diff --git a/adversarial-robustness-toolbox/.coveragerc b/adversarial-robustness-toolbox/.coveragerc
new file mode 100644
index 0000000..b3f39c9
--- /dev/null
+++ b/adversarial-robustness-toolbox/.coveragerc
@@ -0,0 +1,17 @@
+[run]
+branch = True
+source = art
+
+[report]
+exclude_lines =
+    if self.debug:
+    pragma: no cover
+    raise NotImplementedError
+    if __name__ == .__main__.:
+ignore_errors = True
+omit =
+    docs/*
+    examples/*
+    notebooks/*
+    tests/*
+    utils/*
diff --git a/adversarial-robustness-toolbox/.dockerIgnore b/adversarial-robustness-toolbox/.dockerIgnore
new file mode 100644
index 0000000..2ca09b8
--- /dev/null
+++ b/adversarial-robustness-toolbox/.dockerIgnore
@@ -0,0 +1,3 @@
+./venv*
+.git
+./TMP/
diff --git a/adversarial-robustness-toolbox/.gitattributes b/adversarial-robustness-toolbox/.gitattributes
new file mode 100644
index 0000000..3aec19f
--- /dev/null
+++ b/adversarial-robustness-toolbox/.gitattributes
@@ -0,0 +1,2 @@
+# Remove IPython notebooks from language statistics
+*.ipynb linguist-detectable=false
diff --git a/adversarial-robustness-toolbox/.github/ISSUE_TEMPLATE/bug_report.md b/adversarial-robustness-toolbox/.github/ISSUE_TEMPLATE/bug_report.md
new file mode 100644
index 0000000..b009466
--- /dev/null
+++ b/adversarial-robustness-toolbox/.github/ISSUE_TEMPLATE/bug_report.md
@@ -0,0 +1,27 @@
+---
+name: Bug report
+about: Report a bug in ART
+
+---
+
+**Describe the bug**
+A clear and concise description of what the bug is.
+
+**To Reproduce**
+Steps to reproduce the behavior:
+1. Go to '...'
+2. Click on '....'
+3. Scroll down to '....'
+4. See error
+
+**Expected behavior**
+A clear and concise description of what you expected to happen.
+
+**Screenshots**
+If applicable, add screenshots to help explain your problem.
+
+**System information (please complete the following information):**
+ - OS
+ - Python version
+ - ART version or commit number
+ - TensorFlow / Keras / PyTorch / MXNet version
diff --git a/adversarial-robustness-toolbox/.github/ISSUE_TEMPLATE/feature_request.md b/adversarial-robustness-toolbox/.github/ISSUE_TEMPLATE/feature_request.md
new file mode 100644
index 0000000..2a19b33
--- /dev/null
+++ b/adversarial-robustness-toolbox/.github/ISSUE_TEMPLATE/feature_request.md
@@ -0,0 +1,17 @@
+---
+name: Feature request
+about: Suggest an idea for improving ART
+
+---
+
+**Is your feature request related to a problem? Please describe.**
+A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]
+
+**Describe the solution you'd like**
+A clear and concise description of what you want to happen.
+
+**Describe alternatives you've considered**
+A clear and concise description of any alternative solutions or features you've considered.
+
+**Additional context**
+Add any other context or screenshots about the feature request here.
diff --git a/adversarial-robustness-toolbox/.github/dependabot.yml b/adversarial-robustness-toolbox/.github/dependabot.yml
new file mode 100644
index 0000000..b11b8b0
--- /dev/null
+++ b/adversarial-robustness-toolbox/.github/dependabot.yml
@@ -0,0 +1,19 @@
+# To get started with Dependabot version updates, you'll need to specify which
+# package ecosystems to update and where the package manifests are located.
+# Please see the documentation for all configuration options:
+# https://help.github.com/github/administering-a-repository/configuration-options-for-dependency-updates
+
+version: 2
+updates:
+  - package-ecosystem: "pip"
+    directory: "/"
+    schedule:
+      interval: "daily"
+    assignees:
+      - "beat-buesser"
+  - package-ecosystem: "github-actions"
+    directory: "/"
+    schedule:
+      interval: "daily"
+    assignees:
+      - "beat-buesser"
diff --git a/adversarial-robustness-toolbox/.github/workflows/ci-mxnet.yml b/adversarial-robustness-toolbox/.github/workflows/ci-mxnet.yml
new file mode 100644
index 0000000..ba89f0e
--- /dev/null
+++ b/adversarial-robustness-toolbox/.github/workflows/ci-mxnet.yml
@@ -0,0 +1,80 @@
+name: Continuous Integration
+on:
+  # Run on manual trigger
+  workflow_dispatch:
+
+  # Run on pull requests
+  pull_request:
+    paths-ignore:
+      - '*.md'
+
+  # Run when pushing to main or dev branches
+  push:
+    branches:
+      - main
+      - dev*
+
+  # Run scheduled CI flow daily
+  schedule:
+    - cron: '0 8 * * 0'
+
+jobs:
+  test:
+    runs-on: ubuntu-16.04
+    strategy:
+      fail-fast: false
+      matrix:
+        include:
+          - name: mxnet (Python 3.7)
+            framework: mxnet
+            python: 3.7
+          - name: legacy (TensorFlow 2.3.2 Keras 2.4.3  PyTorch 1.7.1 scikit-learn 0.22.2 Python 3.7)
+            framework: legacy
+            python: 3.7
+            tensorflow: 2.3.2
+            keras: 2.4.3
+            scikit-learn: 0.22.2
+            torch: 1.7.1+cpu
+            torchvision: 0.8.2+cpu
+            torchaudio: 0.7.2
+          - name: legacy (TensorFlow 2.4.1 Keras 2.4.3 PyTorch 1.7.1 scikit-learn 0.23.2 Python 3.7)
+            framework: legacy
+            python: 3.7
+            tensorflow: 2.4.1
+            keras: 2.4.3
+            torch: 1.7.1+cpu
+            torchvision: 0.8.2+cpu
+            torchaudio: 0.7.2
+            scikit-learn: 0.23.2
+
+    name: Run ${{ matrix.name }} Tests
+    steps:
+      - name: Checkout Repo
+        uses: actions/checkout@v2
+      - name: Setup Python
+        uses: actions/setup-python@v2
+        with:
+          python-version: ${{ matrix.python }}
+      - name: Install Dependencies
+        run: |
+          sudo apt-get update
+          sudo apt-get -y -q install ffmpeg libavcodec-extra
+          python -m pip install --upgrade pip setuptools wheel
+          pip install tensorflow==2.4.1
+          pip install keras==2.4.3
+          pip3 install -q -r requirements.txt
+          pip list
+      - name: Pre-install legacy
+        if: ${{ matrix.framework == 'legacy' }}
+        run: |
+          pip install tensorflow==${{ matrix.tensorflow }}
+          pip install keras==${{ matrix.keras }}
+          pip install scikit-learn==${{ matrix.scikit-learn }}
+          pip install torch==${{ matrix.torch }} -f https://download.pytorch.org/whl/torch_stable.html
+          pip install torchvision==${{ matrix.torchvision }} -f https://download.pytorch.org/whl/torch_stable.html
+          pip install torchaudio==${{ matrix.torchaudio }} -f https://download.pytorch.org/whl/torch_stable.html
+          pip list
+      - name: Run ${{ matrix.name }} Tests
+        run: ./run_tests.sh ${{ matrix.framework }}
+      - name: Upload coverage to Codecov
+        uses: codecov/codecov-action@v1
diff --git a/adversarial-robustness-toolbox/.github/workflows/ci.yml b/adversarial-robustness-toolbox/.github/workflows/ci.yml
new file mode 100644
index 0000000..85e743c
--- /dev/null
+++ b/adversarial-robustness-toolbox/.github/workflows/ci.yml
@@ -0,0 +1,190 @@
+name: Continuous Integration
+on:
+  # Run on manual trigger
+  workflow_dispatch:
+
+  # Run on pull requests
+  pull_request:
+    paths-ignore:
+      - '*.md'
+
+  # Run when pushing to main or dev branches
+  push:
+    branches:
+      - main
+      - dev*
+
+  # Run scheduled CI flow daily
+  schedule:
+    - cron: '0 8 * * 0'
+
+jobs:
+  test:
+    runs-on: ubuntu-20.04
+    strategy:
+      fail-fast: false
+      matrix:
+        include:
+          - name: TensorFlow 1.15.5 (Keras 2.2.5 Python 3.7)
+            framework: tensorflow
+            python: 3.7
+            tensorflow: 1.15.5
+            tf_version: v1
+            keras: 2.2.5
+          - name: TensorFlow 2.3.2 (Keras 2.4.3 Python 3.7)
+            framework: tensorflow
+            python: 3.7
+            tensorflow: 2.3.2
+            tf_version: v2
+            keras: 2.4.3
+          - name: TensorFlow 2.4.1v1 (Keras 2.4.3 Python 3.8)
+            framework: tensorflow2v1
+            python: 3.8
+            tensorflow: 2.4.1
+            tf_version: v2
+            keras: 2.4.3
+          - name: TensorFlow 2.4.1 (Keras 2.4.3 Python 3.8)
+            framework: tensorflow
+            python: 3.8
+            tensorflow: 2.4.1
+            tf_version: v2
+            keras: 2.4.3
+          - name: Keras 2.3.1 (TensorFlow 2.2.1 Python 3.7)
+            framework: keras
+            python: 3.7
+            tensorflow: 2.2.1
+            keras: 2.3.1
+          - name: TensorFlow-Keras 2.3.2 (Keras 2.4.3 Python 3.7)
+            framework: kerastf
+            python: 3.7
+            tensorflow: 2.3.2
+            keras: 2.4.3
+          - name: PyTorch (Python 3.7)
+            framework: pytorch
+            python: 3.7
+            torch: 1.7.1+cpu
+            torchvision: 0.8.2+cpu
+            torchaudio: 0.7.2
+          - name: scikit-learn 0.22.2 (Python 3.7)
+            framework: scikitlearn
+            scikit-learn: 0.22.2
+            python: 3.7
+          - name: scikit-learn 0.23.2 (Python 3.8)
+            framework: scikitlearn
+            scikit-learn: 0.23.2
+            python: 3.8
+          - name: TensorFlow+Lingvo 2.1.0v1 (Keras 2.3.1 Python 3.6)
+            framework: tensorflow2v1
+            python: 3.6
+            tensorflow: 2.1.0
+            tf_version: v2
+            keras: 2.3.1
+            lingvo: 0.6.4
+#          - name: mxnet (Python 3.7)
+#            framework: mxnet
+#            python: 3.7
+#          - name: legacy (TensorFlow 2.3.2 Keras 2.4.3  PyTorch 1.7.1 scikit-learn 0.22.2 Python 3.7)
+#            framework: legacy
+#            python: 3.7
+#            tensorflow: 2.3.2
+#            keras: 2.4.3
+#            scikit-learn: 0.22.2
+#            torch: 1.7.1+cpu
+#            torchvision: 0.8.2+cpu
+#            torchaudio: 0.7.2
+#          - name: legacy (TensorFlow 2.4.1 Keras 2.4.3 PyTorch 1.7.1 scikit-learn 0.23.2 Python 3.7)
+#            framework: legacy
+#            python: 3.7
+#            tensorflow: 2.4.1
+#            keras: 2.4.3
+#            torch: 1.7.1+cpu
+#            torchvision: 0.8.2+cpu
+#            torchaudio: 0.7.2
+#            scikit-learn: 0.23.2
+
+    name: Run ${{ matrix.name }} Tests
+    steps:
+      - name: Checkout Repo
+        uses: actions/checkout@v2
+      - name: Setup Python
+        uses: actions/setup-python@v2
+        with:
+          python-version: ${{ matrix.python }}
+      - name: Install Dependencies
+        run: |
+          sudo apt-get update
+          sudo apt-get -y -q install ffmpeg libavcodec-extra
+          python -m pip install --upgrade pip setuptools wheel
+          pip install tensorflow==2.4.1
+          pip install keras==2.4.3
+          pip3 install -q -r requirements.txt
+          pip list
+      - name: Pre-install legacy
+        if: ${{ matrix.framework == 'legacy' }}
+        run: |
+          pip install tensorflow==${{ matrix.tensorflow }}
+          pip install keras==${{ matrix.keras }}
+          pip install scikit-learn==${{ matrix.scikit-learn }}
+          pip install torch==${{ matrix.torch }} -f https://download.pytorch.org/whl/torch_stable.html
+          pip install torchvision==${{ matrix.torchvision }} -f https://download.pytorch.org/whl/torch_stable.html
+          pip install torchaudio==${{ matrix.torchaudio }} -f https://download.pytorch.org/whl/torch_stable.html
+          pip list
+      - name: Pre-install tensorflow
+        if: ${{ matrix.framework == 'tensorflow' || matrix.framework == 'keras' || matrix.framework == 'kerastf' || matrix.framework == 'tensorflow2v1' }}
+        run: |
+          pip install tensorflow==${{ matrix.tensorflow }}
+          pip install keras==${{ matrix.keras }}
+          pip list
+      - name: Pre-install scikit-learn
+        if: ${{ matrix.framework == 'scikitlearn' }}
+        run: |
+          pip install scikit-learn==${{ matrix.scikit-learn }}
+          pip list
+      - name: Pre-install torch
+        if: ${{ matrix.framework == 'pytorch' }}
+        run: |
+          pip install torch==${{ matrix.torch }} -f https://download.pytorch.org/whl/torch_stable.html
+          pip install torchvision==${{ matrix.torchvision }} -f https://download.pytorch.org/whl/torch_stable.html
+          pip install torchaudio==${{ matrix.torchaudio }} -f https://download.pytorch.org/whl/torch_stable.html
+      - name: Pre-install Lingvo ASR
+        if: ${{ matrix.tensorflow == '2.1.0' }}
+        run: |
+          pip install tensorflow==${{ matrix.tensorflow }}
+          pip install lingvo==${{ matrix.lingvo }}
+          pip install tensorflow-addons==0.9.1
+          pip install model-pruning-google-research==0.0.3
+          pip list
+      - name: Run ${{ matrix.name }} Tests
+        run: ./run_tests.sh ${{ matrix.framework }}
+      - name: Upload coverage to Codecov
+        uses: codecov/codecov-action@v1
+  style:
+    name: Style Check
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout Repo
+        uses: actions/checkout@v2
+      - name: Setup Python
+        uses: actions/setup-python@v2
+        with:
+          python-version: 3.7
+      - name: Pre-install
+        run: |
+          sudo apt-get update
+          sudo apt-get -y -q install ffmpeg libavcodec-extra
+          pip install tensorflow==2.2.2
+          pip install keras==2.3.1
+      - name: Install Dependencies
+        run: |
+          python -m pip install --upgrade pip setuptools wheel
+          pip install -q pylint pycodestyle
+          pip install -q -r requirements.txt
+          pip list
+      - name: pycodestyle
+        run: (pycodestyle --ignore=C0330,C0415,E203,E231,W503 --max-line-length=120 art || exit 0)
+      - name: pylint
+        run: (pylint --disable=C0330,C0415,E203,E1136 -rn art || exit 0)
+      - name: Check Types (mypy)
+        run: (mypy art || exit 0)
+      - name: pytest
+        run: py.test --pep8 -m pep8
diff --git a/adversarial-robustness-toolbox/.github/workflows/codeql-analysis.yml b/adversarial-robustness-toolbox/.github/workflows/codeql-analysis.yml
new file mode 100644
index 0000000..773feec
--- /dev/null
+++ b/adversarial-robustness-toolbox/.github/workflows/codeql-analysis.yml
@@ -0,0 +1,62 @@
+# For most projects, this workflow file will not need changing; you simply need
+# to commit it to your repository.
+#
+# You may wish to alter this file to override the set of languages analyzed,
+# or to provide custom queries or build logic.
+name: "CodeQL"
+
+on:
+  push:
+    branches: [main, dev_1.6.0, dev_1.6.1]
+  pull_request:
+    # The branches below must be a subset of the branches above
+    branches: [main]
+  schedule:
+    - cron: '0 7 * * 0'
+
+jobs:
+  analyze:
+    name: Analyze
+    runs-on: ubuntu-latest
+
+    strategy:
+      fail-fast: false
+      matrix:
+        # Override automatic language detection by changing the below list
+        # Supported options are ['csharp', 'cpp', 'go', 'java', 'javascript', 'python']
+        language: ['python']
+        # Learn more...
+        # https://docs.github.com/en/github/finding-security-vulnerabilities-and-errors-in-your-code/configuring-code-scanning#overriding-automatic-language-detection
+
+    steps:
+    - name: Checkout repository
+      uses: actions/checkout@v2
+
+    # Initializes the CodeQL tools for scanning.
+    - name: Initialize CodeQL
+      uses: github/codeql-action/init@v1
+      with:
+        languages: ${{ matrix.language }}
+        # If you wish to specify custom queries, you can do so here or in a config file.
+        # By default, queries listed here will override any specified in a config file. 
+        # Prefix the list here with "+" to use these queries and those in the config file.
+        # queries: ./path/to/local/query, your-org/your-repo/queries@main
+
+    # Autobuild attempts to build any compiled languages  (C/C++, C#, or Java).
+    # If this step fails, then you should remove it and run the build manually (see below)
+    - name: Autobuild
+      uses: github/codeql-action/autobuild@v1
+
+    # ℹ️ Command-line programs to run using the OS shell.
+    # 📚 https://git.io/JvXDl
+
+    # ✏️ If the Autobuild fails above, remove it and uncomment the following three lines
+    #    and modify them (or add more) to build your code if your project
+    #    uses a compiled language
+
+    #- run: |
+    #   make bootstrap
+    #   make release
+
+    - name: Perform CodeQL Analysis
+      uses: github/codeql-action/analyze@v1
diff --git a/adversarial-robustness-toolbox/.gitignore b/adversarial-robustness-toolbox/.gitignore
new file mode 100644
index 0000000..426e33f
--- /dev/null
+++ b/adversarial-robustness-toolbox/.gitignore
@@ -0,0 +1,116 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+env/
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports / type hints
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*,cover
+.hypothesis/
+.mypy_cache
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# IPython Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# dotenv
+.env
+
+# virtualenv
+venv/
+ENV/
+
+# Spyder project settings
+.spyderproject
+
+# Rope project settings
+.ropeproject
+
+# ignore local config
+*config.ini
+
+# ignore pictures
+*.jpg
+demo/pics/*
+
+# ignore local config
+*config.ini
+
+# Things TF might pull when testing
+*.gz
+*.npy
+*.npz
+*.ipynb
+.DS_Store
+
+# ignore PyCharm project files
+.idea/
+
+# Exceptions for notebooks/
+!notebooks/*.ipynb
+!notebooks/adaptive_defence_evaluations/*.ipynb
+!notebooks/adversarial_patch/*.ipynb
+!notebooks/art_evaluations/*.ipynb
diff --git a/adversarial-robustness-toolbox/.lgtm.yml b/adversarial-robustness-toolbox/.lgtm.yml
new file mode 100644
index 0000000..9850f6f
--- /dev/null
+++ b/adversarial-robustness-toolbox/.lgtm.yml
@@ -0,0 +1,10 @@
+path_classifiers:
+  test:
+    - tests
+    - examples
+    - notebooks
+    - utils
+
+queries:
+  - exclude: py/import-own-module
+  - exclude: py/similar-function
diff --git a/adversarial-robustness-toolbox/.pylintrc b/adversarial-robustness-toolbox/.pylintrc
new file mode 100644
index 0000000..34f5887
--- /dev/null
+++ b/adversarial-robustness-toolbox/.pylintrc
@@ -0,0 +1,575 @@
+[MASTER]
+
+# A comma-separated list of package or module names from where C extensions may
+# be loaded. Extensions are loading into the active Python interpreter and may
+# run arbitrary code.
+extension-pkg-whitelist=
+
+# Add files or directories to the blacklist. They should be base names, not
+# paths.
+ignore=CVS
+
+# Add files or directories matching the regex patterns to the blacklist. The
+# regex matches against base names, not paths.
+ignore-patterns=
+
+# Python code to execute, usually for sys.path manipulation such as
+# pygtk.require().
+#init-hook=
+
+# Use multiple processes to speed up Pylint. Specifying 0 will auto-detect the
+# number of processors available to use.
+jobs=1
+
+# Control the amount of potential inferred values when inferring a single
+# object. This can help the performance when dealing with large functions or
+# complex, nested conditions.
+limit-inference-results=100
+
+# List of plugins (as comma separated values of python modules names) to load,
+# usually to register additional checkers.
+load-plugins=
+
+# Pickle collected data for later comparisons.
+persistent=yes
+
+# Specify a configuration file.
+#rcfile=
+
+# When enabled, pylint would attempt to guess common misconfiguration and emit
+# user-friendly hints instead of false-positive error messages.
+suggestion-mode=yes
+
+# Allow loading of arbitrary C extensions. Extensions are imported into the
+# active Python interpreter and may run arbitrary code.
+unsafe-load-any-extension=no
+
+
+[MESSAGES CONTROL]
+
+# Only show warnings with the listed confidence levels. Leave empty to show
+# all. Valid levels: HIGH, INFERENCE, INFERENCE_FAILURE, UNDEFINED.
+confidence=
+
+# Disable the message, report, category or checker with the given id(s). You
+# can either give multiple identifiers separated by comma (,) or put this
+# option multiple times (only on the command line, not in the configuration
+# file where it should appear only once). You can also use "--disable=all" to
+# disable everything first and then reenable specific checks. For example, if
+# you want to run only the similarities checker, you can use "--disable=all
+# --enable=similarities". If you want to run only the classes checker, but have
+# no Warning level messages displayed, use "--disable=all --enable=classes
+# --disable=W".
+disable=fixme,
+        misplaced-comparison-constant,
+        no-member,
+        duplicate-code,
+        unnecessary-pass,
+        useless-super-delegation,
+        too-few-public-methods,
+        too-many-instance-attributes,
+        too-many-locals,
+        too-many-arguments,
+        too-many-attributes,
+        too-many-branches,
+        too-many-statements,
+        attribute-defined-outside-init,
+        print-statement,
+        parameter-unpacking,
+        unpacking-in-except,
+        old-raise-syntax,
+        backtick,
+        long-suffix,
+        old-ne-operator,
+        old-octal-literal,
+        import-star-module-level,
+        non-ascii-bytes-literal,
+        raw-checker-failed,
+        bad-inline-option,
+        locally-disabled,
+        file-ignored,
+        suppressed-message,
+        useless-suppression,
+        deprecated-pragma,
+        use-symbolic-message-instead,
+        apply-builtin,
+        basestring-builtin,
+        buffer-builtin,
+        cmp-builtin,
+        coerce-builtin,
+        execfile-builtin,
+        file-builtin,
+        long-builtin,
+        raw_input-builtin,
+        reduce-builtin,
+        standarderror-builtin,
+        unicode-builtin,
+        xrange-builtin,
+        coerce-method,
+        delslice-method,
+        getslice-method,
+        setslice-method,
+        no-absolute-import,
+        old-division,
+        dict-iter-method,
+        dict-view-method,
+        next-method-called,
+        metaclass-assignment,
+        indexing-exception,
+        raising-string,
+        reload-builtin,
+        oct-method,
+        hex-method,
+        nonzero-method,
+        cmp-method,
+        input-builtin,
+        round-builtin,
+        intern-builtin,
+        unichr-builtin,
+        map-builtin-not-iterating,
+        zip-builtin-not-iterating,
+        range-builtin-not-iterating,
+        filter-builtin-not-iterating,
+        using-cmp-argument,
+        eq-without-hash,
+        div-method,
+        idiv-method,
+        rdiv-method,
+        exception-message-attribute,
+        invalid-str-codec,
+        sys-max-int,
+        bad-python3-import,
+        deprecated-string-function,
+        deprecated-str-translate-call,
+        deprecated-itertools-function,
+        deprecated-types-field,
+        next-method-defined,
+        dict-items-not-iterating,
+        dict-keys-not-iterating,
+        dict-values-not-iterating,
+        deprecated-operator-function,
+        deprecated-urllib-function,
+        xreadlines-attribute,
+        deprecated-sys-function,
+        exception-escape,
+        comprehension-escape
+
+# Enable the message, report, category or checker with the given id(s). You can
+# either give multiple identifier separated by comma (,) or put this option
+# multiple time (only on the command line, not in the configuration file where
+# it should appear only once). See also the "--disable" option for examples.
+enable=c-extension-no-member
+
+
+[REPORTS]
+
+# Python expression which should return a note less than 10 (10 is the highest
+# note). You have access to the variables errors warning, statement which
+# respectively contain the number of errors / warnings messages and the total
+# number of statements analyzed. This is used by the global evaluation report
+# (RP0004).
+evaluation=10.0 - ((float(5 * error + warning + refactor + convention) / statement) * 10)
+
+# Template used to display messages. This is a python new-style format string
+# used to format the message information. See doc for all details.
+#msg-template=
+
+# Set the output format. Available formats are text, parseable, colorized, json
+# and msvs (visual studio). You can also give a reporter class, e.g.
+# mypackage.mymodule.MyReporterClass.
+output-format=text
+
+# Tells whether to display a full report or only the messages.
+reports=no
+
+# Activate the evaluation score.
+score=yes
+
+
+[REFACTORING]
+
+# Maximum number of nested blocks for function / method body
+max-nested-blocks=5
+
+# Complete name of functions that never returns. When checking for
+# inconsistent-return-statements if a never returning function is called then
+# it will be considered as an explicit return statement and no message will be
+# printed.
+never-returning-functions=sys.exit
+
+
+[LOGGING]
+
+# Format style used to check logging format string. `old` means using %
+# formatting, while `new` is for `{}` formatting.
+logging-format-style=old
+
+# Logging modules to check that the string format arguments are in logging
+# function parameter format.
+logging-modules=logging
+
+
+[SPELLING]
+
+# Limits count of emitted suggestions for spelling mistakes.
+max-spelling-suggestions=4
+
+# Spelling dictionary name. Available dictionaries: none. To make it working
+# install python-enchant package..
+spelling-dict=
+
+# List of comma separated words that should not be checked.
+spelling-ignore-words=
+
+# A path to a file that contains private dictionary; one word per line.
+spelling-private-dict-file=
+
+# Tells whether to store unknown words to indicated private dictionary in
+# --spelling-private-dict-file option instead of raising a message.
+spelling-store-unknown-words=no
+
+
+[SIMILARITIES]
+
+# Ignore comments when computing similarities.
+ignore-comments=yes
+
+# Ignore docstrings when computing similarities.
+ignore-docstrings=yes
+
+# Ignore imports when computing similarities.
+ignore-imports=no
+
+# Minimum lines number of a similarity.
+min-similarity-lines=4
+
+
+[TYPECHECK]
+
+# List of decorators that produce context managers, such as
+# contextlib.contextmanager. Add to this list to register other decorators that
+# produce valid context managers.
+contextmanager-decorators=contextlib.contextmanager
+
+# List of members which are set dynamically and missed by pylint inference
+# system, and so shouldn't trigger E1101 when accessed. Python regular
+# expressions are accepted.
+generated-members=
+
+# Tells whether missing members accessed in mixin class should be ignored. A
+# mixin class is detected if its name ends with "mixin" (case insensitive).
+ignore-mixin-members=yes
+
+# Tells whether to warn about missing members when the owner of the attribute
+# is inferred to be None.
+ignore-none=yes
+
+# This flag controls whether pylint should warn about no-member and similar
+# checks whenever an opaque object is returned when inferring. The inference
+# can return multiple potential results while evaluating a Python object, but
+# some branches might not be evaluated, which results in partial inference. In
+# that case, it might be useful to still emit no-member and other checks for
+# the rest of the inferred objects.
+ignore-on-opaque-inference=yes
+
+# List of class names for which member attributes should not be checked (useful
+# for classes with dynamically set attributes). This supports the use of
+# qualified names.
+ignored-classes=optparse.Values,thread._local,_thread._local
+
+# List of module names for which member attributes should not be checked
+# (useful for modules/projects where namespaces are manipulated during runtime
+# and thus existing member attributes cannot be deduced by static analysis. It
+# supports qualified module names, as well as Unix pattern matching.
+ignored-modules=
+
+# Show a hint with possible names when a member name was not found. The aspect
+# of finding the hint is based on edit distance.
+missing-member-hint=yes
+
+# The minimum edit distance a name should have in order to be considered a
+# similar match for a missing member name.
+missing-member-hint-distance=1
+
+# The total number of similar names that should be taken in consideration when
+# showing a hint for a missing member.
+missing-member-max-choices=1
+
+
+[VARIABLES]
+
+# List of additional names supposed to be defined in builtins. Remember that
+# you should avoid defining new builtins when possible.
+additional-builtins=
+
+# Tells whether unused global variables should be treated as a violation.
+allow-global-unused-variables=yes
+
+# List of strings which can identify a callback function by name. A callback
+# name must start or end with one of those strings.
+callbacks=cb_,
+          _cb
+
+# A regular expression matching the name of dummy variables (i.e. expected to
+# not be used).
+dummy-variables-rgx=_+$|(_[a-zA-Z0-9_]*[a-zA-Z0-9]+?$)|dummy|^ignored_|^unused_
+
+# Argument names that match this expression will be ignored. Default to name
+# with leading underscore.
+ignored-argument-names=_.*|^ignored_|^unused_
+
+# Tells whether we should check for unused import in __init__ files.
+init-import=no
+
+# List of qualified module names which can have objects that can redefine
+# builtins.
+redefining-builtins-modules=six.moves,past.builtins,future.builtins,builtins,io
+
+
+[MISCELLANEOUS]
+
+# List of note tags to take in consideration, separated by a comma.
+notes=FIXME,
+      XXX,
+      TODO
+
+
+[BASIC]
+
+# Naming style matching correct argument names.
+argument-naming-style=snake_case
+
+# Regular expression matching correct argument names. Overrides argument-
+# naming-style.
+#argument-rgx=
+
+# Naming style matching correct attribute names.
+attr-naming-style=snake_case
+
+# Regular expression matching correct attribute names. Overrides attr-naming-
+# style.
+#attr-rgx=
+
+# Good variable names which should always be accepted, separated by a comma
+# i,j,k = typical indices
+# n,m = typical numbers
+# ex = for exceptions and errors
+# x,y= typical data
+# _ = placeholder name
+good-names=i,j,k,n,m,ex,x,y,_,logger,op
+
+# Bad variable names which should always be refused, separated by a comma.
+bad-names=foo,
+          bar,
+          baz,
+          toto,
+          tutu,
+          tata
+
+# Naming style matching correct class attribute names.
+class-attribute-naming-style=any
+
+# Regular expression matching correct class attribute names. Overrides class-
+# attribute-naming-style.
+#class-attribute-rgx=
+
+# Naming style matching correct class names.
+class-naming-style=PascalCase
+
+# Regular expression matching correct class names. Overrides class-naming-
+# style.
+#class-rgx=
+
+# Naming style matching correct constant names.
+const-naming-style=UPPER_CASE
+
+# Regular expression matching correct constant names. Overrides const-naming-
+# style.
+#const-rgx=
+
+# Minimum line length for functions/classes that require docstrings, shorter
+# ones are exempt.
+docstring-min-length=-1
+
+# Naming style matching correct function names.
+function-naming-style=snake_case
+
+# Regular expression matching correct function names. Overrides function-
+# naming-style.
+#function-rgx=
+
+# Include a hint for the correct naming format with invalid-name.
+include-naming-hint=no
+
+# Naming style matching correct inline iteration names.
+inlinevar-naming-style=any
+
+# Regular expression matching correct inline iteration names. Overrides
+# inlinevar-naming-style.
+#inlinevar-rgx=
+
+# Naming style matching correct method names.
+method-naming-style=snake_case
+
+# Regular expression matching correct method names. Overrides method-naming-
+# style.
+#method-rgx=
+
+# Naming style matching correct module names.
+module-naming-style=snake_case
+
+# Regular expression matching correct module names. Overrides module-naming-
+# style.
+#module-rgx=
+
+# Colon-delimited sets of names that determine each other's naming style when
+# the name regexes allow several styles.
+name-group=
+
+# Regular expression which should only match function or class names that do
+# not require a docstring.
+no-docstring-rgx=^_
+
+# List of decorators that produce properties, such as abc.abstractproperty. Add
+# to this list to register other decorators that produce valid properties.
+# These decorators are taken in consideration only for invalid-name.
+property-classes=abc.abstractproperty
+
+# Naming style matching correct variable names.
+variable-naming-style=snake_case
+
+# Regular expression matching correct variable names. Overrides variable-
+# naming-style.
+#variable-rgx=
+
+
+[FORMAT]
+
+# Expected format of line ending, e.g. empty (any line ending), LF or CRLF.
+expected-line-ending-format=
+
+# Regexp for a line that is allowed to be longer than the limit.
+ignore-long-lines=^\s*(# )?<?https?://\S+>?$
+
+# Number of spaces of indent required inside a hanging or continued line.
+indent-after-paren=4
+
+# String used as indentation unit. This is usually "    " (4 spaces) or "\t" (1
+# tab).
+indent-string='    '
+
+# Maximum number of characters on a single line.
+max-line-length=120
+
+# Maximum number of lines in a module.
+max-module-lines=1000
+
+# List of optional constructs for which whitespace checking is disabled. `dict-
+# separator` is used to allow tabulation in dicts, etc.: {1  : 1,\n222: 2}.
+# `trailing-comma` allows a space between comma and closing bracket: (a, ).
+# `empty-line` allows space-only lines.
+no-space-check=trailing-comma,
+               dict-separator
+
+# Allow the body of a class to be on the same line as the declaration if body
+# contains single statement.
+single-line-class-stmt=no
+
+# Allow the body of an if to be on the same line as the test if there is no
+# else.
+single-line-if-stmt=no
+
+
+[IMPORTS]
+
+# Allow wildcard imports from modules that define __all__.
+allow-wildcard-with-all=no
+
+# Analyse import fallback blocks. This can be used to support both Python 2 and
+# 3 compatible code, which means that the block might have code that exists
+# only in one or another interpreter, leading to false positives when analysed.
+analyse-fallback-blocks=no
+
+# Deprecated modules which should not be used, separated by a comma.
+deprecated-modules=optparse,tkinter.tix
+
+# Create a graph of external dependencies in the given file (report RP0402 must
+# not be disabled).
+ext-import-graph=
+
+# Create a graph of every (i.e. internal and external) dependencies in the
+# given file (report RP0402 must not be disabled).
+import-graph=
+
+# Create a graph of internal dependencies in the given file (report RP0402 must
+# not be disabled).
+int-import-graph=
+
+# Force import order to recognize a module as part of the standard
+# compatibility libraries.
+known-standard-library=
+
+# Force import order to recognize a module as part of a third party library.
+known-third-party=enchant
+
+
+[DESIGN]
+
+# Maximum number of arguments for function / method.
+max-args=5
+
+# Maximum number of attributes for a class (see R0902).
+max-attributes=7
+
+# Maximum number of boolean expressions in an if statement.
+max-bool-expr=5
+
+# Maximum number of branch for function / method body.
+max-branches=12
+
+# Maximum number of locals for function / method body.
+max-locals=15
+
+# Maximum number of parents for a class (see R0901).
+max-parents=7
+
+# Maximum number of public methods for a class (see R0904).
+max-public-methods=20
+
+# Maximum number of return / yield for function / method body.
+max-returns=6
+
+# Maximum number of statements in function / method body.
+max-statements=50
+
+# Minimum number of public methods for a class (see R0903).
+min-public-methods=2
+
+
+[CLASSES]
+
+# List of method names used to declare (i.e. assign) instance attributes.
+defining-attr-methods=__init__,
+                      __new__,
+                      setUp
+
+# List of member names, which should be excluded from the protected access
+# warning.
+exclude-protected=_asdict,
+                  _fields,
+                  _replace,
+                  _source,
+                  _make
+
+# List of valid names for the first argument in a class method.
+valid-classmethod-first-arg=cls
+
+# List of valid names for the first argument in a metaclass class method.
+valid-metaclass-classmethod-first-arg=cls
+
+
+[EXCEPTIONS]
+
+# Exceptions that will emit a warning when being caught. Defaults to
+# "Exception".
+overgeneral-exceptions=Exception
diff --git a/adversarial-robustness-toolbox/AUTHORS b/adversarial-robustness-toolbox/AUTHORS
new file mode 100644
index 0000000..c2376a0
--- /dev/null
+++ b/adversarial-robustness-toolbox/AUTHORS
@@ -0,0 +1,12 @@
+# This is the official list of Adversarial Robustness Toolbox (ART) Authors for copyright purposes.
+
+# Names should be added to this file as:
+# - Organization
+# - Person <email address>
+
+- International Business Machines Corporation (IBM)
+- Two Six Labs, LLC
+- Kyushu University
+- Intel Corporation
+- University of Chicago
+- The MITRE Corporation
diff --git a/adversarial-robustness-toolbox/CODE_OF_CONDUCT.md b/adversarial-robustness-toolbox/CODE_OF_CONDUCT.md
new file mode 100644
index 0000000..3715b23
--- /dev/null
+++ b/adversarial-robustness-toolbox/CODE_OF_CONDUCT.md
@@ -0,0 +1,5 @@
+The Adversarial Robustness Toolbox (ART) is dedicated to providing a harassment-free experience for everyone, regardless of gender, gender identity and expression, sexual orientation, disability, physical appearance, body size, age, race, or religion. We do not tolerate harassment of participants in any form.
+
+This code of conduct applies to all Adversarial Robustness Toolbox spaces, both online and off. Anyone who violates this code of conduct may be sanctioned or expelled from these spaces at the discretion of the Trusted-AI team.
+
+We may add additional rules over time, which will be made clearly available to participants. Participants are responsible for knowing and abiding by these rules.
diff --git a/adversarial-robustness-toolbox/CONTRIBUTING.md b/adversarial-robustness-toolbox/CONTRIBUTING.md
new file mode 100644
index 0000000..e3803ba
--- /dev/null
+++ b/adversarial-robustness-toolbox/CONTRIBUTING.md
@@ -0,0 +1,28 @@
+# Contributing to the Adversarial Robustness Toolbox
+
+## Adding new Features
+Adding new features, improving documentation, fixing bugs, or writing tutorials are all examples of helpful 
+contributions. Furthermore, if you are publishing a new attack or defense, we strongly encourage you to add it to the 
+Adversarial Robustness Toolbox so that others may evaluate it fairly in their own work.
+
+Bug fixes can be initiated through GitHub pull requests. When making code contributions to the Adversarial Robustness 
+Toolbox, we ask that you follow the `PEP 8` coding standard and that you provide unit tests for the new features.
+
+## Development install
+
+We provide a specific set of dependencies that we test and develop against, namely `requirements.txt`. In a virtual
+environment install ART for development in the following way:
+```bash
+pip install -r requirements.txt
+```
+
+## Validating Git Commits
+This project uses [DCO](https://developercertificate.org/). Be sure to sign off your commits using the `-s` flag or 
+adding `Signed-off-By: Name<Email>` in the commit message. Example:
+```bash
+git commit -s -m 'Informative commit message'
+```
+
+## Unit tests
+When submitting additional unit tests for ART, in order to keep the code base maintainable, please make sure each unit 
+test can run ideally in a few seconds.
diff --git a/adversarial-robustness-toolbox/Dockerfile b/adversarial-robustness-toolbox/Dockerfile
new file mode 100644
index 0000000..91c098a
--- /dev/null
+++ b/adversarial-robustness-toolbox/Dockerfile
@@ -0,0 +1,57 @@
+FROM  tensorflow/tensorflow:2.2.0
+RUN pip3 install keras==2.3.1
+#### NOTE: comment these two lines if you wish to use the tensorflow 1 version of ART instead ####
+#FROM tensorflow/tensorflow:1.15.2
+#RUN pip3 install keras==2.2.5
+
+RUN pip3 install numpy==1.19.1 scipy==1.4.1 matplotlib==3.3.1 scikit-learn==0.22.2 six==1.15.0 Pillow==7.2.0 pytest-cov==2.10.1
+RUN pip3 install tqdm==4.48.2 statsmodels==0.11.1 pydub==0.24.1 resampy==0.2.2 ffmpeg-python==0.2.0 cma==3.0.3 mypy==0.770
+RUN pip3 install ffmpeg-python==0.2.0
+RUN pip3 install pandas==1.1.1
+
+#TODO check if jupyter notebook works
+RUN pip3 install jupyter==1.0.0 && pip3 install jupyterlab==2.1.0
+# https://stackoverflow.com/questions/49024624/how-to-dockerize-jupyter-lab
+
+# Lingvo ASR dependencies
+# supported versions: (lingvo==0.6.4 with tensorflow-gpu==2.1.0)
+# note: due to conflicts with other TF1/2 version supported by ART, the dependencies are not installed by default:
+# Replace line 1 with: FROM tensorflow/tensorflow:2.1.0
+# Comment other TF related lines and uncomment:
+# RUN pip3 install tensorflow-gpu==2.1.0
+# RUN pip3 install lingvo==0.6.4
+
+RUN pip3 install h5py==2.10.0
+RUN pip3 install tensorflow-addons==0.11.1
+RUN pip3 install mxnet==1.6.0
+RUN pip3 install torch==1.5.0 torchvision==0.7.0 -f https://download.pytorch.org/whl/torch_stable.html
+RUN pip3 install catboost==0.24
+RUN pip3 install GPy==1.9.9
+RUN pip3 install lightgbm==2.3.1
+RUN pip3 install xgboost==1.1.1
+RUN pip3 install kornia==0.3.1
+
+RUN pip3 install pytest==5.4.1 pytest-pep8==1.0.6 pytest-mock==3.2.0 codecov==2.1.8 requests==2.24.0
+
+RUN mkdir /project; mkdir /project/TMP
+VOLUME /project/TMP
+WORKDIR /project
+
+# IMPORTANT: please double check that the dependencies above are up to date with the following requirements file. We currently still run pip install on dependencies within requirements.txt in order to keep dependencies in agreement (in the rare cases were someone updated the requirements.txt file and forgot to update the dockefile)
+ADD . /project/
+RUN pip3 install --upgrade -r /project/requirements.txt
+
+RUN apt-get update
+RUN apt-get -y -q install ffmpeg libavcodec-extra
+
+RUN echo "You should think about possibly upgrading these outdated packages"
+RUN pip3 list --outdated
+
+EXPOSE 8888
+
+CMD bash run_tests.sh
+
+#Check the Dockerfile here https://www.fromlatest.io/#/
+
+#NOTE to contributors: When changing/adding packages, please make sure that the packages are consitent with those
+# present within the requirements.txt and test_requirements.txt files
diff --git a/adversarial-robustness-toolbox/LICENSE b/adversarial-robustness-toolbox/LICENSE
new file mode 100644
index 0000000..874092c
--- /dev/null
+++ b/adversarial-robustness-toolbox/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/adversarial-robustness-toolbox/MAINTAINERS.md b/adversarial-robustness-toolbox/MAINTAINERS.md
new file mode 100644
index 0000000..d9f5ea5
--- /dev/null
+++ b/adversarial-robustness-toolbox/MAINTAINERS.md
@@ -0,0 +1,72 @@
+# Maintainers Guide
+
+Following is the current list of maintainers on this project
+
+The maintainers are listed in alphabetical order of their Github username.
+- Beat Buesser ([beat-buesser](https://github.com/beat-buesser))
+- Ian Molloy ([imolloy](https://github.com/imolloy))
+- Mathieu Sinn ([mathsinn](https://github.com/mathsinn))
+- Ngoc Minh Tran ([minhitbk](https://github.com/minhitbk))
+- Irina Nicolae ([ririnicolae](https://github.com/ririnicolae))
+
+## Methodology
+
+The quality of the master branch should never be compromised.
+
+The remainder of this document details how to merge pull requests to the
+repositories.
+
+## Merge Approval
+
+The project maintainers use LGTM (Looks Good To Me) in comments on the pull
+request to indicate acceptance prior to merging. A change requires LGTMs from
+two project maintainers. If the code is written by a maintainer, the change
+only requires one additional LGTM.
+
+## Reviewing Pull Requests
+
+We recommend reviewing pull requests directly within GitHub. This allows a
+public commentary on changes, providing transparency for all users. When
+providing feedback be civil, courteous, and kind. Disagreement is fine, so long
+as the discourse is carried out politely. If we see a record of uncivil or
+abusive comments, we will revoke your commit privileges and invite you to leave
+the project.
+
+During your review, consider the following points:
+
+### Does the change have positive impact?
+
+Some proposed changes may not represent a positive impact to the project. Ask
+whether or not the change will make understanding the code easier, or if it
+could simply be a personal preference on the part of the author (see
+[bikeshedding](https://en.wiktionary.org/wiki/bikeshedding)).
+
+Pull requests that do not have a clear positive impact should be closed without
+merging.
+
+### Do the changes make sense?
+
+If you do not understand what the changes are or what they accomplish, ask the
+author for clarification. Ask the author to add comments and/or clarify test
+case names to make the intentions clear.
+
+At times, such clarification will reveal that the author may not be using the
+code correctly, or is unaware of features that accommodate their needs. If you
+feel this is the case, work up a code sample that would address the pull
+request for them, and feel free to close the pull request once they confirm.
+
+### Does the change introduce a new feature?
+
+For any given pull request, ask yourself "is this a new feature?" If so, does
+the pull request (or associated issue) contain narrative indicating the need
+for the feature? If not, ask them to provide that information.
+
+Are new unit tests in place that test all new behaviors introduced? If not, do
+not merge the feature until they are! Is documentation in place for the new
+feature? (See the documentation guidelines). If not, do not merge the feature
+until it is! Is the feature necessary for general use cases? Try and keep the
+scope of any given component narrow. If a proposed feature does not fit that
+scope, recommend to the user that they maintain the feature on their own, and
+close the request. You may also recommend that they see if the feature gains
+traction among other users, and suggest they re-submit when they can show such
+support.
\ No newline at end of file
diff --git a/adversarial-robustness-toolbox/MANIFEST.in b/adversarial-robustness-toolbox/MANIFEST.in
new file mode 100644
index 0000000..09df6d5
--- /dev/null
+++ b/adversarial-robustness-toolbox/MANIFEST.in
@@ -0,0 +1,8 @@
+include *.md
+include CODE_OF_CONDUCT
+include LICENSE
+recursive-include models/ *.npy
+graft docs
+prune docs/_build
+graft examples
+graft tests
diff --git a/adversarial-robustness-toolbox/Makefile b/adversarial-robustness-toolbox/Makefile
new file mode 100644
index 0000000..ddc691d
--- /dev/null
+++ b/adversarial-robustness-toolbox/Makefile
@@ -0,0 +1,25 @@
+PROJECT_HOME_DIR := ${CURDIR}
+
+build:
+    # Builds a TensorFlow 2 ART docker container
+    # IMPORTANT ! If you have an existing python env folder make sure to first add it to the `.dockerIgnore` \
+    to reduce the size of your the art docker image
+	docker build -t project-art-tf2 .
+
+build1:
+	# Builds a TensorFlow 1 ART docker container
+    # IMPORTANT ! If you have an existing python env folder make sure to first add it to the `.dockerIgnore` \
+    to reduce the size of your the art docker image
+	docker build -t project-art-tf1 .
+
+run-bash:
+	docker  run --rm -it --name project-art-run-bash -v ${PWD}:/project/ -v ~/.art/:/root/.art/ project-art-tf2  /bin/bash
+
+run-test:
+	docker  run --rm --name project-art-run-test -v ${PWD}:/project/ -v ~/.art/:/root/.art/  project-art-tf2
+
+run-pep:
+	docker  run --rm --name project-art-run-pep -v ${PWD}:/project/ -v ~/.art/:/root/.art/ project-art-tf2 py.test --pep8 -m pep8
+
+run-jupyter:
+	docker  run --rm  --name project-art-run-jupyter -v ${PWD}:/project/ -v ~/.art/:/root/.art/ -p 8888:8888 project-art-tf2 jupyter notebook --ip 0.0.0.0 --no-browser --allow-root
diff --git a/adversarial-robustness-toolbox/PULL_REQUEST_TEMPLATE.md b/adversarial-robustness-toolbox/PULL_REQUEST_TEMPLATE.md
new file mode 100644
index 0000000..1fe328e
--- /dev/null
+++ b/adversarial-robustness-toolbox/PULL_REQUEST_TEMPLATE.md
@@ -0,0 +1,38 @@
+# Description
+
+Please include a summary of the change, motivation and which issue is fixed. Any dependencies changes should also be included.
+
+Fixes # (issue)
+
+## Type of change
+
+Please check all relevant options.
+
+- [ ] Improvement (non-breaking)
+- [ ] Bug fix (non-breaking)
+- [ ] New feature (non-breaking)
+- [ ] Breaking change (fix or feature that would cause existing functionality to not work as expected)
+- [ ] This change requires a documentation update
+
+# Testing
+
+Please describe the tests that you ran to verify your changes. Consider listing any relevant details of your test configuration.
+
+- [ ] Test A
+- [ ] Test B
+
+**Test Configuration**:
+- OS
+- Python version
+- ART version or commit number
+- TensorFlow / Keras / PyTorch / MXNet version
+
+# Checklist
+
+- [ ] My code follows the style guidelines of this project
+- [ ] I have performed a self-review of my own code
+- [ ] I have commented my code
+- [ ] I have made corresponding changes to the documentation
+- [ ] My changes generate no new warnings
+- [ ] I have added tests that prove my fix is effective or that my feature works
+- [ ] New and existing unit tests pass locally with my changes
diff --git a/adversarial-robustness-toolbox/README-cn.md b/adversarial-robustness-toolbox/README-cn.md
new file mode 100644
index 0000000..27fc770
--- /dev/null
+++ b/adversarial-robustness-toolbox/README-cn.md
@@ -0,0 +1,55 @@
+# Adversarial Robustness Toolbox (ART) v1.6
+<p align="center">
+  <img src="docs/images/art_lfai.png?raw=true" width="467" title="ART logo">
+</p>
+<br />
+
+![Continuous Integration](https://github.com/Trusted-AI/adversarial-robustness-toolbox/workflows/Continuous%20Integration/badge.svg)
+![CodeQL](https://github.com/Trusted-AI/adversarial-robustness-toolbox/workflows/CodeQL/badge.svg)
+[![Documentation Status](https://readthedocs.org/projects/adversarial-robustness-toolbox/badge/?version=latest)](http://adversarial-robustness-toolbox.readthedocs.io/en/latest/?badge=latest)
+[![PyPI](https://badge.fury.io/py/adversarial-robustness-toolbox.svg)](https://badge.fury.io/py/adversarial-robustness-toolbox)
+[![Language grade: Python](https://img.shields.io/lgtm/grade/python/g/Trusted-AI/adversarial-robustness-toolbox.svg?logo=lgtm&logoWidth=18)](https://lgtm.com/projects/g/Trusted-AI/adversarial-robustness-toolbox/context:python)
+[![Total alerts](https://img.shields.io/lgtm/alerts/g/Trusted-AI/adversarial-robustness-toolbox.svg?logo=lgtm&logoWidth=18)](https://lgtm.com/projects/g/Trusted-AI/adversarial-robustness-toolbox/alerts/)
+[![codecov](https://codecov.io/gh/Trusted-AI/adversarial-robustness-toolbox/branch/main/graph/badge.svg)](https://codecov.io/gh/Trusted-AI/adversarial-robustness-toolbox)
+[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
+[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
+[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/adversarial-robustness-toolbox)](https://pypi.org/project/adversarial-robustness-toolbox/)
+[![slack-img](https://img.shields.io/badge/chat-on%20slack-yellow.svg)](https://ibm-art.slack.com/)
+
+
+对抗性鲁棒性工具集（ART）是用于机器学习安全性的Python库。ART提供的工具可
+帮助开发人员和研究人员针对以下方面捍卫和评估机器学习模型和应用程序：
+逃逸，数据污染，模型提取和推断的对抗性威胁。ART支持所有流行的机器学习框架
+（TensorFlow，Keras，PyTorch，MXNet，scikit-learn，XGBoost，LightGBM，CatBoost，GPy等），所有数据类型
+（图像，表格，音频，视频等）和机器学习任务（分类，物体检测，语音识别，
+生成模型，认证等）。
+
+<p align="center">
+  <img src="docs/images/adversarial_threats_attacker.png?raw=true" width="400" title="ART logo">
+  <img src="docs/images/adversarial_threats_art.png?raw=true" width="400" title="ART logo">
+</p>
+<br />
+
+## 学到更多
+
+| **[开始使用][get-started]**     | **[文献资料][documentation]**     | **[贡献][contributing]**           |
+|-------------------------------------|-------------------------------|-----------------------------------|
+| - [安装][installation]<br>- [例子](examples/README.md)<br>- [Notebooks](notebooks/README.md) | - [攻击][attacks]<br>- [防御][defences]<br>- [评估器][estimators]<br>- [指标][metrics]<br>- [技术文档](https://adversarial-robustness-toolbox.readthedocs.io) | - [Slack](https://ibm-art.slack.com), [邀请函](https://join.slack.com/t/ibm-art/shared_invite/enQtMzkyOTkyODE4NzM4LTA4NGQ1OTMxMzFmY2Q1MzE1NWI2MmEzN2FjNGNjOGVlODVkZDE0MjA1NTA4OGVkMjVkNmQ4MTY1NmMyOGM5YTg)<br>- [贡献](CONTRIBUTING.md)<br>- [路线图][roadmap]<br>- [引用][citing] |
+
+[get-started]: https://github.com/Trusted-AI/adversarial-robustness-toolbox/wiki/Get-Started
+[attacks]: https://github.com/Trusted-AI/adversarial-robustness-toolbox/wiki/ART-Attacks
+[defences]: https://github.com/Trusted-AI/adversarial-robustness-toolbox/wiki/ART-Defences
+[estimators]: https://github.com/Trusted-AI/adversarial-robustness-toolbox/wiki/ART-Estimators
+[metrics]: https://github.com/Trusted-AI/adversarial-robustness-toolbox/wiki/ART-Metrics
+[contributing]: https://github.com/Trusted-AI/adversarial-robustness-toolbox/wiki/Contributing
+[documentation]: https://github.com/Trusted-AI/adversarial-robustness-toolbox/wiki/Documentation
+[installation]: https://github.com/Trusted-AI/adversarial-robustness-toolbox/wiki/Get-Started#setup
+[roadmap]: https://github.com/Trusted-AI/adversarial-robustness-toolbox/wiki/Roadmap
+[citing]: https://github.com/Trusted-AI/adversarial-robustness-toolbox/wiki/Contributing#citing-art
+
+该库正在不断开发中。欢迎反馈，错误报告和贡献！
+
+# 致谢
+
+本材料部分基于国防高级研究计划局（DARPA）支持的工作，合同编号HR001120C0013。
+本材料中表达的任何意见，发现和结论或建议均为作者的观点，并不一定反映国防高级研究计划局（DARPA）的观点。
diff --git a/adversarial-robustness-toolbox/README.md b/adversarial-robustness-toolbox/README.md
new file mode 100644
index 0000000..96628de
--- /dev/null
+++ b/adversarial-robustness-toolbox/README.md
@@ -0,0 +1,56 @@
+# Adversarial Robustness Toolbox (ART) v1.6
+<p align="center">
+  <img src="docs/images/art_lfai.png?raw=true" width="467" title="ART logo">
+</p>
+<br />
+
+![Continuous Integration](https://github.com/Trusted-AI/adversarial-robustness-toolbox/workflows/Continuous%20Integration/badge.svg)
+![CodeQL](https://github.com/Trusted-AI/adversarial-robustness-toolbox/workflows/CodeQL/badge.svg)
+[![Documentation Status](https://readthedocs.org/projects/adversarial-robustness-toolbox/badge/?version=latest)](http://adversarial-robustness-toolbox.readthedocs.io/en/latest/?badge=latest)
+[![PyPI](https://badge.fury.io/py/adversarial-robustness-toolbox.svg)](https://badge.fury.io/py/adversarial-robustness-toolbox)
+[![Language grade: Python](https://img.shields.io/lgtm/grade/python/g/Trusted-AI/adversarial-robustness-toolbox.svg?logo=lgtm&logoWidth=18)](https://lgtm.com/projects/g/Trusted-AI/adversarial-robustness-toolbox/context:python)
+[![Total alerts](https://img.shields.io/lgtm/alerts/g/Trusted-AI/adversarial-robustness-toolbox.svg?logo=lgtm&logoWidth=18)](https://lgtm.com/projects/g/Trusted-AI/adversarial-robustness-toolbox/alerts/)
+[![codecov](https://codecov.io/gh/Trusted-AI/adversarial-robustness-toolbox/branch/main/graph/badge.svg)](https://codecov.io/gh/Trusted-AI/adversarial-robustness-toolbox)
+[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
+[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
+[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/adversarial-robustness-toolbox)](https://pypi.org/project/adversarial-robustness-toolbox/)
+[![slack-img](https://img.shields.io/badge/chat-on%20slack-yellow.svg)](https://ibm-art.slack.com/)
+
+[中文README请按此处](README-cn.md)
+
+Adversarial Robustness Toolbox (ART) is a Python library for Machine Learning Security. ART provides tools that enable
+developers and researchers to defend and evaluate Machine Learning models and applications against the
+adversarial threats of Evasion, Poisoning, Extraction, and Inference. ART supports all popular machine learning frameworks
+(TensorFlow, Keras, PyTorch, MXNet, scikit-learn, XGBoost, LightGBM, CatBoost, GPy, etc.), all data types
+(images, tables, audio, video, etc.) and machine learning tasks (classification, object detection, speech recognition,
+generation, certification, etc.).
+
+<p align="center">
+  <img src="docs/images/adversarial_threats_attacker.png?raw=true" width="400" title="ART logo">
+  <img src="docs/images/adversarial_threats_art.png?raw=true" width="400" title="ART logo">
+</p>
+<br />
+
+## Learn more
+
+| **[Get Started][get-started]**     | **[Documentation][documentation]**     | **[Contributing][contributing]**           |
+|-------------------------------------|-------------------------------|-----------------------------------|
+| - [Installation][installation]<br>- [Examples](examples/README.md)<br>- [Notebooks](notebooks/README.md) | - [Attacks][attacks]<br>- [Defences][defences]<br>- [Estimators][estimators]<br>- [Metrics][metrics]<br>- [Technical Documentation](https://adversarial-robustness-toolbox.readthedocs.io) | - [Slack](https://ibm-art.slack.com), [Invitation](https://join.slack.com/t/ibm-art/shared_invite/enQtMzkyOTkyODE4NzM4LTA4NGQ1OTMxMzFmY2Q1MzE1NWI2MmEzN2FjNGNjOGVlODVkZDE0MjA1NTA4OGVkMjVkNmQ4MTY1NmMyOGM5YTg)<br>- [Contributing](CONTRIBUTING.md)<br>- [Roadmap][roadmap]<br>- [Citing][citing] |
+
+[get-started]: https://github.com/Trusted-AI/adversarial-robustness-toolbox/wiki/Get-Started
+[attacks]: https://github.com/Trusted-AI/adversarial-robustness-toolbox/wiki/ART-Attacks
+[defences]: https://github.com/Trusted-AI/adversarial-robustness-toolbox/wiki/ART-Defences
+[estimators]: https://github.com/Trusted-AI/adversarial-robustness-toolbox/wiki/ART-Estimators
+[metrics]: https://github.com/Trusted-AI/adversarial-robustness-toolbox/wiki/ART-Metrics
+[contributing]: https://github.com/Trusted-AI/adversarial-robustness-toolbox/wiki/Contributing
+[documentation]: https://github.com/Trusted-AI/adversarial-robustness-toolbox/wiki/Documentation
+[installation]: https://github.com/Trusted-AI/adversarial-robustness-toolbox/wiki/Get-Started#setup
+[roadmap]: https://github.com/Trusted-AI/adversarial-robustness-toolbox/wiki/Roadmap
+[citing]: https://github.com/Trusted-AI/adversarial-robustness-toolbox/wiki/Contributing#citing-art
+
+The library is under continuous development. Feedback, bug reports and contributions are very welcome!
+
+# Acknowledgment
+This material is partially based upon work supported by the Defense Advanced Research Projects Agency (DARPA) under
+Contract No. HR001120C0013. Any opinions, findings and conclusions or recommendations expressed in this material are
+those of the author(s) and do not necessarily reflect the views of the Defense Advanced Research Projects Agency (DARPA).
diff --git a/adversarial-robustness-toolbox/__init__.py b/adversarial-robustness-toolbox/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/adversarial-robustness-toolbox/art/__init__.py b/adversarial-robustness-toolbox/art/__init__.py
new file mode 100644
index 0000000..36e8f81
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/__init__.py
@@ -0,0 +1,31 @@
+"""
+The Adversarial Robustness Toolbox (ART).
+"""
+import logging.config
+
+# Project Imports
+from art import attacks
+from art import defences
+from art import estimators
+from art import metrics
+from art import wrappers
+
+# Semantic Version
+__version__ = "1.6.0"
+
+# pylint: disable=C0103
+
+LOGGING = {
+    "version": 1,
+    "disable_existing_loggers": False,
+    "formatters": {
+        "std": {"format": "%(asctime)s [%(levelname)s] %(name)s: %(message)s", "datefmt": "%Y-%m-%d %H:%M",}
+    },
+    "handlers": {
+        "default": {"class": "logging.NullHandler",},
+        "test": {"class": "logging.StreamHandler", "formatter": "std", "level": logging.INFO,},
+    },
+    "loggers": {"art": {"handlers": ["default"]}, "tests": {"handlers": ["test"], "level": "INFO", "propagate": True},},
+}
+logging.config.dictConfig(LOGGING)
+logger = logging.getLogger(__name__)
diff --git a/adversarial-robustness-toolbox/art/attacks/__init__.py b/adversarial-robustness-toolbox/art/attacks/__init__.py
new file mode 100644
index 0000000..c7f295f
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/attacks/__init__.py
@@ -0,0 +1,11 @@
+"""
+Module providing adversarial attacks under a common interface.
+"""
+from art.attacks.attack import Attack, EvasionAttack, PoisoningAttack, PoisoningAttackBlackBox, PoisoningAttackWhiteBox
+from art.attacks.attack import PoisoningAttackTransformer, ExtractionAttack, InferenceAttack, AttributeInferenceAttack
+from art.attacks.attack import ReconstructionAttack
+
+from art.attacks import evasion
+from art.attacks import extraction
+from art.attacks import inference
+from art.attacks import poisoning
diff --git a/adversarial-robustness-toolbox/art/attacks/attack.py b/adversarial-robustness-toolbox/art/attacks/attack.py
new file mode 100644
index 0000000..6352f79
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/attacks/attack.py
@@ -0,0 +1,381 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements the abstract base classes for all attacks.
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import abc
+import logging
+from typing import Any, List, Optional, Tuple, Union, TYPE_CHECKING
+
+import numpy as np
+
+from art.exceptions import EstimatorError
+
+if TYPE_CHECKING:
+    from art.utils import CLASSIFIER_TYPE
+
+logger = logging.getLogger(__name__)
+
+
+class input_filter(abc.ABCMeta):
+    """
+    Metaclass to ensure that inputs are ndarray for all of the subclass generate and extract calls
+    """
+
+    def __init__(cls, name, bases, clsdict):
+        """
+        This function overrides any existing generate or extract methods with a new method that
+        ensures the input is an `np.ndarray`. There is an assumption that the input object has implemented
+        __array__ with np.array calls.
+        """
+
+        def make_replacement(fdict, func_name):
+            """
+            This function overrides creates replacement functions dynamically
+            """
+
+            def replacement_function(self, *args, **kwargs):
+                if len(args) > 0:
+                    lst = list(args)
+
+                if "x" in kwargs:
+                    if not isinstance(kwargs["x"], np.ndarray):
+                        kwargs["x"] = np.array(kwargs["x"])
+                else:
+                    if not isinstance(args[0], np.ndarray):
+                        lst[0] = np.array(args[0])
+
+                if "y" in kwargs:
+                    if kwargs["y"] is not None and not isinstance(kwargs["y"], np.ndarray):
+                        kwargs["y"] = np.array(kwargs["y"])
+                elif len(args) == 2:
+                    if not isinstance(args[1], np.ndarray):
+                        lst[1] = np.array(args[1])
+
+                if len(args) > 0:
+                    args = tuple(lst)
+                return fdict[func_name](self, *args, **kwargs)
+
+            replacement_function.__doc__ = fdict[func_name].__doc__
+            replacement_function.__name__ = "new_" + func_name
+            return replacement_function
+
+        replacement_list = ["generate", "extract"]
+        for item in replacement_list:
+            if item in clsdict:
+                new_function = make_replacement(clsdict, item)
+                setattr(cls, item, new_function)
+
+
+class Attack(abc.ABC, metaclass=input_filter):
+    """
+    Abstract base class for all attack abstract base classes.
+    """
+
+    attack_params: List[str] = list()
+    _estimator_requirements: Optional[Union[Tuple[Any, ...], Tuple[()]]] = None
+
+    def __init__(self, estimator):
+        """
+        :param estimator: An estimator.
+        """
+        if self.estimator_requirements is None:
+            raise ValueError("Estimator requirements have not been defined in `_estimator_requirements`.")
+
+        if not all(t in type(estimator).__mro__ for t in self.estimator_requirements):
+            raise EstimatorError(self.__class__, self.estimator_requirements, estimator)
+
+        self._estimator = estimator
+
+    @property
+    def estimator(self):
+        return self._estimator
+
+    @property
+    def estimator_requirements(self):
+        return self._estimator_requirements
+
+    def set_params(self, **kwargs) -> None:
+        """
+        Take in a dictionary of parameters and apply attack-specific checks before saving them as attributes.
+
+        :param kwargs: A dictionary of attack-specific parameters.
+        """
+        for key, value in kwargs.items():
+            if key in self.attack_params:
+                setattr(self, key, value)
+        self._check_params()
+
+    def _check_params(self) -> None:
+        pass
+
+
+class EvasionAttack(Attack):
+    """
+    Abstract base class for evasion attack classes.
+    """
+
+    def __init__(self, **kwargs) -> None:
+        self._targeted = False
+        super().__init__(**kwargs)
+
+    @abc.abstractmethod
+    def generate(  # lgtm [py/inheritance/incorrect-overridden-signature]
+        self, x: np.ndarray, y: Optional[np.ndarray] = None, **kwargs
+    ) -> np.ndarray:
+        """
+        Generate adversarial examples and return them as an array. This method should be overridden by all concrete
+        evasion attack implementations.
+
+        :param x: An array with the original inputs to be attacked.
+        :param y: Correct labels or target labels for `x`, depending if the attack is targeted
+               or not. This parameter is only used by some of the attacks.
+        :return: An array holding the adversarial examples.
+        """
+        raise NotImplementedError
+
+    @property
+    def targeted(self) -> bool:
+        """
+        Return Boolean if attack is targeted. Return None if not applicable.
+        """
+        return self._targeted
+
+    @targeted.setter
+    def targeted(self, targeted) -> None:
+        self._targeted = targeted
+
+
+class PoisoningAttack(Attack):
+    """
+    Abstract base class for poisoning attack classes
+    """
+
+    def __init__(self, classifier: Optional["CLASSIFIER_TYPE"]) -> None:
+        """
+        :param classifier: A trained classifier (or none if no classifier is needed)
+        """
+        super().__init__(classifier)
+
+    @abc.abstractmethod
+    def poison(self, x: np.ndarray, y=Optional[np.ndarray], **kwargs) -> Tuple[np.ndarray, np.ndarray]:
+        """
+        Generate poisoning examples and return them as an array. This method should be overridden by all concrete
+        poisoning attack implementations.
+
+        :param x: An array with the original inputs to be attacked.
+        :param y:  Target labels for `x`. Untargeted attacks set this value to None.
+        :return: An tuple holding the (poisoning examples, poisoning labels).
+        """
+        raise NotImplementedError
+
+
+class PoisoningAttackTransformer(PoisoningAttack):
+    """
+    Abstract base class for poisoning attack classes that return a transformed classifier.
+    These attacks have an additional method, `poison_estimator`, that returns the poisoned classifier.
+    """
+
+    def __init__(self, classifier: Optional["CLASSIFIER_TYPE"], **kwargs) -> None:
+        """
+        :param classifier: A trained classifier (or none if no classifier is needed)
+        """
+        super().__init__(classifier)
+
+    @abc.abstractmethod
+    def poison(self, x: np.ndarray, y=Optional[np.ndarray], **kwargs) -> Tuple[np.ndarray, np.ndarray]:
+        """
+        Generate poisoning examples and return them as an array. This method should be overridden by all concrete
+        poisoning attack implementations.
+
+        :param x: An array with the original inputs to be attacked.
+        :param y:  Target labels for `x`. Untargeted attacks set this value to None.
+        :return: An tuple holding the (poisoning examples, poisoning labels).
+        :rtype: `(np.ndarray, np.ndarray)`
+        """
+        raise NotImplementedError
+
+    @abc.abstractmethod
+    def poison_estimator(self, x: np.ndarray, y: np.ndarray, **kwargs) -> "CLASSIFIER_TYPE":
+        """
+        Returns a poisoned version of the classifier used to initialize the attack
+        :param x: Training data
+        :param y: Training labels
+        :return: A poisoned classifier
+        """
+        raise NotImplementedError
+
+
+class PoisoningAttackBlackBox(PoisoningAttack):
+    """
+    Abstract base class for poisoning attack classes that have no access to the model (classifier object).
+    """
+
+    def __init__(self):
+        """
+        Initializes black-box data poisoning attack.
+        """
+        super().__init__(None)  # type: ignore
+
+    @abc.abstractmethod
+    def poison(self, x: np.ndarray, y: Optional[np.ndarray] = None, **kwargs) -> Tuple[np.ndarray, np.ndarray]:
+        """
+        Generate poisoning examples and return them as an array. This method should be overridden by all concrete
+        poisoning attack implementations.
+
+        :param x: An array with the original inputs to be attacked.
+        :param y:  Target labels for `x`. Untargeted attacks set this value to None.
+        :return: An tuple holding the `(poisoning_examples, poisoning_labels)`.
+        """
+        raise NotImplementedError
+
+
+class PoisoningAttackWhiteBox(PoisoningAttack):
+    """
+    Abstract base class for poisoning attack classes that have white-box access to the model (classifier object).
+    """
+
+    @abc.abstractmethod
+    def poison(self, x: np.ndarray, y: Optional[np.ndarray] = None, **kwargs) -> Tuple[np.ndarray, np.ndarray]:
+        """
+        Generate poisoning examples and return them as an array. This method should be overridden by all concrete
+        poisoning attack implementations.
+
+        :param x: An array with the original inputs to be attacked.
+        :param y: Correct labels or target labels for `x`, depending if the attack is targeted
+               or not. This parameter is only used by some of the attacks.
+        :return: An tuple holding the `(poisoning_examples, poisoning_labels)`.
+        """
+        raise NotImplementedError
+
+
+class ExtractionAttack(Attack):
+    """
+    Abstract base class for extraction attack classes.
+    """
+
+    @abc.abstractmethod
+    def extract(self, x: np.ndarray, y: Optional[np.ndarray] = None, **kwargs) -> "CLASSIFIER_TYPE":
+        """
+        Extract models and return them as an ART classifier. This method should be overridden by all concrete extraction
+        attack implementations.
+
+        :param x: An array with the original inputs to be attacked.
+        :param y: Correct labels or target labels for `x`, depending if the attack is targeted
+               or not. This parameter is only used by some of the attacks.
+        :return: ART classifier of the extracted model.
+        """
+        raise NotImplementedError
+
+
+class InferenceAttack(Attack):
+    """
+    Abstract base class for inference attack classes.
+    """
+
+    def __init__(self, estimator):
+        """
+        :param estimator: A trained estimator targeted for inference attack.
+        :type estimator: :class:`.art.estimators.estimator.BaseEstimator`
+        """
+        super().__init__(estimator)
+
+    @abc.abstractmethod
+    def infer(self, x: np.ndarray, y: Optional[np.ndarray] = None, **kwargs) -> np.ndarray:
+        """
+        Infer sensitive properties (attributes, membership training records) from the targeted estimator. This method
+        should be overridden by all concrete inference attack implementations.
+
+        :param x: An array with reference inputs to be used in the attack.
+        :param y: Labels for `x`. This parameter is only used by some of the attacks.
+        :return: An array holding the inferred properties.
+        """
+        raise NotImplementedError
+
+
+class AttributeInferenceAttack(InferenceAttack):
+    """
+    Abstract base class for attribute inference attack classes.
+    """
+
+    attack_params = InferenceAttack.attack_params + ["attack_feature"]
+
+    def __init__(self, estimator, attack_feature: Union[int, slice] = 0):
+        """
+        :param estimator: A trained estimator targeted for inference attack.
+        :type estimator: :class:`.art.estimators.estimator.BaseEstimator`
+        :param attack_feature: The index of the feature to be attacked.
+        """
+        super().__init__(estimator)
+        self.attack_feature = attack_feature
+
+    @abc.abstractmethod
+    def infer(self, x: np.ndarray, y: Optional[np.ndarray] = None, **kwargs) -> np.ndarray:
+        """
+        Infer sensitive properties (attributes, membership training records) from the targeted estimator. This method
+        should be overridden by all concrete inference attack implementations.
+
+        :param x: An array with reference inputs to be used in the attack.
+        :param y: Labels for `x`. This parameter is only used by some of the attacks.
+        :return: An array holding the inferred properties.
+        """
+        raise NotImplementedError
+
+    def set_params(self, **kwargs) -> None:
+        """
+        Take in a dictionary of parameters and applies attack-specific checks before saving them as attributes.
+        """
+        # Save attack-specific parameters
+        super().set_params(**kwargs)
+        self._check_params()
+
+
+class ReconstructionAttack(Attack):
+    """
+    Abstract base class for reconstruction attack classes.
+    """
+
+    attack_params = InferenceAttack.attack_params
+
+    def __init__(self, estimator):
+        """
+        :param estimator: A trained estimator targeted for reconstruction attack.
+        """
+        super().__init__(estimator)
+
+    @abc.abstractmethod
+    def reconstruct(self, x: np.ndarray, y: Optional[np.ndarray] = None, **kwargs) -> Tuple[np.ndarray, np.ndarray]:
+        """
+        Reconstruct the training dataset of and from the targeted estimator. This method
+        should be overridden by all concrete inference attack implementations.
+
+        :param x: An array with known records of the training set of `estimator`.
+        :param y: An array with known labels of the training set of `estimator`, if None predicted labels will be used.
+        :return: A tuple of two arrays for the reconstructed training input and labels.
+        """
+        raise NotImplementedError
+
+    def set_params(self, **kwargs) -> None:
+        """
+        Take in a dictionary of parameters and applies attack-specific checks before saving them as attributes.
+        """
+        # Save attack-specific parameters
+        super().set_params(**kwargs)
+        self._check_params()
diff --git a/adversarial-robustness-toolbox/art/attacks/evasion/__init__.py b/adversarial-robustness-toolbox/art/attacks/evasion/__init__.py
new file mode 100644
index 0000000..a989fba
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/attacks/evasion/__init__.py
@@ -0,0 +1,50 @@
+"""
+Module providing evasion attacks under a common interface.
+"""
+from art.attacks.evasion.adversarial_patch.adversarial_patch import AdversarialPatch
+from art.attacks.evasion.adversarial_patch.adversarial_patch_numpy import AdversarialPatchNumpy
+from art.attacks.evasion.adversarial_patch.adversarial_patch_tensorflow import AdversarialPatchTensorFlowV2
+from art.attacks.evasion.adversarial_patch.adversarial_patch_pytorch import AdversarialPatchPyTorch
+from art.attacks.evasion.adversarial_asr import CarliniWagnerASR
+from art.attacks.evasion.auto_attack import AutoAttack
+from art.attacks.evasion.auto_projected_gradient_descent import AutoProjectedGradientDescent
+from art.attacks.evasion.brendel_bethge import BrendelBethgeAttack
+from art.attacks.evasion.boundary import BoundaryAttack
+from art.attacks.evasion.carlini import CarliniL2Method, CarliniLInfMethod
+from art.attacks.evasion.decision_tree_attack import DecisionTreeAttack
+from art.attacks.evasion.deepfool import DeepFool
+from art.attacks.evasion.dpatch import DPatch
+from art.attacks.evasion.dpatch_robust import RobustDPatch
+from art.attacks.evasion.elastic_net import ElasticNet
+from art.attacks.evasion.fast_gradient import FastGradientMethod
+from art.attacks.evasion.frame_saliency import FrameSaliencyAttack
+from art.attacks.evasion.feature_adversaries import FeatureAdversaries
+from art.attacks.evasion.hclu import HighConfidenceLowUncertainty
+from art.attacks.evasion.hop_skip_jump import HopSkipJump
+from art.attacks.evasion.imperceptible_asr.imperceptible_asr import ImperceptibleASR
+from art.attacks.evasion.imperceptible_asr.imperceptible_asr_pytorch import ImperceptibleASRPyTorch
+from art.attacks.evasion.iterative_method import BasicIterativeMethod
+from art.attacks.evasion.newtonfool import NewtonFool
+from art.attacks.evasion.pixel_threshold import PixelAttack
+from art.attacks.evasion.projected_gradient_descent.projected_gradient_descent import ProjectedGradientDescent
+from art.attacks.evasion.projected_gradient_descent.projected_gradient_descent_numpy import (
+    ProjectedGradientDescentNumpy,
+)
+from art.attacks.evasion.projected_gradient_descent.projected_gradient_descent_pytorch import (
+    ProjectedGradientDescentPyTorch,
+)
+from art.attacks.evasion.projected_gradient_descent.projected_gradient_descent_tensorflow_v2 import (
+    ProjectedGradientDescentTensorFlowV2,
+)
+from art.attacks.evasion.saliency_map import SaliencyMapMethod
+from art.attacks.evasion.shadow_attack import ShadowAttack
+from art.attacks.evasion.shapeshifter import ShapeShifter
+from art.attacks.evasion.simba import SimBA
+from art.attacks.evasion.spatial_transformation import SpatialTransformation
+from art.attacks.evasion.square_attack import SquareAttack
+from art.attacks.evasion.pixel_threshold import ThresholdAttack
+from art.attacks.evasion.universal_perturbation import UniversalPerturbation
+from art.attacks.evasion.targeted_universal_perturbation import TargetedUniversalPerturbation
+from art.attacks.evasion.virtual_adversarial import VirtualAdversarialMethod
+from art.attacks.evasion.wasserstein import Wasserstein
+from art.attacks.evasion.zoo import ZooAttack
diff --git a/adversarial-robustness-toolbox/art/attacks/evasion/adversarial_asr.py b/adversarial-robustness-toolbox/art/attacks/evasion/adversarial_asr.py
new file mode 100644
index 0000000..67c54f4
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/attacks/evasion/adversarial_asr.py
@@ -0,0 +1,98 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements the audio adversarial attack on automatic speech recognition systems of Carlini and Wagner
+(2018). It generates an adversarial audio example.
+
+| Paper link: https://arxiv.org/abs/1801.01944
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+from typing import TYPE_CHECKING
+
+from art.attacks.attack import EvasionAttack
+from art.attacks.evasion.imperceptible_asr.imperceptible_asr import ImperceptibleASR
+
+if TYPE_CHECKING:
+    from art.utils import SPEECH_RECOGNIZER_TYPE
+
+logger = logging.getLogger(__name__)
+
+
+class CarliniWagnerASR(ImperceptibleASR):
+    """
+    Implementation of the Carlini and Wagner audio adversarial attack against a speech recognition model.
+
+    | Paper link: https://arxiv.org/abs/1801.01944
+    """
+
+    attack_params = EvasionAttack.attack_params + [
+        "eps",
+        "learning_rate",
+        "max_iter",
+        "batch_size",
+        "decrease_factor_eps",
+        "num_iter_decrease_eps",
+    ]
+
+    def __init__(
+        self,
+        estimator: "SPEECH_RECOGNIZER_TYPE",
+        eps: float = 2000.0,
+        learning_rate: float = 100.0,
+        max_iter: int = 1000,
+        decrease_factor_eps: float = 0.8,
+        num_iter_decrease_eps: int = 10,
+        batch_size: int = 16,
+    ):
+        """
+        Create an instance of the :class:`.CarliniWagnerASR`.
+
+        :param estimator: A trained speech recognition estimator.
+        :param eps: Initial max norm bound for adversarial perturbation.
+        :param learning_rate: Learning rate of attack.
+        :param max_iter: Number of iterations.
+        :param decrease_factor_eps: Decrease factor for epsilon (Paper default: 0.8).
+        :param num_iter_decrease_eps: Iterations after which to decrease epsilon if attack succeeds (Paper default: 10).
+        :param batch_size: Batch size.
+        """
+        # pylint: disable=W0231
+
+        # re-implement init such that inherited methods work
+        EvasionAttack.__init__(self, estimator=estimator)  # pylint: disable=W0233
+        self.masker = None
+        self.eps = eps
+        self.learning_rate_1 = learning_rate
+        self.max_iter_1 = max_iter
+        self.max_iter_2 = 0
+        self._targeted = True
+        self.decrease_factor_eps = decrease_factor_eps
+        self.num_iter_decrease_eps = num_iter_decrease_eps
+        self.batch_size = batch_size
+
+        # set remaining stage 2 params to some random values
+        self.alpha = 0.1
+        self.learning_rate_2 = 0.1
+        self.loss_theta_min = 0.0
+        self.increase_factor_alpha: float = 1.0
+        self.num_iter_increase_alpha: int = 1
+        self.decrease_factor_alpha: float = 1.0
+        self.num_iter_decrease_alpha: int = 1
+
+        self._check_params()
diff --git a/adversarial-robustness-toolbox/art/attacks/evasion/adversarial_patch/__init__.py b/adversarial-robustness-toolbox/art/attacks/evasion/adversarial_patch/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/adversarial-robustness-toolbox/art/attacks/evasion/adversarial_patch/adversarial_patch.py b/adversarial-robustness-toolbox/art/attacks/evasion/adversarial_patch/adversarial_patch.py
new file mode 100644
index 0000000..e2cf5b5
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/attacks/evasion/adversarial_patch/adversarial_patch.py
@@ -0,0 +1,222 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements the adversarial patch attack `AdversarialPatch`. This attack generates an adversarial patch that
+can be printed into the physical world with a common printer. The patch can be used to fool image and video classifiers.
+
+| Paper link: https://arxiv.org/abs/1712.09665
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+from typing import Optional, Tuple, Union, TYPE_CHECKING
+
+import numpy as np
+
+from art.attacks.evasion.adversarial_patch.adversarial_patch_numpy import AdversarialPatchNumpy
+from art.attacks.evasion.adversarial_patch.adversarial_patch_tensorflow import AdversarialPatchTensorFlowV2
+from art.estimators.estimator import BaseEstimator, NeuralNetworkMixin
+from art.estimators.classification.classifier import ClassifierMixin
+from art.estimators.classification import TensorFlowV2Classifier
+from art.attacks.attack import EvasionAttack
+
+if TYPE_CHECKING:
+    from art.utils import CLASSIFIER_NEURALNETWORK_TYPE
+
+logger = logging.getLogger(__name__)
+
+
+class AdversarialPatch(EvasionAttack):
+    """
+    Implementation of the adversarial patch attack for square and rectangular images and videos.
+
+    | Paper link: https://arxiv.org/abs/1712.09665
+    """
+
+    attack_params = EvasionAttack.attack_params + [
+        "rotation_max",
+        "scale_min",
+        "scale_max",
+        "learning_rate",
+        "max_iter",
+        "batch_size",
+        "verbose",
+    ]
+
+    _estimator_requirements = (BaseEstimator, NeuralNetworkMixin, ClassifierMixin)
+
+    def __init__(
+        self,
+        classifier: "CLASSIFIER_NEURALNETWORK_TYPE",
+        rotation_max: float = 22.5,
+        scale_min: float = 0.1,
+        scale_max: float = 1.0,
+        learning_rate: float = 5.0,
+        max_iter: int = 500,
+        batch_size: int = 16,
+        patch_shape: Optional[Tuple[int, int, int]] = None,
+        verbose: bool = True,
+    ):
+        """
+        Create an instance of the :class:`.AdversarialPatch`.
+
+        :param classifier: A trained classifier.
+        :param rotation_max: The maximum rotation applied to random patches. The value is expected to be in the
+               range `[0, 180]`.
+        :param scale_min: The minimum scaling applied to random patches. The value should be in the range `[0, 1]`,
+               but less than `scale_max`.
+        :param scale_max: The maximum scaling applied to random patches. The value should be in the range `[0, 1]`, but
+               larger than `scale_min.`
+        :param learning_rate: The learning rate of the optimization.
+        :param max_iter: The number of optimization steps.
+        :param batch_size: The size of the training batch.
+        :param patch_shape: The shape of the adversarial patch as a tuple of shape (width, height, nb_channels).
+                            Currently only supported for `TensorFlowV2Classifier`. For classifiers of other frameworks
+                            the `patch_shape` is set to the shape of the input samples.
+        :param verbose: Show progress bars.
+        """
+        super().__init__(estimator=classifier)
+        if self.estimator.clip_values is None:
+            raise ValueError("Adversarial Patch attack requires a classifier with clip_values.")
+
+        self._attack: Union[AdversarialPatchTensorFlowV2, AdversarialPatchNumpy]
+        if isinstance(self.estimator, TensorFlowV2Classifier):
+            self._attack = AdversarialPatchTensorFlowV2(
+                classifier=classifier,
+                rotation_max=rotation_max,
+                scale_min=scale_min,
+                scale_max=scale_max,
+                learning_rate=learning_rate,
+                max_iter=max_iter,
+                batch_size=batch_size,
+                patch_shape=patch_shape,
+                verbose=verbose,
+            )
+        else:
+            self._attack = AdversarialPatchNumpy(
+                classifier=classifier,
+                rotation_max=rotation_max,
+                scale_min=scale_min,
+                scale_max=scale_max,
+                learning_rate=learning_rate,
+                max_iter=max_iter,
+                batch_size=batch_size,
+                verbose=verbose,
+            )
+        self._check_params()
+
+    def generate(self, x: np.ndarray, y: Optional[np.ndarray] = None, **kwargs) -> Tuple[np.ndarray, np.ndarray]:
+        """
+        Generate an adversarial patch and return the patch and its mask in arrays.
+
+        :param x: An array with the original input images of shape NHWC or NCHW or input videos of shape NFHWC or NFCHW.
+        :param y: An array with the original true labels.
+        :param mask: A boolean array of shape equal to the shape of a single samples (1, H, W) or the shape of `x`
+                     (N, H, W) without their channel dimensions. Any features for which the mask is True can be the
+                     center location of the patch during sampling.
+        :type mask: `np.ndarray`
+        :param reset_patch: If `True` reset patch to initial values of mean of minimal and maximal clip value, else if
+                            `False` (default) restart from previous patch values created by previous call to `generate`
+                            or mean of minimal and maximal clip value if first call to `generate`.
+        :type reset_patch: bool
+        :return: An array with adversarial patch and an array of the patch mask.
+        """
+        logger.info("Creating adversarial patch.")
+
+        if y is None:
+            raise ValueError("Adversarial Patch attack requires target values `y`.")
+
+        if len(x.shape) == 2:
+            raise ValueError(
+                "Feature vectors detected. The adversarial patch can only be applied to data with spatial "
+                "dimensions."
+            )
+
+        return self._attack.generate(x=x, y=y, **kwargs)
+
+    def apply_patch(self, x: np.ndarray, scale: float, patch_external: Optional[np.ndarray] = None) -> np.ndarray:
+        """
+        A function to apply the learned adversarial patch to images or videos.
+
+        :param x: Instances to apply randomly transformed patch.
+        :param scale: Scale of the applied patch in relation to the classifier input shape.
+        :param patch_external: External patch to apply to images `x`.
+        :return: The patched instances.
+        """
+        return self._attack.apply_patch(x, scale, patch_external=patch_external)
+
+    def reset_patch(self, initial_patch_value: Optional[Union[float, np.ndarray]]) -> None:
+        """
+        Reset the adversarial patch.
+
+        :param initial_patch_value: Patch value to use for resetting the patch.
+        """
+        self._attack.reset_patch(initial_patch_value=initial_patch_value)
+
+    def insert_transformed_patch(self, x: np.ndarray, patch: np.ndarray, image_coords: np.ndarray):
+        """
+        Insert patch to image based on given or selected coordinates.
+
+        :param x: The image to insert the patch.
+        :param patch: The patch to be transformed and inserted.
+        :param image_coords: The coordinates of the 4 corners of the transformed, inserted patch of shape
+            [[x1, y1], [x2, y2], [x3, y3], [x4, y4]] in pixel units going in clockwise direction, starting with upper
+            left corner.
+        :return: The input `x` with the patch inserted.
+        """
+        return self._attack.insert_transformed_patch(x, patch, image_coords)
+
+    def set_params(self, **kwargs) -> None:
+        super().set_params(**kwargs)
+        self._attack.set_params(**kwargs)
+
+    def _check_params(self) -> None:
+        if not isinstance(self._attack.rotation_max, (float, int)):
+            raise ValueError("The maximum rotation of the random patches must be of type float.")
+        if self._attack.rotation_max < 0 or self._attack.rotation_max > 180.0:
+            raise ValueError("The maximum rotation of the random patches must be between 0 and 180 degrees.")
+
+        if not isinstance(self._attack.scale_min, float):
+            raise ValueError("The minimum scale of the random patched must be of type float.")
+        if self._attack.scale_min < 0 or self._attack.scale_min >= self._attack.scale_max:
+            raise ValueError(
+                "The minimum scale of the random patched must be greater than 0 and less than the maximum scaling."
+            )
+
+        if not isinstance(self._attack.scale_max, float):
+            raise ValueError("The maximum scale of the random patched must be of type float.")
+        if self._attack.scale_max > 1:
+            raise ValueError("The maximum scale of the random patched must not be greater than 1.")
+
+        if not isinstance(self._attack.learning_rate, float):
+            raise ValueError("The learning rate must be of type float.")
+        if not self._attack.learning_rate > 0.0:
+            raise ValueError("The learning rate must be greater than 0.0.")
+
+        if not isinstance(self._attack.max_iter, int):
+            raise ValueError("The number of optimization steps must be of type int.")
+        if not self._attack.max_iter > 0:
+            raise ValueError("The number of optimization steps must be greater than 0.")
+
+        if not isinstance(self._attack.batch_size, int):
+            raise ValueError("The batch size must be of type int.")
+        if not self._attack.batch_size > 0:
+            raise ValueError("The batch size must be greater than 0.")
+
+        if not isinstance(self._attack.verbose, bool):
+            raise ValueError("The argument `verbose` has to be of type bool.")
diff --git a/adversarial-robustness-toolbox/art/attacks/evasion/adversarial_patch/adversarial_patch_numpy.py b/adversarial-robustness-toolbox/art/attacks/evasion/adversarial_patch/adversarial_patch_numpy.py
new file mode 100644
index 0000000..8e37574
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/attacks/evasion/adversarial_patch/adversarial_patch_numpy.py
@@ -0,0 +1,595 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements the adversarial patch attack `AdversarialPatch`. This attack generates an adversarial patch that
+can be printed into the physical world with a common printer. The patch can be used to fool image and video classifiers.
+
+| Paper link: https://arxiv.org/abs/1712.09665
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import math
+from typing import Optional, Union, Tuple, TYPE_CHECKING
+
+import random
+import numpy as np
+from scipy.ndimage import rotate, shift, zoom
+from tqdm.auto import trange
+
+from art.attacks.attack import EvasionAttack
+from art.attacks.evasion.adversarial_patch.utils import insert_transformed_patch
+from art.estimators.estimator import BaseEstimator, NeuralNetworkMixin
+from art.estimators.classification.classifier import ClassifierMixin
+from art.utils import check_and_transform_label_format
+
+if TYPE_CHECKING:
+    from art.utils import CLASSIFIER_NEURALNETWORK_TYPE
+
+logger = logging.getLogger(__name__)
+
+
+class AdversarialPatchNumpy(EvasionAttack):
+    """
+    Implementation of the adversarial patch attack for square and rectangular images and videos in Numpy.
+
+    | Paper link: https://arxiv.org/abs/1712.09665
+    """
+
+    attack_params = EvasionAttack.attack_params + [
+        "rotation_max",
+        "scale_min",
+        "scale_max",
+        "learning_rate",
+        "max_iter",
+        "batch_size",
+        "verbose",
+    ]
+
+    _estimator_requirements = (BaseEstimator, NeuralNetworkMixin, ClassifierMixin)
+
+    def __init__(
+        self,
+        classifier: "CLASSIFIER_NEURALNETWORK_TYPE",
+        target: int = 0,
+        rotation_max: float = 22.5,
+        scale_min: float = 0.1,
+        scale_max: float = 1.0,
+        learning_rate: float = 5.0,
+        max_iter: int = 500,
+        clip_patch: Union[list, tuple, None] = None,
+        batch_size: int = 16,
+        verbose: bool = True,
+    ) -> None:
+        """
+        Create an instance of the :class:`.AdversarialPatchNumpy`.
+
+        :param classifier: A trained classifier.
+        :param target: The target label for the created patch.
+        :param rotation_max: The maximum rotation applied to random patches. The value is expected to be in the
+               range `[0, 180]`.
+        :param scale_min: The minimum scaling applied to random patches. The value should be in the range `[0, 1]`,
+               but less than `scale_max`.
+        :param scale_max: The maximum scaling applied to random patches. The value should be in the range `[0, 1]`, but
+               larger than `scale_min.`
+        :param learning_rate: The learning rate of the optimization.
+        :param max_iter: The number of optimization steps.
+        :param clip_patch: The minimum and maximum values for each channel in the form
+               [(float, float), (float, float), (float, float)].
+        :param batch_size: The size of the training batch.
+        :param verbose: Show progress bars.
+        """
+        super().__init__(estimator=classifier)
+
+        self.target = target
+        self.rotation_max = rotation_max
+        self.scale_min = scale_min
+        self.scale_max = scale_max
+        self.learning_rate = learning_rate
+        self.max_iter = max_iter
+        self.batch_size = batch_size
+        self.clip_patch = clip_patch
+        self.verbose = verbose
+        self._check_params()
+
+        if len(self.estimator.input_shape) not in [3, 4]:
+            raise ValueError(
+                "Unexpected input_shape in estimator detected. AdversarialPatch is expecting images or videos as input."
+            )
+
+        self.input_shape = self.estimator.input_shape
+
+        self.nb_dims = len(self.input_shape)
+        if self.nb_dims == 3:
+            if self.estimator.channels_first:
+                self.i_c = 0
+                self.i_h = 1
+                self.i_w = 2
+            else:
+                self.i_h = 0
+                self.i_w = 1
+                self.i_c = 2
+        elif self.nb_dims == 4:
+            if self.estimator.channels_first:
+                self.i_c = 1
+                self.i_h = 2
+                self.i_w = 3
+            else:
+                self.i_h = 1
+                self.i_w = 2
+                self.i_c = 3
+
+        smallest_image_edge = np.minimum(self.input_shape[self.i_h], self.input_shape[self.i_w])
+        nb_channels = self.input_shape[self.i_c]
+
+        if self.estimator.channels_first:
+            self.patch_shape = (nb_channels, smallest_image_edge, smallest_image_edge)
+        else:
+            self.patch_shape = (smallest_image_edge, smallest_image_edge, nb_channels)
+
+        self.patch = None
+        self.mean_value = (
+            self.estimator.clip_values[1] - self.estimator.clip_values[0]
+        ) / 2.0 + self.estimator.clip_values[0]
+        self.reset_patch(self.mean_value)
+
+    def generate(self, x: np.ndarray, y: Optional[np.ndarray] = None, **kwargs) -> Tuple[np.ndarray, np.ndarray]:
+        """
+        Generate an adversarial patch and return the patch and its mask in arrays.
+
+        :param x: An array with the original input images of shape NHWC or NCHW or input videos of shape NFHWC or NFCHW.
+        :param y: An array with the original true labels.
+        :param mask: A boolean array of shape equal to the shape of a single samples (1, H, W) or the shape of `x`
+                     (N, H, W) without their channel dimensions. Any features for which the mask is True can be the
+                     center location of the patch during sampling.
+        :type mask: `np.ndarray`
+        :param reset_patch: If `True` reset patch to initial values of mean of minimal and maximal clip value, else if
+                            `False` (default) restart from previous patch values created by previous call to `generate`
+                            or mean of minimal and maximal clip value if first call to `generate`.
+        :type reset_patch: bool
+        :return: An array with adversarial patch and an array of the patch mask.
+        """
+        logger.info("Creating adversarial patch.")
+
+        test_input_shape = list(self.estimator.input_shape)
+
+        for i, size in enumerate(self.estimator.input_shape):
+            if size is None or size != x.shape[i + 1]:
+                test_input_shape[i] = x.shape[i + 1]
+
+        self.input_shape = tuple(test_input_shape)
+
+        mask = kwargs.get("mask")
+        if mask is not None:
+            mask = mask.copy()
+        if mask is not None and (
+            (mask.dtype != np.bool)
+            or not (mask.shape[0] == 1 or mask.shape[0] == x.shape[0])
+            or not (
+                (mask.shape[1] == x.shape[1] and mask.shape[2] == x.shape[2])
+                or (mask.shape[1] == x.shape[2] and mask.shape[2] == x.shape[3])
+            )
+        ):
+            raise ValueError(
+                "The shape of `mask` has to be equal to the shape of a single samples (1, H, W) or the"
+                "shape of `x` (N, H, W) without their channel dimensions."
+            )
+
+        if len(x.shape) == 2:
+            raise ValueError(
+                "Feature vectors detected. The adversarial patch can only be applied to data with spatial "
+                "dimensions."
+            )
+
+        if kwargs.get("reset_patch"):
+            self._reset_patch()
+
+        y_target = check_and_transform_label_format(labels=y, nb_classes=self.estimator.nb_classes)
+
+        for _ in trange(self.max_iter, desc="Adversarial Patch Numpy", disable=not self.verbose):
+            patched_images, patch_mask_transformed, transforms = self._augment_images_with_random_patch(
+                x, self.patch, mask=mask
+            )
+
+            num_batches = int(math.ceil(x.shape[0] / self.batch_size))
+            patch_gradients = np.zeros_like(self.patch)
+
+            for i_batch in range(num_batches):
+                i_batch_start = i_batch * self.batch_size
+                i_batch_end = (i_batch + 1) * self.batch_size
+
+                gradients = self.estimator.loss_gradient(
+                    patched_images[i_batch_start:i_batch_end], y_target[i_batch_start:i_batch_end],
+                )
+
+                for i_image in range(gradients.shape[0]):
+                    patch_gradients_i = self._reverse_transformation(
+                        gradients[i_image, :, :, :], patch_mask_transformed[i_image, :, :, :], transforms[i_image],
+                    )
+                    if self.nb_dims == 4:
+                        patch_gradients_i = np.mean(patch_gradients_i, axis=0)
+                    patch_gradients += patch_gradients_i
+
+            # patch_gradients = patch_gradients / (num_batches * self.batch_size)
+            self.patch -= patch_gradients * self.learning_rate
+            self.patch = np.clip(self.patch, a_min=self.estimator.clip_values[0], a_max=self.estimator.clip_values[1],)
+
+        return self.patch, self._get_circular_patch_mask()
+
+    def apply_patch(
+        self, x: np.ndarray, scale: float, patch_external: np.ndarray = None, mask: Optional[np.ndarray] = None
+    ) -> np.ndarray:
+        """
+        A function to apply the learned adversarial patch to images or videos.
+
+        :param x: Instances to apply randomly transformed patch.
+        :param scale: Scale of the applied patch in relation to the classifier input shape.
+        :param patch_external: External patch to apply to images `x`.
+        :param mask: An boolean array of shape equal to the shape of a single samples (1, H, W) or the shape of `x`
+                     (N, H, W) without their channel dimensions. Any features for which the mask is True can be the
+                     center location of the patch during sampling.
+        :return: The patched instances.
+        """
+        if mask is not None:
+            mask = mask.copy()
+        patch = patch_external if patch_external is not None else self.patch
+        patched_x, _, _ = self._augment_images_with_random_patch(x, patch, mask=mask, scale=scale)
+        return patched_x
+
+    def _check_params(self) -> None:
+        if not isinstance(self.rotation_max, (float, int)):
+            raise ValueError("The maximum rotation of the random patches must be of type float.")
+        if self.rotation_max < 0 or self.rotation_max > 180.0:
+            raise ValueError("The maximum rotation of the random patches must be between 0 and 180 degrees.")
+
+        if not isinstance(self.scale_min, float):
+            raise ValueError("The minimum scale of the random patched must be of type float.")
+        if self.scale_min < 0 or self.scale_min > self.scale_max:
+            raise ValueError(
+                "The minimum scale of the random patched must be greater than 0 and less than the maximum scaling."
+            )
+
+        if not isinstance(self.scale_max, float):
+            raise ValueError("The maximum scale of the random patched must be of type float.")
+        if self.scale_max > 1:
+            raise ValueError("The maximum scale of the random patched must not be greater than 1.")
+
+        if not isinstance(self.learning_rate, float):
+            raise ValueError("The learning rate must be of type float.")
+        if self.learning_rate <= 0.0:
+            raise ValueError("The learning rate must be greater than 0.0.")
+
+        if not isinstance(self.max_iter, int):
+            raise ValueError("The number of optimization steps must be of type int.")
+        if self.max_iter <= 0:
+            raise ValueError("The number of optimization steps must be greater than 0.")
+
+        if not isinstance(self.batch_size, int):
+            raise ValueError("The batch size must be of type int.")
+        if self.batch_size <= 0:
+            raise ValueError("The batch size must be greater than 0.")
+
+        if not isinstance(self.verbose, bool):
+            raise ValueError("The argument `verbose` has to be of type bool.")
+
+    def _get_circular_patch_mask(self, sharpness: int = 40) -> np.ndarray:
+        """
+        Return a circular patch mask
+        """
+        diameter = np.minimum(self.input_shape[self.i_h], self.input_shape[self.i_w])
+
+        x = np.linspace(-1, 1, diameter)
+        y = np.linspace(-1, 1, diameter)
+        x_grid, y_grid = np.meshgrid(x, y, sparse=True)
+        z_grid = (x_grid ** 2 + y_grid ** 2) ** sharpness
+
+        mask = 1 - np.clip(z_grid, -1, 1)
+
+        channel_index = 1 if self.estimator.channels_first else 3
+        axis = channel_index - 1
+        mask = np.expand_dims(mask, axis=axis)
+
+        mask = np.broadcast_to(mask, self.patch_shape).astype(np.float32)
+
+        if self.nb_dims == 4:
+            mask = np.expand_dims(mask, axis=0)
+            mask = np.repeat(mask, axis=0, repeats=self.input_shape[0]).astype(np.float32)
+
+        return mask
+
+    def _augment_images_with_random_patch(self, images, patch, mask=None, scale=None):
+        """
+        Augment images with randomly rotated, shifted and scaled patch.
+        """
+        transformations = list()
+        patched_images = list()
+        patch_mask_transformed_list = list()
+
+        for i_image in range(images.shape[0]):
+            if mask is not None:
+                if mask.shape[0] == 1:
+                    mask_2d = mask[0, :, :]
+                else:
+                    mask_2d = mask[i_image, :, :]
+            else:
+                mask_2d = mask
+
+            (patch_transformed, patch_mask_transformed, transformation,) = self._random_transformation(
+                patch, scale, mask_2d
+            )
+
+            inverted_patch_mask_transformed = 1 - patch_mask_transformed
+
+            patched_image = (
+                images[i_image, :, :, :] * inverted_patch_mask_transformed + patch_transformed * patch_mask_transformed
+            )
+            patched_image = np.expand_dims(patched_image, axis=0)
+            patched_images.append(patched_image)
+
+            patch_mask_transformed = np.expand_dims(patch_mask_transformed, axis=0)
+            patch_mask_transformed_list.append(patch_mask_transformed)
+            transformations.append(transformation)
+
+        patched_images = np.concatenate(patched_images, axis=0)
+        patch_mask_transformed_np = np.concatenate(patch_mask_transformed_list, axis=0)
+
+        return patched_images, patch_mask_transformed_np, transformations
+
+    def _rotate(self, x, angle):
+        axes = (self.i_h, self.i_w)
+        return rotate(x, angle=angle, reshape=False, axes=axes, order=1)
+
+    def _scale(self, x, scale):
+        zooms = None
+        height, width = x.shape[self.i_h], x.shape[self.i_w]
+
+        if self.estimator.channels_first:
+            if self.nb_dims == 3:
+                zooms = (1.0, scale, scale)
+            elif self.nb_dims == 4:
+                zooms = (1.0, 1.0, scale, scale)
+        elif not self.estimator.channels_first:
+            if self.nb_dims == 3:
+                zooms = (scale, scale, 1.0)
+            elif self.nb_dims == 4:
+                zooms = (1.0, scale, scale, 1.0)
+
+        if scale < 1.0:
+            scale_h = int(np.round(height * scale))
+            scale_w = int(np.round(width * scale))
+            top = (height - scale_h) // 2
+            left = (width - scale_w) // 2
+
+            x_out = np.zeros_like(x)
+
+            if self.estimator.channels_first:
+                if self.nb_dims == 3:
+                    x_out[:, top : top + scale_h, left : left + scale_w] = zoom(x, zoom=zooms, order=1)
+                elif self.nb_dims == 4:
+                    x_out[:, :, top : top + scale_h, left : left + scale_w] = zoom(x, zoom=zooms, order=1)
+            else:
+                if self.nb_dims == 3:
+                    x_out[top : top + scale_h, left : left + scale_w, :] = zoom(x, zoom=zooms, order=1)
+                elif self.nb_dims == 4:
+                    x_out[:, top : top + scale_h, left : left + scale_w, :] = zoom(x, zoom=zooms, order=1)
+
+        elif scale > 1.0:
+            scale_h = int(np.round(height / scale)) + 1
+            scale_w = int(np.round(width / scale)) + 1
+            top = (height - scale_h) // 2
+            left = (width - scale_w) // 2
+
+            if scale_h <= height and scale_w <= width and top >= 0 and left >= 0:
+
+                if self.estimator.channels_first:
+                    if self.nb_dims == 3:
+                        x_out = zoom(x[:, top : top + scale_h, left : left + scale_w], zoom=zooms, order=1)
+                    elif self.nb_dims == 4:
+                        x_out = zoom(x[:, :, top : top + scale_h, left : left + scale_w], zoom=zooms, order=1)
+                else:
+                    if self.nb_dims == 3:
+                        x_out = zoom(x[top : top + scale_h, left : left + scale_w, :], zoom=zooms, order=1)
+                    elif self.nb_dims == 4:
+                        x_out = zoom(x[:, top : top + scale_h, left : left + scale_w, :], zoom=zooms, order=1)
+
+            else:
+                x_out = x
+
+            cut_top = (x_out.shape[self.i_h] - height) // 2
+            cut_left = (x_out.shape[self.i_w] - width) // 2
+
+            if self.estimator.channels_first:
+                if self.nb_dims == 3:
+                    x_out = x_out[:, cut_top : cut_top + height, cut_left : cut_left + width]
+                elif self.nb_dims == 4:
+                    x_out = x_out[:, :, cut_top : cut_top + height, cut_left : cut_left + width]
+            else:
+                if self.nb_dims == 3:
+                    x_out = x_out[cut_top : cut_top + height, cut_left : cut_left + width, :]
+                elif self.nb_dims == 4:
+                    x_out = x_out[:, cut_top : cut_top + height, cut_left : cut_left + width, :]
+
+        else:
+            x_out = x
+
+        assert x.shape == x_out.shape
+
+        return x_out
+
+    def _shift(self, x, shift_h, shift_w):
+        if self.estimator.channels_first:
+            if self.nb_dims == 3:
+                shift_hw = (0, shift_h, shift_w)
+            elif self.nb_dims == 4:
+                shift_hw = (0, 0, shift_h, shift_w)
+        else:
+            if self.nb_dims == 3:
+                shift_hw = (shift_h, shift_w, 0)
+            elif self.nb_dims == 4:
+                shift_hw = (0, shift_h, shift_w, 0)
+        return shift(x, shift=shift_hw, order=1)
+
+    def _random_transformation(self, patch, scale, mask_2d):
+        patch_mask = self._get_circular_patch_mask()
+        transformation = dict()
+
+        if self.nb_dims == 4:
+            patch = np.expand_dims(patch, axis=0)
+            patch = np.repeat(patch, axis=0, repeats=self.input_shape[0]).astype(np.float32)
+
+        # rotate
+        angle = random.uniform(-self.rotation_max, self.rotation_max)
+        transformation["rotate"] = angle
+        patch = self._rotate(patch, angle)
+        patch_mask = self._rotate(patch_mask, angle)
+
+        # scale
+        if scale is None:
+            scale = random.uniform(self.scale_min, self.scale_max)
+        patch = self._scale(patch, scale)
+        patch_mask = self._scale(patch_mask, scale)
+        transformation["scale"] = scale
+
+        # pad
+        pad_h_before = int((self.input_shape[self.i_h] - patch.shape[self.i_h]) / 2)
+        pad_h_after = int(self.input_shape[self.i_h] - pad_h_before - patch.shape[self.i_h])
+
+        pad_w_before = int((self.input_shape[self.i_w] - patch.shape[self.i_w]) / 2)
+        pad_w_after = int(self.input_shape[self.i_w] - pad_w_before - patch.shape[self.i_w])
+
+        if self.estimator.channels_first:
+            if self.nb_dims == 3:
+                pad_width = ((0, 0), (pad_h_before, pad_h_after), (pad_w_before, pad_w_after))  # type: ignore
+            elif self.nb_dims == 4:
+                pad_width = ((0, 0), (0, 0), (pad_h_before, pad_h_after), (pad_w_before, pad_w_after))  # type: ignore
+        else:
+            if self.nb_dims == 3:
+                pad_width = ((pad_h_before, pad_h_after), (pad_w_before, pad_w_after), (0, 0))  # type: ignore
+            elif self.nb_dims == 4:
+                pad_width = ((0, 0), (pad_h_before, pad_h_after), (pad_w_before, pad_w_after), (0, 0))  # type: ignore
+
+        transformation["pad_h_before"] = pad_h_before
+        transformation["pad_w_before"] = pad_w_before
+
+        patch = np.pad(patch, pad_width=pad_width, mode="constant", constant_values=(0, 0),)
+        patch_mask = np.pad(patch_mask, pad_width=pad_width, mode="constant", constant_values=(0, 0),)
+
+        # shift
+        if mask_2d is None:
+            shift_max_h = (self.input_shape[self.i_h] - self.patch_shape[self.i_h] * scale) / 2.0
+            shift_max_w = (self.input_shape[self.i_w] - self.patch_shape[self.i_w] * scale) / 2.0
+            if shift_max_h > 0 and shift_max_w > 0:
+                shift_h = random.uniform(-shift_max_h, shift_max_h)
+                shift_w = random.uniform(-shift_max_w, shift_max_w)
+                patch = self._shift(patch, shift_h, shift_w)
+                patch_mask = self._shift(patch_mask, shift_h, shift_w)
+            else:
+                shift_h = 0
+                shift_w = 0
+        else:
+            edge_x_0 = int(self.patch_shape[self.i_h] * scale) // 2
+            edge_x_1 = int(self.patch_shape[self.i_h] * scale) - edge_x_0
+            edge_y_0 = int(self.patch_shape[self.i_w] * scale) // 2
+            edge_y_1 = int(self.patch_shape[self.i_w] * scale) - edge_y_0
+
+            mask_2d[0:edge_x_0, :] = False
+            mask_2d[-edge_x_1:, :] = False
+            mask_2d[:, 0:edge_y_0] = False
+            mask_2d[:, -edge_y_1:] = False
+
+            num_pos = np.argwhere(mask_2d).shape[0]
+            pos_id = np.random.choice(num_pos, size=1)
+            pos = np.argwhere(mask_2d)[pos_id[0]]
+            shift_h = pos[0] - (self.input_shape[self.i_h]) / 2.0
+            shift_w = pos[1] - (self.input_shape[self.i_w]) / 2.0
+
+            patch = self._shift(patch, shift_h, shift_w)
+            patch_mask = self._shift(patch_mask, shift_h, shift_w)
+
+        transformation["shift_h"] = shift_h
+        transformation["shift_w"] = shift_w
+
+        return patch, patch_mask, transformation
+
+    def _reverse_transformation(self, gradients: np.ndarray, patch_mask_transformed, transformation) -> np.ndarray:
+        gradients = gradients * patch_mask_transformed
+
+        # shift
+        shift_h = transformation["shift_h"]
+        shift_w = transformation["shift_w"]
+        gradients = self._shift(gradients, -shift_h, -shift_w)
+
+        # unpad
+
+        pad_h_before = transformation["pad_h_before"]
+        pad_w_before = transformation["pad_w_before"]
+
+        if self.estimator.channels_first:
+            height, width = self.patch_shape[1], self.patch_shape[2]
+        else:
+            height, width = self.patch_shape[0], self.patch_shape[1]
+
+        if self.estimator.channels_first:
+            if self.nb_dims == 3:
+                gradients = gradients[:, pad_h_before : pad_h_before + height, pad_w_before : pad_w_before + width]
+            elif self.nb_dims == 4:
+                gradients = gradients[:, :, pad_h_before : pad_h_before + height, pad_w_before : pad_w_before + width]
+        else:
+            if self.nb_dims == 3:
+                gradients = gradients[pad_h_before : pad_h_before + height, pad_w_before : pad_w_before + width, :]
+            elif self.nb_dims == 4:
+                gradients = gradients[:, pad_h_before : pad_h_before + height, pad_w_before : pad_w_before + width, :]
+
+        # scale
+        scale = transformation["scale"]
+        gradients = self._scale(gradients, 1.0 / scale)
+
+        # rotate
+        angle = transformation["rotate"]
+        gradients = self._rotate(gradients, -angle)
+
+        return gradients
+
+    def reset_patch(self, initial_patch_value: Optional[Union[float, np.ndarray]]) -> None:
+        """
+        Reset the adversarial patch.
+
+        :param initial_patch_value: Patch value to use for resetting the patch.
+        """
+        if initial_patch_value is None:
+            self.patch = np.ones(shape=self.patch_shape).astype(np.float32) * self.mean_value
+        elif isinstance(initial_patch_value, float):
+            self.patch = np.ones(shape=self.patch_shape).astype(np.float32) * initial_patch_value
+        elif self.patch.shape == initial_patch_value.shape:
+            self.patch = initial_patch_value
+        else:
+            raise ValueError("Unexpected value for initial_patch_value.")
+
+    @staticmethod
+    def insert_transformed_patch(x: np.ndarray, patch: np.ndarray, image_coords: np.ndarray):
+        """
+        Insert patch to image based on given or selected coordinates.
+
+        :param x: The image to insert the patch.
+        :param patch: The patch to be transformed and inserted.
+        :param image_coords: The coordinates of the 4 corners of the transformed, inserted patch of shape
+            [[x1, y1], [x2, y2], [x3, y3], [x4, y4]] in pixel units going in clockwise direction, starting with upper
+            left corner.
+        :return: The input `x` with the patch inserted.
+        """
+        return insert_transformed_patch(x, patch, image_coords)
diff --git a/adversarial-robustness-toolbox/art/attacks/evasion/adversarial_patch/adversarial_patch_pytorch.py b/adversarial-robustness-toolbox/art/attacks/evasion/adversarial_patch/adversarial_patch_pytorch.py
new file mode 100644
index 0000000..cc5dc32
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/attacks/evasion/adversarial_patch/adversarial_patch_pytorch.py
@@ -0,0 +1,532 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2021
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements the adversarial patch attack `AdversarialPatch`. This attack generates an adversarial patch that
+can be printed into the physical world with a common printer. The patch can be used to fool image and video classifiers.
+
+| Paper link: https://arxiv.org/abs/1712.09665
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+from typing import Optional, Tuple, Union, TYPE_CHECKING
+
+import numpy as np
+from tqdm.auto import trange
+
+from art.attacks.attack import EvasionAttack
+from art.estimators.estimator import BaseEstimator, NeuralNetworkMixin
+from art.estimators.classification.classifier import ClassifierMixin
+from art.utils import check_and_transform_label_format, is_probability, to_categorical
+
+if TYPE_CHECKING:
+    import torch
+
+    from art.utils import CLASSIFIER_NEURALNETWORK_TYPE
+
+logger = logging.getLogger(__name__)
+
+
+class AdversarialPatchPyTorch(EvasionAttack):
+    """
+    Implementation of the adversarial patch attack for square and rectangular images and videos in PyTorch.
+
+    | Paper link: https://arxiv.org/abs/1712.09665
+    """
+
+    attack_params = EvasionAttack.attack_params + [
+        "rotation_max",
+        "scale_min",
+        "scale_max",
+        "distortion_scale_max",
+        "learning_rate",
+        "max_iter",
+        "batch_size",
+        "patch_shape",
+        "verbose",
+    ]
+
+    _estimator_requirements = (BaseEstimator, NeuralNetworkMixin, ClassifierMixin)
+
+    def __init__(
+        self,
+        classifier: "CLASSIFIER_NEURALNETWORK_TYPE",
+        rotation_max: float = 22.5,
+        scale_min: float = 0.1,
+        scale_max: float = 1.0,
+        distortion_scale_max: float = 0.0,
+        learning_rate: float = 5.0,
+        max_iter: int = 500,
+        batch_size: int = 16,
+        patch_shape: Optional[Tuple[int, int, int]] = None,
+        patch_type: str = "circle",
+        verbose: bool = True,
+    ):
+        """
+        Create an instance of the :class:`.AdversarialPatchPyTorch`.
+
+        :param classifier: A trained classifier.
+        :param rotation_max: The maximum rotation applied to random patches. The value is expected to be in the
+               range `[0, 180]`.
+        :param scale_min: The minimum scaling applied to random patches. The value should be in the range `[0, 1]`,
+               but less than `scale_max`.
+        :param scale_max: The maximum scaling applied to random patches. The value should be in the range `[0, 1]`, but
+               larger than `scale_min`.
+        :param distortion_scale_max: The maximum distortion scale for perspective transformation in range `[0, 1]`. If
+               distortion_scale_max=0.0 the perspective transformation sampling will be disabled.
+        :param learning_rate: The learning rate of the optimization.
+        :param max_iter: The number of optimization steps.
+        :param batch_size: The size of the training batch.
+        :param patch_shape: The shape of the adversarial patch as a tuple of shape HWC (width, height, nb_channels).
+        :param patch_type: The patch type, either circle or square.
+        :param verbose: Show progress bars.
+        """
+        import torch  # lgtm [py/repeated-import]
+        import torchvision
+
+        torch_version = list(map(int, torch.__version__.lower().split(".")))
+        torchvision_version = list(map(int, torchvision.__version__.lower().split(".")))
+        assert torch_version[0] >= 1 and torch_version[1] >= 7, "AdversarialPatchPyTorch requires torch>=1.7.0"
+        assert (
+            torchvision_version[0] >= 0 and torchvision_version[1] >= 8
+        ), "AdversarialPatchPyTorch requires torchvision>=0.8.0"
+
+        super().__init__(estimator=classifier)
+        self.rotation_max = rotation_max
+        self.scale_min = scale_min
+        self.scale_max = scale_max
+        self.distortion_scale_max = distortion_scale_max
+        self.learning_rate = learning_rate
+        self.max_iter = max_iter
+        self.batch_size = batch_size
+        if patch_shape is None:
+            self.patch_shape = self.estimator.input_shape
+        else:
+            self.patch_shape = patch_shape
+        self.patch_type = patch_type
+
+        self.image_shape = classifier.input_shape
+        self.verbose = verbose
+        self._check_params()
+
+        if not self.estimator.channels_first:
+            raise ValueError("Input shape has to be wither NCHW or NFCHW.")
+
+        self.i_h_patch = 1
+        self.i_w_patch = 2
+
+        self.input_shape = self.estimator.input_shape
+
+        self.nb_dims = len(self.image_shape)
+        if self.nb_dims == 3:
+            self.i_h = 1
+            self.i_w = 2
+        elif self.nb_dims == 4:
+            self.i_h = 2
+            self.i_w = 3
+
+        if self.patch_shape[1] != self.patch_shape[2]:
+            raise ValueError("Patch height and width need to be the same.")
+
+        if not (self.estimator.postprocessing_defences is None or self.estimator.postprocessing_defences == []):
+            raise ValueError(
+                "Framework-specific implementation of Adversarial Patch attack does not yet support "
+                + "postprocessing defences."
+            )
+
+        mean_value = (self.estimator.clip_values[1] - self.estimator.clip_values[0]) / 2.0 + self.estimator.clip_values[
+            0
+        ]
+        self._initial_value = np.ones(self.patch_shape) * mean_value
+        self._patch = torch.tensor(self._initial_value, requires_grad=True, device=self.estimator.device)
+
+        self._optimizer = torch.optim.Adam([self._patch], lr=self.learning_rate)
+
+    def _train_step(
+        self, images: "torch.Tensor", target: "torch.Tensor", mask: Optional["torch.Tensor"] = None
+    ) -> "torch.Tensor":
+        import torch  # lgtm [py/repeated-import]
+
+        self._optimizer.zero_grad()
+        loss = self._loss(images, target, mask)
+        loss.backward(retain_graph=True)
+        self._optimizer.step()
+
+        with torch.no_grad():
+            self._patch[:] = torch.clamp(
+                self._patch, min=self.estimator.clip_values[0], max=self.estimator.clip_values[1]
+            )
+
+        return loss
+
+    def _predictions(self, images: "torch.Tensor", mask: Optional["torch.Tensor"]) -> "torch.Tensor":
+        import torch  # lgtm [py/repeated-import]
+
+        patched_input = self._random_overlay(images, self._patch, mask=mask)
+        patched_input = torch.clamp(
+            patched_input, min=self.estimator.clip_values[0], max=self.estimator.clip_values[1],
+        )
+
+        predictions = self.estimator._predict_framework(patched_input)
+
+        return predictions
+
+    def _loss(self, images: "torch.Tensor", target: "torch.Tensor", mask: Optional["torch.Tensor"]) -> "torch.Tensor":
+        import torch  # lgtm [py/repeated-import]
+
+        predictions = self._predictions(images, mask)
+
+        if self.use_logits:
+            loss = torch.nn.functional.cross_entropy(
+                input=predictions, target=torch.argmax(target, dim=1), reduction="mean"
+            )
+        else:
+            loss = torch.nn.functional.nll_loss(input=predictions, target=torch.argmax(target, dim=1), reduction="mean")
+
+        if not self.targeted:
+            loss = -loss
+
+        return loss
+
+    def _get_circular_patch_mask(self, nb_samples: int, sharpness: int = 40) -> "torch.Tensor":
+        """
+        Return a circular patch mask.
+        """
+        import torch  # lgtm [py/repeated-import]
+
+        diameter = np.minimum(self.patch_shape[self.i_h_patch], self.patch_shape[self.i_w_patch])
+
+        if self.patch_type == "circle":
+            x = np.linspace(-1, 1, diameter)
+            y = np.linspace(-1, 1, diameter)
+            x_grid, y_grid = np.meshgrid(x, y, sparse=True)
+            z_grid = (x_grid ** 2 + y_grid ** 2) ** sharpness
+            image_mask = 1 - np.clip(z_grid, -1, 1)
+        elif self.patch_type == "square":
+            image_mask = np.ones((diameter, diameter))
+
+        image_mask = np.expand_dims(image_mask, axis=0)
+        image_mask = np.broadcast_to(image_mask, self.patch_shape)
+        image_mask = torch.Tensor(np.array(image_mask))
+        image_mask = torch.stack([image_mask] * nb_samples, dim=0)
+        return image_mask
+
+    def _random_overlay(
+        self,
+        images: "torch.Tensor",
+        patch: "torch.Tensor",
+        scale: Optional[float] = None,
+        mask: Optional["torch.Tensor"] = None,
+    ) -> "torch.Tensor":
+        import torch  # lgtm [py/repeated-import]
+        import torchvision
+
+        nb_samples = images.shape[0]
+
+        image_mask = self._get_circular_patch_mask(nb_samples=nb_samples)
+        image_mask = image_mask.float()
+
+        smallest_image_edge = np.minimum(self.image_shape[self.i_h], self.image_shape[self.i_w])
+
+        image_mask = torchvision.transforms.functional.resize(
+            img=image_mask, size=(smallest_image_edge, smallest_image_edge), interpolation=2,
+        )
+
+        pad_h_before = int((self.image_shape[self.i_h] - image_mask.shape[self.i_h_patch + 1]) / 2)
+        pad_h_after = int(self.image_shape[self.i_h] - pad_h_before - image_mask.shape[self.i_h_patch + 1])
+
+        pad_w_before = int((self.image_shape[self.i_w] - image_mask.shape[self.i_w_patch + 1]) / 2)
+        pad_w_after = int(self.image_shape[self.i_w] - pad_w_before - image_mask.shape[self.i_w_patch + 1])
+
+        image_mask = torchvision.transforms.functional.pad(
+            img=image_mask,
+            padding=[pad_h_before, pad_w_before, pad_h_after, pad_w_after],
+            fill=0,
+            padding_mode="constant",
+        )
+
+        if self.nb_dims == 4:
+            image_mask = torch.unsqueeze(image_mask, dim=1)
+            image_mask = torch.repeat_interleave(image_mask, dim=1, repeats=self.input_shape[0])
+
+        image_mask = image_mask.float()
+
+        patch = patch.float()
+        padded_patch = torch.stack([patch] * nb_samples)
+
+        padded_patch = torchvision.transforms.functional.resize(
+            img=padded_patch, size=(smallest_image_edge, smallest_image_edge), interpolation=2,
+        )
+
+        padded_patch = torchvision.transforms.functional.pad(
+            img=padded_patch,
+            padding=[pad_h_before, pad_w_before, pad_h_after, pad_w_after],
+            fill=0,
+            padding_mode="constant",
+        )
+
+        if self.nb_dims == 4:
+            padded_patch = torch.unsqueeze(padded_patch, dim=1)
+            padded_patch = torch.repeat_interleave(padded_patch, dim=1, repeats=self.input_shape[0])
+
+        padded_patch = padded_patch.float()
+
+        image_mask_list = list()
+        padded_patch_list = list()
+
+        for i_sample in range(nb_samples):
+            if scale is None:
+                im_scale = np.random.uniform(low=self.scale_min, high=self.scale_max)
+            else:
+                im_scale = scale
+
+            if mask is None:
+                padding_after_scaling_h = (
+                    self.image_shape[self.i_h] - im_scale * padded_patch.shape[self.i_h + 1]
+                ) / 2.0
+                padding_after_scaling_w = (
+                    self.image_shape[self.i_w] - im_scale * padded_patch.shape[self.i_w + 1]
+                ) / 2.0
+                x_shift = np.random.uniform(-padding_after_scaling_w, padding_after_scaling_w)
+                y_shift = np.random.uniform(-padding_after_scaling_h, padding_after_scaling_h)
+            else:
+                mask_2d = mask[i_sample, :, :]
+
+                edge_x_0 = int(im_scale * padded_patch.shape[self.i_w + 1]) // 2
+                edge_x_1 = int(im_scale * padded_patch.shape[self.i_w + 1]) - edge_x_0
+                edge_y_0 = int(im_scale * padded_patch.shape[self.i_h + 1]) // 2
+                edge_y_1 = int(im_scale * padded_patch.shape[self.i_h + 1]) - edge_y_0
+
+                mask_2d[0:edge_x_0, :] = False
+                if edge_x_1 > 0:
+                    mask_2d[-edge_x_1:, :] = False
+                mask_2d[:, 0:edge_y_0] = False
+                if edge_y_1 > 0:
+                    mask_2d[:, -edge_y_1:] = False
+
+                num_pos = np.argwhere(mask_2d).shape[0]
+                pos_id = np.random.choice(num_pos, size=1)
+                pos = np.argwhere(mask_2d)[pos_id[0]]
+                x_shift = pos[1] - self.image_shape[self.i_w] // 2
+                y_shift = pos[0] - self.image_shape[self.i_h] // 2
+
+            phi_rotate = float(np.random.uniform(-self.rotation_max, self.rotation_max))
+
+            image_mask_i = image_mask[i_sample]
+
+            height = padded_patch.shape[self.i_h + 1]
+            width = padded_patch.shape[self.i_w + 1]
+
+            half_height = height // 2
+            half_width = width // 2
+            topleft = [
+                int(torch.randint(0, int(self.distortion_scale_max * half_width) + 1, size=(1,)).item()),
+                int(torch.randint(0, int(self.distortion_scale_max * half_height) + 1, size=(1,)).item()),
+            ]
+            topright = [
+                int(torch.randint(width - int(self.distortion_scale_max * half_width) - 1, width, size=(1,)).item()),
+                int(torch.randint(0, int(self.distortion_scale_max * half_height) + 1, size=(1,)).item()),
+            ]
+            botright = [
+                int(torch.randint(width - int(self.distortion_scale_max * half_width) - 1, width, size=(1,)).item()),
+                int(torch.randint(height - int(self.distortion_scale_max * half_height) - 1, height, size=(1,)).item()),
+            ]
+            botleft = [
+                int(torch.randint(0, int(self.distortion_scale_max * half_width) + 1, size=(1,)).item()),
+                int(torch.randint(height - int(self.distortion_scale_max * half_height) - 1, height, size=(1,)).item()),
+            ]
+            startpoints = [[0, 0], [width - 1, 0], [width - 1, height - 1], [0, height - 1]]
+            endpoints = [topleft, topright, botright, botleft]
+
+            image_mask_i = torchvision.transforms.functional.perspective(
+                img=image_mask_i, startpoints=startpoints, endpoints=endpoints, interpolation=2, fill=None
+            )
+
+            image_mask_i = torchvision.transforms.functional.affine(
+                img=image_mask_i,
+                angle=phi_rotate,
+                translate=[x_shift, y_shift],
+                scale=im_scale,
+                shear=[0, 0],
+                resample=0,
+                fillcolor=None,
+            )
+
+            image_mask_list.append(image_mask_i)
+
+            padded_patch_i = padded_patch[i_sample]
+
+            padded_patch_i = torchvision.transforms.functional.perspective(
+                img=padded_patch_i, startpoints=startpoints, endpoints=endpoints, interpolation=2, fill=None
+            )
+
+            padded_patch_i = torchvision.transforms.functional.affine(
+                img=padded_patch_i,
+                angle=phi_rotate,
+                translate=[x_shift, y_shift],
+                scale=im_scale,
+                shear=[0, 0],
+                resample=0,
+                fillcolor=None,
+            )
+
+            padded_patch_list.append(padded_patch_i)
+
+        image_mask = torch.stack(image_mask_list, dim=0)
+        padded_patch = torch.stack(padded_patch_list, dim=0)
+        inverted_mask = torch.from_numpy(np.ones(shape=image_mask.shape, dtype=np.float32)) - image_mask
+
+        return images * inverted_mask + padded_patch * image_mask
+
+    def generate(self, x: np.ndarray, y: Optional[np.ndarray] = None, **kwargs) -> Tuple[np.ndarray, np.ndarray]:
+        """
+        Generate an adversarial patch and return the patch and its mask in arrays.
+
+        :param x: An array with the original input images of shape NHWC or input videos of shape NFHWC.
+        :param y: An array with the original true labels.
+        :param mask: An boolean array of shape equal to the shape of a single samples (1, H, W) or the shape of `x`
+                     (N, H, W) without their channel dimensions. Any features for which the mask is True can be the
+                     center location of the patch during sampling.
+        :type mask: `np.ndarray`
+        :return: An array with adversarial patch and an array of the patch mask.
+        """
+        import torch  # lgtm [py/repeated-import]
+
+        shuffle = kwargs.get("shuffle", True)
+        mask = kwargs.get("mask")
+        if mask is not None:
+            mask = mask.copy()
+        mask = self._check_mask(mask=mask, x=x)
+
+        if y is None:
+            logger.info("Setting labels to estimator predictions and running untargeted attack because `y=None`.")
+            y = to_categorical(np.argmax(self.estimator.predict(x=x), axis=1), nb_classes=self.estimator.nb_classes)
+            self.targeted = False
+        else:
+            self.targeted = True
+
+        if y is None:
+            raise ValueError("The labels `y` cannot be None, please provide labels `y`.")
+
+        y = check_and_transform_label_format(labels=y, nb_classes=self.estimator.nb_classes)
+
+        # check if logits or probabilities
+        y_pred = self.estimator.predict(x=x[[0]])
+
+        if is_probability(y_pred):
+            self.use_logits = False
+        else:
+            self.use_logits = True
+
+        x_tensor = torch.Tensor(x)
+        y_tensor = torch.Tensor(y)
+
+        if mask is None:
+            dataset = torch.utils.data.TensorDataset(x_tensor, y_tensor)
+            data_loader = torch.utils.data.DataLoader(
+                dataset=dataset, batch_size=self.batch_size, shuffle=shuffle, drop_last=False,
+            )
+        else:
+            mask_tensor = torch.Tensor(mask)
+            dataset = torch.utils.data.TensorDataset(x_tensor, y_tensor, mask_tensor)
+            data_loader = torch.utils.data.DataLoader(
+                dataset=dataset, batch_size=self.batch_size, shuffle=shuffle, drop_last=False,
+            )
+
+        for _ in trange(self.max_iter, desc="Adversarial Patch TensorFlow v2", disable=not self.verbose):
+            if mask is None:
+                for images, target in data_loader:
+                    _ = self._train_step(images=images, target=target, mask=None)
+            else:
+                for images, target, mask_i in data_loader:
+                    _ = self._train_step(images=images, target=target, mask=mask_i)
+
+        return (
+            self._patch.detach().cpu().numpy(),
+            self._get_circular_patch_mask(nb_samples=1).numpy()[0],
+        )
+
+    def _check_mask(self, mask: np.ndarray, x: np.ndarray) -> np.ndarray:
+        if mask is not None and (
+            (mask.dtype != np.bool)
+            or not (mask.shape[0] == 1 or mask.shape[0] == x.shape[0])
+            or not (mask.shape[1] == x.shape[self.i_h + 1] and mask.shape[2] == x.shape[self.i_w + 1])
+        ):
+            raise ValueError(
+                "The shape of `mask` has to be equal to the shape of a single samples (1, H, W) or the"
+                "shape of `x` (N, H, W) without their channel dimensions."
+            )
+
+        if mask is not None and mask.shape[0] == 1:
+            mask = np.repeat(mask, repeats=x.shape[0], axis=0)
+
+        return mask
+
+    def apply_patch(
+        self,
+        x: np.ndarray,
+        scale: float,
+        patch_external: Optional[np.ndarray] = None,
+        mask: Optional[np.ndarray] = None,
+    ) -> np.ndarray:
+        """
+        A function to apply the learned adversarial patch to images or videos.
+
+        :param x: Instances to apply randomly transformed patch.
+        :param scale: Scale of the applied patch in relation to the classifier input shape.
+        :param patch_external: External patch to apply to images `x`.
+        :param mask: An boolean array of shape equal to the shape of a single samples (1, H, W) or the shape of `x`
+                     (N, H, W) without their channel dimensions. Any features for which the mask is True can be the
+                     center location of the patch during sampling.
+        :return: The patched samples.
+        """
+        import torch  # lgtm [py/repeated-import]
+
+        if mask is not None:
+            mask = mask.copy()
+        mask = self._check_mask(mask=mask, x=x)
+        patch = patch_external if patch_external is not None else self._patch
+        x = torch.Tensor(x)
+        return self._random_overlay(images=x, patch=patch, scale=scale, mask=mask).detach().cpu().numpy()
+
+    def reset_patch(self, initial_patch_value: Optional[Union[float, np.ndarray]] = None) -> None:
+        """
+        Reset the adversarial patch.
+
+        :param initial_patch_value: Patch value to use for resetting the patch.
+        """
+        import torch  # lgtm [py/repeated-import]
+
+        if initial_patch_value is None:
+            self._patch.data = torch.Tensor(self._initial_value).double()
+        elif isinstance(initial_patch_value, float):
+            initial_value = np.ones(self.patch_shape) * initial_patch_value
+            self._patch.data = torch.Tensor(initial_value).double()
+        elif self._patch.shape == initial_patch_value.shape:
+            self._patch.data = torch.Tensor(initial_patch_value).double()
+        else:
+            raise ValueError("Unexpected value for initial_patch_value.")
+
+    def _check_params(self) -> None:
+        super()._check_params()
+
+        if not isinstance(self.distortion_scale_max, (float, int)) or 1.0 <= self.distortion_scale_max < 0.0:
+            raise ValueError("The maximum distortion scale has to be greater than or equal 0.0 or smaller than 1.0.")
+
+        if self.patch_type not in ["circle", "square"]:
+            raise ValueError("The patch type has to be either `circle` or `square`.")
diff --git a/adversarial-robustness-toolbox/art/attacks/evasion/adversarial_patch/adversarial_patch_tensorflow.py b/adversarial-robustness-toolbox/art/attacks/evasion/adversarial_patch/adversarial_patch_tensorflow.py
new file mode 100644
index 0000000..ae5760f
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/attacks/evasion/adversarial_patch/adversarial_patch_tensorflow.py
@@ -0,0 +1,512 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements the adversarial patch attack `AdversarialPatch`. This attack generates an adversarial patch that
+can be printed into the physical world with a common printer. The patch can be used to fool image and video classifiers.
+
+| Paper link: https://arxiv.org/abs/1712.09665
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import math
+from typing import Optional, Tuple, Union, TYPE_CHECKING
+
+import numpy as np
+from tqdm.auto import trange
+
+from art.attacks.attack import EvasionAttack
+from art.attacks.evasion.adversarial_patch.utils import insert_transformed_patch
+from art.estimators.estimator import BaseEstimator, NeuralNetworkMixin
+from art.estimators.classification.classifier import ClassifierMixin
+from art.utils import check_and_transform_label_format, is_probability
+
+if TYPE_CHECKING:
+    import tensorflow as tf
+
+    from art.utils import CLASSIFIER_NEURALNETWORK_TYPE
+
+logger = logging.getLogger(__name__)
+
+
+class AdversarialPatchTensorFlowV2(EvasionAttack):
+    """
+    Implementation of the adversarial patch attack for square and rectangular images and videos in TensorFlow v2.
+
+    | Paper link: https://arxiv.org/abs/1712.09665
+    """
+
+    attack_params = EvasionAttack.attack_params + [
+        "rotation_max",
+        "scale_min",
+        "scale_max",
+        "learning_rate",
+        "max_iter",
+        "batch_size",
+        "patch_shape",
+        "verbose",
+    ]
+
+    _estimator_requirements = (BaseEstimator, NeuralNetworkMixin, ClassifierMixin)
+
+    def __init__(
+        self,
+        classifier: "CLASSIFIER_NEURALNETWORK_TYPE",
+        rotation_max: float = 22.5,
+        scale_min: float = 0.1,
+        scale_max: float = 1.0,
+        learning_rate: float = 5.0,
+        max_iter: int = 500,
+        batch_size: int = 16,
+        patch_shape: Optional[Tuple[int, int, int]] = None,
+        verbose: bool = True,
+    ):
+        """
+        Create an instance of the :class:`.AdversarialPatchTensorFlowV2`.
+
+        :param classifier: A trained classifier.
+        :param rotation_max: The maximum rotation applied to random patches. The value is expected to be in the
+               range `[0, 180]`.
+        :param scale_min: The minimum scaling applied to random patches. The value should be in the range `[0, 1]`,
+               but less than `scale_max`.
+        :param scale_max: The maximum scaling applied to random patches. The value should be in the range `[0, 1]`, but
+               larger than `scale_min.`
+        :param learning_rate: The learning rate of the optimization.
+        :param max_iter: The number of optimization steps.
+        :param batch_size: The size of the training batch.
+        :param patch_shape: The shape of the adversarial patch as a tuple of shape HWC (width, height, nb_channels).
+        :param verbose: Show progress bars.
+        """
+        import tensorflow as tf  # lgtm [py/repeated-import]
+
+        super().__init__(estimator=classifier)
+        self.rotation_max = rotation_max
+        self.scale_min = scale_min
+        self.scale_max = scale_max
+        self.learning_rate = learning_rate
+        self.max_iter = max_iter
+        self.batch_size = batch_size
+        if patch_shape is None:
+            self.patch_shape = self.estimator.input_shape
+        else:
+            self.patch_shape = patch_shape
+        self.image_shape = classifier.input_shape
+        self.verbose = verbose
+        self._check_params()
+
+        if self.estimator.channels_first:
+            raise ValueError("Color channel needs to be in last dimension.")
+
+        self.use_logits = None
+
+        self.i_h_patch = 0
+        self.i_w_patch = 1
+
+        self.nb_dims = len(self.image_shape)
+        if self.nb_dims == 3:
+            self.i_h = 0
+            self.i_w = 1
+        elif self.nb_dims == 4:
+            self.i_h = 1
+            self.i_w = 2
+
+        if self.patch_shape[0] != self.patch_shape[1]:
+            raise ValueError("Patch height and width need to be the same.")
+
+        if not (self.estimator.postprocessing_defences is None or self.estimator.postprocessing_defences == []):
+            raise ValueError(
+                "Framework-specific implementation of Adversarial Patch attack does not yet support "
+                + "postprocessing defences."
+            )
+
+        mean_value = (self.estimator.clip_values[1] - self.estimator.clip_values[0]) / 2.0 + self.estimator.clip_values[
+            0
+        ]
+        self._initial_value = np.ones(self.patch_shape) * mean_value
+        self._patch = tf.Variable(
+            initial_value=self._initial_value,
+            shape=self.patch_shape,
+            dtype=tf.float32,
+            constraint=lambda x: tf.clip_by_value(x, self.estimator.clip_values[0], self.estimator.clip_values[1]),
+        )
+
+        self._train_op = tf.keras.optimizers.Adam(
+            learning_rate=self.learning_rate, beta_1=0.9, beta_2=0.999, epsilon=1e-07, amsgrad=False, name="Adam"
+        )
+
+    def _train_step(
+        self, images: "tf.Tensor", target: Optional["tf.Tensor"] = None, mask: Optional["tf.Tensor"] = None
+    ) -> "tf.Tensor":
+        import tensorflow as tf  # lgtm [py/repeated-import]
+
+        if target is None:
+            target = self.estimator.predict(x=images)
+            self.targeted = False
+        else:
+            self.targeted = True
+
+        with tf.GradientTape() as tape:
+            tape.watch(self._patch)
+            loss = self._loss(images, target, mask)
+
+        gradients = tape.gradient(loss, [self._patch])
+
+        if not self.targeted:
+            gradients = [-g for g in gradients]
+
+        self._train_op.apply_gradients(zip(gradients, [self._patch]))
+
+        return loss
+
+    def _predictions(self, images: "tf.Tensor", mask: Optional["tf.Tensor"]) -> "tf.Tensor":
+        import tensorflow as tf  # lgtm [py/repeated-import]
+
+        patched_input = self._random_overlay(images, self._patch, mask=mask)
+
+        patched_input = tf.clip_by_value(
+            patched_input, clip_value_min=self.estimator.clip_values[0], clip_value_max=self.estimator.clip_values[1],
+        )
+
+        predictions = self.estimator._predict_framework(patched_input)
+
+        return predictions
+
+    def _loss(self, images: "tf.Tensor", target: "tf.Tensor", mask: Optional["tf.Tensor"]) -> "tf.Tensor":
+        import tensorflow as tf  # lgtm [py/repeated-import]
+
+        predictions = self._predictions(images, mask)
+
+        self._loss_per_example = tf.keras.losses.categorical_crossentropy(
+            y_true=target, y_pred=predictions, from_logits=self.use_logits, label_smoothing=0
+        )
+
+        loss = tf.reduce_mean(self._loss_per_example)
+
+        return loss
+
+    def _get_circular_patch_mask(self, nb_samples: int, sharpness: int = 40) -> "tf.Tensor":
+        """
+        Return a circular patch mask.
+        """
+        import tensorflow as tf  # lgtm [py/repeated-import]
+
+        diameter = np.minimum(self.patch_shape[self.i_h_patch], self.patch_shape[self.i_w_patch])
+
+        x = np.linspace(-1, 1, diameter)
+        y = np.linspace(-1, 1, diameter)
+        x_grid, y_grid = np.meshgrid(x, y, sparse=True)
+        z_grid = (x_grid ** 2 + y_grid ** 2) ** sharpness
+
+        image_mask = 1 - np.clip(z_grid, -1, 1)
+        image_mask = np.expand_dims(image_mask, axis=2)
+        image_mask = np.broadcast_to(image_mask, self.patch_shape)
+        image_mask = tf.stack([image_mask] * nb_samples)
+        return image_mask
+
+    def _random_overlay(
+        self,
+        images: Union[np.ndarray, "tf.Tensor"],
+        patch: Union[np.ndarray, "tf.Variable"],
+        scale: Optional[float] = None,
+        mask: Optional[Union[np.ndarray, "tf.Tensor"]] = None,
+    ) -> "tf.Tensor":
+        import tensorflow as tf  # lgtm [py/repeated-import]
+        import tensorflow_addons as tfa
+
+        nb_samples = images.shape[0]
+
+        image_mask = self._get_circular_patch_mask(nb_samples=nb_samples)
+        image_mask = tf.cast(image_mask, images.dtype)
+
+        smallest_image_edge = np.minimum(self.image_shape[self.i_h], self.image_shape[self.i_w])
+
+        image_mask = tf.image.resize(
+            image_mask,
+            size=(smallest_image_edge, smallest_image_edge),
+            method=tf.image.ResizeMethod.BILINEAR,
+            preserve_aspect_ratio=False,
+            antialias=False,
+            name=None,
+        )
+
+        pad_h_before = int((self.image_shape[self.i_h] - image_mask.shape[self.i_h_patch + 1]) / 2)
+        pad_h_after = int(self.image_shape[self.i_h] - pad_h_before - image_mask.shape[self.i_h_patch + 1])
+
+        pad_w_before = int((self.image_shape[self.i_w] - image_mask.shape[self.i_w_patch + 1]) / 2)
+        pad_w_after = int(self.image_shape[self.i_w] - pad_w_before - image_mask.shape[self.i_w_patch + 1])
+
+        image_mask = tf.pad(
+            image_mask,
+            paddings=tf.constant([[0, 0], [pad_h_before, pad_h_after], [pad_w_before, pad_w_after], [0, 0]]),
+            mode="CONSTANT",
+            constant_values=0,
+            name=None,
+        )
+
+        image_mask = tf.cast(image_mask, images.dtype)
+
+        patch = tf.cast(patch, images.dtype)
+        padded_patch = tf.stack([patch] * nb_samples)
+
+        padded_patch = tf.image.resize(
+            padded_patch,
+            size=(smallest_image_edge, smallest_image_edge),
+            method=tf.image.ResizeMethod.BILINEAR,
+            preserve_aspect_ratio=False,
+            antialias=False,
+            name=None,
+        )
+
+        padded_patch = tf.pad(
+            padded_patch,
+            paddings=tf.constant([[0, 0], [pad_h_before, pad_h_after], [pad_w_before, pad_w_after], [0, 0]]),
+            mode="CONSTANT",
+            constant_values=0,
+            name=None,
+        )
+
+        padded_patch = tf.cast(padded_patch, images.dtype)
+
+        transform_vectors = list()
+        translation_vectors = list()
+
+        for i_sample in range(nb_samples):
+            if scale is None:
+                im_scale = np.random.uniform(low=self.scale_min, high=self.scale_max)
+            else:
+                im_scale = scale
+
+            if mask is None:
+                padding_after_scaling_h = (
+                    self.image_shape[self.i_h] - im_scale * padded_patch.shape[self.i_h + 1]
+                ) / 2.0
+                padding_after_scaling_w = (
+                    self.image_shape[self.i_w] - im_scale * padded_patch.shape[self.i_w + 1]
+                ) / 2.0
+                x_shift = np.random.uniform(-padding_after_scaling_w, padding_after_scaling_w)
+                y_shift = np.random.uniform(-padding_after_scaling_h, padding_after_scaling_h)
+            else:
+                mask_2d = mask[i_sample, :, :]
+
+                edge_x_0 = int(im_scale * padded_patch.shape[self.i_w + 1]) // 2
+                edge_x_1 = int(im_scale * padded_patch.shape[self.i_w + 1]) - edge_x_0
+                edge_y_0 = int(im_scale * padded_patch.shape[self.i_h + 1]) // 2
+                edge_y_1 = int(im_scale * padded_patch.shape[self.i_h + 1]) - edge_y_0
+
+                mask_2d[0:edge_x_0, :] = False
+                if edge_x_1 > 0:
+                    mask_2d[-edge_x_1:, :] = False
+                mask_2d[:, 0:edge_y_0] = False
+                if edge_y_1 > 0:
+                    mask_2d[:, -edge_y_1:] = False
+
+                num_pos = np.argwhere(mask_2d).shape[0]
+                pos_id = np.random.choice(num_pos, size=1)
+                pos = np.argwhere(mask_2d)[pos_id[0]]
+                x_shift = pos[1] - self.image_shape[self.i_w] // 2
+                y_shift = pos[0] - self.image_shape[self.i_h] // 2
+
+            phi_rotate = float(np.random.uniform(-self.rotation_max, self.rotation_max)) / 180.0 * math.pi
+
+            # Rotation
+            rotation_matrix = np.array(
+                [[math.cos(-phi_rotate), -math.sin(-phi_rotate)], [math.sin(-phi_rotate), math.cos(-phi_rotate)],]
+            )
+
+            # Scale
+            xform_matrix = rotation_matrix * (1.0 / im_scale)
+            a_0, a_1 = xform_matrix[0]
+            b_0, b_1 = xform_matrix[1]
+
+            x_origin = float(self.image_shape[self.i_w]) / 2
+            y_origin = float(self.image_shape[self.i_h]) / 2
+
+            x_origin_shifted, y_origin_shifted = np.matmul(xform_matrix, np.array([x_origin, y_origin]))
+
+            x_origin_delta = x_origin - x_origin_shifted
+            y_origin_delta = y_origin - y_origin_shifted
+
+            # Run translation in a second step to position patch exactly inside of the mask
+            transform_vectors.append([a_0, a_1, x_origin_delta, b_0, b_1, y_origin_delta, 0, 0])
+            translation_vectors.append([1, 0, -x_shift, 0, 1, -y_shift, 0, 0])
+
+        image_mask = tfa.image.transform(image_mask, transform_vectors, "BILINEAR",)
+        padded_patch = tfa.image.transform(padded_patch, transform_vectors, "BILINEAR",)
+
+        image_mask = tfa.image.transform(image_mask, translation_vectors, "BILINEAR",)
+        padded_patch = tfa.image.transform(padded_patch, translation_vectors, "BILINEAR",)
+
+        if self.nb_dims == 4:
+            image_mask = tf.stack([image_mask] * images.shape[1], axis=1)
+            image_mask = tf.cast(image_mask, images.dtype)
+
+            padded_patch = tf.stack([padded_patch] * images.shape[1], axis=1)
+            padded_patch = tf.cast(padded_patch, images.dtype)
+
+        inverted_mask = tf.constant(1, dtype=image_mask.dtype) - image_mask
+
+        return images * inverted_mask + padded_patch * image_mask
+
+    def generate(self, x: np.ndarray, y: Optional[np.ndarray] = None, **kwargs) -> Tuple[np.ndarray, np.ndarray]:
+        """
+        Generate an adversarial patch and return the patch and its mask in arrays.
+
+        :param x: An array with the original input images of shape NHWC or input videos of shape NFHWC.
+        :param y: An array with the original true labels.
+        :param mask: A boolean array of shape equal to the shape of a single samples (1, H, W) or the shape of `x`
+                     (N, H, W) without their channel dimensions. Any features for which the mask is True can be the
+                     center location of the patch during sampling.
+        :type mask: `np.ndarray`
+        :param reset_patch: If `True` reset patch to initial values of mean of minimal and maximal clip value, else if
+                            `False` (default) restart from previous patch values created by previous call to `generate`
+                            or mean of minimal and maximal clip value if first call to `generate`.
+        :type reset_patch: bool
+        :return: An array with adversarial patch and an array of the patch mask.
+        """
+        import tensorflow as tf  # lgtm [py/repeated-import]
+
+        shuffle = kwargs.get("shuffle", True)
+        mask = kwargs.get("mask")
+        if mask is not None:
+            mask = mask.copy()
+        mask = self._check_mask(mask=mask, x=x)
+
+        if kwargs.get("reset_patch"):
+            self.reset_patch(initial_patch_value=self._initial_value)
+
+        y = check_and_transform_label_format(labels=y, nb_classes=self.estimator.nb_classes)
+
+        # check if logits or probabilities
+        y_pred = self.estimator.predict(x=x[[0]])
+
+        if is_probability(y_pred):
+            self.use_logits = False
+        else:
+            self.use_logits = True
+
+        if mask is None:
+            if shuffle:
+                dataset = (
+                    tf.data.Dataset.from_tensor_slices((x, y))
+                    .shuffle(10000)
+                    .batch(self.batch_size)
+                    .repeat(math.ceil(x.shape[0] / self.batch_size))
+                )
+            else:
+                dataset = (
+                    tf.data.Dataset.from_tensor_slices((x, y))
+                    .batch(self.batch_size)
+                    .repeat(math.ceil(x.shape[0] / self.batch_size))
+                )
+        else:
+            if shuffle:
+                dataset = (
+                    tf.data.Dataset.from_tensor_slices((x, y, mask))
+                    .shuffle(10000)
+                    .batch(self.batch_size)
+                    .repeat(math.ceil(x.shape[0] / self.batch_size))
+                )
+            else:
+                dataset = (
+                    tf.data.Dataset.from_tensor_slices((x, y, mask))
+                    .batch(self.batch_size)
+                    .repeat(math.ceil(x.shape[0] / self.batch_size))
+                )
+
+        for _ in trange(self.max_iter, desc="Adversarial Patch TensorFlow v2", disable=not self.verbose):
+            if mask is None:
+                for images, target in dataset:
+                    _ = self._train_step(images=images, target=target, mask=None)
+            else:
+                for images, target, mask_i in dataset:
+                    _ = self._train_step(images=images, target=target, mask=mask_i)
+
+        return (
+            self._patch.numpy(),
+            self._get_circular_patch_mask(nb_samples=1).numpy()[0],
+        )
+
+    def _check_mask(self, mask: np.ndarray, x: np.ndarray) -> np.ndarray:
+        if mask is not None and (
+            (mask.dtype != np.bool)
+            or not (mask.shape[0] == 1 or mask.shape[0] == x.shape[0])
+            or not (mask.shape[1] == x.shape[self.i_h + 1] and mask.shape[2] == x.shape[self.i_w + 1])
+        ):
+            raise ValueError(
+                "The shape of `mask` has to be equal to the shape of a single samples (1, H, W) or the"
+                "shape of `x` (N, H, W) without their channel dimensions."
+            )
+
+        if mask is not None and mask.shape[0] == 1:
+            mask = np.repeat(mask, repeats=x.shape[0], axis=0)
+
+        return mask
+
+    def apply_patch(
+        self,
+        x: np.ndarray,
+        scale: float,
+        patch_external: Optional[np.ndarray] = None,
+        mask: Optional[np.ndarray] = None,
+    ) -> np.ndarray:
+        """
+        A function to apply the learned adversarial patch to images or videos.
+
+        :param x: Instances to apply randomly transformed patch.
+        :param scale: Scale of the applied patch in relation to the classifier input shape.
+        :param patch_external: External patch to apply to images `x`.
+        :param mask: An boolean array of shape equal to the shape of a single samples (1, H, W) or the shape of `x`
+                     (N, H, W) without their channel dimensions. Any features for which the mask is True can be the
+                     center location of the patch during sampling.
+        :return: The patched samples.
+        """
+        if mask is not None:
+            mask = mask.copy()
+        mask = self._check_mask(mask=mask, x=x)
+        patch = patch_external if patch_external is not None else self._patch
+        return self._random_overlay(images=x, patch=patch, scale=scale, mask=mask).numpy()
+
+    def reset_patch(self, initial_patch_value: Optional[Union[float, np.ndarray]] = None) -> None:
+        """
+        Reset the adversarial patch.
+
+        :param initial_patch_value: Patch value to use for resetting the patch.
+        """
+        if initial_patch_value is None:
+            self._patch.assign(self._initial_value)
+        elif isinstance(initial_patch_value, float):
+            initial_value = np.ones(self.patch_shape) * initial_patch_value
+            self._patch.assign(initial_value)
+        elif self._patch.shape == initial_patch_value.shape:
+            self._patch.assign(initial_patch_value)
+        else:
+            raise ValueError("Unexpected value for initial_patch_value.")
+
+    @staticmethod
+    def insert_transformed_patch(x: np.ndarray, patch: np.ndarray, image_coords: np.ndarray):
+        """
+        Insert patch to image based on given or selected coordinates.
+
+        :param x: The image to insert the patch.
+        :param patch: The patch to be transformed and inserted.
+        :param image_coords: The coordinates of the 4 corners of the transformed, inserted patch of shape
+            [[x1, y1], [x2, y2], [x3, y3], [x4, y4]] in pixel units going in clockwise direction, starting with upper
+            left corner.
+        :return: The input `x` with the patch inserted.
+        """
+        return insert_transformed_patch(x, patch, image_coords)
diff --git a/adversarial-robustness-toolbox/art/attacks/evasion/adversarial_patch/utils.py b/adversarial-robustness-toolbox/art/attacks/evasion/adversarial_patch/utils.py
new file mode 100644
index 0000000..bc8315e
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/attacks/evasion/adversarial_patch/utils.py
@@ -0,0 +1,83 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2021
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements utility functions for adversarial patch attacks.
+"""
+import numpy as np
+
+
+def insert_transformed_patch(x: np.ndarray, patch: np.ndarray, image_coords: np.ndarray):
+    """
+    Insert patch to image based on given or selected coordinates.
+
+    :param x: A single image of shape HWC to insert the patch.
+    :param patch: The patch to be transformed and inserted.
+    :param image_coords: The coordinates of the 4 corners of the transformed, inserted patch of shape
+            [[x1, y1], [x2, y2], [x3, y3], [x4, y4]] in pixel units going in clockwise direction, starting with upper
+            left corner.
+    :return: The input `x` with the patch inserted.
+    """
+    import cv2
+
+    scaling = False
+
+    if np.max(x) <= 1.0:
+        scaling = True
+        x = (x * 255).astype(np.uint8)
+        patch = (patch * 255).astype(np.uint8)
+
+    rows = patch.shape[0]
+    cols = patch.shape[1]
+
+    if image_coords.shape[0] == 4:
+        patch_coords = np.array([[0, 0], [cols - 1, 0], [cols - 1, rows - 1], [0, rows - 1]])
+    else:
+        patch_coords = np.array(
+            [
+                [0, 0],
+                [cols - 1, 0],
+                [cols - 1, (rows - 1) // 2],
+                [cols - 1, rows - 1],
+                [0, rows - 1],
+                [0, (rows - 1) // 2],
+            ]
+        )
+
+    # calculate homography
+    height, _ = cv2.findHomography(patch_coords, image_coords)
+
+    # warp patch to destination coordinates
+    x_out = cv2.warpPerspective(patch, height, (x.shape[1], x.shape[0]), cv2.INTER_CUBIC)
+
+    # mask to aid with insertion
+    mask = np.ones(patch.shape)
+    mask_out = cv2.warpPerspective(mask, height, (x.shape[1], x.shape[0]), cv2.INTER_CUBIC)
+
+    # save image before adding shadows
+    x_neg_patch = np.copy(x)
+    x_neg_patch[mask_out != 0] = 0  # negative of the patch space
+
+    if x_neg_patch.shape[2] == 1:
+        x_out = np.expand_dims(x_out, axis=2)
+
+    x_out = x_neg_patch.astype("float32") + x_out.astype("float32")
+
+    if scaling:
+        x_out = x_out / 255
+
+    return x_out
diff --git a/adversarial-robustness-toolbox/art/attacks/evasion/auto_attack.py b/adversarial-robustness-toolbox/art/attacks/evasion/auto_attack.py
new file mode 100644
index 0000000..e345e37
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/attacks/evasion/auto_attack.py
@@ -0,0 +1,267 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements the `AutoAttack` attack.
+
+| Paper link: https://arxiv.org/abs/2003.01690
+"""
+import logging
+from typing import List, Optional, Union, Tuple, TYPE_CHECKING
+
+import numpy as np
+
+from art.config import ART_NUMPY_DTYPE
+from art.attacks.attack import EvasionAttack
+from art.attacks.evasion.auto_projected_gradient_descent import AutoProjectedGradientDescent
+from art.attacks.evasion.deepfool import DeepFool
+from art.attacks.evasion.square_attack import SquareAttack
+from art.estimators.estimator import BaseEstimator
+from art.estimators.classification.classifier import ClassifierMixin
+from art.utils import get_labels_np_array, check_and_transform_label_format
+
+if TYPE_CHECKING:
+    from art.utils import CLASSIFIER_TYPE
+
+logger = logging.getLogger(__name__)
+
+
+class AutoAttack(EvasionAttack):
+    """
+    Implementation of the `AutoAttack` attack.
+
+    | Paper link: https://arxiv.org/abs/2003.01690
+    """
+
+    attack_params = EvasionAttack.attack_params + [
+        "norm",
+        "eps",
+        "eps_step",
+        "attacks",
+        "batch_size",
+        "estimator_orig",
+        "targeted",
+    ]
+
+    _estimator_requirements = (BaseEstimator, ClassifierMixin)
+
+    def __init__(
+        self,
+        estimator: "CLASSIFIER_TYPE",
+        norm: Union[int, float, str] = np.inf,
+        eps: float = 0.3,
+        eps_step: float = 0.1,
+        attacks: Optional[List[EvasionAttack]] = None,
+        batch_size: int = 32,
+        estimator_orig: Optional["CLASSIFIER_TYPE"] = None,
+        targeted: bool = False,
+    ):
+        """
+        Create a :class:`.AutoAttack` instance.
+
+        :param estimator: An trained estimator.
+        :param norm: The norm of the adversarial perturbation. Possible values: "inf", np.inf, 1 or 2.
+        :param eps: Maximum perturbation that the attacker can introduce.
+        :param eps_step: Attack step size (input variation) at each iteration.
+        :param attacks: The list of `art.attacks.EvasionAttack` attacks to be used for AutoAttack. If it is `None` or
+                        empty the standard attacks (PGD, APGD-ce, APGD-dlr, DeepFool, Square) will be used.
+        :param batch_size: Size of the batch on which adversarial samples are generated.
+        :param estimator_orig: Original estimator to be attacked by adversarial examples.
+        :param targeted: If False run only untargeted attacks, if True also run targeted attacks against each possible
+                         target.
+        """
+        super().__init__(estimator=estimator)
+
+        if attacks is None or not attacks:
+            attacks = list()
+            attacks.append(
+                AutoProjectedGradientDescent(
+                    estimator=estimator,  # type: ignore
+                    norm=norm,
+                    eps=eps,
+                    eps_step=eps_step,
+                    max_iter=100,
+                    targeted=False,
+                    nb_random_init=5,
+                    batch_size=batch_size,
+                    loss_type="cross_entropy",
+                )
+            )
+            attacks.append(
+                AutoProjectedGradientDescent(
+                    estimator=estimator,  # type: ignore
+                    norm=norm,
+                    eps=eps,
+                    eps_step=eps_step,
+                    max_iter=100,
+                    targeted=False,
+                    nb_random_init=5,
+                    batch_size=batch_size,
+                    loss_type="difference_logits_ratio",
+                )
+            )
+            attacks.append(
+                (
+                    DeepFool(
+                        classifier=estimator,  # type: ignore
+                        max_iter=100,
+                        epsilon=1e-3,
+                        nb_grads=10,
+                        batch_size=batch_size,
+                    )
+                )
+            )
+            attacks.append(
+                SquareAttack(estimator=estimator, norm=norm, max_iter=5000, eps=eps, p_init=0.8, nb_restarts=5)
+            )
+
+        self.norm = norm
+        self.eps = eps
+        self.eps_step = eps_step
+        self.attacks = attacks
+        self.batch_size = batch_size
+        if estimator_orig is not None:
+            self.estimator_orig = estimator_orig
+        else:
+            self.estimator_orig = estimator
+
+        self._targeted = targeted
+        self._check_params()
+
+    def generate(self, x: np.ndarray, y: Optional[np.ndarray] = None, **kwargs) -> np.ndarray:
+        """
+        Generate adversarial samples and return them in an array.
+
+        :param x: An array with the original inputs.
+        :param y: Target values (class labels) one-hot-encoded of shape `(nb_samples, nb_classes)` or indices of shape
+                  (nb_samples,). Only provide this parameter if you'd like to use true labels when crafting adversarial
+                  samples. Otherwise, model predictions are used as labels to avoid the "label leaking" effect
+                  (explained in this paper: https://arxiv.org/abs/1611.01236). Default is `None`.
+        :param mask: An array with a mask broadcastable to input `x` defining where to apply adversarial perturbations.
+                     Shape needs to be broadcastable to the shape of x and can also be of the same shape as `x`. Any
+                     features for which the mask is zero will not be adversarially perturbed.
+        :type mask: `np.ndarray`
+        :return: An array holding the adversarial examples.
+        """
+        x_adv = x.astype(ART_NUMPY_DTYPE)
+        y = check_and_transform_label_format(y, self.estimator.nb_classes)
+
+        if y is None:
+            y = get_labels_np_array(self.estimator.predict(x, batch_size=self.batch_size))
+
+        # Determine correctly predicted samples
+        y_pred = self.estimator_orig.predict(x.astype(ART_NUMPY_DTYPE))
+        sample_is_robust = np.argmax(y_pred, axis=1) == np.argmax(y, axis=1)
+
+        # Untargeted attacks
+        for attack in self.attacks:
+
+            # Stop if all samples are misclassified
+            if np.sum(sample_is_robust) == 0:
+                break
+
+            if attack.targeted:
+                attack.set_params(targeted=False)
+
+            x_adv, sample_is_robust = self._run_attack(
+                x=x_adv, y=y, sample_is_robust=sample_is_robust, attack=attack, **kwargs,
+            )
+
+        # Targeted attacks
+        if self.targeted:
+            # Labels for targeted attacks
+            y_t = np.array([range(y.shape[1])] * y.shape[0])
+            y_idx = np.argmax(y, axis=1)
+            y_idx = np.expand_dims(y_idx, 1)
+            y_t = y_t[y_t != y_idx]
+            targeted_labels = np.reshape(y_t, (y.shape[0], -1))
+
+            for attack in self.attacks:
+
+                if attack.targeted is not None:
+
+                    if not attack.targeted:
+                        attack.set_params(targeted=True)
+
+                    for i in range(self.estimator.nb_classes - 1):
+                        # Stop if all samples are misclassified
+                        if np.sum(sample_is_robust) == 0:
+                            break
+
+                        target = check_and_transform_label_format(targeted_labels[:, i], self.estimator.nb_classes)
+
+                        x_adv, sample_is_robust = self._run_attack(
+                            x=x_adv, y=target, sample_is_robust=sample_is_robust, attack=attack, **kwargs,
+                        )
+
+        return x_adv
+
+    def _run_attack(
+        self, x: np.ndarray, y: np.ndarray, sample_is_robust: np.ndarray, attack: EvasionAttack, **kwargs,
+    ) -> Tuple[np.ndarray, np.ndarray]:
+        """
+        Run attack.
+
+        :param x: An array of the original inputs.
+        :param y: An array of the labels.
+        :param sample_is_robust: Store the initial robustness of examples.
+        :param attack: Evasion attack to run.
+        :return: An array holding the adversarial examples.
+        """
+        # Attack only correctly classified samples
+        x_robust = x[sample_is_robust]
+        y_robust = y[sample_is_robust]
+
+        # Generate adversarial examples
+        x_robust_adv = attack.generate(x=x_robust, y=y_robust, **kwargs)
+        y_pred_robust_adv = self.estimator_orig.predict(x_robust_adv)
+
+        # Check and update successful examples
+        rel_acc = 1e-4
+        order = np.inf if self.norm == "inf" else self.norm
+        norm_is_smaller_eps = (1 - rel_acc) * np.linalg.norm(
+            (x_robust_adv - x_robust).reshape((x_robust_adv.shape[0], -1)), axis=1, ord=order
+        ) <= self.eps
+
+        if attack.targeted:
+            samples_misclassified = np.argmax(y_pred_robust_adv, axis=1) == np.argmax(y_robust, axis=1)
+        elif not attack.targeted:
+            samples_misclassified = np.argmax(y_pred_robust_adv, axis=1) != np.argmax(y_robust, axis=1)
+        else:
+            raise ValueError
+
+        sample_is_not_robust = np.logical_and(samples_misclassified, norm_is_smaller_eps)
+
+        x_robust[sample_is_not_robust] = x_robust_adv[sample_is_not_robust]
+        x[sample_is_robust] = x_robust
+
+        sample_is_robust[sample_is_robust] = np.invert(sample_is_not_robust)
+
+        return x, sample_is_robust
+
+    def _check_params(self) -> None:
+        if self.norm not in [1, 2, np.inf, "inf"]:
+            raise ValueError('The argument norm has to be either 1, 2, np.inf, "inf".')
+
+        if not isinstance(self.eps, (int, float)) or self.eps <= 0.0:
+            raise ValueError("The argument eps has to be either of type int or float and larger than zero.")
+
+        if not isinstance(self.eps_step, (int, float)) or self.eps_step <= 0.0:
+            raise ValueError("The argument eps_step has to be either of type int or float and larger than zero.")
+
+        if not isinstance(self.batch_size, int) or self.batch_size <= 0:
+            raise ValueError("The argument batch_size has to be of type int and larger than zero.")
diff --git a/adversarial-robustness-toolbox/art/attacks/evasion/auto_projected_gradient_descent.py b/adversarial-robustness-toolbox/art/attacks/evasion/auto_projected_gradient_descent.py
new file mode 100644
index 0000000..dda1435
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/attacks/evasion/auto_projected_gradient_descent.py
@@ -0,0 +1,581 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements the `Auto Projected Gradient Descent` attack.
+
+| Paper link: https://arxiv.org/abs/2003.01690
+"""
+import logging
+import math
+from typing import Optional, Union, TYPE_CHECKING
+
+import numpy as np
+from tqdm.auto import trange
+
+from art.config import ART_NUMPY_DTYPE
+from art.attacks.attack import EvasionAttack
+from art.estimators.estimator import BaseEstimator, LossGradientsMixin
+from art.estimators.classification.classifier import ClassifierMixin
+from art.utils import check_and_transform_label_format, projection, random_sphere, is_probability, get_labels_np_array
+
+if TYPE_CHECKING:
+    from art.utils import CLASSIFIER_LOSS_GRADIENTS_TYPE
+
+logger = logging.getLogger(__name__)
+
+
+class AutoProjectedGradientDescent(EvasionAttack):
+    """
+    Implementation of the `Auto Projected Gradient Descent` attack.
+
+    | Paper link: https://arxiv.org/abs/2003.01690
+    """
+
+    attack_params = EvasionAttack.attack_params + [
+        "norm",
+        "eps",
+        "eps_step",
+        "max_iter",
+        "targeted",
+        "nb_random_init",
+        "batch_size",
+        "loss_type",
+        "verbose",
+    ]
+    _estimator_requirements = (BaseEstimator, LossGradientsMixin, ClassifierMixin)
+    _predefined_losses = [None, "cross_entropy", "difference_logits_ratio"]
+
+    def __init__(
+        self,
+        estimator: "CLASSIFIER_LOSS_GRADIENTS_TYPE",
+        norm: Union[int, float, str] = np.inf,
+        eps: float = 0.3,
+        eps_step: float = 0.1,
+        max_iter: int = 100,
+        targeted: bool = False,
+        nb_random_init: int = 5,
+        batch_size: int = 32,
+        loss_type: Optional[str] = None,
+        verbose: bool = True,
+    ):
+        """
+        Create a :class:`.AutoProjectedGradientDescent` instance.
+
+        :param estimator: An trained estimator.
+        :param norm: The norm of the adversarial perturbation. Possible values: "inf", np.inf, 1 or 2.
+        :param eps: Maximum perturbation that the attacker can introduce.
+        :param eps_step: Attack step size (input variation) at each iteration.
+        :param max_iter: The maximum number of iterations.
+        :param targeted: Indicates whether the attack is targeted (True) or untargeted (False).
+        :param nb_random_init: Number of random initialisations within the epsilon ball. For num_random_init=0
+            starting at the original input.
+        :param batch_size: Size of the batch on which adversarial samples are generated.
+        :param loss_type: Defines the loss to attack. Available options: None (Use loss defined by estimator),
+            "cross_entropy", or "difference_logits_ratio"
+        :param verbose: Show progress bars.
+        """
+        from art.estimators.classification import TensorFlowClassifier, TensorFlowV2Classifier, PyTorchClassifier
+
+        if loss_type not in self._predefined_losses:
+            raise ValueError(
+                "The argument loss_type has an invalid value. The following options for `loss_type` are currently "
+                "supported: {}".format(self._predefined_losses)
+            )
+
+        if loss_type is None:
+            if hasattr(estimator, "predict") and is_probability(
+                estimator.predict(x=np.ones(shape=(1, *estimator.input_shape), dtype=np.float32))
+            ):
+                raise ValueError(
+                    "AutoProjectedGradientDescent is expecting logits as estimator output, the provided "
+                    "estimator seems to predict probabilities."
+                )
+
+            estimator_apgd = estimator
+        else:
+            if isinstance(estimator, TensorFlowClassifier):
+                import tensorflow as tf
+
+                if loss_type == "cross_entropy":
+                    if is_probability(estimator.predict(x=np.ones(shape=(1, *estimator.input_shape)))):
+                        raise NotImplementedError("Cross-entropy loss is not implemented for probability output.")
+
+                    self._loss_object = tf.reduce_mean(
+                        tf.keras.losses.categorical_crossentropy(
+                            y_pred=estimator._output, y_true=estimator._labels_ph, from_logits=True
+                        )
+                    )
+
+                elif loss_type == "difference_logits_ratio":
+                    if is_probability(estimator.predict(x=np.ones(shape=(1, *estimator.input_shape)))):
+                        raise ValueError(
+                            "The provided estimator seems to predict probabilities. "
+                            "If loss_type='difference_logits_ratio' the estimator has to to predict logits."
+                        )
+
+                    raise ValueError(
+                        "The loss `difference_logits_ratio` has not been validate completely. It seems that the "
+                        "commented implemented below is failing to selected the second largest logit for cases "
+                        "where the largest logit is the true logit. For future work `difference_logits_ratio` and "
+                        "loss_fn should return the same loss value."
+                    )
+
+                    # def difference_logits_ratio(y_true, y_pred):
+                    #     i_y_true = tf.cast(tf.math.argmax(tf.cast(y_true, tf.int32), axis=1), tf.int32)
+                    #     i_y_pred_arg = tf.argsort(y_pred, axis=1)
+                    #     # Not completely sure if the following line is correct.
+                    #     # `i_y_pred_arg[:, -2], i_y_pred_arg[:, -1]` seems closer to the output of `loss_fn` than
+                    #     # `i_y_pred_arg[:, -1], i_y_pred_arg[:, -2]`
+                    #     i_z_i = tf.where(i_y_pred_arg[:, -1] != i_y_true[:], i_y_pred_arg[:, -2],
+                    #                      i_y_pred_arg[:, -1])
+                    #
+                    #     z_1 = tf.gather(y_pred, i_y_pred_arg[:, -1], axis=1, batch_dims=0)
+                    #     z_3 = tf.gather(y_pred, i_y_pred_arg[:, -3], axis=1, batch_dims=0)
+                    #     z_i = tf.gather(y_pred, i_z_i, axis=1, batch_dims=0)
+                    #     z_y = tf.gather(y_pred, i_y_true, axis=1, batch_dims=0)
+                    #
+                    #     z_1 = tf.linalg.diag_part(z_1)
+                    #     z_3 = tf.linalg.diag_part(z_3)
+                    #     z_i = tf.linalg.diag_part(z_i)
+                    #     z_y = tf.linalg.diag_part(z_y)
+                    #
+                    #     dlr = -(z_y - z_i) / (z_1 - z_3)
+                    #
+                    #     return tf.reduce_mean(dlr)
+                    #
+                    # def loss_fn(y_true, y_pred):
+                    #     i_y_true = np.argmax(y_true, axis=1)
+                    #     i_y_pred_arg = np.argsort(y_pred, axis=1)
+                    #     i_z_i = np.where(i_y_pred_arg[:, -1] != i_y_true[:], i_y_pred_arg[:, -1],
+                    #                      i_y_pred_arg[:, -2])
+                    #
+                    #     z_1 = y_pred[:, i_y_pred_arg[:, -1]]
+                    #     z_3 = y_pred[:, i_y_pred_arg[:, -3]]
+                    #     z_i = y_pred[:, i_z_i]
+                    #     z_y = y_pred[:, i_y_true]
+                    #
+                    #     z_1 = np.diag(z_1)
+                    #     z_3 = np.diag(z_3)
+                    #     z_i = np.diag(z_i)
+                    #     z_y = np.diag(z_y)
+                    #
+                    #     dlr = -(z_y - z_i) / (z_1 - z_3)
+                    #
+                    #     return np.mean(dlr)
+                    #
+                    # self._loss_fn = loss_fn
+                    # self._loss_object = difference_logits_ratio(y_true=estimator._labels_ph,
+                    #                                             y_pred=estimator._output)
+
+                estimator_apgd = TensorFlowClassifier(
+                    input_ph=estimator._input_ph,
+                    output=estimator._output,
+                    labels_ph=estimator._labels_ph,
+                    train=estimator._train,
+                    loss=self._loss_object,
+                    learning=estimator._learning,
+                    sess=estimator._sess,
+                    channels_first=estimator.channels_first,
+                    clip_values=estimator.clip_values,
+                    preprocessing_defences=estimator.preprocessing_defences,
+                    postprocessing_defences=estimator.postprocessing_defences,
+                    preprocessing=estimator.preprocessing,
+                    feed_dict=estimator._feed_dict,
+                )
+
+            elif isinstance(estimator, TensorFlowV2Classifier):
+                import tensorflow as tf
+
+                if loss_type == "cross_entropy":
+                    if is_probability(estimator.predict(x=np.ones(shape=(1, *estimator.input_shape)))):
+                        self._loss_object = tf.keras.losses.CategoricalCrossentropy(from_logits=False)
+                    else:
+                        self._loss_object = tf.keras.losses.CategoricalCrossentropy(from_logits=True)
+                elif loss_type == "difference_logits_ratio":
+                    if is_probability(estimator.predict(x=np.ones(shape=(1, *estimator.input_shape)))):
+                        raise ValueError(
+                            "The provided estimator seems to predict probabilities. "
+                            "If loss_type='difference_logits_ratio' the estimator has to to predict logits."
+                        )
+
+                    class difference_logits_ratio:
+                        def __init__(self):
+                            self.reduction = "mean"
+
+                        def __call__(self, y_true, y_pred):
+                            i_y_true = tf.cast(tf.math.argmax(tf.cast(y_true, tf.int32), axis=1), tf.int32)
+                            i_y_pred_arg = tf.argsort(y_pred, axis=1)
+                            i_z_i_list = list()
+
+                            for i in range(y_true.shape[0]):
+                                if i_y_pred_arg[i, -1] != i_y_true[i]:
+                                    i_z_i_list.append(i_y_pred_arg[i, -1])
+                                else:
+                                    i_z_i_list.append(i_y_pred_arg[i, -2])
+
+                            i_z_i = tf.stack(i_z_i_list)
+
+                            z_1 = tf.gather(y_pred, i_y_pred_arg[:, -1], axis=1, batch_dims=0)
+                            z_3 = tf.gather(y_pred, i_y_pred_arg[:, -3], axis=1, batch_dims=0)
+                            z_i = tf.gather(y_pred, i_z_i, axis=1, batch_dims=0)
+                            z_y = tf.gather(y_pred, i_y_true, axis=1, batch_dims=0)
+
+                            z_1 = tf.linalg.diag_part(z_1)
+                            z_3 = tf.linalg.diag_part(z_3)
+                            z_i = tf.linalg.diag_part(z_i)
+                            z_y = tf.linalg.diag_part(z_y)
+
+                            dlr = -(z_y - z_i) / (z_1 - z_3)
+
+                            return tf.reduce_mean(dlr)
+
+                    self._loss_fn = difference_logits_ratio()
+                    self._loss_object = difference_logits_ratio()
+
+                estimator_apgd = TensorFlowV2Classifier(
+                    model=estimator.model,
+                    nb_classes=estimator.nb_classes,
+                    input_shape=estimator.input_shape,
+                    loss_object=self._loss_object,
+                    train_step=estimator._train_step,
+                    channels_first=estimator.channels_first,
+                    clip_values=estimator.clip_values,
+                    preprocessing_defences=estimator.preprocessing_defences,
+                    postprocessing_defences=estimator.postprocessing_defences,
+                    preprocessing=estimator.preprocessing,
+                )
+            elif isinstance(estimator, PyTorchClassifier):
+                import torch
+
+                if loss_type == "cross_entropy":
+                    if is_probability(
+                        estimator.predict(x=np.ones(shape=(1, *estimator.input_shape), dtype=np.float32))
+                    ):
+                        raise ValueError(
+                            "The provided estimator seems to predict probabilities. If loss_type='cross_entropy' "
+                            "the estimator has to to predict logits."
+                        )
+
+                    self._loss_object = torch.nn.CrossEntropyLoss(reduction="mean")
+                elif loss_type == "difference_logits_ratio":
+                    if is_probability(
+                        estimator.predict(x=np.ones(shape=(1, *estimator.input_shape), dtype=ART_NUMPY_DTYPE))
+                    ):
+                        raise ValueError(
+                            "The provided estimator seems to predict probabilities. "
+                            "If loss_type='difference_logits_ratio' the estimator has to to predict logits."
+                        )
+
+                    class difference_logits_ratio:
+                        def __init__(self):
+                            self.reduction = "mean"
+
+                        def __call__(self, y_pred, y_true):  # type: ignore
+                            if isinstance(y_true, np.ndarray):
+                                y_true = torch.from_numpy(y_true)
+                            if isinstance(y_pred, np.ndarray):
+                                y_pred = torch.from_numpy(y_pred)
+
+                            y_true = y_true.float()
+
+                            i_y_true = torch.argmax(y_true, axis=1)
+                            i_y_pred_arg = torch.argsort(y_pred, axis=1)
+                            i_z_i_list = list()
+
+                            for i in range(y_true.shape[0]):
+                                if i_y_pred_arg[i, -1] != i_y_true[i]:
+                                    i_z_i_list.append(i_y_pred_arg[i, -1])
+                                else:
+                                    i_z_i_list.append(i_y_pred_arg[i, -2])
+
+                            i_z_i = torch.stack(i_z_i_list)
+
+                            z_1 = y_pred[:, i_y_pred_arg[:, -1]]
+                            z_3 = y_pred[:, i_y_pred_arg[:, -3]]
+                            z_i = y_pred[:, i_z_i]
+                            z_y = y_pred[:, i_y_true]
+
+                            z_1 = torch.diagonal(z_1)
+                            z_3 = torch.diagonal(z_3)
+                            z_i = torch.diagonal(z_i)
+                            z_y = torch.diagonal(z_y)
+
+                            dlr = -(z_y - z_i) / (z_1 - z_3)
+
+                            return torch.mean(dlr.float())
+
+                    self._loss_object = difference_logits_ratio()
+
+                estimator_apgd = PyTorchClassifier(
+                    model=estimator.model,
+                    loss=self._loss_object,
+                    input_shape=estimator.input_shape,
+                    nb_classes=estimator.nb_classes,
+                    optimizer=None,
+                    channels_first=estimator.channels_first,
+                    clip_values=estimator.clip_values,
+                    preprocessing_defences=estimator.preprocessing_defences,
+                    postprocessing_defences=estimator.postprocessing_defences,
+                    preprocessing=estimator.preprocessing,
+                    device_type=estimator._device,
+                )
+
+            else:
+                raise ValueError("The loss type {} is not supported for the provided estimator.".format(loss_type))
+
+        super().__init__(estimator=estimator_apgd)
+        self.norm = norm
+        self.eps = eps
+        self.eps_step = eps_step
+        self.max_iter = max_iter
+        self.targeted = targeted
+        self.nb_random_init = nb_random_init
+        self.batch_size = batch_size
+        self.loss_type = loss_type
+        self.verbose = verbose
+        self._check_params()
+
+    def generate(self, x: np.ndarray, y: Optional[np.ndarray] = None, **kwargs) -> np.ndarray:
+        """
+        Generate adversarial samples and return them in an array.
+
+        :param x: An array with the original inputs.
+        :param y: Target values (class labels) one-hot-encoded of shape `(nb_samples, nb_classes)` or indices of shape
+                  (nb_samples,). Only provide this parameter if you'd like to use true labels when crafting adversarial
+                  samples. Otherwise, model predictions are used as labels to avoid the "label leaking" effect
+                  (explained in this paper: https://arxiv.org/abs/1611.01236). Default is `None`.
+        :param mask: An array with a mask broadcastable to input `x` defining where to apply adversarial perturbations.
+                     Shape needs to be broadcastable to the shape of x and can also be of the same shape as `x`. Any
+                     features for which the mask is zero will not be adversarially perturbed.
+        :type mask: `np.ndarray`
+        :return: An array holding the adversarial examples.
+        """
+        mask = kwargs.get("mask")
+
+        y = check_and_transform_label_format(y, self.estimator.nb_classes)
+
+        if y is None:
+            if self.targeted:
+                raise ValueError("Target labels `y` need to be provided for a targeted attack.")
+            y = get_labels_np_array(self.estimator.predict(x, batch_size=self.batch_size)).astype(np.int32)
+
+        x_adv = x.astype(ART_NUMPY_DTYPE)
+
+        for _ in trange(max(1, self.nb_random_init), desc="AutoPGD - restart", disable=not self.verbose):
+            # Determine correctly predicted samples
+            y_pred = self.estimator.predict(x_adv)
+            if self.targeted:
+                sample_is_robust = np.argmax(y_pred, axis=1) != np.argmax(y, axis=1)
+            elif not self.targeted:
+                sample_is_robust = np.argmax(y_pred, axis=1) == np.argmax(y, axis=1)
+
+            if np.sum(sample_is_robust) == 0:
+                break
+
+            x_robust = x_adv[sample_is_robust]
+            y_robust = y[sample_is_robust]
+            x_init = x[sample_is_robust]
+
+            n = x_robust.shape[0]
+            m = np.prod(x_robust.shape[1:]).item()
+            random_perturbation = (
+                random_sphere(n, m, self.eps, self.norm).reshape(x_robust.shape).astype(ART_NUMPY_DTYPE)
+            )
+
+            x_robust = x_robust + random_perturbation
+
+            if self.estimator.clip_values is not None:
+                clip_min, clip_max = self.estimator.clip_values
+                x_robust = np.clip(x_robust, clip_min, clip_max)
+
+            perturbation = projection(x_robust - x_init, self.eps, self.norm)
+            x_robust = x_init + perturbation
+
+            # Compute perturbation with implicit batching
+            for batch_id in trange(
+                int(np.ceil(x_robust.shape[0] / float(self.batch_size))),
+                desc="AutoPGD - batch",
+                leave=False,
+                disable=not self.verbose,
+            ):
+                self.eta = 2 * self.eps_step
+                batch_index_1, batch_index_2 = batch_id * self.batch_size, (batch_id + 1) * self.batch_size
+                x_k = x_robust[batch_index_1:batch_index_2].astype(ART_NUMPY_DTYPE)
+                x_init_batch = x_init[batch_index_1:batch_index_2].astype(ART_NUMPY_DTYPE)
+                y_batch = y_robust[batch_index_1:batch_index_2]
+
+                p_0 = 0
+                p_1 = 0.22
+                W = [p_0, p_1]
+
+                while True:
+                    p_j_p_1 = W[-1] + max(W[-1] - W[-2] - 0.03, 0.06)
+                    if p_j_p_1 > 1:
+                        break
+                    W.append(p_j_p_1)
+
+                W = [math.ceil(p * self.max_iter) for p in W]
+
+                eta = self.eps_step
+                self.count_condition_1 = 0
+
+                for k_iter in trange(self.max_iter, desc="AutoPGD - iteration", leave=False, disable=not self.verbose):
+
+                    # Get perturbation, use small scalar to avoid division by 0
+                    tol = 10e-8
+
+                    # Get gradient wrt loss; invert it if attack is targeted
+                    grad = self.estimator.loss_gradient(x_k, y_batch) * (1 - 2 * int(self.targeted))
+
+                    # Apply norm bound
+                    if self.norm in [np.inf, "inf"]:
+                        grad = np.sign(grad)
+                    elif self.norm == 1:
+                        ind = tuple(range(1, len(x_k.shape)))
+                        grad = grad / (np.sum(np.abs(grad), axis=ind, keepdims=True) + tol)
+                    elif self.norm == 2:
+                        ind = tuple(range(1, len(x_k.shape)))
+                        grad = grad / (np.sqrt(np.sum(np.square(grad), axis=ind, keepdims=True)) + tol)
+                    assert x_k.shape == grad.shape
+
+                    perturbation = grad
+
+                    if mask is not None:
+                        perturbation = perturbation * (mask.astype(ART_NUMPY_DTYPE))
+
+                    # Apply perturbation and clip
+                    z_k_p_1 = x_k + eta * perturbation
+
+                    if self.estimator.clip_values is not None:
+                        clip_min, clip_max = self.estimator.clip_values
+                        z_k_p_1 = np.clip(z_k_p_1, clip_min, clip_max)
+
+                    if k_iter == 0:
+                        x_1 = z_k_p_1
+                        perturbation = projection(x_1 - x_init_batch, self.eps, self.norm)
+                        x_1 = x_init_batch + perturbation
+
+                        f_0 = self.estimator.compute_loss(x=x_k, y=y_batch, reduction="mean")
+                        f_1 = self.estimator.compute_loss(x=x_1, y=y_batch, reduction="mean")
+
+                        self.eta_w_j_m_1 = eta
+                        self.f_max_w_j_m_1 = f_0
+
+                        if f_1 >= f_0:
+                            self.f_max = f_1
+                            self.x_max = x_1
+                            self.x_max_m_1 = x_init_batch
+                            self.count_condition_1 += 1
+                        else:
+                            self.f_max = f_0
+                            self.x_max = x_k.copy()
+                            self.x_max_m_1 = x_init_batch
+
+                        # Settings for next iteration k
+                        x_k_m_1 = x_k.copy()
+                        x_k = x_1
+
+                    else:
+                        perturbation = projection(z_k_p_1 - x_init_batch, self.eps, self.norm)
+                        z_k_p_1 = x_init_batch + perturbation
+
+                        alpha = 0.75
+
+                        x_k_p_1 = x_k + alpha * (z_k_p_1 - x_k) + (1 - alpha) * (x_k - x_k_m_1)
+
+                        if self.estimator.clip_values is not None:
+                            clip_min, clip_max = self.estimator.clip_values
+                            x_k_p_1 = np.clip(x_k_p_1, clip_min, clip_max)
+
+                        perturbation = projection(x_k_p_1 - x_init_batch, self.eps, self.norm)
+                        x_k_p_1 = x_init_batch + perturbation
+
+                        f_k_p_1 = self.estimator.compute_loss(x=x_k_p_1, y=y_batch, reduction="mean")
+
+                        if f_k_p_1 == 0.0:
+                            x_k = x_k_p_1.copy()
+                            break
+
+                        if (not self.targeted and f_k_p_1 > self.f_max) or (self.targeted and f_k_p_1 < self.f_max):
+                            self.count_condition_1 += 1
+                            self.x_max = x_k_p_1
+                            self.x_max_m_1 = x_k
+                            self.f_max = f_k_p_1
+
+                        if k_iter in W:
+
+                            rho = 0.75
+
+                            condition_1 = self.count_condition_1 < rho * (k_iter - W[W.index(k_iter) - 1])
+                            condition_2 = self.eta_w_j_m_1 == eta and self.f_max_w_j_m_1 == self.f_max
+
+                            if condition_1 or condition_2:
+                                eta = eta / 2
+                                x_k_m_1 = self.x_max_m_1
+                                x_k = self.x_max
+                            else:
+                                x_k_m_1 = x_k
+                                x_k = x_k_p_1.copy()
+
+                            self.count_condition_1 = 0
+                            self.eta_w_j_m_1 = eta
+                            self.f_max_w_j_m_1 = self.f_max
+
+                        else:
+                            x_k_m_1 = x_k
+                            x_k = x_k_p_1.copy()
+
+                y_pred_adv_k = self.estimator.predict(x_k)
+                if self.targeted:
+                    sample_is_not_robust_k = np.invert(np.argmax(y_pred_adv_k, axis=1) != np.argmax(y_batch, axis=1))
+                elif not self.targeted:
+                    sample_is_not_robust_k = np.invert(np.argmax(y_pred_adv_k, axis=1) == np.argmax(y_batch, axis=1))
+
+                x_robust[batch_index_1:batch_index_2][sample_is_not_robust_k] = x_k[sample_is_not_robust_k]
+
+            x_adv[sample_is_robust] = x_robust
+
+        return x_adv
+
+    def _check_params(self) -> None:
+        if self.norm not in [1, 2, np.inf, "inf"]:
+            raise ValueError('The argument norm has to be either 1, 2, np.inf, or "inf".')
+
+        if not isinstance(self.eps, (int, float)) or self.eps <= 0.0:
+            raise ValueError("The argument eps has to be either of type int or float and larger than zero.")
+
+        if not isinstance(self.eps_step, (int, float)) or self.eps_step <= 0.0:
+            raise ValueError("The argument eps_step has to be either of type int or float and larger than zero.")
+
+        if not isinstance(self.max_iter, int) or self.max_iter <= 0:
+            raise ValueError("The argument max_iter has to be of type int and larger than zero.")
+
+        if not isinstance(self.targeted, bool):
+            raise ValueError("The argument targeted has to be of bool.")
+
+        if not isinstance(self.nb_random_init, int) or self.nb_random_init <= 0:
+            raise ValueError("The argument nb_random_init has to be of type int and larger than zero.")
+
+        if not isinstance(self.batch_size, int) or self.batch_size <= 0:
+            raise ValueError("The argument batch_size has to be of type int and larger than zero.")
+
+        if self.loss_type not in self._predefined_losses:
+            raise ValueError("The argument loss_type has to be either {}.".format(self._predefined_losses))
+
+        if not isinstance(self.verbose, bool):
+            raise ValueError("The argument `verbose` has to be of type bool.")
diff --git a/adversarial-robustness-toolbox/art/attacks/evasion/boundary.py b/adversarial-robustness-toolbox/art/attacks/evasion/boundary.py
new file mode 100644
index 0000000..b7ab8e7
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/attacks/evasion/boundary.py
@@ -0,0 +1,441 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2019
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements the boundary attack `BoundaryAttack`. This is a black-box attack which only requires class
+predictions.
+
+| Paper link: https://arxiv.org/abs/1712.04248
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+from typing import Optional, Tuple, TYPE_CHECKING
+
+import numpy as np
+from tqdm.auto import tqdm, trange
+
+from art.attacks.attack import EvasionAttack
+from art.config import ART_NUMPY_DTYPE
+from art.estimators.estimator import BaseEstimator
+from art.estimators.classification.classifier import ClassifierMixin
+from art.utils import compute_success, to_categorical, check_and_transform_label_format
+
+if TYPE_CHECKING:
+    from art.utils import CLASSIFIER_TYPE
+
+logger = logging.getLogger(__name__)
+
+
+class BoundaryAttack(EvasionAttack):
+    """
+    Implementation of the boundary attack from Brendel et al. (2018). This is a powerful black-box attack that
+    only requires final class prediction.
+
+    | Paper link: https://arxiv.org/abs/1712.04248
+    """
+
+    attack_params = EvasionAttack.attack_params + [
+        "targeted",
+        "delta",
+        "epsilon",
+        "step_adapt",
+        "max_iter",
+        "num_trial",
+        "sample_size",
+        "init_size",
+        "batch_size",
+        "verbose",
+    ]
+
+    _estimator_requirements = (BaseEstimator, ClassifierMixin)
+
+    def __init__(
+        self,
+        estimator: "CLASSIFIER_TYPE",
+        targeted: bool = True,
+        delta: float = 0.01,
+        epsilon: float = 0.01,
+        step_adapt: float = 0.667,
+        max_iter: int = 5000,
+        num_trial: int = 25,
+        sample_size: int = 20,
+        init_size: int = 100,
+        min_epsilon: Optional[float] = None,
+        verbose: bool = True,
+    ) -> None:
+        """
+        Create a boundary attack instance.
+
+        :param estimator: A trained classifier.
+        :param targeted: Should the attack target one specific class.
+        :param delta: Initial step size for the orthogonal step.
+        :param epsilon: Initial step size for the step towards the target.
+        :param step_adapt: Factor by which the step sizes are multiplied or divided, must be in the range (0, 1).
+        :param max_iter: Maximum number of iterations.
+        :param num_trial: Maximum number of trials per iteration.
+        :param sample_size: Number of samples per trial.
+        :param init_size: Maximum number of trials for initial generation of adversarial examples.
+        :param min_epsilon: Stop attack if perturbation is smaller than `min_epsilon`.
+        :param verbose: Show progress bars.
+        """
+        super().__init__(estimator=estimator)
+
+        self._targeted = targeted
+        self.delta = delta
+        self.epsilon = epsilon
+        self.step_adapt = step_adapt
+        self.max_iter = max_iter
+        self.num_trial = num_trial
+        self.sample_size = sample_size
+        self.init_size = init_size
+        self.min_epsilon = min_epsilon
+        self.batch_size = 1
+        self.verbose = verbose
+        self._check_params()
+
+        self.curr_adv = None
+
+    def generate(self, x: np.ndarray, y: Optional[np.ndarray] = None, **kwargs) -> np.ndarray:
+        """
+        Generate adversarial samples and return them in an array.
+
+        :param x: An array with the original inputs to be attacked.
+        :param y: Target values (class labels) one-hot-encoded of shape (nb_samples, nb_classes) or indices of shape
+                  (nb_samples,). If `self.targeted` is true, then `y` represents the target labels.
+        :param x_adv_init: Initial array to act as initial adversarial examples. Same shape as `x`.
+        :type x_adv_init: `np.ndarray`
+        :return: An array holding the adversarial examples.
+        """
+        y = check_and_transform_label_format(y, self.estimator.nb_classes, return_one_hot=False)
+
+        # Get clip_min and clip_max from the classifier or infer them from data
+        if self.estimator.clip_values is not None:
+            clip_min, clip_max = self.estimator.clip_values
+        else:
+            clip_min, clip_max = np.min(x), np.max(x)
+
+        # Prediction from the original images
+        preds = np.argmax(self.estimator.predict(x, batch_size=self.batch_size), axis=1)
+
+        # Prediction from the initial adversarial examples if not None
+        x_adv_init = kwargs.get("x_adv_init")
+
+        if x_adv_init is not None:
+            init_preds = np.argmax(self.estimator.predict(x_adv_init, batch_size=self.batch_size), axis=1)
+        else:
+            init_preds = [None] * len(x)
+            x_adv_init = [None] * len(x)
+
+        # Assert that, if attack is targeted, y is provided
+        if self.targeted and y is None:
+            raise ValueError("Target labels `y` need to be provided for a targeted attack.")
+
+        # Some initial setups
+        x_adv = x.astype(ART_NUMPY_DTYPE)
+
+        # Generate the adversarial samples
+        for ind, val in enumerate(tqdm(x_adv, desc="Boundary attack", disable=not self.verbose)):
+            if self.targeted:
+                x_adv[ind] = self._perturb(
+                    x=val,
+                    y=y[ind],
+                    y_p=preds[ind],
+                    init_pred=init_preds[ind],
+                    adv_init=x_adv_init[ind],
+                    clip_min=clip_min,
+                    clip_max=clip_max,
+                )
+            else:
+                x_adv[ind] = self._perturb(
+                    x=val,
+                    y=-1,
+                    y_p=preds[ind],
+                    init_pred=init_preds[ind],
+                    adv_init=x_adv_init[ind],
+                    clip_min=clip_min,
+                    clip_max=clip_max,
+                )
+
+        if y is not None:
+            y = to_categorical(y, self.estimator.nb_classes)
+
+        logger.info(
+            "Success rate of Boundary attack: %.2f%%",
+            100 * compute_success(self.estimator, x, y, x_adv, self.targeted, batch_size=self.batch_size),
+        )
+
+        return x_adv
+
+    def _perturb(
+        self, x: np.ndarray, y: int, y_p: int, init_pred: int, adv_init: np.ndarray, clip_min: float, clip_max: float,
+    ) -> np.ndarray:
+        """
+        Internal attack function for one example.
+
+        :param x: An array with one original input to be attacked.
+        :param y: If `self.targeted` is true, then `y` represents the target label.
+        :param y_p: The predicted label of x.
+        :param init_pred: The predicted label of the initial image.
+        :param adv_init: Initial array to act as an initial adversarial example.
+        :param clip_min: Minimum value of an example.
+        :param clip_max: Maximum value of an example.
+        :return: An adversarial example.
+        """
+        # First, create an initial adversarial sample
+        initial_sample = self._init_sample(x, y, y_p, init_pred, adv_init, clip_min, clip_max)
+
+        # If an initial adversarial example is not found, then return the original image
+        if initial_sample is None:
+            return x
+
+        # If an initial adversarial example found, then go with boundary attack
+        x_adv = self._attack(
+            initial_sample[0], x, y_p, initial_sample[1], self.delta, self.epsilon, clip_min, clip_max,
+        )
+
+        return x_adv
+
+    def _attack(
+        self,
+        initial_sample: np.ndarray,
+        original_sample: np.ndarray,
+        y_p: int,
+        target: int,
+        initial_delta: float,
+        initial_epsilon: float,
+        clip_min: float,
+        clip_max: float,
+    ) -> np.ndarray:
+        """
+        Main function for the boundary attack.
+
+        :param initial_sample: An initial adversarial example.
+        :param original_sample: The original input.
+        :param y_p: The predicted label of the original input.
+        :param target: The target label.
+        :param initial_delta: Initial step size for the orthogonal step.
+        :param initial_epsilon: Initial step size for the step towards the target.
+        :param clip_min: Minimum value of an example.
+        :param clip_max: Maximum value of an example.
+        :return: an adversarial example.
+        """
+        # Get initialization for some variables
+        x_adv = initial_sample
+        self.curr_delta = initial_delta
+        self.curr_epsilon = initial_epsilon
+
+        self.curr_adv = x_adv
+
+        # Main loop to wander around the boundary
+        for _ in trange(self.max_iter, desc="Boundary attack - iterations", disable=not self.verbose):
+            # Trust region method to adjust delta
+            for _ in range(self.num_trial):
+                potential_advs = []
+                for _ in range(self.sample_size):
+                    potential_adv = x_adv + self._orthogonal_perturb(self.curr_delta, x_adv, original_sample)
+                    potential_adv = np.clip(potential_adv, clip_min, clip_max)
+                    potential_advs.append(potential_adv)
+
+                preds = np.argmax(self.estimator.predict(np.array(potential_advs), batch_size=self.batch_size), axis=1,)
+
+                if self.targeted:
+                    satisfied = preds == target
+                else:
+                    satisfied = preds != y_p
+
+                delta_ratio = np.mean(satisfied)
+
+                if delta_ratio < 0.2:
+                    self.curr_delta *= self.step_adapt
+                elif delta_ratio > 0.5:
+                    self.curr_delta /= self.step_adapt
+
+                if delta_ratio > 0:
+                    x_advs = np.array(potential_advs)[np.where(satisfied)[0]]
+                    break
+            else:
+                logger.warning("Adversarial example found but not optimal.")
+                return x_adv
+
+            # Trust region method to adjust epsilon
+            for _ in range(self.num_trial):
+                perturb = np.repeat(np.array([original_sample]), len(x_advs), axis=0) - x_advs
+                perturb *= self.curr_epsilon
+                potential_advs = x_advs + perturb
+                potential_advs = np.clip(potential_advs, clip_min, clip_max)
+                preds = np.argmax(self.estimator.predict(potential_advs, batch_size=self.batch_size), axis=1,)
+
+                if self.targeted:
+                    satisfied = preds == target
+                else:
+                    satisfied = preds != y_p
+
+                epsilon_ratio = np.mean(satisfied)
+
+                if epsilon_ratio < 0.2:
+                    self.curr_epsilon *= self.step_adapt
+                elif epsilon_ratio > 0.5:
+                    self.curr_epsilon /= self.step_adapt
+
+                if epsilon_ratio > 0:
+                    x_adv = self._best_adv(original_sample, potential_advs[np.where(satisfied)[0]])
+                    self.curr_adv = x_adv
+                    break
+            else:
+                logger.warning("Adversarial example found but not optimal.")
+                return self._best_adv(original_sample, x_advs)
+
+            if self.min_epsilon is not None and self.curr_epsilon < self.min_epsilon:
+                return x_adv
+
+        return x_adv
+
+    def _orthogonal_perturb(self, delta: float, current_sample: np.ndarray, original_sample: np.ndarray) -> np.ndarray:
+        """
+        Create an orthogonal perturbation.
+
+        :param delta: Initial step size for the orthogonal step.
+        :param current_sample: Current adversarial example.
+        :param original_sample: The original input.
+        :return: a possible perturbation.
+        """
+        # Generate perturbation randomly
+        perturb = np.random.randn(*self.estimator.input_shape).astype(ART_NUMPY_DTYPE)
+
+        # Rescale the perturbation
+        perturb /= np.linalg.norm(perturb)
+        perturb *= delta * np.linalg.norm(original_sample - current_sample)
+
+        # Project the perturbation onto sphere
+        direction = original_sample - current_sample
+
+        direction_flat = direction.flatten()
+        perturb_flat = perturb.flatten()
+
+        direction_flat /= np.linalg.norm(direction_flat)
+        perturb_flat -= np.dot(perturb_flat, direction_flat.T) * direction_flat
+        perturb = perturb_flat.reshape(self.estimator.input_shape)
+
+        hypotenuse = np.sqrt(1 + delta ** 2)
+        perturb = ((1 - hypotenuse) * (current_sample - original_sample) + perturb) / hypotenuse
+        return perturb
+
+    def _init_sample(
+        self, x: np.ndarray, y: int, y_p: int, init_pred: int, adv_init: np.ndarray, clip_min: float, clip_max: float,
+    ) -> Optional[Tuple[np.ndarray, int]]:
+        """
+        Find initial adversarial example for the attack.
+
+        :param x: An array with one original input to be attacked.
+        :param y: If `self.targeted` is true, then `y` represents the target label.
+        :param y_p: The predicted label of x.
+        :param init_pred: The predicted label of the initial image.
+        :param adv_init: Initial array to act as an initial adversarial example.
+        :param clip_min: Minimum value of an example.
+        :param clip_max: Maximum value of an example.
+        :return: an adversarial example.
+        """
+        nprd = np.random.RandomState()
+        initial_sample = None
+
+        if self.targeted:
+            # Attack satisfied
+            if y == y_p:
+                return None
+
+            # Attack unsatisfied yet and the initial image satisfied
+            if adv_init is not None and init_pred == y:
+                return adv_init.astype(ART_NUMPY_DTYPE), init_pred
+
+            # Attack unsatisfied yet and the initial image unsatisfied
+            for _ in range(self.init_size):
+                random_img = nprd.uniform(clip_min, clip_max, size=x.shape).astype(x.dtype)
+                random_class = np.argmax(
+                    self.estimator.predict(np.array([random_img]), batch_size=self.batch_size), axis=1,
+                )[0]
+
+                if random_class == y:
+                    initial_sample = random_img, random_class
+
+                    logger.info("Found initial adversarial image for targeted attack.")
+                    break
+            else:
+                logger.warning("Failed to draw a random image that is adversarial, attack failed.")
+
+        else:
+            # The initial image satisfied
+            if adv_init is not None and init_pred != y_p:
+                return adv_init.astype(ART_NUMPY_DTYPE), init_pred
+
+            # The initial image unsatisfied
+            for _ in range(self.init_size):
+                random_img = nprd.uniform(clip_min, clip_max, size=x.shape).astype(x.dtype)
+                random_class = np.argmax(
+                    self.estimator.predict(np.array([random_img]), batch_size=self.batch_size), axis=1,
+                )[0]
+
+                if random_class != y_p:
+                    initial_sample = random_img, random_class
+
+                    logger.info("Found initial adversarial image for untargeted attack.")
+                    break
+            else:
+                logger.warning("Failed to draw a random image that is adversarial, attack failed.")
+
+        return initial_sample
+
+    @staticmethod
+    def _best_adv(original_sample: np.ndarray, potential_advs: np.ndarray) -> np.ndarray:
+        """
+        From the potential adversarial examples, find the one that has the minimum L2 distance from the original sample
+
+        :param original_sample: The original input.
+        :param potential_advs: Array containing the potential adversarial examples
+        :return: The adversarial example that has the minimum L2 distance from the original input
+        """
+        shape = potential_advs.shape
+        min_idx = np.linalg.norm(original_sample.flatten() - potential_advs.reshape(shape[0], -1), axis=1).argmin()
+        return potential_advs[min_idx]
+
+    def _check_params(self) -> None:
+        if not isinstance(self.max_iter, (int, np.int)) or self.max_iter < 0:
+            raise ValueError("The number of iterations must be a non-negative integer.")
+
+        if not isinstance(self.num_trial, (int, np.int)) or self.num_trial < 0:
+            raise ValueError("The number of trials must be a non-negative integer.")
+
+        if not isinstance(self.sample_size, (int, np.int)) or self.sample_size <= 0:
+            raise ValueError("The number of samples must be a positive integer.")
+
+        if not isinstance(self.init_size, (int, np.int)) or self.init_size <= 0:
+            raise ValueError("The number of initial trials must be a positive integer.")
+
+        if self.epsilon <= 0:
+            raise ValueError("The initial step size for the step towards the target must be positive.")
+
+        if self.delta <= 0:
+            raise ValueError("The initial step size for the orthogonal step must be positive.")
+
+        if self.step_adapt <= 0 or self.step_adapt >= 1:
+            raise ValueError("The adaptation factor must be in the range (0, 1).")
+
+        if self.min_epsilon is not None and (isinstance(self.min_epsilon, float) or self.min_epsilon <= 0):
+            raise ValueError("The minimum epsilon must be a positive float.")
+
+        if not isinstance(self.verbose, bool):
+            raise ValueError("The argument `verbose` has to be of type bool.")
diff --git a/adversarial-robustness-toolbox/art/attacks/evasion/brendel_bethge.py b/adversarial-robustness-toolbox/art/attacks/evasion/brendel_bethge.py
new file mode 100644
index 0000000..635fa71
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/attacks/evasion/brendel_bethge.py
@@ -0,0 +1,2570 @@
+# Based on reference implementation: https://github.com/bethgelab/foolbox/blob/master/foolbox/attacks/brendel_bethge.py
+
+# MIT License
+#
+# Copyright (c) 2020 Jonas Rauber et al.
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from typing import Union, Optional, Tuple, TYPE_CHECKING
+import logging
+
+import numpy as np
+from numba.experimental import jitclass
+
+from art import config
+from art.utils import get_labels_np_array, check_and_transform_label_format, is_probability
+from art.attacks.attack import EvasionAttack
+from art.estimators.estimator import BaseEstimator, LossGradientsMixin
+from art.estimators.classification.classifier import ClassifierMixin
+
+if TYPE_CHECKING:
+    from art.utils import CLASSIFIER_LOSS_GRADIENTS_TYPE
+
+EPS = 1e-10
+
+logger = logging.getLogger(__name__)
+
+
+@jitclass(spec=[])
+class BFGSB(object):
+    def __init__(self):
+        pass
+
+    def solve(self, fun_and_jac, q0, bounds, args, ftol=1e-10, pgtol=-1e-5, maxiter=None):
+        N = q0.shape[0]
+
+        if maxiter is None:
+            maxiter = N * 200
+
+        l = bounds[:, 0]  # noqa: E741
+        u = bounds[:, 1]
+
+        func_calls = 0
+
+        old_fval, gfk = fun_and_jac(q0, *args)
+        func_calls += 1
+
+        k = 0
+        Hk = np.eye(N)
+
+        # Sets the initial step guess to dx ~ 1
+        qk = q0
+        old_old_fval = old_fval + np.linalg.norm(gfk) / 2
+
+        # gnorm = np.amax(np.abs(gfk))
+        _gfk = gfk
+
+        # Compare with implementation BFGS-B implementation
+        # in https://github.com/andrewhooker/PopED/blob/master/R/bfgsb_min.R
+
+        while k < maxiter:
+            # check if projected gradient is still large enough
+            pg_norm = 0
+            for v in range(N):
+                if _gfk[v] < 0:
+                    gv = max(qk[v] - u[v], _gfk[v])
+                else:
+                    gv = min(qk[v] - l[v], _gfk[v])
+
+                if pg_norm < np.abs(gv):
+                    pg_norm = np.abs(gv)
+
+            if pg_norm < pgtol:
+                break
+
+            # get cauchy point
+            x_cp = self._cauchy_point(qk, l, u, _gfk.copy(), Hk)
+            qk1 = self._subspace_min(qk, l, u, x_cp, _gfk.copy(), Hk)
+            pk = qk1 - qk
+
+            (alpha_k, fc, gc, old_fval, old_old_fval, gfkp1, fnev,) = self._line_search_wolfe(
+                fun_and_jac, qk, pk, _gfk, old_fval, old_old_fval, l, u, args
+            )
+            func_calls += fnev
+
+            if alpha_k is None:
+                break
+
+            if np.abs(old_fval - old_old_fval) <= (ftol + ftol * np.abs(old_fval)):
+                break
+
+            qkp1 = self._project(qk + alpha_k * pk, l, u)
+
+            if gfkp1 is None:
+                _, gfkp1 = fun_and_jac(qkp1, *args)
+
+            sk = qkp1 - qk
+            qk = qkp1
+
+            yk = np.zeros_like(qk)
+            for k3 in range(N):
+                yk[k3] = gfkp1[k3] - _gfk[k3]
+
+                if np.abs(yk[k3]) < 1e-4:
+                    yk[k3] = -1e-4
+
+            _gfk = gfkp1
+
+            k += 1
+
+            # update inverse Hessian matrix
+            Hk_sk = Hk.dot(sk)
+
+            sk_yk = 0
+            sk_Hk_sk = 0
+            for v in range(N):
+                sk_yk += sk[v] * yk[v]
+                sk_Hk_sk += sk[v] * Hk_sk[v]
+
+            if np.abs(sk_yk) >= 1e-8:
+                rhok = 1.0 / sk_yk
+            else:
+                rhok = 100000.0
+
+            if np.abs(sk_Hk_sk) >= 1e-8:
+                rsk_Hk_sk = 1.0 / sk_Hk_sk
+            else:
+                rsk_Hk_sk = 100000.0
+
+            for v in range(N):
+                for w in range(N):
+                    Hk[v, w] += yk[v] * yk[w] * rhok - Hk_sk[v] * Hk_sk[w] * rsk_Hk_sk
+
+        return qk
+
+    def _cauchy_point(self, x, l, u, g, B):  # noqa: E741
+        # finds the cauchy point for q(x)=x'Gx+x'd s$t. l<=x<=u
+        # g=G*x+d #gradient of q(x)
+        # converted from r-code: https://github.com/andrewhooker/PopED/blob/master/R/cauchy_point.R
+        n = x.shape[0]
+        t = np.zeros_like(x)
+        d = np.zeros_like(x)
+
+        for i in range(n):
+            if g[i] < 0:
+                t[i] = (x[i] - u[i]) / g[i]
+            elif g[i] > 0:
+                t[i] = (x[i] - l[i]) / g[i]
+            elif g[i] == 0:
+                t[i] = np.inf
+
+            if t[i] == 0:
+                d[i] = 0
+            else:
+                d[i] = -g[i]
+
+        ts = t.copy()
+        ts = ts[ts != 0]
+        ts = np.sort(ts)
+
+        df = g.dot(d)
+        d2f = d.dot(B.dot(d))
+
+        if d2f < 1e-10:
+            return x
+
+        dt_min = -df / d2f
+        t_old = 0
+        i = 0
+        z = np.zeros_like(x)
+
+        while i < ts.shape[0] and dt_min >= (ts[i] - t_old):
+            ind = ts[i] < t
+            d[~ind] = 0
+            z = z + (ts[i] - t_old) * d
+            df = g.dot(d) + d.dot(B.dot(z))
+            d2f = d.dot(B.dot(d))
+            dt_min = df / (d2f + 1e-8)
+            t_old = ts[i]
+            i += 1
+
+        dt_min = max(dt_min, 0)
+        t_old = t_old + dt_min
+        x_cp = x - t_old * g
+        temp = x - t * g
+        x_cp[t_old > t] = temp[t_old > t]
+
+        return x_cp
+
+    def _subspace_min(self, x, l, u, x_cp, d, G):  # noqa: E741
+        # converted from r-code: https://github.com/andrewhooker/PopED/blob/master/R/subspace_min.R
+        n = x.shape[0]
+        Z = np.eye(n)
+        fixed = (x_cp <= l + 1e-8) + (x_cp >= u - 1e8)
+
+        if np.all(fixed):
+            x = x_cp
+            return x
+
+        Z = Z[:, ~fixed]
+        rgc = Z.T.dot(d + G.dot(x_cp - x))
+        rB = Z.T.dot(G.dot(Z)) + 1e-10 * np.eye(Z.shape[1])
+        d[~fixed] = np.linalg.solve(rB, rgc)
+        d[~fixed] = -d[~fixed]
+        alpha = 1
+        temp1 = alpha
+
+        for i in np.arange(n)[~fixed]:
+            dk = d[i]
+            if dk < 0:
+                temp2 = l[i] - x_cp[i]
+                if temp2 >= 0:
+                    temp1 = 0
+                else:
+                    if dk * alpha < temp2:
+                        temp1 = temp2 / dk
+                    else:
+                        temp2 = u[i] - x_cp[i]  # lgtm [py/multiple-definition]
+            else:
+                temp2 = u[i] - x_cp[i]
+                if temp1 <= 0:
+                    temp1 = 0
+                else:
+                    if dk * alpha > temp2:
+                        temp1 = temp2 / dk
+
+            alpha = min(temp1, alpha)
+
+        return x_cp + alpha * Z.dot(d[~fixed])
+
+    def _project(self, q, l, u):  # noqa: E741
+        N = q.shape[0]
+        for k in range(N):
+            if q[k] < l[k]:
+                q[k] = l[k]
+            elif q[k] > u[k]:
+                q[k] = u[k]
+
+        return q
+
+    def _line_search_armijo(
+        self, fun_and_jac, pt, dpt, func_calls, m, gk, l, u, x0, x, b, min_, max_, c, r,  # noqa: E741
+    ):
+        ls_rho = 0.6
+        ls_c = 1e-4
+        ls_alpha = 1
+
+        t = m * ls_c
+
+        for k2 in range(100):
+            ls_pt = self._project(pt + ls_alpha * dpt, l, u)
+
+            gkp1, dgkp1 = fun_and_jac(ls_pt, x0, x, b, min_, max_, c, r)
+            func_calls += 1
+
+            if gk - gkp1 >= ls_alpha * t:
+                break
+            else:
+                ls_alpha *= ls_rho
+
+        return ls_alpha, ls_pt, gkp1, dgkp1, func_calls
+
+    def _line_search_wolfe(  # noqa: C901
+        self, fun_and_jac, xk, pk, gfk, old_fval, old_old_fval, l, u, args,  # noqa: #E741
+    ):
+        """Find alpha that satisfies strong Wolfe conditions.
+        Uses the line search algorithm to enforce strong Wolfe conditions
+        Wright and Nocedal, 'Numerical Optimization', 1999, pg. 59-60
+        For the zoom phase it uses an algorithm by
+        Outputs: (alpha0, gc, fc)
+        """
+        c1 = 1e-4
+        c2 = 0.9
+        N = xk.shape[0]
+        _ls_fc = 0
+        _ls_ingfk = None
+
+        alpha0 = 0
+        phi0 = old_fval
+
+        derphi0 = 0
+        for v in range(N):
+            derphi0 += gfk[v] * pk[v]
+
+        if derphi0 == 0:
+            derphi0 = 1e-8
+        elif np.abs(derphi0) < 1e-8:
+            derphi0 = np.sign(derphi0) * 1e-8
+
+        alpha1 = min(1.0, 1.01 * 2 * (phi0 - old_old_fval) / derphi0)
+
+        if alpha1 == 0:
+            # This shouldn't happen. Perhaps the increment has slipped below
+            # machine precision?  For now, set the return variables skip the
+            # useless while loop, and raise warnflag=2 due to possible imprecision.
+            # print("Slipped below machine precision.")
+            alpha_star = None
+            fval_star = old_fval
+            old_fval = old_old_fval
+            fprime_star = None
+
+        _xkp1 = self._project(xk + alpha1 * pk, l, u)
+        phi_a1, _ls_ingfk = fun_and_jac(_xkp1, *args)
+        _ls_fc += 1
+        # derphi_a1 = phiprime(alpha1)  evaluated below
+
+        phi_a0 = phi0
+        derphi_a0 = derphi0
+
+        i = 1
+        maxiter = 10
+        while 1:  # bracketing phase
+            # print("   (ls) in while loop: ", alpha1, alpha0)
+            if alpha1 == 0:
+                break
+            if (phi_a1 > phi0 + c1 * alpha1 * derphi0) or ((phi_a1 >= phi_a0) and (i > 1)):
+                # inlining zoom for performance reasons
+                #                 alpha0, alpha1, phi_a0, phi_a1, derphi_a0, phi0, derphi0, pk, xk
+                # zoom signature: (a_lo, a_hi, phi_lo, phi_hi, derphi_lo, phi0, derphi0, pk, xk)
+                # INLINE START
+                k = 0
+                delta1 = 0.2  # cubic interpolant check
+                delta2 = 0.1  # quadratic interpolant check
+                phi_rec = phi0
+                a_rec = 0
+                a_hi = alpha1
+                a_lo = alpha0
+                phi_lo = phi_a0
+                phi_hi = phi_a1
+                derphi_lo = derphi_a0
+                while 1:
+                    # interpolate to find a trial step length between a_lo and a_hi
+                    # Need to choose interpolation here.  Use cubic interpolation and then if the
+                    #  result is within delta * dalpha or outside of the interval bounded by a_lo or a_hi
+                    #  then use quadratic interpolation, if the result is still too close, then use bisection
+
+                    dalpha = a_hi - a_lo
+                    if dalpha < 0:
+                        a, b = a_hi, a_lo
+                    else:
+                        a, b = a_lo, a_hi
+
+                    # minimizer of cubic interpolant
+                    #    (uses phi_lo, derphi_lo, phi_hi, and the most recent value of phi)
+                    #      if the result is too close to the end points (or out of the interval)
+                    #         then use quadratic interpolation with phi_lo, derphi_lo and phi_hi
+                    #      if the result is stil too close to the end points (or out of the interval)
+                    #         then use bisection
+
+                    if k > 0:
+                        cchk = delta1 * dalpha
+                        a_j = self._cubicmin(a_lo, phi_lo, derphi_lo, a_hi, phi_hi, a_rec, phi_rec)
+                    if (k == 0) or (a_j is None) or (a_j > b - cchk) or (a_j < a + cchk):
+                        qchk = delta2 * dalpha
+                        a_j = self._quadmin(a_lo, phi_lo, derphi_lo, a_hi, phi_hi)
+                        if (a_j is None) or (a_j > b - qchk) or (a_j < a + qchk):
+                            a_j = a_lo + 0.5 * dalpha
+
+                    # Check new value of a_j
+                    _xkp1 = self._project(xk + a_j * pk, l, u)
+                    # if _xkp1[1] < 0:
+                    #     _xkp1[1] = 0
+                    phi_aj, _ls_ingfk = fun_and_jac(_xkp1, *args)
+
+                    derphi_aj = 0
+                    for v in range(N):
+                        derphi_aj += _ls_ingfk[v] * pk[v]
+
+                    if (phi_aj > phi0 + c1 * a_j * derphi0) or (phi_aj >= phi_lo):
+                        phi_rec = phi_hi
+                        a_rec = a_hi
+                        a_hi = a_j
+                        phi_hi = phi_aj
+                    else:
+                        if abs(derphi_aj) <= -c2 * derphi0:
+                            a_star = a_j
+                            val_star = phi_aj
+                            valprime_star = _ls_ingfk
+                            break
+                        if derphi_aj * (a_hi - a_lo) >= 0:
+                            phi_rec = phi_hi
+                            a_rec = a_hi
+                            a_hi = a_lo
+                            phi_hi = phi_lo
+                        else:
+                            phi_rec = phi_lo
+                            a_rec = a_lo
+                        a_lo = a_j
+                        phi_lo = phi_aj
+                        derphi_lo = derphi_aj
+                    k += 1
+                    if k > maxiter:
+                        a_star = a_j
+                        val_star = phi_aj
+                        valprime_star = None
+                        break
+
+                alpha_star = a_star
+                fval_star = val_star
+                fprime_star = valprime_star
+                fnev = k
+                # INLINE END
+
+                _ls_fc += fnev
+                break
+
+            i += 1
+            if i > maxiter:
+                break
+
+            _xkp1 = self._project(xk + alpha1 * pk, l, u)
+            _, _ls_ingfk = fun_and_jac(_xkp1, *args)
+            derphi_a1 = 0
+            for v in range(N):
+                derphi_a1 += _ls_ingfk[v] * pk[v]
+            _ls_fc += 1
+            if abs(derphi_a1) <= -c2 * derphi0:
+                alpha_star = alpha1
+                fval_star = phi_a1
+                fprime_star = _ls_ingfk
+                break
+
+            if derphi_a1 >= 0:
+                # alpha_star, fval_star, fprime_star, fnev, _ls_ingfk = _zoom(
+                #     alpha1, alpha0, phi_a1, phi_a0, derphi_a1, phi0, derphi0, pk, xk
+                # )
+                #
+                # INLINE START
+                maxiter = 10
+                k = 0
+                delta1 = 0.2  # cubic interpolant check
+                delta2 = 0.1  # quadratic interpolant check
+                phi_rec = phi0
+                a_rec = 0
+                a_hi = alpha0
+                a_lo = alpha1
+                phi_lo = phi_a1
+                phi_hi = phi_a0
+                derphi_lo = derphi_a1
+                while 1:
+                    # interpolate to find a trial step length between a_lo and a_hi
+                    # Need to choose interpolation here.  Use cubic interpolation and then if the
+                    #  result is within delta * dalpha or outside of the interval bounded by a_lo or a_hi
+                    #  then use quadratic interpolation, if the result is still too close, then use bisection
+
+                    dalpha = a_hi - a_lo
+                    if dalpha < 0:
+                        a, b = a_hi, a_lo
+                    else:
+                        a, b = a_lo, a_hi
+
+                    # minimizer of cubic interpolant
+                    #    (uses phi_lo, derphi_lo, phi_hi, and the most recent value of phi)
+                    #      if the result is too close to the end points (or out of the interval)
+                    #         then use quadratic interpolation with phi_lo, derphi_lo and phi_hi
+                    #      if the result is stil too close to the end points (or out of the interval)
+                    #         then use bisection
+
+                    if k > 0:
+                        cchk = delta1 * dalpha
+                        a_j = self._cubicmin(a_lo, phi_lo, derphi_lo, a_hi, phi_hi, a_rec, phi_rec)
+                    if (k == 0) or (a_j is None) or (a_j > b - cchk) or (a_j < a + cchk):
+                        qchk = delta2 * dalpha
+                        a_j = self._quadmin(a_lo, phi_lo, derphi_lo, a_hi, phi_hi)
+                        if (a_j is None) or (a_j > b - qchk) or (a_j < a + qchk):
+                            a_j = a_lo + 0.5 * dalpha
+
+                    # Check new value of a_j
+                    _xkp1 = self._project(xk + a_j * pk, l, u)
+                    phi_aj, _ls_ingfk = fun_and_jac(_xkp1, *args)
+                    derphi_aj = 0
+                    for v in range(N):
+                        derphi_aj += _ls_ingfk[v] * pk[v]
+                    if (phi_aj > phi0 + c1 * a_j * derphi0) or (phi_aj >= phi_lo):
+                        phi_rec = phi_hi
+                        a_rec = a_hi
+                        a_hi = a_j
+                        phi_hi = phi_aj
+                    else:
+                        if abs(derphi_aj) <= -c2 * derphi0:
+                            a_star = a_j
+                            val_star = phi_aj
+                            valprime_star = _ls_ingfk
+                            break
+                        if derphi_aj * (a_hi - a_lo) >= 0:
+                            phi_rec = phi_hi
+                            a_rec = a_hi
+                            a_hi = a_lo
+                            phi_hi = phi_lo
+                        else:
+                            phi_rec = phi_lo
+                            a_rec = a_lo
+                        a_lo = a_j
+                        phi_lo = phi_aj
+                        derphi_lo = derphi_aj
+                    k += 1
+                    if k > maxiter:
+                        a_star = a_j
+                        val_star = phi_aj
+                        valprime_star = None
+                        break
+
+                alpha_star = a_star
+                fval_star = val_star
+                fprime_star = valprime_star
+                fnev = k
+                # INLINE END
+
+                _ls_fc += fnev
+                break
+
+            alpha2 = 2 * alpha1  # increase by factor of two on each iteration
+            i = i + 1
+            alpha0 = alpha1
+            alpha1 = alpha2
+            phi_a0 = phi_a1
+            _xkp1 = self._project(xk + alpha1 * pk, l, u)
+            phi_a1, _ls_ingfk = fun_and_jac(_xkp1, *args)
+            _ls_fc += 1
+            derphi_a0 = derphi_a1
+
+            # stopping test if lower function not found
+            if i > maxiter:
+                alpha_star = alpha1
+                fval_star = phi_a1
+                fprime_star = None
+                break
+
+        return alpha_star, _ls_fc, _ls_fc, fval_star, old_fval, fprime_star, _ls_fc
+
+    def _cubicmin(self, a, fa, fpa, b, fb, c, fc):
+        # finds the minimizer for a cubic polynomial that goes through the
+        #  points (a,fa), (b,fb), and (c,fc) with derivative at a of fpa.
+        #
+        # if no minimizer can be found return None
+        #
+        # f(x) = A *(x-a)^3 + B*(x-a)^2 + C*(x-a) + D
+
+        C = fpa
+        db = b - a
+        dc = c - a
+        if (db == 0) or (dc == 0) or (b == c):
+            return None
+        denom = (db * dc) ** 2 * (db - dc)
+        A = dc ** 2 * (fb - fa - C * db) - db ** 2 * (fc - fa - C * dc)
+        B = -(dc ** 3) * (fb - fa - C * db) + db ** 3 * (fc - fa - C * dc)
+
+        A /= denom
+        B /= denom
+        radical = B * B - 3 * A * C
+        if radical < 0:
+            return None
+        if A == 0:
+            return None
+        xmin = a + (-B + np.sqrt(radical)) / (3 * A)
+        return xmin
+
+    def _quadmin(self, a, fa, fpa, b, fb):
+        # finds the minimizer for a quadratic polynomial that goes through
+        #  the points (a,fa), (b,fb) with derivative at a of fpa
+        # f(x) = B*(x-a)^2 + C*(x-a) + D
+        D = fa
+        C = fpa
+        db = b - a * 1.0
+        if db == 0:
+            return None
+        B = (fb - D - C * db) / (db * db)
+        if B <= 0:
+            return None
+        xmin = a - C / (2.0 * B)
+        return xmin
+
+
+class Optimizer(object):
+    """
+    Base class for the trust-region optimization. If feasible, this optimizer solves the problem
+
+    min_delta distance(x0, x + delta) s.t. ||delta||_2 <= r AND delta^T b = c AND min_ <= x + delta <= max_
+
+    where x0 is the original sample, x is the current optimisation state, r is the trust-region radius,
+    b is the current estimate of the normal vector of the decision boundary, c is the estimated distance of x
+    to the trust region and [min_, max_] are the value constraints of the input. The function distance(.,.)
+    is the distance measure to be optimised (e.g. L2, L1, L0).
+    """
+
+    def __init__(self):
+        self.bfgsb = BFGSB()  # a box-constrained BFGS solver
+
+    def solve(self, x0, x, b, min_, max_, c, r):
+        x0, x, b = x0.astype(np.float64), x.astype(np.float64), b.astype(np.float64)
+        cmax, cmaxnorm = self._max_logit_diff(x, b, min_, max_, c)
+
+        if np.abs(cmax) < np.abs(c):
+            # problem not solvable (boundary cannot be reached)
+            if np.sqrt(cmaxnorm) < r:
+                # make largest possible step towards boundary while staying within bounds
+                _delta = self.optimize_boundary_s_t_trustregion(x0, x, b, min_, max_, c, r)
+            else:
+                # make largest possible step towards boundary while staying within trust region
+                _delta = self.optimize_boundary_s_t_trustregion(x0, x, b, min_, max_, c, r)
+        else:
+            if cmaxnorm < r:
+                # problem is solvable
+                # proceed with standard optimization
+                _delta = self.optimize_distance_s_t_boundary_and_trustregion(x0, x, b, min_, max_, c, r)
+            else:
+                # problem might not be solvable
+                bnorm = np.linalg.norm(b)
+                minnorm = self._minimum_norm_to_boundary(x, b, min_, max_, c, bnorm)
+
+                if minnorm <= r:
+                    # problem is solvable, proceed with standard optimization
+                    _delta = self.optimize_distance_s_t_boundary_and_trustregion(x0, x, b, min_, max_, c, r)
+                else:
+                    # problem not solvable (boundary cannot be reached)
+                    # make largest step towards boundary within trust region
+                    _delta = self.optimize_boundary_s_t_trustregion(x0, x, b, min_, max_, c, r)
+
+        return _delta
+
+    def _max_logit_diff(self, x, b, _ell, _u, c):
+        """
+        Tests whether the (estimated) boundary can be reached within trust region.
+        """
+        N = x.shape[0]
+        cmax = 0.0
+        norm = 0.0
+
+        if c > 0:
+            for n in range(N):
+                if b[n] > 0:
+                    cmax += b[n] * (_u - x[n])
+                    norm += (_u - x[n]) ** 2
+                else:
+                    cmax += b[n] * (_ell - x[n])
+                    norm += (x[n] - _ell) ** 2
+        else:
+            for n in range(N):
+                if b[n] > 0:
+                    cmax += b[n] * (_ell - x[n])
+                    norm += (x[n] - _ell) ** 2
+                else:
+                    cmax += b[n] * (_u - x[n])
+                    norm += (_u - x[n]) ** 2
+
+        return cmax, np.sqrt(norm)
+
+    def _minimum_norm_to_boundary(self, x, b, _ell, _u, c, bnorm):
+        """
+        Computes the minimum norm necessary to reach the boundary. More precisely, we aim to solve the following
+        optimization problem
+
+            min ||delta||_2^2 s.t. lower <= x + delta <= upper AND b.dot(delta) = c
+
+        Lets forget about the box constraints for a second, i.e.
+
+            min ||delta||_2^2 s.t. b.dot(delta) = c
+
+        The dual of this problem is quite straight-forward to solve,
+
+            g(lambda, delta) = ||delta||_2^2 + lambda * (c - b.dot(delta))
+
+        The minimum of this Lagrangian is delta^* = lambda * b / 2, and so
+
+            inf_delta g(lambda, delta) = lambda^2 / 4 ||b||_2^2 + lambda * c
+
+        and so the optimal lambda, which maximizes inf_delta g(lambda, delta), is given by
+
+            lambda^* = 2c / ||b||_2^2
+
+        which in turn yields the optimal delta:
+
+            delta^* = c * b / ||b||_2^2
+
+        To take into account the box-constraints we perform a binary search over lambda and apply the box
+        constraint in each step.
+        """
+        N = x.shape[0]
+
+        lambda_lower = 2 * c / (bnorm ** 2 + EPS)
+        lambda_upper = np.sign(c) * np.inf  # optimal initial point (if box-constraints are neglected)
+        _lambda = lambda_lower
+        k = 0
+
+        # perform a binary search over lambda
+        while True:
+            # compute _c = b.dot([- _lambda * b / 2]_clip)
+            k += 1
+            _c = 0
+            norm = 0
+
+            if c > 0:
+                for n in range(N):
+                    lam_step = _lambda * b[n] / 2
+                    if b[n] > 0:
+                        max_step = _u - x[n]
+                        delta_step = min(max_step, lam_step)
+                        _c += b[n] * delta_step
+                        norm += delta_step ** 2
+                    else:
+                        max_step = _ell - x[n]
+                        delta_step = max(max_step, lam_step)
+                        _c += b[n] * delta_step
+                        norm += delta_step ** 2
+            else:
+                for n in range(N):
+                    lam_step = _lambda * b[n] / 2
+                    if b[n] > 0:
+                        max_step = _ell - x[n]
+                        delta_step = max(max_step, lam_step)
+                        _c += b[n] * delta_step
+                        norm += delta_step ** 2
+                    else:
+                        max_step = _u - x[n]
+                        delta_step = min(max_step, lam_step)
+                        _c += b[n] * delta_step
+                        norm += delta_step ** 2
+
+            # adjust lambda
+            if np.abs(_c) < np.abs(c):
+                # increase absolute value of lambda
+                if np.isinf(lambda_upper):
+                    _lambda *= 2
+                else:
+                    lambda_lower = _lambda
+                    _lambda = (lambda_upper - lambda_lower) / 2 + lambda_lower
+            else:
+                # decrease lambda
+                lambda_upper = _lambda
+                _lambda = (lambda_upper - lambda_lower) / 2 + lambda_lower
+
+            # stopping condition
+            if 0.999 * np.abs(c) - EPS < np.abs(_c) < 1.001 * np.abs(c) + EPS:
+                break
+
+        return np.sqrt(norm)
+
+    def optimize_distance_s_t_boundary_and_trustregion(self, x0, x, b, min_, max_, c, r):
+        """
+        Find the solution to the optimization problem
+
+        min_delta ||dx - delta||_p^p s.t. ||delta||_2^2 <= r^2 AND b^T delta = c AND min_ <= x + delta <= max_
+        """
+        params0 = np.array([0.0, 0.0])
+        bounds = np.array([(-np.inf, np.inf), (0, np.inf)])
+        args = (x0, x, b, min_, max_, c, r)
+
+        qk = self.bfgsb.solve(self.fun_and_jac, params0, bounds, args)
+        return self._get_final_delta(qk[0], qk[1], x0, x, b, min_, max_, c, r, touchup=True)
+
+    def optimize_boundary_s_t_trustregion_fun_and_jac(self, params, x0, x, b, min_, max_, c, r):
+        N = x0.shape[0]
+        s = -np.sign(c)
+        _mu = params[0]
+        t = 1 / (2 * _mu + EPS)
+
+        g = -_mu * r ** 2
+        grad_mu = -(r ** 2)
+
+        for n in range(N):
+            d = -s * b[n] * t
+
+            if d < min_ - x[n]:
+                d = min_ - x[n]
+            elif d > max_ - x[n]:
+                d = max_ - x[n]
+            else:
+                grad_mu += (b[n] + 2 * _mu * d) * (b[n] / (2 * _mu ** 2 + EPS))
+
+            grad_mu += d ** 2
+            g += (b[n] + _mu * d) * d
+
+        return -g, -np.array([grad_mu])
+
+    def safe_div(self, nominator, denominator):
+        if np.abs(denominator) > EPS:
+            return nominator / denominator
+        elif denominator >= 0:
+            return nominator / EPS
+        else:
+            return -nominator / EPS
+
+    def optimize_boundary_s_t_trustregion(self, x0, x, b, min_, max_, c, r):
+        """
+        Find the solution to the optimization problem
+
+        min_delta sign(c) b^T delta s.t. ||delta||_2^2 <= r^2 AND min_ <= x + delta <= max_
+
+        Note: this optimization problem is independent of the Lp norm being optimized.
+
+        Lagrangian: g(delta) = sign(c) b^T delta + mu * (||delta||_2^2 - r^2)
+        Optimal delta: delta = - sign(c) * b / (2 * mu)
+        """
+        params0 = np.array([1.0])
+        args = (x0, x, b, min_, max_, c, r)
+        bounds = np.array([(0, np.inf)])
+
+        qk = self.bfgsb.solve(self.optimize_boundary_s_t_trustregion_fun_and_jac, params0, bounds, args)
+
+        _delta = self.safe_div(-b, 2 * qk[0])
+
+        for n in range(x0.shape[0]):
+            if _delta[n] < min_ - x[n]:
+                _delta[n] = min_ - x[n]
+            elif _delta[n] > max_ - x[n]:
+                _delta[n] = max_ - x[n]
+
+        return _delta
+
+
+spec = [("bfgsb", BFGSB.class_type.instance_type)]  # type: ignore
+
+
+@jitclass(spec=spec)
+class L2Optimizer(Optimizer):
+    def optimize_distance_s_t_boundary_and_trustregion(self, x0, x, b, min_, max_, c, r):  # noqa: C901
+        """
+        Solves the L2 trust region problem
+
+        min ||x0 - x - delta||_2 s.t. b^top delta = c
+                                    & ell <= x + delta <= u
+                                    & ||delta||_2 <= r
+
+        This is a specialised solver that does not use the generic BFGS-B solver.
+        Instead, this active-set solver computes the active set of indices (those that
+        do not hit the bounds) and then computes that optimal step size in the direction
+        of the boundary and the direction of the original sample over the active indices.
+
+        Parameters
+        ----------
+        x0 : `numpy.ndarray`
+            The original image against which we minimize the perturbation
+            (flattened).
+        x : `numpy.ndarray`
+            The current perturbation (flattened).
+        b : `numpy.ndarray`
+            Normal vector of the local decision boundary (flattened).
+        min_ : float
+            Lower bound on the pixel values.
+        max_ : float
+            Upper bound on the pixel values.
+        c : float
+            Logit difference between the ground truth class of x0 and the
+            leading class different from the ground truth.
+        r : float
+            Size of the trust region.
+        """
+        N = x0.shape[0]
+        clamp_c = 0
+        clamp_norm = 0
+        ck = c
+        rk = r
+        masked_values = 0
+
+        mask = np.zeros(N, dtype=np.uint8)
+        delta = np.empty_like(x0)
+        dx = x0 - x
+
+        for k in range(20):
+            # inner optimization that solves subproblem
+            bnorm = 1e-8
+            bdotDx = 0
+
+            for i in range(N):
+                if mask[i] == 0:
+                    bnorm += b[i] * b[i]
+                    bdotDx += b[i] * dx[i]
+
+            bdotDx = bdotDx / bnorm
+            ck_bnorm = ck / bnorm
+            b_scale = -bdotDx + ck / bnorm
+            new_masked_values = 0
+            delta_norm = 0
+            descent_norm = 0
+            boundary_step_norm = 0
+
+            # make optimal step towards boundary AND minimum
+            for i in range(N):
+                if mask[i] == 0:
+                    delta[i] = dx[i] + b[i] * b_scale
+                    boundary_step_norm = boundary_step_norm + b[i] * ck_bnorm * b[i] * ck_bnorm
+                    delta_norm = delta_norm + delta[i] * delta[i]
+                    descent_norm = descent_norm + (dx[i] - b[i] * bdotDx) * (dx[i] - b[i] * bdotDx)
+
+            # check of step to boundary is already larger than trust region
+            if boundary_step_norm > rk * rk:
+                for i in range(N):
+                    if mask[i] == 0:
+                        delta[i] = b[i] * ck_bnorm
+            else:
+                # check if combined step to large and correct step to minimum if necessary
+                if delta_norm > rk * rk:
+                    region_correct = np.sqrt(rk * rk - boundary_step_norm)
+                    region_correct = region_correct / (np.sqrt(descent_norm) + 1e-8)
+                    b_scale = -region_correct * bdotDx + ck / bnorm
+
+                    for i in range(N):
+                        if mask[i] == 0:
+                            delta[i] = region_correct * dx[i] + b[i] * b_scale
+
+            for i in range(N):
+                if mask[i] == 0:
+                    if x[i] + delta[i] <= min_:
+                        mask[i] = 1
+                        delta[i] = min_ - x[i]
+                        new_masked_values = new_masked_values + 1
+                        clamp_norm = clamp_norm + delta[i] * delta[i]
+                        clamp_c = clamp_c + b[i] * delta[i]
+
+                    if x[i] + delta[i] >= max_:
+                        mask[i] = 1
+                        delta[i] = max_ - x[i]
+                        new_masked_values = new_masked_values + 1
+                        clamp_norm = clamp_norm + delta[i] * delta[i]
+                        clamp_c = clamp_c + b[i] * delta[i]
+
+            # should no additional variable get out of bounds, stop optimization
+            if new_masked_values == 0:
+                break
+
+            masked_values = masked_values + new_masked_values
+
+            if clamp_norm < r * r:
+                rk = np.sqrt(r * r - clamp_norm)
+            else:
+                rk = 0
+
+            ck = c - clamp_c
+
+            if masked_values == N:
+                break
+
+        return delta
+
+    def fun_and_jac(self, params, x0, x, b, min_, max_, c, r):
+        # we need to compute the loss function
+        # g = distance + mu * (norm_d - r ** 2) + lam * (b_dot_d - c)
+        # and its derivative d g / d lam and d g / d mu
+        lam, mu = params
+
+        N = x0.shape[0]
+
+        g = 0
+        d_g_d_lam = 0
+        d_g_d_mu = 0
+
+        distance = 0
+        b_dot_d = 0
+        d_norm = 0
+
+        t = 1 / (2 * mu + 2)
+
+        for n in range(N):
+            dx = x0[n] - x[n]
+            bn = b[n]
+            xn = x[n]
+
+            d = (2 * dx - lam * bn) * t
+
+            if d + xn > max_:
+                d = max_ - xn
+            elif d + xn < min_:
+                d = min_ - xn
+            else:
+                prefac = 2 * (d - dx) + 2 * mu * d + lam * bn
+                d_g_d_lam -= prefac * bn * t
+                d_g_d_mu -= prefac * 2 * d * t
+
+            distance += (d - dx) ** 2
+            b_dot_d += bn * d
+            d_norm += d ** 2
+
+            g += (dx - d) ** 2 + mu * d ** 2 + lam * bn * d
+            d_g_d_lam += bn * d
+            d_g_d_mu += d ** 2
+
+        g += -mu * r ** 2 - lam * c
+        d_g_d_lam -= c
+        d_g_d_mu -= r ** 2
+
+        return -g, -np.array([d_g_d_lam, d_g_d_mu])
+
+    def _get_final_delta(self, lam, mu, x0, x, b, min_, max_, c, r, touchup=True):
+        delta = np.empty_like(x0)
+        N = x0.shape[0]
+
+        t = 1 / (2 * mu + 2)
+
+        for n in range(N):
+            d = (2 * (x0[n] - x[n]) - lam * b[n]) * t
+
+            if d + x[n] > max_:
+                d = max_ - x[n]
+            elif d + x[n] < min_:
+                d = min_ - x[n]
+
+            delta[n] = d
+
+        return delta
+
+    def _distance(self, x0, x):
+        return np.linalg.norm(x0 - x) ** 2
+
+
+@jitclass(spec=spec)
+class L1Optimizer(Optimizer):
+    def fun_and_jac(self, params, x0, x, b, min_, max_, c, r):
+        lam, mu = params
+        # arg min_delta ||delta - dx||_1 + lam * b^T delta + mu * ||delta||_2^2  s.t.  min <= delta + x <= max
+        N = x0.shape[0]
+
+        g = 0
+        d_g_d_lam = 0
+        d_g_d_mu = 0
+
+        if mu > 0:
+            for n in range(N):
+                dx = x0[n] - x[n]
+                bn = b[n]
+                t = 1 / (2 * mu)
+                u = -lam * bn * t - dx
+
+                if np.abs(u) - t < 0:
+                    # value and grad = 0
+                    d = dx
+                else:
+                    d = np.sign(u) * (np.abs(u) - t) + dx
+
+                    if d + x[n] < min_:
+                        d = min_ - x[n]
+                    elif d + x[n] > max_:
+                        d = max_ - x[n]
+                    else:
+                        prefac = np.sign(d - dx) + 2 * mu * d + lam * bn
+                        d_g_d_lam -= prefac * bn * t
+                        d_g_d_mu -= prefac * 2 * d * t
+
+                g += np.abs(dx - d) + mu * d ** 2 + lam * bn * d
+                d_g_d_lam += bn * d
+                d_g_d_mu += d ** 2
+        else:  # mu == 0
+            for n in range(N):
+                dx = x0[n] - x[n]
+                bn = b[n]
+                if np.abs(lam * bn) < 1:
+                    d = dx
+                elif np.sign(lam * bn) < 0:
+                    d = max_ - x[n]
+                else:
+                    d = min_ - x[n]
+
+                g += np.abs(dx - d) + mu * d ** 2 + lam * bn * d
+                d_g_d_lam += bn * d
+                d_g_d_mu += d ** 2
+
+        g += -mu * r ** 2 - lam * c
+        d_g_d_lam -= c
+        d_g_d_mu -= r ** 2
+
+        return -g, -np.array([d_g_d_lam, d_g_d_mu])
+
+    def _get_final_delta(self, lam, mu, x0, x, b, min_, max_, c, r, touchup=True):
+        delta = np.empty_like(x0)
+        N = x0.shape[0]
+
+        b_dot_d = 0
+        norm_d = 0
+        distance = 0
+
+        if mu > 0:
+            for n in range(N):
+                dx = x0[n] - x[n]
+                bn = b[n]
+                t = 1 / (2 * mu)
+                u = -lam * bn * t - dx
+
+                if np.abs(u) - t < 0:
+                    # value and grad = 0
+                    d = dx
+                else:
+                    d = np.sign(u) * (np.abs(u) - t) + dx
+
+                    if d + x[n] < min_:
+                        # grad = 0
+                        d = min_ - x[n]
+                    elif d + x[n] > max_:
+                        # grad = 0
+                        d = max_ - x[n]
+
+                delta[n] = d
+                b_dot_d += b[n] * d
+                norm_d += d ** 2
+                distance += np.abs(d - dx)
+        else:  # mu == 0
+            for n in range(N):
+                dx = x0[n] - x[n]
+                bn = b[n]
+                if np.abs(lam * bn) < 1:
+                    d = dx
+                elif np.sign(lam * bn) < 0:
+                    d = max_ - x[n]
+                else:
+                    d = min_ - x[n]
+
+                delta[n] = d
+                b_dot_d += b[n] * d
+                norm_d += d ** 2
+                distance += np.abs(d - dx)
+
+        if touchup:
+            # search for the one index that (a) we can modify to match boundary constraint, (b) stays within our
+            # trust region and (c) minimize the distance to the original image
+            dc = c - b_dot_d
+            k = 0
+            min_distance = np.inf
+            min_distance_idx = 0
+            for n in range(N):
+                if np.abs(b[n]) > 0:
+                    dx = x0[n] - x[n]
+                    old_d = delta[n]
+                    new_d = old_d + dc / b[n]
+
+                    if x[n] + new_d <= max_ and x[n] + new_d >= min_ and norm_d - old_d ** 2 + new_d ** 2 <= r ** 2:
+                        # conditions (a) and (b) are fulfilled
+                        if k == 0:
+                            min_distance = distance - np.abs(old_d - dx) + np.abs(new_d - dx)
+                            min_distance_idx = n
+                            k += 1
+                        else:
+                            new_distance = distance - np.abs(old_d - dx) + np.abs(new_d - dx)
+                            if min_distance > new_distance:
+                                min_distance = new_distance
+                                min_distance_idx = n
+
+            if k > 0:
+                # touchup successful
+                idx = min_distance_idx
+                old_d = delta[idx]
+
+                new_d = old_d + dc / b[idx]
+                delta[idx] = new_d
+
+        return delta
+
+    def _distance(self, x0, x):
+        return np.abs(x0 - x).sum()
+
+
+@jitclass(spec=spec)
+class LinfOptimizer(Optimizer):
+    def optimize_distance_s_t_boundary_and_trustregion(self, x0, x, b, min_, max_, c, r):
+        """
+        Find the solution to the optimization problem
+
+        min_delta ||dx - delta||_p^p s.t. ||delta||_2^2 <= r^2 AND b^T delta = c AND min_ <= x + delta <= max_
+        """
+        params0 = np.array([0.0, 0.0])
+        bounds = np.array([(-np.inf, np.inf), (0, np.inf)])
+
+        return self.binary_search(params0, bounds, x0, x, b, min_, max_, c, r)
+
+    def binary_search(self, q0, bounds, x0, x, b, min_, max_, c, r, etol=1e-6, maxiter=1000):
+        # perform binary search over epsilon
+        epsilon = (max_ - min_) / 2.0
+        eps_low = min_
+        eps_high = max_
+        func_calls = 0
+
+        bnorm = np.linalg.norm(b)
+        lambda0 = 2 * c / bnorm ** 2
+
+        k = 0
+
+        while eps_high - eps_low > etol:
+            fun, nfev, _lambda0 = self.fun(epsilon, x0, x, b, min_, max_, c, r, lambda0=lambda0)
+            func_calls += nfev
+            if fun > -np.inf:
+                # decrease epsilon
+                eps_high = epsilon
+                lambda0 = _lambda0
+            else:
+                # increase epsilon
+                eps_low = epsilon
+
+            k += 1
+            epsilon = (eps_high - eps_low) / 2.0 + eps_low
+
+            if k > 20:
+                break
+
+        delta = self._get_final_delta(lambda0, eps_high, x0, x, b, min_, max_, c, r, touchup=True)
+        return delta
+
+    def _Linf_bounds(self, x0, epsilon, ell, u):
+        N = x0.shape[0]
+        _ell = np.empty_like(x0)
+        _u = np.empty_like(x0)
+        for i in range(N):
+            nx, px = x0[i] - epsilon, x0[i] + epsilon
+            if nx > ell:
+                _ell[i] = nx
+            else:
+                _ell[i] = ell
+
+            if px < u:
+                _u[i] = px
+            else:
+                _u[i] = u
+
+        return _ell, _u
+
+    def fun(self, epsilon, x0, x, b, ell, u, c, r, lambda0=None):
+        """
+        Computes the minimum norm necessary to reach the boundary. More precisely, we aim to solve the following
+        optimization problem
+
+            min ||delta||_2^2 s.t. lower <= x + delta <= upper AND b.dot(delta) = c
+
+        Lets forget about the box constraints for a second, i.e.
+
+            min ||delta||_2^2 s.t. b.dot(delta) = c
+
+        The dual of this problem is quite straight-forward to solve,
+
+            g(lambda, delta) = ||delta||_2^2 + lambda * (c - b.dot(delta))
+
+        The minimum of this Lagrangian is delta^* = lambda * b / 2, and so
+
+            inf_delta g(lambda, delta) = lambda^2 / 4 ||b||_2^2 + lambda * c
+
+        and so the optimal lambda, which maximizes inf_delta g(lambda, delta), is given by
+
+            lambda^* = 2c / ||b||_2^2
+
+        which in turn yields the optimal delta:
+
+            delta^* = c * b / ||b||_2^2
+
+        To take into account the box-constraints we perform a binary search over lambda and apply the box
+        constraint in each step.
+        """
+        N = x.shape[0]
+
+        # new box constraints
+        _ell, _u = self._Linf_bounds(x0, epsilon, ell, u)
+
+        # initialize lambda
+        _lambda = lambda0
+
+        # compute delta and determine active set
+        k = 0
+
+        lambda_max, lambda_min = 1e10, -1e10
+
+        # check whether problem is actually solvable (i.e. check whether boundary constraint can be reached)
+        max_c = 0
+        min_c = 0
+
+        for n in range(N):
+            if b[n] > 0:
+                max_c += b[n] * (_u[n] - x[n])
+                min_c += b[n] * (_ell[n] - x[n])
+            else:
+                max_c += b[n] * (_ell[n] - x[n])
+                min_c += b[n] * (_u[n] - x[n])
+
+        if c > max_c or c < min_c:
+            return -np.inf, k, _lambda
+
+        while True:
+            k += 1
+            _c = 0
+            norm = 0
+            _active_bnorm = 0
+
+            for n in range(N):
+                lam_step = _lambda * b[n] / 2
+                if lam_step + x[n] < _ell[n]:
+                    delta_step = _ell[n] - x[n]
+                elif lam_step + x[n] > _u[n]:
+                    delta_step = _u[n] - x[n]
+                else:
+                    delta_step = lam_step
+                    _active_bnorm += b[n] ** 2
+
+                _c += b[n] * delta_step
+                norm += delta_step ** 2
+
+            if 0.9999 * np.abs(c) - EPS < np.abs(_c) < 1.0001 * np.abs(c) + EPS:
+                if norm > r ** 2:
+                    return -np.inf, k, _lambda
+                else:
+                    return -epsilon, k, _lambda
+            else:
+                # update lambda according to active variables
+                if _c > c:
+                    lambda_max = _lambda
+                else:
+                    lambda_min = _lambda
+                #
+                if _active_bnorm == 0:
+                    # update is stepping out of feasible region, fallback to binary search
+                    _lambda = (lambda_max - lambda_min) / 2 + lambda_min
+                else:
+                    _lambda += 2 * (c - _c) / _active_bnorm
+
+                dlambda = lambda_max - lambda_min
+                if _lambda > lambda_max - 0.1 * dlambda or _lambda < lambda_min + 0.1 * dlambda:
+                    # update is stepping out of feasible region, fallback to binary search
+                    _lambda = (lambda_max - lambda_min) / 2 + lambda_min
+
+    def _get_final_delta(self, lam, eps, x0, x, b, min_, max_, c, r, touchup=True):
+        N = x.shape[0]
+        delta = np.empty_like(x0)
+
+        # new box constraints
+        _ell, _u = self._Linf_bounds(x0, eps, min_, max_)
+
+        for n in range(N):
+            lam_step = lam * b[n] / 2
+            if lam_step + x[n] < _ell[n]:
+                delta[n] = _ell[n] - x[n]
+            elif lam_step + x[n] > _u[n]:
+                delta[n] = _u[n] - x[n]
+            else:
+                delta[n] = lam_step
+
+        return delta
+
+    def _distance(self, x0, x):
+        return np.abs(x0 - x).max()
+
+
+@jitclass(spec=spec)
+class L0Optimizer(Optimizer):
+    def optimize_distance_s_t_boundary_and_trustregion(self, x0, x, b, min_, max_, c, r):
+        """
+        Find the solution to the optimization problem
+
+        min_delta ||dx - delta||_p^p s.t. ||delta||_2^2 <= r^2 AND b^T delta = c AND min_ <= x + delta <= max_
+        """
+        params0 = np.array([0.0, 0.0])
+        bounds = np.array([(-np.inf, np.inf), (0, np.inf)])
+
+        return self.minimize(params0, bounds, x0, x, b, min_, max_, c, r)
+
+    def minimize(
+        self, q0, bounds, x0, x, b, min_, max_, c, r, ftol=1e-9, xtol=-1e-5, maxiter=1000,
+    ):
+        # First check whether solution can be computed without trust region
+        delta, delta_norm = self.minimize_without_trustregion(x0, x, b, c, r, min_, max_)
+
+        if delta_norm <= r:
+            return delta
+        else:
+            # perform Nelder-Mead optimization
+            args = (x0, x, b, min_, max_, c, r)
+
+            results = self._nelder_mead_algorithm(q0, bounds, args=args, tol_f=ftol, tol_x=xtol, max_iter=maxiter)
+
+            delta = self._get_final_delta(results[0], results[1], x0, x, b, min_, max_, c, r, touchup=True)
+
+        return delta
+
+    def minimize_without_trustregion(self, x0, x, b, c, r, ell, u):
+        # compute maximum direction to b.dot(delta) within box-constraints
+        delta = x0 - x
+        total = np.empty_like(x0)
+        total_b = np.empty_like(x0)
+        bdotdelta = b.dot(delta)
+        delta_bdotdelta = c - bdotdelta
+
+        for k in range(x0.shape[0]):
+            if b[k] > 0 and delta_bdotdelta > 0:
+                total_b[k] = (u - x0[k]) * b[k]  # pos
+                total[k] = u - x0[k]
+            elif b[k] > 0 and delta_bdotdelta < 0:
+                total_b[k] = (ell - x0[k]) * b[k]  # neg
+                total[k] = ell - x0[k]
+            elif b[k] < 0 and delta_bdotdelta > 0:
+                total_b[k] = (ell - x0[k]) * b[k]  # pos
+                total[k] = ell - x0[k]
+            else:
+                total_b[k] = (u - x0[k]) * b[k]  # neg
+                total[k] = u - x0[k]
+
+        b_argsort = np.argsort(np.abs(total_b))[::-1]
+
+        for idx in b_argsort:
+            if np.abs(c - bdotdelta) > np.abs(total_b[idx]):
+                delta[idx] += total[idx]
+                bdotdelta += total_b[idx]
+            else:
+                delta[idx] += (c - bdotdelta) / (b[idx] + 1e-20)
+                break
+
+        delta_norm = np.linalg.norm(delta)
+
+        return delta, delta_norm
+
+    def _nelder_mead_algorithm(
+        self, q0, bounds, args=(), ρ=1.0, χ=2.0, γ=0.5, σ=0.5, tol_f=1e-8, tol_x=1e-8, max_iter=1000,
+    ):
+        """
+        Implements the Nelder-Mead algorithm described in Lagarias et al. (1998)
+        modified to maximize instead of minimizing.
+
+        Parameters
+        ----------
+        vertices : ndarray(float, ndim=2)
+            Initial simplex with shape (n+1, n) to be modified in-place.
+
+        args : tuple, optional
+            Extra arguments passed to the objective function.
+
+        ρ : scalar(float), optional(default=1.)
+            Reflection parameter. Must be strictly greater than 0.
+
+        χ : scalar(float), optional(default=2.)
+            Expansion parameter. Must be strictly greater than max(1, ρ).
+
+        γ : scalar(float), optional(default=0.5)
+            Contraction parameter. Must be stricly between 0 and 1.
+
+        σ : scalar(float), optional(default=0.5)
+            Shrinkage parameter. Must be strictly between 0 and 1.
+
+        tol_f : scalar(float), optional(default=1e-10)
+            Tolerance to be used for the function value convergence test.
+
+        tol_x : scalar(float), optional(default=1e-10)
+            Tolerance to be used for the function domain convergence test.
+
+        max_iter : scalar(float), optional(default=1000)
+            The maximum number of allowed iterations.
+
+        Returns
+        ----------
+        x : Approximate solution
+
+        """
+        vertices = self._initialize_simplex(q0)
+        n = vertices.shape[1]
+        self._check_params(ρ, χ, γ, σ, bounds, n)
+
+        nit = 0
+
+        ργ = ρ * γ
+        ρχ = ρ * χ
+        σ_n = σ ** n
+
+        f_val = np.empty(n + 1, dtype=np.float64)
+        for i in range(n + 1):
+            f_val[i] = self._neg_bounded_fun(bounds, vertices[i], args=args)
+
+        # Step 1: Sort
+        sort_ind = f_val.argsort()
+        LV_ratio = 1
+
+        # Compute centroid
+        x_bar = vertices[sort_ind[:n]].sum(axis=0) / n
+
+        while True:
+            shrink = False
+
+            # Check termination
+            fail = nit >= max_iter
+
+            best_val_idx = sort_ind[0]
+            worst_val_idx = sort_ind[n]
+
+            term_f = f_val[worst_val_idx] - f_val[best_val_idx] < tol_f
+
+            # Linearized volume ratio test (see [2])
+            term_x = LV_ratio < tol_x
+
+            if term_x or term_f or fail:
+                break
+
+            # Step 2: Reflection
+            x_r = x_bar + ρ * (x_bar - vertices[worst_val_idx])
+            f_r = self._neg_bounded_fun(bounds, x_r, args=args)
+
+            if f_r >= f_val[best_val_idx] and f_r < f_val[sort_ind[n - 1]]:
+                # Accept reflection
+                vertices[worst_val_idx] = x_r
+                LV_ratio *= ρ
+
+            # Step 3: Expansion
+            elif f_r < f_val[best_val_idx]:
+                x_e = x_bar + χ * (x_r - x_bar)
+                f_e = self._neg_bounded_fun(bounds, x_e, args=args)
+                if f_e < f_r:  # Greedy minimization
+                    vertices[worst_val_idx] = x_e
+                    LV_ratio *= ρχ
+                else:
+                    vertices[worst_val_idx] = x_r
+                    LV_ratio *= ρ
+
+            # Step 4 & 5: Contraction and Shrink
+            else:
+                # Step 4: Contraction
+                if f_r < f_val[worst_val_idx]:  # Step 4.a: Outside Contraction
+                    x_c = x_bar + γ * (x_r - x_bar)
+                    LV_ratio_update = ργ
+                else:  # Step 4.b: Inside Contraction
+                    x_c = x_bar - γ * (x_r - x_bar)
+                    LV_ratio_update = γ
+
+                f_c = self._neg_bounded_fun(bounds, x_c, args=args)
+                if f_c < min(f_r, f_val[worst_val_idx]):  # Accept contraction
+                    vertices[worst_val_idx] = x_c
+                    LV_ratio *= LV_ratio_update
+
+                # Step 5: Shrink
+                else:
+                    shrink = True
+                    for i in sort_ind[1:]:
+                        vertices[i] = vertices[best_val_idx] + σ * (vertices[i] - vertices[best_val_idx])
+                        f_val[i] = self._neg_bounded_fun(bounds, vertices[i], args=args)
+
+                    sort_ind[1:] = f_val[sort_ind[1:]].argsort() + 1
+
+                    x_bar = (
+                        vertices[best_val_idx]
+                        + σ * (x_bar - vertices[best_val_idx])
+                        + (vertices[worst_val_idx] - vertices[sort_ind[n]]) / n
+                    )
+
+                    LV_ratio *= σ_n
+
+            if not shrink:  # Nonshrink ordering rule
+                f_val[worst_val_idx] = self._neg_bounded_fun(bounds, vertices[worst_val_idx], args=args)
+
+                for i, j in enumerate(sort_ind):
+                    if f_val[worst_val_idx] < f_val[j]:
+                        sort_ind[i + 1 :] = sort_ind[i:-1]
+                        sort_ind[i] = worst_val_idx
+                        break
+
+                x_bar += (vertices[worst_val_idx] - vertices[sort_ind[n]]) / n
+
+            nit += 1
+
+        return vertices[sort_ind[0]]
+
+    def _initialize_simplex(self, x0):
+        """
+        Generates an initial simplex for the Nelder-Mead method.
+
+        Parameters
+        ----------
+        x0 : ndarray(float, ndim=1)
+            Initial guess. Array of real elements of size (n,), where ‘n’ is the
+            number of independent variables.
+
+        bounds: ndarray(float, ndim=2)
+            Sequence of (min, max) pairs for each element in x0.
+
+        Returns
+        ----------
+        vertices : ndarray(float, ndim=2)
+            Initial simplex with shape (n+1, n).
+        """
+        n = x0.size
+
+        vertices = np.empty((n + 1, n), dtype=np.float64)
+
+        # Broadcast x0 on row dimension
+        vertices[:] = x0
+
+        nonzdelt = 0.05
+        zdelt = 0.00025
+
+        for i in range(n):
+            # Generate candidate coordinate
+            if vertices[i + 1, i] != 0.0:
+                vertices[i + 1, i] *= 1 + nonzdelt
+            else:
+                vertices[i + 1, i] = zdelt
+
+        return vertices
+
+    def _check_params(self, ρ, χ, γ, σ, bounds, n):
+        """
+        Checks whether the parameters for the Nelder-Mead algorithm are valid.
+        JIT-compiled in `nopython` mode using Numba.
+
+        Parameters
+        ----------
+        ρ : scalar(float)
+            Reflection parameter. Must be strictly greater than 0.
+
+        χ : scalar(float)
+            Expansion parameter. Must be strictly greater than max(1, ρ).
+
+        γ : scalar(float)
+            Contraction parameter. Must be stricly between 0 and 1.
+
+        σ : scalar(float)
+            Shrinkage parameter. Must be strictly between 0 and 1.
+
+        bounds: ndarray(float, ndim=2)
+            Sequence of (min, max) pairs for each element in x.
+
+        n : scalar(int)
+            Number of independent variables.
+        """
+        if ρ < 0:
+            raise ValueError("ρ must be strictly greater than 0.")
+        if χ < 1:
+            raise ValueError("χ must be strictly greater than 1.")
+        if χ < ρ:
+            raise ValueError("χ must be strictly greater than ρ.")
+        if γ < 0 or γ > 1:
+            raise ValueError("γ must be strictly between 0 and 1.")
+        if σ < 0 or σ > 1:
+            raise ValueError("σ must be strictly between 0 and 1.")
+
+        if not (bounds.shape == (0, 2) or bounds.shape == (n, 2)):
+            raise ValueError("The shape of `bounds` is not valid.")
+        if (np.atleast_2d(bounds)[:, 0] > np.atleast_2d(bounds)[:, 1]).any():
+            raise ValueError("Lower bounds must be greater than upper bounds.")
+
+    def _check_bounds(self, x, bounds):
+        """
+        Checks whether `x` is within `bounds`. JIT-compiled in `nopython` mode
+        using Numba.
+
+        Parameters
+        ----------
+        x : ndarray(float, ndim=1)
+            1-D array with shape (n,) of independent variables.
+
+        bounds: ndarray(float, ndim=2)
+            Sequence of (min, max) pairs for each element in x.
+
+        Returns
+        ----------
+        bool
+            `True` if `x` is within `bounds`, `False` otherwise.
+
+        """
+        if bounds.shape == (0, 2):
+            return True
+        else:
+            return (np.atleast_2d(bounds)[:, 0] <= x).all() and (x <= np.atleast_2d(bounds)[:, 1]).all()
+
+    def _neg_bounded_fun(self, bounds, x, args=()):
+        """
+        Wrapper for bounding and taking the negative of `fun` for the
+        Nelder-Mead algorithm. JIT-compiled in `nopython` mode using Numba.
+
+        Parameters
+        ----------
+        bounds: ndarray(float, ndim=2)
+            Sequence of (min, max) pairs for each element in x.
+
+        x : ndarray(float, ndim=1)
+            1-D array with shape (n,) of independent variables at which `fun` is
+            to be evaluated.
+
+        args : tuple, optional
+            Extra arguments passed to the objective function.
+
+        Returns
+        ----------
+        scalar
+            `-fun(x, *args)` if x is within `bounds`, `np.inf` otherwise.
+
+        """
+        if self._check_bounds(x, bounds):
+            return -self.fun(x, *args)
+        else:
+            return np.inf
+
+    def fun(self, params, x0, x, b, min_, max_, c, r):
+        # arg min_delta ||delta - dx||_0 + lam * b^T delta + mu * ||delta||_2^2  s.t.  min <= delta + x <= max
+        lam, mu = params
+        N = x0.shape[0]
+
+        g = -mu * r ** 2 - lam * c
+
+        if mu > 0:
+            t = 1 / (2 * mu)
+
+            for n in range(N):
+                dx = x0[n] - x[n]
+                bn = b[n]
+
+                case1 = lam * bn * dx + mu * dx ** 2
+
+                optd = -lam * bn * t
+                if optd < min_ - x[n]:
+                    optd = min_ - x[n]
+                elif optd > max_ - x[n]:
+                    optd = max_ - x[n]
+
+                case2 = 1 + lam * bn * optd + mu * optd ** 2
+
+                if case1 <= case2:
+                    g += mu * dx ** 2 + lam * bn * dx
+                else:
+                    g += 1 + mu * optd ** 2 + lam * bn * optd
+        else:
+            # arg min_delta ||delta - dx||_0 + lam * b^T delta
+            # case delta[n] = dx[n]: lam * b[n] * dx[n]
+            # case delta[n] != dx[n]: lam * b[n] * [min_ - x[n], max_ - x[n]]
+            for n in range(N):
+                dx = x0[n] - x[n]
+                bn = b[n]
+                case1 = lam * bn * dx
+                case2 = 1 + lam * bn * (min_ - x[n])
+                case3 = 1 + lam * bn * (max_ - x[n])
+                if case1 <= case2 and case1 <= case3:
+                    g += mu * dx ** 2 + lam * bn * dx
+                elif case2 < case3:
+                    g += 1 + mu * (min_ - x[n]) ** 2 + lam * bn * (min_ - x[n])
+                else:
+                    g += 1 + mu * (max_ - x[n]) ** 2 + lam * bn * (max_ - x[n])
+
+        return g
+
+    def _get_final_delta(self, lam, mu, x0, x, b, min_, max_, c, r, touchup=True):
+        if touchup:
+            delta = self.__get_final_delta(lam, mu, x0, x, b, min_, max_, c, r)
+            if delta is not None:
+                return delta
+            else:
+                # fallback
+                params = [
+                    (lam + 1e-5, mu),
+                    (lam, mu + 1e-5),
+                    (lam - 1e-5, mu),
+                    (lam, mu - 1e-5),
+                    (lam + 1e-5, mu + 1e-5),
+                    (lam - 1e-5, mu - 1e-5),
+                    (lam + 1e-5, mu - 1e-5),
+                    (lam - 1e-5, mu + 1e-5),
+                ]
+                for param in params:
+                    delta = self.__get_final_delta(param[0], param[1], x0, x, b, min_, max_, c, r)
+                    if delta is not None:
+                        return delta
+
+                # 2nd fallback
+                return self.__get_final_delta(lam, mu, x0, x, b, min_, max_, c, r, False)
+        else:
+            return self.__get_final_delta(lam, mu, x0, x, b, min_, max_, c, r, False)
+
+    def __get_final_delta(self, lam, mu, x0, x, b, min_, max_, c, r, touchup=True):
+        delta = np.empty_like(x0)
+        N = x0.shape[0]
+
+        b_dot_d = 0
+        norm_d = 0
+        distance = 0
+
+        if mu > 0:
+            for n in range(N):
+                dx = x0[n] - x[n]
+                bn = b[n]
+                t = 1 / (2 * mu)
+
+                case1 = lam * bn * dx + mu * dx ** 2
+
+                optd = -lam * bn * t
+                if optd < min_ - x[n]:
+                    optd = min_ - x[n]
+                elif optd > max_ - x[n]:
+                    optd = max_ - x[n]
+
+                case2 = 1 + lam * bn * optd + mu * optd ** 2
+
+                if case1 <= case2:
+                    d = dx
+                else:
+                    d = optd
+                    distance += 1
+
+                delta[n] = d
+                b_dot_d += bn * d
+                norm_d += d ** 2
+        else:  # mu == 0
+            for n in range(N):
+                dx = x0[n] - x[n]
+                bn = b[n]
+                case1 = lam * bn * dx
+                case2 = 1 + lam * bn * (min_ - x[n])
+                case3 = 1 + lam * bn * (max_ - x[n])
+                if case1 <= case2 and case1 <= case3:
+                    d = dx
+                elif case2 < case3:
+                    d = min_ - x[n]
+                    distance += 1
+                else:
+                    d = max_ - x[n]
+                    distance += 1
+
+                delta[n] = d
+                norm_d += d ** 2
+                b_dot_d += bn * d
+
+        if touchup:
+            # search for the one index that
+            # (a) we can modify to match boundary constraint
+            # (b) stays within our trust region and
+            # (c) minimize the distance to the original image.
+            dc = c - b_dot_d
+            k = 0
+            min_distance = np.inf
+            min_norm = np.inf
+            min_distance_idx = 0
+            for n in range(N):
+                if np.abs(b[n]) > 0:
+                    dx = x0[n] - x[n]
+                    old_d = delta[n]
+                    new_d = old_d + dc / b[n]
+
+                    if x[n] + new_d <= max_ and x[n] + new_d >= min_ and norm_d - old_d ** 2 + new_d ** 2 <= r ** 2:
+                        # conditions (a) and (b) are fulfilled
+                        if k == 0:
+                            min_distance = distance - (np.abs(old_d - dx) > 1e-10) + (np.abs(new_d - dx) > 1e-10)
+                            min_distance_idx = n
+                            min_norm = norm_d - old_d ** 2 + new_d ** 2
+                            k += 1
+                        else:
+                            new_distance = distance - (np.abs(old_d - dx) > 1e-10) + (np.abs(new_d - dx) > 1e-10)
+                            if (
+                                min_distance > new_distance
+                                or min_distance == new_distance
+                                and min_norm > norm_d - old_d ** 2 + new_d ** 2
+                            ):
+                                min_distance = new_distance
+                                min_norm = norm_d - old_d ** 2 + new_d ** 2
+                                min_distance_idx = n
+
+            if k > 0:
+                # touchup successful
+                idx = min_distance_idx
+                old_d = delta[idx]
+
+                new_d = old_d + dc / b[idx]
+                delta[idx] = new_d
+
+                return delta
+            else:
+                return None
+
+        return delta
+
+    def _distance(self, x0, x):
+        return np.sum(np.abs(x - x0) > EPS)
+
+
+class BrendelBethgeAttack(EvasionAttack):
+
+    attack_params = EvasionAttack.attack_params + [
+        "norm",
+        "targeted",
+        "init_attack",
+        "overshoot",
+        "steps",
+        "lr",
+        "lr_decay",
+        "lr_num_decay",
+        "momentum",
+        "binary_search_steps",
+        "init_size",
+    ]
+    _estimator_requirements = (BaseEstimator, LossGradientsMixin, ClassifierMixin)
+
+    """
+    Base class for the Brendel & Bethge adversarial attack [#Bren19]_, a powerful gradient-based adversarial attack that
+    follows the adversarial boundary (the boundary between the space of adversarial and non-adversarial images as
+    defined by the adversarial criterion) to find the minimum distance to the clean image.
+
+    This is implementation of the Brendel & Bethge attack follows the reference implementation at
+    https://github.com/bethgelab/foolbox/blob/master/foolbox/attacks/brendel_bethge.py.
+
+    Implementation differs from the attack used in the paper in two ways:
+    * The initial binary search is always using the full 10 steps (for ease of implementation).
+    * The adaptation of the trust region over the course of optimisation is less
+      greedy but is more robust, reliable and simpler (decay every K steps)
+
+    Args:
+        estimator : A trained ART classifier providing loss gradients.
+        norm : The norm of the adversarial perturbation. Possible values: "inf", np.inf, 1 or 2.
+        targeted : Flag determining if attack is targeted.
+        overshoot : If 1 the attack tries to return exactly to the adversarial boundary
+            in each iteration. For higher values the attack tries to overshoot
+            over the boundary to ensure that the perturbed sample in each iteration
+            is adversarial.
+        steps : Maximum number of iterations to run. Might converge and stop
+            before that.
+        lr : Trust region radius, behaves similar to a learning rate. Smaller values
+            decrease the step size in each iteration and ensure that the attack
+            follows the boundary more faithfully.
+        lr_decay : The trust region lr is multiplied with lr_decay in regular intervals (see
+            lr_num_decay).
+        lr_num_decay : Number of learning rate decays in regular intervals of
+            length steps / lr_num_decay.
+        momentum : Averaging of the boundary estimation over multiple steps. A momentum of
+            zero would always take the current estimate while values closer to one
+            average over a larger number of iterations.
+        binary_search_steps : Number of binary search steps used to find the adversarial boundary
+            between the starting point and the clean image.
+        batch_size : Batch size for evaluating the model for predictions and gradients.
+        init_size : Maximum number of random search steps to find initial adversarial example.
+
+    References:
+        .. [#Bren19] Wieland Brendel, Jonas Rauber, Matthias Kümmerer,
+            Ivan Ustyuzhaninov, Matthias Bethge,
+            "Accurate, reliable and fast robustness evaluation",
+            33rd Conference on Neural Information Processing Systems (2019)
+            https://arxiv.org/abs/1907.01003
+    """
+
+    def __init__(
+        self,
+        estimator: "CLASSIFIER_LOSS_GRADIENTS_TYPE",
+        norm: Union[int, float, str] = np.inf,
+        targeted: bool = False,
+        overshoot: float = 1.1,
+        steps: int = 1000,
+        lr: float = 1e-3,
+        lr_decay: float = 0.5,
+        lr_num_decay: int = 20,
+        momentum: float = 0.8,
+        binary_search_steps: int = 10,
+        init_size: int = 100,
+        batch_size: int = 32,
+    ):
+        from art.estimators.classification import TensorFlowV2Classifier, PyTorchClassifier
+
+        if isinstance(estimator, TensorFlowV2Classifier):
+            import tensorflow as tf
+
+            if is_probability(estimator.predict(x=np.ones(shape=(1, *estimator.input_shape)))):
+                raise ValueError(
+                    "The provided estimator seems to predict probabilities. If loss_type='difference_logits_ratio' "
+                    "the estimator has to to predict logits."
+                )
+            else:
+
+                def logits_difference(y_true, y_pred):
+                    i_y_true = tf.cast(tf.math.argmax(tf.cast(y_true, tf.int32), axis=1), tf.int32)
+                    i_y_pred_arg = tf.argsort(y_pred, axis=1)
+                    i_z_i_list = list()
+
+                    for i in range(y_true.shape[0]):
+                        if i_y_pred_arg[i, -1] != i_y_true[i]:
+                            i_z_i_list.append(i_y_pred_arg[i, -1])
+                        else:
+                            i_z_i_list.append(i_y_pred_arg[i, -2])
+
+                    i_z_i = tf.stack(i_z_i_list)
+
+                    z_i = tf.gather(y_pred, i_z_i, axis=1, batch_dims=0)
+                    z_y = tf.gather(y_pred, i_y_true, axis=1, batch_dims=0)
+
+                    z_i = tf.linalg.diag_part(z_i)
+                    z_y = tf.linalg.diag_part(z_y)
+
+                    logits_diff = z_y - z_i
+
+                    return tf.reduce_mean(logits_diff)
+
+                self._loss_fn = logits_difference
+                self._loss_object = logits_difference
+
+            estimator_bb = TensorFlowV2Classifier(
+                model=estimator.model,
+                nb_classes=estimator.nb_classes,
+                input_shape=estimator.input_shape,
+                loss_object=self._loss_object,
+                train_step=estimator._train_step,
+                channels_first=estimator.channels_first,
+                clip_values=estimator.clip_values,
+                preprocessing_defences=estimator.preprocessing_defences,
+                postprocessing_defences=estimator.postprocessing_defences,
+                preprocessing=estimator.preprocessing,
+            )
+
+        elif isinstance(estimator, PyTorchClassifier):
+            import torch
+
+            if is_probability(
+                estimator.predict(x=np.ones(shape=(1, *estimator.input_shape), dtype=config.ART_NUMPY_DTYPE))
+            ):
+                raise ValueError(
+                    "The provided estimator seems to predict probabilities. If loss_type='difference_logits_ratio' "
+                    "the estimator has to to predict logits."
+                )
+            else:
+
+                # def difference_logits_ratio(y_true, y_pred):
+                def logits_difference(y_pred, y_true):  # type: ignore
+                    if isinstance(y_true, np.ndarray):
+                        y_true = torch.from_numpy(y_true)
+                    if isinstance(y_pred, np.ndarray):
+                        y_pred = torch.from_numpy(y_pred)
+
+                    y_true = y_true.float()
+
+                    i_y_true = torch.argmax(y_true, axis=1)
+                    i_y_pred_arg = torch.argsort(y_pred, axis=1)
+                    i_z_i_list = list()
+
+                    for i in range(y_true.shape[0]):
+                        if i_y_pred_arg[i, -1] != i_y_true[i]:
+                            i_z_i_list.append(i_y_pred_arg[i, -1])
+                        else:
+                            i_z_i_list.append(i_y_pred_arg[i, -2])
+
+                    i_z_i = torch.stack(i_z_i_list)
+
+                    z_i = y_pred[:, i_z_i]
+                    z_y = y_pred[:, i_y_true]
+
+                    z_i = torch.diagonal(z_i)
+                    z_y = torch.diagonal(z_y)
+
+                    logits_diff = z_y - z_i
+
+                    return torch.mean(logits_diff.float())
+
+                self._loss_fn = logits_difference
+                self._loss_object = logits_difference
+
+            estimator_bb = PyTorchClassifier(
+                model=estimator.model,
+                loss=self._loss_object,
+                input_shape=estimator.input_shape,
+                nb_classes=estimator.nb_classes,
+                optimizer=None,
+                channels_first=estimator.channels_first,
+                clip_values=estimator.clip_values,
+                preprocessing_defences=estimator.preprocessing_defences,
+                postprocessing_defences=estimator.postprocessing_defences,
+                preprocessing=estimator.preprocessing,
+                device_type=estimator._device,
+            )
+
+        else:
+            estimator_bb = None
+
+        super().__init__(estimator=estimator_bb)
+        self.norm = norm
+        self._targeted = targeted
+        self.overshoot = overshoot
+        self.steps = steps
+        self.lr = lr
+        self.lr_decay = lr_decay
+        self.lr_num_decay = lr_num_decay
+        self.momentum = momentum
+        self.binary_search_steps = binary_search_steps
+        self.init_size = init_size
+        self.batch_size = batch_size
+        self._check_params()
+
+        self._optimizer: Optimizer
+        if norm == 0:
+            self._optimizer = L0Optimizer()
+        if norm == 1:
+            self._optimizer = L1Optimizer()
+        elif norm == 2:
+            self._optimizer = L2Optimizer()
+        elif norm in ["inf", np.inf]:
+            self._optimizer = LinfOptimizer()
+
+    def generate(
+        self,
+        x: np.ndarray,
+        y: Optional[np.ndarray] = None,
+        starting_points: Optional[np.ndarray] = None,
+        early_stop: Optional[float] = None,
+        **kwargs,
+    ) -> np.ndarray:
+        """
+        Applies the Brendel & Bethge attack.
+
+        :param x: The original clean inputs.
+        :param y: The labels for inputs `x`.
+        :param starting_points: Adversarial inputs to use as a starting points, in particular for targeted attacks.
+        :param early_stop: Early-stopping criteria.
+        """
+        originals = x.copy()
+
+        y = check_and_transform_label_format(y, self.estimator.nb_classes)
+
+        if y is None:
+            # Throw error if attack is targeted, but no targets are provided
+            if self.targeted:
+                raise ValueError("Target labels `y` need to be provided for a targeted attack.")
+
+            # Use model predictions as correct outputs
+            logger.info("Using model predictions as correct labels for FGM.")
+            y = get_labels_np_array(
+                self.estimator.predict(x, batch_size=self.batch_size)  # type: ignore
+            )
+
+        # Prediction from the initial adversarial examples if not None
+        x_adv_init = kwargs.get("x_adv_init")
+
+        if x_adv_init is not None:
+            init_preds = np.argmax(self.estimator.predict(x_adv_init, batch_size=self.batch_size), axis=1)
+        else:
+            init_preds = [None] * len(x)
+            x_adv_init = [None] * len(x)
+
+        classes = y
+
+        if starting_points is None:
+
+            starting_points = np.zeros_like(x)
+            # First, create an initial adversarial sample
+            clip_min, clip_max = self.estimator.clip_values
+            # Prediction from the original images
+            preds = np.argmax(self.estimator.predict(x, batch_size=self.batch_size), axis=1)
+            y_index = np.argmax(y, axis=1)
+
+            for i_x in range(x.shape[0]):
+                initial_sample = self._init_sample(
+                    x=x[i_x],
+                    y=y_index[i_x],
+                    y_p=preds[i_x],
+                    init_pred=init_preds[i_x],
+                    adv_init=x_adv_init[i_x],
+                    clip_min=clip_min,
+                    clip_max=clip_max,
+                )
+
+                if initial_sample is None:
+                    starting_points[i_x] = x[i_x]
+                else:
+                    starting_points[i_x] = initial_sample[0]
+
+        best_advs = starting_points
+
+        if self.targeted:
+            assert (np.argmax(self.estimator.predict(x=best_advs), axis=1) == np.argmax(y, axis=1)).all()
+        else:
+            assert (np.argmax(self.estimator.predict(x=best_advs), axis=1) != np.argmax(y, axis=1)).all()
+
+        # perform binary search to find adversarial boundary
+        # TODO: Implement more efficient search with breaking condition
+        N = len(originals)
+        rows = range(N)
+
+        bounds = self.estimator.clip_values
+        min_, max_ = bounds
+
+        x0 = originals
+        x0_np_flatten = x0.reshape((N, -1))
+        x1 = best_advs
+
+        lower_bound = np.zeros(shape=(N,))
+        upper_bound = np.ones(shape=(N,))
+
+        for _ in range(self.binary_search_steps):
+            epsilons = (lower_bound + upper_bound) / 2
+            mid_points = self.mid_points(x0, x1, epsilons, bounds)
+            if self.targeted:
+                is_advs = (np.argmax(self.estimator.predict(x=mid_points), axis=1) == np.argmax(y, axis=1)).all()
+            else:
+                is_advs = (np.argmax(self.estimator.predict(x=mid_points), axis=1) != np.argmax(y, axis=1)).all()
+            lower_bound = np.where(is_advs, lower_bound, epsilons)
+            upper_bound = np.where(is_advs, epsilons, upper_bound)
+
+        starting_points = self.mid_points(x0, x1, upper_bound, bounds)
+
+        x = starting_points.astype(config.ART_NUMPY_DTYPE)
+        lrs = self.lr * np.ones(N)
+        lr_reduction_interval = max(1, int(self.steps / self.lr_num_decay))
+        converged = np.zeros(N, dtype=np.bool)
+        rate_normalization = np.prod(x.shape) * (max_ - min_)
+        original_shape = x.shape
+        _best_advs = best_advs.copy()
+
+        from tqdm.auto import trange
+
+        for step in trange(1, self.steps + 1):
+            if converged.all():
+                break  # pragma: no cover
+
+            # get logits and local boundary geometry
+            # TODO: only perform forward pass on non-converged samples
+
+            logits = self.estimator.predict(x=x)
+
+            exclude = classes
+            logits_exclude = logits.copy()
+
+            logits_exclude[:, np.argmax(exclude, axis=1)] = -np.inf
+
+            best_other_classes = np.argmax(logits_exclude, axis=1)
+
+            if self.targeted:
+                c_minimize = best_other_classes
+                c_maximize = np.argmax(classes, axis=1)
+            else:
+                c_minimize = np.argmax(classes, axis=1)
+                c_maximize = best_other_classes
+
+            logits_diffs = logits[rows, c_minimize] - logits[rows, c_maximize]
+
+            _boundary = self.estimator.loss_gradient(x=x, y=y)
+
+            if self.targeted:
+                _boundary = -_boundary
+
+            # record optimal adversarials
+            distances = self.norms(originals - x)
+            source_norms = self.norms(originals - best_advs)
+
+            closer = distances < source_norms
+            closer = np.squeeze(closer)
+            is_advs = logits_diffs < 0
+            closer = np.logical_and(closer, is_advs)
+
+            x_np_flatten = x.reshape((N, -1))
+
+            if closer.any():
+                _best_advs = best_advs.copy()
+                _closer = closer.flatten()
+                for idx in np.arange(N)[_closer]:
+                    _best_advs[idx] = x_np_flatten[idx].reshape(original_shape[1:])
+
+            best_advs = _best_advs.copy()
+
+            # denoise estimate of boundary using a short history of the boundary
+            if step == 1:
+                boundary = _boundary
+            else:
+                boundary = (1 - self.momentum) * _boundary + self.momentum * boundary
+
+            # learning rate adaptation
+            if (step + 1) % lr_reduction_interval == 0:
+                lrs *= self.lr_decay
+
+            # compute optimal step within trust region depending on metric
+            x = x.reshape((N, -1))
+            region = lrs * rate_normalization
+
+            # we aim to slight overshoot over the boundary to stay within the adversarial region
+            corr_logits_diffs = np.where(
+                -logits_diffs < 0, -self.overshoot * logits_diffs, -(2 - self.overshoot) * logits_diffs,
+            )
+
+            # employ solver to find optimal step within trust region for each sample
+            deltas, k = [], 0
+
+            for sample in range(N):
+                if converged[sample]:
+                    # don't perform optimisation on converged samples
+                    deltas.append(np.zeros_like(x0_np_flatten[sample]))  # pragma: no cover
+                else:
+                    _x0 = x0_np_flatten[sample]
+                    _x = x_np_flatten[sample]
+                    _b = boundary[k].flatten()
+                    _c = corr_logits_diffs[k]
+                    r = region[sample]
+
+                    delta = self._optimizer.solve(  # type: ignore
+                        _x0, _x, _b, bounds[0], bounds[1], _c, r
+                    )
+                    deltas.append(delta)
+
+                    k += 1  # idx of masked sample
+
+            deltas = np.stack(deltas)
+            deltas = deltas.astype(np.float32)
+
+            # add step to current perturbation
+            x = (x + deltas).reshape(original_shape)
+
+        return best_advs.astype(config.ART_NUMPY_DTYPE)
+
+    def norms(self, x: np.ndarray) -> np.ndarray:
+        order = self.norm if self.norm != "inf" else np.inf
+        norm = np.linalg.norm(x=x.reshape(x.shape[0], -1), ord=order, axis=1)
+        return norm
+
+    def mid_points(
+        self, x0: np.ndarray, x1: np.ndarray, epsilons: np.ndarray, bounds: Tuple[float, float],
+    ) -> np.ndarray:
+        """
+        returns a point between x0 and x1 where epsilon = 0 returns x0 and epsilon = 1 returns x1
+        """
+        if self.norm == 0:
+            # get epsilons in right shape for broadcasting
+            epsilons = epsilons.reshape(epsilons.shape + (1,) * (x0.ndim - 1))
+
+            threshold = (bounds[1] - bounds[0]) * epsilons
+            mask = np.abs(x1 - x0) < threshold
+            new_x = np.where(mask, x1, x0)
+        if self.norm == 1:
+            # get epsilons in right shape for broadcasting
+            epsilons = epsilons.reshape(epsilons.shape + (1,) * (x0.ndim - 1))
+
+            threshold = (bounds[1] - bounds[0]) * (1 - epsilons)
+            mask = np.abs(x1 - x0) > threshold
+            new_x = np.where(mask, x0 + np.sign(x1 - x0) * (np.abs(x1 - x0) - threshold), x0)
+        if self.norm == 2:
+            # get epsilons in right shape for broadcasting
+            epsilons = epsilons.reshape(epsilons.shape + (1,) * (x0.ndim - 1))
+            new_x = epsilons * x1 + (1 - epsilons) * x0
+        if self.norm in ["inf", np.inf]:
+            delta = x1 - x0
+            min_, max_ = bounds
+            s = max_ - min_
+            # get epsilons in right shape for broadcasting
+            epsilons = epsilons.reshape(epsilons.shape + (1,) * (x0.ndim - 1))
+
+            clipped_delta = np.where(delta < -epsilons * s, -epsilons * s, delta)
+            clipped_delta = np.where(clipped_delta > epsilons * s, epsilons * s, clipped_delta)
+            new_x = x0 + clipped_delta
+
+        return new_x.astype(config.ART_NUMPY_DTYPE)
+
+    def _init_sample(
+        self, x: np.ndarray, y: int, y_p: int, init_pred: int, adv_init: np.ndarray, clip_min: float, clip_max: float,
+    ) -> Optional[Union[np.ndarray, Tuple[np.ndarray, int]]]:
+        """
+        Find initial adversarial example for the attack.
+
+        :param x: An array with 1 original input to be attacked.
+        :param y: If `self.targeted` is true, then `y` represents the target label.
+        :param y_p: The predicted label of x.
+        :param init_pred: The predicted label of the initial image.
+        :param adv_init: Initial array to act as an initial adversarial example.
+        :param clip_min: Minimum value of an example.
+        :param clip_max: Maximum value of an example.
+        :return: An adversarial example.
+        """
+        nprd = np.random.RandomState()
+        initial_sample = None
+
+        if self.targeted:
+            # Attack satisfied
+            if y == y_p:
+                return None
+
+            # Attack unsatisfied yet and the initial image satisfied
+            if adv_init is not None and init_pred == y:
+                return adv_init.astype(config.ART_NUMPY_DTYPE), init_pred
+
+            # Attack unsatisfied yet and the initial image unsatisfied
+            for _ in range(self.init_size):
+                random_img = nprd.uniform(clip_min, clip_max, size=x.shape).astype(x.dtype)
+                random_class = np.argmax(
+                    self.estimator.predict(np.array([random_img]), batch_size=self.batch_size), axis=1,
+                )[0]
+
+                if random_class == y:
+                    # Binary search to reduce the l2 distance to the original image
+                    random_img = self._binary_search(
+                        current_sample=random_img,
+                        original_sample=x,
+                        target=y,
+                        norm=2,
+                        clip_min=clip_min,
+                        clip_max=clip_max,
+                        threshold=0.001,
+                    )
+                    initial_sample = random_img, random_class
+
+                    logger.info("Found initial adversarial image for targeted attack.")
+                    break
+            else:
+                logger.warning("Failed to draw a random image that is adversarial, attack failed.")
+
+        else:
+            # The initial image satisfied
+            if adv_init is not None and init_pred != y_p:
+                return adv_init.astype(config.ART_NUMPY_DTYPE), y_p
+
+            # The initial image unsatisfied
+            for _ in range(self.init_size):
+                random_img = nprd.uniform(clip_min, clip_max, size=x.shape).astype(x.dtype)
+                random_class = np.argmax(
+                    self.estimator.predict(np.array([random_img]), batch_size=self.batch_size), axis=1,
+                )[0]
+
+                if random_class != y_p:
+                    # Binary search to reduce the l2 distance to the original image
+                    random_img = self._binary_search(
+                        current_sample=random_img,
+                        original_sample=x,
+                        target=y_p,
+                        norm=2,
+                        clip_min=clip_min,
+                        clip_max=clip_max,
+                        threshold=0.001,
+                    )
+                    initial_sample = random_img, y_p
+
+                    logger.info("Found initial adversarial image for untargeted attack.")
+                    break
+            else:
+                logger.warning("Failed to draw a random image that is adversarial, attack failed.")
+
+        return initial_sample
+
+    def _binary_search(
+        self,
+        current_sample: np.ndarray,
+        original_sample: np.ndarray,
+        target: int,
+        norm: Union[int, float, str],
+        clip_min: float,
+        clip_max: float,
+        threshold: Optional[float] = None,
+    ) -> np.ndarray:
+        """
+        Binary search to approach the boundary.
+
+        :param current_sample: Current adversarial example.
+        :param original_sample: The original input.
+        :param target: The target label.
+        :param norm: Order of the norm. Possible values: "inf", np.inf or 2.
+        :param clip_min: Minimum value of an example.
+        :param clip_max: Maximum value of an example.
+        :param threshold: The upper threshold in binary search.
+        :return: an adversarial example.
+        """
+        # First set upper and lower bounds as well as the threshold for the binary search
+        if norm == 2:
+            (upper_bound, lower_bound) = (1, 0)
+
+            if threshold is None:
+                threshold = self.theta
+
+        else:
+            (upper_bound, lower_bound) = (
+                np.max(abs(original_sample - current_sample)),
+                0,
+            )
+
+            if threshold is None:
+                threshold = np.minimum(upper_bound * self.theta, self.theta)
+
+        # Then start the binary search
+        while (upper_bound - lower_bound) > threshold:
+            # Interpolation point
+            alpha = (upper_bound + lower_bound) / 2.0
+            interpolated_sample = self._interpolate(
+                current_sample=current_sample, original_sample=original_sample, alpha=alpha, norm=norm,
+            )
+
+            # Update upper_bound and lower_bound
+            satisfied = self._adversarial_satisfactory(
+                samples=interpolated_sample[None], target=target, clip_min=clip_min, clip_max=clip_max,
+            )[0]
+            lower_bound = np.where(satisfied == 0, alpha, lower_bound)
+            upper_bound = np.where(satisfied == 1, alpha, upper_bound)
+
+        result = self._interpolate(
+            current_sample=current_sample, original_sample=original_sample, alpha=upper_bound, norm=norm,
+        )
+
+        return result
+
+    @staticmethod
+    def _interpolate(
+        current_sample: np.ndarray, original_sample: np.ndarray, alpha: float, norm: Union[int, float, str]
+    ) -> np.ndarray:
+        """
+        Interpolate a new sample based on the original and the current samples.
+
+        :param current_sample: Current adversarial example.
+        :param original_sample: The original input.
+        :param alpha: The coefficient of interpolation.
+        :param norm: Order of the norm. Possible values: "inf", np.inf or 2.
+        :return: An adversarial example.
+        """
+        if norm == 2:
+            result = (1 - alpha) * original_sample + alpha * current_sample
+        else:
+            result = np.clip(current_sample, original_sample - alpha, original_sample + alpha)
+
+        return result
+
+    def _adversarial_satisfactory(
+        self, samples: np.ndarray, target: int, clip_min: float, clip_max: float
+    ) -> np.ndarray:
+        """
+        Check whether an image is adversarial.
+
+        :param samples: A batch of examples.
+        :param target: The target label.
+        :param clip_min: Minimum value of an example.
+        :param clip_max: Maximum value of an example.
+        :return: An array of 0/1.
+        """
+        samples = np.clip(samples, clip_min, clip_max)
+        preds = np.argmax(self.estimator.predict(samples, batch_size=self.batch_size), axis=1)
+
+        if self.targeted:
+            result = preds == target
+        else:
+            result = preds != target
+
+        return result
+
+    def _check_params(self) -> None:
+
+        if self.norm not in [1, 2, np.inf, "inf"]:
+            raise ValueError('The argument norm has to be either 1, 2, np.inf, or "inf".')
+
+        if not isinstance(self.targeted, bool):
+            raise ValueError("The argument `targeted` has to be of type `bool`.")
+
+        if not isinstance(self.overshoot, float) or self.overshoot < 1.0:
+            raise ValueError("The argument `overshoot` has to be of `float` and larger than 1.")
+
+        if not isinstance(self.steps, int) or self.steps < 1:
+            raise ValueError("The argument `steps` has to be of `int` and larger than 0.")
+
+        if not isinstance(self.lr, float) or self.lr <= 0.0:
+            raise ValueError("The argument `lr` has to be of `float` and larger than 0.0.")
+
+        if not isinstance(self.lr_decay, float) or self.lr_decay <= 0.0:
+            raise ValueError("The argument `lr_decay` has to be of `float` and larger than 0.0.")
+
+        if not isinstance(self.lr_num_decay, int) or self.lr_num_decay < 1:
+            raise ValueError("The argument `lr_num_decay` has to be of `int` and larger than 0.")
+
+        if not isinstance(self.momentum, float) or self.momentum <= 0.0:
+            raise ValueError("The argument `momentum` has to be of `float` and larger than 0.0.")
+
+        if not isinstance(self.binary_search_steps, int) or self.binary_search_steps < 1:
+            raise ValueError("The argument `binary_search_steps` has to be of `int` and larger than 0.")
+
+        if not isinstance(self.init_size, int) or self.init_size < 1:
+            raise ValueError("The argument `init_size` has to be of `int` and larger than 0.")
diff --git a/adversarial-robustness-toolbox/art/attacks/evasion/carlini.py b/adversarial-robustness-toolbox/art/attacks/evasion/carlini.py
new file mode 100644
index 0000000..2e06b65
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/attacks/evasion/carlini.py
@@ -0,0 +1,799 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements the L2 and LInf optimized attacks `CarliniL2Method` and `CarliniLInfMethod` of Carlini and Wagner
+(2016). These attacks are among the most effective white-box attacks and should be used among the primary attacks to
+evaluate potential defences. A major difference with respect to the original implementation
+(https://github.com/carlini/nn_robust_attacks) is that this implementation uses line search in the optimization of the
+attack objective.
+
+| Paper link: https://arxiv.org/abs/1608.04644
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+from typing import Optional, Tuple, TYPE_CHECKING
+
+import numpy as np
+from tqdm.auto import trange
+
+from art.config import ART_NUMPY_DTYPE
+from art.estimators.estimator import BaseEstimator
+from art.estimators.classification.classifier import ClassGradientsMixin
+from art.attacks.attack import EvasionAttack
+from art.utils import (
+    compute_success,
+    get_labels_np_array,
+    tanh_to_original,
+    original_to_tanh,
+)
+from art.utils import check_and_transform_label_format
+
+if TYPE_CHECKING:
+    from art.utils import CLASSIFIER_CLASS_LOSS_GRADIENTS_TYPE
+
+logger = logging.getLogger(__name__)
+
+
+class CarliniL2Method(EvasionAttack):
+    """
+    The L_2 optimized attack of Carlini and Wagner (2016). This attack is among the most effective and should be used
+    among the primary attacks to evaluate potential defences. A major difference wrt to the original implementation
+    (https://github.com/carlini/nn_robust_attacks) is that we use line search in the optimization of the attack
+    objective.
+
+    | Paper link: https://arxiv.org/abs/1608.04644
+    """
+
+    attack_params = EvasionAttack.attack_params + [
+        "confidence",
+        "targeted",
+        "learning_rate",
+        "max_iter",
+        "binary_search_steps",
+        "initial_const",
+        "max_halving",
+        "max_doubling",
+        "batch_size",
+        "verbose",
+    ]
+    _estimator_requirements = (BaseEstimator, ClassGradientsMixin)
+
+    def __init__(
+        self,
+        classifier: "CLASSIFIER_CLASS_LOSS_GRADIENTS_TYPE",
+        confidence: float = 0.0,
+        targeted: bool = False,
+        learning_rate: float = 0.01,
+        binary_search_steps: int = 10,
+        max_iter: int = 10,
+        initial_const: float = 0.01,
+        max_halving: int = 5,
+        max_doubling: int = 5,
+        batch_size: int = 1,
+        verbose: bool = True,
+    ) -> None:
+        """
+        Create a Carlini L_2 attack instance.
+
+        :param classifier: A trained classifier.
+        :param confidence: Confidence of adversarial examples: a higher value produces examples that are farther away,
+               from the original input, but classified with higher confidence as the target class.
+        :param targeted: Should the attack target one specific class.
+        :param learning_rate: The initial learning rate for the attack algorithm. Smaller values produce better results
+               but are slower to converge.
+        :param binary_search_steps: Number of times to adjust constant with binary search (positive value). If
+                                    `binary_search_steps` is large, then the algorithm is not very sensitive to the
+                                    value of `initial_const`. Note that the values gamma=0.999999 and c_upper=10e10 are
+                                    hardcoded with the same values used by the authors of the method.
+        :param max_iter: The maximum number of iterations.
+        :param initial_const: The initial trade-off constant `c` to use to tune the relative importance of distance and
+                confidence. If `binary_search_steps` is large, the initial constant is not important, as discussed in
+                Carlini and Wagner (2016).
+        :param max_halving: Maximum number of halving steps in the line search optimization.
+        :param max_doubling: Maximum number of doubling steps in the line search optimization.
+        :param batch_size: Size of the batch on which adversarial samples are generated.
+        :param verbose: Show progress bars.
+        """
+        super().__init__(estimator=classifier)
+
+        self.confidence = confidence
+        self._targeted = targeted
+        self.learning_rate = learning_rate
+        self.binary_search_steps = binary_search_steps
+        self.max_iter = max_iter
+        self.initial_const = initial_const
+        self.max_halving = max_halving
+        self.max_doubling = max_doubling
+        self.batch_size = batch_size
+        self.verbose = verbose
+        self._check_params()
+
+        # There are internal hyperparameters:
+        # Abort binary search for c if it exceeds this threshold (suggested in Carlini and Wagner (2016)):
+        self._c_upper_bound = 10e10
+
+        # Smooth arguments of arctanh by multiplying with this constant to avoid division by zero.
+        # It appears this is what Carlini and Wagner (2016) are alluding to in their footnote 8. However, it is not
+        # clear how their proposed trick ("instead of scaling by 1/2 we scale by 1/2 + eps") works in detail.
+        self._tanh_smoother = 0.999999
+
+    def _loss(
+        self, x: np.ndarray, x_adv: np.ndarray, target: np.ndarray, c_weight: float
+    ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
+        """
+        Compute the objective function value.
+
+        :param x: An array with the original input.
+        :param x_adv: An array with the adversarial input.
+        :param target: An array with the target class (one-hot encoded).
+        :param c_weight: Weight of the loss term aiming for classification as target.
+        :return: A tuple holding the current logits, l2 distance and overall loss.
+        """
+        l2dist = np.sum(np.square(x - x_adv).reshape(x.shape[0], -1), axis=1)
+        z_predicted = self.estimator.predict(
+            np.array(x_adv, dtype=ART_NUMPY_DTYPE), logits=True, batch_size=self.batch_size,
+        )
+        z_target = np.sum(z_predicted * target, axis=1)
+        z_other = np.max(
+            z_predicted * (1 - target) + (np.min(z_predicted, axis=1) - 1)[:, np.newaxis] * target, axis=1,
+        )
+
+        # The following differs from the exact definition given in Carlini and Wagner (2016). There (page 9, left
+        # column, last equation), the maximum is taken over Z_other - Z_target (or Z_target - Z_other respectively)
+        # and -confidence. However, it doesn't seem that that would have the desired effect (loss term is <= 0 if and
+        # only if the difference between the logit of the target and any other class differs by at least confidence).
+        # Hence the rearrangement here.
+
+        if self.targeted:
+            # if targeted, optimize for making the target class most likely
+            loss = np.maximum(z_other - z_target + self.confidence, np.zeros(x.shape[0]))
+        else:
+            # if untargeted, optimize for making any other class most likely
+            loss = np.maximum(z_target - z_other + self.confidence, np.zeros(x.shape[0]))
+
+        return z_predicted, l2dist, c_weight * loss + l2dist
+
+    def _loss_gradient(
+        self,
+        z_logits: np.ndarray,
+        target: np.ndarray,
+        x: np.ndarray,
+        x_adv: np.ndarray,
+        x_adv_tanh: np.ndarray,
+        c_weight: np.ndarray,
+        clip_min: float,
+        clip_max: float,
+    ) -> np.ndarray:
+        """
+        Compute the gradient of the loss function.
+
+        :param z_logits: An array with the current logits.
+        :param target: An array with the target class (one-hot encoded).
+        :param x: An array with the original input.
+        :param x_adv: An array with the adversarial input.
+        :param x_adv_tanh: An array with the adversarial input in tanh space.
+        :param c_weight: Weight of the loss term aiming for classification as target.
+        :param clip_min: Minimum clipping value.
+        :param clip_max: Maximum clipping value.
+        :return: An array with the gradient of the loss function.
+        """
+        if self.targeted:
+            i_sub = np.argmax(target, axis=1)
+            i_add = np.argmax(z_logits * (1 - target) + (np.min(z_logits, axis=1) - 1)[:, np.newaxis] * target, axis=1,)
+        else:
+            i_add = np.argmax(target, axis=1)
+            i_sub = np.argmax(z_logits * (1 - target) + (np.min(z_logits, axis=1) - 1)[:, np.newaxis] * target, axis=1,)
+
+        loss_gradient = self.estimator.class_gradient(x_adv, label=i_add)
+        loss_gradient -= self.estimator.class_gradient(x_adv, label=i_sub)
+        loss_gradient = loss_gradient.reshape(x.shape)
+
+        c_mult = c_weight
+        for _ in range(len(x.shape) - 1):
+            c_mult = c_mult[:, np.newaxis]
+
+        loss_gradient *= c_mult
+        loss_gradient += 2 * (x_adv - x)
+        loss_gradient *= clip_max - clip_min
+        loss_gradient *= (1 - np.square(np.tanh(x_adv_tanh))) / (2 * self._tanh_smoother)
+
+        return loss_gradient
+
+    def generate(self, x: np.ndarray, y: Optional[np.ndarray] = None, **kwargs) -> np.ndarray:
+        """
+        Generate adversarial samples and return them in an array.
+
+        :param x: An array with the original inputs to be attacked.
+        :param y: Target values (class labels) one-hot-encoded of shape (nb_samples, nb_classes) or indices of shape
+                  (nb_samples,). If `self.targeted` is true, then `y` represents the target labels. If `self.targeted`
+                  is true, then `y_val` represents the target labels. Otherwise, the targets are the original class
+                  labels.
+        :return: An array holding the adversarial examples.
+        """
+        y = check_and_transform_label_format(y, self.estimator.nb_classes)
+        x_adv = x.astype(ART_NUMPY_DTYPE)
+
+        if self.estimator.clip_values is not None:
+            clip_min, clip_max = self.estimator.clip_values
+        else:
+            clip_min, clip_max = np.amin(x), np.amax(x)
+
+        # Assert that, if attack is targeted, y_val is provided:
+        if self.targeted and y is None:
+            raise ValueError("Target labels `y` need to be provided for a targeted attack.")
+
+        # No labels provided, use model prediction as correct class
+        if y is None:
+            y = get_labels_np_array(self.estimator.predict(x, batch_size=self.batch_size))
+
+        # Compute perturbation with implicit batching
+        nb_batches = int(np.ceil(x_adv.shape[0] / float(self.batch_size)))
+        for batch_id in trange(nb_batches, desc="C&W L_2", disable=not self.verbose):
+            batch_index_1, batch_index_2 = batch_id * self.batch_size, (batch_id + 1) * self.batch_size
+            x_batch = x_adv[batch_index_1:batch_index_2]
+            y_batch = y[batch_index_1:batch_index_2]
+
+            # The optimization is performed in tanh space to keep the adversarial images bounded in correct range
+            x_batch_tanh = original_to_tanh(x_batch, clip_min, clip_max, self._tanh_smoother)
+
+            # Initialize binary search:
+            c_current = self.initial_const * np.ones(x_batch.shape[0])
+            c_lower_bound = np.zeros(x_batch.shape[0])
+            c_double = np.ones(x_batch.shape[0]) > 0
+
+            # Initialize placeholders for best l2 distance and attack found so far
+            best_l2dist = np.inf * np.ones(x_batch.shape[0])
+            best_x_adv_batch = x_batch.copy()
+
+            for bss in range(self.binary_search_steps):
+                logger.debug(
+                    "Binary search step %i out of %i (c_mean==%f)", bss, self.binary_search_steps, np.mean(c_current),
+                )
+                nb_active = int(np.sum(c_current < self._c_upper_bound))
+                logger.debug(
+                    "Number of samples with c_current < _c_upper_bound: %i out of %i", nb_active, x_batch.shape[0],
+                )
+                if nb_active == 0:
+                    break
+                learning_rate = self.learning_rate * np.ones(x_batch.shape[0])
+
+                # Initialize perturbation in tanh space:
+                x_adv_batch = x_batch.copy()
+                x_adv_batch_tanh = x_batch_tanh.copy()
+
+                z_logits, l2dist, loss = self._loss(x_batch, x_adv_batch, y_batch, c_current)
+                attack_success = loss - l2dist <= 0
+                overall_attack_success = attack_success
+
+                for i_iter in range(self.max_iter):
+                    logger.debug("Iteration step %i out of %i", i_iter, self.max_iter)
+                    logger.debug("Average Loss: %f", np.mean(loss))
+                    logger.debug("Average L2Dist: %f", np.mean(l2dist))
+                    logger.debug("Average Margin Loss: %f", np.mean(loss - l2dist))
+                    logger.debug(
+                        "Current number of succeeded attacks: %i out of %i",
+                        int(np.sum(attack_success)),
+                        len(attack_success),
+                    )
+
+                    improved_adv = attack_success & (l2dist < best_l2dist)
+                    logger.debug("Number of improved L2 distances: %i", int(np.sum(improved_adv)))
+                    if np.sum(improved_adv) > 0:
+                        best_l2dist[improved_adv] = l2dist[improved_adv]
+                        best_x_adv_batch[improved_adv] = x_adv_batch[improved_adv]
+
+                    active = (c_current < self._c_upper_bound) & (learning_rate > 0)
+                    nb_active = int(np.sum(active))
+                    logger.debug(
+                        "Number of samples with c_current < _c_upper_bound and learning_rate > 0: %i out of %i",
+                        nb_active,
+                        x_batch.shape[0],
+                    )
+                    if nb_active == 0:
+                        break
+
+                    # compute gradient:
+                    logger.debug("Compute loss gradient")
+                    perturbation_tanh = -self._loss_gradient(
+                        z_logits[active],
+                        y_batch[active],
+                        x_batch[active],
+                        x_adv_batch[active],
+                        x_adv_batch_tanh[active],
+                        c_current[active],
+                        clip_min,
+                        clip_max,
+                    )
+
+                    # perform line search to optimize perturbation
+                    # first, halve the learning rate until perturbation actually decreases the loss:
+                    prev_loss = loss.copy()
+                    best_loss = loss.copy()
+                    best_lr = np.zeros(x_batch.shape[0])
+                    halving = np.zeros(x_batch.shape[0])
+
+                    for i_halve in range(self.max_halving):
+                        logger.debug(
+                            "Perform halving iteration %i out of %i", i_halve, self.max_halving,
+                        )
+                        do_halving = loss[active] >= prev_loss[active]
+                        logger.debug(
+                            "Halving to be performed on %i samples", int(np.sum(do_halving)),
+                        )
+                        if np.sum(do_halving) == 0:
+                            break
+                        active_and_do_halving = active.copy()
+                        active_and_do_halving[active] = do_halving
+
+                        lr_mult = learning_rate[active_and_do_halving]
+                        for _ in range(len(x.shape) - 1):
+                            lr_mult = lr_mult[:, np.newaxis]
+
+                        x_adv1 = x_adv_batch_tanh[active_and_do_halving]
+                        new_x_adv_batch_tanh = x_adv1 + lr_mult * perturbation_tanh[do_halving]
+                        new_x_adv_batch = tanh_to_original(new_x_adv_batch_tanh, clip_min, clip_max)
+                        _, l2dist[active_and_do_halving], loss[active_and_do_halving] = self._loss(
+                            x_batch[active_and_do_halving],
+                            new_x_adv_batch,
+                            y_batch[active_and_do_halving],
+                            c_current[active_and_do_halving],
+                        )
+
+                        logger.debug("New Average Loss: %f", np.mean(loss))
+                        logger.debug("New Average L2Dist: %f", np.mean(l2dist))
+                        logger.debug("New Average Margin Loss: %f", np.mean(loss - l2dist))
+
+                        best_lr[loss < best_loss] = learning_rate[loss < best_loss]
+                        best_loss[loss < best_loss] = loss[loss < best_loss]
+                        learning_rate[active_and_do_halving] /= 2
+                        halving[active_and_do_halving] += 1
+                    learning_rate[active] *= 2
+
+                    # if no halving was actually required, double the learning rate as long as this
+                    # decreases the loss:
+                    for i_double in range(self.max_doubling):
+                        logger.debug(
+                            "Perform doubling iteration %i out of %i", i_double, self.max_doubling,
+                        )
+                        do_doubling = (halving[active] == 1) & (loss[active] <= best_loss[active])
+                        logger.debug(
+                            "Doubling to be performed on %i samples", int(np.sum(do_doubling)),
+                        )
+                        if np.sum(do_doubling) == 0:
+                            break
+                        active_and_do_doubling = active.copy()
+                        active_and_do_doubling[active] = do_doubling
+                        learning_rate[active_and_do_doubling] *= 2
+
+                        lr_mult = learning_rate[active_and_do_doubling]
+                        for _ in range(len(x.shape) - 1):
+                            lr_mult = lr_mult[:, np.newaxis]
+
+                        x_adv2 = x_adv_batch_tanh[active_and_do_doubling]
+                        new_x_adv_batch_tanh = x_adv2 + lr_mult * perturbation_tanh[do_doubling]
+                        new_x_adv_batch = tanh_to_original(new_x_adv_batch_tanh, clip_min, clip_max)
+                        _, l2dist[active_and_do_doubling], loss[active_and_do_doubling] = self._loss(
+                            x_batch[active_and_do_doubling],
+                            new_x_adv_batch,
+                            y_batch[active_and_do_doubling],
+                            c_current[active_and_do_doubling],
+                        )
+                        logger.debug("New Average Loss: %f", np.mean(loss))
+                        logger.debug("New Average L2Dist: %f", np.mean(l2dist))
+                        logger.debug("New Average Margin Loss: %f", np.mean(loss - l2dist))
+                        best_lr[loss < best_loss] = learning_rate[loss < best_loss]
+                        best_loss[loss < best_loss] = loss[loss < best_loss]
+
+                    learning_rate[halving == 1] /= 2
+
+                    update_adv = best_lr[active] > 0
+                    logger.debug(
+                        "Number of adversarial samples to be finally updated: %i", int(np.sum(update_adv)),
+                    )
+
+                    if np.sum(update_adv) > 0:
+                        active_and_update_adv = active.copy()
+                        active_and_update_adv[active] = update_adv
+                        best_lr_mult = best_lr[active_and_update_adv]
+                        for _ in range(len(x.shape) - 1):
+                            best_lr_mult = best_lr_mult[:, np.newaxis]
+
+                        x_adv4 = x_adv_batch_tanh[active_and_update_adv]
+                        best_lr1 = best_lr_mult * perturbation_tanh[update_adv]
+                        x_adv_batch_tanh[active_and_update_adv] = x_adv4 + best_lr1
+
+                        x_adv6 = x_adv_batch_tanh[active_and_update_adv]
+                        x_adv_batch[active_and_update_adv] = tanh_to_original(x_adv6, clip_min, clip_max)
+                        (
+                            z_logits[active_and_update_adv],
+                            l2dist[active_and_update_adv],
+                            loss[active_and_update_adv],
+                        ) = self._loss(
+                            x_batch[active_and_update_adv],
+                            x_adv_batch[active_and_update_adv],
+                            y_batch[active_and_update_adv],
+                            c_current[active_and_update_adv],
+                        )
+                        attack_success = loss - l2dist <= 0
+                        overall_attack_success = overall_attack_success | attack_success
+
+                # Update depending on attack success:
+                improved_adv = attack_success & (l2dist < best_l2dist)
+                logger.debug("Number of improved L2 distances: %i", int(np.sum(improved_adv)))
+
+                if np.sum(improved_adv) > 0:
+                    best_l2dist[improved_adv] = l2dist[improved_adv]
+                    best_x_adv_batch[improved_adv] = x_adv_batch[improved_adv]
+
+                c_double[overall_attack_success] = False
+                c_current[overall_attack_success] = (c_lower_bound + c_current)[overall_attack_success] / 2
+
+                c_old = c_current
+                c_current[~overall_attack_success & c_double] *= 2
+
+                c_current1 = (c_current - c_lower_bound)[~overall_attack_success & ~c_double]
+                c_current[~overall_attack_success & ~c_double] += c_current1 / 2
+                c_lower_bound[~overall_attack_success] = c_old[~overall_attack_success]
+
+            x_adv[batch_index_1:batch_index_2] = best_x_adv_batch
+
+        logger.info(
+            "Success rate of C&W L_2 attack: %.2f%%",
+            100 * compute_success(self.estimator, x, y, x_adv, self.targeted, batch_size=self.batch_size),
+        )
+
+        return x_adv
+
+    def _check_params(self) -> None:
+        if not isinstance(self.binary_search_steps, (int, np.int)) or self.binary_search_steps < 0:
+            raise ValueError("The number of binary search steps must be a non-negative integer.")
+
+        if not isinstance(self.max_iter, (int, np.int)) or self.max_iter < 0:
+            raise ValueError("The number of iterations must be a non-negative integer.")
+
+        if not isinstance(self.max_halving, (int, np.int)) or self.max_halving < 1:
+            raise ValueError("The number of halving steps must be an integer greater than zero.")
+
+        if not isinstance(self.max_doubling, (int, np.int)) or self.max_doubling < 1:
+            raise ValueError("The number of doubling steps must be an integer greater than zero.")
+
+        if not isinstance(self.batch_size, (int, np.int)) or self.batch_size < 1:
+            raise ValueError("The batch size must be an integer greater than zero.")
+
+
+class CarliniLInfMethod(EvasionAttack):
+    """
+    This is a modified version of the L_2 optimized attack of Carlini and Wagner (2016). It controls the L_Inf
+    norm, i.e. the maximum perturbation applied to each pixel.
+    """
+
+    attack_params = EvasionAttack.attack_params + [
+        "confidence",
+        "targeted",
+        "learning_rate",
+        "max_iter",
+        "max_halving",
+        "max_doubling",
+        "eps",
+        "batch_size",
+        "verbose",
+    ]
+    _estimator_requirements = (BaseEstimator, ClassGradientsMixin)
+
+    def __init__(
+        self,
+        classifier: "CLASSIFIER_CLASS_LOSS_GRADIENTS_TYPE",
+        confidence: float = 0.0,
+        targeted: bool = False,
+        learning_rate: float = 0.01,
+        max_iter: int = 10,
+        max_halving: int = 5,
+        max_doubling: int = 5,
+        eps: float = 0.3,
+        batch_size: int = 128,
+        verbose: bool = True,
+    ) -> None:
+        """
+        Create a Carlini L_Inf attack instance.
+
+        :param classifier: A trained classifier.
+        :param confidence: Confidence of adversarial examples: a higher value produces examples that are farther away,
+                from the original input, but classified with higher confidence as the target class.
+        :param targeted: Should the attack target one specific class.
+        :param learning_rate: The initial learning rate for the attack algorithm. Smaller values produce better
+                results but are slower to converge.
+        :param max_iter: The maximum number of iterations.
+        :param max_halving: Maximum number of halving steps in the line search optimization.
+        :param max_doubling: Maximum number of doubling steps in the line search optimization.
+        :param eps: An upper bound for the L_0 norm of the adversarial perturbation.
+        :param batch_size: Size of the batch on which adversarial samples are generated.
+        :param verbose: Show progress bars.
+        """
+        super().__init__(estimator=classifier)
+
+        self.confidence = confidence
+        self._targeted = targeted
+        self.learning_rate = learning_rate
+        self.max_iter = max_iter
+        self.max_halving = max_halving
+        self.max_doubling = max_doubling
+        self.eps = eps
+        self.batch_size = batch_size
+        self.verbose = verbose
+        self._check_params()
+
+        # There is one internal hyperparameter:
+        # Smooth arguments of arctanh by multiplying with this constant to avoid division by zero:
+        self._tanh_smoother = 0.999999
+
+    def _loss(self, x_adv: np.ndarray, target: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
+        """
+        Compute the objective function value.
+
+        :param x_adv: An array with the adversarial input.
+        :param target: An array with the target class (one-hot encoded).
+        :return: A tuple holding the current predictions and overall loss.
+        """
+        z_predicted = self.estimator.predict(np.array(x_adv, dtype=ART_NUMPY_DTYPE), batch_size=self.batch_size)
+        z_target = np.sum(z_predicted * target, axis=1)
+        z_other = np.max(
+            z_predicted * (1 - target) + (np.min(z_predicted, axis=1) - 1)[:, np.newaxis] * target, axis=1,
+        )
+
+        if self.targeted:
+            # if targeted, optimize for making the target class most likely
+            loss = np.maximum(z_other - z_target + self.confidence, np.zeros(x_adv.shape[0]))
+        else:
+            # if untargeted, optimize for making any other class most likely
+            loss = np.maximum(z_target - z_other + self.confidence, np.zeros(x_adv.shape[0]))
+
+        return z_predicted, loss
+
+    def _loss_gradient(
+        self,
+        z_logits: np.ndarray,
+        target: np.ndarray,
+        x_adv: np.ndarray,
+        x_adv_tanh: np.ndarray,
+        clip_min: np.ndarray,
+        clip_max: np.ndarray,
+    ) -> np.ndarray:  # lgtm [py/similar-function]
+        """
+        Compute the gradient of the loss function.
+
+        :param z_logits: An array with the current predictions.
+        :param target: An array with the target class (one-hot encoded).
+        :param x_adv: An array with the adversarial input.
+        :param x_adv_tanh: An array with the adversarial input in tanh space.
+        :param clip_min: Minimum clipping values.
+        :param clip_max: Maximum clipping values.
+        :return: An array with the gradient of the loss function.
+        """
+        if self.targeted:
+            i_sub = np.argmax(target, axis=1)
+            i_add = np.argmax(z_logits * (1 - target) + (np.min(z_logits, axis=1) - 1)[:, np.newaxis] * target, axis=1,)
+        else:
+            i_add = np.argmax(target, axis=1)
+            i_sub = np.argmax(z_logits * (1 - target) + (np.min(z_logits, axis=1) - 1)[:, np.newaxis] * target, axis=1,)
+
+        loss_gradient = self.estimator.class_gradient(x_adv, label=i_add)
+        loss_gradient -= self.estimator.class_gradient(x_adv, label=i_sub)
+        loss_gradient = loss_gradient.reshape(x_adv.shape)
+
+        loss_gradient *= clip_max - clip_min
+        loss_gradient *= (1 - np.square(np.tanh(x_adv_tanh))) / (2 * self._tanh_smoother)
+
+        return loss_gradient
+
+    def generate(self, x: np.ndarray, y: Optional[np.ndarray] = None, **kwargs) -> np.ndarray:
+        """
+        Generate adversarial samples and return them in an array.
+
+        :param x: An array with the original inputs to be attacked.
+        :param y: Target values (class labels) one-hot-encoded of shape (nb_samples, nb_classes) or indices of shape
+                  (nb_samples,). If `self.targeted` is true, then `y_val` represents the target labels. Otherwise, the
+                  targets are the original class labels.
+        :return: An array holding the adversarial examples.
+        """
+        y = check_and_transform_label_format(y, self.estimator.nb_classes)
+        x_adv = x.astype(ART_NUMPY_DTYPE)
+
+        if self.estimator.clip_values is not None:
+            clip_min_per_pixel, clip_max_per_pixel = self.estimator.clip_values
+        else:
+            clip_min_per_pixel, clip_max_per_pixel = np.amin(x), np.amax(x)
+
+        # Assert that, if attack is targeted, y_val is provided:
+        if self.targeted and y is None:
+            raise ValueError("Target labels `y` need to be provided for a targeted attack.")
+
+        # No labels provided, use model prediction as correct class
+        if y is None:
+            y = get_labels_np_array(self.estimator.predict(x, batch_size=self.batch_size))
+
+        # Compute perturbation with implicit batching
+        nb_batches = int(np.ceil(x_adv.shape[0] / float(self.batch_size)))
+        for batch_id in trange(nb_batches, desc="C&W L_inf", disable=not self.verbose):
+            batch_index_1, batch_index_2 = batch_id * self.batch_size, (batch_id + 1) * self.batch_size
+            x_batch = x_adv[batch_index_1:batch_index_2]
+            y_batch = y[batch_index_1:batch_index_2]
+
+            # Determine values for later clipping
+            clip_min = np.clip(x_batch - self.eps, clip_min_per_pixel, clip_max_per_pixel)
+            clip_max = np.clip(x_batch + self.eps, clip_min_per_pixel, clip_max_per_pixel)
+
+            # The optimization is performed in tanh space to keep the
+            # adversarial images bounded from clip_min and clip_max.
+            x_batch_tanh = original_to_tanh(x_batch, clip_min, clip_max, self._tanh_smoother)
+
+            # Initialize perturbation in tanh space:
+            x_adv_batch = x_batch.copy()
+            x_adv_batch_tanh = x_batch_tanh.copy()
+
+            # Initialize optimization:
+            z_logits, loss = self._loss(x_adv_batch, y_batch)
+            attack_success = loss <= 0
+            learning_rate = self.learning_rate * np.ones(x_batch.shape[0])
+
+            for i_iter in range(self.max_iter):
+                logger.debug("Iteration step %i out of %i", i_iter, self.max_iter)
+                logger.debug("Average Loss: %f", np.mean(loss))
+
+                logger.debug(
+                    "Successful attack samples: %i out of %i", int(np.sum(attack_success)), x_batch.shape[0],
+                )
+
+                # only continue optimization for those samples where attack hasn't succeeded yet:
+                active = ~attack_success
+                if np.sum(active) == 0:
+                    break
+
+                # compute gradient:
+                logger.debug("Compute loss gradient")
+                perturbation_tanh = -self._loss_gradient(
+                    z_logits[active],
+                    y_batch[active],
+                    x_adv_batch[active],
+                    x_adv_batch_tanh[active],
+                    clip_min[active],
+                    clip_max[active],
+                )
+
+                # perform line search to optimize perturbation
+                # first, halve the learning rate until perturbation actually decreases the loss:
+                prev_loss = loss.copy()
+                best_loss = loss.copy()
+                best_lr = np.zeros(x_batch.shape[0])
+                halving = np.zeros(x_batch.shape[0])
+
+                for i_halve in range(self.max_halving):
+                    logger.debug(
+                        "Perform halving iteration %i out of %i", i_halve, self.max_halving,
+                    )
+                    do_halving = loss[active] >= prev_loss[active]
+                    logger.debug("Halving to be performed on %i samples", int(np.sum(do_halving)))
+                    if np.sum(do_halving) == 0:
+                        break
+                    active_and_do_halving = active.copy()
+                    active_and_do_halving[active] = do_halving
+
+                    lr_mult = learning_rate[active_and_do_halving]
+                    for _ in range(len(x.shape) - 1):
+                        lr_mult = lr_mult[:, np.newaxis]
+
+                    adv_10 = x_adv_batch_tanh[active_and_do_halving]
+                    new_x_adv_batch_tanh = adv_10 + lr_mult * perturbation_tanh[do_halving]
+
+                    new_x_adv_batch = tanh_to_original(
+                        new_x_adv_batch_tanh, clip_min[active_and_do_halving], clip_max[active_and_do_halving],
+                    )
+                    _, loss[active_and_do_halving] = self._loss(new_x_adv_batch, y_batch[active_and_do_halving])
+                    logger.debug("New Average Loss: %f", np.mean(loss))
+                    logger.debug("Loss: %s", str(loss))
+                    logger.debug("Prev_loss: %s", str(prev_loss))
+                    logger.debug("Best_loss: %s", str(best_loss))
+
+                    best_lr[loss < best_loss] = learning_rate[loss < best_loss]
+                    best_loss[loss < best_loss] = loss[loss < best_loss]
+                    learning_rate[active_and_do_halving] /= 2
+                    halving[active_and_do_halving] += 1
+                learning_rate[active] *= 2
+
+                # if no halving was actually required, double the learning rate as long as this
+                # decreases the loss:
+                for i_double in range(self.max_doubling):
+                    logger.debug(
+                        "Perform doubling iteration %i out of %i", i_double, self.max_doubling,
+                    )
+                    do_doubling = (halving[active] == 1) & (loss[active] <= best_loss[active])
+                    logger.debug(
+                        "Doubling to be performed on %i samples", int(np.sum(do_doubling)),
+                    )
+                    if np.sum(do_doubling) == 0:
+                        break
+                    active_and_do_doubling = active.copy()
+                    active_and_do_doubling[active] = do_doubling
+                    learning_rate[active_and_do_doubling] *= 2
+
+                    lr_mult = learning_rate[active_and_do_doubling]
+                    for _ in range(len(x.shape) - 1):
+                        lr_mult = lr_mult[:, np.newaxis]
+
+                    x_adv15 = x_adv_batch_tanh[active_and_do_doubling]
+                    new_x_adv_batch_tanh = x_adv15 + lr_mult * perturbation_tanh[do_doubling]
+                    new_x_adv_batch = tanh_to_original(
+                        new_x_adv_batch_tanh, clip_min[active_and_do_doubling], clip_max[active_and_do_doubling],
+                    )
+                    _, loss[active_and_do_doubling] = self._loss(new_x_adv_batch, y_batch[active_and_do_doubling])
+                    logger.debug("New Average Loss: %f", np.mean(loss))
+                    best_lr[loss < best_loss] = learning_rate[loss < best_loss]
+                    best_loss[loss < best_loss] = loss[loss < best_loss]
+
+                learning_rate[halving == 1] /= 2
+
+                update_adv = best_lr[active] > 0
+                logger.debug(
+                    "Number of adversarial samples to be finally updated: %i", int(np.sum(update_adv)),
+                )
+
+                if np.sum(update_adv) > 0:
+                    active_and_update_adv = active.copy()
+                    active_and_update_adv[active] = update_adv
+                    best_lr_mult = best_lr[active_and_update_adv]
+                    for _ in range(len(x.shape) - 1):
+                        best_lr_mult = best_lr_mult[:, np.newaxis]
+
+                    best_13 = best_lr_mult * perturbation_tanh[update_adv]
+                    x_adv_batch_tanh[active_and_update_adv] = x_adv_batch_tanh[active_and_update_adv] + best_13
+                    x_adv_batch[active_and_update_adv] = tanh_to_original(
+                        x_adv_batch_tanh[active_and_update_adv],
+                        clip_min[active_and_update_adv],
+                        clip_max[active_and_update_adv],
+                    )
+                    (z_logits[active_and_update_adv], loss[active_and_update_adv],) = self._loss(
+                        x_adv_batch[active_and_update_adv], y_batch[active_and_update_adv],
+                    )
+                    attack_success = loss <= 0
+
+            # Update depending on attack success:
+            x_adv_batch[~attack_success] = x_batch[~attack_success]
+            x_adv[batch_index_1:batch_index_2] = x_adv_batch
+
+        logger.info(
+            "Success rate of C&W L_inf attack: %.2f%%",
+            100 * compute_success(self.estimator, x, y, x_adv, self.targeted, batch_size=self.batch_size),
+        )
+
+        return x_adv
+
+    def _check_params(self) -> None:
+        if self.eps <= 0:
+            raise ValueError("The eps parameter must be strictly positive.")
+
+        if not isinstance(self.max_iter, (int, np.int)) or self.max_iter < 0:
+            raise ValueError("The number of iterations must be a non-negative integer.")
+
+        if not isinstance(self.max_halving, (int, np.int)) or self.max_halving < 1:
+            raise ValueError("The number of halving steps must be an integer greater than zero.")
+
+        if not isinstance(self.max_doubling, (int, np.int)) or self.max_doubling < 1:
+            raise ValueError("The number of doubling steps must be an integer greater than zero.")
+
+        if not isinstance(self.batch_size, (int, np.int)) or self.batch_size < 1:
+            raise ValueError("The batch size must be an integer greater than zero.")
diff --git a/adversarial-robustness-toolbox/art/attacks/evasion/decision_tree_attack.py b/adversarial-robustness-toolbox/art/attacks/evasion/decision_tree_attack.py
new file mode 100644
index 0000000..78b100c
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/attacks/evasion/decision_tree_attack.py
@@ -0,0 +1,165 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements attacks on Decision Trees.
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+from typing import List, Optional, Union
+
+import numpy as np
+from tqdm.auto import trange
+
+from art.attacks.attack import EvasionAttack
+from art.estimators.classification.scikitlearn import ScikitlearnDecisionTreeClassifier
+from art.utils import check_and_transform_label_format, compute_success
+
+logger = logging.getLogger(__name__)
+
+
+class DecisionTreeAttack(EvasionAttack):
+    """
+    Close implementation of Papernot's attack on decision trees following Algorithm 2 and communication with the
+    authors.
+
+    | Paper link: https://arxiv.org/abs/1605.07277
+    """
+
+    attack_params = ["classifier", "offset", "verbose"]
+    _estimator_requirements = (ScikitlearnDecisionTreeClassifier,)
+
+    def __init__(
+        self, classifier: ScikitlearnDecisionTreeClassifier, offset: float = 0.001, verbose: bool = True,
+    ) -> None:
+        """
+        :param classifier: A trained scikit-learn decision tree model.
+        :param offset: How much the value is pushed away from tree's threshold.
+        :param verbose: Show progress bars.
+        """
+        super().__init__(estimator=classifier)
+        self.offset = offset
+        self.verbose = verbose
+        self._check_params()
+
+    def _df_subtree(
+        self, position: int, original_class: Union[int, np.ndarray], target: Optional[int] = None,
+    ) -> List[int]:
+        """
+        Search a decision tree for a mis-classifying instance.
+
+        :param position: An array with the original inputs to be attacked.
+        :param original_class: original label for the instances we are searching mis-classification for.
+        :param target: If the provided, specifies which output the leaf has to have to be accepted.
+        :return: An array specifying the path to the leaf where the classification is either != original class or
+                 ==target class if provided.
+        """
+        # base case, we're at a leaf
+        if self.estimator.get_left_child(position) == self.estimator.get_right_child(position):
+            if target is None:  # untargeted case
+                if self.estimator.get_classes_at_node(position) != original_class:
+                    path = [position]
+                else:
+                    path = [-1]
+            else:  # targeted case
+                if self.estimator.get_classes_at_node(position) == target:
+                    path = [position]
+                else:
+                    path = [-1]
+        else:  # go deeper, depths first
+            res = self._df_subtree(self.estimator.get_left_child(position), original_class, target)
+            if res[0] == -1:
+                # no result, try right subtree
+                res = self._df_subtree(self.estimator.get_right_child(position), original_class, target)
+                if res[0] == -1:
+                    # no desired result
+                    path = [-1]
+                else:
+                    res.append(position)
+                    path = res
+            else:
+                # done, it is returning a path
+                res.append(position)
+                path = res
+
+        return path
+
+    def generate(self, x: np.ndarray, y: Optional[np.ndarray] = None, **kwargs) -> np.ndarray:
+        """
+        Generate adversarial examples and return them as an array.
+
+        :param x: An array with the original inputs to be attacked.
+        :param y: Target values (class labels) one-hot-encoded of shape (nb_samples, nb_classes) or indices of shape
+                  (nb_samples,).
+        :return: An array holding the adversarial examples.
+        """
+        y = check_and_transform_label_format(y, self.estimator.nb_classes, return_one_hot=False)
+        x_adv = x.copy()
+
+        for index in trange(x_adv.shape[0], desc="Decision tree attack", disable=not self.verbose):
+            path = self.estimator.get_decision_path(x_adv[index])
+            legitimate_class = np.argmax(self.estimator.predict(x_adv[index].reshape(1, -1)))
+            position = -2
+            adv_path = [-1]
+            ancestor = path[position]
+            while np.abs(position) < (len(path) - 1) or adv_path[0] == -1:
+                ancestor = path[position]
+                current_child = path[position + 1]
+                # search in right subtree
+                if current_child == self.estimator.get_left_child(ancestor):
+                    if y is None:
+                        adv_path = self._df_subtree(self.estimator.get_right_child(ancestor), legitimate_class)
+                    else:
+                        adv_path = self._df_subtree(
+                            self.estimator.get_right_child(ancestor), legitimate_class, y[index],
+                        )
+                else:  # search in left subtree
+                    if y is None:
+                        adv_path = self._df_subtree(self.estimator.get_left_child(ancestor), legitimate_class)
+                    else:
+                        adv_path = self._df_subtree(
+                            self.estimator.get_left_child(ancestor), legitimate_class, y[index],
+                        )
+                position = position - 1  # we are going the decision path upwards
+            adv_path.append(ancestor)
+            # we figured out which is the way to the target, now perturb
+            # first one is leaf-> no threshold, cannot be perturbed
+            for i in range(1, 1 + len(adv_path[1:])):
+                go_for = adv_path[i - 1]
+                threshold = self.estimator.get_threshold_at_node(adv_path[i])
+                feature = self.estimator.get_feature_at_node(adv_path[i])
+                # only perturb if the feature is actually wrong
+                if x_adv[index][feature] > threshold and go_for == self.estimator.get_left_child(adv_path[i]):
+                    x_adv[index][feature] = threshold - self.offset
+                elif x_adv[index][feature] <= threshold and go_for == self.estimator.get_right_child(adv_path[i]):
+                    x_adv[index][feature] = threshold + self.offset
+
+        logger.info(
+            "Success rate of decision tree attack: %.2f%%", 100 * compute_success(self.estimator, x, y, x_adv),
+        )
+        return x_adv
+
+    def _check_params(self) -> None:
+        if not isinstance(self.estimator, ScikitlearnDecisionTreeClassifier):
+            raise TypeError("Model must be a decision tree model.")
+
+        if self.offset <= 0:
+            raise ValueError("The offset parameter must be strictly positive.")
+
+        if not isinstance(self.verbose, bool):
+            raise ValueError("The argument `verbose` has to be of type bool.")
diff --git a/adversarial-robustness-toolbox/art/attacks/evasion/deepfool.py b/adversarial-robustness-toolbox/art/attacks/evasion/deepfool.py
new file mode 100644
index 0000000..cb9e49e
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/attacks/evasion/deepfool.py
@@ -0,0 +1,223 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements the white-box attack `DeepFool`.
+
+| Paper link: https://arxiv.org/abs/1511.04599
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+from typing import Optional, TYPE_CHECKING
+
+import numpy as np
+from tqdm.auto import trange
+
+from art.config import ART_NUMPY_DTYPE
+from art.estimators.estimator import BaseEstimator
+from art.estimators.classification.classifier import ClassGradientsMixin
+from art.attacks.attack import EvasionAttack
+from art.utils import compute_success, is_probability
+
+if TYPE_CHECKING:
+    from art.utils import CLASSIFIER_CLASS_LOSS_GRADIENTS_TYPE
+
+logger = logging.getLogger(__name__)
+
+
+class DeepFool(EvasionAttack):
+    """
+    Implementation of the attack from Moosavi-Dezfooli et al. (2015).
+
+    | Paper link: https://arxiv.org/abs/1511.04599
+    """
+
+    attack_params = EvasionAttack.attack_params + [
+        "max_iter",
+        "epsilon",
+        "nb_grads",
+        "batch_size",
+        "verbose",
+    ]
+    _estimator_requirements = (BaseEstimator, ClassGradientsMixin)
+
+    def __init__(
+        self,
+        classifier: "CLASSIFIER_CLASS_LOSS_GRADIENTS_TYPE",
+        max_iter: int = 100,
+        epsilon: float = 1e-6,
+        nb_grads: int = 10,
+        batch_size: int = 1,
+        verbose: bool = True,
+    ) -> None:
+        """
+        Create a DeepFool attack instance.
+
+        :param classifier: A trained classifier.
+        :param max_iter: The maximum number of iterations.
+        :param epsilon: Overshoot parameter.
+        :param nb_grads: The number of class gradients (top nb_grads w.r.t. prediction) to compute. This way only the
+                         most likely classes are considered, speeding up the computation.
+        :param batch_size: Batch size
+        :param verbose: Show progress bars.
+        """
+        super().__init__(estimator=classifier)
+        self.max_iter = max_iter
+        self.epsilon = epsilon
+        self.nb_grads = nb_grads
+        self.batch_size = batch_size
+        self.verbose = verbose
+        self._check_params()
+        if self.estimator.clip_values is None:
+            logger.warning(
+                "The `clip_values` attribute of the estimator is `None`, therefore this instance of DeepFool will by "
+                "default generate adversarial perturbations scaled for input values in the range [0, 1] but not clip "
+                "the adversarial example."
+            )
+
+    def generate(self, x: np.ndarray, y: Optional[np.ndarray] = None, **kwargs) -> np.ndarray:
+        """
+        Generate adversarial samples and return them in an array.
+
+        :param x: An array with the original inputs to be attacked.
+        :param y: An array with the original labels to be predicted.
+        :return: An array holding the adversarial examples.
+        """
+        x_adv = x.astype(ART_NUMPY_DTYPE)
+        preds = self.estimator.predict(x, batch_size=self.batch_size)
+
+        if is_probability(preds[0]):
+            logger.warning(
+                "It seems that the attacked model is predicting probabilities. DeepFool expects logits as model output "
+                "to achieve its full attack strength."
+            )
+
+        # Determine the class labels for which to compute the gradients
+        use_grads_subset = self.nb_grads < self.estimator.nb_classes
+        if use_grads_subset:
+            # TODO compute set of unique labels per batch
+            grad_labels = np.argsort(-preds, axis=1)[:, : self.nb_grads]
+            labels_set = np.unique(grad_labels)
+        else:
+            labels_set = np.arange(self.estimator.nb_classes)
+        sorter = np.arange(len(labels_set))
+
+        # Pick a small scalar to avoid division by 0
+        tol = 10e-8
+
+        # Compute perturbation with implicit batching
+        for batch_id in trange(
+            int(np.ceil(x_adv.shape[0] / float(self.batch_size))), desc="DeepFool", disable=not self.verbose
+        ):
+            batch_index_1, batch_index_2 = batch_id * self.batch_size, (batch_id + 1) * self.batch_size
+            batch = x_adv[batch_index_1:batch_index_2].copy()
+
+            # Get predictions and gradients for batch
+            f_batch = preds[batch_index_1:batch_index_2]
+            fk_hat = np.argmax(f_batch, axis=1)
+            if use_grads_subset:
+                # Compute gradients only for top predicted classes
+                grd = np.array([self.estimator.class_gradient(batch, label=_) for _ in labels_set])
+                grd = np.squeeze(np.swapaxes(grd, 0, 2), axis=0)
+            else:
+                # Compute gradients for all classes
+                grd = self.estimator.class_gradient(batch)
+
+            # Get current predictions
+            active_indices = np.arange(len(batch))
+            current_step = 0
+            while active_indices.size > 0 and current_step < self.max_iter:
+                # Compute difference in predictions and gradients only for selected top predictions
+                labels_indices = sorter[np.searchsorted(labels_set, fk_hat, sorter=sorter)]
+                grad_diff = grd - grd[np.arange(len(grd)), labels_indices][:, None]
+                f_diff = f_batch[:, labels_set] - f_batch[np.arange(len(f_batch)), labels_indices][:, None]
+
+                # Choose coordinate and compute perturbation
+                norm = np.linalg.norm(grad_diff.reshape(len(grad_diff), len(labels_set), -1), axis=2) + tol
+                value = np.abs(f_diff) / norm
+                value[np.arange(len(value)), labels_indices] = np.inf
+                l_var = np.argmin(value, axis=1)
+                absolute1 = abs(f_diff[np.arange(len(f_diff)), l_var])
+                draddiff = grad_diff[np.arange(len(grad_diff)), l_var].reshape(len(grad_diff), -1)
+                pow1 = pow(np.linalg.norm(draddiff, axis=1), 2,) + tol
+                r_var = absolute1 / pow1
+                r_var = r_var.reshape((-1,) + (1,) * (len(x.shape) - 1))
+                r_var = r_var * grad_diff[np.arange(len(grad_diff)), l_var]
+
+                # Add perturbation and clip result
+                if self.estimator.clip_values is not None:
+                    batch[active_indices] = np.clip(
+                        batch[active_indices]
+                        + r_var[active_indices] * (self.estimator.clip_values[1] - self.estimator.clip_values[0]),
+                        self.estimator.clip_values[0],
+                        self.estimator.clip_values[1],
+                    )
+                else:
+                    batch[active_indices] += r_var[active_indices]
+
+                # Recompute prediction for new x
+                f_batch = self.estimator.predict(batch)
+                fk_i_hat = np.argmax(f_batch, axis=1)
+
+                # Recompute gradients for new x
+                if use_grads_subset:
+                    # Compute gradients only for (originally) top predicted classes
+                    grd = np.array([self.estimator.class_gradient(batch, label=_) for _ in labels_set])
+                    grd = np.squeeze(np.swapaxes(grd, 0, 2), axis=0)
+                else:
+                    # Compute gradients for all classes
+                    grd = self.estimator.class_gradient(batch)
+
+                # Stop if misclassification has been achieved
+                active_indices = np.where(fk_i_hat == fk_hat)[0]
+
+                current_step += 1
+
+            # Apply overshoot parameter
+            x_adv1 = x_adv[batch_index_1:batch_index_2]
+            x_adv2 = (1 + self.epsilon) * (batch - x_adv[batch_index_1:batch_index_2])
+            x_adv[batch_index_1:batch_index_2] = x_adv1 + x_adv2
+            if self.estimator.clip_values is not None:
+                np.clip(
+                    x_adv[batch_index_1:batch_index_2],
+                    self.estimator.clip_values[0],
+                    self.estimator.clip_values[1],
+                    out=x_adv[batch_index_1:batch_index_2],
+                )
+
+        logger.info(
+            "Success rate of DeepFool attack: %.2f%%",
+            100 * compute_success(self.estimator, x, y, x_adv, batch_size=self.batch_size),
+        )
+        return x_adv
+
+    def _check_params(self) -> None:
+        if not isinstance(self.max_iter, (int, np.int)) or self.max_iter <= 0:
+            raise ValueError("The number of iterations must be a positive integer.")
+
+        if not isinstance(self.nb_grads, (int, np.int)) or self.nb_grads <= 0:
+            raise ValueError("The number of class gradients to compute must be a positive integer.")
+
+        if self.epsilon < 0:
+            raise ValueError("The overshoot parameter must not be negative.")
+
+        if self.batch_size <= 0:
+            raise ValueError("The batch size `batch_size` has to be positive.")
+
+        if not isinstance(self.verbose, bool):
+            raise ValueError("The argument `verbose` has to be of type bool.")
diff --git a/adversarial-robustness-toolbox/art/attacks/evasion/dpatch.py b/adversarial-robustness-toolbox/art/attacks/evasion/dpatch.py
new file mode 100644
index 0000000..60ab204
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/attacks/evasion/dpatch.py
@@ -0,0 +1,355 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements the adversarial patch attack `DPatch` for object detectors.
+
+| Paper link: https://arxiv.org/abs/1806.02299v4
+"""
+import logging
+import math
+import random
+from typing import Dict, List, Optional, Tuple, Union, TYPE_CHECKING
+
+import numpy as np
+from tqdm.auto import trange
+
+from art.attacks.attack import EvasionAttack
+from art.estimators.estimator import BaseEstimator, LossGradientsMixin
+from art.estimators.object_detection.object_detector import ObjectDetectorMixin
+from art import config
+
+if TYPE_CHECKING:
+    from art.utils import OBJECT_DETECTOR_TYPE
+
+logger = logging.getLogger(__name__)
+
+
+class DPatch(EvasionAttack):
+    """
+    Implementation of the DPatch attack.
+
+    | Paper link: https://arxiv.org/abs/1806.02299v4
+    """
+
+    attack_params = EvasionAttack.attack_params + [
+        "patch_shape",
+        "learning_rate",
+        "max_iter",
+        "batch_size",
+        "verbose",
+    ]
+
+    _estimator_requirements = (BaseEstimator, LossGradientsMixin, ObjectDetectorMixin)
+
+    def __init__(
+        self,
+        estimator: "OBJECT_DETECTOR_TYPE",
+        patch_shape: Tuple[int, int, int] = (40, 40, 3),
+        learning_rate: float = 5.0,
+        max_iter: int = 500,
+        batch_size: int = 16,
+        verbose: bool = True,
+    ):
+        """
+        Create an instance of the :class:`.DPatch`.
+
+        :param estimator: A trained object detector.
+        :param patch_shape: The shape of the adversarial path as a tuple of shape (height, width, nb_channels).
+        :param learning_rate: The learning rate of the optimization.
+        :param max_iter: The number of optimization steps.
+        :param batch_size: The size of the training batch.
+        :param verbose: Show progress bars.
+        """
+        super().__init__(estimator=estimator)
+
+        self.patch_shape = patch_shape
+        self.learning_rate = learning_rate
+        self.max_iter = max_iter
+        self.batch_size = batch_size
+        if self.estimator.clip_values is None:
+            self._patch = np.zeros(shape=patch_shape, dtype=config.ART_NUMPY_DTYPE)
+        else:
+            self._patch = (
+                np.random.randint(0, 255, size=patch_shape)
+                / 255
+                * (self.estimator.clip_values[1] - self.estimator.clip_values[0])
+                + self.estimator.clip_values[0]
+            ).astype(config.ART_NUMPY_DTYPE)
+        self.verbose = verbose
+        self._check_params()
+
+        self.target_label = []
+
+    def generate(
+        self,
+        x: np.ndarray,
+        y: Optional[np.ndarray] = None,
+        target_label: Optional[Union[int, List[int], np.ndarray]] = None,
+        **kwargs
+    ) -> np.ndarray:
+        """
+        Generate DPatch.
+
+        :param x: Sample images.
+        :param y: Target labels for object detector.
+        :param target_label: The target label of the DPatch attack.
+        :param mask: An boolean array of shape equal to the shape of a single samples (1, H, W) or the shape of `x`
+                     (N, H, W) without their channel dimensions. Any features for which the mask is True can be the
+                     center location of the patch during sampling.
+        :type mask: `np.ndarray`
+        :return: Adversarial patch.
+        """
+        mask = kwargs.get("mask")
+        if mask is not None:
+            mask = mask.copy()
+        if (
+            mask is not None
+            and (mask.dtype != np.bool)
+            or not (mask.shape[0] == 1 or mask.shape[0] == x.shape[0])
+            or not (
+                (mask.shape[1] == x.shape[1] and mask.shape[2] == x.shape[2])
+                or (mask.shape[1] == x.shape[2] and mask.shape[2] == x.shape[3])
+            )
+        ):
+            raise ValueError(
+                "The shape of `mask` has to be equal to the shape of a single samples (1, H, W) or the"
+                "shape of `x` (N, H, W) without their channel dimensions."
+            )
+
+        channel_index = 1 if self.estimator.channels_first else x.ndim - 1
+        if x.shape[channel_index] != self.patch_shape[channel_index - 1]:
+            raise ValueError("The color channel index of the images and the patch have to be identical.")
+        if y is not None:
+            raise ValueError("The DPatch attack does not use target labels.")
+        if x.ndim != 4:
+            raise ValueError("The adversarial patch can only be applied to images.")
+        if target_label is not None:
+            if isinstance(target_label, int):
+                self.target_label = [target_label] * x.shape[0]
+            elif isinstance(target_label, np.ndarray):
+                if not (target_label.shape == (x.shape[0], 1) or target_label.shape == (x.shape[0],)):
+                    raise ValueError("The target_label has to be a 1-dimensional array.")
+                self.target_label = target_label.tolist()
+            else:
+                if not len(target_label) == x.shape[0] or not isinstance(target_label, list):
+                    raise ValueError("The target_label as list of integers needs to of length number of images in `x`.")
+                self.target_label = target_label
+
+        patched_images, transforms = self._augment_images_with_patch(
+            x, self._patch, random_location=True, channels_first=self.estimator.channels_first, mask=mask
+        )
+        patch_target: List[Dict[str, np.ndarray]] = list()
+
+        if self.target_label:
+
+            for i_image in range(patched_images.shape[0]):
+                i_x_1 = transforms[i_image]["i_x_1"]
+                i_x_2 = transforms[i_image]["i_x_2"]
+                i_y_1 = transforms[i_image]["i_y_1"]
+                i_y_2 = transforms[i_image]["i_y_2"]
+
+                target_dict = dict()
+                target_dict["boxes"] = np.asarray([[i_x_1, i_y_1, i_x_2, i_y_2]])
+                target_dict["labels"] = np.asarray([self.target_label[i_image],])
+                target_dict["scores"] = np.asarray([1.0,])
+
+                patch_target.append(target_dict)
+
+        else:
+
+            predictions = self.estimator.predict(x=patched_images)
+
+            for i_image in range(patched_images.shape[0]):
+                target_dict = dict()
+                target_dict["boxes"] = predictions[i_image]["boxes"]
+                target_dict["labels"] = predictions[i_image]["labels"]
+                target_dict["scores"] = predictions[i_image]["scores"]
+
+                patch_target.append(target_dict)
+
+        for i_step in trange(self.max_iter, desc="DPatch iteration", disable=not self.verbose):
+            if i_step == 0 or (i_step + 1) % 100 == 0:
+                logger.info("Training Step: %i", i_step + 1)
+
+            num_batches = math.ceil(x.shape[0] / self.batch_size)
+            patch_gradients = np.zeros_like(self._patch)
+
+            for i_batch in range(num_batches):
+                i_batch_start = i_batch * self.batch_size
+                i_batch_end = min((i_batch + 1) * self.batch_size, patched_images.shape[0])
+
+                gradients = self.estimator.loss_gradient(
+                    x=patched_images[i_batch_start:i_batch_end], y=patch_target[i_batch_start:i_batch_end],
+                )
+
+                for i_image in range(gradients.shape[0]):
+
+                    i_x_1 = transforms[i_batch_start + i_image]["i_x_1"]
+                    i_x_2 = transforms[i_batch_start + i_image]["i_x_2"]
+                    i_y_1 = transforms[i_batch_start + i_image]["i_y_1"]
+                    i_y_2 = transforms[i_batch_start + i_image]["i_y_2"]
+
+                    if self.estimator.channels_first:
+                        patch_gradients_i = gradients[i_image, :, i_x_1:i_x_2, i_y_1:i_y_2]
+                    else:
+                        patch_gradients_i = gradients[i_image, i_x_1:i_x_2, i_y_1:i_y_2, :]
+
+                    patch_gradients = patch_gradients + patch_gradients_i
+
+            if self.target_label:
+                self._patch = self._patch - np.sign(patch_gradients) * self.learning_rate
+            else:
+                self._patch = self._patch + np.sign(patch_gradients) * self.learning_rate
+
+            if self.estimator.clip_values is not None:
+                self._patch = np.clip(
+                    self._patch, a_min=self.estimator.clip_values[0], a_max=self.estimator.clip_values[1],
+                )
+
+        return self._patch
+
+    @staticmethod
+    def _augment_images_with_patch(
+        x: np.ndarray,
+        patch: np.ndarray,
+        random_location: bool,
+        channels_first: bool,
+        mask: Optional[np.ndarray] = None,
+    ) -> Tuple[np.ndarray, List[Dict[str, int]]]:
+        """
+        Augment images with patch.
+
+        :param x: Sample images.
+        :param patch: The patch to be applied.
+        :param random_location: If True apply patch at randomly shifted locations, otherwise place patch at origin
+                                (top-left corner).
+        :param channels_first: Set channels first or last.
+        :param mask: An boolean array of shape equal to the shape of a single samples (1, H, W) or the shape of `x`
+                     (N, H, W) without their channel dimensions. Any features for which the mask is True can be the
+                     center location of the patch during sampling.
+        :type mask: `np.ndarray`
+        """
+        transformations = list()
+        x_copy = x.copy()
+        patch_copy = patch.copy()
+
+        if channels_first:
+            x_copy = np.transpose(x_copy, (0, 2, 3, 1))
+            patch_copy = np.transpose(patch_copy, (1, 2, 0))
+
+        for i_image in range(x.shape[0]):
+
+            if random_location:
+                if mask is None:
+                    i_x_1 = random.randint(0, x_copy.shape[1] - 1 - patch_copy.shape[0])
+                    i_y_1 = random.randint(0, x_copy.shape[2] - 1 - patch_copy.shape[1])
+                else:
+
+                    if mask.shape[0] == 1:
+                        mask_2d = mask[0, :, :]
+                    else:
+                        mask_2d = mask[i_image, :, :]
+
+                    edge_x_0 = patch_copy.shape[0] // 2
+                    edge_x_1 = patch_copy.shape[0] - edge_x_0
+                    edge_y_0 = patch_copy.shape[1] // 2
+                    edge_y_1 = patch_copy.shape[1] - edge_y_0
+
+                    mask_2d[0:edge_x_0, :] = False
+                    mask_2d[-edge_x_1:, :] = False
+                    mask_2d[:, 0:edge_y_0] = False
+                    mask_2d[:, -edge_y_1:] = False
+
+                    num_pos = np.argwhere(mask_2d).shape[0]
+                    pos_id = np.random.choice(num_pos, size=1)
+                    pos = np.argwhere(mask_2d > 0)[pos_id[0]]
+                    i_x_1 = pos[0] - edge_x_0
+                    i_y_1 = pos[1] - edge_y_0
+
+            else:
+                i_x_1 = 0
+                i_y_1 = 0
+
+            i_x_2 = i_x_1 + patch_copy.shape[0]
+            i_y_2 = i_y_1 + patch_copy.shape[1]
+
+            transformations.append({"i_x_1": i_x_1, "i_y_1": i_y_1, "i_x_2": i_x_2, "i_y_2": i_y_2})
+
+            x_copy[i_image, i_x_1:i_x_2, i_y_1:i_y_2, :] = patch_copy
+
+        if channels_first:
+            x_copy = np.transpose(x_copy, (0, 3, 1, 2))
+
+        return x_copy, transformations
+
+    def apply_patch(
+        self,
+        x: np.ndarray,
+        patch_external: Optional[np.ndarray] = None,
+        random_location: bool = False,
+        mask: Optional[np.ndarray] = None,
+    ) -> np.ndarray:
+        """
+        Apply the adversarial patch to images.
+
+        :param x: Images to be patched.
+        :param patch_external: External patch to apply to images `x`. If None the attacks patch will be applied.
+        :param random_location: True if patch location should be random.
+        :param mask: An boolean array of shape equal to the shape of a single samples (1, H, W) or the shape of `x`
+                     (N, H, W) without their channel dimensions. Any features for which the mask is True can be the
+                     center location of the patch during sampling.
+        :return: The patched images.
+        """
+        if patch_external is not None:
+            patch_local = patch_external
+        else:
+            patch_local = self._patch
+
+        patched_images, _ = self._augment_images_with_patch(
+            x=x,
+            patch=patch_local,
+            random_location=random_location,
+            channels_first=self.estimator.channels_first,
+            mask=mask,
+        )
+
+        return patched_images
+
+    def _check_params(self) -> None:
+        if not isinstance(self.patch_shape, (tuple, list)) or not all(isinstance(s, int) for s in self.patch_shape):
+            raise ValueError("The patch shape must be either a tuple or list of integers.")
+        if len(self.patch_shape) != 3:
+            raise ValueError("The length of patch shape must be 3.")
+
+        if not isinstance(self.learning_rate, float):
+            raise ValueError("The learning rate must be of type float.")
+        if self.learning_rate <= 0.0:
+            raise ValueError("The learning rate must be greater than 0.0.")
+
+        if not isinstance(self.max_iter, int):
+            raise ValueError("The number of optimization steps must be of type int.")
+        if self.max_iter <= 0:
+            raise ValueError("The number of optimization steps must be greater than 0.")
+
+        if not isinstance(self.batch_size, int):
+            raise ValueError("The batch size must be of type int.")
+        if self.batch_size <= 0:
+            raise ValueError("The batch size must be greater than 0.")
+
+        if not isinstance(self.verbose, bool):
+            raise ValueError("The argument `verbose` has to be of type bool.")
diff --git a/adversarial-robustness-toolbox/art/attacks/evasion/dpatch_robust.py b/adversarial-robustness-toolbox/art/attacks/evasion/dpatch_robust.py
new file mode 100644
index 0000000..46583b8
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/attacks/evasion/dpatch_robust.py
@@ -0,0 +1,402 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements a variation of the adversarial patch attack `DPatch` for object detectors.
+It follows Lee & Kolter (2019) in using sign gradients with expectations over transformations.
+The particular transformations supported in this implementation are cropping, rotations by multiples of 90 degrees,
+and changes in the brightness of the image.
+
+| Paper link (original DPatch): https://arxiv.org/abs/1806.02299v4
+| Paper link (physical-world patch from Lee & Kolter): https://arxiv.org/abs/1906.11897
+"""
+import logging
+import math
+import random
+from typing import Dict, List, Optional, Tuple, Union, TYPE_CHECKING
+
+import numpy as np
+from tqdm.auto import trange
+
+from art.attacks.attack import EvasionAttack
+from art.estimators.estimator import BaseEstimator, LossGradientsMixin
+from art.estimators.object_detection.object_detector import ObjectDetectorMixin
+from art import config
+
+if TYPE_CHECKING:
+    from art.utils import OBJECT_DETECTOR_TYPE
+
+logger = logging.getLogger(__name__)
+
+
+class RobustDPatch(EvasionAttack):
+    """
+    Implementation of a particular variation of the DPatch attack.
+    It follows Lee & Kolter (2019) in using sign gradients with expectations over transformations.
+    The particular transformations supported in this implementation are cropping, rotations by multiples of 90 degrees,
+    and changes in the brightness of the image.
+
+    | Paper link (original DPatch): https://arxiv.org/abs/1806.02299v4
+    | Paper link (physical-world patch from Lee & Kolter): https://arxiv.org/abs/1906.11897
+    """
+
+    attack_params = EvasionAttack.attack_params + [
+        "patch_shape",
+        "learning_rate",
+        "max_iter",
+        "batch_size",
+        "verbose",
+        "patch_location",
+        "crop_range",
+        "brightness_range",
+        "rotation_weights",
+        "sample_size",
+    ]
+
+    _estimator_requirements = (BaseEstimator, LossGradientsMixin, ObjectDetectorMixin)
+
+    def __init__(
+        self,
+        estimator: "OBJECT_DETECTOR_TYPE",
+        patch_shape: Tuple[int, int, int] = (40, 40, 3),
+        patch_location: Tuple[int, int] = (0, 0),
+        crop_range: Tuple[int, int] = (0, 0),
+        brightness_range: Tuple[float, float] = (1.0, 1.0),
+        rotation_weights: Union[Tuple[float, float, float, float], Tuple[int, int, int, int]] = (1, 0, 0, 0),
+        sample_size: int = 1,
+        learning_rate: float = 5.0,
+        max_iter: int = 500,
+        batch_size: int = 16,
+        verbose: bool = True,
+    ):
+        """
+        Create an instance of the :class:`.RobustDPatch`.
+
+        :param estimator: A trained object detector.
+        :param patch_shape: The shape of the adversarial patch as a tuple of shape (height, width, nb_channels).
+        :param patch_location: The location of the adversarial patch as a tuple of shape (upper left x, upper left y).
+        :param crop_range: By how much the images may be cropped as a tuple of shape (height, width).
+        :param brightness_range: Range for randomly adjusting the brightness of the image.
+        :param rotation_weights: Sampling weights for random image rotations by (0, 90, 180, 270) degrees clockwise.
+        :param sample_size: Number of samples to be used in expectations over transformation.
+        :param learning_rate: The learning rate of the optimization.
+        :param max_iter: The number of optimization steps.
+        :param batch_size: The size of the training batch.
+        :param verbose: Show progress bars.
+        """
+
+        super().__init__(estimator=estimator)
+
+        self.patch_shape = patch_shape
+        self.learning_rate = learning_rate
+        self.max_iter = max_iter
+        self.batch_size = batch_size
+        if self.estimator.clip_values is None:
+            self._patch = np.zeros(shape=patch_shape, dtype=config.ART_NUMPY_DTYPE)
+        else:
+            self._patch = (
+                np.random.randint(0, 255, size=patch_shape)
+                / 255
+                * (self.estimator.clip_values[1] - self.estimator.clip_values[0])
+                + self.estimator.clip_values[0]
+            ).astype(config.ART_NUMPY_DTYPE)
+        self.verbose = verbose
+        self.patch_location = patch_location
+        self.crop_range = crop_range
+        self.brightness_range = brightness_range
+        self.rotation_weights = rotation_weights
+        self.sample_size = sample_size
+        self._check_params()
+
+    def generate(self, x: np.ndarray, y: Optional[np.ndarray] = None, **kwargs) -> np.ndarray:
+        """
+        Generate RobustDPatch.
+
+        :param x: Sample images.
+        :param y: Target labels for object detector.
+        :return: Adversarial patch.
+        """
+        channel_index = 1 if self.estimator.channels_first else x.ndim - 1
+        if x.shape[channel_index] != self.patch_shape[channel_index - 1]:
+            raise ValueError("The color channel index of the images and the patch have to be identical.")
+        if y is not None:
+            raise ValueError("The RobustDPatch attack does not use target labels.")
+        if x.ndim != 4:
+            raise ValueError("The adversarial patch can only be applied to images.")
+
+        # Check whether patch fits into the cropped images:
+        if self.estimator.channels_first:
+            image_height, image_width = x.shape[2:4]
+        else:
+            image_height, image_width = x.shape[1:3]
+
+        if (
+            self.patch_location[0] + self.patch_shape[0] > image_height - self.crop_range[0]
+            or self.patch_location[1] + self.patch_shape[1] > image_width - self.crop_range[1]
+        ):
+            raise ValueError("The patch (partially) lies outside the cropped image.")
+
+        for i_step in trange(self.max_iter, desc="RobustDPatch iteration", disable=not self.verbose):
+            if i_step == 0 or (i_step + 1) % 100 == 0:
+                logger.info("Training Step: %i", i_step + 1)
+
+            num_batches = math.ceil(x.shape[0] / self.batch_size)
+            patch_gradients_old = np.zeros_like(self._patch)
+
+            for e_step in range(self.sample_size):
+                if e_step == 0 or (e_step + 1) % 100 == 0:
+                    logger.info("EOT Step: %i", e_step + 1)
+
+                for i_batch in range(num_batches):
+                    i_batch_start = i_batch * self.batch_size
+                    i_batch_end = min((i_batch + 1) * self.batch_size, x.shape[0])
+
+                    # Sample and apply the random transformations:
+                    patched_images, patch_target, transforms = self._augment_images_with_patch(
+                        x[i_batch_start:i_batch_end], self._patch, channels_first=self.estimator.channels_first
+                    )
+
+                    gradients = self.estimator.loss_gradient(x=patched_images, y=patch_target,)
+
+                    gradients = self._untransform_gradients(
+                        gradients, transforms, channels_first=self.estimator.channels_first
+                    )
+
+                    patch_gradients = patch_gradients_old + np.sum(gradients, axis=0)
+                    logger.debug(
+                        "Gradient percentage diff: %f)",
+                        np.mean(np.sign(patch_gradients) != np.sign(patch_gradients_old)),
+                    )
+
+                    patch_gradients_old = patch_gradients
+
+            self._patch = self._patch + np.sign(patch_gradients) * self.learning_rate
+
+            if self.estimator.clip_values is not None:
+                self._patch = np.clip(
+                    self._patch, a_min=self.estimator.clip_values[0], a_max=self.estimator.clip_values[1],
+                )
+
+        return self._patch
+
+    def _augment_images_with_patch(
+        self, x: np.ndarray, patch: np.ndarray, channels_first: bool
+    ) -> Tuple[np.ndarray, List[Dict[str, np.ndarray]], Dict[str, Union[int, float]]]:
+        """
+        Augment images with patch.
+
+        :param x: Sample images.
+        :param patch: The patch to be applied.
+        :param channels_first: Set channels first or last.
+        """
+
+        transformations: Dict[str, Union[float, int]] = dict()
+        x_copy = x.copy()
+        patch_copy = patch.copy()
+        x_patch = x.copy()
+
+        if channels_first:
+            x_copy = np.transpose(x_copy, (0, 2, 3, 1))
+            x_patch = np.transpose(x_patch, (0, 2, 3, 1))
+            patch_copy = np.transpose(patch_copy, (1, 2, 0))
+
+        # Apply patch:
+        x_1, y_1 = self.patch_location
+        x_2, y_2 = x_1 + patch_copy.shape[0], y_1 + patch_copy.shape[1]
+        x_patch[:, x_1:x_2, y_1:y_2, :] = patch_copy
+
+        # 1) crop images:
+        crop_x = random.randint(0, self.crop_range[0])
+        crop_y = random.randint(0, self.crop_range[1])
+        x_1, y_1 = crop_x, crop_y
+        x_2, y_2 = x_copy.shape[1] - crop_x + 1, x_copy.shape[2] - crop_y + 1
+        x_copy = x_copy[:, x_1:x_2, y_1:y_2, :]
+        x_patch = x_patch[:, x_1:x_2, y_1:y_2, :]
+
+        transformations.update({"crop_x": crop_x, "crop_y": crop_y})
+
+        # 2) rotate images:
+        rot90 = random.choices([0, 1, 2, 3], weights=self.rotation_weights)[0]
+
+        x_copy = np.rot90(x_copy, rot90, (1, 2))
+        x_patch = np.rot90(x_patch, rot90, (1, 2))
+
+        transformations.update({"rot90": rot90})
+
+        # 3) adjust brightness:
+        brightness = random.uniform(*self.brightness_range)
+        x_copy = np.round(brightness * x_copy)
+        x_patch = np.round(brightness * x_patch)
+
+        transformations.update({"brightness": brightness})
+
+        logger.debug("Transformations: %s", str(transformations))
+
+        patch_target: List[Dict[str, np.ndarray]] = list()
+        predictions = self.estimator.predict(x=x_copy)
+        for i_image in range(x_copy.shape[0]):
+            target_dict = dict()
+            target_dict["boxes"] = predictions[i_image]["boxes"]
+            target_dict["labels"] = predictions[i_image]["labels"]
+            target_dict["scores"] = predictions[i_image]["scores"]
+
+            patch_target.append(target_dict)
+
+        if channels_first:
+            x_patch = np.transpose(x_patch, (0, 3, 1, 2))
+
+        return x_patch, patch_target, transformations
+
+    def _untransform_gradients(
+        self, gradients: np.ndarray, transforms: Dict[str, Union[int, float]], channels_first: bool,
+    ) -> np.ndarray:
+        """
+        Revert transformation on gradients.
+
+        :param gradients: The gradients to be reverse transformed.
+        :param transforms: The transformations in forward direction.
+        :param channels_first: Set channels first or last.
+        """
+
+        if channels_first:
+            gradients = np.transpose(gradients, (0, 2, 3, 1))
+
+        # Account for brightness adjustment:
+        gradients = transforms["brightness"] * gradients
+
+        # Undo rotations:
+        rot90 = (4 - transforms["rot90"]) % 4
+        gradients = np.rot90(gradients, rot90, (1, 2))
+
+        # Account for cropping when considering the upper left point of the patch:
+        x_1 = self.patch_location[0] - int(transforms["crop_x"])
+        y_1 = self.patch_location[1] - int(transforms["crop_y"])
+        x_2 = x_1 + self.patch_shape[0]
+        y_2 = y_1 + self.patch_shape[1]
+        gradients = gradients[:, x_1:x_2, y_1:y_2, :]
+
+        if channels_first:
+            gradients = np.transpose(gradients, (0, 3, 1, 2))
+
+        return gradients
+
+    def apply_patch(self, x: np.ndarray, patch_external: Optional[np.ndarray] = None) -> np.ndarray:
+        """
+        Apply the adversarial patch to images.
+
+        :param x: Images to be patched.
+        :param patch_external: External patch to apply to images `x`. If None the attacks patch will be applied.
+        :return: The patched images.
+        """
+
+        x_patch = x.copy()
+
+        if patch_external is not None:
+            patch_local = patch_external.copy()
+        else:
+            patch_local = self._patch.copy()
+
+        if self.estimator.channels_first:
+            x_patch = np.transpose(x_patch, (0, 2, 3, 1))
+            patch_local = np.transpose(patch_local, (1, 2, 0))
+
+        # Apply patch:
+        x_1, y_1 = self.patch_location
+        x_2, y_2 = x_1 + patch_local.shape[0], y_1 + patch_local.shape[1]
+
+        if x_2 > x_patch.shape[1] or y_2 > x_patch.shape[2]:
+            raise ValueError("The patch (partially) lies outside the image.")
+
+        x_patch[:, x_1:x_2, y_1:y_2, :] = patch_local
+
+        if self.estimator.channels_first:
+            x_patch = np.transpose(x_patch, (0, 3, 1, 2))
+
+        return x_patch
+
+    def _check_params(self) -> None:
+        if not isinstance(self.patch_shape, (tuple, list)) or not all(isinstance(s, int) for s in self.patch_shape):
+            raise ValueError("The patch shape must be either a tuple or list of integers.")
+        if len(self.patch_shape) != 3:
+            raise ValueError("The length of patch shape must be 3.")
+
+        if not isinstance(self.learning_rate, float):
+            raise ValueError("The learning rate must be of type float.")
+        if self.learning_rate <= 0.0:
+            raise ValueError("The learning rate must be greater than 0.0.")
+
+        if not isinstance(self.max_iter, int):
+            raise ValueError("The number of optimization steps must be of type int.")
+        if self.max_iter <= 0:
+            raise ValueError("The number of optimization steps must be greater than 0.")
+
+        if not isinstance(self.batch_size, int):
+            raise ValueError("The batch size must be of type int.")
+        if self.batch_size <= 0:
+            raise ValueError("The batch size must be greater than 0.")
+
+        if not isinstance(self.verbose, bool):
+            raise ValueError("The argument `verbose` has to be of type bool.")
+
+        if not isinstance(self.patch_location, (tuple, list)) or not all(
+            isinstance(s, int) for s in self.patch_location
+        ):
+            raise ValueError("The patch location must be either a tuple or list of integers.")
+        if len(self.patch_location) != 2:
+            raise ValueError("The length of patch location must be 2.")
+
+        if not isinstance(self.crop_range, (tuple, list)) or not all(isinstance(s, int) for s in self.crop_range):
+            raise ValueError("The crop range must be either a tuple or list of integers.")
+        if len(self.crop_range) != 2:
+            raise ValueError("The length of crop range must be 2.")
+
+        if self.crop_range[0] > self.crop_range[1]:
+            raise ValueError("The first element of the crop range must be less or equal to the second one.")
+
+        if self.patch_location[0] < self.crop_range[0] or self.patch_location[1] < self.crop_range[1]:
+            raise ValueError("The patch location must be outside the crop range.")
+
+        if not isinstance(self.brightness_range, (tuple, list)) or not all(
+            isinstance(s, float) for s in self.brightness_range
+        ):
+            raise ValueError("The brightness range must be either a tuple or list of floats.")
+        if len(self.brightness_range) != 2:
+            raise ValueError("The length of brightness range must be 2.")
+
+        if self.brightness_range[0] < 0.0 or self.brightness_range[1] > 1.0:
+            raise ValueError("The brightness range must be between 0.0 and 1.0.")
+
+        if self.brightness_range[0] > self.brightness_range[1]:
+            raise ValueError("The first element of the brightness range must be less or equal to the second one.")
+
+        if not isinstance(self.rotation_weights, (tuple, list)) or not all(
+            isinstance(s, (float, int)) for s in self.rotation_weights
+        ):
+            raise ValueError("The rotation sampling weights must be provided as tuple or list of float or int values.")
+        if len(self.rotation_weights) != 4:
+            raise ValueError("The number of rotation sampling weights must be 4.")
+
+        if not all(s >= 0.0 for s in self.rotation_weights):
+            raise ValueError("The rotation sampling weights must be non-negative.")
+
+        if all(s == 0.0 for s in self.rotation_weights):
+            raise ValueError("At least one of the rotation sampling weights must be strictly greater than zero.")
+
+        if not isinstance(self.sample_size, int):
+            raise ValueError("The EOT sample size must be of type int.")
+        if self.sample_size <= 0:
+            raise ValueError("The EOT sample size must be greater than 0.")
diff --git a/adversarial-robustness-toolbox/art/attacks/evasion/elastic_net.py b/adversarial-robustness-toolbox/art/attacks/evasion/elastic_net.py
new file mode 100644
index 0000000..a90cf92
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/attacks/evasion/elastic_net.py
@@ -0,0 +1,395 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements the elastic net attack `ElasticNet`. This is a white-box attack.
+
+| Paper link: https://arxiv.org/abs/1709.04114
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+from typing import Optional, Tuple, TYPE_CHECKING
+
+import numpy as np
+import six
+from tqdm.auto import trange
+
+from art.config import ART_NUMPY_DTYPE
+from art.attacks.attack import EvasionAttack
+from art.estimators.estimator import BaseEstimator
+from art.estimators.classification.classifier import ClassGradientsMixin
+from art.utils import (
+    compute_success,
+    get_labels_np_array,
+    check_and_transform_label_format,
+)
+
+if TYPE_CHECKING:
+    from art.utils import CLASSIFIER_CLASS_LOSS_GRADIENTS_TYPE
+
+logger = logging.getLogger(__name__)
+
+
+class ElasticNet(EvasionAttack):
+    """
+    The elastic net attack of Pin-Yu Chen et al. (2018).
+
+    | Paper link: https://arxiv.org/abs/1709.04114
+    """
+
+    attack_params = EvasionAttack.attack_params + [
+        "confidence",
+        "targeted",
+        "learning_rate",
+        "max_iter",
+        "beta",
+        "binary_search_steps",
+        "initial_const",
+        "batch_size",
+        "decision_rule",
+        "verbose",
+    ]
+
+    _estimator_requirements = (BaseEstimator, ClassGradientsMixin)
+
+    def __init__(
+        self,
+        classifier: "CLASSIFIER_CLASS_LOSS_GRADIENTS_TYPE",
+        confidence: float = 0.0,
+        targeted: bool = False,
+        learning_rate: float = 1e-2,
+        binary_search_steps: int = 9,
+        max_iter: int = 100,
+        beta: float = 1e-3,
+        initial_const: float = 1e-3,
+        batch_size: int = 1,
+        decision_rule: str = "EN",
+        verbose: bool = True,
+    ) -> None:
+        """
+        Create an ElasticNet attack instance.
+
+        :param classifier: A trained classifier.
+        :param confidence: Confidence of adversarial examples: a higher value produces examples that are farther
+               away, from the original input, but classified with higher confidence as the target class.
+        :param targeted: Should the attack target one specific class.
+        :param learning_rate: The initial learning rate for the attack algorithm. Smaller values produce better
+               results but are slower to converge.
+        :param binary_search_steps: Number of times to adjust constant with binary search (positive value).
+        :param max_iter: The maximum number of iterations.
+        :param beta: Hyperparameter trading off L2 minimization for L1 minimization.
+        :param initial_const: The initial trade-off constant `c` to use to tune the relative importance of distance
+               and confidence. If `binary_search_steps` is large, the initial constant is not important, as discussed in
+               Carlini and Wagner (2016).
+        :param batch_size: Internal size of batches on which adversarial samples are generated.
+        :param decision_rule: Decision rule. 'EN' means Elastic Net rule, 'L1' means L1 rule, 'L2' means L2 rule.
+        :param verbose: Show progress bars.
+        """
+        super().__init__(estimator=classifier)
+        self.confidence = confidence
+        self._targeted = targeted
+        self.learning_rate = learning_rate
+        self.binary_search_steps = binary_search_steps
+        self.max_iter = max_iter
+        self.beta = beta
+        self.initial_const = initial_const
+        self.batch_size = batch_size
+        self.decision_rule = decision_rule
+        self.verbose = verbose
+        self._check_params()
+
+    def _loss(self, x: np.ndarray, x_adv: np.ndarray) -> tuple:
+        """
+        Compute the loss function values.
+
+        :param x: An array with the original input.
+        :param x_adv: An array with the adversarial input.
+        :return: A tuple of shape `(np.ndarray, float, float, float)` holding the current predictions, l1 distance,
+                 l2 distance and elastic net loss.
+        """
+        l1dist = np.sum(np.abs(x - x_adv).reshape(x.shape[0], -1), axis=1)
+        l2dist = np.sum(np.square(x - x_adv).reshape(x.shape[0], -1), axis=1)
+        endist = self.beta * l1dist + l2dist
+        predictions = self.estimator.predict(np.array(x_adv, dtype=ART_NUMPY_DTYPE), batch_size=self.batch_size)
+
+        return np.argmax(predictions, axis=1), l1dist, l2dist, endist
+
+    def _gradient_of_loss(
+        self, target: np.ndarray, x: np.ndarray, x_adv: np.ndarray, c_weight: np.ndarray,
+    ) -> np.ndarray:
+        """
+        Compute the gradient of the loss function.
+
+        :param target: An array with the target class (one-hot encoded).
+        :param x: An array with the original input.
+        :param x_adv: An array with the adversarial input.
+        :param c_weight: Weight of the loss term aiming for classification as target.
+        :return: An array with the gradient of the loss function.
+        """
+        # Compute the current predictions
+        predictions = self.estimator.predict(np.array(x_adv, dtype=ART_NUMPY_DTYPE), batch_size=self.batch_size)
+
+        if self.targeted:
+            i_sub = np.argmax(target, axis=1)
+            i_add = np.argmax(
+                predictions * (1 - target) + (np.min(predictions, axis=1) - 1)[:, np.newaxis] * target, axis=1,
+            )
+        else:
+            i_add = np.argmax(target, axis=1)
+            i_sub = np.argmax(
+                predictions * (1 - target) + (np.min(predictions, axis=1) - 1)[:, np.newaxis] * target, axis=1,
+            )
+
+        loss_gradient = self.estimator.class_gradient(x_adv, label=i_add)
+        loss_gradient -= self.estimator.class_gradient(x_adv, label=i_sub)
+        loss_gradient = loss_gradient.reshape(x.shape)
+
+        c_mult = c_weight
+        for _ in range(len(x.shape) - 1):
+            c_mult = c_mult[:, np.newaxis]
+
+        loss_gradient *= c_mult
+        loss_gradient += 2 * (x_adv - x)
+
+        return loss_gradient
+
+    def _decay_learning_rate(self, global_step: int, end_learning_rate: float, decay_steps: int) -> float:
+        """
+        Applies a square-root decay to the learning rate.
+
+        :param global_step: Global step to use for the decay computation.
+        :param end_learning_rate: The minimal end learning rate.
+        :param decay_steps: Number of decayed steps.
+        :return: The decayed learning rate
+        """
+        learn_rate = self.learning_rate - end_learning_rate
+        decayed_learning_rate = learn_rate * (1 - global_step / decay_steps) ** 2 + end_learning_rate
+
+        return decayed_learning_rate
+
+    def generate(self, x: np.ndarray, y: Optional[np.ndarray] = None, **kwargs) -> np.ndarray:
+        """
+        Generate adversarial samples and return them in an array.
+
+        :param x: An array with the original inputs to be attacked.
+        :param y: Target values (class labels) one-hot-encoded of shape (nb_samples, nb_classes) or indices of shape
+                  (nb_samples,). If `self.targeted` is true, then `y` represents the target labels. Otherwise, the
+                  targets are the original class labels.
+        :return: An array holding the adversarial examples.
+        """
+        y = check_and_transform_label_format(y, self.estimator.nb_classes)
+        x_adv = x.astype(ART_NUMPY_DTYPE)
+
+        # Assert that, if attack is targeted, y is provided:
+        if self.targeted and y is None:
+            raise ValueError("Target labels `y` need to be provided for a targeted attack.")
+
+        # No labels provided, use model prediction as correct class
+        if y is None:
+            y = get_labels_np_array(self.estimator.predict(x, batch_size=self.batch_size))
+
+        # Compute adversarial examples with implicit batching
+        nb_batches = int(np.ceil(x_adv.shape[0] / float(self.batch_size)))
+        for batch_id in trange(nb_batches, desc="EAD", disable=not self.verbose):
+            batch_index_1, batch_index_2 = batch_id * self.batch_size, (batch_id + 1) * self.batch_size
+            x_batch = x_adv[batch_index_1:batch_index_2]
+            y_batch = y[batch_index_1:batch_index_2]
+            x_adv[batch_index_1:batch_index_2] = self._generate_batch(x_batch, y_batch)
+
+        # Apply clip
+        if self.estimator.clip_values is not None:
+            x_adv = np.clip(x_adv, self.estimator.clip_values[0], self.estimator.clip_values[1])
+
+        # Compute success rate of the EAD attack
+        logger.info(
+            "Success rate of EAD attack: %.2f%%",
+            100 * compute_success(self.estimator, x, y, x_adv, self.targeted, batch_size=self.batch_size),
+        )
+
+        return x_adv
+
+    def _generate_batch(self, x_batch: np.ndarray, y_batch: np.ndarray) -> np.ndarray:
+        """
+        Run the attack on a batch of images and labels.
+
+        :param x_batch: A batch of original examples.
+        :param y_batch: A batch of targets (0-1 hot).
+        :return: A batch of adversarial examples.
+        """
+        # Initialize binary search:
+        c_current = self.initial_const * np.ones(x_batch.shape[0])
+        c_lower_bound = np.zeros(x_batch.shape[0])
+        c_upper_bound = 10e10 * np.ones(x_batch.shape[0])
+
+        # Initialize best distortions and best attacks globally
+        o_best_dist = np.inf * np.ones(x_batch.shape[0])
+        o_best_attack = x_batch.copy()
+
+        # Start with a binary search
+        for bss in range(self.binary_search_steps):
+            logger.debug(
+                "Binary search step %i out of %i (c_mean==%f)", bss, self.binary_search_steps, np.mean(c_current),
+            )
+
+            # Run with 1 specific binary search step
+            best_dist, best_label, best_attack = self._generate_bss(x_batch, y_batch, c_current)
+
+            # Update best results so far
+            o_best_attack[best_dist < o_best_dist] = best_attack[best_dist < o_best_dist]
+            o_best_dist[best_dist < o_best_dist] = best_dist[best_dist < o_best_dist]
+
+            # Adjust the constant as needed
+            c_current, c_lower_bound, c_upper_bound = self._update_const(
+                y_batch, best_label, c_current, c_lower_bound, c_upper_bound
+            )
+
+        return o_best_attack
+
+    def _update_const(
+        self,
+        y_batch: np.ndarray,
+        best_label: np.ndarray,
+        c_batch: np.ndarray,
+        c_lower_bound: np.ndarray,
+        c_upper_bound: np.ndarray,
+    ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
+        """
+        Update constants.
+
+        :param y_batch: A batch of targets (0-1 hot).
+        :param best_label: A batch of best labels.
+        :param c_batch: A batch of constants.
+        :param c_lower_bound: A batch of lower bound constants.
+        :param c_upper_bound: A batch of upper bound constants.
+        :return: A tuple of three batches of updated constants and lower/upper bounds.
+        """
+
+        def compare(o_1, o_2):
+            if self.targeted:
+                return o_1 == o_2
+            return o_1 != o_2
+
+        for i in range(c_batch.shape[0]):
+            if compare(best_label[i], np.argmax(y_batch[i])) and best_label[i] != -np.inf:
+                # Successful attack
+                c_upper_bound[i] = min(c_upper_bound[i], c_batch[i])
+                if c_upper_bound[i] < 1e9:
+                    c_batch[i] = (c_lower_bound[i] + c_upper_bound[i]) / 2.0
+
+            else:
+                # Failure attack
+                c_lower_bound[i] = max(c_lower_bound[i], c_batch[i])
+                if c_upper_bound[i] < 1e9:
+                    c_batch[i] = (c_lower_bound[i] + c_upper_bound[i]) / 2.0
+                else:
+                    c_batch[i] *= 10
+
+        return c_batch, c_lower_bound, c_upper_bound
+
+    def _generate_bss(self, x_batch: np.ndarray, y_batch: np.ndarray, c_batch: np.ndarray) -> tuple:
+        """
+        Generate adversarial examples for a batch of inputs with a specific batch of constants.
+
+        :param x_batch: A batch of original examples.
+        :param y_batch: A batch of targets (0-1 hot).
+        :param c_batch: A batch of constants.
+        :return: A tuple of best elastic distances, best labels, best attacks
+        """
+
+        def compare(o_1, o_2):
+            if self.targeted:
+                return o_1 == o_2
+            return o_1 != o_2
+
+        # Initialize best distortions and best changed labels and best attacks
+        best_dist = np.inf * np.ones(x_batch.shape[0])
+        best_label = [-np.inf] * x_batch.shape[0]
+        best_attack = x_batch.copy()
+
+        # Implement the algorithm 1 in the EAD paper
+        x_adv = x_batch.copy()
+        y_adv = x_batch.copy()
+        for i_iter in range(self.max_iter):
+            logger.debug("Iteration step %i out of %i", i_iter, self.max_iter)
+
+            # Update learning rate
+            learning_rate = self._decay_learning_rate(
+                global_step=i_iter, end_learning_rate=0, decay_steps=self.max_iter
+            )
+
+            # Compute adversarial examples
+            grad = self._gradient_of_loss(target=y_batch, x=x_batch, x_adv=y_adv, c_weight=c_batch)
+            x_adv_next = self._shrinkage_threshold(y_adv - learning_rate * grad, x_batch, self.beta)
+            y_adv = x_adv_next + (1.0 * i_iter / (i_iter + 3)) * (x_adv_next - x_adv)
+            x_adv = x_adv_next
+
+            # Adjust the best result
+            (logits, l1dist, l2dist, endist) = self._loss(x=x_batch, x_adv=x_adv)
+
+            if self.decision_rule == "EN":
+                zip_set = zip(endist, logits)
+            elif self.decision_rule == "L1":
+                zip_set = zip(l1dist, logits)
+            elif self.decision_rule == "L2":
+                zip_set = zip(l2dist, logits)
+            else:
+                raise ValueError("The decision rule only supports `EN`, `L1`, `L2`.")
+
+            for j, (distance, label) in enumerate(zip_set):
+                if distance < best_dist[j] and compare(label, np.argmax(y_batch[j])):
+                    best_dist[j] = distance
+                    best_attack[j] = x_adv[j]
+                    best_label[j] = label
+
+        return best_dist, best_label, best_attack
+
+    @staticmethod
+    def _shrinkage_threshold(z_batch: np.ndarray, x_batch: np.ndarray, beta: float) -> np.ndarray:
+        """
+        Implement the element-wise projected shrinkage-threshold function.
+
+        :param z_batch: A batch of examples.
+        :param x_batch: A batch of original examples.
+        :param beta: The shrink parameter.
+        :return: A shrinked version of z.
+        """
+        cond1 = (z_batch - x_batch) > beta
+        cond2 = np.abs(z_batch - x_batch) <= beta
+        cond3 = (z_batch - x_batch) < -beta
+
+        upper = np.minimum(z_batch - beta, 1.0)
+        lower = np.maximum(z_batch + beta, 0.0)
+        result = cond1 * upper + cond2 * x_batch + cond3 * lower
+
+        return result
+
+    def _check_params(self) -> None:
+        if not isinstance(self.binary_search_steps, int) or self.binary_search_steps < 0:
+            raise ValueError("The number of binary search steps must be a non-negative integer.")
+
+        if not isinstance(self.max_iter, int) or self.max_iter < 0:
+            raise ValueError("The number of iterations must be a non-negative integer.")
+
+        if not isinstance(self.batch_size, int) or self.batch_size < 1:
+            raise ValueError("The batch size must be an integer greater than zero.")
+
+        if not isinstance(self.decision_rule, six.string_types) or self.decision_rule not in ["EN", "L1", "L2"]:
+            raise ValueError("The decision rule only supports `EN`, `L1`, `L2`.")
+
+        if not isinstance(self.verbose, bool):
+            raise ValueError("The argument `verbose` has to be of type bool.")
diff --git a/adversarial-robustness-toolbox/art/attacks/evasion/fast_gradient.py b/adversarial-robustness-toolbox/art/attacks/evasion/fast_gradient.py
new file mode 100644
index 0000000..72d3c4a
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/attacks/evasion/fast_gradient.py
@@ -0,0 +1,510 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements the Fast Gradient Method attack. This implementation includes the original Fast Gradient Sign
+Method attack and extends it to other norms, therefore it is called the Fast Gradient Method.
+
+| Paper link: https://arxiv.org/abs/1412.6572
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+from typing import Optional, Union, TYPE_CHECKING
+
+import numpy as np
+
+from art.config import ART_NUMPY_DTYPE
+from art.attacks.attack import EvasionAttack
+from art.estimators.estimator import BaseEstimator, LossGradientsMixin
+from art.estimators.classification.classifier import ClassifierMixin
+from art.utils import (
+    compute_success,
+    get_labels_np_array,
+    random_sphere,
+    projection,
+    check_and_transform_label_format,
+)
+
+if TYPE_CHECKING:
+    from art.utils import CLASSIFIER_LOSS_GRADIENTS_TYPE
+
+logger = logging.getLogger(__name__)
+
+
+class FastGradientMethod(EvasionAttack):
+    """
+    This attack was originally implemented by Goodfellow et al. (2015) with the infinity norm (and is known as the "Fast
+    Gradient Sign Method"). This implementation extends the attack to other norms, and is therefore called the Fast
+    Gradient Method.
+
+    | Paper link: https://arxiv.org/abs/1412.6572
+    """
+
+    attack_params = EvasionAttack.attack_params + [
+        "norm",
+        "eps",
+        "eps_step",
+        "targeted",
+        "num_random_init",
+        "batch_size",
+        "minimal",
+    ]
+    _estimator_requirements = (BaseEstimator, LossGradientsMixin)
+
+    def __init__(
+        self,
+        estimator: "CLASSIFIER_LOSS_GRADIENTS_TYPE",
+        norm: Union[int, float, str] = np.inf,
+        eps: Union[int, float, np.ndarray] = 0.3,
+        eps_step: Union[int, float, np.ndarray] = 0.1,
+        targeted: bool = False,
+        num_random_init: int = 0,
+        batch_size: int = 32,
+        minimal: bool = False,
+    ) -> None:
+        """
+        Create a :class:`.FastGradientMethod` instance.
+
+        :param estimator: A trained classifier.
+        :param norm: The norm of the adversarial perturbation. Possible values: "inf", np.inf, 1 or 2.
+        :param eps: Attack step size (input variation).
+        :param eps_step: Step size of input variation for minimal perturbation computation.
+        :param targeted: Indicates whether the attack is targeted (True) or untargeted (False)
+        :param num_random_init: Number of random initialisations within the epsilon ball. For random_init=0 starting at
+            the original input.
+        :param batch_size: Size of the batch on which adversarial samples are generated.
+        :param minimal: Indicates if computing the minimal perturbation (True). If True, also define `eps_step` for
+                        the step size and eps for the maximum perturbation.
+        """
+        super().__init__(estimator=estimator)
+        self.norm = norm
+        self.eps = eps
+        self.eps_step = eps_step
+        self._targeted = targeted
+        self.num_random_init = num_random_init
+        self.batch_size = batch_size
+        self.minimal = minimal
+        self._project = True
+        FastGradientMethod._check_params(self)
+
+    def _check_compatibility_input_and_eps(self, x: np.ndarray):
+        """
+        Check the compatibility of the input with `eps` and `eps_step` which are of the same shape.
+
+        :param x: An array with the original inputs.
+        """
+        if isinstance(self.eps, np.ndarray):
+            # Ensure the eps array is broadcastable
+            if self.eps.ndim > x.ndim:
+                raise ValueError("The `eps` shape must be broadcastable to input shape.")
+
+    def _minimal_perturbation(self, x: np.ndarray, y: np.ndarray, mask: np.ndarray) -> np.ndarray:
+        """
+        Iteratively compute the minimal perturbation necessary to make the class prediction change. Stop when the
+        first adversarial example was found.
+
+        :param x: An array with the original inputs.
+        :param y: Target values (class labels) one-hot-encoded of shape (nb_samples, nb_classes).
+        :return: An array holding the adversarial examples.
+        """
+        adv_x = x.copy()
+
+        # Compute perturbation with implicit batching
+        for batch_id in range(int(np.ceil(adv_x.shape[0] / float(self.batch_size)))):
+            batch_index_1, batch_index_2 = (
+                batch_id * self.batch_size,
+                (batch_id + 1) * self.batch_size,
+            )
+            batch = adv_x[batch_index_1:batch_index_2]
+            batch_labels = y[batch_index_1:batch_index_2]
+
+            mask_batch = mask
+            if mask is not None:
+                # Here we need to make a distinction: if the masks are different for each input, we need to index
+                # those for the current batch. Otherwise (i.e. mask is meant to be broadcasted), keep it as it is.
+                if len(mask.shape) == len(x.shape):
+                    mask_batch = mask[batch_index_1:batch_index_2]
+
+            # Get perturbation
+            perturbation = self._compute_perturbation(batch, batch_labels, mask_batch)
+
+            # Get current predictions
+            active_indices = np.arange(len(batch))
+
+            if isinstance(self.eps, np.ndarray):
+                if len(self.eps.shape) == len(x.shape) and self.eps.shape[0] == x.shape[0]:
+                    current_eps = self.eps_step[batch_index_1:batch_index_2]
+                    partial_stop_condition = (current_eps <= self.eps[batch_index_1:batch_index_2]).all()
+
+                else:
+                    current_eps = self.eps_step
+                    partial_stop_condition = (current_eps <= self.eps).all()
+
+            else:
+                current_eps = self.eps_step
+                partial_stop_condition = current_eps <= self.eps
+
+            while active_indices.size > 0 and partial_stop_condition:
+                # Adversarial crafting
+                current_x = self._apply_perturbation(x[batch_index_1:batch_index_2], perturbation, current_eps)
+
+                # Update
+                batch[active_indices] = current_x[active_indices]
+                adv_preds = self.estimator.predict(batch)
+
+                # If targeted active check to see whether we have hit the target, otherwise head to anything but
+                if self.targeted:
+                    active_indices = np.where(np.argmax(batch_labels, axis=1) != np.argmax(adv_preds, axis=1))[0]
+                else:
+                    active_indices = np.where(np.argmax(batch_labels, axis=1) == np.argmax(adv_preds, axis=1))[0]
+
+                # Update current eps and check the stop condition
+                if isinstance(self.eps, np.ndarray):
+                    if len(self.eps.shape) == len(x.shape) and self.eps.shape[0] == x.shape[0]:
+                        current_eps = current_eps + self.eps_step[batch_index_1:batch_index_2]
+                        partial_stop_condition = (current_eps <= self.eps[batch_index_1:batch_index_2]).all()
+
+                    else:
+                        current_eps = current_eps + self.eps_step
+                        partial_stop_condition = (current_eps <= self.eps).all()
+
+                else:
+                    current_eps = current_eps + self.eps_step
+                    partial_stop_condition = current_eps <= self.eps
+
+            adv_x[batch_index_1:batch_index_2] = batch
+
+        return adv_x
+
+    def generate(self, x: np.ndarray, y: Optional[np.ndarray] = None, **kwargs) -> np.ndarray:
+        """Generate adversarial samples and return them in an array.
+
+        :param x: An array with the original inputs.
+        :param y: Target values (class labels) one-hot-encoded of shape (nb_samples, nb_classes) or indices of shape
+                  (nb_samples,). Only provide this parameter if you'd like to use true labels when crafting adversarial
+                  samples. Otherwise, model predictions are used as labels to avoid the "label leaking" effect
+                  (explained in this paper: https://arxiv.org/abs/1611.01236). Default is `None`.
+        :param mask: An array with a mask broadcastable to input `x` defining where to apply adversarial perturbations.
+                     Shape needs to be broadcastable to the shape of x and can also be of the same shape as `x`. Any
+                     features for which the mask is zero will not be adversarially perturbed.
+        :type mask: `np.ndarray`
+        :return: An array holding the adversarial examples.
+        """
+        mask = self._get_mask(x, **kwargs)
+
+        # Ensure eps is broadcastable
+        self._check_compatibility_input_and_eps(x=x)
+
+        if isinstance(self.estimator, ClassifierMixin):
+            y = check_and_transform_label_format(y, self.estimator.nb_classes)
+
+            if y is None:
+                # Throw error if attack is targeted, but no targets are provided
+                if self.targeted:
+                    raise ValueError("Target labels `y` need to be provided for a targeted attack.")
+
+                # Use model predictions as correct outputs
+                logger.info("Using model predictions as correct labels for FGM.")
+                y = get_labels_np_array(self.estimator.predict(x, batch_size=self.batch_size))  # type: ignore
+            y = y / np.sum(y, axis=1, keepdims=True)
+
+            # Return adversarial examples computed with minimal perturbation if option is active
+            rate_best: Optional[float]
+            if self.minimal:
+                logger.info("Performing minimal perturbation FGM.")
+                adv_x_best = self._minimal_perturbation(x, y, mask)
+                rate_best = 100 * compute_success(
+                    self.estimator, x, y, adv_x_best, self.targeted, batch_size=self.batch_size,  # type: ignore
+                )
+            else:
+                adv_x_best = None
+                rate_best = None
+
+                for _ in range(max(1, self.num_random_init)):
+                    adv_x = self._compute(x, x, y, mask, self.eps, self.eps, self._project, self.num_random_init > 0,)
+
+                    if self.num_random_init > 1:
+                        rate = 100 * compute_success(
+                            self.estimator, x, y, adv_x, self.targeted, batch_size=self.batch_size,  # type: ignore
+                        )
+                        if rate_best is None or rate > rate_best or adv_x_best is None:
+                            rate_best = rate
+                            adv_x_best = adv_x
+                    else:
+                        adv_x_best = adv_x
+
+            logger.info(
+                "Success rate of FGM attack: %.2f%%",
+                rate_best
+                if rate_best is not None
+                else 100
+                * compute_success(
+                    self.estimator,  # type: ignore
+                    x,
+                    y,
+                    adv_x_best,
+                    self.targeted,
+                    batch_size=self.batch_size,
+                ),
+            )
+
+        else:
+            if self.minimal:
+                raise ValueError("Minimal perturbation is only supported for classification.")
+
+            if y is None:
+                # Throw error if attack is targeted, but no targets are provided
+                if self.targeted:
+                    raise ValueError("Target labels `y` need to be provided for a targeted attack.")
+
+                # Use model predictions as correct outputs
+                logger.info("Using model predictions as correct labels for FGM.")
+                y = self.estimator.predict(x, batch_size=self.batch_size)
+
+            adv_x_best = self._compute(x, x, y, None, self.eps, self.eps, self._project, self.num_random_init > 0,)
+
+        return adv_x_best
+
+    def _check_params(self) -> None:
+
+        if self.norm not in [1, 2, np.inf, "inf"]:
+            raise ValueError('Norm order must be either 1, 2, `np.inf` or "inf".')
+
+        if not (
+            isinstance(self.eps, (int, float))
+            and isinstance(self.eps_step, (int, float))
+            or isinstance(self.eps, np.ndarray)
+            and isinstance(self.eps_step, np.ndarray)
+        ):
+            raise TypeError(
+                "The perturbation size `eps` and the perturbation step-size `eps_step` must have the same type of `int`"
+                ", `float`, or `np.ndarray`."
+            )
+
+        if isinstance(self.eps, (int, float)):
+            if self.eps < 0:
+                raise ValueError("The perturbation size `eps` has to be nonnegative.")
+        else:
+            if (self.eps < 0).any():
+                raise ValueError("The perturbation size `eps` has to be nonnegative.")
+
+        if isinstance(self.eps_step, (int, float)):
+            if self.eps_step <= 0:
+                raise ValueError("The perturbation step-size `eps_step` has to be positive.")
+        else:
+            if (self.eps_step <= 0).any():
+                raise ValueError("The perturbation step-size `eps_step` has to be positive.")
+
+        if isinstance(self.eps, np.ndarray) and isinstance(self.eps_step, np.ndarray):
+            if self.eps.shape != self.eps_step.shape:
+                raise ValueError(
+                    "The perturbation size `eps` and the perturbation step-size `eps_step` must have the same shape."
+                )
+
+        if not isinstance(self.targeted, bool):
+            raise ValueError("The flag `targeted` has to be of type bool.")
+
+        if not isinstance(self.num_random_init, (int, np.int)):
+            raise TypeError("The number of random initialisations has to be of type integer")
+
+        if self.num_random_init < 0:
+            raise ValueError("The number of random initialisations `random_init` has to be greater than or equal to 0.")
+
+        if self.batch_size <= 0:
+            raise ValueError("The batch size `batch_size` has to be positive.")
+
+        if not isinstance(self.minimal, bool):
+            raise ValueError("The flag `minimal` has to be of type bool.")
+
+    def _compute_perturbation(
+        self, batch: np.ndarray, batch_labels: np.ndarray, mask: Optional[np.ndarray]
+    ) -> np.ndarray:
+        # Pick a small scalar to avoid division by 0
+        tol = 10e-8
+
+        # Get gradient wrt loss; invert it if attack is targeted
+        grad = self.estimator.loss_gradient(batch, batch_labels) * (1 - 2 * int(self.targeted))
+
+        # Check for NaN before normalisation an replace with 0
+        if np.isnan(grad).any():
+            logger.warning("Elements of the loss gradient are NaN and have been replaced with 0.0.")
+            grad = np.where(np.isnan(grad), 0.0, grad)
+
+        # Apply mask
+        if mask is not None:
+            grad = np.where(mask == 0.0, 0.0, grad)
+
+        # Apply norm bound
+        def _apply_norm(grad, object_type=False):
+            if np.isinf(grad).any():
+                logger.info("The loss gradient array contains at least one positive or negative infinity.")
+
+            if self.norm in [np.inf, "inf"]:
+                grad = np.sign(grad)
+            elif self.norm == 1:
+                if not object_type:
+                    ind = tuple(range(1, len(batch.shape)))
+                else:
+                    ind = None
+                grad = grad / (np.sum(np.abs(grad), axis=ind, keepdims=True) + tol)
+            elif self.norm == 2:
+                if not object_type:
+                    ind = tuple(range(1, len(batch.shape)))
+                else:
+                    ind = None
+                grad = grad / (np.sqrt(np.sum(np.square(grad), axis=ind, keepdims=True)) + tol)
+            return grad
+
+        if batch.dtype == np.object:
+            for i_sample in range(batch.shape[0]):
+                grad[i_sample] = _apply_norm(grad[i_sample], object_type=True)
+                assert batch[i_sample].shape == grad[i_sample].shape
+        else:
+            grad = _apply_norm(grad)
+
+        assert batch.shape == grad.shape
+
+        return grad
+
+    def _apply_perturbation(
+        self, batch: np.ndarray, perturbation: np.ndarray, eps_step: Union[int, float, np.ndarray]
+    ) -> np.ndarray:
+
+        perturbation_step = eps_step * perturbation
+        perturbation_step[np.isnan(perturbation_step)] = 0
+        batch = batch + perturbation_step
+        if self.estimator.clip_values is not None:
+            clip_min, clip_max = self.estimator.clip_values
+            batch = np.clip(batch, clip_min, clip_max)
+
+        return batch
+
+    def _compute(
+        self,
+        x: np.ndarray,
+        x_init: np.ndarray,
+        y: np.ndarray,
+        mask: Optional[np.ndarray],
+        eps: Union[int, float, np.ndarray],
+        eps_step: Union[int, float, np.ndarray],
+        project: bool,
+        random_init: bool,
+    ) -> np.ndarray:
+        if random_init:
+            n = x.shape[0]
+            m = np.prod(x.shape[1:]).item()
+            random_perturbation = random_sphere(n, m, eps, self.norm).reshape(x.shape).astype(ART_NUMPY_DTYPE)
+            if mask is not None:
+                random_perturbation = random_perturbation * (mask.astype(ART_NUMPY_DTYPE))
+            x_adv = x.astype(ART_NUMPY_DTYPE) + random_perturbation
+
+            if self.estimator.clip_values is not None:
+                clip_min, clip_max = self.estimator.clip_values
+                x_adv = np.clip(x_adv, clip_min, clip_max)
+        else:
+            if x.dtype == np.object:
+                x_adv = x.copy()
+            else:
+                x_adv = x.astype(ART_NUMPY_DTYPE)
+
+        # Compute perturbation with implicit batching
+        for batch_id in range(int(np.ceil(x.shape[0] / float(self.batch_size)))):
+            batch_index_1, batch_index_2 = batch_id * self.batch_size, (batch_id + 1) * self.batch_size
+            batch_index_2 = min(batch_index_2, x.shape[0])
+            batch = x_adv[batch_index_1:batch_index_2]
+            batch_labels = y[batch_index_1:batch_index_2]
+
+            mask_batch = mask
+            if mask is not None:
+                # Here we need to make a distinction: if the masks are different for each input, we need to index
+                # those for the current batch. Otherwise (i.e. mask is meant to be broadcasted), keep it as it is.
+                if len(mask.shape) == len(x.shape):
+                    mask_batch = mask[batch_index_1:batch_index_2]
+
+            # Get perturbation
+            perturbation = self._compute_perturbation(batch, batch_labels, mask_batch)
+
+            # Compute batch_eps and batch_eps_step
+            if isinstance(eps, np.ndarray):
+                if len(eps.shape) == len(x.shape) and eps.shape[0] == x.shape[0]:
+                    batch_eps = eps[batch_index_1:batch_index_2]
+                    batch_eps_step = eps_step[batch_index_1:batch_index_2]
+
+                else:
+                    batch_eps = eps
+                    batch_eps_step = eps_step
+
+            else:
+                batch_eps = eps
+                batch_eps_step = eps_step
+
+            # Apply perturbation and clip
+            x_adv[batch_index_1:batch_index_2] = self._apply_perturbation(batch, perturbation, batch_eps_step)
+
+            if project:
+                if x_adv.dtype == np.object:
+                    for i_sample in range(batch_index_1, batch_index_2):
+                        if isinstance(batch_eps, np.ndarray) and batch_eps.shape[0] == x_adv.shape[0]:
+                            perturbation = projection(
+                                x_adv[i_sample] - x_init[i_sample], batch_eps[i_sample], self.norm
+                            )
+
+                        else:
+                            perturbation = projection(x_adv[i_sample] - x_init[i_sample], batch_eps, self.norm)
+
+                        x_adv[i_sample] = x_init[i_sample] + perturbation
+
+                else:
+                    perturbation = projection(
+                        x_adv[batch_index_1:batch_index_2] - x_init[batch_index_1:batch_index_2], batch_eps, self.norm
+                    )
+                    x_adv[batch_index_1:batch_index_2] = x_init[batch_index_1:batch_index_2] + perturbation
+
+        return x_adv
+
+    @staticmethod
+    def _get_mask(x: np.ndarray, **kwargs) -> np.ndarray:
+        """
+        Get the mask from the kwargs.
+
+        :param x: An array with the original inputs.
+        :param mask: An array with a mask to be applied to the adversarial perturbations. Shape needs to be
+                     broadcastable to the shape of x. Any features for which the mask is zero will not be adversarially
+                     perturbed.
+        :type mask: `np.ndarray`
+        :return: The mask.
+        """
+        mask = kwargs.get("mask")
+
+        if mask is not None:
+            if mask.ndim > x.ndim:
+                raise ValueError("Mask shape must be broadcastable to input shape.")
+
+            if not (np.issubdtype(mask.dtype, np.floating) or mask.dtype == np.bool):
+                raise ValueError(
+                    "The `mask` has to be either of type np.float32, np.float64 or np.bool. The provided"
+                    "`mask` is of type {}.".format(mask.dtype)
+                )
+
+            if np.issubdtype(mask.dtype, np.floating) and np.amin(mask) < 0.0:
+                raise ValueError(
+                    "The `mask` of type np.float32 or np.float64 requires all elements to be either zero"
+                    "or positive values."
+                )
+
+        return mask
diff --git a/adversarial-robustness-toolbox/art/attacks/evasion/feature_adversaries.py b/adversarial-robustness-toolbox/art/attacks/evasion/feature_adversaries.py
new file mode 100644
index 0000000..d88f83e
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/attacks/evasion/feature_adversaries.py
@@ -0,0 +1,194 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements the Feature Adversaries attack.
+
+| Paper link: https://arxiv.org/abs/1511.05122
+"""
+import logging
+from typing import Optional, TYPE_CHECKING
+
+import numpy as np
+
+from art.attacks.attack import EvasionAttack
+from art.estimators.estimator import BaseEstimator, NeuralNetworkMixin
+
+if TYPE_CHECKING:
+    from art.utils import CLASSIFIER_NEURALNETWORK_TYPE
+
+logger = logging.getLogger(__name__)
+
+
+class FeatureAdversaries(EvasionAttack):
+    """
+    This class represent a Feature Adversaries evasion attack.
+
+    | Paper link: https://arxiv.org/abs/1511.05122
+    """
+
+    attack_params = EvasionAttack.attack_params + [
+        "delta",
+        "layer",
+        "batch_size",
+    ]
+
+    _estimator_requirements = (BaseEstimator, NeuralNetworkMixin)
+
+    def __init__(
+        self,
+        classifier: "CLASSIFIER_NEURALNETWORK_TYPE",
+        delta: Optional[float] = None,
+        layer: Optional[int] = None,
+        batch_size: int = 32,
+    ):
+        """
+        Create a :class:`.FeatureAdversaries` instance.
+
+        :param classifier: A trained classifier.
+        :param delta: The maximum deviation between source and guide images.
+        :param layer: Index of the representation layer.
+        :param batch_size: Batch size.
+        """
+        super().__init__(estimator=classifier)
+
+        self.delta = delta
+        self.layer = layer
+        self.batch_size = batch_size
+        self.norm = np.inf
+        self._check_params()
+
+    def generate(self, x: np.ndarray, y: Optional[np.ndarray] = None, **kwargs) -> np.ndarray:
+        """
+        Generate adversarial samples and return them in an array.
+
+        :param x: Source samples.
+        :param y: Guide samples.
+        :param kwargs: The kwargs are used as `options` for the minimisation with `scipy.optimize.minimize` using
+                       `method="L-BFGS-B"`. Valid options are based on the output of
+                       `scipy.optimize.show_options(solver='minimize', method='L-BFGS-B')`:
+                       Minimize a scalar function of one or more variables using the L-BFGS-B algorithm.
+
+                       disp : None or int
+                           If `disp is None` (the default), then the supplied version of `iprint`
+                           is used. If `disp is not None`, then it overrides the supplied version
+                           of `iprint` with the behaviour you outlined.
+                       maxcor : int
+                           The maximum number of variable metric corrections used to
+                           define the limited memory matrix. (The limited memory BFGS
+                           method does not store the full hessian but uses this many terms
+                           in an approximation to it.)
+                       ftol : float
+                           The iteration stops when ``(f^k -
+                           f^{k+1})/max{|f^k|,|f^{k+1}|,1} <= ftol``.
+                       gtol : float
+                           The iteration will stop when ``max{|proj g_i | i = 1, ..., n}
+                           <= gtol`` where ``pg_i`` is the i-th component of the
+                           projected gradient.
+                       eps : float
+                           Step size used for numerical approximation of the Jacobian.
+                       maxfun : int
+                           Maximum number of function evaluations.
+                       maxiter : int
+                           Maximum number of iterations.
+                       iprint : int, optional
+                           Controls the frequency of output. ``iprint < 0`` means no output;
+                           ``iprint = 0``    print only one line at the last iteration;
+                           ``0 < iprint < 99`` print also f and ``|proj g|`` every iprint iterations;
+                           ``iprint = 99``   print details of every iteration except n-vectors;
+                           ``iprint = 100``  print also the changes of active set and final x;
+                           ``iprint > 100``  print details of every iteration including x and g.
+                       callback : callable, optional
+                           Called after each iteration, as ``callback(xk)``, where ``xk`` is the
+                           current parameter vector.
+                       maxls : int, optional
+                           Maximum number of line search steps (per iteration). Default is 20.
+
+                       The option `ftol` is exposed via the `scipy.optimize.minimize` interface,
+                       but calling `scipy.optimize.fmin_l_bfgs_b` directly exposes `factr`. The
+                       relationship between the two is ``ftol = factr * numpy.finfo(float).eps``.
+                       I.e., `factr` multiplies the default machine floating-point precision to
+                       arrive at `ftol`.
+        :return: Adversarial examples.
+        :raises KeyError: The argument {} in kwargs is not allowed as option for `scipy.optimize.minimize` using
+                          `method="L-BFGS-B".`
+        """
+        from scipy.optimize import minimize, Bounds
+        from scipy.linalg import norm
+
+        l_b = x.flatten() - self.delta
+        l_b[l_b < self.estimator.clip_values[0]] = self.estimator.clip_values[0]
+
+        u_b = x.flatten() + self.delta
+        u_b[u_b > self.estimator.clip_values[1]] = self.estimator.clip_values[1]
+
+        bound = Bounds(lb=l_b, ub=u_b, keep_feasible=False)
+
+        guide_representation = self.estimator.get_activations(
+            x=y.reshape(-1, *self.estimator.input_shape),  # type: ignore
+            layer=self.layer,
+            batch_size=self.batch_size,
+        )
+
+        def func(x_i):
+            source_representation = self.estimator.get_activations(
+                x=x_i.reshape(-1, *self.estimator.input_shape), layer=self.layer, batch_size=self.batch_size,
+            )
+
+            n = norm(source_representation.flatten() - guide_representation.flatten(), ord=2,) ** 2
+
+            return n
+
+        x_0 = x.copy()
+
+        options = {"eps": 1e-3, "ftol": 1e-3}
+        options_allowed_keys = [
+            "disp",
+            "maxcor",
+            "ftol",
+            "gtol",
+            "eps",
+            "maxfun",
+            "maxiter",
+            "iprint",
+            "callback",
+            "maxls",
+        ]
+
+        for key in kwargs:
+            if key not in options_allowed_keys:
+                raise KeyError(
+                    "The argument `{}` in kwargs is not allowed as option for `scipy.optimize.minimize` using "
+                    '`method="L-BFGS-B".`'.format(key)
+                )
+        options.update(kwargs)
+
+        res = minimize(func, x_0, method="L-BFGS-B", bounds=bound, options=options)
+        x_adv = res.x
+        logger.info(res)
+
+        return x_adv.reshape(-1, *self.estimator.input_shape)
+
+    def _check_params(self) -> None:
+        if self.delta is not None and self.delta <= 0:
+            raise ValueError("The maximum deviation `delta` has to be positive.")
+
+        if not isinstance(self.layer, int):
+            raise ValueError("The index of the representation layer `layer` has to be integer.")
+
+        if self.batch_size <= 0:
+            raise ValueError("The batch size `batch_size` has to be positive.")
diff --git a/adversarial-robustness-toolbox/art/attacks/evasion/frame_saliency.py b/adversarial-robustness-toolbox/art/attacks/evasion/frame_saliency.py
new file mode 100644
index 0000000..c9b9773
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/attacks/evasion/frame_saliency.py
@@ -0,0 +1,226 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements the frame saliency attack framework. Originally designed for video data, this framework will
+prioritize which parts of a sequential input should be perturbed based on saliency scores.
+
+| Paper link: https://arxiv.org/abs/1811.11875
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+from typing import Optional, TYPE_CHECKING
+
+import numpy as np
+from tqdm.auto import trange
+
+from art.config import ART_NUMPY_DTYPE
+from art.estimators.estimator import BaseEstimator, NeuralNetworkMixin
+from art.estimators.classification.classifier import ClassGradientsMixin
+from art.attacks.attack import EvasionAttack
+from art.utils import (
+    compute_success_array,
+    get_labels_np_array,
+    check_and_transform_label_format,
+)
+
+if TYPE_CHECKING:
+    from art.utils import CLASSIFIER_NEURALNETWORK_TYPE
+
+logger = logging.getLogger(__name__)
+
+
+class FrameSaliencyAttack(EvasionAttack):
+    """
+    Implementation of the attack framework proposed by Inkawhich et al. (2018). Prioritizes the frame of a sequential
+    input to be adversarially perturbed based on the saliency score of each frame.
+
+    | Paper link: https://arxiv.org/abs/1811.11875
+    """
+
+    method_list = ["iterative_saliency", "iterative_saliency_refresh", "one_shot"]
+    attack_params = EvasionAttack.attack_params + [
+        "attacker",
+        "method",
+        "frame_index",
+        "batch_size",
+        "verbose",
+    ]
+    _estimator_requirements = (BaseEstimator, NeuralNetworkMixin, ClassGradientsMixin)
+
+    def __init__(
+        self,
+        classifier: "CLASSIFIER_NEURALNETWORK_TYPE",
+        attacker: EvasionAttack,
+        method: str = "iterative_saliency",
+        frame_index: int = 1,
+        batch_size: int = 1,
+        verbose: bool = True,
+    ):
+        """
+        :param classifier: A trained classifier.
+        :param attacker: An adversarial evasion attacker which supports masking. Currently supported:
+                         ProjectedGradientDescent, BasicIterativeMethod, FastGradientMethod.
+        :param method: Specifies which method to use: "iterative_saliency" (adds perturbation iteratively to frame
+                       with highest saliency score until attack is successful), "iterative_saliency_refresh" (updates
+                       perturbation after each iteration), "one_shot" (adds all perturbations at once, i.e. defaults to
+                       original attack).
+        :param frame_index: Index of the axis in input (feature) array `x` representing the frame dimension.
+        :param batch_size: Size of the batch on which adversarial samples are generated.
+        :param verbose: Show progress bars.
+        """
+        super().__init__(estimator=classifier)
+
+        self.attacker = attacker
+        self.method = method
+        self.frame_index = frame_index
+        self.batch_size = batch_size
+        self.verbose = verbose
+        self._check_params()
+
+    def generate(self, x: np.ndarray, y: Optional[np.ndarray] = None, **kwargs) -> np.ndarray:
+        """
+        Generate adversarial samples and return them in an array.
+
+        :param x: An array with the original inputs.
+        :param y: An array with the original labels to be predicted.
+        :return: An array holding the adversarial examples.
+        """
+        if len(x.shape) < 3:
+            raise ValueError("Frame saliency attack works only on inputs of dimension greater than 2.")
+
+        if self.frame_index >= len(x.shape):
+            raise ValueError("Frame index is out of bounds for the given input shape.")
+
+        y = check_and_transform_label_format(y, nb_classes=self.estimator.nb_classes)
+
+        if self.method == "one_shot":
+            if y is None:
+                return self.attacker.generate(x)
+
+            return self.attacker.generate(x, y)
+
+        if y is None:
+            # Throw error if attack is targeted, but no targets are provided
+            if hasattr(self.attacker, "targeted") and self.attacker.targeted:  # type: ignore
+                raise ValueError("Target labels `y` need to be provided for a targeted attack.")
+
+            # Use model predictions as correct outputs
+            targets = get_labels_np_array(self.estimator.predict(x, batch_size=self.batch_size))
+        else:
+            targets = y
+
+        nb_samples = x.shape[0]
+        nb_frames = x.shape[self.frame_index]
+        x_adv = x.astype(ART_NUMPY_DTYPE)
+
+        # Determine for which adversarial examples the attack fails:
+        attack_failure = self._compute_attack_failure_array(x, targets, x_adv)
+
+        # Determine the order in which to perturb frames, based on saliency scores:
+        frames_to_perturb = self._compute_frames_to_perturb(x_adv, targets)
+
+        # Generate adversarial perturbations. If the method is "iterative_saliency_refresh", we will use a mask so that
+        # only the next frame to be perturbed is considered in the attack; moreover we keep track of the next frames to
+        # be perturbed so they will not be perturbed again later on.
+        mask = np.ones(x.shape)
+        if self.method == "iterative_saliency_refresh":
+            mask = np.zeros(x.shape)
+            mask = np.swapaxes(mask, 1, self.frame_index)
+            mask[:, frames_to_perturb[:, 0], ::] = 1
+            mask = np.swapaxes(mask, 1, self.frame_index)
+            disregard = np.zeros((nb_samples, nb_frames))
+            disregard[:, frames_to_perturb[:, 0]] = np.inf
+
+        x_adv_new = self.attacker.generate(x, targets, mask=mask)
+
+        # Here starts the main iteration:
+        for i in trange(nb_frames, desc="Frame saliency", disable=not self.verbose):
+            # Check if attack has already succeeded for all inputs:
+            if sum(attack_failure) == 0:
+                break
+
+            # Update designated frames with adversarial perturbations:
+            x_adv = np.swapaxes(x_adv, 1, self.frame_index)
+            x_adv_new = np.swapaxes(x_adv_new, 1, self.frame_index)
+            x_adv[attack_failure, frames_to_perturb[:, i][attack_failure], ::] = x_adv_new[
+                attack_failure, frames_to_perturb[:, i][attack_failure], ::
+            ]
+            x_adv = np.swapaxes(x_adv, 1, self.frame_index)
+            x_adv_new = np.swapaxes(x_adv_new, 1, self.frame_index)
+
+            # Update for which adversarial examples the attack still fails:
+            attack_failure = self._compute_attack_failure_array(x, targets, x_adv)
+
+            # For the "refresh" method, update the next frames to be perturbed (disregarding the frames that were
+            # perturbed already) and also refresh the adversarial perturbations:
+            if self.method == "iterative_saliency_refresh" and i < nb_frames - 1:
+                frames_to_perturb = self._compute_frames_to_perturb(x_adv, targets, disregard)
+                mask = np.zeros(x.shape)
+                mask = np.swapaxes(mask, 1, self.frame_index)
+                mask[:, frames_to_perturb[:, i + 1], ::] = 1
+                mask = np.swapaxes(mask, 1, self.frame_index)
+                disregard[:, frames_to_perturb[:, i + 1]] = np.inf
+                x_adv_new = self.attacker.generate(x_adv, targets, mask=mask)
+
+        return x_adv
+
+    def _compute_attack_failure_array(self, x: np.ndarray, targets: np.ndarray, x_adv: np.ndarray) -> np.ndarray:
+        attack_success = compute_success_array(
+            self.attacker.estimator, x, targets, x_adv, self.attacker.targeted  # type: ignore
+        )
+        return np.invert(attack_success)
+
+    def _compute_frames_to_perturb(
+        self, x_adv: np.ndarray, targets: np.ndarray, disregard: Optional[np.ndarray] = None
+    ) -> np.ndarray:
+        saliency_score = self.estimator.loss_gradient(x_adv, targets)
+        saliency_score = np.swapaxes(saliency_score, 1, self.frame_index)
+        saliency_score = saliency_score.reshape((saliency_score.shape[:2] + (np.prod(saliency_score.shape[2:]),)))
+        saliency_score = np.mean(np.abs(saliency_score), axis=2)
+
+        if disregard is not None:
+            saliency_score += disregard
+
+        return np.argsort(-saliency_score, axis=1)
+
+    def _check_params(self) -> None:
+        from art.attacks.evasion.projected_gradient_descent.projected_gradient_descent import ProjectedGradientDescent
+        from art.attacks.evasion.iterative_method import BasicIterativeMethod
+        from art.attacks.evasion.fast_gradient import FastGradientMethod
+
+        if not isinstance(self.attacker, (ProjectedGradientDescent, BasicIterativeMethod, FastGradientMethod)):
+            raise ValueError(
+                "The attacker must be either of class 'ProjectedGradientDescent', 'BasicIterativeMethod' or "
+                "'FastGradientMethod'"
+            )
+
+        if self.method not in self.method_list:
+            raise ValueError("Method must be either 'iterative_saliency', 'iterative_saliency_refresh' or 'one_shot'.")
+
+        if self.frame_index < 1:
+            raise ValueError("The index `frame_index` of the frame dimension has to be >=1.")
+
+        if self.batch_size <= 0:
+            raise ValueError("The batch size `batch_size` has to be positive.")
+
+        if not self.estimator == self.attacker.estimator:
+            raise Warning("Different classifiers given for computation of saliency scores and adversarial noise.")
+
+        if not isinstance(self.verbose, bool):
+            raise ValueError("The argument `verbose` has to be of type bool.")
diff --git a/adversarial-robustness-toolbox/art/attacks/evasion/hclu.py b/adversarial-robustness-toolbox/art/attacks/evasion/hclu.py
new file mode 100644
index 0000000..442abfc
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/attacks/evasion/hclu.py
@@ -0,0 +1,137 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2019
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+Implementation of the High-Confidence-Low-Uncertainty (HCLU) adversarial example formulation by Grosse et al. (2018)
+
+| Paper link: https://arxiv.org/abs/1812.02606
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import copy
+import logging
+from typing import Optional
+
+import numpy as np
+from scipy.optimize import minimize
+from tqdm.auto import trange
+
+from art.attacks.attack import EvasionAttack
+from art.estimators.classification.GPy import GPyGaussianProcessClassifier
+from art.utils import compute_success
+
+logger = logging.getLogger(__name__)
+
+
+class HighConfidenceLowUncertainty(EvasionAttack):
+    """
+    Implementation of the High-Confidence-Low-Uncertainty (HCLU) adversarial example formulation by Grosse et al. (2018)
+
+    | Paper link: https://arxiv.org/abs/1812.02606
+    """
+
+    attack_params = ["conf", "unc_increase", "min_val", "max_val", "verbose"]
+    _estimator_requirements = (GPyGaussianProcessClassifier,)
+
+    def __init__(
+        self,
+        classifier: GPyGaussianProcessClassifier,
+        conf: float = 0.95,
+        unc_increase: float = 100.0,
+        min_val: float = 0.0,
+        max_val: float = 1.0,
+        verbose: bool = True,
+    ) -> None:
+        """
+        :param classifier: A trained model of type GPYGaussianProcessClassifier.
+        :param conf: Confidence that examples should have, if there were to be classified as 1.0 maximally.
+        :param unc_increase: Value uncertainty is allowed to deviate, where 1.0 is original value.
+        :param min_val: minimal value any feature can take.
+        :param max_val: maximal value any feature can take.
+        :param verbose: Show progress bars.
+        """
+        super().__init__(estimator=classifier)
+        self.conf = conf
+        self.unc_increase = unc_increase
+        self.min_val = min_val
+        self.max_val = max_val
+        self.verbose = verbose
+        self._check_params()
+
+    def generate(self, x: np.ndarray, y: Optional[np.ndarray] = None, **kwargs) -> np.ndarray:
+        """
+        Generate adversarial examples and return them as an array.
+
+        :param x: An array with the original inputs to be attacked.
+        :param y: Target values (class labels) one-hot-encoded of shape (nb_samples, nb_classes) or indices of shape
+                  (nb_samples,).
+        :return: An array holding the adversarial examples.
+        """
+        x_adv = copy.copy(x)
+
+        def minfun(x, args):  # minimize L2 norm
+            return np.sum(np.sqrt((x - args["orig"]) ** 2))
+
+        def constraint_conf(x, args):  # constraint for confidence
+            pred = args["classifier"].predict(x.reshape(1, -1))[0, 0]
+            if args["class_zero"]:
+                pred = 1.0 - pred
+            return (pred - args["conf"]).reshape(-1)
+
+        def constraint_unc(x, args):  # constraint for uncertainty
+            cur_unc = (args["classifier"].predict_uncertainty(x.reshape(1, -1))).reshape(-1)
+            return (args["max_uncertainty"] - cur_unc)[0]
+
+        bounds = []
+        # adding bounds, to not go away from original data
+        for i in range(np.shape(x)[1]):
+            bounds.append((self.min_val, self.max_val))
+        for i in trange(x.shape[0], desc="HCLU", disable=not self.verbose):  # go through data and craft
+            # get properties for attack
+            max_uncertainty = self.unc_increase * self.estimator.predict_uncertainty(x_adv[i].reshape(1, -1))
+            class_zero = not self.estimator.predict(x_adv[i].reshape(1, -1))[0, 0] < 0.5
+            init_args = {
+                "classifier": self.estimator,
+                "class_zero": class_zero,
+                "max_uncertainty": max_uncertainty,
+                "conf": self.conf,
+            }
+            constr_conf = {"type": "ineq", "fun": constraint_conf, "args": (init_args,)}
+            constr_unc = {"type": "ineq", "fun": constraint_unc, "args": (init_args,)}
+            args = {"args": init_args, "orig": x[i].reshape(-1)}
+            # finally, run optimization
+            x_adv[i] = minimize(minfun, x_adv[i], args=args, bounds=bounds, constraints=[constr_conf, constr_unc],)["x"]
+        logger.info(
+            "Success rate of HCLU attack: %.2f%%", 100 * compute_success(self.estimator, x, y, x_adv),
+        )
+        return x_adv
+
+    def _check_params(self) -> None:
+        if not isinstance(self.estimator, GPyGaussianProcessClassifier):
+            raise TypeError("Model must be a GPy Gaussian Process classifier.")
+
+        if self.conf <= 0.5 or self.conf > 1.0:
+            raise ValueError("Confidence value has to be a value between 0.5 and 1.0.")
+
+        if self.unc_increase <= 0.0:
+            raise ValueError("Value to increase uncertainty must be positive.")
+
+        if self.min_val > self.max_val:
+            raise ValueError("Maximum has to be larger than minimum.")
+
+        if not isinstance(self.verbose, bool):
+            raise ValueError("The argument `verbose` has to be of type bool.")
diff --git a/code_vmr/art/attacks/evasion/hop_skip_jump.py b/adversarial-robustness-toolbox/art/attacks/evasion/hop_skip_jump.py
similarity index 100%
rename from code_vmr/art/attacks/evasion/hop_skip_jump.py
rename to adversarial-robustness-toolbox/art/attacks/evasion/hop_skip_jump.py
diff --git a/adversarial-robustness-toolbox/art/attacks/evasion/imperceptible_asr/__init__.py b/adversarial-robustness-toolbox/art/attacks/evasion/imperceptible_asr/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/adversarial-robustness-toolbox/art/attacks/evasion/imperceptible_asr/imperceptible_asr.py b/adversarial-robustness-toolbox/art/attacks/evasion/imperceptible_asr/imperceptible_asr.py
new file mode 100644
index 0000000..36ebc86
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/attacks/evasion/imperceptible_asr/imperceptible_asr.py
@@ -0,0 +1,861 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements the adversarial and imperceptible attack on automatic speech recognition systems of Qin et al.
+(2019). It generates an adversarial audio example.
+
+| Paper link: http://proceedings.mlr.press/v97/qin19a.html
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+from typing import TYPE_CHECKING, Optional, Tuple, Union
+
+import numpy as np
+import scipy.signal as ss
+
+from art.attacks.attack import EvasionAttack
+from art.estimators.estimator import BaseEstimator, LossGradientsMixin, NeuralNetworkMixin
+from art.estimators.pytorch import PyTorchEstimator
+from art.estimators.speech_recognition.speech_recognizer import SpeechRecognizerMixin
+from art.estimators.tensorflow import TensorFlowV2Estimator
+from art.utils import pad_sequence_input
+
+if TYPE_CHECKING:
+    from tensorflow.compat.v1 import Tensor
+    from torch import Tensor as PTensor
+
+    from art.utils import SPEECH_RECOGNIZER_TYPE
+
+logger = logging.getLogger(__name__)
+
+
+class ImperceptibleASR(EvasionAttack):
+    """
+    Implementation of the imperceptible attack against a speech recognition model.
+
+    | Paper link: http://proceedings.mlr.press/v97/qin19a.html
+    """
+
+    attack_params = EvasionAttack.attack_params + [
+        "masker",
+        "eps",
+        "learning_rate_1",
+        "max_iter_1",
+        "alpha",
+        "learning_rate_2",
+        "max_iter_2",
+        "batch_size",
+        "loss_theta_min",
+        "decrease_factor_eps",
+        "num_iter_decrease_eps",
+        "increase_factor_alpha",
+        "num_iter_increase_alpha",
+        "decrease_factor_alpha",
+        "num_iter_decrease_alpha",
+    ]
+
+    _estimator_requirements = (NeuralNetworkMixin, LossGradientsMixin, BaseEstimator, SpeechRecognizerMixin)
+
+    def __init__(
+        self,
+        estimator: "SPEECH_RECOGNIZER_TYPE",
+        masker: Optional["PsychoacousticMasker"],
+        eps: float = 2000.0,
+        learning_rate_1: float = 100.0,
+        max_iter_1: int = 1000,
+        alpha: float = 0.05,
+        learning_rate_2: float = 1.0,
+        max_iter_2: int = 4000,
+        loss_theta_min: float = 0.05,
+        decrease_factor_eps: float = 0.8,
+        num_iter_decrease_eps: int = 10,
+        increase_factor_alpha: float = 1.2,
+        num_iter_increase_alpha: int = 20,
+        decrease_factor_alpha: float = 0.8,
+        num_iter_decrease_alpha: int = 50,
+        batch_size: int = 1,
+    ) -> None:
+        """
+        Create an instance of the :class:`.ImperceptibleASR`.
+
+        The default parameters assume that audio input is in `int16` range. If using normalized audio input, parameters
+        `eps` and `learning_rate_{1,2}` need to be scaled with a factor `2^-15`
+
+        :param estimator: A trained speech recognition estimator.
+        :param masker: A Psychoacoustic masker.
+        :param eps: Initial max norm bound for adversarial perturbation.
+        :param learning_rate_1: Learning rate for stage 1 of attack.
+        :param max_iter_1: Number of iterations for stage 1 of attack.
+        :param alpha: Initial alpha value for balancing stage 2 loss.
+        :param learning_rate_2: Learning rate for stage 2 of attack.
+        :param max_iter_2: Number of iterations for stage 2 of attack.
+        :param loss_theta_min: If imperceptible loss reaches minimum, stop early. Works best with `batch_size=1`.
+        :param decrease_factor_eps: Decrease factor for epsilon (Paper default: 0.8).
+        :param num_iter_decrease_eps: Iterations after which to decrease epsilon if attack succeeds (Paper default: 10).
+        :param increase_factor_alpha: Increase factor for alpha (Paper default: 1.2).
+        :param num_iter_increase_alpha: Iterations after which to increase alpha if attack succeeds (Paper default: 20).
+        :param decrease_factor_alpha: Decrease factor for alpha (Paper default: 0.8).
+        :param num_iter_decrease_alpha: Iterations after which to decrease alpha if attack fails (Paper default: 50).
+        :param batch_size: Batch size.
+        """
+
+        # Super initialization
+        super().__init__(estimator=estimator)
+        self.masker = masker
+        self.eps = eps
+        self.learning_rate_1 = learning_rate_1
+        self.max_iter_1 = max_iter_1
+        self.alpha = alpha
+        self.learning_rate_2 = learning_rate_2
+        self.max_iter_2 = max_iter_2
+        self._targeted = True
+        self.batch_size = batch_size
+        self.loss_theta_min = loss_theta_min
+        self.decrease_factor_eps = decrease_factor_eps
+        self.num_iter_decrease_eps = num_iter_decrease_eps
+        self.increase_factor_alpha = increase_factor_alpha
+        self.num_iter_increase_alpha = num_iter_increase_alpha
+        self.decrease_factor_alpha = decrease_factor_alpha
+        self.num_iter_decrease_alpha = num_iter_decrease_alpha
+        self._check_params()
+
+        # init some aliases
+        self._window_size = masker.window_size
+        self._hop_size = masker.hop_size
+        self._sample_rate = masker.sample_rate
+
+        if isinstance(self.estimator, TensorFlowV2Estimator):
+            import tensorflow.compat.v1 as tf1
+
+            # set framework attribute
+            self._framework = "tensorflow"
+
+            # disable eager execution and use tensorflow.compat.v1 API, e.g. Lingvo uses TF2v1 AP
+            tf1.disable_eager_execution()
+
+            # TensorFlow placeholders
+            self._delta = tf1.placeholder(tf1.float32, shape=[None, None], name="art_delta")
+            self._power_spectral_density_maximum_tf = tf1.placeholder(tf1.float32, shape=[None], name="art_psd_max")
+            self._masking_threshold_tf = tf1.placeholder(
+                tf1.float32, shape=[None, None, None], name="art_masking_threshold"
+            )
+            # TensorFlow loss gradient ops
+            self._loss_gradient_masking_threshold_op_tf = self._loss_gradient_masking_threshold_tf(
+                self._delta, self._power_spectral_density_maximum_tf, self._masking_threshold_tf
+            )
+
+        elif isinstance(self.estimator, PyTorchEstimator):
+            # set framework attribute
+            self._framework = "pytorch"
+        else:
+            # set framework attribute
+            self._framework = None
+
+    def generate(self, x: np.ndarray, y: np.ndarray, **kwargs) -> np.ndarray:
+        """
+        Generate imperceptible, adversarial examples.
+
+        :param x: An array with the original inputs to be attacked.
+        :param y: Target values of shape (batch_size,). Each sample in `y` is a string and it may possess different
+            lengths. A possible example of `y` could be: `y = np.array(['SIXTY ONE', 'HELLO'])`.
+        :return: An array holding the adversarial examples.
+        """
+        nb_samples = x.shape[0]
+
+        x_imperceptible = [None] * nb_samples
+
+        nb_batches = int(np.ceil(nb_samples / float(self.batch_size)))
+        for m in range(nb_batches):
+            # batch indices
+            begin, end = m * self.batch_size, min((m + 1) * self.batch_size, nb_samples)
+
+            # create batch of adversarial examples
+            x_imperceptible[begin:end] = self._generate_batch(x[begin:end], y[begin:end])
+
+        # for ragged input, use np.object dtype
+        dtype = np.float32 if x.ndim != 1 else np.object
+        return np.array(x_imperceptible, dtype=dtype)
+
+    def _generate_batch(self, x: np.ndarray, y: np.ndarray) -> np.ndarray:
+        """
+        Create imperceptible, adversarial sample.
+
+        This is a helper method that calls the methods to create an adversarial (`ImperceptibleASR._create_adversarial`)
+        and imperceptible (`ImperceptibleASR._create_imperceptible`) example subsequently.
+        """
+        # create adversarial example
+        x_adversarial = self._create_adversarial(x, y)
+        if self.max_iter_2 == 0:
+            return x_adversarial
+
+        # make adversarial example imperceptible
+        x_imperceptible = self._create_imperceptible(x, x_adversarial, y)
+        return x_imperceptible
+
+    def _create_adversarial(self, x, y) -> np.ndarray:
+        """
+        Create adversarial example with small perturbation that successfully deceives the estimator.
+
+        The method implements the part of the paper by Qin et al. (2019) that is referred to as the first stage of the
+        attack. The authors basically follow Carlini and Wagner (2018).
+
+        | Paper link: https://arxiv.org/abs/1801.01944.
+
+        :param x: An array with the original inputs to be attacked.
+        :param y: Target values of shape (batch_size,). Each sample in `y` is a string and it may possess different
+            lengths. A possible example of `y` could be: `y = np.array(['SIXTY ONE', 'HELLO'])`.
+        :return: An array with the adversarial outputs.
+        """
+        batch_size = x.shape[0]
+
+        # for ragged input, use np.object dtype
+        dtype = np.float32 if x.ndim != 1 else np.object
+
+        epsilon = [self.eps] * batch_size
+        x_adversarial = [None] * batch_size
+
+        x_perturbed = x.copy()
+
+        for i in range(1, self.max_iter_1 + 1):
+            # perform FGSM step for x
+            gradients = self.estimator.loss_gradient(x_perturbed, y, batch_mode=True)
+            x_perturbed = x_perturbed - self.learning_rate_1 * np.array([np.sign(g) for g in gradients], dtype=dtype)
+
+            # clip perturbation
+            perturbation = x_perturbed - x
+            perturbation = np.array([np.clip(p, -e, e) for p, e in zip(perturbation, epsilon)], dtype=dtype)
+
+            # re-apply clipped perturbation to x
+            x_perturbed = x + perturbation
+
+            if i % self.num_iter_decrease_eps == 0:
+                prediction = self.estimator.predict(x_perturbed, batch_size=batch_size)
+                for j in range(batch_size):
+                    # validate adversarial target, i.e. f(x_perturbed)=y
+                    if prediction[j] == y[j].upper():
+                        # decrease max norm bound epsilon
+                        perturbation_norm = np.max(np.abs(perturbation[j]))
+                        if epsilon[j] > perturbation_norm:
+                            epsilon[j] = perturbation_norm
+                        epsilon[j] *= self.decrease_factor_eps
+                        # save current best adversarial example
+                        x_adversarial[j] = x_perturbed[j]
+                logger.info("Current iteration %s, epsilon %s", i, epsilon)
+
+        # return perturbed x if no adversarial example found
+        for j in range(batch_size):
+            if x_adversarial[j] is None:
+                logger.critical("Adversarial attack stage 1 for x_%s was not successful", j)
+                x_adversarial[j] = x_perturbed[j]
+
+        return np.array(x_adversarial, dtype=dtype)
+
+    def _create_imperceptible(self, x: np.ndarray, x_adversarial: np.ndarray, y: np.ndarray) -> np.ndarray:
+        """
+        Create imperceptible, adversarial example with small perturbation.
+
+        This method implements the part of the paper by Qin et al. (2019) that is described as the second stage of the
+        attack. The resulting adversarial audio samples are able to successfully deceive the ASR estimator and are
+        imperceptible to the human ear.
+
+        :param x: An array with the original inputs to be attacked.
+        :param x_adversarial: An array with the adversarial examples.
+        :param y: Target values of shape (batch_size,). Each sample in `y` is a string and it may possess different
+            lengths. A possible example of `y` could be: `y = np.array(['SIXTY ONE', 'HELLO'])`.
+        :return: An array with the imperceptible, adversarial outputs.
+        """
+        batch_size = x.shape[0]
+        alpha_min = 0.0005
+
+        # for ragged input, use np.object dtype
+        dtype = np.float32 if x.ndim != 1 else np.object
+
+        early_stop = [False] * batch_size
+
+        alpha = np.array([self.alpha] * batch_size, dtype=np.float32)
+        loss_theta_previous = [np.inf] * batch_size
+        x_imperceptible = [None] * batch_size
+        # if inputs are *not* ragged, we can't multiply alpha * gradients_theta
+        if x.ndim != 1:
+            alpha = np.expand_dims(alpha, axis=-1)
+
+        masking_threshold, psd_maximum = self._stabilized_threshold_and_psd_maximum(x)
+
+        x_perturbed = x_adversarial.copy()
+
+        for i in range(1, self.max_iter_2 + 1):
+            # get perturbation
+            perturbation = x_perturbed - x
+
+            # get loss gradients of both losses
+            gradients_net = self.estimator.loss_gradient(x_perturbed, y, batch_mode=True)
+            gradients_theta, loss_theta = self._loss_gradient_masking_threshold(
+                perturbation, x, masking_threshold, psd_maximum
+            )
+
+            # check shapes match, otherwise unexpected errors can occur
+            assert gradients_net.shape == gradients_theta.shape
+
+            # perform gradient descent steps
+            x_perturbed = x_perturbed - self.learning_rate_2 * (gradients_net + alpha * gradients_theta)
+
+            if i % self.num_iter_increase_alpha == 0 or i % self.num_iter_decrease_alpha == 0:
+                prediction = self.estimator.predict(x_perturbed, batch_size=batch_size)
+                for j in range(batch_size):
+                    # validate if adversarial target succeeds, i.e. f(x_perturbed)=y
+                    if i % self.num_iter_increase_alpha == 0 and prediction[j] == y[j].upper():
+                        # increase alpha
+                        alpha[j] *= self.increase_factor_alpha
+                        # save current best imperceptible, adversarial example
+                        if loss_theta[j] < loss_theta_previous[j]:
+                            x_imperceptible[j] = x_perturbed[j]
+                            loss_theta_previous[j] = loss_theta[j]
+
+                    # validate if adversarial target fails, i.e. f(x_perturbed)!=y
+                    if i % self.num_iter_decrease_alpha == 0 and prediction[j] != y[j].upper():
+                        # decrease alpha
+                        alpha[j] = max(alpha[j] * self.decrease_factor_alpha, alpha_min)
+                logger.info("Current iteration %s, alpha %s, loss theta %s", i, alpha, loss_theta)
+
+            # note: avoids nan values in loss theta, which can occur when loss converges to zero.
+            for j in range(batch_size):
+                if loss_theta[j] < self.loss_theta_min and not early_stop[j]:
+                    logger.warning(
+                        "Batch sample %s reached minimum threshold of %s for theta loss.", j, self.loss_theta_min
+                    )
+                    early_stop[j] = True
+            if all(early_stop):
+                logger.warning(
+                    "All batch samples reached minimum threshold for theta loss. Stopping early at iteration %s.", i
+                )
+                break
+
+        # return perturbed x if no adversarial example found
+        for j in range(batch_size):
+            if x_imperceptible[j] is None:
+                logger.critical("Adversarial attack stage 2 for x_%s was not successful", j)
+                x_imperceptible[j] = x_perturbed[j]
+
+        return np.array(x_imperceptible, dtype=dtype)
+
+    def _stabilized_threshold_and_psd_maximum(self, x: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
+        """
+        Return batch of stabilized masking thresholds and PSD maxima.
+
+        :param x: An array with the original inputs to be attacked.
+        :return: Tuple consisting of stabilized masking thresholds and PSD maxima.
+        """
+        masking_threshold = []
+        psd_maximum = []
+        x_padded, _ = pad_sequence_input(x)
+
+        for x_i in x_padded:
+            mt, pm = self.masker.calculate_threshold_and_psd_maximum(x_i)
+            masking_threshold.append(mt)
+            psd_maximum.append(pm)
+        # stabilize imperceptible loss by canceling out the "10*log" term in power spectral density maximum and
+        # masking threshold
+        masking_threshold_stabilized = 10 ** (np.array(masking_threshold) * 0.1)
+        psd_maximum_stabilized = 10 ** (np.array(psd_maximum) * 0.1)
+        return masking_threshold_stabilized, psd_maximum_stabilized
+
+    def _loss_gradient_masking_threshold(
+        self,
+        perturbation: np.ndarray,
+        x: np.ndarray,
+        masking_threshold_stabilized: np.ndarray,
+        psd_maximum_stabilized: np.ndarray,
+    ) -> Tuple[np.ndarray, np.ndarray]:
+        """
+        Compute loss gradient of the global masking threshold w.r.t. the PSD approximate of the perturbation.
+
+        The loss is defined as the hinge loss w.r.t. to the frequency masking threshold of the original audio input `x`
+        and the normalized power spectral density estimate of the perturbation. In order to stabilize the optimization
+        problem during back-propagation, the `10*log`-terms are canceled out.
+
+        :param perturbation: Adversarial perturbation.
+        :param x: An array with the original inputs to be attacked.
+        :param masking_threshold_stabilized: Stabilized masking threshold for the original input `x`.
+        :param psd_maximum_stabilized: Stabilized maximum across frames, i.e. shape is `(batch_size, frame_length)`, of
+            the original unnormalized PSD of `x`.
+        :return: Tuple consisting of the loss gradient, which has same shape as `perturbation`, and loss value.
+        """
+        # pad input
+        perturbation_padded, delta_mask = pad_sequence_input(perturbation)
+
+        if self._framework == "tensorflow":
+            # get loss gradients (TensorFlow)
+            feed_dict = {
+                self._delta: perturbation_padded,
+                self._power_spectral_density_maximum_tf: psd_maximum_stabilized,
+                self._masking_threshold_tf: masking_threshold_stabilized,
+            }
+            gradients_padded, loss = self.estimator._sess.run(self._loss_gradient_masking_threshold_op_tf, feed_dict)
+        elif self._framework == "pytorch":
+            # get loss gradients (TensorFlow)
+            gradients_padded, loss = self._loss_gradient_masking_threshold_torch(
+                perturbation_padded, psd_maximum_stabilized, masking_threshold_stabilized
+            )
+        else:
+            raise NotImplementedError
+
+        # undo padding, i.e. change gradients shape from (nb_samples, max_length) to (nb_samples)
+        lengths = delta_mask.sum(axis=1)
+        gradients = list()
+        for gradient_padded, length in zip(gradients_padded, lengths):
+            gradient = gradient_padded[:length]
+            gradients.append(gradient)
+
+        # for ragged input, use np.object dtype
+        dtype = np.float32 if x.ndim != 1 else np.object
+        return np.array(gradients, dtype=dtype), loss
+
+    def _loss_gradient_masking_threshold_tf(
+        self, perturbation: "Tensor", psd_maximum_stabilized: "Tensor", masking_threshold_stabilized: "Tensor"
+    ) -> Union["Tensor", "Tensor"]:
+        """
+        Compute loss gradient of the masking threshold loss in TensorFlow.
+
+        Note that the PSD maximum and masking threshold are required to be stabilized, i.e. have the `10*log10`-term
+        canceled out. Following Qin et al (2019) this mitigates optimization instabilities.
+
+        :param perturbation: Adversarial perturbation.
+        :param psd_maximum_stabilized: Stabilized maximum across frames, i.e. shape is `(batch_size, frame_length)`, of
+            the original unnormalized PSD of `x`.
+        :param masking_threshold_stabilized: Stabilized masking threshold for the original input `x`.
+        :return: Approximate PSD tensor of shape `(batch_size, window_size // 2 + 1, frame_length)`.
+        """
+        import tensorflow.compat.v1 as tf1
+
+        # calculate approximate power spectral density
+        psd_perturbation = self._approximate_power_spectral_density_tf(perturbation, psd_maximum_stabilized)
+
+        # calculate hinge loss
+        loss = tf1.reduce_mean(
+            tf1.nn.relu(psd_perturbation - masking_threshold_stabilized), axis=[1, 2], keepdims=False
+        )
+
+        # compute loss gradient
+        loss_gradient = tf1.gradients(loss, [perturbation])[0]
+        return loss_gradient, loss
+
+    def _loss_gradient_masking_threshold_torch(
+        self, perturbation: np.ndarray, psd_maximum_stabilized: np.ndarray, masking_threshold_stabilized: np.ndarray
+    ) -> Union[np.ndarray, np.ndarray]:
+        """
+        Compute loss gradient of the masking threshold loss in PyTorch.
+
+        See also `ImperceptibleASR._loss_gradient_masking_threshold_tf`.
+        """
+        import torch  # lgtm [py/import-and-import-from]
+
+        # define tensors
+        perturbation_torch = torch.from_numpy(perturbation).to(self.estimator._device)
+        masking_threshold_stabilized_torch = torch.from_numpy(masking_threshold_stabilized).to(self.estimator._device)
+        psd_maximum_stabilized_torch = torch.from_numpy(psd_maximum_stabilized).to(self.estimator._device)
+
+        # track gradient of perturbation
+        perturbation_torch.requires_grad = True
+
+        # calculate approximate power spectral density
+        psd_perturbation = self._approximate_power_spectral_density_torch(
+            perturbation_torch, psd_maximum_stabilized_torch
+        )
+
+        # calculate hinge loss
+        loss = torch.mean(
+            torch.nn.functional.relu(psd_perturbation - masking_threshold_stabilized_torch), dim=(1, 2), keepdims=False
+        )
+
+        # compute loss gradient
+        loss.sum().backward()
+        loss_gradient = perturbation_torch.grad.cpu().numpy()
+        loss_value = loss.detach().cpu().numpy()
+
+        return loss_gradient, loss_value
+
+    def _approximate_power_spectral_density_tf(
+        self, perturbation: "Tensor", psd_maximum_stabilized: "Tensor"
+    ) -> "Tensor":
+        """
+        Approximate the power spectral density for a perturbation `perturbation` in TensorFlow.
+
+        Note that a stabilized PSD approximate is returned, where the `10*log10`-term has been canceled out.
+        Following Qin et al (2019) this mitigates optimization instabilities.
+
+        :param perturbation: Adversarial perturbation.
+        :param psd_maximum_stabilized: Stabilized maximum across frames, i.e. shape is `(batch_size, frame_length)`, of
+            the original unnormalized PSD of `x`.
+        :return: Approximate PSD tensor of shape `(batch_size, window_size // 2 + 1, frame_length)`.
+        """
+        import tensorflow.compat.v1 as tf1
+
+        # compute short-time Fourier transform (STFT)
+        stft_matrix = tf1.signal.stft(perturbation, self._window_size, self._hop_size, fft_length=self._window_size)
+
+        # compute power spectral density (PSD)
+        # note: fixes implementation of Qin et al. by also considering the square root of gain_factor
+        gain_factor = np.sqrt(8.0 / 3.0)
+        psd_matrix = tf1.square(tf1.abs(gain_factor * stft_matrix / self._window_size))
+
+        # approximate normalized psd: psd_matrix_approximated = 10^((96.0 - psd_matrix_max + psd_matrix)/10)
+        psd_matrix_approximated = tf1.pow(10.0, 9.6) / tf1.reshape(psd_maximum_stabilized, [-1, 1, 1]) * psd_matrix
+
+        # return PSD matrix such that shape is (batch_size, window_size // 2 + 1, frame_length)
+        return tf1.transpose(psd_matrix_approximated, [0, 2, 1])
+
+    def _approximate_power_spectral_density_torch(
+        self, perturbation: "PTensor", psd_maximum_stabilized: "PTensor"
+    ) -> "PTensor":
+        """
+        Approximate the power spectral density for a perturbation `perturbation` in PyTorch.
+
+        See also `ImperceptibleASR._approximate_power_spectral_density_tf`.
+        """
+        import torch  # lgtm [py/import-and-import-from]
+
+        # compute short-time Fourier transform (STFT)
+        stft_matrix = torch.stft(
+            perturbation,
+            n_fft=self._window_size,
+            hop_length=self._hop_size,
+            win_length=self._window_size,
+            center=False,
+            window=torch.hann_window(self._window_size).to(self.estimator._device),
+        ).to(self.estimator._device)
+
+        # compute power spectral density (PSD)
+        # note: fixes implementation of Qin et al. by also considering the square root of gain_factor
+        gain_factor = np.sqrt(8.0 / 3.0)
+        stft_matrix_abs = torch.sqrt(torch.sum(torch.square(gain_factor * stft_matrix / self._window_size), -1))
+        psd_matrix = torch.square(stft_matrix_abs)
+
+        # approximate normalized psd: psd_matrix_approximated = 10^((96.0 - psd_matrix_max + psd_matrix)/10)
+        psd_matrix_approximated = pow(10.0, 9.6) / psd_maximum_stabilized.reshape(-1, 1, 1) * psd_matrix
+
+        # return PSD matrix such that shape is (batch_size, window_size // 2 + 1, frame_length)
+        return psd_matrix_approximated
+
+    def _check_params(self) -> None:
+        """
+        Apply attack-specific checks.
+        """
+        if self.eps <= 0:
+            raise ValueError("The perturbation max norm bound `eps` has to be positive.")
+
+        if not isinstance(self.alpha, float):
+            raise ValueError("The value of alpha must be of type float.")
+        if self.alpha <= 0.0:
+            raise ValueError("The value of alpha must be positive")
+
+        if not isinstance(self.max_iter_1, int):
+            raise ValueError("The maximum number of iterations for stage 1 must be of type int.")
+        if self.max_iter_1 <= 0:
+            raise ValueError("The maximum number of iterations for stage 1 must be greater than 0.")
+
+        if not isinstance(self.max_iter_2, int):
+            raise ValueError("The maximum number of iterations for stage 2 must be of type int.")
+        if self.max_iter_2 < 0:
+            raise ValueError("The maximum number of iterations for stage 2 must be non-negative.")
+
+        if not isinstance(self.learning_rate_1, float):
+            raise ValueError("The learning rate for stage 1 must be of type float.")
+        if self.learning_rate_1 <= 0.0:
+            raise ValueError("The learning rate for stage 1 must be greater than 0.0.")
+
+        if not isinstance(self.learning_rate_2, float):
+            raise ValueError("The learning rate for stage 2 must be of type float.")
+        if self.learning_rate_2 <= 0.0:
+            raise ValueError("The learning rate for stage 2 must be greater than 0.0.")
+
+        if not isinstance(self.loss_theta_min, float):
+            raise ValueError("The loss_theta_min threshold must be of type float.")
+
+        if not isinstance(self.decrease_factor_eps, float):
+            raise ValueError("The factor to decrease eps must be of type float.")
+        if self.decrease_factor_eps <= 0.0:
+            raise ValueError("The factor to decrease eps must be greater than 0.0.")
+
+        if not isinstance(self.num_iter_decrease_alpha, int):
+            raise ValueError("The number of iterations must be of type int.")
+        if self.num_iter_decrease_alpha <= 0:
+            raise ValueError("The number of iterations must be greater than 0.")
+
+        if not isinstance(self.increase_factor_alpha, float):
+            raise ValueError("The factor to increase alpha must be of type float.")
+        if self.increase_factor_alpha <= 0.0:
+            raise ValueError("The factor to increase alpha must be greater than 0.0.")
+
+        if not isinstance(self.num_iter_increase_alpha, int):
+            raise ValueError("The number of iterations must be of type int.")
+        if self.num_iter_increase_alpha <= 0:
+            raise ValueError("The number of iterations must be greater than 0.")
+
+        if not isinstance(self.decrease_factor_alpha, float):
+            raise ValueError("The factor to decrease alpha must be of type float.")
+        if self.decrease_factor_alpha <= 0.0:
+            raise ValueError("The factor to decrease alpha must be greater than 0.0.")
+
+        if not isinstance(self.num_iter_decrease_alpha, int):
+            raise ValueError("The number of iterations must be of type int.")
+        if self.num_iter_decrease_alpha <= 0:
+            raise ValueError("The number of iterations must be greater than 0.")
+
+        if self.batch_size <= 0:
+            raise ValueError("The batch size `batch_size` has to be positive.")
+
+
+class PsychoacousticMasker:
+    """
+    Implements psychoacoustic model of Lin and Abdulla (2015) following Qin et al. (2019) simplifications.
+
+    | Paper link: Lin and Abdulla (2015), https://www.springer.com/gp/book/9783319079738
+    | Paper link: Qin et al. (2019), http://proceedings.mlr.press/v97/qin19a.html
+    """
+
+    def __init__(self, window_size: int = 2048, hop_size: int = 512, sample_rate: int = 16000) -> None:
+        """
+        Initialization.
+
+        :param window_size: Length of the window. The number of STFT rows is `(window_size // 2 + 1)`.
+        :param hop_size: Number of audio samples between adjacent STFT columns.
+        :param sample_rate: Sampling frequency of audio inputs.
+        """
+        self._window_size = window_size
+        self._hop_size = hop_size
+        self._sample_rate = sample_rate
+
+        # init some private properties for lazy loading
+        self._fft_frequencies = None
+        self._bark = None
+        self._absolute_threshold_hearing = None
+
+    def calculate_threshold_and_psd_maximum(self, audio: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
+        """
+        Compute the global masking threshold for an audio input and also return its maximum power spectral density.
+
+        This method is the main method to call in order to obtain global masking thresholds for an audio input. It also
+        returns the maximum power spectral density (PSD) for each frame. Given an audio input, the following steps are
+        performed:
+
+        1. STFT analysis and sound pressure level normalization
+        2. Identification and filtering of maskers
+        3. Calculation of individual masking thresholds
+        4. Calculation of global masking tresholds
+
+        :param audio: Audio samples of shape `(length,)`.
+        :return: Global masking thresholds of shape `(window_size // 2 + 1, frame_length)` and the PSD maximum for each
+            frame of shape `(frame_length)`.
+        """
+        psd_matrix, psd_max = self.power_spectral_density(audio)
+        threshold = np.zeros_like(psd_matrix)
+        for frame in range(psd_matrix.shape[1]):
+            # apply methods for finding and filtering maskers
+            maskers, masker_idx = self.filter_maskers(*self.find_maskers(psd_matrix[:, frame]))
+            # apply methods for calculating global threshold
+            threshold[:, frame] = self.calculate_global_threshold(
+                self.calculate_individual_threshold(maskers, masker_idx)
+            )
+        return threshold, psd_max
+
+    @property
+    def window_size(self) -> int:
+        """
+        :return: Window size of the masker.
+        """
+        return self._window_size
+
+    @property
+    def hop_size(self) -> int:
+        """
+        :return: Hop size of the masker.
+        """
+        return self._hop_size
+
+    @property
+    def sample_rate(self) -> int:
+        """
+        :return: Sample rate of the masker.
+        """
+        return self._sample_rate
+
+    @property
+    def fft_frequencies(self) -> np.ndarray:
+        """
+        :return: Discrete fourier transform sample frequencies.
+        """
+        if self._fft_frequencies is None:
+            self._fft_frequencies = np.linspace(0, self.sample_rate / 2, self.window_size // 2 + 1)
+        return self._fft_frequencies
+
+    @property
+    def bark(self) -> np.ndarray:
+        """
+        :return: Bark scale for discrete fourier transform sample frequencies.
+        """
+        if self._bark is None:
+            self._bark = 13 * np.arctan(0.00076 * self.fft_frequencies) + 3.5 * np.arctan(
+                np.square(self.fft_frequencies / 7500.0)
+            )
+        return self._bark
+
+    @property
+    def absolute_threshold_hearing(self) -> np.ndarray:
+        """
+        :return: Absolute threshold of hearing (ATH) for discrete fourier transform sample frequencies.
+        """
+        if self._absolute_threshold_hearing is None:
+            # ATH applies only to frequency range 20Hz<=f<=20kHz
+            # note: deviates from Qin et al. implementation by using the Hz range as valid domain
+            valid_domain = np.logical_and(20 <= self.fft_frequencies, self.fft_frequencies <= 2e4)
+            freq = self.fft_frequencies[valid_domain] * 0.001
+
+            # outside valid ATH domain, set values to -np.inf
+            # note: This ensures that every possible masker in the bins <=20Hz is valid. As a consequence, the global
+            # masking threshold formula will always return a value different to np.inf
+            self._absolute_threshold_hearing = np.ones(valid_domain.shape) * -np.inf
+
+            self._absolute_threshold_hearing[valid_domain] = (
+                3.64 * pow(freq, -0.8) - 6.5 * np.exp(-0.6 * np.square(freq - 3.3)) + 0.001 * pow(freq, 4) - 12
+            )
+        return self._absolute_threshold_hearing
+
+    def power_spectral_density(self, audio: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
+        """
+        Compute the power spectral density matrix for an audio input.
+
+        :param audio: Audio sample of shape `(length,)`.
+        :return: PSD matrix of shape `(window_size // 2 + 1, frame_length)` and maximum vector of shape
+        `(frame_length)`.
+        """
+        import librosa
+
+        # compute short-time Fourier transform (STFT)
+        audio_float = audio.astype(np.float32)
+        stft_params = {
+            "n_fft": self.window_size,
+            "hop_length": self.hop_size,
+            "win_length": self.window_size,
+            "window": ss.get_window("hann", self.window_size, fftbins=True),
+            "center": False,
+        }
+        stft_matrix = librosa.core.stft(audio_float, **stft_params)
+
+        # compute power spectral density (PSD)
+        with np.errstate(divide="ignore"):
+            gain_factor = np.sqrt(8.0 / 3.0)
+            psd_matrix = 20 * np.log10(np.abs(gain_factor * stft_matrix / self.window_size))
+            psd_matrix = psd_matrix.clip(min=-200)
+
+        # normalize PSD at 96dB
+        psd_matrix_max = np.max(psd_matrix)
+        psd_matrix_normalized = 96.0 - psd_matrix_max + psd_matrix
+
+        return psd_matrix_normalized, psd_matrix_max
+
+    @staticmethod
+    def find_maskers(psd_vector: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
+        """
+        Identify maskers.
+
+        Possible maskers are local PSD maxima. Following Qin et al., all maskers are treated as tonal. Thus neglecting
+        the nontonal type.
+
+        :param psd_vector: PSD vector of shape `(window_size // 2 + 1)`.
+        :return: Possible PSD maskers and indices.
+        """
+        # identify maskers. For simplification it is assumed that all maskers are tonal (vs. nontonal).
+        masker_idx = ss.argrelmax(psd_vector)[0]
+
+        # smooth maskers with their direct neighbors
+        psd_maskers = 10 * np.log10(np.sum([10 ** (psd_vector[masker_idx + i] / 10) for i in range(-1, 2)], axis=0))
+        return psd_maskers, masker_idx
+
+    def filter_maskers(self, maskers: np.ndarray, masker_idx: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
+        """
+        Filter maskers.
+
+        First, discard all maskers that are below the absolute threshold of hearing. Second, reduce pairs of maskers
+        that are within 0.5 bark distance of each other by keeping the larger masker.
+
+        :param maskers: Masker PSD values.
+        :param masker_idx: Masker indices.
+        :return: Filtered PSD maskers and indices.
+        """
+        # filter on the absolute threshold of hearing
+        # note: deviates from Qin et al. implementation by filtering first on ATH and only then on bark distance
+        ath_condition = maskers > self.absolute_threshold_hearing[masker_idx]
+        masker_idx = masker_idx[ath_condition]
+        maskers = maskers[ath_condition]
+
+        # filter on the bark distance
+        bark_condition = np.ones(masker_idx.shape, dtype=bool)
+        i_prev = 0
+        for i in range(1, len(masker_idx)):
+            # find pairs of maskers that are within 0.5 bark distance of each other
+            if self.bark[i] - self.bark[i_prev] < 0.5:
+                # discard the smaller masker
+                i_todelete, i_prev = (i_prev, i_prev + 1) if maskers[i_prev] < maskers[i] else (i, i_prev)
+                bark_condition[i_todelete] = False
+            else:
+                i_prev = i
+        masker_idx = masker_idx[bark_condition]
+        maskers = maskers[bark_condition]
+
+        return maskers, masker_idx
+
+    def calculate_individual_threshold(self, maskers: np.ndarray, masker_idx: np.ndarray) -> np.ndarray:
+        """
+        Calculate individual masking threshold with frequency denoted at bark scale.
+
+        :param maskers: Masker PSD values.
+        :param masker_idx: Masker indices.
+        :return: Individual threshold vector of shape `(window_size // 2 + 1)`.
+        """
+        delta_shift = -6.025 - 0.275 * self.bark
+        threshold = np.zeros(masker_idx.shape + self.bark.shape)
+        # TODO reduce for loop
+        for k, (masker_j, masker) in enumerate(zip(masker_idx, maskers)):
+            # critical band rate of the masker
+            z_j = self.bark[masker_j]
+            # distance maskees to masker in bark
+            delta_z = self.bark - z_j
+            # define two-slope spread function:
+            #   if delta_z <= 0, spread_function = 27*delta_z
+            #   if delta_z > 0, spread_function = [-27+0.37*max(PSD_masker-40,0]*delta_z
+            spread_function = 27 * delta_z
+            spread_function[delta_z > 0] = (-27 + 0.37 * max(masker - 40, 0)) * delta_z[delta_z > 0]
+
+            # calculate threshold
+            threshold[k, :] = masker + delta_shift[masker_j] + spread_function
+        return threshold
+
+    def calculate_global_threshold(self, individual_threshold):
+        """
+        Calculate global masking threshold.
+
+        :param individual_threshold: Individual masking threshold vector.
+        :return: Global threshold vector of shape `(window_size // 2 + 1)`.
+        """
+        # note: deviates from Qin et al. implementation by taking the log of the summation, which they do for numerical
+        #       stability of the stage 2 optimization. We stabilize the optimization in the loss itself.
+        with np.errstate(divide="ignore"):
+            return 10 * np.log10(
+                np.sum(10 ** (individual_threshold / 10), axis=0) + 10 ** (self.absolute_threshold_hearing / 10)
+            )
diff --git a/adversarial-robustness-toolbox/art/attacks/evasion/imperceptible_asr/imperceptible_asr_pytorch.py b/adversarial-robustness-toolbox/art/attacks/evasion/imperceptible_asr/imperceptible_asr_pytorch.py
new file mode 100644
index 0000000..0a3eaba
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/attacks/evasion/imperceptible_asr/imperceptible_asr_pytorch.py
@@ -0,0 +1,880 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements the imperceptible, robust, and targeted attack to generate adversarial examples for automatic
+speech recognition models. This attack will be implemented specifically for DeepSpeech model and is framework dependent,
+specifically for PyTorch.
+
+| Paper link: https://arxiv.org/abs/1903.10346
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+from typing import TYPE_CHECKING, Optional, Tuple
+
+import numpy as np
+import scipy
+
+from art.attacks.attack import EvasionAttack
+from art.estimators.estimator import BaseEstimator, LossGradientsMixin, NeuralNetworkMixin
+from art.estimators.pytorch import PyTorchEstimator
+from art.estimators.speech_recognition.pytorch_deep_speech import PyTorchDeepSpeech
+from art.estimators.speech_recognition.speech_recognizer import SpeechRecognizerMixin
+
+if TYPE_CHECKING:
+    import torch
+
+logger = logging.getLogger(__name__)
+
+
+class ImperceptibleASRPyTorch(EvasionAttack):
+    """
+    This class implements the imperceptible, robust, and targeted attack to generate adversarial examples for automatic
+    speech recognition models. This attack will be implemented specifically for DeepSpeech model and is framework
+    dependent, specifically for PyTorch.
+
+    | Paper link: https://arxiv.org/abs/1903.10346
+    """
+
+    attack_params = EvasionAttack.attack_params + [
+        "eps",
+        "max_iter_1",
+        "max_iter_2",
+        "learning_rate_1",
+        "learning_rate_2",
+        "optimizer_1",
+        "optimizer_2",
+        "global_max_length",
+        "initial_rescale",
+        "decrease_factor_eps",
+        "num_iter_decrease_eps",
+        "alpha",
+        "increase_factor_alpha",
+        "num_iter_increase_alpha",
+        "decrease_factor_alpha",
+        "num_iter_decrease_alpha",
+        "batch_size",
+        "use_amp",
+        "opt_level",
+    ]
+
+    _estimator_requirements = (
+        BaseEstimator,
+        LossGradientsMixin,
+        NeuralNetworkMixin,
+        SpeechRecognizerMixin,
+        PyTorchEstimator,
+        PyTorchDeepSpeech,
+    )
+
+    def __init__(
+        self,
+        estimator: PyTorchDeepSpeech,
+        eps: float = 0.05,
+        max_iter_1: int = 10,
+        max_iter_2: int = 4000,
+        learning_rate_1: float = 0.001,
+        learning_rate_2: float = 5e-4,
+        optimizer_1: Optional["torch.optim.Optimizer"] = None,
+        optimizer_2: Optional["torch.optim.Optimizer"] = None,
+        global_max_length: int = 200000,
+        initial_rescale: float = 1.0,
+        decrease_factor_eps: float = 0.8,
+        num_iter_decrease_eps: int = 1,
+        alpha: float = 1.2,
+        increase_factor_alpha: float = 1.2,
+        num_iter_increase_alpha: int = 20,
+        decrease_factor_alpha: float = 0.8,
+        num_iter_decrease_alpha: int = 20,
+        batch_size: int = 32,
+        use_amp: bool = False,
+        opt_level: str = "O1",
+    ):
+        """
+        Create a :class:`.ImperceptibleASRPyTorch` instance.
+
+        :param estimator: A trained estimator.
+        :param eps: Maximum perturbation that the attacker can introduce.
+        :param max_iter_1: The maximum number of iterations applied for the first stage of the optimization of the
+                           attack.
+        :param max_iter_2: The maximum number of iterations applied for the second stage of the optimization of the
+                           attack.
+        :param learning_rate_1: The learning rate applied for the first stage of the optimization of the attack.
+        :param learning_rate_2: The learning rate applied for the second stage of the optimization of the attack.
+        :param optimizer_1: The optimizer applied for the first stage of the optimization of the attack. If `None`
+                            attack will use `torch.optim.Adam`.
+        :param optimizer_2: The optimizer applied for the second stage of the optimization of the attack. If `None`
+                            attack will use `torch.optim.Adam`.
+        :param global_max_length: The length of the longest audio signal allowed by this attack.
+        :param initial_rescale: Initial rescale coefficient to speedup the decrease of the perturbation size during
+                                the first stage of the optimization of the attack.
+        :param decrease_factor_eps: The factor to adjust the rescale coefficient during the first stage of the
+                                    optimization of the attack.
+        :param num_iter_decrease_eps: Number of iterations to adjust the rescale coefficient, and therefore adjust the
+                                      perturbation size.
+        :param alpha: Value of the alpha coefficient used in the second stage of the optimization of the attack.
+        :param increase_factor_alpha: The factor to increase the alpha coefficient used in the second stage of the
+                                      optimization of the attack.
+        :param num_iter_increase_alpha: Number of iterations to increase alpha.
+        :param decrease_factor_alpha: The factor to decrease the alpha coefficient used in the second stage of the
+                                      optimization of the attack.
+        :param num_iter_decrease_alpha: Number of iterations to decrease alpha.
+        :param batch_size: Size of the batch on which adversarial samples are generated.
+        :param use_amp: Whether to use the automatic mixed precision tool to enable mixed precision training or
+                        gradient computation, e.g. with loss gradient computation. When set to True, this option is
+                        only triggered if there are GPUs available.
+        :param opt_level: Specify a pure or mixed precision optimization level. Used when use_amp is True. Accepted
+                          values are `O0`, `O1`, `O2`, and `O3`.
+        """
+        import torch  # lgtm [py/repeated-import]
+        from torch.autograd import Variable
+
+        super().__init__(estimator=estimator)
+
+        # Set attack attributes
+        self._targeted = True
+        self.eps = eps
+        self.max_iter_1 = max_iter_1
+        self.max_iter_2 = max_iter_2
+        self.learning_rate_1 = learning_rate_1
+        self.learning_rate_2 = learning_rate_2
+        self.global_max_length = global_max_length
+        self.initial_rescale = initial_rescale
+        self.decrease_factor_eps = decrease_factor_eps
+        self.num_iter_decrease_eps = num_iter_decrease_eps
+        self.alpha = alpha
+        self.increase_factor_alpha = increase_factor_alpha
+        self.num_iter_increase_alpha = num_iter_increase_alpha
+        self.decrease_factor_alpha = decrease_factor_alpha
+        self.num_iter_decrease_alpha = num_iter_decrease_alpha
+        self.batch_size = batch_size
+        self._use_amp = use_amp
+
+        # Create the main variable to optimize
+        if self.estimator.device.type == "cpu":
+            self.global_optimal_delta = Variable(
+                torch.zeros(self.batch_size, self.global_max_length).type(torch.FloatTensor), requires_grad=True
+            )
+        else:
+            self.global_optimal_delta = Variable(
+                torch.zeros(self.batch_size, self.global_max_length).type(torch.cuda.FloatTensor), requires_grad=True
+            )
+
+        self.global_optimal_delta.to(self.estimator.device)
+
+        # Create the optimizers
+        self._optimizer_arg_1 = optimizer_1
+        if self._optimizer_arg_1 is None:
+            self.optimizer_1 = torch.optim.Adam(params=[self.global_optimal_delta], lr=self.learning_rate_1)
+        else:
+            self.optimizer_1 = self._optimizer_arg_1(params=[self.global_optimal_delta], lr=self.learning_rate_1)
+
+        self._optimizer_arg_2 = optimizer_2
+        if self._optimizer_arg_2 is None:
+            self.optimizer_2 = torch.optim.Adam(params=[self.global_optimal_delta], lr=self.learning_rate_2)
+        else:
+            self.optimizer_2 = self._optimizer_arg_2(params=[self.global_optimal_delta], lr=self.learning_rate_2)
+
+        # Setup for AMP use
+        if self._use_amp:
+            from apex import amp
+
+            if self.estimator.device.type == "cpu":
+                enabled = False
+            else:
+                enabled = True
+
+            self.estimator._model, [self.optimizer_1, self.optimizer_2] = amp.initialize(
+                models=self.estimator._model,
+                optimizers=[self.optimizer_1, self.optimizer_2],
+                enabled=enabled,
+                opt_level=opt_level,
+                loss_scale=1.0,
+            )
+
+        # Check validity of attack attributes
+        self._check_params()
+
+    def generate(self, x: np.ndarray, y: Optional[np.ndarray] = None, **kwargs) -> np.ndarray:
+        """
+        Generate adversarial samples and return them in an array.
+
+        :param x: Samples of shape (nb_samples, seq_length). Note that, it is allowable that sequences in the batch
+                  could have different lengths. A possible example of `x` could be:
+                  `x = np.array([np.array([0.1, 0.2, 0.1, 0.4]), np.array([0.3, 0.1])])`.
+        :param y: Target values of shape (nb_samples). Each sample in `y` is a string and it may possess different
+                  lengths. A possible example of `y` could be: `y = np.array(['SIXTY ONE', 'HELLO'])`. Note that, this
+                  class only supports targeted attack.
+        :return: An array holding the adversarial examples.
+        """
+        import torch  # lgtm [py/repeated-import]
+
+        if y is None:
+            raise ValueError(
+                "`ImperceptibleASRPyTorch` is a targeted attack and requires the definition of target"
+                "labels `y`. Currently `y` is set to `None`."
+            )
+
+        # Start to compute adversarial examples
+        dtype = x.dtype
+
+        # Cast to type float64 to avoid overflow
+        if dtype.type == np.float64:
+            adv_x = x.copy()
+        else:
+            adv_x = x.copy().astype(np.float64)
+
+        # Put the estimator in the training mode, otherwise CUDA can't backpropagate through the model.
+        # However, estimator uses batch norm layers which need to be frozen
+        self.estimator.model.train()
+        self.estimator.set_batchnorm(train=False)
+
+        # Compute perturbation with batching
+        num_batch = int(np.ceil(len(x) / float(self.batch_size)))
+
+        for m in range(num_batch):
+            # Batch indexes
+            batch_index_1, batch_index_2 = (m * self.batch_size, min((m + 1) * self.batch_size, len(x)))
+
+            # First reset delta
+            self.global_optimal_delta.data = torch.zeros(self.batch_size, self.global_max_length).type(torch.float64)
+
+            # Next, reset optimizers
+            if self._optimizer_arg_1 is None:
+                self.optimizer_1 = torch.optim.Adam(params=[self.global_optimal_delta], lr=self.learning_rate_1)
+            else:
+                self.optimizer_1 = self._optimizer_arg_1(params=[self.global_optimal_delta], lr=self.learning_rate_1)
+
+            if self._optimizer_arg_2 is None:
+                self.optimizer_2 = torch.optim.Adam(params=[self.global_optimal_delta], lr=self.learning_rate_2)
+            else:
+                self.optimizer_2 = self._optimizer_arg_2(params=[self.global_optimal_delta], lr=self.learning_rate_2)
+
+            # Then compute the batch
+            adv_x_batch = self._generate_batch(adv_x[batch_index_1:batch_index_2], y[batch_index_1:batch_index_2])
+
+            for i in range(len(adv_x_batch)):
+                adv_x[batch_index_1 + i] = adv_x_batch[i, : len(adv_x[batch_index_1 + i])]
+
+        # Unfreeze batch norm layers again
+        self.estimator.set_batchnorm(train=True)
+
+        # Recast to the original type if needed
+        if dtype.type == np.float32:
+            adv_x = adv_x.astype(dtype)
+
+        return adv_x
+
+    def _generate_batch(self, x: np.ndarray, y: np.ndarray) -> np.ndarray:
+        """
+        Generate a batch of adversarial samples and return them in an array.
+
+        :param x: Samples of shape (nb_samples, seq_length). Note that, it is allowable that sequences in the batch
+                  could have different lengths. A possible example of `x` could be:
+                  `x = np.array([np.array([0.1, 0.2, 0.1, 0.4]), np.array([0.3, 0.1])])`.
+        :param y: Target values of shape (nb_samples). Each sample in `y` is a string and it may possess different
+                  lengths. A possible example of `y` could be: `y = np.array(['SIXTY ONE', 'HELLO'])`. Note that, this
+                  class only supports targeted attack.
+        :return: A batch of adversarial examples.
+        """
+        import torch  # lgtm [py/repeated-import]
+
+        # First stage of attack
+        successful_adv_input_1st_stage, original_input = self._attack_1st_stage(x=x, y=y)
+        successful_perturbation_1st_stage = successful_adv_input_1st_stage - torch.tensor(original_input).to(
+            self.estimator.device
+        )
+
+        # Compute original masking threshold and maximum psd
+        theta_batch = []
+        original_max_psd_batch = []
+
+        for i in range(len(x)):
+            theta, original_max_psd = self._compute_masking_threshold(original_input[i])
+            theta = theta.transpose(1, 0)
+            theta_batch.append(theta)
+            original_max_psd_batch.append(original_max_psd)
+
+        theta_batch = np.array(theta_batch)
+        original_max_psd_batch = np.array(original_max_psd_batch)
+
+        # Reset delta with new result
+        local_batch_shape = successful_adv_input_1st_stage.shape
+        self.global_optimal_delta.data = torch.zeros(self.batch_size, self.global_max_length).type(torch.float64)
+        self.global_optimal_delta.data[
+            : local_batch_shape[0], : local_batch_shape[1]
+        ] = successful_perturbation_1st_stage
+
+        # Second stage of attack
+        successful_adv_input_2nd_stage = self._attack_2nd_stage(
+            x=x, y=y, theta_batch=theta_batch, original_max_psd_batch=original_max_psd_batch
+        )
+
+        results = successful_adv_input_2nd_stage.detach().cpu().numpy()
+
+        return results
+
+    def _attack_1st_stage(self, x: np.ndarray, y: np.ndarray) -> Tuple["torch.Tensor", np.ndarray]:
+        """
+        The first stage of the attack.
+
+        :param x: Samples of shape (nb_samples, seq_length). Note that, it is allowable that sequences in the batch
+                  could have different lengths. A possible example of `x` could be:
+                  `x = np.array([np.array([0.1, 0.2, 0.1, 0.4]), np.array([0.3, 0.1])])`.
+        :param y: Target values of shape (nb_samples). Each sample in `y` is a string and it may possess different
+                  lengths. A possible example of `y` could be: `y = np.array(['SIXTY ONE', 'HELLO'])`. Note that, this
+                  class only supports targeted attack.
+        :return: A tuple of two tensors:
+                    - A tensor holding the candidate adversarial examples.
+                    - An array holding the original inputs.
+        """
+        import torch  # lgtm [py/repeated-import]
+
+        # Compute local shape
+        local_batch_size = len(x)
+        real_lengths = np.array([x_.shape[0] for x_ in x])
+        local_max_length = np.max(real_lengths)
+
+        # Initialize rescale
+        rescale = np.ones([local_batch_size, local_max_length], dtype=np.float64) * self.initial_rescale
+
+        # Reformat input
+        input_mask = np.zeros([local_batch_size, local_max_length], dtype=np.float64)
+        original_input = np.zeros([local_batch_size, local_max_length], dtype=np.float64)
+
+        for local_batch_size_idx in range(local_batch_size):
+            input_mask[local_batch_size_idx, : len(x[local_batch_size_idx])] = 1
+            original_input[local_batch_size_idx, : len(x[local_batch_size_idx])] = x[local_batch_size_idx]
+
+        # Optimization loop
+        successful_adv_input = [None] * local_batch_size
+        trans = [None] * local_batch_size
+
+        for iter_1st_stage_idx in range(self.max_iter_1):
+            # Zero the parameter gradients
+            self.optimizer_1.zero_grad()
+
+            # Call to forward pass
+            loss, local_delta, decoded_output, masked_adv_input, _ = self._forward_1st_stage(
+                original_input=original_input,
+                original_output=y,
+                local_batch_size=local_batch_size,
+                local_max_length=local_max_length,
+                rescale=rescale,
+                input_mask=input_mask,
+                real_lengths=real_lengths,
+            )
+
+            # Actual training
+            if self._use_amp:
+                from apex import amp
+
+                with amp.scale_loss(loss, self.optimizer_1) as scaled_loss:
+                    scaled_loss.backward()
+
+            else:
+                loss.backward()
+
+            # Get sign of the gradients
+            self.global_optimal_delta.grad = torch.sign(self.global_optimal_delta.grad)
+
+            # Do optimization
+            self.optimizer_1.step()
+
+            # Save the best adversarial example and adjust the rescale coefficient if successful
+            if iter_1st_stage_idx % self.num_iter_decrease_eps == 0:
+                for local_batch_size_idx in range(local_batch_size):
+                    if decoded_output[local_batch_size_idx] == y[local_batch_size_idx]:
+                        # Adjust the rescale coefficient
+                        max_local_delta = np.max(np.abs(local_delta[local_batch_size_idx].detach().numpy()))
+
+                        if rescale[local_batch_size_idx][0] * self.eps > max_local_delta:
+                            rescale[local_batch_size_idx] = max_local_delta / self.eps
+                        rescale[local_batch_size_idx] *= self.decrease_factor_eps
+
+                        # Save the best adversarial example
+                        successful_adv_input[local_batch_size_idx] = masked_adv_input[local_batch_size_idx]
+                        trans[local_batch_size_idx] = decoded_output[local_batch_size_idx]
+
+            # If attack is unsuccessful
+            if iter_1st_stage_idx == self.max_iter_1 - 1:
+                for local_batch_size_idx in range(local_batch_size):
+                    if successful_adv_input[local_batch_size_idx] is None:
+                        successful_adv_input[local_batch_size_idx] = masked_adv_input[local_batch_size_idx]
+                        trans[local_batch_size_idx] = decoded_output[local_batch_size_idx]
+
+        result = torch.stack(successful_adv_input)
+
+        return result, original_input
+
+    def _forward_1st_stage(
+        self,
+        original_input: np.ndarray,
+        original_output: np.ndarray,
+        local_batch_size: int,
+        local_max_length: int,
+        rescale: np.ndarray,
+        input_mask: np.ndarray,
+        real_lengths: np.ndarray,
+    ) -> Tuple["torch.Tensor", "torch.Tensor", np.ndarray, "torch.Tensor", "torch.Tensor"]:
+        """
+        The forward pass of the first stage of the attack.
+
+        :param original_input: Samples of shape (nb_samples, seq_length). Note that, sequences in the batch must have
+                               equal lengths. A possible example of `original_input` could be:
+                               `original_input = np.array([np.array([0.1, 0.2, 0.1]), np.array([0.3, 0.1, 0.0])])`.
+        :param original_output: Target values of shape (nb_samples). Each sample in `original_output` is a string and
+                                it may possess different lengths. A possible example of `original_output` could be:
+                                `original_output = np.array(['SIXTY ONE', 'HELLO'])`.
+        :param local_batch_size: Current batch size.
+        :param local_max_length: Max length of the current batch.
+        :param rescale: Current rescale coefficients.
+        :param input_mask: Masks of true inputs.
+        :param real_lengths: Real lengths of original sequences.
+        :return: A tuple of (loss, local_delta, decoded_output, masked_adv_input)
+                    - loss: The loss tensor of the first stage of the attack.
+                    - local_delta: The delta of the current batch.
+                    - decoded_output: Transcription output.
+                    - masked_adv_input: Perturbed inputs.
+        """
+        import torch  # lgtm [py/repeated-import]
+        from warpctc_pytorch import CTCLoss
+
+        # Compute perturbed inputs
+        local_delta = self.global_optimal_delta[:local_batch_size, :local_max_length]
+        local_delta_rescale = torch.clamp(local_delta, -self.eps, self.eps).to(self.estimator.device)
+        local_delta_rescale *= torch.tensor(rescale).to(self.estimator.device)
+        adv_input = local_delta_rescale + torch.tensor(original_input).to(self.estimator.device)
+        masked_adv_input = adv_input * torch.tensor(input_mask).to(self.estimator.device)
+
+        # Transform data into the model input space
+        inputs, targets, input_rates, target_sizes, batch_idx = self.estimator.preprocess_transform_model_input(
+            x=masked_adv_input.to(self.estimator.device), y=original_output, real_lengths=real_lengths,
+        )
+
+        # Compute real input sizes
+        input_sizes = input_rates.mul_(inputs.size()[-1]).int()
+
+        # Call to DeepSpeech model for prediction
+        outputs, output_sizes = self.estimator.model(
+            inputs.to(self.estimator.device), input_sizes.to(self.estimator.device)
+        )
+        outputs_ = outputs.transpose(0, 1)
+        float_outputs = outputs_.float()
+
+        # Loss function
+        criterion = CTCLoss()
+        loss = criterion(float_outputs, targets, output_sizes, target_sizes).to(self.estimator.device)
+        loss = loss / inputs.size(0)
+
+        # Compute transcription
+        decoded_output, _ = self.estimator.decoder.decode(outputs, output_sizes)
+        decoded_output = [do[0] for do in decoded_output]
+        decoded_output = np.array(decoded_output)
+
+        # Rearrange to the original order
+        decoded_output_ = decoded_output.copy()
+        decoded_output[batch_idx] = decoded_output_
+
+        return loss, local_delta, decoded_output, masked_adv_input, local_delta_rescale
+
+    def _attack_2nd_stage(
+        self, x: np.ndarray, y: np.ndarray, theta_batch: np.ndarray, original_max_psd_batch: np.ndarray
+    ) -> "torch.Tensor":
+        """
+        The second stage of the attack.
+
+        :param x: Samples of shape (nb_samples, seq_length). Note that, it is allowable that sequences in the batch
+                  could have different lengths. A possible example of `x` could be:
+                  `x = np.array([np.array([0.1, 0.2, 0.1, 0.4]), np.array([0.3, 0.1])])`.
+        :param y: Target values of shape (nb_samples). Each sample in `y` is a string and it may possess different
+                  lengths. A possible example of `y` could be: `y = np.array(['SIXTY ONE', 'HELLO'])`. Note that, this
+                  class only supports targeted attack.
+        :param theta_batch: Original thresholds.
+        :param original_max_psd_batch: Original maximum psd.
+        :return: An array holding the candidate adversarial examples.
+        """
+        import torch  # lgtm [py/repeated-import]
+
+        # Compute local shape
+        local_batch_size = len(x)
+        real_lengths = np.array([x_.shape[0] for x_ in x])
+        local_max_length = np.max(real_lengths)
+
+        # Initialize alpha and rescale
+        alpha = np.array([self.alpha] * local_batch_size, dtype=np.float64)
+        rescale = np.ones([local_batch_size, local_max_length], dtype=np.float64) * self.initial_rescale
+
+        # Reformat input
+        input_mask = np.zeros([local_batch_size, local_max_length], dtype=np.float64)
+        original_input = np.zeros([local_batch_size, local_max_length], dtype=np.float64)
+
+        for local_batch_size_idx in range(local_batch_size):
+            input_mask[local_batch_size_idx, : len(x[local_batch_size_idx])] = 1
+            original_input[local_batch_size_idx, : len(x[local_batch_size_idx])] = x[local_batch_size_idx]
+
+        # Optimization loop
+        successful_adv_input = [None] * local_batch_size
+        best_loss_2nd_stage = [np.inf] * local_batch_size
+        trans = [None] * local_batch_size
+
+        for iter_2nd_stage_idx in range(self.max_iter_2):
+            # Zero the parameter gradients
+            self.optimizer_2.zero_grad()
+
+            # Call to forward pass of the first stage
+            loss_1st_stage, _, decoded_output, masked_adv_input, local_delta_rescale = self._forward_1st_stage(
+                original_input=original_input,
+                original_output=y,
+                local_batch_size=local_batch_size,
+                local_max_length=local_max_length,
+                rescale=rescale,
+                input_mask=input_mask,
+                real_lengths=real_lengths,
+            )
+
+            # Call to forward pass of the first stage
+            loss_2nd_stage = self._forward_2nd_stage(
+                local_delta_rescale=local_delta_rescale,
+                theta_batch=theta_batch,
+                original_max_psd_batch=original_max_psd_batch,
+            )
+
+            # Total loss
+            loss = loss_1st_stage + torch.tensor(alpha).to(self.estimator.device) * loss_2nd_stage
+            loss = torch.mean(loss)
+
+            # Actual training
+            if self._use_amp:
+                from apex import amp
+
+                with amp.scale_loss(loss, self.optimizer_2) as scaled_loss:
+                    scaled_loss.backward()
+
+            else:
+                loss.backward()
+
+            # Do optimization
+            self.optimizer_2.step()
+
+            # Save the best adversarial example and adjust the alpha coefficient
+            for local_batch_size_idx in range(local_batch_size):
+                if decoded_output[local_batch_size_idx] == y[local_batch_size_idx]:
+                    if loss_2nd_stage[local_batch_size_idx] < best_loss_2nd_stage[local_batch_size_idx]:
+                        # Update best loss at 2nd stage
+                        best_loss_2nd_stage[local_batch_size_idx] = loss_2nd_stage[local_batch_size_idx]
+
+                        # Save the best adversarial example
+                        successful_adv_input[local_batch_size_idx] = masked_adv_input[local_batch_size_idx]
+                        trans[local_batch_size_idx] = decoded_output[local_batch_size_idx]
+
+                    # Adjust to increase the alpha coefficient
+                    if iter_2nd_stage_idx % self.num_iter_increase_alpha == 0:
+                        alpha[local_batch_size_idx] *= self.increase_factor_alpha
+
+                # Adjust to decrease the alpha coefficient
+                elif iter_2nd_stage_idx % self.num_iter_decrease_alpha == 0:
+                    alpha[local_batch_size_idx] *= self.decrease_factor_alpha
+                    alpha[local_batch_size_idx] = max(alpha[local_batch_size_idx], 0.0005)
+
+            # If attack is unsuccessful
+            if iter_2nd_stage_idx == self.max_iter_2 - 1:
+                for local_batch_size_idx in range(local_batch_size):
+                    if successful_adv_input[local_batch_size_idx] is None:
+                        successful_adv_input[local_batch_size_idx] = masked_adv_input[local_batch_size_idx]
+                        trans[local_batch_size_idx] = decoded_output[local_batch_size_idx]
+
+        result = torch.stack(successful_adv_input)
+
+        return result
+
+    def _forward_2nd_stage(
+        self, local_delta_rescale: "torch.Tensor", theta_batch: np.ndarray, original_max_psd_batch: np.ndarray,
+    ) -> "torch.Tensor":
+        """
+        The forward pass of the second stage of the attack.
+
+        :param local_delta_rescale: Local delta after rescaled.
+        :param theta_batch: Original thresholds.
+        :param original_max_psd_batch: Original maximum psd.
+        :return: The loss tensor of the second stage of the attack.
+        """
+        import torch  # lgtm [py/repeated-import]
+
+        # Compute loss for masking threshold
+        losses = []
+        relu = torch.nn.ReLU()
+
+        for i, _ in enumerate(theta_batch):
+            psd_transform_delta = self._psd_transform(
+                delta=local_delta_rescale[i, :], original_max_psd=original_max_psd_batch[i]
+            )
+
+            loss = torch.mean(relu(psd_transform_delta - torch.tensor(theta_batch[i]).to(self.estimator.device)))
+            losses.append(loss)
+
+        losses = torch.stack(losses)
+
+        return losses
+
+    def _compute_masking_threshold(self, x: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
+        """
+        Compute the masking threshold and the maximum psd of the original audio.
+
+        :param x: Samples of shape (seq_length,).
+        :return: A tuple of the masking threshold and the maximum psd.
+        """
+        import librosa
+
+        # First compute the psd matrix
+        # These parameters are needed for the transformation
+        sample_rate = self.estimator.model.audio_conf.sample_rate
+        window_size = self.estimator.model.audio_conf.window_size
+        window_stride = self.estimator.model.audio_conf.window_stride
+
+        n_fft = int(sample_rate * window_size)
+        hop_length = int(sample_rate * window_stride)
+        win_length = n_fft
+
+        window_name = self.estimator.model.audio_conf.window.value
+
+        window = scipy.signal.get_window(window_name, win_length, fftbins=True)
+
+        transformed_x = librosa.core.stft(
+            y=x, n_fft=n_fft, hop_length=hop_length, win_length=win_length, window=window, center=False
+        )
+        transformed_x *= np.sqrt(8.0 / 3.0)
+
+        psd = abs(transformed_x / win_length)
+        original_max_psd = np.max(psd * psd)
+        with np.errstate(divide="ignore"):
+            psd = (20 * np.log10(psd)).clip(min=-200)
+        psd = 96 - np.max(psd) + psd
+
+        # Compute freqs and barks
+        freqs = librosa.core.fft_frequencies(sample_rate, win_length)
+        barks = 13 * np.arctan(0.00076 * freqs) + 3.5 * np.arctan(pow(freqs / 7500.0, 2))
+
+        # Compute quiet threshold
+        ath = np.zeros(len(barks), dtype=np.float64) - np.inf
+        bark_idx = np.argmax(barks > 1)
+        ath[bark_idx:] = (
+            3.64 * pow(freqs[bark_idx:] * 0.001, -0.8)
+            - 6.5 * np.exp(-0.6 * pow(0.001 * freqs[bark_idx:] - 3.3, 2))
+            + 0.001 * pow(0.001 * freqs[bark_idx:], 4)
+            - 12
+        )
+
+        # Compute the global masking threshold theta
+        theta = []
+
+        for i in range(psd.shape[1]):
+            # Compute masker index
+            masker_idx = scipy.signal.argrelextrema(psd[:, i], np.greater)[0]
+
+            if 0 in masker_idx:
+                masker_idx = np.delete(masker_idx, 0)
+
+            if len(psd[:, i]) - 1 in masker_idx:
+                masker_idx = np.delete(masker_idx, len(psd[:, i]) - 1)
+
+            barks_psd = np.zeros([len(masker_idx), 3], dtype=np.float64)
+            barks_psd[:, 0] = barks[masker_idx]
+            barks_psd[:, 1] = 10 * np.log10(
+                pow(10, psd[:, i][masker_idx - 1] / 10.0)
+                + pow(10, psd[:, i][masker_idx] / 10.0)
+                + pow(10, psd[:, i][masker_idx + 1] / 10.0)
+            )
+            barks_psd[:, 2] = masker_idx
+
+            for j in range(len(masker_idx)):
+                if barks_psd.shape[0] <= j + 1:
+                    break
+
+                while barks_psd[j + 1, 0] - barks_psd[j, 0] < 0.5:
+                    quiet_threshold = (
+                        3.64 * pow(freqs[int(barks_psd[j, 2])] * 0.001, -0.8)
+                        - 6.5 * np.exp(-0.6 * pow(0.001 * freqs[int(barks_psd[j, 2])] - 3.3, 2))
+                        + 0.001 * pow(0.001 * freqs[int(barks_psd[j, 2])], 4)
+                        - 12
+                    )
+                    if barks_psd[j, 1] < quiet_threshold:
+                        barks_psd = np.delete(barks_psd, j, axis=0)
+
+                    if barks_psd.shape[0] == j + 1:
+                        break
+
+                    if barks_psd[j, 1] < barks_psd[j + 1, 1]:
+                        barks_psd = np.delete(barks_psd, j, axis=0)
+                    else:
+                        barks_psd = np.delete(barks_psd, j + 1, axis=0)
+
+                    if barks_psd.shape[0] == j + 1:
+                        break
+
+            # Compute the global masking threshold
+            delta = 1 * (-6.025 - 0.275 * barks_psd[:, 0])
+
+            t_s = []
+
+            for m in range(barks_psd.shape[0]):
+                d_z = barks - barks_psd[m, 0]
+                zero_idx = np.argmax(d_z > 0)
+                s_f = np.zeros(len(d_z), dtype=np.float64)
+                s_f[:zero_idx] = 27 * d_z[:zero_idx]
+                s_f[zero_idx:] = (-27 + 0.37 * max(barks_psd[m, 1] - 40, 0)) * d_z[zero_idx:]
+                t_s.append(barks_psd[m, 1] + delta[m] + s_f)
+
+            t_s = np.array(t_s)
+
+            theta.append(np.sum(pow(10, t_s / 10.0), axis=0) + pow(10, ath / 10.0))
+
+        theta = np.array(theta)
+
+        return theta, original_max_psd
+
+    def _psd_transform(self, delta: "torch.Tensor", original_max_psd: "torch.Tensor") -> "torch.Tensor":
+        """
+        Compute the psd matrix of the perturbation.
+
+        :param delta: The perturbation.
+        :param original_max_psd: The maximum psd of the original audio.
+        :return: The psd matrix.
+        """
+        import torch  # lgtm [py/repeated-import]
+
+        # These parameters are needed for the transformation
+        sample_rate = self.estimator.model.audio_conf.sample_rate
+        window_size = self.estimator.model.audio_conf.window_size
+        window_stride = self.estimator.model.audio_conf.window_stride
+
+        n_fft = int(sample_rate * window_size)
+        hop_length = int(sample_rate * window_stride)
+        win_length = n_fft
+
+        window = self.estimator.model.audio_conf.window.value
+
+        if window == "hamming":
+            window_fn = torch.hamming_window
+        elif window == "hann":
+            window_fn = torch.hann_window
+        elif window == "blackman":
+            window_fn = torch.blackman_window
+        elif window == "bartlett":
+            window_fn = torch.bartlett_window
+        else:
+            raise NotImplementedError("Spectrogram window %s not supported." % window)
+
+        # Return STFT of delta
+        delta_stft = torch.stft(
+            delta,
+            n_fft=n_fft,
+            hop_length=hop_length,
+            win_length=win_length,
+            center=False,
+            window=window_fn(win_length).to(self.estimator.device),
+        ).to(self.estimator.device)
+
+        # Take abs of complex STFT results
+        transformed_delta = torch.sqrt(torch.sum(torch.square(delta_stft), -1))
+
+        # Compute the psd matrix
+        psd = (8.0 / 3.0) * transformed_delta / win_length
+        psd = psd ** 2
+        psd = (
+            torch.pow(torch.tensor(10.0), torch.tensor(9.6)).to(self.estimator.device)
+            / torch.reshape(torch.tensor(original_max_psd).to(self.estimator.device), [-1, 1, 1])
+            * psd
+        )
+
+        return psd
+
+    def _check_params(self) -> None:
+        """
+        Apply attack-specific checks.
+        """
+        if self.eps <= 0:
+            raise ValueError("The perturbation size `eps` has to be positive.")
+
+        if not isinstance(self.max_iter_1, int):
+            raise ValueError("The maximum number of iterations must be of type int.")
+        if self.max_iter_1 <= 0:
+            raise ValueError("The maximum number of iterations must be greater than 0.")
+
+        if not isinstance(self.max_iter_2, int):
+            raise ValueError("The maximum number of iterations must be of type int.")
+        if self.max_iter_2 <= 0:
+            raise ValueError("The maximum number of iterations must be greater than 0.")
+
+        if not isinstance(self.learning_rate_1, float):
+            raise ValueError("The learning rate must be of type float.")
+        if self.learning_rate_1 <= 0.0:
+            raise ValueError("The learning rate must be greater than 0.0.")
+
+        if not isinstance(self.learning_rate_2, float):
+            raise ValueError("The learning rate must be of type float.")
+        if self.learning_rate_2 <= 0.0:
+            raise ValueError("The learning rate must be greater than 0.0.")
+
+        if not isinstance(self.global_max_length, int):
+            raise ValueError("The length of the longest audio signal must be of type int.")
+        if self.global_max_length <= 0:
+            raise ValueError("The length of the longest audio signal must be greater than 0.")
+
+        if not isinstance(self.initial_rescale, float):
+            raise ValueError("The initial rescale coefficient must be of type float.")
+        if self.initial_rescale <= 0.0:
+            raise ValueError("The initial rescale coefficient must be greater than 0.0.")
+
+        if not isinstance(self.decrease_factor_eps, float):
+            raise ValueError("The rescale factor of `eps` must be of type float.")
+        if self.decrease_factor_eps <= 0.0:
+            raise ValueError("The rescale factor of `eps` must be greater than 0.0.")
+
+        if not isinstance(self.num_iter_decrease_eps, int):
+            raise ValueError("The number of iterations must be of type int.")
+        if self.num_iter_decrease_eps <= 0:
+            raise ValueError("The number of iterations must be greater than 0.")
+
+        if not isinstance(self.alpha, float):
+            raise ValueError("The value of alpha must be of type float.")
+        if self.alpha <= 0.0:
+            raise ValueError("The value of alpha must be greater than 0.0.")
+
+        if not isinstance(self.increase_factor_alpha, float):
+            raise ValueError("The factor to increase alpha must be of type float.")
+        if self.increase_factor_alpha <= 0.0:
+            raise ValueError("The factor to increase alpha must be greater than 0.0.")
+
+        if not isinstance(self.num_iter_increase_alpha, int):
+            raise ValueError("The number of iterations must be of type int.")
+        if self.num_iter_increase_alpha <= 0:
+            raise ValueError("The number of iterations must be greater than 0.")
+
+        if not isinstance(self.decrease_factor_alpha, float):
+            raise ValueError("The factor to decrease alpha must be of type float.")
+        if self.decrease_factor_alpha <= 0.0:
+            raise ValueError("The factor to decrease alpha must be greater than 0.0.")
+
+        if not isinstance(self.num_iter_decrease_alpha, int):
+            raise ValueError("The number of iterations must be of type int.")
+        if self.num_iter_decrease_alpha <= 0:
+            raise ValueError("The number of iterations must be greater than 0.")
+
+        if self.batch_size <= 0:
+            raise ValueError("The batch size `batch_size` has to be positive.")
diff --git a/adversarial-robustness-toolbox/art/attacks/evasion/iterative_method.py b/adversarial-robustness-toolbox/art/attacks/evasion/iterative_method.py
new file mode 100644
index 0000000..0d36ed5
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/attacks/evasion/iterative_method.py
@@ -0,0 +1,76 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements the Basic Iterative Method attack `BasicIterativeMethod` as the iterative version of FGM and
+FGSM. This is a white-box attack.
+
+| Paper link: https://arxiv.org/abs/1607.02533
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+from typing import Union, TYPE_CHECKING
+
+import numpy as np
+
+from art.attacks.evasion.projected_gradient_descent.projected_gradient_descent import ProjectedGradientDescent
+
+if TYPE_CHECKING:
+    from art.utils import CLASSIFIER_LOSS_GRADIENTS_TYPE
+
+logger = logging.getLogger(__name__)
+
+
+class BasicIterativeMethod(ProjectedGradientDescent):
+    """
+    The Basic Iterative Method is the iterative version of FGM and FGSM.
+
+    | Paper link: https://arxiv.org/abs/1607.02533
+    """
+
+    attack_params = ProjectedGradientDescent.attack_params
+
+    def __init__(
+        self,
+        estimator: "CLASSIFIER_LOSS_GRADIENTS_TYPE",
+        eps: Union[int, float, np.ndarray] = 0.3,
+        eps_step: Union[int, float, np.ndarray] = 0.1,
+        max_iter: int = 100,
+        targeted: bool = False,
+        batch_size: int = 32,
+    ) -> None:
+        """
+        Create a :class:`.ProjectedGradientDescent` instance.
+
+        :param estimator: A trained classifier.
+        :param eps: Maximum perturbation that the attacker can introduce.
+        :param eps_step: Attack step size (input variation) at each iteration.
+        :param max_iter: The maximum number of iterations.
+        :param targeted: Indicates whether the attack is targeted (True) or untargeted (False).
+        :param batch_size: Size of the batch on which adversarial samples are generated.
+        """
+        super().__init__(
+            estimator=estimator,
+            norm=np.inf,
+            eps=eps,
+            eps_step=eps_step,
+            max_iter=max_iter,
+            targeted=targeted,
+            num_random_init=0,
+            batch_size=batch_size,
+        )
diff --git a/adversarial-robustness-toolbox/art/attacks/evasion/newtonfool.py b/adversarial-robustness-toolbox/art/attacks/evasion/newtonfool.py
new file mode 100644
index 0000000..405f8aa
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/attacks/evasion/newtonfool.py
@@ -0,0 +1,182 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements the white-box attack `NewtonFool`.
+
+| Paper link: http://doi.acm.org/10.1145/3134600.3134635
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+from typing import Optional, TYPE_CHECKING
+
+import numpy as np
+from tqdm.auto import trange
+
+from art.attacks.attack import EvasionAttack
+from art.config import ART_NUMPY_DTYPE
+from art.estimators.estimator import BaseEstimator
+from art.estimators.classification.classifier import ClassGradientsMixin
+from art.utils import to_categorical, compute_success
+
+if TYPE_CHECKING:
+    from art.utils import CLASSIFIER_CLASS_LOSS_GRADIENTS_TYPE
+
+logger = logging.getLogger(__name__)
+
+
+class NewtonFool(EvasionAttack):
+    """
+    Implementation of the attack from Uyeong Jang et al. (2017).
+
+    | Paper link: http://doi.acm.org/10.1145/3134600.3134635
+    """
+
+    attack_params = EvasionAttack.attack_params + ["max_iter", "eta", "batch_size", "verbose"]
+    _estimator_requirements = (BaseEstimator, ClassGradientsMixin)
+
+    def __init__(
+        self,
+        classifier: "CLASSIFIER_CLASS_LOSS_GRADIENTS_TYPE",
+        max_iter: int = 100,
+        eta: float = 0.01,
+        batch_size: int = 1,
+        verbose: bool = True,
+    ) -> None:
+        """
+        Create a NewtonFool attack instance.
+
+        :param classifier: A trained classifier.
+        :param max_iter: The maximum number of iterations.
+        :param eta: The eta coefficient.
+        :param batch_size: Size of the batch on which adversarial samples are generated.
+        :param verbose: Show progress bars.
+        """
+        super().__init__(estimator=classifier)
+        self.max_iter = max_iter
+        self.eta = eta
+        self.batch_size = batch_size
+        self.verbose = verbose
+        self._check_params()
+
+    def generate(self, x: np.ndarray, y: Optional[np.ndarray] = None, **kwargs) -> np.ndarray:
+        """
+        Generate adversarial samples and return them in a Numpy array.
+
+        :param x: An array with the original inputs to be attacked.
+        :param y: An array with the original labels to be predicted.
+        :return: An array holding the adversarial examples.
+        """
+        x_adv = x.astype(ART_NUMPY_DTYPE)
+
+        # Initialize variables
+        y_pred = self.estimator.predict(x, batch_size=self.batch_size)
+        pred_class = np.argmax(y_pred, axis=1)
+
+        # Compute perturbation with implicit batching
+        for batch_id in trange(
+            int(np.ceil(x_adv.shape[0] / float(self.batch_size))), desc="NewtonFool", disable=not self.verbose
+        ):
+            batch_index_1, batch_index_2 = batch_id * self.batch_size, (batch_id + 1) * self.batch_size
+            batch = x_adv[batch_index_1:batch_index_2]
+
+            # Main algorithm for each batch
+            norm_batch = np.linalg.norm(np.reshape(batch, (batch.shape[0], -1)), axis=1)
+            l_batch = pred_class[batch_index_1:batch_index_2]
+            l_b = to_categorical(l_batch, self.estimator.nb_classes).astype(bool)
+
+            # Main loop of the algorithm
+            for _ in range(self.max_iter):
+                # Compute score
+                score = self.estimator.predict(batch)[l_b]
+
+                # Compute the gradients and norm
+                grads = self.estimator.class_gradient(batch, label=l_batch)
+                if grads.shape[1] == 1:
+                    grads = np.squeeze(grads, axis=1)
+                norm_grad = np.linalg.norm(np.reshape(grads, (batch.shape[0], -1)), axis=1)
+
+                # Theta
+                theta = self._compute_theta(norm_batch, score, norm_grad)
+
+                # Perturbation
+                di_batch = self._compute_pert(theta, grads, norm_grad)
+
+                # Update xi and perturbation
+                batch += di_batch
+
+            # Apply clip
+            if self.estimator.clip_values is not None:
+                clip_min, clip_max = self.estimator.clip_values
+                x_adv[batch_index_1:batch_index_2] = np.clip(batch, clip_min, clip_max)
+            else:
+                x_adv[batch_index_1:batch_index_2] = batch
+
+        logger.info(
+            "Success rate of NewtonFool attack: %.2f%%",
+            100 * compute_success(self.estimator, x, y, x_adv, batch_size=self.batch_size),
+        )
+        return x_adv
+
+    def _compute_theta(self, norm_batch: np.ndarray, score: np.ndarray, norm_grad: np.ndarray) -> np.ndarray:
+        """
+        Function to compute the theta at each step.
+
+        :param norm_batch: Norm of a batch.
+        :param score: Softmax value at the attacked class.
+        :param norm_grad: Norm of gradient values at the attacked class.
+        :return: Theta value.
+        """
+        equ1 = self.eta * norm_batch * norm_grad
+        equ2 = score - 1.0 / self.estimator.nb_classes
+        result = np.minimum.reduce([equ1, equ2])
+
+        return result
+
+    @staticmethod
+    def _compute_pert(theta: np.ndarray, grads: np.ndarray, norm_grad: np.ndarray) -> np.ndarray:
+        """
+        Function to compute the perturbation at each step.
+
+        :param theta: Theta value at the current step.
+        :param grads: Gradient values at the attacked class.
+        :param norm_grad: Norm of gradient values at the attacked class.
+        :return: Computed perturbation.
+        """
+        # Pick a small scalar to avoid division by 0
+        tol = 10e-8
+
+        nom = -theta.reshape((-1,) + (1,) * (len(grads.shape) - 1)) * grads
+        denom = norm_grad ** 2
+        denom[denom < tol] = tol
+        result = nom / denom.reshape((-1,) + (1,) * (len(grads.shape) - 1))
+
+        return result
+
+    def _check_params(self) -> None:
+        if not isinstance(self.max_iter, (int, np.int)) or self.max_iter <= 0:
+            raise ValueError("The number of iterations must be a positive integer.")
+
+        if not isinstance(self.eta, (float, int, np.int)) or self.eta <= 0:
+            raise ValueError("The eta coefficient must be a positive float.")
+
+        if self.batch_size <= 0:
+            raise ValueError("The batch size `batch_size` has to be positive.")
+
+        if not isinstance(self.verbose, bool):
+            raise ValueError("The argument `verbose` has to be of type bool.")
diff --git a/adversarial-robustness-toolbox/art/attacks/evasion/pixel_threshold.py b/adversarial-robustness-toolbox/art/attacks/evasion/pixel_threshold.py
new file mode 100644
index 0000000..efdb8de
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/attacks/evasion/pixel_threshold.py
@@ -0,0 +1,1455 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements the Threshold Attack and Pixel Attack.
+The Pixel Attack is a generalisation of One Pixel Attack.
+
+| One Pixel Attack Paper link:
+    https://ieeexplore.ieee.org/abstract/document/8601309/citations#citations
+    (arXiv link: https://arxiv.org/pdf/1710.08864.pdf)
+| Pixel and Threshold Attack Paper link:
+    https://arxiv.org/abs/1906.06026
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+from itertools import product
+from typing import List, Optional, Tuple, Union, TYPE_CHECKING
+
+import numpy as np
+
+# Currently, a modified version of SciPy's differential evolution is used in
+# code. An ideal version would be using the import as follows,
+# from scipy.optimize import differential_evolution
+# In the meantime, the modified implementation is used which is defined in the
+# lines `453-1457`.
+
+from six import string_types
+from scipy._lib._util import check_random_state
+from scipy.optimize.optimize import _status_message
+from scipy.optimize import OptimizeResult, minimize
+from tqdm.auto import tqdm
+
+from art.attacks.attack import EvasionAttack
+from art.estimators.estimator import BaseEstimator, NeuralNetworkMixin
+from art.estimators.classification.classifier import ClassifierMixin
+from art.utils import compute_success, to_categorical, check_and_transform_label_format
+
+if TYPE_CHECKING:
+    from art.utils import CLASSIFIER_NEURALNETWORK_TYPE
+
+logger = logging.getLogger(__name__)
+
+
+class PixelThreshold(EvasionAttack):
+    """
+    These attacks were originally implemented by Vargas et al. (2019) & Su et al.(2019).
+
+    | One Pixel Attack Paper link:
+        https://ieeexplore.ieee.org/abstract/document/8601309/citations#citations
+        (arXiv link: https://arxiv.org/pdf/1710.08864.pdf)
+    | Pixel and Threshold Attack Paper link:
+        https://arxiv.org/abs/1906.06026
+    """
+
+    attack_params = EvasionAttack.attack_params + ["th", "es", "targeted", "verbose"]
+    _estimator_requirements = (BaseEstimator, NeuralNetworkMixin, ClassifierMixin)
+
+    def __init__(
+        self,
+        classifier: "CLASSIFIER_NEURALNETWORK_TYPE",
+        th: Optional[int],
+        es: int,
+        targeted: bool,
+        verbose: bool = True,
+    ) -> None:
+        """
+        Create a :class:`.PixelThreshold` instance.
+
+        :param classifier: A trained classifier.
+        :param th: threshold value of the Pixel/ Threshold attack. th=None indicates finding a minimum threshold.
+        :param es: Indicates whether the attack uses CMAES (0) or DE (1) as Evolutionary Strategy.
+        :param targeted: Indicates whether the attack is targeted (True) or untargeted (False).
+        :param verbose: Print verbose messages of ES and show progress bars.
+        """
+        super().__init__(estimator=classifier)
+
+        self._project = True
+        self.type_attack = -1
+        self.th = th
+        self.es = es
+        self._targeted = targeted
+        self.verbose = verbose
+        PixelThreshold._check_params(self)
+
+        if self.estimator.channels_first:
+            self.img_rows = self.estimator.input_shape[-2]
+            self.img_cols = self.estimator.input_shape[-1]
+            self.img_channels = self.estimator.input_shape[-3]
+        else:
+            self.img_rows = self.estimator.input_shape[-3]
+            self.img_cols = self.estimator.input_shape[-2]
+            self.img_channels = self.estimator.input_shape[-1]
+
+    def _check_params(self) -> None:
+        if self.th is not None:
+            if self.th <= 0:
+                raise ValueError("The perturbation size `eps` has to be positive.")
+
+        if not isinstance(self.es, int):
+            raise ValueError("The flag `es` has to be of type int.")
+
+        if not isinstance(self.targeted, bool):
+            raise ValueError("The flag `targeted` has to be of type bool.")
+
+        if not isinstance(self.verbose, bool):
+            raise ValueError("The flag `verbose` has to be of type bool.")
+
+        if not isinstance(self.verbose, bool):
+            raise ValueError("The argument `verbose` has to be of type bool.")
+
+    def generate(self, x: np.ndarray, y: Optional[np.ndarray] = None, max_iter: int = 100, **kwargs) -> np.ndarray:
+        """
+        Generate adversarial samples and return them in an array.
+
+        :param x: An array with the original inputs.
+        :param y: Target values (class labels) one-hot-encoded of shape (nb_samples, nb_classes) or indices of shape
+                  (nb_samples,). Only provide this parameter if you'd like to use true labels when crafting adversarial
+                  samples. Otherwise, model predictions are used as labels to avoid the "label leaking" effect
+                  (explained in this paper: https://arxiv.org/abs/1611.01236). Default is `None`.
+        :param max_iter: Maximum number of optimisation iterations.
+        :return: An array holding the adversarial examples.
+        """
+        y = check_and_transform_label_format(y, self.estimator.nb_classes, return_one_hot=False)
+
+        if y is None:
+            if self.targeted:
+                raise ValueError("Target labels `y` need to be provided for a targeted attack.")
+            y = np.argmax(self.estimator.predict(x), axis=1)
+        else:
+            if len(y.shape) > 1:
+                y = np.argmax(y, axis=1)
+
+        if self.th is None:
+            logger.info("Performing minimal perturbation Attack.")
+
+        if np.max(x) <= 1:
+            scale_input = True
+        else:
+            scale_input = False
+
+        if scale_input:
+            x = x * 255.0
+
+        adv_x_best = []
+        for image, target_class in tqdm(zip(x, y), desc="Pixel threshold", disable=not self.verbose):
+            if self.th is None:
+                self.min_th = 127
+                start, end = 1, 127
+                while True:
+                    image_result: Union[List[np.ndarray], np.ndarray] = []
+                    threshold = (start + end) // 2
+                    success, trial_image_result = self._attack(image, target_class, threshold, max_iter)
+                    if image_result or success:
+                        image_result = trial_image_result
+                    if success:
+                        end = threshold - 1
+                    else:
+                        start = threshold + 1
+                    if success:
+                        self.min_th = threshold
+                    if end < start:
+                        if isinstance(image_result, list) and not image_result:
+                            # success = False
+                            image_result = image
+                        break
+            else:
+                success, image_result = self._attack(image, target_class, self.th, max_iter)
+            adv_x_best += [image_result]
+
+        adv_x_best = np.array(adv_x_best)
+
+        if scale_input:
+            adv_x_best = adv_x_best / 255.0
+
+        if y is not None:
+            y = to_categorical(y, self.estimator.nb_classes)
+
+        logger.info(
+            "Success rate of Attack: %.2f%%", 100 * compute_success(self.estimator, x, y, adv_x_best, self.targeted, 1),
+        )
+        return adv_x_best
+
+    def _get_bounds(self, img: np.ndarray, limit) -> Tuple[List[list], list]:
+        """
+        Define the bounds for the image `img` within the limits `limit`.
+        """
+
+        def bound_limit(value):
+            return np.clip(value - limit, 0, 255), np.clip(value + limit, 0, 255)
+
+        minbounds, maxbounds, bounds, initial = [], [], [], []
+
+        for i, j, k in product(range(img.shape[-3]), range(img.shape[-2]), range(img.shape[-1])):
+            temp = img[i, j, k]
+            initial += [temp]
+            bound = bound_limit(temp)
+            if self.es == 0:
+                minbounds += [bound[0]]
+                maxbounds += [bound[1]]
+            else:
+                bounds += [bound]
+        if self.es == 0:
+            bounds = [minbounds, maxbounds]
+
+        return bounds, initial
+
+    def _perturb_image(self, x: np.ndarray, img: np.ndarray) -> np.ndarray:
+        """
+        Perturbs the given image `img` with the given perturbation `x`.
+        """
+        return img
+
+    def _attack_success(self, adv_x, x, target_class):
+        """
+        Checks whether the given perturbation `adv_x` for the image `img` is successful.
+        """
+        predicted_class = np.argmax(self.estimator.predict(self._perturb_image(adv_x, x))[0])
+        return bool(
+            (self.targeted and predicted_class == target_class)
+            or (not self.targeted and predicted_class != target_class)
+        )
+
+    def _attack(
+        self, image: np.ndarray, target_class: np.ndarray, limit: int, max_iter: int
+    ) -> Tuple[bool, np.ndarray]:
+        """
+        Attack the given image `image` with the threshold `limit` for the `target_class` which is true label for
+        untargeted attack and targeted label for targeted attack.
+        """
+        bounds, initial = self._get_bounds(image, limit)
+
+        def predict_fn(x):
+            predictions = self.estimator.predict(self._perturb_image(x, image))[:, target_class]
+            return predictions if not self.targeted else 1 - predictions
+
+        def callback_fn(x, convergence=None):
+            if self.es == 0:
+                if self._attack_success(x.result[0], image, target_class):
+                    raise Exception("Attack Completed :) Earlier than expected")
+            else:
+                return self._attack_success(x, image, target_class)
+
+        if self.es == 0:
+            from cma import CMAOptions
+
+            opts = CMAOptions()
+            if not self.verbose:
+                opts.set("verbose", -9)
+                opts.set("verb_disp", 40000)
+                opts.set("verb_log", 40000)
+                opts.set("verb_time", False)
+
+            opts.set("bounds", bounds)
+
+            if self.type_attack == 0:
+                std = 63
+            else:
+                std = limit
+
+            from cma import CMAEvolutionStrategy
+
+            strategy = CMAEvolutionStrategy(initial, std / 4, opts)
+
+            try:
+                strategy.optimize(
+                    predict_fn,
+                    maxfun=max(1, 400 // len(bounds)) * len(bounds) * 100,
+                    callback=callback_fn,
+                    iterations=max_iter,
+                )
+            except Exception as exception:
+                if self.verbose:
+                    print(exception)
+
+            adv_x = strategy.result[0]
+        else:
+            strategy = differential_evolution(
+                predict_fn,
+                bounds,
+                disp=self.verbose,
+                maxiter=max_iter,
+                popsize=max(1, 400 // len(bounds)),
+                recombination=1,
+                atol=-1,
+                callback=callback_fn,
+                polish=False,
+            )
+            adv_x = strategy.x
+
+        if self._attack_success(adv_x, image, target_class):
+            return True, self._perturb_image(adv_x, image)[0]
+        else:
+            return False, image
+
+
+class PixelAttack(PixelThreshold):
+    """
+    This attack was originally implemented by Vargas et al. (2019). It is generalisation of One Pixel Attack originally
+    implemented by Su et al. (2019).
+
+    | One Pixel Attack Paper link:
+        https://ieeexplore.ieee.org/abstract/document/8601309/citations#citations
+        (arXiv link: https://arxiv.org/pdf/1710.08864.pdf)
+    | Pixel Attack Paper link:
+        https://arxiv.org/abs/1906.06026
+    """
+
+    def __init__(
+        self,
+        classifier: "CLASSIFIER_NEURALNETWORK_TYPE",
+        th: Optional[int] = None,
+        es: int = 0,
+        targeted: bool = False,
+        verbose: bool = False,
+    ) -> None:
+        """
+        Create a :class:`.PixelAttack` instance.
+
+        :param classifier: A trained classifier.
+        :param th: threshold value of the Pixel/ Threshold attack. th=None indicates finding a minimum threshold.
+        :param es: Indicates whether the attack uses CMAES (0) or DE (1) as Evolutionary Strategy.
+        :param targeted: Indicates whether the attack is targeted (True) or untargeted (False).
+        :param verbose: Indicates whether to print verbose messages of ES used.
+        """
+        super().__init__(classifier, th, es, targeted, verbose)
+        self.type_attack = 0
+
+    def _perturb_image(self, x: np.ndarray, img: np.ndarray) -> np.ndarray:
+        """
+        Perturbs the given image `img` with the given perturbation `x`.
+        """
+        if x.ndim < 2:
+            x = np.array([x])
+        imgs = np.tile(img, [len(x)] + [1] * (x.ndim + 1))
+        x = x.astype(int)
+        for adv, image in zip(x, imgs):
+            for pixel in np.split(adv, len(adv) // (2 + self.img_channels)):
+                x_pos, y_pos, *rgb = pixel
+                if not self.estimator.channels_first:
+                    image[x_pos % self.img_rows, y_pos % self.img_cols] = rgb
+                else:
+                    image[:, x_pos % self.img_rows, y_pos % self.img_cols] = rgb
+        return imgs
+
+    def _get_bounds(self, img: np.ndarray, limit) -> Tuple[List[list], list]:
+        """
+        Define the bounds for the image `img` within the limits `limit`.
+        """
+        initial: List[np.ndarray] = []
+        bounds: List[List[int]]
+        if self.es == 0:
+            for count, (i, j) in enumerate(product(range(self.img_rows), range(self.img_cols))):
+                initial += [i, j]
+                for k in range(self.img_channels):
+                    if not self.estimator.channels_first:
+                        initial += [img[i, j, k]]
+                    else:
+                        initial += [img[k, i, j]]
+
+                if count == limit - 1:
+                    break
+                else:
+                    continue
+            min_bounds = [0, 0]
+            for _ in range(self.img_channels):
+                min_bounds += [0]
+            min_bounds = min_bounds * limit
+            max_bounds = [self.img_rows, self.img_cols]
+            for _ in range(self.img_channels):
+                max_bounds += [255]
+            max_bounds = max_bounds * limit
+            bounds = [min_bounds, max_bounds]
+        else:
+            bounds = [[0, self.img_rows], [0, self.img_cols]]
+            for _ in range(self.img_channels):
+                bounds += [[0, 255]]
+            bounds = bounds * limit
+        return bounds, initial
+
+
+class ThresholdAttack(PixelThreshold):
+    """
+    This attack was originally implemented by Vargas et al. (2019).
+
+    | Paper link:
+        https://arxiv.org/abs/1906.06026
+    """
+
+    def __init__(
+        self,
+        classifier: "CLASSIFIER_NEURALNETWORK_TYPE",
+        th: Optional[int] = None,
+        es: int = 0,
+        targeted: bool = False,
+        verbose: bool = False,
+    ) -> None:
+        """
+        Create a :class:`.PixelThreshold` instance.
+
+        :param classifier: A trained classifier.
+        :param th: threshold value of the Pixel/ Threshold attack. th=None indicates finding a minimum threshold.
+        :param es: Indicates whether the attack uses CMAES (0) or DE (1) as Evolutionary Strategy.
+        :param targeted: Indicates whether the attack is targeted (True) or untargeted (False).
+        :param verbose: Indicates whether to print verbose messages of ES used.
+        """
+        super().__init__(classifier, th, es, targeted, verbose)
+        self.type_attack = 1
+
+    def _perturb_image(self, x: np.ndarray, img: np.ndarray) -> np.ndarray:
+        """
+        Perturbs the given image `img` with the given perturbation `x`.
+        """
+        if x.ndim < 2:
+            x = x[None, ...]
+        imgs = np.tile(img, [len(x)] + [1] * (x.ndim + 1))
+        x = x.astype(int)
+        for adv, image in zip(x, imgs):
+            for count, (i, j, k) in enumerate(
+                product(range(image.shape[-3]), range(image.shape[-2]), range(image.shape[-1]),)
+            ):
+                image[i, j, k] = adv[count]
+        return imgs
+
+
+# TODO: Make the attack compatible with current version of SciPy Optimize
+# Differential Evolution
+
+"""
+A slight modification to Scipy's implementation of differential evolution.
+To speed up predictions, the entire parameters array is passed to `self.func`,
+where a neural network model can batch its computations and execute in parallel
+Search for `CHANGES` to find all code changes.
+
+Dan Kondratyuk 2018
+
+Original code adapted from
+https://github.com/scipy/scipy/blob/70e61dee181de23fdd8d893eaa9491100e2218d7/scipy/optimize/_differentialevolution.py
+----------
+differential_evolution:The differential evolution global optimization algorithm
+Added by Andrew Nelson 2014
+"""
+
+# Copyright (c) 2001, 2002 Enthought, Inc.
+# All rights reserved.
+# Copyright (c) 2003-2017 SciPy Developers.
+# All rights reserved.
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+#   a. Redistributions of source code must retain the above copyright notice,
+#      this list of conditions and the following disclaimer.
+#   b. Redistributions in binary form must reproduce the above copyright
+#      notice, this list of conditions and the following disclaimer in the
+#      documentation and/or other materials provided with the distribution.
+#   c. Neither the name of Enthought nor the names of the SciPy Developers
+#      may be used to endorse or promote products derived from this software
+#      without specific prior written permission.
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS
+# BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY,
+# OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF
+# THE POSSIBILITY OF SUCH DAMAGE.
+# SciPy bundles a number of libraries that are compatibly licensed.  We list
+# these here.
+# Name: Numpydoc
+# Files: doc/sphinxext/numpydoc/*
+# License: 2-clause BSD
+#   For details, see doc/sphinxext/LICENSE.txt
+# Name: scipy-sphinx-theme
+# Files: doc/scipy-sphinx-theme/*
+# License: 3-clause BSD, PSF and Apache 2.0
+#   For details, see doc/sphinxext/LICENSE.txt
+# Name: Six
+# Files: scipy/_lib/six.py
+# License: MIT
+#   For details, see the header inside scipy/_lib/six.py
+# Name: Decorator
+# Files: scipy/_lib/decorator.py
+# License: 2-clause BSD
+#   For details, see the header inside scipy/_lib/decorator.py
+# Name: ID
+# Files: scipy/linalg/src/id_dist/*
+# License: 3-clause BSD
+#   For details, see scipy/linalg/src/id_dist/doc/doc.tex
+# Name: L-BFGS-B
+# Files: scipy/optimize/lbfgsb/*
+# License: BSD license
+#   For details, see scipy/optimize/lbfgsb/README
+# Name: SuperLU
+# Files: scipy/sparse/linalg/dsolve/SuperLU/*
+# License: 3-clause BSD
+#   For details, see scipy/sparse/linalg/dsolve/SuperLU/License.txt
+# Name: ARPACK
+# Files: scipy/sparse/linalg/eigen/arpack/ARPACK/*
+# License: 3-clause BSD
+#   For details, see scipy/sparse/linalg/eigen/arpack/ARPACK/COPYING
+# Name: Qhull
+# Files: scipy/spatial/qhull/*
+# License: Qhull license (BSD-like)
+#   For details, see scipy/spatial/qhull/COPYING.txt
+# Name: Cephes
+# Files: scipy/special/cephes/*
+# License: 3-clause BSD
+#   Distributed under 3-clause BSD license with permission from the author,
+#   see https://lists.debian.org/debian-legal/2004/12/msg00295.html
+#   Cephes Math Library Release 2.8:  June, 2000
+#   Copyright 1984, 1995, 2000 by Stephen L. Moshier
+#   This software is derived from the Cephes Math Library and is
+#   incorporated herein by permission of the author.
+#   All rights reserved.
+#   Redistribution and use in source and binary forms, with or without
+#   modification, are permitted provided that the following conditions are met:
+#       * Redistributions of source code must retain the above copyright
+#         notice, this list of conditions and the following disclaimer.
+#       * Redistributions in binary form must reproduce the above copyright
+#         notice, this list of conditions and the following disclaimer in the
+#         documentation and/or other materials provided with the distribution.
+#       * Neither the name of the <organization> nor the
+#         names of its contributors may be used to endorse or promote products
+#         derived from this software without specific prior written permission.
+#   THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+#   ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+#   WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+#   DISCLAIMED. IN NO EVENT SHALL <COPYRIGHT HOLDER> BE LIABLE FOR ANY
+#   DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+#   (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+#   LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
+#   ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+#   (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+#   SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+# Name: Faddeeva
+# Files: scipy/special/Faddeeva.*
+# License: MIT
+#   Copyright (c) 2012 Massachusetts Institute of Technology
+#   Permission is hereby granted, free of charge, to any person obtaining
+#   a copy of this software and associated documentation files (the
+#   "Software"), to deal in the Software without restriction, including
+#   without limitation the rights to use, copy, modify, merge, publish,
+#   distribute, sublicense, and/or sell copies of the Software, and to
+#   permit persons to whom the Software is furnished to do so, subject to
+#   the following conditions:
+#   The above copyright notice and this permission notice shall be
+#   included in all copies or substantial portions of the Software.
+#   THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+#   EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+#   MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+#   NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE
+#   LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
+#   OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
+#   WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+__all__ = ["differential_evolution"]
+
+_MACHEPS = np.finfo(np.float64).eps
+
+
+def differential_evolution(
+    func,
+    bounds,
+    args=(),
+    strategy="best1bin",
+    maxiter=1000,
+    popsize=15,
+    tol=0.01,
+    mutation=(0.5, 1),
+    recombination=0.7,
+    seed=None,
+    callback=None,
+    disp=False,
+    polish=True,
+    init="latinhypercube",
+    atol=0,
+):
+    """Finds the global minimum of a multivariate function.
+    Differential Evolution is stochastic in nature (does not use gradient
+    methods) to find the minimium, and can search large areas of candidate
+    space, but often requires larger numbers of function evaluations than
+    conventional gradient based techniques.
+    The algorithm is due to Storn and Price [1]_.
+    Parameters
+    ----------
+    func : callable
+        The objective function to be minimized.  Must be in the form
+        ``f(x, *args)``, where ``x`` is the argument in the form of a 1-D array
+        and ``args`` is a  tuple of any additional fixed parameters needed to
+        completely specify the function.
+    bounds : sequence
+        Bounds for variables.  ``(min, max)`` pairs for each element in ``x``,
+        defining the lower and upper bounds for the optimizing argument of
+        `func`. It is required to have ``len(bounds) == len(x)``.
+        ``len(bounds)`` is used to determine the number of parameters in ``x``.
+    args : tuple, optional
+        Any additional fixed parameters needed to
+        completely specify the objective function.
+    strategy : str, optional
+        The differential evolution strategy to use. Should be one of:
+            - 'best1bin'
+            - 'best1exp'
+            - 'rand1exp'
+            - 'randtobest1exp'
+            - 'currenttobest1exp'
+            - 'best2exp'
+            - 'rand2exp'
+            - 'randtobest1bin'
+            - 'currenttobest1bin'
+            - 'best2bin'
+            - 'rand2bin'
+            - 'rand1bin'
+        The default is 'best1bin'.
+    maxiter : int, optional
+        The maximum number of generations over which the entire population is
+        evolved. The maximum number of function evaluations (with no polishing)
+        is: ``(maxiter + 1) * popsize * len(x)``
+    popsize : int, optional
+        A multiplier for setting the total population size.  The population has
+        ``popsize * len(x)`` individuals (unless the initial population is
+        supplied via the `init` keyword).
+    tol : float, optional
+        Relative tolerance for convergence, the solving stops when
+        ``np.std(pop) <= atol + tol * np.abs(np.mean(population_energies))``,
+        where and `atol` and `tol` are the absolute and relative tolerance
+        respectively.
+    mutation : float or tuple(float, float), optional
+        The mutation constant. In the literature this is also known as
+        differential weight, being denoted by F.
+        If specified as a float it should be in the range [0, 2].
+        If specified as a tuple ``(min, max)`` dithering is employed. Dithering
+        randomly changes the mutation constant on a generation by generation
+        basis. The mutation constant for that generation is taken from
+        ``U[min, max)``. Dithering can help speed convergence significantly.
+        Increasing the mutation constant increases the search radius, but will
+        slow down convergence.
+    recombination : float, optional
+        The recombination constant, should be in the range [0, 1]. In the
+        literature this is also known as the crossover probability, being
+        denoted by CR. Increasing this value allows a larger number of mutants
+        to progress into the next generation, but at the risk of population
+        stability.
+    seed : int or `np.random.RandomState`, optional
+        If `seed` is not specified the `np.RandomState` singleton is used.
+        If `seed` is an int, a new `np.random.RandomState` instance is used,
+        seeded with seed.
+        If `seed` is already a `np.random.RandomState instance`, then that
+        `np.random.RandomState` instance is used.
+        Specify `seed` for repeatable minimizations.
+    disp : bool, optional
+        Display status messages
+    callback : callable, `callback(xk, convergence=val)`, optional
+        A function to follow the progress of the minimization. ``xk`` is
+        the current value of ``x0``. ``val`` represents the fractional
+        value of the population convergence.  When ``val`` is greater than one
+        the function halts. If callback returns `True`, then the minimization
+        is halted (any polishing is still carried out).
+    polish : bool, optional
+        If True (default), then `scipy.optimize.minimize` with the `L-BFGS-B`
+        method is used to polish the best population member at the end, which
+        can improve the minimization slightly.
+    init : str or array-like, optional
+        Specify which type of population initialization is performed. Should be
+        one of:
+            - 'latinhypercube'
+            - 'random'
+            - array specifying the initial population. The array should have
+              shape ``(M, len(x))``, where len(x) is the number of parameters.
+              `init` is clipped to `bounds` before use.
+        The default is 'latinhypercube'. Latin Hypercube sampling tries to
+        maximize coverage of the available parameter space. 'random'
+        initializes the population randomly - this has the drawback that
+        clustering can occur, preventing the whole of parameter space being
+        covered. Use of an array to specify a population subset could be used,
+        for example, to create a tight bunch of initial guesses in an location
+        where the solution is known to exist, thereby reducing time for
+        convergence.
+    atol : float, optional
+        Absolute tolerance for convergence, the solving stops when
+        ``np.std(pop) <= atol + tol * np.abs(np.mean(population_energies))``,
+        where and `atol` and `tol` are the absolute and relative tolerance
+        respectively.
+    Returns
+    -------
+    res : OptimizeResult
+        The optimization result represented as a `OptimizeResult` object.
+        Important attributes are: ``x`` the solution array, ``success`` a
+        Boolean flag indicating if the optimizer exited successfully and
+        ``message`` which describes the cause of the termination. See
+        `OptimizeResult` for a description of other attributes.  If `polish`
+        was employed, and a lower minimum was obtained by the polishing, then
+        OptimizeResult also contains the ``jac`` attribute.
+    Notes
+    -----
+    Differential evolution is a stochastic population based method that is
+    useful for global optimization problems. At each pass through the
+    population the algorithm mutates each candidate solution by mixing with
+    other candidate solutions to create a trial candidate. There are several
+    strategies [2]_ for creating trial candidates, which suit some problems
+    more than others. The 'best1bin' strategy is a good starting point for many
+    systems. In this strategy two members of the population are randomly
+    chosen. Their difference is used to mutate the best member (the `best` in
+    `best1bin`), :math:`b_0`,
+    so far:
+    .. math::
+        b' = b_0 + mutation * (population[rand0] - population[rand1])
+    A trial vector is then constructed. Starting with a randomly chosen 'i'th
+    parameter the trial is sequentially filled (in modulo) with parameters from
+    `b'` or the original candidate. The choice of whether to use `b'` or the
+    original candidate is made with a binomial distribution (the 'bin' in
+    'best1bin') - a random number in [0, 1) is generated.  If this number is
+    less than the `recombination` constant then the parameter is loaded from
+    `b'`, otherwise it is loaded from the original candidate.  The final
+    parameter is always loaded from `b'`.  Once the trial candidate is built
+    its fitness is assessed. If the trial is better than the original candidate
+    then it takes its place. If it is also better than the best overall
+    candidate it also replaces that.
+    To improve your chances of finding a global minimum use higher `popsize`
+    values, with higher `mutation` and (dithering), but lower `recombination`
+    values. This has the effect of widening the search radius, but slowing
+    convergence.
+    .. versionadded:: 0.15.0
+    Examples
+    --------
+    Let us consider the problem of minimizing the Rosenbrock function. This
+    function is implemented in `rosen` in `scipy.optimize`.
+    >>> from scipy.optimize import rosen, differential_evolution
+    >>> bounds = [(0,2), (0, 2), (0, 2), (0, 2), (0, 2)]
+    >>> result = differential_evolution(rosen, bounds)
+    >>> result.x, result.fun
+    (array([1., 1., 1., 1., 1.]), 1.9216496320061384e-19)
+    Next find the minimum of the Ackley function
+    (http://en.wikipedia.org/wiki/Test_functions_for_optimization).
+    >>> from scipy.optimize import differential_evolution
+    >>> import numpy as np
+    >>> def ackley(x):
+    ...     arg1 = -0.2 * np.sqrt(0.5 * (x[0] ** 2 + x[1] ** 2))
+    ...     arg2 = 0.5 * (np.cos(2. * np.pi * x[0]) + np.cos(2. * np.pi *x[1]))
+    ...     return -20. * np.exp(arg1) - np.exp(arg2) + 20. + np.e
+    >>> bounds = [(-5, 5), (-5, 5)]
+    >>> result = differential_evolution(ackley, bounds)
+    >>> result.x, result.fun
+    (array([ 0.,  0.]), 4.4408920985006262e-16)
+    References
+    ----------
+    .. [1] Storn, R and Price, K, Differential Evolution - a Simple and
+           Efficient Heuristic for Global Optimization over Continuous Spaces,
+           Journal of Global Optimization, 1997, 11, 341 - 359.
+    .. [2] http://www1.icsi.berkeley.edu/~storn/code.html
+    .. [3] http://en.wikipedia.org/wiki/Differential_evolution
+    """
+
+    solver = DifferentialEvolutionSolver(
+        func,
+        bounds,
+        args=args,
+        strategy=strategy,
+        maxiter=maxiter,
+        popsize=popsize,
+        tol=tol,
+        mutation=mutation,
+        recombination=recombination,
+        seed=seed,
+        polish=polish,
+        callback=callback,
+        disp=disp,
+        init=init,
+        atol=atol,
+    )
+    return solver.solve()
+
+
+class DifferentialEvolutionSolver:
+    """This class implements the differential evolution solver
+    Parameters
+    ----------
+    func : callable
+        The objective function to be minimized.  Must be in the form
+        ``f(x, *args)``, where ``x`` is the argument in the form of a 1-D array
+        and ``args`` is a  tuple of any additional fixed parameters needed to
+        completely specify the function.
+    bounds : sequence
+        Bounds for variables.  ``(min, max)`` pairs for each element in ``x``,
+        defining the lower and upper bounds for the optimizing argument of
+        `func`. It is required to have ``len(bounds) == len(x)``.
+        ``len(bounds)`` is used to determine the number of parameters in ``x``.
+    args : tuple, optional
+        Any additional fixed parameters needed to
+        completely specify the objective function.
+    strategy : str, optional
+        The differential evolution strategy to use. Should be one of:
+            - 'best1bin'
+            - 'best1exp'
+            - 'rand1exp'
+            - 'randtobest1exp'
+            - 'currenttobest1exp'
+            - 'best2exp'
+            - 'rand2exp'
+            - 'randtobest1bin'
+            - 'currenttobest1bin'
+            - 'best2bin'
+            - 'rand2bin'
+            - 'rand1bin'
+        The default is 'best1bin'
+    maxiter : int, optional
+        The maximum number of generations over which the entire population is
+        evolved. The maximum number of function evaluations (with no polishing)
+        is: ``(maxiter + 1) * popsize * len(x)``
+    popsize : int, optional
+        A multiplier for setting the total population size.  The population has
+        ``popsize * len(x)`` individuals (unless the initial population is
+        supplied via the `init` keyword).
+    tol : float, optional
+        Relative tolerance for convergence, the solving stops when
+        ``np.std(pop) <= atol + tol * np.abs(np.mean(population_energies))``,
+        where and `atol` and `tol` are the absolute and relative tolerance
+        respectively.
+    mutation : float or tuple(float, float), optional
+        The mutation constant. In the literature this is also known as
+        differential weight, being denoted by F.
+        If specified as a float it should be in the range [0, 2].
+        If specified as a tuple ``(min, max)`` dithering is employed. Dithering
+        randomly changes the mutation constant on a generation by generation
+        basis. The mutation constant for that generation is taken from
+        U[min, max). Dithering can help speed convergence significantly.
+        Increasing the mutation constant increases the search radius, but will
+        slow down convergence.
+    recombination : float, optional
+        The recombination constant, should be in the range [0, 1]. In the
+        literature this is also known as the crossover probability, being
+        denoted by CR. Increasing this value allows a larger number of mutants
+        to progress into the next generation, but at the risk of population
+        stability.
+    seed : int or `np.random.RandomState`, optional
+        If `seed` is not specified the `np.random.RandomState` singleton is
+        used.
+        If `seed` is an int, a new `np.random.RandomState` instance is used,
+        seeded with `seed`.
+        If `seed` is already a `np.random.RandomState` instance, then that
+        `np.random.RandomState` instance is used.
+        Specify `seed` for repeatable minimizations.
+    disp : bool, optional
+        Display status messages
+    callback : callable, `callback(xk, convergence=val)`, optional
+        A function to follow the progress of the minimization. ``xk`` is
+        the current value of ``x0``. ``val`` represents the fractional
+        value of the population convergence.  When ``val`` is greater than one
+        the function halts. If callback returns `True`, then the minimization
+        is halted (any polishing is still carried out).
+    polish : bool, optional
+        If True, then `scipy.optimize.minimize` with the `L-BFGS-B` method
+        is used to polish the best population member at the end. This requires
+        a few more function evaluations.
+    maxfun : int, optional
+        Set the maximum number of function evaluations. However, it probably
+        makes more sense to set `maxiter` instead.
+    init : str or array-like, optional
+        Specify which type of population initialization is performed. Should be
+        one of:
+            - 'latinhypercube'
+            - 'random'
+            - array specifying the initial population. The array should have
+              shape ``(M, len(x))``, where len(x) is the number of parameters.
+              `init` is clipped to `bounds` before use.
+        The default is 'latinhypercube'. Latin Hypercube sampling tries to
+        maximize coverage of the available parameter space. 'random'
+        initializes the population randomly - this has the drawback that
+        clustering can occur, preventing the whole of parameter space being
+        covered. Use of an array to specify a population could be used, for
+        example, to create a tight bunch of initial guesses in an location
+        where the solution is known to exist, thereby reducing time for
+        convergence.
+    atol : float, optional
+        Absolute tolerance for convergence, the solving stops when
+        ``np.std(pop) <= atol + tol * np.abs(np.mean(population_energies))``,
+        where and `atol` and `tol` are the absolute and relative tolerance
+        respectively.
+    """
+
+    # Dispatch of mutation strategy method (binomial or exponential).
+    _binomial = {
+        "best1bin": "_best1",
+        "randtobest1bin": "_randtobest1",
+        "currenttobest1bin": "_currenttobest1",
+        "best2bin": "_best2",
+        "rand2bin": "_rand2",
+        "rand1bin": "_rand1",
+    }
+    _exponential = {
+        "best1exp": "_best1",
+        "rand1exp": "_rand1",
+        "randtobest1exp": "_randtobest1",
+        "currenttobest1exp": "_currenttobest1",
+        "best2exp": "_best2",
+        "rand2exp": "_rand2",
+    }
+
+    __init_error_msg = (
+        "The population initialization method must be one of "
+        "'latinhypercube' or 'random', or an array of shape "
+        "(M, N) where N is the number of parameters and M>5"
+    )
+
+    def __init__(
+        self,
+        func,
+        bounds,
+        args=(),
+        strategy="best1bin",
+        maxiter=1000,
+        popsize=15,
+        tol=0.01,
+        mutation=(0.5, 1),
+        recombination=0.7,
+        seed=None,
+        maxfun=np.inf,
+        callback=None,
+        disp=False,
+        polish=True,
+        init="latinhypercube",
+        atol=0,
+    ):
+
+        if strategy in self._binomial:
+            self.mutation_func = getattr(self, self._binomial[strategy])
+        elif strategy in self._exponential:
+            self.mutation_func = getattr(self, self._exponential[strategy])
+        else:
+            raise ValueError("Please select a valid mutation strategy")
+        self.strategy = strategy
+
+        self.callback = callback
+        self.polish = polish
+
+        # relative and absolute tolerances for convergence
+        self.tol, self.atol = tol, atol
+
+        # Mutation constant should be in [0, 2). If specified as a sequence
+        # then dithering is performed.
+        self.scale = mutation
+        if not np.all(np.isfinite(mutation)) or np.any(np.array(mutation) >= 2) or np.any(np.array(mutation) < 0):
+            raise ValueError(
+                "The mutation constant must be a float in "
+                "U[0, 2), or specified as a tuple(min, max)"
+                " where min < max and min, max are in U[0, 2)."
+            )
+
+        self.dither = None
+        if hasattr(mutation, "__iter__") and len(mutation) > 1:
+            self.dither = [mutation[0], mutation[1]]
+            self.dither.sort()
+
+        self.cross_over_probability = recombination
+
+        self.func = func
+        self.args = args
+
+        # convert tuple of lower and upper bounds to limits
+        # [(low_0, high_0), ..., (low_n, high_n]
+        #     -> [[low_0, ..., low_n], [high_0, ..., high_n]]
+        self.limits = np.array(bounds, dtype="float").T
+        if np.size(self.limits, 0) != 2 or not np.all(np.isfinite(self.limits)):
+            raise ValueError(
+                "bounds should be a sequence containing " "real valued (min, max) pairs for each value" " in x"
+            )
+
+        if maxiter is None:  # the default used to be None
+            maxiter = 1000
+        self.maxiter = maxiter
+        if maxfun is None:  # the default used to be None
+            maxfun = np.inf
+        self.maxfun = maxfun
+
+        # population is scaled to between [0, 1].
+        # We have to scale between parameter <-> population
+        # save these arguments for _scale_parameter and
+        # _unscale_parameter. This is an optimization
+        self.__scale_arg1 = 0.5 * (self.limits[0] + self.limits[1])
+        self.__scale_arg2 = np.fabs(self.limits[0] - self.limits[1])
+
+        self.parameter_count = np.size(self.limits, 1)
+
+        self.random_number_generator = check_random_state(seed)
+
+        # default population initialization is a latin hypercube design, but
+        # there are other population initializations possible.
+        # the minimum is 5 because 'best2bin' requires a population that's at
+        # least 5 long
+        self.num_population_members = max(5, popsize * self.parameter_count)
+
+        self.population_shape = (self.num_population_members, self.parameter_count)
+
+        self._nfev = 0
+        if isinstance(init, string_types):
+            if init == "latinhypercube":
+                self.init_population_lhs()
+            elif init == "random":
+                self.init_population_random()
+            else:
+                raise ValueError(self.__init_error_msg)
+        else:
+            self.init_population_array(init)
+
+        self.disp = disp
+
+    def init_population_lhs(self):
+        """
+        Initializes the population with Latin Hypercube Sampling.
+        Latin Hypercube Sampling ensures that each parameter is uniformly
+        sampled over its range.
+        """
+        rng = self.random_number_generator
+
+        # Each parameter range needs to be sampled uniformly. The scaled
+        # parameter range ([0, 1)) needs to be split into
+        # `self.num_population_members` segments, each of which has the
+        # following size:
+        segsize = 1.0 / self.num_population_members
+
+        # Within each segment we sample from a uniform random distribution.
+        # We need to do this sampling for each parameter.
+        samples = (
+            segsize * rng.random_sample(self.population_shape)
+            # Offset each segment to cover the entire parameter range
+            # [0, 1)
+            + np.linspace(0.0, 1.0, self.num_population_members, endpoint=False)[:, np.newaxis]
+        )
+
+        # Create an array for population of candidate solutions.
+        self.population = np.zeros_like(samples)
+
+        # Initialize population of candidate solutions by permutation of the
+        # random samples.
+        for j in range(self.parameter_count):
+            order = rng.permutation(range(self.num_population_members))
+            self.population[:, j] = samples[order, j]
+
+        # reset population energies
+        self.population_energies = np.ones(self.num_population_members) * np.inf
+
+        # reset number of function evaluations counter
+        self._nfev = 0
+
+    def init_population_random(self):
+        """
+        Initialises the population at random.  This type of initialization
+        can possess clustering, Latin Hypercube sampling is generally better.
+        """
+        rng = self.random_number_generator
+        self.population = rng.random_sample(self.population_shape)
+
+        # reset population energies
+        self.population_energies = np.ones(self.num_population_members) * np.inf
+
+        # reset number of function evaluations counter
+        self._nfev = 0
+
+    def init_population_array(self, init):
+        """
+        Initialises the population with a user specified population.
+        Parameters
+        ----------
+        init : np.ndarray
+            Array specifying subset of the initial population. The array should
+            have shape (M, len(x)), where len(x) is the number of parameters.
+            The population is clipped to the lower and upper `bounds`.
+        """
+        # make sure you're using a float array
+        popn = np.asfarray(init)
+
+        if np.size(popn, 0) < 5 or popn.shape[1] != self.parameter_count or len(popn.shape) != 2:
+            raise ValueError("The population supplied needs to have shape" " (M, len(x)), where M > 4.")
+
+        # scale values and clip to bounds, assigning to population
+        self.population = np.clip(self._unscale_parameters(popn), 0, 1)
+
+        self.num_population_members = np.size(self.population, 0)
+
+        self.population_shape = (self.num_population_members, self.parameter_count)
+
+        # reset population energies
+        self.population_energies = np.ones(self.num_population_members) * np.inf
+
+        # reset number of function evaluations counter
+        self._nfev = 0
+
+    @property
+    def x(self):
+        """
+        The best solution from the solver
+        Returns
+        -------
+        x : ndarray
+            The best solution from the solver.
+        """
+        return self._scale_parameters(self.population[0])
+
+    @property
+    def convergence(self):
+        """
+        The standard deviation of the population energies divided by their
+        mean.
+        """
+        return np.std(self.population_energies) / np.abs(np.mean(self.population_energies) + _MACHEPS)
+
+    def solve(self):
+        """
+        Runs the DifferentialEvolutionSolver.
+        Returns
+        -------
+        res : OptimizeResult
+            The optimization result represented as a ``OptimizeResult`` object.
+            Important attributes are: ``x`` the solution array, ``success`` a
+            Boolean flag indicating if the optimizer exited successfully and
+            ``message`` which describes the cause of the termination. See
+            `OptimizeResult` for a description of other attributes. If `polish`
+            was employed, and a lower minimum was obtained by the polishing,
+            then OptimizeResult also contains the ``jac`` attribute.
+        """
+        nit, warning_flag = 0, False
+        status_message = _status_message["success"]
+
+        # The population may have just been initialized (all entries are
+        # np.inf). If it has you have to calculate the initial energies.
+        # Although this is also done in the evolve generator it's possible
+        # that someone can set maxiter=0, at which point we still want the
+        # initial energies to be calculated (the following loop isn't run).
+        if np.all(np.isinf(self.population_energies)):
+            self._calculate_population_energies()
+
+        # do the optimisation.
+        for nit in range(1, self.maxiter + 1):
+            # evolve the population by a generation
+            try:
+                next(self)
+            except StopIteration:
+                warning_flag = True
+                status_message = _status_message["maxfev"]
+                break
+
+            if self.disp:
+                print("differential_evolution step %d: f(x)= %g" % (nit, self.population_energies[0]))
+
+            # should the solver terminate?
+            convergence = self.convergence
+
+            if (
+                self.callback
+                and self.callback(self._scale_parameters(self.population[0]), convergence=self.tol / convergence,)
+                is True
+            ):
+                warning_flag = True
+                status_message = "callback function requested stop early " "by returning True"
+                break
+
+            intol = np.std(self.population_energies) <= self.atol + self.tol * np.abs(np.mean(self.population_energies))
+            if warning_flag or intol:
+                break
+
+        else:
+            status_message = _status_message["maxiter"]
+            warning_flag = True
+
+        de_result = OptimizeResult(
+            x=self.x,
+            fun=self.population_energies[0],
+            nfev=self._nfev,
+            nit=nit,
+            message=status_message,
+            success=(warning_flag is not True),
+        )
+
+        if self.polish:
+            result = minimize(self.func, np.copy(de_result.x), method="L-BFGS-B", bounds=self.limits.T, args=self.args,)
+
+            self._nfev += result.nfev
+            de_result.nfev = self._nfev
+
+            if result.fun < de_result.fun:
+                de_result.fun = result.fun
+                de_result.x = result.x
+                de_result.jac = result.jac
+                # to keep internal state consistent
+                self.population_energies[0] = result.fun
+                self.population[0] = self._unscale_parameters(result.x)
+
+        return de_result
+
+    def _calculate_population_energies(self):
+        """
+        Calculate the energies of all the population members at the same time.
+        Puts the best member in first place. Useful if the population has just
+        been initialised.
+        """
+
+        ##############
+        # CHANGES: self.func operates on the entire parameters array
+        ##############
+        itersize = max(0, min(len(self.population), self.maxfun - self._nfev + 1))
+        candidates = self.population[:itersize]
+        parameters = np.array([self._scale_parameters(c) for c in candidates])  # TODO: can be vectorized
+        energies = self.func(parameters, *self.args)
+        self.population_energies = energies
+        self._nfev += itersize
+
+        # for index, candidate in enumerate(self.population):
+        #     if self._nfev > self.maxfun:
+        #         break
+
+        #     parameters = self._scale_parameters(candidate)
+        #     self.population_energies[index] = self.func(parameters,
+        #                                                 *self.args)
+        #     self._nfev += 1
+
+        ##############
+        ##############
+
+        minval = np.argmin(self.population_energies)
+
+        # put the lowest energy into the best solution position.
+        lowest_energy = self.population_energies[minval]
+        self.population_energies[minval] = self.population_energies[0]
+        self.population_energies[0] = lowest_energy
+
+        self.population[[0, minval], :] = self.population[[minval, 0], :]
+
+    def __iter__(self):
+        return self
+
+    def __next__(self):
+        """
+        Evolve the population by a single generation
+        Returns
+        -------
+        x : ndarray
+            The best solution from the solver.
+        fun : float
+            Value of objective function obtained from the best solution.
+        """
+        # the population may have just been initialized (all entries are
+        # np.inf). If it has you have to calculate the initial energies
+        if np.all(np.isinf(self.population_energies)):
+            self._calculate_population_energies()
+
+        if self.dither is not None:
+            self.scale = self.random_number_generator.rand() * (self.dither[1] - self.dither[0]) + self.dither[0]
+
+        ##############
+        # CHANGES: self.func operates on the entire parameters array
+        ##############
+
+        itersize = max(0, min(self.num_population_members, self.maxfun - self._nfev + 1))
+        trials = np.array([self._mutate(c) for c in range(itersize)])  # TODO:can be vectorized
+        for trial in trials:
+            self._ensure_constraint(trial)
+        parameters = np.array([self._scale_parameters(trial) for trial in trials])
+        energies = self.func(parameters, *self.args)
+        self._nfev += itersize
+
+        for candidate, (energy, trial) in enumerate(zip(energies, trials)):
+            # if the energy of the trial candidate is lower than the
+            # original population member then replace it
+            if energy < self.population_energies[candidate]:
+                self.population[candidate] = trial
+                self.population_energies[candidate] = energy
+
+                # if the trial candidate also has a lower energy than the
+                # best solution then replace that as well
+                if energy < self.population_energies[0]:
+                    self.population_energies[0] = energy
+                    self.population[0] = trial
+
+        # for candidate in range(self.num_population_members):
+        #     if self._nfev > self.maxfun:
+        #         raise StopIteration
+
+        #     # create a trial solution
+        #     trial = self._mutate(candidate)
+
+        #     # ensuring that it's in the range [0, 1)
+        #     self._ensure_constraint(trial)
+
+        #     # scale from [0, 1) to the actual parameter value
+        #     parameters = self._scale_parameters(trial)
+
+        #     # determine the energy of the objective function
+        #     energy = self.func(parameters, *self.args)
+        #     self._nfev += 1
+
+        #     # if the energy of the trial candidate is lower than the
+        #     # original population member then replace it
+        #     if energy < self.population_energies[candidate]:
+        #         self.population[candidate] = trial
+        #         self.population_energies[candidate] = energy
+
+        #         # if the trial candidate also has a lower energy than the
+        #         # best solution then replace that as well
+        #         if energy < self.population_energies[0]:
+        #             self.population_energies[0] = energy
+        #             self.population[0] = trial
+
+        ##############
+        ##############
+
+        return self.x, self.population_energies[0]
+
+    def next(self):
+        """
+        Evolve the population by a single generation
+        Returns
+        -------
+        x : ndarray
+            The best solution from the solver.
+        fun : float
+            Value of objective function obtained from the best solution.
+        """
+        # next() is required for compatibility with Python2.7.
+        return self.__next__()
+
+    def _scale_parameters(self, trial):
+        """
+        scale from a number between 0 and 1 to parameters.
+        """
+        return self.__scale_arg1 + (trial - 0.5) * self.__scale_arg2
+
+    def _unscale_parameters(self, parameters):
+        """
+        scale from parameters to a number between 0 and 1.
+        """
+        return (parameters - self.__scale_arg1) / self.__scale_arg2 + 0.5
+
+    def _ensure_constraint(self, trial):
+        """
+        make sure the parameters lie between the limits
+        """
+        for index in np.where((trial < 0) | (trial > 1))[0]:
+            trial[index] = self.random_number_generator.rand()
+
+    def _mutate(self, candidate):
+        """
+        create a trial vector based on a mutation strategy
+        """
+        trial = np.copy(self.population[candidate])
+
+        rng = self.random_number_generator
+
+        fill_point = rng.randint(0, self.parameter_count)
+
+        if self.strategy in ["currenttobest1exp", "currenttobest1bin"]:
+            bprime = self.mutation_func(candidate, self._select_samples(candidate, 5))
+        else:
+            bprime = self.mutation_func(self._select_samples(candidate, 5))
+
+        if self.strategy in self._binomial:
+            crossovers = rng.rand(self.parameter_count)
+            crossovers = crossovers < self.cross_over_probability
+            # the last one is always from the bprime vector for binomial
+            # If you fill in modulo with a loop you have to set the last one to
+            # true. If you don't use a loop then you can have any random entry
+            # be True.
+            crossovers[fill_point] = True
+            trial = np.where(crossovers, bprime, trial)
+            return trial
+
+        elif self.strategy in self._exponential:
+            i = 0
+            while i < self.parameter_count and rng.rand() < self.cross_over_probability:
+                trial[fill_point] = bprime[fill_point]
+                fill_point = (fill_point + 1) % self.parameter_count
+                i += 1
+
+            return trial
+
+    def _best1(self, samples):
+        """
+        best1bin, best1exp
+        """
+        r_0, r_1 = samples[:2]
+        return self.population[0] + self.scale * (self.population[r_0] - self.population[r_1])
+
+    def _rand1(self, samples):
+        """
+        rand1bin, rand1exp
+        """
+        r_0, r_1, r_2 = samples[:3]
+        return self.population[r_0] + self.scale * (self.population[r_1] - self.population[r_2])
+
+    def _randtobest1(self, samples):
+        """
+        randtobest1bin, randtobest1exp
+        """
+        r_0, r_1, r_2 = samples[:3]
+        bprime = np.copy(self.population[r_0])
+        bprime += self.scale * (self.population[0] - bprime)
+        bprime += self.scale * (self.population[r_1] - self.population[r_2])
+        return bprime
+
+    def _currenttobest1(self, candidate, samples):
+        """
+        currenttobest1bin, currenttobest1exp
+        """
+        r_0, r_1 = samples[:2]
+        bprime = self.population[candidate] + self.scale * (
+            self.population[0] - self.population[candidate] + self.population[r_0] - self.population[r_1]
+        )
+        return bprime
+
+    def _best2(self, samples):
+        """
+        best2bin, best2exp
+        """
+        r_0, r_1, r_2, r_3 = samples[:4]
+        bprime = self.population[0] + self.scale * (
+            self.population[r_0] + self.population[r_1] - self.population[r_2] - self.population[r_3]
+        )
+
+        return bprime
+
+    def _rand2(self, samples):
+        """
+        rand2bin, rand2exp
+        """
+        r_0, r_1, r_2, r_3, r_4 = samples
+        bprime = self.population[r_0] + self.scale * (
+            self.population[r_1] + self.population[r_2] - self.population[r_3] - self.population[r_4]
+        )
+
+        return bprime
+
+    def _select_samples(self, candidate, number_samples):
+        """
+        obtain random integers from range(self.num_population_members),
+        without replacement.  You can't have the original candidate either.
+        """
+        idxs = list(range(self.num_population_members))
+        idxs.remove(candidate)
+        self.random_number_generator.shuffle(idxs)
+        idxs = idxs[:number_samples]
+        return idxs
diff --git a/adversarial-robustness-toolbox/art/attacks/evasion/projected_gradient_descent/__init__.py b/adversarial-robustness-toolbox/art/attacks/evasion/projected_gradient_descent/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/adversarial-robustness-toolbox/art/attacks/evasion/projected_gradient_descent/projected_gradient_descent.py b/adversarial-robustness-toolbox/art/attacks/evasion/projected_gradient_descent/projected_gradient_descent.py
new file mode 100644
index 0000000..d77bde9
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/attacks/evasion/projected_gradient_descent/projected_gradient_descent.py
@@ -0,0 +1,239 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements the Projected Gradient Descent attack `ProjectedGradientDescent` as an iterative method in which,
+after each iteration, the perturbation is projected on an lp-ball of specified radius (in addition to clipping the
+values of the adversarial sample so that it lies in the permitted data range). This is the attack proposed by Madry et
+al. for adversarial training.
+
+| Paper link: https://arxiv.org/abs/1706.06083
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+from typing import Optional, Union, TYPE_CHECKING
+
+import numpy as np
+
+from art.estimators.classification.pytorch import PyTorchClassifier
+from art.estimators.classification.tensorflow import TensorFlowV2Classifier
+from art.estimators.estimator import BaseEstimator, LossGradientsMixin
+from art.attacks.attack import EvasionAttack
+from art.attacks.evasion.projected_gradient_descent.projected_gradient_descent_numpy import (
+    ProjectedGradientDescentNumpy,
+)
+from art.attacks.evasion.projected_gradient_descent.projected_gradient_descent_pytorch import (
+    ProjectedGradientDescentPyTorch,
+)
+from art.attacks.evasion.projected_gradient_descent.projected_gradient_descent_tensorflow_v2 import (
+    ProjectedGradientDescentTensorFlowV2,
+)
+
+if TYPE_CHECKING:
+    from art.utils import CLASSIFIER_LOSS_GRADIENTS_TYPE
+
+logger = logging.getLogger(__name__)
+
+
+class ProjectedGradientDescent(EvasionAttack):
+    """
+    The Projected Gradient Descent attack is an iterative method in which, after each iteration, the perturbation is
+    projected on an lp-ball of specified radius (in addition to clipping the values of the adversarial sample so that it
+    lies in the permitted data range). This is the attack proposed by Madry et al. for adversarial training.
+
+    | Paper link: https://arxiv.org/abs/1706.06083
+    """
+
+    attack_params = EvasionAttack.attack_params + [
+        "norm",
+        "eps",
+        "eps_step",
+        "targeted",
+        "num_random_init",
+        "batch_size",
+        "max_iter",
+        "random_eps",
+        "verbose",
+    ]
+
+    _estimator_requirements = (BaseEstimator, LossGradientsMixin)
+
+    def __init__(
+        self,
+        estimator: "CLASSIFIER_LOSS_GRADIENTS_TYPE",
+        norm: Union[int, float, str] = np.inf,
+        eps: Union[int, float, np.ndarray] = 0.3,
+        eps_step: Union[int, float, np.ndarray] = 0.1,
+        max_iter: int = 100,
+        targeted: bool = False,
+        num_random_init: int = 0,
+        batch_size: int = 32,
+        random_eps: bool = False,
+        verbose: bool = True,
+    ):
+        """
+        Create a :class:`.ProjectedGradientDescent` instance.
+
+        :param estimator: An trained estimator.
+        :param norm: The norm of the adversarial perturbation supporting "inf", np.inf, 1 or 2.
+        :param eps: Maximum perturbation that the attacker can introduce.
+        :param eps_step: Attack step size (input variation) at each iteration.
+        :param random_eps: When True, epsilon is drawn randomly from truncated normal distribution. The literature
+                           suggests this for FGSM based training to generalize across different epsilons. eps_step
+                           is modified to preserve the ratio of eps / eps_step. The effectiveness of this
+                           method with PGD is untested (https://arxiv.org/pdf/1611.01236.pdf).
+        :param max_iter: The maximum number of iterations.
+        :param targeted: Indicates whether the attack is targeted (True) or untargeted (False).
+        :param num_random_init: Number of random initialisations within the epsilon ball. For num_random_init=0 starting
+                                at the original input.
+        :param batch_size: Size of the batch on which adversarial samples are generated.
+        :param verbose: Show progress bars.
+        """
+        super().__init__(estimator=estimator)
+
+        self.norm = norm
+        self.eps = eps
+        self.eps_step = eps_step
+        self.max_iter = max_iter
+        self.targeted = targeted
+        self.num_random_init = num_random_init
+        self.batch_size = batch_size
+        self.random_eps = random_eps
+        self.verbose = verbose
+        ProjectedGradientDescent._check_params(self)
+
+        self._attack: Union[
+            ProjectedGradientDescentPyTorch, ProjectedGradientDescentTensorFlowV2, ProjectedGradientDescentNumpy
+        ]
+        if isinstance(self.estimator, PyTorchClassifier) and self.estimator.all_framework_preprocessing:
+            self._attack = ProjectedGradientDescentPyTorch(
+                estimator=estimator,  # type: ignore
+                norm=norm,
+                eps=eps,
+                eps_step=eps_step,
+                max_iter=max_iter,
+                targeted=targeted,
+                num_random_init=num_random_init,
+                batch_size=batch_size,
+                random_eps=random_eps,
+                verbose=verbose,
+            )
+
+        elif isinstance(self.estimator, TensorFlowV2Classifier) and self.estimator.all_framework_preprocessing:
+            self._attack = ProjectedGradientDescentTensorFlowV2(
+                estimator=estimator,  # type: ignore
+                norm=norm,
+                eps=eps,
+                eps_step=eps_step,
+                max_iter=max_iter,
+                targeted=targeted,
+                num_random_init=num_random_init,
+                batch_size=batch_size,
+                random_eps=random_eps,
+                verbose=verbose,
+            )
+
+        else:
+            self._attack = ProjectedGradientDescentNumpy(
+                estimator=estimator,
+                norm=norm,
+                eps=eps,
+                eps_step=eps_step,
+                max_iter=max_iter,
+                targeted=targeted,
+                num_random_init=num_random_init,
+                batch_size=batch_size,
+                random_eps=random_eps,
+                verbose=verbose,
+            )
+
+    def generate(self, x: np.ndarray, y: Optional[np.ndarray] = None, **kwargs) -> np.ndarray:
+        """
+        Generate adversarial samples and return them in an array.
+
+        :param x: An array with the original inputs.
+        :param y: Target values (class labels) one-hot-encoded of shape `(nb_samples, nb_classes)` or indices of shape
+                  (nb_samples,). Only provide this parameter if you'd like to use true labels when crafting adversarial
+                  samples. Otherwise, model predictions are used as labels to avoid the "label leaking" effect
+                  (explained in this paper: https://arxiv.org/abs/1611.01236). Default is `None`.
+        :param mask: An array with a mask broadcastable to input `x` defining where to apply adversarial perturbations.
+                     Shape needs to be broadcastable to the shape of x and can also be of the same shape as `x`. Any
+                     features for which the mask is zero will not be adversarially perturbed.
+        :type mask: `np.ndarray`
+        :return: An array holding the adversarial examples.
+        """
+        logger.info("Creating adversarial samples.")
+        return self._attack.generate(x=x, y=y, **kwargs)
+
+    def set_params(self, **kwargs) -> None:
+        super().set_params(**kwargs)
+        self._attack.set_params(**kwargs)
+
+    def _check_params(self) -> None:
+
+        if self.norm not in [1, 2, np.inf, "inf"]:
+            raise ValueError('Norm order must be either 1, 2, `np.inf` or "inf".')
+
+        if not (
+            isinstance(self.eps, (int, float))
+            and isinstance(self.eps_step, (int, float))
+            or isinstance(self.eps, np.ndarray)
+            and isinstance(self.eps_step, np.ndarray)
+        ):
+            raise TypeError(
+                "The perturbation size `eps` and the perturbation step-size `eps_step` must have the same type of `int`"
+                ", `float`, or `np.ndarray`."
+            )
+
+        if isinstance(self.eps, (int, float)):
+            if self.eps < 0:
+                raise ValueError("The perturbation size `eps` has to be nonnegative.")
+        else:
+            if (self.eps < 0).any():
+                raise ValueError("The perturbation size `eps` has to be nonnegative.")
+
+        if isinstance(self.eps_step, (int, float)):
+            if self.eps_step <= 0:
+                raise ValueError("The perturbation step-size `eps_step` has to be positive.")
+        else:
+            if (self.eps_step <= 0).any():
+                raise ValueError("The perturbation step-size `eps_step` has to be positive.")
+
+        if isinstance(self.eps, np.ndarray) and isinstance(self.eps_step, np.ndarray):
+            if self.eps.shape != self.eps_step.shape:
+                raise ValueError(
+                    "The perturbation size `eps` and the perturbation step-size `eps_step` must have the same shape."
+                )
+
+        if not isinstance(self.targeted, bool):
+            raise ValueError("The flag `targeted` has to be of type bool.")
+
+        if not isinstance(self.num_random_init, (int, np.int)):
+            raise TypeError("The number of random initialisations has to be of type integer.")
+
+        if self.num_random_init < 0:
+            raise ValueError("The number of random initialisations `random_init` has to be greater than or equal to 0.")
+
+        if self.batch_size <= 0:
+            raise ValueError("The batch size `batch_size` has to be positive.")
+
+        if self.max_iter < 0:
+            raise ValueError("The number of iterations `max_iter` has to be a nonnegative integer.")
+
+        if not isinstance(self.verbose, bool):
+            raise ValueError("The verbose has to be a Boolean.")
diff --git a/adversarial-robustness-toolbox/art/attacks/evasion/projected_gradient_descent/projected_gradient_descent_numpy.py b/adversarial-robustness-toolbox/art/attacks/evasion/projected_gradient_descent/projected_gradient_descent_numpy.py
new file mode 100644
index 0000000..6e546f2
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/attacks/evasion/projected_gradient_descent/projected_gradient_descent_numpy.py
@@ -0,0 +1,378 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements the Projected Gradient Descent attack `ProjectedGradientDescent` as an iterative method in which,
+after each iteration, the perturbation is projected on an lp-ball of specified radius (in addition to clipping the
+values of the adversarial sample so that it lies in the permitted data range). This is the attack proposed by Madry et
+al. for adversarial training.
+
+| Paper link: https://arxiv.org/abs/1706.06083
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+from typing import Optional, Union, TYPE_CHECKING
+
+import numpy as np
+from scipy.stats import truncnorm
+from tqdm.auto import trange
+
+from art.attacks.evasion.fast_gradient import FastGradientMethod
+from art.config import ART_NUMPY_DTYPE
+from art.estimators.classification.classifier import ClassifierMixin
+from art.estimators.estimator import BaseEstimator, LossGradientsMixin
+from art.utils import compute_success, get_labels_np_array, check_and_transform_label_format, compute_success_array
+
+if TYPE_CHECKING:
+    from art.utils import CLASSIFIER_LOSS_GRADIENTS_TYPE, OBJECT_DETECTOR_TYPE
+
+logger = logging.getLogger(__name__)
+
+
+class ProjectedGradientDescentCommon(FastGradientMethod):
+    """
+    Common class for different variations of implementation of the Projected Gradient Descent attack. The attack is an
+    iterative method in which, after each iteration, the perturbation is projected on an lp-ball of specified radius (in
+    addition to clipping the values of the adversarial sample so that it lies in the permitted data range). This is the
+    attack proposed by Madry et al. for adversarial training.
+
+    | Paper link: https://arxiv.org/abs/1706.06083
+    """
+
+    attack_params = FastGradientMethod.attack_params + ["max_iter", "random_eps", "verbose"]
+    _estimator_requirements = (BaseEstimator, LossGradientsMixin)
+
+    def __init__(
+        self,
+        estimator: Union["CLASSIFIER_LOSS_GRADIENTS_TYPE", "OBJECT_DETECTOR_TYPE"],
+        norm: Union[int, float, str] = np.inf,
+        eps: Union[int, float, np.ndarray] = 0.3,
+        eps_step: Union[int, float, np.ndarray] = 0.1,
+        max_iter: int = 100,
+        targeted: bool = False,
+        num_random_init: int = 0,
+        batch_size: int = 32,
+        random_eps: bool = False,
+        verbose: bool = True,
+    ) -> None:
+        """
+        Create a :class:`.ProjectedGradientDescentCommon` instance.
+
+        :param estimator: A trained classifier.
+        :param norm: The norm of the adversarial perturbation supporting "inf", np.inf, 1 or 2.
+        :param eps: Maximum perturbation that the attacker can introduce.
+        :param eps_step: Attack step size (input variation) at each iteration.
+        :param random_eps: When True, epsilon is drawn randomly from truncated normal distribution. The literature
+            suggests this for FGSM based training to generalize across different epsilons. eps_step is
+            modified to preserve the ratio of eps / eps_step. The effectiveness of this method with PGD
+            is untested (https://arxiv.org/pdf/1611.01236.pdf).
+        :param max_iter: The maximum number of iterations.
+        :param targeted: Indicates whether the attack is targeted (True) or untargeted (False).
+        :param num_random_init: Number of random initialisations within the epsilon ball. For num_random_init=0
+            starting at the original input.
+        :param batch_size: Size of the batch on which adversarial samples are generated.
+        :param verbose: Show progress bars.
+        """
+        super().__init__(
+            estimator=estimator,  # type: ignore
+            norm=norm,
+            eps=eps,
+            eps_step=eps_step,
+            targeted=targeted,
+            num_random_init=num_random_init,
+            batch_size=batch_size,
+            minimal=False,
+        )
+        self.max_iter = max_iter
+        self.random_eps = random_eps
+        self.verbose = verbose
+        ProjectedGradientDescentCommon._check_params(self)
+
+        if self.random_eps:
+            if isinstance(eps, (int, float)):
+                lower, upper = 0, eps
+                mu, sigma = 0, (eps / 2)
+            else:
+                lower, upper = np.zeros_like(eps), eps
+                mu, sigma = np.zeros_like(eps), (eps / 2)
+
+            self.norm_dist = truncnorm((lower - mu) / sigma, (upper - mu) / sigma, loc=mu, scale=sigma)
+
+    def _random_eps(self):
+        """
+        Check whether random eps is enabled, then scale eps and eps_step appropriately.
+        """
+        if self.random_eps:
+            ratio = self.eps_step / self.eps
+
+            if isinstance(self.eps, (int, float)):
+                self.eps = np.round(self.norm_dist.rvs(1)[0], 10)
+            else:
+                self.eps = np.round(self.norm_dist.rvs(size=self.eps.shape), 10)
+
+            self.eps_step = ratio * self.eps
+
+    def _set_targets(self, x: np.ndarray, y: np.ndarray, classifier_mixin: bool = True) -> np.ndarray:
+        """
+        Check and set up targets.
+
+        :param x: An array with the original inputs.
+        :param y: Target values (class labels) one-hot-encoded of shape `(nb_samples, nb_classes)` or indices of shape
+                  (nb_samples,). Only provide this parameter if you'd like to use true labels when crafting adversarial
+                  samples. Otherwise, model predictions are used as labels to avoid the "label leaking" effect
+                  (explained in this paper: https://arxiv.org/abs/1611.01236). Default is `None`.
+        :param classifier_mixin: Whether the estimator is of type `ClassifierMixin`.
+        :return: The targets.
+        """
+        if classifier_mixin:
+            y = check_and_transform_label_format(y, self.estimator.nb_classes)
+
+        if y is None:
+            # Throw error if attack is targeted, but no targets are provided
+            if self.targeted:
+                raise ValueError("Target labels `y` need to be provided for a targeted attack.")
+
+            # Use model predictions as correct outputs
+            if classifier_mixin:
+                targets = get_labels_np_array(self.estimator.predict(x, batch_size=self.batch_size))
+            else:
+                targets = self.estimator.predict(x, batch_size=self.batch_size)
+
+        else:
+            targets = y
+
+        return targets
+
+    def _check_params(self) -> None:
+
+        if self.norm not in [1, 2, np.inf, "inf"]:
+            raise ValueError('Norm order must be either 1, 2, `np.inf` or "inf".')
+
+        if not (
+            isinstance(self.eps, (int, float))
+            and isinstance(self.eps_step, (int, float))
+            or isinstance(self.eps, np.ndarray)
+            and isinstance(self.eps_step, np.ndarray)
+        ):
+            raise TypeError(
+                "The perturbation size `eps` and the perturbation step-size `eps_step` must have the same type of `int`"
+                ", `float`, or `np.ndarray`."
+            )
+
+        if isinstance(self.eps, (int, float)):
+            if self.eps < 0:
+                raise ValueError("The perturbation size `eps` has to be nonnegative.")
+        else:
+            if (self.eps < 0).any():
+                raise ValueError("The perturbation size `eps` has to be nonnegative.")
+
+        if isinstance(self.eps_step, (int, float)):
+            if self.eps_step <= 0:
+                raise ValueError("The perturbation step-size `eps_step` has to be positive.")
+        else:
+            if (self.eps_step <= 0).any():
+                raise ValueError("The perturbation step-size `eps_step` has to be positive.")
+
+        if isinstance(self.eps, np.ndarray) and isinstance(self.eps_step, np.ndarray):
+            if self.eps.shape != self.eps_step.shape:
+                raise ValueError(
+                    "The perturbation size `eps` and the perturbation step-size `eps_step` must have the same shape."
+                )
+
+        if not isinstance(self.targeted, bool):
+            raise ValueError("The flag `targeted` has to be of type bool.")
+
+        if not isinstance(self.num_random_init, (int, np.int)):
+            raise TypeError("The number of random initialisations has to be of type integer.")
+
+        if self.num_random_init < 0:
+            raise ValueError("The number of random initialisations `random_init` has to be greater than or equal to 0.")
+
+        if self.batch_size <= 0:
+            raise ValueError("The batch size `batch_size` has to be positive.")
+
+        if self.max_iter < 0:
+            raise ValueError("The number of iterations `max_iter` has to be a nonnegative integer.")
+
+        if not isinstance(self.verbose, bool):
+            raise ValueError("The verbose has to be a Boolean.")
+
+
+class ProjectedGradientDescentNumpy(ProjectedGradientDescentCommon):
+    """
+    The Projected Gradient Descent attack is an iterative method in which, after each iteration, the perturbation is
+    projected on an lp-ball of specified radius (in addition to clipping the values of the adversarial sample so that it
+    lies in the permitted data range). This is the attack proposed by Madry et al. for adversarial training.
+
+    | Paper link: https://arxiv.org/abs/1706.06083
+    """
+
+    def __init__(
+        self,
+        estimator: Union["CLASSIFIER_LOSS_GRADIENTS_TYPE", "OBJECT_DETECTOR_TYPE"],
+        norm: Union[int, float, str] = np.inf,
+        eps: Union[int, float, np.ndarray] = 0.3,
+        eps_step: Union[int, float, np.ndarray] = 0.1,
+        max_iter: int = 100,
+        targeted: bool = False,
+        num_random_init: int = 0,
+        batch_size: int = 32,
+        random_eps: bool = False,
+        verbose: bool = True,
+    ) -> None:
+        """
+        Create a :class:`.ProjectedGradientDescentNumpy` instance.
+
+        :param estimator: An trained estimator.
+        :param norm: The norm of the adversarial perturbation supporting "inf", np.inf, 1 or 2.
+        :param eps: Maximum perturbation that the attacker can introduce.
+        :param eps_step: Attack step size (input variation) at each iteration.
+        :param random_eps: When True, epsilon is drawn randomly from truncated normal distribution. The literature
+                           suggests this for FGSM based training to generalize across different epsilons. eps_step
+                           is modified to preserve the ratio of eps / eps_step. The effectiveness of this method with
+                           PGD is untested (https://arxiv.org/pdf/1611.01236.pdf).
+        :param max_iter: The maximum number of iterations.
+        :param targeted: Indicates whether the attack is targeted (True) or untargeted (False)
+        :param num_random_init: Number of random initialisations within the epsilon ball. For num_random_init=0 starting
+                                at the original input.
+        :param batch_size: Size of the batch on which adversarial samples are generated.
+        :param verbose: Show progress bars.
+        """
+        super().__init__(
+            estimator=estimator,
+            norm=norm,
+            eps=eps,
+            eps_step=eps_step,
+            max_iter=max_iter,
+            targeted=targeted,
+            num_random_init=num_random_init,
+            batch_size=batch_size,
+            random_eps=random_eps,
+            verbose=verbose,
+        )
+
+        self._project = True
+
+    def generate(self, x: np.ndarray, y: Optional[np.ndarray] = None, **kwargs) -> np.ndarray:
+        """
+        Generate adversarial samples and return them in an array.
+
+        :param x: An array with the original inputs.
+        :param y: Target values (class labels) one-hot-encoded of shape `(nb_samples, nb_classes)` or indices of shape
+                  (nb_samples,). Only provide this parameter if you'd like to use true labels when crafting adversarial
+                  samples. Otherwise, model predictions are used as labels to avoid the "label leaking" effect
+                  (explained in this paper: https://arxiv.org/abs/1611.01236). Default is `None`.
+
+        :param mask: An array with a mask broadcastable to input `x` defining where to apply adversarial perturbations.
+                     Shape needs to be broadcastable to the shape of x and can also be of the same shape as `x`. Any
+                     features for which the mask is zero will not be adversarially perturbed.
+        :type mask: `np.ndarray`
+        :return: An array holding the adversarial examples.
+        """
+        mask = self._get_mask(x, **kwargs)
+
+        # Ensure eps is broadcastable
+        self._check_compatibility_input_and_eps(x=x)
+
+        # Check whether random eps is enabled
+        self._random_eps()
+
+        if isinstance(self.estimator, ClassifierMixin):
+            # Set up targets
+            targets = self._set_targets(x, y)
+
+            # Start to compute adversarial examples
+            adv_x = x.astype(ART_NUMPY_DTYPE)
+
+            for batch_id in range(int(np.ceil(x.shape[0] / float(self.batch_size)))):
+                for rand_init_num in trange(
+                    max(1, self.num_random_init), desc="PGD - Random Initializations", disable=not self.verbose
+                ):
+                    batch_index_1, batch_index_2 = batch_id * self.batch_size, (batch_id + 1) * self.batch_size
+                    batch_index_2 = min(batch_index_2, x.shape[0])
+                    batch = x[batch_index_1:batch_index_2]
+                    batch_labels = targets[batch_index_1:batch_index_2]
+                    mask_batch = mask
+
+                    if mask is not None:
+                        if len(mask.shape) == len(x.shape):
+                            mask_batch = mask[batch_index_1:batch_index_2]
+
+                    for i_max_iter in trange(
+                        self.max_iter, desc="PGD - Iterations", leave=False, disable=not self.verbose
+                    ):
+                        batch = self._compute(
+                            batch,
+                            x[batch_index_1:batch_index_2],
+                            batch_labels,
+                            mask_batch,
+                            self.eps,
+                            self.eps_step,
+                            self._project,
+                            self.num_random_init > 0 and i_max_iter == 0,
+                        )
+
+                    if rand_init_num == 0:
+                        # initial (and possibly only) random restart: we only have this set of
+                        # adversarial examples for now
+                        adv_x[batch_index_1:batch_index_2] = np.copy(batch)
+                    else:
+                        # replace adversarial examples if they are successful
+                        attack_success = compute_success_array(
+                            self.estimator,
+                            x[batch_index_1:batch_index_2],
+                            targets[batch_index_1:batch_index_2],
+                            batch,
+                            self.targeted,
+                            batch_size=self.batch_size,
+                        )
+                        adv_x[batch_index_1:batch_index_2][attack_success] = batch[attack_success]
+
+            logger.info(
+                "Success rate of attack: %.2f%%",
+                100
+                * compute_success(
+                    self.estimator, x, targets, adv_x, self.targeted, batch_size=self.batch_size,  # type: ignore
+                ),
+            )
+        else:
+            if self.num_random_init > 0:
+                raise ValueError("Random initialisation is only supported for classification.")
+
+            # Set up targets
+            targets = self._set_targets(x, y, classifier_mixin=False)
+
+            # Start to compute adversarial examples
+            if x.dtype == np.object:
+                adv_x = x.copy()
+            else:
+                adv_x = x.astype(ART_NUMPY_DTYPE)
+
+            for i_max_iter in trange(self.max_iter, desc="PGD - Iterations", disable=not self.verbose):
+                adv_x = self._compute(
+                    adv_x,
+                    x,
+                    targets,
+                    mask,
+                    self.eps,
+                    self.eps_step,
+                    self._project,
+                    self.num_random_init > 0 and i_max_iter == 0,
+                )
+
+        return adv_x
diff --git a/adversarial-robustness-toolbox/art/attacks/evasion/projected_gradient_descent/projected_gradient_descent_pytorch.py b/adversarial-robustness-toolbox/art/attacks/evasion/projected_gradient_descent/projected_gradient_descent_pytorch.py
new file mode 100644
index 0000000..eca0466
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/attacks/evasion/projected_gradient_descent/projected_gradient_descent_pytorch.py
@@ -0,0 +1,453 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements the Projected Gradient Descent attack `ProjectedGradientDescent` as an iterative method in which,
+after each iteration, the perturbation is projected on an lp-ball of specified radius (in addition to clipping the
+values of the adversarial sample so that it lies in the permitted data range). This is the attack proposed by Madry et
+al. for adversarial training.
+
+| Paper link: https://arxiv.org/abs/1706.06083
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+from typing import Optional, Union, TYPE_CHECKING
+
+import numpy as np
+from tqdm.auto import tqdm
+
+from art.config import ART_NUMPY_DTYPE
+from art.estimators.estimator import BaseEstimator, LossGradientsMixin
+from art.estimators.classification.classifier import ClassifierMixin
+from art.attacks.evasion.projected_gradient_descent.projected_gradient_descent_numpy import (
+    ProjectedGradientDescentCommon,
+)
+from art.utils import compute_success, random_sphere, compute_success_array
+
+if TYPE_CHECKING:
+    import torch
+    from art.estimators.classification.pytorch import PyTorchClassifier
+
+logger = logging.getLogger(__name__)
+
+
+class ProjectedGradientDescentPyTorch(ProjectedGradientDescentCommon):
+    """
+    The Projected Gradient Descent attack is an iterative method in which, after each iteration, the perturbation is
+    projected on an lp-ball of specified radius (in addition to clipping the values of the adversarial sample so that it
+    lies in the permitted data range). This is the attack proposed by Madry et al. for adversarial training.
+
+    | Paper link: https://arxiv.org/abs/1706.06083
+    """
+
+    _estimator_requirements = (BaseEstimator, LossGradientsMixin, ClassifierMixin)
+
+    def __init__(
+        self,
+        estimator: Union["PyTorchClassifier"],
+        norm: Union[int, float, str] = np.inf,
+        eps: Union[int, float, np.ndarray] = 0.3,
+        eps_step: Union[int, float, np.ndarray] = 0.1,
+        max_iter: int = 100,
+        targeted: bool = False,
+        num_random_init: int = 0,
+        batch_size: int = 32,
+        random_eps: bool = False,
+        verbose: bool = True,
+    ):
+        """
+        Create a :class:`.ProjectedGradientDescentPyTorch` instance.
+
+        :param estimator: An trained estimator.
+        :param norm: The norm of the adversarial perturbation. Possible values: "inf", np.inf, 1 or 2.
+        :param eps: Maximum perturbation that the attacker can introduce.
+        :param eps_step: Attack step size (input variation) at each iteration.
+        :param random_eps: When True, epsilon is drawn randomly from truncated normal distribution. The literature
+                           suggests this for FGSM based training to generalize across different epsilons. eps_step is
+                           modified to preserve the ratio of eps / eps_step. The effectiveness of this method with PGD
+                           is untested (https://arxiv.org/pdf/1611.01236.pdf).
+        :param max_iter: The maximum number of iterations.
+        :param targeted: Indicates whether the attack is targeted (True) or untargeted (False).
+        :param num_random_init: Number of random initialisations within the epsilon ball. For num_random_init=0 starting
+                                at the original input.
+        :param batch_size: Size of the batch on which adversarial samples are generated.
+        :param verbose: Show progress bars.
+        """
+        if not estimator.all_framework_preprocessing:
+            raise NotImplementedError(
+                "The framework-specific implementation only supports framework-specific preprocessing."
+            )
+
+        super().__init__(
+            estimator=estimator,
+            norm=norm,
+            eps=eps,
+            eps_step=eps_step,
+            max_iter=max_iter,
+            targeted=targeted,
+            num_random_init=num_random_init,
+            batch_size=batch_size,
+            random_eps=random_eps,
+            verbose=verbose,
+        )
+
+    def generate(self, x: np.ndarray, y: Optional[np.ndarray] = None, **kwargs) -> np.ndarray:
+        """
+        Generate adversarial samples and return them in an array.
+
+        :param x: An array with the original inputs.
+        :param y: Target values (class labels) one-hot-encoded of shape `(nb_samples, nb_classes)` or indices of shape
+                  (nb_samples,). Only provide this parameter if you'd like to use true labels when crafting adversarial
+                  samples. Otherwise, model predictions are used as labels to avoid the "label leaking" effect
+                  (explained in this paper: https://arxiv.org/abs/1611.01236). Default is `None`.
+        :param mask: An array with a mask broadcastable to input `x` defining where to apply adversarial perturbations.
+                     Shape needs to be broadcastable to the shape of x and can also be of the same shape as `x`. Any
+                     features for which the mask is zero will not be adversarially perturbed.
+        :type mask: `np.ndarray`
+        :return: An array holding the adversarial examples.
+        """
+        import torch  # lgtm [py/repeated-import]
+
+        mask = self._get_mask(x, **kwargs)
+
+        # Ensure eps is broadcastable
+        self._check_compatibility_input_and_eps(x=x)
+
+        # Check whether random eps is enabled
+        self._random_eps()
+
+        # Set up targets
+        targets = self._set_targets(x, y)
+
+        # Create dataset
+        if mask is not None:
+            # Here we need to make a distinction: if the masks are different for each input, we need to index
+            # those for the current batch. Otherwise (i.e. mask is meant to be broadcasted), keep it as it is.
+            if len(mask.shape) == len(x.shape):
+                dataset = torch.utils.data.TensorDataset(
+                    torch.from_numpy(x.astype(ART_NUMPY_DTYPE)),
+                    torch.from_numpy(targets.astype(ART_NUMPY_DTYPE)),
+                    torch.from_numpy(mask.astype(ART_NUMPY_DTYPE)),
+                )
+
+            else:
+                dataset = torch.utils.data.TensorDataset(
+                    torch.from_numpy(x.astype(ART_NUMPY_DTYPE)),
+                    torch.from_numpy(targets.astype(ART_NUMPY_DTYPE)),
+                    torch.from_numpy(np.array([mask.astype(ART_NUMPY_DTYPE)] * x.shape[0])),
+                )
+
+        else:
+            dataset = torch.utils.data.TensorDataset(
+                torch.from_numpy(x.astype(ART_NUMPY_DTYPE)), torch.from_numpy(targets.astype(ART_NUMPY_DTYPE)),
+            )
+
+        data_loader = torch.utils.data.DataLoader(
+            dataset=dataset, batch_size=self.batch_size, shuffle=False, drop_last=False
+        )
+
+        # Start to compute adversarial examples
+        adv_x = x.astype(ART_NUMPY_DTYPE)
+
+        # Compute perturbation with batching
+        for (batch_id, batch_all) in enumerate(
+            tqdm(data_loader, desc="PGD - Batches", leave=False, disable=not self.verbose)
+        ):
+            if mask is not None:
+                (batch, batch_labels, mask_batch) = batch_all[0], batch_all[1], batch_all[2]
+            else:
+                (batch, batch_labels, mask_batch) = batch_all[0], batch_all[1], None
+
+            batch_index_1, batch_index_2 = batch_id * self.batch_size, (batch_id + 1) * self.batch_size
+
+            # Compute batch_eps and batch_eps_step
+            if isinstance(self.eps, np.ndarray):
+                if len(self.eps.shape) == len(x.shape) and self.eps.shape[0] == x.shape[0]:
+                    batch_eps = self.eps[batch_index_1:batch_index_2]
+                    batch_eps_step = self.eps_step[batch_index_1:batch_index_2]
+
+                else:
+                    batch_eps = self.eps
+                    batch_eps_step = self.eps_step
+
+            else:
+                batch_eps = self.eps
+                batch_eps_step = self.eps_step
+
+            for rand_init_num in range(max(1, self.num_random_init)):
+                if rand_init_num == 0:
+                    # first iteration: use the adversarial examples as they are the only ones we have now
+                    adv_x[batch_index_1:batch_index_2] = self._generate_batch(
+                        x=batch, targets=batch_labels, mask=mask_batch, eps=batch_eps, eps_step=batch_eps_step
+                    )
+                else:
+                    adversarial_batch = self._generate_batch(
+                        x=batch, targets=batch_labels, mask=mask_batch, eps=batch_eps, eps_step=batch_eps_step
+                    )
+
+                    # return the successful adversarial examples
+                    attack_success = compute_success_array(
+                        self.estimator,
+                        batch,
+                        batch_labels,
+                        adversarial_batch,
+                        self.targeted,
+                        batch_size=self.batch_size,
+                    )
+                    adv_x[batch_index_1:batch_index_2][attack_success] = adversarial_batch[attack_success]
+
+        logger.info(
+            "Success rate of attack: %.2f%%",
+            100 * compute_success(self.estimator, x, targets, adv_x, self.targeted, batch_size=self.batch_size),
+        )
+
+        return adv_x
+
+    def _generate_batch(
+        self,
+        x: "torch.Tensor",
+        targets: "torch.Tensor",
+        mask: "torch.Tensor",
+        eps: Union[int, float, np.ndarray],
+        eps_step: Union[int, float, np.ndarray],
+    ) -> np.ndarray:
+        """
+        Generate a batch of adversarial samples and return them in an array.
+
+        :param x: An array with the original inputs.
+        :param targets: Target values (class labels) one-hot-encoded of shape `(nb_samples, nb_classes)`.
+        :param mask: An array with a mask to be applied to the adversarial perturbations. Shape needs to be
+                     broadcastable to the shape of x. Any features for which the mask is zero will not be adversarially
+                     perturbed.
+        :param eps: Maximum perturbation that the attacker can introduce.
+        :param eps_step: Attack step size (input variation) at each iteration.
+        :return: Adversarial examples.
+        """
+        import torch  # lgtm [py/repeated-import]
+
+        inputs = x.to(self.estimator.device)
+        targets = targets.to(self.estimator.device)
+        adv_x = torch.clone(inputs)
+
+        if mask is not None:
+            mask = mask.to(self.estimator.device)
+
+        for i_max_iter in range(self.max_iter):
+            adv_x = self._compute_torch(
+                adv_x, inputs, targets, mask, eps, eps_step, self.num_random_init > 0 and i_max_iter == 0,
+            )
+
+        return adv_x.cpu().detach().numpy()
+
+    def _compute_perturbation(
+        self, x: "torch.Tensor", y: "torch.Tensor", mask: Optional["torch.Tensor"]
+    ) -> "torch.Tensor":
+        """
+        Compute perturbations.
+
+        :param x: Current adversarial examples.
+        :param y: Target values (class labels) one-hot-encoded of shape `(nb_samples, nb_classes)` or indices of shape
+                  (nb_samples,). Only provide this parameter if you'd like to use true labels when crafting adversarial
+                  samples. Otherwise, model predictions are used as labels to avoid the "label leaking" effect
+                  (explained in this paper: https://arxiv.org/abs/1611.01236). Default is `None`.
+        :param mask: An array with a mask broadcastable to input `x` defining where to apply adversarial perturbations.
+                     Shape needs to be broadcastable to the shape of x and can also be of the same shape as `x`. Any
+                     features for which the mask is zero will not be adversarially perturbed.
+        :return: Perturbations.
+        """
+        import torch  # lgtm [py/repeated-import]
+
+        # Pick a small scalar to avoid division by 0
+        tol = 10e-8
+
+        # Get gradient wrt loss; invert it if attack is targeted
+        grad = self.estimator.loss_gradient(x=x, y=y) * (1 - 2 * int(self.targeted))
+
+        # Check for nan before normalisation an replace with 0
+        if torch.any(grad.isnan()):
+            logger.warning("Elements of the loss gradient are NaN and have been replaced with 0.0.")
+            grad[grad.isnan()] = 0.0
+
+        # Apply mask
+        if mask is not None:
+            grad = torch.where(mask == 0.0, torch.tensor(0.0), grad)
+
+        # Apply norm bound
+        if self.norm in ["inf", np.inf]:
+            grad = grad.sign()
+
+        elif self.norm == 1:
+            ind = tuple(range(1, len(x.shape)))
+            grad = grad / (torch.sum(grad.abs(), dim=ind, keepdims=True) + tol)  # type: ignore
+
+        elif self.norm == 2:
+            ind = tuple(range(1, len(x.shape)))
+            grad = grad / (torch.sqrt(torch.sum(grad * grad, axis=ind, keepdims=True)) + tol)  # type: ignore
+
+        assert x.shape == grad.shape
+
+        return grad
+
+    def _apply_perturbation(
+        self, x: "torch.Tensor", perturbation: "torch.Tensor", eps_step: Union[int, float, np.ndarray]
+    ) -> "torch.Tensor":
+        """
+        Apply perturbation on examples.
+
+        :param x: Current adversarial examples.
+        :param perturbation: Current perturbations.
+        :param eps_step: Attack step size (input variation) at each iteration.
+        :return: Adversarial examples.
+        """
+        import torch  # lgtm [py/repeated-import]
+
+        eps_step = np.array(eps_step, dtype=ART_NUMPY_DTYPE)
+        perturbation_step = torch.tensor(eps_step).to(self.estimator.device) * perturbation
+        perturbation_step[torch.isnan(perturbation_step)] = 0
+        x = x + perturbation_step
+        if self.estimator.clip_values is not None:
+            clip_min, clip_max = self.estimator.clip_values
+            x = torch.max(
+                torch.min(x, torch.tensor(clip_max).to(self.estimator.device)),
+                torch.tensor(clip_min).to(self.estimator.device),
+            )
+
+        return x
+
+    def _compute_torch(
+        self,
+        x: "torch.Tensor",
+        x_init: "torch.Tensor",
+        y: "torch.Tensor",
+        mask: "torch.Tensor",
+        eps: Union[int, float, np.ndarray],
+        eps_step: Union[int, float, np.ndarray],
+        random_init: bool,
+    ) -> "torch.Tensor":
+        """
+        Compute adversarial examples for one iteration.
+
+        :param x: Current adversarial examples.
+        :param x_init: An array with the original inputs.
+        :param y: Target values (class labels) one-hot-encoded of shape `(nb_samples, nb_classes)` or indices of shape
+                  (nb_samples,). Only provide this parameter if you'd like to use true labels when crafting adversarial
+                  samples. Otherwise, model predictions are used as labels to avoid the "label leaking" effect
+                  (explained in this paper: https://arxiv.org/abs/1611.01236).
+        :param mask: An array with a mask broadcastable to input `x` defining where to apply adversarial perturbations.
+                     Shape needs to be broadcastable to the shape of x and can also be of the same shape as `x`. Any
+                     features for which the mask is zero will not be adversarially perturbed.
+        :param eps: Maximum perturbation that the attacker can introduce.
+        :param eps_step: Attack step size (input variation) at each iteration.
+        :param random_init: Random initialisation within the epsilon ball. For random_init=False starting at the
+                            original input.
+        :return: Adversarial examples.
+        """
+        import torch  # lgtm [py/repeated-import]
+
+        if random_init:
+            n = x.shape[0]
+            m = np.prod(x.shape[1:]).item()
+
+            random_perturbation = random_sphere(n, m, eps, self.norm).reshape(x.shape).astype(ART_NUMPY_DTYPE)
+            random_perturbation = torch.from_numpy(random_perturbation).to(self.estimator.device)
+
+            if mask is not None:
+                random_perturbation = random_perturbation * mask
+
+            x_adv = x + random_perturbation
+
+            if self.estimator.clip_values is not None:
+                clip_min, clip_max = self.estimator.clip_values
+                x_adv = torch.max(
+                    torch.min(x_adv, torch.tensor(clip_max).to(self.estimator.device)),
+                    torch.tensor(clip_min).to(self.estimator.device),
+                )
+
+        else:
+            x_adv = x
+
+        # Get perturbation
+        perturbation = self._compute_perturbation(x_adv, y, mask)
+
+        # Apply perturbation and clip
+        x_adv = self._apply_perturbation(x_adv, perturbation, eps_step)
+
+        # Do projection
+        perturbation = self._projection(x_adv - x_init, eps, self.norm)
+
+        # Recompute x_adv
+        x_adv = perturbation + x_init
+
+        return x_adv
+
+    def _projection(
+        self, values: "torch.Tensor", eps: Union[int, float, np.ndarray], norm_p: Union[int, float, str]
+    ) -> "torch.Tensor":
+        """
+        Project `values` on the L_p norm ball of size `eps`.
+
+        :param values: Values to clip.
+        :param eps: Maximum norm allowed.
+        :param norm_p: L_p norm to use for clipping supporting 1, 2, `np.Inf` and "inf".
+        :return: Values of `values` after projection.
+        """
+        import torch  # lgtm [py/repeated-import]
+
+        # Pick a small scalar to avoid division by 0
+        tol = 10e-8
+        values_tmp = values.reshape(values.shape[0], -1)
+
+        if norm_p == 2:
+            if isinstance(eps, np.ndarray):
+                raise NotImplementedError(
+                    "The parameter `eps` of type `np.ndarray` is not supported to use with norm 2."
+                )
+
+            values_tmp = values_tmp * torch.min(
+                torch.tensor([1.0], dtype=torch.float32).to(self.estimator.device),
+                eps / (torch.norm(values_tmp, p=2, dim=1) + tol),
+            ).unsqueeze_(-1)
+
+        elif norm_p == 1:
+            if isinstance(eps, np.ndarray):
+                raise NotImplementedError(
+                    "The parameter `eps` of type `np.ndarray` is not supported to use with norm 1."
+                )
+
+            values_tmp = values_tmp * torch.min(
+                torch.tensor([1.0], dtype=torch.float32).to(self.estimator.device),
+                eps / (torch.norm(values_tmp, p=1, dim=1) + tol),
+            ).unsqueeze_(-1)
+
+        elif norm_p in [np.inf, "inf"]:
+            if isinstance(eps, np.ndarray):
+                eps = eps * np.ones_like(values.cpu())
+                eps = eps.reshape([eps.shape[0], -1])
+
+            values_tmp = values_tmp.sign() * torch.min(
+                values_tmp.abs(), torch.tensor([eps], dtype=torch.float32).to(self.estimator.device)
+            )
+
+        else:
+            raise NotImplementedError(
+                "Values of `norm_p` different from 1, 2 and `np.inf` are currently not supported."
+            )
+
+        values = values_tmp.reshape(values.shape)
+
+        return values
diff --git a/adversarial-robustness-toolbox/art/attacks/evasion/projected_gradient_descent/projected_gradient_descent_tensorflow_v2.py b/adversarial-robustness-toolbox/art/attacks/evasion/projected_gradient_descent/projected_gradient_descent_tensorflow_v2.py
new file mode 100644
index 0000000..d423db0
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/attacks/evasion/projected_gradient_descent/projected_gradient_descent_tensorflow_v2.py
@@ -0,0 +1,435 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements the Projected Gradient Descent attack `ProjectedGradientDescent` as an iterative method in which,
+after each iteration, the perturbation is projected on an lp-ball of specified radius (in addition to clipping the
+values of the adversarial sample so that it lies in the permitted data range). This is the attack proposed by Madry et
+al. for adversarial training.
+
+| Paper link: https://arxiv.org/abs/1706.06083
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+from typing import Optional, Union, TYPE_CHECKING
+
+import numpy as np
+from tqdm.auto import tqdm
+
+from art.config import ART_NUMPY_DTYPE
+from art.estimators.estimator import BaseEstimator, LossGradientsMixin
+from art.estimators.classification.classifier import ClassifierMixin
+from art.attacks.evasion.projected_gradient_descent.projected_gradient_descent_numpy import (
+    ProjectedGradientDescentCommon,
+)
+from art.utils import compute_success, random_sphere, compute_success_array
+
+if TYPE_CHECKING:
+    import tensorflow as tf
+    from art.estimators.classification.tensorflow import TensorFlowV2Classifier
+
+logger = logging.getLogger(__name__)
+
+
+class ProjectedGradientDescentTensorFlowV2(ProjectedGradientDescentCommon):
+    """
+    The Projected Gradient Descent attack is an iterative method in which, after each iteration, the perturbation is
+    projected on an lp-ball of specified radius (in addition to clipping the values of the adversarial sample so that it
+    lies in the permitted data range). This is the attack proposed by Madry et al. for adversarial training.
+
+    | Paper link: https://arxiv.org/abs/1706.06083
+    """
+
+    _estimator_requirements = (BaseEstimator, LossGradientsMixin, ClassifierMixin)
+
+    def __init__(
+        self,
+        estimator: "TensorFlowV2Classifier",
+        norm: Union[int, float, str] = np.inf,
+        eps: Union[int, float, np.ndarray] = 0.3,
+        eps_step: Union[int, float, np.ndarray] = 0.1,
+        max_iter: int = 100,
+        targeted: bool = False,
+        num_random_init: int = 0,
+        batch_size: int = 32,
+        random_eps: bool = False,
+        verbose: bool = True,
+    ):
+        """
+        Create a :class:`.ProjectedGradientDescentTensorFlowV2` instance.
+
+        :param estimator: An trained estimator.
+        :param norm: The norm of the adversarial perturbation. Possible values: np.inf, 1 or 2.
+        :param eps: Maximum perturbation that the attacker can introduce.
+        :param eps_step: Attack step size (input variation) at each iteration.
+        :param random_eps: When True, epsilon is drawn randomly from truncated normal distribution. The literature
+                           suggests this for FGSM based training to generalize across different epsilons. eps_step is
+                           modified to preserve the ratio of eps / eps_step. The effectiveness of this method with PGD
+                           is untested (https://arxiv.org/pdf/1611.01236.pdf).
+        :param max_iter: The maximum number of iterations.
+        :param targeted: Indicates whether the attack is targeted (True) or untargeted (False).
+        :param num_random_init: Number of random initialisations within the epsilon ball. For num_random_init=0 starting
+                                at the original input.
+        :param batch_size: Size of the batch on which adversarial samples are generated.
+        :param verbose: Show progress bars.
+        """
+        if not estimator.all_framework_preprocessing:
+            raise NotImplementedError(
+                "The framework-specific implementation only supports framework-specific preprocessing."
+            )
+
+        super().__init__(
+            estimator=estimator,
+            norm=norm,
+            eps=eps,
+            eps_step=eps_step,
+            max_iter=max_iter,
+            targeted=targeted,
+            num_random_init=num_random_init,
+            batch_size=batch_size,
+            random_eps=random_eps,
+            verbose=verbose,
+        )
+
+    def generate(self, x: np.ndarray, y: Optional[np.ndarray] = None, **kwargs) -> np.ndarray:
+        """
+        Generate adversarial samples and return them in an array.
+
+        :param x: An array with the original inputs.
+        :param y: Target values (class labels) one-hot-encoded of shape `(nb_samples, nb_classes)` or indices of shape
+                  (nb_samples,). Only provide this parameter if you'd like to use true labels when crafting adversarial
+                  samples. Otherwise, model predictions are used as labels to avoid the "label leaking" effect
+                  (explained in this paper: https://arxiv.org/abs/1611.01236). Default is `None`.
+        :param mask: An array with a mask broadcastable to input `x` defining where to apply adversarial perturbations.
+                     Shape needs to be broadcastable to the shape of x and can also be of the same shape as `x`. Any
+                     features for which the mask is zero will not be adversarially perturbed.
+        :type mask: `np.ndarray`
+        :return: An array holding the adversarial examples.
+        """
+        import tensorflow as tf  # lgtm [py/repeated-import]
+
+        mask = self._get_mask(x, **kwargs)
+
+        # Ensure eps is broadcastable
+        self._check_compatibility_input_and_eps(x=x)
+
+        # Check whether random eps is enabled
+        self._random_eps()
+
+        # Set up targets
+        targets = self._set_targets(x, y)
+
+        # Create dataset
+        if mask is not None:
+            # Here we need to make a distinction: if the masks are different for each input, we need to index
+            # those for the current batch. Otherwise (i.e. mask is meant to be broadcasted), keep it as it is.
+            if len(mask.shape) == len(x.shape):
+                dataset = tf.data.Dataset.from_tensor_slices(
+                    (x.astype(ART_NUMPY_DTYPE), targets.astype(ART_NUMPY_DTYPE), mask.astype(ART_NUMPY_DTYPE),)
+                ).batch(self.batch_size, drop_remainder=False)
+
+            else:
+                dataset = tf.data.Dataset.from_tensor_slices(
+                    (
+                        x.astype(ART_NUMPY_DTYPE),
+                        targets.astype(ART_NUMPY_DTYPE),
+                        np.array([mask.astype(ART_NUMPY_DTYPE)] * x.shape[0]),
+                    )
+                ).batch(self.batch_size, drop_remainder=False)
+
+        else:
+            dataset = tf.data.Dataset.from_tensor_slices(
+                (x.astype(ART_NUMPY_DTYPE), targets.astype(ART_NUMPY_DTYPE),)
+            ).batch(self.batch_size, drop_remainder=False)
+
+        # Start to compute adversarial examples
+        adv_x = x.astype(ART_NUMPY_DTYPE)
+        data_loader = iter(dataset)
+
+        # Compute perturbation with batching
+        for (batch_id, batch_all) in enumerate(
+            tqdm(data_loader, desc="PGD - Batches", leave=False, disable=not self.verbose)
+        ):
+            if mask is not None:
+                (batch, batch_labels, mask_batch) = batch_all[0], batch_all[1], batch_all[2]
+            else:
+                (batch, batch_labels, mask_batch) = batch_all[0], batch_all[1], None
+
+            batch_index_1, batch_index_2 = batch_id * self.batch_size, (batch_id + 1) * self.batch_size
+
+            # Compute batch_eps and batch_eps_step
+            if isinstance(self.eps, np.ndarray):
+                if len(self.eps.shape) == len(x.shape) and self.eps.shape[0] == x.shape[0]:
+                    batch_eps = self.eps[batch_index_1:batch_index_2]
+                    batch_eps_step = self.eps_step[batch_index_1:batch_index_2]
+
+                else:
+                    batch_eps = self.eps
+                    batch_eps_step = self.eps_step
+
+            else:
+                batch_eps = self.eps
+                batch_eps_step = self.eps_step
+
+            for rand_init_num in range(max(1, self.num_random_init)):
+                if rand_init_num == 0:
+                    # first iteration: use the adversarial examples as they are the only ones we have now
+                    adv_x[batch_index_1:batch_index_2] = self._generate_batch(
+                        x=batch, targets=batch_labels, mask=mask_batch, eps=batch_eps, eps_step=batch_eps_step
+                    )
+                else:
+                    adversarial_batch = self._generate_batch(
+                        x=batch, targets=batch_labels, mask=mask_batch, eps=batch_eps, eps_step=batch_eps_step
+                    )
+                    attack_success = compute_success_array(
+                        self.estimator,
+                        batch,
+                        batch_labels,
+                        adversarial_batch,
+                        self.targeted,
+                        batch_size=self.batch_size,
+                    )
+                    # return the successful adversarial examples
+                    adv_x[batch_index_1:batch_index_2][attack_success] = adversarial_batch[attack_success]
+
+        logger.info(
+            "Success rate of attack: %.2f%%",
+            100 * compute_success(self.estimator, x, targets, adv_x, self.targeted, batch_size=self.batch_size),
+        )
+
+        return adv_x
+
+    def _generate_batch(
+        self,
+        x: "tf.Tensor",
+        targets: "tf.Tensor",
+        mask: "tf.Tensor",
+        eps: Union[int, float, np.ndarray],
+        eps_step: Union[int, float, np.ndarray],
+    ) -> "tf.Tensor":
+        """
+        Generate a batch of adversarial samples and return them in an array.
+
+        :param x: An array with the original inputs.
+        :param targets: Target values (class labels) one-hot-encoded of shape `(nb_samples, nb_classes)`.
+        :param mask: An array with a mask to be applied to the adversarial perturbations. Shape needs to be
+                     broadcastable to the shape of x. Any features for which the mask is zero will not be adversarially
+                     perturbed.
+        :param eps: Maximum perturbation that the attacker can introduce.
+        :param eps_step: Attack step size (input variation) at each iteration.
+        :return: Adversarial examples.
+        """
+        import tensorflow as tf  # lgtm [py/repeated-import]
+
+        adv_x = tf.identity(x)
+
+        for i_max_iter in range(self.max_iter):
+            adv_x = self._compute_tf(
+                adv_x, x, targets, mask, eps, eps_step, self.num_random_init > 0 and i_max_iter == 0,
+            )
+
+        return adv_x
+
+    def _compute_perturbation(self, x: "tf.Tensor", y: "tf.Tensor", mask: Optional["tf.Tensor"]) -> "tf.Tensor":
+        """
+        Compute perturbations.
+
+        :param x: Current adversarial examples.
+        :param y: Target values (class labels) one-hot-encoded of shape `(nb_samples, nb_classes)` or indices of shape
+                  (nb_samples,). Only provide this parameter if you'd like to use true labels when crafting adversarial
+                  samples. Otherwise, model predictions are used as labels to avoid the "label leaking" effect
+                  (explained in this paper: https://arxiv.org/abs/1611.01236). Default is `None`.
+        :param mask: An array with a mask broadcastable to input `x` defining where to apply adversarial perturbations.
+                     Shape needs to be broadcastable to the shape of x and can also be of the same shape as `x`. Any
+                     features for which the mask is zero will not be adversarially perturbed.
+        :return: Perturbations.
+        """
+        import tensorflow as tf  # lgtm [py/repeated-import]
+
+        # Pick a small scalar to avoid division by 0
+        tol = 10e-8
+
+        # Get gradient wrt loss; invert it if attack is targeted
+        grad: tf.Tensor = self.estimator.loss_gradient(x, y) * tf.constant(
+            1 - 2 * int(self.targeted), dtype=ART_NUMPY_DTYPE
+        )
+
+        # Check for NaN before normalisation an replace with 0
+        if tf.reduce_any(tf.math.is_nan(grad)):
+            logger.warning("Elements of the loss gradient are NaN and have been replaced with 0.0.")
+            grad = tf.where(tf.math.is_nan(grad), tf.zeros_like(grad), grad)
+
+        # Apply mask
+        if mask is not None:
+            grad = tf.where(mask == 0.0, 0.0, grad)
+
+        # Apply norm bound
+        if self.norm == np.inf:
+            grad = tf.sign(grad)
+
+        elif self.norm == 1:
+            ind = tuple(range(1, len(x.shape)))
+            grad = tf.divide(grad, (tf.math.reduce_sum(tf.abs(grad), axis=ind, keepdims=True) + tol))
+
+        elif self.norm == 2:
+            ind = tuple(range(1, len(x.shape)))
+            grad = tf.divide(
+                grad, (tf.math.sqrt(tf.math.reduce_sum(tf.math.square(grad), axis=ind, keepdims=True)) + tol)
+            )
+
+        assert x.shape == grad.shape
+
+        return grad
+
+    def _apply_perturbation(
+        self, x: "tf.Tensor", perturbation: "tf.Tensor", eps_step: Union[int, float, np.ndarray]
+    ) -> "tf.Tensor":
+        """
+        Apply perturbation on examples.
+
+        :param x: Current adversarial examples.
+        :param perturbation: Current perturbations.
+        :param eps_step: Attack step size (input variation) at each iteration.
+        :return: Adversarial examples.
+        """
+        import tensorflow as tf  # lgtm [py/repeated-import]
+
+        perturbation_step = tf.constant(eps_step, dtype=ART_NUMPY_DTYPE) * perturbation
+        perturbation_step = tf.where(tf.math.is_nan(perturbation_step), 0, perturbation_step)
+        x = x + perturbation_step
+        if self.estimator.clip_values is not None:
+            clip_min, clip_max = self.estimator.clip_values
+            x = tf.clip_by_value(x, clip_value_min=clip_min, clip_value_max=clip_max)
+
+        return x
+
+    def _compute_tf(
+        self,
+        x: "tf.Tensor",
+        x_init: "tf.Tensor",
+        y: "tf.Tensor",
+        mask: "tf.Tensor",
+        eps: Union[int, float, np.ndarray],
+        eps_step: Union[int, float, np.ndarray],
+        random_init: bool,
+    ) -> "tf.Tensor":
+        """
+        Compute adversarial examples for one iteration.
+
+        :param x: Current adversarial examples.
+        :param x_init: An array with the original inputs.
+        :param y: Target values (class labels) one-hot-encoded of shape `(nb_samples, nb_classes)` or indices of shape
+                  (nb_samples,). Only provide this parameter if you'd like to use true labels when crafting adversarial
+                  samples. Otherwise, model predictions are used as labels to avoid the "label leaking" effect
+                  (explained in this paper: https://arxiv.org/abs/1611.01236). Default is `None`.
+        :param mask: An array with a mask broadcastable to input `x` defining where to apply adversarial perturbations.
+                     Shape needs to be broadcastable to the shape of x and can also be of the same shape as `x`. Any
+                     features for which the mask is zero will not be adversarially perturbed.
+        :param eps: Maximum perturbation that the attacker can introduce.
+        :param eps_step: Attack step size (input variation) at each iteration.
+        :param random_init: Random initialisation within the epsilon ball. For random_init=False starting at the
+                            original input.
+        :return: Adversarial examples.
+        """
+        import tensorflow as tf  # lgtm [py/repeated-import]
+
+        if random_init:
+            n = x.shape[0]
+            m = np.prod(x.shape[1:]).item()
+
+            random_perturbation = random_sphere(n, m, eps, self.norm).reshape(x.shape).astype(ART_NUMPY_DTYPE)
+            random_perturbation = tf.convert_to_tensor(random_perturbation)
+            if mask is not None:
+                random_perturbation = random_perturbation * mask
+
+            x_adv = x + random_perturbation
+
+            if self.estimator.clip_values is not None:
+                clip_min, clip_max = self.estimator.clip_values
+                x_adv = tf.clip_by_value(x_adv, clip_min, clip_max)
+
+        else:
+            x_adv = x
+
+        # Get perturbation
+        perturbation = self._compute_perturbation(x_adv, y, mask)
+
+        # Apply perturbation and clip
+        x_adv = self._apply_perturbation(x_adv, perturbation, eps_step)
+
+        # Do projection
+        perturbation = self._projection(x_adv - x_init, eps, self.norm)
+
+        # Recompute x_adv
+        x_adv = tf.add(perturbation, x_init)
+
+        return x_adv
+
+    @staticmethod
+    def _projection(
+        values: "tf.Tensor", eps: Union[int, float, np.ndarray], norm_p: Union[int, float, str]
+    ) -> "tf.Tensor":
+        """
+        Project `values` on the L_p norm ball of size `eps`.
+
+        :param values: Values to clip.
+        :param eps: Maximum norm allowed.
+        :param norm_p: L_p norm to use for clipping supporting 1, 2 and `np.Inf`.
+        :return: Values of `values` after projection.
+        """
+        import tensorflow as tf  # lgtm [py/repeated-import]
+
+        # Pick a small scalar to avoid division by 0
+        tol = 10e-8
+        values_tmp = tf.reshape(values, (values.shape[0], -1))
+
+        if norm_p == 2:
+            if isinstance(eps, np.ndarray):
+                raise NotImplementedError(
+                    "The parameter `eps` of type `np.ndarray` is not supported to use with norm 2."
+                )
+
+            values_tmp = values_tmp * tf.expand_dims(
+                tf.minimum(1.0, eps / (tf.norm(values_tmp, ord=2, axis=1) + tol)), axis=1
+            )
+
+        elif norm_p == 1:
+            if isinstance(eps, np.ndarray):
+                raise NotImplementedError(
+                    "The parameter `eps` of type `np.ndarray` is not supported to use with norm 1."
+                )
+
+            values_tmp = values_tmp * tf.expand_dims(
+                tf.minimum(1.0, eps / (tf.norm(values_tmp, ord=1, axis=1) + tol)), axis=1
+            )
+
+        elif norm_p in ["inf", np.inf]:
+            if isinstance(eps, np.ndarray):
+                eps = eps * np.ones(shape=values.shape)
+                eps = eps.reshape([eps.shape[0], -1])
+
+            values_tmp = tf.sign(values_tmp) * tf.minimum(tf.math.abs(values_tmp), eps)
+
+        else:
+            raise NotImplementedError(
+                'Values of `norm_p` different from 1, 2 "inf" and `np.inf` are currently not supported.'
+            )
+
+        values = tf.reshape(values_tmp, values.shape)
+
+        return values
diff --git a/adversarial-robustness-toolbox/art/attacks/evasion/saliency_map.py b/adversarial-robustness-toolbox/art/attacks/evasion/saliency_map.py
new file mode 100644
index 0000000..8f89124
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/attacks/evasion/saliency_map.py
@@ -0,0 +1,222 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements the Jacobian-based Saliency Map attack `SaliencyMapMethod`. This is a white-box attack.
+
+| Paper link: https://arxiv.org/abs/1511.07528
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+from typing import Optional, Union, TYPE_CHECKING
+
+import numpy as np
+from tqdm.auto import trange
+
+from art.attacks.attack import EvasionAttack
+from art.config import ART_NUMPY_DTYPE
+from art.estimators.estimator import BaseEstimator
+from art.estimators.classification.classifier import ClassGradientsMixin
+from art.utils import check_and_transform_label_format, compute_success
+
+if TYPE_CHECKING:
+    from art.utils import CLASSIFIER_CLASS_LOSS_GRADIENTS_TYPE
+
+logger = logging.getLogger(__name__)
+
+
+class SaliencyMapMethod(EvasionAttack):
+    """
+    Implementation of the Jacobian-based Saliency Map Attack (Papernot et al. 2016).
+
+    | Paper link: https://arxiv.org/abs/1511.07528
+    """
+
+    attack_params = EvasionAttack.attack_params + ["theta", "gamma", "batch_size", "verbose"]
+    _estimator_requirements = (BaseEstimator, ClassGradientsMixin)
+
+    def __init__(
+        self,
+        classifier: "CLASSIFIER_CLASS_LOSS_GRADIENTS_TYPE",
+        theta: float = 0.1,
+        gamma: float = 1.0,
+        batch_size: int = 1,
+        verbose: bool = True,
+    ) -> None:
+        """
+        Create a SaliencyMapMethod instance.
+
+        :param classifier: A trained classifier.
+        :param theta: Amount of Perturbation introduced to each modified feature per step (can be positive or negative).
+        :param gamma: Maximum fraction of features being perturbed (between 0 and 1).
+        :param batch_size: Size of the batch on which adversarial samples are generated.
+        :param verbose: Show progress bars.
+        """
+        super().__init__(estimator=classifier)
+        self.theta = theta
+        self.gamma = gamma
+        self.batch_size = batch_size
+        self.verbose = verbose
+        self._check_params()
+
+    def generate(self, x: np.ndarray, y: Optional[np.ndarray] = None, **kwargs) -> np.ndarray:
+        """
+        Generate adversarial samples and return them in an array.
+
+        :param x: An array with the original inputs to be attacked.
+        :param y: Target values (class labels) one-hot-encoded of shape `(nb_samples, nb_classes)` or indices of shape
+                  `(nb_samples,)`.
+        :return: An array holding the adversarial examples.
+        """
+        y = check_and_transform_label_format(y, self.estimator.nb_classes)
+
+        # Initialize variables
+        dims = list(x.shape[1:])
+        self._nb_features = np.product(dims)
+        x_adv = np.reshape(x.astype(ART_NUMPY_DTYPE), (-1, self._nb_features))
+        preds = np.argmax(self.estimator.predict(x, batch_size=self.batch_size), axis=1)
+
+        # Determine target classes for attack
+        if y is None:
+            # Randomly choose target from the incorrect classes for each sample
+            from art.utils import random_targets
+
+            targets = np.argmax(random_targets(preds, self.estimator.nb_classes), axis=1)
+        else:
+            targets = np.argmax(y, axis=1)
+
+        # Compute perturbation with implicit batching
+        for batch_id in trange(
+            int(np.ceil(x_adv.shape[0] / float(self.batch_size))), desc="JSMA", disable=not self.verbose
+        ):
+            batch_index_1, batch_index_2 = batch_id * self.batch_size, (batch_id + 1) * self.batch_size
+            batch = x_adv[batch_index_1:batch_index_2]
+
+            # Main algorithm for each batch
+            # Initialize the search space; optimize to remove features that can't be changed
+            if self.estimator.clip_values is not None:
+                search_space = np.zeros(batch.shape)
+                clip_min, clip_max = self.estimator.clip_values
+                if self.theta > 0:
+                    search_space[batch < clip_max] = 1
+                else:
+                    search_space[batch > clip_min] = 1
+            else:
+                search_space = np.ones(batch.shape)
+
+            # Get current predictions
+            current_pred = preds[batch_index_1:batch_index_2]
+            target = targets[batch_index_1:batch_index_2]
+            active_indices = np.where(current_pred != target)[0]
+            all_feat = np.zeros_like(batch)
+
+            while active_indices.size != 0:
+                # Compute saliency map
+                feat_ind = self._saliency_map(
+                    np.reshape(batch, [batch.shape[0]] + dims)[active_indices],
+                    target[active_indices],
+                    search_space[active_indices],
+                )
+
+                # Update used features
+                all_feat[active_indices, feat_ind[:, 0]] = 1
+                all_feat[active_indices, feat_ind[:, 1]] = 1
+
+                # Apply attack with clipping
+                if self.estimator.clip_values is not None:
+                    # Prepare update depending of theta
+                    if self.theta > 0:
+                        clip_func, clip_value = np.minimum, clip_max
+                    else:
+                        clip_func, clip_value = np.maximum, clip_min
+
+                    # Update adversarial examples
+                    tmp_batch = batch[active_indices]
+                    tmp_batch[np.arange(len(active_indices)), feat_ind[:, 0]] = clip_func(
+                        clip_value, tmp_batch[np.arange(len(active_indices)), feat_ind[:, 0]] + self.theta,
+                    )
+                    tmp_batch[np.arange(len(active_indices)), feat_ind[:, 1]] = clip_func(
+                        clip_value, tmp_batch[np.arange(len(active_indices)), feat_ind[:, 1]] + self.theta,
+                    )
+                    batch[active_indices] = tmp_batch
+
+                    # Remove indices from search space if max/min values were reached
+                    search_space[batch == clip_value] = 0
+
+                # Apply attack without clipping
+                else:
+                    tmp_batch = batch[active_indices]
+                    tmp_batch[np.arange(len(active_indices)), feat_ind[:, 0]] += self.theta
+                    tmp_batch[np.arange(len(active_indices)), feat_ind[:, 1]] += self.theta
+                    batch[active_indices] = tmp_batch
+
+                # Recompute model prediction
+                current_pred = np.argmax(self.estimator.predict(np.reshape(batch, [batch.shape[0]] + dims)), axis=1,)
+
+                # Update active_indices
+                active_indices = np.where(
+                    (current_pred != target)
+                    * (np.sum(all_feat, axis=1) / self._nb_features <= self.gamma)
+                    * (np.sum(search_space, axis=1) > 0)
+                )[0]
+
+            x_adv[batch_index_1:batch_index_2] = batch
+
+        x_adv = np.reshape(x_adv, x.shape)
+
+        logger.info(
+            "Success rate of JSMA attack: %.2f%%",
+            100 * compute_success(self.estimator, x, y, x_adv, batch_size=self.batch_size),
+        )
+
+        return x_adv
+
+    def _saliency_map(self, x: np.ndarray, target: Union[np.ndarray, int], search_space: np.ndarray) -> np.ndarray:
+        """
+        Compute the saliency map of `x`. Return the top 2 coefficients in `search_space` that maximize / minimize
+        the saliency map.
+
+        :param x: A batch of input samples.
+        :param target: Target class for `x`.
+        :param search_space: The set of valid pairs of feature indices to search.
+        :return: The top 2 coefficients in `search_space` that maximize / minimize the saliency map.
+        """
+        grads = self.estimator.class_gradient(x, label=target)
+        grads = np.reshape(grads, (-1, self._nb_features))
+
+        # Remove gradients for already used features
+        used_features = 1 - search_space
+        coeff = 2 * int(self.theta > 0) - 1
+        grads[used_features == 1] = -np.inf * coeff
+
+        if self.theta > 0:
+            ind = np.argpartition(grads, -2, axis=1)[:, -2:]
+        else:
+            ind = np.argpartition(-grads, -2, axis=1)[:, -2:]
+
+        return ind
+
+    def _check_params(self) -> None:
+        if self.gamma <= 0 or self.gamma > 1:
+            raise ValueError("The total perturbation percentage `gamma` must be between 0 and 1.")
+
+        if self.batch_size <= 0:
+            raise ValueError("The batch size `batch_size` has to be positive.")
+
+        if not isinstance(self.verbose, bool):
+            raise ValueError("The argument `verbose` has to be of type bool.")
diff --git a/adversarial-robustness-toolbox/art/attacks/evasion/shadow_attack.py b/adversarial-robustness-toolbox/art/attacks/evasion/shadow_attack.py
new file mode 100644
index 0000000..24613b0
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/attacks/evasion/shadow_attack.py
@@ -0,0 +1,290 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements the evasion attack `ShadowAttack`.
+
+| Paper link: https://arxiv.org/abs/2003.08937
+"""
+import logging
+from typing import Optional, Union
+
+import numpy as np
+from tqdm.auto import trange
+
+from art.config import ART_NUMPY_DTYPE
+from art.estimators.estimator import BaseEstimator, LossGradientsMixin
+from art.estimators.classification.classifier import ClassifierMixin
+from art.estimators.classification import TensorFlowV2Classifier, PyTorchClassifier
+from art.estimators.certification.randomized_smoothing import (
+    TensorFlowV2RandomizedSmoothing,
+    PyTorchRandomizedSmoothing,
+)
+from art.attacks.attack import EvasionAttack
+from art.utils import get_labels_np_array, check_and_transform_label_format
+
+logger = logging.getLogger(__name__)
+
+
+class ShadowAttack(EvasionAttack):
+    """
+    Implementation of the Shadow Attack.
+
+    | Paper link: https://arxiv.org/abs/2003.08937
+    """
+
+    attack_params = EvasionAttack.attack_params + [
+        "sigma",
+        "nb_steps",
+        "learning_rate",
+        "lambda_tv",
+        "lambda_c",
+        "lambda_s",
+        "batch_size",
+        "targeted",
+        "verbose",
+    ]
+
+    _estimator_requirements = (BaseEstimator, LossGradientsMixin, ClassifierMixin)
+
+    def __init__(
+        self,
+        estimator: Union[
+            TensorFlowV2Classifier, TensorFlowV2RandomizedSmoothing, PyTorchClassifier, PyTorchRandomizedSmoothing
+        ],
+        sigma: float = 0.5,
+        nb_steps: int = 300,
+        learning_rate: float = 0.1,
+        lambda_tv: float = 0.3,
+        lambda_c: float = 1.0,
+        lambda_s: float = 0.5,
+        batch_size: int = 400,
+        targeted: bool = False,
+        verbose: bool = True,
+    ):
+        """
+        Create an instance of the :class:`.ShadowAttack`.
+
+        :param estimator: A trained classifier.
+        :param sigma: Standard deviation random Gaussian Noise.
+        :param nb_steps: Number of SGD steps.
+        :param learning_rate: Learning rate for SGD.
+        :param lambda_tv: Scalar penalty weight for total variation of the perturbation.
+        :param lambda_c: Scalar penalty weight for change in the mean of each color channel of the perturbation.
+        :param lambda_s: Scalar penalty weight for similarity of color channels in perturbation.
+        :param batch_size: The size of the training batch.
+        :param targeted: True if the attack is targeted.
+        :param verbose: Show progress bars.
+        """
+        super().__init__(estimator=estimator)
+
+        self.sigma = sigma
+        self.batch_size = batch_size
+        self.nb_steps = nb_steps
+        self.learning_rate = learning_rate
+        self.lambda_tv = lambda_tv
+        self.lambda_c = lambda_c
+        self.lambda_s = lambda_s
+        self._targeted = targeted
+        self.verbose = verbose
+        self._check_params()
+
+        self.framework: Optional[str]
+        if isinstance(self.estimator, (TensorFlowV2Classifier, TensorFlowV2RandomizedSmoothing)):
+            self.framework = "tensorflow"
+        elif isinstance(self.estimator, (PyTorchClassifier, PyTorchRandomizedSmoothing)):
+            self.framework = "pytorch"
+        else:
+            self.framework = None
+
+    def generate(self, x: np.ndarray, y: Optional[np.ndarray] = None, **kwargs) -> np.ndarray:
+        """
+        Generate adversarial samples and return them in an array. This requires a lot of memory, therefore it accepts
+        only a single samples as input, e.g. a batch of size 1.
+
+        :param x: An array of a single original input sample.
+        :param y: An array of a single target label.
+        :return: An array with the adversarial examples.
+        """
+        y = check_and_transform_label_format(y, self.estimator.nb_classes)
+
+        if y is None:
+            # Throw error if attack is targeted, but no targets are provided
+            if self.targeted:
+                raise ValueError("Target labels `y` need to be provided for a targeted attack.")
+
+            logger.info("Using model predictions as correct labels for FGM.")
+            y = get_labels_np_array(self.estimator.predict(x, batch_size=self.batch_size))
+        else:
+            self.targeted = True
+
+        if x.shape[0] > 1 or y.shape[0] > 1:
+            raise ValueError("This attack only accepts a single sample as input.")
+
+        if x.ndim != 4:
+            raise ValueError("Unrecognized input dimension. Shadow Attack can only be applied to image data.")
+
+        x = x.astype(ART_NUMPY_DTYPE)
+        x_batch = np.repeat(x, repeats=self.batch_size, axis=0).astype(ART_NUMPY_DTYPE)
+        x_batch = x_batch + np.random.normal(scale=self.sigma, size=x_batch.shape).astype(ART_NUMPY_DTYPE)
+        y_batch = np.repeat(y, repeats=self.batch_size, axis=0)
+
+        perturbation = (
+            np.random.uniform(
+                low=self.estimator.clip_values[0], high=self.estimator.clip_values[1], size=x.shape
+            ).astype(ART_NUMPY_DTYPE)
+            - (self.estimator.clip_values[1] - self.estimator.clip_values[0]) / 2
+        )
+
+        for _ in trange(self.nb_steps, desc="Shadow attack", disable=not self.verbose):
+            gradients_ce = np.mean(
+                self.estimator.loss_gradient(x=x_batch + perturbation, y=y_batch, sampling=False)
+                * (1 - 2 * int(self.targeted)),
+                axis=0,
+                keepdims=True,
+            )
+            gradients = gradients_ce - self._get_regularisation_loss_gradients(perturbation)
+            perturbation += self.learning_rate * gradients
+
+        x_p = x + perturbation
+        x_adv = np.clip(x_p, a_min=self.estimator.clip_values[0], a_max=self.estimator.clip_values[1]).astype(
+            ART_NUMPY_DTYPE
+        )
+
+        return x_adv
+
+    def _get_regularisation_loss_gradients(self, perturbation: np.ndarray) -> np.ndarray:
+        """
+        Get regularisation loss gradients.
+
+        :param perturbation: The perturbation to be regularised.
+        :return: The loss gradients of the perturbation.
+        """
+        if not self.estimator.channels_first:
+            perturbation = perturbation.transpose((0, 3, 1, 2))
+
+        if self.framework == "tensorflow":
+            import tensorflow as tf
+
+            if tf.executing_eagerly():
+                with tf.GradientTape() as tape:
+
+                    perturbation_t = tf.convert_to_tensor(perturbation)
+                    tape.watch(perturbation_t)
+
+                    x_t = perturbation_t[:, :, :, 1:] - perturbation_t[:, :, :, :-1]
+                    y_t = perturbation_t[:, :, 1:, :] - perturbation_t[:, :, :-1, :]
+                    loss_tv = tf.reduce_sum(x_t * x_t, axis=(1, 2, 3)) + tf.reduce_sum(y_t * y_t, axis=(1, 2, 3))
+
+                    if perturbation_t.shape[1] == 1:
+                        loss_s = 0.0
+                    elif perturbation_t.shape[1] == 3:
+                        loss_s = tf.norm(
+                            (perturbation_t[:, 0, :, :] - perturbation_t[:, 1, :, :]) ** 2
+                            + (perturbation_t[:, 1, :, :] - perturbation_t[:, 2, :, :]) ** 2
+                            + (perturbation_t[:, 0, :, :] - perturbation_t[:, 2, :, :]) ** 2,
+                            ord=2,
+                            axis=(1, 2),
+                        )
+
+                    loss_c = tf.norm(tf.reduce_mean(tf.abs(perturbation_t), axis=[2, 3]), ord=2, axis=1) ** 2
+                    loss = self.lambda_tv * loss_tv + self.lambda_s * loss_s + self.lambda_c * loss_c
+                    gradients = tape.gradient(loss, perturbation_t).numpy()
+
+            else:
+                raise ValueError("Expecting eager execution.")
+
+        elif self.framework == "pytorch":
+            import torch
+
+            perturbation_t = torch.from_numpy(perturbation).to("cpu")
+            perturbation_t.requires_grad = True
+
+            x_t = perturbation_t[:, :, :, 1:] - perturbation_t[:, :, :, :-1]
+            y_t = perturbation_t[:, :, 1:, :] - perturbation_t[:, :, :-1, :]
+
+            loss_tv = (x_t * x_t).sum(dim=(1, 2, 3)) + (y_t * y_t).sum(dim=(1, 2, 3))
+
+            if perturbation_t.shape[1] == 1:
+                loss_s = 0.0
+            elif perturbation_t.shape[1] == 3:
+                loss_s = (
+                    (perturbation_t[:, 0, :, :] - perturbation_t[:, 1, :, :]) ** 2
+                    + (perturbation_t[:, 1, :, :] - perturbation_t[:, 2, :, :]) ** 2
+                    + (perturbation_t[:, 0, :, :] - perturbation_t[:, 2, :, :]) ** 2
+                ).norm(p=2, dim=(1, 2))
+
+            loss_c = perturbation_t.abs().mean([2, 3]).norm(dim=1) ** 2
+            loss = torch.mean(self.lambda_tv * loss_tv + self.lambda_s * loss_s + self.lambda_c * loss_c)
+            loss.backward()
+            gradients = perturbation_t.grad.numpy()
+        else:
+            raise NotImplementedError
+
+        if not self.estimator.channels_first:
+            gradients = gradients.transpose(0, 2, 3, 1)
+
+        return gradients
+
+    def _check_params(self) -> None:
+        if not isinstance(self.sigma, (int, float)):
+            raise ValueError("The sigma must be of type int or float.")
+
+        if self.sigma <= 0:
+            raise ValueError("The sigma must larger than zero.")
+
+        if not isinstance(self.nb_steps, int):
+            raise ValueError("The number of steps must be of type int.")
+
+        if self.nb_steps <= 0:
+            raise ValueError("The number of steps must larger than zero.")
+
+        if not isinstance(self.learning_rate, float):
+            raise ValueError("The learning rate must be of type float.")
+
+        if self.learning_rate <= 0:
+            raise ValueError("The learning rate must larger than zero.")
+
+        if not isinstance(self.lambda_tv, float):
+            raise ValueError("The lambda_tv must be of type float.")
+
+        if self.lambda_tv < 0:
+            raise ValueError("The lambda_tv must larger than zero.")
+
+        if not isinstance(self.lambda_c, float):
+            raise ValueError("The lambda_c must be of type float.")
+
+        if self.lambda_c < 0:
+            raise ValueError("The lambda_c must larger than zero.")
+
+        if not isinstance(self.lambda_s, float):
+            raise ValueError("The lambda_s must be of type float.")
+
+        if self.lambda_s < 0:
+            raise ValueError("The lambda_s must larger than zero.")
+
+        if not isinstance(self.batch_size, int):
+            raise ValueError("The batch size must be of type int.")
+
+        if self.batch_size <= 0:
+            raise ValueError("The sigma must larger than zero.")
+
+        if not isinstance(self.targeted, bool):
+            raise ValueError("The targeted argument must be of type bool.")
+
+        if not isinstance(self.verbose, bool):
+            raise ValueError("The argument `verbose` has to be of type bool.")
diff --git a/adversarial-robustness-toolbox/art/attacks/evasion/shapeshifter.py b/adversarial-robustness-toolbox/art/attacks/evasion/shapeshifter.py
new file mode 100644
index 0000000..bc38720
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/attacks/evasion/shapeshifter.py
@@ -0,0 +1,1055 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements ShapeShifter, a robust physical adversarial attack on Faster R-CNN object detector.
+
+| Paper link: https://arxiv.org/abs/1804.05810
+"""
+import logging
+from typing import List, Dict, Optional, Tuple, TYPE_CHECKING
+from collections import Callable
+
+import numpy as np
+
+from art.attacks.attack import EvasionAttack
+from art.estimators.object_detection.tensorflow_faster_rcnn import TensorFlowFasterRCNN
+
+if TYPE_CHECKING:
+    from tensorflow.python.framework.ops import Tensor
+    from tensorflow.python.training.optimizer import Optimizer
+
+logger = logging.getLogger(__name__)
+
+
+class ShapeShifter(EvasionAttack):
+    """
+    Implementation of the ShapeShifter attack. This is a robust physical adversarial attack on Faster R-CNN object
+    detector and is developed in TensorFlow.
+
+    | Paper link: https://arxiv.org/abs/1804.05810
+    """
+
+    attack_params = EvasionAttack.attack_params + [
+        "random_transform",
+        "box_classifier_weight",
+        "box_localizer_weight",
+        "rpn_classifier_weight",
+        "rpn_localizer_weight",
+        "box_iou_threshold",
+        "box_victim_weight",
+        "box_target_weight",
+        "box_victim_cw_weight",
+        "box_victim_cw_confidence",
+        "box_target_cw_weight",
+        "box_target_cw_confidence",
+        "rpn_iou_threshold",
+        "rpn_background_weight",
+        "rpn_foreground_weight",
+        "rpn_cw_weight",
+        "rpn_cw_confidence",
+        "similarity_weight",
+        "learning_rate",
+        "optimizer",
+        "momentum",
+        "decay",
+        "sign_gradients",
+        "random_size",
+        "max_iter",
+        "texture_as_input",
+        "use_spectral",
+        "soft_clip",
+    ]
+
+    _estimator_requirements = (TensorFlowFasterRCNN,)
+
+    def __init__(
+        self,
+        estimator: TensorFlowFasterRCNN,
+        random_transform: "Callable",
+        box_classifier_weight: float = 1.0,
+        box_localizer_weight: float = 2.0,
+        rpn_classifier_weight: float = 1.0,
+        rpn_localizer_weight: float = 2.0,
+        box_iou_threshold: float = 0.5,
+        box_victim_weight: float = 0.0,
+        box_target_weight: float = 0.0,
+        box_victim_cw_weight: float = 0.0,
+        box_victim_cw_confidence: float = 0.0,
+        box_target_cw_weight: float = 0.0,
+        box_target_cw_confidence: float = 0.0,
+        rpn_iou_threshold: float = 0.5,
+        rpn_background_weight: float = 0.0,
+        rpn_foreground_weight: float = 0.0,
+        rpn_cw_weight: float = 0.0,
+        rpn_cw_confidence: float = 0.0,
+        similarity_weight: float = 0.0,
+        learning_rate: float = 1.0,
+        optimizer: str = "GradientDescentOptimizer",
+        momentum: float = 0.0,
+        decay: float = 0.0,
+        sign_gradients: bool = False,
+        random_size: int = 10,
+        max_iter: int = 10,
+        texture_as_input: bool = False,
+        use_spectral: bool = True,
+        soft_clip: bool = False,
+    ):
+        """
+        Create an instance of the :class:`.ShapeShifter`.
+
+        :param estimator: A trained object detector.
+        :param random_transform: A function applies random transformations to images/textures.
+        :param box_classifier_weight: Weight of box classifier loss.
+        :param box_localizer_weight: Weight of box localizer loss.
+        :param rpn_classifier_weight: Weight of RPN classifier loss.
+        :param rpn_localizer_weight: Weight of RPN localizer loss.
+        :param box_iou_threshold: Box intersection over union threshold.
+        :param box_victim_weight: Weight of box victim loss.
+        :param box_target_weight: Weight of box target loss.
+        :param box_victim_cw_weight: Weight of box victim CW loss.
+        :param box_victim_cw_confidence: Confidence of box victim CW loss.
+        :param box_target_cw_weight: Weight of box target CW loss.
+        :param box_target_cw_confidence: Confidence of box target CW loss.
+        :param rpn_iou_threshold: RPN intersection over union threshold.
+        :param rpn_background_weight: Weight of RPN background loss.
+        :param rpn_foreground_weight: Weight of RPN foreground loss.
+        :param rpn_cw_weight: Weight of RPN CW loss.
+        :param rpn_cw_confidence: Confidence of RPN CW loss.
+        :param similarity_weight: Weight of similarity loss.
+        :param learning_rate: Learning rate.
+        :param optimizer: Optimizer including one of the following choices: `GradientDescentOptimizer`,
+                          `MomentumOptimizer`, `RMSPropOptimizer`, `AdamOptimizer`.
+        :param momentum: Momentum for `RMSPropOptimizer`, `MomentumOptimizer`.
+        :param decay: Learning rate decay for `RMSPropOptimizer`.
+        :param sign_gradients: Whether to use the sign of gradients for optimization.
+        :param random_size: Random sample size.
+        :param max_iter: Maximum number of iterations.
+        :param texture_as_input: Whether textures are used as inputs instead of images.
+        :param use_spectral: Whether to use spectral with textures.
+        :param soft_clip: Whether to apply soft clipping on textures.
+        """
+        super().__init__(estimator=estimator)
+
+        # Set attack attributes
+        self.random_transform = random_transform
+        self.box_classifier_weight = box_classifier_weight
+        self.box_localizer_weight = box_localizer_weight
+        self.rpn_classifier_weight = rpn_classifier_weight
+        self.rpn_localizer_weight = rpn_localizer_weight
+        self.box_iou_threshold = box_iou_threshold
+        self.box_victim_weight = box_victim_weight
+        self.box_target_weight = box_target_weight
+        self.box_victim_cw_weight = box_victim_cw_weight
+        self.box_victim_cw_confidence = box_victim_cw_confidence
+        self.box_target_cw_weight = box_target_cw_weight
+        self.box_target_cw_confidence = box_target_cw_confidence
+        self.rpn_iou_threshold = rpn_iou_threshold
+        self.rpn_background_weight = rpn_background_weight
+        self.rpn_foreground_weight = rpn_foreground_weight
+        self.rpn_cw_weight = rpn_cw_weight
+        self.rpn_cw_confidence = rpn_cw_confidence
+        self.similarity_weight = similarity_weight
+        self.learning_rate = learning_rate
+        self.optimizer = optimizer
+        self.momentum = momentum
+        self.decay = decay
+        self.sign_gradients = sign_gradients
+        self.random_size = random_size
+        self.max_iter = max_iter
+        self.texture_as_input = texture_as_input
+        self.use_spectral = use_spectral
+        self.soft_clip = soft_clip
+        self.graph_available: bool = False
+
+        # Check validity of attack attributes
+        self._check_params()
+
+    def generate(self, x: np.ndarray, y: Optional[np.ndarray] = None, **kwargs) -> np.ndarray:
+        """
+        Generate adversarial samples and return them in an array.
+
+        :param x: Sample image/texture.
+        :param y: Not used.
+        :param label: A dictionary of target labels for object detector. The fields of the dictionary are as follows:
+
+                    - `groundtruth_boxes_list`: A list of `nb_samples` size of 2-D tf.float32 tensors of shape
+                                                [num_boxes, 4] containing coordinates of the groundtruth boxes.
+                                                Groundtruth boxes are provided in [y_min, x_min, y_max, x_max]
+                                                format and also assumed to be normalized as well as clipped
+                                                relative to the image window with conditions y_min <= y_max and
+                                                x_min <= x_max.
+                    - `groundtruth_classes_list`: A list of `nb_samples` size of 1-D tf.float32 tensors of shape
+                                                  [num_boxes] containing the class targets with the zero index
+                                                  assumed to map to the first non-background class.
+                    - `groundtruth_weights_list`: A list of `nb_samples` size of 1-D tf.float32 tensors of shape
+                                                  [num_boxes] containing weights for groundtruth boxes.
+        :type label: Dict[str, List[np.ndarray]]
+        :param mask: Input mask.
+        :type mask: `np.ndarray`.
+        :param target_class: Target class.
+        :type target_class: int
+        :param victim_class: Victim class.
+        :type victim_class: int
+        :param custom_loss: Custom loss function from users.
+        :type custom_loss: Tensor
+        :param rendering_function: A rendering function to use textures as input.
+        :type rendering_function: Callable
+        :return: Adversarial image/texture.
+        """
+        # Check input shape
+        assert x.ndim == 4, "The ShapeShifter attack can only be applied to images."
+        assert x.shape[0] == 1, "The ShapeShifter attack can only be applied to one image."
+        assert list(x.shape[1:]) == self.estimator.input_shape
+
+        # Check if label is provided
+        label = kwargs.get("label")
+        if label is None and not self.texture_as_input:
+            raise ValueError("Need the target labels for image as input.")
+
+        # Check whether users have a custom loss
+        custom_loss = kwargs.get("custom_loss")
+
+        # Build the TensorFlow graph
+        if not self.graph_available:
+            self.graph_available = True
+
+            if self.texture_as_input:
+                # Check whether users provide a rendering function
+                rendering_function = kwargs.get("rendering_function")
+                if rendering_function is None:
+                    raise ValueError("Need a rendering function to use textures as input.")
+
+                # Build the TensorFlow graph
+                (
+                    self.project_texture_op,
+                    self.current_image_assign_to_input_image_op,
+                    self.accumulated_gradients_op,
+                    self.final_attack_optimization_op,
+                    self.current_variable,
+                    self.current_value,
+                ) = self._build_graph(
+                    initial_shape=x.shape, custom_loss=custom_loss, rendering_function=rendering_function
+                )
+
+            else:
+                (
+                    self.project_texture_op,
+                    self.current_image_assign_to_input_image_op,
+                    self.accumulated_gradients_op,
+                    self.final_attack_optimization_op,
+                    self.current_variable,
+                    self.current_value,
+                ) = self._build_graph(initial_shape=x.shape, custom_loss=custom_loss)
+
+        # Use mask or not
+        if self.texture_as_input:
+            # Check whether users have a mask
+            mask = kwargs.get("mask")
+            if mask is None:
+                mask = np.ones_like(x)
+        else:
+            mask = None
+
+        # Get victim class
+        victim_class = kwargs.get("victim_class")
+        if victim_class is None:
+            raise ValueError("Need to provide a victim class.")
+
+        if not isinstance(victim_class, int):
+            raise TypeError("Victim class must be of type `int`.")
+
+        # Get target class
+        target_class = kwargs.get("target_class")
+        if target_class is None:
+            logger.warning("Target class not provided, an untargeted attack is defaulted.")
+            target_class = victim_class
+
+        if not isinstance(target_class, int):
+            raise TypeError("Target class must be of type `int`.")
+
+        # Do attack
+        result = self._attack_training(
+            x=x,
+            y=label,
+            mask=mask,
+            target_class=target_class,
+            victim_class=victim_class,
+            project_texture_op=self.project_texture_op,
+            current_image_assign_to_input_image_op=self.current_image_assign_to_input_image_op,
+            accumulated_gradients_op=self.accumulated_gradients_op,
+            final_attack_optimization_op=self.final_attack_optimization_op,
+            current_variable=self.current_variable,
+            current_value=self.current_value,
+        )
+
+        return result[1]
+
+    def _attack_training(
+        self,
+        x: np.ndarray,
+        y: Dict[str, List[np.ndarray]],
+        mask: np.ndarray,
+        target_class: int,
+        victim_class: int,
+        project_texture_op: "Tensor",
+        current_image_assign_to_input_image_op: "Tensor",
+        accumulated_gradients_op: "Tensor",
+        final_attack_optimization_op: "Tensor",
+        current_variable: "Tensor",
+        current_value: "Tensor",
+    ) -> List[np.ndarray]:
+        """
+        Do attack optimization.
+
+        :param x: Sample image/texture.
+        :param y: A dictionary of target labels for object detector. The fields of the dictionary are as follows:
+
+                    - `groundtruth_boxes_list`: A list of `nb_samples` size of 2-D tf.float32 tensors of shape
+                                                [num_boxes, 4] containing coordinates of the groundtruth boxes.
+                                                Groundtruth boxes are provided in [y_min, x_min, y_max, x_max]
+                                                format and also assumed to be normalized as well as clipped
+                                                relative to the image window with conditions y_min <= y_max and
+                                                x_min <= x_max.
+                    - `groundtruth_classes_list`: A list of `nb_samples` size of 1-D tf.float32 tensors of shape
+                                                  [num_boxes] containing the class targets with the zero index
+                                                  assumed to map to the first non-background class.
+                    - `groundtruth_weights_list`: A list of `nb_samples` size of 1-D tf.float32 tensors of shape
+                                                  [num_boxes] containing weights for groundtruth boxes.
+        :param mask: Input mask.
+        :param target_class: Target class.
+        :param victim_class: Victim class.
+        :param project_texture_op: The project texture operator in the TensorFlow graph.
+        :param current_image_assign_to_input_image_op: The current_image assigned to input_image operator in the
+                                                       TensorFlow graph.
+        :param accumulated_gradients_op: The accumulated gradients operator in the TensorFlow graph.
+        :param final_attack_optimization_op: The final attack optimization operator in the TensorFlow graph.
+        :param current_value: The current image/texture in the TensorFlow graph.
+        :param current_variable: The current image/texture variable in the TensorFlow graph.
+
+        :return: Adversarial image/texture.
+        """
+        import tensorflow as tf
+
+        # Initialize session
+        self.estimator.sess.run(tf.global_variables_initializer())
+        self.estimator.sess.run(tf.local_variables_initializer())
+
+        # Create common feed_dict
+        feed_dict = {
+            "initial_input:0": x,
+            "learning_rate:0": self.learning_rate,
+            "rpn_classifier_weight:0": self.rpn_classifier_weight,
+            "rpn_localizer_weight:0": self.rpn_localizer_weight,
+            "box_classifier_weight:0": self.box_classifier_weight,
+            "box_localizer_weight:0": self.box_localizer_weight,
+            "target_class_phd:0": target_class,
+            "victim_class_phd:0": victim_class,
+            "box_iou_threshold:0": self.box_iou_threshold,
+            "box_target_weight:0": self.box_target_weight,
+            "box_victim_weight:0": self.box_victim_weight,
+            "box_target_cw_weight:0": self.box_target_cw_weight,
+            "box_target_cw_confidence:0": self.box_target_cw_confidence,
+            "box_victim_cw_weight:0": self.box_victim_cw_weight,
+            "box_victim_cw_confidence:0": self.box_victim_cw_confidence,
+            "rpn_iou_threshold:0": self.rpn_iou_threshold,
+            "rpn_background_weight:0": self.rpn_background_weight,
+            "rpn_foreground_weight:0": self.rpn_foreground_weight,
+            "rpn_cw_weight:0": self.rpn_cw_weight,
+            "rpn_cw_confidence:0": self.rpn_cw_confidence,
+            "similarity_weight:0": self.similarity_weight,
+        }
+
+        # Add momentum to feed_dict
+        if self.optimizer in ["RMSPropOptimizer", "MomentumOptimizer"]:
+            feed_dict["momentum:0"] = self.momentum
+
+        # Add decay to feed_dict
+        if self.optimizer == "RMSPropOptimizer":
+            feed_dict["decay:0"] = self.decay
+
+        # Add mask to feed_dict
+        if self.texture_as_input:
+            feed_dict["mask_input:0"] = mask
+
+        # Training loop for attack optimization
+        for _ in range(self.max_iter):
+            # Accumulate gradients
+            for _ in range(self.random_size):
+                if self.texture_as_input:
+                    # Random transformation
+                    background, image_frame, y_transform = self.random_transform(x)
+
+                    # Add more to feed_dict
+                    feed_dict["background_phd:0"] = background
+                    feed_dict["image_frame_phd:0"] = image_frame
+
+                    for i in range(x.shape[0]):
+                        feed_dict["groundtruth_boxes_{}:0".format(i)] = y_transform["groundtruth_boxes_list"][i]
+                        feed_dict["groundtruth_classes_{}:0".format(i)] = y_transform["groundtruth_classes_list"][i]
+                        feed_dict["groundtruth_weights_{}:0".format(i)] = y_transform["groundtruth_weights_list"][i]
+
+                else:
+                    # Random transformation
+                    random_transformation = self.random_transform(x)
+
+                    # Add more to feed_dict
+                    feed_dict["random_transformation_phd:0"] = random_transformation
+
+                    for i in range(x.shape[0]):
+                        feed_dict["groundtruth_boxes_{}:0".format(i)] = y["groundtruth_boxes_list"][i]
+                        feed_dict["groundtruth_classes_{}:0".format(i)] = y["groundtruth_classes_list"][i]
+                        feed_dict["groundtruth_weights_{}:0".format(i)] = y["groundtruth_weights_list"][i]
+
+                # Accumulate gradients
+                self.estimator.sess.run(current_image_assign_to_input_image_op, feed_dict)
+                self.estimator.sess.run(accumulated_gradients_op, feed_dict)
+
+            # Update image/texture variable
+            self.estimator.sess.run(final_attack_optimization_op, feed_dict)
+
+            if self.texture_as_input:
+                self.estimator.sess.run(project_texture_op, feed_dict)
+
+        result = self.estimator.sess.run([current_variable, current_value], feed_dict)
+
+        return result
+
+    def _build_graph(
+        self,
+        initial_shape: Tuple[int, ...],
+        custom_loss: Optional["Tensor"] = None,
+        rendering_function: Optional["Callable"] = None,
+    ) -> Tuple["Tensor", ...]:
+        """
+        Build the TensorFlow graph for the attack.
+
+        :param initial_shape: Image/texture shape.
+        :param custom_loss: Custom loss function from users.
+        :param rendering_function: A rendering function to use textures as input.
+        :return: A tuple of tensors.
+        """
+        import tensorflow as tf
+
+        # Create a placeholder to pass input image/texture
+        initial_input = tf.placeholder(dtype=tf.float32, shape=initial_shape, name="initial_input")
+
+        # Create adversarial image
+        project_texture_op = None
+
+        if self.texture_as_input:
+            # Create a placeholder to pass input texture mask
+            mask_input = tf.placeholder(dtype=tf.float32, shape=initial_shape, name="mask_input")
+
+            # Create texture variable
+            if self.use_spectral:
+                initial_value = np.zeros(
+                    (2, initial_shape[0], initial_shape[3], initial_shape[1], int(np.ceil(initial_shape[2] / 2) + 1))
+                )
+
+                current_texture_variable = tf.Variable(
+                    initial_value=initial_value, dtype=tf.float32, name="current_texture_variable"
+                )
+
+                current_texture = current_texture_variable
+                current_texture = tf.complex(current_texture[0], current_texture[1])
+                current_texture = tf.map_fn(tf.spectral.irfft2d, current_texture, dtype=tf.float32)
+                current_texture = tf.transpose(current_texture, (0, 2, 3, 1))
+
+            else:
+                initial_value = np.zeros((initial_shape[0], initial_shape[1], initial_shape[2], initial_shape[3]))
+
+                current_texture_variable = tf.Variable(
+                    initial_value=initial_value, dtype=tf.float32, name="current_texture_variable"
+                )
+
+                current_texture = current_texture_variable
+
+            # Invert texture for projection
+            project_texture = initial_input * (1.0 - mask_input) + current_texture * mask_input
+
+            if self.soft_clip:
+                project_texture = tf.nn.sigmoid(project_texture)
+            else:
+                project_texture = tf.clip_by_value(project_texture, 0.0, 1.0)
+
+            if self.use_spectral:
+                project_texture = tf.transpose(project_texture, (0, 3, 1, 2))
+                project_texture = tf.map_fn(tf.spectral.rfft2d, project_texture, dtype=tf.complex64)
+                project_texture = tf.stack([tf.real(project_texture), tf.imag(project_texture)])
+
+            # Update texture variable
+            project_texture_op = tf.assign(current_texture_variable, project_texture, name="project_texture_op")
+
+            # Create a placeholder to pass the background
+            background_phd = tf.placeholder(
+                dtype=tf.float32, shape=initial_input.shape.as_list(), name="background_phd"
+            )
+
+            # Create a placeholder to pass the image frame
+            image_frame_phd = tf.placeholder(
+                dtype=tf.float32, shape=[initial_shape[0], None, None, 4], name="image_frame_phd"
+            )
+
+            # Create adversarial image
+            if rendering_function is not None:
+                current_image = rendering_function(background_phd, image_frame_phd, current_texture)
+            else:
+                ValueError("Callable rendering_function is None.")
+
+        else:
+            # Create image variable
+            current_image_variable = tf.Variable(
+                initial_value=np.zeros(initial_input.shape.as_list()), dtype=tf.float32, name="current_image_variable"
+            )
+
+            # Create a placeholder to pass random transformation
+            random_transformation_phd = tf.placeholder(
+                dtype=tf.float32, shape=initial_input.shape.as_list(), name="random_transformation_phd"
+            )
+
+            # Update current image
+            current_image = current_image_variable + random_transformation_phd
+            current_image = (tf.tanh(current_image) + 1) / 2
+            current_image = tf.identity(current_image, name="current_image")
+
+        # Assign current image to the input of the object detector
+        current_image_assign_to_input_image_op = tf.assign(
+            ref=self.estimator.input_images, value=current_image, name="current_image_assign_to_input_image_op"
+        )
+
+        # Create attack loss
+        if self.texture_as_input:
+            total_loss = self._create_attack_loss(
+                initial_input=initial_input, current_value=current_texture, custom_loss=custom_loss
+            )
+        else:
+            total_loss = self._create_attack_loss(
+                initial_input=initial_input, current_value=current_image, custom_loss=custom_loss
+            )
+
+        # Create optimizer
+        optimizer = self._create_optimizer()
+
+        # Create gradients
+        if self.texture_as_input:
+            gradients = optimizer.compute_gradients(total_loss, var_list=[current_texture_variable])[0][0]
+        else:
+            gradients = optimizer.compute_gradients(total_loss, var_list=[current_image_variable])[0][0]
+
+        # Create variables to store gradients
+        if self.texture_as_input:
+            sum_gradients = tf.Variable(
+                initial_value=np.zeros(current_texture_variable.shape.as_list()),
+                trainable=False,
+                name="sum_gradients",
+                dtype=tf.float32,
+                collections=[tf.GraphKeys.GLOBAL_VARIABLES, tf.GraphKeys.LOCAL_VARIABLES],
+            )
+
+        else:
+            sum_gradients = tf.Variable(
+                initial_value=np.zeros(current_image_variable.shape.as_list()),
+                trainable=False,
+                name="sum_gradients",
+                dtype=tf.float32,
+                collections=[tf.GraphKeys.GLOBAL_VARIABLES, tf.GraphKeys.LOCAL_VARIABLES],
+            )
+
+        num_gradients = tf.Variable(
+            initial_value=0.0,
+            trainable=False,
+            name="count_gradients",
+            collections=[tf.GraphKeys.GLOBAL_VARIABLES, tf.GraphKeys.LOCAL_VARIABLES],
+        )
+
+        # Accumulate gradients
+        accumulated_sum_gradients = tf.assign_add(sum_gradients, gradients)
+        accumulated_num_gradients = tf.assign_add(num_gradients, 1.0)
+
+        # Final gradients
+        final_gradients = tf.div(
+            accumulated_sum_gradients, tf.maximum(accumulated_num_gradients, 1.0), name="final_gradients"
+        )
+
+        if self.sign_gradients:
+            final_gradients = tf.sign(final_gradients)
+
+        # Create accumulated gradients operator
+        accumulated_gradients_op = tf.group(
+            [accumulated_sum_gradients, accumulated_num_gradients], name="accumulated_gradients_op"
+        )
+
+        # Create final attack optimization operator and return
+        if self.texture_as_input:
+            # Create final attack optimization operator
+            final_attack_optimization_op = optimizer.apply_gradients(
+                grads_and_vars=[(final_gradients, current_texture_variable)], name="final_attack_optimization_op"
+            )
+
+            return (
+                project_texture_op,
+                current_image_assign_to_input_image_op,
+                accumulated_gradients_op,
+                final_attack_optimization_op,
+                current_texture_variable,
+                current_texture,
+            )
+
+        # Create final attack optimization operator
+        final_attack_optimization_op = optimizer.apply_gradients(
+            grads_and_vars=[(final_gradients, current_image_variable)], name="final_attack_optimization_op"
+        )
+
+        return (
+            project_texture_op,
+            current_image_assign_to_input_image_op,
+            accumulated_gradients_op,
+            final_attack_optimization_op,
+            current_image_variable,
+            current_image,
+        )
+
+    def _create_optimizer(self) -> "Optimizer":
+        """
+        Create an optimizer of this attack.
+
+        :return: Attack optimizer.
+        """
+        import tensorflow as tf
+
+        # Create placeholder for learning rate
+        learning_rate = tf.placeholder(dtype=tf.float32, shape=[], name="learning_rate")
+
+        # Create placeholder for momentum
+        if self.optimizer in ["RMSPropOptimizer", "MomentumOptimizer"]:
+            momentum = tf.placeholder(dtype=tf.float32, shape=[], name="momentum")
+
+        # Create placeholder for decay
+        if self.optimizer == "RMSPropOptimizer":
+            decay = tf.placeholder(dtype=tf.float32, shape=[], name="decay")
+
+        # Create optimizer
+        if self.optimizer == "GradientDescentOptimizer":
+            optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)
+        elif self.optimizer == "MomentumOptimizer":
+            optimizer = tf.train.MomentumOptimizer(learning_rate=learning_rate, momentum=momentum)
+        elif self.optimizer == "RMSPropOptimizer":
+            optimizer = tf.train.RMSPropOptimizer(learning_rate=learning_rate, momentum=momentum, decay=decay)
+        elif self.optimizer == "AdamOptimizer":
+            optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
+        else:
+            raise NotImplementedError("Unknown optimizer.")
+
+        return optimizer
+
+    def _create_attack_loss(
+        self, initial_input: "Tensor", current_value: "Tensor", custom_loss: Optional["Tensor"] = None,
+    ) -> "Tensor":
+        """
+        Create the loss tensor of this attack.
+
+        :param initial_input: Initial input.
+        :param current_value: Current image/texture.
+        :param custom_loss: Custom loss function from users.
+        :return: Attack loss tensor.
+        """
+        import tensorflow as tf
+
+        # Compute faster rcnn loss
+        partial_faster_rcnn_loss = self._create_faster_rcnn_loss()
+
+        # Compute box loss
+        partial_box_loss = self._create_box_loss()
+
+        # Compute RPN loss
+        partial_rpn_loss = self._create_rpn_loss()
+
+        # Compute similarity loss
+        weight_similarity_loss = self._create_similarity_loss(initial_input=initial_input, current_value=current_value)
+
+        # Compute total loss
+        if custom_loss is not None:
+            total_loss = tf.add_n(
+                [partial_faster_rcnn_loss, partial_box_loss, partial_rpn_loss, weight_similarity_loss, custom_loss],
+                name="total_loss",
+            )
+
+        else:
+            total_loss = tf.add_n(
+                [partial_faster_rcnn_loss, partial_box_loss, partial_rpn_loss, weight_similarity_loss],
+                name="total_loss",
+            )
+
+        return total_loss
+
+    def _create_faster_rcnn_loss(self) -> "Tensor":
+        """
+        Create the partial loss tensor of this attack from losses of the object detector.
+
+        :return: Attack partial loss tensor.
+        """
+        import tensorflow as tf
+
+        # Compute RPN classifier loss
+        rpn_classifier_weight = tf.placeholder(dtype=tf.float32, shape=[], name="rpn_classifier_weight")
+
+        rpn_classifier_loss = self.estimator.losses["Loss/RPNLoss/objectness_loss"]
+        weight_rpn_classifier_loss = tf.multiply(
+            x=rpn_classifier_loss, y=rpn_classifier_weight, name="weight_rpn_classifier_loss"
+        )
+
+        # Compute RPN localizer loss
+        rpn_localizer_weight = tf.placeholder(dtype=tf.float32, shape=[], name="rpn_localizer_weight")
+
+        rpn_localizer_loss = self.estimator.losses["Loss/RPNLoss/localization_loss"]
+        weight_rpn_localizer_loss = tf.multiply(
+            x=rpn_localizer_loss, y=rpn_localizer_weight, name="weight_rpn_localizer_loss"
+        )
+
+        # Compute box classifier loss
+        box_classifier_weight = tf.placeholder(dtype=tf.float32, shape=[], name="box_classifier_weight")
+
+        box_classifier_loss = self.estimator.losses["Loss/BoxClassifierLoss/classification_loss"]
+        weight_box_classifier_loss = tf.multiply(
+            x=box_classifier_loss, y=box_classifier_weight, name="weight_box_classifier_loss"
+        )
+
+        # Compute box localizer loss
+        box_localizer_weight = tf.placeholder(dtype=tf.float32, shape=[], name="box_localizer_weight")
+
+        box_localizer_loss = self.estimator.losses["Loss/BoxClassifierLoss/localization_loss"]
+        weight_box_localizer_loss = tf.multiply(
+            x=box_localizer_loss, y=box_localizer_weight, name="weight_box_localizer_loss"
+        )
+
+        # Compute partial loss
+        partial_loss = tf.add_n(
+            [
+                weight_rpn_classifier_loss,
+                weight_rpn_localizer_loss,
+                weight_box_classifier_loss,
+                weight_box_localizer_loss,
+            ],
+            name="partial_faster_rcnn_loss",
+        )
+
+        return partial_loss
+
+    def _create_box_loss(self) -> "Tensor":
+        """
+        Create the partial loss tensor of this attack from box losses.
+
+        :return: Attack partial loss tensor.
+        """
+        import tensorflow as tf
+
+        # Get default graph
+        default_graph = tf.get_default_graph()
+
+        # Compute box losses
+        target_class_phd = tf.placeholder(dtype=tf.int32, shape=[], name="target_class_phd")
+        victim_class_phd = tf.placeholder(dtype=tf.int32, shape=[], name="victim_class_phd")
+        box_iou_threshold = tf.placeholder(dtype=tf.float32, shape=[], name="box_iou_threshold")
+
+        # Ignore background class
+        class_predictions_with_background = self.estimator.predictions["class_predictions_with_background"]
+        class_predictions_with_background = class_predictions_with_background[:, 1:]
+
+        # Convert to 1-hot
+        target_class_one_hot = tf.one_hot([target_class_phd - 1], class_predictions_with_background.shape[-1])
+        victim_class_one_hot = tf.one_hot([victim_class_phd - 1], class_predictions_with_background.shape[-1])
+
+        box_iou_tensor = default_graph.get_tensor_by_name("Loss/BoxClassifierLoss/Compare/IOU/Select:0")
+        box_iou_tensor = tf.reshape(box_iou_tensor, (-1,))
+        box_target = tf.cast(box_iou_tensor >= box_iou_threshold, dtype=tf.float32)
+
+        # Compute box target loss
+        box_target_weight = tf.placeholder(dtype=tf.float32, shape=[], name="box_target_weight")
+
+        box_target_logit = class_predictions_with_background[:, target_class_phd - 1]
+        box_target_loss = box_target_logit * box_target
+        box_target_loss = -1 * tf.reduce_sum(box_target_loss)
+        weight_box_target_loss = tf.multiply(x=box_target_loss, y=box_target_weight, name="weight_box_target_loss")
+
+        # Compute box victim loss
+        box_victim_weight = tf.placeholder(dtype=tf.float32, shape=[], name="box_victim_weight")
+
+        box_victim_logit = class_predictions_with_background[:, victim_class_phd - 1]
+        box_victim_loss = box_victim_logit * box_target
+        box_victim_loss = tf.reduce_sum(box_victim_loss)
+        weight_box_victim_loss = tf.multiply(x=box_victim_loss, y=box_victim_weight, name="weight_box_victim_loss")
+
+        # Compute box target CW loss
+        box_target_cw_weight = tf.placeholder(dtype=tf.float32, shape=[], name="box_target_cw_weight")
+        box_target_cw_confidence = tf.placeholder(dtype=tf.float32, shape=[], name="box_target_cw_confidence")
+
+        box_nontarget_logit = tf.reduce_max(
+            class_predictions_with_background * (1 - target_class_one_hot) - 10000 * target_class_one_hot, axis=-1
+        )
+        box_target_cw_loss = tf.nn.relu(box_nontarget_logit - box_target_logit + box_target_cw_confidence)
+        box_target_cw_loss = box_target_cw_loss * box_target
+        box_target_cw_loss = tf.reduce_sum(box_target_cw_loss)
+        weight_box_target_cw_loss = tf.multiply(
+            x=box_target_cw_loss, y=box_target_cw_weight, name="weight_box_target_cw_loss"
+        )
+
+        # Compute box victim CW loss
+        box_victim_cw_weight = tf.placeholder(dtype=tf.float32, shape=[], name="box_victim_cw_weight")
+        box_victim_cw_confidence = tf.placeholder(dtype=tf.float32, shape=[], name="box_victim_cw_confidence")
+
+        box_nonvictim_logit = tf.reduce_max(
+            class_predictions_with_background * (1 - victim_class_one_hot) - 10000 * victim_class_one_hot, axis=-1
+        )
+        box_victim_cw_loss = tf.nn.relu(box_victim_logit - box_nonvictim_logit + box_victim_cw_confidence)
+        box_victim_cw_loss = box_victim_cw_loss * box_target
+        box_victim_cw_loss = tf.reduce_sum(box_victim_cw_loss)
+        weight_box_victim_cw_loss = tf.multiply(
+            x=box_victim_cw_loss, y=box_victim_cw_weight, name="weight_box_victim_cw_loss"
+        )
+
+        # Compute partial loss
+        partial_loss = tf.add_n(
+            [weight_box_target_loss, weight_box_victim_loss, weight_box_target_cw_loss, weight_box_victim_cw_loss],
+            name="partial_box_loss",
+        )
+
+        return partial_loss
+
+    def _create_rpn_loss(self) -> "Tensor":
+        """
+        Create the partial loss tensor of this attack from RPN losses.
+
+        :return: Attack partial loss tensor.
+        """
+        import tensorflow as tf
+
+        # Get default graph
+        default_graph = tf.get_default_graph()
+
+        # Compute RPN losses
+        rpn_iou_threshold = tf.placeholder(dtype=tf.float32, shape=[], name="rpn_iou_threshold")
+
+        # RPN background
+        rpn_objectness_predictions_with_background = self.estimator.predictions[
+            "rpn_objectness_predictions_with_background"
+        ]
+        rpn_objectness_predictions_with_background = tf.reshape(
+            rpn_objectness_predictions_with_background, (-1, rpn_objectness_predictions_with_background.shape[-1])
+        )
+        rpn_iou_tensor = default_graph.get_tensor_by_name("Loss/RPNLoss/Compare/IOU/Select:0")
+        rpn_iou_tensor = tf.reshape(rpn_iou_tensor, (-1,))
+        rpn_target = tf.cast(rpn_iou_tensor >= rpn_iou_threshold, dtype=tf.float32)
+
+        # Compute RPN background loss
+        rpn_background_weight = tf.placeholder(dtype=tf.float32, shape=[], name="rpn_background_weight")
+
+        rpn_background_logit = rpn_objectness_predictions_with_background[:, 0]
+        rpn_background_loss = rpn_background_logit * rpn_target
+        rpn_background_loss = -1 * tf.reduce_sum(rpn_background_loss)
+        weight_rpn_background_loss = tf.multiply(
+            x=rpn_background_loss, y=rpn_background_weight, name="weight_rpn_background_loss"
+        )
+
+        # Compute RPN foreground loss
+        rpn_foreground_weight = tf.placeholder(dtype=tf.float32, shape=[], name="rpn_foreground_weight")
+
+        rpn_foreground_logit = rpn_objectness_predictions_with_background[:, 1]
+        rpn_foreground_loss = rpn_foreground_logit * rpn_target
+        rpn_foreground_loss = tf.reduce_sum(rpn_foreground_loss)
+        weight_rpn_foreground_loss = tf.multiply(
+            x=rpn_foreground_loss, y=rpn_foreground_weight, name="weight_rpn_foreground_loss"
+        )
+
+        # Compute RPN CW loss
+        rpn_cw_weight = tf.placeholder(dtype=tf.float32, shape=[], name="rpn_cw_weight")
+        rpn_cw_confidence = tf.placeholder(dtype=tf.float32, shape=[], name="rpn_cw_confidence")
+
+        rpn_cw_loss = tf.nn.relu(rpn_foreground_logit - rpn_background_logit + rpn_cw_confidence)
+        rpn_cw_loss = rpn_cw_loss * rpn_target
+        rpn_cw_loss = tf.reduce_sum(rpn_cw_loss)
+        weight_rpn_cw_loss = tf.multiply(x=rpn_cw_loss, y=rpn_cw_weight, name="weight_rpn_cw_loss")
+
+        # Compute partial loss
+        partial_loss = tf.add_n(
+            [weight_rpn_background_loss, weight_rpn_foreground_loss, weight_rpn_cw_loss,], name="partial_rpn_loss"
+        )
+
+        return partial_loss
+
+    @staticmethod
+    def _create_similarity_loss(initial_input: "Tensor", current_value: "Tensor") -> "Tensor":
+        """
+        Create the partial loss tensor of this attack from the similarity loss.
+
+        :param initial_input: Initial input.
+        :param current_value: Current image/texture.
+        :return: Attack partial loss tensor.
+        """
+        import tensorflow as tf
+
+        # Create a placeholder for the similarity weight
+        similarity_weight = tf.placeholder(dtype=tf.float32, shape=[], name="similarity_weight")
+
+        # Compute similarity loss
+        similarity_loss = tf.nn.l2_loss(initial_input - current_value)
+        weight_similarity_loss = tf.multiply(x=similarity_loss, y=similarity_weight, name="weight_similarity_loss")
+
+        return weight_similarity_loss
+
+    def _check_params(self) -> None:
+        """
+        Apply attack-specific checks.
+        """
+        if not isinstance(self.random_transform, Callable):
+            raise ValueError("The applied random transformation function must be of type Callable.")
+
+        if not isinstance(self.box_classifier_weight, float):
+            raise ValueError("The weight of box classifier loss must be of type float.")
+        if self.box_classifier_weight < 0.0:
+            raise ValueError("The weight of box classifier loss must be greater than or equal to 0.0.")
+
+        if not isinstance(self.box_localizer_weight, float):
+            raise ValueError("The weight of box localizer loss must be of type float.")
+        if self.box_localizer_weight < 0.0:
+            raise ValueError("The weight of box localizer loss must be greater than or equal to 0.0.")
+
+        if not isinstance(self.rpn_classifier_weight, float):
+            raise ValueError("The weight of RPN classifier loss must be of type float.")
+        if self.rpn_classifier_weight < 0.0:
+            raise ValueError("The weight of RPN classifier loss must be greater than or equal to 0.0.")
+
+        if not isinstance(self.rpn_localizer_weight, float):
+            raise ValueError("The weight of RPN localizer loss must be of type float.")
+        if self.rpn_localizer_weight < 0.0:
+            raise ValueError("The weight of RPN localizer loss must be greater than or equal to 0.0.")
+
+        if not isinstance(self.box_iou_threshold, float):
+            raise ValueError("The box intersection over union threshold must be of type float.")
+        if self.box_iou_threshold < 0.0:
+            raise ValueError("The box intersection over union threshold must be greater than or equal to 0.0.")
+
+        if not isinstance(self.box_victim_weight, float):
+            raise ValueError("The weight of box victim loss must be of type float.")
+        if self.box_victim_weight < 0.0:
+            raise ValueError("The weight of box victim loss must be greater than or equal to 0.0.")
+
+        if not isinstance(self.box_target_weight, float):
+            raise ValueError("The weight of box target loss must be of type float.")
+        if self.box_target_weight < 0.0:
+            raise ValueError("The weight of box target loss must be greater than or equal to 0.0.")
+
+        if not isinstance(self.box_victim_cw_weight, float):
+            raise ValueError("The weight of box victim CW loss must be of type float.")
+        if self.box_victim_cw_weight < 0.0:
+            raise ValueError("The weight of box victim CW loss must be greater than or equal to 0.0.")
+
+        if not isinstance(self.box_victim_cw_confidence, float):
+            raise ValueError("The confidence of box victim CW loss must be of type float.")
+        if self.box_victim_cw_confidence < 0.0:
+            raise ValueError("The confidence of box victim CW loss must be greater than or equal to 0.0.")
+
+        if not isinstance(self.box_target_cw_weight, float):
+            raise ValueError("The weight of box target CW loss must be of type float.")
+        if self.box_target_cw_weight < 0.0:
+            raise ValueError("The weight of box target CW loss must be greater than or equal to 0.0.")
+
+        if not isinstance(self.box_target_cw_confidence, float):
+            raise ValueError("The confidence of box target CW loss must be of type float.")
+        if self.box_target_cw_confidence < 0.0:
+            raise ValueError("The confidence of box target CW loss must be greater than or equal to 0.0.")
+
+        if not isinstance(self.rpn_iou_threshold, float):
+            raise ValueError("The RPN intersection over union threshold must be of type float.")
+        if self.rpn_iou_threshold < 0.0:
+            raise ValueError("The RPN intersection over union threshold must be greater than or equal to 0.0.")
+
+        if not isinstance(self.rpn_background_weight, float):
+            raise ValueError("The weight of RPN background loss must be of type float.")
+        if self.rpn_background_weight < 0.0:
+            raise ValueError("The weight of RPN background loss must be greater than or equal to 0.0.")
+
+        if not isinstance(self.rpn_foreground_weight, float):
+            raise ValueError("The weight of RPN foreground loss must be of type float.")
+        if self.rpn_foreground_weight < 0.0:
+            raise ValueError("The weight of RPN foreground loss must be greater than or equal to 0.0.")
+
+        if not isinstance(self.rpn_cw_weight, float):
+            raise ValueError("The weight of RPN CW loss must be of type float.")
+        if self.rpn_cw_weight < 0.0:
+            raise ValueError("The weight of RPN CW loss must be greater than or equal to 0.0.")
+
+        if not isinstance(self.rpn_cw_confidence, float):
+            raise ValueError("The confidence of RPN CW loss must be of type float.")
+        if self.rpn_cw_confidence < 0.0:
+            raise ValueError("The confidence of RPN CW loss must be greater than or equal to 0.0.")
+
+        if not isinstance(self.similarity_weight, float):
+            raise ValueError("The weight of similarity loss must be of type float.")
+        if self.similarity_weight < 0.0:
+            raise ValueError("The weight of similarity loss must be greater than or equal to 0.0.")
+
+        if not isinstance(self.learning_rate, float):
+            raise ValueError("The learning rate must be of type float.")
+        if self.learning_rate <= 0.0:
+            raise ValueError("The learning rate must be greater than 0.0.")
+
+        if self.optimizer not in ["RMSPropOptimizer", "MomentumOptimizer", "GradientDescentOptimizer", "AdamOptimizer"]:
+            raise ValueError(
+                "Optimizer only includes one of the following choices: `GradientDescentOptimizer`, "
+                "`MomentumOptimizer`, `RMSPropOptimizer`, `AdamOptimizer`."
+            )
+
+        if self.optimizer in ["RMSPropOptimizer", "MomentumOptimizer"]:
+            if not isinstance(self.momentum, float):
+                raise ValueError("The momentum must be of type float.")
+            if self.momentum <= 0.0:
+                raise ValueError("The momentum must be greater than 0.0.")
+
+        if self.optimizer == "RMSPropOptimizer":
+            if not isinstance(self.decay, float):
+                raise ValueError("The learning rate decay must be of type float.")
+            if self.decay <= 0.0:
+                raise ValueError("The learning rate decay must be greater than 0.0.")
+            if self.decay >= 1.0:
+                raise ValueError("The learning rate decay must be smaller than 1.0.")
+
+        if not isinstance(self.sign_gradients, bool):
+            raise ValueError(
+                "The choice of whether to use the sign of gradients for the optimization must be of type bool."
+            )
+
+        if not isinstance(self.random_size, int):
+            raise ValueError("The random sample size must be of type int.")
+        if self.random_size <= 0:
+            raise ValueError("The random sample size must be greater than 0.")
+
+        if not isinstance(self.max_iter, int):
+            raise ValueError("The maximum number of iterations must be of type int.")
+        if self.max_iter <= 0:
+            raise ValueError("The maximum number of iterations must be greater than 0.")
+
+        if not isinstance(self.texture_as_input, bool):
+            raise ValueError(
+                "The choice of whether textures are used as inputs instead of images must be of type bool."
+            )
+
+        if not isinstance(self.use_spectral, bool):
+            raise ValueError("The choice of whether to use spectral with textures must be of type bool.")
+
+        if not isinstance(self.soft_clip, bool):
+            raise ValueError("The choice of whether to apply soft clipping on textures must be of type bool.")
diff --git a/adversarial-robustness-toolbox/art/attacks/evasion/simba.py b/adversarial-robustness-toolbox/art/attacks/evasion/simba.py
new file mode 100644
index 0000000..0446481
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/attacks/evasion/simba.py
@@ -0,0 +1,387 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements the black-box attack `SimBA`.
+
+| Paper link: https://arxiv.org/abs/1905.07121
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+from typing import Optional, TYPE_CHECKING
+
+import numpy as np
+from scipy.fftpack import idct
+
+from art.attacks.attack import EvasionAttack
+from art.estimators.estimator import BaseEstimator, NeuralNetworkMixin
+from art.estimators.classification.classifier import ClassifierMixin
+from art.config import ART_NUMPY_DTYPE
+
+if TYPE_CHECKING:
+    from art.utils import CLASSIFIER_TYPE
+
+logger = logging.getLogger(__name__)
+
+
+class SimBA(EvasionAttack):
+    """
+    This class implements the black-box attack `SimBA`.
+
+    | Paper link: https://arxiv.org/abs/1905.07121
+    """
+
+    attack_params = EvasionAttack.attack_params + [
+        "attack",
+        "max_iter",
+        "epsilon",
+        "order",
+        "freq_dim",
+        "stride",
+        "targeted",
+        "batch_size",
+    ]
+
+    _estimator_requirements = (BaseEstimator, ClassifierMixin, NeuralNetworkMixin)
+
+    def __init__(
+        self,
+        classifier: "CLASSIFIER_TYPE",
+        attack: str = "dct",
+        max_iter: int = 3000,
+        order: str = "random",
+        epsilon: float = 0.1,
+        freq_dim: int = 4,
+        stride: int = 1,
+        targeted: bool = False,
+        batch_size: int = 1,
+    ):
+        """
+        Create a SimBA (dct) attack instance.
+
+        :param classifier: A trained classifier.
+        :param attack: attack type: pixel (px) or DCT (dct) attacks
+        :param max_iter: The maximum number of iterations.
+        :param epsilon: Overshoot parameter.
+        :param order: order of pixel attacks: random or diagonal (diag)
+        :param freq_dim: dimensionality of 2D frequency space (DCT).
+        :param stride: stride for block order (DCT).
+        :param targeted: perform targeted attack
+        :param batch_size: Batch size (but, batch process unavailable in this implementation)
+        """
+        super().__init__(estimator=classifier)
+
+        self.attack = attack
+        self.max_iter = max_iter
+        self.epsilon = epsilon
+        self.order = order
+        self.freq_dim = freq_dim
+        self.stride = stride
+        self._targeted = targeted
+        self.batch_size = batch_size
+        self._check_params()
+
+    def generate(self, x: np.ndarray, y: Optional[np.ndarray] = None, **kwargs) -> np.ndarray:
+        """
+        Generate adversarial samples and return them in an array.
+
+        :param x: An array with the original inputs to be attacked.
+        :param y: An array with the original labels to be predicted.
+        :return: An array holding the adversarial examples.
+        """
+        x = x.astype(ART_NUMPY_DTYPE)
+        preds = self.estimator.predict(x, batch_size=self.batch_size)
+
+        if y is None:
+            if self.targeted:
+                raise ValueError("Target labels `y` need to be provided for a targeted attack.")
+
+            # Use model predictions as correct outputs
+            logger.info("Using the model prediction as the correct label for SimBA.")
+            y_i = np.argmax(preds, axis=1)
+        else:
+            y_i = np.argmax(y, axis=1)
+
+        desired_label = y_i[0]
+        current_label = np.argmax(preds, axis=1)[0]
+        last_prob = preds.reshape(-1)[desired_label]
+
+        if self.estimator.channels_first:
+            nb_channels = x.shape[1]
+        else:
+            nb_channels = x.shape[3]
+
+        n_dims = np.prod(x.shape)
+
+        if self.attack == "px":
+            if self.order == "diag":
+                indices = self.diagonal_order(x.shape[2], nb_channels)[: self.max_iter]
+            elif self.order == "random":
+                indices = np.random.permutation(n_dims)[: self.max_iter]
+            indices_size = len(indices)
+            while indices_size < self.max_iter:
+                if self.order == "diag":
+                    tmp_indices = self.diagonal_order(x.shape[2], nb_channels)
+                elif self.order == "random":
+                    tmp_indices = np.random.permutation(n_dims)
+                indices = np.hstack((indices, tmp_indices))[: self.max_iter]
+                indices_size = len(indices)
+        elif self.attack == "dct":
+            indices = self._block_order(x.shape[2], nb_channels, initial_size=self.freq_dim, stride=self.stride)[
+                : self.max_iter
+            ]
+            indices_size = len(indices)
+            while indices_size < self.max_iter:
+                tmp_indices = self._block_order(x.shape[2], nb_channels, initial_size=self.freq_dim, stride=self.stride)
+                indices = np.hstack((indices, tmp_indices))[: self.max_iter]
+                indices_size = len(indices)
+
+            def trans(z):
+                return self._block_idct(z, block_size=x.shape[2])
+
+        clip_min = -np.inf
+        clip_max = np.inf
+        if self.estimator.clip_values is not None:
+            clip_min, clip_max = self.estimator.clip_values
+
+        term_flag = 1
+        if self.targeted:
+            if desired_label != current_label:
+                term_flag = 0
+        else:
+            if desired_label == current_label:
+                term_flag = 0
+
+        nb_iter = 0
+        while term_flag == 0 and nb_iter < self.max_iter:
+            diff = np.zeros(n_dims).astype(ART_NUMPY_DTYPE)
+            diff[indices[nb_iter]] = self.epsilon
+
+            if self.attack == "dct":
+                left_preds = self.estimator.predict(
+                    np.clip(x - trans(diff.reshape(x.shape)), clip_min, clip_max), batch_size=self.batch_size
+                )
+            elif self.attack == "px":
+                left_preds = self.estimator.predict(
+                    np.clip(x - diff.reshape(x.shape), clip_min, clip_max), batch_size=self.batch_size
+                )
+            left_prob = left_preds.reshape(-1)[desired_label]
+
+            if self.attack == "dct":
+                right_preds = self.estimator.predict(
+                    np.clip(x + trans(diff.reshape(x.shape)), clip_min, clip_max), batch_size=self.batch_size
+                )
+            elif self.attack == "px":
+                right_preds = self.estimator.predict(
+                    np.clip(x + diff.reshape(x.shape), clip_min, clip_max), batch_size=self.batch_size
+                )
+            right_prob = right_preds.reshape(-1)[desired_label]
+
+            # Use (2 * int(self.targeted) - 1) to shorten code?
+            if self.targeted:
+                if left_prob > last_prob:
+                    if left_prob > right_prob:
+                        if self.attack == "dct":
+                            x = np.clip(x - trans(diff.reshape(x.shape)), clip_min, clip_max)
+                        elif self.attack == "px":
+                            x = np.clip(x - diff.reshape(x.shape), clip_min, clip_max)
+                        last_prob = left_prob
+                        current_label = np.argmax(left_preds, axis=1)[0]
+                    else:
+                        if self.attack == "dct":
+                            x = np.clip(x + trans(diff.reshape(x.shape)), clip_min, clip_max)
+                        elif self.attack == "px":
+                            x = np.clip(x + diff.reshape(x.shape), clip_min, clip_max)
+                        last_prob = right_prob
+                        current_label = np.argmax(right_preds, axis=1)[0]
+                else:
+                    if right_prob > last_prob:
+                        if self.attack == "dct":
+                            x = np.clip(x + trans(diff.reshape(x.shape)), clip_min, clip_max)
+                        elif self.attack == "px":
+                            x = np.clip(x + diff.reshape(x.shape), clip_min, clip_max)
+                        last_prob = right_prob
+                        current_label = np.argmax(right_preds, axis=1)[0]
+            else:
+                if left_prob < last_prob:
+                    if left_prob < right_prob:
+                        if self.attack == "dct":
+                            x = np.clip(x - trans(diff.reshape(x.shape)), clip_min, clip_max)
+                        elif self.attack == "px":
+                            x = np.clip(x - diff.reshape(x.shape), clip_min, clip_max)
+                        last_prob = left_prob
+                        current_label = np.argmax(left_preds, axis=1)[0]
+                    else:
+                        if self.attack == "dct":
+                            x = np.clip(x + trans(diff.reshape(x.shape)), clip_min, clip_max)
+                        elif self.attack == "px":
+                            x = np.clip(x + diff.reshape(x.shape), clip_min, clip_max)
+                        last_prob = right_prob
+                        current_label = np.argmax(right_preds, axis=1)[0]
+                else:
+                    if right_prob < last_prob:
+                        if self.attack == "dct":
+                            x = np.clip(x + trans(diff.reshape(x.shape)), clip_min, clip_max)
+                        elif self.attack == "px":
+                            x = np.clip(x + diff.reshape(x.shape), clip_min, clip_max)
+                        last_prob = right_prob
+                        current_label = np.argmax(right_preds, axis=1)[0]
+
+            if self.targeted:
+                if desired_label == current_label:
+                    term_flag = 1
+            else:
+                if desired_label != current_label:
+                    term_flag = 1
+
+            nb_iter = nb_iter + 1
+
+        if nb_iter < self.max_iter:
+            logger.info("SimBA (%s) %s attack succeed", self.attack, ["non-targeted", "targeted"][int(self.targeted)])
+        else:
+            logger.info("SimBA (%s) %s attack failed", self.attack, ["non-targeted", "targeted"][int(self.targeted)])
+
+        return x
+
+    def _check_params(self) -> None:
+
+        if not isinstance(self.max_iter, (int, np.int)) or self.max_iter <= 0:
+            raise ValueError("The number of iterations must be a positive integer.")
+
+        if self.epsilon < 0:
+            raise ValueError("The overshoot parameter must not be negative.")
+
+        if self.batch_size != 1:
+            raise ValueError("The batch size `batch_size` has to be 1 in this implementation.")
+
+        if not isinstance(self.stride, (int, np.int)) or self.stride <= 0:
+            raise ValueError("The `stride` value must be a positive integer.")
+
+        if not isinstance(self.freq_dim, (int, np.int)) or self.freq_dim <= 0:
+            raise ValueError("The `freq_dim` value must be a positive integer.")
+
+        if self.order != "random" and self.order != "diag":
+            raise ValueError("The order of pixel attacks has to be `random` or `diag`.")
+
+        if self.attack != "px" and self.attack != "dct":
+            raise ValueError("The attack type has to be `px` or `dct`.")
+
+        if not isinstance(self.targeted, int) or (self.targeted != 0 and self.targeted != 1):
+            raise ValueError("`targeted` has to be a logical value.")
+
+    def _block_order(self, img_size, channels, initial_size=2, stride=1):
+        """
+        Defines a block order, starting with top-left (initial_size x initial_size) submatrix
+        expanding by stride rows and columns whenever exhausted
+        randomized within the block and across channels.
+        e.g. (initial_size=2, stride=1)
+        [1, 3, 6]
+        [2, 4, 9]
+        [5, 7, 8]
+
+        :param img_size: image size (i.e., width or height).
+        :param channels: the number of channels.
+        :param initial size: initial size for submatrix.
+        :param stride: stride size for expansion.
+
+        :return z: An array holding the block order of DCT attacks.
+        """
+        order = np.zeros((channels, img_size, img_size)).astype(ART_NUMPY_DTYPE)
+        total_elems = channels * initial_size * initial_size
+        perm = np.random.permutation(total_elems)
+        order[:, :initial_size, :initial_size] = perm.reshape((channels, initial_size, initial_size))
+        for i in range(initial_size, img_size, stride):
+            num_elems = channels * (2 * stride * i + stride * stride)
+            perm = np.random.permutation(num_elems) + total_elems
+            num_first = channels * stride * (stride + i)
+            order[:, : (i + stride), i : (i + stride)] = perm[:num_first].reshape((channels, -1, stride))
+            order[:, i : (i + stride), :i] = perm[num_first:].reshape((channels, stride, -1))
+            total_elems += num_elems
+
+        if self.estimator.channels_first:
+            return order.reshape(1, -1).squeeze().argsort()
+
+        return order.transpose((1, 2, 0)).reshape(1, -1).squeeze().argsort()
+
+    def _block_idct(self, x, block_size=8, masked=False, ratio=0.5):
+        """
+        Applies IDCT to each block of size block_size.
+
+        :param x: An array with the inputs to be attacked.
+        :param block_size: block size for DCT attacks.
+        :param masked: use the mask.
+        :param ratio: Ratio of the lowest frequency directions in order to make the adversarial perturbation in the low
+                      frequency space.
+
+        :return z: An array holding the order of DCT attacks.
+        """
+        if not self.estimator.channels_first:
+            x = x.transpose(0, 3, 1, 2)
+        z = np.zeros(x.shape).astype(ART_NUMPY_DTYPE)
+        num_blocks = int(x.shape[2] / block_size)
+        mask = np.zeros((x.shape[0], x.shape[1], block_size, block_size))
+        if not isinstance(ratio, float):
+            for i in range(x.shape[0]):
+                mask[i, :, : int(block_size * ratio[i]), : int(block_size * ratio[i])] = 1
+        else:
+            mask[:, :, : int(block_size * ratio), : int(block_size * ratio)] = 1
+        for i in range(num_blocks):
+            for j in range(num_blocks):
+                submat = x[:, :, (i * block_size) : ((i + 1) * block_size), (j * block_size) : ((j + 1) * block_size)]
+                if masked:
+                    submat = submat * mask
+                z[:, :, (i * block_size) : ((i + 1) * block_size), (j * block_size) : ((j + 1) * block_size)] = idct(
+                    idct(submat, axis=3, norm="ortho"), axis=2, norm="ortho"
+                )
+
+        if self.estimator.channels_first:
+            return z
+
+        return z.transpose((0, 2, 3, 1))
+
+    def diagonal_order(self, image_size, channels):
+        """
+        Defines a diagonal order for pixel attacks.
+        order is fixed across diagonals but are randomized across channels and within the diagonal
+        e.g.
+        [1, 2, 5]
+        [3, 4, 8]
+        [6, 7, 9]
+
+        :param image_size: image size (i.e., width or height)
+        :param channels: the number of channels
+
+        :return z: An array holding the diagonal order of pixel attacks.
+        """
+        x = np.arange(0, image_size).cumsum()
+        order = np.zeros((image_size, image_size)).astype(ART_NUMPY_DTYPE)
+        for i in range(image_size):
+            order[i, : (image_size - i)] = i + x[i:]
+        for i in range(1, image_size):
+            reverse = order[image_size - i - 1].take([i for i in range(i - 1, -1, -1)])
+            order[i, (image_size - i) :] = image_size * image_size - 1 - reverse
+        if channels > 1:
+            order_2d = order
+            order = np.zeros((channels, image_size, image_size))
+            for i in range(channels):
+                order[i, :, :] = 3 * order_2d + i
+
+        if self.estimator.channels_first:
+            return order.reshape(1, -1).squeeze().argsort()
+
+        return order.transpose((1, 2, 0)).reshape(1, -1).squeeze().argsort()
diff --git a/adversarial-robustness-toolbox/art/attacks/evasion/spatial_transformation.py b/adversarial-robustness-toolbox/art/attacks/evasion/spatial_transformation.py
new file mode 100644
index 0000000..82750d9
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/attacks/evasion/spatial_transformation.py
@@ -0,0 +1,220 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements the spatial transformation attack `SpatialTransformation` using translation and rotation of
+inputs. The attack conducts black-box queries to the target model in a grid search over possible translations and
+rotations to find optimal attack parameters.
+
+| Paper link: https://arxiv.org/abs/1712.02779
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+from typing import Optional, TYPE_CHECKING
+
+import numpy as np
+from scipy.ndimage import rotate, shift
+from tqdm.auto import tqdm
+
+from art.attacks.attack import EvasionAttack
+from art.estimators.estimator import BaseEstimator, NeuralNetworkMixin
+
+if TYPE_CHECKING:
+    from art.utils import CLASSIFIER_NEURALNETWORK_TYPE
+
+logger = logging.getLogger(__name__)
+
+
+class SpatialTransformation(EvasionAttack):
+    """
+    Implementation of the spatial transformation attack using translation and rotation of inputs. The attack conducts
+    black-box queries to the target model in a grid search over possible translations and rotations to find optimal
+    attack parameters.
+
+    | Paper link: https://arxiv.org/abs/1712.02779
+    """
+
+    attack_params = EvasionAttack.attack_params + [
+        "max_translation",
+        "num_translations",
+        "max_rotation",
+        "num_rotations",
+        "verbose",
+    ]
+    _estimator_requirements = (BaseEstimator, NeuralNetworkMixin)
+
+    def __init__(
+        self,
+        classifier: "CLASSIFIER_NEURALNETWORK_TYPE",
+        max_translation: float = 0.0,
+        num_translations: int = 1,
+        max_rotation: float = 0.0,
+        num_rotations: int = 1,
+        verbose: bool = True,
+    ) -> None:
+        """
+        :param classifier: A trained classifier.
+        :param max_translation: The maximum translation in any direction as percentage of image size. The value is
+               expected to be in the range `[0, 100]`.
+        :param num_translations: The number of translations to search on grid spacing per direction.
+        :param max_rotation: The maximum rotation in either direction in degrees. The value is expected to be in the
+               range `[0, 180]`.
+        :param num_rotations: The number of rotations to search on grid spacing.
+        :param verbose: Show progress bars.
+        """
+        super().__init__(estimator=classifier)
+        self.max_translation = max_translation
+        self.num_translations = num_translations
+        self.max_rotation = max_rotation
+        self.num_rotations = num_rotations
+        self.verbose = verbose
+        self._check_params()
+
+        self.fooling_rate: Optional[float] = None
+        self.attack_trans_x: Optional[np.ndarray] = None
+        self.attack_trans_y: Optional[np.ndarray] = None
+        self.attack_rot: Optional[np.ndarray] = None
+
+    def generate(self, x: np.ndarray, y: Optional[np.ndarray] = None, **kwargs) -> np.ndarray:
+        """
+        Generate adversarial samples and return them in an array.
+
+        :param x: An array with the original inputs.
+        :param y: An array with the original labels to be predicted.
+        :return: An array holding the adversarial examples.
+        """
+        logger.info("Computing spatial transformation based on grid search.")
+
+        if len(x.shape) == 2:
+            raise ValueError(
+                "Feature vectors detected. The attack can only be applied to data with spatial" "dimensions."
+            )
+
+        if self.attack_trans_x is None or self.attack_trans_y is None or self.attack_rot is None:
+
+            y_pred = self.estimator.predict(x, batch_size=1)
+            y_pred_max = np.argmax(y_pred, axis=1)
+
+            nb_instances = len(x)
+
+            # Determine grids
+            max_num_pixel_trans_x = int(round((x.shape[1] * self.max_translation / 100.0)))
+            max_num_pixel_trans_y = int(round((x.shape[2] * self.max_translation / 100.0)))
+
+            grid_trans_x = [
+                int(round(g))
+                for g in list(np.linspace(-max_num_pixel_trans_x, max_num_pixel_trans_x, num=self.num_translations,))
+            ]
+            grid_trans_y = [
+                int(round(g))
+                for g in list(np.linspace(-max_num_pixel_trans_y, max_num_pixel_trans_y, num=self.num_translations,))
+            ]
+            grid_rot = list(np.linspace(-self.max_rotation, self.max_rotation, num=self.num_rotations))
+
+            # Remove duplicates
+            grid_trans_x = list(set(grid_trans_x))
+            grid_trans_y = list(set(grid_trans_y))
+            grid_rot = list(set(grid_rot))
+
+            grid_trans_x.sort()
+            grid_trans_y.sort()
+            grid_rot.sort()
+
+            # Search for worst case
+            fooling_rate = 0.0
+            x_adv = np.copy(x)
+            trans_x = 0
+            trans_y = 0
+            rot = 0.0
+
+            # Initialize progress bar
+            pbar = tqdm(
+                total=len(grid_trans_x) * len(grid_trans_y) * len(grid_rot),
+                desc="Spatial transformation",
+                disable=not self.verbose,
+            )
+
+            for trans_x_i in grid_trans_x:
+                for trans_y_i in grid_trans_y:
+                    for rot_i in grid_rot:
+
+                        # Generate the adversarial examples
+                        x_adv_i = self._perturb(x, trans_x_i, trans_y_i, rot_i)
+
+                        # Compute the error rate
+                        y_adv_i = np.argmax(self.estimator.predict(x_adv_i, batch_size=1), axis=1)
+                        fooling_rate_i = np.sum(y_pred_max != y_adv_i) / nb_instances
+
+                        if fooling_rate_i > fooling_rate:
+                            fooling_rate = fooling_rate_i
+                            trans_x = trans_x_i
+                            trans_y = trans_y_i
+                            rot = rot_i
+                            x_adv = np.copy(x_adv_i)
+                        pbar.update(1)
+            pbar.close()
+
+            self.fooling_rate = fooling_rate
+            self.attack_trans_x = trans_x
+            self.attack_trans_y = trans_y
+            self.attack_rot = rot
+
+            logger.info(
+                "Success rate of spatial transformation attack: %.2f%%", 100 * self.fooling_rate,
+            )
+            logger.info("Attack-translation in x: %.2f%%", self.attack_trans_x)
+            logger.info("Attack-translation in y: %.2f%%", self.attack_trans_y)
+            logger.info("Attack-rotation: %.2f%%", self.attack_rot)
+
+        else:
+            x_adv = self._perturb(x, self.attack_trans_x, self.attack_trans_y, self.attack_rot)
+
+        return x_adv
+
+    def _perturb(self, x: np.ndarray, trans_x: int, trans_y: int, rot: float) -> np.ndarray:
+        if not self.estimator.channels_first:
+            x_adv = shift(x, [0, trans_x, trans_y, 0])
+            x_adv = rotate(x_adv, angle=rot, axes=(1, 2), reshape=False)
+        elif self.estimator.channels_first:
+            x_adv = shift(x, [0, 0, trans_x, trans_y])
+            x_adv = rotate(x_adv, angle=rot, axes=(2, 3), reshape=False)
+        else:
+            raise ValueError("Unsupported channel_first value.")
+
+        if self.estimator.clip_values is not None:
+            np.clip(
+                x_adv, self.estimator.clip_values[0], self.estimator.clip_values[1], out=x_adv,
+            )
+
+        return x_adv
+
+    def _check_params(self) -> None:
+        if not isinstance(self.max_translation, (float, int)) or self.max_translation < 0 or self.max_translation > 100:
+            raise ValueError("The maximum translation must be in the range [0, 100].")
+
+        if not isinstance(self.num_translations, int) or self.num_translations <= 0:
+            raise ValueError("The number of translations must be a positive integer.")
+
+        if not isinstance(self.max_rotation, (float, int)) or self.max_rotation < 0 or self.max_translation > 180:
+            raise ValueError("The maximum rotation must be in the range [0, 180].")
+
+        if not isinstance(self.num_rotations, int) or self.num_rotations <= 0:
+            raise ValueError("The number of rotations must be a positive integer.")
+
+        if not isinstance(self.verbose, bool):
+            raise ValueError("The argument `verbose` has to be of type bool.")
diff --git a/code_vmr/art/attacks/evasion/square_attack.py b/adversarial-robustness-toolbox/art/attacks/evasion/square_attack.py
similarity index 100%
rename from code_vmr/art/attacks/evasion/square_attack.py
rename to adversarial-robustness-toolbox/art/attacks/evasion/square_attack.py
diff --git a/adversarial-robustness-toolbox/art/attacks/evasion/targeted_universal_perturbation.py b/adversarial-robustness-toolbox/art/attacks/evasion/targeted_universal_perturbation.py
new file mode 100644
index 0000000..429ef22
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/attacks/evasion/targeted_universal_perturbation.py
@@ -0,0 +1,205 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements the universal adversarial perturbations attack `TargetedUniversalPerturbation`.
+
+| Paper link: https://arxiv.org/abs/1911.06502
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import random
+import types
+from typing import Any, Dict, Optional, Union, TYPE_CHECKING
+
+import numpy as np
+
+from art.attacks.attack import EvasionAttack
+from art.estimators.estimator import BaseEstimator
+from art.estimators.classification.classifier import ClassifierMixin
+from art.utils import projection
+
+if TYPE_CHECKING:
+    from art.utils import CLASSIFIER_TYPE
+
+logger = logging.getLogger(__name__)
+
+
+class TargetedUniversalPerturbation(EvasionAttack):
+    """
+    Implementation of the attack from Hirano and Takemoto (2019). Computes a fixed perturbation to be applied to all
+    future inputs. To this end, it can use any adversarial attack method.
+
+    | Paper link: https://arxiv.org/abs/1911.06502
+    """
+
+    attacks_dict = {
+        "fgsm": "art.attacks.evasion.fast_gradient.FastGradientMethod",
+        "simba": "art.attacks.evasion.simba.SimBA",
+    }
+    attack_params = EvasionAttack.attack_params + ["attacker", "attacker_params", "delta", "max_iter", "eps", "norm"]
+
+    _estimator_requirements = (BaseEstimator, ClassifierMixin)
+
+    def __init__(
+        self,
+        classifier: "CLASSIFIER_TYPE",
+        attacker: str = "fgsm",
+        attacker_params: Optional[Dict[str, Any]] = None,
+        delta: float = 0.2,
+        max_iter: int = 20,
+        eps: float = 10.0,
+        norm: Union[int, float, str] = np.inf,
+    ):
+        """
+        :param classifier: A trained classifier.
+        :param attacker: Adversarial attack name. Default is 'deepfool'. Supported names: 'fgsm'.
+        :param attacker_params: Parameters specific to the adversarial attack. If this parameter is not specified,
+                                the default parameters of the chosen attack will be used.
+        :param delta: desired accuracy
+        :param max_iter: The maximum number of iterations for computing universal perturbation.
+        :param eps: Attack step size (input variation)
+        :param norm: The norm of the adversarial perturbation. Possible values: "inf", np.inf, 2
+        """
+        super().__init__(estimator=classifier)
+
+        self.attacker = attacker
+        self.attacker_params = attacker_params
+        self.delta = delta
+        self.max_iter = max_iter
+        self.eps = eps
+        self.norm = norm
+        self._targeted = True
+        self._check_params()
+
+    def generate(self, x: np.ndarray, y: Optional[np.ndarray] = None, **kwargs) -> np.ndarray:
+        """
+        Generate adversarial samples and return them in an array.
+
+        :param x: An array with the original inputs.
+        :param y: An array with the targeted labels.
+        :return: An array holding the adversarial examples.
+        """
+        if y is None:
+            raise ValueError("Labels `y` cannot be None.")
+
+        logger.info("Computing targeted universal perturbation based on %s attack.", self.attacker)
+
+        # Init universal perturbation
+        noise = 0
+        fooling_rate = 0.0
+        targeted_success_rate = 0.0
+        nb_instances = len(x)
+
+        # Instantiate the middle attacker and get the predicted labels
+        attacker = self._get_attack(self.attacker, self.attacker_params)
+        pred_y = self.estimator.predict(x, batch_size=1)
+        pred_y_max = np.argmax(pred_y, axis=1)
+
+        # Start to generate the adversarial examples
+        nb_iter = 0
+        while targeted_success_rate < 1.0 - self.delta and nb_iter < self.max_iter:
+            # Go through all the examples randomly
+            rnd_idx = random.sample(range(nb_instances), nb_instances)
+
+            # Go through the data set and compute the perturbation increments sequentially
+            for _, (e_x, e_y) in enumerate(zip(x[rnd_idx], y[rnd_idx])):
+                x_i = e_x[None, ...]
+                y_i = e_y[None, ...]
+
+                current_label = np.argmax(self.estimator.predict(x_i + noise)[0])
+                target_label = np.argmax(y_i)
+
+                if current_label != target_label:
+                    # Compute adversarial perturbation
+                    adv_xi = attacker.generate(x_i + noise, y=y_i)
+
+                    new_label = np.argmax(self.estimator.predict(adv_xi)[0])
+
+                    # If the class has changed, update v
+                    if new_label == target_label:
+                        noise = adv_xi - x_i
+
+                        # Project on L_p ball
+                        noise = projection(noise, self.eps, self.norm)
+            nb_iter += 1
+
+            # Apply attack and clip
+            x_adv = x + noise
+            if hasattr(self.estimator, "clip_values") and self.estimator.clip_values is not None:
+                clip_min, clip_max = self.estimator.clip_values
+                x_adv = np.clip(x_adv, clip_min, clip_max)
+
+            # Compute the error rate
+            y_adv = np.argmax(self.estimator.predict(x_adv, batch_size=1), axis=1)
+            fooling_rate = np.sum(pred_y_max != y_adv) / nb_instances
+            targeted_success_rate = np.sum(y_adv == np.argmax(y, axis=1)) / nb_instances
+
+        self.fooling_rate = fooling_rate
+        self.targeted_success_rate = targeted_success_rate
+        self.converged = nb_iter < self.max_iter
+        self.noise = noise
+        logger.info("Fooling rate of universal perturbation attack: %.2f%%", 100 * fooling_rate)
+        logger.info("Targeted success rate of universal perturbation attack: %.2f%%", 100 * targeted_success_rate)
+
+        return x_adv
+
+    def _check_params(self) -> None:
+
+        if not isinstance(self.delta, (float, int)) or self.delta < 0 or self.delta > 1:
+            raise ValueError("The desired accuracy must be in the range [0, 1].")
+
+        if not isinstance(self.max_iter, (int, np.int)) or self.max_iter <= 0:
+            raise ValueError("The number of iterations must be a positive integer.")
+
+        if not isinstance(self.eps, (float, int)) or self.eps <= 0:
+            raise ValueError("The eps coefficient must be a positive float.")
+
+    def _get_attack(self, a_name: str, params: Optional[Dict[str, Any]] = None) -> EvasionAttack:
+        """
+        Get an attack object from its name.
+
+        :param a_name: attack name.
+        :param params: attack params.
+        :return: attack object
+        """
+        try:
+            attack_class = self._get_class(self.attacks_dict[a_name])
+            a_instance = attack_class(self.estimator)  # type: ignore
+
+            if params:
+                a_instance.set_params(**params)
+
+            return a_instance
+
+        except KeyError:
+            raise NotImplementedError("{} attack not supported".format(a_name)) from KeyError
+
+    @staticmethod
+    def _get_class(class_name: str) -> types.ModuleType:
+        """
+        Get a class module from its name.
+
+        :param class_name: Full name of a class.
+        :return: The class `module`.
+        """
+        sub_mods = class_name.split(".")
+        module_ = __import__(".".join(sub_mods[:-1]), fromlist=sub_mods[-1])
+        class_module = getattr(module_, sub_mods[-1])
+
+        return class_module
diff --git a/adversarial-robustness-toolbox/art/attacks/evasion/universal_perturbation.py b/adversarial-robustness-toolbox/art/attacks/evasion/universal_perturbation.py
new file mode 100644
index 0000000..0cf94a0
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/attacks/evasion/universal_perturbation.py
@@ -0,0 +1,268 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements the universal adversarial perturbations attack `UniversalPerturbation`. This is a white-box
+attack.
+
+| Paper link: https://arxiv.org/abs/1610.08401
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import random
+import types
+from typing import Any, Dict, Optional, Union, TYPE_CHECKING
+
+import numpy as np
+from tqdm.auto import tqdm
+
+from art.attacks.attack import EvasionAttack
+from art.estimators.estimator import BaseEstimator
+from art.estimators.classification.classifier import ClassifierMixin
+from art.utils import projection, get_labels_np_array, check_and_transform_label_format
+
+if TYPE_CHECKING:
+    from art.utils import CLASSIFIER_TYPE
+
+logger = logging.getLogger(__name__)
+
+
+class UniversalPerturbation(EvasionAttack):
+    """
+    Implementation of the attack from Moosavi-Dezfooli et al. (2016). Computes a fixed perturbation to be applied to all
+    future inputs. To this end, it can use any adversarial attack method.
+
+    | Paper link: https://arxiv.org/abs/1610.08401
+    """
+
+    attacks_dict = {
+        "carlini": "art.attacks.evasion.carlini.CarliniL2Method",
+        "carlini_inf": "art.attacks.evasion.carlini.CarliniLInfMethod",
+        "deepfool": "art.attacks.evasion.deepfool.DeepFool",
+        "ead": "art.attacks.evasion.elastic_net.ElasticNet",
+        "fgsm": "art.attacks.evasion.fast_gradient.FastGradientMethod",
+        "bim": "art.attacks.evasion.iterative_method.BasicIterativeMethod",
+        "pgd": "art.attacks.evasion.projected_gradient_descent.projected_gradient_descent.ProjectedGradientDescent",
+        "newtonfool": "art.attacks.evasion.newtonfool.NewtonFool",
+        "jsma": "art.attacks.evasion.saliency_map.SaliencyMapMethod",
+        "vat": "art.attacks.evasion.virtual_adversarial.VirtualAdversarialMethod",
+        "simba": "art.attacks.evasion.simba.SimBA",
+    }
+    attack_params = EvasionAttack.attack_params + [
+        "attacker",
+        "attacker_params",
+        "delta",
+        "max_iter",
+        "eps",
+        "norm",
+        "batch_size",
+        "verbose",
+    ]
+    _estimator_requirements = (BaseEstimator, ClassifierMixin)
+
+    def __init__(
+        self,
+        classifier: "CLASSIFIER_TYPE",
+        attacker: str = "deepfool",
+        attacker_params: Optional[Dict[str, Any]] = None,
+        delta: float = 0.2,
+        max_iter: int = 20,
+        eps: float = 10.0,
+        norm: Union[int, float, str] = np.inf,
+        batch_size: int = 32,
+        verbose: bool = True,
+    ) -> None:
+        """
+        :param classifier: A trained classifier.
+        :param attacker: Adversarial attack name. Default is 'deepfool'. Supported names: 'carlini', 'carlini_inf',
+                         'deepfool', 'fgsm', 'bim', 'pgd', 'margin', 'ead', 'newtonfool', 'jsma', 'vat', 'simba'.
+        :param attacker_params: Parameters specific to the adversarial attack. If this parameter is not specified,
+                                the default parameters of the chosen attack will be used.
+        :param delta: desired accuracy
+        :param max_iter: The maximum number of iterations for computing universal perturbation.
+        :param eps: Attack step size (input variation).
+        :param norm: The norm of the adversarial perturbation. Possible values: "inf", np.inf, 2.
+        :param batch_size: Batch size for model evaluations in UniversalPerturbation.
+        :param verbose: Show progress bars.
+        """
+        super().__init__(estimator=classifier)
+        self.attacker = attacker
+        self.attacker_params = attacker_params
+        self.delta = delta
+        self.max_iter = max_iter
+        self.eps = eps
+        self.norm = norm
+        self.batch_size = batch_size
+        self.verbose = verbose
+        self._check_params()
+
+        # Attack properties
+        self._fooling_rate: Optional[float] = None
+        self._converged: Optional[bool] = None
+        self._noise: Optional[np.ndarray] = None
+
+    @property
+    def fooling_rate(self) -> Optional[float]:
+        """
+        The fooling rate of the universal perturbation on the most recent call to `generate`.
+
+        :return: Fooling Rate.
+        """
+        return self._fooling_rate
+
+    @property
+    def converged(self) -> Optional[bool]:
+        """
+        The convergence of universal perturbation generation.
+
+        :return: `True` if generation of universal perturbation has converged.
+        """
+        return self._converged
+
+    @property
+    def noise(self) -> Optional[np.ndarray]:
+        """
+        The universal perturbation.
+
+        :return: Universal perturbation.
+        """
+        return self._noise
+
+    def generate(self, x: np.ndarray, y: Optional[np.ndarray] = None, **kwargs) -> np.ndarray:
+        """
+        Generate adversarial samples and return them in an array.
+
+        :param x: An array with the original inputs.
+        :param y: An array with the original labels to be predicted.
+        :return: An array holding the adversarial examples.
+        """
+        logger.info("Computing universal perturbation based on %s attack.", self.attacker)
+
+        y = check_and_transform_label_format(y, self.estimator.nb_classes)
+
+        if y is None:
+            # Use model predictions as true labels
+            logger.info("Using model predictions as true labels.")
+            y = get_labels_np_array(self.estimator.predict(x, batch_size=self.batch_size))
+
+        y_index = np.argmax(y, axis=1)
+
+        # Init universal perturbation
+        noise = 0
+        fooling_rate = 0.0
+        nb_instances = len(x)
+
+        # Instantiate the middle attacker
+        attacker = self._get_attack(self.attacker, self.attacker_params)
+
+        # Generate the adversarial examples
+        nb_iter = 0
+        pbar = tqdm(total=self.max_iter, desc="Universal perturbation", disable=not self.verbose)
+
+        while fooling_rate < 1.0 - self.delta and nb_iter < self.max_iter:
+            # Go through all the examples randomly
+            rnd_idx = random.sample(range(nb_instances), nb_instances)
+
+            # Go through the data set and compute the perturbation increments sequentially
+            for j, ex in enumerate(x[rnd_idx]):
+                x_i = ex[None, ...]
+
+                current_label = np.argmax(self.estimator.predict(x_i + noise)[0])
+                original_label = y_index[rnd_idx][j]
+
+                if current_label == original_label:
+                    # Compute adversarial perturbation
+                    adv_xi = attacker.generate(x_i + noise, y=y[rnd_idx][[j]])
+                    new_label = np.argmax(self.estimator.predict(adv_xi)[0])
+
+                    # If the class has changed, update v
+                    if current_label != new_label:
+                        noise = adv_xi - x_i
+
+                        # Project on L_p ball
+                        noise = projection(noise, self.eps, self.norm)
+            nb_iter += 1
+            pbar.update(1)
+
+            # Apply attack and clip
+            x_adv = x + noise
+            if self.estimator.clip_values is not None:
+                clip_min, clip_max = self.estimator.clip_values
+                x_adv = np.clip(x_adv, clip_min, clip_max)
+
+            # Compute the error rate
+            y_adv = np.argmax(self.estimator.predict(x_adv, batch_size=1), axis=1)
+            fooling_rate = np.sum(y_index != y_adv) / nb_instances
+
+        pbar.close()
+        self._fooling_rate = fooling_rate
+        self._converged = nb_iter < self.max_iter
+        self._noise = noise
+        logger.info("Success rate of universal perturbation attack: %.2f%%", 100 * fooling_rate)
+
+        return x_adv
+
+    def _get_attack(self, a_name: str, params: Optional[Dict[str, Any]] = None) -> EvasionAttack:
+        """
+        Get an attack object from its name.
+
+        :param a_name: Attack name.
+        :param params: Attack params.
+        :return: Attack object.
+        :raises NotImplementedError: If the attack is not supported.
+        """
+        try:
+            attack_class = self._get_class(self.attacks_dict[a_name])
+            a_instance = attack_class(self.estimator)  # type: ignore
+
+            if params:
+                a_instance.set_params(**params)
+
+            return a_instance
+        except KeyError:
+            raise NotImplementedError("{} attack not supported".format(a_name)) from KeyError
+
+    @staticmethod
+    def _get_class(class_name: str) -> types.ModuleType:
+        """
+        Get a class module from its name.
+
+        :param class_name: Full name of a class.
+        :return: The class `module`.
+        """
+        sub_mods = class_name.split(".")
+        module_ = __import__(".".join(sub_mods[:-1]), fromlist=sub_mods[-1])
+        class_module = getattr(module_, sub_mods[-1])
+
+        return class_module
+
+    def _check_params(self) -> None:
+        if not isinstance(self.delta, (float, int)) or self.delta < 0 or self.delta > 1:
+            raise ValueError("The desired accuracy must be in the range [0, 1].")
+
+        if not isinstance(self.max_iter, (int, np.int)) or self.max_iter <= 0:
+            raise ValueError("The number of iterations must be a positive integer.")
+
+        if not isinstance(self.eps, (float, int)) or self.eps <= 0:
+            raise ValueError("The eps coefficient must be a positive float.")
+
+        if not isinstance(self.batch_size, (int, np.int)) or self.batch_size <= 0:
+            raise ValueError("The batch_size must be a positive integer.")
+
+        if not isinstance(self.verbose, bool):
+            raise ValueError("The argument `verbose` has to be of type bool.")
diff --git a/adversarial-robustness-toolbox/art/attacks/evasion/virtual_adversarial.py b/adversarial-robustness-toolbox/art/attacks/evasion/virtual_adversarial.py
new file mode 100644
index 0000000..91649c8
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/attacks/evasion/virtual_adversarial.py
@@ -0,0 +1,215 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements the virtual adversarial attack. It was originally used for virtual adversarial training.
+
+| Paper link: https://arxiv.org/abs/1507.00677
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+from typing import Optional, TYPE_CHECKING
+
+import numpy as np
+from tqdm.auto import trange
+
+from art.attacks.attack import EvasionAttack
+from art.config import ART_NUMPY_DTYPE
+from art.estimators.estimator import BaseEstimator
+from art.estimators.classification.classifier import ClassifierMixin
+from art.utils import compute_success
+
+if TYPE_CHECKING:
+    from art.utils import CLASSIFIER_TYPE
+
+logger = logging.getLogger(__name__)
+
+
+class VirtualAdversarialMethod(EvasionAttack):
+    """
+    This attack was originally proposed by Miyato et al. (2016) and was used for virtual adversarial training.
+
+    | Paper link: https://arxiv.org/abs/1507.00677
+    """
+
+    attack_params = EvasionAttack.attack_params + [
+        "eps",
+        "finite_diff",
+        "max_iter",
+        "batch_size",
+        "verbose",
+    ]
+    _estimator_requirements = (BaseEstimator, ClassifierMixin)
+
+    def __init__(
+        self,
+        classifier: "CLASSIFIER_TYPE",
+        max_iter: int = 10,
+        finite_diff: float = 1e-6,
+        eps: float = 0.1,
+        batch_size: int = 1,
+        verbose: bool = True,
+    ) -> None:
+        """
+        Create a :class:`.VirtualAdversarialMethod` instance.
+
+        :param classifier: A trained classifier.
+        :param eps: Attack step (max input variation).
+        :param finite_diff: The finite difference parameter.
+        :param max_iter: The maximum number of iterations.
+        :param batch_size: Size of the batch on which adversarial samples are generated.
+        :param verbose: Show progress bars.
+        """
+        super().__init__(estimator=classifier)
+        self.finite_diff = finite_diff
+        self.eps = eps
+        self.max_iter = max_iter
+        self.batch_size = batch_size
+        self.verbose = verbose
+        self._check_params()
+
+    def generate(self, x: np.ndarray, y: Optional[np.ndarray] = None, **kwargs) -> np.ndarray:
+        """
+        Generate adversarial samples and return them in an array.
+
+        :param x: An array with the original inputs to be attacked.
+        :param y: An array with the original labels to be predicted.
+        :return: An array holding the adversarial examples.
+        """
+        x_adv = x.astype(ART_NUMPY_DTYPE)
+        preds = self.estimator.predict(x_adv, batch_size=self.batch_size)
+        if (preds < 0.0).any() or (preds > 1.0).any():
+            raise TypeError(
+                "This attack requires a classifier predicting probabilities in the range [0, 1] as output."
+                "Values smaller than 0.0 or larger than 1.0 have been detected."
+            )
+        # preds_rescaled = self._rescale(preds) # Rescaling needs more testing
+        preds_rescaled = preds
+
+        # Compute perturbation with implicit batching
+        for batch_id in trange(
+            int(np.ceil(x_adv.shape[0] / float(self.batch_size))), desc="VAT", disable=not self.verbose
+        ):
+            batch_index_1, batch_index_2 = batch_id * self.batch_size, (batch_id + 1) * self.batch_size
+            batch = x_adv[batch_index_1:batch_index_2]
+            batch = batch.reshape((batch.shape[0], -1))
+
+            # Main algorithm for each batch
+            var_d = np.random.randn(*batch.shape).astype(ART_NUMPY_DTYPE)
+
+            # Main loop of the algorithm
+            for _ in range(self.max_iter):
+                var_d = self._normalize(var_d)
+                preds_new = self.estimator.predict((batch + var_d).reshape((-1,) + self.estimator.input_shape))
+                if (preds_new < 0.0).any() or (preds_new > 1.0).any():
+                    raise TypeError(
+                        "This attack requires a classifier predicting probabilities in the range [0, 1] as "
+                        "output. Values smaller than 0.0 or larger than 1.0 have been detected."
+                    )
+                # preds_new_rescaled = self._rescale(preds_new) # Rescaling needs more testing
+                preds_new_rescaled = preds_new
+
+                from scipy.stats import entropy
+
+                kl_div1 = entropy(
+                    np.transpose(preds_rescaled[batch_index_1:batch_index_2]), np.transpose(preds_new_rescaled),
+                )
+
+                var_d_new = np.zeros(var_d.shape).astype(ART_NUMPY_DTYPE)
+                for current_index in range(var_d.shape[1]):
+                    var_d[:, current_index] += self.finite_diff
+                    preds_new = self.estimator.predict((batch + var_d).reshape((-1,) + self.estimator.input_shape))
+                    if (preds_new < 0.0).any() or (preds_new > 1.0).any():
+                        raise TypeError(
+                            "This attack requires a classifier predicting probabilities in the range [0, 1]"
+                            "as output. Values smaller than 0.0 or larger than 1.0 have been detected."
+                        )
+                    # preds_new_rescaled = self._rescale(preds_new) # Rescaling needs more testing
+                    preds_new_rescaled = preds_new
+
+                    kl_div2 = entropy(
+                        np.transpose(preds_rescaled[batch_index_1:batch_index_2]), np.transpose(preds_new_rescaled),
+                    )
+                    var_d_new[:, current_index] = (kl_div2 - kl_div1) / self.finite_diff
+                    var_d[:, current_index] -= self.finite_diff
+                var_d = var_d_new
+
+            # Apply perturbation and clip
+            if self.estimator.clip_values is not None:
+                clip_min, clip_max = self.estimator.clip_values
+                x_adv[batch_index_1:batch_index_2] = np.clip(
+                    batch + self.eps * self._normalize(var_d), clip_min, clip_max
+                ).reshape((-1,) + self.estimator.input_shape)
+            else:
+                x_adv[batch_index_1:batch_index_2] = (batch + self.eps * self._normalize(var_d)).reshape(
+                    (-1,) + self.estimator.input_shape
+                )
+
+        logger.info(
+            "Success rate of virtual adversarial attack: %.2f%%",
+            100 * compute_success(self.estimator, x, y, x_adv, batch_size=self.batch_size),
+        )
+
+        return x_adv
+
+    @staticmethod
+    def _normalize(x: np.ndarray) -> np.ndarray:
+        """
+        Apply L_2 batch normalization on `x`.
+
+        :param x: The input array batch to normalize.
+        :return: The normalized version of `x`.
+        """
+        norm = np.atleast_1d(np.linalg.norm(x, axis=1))
+        norm[norm == 0] = 1
+        normalized_x = x / np.expand_dims(norm, axis=1)
+
+        return normalized_x
+
+    @staticmethod
+    def _rescale(x: np.ndarray) -> np.ndarray:
+        """
+        Rescale values of `x` to the range (0, 1]. The interval is open on the left side, using values close to zero
+        instead. This is to avoid values that are invalid for further KL divergence computation.
+
+        :param x: Input array.
+        :return: Rescaled value of `x`.
+        """
+        # Tolerance range avoids actually setting minimum value to 0, as this value is invalid for KL divergence
+        tol = 1e-5
+
+        current_range = np.amax(x, axis=1, keepdims=True) - np.amin(x, axis=1, keepdims=True)
+        current_range[current_range == 0] = 1
+        res = (x - np.amin(x, axis=1, keepdims=True) + tol) / current_range
+        return res
+
+    def _check_params(self) -> None:
+        if not isinstance(self.max_iter, (int, np.int)) or self.max_iter <= 0:
+            raise ValueError("The number of iterations must be a positive integer.")
+
+        if self.eps <= 0:
+            raise ValueError("The attack step must be positive.")
+
+        if not isinstance(self.finite_diff, float) or self.finite_diff <= 0:
+            raise ValueError("The finite difference parameter must be a positive float.")
+
+        if self.batch_size <= 0:
+            raise ValueError("The batch size `batch_size` has to be positive.")
+
+        if not isinstance(self.verbose, bool):
+            raise ValueError("The argument `verbose` has to be of type bool.")
diff --git a/adversarial-robustness-toolbox/art/attacks/evasion/wasserstein.py b/adversarial-robustness-toolbox/art/attacks/evasion/wasserstein.py
new file mode 100644
index 0000000..73d8e39
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/attacks/evasion/wasserstein.py
@@ -0,0 +1,765 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements ``Wasserstein Adversarial Examples via Projected Sinkhorn Iterations`` as evasion attack.
+
+| Paper link: https://arxiv.org/abs/1902.07906
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+from typing import Optional, TYPE_CHECKING
+
+import numpy as np
+from scipy.special import lambertw
+from tqdm.auto import trange
+
+from art.config import ART_NUMPY_DTYPE
+from art.estimators.estimator import BaseEstimator, LossGradientsMixin
+from art.estimators.classification.classifier import ClassifierMixin
+from art.attacks.attack import EvasionAttack
+from art.utils import compute_success, get_labels_np_array, check_and_transform_label_format
+
+if TYPE_CHECKING:
+    from art.utils import CLASSIFIER_LOSS_GRADIENTS_TYPE
+
+logger = logging.getLogger(__name__)
+
+EPS_LOG = 10 ** -10
+
+
+class Wasserstein(EvasionAttack):
+    """
+    Implements ``Wasserstein Adversarial Examples via Projected Sinkhorn Iterations`` as evasion attack.
+
+    | Paper link: https://arxiv.org/abs/1902.07906
+    """
+
+    attack_params = EvasionAttack.attack_params + [
+        "targeted",
+        "regularization",
+        "p",
+        "kernel_size",
+        "eps_step",
+        "norm",
+        "ball",
+        "eps",
+        "eps_iter",
+        "eps_factor",
+        "max_iter",
+        "conjugate_sinkhorn_max_iter",
+        "projected_sinkhorn_max_iter",
+        "batch_size",
+        "verbose",
+    ]
+
+    _estimator_requirements = (BaseEstimator, LossGradientsMixin, ClassifierMixin)
+
+    def __init__(
+        self,
+        estimator: "CLASSIFIER_LOSS_GRADIENTS_TYPE",
+        targeted: bool = False,
+        regularization: float = 3000.0,
+        p: int = 2,
+        kernel_size: int = 5,
+        eps_step: float = 0.1,
+        norm: str = "wasserstein",
+        ball: str = "wasserstein",
+        eps: float = 0.3,
+        eps_iter: int = 10,
+        eps_factor: float = 1.1,
+        max_iter: int = 400,
+        conjugate_sinkhorn_max_iter: int = 400,
+        projected_sinkhorn_max_iter: int = 400,
+        batch_size: int = 1,
+        verbose: bool = True,
+    ):
+        """
+        Create a Wasserstein attack instance.
+
+        :param estimator: A trained estimator.
+        :param targeted: Indicates whether the attack is targeted (True) or untargeted (False).
+        :param regularization: Entropy regularization.
+        :param p: The p-wasserstein distance.
+        :param kernel_size: Kernel size for computing the cost matrix.
+        :param eps_step: Attack step size (input variation) at each iteration.
+        :param norm: The norm of the adversarial perturbation. Possible values: `inf`, `1`, `2` or `wasserstein`.
+        :param ball: The ball of the adversarial perturbation. Possible values: `inf`, `1`, `2` or `wasserstein`.
+        :param eps: Maximum perturbation that the attacker can introduce.
+        :param eps_iter: Number of iterations to increase the epsilon.
+        :param eps_factor: Factor to increase the epsilon.
+        :param max_iter: The maximum number of iterations.
+        :param conjugate_sinkhorn_max_iter: The maximum number of iterations for the conjugate sinkhorn optimizer.
+        :param projected_sinkhorn_max_iter: The maximum number of iterations for the projected sinkhorn optimizer.
+        :param batch_size: Size of batches.
+        :param verbose: Show progress bars.
+        """
+        super().__init__(estimator=estimator)
+
+        self._targeted = targeted
+        self.regularization = regularization
+        self.p = p
+        self.kernel_size = kernel_size
+        self.eps_step = eps_step
+        self.norm = norm
+        self.ball = ball
+        self.eps = eps
+        self.eps_iter = eps_iter
+        self.eps_factor = eps_factor
+        self.max_iter = max_iter
+        self.conjugate_sinkhorn_max_iter = conjugate_sinkhorn_max_iter
+        self.projected_sinkhorn_max_iter = projected_sinkhorn_max_iter
+        self.batch_size = batch_size
+        self.verbose = verbose
+        self._check_params()
+
+    def generate(self, x: np.ndarray, y: Optional[np.ndarray] = None, **kwargs) -> np.ndarray:
+        """
+        Generate adversarial samples and return them in an array.
+
+        :param x: An array with the original inputs.
+        :param y: Target values (class labels) one-hot-encoded of shape `(nb_samples, nb_classes)` or indices of shape
+                  (nb_samples,). Only provide this parameter if you'd like to use true labels when crafting adversarial
+                  samples. Otherwise, model predictions are used as labels to avoid the "label leaking" effect
+                  (explained in this paper: https://arxiv.org/abs/1611.01236). Default is `None`.
+        :param cost_matrix: A non-negative cost matrix.
+        :type cost_matrix: `np.ndarray`
+        :return: An array holding the adversarial examples.
+        """
+        y = check_and_transform_label_format(y, self.estimator.nb_classes)
+        x_adv = x.copy().astype(ART_NUMPY_DTYPE)
+
+        if y is None:
+            # Throw error if attack is targeted, but no targets are provided
+            if self.targeted:
+                raise ValueError("Target labels `y` need to be provided for a targeted attack.")
+
+            # Use model predictions as correct outputs
+            targets = get_labels_np_array(self.estimator.predict(x, batch_size=self.batch_size))
+        else:
+            targets = y
+
+        # Compute the cost matrix if needed
+        cost_matrix = kwargs.get("cost_matrix")
+        if cost_matrix is None:
+            cost_matrix = self._compute_cost_matrix(self.p, self.kernel_size)
+
+        # Compute perturbation with implicit batching
+        nb_batches = int(np.ceil(x.shape[0] / float(self.batch_size)))
+        for batch_id in trange(nb_batches, desc="Wasserstein", disable=not self.verbose):
+            logger.debug("Processing batch %i out of %i", batch_id, nb_batches)
+
+            batch_index_1, batch_index_2 = batch_id * self.batch_size, (batch_id + 1) * self.batch_size
+            batch = x_adv[batch_index_1:batch_index_2]
+            batch_labels = targets[batch_index_1:batch_index_2]
+
+            x_adv[batch_index_1:batch_index_2] = self._generate_batch(batch, batch_labels, cost_matrix)
+
+        logger.info(
+            "Success rate of attack: %.2f%%",
+            100 * compute_success(self.estimator, x, y, x_adv, self.targeted, batch_size=self.batch_size),
+        )
+
+        return x_adv
+
+    def _generate_batch(self, x: np.ndarray, targets: np.ndarray, cost_matrix: np.ndarray) -> np.ndarray:
+        """
+        Generate a batch of adversarial samples and return them in an array.
+
+        :param x: An array with the original inputs.
+        :param targets: Target values (class labels) one-hot-encoded of shape `(nb_samples, nb_classes)`.
+        :param cost_matrix: A non-negative cost matrix.
+        :return: Adversarial examples.
+        """
+        adv_x = x.copy().astype(ART_NUMPY_DTYPE)
+        adv_x_best = x.copy().astype(ART_NUMPY_DTYPE)
+
+        if self.targeted:
+            err = np.argmax(self.estimator.predict(adv_x, batch_size=x.shape[0]), axis=1) == np.argmax(targets, axis=1)
+        else:
+            err = np.argmax(self.estimator.predict(adv_x, batch_size=x.shape[0]), axis=1) != np.argmax(targets, axis=1)
+
+        err_best = err
+        eps_ = np.ones(x.shape[0]) * self.eps
+
+        for i in range(self.max_iter):
+            adv_x = self._compute(adv_x, x, targets, cost_matrix, eps_, err)
+
+            if self.targeted:
+                err = np.argmax(self.estimator.predict(adv_x, batch_size=x.shape[0]), axis=1) == np.argmax(
+                    targets, axis=1
+                )
+
+            else:
+                err = np.argmax(self.estimator.predict(adv_x, batch_size=x.shape[0]), axis=1) != np.argmax(
+                    targets, axis=1
+                )
+
+            if np.mean(err) > np.mean(err_best):
+                err_best = err
+                adv_x_best = adv_x.copy()
+
+            if np.mean(err) == 1:
+                break
+
+            if (i + 1) % self.eps_iter == 0:
+                eps_[~err] *= self.eps_factor
+
+        return adv_x_best
+
+    def _compute(
+        self,
+        x_adv: np.ndarray,
+        x_init: np.ndarray,
+        y: np.ndarray,
+        cost_matrix: np.ndarray,
+        eps: np.ndarray,
+        err: np.ndarray,
+    ) -> np.ndarray:
+        """
+        Compute adversarial examples for one iteration.
+
+        :param x_adv: Current adversarial examples.
+        :param x_init: An array with the original inputs.
+        :param y: Target values (class labels) one-hot-encoded of shape `(nb_samples, nb_classes)` or indices of shape
+                  (nb_samples,). Only provide this parameter if you'd like to use true labels when crafting adversarial
+                  samples. Otherwise, model predictions are used as labels to avoid the "label leaking" effect
+                  (explained in this paper: https://arxiv.org/abs/1611.01236). Default is `None`.
+        :param cost_matrix: A non-negative cost matrix.
+        :param eps: Maximum perturbation that the attacker can introduce.
+        :param err: Current successful adversarial examples.
+        :return: Adversarial examples.
+        """
+        # Compute and apply perturbation
+        x_adv[~err] = self._compute_apply_perturbation(x_adv, y, cost_matrix)[~err]
+
+        # Do projection
+        x_adv[~err] = self._apply_projection(x_adv, x_init, cost_matrix, eps)[~err]
+
+        # Clip x_adv
+        if self.estimator.clip_values is not None:
+            clip_min, clip_max = self.estimator.clip_values
+            x_adv = np.clip(x_adv, clip_min, clip_max)
+
+        return x_adv
+
+    def _compute_apply_perturbation(self, x: np.ndarray, y: np.ndarray, cost_matrix: np.ndarray) -> np.ndarray:
+        """
+        Compute and apply perturbations.
+
+        :param x: Current adversarial examples.
+        :param y: Target values (class labels) one-hot-encoded of shape `(nb_samples, nb_classes)` or indices of shape
+                  (nb_samples,). Only provide this parameter if you'd like to use true labels when crafting adversarial
+                  samples. Otherwise, model predictions are used as labels to avoid the "label leaking" effect
+                  (explained in this paper: https://arxiv.org/abs/1611.01236). Default is `None`.
+        :param cost_matrix: A non-negative cost matrix.
+        :return: Adversarial examples.
+        """
+        # Pick a small scalar to avoid division by 0
+        tol = 10e-8
+
+        # Get gradient wrt loss; invert it if attack is targeted
+        grad = self.estimator.loss_gradient(x, y) * (1 - 2 * int(self.targeted))
+
+        # Apply norm bound
+        if self.norm == "inf":
+            grad = np.sign(grad)
+            x_adv = x + self.eps_step * grad
+
+        elif self.norm == "1":
+            ind = tuple(range(1, len(x.shape)))
+            grad = grad / (np.sum(np.abs(grad), axis=ind, keepdims=True) + tol)
+            x_adv = x + self.eps_step * grad
+
+        elif self.norm == "2":
+            ind = tuple(range(1, len(x.shape)))
+            grad = grad / (np.sqrt(np.sum(np.square(grad), axis=ind, keepdims=True)) + tol)
+            x_adv = x + self.eps_step * grad
+
+        elif self.norm == "wasserstein":
+            x_adv = self._conjugate_sinkhorn(x, grad, cost_matrix)
+
+        else:
+            raise NotImplementedError(
+                "Values of `norm` different from `1`, `2`, `inf` and `wasserstein` are currently not supported."
+            )
+
+        return x_adv
+
+    def _apply_projection(
+        self, x: np.ndarray, x_init: np.ndarray, cost_matrix: np.ndarray, eps: np.ndarray
+    ) -> np.ndarray:
+        """
+        Apply projection on the ball of size `eps`.
+
+        :param x: Current adversarial examples.
+        :param x_init: An array with the original inputs.
+        :param cost_matrix: A non-negative cost matrix.
+        :param eps: Maximum perturbation that the attacker can introduce.
+        :return: Adversarial examples.
+        """
+        # Pick a small scalar to avoid division by 0
+        tol = 10e-8
+
+        if self.ball == "2":
+            values = x - x_init
+            values_tmp = values.reshape((values.shape[0], -1))
+
+            values_tmp = values_tmp * np.expand_dims(
+                np.minimum(1.0, eps / (np.linalg.norm(values_tmp, axis=1) + tol)), axis=1
+            )
+
+            values = values_tmp.reshape(values.shape)
+
+            x_adv = values + x_init
+
+        elif self.ball == "1":
+            values = x - x_init
+            values_tmp = values.reshape((values.shape[0], -1))
+
+            values_tmp = values_tmp * np.expand_dims(
+                np.minimum(1.0, eps / (np.linalg.norm(values_tmp, axis=1, ord=1) + tol)), axis=1
+            )
+
+            values = values_tmp.reshape(values.shape)
+            x_adv = values + x_init
+
+        elif self.ball == "inf":
+            values = x - x_init
+            values_tmp = values.reshape((values.shape[0], -1))
+
+            values_tmp = np.sign(values_tmp) * np.minimum(abs(values_tmp), np.expand_dims(eps, -1))
+
+            values = values_tmp.reshape(values.shape)
+            x_adv = values + x_init
+
+        elif self.ball == "wasserstein":
+            x_adv = self._projected_sinkhorn(x, x_init, cost_matrix, eps)
+
+        else:
+            raise NotImplementedError(
+                "Values of `ball` different from `1`, `2`, `inf` and `wasserstein` are currently not supported."
+            )
+
+        return x_adv
+
+    def _conjugate_sinkhorn(self, x: np.ndarray, grad: np.ndarray, cost_matrix: np.ndarray) -> np.ndarray:
+        """
+        The conjugate sinkhorn_optimizer.
+
+        :param x: Current adversarial examples.
+        :param grad: The loss gradients.
+        :param cost_matrix: A non-negative cost matrix.
+        :return: Adversarial examples.
+        """
+        # Normalize inputs
+        normalization = x.reshape(x.shape[0], -1).sum(-1).reshape(x.shape[0], 1, 1, 1)
+        x = x.copy() / normalization
+
+        # Dimension size for each example
+        m = np.prod(x.shape[1:])
+
+        # Initialize
+        alpha = np.log(np.ones(x.shape) / m) + 0.5
+        exp_alpha = np.exp(-alpha)
+
+        beta = -self.regularization * grad
+        beta = beta.astype(np.float64)
+        exp_beta = np.exp(-beta)
+
+        # Check for overflow
+        if (exp_beta == np.inf).any():
+            raise ValueError("Overflow error in `_conjugate_sinkhorn` for exponential beta.")
+
+        cost_matrix_new = cost_matrix.copy() + 1
+        cost_matrix_new = np.expand_dims(np.expand_dims(cost_matrix_new, 0), 0)
+
+        I_nonzero = self._batch_dot(x, self._local_transport(cost_matrix_new, grad, self.kernel_size)) != 0
+        I_nonzero_ = np.zeros(alpha.shape).astype(bool)
+        I_nonzero_[:, :, :, :] = np.expand_dims(np.expand_dims(np.expand_dims(I_nonzero, -1), -1), -1)
+
+        psi = np.ones(x.shape[0])
+
+        K = np.expand_dims(np.expand_dims(np.expand_dims(psi, -1), -1), -1)
+        K = np.exp(-K * cost_matrix - 1)
+
+        convergence = -np.inf
+
+        for _ in range(self.conjugate_sinkhorn_max_iter):
+            # Block coordinate descent iterates
+            x[x == 0.0] = EPS_LOG  # Prevent divide by zero in np.log
+            alpha[I_nonzero_] = (np.log(self._local_transport(K, exp_beta, self.kernel_size)) - np.log(x))[I_nonzero_]
+            exp_alpha = np.exp(-alpha)
+
+            # Newton step
+            g = -self.eps_step + self._batch_dot(
+                exp_alpha, self._local_transport(cost_matrix * K, exp_beta, self.kernel_size)
+            )
+
+            h = -self._batch_dot(
+                exp_alpha, self._local_transport(cost_matrix * cost_matrix * K, exp_beta, self.kernel_size)
+            )
+
+            delta = g / h
+
+            # Ensure psi >= 0
+            tmp = np.ones(delta.shape)
+            neg = psi - tmp * delta < 0
+
+            while neg.any() and np.min(tmp) > 1e-2:
+                tmp[neg] /= 2
+                neg = psi - tmp * delta < 0
+
+            psi[I_nonzero] = np.maximum(psi - tmp * delta, 0)[I_nonzero]
+
+            # Update K
+            K = np.expand_dims(np.expand_dims(np.expand_dims(psi, -1), -1), -1)
+            K = np.exp(-K * cost_matrix - 1)
+
+            # Check for convergence
+            next_convergence = self._conjugated_sinkhorn_evaluation(x, alpha, exp_alpha, exp_beta, psi, K)
+
+            if (np.abs(convergence - next_convergence) <= 1e-4 + 1e-4 * np.abs(next_convergence)).all():
+                break
+            else:
+                convergence = next_convergence
+
+        result = exp_beta * self._local_transport(K, exp_alpha, self.kernel_size)
+        result[~I_nonzero] = 0
+        result *= normalization
+
+        return result
+
+    def _projected_sinkhorn(
+        self, x: np.ndarray, x_init: np.ndarray, cost_matrix: np.ndarray, eps: np.ndarray
+    ) -> np.ndarray:
+        """
+        The projected sinkhorn_optimizer.
+
+        :param x: Current adversarial examples.
+        :param x_init: An array with the original inputs.
+        :param cost_matrix: A non-negative cost matrix.
+        :param eps: Maximum perturbation that the attacker can introduce.
+        :return: Adversarial examples.
+        """
+        # Normalize inputs
+        normalization = x_init.reshape(x.shape[0], -1).sum(-1).reshape(x.shape[0], 1, 1, 1)
+        x = x.copy() / normalization
+        x_init = x_init.copy() / normalization
+
+        # Dimension size for each example
+        m = np.prod(x_init.shape[1:])
+
+        # Initialize
+        beta = np.log(np.ones(x.shape) / m)
+        exp_beta = np.exp(-beta)
+
+        psi = np.ones(x.shape[0])
+
+        K = np.expand_dims(np.expand_dims(np.expand_dims(psi, -1), -1), -1)
+        K = np.exp(-K * cost_matrix - 1)
+
+        convergence = -np.inf
+
+        for _ in range(self.projected_sinkhorn_max_iter):
+            # Block coordinate descent iterates
+            x_init[x_init == 0.0] = EPS_LOG  # Prevent divide by zero in np.log
+            alpha = np.log(self._local_transport(K, exp_beta, self.kernel_size)) - np.log(x_init)
+            exp_alpha = np.exp(-alpha)
+
+            beta = (
+                self.regularization
+                * np.exp(self.regularization * x)
+                * self._local_transport(K, exp_alpha, self.kernel_size)
+            )
+            beta[beta > 1e-10] = np.real(lambertw(beta[beta > 1e-10]))
+            beta -= self.regularization * x
+            exp_beta = np.exp(-beta)
+
+            # Newton step
+            g = -eps + self._batch_dot(exp_alpha, self._local_transport(cost_matrix * K, exp_beta, self.kernel_size))
+
+            h = -self._batch_dot(
+                exp_alpha, self._local_transport(cost_matrix * cost_matrix * K, exp_beta, self.kernel_size)
+            )
+
+            delta = g / h
+
+            # Ensure psi >= 0
+            tmp = np.ones(delta.shape)
+            neg = psi - tmp * delta < 0
+
+            while neg.any() and np.min(tmp) > 1e-2:
+                tmp[neg] /= 2
+                neg = psi - tmp * delta < 0
+
+            psi = np.maximum(psi - tmp * delta, 0)
+
+            # Update K
+            K = np.expand_dims(np.expand_dims(np.expand_dims(psi, -1), -1), -1)
+            K = np.exp(-K * cost_matrix - 1)
+
+            # Check for convergence
+            next_convergence = self._projected_sinkhorn_evaluation(
+                x, x_init, alpha, exp_alpha, beta, exp_beta, psi, K, eps,
+            )
+
+            if (np.abs(convergence - next_convergence) <= 1e-4 + 1e-4 * np.abs(next_convergence)).all():
+                break
+            else:
+                convergence = next_convergence
+
+        result = (beta / self.regularization + x) * normalization
+
+        return result
+
+    @staticmethod
+    def _compute_cost_matrix(p: int, kernel_size: int) -> np.ndarray:
+        """
+        Compute the default cost matrix.
+
+        :param p: The p-wasserstein distance.
+        :param kernel_size: Kernel size for computing the cost matrix.
+        :return: The cost matrix.
+        """
+        center = kernel_size // 2
+        cost_matrix = np.zeros((kernel_size, kernel_size))
+
+        for i in range(kernel_size):
+            for j in range(kernel_size):
+                # The code of the paper of this attack (https://arxiv.org/abs/1902.07906) implements the cost as:
+                # cost_matrix[i, j] = (abs(i - center) ** 2 + abs(j - center) ** 2) ** (p / 2)
+                # which only can reproduce L2-norm for p=1 correctly
+                cost_matrix[i, j] = (abs(i - center) ** p + abs(j - center) ** p) ** (1 / p)
+
+        return cost_matrix
+
+    @staticmethod
+    def _batch_dot(x: np.ndarray, y: np.ndarray) -> np.ndarray:
+        """
+        Compute batch dot product.
+
+        :param x: Sample batch.
+        :param y: Sample batch.
+        :return: Batch dot product.
+        """
+        batch_size = x.shape[0]
+        assert batch_size == y.shape[0]
+
+        x_ = x.reshape(batch_size, 1, -1)
+        y_ = y.reshape(batch_size, -1, 1)
+
+        result = np.matmul(x_, y_).reshape(batch_size)
+
+        return result
+
+    @staticmethod
+    def _unfold(x: np.ndarray, kernel_size: int, padding: int) -> np.ndarray:
+        """
+        Extract sliding local blocks from a batched input.
+
+        :param x: A batched input of shape `batch x channel x width x height`.
+        :param kernel_size: Kernel size for computing the cost matrix.
+        :param padding: Controls the amount of implicit zero-paddings on both sides for padding number of points
+            for each dimension before reshaping.
+        :return: Sliding local blocks.
+        """
+        # Do padding
+        shape = tuple(np.array(x.shape[2:]) + padding * 2)
+        x_pad = np.zeros(x.shape[:2] + shape)
+        x_pad[:, :, padding : (shape[0] - padding), padding : (shape[1] - padding)] = x
+
+        # Do unfolding
+        res_dim_0 = x.shape[0]
+        res_dim_1 = x.shape[1] * kernel_size ** 2
+        res_dim_2 = (shape[0] - kernel_size + 1) * (shape[1] - kernel_size + 1)
+        result = np.zeros((res_dim_0, res_dim_1, res_dim_2))
+
+        for i in range(shape[0] - kernel_size + 1):
+            for j in range(shape[1] - kernel_size + 1):
+                patch = x_pad[:, :, i : (i + kernel_size), j : (j + kernel_size)]
+                patch = patch.reshape(x.shape[0], -1)
+                result[:, :, i * (shape[1] - kernel_size + 1) + j] = patch
+
+        return result
+
+    def _local_transport(self, K: np.ndarray, x: np.ndarray, kernel_size: int) -> np.ndarray:
+        """
+        Compute local transport.
+
+        :param K: K parameter in Algorithm 2 of the paper ``Wasserstein Adversarial Examples via Projected
+            Sinkhorn Iterations``.
+        :param x: An array to apply local transport.
+        :param kernel_size: Kernel size for computing the cost matrix.
+        :return: Local transport result.
+        """
+        # Compute number of channels
+        num_channels = x.shape[1 if self.estimator.channels_first else 3]
+
+        # Expand channels
+        K = np.repeat(K, num_channels, axis=1)
+
+        # Swap channels to prepare for local transport computation
+        if not self.estimator.channels_first:
+            x = np.swapaxes(x, 1, 3)
+
+        # Compute local transport
+        unfold_x = self._unfold(x=x, kernel_size=kernel_size, padding=kernel_size // 2)
+        unfold_x = unfold_x.swapaxes(-1, -2)
+        unfold_x = unfold_x.reshape(*unfold_x.shape[:-1], num_channels, kernel_size ** 2)
+        unfold_x = unfold_x.swapaxes(-2, -3)
+
+        tmp_K = K.reshape(K.shape[0], num_channels, -1)
+        tmp_K = np.expand_dims(tmp_K, -1)
+
+        result = np.matmul(unfold_x, tmp_K)
+        result = np.squeeze(result, -1)
+        result = result.reshape(*result.shape[:-1], x.shape[-2], x.shape[-1])
+
+        # Swap channels for final result
+        if not self.estimator.channels_first:
+            result = np.swapaxes(result, 1, 3)
+
+        return result
+
+    def _projected_sinkhorn_evaluation(
+        self,
+        x: np.ndarray,
+        x_init: np.ndarray,
+        alpha: np.ndarray,
+        exp_alpha: np.ndarray,
+        beta: np.ndarray,
+        exp_beta: np.ndarray,
+        psi: np.ndarray,
+        K: np.ndarray,
+        eps: np.ndarray,
+    ) -> np.ndarray:
+        """
+        Function to evaluate the objective of the projected sinkhorn optimizer.
+
+        :param x: Current adversarial examples.
+        :param x_init: An array with the original inputs.
+        :param alpha: Alpha parameter in Algorithm 2 of the paper ``Wasserstein Adversarial Examples via Projected
+            Sinkhorn Iterations``.
+        :param exp_alpha: Exponential of alpha.
+        :param beta: Beta parameter in Algorithm 2 of the paper ``Wasserstein Adversarial Examples via Projected
+            Sinkhorn Iterations``.
+        :param exp_beta: Exponential of beta.
+        :param psi: Psi parameter in Algorithm 2 of the paper ``Wasserstein Adversarial Examples via Projected
+            Sinkhorn Iterations``.
+        :param K: K parameter in Algorithm 2 of the paper ``Wasserstein Adversarial Examples via Projected
+            Sinkhorn Iterations``.
+        :param eps: Maximum perturbation that the attacker can introduce.
+        :return: Evaluation result.
+        """
+        return (
+            -0.5 / self.regularization * self._batch_dot(beta, beta)
+            - psi * eps
+            - self._batch_dot(np.minimum(alpha, 1e10), x_init)
+            - self._batch_dot(np.minimum(beta, 1e10), x)
+            - self._batch_dot(exp_alpha, self._local_transport(K, exp_beta, self.kernel_size))
+        )
+
+    def _conjugated_sinkhorn_evaluation(
+        self,
+        x: np.ndarray,
+        alpha: np.ndarray,
+        exp_alpha: np.ndarray,
+        exp_beta: np.ndarray,
+        psi: np.ndarray,
+        K: np.ndarray,
+    ) -> np.ndarray:
+        """
+        Function to evaluate the objective of the conjugated sinkhorn optimizer.
+
+        :param x: Current adversarial examples.
+        :param alpha: Alpha parameter in the conjugated sinkhorn optimizer of the paper ``Wasserstein Adversarial
+            Examples via Projected Sinkhorn Iterations``.
+        :param exp_alpha: Exponential of alpha.
+        :param exp_beta: Exponential of beta parameter in the conjugated sinkhorn optimizer of the paper ``Wasserstein
+            Adversarial Examples via Projected Sinkhorn Iterations``.
+        :param psi: Psi parameter in the conjugated sinkhorn optimizer of the paper ``Wasserstein Adversarial
+            Examples via Projected Sinkhorn Iterations``.
+        :param K: K parameter in the conjugated sinkhorn optimizer of the paper ``Wasserstein Adversarial Examples
+            via Projected Sinkhorn Iterations``.
+        :return: Evaluation result.
+        """
+        return (
+            -psi * self.eps_step
+            - self._batch_dot(np.minimum(alpha, 1e38), x)
+            - self._batch_dot(exp_alpha, self._local_transport(K, exp_beta, self.kernel_size))
+        )
+
+    def _check_params(self) -> None:
+        if not isinstance(self.targeted, bool):
+            raise ValueError("The flag `targeted` has to be of type bool.")
+
+        if self.regularization <= 0:
+            raise ValueError("The entropy regularization has to be greater than 0.")
+
+        if not isinstance(self.p, (int, np.int)):
+            raise TypeError("The p-wasserstein has to be of type integer.")
+
+        if self.p < 1:
+            raise ValueError("The p-wasserstein must be larger or equal to 1.")
+
+        if not isinstance(self.kernel_size, (int, np.int)):
+            raise TypeError("The kernel size has to be of type integer.")
+
+        if self.kernel_size % 2 != 1:
+            raise ValueError("Need odd kernel size.")
+
+        # Check if order of the norm is acceptable given current implementation
+        if self.norm not in ["inf", "1", "2", "wasserstein"]:
+            raise ValueError("Norm order must be either `inf`, `1`, `2` or `wasserstein`.")
+
+        # Check if order of the ball is acceptable given current implementation
+        if self.ball not in ["inf", "1", "2", "wasserstein"]:
+            raise ValueError("Ball order must be either `inf`, `1`, `2` or `wasserstein`.")
+
+        if self.eps <= 0:
+            raise ValueError("The perturbation size `eps` has to be positive.")
+
+        if self.eps_step <= 0:
+            raise ValueError("The perturbation step-size `eps_step` has to be positive.")
+
+        if self.norm == "inf" and self.eps_step > self.eps:
+            raise ValueError(
+                "The iteration step `eps_step` has to be smaller than or equal to the total attack budget `eps`."
+            )
+
+        if self.eps_iter <= 0:
+            raise ValueError("The number of epsilon iterations `eps_iter` has to be a positive integer.")
+
+        if self.eps_factor <= 1:
+            raise ValueError("The epsilon factor must be larger than 1.")
+
+        if self.max_iter <= 0:
+            raise ValueError("The number of iterations `max_iter` has to be a positive integer.")
+
+        if self.conjugate_sinkhorn_max_iter <= 0:
+            raise ValueError("The number of iterations `conjugate_sinkhorn_max_iter` has to be a positive integer.")
+
+        if self.projected_sinkhorn_max_iter <= 0:
+            raise ValueError("The number of iterations `projected_sinkhorn_max_iter` has to be a positive integer.")
+
+        if self.batch_size <= 0:
+            raise ValueError("The batch size `batch_size` has to be positive.")
+
+        if not isinstance(self.verbose, bool):
+            raise ValueError("The argument `verbose` has to be of type bool.")
diff --git a/adversarial-robustness-toolbox/art/attacks/evasion/zoo.py b/adversarial-robustness-toolbox/art/attacks/evasion/zoo.py
new file mode 100644
index 0000000..7896a5f
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/attacks/evasion/zoo.py
@@ -0,0 +1,616 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements the zeroth-order optimization attack `ZooAttack`. This is a black-box attack. This attack is a
+variant of the Carlini and Wagner attack which uses ADAM coordinate descent to perform numerical estimation of
+gradients.
+
+| Paper link: https://arxiv.org/abs/1708.03999
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+from typing import Optional, Tuple, TYPE_CHECKING
+
+import numpy as np
+from scipy.ndimage import zoom
+from tqdm.auto import trange
+
+from art.config import ART_NUMPY_DTYPE
+from art.attacks.attack import EvasionAttack
+from art.estimators.estimator import BaseEstimator
+from art.estimators.classification.classifier import ClassifierMixin
+from art.utils import (
+    compute_success,
+    get_labels_np_array,
+    check_and_transform_label_format,
+)
+
+if TYPE_CHECKING:
+    from art.utils import CLASSIFIER_TYPE
+
+logger = logging.getLogger(__name__)
+
+
+class ZooAttack(EvasionAttack):
+    """
+    The black-box zeroth-order optimization attack from Pin-Yu Chen et al. (2018). This attack is a variant of the
+    C&W attack which uses ADAM coordinate descent to perform numerical estimation of gradients.
+
+    | Paper link: https://arxiv.org/abs/1708.03999
+    """
+
+    attack_params = EvasionAttack.attack_params + [
+        "confidence",
+        "targeted",
+        "learning_rate",
+        "max_iter",
+        "binary_search_steps",
+        "initial_const",
+        "abort_early",
+        "use_resize",
+        "use_importance",
+        "nb_parallel",
+        "batch_size",
+        "variable_h",
+        "verbose",
+    ]
+    _estimator_requirements = (BaseEstimator, ClassifierMixin)
+
+    def __init__(
+        self,
+        classifier: "CLASSIFIER_TYPE",
+        confidence: float = 0.0,
+        targeted: bool = False,
+        learning_rate: float = 1e-2,
+        max_iter: int = 10,
+        binary_search_steps: int = 1,
+        initial_const: float = 1e-3,
+        abort_early: bool = True,
+        use_resize: bool = True,
+        use_importance: bool = True,
+        nb_parallel: int = 128,
+        batch_size: int = 1,
+        variable_h: float = 1e-4,
+        verbose: bool = True,
+    ):
+        """
+        Create a ZOO attack instance.
+
+        :param classifier: A trained classifier.
+        :param confidence: Confidence of adversarial examples: a higher value produces examples that are farther
+               away, from the original input, but classified with higher confidence as the target class.
+        :param targeted: Should the attack target one specific class.
+        :param learning_rate: The initial learning rate for the attack algorithm. Smaller values produce better
+               results but are slower to converge.
+        :param max_iter: The maximum number of iterations.
+        :param binary_search_steps: Number of times to adjust constant with binary search (positive value).
+        :param initial_const: The initial trade-off constant `c` to use to tune the relative importance of distance
+               and confidence. If `binary_search_steps` is large, the initial constant is not important, as discussed in
+               Carlini and Wagner (2016).
+        :param abort_early: `True` if gradient descent should be abandoned when it gets stuck.
+        :param use_resize: `True` if to use the resizing strategy from the paper: first, compute attack on inputs
+               resized to 32x32, then increase size if needed to 64x64, followed by 128x128.
+        :param use_importance: `True` if to use importance sampling when choosing coordinates to update.
+        :param nb_parallel: Number of coordinate updates to run in parallel. A higher value for `nb_parallel` should
+               be preferred over a large batch size.
+        :param batch_size: Internal size of batches on which adversarial samples are generated. Small batch sizes are
+               encouraged for ZOO, as the algorithm already runs `nb_parallel` coordinate updates in parallel for each
+               sample. The batch size is a multiplier of `nb_parallel` in terms of memory consumption.
+        :param variable_h: Step size for numerical estimation of derivatives.
+        :param verbose: Show progress bars.
+        """
+        super().__init__(estimator=classifier)
+
+        if len(classifier.input_shape) == 1:
+            self.input_is_feature_vector = True
+            if batch_size != 1:
+                raise ValueError(
+                    "The current implementation of Zeroth-Order Optimisation attack only supports "
+                    "`batch_size=1` with feature vectors as input."
+                )
+        else:
+            self.input_is_feature_vector = False
+
+        self.confidence = confidence
+        self._targeted = targeted
+        self.learning_rate = learning_rate
+        self.max_iter = max_iter
+        self.binary_search_steps = binary_search_steps
+        self.initial_const = initial_const
+        self.abort_early = abort_early
+        self.use_resize = use_resize
+        self.use_importance = use_importance
+        self.nb_parallel = nb_parallel
+        self.batch_size = batch_size
+        self.variable_h = variable_h
+        self.verbose = verbose
+        self._check_params()
+
+        # Initialize some internal variables
+        self._init_size = 32
+        if self.abort_early:
+            self._early_stop_iters = self.max_iter // 10 if self.max_iter >= 10 else self.max_iter
+        self.nb_parallel = nb_parallel
+
+        # Initialize noise variable to zero
+        if self.input_is_feature_vector:
+            self.use_resize = False
+            self.use_importance = False
+            logger.info("Disable resizing and importance sampling because feature vector input has been detected.")
+
+        if self.use_resize:
+            if not self.estimator.channels_first:
+                dims = (batch_size, self._init_size, self._init_size, self.estimator.input_shape[-1])
+            else:
+                dims = (batch_size, self.estimator.input_shape[0], self._init_size, self._init_size)
+            self._current_noise = np.zeros(dims, dtype=ART_NUMPY_DTYPE)
+        else:
+            self._current_noise = np.zeros((batch_size,) + self.estimator.input_shape, dtype=ART_NUMPY_DTYPE)
+        self._sample_prob = np.ones(self._current_noise.size, dtype=ART_NUMPY_DTYPE) / self._current_noise.size
+
+        self.adam_mean = None
+        self.adam_var = None
+        self.adam_epochs = None
+
+    def _loss(
+        self, x: np.ndarray, x_adv: np.ndarray, target: np.ndarray, c_weight: np.ndarray
+    ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
+        """
+        Compute the loss function values.
+
+        :param x: An array with the original input.
+        :param x_adv: An array with the adversarial input.
+        :param target: An array with the target class (one-hot encoded).
+        :param c_weight: Weight of the loss term aiming for classification as target.
+        :return: A tuple holding the current logits, `L_2` distortion and overall loss.
+        """
+        l2dist = np.sum(np.square(x - x_adv).reshape(x_adv.shape[0], -1), axis=1)
+        ratios = [1.0] + [
+            int(new_size) / int(old_size) for new_size, old_size in zip(self.estimator.input_shape, x.shape[1:])
+        ]
+        preds = self.estimator.predict(np.array(zoom(x_adv, zoom=ratios)), batch_size=self.batch_size)
+        z_target = np.sum(preds * target, axis=1)
+        z_other = np.max(preds * (1 - target) + (np.min(preds, axis=1) - 1)[:, np.newaxis] * target, axis=1,)
+
+        if self.targeted:
+            # If targeted, optimize for making the target class most likely
+            loss = np.maximum(z_other - z_target + self.confidence, 0)
+        else:
+            # If untargeted, optimize for making any other class most likely
+            loss = np.maximum(z_target - z_other + self.confidence, 0)
+
+        return preds, l2dist, c_weight * loss + l2dist
+
+    def generate(self, x: np.ndarray, y: Optional[np.ndarray] = None, **kwargs) -> np.ndarray:
+        """
+        Generate adversarial samples and return them in an array.
+
+        :param x: An array with the original inputs to be attacked.
+        :param y: Target values (class labels) one-hot-encoded of shape (nb_samples, nb_classes) or indices of shape
+                  (nb_samples,).
+        :return: An array holding the adversarial examples.
+        """
+        y = check_and_transform_label_format(y, self.estimator.nb_classes)
+
+        # Check that `y` is provided for targeted attacks
+        if self.targeted and y is None:
+            raise ValueError("Target labels `y` need to be provided for a targeted attack.")
+
+        # No labels provided, use model prediction as correct class
+        if y is None:
+            y = get_labels_np_array(self.estimator.predict(x, batch_size=self.batch_size))
+
+        # Compute adversarial examples with implicit batching
+        nb_batches = int(np.ceil(x.shape[0] / float(self.batch_size)))
+        x_adv = []
+        for batch_id in trange(nb_batches, desc="ZOO", disable=not self.verbose):
+            batch_index_1, batch_index_2 = batch_id * self.batch_size, (batch_id + 1) * self.batch_size
+            x_batch = x[batch_index_1:batch_index_2]
+            y_batch = y[batch_index_1:batch_index_2]
+            res = self._generate_batch(x_batch, y_batch)
+            x_adv.append(res)
+        x_adv = np.vstack(x_adv)
+
+        # Apply clip
+        if self.estimator.clip_values is not None:
+            clip_min, clip_max = self.estimator.clip_values
+            np.clip(x_adv, clip_min, clip_max, out=x_adv)
+
+        # Log success rate of the ZOO attack
+        logger.info(
+            "Success rate of ZOO attack: %.2f%%",
+            100 * compute_success(self.estimator, x, y, x_adv, self.targeted, batch_size=self.batch_size),
+        )
+
+        return x_adv
+
+    def _generate_batch(self, x_batch: np.ndarray, y_batch: np.ndarray) -> np.ndarray:
+        """
+        Run the attack on a batch of images and labels.
+
+        :param x_batch: A batch of original examples.
+        :param y_batch: A batch of targets (0-1 hot).
+        :return: A batch of adversarial examples.
+        """
+        # Initialize binary search
+        c_current = self.initial_const * np.ones(x_batch.shape[0])
+        c_lower_bound = np.zeros(x_batch.shape[0])
+        c_upper_bound = 1e10 * np.ones(x_batch.shape[0])
+
+        # Initialize best distortions and best attacks globally
+        o_best_dist = np.inf * np.ones(x_batch.shape[0])
+        o_best_attack = x_batch.copy()
+
+        # Start with a binary search
+        for bss in range(self.binary_search_steps):
+            logger.debug(
+                "Binary search step %i out of %i (c_mean==%f)", bss, self.binary_search_steps, np.mean(c_current),
+            )
+
+            # Run with 1 specific binary search step
+            best_dist, best_label, best_attack = self._generate_bss(x_batch, y_batch, c_current)
+
+            # Update best results so far
+            o_best_attack[best_dist < o_best_dist] = best_attack[best_dist < o_best_dist]
+            o_best_dist[best_dist < o_best_dist] = best_dist[best_dist < o_best_dist]
+
+            # Adjust the constant as needed
+            c_current, c_lower_bound, c_upper_bound = self._update_const(
+                y_batch, best_label, c_current, c_lower_bound, c_upper_bound
+            )
+
+        return o_best_attack
+
+    def _update_const(
+        self,
+        y_batch: np.ndarray,
+        best_label: np.ndarray,
+        c_batch: np.ndarray,
+        c_lower_bound: np.ndarray,
+        c_upper_bound: np.ndarray,
+    ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
+        """
+        Update constant `c_batch` from the ZOO objective. This characterizes the trade-off between attack strength and
+        amount of noise introduced.
+
+        :param y_batch: A batch of targets (0-1 hot).
+        :param best_label: A batch of best labels.
+        :param c_batch: A batch of constants.
+        :param c_lower_bound: A batch of lower bound constants.
+        :param c_upper_bound: A batch of upper bound constants.
+        :return: A tuple of three batches of updated constants and lower/upper bounds.
+        """
+
+        def compare(object1, object2):
+            return object1 == object2 if self.targeted else object1 != object2
+
+        comparison = [
+            compare(best_label[i], np.argmax(y_batch[i])) and best_label[i] != -np.inf for i in range(len(c_batch))
+        ]
+        for i, comp in enumerate(comparison):
+            if comp:
+                # Successful attack
+                c_upper_bound[i] = min(c_upper_bound[i], c_batch[i])
+                if c_upper_bound[i] < 1e9:
+                    c_batch[i] = (c_lower_bound[i] + c_upper_bound[i]) / 2
+            else:
+                # Failure attack
+                c_lower_bound[i] = max(c_lower_bound[i], c_batch[i])
+                c_batch[i] = (c_lower_bound[i] + c_upper_bound[i]) / 2 if c_upper_bound[i] < 1e9 else c_batch[i] * 10
+
+        return c_batch, c_lower_bound, c_upper_bound
+
+    def _generate_bss(
+        self, x_batch: np.ndarray, y_batch: np.ndarray, c_batch: np.ndarray
+    ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
+        """
+        Generate adversarial examples for a batch of inputs with a specific batch of constants.
+
+        :param x_batch: A batch of original examples.
+        :param y_batch: A batch of targets (0-1 hot).
+        :param c_batch: A batch of constants.
+        :return: A tuple of best elastic distances, best labels, best attacks.
+        """
+
+        def compare(object1, object2):
+            return object1 == object2 if self.targeted else object1 != object2
+
+        x_orig = x_batch.astype(ART_NUMPY_DTYPE)
+        fine_tuning = np.full(x_batch.shape[0], False, dtype=bool)
+        prev_loss = 1e6 * np.ones(x_batch.shape[0])
+        prev_l2dist = np.zeros(x_batch.shape[0])
+
+        # Resize and initialize Adam
+        if self.use_resize:
+            x_orig = self._resize_image(x_orig, self._init_size, self._init_size, True)
+            assert (x_orig != 0).any()
+            x_adv = x_orig.copy()
+        else:
+            x_orig = x_batch
+            self._reset_adam(np.prod(self.estimator.input_shape).item())
+            if x_batch.shape == self._current_noise.shape:
+                self._current_noise.fill(0)
+            else:
+                self._current_noise = np.zeros(x_batch.shape, dtype=ART_NUMPY_DTYPE)
+            x_adv = x_orig.copy()
+
+        # Initialize best distortions, best changed labels and best attacks
+        best_dist = np.inf * np.ones(x_adv.shape[0])
+        best_label = -np.inf * np.ones(x_adv.shape[0])
+        best_attack = np.array([x_adv[i] for i in range(x_adv.shape[0])])
+
+        for iter_ in range(self.max_iter):
+            logger.debug("Iteration step %i out of %i", iter_, self.max_iter)
+
+            # Upscaling for very large number of iterations
+            if self.use_resize:
+                if iter_ == 2000:
+                    x_adv = self._resize_image(x_adv, 64, 64)
+                    x_orig = zoom(
+                        x_orig,
+                        [
+                            1,
+                            x_adv.shape[1] / x_orig.shape[1],
+                            x_adv.shape[2] / x_orig.shape[2],
+                            x_adv.shape[3] / x_orig.shape[3],
+                        ],
+                    )
+                elif iter_ == 10000:
+                    x_adv = self._resize_image(x_adv, 128, 128)
+                    x_orig = zoom(
+                        x_orig,
+                        [
+                            1,
+                            x_adv.shape[1] / x_orig.shape[1],
+                            x_adv.shape[2] / x_orig.shape[2],
+                            x_adv.shape[3] / x_orig.shape[3],
+                        ],
+                    )
+
+            # Compute adversarial examples and loss
+            x_adv = self._optimizer(x_adv, y_batch, c_batch)
+            preds, l2dist, loss = self._loss(x_orig, x_adv, y_batch, c_batch)
+
+            # Reset Adam if a valid example has been found to avoid overshoot
+            mask_fine_tune = (~fine_tuning) & (loss == l2dist) & (prev_loss != prev_l2dist)
+            fine_tuning[mask_fine_tune] = True
+            self._reset_adam(
+                self.adam_mean.size, np.repeat(mask_fine_tune, x_adv[0].size)  # type: ignore
+            )
+            prev_l2dist = l2dist
+
+            # Abort early if no improvement is obtained
+            if self.abort_early and iter_ % self._early_stop_iters == 0:
+                if (loss > 0.9999 * prev_loss).all():
+                    break
+                prev_loss = loss
+
+            # Adjust the best result
+            labels_batch = np.argmax(y_batch, axis=1)
+            for i, (dist, pred) in enumerate(zip(l2dist, np.argmax(preds, axis=1))):
+                if dist < best_dist[i] and compare(pred, labels_batch[i]):
+                    best_dist[i] = dist
+                    best_attack[i] = x_adv[i]
+                    best_label[i] = pred
+
+        # Resize images to original size before returning
+        best_attack = np.array(best_attack)
+        if self.use_resize:
+            if not self.estimator.channels_first:
+                best_attack = zoom(
+                    best_attack,
+                    [1, int(x_batch.shape[1]) / best_attack.shape[1], int(x_batch.shape[2]) / best_attack.shape[2], 1,],
+                )
+            else:
+                best_attack = zoom(
+                    best_attack,
+                    [1, 1, int(x_batch.shape[2]) / best_attack.shape[2], int(x_batch.shape[2]) / best_attack.shape[3],],
+                )
+
+        return best_dist, best_label, best_attack
+
+    def _optimizer(self, x: np.ndarray, targets: np.ndarray, c_batch: np.ndarray) -> np.ndarray:
+        # Variation of input for computing loss, same as in original implementation
+        coord_batch = np.repeat(self._current_noise, 2 * self.nb_parallel, axis=0)
+        coord_batch = coord_batch.reshape(2 * self.nb_parallel * self._current_noise.shape[0], -1)
+
+        # Sample indices to prioritize for optimization
+        if self.use_importance and np.unique(self._sample_prob).size != 1:
+            indices = (
+                np.random.choice(
+                    coord_batch.shape[-1] * x.shape[0],
+                    self.nb_parallel * self._current_noise.shape[0],
+                    replace=False,
+                    p=self._sample_prob.flatten(),
+                )
+                % coord_batch.shape[-1]
+            )
+        else:
+            indices = (
+                np.random.choice(
+                    coord_batch.shape[-1] * x.shape[0], self.nb_parallel * self._current_noise.shape[0], replace=False,
+                )
+                % coord_batch.shape[-1]
+            )
+
+        # Create the batch of modifications to run
+        for i in range(self.nb_parallel * self._current_noise.shape[0]):
+            coord_batch[2 * i, indices[i]] += self.variable_h
+            coord_batch[2 * i + 1, indices[i]] -= self.variable_h
+
+        # Compute loss for all samples and coordinates, then optimize
+        expanded_x = np.repeat(x, 2 * self.nb_parallel, axis=0).reshape((-1,) + x.shape[1:])
+        expanded_targets = np.repeat(targets, 2 * self.nb_parallel, axis=0).reshape((-1,) + targets.shape[1:])
+        expanded_c = np.repeat(c_batch, 2 * self.nb_parallel)
+        _, _, loss = self._loss(
+            expanded_x, expanded_x + coord_batch.reshape(expanded_x.shape), expanded_targets, expanded_c,
+        )
+        self._current_noise = self._optimizer_adam_coordinate(
+            loss,
+            indices,
+            self.adam_mean,
+            self.adam_var,
+            self._current_noise,
+            self.learning_rate,
+            self.adam_epochs,
+            True,
+        )
+
+        if self.use_importance and self._current_noise.shape[2] > self._init_size:
+            self._sample_prob = self._get_prob(self._current_noise).flatten()
+
+        return x + self._current_noise
+
+    def _optimizer_adam_coordinate(
+        self,
+        losses: np.ndarray,
+        index: int,
+        mean: np.ndarray,
+        var: np.ndarray,
+        current_noise: np.ndarray,
+        learning_rate: float,
+        adam_epochs: np.ndarray,
+        proj: bool,
+    ) -> np.ndarray:
+        """
+        Implementation of the ADAM optimizer for coordinate descent.
+        """
+        beta1, beta2 = 0.9, 0.999
+
+        # Estimate grads from loss variation (constant `h` from the paper is fixed to .0001)
+        grads = np.array([(losses[i] - losses[i + 1]) / (2 * self.variable_h) for i in range(0, len(losses), 2)])
+
+        # ADAM update
+        mean[index] = beta1 * mean[index] + (1 - beta1) * grads
+        var[index] = beta2 * var[index] + (1 - beta2) * grads ** 2
+
+        corr = (np.sqrt(1 - np.power(beta2, adam_epochs[index]))) / (1 - np.power(beta1, adam_epochs[index]))
+        orig_shape = current_noise.shape
+        current_noise = current_noise.reshape(-1)
+        current_noise[index] -= learning_rate * corr * mean[index] / (np.sqrt(var[index]) + 1e-8)
+        adam_epochs[index] += 1
+
+        if proj and hasattr(self.estimator, "clip_values") and self.estimator.clip_values is not None:
+            clip_min, clip_max = self.estimator.clip_values
+            current_noise[index] = np.clip(current_noise[index], clip_min, clip_max)
+
+        return current_noise.reshape(orig_shape)
+
+    def _reset_adam(self, nb_vars: int, indices: Optional[np.ndarray] = None) -> None:
+        # If variables are already there and at the right size, reset values
+        if self.adam_mean is not None and self.adam_mean.size == nb_vars:
+            if indices is None:
+                self.adam_mean.fill(0)
+                self.adam_var.fill(0)  # type: ignore
+                self.adam_epochs.fill(1)  # type: ignore
+            else:
+                self.adam_mean[indices] = 0
+                self.adam_var[indices] = 0  # type: ignore
+                self.adam_epochs[indices] = 1  # type: ignore
+        else:
+            # Allocate Adam variables
+            self.adam_mean = np.zeros(nb_vars, dtype=ART_NUMPY_DTYPE)
+            self.adam_var = np.zeros(nb_vars, dtype=ART_NUMPY_DTYPE)
+            self.adam_epochs = np.ones(nb_vars, dtype=np.int32)
+
+    def _resize_image(self, x: np.ndarray, size_x: int, size_y: int, reset: bool = False) -> np.ndarray:
+        if not self.estimator.channels_first:
+            dims = (x.shape[0], size_x, size_y, x.shape[-1])
+        else:
+            dims = (x.shape[0], x.shape[1], size_x, size_y)
+        nb_vars = np.prod(dims).item()
+
+        if reset:
+            # Reset variables to original size and value
+            if dims == x.shape:
+                resized_x = x
+                if x.shape == self._current_noise.shape:
+                    self._current_noise.fill(0)
+                else:
+                    self._current_noise = np.zeros(x.shape, dtype=ART_NUMPY_DTYPE)
+            else:
+                resized_x = zoom(x, (1, dims[1] / x.shape[1], dims[2] / x.shape[2], dims[3] / x.shape[3],),)
+                self._current_noise = np.zeros(dims, dtype=ART_NUMPY_DTYPE)
+            self._sample_prob = np.ones(nb_vars, dtype=ART_NUMPY_DTYPE) / nb_vars
+        else:
+            # Rescale variables and reset values
+            resized_x = zoom(x, (1, dims[1] / x.shape[1], dims[2] / x.shape[2], dims[3] / x.shape[3]))
+            self._sample_prob = self._get_prob(self._current_noise, double=True).flatten()
+            self._current_noise = np.zeros(dims, dtype=ART_NUMPY_DTYPE)
+
+        # Reset Adam
+        self._reset_adam(nb_vars)
+
+        return resized_x
+
+    def _get_prob(self, prev_noise: np.ndarray, double: bool = False) -> np.ndarray:
+        dims = list(prev_noise.shape)
+        channel_index = 1 if self.estimator.channels_first else 3
+
+        # Double size if needed
+        if double:
+            dims = [2 * size if i not in [0, channel_index] else size for i, size in enumerate(dims)]
+
+        prob = np.empty(shape=dims, dtype=np.float32)
+        image = np.abs(prev_noise)
+
+        for channel in range(prev_noise.shape[channel_index]):
+            if not self.estimator.channels_first:
+                image_pool = self._max_pooling(image[:, :, :, channel], dims[1] // 8)
+                if double:
+                    prob[:, :, :, channel] = np.abs(zoom(image_pool, [1, 2, 2]))
+                else:
+                    prob[:, :, :, channel] = image_pool
+            elif self.estimator.channels_first:
+                image_pool = self._max_pooling(image[:, channel, :, :], dims[2] // 8)
+                if double:
+                    prob[:, channel, :, :] = np.abs(zoom(image_pool, [1, 2, 2]))
+                else:
+                    prob[:, channel, :, :] = image_pool
+
+        prob /= np.sum(prob)
+
+        return prob
+
+    @staticmethod
+    def _max_pooling(image: np.ndarray, kernel_size: int) -> np.ndarray:
+        img_pool = np.copy(image)
+        for i in range(0, image.shape[1], kernel_size):
+            for j in range(0, image.shape[2], kernel_size):
+                img_pool[:, i : i + kernel_size, j : j + kernel_size] = np.max(
+                    image[:, i : i + kernel_size, j : j + kernel_size], axis=(1, 2), keepdims=True,
+                )
+
+        return img_pool
+
+    def _check_params(self) -> None:
+        if not isinstance(self.binary_search_steps, (int, np.int)) or self.binary_search_steps < 0:
+            raise ValueError("The number of binary search steps must be a non-negative integer.")
+
+        if not isinstance(self.max_iter, (int, np.int)) or self.max_iter < 0:
+            raise ValueError("The number of iterations must be a non-negative integer.")
+
+        if not isinstance(self.nb_parallel, (int, np.int)) or self.nb_parallel < 1:
+            raise ValueError("The number of parallel coordinates must be an integer greater than zero.")
+
+        if not isinstance(self.batch_size, (int, np.int)) or self.batch_size < 1:
+            raise ValueError("The batch size must be an integer greater than zero.")
+
+        if not isinstance(self.verbose, bool):
+            raise ValueError("The argument `verbose` has to be of type bool.")
diff --git a/adversarial-robustness-toolbox/art/attacks/extraction/__init__.py b/adversarial-robustness-toolbox/art/attacks/extraction/__init__.py
new file mode 100644
index 0000000..e0fd6f8
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/attacks/extraction/__init__.py
@@ -0,0 +1,6 @@
+"""
+Module providing extraction attacks under a common interface.
+"""
+from art.attacks.extraction.functionally_equivalent_extraction import FunctionallyEquivalentExtraction
+from art.attacks.extraction.copycat_cnn import CopycatCNN
+from art.attacks.extraction.knockoff_nets import KnockoffNets
diff --git a/adversarial-robustness-toolbox/art/attacks/extraction/copycat_cnn.py b/adversarial-robustness-toolbox/art/attacks/extraction/copycat_cnn.py
new file mode 100644
index 0000000..5dee006
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/attacks/extraction/copycat_cnn.py
@@ -0,0 +1,165 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2019
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements the copycat cnn attack `CopycatCNN`.
+
+| Paper link: https://arxiv.org/abs/1806.05476
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+from typing import Optional, TYPE_CHECKING
+
+import numpy as np
+
+from art.config import ART_NUMPY_DTYPE
+from art.attacks.attack import ExtractionAttack
+from art.estimators.estimator import BaseEstimator
+from art.estimators.classification.classifier import ClassifierMixin
+from art.utils import to_categorical
+
+if TYPE_CHECKING:
+    from art.utils import CLASSIFIER_TYPE
+
+logger = logging.getLogger(__name__)
+
+
+class CopycatCNN(ExtractionAttack):
+    """
+    Implementation of the Copycat CNN attack from Rodrigues Correia-Silva et al. (2018).
+
+    | Paper link: https://arxiv.org/abs/1806.05476
+    """
+
+    attack_params = ExtractionAttack.attack_params + [
+        "batch_size_fit",
+        "batch_size_query",
+        "nb_epochs",
+        "nb_stolen",
+        "use_probability",
+    ]
+    _estimator_requirements = (BaseEstimator, ClassifierMixin)
+
+    def __init__(
+        self,
+        classifier: "CLASSIFIER_TYPE",
+        batch_size_fit: int = 1,
+        batch_size_query: int = 1,
+        nb_epochs: int = 10,
+        nb_stolen: int = 1,
+        use_probability: bool = False,
+    ) -> None:
+        """
+        Create a Copycat CNN attack instance.
+
+        :param classifier: A victim classifier.
+        :param batch_size_fit: Size of batches for fitting the thieved classifier.
+        :param batch_size_query: Size of batches for querying the victim classifier.
+        :param nb_epochs: Number of epochs to use for training.
+        :param nb_stolen: Number of queries submitted to the victim classifier to steal it.
+        """
+        super().__init__(estimator=classifier)
+
+        self.batch_size_fit = batch_size_fit
+        self.batch_size_query = batch_size_query
+        self.nb_epochs = nb_epochs
+        self.nb_stolen = nb_stolen
+        self.use_probability = use_probability
+        self._check_params()
+
+    def extract(self, x: np.ndarray, y: Optional[np.ndarray] = None, **kwargs) -> "CLASSIFIER_TYPE":
+        """
+        Extract a thieved classifier.
+
+        :param x: An array with the source input to the victim classifier.
+        :param y: Target values (class labels) one-hot-encoded of shape (nb_samples, nb_classes) or indices of shape
+                  (nb_samples,). Not used in this attack.
+        :param thieved_classifier: A classifier to be stolen, currently always trained on one-hot labels.
+        :type thieved_classifier: :class:`.Classifier`
+        :return: The stolen classifier.
+        """
+        # Warning to users if y is not None
+        if y is not None:
+            logger.warning("This attack does not use the provided label y.")
+
+        # Check the size of the source input vs nb_stolen
+        if x.shape[0] < self.nb_stolen:
+            logger.warning(
+                "The size of the source input is smaller than the expected number of queries submitted "
+                "to the victim classifier."
+            )
+
+        # Check if there is a thieved classifier provided for training
+        thieved_classifier = kwargs["thieved_classifier"]
+        if thieved_classifier is None or not isinstance(thieved_classifier, ClassifierMixin):
+            raise ValueError("A thieved classifier is needed.")
+
+        # Select data to attack
+        selected_x = self._select_data(x)
+
+        # Query the victim classifier
+        fake_labels = self._query_label(selected_x)
+
+        # Train the thieved classifier
+        thieved_classifier.fit(  # type: ignore
+            x=selected_x, y=fake_labels, batch_size=self.batch_size_fit, nb_epochs=self.nb_epochs,
+        )
+
+        return thieved_classifier  # type: ignore
+
+    def _select_data(self, x: np.ndarray) -> np.ndarray:
+        """
+        Select data to attack.
+
+        :param x: An array with the source input to the victim classifier.
+        :return: An array with the selected input to the victim classifier.
+        """
+        nb_stolen = np.minimum(self.nb_stolen, x.shape[0])
+        rnd_index = np.random.choice(x.shape[0], nb_stolen, replace=False)
+
+        return x[rnd_index].astype(ART_NUMPY_DTYPE)
+
+    def _query_label(self, x: np.ndarray) -> np.ndarray:
+        """
+        Query the victim classifier.
+
+        :param x: An array with the source input to the victim classifier.
+        :return: Target values (class labels) one-hot-encoded of shape (nb_samples, nb_classes).
+        """
+        labels = self.estimator.predict(x=x, batch_size=self.batch_size_query)
+        if not self.use_probability:
+            labels = np.argmax(labels, axis=1)
+            labels = to_categorical(labels=labels, nb_classes=self.estimator.nb_classes)
+
+        return labels
+
+    def _check_params(self) -> None:
+        if not isinstance(self.batch_size_fit, (int, np.int)) or self.batch_size_fit <= 0:
+            raise ValueError("The size of batches for fitting the thieved classifier must be a positive integer.")
+
+        if not isinstance(self.batch_size_query, (int, np.int)) or self.batch_size_query <= 0:
+            raise ValueError("The size of batches for querying the victim classifier must be a positive integer.")
+
+        if not isinstance(self.nb_epochs, (int, np.int)) or self.nb_epochs <= 0:
+            raise ValueError("The number of epochs must be a positive integer.")
+
+        if not isinstance(self.nb_stolen, (int, np.int)) or self.nb_stolen <= 0:
+            raise ValueError("The number of queries submitted to the victim classifier must be a positive integer.")
+
+        if not isinstance(self.use_probability, bool):
+            raise ValueError("The argument `use_probability` has to be of type bool.")
diff --git a/adversarial-robustness-toolbox/art/attacks/extraction/functionally_equivalent_extraction.py b/adversarial-robustness-toolbox/art/attacks/extraction/functionally_equivalent_extraction.py
new file mode 100644
index 0000000..16a69e1
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/attacks/extraction/functionally_equivalent_extraction.py
@@ -0,0 +1,488 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2019
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements the Functionally Equivalent Extraction attack mainly following Jagielski et al, 2019.
+
+This module contains en example application for MNIST which can be run as `python functionally_equivalent_extraction.py`
+producing output like:
+
+Target model - Test accuracy: 0.9259
+Extracted model - Test accuracy: 0.9259
+Extracted model - Test Fidelity: 0.9977
+
+| Paper link: https://arxiv.org/abs/1909.01838
+"""
+import logging
+import os
+from typing import List, Optional, TYPE_CHECKING
+
+import numpy as np
+from scipy.optimize import least_squares
+
+from art.attacks.attack import ExtractionAttack
+from art.estimators.estimator import BaseEstimator, NeuralNetworkMixin
+from art.estimators.classification.classifier import ClassifierMixin
+from art.estimators.classification.keras import KerasClassifier
+from art.estimators.classification.blackbox import BlackBoxClassifier
+
+if TYPE_CHECKING:
+    from art.utils import CLASSIFIER_TYPE
+
+NUMPY_DTYPE = np.float64
+
+logger = logging.getLogger(__name__)
+
+
+class FunctionallyEquivalentExtraction(ExtractionAttack):
+    """
+    This module implements the Functionally Equivalent Extraction attack for neural networks with two dense layers,
+    ReLU activation at the first layer and logits output after the second layer.
+
+    | Paper link: https://arxiv.org/abs/1909.01838
+    """
+
+    _estimator_requirements = (BaseEstimator, NeuralNetworkMixin, ClassifierMixin)
+
+    def __init__(self, classifier: "CLASSIFIER_TYPE", num_neurons: Optional[int] = None) -> None:
+        """
+        Create a `FunctionallyEquivalentExtraction` instance.
+
+        :param classifier: A trained ART classifier.
+        :param num_neurons: The number of neurons in the first dense layer.
+        """
+        super().__init__(estimator=classifier)
+        self.num_neurons = num_neurons
+        self.num_classes = classifier.nb_classes
+        self.num_features = int(np.prod(classifier.input_shape))
+
+        self.vector_u = np.random.normal(0, 1, (1, self.num_features)).astype(dtype=NUMPY_DTYPE)
+        self.vector_v = np.random.normal(0, 1, (1, self.num_features)).astype(dtype=NUMPY_DTYPE)
+
+        self.critical_points: List[np.ndarray] = list()
+
+        self.w_0: Optional[np.ndarray] = None  # Weight matrix of first dense layer
+        self.b_0: Optional[np.ndarray] = None  # Bias vector of first dense layer
+        self.w_1: Optional[np.ndarray] = None  # Weight matrix of second dense layer
+        self.b_1: Optional[np.ndarray] = None  # Bias vector of second dense layer
+
+    def extract(
+        self,
+        x: np.ndarray,
+        y: Optional[np.ndarray] = None,
+        delta_0: float = 0.05,
+        fraction_true: float = 0.3,
+        rel_diff_slope: float = 0.00001,
+        rel_diff_value: float = 0.000001,
+        delta_init_value: float = 0.1,
+        delta_value_max: int = 50,
+        d2_min: float = 0.0004,
+        d_step: float = 0.01,
+        delta_sign: float = 0.02,
+        unit_vector_scale: int = 10000,
+        **kwargs
+    ) -> BlackBoxClassifier:
+        """
+        Extract the targeted model.
+
+        :param x: Samples of input data of shape (num_samples, num_features).
+        :param y: Correct labels or target labels for `x`, depending if the attack is targeted
+               or not. This parameter is only used by some of the attacks.
+        :param delta_0: Initial step size of binary search.
+        :param fraction_true: Fraction of output predictions that have to fulfill criteria for critical point.
+        :param rel_diff_slope: Relative slope difference at critical points.
+        :param rel_diff_value: Relative value difference at critical points.
+        :param delta_init_value: Initial delta of weight value search.
+        :param delta_value_max: Maximum delta  of weight value search.
+        :param d2_min: Minimum acceptable value of sum of absolute second derivatives.
+        :param d_step:  Step size of delta increase.
+        :param delta_sign: Delta of weight sign search.
+        :param unit_vector_scale: Multiplicative scale of the unit vector `e_j`.
+        :return: ART :class:`.BlackBoxClassifier` of the extracted model.
+        """
+        self._critical_point_search(
+            delta_0=delta_0, fraction_true=fraction_true, rel_diff_slope=rel_diff_slope, rel_diff_value=rel_diff_value,
+        )
+        self._weight_recovery(
+            delta_init_value=delta_init_value,
+            delta_value_max=delta_value_max,
+            d2_min=d2_min,
+            d_step=d_step,
+            delta_sign=delta_sign,
+        )
+        self._sign_recovery(unit_vector_scale=unit_vector_scale)
+        self._last_layer_extraction(x)
+
+        def predict(x: np.ndarray) -> np.ndarray:
+            """
+            Predict extracted model.
+
+            :param x: Samples of input data of shape `(num_samples, num_features)`.
+            :return: Predictions with the extracted model of shape `(num_samples, num_classes)`.
+            """
+            layer_0 = np.maximum(np.matmul(self.w_0.T, x.T) + self.b_0, 0.0)  # type: ignore
+            layer_1 = np.matmul(self.w_1.T, layer_0) + self.b_1  # type: ignore
+            return layer_1.T
+
+        extracted_classifier = BlackBoxClassifier(
+            predict,
+            input_shape=self.estimator.input_shape,
+            nb_classes=self.estimator.nb_classes,
+            clip_values=self.estimator.clip_values,
+            preprocessing_defences=self.estimator.preprocessing_defences,
+            preprocessing=self.estimator.preprocessing,
+        )
+
+        return extracted_classifier
+
+    def _o_l(self, x: np.ndarray, e_j: Optional[np.ndarray] = None) -> np.ndarray:
+        """
+        Predict the target model.
+
+        :param x: Samples of input data of shape `(num_samples, num_features)`.
+        :param e_j: Additive delta vector of shape `(1, num_features)`.
+        :return: Prediction of the target model of shape `(num_samples, num_classes)`.
+        """
+        if e_j is not None:
+            x = x + e_j
+        return self.estimator.predict(x).astype(NUMPY_DTYPE)
+
+    def _get_x(self, var_t: float) -> np.ndarray:
+        """
+        Get input sample as function of multiplicative factor of random vector.
+
+        :param var_t: Multiplicative factor of second random vector for critical point search.
+        :return: Input sample of shape `(1, num_features)`.
+        """
+        return self.vector_u + var_t * self.vector_v
+
+    def _critical_point_search(
+        self, delta_0: float, fraction_true: float, rel_diff_slope: float, rel_diff_value: float,
+    ) -> None:
+        """
+        Search for critical points.
+
+        :param delta_0: Initial step size of binary search.
+        :param fraction_true: Fraction of output predictions that have to fulfill criteria for critical point.
+        :param rel_diff_slope: Relative slope difference at critical points.
+        :param rel_diff_value: Relative value difference at critical points.
+        """
+        logger.info("Searching for critical points.")
+
+        if self.num_neurons is None:
+            raise ValueError("The value of `num_neurons` is required for critical point search.")
+        h_square = self.num_neurons * self.num_neurons
+
+        t_current = float(-h_square)
+        while t_current < h_square:
+            delta = delta_0
+            found_critical_point = False
+
+            while not found_critical_point:
+                epsilon = delta / 10
+
+                t_1 = t_current
+                t_2 = t_current + delta
+
+                x_1 = self._get_x(t_1)
+                x_1_p = self._get_x(t_1 + epsilon)
+                x_2 = self._get_x(t_2)
+                x_2_m = self._get_x(t_2 - epsilon)
+
+                m_1 = (self._o_l(x_1_p) - self._o_l(x_1)) / epsilon
+                m_2 = (self._o_l(x_2) - self._o_l(x_2_m)) / epsilon
+
+                y_1 = self._o_l(x_1)
+                y_2 = self._o_l(x_2)
+
+                if np.sum(np.abs((m_1 - m_2) / m_1) < rel_diff_slope) > fraction_true * self.num_classes:
+                    t_current = t_2
+                    break
+
+                t_hat = t_1 + np.divide(y_2 - y_1 - (t_2 - t_1) * m_2, m_1 - m_2)
+                y_hat = y_1 + m_1 * np.divide(y_2 - y_1 - (t_2 - t_1) * m_2, m_1 - m_2)
+
+                t_mean = np.mean(t_hat[t_hat != -np.inf])
+
+                x_mean = self._get_x(t_mean)
+                x_mean_p = self._get_x(t_mean + epsilon)
+                x_mean_m = self._get_x(t_mean - epsilon)
+
+                y = self._o_l(x_mean)
+
+                m_x_1 = (self._o_l(x_mean_p) - self._o_l(x_mean)) / epsilon
+                m_x_2 = (self._o_l(x_mean) - self._o_l(x_mean_m)) / epsilon
+
+                if (
+                    np.sum(np.abs((y_hat - y) / y) < rel_diff_value) > fraction_true * self.num_classes
+                    and t_1 < t_mean < t_2
+                    and np.sum(np.abs((m_x_1 - m_x_2) / m_x_1) > rel_diff_slope) > fraction_true * self.num_classes
+                ):
+                    found_critical_point = True
+                    self.critical_points.append(x_mean)
+                    t_current = t_2
+                else:
+                    delta = delta / 2
+
+        if len(self.critical_points) != self.num_neurons:
+            raise AssertionError(
+                "The number of critical points found ({}) does not equal the number of expected"
+                "neurons in the first layer ({}).".format(len(self.critical_points), self.num_neurons)
+            )
+
+    def _weight_recovery(
+        self, delta_init_value: float, delta_value_max: float, d2_min: float, d_step: float, delta_sign: float,
+    ) -> None:
+        """
+        Recover the weights and biases of the first layer.
+
+        :param delta_init_value: Initial delta of weight value search.
+        :param delta_value_max: Maximum delta  of weight value search.
+        :param d2_min: Minimum acceptable value of sum of absolute second derivatives.
+        :param d_step:  Step size of delta increase.
+        :param delta_sign: Delta of weight sign search.
+        """
+        logger.info("Recovering weights of first layer.")
+
+        if self.num_neurons is None:
+            raise ValueError("The value of `num_neurons` is required for critical point search.")
+
+        # Absolute Value Recovery
+        d2_ol_d2ej_xi = np.zeros((self.num_features, self.num_neurons), dtype=NUMPY_DTYPE)
+
+        for i in range(self.num_neurons):
+            for j in range(self.num_features):
+
+                delta = delta_init_value
+                e_j = np.zeros((1, self.num_features))
+                d2_ol_d2ej_xi_ok = False
+
+                while not d2_ol_d2ej_xi_ok:
+
+                    e_j[0, j] = delta
+
+                    d_ol_dej_xi_p_cej = (
+                        self._o_l(self.critical_points[i], e_j=e_j) - self._o_l(self.critical_points[i])
+                    ) / delta
+                    d_ol_dej_xi_m_cej = (
+                        self._o_l(self.critical_points[i]) - self._o_l(self.critical_points[i], e_j=-e_j)
+                    ) / delta
+
+                    d2_ol_d2ej_xi[j, i] = np.sum(np.abs(d_ol_dej_xi_p_cej - d_ol_dej_xi_m_cej)) / delta
+
+                    if d2_ol_d2ej_xi[j, i] < d2_min and delta < delta_value_max:
+                        delta = delta + d_step
+                    else:
+                        d2_ol_d2ej_xi_ok = True
+
+        self.a0_pairwise_ratios = np.zeros((self.num_features, self.num_neurons), dtype=NUMPY_DTYPE)
+
+        for i in range(self.num_neurons):
+            for k in range(self.num_features):
+                self.a0_pairwise_ratios[k, i] = d2_ol_d2ej_xi[0, i] / d2_ol_d2ej_xi[k, i]
+
+        # Weight Sign Recovery
+
+        for i in range(self.num_neurons):
+            d2_ol_dejek_xi_0 = None
+            for j in range(self.num_features):
+
+                e_j = np.zeros((1, self.num_features), dtype=NUMPY_DTYPE)
+
+                e_j[0, 0] += delta_sign
+                e_j[0, j] += delta_sign
+
+                d_ol_dejek_xi_p_cejek = (
+                    self._o_l(self.critical_points[i], e_j=e_j) - self._o_l(self.critical_points[i])
+                ) / delta_sign
+                d_ol_dejek_xi_m_cejek = (
+                    self._o_l(self.critical_points[i]) - self._o_l(self.critical_points[i], e_j=-e_j)
+                ) / delta_sign
+
+                d2_ol_dejek_xi = d_ol_dejek_xi_p_cejek - d_ol_dejek_xi_m_cejek
+
+                if j == 0:
+                    d2_ol_dejek_xi_0 = d2_ol_dejek_xi / 2.0
+
+                co_p = np.sum(np.abs(d2_ol_dejek_xi_0 * (1 + 1 / self.a0_pairwise_ratios[j, i]) - d2_ol_dejek_xi))
+                co_m = np.sum(np.abs(d2_ol_dejek_xi_0 * (1 - 1 / self.a0_pairwise_ratios[j, i]) - d2_ol_dejek_xi))
+
+                if co_m < co_p * np.max(1 / self.a0_pairwise_ratios[:, i]):
+                    self.a0_pairwise_ratios[j, i] *= -1
+
+    def _sign_recovery(self, unit_vector_scale: int) -> None:
+        """
+        Recover the sign of weights in the first layer.
+
+        :param unit_vector_scale: Multiplicative scale of the unit vector e_j.
+        """
+        logger.info("Recover sign of the weights of the first layer.")
+
+        if self.num_neurons is None:
+            raise ValueError("The value of `num_neurons` is required for critical point search.")
+
+        a0_pairwise_ratios_inverse = 1.0 / self.a0_pairwise_ratios
+        self.b_0 = np.zeros((self.num_neurons, 1), dtype=NUMPY_DTYPE)
+
+        for i in range(self.num_neurons):
+            x_i = self.critical_points[i].flatten()
+            self.b_0[i] = -np.matmul(a0_pairwise_ratios_inverse[:, i], x_i)
+
+        z_0 = np.random.normal(0, 1, (self.num_features,)).astype(dtype=NUMPY_DTYPE)
+
+        def f_z(z_i):
+            return np.squeeze(np.matmul(a0_pairwise_ratios_inverse.T, np.expand_dims(z_i, axis=0).T) + self.b_0)
+
+        result_z = least_squares(f_z, z_0)
+
+        for i in range(self.num_neurons):
+            e_i = np.zeros((self.num_neurons, 1), dtype=NUMPY_DTYPE)
+            e_i[i, 0] = unit_vector_scale
+
+            def f_v(v_i):
+                # pylint: disable=W0640
+                return np.squeeze(np.matmul(-a0_pairwise_ratios_inverse.T, np.expand_dims(v_i, axis=0).T) - e_i)
+
+            v_0 = np.random.normal(0, 1, self.num_features)
+            result_v_i = least_squares(f_v, v_0)
+            value_p = np.sum(
+                np.abs(
+                    self._o_l(np.expand_dims(result_z.x, axis=0))
+                    - (self._o_l(np.expand_dims(result_z.x + result_v_i.x, axis=0)))
+                )
+            )
+            value_m = np.sum(
+                np.abs(
+                    self._o_l(np.expand_dims(result_z.x, axis=0))
+                    - (self._o_l(np.expand_dims(result_z.x - result_v_i.x, axis=0)))
+                )
+            )
+
+            if value_m < value_p:
+                a0_pairwise_ratios_inverse[:, i] *= -1
+                self.b_0[i, 0] *= -1
+
+        self.w_0 = a0_pairwise_ratios_inverse
+
+    def _last_layer_extraction(self, x: np.ndarray) -> None:
+        """
+        Extract weights and biases of the second layer.
+
+        :param x: Samples of input data of shape `(num_samples, num_features)`.
+        """
+        logger.info("Extract second layer.")
+
+        if self.num_neurons is None:
+            raise ValueError("The value of `num_neurons` is required for critical point search.")
+
+        predictions = self._o_l(x)
+        w_1_b_1_0 = np.random.normal(0, 1, ((self.num_neurons + 1) * self.num_classes)).astype(dtype=NUMPY_DTYPE)
+
+        def f_w_1_b_1(w_1_b_1_i):
+            layer_0 = np.maximum(np.matmul(self.w_0.T, x.T) + self.b_0, 0.0)
+
+            w_1 = w_1_b_1_i[0 : self.num_neurons * self.num_classes].reshape(self.num_neurons, self.num_classes)
+            b_1 = w_1_b_1_i[self.num_neurons * self.num_classes :].reshape(self.num_classes, 1)
+
+            layer_1 = np.matmul(w_1.T, layer_0) + b_1
+
+            return np.squeeze((layer_1.T - predictions).flatten())
+
+        result_a1_b1 = least_squares(f_w_1_b_1, w_1_b_1_0)
+
+        self.w_1 = result_a1_b1.x[0 : self.num_neurons * self.num_classes].reshape(self.num_neurons, self.num_classes)
+        self.b_1 = result_a1_b1.x[self.num_neurons * self.num_classes :].reshape(self.num_classes, 1)
+
+
+# pylint: disable=C0103, E0401
+if __name__ == "__main__":
+    import tensorflow as tf
+
+    tf.compat.v1.disable_eager_execution()
+    tf.keras.backend.set_floatx("float64")
+
+    from tensorflow.keras.datasets import mnist
+    from tensorflow.keras.models import Sequential
+    from tensorflow.keras.layers import Dense
+
+    np.random.seed(1)
+    number_neurons = 16
+    batch_size = 128
+    number_classes = 10
+    epochs = 10
+    img_rows = 28
+    img_cols = 28
+    number_channels = 1
+
+    (x_train, y_train), (x_test, y_test) = mnist.load_data()
+    x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, number_channels)
+    x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, number_channels)
+    input_shape = (number_channels * img_rows * img_cols,)
+
+    x_train = x_train.reshape((x_train.shape[0], number_channels * img_rows * img_cols)).astype("float64")
+    x_test = x_test.reshape((x_test.shape[0], number_channels * img_rows * img_cols)).astype("float64")
+
+    mean = np.mean(x_train)
+    std = np.std(x_train)
+
+    x_train = (x_train - mean) / std
+    x_test = (x_test - mean) / std
+
+    y_train = tf.keras.utils.to_categorical(y_train, number_classes)
+    y_test = tf.keras.utils.to_categorical(y_test, number_classes)
+
+    if os.path.isfile("./model.h5"):
+        model = tf.keras.models.load_model("./model.h5")
+    else:
+        model = Sequential()
+        model.add(Dense(number_neurons, activation="relu", input_shape=input_shape))
+        model.add(Dense(number_classes, activation="linear"))
+
+        model.compile(
+            loss=tf.keras.losses.CategoricalCrossentropy(from_logits=True),
+            optimizer=tf.keras.optimizers.Adam(learning_rate=0.0001,),
+            metrics=["accuracy"],
+        )
+
+        model.fit(
+            x_train, y_train, batch_size=batch_size, epochs=epochs, verbose=1, validation_data=(x_test, y_test),
+        )
+
+        model.save("./model.h5")
+
+    score_target = model.evaluate(x_test, y_test, verbose=0)
+
+    target_classifier = KerasClassifier(model=model, use_logits=True, clip_values=(0, 1))
+
+    fee = FunctionallyEquivalentExtraction(
+        classifier=target_classifier, num_neurons=number_neurons  # type: ignore
+    )
+    bbc = fee.extract(x_test[0:100])
+
+    y_test_predicted_extracted = bbc.predict(x_test)
+    y_test_predicted_target = target_classifier.predict(x_test)
+
+    print("Target model - Test accuracy:", score_target[1])
+    print(
+        "Extracted model - Test accuracy:",
+        np.sum(np.argmax(y_test_predicted_extracted, axis=1) == np.argmax(y_test, axis=1)) / y_test.shape[0],
+    )
+    print(
+        "Extracted model - Test Fidelity:",
+        np.sum(np.argmax(y_test_predicted_extracted, axis=1) == np.argmax(y_test_predicted_target, axis=1))
+        / y_test_predicted_target.shape[0],
+    )
diff --git a/adversarial-robustness-toolbox/art/attacks/extraction/knockoff_nets.py b/adversarial-robustness-toolbox/art/attacks/extraction/knockoff_nets.py
new file mode 100644
index 0000000..7c5258d
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/attacks/extraction/knockoff_nets.py
@@ -0,0 +1,410 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements the Knockoff Nets attack `KnockoffNets`.
+
+| Paper link: https://arxiv.org/abs/1812.02766
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+from typing import Optional, TYPE_CHECKING
+
+import numpy as np
+from tqdm.auto import trange
+
+from art.config import ART_NUMPY_DTYPE
+from art.attacks.attack import ExtractionAttack
+from art.estimators.estimator import BaseEstimator
+from art.estimators.classification.classifier import ClassifierMixin
+from art.utils import to_categorical
+
+if TYPE_CHECKING:
+    from art.utils import CLASSIFIER_TYPE
+
+logger = logging.getLogger(__name__)
+
+
+class KnockoffNets(ExtractionAttack):
+    """
+    Implementation of the Knockoff Nets attack from Orekondy et al. (2018).
+
+    | Paper link: https://arxiv.org/abs/1812.02766
+    """
+
+    attack_params = ExtractionAttack.attack_params + [
+        "batch_size_fit",
+        "batch_size_query",
+        "nb_epochs",
+        "nb_stolen",
+        "sampling_strategy",
+        "reward",
+        "verbose",
+        "use_probability",
+    ]
+
+    _estimator_requirements = (BaseEstimator, ClassifierMixin)
+
+    def __init__(
+        self,
+        classifier: "CLASSIFIER_TYPE",
+        batch_size_fit: int = 1,
+        batch_size_query: int = 1,
+        nb_epochs: int = 10,
+        nb_stolen: int = 1,
+        sampling_strategy: str = "random",
+        reward: str = "all",
+        verbose: bool = True,
+        use_probability: bool = False,
+    ) -> None:
+        """
+        Create a KnockoffNets attack instance. Note, it is assumed that both the victim classifier and the thieved
+        classifier produce logit outputs.
+
+        :param classifier: A victim classifier.
+        :param batch_size_fit: Size of batches for fitting the thieved classifier.
+        :param batch_size_query: Size of batches for querying the victim classifier.
+        :param nb_epochs: Number of epochs to use for training.
+        :param nb_stolen: Number of queries submitted to the victim classifier to steal it.
+        :param sampling_strategy: Sampling strategy, either `random` or `adaptive`.
+        :param reward: Reward type, in ['cert', 'div', 'loss', 'all'].
+        :param verbose: Show progress bars.
+        """
+        super().__init__(estimator=classifier)
+
+        self.batch_size_fit = batch_size_fit
+        self.batch_size_query = batch_size_query
+        self.nb_epochs = nb_epochs
+        self.nb_stolen = nb_stolen
+        self.sampling_strategy = sampling_strategy
+        self.reward = reward
+        self.verbose = verbose
+        self.use_probability = use_probability
+        self._check_params()
+
+    def extract(self, x: np.ndarray, y: Optional[np.ndarray] = None, **kwargs) -> "CLASSIFIER_TYPE":
+        """
+        Extract a thieved classifier.
+
+        :param x: An array with the source input to the victim classifier.
+        :param y: Target values (class labels) one-hot-encoded of shape (nb_samples, nb_classes) or indices of shape
+                  `(nb_samples,)`.
+        :param thieved_classifier: A thieved classifier to be stolen.
+        :return: The stolen classifier.
+        """
+        # Check prerequisite for random strategy
+        if self.sampling_strategy == "random" and y is not None:
+            logger.warning("This attack with random sampling strategy does not use the provided label y.")
+
+        # Check prerequisite for adaptive strategy
+        if self.sampling_strategy == "adaptive" and y is None:
+            raise ValueError("This attack with adaptive sampling strategy needs label y.")
+
+        # Check the size of the source input vs nb_stolen
+        if x.shape[0] < self.nb_stolen:
+            logger.warning(
+                "The size of the source input is smaller than the expected number of queries submitted "
+                "to the victim classifier."
+            )
+
+        # Check if there is a thieved classifier provided for training
+        thieved_classifier = kwargs.get("thieved_classifier")
+        if thieved_classifier is None or not isinstance(thieved_classifier, ClassifierMixin):
+            raise ValueError("A thieved classifier is needed.")
+
+        # Implement model extractions
+        if self.sampling_strategy == "random":
+            thieved_classifier = self._random_extraction(x, thieved_classifier)  # type: ignore
+        else:
+            thieved_classifier = self._adaptive_extraction(x, y, thieved_classifier)  # type: ignore
+
+        return thieved_classifier
+
+    def _random_extraction(self, x: np.ndarray, thieved_classifier: "CLASSIFIER_TYPE") -> "CLASSIFIER_TYPE":
+        """
+        Extract with the random sampling strategy.
+
+        :param x: An array with the source input to the victim classifier.
+        :param thieved_classifier: A thieved classifier to be stolen.
+        :return: The stolen classifier.
+        """
+        # Select data to attack
+        selected_x = self._select_data(x)
+
+        # Query the victim classifier
+        fake_labels = self._query_label(selected_x)
+
+        # Train the thieved classifier
+        thieved_classifier.fit(
+            x=selected_x, y=fake_labels, batch_size=self.batch_size_fit, nb_epochs=self.nb_epochs, verbose=0,
+        )
+
+        return thieved_classifier
+
+    def _select_data(self, x: np.ndarray) -> np.ndarray:
+        """
+        Select data to attack.
+
+        :param x: An array with the source input to the victim classifier.
+        :return: An array with the selected input to the victim classifier.
+        """
+        nb_stolen = np.minimum(self.nb_stolen, x.shape[0])
+        rnd_index = np.random.choice(x.shape[0], nb_stolen, replace=False)
+
+        return x[rnd_index].astype(ART_NUMPY_DTYPE)
+
+    def _query_label(self, x: np.ndarray) -> np.ndarray:
+        """
+        Query the victim classifier.
+
+        :param x: An array with the source input to the victim classifier.
+        :return: Target values (class labels) one-hot-encoded of shape `(nb_samples, nb_classes)`.
+        """
+        labels = self.estimator.predict(x=x, batch_size=self.batch_size_query)
+        if not self.use_probability:
+            labels = np.argmax(labels, axis=1)
+            labels = to_categorical(labels=labels, nb_classes=self.estimator.nb_classes)
+
+        return labels
+
+    def _adaptive_extraction(
+        self, x: np.ndarray, y: np.ndarray, thieved_classifier: "CLASSIFIER_TYPE"
+    ) -> "CLASSIFIER_TYPE":
+        """
+        Extract with the adaptive sampling strategy.
+
+        :param x: An array with the source input to the victim classifier.
+        :param y: Target values (class labels) one-hot-encoded of shape (nb_samples, nb_classes) or indices of shape
+                  (nb_samples,).
+        :param thieved_classifier: A thieved classifier to be stolen.
+        :return: The stolen classifier.
+        """
+        # Compute number of actions
+        if len(y.shape) == 2:
+            nb_actions = len(np.unique(np.argmax(y, axis=1)))
+        elif len(y.shape) == 1:
+            nb_actions = len(np.unique(y))
+        else:
+            raise ValueError("Target values `y` has a wrong shape.")
+
+        # We need to keep an average version of the victim output
+        if self.reward == "div" or self.reward == "all":
+            self.y_avg = np.zeros(self.estimator.nb_classes)
+
+        # We need to keep an average and variance version of rewards
+        if self.reward == "all":
+            self.reward_avg = np.zeros(3)
+            self.reward_var = np.zeros(3)
+
+        # Implement the bandit gradients algorithm
+        h_func = np.zeros(nb_actions)
+        learning_rate = np.zeros(nb_actions)
+        probs = np.ones(nb_actions) / nb_actions
+        selected_x = []
+        queried_labels = []
+
+        avg_reward = 0.0
+        for it in trange(1, self.nb_stolen + 1, desc="Knock-off nets", disable=not self.verbose):
+            # Sample an action
+            action = np.random.choice(np.arange(0, nb_actions), p=probs)
+
+            # Sample data to attack
+            sampled_x = self._sample_data(x, y, action)
+            selected_x.append(sampled_x)
+
+            # Query the victim classifier
+            y_output = self.estimator.predict(x=np.array([sampled_x]), batch_size=self.batch_size_query)
+            fake_label = np.argmax(y_output, axis=1)
+            fake_label = to_categorical(labels=fake_label, nb_classes=self.estimator.nb_classes)
+            queried_labels.append(fake_label[0])
+
+            # Train the thieved classifier
+            thieved_classifier.fit(
+                x=np.array([sampled_x]), y=fake_label, batch_size=self.batch_size_fit, nb_epochs=1, verbose=0,
+            )
+
+            # Test new labels
+            y_hat = thieved_classifier.predict(x=np.array([sampled_x]), batch_size=self.batch_size_query)
+
+            # Compute rewards
+            reward = self._reward(y_output, y_hat, it)
+            avg_reward = avg_reward + (1.0 / it) * (reward - avg_reward)
+
+            # Update learning rate
+            learning_rate[action] += 1
+
+            # Update H function
+            for a in range(nb_actions):
+                if a != action:
+                    h_func[a] = h_func[a] - 1.0 / learning_rate[action] * (reward - avg_reward) * probs[a]
+                else:
+                    h_func[a] = h_func[a] + 1.0 / learning_rate[action] * (reward - avg_reward) * (1 - probs[a])
+
+            # Update probs
+            aux_exp = np.exp(h_func)
+            probs = aux_exp / np.sum(aux_exp)
+
+        # Train the thieved classifier the final time
+        thieved_classifier.fit(
+            x=np.array(selected_x),
+            y=np.array(queried_labels),
+            batch_size=self.batch_size_fit,
+            nb_epochs=self.nb_epochs,
+        )
+
+        return thieved_classifier
+
+    @staticmethod
+    def _sample_data(x: np.ndarray, y: np.ndarray, action: int) -> np.ndarray:
+        """
+        Sample data with a specific action.
+
+        :param x: An array with the source input to the victim classifier.
+        :param y: Target values (class labels) one-hot-encoded of shape (nb_samples, nb_classes) or indices of shape
+                  (nb_samples,).
+        :param action: The action index returned from the action sampling.
+        :return: An array with one input to the victim classifier.
+        """
+        if len(y.shape) == 2:
+            y_ = np.argmax(y, axis=1)
+        else:
+            y_ = y
+
+        x_ = x[y_ == action]
+        rnd_idx = np.random.choice(len(x_))
+
+        return x_[rnd_idx]
+
+    def _reward(self, y_output: np.ndarray, y_hat: np.ndarray, n: int) -> float:
+        """
+        Compute reward value.
+
+        :param y_output: Output of the victim classifier.
+        :param y_hat: Output of the thieved classifier.
+        :param n: Current iteration.
+        :return: Reward value.
+        """
+        if self.reward == "cert":
+            return self._reward_cert(y_output)
+        if self.reward == "div":
+            return self._reward_div(y_output, n)
+        if self.reward == "loss":
+            return self._reward_loss(y_output, y_hat)
+        return self._reward_all(y_output, y_hat, n)
+
+    @staticmethod
+    def _reward_cert(y_output: np.ndarray) -> float:
+        """
+        Compute `cert` reward value.
+
+        :param y_output: Output of the victim classifier.
+        :return: Reward value.
+        """
+        largests = np.partition(y_output.flatten(), -2)[-2:]
+        reward = largests[1] - largests[0]
+
+        return reward
+
+    def _reward_div(self, y_output: np.ndarray, n: int) -> float:
+        """
+        Compute `div` reward value.
+
+        :param y_output: Output of the victim classifier.
+        :param n: Current iteration.
+        :return: Reward value.
+        """
+        # First update y_avg
+        self.y_avg = self.y_avg + (1.0 / n) * (y_output[0] - self.y_avg)
+
+        # Then compute reward
+        reward = 0
+        for k in range(self.estimator.nb_classes):
+            reward += np.maximum(0, y_output[0][k] - self.y_avg[k])
+
+        return reward
+
+    def _reward_loss(self, y_output: np.ndarray, y_hat: np.ndarray) -> float:
+        """
+        Compute `loss` reward value.
+
+        :param y_output: Output of the victim classifier.
+        :param y_hat: Output of the thieved classifier.
+        :return: Reward value.
+        """
+        # Compute victim probs
+        aux_exp = np.exp(y_output[0])
+        probs_output = aux_exp / np.sum(aux_exp)
+
+        # Compute thieved probs
+        aux_exp = np.exp(y_hat[0])
+        probs_hat = aux_exp / np.sum(aux_exp)
+
+        # Compute reward
+        reward = 0
+        for k in range(self.estimator.nb_classes):
+            reward += -probs_output[k] * np.log(probs_hat[k])
+
+        return reward
+
+    def _reward_all(self, y_output: np.ndarray, y_hat: np.ndarray, n: int) -> np.ndarray:
+        """
+        Compute `all` reward value.
+
+        :param y_output: Output of the victim classifier.
+        :param y_hat: Output of the thieved classifier.
+        :param n: Current iteration.
+        :return: Reward value.
+        """
+        reward_cert = self._reward_cert(y_output)
+        reward_div = self._reward_div(y_output, n)
+        reward_loss = self._reward_loss(y_output, y_hat)
+        reward = [reward_cert, reward_div, reward_loss]
+        self.reward_avg = self.reward_avg + (1.0 / n) * (reward - self.reward_avg)
+        self.reward_var = self.reward_var + (1.0 / n) * ((reward - self.reward_avg) ** 2 - self.reward_var)
+
+        # Normalize rewards
+        if n > 1:
+            reward = (reward - self.reward_avg) / np.sqrt(self.reward_var)
+        else:
+            reward = [max(min(r, 1), 0) for r in reward]
+
+        return np.mean(reward)
+
+    def _check_params(self) -> None:
+        if not isinstance(self.batch_size_fit, (int, np.int)) or self.batch_size_fit <= 0:
+            raise ValueError("The size of batches for fitting the thieved classifier must be a positive integer.")
+
+        if not isinstance(self.batch_size_query, (int, np.int)) or self.batch_size_query <= 0:
+            raise ValueError("The size of batches for querying the victim classifier must be a positive integer.")
+
+        if not isinstance(self.nb_epochs, (int, np.int)) or self.nb_epochs <= 0:
+            raise ValueError("The number of epochs must be a positive integer.")
+
+        if not isinstance(self.nb_stolen, (int, np.int)) or self.nb_stolen <= 0:
+            raise ValueError("The number of queries submitted to the victim classifier must be a positive integer.")
+
+        if self.sampling_strategy not in ["random", "adaptive"]:
+            raise ValueError("Sampling strategy must be either `random` or `adaptive`.")
+
+        if self.reward not in ["cert", "div", "loss", "all"]:
+            raise ValueError("Reward type must be in ['cert', 'div', 'loss', 'all'].")
+
+        if not isinstance(self.verbose, bool):
+            raise ValueError("The argument `verbose` has to be of type bool.")
+        if not isinstance(self.use_probability, bool):
+            raise ValueError("The argument `use_probability` has to be of type bool.")
diff --git a/adversarial-robustness-toolbox/art/attacks/inference/__init__.py b/adversarial-robustness-toolbox/art/attacks/inference/__init__.py
new file mode 100644
index 0000000..b0ea556
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/attacks/inference/__init__.py
@@ -0,0 +1,7 @@
+"""
+Module providing inference attacks.
+"""
+from art.attacks.inference import attribute_inference
+from art.attacks.inference import membership_inference
+from art.attacks.inference import model_inversion
+from art.attacks.inference import reconstruction
diff --git a/adversarial-robustness-toolbox/art/attacks/inference/attribute_inference/__init__.py b/adversarial-robustness-toolbox/art/attacks/inference/attribute_inference/__init__.py
new file mode 100644
index 0000000..01557dc
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/attacks/inference/attribute_inference/__init__.py
@@ -0,0 +1,9 @@
+"""
+Module providing attribute inference attacks.
+"""
+from art.attacks.inference.attribute_inference.black_box import AttributeInferenceBlackBox
+from art.attacks.inference.attribute_inference.baseline import AttributeInferenceBaseline
+from art.attacks.inference.attribute_inference.white_box_decision_tree import AttributeInferenceWhiteBoxDecisionTree
+from art.attacks.inference.attribute_inference.white_box_lifestyle_decision_tree import (
+    AttributeInferenceWhiteBoxLifestyleDecisionTree,
+)
diff --git a/adversarial-robustness-toolbox/art/attacks/inference/attribute_inference/baseline.py b/adversarial-robustness-toolbox/art/attacks/inference/attribute_inference/baseline.py
new file mode 100644
index 0000000..9d28061
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/attacks/inference/attribute_inference/baseline.py
@@ -0,0 +1,157 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2021
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements attribute inference attacks.
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+from typing import Optional, Union, TYPE_CHECKING
+
+import numpy as np
+from sklearn.neural_network import MLPClassifier
+
+from art.estimators.classification.classifier import ClassifierMixin
+from art.attacks.attack import AttributeInferenceAttack
+from art.utils import check_and_transform_label_format, float_to_categorical, floats_to_one_hot
+
+if TYPE_CHECKING:
+    from art.utils import CLASSIFIER_TYPE
+
+logger = logging.getLogger(__name__)
+
+
+class AttributeInferenceBaseline(AttributeInferenceAttack):
+    """
+    Implementation of a baseline attribute inference, not using a model.
+
+    The idea is to train a simple neural network to learn the attacked feature from the rest of the features. Should
+    be used to compare with other attribute inference results.
+    """
+
+    _estimator_requirements = ()
+
+    def __init__(
+        self, attack_model: Optional["CLASSIFIER_TYPE"] = None, attack_feature: Union[int, slice] = 0,
+    ):
+        """
+        Create an AttributeInferenceBaseline attack instance.
+
+        :param attack_model: The attack model to train, optional. If none is provided, a default model will be created.
+        :param attack_feature: The index of the feature to be attacked or a slice representing multiple indexes in
+                               case of a one-hot encoded feature.
+        """
+        super().__init__(estimator=None, attack_feature=attack_feature)
+
+        if isinstance(self.attack_feature, int):
+            self.single_index_feature = True
+        else:
+            self.single_index_feature = False
+
+        if attack_model:
+            if ClassifierMixin not in type(attack_model).__mro__:
+                raise ValueError("Attack model must be of type Classifier.")
+            self.attack_model = attack_model
+        else:
+            self.attack_model = MLPClassifier(
+                hidden_layer_sizes=(100,),
+                activation="relu",
+                solver="adam",
+                alpha=0.0001,
+                batch_size="auto",
+                learning_rate="constant",
+                learning_rate_init=0.001,
+                power_t=0.5,
+                max_iter=2000,
+                shuffle=True,
+                random_state=None,
+                tol=0.0001,
+                verbose=False,
+                warm_start=False,
+                momentum=0.9,
+                nesterovs_momentum=True,
+                early_stopping=False,
+                validation_fraction=0.1,
+                beta_1=0.9,
+                beta_2=0.999,
+                epsilon=1e-08,
+                n_iter_no_change=10,
+                max_fun=15000,
+            )
+        self._check_params()
+
+    def fit(self, x: np.ndarray) -> None:
+        """
+        Train the attack model.
+
+        :param x: Input to training process. Includes all features used to train the original model.
+        """
+
+        # Checks:
+        if self.single_index_feature and self.attack_feature >= x.shape[1]:
+            raise ValueError("attack_feature must be a valid index to a feature in x")
+
+        # get vector of attacked feature
+        y = x[:, self.attack_feature]
+        if self.single_index_feature:
+            y_one_hot = float_to_categorical(y)
+        else:
+            y_one_hot = floats_to_one_hot(y)
+        y_ready = check_and_transform_label_format(y_one_hot, len(np.unique(y)), return_one_hot=True)
+
+        # create training set for attack model
+        x_train = np.delete(x, self.attack_feature, 1).astype(np.float32)
+
+        # train attack model
+        self.attack_model.fit(x_train, y_ready)
+
+    def infer(self, x: np.ndarray, y: Optional[np.ndarray] = None, **kwargs) -> np.ndarray:
+        """
+        Infer the attacked feature.
+
+        :param x: Input to attack. Includes all features except the attacked feature.
+        :param y: Not used in this attack.
+        :param values: Possible values for attacked feature. Only needed in case of categorical feature (not one-hot).
+        :type values: `np.ndarray`
+        :return: The inferred feature values.
+        """
+        x_test = x.astype(np.float32)
+
+        if self.single_index_feature:
+            if "values" not in kwargs.keys():
+                raise ValueError("Missing parameter `values`.")
+            values: np.ndarray = kwargs.get("values")
+            return np.array([values[np.argmax(arr)] for arr in self.attack_model.predict(x_test)])
+
+        if "values" in kwargs.keys():
+            values = kwargs.get("values")
+            predictions = self.attack_model.predict(x_test).astype(np.float32)
+            i = 0
+            for column in predictions.T:
+                for index in range(len(values[i])):
+                    np.place(column, [column == index], values[i][index])
+                i += 1
+            return np.array(predictions)
+
+        return np.array(self.attack_model.predict(x_test))
+
+    def _check_params(self) -> None:
+        if not isinstance(self.attack_feature, int) and not isinstance(self.attack_feature, slice):
+            raise ValueError("Attack feature must be either an integer or a slice object.")
+        if isinstance(self.attack_feature, int) and self.attack_feature < 0:
+            raise ValueError("Attack feature index must be positive.")
diff --git a/adversarial-robustness-toolbox/art/attacks/inference/attribute_inference/black_box.py b/adversarial-robustness-toolbox/art/attacks/inference/attribute_inference/black_box.py
new file mode 100644
index 0000000..47cf74e
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/attacks/inference/attribute_inference/black_box.py
@@ -0,0 +1,175 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements attribute inference attacks.
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+from typing import Optional, Union, TYPE_CHECKING
+
+import numpy as np
+from sklearn.neural_network import MLPClassifier
+
+from art.estimators.estimator import BaseEstimator
+from art.estimators.classification.classifier import ClassifierMixin
+from art.attacks.attack import AttributeInferenceAttack
+from art.utils import check_and_transform_label_format, float_to_categorical, floats_to_one_hot
+
+if TYPE_CHECKING:
+    from art.utils import CLASSIFIER_TYPE
+
+logger = logging.getLogger(__name__)
+
+
+class AttributeInferenceBlackBox(AttributeInferenceAttack):
+    """
+    Implementation of a simple black-box attribute inference attack.
+
+    The idea is to train a simple neural network to learn the attacked feature from the rest of the features and the
+    model's predictions. Assumes the availability of the attacked model's predictions for the samples under attack,
+    in addition to the rest of the feature values. If this is not available, the true class label of the samples may be
+    used as a proxy.
+    """
+
+    _estimator_requirements = (BaseEstimator, ClassifierMixin)
+
+    def __init__(
+        self,
+        classifier: "CLASSIFIER_TYPE",
+        attack_model: Optional["CLASSIFIER_TYPE"] = None,
+        attack_feature: Union[int, slice] = 0,
+    ):
+        """
+        Create an AttributeInferenceBlackBox attack instance.
+
+        :param classifier: Target classifier.
+        :param attack_model: The attack model to train, optional. If none is provided, a default model will be created.
+        :param attack_feature: The index of the feature to be attacked or a slice representing multiple indexes in
+                               case of a one-hot encoded feature.
+        """
+        super().__init__(estimator=classifier, attack_feature=attack_feature)
+        if isinstance(self.attack_feature, int):
+            self.single_index_feature = True
+        else:
+            self.single_index_feature = False
+
+        if attack_model:
+            if ClassifierMixin not in type(attack_model).__mro__:
+                raise ValueError("Attack model must be of type Classifier.")
+            self.attack_model = attack_model
+        else:
+            self.attack_model = MLPClassifier(
+                hidden_layer_sizes=(100,),
+                activation="relu",
+                solver="adam",
+                alpha=0.0001,
+                batch_size="auto",
+                learning_rate="constant",
+                learning_rate_init=0.001,
+                power_t=0.5,
+                max_iter=2000,
+                shuffle=True,
+                random_state=None,
+                tol=0.0001,
+                verbose=False,
+                warm_start=False,
+                momentum=0.9,
+                nesterovs_momentum=True,
+                early_stopping=False,
+                validation_fraction=0.1,
+                beta_1=0.9,
+                beta_2=0.999,
+                epsilon=1e-08,
+                n_iter_no_change=10,
+                max_fun=15000,
+            )
+        self._check_params()
+
+    def fit(self, x: np.ndarray) -> None:
+        """
+        Train the attack model.
+
+        :param x: Input to training process. Includes all features used to train the original model.
+        """
+
+        # Checks:
+        if self.estimator.input_shape is not None:
+            if self.estimator.input_shape[0] != x.shape[1]:
+                raise ValueError("Shape of x does not match input_shape of classifier")
+        if self.single_index_feature and self.attack_feature >= x.shape[1]:
+            raise ValueError("attack_feature must be a valid index to a feature in x")
+
+        # get model's predictions for x
+        predictions = np.array([np.argmax(arr) for arr in self.estimator.predict(x)]).reshape(-1, 1)
+
+        # get vector of attacked feature
+        y = x[:, self.attack_feature]
+        if self.single_index_feature:
+            y_one_hot = float_to_categorical(y)
+        else:
+            y_one_hot = floats_to_one_hot(y)
+        y_ready = check_and_transform_label_format(y_one_hot, len(np.unique(y)), return_one_hot=True)
+
+        # create training set for attack model
+        x_train = np.concatenate((np.delete(x, self.attack_feature, 1), predictions), axis=1).astype(np.float32)
+
+        # train attack model
+        self.attack_model.fit(x_train, y_ready)
+
+    def infer(self, x: np.ndarray, y: np.ndarray, **kwargs) -> np.ndarray:
+        """
+        Infer the attacked feature.
+
+        :param x: Input to attack. Includes all features except the attacked feature.
+        :param y: Original model's predictions for x.
+        :param values: Possible values for attacked feature. Only needed in case of categorical feature (not one-hot).
+        :type values: `np.ndarray`
+        :return: The inferred feature values.
+        """
+        if y.shape[0] != x.shape[0]:
+            raise ValueError("Number of rows in x and y do not match")
+        if self.estimator.input_shape is not None:
+            if self.single_index_feature and self.estimator.input_shape[0] != x.shape[1] + 1:
+                raise ValueError("Number of features in x + 1 does not match input_shape of classifier")
+
+        x_test = np.concatenate((x, y), axis=1).astype(np.float32)
+
+        if self.single_index_feature:
+            if "values" not in kwargs.keys():
+                raise ValueError("Missing parameter `values`.")
+            values = kwargs.get("values")
+            return np.array([values[np.argmax(arr)] for arr in self.attack_model.predict(x_test)])
+
+        if "values" in kwargs.keys():
+            values = kwargs.get("values")
+            predictions = self.attack_model.predict(x_test).astype(np.float32)
+            i = 0
+            for column in predictions.T:
+                for index in range(len(values[i])):
+                    np.place(column, [column == index], values[i][index])
+                i += 1
+            return np.array(predictions)
+
+        return np.array(self.attack_model.predict(x_test))
+
+    def _check_params(self) -> None:
+        if not isinstance(self.attack_feature, int) and not isinstance(self.attack_feature, slice):
+            raise ValueError("Attack feature must be either an integer or a slice object.")
+        if isinstance(self.attack_feature, int) and self.attack_feature < 0:
+            raise ValueError("Attack feature index must be positive.")
diff --git a/adversarial-robustness-toolbox/art/attacks/inference/attribute_inference/white_box_decision_tree.py b/adversarial-robustness-toolbox/art/attacks/inference/attribute_inference/white_box_decision_tree.py
new file mode 100644
index 0000000..986a826
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/attacks/inference/attribute_inference/white_box_decision_tree.py
@@ -0,0 +1,144 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements attribute inference attacks.
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+from typing import Optional
+
+import numpy as np
+
+from art.estimators.classification.scikitlearn import ScikitlearnDecisionTreeClassifier
+from art.attacks.attack import AttributeInferenceAttack
+
+logger = logging.getLogger(__name__)
+
+
+class AttributeInferenceWhiteBoxDecisionTree(AttributeInferenceAttack):
+    """
+    A variation of the method proposed by of Fredrikson et al. in:
+    https://dl.acm.org/doi/10.1145/2810103.2813677
+
+    Assumes the availability of the attacked model's predictions for the samples under attack, in addition to access to
+    the model itself and the rest of the feature values. If this is not available, the true class label of the samples
+    may be used as a proxy. Also assumes that the attacked feature is discrete or categorical, with limited number of
+    possible values. For example: a boolean feature.
+
+    | Paper link: https://dl.acm.org/doi/10.1145/2810103.2813677
+    """
+
+    _estimator_requirements = (ScikitlearnDecisionTreeClassifier,)
+
+    def __init__(self, classifier: ScikitlearnDecisionTreeClassifier, attack_feature: int = 0):
+        """
+        Create an AttributeInferenceWhiteBox attack instance.
+
+        :param classifier: Target classifier.
+        :param attack_feature: The index of the feature to be attacked.
+        """
+        super().__init__(estimator=classifier, attack_feature=attack_feature)
+        self._check_params()
+
+    def infer(self, x: np.ndarray, y: Optional[np.ndarray] = None, **kwargs) -> np.ndarray:
+        """
+        Infer the attacked feature.
+
+        If the model's prediction coincides with the real prediction for the sample for a single value, choose it as the
+        predicted value. If not, fall back to the Fredrikson method (without phi)
+
+        :param x: Input to attack. Includes all features except the attacked feature.
+        :param y: Original model's predictions for x.
+        :param values: Possible values for attacked feature.
+        :type values: `np.ndarray`
+        :param priors: Prior distributions of attacked feature values. Same size array as `values`.
+        :type priors: `np.ndarray`
+        :return: The inferred feature values.
+        """
+        if "priors" not in kwargs.keys():
+            raise ValueError("Missing parameter `priors`.")
+        if "values" not in kwargs.keys():
+            raise ValueError("Missing parameter `values`.")
+        priors: np.ndarray = kwargs.get("priors")
+        values: np.ndarray = kwargs.get("values")
+
+        if self.estimator.input_shape[0] != x.shape[1] + 1:
+            raise ValueError("Number of features in x + 1 does not match input_shape of classifier")
+        if len(priors) != len(values):
+            raise ValueError("Number of priors does not match number of values")
+        if y is not None and y.shape[0] != x.shape[0]:
+            raise ValueError("Number of rows in x and y do not match")
+        if self.attack_feature >= x.shape[1]:
+            raise ValueError("attack_feature must be a valid index to a feature in x")
+
+        n_values = len(values)
+        n_samples = x.shape[0]
+
+        # Will contain the model's predictions for each value
+        pred_values = []
+        # Will contain the probability of each value
+        prob_values = []
+
+        for i, value in enumerate(values):
+            # prepare data with the given value in the attacked feature
+            v_full = np.full((n_samples, 1), value)
+            x_value = np.concatenate((x[:, : self.attack_feature], v_full), axis=1)
+            x_value = np.concatenate((x_value, x[:, self.attack_feature :]), axis=1)
+
+            # Obtain the model's prediction for each possible value of the attacked feature
+            pred_value = [np.argmax(arr) for arr in self.estimator.predict(x_value)]
+            pred_values.append(pred_value)
+
+            # find the relative probability of this value for all samples being attacked
+            prob_value = [
+                (
+                    (self.estimator.get_samples_at_node(self.estimator.get_decision_path([row])[-1]) / n_samples)
+                    * priors[i]
+                )
+                for row in x_value
+            ]
+            prob_values.append(prob_value)
+
+        # Find the single value that coincides with the real prediction for the sample (if it exists)
+        pred_rows = zip(*pred_values)
+        predicted_pred = []
+        for row_index, row in enumerate(pred_rows):
+            if y is not None:
+                matches = [1 if row[value_index] == y[row_index] else 0 for value_index in range(n_values)]
+                match_values = [
+                    values[value_index] if row[value_index] == y[row_index] else 0 for value_index in range(n_values)
+                ]
+            else:
+                matches = [0 for _ in range(n_values)]
+                match_values = [0 for _ in range(n_values)]
+            predicted_pred.append(sum(match_values) if sum(matches) == 1 else None)
+
+        # Choose the value with highest probability for each sample
+        predicted_prob = [np.argmax(list(prob)) for prob in zip(*prob_values)]
+
+        return np.array(
+            [
+                value if value is not None else values[predicted_prob[index]]
+                for index, value in enumerate(predicted_pred)
+            ]
+        )
+
+    def _check_params(self) -> None:
+        if self.attack_feature < 0:
+            raise ValueError("Attack feature must be positive.")
diff --git a/adversarial-robustness-toolbox/art/attacks/inference/attribute_inference/white_box_lifestyle_decision_tree.py b/adversarial-robustness-toolbox/art/attacks/inference/attribute_inference/white_box_lifestyle_decision_tree.py
new file mode 100644
index 0000000..f0d2a1b
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/attacks/inference/attribute_inference/white_box_lifestyle_decision_tree.py
@@ -0,0 +1,136 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements attribute inference attacks.
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+from typing import Optional, TYPE_CHECKING
+
+import numpy as np
+
+from art.estimators.classification.scikitlearn import ScikitlearnDecisionTreeClassifier
+from art.attacks.attack import AttributeInferenceAttack
+
+if TYPE_CHECKING:
+    from art.utils import CLASSIFIER_TYPE
+
+logger = logging.getLogger(__name__)
+
+
+class AttributeInferenceWhiteBoxLifestyleDecisionTree(AttributeInferenceAttack):
+    """
+    Implementation of Fredrikson et al. white box inference attack for decision trees.
+
+    Assumes that the attacked feature is discrete or categorical, with limited number of possible values. For example:
+    a boolean feature.
+
+    | Paper link: https://dl.acm.org/doi/10.1145/2810103.2813677
+    """
+
+    _estimator_requirements = (ScikitlearnDecisionTreeClassifier,)
+
+    def __init__(self, classifier: "CLASSIFIER_TYPE", attack_feature: int = 0):
+        """
+        Create an AttributeInferenceWhiteBoxLifestyle attack instance.
+
+        :param classifier: Target classifier.
+        :param attack_feature: The index of the feature to be attacked.
+        """
+        super().__init__(estimator=classifier, attack_feature=attack_feature)
+        self._check_params()
+
+    def infer(self, x: np.ndarray, y: Optional[np.ndarray] = None, **kwargs) -> np.ndarray:
+        """
+        Infer the attacked feature.
+
+        :param x: Input to attack. Includes all features except the attacked feature.
+        :param y: Not used.
+        :param values: Possible values for attacked feature.
+        :type values: `np.ndarray`
+        :param priors: Prior distributions of attacked feature values. Same size array as `values`.
+        :type priors: `np.ndarray`
+        :return: The inferred feature values.
+        :rtype: `np.ndarray`
+        """
+        if "priors" not in kwargs.keys():
+            raise ValueError("Missing parameter `priors`.")
+        if "values" not in kwargs.keys():
+            raise ValueError("Missing parameter `values`.")
+        priors: np.ndarray = kwargs.get("priors")
+        values: np.ndarray = kwargs.get("values")
+
+        # Checks:
+        if self.estimator.input_shape[0] != x.shape[1] + 1:
+            raise ValueError("Number of features in x + 1 does not match input_shape of classifier")
+        if len(priors) != len(values):
+            raise ValueError("Number of priors does not match number of values")
+        if self.attack_feature >= x.shape[1]:
+            raise ValueError("attack_feature must be a valid index to a feature in x")
+
+        n_samples = x.shape[0]
+
+        # Calculate phi for each possible value of the attacked feature
+        # phi is the total number of samples in all tree leaves corresponding to this value
+        phi = self._calculate_phi(x, values, n_samples)
+
+        # Will contain the probability of each value
+        prob_values = []
+
+        for i, value in enumerate(values):
+            # prepare data with the given value in the attacked feature
+            v_full = np.full((n_samples, 1), value)
+            x_value = np.concatenate((x[:, : self.attack_feature], v_full), axis=1)
+            x_value = np.concatenate((x_value, x[:, self.attack_feature :]), axis=1)
+
+            # find the relative probability of this value for all samples being attacked
+            prob_value = [
+                (
+                    (self.estimator.get_samples_at_node(self.estimator.get_decision_path([row])[-1]) / n_samples)
+                    * priors[i]
+                    / phi[i]
+                )
+                for row in x_value
+            ]
+            prob_values.append(prob_value)
+
+        # Choose the value with highest probability for each sample
+        return np.array([values[np.argmax(list(prob))] for prob in zip(*prob_values)])
+
+    def _calculate_phi(self, x, values, n_samples):
+        phi = []
+        for value in values:
+            v_full = np.full((n_samples, 1), value)
+            x_value = np.concatenate((x[:, : self.attack_feature], v_full), axis=1)
+            x_value = np.concatenate((x_value, x[:, self.attack_feature :]), axis=1)
+            nodes_value = {}
+
+            for row in x_value:
+                # get leaf ids (no duplicates)
+                node_id = self.estimator.get_decision_path([row])[0]
+                nodes_value[node_id] = self.estimator.get_samples_at_node(node_id)
+            # sum sample numbers
+            num_value = sum(nodes_value.values()) / n_samples
+            phi.append(num_value)
+
+        return phi
+
+    def _check_params(self) -> None:
+        if self.attack_feature < 0:
+            raise ValueError("Attack feature must be positive.")
diff --git a/adversarial-robustness-toolbox/art/attacks/inference/membership_inference/__init__.py b/adversarial-robustness-toolbox/art/attacks/inference/membership_inference/__init__.py
new file mode 100644
index 0000000..0cf74f7
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/attacks/inference/membership_inference/__init__.py
@@ -0,0 +1,7 @@
+"""
+Module providing membership inference attacks.
+"""
+from art.attacks.inference.membership_inference.black_box import MembershipInferenceBlackBox
+from art.attacks.inference.membership_inference.black_box_rule_based import MembershipInferenceBlackBoxRuleBased
+from art.attacks.inference.membership_inference.label_only_gap_attack import LabelOnlyGapAttack
+from art.attacks.inference.membership_inference.label_only_boundary_distance import LabelOnlyDecisionBoundary
diff --git a/adversarial-robustness-toolbox/art/attacks/inference/membership_inference/black_box.py b/adversarial-robustness-toolbox/art/attacks/inference/membership_inference/black_box.py
new file mode 100644
index 0000000..0a1ed72
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/attacks/inference/membership_inference/black_box.py
@@ -0,0 +1,324 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+
+"""
+This module implements membership inference attacks.
+"""
+
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+from typing import Any, Optional, Union, TYPE_CHECKING
+
+import numpy as np
+from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
+
+from art.attacks.attack import InferenceAttack
+from art.estimators.estimator import BaseEstimator, NeuralNetworkMixin
+from art.estimators.classification.classifier import ClassifierMixin
+from art.utils import check_and_transform_label_format
+
+if TYPE_CHECKING:
+    from art.utils import CLASSIFIER_TYPE
+
+logger = logging.getLogger(__name__)
+
+
+class MembershipInferenceBlackBox(InferenceAttack):
+    """
+        Implementation of a learned black-box membership inference attack.
+
+        This implementation can use as input to the learning process probabilities/logits or losses,
+        depending on the type of model and provided configuration.
+    """
+
+    attack_params = InferenceAttack.attack_params + [
+        "input_type",
+        "attack_model_type",
+        "attack_model",
+    ]
+    _estimator_requirements = (BaseEstimator, ClassifierMixin)
+
+    def __init__(
+        self,
+        classifier: Union["CLASSIFIER_TYPE"],
+        input_type: str = "prediction",
+        attack_model_type: str = "nn",
+        attack_model: Optional[Any] = None,
+    ):
+        """
+        Create a MembershipInferenceBlackBox attack instance.
+
+        :param classifier: Target classifier.
+        :param attack_model_type: the type of default attack model to train, optional. Should be one of `nn` (for neural
+                                  network, default), `rf` (for random forest) or `gb` (gradient boosting). If
+                                  `attack_model` is supplied, this option will be ignored.
+        :param input_type: the type of input to train the attack on. Can be one of: 'prediction' or 'loss'. Default is
+                           `prediction`. Predictions can be either probabilities or logits, depending on the return type
+                           of the model.
+        :param attack_model: The attack model to train, optional. If none is provided, a default model will be created.
+        """
+
+        super().__init__(estimator=classifier)
+        self.input_type = input_type
+        self.attack_model_type = attack_model_type
+        self.attack_model = attack_model
+
+        self._check_params()
+
+        if self.attack_model:
+            self.default_model = False
+            self.attack_model_type = "None"
+        else:
+            self.default_model = True
+            if self.attack_model_type == "nn":
+                import torch  # lgtm [py/repeated-import]
+                import torch.nn as nn  # lgtm [py/repeated-import]
+
+                class MembershipInferenceAttackModel(nn.Module):
+                    """
+                        Implementation of a pytorch model for learning a membership inference attack.
+
+                        The features used are probabilities/logits or losses for the attack training data along with
+                        its true labels.
+                    """
+
+                    def __init__(self, num_classes, num_features=None):
+
+                        self.num_classes = num_classes
+                        if num_features:
+                            self.num_features = num_features
+                        else:
+                            self.num_features = num_classes
+
+                        super().__init__()
+
+                        self.features = nn.Sequential(
+                            nn.Linear(self.num_features, 512),
+                            nn.ReLU(),
+                            nn.Linear(512, 100),
+                            nn.ReLU(),
+                            nn.Linear(100, 64),
+                            nn.ReLU(),
+                        )
+
+                        self.labels = nn.Sequential(
+                            nn.Linear(self.num_classes, 256), nn.ReLU(), nn.Linear(256, 64), nn.ReLU(),
+                        )
+
+                        self.combine = nn.Sequential(nn.Linear(64 * 2, 1),)
+
+                        self.output = nn.Sigmoid()
+
+                    def forward(self, x_1, label):
+                        """Forward the model."""
+                        out_x1 = self.features(x_1)
+                        out_l = self.labels(label)
+                        is_member = self.combine(torch.cat((out_x1, out_l), 1))
+                        return self.output(is_member)
+
+                if self.input_type == "prediction":
+                    self.attack_model = MembershipInferenceAttackModel(classifier.nb_classes)
+                else:
+                    self.attack_model = MembershipInferenceAttackModel(classifier.nb_classes, num_features=1)
+                self.epochs = 100
+                self.batch_size = 100
+                self.learning_rate = 0.0001
+            elif self.attack_model_type == "rf":
+                self.attack_model = RandomForestClassifier()
+            elif self.attack_model_type == "gb":
+                self.attack_model = GradientBoostingClassifier()
+
+    def fit(self, x: np.ndarray, y: np.ndarray, test_x: np.ndarray, test_y: np.ndarray, **kwargs):
+        """
+        Infer membership in the training set of the target estimator.
+
+        :param x: Records that were used in training the target model.
+        :param y: True labels for `x`.
+        :param test_x: Records that were not used in training the target model.
+        :param test_y: True labels for `test_x`.
+        :return: An array holding the inferred membership status, 1 indicates a member and 0 indicates non-member.
+        """
+        if self.estimator.input_shape is not None:
+            if self.estimator.input_shape[0] != x.shape[1]:
+                raise ValueError("Shape of x does not match input_shape of classifier")
+            if self.estimator.input_shape[0] != test_x.shape[1]:
+                raise ValueError("Shape of test_x does not match input_shape of classifier")
+
+        y = check_and_transform_label_format(y, len(np.unique(y)), return_one_hot=True)
+        test_y = check_and_transform_label_format(test_y, len(np.unique(test_y)), return_one_hot=True)
+
+        if y.shape[0] != x.shape[0]:
+            raise ValueError("Number of rows in x and y do not match")
+        if test_y.shape[0] != test_x.shape[0]:
+            raise ValueError("Number of rows in test_x and test_y do not match")
+
+        # Create attack dataset
+        # uses final probabilities/logits
+        if self.input_type == "prediction":
+            # members
+            features = self.estimator.predict(x).astype(np.float32)
+            # non-members
+            test_features = self.estimator.predict(test_x).astype(np.float32)
+        # only for models with loss
+        elif self.input_type == "loss":
+            if NeuralNetworkMixin not in type(self.estimator).__mro__:
+                raise TypeError("loss input_type can only be used with neural networks")
+            # members
+            features = self.estimator.compute_loss(x, y).astype(np.float32).reshape(-1, 1)
+            # non-members
+            test_features = self.estimator.compute_loss(test_x, test_y).astype(np.float32).reshape(-1, 1)
+        else:
+            raise ValueError("Illegal value for parameter `input_type`.")
+
+        # members
+        labels = np.ones(x.shape[0])
+        # non-members
+        test_labels = np.zeros(test_x.shape[0])
+
+        x_1 = np.concatenate((features, test_features))
+        x_2 = np.concatenate((y, test_y))
+        y_new = np.concatenate((labels, test_labels))
+
+        if self.default_model and self.attack_model_type == "nn":
+            import torch  # lgtm [py/repeated-import]
+            import torch.nn as nn  # lgtm [py/repeated-import]
+            import torch.optim as optim  # lgtm [py/repeated-import]
+            from torch.utils.data import DataLoader  # lgtm [py/repeated-import]
+            from art.utils import to_cuda
+
+            loss_fn = nn.BCELoss()
+            optimizer = optim.Adam(self.attack_model.parameters(), lr=self.learning_rate)
+
+            attack_train_set = self._get_attack_dataset(f_1=x_1, f_2=x_2, label=y_new)
+            train_loader = DataLoader(attack_train_set, batch_size=self.batch_size, shuffle=True, num_workers=0)
+
+            self.attack_model = to_cuda(self.attack_model)
+            self.attack_model.train()
+
+            for _ in range(self.epochs):
+                for (input1, input2, targets) in train_loader:
+                    input1, input2, targets = to_cuda(input1), to_cuda(input2), to_cuda(targets)
+                    _, input2 = torch.autograd.Variable(input1), torch.autograd.Variable(input2)
+                    targets = torch.autograd.Variable(targets)
+
+                    optimizer.zero_grad()
+                    outputs = self.attack_model(input1, input2)
+                    loss = loss_fn(outputs, targets.unsqueeze(1))  # lgtm [py/call-to-non-callable]
+
+                    loss.backward()
+                    optimizer.step()
+        else:
+            if self.attack_model_type == "gb":
+                y_ready = check_and_transform_label_format(y_new, len(np.unique(y_new)), return_one_hot=False)
+            else:
+                y_ready = check_and_transform_label_format(y_new, len(np.unique(y_new)), return_one_hot=True)
+            self.attack_model.fit(np.c_[x_1, x_2], y_ready)
+
+    def infer(self, x: np.ndarray, y: Optional[np.ndarray] = None, **kwargs) -> np.ndarray:
+        """
+        Infer membership in the training set of the target estimator.
+
+        :param x: Input records to attack.
+        :param y: True labels for `x`.
+        :return: An array holding the inferred membership status, 1 indicates a member and 0 indicates non-member.
+        """
+        if y is None:
+            raise ValueError("MembershipInferenceBlackBox requires true labels `y`.")
+
+        if self.estimator.input_shape is not None:
+            if self.estimator.input_shape[0] != x.shape[1]:
+                raise ValueError("Shape of x does not match input_shape of classifier")
+
+        y = check_and_transform_label_format(y, len(np.unique(y)), return_one_hot=True)
+
+        if y.shape[0] != x.shape[0]:
+            raise ValueError("Number of rows in x and y do not match")
+
+        if self.input_type == "prediction":
+            features = self.estimator.predict(x).astype(np.float32)
+        elif self.input_type == "loss":
+            features = self.estimator.compute_loss(x, y).astype(np.float32).reshape(-1, 1)
+
+        if self.default_model and self.attack_model_type == "nn":
+            import torch  # lgtm [py/repeated-import]
+            from torch.utils.data import DataLoader  # lgtm [py/repeated-import]
+            from art.utils import to_cuda, from_cuda
+
+            self.attack_model.eval()
+            inferred = None
+            test_set = self._get_attack_dataset(f_1=features, f_2=y)
+            test_loader = DataLoader(test_set, batch_size=self.batch_size, shuffle=True, num_workers=0)
+            for input1, input2, _ in test_loader:
+                input1, input2 = to_cuda(input1), to_cuda(input2)
+                outputs = self.attack_model(input1, input2)
+                predicted = torch.round(outputs)
+                predicted = from_cuda(predicted)
+
+                if inferred is None:
+                    inferred = predicted.detach().numpy()
+                else:
+                    inferred = np.vstack((inferred, predicted.detach().numpy()))
+            inferred = inferred.reshape(-1).astype(np.int)
+        else:
+            inferred = np.array([np.argmax(arr) for arr in self.attack_model.predict(np.c_[features, y])])
+        return inferred
+
+    def _get_attack_dataset(self, f_1, f_2, label=None):
+        from torch.utils.data.dataset import Dataset
+
+        class AttackDataset(Dataset):
+            """
+                Implementation of a pytorch dataset for membership inference attack.
+
+                The features are probabilities/logits or losses for the attack training data (`x_1`) along with
+                its true labels (`x_2`). The labels (`y`) are a boolean representing whether this is a member.
+            """
+
+            def __init__(self, x_1, x_2, y=None):
+                import torch  # lgtm [py/repeated-import]
+
+                self.x_1 = torch.from_numpy(x_1.astype(np.float64)).type(torch.FloatTensor)
+                self.x_2 = torch.from_numpy(x_2.astype(np.int32)).type(torch.FloatTensor)
+
+                if y is not None:
+                    self.y = torch.from_numpy(y.astype(np.int8)).type(torch.FloatTensor)
+                else:
+                    self.y = torch.zeros(x_1.shape[0])
+
+            def __len__(self):
+                return len(self.x_1)
+
+            def __getitem__(self, idx):
+                if idx >= len(self.x_1):
+                    raise IndexError("Invalid Index")
+
+                return self.x_1[idx], self.x_2[idx], self.y[idx]
+
+        return AttackDataset(x_1=f_1, x_2=f_2, y=label)
+
+    def _check_params(self) -> None:
+        if self.input_type not in ["prediction", "loss"]:
+            raise ValueError("Illegal value for parameter `input_type`.")
+
+        if self.attack_model_type not in ["nn", "rf", "gb"]:
+            raise ValueError("Illegal value for parameter `attack_model_type`.")
+
+        if self.attack_model:
+            if ClassifierMixin not in type(self.attack_model).__mro__:
+                raise TypeError("Attack model must be of type Classifier.")
diff --git a/adversarial-robustness-toolbox/art/attacks/inference/membership_inference/black_box_rule_based.py b/adversarial-robustness-toolbox/art/attacks/inference/membership_inference/black_box_rule_based.py
new file mode 100644
index 0000000..7b31f5a
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/attacks/inference/membership_inference/black_box_rule_based.py
@@ -0,0 +1,80 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements membership inference attacks.
+"""
+
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+from typing import Optional, TYPE_CHECKING
+
+import numpy as np
+
+from art.attacks.attack import InferenceAttack
+from art.estimators.estimator import BaseEstimator
+from art.estimators.classification.classifier import ClassifierMixin
+from art.utils import check_and_transform_label_format
+
+if TYPE_CHECKING:
+    from art.utils import CLASSIFIER_TYPE
+
+logger = logging.getLogger(__name__)
+
+
+class MembershipInferenceBlackBoxRuleBased(InferenceAttack):
+    """
+        Implementation of a simple, rule-based black-box membership inference attack.
+
+        This implementation uses the simple rule: if the model's prediction for a sample is correct, then it is a
+        member. Otherwise, it is not a member.
+    """
+
+    attack_params = InferenceAttack.attack_params
+    _estimator_requirements = (BaseEstimator, ClassifierMixin)
+
+    def __init__(self, classifier: "CLASSIFIER_TYPE"):
+        """
+        Create a MembershipInferenceBlackBoxRuleBased attack instance.
+
+        :param classifier: Target classifier.
+        """
+        super().__init__(estimator=classifier)
+
+    def infer(self, x: np.ndarray, y: Optional[np.ndarray] = None, **kwargs) -> np.ndarray:
+        """
+        Infer membership in the training set of the target estimator.
+
+        :param x: Input records to attack.
+        :param y: True labels for `x`.
+        :return: An array holding the inferred membership status, 1 indicates a member and 0 indicates non-member.
+        """
+        if y is None:
+            raise ValueError("MembershipInferenceBlackBoxRuleBased requires true labels `y`.")
+
+        if self.estimator.input_shape is not None:
+            if self.estimator.input_shape[0] != x.shape[1]:
+                raise ValueError("Shape of x does not match input_shape of classifier")
+
+        y = check_and_transform_label_format(y, len(np.unique(y)), return_one_hot=True)
+        if y.shape[0] != x.shape[0]:
+            raise ValueError("Number of rows in x and y do not match")
+
+        # get model's predictions for x
+        y_pred = self.estimator.predict(x=x)
+        return (np.argmax(y, axis=1) == np.argmax(y_pred, axis=1)).astype(np.int)
diff --git a/adversarial-robustness-toolbox/art/attacks/inference/membership_inference/label_only_boundary_distance.py b/adversarial-robustness-toolbox/art/attacks/inference/membership_inference/label_only_boundary_distance.py
new file mode 100644
index 0000000..45101cb
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/attacks/inference/membership_inference/label_only_boundary_distance.py
@@ -0,0 +1,182 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements the Label-Only Inference Attack based on Decision Boundary.
+
+| Paper link: https://arxiv.org/abs/2007.14321
+"""
+import logging
+from typing import Optional, NoReturn, TYPE_CHECKING
+
+import numpy as np
+
+from art.attacks.attack import InferenceAttack
+from art.estimators.estimator import BaseEstimator
+from art.estimators.classification.classifier import ClassifierMixin
+from art.utils import check_and_transform_label_format
+
+if TYPE_CHECKING:
+    from art.utils import CLASSIFIER_TYPE
+
+logger = logging.getLogger(__name__)
+
+
+class LabelOnlyDecisionBoundary(InferenceAttack):
+    """
+    Implementation of Label-Only Inference Attack based on Decision Boundary.
+
+    | Paper link: https://arxiv.org/abs/2007.14321
+    """
+
+    attack_params = InferenceAttack.attack_params + [
+        "distance_threshold_tau",
+    ]
+    _estimator_requirements = (BaseEstimator, ClassifierMixin)
+
+    def __init__(self, estimator: "CLASSIFIER_TYPE", distance_threshold_tau: Optional[float] = None):
+        """
+        Create a `LabelOnlyDecisionBoundary` instance for Label-Only Inference Attack based on Decision Boundary.
+
+        :param estimator: A trained classification estimator.
+        :param distance_threshold_tau: Threshold distance for decision boundary. Samples with boundary distances larger
+                                       than threshold are considered members of the training dataset.
+        """
+        super().__init__(estimator=estimator)
+        self.distance_threshold_tau = distance_threshold_tau
+        self._check_params()
+
+    def infer(self, x: np.ndarray, y: Optional[np.ndarray] = None, **kwargs) -> np.ndarray:
+        """
+        Infer membership of input `x` in estimator's training data.
+
+        :param x: Input data.
+        :param y: True labels for `x`.
+
+        :Keyword Arguments for HopSkipJump:
+            * *norm*: Order of the norm. Possible values: "inf", np.inf or 2.
+            * *max_iter*: Maximum number of iterations.
+            * *max_eval*: Maximum number of evaluations for estimating gradient.
+            * *init_eval*: Initial number of evaluations for estimating gradient.
+            * *init_size*: Maximum number of trials for initial generation of adversarial examples.
+            * *verbose*: Show progress bars.
+
+        :return: An array holding the inferred membership status, 1 indicates a member and 0 indicates non-member.
+        """
+        from art.attacks.evasion.hop_skip_jump import HopSkipJump
+
+        if y is None:
+            raise ValueError("Argument `y` is None, but this attack requires true labels `y` to be provided.")
+
+        if self.distance_threshold_tau is None:
+            raise ValueError(
+                "No value for distance threshold `distance_threshold_tau` provided. Please set"
+                "`distance_threshold_tau` or run method `calibrate_distance_threshold` on known training and test"
+                "dataset."
+            )
+
+        if "classifier" in kwargs:
+            raise ValueError("Keyword `classifier` in kwargs is not supported.")
+
+        if "targeted" in kwargs:
+            raise ValueError("Keyword `targeted` in kwargs is not supported.")
+
+        y = check_and_transform_label_format(y, self.estimator.nb_classes)
+
+        hsj = HopSkipJump(classifier=self.estimator, targeted=False, **kwargs)
+        x_adv = hsj.generate(x=x, y=y)
+
+        distance = np.linalg.norm((x_adv - x).reshape((x.shape[0], -1)), ord=2, axis=1)
+
+        y_pred = self.estimator.predict(x=x)
+
+        distance[np.argmax(y_pred, axis=1) != np.argmax(y, axis=1)] = 0
+
+        is_member = np.where(distance > self.distance_threshold_tau, 1, 0)
+
+        return is_member
+
+    def calibrate_distance_threshold(
+        self, x_train: np.ndarray, y_train: np.ndarray, x_test: np.ndarray, y_test: np.ndarray, **kwargs
+    ) -> NoReturn:
+        """
+        Calibrate distance threshold maximising the membership inference accuracy on `x_train` and `x_test`.
+
+        :param x_train: Training data.
+        :param y_train: Labels of training data `x_train`.
+        :param x_test: Test data.
+        :param y_test: Labels of test data `x_test`.
+
+        :Keyword Arguments for HopSkipJump:
+            * *norm*: Order of the norm. Possible values: "inf", np.inf or 2.
+            * *max_iter*: Maximum number of iterations.
+            * *max_eval*: Maximum number of evaluations for estimating gradient.
+            * *init_eval*: Initial number of evaluations for estimating gradient.
+            * *init_size*: Maximum number of trials for initial generation of adversarial examples.
+            * *verbose*: Show progress bars.
+        """
+        from art.attacks.evasion.hop_skip_jump import HopSkipJump
+
+        if "classifier" in kwargs:
+            raise ValueError("Keyword `classifier` in kwargs is not supported.")
+
+        if "targeted" in kwargs:
+            raise ValueError("Keyword `targeted` in kwargs is not supported.")
+
+        y_train = check_and_transform_label_format(y_train, self.estimator.nb_classes)
+        y_test = check_and_transform_label_format(y_test, self.estimator.nb_classes)
+
+        hsj = HopSkipJump(classifier=self.estimator, targeted=False, **kwargs)
+
+        x_train_adv = hsj.generate(x=x_train, y=y_train)
+        x_test_adv = hsj.generate(x=x_test, y=y_test)
+
+        distance_train = np.linalg.norm((x_train_adv - x_train).reshape((x_train.shape[0], -1)), ord=2, axis=1)
+        distance_test = np.linalg.norm((x_test_adv - x_test).reshape((x_test.shape[0], -1)), ord=2, axis=1)
+
+        y_train_pred = self.estimator.predict(x=x_train)
+        y_test_pred = self.estimator.predict(x=x_test)
+
+        distance_train[np.argmax(y_train_pred, axis=1) != np.argmax(y_train, axis=1)] = 0
+        distance_test[np.argmax(y_test_pred, axis=1) != np.argmax(y_test, axis=1)] = 0
+
+        num_increments = 100
+        tau_increment = np.amax([np.amax(distance_train), np.amax(distance_test)]) / num_increments
+
+        acc_max = 0.0
+        distance_threshold_tau = 0.0
+
+        for i_tau in range(1, num_increments):
+
+            is_member_train = np.where(distance_train > i_tau * tau_increment, 1, 0)
+            is_member_test = np.where(distance_test > i_tau * tau_increment, 1, 0)
+
+            acc = (np.sum(is_member_train) + (is_member_test.shape[0] - np.sum(is_member_test))) / (
+                is_member_train.shape[0] + is_member_test.shape[0]
+            )
+
+            if acc > acc_max:
+                distance_threshold_tau = i_tau * tau_increment
+                acc_max = acc
+
+        self.distance_threshold_tau = distance_threshold_tau
+
+    def _check_params(self) -> None:
+        if self.distance_threshold_tau is not None and (
+            not isinstance(self.distance_threshold_tau, (int, float)) or self.distance_threshold_tau <= 0.0
+        ):
+            raise ValueError("The distance threshold `distance_threshold_tau` needs to be a positive float.")
diff --git a/adversarial-robustness-toolbox/art/attacks/inference/membership_inference/label_only_gap_attack.py b/adversarial-robustness-toolbox/art/attacks/inference/membership_inference/label_only_gap_attack.py
new file mode 100644
index 0000000..72dbd21
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/attacks/inference/membership_inference/label_only_gap_attack.py
@@ -0,0 +1,31 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements the Label Only Gap Attack `.
+
+| Paper link: https://arxiv.org/abs/2007.14321
+"""
+import logging
+
+from art.attacks.inference.membership_inference.black_box_rule_based import MembershipInferenceBlackBoxRuleBased
+
+
+logger = logging.getLogger(__name__)
+
+
+LabelOnlyGapAttack = MembershipInferenceBlackBoxRuleBased
diff --git a/adversarial-robustness-toolbox/art/attacks/inference/model_inversion/__init__.py b/adversarial-robustness-toolbox/art/attacks/inference/model_inversion/__init__.py
new file mode 100644
index 0000000..73fc935
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/attacks/inference/model_inversion/__init__.py
@@ -0,0 +1,4 @@
+"""
+Module providing model inversion attacks.
+"""
+from art.attacks.inference.model_inversion.mi_face import MIFace
diff --git a/adversarial-robustness-toolbox/art/attacks/inference/model_inversion/mi_face.py b/adversarial-robustness-toolbox/art/attacks/inference/model_inversion/mi_face.py
new file mode 100644
index 0000000..ddf48d0
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/attacks/inference/model_inversion/mi_face.py
@@ -0,0 +1,166 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements model inversion attacks.
+
+| Paper link: https://dl.acm.org/doi/10.1145/2810103.2813677
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+from typing import Optional, TYPE_CHECKING
+
+import numpy as np
+from tqdm.auto import trange
+
+from art.config import ART_NUMPY_DTYPE
+from art.estimators.classification.classifier import ClassifierMixin, ClassGradientsMixin
+from art.estimators.estimator import BaseEstimator
+from art.attacks.attack import InferenceAttack
+from art.utils import get_labels_np_array, check_and_transform_label_format
+
+if TYPE_CHECKING:
+    from art.utils import CLASSIFIER_CLASS_LOSS_GRADIENTS_TYPE
+
+logger = logging.getLogger(__name__)
+
+
+class MIFace(InferenceAttack):
+    """
+    Implementation of the MIFace algorithm from Fredrikson et al. (2015). While in that paper the attack is demonstrated
+    specifically against face recognition models, it is applicable more broadly to classifiers with continuous features
+    which expose class gradients.
+
+    | Paper link: https://dl.acm.org/doi/10.1145/2810103.2813677
+    """
+
+    attack_params = InferenceAttack.attack_params + [
+        "max_iter",
+        "window_length",
+        "threshold",
+        "learning_rate",
+        "batch_size",
+        "verbose",
+    ]
+
+    _estimator_requirements = (BaseEstimator, ClassifierMixin, ClassGradientsMixin)
+
+    def __init__(
+        self,
+        classifier: "CLASSIFIER_CLASS_LOSS_GRADIENTS_TYPE",
+        max_iter: int = 10000,
+        window_length: int = 100,
+        threshold: float = 0.99,
+        learning_rate: float = 0.1,
+        batch_size: int = 1,
+        verbose: bool = True,
+    ):
+        """
+        Create an MIFace attack instance.
+
+        :param classifier: Target classifier.
+        :param max_iter: Maximum number of gradient descent iterations for the model inversion.
+        :param window_length: Length of window for checking whether descent should be aborted.
+        :param threshold: Threshold for descent stopping criterion.
+        :param batch_size: Size of internal batches.
+        :param verbose: Show progress bars.
+        """
+        super().__init__(estimator=classifier)
+
+        self.max_iter = max_iter
+        self.window_length = window_length
+        self.threshold = threshold
+        self.learning_rate = learning_rate
+        self.batch_size = batch_size
+        self.verbose = verbose
+        self._check_params()
+
+    def infer(self, x: Optional[np.ndarray], y: Optional[np.ndarray] = None, **kwargs) -> np.ndarray:
+        """
+        Extract a thieved classifier.
+
+        :param x: An array with the initial input to the victim classifier. If `None`, then initial input will be
+                  initialized as zero array.
+        :param y: Target values (class labels) one-hot-encoded of shape (nb_samples, nb_classes) or indices of shape
+                  (nb_samples,).
+        :return: The inferred training samples.
+        """
+        if x is None and y is None:
+            raise ValueError("Either `x` or `y` should be provided.")
+
+        y = check_and_transform_label_format(y, self.estimator.nb_classes)
+        if x is None:
+            x = np.zeros((len(y),) + self.estimator.input_shape)
+
+        if y is None:
+            y = get_labels_np_array(self.estimator.predict(x, batch_size=self.batch_size))
+
+        x_infer = x.astype(ART_NUMPY_DTYPE)
+
+        # Compute inversions with implicit batching
+        for batch_id in trange(
+            int(np.ceil(x.shape[0] / float(self.batch_size))), desc="Model inversion", disable=not self.verbose
+        ):
+            batch_index_1, batch_index_2 = batch_id * self.batch_size, (batch_id + 1) * self.batch_size
+            batch = x_infer[batch_index_1:batch_index_2]
+            batch_labels = y[batch_index_1:batch_index_2]
+
+            active = np.array([True] * len(batch))
+            window = np.inf * np.ones((len(batch), self.window_length))
+
+            i = 0
+
+            while i < self.max_iter and sum(active) > 0:
+                grads = self.estimator.class_gradient(batch[active], np.argmax(batch_labels[active], axis=1))
+                grads = np.reshape(grads, (grads.shape[0],) + grads.shape[2:])
+                batch[active] = batch[active] + self.learning_rate * grads
+
+                if self.estimator.clip_values is not None:
+                    clip_min, clip_max = self.estimator.clip_values
+                    batch[active] = np.clip(batch[active], clip_min, clip_max)
+
+                cost = 1 - self.estimator.predict(batch)[np.arange(len(batch)), np.argmax(batch_labels, axis=1)]
+                active = (cost <= self.threshold) + (cost >= np.max(window, axis=1))
+
+                i_window = i % self.window_length
+                window[::, i_window] = cost
+
+                i = i + 1
+
+            x_infer[batch_index_1:batch_index_2] = batch
+
+        return x_infer
+
+    def _check_params(self) -> None:
+        if not isinstance(self.max_iter, (int, np.int)) or self.max_iter < 0:
+            raise ValueError("The number of iterations must be a non-negative integer.")
+
+        if not isinstance(self.window_length, (int, np.int)) or self.window_length < 0:
+            raise ValueError("The window length must be a non-negative integer.")
+
+        if not isinstance(self.threshold, float) or self.threshold < 0.0:
+            raise ValueError("The threshold must be a non-negative float.")
+
+        if not isinstance(self.learning_rate, float) or self.learning_rate < 0.0:
+            raise ValueError("The learning rate must be a non-negative float.")
+
+        if not isinstance(self.batch_size, (int, np.int)) or self.batch_size < 0:
+            raise ValueError("The batch size must be a non-negative integer.")
+
+        if not isinstance(self.verbose, bool):
+            raise ValueError("The argument `verbose` has to be of type bool.")
diff --git a/adversarial-robustness-toolbox/art/attacks/inference/reconstruction/__init__.py b/adversarial-robustness-toolbox/art/attacks/inference/reconstruction/__init__.py
new file mode 100644
index 0000000..b818ab0
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/attacks/inference/reconstruction/__init__.py
@@ -0,0 +1,4 @@
+"""
+Module providing model inversion attacks.
+"""
+from art.attacks.inference.reconstruction.white_box import DatabaseReconstruction
diff --git a/adversarial-robustness-toolbox/art/attacks/inference/reconstruction/white_box.py b/adversarial-robustness-toolbox/art/attacks/inference/reconstruction/white_box.py
new file mode 100644
index 0000000..551479f
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/attacks/inference/reconstruction/white_box.py
@@ -0,0 +1,104 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements reconstruction attacks.
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+from typing import Optional, Tuple
+
+import numpy as np
+import sklearn
+from scipy.optimize import fmin_l_bfgs_b
+
+from art.attacks.attack import ReconstructionAttack
+from art.estimators.estimator import BaseEstimator
+from art.estimators.classification.classifier import ClassifierMixin
+from art.estimators.classification.scikitlearn import ScikitlearnEstimator
+
+logger = logging.getLogger(__name__)
+
+
+class DatabaseReconstruction(ReconstructionAttack):
+    """
+    Implementation of a database reconstruction attack. In this case, the adversary is assumed to have in his/her
+    possession a model trained on a dataset, and all but one row of that training dataset. This attack attempts to
+    reconstruct the missing row.
+    """
+
+    _estimator_requirements = (BaseEstimator, ClassifierMixin, ScikitlearnEstimator)
+
+    def __init__(self, estimator):
+        """
+        Create a DatabaseReconstruction instance.
+
+        :param estimator: Trained target estimator.
+        """
+        super().__init__(estimator=estimator)
+
+        self.params = self.estimator.get_trainable_attribute_names()
+
+    @staticmethod
+    def objective(x, y, x_train, y_train, private_estimator, parent_model, params):
+        """Objective function which we seek to minimise"""
+
+        model = sklearn.base.clone(parent_model.model, safe=True)
+        model.fit(np.vstack((x_train, x)), np.hstack((y_train, y)))
+
+        residual = 0.0
+
+        for param in params:
+            residual += np.sum((model.__getattribute__(param) - private_estimator.model.__getattribute__(param)) ** 2)
+
+        return residual
+
+    def reconstruct(self, x: np.ndarray, y: Optional[np.ndarray] = None, **kwargs) -> Tuple[np.ndarray, np.ndarray]:
+        """
+        Infer the missing row from x, y with which `estimator` was trained with.
+
+        :param x: Known records of the training set of `estimator`.
+        :param y: Known labels of the training set of `estimator`.
+        """
+        if y is None:
+            y = self.estimator.predict(x=x)
+
+        if y.ndim == 2:
+            y = np.argmax(y, axis=1)
+
+        tol = float("inf")
+        x_0 = x[0, :]
+        x_guess = None
+        y_guess = None
+
+        for _y in range(self.estimator.nb_classes):
+            args = (_y, x, y, self._estimator, self.estimator, self.params)
+            _x, _tol, _ = fmin_l_bfgs_b(
+                self.objective, x_0, args=args, approx_grad=True, factr=100, pgtol=1e-10, bounds=None
+            )
+
+            if _tol < tol:
+                tol = _tol
+                x_guess = _x
+                y_guess = _y
+
+        x_reconstructed = np.expand_dims(x_guess, axis=0)
+        y_reconstructed = np.zeros(shape=(1, self.estimator.nb_classes))
+        y_reconstructed[0, y_guess] = 1
+
+        return x_reconstructed, y_reconstructed
diff --git a/adversarial-robustness-toolbox/art/attacks/poisoning/__init__.py b/adversarial-robustness-toolbox/art/attacks/poisoning/__init__.py
new file mode 100644
index 0000000..523153e
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/attacks/poisoning/__init__.py
@@ -0,0 +1,9 @@
+"""
+Module providing poisoning attacks under a common interface.
+"""
+from art.attacks.poisoning.backdoor_attack import PoisoningAttackBackdoor
+from art.attacks.poisoning.poisoning_attack_svm import PoisoningAttackSVM
+from art.attacks.poisoning.feature_collision_attack import FeatureCollisionAttack
+from art.attacks.poisoning.adversarial_embedding_attack import PoisoningAttackAdversarialEmbedding
+from art.attacks.poisoning.clean_label_backdoor_attack import PoisoningAttackCleanLabelBackdoor
+from art.attacks.poisoning.bullseye_polytope_attack import BullseyePolytopeAttackPyTorch
diff --git a/adversarial-robustness-toolbox/art/attacks/poisoning/adversarial_embedding_attack.py b/adversarial-robustness-toolbox/art/attacks/poisoning/adversarial_embedding_attack.py
new file mode 100644
index 0000000..16aa681
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/attacks/poisoning/adversarial_embedding_attack.py
@@ -0,0 +1,326 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements clean-label attacks on Neural Networks.
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+from typing import Tuple, Union, List, Optional, TYPE_CHECKING
+
+import numpy as np
+
+from art.attacks.attack import PoisoningAttackTransformer
+from art.attacks.poisoning.backdoor_attack import PoisoningAttackBackdoor
+from art.estimators.classification.keras import KerasClassifier
+
+if TYPE_CHECKING:
+    from art.utils import CLASSIFIER_TYPE
+
+logger = logging.getLogger(__name__)
+
+
+class PoisoningAttackAdversarialEmbedding(PoisoningAttackTransformer):
+    """
+    Implementation of Adversarial Embedding attack by Tan, Shokri (2019).
+    "Bypassing Backdoor Detection Algorithms in Deep Learning"
+
+    This attack trains a classifier with an additional discriminator and loss function that aims
+    to create non-differentiable latent representations between backdoored and benign examples.
+
+    | Paper link: https://arxiv.org/abs/1905.13409
+    """
+
+    attack_params = PoisoningAttackTransformer.attack_params + [
+        "backdoor",
+        "feature_layer",
+        "target",
+        "pp_poison",
+        "discriminator_layer_1",
+        "discriminator_layer_2",
+        "regularization",
+        "learning_rate",
+    ]
+
+    _estimator_requirements = (KerasClassifier,)
+
+    def __init__(
+        self,
+        classifier: "CLASSIFIER_TYPE",
+        backdoor: PoisoningAttackBackdoor,
+        feature_layer: Union[int, str],
+        target: Union[np.ndarray, List[Tuple[np.ndarray, np.ndarray]]],
+        pp_poison: Union[float, List[float]] = 0.05,
+        discriminator_layer_1: int = 256,
+        discriminator_layer_2: int = 128,
+        regularization: float = 10,
+        learning_rate: float = 1e-4,
+        clone=True,
+    ):
+        """
+        Initialize an Feature Collision Clean-Label poisoning attack
+
+        :param classifier: A neural network classifier.
+        :param backdoor: The backdoor attack used to poison samples
+        :param feature_layer: The layer of the original network to extract features from
+        :param target: The target label to poison
+        :param pp_poison: The percentage of training data to poison
+        :param discriminator_layer_1: The size of the first discriminator layer
+        :param discriminator_layer_2: The size of the second discriminator layer
+        :param regularization: The regularization constant for the backdoor recognition part of the loss function
+        :param learning_rate: The learning rate of clean-label attack optimization.
+        :param clone: Whether or not to clone the model or apply the attack on the original model
+        """
+        super().__init__(classifier=classifier)
+        self.backdoor = backdoor
+        self.feature_layer = feature_layer
+        self.target = target
+        if isinstance(pp_poison, float):
+            self.pp_poison = [pp_poison]
+        else:
+            self.pp_poison = pp_poison
+        self.discriminator_layer_1 = discriminator_layer_1
+        self.discriminator_layer_2 = discriminator_layer_2
+        self.regularization = regularization
+        self.train_data: Optional[np.ndarray] = None
+        self.train_labels: Optional[np.ndarray] = None
+        self.is_backdoor: Optional[np.ndarray] = None
+        self.learning_rate = learning_rate
+        self._check_params()
+
+        if isinstance(self.estimator, KerasClassifier):
+            using_tf_keras = "tensorflow.python.keras" in str(type(self.estimator.model))
+            if using_tf_keras:
+                from tensorflow.keras.models import Model, clone_model
+                from tensorflow.keras.layers import GaussianNoise, Dense, BatchNormalization, LeakyReLU
+                from tensorflow.keras.optimizers import Adam
+
+            else:
+                from keras import Model
+                from keras.models import clone_model
+                from keras.layers import GaussianNoise, Dense, BatchNormalization, LeakyReLU
+                from keras.optimizers import Adam
+
+            if clone:
+                self.orig_model = clone_model(self.estimator.model, input_tensors=self.estimator.model.inputs)
+            else:
+                self.orig_model = self.estimator.model
+            model_input = self.orig_model.input
+            init_model_output = self.orig_model(model_input)
+
+            # Extracting feature tensor
+            if isinstance(self.feature_layer, int):
+                feature_layer_tensor = self.orig_model.layers[self.feature_layer].output
+            else:
+                feature_layer_tensor = self.orig_model.get_layer(name=feature_layer).output
+            feature_layer_output = Model(inputs=[model_input], outputs=[feature_layer_tensor])
+
+            # Architecture for discriminator
+            discriminator_input = feature_layer_output(model_input)
+            discriminator_input = GaussianNoise(stddev=1)(discriminator_input)
+            dense_layer_1 = Dense(self.discriminator_layer_1)(discriminator_input)
+            norm_1_layer = BatchNormalization()(dense_layer_1)
+            leaky_layer_1 = LeakyReLU(alpha=0.2)(norm_1_layer)
+            dense_layer_2 = Dense(self.discriminator_layer_2)(leaky_layer_1)
+            norm_2_layer = BatchNormalization()(dense_layer_2)
+            leaky_layer_2 = LeakyReLU(alpha=0.2)(norm_2_layer)
+            backdoor_detect = Dense(2, activation="softmax", name="backdoor_detect")(leaky_layer_2)
+
+            # Creating embedded model
+            self.embed_model = Model(inputs=self.orig_model.inputs, outputs=[init_model_output, backdoor_detect])
+
+            # Add backdoor detection loss
+            model_name = self.orig_model.name
+            model_loss = self.estimator.model.loss
+            loss_name = "backdoor_detect"
+            loss_type = "binary_crossentropy"
+            if isinstance(model_loss, str):
+                losses = {model_name: model_loss, loss_name: loss_type}
+                loss_weights = {model_name: 1.0, loss_name: -self.regularization}
+            elif isinstance(model_loss, dict):
+                losses = model_loss
+                losses[loss_name] = loss_type
+                loss_weights = self.orig_model.loss_weights
+                loss_weights[loss_name] = -self.regularization
+            else:
+                raise TypeError("Cannot read model loss value of type {}".format(type(model_loss)))
+
+            opt = Adam(lr=self.learning_rate)
+            self.embed_model.compile(optimizer=opt, loss=losses, loss_weights=loss_weights, metrics=["accuracy"])
+        else:
+            raise NotImplementedError("This attack currently only supports Keras.")
+
+    def poison(
+        self, x: np.ndarray, y: Optional[np.ndarray] = None, broadcast=False, **kwargs
+    ) -> Tuple[np.ndarray, np.ndarray]:
+        """
+        Calls perturbation function on input x and target labels y
+
+        :param x: An array with the points that initialize attack points.
+        :param y: The target labels for the attack.
+        :param broadcast: whether or not to broadcast single target label
+        :return: An tuple holding the `(poisoning_examples, poisoning_labels)`.
+        """
+        return self.backdoor.poison(x, y, broadcast=broadcast)
+
+    def poison_estimator(
+        self, x: np.ndarray, y: np.ndarray, batch_size: int = 64, nb_epochs: int = 10, **kwargs
+    ) -> "CLASSIFIER_TYPE":
+        """
+        Train a poisoned model and return it
+        :param x: Training data
+        :param y: Training labels
+        :param batch_size: The size of the batches used for training
+        :param nb_epochs: The number of epochs to train for
+        :return: A classifier with embedded backdoors
+        """
+        train_data = np.copy(x)
+        train_labels = np.copy(y)
+
+        # Select indices to poison
+        selected_indices = np.zeros(len(x)).astype(bool)
+
+        if len(self.pp_poison) == 1:
+            if isinstance(self.target, np.ndarray):
+                not_target = np.logical_not(np.all(y == self.target, axis=1))
+                selected_indices[not_target] = np.random.uniform(size=sum(not_target)) < self.pp_poison[0]
+            else:
+                for src, _ in self.target:
+                    all_src = np.all(y == src, axis=1)
+                    selected_indices[all_src] = np.random.uniform(size=sum(all_src)) < self.pp_poison[0]
+        else:
+            for pp, (src, _) in zip(self.pp_poison, self.target):
+                all_src = np.all(y == src, axis=1)
+                selected_indices[all_src] = np.random.uniform(size=sum(all_src)) < pp
+
+        # Poison selected indices
+        if isinstance(self.target, np.ndarray):
+            to_be_poisoned = train_data[selected_indices]
+            poison_data, poison_labels = self.poison(to_be_poisoned, y=self.target, broadcast=True)
+
+            poison_idxs = np.arange(len(x))[selected_indices]
+            for i, idx in enumerate(poison_idxs):
+                train_data[idx] = poison_data[i]
+                train_labels[idx] = poison_labels[i]
+        else:
+            for src, tgt in self.target:
+                poison_mask = np.logical_and(selected_indices, np.all(y == src, axis=1))
+                to_be_poisoned = train_data[poison_mask]
+                src_poison_data, src_poison_labels = self.poison(to_be_poisoned, y=shape_labels(tgt), broadcast=True)
+                train_data[poison_mask] = src_poison_data
+                train_labels[poison_mask] = src_poison_labels
+
+        # label 1 if is backdoor 0 otherwise
+        is_backdoor = selected_indices.astype(int)
+
+        # convert to one-hot
+        is_backdoor = np.fromfunction(lambda b_idx: np.eye(2)[is_backdoor[b_idx]], shape=(len(x),), dtype=int)
+
+        # Save current training data
+        self.train_data = train_data
+        self.train_labels = train_labels
+        self.is_backdoor = is_backdoor
+
+        if isinstance(self.estimator, KerasClassifier):
+            # Call fit with both y and is_backdoor labels
+            self.embed_model.fit(
+                train_data, y=[train_labels, is_backdoor], batch_size=batch_size, epochs=nb_epochs, **kwargs
+            )
+            params = self.estimator.get_params()
+            del params["model"]
+            del params["nb_classes"]
+            return KerasClassifier(self.orig_model, **params)
+
+        raise NotImplementedError("Currently only Keras is supported")
+
+    def get_training_data(self) -> Optional[Tuple[np.ndarray, np.ndarray, np.ndarray]]:
+        """
+        Returns the training data generated from the last call to fit
+
+        :return: If fit has been called, return the last data, labels, and backdoor labels used to train model
+                 otherwise return None
+        """
+        if self.train_data is not None:
+            return self.train_data, self.train_labels, self.is_backdoor
+
+        return None
+
+    def _check_params(self) -> None:
+        if isinstance(self.feature_layer, str):
+            layer_names = {l.name for l in self.estimator.model.layers}
+            if self.feature_layer not in layer_names:
+                raise ValueError("Layer {} not found in model".format(self.feature_layer))
+        elif isinstance(self.feature_layer, int):
+            num_layers = len(self.estimator.model.layers)
+            if num_layers <= int(self.feature_layer) or int(self.feature_layer) < 0:
+                raise ValueError(
+                    "Feature layer {} is out of range. Network only has {} layers".format(
+                        self.feature_layer, num_layers
+                    )
+                )
+
+        if isinstance(self.target, np.ndarray):
+            self._check_valid_label_shape(self.target)
+        else:
+            for source, target in self.target:
+                self._check_valid_label_shape(shape_labels(source))
+                self._check_valid_label_shape(shape_labels(target))
+
+        if len(self.pp_poison) == 1:
+            _check_pp_poison(self.pp_poison[0])
+        else:
+            if not isinstance(self.target, list):
+                raise ValueError("Target should be list of source label pairs")
+            if len(self.pp_poison) != len(self.target):
+                raise ValueError("pp_poison and target lists should be the same length")
+            for pp in self.pp_poison:
+                _check_pp_poison(pp)
+
+        if self.regularization <= 0:
+            raise ValueError("Regularization constant must be positive")
+
+        if self.discriminator_layer_1 <= 0 or self.discriminator_layer_2 <= 0:
+            raise ValueError("Discriminator layer size must be positive")
+
+    def _check_valid_label_shape(self, label: np.ndarray) -> None:
+        if label.shape != self.estimator.model.output_shape[1:]:
+            raise ValueError(
+                "Invalid shape for target array. Should be {} received {}".format(
+                    self.estimator.model.output_shape[1:], label.shape
+                )
+            )
+
+
+def _check_pp_poison(pp_poison: float) -> None:
+    """
+    Return an error when a poison value is invalid
+    """
+    if 1 < pp_poison < 0:
+        raise ValueError("pp_poison must be between 0 and 1")
+
+
+def shape_labels(lbl: np.ndarray) -> np.ndarray:
+    """
+    Reshape a labels array
+
+    :param lbl: a label array
+    :return:
+    """
+    if lbl.shape[0] == 1:
+        return lbl.squeeze(axis=0)
+    return lbl
diff --git a/adversarial-robustness-toolbox/art/attacks/poisoning/backdoor_attack.py b/adversarial-robustness-toolbox/art/attacks/poisoning/backdoor_attack.py
new file mode 100644
index 0000000..b0316eb
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/attacks/poisoning/backdoor_attack.py
@@ -0,0 +1,90 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements Backdoor Attacks to poison data used in ML models.
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+from typing import Callable, List, Optional, Tuple, Union
+
+import numpy as np
+
+from art.attacks.attack import PoisoningAttackBlackBox
+
+
+logger = logging.getLogger(__name__)
+
+
+class PoisoningAttackBackdoor(PoisoningAttackBlackBox):
+    """
+    Implementation of backdoor attacks introduced in Gu, et. al. 2017
+
+    Applies a number of backdoor perturbation functions and switches label to target label
+
+    | Paper link: https://arxiv.org/abs/1708.06733
+    """
+
+    attack_params = PoisoningAttackBlackBox.attack_params + ["perturbation"]
+    _estimator_requirements = ()
+
+    def __init__(self, perturbation: Union[Callable, List[Callable]]) -> None:
+        """
+        Initialize a backdoor poisoning attack.
+
+        :param perturbation: A single perturbation function or list of perturbation functions that modify input.
+        """
+        super().__init__()
+        self.perturbation = perturbation
+        self._check_params()
+
+    def poison(
+        self, x: np.ndarray, y: Optional[np.ndarray] = None, broadcast=False, **kwargs
+    ) -> Tuple[np.ndarray, np.ndarray]:
+        """
+        Calls perturbation function on input x and returns the perturbed input and poison labels for the data.
+
+        :param x: An array with the points that initialize attack points.
+        :param y: The target labels for the attack.
+        :param broadcast: whether or not to broadcast single target label
+        :return: An tuple holding the `(poisoning_examples, poisoning_labels)`.
+        """
+        if y is None:
+            raise ValueError("Target labels `y` need to be provided for a targeted attack.")
+
+        if broadcast:
+            y_attack = np.broadcast_to(y, (x.shape[0], y.shape[0]))
+        else:
+            y_attack = np.copy(y)
+
+        num_poison = len(x)
+        if num_poison == 0:
+            raise ValueError("Must input at least one poison point.")
+        poisoned = np.copy(x)
+
+        if callable(self.perturbation):
+            return self.perturbation(poisoned), y_attack
+
+        for perturb in self.perturbation:
+            poisoned = perturb(poisoned)
+
+        return poisoned, y_attack
+
+    def _check_params(self) -> None:
+        if not (callable(self.perturbation) or all((callable(perturb) for perturb in self.perturbation))):
+            raise ValueError("Perturbation must be a function or a list of functions.")
diff --git a/adversarial-robustness-toolbox/art/attacks/poisoning/bullseye_polytope_attack.py b/adversarial-robustness-toolbox/art/attacks/poisoning/bullseye_polytope_attack.py
new file mode 100644
index 0000000..72aa9bb
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/attacks/poisoning/bullseye_polytope_attack.py
@@ -0,0 +1,318 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2021
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements Bullseye Polytope clean-label attacks on Neural Networks.
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+from typing import Optional, Tuple, Union, TYPE_CHECKING, List
+
+import numpy as np
+import time
+from tqdm.auto import trange
+
+from art.attacks.attack import PoisoningAttackWhiteBox
+from art.estimators import BaseEstimator, NeuralNetworkMixin
+from art.estimators.classification.classifier import ClassifierMixin
+from art.estimators.classification.pytorch import PyTorchClassifier
+
+if TYPE_CHECKING:
+    import torch
+    from art.utils import CLASSIFIER_NEURALNETWORK_TYPE
+
+logger = logging.getLogger(__name__)
+
+
+class BullseyePolytopeAttackPyTorch(PoisoningAttackWhiteBox):
+    """
+    Implementation of Bullseye Polytope Attack by Aghakhani, et. al. 2020.
+    "Bullseye Polytope: A Scalable Clean-Label Poisoning Attack with Improved Transferability"
+
+    This implementation is based on UCSB's original code here: https://github.com/ucsb-seclab/BullseyePoison
+
+    | Paper link: https://arxiv.org/abs/2005.00191
+    """
+
+    attack_params = PoisoningAttackWhiteBox.attack_params + [
+        "target",
+        "feature_layer",
+        "opt",
+        "max_iter",
+        "learning_rate",
+        "momentum",
+        "decay_iter",
+        "decay_coeff",
+        "epsilon",
+        "norm",
+        "dropout",
+        "endtoend",
+        "verbose",
+    ]
+
+    _estimator_requirements = (BaseEstimator, NeuralNetworkMixin, ClassifierMixin, PyTorchClassifier)
+
+    def __init__(
+        self,
+        classifier: Union["CLASSIFIER_NEURALNETWORK_TYPE", List["CLASSIFIER_NEURALNETWORK_TYPE"]],
+        target: np.ndarray,
+        feature_layer: Union[Union[str, int], List[Union[str, int]]],
+        opt: str = "adam",
+        max_iter: int = 4000,
+        learning_rate: float = 4e-2,
+        momentum: float = 0.9,
+        decay_iter: Union[int, List[int]] = 10000,
+        decay_coeff: float = 0.5,
+        epsilon: float = 0.1,
+        dropout: int = 0.3,
+        net_repeat: int = 1,
+        endtoend: bool = True,
+        verbose: bool = True,
+    ):
+        """
+        Initialize an Feature Collision Clean-Label poisoning attack
+
+        :param classifier: The proxy classifiers used for the attack. Can be a single classifier or list of classifiers
+                           with varying architectures.
+        :param target: The target input(s) of shape (N, W, H, C) to misclassify at test time. Multiple targets will be
+                       averaged.
+        :param feature_layer: The name(s) of the feature representation layer(s).
+        :param opt: The optimizer to use for the attack. Can be 'adam' or 'sgd'
+        :param max_iter: The maximum number of iterations for the attack.
+        :param learning_rate: The learning rate of clean-label attack optimization.
+        :param momentum: The momentum of clean-label attack optimization.
+        :param decay_iter: Which iterations to decay the learning rate.
+                           Can be a integer (every N iterations) or list of integers [0, 500, 1500]
+        :param decay_coeff: The decay coefficient of the learning rate.
+        :param epsilon: The perturbation budget
+        :param dropout: Dropout to apply while training
+        :param net_repeat: The number of times to repeat prediction on each network
+        :param endtoend: True for end-to-end training. False for transfer learning.
+        :param verbose: Show progress bars.
+        """
+        self.subsistute_networks: List["CLASSIFIER_NEURALNETWORK_TYPE"] = [classifier] if not isinstance(
+            classifier, list
+        ) else classifier
+
+        super().__init__(classifier=self.subsistute_networks[0])  # type: ignore
+        self.target = target
+        self.opt = opt
+        self.momentum = momentum
+        self.decay_iter = decay_iter
+        self.epsilon = epsilon
+        self.dropout = dropout
+        self.net_repeat = net_repeat
+        self.endtoend = endtoend
+        self.feature_layer = feature_layer
+        self.learning_rate = learning_rate
+        self.decay_coeff = decay_coeff
+        self.max_iter = max_iter
+        self.verbose = verbose
+        self._check_params()
+
+    def poison(self, x: np.ndarray, y: Optional[np.ndarray] = None, **kwargs) -> Tuple[np.ndarray, np.ndarray]:
+        """
+        Iteratively finds optimal attack points starting at values at x
+
+        :param x: The base images to begin the poison process.
+        :param y: Target label
+        :return: An tuple holding the (poisoning examples, poisoning labels).
+        """
+        import torch  # lgtm [py/repeated-import]
+
+        class PoisonBatch(torch.nn.Module):
+            """
+            Implementing this to work with PyTorch optimizers.
+            """
+
+            def __init__(self, base_list):
+                super(PoisonBatch, self).__init__()
+                base_batch = torch.stack(base_list, 0)
+                self.poison = torch.nn.Parameter(base_batch.clone())
+
+            def forward(self):
+                return self.poison
+
+        base_tensor_list = [torch.from_numpy(sample).to(self.estimator.device) for sample in x]
+        poison_batch = PoisonBatch([torch.from_numpy(np.copy(sample)).to(self.estimator.device) for sample in x])
+        opt_method = self.opt.lower()
+
+        if opt_method == "sgd":
+            logger.info("Using SGD to craft poison samples")
+            optimizer = torch.optim.SGD(poison_batch.parameters(), lr=self.learning_rate, momentum=self.momentum)
+        elif opt_method == "adam":
+            logger.info("Using Adam to craft poison samples")
+            optimizer = torch.optim.Adam(poison_batch.parameters(), lr=self.learning_rate, betas=(self.momentum, 0.999))
+
+        base_tensor_batch = torch.stack(base_tensor_list, 0)
+        base_range01_batch = base_tensor_batch
+
+        # Because we have turned on DP for the substitute networks,
+        # the target image's feature becomes random.
+        # We can try enforcing the convex polytope in one of the multiple realizations of the feature,
+        # but empirically one realization is enough.
+        target_feat_list = []
+        # Coefficients for the convex combination.
+        # Initializing from the coefficients of last step gives faster convergence.
+        s_init_coeff_list = []
+        n_poisons = len(x)
+        for n, net in enumerate(self.subsistute_networks):
+            # End to end training
+            if self.endtoend:
+                block_feats = [
+                    feat.detach() for feat in net.get_activations(x, layer=self.feature_layer, framework=True)
+                ]
+                target_feat_list.append(block_feats)
+                s_coeff = [
+                    torch.ones(n_poisons, 1).to(self.estimator.device) / n_poisons for _ in range(len(block_feats))
+                ]
+            else:
+                target_feat_list.append(net.get_activations(x, layer=self.feature_layer, framework=True).detach())
+                s_coeff = torch.ones(n_poisons, 1).to(self.estimator.device) / n_poisons
+
+            s_init_coeff_list.append(s_coeff)
+
+        for ite in trange(self.max_iter):
+            if ite % self.decay_iter == 0 and ite != 0:
+                for param_group in optimizer.param_groups:
+                    param_group["lr"] *= self.decay_coeff
+                print(
+                    "%s Iteration %d, Adjusted lr to %.2e"
+                    % (time.strftime("%Y-%m-%d %H:%M:%S"), ite, self.learning_rate)
+                )
+
+            poison_batch.zero_grad()
+            total_loss = loss_from_center(
+                self.subsistute_networks,
+                target_feat_list,
+                poison_batch,
+                self.net_repeat,
+                self.endtoend,
+                self.feature_layer,
+            )
+            total_loss.backward()
+            optimizer.step()
+
+            # clip the perturbations into the range
+            perturb_range01 = torch.clamp((poison_batch.poison.data - base_tensor_batch), -self.epsilon, self.epsilon)
+            perturbed_range01 = torch.clamp(
+                base_range01_batch.data + perturb_range01.data,
+                self.estimator.clip_values[0],
+                self.estimator.clip_values[1],
+            )
+            poison_batch.poison.data = perturbed_range01
+
+        if y is None:
+            raise ValueError("You must pass in the target label as y")
+
+        return get_poison_tuples(poison_batch, y)
+
+    def _check_params(self) -> None:
+        if self.learning_rate <= 0:
+            raise ValueError("Learning rate must be strictly positive")
+
+        if self.max_iter < 1:
+            raise ValueError("Value of max_iter at least 1")
+
+        if not isinstance(self.feature_layer, (str, int)):
+            raise TypeError("Feature layer should be a string or int")
+
+        if self.opt.lower() not in ["adam", "sgd"]:
+            raise ValueError("Optimizer must be 'adam' or 'sgd'")
+
+        if 1 < self.momentum < 0:
+            raise ValueError("Momentum must be between 0 and 1")
+
+        if self.decay_iter < 0:
+            raise ValueError("decay_iter must be at least 0")
+
+        if self.epsilon <= 0:
+            raise ValueError("epsilon must be at least 0")
+
+        if 1 < self.dropout < 0:
+            raise ValueError("dropout must be between 0 and 1")
+
+        if self.net_repeat < 1:
+            raise ValueError("net_repeat must be at least 1")
+
+        if not 0 <= self.feature_layer < len(self.estimator.layer_names):
+            raise ValueError("Invalid feature layer")
+
+        if 1 < self.decay_coeff < 0:
+            raise ValueError("Decay coefficient must be between zero and one")
+
+
+def get_poison_tuples(poison_batch, poison_label):
+    """
+    Includes the labels
+    """
+    poison = [
+        poison_batch.poison.data[num_p].unsqueeze(0).detach().cpu().numpy()
+        for num_p in range(poison_batch.poison.size(0))
+    ]
+    return np.vstack(poison), poison_label
+
+
+def loss_from_center(
+    subs_net_list, target_feat_list, poison_batch, net_repeat, end2end, feature_layer
+) -> "torch.Tensor":
+    import torch  # lgtm [py/repeated-import]
+
+    if end2end:
+        loss = 0
+        for net, center_feats in zip(subs_net_list, target_feat_list):
+            if net_repeat > 1:
+                poisons_feats_repeats = [
+                    net.get_activations(poison_batch(), layer=feature_layer, framework=True) for _ in range(net_repeat)
+                ]
+                BLOCK_NUM = len(poisons_feats_repeats[0])
+                poisons_feats = []
+                for block_idx in range(BLOCK_NUM):
+                    poisons_feats.append(
+                        sum([poisons_feat_r[block_idx] for poisons_feat_r in poisons_feats_repeats]) / net_repeat
+                    )
+            elif net_repeat == 1:
+                poisons_feats = net.get_activations(poison_batch(), layer=feature_layer, framework=True)
+            else:
+                assert False, "net_repeat set to {}".format(net_repeat)
+
+            net_loss = 0
+            for pfeat, cfeat in zip(poisons_feats, center_feats):
+                diff = torch.mean(pfeat, dim=0) - cfeat
+                diff_norm = torch.norm(diff, dim=0)
+                cfeat_norm = torch.norm(cfeat, dim=0)
+                diff_norm = diff_norm / cfeat_norm
+                net_loss += torch.mean(diff_norm)
+            loss += net_loss / len(center_feats)
+        loss = loss / len(subs_net_list)
+
+    else:
+        loss = 0
+        for net, center in zip(subs_net_list, target_feat_list):
+            poisons = [
+                net.get_activations(poison_batch(), layer=feature_layer, framework=True) for _ in range(net_repeat)
+            ]
+            poisons = sum(poisons) / len(poisons)
+
+            diff = torch.mean(poisons, dim=0) - center
+            diff_norm = torch.norm(diff, dim=1) / torch.norm(center, dim=1)
+            loss += torch.mean(diff_norm)
+
+        loss = loss / len(subs_net_list)
+
+    return loss
diff --git a/adversarial-robustness-toolbox/art/attacks/poisoning/clean_label_backdoor_attack.py b/adversarial-robustness-toolbox/art/attacks/poisoning/clean_label_backdoor_attack.py
new file mode 100644
index 0000000..1248dee
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/attacks/poisoning/clean_label_backdoor_attack.py
@@ -0,0 +1,150 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements Backdoor Attacks to poison data used in ML models.
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+from typing import Optional, Tuple, TYPE_CHECKING, Union
+
+import numpy as np
+
+from art.attacks.attack import PoisoningAttackBlackBox
+from art.attacks.evasion.projected_gradient_descent.projected_gradient_descent import ProjectedGradientDescent
+from art.attacks.poisoning.backdoor_attack import PoisoningAttackBackdoor
+
+if TYPE_CHECKING:
+    from art.utils import CLASSIFIER_LOSS_GRADIENTS_TYPE
+
+
+logger = logging.getLogger(__name__)
+
+
+class PoisoningAttackCleanLabelBackdoor(PoisoningAttackBlackBox):
+    """
+    Implementation of Clean-Label Backdoor Attacks introduced in Gu, et. al. 2017
+
+    Applies a number of backdoor perturbation functions and switches label to target label
+
+    | Paper link: https://arxiv.org/abs/1708.06733
+    """
+
+    attack_params = PoisoningAttackBlackBox.attack_params + [
+        "backdoor",
+        "proxy_classifier",
+        "target",
+        "pp_poison",
+        "norm",
+        "eps",
+        "eps_step",
+        "max_iter",
+        "num_random_init",
+    ]
+    _estimator_requirements = ()
+
+    def __init__(
+        self,
+        backdoor: PoisoningAttackBackdoor,
+        proxy_classifier: "CLASSIFIER_LOSS_GRADIENTS_TYPE",
+        target: np.ndarray,
+        pp_poison: float = 0.33,
+        norm: Union[int, float, str] = np.inf,
+        eps: float = 0.3,
+        eps_step: float = 0.1,
+        max_iter: int = 100,
+        num_random_init: int = 0,
+    ) -> None:
+        """
+        Creates a new Clean Label Backdoor poisoning attack
+
+        :param backdoor: the backdoor chosen for this attack
+        :param proxy_classifier: the classifier for this attack ideally it solves the same or similar classification
+                                 task as the original classifier
+        :param target: The target label to poison
+        :param pp_poison: The percentage of the data to poison. Note: Only data within the target label is poisoned
+        :param norm: The norm of the adversarial perturbation supporting "inf", np.inf, 1 or 2.
+        :param eps: Maximum perturbation that the attacker can introduce.
+        :param eps_step: Attack step size (input variation) at each iteration.
+        :param max_iter: The maximum number of iterations.
+        :param num_random_init: Number of random initialisations within the epsilon ball. For num_random_init=0 starting
+                                at the original input.
+        """
+        super().__init__()
+        self.backdoor = backdoor
+        self.proxy_classifier = proxy_classifier
+        self.target = target
+        self.pp_poison = pp_poison
+        self.attack = ProjectedGradientDescent(
+            proxy_classifier,
+            norm=norm,
+            eps=eps,
+            eps_step=eps_step,
+            max_iter=max_iter,
+            targeted=False,
+            num_random_init=num_random_init,
+        )
+        self._check_params()
+
+    def poison(
+        self, x: np.ndarray, y: Optional[np.ndarray] = None, broadcast: bool = True, **kwargs
+    ) -> Tuple[np.ndarray, np.ndarray]:
+        """
+        Calls perturbation function on input x and returns the perturbed input and poison labels for the data.
+
+        :param x: An array with the points that initialize attack points.
+        :param y: The target labels for the attack.
+        :param broadcast: whether or not to broadcast single target label
+        :return: An tuple holding the `(poisoning_examples, poisoning_labels)`.
+        """
+        data = np.copy(x)
+        estimated_labels = self.proxy_classifier.predict(data) if y is None else np.copy(y)
+
+        # Selected target indices to poison
+        all_indices = np.arange(len(data))
+        target_indices = all_indices[np.all(estimated_labels == self.target, axis=1)]
+        num_poison = int(self.pp_poison * len(target_indices))
+        selected_indices = np.random.choice(target_indices, num_poison)
+
+        # Run untargeted PGD on selected points, making it hard to classify correctly
+        perturbed_input = self.attack.generate(data[selected_indices])
+        no_change_detected = np.array(
+            [
+                np.all(data[selected_indices][poison_idx] == perturbed_input[poison_idx])
+                for poison_idx in range(len(perturbed_input))
+            ]
+        )
+
+        if any(no_change_detected):
+            logger.warning("Perturbed input is the same as original data after PGD. Check params.")
+            idx_no_change = np.arange(len(no_change_detected))[no_change_detected]
+            logger.warning(f"%d indices without change: %d", len(idx_no_change), idx_no_change)
+
+        # Add backdoor and poison with the same label
+        poisoned_input, _ = self.backdoor.poison(perturbed_input, self.target, broadcast=broadcast)
+        data[selected_indices] = poisoned_input
+
+        return data, estimated_labels
+
+    def _check_params(self) -> None:
+        if not isinstance(self.backdoor, PoisoningAttackBackdoor):
+            raise ValueError("Backdoor must be of type PoisoningAttackBackdoor")
+        if not isinstance(self.attack, ProjectedGradientDescent):
+            raise ValueError("There was an issue creating the PGD attack")
+        if not 0 < self.pp_poison < 1:
+            raise ValueError("pp_poison must be between 0 and 1")
diff --git a/adversarial-robustness-toolbox/art/attacks/poisoning/feature_collision_attack.py b/adversarial-robustness-toolbox/art/attacks/poisoning/feature_collision_attack.py
new file mode 100644
index 0000000..03e00d5
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/attacks/poisoning/feature_collision_attack.py
@@ -0,0 +1,306 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements clean-label attacks on Neural Networks.
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+from functools import reduce
+import logging
+from typing import Optional, Tuple, Union, TYPE_CHECKING
+
+import numpy as np
+from tqdm.auto import trange
+
+from art.attacks.attack import PoisoningAttackWhiteBox
+from art.estimators import BaseEstimator, NeuralNetworkMixin
+from art.estimators.classification.classifier import ClassifierMixin
+from art.estimators.classification.keras import KerasClassifier
+
+if TYPE_CHECKING:
+    from art.utils import CLASSIFIER_NEURALNETWORK_TYPE
+
+logger = logging.getLogger(__name__)
+
+
+class FeatureCollisionAttack(PoisoningAttackWhiteBox):
+    """
+    Close implementation of Feature Collision Poisoning Attack by Shafahi, Huang, et al 2018.
+    "Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks"
+
+    This implementation dynamically calculates the dimension of the feature layer, and doesn't hardcode this
+    value to 2048 as done in the paper. Thus we recommend using larger values for the similarity_coefficient.
+
+    | Paper link: https://arxiv.org/abs/1804.00792
+    """
+
+    attack_params = PoisoningAttackWhiteBox.attack_params + [
+        "target",
+        "feature_layer",
+        "learning_rate",
+        "decay_coeff",
+        "stopping_tol",
+        "obj_threshold",
+        "num_old_obj",
+        "max_iter",
+        "similarity_coeff",
+        "watermark",
+        "verbose",
+    ]
+
+    _estimator_requirements = (BaseEstimator, NeuralNetworkMixin, ClassifierMixin, KerasClassifier)
+
+    def __init__(
+        self,
+        classifier: "CLASSIFIER_NEURALNETWORK_TYPE",
+        target: np.ndarray,
+        feature_layer: Union[str, int],
+        learning_rate: float = 500 * 255.0,
+        decay_coeff: float = 0.5,
+        stopping_tol: float = 1e-10,
+        obj_threshold: Optional[float] = None,
+        num_old_obj: int = 40,
+        max_iter: int = 120,
+        similarity_coeff: float = 256.0,
+        watermark: Optional[float] = None,
+        verbose: bool = True,
+    ):
+        """
+        Initialize an Feature Collision Clean-Label poisoning attack
+
+        :param classifier: A trained neural network classifier.
+        :param target: The target input to misclassify at test time.
+        :param feature_layer: The name of the feature representation layer.
+        :param learning_rate: The learning rate of clean-label attack optimization.
+        :param decay_coeff: The decay coefficient of the learning rate.
+        :param stopping_tol: Stop iterations after changes in attacks in less than this threshold.
+        :param obj_threshold: Stop iterations after changes in objectives values are less than this threshold.
+        :param num_old_obj: The number of old objective values to store.
+        :param max_iter: The maximum number of iterations for the attack.
+        :param similarity_coeff: The maximum number of iterations for the attack.
+        :param watermark: Whether The opacity of the watermarked target image.
+        :param verbose: Show progress bars.
+        """
+        super().__init__(classifier=classifier)  # type: ignore
+        self.target = target
+        self.feature_layer = feature_layer
+        self.learning_rate = learning_rate
+        self.decay_coeff = decay_coeff
+        self.stopping_tol = stopping_tol
+        self.obj_threshold = obj_threshold
+        self.num_old_obj = num_old_obj
+        self.max_iter = max_iter
+        self.similarity_coeff = similarity_coeff
+        self.watermark = watermark
+        self.verbose = verbose
+        self._check_params()
+
+        self.target_placeholder, self.target_feature_rep = self.estimator.get_activations(
+            self.target, self.feature_layer, 1, framework=True
+        )
+        self.poison_placeholder, self.poison_feature_rep = self.estimator.get_activations(
+            self.target, self.feature_layer, 1, framework=True
+        )
+        self.attack_loss = tensor_norm(self.poison_feature_rep - self.target_feature_rep)
+
+    def poison(self, x: np.ndarray, y: Optional[np.ndarray] = None, **kwargs) -> Tuple[np.ndarray, np.ndarray]:
+        """
+        Iteratively finds optimal attack points starting at values at x
+
+        :param x: The base images to begin the poison process.
+        :param y: Not used in this attack (clean-label).
+        :return: An tuple holding the (poisoning examples, poisoning labels).
+        """
+        num_poison = len(x)
+        final_attacks = []
+        if num_poison == 0:
+            raise ValueError("Must input at least one poison point")
+
+        target_features = self.estimator.get_activations(self.target, self.feature_layer, 1)
+        for init_attack in x:
+            old_attack = np.expand_dims(np.copy(init_attack), axis=0)
+            poison_features = self.estimator.get_activations(old_attack, self.feature_layer, 1)
+            old_objective = self.objective(poison_features, target_features, init_attack, old_attack)
+            last_m_objectives = [old_objective]
+
+            for i in trange(self.max_iter, desc="Feature collision", disable=not self.verbose):
+                # forward step
+                new_attack = self.forward_step(old_attack)
+
+                # backward step
+                new_attack = self.backward_step(np.expand_dims(init_attack, axis=0), poison_features, new_attack)
+
+                rel_change_val = np.linalg.norm(new_attack - old_attack) / np.linalg.norm(new_attack)
+                if rel_change_val < self.stopping_tol or self.obj_threshold and old_objective <= self.obj_threshold:
+                    logger.info("stopped after %d iterations due to small changes", i)
+                    break
+
+                np.expand_dims(new_attack, axis=0)
+                new_feature_rep = self.estimator.get_activations(new_attack, self.feature_layer, 1)
+                new_objective = self.objective(new_feature_rep, target_features, init_attack, new_attack)
+
+                avg_of_last_m = sum(last_m_objectives) / float(min(self.num_old_obj, i + 1))
+
+                # Increasing objective means then learning rate is too big.  Chop it, and throw out the latest iteration
+                if new_objective >= avg_of_last_m and (i % self.num_old_obj / 2 == 0):
+                    self.learning_rate *= self.decay_coeff
+                else:
+                    old_attack = new_attack
+                    old_objective = new_objective
+
+                if i < self.num_old_obj - 1:
+                    last_m_objectives.append(new_objective)
+                else:
+                    # first remove the oldest obj then append the new obj
+                    del last_m_objectives[0]
+                    last_m_objectives.append(new_objective)
+
+            # Watermarking
+            watermark = self.watermark * self.target if self.watermark else 0
+            final_poison = np.clip(old_attack + watermark, *self.estimator.clip_values)
+            final_attacks.append(final_poison)
+
+        return np.vstack(final_attacks), self.estimator.predict(x)
+
+    def forward_step(self, poison: np.ndarray) -> np.ndarray:
+        """
+        Forward part of forward-backward splitting algorithm.
+
+        :param poison: the current poison samples.
+        :return: poison example closer in feature representation to target space.
+        """
+        (attack_grad,) = self.estimator.custom_loss_gradient(
+            self.attack_loss,
+            [self.poison_placeholder, self.target_placeholder],
+            [poison, self.target],
+            name="feature_collision_" + str(self.feature_layer),
+        )
+        poison -= self.learning_rate * attack_grad[0]
+
+        return poison
+
+    def backward_step(self, base: np.ndarray, feature_rep: np.ndarray, poison: np.ndarray) -> np.ndarray:
+        """
+        Backward part of forward-backward splitting algorithm
+
+        :param base: The base image that the poison was initialized with.
+        :param feature_rep: Numpy activations at the target layer.
+        :param poison: The current poison samples.
+        :return: Poison example closer in feature representation to target space.
+        """
+        num_features = reduce(lambda x, y: x * y, base.shape)
+        dim_features = feature_rep.shape[-1]
+        beta = self.similarity_coeff * (dim_features / num_features) ** 2
+        poison = (poison + self.learning_rate * beta * base) / (1 + beta * self.learning_rate)
+        low, high = self.estimator.clip_values
+        return np.clip(poison, low, high)
+
+    def objective(
+        self, poison_feature_rep: np.ndarray, target_feature_rep: np.ndarray, base_image: np.ndarray, poison: np.ndarray
+    ) -> float:
+        """
+        Objective function of the attack
+
+        :param poison_feature_rep: The numpy activations of the poison image.
+        :param target_feature_rep: The numpy activations of the target image.
+        :param base_image: The initial image used to poison.
+        :param poison: The current poison image.
+        :return: The objective of the optimization.
+        """
+        num_features = base_image.size
+        num_activations = poison_feature_rep.size
+        beta = self.similarity_coeff * (num_activations / num_features) ** 2
+        return np.linalg.norm(poison_feature_rep - target_feature_rep) + beta * np.linalg.norm(poison - base_image)
+
+    def _check_params(self) -> None:
+        if self.learning_rate <= 0:
+            raise ValueError("Learning rate must be strictly positive")
+
+        if self.max_iter < 1:
+            raise ValueError("Value of max_iter at least 1")
+
+        if not isinstance(self.feature_layer, (str, int)):
+            raise TypeError("Feature layer should be a string or int")
+
+        if self.decay_coeff <= 0:
+            raise ValueError("Decay coefficient must be positive")
+
+        if self.stopping_tol <= 0:
+            raise ValueError("Stopping tolerance must be positive")
+
+        if self.obj_threshold and self.obj_threshold <= 0:
+            raise ValueError("Objective threshold must be positive")
+
+        if self.num_old_obj <= 0:
+            raise ValueError("Number of old stored objectives must be positive")
+
+        if self.max_iter <= 0:
+            raise ValueError("Number of old stored objectives must be positive")
+
+        if self.watermark and not (isinstance(self.watermark, float) and 0 <= self.watermark < 1):
+            raise ValueError("Watermark must be between 0 and 1")
+
+        if not isinstance(self.verbose, bool):
+            raise ValueError("The argument `verbose` has to be of type bool.")
+
+
+def get_class_name(obj: object) -> str:
+    """
+    Get the full class name of an object.
+
+    :param obj: A Python object.
+    :return: A qualified class name.
+    """
+    module = obj.__class__.__module__
+
+    if module is None or module == str.__class__.__module__:
+        return obj.__class__.__name__
+
+    return module + "." + obj.__class__.__name__
+
+
+def tensor_norm(tensor, norm_type: Union[int, float, str] = 2):
+    """
+    Compute the norm of a tensor.
+
+    :param tensor: A tensor from a supported ART neural network.
+    :param norm_type: Order of the norm.
+    :return: A tensor with the norm applied.
+    """
+    tf_tensor_types = ("tensorflow.python.framework.ops.Tensor", "tensorflow.python.framework.ops.EagerTensor")
+    torch_tensor_types = ()
+    mxnet_tensor_types = ()
+    supported_types = tf_tensor_types + torch_tensor_types + mxnet_tensor_types
+    tensor_type = get_class_name(tensor)
+    if tensor_type not in supported_types:
+        raise TypeError("Tensor type `" + tensor_type + "` is not supported")
+
+    if tensor_type in tf_tensor_types:
+        import tensorflow as tf
+
+        return tf.norm(tensor, ord=norm_type)
+
+    if tensor_type in torch_tensor_types:
+        import torch
+
+        return torch.norm(tensor, p=norm_type)
+
+    if tensor_type in mxnet_tensor_types:
+        import mxnet
+
+        return mxnet.ndarray.norm(tensor, ord=norm_type)
diff --git a/adversarial-robustness-toolbox/art/attacks/poisoning/perturbations/__init__.py b/adversarial-robustness-toolbox/art/attacks/poisoning/perturbations/__init__.py
new file mode 100644
index 0000000..0e608d5
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/attacks/poisoning/perturbations/__init__.py
@@ -0,0 +1,8 @@
+"""
+Module providing perturbation functions under a common interface
+"""
+from art.attacks.poisoning.perturbations.image_perturbations import (
+    add_pattern_bd,
+    add_single_bd,
+    insert_image,
+)
diff --git a/adversarial-robustness-toolbox/art/attacks/poisoning/perturbations/image_perturbations.py b/adversarial-robustness-toolbox/art/attacks/poisoning/perturbations/image_perturbations.py
new file mode 100644
index 0000000..93aa412
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/attacks/poisoning/perturbations/image_perturbations.py
@@ -0,0 +1,171 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+Adversarial perturbations designed to work for images.
+"""
+from typing import Optional, Tuple
+
+import numpy as np
+from PIL import Image
+
+
+def add_single_bd(x: np.ndarray, distance: int = 2, pixel_value: int = 1) -> np.ndarray:
+    """
+    Augments a matrix by setting value some `distance` away from the bottom-right edge to 1. Works for single images
+    or a batch of images.
+
+    :param x: N X W X H matrix or W X H matrix
+    :param distance: Distance from bottom-right walls.
+    :param pixel_value: Value used to replace the entries of the image matrix.
+    :return: Backdoored image.
+    """
+    x = np.array(x)
+    shape = x.shape
+    if len(shape) == 4:
+        width, height = x.shape[1:3]
+        x[:, width - distance, height - distance, :] = pixel_value
+    elif len(shape) == 3:
+        width, height = x.shape[1:]
+        x[:, width - distance, height - distance] = pixel_value
+    elif len(shape) == 2:
+        width, height = x.shape
+        x[width - distance, height - distance] = pixel_value
+    else:
+        raise ValueError("Invalid array shape: " + str(shape))
+    return x
+
+
+def add_pattern_bd(x: np.ndarray, distance: int = 2, pixel_value: int = 1) -> np.ndarray:
+    """
+    Augments a matrix by setting a checkboard-like pattern of values some `distance` away from the bottom-right
+    edge to 1. Works for single images or a batch of images.
+
+    :param x: N X W X H matrix or W X H matrix or N X W X H X C matrix, pixels will ne added to all channels
+    :param distance: Distance from bottom-right walls.
+    :param pixel_value: Value used to replace the entries of the image matrix.
+    :return: Backdoored image.
+    """
+    x = np.array(x)
+    shape = x.shape
+    if len(shape) == 4:
+        width, height = x.shape[1:3]
+        x[:, width - distance, height - distance, :] = pixel_value
+        x[:, width - distance - 1, height - distance - 1, :] = pixel_value
+        x[:, width - distance, height - distance - 2, :] = pixel_value
+        x[:, width - distance - 2, height - distance, :] = pixel_value
+    elif len(shape) == 3:
+        width, height = x.shape[1:]
+        x[:, width - distance, height - distance] = pixel_value
+        x[:, width - distance - 1, height - distance - 1] = pixel_value
+        x[:, width - distance, height - distance - 2] = pixel_value
+        x[:, width - distance - 2, height - distance] = pixel_value
+    elif len(shape) == 2:
+        width, height = x.shape
+        x[width - distance, height - distance] = pixel_value
+        x[width - distance - 1, height - distance - 1] = pixel_value
+        x[width - distance, height - distance - 2] = pixel_value
+        x[width - distance - 2, height - distance] = pixel_value
+    else:
+        raise ValueError("Invalid array shape: " + str(shape))
+    return x
+
+
+def insert_image(
+    x: np.ndarray,
+    backdoor_path: str = "../utils/data/backdoors/alert.png",
+    channels_first: bool = False,
+    random: bool = True,
+    x_shift: int = 0,
+    y_shift: int = 0,
+    size: Optional[Tuple[int, int]] = None,
+    mode: str = "L",
+    blend=0.8,
+) -> np.ndarray:
+    """
+    Augments a matrix by setting a checkboard-like pattern of values some `distance` away from the bottom-right
+    edge to 1. Works for single images or a batch of images.
+
+    :param x: N x W x H x C or N x C x W x H or N x W x H x C matrix or W x H x C matrix. X is in range [0,1]
+    :param backdoor_path: The path to the image to insert as a trigger.
+    :param channels_first: Whether the channels axis is in the first or last dimension
+    :param random: Whether or not the image should be randomly placed somewhere on the image.
+    :param x_shift: Number of pixels from the left to shift the trigger (when not using random placement).
+    :param y_shift: Number of pixels from the right to shift the trigger (when not using random placement).
+    :param size: The size the trigger image should be (width, height). Default `None` if no resizing necessary.
+    :param mode: The mode the image should be read in. See PIL documentation
+                 (https://pillow.readthedocs.io/en/stable/handbook/concepts.html#concept-modes).
+    :param blend: The blending factor
+    :return: Backdoored image.
+    """
+    n_dim = len(x.shape)
+    if n_dim == 4:
+        return np.array(
+            [
+                insert_image(single_img, backdoor_path, channels_first, random, x_shift, y_shift, size, mode, blend)
+                for single_img in x
+            ]
+        )
+
+    if n_dim != 3:
+        raise ValueError("Invalid array shape " + str(x.shape))
+
+    data = np.copy(x)
+    if channels_first:
+        data = data.transpose([2, 0, 1])
+
+    width, height, num_channels = x.shape
+
+    no_color = num_channels == 1
+    orig_img = Image.new("RGBA", (width, height), 0)
+    backdoored_img = Image.new("RGBA", (width, height), 0)
+
+    if no_color:
+        backdoored_input = Image.fromarray((data * 255).astype("uint8").squeeze(axis=2), mode=mode)
+    else:
+        backdoored_input = Image.fromarray((data * 255).astype("uint8"), mode=mode)
+
+    orig_img.paste(backdoored_input)
+
+    trigger = Image.open(backdoor_path).convert("RGBA")
+    if size:
+        trigger = trigger.resize(size)
+
+    backdoor_width, backdoor_height = trigger.size
+
+    if backdoor_width > width or backdoor_height > height:
+        raise ValueError("Backdoor does not fit inside original image")
+
+    if random:
+        x_shift = np.random.randint(width - backdoor_width)
+        y_shift = np.random.randint(height - backdoor_height)
+
+    backdoored_img.paste(trigger, (x_shift, y_shift), mask=trigger)
+    composite = Image.alpha_composite(orig_img, backdoored_img)
+    backdoored_img = Image.blend(orig_img, composite, blend)
+
+    backdoored_img = backdoored_img.convert(mode)
+
+    res = np.array(backdoored_img) / 255.0
+
+    if no_color:
+        res = np.expand_dims(res, 2)
+
+    if channels_first:
+        res = res.transpose([2, 0, 1])
+
+    return res
diff --git a/adversarial-robustness-toolbox/art/attacks/poisoning/poisoning_attack_svm.py b/adversarial-robustness-toolbox/art/attacks/poisoning/poisoning_attack_svm.py
new file mode 100644
index 0000000..cc13043
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/attacks/poisoning/poisoning_attack_svm.py
@@ -0,0 +1,256 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2019
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements poisoning attacks on Support Vector Machines.
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+from typing import Optional, Tuple
+
+import numpy as np
+from tqdm.auto import tqdm
+
+from art.attacks.attack import PoisoningAttackWhiteBox
+from art.estimators.classification.scikitlearn import ScikitlearnSVC
+from art.utils import compute_success
+
+
+logger = logging.getLogger(__name__)
+
+
+class PoisoningAttackSVM(PoisoningAttackWhiteBox):
+    """
+    Close implementation of poisoning attack on Support Vector Machines (SVM) by Biggio et al.
+
+    | Paper link: https://arxiv.org/pdf/1206.6389.pdf
+    """
+
+    attack_params = PoisoningAttackWhiteBox.attack_params + [
+        "classifier",
+        "step",
+        "eps",
+        "x_train",
+        "y_train",
+        "x_val",
+        "y_val",
+        "verbose",
+    ]
+    _estimator_requirements = (ScikitlearnSVC,)
+
+    def __init__(
+        self,
+        classifier: "ScikitlearnSVC",
+        step: Optional[float] = None,
+        eps: Optional[float] = None,
+        x_train: Optional[np.ndarray] = None,
+        y_train: Optional[np.ndarray] = None,
+        x_val: Optional[np.ndarray] = None,
+        y_val: Optional[np.ndarray] = None,
+        max_iter: int = 100,
+        verbose: bool = True,
+    ) -> None:
+        """
+        Initialize an SVM poisoning attack.
+
+        :param classifier: A trained :class:`.ScikitlearnSVC` classifier.
+        :param step: The step size of the classifier.
+        :param eps: The minimum difference in loss before convergence of the classifier.
+        :param x_train: The training data used for classification.
+        :param y_train: The training labels used for classification.
+        :param x_val: The validation data used to test the attack.
+        :param y_val: The validation labels used to test the attack.
+        :param max_iter: The maximum number of iterations for the attack.
+        :raises `NotImplementedError`, `TypeError`: If the argument classifier has the wrong type.
+        :param verbose: Show progress bars.
+        """
+        # pylint: disable=W0212
+        from sklearn.svm import LinearSVC, SVC
+
+        super().__init__(classifier=classifier)
+
+        if isinstance(self.estimator.model, LinearSVC):
+            self._estimator = ScikitlearnSVC(
+                model=SVC(C=self.estimator.model.C, kernel="linear"), clip_values=self.estimator.clip_values,
+            )
+            self.estimator.fit(x_train, y_train)
+        elif not isinstance(self.estimator.model, SVC):
+            raise NotImplementedError("Model type '{}' not yet supported".format(type(self.estimator.model)))
+
+        self.step = step
+        self.eps = eps
+        self.x_train = x_train
+        self.y_train = y_train
+        self.x_val = x_val
+        self.y_val = y_val
+        self.max_iter = max_iter
+        self.verbose = verbose
+        self._check_params()
+
+    def poison(self, x: np.ndarray, y: Optional[np.ndarray] = None, **kwargs) -> Tuple[np.ndarray, np.ndarray]:
+        """
+        Iteratively finds optimal attack points starting at values at `x`.
+
+        :param x: An array with the points that initialize attack points.
+        :param y: The target labels for the attack.
+        :return: A tuple holding the `(poisoning_examples, poisoning_labels)`.
+        """
+        if y is None:
+            raise ValueError("Target labels `y` need to be provided for a targeted attack.")
+
+        y_attack = np.copy(y)
+
+        num_poison = len(x)
+        if num_poison == 0:
+            raise ValueError("Must input at least one poison point")
+
+        num_features = len(x[0])
+        train_data = np.copy(self.x_train)
+        train_labels = np.copy(self.y_train)
+        all_poison = []
+
+        for attack_point, attack_label in tqdm(zip(x, y_attack), desc="SVM poisoning", disable=not self.verbose):
+            poison = self.generate_attack_point(attack_point, attack_label)
+            all_poison.append(poison)
+            train_data = np.vstack([train_data, poison])
+            train_labels = np.vstack([train_labels, attack_label])
+
+        x_adv = np.array(all_poison).reshape((num_poison, num_features))
+        targeted = y is not None
+
+        logger.info(
+            "Success rate of poisoning attack SVM attack: %.2f%%",
+            100 * compute_success(self.estimator, x, y, x_adv, targeted=targeted),
+        )
+
+        return x_adv, y_attack
+
+    def generate_attack_point(self, x_attack: np.ndarray, y_attack: np.ndarray) -> np.ndarray:
+        """
+        Generate a single poison attack the model, using `x_val` and `y_val` as validation points.
+        The attack begins at the point init_attack. The attack class will be the opposite of the model's
+        classification for `init_attack`.
+
+        :param x_attack: The initial attack point.
+        :param y_attack: The initial attack label.
+        :return: A tuple containing the final attack point and the poisoned model.
+        """
+        # pylint: disable=W0212
+        from sklearn.preprocessing import normalize
+
+        poisoned_model = self.estimator.model
+        y_t = np.argmax(self.y_train, axis=1)
+        poisoned_model.fit(self.x_train, y_t)
+        y_a = np.argmax(y_attack)
+        attack_point = np.expand_dims(x_attack, axis=0)
+        var_g = poisoned_model.decision_function(self.x_val)
+        k_values = np.where(-var_g > 0)
+        new_p = np.sum(var_g[k_values])
+        old_p = np.copy(new_p)
+        i = 0
+
+        while new_p - old_p < self.eps and i < self.max_iter:
+            old_p = new_p
+            poisoned_input = np.vstack([self.x_train, attack_point])
+            poisoned_labels = np.append(y_t, y_a)
+            poisoned_model.fit(poisoned_input, poisoned_labels)
+
+            unit_grad = normalize(self.attack_gradient(attack_point))
+            attack_point += self.step * unit_grad
+            lower, upper = self.estimator.clip_values
+            new_attack = np.clip(attack_point, lower, upper)
+            new_g = poisoned_model.decision_function(self.x_val)
+            k_values = np.where(-new_g > 0)
+            new_p = np.sum(new_g[k_values])
+            i += 1
+            attack_point = new_attack
+
+        poisoned_input = np.vstack([self.x_train, attack_point])
+        poisoned_labels = np.append(y_t, y_a)
+        poisoned_model.fit(poisoned_input, poisoned_labels)
+        return attack_point
+
+    def predict_sign(self, vec: np.ndarray) -> np.ndarray:
+        """
+        Predicts the inputs by binary classifier and outputs -1 and 1 instead of 0 and 1.
+
+        :param vec: An input array.
+        :return: An array of -1/1 predictions.
+        """
+        # pylint: disable=W0212
+        preds = self.estimator.model.predict(vec)
+        return 2 * preds - 1
+
+    def attack_gradient(self, attack_point: np.ndarray, tol: float = 0.0001) -> np.ndarray:
+        """
+        Calculates the attack gradient, or dP for this attack.
+        See equation 8 in Biggio et al. Ch. 14
+
+        :param attack_point: The current attack point.
+        :param tol: Tolerance level.
+        :return: The attack gradient.
+        """
+        # pylint: disable=W0212
+        if self.x_val is None or self.y_val is None:
+            raise ValueError("The values of `x_val` and `y_val` are required for computing the gradients.")
+
+        art_model = self.estimator
+        model = self.estimator.model
+        grad = np.zeros((1, self.x_val.shape[1]))
+        support_vectors = model.support_vectors_
+        num_support = len(support_vectors)
+        support_labels = np.expand_dims(self.predict_sign(support_vectors), axis=1)
+        c_idx = np.isin(support_vectors, attack_point).all(axis=1)
+
+        if not c_idx.any():
+            return grad
+
+        c_idx = np.where(c_idx > 0)[0][0]
+        alpha_c = model.dual_coef_[0, c_idx]
+
+        assert support_labels.shape == (num_support, 1)
+        qss = art_model.q_submatrix(support_vectors, support_vectors)
+        qss_inv = np.linalg.inv(qss + np.random.uniform(0, 0.01 * np.min(qss) + tol, (num_support, num_support)))
+        zeta = np.matmul(qss_inv, support_labels)
+        zeta = np.matmul(support_labels.T, zeta)
+        nu_k = np.matmul(qss_inv, support_labels)
+        for x_k, y_k in zip(self.x_val, self.y_val):
+            y_k = 2 * np.expand_dims(np.argmax(y_k), axis=0) - 1
+
+            q_ks = art_model.q_submatrix(np.array([x_k]), support_vectors)
+            m_k = (1.0 / zeta) * np.matmul(q_ks, zeta * qss_inv - np.matmul(nu_k, nu_k.T)) + np.matmul(y_k, nu_k.T)
+            d_q_sc = np.fromfunction(
+                lambda i: art_model._get_kernel_gradient_sv(i, attack_point), (len(support_vectors),), dtype=int,
+            )
+            d_q_kc = art_model._kernel_grad(x_k, attack_point)
+            grad += (np.matmul(m_k, d_q_sc) + d_q_kc) * alpha_c
+
+        return grad
+
+    def _check_params(self) -> None:
+        if self.step is not None and self.step <= 0:
+            raise ValueError("Step size must be strictly positive.")
+
+        if self.eps is not None and self.eps <= 0:
+            raise ValueError("Value of eps must be strictly positive.")
+
+        if self.max_iter <= 1:
+            raise ValueError("Value of max_iter must be strictly positive.")
+
+        if not isinstance(self.verbose, bool):
+            raise ValueError("The argument `verbose` has to be of type bool.")
diff --git a/adversarial-robustness-toolbox/art/classifiers/__init__.py b/adversarial-robustness-toolbox/art/classifiers/__init__.py
new file mode 100644
index 0000000..5dba5e1
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/classifiers/__init__.py
@@ -0,0 +1,20 @@
+"""
+Classifier API for applying all attacks. Use the :class:`.Classifier` wrapper to be able to apply an attack to a
+existing model.
+"""
+from art.estimators.classification.blackbox import BlackBoxClassifier
+from art.estimators.classification.catboost import CatBoostARTClassifier
+from art.estimators.classification.detector_classifier import DetectorClassifier
+from art.estimators.classification.ensemble import EnsembleClassifier
+from art.estimators.classification.GPy import GPyGaussianProcessClassifier
+from art.estimators.classification.keras import KerasClassifier
+from art.estimators.classification.lightgbm import LightGBMClassifier
+from art.estimators.classification.mxnet import MXClassifier
+from art.estimators.classification.pytorch import PyTorchClassifier
+from art.estimators.classification.scikitlearn import SklearnClassifier
+from art.estimators.classification.tensorflow import (
+    TFClassifier,
+    TensorFlowClassifier,
+    TensorFlowV2Classifier,
+)
+from art.estimators.classification.xgboost import XGBoostClassifier
diff --git a/adversarial-robustness-toolbox/art/classifiers/scikitlearn/__init__.py b/adversarial-robustness-toolbox/art/classifiers/scikitlearn/__init__.py
new file mode 100644
index 0000000..1c984b2
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/classifiers/scikitlearn/__init__.py
@@ -0,0 +1,17 @@
+"""
+Classifier API for abstracting classification models for testing purposes. Soon to be replaced by Estimator API.
+Use `art.estimators.classification instead.
+"""
+from art.estimators.classification.scikitlearn import SklearnClassifier
+from art.estimators.classification.scikitlearn import ScikitlearnClassifier
+from art.estimators.classification.scikitlearn import ScikitlearnDecisionTreeClassifier
+from art.estimators.classification.scikitlearn import ScikitlearnDecisionTreeRegressor
+from art.estimators.classification.scikitlearn import ScikitlearnExtraTreeClassifier
+from art.estimators.classification.scikitlearn import ScikitlearnAdaBoostClassifier
+from art.estimators.classification.scikitlearn import ScikitlearnBaggingClassifier
+from art.estimators.classification.scikitlearn import ScikitlearnExtraTreesClassifier
+from art.estimators.classification.scikitlearn import ScikitlearnGradientBoostingClassifier
+from art.estimators.classification.scikitlearn import ScikitlearnRandomForestClassifier
+from art.estimators.classification.scikitlearn import ScikitlearnLogisticRegression
+from art.estimators.classification.scikitlearn import ScikitlearnSVC
+from art.estimators.classification.scikitlearn import ScikitlearnLinearSVC
diff --git a/adversarial-robustness-toolbox/art/config.py b/adversarial-robustness-toolbox/art/config.py
new file mode 100644
index 0000000..244fdb6
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/config.py
@@ -0,0 +1,94 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module loads and provides configuration parameters for ART.
+"""
+import json
+import logging
+import os
+
+import numpy as np
+
+logger = logging.getLogger(__name__)
+
+# ------------------------------------------------------------------------------------------------- CONSTANTS AND TYPES
+
+ART_NUMPY_DTYPE = np.float32
+ART_DATA_PATH: str
+
+# --------------------------------------------------------------------------------------------- DEFAULT PACKAGE CONFIGS
+
+_folder = os.path.expanduser("~")
+if not os.access(_folder, os.W_OK):
+    _folder = "/tmp"
+_folder = os.path.join(_folder, ".art")
+
+
+def set_data_path(path):
+    """
+    Set the path for ART's data directory (ART_DATA_PATH).
+    """
+    expanded_path = os.path.abspath(os.path.expanduser(path))
+    os.makedirs(expanded_path, exist_ok=True)
+    if not os.access(expanded_path, os.R_OK):
+        raise OSError(f"path {expanded_path} cannot be read from")
+    if not os.access(expanded_path, os.W_OK):
+        logger.warning(f"path %s is read only", expanded_path)
+
+    global ART_DATA_PATH
+    ART_DATA_PATH = expanded_path
+    logger.info(f"set ART_DATA_PATH to %s", expanded_path)
+
+
+# Load data from configuration file if it exists. Otherwise create one.
+_config_path = os.path.expanduser(os.path.join(_folder, "config.json"))
+if os.path.exists(_config_path):
+    try:
+        with open(_config_path) as f:
+            _config = json.load(f)
+
+            # Since renaming this variable we must update existing config files
+            if "DATA_PATH" in _config:
+                _config["ART_DATA_PATH"] = _config.pop("DATA_PATH")
+                try:
+                    with open(_config_path, "w") as f:
+                        f.write(json.dumps(_config, indent=4))
+                except IOError:
+                    logger.warning("Unable to update configuration file", exc_info=True)
+
+    except ValueError:
+        _config = {}
+
+if not os.path.exists(_folder):
+    try:
+        os.makedirs(_folder)
+    except OSError:
+        logger.warning("Unable to create folder for configuration file.", exc_info=True)
+
+if not os.path.exists(_config_path):
+    # Generate default config
+    _config = {"ART_DATA_PATH": os.path.join(_folder, "data")}
+
+    try:
+        with open(_config_path, "w") as f:
+            f.write(json.dumps(_config, indent=4))
+    except IOError:
+        logger.warning("Unable to create configuration file", exc_info=True)
+
+if "ART_DATA_PATH" in _config:
+    set_data_path(_config["ART_DATA_PATH"])
diff --git a/adversarial-robustness-toolbox/art/data_generators.py b/adversarial-robustness-toolbox/art/data_generators.py
new file mode 100644
index 0000000..da9ccbc
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/data_generators.py
@@ -0,0 +1,339 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+Module defining an interface for data generators and providing concrete implementations for the supported frameworks.
+Their purpose is to allow for data loading and batching on the fly, as well as dynamic data augmentation.
+The generators can be used with the `fit_generator` function in the :class:`.Classifier` interface. Users can define
+their own generators following the :class:`.DataGenerator` interface.
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import abc
+import inspect
+import logging
+from typing import Any, Dict, Generator, Optional, Tuple, Union, TYPE_CHECKING
+
+if TYPE_CHECKING:
+    import keras
+    import mxnet
+    import tensorflow as tf
+    import torch
+
+logger = logging.getLogger(__name__)
+
+
+class DataGenerator(abc.ABC):
+    """
+    Base class for data generators.
+    """
+
+    def __init__(self, size: Optional[int], batch_size: int) -> None:
+        """
+        Base initializer for data generators.
+
+        :param size: Total size of the dataset.
+        :param batch_size: Size of the minibatches.
+        """
+        if size is not None and (not isinstance(size, int) or size < 1):
+            raise ValueError("The total size of the dataset must be an integer greater than zero.")
+        self._size = size
+
+        if not isinstance(batch_size, int) or batch_size < 1:
+            raise ValueError("The batch size must be an integer greater than zero.")
+        self._batch_size = batch_size
+
+        if size is not None and batch_size > size:
+            raise ValueError("The batch size must be smaller than the dataset size.")
+
+        self._iterator: Optional[Any] = None
+
+    @abc.abstractmethod
+    def get_batch(self) -> tuple:
+        """
+        Provide the next batch for training in the form of a tuple `(x, y)`. The generator should loop over the data
+        indefinitely.
+
+        :return: A tuple containing a batch of data `(x, y)`.
+        """
+        raise NotImplementedError
+
+    @property
+    def iterator(self):
+        """
+        :return: Return the framework's iterable data generator.
+        """
+        return self._iterator
+
+    @property
+    def batch_size(self) -> int:
+        """
+        :return: Return the batch size.
+        """
+        return self._batch_size
+
+    @property
+    def size(self) -> Optional[int]:
+        """
+        :return: Return the dataset size.
+        """
+        return self._size
+
+
+class KerasDataGenerator(DataGenerator):
+    """
+    Wrapper class on top of the Keras-native data generators. These can either be generator functions,
+    `keras.utils.Sequence` or Keras-specific data generators (`keras.preprocessing.image.ImageDataGenerator`).
+    """
+
+    def __init__(
+        self,
+        iterator: Union[
+            "keras.utils.Sequence",
+            "tf.keras.utils.Sequence",
+            "keras.preprocessing.image.ImageDataGenerator",
+            "tf.keras.preprocessing.image.ImageDataGenerator",
+            Generator,
+        ],
+        size: Optional[int],
+        batch_size: int,
+    ) -> None:
+        """
+        Create a Keras data generator wrapper instance.
+
+        :param iterator: A generator as specified by Keras documentation. Its output must be a tuple of either
+                         `(inputs, targets)` or `(inputs, targets, sample_weights)`. All arrays in this tuple must have
+                         the same length. The generator is expected to loop over its data indefinitely.
+        :param size: Total size of the dataset.
+        :param batch_size: Size of the minibatches.
+        """
+        super().__init__(size=size, batch_size=batch_size)
+        self._iterator = iterator
+
+    def get_batch(self) -> tuple:
+        """
+        Provide the next batch for training in the form of a tuple `(x, y)`. The generator should loop over the data
+        indefinitely.
+
+        :return: A tuple containing a batch of data `(x, y)`.
+        """
+        if inspect.isgeneratorfunction(self.iterator):
+            return next(self.iterator)
+
+        iter_ = iter(self.iterator)
+        return next(iter_)
+
+
+class PyTorchDataGenerator(DataGenerator):
+    """
+    Wrapper class on top of the PyTorch native data loader :class:`torch.utils.data.DataLoader`.
+    """
+
+    def __init__(self, iterator: "torch.utils.data.DataLoader", size: int, batch_size: int) -> None:
+        """
+        Create a data generator wrapper on top of a PyTorch :class:`DataLoader`.
+
+        :param iterator: A PyTorch data generator.
+        :param size: Total size of the dataset.
+        :param batch_size: Size of the minibatches.
+        """
+        from torch.utils.data import DataLoader
+
+        super().__init__(size=size, batch_size=batch_size)
+        if not isinstance(iterator, DataLoader):
+            raise TypeError("Expected instance of PyTorch `DataLoader, received %s instead.`" % str(type(iterator)))
+
+        self._iterator: DataLoader = iterator
+        self._current = iter(self.iterator)
+
+    def get_batch(self) -> tuple:
+        """
+        Provide the next batch for training in the form of a tuple `(x, y)`. The generator should loop over the data
+        indefinitely.
+
+        :return: A tuple containing a batch of data `(x, y)`.
+        :rtype: `tuple`
+        """
+        try:
+            batch = list(next(self._current))
+        except StopIteration:
+            self._current = iter(self.iterator)
+            batch = list(next(self._current))
+
+        for i, item in enumerate(batch):
+            batch[i] = item.data.cpu().numpy()
+
+        return tuple(batch)
+
+
+class MXDataGenerator(DataGenerator):
+    """
+    Wrapper class on top of the MXNet/Gluon native data loader :class:`mxnet.gluon.data.DataLoader`.
+    """
+
+    def __init__(self, iterator: "mxnet.gluon.data.DataLoader", size: int, batch_size: int) -> None:
+        """
+        Create a data generator wrapper on top of an MXNet :class:`DataLoader`.
+
+        :param iterator: A MXNet DataLoader instance.
+        :param size: Total size of the dataset.
+        :param batch_size: Size of the minibatches.
+        """
+        import mxnet  # lgtm [py/repeated-import]
+
+        super().__init__(size=size, batch_size=batch_size)
+        if not isinstance(iterator, mxnet.gluon.data.DataLoader):
+            raise TypeError("Expected instance of Gluon `DataLoader, received %s instead.`" % str(type(iterator)))
+
+        self._iterator = iterator
+        self._current = iter(self.iterator)
+
+    def get_batch(self) -> tuple:
+        """
+        Provide the next batch for training in the form of a tuple `(x, y)`. The generator should loop over the data
+        indefinitely.
+
+        :return: A tuple containing a batch of data `(x, y)`.
+        """
+        try:
+            batch = list(next(self._current))
+        except StopIteration:
+            self._current = iter(self.iterator)
+            batch = list(next(self._current))
+
+        for i, item in enumerate(batch):
+            batch[i] = item.asnumpy()
+
+        return tuple(batch)
+
+
+class TensorFlowDataGenerator(DataGenerator):
+    """
+    Wrapper class on top of the TensorFlow native iterators :class:`tf.data.Iterator`.
+    """
+
+    def __init__(
+        self,
+        sess: "tf.Session",
+        iterator: "tf.data.Iterator",
+        iterator_type: str,
+        iterator_arg: Union[Dict, Tuple, "tf.Operation"],
+        size: int,
+        batch_size: int,
+    ) -> None:
+        """
+        Create a data generator wrapper for TensorFlow. Supported iterators: initializable, reinitializable, feedable.
+
+        :param sess: TensorFlow session.
+        :param iterator: Data iterator from TensorFlow.
+        :param iterator_type: Type of the iterator. Supported types: `initializable`, `reinitializable`, `feedable`.
+        :param iterator_arg: Argument to initialize the iterator. It is either a feed_dict used for the initializable
+        and feedable mode, or an init_op used for the reinitializable mode.
+        :param size: Total size of the dataset.
+        :param batch_size: Size of the minibatches.
+        :raises `TypeError`, `ValueError`: If input parameters are not valid.
+        """
+        # pylint: disable=E0401
+        import tensorflow as tf  # lgtm [py/repeated-import]
+
+        super().__init__(size=size, batch_size=batch_size)
+        self.sess = sess
+        self._iterator = iterator
+        self.iterator_type = iterator_type
+        self.iterator_arg = iterator_arg
+
+        if not isinstance(iterator, tf.data.Iterator):
+            raise TypeError("Only support object tf.data.Iterator")
+
+        if iterator_type == "initializable":
+            if not isinstance(iterator_arg, dict):
+                raise TypeError("Need to pass a dictionary for iterator type %s" % iterator_type)
+        elif iterator_type == "reinitializable":
+            if not isinstance(iterator_arg, tf.Operation):
+                raise TypeError("Need to pass a TensorFlow operation for iterator type %s" % iterator_type)
+        elif iterator_type == "feedable":
+            if not isinstance(iterator_arg, tuple):
+                raise TypeError("Need to pass a tuple for iterator type %s" % iterator_type)
+        else:
+            raise TypeError("Iterator type %s not supported" % iterator_type)
+
+    def get_batch(self) -> tuple:
+        """
+        Provide the next batch for training in the form of a tuple `(x, y)`. The generator should loop over the data
+        indefinitely.
+
+        :return: A tuple containing a batch of data `(x, y)`.
+        :raises `ValueError`: If the iterator has reached the end.
+        """
+        import tensorflow as tf  # lgtm [py/repeated-import]
+
+        # Get next batch
+        next_batch = self.iterator.get_next()
+
+        # Process to get the batch
+        try:
+            if self.iterator_type in ("initializable", "reinitializable"):
+                return self.sess.run(next_batch)
+            return self.sess.run(next_batch, feed_dict=self.iterator_arg[1])
+        except (tf.errors.FailedPreconditionError, tf.errors.OutOfRangeError):
+            if self.iterator_type == "initializable":
+                self.sess.run(self.iterator.initializer, feed_dict=self.iterator_arg)
+                return self.sess.run(next_batch)
+
+            if self.iterator_type == "reinitializable":
+                self.sess.run(self.iterator_arg)
+                return self.sess.run(next_batch)
+
+            self.sess.run(self.iterator_arg[0].initializer)
+            return self.sess.run(next_batch, feed_dict=self.iterator_arg[1])
+
+
+class TensorFlowV2DataGenerator(DataGenerator):
+    """
+    Wrapper class on top of the TensorFlow v2 native iterators :class:`tf.data.Iterator`.
+    """
+
+    def __init__(self, iterator: "tf.data.Dataset", size: int, batch_size: int) -> None:
+        """
+        Create a data generator wrapper for TensorFlow. Supported iterators: initializable, reinitializable, feedable.
+
+        :param iterator: TensorFlow Dataset.
+        :param size: Total size of the dataset.
+        :param batch_size: Size of the minibatches.
+        :raises `TypeError`, `ValueError`: If input parameters are not valid.
+        """
+        # pylint: disable=E0401
+        import tensorflow as tf  # lgtm [py/repeated-import]
+
+        super().__init__(size=size, batch_size=batch_size)
+        self._iterator = iterator
+        self._iterator_iter = iter(iterator)
+
+        if not isinstance(iterator, tf.data.Dataset):
+            raise TypeError("Only support object tf.data.Dataset")
+
+    def get_batch(self) -> tuple:
+        """
+        Provide the next batch for training in the form of a tuple `(x, y)`. The generator should loop over the data
+        indefinitely.
+
+        :return: A tuple containing a batch of data `(x, y)`.
+        :raises `ValueError`: If the iterator has reached the end.
+        """
+        # Get next batch
+        x, y = next(self._iterator_iter)
+        return x.numpy(), y.numpy()
diff --git a/adversarial-robustness-toolbox/art/defences/__init__.py b/adversarial-robustness-toolbox/art/defences/__init__.py
new file mode 100644
index 0000000..65d8b10
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/defences/__init__.py
@@ -0,0 +1,8 @@
+"""
+Module implementing multiple types of defences against adversarial attacks.
+"""
+from art.defences import detector
+from art.defences import postprocessor
+from art.defences import preprocessor
+from art.defences import trainer
+from art.defences import transformer
diff --git a/adversarial-robustness-toolbox/art/defences/detector/__init__.py b/adversarial-robustness-toolbox/art/defences/detector/__init__.py
new file mode 100644
index 0000000..f2f7937
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/defences/detector/__init__.py
@@ -0,0 +1,5 @@
+"""
+Module implementing detector-based defences against adversarial attacks.
+"""
+from art.defences.detector import evasion
+from art.defences.detector import poison
diff --git a/adversarial-robustness-toolbox/art/defences/detector/evasion/__init__.py b/adversarial-robustness-toolbox/art/defences/detector/evasion/__init__.py
new file mode 100644
index 0000000..560f66f
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/defences/detector/evasion/__init__.py
@@ -0,0 +1,6 @@
+"""
+Module implementing detector-based defences against evasion attacks.
+"""
+from art.defences.detector.evasion import subsetscanning
+
+from art.defences.detector.evasion.detector import BinaryInputDetector, BinaryActivationDetector
diff --git a/adversarial-robustness-toolbox/art/defences/detector/evasion/detector.py b/adversarial-robustness-toolbox/art/defences/detector/evasion/detector.py
new file mode 100644
index 0000000..54e1b23
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/defences/detector/evasion/detector.py
@@ -0,0 +1,350 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+Module containing different methods for the detection of adversarial examples. All models are considered to be binary
+detectors.
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+from typing import List, Optional, Tuple, Union, TYPE_CHECKING
+
+import numpy as np
+
+from art.estimators.estimator import BaseEstimator, NeuralNetworkMixin, LossGradientsMixin
+from art.estimators.classification.classifier import ClassifierMixin, ClassGradientsMixin
+
+if TYPE_CHECKING:
+    from art.utils import CLIP_VALUES_TYPE
+    from art.data_generators import DataGenerator
+    from art.estimators.classification.classifier import ClassifierNeuralNetwork
+
+logger = logging.getLogger(__name__)
+
+
+class BinaryInputDetector(ClassGradientsMixin, ClassifierMixin, LossGradientsMixin, NeuralNetworkMixin, BaseEstimator):
+    """
+    Binary detector of adversarial samples coming from evasion attacks. The detector uses an architecture provided by
+    the user and trains it on data labeled as clean (label 0) or adversarial (label 1).
+    """
+
+    estimator_params = (
+        BaseEstimator.estimator_params
+        + NeuralNetworkMixin.estimator_params
+        + ClassifierMixin.estimator_params
+        + ["detector"]
+    )
+
+    def __init__(self, detector: "ClassifierNeuralNetwork") -> None:
+        """
+        Create a `BinaryInputDetector` instance which performs binary classification on input data.
+
+        :param detector: The detector architecture to be trained and applied for the binary classification.
+        """
+        super().__init__(
+            model=None,
+            clip_values=detector.clip_values,
+            channels_first=detector.channels_first,
+            preprocessing_defences=detector.preprocessing_defences,
+            preprocessing=detector.preprocessing,
+        )
+        self.detector = detector
+
+    def fit(self, x: np.ndarray, y: np.ndarray, batch_size: int = 128, nb_epochs: int = 20, **kwargs) -> None:
+        """
+        Fit the detector using clean and adversarial samples.
+
+        :param x: Training set to fit the detector.
+        :param y: Labels for the training set.
+        :param batch_size: Size of batches.
+        :param nb_epochs: Number of epochs to use for training.
+        :param kwargs: Other parameters.
+        """
+        self.detector.fit(x, y, batch_size=batch_size, nb_epochs=nb_epochs, **kwargs)
+
+    def predict(self, x: np.ndarray, batch_size: int = 128, **kwargs) -> np.ndarray:
+        """
+        Perform detection of adversarial data and return prediction as tuple.
+
+        :param x: Data sample on which to perform detection.
+        :param batch_size: Size of batches.
+        :return: Per-sample prediction whether data is adversarial or not, where `0` means non-adversarial.
+                 Return variable has the same `batch_size` (first dimension) as `x`.
+        """
+        return self.detector.predict(x, batch_size=batch_size)
+
+    def fit_generator(self, generator: "DataGenerator", nb_epochs: int = 20, **kwargs) -> None:
+        """
+        Fit the classifier using the generator gen that yields batches as specified. This function is not supported
+        for this detector.
+
+        :raises `NotImplementedException`: This method is not supported for detectors.
+        """
+        raise NotImplementedError
+
+    def compute_loss(self, x: np.ndarray, y: np.ndarray, **kwargs) -> np.ndarray:
+        """
+        Compute the loss of the neural network for samples `x`.
+
+        :param x: Samples of shape (nb_samples, nb_features) or (nb_samples, nb_pixels_1, nb_pixels_2,
+                  nb_channels) or (nb_samples, nb_channels, nb_pixels_1, nb_pixels_2).
+        :param y: Target values (class labels) one-hot-encoded of shape `(nb_samples, nb_classes)` or indices
+                  of shape `(nb_samples,)`.
+        :return: Loss values.
+        :rtype: Format as expected by the `model`
+        """
+        raise NotImplementedError
+
+    @property
+    def nb_classes(self) -> int:
+        return self.detector.nb_classes
+
+    @property
+    def input_shape(self) -> Tuple[int, ...]:
+        return self.detector.input_shape
+
+    @property
+    def clip_values(self) -> Optional["CLIP_VALUES_TYPE"]:
+        return self.detector.clip_values
+
+    @property
+    def channels_first(self) -> Optional[bool]:
+        """
+        :return: Boolean to indicate index of the color channels in the sample `x`.
+        """
+        return self._channels_first
+
+    def class_gradient(
+        self, x: np.ndarray, label: Union[int, List[int], None] = None, training_mode: bool = False, **kwargs
+    ) -> np.ndarray:
+        """
+        Compute per-class derivatives w.r.t. `x`.
+
+        :param x: Sample input with shape as expected by the model.
+        :param label: Index of a specific per-class derivative. If an integer is provided, the gradient of that class
+                      output is computed for all samples. If multiple values as provided, the first dimension should
+                      match the batch size of `x`, and each value will be used as target for its corresponding sample in
+                      `x`. If `None`, then gradients for all classes will be computed for each sample.
+        :param training_mode: `True` for model set to training mode and `'False` for model set to evaluation mode.
+        :return: Array of gradients of input features w.r.t. each class in the form
+                 `(batch_size, nb_classes, input_shape)` when computing for all classes, otherwise shape becomes
+                 `(batch_size, 1, input_shape)` when `label` parameter is specified.
+        """
+        return self.detector.class_gradient(x, label=label, training_mode=training_mode, **kwargs)
+
+    def loss_gradient(self, x: np.ndarray, y: np.ndarray, training_mode: bool = False, **kwargs) -> np.ndarray:
+        """
+        Compute the gradient of the loss function w.r.t. `x`.
+
+        :param x: Sample input with shape as expected by the model.
+        :param y: Target values (class labels) one-hot-encoded of shape `(nb_samples, nb_classes)` or indices of shape
+                  `(nb_samples,)`.
+        :param training_mode: `True` for model set to training mode and `'False` for model set to evaluation mode.
+        :return: Array of gradients of the same shape as `x`.
+        """
+        return self.detector.loss_gradient(x=x, y=y, training_mode=training_mode, **kwargs)
+
+    def get_activations(
+        self, x: np.ndarray, layer: Union[int, str], batch_size: int, framework: bool = False
+    ) -> np.ndarray:
+        """
+        Return the output of the specified layer for input `x`. `layer` is specified by layer index (between 0 and
+        `nb_layers - 1`) or by name. The number of layers can be determined by counting the results returned by
+        calling `layer_names`. This function is not supported for this detector.
+
+        :raises `NotImplementedException`: This method is not supported for detectors.
+        """
+        raise NotImplementedError
+
+    def save(self, filename: str, path: Optional[str] = None) -> None:
+        """
+        Save the detector model.
+
+        param filename: The name of the saved file.
+        param path: The path to the location of the saved file.
+        """
+        self.detector.save(filename, path)
+
+
+class BinaryActivationDetector(
+    ClassGradientsMixin, ClassifierMixin, LossGradientsMixin, NeuralNetworkMixin, BaseEstimator
+):
+    """
+    Binary detector of adversarial samples coming from evasion attacks. The detector uses an architecture provided by
+    the user and is trained on the values of the activations of a classifier at a given layer.
+    """
+
+    estimator_params = (
+        BaseEstimator.estimator_params + NeuralNetworkMixin.estimator_params + ClassifierMixin.estimator_params
+    )
+
+    def __init__(
+        self, classifier: "ClassifierNeuralNetwork", detector: "ClassifierNeuralNetwork", layer: Union[int, str],
+    ) -> None:  # lgtm [py/similar-function]
+        """
+        Create a `BinaryActivationDetector` instance which performs binary classification on activation information.
+        The shape of the input of the detector has to match that of the output of the chosen layer.
+
+        :param classifier: The classifier of which the activation information is to be used for detection.
+        :param detector: The detector architecture to be trained and applied for the binary classification.
+        :param layer: Layer for computing the activations to use for training the detector.
+        """
+        super().__init__(
+            model=None,
+            clip_values=detector.clip_values,
+            channels_first=detector.channels_first,
+            preprocessing_defences=detector.preprocessing_defences,
+            preprocessing=detector.preprocessing,
+        )
+        self.classifier = classifier
+        self.detector = detector
+
+        # Ensure that layer is well-defined:
+        if classifier.layer_names is None:
+            raise ValueError("No layer names identified.")
+
+        if isinstance(layer, int):
+            if layer < 0 or layer >= len(classifier.layer_names):
+                raise ValueError(
+                    "Layer index %d is outside of range (0 to %d included)." % (layer, len(classifier.layer_names) - 1)
+                )
+            self._layer_name = classifier.layer_names[layer]
+        else:
+            if layer not in classifier.layer_names:
+                raise ValueError("Layer name %s is not part of the graph." % layer)
+            self._layer_name = layer
+
+    def fit(self, x: np.ndarray, y: np.ndarray, batch_size: int = 128, nb_epochs: int = 20, **kwargs) -> None:
+        """
+        Fit the detector using training data.
+
+        :param x: Training set to fit the detector.
+        :param y: Labels for the training set.
+        :param batch_size: Size of batches.
+        :param nb_epochs: Number of epochs to use for training.
+        :param kwargs: Other parameters.
+        """
+        x_activations = self.classifier.get_activations(x, self._layer_name, batch_size)
+        self.detector.fit(x_activations, y, batch_size=batch_size, nb_epochs=nb_epochs, **kwargs)
+
+    def predict(self, x: np.ndarray, batch_size: int = 128, **kwargs) -> np.ndarray:
+        """
+        Perform detection of adversarial data and return prediction as tuple.
+
+        :param x: Data sample on which to perform detection.
+        :param batch_size: Size of batches.
+        :return: Per-sample prediction whether data is adversarial or not, where `0` means non-adversarial.
+                 Return variable has the same `batch_size` (first dimension) as `x`.
+        """
+        return self.detector.predict(self.classifier.get_activations(x, self._layer_name, batch_size))
+
+    def fit_generator(self, generator: "DataGenerator", nb_epochs: int = 20, **kwargs) -> None:
+        """
+        Fit the classifier using the generator gen that yields batches as specified. This function is not supported
+        for this detector.
+
+        :raises `NotImplementedException`: This method is not supported for detectors.
+        """
+        raise NotImplementedError
+
+    def compute_loss(self, x: np.ndarray, y: np.ndarray, **kwargs) -> np.ndarray:
+        """
+        Compute the loss of the neural network for samples `x`.
+
+        :param x: Samples of shape (nb_samples, nb_features) or (nb_samples, nb_pixels_1, nb_pixels_2,
+                  nb_channels) or (nb_samples, nb_channels, nb_pixels_1, nb_pixels_2).
+        :param y: Target values (class labels) one-hot-encoded of shape `(nb_samples, nb_classes)` or indices
+                  of shape `(nb_samples,)`.
+        :return: Loss values.
+        :rtype: Format as expected by the `model`
+        """
+        raise NotImplementedError
+
+    @property
+    def nb_classes(self) -> int:
+        return self.detector.nb_classes
+
+    @property
+    def input_shape(self) -> Tuple[int, ...]:
+        return self.detector.input_shape
+
+    @property
+    def clip_values(self) -> Optional["CLIP_VALUES_TYPE"]:
+        return self.detector.clip_values
+
+    @property
+    def channels_first(self) -> Optional[bool]:
+        """
+        :return: Boolean to indicate index of the color channels in the sample `x`.
+        """
+        return self._channels_first
+
+    @property
+    def layer_names(self) -> List[str]:
+        raise NotImplementedError
+
+    def class_gradient(
+        self, x: np.ndarray, label: Union[int, List[int], None] = None, training_mode: bool = False, **kwargs
+    ) -> np.ndarray:
+        """
+        Compute per-class derivatives w.r.t. `x`.
+
+        :param x: Sample input with shape as expected by the model.
+        :param label: Index of a specific per-class derivative. If an integer is provided, the gradient of that class
+                      output is computed for all samples. If multiple values as provided, the first dimension should
+                      match the batch size of `x`, and each value will be used as target for its corresponding sample in
+                      `x`. If `None`, then gradients for all classes will be computed for each sample.
+        :param training_mode: `True` for model set to training mode and `'False` for model set to evaluation mode.
+        :return: Array of gradients of input features w.r.t. each class in the form
+                 `(batch_size, nb_classes, input_shape)` when computing for all classes, otherwise shape becomes
+                 `(batch_size, 1, input_shape)` when `label` parameter is specified.
+        """
+        return self.detector.class_gradient(x=x, label=label, training_mode=training_mode, **kwargs)
+
+    def loss_gradient(self, x: np.ndarray, y: np.ndarray, training_mode: bool = False, **kwargs) -> np.ndarray:
+        """
+        Compute the gradient of the loss function w.r.t. `x`.
+
+        :param x: Sample input with shape as expected by the model.
+        :param y: Target values (class labels) one-hot-encoded of shape `(nb_samples, nb_classes)` or indices of shape
+                  `(nb_samples,)`.
+        :param training_mode: `True` for model set to training mode and `'False` for model set to evaluation mode.
+        :return: Array of gradients of the same shape as `x`.
+        """
+        return self.detector.loss_gradient(x=x, y=y, training_mode=training_mode, **kwargs)
+
+    def get_activations(
+        self, x: np.ndarray, layer: Union[int, str], batch_size: int, framework: bool = False
+    ) -> np.ndarray:
+        """
+        Return the output of the specified layer for input `x`. `layer` is specified by layer index (between 0 and
+        `nb_layers - 1`) or by name. The number of layers can be determined by counting the results returned by
+        calling `layer_names`. This function is not supported for this detector.
+
+        :raises `NotImplementedException`: This method is not supported for detectors.
+        """
+        raise NotImplementedError
+
+    def save(self, filename: str, path: Optional[str] = None) -> None:
+        """
+        Save the detector model.
+
+        param filename: The name of the saved file.
+        param path: The path to the location of the saved file.
+        """
+        self.detector.save(filename, path)
diff --git a/adversarial-robustness-toolbox/art/defences/detector/evasion/subsetscanning/__init__.py b/adversarial-robustness-toolbox/art/defences/detector/evasion/subsetscanning/__init__.py
new file mode 100644
index 0000000..dd0f35e
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/defences/detector/evasion/subsetscanning/__init__.py
@@ -0,0 +1,6 @@
+"""
+This module implements the fast generalized subset scan based detector.
+"""
+from art.defences.detector.evasion.subsetscanning.scanningops import ScanningOps
+from art.defences.detector.evasion.subsetscanning.scanner import Scanner
+from art.defences.detector.evasion.subsetscanning.detector import SubsetScanningDetector
diff --git a/adversarial-robustness-toolbox/art/defences/detector/evasion/subsetscanning/detector.py b/adversarial-robustness-toolbox/art/defences/detector/evasion/subsetscanning/detector.py
new file mode 100644
index 0000000..5691fca
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/defences/detector/evasion/subsetscanning/detector.py
@@ -0,0 +1,296 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements the fast generalized subset scan based detector.
+"""
+
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+from typing import List, Optional, Tuple, Union, TYPE_CHECKING
+
+# pylint: disable=E0001
+import numpy as np
+from sklearn import metrics
+from tqdm.auto import trange, tqdm
+
+from art.defences.detector.evasion.subsetscanning.scanner import Scanner
+from art.estimators.classification.classifier import ClassifierNeuralNetwork
+
+
+if TYPE_CHECKING:
+    from art.utils import CLIP_VALUES_TYPE
+    from art.data_generators import DataGenerator
+
+logger = logging.getLogger(__name__)
+
+
+class SubsetScanningDetector(ClassifierNeuralNetwork):
+    """
+    Fast generalized subset scan based detector by McFowland, E., Speakman, S., and Neill, D. B. (2013).
+
+    | Paper link: https://www.cs.cmu.edu/~neill/papers/mcfowland13a.pdf
+    """
+
+    estimator_params = ClassifierNeuralNetwork.estimator_params + ["classifier", "bgd_data", "layer", "verbose"]
+
+    def __init__(
+        self, classifier: ClassifierNeuralNetwork, bgd_data: np.ndarray, layer: Union[int, str], verbose: bool = True
+    ) -> None:
+        """
+        Create a `SubsetScanningDetector` instance which is used to the detect the presence of adversarial samples.
+
+        :param classifier: The model being evaluated for its robustness to anomalies (e.g. adversarial samples).
+        :param bgd_data: The background data used to learn a null model. Typically dataset used to train the classifier.
+        :param layer: The layer from which to extract activations to perform scan.
+        :param verbose: Show progress bars.
+        """
+        super().__init__(
+            model=None,
+            clip_values=classifier.clip_values,
+            channels_first=classifier.channels_first,
+            preprocessing_defences=classifier.preprocessing_defences,
+            preprocessing=classifier.preprocessing,
+        )
+        self.detector = classifier
+        self.bgd_data = bgd_data
+        self.verbose = verbose
+        self.layer = layer
+
+        # Ensure that layer is well-defined
+        if classifier.layer_names is None:
+            raise ValueError("No layer names identified.")
+
+        if isinstance(layer, int):
+            if layer < 0 or layer >= len(classifier.layer_names):
+                raise ValueError(
+                    "Layer index %d is outside of range (0 to %d included)." % (layer, len(classifier.layer_names) - 1)
+                )
+            self._layer_name = classifier.layer_names[layer]
+        else:
+            if layer not in classifier.layer_names:
+                raise ValueError("Layer name %s is not part of the graph." % layer)
+            self._layer_name = layer
+
+        bgd_activations = classifier.get_activations(bgd_data, self._layer_name, batch_size=128)
+        if len(bgd_activations.shape) == 4:
+            dim2 = bgd_activations.shape[1] * bgd_activations.shape[2] * bgd_activations.shape[3]
+            bgd_activations = np.reshape(bgd_activations, (bgd_activations.shape[0], dim2))
+
+        self.sorted_bgd_activations = np.sort(bgd_activations, axis=0)
+
+    def calculate_pvalue_ranges(self, eval_x: np.ndarray) -> np.ndarray:
+        """
+        Returns computed p-value ranges.
+
+        :param eval_x: Data being evaluated for anomalies.
+        :return: P-value ranges.
+        """
+        bgd_activations = self.sorted_bgd_activations
+        eval_activations = self.detector.get_activations(eval_x, self._layer_name, batch_size=128)
+
+        if len(eval_activations.shape) == 4:
+            dim2 = eval_activations.shape[1] * eval_activations.shape[2] * eval_activations.shape[3]
+            eval_activations = np.reshape(eval_activations, (eval_activations.shape[0], dim2))
+
+        bgrecords_n = bgd_activations.shape[0]
+        records_n = eval_activations.shape[0]
+        atrr_n = eval_activations.shape[1]
+
+        pvalue_ranges = np.empty((records_n, atrr_n, 2))
+
+        for j in range(atrr_n):
+            pvalue_ranges[:, j, 0] = np.searchsorted(bgd_activations[:, j], eval_activations[:, j], side="right")
+            pvalue_ranges[:, j, 1] = np.searchsorted(bgd_activations[:, j], eval_activations[:, j], side="left")
+
+        pvalue_ranges = bgrecords_n - pvalue_ranges
+
+        pvalue_ranges[:, :, 0] = np.divide(pvalue_ranges[:, :, 0], bgrecords_n + 1)
+        pvalue_ranges[:, :, 1] = np.divide(pvalue_ranges[:, :, 1] + 1, bgrecords_n + 1)
+
+        return pvalue_ranges
+
+    def scan(
+        self,
+        clean_x: np.ndarray,
+        adv_x: np.ndarray,
+        clean_size: Optional[int] = None,
+        advs_size: Optional[int] = None,
+        run: int = 10,
+    ) -> Tuple[list, list, float]:
+        """
+        Returns scores of highest scoring subsets.
+
+        :param clean_x: Data presumably without anomalies.
+        :param adv_x: Data presumably with anomalies (adversarial samples).
+        :param clean_size:
+        :param advs_size:
+        :param run:
+        :return: (clean_scores, adv_scores, detectionpower).
+        """
+        clean_pvalranges = self.calculate_pvalue_ranges(clean_x)
+        adv_pvalranges = self.calculate_pvalue_ranges(adv_x)
+
+        clean_scores = []
+        adv_scores = []
+
+        if clean_size is None and advs_size is None:
+            # Individual scan
+            with tqdm(
+                total=len(clean_pvalranges) + len(adv_pvalranges), desc="Subset scanning", disable=not self.verbose
+            ) as pbar:
+                for _, c_p in enumerate(clean_pvalranges):
+                    best_score, _, _, _ = Scanner.fgss_individ_for_nets(c_p)
+                    clean_scores.append(best_score)
+                    pbar.update(1)
+                for j, a_p in enumerate(adv_pvalranges):
+                    best_score, _, _, _ = Scanner.fgss_individ_for_nets(a_p)
+                    adv_scores.append(best_score)
+                    pbar.update(1)
+
+        else:
+            len_adv_x = len(adv_x)
+            len_clean_x = len(clean_x)
+
+            for _ in trange(run, desc="Subset scanning", disable=not self.verbose):
+                np.random.seed()
+
+                advchoice = np.random.choice(range(len_adv_x), advs_size, replace=False)
+                cleanchoice = np.random.choice(range(len_clean_x), clean_size, replace=False)
+
+                combined_pvals = np.concatenate((clean_pvalranges[cleanchoice], adv_pvalranges[advchoice]), axis=0)
+
+                best_score, _, _, _ = Scanner.fgss_for_nets(clean_pvalranges[cleanchoice])
+                clean_scores.append(best_score)
+                best_score, _, _, _ = Scanner.fgss_for_nets(combined_pvals)
+                adv_scores.append(best_score)
+
+        y_true = np.append([np.ones(len(adv_scores))], [np.zeros(len(clean_scores))])
+        all_scores = np.append([adv_scores], [clean_scores])
+
+        fpr, tpr, _ = metrics.roc_curve(y_true, all_scores)
+        roc_auc = metrics.auc(fpr, tpr)
+        detectionpower = roc_auc
+
+        return clean_scores, adv_scores, detectionpower
+
+    def fit(self, x: np.ndarray, y: np.ndarray, batch_size: int = 128, nb_epochs: int = 20, **kwargs) -> None:
+        """
+        Fit the detector using training data. Assumes that the classifier is already trained.
+
+        :raises `NotImplementedException`: This method is not supported for detectors.
+        """
+        raise NotImplementedError
+
+    def predict(self, x: np.ndarray, batch_size: int = 128, **kwargs) -> np.ndarray:
+        """
+        Perform detection of adversarial data and return prediction as tuple.
+
+        :raises `NotImplementedException`: This method is not supported for detectors.
+        """
+        raise NotImplementedError
+
+    def fit_generator(self, generator: "DataGenerator", nb_epochs: int = 20, **kwargs) -> None:
+        """
+        Fit the classifier using the generator gen that yields batches as specified. This function is not supported
+        for this detector.
+
+        :raises `NotImplementedException`: This method is not supported for detectors.
+        """
+        raise NotImplementedError
+
+    def compute_loss(self, x: np.ndarray, y: np.ndarray, **kwargs) -> np.ndarray:
+        """
+        Compute the loss of the neural network for samples `x`.
+
+        :param x: Samples of shape (nb_samples, nb_features) or (nb_samples, nb_pixels_1, nb_pixels_2,
+                  nb_channels) or (nb_samples, nb_channels, nb_pixels_1, nb_pixels_2).
+        :param y: Target values (class labels) one-hot-encoded of shape `(nb_samples, nb_classes)` or indices
+                  of shape `(nb_samples,)`.
+        :return: Loss values.
+        :rtype: Format as expected by the `model`
+        """
+        raise NotImplementedError
+
+    @property
+    def nb_classes(self) -> int:
+        return self.detector.nb_classes
+
+    @property
+    def input_shape(self) -> Tuple[int, ...]:
+        return self.detector.input_shape
+
+    @property
+    def clip_values(self) -> Optional["CLIP_VALUES_TYPE"]:
+        return self.detector.clip_values
+
+    @property
+    def channels_first(self) -> bool:
+        """
+        :return: Boolean to indicate index of the color channels in the sample `x`.
+        """
+        return self.channels_first
+
+    @property
+    def classifier(self) -> int:
+        return self.detector
+
+    def class_gradient(
+        self, x: np.ndarray, label: Union[int, List[int], None] = None, training_mode: bool = False, **kwargs
+    ) -> np.ndarray:
+        """
+        Compute per-class derivatives w.r.t. `x`.
+
+        :param x: Sample input with shape as expected by the model.
+        :param label: Index of a specific per-class derivative. If an integer is provided, the gradient of that class
+                      output is computed for all samples. If multiple values as provided, the first dimension should
+                      match the batch size of `x`, and each value will be used as target for its corresponding sample in
+                      `x`. If `None`, then gradients for all classes will be computed for each sample.
+        :param training_mode: `True` for model set to training mode and `'False` for model set to evaluation mode.
+        :return: Array of gradients of input features w.r.t. each class in the form
+                 `(batch_size, nb_classes, input_shape)` when computing for all classes, otherwise shape becomes
+                 `(batch_size, 1, input_shape)` when `label` parameter is specified.
+        """
+        return self.detector.class_gradient(x=x, label=label, training_mode=training_mode, **kwargs)
+
+    def loss_gradient(self, x: np.ndarray, y: np.ndarray, training_mode: bool = False, **kwargs) -> np.ndarray:
+        """
+        Compute the gradient of the loss function w.r.t. `x`.
+
+        :param x: Sample input with shape as expected by the model.
+        :param y: Target values (class labels) one-hot-encoded of shape `(nb_samples, nb_classes)` or indices of shape
+                  `(nb_samples,)`.
+        :param training_mode: `True` for model set to training mode and `'False` for model set to evaluation mode.
+        :return: Array of gradients of the same shape as `x`.
+        """
+        return self.detector.loss_gradient(x=x, y=y, training_mode=training_mode, **kwargs)
+
+    def get_activations(
+        self, x: np.ndarray, layer: Union[int, str], batch_size: int, framework: bool = False
+    ) -> np.ndarray:
+        """
+        Return the output of the specified layer for input `x`. `layer` is specified by layer index (between 0 and
+        `nb_layers - 1`) or by name. The number of layers can be determined by counting the results returned by
+        calling `layer_names`. This function is not supported for this detector.
+
+        :raises `NotImplementedException`: This method is not supported for detectors.
+        """
+        raise NotImplementedError
+
+    def save(self, filename: str, path: Optional[str] = None) -> None:
+        self.detector.save(filename, path)
diff --git a/adversarial-robustness-toolbox/art/defences/detector/evasion/subsetscanning/scanner.py b/adversarial-robustness-toolbox/art/defences/detector/evasion/subsetscanning/scanner.py
new file mode 100644
index 0000000..d66508d
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/defences/detector/evasion/subsetscanning/scanner.py
@@ -0,0 +1,176 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+Subset scanning based on FGSS
+"""
+from typing import Callable, Tuple
+
+import numpy as np
+
+from art.defences.detector.evasion.subsetscanning.scoring_functions import ScoringFunctions
+from art.defences.detector.evasion.subsetscanning.scanningops import ScanningOps
+
+
+class Scanner:
+    """
+    Fast generalized subset scan
+
+    | Paper link: https://www.cs.cmu.edu/~neill/papers/mcfowland13a.pdf
+    """
+
+    @staticmethod
+    def fgss_individ_for_nets(
+        pvalues: np.ndarray,
+        a_max: float = 0.5,
+        score_function: Callable[[list, list, np.ndarray], np.ndarray] = ScoringFunctions.get_score_bj_fast,
+    ) -> Tuple[float, np.ndarray, np.ndarray, float]:
+        """
+        Finds the highest scoring subset of records and attribute. Return the subsets, the score, and the alpha that
+        maximizes the score.
+
+        A simplified, faster, exact method but only useable when scoring an individual input. This method recognizes
+        that for an individual input, the priority function for a fixed alpha threshold results in all nodes having
+        either priority 1 or 0. That is, the pmax is either below the threshold or not. Due to convexity of the scoring
+        function we know elements with tied priority are either all included or all excluded. Therefore, each alpha
+        threshold uniquely defines a single subset of nodes to be scored. These are the nodes that have pmax less than
+        threshold. This means the individual-input scanner is equivalent to sorting pmax values and iteratively adding
+        the next largest pmax. There are at most O(N) of these subsets to consider. Sorting requires O(N ln N). There is
+        no iterative ascent required and no special choice of alpha thresholds for speed improvements.
+
+        :param pvalues: pvalue ranges.
+        :param a_max: alpha max. determines the significance level threshold
+        :param score_function: scoring function
+        :return: (best_score, image_sub, node_sub, optimal_alpha)
+        """
+        pmaxes = np.reshape(pvalues[:, 1], pvalues.shape[0])  # should be number of columns/nodes
+        # we can ignore any pmax that is greater than a_max; this makes sorting faster
+        # all the pvalues less than equal a_max are kept by nonzero result of the comparison
+        potential_thresholds = pmaxes[np.flatnonzero(pmaxes <= a_max)]
+
+        # sorrted_unique provides our alpha thresholds that we will scan
+        # count_unique (in cumulative format) will provide the number of observations less than corresponding alpha
+        sorted_unique, count_unique = np.unique(potential_thresholds, return_counts=True)
+
+        cumulative_count = np.cumsum(count_unique)
+        # In individual input case we have n_a = N, so cumulative count is used for both.
+        # sorted_unique provides the increasing alpha values that need to be checked.
+        vector_of_scores = score_function(cumulative_count, cumulative_count, sorted_unique)
+
+        # scoring completed, now grab the max (and index)
+        best_score_idx = np.argmax(vector_of_scores)
+        best_score = vector_of_scores[best_score_idx]
+        optimal_alpha = sorted_unique[best_score_idx]
+        # best_size = cumulative_count[best_score_idx]
+
+        # In order to determine which nodes are included, we look for all pmaxes less than or equal best alpha
+        node_sub = np.flatnonzero(pvalues[:, 1] <= optimal_alpha)
+        # in the individual input case there's only 1 possible subset of inputs to return - a 1x1 with index 0
+        image_sub = np.array([0])
+
+        return best_score, image_sub, node_sub, optimal_alpha
+
+    @staticmethod
+    def fgss_for_nets(
+        pvalues: np.ndarray,
+        a_max: float = 0.5,
+        restarts: int = 10,
+        image_to_node_init: bool = False,
+        score_function: Callable[[list, list, np.ndarray], np.ndarray] = ScoringFunctions.get_score_bj_fast,
+    ) -> Tuple[float, np.ndarray, np.ndarray, float]:
+        """
+        Finds the highest scoring subset of records and attribute. Return the subsets, the score, and the alpha that
+        maximizes the score iterates between images and nodes, each time performing NPSS efficient maximization.
+
+        :param pvalues: pvalue ranges.
+        :param a_max: alpha threshold
+        :param restarts: number of iterative restarts
+        :param image_to_node_init: intializes what direction to begin the search: image to node or vice-versa
+        :param score_function: scoring function
+        :return: (best_score, image_sub, node_sub, optimal_alpha)
+        """
+        best_score = -100000.0
+
+        if len(pvalues) < restarts:
+            restarts = len(pvalues)
+
+        for r_indx in range(0, restarts):  # do random restarts to come close to global maximum
+            image_to_node = image_to_node_init
+            if r_indx == 0:
+                if image_to_node:
+                    # all 1's for number of rows
+                    indices_of_seeds = np.arange(pvalues.shape[0])
+                else:
+                    # all 1's for number of cols
+                    indices_of_seeds = np.arange(pvalues.shape[1])
+
+                (
+                    best_score_from_restart,
+                    best_image_sub_from_restart,
+                    best_node_sub_from_restart,
+                    best_alpha_from_restart,
+                ) = ScanningOps.single_restart(pvalues, a_max, indices_of_seeds, image_to_node, score_function)
+
+                if best_score_from_restart > best_score:
+                    best_score = best_score_from_restart
+                    image_sub = best_image_sub_from_restart
+                    node_sub = best_node_sub_from_restart
+                    optimal_alpha = best_alpha_from_restart
+
+                # Finished A Restart
+            else:
+                # New Restart
+                # some some randomizing and only leave in a random number of rows of pvalues TODO
+                prob = np.random.uniform(0, 1)
+                if image_to_node:
+                    indices_of_seeds = np.random.choice(
+                        np.arange(pvalues.shape[0]), int(pvalues.shape[0] * prob), replace=False,
+                    )
+                else:
+                    indices_of_seeds = np.random.choice(
+                        np.arange(pvalues.shape[1]), int(pvalues.shape[1] * prob), replace=False,
+                    )
+                while indices_of_seeds.size == 0:
+                    # eventually will make non zero
+                    prob = np.random.uniform(0, 1)
+                    if image_to_node:
+                        indices_of_seeds = np.random.choice(
+                            np.arange(pvalues.shape[0]), int(pvalues.shape[0] * prob), replace=False,
+                        )
+                    else:
+                        indices_of_seeds = np.random.choice(
+                            np.arange(pvalues.shape[1]), int(pvalues.shape[1] * prob), replace=False,
+                        )
+
+                indices_of_seeds.astype(int)
+                # process a random subset of rows of pvalues array
+                (
+                    best_score_from_restart,
+                    best_image_sub_from_restart,
+                    best_node_sub_from_restart,
+                    best_alpha_from_restart,
+                ) = ScanningOps.single_restart(pvalues, a_max, indices_of_seeds, image_to_node, score_function)
+
+                if best_score_from_restart > best_score:
+                    best_score = best_score_from_restart
+                    image_sub = best_image_sub_from_restart
+                    node_sub = best_node_sub_from_restart
+                    optimal_alpha = best_alpha_from_restart
+
+                # Finished A Restart
+
+        return best_score, image_sub, node_sub, optimal_alpha
diff --git a/adversarial-robustness-toolbox/art/defences/detector/evasion/subsetscanning/scanningops.py b/adversarial-robustness-toolbox/art/defences/detector/evasion/subsetscanning/scanningops.py
new file mode 100644
index 0000000..653e132
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/defences/detector/evasion/subsetscanning/scanningops.py
@@ -0,0 +1,188 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+Scanning operations
+"""
+from typing import Callable, Tuple
+
+import numpy as np
+
+
+class ScanningOps:
+    """
+    Specific operations done during scanning
+    """
+
+    @staticmethod
+    def optimize_in_single_dimension(
+        pvalues: np.ndarray,
+        a_max: float,
+        image_to_node: bool,
+        score_function: Callable[[list, list, np.ndarray], np.ndarray],
+    ) -> Tuple[float, np.ndarray, float]:
+        """
+        Optimizes over all subsets of nodes for a given subset of images or over all subsets of images for a given
+        subset of nodes.
+
+        :param pvalues: pvalue ranges.
+        :param a_max: Determines the significance level threshold.
+        :param image_to_node: Informs the direction for optimization.
+        :param score_function: Scoring function.
+        :return: (best_score_so_far, subset, best_alpha).
+        """
+        alpha_thresholds = np.unique(pvalues[:, :, 1])
+
+        # alpha_thresholds = alpha_thresholds[0::5] #take every 5th for speed purposes
+        # where does a_max fall in check
+        last_alpha_index = np.searchsorted(alpha_thresholds, a_max)
+        # resize check for only ones smaller than a_max
+        alpha_thresholds = alpha_thresholds[0:last_alpha_index]
+
+        step_for_50 = len(alpha_thresholds) / 50
+        alpha_thresholds = alpha_thresholds[0 :: int(step_for_50) + 1]
+        # add on the max value to check as well as it may not have been part of unique
+        alpha_thresholds = np.append(alpha_thresholds, a_max)
+
+        # alpha_thresholds = np.arange(a_max/50, a_max, a_max/50)
+
+        if image_to_node:
+            number_of_elements = pvalues.shape[1]  # searching over j columns
+            size_of_given = pvalues.shape[0]  # for fixed this many images
+            unsort_priority = np.zeros((pvalues.shape[1], alpha_thresholds.shape[0]))  # number of columns
+        else:
+            number_of_elements = pvalues.shape[0]  # searching over i rows
+            size_of_given = pvalues.shape[1]  # for this many fixed nodes
+            unsort_priority = np.zeros((pvalues.shape[0], alpha_thresholds.shape[0]))  # number of rows
+
+        for elem_indx in range(0, number_of_elements):
+            # sort all the range maxes
+            if image_to_node:
+                # collect ranges over images(rows)
+                arg_sort_max = np.argsort(pvalues[:, elem_indx, 1])
+                # arg_sort_min = np.argsort(pvalues[:,e,0]) #collect ranges over images(rows)
+                completely_included = np.searchsorted(
+                    pvalues[:, elem_indx, 1][arg_sort_max], alpha_thresholds, side="right",
+                )
+            else:
+                # collect ranges over nodes(columns)
+                arg_sort_max = np.argsort(pvalues[elem_indx, :, 1])
+                # arg_sort_min = np.argsort(pvalues[elem_indx,:,0])
+
+                completely_included = np.searchsorted(
+                    pvalues[elem_indx, :, 1][arg_sort_max], alpha_thresholds, side="right",
+                )
+
+            # should be num elements by num thresh
+            unsort_priority[elem_indx, :] = completely_included
+
+        # want to sort for a fixed thresh (across?)
+        arg_sort_priority = np.argsort(-unsort_priority, axis=0)
+
+        best_score_so_far = -10000
+        best_alpha = -2
+
+        alpha_count = 0
+        for alpha_threshold in alpha_thresholds:
+
+            # score each threshold by itself, cumulating priority,
+            # cumulating count, alpha stays same.
+            alpha_v = np.ones(number_of_elements) * alpha_threshold
+
+            n_alpha_v = np.cumsum(unsort_priority[:, alpha_count][arg_sort_priority][:, alpha_count])
+            count_increments_this = np.ones(number_of_elements) * size_of_given
+            n_v = np.cumsum(count_increments_this)
+
+            vector_of_scores = score_function(n_alpha_v, n_v, alpha_v)
+
+            best_score_for_this_alpha_idx = np.argmax(vector_of_scores)
+            best_score_for_this_alpha = vector_of_scores[best_score_for_this_alpha_idx]
+
+            if best_score_for_this_alpha > best_score_so_far:
+                best_score_so_far = best_score_for_this_alpha
+                best_size = best_score_for_this_alpha_idx + 1  # not sure 1 is needed?
+                best_alpha = alpha_threshold
+                best_alpha_count = alpha_count
+            alpha_count = alpha_count + 1
+
+        # after the alpha for loop we now have best score, best alpha, size of best subset,
+        # and alpha counter use these with the priority argsort to reconstruct the best subset
+        unsort = arg_sort_priority[:, best_alpha_count]
+
+        subset = np.zeros(best_size).astype(int)
+        for loc in range(0, best_size):
+            subset[loc] = unsort[loc]
+
+        return best_score_so_far, subset, best_alpha
+
+    @staticmethod
+    def single_restart(
+        pvalues: np.ndarray,
+        a_max: float,
+        indices_of_seeds: np.ndarray,
+        image_to_node: bool,
+        score_function: Callable[[list, list, np.ndarray], np.ndarray],
+    ) -> Tuple[float, np.ndarray, np.ndarray, float]:
+        """
+        Here we control the iteration between images->nodes and nodes->images. It starts with a fixed subset of nodes by
+        default.
+
+        :param pvalues: pvalue ranges.
+        :param a_max: Determines the significance level threshold.
+        :param indices_of_seeds: Indices of initial sets of images or nodes to perform optimization.
+        :param image_to_node: Informs the direction for optimization.
+        :param score_function: Scoring function.
+        :return: (best_score_so_far, best_sub_of_images, best_sub_of_nodes, best_alpha).
+        """
+        best_score_so_far = -100000.0
+        count = 0
+
+        while True:
+            # These can be moved outside the while loop as only executed first time through??
+            if count == 0:  # first time through, we need something initialized depending on direction.
+                if image_to_node:
+                    sub_of_images = indices_of_seeds
+                else:
+                    sub_of_nodes = indices_of_seeds
+
+            if image_to_node:  # passed pvalues are only those belonging to fixed images, update nodes in return
+                # only sending sub of images
+                (score_from_optimization, sub_of_nodes, optimal_alpha,) = ScanningOps.optimize_in_single_dimension(
+                    pvalues[sub_of_images, :, :], a_max, image_to_node, score_function
+                )
+            else:  # passed pvalues are only those belonging to fixed nodes, update images in return
+                # only sending sub of nodes
+                (score_from_optimization, sub_of_images, optimal_alpha,) = ScanningOps.optimize_in_single_dimension(
+                    pvalues[:, sub_of_nodes, :], a_max, image_to_node, score_function
+                )
+
+            if score_from_optimization > best_score_so_far:  # haven't converged yet
+                # update
+                best_score_so_far = score_from_optimization
+                best_sub_of_nodes = sub_of_nodes
+                best_sub_of_images = sub_of_images
+                best_alpha = optimal_alpha
+
+                image_to_node = not image_to_node  # switch direction!
+                count = count + 1  # for printing and
+            else:  # converged!  Don't update from most recent optimization, return current best
+                return (
+                    best_score_so_far,
+                    best_sub_of_images,
+                    best_sub_of_nodes,
+                    best_alpha,
+                )
diff --git a/adversarial-robustness-toolbox/art/defences/detector/evasion/subsetscanning/scoring_functions.py b/adversarial-robustness-toolbox/art/defences/detector/evasion/subsetscanning/scoring_functions.py
new file mode 100644
index 0000000..c99ce9d
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/defences/detector/evasion/subsetscanning/scoring_functions.py
@@ -0,0 +1,95 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+Scanner scoring functions.
+"""
+import numpy as np
+
+
+class ScoringFunctions:
+    """
+    Scanner scoring functions. These functions are used in the scanner to determine the score of a subset.
+    """
+
+    @staticmethod
+    def get_score_bj_fast(n_alpha: list, no_records: list, alpha: np.ndarray) -> np.ndarray:
+        """
+        BerkJones
+
+        :param n_alpha: Number of records less than alpha.
+        :param no_records: Number of records.
+        :param alpha: Alpha threshold.
+        :return: Score.
+        """
+        score = np.zeros(alpha.shape[0])
+        inds_tie = n_alpha == no_records
+        inds_not_tie = np.logical_not(inds_tie)
+        inds_pos = n_alpha > no_records * alpha
+        inds_pos_not_tie = np.logical_and(inds_pos, inds_not_tie)
+        score[inds_tie] = no_records[inds_tie] * np.log(np.true_divide(1, alpha[inds_tie]))
+
+        factor1 = n_alpha[inds_pos_not_tie] * np.log(
+            np.true_divide(n_alpha[inds_pos_not_tie], no_records[inds_pos_not_tie] * alpha[inds_pos_not_tie],)
+        )
+
+        factor2 = no_records[inds_pos_not_tie] - n_alpha[inds_pos_not_tie]
+
+        factor3 = np.log(
+            np.true_divide(
+                no_records[inds_pos_not_tie] - n_alpha[inds_pos_not_tie],
+                no_records[inds_pos_not_tie] * (1 - alpha[inds_pos_not_tie]),
+            )
+        )
+
+        score[inds_pos_not_tie] = factor1 + factor2 * factor3
+        return score
+
+    @staticmethod
+    def get_score_hc_fast(n_alpha: list, no_records: list, alpha: np.ndarray) -> np.ndarray:
+        """
+        Higher criticism
+        Similar to a traditional wald test statistic: (Observed - expected) / standard deviation.
+        In this case we use the binomial distribution. The observed is N_a.  The expected (under null) is N*a
+        and the standard deviation is sqrt(N*a(1-a)).
+
+        :param n_alpha: Number of records less than alpha.
+        :param no_records: Number of records.
+        :param alpha: Alpha threshold.
+        :return: Score.
+        """
+        score = np.zeros(alpha.shape[0])
+        inds = n_alpha > no_records * alpha
+        factor1 = n_alpha[inds] - no_records[inds] * alpha[inds]
+        factor2 = np.sqrt(no_records[inds] * alpha[inds] * (1.0 - alpha[inds]))
+        score[inds] = np.true_divide(factor1, factor2)
+        return score
+
+    @staticmethod
+    def get_score_ks_fast(n_alpha: list, no_records: list, alpha: np.ndarray) -> np.ndarray:
+        """
+        KolmarovSmirnov
+
+        :param n_alpha: Number of records less than alpha.
+        :param no_records: Number of records.
+        :param alpha: Alpha threshold.
+        :return: Score.
+        """
+        score = np.zeros(alpha.shape[0])
+        inds = n_alpha > no_records * alpha
+        score[inds] = np.true_divide(n_alpha[inds] - no_records[inds] * alpha[inds], np.sqrt(no_records[inds]))
+        return score
diff --git a/adversarial-robustness-toolbox/art/defences/detector/poison/__init__.py b/adversarial-robustness-toolbox/art/defences/detector/poison/__init__.py
new file mode 100644
index 0000000..228957b
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/defences/detector/poison/__init__.py
@@ -0,0 +1,10 @@
+"""
+Module implementing detector-based defences against poisoning attacks.
+"""
+from art.defences.detector.poison.poison_filtering_defence import PoisonFilteringDefence
+from art.defences.detector.poison.ground_truth_evaluator import GroundTruthEvaluator
+from art.defences.detector.poison.activation_defence import ActivationDefence
+from art.defences.detector.poison.clustering_analyzer import ClusteringAnalyzer
+from art.defences.detector.poison.provenance_defense import ProvenanceDefense
+from art.defences.detector.poison.roni import RONIDefense
+from art.defences.detector.poison.spectral_signature_defense import SpectralSignatureDefense
diff --git a/adversarial-robustness-toolbox/art/defences/detector/poison/activation_defence.py b/adversarial-robustness-toolbox/art/defences/detector/poison/activation_defence.py
new file mode 100644
index 0000000..bb7fb3b
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/defences/detector/poison/activation_defence.py
@@ -0,0 +1,740 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements methods performing poisoning detection based on activations clustering.
+
+| Paper link: https://arxiv.org/abs/1811.03728
+
+| Please keep in mind the limitations of defences. For more information on the limitations of this
+    defence, see https://arxiv.org/abs/1905.13409 . For details on how to evaluate classifier security
+    in general, see https://arxiv.org/abs/1902.06705
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import os
+import pickle
+import time
+from typing import Any, Dict, List, Optional, Tuple, TYPE_CHECKING
+
+import numpy as np
+
+from sklearn.cluster import KMeans, MiniBatchKMeans
+
+from art import config
+from art.data_generators import DataGenerator
+from art.defences.detector.poison.clustering_analyzer import ClusteringAnalyzer
+from art.defences.detector.poison.ground_truth_evaluator import GroundTruthEvaluator
+from art.defences.detector.poison.poison_filtering_defence import PoisonFilteringDefence
+from art.utils import segment_by_class
+from art.visualization import create_sprite, save_image, plot_3d
+
+if TYPE_CHECKING:
+    from art.utils import CLASSIFIER_NEURALNETWORK_TYPE
+
+logger = logging.getLogger(__name__)
+
+
+class ActivationDefence(PoisonFilteringDefence):
+    """
+    Method from Chen et al., 2018 performing poisoning detection based on activations clustering.
+
+    | Paper link: https://arxiv.org/abs/1811.03728
+
+    | Please keep in mind the limitations of defences. For more information on the limitations of this
+        defence, see https://arxiv.org/abs/1905.13409 . For details on how to evaluate classifier security
+        in general, see https://arxiv.org/abs/1902.06705
+    """
+
+    defence_params = ["nb_clusters", "clustering_method", "nb_dims", "reduce", "cluster_analysis", "generator"]
+    valid_clustering = ["KMeans"]
+    valid_reduce = ["PCA", "FastICA", "TSNE"]
+    valid_analysis = ["smaller", "distance", "relative-size", "silhouette-scores"]
+
+    TOO_SMALL_ACTIVATIONS = 32  # Threshold used to print a warning when activations are not enough
+
+    def __init__(
+        self,
+        classifier: "CLASSIFIER_NEURALNETWORK_TYPE",
+        x_train: Optional[np.ndarray],
+        y_train: Optional[np.ndarray],
+        generator: Optional[DataGenerator] = None,
+    ) -> None:
+        """
+        Create an :class:`.ActivationDefence` object with the provided classifier.
+
+        :param classifier: Model evaluated for poison.
+        :param x_train: A dataset used to train the classifier.
+        :param y_train: Labels used to train the classifier.
+        :param generator: A data generator to be used instead of `x_train` and `y_train`.
+        """
+        super().__init__(classifier, x_train, y_train)
+        self.classifier: "CLASSIFIER_NEURALNETWORK_TYPE" = classifier
+        self.nb_clusters = 2
+        self.clustering_method = "KMeans"
+        self.nb_dims = 10
+        self.reduce = "PCA"
+        self.cluster_analysis = "smaller"
+        self.generator = generator
+        self.activations_by_class: List[np.ndarray] = []
+        self.clusters_by_class: List[np.ndarray] = []
+        self.assigned_clean_by_class: List[np.ndarray] = []
+        self.is_clean_by_class: List[np.ndarray] = []
+        self.errors_by_class: List[np.ndarray] = []
+        self.red_activations_by_class: List[np.ndarray] = []  # Activations reduced by class
+        self.evaluator = GroundTruthEvaluator()
+        self.is_clean_lst: List[int] = []
+        self.confidence_level: List[float] = []
+        self.poisonous_clusters: List[List[np.ndarray]] = []
+        self.clusterer = MiniBatchKMeans(n_clusters=self.nb_clusters)
+        self._check_params()
+
+    def evaluate_defence(self, is_clean: np.ndarray, **kwargs) -> str:
+        """
+        If ground truth is known, this function returns a confusion matrix in the form of a JSON object.
+
+        :param is_clean: Ground truth, where is_clean[i]=1 means that x_train[i] is clean and is_clean[i]=0 means
+                         x_train[i] is poisonous.
+        :param kwargs: A dictionary of defence-specific parameters.
+        :return: JSON object with confusion matrix.
+        """
+        if is_clean is None or is_clean.size == 0:
+            raise ValueError("is_clean was not provided while invoking evaluate_defence.")
+
+        self.set_params(**kwargs)
+
+        if not self.activations_by_class and self.generator is None:
+            activations = self._get_activations()
+            self.activations_by_class = self._segment_by_class(activations, self.y_train)
+
+        (self.clusters_by_class, self.red_activations_by_class,) = self.cluster_activations()
+        _, self.assigned_clean_by_class = self.analyze_clusters()
+
+        # Now check ground truth:
+        if self.generator is not None:
+            batch_size = self.generator.batch_size
+            num_samples = self.generator.size
+            num_classes = self.classifier.nb_classes
+            self.is_clean_by_class = [np.empty(0, dtype=int) for _ in range(num_classes)]
+
+            # calculate is_clean_by_class for each batch
+            for batch_idx in range(num_samples // batch_size):  # type: ignore
+                _, y_batch = self.generator.get_batch()
+                is_clean_batch = is_clean[batch_idx * batch_size : batch_idx * batch_size + batch_size]
+                clean_by_class_batch = self._segment_by_class(is_clean_batch, y_batch)
+                self.is_clean_by_class = [
+                    np.append(self.is_clean_by_class[class_idx], clean_by_class_batch[class_idx])
+                    for class_idx in range(num_classes)
+                ]
+
+        else:
+            self.is_clean_by_class = self._segment_by_class(is_clean, self.y_train)
+        self.errors_by_class, conf_matrix_json = self.evaluator.analyze_correctness(
+            self.assigned_clean_by_class, self.is_clean_by_class
+        )
+        return conf_matrix_json
+
+    # pylint: disable=W0221
+    def detect_poison(self, **kwargs) -> Tuple[Dict[str, Any], List[int]]:
+        """
+        Returns poison detected and a report.
+
+        :param clustering_method: clustering algorithm to be used. Currently `KMeans` is the only method supported
+        :type clustering_method: `str`
+        :param nb_clusters: number of clusters to find. This value needs to be greater or equal to one
+        :type nb_clusters: `int`
+        :param reduce: method used to reduce dimensionality of the activations. Supported methods include  `PCA`,
+                       `FastICA` and `TSNE`
+        :type reduce: `str`
+        :param nb_dims: number of dimensions to be reduced
+        :type nb_dims: `int`
+        :param cluster_analysis: heuristic to automatically determine if a cluster contains poisonous data. Supported
+                                 methods include `smaller` and `distance`. The `smaller` method defines as poisonous the
+                                 cluster with less number of data points, while the `distance` heuristic uses the
+                                 distance between the clusters.
+        :type cluster_analysis: `str`
+        :return: (report, is_clean_lst):
+                where a report is a dict object that contains information specified by the clustering analysis technique
+                where is_clean is a list, where is_clean_lst[i]=1 means that x_train[i]
+                there is clean and is_clean_lst[i]=0, means that x_train[i] was classified as poison.
+        """
+        old_nb_clusters = self.nb_clusters
+        self.set_params(**kwargs)
+        if self.nb_clusters != old_nb_clusters:
+            self.clusterer = MiniBatchKMeans(n_clusters=self.nb_clusters)
+
+        if self.generator is not None:
+            self.clusters_by_class, self.red_activations_by_class = self.cluster_activations()
+            report, self.assigned_clean_by_class = self.analyze_clusters()
+
+            batch_size = self.generator.batch_size
+            num_samples = self.generator.size
+            self.is_clean_lst = []
+
+            # loop though the generator to generator a report
+            for _ in range(num_samples // batch_size):  # type: ignore
+                _, y_batch = self.generator.get_batch()
+                indices_by_class = self._segment_by_class(np.arange(batch_size), y_batch)
+                is_clean_lst = [0] * batch_size
+                for class_idx, idxs in enumerate(indices_by_class):
+                    for idx_in_class, idx in enumerate(idxs):
+                        is_clean_lst[idx] = self.assigned_clean_by_class[class_idx][idx_in_class]
+                self.is_clean_lst += is_clean_lst
+            return report, self.is_clean_lst
+
+        if not self.activations_by_class:
+            activations = self._get_activations()
+            self.activations_by_class = self._segment_by_class(activations, self.y_train)
+        (self.clusters_by_class, self.red_activations_by_class,) = self.cluster_activations()
+        report, self.assigned_clean_by_class = self.analyze_clusters()
+        # Here, assigned_clean_by_class[i][j] is 1 if the jth data point in the ith class was
+        # determined to be clean by activation cluster
+
+        # Build an array that matches the original indexes of x_train
+        n_train = len(self.x_train)
+        indices_by_class = self._segment_by_class(np.arange(n_train), self.y_train)
+        self.is_clean_lst = [0] * n_train
+
+        for assigned_clean, indices_dp in zip(self.assigned_clean_by_class, indices_by_class):
+            for assignment, index_dp in zip(assigned_clean, indices_dp):
+                if assignment == 1:
+                    self.is_clean_lst[index_dp] = 1
+
+        return report, self.is_clean_lst
+
+    def cluster_activations(self, **kwargs) -> Tuple[List[List[int]], List[List[int]]]:
+        """
+        Clusters activations and returns cluster_by_class and red_activations_by_class, where cluster_by_class[i][j] is
+        the cluster to which the j-th data point in the ith class belongs and the correspondent activations reduced by
+        class red_activations_by_class[i][j].
+
+        :param kwargs: A dictionary of cluster-specific parameters.
+        :return: Clusters per class and activations by class.
+        """
+        self.set_params(**kwargs)
+
+        if self.generator is not None:
+            batch_size = self.generator.batch_size
+            num_samples = self.generator.size
+            num_classes = self.classifier.nb_classes
+            for batch_idx in range(num_samples // batch_size):  # type: ignore
+                x_batch, y_batch = self.generator.get_batch()
+
+                batch_activations = self._get_activations(x_batch)
+                activation_dim = batch_activations.shape[-1]
+
+                # initialize values list of lists on first run
+                if batch_idx == 0:
+                    self.activations_by_class = [np.empty((0, activation_dim)) for _ in range(num_classes)]
+                    self.clusters_by_class = [np.empty(0, dtype=int) for _ in range(num_classes)]
+                    self.red_activations_by_class = [np.empty((0, self.nb_dims)) for _ in range(num_classes)]
+
+                activations_by_class = self._segment_by_class(batch_activations, y_batch)
+                clusters_by_class, red_activations_by_class = cluster_activations(
+                    activations_by_class,
+                    nb_clusters=self.nb_clusters,
+                    nb_dims=self.nb_dims,
+                    reduce=self.reduce,
+                    clustering_method=self.clustering_method,
+                    generator=self.generator,
+                    clusterer_new=self.clusterer,
+                )
+
+                for class_idx in range(num_classes):
+                    self.activations_by_class[class_idx] = np.vstack(
+                        [self.activations_by_class[class_idx], activations_by_class[class_idx]]
+                    )
+                    self.clusters_by_class[class_idx] = np.append(
+                        self.clusters_by_class[class_idx], clusters_by_class[class_idx]
+                    )
+                    self.red_activations_by_class[class_idx] = np.vstack(
+                        [self.red_activations_by_class[class_idx], red_activations_by_class[class_idx]]
+                    )
+            return self.clusters_by_class, self.red_activations_by_class
+
+        if not self.activations_by_class:
+            activations = self._get_activations()
+            self.activations_by_class = self._segment_by_class(activations, self.y_train)
+
+        [self.clusters_by_class, self.red_activations_by_class] = cluster_activations(
+            self.activations_by_class,
+            nb_clusters=self.nb_clusters,
+            nb_dims=self.nb_dims,
+            reduce=self.reduce,
+            clustering_method=self.clustering_method,
+        )
+
+        return self.clusters_by_class, self.red_activations_by_class
+
+    def analyze_clusters(self, **kwargs) -> Tuple[Dict[str, Any], np.ndarray]:
+        """
+        This function analyzes the clusters according to the provided method.
+
+        :param kwargs: A dictionary of cluster-analysis-specific parameters.
+        :return: (report, assigned_clean_by_class), where the report is a dict object and assigned_clean_by_class
+                 is an array of arrays that contains what data points where classified as clean.
+        """
+        self.set_params(**kwargs)
+
+        if not self.clusters_by_class:
+            self.cluster_activations()
+
+        analyzer = ClusteringAnalyzer()
+        if self.cluster_analysis == "smaller":
+            (self.assigned_clean_by_class, self.poisonous_clusters, report,) = analyzer.analyze_by_size(
+                self.clusters_by_class
+            )
+        elif self.cluster_analysis == "relative-size":
+            (self.assigned_clean_by_class, self.poisonous_clusters, report,) = analyzer.analyze_by_relative_size(
+                self.clusters_by_class
+            )
+        elif self.cluster_analysis == "distance":
+            (self.assigned_clean_by_class, self.poisonous_clusters, report,) = analyzer.analyze_by_distance(
+                self.clusters_by_class, separated_activations=self.red_activations_by_class,
+            )
+        elif self.cluster_analysis == "silhouette-scores":
+            (self.assigned_clean_by_class, self.poisonous_clusters, report,) = analyzer.analyze_by_silhouette_score(
+                self.clusters_by_class, reduced_activations_by_class=self.red_activations_by_class,
+            )
+        else:
+            raise ValueError("Unsupported cluster analysis technique " + self.cluster_analysis)
+
+        # Add to the report current parameters used to run the defence and the analysis summary
+        report = dict(list(report.items()) + list(self.get_params().items()))
+
+        return report, self.assigned_clean_by_class
+
+    @staticmethod
+    def relabel_poison_ground_truth(
+        classifier: "CLASSIFIER_NEURALNETWORK_TYPE",
+        x: np.ndarray,
+        y_fix: np.ndarray,
+        test_set_split: float = 0.7,
+        tolerable_backdoor: float = 0.01,
+        max_epochs: int = 50,
+        batch_epochs: int = 10,
+    ) -> Tuple[float, "CLASSIFIER_NEURALNETWORK_TYPE"]:
+        """
+        Revert poison attack by continue training the current classifier with `x`, `y_fix`. `test_set_split` determines
+        the percentage in x that will be used as training set, while `1-test_set_split` determines how many data points
+        to use for test set.
+
+        :param classifier: Classifier to be fixed.
+        :param x: Samples.
+        :param y_fix: True label of `x_poison`.
+        :param test_set_split: this parameter determine how much data goes to the training set.
+               Here `test_set_split*len(y_fix)` determines the number of data points in `x_train`
+               and `(1-test_set_split) * len(y_fix)` the number of data points in `x_test`.
+        :param tolerable_backdoor: Threshold that determines what is the maximum tolerable backdoor success rate.
+        :param max_epochs: Maximum number of epochs that the model will be trained.
+        :param batch_epochs: Number of epochs to be trained before checking current state of model.
+        :return: (improve_factor, classifier).
+        """
+        # Split data into testing and training:
+        n_train = int(len(x) * test_set_split)
+        x_train, x_test = x[:n_train], x[n_train:]
+        y_train, y_test = y_fix[:n_train], y_fix[n_train:]
+
+        filename = "original_classifier" + str(time.time()) + ".p"
+        ActivationDefence._pickle_classifier(classifier, filename)
+
+        # Now train using y_fix:
+        improve_factor, _ = train_remove_backdoor(
+            classifier,
+            x_train,
+            y_train,
+            x_test,
+            y_test,
+            tolerable_backdoor=tolerable_backdoor,
+            max_epochs=max_epochs,
+            batch_epochs=batch_epochs,
+        )
+
+        # Only update classifier if there was an improvement:
+        if improve_factor < 0:
+            classifier = ActivationDefence._unpickle_classifier(filename)
+            return 0, classifier
+
+        ActivationDefence._remove_pickle(filename)
+        return improve_factor, classifier
+
+    @staticmethod
+    def relabel_poison_cross_validation(
+        classifier: "CLASSIFIER_NEURALNETWORK_TYPE",
+        x: np.ndarray,
+        y_fix: np.ndarray,
+        n_splits: int = 10,
+        tolerable_backdoor: float = 0.01,
+        max_epochs: int = 50,
+        batch_epochs: int = 10,
+    ) -> Tuple[float, "CLASSIFIER_NEURALNETWORK_TYPE"]:
+        """
+        Revert poison attack by continue training the current classifier with `x`, `y_fix`. `n_splits` determines the
+        number of cross validation splits.
+
+        :param classifier: Classifier to be fixed.
+        :param x: Samples that were miss-labeled.
+        :param y_fix: True label of `x`.
+        :param n_splits: Determines how many splits to use in cross validation (only used if `cross_validation=True`).
+        :param tolerable_backdoor: Threshold that determines what is the maximum tolerable backdoor success rate.
+        :param max_epochs: Maximum number of epochs that the model will be trained.
+        :param batch_epochs: Number of epochs to be trained before checking current state of model.
+        :return: (improve_factor, classifier)
+        """
+        # pylint: disable=E0001
+        from sklearn.model_selection import KFold
+
+        # Train using cross validation
+        k_fold = KFold(n_splits=n_splits)
+        KFold(n_splits=n_splits, random_state=None, shuffle=True)
+
+        filename = "original_classifier" + str(time.time()) + ".p"
+        ActivationDefence._pickle_classifier(classifier, filename)
+        curr_improvement = 0
+
+        for train_index, test_index in k_fold.split(x):
+            # Obtain partition:
+            x_train, x_test = x[train_index], x[test_index]
+            y_train, y_test = y_fix[train_index], y_fix[test_index]
+            # Unpickle original model:
+            curr_classifier = ActivationDefence._unpickle_classifier(filename)
+
+            new_improvement, fixed_classifier = train_remove_backdoor(
+                curr_classifier,
+                x_train,
+                y_train,
+                x_test,
+                y_test,
+                tolerable_backdoor=tolerable_backdoor,
+                max_epochs=max_epochs,
+                batch_epochs=batch_epochs,
+            )
+            if curr_improvement < new_improvement and new_improvement > 0:
+                curr_improvement = new_improvement
+                classifier = fixed_classifier
+                logger.info("Selected as best model so far: %s", curr_improvement)
+
+        ActivationDefence._remove_pickle(filename)
+        return curr_improvement, classifier
+
+    @staticmethod
+    def _pickle_classifier(classifier: "CLASSIFIER_NEURALNETWORK_TYPE", file_name: str) -> None:
+        """
+        Pickles the self.classifier and stores it using the provided file_name in folder `art.config.ART_DATA_PATH`.
+
+        :param classifier: Classifier to be pickled.
+        :param file_name: Name of the file where the classifier will be pickled.
+        """
+        full_path = os.path.join(config.ART_DATA_PATH, file_name)
+        folder = os.path.split(full_path)[0]
+        if not os.path.exists(folder):
+            os.makedirs(folder)
+
+        with open(full_path, "wb") as f_classifier:
+            pickle.dump(classifier, f_classifier)
+
+    @staticmethod
+    def _unpickle_classifier(file_name: str) -> "CLASSIFIER_NEURALNETWORK_TYPE":
+        """
+        Unpickles classifier using the filename provided. Function assumes that the pickle is in
+        `art.config.ART_DATA_PATH`.
+
+        :param file_name: Path of the pickled classifier relative to `ART_DATA_PATH`.
+        :return: The loaded classifier.
+        """
+        full_path = os.path.join(config.ART_DATA_PATH, file_name)
+        logger.info("Loading classifier from %s", full_path)
+        with open(full_path, "rb") as f_classifier:
+            loaded_classifier = pickle.load(f_classifier)
+            return loaded_classifier
+
+    @staticmethod
+    def _remove_pickle(file_name: str) -> None:
+        """
+        Erases the pickle with the provided file name.
+
+        :param file_name: File name without directory.
+        """
+        full_path = os.path.join(config.ART_DATA_PATH, file_name)
+        os.remove(full_path)
+
+    def visualize_clusters(
+        self, x_raw: np.ndarray, save: bool = True, folder: str = ".", **kwargs
+    ) -> List[List[List[np.ndarray]]]:
+        """
+        This function creates the sprite/mosaic visualization for clusters. When save=True,
+        it also stores a sprite (mosaic) per cluster in art.config.ART_DATA_PATH.
+
+        :param x_raw: Images used to train the classifier (before pre-processing).
+        :param save: Boolean specifying if image should be saved.
+        :param folder: Directory where the sprites will be saved inside art.config.ART_DATA_PATH folder.
+        :param kwargs: a dictionary of cluster-analysis-specific parameters.
+        :return: Array with sprite images sprites_by_class, where sprites_by_class[i][j] contains the
+                                  sprite of class i cluster j.
+        """
+        self.set_params(**kwargs)
+
+        if not self.clusters_by_class:
+            self.cluster_activations()
+
+        x_raw_by_class = self._segment_by_class(x_raw, self.y_train)
+        x_raw_by_cluster: List[List[List[np.ndarray]]] = [
+            [[] for _ in range(self.nb_clusters)] for _ in range(self.classifier.nb_classes)
+        ]
+
+        # Get all data in x_raw in the right cluster
+        for n_class, cluster in enumerate(self.clusters_by_class):
+            for j, assigned_cluster in enumerate(cluster):
+                x_raw_by_cluster[n_class][assigned_cluster].append(x_raw_by_class[n_class][j])
+
+        # Now create sprites:
+        sprites_by_class: List[List[List[np.ndarray]]] = [
+            [[] for _ in range(self.nb_clusters)] for _ in range(self.classifier.nb_classes)
+        ]
+        for i, class_i in enumerate(x_raw_by_cluster):
+            for j, images_cluster in enumerate(class_i):
+                title = "Class_" + str(i) + "_cluster_" + str(j) + "_clusterSize_" + str(len(images_cluster))
+                f_name = title + ".png"
+                f_name = os.path.join(folder, f_name)
+                sprite = create_sprite(np.array(images_cluster))
+                if save:
+                    save_image(sprite, f_name)
+                sprites_by_class[i][j] = sprite
+
+        return sprites_by_class
+
+    def plot_clusters(self, save: bool = True, folder: str = ".", **kwargs) -> None:
+        """
+        Creates a 3D-plot to visualize each cluster each cluster is assigned a different color in the plot. When
+        save=True, it also stores the 3D-plot per cluster in art.config.ART_DATA_PATH.
+
+        :param save: Boolean specifying if image should be saved.
+        :param folder: Directory where the sprites will be saved inside art.config.ART_DATA_PATH folder.
+        :param kwargs: a dictionary of cluster-analysis-specific parameters.
+        """
+        self.set_params(**kwargs)
+
+        if not self.clusters_by_class:
+            self.cluster_activations()
+
+        # Get activations reduced to 3-components:
+        separated_reduced_activations = []
+        for activation in self.activations_by_class:
+            reduced_activations = reduce_dimensionality(activation, nb_dims=3)
+            separated_reduced_activations.append(reduced_activations)
+
+        # For each class generate a plot:
+        for class_id, (labels, coordinates) in enumerate(zip(self.clusters_by_class, separated_reduced_activations)):
+            f_name = ""
+            if save:
+                f_name = os.path.join(folder, "plot_class_" + str(class_id) + ".png")
+            plot_3d(coordinates, labels, save=save, f_name=f_name)
+
+    def _check_params(self):
+        if self.nb_clusters <= 1:
+            raise ValueError(
+                "Wrong number of clusters, should be greater or equal to 2. Provided: " + str(self.nb_clusters)
+            )
+        if self.nb_dims <= 0:
+            raise ValueError("Wrong number of dimensions.")
+        if self.clustering_method not in self.valid_clustering:
+            raise ValueError("Unsupported clustering method: " + self.clustering_method)
+        if self.reduce not in self.valid_reduce:
+            raise ValueError("Unsupported reduction method: " + self.reduce)
+        if self.cluster_analysis not in self.valid_analysis:
+            raise ValueError("Unsupported method for cluster analysis method: " + self.cluster_analysis)
+        if self.generator and not isinstance(self.generator, DataGenerator):
+            raise TypeError("Generator must a an instance of DataGenerator")
+
+    def _get_activations(self, x_train: Optional[np.ndarray] = None) -> np.ndarray:
+        """
+        Find activations from :class:`.Classifier`.
+        """
+        logger.info("Getting activations")
+
+        if self.classifier.layer_names is not None:
+            nb_layers = len(self.classifier.layer_names)
+        else:
+            raise ValueError("No layer names identified.")
+        protected_layer = nb_layers - 1
+
+        if self.generator is not None:
+            activations = self.classifier.get_activations(
+                x_train, layer=protected_layer, batch_size=self.generator.batch_size
+            )
+        else:
+            activations = self.classifier.get_activations(self.x_train, layer=protected_layer, batch_size=128)
+
+        # wrong way to get activations activations = self.classifier.predict(self.x_train)
+        nodes_last_layer = np.shape(activations)[1]
+
+        if nodes_last_layer <= self.TOO_SMALL_ACTIVATIONS:
+            logger.warning(
+                "Number of activations in last hidden layer is too small. Method may not work properly. " "Size: %s",
+                str(nodes_last_layer),
+            )
+        return activations
+
+    def _segment_by_class(self, data: np.ndarray, features: np.ndarray) -> List[np.ndarray]:
+        """
+        Returns segmented data according to specified features.
+
+        :param data: Data to be segmented.
+        :param features: Features used to segment data, e.g., segment according to predicted label or to `y_train`.
+        :return: Segmented data according to specified features.
+        """
+        n_classes = self.classifier.nb_classes
+        return segment_by_class(data, features, n_classes)
+
+
+def measure_misclassification(
+    classifier: "CLASSIFIER_NEURALNETWORK_TYPE", x_test: np.ndarray, y_test: np.ndarray
+) -> float:
+    """
+    Computes 1-accuracy given x_test and y_test
+
+    :param classifier: Classifier to be used for predictions.
+    :param x_test: Test set.
+    :param y_test: Labels for test set.
+    :return: 1-accuracy.
+    """
+    predictions = np.argmax(classifier.predict(x_test), axis=1)
+    return 1.0 - np.sum(predictions == np.argmax(y_test, axis=1)) / y_test.shape[0]
+
+
+def train_remove_backdoor(
+    classifier: "CLASSIFIER_NEURALNETWORK_TYPE",
+    x_train: np.ndarray,
+    y_train: np.ndarray,
+    x_test: np.ndarray,
+    y_test: np.ndarray,
+    tolerable_backdoor: float,
+    max_epochs: int,
+    batch_epochs: int,
+) -> tuple:
+    """
+    Trains the provider classifier until the tolerance or number of maximum epochs are reached.
+
+    :param classifier: Classifier to be used for predictions.
+    :param x_train: Training set.
+    :param y_train: Labels used for training.
+    :param x_test: Samples in test set.
+    :param y_test: Labels in test set.
+    :param tolerable_backdoor: Parameter that determines how many misclassifications are acceptable.
+    :param max_epochs: maximum number of epochs to be run.
+    :param batch_epochs: groups of epochs that will be run together before checking for termination.
+    :return: (improve_factor, classifier).
+    """
+    # Measure poison success in current model:
+    initial_missed = measure_misclassification(classifier, x_test, y_test)
+
+    curr_epochs = 0
+    curr_missed = 1.0
+    while curr_epochs < max_epochs and curr_missed > tolerable_backdoor:
+        classifier.fit(x_train, y_train, nb_epochs=batch_epochs)
+        curr_epochs += batch_epochs
+        curr_missed = measure_misclassification(classifier, x_test, y_test)
+        logger.info("Current epoch: %s", curr_epochs)
+        logger.info("Misclassifications: %s", curr_missed)
+
+    improve_factor = initial_missed - curr_missed
+    return improve_factor, classifier
+
+
+def cluster_activations(
+    separated_activations: List[np.ndarray],
+    nb_clusters: int = 2,
+    nb_dims: int = 10,
+    reduce: str = "FastICA",
+    clustering_method: str = "KMeans",
+    generator: Optional[DataGenerator] = None,
+    clusterer_new: Optional[MiniBatchKMeans] = None,
+) -> Tuple[List[np.ndarray], List[np.ndarray]]:
+    """
+    Clusters activations and returns two arrays.
+    1) separated_clusters: where separated_clusters[i] is a 1D array indicating which cluster each data point
+    in the class has been assigned.
+    2) separated_reduced_activations: activations with dimensionality reduced using the specified reduce method.
+
+    :param separated_activations: List where separated_activations[i] is a np matrix for the ith class where
+           each row corresponds to activations for a given data point.
+    :param nb_clusters: number of clusters (defaults to 2 for poison/clean).
+    :param nb_dims: number of dimensions to reduce activation to via PCA.
+    :param reduce: Method to perform dimensionality reduction, default is FastICA.
+    :param clustering_method: Clustering method to use, default is KMeans.
+    :param generator: whether or not a the activations are a batch or full activations
+    :return: (separated_clusters, separated_reduced_activations).
+    :param clusterer_new: whether or not a the activations are a batch or full activations
+    :return: (separated_clusters, separated_reduced_activations)
+    """
+    separated_clusters = []
+    separated_reduced_activations = []
+
+    if clustering_method == "KMeans":
+        clusterer = KMeans(n_clusters=nb_clusters)
+    else:
+        raise ValueError(clustering_method + " clustering method not supported.")
+
+    for activation in separated_activations:
+        # Apply dimensionality reduction
+        nb_activations = np.shape(activation)[1]
+        if nb_activations > nb_dims:
+            # TODO: address issue where if fewer samples than nb_dims this fails
+            reduced_activations = reduce_dimensionality(activation, nb_dims=nb_dims, reduce=reduce)
+        else:
+            logger.info(
+                "Dimensionality of activations = %i less than nb_dims = %i. Not applying dimensionality " "reduction.",
+                nb_activations,
+                nb_dims,
+            )
+            reduced_activations = activation
+        separated_reduced_activations.append(reduced_activations)
+
+        # Get cluster assignments
+        if generator is not None and clusterer_new is not None:
+            clusterer_new = clusterer_new.partial_fit(reduced_activations)
+            # NOTE: this may cause earlier predictions to be less accurate
+            clusters = clusterer_new.predict(reduced_activations)
+        else:
+            clusters = clusterer.fit_predict(reduced_activations)
+        separated_clusters.append(clusters)
+
+    return separated_clusters, separated_reduced_activations
+
+
+def reduce_dimensionality(activations: np.ndarray, nb_dims: int = 10, reduce: str = "FastICA") -> np.ndarray:
+    """
+    Reduces dimensionality of the activations provided using the specified number of dimensions and reduction technique.
+
+    :param activations: Activations to be reduced.
+    :param nb_dims: number of dimensions to reduce activation to via PCA.
+    :param reduce: Method to perform dimensionality reduction, default is FastICA.
+    :return: Array with the reduced activations.
+    """
+    # pylint: disable=E0001
+    from sklearn.decomposition import FastICA, PCA
+
+    if reduce == "FastICA":
+        projector = FastICA(n_components=nb_dims, max_iter=1000, tol=0.005)
+    elif reduce == "PCA":
+        projector = PCA(n_components=nb_dims)
+    else:
+        raise ValueError(reduce + " dimensionality reduction method not supported.")
+
+    reduced_activations = projector.fit_transform(activations)
+    return reduced_activations
diff --git a/adversarial-robustness-toolbox/art/defences/detector/poison/clustering_analyzer.py b/adversarial-robustness-toolbox/art/defences/detector/poison/clustering_analyzer.py
new file mode 100644
index 0000000..6134e8a
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/defences/detector/poison/clustering_analyzer.py
@@ -0,0 +1,345 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements methodologies to analyze clusters and determine whether they are poisonous.
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+from typing import Any, Dict, List, Tuple
+
+import numpy as np
+
+logger = logging.getLogger(__name__)
+
+
+class ClusteringAnalyzer:
+    """
+    Class for all methodologies implemented to analyze clusters and determine whether they are poisonous.
+    """
+
+    @staticmethod
+    def assign_class(clusters: np.ndarray, clean_clusters: List[int], poison_clusters: List[int]) -> np.ndarray:
+        """
+        Determines whether each data point in the class is in a clean or poisonous cluster
+
+        :param clusters: `clusters[i]` indicates which cluster the i'th data point is in.
+        :param clean_clusters: List containing the clusters designated as clean.
+        :param poison_clusters: List containing the clusters designated as poisonous.
+        :return: assigned_clean: `assigned_clean[i]` is a boolean indicating whether the ith data point is clean.
+        """
+        assigned_clean = np.empty(np.shape(clusters))
+        assigned_clean[np.isin(clusters, clean_clusters)] = 1
+        assigned_clean[np.isin(clusters, poison_clusters)] = 0
+        return assigned_clean
+
+    def analyze_by_size(
+        self, separated_clusters: List[np.ndarray]
+    ) -> Tuple[np.ndarray, List[List[int]], Dict[str, int]]:
+        """
+        Designates as poisonous the cluster with less number of items on it.
+
+        :param separated_clusters: list where separated_clusters[i] is the cluster assignments for the ith class.
+        :return: all_assigned_clean, summary_poison_clusters, report:
+                 where all_assigned_clean[i] is a 1D boolean array indicating whether
+                 a given data point was determined to be clean (as opposed to poisonous) and
+                 summary_poison_clusters: array, where summary_poison_clusters[i][j]=1 if cluster j of class i was
+                 classified as poison, otherwise 0
+                 report: Dictionary with summary of the analysis
+        """
+        report: Dict[str, Any] = {
+            "cluster_analysis": "smaller",
+            "suspicious_clusters": 0,
+        }
+
+        all_assigned_clean = []
+        nb_classes = len(separated_clusters)
+        nb_clusters = len(np.unique(separated_clusters[0]))
+        summary_poison_clusters: List[List[int]] = [[0 for _ in range(nb_clusters)] for _ in range(nb_classes)]
+
+        for i, clusters in enumerate(separated_clusters):
+
+            # assume that smallest cluster is poisonous and all others are clean
+            sizes = np.bincount(clusters)
+            total_dp_in_class = np.sum(sizes)
+            poison_clusters: List[int] = [int(np.argmin(sizes))]
+            clean_clusters = list(set(clusters) - set(poison_clusters))
+
+            for p_id in poison_clusters:
+                summary_poison_clusters[i][p_id] = 1
+            for c_id in clean_clusters:
+                summary_poison_clusters[i][c_id] = 0
+
+            assigned_clean = self.assign_class(clusters, clean_clusters, poison_clusters)
+            all_assigned_clean.append(assigned_clean)
+
+            # Generate report for this class:
+            report_class = dict()
+            for cluster_id in range(nb_clusters):
+                ptc = sizes[cluster_id] / total_dp_in_class
+                susp = cluster_id in poison_clusters
+                dict_i = dict(ptc_data_in_cluster=round(ptc, 2), suspicious_cluster=susp)
+
+                dict_cluster: Dict[str, Dict[str, int]] = {"cluster_" + str(cluster_id): dict_i}
+                report_class.update(dict_cluster)
+
+            report["Class_" + str(i)] = report_class
+
+        report["suspicious_clusters"] = report["suspicious_clusters"] + np.sum(summary_poison_clusters).item()
+        return np.asarray(all_assigned_clean), summary_poison_clusters, report
+
+    def analyze_by_distance(
+        self, separated_clusters: List[np.ndarray], separated_activations: List[np.ndarray],
+    ) -> Tuple[np.ndarray, List[List[int]], Dict[str, int]]:
+        """
+        Assigns a cluster as poisonous if its median activation is closer to the median activation for another class
+        than it is to the median activation of its own class. Currently, this function assumes there are only two
+        clusters per class.
+
+        :param separated_clusters: list where separated_clusters[i] is the cluster assignments for the ith class.
+        :param separated_activations: list where separated_activations[i] is a 1D array of [0,1] for [poison,clean].
+        :return: all_assigned_clean, summary_poison_clusters, report:
+                 where all_assigned_clean[i] is a 1D boolean array indicating whether a given data point was determined
+                 to be clean (as opposed to poisonous) and summary_poison_clusters: array, where
+                 summary_poison_clusters[i][j]=1 if cluster j of class i was classified as poison, otherwise 0
+                 report: Dictionary with summary of the analysis.
+        """
+        report: Dict[str, Any] = {"cluster_analysis": 0.0}
+        all_assigned_clean = []
+        cluster_centers = []
+
+        nb_classes = len(separated_clusters)
+        nb_clusters = len(np.unique(separated_clusters[0]))
+        summary_poison_clusters: List[List[int]] = [[0 for _ in range(nb_clusters)] for _ in range(nb_classes)]
+
+        # assign centers
+        for _, activations in enumerate(separated_activations):
+            cluster_centers.append(np.median(activations, axis=0))
+
+        for i, (clusters, activation) in enumerate(zip(separated_clusters, separated_activations)):
+            clusters = np.array(clusters)
+
+            cluster0_center = np.median(activation[np.where(clusters == 0)], axis=0)
+            cluster1_center = np.median(activation[np.where(clusters == 1)], axis=0)
+
+            cluster0_distance = np.linalg.norm(cluster0_center - cluster_centers[i])
+            cluster1_distance = np.linalg.norm(cluster1_center - cluster_centers[i])
+
+            cluster0_is_poison = False
+            cluster1_is_poison = False
+
+            dict_k = dict()
+            dict_cluster_0 = dict(cluster0_distance_to_its_class=str(cluster0_distance))
+            dict_cluster_1 = dict(cluster1_distance_to_its_class=str(cluster1_distance))
+            for k, center in enumerate(cluster_centers):
+                if k == i:
+                    pass
+                else:
+                    cluster0_distance_to_k = np.linalg.norm(cluster0_center - center)
+                    cluster1_distance_to_k = np.linalg.norm(cluster1_center - center)
+
+                    if cluster0_distance_to_k < cluster0_distance and cluster1_distance_to_k > cluster1_distance:
+                        cluster0_is_poison = True
+                    if cluster1_distance_to_k < cluster1_distance and cluster0_distance_to_k > cluster0_distance:
+                        cluster1_is_poison = True
+
+                    dict_cluster_0["distance_to_class_" + str(k)] = str(cluster0_distance_to_k)
+                    dict_cluster_0["suspicious"] = str(cluster0_is_poison)
+
+                    dict_cluster_1["distance_to_class_" + str(k)] = str(cluster1_distance_to_k)
+                    dict_cluster_1["suspicious"] = str(cluster1_is_poison)
+
+                    dict_k.update(dict_cluster_0)
+                    dict_k.update(dict_cluster_1)
+
+            report_class = dict(cluster_0=dict_cluster_0, cluster_1=dict_cluster_1)
+            report["Class_" + str(i)] = report_class
+
+            poison_clusters = []
+            if cluster0_is_poison:
+                poison_clusters.append(0)
+                summary_poison_clusters[i][0] = 1
+            else:
+                summary_poison_clusters[i][0] = 0
+
+            if cluster1_is_poison:
+                poison_clusters.append(1)
+                summary_poison_clusters[i][1] = 1
+            else:
+                summary_poison_clusters[i][1] = 0
+
+            clean_clusters = list(set(clusters) - set(poison_clusters))
+            assigned_clean = self.assign_class(clusters, clean_clusters, poison_clusters)
+            all_assigned_clean.append(assigned_clean)
+
+        all_assigned_clean = np.asarray(all_assigned_clean)
+        return all_assigned_clean, summary_poison_clusters, report
+
+    def analyze_by_relative_size(
+        self, separated_clusters: List[np.ndarray], size_threshold: float = 0.35, r_size: int = 2,
+    ) -> Tuple[np.ndarray, List[List[int]], Dict[str, int]]:
+        """
+        Assigns a cluster as poisonous if the smaller one contains less than threshold of the data.
+        This method assumes only 2 clusters
+
+        :param separated_clusters: List where `separated_clusters[i]` is the cluster assignments for the ith class.
+        :param size_threshold: Threshold used to define when a cluster is substantially smaller.
+        :param r_size: Round number used for size rate comparisons.
+        :return: all_assigned_clean, summary_poison_clusters, report:
+                 where all_assigned_clean[i] is a 1D boolean array indicating whether a given data point was determined
+                 to be clean (as opposed to poisonous) and summary_poison_clusters: array, where
+                 summary_poison_clusters[i][j]=1 if cluster j of class i was classified as poison, otherwise 0
+                 report: Dictionary with summary of the analysis.
+        """
+        size_threshold = round(size_threshold, r_size)
+        report: Dict[str, Any] = {
+            "cluster_analysis": "relative_size",
+            "suspicious_clusters": 0,
+            "size_threshold": size_threshold,
+        }
+
+        all_assigned_clean = []
+        nb_classes = len(separated_clusters)
+        nb_clusters = len(np.unique(separated_clusters[0]))
+        summary_poison_clusters: List[List[int]] = [[0 for _ in range(nb_clusters)] for _ in range(nb_classes)]
+
+        for i, clusters in enumerate(separated_clusters):
+            sizes = np.bincount(clusters)
+            total_dp_in_class = np.sum(sizes)
+
+            if np.size(sizes) > 2:
+                raise ValueError(" RelativeSizeAnalyzer does not support more than two clusters.")
+            percentages = np.round(sizes / float(np.sum(sizes)), r_size)
+            poison_clusters = np.where(percentages < size_threshold)
+            clean_clusters = np.where(percentages >= size_threshold)
+
+            for p_id in poison_clusters[0]:
+                summary_poison_clusters[i][p_id] = 1
+            for c_id in clean_clusters[0]:
+                summary_poison_clusters[i][c_id] = 0
+
+            assigned_clean = self.assign_class(clusters, clean_clusters, poison_clusters)
+            all_assigned_clean.append(assigned_clean)
+
+            # Generate report for this class:
+            report_class = dict()
+            for cluster_id in range(nb_clusters):
+                ptc = sizes[cluster_id] / total_dp_in_class
+                susp = cluster_id in poison_clusters
+                dict_i = dict(ptc_data_in_cluster=round(ptc, 2), suspicious_cluster=susp)
+
+                dict_cluster = {"cluster_" + str(cluster_id): dict_i}
+                report_class.update(dict_cluster)
+
+            report["Class_" + str(i)] = report_class
+
+        report["suspicious_clusters"] = report["suspicious_clusters"] + np.sum(summary_poison_clusters).item()
+        return np.asarray(all_assigned_clean), summary_poison_clusters, report
+
+    def analyze_by_silhouette_score(
+        self,
+        separated_clusters: list,
+        reduced_activations_by_class: list,
+        size_threshold: float = 0.35,
+        silhouette_threshold: float = 0.1,
+        r_size: int = 2,
+        r_silhouette: int = 4,
+    ) -> Tuple[np.ndarray, List[List[int]], Dict[str, int]]:
+        """
+        Analyzes clusters to determine level of suspiciousness of poison based on the cluster's relative size
+        and silhouette score.
+        Computes a silhouette score for each class to determine how cohesive resulting clusters are.
+        A low silhouette score indicates that the clustering does not fit the data well, and the class can be considered
+        to be un-poisoned. Conversely, a high silhouette score indicates that the clusters reflect true splits in the
+        data.
+        The method concludes that a cluster is poison based on the silhouette score and the cluster relative size.
+        If the relative size is too small, below a size_threshold and at the same time
+        the silhouette score is higher than silhouette_threshold, the cluster is classified as poisonous.
+        If the above thresholds are not provided, the default ones will be used.
+
+        :param separated_clusters: list where `separated_clusters[i]` is the cluster assignments for the ith class.
+        :param reduced_activations_by_class: list where separated_activations[i] is a 1D array of [0,1] for
+               [poison,clean].
+        :param size_threshold: (optional) threshold used to define when a cluster is substantially smaller. A default
+        value is used if the parameter is not provided.
+        :param silhouette_threshold: (optional) threshold used to define when a cluster is cohesive. Default
+        value is used if the parameter is not provided.
+        :param r_size: Round number used for size rate comparisons.
+        :param r_silhouette: Round number used for silhouette rate comparisons.
+        :return: all_assigned_clean, summary_poison_clusters, report:
+                 where all_assigned_clean[i] is a 1D boolean array indicating whether a given data point was determined
+                 to be clean (as opposed to poisonous) summary_poison_clusters: array, where
+                 summary_poison_clusters[i][j]=1 if cluster j of class j was classified as poison
+                 report: Dictionary with summary of the analysis.
+        """
+        # pylint: disable=E0001
+        from sklearn.metrics import silhouette_score
+
+        size_threshold = round(size_threshold, r_size)
+        silhouette_threshold = round(silhouette_threshold, r_silhouette)
+        report: Dict[str, Any] = {
+            "cluster_analysis": "silhouette_score",
+            "size_threshold": str(size_threshold),
+            "silhouette_threshold": str(silhouette_threshold),
+        }
+        all_assigned_clean = []
+        nb_classes = len(separated_clusters)
+        nb_clusters = len(np.unique(separated_clusters[0]))
+        summary_poison_clusters: List[List[int]] = [[0 for _ in range(nb_clusters)] for _ in range(nb_classes)]
+
+        for i, (clusters, activations) in enumerate(zip(separated_clusters, reduced_activations_by_class)):
+            bins = np.bincount(clusters)
+            if np.size(bins) > 2:
+                raise ValueError("Analyzer does not support more than two clusters.")
+            percentages = np.round(bins / float(np.sum(bins)), r_size)
+            poison_clusters = np.where(percentages < size_threshold)
+            clean_clusters = np.where(percentages >= size_threshold)
+
+            # Generate report for class
+            silhouette_avg = round(silhouette_score(activations, clusters), r_silhouette)
+            dict_i: Dict[str, Any] = dict(
+                sizes_clusters=str(bins), ptc_cluster=str(percentages), avg_silhouette_score=str(silhouette_avg),
+            )
+
+            if np.shape(poison_clusters)[1] != 0:
+                # Relative size of the clusters is suspicious
+                if silhouette_avg > silhouette_threshold:
+                    # In this case the cluster is considered poisonous
+                    clean_clusters = np.where(percentages < size_threshold)
+                    logger.info("computed silhouette score: %s", silhouette_avg)
+                    dict_i.update(suspicious=True)
+                else:
+                    poison_clusters = [[]]
+                    clean_clusters = np.where(percentages >= 0)
+                    dict_i.update(suspicious=False)
+            else:
+                # If relative size of the clusters is Not suspicious, we conclude it's not suspicious.
+                dict_i.update(suspicious=False)
+
+            report_class: Dict[str, Dict[str, bool]] = {"class_" + str(i): dict_i}
+
+            for p_id in poison_clusters[0]:
+                summary_poison_clusters[i][p_id] = 1
+            for c_id in clean_clusters[0]:
+                summary_poison_clusters[i][c_id] = 0
+
+            assigned_clean = self.assign_class(clusters, clean_clusters, poison_clusters)
+            all_assigned_clean.append(assigned_clean)
+            report.update(report_class)
+
+        return np.asarray(all_assigned_clean), summary_poison_clusters, report
diff --git a/adversarial-robustness-toolbox/art/defences/detector/poison/ground_truth_evaluator.py b/adversarial-robustness-toolbox/art/defences/detector/poison/ground_truth_evaluator.py
new file mode 100644
index 0000000..d8a34cd
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/defences/detector/poison/ground_truth_evaluator.py
@@ -0,0 +1,160 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements classes to evaluate the performance of poison detection methods.
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import json
+import logging
+from typing import Tuple, Union, List
+
+import numpy as np
+
+logger = logging.getLogger(__name__)
+
+
+class GroundTruthEvaluator:
+    """
+    Class to evaluate the performance of the poison detection method.
+    """
+
+    def __init__(self):
+        """
+        Evaluates ground truth constructor
+        """
+
+    def analyze_correctness(
+        self, assigned_clean_by_class: Union[np.ndarray, List[np.ndarray]], is_clean_by_class: list
+    ) -> Tuple[np.ndarray, str]:
+        """
+        For each training sample, determine whether the activation clustering method was correct.
+
+        :param assigned_clean_by_class: Result of clustering.
+        :param is_clean_by_class: is clean separated by class.
+        :return: Two variables are returned:
+                 1) all_errors_by_class[i]: an array indicating the correctness of each assignment
+                 in the ith class. Such that:
+                 all_errors_by_class[i] = 0 if marked poison, is poison
+                 all_errors_by_class[i] = 1 if marked clean, is clean
+                 all_errors_by_class[i] = 2 if marked poison, is clean
+                 all_errors_by_class[i] = 3 marked clean, is poison
+                 2) Json object with confusion matrix per-class.
+        """
+        all_errors_by_class = []
+        poison = 0
+        clean = 1
+        dic_json = {}
+
+        logger.debug("Error rates per class:")
+        for class_i, (assigned_clean, is_clean) in enumerate(zip(assigned_clean_by_class, is_clean_by_class)):
+            errors = []
+            for assignment, bl_var in zip(assigned_clean, is_clean):
+                bl_var = int(bl_var)
+                # marked poison, is poison = 0
+                # true positive
+                if assignment == poison and bl_var == poison:
+                    errors.append(0)
+
+                # marked clean, is clean = 1
+                # true negative
+                elif assignment == clean and bl_var == clean:
+                    errors.append(1)
+
+                # marked poison, is clean = 2
+                # false positive
+                elif assignment == poison and bl_var == clean:
+                    errors.append(2)
+
+                # marked clean, is poison = 3
+                # false negative
+                elif assignment == clean and bl_var == poison:
+                    errors.append(3)
+                else:
+                    raise Exception("Analyze_correctness entered wrong class")
+
+            errors = np.asarray(errors)
+            logger.debug("-------------------%d---------------", class_i)
+            key_i = "class_" + str(class_i)
+            matrix_i = self.get_confusion_matrix(errors)
+            dic_json.update({key_i: matrix_i})
+            all_errors_by_class.append(errors)
+
+        all_errors_by_class = np.asarray(all_errors_by_class)
+        conf_matrix_json = json.dumps(dic_json)
+
+        return all_errors_by_class, conf_matrix_json
+
+    def get_confusion_matrix(self, values: np.ndarray) -> dict:
+        """
+        Computes and returns a json object that contains the confusion matrix for each class.
+
+        :param values: Array indicating the correctness of each assignment in the ith class.
+        :return: Json object with confusion matrix per-class.
+        """
+        dic_class = {}
+        true_positive = np.where(values == 0)[0].shape[0]
+        true_negative = np.where(values == 1)[0].shape[0]
+        false_positive = np.where(values == 2)[0].shape[0]
+        false_negative = np.where(values == 3)[0].shape[0]
+
+        tp_rate = self.calculate_and_print(true_positive, true_positive + false_negative, "true-positive rate")
+        tn_rate = self.calculate_and_print(true_negative, false_positive + true_negative, "true-negative rate")
+        fp_rate = self.calculate_and_print(false_positive, false_positive + true_negative, "false-positive rate")
+        fn_rate = self.calculate_and_print(false_negative, true_positive + false_negative, "false-negative rate")
+
+        dic_tp = dict(rate=round(tp_rate, 2), numerator=true_positive, denominator=(true_positive + false_negative),)
+        if (true_positive + false_negative) == 0:
+            dic_tp = dict(rate="N/A", numerator=true_positive, denominator=(true_positive + false_negative),)
+
+        dic_tn = dict(rate=round(tn_rate, 2), numerator=true_negative, denominator=(false_positive + true_negative),)
+        if (false_positive + true_negative) == 0:
+            dic_tn = dict(rate="N/A", numerator=true_negative, denominator=(false_positive + true_negative),)
+
+        dic_fp = dict(rate=round(fp_rate, 2), numerator=false_positive, denominator=(false_positive + true_negative),)
+        if (false_positive + true_negative) == 0:
+            dic_fp = dict(rate="N/A", numerator=false_positive, denominator=(false_positive + true_negative),)
+
+        dic_fn = dict(rate=round(fn_rate, 2), numerator=false_negative, denominator=(true_positive + false_negative),)
+        if (true_positive + false_negative) == 0:
+            dic_fn = dict(rate="N/A", numerator=false_negative, denominator=(true_positive + false_negative),)
+
+        dic_class.update(dict(TruePositive=dic_tp))
+        dic_class.update(dict(TrueNegative=dic_tn))
+        dic_class.update(dict(FalsePositive=dic_fp))
+        dic_class.update(dict(FalseNegative=dic_fn))
+
+        return dic_class
+
+    @staticmethod
+    def calculate_and_print(numerator: int, denominator: int, name: str) -> float:
+        """
+        Computes and prints the rates based on the denominator provided.
+
+        :param numerator: number used to compute the rate.
+        :param denominator: number used to compute the rate.
+        :param name: Rate name being computed e.g., false-positive rate.
+        :return: Computed rate
+        """
+        try:
+            res = 100 * (numerator / float(denominator))
+            logger.debug("%s: %d/%d=%.3g", name, numerator, denominator, res)
+            return res
+        except ZeroDivisionError:
+            logger.debug("%s: couldn't calculate %d/%d", name, numerator, denominator)
+            return 0.0
diff --git a/adversarial-robustness-toolbox/art/defences/detector/poison/poison_filtering_defence.py b/adversarial-robustness-toolbox/art/defences/detector/poison/poison_filtering_defence.py
new file mode 100644
index 0000000..0d10e76
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/defences/detector/poison/poison_filtering_defence.py
@@ -0,0 +1,100 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements the abstract base class for all poison filtering defences.
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import abc
+import sys
+from typing import Any, Dict, List, Tuple, TYPE_CHECKING
+
+import numpy as np
+
+# Ensure compatibility with Python 2 and 3 when using ABCMeta
+if sys.version_info >= (3, 4):
+    ABC = abc.ABC
+else:
+    ABC = abc.ABCMeta(str("ABC"), (), {})
+
+if TYPE_CHECKING:
+    from art.utils import CLASSIFIER_TYPE
+
+
+class PoisonFilteringDefence(ABC):
+    """
+    Base class for all poison filtering defences.
+    """
+
+    defence_params = ["classifier"]
+
+    def __init__(self, classifier: "CLASSIFIER_TYPE", x_train: np.ndarray, y_train: np.ndarray) -> None:
+        """
+        Create an :class:`.ActivationDefence` object with the provided classifier.
+
+        :param classifier: Model evaluated for poison.
+        :param x_train: dataset used to train the classifier.
+        :param y_train: labels used to train the classifier.
+        """
+        self.classifier = classifier
+        self.x_train = x_train
+        self.y_train = y_train
+
+    @abc.abstractmethod
+    def detect_poison(self, **kwargs) -> Tuple[dict, List[int]]:
+        """
+        Detect poison.
+
+        :param kwargs: Defence-specific parameters used by child classes.
+        :return: Dictionary with report and list with items identified as poison.
+        """
+        raise NotImplementedError
+
+    @abc.abstractmethod
+    def evaluate_defence(self, is_clean: np.ndarray, **kwargs) -> str:
+        """
+        Evaluate the defence given the labels specifying if the data is poisoned or not.
+
+        :param is_clean: 1-D array where is_clean[i]=1 means x_train[i] is clean and is_clean[i]=0 that it's poison.
+        :param kwargs: Defence-specific parameters used by child classes.
+        :return: JSON object with confusion matrix.
+        """
+        raise NotImplementedError
+
+    def set_params(self, **kwargs) -> None:
+        """
+        Take in a dictionary of parameters and apply attack-specific checks before saving them as attributes.
+
+        :param kwargs: A dictionary of defence-specific parameters.
+        """
+        for key, value in kwargs.items():
+            if key in self.defence_params:
+                setattr(self, key, value)
+        self._check_params()
+
+    def get_params(self) -> Dict[str, Any]:
+        """
+        Returns dictionary of parameters used to run defence.
+
+        :return: Dictionary of parameters of the method.
+        """
+        dictionary = {param: getattr(self, param) for param in self.defence_params}
+        return dictionary
+
+    def _check_params(self) -> None:
+        pass
diff --git a/adversarial-robustness-toolbox/art/defences/detector/poison/provenance_defense.py b/adversarial-robustness-toolbox/art/defences/detector/poison/provenance_defense.py
new file mode 100644
index 0000000..eb6520e
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/defences/detector/poison/provenance_defense.py
@@ -0,0 +1,256 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements methods performing poisoning detection based on data provenance.
+
+| Paper link: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8473440
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+from copy import deepcopy
+from typing import Dict, List, Optional, Tuple, TYPE_CHECKING
+
+import numpy as np
+from sklearn.model_selection import train_test_split
+
+from art.defences.detector.poison.ground_truth_evaluator import GroundTruthEvaluator
+from art.defences.detector.poison.poison_filtering_defence import PoisonFilteringDefence
+from art.utils import segment_by_class, performance_diff
+
+if TYPE_CHECKING:
+    from art.utils import CLASSIFIER_TYPE
+
+logger = logging.getLogger(__name__)
+
+
+class ProvenanceDefense(PoisonFilteringDefence):
+    """
+    Implements methods performing poisoning detection based on data provenance.
+
+    | Paper link: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8473440
+    """
+
+    defence_params = [
+        "classifier",
+        "x_train",
+        "y_train",
+        "p_train",
+        "x_val",
+        "y_val",
+        "eps",
+        "perf_func",
+        "pp_valid",
+    ]
+
+    def __init__(
+        self,
+        classifier: "CLASSIFIER_TYPE",
+        x_train: np.ndarray,
+        y_train: np.ndarray,
+        p_train: np.ndarray,
+        x_val: Optional[np.ndarray] = None,
+        y_val: Optional[np.ndarray] = None,
+        eps: float = 0.2,
+        perf_func: str = "accuracy",
+        pp_valid: float = 0.2,
+    ) -> None:
+        """
+        Create an :class:`.ProvenanceDefense` object with the provided classifier.
+
+        :param classifier: Model evaluated for poison.
+        :param x_train: dataset used to train the classifier.
+        :param y_train: labels used to train the classifier.
+        :param p_train: provenance features for each training data point as one hot vectors.
+        :param x_val: Validation data for defense.
+        :param y_val: Validation labels for defense.
+        :param eps: Threshold for performance shift in suspicious data.
+        :param perf_func: performance function used to evaluate effectiveness of defense.
+        :param pp_valid: The percent of training data to use as validation data (for defense without validation data).
+        """
+        super().__init__(classifier, x_train, y_train)
+        self.p_train = p_train
+        self.num_devices = self.p_train.shape[1]
+        self.x_val = x_val
+        self.y_val = y_val
+        self.eps = eps
+        self.perf_func = perf_func
+        self.pp_valid = pp_valid
+        self.assigned_clean_by_device: List[np.ndarray] = []
+        self.is_clean_by_device: List[np.ndarray] = []
+        self.errors_by_device: Optional[np.ndarray] = None
+        self.evaluator = GroundTruthEvaluator()
+        self.is_clean_lst: Optional[np.ndarray] = None
+        self._check_params()
+
+    def evaluate_defence(self, is_clean: np.ndarray, **kwargs) -> str:
+        """
+        Returns confusion matrix.
+
+        :param is_clean: Ground truth, where is_clean[i]=1 means that x_train[i] is clean and is_clean[i]=0 means
+                         x_train[i] is poisonous.
+        :param kwargs: A dictionary of defence-specific parameters.
+        :return: JSON object with confusion matrix.
+        """
+        if is_clean is None or is_clean.size == 0:
+            raise ValueError("is_clean was not provided while invoking evaluate_defence.")
+        self.set_params(**kwargs)
+
+        if not self.assigned_clean_by_device:
+            self.detect_poison()
+
+        self.is_clean_by_device = segment_by_class(is_clean, self.p_train, self.num_devices)
+        self.errors_by_device, conf_matrix_json = self.evaluator.analyze_correctness(
+            self.assigned_clean_by_device, self.is_clean_by_device
+        )
+        return conf_matrix_json
+
+    def detect_poison(self, **kwargs) -> Tuple[dict, np.ndarray]:
+        """
+        Returns poison detected and a report.
+
+        :param kwargs: A dictionary of detection-specific parameters.
+        :return: (report, is_clean_lst):
+                where a report is a dict object that contains information specified by the provenance detection method
+                where is_clean is a list, where is_clean_lst[i]=1 means that x_train[i]
+                there is clean and is_clean_lst[i]=0, means that x_train[i] was classified as poison.
+        :rtype: `tuple`
+        """
+        self.set_params(**kwargs)
+
+        if self.x_val is None:
+            report = self.detect_poison_untrusted()
+        else:
+            report = self.detect_poison_partially_trusted()
+
+        n_train = len(self.x_train)
+        indices_by_provenance = segment_by_class(np.arange(n_train), self.p_train, self.num_devices)
+        self.is_clean_lst = np.array([1] * n_train)
+
+        for device in report:
+            self.is_clean_lst[indices_by_provenance[device]] = 0
+        self.assigned_clean_by_device = segment_by_class(np.array(self.is_clean_lst), self.p_train, self.num_devices)
+
+        return report, self.is_clean_lst
+
+    def detect_poison_partially_trusted(self, **kwargs) -> Dict[int, float]:
+        """
+        Detect poison given trusted validation data
+
+        :return: dictionary where keys are suspected poisonous device indices and values are performance differences
+        """
+        self.set_params(**kwargs)
+
+        if self.x_val is None or self.y_val is None:
+            raise ValueError("Trusted data unavailable.")
+
+        suspected = {}
+        unfiltered_data = np.copy(self.x_train)
+        unfiltered_labels = np.copy(self.y_train)
+
+        segments = segment_by_class(self.x_train, self.p_train, self.num_devices)
+        for device_idx, segment in enumerate(segments):
+            filtered_data, filtered_labels = self.filter_input(unfiltered_data, unfiltered_labels, segment)
+
+            unfiltered_model = deepcopy(self.classifier)
+            filtered_model = deepcopy(self.classifier)
+
+            unfiltered_model.fit(unfiltered_data, unfiltered_labels)
+            filtered_model.fit(filtered_data, filtered_labels)
+
+            var_w = performance_diff(
+                filtered_model, unfiltered_model, self.x_val, self.y_val, perf_function=self.perf_func,
+            )
+            if self.eps < var_w:
+                suspected[device_idx] = var_w
+                unfiltered_data = filtered_data
+                unfiltered_labels = filtered_labels
+
+        return suspected
+
+    def detect_poison_untrusted(self, **kwargs) -> Dict[int, float]:
+        """
+        Detect poison given no trusted validation data
+
+        :return: dictionary where keys are suspected poisonous device indices and values are performance differences
+        """
+        self.set_params(**kwargs)
+
+        suspected = {}
+        (train_data, valid_data, train_labels, valid_labels, train_prov, valid_prov,) = train_test_split(
+            self.x_train, self.y_train, self.p_train, test_size=self.pp_valid
+        )
+
+        train_segments = segment_by_class(train_data, train_prov, self.num_devices)
+        valid_segments = segment_by_class(valid_data, valid_prov, self.num_devices)
+
+        for device_idx, (train_segment, valid_segment) in enumerate(zip(train_segments, valid_segments)):
+            filtered_data, filtered_labels = self.filter_input(train_data, train_labels, train_segment)
+
+            unfiltered_model = deepcopy(self.classifier)
+            filtered_model = deepcopy(self.classifier)
+
+            unfiltered_model.fit(train_data, train_labels)
+            filtered_model.fit(filtered_data, filtered_labels)
+
+            valid_non_device_data, valid_non_device_labels = self.filter_input(valid_data, valid_labels, valid_segment)
+            var_w = performance_diff(
+                filtered_model,
+                unfiltered_model,
+                valid_non_device_data,
+                valid_non_device_labels,
+                perf_function=self.perf_func,
+            )
+
+            if self.eps < var_w:
+                suspected[device_idx] = var_w
+                train_data = filtered_data
+                train_labels = filtered_labels
+                valid_data = valid_non_device_data
+                valid_labels = valid_non_device_labels
+
+        return suspected
+
+    @staticmethod
+    def filter_input(data: np.ndarray, labels: np.ndarray, segment: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
+        """
+        Return the data and labels that are not part of a specified segment
+
+        :param data: The data to segment.
+        :param labels: The corresponding labels to segment
+        :param segment:
+        :return: Tuple of (filtered_data, filtered_labels).
+        """
+        filter_mask = np.array([np.isin(data[i, :], segment, invert=True).any() for i in range(data.shape[0])])
+        filtered_data = data[filter_mask]
+        filtered_labels = labels[filter_mask]
+
+        return filtered_data, filtered_labels
+
+    def _check_params(self) -> None:
+        if self.eps < 0:
+            raise ValueError("Value of epsilon must be at least 0.")
+
+        if self.pp_valid < 0:
+            raise ValueError("Value of pp_valid must be at least 0.")
+
+        if len(self.x_train) != len(self.y_train):
+            raise ValueError("x_train and y_train do not match in shape.")
+
+        if len(self.x_train) != len(self.p_train):
+            raise ValueError("Provenance features do not match data.")
diff --git a/adversarial-robustness-toolbox/art/defences/detector/poison/roni.py b/adversarial-robustness-toolbox/art/defences/detector/poison/roni.py
new file mode 100644
index 0000000..8eef136
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/defences/detector/poison/roni.py
@@ -0,0 +1,207 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements the Reject on Negative Impact (RONI) defense by Nelson et al. (2019)
+
+| Paper link: https://people.eecs.berkeley.edu/~tygar/papers/SML/misleading.learners.pdf
+"""
+
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+from copy import deepcopy
+from typing import Callable, List, Tuple, Union, TYPE_CHECKING
+
+import numpy as np
+from sklearn.model_selection import train_test_split
+
+from art.defences.detector.poison.ground_truth_evaluator import GroundTruthEvaluator
+from art.defences.detector.poison.poison_filtering_defence import PoisonFilteringDefence
+from art.utils import performance_diff
+
+if TYPE_CHECKING:
+    from art.utils import CLASSIFIER_TYPE
+
+logger = logging.getLogger(__name__)
+
+
+class RONIDefense(PoisonFilteringDefence):
+    """
+    Close implementation based on description in Nelson
+    'Behavior of Machine Learning Algorithms in Adversarial Environments' Ch. 4.4
+
+    | Textbook link: https://people.eecs.berkeley.edu/~adj/publications/paper-files/EECS-2010-140.pdf
+    """
+
+    defence_params = [
+        "classifier",
+        "x_train",
+        "y_train",
+        "x_val",
+        "y_val",
+        "perf_func",
+        "calibrated",
+        "eps",
+    ]
+
+    def __init__(
+        self,
+        classifier: "CLASSIFIER_TYPE",
+        x_train: np.ndarray,
+        y_train: np.ndarray,
+        x_val: np.ndarray,
+        y_val: np.ndarray,
+        perf_func: Union[str, Callable] = "accuracy",
+        pp_cal: float = 0.2,
+        pp_quiz: float = 0.2,
+        calibrated: bool = True,
+        eps: float = 0.1,
+    ):
+        """
+        Create an :class:`.RONIDefense` object with the provided classifier.
+
+        :param classifier: Model evaluated for poison.
+        :param x_train: Dataset used to train the classifier.
+        :param y_train: Labels used to train the classifier.
+        :param x_val: Trusted data points.
+        :param y_train: Trusted data labels.
+        :param perf_func: Performance function to use.
+        :param pp_cal: Percent of training data used for calibration.
+        :param pp_quiz: Percent of training data used for quiz set.
+        :param calibrated: True if using the calibrated form of RONI.
+        :param eps: performance threshold if using uncalibrated RONI.
+        """
+        super().__init__(classifier, x_train, y_train)
+        n_points = len(x_train)
+        quiz_idx = np.random.randint(n_points, size=int(pp_quiz * n_points))
+        self.calibrated = calibrated
+        self.x_quiz = np.copy(self.x_train[quiz_idx])
+        self.y_quiz = np.copy(self.y_train[quiz_idx])
+        if self.calibrated:
+            _, self.x_cal, _, self.y_cal = train_test_split(self.x_train, self.y_train, test_size=pp_cal, shuffle=True)
+        self.eps = eps
+        self.evaluator = GroundTruthEvaluator()
+        self.x_val = x_val
+        self.y_val = y_val
+        self.perf_func = perf_func
+        self.is_clean_lst: List[int] = list()
+        self._check_params()
+
+    def evaluate_defence(self, is_clean: np.ndarray, **kwargs) -> str:
+        """
+        Returns confusion matrix.
+
+        :param is_clean: Ground truth, where is_clean[i]=1 means that x_train[i] is clean and is_clean[i]=0 means
+                         x_train[i] is poisonous.
+        :param kwargs: A dictionary of defence-specific parameters.
+        :return: JSON object with confusion matrix.
+        """
+        self.set_params(**kwargs)
+        if len(self.is_clean_lst) == 0:
+            self.detect_poison()
+
+        if is_clean is None or len(is_clean) != len(self.is_clean_lst):
+            raise ValueError("Invalid value for is_clean.")
+
+        _, conf_matrix = self.evaluator.analyze_correctness([self.is_clean_lst], [is_clean])
+        return conf_matrix
+
+    def detect_poison(self, **kwargs) -> Tuple[dict, List[int]]:
+        """
+        Returns poison detected and a report.
+
+        :param kwargs: A dictionary of detection-specific parameters.
+        :return: (report, is_clean_lst):
+                where a report is a dict object that contains information specified by the provenance detection method
+                where is_clean is a list, where is_clean_lst[i]=1 means that x_train[i]
+                there is clean and is_clean_lst[i]=0, means that x_train[i] was classified as poison.
+        """
+        self.set_params(**kwargs)
+
+        x_suspect = self.x_train
+        y_suspect = self.y_train
+        x_trusted = self.x_val
+        y_trusted = self.y_val
+
+        self.is_clean_lst = [1 for _ in range(len(x_suspect))]
+        report = {}
+
+        before_classifier = deepcopy(self.classifier)
+        before_classifier.fit(x_suspect, y_suspect)
+
+        for idx in np.random.permutation(len(x_suspect)):
+            x_i = x_suspect[idx]
+            y_i = y_suspect[idx]
+
+            after_classifier = deepcopy(before_classifier)
+            after_classifier.fit(x=np.vstack([x_trusted, x_i]), y=np.vstack([y_trusted, y_i]))
+            acc_shift = performance_diff(
+                before_classifier, after_classifier, self.x_quiz, self.y_quiz, perf_function=self.perf_func,
+            )
+            # print(acc_shift, median, std_dev)
+            if self.is_suspicious(before_classifier, acc_shift):
+                self.is_clean_lst[idx] = 0
+                report[idx] = acc_shift
+            else:
+                before_classifier = after_classifier
+                x_trusted = np.vstack([x_trusted, x_i])
+                y_trusted = np.vstack([y_trusted, y_i])
+
+        return report, self.is_clean_lst
+
+    def is_suspicious(self, before_classifier: "CLASSIFIER_TYPE", perf_shift: float) -> bool:
+        """
+        Returns True if a given performance shift is suspicious
+
+        :param before_classifier: The classifier without untrusted data.
+        :param perf_shift: A shift in performance.
+        :return: True if a given performance shift is suspicious, false otherwise.
+        """
+        if self.calibrated:
+            median, std_dev = self.get_calibration_info(before_classifier)
+            return perf_shift < median - 3 * std_dev
+
+        return perf_shift < -self.eps
+
+    def get_calibration_info(self, before_classifier: "CLASSIFIER_TYPE") -> Tuple[np.ndarray, np.ndarray]:
+        """
+        Calculate the median and standard deviation of the accuracy shifts caused
+        by the calibration set.
+
+        :param before_classifier: The classifier trained without suspicious point.
+        :return: A tuple consisting of `(median, std_dev)`.
+        """
+        accs = []
+
+        for x_c, y_c in zip(self.x_cal, self.y_cal):
+            after_classifier = deepcopy(before_classifier)
+            after_classifier.fit(x=np.vstack([self.x_val, x_c]), y=np.vstack([self.y_val, y_c]))
+            accs.append(
+                performance_diff(
+                    before_classifier, after_classifier, self.x_quiz, self.y_quiz, perf_function=self.perf_func,
+                )
+            )
+
+        return np.median(accs), np.std(accs)
+
+    def _check_params(self) -> None:
+        if len(self.x_train) != len(self.y_train):
+            raise ValueError("`x_train` and `y_train` do not match shape.")
+
+        if self.eps < 0:
+            raise ValueError("Value of `eps` must be at least 0.")
diff --git a/adversarial-robustness-toolbox/art/defences/detector/poison/spectral_signature_defense.py b/adversarial-robustness-toolbox/art/defences/detector/poison/spectral_signature_defense.py
new file mode 100644
index 0000000..dbb7397
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/defences/detector/poison/spectral_signature_defense.py
@@ -0,0 +1,173 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements methods performing backdoor poisoning detection based on spectral signatures.
+
+| Paper link: https://papers.nips.cc/paper/8024-spectral-signatures-in-backdoor-attacks.pdf
+
+| Please keep in mind the limitations of defenses. For more information on the limitations of this
+    specific defense, see https://arxiv.org/abs/1905.13409 .
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+from typing import List, Tuple, TYPE_CHECKING
+
+import numpy as np
+
+from art.defences.detector.poison.ground_truth_evaluator import GroundTruthEvaluator
+from art.defences.detector.poison.poison_filtering_defence import PoisonFilteringDefence
+
+from art.utils import segment_by_class
+
+if TYPE_CHECKING:
+    from art.utils import CLASSIFIER_NEURALNETWORK_TYPE
+
+
+class SpectralSignatureDefense(PoisonFilteringDefence):
+    """
+    Method from Tran et al., 2018 performing poisoning detection based on Spectral Signatures
+    """
+
+    defence_params = PoisonFilteringDefence.defence_params + [
+        "x_train",
+        "y_train",
+        "batch_size",
+        "eps_multiplier",
+        "expected_pp_poison",
+    ]
+
+    def __init__(
+        self,
+        classifier: "CLASSIFIER_NEURALNETWORK_TYPE",
+        x_train: np.ndarray,
+        y_train: np.ndarray,
+        expected_pp_poison: float = 0.33,
+        batch_size: int = 128,
+        eps_multiplier: float = 1.5,
+    ) -> None:
+        """
+        Create an :class:`.SpectralSignatureDefense` object with the provided classifier.
+
+        :param classifier: Model evaluated for poison.
+        :param x_train: Dataset used to train the classifier.
+        :param y_train: Labels used to train the classifier.
+        :param expected_pp_poison: The expected percentage of poison in the dataset
+        :param batch_size: The batch size for predictions
+        :param eps_multiplier: The multiplier to add to the previous expectation. Numbers higher than one represent
+                               a potentially higher false positive rate, but may detect more poison samples
+        """
+        super().__init__(classifier, x_train, y_train)
+        self.classifier: "CLASSIFIER_NEURALNETWORK_TYPE" = classifier
+        self.batch_size = batch_size
+        self.eps_multiplier = eps_multiplier
+        self.expected_pp_poison = expected_pp_poison
+        self.y_train_sparse = np.argmax(y_train, axis=1)
+        self.evaluator = GroundTruthEvaluator()
+        self._check_params()
+
+    def evaluate_defence(self, is_clean: np.ndarray, **kwargs) -> str:
+        """
+        If ground truth is known, this function returns a confusion matrix in the form of a JSON object.
+
+        :param is_clean: Ground truth, where is_clean[i]=1 means that x_train[i] is clean and is_clean[i]=0 means
+                         x_train[i] is poisonous.
+        :param kwargs: A dictionary of defence-specific parameters.
+        :return: JSON object with confusion matrix.
+        """
+        if is_clean is None or is_clean.size == 0:
+            raise ValueError("is_clean was not provided while invoking evaluate_defence.")
+        is_clean_by_class = segment_by_class(is_clean, self.y_train_sparse, self.classifier.nb_classes)
+        _, predicted_clean = self.detect_poison()
+        predicted_clean_by_class = segment_by_class(predicted_clean, self.y_train_sparse, self.classifier.nb_classes)
+
+        _, conf_matrix_json = self.evaluator.analyze_correctness(predicted_clean_by_class, is_clean_by_class)
+
+        return conf_matrix_json
+
+    def detect_poison(self, **kwargs) -> Tuple[dict, List[int]]:
+        """
+        Returns poison detected and a report.
+
+        :return: (report, is_clean_lst):
+                where a report is a dictionary containing the index as keys the outlier score of suspected poisons as
+                values where is_clean is a list, where is_clean_lst[i]=1 means that x_train[i] there is clean and
+                is_clean_lst[i]=0, means that x_train[i] was classified as poison.
+        """
+        self.set_params(**kwargs)
+
+        if self.classifier.layer_names is not None:
+            nb_layers = len(self.classifier.layer_names)
+        else:
+            raise ValueError("No layer names identified.")
+        features_x_poisoned = self.classifier.get_activations(
+            self.x_train, layer=nb_layers - 1, batch_size=self.batch_size
+        )
+
+        features_split = segment_by_class(features_x_poisoned, self.y_train_sparse, self.classifier.nb_classes)
+        score_by_class = []
+        keep_by_class = []
+
+        for idx, feature in enumerate(features_split):
+            # Check for empty list
+            if len(feature):
+                score = SpectralSignatureDefense.spectral_signature_scores(np.vstack(feature))
+                score_cutoff = np.quantile(score, max(1 - self.eps_multiplier * self.expected_pp_poison, 0.0))
+                score_by_class.append(score)
+                keep_by_class.append(score < score_cutoff)
+            else:
+                score_by_class.append([0])
+                keep_by_class.append([True])
+
+        base_indices_by_class = segment_by_class(
+            np.arange(len(self.y_train_sparse)), self.y_train_sparse, self.classifier.nb_classes,
+        )
+        is_clean_lst = [0] * len(self.y_train_sparse)
+        report = {}
+
+        for keep_booleans, all_scores, indices in zip(keep_by_class, score_by_class, base_indices_by_class):
+            for keep_boolean, all_score, idx in zip(keep_booleans, all_scores, indices):
+                if keep_boolean:
+                    is_clean_lst[idx] = 1
+                else:
+                    report[idx] = all_score[0]
+
+        return report, is_clean_lst
+
+    def _check_params(self) -> None:
+        if self.batch_size < 0:
+            raise ValueError("Batch size must be positive integer. Unsupported batch size: " + str(self.batch_size))
+        if self.eps_multiplier < 0:
+            raise ValueError("eps_multiplier must be positive. Unsupported value: " + str(self.eps_multiplier))
+        if self.expected_pp_poison < 0 or self.expected_pp_poison > 1:
+            raise ValueError(
+                "expected_pp_poison must be between 0 and 1. Unsupported value: " + str(self.expected_pp_poison)
+            )
+
+    @staticmethod
+    def spectral_signature_scores(matrix_r: np.ndarray) -> np.ndarray:
+        """
+        :param matrix_r: Matrix of feature representations.
+        :return: Outlier scores for each observation based on spectral signature.
+        """
+        matrix_m = matrix_r - np.mean(matrix_r, axis=0)
+        # Following Algorithm #1 in paper, use SVD of centered features, not of covariance
+        _, _, matrix_v = np.linalg.svd(matrix_m, full_matrices=False)
+        eigs = matrix_v[:1]
+        corrs = np.matmul(eigs, np.transpose(matrix_r))
+        score = np.expand_dims(np.linalg.norm(corrs, axis=1), axis=1)
+        return score
diff --git a/adversarial-robustness-toolbox/art/defences/postprocessor/__init__.py b/adversarial-robustness-toolbox/art/defences/postprocessor/__init__.py
new file mode 100644
index 0000000..dec23dc
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/defences/postprocessor/__init__.py
@@ -0,0 +1,9 @@
+"""
+Module implementing postprocessing defences against adversarial attacks.
+"""
+from art.defences.postprocessor.class_labels import ClassLabels
+from art.defences.postprocessor.gaussian_noise import GaussianNoise
+from art.defences.postprocessor.high_confidence import HighConfidence
+from art.defences.postprocessor.postprocessor import Postprocessor
+from art.defences.postprocessor.reverse_sigmoid import ReverseSigmoid
+from art.defences.postprocessor.rounded import Rounded
diff --git a/adversarial-robustness-toolbox/art/defences/postprocessor/class_labels.py b/adversarial-robustness-toolbox/art/defences/postprocessor/class_labels.py
new file mode 100644
index 0000000..e8679ed
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/defences/postprocessor/class_labels.py
@@ -0,0 +1,58 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements class labels added to the classifier output.
+"""
+import logging
+
+import numpy as np
+
+from art.defences.postprocessor.postprocessor import Postprocessor
+
+logger = logging.getLogger(__name__)
+
+
+class ClassLabels(Postprocessor):
+    """
+    Implementation of a postprocessor based on adding class labels to classifier output.
+    """
+
+    def __init__(self, apply_fit: bool = False, apply_predict: bool = True) -> None:
+        """
+        Create a ClassLabels postprocessor.
+
+        :param apply_fit: True if applied during fitting/training.
+        :param apply_predict: True if applied during predicting.
+        """
+        super().__init__(is_fitted=True, apply_fit=apply_fit, apply_predict=apply_predict)
+
+    def __call__(self, preds: np.ndarray) -> np.ndarray:
+        """
+        Perform model postprocessing and return postprocessed output.
+
+        :param preds: model output to be postprocessed.
+        :return: Postprocessed model output.
+        """
+        class_labels = np.zeros_like(preds)
+        if preds.shape[1] > 1:
+            index_labels = np.argmax(preds, axis=1)
+            class_labels[:, index_labels] = 1
+        else:
+            class_labels[preds > 0.5] = 1
+
+        return class_labels
diff --git a/adversarial-robustness-toolbox/art/defences/postprocessor/gaussian_noise.py b/adversarial-robustness-toolbox/art/defences/postprocessor/gaussian_noise.py
new file mode 100644
index 0000000..6657ce5
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/defences/postprocessor/gaussian_noise.py
@@ -0,0 +1,81 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements Gaussian noise added to the classifier output.
+"""
+import logging
+
+import numpy as np
+
+from art.defences.postprocessor.postprocessor import Postprocessor
+from art.utils import is_probability
+
+logger = logging.getLogger(__name__)
+
+
+class GaussianNoise(Postprocessor):
+    """
+    Implementation of a postprocessor based on adding Gaussian noise to classifier output.
+    """
+
+    params = ["scale"]
+
+    def __init__(self, scale: float = 0.2, apply_fit: bool = False, apply_predict: bool = True) -> None:
+        """
+        Create a GaussianNoise postprocessor.
+
+        :param scale: Standard deviation of the distribution.
+        :param apply_fit: True if applied during fitting/training.
+        :param apply_predict: True if applied during predicting.
+        """
+        super().__init__(is_fitted=True, apply_fit=apply_fit, apply_predict=apply_predict)
+        self.scale = scale
+        self._check_params()
+
+    def __call__(self, preds: np.ndarray) -> np.ndarray:
+        """
+        Perform model postprocessing and return postprocessed output.
+
+        :param preds: model output to be postprocessed.
+        :return: Postprocessed model output.
+        """
+        # Generate random noise
+        noise = np.random.normal(loc=0.0, scale=self.scale, size=preds.shape)
+
+        # Add noise to model output
+        post_preds = preds.copy()
+        post_preds += noise
+
+        if preds.shape[1] > 1:
+            # Check if model output is logits or probability
+            are_probability = [is_probability(x) for x in preds]
+            all_probability = np.sum(are_probability) == preds.shape[0]
+
+            # Finally normalize probability output
+            if all_probability:
+                post_preds[post_preds < 0.0] = 0.0
+                sums = np.sum(post_preds, axis=1)
+                post_preds /= sums
+        else:
+            post_preds[post_preds < 0.0] = 0.0
+
+        return post_preds
+
+    def _check_params(self) -> None:
+        if self.scale <= 0:
+            raise ValueError("Standard deviation must be positive.")
diff --git a/adversarial-robustness-toolbox/art/defences/postprocessor/high_confidence.py b/adversarial-robustness-toolbox/art/defences/postprocessor/high_confidence.py
new file mode 100644
index 0000000..736c872
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/defences/postprocessor/high_confidence.py
@@ -0,0 +1,63 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements confidence added to the classifier output.
+"""
+import logging
+
+import numpy as np
+
+from art.defences.postprocessor.postprocessor import Postprocessor
+
+logger = logging.getLogger(__name__)
+
+
+class HighConfidence(Postprocessor):
+    """
+    Implementation of a postprocessor based on selecting high confidence predictions to return as classifier output.
+    """
+
+    params = ["cutoff"]
+
+    def __init__(self, cutoff: float = 0.25, apply_fit: bool = False, apply_predict: bool = True) -> None:
+        """
+        Create a HighConfidence postprocessor.
+
+        :param cutoff: Minimal value for returned prediction output.
+        :param apply_fit: True if applied during fitting/training.
+        :param apply_predict: True if applied during predicting.
+        """
+        super().__init__(is_fitted=True, apply_fit=apply_fit, apply_predict=apply_predict)
+        self.cutoff = cutoff
+        self._check_params()
+
+    def __call__(self, preds: np.ndarray) -> np.ndarray:
+        """
+        Perform model postprocessing and return postprocessed output.
+
+        :param preds: model output to be postprocessed.
+        :return: Postprocessed model output.
+        """
+        post_preds = preds.copy()
+        post_preds[post_preds < self.cutoff] = 0.0
+
+        return post_preds
+
+    def _check_params(self) -> None:
+        if self.cutoff <= 0:
+            raise ValueError("Minimal value must be positive.")
diff --git a/adversarial-robustness-toolbox/art/defences/postprocessor/postprocessor.py b/adversarial-robustness-toolbox/art/defences/postprocessor/postprocessor.py
new file mode 100644
index 0000000..bb9fd10
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/defences/postprocessor/postprocessor.py
@@ -0,0 +1,103 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements the abstract base class for defences that post-process classifier output.
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+from typing import List
+
+import abc
+
+import numpy as np
+
+
+class Postprocessor(abc.ABC):
+    """
+    Abstract base class for postprocessing defences. Postprocessing defences are not included in the loss function
+    evaluation for loss gradients or the calculation of class gradients.
+    """
+
+    params: List[str] = []
+
+    def __init__(self, is_fitted: bool = False, apply_fit: bool = True, apply_predict: bool = True) -> None:
+        """
+        Create a postprocessing object.
+
+        Optionally, set attributes.
+        """
+        self._is_fitted = bool(is_fitted)
+        self._apply_fit = bool(apply_fit)
+        self._apply_predict = bool(apply_predict)
+
+    @property
+    def is_fitted(self) -> bool:
+        """
+        Return the state of the postprocessing object.
+
+        :return: `True` if the postprocessing model has been fitted (if this applies).
+        """
+        return self._is_fitted
+
+    @property
+    def apply_fit(self) -> bool:
+        """
+        Property of the defence indicating if it should be applied at training time.
+
+        :return: `True` if the defence should be applied when fitting a model, `False` otherwise.
+        """
+        return self._apply_fit
+
+    @property
+    def apply_predict(self) -> bool:
+        """
+        Property of the defence indicating if it should be applied at test time.
+
+        :return: `True` if the defence should be applied at prediction time, `False` otherwise.
+        """
+        return self._apply_predict
+
+    @abc.abstractmethod
+    def __call__(self, preds: np.ndarray) -> np.ndarray:
+        """
+        Perform model postprocessing and return postprocessed output.
+
+        :param preds: model output to be postprocessed.
+        :return: Postprocessed model output.
+        """
+        raise NotImplementedError
+
+    def fit(self, preds: np.ndarray, **kwargs) -> None:
+        """
+        Fit the parameters of the postprocessor if it has any.
+
+        :param preds: Training set to fit the postprocessor.
+        :param kwargs: Other parameters.
+        """
+        pass
+
+    def set_params(self, **kwargs) -> None:
+        """
+        Take in a dictionary of parameters and apply checks before saving them as attributes.
+        """
+        for key, value in kwargs.items():
+            if key in self.params:
+                setattr(self, key, value)
+        self._check_params()
+
+    def _check_params(self) -> None:
+        pass
diff --git a/adversarial-robustness-toolbox/art/defences/postprocessor/reverse_sigmoid.py b/adversarial-robustness-toolbox/art/defences/postprocessor/reverse_sigmoid.py
new file mode 100644
index 0000000..9a537ce
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/defences/postprocessor/reverse_sigmoid.py
@@ -0,0 +1,106 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements the Reverse Sigmoid perturbation for the classifier output.
+
+| Paper link: https://arxiv.org/abs/1806.00054
+"""
+import logging
+
+import numpy as np
+
+from art.defences.postprocessor.postprocessor import Postprocessor
+
+logger = logging.getLogger(__name__)
+
+
+class ReverseSigmoid(Postprocessor):
+    """
+    Implementation of a postprocessor based on adding the Reverse Sigmoid perturbation to classifier output.
+    """
+
+    params = ["beta", "gamma"]
+
+    def __init__(
+        self, beta: float = 1.0, gamma: float = 0.1, apply_fit: bool = False, apply_predict: bool = True,
+    ) -> None:
+        """
+        Create a ReverseSigmoid postprocessor.
+
+        :param beta: A positive magnitude parameter.
+        :param gamma: A positive dataset and model specific convergence parameter.
+        :param apply_fit: True if applied during fitting/training.
+        :param apply_predict: True if applied during predicting.
+        """
+        super().__init__(is_fitted=True, apply_fit=apply_fit, apply_predict=apply_predict)
+        self.beta = beta
+        self.gamma = gamma
+        self._check_params()
+
+    def __call__(self, preds: np.ndarray) -> np.ndarray:
+        """
+        Perform model postprocessing and return postprocessed output.
+
+        :param preds: model output to be postprocessed.
+        :return: Postprocessed model output.
+        """
+        clip_min = 1e-9
+        clip_max = 1.0 - clip_min
+
+        def sigmoid(var_z):
+            return 1.0 / (1.0 + np.exp(-var_z))
+
+        preds_clipped = np.clip(preds, clip_min, clip_max)
+
+        if preds.shape[1] > 1:
+            perturbation_r = self.beta * (sigmoid(-self.gamma * np.log((1.0 - preds_clipped) / preds_clipped)) - 0.5)
+            preds_perturbed = preds - perturbation_r
+            preds_perturbed = np.clip(preds_perturbed, 0.0, 1.0)
+            alpha = 1.0 / np.sum(preds_perturbed, axis=-1, keepdims=True)
+            reverse_sigmoid = alpha * preds_perturbed
+        else:
+            preds_1 = preds
+            preds_2 = 1.0 - preds
+
+            preds_clipped_1 = preds_clipped
+            preds_clipped_2 = 1.0 - preds_clipped
+
+            perturbation_r_1 = self.beta * (
+                sigmoid(-self.gamma * np.log((1.0 - preds_clipped_1) / preds_clipped_1)) - 0.5
+            )
+            perturbation_r_2 = self.beta * (
+                sigmoid(-self.gamma * np.log((1.0 - preds_clipped_2) / preds_clipped_2)) - 0.5
+            )
+
+            preds_perturbed_1 = preds_1 - perturbation_r_1
+            preds_perturbed_2 = preds_2 - perturbation_r_2
+
+            preds_perturbed_1 = np.clip(preds_perturbed_1, 0.0, 1.0)
+            preds_perturbed_2 = np.clip(preds_perturbed_2, 0.0, 1.0)
+
+            alpha = 1.0 / (preds_perturbed_1 + preds_perturbed_2)
+            reverse_sigmoid = alpha * preds_perturbed_1
+
+        return reverse_sigmoid
+
+    def _check_params(self) -> None:
+        if self.beta <= 0:
+            raise ValueError("Magnitude parameter must be positive.")
+
+        if self.gamma <= 0:
+            raise ValueError("Convergence parameter must be positive.")
diff --git a/adversarial-robustness-toolbox/art/defences/postprocessor/rounded.py b/adversarial-robustness-toolbox/art/defences/postprocessor/rounded.py
new file mode 100644
index 0000000..6602400
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/defences/postprocessor/rounded.py
@@ -0,0 +1,60 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements a rounding to the classifier output.
+"""
+import logging
+
+import numpy as np
+
+from art.defences.postprocessor.postprocessor import Postprocessor
+
+logger = logging.getLogger(__name__)
+
+
+class Rounded(Postprocessor):
+    """
+    Implementation of a postprocessor based on rounding classifier output.
+    """
+
+    params = ["decimals"]
+
+    def __init__(self, decimals: int = 3, apply_fit: bool = False, apply_predict: bool = True) -> None:
+        """
+        Create a Rounded postprocessor.
+
+        :param decimals: Number of decimal places after the decimal point.
+        :param apply_fit: True if applied during fitting/training.
+        :param apply_predict: True if applied during predicting.
+        """
+        super().__init__(is_fitted=True, apply_fit=apply_fit, apply_predict=apply_predict)
+        self.decimals = decimals
+        self._check_params()
+
+    def __call__(self, preds: np.ndarray) -> np.ndarray:
+        """
+        Perform model postprocessing and return postprocessed output.
+
+        :param preds: model output to be postprocessed.
+        :return: Postprocessed model output.
+        """
+        return np.around(preds, decimals=self.decimals)
+
+    def _check_params(self) -> None:
+        if not isinstance(self.decimals, (int, np.int)) or self.decimals <= 0:
+            raise ValueError("Number of decimal places must be a positive integer.")
diff --git a/adversarial-robustness-toolbox/art/defences/preprocessor/__init__.py b/adversarial-robustness-toolbox/art/defences/preprocessor/__init__.py
new file mode 100644
index 0000000..3a8bc38
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/defences/preprocessor/__init__.py
@@ -0,0 +1,18 @@
+"""
+Module implementing preprocessing defences against adversarial attacks.
+"""
+from art.defences.preprocessor.feature_squeezing import FeatureSqueezing
+from art.defences.preprocessor.gaussian_augmentation import GaussianAugmentation
+from art.defences.preprocessor.inverse_gan import InverseGAN, DefenseGAN
+from art.defences.preprocessor.jpeg_compression import JpegCompression
+from art.defences.preprocessor.label_smoothing import LabelSmoothing
+from art.defences.preprocessor.mp3_compression import Mp3Compression
+from art.defences.preprocessor.pixel_defend import PixelDefend
+from art.defences.preprocessor.preprocessor import Preprocessor
+from art.defences.preprocessor.resample import Resample
+from art.defences.preprocessor.spatial_smoothing import SpatialSmoothing
+from art.defences.preprocessor.spatial_smoothing_pytorch import SpatialSmoothingPyTorch
+from art.defences.preprocessor.spatial_smoothing_tensorflow import SpatialSmoothingTensorFlowV2
+from art.defences.preprocessor.thermometer_encoding import ThermometerEncoding
+from art.defences.preprocessor.variance_minimization import TotalVarMin
+from art.defences.preprocessor.video_compression import VideoCompression
diff --git a/adversarial-robustness-toolbox/art/defences/preprocessor/feature_squeezing.py b/adversarial-robustness-toolbox/art/defences/preprocessor/feature_squeezing.py
new file mode 100644
index 0000000..0a3cf35
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/defences/preprocessor/feature_squeezing.py
@@ -0,0 +1,97 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements the feature squeezing defence in `FeatureSqueezing`.
+
+| Paper link: https://arxiv.org/abs/1704.01155
+
+| Please keep in mind the limitations of defences. For more information on the limitations of this defence, see
+    https://arxiv.org/abs/1803.09868 . For details on how to evaluate classifier security in general, see
+    https://arxiv.org/abs/1902.06705
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+from typing import Optional, Tuple
+
+import numpy as np
+
+from art.utils import CLIP_VALUES_TYPE
+from art.defences.preprocessor.preprocessor import Preprocessor
+
+logger = logging.getLogger(__name__)
+
+
+class FeatureSqueezing(Preprocessor):
+    """
+    Reduces the sensibility of the features of a sample.
+
+    | Paper link: https://arxiv.org/abs/1704.01155
+
+    | Please keep in mind the limitations of defences. For more information on the limitations of this defence,
+        see https://arxiv.org/abs/1803.09868 . For details on how to evaluate classifier security in general,
+        see https://arxiv.org/abs/1902.06705
+    """
+
+    params = ["clip_values", "bit_depth"]
+
+    def __init__(
+        self, clip_values: CLIP_VALUES_TYPE, bit_depth: int = 8, apply_fit: bool = False, apply_predict: bool = True,
+    ) -> None:
+        """
+        Create an instance of feature squeezing.
+
+        :param clip_values: Tuple of the form `(min, max)` representing the minimum and maximum values allowed
+               for features.
+        :param bit_depth: The number of bits per channel for encoding the data.
+        :param apply_fit: True if applied during fitting/training.
+        :param apply_predict: True if applied during predicting.
+        """
+        super().__init__(is_fitted=True, apply_fit=apply_fit, apply_predict=apply_predict)
+        self.clip_values = clip_values
+        self.bit_depth = bit_depth
+        self._check_params()
+
+    def __call__(self, x: np.ndarray, y: Optional[np.ndarray] = None) -> Tuple[np.ndarray, Optional[np.ndarray]]:
+        """
+        Apply feature squeezing to sample `x`.
+
+        :param x: Sample to squeeze. `x` values are expected to be in the data range provided by `clip_values`.
+        :param y: Labels of the sample `x`. This function does not affect them in any way.
+        :return: Squeezed sample.
+        """
+        x_normalized = x - self.clip_values[0]
+        x_normalized = x_normalized / (self.clip_values[1] - self.clip_values[0])
+
+        max_value = np.rint(2 ** self.bit_depth - 1)
+        res = np.rint(x_normalized * max_value) / max_value
+
+        res = res * (self.clip_values[1] - self.clip_values[0])
+        res = res + self.clip_values[0]
+
+        return res, y
+
+    def _check_params(self) -> None:
+        if not isinstance(self.bit_depth, (int, np.int)) or self.bit_depth <= 0 or self.bit_depth > 64:
+            raise ValueError("The bit depth must be between 1 and 64.")
+
+        if len(self.clip_values) != 2:
+            raise ValueError("`clip_values` should be a tuple of 2 floats containing the allowed data range.")
+
+        if np.array(self.clip_values[0] >= self.clip_values[1]).any():
+            raise ValueError("Invalid `clip_values`: min >= max.")
diff --git a/adversarial-robustness-toolbox/art/defences/preprocessor/gaussian_augmentation.py b/adversarial-robustness-toolbox/art/defences/preprocessor/gaussian_augmentation.py
new file mode 100644
index 0000000..92b4f78
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/defences/preprocessor/gaussian_augmentation.py
@@ -0,0 +1,137 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements the Gaussian augmentation defence in `GaussianAugmentation`.
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+from typing import Optional, Tuple, TYPE_CHECKING
+
+import numpy as np
+
+from art.config import ART_NUMPY_DTYPE
+from art.defences.preprocessor.preprocessor import Preprocessor
+
+if TYPE_CHECKING:
+    from art.utils import CLIP_VALUES_TYPE
+
+logger = logging.getLogger(__name__)
+
+
+class GaussianAugmentation(Preprocessor):
+    """
+    Add Gaussian noise to a dataset in one of two ways: either add noise to each sample (keeping the size of the
+    original dataset) or perform augmentation by keeping all original samples and adding noisy counterparts. When used
+    as part of a :class:`.Classifier` instance, the defense will be applied automatically only when training if
+    `augmentation` is true, and only when performing prediction otherwise.
+    """
+
+    params = [
+        "sigma",
+        "augmentation",
+        "ratio",
+        "clip_values",
+        "_apply_fit",
+        "_apply_predict",
+    ]
+
+    def __init__(
+        self,
+        sigma: float = 1.0,
+        augmentation: bool = True,
+        ratio: float = 1.0,
+        clip_values: Optional["CLIP_VALUES_TYPE"] = None,
+        apply_fit: bool = True,
+        apply_predict: bool = False,
+    ):
+        """
+        Initialize a Gaussian augmentation object.
+
+        :param sigma: Standard deviation of Gaussian noise to be added.
+        :param augmentation: If true, perform dataset augmentation using `ratio`, otherwise replace samples with noisy
+                            counterparts.
+        :param ratio: Percentage of data augmentation. E.g. for a rate of 1, the size of the dataset will double.
+                      If `augmentation` is false, `ratio` value is ignored.
+        :param clip_values: Tuple of the form `(min, max)` representing the minimum and maximum values allowed
+               for features.
+        :param apply_fit: True if applied during fitting/training.
+        :param apply_predict: True if applied during predicting.
+        """
+        super().__init__(is_fitted=True, apply_fit=apply_fit, apply_predict=apply_predict)
+        if augmentation and not apply_fit and apply_predict:
+            raise ValueError(
+                "If `augmentation` is `True`, then `apply_fit` must be `True` and `apply_predict` must be `False`."
+            )
+        if augmentation and not (apply_fit or apply_predict):
+            raise ValueError("If `augmentation` is `True`, then `apply_fit` and `apply_predict` can't be both `False`.")
+
+        self.sigma = sigma
+        self.augmentation = augmentation
+        self.ratio = ratio
+        self.clip_values = clip_values
+        self._check_params()
+
+    def __call__(self, x: np.ndarray, y: Optional[np.ndarray] = None) -> Tuple[np.ndarray, Optional[np.ndarray]]:
+        """
+        Augment the sample `(x, y)` with Gaussian noise. The result is either an extended dataset containing the
+        original sample, as well as the newly created noisy samples (augmentation=True) or just the noisy counterparts
+        to the original samples.
+
+        :param x: Sample to augment with shape `(batch_size, width, height, depth)`.
+        :param y: Labels for the sample. If this argument is provided, it will be augmented with the corresponded
+                  original labels of each sample point.
+        :return: The augmented dataset and (if provided) corresponding labels.
+        """
+        logger.info("Original dataset size: %d", x.shape[0])
+
+        # Select indices to augment
+        if self.augmentation:
+            size = int(x.shape[0] * self.ratio)
+            indices = np.random.randint(0, x.shape[0], size=size)
+
+            # Generate noisy samples
+            x_aug = np.random.normal(x[indices], scale=self.sigma, size=(size,) + x.shape[1:]).astype(ART_NUMPY_DTYPE)
+            x_aug = np.vstack((x, x_aug))
+            if y is not None:
+                y_aug = np.concatenate((y, y[indices]))
+            else:
+                y_aug = y
+            logger.info("Augmented dataset size: %d", x_aug.shape[0])
+        else:
+            x_aug = np.random.normal(x, scale=self.sigma, size=x.shape).astype(ART_NUMPY_DTYPE)
+            y_aug = y
+            logger.info("Created %i samples with Gaussian noise.")
+
+        if self.clip_values is not None:
+            x_aug = np.clip(x_aug, self.clip_values[0], self.clip_values[1])
+
+        return x_aug, y_aug
+
+    def _check_params(self) -> None:
+        if self.augmentation and self.ratio <= 0:
+            raise ValueError("The augmentation ratio must be positive.")
+
+        if self.clip_values is not None:
+
+            if len(self.clip_values) != 2:
+                raise ValueError(
+                    "`clip_values` should be a tuple of 2 floats or arrays containing the allowed data range."
+                )
+            if np.array(self.clip_values[0] >= self.clip_values[1]).any():
+                raise ValueError("Invalid `clip_values`: min >= max.")
diff --git a/adversarial-robustness-toolbox/art/defences/preprocessor/inverse_gan.py b/adversarial-robustness-toolbox/art/defences/preprocessor/inverse_gan.py
new file mode 100644
index 0000000..9b1e076
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/defences/preprocessor/inverse_gan.py
@@ -0,0 +1,191 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements the InverseGAN defence.
+
+| Paper link: https://arxiv.org/abs/1911.10291
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+from typing import Optional, Tuple, TYPE_CHECKING
+
+import numpy as np
+from scipy.optimize import minimize
+
+from art.defences.preprocessor.preprocessor import Preprocessor
+
+if TYPE_CHECKING:
+    # pylint: disable=C0412,R0401
+    import tensorflow as tf
+
+    from art.estimators.encoding.tensorflow import TensorFlowEncoder
+    from art.estimators.generation.tensorflow import TensorFlowGenerator
+
+logger = logging.getLogger(__name__)
+
+
+class InverseGAN(Preprocessor):
+    """
+    Given a latent variable generating a given adversarial sample, either inferred by an inverse GAN or randomly
+    generated, the InverseGAN optimizes that latent variable to project a sample as close as possible to
+    the adversarial sample without the adversarial noise.
+    """
+
+    params = ["sess", "gan", "inverse_gan"]
+
+    def __init__(
+        self,
+        sess: "tf.compat.v1.Session",
+        gan: "TensorFlowGenerator",
+        inverse_gan: Optional["TensorFlowEncoder"],
+        apply_fit: bool = False,
+        apply_predict: bool = False,
+    ):
+        """
+        Create an instance of an InverseGAN.
+
+        :param sess: TF session for computations.
+        :param gan: GAN model.
+        :param inverse_gan: Inverse GAN model.
+        :param apply_fit: True if applied during fitting/training.
+        :param apply_predict: True if applied during predicting.
+        """
+        import tensorflow as tf  # lgtm [py/repeated-import]
+
+        super().__init__(is_fitted=True, apply_fit=apply_fit, apply_predict=apply_predict)
+        self.gan = gan
+        self.inverse_gan = inverse_gan
+        self.sess = sess
+        self._image_adv = tf.placeholder(tf.float32, shape=self.gan.model.get_shape().as_list(), name="image_adv_ph")
+
+        num_dim = len(self._image_adv.get_shape())
+        image_loss = tf.reduce_mean(tf.square(self.gan.model - self._image_adv), axis=list(range(1, num_dim)))
+        self._loss = tf.reduce_sum(image_loss)
+        self._grad = tf.gradients(self._loss, self.gan.input_ph)
+        self._check_params()
+
+    def __call__(
+        self, x: np.ndarray, y: Optional[np.ndarray] = None, **kwargs
+    ) -> Tuple[np.ndarray, Optional[np.ndarray]]:
+        """
+        Applies the :class:`.InverseGAN` defence upon the sample input.
+
+        :param x: Sample input.
+        :param y: Labels of the sample `x`. This function does not affect them in any way.
+        :return: Defended input.
+        """
+        batch_size = x.shape[0]
+        iteration_count = 0
+
+        if self.inverse_gan is not None:
+            logger.info("Encoding x_adv into starting z encoding")
+            initial_z_encoding = self.inverse_gan.predict(x)
+        else:
+            logger.info("Choosing a random starting z encoding")
+            initial_z_encoding = np.random.rand(batch_size, self.gan.encoding_length)
+
+        def func_gen_gradients(z_i):
+            z_i_reshaped = np.reshape(z_i, [batch_size, self.gan.encoding_length])
+            grad = self.estimate_gradient(z_i_reshaped, x)
+            grad = np.float64(
+                grad
+            )  # scipy fortran code seems to expect float64 not 32 https://github.com/scipy/scipy/issues/5832
+
+            return grad.flatten()
+
+        def func_loss(z_i):
+            nonlocal iteration_count
+            iteration_count += 1
+            logging.info("Iteration: %d", iteration_count)
+            z_i_reshaped = np.reshape(z_i, [batch_size, self.gan.encoding_length])
+            loss = self.compute_loss(z_i_reshaped, x)
+
+            return loss
+
+        options_allowed_keys = [
+            "disp",
+            "maxcor",
+            "ftol",
+            "gtol",
+            "eps",
+            "maxfun",
+            "maxiter",
+            "iprint",
+            "callback",
+            "maxls",
+        ]
+
+        for key in kwargs:
+            if key not in options_allowed_keys:
+                raise KeyError(
+                    "The argument `{}` in kwargs is not allowed as option for `scipy.optimize.minimize` using "
+                    '`method="L-BFGS-B".`'.format(key)
+                )
+
+        options = kwargs.copy()
+        optimized_z_encoding_flat = minimize(
+            func_loss, initial_z_encoding, jac=func_gen_gradients, method="L-BFGS-B", options=options
+        )
+        optimized_z_encoding = np.reshape(optimized_z_encoding_flat.x, [batch_size, self.gan.encoding_length])
+        y = self.gan.predict(optimized_z_encoding)
+
+        return y
+
+    def compute_loss(self, z_encoding: np.ndarray, image_adv: np.ndarray) -> np.ndarray:
+        """
+        Given a encoding z, computes the loss between the projected sample and the original sample.
+
+        :param z_encoding: The encoding z.
+        :param image_adv: The adversarial image.
+        :return: The loss value
+        """
+        logging.info("Calculating Loss")
+
+        loss = self.sess.run(self._loss, feed_dict={self.gan.input_ph: z_encoding, self._image_adv: image_adv})
+        return loss
+
+    def estimate_gradient(self, z_encoding: np.ndarray, y: np.ndarray) -> np.ndarray:
+        """
+        Compute the gradient of the loss function w.r.t. a `z_encoding` input within a GAN against a
+        corresponding adversarial sample.
+
+        :param z_encoding: The encoding z.
+        :param y: Target values of shape `(nb_samples, nb_classes)`.
+        :return: Array of gradients of the same shape as `z_encoding`.
+        """
+        logging.info("Calculating Gradients")
+
+        gradient = self.sess.run(self._grad, feed_dict={self._image_adv: y, self.gan.input_ph: z_encoding})
+        return gradient
+
+    def _check_params(self) -> None:
+        if self.inverse_gan is not None and self.gan.encoding_length != self.inverse_gan.encoding_length:
+            raise ValueError("Both GAN and InverseGAN must use the same size encoding.")
+
+
+class DefenseGAN(InverseGAN):
+    """
+    Implementation of DefenseGAN.
+    """
+
+    def __init__(self, sess, gan):
+        """
+        Create an instance of DefenseGAN.
+        """
+        super().__init__(sess=sess, gan=gan, inverse_gan=None)
diff --git a/adversarial-robustness-toolbox/art/defences/preprocessor/jpeg_compression.py b/adversarial-robustness-toolbox/art/defences/preprocessor/jpeg_compression.py
new file mode 100644
index 0000000..300f358
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/defences/preprocessor/jpeg_compression.py
@@ -0,0 +1,191 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements the JPEG compression defence `JpegCompression`.
+
+| Paper link: https://arxiv.org/abs/1705.02900, https://arxiv.org/abs/1608.00853
+
+| Please keep in mind the limitations of defences. For more information on the limitations of this defence, see
+    https://arxiv.org/abs/1802.00420 . For details on how to evaluate classifier security in general, see
+    https://arxiv.org/abs/1902.06705
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+from io import BytesIO
+import logging
+from typing import Optional, Tuple, TYPE_CHECKING
+
+import numpy as np
+from tqdm.auto import tqdm
+
+from art.config import ART_NUMPY_DTYPE
+from art.defences.preprocessor.preprocessor import Preprocessor
+
+if TYPE_CHECKING:
+    from art.utils import CLIP_VALUES_TYPE
+
+logger = logging.getLogger(__name__)
+
+
+class JpegCompression(Preprocessor):
+    """
+    Implement the JPEG compression defence approach.
+
+    For input images or videos with 3 color channels the compression is applied in mode `RGB`
+    (3x8-bit pixels, true color), for all other numbers of channels the compression is applied for each channel with
+    mode `L` (8-bit pixels, black and white).
+
+    | Paper link: https://arxiv.org/abs/1705.02900, https://arxiv.org/abs/1608.00853
+
+
+    | Please keep in mind the limitations of defences. For more information on the limitations of this defence,
+        see https://arxiv.org/abs/1802.00420 . For details on how to evaluate classifier security in general, see
+        https://arxiv.org/abs/1902.06705
+    """
+
+    params = ["quality", "channels_first", "clip_values", "verbose"]
+
+    def __init__(
+        self,
+        clip_values: "CLIP_VALUES_TYPE",
+        quality: int = 50,
+        channels_first: bool = False,
+        apply_fit: bool = True,
+        apply_predict: bool = True,
+        verbose: bool = False,
+    ):
+        """
+        Create an instance of JPEG compression.
+
+        :param clip_values: Tuple of the form `(min, max)` representing the minimum and maximum values allowed
+               for features.
+        :param quality: The image quality, on a scale from 1 (worst) to 95 (best). Values above 95 should be avoided.
+        :param channels_first: Set channels first or last.
+        :param apply_fit: True if applied during fitting/training.
+        :param apply_predict: True if applied during predicting.
+        :param verbose: Show progress bars.
+        """
+
+        super().__init__(is_fitted=True, apply_fit=apply_fit, apply_predict=apply_predict)
+        self.quality = quality
+        self.channels_first = channels_first
+        self.clip_values = clip_values
+        self.verbose = verbose
+        self._check_params()
+
+    def _compress(self, x: np.ndarray, mode: str) -> np.ndarray:
+        """
+        Apply JPEG compression to image input.
+        """
+        from PIL import Image
+
+        tmp_jpeg = BytesIO()
+        x_image = Image.fromarray(x, mode=mode)
+        x_image.save(tmp_jpeg, format="jpeg", quality=self.quality)
+        x_jpeg = np.array(Image.open(tmp_jpeg))
+        tmp_jpeg.close()
+        return x_jpeg
+
+    def __call__(self, x: np.ndarray, y: Optional[np.ndarray] = None) -> Tuple[np.ndarray, Optional[np.ndarray]]:
+        """
+        Apply JPEG compression to sample `x`.
+
+        For input images or videos with 3 color channels the compression is applied in mode `RGB`
+        (3x8-bit pixels, true color), for all other numbers of channels the compression is applied for each channel with
+        mode `L` (8-bit pixels, black and white).
+
+        :param x: Sample to compress with shape of `NCHW`, `NHWC`, `NCFHW` or `NFHWC`. `x` values are expected to be in
+                  the data range [0, 1] or [0, 255].
+        :param y: Labels of the sample `x`. This function does not affect them in any way.
+        :return: compressed sample.
+        """
+        x_ndim = x.ndim
+        if x_ndim not in [4, 5]:
+            raise ValueError(
+                "Unrecognized input dimension. JPEG compression can only be applied to image and video data."
+            )
+
+        if x.min() < 0.0:
+            raise ValueError(
+                "Negative values in input `x` detected. The JPEG compression defence requires unnormalized input."
+            )
+
+        # Swap channel index
+        if self.channels_first and x_ndim == 4:
+            # image shape NCHW to NHWC
+            x = np.transpose(x, (0, 2, 3, 1))
+        elif self.channels_first and x_ndim == 5:
+            # video shape NCFHW to NFHWC
+            x = np.transpose(x, (0, 2, 3, 4, 1))
+
+        # insert temporal dimension to image data
+        if x_ndim == 4:
+            x = np.expand_dims(x, axis=1)
+
+        # Convert into uint8
+        if self.clip_values[1] == 1.0:
+            x = x * 255
+        x = x.astype("uint8")
+
+        # Compress one image at a time
+        x_jpeg = x.copy()
+        for idx in tqdm(np.ndindex(x.shape[:2]), desc="JPEG compression", disable=not self.verbose):
+            if x.shape[-1] == 3:
+                x_jpeg[idx] = self._compress(x[idx], mode="RGB")
+            else:
+                for i_channel in range(x.shape[-1]):
+                    x_channel = x[idx[0], idx[1], ..., i_channel]
+                    x_channel = self._compress(x_channel, mode="L")
+                    x_jpeg[idx[0], idx[1], :, :, i_channel] = x_channel
+
+        # Convert to ART dtype
+        if self.clip_values[1] == 1.0:
+            x_jpeg = x_jpeg / 255.0
+        x_jpeg = x_jpeg.astype(ART_NUMPY_DTYPE)
+
+        # remove temporal dimension for image data
+        if x_ndim == 4:
+            x_jpeg = np.squeeze(x_jpeg, axis=1)
+
+        # Swap channel index
+        if self.channels_first and x_jpeg.ndim == 4:
+            # image shape NHWC to NCHW
+            x_jpeg = np.transpose(x_jpeg, (0, 3, 1, 2))
+        elif self.channels_first and x_ndim == 5:
+            # video shape NFHWC to NCFHW
+            x_jpeg = np.transpose(x_jpeg, (0, 4, 1, 2, 3))
+        return x_jpeg, y
+
+    def _check_params(self) -> None:
+        if not isinstance(self.quality, (int, np.int)) or self.quality <= 0 or self.quality > 100:
+            raise ValueError("Image quality must be a positive integer <= 100.")
+
+        if len(self.clip_values) != 2:
+            raise ValueError("'clip_values' should be a tuple of 2 floats or arrays containing the allowed data range.")
+
+        if np.array(self.clip_values[0] >= self.clip_values[1]).any():
+            raise ValueError("Invalid 'clip_values': min >= max.")
+
+        if self.clip_values[0] != 0:
+            raise ValueError("'clip_values' min value must be 0.")
+
+        if self.clip_values[1] != 1.0 and self.clip_values[1] != 255:
+            raise ValueError("'clip_values' max value must be either 1 or 255.")
+
+        if not isinstance(self.verbose, bool):
+            raise ValueError("The argument `verbose` has to be of type bool.")
diff --git a/adversarial-robustness-toolbox/art/defences/preprocessor/label_smoothing.py b/adversarial-robustness-toolbox/art/defences/preprocessor/label_smoothing.py
new file mode 100644
index 0000000..9720381
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/defences/preprocessor/label_smoothing.py
@@ -0,0 +1,88 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements the label smoothing defence in `LabelSmoothing`. It computes a vector of smooth labels from a
+vector of hard labels.
+
+| Paper link: https://pdfs.semanticscholar.org/b5ec/486044c6218dd41b17d8bba502b32a12b91a.pdf
+
+| Please keep in mind the limitations of defences. For details on how to evaluate classifier security in general,
+    see https://arxiv.org/abs/1902.06705  .
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+from typing import Optional, Tuple
+
+import numpy as np
+
+from art.defences.preprocessor.preprocessor import Preprocessor
+
+logger = logging.getLogger(__name__)
+
+
+class LabelSmoothing(Preprocessor):
+    """
+    Computes a vector of smooth labels from a vector of hard ones. The hard labels have to contain ones for the
+    correct classes and zeros for all the others. The remaining probability mass between `max_value` and 1 is
+    distributed uniformly between the incorrect classes for each instance.
+
+
+    | Paper link: https://pdfs.semanticscholar.org/b5ec/486044c6218dd41b17d8bba502b32a12b91a.pdf
+
+    | Please keep in mind the limitations of defences. For details on how to evaluate classifier security in general,
+        see https://arxiv.org/abs/1902.06705  .
+    """
+
+    params = ["max_value"]
+
+    def __init__(self, max_value: float = 0.9, apply_fit: bool = True, apply_predict: bool = False,) -> None:
+        """
+        Create an instance of label smoothing.
+
+        :param max_value: Value to affect to correct label
+        :param apply_fit: True if applied during fitting/training.
+        :param apply_predict: True if applied during predicting.
+        """
+        super().__init__(is_fitted=True, apply_fit=apply_fit, apply_predict=apply_predict)
+        self.max_value = max_value
+        self._check_params()
+
+    def __call__(self, x: np.ndarray, y: Optional[np.ndarray] = None) -> Tuple[np.ndarray, Optional[np.ndarray]]:
+        """
+        Apply label smoothing.
+
+        :param x: Input data, will not be modified by this method.
+        :param y: Original vector of label probabilities (one-vs-rest).
+        :return: Unmodified input data and the vector of smooth probabilities as correct labels.
+        :raises `ValueError`: If no labels are provided.
+        """
+        if y is None:
+            raise ValueError("Labels `y` cannot be None.")
+
+        min_value = (1 - self.max_value) / (y.shape[1] - 1)
+        assert self.max_value >= min_value
+
+        smooth_y = y.copy()
+        smooth_y[smooth_y == 1.0] = self.max_value
+        smooth_y[smooth_y == 0.0] = min_value
+        return x, smooth_y
+
+    def _check_params(self) -> None:
+        if self.max_value <= 0 or self.max_value > 1:
+            raise ValueError("The maximum value for correct labels must be between 0 and 1.")
diff --git a/adversarial-robustness-toolbox/art/defences/preprocessor/mp3_compression.py b/adversarial-robustness-toolbox/art/defences/preprocessor/mp3_compression.py
new file mode 100644
index 0000000..dc2474b
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/defences/preprocessor/mp3_compression.py
@@ -0,0 +1,156 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements the MP3 compression defence `Mp3Compression`.
+
+| Paper link: https://arxiv.org/abs/1801.01944
+
+| Please keep in mind the limitations of defences. For details on how to evaluate classifier security in general,
+    see https://arxiv.org/abs/1902.06705.
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+from io import BytesIO
+from typing import Optional, Tuple
+
+import numpy as np
+from tqdm.auto import tqdm
+
+from art.defences.preprocessor.preprocessor import Preprocessor
+
+logger = logging.getLogger(__name__)
+
+
+class Mp3Compression(Preprocessor):
+    """
+    Implement the MP3 compression defense approach.
+    """
+
+    params = ["channels_first", "sample_rate", "verbose"]
+
+    def __init__(
+        self,
+        sample_rate: int,
+        channels_first: bool = False,
+        apply_fit: bool = False,
+        apply_predict: bool = True,
+        verbose: bool = False,
+    ) -> None:
+        """
+        Create an instance of MP3 compression.
+
+        :param sample_rate: Specifies the sampling rate of sample.
+        :param channels_first: Set channels first or last.
+        :param apply_fit: True if applied during fitting/training.
+        :param apply_predict: True if applied during predicting.
+        :param verbose: Show progress bars.
+        """
+        super().__init__(is_fitted=True, apply_fit=apply_fit, apply_predict=apply_predict)
+        self.channels_first = channels_first
+        self.sample_rate = sample_rate
+        self.verbose = verbose
+        self._check_params()
+
+    def __call__(self, x: np.ndarray, y: Optional[np.ndarray] = None) -> Tuple[np.ndarray, Optional[np.ndarray]]:
+        """
+        Apply MP3 compression to sample `x`.
+
+        :param x: Sample to compress with shape `(batch_size, length, channel)` or an array of sample arrays with shape
+                  (length,) or (length, channel). `x` values are recommended to be of type `np.int16`.
+        :param y: Labels of the sample `x`. This function does not affect them in any way.
+        :return: Compressed sample.
+        """
+
+        def wav_to_mp3(x, sample_rate):
+            """
+            Apply MP3 compression to audio input of shape (samples, channel).
+            """
+            # WARNING: Writing and reading MP3 from byte stream causes pydub to extend the original
+            # length. Writing and reading MP3 from local file system works without problems. It is
+            # easy to move from using BytesIO to local read/writes with the following:
+            # import os
+            # from art import config
+            # tmp_wav = os.path.join(config.ART_DATA_PATH, "tmp.wav")
+            # tmp_mp3 = os.path.join(config.ART_DATA_PATH, "tmp.mp3")
+            from pydub import AudioSegment
+            from scipy.io.wavfile import write
+
+            normalized = bool(x.min() >= -1.0 and x.max() <= 1.0)
+            if x.dtype != np.int16 and not normalized:
+                # input is not of type np.int16 and seems to be unnormalized. Therefore casting to np.int16.
+                x = x.astype(np.int16)
+            elif x.dtype != np.int16 and normalized:
+                # x is not of type np.int16 and seems to be normalized. Therefore undoing normalization and
+                # casting to np.int16.
+                x = (x * 2 ** 15).astype(np.int16)
+
+            tmp_wav, tmp_mp3 = BytesIO(), BytesIO()
+            write(tmp_wav, sample_rate, x)
+            AudioSegment.from_wav(tmp_wav).export(tmp_mp3)
+            audio_segment = AudioSegment.from_mp3(tmp_mp3)
+            tmp_wav.close()
+            tmp_mp3.close()
+            x_mp3 = np.array(audio_segment.get_array_of_samples()).reshape((-1, audio_segment.channels))
+            # WARNING: Due to above problem, we need to manually resize x_mp3 to original length.
+            x_mp3 = x_mp3[: x.shape[0]]
+
+            if normalized:
+                # x was normalized. Therefore normalizing x_mp3.
+                x_mp3 = x_mp3 * 2 ** -15
+            return x_mp3
+
+        if x.dtype != np.object and x.ndim != 3:
+            raise ValueError("Mp3 compression can only be applied to temporal data across at least one channel.")
+
+        if x.dtype != np.object and self.channels_first:
+            x = np.swapaxes(x, 1, 2)
+
+        # apply mp3 compression per audio item
+        x_mp3 = x.copy()
+        for i, x_i in enumerate(tqdm(x, desc="MP3 compression", disable=not self.verbose)):
+            x_i_ndim_0 = x_i.ndim
+            if x.dtype == np.object:
+                if x_i.ndim == 1:
+                    x_i = np.expand_dims(x_i, axis=1)
+
+                if x_i_ndim_0 == 2 and self.channels_first:
+                    x_i = np.swapaxes(x_i, 0, 1)
+
+            x_i = wav_to_mp3(x_i, self.sample_rate)
+
+            if x.dtype == np.object:
+                if x_i_ndim_0 == 2 and self.channels_first:
+                    x_i = np.swapaxes(x_i, 0, 1)
+
+                if x_i_ndim_0 == 1:
+                    x_i = np.squeeze(x_i)
+
+            x_mp3[i] = x_i
+
+        if x.dtype != np.object and self.channels_first:
+            x_mp3 = np.swapaxes(x_mp3, 1, 2)
+
+        return x_mp3, y
+
+    def _check_params(self) -> None:
+        if not (isinstance(self.sample_rate, (int, np.int)) and self.sample_rate > 0):
+            raise ValueError("Sample rate be must a positive integer.")
+
+        if not isinstance(self.verbose, bool):
+            raise ValueError("The argument `verbose` has to be of type bool.")
diff --git a/adversarial-robustness-toolbox/art/defences/preprocessor/pixel_defend.py b/adversarial-robustness-toolbox/art/defences/preprocessor/pixel_defend.py
new file mode 100644
index 0000000..272b7fd
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/defences/preprocessor/pixel_defend.py
@@ -0,0 +1,164 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implement the pixel defence in `PixelDefend`. It is based on PixelCNN that projects samples back to the data
+manifold.
+
+| Paper link: https://arxiv.org/abs/1710.10766
+
+| Please keep in mind the limitations of defences. For more information on the limitations of this defence,
+    see https://arxiv.org/abs/1802.00420 . For details on how to evaluate classifier security in general, see
+    https://arxiv.org/abs/1902.06705
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+from typing import Optional, Tuple, TYPE_CHECKING
+
+import numpy as np
+from tqdm.auto import tqdm
+
+from art.config import ART_NUMPY_DTYPE
+from art.defences.preprocessor.preprocessor import Preprocessor
+
+if TYPE_CHECKING:
+    from art.utils import CLIP_VALUES_TYPE, CLASSIFIER_NEURALNETWORK_TYPE
+
+
+logger = logging.getLogger(__name__)
+
+
+class PixelDefend(Preprocessor):
+    """
+    Implement the pixel defence approach. Defense based on PixelCNN that projects samples back to the data manifold.
+
+    | Paper link: https://arxiv.org/abs/1710.10766
+
+    | Please keep in mind the limitations of defences. For more information on the limitations of this defence,
+        see https://arxiv.org/abs/1802.00420 . For details on how to evaluate classifier security in general, see
+        https://arxiv.org/abs/1902.06705
+    """
+
+    params = ["clip_values", "eps", "pixel_cnn", "verbose"]
+
+    def __init__(
+        self,
+        clip_values: "CLIP_VALUES_TYPE" = (0.0, 1.0),
+        eps: int = 16,
+        pixel_cnn: Optional["CLASSIFIER_NEURALNETWORK_TYPE"] = None,
+        batch_size: int = 128,
+        apply_fit: bool = False,
+        apply_predict: bool = True,
+        verbose: bool = False,
+    ) -> None:
+        """
+        Create an instance of pixel defence.
+
+        :param clip_values: Tuple of the form `(min, max)` representing the minimum and maximum values allowed
+               for features.
+        :param eps: Defense parameter 0-255.
+        :param pixel_cnn: Pre-trained PixelCNN model.
+        :param verbose: Show progress bars.
+        """
+        super().__init__(is_fitted=True, apply_fit=apply_fit, apply_predict=apply_predict)
+        self.clip_values = clip_values
+        self.eps = eps
+        self.batch_size = batch_size
+        self.pixel_cnn = pixel_cnn
+        self.verbose = verbose
+        self._check_params()
+
+    def __call__(self, x: np.ndarray, y: Optional[np.ndarray] = None) -> Tuple[np.ndarray, Optional[np.ndarray]]:
+        """
+        Apply pixel defence to sample `x`.
+
+        :param x: Sample to defense with shape `(batch_size, width, height, depth)`. `x` values are expected to be in
+                the data range [0, 1].
+        :param y: Labels of the sample `x`. This function does not affect them in any way.
+        :return: Purified sample.
+        """
+        # Convert into `uint8`
+        original_shape = x.shape
+        if self.pixel_cnn is not None:
+            probs = self.pixel_cnn.get_activations(x, layer=-1, batch_size=self.batch_size).reshape(
+                (x.shape[0], -1, 256)
+            )
+        else:
+            raise ValueError("No model received for `pixel_cnn`.")
+
+        x = x * 255
+        x = x.astype("uint8")
+        x = x.reshape((x.shape[0], -1))
+
+        # Start defence one image at a time
+        for i, x_i in enumerate(tqdm(x, desc="PixelDefend", disable=not self.verbose)):
+            for feat_index in range(x.shape[1]):
+                # Setup the search space
+                f_probs = probs[i, feat_index, :]
+                f_range = range(int(max(x_i[feat_index] - self.eps, 0)), int(min(x_i[feat_index] + self.eps, 255) + 1),)
+
+                # Look in the search space
+                best_prob = -1
+                best_idx = -1
+                for idx in f_range:
+                    if f_probs[idx] > best_prob:
+                        best_prob = f_probs[idx]
+                        best_idx = idx
+
+                # Update result
+                x_i[feat_index] = best_idx
+
+            # Update in batch
+            x[i] = x_i
+
+        # Convert to old dtype
+        x = x / 255.0
+        x = x.astype(ART_NUMPY_DTYPE).reshape(original_shape)
+
+        # Clip to clip_values
+        x = np.clip(x, self.clip_values[0], self.clip_values[1])
+
+        return x, y
+
+    def _check_params(self) -> None:
+
+        if not isinstance(self.eps, (int, np.int)) or self.eps < 0 or self.eps > 255:
+            raise ValueError("The defense parameter must be between 0 and 255.")
+
+        from art.estimators.classification.classifier import ClassifierMixin
+        from art.estimators.estimator import NeuralNetworkMixin
+
+        if hasattr(self, "pixel_cnn") and not (
+            isinstance(self.pixel_cnn, ClassifierMixin) and isinstance(self.pixel_cnn, NeuralNetworkMixin)
+        ):
+            raise TypeError("PixelCNN model must be of type Classifier.")
+
+        if np.array(self.clip_values[0] >= self.clip_values[1]).any():
+            raise ValueError("Invalid `clip_values`: min >= max.")
+
+        if self.clip_values[0] != 0:
+            raise ValueError("`clip_values` min value must be 0.")
+
+        if self.clip_values[1] != 1:
+            raise ValueError("`clip_values` max value must be 1.")
+
+        if self.batch_size <= 0:
+            raise ValueError("The batch size `batch_size` has to be positive.")
+
+        if not isinstance(self.verbose, bool):
+            raise ValueError("The argument `verbose` has to be of type bool.")
diff --git a/adversarial-robustness-toolbox/art/defences/preprocessor/preprocessor.py b/adversarial-robustness-toolbox/art/defences/preprocessor/preprocessor.py
new file mode 100644
index 0000000..3191c29
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/defences/preprocessor/preprocessor.py
@@ -0,0 +1,321 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements the abstract base class for defences that pre-process input data.
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import abc
+from typing import List, Optional, Tuple, Any, TYPE_CHECKING
+
+import numpy as np
+
+from art import config
+
+if TYPE_CHECKING:
+    import torch
+    import tensorflow as tf
+
+
+class Preprocessor(abc.ABC):
+    """
+    Abstract base class for preprocessing defences.
+
+    By default, the gradient is estimated using BPDA with the identity function.
+        To modify, override `estimate_gradient`
+    """
+
+    params: List[str] = []
+
+    def __init__(self, is_fitted: bool = False, apply_fit: bool = True, apply_predict: bool = True) -> None:
+        """
+        Create a preprocessing object.
+
+        Optionally, set attributes.
+        """
+        self._is_fitted = bool(is_fitted)
+        self._apply_fit = bool(apply_fit)
+        self._apply_predict = bool(apply_predict)
+
+    @property
+    def is_fitted(self) -> bool:
+        """
+        Return the state of the preprocessing object.
+
+        :return: `True` if the preprocessing model has been fitted (if this applies).
+        """
+        return self._is_fitted
+
+    @property
+    def apply_fit(self) -> bool:
+        """
+        Property of the defence indicating if it should be applied at training time.
+
+        :return: `True` if the defence should be applied when fitting a model, `False` otherwise.
+        """
+        return self._apply_fit
+
+    @property
+    def apply_predict(self) -> bool:
+        """
+        Property of the defence indicating if it should be applied at test time.
+
+        :return: `True` if the defence should be applied at prediction time, `False` otherwise.
+        """
+        return self._apply_predict
+
+    @abc.abstractmethod
+    def __call__(self, x: np.ndarray, y: Optional[np.ndarray] = None) -> Tuple[np.ndarray, Optional[np.ndarray]]:
+        """
+        Perform data preprocessing and return preprocessed data as tuple.
+
+        :param x: Dataset to be preprocessed.
+        :param y: Labels to be preprocessed.
+        :return: Preprocessed data.
+        """
+        raise NotImplementedError
+
+    def fit(self, x: np.ndarray, y: Optional[np.ndarray] = None, **kwargs) -> None:
+        """
+        Fit the parameters of the data preprocessor if it has any.
+
+        :param x: Training set to fit the preprocessor.
+        :param y: Labels for the training set.
+        :param kwargs: Other parameters.
+        """
+        pass
+
+    def estimate_gradient(self, x: np.ndarray, grad: np.ndarray) -> np.ndarray:
+        """
+        Provide an estimate of the gradients of the defence for the backward pass. If the defence is not differentiable,
+        this is an estimate of the gradient, most often replacing the computation performed by the defence with the
+        identity function (the default).
+
+        :param x: Input data for which the gradient is estimated. First dimension is the batch size.
+        :param grad: Gradient value so far.
+        :return: The gradient (estimate) of the defence.
+        """
+        return grad
+
+    def set_params(self, **kwargs) -> None:
+        """
+        Take in a dictionary of parameters and apply checks before saving them as attributes.
+        """
+        for key, value in kwargs.items():
+            if key in self.params:
+                setattr(self, key, value)
+        self._check_params()
+
+    def _check_params(self) -> None:
+        pass
+
+    def forward(self, x: Any, y: Any = None) -> Tuple[Any, Any]:
+        """
+        Perform data preprocessing and return preprocessed data.
+
+        :param x: Dataset to be preprocessed.
+        :param y: Labels to be preprocessed.
+        :return: Preprocessed data.
+        """
+        raise NotImplementedError
+
+
+class PreprocessorPyTorch(Preprocessor):
+    """
+    Abstract base class for preprocessing defences implemented in PyTorch that support efficient preprocessor-chaining.
+    """
+
+    @abc.abstractmethod
+    def forward(
+        self, x: "torch.Tensor", y: Optional["torch.Tensor"] = None
+    ) -> Tuple["torch.Tensor", Optional["torch.Tensor"]]:
+        """
+        Perform data preprocessing in PyTorch and return preprocessed data as tuple.
+
+        :param x: Dataset to be preprocessed.
+        :param y: Labels to be preprocessed.
+        :return: Preprocessed data.
+        """
+        raise NotImplementedError
+
+    def estimate_forward(self, x: "torch.Tensor", y: Optional["torch.Tensor"] = None) -> "torch.Tensor":
+        """
+        Provide a differentiable estimate of the forward function, so that autograd can calculate gradients
+        of the defence for the backward pass. If the defence is differentiable, just call `self.forward()`.
+        If the defence is not differentiable and a differentiable estimate is not available, replace with
+        an identity function.
+
+        :param x: Dataset to be preprocessed.
+        :param y: Labels to be preprocessed.
+        :return: Preprocessed data.
+        """
+        return self.forward(x, y=y)[0]
+
+    @property
+    def device(self):
+        """
+        Type of device on which the classifier is run, either `gpu` or `cpu`.
+        """
+        return self._device
+
+    def __call__(self, x: np.ndarray, y: Optional[np.ndarray] = None) -> Tuple[np.ndarray, Optional[np.ndarray]]:
+        """
+        Apply preprocessing to input `x` and labels `y`.
+
+        :param x: Sample to smooth with shape `(batch_size, width, height, depth)`.
+        :param y: Labels of the sample `x`. This function does not affect them in any way.
+        :return: Smoothed sample.
+        """
+        import torch  # lgtm [py/repeated-import]
+
+        x = torch.tensor(x, device=self.device)
+        if y is not None:
+            y = torch.tensor(y, device=self.device)
+
+        with torch.no_grad():
+            x, y = self.forward(x, y)
+
+        result = x.cpu().numpy()
+        if y is not None:
+            y = y.cpu().numpy()
+        return result, y
+
+    # Backward compatibility.
+    def estimate_gradient(self, x: np.ndarray, grad: np.ndarray) -> np.ndarray:
+        import torch  # lgtm [py/repeated-import]
+
+        def get_gradient(x, grad):
+            x = torch.tensor(x, device=self.device, requires_grad=True)
+            grad = torch.tensor(grad, device=self.device)
+
+            x_prime = self.estimate_forward(x)
+            x_prime.backward(grad)
+            x_grad = x.grad.detach().cpu().numpy()
+
+            if x_grad.shape != x.shape:
+                raise ValueError("The input shape is {} while the gradient shape is {}".format(x.shape, x_grad.shape))
+
+            return x_grad
+
+        if x.dtype == np.object:
+            x_grad_list = list()
+            for i, x_i in enumerate(x):
+                x_grad_list.append(get_gradient(x=x_i, grad=grad[i]))
+            x_grad = np.empty(x.shape[0], dtype=object)
+            x_grad[:] = list(x_grad_list)
+        elif x.shape == grad.shape:
+            x_grad = get_gradient(x=x, grad=grad)
+        else:
+            # Special case for loss gradients
+            x_grad = np.zeros_like(grad)
+            for i in range(grad.shape[1]):
+                x_grad[:, i, ...] = get_gradient(x=x, grad=grad[:, i, ...])
+
+        return x_grad
+
+
+class PreprocessorTensorFlowV2(Preprocessor):
+    """
+    Abstract base class for preprocessing defences implemented in TensorFlow v2 that support efficient
+    preprocessor-chaining.
+    """
+
+    @abc.abstractmethod
+    def forward(self, x: "tf.Tensor", y: Optional["tf.Tensor"] = None) -> Tuple["tf.Tensor", Optional["tf.Tensor"]]:
+        """
+        Perform data preprocessing in TensorFlow v2 and return preprocessed data as tuple.
+
+        :param x: Dataset to be preprocessed.
+        :param y: Labels to be preprocessed.
+        :return: Preprocessed data.
+        """
+        raise NotImplementedError
+
+    def estimate_forward(self, x: "tf.Tensor", y: Optional["tf.Tensor"] = None) -> "tf.Tensor":
+        """
+        Provide a differentiable estimate of the forward function, so that autograd can calculate gradients
+        of the defence for the backward pass. If the defence is differentiable, just call `self.forward()`.
+        If the defence is not differentiable and a differentiable estimate is not available, replace with
+        an identity function.
+
+        :param x: Dataset to be preprocessed.
+        :param y: Labels to be preprocessed.
+        :return: Preprocessed data.
+        """
+        return self.forward(x, y=y)[0]
+
+    def __call__(self, x: np.ndarray, y: Optional[np.ndarray] = None) -> Tuple[np.ndarray, Optional[np.ndarray]]:
+        """
+        Apply preprocessing to input `x` and labels `y`.
+
+        :param x: Sample to smooth with shape `(batch_size, width, height, depth)`.
+        :param y: Labels of the sample `x`. This function does not affect them in any way.
+        :return: Smoothed sample.
+        """
+        import tensorflow as tf  # lgtm [py/repeated-import]
+
+        x = tf.convert_to_tensor(x)
+        if y is not None:
+            y = tf.convert_to_tensor(y)
+
+        x, y = self.forward(x, y)
+
+        result = x.numpy()
+        if y is not None:
+            y = y.numpy()
+        return result, y
+
+    # Backward compatibility.
+    def estimate_gradient(self, x: np.ndarray, grad: np.ndarray) -> np.ndarray:
+        import tensorflow as tf  # lgtm [py/repeated-import]
+
+        def get_gradient(x: np.ndarray, grad: np.ndarray) -> np.ndarray:
+            """
+            Helper function for estimate_gradient
+            """
+
+            with tf.GradientTape() as tape:
+                x = tf.convert_to_tensor(x, dtype=config.ART_NUMPY_DTYPE)
+                tape.watch(x)
+                grad = tf.convert_to_tensor(grad, dtype=config.ART_NUMPY_DTYPE)
+
+                x_prime = self.estimate_forward(x)
+
+            x_grad = tape.gradient(target=x_prime, sources=x, output_gradients=grad)
+
+            x_grad = x_grad.numpy()
+            if x_grad.shape != x.shape:
+                raise ValueError("The input shape is {} while the gradient shape is {}".format(x.shape, x_grad.shape))
+
+            return x_grad
+
+        if x.dtype == np.object:
+            x_grad_list = list()
+            for i, x_i in enumerate(x):
+                x_grad_list.append(get_gradient(x=x_i, grad=grad[i]))
+            x_grad = np.empty(x.shape[0], dtype=object)
+            x_grad[:] = list(x_grad_list)
+        elif x.shape == grad.shape:
+            x_grad = get_gradient(x=x, grad=grad)
+        else:
+            # Special case for loss gradients
+            x_grad = np.zeros_like(grad)
+            for i in range(grad.shape[1]):
+                x_grad[:, i, ...] = get_gradient(x=x, grad=grad[:, i, ...])
+
+        return x_grad
diff --git a/adversarial-robustness-toolbox/art/defences/preprocessor/resample.py b/adversarial-robustness-toolbox/art/defences/preprocessor/resample.py
new file mode 100644
index 0000000..9709a0b
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/defences/preprocessor/resample.py
@@ -0,0 +1,91 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements the resampling defence `Resample`.
+
+| Paper link: https://arxiv.org/abs/1809.10875
+
+| Please keep in mind the limitations of defences. For details on how to evaluate classifier security in general,
+    see https://arxiv.org/abs/1902.06705.
+"""
+import logging
+from typing import Optional, Tuple
+
+import numpy as np
+
+from art.defences.preprocessor.preprocessor import Preprocessor
+
+logger = logging.getLogger(__name__)
+
+
+class Resample(Preprocessor):
+    """
+    Implement the resampling defense approach.
+
+    Resampling implicitly consists of a step that applies a low-pass filter. The underlying filter in this
+    implementation is a Windowed Sinc Interpolation function.
+    """
+
+    params = ["sr_original", "sr_new", "channels_first"]
+
+    def __init__(
+        self,
+        sr_original: int,
+        sr_new: int,
+        channels_first: bool = False,
+        apply_fit: bool = False,
+        apply_predict: bool = True,
+    ):
+        """
+        Create an instance of the resample preprocessor.
+
+        :param sr_original: Original sampling rate of sample.
+        :param sr_new: New sampling rate of sample.
+        :param channels_first: Set channels first or last.
+        :param apply_fit: True if applied during fitting/training.
+        :param apply_predict: True if applied during predicting.
+        """
+        super().__init__(is_fitted=True, apply_fit=apply_fit, apply_predict=apply_predict)
+        self.sr_original = sr_original
+        self.sr_new = sr_new
+        self.channels_first = channels_first
+        self._check_params()
+
+    def __call__(self, x: np.ndarray, y: Optional[np.ndarray] = None) -> Tuple[np.ndarray, Optional[np.ndarray]]:
+        """
+        Resample `x` to a new sampling rate.
+
+        :param x: Sample to resample of shape `(batch_size, length, channel)` or `(batch_size, channel, length)`.
+        :param y: Labels of the sample `x`. This function does not affect them in any way.
+        :return: Resampled audio sample.
+        """
+        import resampy
+
+        if x.ndim != 3:
+            raise ValueError("Resampling can only be applied to temporal data across at least one channel.")
+
+        sample_index = 2 if self.channels_first else 1
+
+        return resampy.resample(x, self.sr_original, self.sr_new, axis=sample_index, filter="sinc_window"), y
+
+    def _check_params(self) -> None:
+        if not (isinstance(self.sr_original, (int, np.int)) and self.sr_original > 0):
+            raise ValueError("Original sampling rate be must a positive integer.")
+
+        if not (isinstance(self.sr_new, (int, np.int)) and self.sr_new > 0):
+            raise ValueError("New sampling rate be must a positive integer.")
diff --git a/adversarial-robustness-toolbox/art/defences/preprocessor/spatial_smoothing.py b/adversarial-robustness-toolbox/art/defences/preprocessor/spatial_smoothing.py
new file mode 100644
index 0000000..107c01b
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/defences/preprocessor/spatial_smoothing.py
@@ -0,0 +1,123 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements the local spatial smoothing defence in `SpatialSmoothing`.
+
+| Paper link: https://arxiv.org/abs/1704.01155
+
+| Please keep in mind the limitations of defences. For more information on the limitations of this defence,
+    see https://arxiv.org/abs/1803.09868 . For details on how to evaluate classifier security in general, see
+    https://arxiv.org/abs/1902.06705
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+from typing import Optional, Tuple
+
+import numpy as np
+from scipy.ndimage.filters import median_filter
+
+from art.utils import CLIP_VALUES_TYPE
+from art.defences.preprocessor.preprocessor import Preprocessor
+
+logger = logging.getLogger(__name__)
+
+
+class SpatialSmoothing(Preprocessor):
+    """
+    Implement the local spatial smoothing defence approach.
+
+    | Paper link: https://arxiv.org/abs/1704.01155
+
+    | Please keep in mind the limitations of defences. For more information on the limitations of this defence,
+        see https://arxiv.org/abs/1803.09868 . For details on how to evaluate classifier security in general, see
+        https://arxiv.org/abs/1902.06705
+    """
+
+    params = ["window_size", "channels_first", "clip_values"]
+
+    def __init__(
+        self,
+        window_size: int = 3,
+        channels_first: bool = False,
+        clip_values: Optional[CLIP_VALUES_TYPE] = None,
+        apply_fit: bool = False,
+        apply_predict: bool = True,
+    ) -> None:
+        """
+        Create an instance of local spatial smoothing.
+
+        :param channels_first: Set channels first or last.
+        :param window_size: The size of the sliding window.
+        :param clip_values: Tuple of the form `(min, max)` representing the minimum and maximum values allowed
+               for features.
+        :param apply_fit: True if applied during fitting/training.
+        :param apply_predict: True if applied during predicting.
+        """
+        super().__init__(is_fitted=True, apply_fit=apply_fit, apply_predict=apply_predict)
+
+        self.channels_first = channels_first
+        self.window_size = window_size
+        self.clip_values = clip_values
+        self._check_params()
+
+    def __call__(self, x: np.ndarray, y: Optional[np.ndarray] = None) -> Tuple[np.ndarray, Optional[np.ndarray]]:
+        """
+        Apply local spatial smoothing to sample `x`.
+
+        :param x: Sample to smooth with shape `(batch_size, width, height, depth)`.
+        :param y: Labels of the sample `x`. This function does not affect them in any way.
+        :return: Smoothed sample.
+        """
+        x_ndim = x.ndim
+        if x_ndim not in [4, 5]:
+            raise ValueError(
+                "Unrecognized input dimension. Spatial smoothing can only be applied to image and video data."
+            )
+
+        # get channel index
+        channel_index = 1 if self.channels_first else x_ndim - 1
+
+        filter_size = [self.window_size] * x_ndim
+        # set filter_size at batch and channel indices to 1
+        filter_size[0] = 1
+        filter_size[channel_index] = 1
+        # set filter_size at temporal index to 1
+        if x_ndim == 5:
+            # check if NCFHW or NFHWC
+            temporal_index = 2 if self.channels_first else 1
+            filter_size[temporal_index] = 1
+        # Note median_filter:
+        # * center pixel located lower right
+        # * if window size even, use larger value (e.g. median(4,5)=5)
+        result = median_filter(x, size=tuple(filter_size), mode="reflect")
+
+        if self.clip_values is not None:
+            np.clip(result, self.clip_values[0], self.clip_values[1], out=result)
+
+        return result, y
+
+    def _check_params(self) -> None:
+        if not (isinstance(self.window_size, (int, np.int)) and self.window_size > 0):
+            raise ValueError("Sliding window size must be a positive integer.")
+
+        if self.clip_values is not None and len(self.clip_values) != 2:
+            raise ValueError("'clip_values' should be a tuple of 2 floats or arrays containing the allowed data range.")
+
+        if self.clip_values is not None and np.array(self.clip_values[0] >= self.clip_values[1]).any():
+            raise ValueError("Invalid 'clip_values': min >= max.")
diff --git a/adversarial-robustness-toolbox/art/defences/preprocessor/spatial_smoothing_pytorch.py b/adversarial-robustness-toolbox/art/defences/preprocessor/spatial_smoothing_pytorch.py
new file mode 100644
index 0000000..1887966
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/defences/preprocessor/spatial_smoothing_pytorch.py
@@ -0,0 +1,206 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements the local spatial smoothing defence in `SpatialSmoothing` in PyTorch.
+
+| Paper link: https://arxiv.org/abs/1704.01155
+
+| Please keep in mind the limitations of defences. For more information on the limitations of this defence,
+    see https://arxiv.org/abs/1803.09868 . For details on how to evaluate classifier security in general, see
+    https://arxiv.org/abs/1902.06705
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+from typing import Optional, Tuple, TYPE_CHECKING
+
+import numpy as np
+
+from art.defences.preprocessor.preprocessor import PreprocessorPyTorch
+
+if TYPE_CHECKING:
+    import torch
+    from art.utils import CLIP_VALUES_TYPE
+
+logger = logging.getLogger(__name__)
+
+
+class SpatialSmoothingPyTorch(PreprocessorPyTorch):
+    """
+    Implement the local spatial smoothing defence approach in PyTorch.
+
+    | Paper link: https://arxiv.org/abs/1704.01155
+
+    | Please keep in mind the limitations of defences. For more information on the limitations of this defence,
+        see https://arxiv.org/abs/1803.09868 . For details on how to evaluate classifier security in general, see
+        https://arxiv.org/abs/1902.06705
+    """
+
+    def __init__(
+        self,
+        window_size: int = 3,
+        channels_first: bool = False,
+        clip_values: Optional["CLIP_VALUES_TYPE"] = None,
+        apply_fit: bool = False,
+        apply_predict: bool = True,
+        device_type: str = "gpu",
+    ) -> None:
+        """
+        Create an instance of local spatial smoothing.
+
+        :param window_size: Size of spatial smoothing window.
+        :param channels_first: Set channels first or last.
+        :param clip_values: Tuple of the form `(min, max)` representing the minimum and maximum values allowed
+               for features.
+        :param apply_fit: True if applied during fitting/training.
+        :param apply_predict: True if applied during predicting.
+        :param device_type: Type of device on which the classifier is run, either `gpu` or `cpu`.
+        """
+        import torch  # lgtm [py/repeated-import]
+
+        super().__init__(apply_fit=apply_fit, apply_predict=apply_predict)
+
+        self.channels_first = channels_first
+        self.window_size = window_size
+        self.clip_values = clip_values
+        self._check_params()
+
+        # Set device
+        if device_type == "cpu" or not torch.cuda.is_available():
+            self._device = torch.device("cpu")
+        else:
+            cuda_idx = torch.cuda.current_device()
+            self._device = torch.device("cuda:{}".format(cuda_idx))
+
+        from kornia.filters import MedianBlur
+
+        class MedianBlurCustom(MedianBlur):
+            """
+            An ongoing effort to reproduce the median blur function in SciPy.
+            """
+
+            def __init__(self, kernel_size: Tuple[int, int]) -> None:
+                super().__init__(kernel_size)
+
+                # Half-pad the input so that the output keeps the same shape.
+                # * center pixel located lower right
+                half_pad = [int(k % 2 == 0) for k in kernel_size]
+                self.p2d = [
+                    int(self.padding[-1]) + half_pad[-1],
+                    int(self.padding[-1]),
+                    int(self.padding[-2]) + half_pad[-2],
+                    int(self.padding[-2]),
+                ]
+                # PyTorch requires Padding size should be less than the corresponding input dimension,
+
+            def forward(self, input: "torch.Tensor"):  # type: ignore
+                import torch  # lgtm [py/repeated-import]
+                import torch.nn.functional as F
+
+                if not torch.is_tensor(input):
+                    raise TypeError("Input type is not a torch.Tensor. Got {}".format(type(input)))
+                if not len(input.shape) == 4:
+                    raise ValueError("Invalid input shape, we expect BxCxHxW. Got: {}".format(input.shape))
+                # prepare kernel
+                batch_size, channels, height, width = input.shape
+                kernel: torch.Tensor = self.kernel.to(input.device).to(input.dtype)
+                # map the local window to single vector
+
+                _input = input.reshape(batch_size * channels, 1, height, width)
+                if input.dtype == torch.int64:
+                    # "reflection_pad2d" not implemented for 'Long'
+                    # "reflect" in scipy.ndimage.median_filter has no equivalence in F.pad.
+                    # "reflect" in PyTorch maps to "mirror" in scipy.ndimage.median_filter.
+                    _input = _input.to(torch.float32)
+                    _input = F.pad(_input, self.p2d, "reflect")
+                    _input = _input.to(torch.int64)
+                else:
+                    _input = F.pad(_input, self.p2d, "reflect")
+
+                features: torch.Tensor = F.conv2d(_input, kernel, stride=1)
+                features = features.view(batch_size, channels, -1, height, width)  # BxCx(K_h * K_w)xHxW
+
+                # compute the median along the feature axis
+                # * torch.median(), if window size even, use smaller value (e.g. median(4,5)=4)
+                median: torch.Tensor = torch.median(features, dim=2)[0]
+                return median
+
+        self.median_blur = MedianBlurCustom(kernel_size=(self.window_size, self.window_size))
+
+    def forward(
+        self, x: "torch.Tensor", y: Optional["torch.Tensor"] = None
+    ) -> Tuple["torch.Tensor", Optional["torch.Tensor"]]:
+        """
+        Apply local spatial smoothing to sample `x`.
+        """
+        x_ndim = x.ndim
+
+        # NHWC/NCFHW/NFHWC --> NCHW.
+        if x_ndim == 4:
+            if self.channels_first:
+                x_nchw = x
+            else:
+                # NHWC --> NCHW
+                x_nchw = x.permute(0, 3, 1, 2)
+        elif x_ndim == 5:
+            if self.channels_first:
+                # NCFHW --> NFCHW --> NCHW
+                nb_clips, channels, clip_size, height, width = x.shape
+                x_nchw = x.permute(0, 2, 1, 3, 4).reshape(nb_clips * clip_size, channels, height, width)
+            else:
+                # NFHWC --> NHWC --> NCHW
+                nb_clips, clip_size, height, width, channels = x.shape
+                x_nchw = x.reshape(nb_clips * clip_size, height, width, channels).permute(0, 3, 1, 2)
+        else:
+            raise ValueError(
+                "Unrecognized input dimension. Spatial smoothing can only be applied to image and video data."
+            )
+
+        x_nchw = self.median_blur(x_nchw)
+
+        # NHWC/NCFHW/NFHWC <-- NCHW.
+        if x_ndim == 4:
+            if self.channels_first:
+                x = x_nchw
+            else:
+                #   NHWC <-- NCHW
+                x = x_nchw.permute(0, 2, 3, 1)
+        elif x_ndim == 5:  # lgtm [py/redundant-comparison]
+            if self.channels_first:
+                # NCFHW <-- NFCHW <-- NCHW
+                x_nfchw = x_nchw.reshape(nb_clips, clip_size, channels, height, width)
+                x = x_nfchw.permute(0, 2, 1, 3, 4)
+            else:
+                # NFHWC <-- NHWC <-- NCHW
+                x_nhwc = x_nchw.permute(0, 2, 3, 1)
+                x = x_nhwc.reshape(nb_clips, clip_size, height, width, channels)
+
+        if self.clip_values is not None:
+            x = x.clamp(min=self.clip_values[0], max=self.clip_values[1])
+
+        return x, y
+
+    def _check_params(self) -> None:
+        if not (isinstance(self.window_size, (int, np.int)) and self.window_size > 0):
+            raise ValueError("Sliding window size must be a positive integer.")
+
+        if self.clip_values is not None and len(self.clip_values) != 2:
+            raise ValueError("'clip_values' should be a tuple of 2 floats or arrays containing the allowed data range.")
+
+        if self.clip_values is not None and np.array(self.clip_values[0] >= self.clip_values[1]).any():
+            raise ValueError("Invalid 'clip_values': min >= max.")
diff --git a/adversarial-robustness-toolbox/art/defences/preprocessor/spatial_smoothing_tensorflow.py b/adversarial-robustness-toolbox/art/defences/preprocessor/spatial_smoothing_tensorflow.py
new file mode 100644
index 0000000..a482dc9
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/defences/preprocessor/spatial_smoothing_tensorflow.py
@@ -0,0 +1,126 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements the local spatial smoothing defence in `SpatialSmoothing` in PyTorch.
+
+| Paper link: https://arxiv.org/abs/1704.01155
+
+| Please keep in mind the limitations of defences. For more information on the limitations of this defence,
+    see https://arxiv.org/abs/1803.09868 . For details on how to evaluate classifier security in general, see
+    https://arxiv.org/abs/1902.06705
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+from typing import Optional, Tuple, TYPE_CHECKING
+
+import numpy as np
+
+from art.defences.preprocessor.preprocessor import PreprocessorTensorFlowV2
+
+if TYPE_CHECKING:
+    import tensorflow as tf
+    from art.utils import CLIP_VALUES_TYPE
+
+logger = logging.getLogger(__name__)
+
+
+class SpatialSmoothingTensorFlowV2(PreprocessorTensorFlowV2):
+    """
+    Implement the local spatial smoothing defence approach in TensorFlow v2.
+
+    | Paper link: https://arxiv.org/abs/1704.01155
+
+    | Please keep in mind the limitations of defences. For more information on the limitations of this defence,
+        see https://arxiv.org/abs/1803.09868 . For details on how to evaluate classifier security in general, see
+        https://arxiv.org/abs/1902.06705
+    """
+
+    def __init__(
+        self,
+        window_size: int = 3,
+        channels_first: bool = False,
+        clip_values: Optional["CLIP_VALUES_TYPE"] = None,
+        apply_fit: bool = False,
+        apply_predict: bool = True,
+    ) -> None:
+        """
+        Create an instance of local spatial smoothing.
+
+        :window_size: Size of spatial smoothing window.
+        :param channels_first: Set channels first or last.
+        :param clip_values: Tuple of the form `(min, max)` representing the minimum and maximum values allowed
+               for features.
+        :param apply_fit: True if applied during fitting/training.
+        :param apply_predict: True if applied during predicting.
+        """
+        super().__init__(apply_fit=apply_fit, apply_predict=apply_predict)
+
+        self.channels_first = channels_first
+        self.window_size = window_size
+        self.clip_values = clip_values
+        self._check_params()
+
+    def forward(self, x: "tf.Tensor", y: Optional["tf.Tensor"] = None) -> Tuple["tf.Tensor", Optional["tf.Tensor"]]:
+        """
+        Apply local spatial smoothing to sample `x`.
+        """
+        import tensorflow as tf  # lgtm [py/repeated-import]
+        import tensorflow_addons as tfa
+
+        x_ndim = x.ndim
+
+        if x_ndim == 4:
+            x_nhwc = x
+        elif x_ndim == 5:
+            # NFHWC --> NHWC
+            nb_clips, clip_size, height, width, channels = x.shape
+            x_nhwc = tf.reshape(x, (nb_clips * clip_size, height, width, channels))
+        else:
+            raise ValueError(
+                "Unrecognized input dimension. Spatial smoothing can only be applied to image (NHWC) and video (NFHWC) "
+                "data."
+            )
+
+        x_nhwc = tfa.image.median_filter2d(
+            x_nhwc, filter_shape=[self.window_size, self.window_size], padding="REFLECT", constant_values=0, name=None
+        )
+
+        if x_ndim == 4:
+            x = x_nhwc
+        elif x_ndim == 5:  # lgtm [py/redundant-comparison]
+            # NFHWC <-- NHWC
+            x = tf.reshape(x_nhwc, (nb_clips, clip_size, height, width, channels))
+
+        if self.clip_values is not None:
+            x = x.clip_by_value(min=self.clip_values[0], max=self.clip_values[1])
+
+        return x, y
+
+    def _check_params(self) -> None:
+        if not (isinstance(self.window_size, (int, np.int)) and self.window_size > 0):
+            raise ValueError("Sliding window size must be a positive integer.")
+
+        if self.clip_values is not None and len(self.clip_values) != 2:
+            raise ValueError("'clip_values' should be a tuple of 2 floats or arrays containing the allowed data range.")
+
+        if self.clip_values is not None and np.array(self.clip_values[0] >= self.clip_values[1]).any():
+            raise ValueError("Invalid 'clip_values': min >= max.")
+
+        if self.channels_first:
+            raise ValueError("Only channels last input data is supported (`channels_first=False`)")
diff --git a/adversarial-robustness-toolbox/art/defences/preprocessor/thermometer_encoding.py b/adversarial-robustness-toolbox/art/defences/preprocessor/thermometer_encoding.py
new file mode 100644
index 0000000..0d7eee2
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/defences/preprocessor/thermometer_encoding.py
@@ -0,0 +1,157 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements the thermometer encoding defence `ThermometerEncoding`.
+
+| Paper link: https://openreview.net/forum?id=S18Su--CW
+
+| Please keep in mind the limitations of defences. For more information on the limitations of this defence,
+    see https://arxiv.org/abs/1802.00420 . For details on how to evaluate classifier security in general, see
+    https://arxiv.org/abs/1902.06705
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+from typing import Optional, Tuple, TYPE_CHECKING
+
+import numpy as np
+
+from art.config import ART_NUMPY_DTYPE
+from art.defences.preprocessor.preprocessor import Preprocessor
+from art.utils import to_categorical
+
+if TYPE_CHECKING:
+    from art.utils import CLIP_VALUES_TYPE
+
+logger = logging.getLogger(__name__)
+
+
+class ThermometerEncoding(Preprocessor):
+    """
+    Implement the thermometer encoding defence approach.
+
+    | Paper link: https://openreview.net/forum?id=S18Su--CW
+
+    | Please keep in mind the limitations of defences. For more information on the limitations of this defence,
+        see https://arxiv.org/abs/1802.00420 . For details on how to evaluate classifier security in general, see
+        https://arxiv.org/abs/1902.06705
+    """
+
+    params = ["clip_values", "num_space", "channels_first"]
+
+    def __init__(
+        self,
+        clip_values: "CLIP_VALUES_TYPE",
+        num_space: int = 10,
+        channels_first: bool = False,
+        apply_fit: bool = True,
+        apply_predict: bool = True,
+    ) -> None:
+        """
+        Create an instance of thermometer encoding.
+
+        :param clip_values: Tuple of the form `(min, max)` representing the minimum and maximum values allowed
+               for features.
+        :param num_space: Number of evenly spaced levels within the interval of minimum and maximum clip values.
+        :param channels_first: Set channels first or last.
+        :param apply_fit: True if applied during fitting/training.
+        :param apply_predict: True if applied during predicting.
+        """
+        super().__init__(is_fitted=True, apply_fit=apply_fit, apply_predict=apply_predict)
+        self.clip_values = clip_values
+        self.num_space = num_space
+        self.channels_first = channels_first
+        self._check_params()
+
+    def __call__(self, x: np.ndarray, y: Optional[np.ndarray] = None) -> Tuple[np.ndarray, Optional[np.ndarray]]:
+        """
+        Apply thermometer encoding to sample `x`. The new axis with the encoding is added as last dimension.
+
+        :param x: Sample to encode with shape `(batch_size, width, height, depth)`.
+        :param y: Labels of the sample `x`. This function does not affect them in any way.
+        :return: Encoded sample with shape `(batch_size, width, height, depth x num_space)`.
+        """
+        # First normalize the input to be in [0, 1]:
+        np.clip(x, self.clip_values[0], self.clip_values[1], out=x)
+        x = (x - self.clip_values[0]) / (self.clip_values[1] - self.clip_values[0])
+
+        # Now apply the encoding:
+        channel_index = 1 if self.channels_first else x.ndim - 1
+        result = np.apply_along_axis(self._perchannel, channel_index, x)
+        np.clip(result, 0, 1, out=result)
+        return result.astype(ART_NUMPY_DTYPE), y
+
+    def _perchannel(self, x: np.ndarray) -> np.ndarray:
+        """
+        Apply thermometer encoding to one channel.
+
+        :param x: Sample to encode with shape `(batch_size, width, height)`.
+        :return: Encoded sample with shape `(batch_size, width, height, num_space)`.
+        """
+        pos = np.zeros(shape=x.shape)
+        for i in range(1, self.num_space):
+            pos[x > float(i) / self.num_space] += 1
+
+        onehot_rep = to_categorical(pos.reshape(-1), self.num_space)
+
+        for i in range(self.num_space - 1):
+            onehot_rep[:, i] += np.sum(onehot_rep[:, i + 1 :], axis=1)
+
+        return onehot_rep.flatten()
+
+    def estimate_gradient(self, x: np.ndarray, grad: np.ndarray) -> np.ndarray:
+        """
+        Provide an estimate of the gradients of the defence for the backward pass. For thermometer encoding,
+        the gradient estimate is the one used in https://arxiv.org/abs/1802.00420, where the thermometer encoding
+        is replaced with a differentiable approximation:
+        `g(x_{i,j,c})_k = min(max(x_{i,j,c} - k / self.num_space, 0), 1)`.
+
+        :param x: Input data for which the gradient is estimated. First dimension is the batch size.
+        :param grad: Gradient value so far.
+        :return: The gradient (estimate) of the defence.
+        """
+        if self.channels_first:
+            x = np.transpose(x, (0,) + tuple(range(2, len(x.shape))) + (1,))
+            grad = np.transpose(grad, (0,) + tuple(range(2, len(x.shape))) + (1,))
+
+        thermometer_grad = np.zeros(x.shape[:-1] + (x.shape[-1] * self.num_space,))
+        mask = np.array([x > k / self.num_space for k in range(self.num_space)])
+        mask = np.moveaxis(mask, 0, -1)
+        mask = mask.reshape(thermometer_grad.shape)
+        thermometer_grad[mask] = 1
+
+        grad = grad * thermometer_grad
+        grad = np.reshape(grad, grad.shape[:-1] + (grad.shape[-1] // self.num_space, self.num_space))
+        grad = np.sum(grad, -1)
+
+        if self.channels_first:
+            x = np.transpose(x, (0,) + (len(x.shape) - 1,) + tuple(range(1, len(x.shape) - 1)))
+            grad = np.transpose(grad, (0,) + (len(x.shape) - 1,) + tuple(range(1, len(x.shape) - 1)))
+
+        return grad / (self.clip_values[1] - self.clip_values[0])
+
+    def _check_params(self) -> None:
+        if not isinstance(self.num_space, (int, np.int)) or self.num_space <= 0:
+            logger.error("Number of evenly spaced levels must be a positive integer.")
+            raise ValueError("Number of evenly spaced levels must be a positive integer.")
+
+        if len(self.clip_values) != 2:
+            raise ValueError("`clip_values` should be a tuple of 2 floats containing the allowed data range.")
+
+        if self.clip_values[0] >= self.clip_values[1]:
+            raise ValueError("first entry of `clip_values` should be strictly smaller than the second one.")
diff --git a/adversarial-robustness-toolbox/art/defences/preprocessor/variance_minimization.py b/adversarial-robustness-toolbox/art/defences/preprocessor/variance_minimization.py
new file mode 100644
index 0000000..8f75389
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/defences/preprocessor/variance_minimization.py
@@ -0,0 +1,230 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements the total variance minimization defence `TotalVarMin`.
+
+| Paper link: https://openreview.net/forum?id=SyJ7ClWCb
+
+| Please keep in mind the limitations of defences. For more information on the limitations of this defence,
+    see https://arxiv.org/abs/1802.00420 . For details on how to evaluate classifier security in general, see
+    https://arxiv.org/abs/1902.06705
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+from typing import Optional, Tuple, TYPE_CHECKING
+
+import numpy as np
+from scipy.optimize import minimize
+from tqdm.auto import tqdm
+
+from art.config import ART_NUMPY_DTYPE
+from art.defences.preprocessor.preprocessor import Preprocessor
+
+if TYPE_CHECKING:
+    from art.utils import CLIP_VALUES_TYPE
+
+logger = logging.getLogger(__name__)
+
+
+class TotalVarMin(Preprocessor):
+    """
+    Implement the total variance minimization defence approach.
+
+    | Paper link: https://openreview.net/forum?id=SyJ7ClWCb
+
+    | Please keep in mind the limitations of defences. For more information on the limitations of this
+        defence, see https://arxiv.org/abs/1802.00420 . For details on how to evaluate classifier security in general,
+        see https://arxiv.org/abs/1902.06705
+    """
+
+    params = ["prob", "norm", "lamb", "solver", "max_iter", "clip_values", "verbose"]
+
+    def __init__(
+        self,
+        prob: float = 0.3,
+        norm: int = 2,
+        lamb: float = 0.5,
+        solver: str = "L-BFGS-B",
+        max_iter: int = 10,
+        clip_values: Optional["CLIP_VALUES_TYPE"] = None,
+        apply_fit: bool = False,
+        apply_predict: bool = True,
+        verbose: bool = False,
+    ):
+        """
+        Create an instance of total variance minimization.
+
+        :param prob: Probability of the Bernoulli distribution.
+        :param norm: The norm (positive integer).
+        :param lamb: The lambda parameter in the objective function.
+        :param solver: Current support: `L-BFGS-B`, `CG`, `Newton-CG`.
+        :param max_iter: Maximum number of iterations when performing optimization.
+        :param clip_values: Tuple of the form `(min, max)` representing the minimum and maximum values allowed
+               for features.
+        :param apply_fit: True if applied during fitting/training.
+        :param apply_predict: True if applied during predicting.
+        :param verbose: Show progress bars.
+        """
+        super().__init__(is_fitted=True, apply_fit=apply_fit, apply_predict=apply_predict)
+        self.prob = prob
+        self.norm = norm
+        self.lamb = lamb
+        self.solver = solver
+        self.max_iter = max_iter
+        self.clip_values = clip_values
+        self.verbose = verbose
+        self._check_params()
+
+    def __call__(self, x: np.ndarray, y: Optional[np.ndarray] = None) -> Tuple[np.ndarray, Optional[np.ndarray]]:
+        """
+        Apply total variance minimization to sample `x`.
+
+        :param x: Sample to compress with shape `(batch_size, width, height, depth)`.
+        :param y: Labels of the sample `x`. This function does not affect them in any way.
+        :return: Similar samples.
+        """
+        if len(x.shape) == 2:
+            raise ValueError(
+                "Feature vectors detected. Variance minimization can only be applied to data with spatial dimensions."
+            )
+        x_preproc = x.copy()
+
+        # Minimize one input at a time
+        for i, x_i in enumerate(tqdm(x_preproc, desc="Variance minimization", disable=not self.verbose)):
+            mask = (np.random.rand(*x_i.shape) < self.prob).astype("int")
+            x_preproc[i] = self._minimize(x_i, mask)
+
+        if self.clip_values is not None:
+            np.clip(x_preproc, self.clip_values[0], self.clip_values[1], out=x_preproc)
+
+        return x_preproc.astype(ART_NUMPY_DTYPE), y
+
+    def _minimize(self, x: np.ndarray, mask: np.ndarray) -> np.ndarray:
+        """
+        Minimize the total variance objective function.
+
+        :param x: Original image.
+        :param mask: A matrix that decides which points are kept.
+        :return: A new image.
+        """
+        z_min = x.copy()
+
+        for i in range(x.shape[2]):
+            res = minimize(
+                self._loss_func,
+                z_min[:, :, i].flatten(),
+                (x[:, :, i], mask[:, :, i], self.norm, self.lamb),
+                method=self.solver,
+                jac=self._deri_loss_func,
+                options={"maxiter": self.max_iter},
+            )
+            z_min[:, :, i] = np.reshape(res.x, z_min[:, :, i].shape)
+
+        return z_min
+
+    @staticmethod
+    def _loss_func(z_init: np.ndarray, x: np.ndarray, mask: np.ndarray, norm: int, lamb: float) -> float:
+        """
+        Loss function to be minimized.
+
+        :param z_init: Initial guess.
+        :param x: Original image.
+        :param mask: A matrix that decides which points are kept.
+        :param norm: The norm (positive integer).
+        :param lamb: The lambda parameter in the objective function.
+        :return: Loss value.
+        """
+        res = np.sqrt(np.power(z_init - x.flatten(), 2).dot(mask.flatten()))
+        z_init = np.reshape(z_init, x.shape)
+        res += lamb * np.linalg.norm(z_init[1:, :] - z_init[:-1, :], norm, axis=1).sum()
+        res += lamb * np.linalg.norm(z_init[:, 1:] - z_init[:, :-1], norm, axis=0).sum()
+
+        return res
+
+    @staticmethod
+    def _deri_loss_func(z_init: np.ndarray, x: np.ndarray, mask: np.ndarray, norm: int, lamb: float) -> float:
+        """
+        Derivative of loss function to be minimized.
+
+        :param z_init: Initial guess.
+        :param x: Original image.
+        :param mask: A matrix that decides which points are kept.
+        :param norm: The norm (positive integer).
+        :param lamb: The lambda parameter in the objective function.
+        :return: Derivative value.
+        """
+        # First compute the derivative of the first component of the loss function
+        nor1 = np.sqrt(np.power(z_init - x.flatten(), 2).dot(mask.flatten()))
+        if nor1 < 1e-6:
+            nor1 = 1e-6
+        der1 = ((z_init - x.flatten()) * mask.flatten()) / (nor1 * 1.0)
+
+        # Then compute the derivative of the second component of the loss function
+        z_init = np.reshape(z_init, x.shape)
+
+        if norm == 1:
+            z_d1 = np.sign(z_init[1:, :] - z_init[:-1, :])
+            z_d2 = np.sign(z_init[:, 1:] - z_init[:, :-1])
+        else:
+            z_d1_norm = np.power(np.linalg.norm(z_init[1:, :] - z_init[:-1, :], norm, axis=1), norm - 1)
+            z_d2_norm = np.power(np.linalg.norm(z_init[:, 1:] - z_init[:, :-1], norm, axis=0), norm - 1)
+            z_d1_norm[z_d1_norm < 1e-6] = 1e-6
+            z_d2_norm[z_d2_norm < 1e-6] = 1e-6
+            z_d1_norm = np.repeat(z_d1_norm[:, np.newaxis], z_init.shape[1], axis=1)
+            z_d2_norm = np.repeat(z_d2_norm[np.newaxis, :], z_init.shape[0], axis=0)
+            z_d1 = norm * np.power(z_init[1:, :] - z_init[:-1, :], norm - 1) / z_d1_norm
+            z_d2 = norm * np.power(z_init[:, 1:] - z_init[:, :-1], norm - 1) / z_d2_norm
+
+        der2 = np.zeros(z_init.shape)
+        der2[:-1, :] -= z_d1
+        der2[1:, :] += z_d1
+        der2[:, :-1] -= z_d2
+        der2[:, 1:] += z_d2
+        der2 = lamb * der2.flatten()
+
+        # Total derivative
+        return der1 + der2
+
+    def _check_params(self) -> None:
+        if not isinstance(self.prob, (float, int)) or self.prob < 0.0 or self.prob > 1.0:
+            logger.error("Probability must be between 0 and 1.")
+            raise ValueError("Probability must be between 0 and 1.")
+
+        if not isinstance(self.norm, (int, np.int)) or self.norm <= 0:
+            logger.error("Norm must be a positive integer.")
+            raise ValueError("Norm must be a positive integer.")
+
+        if not (self.solver == "L-BFGS-B" or self.solver == "CG" or self.solver == "Newton-CG"):
+            logger.error("Current support only L-BFGS-B, CG, Newton-CG.")
+            raise ValueError("Current support only L-BFGS-B, CG, Newton-CG.")
+
+        if not isinstance(self.max_iter, (int, np.int)) or self.max_iter <= 0:
+            logger.error("Number of iterations must be a positive integer.")
+            raise ValueError("Number of iterations must be a positive integer.")
+
+        if self.clip_values is not None:
+
+            if len(self.clip_values) != 2:
+                raise ValueError("`clip_values` should be a tuple of 2 floats containing the allowed data range.")
+
+            if np.array(self.clip_values[0] >= self.clip_values[1]).any():
+                raise ValueError("Invalid `clip_values`: min >= max.")
+
+        if not isinstance(self.verbose, bool):
+            raise ValueError("The argument `verbose` has to be of type bool.")
diff --git a/adversarial-robustness-toolbox/art/defences/preprocessor/video_compression.py b/adversarial-robustness-toolbox/art/defences/preprocessor/video_compression.py
new file mode 100644
index 0000000..eef27fe
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/defences/preprocessor/video_compression.py
@@ -0,0 +1,136 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements a wrapper for video compression defence with FFmpeg.
+
+| Please keep in mind the limitations of defences. For details on how to evaluate classifier security in general,
+    see https://arxiv.org/abs/1902.06705.
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import os
+from tempfile import TemporaryDirectory
+from typing import Optional, Tuple
+
+import numpy as np
+from tqdm.auto import tqdm
+
+from art import config
+from art.defences.preprocessor.preprocessor import Preprocessor
+
+logger = logging.getLogger(__name__)
+
+
+class VideoCompression(Preprocessor):
+    """
+    Implement FFmpeg wrapper for video compression defence based on H.264/MPEG-4 AVC.
+
+    Video compression uses H.264 video encoding. The video quality is controlled with the constant rate factor
+    parameter. More information on the constant rate factor: https://trac.ffmpeg.org/wiki/Encode/H.264.
+    """
+
+    params = ["video_format", "constant_rate_factor", "channels_first", "verbose"]
+
+    def __init__(
+        self,
+        *,
+        video_format: str,
+        constant_rate_factor: int = 28,
+        channels_first: bool = False,
+        apply_fit: bool = False,
+        apply_predict: bool = True,
+        verbose: bool = False,
+    ):
+        """
+        Create an instance of VideoCompression.
+
+        :param video_format: Specify one of supported video file extensions, e.g. `avi`, `mp4` or `mkv`.
+        :param constant_rate_factor: Specify constant rate factor (range 0 to 51, where 0 is lossless).
+        :param channels_first: Set channels first or last.
+        :param apply_fit: True if applied during fitting/training.
+        :param apply_predict: True if applied during predicting.
+        :param verbose: Show progress bars.
+        """
+        super().__init__(is_fitted=True, apply_fit=apply_fit, apply_predict=apply_predict)
+        self.video_format = video_format
+        self.constant_rate_factor = constant_rate_factor
+        self.channels_first = channels_first
+        self.verbose = verbose
+        self._check_params()
+
+    def __call__(self, x: np.ndarray, y: Optional[np.ndarray] = None) -> Tuple[np.ndarray, Optional[np.ndarray]]:
+        """
+        Apply video compression to sample `x`.
+
+        :param x: Sample to compress of shape NCFHW or NFHWC. `x` values are expected to be in the data range [0, 255].
+        :param y: Labels of the sample `x`. This function does not affect them in any way.
+        :return: Compressed sample.
+        """
+
+        def compress_video(x: np.ndarray, video_format: str, constant_rate_factor: int, dir_: str = ""):
+            """
+            Apply video compression to video input of shape (frames, height, width, channel).
+            """
+            import ffmpeg
+
+            video_path = os.path.join(dir_, f"tmp_video.{video_format}")
+            _, height, width, _ = x.shape
+
+            # numpy to local video file
+            process = (
+                ffmpeg.input("pipe:", format="rawvideo", pix_fmt="rgb24", s=f"{width}x{height}")
+                .output(video_path, pix_fmt="yuv420p", vcodec="libx264", crf=constant_rate_factor)
+                .overwrite_output()
+                .run_async(pipe_stdin=True, quiet=True)
+            )
+            process.stdin.write(x.flatten().astype(np.uint8).tobytes())
+            process.stdin.close()
+            process.wait()
+
+            # local video file to numpy
+            stdout, _ = (
+                ffmpeg.input(video_path)
+                .output("pipe:", format="rawvideo", pix_fmt="rgb24")
+                .run(capture_stdout=True, quiet=True)
+            )
+            return np.frombuffer(stdout, np.uint8).reshape(x.shape)
+
+        if x.ndim != 5:
+            raise ValueError("Video compression can only be applied to spatio-temporal data.")
+
+        if self.channels_first:
+            x = np.transpose(x, (0, 2, 3, 4, 1))
+
+        # apply video compression per video item
+        x_compressed = x.copy()
+        with TemporaryDirectory(dir=config.ART_DATA_PATH) as tmp_dir:
+            for i, x_i in enumerate(tqdm(x, desc="Video compression", disable=not self.verbose)):
+                x_compressed[i] = compress_video(x_i, self.video_format, self.constant_rate_factor, dir_=tmp_dir)
+
+        if self.channels_first:
+            x_compressed = np.transpose(x_compressed, (0, 4, 1, 2, 3))
+
+        return x_compressed, y
+
+    def _check_params(self) -> None:
+        if not (isinstance(self.constant_rate_factor, (int, np.int)) and 0 <= self.constant_rate_factor < 52):
+            raise ValueError("Constant rate factor must be an integer in the range [0, 51].")
+
+        if not isinstance(self.verbose, bool):
+            raise ValueError("The argument `verbose` has to be of type bool.")
diff --git a/adversarial-robustness-toolbox/art/defences/trainer/__init__.py b/adversarial-robustness-toolbox/art/defences/trainer/__init__.py
new file mode 100644
index 0000000..a24722a
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/defences/trainer/__init__.py
@@ -0,0 +1,8 @@
+"""
+Module implementing train-based defences against adversarial attacks.
+"""
+from art.defences.trainer.trainer import Trainer
+from art.defences.trainer.adversarial_trainer import AdversarialTrainer
+from art.defences.trainer.adversarial_trainer_madry_pgd import AdversarialTrainerMadryPGD
+from art.defences.trainer.adversarial_trainer_fbf import AdversarialTrainerFBF
+from art.defences.trainer.adversarial_trainer_fbf_pytorch import AdversarialTrainerFBFPyTorch
diff --git a/adversarial-robustness-toolbox/art/defences/trainer/adversarial_trainer.py b/adversarial-robustness-toolbox/art/defences/trainer/adversarial_trainer.py
new file mode 100644
index 0000000..0a8db66
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/defences/trainer/adversarial_trainer.py
@@ -0,0 +1,252 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements adversarial training based on a model and one or multiple attack methods. It incorporates
+original adversarial training, ensemble adversarial training, training on all adversarial data and other common setups.
+If multiple attacks are specified, they are rotated for each batch. If the specified attacks have as target a different
+model, then the attack is transferred. The `ratio` determines how many of the clean samples in each batch are replaced
+with their adversarial counterpart.
+
+.. warning:: Both successful and unsuccessful adversarial samples are used for training. In the case of
+              unbounded attacks (e.g., DeepFool), this can result in invalid (very noisy) samples being included.
+
+| Paper link: https://arxiv.org/abs/1705.07204
+
+| Please keep in mind the limitations of defences. While adversarial training is widely regarded as a promising,
+    principled approach to making classifiers more robust (see https://arxiv.org/abs/1802.00420), very careful
+    evaluations are required to assess its effectiveness case by case (see https://arxiv.org/abs/1902.06705).
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+from typing import List, Optional, Union, TYPE_CHECKING
+
+import numpy as np
+from tqdm.auto import trange, tqdm
+
+from art.defences.trainer.trainer import Trainer
+
+if TYPE_CHECKING:
+    from art.utils import CLASSIFIER_LOSS_GRADIENTS_TYPE
+    from art.attacks.attack import EvasionAttack
+    from art.data_generators import DataGenerator
+
+logger = logging.getLogger(__name__)
+
+
+class AdversarialTrainer(Trainer):
+    """
+    Class performing adversarial training based on a model architecture and one or multiple attack methods.
+
+    Incorporates original adversarial training, ensemble adversarial training (https://arxiv.org/abs/1705.07204),
+    training on all adversarial data and other common setups. If multiple attacks are specified, they are rotated
+    for each batch. If the specified attacks have as target a different model, then the attack is transferred. The
+    `ratio` determines how many of the clean samples in each batch are replaced with their adversarial counterpart.
+
+     .. warning:: Both successful and unsuccessful adversarial samples are used for training. In the case of
+                  unbounded attacks (e.g., DeepFool), this can result in invalid (very noisy) samples being included.
+
+    | Paper link: https://arxiv.org/abs/1705.07204
+
+    | Please keep in mind the limitations of defences. While adversarial training is widely regarded as a promising,
+        principled approach to making classifiers more robust (see https://arxiv.org/abs/1802.00420), very careful
+        evaluations are required to assess its effectiveness case by case (see https://arxiv.org/abs/1902.06705).
+    """
+
+    def __init__(
+        self,
+        classifier: "CLASSIFIER_LOSS_GRADIENTS_TYPE",
+        attacks: Union["EvasionAttack", List["EvasionAttack"]],
+        ratio: float = 0.5,
+    ) -> None:
+        """
+        Create an :class:`.AdversarialTrainer` instance.
+
+        :param classifier: Model to train adversarially.
+        :param attacks: attacks to use for data augmentation in adversarial training
+        :param ratio: The proportion of samples in each batch to be replaced with their adversarial counterparts.
+                      Setting this value to 1 allows to train only on adversarial samples.
+        """
+        from art.attacks.attack import EvasionAttack
+
+        super().__init__(classifier=classifier)
+        if isinstance(attacks, EvasionAttack):
+            self.attacks = [attacks]
+        elif isinstance(attacks, list):
+            self.attacks = attacks
+        else:
+            raise ValueError("Only EvasionAttack instances or list of attacks supported.")
+
+        if ratio <= 0 or ratio > 1:
+            raise ValueError("The `ratio` of adversarial samples in each batch has to be between 0 and 1.")
+        self.ratio = ratio
+
+        self._precomputed_adv_samples: List[np.ndarray] = []
+        self.x_augmented: Optional[np.ndarray] = None
+        self.y_augmented: Optional[np.ndarray] = None
+
+    def fit_generator(self, generator: "DataGenerator", nb_epochs: int = 20, **kwargs) -> None:
+        """
+        Train a model adversarially using a data generator.
+        See class documentation for more information on the exact procedure.
+
+        :param generator: Data generator.
+        :param nb_epochs: Number of epochs to use for trainings.
+        :param kwargs: Dictionary of framework-specific arguments. These will be passed as such to the `fit` function of
+               the target classifier.
+        """
+        logger.info("Performing adversarial training using %i attacks.", len(self.attacks))
+        size = generator.size
+        batch_size = generator.batch_size
+        nb_batches = int(np.ceil(size / batch_size))  # type: ignore
+        ind = np.arange(generator.size)
+        attack_id = 0
+
+        # Precompute adversarial samples for transferred attacks
+        logged = False
+        self._precomputed_adv_samples = []
+        for attack in tqdm(self.attacks, desc="Precompute adversarial examples."):
+            attack.set_params(verbose=False)
+            if "targeted" in attack.attack_params and attack.targeted:  # type: ignore
+                raise NotImplementedError("Adversarial training with targeted attacks is currently not implemented")
+
+            if attack.estimator != self._classifier:
+                if not logged:
+                    logger.info("Precomputing transferred adversarial samples.")
+                    logged = True
+
+                next_precomputed_adv_samples: Optional[np.ndarray] = None
+                for batch_id in range(nb_batches):
+                    # Create batch data
+                    x_batch, y_batch = generator.get_batch()
+                    x_adv_batch = attack.generate(x_batch, y=y_batch)
+                    if next_precomputed_adv_samples is None:
+                        next_precomputed_adv_samples = x_adv_batch
+                    else:
+                        next_precomputed_adv_samples = np.append(next_precomputed_adv_samples, x_adv_batch, axis=0)
+                self._precomputed_adv_samples.append(next_precomputed_adv_samples)
+            else:
+                self._precomputed_adv_samples.append(None)
+
+        for _ in trange(nb_epochs, desc="Adversarial training epochs"):
+            # Shuffle the indices of precomputed examples
+            np.random.shuffle(ind)
+
+            for batch_id in range(nb_batches):
+                # Create batch data
+                x_batch, y_batch = generator.get_batch()
+                x_batch = x_batch.copy()
+
+                # Choose indices to replace with adversarial samples
+                nb_adv = int(np.ceil(self.ratio * x_batch.shape[0]))
+                attack = self.attacks[attack_id]
+                attack.set_params(verbose=False)
+                if self.ratio < 1:
+                    adv_ids = np.random.choice(x_batch.shape[0], size=nb_adv, replace=False)
+                else:
+                    adv_ids = list(range(x_batch.shape[0]))
+                    np.random.shuffle(adv_ids)
+
+                # If source and target models are the same, craft fresh adversarial samples
+                if attack.estimator == self._classifier:
+                    x_batch[adv_ids] = attack.generate(x_batch[adv_ids], y=y_batch[adv_ids])
+
+                # Otherwise, use precomputed adversarial samples
+                else:
+                    x_adv = self._precomputed_adv_samples[attack_id]
+                    x_adv = x_adv[ind[batch_id * batch_size : min((batch_id + 1) * batch_size, size)]][adv_ids]
+                    x_batch[adv_ids] = x_adv
+
+                # Fit batch
+                self._classifier.fit(x_batch, y_batch, nb_epochs=1, batch_size=x_batch.shape[0], verbose=0, **kwargs)
+                attack_id = (attack_id + 1) % len(self.attacks)
+
+    def fit(self, x: np.ndarray, y: np.ndarray, batch_size: int = 128, nb_epochs: int = 20, **kwargs) -> None:
+        """
+        Train a model adversarially. See class documentation for more information on the exact procedure.
+
+        :param x: Training set.
+        :param y: Labels for the training set.
+        :param batch_size: Size of batches.
+        :param nb_epochs: Number of epochs to use for trainings.
+        :param kwargs: Dictionary of framework-specific arguments. These will be passed as such to the `fit` function of
+               the target classifier.
+        """
+        logger.info("Performing adversarial training using %i attacks.", len(self.attacks))
+        nb_batches = int(np.ceil(len(x) / batch_size))
+        ind = np.arange(len(x))
+        attack_id = 0
+
+        # Precompute adversarial samples for transferred attacks
+        logged = False
+        self._precomputed_adv_samples = []
+        for attack in tqdm(self.attacks, desc="Precompute adv samples"):
+            attack.set_params(verbose=False)
+            if "targeted" in attack.attack_params and attack.targeted:  # type: ignore
+                raise NotImplementedError("Adversarial training with targeted attacks is currently not implemented")
+
+            if attack.estimator != self._classifier:
+                if not logged:
+                    logger.info("Precomputing transferred adversarial samples.")
+                    logged = True
+                self._precomputed_adv_samples.append(attack.generate(x, y=y))
+            else:
+                self._precomputed_adv_samples.append(None)
+
+        for _ in trange(nb_epochs, desc="Adversarial training epochs"):
+            # Shuffle the examples
+            np.random.shuffle(ind)
+
+            for batch_id in range(nb_batches):
+                # Create batch data
+                x_batch = x[ind[batch_id * batch_size : min((batch_id + 1) * batch_size, x.shape[0])]].copy()
+                y_batch = y[ind[batch_id * batch_size : min((batch_id + 1) * batch_size, x.shape[0])]]
+
+                # Choose indices to replace with adversarial samples
+                nb_adv = int(np.ceil(self.ratio * x_batch.shape[0]))
+                attack = self.attacks[attack_id]
+                attack.set_params(verbose=False)
+                if self.ratio < 1:
+                    adv_ids = np.random.choice(x_batch.shape[0], size=nb_adv, replace=False)
+                else:
+                    adv_ids = list(range(x_batch.shape[0]))
+                    np.random.shuffle(adv_ids)
+
+                # If source and target models are the same, craft fresh adversarial samples
+                if attack.estimator == self._classifier:
+                    x_batch[adv_ids] = attack.generate(x_batch[adv_ids], y=y_batch[adv_ids])
+
+                # Otherwise, use precomputed adversarial samples
+                else:
+                    x_adv = self._precomputed_adv_samples[attack_id]
+                    x_adv = x_adv[ind[batch_id * batch_size : min((batch_id + 1) * batch_size, x.shape[0])]][adv_ids]
+                    x_batch[adv_ids] = x_adv
+
+                # Fit batch
+                self._classifier.fit(x_batch, y_batch, nb_epochs=1, batch_size=x_batch.shape[0], verbose=0, **kwargs)
+                attack_id = (attack_id + 1) % len(self.attacks)
+
+    def predict(self, x: np.ndarray, **kwargs) -> np.ndarray:
+        """
+        Perform prediction using the adversarially trained classifier.
+
+        :param x: Input samples.
+        :param kwargs: Other parameters to be passed on to the `predict` function of the classifier.
+        :return: Predictions for test set.
+        """
+        return self._classifier.predict(x, **kwargs)
diff --git a/adversarial-robustness-toolbox/art/defences/trainer/adversarial_trainer_fbf.py b/adversarial-robustness-toolbox/art/defences/trainer/adversarial_trainer_fbf.py
new file mode 100644
index 0000000..e428955
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/defences/trainer/adversarial_trainer_fbf.py
@@ -0,0 +1,104 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements adversarial training with Fast is better than free protocol.
+
+| Paper link: https://openreview.net/forum?id=BJx040EFvH
+
+| It was noted that this protocol is sensitive to the use of techniques like data augmentation, gradient clipping,
+    and learning rate schedules. Consequently, framework specific implementations are being provided in ART.
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import abc
+from typing import Optional, Union, Tuple, TYPE_CHECKING
+
+import numpy as np
+
+from art.defences.trainer.trainer import Trainer
+
+if TYPE_CHECKING:
+    from art.utils import CLASSIFIER_LOSS_GRADIENTS_TYPE
+    from art.data_generators import DataGenerator
+
+
+class AdversarialTrainerFBF(Trainer, abc.ABC):
+    """
+    This is abstract class for different backend-specific implementations of Fast is Better than Free protocol
+    for adversarial training.
+
+    | Paper link: https://openreview.net/forum?id=BJx040EFvH
+    """
+
+    def __init__(
+        self, classifier: "CLASSIFIER_LOSS_GRADIENTS_TYPE", eps: Union[int, float] = 8,
+    ):
+        """
+        Create an :class:`.AdversarialTrainerFBF` instance.
+
+        :param classifier: Model to train adversarially.
+        :param eps: Maximum perturbation that the attacker can introduce.
+        """
+        self._eps = eps
+        super().__init__(classifier)
+
+    @abc.abstractmethod
+    def fit(
+        self,
+        x: np.ndarray,
+        y: np.ndarray,
+        validation_data: Optional[Tuple[np.ndarray, np.ndarray]] = None,
+        batch_size: int = 128,
+        nb_epochs: int = 20,
+        **kwargs
+    ):
+        """
+        Train a model adversarially with FBF. See class documentation for more information on the exact procedure.
+
+        :param x: Training set.
+        :param y: Labels for the training set.
+        :param validation_data: Tuple consisting of validation data, (x_val, y_val)
+        :param batch_size: Size of batches.
+        :param nb_epochs: Number of epochs to use for trainings.
+        :param kwargs: Dictionary of framework-specific arguments. These will be passed as such to the `fit` function of
+               the target classifier.
+        """
+        raise NotImplementedError
+
+    @abc.abstractmethod
+    def fit_generator(self, generator: "DataGenerator", nb_epochs: int = 20, **kwargs):
+        """
+        Train a model adversarially using a data generator.
+        See class documentation for more information on the exact procedure.
+
+        :param generator: Data generator.
+        :param nb_epochs: Number of epochs to use for trainings.
+        :param kwargs: Dictionary of framework-specific arguments. These will be passed as such to the `fit` function of
+               the target classifier.
+        """
+        raise NotImplementedError
+
+    def predict(self, x: np.ndarray, **kwargs) -> np.ndarray:
+        """
+        Perform prediction using the adversarially trained classifier.
+
+        :param x: Input samples.
+        :param kwargs: Other parameters to be passed on to the `predict` function of the classifier.
+        :return: Predictions for test set.
+        """
+        return self._classifier.predict(x, **kwargs)
diff --git a/adversarial-robustness-toolbox/art/defences/trainer/adversarial_trainer_fbf_pytorch.py b/adversarial-robustness-toolbox/art/defences/trainer/adversarial_trainer_fbf_pytorch.py
new file mode 100644
index 0000000..68aa7ea
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/defences/trainer/adversarial_trainer_fbf_pytorch.py
@@ -0,0 +1,254 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This is a PyTorch implementation of the Fast is better than free protocol.
+
+| Paper link: https://openreview.net/forum?id=BJx040EFvH
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import time
+from typing import Optional, Tuple, Union, TYPE_CHECKING
+
+import numpy as np
+from tqdm.auto import trange
+
+from art.config import ART_NUMPY_DTYPE
+from art.defences.trainer.adversarial_trainer_fbf import AdversarialTrainerFBF
+from art.utils import random_sphere
+
+if TYPE_CHECKING:
+    from art.data_generators import DataGenerator
+    from art.estimators.classification.pytorch import PyTorchClassifier
+
+logger = logging.getLogger(__name__)
+
+
+class AdversarialTrainerFBFPyTorch(AdversarialTrainerFBF):
+    """
+    Class performing adversarial training following Fast is Better Than Free protocol.
+
+    | Paper link: https://openreview.net/forum?id=BJx040EFvH
+
+    | The effectiveness of this protocol is found to be sensitive to the use of techniques like
+        data augmentation, gradient clipping and learning rate schedules. Optionally, the use of
+        mixed precision arithmetic operation via apex library can significantly reduce the training
+        time making this one of the fastest adversarial training protocol.
+    """
+
+    def __init__(self, classifier: "PyTorchClassifier", eps: Union[int, float] = 8, use_amp: bool = False):
+        """
+        Create an :class:`.AdversarialTrainerFBFPyTorch` instance.
+
+        :param classifier: Model to train adversarially.
+        :param eps: Maximum perturbation that the attacker can introduce.
+        :param use_amp: Boolean that decides if apex should be used for mixed precision arithmetic during training
+        """
+        super().__init__(classifier, eps)
+        self._classifier: "PyTorchClassifier"
+        self._use_amp = use_amp
+
+    def fit(
+        self,
+        x: np.ndarray,
+        y: np.ndarray,
+        validation_data: Optional[Tuple[np.ndarray, np.ndarray]] = None,
+        batch_size: int = 128,
+        nb_epochs: int = 20,
+        **kwargs
+    ):
+        """
+        Train a model adversarially with FBF protocol.
+        See class documentation for more information on the exact procedure.
+
+        :param x: Training set.
+        :param y: Labels for the training set.
+        :param validation_data: Tuple consisting of validation data, (x_val, y_val)
+        :param batch_size: Size of batches.
+        :param nb_epochs: Number of epochs to use for trainings.
+        :param kwargs: Dictionary of framework-specific arguments. These will be passed as such to the `fit` function of
+                                  the target classifier.
+        """
+        logger.info("Performing adversarial training with Fast is better than Free protocol")
+
+        nb_batches = int(np.ceil(len(x) / batch_size))
+        ind = np.arange(len(x))
+
+        def lr_schedule(t):
+            return np.interp([t], [0, nb_epochs * 2 // 5, nb_epochs], [0, 0.21, 0])[0]
+
+        logger.info("Adversarial Training FBF")
+
+        for i_epoch in trange(nb_epochs, desc="Adversarial Training FBF - Epochs"):
+            # Shuffle the examples
+            np.random.shuffle(ind)
+            start_time = time.time()
+            train_loss = 0.0
+            train_acc = 0.0
+            train_n = 0.0
+
+            for batch_id in range(nb_batches):
+                l_r = lr_schedule(i_epoch + (batch_id + 1) / nb_batches)
+
+                # Create batch data
+                x_batch = x[ind[batch_id * batch_size : min((batch_id + 1) * batch_size, x.shape[0])]].copy()
+                y_batch = y[ind[batch_id * batch_size : min((batch_id + 1) * batch_size, x.shape[0])]]
+
+                _train_loss, _train_acc, _train_n = self._batch_process(x_batch, y_batch, l_r)
+
+                train_loss += _train_loss
+                train_acc += _train_acc
+                train_n += _train_n
+
+            train_time = time.time()
+
+            # compute accuracy
+            if validation_data is not None:
+                (x_test, y_test) = validation_data
+                output = np.argmax(self.predict(x_test), axis=1)
+                nb_correct_pred = np.sum(output == np.argmax(y_test, axis=1))
+                logger.info(
+                    "epoch {}\ttime(s) {:.1f}\tl_r {:.4f}\tloss {:.4f}\tacc(tr) {:.4f}\tacc(val) {:.4f}".format(
+                        i_epoch,
+                        train_time - start_time,
+                        l_r,
+                        train_loss / train_n,
+                        train_acc / train_n,
+                        nb_correct_pred / x_test.shape[0],
+                    )
+                )
+            else:
+                logger.info(
+                    "epoch {}\t time(s) {:.1f}\t l_r {:.4f}\t loss {:.4f}\t acc {:.4f}".format(
+                        i_epoch, train_time - start_time, l_r, train_loss / train_n, train_acc / train_n
+                    )
+                )
+
+    def fit_generator(self, generator: "DataGenerator", nb_epochs: int = 20, **kwargs):
+        """
+        Train a model adversarially with FBF protocol using a data generator.
+        See class documentation for more information on the exact procedure.
+
+        :param generator: Data generator.
+        :param nb_epochs: Number of epochs to use for trainings.
+        :param kwargs: Dictionary of framework-specific arguments. These will be passed as such to the `fit` function of
+                                  the target classifier.
+        """
+        logger.info("Performing adversarial training with Fast is better than Free protocol")
+        size = generator.size
+        batch_size = generator.batch_size
+        if size is not None:
+            nb_batches = int(np.ceil(size / batch_size))
+        else:
+            raise ValueError("Size is None.")
+
+        def lr_schedule(t):
+            return np.interp([t], [0, nb_epochs * 2 // 5, nb_epochs], [0, 0.21, 0])[0]
+
+        logger.info("Adversarial Training FBF")
+
+        for i_epoch in trange(nb_epochs, desc="Adversarial Training FBF - Epochs"):
+            start_time = time.time()
+            train_loss = 0.0
+            train_acc = 0.0
+            train_n = 0.0
+
+            for batch_id in range(nb_batches):
+                l_r = lr_schedule(i_epoch + (batch_id + 1) / nb_batches)
+
+                # Create batch data
+                x_batch, y_batch = generator.get_batch()
+                x_batch = x_batch.copy()
+
+                _train_loss, _train_acc, _train_n = self._batch_process(x_batch, y_batch, l_r)
+
+                train_loss += _train_loss
+                train_acc += _train_acc
+                train_n += _train_n
+
+            train_time = time.time()
+            logger.info(
+                "epoch {}\t time(s) {:.1f}\t l_r {:.4f}\t loss {:.4f}\t acc {:.4f}".format(
+                    i_epoch, train_time - start_time, l_r, train_loss / train_n, train_acc / train_n
+                )
+            )
+
+    def _batch_process(self, x_batch: np.ndarray, y_batch: np.ndarray, l_r: float) -> Tuple[float, float, float]:
+        """
+        Perform the operations of FBF for a batch of data.
+        See class documentation for more information on the exact procedure.
+
+        :param x_batch: batch of x.
+        :param y_batch: batch of y.
+        :param l_r: learning rate for the optimisation step.
+        """
+        import torch
+
+        if self._classifier._optimizer is None:
+            raise ValueError("Optimizer of classifier is currently None, but is required for adversarial training.")
+
+        n = x_batch.shape[0]
+        m = np.prod(x_batch.shape[1:]).item()
+        delta = random_sphere(n, m, self._eps, np.inf).reshape(x_batch.shape).astype(ART_NUMPY_DTYPE)
+        delta_grad = self._classifier.loss_gradient(x_batch + delta, y_batch)
+        delta = np.clip(delta + 1.25 * self._eps * np.sign(delta_grad), -self._eps, +self._eps)
+        if self._classifier.clip_values is not None:
+            x_batch_pert = np.clip(x_batch + delta, self._classifier.clip_values[0], self._classifier.clip_values[1])
+        else:
+            x_batch_pert = x_batch + delta
+
+        # Apply preprocessing
+        x_preprocessed, y_preprocessed = self._classifier._apply_preprocessing(x_batch_pert, y_batch, fit=True)
+
+        # Check label shape
+        if self._classifier._reduce_labels:
+            y_preprocessed = np.argmax(y_preprocessed, axis=1)
+
+        i_batch = torch.from_numpy(x_preprocessed).to(self._classifier._device)
+        o_batch = torch.from_numpy(y_preprocessed).to(self._classifier._device)
+
+        # Zero the parameter gradients
+        self._classifier._optimizer.zero_grad()
+
+        # Perform prediction
+        model_outputs = self._classifier._model(i_batch)
+
+        # Form the loss function
+        loss = self._classifier._loss(model_outputs[-1], o_batch)
+
+        self._classifier._optimizer.param_groups[0].update(lr=l_r)
+
+        # Actual training
+        if self._use_amp:
+            import apex.amp as amp
+
+            with amp.scale_loss(loss, self._classifier._optimizer) as scaled_loss:
+                scaled_loss.backward()
+        else:
+            loss.backward()
+
+        # clip the gradients
+        torch.nn.utils.clip_grad_norm_(self._classifier._model.parameters(), 0.5)
+        self._classifier._optimizer.step()
+
+        train_loss = loss.item() * o_batch.size(0)
+        train_acc = (model_outputs[0].max(1)[1] == o_batch).sum().item()
+        train_n = o_batch.size(0)
+
+        return train_loss, train_acc, train_n
diff --git a/adversarial-robustness-toolbox/art/defences/trainer/adversarial_trainer_madry_pgd.py b/adversarial-robustness-toolbox/art/defences/trainer/adversarial_trainer_madry_pgd.py
new file mode 100644
index 0000000..32dd3e3
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/defences/trainer/adversarial_trainer_madry_pgd.py
@@ -0,0 +1,103 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements adversarial training following Madry's Protocol.
+
+| Paper link: https://arxiv.org/abs/1706.06083
+
+| Please keep in mind the limitations of defences. While adversarial training is widely regarded as a promising,
+    principled approach to making classifiers more robust (see https://arxiv.org/abs/1802.00420), very careful
+    evaluations are required to assess its effectiveness case by case (see https://arxiv.org/abs/1902.06705).
+"""
+import logging
+from typing import Optional, Union, TYPE_CHECKING
+
+import numpy as np
+
+from art.defences.trainer.trainer import Trainer
+from art.defences.trainer.adversarial_trainer import AdversarialTrainer
+from art.attacks.evasion.projected_gradient_descent.projected_gradient_descent import ProjectedGradientDescent
+
+if TYPE_CHECKING:
+    from art.utils import CLASSIFIER_LOSS_GRADIENTS_TYPE
+
+
+logger = logging.getLogger(__name__)
+
+
+class AdversarialTrainerMadryPGD(Trainer):
+    """
+    Class performing adversarial training following Madry's Protocol.
+
+    | Paper link: https://arxiv.org/abs/1706.06083
+
+    | Please keep in mind the limitations of defences. While adversarial training is widely regarded as a promising,
+        principled approach to making classifiers more robust (see https://arxiv.org/abs/1802.00420), very careful
+        evaluations are required to assess its effectiveness case by case (see https://arxiv.org/abs/1902.06705).
+    """
+
+    def __init__(
+        self,
+        classifier: "CLASSIFIER_LOSS_GRADIENTS_TYPE",
+        nb_epochs: int = 391,
+        batch_size: int = 128,
+        eps: Union[int, float] = 8,
+        eps_step: Union[int, float] = 2,
+        max_iter: int = 7,
+        num_random_init: int = 1,
+    ) -> None:
+        """
+        Create an :class:`.AdversarialTrainerMadryPGD` instance.
+
+        Default values are for CIFAR-10 in pixel range 0-255.
+
+        :param classifier: Classifier to train adversarially.
+        :param nb_epochs: Number of training epochs.
+        :param batch_size: Size of the batch on which adversarial samples are generated.
+        :param eps: Maximum perturbation that the attacker can introduce.
+        :param eps_step: Attack step size (input variation) at each iteration.
+        :param max_iter: The maximum number of iterations.
+        :param num_random_init: Number of random initialisations within the epsilon ball. For num_random_init=0
+                                starting at the original input.
+        """
+        super().__init__(classifier=classifier)  # type: ignore
+        self.batch_size = batch_size
+        self.nb_epochs = nb_epochs
+
+        # Setting up adversary and perform adversarial training:
+        self.attack = ProjectedGradientDescent(
+            classifier, eps=eps, eps_step=eps_step, max_iter=max_iter, num_random_init=num_random_init,
+        )
+
+        self.trainer = AdversarialTrainer(classifier, self.attack, ratio=1.0)  # type: ignore
+
+    def fit(self, x: np.ndarray, y: np.ndarray, validation_data: Optional[np.ndarray] = None, **kwargs) -> None:
+        """
+        Train a model adversarially. See class documentation for more information on the exact procedure.
+
+        :param x: Training data.
+        :param y: Labels for the training data.
+        :param validation_data: Validation data.
+        :param kwargs: Dictionary of framework-specific arguments.
+        """
+        self.trainer.fit(
+            x, y, validation_data=validation_data, nb_epochs=self.nb_epochs, batch_size=self.batch_size, **kwargs
+        )
+
+    def get_classifier(self) -> "CLASSIFIER_LOSS_GRADIENTS_TYPE":
+        return self.trainer.get_classifier()
diff --git a/adversarial-robustness-toolbox/art/defences/trainer/trainer.py b/adversarial-robustness-toolbox/art/defences/trainer/trainer.py
new file mode 100644
index 0000000..bd0d856
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/defences/trainer/trainer.py
@@ -0,0 +1,62 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements the abstract base class for defences that adversarially train models.
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import abc
+from typing import TYPE_CHECKING
+
+import numpy as np
+
+if TYPE_CHECKING:
+    from art.utils import CLASSIFIER_LOSS_GRADIENTS_TYPE
+
+
+class Trainer(abc.ABC):
+    """
+    Abstract base class for training defences.
+    """
+
+    def __init__(self, classifier: "CLASSIFIER_LOSS_GRADIENTS_TYPE") -> None:
+        """
+        Create a adversarial training object
+        """
+        self._classifier = classifier
+
+    @abc.abstractmethod
+    def fit(  # lgtm [py/inheritance/incorrect-overridden-signature]
+        self, x: np.ndarray, y: np.ndarray, **kwargs
+    ) -> None:
+        """
+        Train the model.
+
+        :param x: Training data.
+        :param y: Labels for the training data.
+        :param kwargs: Other parameters.
+        """
+        raise NotImplementedError
+
+    def get_classifier(self) -> "CLASSIFIER_LOSS_GRADIENTS_TYPE":
+        """
+        Return the classifier trained via adversarial training.
+
+        :return: The classifier.
+        """
+        return self._classifier
diff --git a/adversarial-robustness-toolbox/art/defences/transformer/__init__.py b/adversarial-robustness-toolbox/art/defences/transformer/__init__.py
new file mode 100644
index 0000000..ddb4188
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/defences/transformer/__init__.py
@@ -0,0 +1,7 @@
+"""
+Module implementing transformer-based defences against adversarial attacks.
+"""
+from art.defences.transformer.transformer import Transformer
+
+from art.defences.transformer import evasion
+from art.defences.transformer import poisoning
diff --git a/adversarial-robustness-toolbox/art/defences/transformer/evasion/__init__.py b/adversarial-robustness-toolbox/art/defences/transformer/evasion/__init__.py
new file mode 100644
index 0000000..d2a6d92
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/defences/transformer/evasion/__init__.py
@@ -0,0 +1,4 @@
+"""
+Module implementing transformer-based defences against evasion attacks.
+"""
+from art.defences.transformer.evasion.defensive_distillation import DefensiveDistillation
diff --git a/adversarial-robustness-toolbox/art/defences/transformer/evasion/defensive_distillation.py b/adversarial-robustness-toolbox/art/defences/transformer/evasion/defensive_distillation.py
new file mode 100644
index 0000000..f87f7c0
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/defences/transformer/evasion/defensive_distillation.py
@@ -0,0 +1,104 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements the transforming defence mechanism of defensive distillation.
+
+| Paper link: https://arxiv.org/abs/1511.04508
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+from typing import Optional, TYPE_CHECKING
+
+import numpy as np
+
+from art.defences.transformer.transformer import Transformer
+from art.utils import is_probability
+
+if TYPE_CHECKING:
+    from art.utils import CLASSIFIER_TYPE
+
+logger = logging.getLogger(__name__)
+
+
+class DefensiveDistillation(Transformer):
+    """
+    Implement the defensive distillation mechanism.
+
+    | Paper link: https://arxiv.org/abs/1511.04508
+    """
+
+    params = ["batch_size", "nb_epochs"]
+
+    def __init__(self, classifier: "CLASSIFIER_TYPE", batch_size: int = 128, nb_epochs: int = 10) -> None:
+        """
+        Create an instance of the defensive distillation defence.
+
+        :param classifier: A trained classifier.
+        :param batch_size: Size of batches.
+        :param nb_epochs: Number of epochs to use for training.
+        """
+        super().__init__(classifier=classifier)
+        self._is_fitted = True
+        self.batch_size = batch_size
+        self.nb_epochs = nb_epochs
+        self._check_params()
+
+    def __call__(self, x: np.ndarray, transformed_classifier: "CLASSIFIER_TYPE") -> "CLASSIFIER_TYPE":
+        """
+        Perform the defensive distillation defence mechanism and return a robuster classifier.
+
+        :param x: Dataset for training the transformed classifier.
+        :param transformed_classifier: A classifier to be transformed for increased robustness. Note that, the
+            objective loss function used for fitting inside the input transformed_classifier must support soft labels,
+            i.e. probability labels.
+        :return: The transformed classifier.
+        """
+        # Check if the trained classifier produces probability outputs
+        preds = self.classifier.predict(x=x, batch_size=self.batch_size)
+        are_probability = [is_probability(y) for y in preds]
+        all_probability = np.sum(are_probability) == preds.shape[0]
+
+        if not all_probability:
+            raise ValueError("The input trained classifier do not produce probability outputs.")
+
+        # Check if the transformed classifier produces probability outputs
+        transformed_preds = transformed_classifier.predict(x=x, batch_size=self.batch_size)
+        are_probability = [is_probability(y) for y in transformed_preds]
+        all_probability = np.sum(are_probability) == transformed_preds.shape[0]
+
+        if not all_probability:
+            raise ValueError("The input transformed classifier do not produce probability outputs.")
+
+        # Train the transformed classifier with soft labels
+        transformed_classifier.fit(x=x, y=preds, batch_size=self.batch_size, nb_epochs=self.nb_epochs)
+
+        return transformed_classifier
+
+    def fit(self, x: np.ndarray, y: Optional[np.ndarray] = None, **kwargs) -> None:
+        """
+        No parameters to learn for this method; do nothing.
+        """
+        pass
+
+    def _check_params(self) -> None:
+        if not isinstance(self.batch_size, (int, np.int)) or self.batch_size <= 0:
+            raise ValueError("The size of batches must be a positive integer.")
+
+        if not isinstance(self.nb_epochs, (int, np.int)) or self.nb_epochs <= 0:
+            raise ValueError("The number of epochs must be a positive integer.")
diff --git a/adversarial-robustness-toolbox/art/defences/transformer/poisoning/__init__.py b/adversarial-robustness-toolbox/art/defences/transformer/poisoning/__init__.py
new file mode 100644
index 0000000..6657651
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/defences/transformer/poisoning/__init__.py
@@ -0,0 +1,5 @@
+"""
+Module implementing transformer-based defences against poisoning attacks.
+"""
+from art.defences.transformer.poisoning.neural_cleanse import NeuralCleanse
+from art.defences.transformer.poisoning.strip import STRIP
diff --git a/adversarial-robustness-toolbox/art/defences/transformer/poisoning/neural_cleanse.py b/adversarial-robustness-toolbox/art/defences/transformer/poisoning/neural_cleanse.py
new file mode 100644
index 0000000..e2bcec7
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/defences/transformer/poisoning/neural_cleanse.py
@@ -0,0 +1,137 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements Neural Cleanse (Wang et. al. 2019)
+
+| Paper link: http://people.cs.uchicago.edu/~ravenben/publications/abstracts/backdoor-sp19.html
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+from typing import Optional, TYPE_CHECKING, Union
+
+import numpy as np
+
+from art.defences.transformer.transformer import Transformer
+from art.estimators.poison_mitigation.neural_cleanse import KerasNeuralCleanse
+from art.estimators.classification.keras import KerasClassifier
+
+if TYPE_CHECKING:
+    from art.utils import CLASSIFIER_TYPE
+
+logger = logging.getLogger(__name__)
+
+
+class NeuralCleanse(Transformer):
+    """
+    Implementation of methods in Neural Cleanse: Identifying and Mitigating Backdoor Attacks in Neural Networks.
+    Wang et al. (2019).
+
+    | Paper link: https://people.cs.uchicago.edu/~ravenben/publications/pdf/backdoor-sp19.pdf
+    """
+
+    params = [
+        "steps",
+        "init_cost",
+        "norm",
+        "learning_rate",
+        "attack_success_threshold",
+        "patience",
+        "early_stop",
+        "early_stop_threshold",
+        "early_stop_patience",
+        "cost_multiplier",
+        "batch_size",
+    ]
+
+    def __init__(self, classifier: "CLASSIFIER_TYPE") -> None:
+        """
+        Create an instance of the neural cleanse defence.
+
+        :param classifier: A trained classifier.
+        """
+        super().__init__(classifier=classifier)
+        self._check_params()
+
+    def __call__(
+        self,
+        transformed_classifier: "CLASSIFIER_TYPE",
+        steps: int = 1000,
+        init_cost: float = 1e-3,
+        norm: Union[int, float] = 2,
+        learning_rate: float = 0.1,
+        attack_success_threshold: float = 0.99,
+        patience: int = 5,
+        early_stop: bool = True,
+        early_stop_threshold: float = 0.99,
+        early_stop_patience: int = 10,
+        cost_multiplier: float = 1.5,
+        batch_size: int = 32,
+    ) -> KerasNeuralCleanse:
+        """
+        Returns an new classifier with implementation of methods in Neural Cleanse: Identifying and Mitigating Backdoor
+        Attacks in Neural Networks. Wang et al. (2019).
+
+        Namely, the new classifier has a new method mitigate(). This can also affect the predict() function.
+
+        | Paper link: https://people.cs.uchicago.edu/~ravenben/publications/pdf/backdoor-sp19.pdf
+
+        :param transformed_classifier: An ART classifier
+        :param steps: The maximum number of steps to run the Neural Cleanse optimization
+        :param init_cost: The initial value for the cost tensor in the Neural Cleanse optimization
+        :param norm: The norm to use for the Neural Cleanse optimization, can be 1, 2, or np.inf
+        :param learning_rate: The learning rate for the Neural Cleanse optimization
+        :param attack_success_threshold: The threshold at which the generated backdoor is successful enough to stop the
+                                         Neural Cleanse optimization
+        :param patience: How long to wait for changing the cost multiplier in the Neural Cleanse optimization
+        :param early_stop: Whether or not to allow early stopping in the Neural Cleanse optimization
+        :param early_stop_threshold: How close values need to come to max value to start counting early stop
+        :param early_stop_patience: How long to wait to determine early stopping in the Neural Cleanse optimization
+        :param cost_multiplier: How much to change the cost in the Neural Cleanse optimization
+        :param batch_size: The batch size for optimizations in the Neural Cleanse optimization
+        """
+        import keras
+
+        if isinstance(transformed_classifier, KerasClassifier) and keras.__version__ == "2.2.4":
+            transformed_classifier = KerasNeuralCleanse(
+                model=transformed_classifier.model,
+                steps=steps,
+                init_cost=init_cost,
+                norm=norm,
+                learning_rate=learning_rate,
+                attack_success_threshold=attack_success_threshold,
+                patience=patience,
+                early_stop=early_stop,
+                early_stop_threshold=early_stop_threshold,
+                early_stop_patience=early_stop_patience,
+                cost_multiplier=cost_multiplier,
+                batch_size=batch_size,
+            )
+            return transformed_classifier
+
+        raise NotImplementedError("Only Keras classifiers (v2.2.4) are supported for this defence.")
+
+    def fit(self, x: np.ndarray, y: Optional[np.ndarray] = None, **kwargs) -> None:
+        """
+        No parameters to learn for this method; do nothing.
+        """
+        raise NotImplementedError
+
+    def _check_params(self) -> None:
+        if not isinstance(self.classifier, KerasClassifier):
+            raise NotImplementedError("Only Keras classifiers are supported for this defence.")
diff --git a/adversarial-robustness-toolbox/art/defences/transformer/poisoning/strip.py b/adversarial-robustness-toolbox/art/defences/transformer/poisoning/strip.py
new file mode 100644
index 0000000..416ae54
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/defences/transformer/poisoning/strip.py
@@ -0,0 +1,88 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements STRIP: A Defence Against Trojan Attacks on Deep Neural Networks.
+
+| Paper link: https://arxiv.org/abs/1902.06531
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+from typing import Optional, TypeVar, TYPE_CHECKING
+
+import numpy as np
+
+from art.defences.transformer.transformer import Transformer
+from art.estimators.poison_mitigation.strip import STRIPMixin
+
+if TYPE_CHECKING:
+    from art.utils import CLASSIFIER_TYPE
+
+    ClassifierWithStrip = TypeVar("ClassifierWithStrip", CLASSIFIER_TYPE, STRIPMixin)
+
+logger = logging.getLogger(__name__)
+
+
+class STRIP(Transformer):
+    """
+    Implementation of STRIP: A Defence Against Trojan Attacks on Deep Neural Networks (Gao et. al. 2020)
+
+    | Paper link: https://arxiv.org/abs/1902.06531
+    """
+
+    params = [
+        "num_samples",
+        "false_acceptance_rate",
+    ]
+
+    def __init__(self, classifier: "CLASSIFIER_TYPE"):
+        """
+        Create an instance of the neural cleanse defence.
+
+        :param classifier: A trained classifier.
+        """
+        super().__init__(classifier=classifier)
+        self._check_params()
+
+    def __call__(self, num_samples: int = 20, false_acceptance_rate: float = 0.01,) -> "ClassifierWithStrip":
+        """
+        Create a STRIP defense
+
+        :param num_samples: The number of samples to use to test entropy at inference time
+        :param false_acceptance_rate: The percentage of acceptable false acceptance
+        """
+        base_cls = self.classifier.__class__
+        base_cls_name = self.classifier.__class__.__name__
+        self.classifier.__class__ = type(
+            base_cls_name,
+            (STRIPMixin, base_cls),
+            dict(
+                num_samples=num_samples, false_acceptance_rate=false_acceptance_rate, predict_fn=self.classifier.predict
+            ),
+        )
+
+        return self.classifier
+
+    def fit(self, x: np.ndarray, y: Optional[np.ndarray] = None, **kwargs) -> None:
+        """
+        No parameters to learn for this method; do nothing.
+        """
+        raise NotImplementedError
+
+    def _check_params(self) -> None:
+        pass
diff --git a/adversarial-robustness-toolbox/art/defences/transformer/transformer.py b/adversarial-robustness-toolbox/art/defences/transformer/transformer.py
new file mode 100644
index 0000000..0a8fb75
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/defences/transformer/transformer.py
@@ -0,0 +1,97 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements the abstract base class for defences that transform a classifier into a more robust classifier.
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import abc
+from typing import List, Optional, TYPE_CHECKING
+
+import numpy as np
+
+if TYPE_CHECKING:
+    from art.utils import CLASSIFIER_TYPE
+
+
+class Transformer(abc.ABC):
+    """
+    Abstract base class for transformation defences.
+    """
+
+    params: List[str] = list()
+
+    def __init__(self, classifier: "CLASSIFIER_TYPE") -> None:
+        """
+        Create a transformation object.
+
+        :param classifier: A trained classifier.
+        """
+        self.classifier = classifier
+        self._is_fitted = False
+
+    @property
+    def is_fitted(self) -> bool:
+        """
+        Return the state of the transformation object.
+
+        :return: `True` if the transformation model has been fitted (if this applies).
+        """
+        return self._is_fitted
+
+    def get_classifier(self) -> "CLASSIFIER_TYPE":
+        """
+        Get the internal classifier.
+
+        :return: The internal classifier.
+        """
+        return self.classifier
+
+    @abc.abstractmethod
+    def __call__(self, x: np.ndarray, transformed_classifier: "CLASSIFIER_TYPE") -> "CLASSIFIER_TYPE":
+        """
+        Perform the transformation defence and return a robuster classifier.
+
+        :param x: Dataset for training the transformed classifier.
+        :param transformed_classifier: A classifier to be transformed for increased robustness.
+        :return: The transformed classifier.
+        """
+        raise NotImplementedError
+
+    @abc.abstractmethod
+    def fit(self, x: np.ndarray, y: Optional[np.ndarray] = None, **kwargs) -> None:
+        """
+        Fit the parameters of the transformer if it has any.
+
+        :param x: Training set to fit the transformer.
+        :param y: Labels for the training set.
+        :param kwargs: Other parameters.
+        """
+        raise NotImplementedError
+
+    def set_params(self, **kwargs) -> None:
+        """
+        Take in a dictionary of parameters and apply checks before saving them as attributes.
+        """
+        for key, value in kwargs.items():
+            if key in self.params:
+                setattr(self, key, value)
+        self._check_params()
+
+    def _check_params(self) -> None:
+        pass
diff --git a/adversarial-robustness-toolbox/art/estimators/__init__.py b/adversarial-robustness-toolbox/art/estimators/__init__.py
new file mode 100644
index 0000000..f9a7d41
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/estimators/__init__.py
@@ -0,0 +1,24 @@
+"""
+This module contains the Estimator API.
+"""
+from art.estimators.estimator import (
+    BaseEstimator,
+    LossGradientsMixin,
+    NeuralNetworkMixin,
+    DecisionTreeMixin,
+)
+
+from art.estimators.keras import KerasEstimator
+from art.estimators.mxnet import MXEstimator
+from art.estimators.pytorch import PyTorchEstimator
+from art.estimators.scikitlearn import ScikitlearnEstimator
+from art.estimators.tensorflow import TensorFlowEstimator, TensorFlowV2Estimator
+
+from art.estimators import certification
+from art.estimators import classification
+from art.estimators import encoding
+from art.estimators import generation
+from art.estimators import object_detection
+from art.estimators import poison_mitigation
+from art.estimators import regression
+from art.estimators import speech_recognition
diff --git a/adversarial-robustness-toolbox/art/estimators/certification/__init__.py b/adversarial-robustness-toolbox/art/estimators/certification/__init__.py
new file mode 100644
index 0000000..fcff7b8
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/estimators/certification/__init__.py
@@ -0,0 +1,4 @@
+"""
+This module contains certified classifiers.
+"""
+from art.estimators.certification import randomized_smoothing
diff --git a/adversarial-robustness-toolbox/art/estimators/certification/abstain.py b/adversarial-robustness-toolbox/art/estimators/certification/abstain.py
new file mode 100644
index 0000000..f4fb29d
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/estimators/certification/abstain.py
@@ -0,0 +1,50 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements a mixin to be added to classifier so that they may abstain from classification.
+
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+
+import numpy as np
+
+from art.estimators.classification.classifier import ClassifierMixin
+
+logger = logging.getLogger(__name__)
+
+
+class AbstainPredictorMixin(ClassifierMixin):
+    """
+    A mixin class that gives classifiers the ability to abstain
+    """
+
+    def __init__(self, **kwargs):
+        """
+        Creates a predictor that can abstain from predictions
+
+        """
+        super().__init__(**kwargs)
+
+    def abstain(self) -> np.ndarray:
+        """
+        Abstain from a prediction
+        :return: A numpy array of zeros
+        """
+        return np.zeros(self.nb_classes)
diff --git a/adversarial-robustness-toolbox/art/estimators/certification/randomized_smoothing/__init__.py b/adversarial-robustness-toolbox/art/estimators/certification/randomized_smoothing/__init__.py
new file mode 100644
index 0000000..bd4ceae
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/estimators/certification/randomized_smoothing/__init__.py
@@ -0,0 +1,8 @@
+"""
+Randomized smoothing estimators.
+"""
+from art.estimators.certification.randomized_smoothing.randomized_smoothing import RandomizedSmoothingMixin
+
+from art.estimators.certification.randomized_smoothing.numpy import NumpyRandomizedSmoothing
+from art.estimators.certification.randomized_smoothing.tensorflow import TensorFlowV2RandomizedSmoothing
+from art.estimators.certification.randomized_smoothing.pytorch import PyTorchRandomizedSmoothing
diff --git a/adversarial-robustness-toolbox/art/estimators/certification/randomized_smoothing/numpy.py b/adversarial-robustness-toolbox/art/estimators/certification/randomized_smoothing/numpy.py
new file mode 100644
index 0000000..876025d
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/estimators/certification/randomized_smoothing/numpy.py
@@ -0,0 +1,140 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements Randomized Smoothing applied to classifier predictions.
+
+| Paper link: https://arxiv.org/abs/1902.02918
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+from typing import List, Union, TYPE_CHECKING, Tuple
+
+import numpy as np
+
+from art.estimators.estimator import BaseEstimator, LossGradientsMixin, NeuralNetworkMixin
+from art.estimators.certification.randomized_smoothing.randomized_smoothing import RandomizedSmoothingMixin
+from art.estimators.classification import ClassifierMixin, ClassGradientsMixin
+
+if TYPE_CHECKING:
+    from art.utils import CLASSIFIER_LOSS_GRADIENTS_TYPE
+
+logger = logging.getLogger(__name__)
+
+
+class NumpyRandomizedSmoothing(  # lgtm [py/conflicting-attributes]
+    RandomizedSmoothingMixin,
+    ClassGradientsMixin,
+    ClassifierMixin,
+    NeuralNetworkMixin,
+    LossGradientsMixin,
+    BaseEstimator,
+):
+    """
+    Implementation of Randomized Smoothing applied to classifier predictions and gradients, as introduced
+    in Cohen et al. (2019).
+
+    | Paper link: https://arxiv.org/abs/1902.02918
+    """
+
+    estimator_params = (
+        BaseEstimator.estimator_params
+        + NeuralNetworkMixin.estimator_params
+        + ClassifierMixin.estimator_params
+        + ["classifier", "sample_size", "scale", "alpha"]
+    )
+
+    def __init__(
+        self, classifier: "CLASSIFIER_LOSS_GRADIENTS_TYPE", sample_size: int, scale: float = 0.1, alpha: float = 0.001
+    ):
+        """
+        Create a randomized smoothing wrapper.
+        :param classifier: The Classifier we want to wrap the functionality for the purpose of smoothing.
+        :param sample_size: Number of samples for smoothing
+        :param scale: Standard deviation of Gaussian noise added.
+        :param alpha: The failure probability of smoothing
+        """
+        super().__init__(
+            model=classifier.model,
+            clip_values=classifier.clip_values,
+            preprocessing_defences=classifier.preprocessing_defences,
+            postprocessing_defences=classifier.postprocessing_defences,
+            preprocessing=classifier.preprocessing,
+            sample_size=sample_size,
+            scale=scale,
+            alpha=alpha,
+        )
+        self._input_shape = classifier.input_shape
+        self._nb_classes = classifier.nb_classes
+
+        self.classifier = classifier
+
+    @property
+    def input_shape(self) -> Tuple[int, ...]:
+        return self._input_shape
+
+    def _predict_classifier(self, x: np.ndarray, batch_size: int) -> np.ndarray:
+        """
+        Perform prediction for a batch of inputs.
+
+        :param x: Input samples.
+        :param batch_size: Size of batches.
+        :return: Array of predictions of shape `(nb_inputs, nb_classes)`.
+        """
+        return self.classifier.predict(x=x, batch_size=batch_size)
+
+    def _fit_classifier(self, x: np.ndarray, y: np.ndarray, batch_size: int, nb_epochs: int, **kwargs) -> None:
+        """
+         Fit the classifier on the training set `(x, y)`.
+
+        :param x: Training data.
+        :param y: Target values (class labels) one-hot-encoded of shape (nb_samples, nb_classes) or indices of shape
+                  (nb_samples,).
+        :param batch_size: Batch size.
+        :param nb_epochs: Number of epochs to use for training.
+        :param kwargs: Dictionary of framework-specific arguments. This parameter is not currently supported for PyTorch
+                       and providing it takes no effect.
+        """
+        return self.classifier.fit(x, y, batch_size=batch_size, nb_epochs=nb_epochs, **kwargs)
+
+    def loss_gradient(self, x: np.ndarray, y: np.ndarray, training_mode: bool = False, **kwargs) -> np.ndarray:
+        """
+        Compute the gradient of the given classifier's loss function w.r.t. `x` of the original classifier.
+        :param x: Sample input with shape as expected by the model.
+        :param y: Correct labels, one-hot encoded.
+        :param training_mode: `True` for model set to training mode and `'False` for model set to evaluation mode.
+        :return: Array of gradients of the same shape as `x`.
+        """
+        return self.classifier.loss_gradient(x=x, y=y, training_mode=training_mode, **kwargs)
+
+    def class_gradient(
+        self, x: np.ndarray, label: Union[int, List[int]] = None, training_mode: bool = False, **kwargs
+    ) -> np.ndarray:
+        """
+        Compute per-class derivatives of the given classifier w.r.t. `x` of original classifier.
+        :param x: Sample input with shape as expected by the model.
+        :param label: Index of a specific per-class derivative. If an integer is provided, the gradient of that class
+                      output is computed for all samples. If multiple values as provided, the first dimension should
+                      match the batch size of `x`, and each value will be used as target for its corresponding sample in
+                      `x`. If `None`, then gradients for all classes will be computed for each sample.
+        :param training_mode: `True` for model set to training mode and `'False` for model set to evaluation mode.
+        :return: Array of gradients of input features w.r.t. each class in the form
+                 `(batch_size, nb_classes, input_shape)` when computing for all classes, otherwise shape becomes
+                 `(batch_size, 1, input_shape)` when `label` parameter is specified.
+        """
+        return self.classifier.class_gradient(x=x, label=label, training_mode=training_mode, **kwargs)
diff --git a/adversarial-robustness-toolbox/art/estimators/certification/randomized_smoothing/pytorch.py b/adversarial-robustness-toolbox/art/estimators/certification/randomized_smoothing/pytorch.py
new file mode 100644
index 0000000..7015345
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/estimators/certification/randomized_smoothing/pytorch.py
@@ -0,0 +1,227 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements Randomized Smoothing applied to classifier predictions.
+
+| Paper link: https://arxiv.org/abs/1902.02918
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+from typing import List, Optional, Tuple, Union, TYPE_CHECKING
+
+import numpy as np
+
+from art.config import ART_NUMPY_DTYPE
+from art.estimators.classification.pytorch import PyTorchClassifier
+from art.estimators.certification.randomized_smoothing.randomized_smoothing import RandomizedSmoothingMixin
+
+if TYPE_CHECKING:
+    # pylint: disable=C0412
+    import torch
+
+    from art.utils import CLIP_VALUES_TYPE, PREPROCESSING_TYPE
+    from art.defences.preprocessor import Preprocessor
+    from art.defences.postprocessor import Postprocessor
+
+logger = logging.getLogger(__name__)
+
+
+class PyTorchRandomizedSmoothing(RandomizedSmoothingMixin, PyTorchClassifier):
+    """
+    Implementation of Randomized Smoothing applied to classifier predictions and gradients, as introduced
+    in Cohen et al. (2019).
+
+    | Paper link: https://arxiv.org/abs/1902.02918
+    """
+
+    estimator_params = PyTorchClassifier.estimator_params + ["sample_size", "scale", "alpha"]
+
+    def __init__(
+        self,
+        model: "torch.nn.Module",
+        loss: "torch.nn.modules.loss._Loss",
+        input_shape: Tuple[int, ...],
+        nb_classes: int,
+        optimizer: Optional["torch.optim.Optimizer"] = None,  # type: ignore
+        channels_first: bool = True,
+        clip_values: Optional["CLIP_VALUES_TYPE"] = None,
+        preprocessing_defences: Union["Preprocessor", List["Preprocessor"], None] = None,
+        postprocessing_defences: Union["Postprocessor", List["Postprocessor"], None] = None,
+        preprocessing: "PREPROCESSING_TYPE" = (0.0, 1.0),
+        device_type: str = "gpu",
+        sample_size: int = 32,
+        scale: float = 0.1,
+        alpha: float = 0.001,
+    ):
+        """
+        Create a randomized smoothing classifier.
+
+        :param model: PyTorch model. The output of the model can be logits, probabilities or anything else. Logits
+               output should be preferred where possible to ensure attack efficiency.
+        :param loss: The loss function for which to compute gradients for training. The target label must be raw
+               categorical, i.e. not converted to one-hot encoding.
+        :param input_shape: The shape of one input instance.
+        :param nb_classes: The number of classes of the model.
+        :param optimizer: The optimizer used to train the classifier.
+        :param channels_first: Set channels first or last.
+        :param clip_values: Tuple of the form `(min, max)` of floats or `np.ndarray` representing the minimum and
+               maximum values allowed for features. If floats are provided, these will be used as the range of all
+               features. If arrays are provided, each value will be considered the bound for a feature, thus
+               the shape of clip values needs to match the total number of features.
+        :param preprocessing_defences: Preprocessing defence(s) to be applied by the classifier.
+        :param postprocessing_defences: Postprocessing defence(s) to be applied by the classifier.
+        :param preprocessing: Tuple of the form `(subtrahend, divisor)` of floats or `np.ndarray` of values to be
+               used for data preprocessing. The first value will be subtracted from the input. The input will then
+               be divided by the second one.
+        :param device_type: Type of device on which the classifier is run, either `gpu` or `cpu`.
+        :param sample_size: Number of samples for smoothing.
+        :param scale: Standard deviation of Gaussian noise added.
+        :param alpha: The failure probability of smoothing.
+        """
+        super().__init__(
+            model=model,
+            loss=loss,
+            input_shape=input_shape,
+            nb_classes=nb_classes,
+            optimizer=optimizer,
+            channels_first=channels_first,
+            clip_values=clip_values,
+            preprocessing_defences=preprocessing_defences,
+            postprocessing_defences=postprocessing_defences,
+            preprocessing=preprocessing,
+            device_type=device_type,
+            sample_size=sample_size,
+            scale=scale,
+            alpha=alpha,
+        )
+
+    def _predict_classifier(self, x: np.ndarray, batch_size: int) -> np.ndarray:
+        x = x.astype(ART_NUMPY_DTYPE)
+        return PyTorchClassifier.predict(self, x=x, batch_size=batch_size)
+
+    def _fit_classifier(self, x: np.ndarray, y: np.ndarray, batch_size: int, nb_epochs: int, **kwargs) -> None:
+        x = x.astype(ART_NUMPY_DTYPE)
+        return PyTorchClassifier.fit(self, x, y, batch_size=batch_size, nb_epochs=nb_epochs, **kwargs)
+
+    def fit(self, x: np.ndarray, y: np.ndarray, batch_size: int = 128, nb_epochs: int = 10, **kwargs):
+        """
+        Fit the classifier on the training set `(x, y)`.
+
+        :param x: Training data.
+        :param y: Target values (class labels) one-hot-encoded of shape (nb_samples, nb_classes) or indices of shape
+                  (nb_samples,).
+        :param batch_size: Batch size.
+        :key nb_epochs: Number of epochs to use for training
+        :param kwargs: Dictionary of framework-specific arguments. This parameter is not currently supported for PyTorch
+               and providing it takes no effect.
+        :type kwargs: `dict`
+        :return: `None`
+        """
+        RandomizedSmoothingMixin.fit(self, x, y, batch_size=128, nb_epochs=10, **kwargs)
+
+    def predict(self, x: np.ndarray, batch_size: int = 128, **kwargs) -> np.ndarray:
+        """
+        Perform prediction of the given classifier for a batch of inputs, taking an expectation over transformations.
+
+        :param x: Input samples.
+        :param batch_size: Batch size.
+        :param is_abstain: True if function will abstain from prediction and return 0s. Default: True
+        :type is_abstain: `boolean`
+        :return: Array of predictions of shape `(nb_inputs, nb_classes)`.
+        """
+        return RandomizedSmoothingMixin.predict(self, x, batch_size=128, **kwargs)
+
+    def loss_gradient(self, x: np.ndarray, y: np.ndarray, training_mode: bool = False, **kwargs) -> np.ndarray:
+        """
+        Compute the gradient of the loss function w.r.t. `x`.
+
+        :param x: Sample input with shape as expected by the model.
+        :param y: Target values (class labels) one-hot-encoded of shape (nb_samples, nb_classes) or indices of shape
+                  (nb_samples,).
+        :param training_mode: `True` for model set to training mode and `'False` for model set to evaluation mode.
+        :param sampling: True if loss gradients should be determined with Monte Carlo sampling.
+        :type sampling: `bool`
+        :return: Array of gradients of the same shape as `x`.
+        """
+        import torch  # lgtm [py/repeated-import]
+
+        sampling = kwargs.get("sampling")
+
+        if sampling:
+            self._model.train(mode=training_mode)
+
+            # Apply preprocessing
+            x_preprocessed, y_preprocessed = self._apply_preprocessing(x, y, fit=False)
+
+            # Check label shape
+            if self._reduce_labels:
+                y_preprocessed = np.argmax(y_preprocessed, axis=1)
+
+            # Convert the inputs to Tensors
+            inputs_t = torch.from_numpy(x_preprocessed).to(self._device)
+            inputs_t.requires_grad = True
+
+            # Convert the labels to Tensors
+            labels_t = torch.from_numpy(y_preprocessed).to(self._device)
+            inputs_repeat_t = inputs_t.repeat_interleave(self.sample_size, 0)
+
+            noise = torch.randn_like(inputs_repeat_t, device=self._device) * self.scale
+            inputs_noise_t = inputs_repeat_t + noise
+            if self.clip_values is not None:
+                inputs_noise_t.clamp(self.clip_values[0], self.clip_values[1])
+
+            model_outputs = self._model(inputs_noise_t)[-1]
+            softmax = torch.nn.functional.softmax(model_outputs, dim=1)
+            average_softmax = (
+                softmax.reshape(-1, self.sample_size, model_outputs.shape[-1]).mean(1, keepdim=True).squeeze(1)
+            )
+            log_softmax = torch.log(average_softmax.clamp(min=1e-20))
+            loss = torch.nn.functional.nll_loss(log_softmax, labels_t)
+
+            # Clean gradients
+            self._model.zero_grad()
+
+            # Compute gradients
+            loss.backward()
+            gradients = inputs_t.grad.cpu().numpy().copy()  # type: ignore
+            gradients = self._apply_preprocessing_gradient(x, gradients)
+            assert gradients.shape == x.shape
+
+        else:
+            gradients = PyTorchClassifier.loss_gradient(self, x=x, y=y, training_mode=training_mode, **kwargs)
+
+        return gradients
+
+    def class_gradient(
+        self, x: np.ndarray, label: Union[int, List[int], None] = None, training_mode: bool = False, **kwargs
+    ) -> np.ndarray:
+        """
+        Compute per-class derivatives of the given classifier w.r.t. `x` of original classifier.
+
+        :param x: Sample input with shape as expected by the model.
+        :param label: Index of a specific per-class derivative. If an integer is provided, the gradient of that class
+                      output is computed for all samples. If multiple values as provided, the first dimension should
+                      match the batch size of `x`, and each value will be used as target for its corresponding sample in
+                      `x`. If `None`, then gradients for all classes will be computed for each sample.
+        :param training_mode: `True` for model set to training mode and `'False` for model set to evaluation mode.
+        :return: Array of gradients of input features w.r.t. each class in the form
+                 `(batch_size, nb_classes, input_shape)` when computing for all classes, otherwise shape becomes
+                 `(batch_size, 1, input_shape)` when `label` parameter is specified.
+        """
+        raise NotImplementedError
diff --git a/adversarial-robustness-toolbox/art/estimators/certification/randomized_smoothing/randomized_smoothing.py b/adversarial-robustness-toolbox/art/estimators/certification/randomized_smoothing/randomized_smoothing.py
new file mode 100644
index 0000000..cce0e10
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/estimators/certification/randomized_smoothing/randomized_smoothing.py
@@ -0,0 +1,225 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements Randomized Smoothing applied to classifier predictions.
+
+| Paper link: https://arxiv.org/abs/1902.02918
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+from abc import ABC
+import logging
+from typing import Optional, Tuple
+
+import numpy as np
+from scipy.stats import norm
+from tqdm.auto import tqdm
+
+from art.config import ART_NUMPY_DTYPE
+from art.defences.preprocessor.gaussian_augmentation import GaussianAugmentation
+
+logger = logging.getLogger(__name__)
+
+
+class RandomizedSmoothingMixin(ABC):
+    """
+    Implementation of Randomized Smoothing applied to classifier predictions and gradients, as introduced
+    in Cohen et al. (2019).
+
+    | Paper link: https://arxiv.org/abs/1902.02918
+    """
+
+    def __init__(self, sample_size: int, *args, scale: float = 0.1, alpha: float = 0.001, **kwargs,) -> None:
+        """
+        Create a randomized smoothing wrapper.
+
+        :param sample_size: Number of samples for smoothing.
+        :param scale: Standard deviation of Gaussian noise added.
+        :param alpha: The failure probability of smoothing.
+        """
+        super().__init__(*args, **kwargs)  # type: ignore
+        self.sample_size = sample_size
+        self.scale = scale
+        self.alpha = alpha
+
+    def _predict_classifier(self, x: np.ndarray, batch_size: int) -> np.ndarray:
+        """
+        Perform prediction for a batch of inputs.
+
+        :param x: Input samples.
+        :param batch_size: Size of batches.
+        :return: Array of predictions of shape `(nb_inputs, nb_classes)`.
+        """
+        raise NotImplementedError
+
+    def predict(self, x: np.ndarray, batch_size: int = 128, **kwargs) -> np.ndarray:
+        """
+        Perform prediction of the given classifier for a batch of inputs, taking an expectation over transformations.
+
+        :param x: Input samples.
+        :param batch_size: Batch size.
+        :param is_abstain: True if function will abstain from prediction and return 0s. Default: True
+        :type is_abstain: `boolean`
+        :return: Array of predictions of shape `(nb_inputs, nb_classes)`.
+        """
+        from scipy.stats import binom_test
+
+        is_abstain = kwargs.get("is_abstain")
+        if is_abstain is not None and not isinstance(is_abstain, bool):
+            raise ValueError("The argument is_abstain needs to be of type bool.")
+        if is_abstain is None:
+            is_abstain = True
+
+        logger.info("Applying randomized smoothing.")
+        n_abstained = 0
+        prediction = []
+        for x_i in tqdm(x, desc="Randomized smoothing"):
+            # get class counts
+            counts_pred = self._prediction_counts(x_i, batch_size=batch_size)
+            top = counts_pred.argsort()[::-1]
+            count1 = np.max(counts_pred)
+            count2 = counts_pred[top[1]]
+
+            # predict or abstain
+            smooth_prediction = np.zeros(counts_pred.shape)
+            if (not is_abstain) or (binom_test(count1, count1 + count2, p=0.5) <= self.alpha):
+                smooth_prediction[np.argmax(counts_pred)] = 1
+            elif is_abstain:
+                n_abstained += 1
+
+            prediction.append(smooth_prediction)
+        if n_abstained > 0:
+            logger.info("%s prediction(s) abstained.", n_abstained)
+        return np.array(prediction)
+
+    def _fit_classifier(self, x: np.ndarray, y: np.ndarray, batch_size: int, nb_epochs: int, **kwargs) -> None:
+        """
+         Fit the classifier on the training set `(x, y)`.
+
+        :param x: Training data.
+        :param y: Target values (class labels) one-hot-encoded of shape (nb_samples, nb_classes) or indices of shape
+                  (nb_samples,).
+        :param batch_size: Batch size.
+        :param nb_epochs: Number of epochs to use for training.
+        :param kwargs: Dictionary of framework-specific arguments. This parameter is not currently supported for PyTorch
+               and providing it takes no effect.
+        """
+        raise NotImplementedError
+
+    def fit(self, x: np.ndarray, y: np.ndarray, batch_size: int = 128, nb_epochs: int = 10, **kwargs) -> None:
+        """
+        Fit the classifier on the training set `(x, y)`.
+
+        :param x: Training data.
+        :param y: Target values (class labels) one-hot-encoded of shape (nb_samples, nb_classes) or indices of shape
+                  (nb_samples,).
+        :param batch_size: Batch size.
+        :param nb_epochs: Number of epochs to use for training.
+        :param kwargs: Dictionary of framework-specific arguments. This parameter is not currently supported for PyTorch
+               and providing it takes no effect.
+        """
+        g_a = GaussianAugmentation(sigma=self.scale, augmentation=False)
+        x_rs, _ = g_a(x)
+        self._fit_classifier(x_rs, y, batch_size=batch_size, nb_epochs=nb_epochs, **kwargs)
+
+    def certify(self, x: np.ndarray, n: int, batch_size: int = 32) -> Tuple[np.ndarray, np.ndarray]:
+        """
+        Computes certifiable radius around input `x` and returns radius `r` and prediction.
+
+        :param x: Sample input with shape as expected by the model.
+        :param n: Number of samples for estimate certifiable radius.
+        :param batch_size: Batch size.
+        :return: Tuple of length 2 of the selected class and certified radius.
+        """
+        prediction = []
+        radius = []
+
+        for x_i in x:
+
+            # get sample prediction for classification
+            counts_pred = self._prediction_counts(x_i, n=self.sample_size, batch_size=batch_size)
+            class_select = np.argmax(counts_pred)
+
+            # get sample prediction for certification
+            counts_est = self._prediction_counts(x_i, n=n, batch_size=batch_size)
+            count_class = counts_est[class_select]
+
+            prob_class = self._lower_confidence_bound(count_class, n)
+
+            if prob_class < 0.5:
+                prediction.append(-1)
+                radius.append(0.0)
+            else:
+                prediction.append(class_select)
+                radius.append(self.scale * norm.ppf(prob_class))
+
+        return np.array(prediction), np.array(radius)
+
+    def _noisy_samples(self, x: np.ndarray, n: Optional[int] = None) -> np.ndarray:
+        """
+        Adds Gaussian noise to `x` to generate samples. Optionally augments `y` similarly.
+
+        :param x: Sample input with shape as expected by the model.
+        :param n: Number of noisy samples to create.
+        :return: Array of samples of the same shape as `x`.
+        """
+        # set default value to sample_size
+        if n is None:
+            n = self.sample_size
+
+        # augment x
+        x = np.expand_dims(x, axis=0)
+        x = np.repeat(x, n, axis=0)
+        x = x + np.random.normal(scale=self.scale, size=x.shape).astype(ART_NUMPY_DTYPE)
+
+        return x
+
+    def _prediction_counts(self, x: np.ndarray, n: Optional[int] = None, batch_size: int = 128) -> np.ndarray:
+        """
+        Makes predictions and then converts probability distribution to counts.
+
+        :param x: Sample input with shape as expected by the model.
+        :param n: Number of noisy samples to create.
+        :param batch_size: Size of batches.
+        :return: Array of counts with length equal to number of columns of `x`.
+        """
+        # sample and predict
+        x_new = self._noisy_samples(x, n=n)
+        predictions = self._predict_classifier(x=x_new, batch_size=batch_size)
+
+        # convert to binary predictions
+        idx = np.argmax(predictions, axis=-1)
+        pred = np.zeros(predictions.shape)
+        pred[np.arange(pred.shape[0]), idx] = 1
+
+        # get class counts
+        counts = np.sum(pred, axis=0)
+
+        return counts
+
+    def _lower_confidence_bound(self, n_class_samples: int, n_total_samples: int) -> float:
+        """
+        Uses Clopper-Pearson method to return a (1-alpha) lower confidence bound on bernoulli proportion
+
+        :param n_class_samples: Number of samples of a specific class.
+        :param n_total_samples: Number of samples for certification.
+        :return: Lower bound on the binomial proportion w.p. (1-alpha) over samples.
+        """
+        from statsmodels.stats.proportion import proportion_confint
+
+        return proportion_confint(n_class_samples, n_total_samples, alpha=2 * self.alpha, method="beta")[0]
diff --git a/adversarial-robustness-toolbox/art/estimators/certification/randomized_smoothing/tensorflow.py b/adversarial-robustness-toolbox/art/estimators/certification/randomized_smoothing/tensorflow.py
new file mode 100644
index 0000000..ca58c57
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/estimators/certification/randomized_smoothing/tensorflow.py
@@ -0,0 +1,245 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements Randomized Smoothing applied to classifier predictions.
+
+| Paper link: https://arxiv.org/abs/1902.02918
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+from typing import Callable, List, Optional, Tuple, Union, TYPE_CHECKING
+
+import numpy as np
+
+from art.estimators.classification.tensorflow import TensorFlowV2Classifier
+from art.estimators.certification.randomized_smoothing.randomized_smoothing import RandomizedSmoothingMixin
+
+if TYPE_CHECKING:
+    # pylint: disable=C0412
+    import tensorflow as tf
+
+    from art.utils import CLIP_VALUES_TYPE, PREPROCESSING_TYPE
+    from art.defences.preprocessor import Preprocessor
+    from art.defences.postprocessor import Postprocessor
+
+logger = logging.getLogger(__name__)
+
+
+class TensorFlowV2RandomizedSmoothing(RandomizedSmoothingMixin, TensorFlowV2Classifier):
+    """
+    Implementation of Randomized Smoothing applied to classifier predictions and gradients, as introduced
+    in Cohen et al. (2019).
+
+    | Paper link: https://arxiv.org/abs/1902.02918
+    """
+
+    estimator_params = TensorFlowV2Classifier.estimator_params + ["sample_size", "scale", "alpha"]
+
+    def __init__(
+        self,
+        model,
+        nb_classes: int,
+        input_shape: Tuple[int, ...],
+        loss_object: Optional["tf.Tensor"] = None,
+        train_step: Optional[Callable] = None,
+        channels_first: bool = False,
+        clip_values: Optional["CLIP_VALUES_TYPE"] = None,
+        preprocessing_defences: Union["Preprocessor", List["Preprocessor"], None] = None,
+        postprocessing_defences: Union["Postprocessor", List["Postprocessor"], None] = None,
+        preprocessing: "PREPROCESSING_TYPE" = (0.0, 1.0),
+        sample_size: int = 32,
+        scale: float = 0.1,
+        alpha: float = 0.001,
+    ):
+        """
+        Create a randomized smoothing classifier.
+
+        :param model: a python functions or callable class defining the model and providing it prediction as output.
+        :type model: `function` or `callable class`
+        :param nb_classes: the number of classes in the classification task.
+        :param input_shape: Shape of one input for the classifier, e.g. for MNIST input_shape=(28, 28, 1).
+        :param loss_object: The loss function for which to compute gradients. This parameter is applied for training
+            the model and computing gradients of the loss w.r.t. the input.
+        :param train_step: A function that applies a gradient update to the trainable variables.
+        :param channels_first: Set channels first or last.
+        :param clip_values: Tuple of the form `(min, max)` of floats or `np.ndarray` representing the minimum and
+               maximum values allowed for features. If floats are provided, these will be used as the range of all
+               features. If arrays are provided, each value will be considered the bound for a feature, thus
+               the shape of clip values needs to match the total number of features.
+        :param preprocessing_defences: Preprocessing defence(s) to be applied by the classifier.
+        :param postprocessing_defences: Postprocessing defence(s) to be applied by the classifier.
+        :param preprocessing: Tuple of the form `(subtrahend, divisor)` of floats or `np.ndarray` of values to be
+               used for data preprocessing. The first value will be subtracted from the input. The input will then
+               be divided by the second one.
+        :param sample_size: Number of samples for smoothing.
+        :param scale: Standard deviation of Gaussian noise added.
+        :param alpha: The failure probability of smoothing.
+        """
+        super().__init__(
+            model=model,
+            nb_classes=nb_classes,
+            input_shape=input_shape,
+            loss_object=loss_object,
+            train_step=train_step,
+            channels_first=channels_first,
+            clip_values=clip_values,
+            preprocessing_defences=preprocessing_defences,
+            postprocessing_defences=postprocessing_defences,
+            preprocessing=preprocessing,
+            sample_size=sample_size,
+            scale=scale,
+            alpha=alpha,
+        )
+
+    def _predict_classifier(self, x: np.ndarray, batch_size: int) -> np.ndarray:
+        return TensorFlowV2Classifier.predict(self, x=x, batch_size=batch_size)
+
+    def _fit_classifier(self, x: np.ndarray, y: np.ndarray, batch_size: int, nb_epochs: int, **kwargs) -> None:
+        return TensorFlowV2Classifier.fit(self, x, y, batch_size=batch_size, nb_epochs=nb_epochs, **kwargs)
+
+    def fit(self, x: np.ndarray, y: np.ndarray, batch_size: int = 128, nb_epochs: int = 10, **kwargs):
+        """
+        Fit the classifier on the training set `(x, y)`.
+
+        :param x: Training data.
+        :param y: Target values (class labels) one-hot-encoded of shape (nb_samples, nb_classes) or indices of shape
+                  (nb_samples,).
+        :param batch_size: Batch size.
+        :key nb_epochs: Number of epochs to use for training
+        :param kwargs: Dictionary of framework-specific arguments. This parameter is not currently supported for PyTorch
+               and providing it takes no effect.
+        :type kwargs: `dict`
+        :return: `None`
+        """
+        RandomizedSmoothingMixin.fit(self, x, y, batch_size=128, nb_epochs=10, **kwargs)
+
+    def predict(self, x: np.ndarray, batch_size: int = 128, **kwargs) -> np.ndarray:
+        """
+        Perform prediction of the given classifier for a batch of inputs, taking an expectation over transformations.
+
+        :param x: Input samples.
+        :param batch_size: Batch size.
+        :param is_abstain: True if function will abstain from prediction and return 0s. Default: True
+        :type is_abstain: `boolean`
+        :return: Array of predictions of shape `(nb_inputs, nb_classes)`.
+        """
+        return RandomizedSmoothingMixin.predict(self, x, batch_size=128, **kwargs)
+
+    def loss_gradient(self, x: np.ndarray, y: np.ndarray, training_mode: bool = False, **kwargs) -> np.ndarray:
+        """
+        Compute the gradient of the loss function w.r.t. `x`.
+
+        :param x: Sample input with shape as expected by the model.
+        :param y: Correct labels, one-vs-rest encoding.
+        :param training_mode: `True` for model set to training mode and `'False` for model set to evaluation mode.
+        :param sampling: True if loss gradients should be determined with Monte Carlo sampling.
+        :type sampling: `bool`
+        :return: Array of gradients of the same shape as `x`.
+        """
+        import tensorflow as tf  # lgtm [py/repeated-import]
+
+        sampling = kwargs.get("sampling")
+
+        if sampling:
+            # Apply preprocessing
+            x_preprocessed, _ = self._apply_preprocessing(x, y, fit=False)
+
+            if tf.executing_eagerly():
+                with tf.GradientTape() as tape:
+                    inputs_t = tf.convert_to_tensor(x_preprocessed)
+                    tape.watch(inputs_t)
+
+                    inputs_repeat_t = tf.repeat(inputs_t, repeats=self.sample_size, axis=0)
+
+                    noise = tf.random.normal(
+                        shape=inputs_repeat_t.shape,
+                        mean=0.0,
+                        stddev=self.scale,
+                        dtype=inputs_repeat_t.dtype,
+                        seed=None,
+                        name=None,
+                    )
+
+                    inputs_noise_t = inputs_repeat_t + noise
+                    if self.clip_values is not None:
+                        inputs_noise_t = tf.clip_by_value(
+                            inputs_noise_t,
+                            clip_value_min=self.clip_values[0],
+                            clip_value_max=self.clip_values[1],
+                            name=None,
+                        )
+
+                    model_outputs = self._model(inputs_noise_t, training=training_mode)
+                    softmax = tf.nn.softmax(model_outputs, axis=1, name=None)
+                    average_softmax = tf.reduce_mean(
+                        tf.reshape(softmax, shape=(-1, self.sample_size, model_outputs.shape[-1])), axis=1
+                    )
+
+                    loss = tf.reduce_mean(
+                        tf.keras.losses.categorical_crossentropy(
+                            y_true=y, y_pred=average_softmax, from_logits=False, label_smoothing=0
+                        )
+                    )
+
+                gradients = tape.gradient(loss, inputs_t).numpy()
+            else:
+                raise ValueError("Expecting eager execution.")
+
+            # Apply preprocessing gradients
+            gradients = self._apply_preprocessing_gradient(x, gradients)
+
+        else:
+            gradients = TensorFlowV2Classifier.loss_gradient(self, x=x, y=y, training_mode=training_mode, **kwargs)
+
+        return gradients
+
+    def class_gradient(
+        self, x: np.ndarray, label: Union[int, List[int], None] = None, training_mode: bool = False, **kwargs
+    ) -> np.ndarray:
+        """
+        Compute per-class derivatives of the given classifier w.r.t. `x` of original classifier.
+
+        :param x: Sample input with shape as expected by the model.
+        :param label: Index of a specific per-class derivative. If an integer is provided, the gradient of that class
+                      output is computed for all samples. If multiple values as provided, the first dimension should
+                      match the batch size of `x`, and each value will be used as target for its corresponding sample in
+                      `x`. If `None`, then gradients for all classes will be computed for each sample.
+        :param training_mode: `True` for model set to training mode and `'False` for model set to evaluation mode.
+        :return: Array of gradients of input features w.r.t. each class in the form
+                 `(batch_size, nb_classes, input_shape)` when computing for all classes, otherwise shape becomes
+                 `(batch_size, 1, input_shape)` when `label` parameter is specified.
+        """
+        raise NotImplementedError
+
+    def compute_loss(self, x: np.ndarray, y: np.ndarray, reduction: str = "none", **kwargs) -> np.ndarray:
+        """
+        Compute the loss of the neural network for samples `x`.
+
+        :param x: Samples of shape (nb_samples, nb_features) or (nb_samples, nb_pixels_1, nb_pixels_2,
+                  nb_channels) or (nb_samples, nb_channels, nb_pixels_1, nb_pixels_2).
+        :param y: Target values (class labels) one-hot-encoded of shape `(nb_samples, nb_classes)` or indices
+                  of shape `(nb_samples,)`.
+        :param reduction: Specifies the reduction to apply to the output: 'none' | 'mean' | 'sum'.
+                   'none': no reduction will be applied
+                   'mean': the sum of the output will be divided by the number of elements in the output,
+                   'sum': the output will be summed.
+        :return: Loss values.
+        :rtype: Format as expected by the `model`
+        """
+        raise NotImplementedError
diff --git a/adversarial-robustness-toolbox/art/estimators/classification/GPy.py b/adversarial-robustness-toolbox/art/estimators/classification/GPy.py
new file mode 100644
index 0000000..e1027be
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/estimators/classification/GPy.py
@@ -0,0 +1,231 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements a wrapper class for GPy Gaussian Process classification models.
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import os
+from typing import List, Optional, Union, Tuple, TYPE_CHECKING
+
+import numpy as np
+
+from art.estimators.classification.classifier import ClassifierClassLossGradients
+from art import config
+
+if TYPE_CHECKING:
+    # pylint: disable=C0412
+    from GPy.models import GPClassification
+
+    from art.utils import CLIP_VALUES_TYPE, PREPROCESSING_TYPE
+    from art.defences.preprocessor import Preprocessor
+    from art.defences.postprocessor import Postprocessor
+
+logger = logging.getLogger(__name__)
+
+
+# pylint: disable=C0103
+class GPyGaussianProcessClassifier(ClassifierClassLossGradients):
+    """
+    Wrapper class for GPy Gaussian Process classification models.
+    """
+
+    def __init__(
+        self,
+        model: Optional["GPClassification"] = None,
+        clip_values: Optional["CLIP_VALUES_TYPE"] = None,
+        preprocessing_defences: Union["Preprocessor", List["Preprocessor"], None] = None,
+        postprocessing_defences: Union["Postprocessor", List["Postprocessor"], None] = None,
+        preprocessing: "PREPROCESSING_TYPE" = (0.0, 1.0),
+    ) -> None:
+        """
+        Create a `Classifier` instance GPY Gaussian Process classification models.
+
+        :param model: GPY Gaussian Process Classification model.
+        :param clip_values: Tuple of the form `(min, max)` representing the minimum and maximum values allowed
+               for features.
+        :param preprocessing_defences: Preprocessing defence(s) to be applied by the classifier.
+        :param postprocessing_defences: Postprocessing defence(s) to be applied by the classifier.
+        :param preprocessing: Tuple of the form `(subtrahend, divisor)` of floats or `np.ndarray` of values to be
+               used for data preprocessing. The first value will be subtracted from the input. The input will then
+               be divided by the second one.
+        """
+        from GPy.models import GPClassification
+
+        if not isinstance(model, GPClassification):
+            raise TypeError("Model must be of type GPy.models.GPClassification")
+
+        super().__init__(
+            model=model,
+            clip_values=clip_values,
+            preprocessing_defences=preprocessing_defences,
+            postprocessing_defences=postprocessing_defences,
+            preprocessing=preprocessing,
+        )
+        self._nb_classes = 2  # always binary
+
+    @property
+    def input_shape(self) -> Tuple[int, ...]:
+        """
+        Return the shape of one input sample.
+
+        :return: Shape of one input sample.
+        """
+        return self._input_shape  # type: ignore
+
+    # pylint: disable=W0221
+    def class_gradient(  # type: ignore
+        self, x: np.ndarray, label: Union[int, List[int], None] = None, eps: float = 0.0001,
+    ) -> np.ndarray:
+        """
+        Compute per-class derivatives w.r.t. `x`.
+
+        :param x: Sample input with shape as expected by the model.
+        :param label: Index of a specific per-class derivative. If an integer is provided, the gradient of that class
+                      output is computed for all samples. If multiple values as provided, the first dimension should
+                      match the batch size of `x`, and each value will be used as target for its corresponding sample in
+                      `x`. If `None`, then gradients for all classes will be computed for each sample.
+        :param eps: Fraction added to the diagonal elements of the input `x`.
+        :return: Array of gradients of input features w.r.t. each class in the form
+                 `(batch_size, nb_classes, input_shape)` when computing for all classes, otherwise shape becomes
+                 `(batch_size, 1, input_shape)` when `label` parameter is specified.
+        """
+        # Apply preprocessing
+        x_preprocessed, _ = self._apply_preprocessing(x, y=None, fit=False)
+
+        grads = np.zeros((np.shape(x_preprocessed)[0], 2, np.shape(x)[1]))
+        for i in range(np.shape(x_preprocessed)[0]):
+            # Get gradient for the two classes GPC can maximally have
+            for i_c in range(2):
+                ind = self.predict(x[i].reshape(1, -1))[0, i_c]
+                sur = self.predict(
+                    np.repeat(x_preprocessed[i].reshape(1, -1), np.shape(x_preprocessed)[1], 0)
+                    + eps * np.eye(np.shape(x_preprocessed)[1])
+                )[:, i_c]
+                grads[i, i_c] = ((sur - ind) * eps).reshape(1, -1)
+
+        grads = self._apply_preprocessing_gradient(x, grads)
+
+        if label is not None:
+            return grads[:, label, :].reshape(np.shape(x_preprocessed)[0], 1, np.shape(x_preprocessed)[1])
+
+        return grads
+
+    def loss_gradient(self, x: np.ndarray, y: np.ndarray, **kwargs) -> np.ndarray:
+        """
+        Compute the gradient of the loss function w.r.t. `x`.
+
+        :param x: Sample input with shape as expected by the model.
+        :param y: Target values (class labels) one-hot-encoded of shape `(nb_samples, nb_classes)` or indices of shape
+                  `(nb_samples,)`.
+        :return: Array of gradients of the same shape as `x`.
+        """
+        # Apply preprocessing
+        x_preprocessed, _ = self._apply_preprocessing(x, y, fit=False)
+
+        eps = 0.00001
+        grads = np.zeros(np.shape(x))
+        for i in range(np.shape(x)[0]):
+            # 1.0 - to mimic loss, [0,np.argmax] to get right class
+            ind = 1.0 - self.predict(x_preprocessed[i].reshape(1, -1))[0, np.argmax(y[i])]
+            sur = (
+                1.0
+                - self.predict(
+                    np.repeat(x_preprocessed[i].reshape(1, -1), np.shape(x_preprocessed)[1], 0)
+                    + eps * np.eye(np.shape(x_preprocessed)[1])
+                )[:, np.argmax(y[i])]
+            )
+            grads[i] = ((sur - ind) * eps).reshape(1, -1)
+
+        grads = self._apply_preprocessing_gradient(x, grads)
+
+        return grads
+
+    # pylint: disable=W0221
+    def predict(self, x: np.ndarray, logits: bool = False, **kwargs) -> np.ndarray:
+        """
+        Perform prediction for a batch of inputs.
+
+        :param x: Input samples.
+        :param logits: `True` if the prediction should be done without squashing function.
+        :return: Array of predictions of shape `(nb_inputs, nb_classes)`.
+        """
+        # Apply preprocessing
+        x_preprocessed, _ = self._apply_preprocessing(x, y=None, fit=False)
+
+        # Perform prediction
+        out = np.zeros((np.shape(x_preprocessed)[0], 2))
+        if logits:
+            # output the non-squashed version
+            out[:, 0] = self.model.predict_noiseless(x_preprocessed)[0].reshape(-1)
+            out[:, 1] = -1.0 * out[:, 0]
+        else:
+            # output normal prediction, scale up to two values
+            out[:, 0] = self.model.predict(x_preprocessed)[0].reshape(-1)
+            out[:, 1] = 1.0 - out[:, 0]
+
+        # Apply postprocessing
+        predictions = self._apply_postprocessing(preds=out, fit=False)
+
+        return predictions
+
+    def predict_uncertainty(self, x: np.ndarray) -> np.ndarray:
+        """
+        Perform uncertainty prediction for a batch of inputs.
+
+        :param x: Input samples.
+        :return: Array of uncertainty predictions of shape `(nb_inputs)`.
+        """
+        # Apply preprocessing
+        x_preprocessed, _ = self._apply_preprocessing(x, y=None, fit=False)
+
+        # Perform prediction
+        out = self.model.predict_noiseless(x_preprocessed)[1]
+
+        # Apply postprocessing
+        predictions = self._apply_postprocessing(preds=out, fit=False)
+
+        return predictions
+
+    def fit(self, x: np.ndarray, y: np.ndarray, **kwargs) -> None:
+        """
+        Fit the classifier on the training set `(x, y)`.
+
+        :param x: Training data. Not used, as given to model in initialized earlier.
+        :param y: Target values (class labels) one-hot-encoded of shape (nb_samples, nb_classes).
+        """
+        raise NotImplementedError
+
+    def save(self, filename: str, path: Optional[str] = None) -> None:
+        """
+        Save a model to file in the format specific to the backend framework.
+
+        :param filename: Name of the file where to store the model.
+        :param path: Path of the folder where to store the model. If no path is specified, the model will be stored in
+                     the default data location of the library `ART_DATA_PATH`.
+        """
+        if path is None:
+            full_path = os.path.join(config.ART_DATA_PATH, filename)
+        else:
+            full_path = os.path.join(path, filename)
+        folder = os.path.split(full_path)[0]
+        if not os.path.exists(folder):
+            os.makedirs(folder)
+
+        self.model.save_model(full_path, save_data=False)
diff --git a/adversarial-robustness-toolbox/art/estimators/classification/__init__.py b/adversarial-robustness-toolbox/art/estimators/classification/__init__.py
new file mode 100644
index 0000000..2db485f
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/estimators/classification/__init__.py
@@ -0,0 +1,25 @@
+"""
+Classifier API for applying all attacks. Use the :class:`.Classifier` wrapper to be able to apply an attack to a
+preexisting model.
+"""
+from art.estimators.classification.classifier import (
+    ClassifierMixin,
+    ClassGradientsMixin,
+)
+
+from art.estimators.classification.blackbox import BlackBoxClassifier, BlackBoxClassifierNeuralNetwork
+from art.estimators.classification.catboost import CatBoostARTClassifier
+from art.estimators.classification.detector_classifier import DetectorClassifier
+from art.estimators.classification.ensemble import EnsembleClassifier
+from art.estimators.classification.GPy import GPyGaussianProcessClassifier
+from art.estimators.classification.keras import KerasClassifier
+from art.estimators.classification.lightgbm import LightGBMClassifier
+from art.estimators.classification.mxnet import MXClassifier
+from art.estimators.classification.pytorch import PyTorchClassifier
+from art.estimators.classification.scikitlearn import SklearnClassifier
+from art.estimators.classification.tensorflow import (
+    TFClassifier,
+    TensorFlowClassifier,
+    TensorFlowV2Classifier,
+)
+from art.estimators.classification.xgboost import XGBoostClassifier
diff --git a/adversarial-robustness-toolbox/art/estimators/classification/blackbox.py b/adversarial-robustness-toolbox/art/estimators/classification/blackbox.py
new file mode 100644
index 0000000..928aaae
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/estimators/classification/blackbox.py
@@ -0,0 +1,287 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements the classifier `BlackBoxClassifier` for black-box classifiers.
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+from typing import Callable, List, Optional, Union, Tuple, TYPE_CHECKING
+
+import numpy as np
+
+from art.estimators.estimator import BaseEstimator, NeuralNetworkMixin
+from art.estimators.classification.classifier import ClassifierMixin, Classifier
+
+if TYPE_CHECKING:
+    from art.utils import CLIP_VALUES_TYPE, PREPROCESSING_TYPE
+    from art.defences.preprocessor import Preprocessor
+    from art.defences.postprocessor import Postprocessor
+
+logger = logging.getLogger(__name__)
+
+
+class BlackBoxClassifier(ClassifierMixin, BaseEstimator):
+    """
+    Wrapper class for black-box classifiers.
+    """
+
+    estimator_params = Classifier.estimator_params + ["nb_classes", "input_shape", "predict"]
+
+    def __init__(
+        self,
+        predict_fn: Callable,
+        input_shape: Tuple[int, ...],
+        nb_classes: int,
+        clip_values: Optional["CLIP_VALUES_TYPE"] = None,
+        preprocessing_defences: Union["Preprocessor", List["Preprocessor"], None] = None,
+        postprocessing_defences: Union["Postprocessor", List["Postprocessor"], None] = None,
+        preprocessing: "PREPROCESSING_TYPE" = (0.0, 1.0),
+    ):
+        """
+        Create a `Classifier` instance for a black-box model.
+
+        :param predict_fn: Function that takes in one input of the data and returns the one-hot encoded predicted class.
+        :param input_shape: Size of input.
+        :param nb_classes: Number of prediction classes.
+        :param clip_values: Tuple of the form `(min, max)` of floats or `np.ndarray` representing the minimum and
+               maximum values allowed for features. If floats are provided, these will be used as the range of all
+               features. If arrays are provided, each value will be considered the bound for a feature, thus
+               the shape of clip values needs to match the total number of features.
+        :param preprocessing_defences: Preprocessing defence(s) to be applied by the classifier.
+        :param postprocessing_defences: Postprocessing defence(s) to be applied by the classifier.
+        :param preprocessing: Tuple of the form `(subtrahend, divisor)` of floats or `np.ndarray` of values to be
+               used for data preprocessing. The first value will be subtracted from the input. The input will then
+               be divided by the second one.
+        """
+        super().__init__(
+            model=None,
+            clip_values=clip_values,
+            preprocessing_defences=preprocessing_defences,
+            postprocessing_defences=postprocessing_defences,
+            preprocessing=preprocessing,
+        )
+
+        self._predict_fn = predict_fn
+        self._input_shape = input_shape
+        self._nb_classes = nb_classes
+
+    @property
+    def input_shape(self) -> Tuple[int, ...]:
+        """
+        Return the shape of one input sample.
+
+        :return: Shape of one input sample.
+        """
+        return self._input_shape  # type: ignore
+
+    @property
+    def predict_fn(self) -> Callable:
+        """
+        Return the prediction function.
+
+        :return: The prediction function.
+        """
+        return self._predict_fn  # type: ignore
+
+    # pylint: disable=W0221
+    def predict(self, x: np.ndarray, batch_size: int = 128, **kwargs) -> np.ndarray:
+        """
+        Perform prediction for a batch of inputs.
+
+        :param x: Input samples.
+        :param batch_size: Size of batches.
+        :return: Array of predictions of shape `(nb_inputs, nb_classes)`.
+        """
+        from art.config import ART_NUMPY_DTYPE
+
+        # Apply preprocessing
+        x_preprocessed, _ = self._apply_preprocessing(x, y=None, fit=False)
+
+        # Run predictions with batching
+        predictions = np.zeros((x_preprocessed.shape[0], self.nb_classes), dtype=ART_NUMPY_DTYPE)
+        for batch_index in range(int(np.ceil(x_preprocessed.shape[0] / float(batch_size)))):
+            begin, end = (
+                batch_index * batch_size,
+                min((batch_index + 1) * batch_size, x_preprocessed.shape[0]),
+            )
+            predictions[begin:end] = self._predict_fn(x_preprocessed[begin:end])
+
+        # Apply postprocessing
+        predictions = self._apply_postprocessing(preds=predictions, fit=False)
+
+        return predictions
+
+    def fit(self, x: np.ndarray, y: np.ndarray, **kwargs) -> None:
+        """
+        Fit the classifier on the training set `(x, y)`.
+
+        :param x: Training data.
+        :param y: Labels, one-vs-rest encoding.
+        :param kwargs: Dictionary of framework-specific arguments. These should be parameters supported by the
+               `fit_generator` function in Keras and will be passed to this function as such. Including the number of
+               epochs or the number of steps per epoch as part of this argument will result in as error.
+        :raises `NotImplementedException`: This method is not supported for black-box classifiers.
+        """
+        raise NotImplementedError
+
+    def save(self, filename: str, path: Optional[str] = None) -> None:
+        """
+        Save a model to file in the format specific to the backend framework. For Keras, .h5 format is used.
+
+        :param filename: Name of the file where to store the model.
+        :param path: Path of the folder where to store the model. If no path is specified, the model will be stored in
+                     the default data location of the library `ART_DATA_PATH`.
+        :raises `NotImplementedException`: This method is not supported for black-box classifiers.
+        """
+        raise NotImplementedError
+
+
+class BlackBoxClassifierNeuralNetwork(NeuralNetworkMixin, ClassifierMixin, BaseEstimator):
+    """
+    Wrapper class for black-box neural network classifiers.
+    """
+
+    def __init__(
+        self,
+        predict: Callable,
+        input_shape: Tuple[int, ...],
+        nb_classes: int,
+        channels_first: bool = True,
+        clip_values: Optional["CLIP_VALUES_TYPE"] = None,
+        preprocessing_defences: Union["Preprocessor", List["Preprocessor"], None] = None,
+        postprocessing_defences: Union["Postprocessor", List["Postprocessor"], None] = None,
+        preprocessing: "PREPROCESSING_TYPE" = (0, 1),
+    ):
+        """
+        Create a `Classifier` instance for a black-box model.
+
+        :param predict: Function that takes in one input of the data and returns the one-hot encoded predicted class.
+        :param input_shape: Size of input.
+        :param nb_classes: Number of prediction classes.
+        :param channels_first: Set channels first or last.
+        :param clip_values: Tuple of the form `(min, max)` of floats or `np.ndarray` representing the minimum and
+               maximum values allowed for features. If floats are provided, these will be used as the range of all
+               features. If arrays are provided, each value will be considered the bound for a feature, thus
+               the shape of clip values needs to match the total number of features.
+        :param preprocessing_defences: Preprocessing defence(s) to be applied by the classifier.
+        :param postprocessing_defences: Postprocessing defence(s) to be applied by the classifier.
+        :param preprocessing: Tuple of the form `(subtrahend, divisor)` of floats or `np.ndarray` of values to be
+               used for data preprocessing. The first value will be subtracted from the input. The input will then
+               be divided by the second one.
+        """
+        super().__init__(
+            model=None,
+            channels_first=channels_first,
+            clip_values=clip_values,
+            preprocessing_defences=preprocessing_defences,
+            postprocessing_defences=postprocessing_defences,
+            preprocessing=preprocessing,
+        )
+
+        self._predictions = predict
+        self._input_shape = input_shape
+        self._nb_classes = nb_classes
+        self._learning_phase = None
+        self._layer_names = None
+
+    @property
+    def input_shape(self) -> Tuple[int, ...]:
+        """
+        Return the shape of one input sample.
+
+        :return: Shape of one input sample.
+        """
+        return self._input_shape  # type: ignore
+
+    def predict(self, x: np.ndarray, batch_size: int = 128, **kwargs):
+        """
+        Perform prediction for a batch of inputs.
+
+        :param x: Test set.
+        :param batch_size: Size of batches.
+        :return: Array of predictions of shape `(nb_inputs, nb_classes)`.
+        """
+        from art.config import ART_NUMPY_DTYPE
+
+        # Apply preprocessing
+        x_preprocessed, _ = self._apply_preprocessing(x, y=None, fit=False)
+
+        # Run predictions with batching
+        predictions = np.zeros((x_preprocessed.shape[0], self.nb_classes), dtype=ART_NUMPY_DTYPE)
+        for batch_index in range(int(np.ceil(x_preprocessed.shape[0] / float(batch_size)))):
+            begin, end = (
+                batch_index * batch_size,
+                min((batch_index + 1) * batch_size, x_preprocessed.shape[0]),
+            )
+            predictions[begin:end] = self._predictions(x_preprocessed[begin:end])
+
+        # Apply postprocessing
+        predictions = self._apply_postprocessing(preds=predictions, fit=False)
+
+        return predictions
+
+    def fit(self, x: np.ndarray, y, batch_size: int = 128, nb_epochs: int = 20, **kwargs) -> None:
+        """
+        Fit the model of the estimator on the training data `x` and `y`.
+
+        :param x: Samples of shape (nb_samples, nb_features) or (nb_samples, nb_pixels_1, nb_pixels_2,
+                  nb_channels) or (nb_samples, nb_channels, nb_pixels_1, nb_pixels_2).
+        :param y: Target values.
+        :type y: Format as expected by the `model`
+        :param batch_size: Batch size.
+        :param nb_epochs: Number of training epochs.
+        """
+        raise NotImplementedError
+
+    def get_activations(
+        self, x: np.ndarray, layer: Union[int, str], batch_size: int, framework: bool = False
+    ) -> np.ndarray:
+        """
+        Return the output of a specific layer for samples `x` where `layer` is the index of the layer between 0 and
+        `nb_layers - 1 or the name of the layer. The number of layers can be determined by counting the results
+        returned by calling `layer_names`.
+
+        :param x: Samples
+        :param layer: Index or name of the layer.
+        :param batch_size: Batch size.
+        :param framework: If true, return the intermediate tensor representation of the activation.
+        :return: The output of `layer`, where the first dimension is the batch size corresponding to `x`.
+        """
+        raise NotImplementedError
+
+    def set_learning_phase(self, train: bool) -> None:
+        """
+        Set the learning phase for the backend framework.
+
+        :param train: `True` if the learning phase is training, otherwise `False`.
+        """
+        raise NotImplementedError
+
+    def loss(self, x: np.ndarray, y: np.ndarray, **kwargs) -> np.ndarray:
+        """
+        Compute the loss of the neural network for samples `x`.
+
+        :param x: Samples of shape (nb_samples, nb_features) or (nb_samples, nb_pixels_1, nb_pixels_2,
+                  nb_channels) or (nb_samples, nb_channels, nb_pixels_1, nb_pixels_2).
+        :param y: Target values (class labels) one-hot-encoded of shape `(nb_samples, nb_classes)` or indices
+                  of shape `(nb_samples,)`.
+        :return: Loss values.
+        :rtype: Format as expected by the `model`
+        """
+        raise NotImplementedError
diff --git a/adversarial-robustness-toolbox/art/estimators/classification/catboost.py b/adversarial-robustness-toolbox/art/estimators/classification/catboost.py
new file mode 100644
index 0000000..d7affae
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/estimators/classification/catboost.py
@@ -0,0 +1,172 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements the classifier `CatBoostARTClassifier` for CatBoost models.
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import os
+import pickle
+from typing import List, Optional, Union, Tuple, TYPE_CHECKING
+
+import numpy as np
+
+from art.estimators.classification.classifier import ClassifierDecisionTree
+from art import config
+
+if TYPE_CHECKING:
+    # pylint: disable=C0412
+    from catboost.core import CatBoostClassifier
+
+    from art.utils import CLIP_VALUES_TYPE, PREPROCESSING_TYPE
+    from art.defences.preprocessor import Preprocessor
+    from art.defences.postprocessor import Postprocessor
+
+logger = logging.getLogger(__name__)
+
+
+class CatBoostARTClassifier(ClassifierDecisionTree):
+    """
+    Wrapper class for importing CatBoost models.
+    """
+
+    estimator_params = ClassifierDecisionTree.estimator_params + ["nb_features"]
+
+    def __init__(
+        self,
+        model: Optional["CatBoostClassifier"] = None,
+        preprocessing_defences: Union["Preprocessor", List["Preprocessor"], None] = None,
+        postprocessing_defences: Union["Postprocessor", List["Postprocessor"], None] = None,
+        preprocessing: "PREPROCESSING_TYPE" = (0.0, 1.0),
+        clip_values: Optional["CLIP_VALUES_TYPE"] = None,
+        nb_features: Optional[int] = None,
+    ) -> None:
+        """
+        Create a `Classifier` instance from a CatBoost model.
+
+        :param model: CatBoost model.
+        :param preprocessing_defences: Preprocessing defence(s) to be applied by the classifier.
+        :param postprocessing_defences: Postprocessing defence(s) to be applied by the classifier.
+        :param preprocessing: Tuple of the form `(subtrahend, divisor)` of floats or `np.ndarray` of values to be
+               used for data preprocessing. The first value will be subtracted from the input. The input will then
+               be divided by the second one.
+        :param clip_values: Tuple of the form `(min, max)` representing the minimum and maximum values allowed
+               for features.
+        :param nb_features: Number of features.
+        """
+        # pylint: disable=E0611,E0401
+        from catboost.core import CatBoostClassifier
+
+        if not isinstance(model, CatBoostClassifier):
+            raise TypeError("Model must be of type catboost.core.CatBoostClassifier")
+
+        super().__init__(
+            model=model,
+            clip_values=clip_values,
+            preprocessing_defences=preprocessing_defences,
+            postprocessing_defences=postprocessing_defences,
+            preprocessing=preprocessing,
+        )
+
+        self._input_shape = (nb_features,)
+        self._nb_classes = self._get_nb_classes()
+
+    @property
+    def input_shape(self) -> Tuple[int, ...]:
+        """
+        Return the shape of one input sample.
+
+        :return: Shape of one input sample.
+        """
+        return self._input_shape  # type: ignore
+
+    @property
+    def nb_features(self) -> int:
+        """
+        Return the number of features.
+
+        :return: The number of features.
+        """
+        return self._input_shape[0]  # type: ignore
+
+    def fit(self, x: np.ndarray, y: np.ndarray, **kwargs) -> None:
+        """
+        Fit the classifier on the training set `(x, y)`.
+
+        :param x: Training data.
+        :param y: Target values (class labels) one-hot-encoded of shape (nb_samples, nb_classes).
+        :param kwargs: Dictionary of framework-specific arguments. These should be parameters supported by the
+               `fit` function in `catboost.core.CatBoostClassifier` and will be passed to this function as such.
+        """
+        # Apply preprocessing
+        x_preprocessed, y_preprocessed = self._apply_preprocessing(x, y, fit=True)
+
+        self._model.fit(x_preprocessed, y_preprocessed, **kwargs)
+        self._nb_classes = self._get_nb_classes()
+
+    def predict(self, x: np.ndarray, **kwargs) -> np.ndarray:
+        """
+        Perform prediction for a batch of inputs.
+
+        :param x: Input samples.
+        :return: Array of predictions of shape `(nb_inputs, nb_classes)`.
+        """
+        # Apply preprocessing
+        x_preprocessed, _ = self._apply_preprocessing(x, y=None, fit=False)
+
+        # Perform prediction
+        predictions = self._model.predict_proba(x_preprocessed)
+
+        # Apply postprocessing
+        predictions = self._apply_postprocessing(preds=predictions, fit=False)
+
+        return predictions
+
+    def _get_nb_classes(self) -> int:
+        """
+        Return the number of output classes.
+
+        :return: Number of classes in the data.
+        """
+        if self._model.classes_ is not None:
+            return len(self._model.classes_)
+
+        return -1
+
+    def save(self, filename: str, path: Optional[str] = None) -> None:
+        """
+        Save a model to file in the format specific to the backend framework.
+
+        :param filename: Name of the file where to store the model.
+        :param path: Path of the folder where to store the model. If no path is specified, the model will be stored in
+                     the default data location of the library `ART_DATA_PATH`.
+        """
+        if path is None:
+            full_path = os.path.join(config.ART_DATA_PATH, filename)
+        else:
+            full_path = os.path.join(path, filename)
+        folder = os.path.split(full_path)[0]
+        if not os.path.exists(folder):
+            os.makedirs(folder)
+
+        with open(full_path + ".pickle", "wb") as file_pickle:
+            pickle.dump(self._model, file=file_pickle)
+
+    def get_trees(self):
+        raise NotImplementedError
diff --git a/adversarial-robustness-toolbox/art/estimators/classification/classifier.py b/adversarial-robustness-toolbox/art/estimators/classification/classifier.py
new file mode 100644
index 0000000..fa62d3b
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/estimators/classification/classifier.py
@@ -0,0 +1,189 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements mixin abstract base classes defining properties for all classifiers in ART.
+"""
+from abc import ABC, ABCMeta, abstractmethod
+from typing import List, Optional, Union
+
+import numpy as np
+
+from art.estimators.estimator import (
+    BaseEstimator,
+    NeuralNetworkMixin,
+    LossGradientsMixin,
+    DecisionTreeMixin,
+)
+
+
+class InputFilter(ABCMeta):
+    """
+    Metaclass to ensure that inputs are ndarray for all of the subclass generate and extract calls.
+    """
+
+    def __init__(cls, name, bases, clsdict):
+        """
+        This function overrides any existing generate or extract methods with a new method that
+        ensures the input is an ndarray. There is an assumption that the input object has implemented
+        __array__ with np.array calls.
+        """
+
+        def make_replacement(fdict, func_name, has_y):
+            """
+            This function overrides creates replacement functions dynamically.
+            """
+
+            def replacement_function(self, *args, **kwargs):
+                if len(args) > 0:
+                    lst = list(args)
+
+                if "x" in kwargs:
+                    if not isinstance(kwargs["x"], np.ndarray):
+                        kwargs["x"] = np.array(kwargs["x"])
+                else:
+                    if not isinstance(args[0], np.ndarray):
+                        lst[0] = np.array(args[0])
+
+                if "y" in kwargs:
+                    if kwargs["y"] is not None and not isinstance(kwargs["y"], np.ndarray):
+                        kwargs["y"] = np.array(kwargs["y"])
+                elif has_y:
+                    if not isinstance(args[1], np.ndarray):
+                        lst[1] = np.array(args[1])
+
+                if len(args) > 0:
+                    args = tuple(lst)
+                return fdict[func_name](self, *args, **kwargs)
+
+            replacement_function.__doc__ = fdict[func_name].__doc__
+            replacement_function.__name__ = "new_" + func_name
+            return replacement_function
+
+        replacement_list_no_y = ["predict"]
+        replacement_list_has_y = ["fit"]
+
+        for item in replacement_list_no_y:
+            if item in clsdict:
+                new_function = make_replacement(clsdict, item, False)
+                setattr(cls, item, new_function)
+        for item in replacement_list_has_y:
+            if item in clsdict:
+                new_function = make_replacement(clsdict, item, True)
+                setattr(cls, item, new_function)
+
+
+class ClassifierMixin(ABC, metaclass=InputFilter):
+    """
+    Mixin abstract base class defining functionality for classifiers.
+    """
+
+    estimator_params = ["nb_classes"]
+
+    def __init__(self, **kwargs) -> None:
+        super().__init__(**kwargs)
+        self._nb_classes: int = -1
+
+    @property
+    def nb_classes(self) -> int:
+        """
+        Return the number of output classes.
+
+        :return: Number of classes in the data.
+        """
+        return self._nb_classes  # type: ignore
+
+
+class ClassGradientsMixin(ABC):
+    """
+    Mixin abstract base class defining classifiers providing access to class gradients. A classifier of this type can
+    be combined with certain white-box attacks. This mixin abstract base class has to be mixed in with
+    class `Classifier`.
+    """
+
+    @abstractmethod
+    def class_gradient(self, x: np.ndarray, label: Union[int, List[int], None] = None, **kwargs) -> np.ndarray:
+        """
+        Compute per-class derivatives w.r.t. `x`.
+
+        :param x: Samples.
+        :type x: `np.ndarray` or `pandas.DataFrame`
+        :param label: Index of a specific per-class derivative. If an integer is provided, the gradient of that class
+                      output is computed for all samples. If multiple values as provided, the first dimension should
+                      match the batch size of `x`, and each value will be used as target for its corresponding sample in
+                      `x`. If `None`, then gradients for all classes will be computed for each sample.
+        :return: Gradients of input features w.r.t. each class in the form `(batch_size, nb_classes, input_shape)` when
+                 computing for all classes, otherwise shape becomes `(batch_size, 1, input_shape)` when `label`
+                 parameter is specified.
+        """
+        raise NotImplementedError
+
+
+class Classifier(ClassifierMixin, BaseEstimator, ABC):
+    """
+    Typing variable definition.
+    """
+
+    estimator_params = BaseEstimator.estimator_params + ClassifierMixin.estimator_params
+
+
+class ClassifierLossGradients(ClassifierMixin, LossGradientsMixin, BaseEstimator, ABC):
+    """
+    Typing variable definition.
+    """
+
+    estimator_params = BaseEstimator.estimator_params + ClassifierMixin.estimator_params
+
+
+class ClassifierClassLossGradients(ClassGradientsMixin, ClassifierMixin, LossGradientsMixin, BaseEstimator, ABC):
+    """
+    Typing variable definition.
+    """
+
+    estimator_params = BaseEstimator.estimator_params + ClassifierMixin.estimator_params
+
+
+class ClassifierNeuralNetwork(  # lgtm [py/conflicting-attributes]
+    ClassGradientsMixin, ClassifierMixin, LossGradientsMixin, NeuralNetworkMixin, BaseEstimator, ABC
+):
+    """
+    Typing variable definition.
+    """
+
+    estimator_params = (
+        BaseEstimator.estimator_params + NeuralNetworkMixin.estimator_params + ClassifierMixin.estimator_params
+    )
+
+    @abstractmethod
+    def save(self, filename: str, path: Optional[str] = None) -> None:
+        """
+        Save a model to file in the format specific to the backend framework. This function is not supported for
+        ensembles.
+
+        :param filename: Name of the file where to store the model.
+        :param path: Path of the folder where to store the model. If no path is specified, the model will be stored in
+                     the default data location of the library `ART_DATA_PATH`.
+        """
+        raise NotImplementedError
+
+
+class ClassifierDecisionTree(DecisionTreeMixin, ClassifierMixin, BaseEstimator, ABC):
+    """
+    Typing variable definition.
+    """
+
+    estimator_params = BaseEstimator.estimator_params + ClassifierMixin.estimator_params
diff --git a/adversarial-robustness-toolbox/art/estimators/classification/detector_classifier.py b/adversarial-robustness-toolbox/art/estimators/classification/detector_classifier.py
new file mode 100644
index 0000000..fb8a538
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/estimators/classification/detector_classifier.py
@@ -0,0 +1,359 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements the base class `DetectorClassifier` for classifier and detector combinations.
+
+Paper link:
+    https://arxiv.org/abs/1705.07263
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+from typing import List, Optional, Union, Tuple, TYPE_CHECKING
+
+import numpy as np
+
+from art.estimators.classification.classifier import ClassifierNeuralNetwork
+
+if TYPE_CHECKING:
+    from art.utils import PREPROCESSING_TYPE
+    from art.data_generators import DataGenerator
+    from art.defences.preprocessor import Preprocessor
+    from art.defences.postprocessor import Postprocessor
+
+logger = logging.getLogger(__name__)
+
+
+class DetectorClassifier(ClassifierNeuralNetwork):
+    """
+    This class implements a Classifier extension that wraps a classifier and a detector.
+    More details in https://arxiv.org/abs/1705.07263
+    """
+
+    estimator_params = ClassifierNeuralNetwork.estimator_params + ["classifier", "detector"]
+
+    def __init__(
+        self,
+        classifier: ClassifierNeuralNetwork,
+        detector: ClassifierNeuralNetwork,
+        preprocessing_defences: Union["Preprocessor", List["Preprocessor"], None] = None,
+        postprocessing_defences: Union["Postprocessor", List["Postprocessor"], None] = None,
+        preprocessing: "PREPROCESSING_TYPE" = (0.0, 1.0),
+    ) -> None:
+        """
+        Initialization for the DetectorClassifier.
+
+        :param classifier: A trained classifier.
+        :param detector: A trained detector applied for the binary classification.
+        :param preprocessing_defences: Preprocessing defence(s) to be applied by the classifier. Not applicable
+               in this classifier.
+        :param postprocessing_defences: Postprocessing defence(s) to be applied by the classifier.
+        :param preprocessing: Tuple of the form `(subtrahend, divisor)` of floats or `np.ndarray` of values to be
+               used for data preprocessing. The first value will be subtracted from the input. The input will then
+               be divided by the second one. Not applicable in this classifier.
+        """
+        if preprocessing_defences is not None:
+            raise NotImplementedError("Preprocessing is not applicable in this classifier.")
+
+        super().__init__(
+            model=None,
+            clip_values=classifier.clip_values,
+            preprocessing=preprocessing,
+            channels_first=classifier.channels_first,
+            preprocessing_defences=preprocessing_defences,
+            postprocessing_defences=postprocessing_defences,
+        )
+
+        self.classifier = classifier
+        self.detector = detector
+        self._nb_classes = classifier.nb_classes + 1
+        self._input_shape = classifier.input_shape
+
+    @property
+    def input_shape(self) -> Tuple[int, ...]:
+        """
+        Return the shape of one input sample.
+
+        :return: Shape of one input sample.
+        """
+        return self._input_shape  # type: ignore
+
+    def predict(self, x: np.ndarray, batch_size: int = 128, **kwargs) -> np.ndarray:
+        """
+        Perform prediction for a batch of inputs.
+
+        :param x: Input samples.
+        :param batch_size: Size of batches.
+        :return: Array of predictions of shape `(nb_inputs, nb_classes)`.
+        """
+        # Compute the prediction logits
+        classifier_outputs = self.classifier.predict(x=x, batch_size=batch_size)
+        detector_outputs = self.detector.predict(x=x, batch_size=batch_size)
+        detector_outputs = (np.reshape(detector_outputs, [-1]) + 1) * np.max(classifier_outputs, axis=1)
+        detector_outputs = np.reshape(detector_outputs, [-1, 1])
+        combined_outputs = np.concatenate([classifier_outputs, detector_outputs], axis=1)
+
+        # Apply postprocessing
+        predictions = self._apply_postprocessing(preds=combined_outputs, fit=False)
+
+        return predictions
+
+    def fit(self, x: np.ndarray, y: np.ndarray, batch_size: int = 128, nb_epochs: int = 10, **kwargs) -> None:
+        """
+        Fit the classifier on the training set `(x, y)`.
+
+        :param x: Training data.
+        :param y: Target values (class labels) one-hot-encoded of shape (nb_samples, nb_classes).
+        :param batch_size: Size of batches.
+        :param nb_epochs: Number of epochs to use for training.
+        :param kwargs: Dictionary of framework-specific arguments. This parameter is not currently supported for PyTorch
+               and providing it takes no effect.
+        :raises `NotImplementedException`: This method is not supported for detector-classifiers.
+        """
+        raise NotImplementedError
+
+    def fit_generator(self, generator: "DataGenerator", nb_epochs: int = 20, **kwargs) -> None:
+        """
+        Fit the classifier using the generator that yields batches as specified.
+
+        :param generator: Batch generator providing `(x, y)` for each epoch.
+        :param nb_epochs: Number of epochs to use for training.
+        :param kwargs: Dictionary of framework-specific arguments. This parameter is not currently supported for PyTorch
+               and providing it takes no effect.
+        :raises `NotImplementedException`: This method is not supported for detector-classifiers.
+        """
+        raise NotImplementedError
+
+    def class_gradient(
+        self,
+        x: np.ndarray,
+        label: Union[int, List[int], np.ndarray, None] = None,
+        training_mode: bool = False,
+        **kwargs
+    ) -> np.ndarray:
+        """
+        Compute per-class derivatives w.r.t. `x`.
+
+        :param x: Sample input with shape as expected by the model.
+        :param label: Index of a specific per-class derivative. If an integer is provided, the gradient of that class
+                      output is computed for all samples. If multiple values as provided, the first dimension should
+                      match the batch size of `x`, and each value will be used as target for its corresponding sample in
+                      `x`. If `None`, then gradients for all classes will be computed for each sample.
+        :param training_mode: `True` for model set to training mode and `'False` for model set to evaluation mode.
+        :return: Array of gradients of input features w.r.t. each class in the form
+                 `(batch_size, nb_classes, input_shape)` when computing for all classes, otherwise shape becomes
+                 `(batch_size, 1, input_shape)` when `label` parameter is specified.
+        """
+        if not (
+            (label is None)
+            or (isinstance(label, (int, np.integer)) and label in range(self.nb_classes))
+            or (
+                isinstance(label, np.ndarray)
+                and len(label.shape) == 1
+                and (label < self.nb_classes).all()
+                and label.shape[0] == x.shape[0]
+            )
+        ):
+            raise ValueError("Label %s is out of range." % label)
+
+        # Compute the gradient and return
+        if label is None:
+            combined_grads = self._compute_combined_grads(x, label=None)
+
+        elif isinstance(label, (int, np.int)):
+            if label < self.nb_classes - 1:
+                # Compute and return from the classifier gradients
+                combined_grads = self.classifier.class_gradient(x=x, label=label, training_mode=training_mode, **kwargs)
+
+            else:
+                # First compute the classifier gradients
+                classifier_grads = self.classifier.class_gradient(
+                    x=x, label=None, training_mode=training_mode, **kwargs
+                )
+
+                # Then compute the detector gradients
+                detector_grads = self.detector.class_gradient(x=x, label=0, training_mode=training_mode, **kwargs)
+
+                # Chain the detector gradients for the first component
+                classifier_preds = self.classifier.predict(x=x)
+                maxind_classifier_preds = np.argmax(classifier_preds, axis=1)
+                max_classifier_preds = classifier_preds[np.arange(x.shape[0]), maxind_classifier_preds]
+                first_detector_grads = max_classifier_preds[:, None, None, None, None] * detector_grads
+
+                # Chain the detector gradients for the second component
+                max_classifier_grads = classifier_grads[np.arange(len(classifier_grads)), maxind_classifier_preds]
+                detector_preds = self.detector.predict(x=x)
+                second_detector_grads = max_classifier_grads * (detector_preds + 1)[:, None, None]
+                second_detector_grads = second_detector_grads[None, ...]
+                second_detector_grads = np.swapaxes(second_detector_grads, 0, 1)
+
+                # Update detector gradients
+                combined_grads = first_detector_grads + second_detector_grads
+
+        else:
+            # Compute indexes for classifier labels and detector labels
+            classifier_idx = np.where(label < self.nb_classes - 1)
+            detector_idx = np.where(label == self.nb_classes - 1)
+
+            # Initialize the combined gradients
+            combined_grads = np.zeros(shape=(x.shape[0], 1, x.shape[1], x.shape[2], x.shape[3]))
+
+            # First compute the classifier gradients for classifier_idx
+            if classifier_idx:
+                combined_grads[classifier_idx] = self.classifier.class_gradient(
+                    x=x[classifier_idx], label=label[classifier_idx], training_mode=training_mode, **kwargs
+                )
+
+            # Then compute the detector gradients for detector_idx
+            if detector_idx:
+                # First compute the classifier gradients for detector_idx
+                classifier_grads = self.classifier.class_gradient(
+                    x=x[detector_idx], label=None, training_mode=training_mode, **kwargs
+                )
+
+                # Then compute the detector gradients for detector_idx
+                detector_grads = self.detector.class_gradient(
+                    x=x[detector_idx], label=0, training_mode=training_mode, **kwargs
+                )
+
+                # Chain the detector gradients for the first component
+                classifier_preds = self.classifier.predict(x=x[detector_idx])
+                maxind_classifier_preds = np.argmax(classifier_preds, axis=1)
+                max_classifier_preds = classifier_preds[np.arange(len(detector_idx)), maxind_classifier_preds]
+                first_detector_grads = max_classifier_preds[:, None, None, None, None] * detector_grads
+
+                # Chain the detector gradients for the second component
+                max_classifier_grads = classifier_grads[np.arange(len(classifier_grads)), maxind_classifier_preds]
+                detector_preds = self.detector.predict(x=x[detector_idx])
+                second_detector_grads = max_classifier_grads * (detector_preds + 1)[:, None, None]
+                second_detector_grads = second_detector_grads[None, ...]
+                second_detector_grads = np.swapaxes(second_detector_grads, 0, 1)
+
+                # Update detector gradients
+                detector_grads = first_detector_grads + second_detector_grads
+
+                # Reassign the combined gradients
+                combined_grads[detector_idx] = detector_grads
+
+        return combined_grads
+
+    def compute_loss(self, x: np.ndarray, y: np.ndarray, **kwargs) -> np.ndarray:
+        """
+        Compute the loss of the neural network for samples `x`.
+
+        :param x: Samples of shape (nb_samples, nb_features) or (nb_samples, nb_pixels_1, nb_pixels_2,
+                  nb_channels) or (nb_samples, nb_channels, nb_pixels_1, nb_pixels_2).
+        :param y: Target values (class labels) one-hot-encoded of shape `(nb_samples, nb_classes)` or indices
+                  of shape `(nb_samples,)`.
+        :return: Loss values.
+        :rtype: Format as expected by the `model`
+        """
+        raise NotImplementedError
+
+    def loss_gradient(self, x: np.ndarray, y: np.ndarray, training_mode: bool = False, **kwargs) -> np.ndarray:
+        """
+        Compute the gradient of the loss function w.r.t. `x`.
+
+        :param x: Sample input with shape as expected by the model.
+        :param y: Target values (class labels) one-hot-encoded of shape (nb_samples, nb_classes) or indices of shape
+                  (nb_samples,).
+        :param training_mode: `True` for model set to training mode and `'False` for model set to evaluation mode.
+        :return: Array of gradients of the same shape as `x`.
+        :raises `NotImplementedException`: This method is not supported for detector-classifiers.
+        """
+        raise NotImplementedError
+
+    @property
+    def layer_names(self) -> List[str]:
+        """
+        Return the hidden layers in the model, if applicable. This function is not supported for the
+        Classifier and Detector wrapper.
+
+        :return: The hidden layers in the model, input and output layers excluded.
+        :raises `NotImplementedException`: This method is not supported for detector-classifiers.
+        """
+        raise NotImplementedError
+
+    def get_activations(
+        self, x: np.ndarray, layer: Union[int, str], batch_size: int = 128, framework: bool = False
+    ) -> np.ndarray:
+        """
+        Return the output of the specified layer for input `x`. `layer` is specified by layer index (between 0 and
+        `nb_layers - 1`) or by name. The number of layers can be determined by counting the results returned by
+        calling `layer_names`.
+
+        :param x: Input for computing the activations.
+        :param layer: Layer for computing the activations.
+        :param batch_size: Size of batches.
+        :param framework: If true, return the intermediate tensor representation of the activation.
+        :return: The output of `layer`, where the first dimension is the batch size corresponding to `x`.
+        :raises `NotImplementedException`: This method is not supported for detector-classifiers.
+        """
+        raise NotImplementedError
+
+    def save(self, filename: str, path: Optional[str] = None) -> None:
+        """
+        Save a model to file in the format specific to the backend framework.
+
+        :param filename: Name of the file where to store the model.
+        :param path: Path of the folder where to store the model. If no path is specified, the model will be stored in
+                     the default data location of the library `ART_DATA_PATH`.
+        """
+        self.classifier.save(filename=filename + "_classifier", path=path)
+        self.detector.save(filename=filename + "_detector", path=path)
+
+    def __repr__(self):
+        repr_ = "%s(classifier=%r, detector=%r, postprocessing_defences=%r, " "preprocessing=%r)" % (
+            self.__module__ + "." + self.__class__.__name__,
+            self.classifier,
+            self.detector,
+            self.postprocessing_defences,
+            self.preprocessing,
+        )
+
+        return repr_
+
+    def _compute_combined_grads(
+        self, x: np.ndarray, label: Union[int, List[int], np.ndarray, None] = None
+    ) -> np.ndarray:
+        # Compute the classifier gradients
+        classifier_grads = self.classifier.class_gradient(x=x, label=label)
+
+        # Then compute the detector gradients
+        detector_grads = self.detector.class_gradient(x=x, label=label)
+
+        # Chain the detector gradients for the first component
+        classifier_preds = self.classifier.predict(x=x)
+        maxind_classifier_preds = np.argmax(classifier_preds, axis=1)
+        max_classifier_preds = classifier_preds[np.arange(classifier_preds.shape[0]), maxind_classifier_preds]
+        first_detector_grads = max_classifier_preds[:, None, None, None, None] * detector_grads
+
+        # Chain the detector gradients for the second component
+        max_classifier_grads = classifier_grads[np.arange(len(classifier_grads)), maxind_classifier_preds]
+        detector_preds = self.detector.predict(x=x)
+        second_detector_grads = max_classifier_grads * (detector_preds + 1)[:, None, None]
+        second_detector_grads = second_detector_grads[None, ...]
+        second_detector_grads = np.swapaxes(second_detector_grads, 0, 1)
+
+        # Update detector gradients
+        detector_grads = first_detector_grads + second_detector_grads
+
+        # Combine the gradients
+        combined_logits_grads = np.concatenate([classifier_grads, detector_grads], axis=1)
+
+        return combined_logits_grads
diff --git a/adversarial-robustness-toolbox/art/estimators/classification/ensemble.py b/adversarial-robustness-toolbox/art/estimators/classification/ensemble.py
new file mode 100644
index 0000000..5797ace
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/estimators/classification/ensemble.py
@@ -0,0 +1,341 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements the classifier `EnsembleClassifier` for ensembles of multiple classifiers.
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+from typing import List, Optional, Union, Tuple, TYPE_CHECKING
+
+import numpy as np
+
+from art.estimators.classification.classifier import ClassifierNeuralNetwork
+from art.estimators.estimator import NeuralNetworkMixin
+
+if TYPE_CHECKING:
+    from art.utils import CLIP_VALUES_TYPE, PREPROCESSING_TYPE
+    from art.data_generators import DataGenerator
+    from art.defences.preprocessor import Preprocessor
+    from art.defences.postprocessor import Postprocessor
+
+logger = logging.getLogger(__name__)
+
+
+class EnsembleClassifier(ClassifierNeuralNetwork):
+    """
+    Class allowing to aggregate multiple classifiers as an ensemble. The individual classifiers are expected to be
+    trained when the ensemble is created and no training procedures are provided through this class.
+    """
+
+    estimator_params = ClassifierNeuralNetwork.estimator_params + [
+        "classifiers",
+        "classifier_weights",
+    ]
+
+    def __init__(
+        self,
+        classifiers: List[ClassifierNeuralNetwork],
+        classifier_weights: Union[list, np.ndarray, None] = None,
+        channels_first: bool = False,
+        clip_values: Optional["CLIP_VALUES_TYPE"] = None,
+        preprocessing_defences: Union["Preprocessor", List["Preprocessor"], None] = None,
+        postprocessing_defences: Union["Postprocessor", List["Postprocessor"], None] = None,
+        preprocessing: "PREPROCESSING_TYPE" = (0.0, 1.0),
+    ) -> None:
+        """
+        Initialize a :class:`.EnsembleClassifier` object. The data range values and colour channel index have to
+        be consistent for all the classifiers in the ensemble.
+
+        :param classifiers: List of :class:`.Classifier` instances to be ensembled together.
+        :param classifier_weights: List of weights, one scalar per classifier, to assign to their prediction when
+               aggregating results. If `None`, all classifiers are assigned the same weight.
+        :param channels_first: Set channels first or last.
+        :param clip_values: Tuple of the form `(min, max)` of floats or `np.ndarray` representing the minimum and
+               maximum values allowed for features. If floats are provided, these will be used as the range of all
+               features. If arrays are provided, each value will be considered the bound for a feature, thus
+               the shape of clip values needs to match the total number of features.
+        :param preprocessing_defences: Preprocessing defence(s) to be applied by the classifier. Not applicable
+               in this classifier.
+        :param postprocessing_defences: Postprocessing defence(s) to be applied by the classifier.
+        :param preprocessing: Tuple of the form `(subtrahend, divisor)` of floats or `np.ndarray` of values to be
+               used for data preprocessing. The first value will be subtracted from the input. The input will then
+               be divided by the second one. Not applicable in this classifier.
+        """
+        if preprocessing_defences is not None:
+            raise NotImplementedError("Preprocessing is not applicable in this classifier.")
+
+        super().__init__(
+            model=None,
+            clip_values=clip_values,
+            channels_first=channels_first,
+            preprocessing_defences=preprocessing_defences,
+            postprocessing_defences=postprocessing_defences,
+            preprocessing=preprocessing,
+        )
+        self._nb_classifiers = len(classifiers)
+
+        # Assert all classifiers are the right shape(s)
+        for classifier in classifiers:
+            if not isinstance(classifier, NeuralNetworkMixin):
+                raise TypeError("Expected type `Classifier`, found %s instead." % type(classifier))
+
+            if not np.array_equal(self.clip_values, classifier.clip_values):
+                raise ValueError(
+                    "Incompatible `clip_values` between classifiers in the ensemble. Found %s and %s."
+                    % (str(self.clip_values), str(classifier.clip_values))
+                )
+
+            if classifier.nb_classes != classifiers[0].nb_classes:
+                raise ValueError(
+                    "Incompatible output shapes between classifiers in the ensemble. Found %s and %s."
+                    % (str(classifier.nb_classes), str(classifiers[0].nb_classes))
+                )
+
+            if classifier.input_shape != classifiers[0].input_shape:
+                raise ValueError(
+                    "Incompatible input shapes between classifiers in the ensemble. Found %s and %s."
+                    % (str(classifier.input_shape), str(classifiers[0].input_shape))
+                )
+
+        self._input_shape = classifiers[0].input_shape
+        self._nb_classes = classifiers[0].nb_classes
+
+        # Set weights for classifiers
+        if classifier_weights is None:
+            classifier_weights = np.ones(self._nb_classifiers) / self._nb_classifiers
+        self._classifier_weights = classifier_weights
+
+        # check for consistent channels_first in ensemble members
+        for i_cls, cls in enumerate(classifiers):
+            if cls.channels_first != self.channels_first:
+                raise ValueError(
+                    "The channels_first boolean of classifier {} is {} while this ensemble expects a "
+                    "channels_first boolean of {}. The channels_first booleans of all classifiers and the "
+                    "ensemble need ot be identical.".format(i_cls, cls.channels_first, self.channels_first)
+                )
+
+        self._classifiers = classifiers
+
+    @property
+    def input_shape(self) -> Tuple[int, ...]:
+        """
+        Return the shape of one input sample.
+
+        :return: Shape of one input sample.
+        """
+        return self._input_shape  # type: ignore
+
+    @property
+    def classifiers(self) -> List[ClassifierNeuralNetwork]:
+        """
+        Return the Classifier instances that are ensembled together.
+
+        :return: Classifier instances that are ensembled together.
+        """
+        return self._classifiers  # type: ignore
+
+    @property
+    def classifier_weights(self) -> Union[list, np.ndarray, None]:
+        """
+        Return the list of classifier weights to assign to their prediction when aggregating results.
+
+        :return: The list of classifier weights to assign to their prediction when aggregating results.
+        """
+        return self._classifier_weights  # type: ignore
+
+    def predict(self, x: np.ndarray, batch_size: int = 128, raw: bool = False, **kwargs) -> np.ndarray:
+        """
+        Perform prediction for a batch of inputs. Predictions from classifiers should only be aggregated if they all
+        have the same type of output (e.g., probabilities). Otherwise, use `raw=True` to get predictions from all
+        models without aggregation. The same option should be used for logits output, as logits are not comparable
+        between models and should not be aggregated.
+
+        :param x: Input samples.
+        :param batch_size: Size of batches.
+        :param raw: Return the individual classifier raw outputs (not aggregated).
+        :return: Array of predictions of shape `(nb_inputs, nb_classes)`, or of shape
+                 `(nb_classifiers, nb_inputs, nb_classes)` if `raw=True`.
+        """
+        preds = np.array(
+            [self._classifier_weights[i] * self._classifiers[i].predict(x) for i in range(self._nb_classifiers)]
+        )
+        if raw:
+            return preds
+
+        # Aggregate predictions only at probabilities level, as logits are not comparable between models
+        var_z = np.sum(preds, axis=0)
+
+        # Apply postprocessing
+        predictions = self._apply_postprocessing(preds=var_z, fit=False)
+
+        return predictions
+
+    def fit(self, x: np.ndarray, y: np.ndarray, batch_size: int = 128, nb_epochs: int = 20, **kwargs) -> None:
+        """
+        Fit the classifier on the training set `(x, y)`. This function is not supported for ensembles.
+
+        :param x: Training data.
+        :param y: Target values (class labels) one-hot-encoded of shape (nb_samples, nb_classes).
+        :param batch_size: Size of batches.
+        :param nb_epochs: Number of epochs to use for training.
+        :param kwargs: Dictionary of framework-specific arguments.
+        :raises `NotImplementedException`: This method is not supported for ensembles.
+        """
+        raise NotImplementedError
+
+    def fit_generator(self, generator: "DataGenerator", nb_epochs: int = 20, **kwargs) -> None:
+        """
+        Fit the classifier using the generator that yields batches as specified. This function is not supported for
+        ensembles.
+
+        :param generator: Batch generator providing `(x, y)` for each epoch. If the generator can be used for native
+                          training in Keras, it will.
+        :param nb_epochs: Number of epochs to use for trainings.
+        :param kwargs: Dictionary of framework-specific argument.
+        :raises `NotImplementedException`: This method is not supported for ensembles.
+        """
+        raise NotImplementedError
+
+    @property
+    def layer_names(self) -> List[str]:
+        """
+        Return the hidden layers in the model, if applicable. This function is not supported for ensembles.
+
+        :return: The hidden layers in the model, input and output layers excluded.
+        :raises `NotImplementedException`: This method is not supported for ensembles.
+        """
+        raise NotImplementedError
+
+    def get_activations(
+        self, x: np.ndarray, layer: Union[int, str], batch_size: int = 128, framework: bool = False
+    ) -> np.ndarray:
+        """
+        Return the output of the specified layer for input `x`. `layer` is specified by layer index (between 0 and
+        `nb_layers - 1`) or by name. The number of layers can be determined by counting the results returned by
+        calling `layer_names`. This function is not supported for ensembles.
+
+        :param x: Input for computing the activations.
+        :param layer: Layer for computing the activations.
+        :param batch_size: Size of batches.
+        :param framework: If true, return the intermediate tensor representation of the activation.
+        :return: The output of `layer`, where the first dimension is the batch size corresponding to `x`.
+        :raises `NotImplementedException`: This method is not supported for ensembles.
+        """
+        raise NotImplementedError
+
+    def class_gradient(
+        self,
+        x: np.ndarray,
+        label: Union[int, List[int], None] = None,
+        training_mode: bool = False,
+        raw: bool = False,
+        **kwargs
+    ) -> np.ndarray:
+        """
+        Compute per-class derivatives w.r.t. `x`.
+
+        :param x: Sample input with shape as expected by the model.
+        :param label: Index of a specific per-class derivative. If `None`, then gradients for all
+                      classes will be computed.
+        :param training_mode: `True` for model set to training mode and `'False` for model set to evaluation mode.
+        :param raw: Return the individual classifier raw outputs (not aggregated).
+        :return: Array of gradients of input features w.r.t. each class in the form
+                 `(batch_size, nb_classes, input_shape)` when computing for all classes, otherwise shape becomes
+                 `(batch_size, 1, input_shape)` when `label` parameter is specified. If `raw=True`, an additional
+                 dimension is added at the beginning of the array, indexing the different classifiers.
+        """
+        grads = np.array(
+            [
+                self._classifier_weights[i]
+                * self._classifiers[i].class_gradient(x=x, label=label, training_mode=training_mode, **kwargs)
+                for i in range(self._nb_classifiers)
+            ]
+        )
+        if raw:
+            return grads
+
+        return np.sum(grads, axis=0)
+
+    def loss_gradient(
+        self, x: np.ndarray, y: np.ndarray, training_mode: bool = False, raw: bool = False, **kwargs
+    ) -> np.ndarray:
+        """
+        Compute the gradient of the loss function w.r.t. `x`.
+
+        :param x: Sample input with shape as expected by the model.
+        :param y: Target values (class labels) one-hot-encoded of shape (nb_samples, nb_classes) or indices of shape
+                  (nb_samples,).
+        :param training_mode: `True` for model set to training mode and `'False` for model set to evaluation mode.
+        :param raw: Return the individual classifier raw outputs (not aggregated).
+        :return: Array of gradients of the same shape as `x`. If `raw=True`, shape becomes `[nb_classifiers, x.shape]`.
+        """
+        grads = np.array(
+            [
+                self._classifier_weights[i]
+                * self._classifiers[i].loss_gradient(x=x, y=y, training_mode=training_mode, **kwargs)
+                for i in range(self._nb_classifiers)
+            ]
+        )
+        if raw:
+            return grads
+
+        return np.sum(grads, axis=0)
+
+    def __repr__(self):
+        repr_ = (
+            "%s(classifiers=%r, classifier_weights=%r, channels_first=%r, clip_values=%r, "
+            "preprocessing_defences=%r, postprocessing_defences=%r, preprocessing=%r)"
+            % (
+                self.__module__ + "." + self.__class__.__name__,
+                self._classifiers,
+                self._classifier_weights,
+                self.channels_first,
+                self.clip_values,
+                self.preprocessing_defences,
+                self.postprocessing_defences,
+                self.preprocessing,
+            )
+        )
+
+        return repr_
+
+    def save(self, filename: str, path: Optional[str] = None) -> None:
+        """
+        Save a model to file in the format specific to the backend framework. This function is not supported for
+        ensembles.
+
+        :param filename: Name of the file where to store the model.
+        :param path: Path of the folder where to store the model. If no path is specified, the model will be stored in
+                     the default data location of the library `ART_DATA_PATH`.
+        :raises `NotImplementedException`: This method is not supported for ensembles.
+        """
+        raise NotImplementedError
+
+    def compute_loss(self, x: np.ndarray, y: np.ndarray, **kwargs) -> np.ndarray:
+        """
+        Compute the loss of the neural network for samples `x`.
+
+        :param x: Samples of shape (nb_samples, nb_features) or (nb_samples, nb_pixels_1, nb_pixels_2,
+                  nb_channels) or (nb_samples, nb_channels, nb_pixels_1, nb_pixels_2).
+        :param y: Target values (class labels) one-hot-encoded of shape `(nb_samples, nb_classes)` or indices
+                  of shape `(nb_samples,)`.
+        :return: Loss values.
+        :rtype: Format as expected by the `model`
+        """
+        raise NotImplementedError
diff --git a/adversarial-robustness-toolbox/art/estimators/classification/keras.py b/adversarial-robustness-toolbox/art/estimators/classification/keras.py
new file mode 100644
index 0000000..a6e5807
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/estimators/classification/keras.py
@@ -0,0 +1,835 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements the classifier `KerasClassifier` for Keras models.
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import os
+import time
+from typing import (
+    Any,
+    Callable,
+    Dict,
+    Iterator,
+    List,
+    Optional,
+    Tuple,
+    Union,
+    TYPE_CHECKING,
+)
+
+import numpy as np
+import six
+
+from art import config
+from art.estimators.keras import KerasEstimator
+from art.estimators.classification.classifier import (
+    ClassifierMixin,
+    ClassGradientsMixin,
+)
+from art.utils import check_and_transform_label_format
+
+if TYPE_CHECKING:
+    # pylint: disable=C0412
+    import keras
+    import tensorflow as tf
+
+    from art.utils import CLIP_VALUES_TYPE, PREPROCESSING_TYPE
+    from art.data_generators import DataGenerator
+    from art.defences.preprocessor import Preprocessor
+    from art.defences.postprocessor import Postprocessor
+
+logger = logging.getLogger(__name__)
+
+KERAS_MODEL_TYPE = Union["keras.models.Model", "tf.keras.models.Model"]
+
+
+class KerasClassifier(ClassGradientsMixin, ClassifierMixin, KerasEstimator):
+    """
+    Wrapper class for importing Keras models.
+    """
+
+    estimator_params = (
+        KerasEstimator.estimator_params
+        + ClassifierMixin.estimator_params
+        + ["use_logits", "input_layer", "output_layer"]
+    )
+
+    def __init__(
+        self,
+        model: KERAS_MODEL_TYPE,
+        use_logits: bool = False,
+        channels_first: bool = False,
+        clip_values: Optional["CLIP_VALUES_TYPE"] = None,
+        preprocessing_defences: Union["Preprocessor", List["Preprocessor"], None] = None,
+        postprocessing_defences: Union["Postprocessor", List["Postprocessor"], None] = None,
+        preprocessing: "PREPROCESSING_TYPE" = (0.0, 1.0),
+        input_layer: int = 0,
+        output_layer: int = 0,
+    ) -> None:
+        """
+        Create a `Classifier` instance from a Keras model. Assumes the `model` passed as argument is compiled.
+
+        :param model: Keras model, neural network or other.
+        :param use_logits: True if the output of the model are logits; false for probabilities or any other type of
+               outputs. Logits output should be favored when possible to ensure attack efficiency.
+        :param channels_first: Set channels first or last.
+        :param clip_values: Tuple of the form `(min, max)` of floats or `np.ndarray` representing the minimum and
+               maximum values allowed for features. If floats are provided, these will be used as the range of all
+               features. If arrays are provided, each value will be considered the bound for a feature, thus
+               the shape of clip values needs to match the total number of features.
+        :param preprocessing_defences: Preprocessing defence(s) to be applied by the classifier.
+        :param postprocessing_defences: Postprocessing defence(s) to be applied by the classifier.
+        :param preprocessing: Tuple of the form `(subtrahend, divisor)` of floats or `np.ndarray` of values to be
+               used for data preprocessing. The first value will be subtracted from the input. The input will then
+               be divided by the second one.
+        :param input_layer: The index of the layer to consider as input for models with multiple input layers. The layer
+                            with this index will be considered for computing gradients. For models with only one input
+                            layer this values is not required.
+        :param output_layer: Which layer to consider as the output when the models has multiple output layers. The layer
+                             with this index will be considered for computing gradients. For models with only one output
+                             layer this values is not required.
+        """
+        super().__init__(
+            model=model,
+            clip_values=clip_values,
+            preprocessing_defences=preprocessing_defences,
+            postprocessing_defences=postprocessing_defences,
+            preprocessing=preprocessing,
+            channels_first=channels_first,
+        )
+
+        self._input_layer = input_layer
+        self._output_layer = output_layer
+
+        if "<class 'tensorflow" in str(type(model).__mro__):
+            self.is_tensorflow = True
+        elif "<class 'keras" in str(type(model).__mro__):
+            self.is_tensorflow = False
+        else:
+            raise TypeError("Type of model not recognized:" + str(type(model)))
+
+        self._initialize_params(model, use_logits, input_layer, output_layer)
+
+    def _initialize_params(
+        self, model: KERAS_MODEL_TYPE, use_logits: bool, input_layer: int, output_layer: int,
+    ):
+        """
+        Initialize most parameters of the classifier. This is a convenience function called by `__init__` and
+        `__setstate__` to avoid code duplication.
+
+        :param model: Keras model
+        :param use_logits: True if the output of the model are logits.
+        :param input_layer: Which layer to consider as the Input when the model has multiple input layers.
+        :param output_layer: Which layer to consider as the Output when the model has multiple output layers.
+        """
+        # pylint: disable=E0401
+        if self.is_tensorflow:
+            import tensorflow as tf  # lgtm [py/repeated-import]
+
+            if tf.executing_eagerly():
+                raise ValueError("TensorFlow is executing eagerly. Please disable eager execution.")
+            import tensorflow.keras as keras
+            import tensorflow.keras.backend as k
+
+            self._losses = keras.losses
+        else:
+            import keras  # lgtm [py/repeated-import]
+            import keras.backend as k
+
+            if hasattr(keras.utils, "losses_utils"):
+                self._losses = keras.utils.losses_utils
+            else:
+                self._losses = None
+
+        if hasattr(model, "inputs"):
+            self._input_layer = input_layer
+            self._input = model.inputs[input_layer]
+        else:
+            self._input = model.input
+            self._input_layer = 0
+
+        if hasattr(model, "outputs"):
+            self._output = model.outputs[output_layer]
+            self._output_layer = output_layer
+        else:
+            self._output = model.output
+            self._output_layer = 0
+
+        _, self._nb_classes = k.int_shape(self._output)
+        self._input_shape = k.int_shape(self._input)[1:]
+        logger.debug(
+            "Inferred %i classes and %s as input shape for Keras classifier.", self.nb_classes, str(self.input_shape),
+        )
+
+        self._use_logits = use_logits
+
+        # Get loss function
+        if not hasattr(self._model, "loss"):
+            logger.warning("Keras model has no loss set. Classifier tries to use `k.sparse_categorical_crossentropy`.")
+            loss_function = k.sparse_categorical_crossentropy
+        else:
+            self._orig_loss = self._model.loss
+            if isinstance(self._model.loss, six.string_types):
+                loss_function = getattr(k, self._model.loss)
+
+            elif "__name__" in dir(self._model.loss) and self._model.loss.__name__ in [
+                "categorical_hinge",
+                "categorical_crossentropy",
+                "sparse_categorical_crossentropy",
+                "binary_crossentropy",
+                "kullback_leibler_divergence",
+            ]:
+                if self._model.loss.__name__ in [
+                    "categorical_hinge",
+                    "kullback_leibler_divergence",
+                ]:
+                    loss_function = getattr(keras.losses, self._model.loss.__name__)
+                else:
+                    loss_function = getattr(keras.backend, self._model.loss.__name__)
+
+            elif isinstance(
+                self._model.loss,
+                (
+                    keras.losses.CategoricalHinge,
+                    keras.losses.CategoricalCrossentropy,
+                    keras.losses.SparseCategoricalCrossentropy,
+                    keras.losses.BinaryCrossentropy,
+                    keras.losses.KLDivergence,
+                ),
+            ):
+                loss_function = self._model.loss
+            else:
+                loss_function = getattr(k, self._model.loss.__name__)
+
+        # Check if loss function is an instance of loss function generator, the try is required because some of the
+        # modules are not available in older Keras versions
+        try:
+            flag_is_instance = isinstance(
+                loss_function,
+                (
+                    keras.losses.CategoricalHinge,
+                    keras.losses.CategoricalCrossentropy,
+                    keras.losses.BinaryCrossentropy,
+                    keras.losses.KLDivergence,
+                ),
+            )
+        except AttributeError:
+            flag_is_instance = False
+
+        # Check if the labels have to be reduced to index labels and create placeholder for labels
+        if (
+            "__name__" in dir(loss_function)
+            and loss_function.__name__
+            in ["categorical_hinge", "categorical_crossentropy", "binary_crossentropy", "kullback_leibler_divergence",]
+        ) or flag_is_instance:
+            self._reduce_labels = False
+            label_ph = k.placeholder(shape=self._output.shape)
+        elif (
+            "__name__" in dir(loss_function) and loss_function.__name__ in ["sparse_categorical_crossentropy"]
+        ) or isinstance(loss_function, keras.losses.SparseCategoricalCrossentropy):
+            self._reduce_labels = True
+            label_ph = k.placeholder(shape=[None,])
+        else:
+            raise ValueError("Loss function not recognised.")
+
+        # Define the loss using the loss function
+        if "__name__" in dir(loss_function,) and loss_function.__name__ in [
+            "categorical_crossentropy",
+            "sparse_categorical_crossentropy",
+            "binary_crossentropy",
+        ]:
+            loss_ = loss_function(label_ph, self._output, from_logits=self._use_logits)
+
+        elif "__name__" in dir(loss_function) and loss_function.__name__ in [
+            "categorical_hinge",
+            "kullback_leibler_divergence",
+        ]:
+            loss_ = loss_function(label_ph, self._output)
+
+        elif isinstance(
+            loss_function,
+            (
+                keras.losses.CategoricalHinge,
+                keras.losses.CategoricalCrossentropy,
+                keras.losses.SparseCategoricalCrossentropy,
+                keras.losses.KLDivergence,
+                keras.losses.BinaryCrossentropy,
+            ),
+        ):
+            loss_ = loss_function(label_ph, self._output)
+
+        # Define loss gradients
+        loss_gradients = k.gradients(loss_, self._input)
+
+        if k.backend() == "tensorflow":
+            loss_gradients = loss_gradients[0]
+        elif k.backend() == "cntk":
+            raise NotImplementedError("Only TensorFlow is supported as backend for Keras.")
+
+        # Set loss, gradients and prediction functions
+        self._predictions_op = self._output
+        self._loss_function = loss_function
+        self._loss = loss_
+        self._loss_gradients = k.function([self._input, label_ph, k.learning_phase()], [loss_gradients])
+
+        # Get the internal layer
+        self._layer_names = self._get_layers()
+
+    @property
+    def input_shape(self) -> Tuple[int, ...]:
+        """
+        Return the shape of one input sample.
+
+        :return: Shape of one input sample.
+        """
+        return self._input_shape  # type: ignore
+
+    @property
+    def use_logits(self) -> bool:
+        """
+        A boolean representing whether the outputs of the model are logits.
+
+        :return: a boolean representing whether the outputs of the model are logits.
+        """
+        return self._use_logits  # type: ignore
+
+    @property
+    def input_layer(self) -> int:
+        """
+        The index of the layer considered as input for models with multiple input layers.
+        For models with only one input layer the index is 0.
+
+        :return: The index of the layer considered as input for models with multiple input layers.
+        """
+        return self._input_layer  # type: ignore
+
+    @property
+    def output_layer(self) -> int:
+        """
+        The index of the layer considered as output for models with multiple output layers.
+        For models with only one output layer the index is 0.
+
+        :return: The index of the layer considered as output for models with multiple output layers.
+        """
+        return self._output_layer  # type: ignore
+
+    def compute_loss(self, x: np.ndarray, y: np.ndarray, reduction: str = "none", **kwargs) -> np.ndarray:
+        """
+        Compute the loss of the neural network for samples `x`.
+
+        :param x: Samples of shape (nb_samples, nb_features) or (nb_samples, nb_pixels_1, nb_pixels_2,
+                  nb_channels) or (nb_samples, nb_channels, nb_pixels_1, nb_pixels_2).
+        :param y: Target values (class labels) one-hot-encoded of shape `(nb_samples, nb_classes)` or indices
+                  of shape `(nb_samples,)`.
+        :param reduction: Specifies the reduction to apply to the output: 'none' | 'mean' | 'sum'.
+                   'none': no reduction will be applied
+                   'mean': the sum of the output will be divided by the number of elements in the output,
+                   'sum': the output will be summed.
+        :return: Loss values.
+        :rtype: Format as expected by the `model`
+        """
+        if not self._losses:
+            raise NotImplementedError("loss method is only supported for keras versions >= 2.3.1")
+
+        if self.is_tensorflow:
+            import tensorflow.keras.backend as k
+        else:
+            import keras.backend as k
+
+        x_preprocessed, y_preprocessed = self._apply_preprocessing(x, y, fit=False)
+        shape_match = [i is None or i == j for i, j in zip(self._input_shape, x_preprocessed.shape[1:])]
+        if not all(shape_match):
+            raise ValueError(
+                "Error when checking x: expected preprocessed x to have shape {} but got array with "
+                "shape {}.".format(self._input_shape, x_preprocessed.shape[1:])
+            )
+
+        # Adjust the shape of y for loss functions that do not take labels in one-hot encoding
+        if self._reduce_labels:
+            y_preprocessed = np.argmax(y_preprocessed, axis=1)
+
+        predictions = self._model.predict(x_preprocessed)
+
+        if self._orig_loss and hasattr(self._orig_loss, "reduction"):
+            prev_reduction = self._orig_loss.reduction
+            self._orig_loss.reduction = self._losses.Reduction.NONE
+            loss = self._orig_loss(y_preprocessed, predictions)
+            self._orig_loss.reduction = prev_reduction
+        else:
+            prev_reduction = []
+            predictions = k.constant(predictions)
+            y_preprocessed = k.constant(y_preprocessed)
+            for loss_function in self._model.loss_functions:
+                prev_reduction.append(loss_function.reduction)
+                loss_function.reduction = self._losses.Reduction.NONE
+            loss = self._loss_function(y_preprocessed, predictions)
+            for i, loss_function in enumerate(self._model.loss_functions):
+                loss_function.reduction = prev_reduction[i]
+
+        loss_value = k.eval(loss)
+
+        if reduction == "none":
+            pass
+        elif reduction == "mean":
+            loss_value = np.mean(loss_value, axis=0)
+        elif reduction == "sum":
+            loss_value = np.sum(loss_value, axis=0)
+
+        return loss_value
+
+    def loss_gradient(self, x: np.ndarray, y: np.ndarray, training_mode: bool = False, **kwargs) -> np.ndarray:
+        """
+        Compute the gradient of the loss function w.r.t. `x`.
+
+        :param x: Sample input with shape as expected by the model.
+        :param y: Target values (class labels) one-hot-encoded of shape (nb_samples, nb_classes) or indices of shape
+                  (nb_samples,).
+        :param training_mode: `True` for model set to training mode and `'False` for model set to evaluation mode.
+        :return: Array of gradients of the same shape as `x`.
+        """
+        # Check shape of preprocessed `x` because of custom function for `_loss_gradients`
+        x_preprocessed, y_preprocessed = self._apply_preprocessing(x, y, fit=False)
+        shape_match = [i is None or i == j for i, j in zip(self._input_shape, x_preprocessed.shape[1:])]
+        if not all(shape_match):
+            raise ValueError(
+                "Error when checking x: expected preprocessed x to have shape {} but got array with shape {}".format(
+                    self._input_shape, x_preprocessed.shape[1:]
+                )
+            )
+
+        # Adjust the shape of y for loss functions that do not take labels in one-hot encoding
+        if self._reduce_labels:
+            y_preprocessed = np.argmax(y_preprocessed, axis=1)
+
+        # Compute gradients
+        gradients = self._loss_gradients([x_preprocessed, y_preprocessed, int(training_mode)])[0]
+        assert gradients.shape == x_preprocessed.shape
+        gradients = self._apply_preprocessing_gradient(x, gradients)
+        assert gradients.shape == x.shape
+
+        return gradients
+
+    def class_gradient(
+        self, x: np.ndarray, label: Optional[Union[int, List[int]]] = None, training_mode: bool = False, **kwargs
+    ) -> np.ndarray:
+        """
+        Compute per-class derivatives w.r.t. `x`.
+
+        :param x: Sample input with shape as expected by the model.
+        :param label: Index of a specific per-class derivative. If an integer is provided, the gradient of that class
+                      output is computed for all samples. If multiple values are provided, the first dimension should
+                      match the batch size of `x`, and each value will be used as target for its corresponding sample in
+                      `x`. If `None`, then gradients for all classes will be computed for each sample.
+        :param training_mode: `True` for model set to training mode and `'False` for model set to evaluation mode.
+        :return: Array of gradients of input features w.r.t. each class in the form
+                 `(batch_size, nb_classes, input_shape)` when computing for all classes, otherwise shape becomes
+                 `(batch_size, 1, input_shape)` when `label` parameter is specified.
+        """
+        # Check value of label for computing gradients
+        if not (
+            label is None
+            or (isinstance(label, (int, np.integer)) and label in range(self.nb_classes))
+            or (
+                isinstance(label, np.ndarray)
+                and len(label.shape) == 1
+                and (label < self.nb_classes).all()
+                and label.shape[0] == x.shape[0]
+            )
+        ):
+            raise ValueError("Label %s is out of range." % str(label))
+
+        # Check shape of preprocessed `x` because of custom function for `_class_gradients`
+        x_preprocessed, _ = self._apply_preprocessing(x, y=None, fit=False)
+        shape_match = [i is None or i == j for i, j in zip(self._input_shape, x_preprocessed.shape[1:])]
+        if not all(shape_match):
+            raise ValueError(
+                "Error when checking x: expected preprocessed x to have shape {} but got array with shape {}".format(
+                    self._input_shape, x_preprocessed.shape[1:]
+                )
+            )
+
+        self._init_class_gradients(label=label)
+
+        if label is None:
+            # Compute the gradients w.r.t. all classes
+            gradients = np.swapaxes(np.array(self._class_gradients([x_preprocessed])), 0, 1)
+
+        elif isinstance(label, (int, np.integer)):
+            # Compute the gradients only w.r.t. the provided label
+            gradients = np.swapaxes(
+                np.array(self._class_gradients_idx[label]([x_preprocessed, int(training_mode)])), axis1=0, axis2=1
+            )  # type: ignore
+            assert gradients.shape == (x_preprocessed.shape[0], 1) + x_preprocessed.shape[1:]
+
+        else:
+            # For each sample, compute the gradients w.r.t. the indicated target class (possibly distinct)
+            unique_label = list(np.unique(label))
+            gradients = np.array(
+                [self._class_gradients_idx[l]([x_preprocessed, int(training_mode)]) for l in unique_label]
+            )
+            gradients = np.swapaxes(np.squeeze(gradients, axis=1), 0, 1)
+            lst = [unique_label.index(i) for i in label]
+            gradients = np.expand_dims(gradients[np.arange(len(gradients)), lst], axis=1)
+
+        gradients = self._apply_preprocessing_gradient(x, gradients)
+
+        return gradients
+
+    def predict(self, x: np.ndarray, batch_size: int = 128, training_mode: bool = False, **kwargs) -> np.ndarray:
+        """
+        Perform prediction for a batch of inputs.
+
+        :param x: Input samples.
+        :param batch_size: Size of batches.
+        :param training_mode: `True` for model set to training mode and `'False` for model set to evaluation mode.
+        :return: Array of predictions of shape `(nb_inputs, nb_classes)`.
+        """
+        # Apply preprocessing
+        x_preprocessed, _ = self._apply_preprocessing(x, y=None, fit=False)
+
+        # Run predictions with batching
+        if training_mode:
+            predictions = self._model(x_preprocessed, training=training_mode)
+        else:
+            predictions = self._model.predict(x_preprocessed, batch_size=batch_size)
+
+        # Apply postprocessing
+        predictions = self._apply_postprocessing(preds=predictions, fit=False)
+
+        return predictions
+
+    def fit(self, x: np.ndarray, y: np.ndarray, batch_size: int = 128, nb_epochs: int = 20, **kwargs) -> None:
+        """
+        Fit the classifier on the training set `(x, y)`.
+
+        :param x: Training data.
+        :param y: Target values (class labels) one-hot-encoded of shape (nb_samples, nb_classes) or index labels of
+                  shape (nb_samples,).
+        :param batch_size: Size of batches.
+        :param nb_epochs: Number of epochs to use for training.
+        :param kwargs: Dictionary of framework-specific arguments. These should be parameters supported by the
+               `fit_generator` function in Keras and will be passed to this function as such. Including the number of
+               epochs or the number of steps per epoch as part of this argument will result in as error.
+        """
+        y = check_and_transform_label_format(y, self.nb_classes)
+
+        # Apply preprocessing
+        x_preprocessed, y_preprocessed = self._apply_preprocessing(x, y, fit=True)
+
+        # Adjust the shape of y for loss functions that do not take labels in one-hot encoding
+        if self._reduce_labels:
+            y_preprocessed = np.argmax(y_preprocessed, axis=1)
+
+        gen = generator_fit(x_preprocessed, y_preprocessed, batch_size)
+        steps_per_epoch = max(int(x_preprocessed.shape[0] / batch_size), 1)
+        self._model.fit_generator(gen, steps_per_epoch=steps_per_epoch, epochs=nb_epochs, **kwargs)
+
+    def fit_generator(self, generator: "DataGenerator", nb_epochs: int = 20, **kwargs) -> None:
+        """
+        Fit the classifier using the generator that yields batches as specified.
+
+        :param generator: Batch generator providing `(x, y)` for each epoch. If the generator can be used for native
+                          training in Keras, it will.
+        :param nb_epochs: Number of epochs to use for training.
+        :param kwargs: Dictionary of framework-specific arguments. These should be parameters supported by the
+               `fit_generator` function in Keras and will be passed to this function as such. Including the number of
+               epochs as part of this argument will result in as error.
+        """
+        from art.data_generators import KerasDataGenerator
+
+        # Try to use the generator as a Keras native generator, otherwise use it through the `DataGenerator` interface
+        if isinstance(generator, KerasDataGenerator) and not self.preprocessing:
+            try:
+                self._model.fit_generator(generator.iterator, epochs=nb_epochs, **kwargs)
+            except ValueError:
+                logger.info("Unable to use data generator as Keras generator. Now treating as framework-independent.")
+                if "verbose" not in kwargs.keys():
+                    kwargs["verbose"] = 0
+                super().fit_generator(generator, nb_epochs=nb_epochs, **kwargs)
+        else:
+            if "verbose" not in kwargs.keys():
+                kwargs["verbose"] = 0
+            super().fit_generator(generator, nb_epochs=nb_epochs, **kwargs)
+
+    def get_activations(
+        self, x: np.ndarray, layer: Union[int, str], batch_size: int, framework: bool = False
+    ) -> np.ndarray:
+        """
+        Return the output of the specified layer for input `x`. `layer` is specified by layer index (between 0 and
+        `nb_layers - 1`) or by name. The number of layers can be determined by counting the results returned by
+        calling `layer_names`.
+
+        :param x: Input for computing the activations.
+        :param layer: Layer for computing the activations.
+        :param batch_size: Size of batches.
+        :param framework: If true, return the intermediate tensor representation of the activation.
+        :return: The output of `layer`, where the first dimension is the batch size corresponding to `x`.
+        """
+        # pylint: disable=E0401
+        if self.is_tensorflow:
+            import tensorflow.keras.backend as k
+        else:
+            import keras.backend as k
+        from art.config import ART_NUMPY_DTYPE
+
+        if isinstance(layer, six.string_types):
+            if layer not in self._layer_names:
+                raise ValueError("Layer name %s is not part of the graph." % layer)
+            layer_name = layer
+        elif isinstance(layer, int):
+            if layer < 0 or layer >= len(self._layer_names):
+                raise ValueError(
+                    "Layer index %d is outside of range (0 to %d included)." % (layer, len(self._layer_names) - 1)
+                )
+            layer_name = self._layer_names[layer]
+        else:
+            raise TypeError("Layer must be of type `str` or `int`.")
+
+        if x.shape == self.input_shape:
+            x_expanded = np.expand_dims(x, 0)
+        else:
+            x_expanded = x
+
+        # Apply preprocessing
+        x_preprocessed, _ = self._apply_preprocessing(x=x_expanded, y=None, fit=False)
+
+        if not hasattr(self, "_activations_func"):
+            self._activations_func: Dict[str, Callable] = {}
+
+        keras_layer = self._model.get_layer(layer_name)
+        if layer_name not in self._activations_func:
+            num_inbound_nodes = len(getattr(keras_layer, "_inbound_nodes", []))
+            if num_inbound_nodes > 1:
+                layer_output = keras_layer.get_output_at(0)
+            else:
+                layer_output = keras_layer.output
+            self._activations_func[layer_name] = k.function([self._input, k.learning_phase()], [layer_output])
+
+        # Determine shape of expected output and prepare array
+        output_shape = self._activations_func[layer_name]([x_preprocessed[0][None, ...], int(False)])[0].shape
+        activations = np.zeros((x_preprocessed.shape[0],) + output_shape[1:], dtype=ART_NUMPY_DTYPE)
+
+        # Get activations with batching
+        for batch_index in range(int(np.ceil(x_preprocessed.shape[0] / float(batch_size)))):
+            begin, end = (
+                batch_index * batch_size,
+                min((batch_index + 1) * batch_size, x_preprocessed.shape[0]),
+            )
+            activations[begin:end] = self._activations_func[layer_name]([x_preprocessed[begin:end], 0])[0]
+
+        if framework:
+            placeholder = k.placeholder(shape=x.shape)
+            return placeholder, keras_layer(placeholder)
+
+        return activations
+
+    def custom_loss_gradient(self, nn_function, tensors, input_values, name="default"):
+        """
+        Returns the gradient of the nn_function with respect to model input
+
+        :param nn_function: an intermediate tensor representation of the function to differentiate
+        :type nn_function: a Keras tensor
+        :param tensors: the tensors or variables to differentiate with respect to
+        :type tensors: `list`
+        :param input_values: the inputs to evaluate the gradient
+        :type input_values: `list`
+        :param name: The name of the function. Functions of the same name are cached
+        :type name: `str`
+        :return: the gradient of the function w.r.t vars
+        :rtype: `np.ndarray`
+        """
+        if self.is_tensorflow:
+            import tensorflow.keras.backend as k
+        else:
+            import keras.backend as k
+
+        if not hasattr(self, "_custom_loss_func"):
+            self._custom_loss_func = {}
+
+        if name not in self._custom_loss_func:
+            grads = k.gradients(nn_function, tensors[0])[0]
+            self._custom_loss_func[name] = k.function(tensors, [grads])
+
+        outputs = self._custom_loss_func[name]
+        return outputs(input_values)
+
+    def _init_class_gradients(self, label: Optional[Union[int, List[int], np.ndarray]] = None) -> None:
+        # pylint: disable=E0401
+        if self.is_tensorflow:
+            import tensorflow.keras.backend as k
+        else:
+            import keras.backend as k
+
+        if len(self._output.shape) == 2:
+            nb_outputs = self._output.shape[1]
+        else:
+            raise ValueError("Unexpected output shape for classification in Keras model.")
+
+        if label is None:
+            logger.debug("Computing class gradients for all %i classes.", self.nb_classes)
+            if not hasattr(self, "_class_gradients"):
+                class_gradients = [k.gradients(self._predictions_op[:, i], self._input)[0] for i in range(nb_outputs)]
+                self._class_gradients = k.function([self._input], class_gradients)
+
+        else:
+            if isinstance(label, int):
+                unique_labels = [label]
+            else:
+                unique_labels = np.unique(label)
+            logger.debug("Computing class gradients for classes %s.", str(unique_labels))
+
+            if not hasattr(self, "_class_gradients_idx"):
+                self._class_gradients_idx = [None for _ in range(nb_outputs)]
+
+            for current_label in unique_labels:
+                if self._class_gradients_idx[current_label] is None:
+                    class_gradients = [k.gradients(self._predictions_op[:, current_label], self._input)[0]]
+                    self._class_gradients_idx[current_label] = k.function(
+                        [self._input, k.learning_phase()], class_gradients
+                    )
+
+    def _get_layers(self) -> List[str]:
+        """
+        Return the hidden layers in the model, if applicable.
+
+        :return: The hidden layers in the model, input and output layers excluded.
+        """
+        # pylint: disable=E0401
+        if self.is_tensorflow:
+            from tensorflow.keras.layers import InputLayer
+        else:
+            from keras.engine.topology import InputLayer
+
+        layer_names = [layer.name for layer in self._model.layers[:-1] if not isinstance(layer, InputLayer)]
+        logger.info("Inferred %i hidden layers on Keras classifier.", len(layer_names))
+
+        return layer_names
+
+    def save(self, filename: str, path: Optional[str] = None) -> None:
+        """
+        Save a model to file in the format specific to the backend framework. For Keras, .h5 format is used.
+
+        :param filename: Name of the file where to store the model.
+        :param path: Path of the folder where to store the model. If no path is specified, the model will be stored in
+                     the default data location of the library `ART_DATA_PATH`.
+        """
+        if path is None:
+            full_path = os.path.join(config.ART_DATA_PATH, filename)
+        else:
+            full_path = os.path.join(path, filename)
+        folder = os.path.split(full_path)[0]
+        if not os.path.exists(folder):
+            os.makedirs(folder)
+
+        self._model.save(str(full_path))
+        logger.info("Model saved in path: %s.", full_path)
+
+    def __getstate__(self) -> Dict[str, Any]:
+        """
+        Use to ensure `KerasClassifier` can be pickled.
+
+        :return: State dictionary with instance parameters.
+        """
+        state = self.__dict__.copy()
+
+        # Remove the unpicklable entries
+        del state["_model"]
+        del state["_input"]
+        del state["_output"]
+        del state["_predictions_op"]
+        del state["_loss"]
+        del state["_loss_gradients"]
+        del state["_layer_names"]
+        del state["_losses"]
+        del state["_loss_function"]
+
+        if "_orig_loss" in state:
+            del state["_orig_loss"]
+
+        if "_class_gradients" in state:
+            del state["_class_gradients"]
+
+        if "_class_gradients_idx" in state:
+            del state["_class_gradients_idx"]
+
+        if "_activations_func" in state:
+            del state["_activations_func"]
+
+        if "_custom_loss_func" in state:
+            del state["_custom_loss_func"]
+
+        model_name = str(time.time()) + ".h5"
+        state["model_name"] = model_name
+        self.save(model_name)
+        return state
+
+    def __setstate__(self, state: Dict[str, Any]) -> None:
+        """
+        Use to ensure `KerasClassifier` can be unpickled.
+
+        :param state: State dictionary with instance parameters to restore.
+        """
+        self.__dict__.update(state)
+
+        if self.is_tensorflow:
+            from tensorflow.keras.models import load_model
+        else:
+            from keras.models import load_model
+
+        full_path = os.path.join(config.ART_DATA_PATH, state["model_name"])
+        model = load_model(str(full_path))
+
+        self._model = model
+        self._initialize_params(model, state["_use_logits"], state["_input_layer"], state["_output_layer"])
+
+    def __repr__(self):
+        repr_ = (
+            "%s(model=%r, use_logits=%r, channels_first=%r, clip_values=%r, preprocessing_defences=%r"
+            ", postprocessing_defences=%r, preprocessing=%r, input_layer=%r, output_layer=%r)"
+            % (
+                self.__module__ + "." + self.__class__.__name__,
+                self._model,
+                self._use_logits,
+                self.channels_first,
+                self.clip_values,
+                self.preprocessing_defences,
+                self.postprocessing_defences,
+                self.preprocessing,
+                self._input_layer,
+                self._output_layer,
+            )
+        )
+
+        return repr_
+
+
+def generator_fit(x: np.ndarray, y: np.ndarray, batch_size: int = 128) -> Iterator[Tuple[np.ndarray, np.ndarray]]:
+    """
+    Minimal data generator for randomly batching large datasets.
+
+    :param x: The data sample to batch.
+    :param y: The labels for `x`. The first dimension has to match the first dimension of `x`.
+    :param batch_size: The size of the batches to produce.
+    :return: A batch of size `batch_size` of random samples from `(x, y)`.
+    """
+    while True:
+        indices = np.random.randint(x.shape[0], size=batch_size)
+        yield x[indices], y[indices]
diff --git a/adversarial-robustness-toolbox/art/estimators/classification/lightgbm.py b/adversarial-robustness-toolbox/art/estimators/classification/lightgbm.py
new file mode 100644
index 0000000..20f97be
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/estimators/classification/lightgbm.py
@@ -0,0 +1,220 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements the classifier `LightGBMClassifier` for LightGBM models.
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+from copy import deepcopy
+import logging
+import os
+import pickle
+from typing import List, Optional, Union, Tuple, TYPE_CHECKING
+
+import numpy as np
+
+from art.estimators.classification.classifier import ClassifierDecisionTree
+from art import config
+
+if TYPE_CHECKING:
+    # pylint: disable=C0412
+    import lightgbm  # lgtm [py/import-and-import-from]
+
+    from art.utils import CLIP_VALUES_TYPE, PREPROCESSING_TYPE
+    from art.defences.preprocessor import Preprocessor
+    from art.defences.postprocessor import Postprocessor
+    from art.metrics.verification_decisions_trees import LeafNode
+
+logger = logging.getLogger(__name__)
+
+
+class LightGBMClassifier(ClassifierDecisionTree):
+    """
+    Wrapper class for importing LightGBM models.
+    """
+
+    def __init__(
+        self,
+        model: Optional["lightgbm.Booster"] = None,  # type: ignore
+        clip_values: Optional["CLIP_VALUES_TYPE"] = None,
+        preprocessing_defences: Union["Preprocessor", List["Preprocessor"], None] = None,
+        postprocessing_defences: Union["Postprocessor", List["Postprocessor"], None] = None,
+        preprocessing: "PREPROCESSING_TYPE" = (0.0, 1.0),
+    ) -> None:
+        """
+        Create a `Classifier` instance from a LightGBM model.
+
+        :param model: LightGBM model.
+        :param clip_values: Tuple of the form `(min, max)` representing the minimum and maximum values allowed
+               for features.
+        :param preprocessing_defences: Preprocessing defence(s) to be applied by the classifier.
+        :param postprocessing_defences: Postprocessing defence(s) to be applied by the classifier.
+        :param preprocessing: Tuple of the form `(subtrahend, divisor)` of floats or `np.ndarray` of values to be
+               used for data preprocessing. The first value will be subtracted from the input. The input will then
+               be divided by the second one.
+        """
+        from lightgbm import Booster  # type: ignore
+
+        if not isinstance(model, Booster):
+            raise TypeError("Model must be of type lightgbm.Booster")
+
+        super().__init__(
+            model=model,
+            clip_values=clip_values,
+            preprocessing_defences=preprocessing_defences,
+            postprocessing_defences=postprocessing_defences,
+            preprocessing=preprocessing,
+        )
+
+        self._input_shape = (self._model.num_feature(),)
+        self._nb_classes = self._get_nb_classes()
+
+    @property
+    def input_shape(self) -> Tuple[int, ...]:
+        """
+        Return the shape of one input sample.
+
+        :return: Shape of one input sample.
+        """
+        return self._input_shape  # type: ignore
+
+    def fit(self, x: np.ndarray, y: np.ndarray, **kwargs) -> None:
+        """
+        Fit the classifier on the training set `(x, y)`.
+
+        :param x: Training data.
+        :param y: Target values (class labels) one-hot-encoded of shape (nb_samples, nb_classes).
+        :param kwargs: Dictionary of framework-specific arguments. These should be parameters supported by the
+               `fit` function in `lightgbm.Booster` and will be passed to this function as such.
+        :raises `NotImplementedException`: This method is not supported for LightGBM classifiers.
+        """
+        raise NotImplementedError
+
+    def predict(self, x: np.ndarray, **kwargs) -> np.ndarray:
+        """
+        Perform prediction for a batch of inputs.
+
+        :param x: Input samples.
+        :return: Array of predictions of shape `(nb_inputs, nb_classes)`.
+        """
+        # Apply preprocessing
+        x_preprocessed, _ = self._apply_preprocessing(x, y=None, fit=False)
+
+        # Perform prediction
+        predictions = self._model.predict(x_preprocessed)
+
+        # Apply postprocessing
+        predictions = self._apply_postprocessing(preds=predictions, fit=False)
+
+        return predictions
+
+    def _get_nb_classes(self) -> int:
+        """
+        Return the number of output classes.
+
+        :return: Number of classes in the data.
+        """
+        # pylint: disable=W0212
+        return self._model._Booster__num_class
+
+    def save(self, filename: str, path: Optional[str] = None) -> None:
+        """
+        Save a model to file in the format specific to the backend framework.
+
+        :param filename: Name of the file where to store the model.
+        :param path: Path of the folder where to store the model. If no path is specified, the model will be stored in
+                     the default data location of the library `ART_DATA_PATH`.
+        """
+        if path is None:
+            full_path = os.path.join(config.ART_DATA_PATH, filename)
+        else:
+            full_path = os.path.join(path, filename)
+        folder = os.path.split(full_path)[0]
+        if not os.path.exists(folder):
+            os.makedirs(folder)
+
+        with open(full_path + ".pickle", "wb") as file_pickle:
+            pickle.dump(self._model, file=file_pickle)
+
+    def get_trees(self) -> list:
+        """
+        Get the decision trees.
+
+        :return: A list of decision trees.
+        """
+        from art.metrics.verification_decisions_trees import Box, Tree
+
+        booster_dump = self._model.dump_model()["tree_info"]
+        trees = list()
+
+        for i_tree, tree_dump in enumerate(booster_dump):
+            box = Box()
+
+            # pylint: disable=W0212
+            if self._model._Booster__num_class == 2:
+                class_label = -1
+            else:
+                class_label = i_tree % self._model._Booster__num_class
+
+            trees.append(
+                Tree(
+                    class_id=class_label,
+                    leaf_nodes=self._get_leaf_nodes(tree_dump["tree_structure"], i_tree, class_label, box),
+                )
+            )
+
+        return trees
+
+    def _get_leaf_nodes(self, node, i_tree, class_label, box) -> List["LeafNode"]:
+        from art.metrics.verification_decisions_trees import Box, Interval, LeafNode
+
+        leaf_nodes: List[LeafNode] = list()
+
+        if "split_index" in node:
+            node_left = node["left_child"]
+            node_right = node["right_child"]
+
+            box_left = deepcopy(box)
+            box_right = deepcopy(box)
+
+            feature = node["split_feature"]
+            box_split_left = Box(intervals={feature: Interval(-np.inf, node["threshold"])})
+            box_split_right = Box(intervals={feature: Interval(node["threshold"], np.inf)})
+
+            if box.intervals:
+                box_left.intersect_with_box(box_split_left)
+                box_right.intersect_with_box(box_split_right)
+            else:
+                box_left = box_split_left
+                box_right = box_split_right
+
+            leaf_nodes += self._get_leaf_nodes(node_left, i_tree, class_label, box_left)
+            leaf_nodes += self._get_leaf_nodes(node_right, i_tree, class_label, box_right)
+
+        if "leaf_index" in node:
+            leaf_nodes.append(
+                LeafNode(
+                    tree_id=i_tree,
+                    class_label=class_label,
+                    node_id=node["leaf_index"],
+                    box=box,
+                    value=node["leaf_value"],
+                )
+            )
+
+        return leaf_nodes
diff --git a/adversarial-robustness-toolbox/art/estimators/classification/mxnet.py b/adversarial-robustness-toolbox/art/estimators/classification/mxnet.py
new file mode 100644
index 0000000..15623fe
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/estimators/classification/mxnet.py
@@ -0,0 +1,544 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements the classifier `MXClassifier` for MXNet Gluon models.
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import os
+from typing import List, Optional, Tuple, Union, TYPE_CHECKING
+
+import numpy as np
+import six
+
+from art import config
+from art.estimators.mxnet import MXEstimator
+from art.estimators.classification.classifier import ClassGradientsMixin, ClassifierMixin
+from art.utils import check_and_transform_label_format
+
+if TYPE_CHECKING:
+    # pylint: disable=C0412
+    import mxnet as mx
+
+    from art.utils import CLIP_VALUES_TYPE, PREPROCESSING_TYPE
+    from art.data_generators import DataGenerator
+    from art.defences.preprocessor import Preprocessor
+    from art.defences.postprocessor import Postprocessor
+
+logger = logging.getLogger(__name__)
+
+
+class MXClassifier(ClassGradientsMixin, ClassifierMixin, MXEstimator):  # lgtm [py/missing-call-to-init]
+    """
+    Wrapper class for importing MXNet Gluon models.
+    """
+
+    estimator_params = (
+        MXEstimator.estimator_params
+        + ClassifierMixin.estimator_params
+        + ["loss", "input_shape", "nb_classes", "optimizer", "ctx", "channels_first",]
+    )
+
+    def __init__(
+        self,
+        model: "mx.gluon.Block",
+        loss: Union["mx.nd.loss", "mx.gluon.loss"],
+        input_shape: Tuple[int, ...],
+        nb_classes: int,
+        optimizer: Optional["mx.gluon.Trainer"] = None,
+        ctx: Optional["mx.context.Context"] = None,
+        channels_first: bool = True,
+        clip_values: Optional["CLIP_VALUES_TYPE"] = None,
+        preprocessing_defences: Union["Preprocessor", List["Preprocessor"], None] = None,
+        postprocessing_defences: Union["Postprocessor", List["Postprocessor"], None] = None,
+        preprocessing: "PREPROCESSING_TYPE" = (0.0, 1.0),
+    ) -> None:
+        """
+        Initialize an `MXClassifier` object. Assumes the `model` passed as parameter is a Gluon model.
+
+        :param model: The Gluon model. The output of the model can be logits, probabilities or anything else. Logits
+               output should be preferred where possible to ensure attack efficiency.
+        :param loss: The loss function for which to compute gradients for training.
+        :param input_shape: The shape of one input instance.
+        :param nb_classes: The number of classes of the model.
+        :param optimizer: The optimizer used to train the classifier. This parameter is only required if fitting will
+                          be done with method fit.
+        :param ctx: The device on which the model runs (CPU or GPU). If not provided, CPU is assumed.
+        :param channels_first: Set channels first or last.
+        :param clip_values: Tuple of the form `(min, max)` of floats or `np.ndarray` representing the minimum and
+               maximum values allowed for features. If floats are provided, these will be used as the range of all
+               features. If arrays are provided, each value will be considered the bound for a feature, thus
+               the shape of clip values needs to match the total number of features.
+        :param preprocessing_defences: Preprocessing defence(s) to be applied by the classifier.
+        :param postprocessing_defences: Postprocessing defence(s) to be applied by the classifier.
+        :param preprocessing: Tuple of the form `(subtrahend, divisor)` of floats or `np.ndarray` of values to be
+               used for data preprocessing. The first value will be subtracted from the input. The input will then
+               be divided by the second one.
+        """
+        import mxnet as mx  # lgtm [py/repeated-import]
+
+        super().__init__(
+            model=model,
+            clip_values=clip_values,
+            channels_first=channels_first,
+            preprocessing_defences=preprocessing_defences,
+            postprocessing_defences=postprocessing_defences,
+            preprocessing=preprocessing,
+        )
+
+        self._loss = loss
+        self._nb_classes = nb_classes
+        self._input_shape = input_shape
+        self._device = ctx
+        self._optimizer = optimizer
+
+        if ctx is None:
+            self._ctx = mx.cpu()
+        else:
+            self._ctx = ctx
+
+        # Get the internal layer
+        self._layer_names = self._get_layers()
+
+    @property
+    def input_shape(self) -> Tuple[int, ...]:
+        """
+        Return the shape of one input sample.
+
+        :return: Shape of one input sample.
+        """
+        return self._input_shape  # type: ignore
+
+    @property
+    def loss(self) -> Union["mx.nd.loss", "mx.gluon.loss"]:
+        """
+        Return the loss function.
+
+        :return: The loss function.
+        """
+        return self._loss  # type: ignore
+
+    @property
+    def optimizer(self) -> "mx.gluon.Trainer":
+        """
+        Return the optimizer used to train the classifier.
+
+        :return: The optimizer used to train the classifier.
+        """
+        return self._optimizer  # type: ignore
+
+    @property
+    def ctx(self) -> "mx.context.Context":
+        """
+        Return the device on which the model runs.
+
+        :return: The device on which the model runs (CPU or GPU).
+        """
+        return self._ctx  # type: ignore
+
+    def fit(self, x: np.ndarray, y: np.ndarray, batch_size: int = 128, nb_epochs: int = 20, **kwargs) -> None:
+        """
+        Fit the classifier on the training set `(inputs, outputs)`.
+
+        :param x: Training data.
+        :param y: Target values (class labels) one-hot-encoded of shape (nb_samples, nb_classes) or index labels of
+                  shape (nb_samples,).
+        :param batch_size: Size of batches.
+        :param nb_epochs: Number of epochs to use for training.
+        :param kwargs: Dictionary of framework-specific arguments. This parameter is not currently supported for MXNet
+               and providing it takes no effect.
+        """
+        import mxnet as mx  # lgtm [py/repeated-import]
+
+        if self._optimizer is None:
+            raise ValueError("An MXNet optimizer is required for fitting the model.")
+
+        training_mode = True
+
+        y = check_and_transform_label_format(y, self.nb_classes)
+
+        # Apply preprocessing
+        x_preprocessed, y_preprocessed = self._apply_preprocessing(x, y, fit=True)
+        y_preprocessed = np.argmax(y_preprocessed, axis=1)
+        nb_batch = int(np.ceil(len(x_preprocessed) / batch_size))
+        ind = np.arange(len(x_preprocessed))
+
+        for _ in range(nb_epochs):
+            # Shuffle the examples
+            np.random.shuffle(ind)
+
+            # Train for one epoch
+            for m in range(nb_batch):
+                x_batch = mx.nd.array(
+                    x_preprocessed[ind[m * batch_size : (m + 1) * batch_size]].astype(config.ART_NUMPY_DTYPE)
+                ).as_in_context(self._ctx)
+                y_batch = mx.nd.array(y_preprocessed[ind[m * batch_size : (m + 1) * batch_size]]).as_in_context(
+                    self._ctx
+                )
+
+                with mx.autograd.record(train_mode=training_mode):
+                    # Perform prediction
+                    preds = self._model(x_batch)
+
+                    # Apply postprocessing
+                    preds = self._apply_postprocessing(preds=preds, fit=True)
+
+                    # Form the loss function
+                    loss = self._loss(preds, y_batch)
+
+                loss.backward()
+
+                # Update parameters
+                self._optimizer.step(batch_size)
+
+    def fit_generator(self, generator: "DataGenerator", nb_epochs: int = 20, **kwargs) -> None:
+        """
+        Fit the classifier using the generator that yields batches as specified.
+
+        :param generator: Batch generator providing `(x, y)` for each epoch.
+        :param nb_epochs: Number of epochs to use for training.
+        :param kwargs: Dictionary of framework-specific arguments. This parameter is not currently supported for MXNet
+               and providing it takes no effect.
+        """
+        import mxnet as mx  # lgtm [py/repeated-import]
+        from art.data_generators import MXDataGenerator
+
+        if self._optimizer is None:
+            raise ValueError("An MXNet optimizer is required for fitting the model.")
+
+        training_mode = True
+
+        if (
+            isinstance(generator, MXDataGenerator)
+            and (self.preprocessing is None or self.preprocessing == [])
+            and self.preprocessing == (0, 1)
+        ):
+            # Train directly in MXNet
+            for _ in range(nb_epochs):
+                for x_batch, y_batch in generator.iterator:
+                    x_batch = mx.nd.array(x_batch.astype(config.ART_NUMPY_DTYPE)).as_in_context(self._ctx)
+                    y_batch = mx.nd.argmax(y_batch, axis=1)
+                    y_batch = mx.nd.array(y_batch).as_in_context(self._ctx)
+
+                    with mx.autograd.record(train_mode=training_mode):
+                        # Perform prediction
+                        preds = self._model(x_batch)
+
+                        # Form the loss function
+                        loss = self._loss(preds, y_batch)
+
+                    loss.backward()
+
+                    # Update parameters
+                    self._optimizer.step(x_batch.shape[0])
+        else:
+            # Fit a generic data generator through the API
+            super().fit_generator(generator, nb_epochs=nb_epochs)
+
+    def predict(self, x: np.ndarray, batch_size: int = 128, training_mode: bool = False, **kwargs) -> np.ndarray:
+        """
+        Perform prediction for a batch of inputs.
+
+        :param x: Input samples.
+        :param batch_size: Size of batches.
+        :param training_mode: `True` for model set to training mode and `'False` for model set to evaluation mode.
+        :return: Array of predictions of shape `(nb_inputs, nb_classes)`.
+        """
+        import mxnet as mx  # lgtm [py/repeated-import]
+
+        # Apply preprocessing
+        x_preprocessed, _ = self._apply_preprocessing(x, y=None, fit=False)
+
+        # Run prediction with batch processing
+        results = np.zeros((x_preprocessed.shape[0], self.nb_classes), dtype=np.float32)
+        num_batch = int(np.ceil(len(x_preprocessed) / float(batch_size)))
+        for m in range(num_batch):
+            # Batch indexes
+            begin, end = (
+                m * batch_size,
+                min((m + 1) * batch_size, x_preprocessed.shape[0]),
+            )
+
+            # Predict
+            x_batch = mx.nd.array(x_preprocessed[begin:end].astype(config.ART_NUMPY_DTYPE), ctx=self._ctx)
+            x_batch.attach_grad()
+            with mx.autograd.record(train_mode=training_mode):
+                preds = self._model(x_batch)
+
+            results[begin:end] = preds.asnumpy()
+
+        # Apply postprocessing
+        predictions = self._apply_postprocessing(preds=results, fit=False)
+
+        return predictions
+
+    def class_gradient(
+        self, x: np.ndarray, label: Union[int, List[int], None] = None, training_mode: bool = False, **kwargs
+    ) -> np.ndarray:
+        """
+        Compute per-class derivatives w.r.t. `x`.
+
+        :param x: Sample input with shape as expected by the model.
+        :param label: Index of a specific per-class derivative. If an integer is provided, the gradient of that class
+                      output is computed for all samples. If multiple values as provided, the first dimension should
+                      match the batch size of `x`, and each value will be used as target for its corresponding sample in
+                      `x`. If `None`, then gradients for all classes will be computed for each sample.
+        :param training_mode: `True` for model set to training mode and `'False` for model set to evaluation mode.
+        :return: Array of gradients of input features w.r.t. each class in the form
+                 `(batch_size, nb_classes, input_shape)` when computing for all classes, otherwise shape becomes
+                 `(batch_size, 1, input_shape)` when `label` parameter is specified.
+        """
+        import mxnet as mx  # lgtm [py/repeated-import]
+
+        # Check value of label for computing gradients
+        if not (
+            label is None
+            or (isinstance(label, (int, np.integer)) and label in range(self.nb_classes))
+            or (
+                isinstance(label, np.ndarray)
+                and len(label.shape) == 1
+                and (label < self.nb_classes).all()
+                and label.shape[0] == x.shape[0]
+            )
+        ):
+            raise ValueError("Label %s is out of range." % str(label))
+
+        # Apply preprocessing
+        x_preprocessed, _ = self._apply_preprocessing(x, y=None, fit=False)
+        x_preprocessed = mx.nd.array(x_preprocessed.astype(config.ART_NUMPY_DTYPE), ctx=self._ctx)
+        x_preprocessed.attach_grad()
+
+        if label is None:
+            with mx.autograd.record(train_mode=False):
+                preds = self._model(x_preprocessed)
+                class_slices = [preds[:, i] for i in range(self.nb_classes)]
+
+            grads = []
+            for slice_ in class_slices:
+                slice_.backward(retain_graph=True)
+                grad = x_preprocessed.grad.asnumpy()
+                grads.append(grad)
+            grads = np.swapaxes(np.array(grads), 0, 1)
+        elif isinstance(label, (int, np.integer)):
+            with mx.autograd.record(train_mode=training_mode):
+                preds = self._model(x_preprocessed)
+                class_slice = preds[:, label]
+
+            class_slice.backward()
+            grads = np.expand_dims(x_preprocessed.grad.asnumpy(), axis=1)
+        else:
+            unique_labels = list(np.unique(label))
+
+            with mx.autograd.record(train_mode=training_mode):
+                preds = self._model(x_preprocessed)
+                class_slices = [preds[:, i] for i in unique_labels]
+
+            grads = []
+            for slice_ in class_slices:
+                slice_.backward(retain_graph=True)
+                grad = x_preprocessed.grad.asnumpy()
+                grads.append(grad)
+
+            grads = np.swapaxes(np.array(grads), 0, 1)
+            lst = [unique_labels.index(i) for i in label]
+            grads = grads[np.arange(len(grads)), lst]
+            grads = np.expand_dims(grads, axis=1)
+
+        grads = self._apply_preprocessing_gradient(x, grads)
+
+        return grads
+
+    def loss_gradient(self, x: np.ndarray, y: np.ndarray, training_mode: bool = False, **kwargs) -> np.ndarray:
+        """
+        Compute the gradient of the loss function w.r.t. `x`.
+
+        :param x: Sample input with shape as expected by the model.
+        :param y: Target values (class labels) one-hot-encoded of shape `(nb_samples, nb_classes)` or indices of shape
+                  `(nb_samples,)`.
+        :param training_mode: `True` for model set to training mode and `'False` for model set to evaluation mode.
+        :return: Array of gradients of the same shape as `x`.
+        """
+        import mxnet as mx  # lgtm [py/repeated-import]
+
+        # Apply preprocessing
+        x_preprocessed, y_preprocessed = self._apply_preprocessing(x, y, fit=False)
+        y_preprocessed = mx.nd.array([np.argmax(y_preprocessed, axis=1)], ctx=self._ctx).T
+        x_preprocessed = mx.nd.array(x_preprocessed.astype(config.ART_NUMPY_DTYPE), ctx=self._ctx)
+        x_preprocessed.attach_grad()
+
+        with mx.autograd.record(train_mode=training_mode):
+            preds = self._model(x_preprocessed)
+            loss = self._loss(preds, y_preprocessed)
+
+        loss.backward()
+
+        # Compute gradients
+        grads = x_preprocessed.grad.asnumpy()
+        grads = self._apply_preprocessing_gradient(x, grads)
+        assert grads.shape == x.shape
+
+        return grads
+
+    def compute_loss(self, x: np.ndarray, y: np.ndarray, **kwargs) -> np.ndarray:
+        """
+        Compute the loss of the neural network for samples `x`.
+
+        :param x: Samples of shape (nb_samples, nb_features) or (nb_samples, nb_pixels_1, nb_pixels_2,
+                  nb_channels) or (nb_samples, nb_channels, nb_pixels_1, nb_pixels_2).
+        :param y: Target values (class labels) one-hot-encoded of shape `(nb_samples, nb_classes)` or indices
+                  of shape `(nb_samples,)`.
+        :return: Loss values.
+        :rtype: Format as expected by the `model`
+        """
+        raise NotImplementedError
+
+    @property
+    def layer_names(self) -> List[str]:
+        """
+        Return the hidden layers in the model, if applicable.
+
+        :return: The hidden layers in the model, input and output layers excluded.
+
+        .. warning:: `layer_names` tries to infer the internal structure of the model.
+                     This feature comes with no guarantees on the correctness of the result.
+                     The intended order of the layers tries to match their order in the model, but this is not
+                     guaranteed either.
+        """
+        return self._layer_names
+
+    def get_activations(
+        self, x: np.ndarray, layer: Union[int, str], batch_size: int = 128, framework: bool = False
+    ) -> np.ndarray:
+        """
+        Return the output of the specified layer for input `x`. `layer` is specified by layer index (between 0 and
+        `nb_layers - 1`) or by name. The number of layers can be determined by counting the results returned by
+        calling `layer_names`.
+
+        :param x: Input for computing the activations.
+        :param layer: Layer for computing the activations
+        :param batch_size: Size of batches.
+        :param framework: If true, return the intermediate tensor representation of the activation.
+        :return: The output of `layer`, where the first dimension is the batch size corresponding to `x`.
+        """
+        import mxnet as mx  # lgtm [py/repeated-import]
+
+        if isinstance(layer, six.string_types):
+            if layer not in self._layer_names:
+                raise ValueError("Layer name %s is not part of the model." % layer)
+            layer_ind = self._layer_names.index(layer)
+        elif isinstance(layer, int):
+            if layer < 0 or layer >= len(self._layer_names):
+                raise ValueError(
+                    "Layer index %d is outside of range (0 to %d included)." % (layer, len(self._layer_names) - 1)
+                )
+            layer_ind = layer
+        else:
+            raise TypeError("Layer must be of type `str` or `int`.")
+
+        # Apply preprocessing and defences
+        if x.shape == self.input_shape:
+            x_expanded = np.expand_dims(x, 0)
+        else:
+            x_expanded = x
+
+        x_preprocessed, _ = self._apply_preprocessing(x=x_expanded, y=None, fit=False)
+
+        if framework:
+            return self._model[layer_ind]
+
+        # Compute activations with batching
+        activations = []
+        nb_batches = int(np.ceil(len(x_preprocessed) / float(batch_size)))
+        for batch_index in range(nb_batches):
+            # Batch indexes
+            begin, end = (
+                batch_index * batch_size,
+                min((batch_index + 1) * batch_size, x_preprocessed.shape[0]),
+            )
+
+            # Predict
+            x_batch = mx.nd.array(x_preprocessed[begin:end].astype(config.ART_NUMPY_DTYPE), ctx=self._ctx)
+            x_batch.attach_grad()
+            with mx.autograd.record(train_mode=False):
+                preds = self._model[layer_ind](x_batch)
+
+            activations.append(preds.asnumpy())
+
+        activations = np.vstack(activations)
+        return activations
+
+    def save(self, filename: str, path: Optional[str] = None) -> None:
+        """
+        Save a model to file in the format specific to the backend framework. For Gluon, only parameters are saved in
+        file with name `<filename>.params` at the specified path. To load the saved model, the original model code needs
+        to be run before calling `load_parameters` on the generated Gluon model.
+
+        :param filename: Name of the file where to store the model.
+        :param path: Path of the folder where to store the model. If no path is specified, the model will be stored in
+                     the default data location of the library `ART_DATA_PATH`.
+        """
+        if path is None:
+            full_path = os.path.join(config.ART_DATA_PATH, filename)
+        else:
+            full_path = os.path.join(path, filename)
+        folder = os.path.split(full_path)[0]
+        if not os.path.exists(folder):
+            os.makedirs(folder)
+
+        self._model.save_parameters(full_path + ".params")
+        logger.info("Model parameters saved in path: %s.params.", full_path)
+
+    def __repr__(self):
+        repr_ = (
+            "%s(model=%r, loss=%r, input_shape=%r, nb_classes=%r, optimizer=%r, ctx=%r, "
+            " channels_first=%r, clip_values=%r, preprocessing=%r, postprocessing_defences=%r,"
+            " preprocessing=%r)"
+            % (
+                self.__module__ + "." + self.__class__.__name__,
+                self._model,
+                self._loss,
+                self.input_shape,
+                self.nb_classes,
+                self._optimizer,
+                self._ctx,
+                self.channels_first,
+                self.clip_values,
+                self.preprocessing,
+                self.postprocessing_defences,
+                self.preprocessing,
+            )
+        )
+
+        return repr_
+
+    def _get_layers(self) -> list:
+        """
+        Return the hidden layers in the model, if applicable.
+
+        :return: The hidden layers in the model, input and output layers excluded.
+        """
+        import mxnet
+
+        if isinstance(self._model, mxnet.gluon.nn.Sequential):
+            layer_names = [layer.name for layer in self._model[:-1]]
+            logger.info("Inferred %i hidden layers on MXNet classifier.", len(layer_names))
+        else:
+            layer_names = []
+
+        return layer_names
diff --git a/adversarial-robustness-toolbox/art/estimators/classification/pytorch.py b/adversarial-robustness-toolbox/art/estimators/classification/pytorch.py
new file mode 100644
index 0000000..903ccd4
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/estimators/classification/pytorch.py
@@ -0,0 +1,969 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements the classifier `PyTorchClassifier` for PyTorch models.
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import copy
+import logging
+import os
+import random
+import time
+from typing import Any, Dict, List, Optional, Tuple, Union, TYPE_CHECKING
+
+import numpy as np
+import six
+
+from art import config
+from art.estimators.classification.classifier import (
+    ClassGradientsMixin,
+    ClassifierMixin,
+)
+from art.estimators.pytorch import PyTorchEstimator
+from art.utils import check_and_transform_label_format
+
+if TYPE_CHECKING:
+    # pylint: disable=C0412
+    import torch
+
+    from art.utils import CLIP_VALUES_TYPE, PREPROCESSING_TYPE
+    from art.data_generators import DataGenerator
+    from art.defences.preprocessor import Preprocessor
+    from art.defences.postprocessor import Postprocessor
+
+logger = logging.getLogger(__name__)
+
+
+class PyTorchClassifier(ClassGradientsMixin, ClassifierMixin, PyTorchEstimator):  # lgtm [py/missing-call-to-init]
+    """
+    This class implements a classifier with the PyTorch framework.
+    """
+
+    estimator_params = (
+        PyTorchEstimator.estimator_params
+        + ClassifierMixin.estimator_params
+        + ["loss", "input_shape", "optimizer", "use_amp", "opt_level", "loss_scale",]
+    )
+
+    def __init__(
+        self,
+        model: "torch.nn.Module",
+        loss: "torch.nn.modules.loss._Loss",
+        input_shape: Tuple[int, ...],
+        nb_classes: int,
+        optimizer: Optional["torch.optim.Optimizer"] = None,  # type: ignore
+        use_amp: bool = False,
+        opt_level: str = "O1",
+        loss_scale: Optional[Union[float, str]] = "dynamic",
+        channels_first: bool = True,
+        clip_values: Optional["CLIP_VALUES_TYPE"] = None,
+        preprocessing_defences: Union["Preprocessor", List["Preprocessor"], None] = None,
+        postprocessing_defences: Union["Postprocessor", List["Postprocessor"], None] = None,
+        preprocessing: "PREPROCESSING_TYPE" = (0.0, 1.0),
+        device_type: str = "gpu",
+    ) -> None:
+        """
+        Initialization specifically for the PyTorch-based implementation.
+
+        :param model: PyTorch model. The output of the model can be logits, probabilities or anything else. Logits
+               output should be preferred where possible to ensure attack efficiency.
+        :param loss: The loss function for which to compute gradients for training. The target label must be raw
+               categorical, i.e. not converted to one-hot encoding.
+        :param input_shape: The shape of one input instance.
+        :param optimizer: The optimizer used to train the classifier.
+        :param use_amp: Whether to use the automatic mixed precision tool to enable mixed precision training or
+                        gradient computation, e.g. with loss gradient computation. When set to True, this option is
+                        only triggered if there are GPUs available.
+        :param opt_level: Specify a pure or mixed precision optimization level. Used when use_amp is True. Accepted
+                          values are `O0`, `O1`, `O2`, and `O3`.
+        :param loss_scale: Loss scaling. Used when use_amp is True. If passed as a string, must be a string
+                           representing a number, e.g., “1.0”, or the string “dynamic”.
+        :param nb_classes: The number of classes of the model.
+        :param optimizer: The optimizer used to train the classifier.
+        :param channels_first: Set channels first or last.
+        :param clip_values: Tuple of the form `(min, max)` of floats or `np.ndarray` representing the minimum and
+               maximum values allowed for features. If floats are provided, these will be used as the range of all
+               features. If arrays are provided, each value will be considered the bound for a feature, thus
+               the shape of clip values needs to match the total number of features.
+        :param preprocessing_defences: Preprocessing defence(s) to be applied by the classifier.
+        :param postprocessing_defences: Postprocessing defence(s) to be applied by the classifier.
+        :param preprocessing: Tuple of the form `(subtrahend, divisor)` of floats or `np.ndarray` of values to be
+               used for data preprocessing. The first value will be subtracted from the input. The input will then
+               be divided by the second one.
+        :param device_type: Type of device on which the classifier is run, either `gpu` or `cpu`.
+        """
+        import torch  # lgtm [py/repeated-import]
+
+        super().__init__(
+            model=model,
+            clip_values=clip_values,
+            channels_first=channels_first,
+            preprocessing_defences=preprocessing_defences,
+            postprocessing_defences=postprocessing_defences,
+            preprocessing=preprocessing,
+            device_type=device_type,
+        )
+        self._nb_classes = nb_classes
+        self._input_shape = input_shape
+        self._model = self._make_model_wrapper(model)
+        self._loss = loss
+        self._optimizer = optimizer
+        self._use_amp = use_amp
+        self._learning_phase: Optional[bool] = None
+        self._opt_level = opt_level
+        self._loss_scale = loss_scale
+
+        # Check if model is RNN-like to decide if freezing batch-norm and dropout layers might be required for loss and
+        # class gradient calculation
+        self.is_rnn = any([isinstance(m, torch.nn.modules.RNNBase) for m in self._model.modules()])
+
+        # Get the internal layers
+        self._layer_names = self._model.get_layers
+
+        self._model.to(self._device)
+
+        # Index of layer at which the class gradients should be calculated
+        self._layer_idx_gradients = -1
+
+        if isinstance(self._loss, (torch.nn.CrossEntropyLoss, torch.nn.NLLLoss, torch.nn.MultiMarginLoss),):
+            self._reduce_labels = True
+            self._int_labels = True
+        elif isinstance(self._loss, (torch.nn.BCELoss),):
+            self._reduce_labels = True
+            self._int_labels = False
+        else:
+            self._reduce_labels = False
+            self._int_labels = False
+
+        # Setup for AMP use
+        if self._use_amp:
+            from apex import amp
+
+            if self._optimizer is None:
+                logger.warning(
+                    "An optimizer is needed to use the automatic mixed precision tool, but none for provided. "
+                    "A default optimizer is used."
+                )
+
+                # Create the optimizers
+                parameters = self._model.parameters()
+                self._optimizer = torch.optim.SGD(parameters, lr=0.01)
+
+            if self.device.type == "cpu":
+                enabled = False
+            else:
+                enabled = True
+
+            self._model, self._optimizer = amp.initialize(
+                models=self._model,
+                optimizers=self._optimizer,
+                enabled=enabled,
+                opt_level=opt_level,
+                loss_scale=loss_scale,
+            )
+
+    @property
+    def device(self) -> "torch.device":
+        """
+        Get current used device.
+
+        :return: Current used device.
+        """
+        return self._device
+
+    @property
+    def model(self) -> "torch.nn.Module":
+        return self._model._model
+
+    @property
+    def input_shape(self) -> Tuple[int, ...]:
+        """
+        Return the shape of one input sample.
+
+        :return: Shape of one input sample.
+        """
+        return self._input_shape  # type: ignore
+
+    @property
+    def loss(self) -> "torch.nn.modules.loss._Loss":
+        """
+        Return the loss function.
+
+        :return: The loss function.
+        """
+        return self._loss  # type: ignore
+
+    @property
+    def optimizer(self) -> "torch.optim.Optimizer":
+        """
+        Return the optimizer.
+
+        :return: The optimizer.
+        """
+        return self._optimizer  # type: ignore
+
+    @property
+    def use_amp(self) -> bool:
+        """
+        Return a boolean indicating whether to use the automatic mixed precision tool.
+
+        :return: Whether to use the automatic mixed precision tool.
+        """
+        return self._use_amp  # type: ignore
+
+    @property
+    def opt_level(self) -> str:
+        """
+        Return a string specifying a pure or mixed precision optimization level.
+
+        :return: A string specifying a pure or mixed precision optimization level. Possible
+                 values are `O0`, `O1`, `O2`, and `O3`.
+        """
+        return self._opt_level  # type: ignore
+
+    @property
+    def loss_scale(self) -> Union[float, str]:
+        """
+        Return the loss scaling value.
+
+        :return: Loss scaling. Possible values for string: a string representing a number, e.g., “1.0”,
+                 or the string “dynamic”.
+        """
+        return self._loss_scale  # type: ignore
+
+    def reduce_labels(self, y: Union[np.ndarray, "torch.Tensor"]) -> Union[np.ndarray, "torch.Tensor"]:
+        """
+        Reduce labels from one-hot encoded to index labels.
+        """
+        import torch  # lgtm [py/repeated-import]
+
+        # Check if the loss function requires as input index labels instead of one-hot-encoded labels
+        if self._reduce_labels and self._int_labels:
+            if isinstance(y, torch.Tensor):
+                return torch.argmax(y, dim=1)
+            return np.argmax(y, axis=1)
+        elif self._reduce_labels:  # float labels
+            if isinstance(y, torch.Tensor):
+                return torch.argmax(y, dim=1).type("torch.FloatTensor")
+            y_index = np.argmax(y, axis=1).astype(np.float32)
+            y_index = np.expand_dims(y_index, axis=1)
+            return y_index
+        else:
+            return y
+
+    def predict(self, x: np.ndarray, batch_size: int = 128, training_mode: bool = False, **kwargs) -> np.ndarray:
+        """
+        Perform prediction for a batch of inputs.
+
+        :param x: Input samples.
+        :param batch_size: Size of batches.
+        :param training_mode: `True` for model set to training mode and `'False` for model set to evaluation mode.
+        :return: Array of predictions of shape `(nb_inputs, nb_classes)`.
+        """
+        import torch  # lgtm [py/repeated-import]
+
+        # Set model mode
+        self._model.train(mode=training_mode)
+
+        # Apply preprocessing
+        x_preprocessed, _ = self._apply_preprocessing(x, y=None, fit=False)
+
+        # Run prediction with batch processing
+        results = np.zeros((x_preprocessed.shape[0], self.nb_classes), dtype=np.float32)
+        num_batch = int(np.ceil(len(x_preprocessed) / float(batch_size)))
+        for m in range(num_batch):
+            # Batch indexes
+            begin, end = (
+                m * batch_size,
+                min((m + 1) * batch_size, x_preprocessed.shape[0]),
+            )
+
+            with torch.no_grad():
+                model_outputs = self._model(torch.from_numpy(x_preprocessed[begin:end]).to(self._device))
+            output = model_outputs[-1]
+            results[begin:end] = output.detach().cpu().numpy()
+
+        # Apply postprocessing
+        predictions = self._apply_postprocessing(preds=results, fit=False)
+
+        return predictions
+
+    def _predict_framework(self, x: "torch.Tensor", **kwargs) -> "torch.Tensor":
+        """
+        Perform prediction for a batch of inputs.
+
+        :param x: Test set.
+        :return: Tensor of predictions of shape `(nb_inputs, nb_classes)`.
+        """
+        # Apply preprocessing
+        x_preprocessed, _ = self._apply_preprocessing(x, y=None, fit=False, no_grad=False)
+
+        # Put the model in the eval mode
+        self._model.eval()
+
+        model_outputs = self._model(x_preprocessed)
+        output = model_outputs[-1]
+
+        return output
+
+    def fit(self, x: np.ndarray, y: np.ndarray, batch_size: int = 128, nb_epochs: int = 10, **kwargs) -> None:
+        """
+        Fit the classifier on the training set `(x, y)`.
+
+        :param x: Training data.
+        :param y: Target values (class labels) one-hot-encoded of shape (nb_samples, nb_classes) or index labels of
+                  shape (nb_samples,).
+        :param batch_size: Size of batches.
+        :param nb_epochs: Number of epochs to use for training.
+        :param kwargs: Dictionary of framework-specific arguments. This parameter is not currently supported for PyTorch
+               and providing it takes no effect.
+        """
+        import torch  # lgtm [py/repeated-import]
+
+        # Put the model in the training mode
+        self._model.train()
+
+        if self._optimizer is None:
+            raise ValueError("An optimizer is needed to train the model, but none for provided.")
+
+        y = check_and_transform_label_format(y, self.nb_classes)
+
+        # Apply preprocessing
+        x_preprocessed, y_preprocessed = self._apply_preprocessing(x, y, fit=True)
+
+        # Check label shape
+        y_preprocessed = self.reduce_labels(y_preprocessed)
+
+        num_batch = int(np.ceil(len(x_preprocessed) / float(batch_size)))
+        ind = np.arange(len(x_preprocessed))
+
+        # Start training
+        for _ in range(nb_epochs):
+            # Shuffle the examples
+            random.shuffle(ind)
+
+            # Train for one epoch
+            for m in range(num_batch):
+                i_batch = torch.from_numpy(x_preprocessed[ind[m * batch_size : (m + 1) * batch_size]]).to(self._device)
+                o_batch = torch.from_numpy(y_preprocessed[ind[m * batch_size : (m + 1) * batch_size]]).to(self._device)
+
+                # Zero the parameter gradients
+                self._optimizer.zero_grad()
+
+                # Perform prediction
+                model_outputs = self._model(i_batch)
+
+                # Form the loss function
+                loss = self._loss(model_outputs[-1], o_batch)  # lgtm [py/call-to-non-callable]
+
+                # Do training
+                if self._use_amp:
+                    from apex import amp
+
+                    with amp.scale_loss(loss, self._optimizer) as scaled_loss:
+                        scaled_loss.backward()
+
+                else:
+                    loss.backward()
+
+                self._optimizer.step()
+
+    def fit_generator(self, generator: "DataGenerator", nb_epochs: int = 20, **kwargs) -> None:
+        """
+        Fit the classifier using the generator that yields batches as specified.
+
+        :param generator: Batch generator providing `(x, y)` for each epoch.
+        :param nb_epochs: Number of epochs to use for training.
+        :param kwargs: Dictionary of framework-specific arguments. This parameter is not currently supported for PyTorch
+               and providing it takes no effect.
+        """
+        import torch  # lgtm [py/repeated-import]
+        from art.data_generators import PyTorchDataGenerator
+
+        # Put the model in the training mode
+        self._model.train()
+
+        if self._optimizer is None:
+            raise ValueError("An optimizer is needed to train the model, but none for provided.")
+
+        # Train directly in PyTorch
+        if isinstance(generator, PyTorchDataGenerator) and not self.preprocessing:
+            for _ in range(nb_epochs):
+                for i_batch, o_batch in generator.iterator:
+                    if isinstance(i_batch, np.ndarray):
+                        i_batch = torch.from_numpy(i_batch).to(self._device)
+                    else:
+                        i_batch = i_batch.to(self._device)
+
+                    if isinstance(o_batch, np.ndarray):
+                        o_batch = torch.argmax(torch.from_numpy(o_batch).to(self._device), dim=1)
+                    else:
+                        o_batch = torch.argmax(o_batch.to(self._device), dim=1)
+
+                    # Zero the parameter gradients
+                    self._optimizer.zero_grad()
+
+                    # Perform prediction
+                    model_outputs = self._model(i_batch)
+
+                    # Form the loss function
+                    loss = self._loss(model_outputs[-1], o_batch)
+
+                    # Do training
+                    if self._use_amp:
+                        from apex import amp
+
+                        with amp.scale_loss(loss, self._optimizer) as scaled_loss:
+                            scaled_loss.backward()
+
+                    else:
+                        loss.backward()
+
+                    self._optimizer.step()
+
+        else:
+            # Fit a generic data generator through the API
+            super().fit_generator(generator, nb_epochs=nb_epochs)
+
+    def clone_for_refitting(self) -> "PyTorchClassifier":  # lgtm [py/inheritance/incorrect-overridden-signature]
+        """
+        Create a copy of the classifier that can be refit from scratch. Will inherit same architecture, optimizer and
+        initialization as cloned model, but without weights.
+
+        :return: new estimator
+        """
+        model = copy.deepcopy(self.model)
+        clone = type(self)(model, self._loss, self.input_shape, self.nb_classes, optimizer=self._optimizer)
+        # reset weights
+        clone.reset()
+        params = self.get_params()
+        del params["model"]
+        clone.set_params(**params)
+        return clone
+
+    def reset(self) -> None:
+        """
+        Resets the weights of the classifier so that it can be refit from scratch.
+
+        """
+
+        def weight_reset(module):
+            reset_parameters = getattr(module, "reset_parameters", None)
+            if reset_parameters and callable(reset_parameters):
+                module.reset_parameters()
+
+        self.model.apply(weight_reset)
+
+    def class_gradient(
+        self, x: np.ndarray, label: Union[int, List[int], None] = None, training_mode: bool = False, **kwargs
+    ) -> np.ndarray:
+        """
+        Compute per-class derivatives w.r.t. `x`.
+
+        :param x: Sample input with shape as expected by the model.
+        :param label: Index of a specific per-class derivative. If an integer is provided, the gradient of that class
+                      output is computed for all samples. If multiple values as provided, the first dimension should
+                      match the batch size of `x`, and each value will be used as target for its corresponding sample in
+                      `x`. If `None`, then gradients for all classes will be computed for each sample.
+        :param training_mode: `True` for model set to training mode and `'False` for model set to evaluation mode.
+                              Note on RNN-like models: Backpropagation through RNN modules in eval mode raises
+                              RuntimeError due to cudnn issues and require training mode, i.e. RuntimeError: cudnn RNN
+                              backward can only be called in training mode. Therefore, if the model is an RNN type we
+                              always use training mode but freeze batch-norm and dropout layers if
+                              `training_mode=False.`
+        :return: Array of gradients of input features w.r.t. each class in the form
+                 `(batch_size, nb_classes, input_shape)` when computing for all classes, otherwise shape becomes
+                 `(batch_size, 1, input_shape)` when `label` parameter is specified.
+        """
+        import torch  # lgtm [py/repeated-import]
+
+        self._model.train(mode=training_mode)
+
+        # Backpropagation through RNN modules in eval mode raises RuntimeError due to cudnn issues and require training
+        # mode, i.e. RuntimeError: cudnn RNN backward can only be called in training mode. Therefore, if the model is
+        # an RNN type we always use training mode but freeze batch-norm and dropout layers if training_mode=False.
+        if self.is_rnn:
+            self._model.train(mode=True)
+            if not training_mode:
+                logger.debug(
+                    "Freezing batch-norm and dropout layers for gradient calculation in train mode with eval parameters"
+                    "of batch-norm and dropout."
+                )
+                self.set_batchnorm(train=False)
+                self.set_dropout(train=False)
+
+        if not (
+            (label is None)
+            or (isinstance(label, (int, np.integer)) and label in range(self._nb_classes))
+            or (
+                isinstance(label, np.ndarray)
+                and len(label.shape) == 1
+                and (label < self._nb_classes).all()
+                and label.shape[0] == x.shape[0]
+            )
+        ):
+            raise ValueError("Label %s is out of range." % label)
+
+        # Apply preprocessing
+        if self.all_framework_preprocessing:
+            x_grad = torch.from_numpy(x).to(self._device)
+            if self._layer_idx_gradients < 0:
+                x_grad.requires_grad = True
+            x_input, _ = self._apply_preprocessing(x_grad, y=None, fit=False, no_grad=False)
+        else:
+            x_preprocessed, _ = self._apply_preprocessing(x, y=None, fit=False, no_grad=True)
+            x_grad = torch.from_numpy(x_preprocessed).to(self._device)
+            if self._layer_idx_gradients < 0:
+                x_grad.requires_grad = True
+            x_input = x_grad
+
+        # Run prediction
+        model_outputs = self._model(x_input)
+
+        # Set where to get gradient
+        if self._layer_idx_gradients >= 0:
+            input_grad = model_outputs[self._layer_idx_gradients]
+        else:
+            input_grad = x_grad
+
+        # Set where to get gradient from
+        preds = model_outputs[-1]
+
+        # Compute the gradient
+        grads = []
+
+        def save_grad():
+            def hook(grad):
+                grads.append(grad.cpu().numpy().copy())
+                grad.data.zero_()
+
+            return hook
+
+        input_grad.register_hook(save_grad())
+
+        self._model.zero_grad()
+        if label is None:
+            for i in range(self.nb_classes):
+                torch.autograd.backward(
+                    preds[:, i], torch.tensor([1.0] * len(preds[:, 0])).to(self._device), retain_graph=True,
+                )
+
+        elif isinstance(label, (int, np.integer)):
+            torch.autograd.backward(
+                preds[:, label], torch.tensor([1.0] * len(preds[:, 0])).to(self._device), retain_graph=True,
+            )
+        else:
+            unique_label = list(np.unique(label))
+            for i in unique_label:
+                torch.autograd.backward(
+                    preds[:, i], torch.tensor([1.0] * len(preds[:, 0])).to(self._device), retain_graph=True,
+                )
+
+            grads = np.swapaxes(np.array(grads), 0, 1)
+            lst = [unique_label.index(i) for i in label]
+            grads = grads[np.arange(len(grads)), lst]
+
+            grads = grads[None, ...]
+
+        grads = np.swapaxes(np.array(grads), 0, 1)
+        if not self.all_framework_preprocessing:
+            grads = self._apply_preprocessing_gradient(x, grads)
+
+        return grads
+
+    def compute_loss(self, x: np.ndarray, y: np.ndarray, reduction: str = "none", **kwargs) -> np.ndarray:
+        """
+        Compute the loss function w.r.t. `x`.
+
+        :param x: Sample input with shape as expected by the model.
+        :param y: Target values (class labels) one-hot-encoded of shape `(nb_samples, nb_classes)` or indices
+                  of shape `(nb_samples,)`.
+        :param reduction: Specifies the reduction to apply to the output: 'none' | 'mean' | 'sum'.
+                   'none': no reduction will be applied
+                   'mean': the sum of the output will be divided by the number of elements in the output,
+                   'sum': the output will be summed.
+        :return: Array of losses of the same shape as `x`.
+        """
+        import torch  # lgtm [py/repeated-import]
+
+        self._model.eval()
+
+        # Apply preprocessing
+        x_preprocessed, y_preprocessed = self._apply_preprocessing(x, y, fit=False)
+
+        # Check label shape
+        y_preprocessed = self.reduce_labels(y_preprocessed)
+
+        # Convert the inputs to Tensors
+        inputs_t = torch.from_numpy(x_preprocessed).to(self._device)
+
+        # Convert the labels to Tensors
+        labels_t = torch.from_numpy(y_preprocessed).to(self._device)
+
+        # Compute the loss and return
+        model_outputs = self._model(inputs_t)
+        prev_reduction = self._loss.reduction
+
+        # Return individual loss values
+        self._loss.reduction = reduction
+        loss = self._loss(model_outputs[-1], labels_t)
+        self._loss.reduction = prev_reduction
+
+        return loss.detach().cpu().numpy()
+
+    def loss_gradient(
+        self,
+        x: Union[np.ndarray, "torch.Tensor"],
+        y: Union[np.ndarray, "torch.Tensor"],
+        training_mode: bool = False,
+        **kwargs
+    ) -> Union[np.ndarray, "torch.Tensor"]:
+        """
+        Compute the gradient of the loss function w.r.t. `x`.
+
+        :param x: Sample input with shape as expected by the model.
+        :param y: Target values (class labels) one-hot-encoded of shape `(nb_samples, nb_classes)` or indices of shape
+                  `(nb_samples,)`.
+        :param training_mode: `True` for model set to training mode and `'False` for model set to evaluation mode.
+                              Note on RNN-like models: Backpropagation through RNN modules in eval mode raises
+                              RuntimeError due to cudnn issues and require training mode, i.e. RuntimeError: cudnn RNN
+                              backward can only be called in training mode. Therefore, if the model is an RNN type we
+                              always use training mode but freeze batch-norm and dropout layers if
+                              `training_mode=False.`
+        :return: Array of gradients of the same shape as `x`.
+        """
+        import torch  # lgtm [py/repeated-import]
+
+        self._model.train(mode=training_mode)
+
+        # Backpropagation through RNN modules in eval mode raises RuntimeError due to cudnn issues and require training
+        # mode, i.e. RuntimeError: cudnn RNN backward can only be called in training mode. Therefore, if the model is
+        # an RNN type we always use training mode but freeze batch-norm and dropout layers if training_mode=False.
+        if self.is_rnn:
+            self._model.train(mode=True)
+            if not training_mode:
+                logger.debug(
+                    "Freezing batch-norm and dropout layers for gradient calculation in train mode with eval parameters"
+                    "of batch-norm and dropout."
+                )
+                self.set_batchnorm(train=False)
+                self.set_dropout(train=False)
+
+        # Apply preprocessing
+        if self.all_framework_preprocessing:
+            x_grad = torch.tensor(x).to(self._device)
+            y_grad = torch.tensor(y).to(self._device)
+            x_grad.requires_grad = True
+            inputs_t, y_preprocessed = self._apply_preprocessing(x_grad, y=y_grad, fit=False, no_grad=False)
+        elif isinstance(x, np.ndarray):
+            x_preprocessed, y_preprocessed = self._apply_preprocessing(x, y=y, fit=False, no_grad=True)
+            x_grad = torch.from_numpy(x_preprocessed).to(self._device)
+            x_grad.requires_grad = True
+            inputs_t = x_grad
+        else:
+            raise NotImplementedError("Combination of inputs and preprocessing not supported.")
+
+        # Check label shape
+        y_preprocessed = self.reduce_labels(y_preprocessed)
+
+        if isinstance(y_preprocessed, np.ndarray):
+            labels_t = torch.from_numpy(y_preprocessed).to(self._device)
+        else:
+            labels_t = y_preprocessed
+
+        # Compute the gradient and return
+        model_outputs = self._model(inputs_t)
+        loss = self._loss(model_outputs[-1], labels_t)  # lgtm [py/call-to-non-callable]
+
+        # Clean gradients
+        self._model.zero_grad()
+
+        # Compute gradients
+        if self._use_amp:
+            from apex import amp
+
+            with amp.scale_loss(loss, self._optimizer) as scaled_loss:
+                scaled_loss.backward()
+
+        else:
+            loss.backward()
+
+        if isinstance(x, torch.Tensor):
+            grads = x_grad.grad
+        else:
+            grads = x_grad.grad.cpu().numpy().copy()  # type: ignore
+
+        if not self.all_framework_preprocessing:
+            grads = self._apply_preprocessing_gradient(x, grads)
+
+        assert grads.shape == x.shape
+
+        return grads
+
+    def get_activations(
+        self,
+        x: Union[np.ndarray, "torch.Tensor"],
+        layer: Optional[Union[int, str]] = None,
+        batch_size: int = 128,
+        framework: bool = False,
+    ) -> np.ndarray:
+        """
+        Return the output of the specified layer for input `x`. `layer` is specified by layer index (between 0 and
+        `nb_layers - 1`) or by name. The number of layers can be determined by counting the results returned by
+        calling `layer_names`.
+
+        :param x: Input for computing the activations.
+        :param layer: Layer for computing the activations
+        :param batch_size: Size of batches.
+        :param framework: If true, return the intermediate tensor representation of the activation.
+        :return: The output of `layer`, where the first dimension is the batch size corresponding to `x`.
+        """
+        import torch  # lgtm [py/repeated-import]
+
+        self._model.eval()
+
+        # Apply defences
+        x_preprocessed, _ = self._apply_preprocessing(x=x, y=None, fit=False)
+
+        # Get index of the extracted layer
+        if isinstance(layer, six.string_types):
+            if layer not in self._layer_names:
+                raise ValueError("Layer name %s not supported" % layer)
+            layer_index = self._layer_names.index(layer)
+
+        elif isinstance(layer, (int, np.integer)):
+            layer_index = layer
+
+        else:
+            raise TypeError("Layer must be of type str or int")
+
+        if framework:
+            if isinstance(x, torch.Tensor):
+                return self._model(x)[layer_index]
+            return self._model(torch.from_numpy(x).to(self._device))[layer_index]
+
+        # Run prediction with batch processing
+        results = []
+        num_batch = int(np.ceil(len(x_preprocessed) / float(batch_size)))
+
+        for m in range(num_batch):
+            # Batch indexes
+            begin, end = (
+                m * batch_size,
+                min((m + 1) * batch_size, x_preprocessed.shape[0]),
+            )
+
+            # Run prediction for the current batch
+            layer_output = self._model(torch.from_numpy(x_preprocessed[begin:end]).to(self._device))[layer_index]
+            results.append(layer_output.detach().cpu().numpy())
+
+        results = np.concatenate(results)
+
+        return results
+
+    def save(self, filename: str, path: Optional[str] = None) -> None:
+        """
+        Save a model to file in the format specific to the backend framework.
+
+        :param filename: Name of the file where to store the model.
+        :param path: Path of the folder where to store the model. If no path is specified, the model will be stored in
+                     the default data location of the library `ART_DATA_PATH`.
+        """
+        import torch  # lgtm [py/repeated-import]
+
+        if path is None:
+            full_path = os.path.join(config.ART_DATA_PATH, filename)
+        else:
+            full_path = os.path.join(path, filename)
+        folder = os.path.split(full_path)[0]
+        if not os.path.exists(folder):
+            os.makedirs(folder)
+
+        # pylint: disable=W0212
+        # disable pylint because access to _modules required
+        torch.save(self._model._model.state_dict(), full_path + ".model")
+        torch.save(self._optimizer.state_dict(), full_path + ".optimizer")  # type: ignore
+        logger.info("Model state dict saved in path: %s.", full_path + ".model")
+        logger.info("Optimizer state dict saved in path: %s.", full_path + ".optimizer")
+
+    def __getstate__(self) -> Dict[str, Any]:
+        """
+        Use to ensure `PyTorchClassifier` can be pickled.
+
+        :return: State dictionary with instance parameters.
+        """
+        # pylint: disable=W0212
+        # disable pylint because access to _model required
+        state = self.__dict__.copy()
+        state["inner_model"] = copy.copy(state["_model"]._model)
+
+        # Remove the unpicklable entries
+        del state["_model_wrapper"]
+        del state["_device"]
+        del state["_model"]
+
+        model_name = str(time.time())
+        state["model_name"] = model_name
+        self.save(model_name)
+
+        return state
+
+    def __setstate__(self, state: Dict[str, Any]) -> None:
+        """
+        Use to ensure `PyTorchClassifier` can be unpickled.
+
+        :param state: State dictionary with instance parameters to restore.
+        """
+        import torch  # lgtm [py/repeated-import]
+
+        # Recover model
+        self.__dict__.update(state)
+        full_path = os.path.join(config.ART_DATA_PATH, state["model_name"])
+        model = state["inner_model"]
+        model.load_state_dict(torch.load(str(full_path) + ".model"))
+        model.eval()
+        self._model = self._make_model_wrapper(model)
+
+        # Recover device
+        self._device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+        self._model.to(self._device)
+
+        # Recover optimizer
+        self._optimizer.load_state_dict(torch.load(str(full_path) + ".optimizer"))  # type: ignore
+
+        self.__dict__.pop("model_name", None)
+        self.__dict__.pop("inner_model", None)
+
+    def __repr__(self):
+        repr_ = (
+            "%s(model=%r, loss=%r, optimizer=%r, input_shape=%r, nb_classes=%r, channels_first=%r, "
+            "clip_values=%r, preprocessing_defences=%r, postprocessing_defences=%r, preprocessing=%r)"
+            % (
+                self.__module__ + "." + self.__class__.__name__,
+                self._model,
+                self._loss,
+                self._optimizer,
+                self._input_shape,
+                self.nb_classes,
+                self.channels_first,
+                self.clip_values,
+                self.preprocessing_defences,
+                self.postprocessing_defences,
+                self.preprocessing,
+            )
+        )
+
+        return repr_
+
+    def _make_model_wrapper(self, model: "torch.nn.Module") -> "torch.nn.Module":
+        # Try to import PyTorch and create an internal class that acts like a model wrapper extending torch.nn.Module
+        try:
+            import torch.nn as nn
+
+            # Define model wrapping class only if not defined before
+            if not hasattr(self, "_model_wrapper"):
+
+                class ModelWrapper(nn.Module):
+                    """
+                    This is a wrapper for the input model.
+                    """
+
+                    import torch  # lgtm [py/repeated-import]
+
+                    def __init__(self, model: torch.nn.Module):
+                        """
+                        Initialization by storing the input model.
+
+                        :param model: PyTorch model. The forward function of the model must return the logit output.
+                        """
+                        super().__init__()
+                        self._model = model
+
+                    # pylint: disable=W0221
+                    # disable pylint because of API requirements for function
+                    def forward(self, x):
+                        """
+                        This is where we get outputs from the input model.
+
+                        :param x: Input data.
+                        :type x: `torch.Tensor`
+                        :return: a list of output layers, where the last 2 layers are logit and final outputs.
+                        :rtype: `list`
+                        """
+                        # pylint: disable=W0212
+                        # disable pylint because access to _model required
+                        import torch.nn as nn
+
+                        result = []
+                        if isinstance(self._model, nn.Sequential):
+                            for _, module_ in self._model._modules.items():
+                                x = module_(x)
+                                result.append(x)
+
+                        elif isinstance(self._model, nn.Module):
+                            x = self._model(x)
+                            result.append(x)
+
+                        else:
+                            raise TypeError("The input model must inherit from `nn.Module`.")
+
+                        return result
+
+                    @property
+                    def get_layers(self) -> List[str]:
+                        """
+                        Return the hidden layers in the model, if applicable.
+
+                        :return: The hidden layers in the model, input and output layers excluded.
+
+                        .. warning:: `get_layers` tries to infer the internal structure of the model.
+                                     This feature comes with no guarantees on the correctness of the result.
+                                     The intended order of the layers tries to match their order in the model, but this
+                                     is not guaranteed either. In addition, the function can only infer the internal
+                                     layers if the input model is of type `nn.Sequential`, otherwise, it will only
+                                     return the logit layer.
+                        """
+                        import torch.nn as nn
+
+                        result = []
+                        if isinstance(self._model, nn.Sequential):
+                            # pylint: disable=W0212
+                            # disable pylint because access to _modules required
+                            for name, module_ in self._model._modules.items():  # type: ignore
+                                result.append(name + "_" + str(module_))
+
+                        elif isinstance(self._model, nn.Module):
+                            result.append("final_layer")
+
+                        else:
+                            raise TypeError("The input model must inherit from `nn.Module`.")
+                        logger.info(
+                            "Inferred %i hidden layers on PyTorch classifier.", len(result),
+                        )
+
+                        return result
+
+                # Set newly created class as private attribute
+                self._model_wrapper = ModelWrapper
+
+            # Use model wrapping class to wrap the PyTorch model received as argument
+            return self._model_wrapper(model)
+
+        except ImportError:
+            raise ImportError("Could not find PyTorch (`torch`) installation.") from ImportError
diff --git a/adversarial-robustness-toolbox/art/estimators/classification/scikitlearn.py b/adversarial-robustness-toolbox/art/estimators/classification/scikitlearn.py
new file mode 100644
index 0000000..44f0a32
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/estimators/classification/scikitlearn.py
@@ -0,0 +1,1485 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements the classifiers for scikit-learn models.
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+from copy import deepcopy
+import importlib
+import logging
+import os
+import pickle
+from typing import Callable, List, Optional, Tuple, Union, TYPE_CHECKING
+
+import numpy as np
+
+from art.estimators.estimator import DecisionTreeMixin, LossGradientsMixin
+from art.estimators.classification.classifier import (
+    ClassGradientsMixin,
+    ClassifierMixin,
+)
+from art.estimators.scikitlearn import ScikitlearnEstimator
+from art.utils import to_categorical
+from art import config
+
+if TYPE_CHECKING:
+    # pylint: disable=C0412
+    import sklearn
+
+    from art.utils import CLIP_VALUES_TYPE, PREPROCESSING_TYPE
+    from art.defences.preprocessor import Preprocessor
+    from art.defences.postprocessor import Postprocessor
+    from art.metrics.verification_decisions_trees import LeafNode, Tree
+
+logger = logging.getLogger(__name__)
+
+
+# pylint: disable=C0103
+def SklearnClassifier(
+    model: "sklearn.base.BaseEstimator",
+    clip_values: Optional["CLIP_VALUES_TYPE"] = None,
+    preprocessing_defences: Union["Preprocessor", List["Preprocessor"], None] = None,
+    postprocessing_defences: Union["Postprocessor", List["Postprocessor"], None] = None,
+    preprocessing: "PREPROCESSING_TYPE" = (0.0, 1.0),
+    use_logits: bool = False,
+) -> "ScikitlearnClassifier":
+    """
+    Create a `Classifier` instance from a scikit-learn Classifier model. This is a convenience function that
+    instantiates the correct wrapper class for the given scikit-learn model.
+
+    :param model: scikit-learn Classifier model.
+    :param clip_values: Tuple of the form `(min, max)` representing the minimum and maximum values allowed
+            for features.
+    :param preprocessing_defences: Preprocessing defence(s) to be applied by the classifier.
+    :param postprocessing_defences: Postprocessing defence(s) to be applied by the classifier.
+    :param preprocessing: Tuple of the form `(subtrahend, divisor)` of floats or `np.ndarray` of values to be
+            used for data preprocessing. The first value will be subtracted from the input. The input will then
+            be divided by the second one.
+    """
+    if model.__class__.__module__.split(".")[0] != "sklearn":
+        raise TypeError("Model is not an sklearn model. Received '%s'" % model.__class__)
+
+    sklearn_name = model.__class__.__name__
+    module = importlib.import_module("art.estimators.classification.scikitlearn")
+    if hasattr(module, "Scikitlearn%s" % sklearn_name):
+        return getattr(module, "Scikitlearn%s" % sklearn_name)(
+            model=model,
+            clip_values=clip_values,
+            preprocessing_defences=preprocessing_defences,
+            postprocessing_defences=postprocessing_defences,
+            preprocessing=preprocessing,
+        )
+
+    # This basic class at least generically handles `fit`, `predict` and `save`
+    return ScikitlearnClassifier(
+        model, clip_values, preprocessing_defences, postprocessing_defences, preprocessing, use_logits,
+    )
+
+
+class ScikitlearnClassifier(ClassifierMixin, ScikitlearnEstimator):  # lgtm [py/missing-call-to-init]
+    """
+    Wrapper class for scikit-learn classifier models.
+    """
+
+    def __init__(
+        self,
+        model: "sklearn.base.BaseEstimator",
+        clip_values: Optional["CLIP_VALUES_TYPE"] = None,
+        preprocessing_defences: Union["Preprocessor", List["Preprocessor"], None] = None,
+        postprocessing_defences: Union["Postprocessor", List["Postprocessor"], None] = None,
+        preprocessing: "PREPROCESSING_TYPE" = (0.0, 1.0),
+        use_logits: bool = False,
+    ) -> None:
+        """
+        Create a `Classifier` instance from a scikit-learn classifier model.
+
+        :param model: scikit-learn classifier model.
+        :param clip_values: Tuple of the form `(min, max)` representing the minimum and maximum values allowed
+               for features.
+        :param preprocessing_defences: Preprocessing defence(s) to be applied by the classifier.
+        :param postprocessing_defences: Postprocessing defence(s) to be applied by the classifier.
+        :param preprocessing: Tuple of the form `(subtrahend, divisor)` of floats or `np.ndarray` of values to be
+               used for data preprocessing. The first value will be subtracted from the input. The input will then
+               be divided by the second one.
+        :param use_logits: Determines whether predict() returns logits instead of probabilities if available. Some
+               adversarial attacks (DeepFool) may perform better if logits are used.
+        """
+        super().__init__(
+            model=model,
+            clip_values=clip_values,
+            preprocessing_defences=preprocessing_defences,
+            postprocessing_defences=postprocessing_defences,
+            preprocessing=preprocessing,
+        )
+        self._input_shape = self._get_input_shape(model)
+        self._nb_classes = self._get_nb_classes()
+        self._use_logits = use_logits
+
+    @property
+    def input_shape(self) -> Tuple[int, ...]:
+        """
+        Return the shape of one input sample.
+
+        :return: Shape of one input sample.
+        """
+        return self._input_shape  # type: ignore
+
+    def fit(self, x: np.ndarray, y: np.ndarray, **kwargs) -> None:
+        """
+        Fit the classifier on the training set `(x, y)`.
+
+        :param x: Training data.
+        :param y: Target values (class labels) one-hot-encoded of shape (nb_samples, nb_classes).
+        :param kwargs: Dictionary of framework-specific arguments. These should be parameters supported by the
+               `fit` function in `sklearn` classifier and will be passed to this function as such.
+        """
+        # Apply preprocessing
+        x_preprocessed, y_preprocessed = self._apply_preprocessing(x, y, fit=True)
+        y_preprocessed = np.argmax(y_preprocessed, axis=1)
+
+        self.model.fit(x_preprocessed, y_preprocessed, **kwargs)
+        self._input_shape = self._get_input_shape(self.model)
+        self._nb_classes = self._get_nb_classes()
+
+    def predict(self, x: np.ndarray, **kwargs) -> np.ndarray:
+        """
+        Perform prediction for a batch of inputs.
+
+        :param x: Input samples.
+        :return: Array of predictions of shape `(nb_inputs, nb_classes)`.
+        :raises `ValueError`: If the classifier does not have methods `predict` or `predict_proba`.
+        """
+        # Apply defences
+        x_preprocessed, _ = self._apply_preprocessing(x, y=None, fit=False)
+
+        if self._use_logits:
+            if callable(getattr(self.model, "predict_log_proba", None)):
+                y_pred = self.model.predict_log_proba(x_preprocessed)
+            else:
+                logger.warning(
+                    "use_logits was True but classifier did not have callable predict_log_proba member. Falling back to"
+                    " probabilities"
+                )
+        elif callable(getattr(self.model, "predict_proba", None)):
+            y_pred = self.model.predict_proba(x_preprocessed)
+        elif callable(getattr(self.model, "predict", None)):
+            y_pred = to_categorical(self.model.predict(x_preprocessed), nb_classes=self.model.classes_.shape[0],)
+        else:
+            raise ValueError("The provided model does not have methods `predict_proba` or `predict`.")
+
+        # Apply postprocessing
+        predictions = self._apply_postprocessing(preds=y_pred, fit=False)
+
+        return predictions
+
+    def save(self, filename: str, path: Optional[str] = None) -> None:
+        """
+        Save a model to file in the format specific to the backend framework.
+
+        :param filename: Name of the file where to store the model.
+        :param path: Path of the folder where to store the model. If no path is specified, the model will be stored in
+                     the default data location of the library `ART_DATA_PATH`.
+        """
+        if path is None:
+            full_path = os.path.join(config.ART_DATA_PATH, filename)
+        else:
+            full_path = os.path.join(path, filename)
+        folder = os.path.split(full_path)[0]
+        if not os.path.exists(folder):
+            os.makedirs(folder)
+
+        with open(full_path + ".pickle", "wb") as file_pickle:
+            pickle.dump(self.model, file=file_pickle)
+
+    def clone_for_refitting(self) -> "ScikitlearnClassifier":  # lgtm [py/inheritance/incorrect-overridden-signature]
+        """
+        Create a copy of the classifier that can be refit from scratch.
+
+        :return: new estimator
+        """
+        import sklearn  # lgtm [py/repeated-import]
+
+        clone = type(self)(sklearn.base.clone(self.model))
+        params = self.get_params()
+        del params["model"]
+        clone.set_params(**params)
+        return clone
+
+    def reset(self) -> None:
+        """
+        Resets the weights of the classifier so that it can be refit from scratch.
+
+        """
+        # No need to do anything since scikitlearn models start from scratch each time fit() is called
+        pass
+
+    def _get_input_shape(self, model) -> Optional[Tuple[int, ...]]:
+        _input_shape: Optional[Tuple[int, ...]]
+        if hasattr(model, "n_features_"):
+            _input_shape = (model.n_features_,)
+        elif hasattr(model, "n_features_in_"):
+            _input_shape = (model.n_features_in_,)
+        elif hasattr(model, "feature_importances_"):
+            _input_shape = (len(model.feature_importances_),)
+        elif hasattr(model, "coef_"):
+            if len(model.coef_.shape) == 1:
+                _input_shape = (model.coef_.shape[0],)
+            else:
+                _input_shape = (model.coef_.shape[1],)
+        elif hasattr(model, "support_vectors_"):
+            _input_shape = (model.support_vectors_.shape[1],)
+        elif hasattr(model, "steps"):
+            _input_shape = self._get_input_shape(model.steps[0][1])
+        else:
+            logger.warning("Input shape not recognised. The model might not have been fitted.")
+            _input_shape = None
+        return _input_shape
+
+    def _get_nb_classes(self) -> int:
+        if hasattr(self.model, "n_classes_"):
+            _nb_classes = self.model.n_classes_
+        elif hasattr(self.model, "classes_"):
+            _nb_classes = self.model.classes_.shape[0]
+        else:
+            logger.warning("Number of classes not recognised. The model might not have been fitted.")
+            _nb_classes = None
+        return _nb_classes
+
+
+class ScikitlearnDecisionTreeClassifier(ScikitlearnClassifier):
+    """
+    Wrapper class for scikit-learn Decision Tree Classifier models.
+    """
+
+    def __init__(
+        self,
+        model: "sklearn.tree.DecisionTreeClassifier",
+        clip_values: Optional["CLIP_VALUES_TYPE"] = None,
+        preprocessing_defences: Union["Preprocessor", List["Preprocessor"], None] = None,
+        postprocessing_defences: Union["Postprocessor", List["Postprocessor"], None] = None,
+        preprocessing: "PREPROCESSING_TYPE" = (0.0, 1.0),
+    ) -> None:
+        """
+        Create a `Classifier` instance from a scikit-learn Decision Tree Classifier model.
+
+        :param model: scikit-learn Decision Tree Classifier model.
+        :param clip_values: Tuple of the form `(min, max)` representing the minimum and maximum values allowed
+               for features.
+        :param preprocessing_defences: Preprocessing defence(s) to be applied by the classifier.
+        :param postprocessing_defences: Postprocessing defence(s) to be applied by the classifier.
+        :param preprocessing: Tuple of the form `(subtrahend, divisor)` of floats or `np.ndarray` of values to be
+               used for data preprocessing. The first value will be subtracted from the input. The input will then
+               be divided by the second one.
+        """
+        import sklearn  # lgtm [py/repeated-import]
+
+        if not isinstance(model, sklearn.tree.DecisionTreeClassifier) and model is not None:
+            raise TypeError("Model must be of type sklearn.tree.DecisionTreeClassifier.")
+
+        super().__init__(
+            model=model,
+            clip_values=clip_values,
+            preprocessing_defences=preprocessing_defences,
+            postprocessing_defences=postprocessing_defences,
+            preprocessing=preprocessing,
+        )
+
+    def get_classes_at_node(self, node_id: int) -> np.ndarray:
+        """
+        Returns the classification for a given node.
+
+        :return: Major class in node.
+        """
+        return np.argmax(self.model.tree_.value[node_id])
+
+    def get_threshold_at_node(self, node_id: int) -> float:
+        """
+        Returns the threshold of given id for a node.
+
+        :return: Threshold value of feature split in this node.
+        """
+        return self.model.tree_.threshold[node_id]
+
+    def get_feature_at_node(self, node_id: int) -> int:
+        """
+        Returns the feature of given id for a node.
+
+        :return: Feature index of feature split in this node.
+        """
+        return self.model.tree_.feature[node_id]
+
+    def get_samples_at_node(self, node_id: int) -> int:
+        """
+        Returns the number of training samples mapped to a node.
+
+        :return: Number of samples mapped this node.
+        """
+        return self.model.tree_.n_node_samples[node_id]
+
+    def get_left_child(self, node_id: int) -> int:
+        """
+        Returns the id of the left child node of node_id.
+
+        :return: The indices of the left child in the tree.
+        """
+        return self.model.tree_.children_left[node_id]
+
+    def get_right_child(self, node_id: int) -> int:
+        """
+        Returns the id of the right child node of node_id.
+
+        :return: The indices of the right child in the tree.
+        """
+        return self.model.tree_.children_right[node_id]
+
+    def get_decision_path(self, x: np.ndarray) -> np.ndarray:
+        """
+        Returns the path through nodes in the tree when classifying x. Last one is leaf, first one root node.
+
+        :return: The indices of the nodes in the array structure of the tree.
+        """
+        if len(np.shape(x)) == 1:
+            return self.model.decision_path(x.reshape(1, -1)).indices
+
+        return self.model.decision_path(x).indices
+
+    def get_values_at_node(self, node_id: int) -> np.ndarray:
+        """
+        Returns the feature of given id for a node.
+
+        :return: Normalized values at node node_id.
+        """
+        return self.model.tree_.value[node_id] / np.linalg.norm(self.model.tree_.value[node_id])
+
+    def _get_leaf_nodes(self, node_id, i_tree, class_label, box) -> List["LeafNode"]:
+        from art.metrics.verification_decisions_trees import LeafNode, Box, Interval
+
+        leaf_nodes = list()
+
+        if self.get_left_child(node_id) != self.get_right_child(node_id):
+
+            node_left = self.get_left_child(node_id)
+            node_right = self.get_right_child(node_id)
+
+            box_left = deepcopy(box)
+            box_right = deepcopy(box)
+
+            feature = self.get_feature_at_node(node_id)
+            box_split_left = Box(intervals={feature: Interval(-np.inf, self.get_threshold_at_node(node_id))})
+            box_split_right = Box(intervals={feature: Interval(self.get_threshold_at_node(node_id), np.inf)})
+
+            if box.intervals:
+                box_left.intersect_with_box(box_split_left)
+                box_right.intersect_with_box(box_split_right)
+            else:
+                box_left = box_split_left
+                box_right = box_split_right
+
+            leaf_nodes += self._get_leaf_nodes(node_left, i_tree, class_label, box_left)
+            leaf_nodes += self._get_leaf_nodes(node_right, i_tree, class_label, box_right)
+
+        else:
+            leaf_nodes.append(
+                LeafNode(
+                    tree_id=i_tree,
+                    class_label=class_label,
+                    node_id=node_id,
+                    box=box,
+                    value=self.get_values_at_node(node_id)[0, class_label],
+                )
+            )
+
+        return leaf_nodes
+
+
+class ScikitlearnDecisionTreeRegressor(ScikitlearnDecisionTreeClassifier):
+    """
+    Wrapper class for scikit-learn Decision Tree Regressor models.
+    """
+
+    def __init__(
+        self,
+        model: "sklearn.tree.DecisionTreeRegressor",
+        clip_values: Optional["CLIP_VALUES_TYPE"] = None,
+        preprocessing_defences: Union["Preprocessor", List["Preprocessor"], None] = None,
+        postprocessing_defences: Union["Postprocessor", List["Postprocessor"], None] = None,
+        preprocessing: "PREPROCESSING_TYPE" = (0.0, 1.0),
+    ) -> None:
+        """
+        Create a `Regressor` instance from a scikit-learn Decision Tree Regressor model.
+
+        :param model: scikit-learn Decision Tree Regressor model.
+        :param clip_values: Tuple of the form `(min, max)` representing the minimum and maximum values allowed
+               for features.
+        :param preprocessing_defences: Preprocessing defence(s) to be applied by the classifier.
+        :param postprocessing_defences: Postprocessing defence(s) to be applied by the classifier.
+        :param preprocessing: Tuple of the form `(subtrahend, divisor)` of floats or `np.ndarray` of values to be
+               used for data preprocessing. The first value will be subtracted from the input. The input will then
+               be divided by the second one.
+        """
+        # pylint: disable=E0001
+        import sklearn  # lgtm [py/repeated-import]
+
+        if not isinstance(model, sklearn.tree.DecisionTreeRegressor):
+            raise TypeError("Model must be of type sklearn.tree.DecisionTreeRegressor.")
+
+        ScikitlearnDecisionTreeClassifier.__init__(
+            self,
+            model=None,
+            clip_values=clip_values,
+            preprocessing_defences=preprocessing_defences,
+            postprocessing_defences=postprocessing_defences,
+            preprocessing=preprocessing,
+        )
+        self._model = model
+
+    def get_values_at_node(self, node_id: int) -> np.ndarray:
+        """
+        Returns the feature of given id for a node.
+
+        :return: Normalized values at node node_id.
+        """
+        return self.model.tree_.value[node_id]
+
+    def _get_leaf_nodes(self, node_id, i_tree, class_label, box) -> List["LeafNode"]:
+        from art.metrics.verification_decisions_trees import LeafNode, Box, Interval
+
+        leaf_nodes: List[LeafNode] = list()
+
+        if self.get_left_child(node_id) != self.get_right_child(node_id):
+
+            node_left = self.get_left_child(node_id)
+            node_right = self.get_right_child(node_id)
+
+            box_left = deepcopy(box)
+            box_right = deepcopy(box)
+
+            feature = self.get_feature_at_node(node_id)
+            box_split_left = Box(intervals={feature: Interval(-np.inf, self.get_threshold_at_node(node_id))})
+            box_split_right = Box(intervals={feature: Interval(self.get_threshold_at_node(node_id), np.inf)})
+
+            if box.intervals:
+                box_left.intersect_with_box(box_split_left)
+                box_right.intersect_with_box(box_split_right)
+            else:
+                box_left = box_split_left
+                box_right = box_split_right
+
+            leaf_nodes += self._get_leaf_nodes(node_left, i_tree, class_label, box_left)
+            leaf_nodes += self._get_leaf_nodes(node_right, i_tree, class_label, box_right)
+
+        else:
+            leaf_nodes.append(
+                LeafNode(
+                    tree_id=i_tree,
+                    class_label=class_label,
+                    node_id=node_id,
+                    box=box,
+                    value=self.get_values_at_node(node_id)[0, 0],
+                )
+            )
+
+        return leaf_nodes
+
+
+class ScikitlearnExtraTreeClassifier(ScikitlearnDecisionTreeClassifier):
+    """
+    Wrapper class for scikit-learn Extra TreeClassifier Classifier models.
+    """
+
+    def __init__(
+        self,
+        model: "sklearn.tree.ExtraTreeClassifier",
+        clip_values: Optional["CLIP_VALUES_TYPE"] = None,
+        preprocessing_defences: Union["Preprocessor", List["Preprocessor"], None] = None,
+        postprocessing_defences: Union["Postprocessor", List["Postprocessor"], None] = None,
+        preprocessing: "PREPROCESSING_TYPE" = (0.0, 1.0),
+    ) -> None:
+        """
+        Create a `Classifier` instance from a scikit-learn Extra TreeClassifier Classifier model.
+
+        :param model: scikit-learn Extra TreeClassifier Classifier model.
+        :param clip_values: Tuple of the form `(min, max)` representing the minimum and maximum values allowed
+               for features.
+        :param preprocessing_defences: Preprocessing defence(s) to be applied by the classifier.
+        :param postprocessing_defences: Postprocessing defence(s) to be applied by the classifier.
+        :param preprocessing: Tuple of the form `(subtrahend, divisor)` of floats or `np.ndarray` of values to be
+               used for data preprocessing. The first value will be subtracted from the input. The input will then
+               be divided by the second one.
+        """
+        import sklearn  # lgtm [py/repeated-import]
+
+        if not isinstance(model, sklearn.tree.ExtraTreeClassifier):
+            raise TypeError("Model must be of type sklearn.tree.ExtraTreeClassifier.")
+
+        super().__init__(
+            model=model,
+            clip_values=clip_values,
+            preprocessing_defences=preprocessing_defences,
+            postprocessing_defences=postprocessing_defences,
+            preprocessing=preprocessing,
+        )
+
+
+class ScikitlearnAdaBoostClassifier(ScikitlearnClassifier):
+    """
+    Wrapper class for scikit-learn AdaBoost Classifier models.
+    """
+
+    def __init__(
+        self,
+        model: "sklearn.ensemble.AdaBoostClassifier",
+        clip_values: Optional["CLIP_VALUES_TYPE"] = None,
+        preprocessing_defences: Union["Preprocessor", List["Preprocessor"], None] = None,
+        postprocessing_defences: Union["Postprocessor", List["Postprocessor"], None] = None,
+        preprocessing: "PREPROCESSING_TYPE" = (0.0, 1.0),
+    ) -> None:
+        """
+        Create a `Classifier` instance from a scikit-learn AdaBoost Classifier model.
+
+        :param model: scikit-learn AdaBoost Classifier model.
+        :param clip_values: Tuple of the form `(min, max)` representing the minimum and maximum values allowed
+               for features.
+        :param preprocessing_defences: Preprocessing defence(s) to be applied by the classifier.
+        :param postprocessing_defences: Postprocessing defence(s) to be applied by the classifier.
+        :param preprocessing: Tuple of the form `(subtrahend, divisor)` of floats or `np.ndarray` of values to be
+               used for data preprocessing. The first value will be subtracted from the input. The input will then
+               be divided by the second one.
+        """
+        import sklearn  # lgtm [py/repeated-import]
+
+        if not isinstance(model, sklearn.ensemble.AdaBoostClassifier):
+            raise TypeError("Model must be of type sklearn.ensemble.AdaBoostClassifier.")
+
+        super().__init__(
+            model=model,
+            clip_values=clip_values,
+            preprocessing_defences=preprocessing_defences,
+            postprocessing_defences=postprocessing_defences,
+            preprocessing=preprocessing,
+        )
+
+
+class ScikitlearnBaggingClassifier(ScikitlearnClassifier):
+    """
+    Wrapper class for scikit-learn Bagging Classifier models.
+    """
+
+    def __init__(
+        self,
+        model: "sklearn.ensemble.BaggingClassifier",
+        clip_values: Optional["CLIP_VALUES_TYPE"] = None,
+        preprocessing_defences: Union["Preprocessor", List["Preprocessor"], None] = None,
+        postprocessing_defences: Union["Postprocessor", List["Postprocessor"], None] = None,
+        preprocessing: "PREPROCESSING_TYPE" = (0.0, 1.0),
+    ) -> None:
+        """
+        Create a `Classifier` instance from a scikit-learn Bagging Classifier model.
+
+        :param model: scikit-learn Bagging Classifier model.
+        :param clip_values: Tuple of the form `(min, max)` representing the minimum and maximum values allowed
+               for features.
+        :param preprocessing_defences: Preprocessing defence(s) to be applied by the classifier.
+        :param postprocessing_defences: Postprocessing defence(s) to be applied by the classifier.
+        :param preprocessing: Tuple of the form `(subtrahend, divisor)` of floats or `np.ndarray` of values to be
+               used for data preprocessing. The first value will be subtracted from the input. The input will then
+               be divided by the second one.
+        """
+        # pylint: disable=E0001
+        import sklearn  # lgtm [py/repeated-import]
+
+        if not isinstance(model, sklearn.ensemble.BaggingClassifier):
+            raise TypeError("Model must be of type sklearn.ensemble.BaggingClassifier.")
+
+        super().__init__(
+            model=model,
+            clip_values=clip_values,
+            preprocessing_defences=preprocessing_defences,
+            postprocessing_defences=postprocessing_defences,
+            preprocessing=preprocessing,
+        )
+
+
+class ScikitlearnExtraTreesClassifier(ScikitlearnClassifier, DecisionTreeMixin):
+    """
+    Wrapper class for scikit-learn Extra Trees Classifier models.
+    """
+
+    def __init__(
+        self,
+        model: "sklearn.ensemble.ExtraTreesClassifier",
+        clip_values: Optional["CLIP_VALUES_TYPE"] = None,
+        preprocessing_defences: Union["Preprocessor", List["Preprocessor"], None] = None,
+        postprocessing_defences: Union["Postprocessor", List["Postprocessor"], None] = None,
+        preprocessing: "PREPROCESSING_TYPE" = (0.0, 1.0),
+    ):
+        """
+        Create a `Classifier` instance from a scikit-learn Extra Trees Classifier model.
+
+        :param model: scikit-learn Extra Trees Classifier model.
+        :param clip_values: Tuple of the form `(min, max)` representing the minimum and maximum values allowed
+               for features.
+        :param preprocessing_defences: Preprocessing defence(s) to be applied by the classifier.
+        :param postprocessing_defences: Postprocessing defence(s) to be applied by the classifier.
+        :param preprocessing: Tuple of the form `(subtrahend, divisor)` of floats or `np.ndarray` of values to be
+               used for data preprocessing. The first value will be subtracted from the input. The input will then
+               be divided by the second one.
+        """
+        # pylint: disable=E0001
+        import sklearn  # lgtm [py/repeated-import]
+
+        if not isinstance(model, sklearn.ensemble.ExtraTreesClassifier):
+            raise TypeError("Model must be of type sklearn.ensemble.ExtraTreesClassifier.")
+
+        super().__init__(
+            model=model,
+            clip_values=clip_values,
+            preprocessing_defences=preprocessing_defences,
+            postprocessing_defences=postprocessing_defences,
+            preprocessing=preprocessing,
+        )
+
+    def get_trees(self) -> List["Tree"]:  # lgtm [py/similar-function]
+        """
+        Get the decision trees.
+
+        :return: A list of decision trees.
+        """
+        from art.metrics.verification_decisions_trees import Box, Tree
+
+        trees = list()
+
+        for i_tree, decision_tree_model in enumerate(self.model.estimators_):
+            box = Box()
+
+            #     if num_classes == 2:
+            #         class_label = -1
+            #     else:
+            #         class_label = i_tree % num_classes
+
+            extra_tree_classifier = ScikitlearnExtraTreeClassifier(model=decision_tree_model)
+
+            for i_class in range(self.model.n_classes_):
+                class_label = i_class
+
+                # pylint: disable=W0212
+                trees.append(
+                    Tree(
+                        class_id=class_label,
+                        leaf_nodes=extra_tree_classifier._get_leaf_nodes(0, i_tree, class_label, box),
+                    )
+                )
+
+        return trees
+
+
+class ScikitlearnGradientBoostingClassifier(ScikitlearnClassifier, DecisionTreeMixin):
+    """
+    Wrapper class for scikit-learn Gradient Boosting Classifier models.
+    """
+
+    def __init__(
+        self,
+        model: "sklearn.ensemble.GradientBoostingClassifier",
+        clip_values: Optional["CLIP_VALUES_TYPE"] = None,
+        preprocessing_defences: Union["Preprocessor", List["Preprocessor"], None] = None,
+        postprocessing_defences: Union["Postprocessor", List["Postprocessor"], None] = None,
+        preprocessing: "PREPROCESSING_TYPE" = (0.0, 1.0),
+    ) -> None:
+        """
+        Create a `Classifier` instance from a scikit-learn Gradient Boosting Classifier model.
+
+        :param model: scikit-learn Gradient Boosting Classifier model.
+        :param clip_values: Tuple of the form `(min, max)` representing the minimum and maximum values allowed
+               for features.
+        :param preprocessing_defences: Preprocessing defence(s) to be applied by the classifier.
+        :param postprocessing_defences: Postprocessing defence(s) to be applied by the classifier.
+        :param preprocessing: Tuple of the form `(subtrahend, divisor)` of floats or `np.ndarray` of values to be
+               used for data preprocessing. The first value will be subtracted from the input. The input will then
+               be divided by the second one.
+        """
+        # pylint: disable=E0001
+        import sklearn  # lgtm [py/repeated-import]
+
+        if not isinstance(model, sklearn.ensemble.GradientBoostingClassifier):
+            raise TypeError("Model must be of type sklearn.ensemble.GradientBoostingClassifier.")
+
+        super().__init__(
+            model=model,
+            clip_values=clip_values,
+            preprocessing_defences=preprocessing_defences,
+            postprocessing_defences=postprocessing_defences,
+            preprocessing=preprocessing,
+        )
+
+    def get_trees(self) -> List["Tree"]:
+        """
+        Get the decision trees.
+
+        :return: A list of decision trees.
+        """
+        from art.metrics.verification_decisions_trees import Box, Tree
+
+        trees = list()
+        num_trees, num_classes = self.model.estimators_.shape
+
+        for i_tree in range(num_trees):
+            box = Box()
+
+            for i_class in range(num_classes):
+                decision_tree_classifier = ScikitlearnDecisionTreeRegressor(
+                    model=self.model.estimators_[i_tree, i_class]
+                )
+
+                if num_classes == 2:
+                    class_label = None
+                else:
+                    class_label = i_class
+
+                # pylint: disable=W0212
+                trees.append(
+                    Tree(
+                        class_id=class_label,
+                        leaf_nodes=decision_tree_classifier._get_leaf_nodes(0, i_tree, class_label, box),
+                    )
+                )
+
+        return trees
+
+
+class ScikitlearnRandomForestClassifier(ScikitlearnClassifier):
+    """
+    Wrapper class for scikit-learn Random Forest Classifier models.
+    """
+
+    def __init__(
+        self,
+        model: "sklearn.ensemble.RandomForestClassifier",
+        clip_values: Optional["CLIP_VALUES_TYPE"] = None,
+        preprocessing_defences: Union["Preprocessor", List["Preprocessor"], None] = None,
+        postprocessing_defences: Union["Postprocessor", List["Postprocessor"], None] = None,
+        preprocessing: "PREPROCESSING_TYPE" = (0.0, 1.0),
+    ) -> None:
+        """
+        Create a `Classifier` instance from a scikit-learn Random Forest Classifier model.
+
+        :param model: scikit-learn Random Forest Classifier model.
+        :param clip_values: Tuple of the form `(min, max)` representing the minimum and maximum values allowed
+               for features.
+        :param preprocessing_defences: Preprocessing defence(s) to be applied by the classifier.
+        :param postprocessing_defences: Postprocessing defence(s) to be applied by the classifier.
+        :param preprocessing: Tuple of the form `(subtrahend, divisor)` of floats or `np.ndarray` of values to be
+               used for data preprocessing. The first value will be subtracted from the input. The input will then
+               be divided by the second one.
+        """
+        # pylint: disable=E0001
+        import sklearn  # lgtm [py/repeated-import]
+
+        if not isinstance(model, sklearn.ensemble.RandomForestClassifier):
+            raise TypeError("Model must be of type sklearn.ensemble.RandomForestClassifier.")
+
+        super().__init__(
+            model=model,
+            clip_values=clip_values,
+            preprocessing_defences=preprocessing_defences,
+            postprocessing_defences=postprocessing_defences,
+            preprocessing=preprocessing,
+        )
+
+    def get_trees(self) -> List["Tree"]:  # lgtm [py/similar-function]
+        """
+        Get the decision trees.
+
+        :return: A list of decision trees.
+        """
+        from art.metrics.verification_decisions_trees import Box, Tree
+
+        trees = list()
+
+        for i_tree, decision_tree_model in enumerate(self.model.estimators_):
+            box = Box()
+
+            #     if num_classes == 2:
+            #         class_label = -1
+            #     else:
+            #         class_label = i_tree % num_classes
+
+            decision_tree_classifier = ScikitlearnDecisionTreeClassifier(model=decision_tree_model)
+
+            for i_class in range(self.model.n_classes_):
+                class_label = i_class
+
+                # pylint: disable=W0212
+                trees.append(
+                    Tree(
+                        class_id=class_label,
+                        leaf_nodes=decision_tree_classifier._get_leaf_nodes(0, i_tree, class_label, box),
+                    )
+                )
+
+        return trees
+
+
+class ScikitlearnLogisticRegression(ClassGradientsMixin, LossGradientsMixin, ScikitlearnClassifier):
+    """
+    Wrapper class for scikit-learn Logistic Regression models.
+    """
+
+    def __init__(
+        self,
+        model: "sklearn.linear_model.LogisticRegression",
+        clip_values: Optional["CLIP_VALUES_TYPE"] = None,
+        preprocessing_defences: Union["Preprocessor", List["Preprocessor"], None] = None,
+        postprocessing_defences: Union["Postprocessor", List["Postprocessor"], None] = None,
+        preprocessing: "PREPROCESSING_TYPE" = (0.0, 1.0),
+    ) -> None:
+        """
+        Create a `Classifier` instance from a scikit-learn Logistic Regression model.
+
+        :param model: scikit-learn LogisticRegression model
+        :param clip_values: Tuple of the form `(min, max)` representing the minimum and maximum values allowed
+               for features.
+        :param preprocessing_defences: Preprocessing defence(s) to be applied by the classifier.
+        :param postprocessing_defences: Postprocessing defence(s) to be applied by the classifier.
+        :param preprocessing: Tuple of the form `(subtrahend, divisor)` of floats or `np.ndarray` of values to be
+               used for data preprocessing. The first value will be subtracted from the input. The input will then
+               be divided by the second one.
+        """
+        super().__init__(
+            model=model,
+            clip_values=clip_values,
+            preprocessing_defences=preprocessing_defences,
+            postprocessing_defences=postprocessing_defences,
+            preprocessing=preprocessing,
+        )
+
+    def class_gradient(self, x: np.ndarray, label: Union[int, List[int], None] = None, **kwargs) -> np.ndarray:
+        """
+        Compute per-class derivatives w.r.t. `x`.
+
+        | Paper link: http://cs229.stanford.edu/proj2016/report/ItkinaWu-AdversarialAttacksonImageRecognition-report.pdf
+        | Typo in https://arxiv.org/abs/1605.07277 (equation 6)
+
+        :param x: Sample input with shape as expected by the model.
+        :param label: Index of a specific per-class derivative. If an integer is provided, the gradient of that class
+                      output is computed for all samples. If multiple values as provided, the first dimension should
+                      match the batch size of `x`, and each value will be used as target for its corresponding sample in
+                      `x`. If `None`, then gradients for all classes will be computed for each sample.
+        :return: Array of gradients of input features w.r.t. each class in the form
+                 `(batch_size, nb_classes, input_shape)` when computing for all classes, otherwise shape becomes
+                 `(batch_size, 1, input_shape)` when `label` parameter is specified.
+        :raises `ValueError`: If the model has not been fitted prior to calling this method or if the number of
+            classes in the classifier is not known.
+        :raises `TypeError`: If the requested label cannot be processed.
+        """
+        if not hasattr(self.model, "coef_"):
+            raise ValueError(
+                """Model has not been fitted. Run function `fit(x, y)` of classifier first or provide a
+            fitted model."""
+            )
+        if self.nb_classes is None:
+            raise ValueError("Unknown number of classes in classifier.")
+        nb_samples = x.shape[0]
+
+        # Apply preprocessing
+        x_preprocessed, _ = self._apply_preprocessing(x, y=None, fit=False)
+
+        y_pred = self.model.predict_proba(X=x_preprocessed)
+        weights = self.model.coef_
+
+        if self.nb_classes > 2:  # type: ignore
+            w_weighted = np.matmul(y_pred, weights)
+
+        def _f_class_gradient(i_class, i_sample):
+            if self.nb_classes == 2:
+                return (-1.0) ** (i_class + 1.0) * y_pred[i_sample, 0] * y_pred[i_sample, 1] * weights[0, :]
+
+            return weights[i_class, :] - w_weighted[i_sample, :]
+
+        if label is None:
+            # Compute the gradients w.r.t. all classes
+            class_gradients = list()
+
+            for i_class in range(self.nb_classes):  # type: ignore
+                class_gradient = np.zeros(x.shape)
+                for i_sample in range(nb_samples):
+                    class_gradient[i_sample, :] += _f_class_gradient(i_class, i_sample)
+                class_gradients.append(class_gradient)
+
+            gradients = np.swapaxes(np.array(class_gradients), 0, 1)
+
+        elif isinstance(label, (int, np.integer)):
+            # Compute the gradients only w.r.t. the provided label
+            class_gradient = np.zeros(x.shape)
+            for i_sample in range(nb_samples):
+                class_gradient[i_sample, :] += _f_class_gradient(label, i_sample)
+
+            gradients = np.swapaxes(np.array([class_gradient]), 0, 1)
+
+        elif (
+            (isinstance(label, list) and len(label) == nb_samples)
+            or isinstance(label, np.ndarray)
+            and label.shape == (nb_samples,)
+        ):
+            # For each sample, compute the gradients w.r.t. the indicated target class (possibly distinct)
+            class_gradients = list()
+            unique_labels = list(np.unique(label))
+
+            for unique_label in unique_labels:
+                class_gradient = np.zeros(x.shape)
+                for i_sample in range(nb_samples):
+                    # class_gradient[i_sample, :] += label[i_sample, unique_label] * (weights[unique_label, :]
+                    # - w_weighted[i_sample, :])
+                    class_gradient[i_sample, :] += _f_class_gradient(unique_label, i_sample)
+
+                class_gradients.append(class_gradient)
+
+            gradients = np.swapaxes(np.array(class_gradients), 0, 1)
+            lst = [unique_labels.index(i) for i in label]
+            gradients = np.expand_dims(gradients[np.arange(len(gradients)), lst], axis=1)
+
+        else:
+            raise TypeError("Unrecognized type for argument `label` with type " + str(type(label)))
+
+        gradients = self._apply_preprocessing_gradient(x, gradients)
+
+        return gradients
+
+    def loss_gradient(self, x: np.ndarray, y: np.ndarray, **kwargs) -> np.ndarray:
+        """
+        Compute the gradient of the loss function w.r.t. `x`.
+
+        | Paper link: http://cs229.stanford.edu/proj2016/report/ItkinaWu-AdversarialAttacksonImageRecognition-report.pdf
+        | Typo in https://arxiv.org/abs/1605.07277 (equation 6)
+
+        :param x: Sample input with shape as expected by the model.
+        :param y: Target values (class labels) one-hot-encoded of shape `(nb_samples, nb_classes)` or indices of shape
+                  `(nb_samples,)`.
+        :return: Array of gradients of the same shape as `x`.
+        :raises `ValueError`: If the model has not been fitted prior to calling this method.
+        """
+        # pylint: disable=E0001
+        from sklearn.utils.class_weight import compute_class_weight
+
+        if not hasattr(self.model, "coef_"):
+            raise ValueError(
+                """Model has not been fitted. Run function `fit(x, y)` of classifier first or provide a
+            fitted model."""
+            )
+
+        # Apply preprocessing
+        x_preprocessed, y_preprocessed = self._apply_preprocessing(x, y, fit=False)
+
+        num_samples, _ = x_preprocessed.shape
+        gradients = np.zeros(x_preprocessed.shape)
+
+        y_index = np.argmax(y_preprocessed, axis=1)
+        if self.model.class_weight is None or self.model.class_weight == "balanced":
+            class_weight = np.ones(self.nb_classes)
+        else:
+            class_weight = compute_class_weight(
+                class_weight=self.model.class_weight, classes=self.model.classes_, y=y_index,
+            )
+
+        y_pred = self.model.predict_proba(X=x_preprocessed)
+        weights = self.model.coef_
+
+        # Consider the special case of a binary logistic regression model:
+        if self.nb_classes == 2:
+            for i_sample in range(num_samples):
+                gradients[i_sample, :] += (
+                    class_weight[1] * (1.0 - y_preprocessed[i_sample, 1])
+                    - class_weight[0] * (1.0 - y_preprocessed[i_sample, 0])
+                ) * (y_pred[i_sample, 0] * y_pred[i_sample, 1] * weights[0, :])
+        else:
+            w_weighted = np.matmul(y_pred, weights)
+
+            for i_sample in range(num_samples):
+                for i_class in range(self.nb_classes):  # type: ignore
+                    gradients[i_sample, :] += (
+                        class_weight[i_class]
+                        * (1.0 - y_preprocessed[i_sample, i_class])
+                        * (weights[i_class, :] - w_weighted[i_sample, :])
+                    )
+
+        gradients = self._apply_preprocessing_gradient(x, gradients)
+        return gradients
+
+    @staticmethod
+    def get_trainable_attribute_names() -> Tuple[str, str]:
+        """
+        Get the names of trainable attributes.
+
+        :return: A tuple of trainable attributes.
+        """
+        return "intercept_", "coef_"
+
+
+class ScikitlearnGaussianNB(ScikitlearnClassifier):
+    """
+    Wrapper class for scikit-learn Gaussian Naive Bayes models.
+    """
+
+    def __init__(
+        self,
+        model: Union["sklearn.naive_bayes.GaussianNB"],
+        clip_values: Optional["CLIP_VALUES_TYPE"] = None,
+        preprocessing_defences: Union["Preprocessor", List["Preprocessor"], None] = None,
+        postprocessing_defences: Union["Postprocessor", List["Postprocessor"], None] = None,
+        preprocessing: "PREPROCESSING_TYPE" = (0.0, 1.0),
+    ) -> None:
+        """
+        Create a `Classifier` instance from a scikit-learn Gaussian Naive Bayes (GaussianNB) model.
+
+        :param model: scikit-learn Gaussian Naive Bayes (GaussianNB) model.
+        :param clip_values: Tuple of the form `(min, max)` representing the minimum and maximum values allowed
+               for features.
+        :param preprocessing_defences: Preprocessing defence(s) to be applied by the classifier.
+        :param postprocessing_defences: Postprocessing defence(s) to be applied by the classifier.
+        :param preprocessing: Tuple of the form `(subtrahend, divisor)` of floats or `np.ndarray` of values to be
+               used for data preprocessing. The first value will be subtracted from the input. The input will then
+               be divided by the second one.
+        """
+        # pylint: disable=E0001
+        import sklearn  # lgtm [py/repeated-import]
+
+        if not isinstance(model, sklearn.naive_bayes.GaussianNB):
+            raise TypeError("Model must be of type sklearn.naive_bayes.GaussianNB. Found type {}".format(type(model)))
+
+        super().__init__(
+            model=model,
+            clip_values=clip_values,
+            preprocessing_defences=preprocessing_defences,
+            postprocessing_defences=postprocessing_defences,
+            preprocessing=preprocessing,
+        )
+
+    @staticmethod
+    def get_trainable_attribute_names() -> Tuple[str, str]:
+        """
+        Get the names of trainable attributes.
+
+        :return: A tuple of trainable attributes.
+        """
+        return "sigma_", "theta_"
+
+
+class ScikitlearnSVC(ClassGradientsMixin, LossGradientsMixin, ScikitlearnClassifier):
+    """
+    Wrapper class for scikit-learn C-Support Vector Classification models.
+    """
+
+    def __init__(
+        self,
+        model: Union["sklearn.svm.SVC", "sklearn.svm.LinearSVC"],
+        clip_values: Optional["CLIP_VALUES_TYPE"] = None,
+        preprocessing_defences: Union["Preprocessor", List["Preprocessor"], None] = None,
+        postprocessing_defences: Union["Postprocessor", List["Postprocessor"], None] = None,
+        preprocessing: "PREPROCESSING_TYPE" = (0.0, 1.0),
+    ) -> None:
+        """
+        Create a `Classifier` instance from a scikit-learn C-Support Vector Classification model.
+
+        :param model: scikit-learn C-Support Vector Classification model.
+        :param clip_values: Tuple of the form `(min, max)` representing the minimum and maximum values allowed
+               for features.
+        :param preprocessing_defences: Preprocessing defence(s) to be applied by the classifier.
+        :param postprocessing_defences: Postprocessing defence(s) to be applied by the classifier.
+        :param preprocessing: Tuple of the form `(subtrahend, divisor)` of floats or `np.ndarray` of values to be
+               used for data preprocessing. The first value will be subtracted from the input. The input will then
+               be divided by the second one.
+        """
+        # pylint: disable=E0001
+        import sklearn  # lgtm [py/repeated-import]
+
+        if not isinstance(model, sklearn.svm.SVC) and not isinstance(model, sklearn.svm.LinearSVC):
+            raise TypeError(
+                "Model must be of type sklearn.svm.SVC or sklearn.svm.LinearSVC. Found type {}".format(type(model))
+            )
+
+        super().__init__(
+            model=model,
+            clip_values=clip_values,
+            preprocessing_defences=preprocessing_defences,
+            postprocessing_defences=postprocessing_defences,
+            preprocessing=preprocessing,
+        )
+        self._kernel = self._kernel_func()
+
+    def class_gradient(self, x: np.ndarray, label: Union[int, List[int], None] = None, **kwargs) -> np.ndarray:
+        """
+        Compute per-class derivatives w.r.t. `x`.
+
+        :param x: Sample input with shape as expected by the model.
+        :param label: Index of a specific per-class derivative. If an integer is provided, the gradient of that class
+                      output is computed for all samples. If multiple values as provided, the first dimension should
+                      match the batch size of `x`, and each value will be used as target for its corresponding sample in
+                      `x`. If `None`, then gradients for all classes will be computed for each sample.
+        :return: Array of gradients of input features w.r.t. each class in the form
+                 `(batch_size, nb_classes, input_shape)` when computing for all classes, otherwise shape becomes
+                 `(batch_size, 1, input_shape)` when `label` parameter is specified.
+        """
+        # pylint: disable=E0001
+        import sklearn  # lgtm [py/repeated-import]
+
+        # Apply preprocessing
+        x_preprocessed, _ = self._apply_preprocessing(x, y=None, fit=False)
+
+        num_samples, _ = x_preprocessed.shape
+
+        if isinstance(self.model, sklearn.svm.SVC):
+            if self.model.fit_status_:
+                raise AssertionError("Model has not been fitted correctly.")
+
+            support_indices = [0] + list(np.cumsum(self.model.n_support_))
+
+            if self.nb_classes == 2:
+                sign_multiplier = -1
+            else:
+                sign_multiplier = 1
+
+            if label is None:
+                gradients = np.zeros((x_preprocessed.shape[0], self.nb_classes, x_preprocessed.shape[1],))
+
+                for i_label in range(self.nb_classes):  # type: ignore
+                    for i_sample in range(num_samples):
+                        for not_label in range(self.nb_classes):  # type: ignore
+                            if i_label != not_label:
+                                if not_label < i_label:
+                                    label_multiplier = -1
+                                else:
+                                    label_multiplier = 1
+
+                                for label_sv in range(support_indices[i_label], support_indices[i_label + 1],):
+                                    alpha_i_k_y_i = self.model.dual_coef_[
+                                        not_label if not_label < i_label else not_label - 1, label_sv,
+                                    ]
+                                    grad_kernel = self._get_kernel_gradient_sv(label_sv, x_preprocessed[i_sample])
+                                    gradients[i_sample, i_label] += label_multiplier * alpha_i_k_y_i * grad_kernel
+
+                                for not_label_sv in range(support_indices[not_label], support_indices[not_label + 1],):
+                                    alpha_i_k_y_i = self.model.dual_coef_[
+                                        i_label if i_label < not_label else i_label - 1, not_label_sv,
+                                    ]
+                                    grad_kernel = self._get_kernel_gradient_sv(not_label_sv, x_preprocessed[i_sample])
+                                    gradients[i_sample, i_label] += label_multiplier * alpha_i_k_y_i * grad_kernel
+
+            elif isinstance(label, (int, np.integer)):
+                gradients = np.zeros((x_preprocessed.shape[0], 1, x_preprocessed.shape[1]))
+
+                for i_sample in range(num_samples):
+                    for not_label in range(self.nb_classes):  # type: ignore
+                        if label != not_label:
+                            if not_label < label:
+                                label_multiplier = -1
+                            else:
+                                label_multiplier = 1
+
+                            for label_sv in range(support_indices[label], support_indices[label + 1]):
+                                alpha_i_k_y_i = self.model.dual_coef_[
+                                    not_label if not_label < label else not_label - 1, label_sv,
+                                ]
+                                grad_kernel = self._get_kernel_gradient_sv(label_sv, x_preprocessed[i_sample])
+                                gradients[i_sample, 0] += label_multiplier * alpha_i_k_y_i * grad_kernel
+
+                            for not_label_sv in range(support_indices[not_label], support_indices[not_label + 1],):
+                                alpha_i_k_y_i = self.model.dual_coef_[
+                                    label if label < not_label else label - 1, not_label_sv,
+                                ]
+                                grad_kernel = self._get_kernel_gradient_sv(not_label_sv, x_preprocessed[i_sample])
+                                gradients[i_sample, 0] += label_multiplier * alpha_i_k_y_i * grad_kernel
+
+            elif (
+                (isinstance(label, list) and len(label) == num_samples)
+                or isinstance(label, np.ndarray)
+                and label.shape == (num_samples,)
+            ):
+                gradients = np.zeros((x_preprocessed.shape[0], 1, x_preprocessed.shape[1]))
+
+                for i_sample in range(num_samples):
+                    for not_label in range(self.nb_classes):  # type: ignore
+                        if label[i_sample] != not_label:
+                            if not_label < label[i_sample]:
+                                label_multiplier = -1
+                            else:
+                                label_multiplier = 1
+
+                            for label_sv in range(
+                                support_indices[label[i_sample]], support_indices[label[i_sample] + 1],
+                            ):
+                                alpha_i_k_y_i = self.model.dual_coef_[
+                                    not_label if not_label < label[i_sample] else not_label - 1, label_sv,
+                                ]
+                                grad_kernel = self._get_kernel_gradient_sv(label_sv, x_preprocessed[i_sample])
+                                gradients[i_sample, 0] += label_multiplier * alpha_i_k_y_i * grad_kernel
+
+                            for not_label_sv in range(support_indices[not_label], support_indices[not_label + 1],):
+                                alpha_i_k_y_i = self.model.dual_coef_[
+                                    label[i_sample] if label[i_sample] < not_label else label[i_sample] - 1,
+                                    not_label_sv,
+                                ]
+                                grad_kernel = self._get_kernel_gradient_sv(not_label_sv, x_preprocessed[i_sample])
+                                gradients[i_sample, 0] += label_multiplier * alpha_i_k_y_i * grad_kernel
+
+            else:
+                raise TypeError("Unrecognized type for argument `label` with type " + str(type(label)))
+
+            gradients = self._apply_preprocessing_gradient(x, gradients * sign_multiplier)
+
+        elif isinstance(self.model, sklearn.svm.LinearSVC):
+            if label is None:
+                gradients = np.zeros((x_preprocessed.shape[0], self.nb_classes, x_preprocessed.shape[1],))
+
+                for i in range(self.nb_classes):  # type: ignore
+                    for i_sample in range(num_samples):
+                        if self.nb_classes == 2:
+                            gradients[i_sample, i] = self.model.coef_[0] * (2 * i - 1)
+                        else:
+                            gradients[i_sample, i] = self.model.coef_[i]
+
+            elif isinstance(label, (int, np.integer)):
+                gradients = np.zeros((x_preprocessed.shape[0], 1, x_preprocessed.shape[1]))
+
+                for i_sample in range(num_samples):
+                    if self.nb_classes == 2:
+                        gradients[i_sample, 0] = self.model.coef_[0] * (2 * label - 1)
+                    else:
+                        gradients[i_sample, 0] = self.model.coef_[label]
+
+            elif (
+                (isinstance(label, list) and len(label) == num_samples)
+                or isinstance(label, np.ndarray)
+                and label.shape == (num_samples,)
+            ):
+                gradients = np.zeros((x_preprocessed.shape[0], 1, x_preprocessed.shape[1]))
+
+                for i_sample in range(num_samples):
+                    if self.nb_classes == 2:
+                        gradients[i_sample, 0] = self.model.coef_[0] * (2 * label[i_sample] - 1)
+                    else:
+                        gradients[i_sample, 0] = self.model.coef_[label[i_sample]]
+
+            else:
+                raise TypeError("Unrecognized type for argument `label` with type " + str(type(label)))
+
+            gradients = self._apply_preprocessing_gradient(x, gradients)
+
+        return gradients
+
+    def _kernel_grad(self, sv: np.ndarray, x_sample: np.ndarray) -> np.ndarray:
+        """
+        Applies the kernel gradient to a support vector.
+
+        :param sv: A support vector.
+        :param x_sample: The sample the gradient is taken with respect to.
+        :return: the kernel gradient.
+        """
+        # pylint: disable=W0212
+        if self.model.kernel == "linear":
+            grad = sv
+        elif self.model.kernel == "poly":
+            grad = (
+                self.model.degree
+                * (self.model._gamma * np.sum(x_sample * sv) + self.model.coef0) ** (self.model.degree - 1)
+                * sv
+            )
+        elif self.model.kernel == "rbf":
+            grad = (
+                2
+                * self.model._gamma
+                * (-1)
+                * np.exp(-self.model._gamma * np.linalg.norm(x_sample - sv, ord=2) ** 2)
+                * (x_sample - sv)
+            )
+        elif self.model.kernel == "sigmoid":
+            raise NotImplementedError
+        else:
+            raise NotImplementedError("Loss gradients for kernel '{}' are not implemented.".format(self.model.kernel))
+        return grad
+
+    def _get_kernel_gradient_sv(self, i_sv: int, x_sample: np.ndarray) -> np.ndarray:
+        """
+        Applies the kernel gradient to all of a model's support vectors.
+
+        :param i_sv: A support vector index.
+        :param x_sample: A sample vector.
+        :return: The kernelized product of the vectors.
+        """
+        x_i = self.model.support_vectors_[i_sv, :]
+        return self._kernel_grad(x_i, x_sample)
+
+    def loss_gradient(self, x: np.ndarray, y: np.ndarray, **kwargs) -> np.ndarray:
+        """
+        Compute the gradient of the loss function w.r.t. `x`.
+        Following equation (1) with lambda=0.
+
+        | Paper link: https://pralab.diee.unica.it/sites/default/files/biggio14-svm-chapter.pdf
+
+        :param x: Sample input with shape as expected by the model.
+        :param y: Target values (class labels) one-hot-encoded of shape `(nb_samples, nb_classes)` or indices of shape
+                  `(nb_samples,)`.
+        :return: Array of gradients of the same shape as `x`.
+        """
+        # pylint: disable=E0001
+        import sklearn  # lgtm [py/repeated-import]
+
+        # Apply preprocessing
+        x_preprocessed, y_preprocessed = self._apply_preprocessing(x, y, fit=False)
+
+        num_samples, _ = x_preprocessed.shape
+        gradients = np.zeros_like(x_preprocessed)
+        y_index = np.argmax(y_preprocessed, axis=1)
+
+        if isinstance(self.model, sklearn.svm.SVC):
+
+            if self.model.fit_status_:
+                raise AssertionError("Model has not been fitted correctly.")
+
+            if y_preprocessed.shape[1] == 2:
+                sign_multiplier = 1
+            else:
+                sign_multiplier = -1
+
+            i_not_label_i = None
+            label_multiplier = None
+            support_indices = [0] + list(np.cumsum(self.model.n_support_))
+
+            for i_sample in range(num_samples):
+                i_label = y_index[i_sample]
+
+                for i_not_label in range(self.nb_classes):  # type: ignore
+                    if i_label != i_not_label:
+                        if i_not_label < i_label:
+                            i_not_label_i = i_not_label
+                            label_multiplier = -1
+                        elif i_not_label > i_label:
+                            i_not_label_i = i_not_label - 1
+                            label_multiplier = 1
+
+                        for i_label_sv in range(support_indices[i_label], support_indices[i_label + 1]):
+                            alpha_i_k_y_i = self.model.dual_coef_[i_not_label_i, i_label_sv] * label_multiplier
+                            grad_kernel = self._get_kernel_gradient_sv(i_label_sv, x_preprocessed[i_sample])
+                            gradients[i_sample, :] += sign_multiplier * alpha_i_k_y_i * grad_kernel
+
+                        for i_not_label_sv in range(support_indices[i_not_label], support_indices[i_not_label + 1],):
+                            alpha_i_k_y_i = self.model.dual_coef_[i_not_label_i, i_not_label_sv] * label_multiplier
+                            grad_kernel = self._get_kernel_gradient_sv(i_not_label_sv, x_preprocessed[i_sample])
+                            gradients[i_sample, :] += sign_multiplier * alpha_i_k_y_i * grad_kernel
+
+        elif isinstance(self.model, sklearn.svm.LinearSVC):
+            for i_sample in range(num_samples):
+                i_label = y_index[i_sample]
+                if self.nb_classes == 2:
+                    i_label_i = 0
+                    if i_label == 0:
+                        label_multiplier = 1
+                    elif i_label == 1:
+                        label_multiplier = -1
+                    else:
+                        raise ValueError("Label index not recognized because it is not 0 or 1.")
+                else:
+                    i_label_i = i_label
+                    label_multiplier = -1
+
+                gradients[i_sample] = label_multiplier * self.model.coef_[i_label_i]
+        else:
+            raise TypeError("Model not recognized.")
+
+        gradients = self._apply_preprocessing_gradient(x, gradients)
+        return gradients
+
+    def _kernel_func(self) -> Callable:
+        """
+        Return the function for the kernel of this SVM.
+
+        :return: A callable kernel function.
+        """
+        # pylint: disable=E0001
+        import sklearn  # lgtm [py/repeated-import]
+        from sklearn.metrics.pairwise import (
+            polynomial_kernel,
+            linear_kernel,
+            rbf_kernel,
+        )
+
+        if isinstance(self.model, sklearn.svm.LinearSVC):
+            kernel = "linear"
+        elif isinstance(self.model, sklearn.svm.SVC):
+            kernel = self.model.kernel
+        else:
+            raise NotImplementedError("SVM model not yet supported.")
+
+        if kernel == "linear":
+            kernel_func = linear_kernel
+        elif kernel == "poly":
+            kernel_func = polynomial_kernel
+        elif kernel == "rbf":
+            kernel_func = rbf_kernel
+        elif callable(kernel):
+            kernel_func = kernel
+        else:
+            raise NotImplementedError("Kernel '{}' not yet supported.".format(kernel))
+
+        return kernel_func
+
+    def q_submatrix(self, rows: np.ndarray, cols: np.ndarray) -> np.ndarray:
+        """
+        Returns the q submatrix of this SVM indexed by the arrays at rows and columns.
+
+        :param rows: The row vectors.
+        :param cols: The column vectors.
+        :return: A submatrix of Q.
+        """
+        submatrix_shape = (rows.shape[0], cols.shape[0])
+        y_row = self.model.predict(rows)
+        y_col = self.model.predict(cols)
+        y_row[y_row == 0] = -1
+        y_col[y_col == 0] = -1
+        q_rc = np.zeros(submatrix_shape)
+        for row in range(q_rc.shape[0]):
+            for col in range(q_rc.shape[1]):
+                q_rc[row][col] = self._kernel([rows[row]], [cols[col]])[0][0] * y_row[row] * y_col[col]
+
+        return q_rc
+
+    def predict(self, x: np.ndarray, **kwargs) -> np.ndarray:
+        """
+        Perform prediction for a batch of inputs.
+
+        :param x: Input samples.
+        :return: Array of predictions of shape `(nb_inputs, nb_classes)`.
+        """
+        # pylint: disable=E0001
+        import sklearn  # lgtm [py/repeated-import]
+
+        # Apply defences
+        x_preprocessed, _ = self._apply_preprocessing(x, y=None, fit=False)
+
+        if isinstance(self.model, sklearn.svm.SVC) and self.model.probability:
+            y_pred = self.model.predict_proba(X=x_preprocessed)
+        else:
+            y_pred_label = self.model.predict(X=x_preprocessed)
+            targets = np.array(y_pred_label).reshape(-1)
+            one_hot_targets = np.eye(self.nb_classes)[targets]
+            y_pred = one_hot_targets
+
+        return y_pred
+
+
+ScikitlearnLinearSVC = ScikitlearnSVC
diff --git a/adversarial-robustness-toolbox/art/estimators/classification/tensorflow.py b/adversarial-robustness-toolbox/art/estimators/classification/tensorflow.py
new file mode 100644
index 0000000..ed763e8
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/estimators/classification/tensorflow.py
@@ -0,0 +1,1337 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements the classifier `TensorFlowClassifier` for TensorFlow models.
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import os
+import random
+import shutil
+import time
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union, TYPE_CHECKING
+
+import numpy as np
+import six
+
+from art import config
+from art.estimators.classification.classifier import ClassGradientsMixin, ClassifierMixin
+from art.estimators.tensorflow import TensorFlowEstimator, TensorFlowV2Estimator
+from art.utils import check_and_transform_label_format
+
+if TYPE_CHECKING:
+    # pylint: disable=C0412
+    import tensorflow as tf
+
+    from art.utils import CLIP_VALUES_TYPE, PREPROCESSING_TYPE
+    from art.data_generators import DataGenerator
+    from art.defences.preprocessor import Preprocessor
+    from art.defences.postprocessor import Postprocessor
+
+logger = logging.getLogger(__name__)
+
+
+class TensorFlowClassifier(ClassGradientsMixin, ClassifierMixin, TensorFlowEstimator):  # lgtm [py/missing-call-to-init]
+    """
+    This class implements a classifier with the TensorFlow framework.
+    """
+
+    estimator_params = (
+        TensorFlowEstimator.estimator_params
+        + ClassifierMixin.estimator_params
+        + ["input_ph", "output", "labels_ph", "train", "loss", "learning", "sess", "feed_dict",]
+    )
+
+    def __init__(
+        self,
+        input_ph: "tf.Placeholder",
+        output: "tf.Tensor",
+        labels_ph: Optional["tf.Placeholder"] = None,
+        train: Optional["tf.Tensor"] = None,
+        loss: Optional["tf.Tensor"] = None,
+        learning: Optional["tf.Placeholder"] = None,
+        sess: Optional["tf.Session"] = None,
+        channels_first: bool = False,
+        clip_values: Optional["CLIP_VALUES_TYPE"] = None,
+        preprocessing_defences: Union["Preprocessor", List["Preprocessor"], None] = None,
+        postprocessing_defences: Union["Postprocessor", List["Postprocessor"], None] = None,
+        preprocessing: "PREPROCESSING_TYPE" = (0.0, 1.0),
+        feed_dict: Optional[Dict[Any, Any]] = None,
+    ) -> None:
+        """
+        Initialization specific to TensorFlow models implementation.
+
+        :param input_ph: The input placeholder.
+        :param output: The output layer of the model. This can be logits, probabilities or anything else. Logits
+               output should be preferred where possible to ensure attack efficiency.
+        :param labels_ph: The labels placeholder of the model. This parameter is necessary when training the model and
+               when computing gradients w.r.t. the loss function.
+        :param train: The train tensor for fitting, including an optimizer. Use this parameter only when training the
+               model.
+        :param loss: The loss function for which to compute gradients. This parameter is necessary when training the
+               model and when computing gradients w.r.t. the loss function.
+        :param learning: The placeholder to indicate if the model is training.
+        :param sess: Computation session.
+        :param channels_first: Set channels first or last.
+        :param clip_values: Tuple of the form `(min, max)` of floats or `np.ndarray` representing the minimum and
+               maximum values allowed for features. If floats are provided, these will be used as the range of all
+               features. If arrays are provided, each value will be considered the bound for a feature, thus
+               the shape of clip values needs to match the total number of features.
+        :param preprocessing_defences: Preprocessing defence(s) to be applied by the classifier.
+        :param postprocessing_defences: Postprocessing defence(s) to be applied by the classifier.
+        :param preprocessing: Tuple of the form `(subtrahend, divisor)` of floats or `np.ndarray` of values to be
+               used for data preprocessing. The first value will be subtracted from the input. The input will then
+               be divided by the second one.
+        :param feed_dict: A feed dictionary for the session run evaluating the classifier. This dictionary includes all
+                          additionally required placeholders except the placeholders defined in this class.
+        """
+        # pylint: disable=E0401
+        import tensorflow as tf  # lgtm [py/repeated-import]
+
+        super().__init__(
+            model=None,
+            clip_values=clip_values,
+            channels_first=channels_first,
+            preprocessing_defences=preprocessing_defences,
+            postprocessing_defences=postprocessing_defences,
+            preprocessing=preprocessing,
+        )
+
+        self._nb_classes = int(output.get_shape()[-1])
+        self._input_shape = tuple(input_ph.get_shape().as_list()[1:])
+        self._input_ph = input_ph
+        self._output = output
+        self._labels_ph = labels_ph
+        self._train = train
+        self._loss = loss
+        self._learning = learning
+        if feed_dict is None:
+            self._feed_dict = dict()
+        else:
+            self._feed_dict = feed_dict
+
+        # Assign session
+        if sess is None:
+            raise ValueError("A session cannot be None.")
+        self._sess = sess
+
+        # Get the internal layers
+        self._layer_names = self._get_layers()
+
+        # Get the loss gradients graph
+        if self._loss is not None:
+            self._loss_grads = tf.gradients(self._loss, self._input_ph)[0]
+
+        # Check if the loss function requires as input index labels instead of one-hot-encoded labels
+        if self._labels_ph is not None and len(self._labels_ph.shape) == 1:
+            self._reduce_labels = True
+        else:
+            self._reduce_labels = False
+
+    @property
+    def input_shape(self) -> Tuple[int, ...]:
+        """
+        Return the shape of one input sample.
+
+        :return: Shape of one input sample.
+        """
+        return self._input_shape  # type: ignore
+
+    @property
+    def input_ph(self) -> "tf.Placeholder":
+        """
+        Return the input placeholder.
+
+        :return: The input placeholder.
+        """
+        return self._input_ph  # type: ignore
+
+    @property
+    def output(self) -> "tf.Tensor":
+        """
+        Return the output layer of the model.
+
+        :return: The output layer of the model.
+        """
+        return self._output  # type: ignore
+
+    @property
+    def labels_ph(self) -> "tf.Placeholder":
+        """
+        Return the labels placeholder of the model.
+
+        :return: The labels placeholder of the model.
+        """
+        return self._labels_ph  # type: ignore
+
+    @property
+    def train(self) -> "tf.Tensor":
+        """
+        Return the train tensor for fitting.
+
+        :return: The train tensor for fitting.
+        """
+        return self._train  # type: ignore
+
+    @property
+    def loss(self) -> "tf.Tensor":
+        """
+        Return the loss function.
+
+        :return: The loss function.
+        """
+        return self._loss  # type: ignore
+
+    @property
+    def learning(self) -> "tf.Placeholder":
+        """
+        Return the placeholder to indicate if the model is training.
+
+        :return: The placeholder to indicate if the model is training.
+        """
+        return self._learning  # type: ignore
+
+    @property
+    def feed_dict(self) -> Dict[Any, Any]:
+        """
+        Return the feed dictionary for the session run evaluating the classifier.
+
+        :return: The feed dictionary for the session run evaluating the classifier.
+        """
+        return self._feed_dict  # type: ignore
+
+    def predict(self, x: np.ndarray, batch_size: int = 128, training_mode: bool = False, **kwargs) -> np.ndarray:
+        """
+        Perform prediction for a batch of inputs.
+
+        :param x: Input samples.
+        :param batch_size: Size of batches.
+        :param training_mode: `True` for model set to training mode and `'False` for model set to evaluation mode.
+        :return: Array of predictions of shape `(num_inputs, nb_classes)`.
+        """
+        if self._learning is not None:
+            self._feed_dict[self._learning] = training_mode
+
+        # Apply preprocessing
+        x_preprocessed, _ = self._apply_preprocessing(x, y=None, fit=False)
+
+        # Run prediction with batch processing
+        results = np.zeros((x_preprocessed.shape[0], self.nb_classes), dtype=np.float32)
+        num_batch = int(np.ceil(len(x_preprocessed) / float(batch_size)))
+        for m in range(num_batch):
+            # Batch indexes
+            begin, end = (
+                m * batch_size,
+                min((m + 1) * batch_size, x_preprocessed.shape[0]),
+            )
+
+            # Create feed_dict
+            feed_dict = {self._input_ph: x_preprocessed[begin:end]}
+            feed_dict.update(self._feed_dict)
+
+            # Run prediction
+            results[begin:end] = self._sess.run(self._output, feed_dict=feed_dict)
+
+        # Apply postprocessing
+        predictions = self._apply_postprocessing(preds=results, fit=False)
+
+        return predictions
+
+    def fit(self, x: np.ndarray, y: np.ndarray, batch_size: int = 128, nb_epochs: int = 10, **kwargs) -> None:
+        """
+        Fit the classifier on the training set `(x, y)`.
+
+        :param x: Training data.
+        :param y: Target values (class labels) one-hot-encoded of shape (nb_samples, nb_classes) or index labels of
+                  shape (nb_samples,).
+        :param batch_size: Size of batches.
+        :param nb_epochs: Number of epochs to use for training.
+        :param kwargs: Dictionary of framework-specific arguments. This parameter is not currently supported for
+               TensorFlow and providing it takes no effect.
+        """
+        if self._learning is not None:
+            self._feed_dict[self._learning] = True
+
+        # Check if train and output_ph available
+        if self._train is None or self._labels_ph is None:
+            raise ValueError("Need the training objective and the output placeholder to train the model.")
+
+        y = check_and_transform_label_format(y, self.nb_classes)
+
+        # Apply preprocessing
+        x_preprocessed, y_preprocessed = self._apply_preprocessing(x, y, fit=True)
+
+        # Check label shape
+        if self._reduce_labels:
+            y_preprocessed = np.argmax(y_preprocessed, axis=1)
+
+        num_batch = int(np.ceil(len(x_preprocessed) / float(batch_size)))
+        ind = np.arange(len(x_preprocessed))
+
+        # Start training
+        for _ in range(nb_epochs):
+            # Shuffle the examples
+            random.shuffle(ind)
+
+            # Train for one epoch
+            for m in range(num_batch):
+                i_batch = x_preprocessed[ind[m * batch_size : (m + 1) * batch_size]]
+                o_batch = y_preprocessed[ind[m * batch_size : (m + 1) * batch_size]]
+
+                # Create feed_dict
+                feed_dict = {self._input_ph: i_batch, self._labels_ph: o_batch}
+                feed_dict.update(self._feed_dict)
+
+                # Run train step
+                self._sess.run(self._train, feed_dict=feed_dict)
+
+    def fit_generator(self, generator: "DataGenerator", nb_epochs: int = 20, **kwargs) -> None:
+        """
+        Fit the classifier using the generator that yields batches as specified.
+
+        :param generator: Batch generator providing `(x, y)` for each epoch. If the generator can be used for native
+                          training in TensorFlow, it will.
+        :param nb_epochs: Number of epochs to use for training.
+        :param kwargs: Dictionary of framework-specific arguments. This parameter is not currently supported for
+               TensorFlow and providing it takes no effect.
+        """
+        from art.data_generators import TensorFlowDataGenerator
+
+        if self._learning is not None:
+            self._feed_dict[self._learning] = True
+
+        # Train directly in TensorFlow
+        if isinstance(generator, TensorFlowDataGenerator) and not self.preprocessing and self.preprocessing == (0, 1):
+            for _ in range(nb_epochs):
+                for _ in range(int(generator.size / generator.batch_size)):  # type: ignore
+                    i_batch, o_batch = generator.get_batch()
+
+                    if self._reduce_labels:
+                        o_batch = np.argmax(o_batch, axis=1)
+
+                    # Create feed_dict
+                    feed_dict = {self._input_ph: i_batch, self._labels_ph: o_batch}
+                    feed_dict.update(self._feed_dict)
+
+                    # Run train step
+                    self._sess.run(self._train, feed_dict=feed_dict)
+        else:
+            super().fit_generator(generator, nb_epochs=nb_epochs, **kwargs)
+
+    def class_gradient(
+        self, x: np.ndarray, label: Union[int, List[int], None] = None, training_mode: bool = False, **kwargs
+    ) -> np.ndarray:
+        """
+        Compute per-class derivatives w.r.t. `x`.
+
+        :param x: Sample input with shape as expected by the model.
+        :param label: Index of a specific per-class derivative. If an integer is provided, the gradient of that class
+                      output is computed for all samples. If multiple values as provided, the first dimension should
+                      match the batch size of `x`, and each value will be used as target for its corresponding sample in
+                      `x`. If `None`, then gradients for all classes will be computed for each sample.
+        :param training_mode: `True` for model set to training mode and `'False` for model set to evaluation mode.
+        :return: Array of gradients of input features w.r.t. each class in the form
+                 `(batch_size, nb_classes, input_shape)` when computing for all classes, otherwise shape becomes
+                 `(batch_size, 1, input_shape)` when `label` parameter is specified.
+        """
+        if self._learning is not None:
+            self._feed_dict[self._learning] = training_mode
+
+        # Check value of label for computing gradients
+        if not (
+            label is None
+            or (isinstance(label, (int, np.integer)) and label in range(self.nb_classes))
+            or (
+                isinstance(label, np.ndarray)
+                and len(label.shape) == 1
+                and (label < self.nb_classes).all()
+                and label.shape[0] == x.shape[0]
+            )
+        ):
+            raise ValueError("Label %s is out of range." % label)
+
+        self._init_class_grads(label=label)
+
+        # Apply preprocessing
+        x_preprocessed, _ = self._apply_preprocessing(x, y=None, fit=False)
+
+        # Create feed_dict
+        feed_dict = {self._input_ph: x_preprocessed}
+        feed_dict.update(self._feed_dict)
+
+        # Compute the gradient and return
+        if label is None:
+            # Compute the gradients w.r.t. all classes
+            grads = self._sess.run(self._class_grads, feed_dict=feed_dict)
+            grads = np.swapaxes(np.array(grads), 0, 1)
+
+        elif isinstance(label, (int, np.integer)):
+            # Compute the gradients only w.r.t. the provided label
+            grads = self._sess.run(self._class_grads[label], feed_dict=feed_dict)
+            grads = grads[None, ...]
+            grads = np.swapaxes(np.array(grads), 0, 1)
+
+        else:
+            # For each sample, compute the gradients w.r.t. the indicated target class (possibly distinct)
+            unique_label = list(np.unique(label))
+            grads = self._sess.run([self._class_grads[l] for l in unique_label], feed_dict=feed_dict)
+            grads = np.swapaxes(np.array(grads), 0, 1)
+            lst = [unique_label.index(i) for i in label]
+            grads = np.expand_dims(grads[np.arange(len(grads)), lst], axis=1)
+
+        grads = self._apply_preprocessing_gradient(x, grads)
+
+        return grads
+
+    def loss_gradient(self, x: np.ndarray, y: np.ndarray, training_mode: bool = False, **kwargs) -> np.ndarray:
+        """
+        Compute the gradient of the loss function w.r.t. `x`.
+
+        :param x: Sample input with shape as expected by the model.
+        :param y: Target values (class labels) one-hot-encoded of shape `(nb_samples, nb_classes)` or indices of shape
+                  `(nb_samples,)`.
+        :param training_mode: `True` for model set to training mode and `'False` for model set to evaluation mode.
+        :return: Array of gradients of the same shape as `x`.
+        """
+        if self._learning is not None:
+            self._feed_dict[self._learning] = training_mode
+
+        # Apply preprocessing
+        x_preprocessed, y_preprocessed = self._apply_preprocessing(x, y, fit=False)
+
+        # Check if loss available
+        if not hasattr(self, "_loss_grads") or self._loss_grads is None or self._labels_ph is None:
+            raise ValueError("Need the loss function and the labels placeholder to compute the loss gradient.")
+
+        # Check label shape
+        if self._reduce_labels:
+            y_preprocessed = np.argmax(y_preprocessed, axis=1)
+
+        # Create feed_dict
+        feed_dict = {self._input_ph: x_preprocessed, self._labels_ph: y_preprocessed}
+        feed_dict.update(self._feed_dict)
+
+        # Compute gradients
+        grads = self._sess.run(self._loss_grads, feed_dict=feed_dict)
+        grads = self._apply_preprocessing_gradient(x, grads)
+        assert grads.shape == x_preprocessed.shape
+
+        return grads
+
+    def compute_loss(self, x: np.ndarray, y: np.ndarray, reduction: str = "none", **kwargs) -> np.ndarray:
+        """
+        Compute the loss of the neural network for samples `x`.
+
+        :param x: Samples of shape (nb_samples, nb_features) or (nb_samples, nb_pixels_1, nb_pixels_2,
+                  nb_channels) or (nb_samples, nb_channels, nb_pixels_1, nb_pixels_2).
+        :param y: Target values (class labels) one-hot-encoded of shape `(nb_samples, nb_classes)` or indices
+                  of shape `(nb_samples,)`.
+        :param reduction: Specifies the reduction to apply to the output: 'none' | 'mean' | 'sum'.
+                   'none': no reduction will be applied
+                   'mean': Not supported
+                   'sum': Not supported
+        :return: Loss values.
+        :rtype: Format as expected by the `model`
+        """
+        import tensorflow as tf  # lgtm [py/repeated-import]
+
+        if self._learning is not None:
+            self._feed_dict[self._learning] = False
+
+        if self.loss is None:
+            raise TypeError("The loss placeholder `loss` is required for computing losses, but it is not defined.")
+
+        if reduction == "none":
+            _loss = self._loss
+        elif reduction == "mean":
+            _loss = tf.reduce_mean(self._loss)
+        elif reduction == "sum":
+            _loss = tf.reduce_sum(self._loss)
+
+        # Apply preprocessing
+        x_preprocessed, _ = self._apply_preprocessing(x, y, fit=False)
+
+        # Create feed_dict
+        feed_dict = {self._input_ph: x_preprocessed, self._labels_ph: y}
+        feed_dict.update(self._feed_dict)
+
+        # Run train step
+        loss_value = self._sess.run(_loss, feed_dict=feed_dict)
+
+        return loss_value
+
+    def _init_class_grads(self, label=None):
+        # pylint: disable=E0401
+        import tensorflow as tf  # lgtm [py/repeated-import]
+
+        if not hasattr(self, "_class_grads"):
+            self._class_grads = [None for _ in range(self.nb_classes)]
+
+        # Construct the class gradients graph
+        if label is None:
+            if None in self._class_grads:
+                self._class_grads = [
+                    tf.gradients(self._output[:, i], self._input_ph)[0] for i in range(self.nb_classes)
+                ]
+
+        elif isinstance(label, int):
+            if self._class_grads[label] is None:
+                self._class_grads[label] = tf.gradients(self._output[:, label], self._input_ph)[0]
+
+        else:
+            for unique_label in np.unique(label):
+                if self._class_grads[unique_label] is None:
+                    self._class_grads[unique_label] = tf.gradients(self._output[:, unique_label], self._input_ph)[0]
+
+    def _get_layers(self) -> List[str]:
+        """
+        Return the hidden layers in the model, if applicable.
+
+        :return: The hidden layers in the model, input and output layers excluded.
+        """
+        # pylint: disable=E0401
+        import tensorflow as tf  # lgtm [py/repeated-import]
+
+        # Get the computational graph
+        with self._sess.graph.as_default():
+            graph = tf.get_default_graph()
+
+        # Get the list of operators and heuristically filter them
+        tmp_list = []
+        ops = graph.get_operations()
+
+        # pylint: disable=R1702
+        for op in ops:
+            if op.values():
+                if op.values()[0].get_shape() is not None:
+                    if op.values()[0].get_shape().ndims is not None:
+                        if len(op.values()[0].get_shape().as_list()) > 1:
+                            if op.values()[0].get_shape().as_list()[0] is None:
+                                if op.values()[0].get_shape().as_list()[1] is not None:
+                                    if not op.values()[0].name.startswith("gradients"):
+                                        if not op.values()[0].name.startswith("softmax_cross_entropy_loss"):
+                                            if not op.type == "Placeholder":
+                                                tmp_list.append(op.values()[0].name)
+
+        # Shorten the list
+        if not tmp_list:
+            return tmp_list
+
+        result = [tmp_list[-1]]
+        for name in reversed(tmp_list[:-1]):
+            if result[0].split("/")[0] != name.split("/")[0]:
+                result = [name] + result
+        logger.info("Inferred %i hidden layers on TensorFlow classifier.", len(result))
+
+        return result
+
+    def get_activations(
+        self, x: np.ndarray, layer: Union[int, str], batch_size: int = 128, framework: bool = False
+    ) -> np.ndarray:
+        """
+        Return the output of the specified layer for input `x`. `layer` is specified by layer index (between 0 and
+        `nb_layers - 1`) or by name. The number of layers can be determined by counting the results returned by
+        calling `layer_names`.
+
+        :param x: Input for computing the activations.
+        :param layer: Layer for computing the activations.
+        :param batch_size: Size of batches.
+        :param framework: If true, return the intermediate tensor representation of the activation.
+        :return: The output of `layer`, where the first dimension is the batch size corresponding to `x`.
+        """
+        # pylint: disable=E0401
+        import tensorflow as tf  # lgtm [py/repeated-import]
+
+        if self._learning is not None:
+            self._feed_dict[self._learning] = False
+
+        # Get the computational graph
+        with self._sess.graph.as_default():
+            graph = tf.get_default_graph()
+
+        if isinstance(layer, six.string_types):  # basestring for Python 2 (str, unicode) support
+            if layer not in self._layer_names:
+                raise ValueError("Layer name %s is not part of the graph." % layer)
+            layer_tensor = graph.get_tensor_by_name(layer)
+
+        elif isinstance(layer, (int, np.integer)):
+            layer_tensor = graph.get_tensor_by_name(self._layer_names[layer])
+
+        else:
+            raise TypeError("Layer must be of type `str` or `int`. Received %s." % layer)
+
+        if framework:
+            return layer_tensor
+
+        # Apply preprocessing
+        x_preprocessed, _ = self._apply_preprocessing(x, y=None, fit=False)
+
+        # Run prediction with batch processing
+        results = []
+        num_batch = int(np.ceil(len(x_preprocessed) / float(batch_size)))
+        for m in range(num_batch):
+            # Batch indexes
+            begin, end = (
+                m * batch_size,
+                min((m + 1) * batch_size, x_preprocessed.shape[0]),
+            )
+
+            # Create feed_dict
+            feed_dict = {self._input_ph: x_preprocessed[begin:end]}
+            feed_dict.update(self._feed_dict)
+
+            # Run prediction for the current batch
+            layer_output = self._sess.run(layer_tensor, feed_dict=feed_dict)
+            results.append(layer_output)
+
+        results = np.concatenate(results)
+
+        return results
+
+    def save(self, filename: str, path: Optional[str] = None) -> None:
+        """
+        Save a model to file in the format specific to the backend framework. For TensorFlow, .ckpt is used.
+
+        :param filename: Name of the file where to store the model.
+        :param path: Path of the folder where to store the model. If no path is specified, the model will be stored in
+                     the default data location of the library `ART_DATA_PATH`.
+        """
+        # pylint: disable=E0611
+        from tensorflow.python import saved_model
+        from tensorflow.python.saved_model import tag_constants
+        from tensorflow.python.saved_model.signature_def_utils_impl import predict_signature_def
+
+        if path is None:
+            full_path = os.path.join(config.ART_DATA_PATH, filename)
+        else:
+            full_path = os.path.join(path, filename)
+
+        if os.path.exists(full_path):
+            shutil.rmtree(full_path)
+
+        builder = saved_model.builder.SavedModelBuilder(full_path)
+        signature = predict_signature_def(
+            inputs={"SavedInputPhD": self._input_ph}, outputs={"SavedOutput": self._output},
+        )
+        builder.add_meta_graph_and_variables(
+            sess=self._sess, tags=[tag_constants.SERVING], signature_def_map={"predict": signature},
+        )
+        builder.save()
+
+        logger.info("Model saved in path: %s.", full_path)
+
+    def __getstate__(self) -> Dict[str, Any]:
+        """
+        Use to ensure `TensorFlowClassifier` can be pickled.
+
+        :return: State dictionary with instance parameters.
+        """
+        state = self.__dict__.copy()
+
+        # Remove the unpicklable entries
+        del state["_sess"]
+        del state["_input_ph"]
+        state["_output"] = self._output.name
+
+        if self._labels_ph is not None:
+            state["_labels_ph"] = self._labels_ph.name
+
+        if self._loss is not None:
+            state["_loss"] = self._loss.name
+
+        if hasattr(self, "_loss_grads"):
+            state["_loss_grads"] = self._loss_grads.name
+        else:
+            state["_loss_grads"] = False
+
+        if self._learning is not None:
+            state["_learning"] = self._learning.name
+
+        if self._train is not None:
+            state["_train"] = self._train.name
+
+        if hasattr(self, "_class_grads"):
+            state["_class_grads"] = [ts if ts is None else ts.name for ts in self._class_grads]
+        else:
+            state["_class_grads"] = False
+
+        model_name = str(time.time())
+        state["model_name"] = model_name
+        self.save(model_name)
+
+        return state
+
+    def __setstate__(self, state: Dict[str, Any]) -> None:
+        """
+        Use to ensure `TensorFlowClassifier` can be unpickled.
+
+        :param state: State dictionary with instance parameters to restore.
+        """
+        self.__dict__.update(state)
+
+        # Load and update all functionality related to TensorFlow
+        # pylint: disable=E0611, E0401
+        import tensorflow as tf  # lgtm [py/repeated-import]
+        from tensorflow.python.saved_model import tag_constants
+
+        full_path = os.path.join(config.ART_DATA_PATH, state["model_name"])
+
+        graph = tf.Graph()
+        sess = tf.Session(graph=graph)
+        loaded = tf.saved_model.loader.load(sess, [tag_constants.SERVING], full_path)
+
+        # Recover session
+        self._sess = sess
+
+        # Recover input_ph
+        input_tensor_name = loaded.signature_def["predict"].inputs["SavedInputPhD"].name
+        self._input_ph = graph.get_tensor_by_name(input_tensor_name)
+
+        # Recover output layer
+        self._output = graph.get_tensor_by_name(state["_output"])
+
+        # Recover labels' placeholder if any
+        if state["_labels_ph"] is not None:
+            self._labels_ph = graph.get_tensor_by_name(state["_labels_ph"])
+
+        # Recover loss if any
+        if state["_loss"] is not None:
+            self._loss = graph.get_tensor_by_name(state["_loss"])
+
+        # Recover loss_grads if any
+        if state["_loss_grads"]:
+            self._loss_grads = graph.get_tensor_by_name(state["_loss_grads"])
+        else:
+            self.__dict__.pop("_loss_grads", None)
+
+        # Recover learning if any
+        if state["_learning"] is not None:
+            self._learning = graph.get_tensor_by_name(state["_learning"])
+
+        # Recover train if any
+        if state["_train"] is not None:
+            self._train = graph.get_operation_by_name(state["_train"])
+
+        # Recover class_grads if any
+        if state["_class_grads"]:
+            self._class_grads = [ts if ts is None else graph.get_tensor_by_name(ts) for ts in state["_class_grads"]]
+        else:
+            self.__dict__.pop("_class_grads", None)
+
+        self.__dict__.pop("model_name", None)
+
+    def __repr__(self):
+        repr_ = (
+            "%s(input_ph=%r, output=%r, labels_ph=%r, train=%r, loss=%r, learning=%r, sess=%r, "
+            "channels_first=%r, clip_values=%r, preprocessing_defences=%r, postprocessing_defences=%r, "
+            "preprocessing=%r)"
+            % (
+                self.__module__ + "." + self.__class__.__name__,
+                self._input_ph,
+                self._output,
+                self._labels_ph,
+                self._train,
+                self._loss,
+                self._learning,
+                self._sess,
+                self.channels_first,
+                self.clip_values,
+                self.preprocessing_defences,
+                self.postprocessing_defences,
+                self.preprocessing,
+            )
+        )
+
+        return repr_
+
+
+# backward compatibility for ART v0.10 and earlier
+TFClassifier = TensorFlowClassifier
+
+
+class TensorFlowV2Classifier(ClassGradientsMixin, ClassifierMixin, TensorFlowV2Estimator):
+    """
+    This class implements a classifier with the TensorFlow v2 framework.
+    """
+
+    estimator_params = (
+        TensorFlowV2Estimator.estimator_params
+        + ClassifierMixin.estimator_params
+        + ["input_shape", "loss_object", "train_step",]
+    )
+
+    def __init__(
+        self,
+        model: Callable,
+        nb_classes: int,
+        input_shape: Tuple[int, ...],
+        loss_object: Optional["tf.keras.losses.Loss"] = None,
+        train_step: Optional[Callable] = None,
+        channels_first: bool = False,
+        clip_values: Optional["CLIP_VALUES_TYPE"] = None,
+        preprocessing_defences: Union["Preprocessor", List["Preprocessor"], None] = None,
+        postprocessing_defences: Union["Postprocessor", List["Postprocessor"], None] = None,
+        preprocessing: "PREPROCESSING_TYPE" = (0.0, 1.0),
+    ) -> None:
+        """
+        Initialization specific to TensorFlow v2 models.
+
+        :param model: a python functions or callable class defining the model and providing it prediction as output.
+        :param nb_classes: the number of classes in the classification task.
+        :param input_shape: shape of one input for the classifier, e.g. for MNIST input_shape=(28, 28, 1).
+        :param loss_object: The loss function for which to compute gradients. This parameter is applied for training
+            the model and computing gradients of the loss w.r.t. the input.
+        :type loss_object: `tf.keras.losses`
+        :param train_step: A function that applies a gradient update to the trainable variables with signature
+                           train_step(model, images, labels).
+        :param channels_first: Set channels first or last.
+        :param clip_values: Tuple of the form `(min, max)` of floats or `np.ndarray` representing the minimum and
+               maximum values allowed for features. If floats are provided, these will be used as the range of all
+               features. If arrays are provided, each value will be considered the bound for a feature, thus
+               the shape of clip values needs to match the total number of features.
+        :param preprocessing_defences: Preprocessing defence(s) to be applied by the classifier.
+        :param postprocessing_defences: Postprocessing defence(s) to be applied by the classifier.
+        :param preprocessing: Tuple of the form `(subtrahend, divisor)` of floats or `np.ndarray` of values to be
+               used for data preprocessing. The first value will be subtracted from the input. The input will then
+               be divided by the second one.
+        """
+        import tensorflow as tf  # lgtm [py/repeated-import]
+
+        super().__init__(
+            model=model,
+            clip_values=clip_values,
+            channels_first=channels_first,
+            preprocessing_defences=preprocessing_defences,
+            postprocessing_defences=postprocessing_defences,
+            preprocessing=preprocessing,
+        )
+
+        self._nb_classes = nb_classes
+        self._input_shape = input_shape
+        self._loss_object = loss_object
+        self._train_step = train_step
+
+        # Check if the loss function requires as input index labels instead of one-hot-encoded labels
+        if isinstance(self._loss_object, tf.keras.losses.SparseCategoricalCrossentropy):
+            self._reduce_labels = True
+        else:
+            self._reduce_labels = False
+
+    @property
+    def input_shape(self) -> Tuple[int, ...]:
+        """
+        Return the shape of one input sample.
+
+        :return: Shape of one input sample.
+        """
+        return self._input_shape  # type: ignore
+
+    @property
+    def loss_object(self) -> "tf.keras.losses.Loss":
+        """
+        Return the loss function.
+
+        :return: The loss function.
+        """
+        return self._loss_object  # type: ignore
+
+    @property
+    def train_step(self) -> Callable:
+        """
+        Return the function that applies a gradient update to the trainable variables.
+
+        :return: The function that applies a gradient update to the trainable variables.
+        """
+        return self._train_step  # type: ignore
+
+    def predict(self, x: np.ndarray, batch_size: int = 128, training_mode: bool = False, **kwargs) -> np.ndarray:
+        """
+        Perform prediction for a batch of inputs.
+
+        :param x: Input samples.
+        :param batch_size: Size of batches.
+        :param training_mode: `True` for model set to training mode and `'False` for model set to evaluation mode.
+        :return: Array of predictions of shape `(nb_inputs, nb_classes)`.
+        """
+        # Apply preprocessing
+        x_preprocessed, _ = self._apply_preprocessing(x, y=None, fit=False)
+
+        # Run prediction with batch processing
+        results = np.zeros((x_preprocessed.shape[0], self.nb_classes), dtype=np.float32)
+        num_batch = int(np.ceil(len(x_preprocessed) / float(batch_size)))
+        for m in range(num_batch):
+            # Batch indexes
+            begin, end = (
+                m * batch_size,
+                min((m + 1) * batch_size, x_preprocessed.shape[0]),
+            )
+
+            # Run prediction
+            results[begin:end] = self._model(x_preprocessed[begin:end], training=training_mode)
+
+        # Apply postprocessing
+        predictions = self._apply_postprocessing(preds=results, fit=False)
+        return predictions
+
+    def _predict_framework(self, x: "tf.Tensor", training_mode: bool = False, **kwargs) -> "tf.Tensor":
+        """
+        Perform prediction for a batch of inputs.
+
+        :param x: Input samples.
+        :param training_mode: `True` for model set to training mode and `'False` for model set to evaluation mode.
+        :return: Array of predictions of shape `(nb_inputs, nb_classes)`.
+        """
+        # Apply preprocessing
+        x_preprocessed, _ = self._apply_preprocessing(x, y=None, fit=False)
+
+        return self._model(x_preprocessed, training=training_mode)
+
+    def fit(self, x: np.ndarray, y: np.ndarray, batch_size: int = 128, nb_epochs: int = 10, **kwargs) -> None:
+        """
+        Fit the classifier on the training set `(x, y)`.
+
+        :param x: Training data.
+        :param y: Labels, one-hot-encoded of shape (nb_samples, nb_classes) or index labels of
+                  shape (nb_samples,).
+        :param batch_size: Size of batches.
+        :param nb_epochs: Number of epochs to use for training.
+        :param kwargs: Dictionary of framework-specific arguments. This parameter is not currently supported for
+               TensorFlow and providing it takes no effect.
+        """
+        import tensorflow as tf  # lgtm [py/repeated-import]
+
+        if self._train_step is None:
+            raise TypeError(
+                "The training function `train_step` is required for fitting a model but it has not been " "defined."
+            )
+
+        y = check_and_transform_label_format(y, self.nb_classes)
+
+        # Apply preprocessing
+        x_preprocessed, y_preprocessed = self._apply_preprocessing(x, y, fit=True)
+
+        # Check label shape
+        if self._reduce_labels:
+            y_preprocessed = np.argmax(y_preprocessed, axis=1)
+
+        train_ds = tf.data.Dataset.from_tensor_slices((x_preprocessed, y_preprocessed)).shuffle(10000).batch(batch_size)
+
+        for _ in range(nb_epochs):
+            for images, labels in train_ds:
+                self._train_step(self.model, images, labels)
+
+    def fit_generator(self, generator: "DataGenerator", nb_epochs: int = 20, **kwargs) -> None:
+        """
+        Fit the classifier using the generator that yields batches as specified.
+
+        :param generator: Batch generator providing `(x, y)` for each epoch. If the generator can be used for native
+                          training in TensorFlow, it will.
+        :param nb_epochs: Number of epochs to use for training.
+        :param kwargs: Dictionary of framework-specific arguments. This parameter is not currently supported for
+               TensorFlow and providing it takes no effect.
+        """
+        import tensorflow as tf  # lgtm [py/repeated-import]
+        from art.data_generators import TensorFlowV2DataGenerator
+
+        if self._train_step is None:
+            raise TypeError(
+                "The training function `train_step` is required for fitting a model but it has not been " "defined."
+            )
+
+        # Train directly in TensorFlow
+        if isinstance(generator, TensorFlowV2DataGenerator) and not self.preprocessing:
+            for _ in range(nb_epochs):
+                for i_batch, o_batch in generator.iterator:
+                    if self._reduce_labels:
+                        o_batch = tf.math.argmax(o_batch, axis=1)
+                    self._train_step(i_batch, o_batch)
+        else:
+            # Fit a generic data generator through the API
+            super().fit_generator(generator, nb_epochs=nb_epochs)
+
+    def class_gradient(
+        self, x: np.ndarray, label: Union[int, List[int], None] = None, training_mode: bool = False, **kwargs
+    ) -> np.ndarray:
+        """
+        Compute per-class derivatives w.r.t. `x`.
+
+        :param x: Sample input with shape as expected by the model.
+        :param label: Index of a specific per-class derivative. If an integer is provided, the gradient of that class
+                      output is computed for all samples. If multiple values as provided, the first dimension should
+                      match the batch size of `x`, and each value will be used as target for its corresponding sample in
+                      `x`. If `None`, then gradients for all classes will be computed for each sample.
+        :param training_mode: `True` for model set to training mode and `'False` for model set to evaluation mode.
+        :return: Array of gradients of input features w.r.t. each class in the form
+                 `(batch_size, nb_classes, input_shape)` when computing for all classes, otherwise shape becomes
+                 `(batch_size, 1, input_shape)` when `label` parameter is specified.
+        """
+        import tensorflow as tf  # lgtm [py/repeated-import]
+
+        x = tf.convert_to_tensor(x)
+
+        with tf.GradientTape(persistent=True) as tape:
+            # Apply preprocessing
+            if self.all_framework_preprocessing:
+                x_grad = tf.convert_to_tensor(x)
+                tape.watch(x_grad)
+                x_input, _ = self._apply_preprocessing(x_grad, y=None, fit=False)
+            else:
+                x_preprocessed, _ = self._apply_preprocessing(x, y=None, fit=False)
+                x_grad = tf.convert_to_tensor(x_preprocessed)
+                tape.watch(x_grad)
+                x_input = x_grad
+
+            tape.watch(x_input)
+
+            # Compute the gradients
+            if tf.executing_eagerly():
+                if label is None:
+                    # Compute the gradients w.r.t. all classes
+                    class_gradients = list()
+
+                    for i in range(self.nb_classes):
+                        predictions = self.model(x_input, training=training_mode)
+                        prediction = predictions[:, i]
+                        tape.watch(prediction)
+
+                        class_gradient = tape.gradient(prediction, x_input).numpy()
+                        class_gradients.append(class_gradient)
+
+                    gradients = np.swapaxes(np.array(class_gradients), 0, 1)
+
+                elif isinstance(label, (int, np.integer)):
+                    # Compute the gradients only w.r.t. the provided label
+                    predictions = self.model(x_input, training=training_mode)
+                    prediction = predictions[:, label]
+                    tape.watch(prediction)
+
+                    class_gradient = tape.gradient(prediction, x_grad).numpy()
+                    gradients = np.expand_dims(class_gradient, axis=1)
+
+                else:
+                    # For each sample, compute the gradients w.r.t. the indicated target class (possibly distinct)
+                    class_gradients = list()
+                    unique_labels = list(np.unique(label))
+
+                    for unique_label in unique_labels:
+                        predictions = self.model(x_input, training=training_mode)
+                        prediction = predictions[:, unique_label]
+                        tape.watch(prediction)
+
+                        class_gradient = tape.gradient(prediction, x_grad).numpy()
+                        class_gradients.append(class_gradient)
+
+                    gradients = np.swapaxes(np.array(class_gradients), 0, 1)
+                    lst = [unique_labels.index(i) for i in label]
+                    gradients = np.expand_dims(gradients[np.arange(len(gradients)), lst], axis=1)
+
+                if not self.all_framework_preprocessing:
+                    gradients = self._apply_preprocessing_gradient(x, gradients)
+
+            else:
+                raise NotImplementedError("Expecting eager execution.")
+
+        return gradients
+
+    def compute_loss(
+        self, x: np.ndarray, y: np.ndarray, reduction: str = "none", training_mode: bool = False, **kwargs
+    ) -> np.ndarray:
+        """
+        Compute the loss function w.r.t. `x`.
+
+        :param x: Sample input with shape as expected by the model.
+        :param y: Target values (class labels) one-hot-encoded of shape `(nb_samples, nb_classes)` or indices
+                  of shape `(nb_samples,)`.
+        :param reduction: Specifies the reduction to apply to the output: 'none' | 'mean' | 'sum'.
+                   'none': no reduction will be applied
+                   'mean': the sum of the output will be divided by the number of elements in the output,
+                   'sum': the output will be summed.
+        :param training_mode: `True` for model set to training mode and `'False` for model set to evaluation mode.
+        :return: Array of losses of the same shape as `x`.
+        """
+        import tensorflow as tf  # lgtm [py/repeated-import]
+
+        if self._loss_object is None:
+            raise TypeError("The loss function `loss_object` is required for computing losses, but it is not defined.")
+        prev_reduction = self._loss_object.reduction
+        if reduction == "none":
+            self._loss_object.reduction = tf.keras.losses.Reduction.NONE
+        elif reduction == "mean":
+            self._loss_object.reduction = tf.keras.losses.Reduction.SUM_OVER_BATCH_SIZE
+        elif reduction == "sum":
+            self._loss_object.reduction = tf.keras.losses.Reduction.SUM
+
+        # Apply preprocessing
+        x_preprocessed, _ = self._apply_preprocessing(x, y, fit=False)
+
+        if tf.executing_eagerly():
+            x_preprocessed_tf = tf.convert_to_tensor(x_preprocessed)
+            predictions = self.model(x_preprocessed_tf, training=training_mode)
+            if self._reduce_labels:
+                loss = self._loss_object(np.argmax(y, axis=1), predictions)
+            else:
+                loss = self._loss_object(y, predictions)
+        else:
+            raise NotImplementedError("Expecting eager execution.")
+
+        self._loss_object.reduction = prev_reduction
+        return loss.numpy()
+
+    def loss_gradient(
+        self,
+        x: Union[np.ndarray, "tf.Tensor"],
+        y: Union[np.ndarray, "tf.Tensor"],
+        training_mode: bool = False,
+        **kwargs
+    ) -> Union[np.ndarray, "tf.Tensor"]:
+        """
+        Compute the gradient of the loss function w.r.t. `x`.
+
+        :param x: Sample input with shape as expected by the model.
+        :param y: Correct labels, one-vs-rest encoding.
+        :param training_mode: `True` for model set to training mode and `'False` for model set to evaluation mode.
+        :return: Array of gradients of the same shape as `x`.
+        """
+        import tensorflow as tf  # lgtm [py/repeated-import]
+
+        if self._loss_object is None:
+            raise TypeError(
+                "The loss function `loss_object` is required for computing loss gradients, but it has not been "
+                "defined."
+            )
+
+        if tf.executing_eagerly():
+            with tf.GradientTape() as tape:
+                # Apply preprocessing
+                if self.all_framework_preprocessing:
+                    x_grad = tf.convert_to_tensor(x)
+                    tape.watch(x_grad)
+                    x_input, _ = self._apply_preprocessing(x_grad, y=None, fit=False)
+                else:
+                    x_preprocessed, _ = self._apply_preprocessing(x, y=None, fit=False)
+                    x_grad = tf.convert_to_tensor(x_preprocessed)
+                    tape.watch(x_grad)
+                    x_input = x_grad
+
+                predictions = self.model(x_input, training=training_mode)
+
+                if self._reduce_labels:
+                    loss = self._loss_object(np.argmax(y, axis=1), predictions)
+                else:
+                    loss = self._loss_object(y, predictions)
+
+            gradients = tape.gradient(loss, x_grad)
+
+            if isinstance(x, np.ndarray):
+                gradients = gradients.numpy()
+
+        else:
+            raise NotImplementedError("Expecting eager execution.")
+
+        # Apply preprocessing gradients
+        if not self.all_framework_preprocessing:
+            gradients = self._apply_preprocessing_gradient(x, gradients)
+
+        return gradients
+
+    def clone_for_refitting(self) -> "TensorFlowV2Classifier":  # lgtm [py/inheritance/incorrect-overridden-signature]
+        """
+        Create a copy of the classifier that can be refit from scratch. Will inherit same architecture, optimizer and
+        initialization as cloned model, but without weights.
+
+        :return: new estimator
+        """
+        import tensorflow as tf  # lgtm [py/repeated-import]
+
+        try:
+            # only works for functionally defined models
+            model = tf.keras.models.clone_model(self.model, input_tensors=self.model.inputs)
+        except ValueError as e:
+            raise ValueError("Cannot clone custom tensorflow models") from e
+
+        optimizer = self.model.optimizer
+        # reset optimizer variables
+        for var in optimizer.variables():
+            var.assign(tf.zeros_like(var))
+
+        model.compile(
+            optimizer=optimizer,
+            loss=self.model.loss,
+            metrics=self.model.metrics,
+            loss_weights=self.model.compiled_loss._loss_weights,
+            weighted_metrics=self.model.compiled_metrics._weighted_metrics,
+            run_eagerly=self.model.run_eagerly,
+        )
+
+        clone = type(self)(model, self.nb_classes, self.input_shape)
+        params = self.get_params()
+        del params["model"]
+        clone.set_params(**params)
+        clone._train_step = self._train_step
+        clone._reduce_labels = self._reduce_labels
+        clone._loss_object = self._loss_object
+        return clone
+
+    def reset(self) -> None:
+        """
+        Resets the weights of the classifier so that it can be refit from scratch.
+        """
+        import tensorflow as tf  # lgtm [py/repeated-import]
+
+        for layer in self.model.layers:
+            if isinstance(layer, (tf.keras.Model, tf.keras.models.Sequential)):  # if there is a model as a layer
+                raise NotImplementedError("Resetting of models with models as layers has not been tested.")
+            #     self.reset(layer)  # apply recursively
+            #     continue
+
+            # find initializers
+            if hasattr(layer, "cell"):
+                init_container = layer.cell
+            else:
+                init_container = layer
+
+            for key, initializer in init_container.__dict__.items():
+                if "initializer" not in key:  # not an initializer skip
+                    continue
+
+                # find the corresponding variable, like the kernel or the bias
+                if key == "recurrent_initializer":  # special case check
+                    var = getattr(init_container, "recurrent_kernel", None)
+                else:
+                    var = getattr(init_container, key.replace("_initializer", ""), None)
+
+                if var is not None:
+                    var.assign(initializer(var.shape, var.dtype))
+
+    def _get_layers(self) -> list:
+        """
+        Return the hidden layers in the model, if applicable.
+
+        :return: The hidden layers in the model, input and output layers excluded.
+        """
+        raise NotImplementedError
+
+    @property
+    def layer_names(self) -> Optional[List[str]]:
+        """
+        Return the hidden layers in the model, if applicable.
+
+        :return: The hidden layers in the model, input and output layers excluded.
+
+        .. warning:: `layer_names` tries to infer the internal structure of the model.
+                     This feature comes with no guarantees on the correctness of the result.
+                     The intended order of the layers tries to match their order in the model, but this is not
+                     guaranteed either.
+        """
+        import tensorflow as tf  # lgtm [py/repeated-import]
+
+        if isinstance(self._model, (tf.keras.Model, tf.keras.models.Sequential)):
+            return self._model.layers
+
+        return None  # type: ignore
+
+    def get_activations(
+        self, x: np.ndarray, layer: Union[int, str], batch_size: int = 128, framework: bool = False
+    ) -> Optional[np.ndarray]:
+        """
+        Return the output of the specified layer for input `x`. `layer` is specified by layer index (between 0 and
+        `nb_layers - 1`) or by name. The number of layers can be determined by counting the results returned by
+        calling `layer_names`.
+
+        :param x: Input for computing the activations.
+        :param layer: Layer for computing the activations.
+        :param batch_size: Batch size.
+        :return: The output of `layer`, where the first dimension is the batch size corresponding to `x`.
+        """
+        import tensorflow as tf  # lgtm [py/repeated-import]
+        from art.config import ART_NUMPY_DTYPE
+
+        if isinstance(self._model, tf.keras.models.Sequential):
+            i_layer = None
+            if self.layer_names is None:
+                raise ValueError("No layer names identified.")
+
+            if isinstance(layer, six.string_types):
+                if layer not in self.layer_names:
+                    raise ValueError("Layer name %s is not part of the graph." % layer)
+                for i_name, name in enumerate(self.layer_names):
+                    if name == layer:
+                        i_layer = i_name
+                        break
+            elif isinstance(layer, int):
+                if layer < 0 or layer >= len(self.layer_names):
+                    raise ValueError(
+                        "Layer index %d is outside of range (0 to %d included)." % (layer, len(self.layer_names) - 1)
+                    )
+                i_layer = layer
+            else:
+                raise TypeError("Layer must be of type `str` or `int`.")
+
+            activation_model = tf.keras.Model(self._model.layers[0].input, self._model.layers[i_layer].output)
+
+            # Apply preprocessing
+            x_preprocessed, _ = self._apply_preprocessing(x=x, y=None, fit=False)
+
+            # Determine shape of expected output and prepare array
+            output_shape = self._model.layers[i_layer].output_shape
+            activations = np.zeros((x_preprocessed.shape[0],) + output_shape[1:], dtype=ART_NUMPY_DTYPE)
+
+            # Get activations with batching
+            for batch_index in range(int(np.ceil(x_preprocessed.shape[0] / float(batch_size)))):
+                begin, end = (
+                    batch_index * batch_size,
+                    min((batch_index + 1) * batch_size, x_preprocessed.shape[0]),
+                )
+                activations[begin:end] = activation_model([x_preprocessed[begin:end]], training=False).numpy()
+
+            return activations
+
+        return None
+
+    def save(self, filename: str, path: Optional[str] = None) -> None:
+        """
+        Save a model to file in the format specific to the backend framework. For TensorFlow, .ckpt is used.
+
+        :param filename: Name of the file where to store the model.
+        :param path: Path of the folder where to store the model. If no path is specified, the model will be stored in
+                     the default data location of the library `ART_DATA_PATH`.
+        """
+        raise NotImplementedError
+
+    def __repr__(self):
+        repr_ = (
+            "%s(model=%r, nb_classes=%r, input_shape=%r, loss_object=%r, train_step=%r, "
+            "channels_first=%r, clip_values=%r, preprocessing_defences=%r, postprocessing_defences=%r, "
+            "preprocessing=%r)"
+            % (
+                self.__module__ + "." + self.__class__.__name__,
+                self._model,
+                self._nb_classes,
+                self._input_shape,
+                self._loss_object,
+                self._train_step,
+                self.channels_first,
+                self.clip_values,
+                self.preprocessing_defences,
+                self.postprocessing_defences,
+                self.preprocessing,
+            )
+        )
+
+        return repr_
diff --git a/adversarial-robustness-toolbox/art/estimators/classification/xgboost.py b/adversarial-robustness-toolbox/art/estimators/classification/xgboost.py
new file mode 100644
index 0000000..7a62e1b
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/estimators/classification/xgboost.py
@@ -0,0 +1,260 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements the classifier `XGBoostClassifier` for XGBoost models.
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+from copy import deepcopy
+import json
+import logging
+import os
+import pickle
+from typing import List, Optional, Union, Tuple, TYPE_CHECKING
+
+import numpy as np
+
+from art.estimators.classification.classifier import ClassifierDecisionTree
+from art.utils import to_categorical
+from art import config
+
+if TYPE_CHECKING:
+    # pylint: disable=C0412
+    import xgboost  # lgtm [py/import-and-import-from]
+
+    from art.utils import CLIP_VALUES_TYPE, PREPROCESSING_TYPE
+    from art.defences.preprocessor import Preprocessor
+    from art.defences.postprocessor import Postprocessor
+    from art.metrics.verification_decisions_trees import LeafNode, Tree
+
+logger = logging.getLogger(__name__)
+
+
+class XGBoostClassifier(ClassifierDecisionTree):
+    """
+    Wrapper class for importing XGBoost models.
+    """
+
+    estimator_params = ClassifierDecisionTree.estimator_params + [
+        "nb_features",
+    ]
+
+    def __init__(
+        self,
+        model: Union["xgboost.Booster", "xgboost.XGBClassifier", None] = None,
+        clip_values: Optional["CLIP_VALUES_TYPE"] = None,
+        preprocessing_defences: Union["Preprocessor", List["Preprocessor"], None] = None,
+        postprocessing_defences: Union["Postprocessor", List["Postprocessor"], None] = None,
+        preprocessing: "PREPROCESSING_TYPE" = (0.0, 1.0),
+        nb_features: Optional[int] = None,
+        nb_classes: Optional[int] = None,
+    ) -> None:
+        """
+        Create a `Classifier` instance from a XGBoost model.
+
+        :param model: XGBoost model.
+        :param clip_values: Tuple of the form `(min, max)` representing the minimum and maximum values allowed
+               for features.
+        :param preprocessing_defences: Preprocessing defence(s) to be applied by the classifier.
+        :param postprocessing_defences: Postprocessing defence(s) to be applied by the classifier.
+        :param preprocessing: Tuple of the form `(subtrahend, divisor)` of floats or `np.ndarray` of values to be
+               used for data preprocessing. The first value will be subtracted from the input. The input will then
+               be divided by the second one.
+        :param nb_features: The number of features in the training data. Only used if it cannot be extracted from
+                             model.
+        :param nb_classes: The number of classes in the training data. Only used if it cannot be extracted from model.
+        """
+        from xgboost import Booster, XGBClassifier
+
+        if not isinstance(model, Booster) and not isinstance(model, XGBClassifier):
+            raise TypeError("Model must be of type xgboost.Booster or xgboost.XGBClassifier.")
+
+        super().__init__(
+            model=model,
+            clip_values=clip_values,
+            preprocessing_defences=preprocessing_defences,
+            postprocessing_defences=postprocessing_defences,
+            preprocessing=preprocessing,
+        )
+        self._input_shape = (nb_features,)
+        self._nb_classes = self._get_nb_classes(nb_classes)
+
+    @property
+    def input_shape(self) -> Tuple[int, ...]:
+        """
+        Return the shape of one input sample.
+
+        :return: Shape of one input sample.
+        """
+        return self._input_shape  # type: ignore
+
+    @property
+    def nb_features(self) -> int:
+        """
+        Return the number of features.
+
+        :return: The number of features.
+        """
+        return self._input_shape[0]  # type: ignore
+
+    def fit(self, x: np.ndarray, y: np.ndarray, **kwargs) -> None:
+        """
+        Fit the classifier on the training set `(x, y)`.
+
+        :param x: Training data.
+        :param y: Target values (class labels) one-hot-encoded of shape (nb_samples, nb_classes).
+        :param kwargs: Dictionary of framework-specific arguments. These should be parameters supported by the
+                       `fit` function in `xgboost.Booster` or `xgboost.XGBClassifier` and will be passed to this
+                       function as such.
+        :raises `NotImplementedException`: This method is not supported for XGBoost classifiers.
+        """
+        raise NotImplementedError
+
+    def predict(self, x: np.ndarray, **kwargs) -> np.ndarray:
+        """
+        Perform prediction for a batch of inputs.
+
+        :param x: Input samples.
+        :return: Array of predictions of shape `(nb_inputs, nb_classes)`.
+        """
+        import xgboost  # lgtm [py/repeated-import] lgtm [py/import-and-import-from]
+
+        # Apply preprocessing
+        x_preprocessed, _ = self._apply_preprocessing(x, y=None, fit=False)
+
+        if isinstance(self._model, xgboost.Booster):
+            train_data = xgboost.DMatrix(x_preprocessed, label=None)
+            predictions = self._model.predict(train_data)
+            y_prediction = np.asarray([line for line in predictions])
+            if len(y_prediction.shape) == 1:
+                y_prediction = to_categorical(labels=y_prediction, nb_classes=self.nb_classes)
+        elif isinstance(self._model, xgboost.XGBClassifier):
+            y_prediction = self._model.predict_proba(x_preprocessed)
+
+        # Apply postprocessing
+        y_prediction = self._apply_postprocessing(preds=y_prediction, fit=False)
+
+        return y_prediction
+
+    def _get_nb_classes(self, nb_classes: Optional[int]) -> int:
+        """
+        Return the number of output classes.
+
+        :return: Number of classes in the data.
+        """
+        from xgboost import Booster, XGBClassifier
+
+        if isinstance(self._model, Booster):
+            try:
+                return int(len(self._model.get_dump(dump_format="json")) / self._model.n_estimators)
+            except AttributeError:
+                if nb_classes is not None:
+                    return nb_classes
+                raise NotImplementedError(
+                    "Number of classes cannot be determined automatically. "
+                    + "Please manually set argument nb_classes in XGBoostClassifier."
+                ) from AttributeError
+
+        if isinstance(self._model, XGBClassifier):
+            return self._model.n_classes_
+
+        return -1
+
+    def save(self, filename: str, path: Optional[str] = None) -> None:
+        """
+        Save a model to file in the format specific to the backend framework.
+
+        :param filename: Name of the file where to store the model.
+        :param path: Path of the folder where to store the model. If no path is specified, the model will be stored in
+                     the default data location of the library `ART_DATA_PATH`.
+        """
+        if path is None:
+            full_path = os.path.join(config.ART_DATA_PATH, filename)
+        else:
+            full_path = os.path.join(path, filename)
+        folder = os.path.split(full_path)[0]
+        if not os.path.exists(folder):
+            os.makedirs(folder)
+
+        with open(full_path + ".pickle", "wb") as file_pickle:
+            pickle.dump(self._model, file=file_pickle)
+
+    def get_trees(self) -> List["Tree"]:
+        """
+        Get the decision trees.
+
+        :return: A list of decision trees.
+        """
+        from art.metrics.verification_decisions_trees import Box, Tree
+
+        booster_dump = self._model.get_booster().get_dump(dump_format="json")
+        trees = list()
+
+        for i_tree, tree_dump in enumerate(booster_dump):
+            box = Box()
+
+            if self._model.n_classes_ == 2:
+                class_label = -1
+            else:
+                class_label = i_tree % self._model.n_classes_
+
+            tree_json = json.loads(tree_dump)
+            trees.append(
+                Tree(class_id=class_label, leaf_nodes=self._get_leaf_nodes(tree_json, i_tree, class_label, box),)
+            )
+
+        return trees
+
+    def _get_leaf_nodes(self, node, i_tree, class_label, box) -> List["LeafNode"]:
+        from art.metrics.verification_decisions_trees import LeafNode, Box, Interval
+
+        leaf_nodes: List[LeafNode] = list()
+
+        if "children" in node:
+            if node["children"][0]["nodeid"] == node["yes"] and node["children"][1]["nodeid"] == node["no"]:
+                node_left = node["children"][0]
+                node_right = node["children"][1]
+            elif node["children"][1]["nodeid"] == node["yes"] and node["children"][0]["nodeid"] == node["no"]:
+                node_left = node["children"][1]
+                node_right = node["children"][0]
+            else:
+                raise ValueError
+
+            box_left = deepcopy(box)
+            box_right = deepcopy(box)
+
+            feature = int(node["split"][1:])
+            box_split_left = Box(intervals={feature: Interval(-np.inf, node["split_condition"])})
+            box_split_right = Box(intervals={feature: Interval(node["split_condition"], np.inf)})
+
+            if box.intervals:
+                box_left.intersect_with_box(box_split_left)
+                box_right.intersect_with_box(box_split_right)
+            else:
+                box_left = box_split_left
+                box_right = box_split_right
+
+            leaf_nodes += self._get_leaf_nodes(node_left, i_tree, class_label, box_left)
+            leaf_nodes += self._get_leaf_nodes(node_right, i_tree, class_label, box_right)
+
+        if "leaf" in node:
+            leaf_nodes.append(
+                LeafNode(tree_id=i_tree, class_label=class_label, node_id=node["nodeid"], box=box, value=node["leaf"],)
+            )
+
+        return leaf_nodes
diff --git a/adversarial-robustness-toolbox/art/estimators/encoding/__init__.py b/adversarial-robustness-toolbox/art/estimators/encoding/__init__.py
new file mode 100644
index 0000000..9e40f8a
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/estimators/encoding/__init__.py
@@ -0,0 +1,6 @@
+"""
+Encoder API.
+"""
+from art.estimators.encoding.encoder import EncoderMixin
+
+from art.estimators.encoding.tensorflow import TensorFlowEncoder
diff --git a/adversarial-robustness-toolbox/art/estimators/encoding/encoder.py b/adversarial-robustness-toolbox/art/estimators/encoding/encoder.py
new file mode 100644
index 0000000..b9c0225
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/estimators/encoding/encoder.py
@@ -0,0 +1,38 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements mixin abstract base classes defining properties for all encoders in ART.
+"""
+
+import abc
+
+
+class EncoderMixin(abc.ABC):
+    """
+    Mixin abstract base class defining functionality for encoders.
+    """
+
+    @property
+    @abc.abstractmethod
+    def encoding_length(self) -> int:
+        """
+        Returns the length of the encoding size output.
+
+        :return: The length of the encoding size output.
+        """
+        raise NotImplementedError
diff --git a/adversarial-robustness-toolbox/art/estimators/encoding/tensorflow.py b/adversarial-robustness-toolbox/art/estimators/encoding/tensorflow.py
new file mode 100644
index 0000000..53ed5a1
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/estimators/encoding/tensorflow.py
@@ -0,0 +1,199 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements the classifier `TensorFlowEncoder` for TensorFlow models.
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+from typing import Any, Dict, List, Optional, Union, Tuple, TYPE_CHECKING
+
+from art.estimators.encoding.encoder import EncoderMixin
+from art.estimators.tensorflow import TensorFlowEstimator
+
+if TYPE_CHECKING:
+    # pylint: disable=C0412
+    import numpy as np
+    import tensorflow.compat.v1 as tf
+
+    from art.utils import CLIP_VALUES_TYPE, PREPROCESSING_TYPE
+    from art.defences.preprocessor import Preprocessor
+    from art.defences.postprocessor import Postprocessor
+
+logger = logging.getLogger(__name__)
+
+
+class TensorFlowEncoder(EncoderMixin, TensorFlowEstimator):  # lgtm [py/missing-call-to-init]
+    """
+    This class implements an encoder model using the TensorFlow framework.
+    """
+
+    estimator_params = TensorFlowEstimator.estimator_params + [
+        "input_ph",
+        "loss",
+        "sess",
+        "feed_dict",
+        "channels_first",
+    ]
+
+    def __init__(
+        self,
+        input_ph: "tf.Placeholder",
+        model: "tf.Tensor",
+        loss: Optional["tf.Tensor"] = None,
+        sess: Optional["tf.compat.v1.Session"] = None,
+        channels_first: bool = False,
+        clip_values: Optional["CLIP_VALUES_TYPE"] = None,
+        preprocessing_defences: Union["Preprocessor", List["Preprocessor"], None] = None,
+        postprocessing_defences: Union["Postprocessor", List["Postprocessor"], None] = None,
+        preprocessing: "PREPROCESSING_TYPE" = (0.0, 1.0),
+        feed_dict: Optional[Dict[Any, Any]] = None,
+    ):
+        """
+        Initialization specific to encoder estimator implementation in TensorFlow.
+
+        :param input_ph: The input placeholder.
+        :param model: TensorFlow model, neural network or other.
+        :param loss: The loss function for which to compute gradients. This parameter is necessary when training the
+                     model and when computing gradients w.r.t. the loss function.
+        :param sess: Computation session.
+        :param channels_first: Set channels first or last.
+        :param clip_values: Tuple of the form `(min, max)` of floats or `np.ndarray` representing the minimum and
+                            maximum values allowed for features. If floats are provided, these will be used as the range
+                            of all features. If arrays are provided, each value will be considered the bound for a
+                            feature, thus the shape of clip values needs to match the total number of features.
+        :param preprocessing_defences: Preprocessing defence(s) to be applied by the classifier.
+        :param postprocessing_defences: Postprocessing defence(s) to be applied by the classifier.
+        :param preprocessing: Tuple of the form `(subtrahend, divisor)` of floats or `np.ndarray` of values to be
+                              used for data preprocessing. The first value will be subtracted from the input. The input
+                              will then be divided by the second one.
+        :param feed_dict: A feed dictionary for the session run evaluating the classifier. This dictionary includes all
+                          additionally required placeholders except the placeholders defined in this class.
+        """
+        import tensorflow.compat.v1 as tf  # lgtm [py/repeated-import]
+
+        super().__init__(
+            model=model,
+            clip_values=clip_values,
+            channels_first=channels_first,
+            preprocessing_defences=preprocessing_defences,
+            postprocessing_defences=postprocessing_defences,
+            preprocessing=preprocessing,
+        )
+
+        self._nb_classes = int(model.get_shape()[-1])
+        self._input_shape = tuple(input_ph.get_shape().as_list()[1:])
+        self._input_ph = input_ph
+        self._encoding_length = self._model.shape[1]
+        self._loss = loss
+        if feed_dict is None:
+            self._feed_dict = dict()
+        else:
+            self._feed_dict = feed_dict
+
+        # Assign session
+        if sess is None:
+            raise ValueError("A session cannot be None.")
+        self._sess = sess
+
+        # Get the loss gradients graph
+        if self._loss is not None:
+            self._loss_grads = tf.gradients(self._loss, self._input_ph)[0]
+
+    @property
+    def input_shape(self) -> Tuple[int, ...]:
+        """
+        Return the shape of one input sample.
+
+        :return: Shape of one input sample.
+        """
+        return self._input_shape  # type: ignore
+
+    @property
+    def input_ph(self) -> "tf.Placeholder":
+        """
+        Return the input placeholder.
+
+        :return: The input placeholder.
+        """
+        return self._input_ph  # type: ignore
+
+    @property
+    def loss(self) -> "tf.Tensor":
+        """
+        Return the loss function.
+
+        :return: The loss function.
+        """
+        return self._loss  # type: ignore
+
+    @property
+    def feed_dict(self) -> Dict[Any, Any]:
+        """
+        Return the feed dictionary for the session run evaluating the classifier.
+
+        :return: The feed dictionary for the session run evaluating the classifier.
+        """
+        return self._feed_dict  # type: ignore
+
+    def predict(self, x: "np.ndarray", batch_size: int = 128, **kwargs):
+        """
+        Perform prediction for a batch of inputs.
+
+        :param x: Input samples.
+        :param batch_size: Batch size.
+        :return: Array of encoding predictions of shape `(num_inputs, encoding_length)`.
+        """
+        logger.info("Encoding input")
+        feed_dict = {self._input_ph: x}
+        if self._feed_dict is not None:
+            feed_dict.update(self._feed_dict)
+        y = self._sess.run(self._model, feed_dict=feed_dict)
+        return y
+
+    def fit(self, x: "np.ndarray", y: "np.ndarray", batch_size: int = 128, nb_epochs: int = 10, **kwargs) -> None:
+        """
+        Do nothing.
+        """
+        raise NotImplementedError
+
+    def get_activations(
+        self, x: "np.ndarray", layer: Union[int, str], batch_size: int, framework: bool = False
+    ) -> "np.ndarray":
+        """
+        Do nothing.
+        """
+        raise NotImplementedError
+
+    def compute_loss(self, x: "np.ndarray", y: "np.ndarray", **kwargs) -> "np.ndarray":
+        raise NotImplementedError
+
+    def loss_gradient(self, x: "np.ndarray", y: "np.ndarray", training_mode: bool = False, **kwargs) -> "np.ndarray":
+        """
+        No gradients to compute for this method; do nothing.
+        """
+        raise NotImplementedError
+
+    @property
+    def encoding_length(self) -> int:
+        """
+        Returns the length of the encoding size output.
+
+        :return: The length of the encoding size output.
+        """
+        return self._encoding_length
diff --git a/adversarial-robustness-toolbox/art/estimators/estimator.py b/adversarial-robustness-toolbox/art/estimators/estimator.py
new file mode 100644
index 0000000..9abe6cd
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/estimators/estimator.py
@@ -0,0 +1,530 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements abstract base and mixin classes for estimators in ART.
+"""
+from abc import ABC, abstractmethod
+from typing import Any, Dict, List, Optional, Tuple, Union, TYPE_CHECKING
+
+import numpy as np
+from tqdm.auto import trange
+
+from art.config import ART_NUMPY_DTYPE
+
+if TYPE_CHECKING:
+    # pylint: disable=R0401
+    from art.utils import CLIP_VALUES_TYPE, PREPROCESSING_TYPE
+    from art.data_generators import DataGenerator
+    from art.metrics.verification_decisions_trees import Tree
+    from art.defences.postprocessor.postprocessor import Postprocessor
+    from art.defences.preprocessor.preprocessor import Preprocessor
+
+
+class BaseEstimator(ABC):
+    """
+    The abstract base class `BaseEstimator` defines the basic requirements of an estimator in ART. The BaseEstimator is
+    is the highest abstraction of a machine learning model in ART.
+    """
+
+    estimator_params = [
+        "model",
+        "clip_values",
+        "preprocessing_defences",
+        "postprocessing_defences",
+        "preprocessing",
+    ]
+
+    def __init__(
+        self,
+        model,
+        clip_values: Optional["CLIP_VALUES_TYPE"],
+        preprocessing_defences: Union["Preprocessor", List["Preprocessor"], None] = None,
+        postprocessing_defences: Union["Postprocessor", List["Postprocessor"], None] = None,
+        preprocessing: Union["PREPROCESSING_TYPE", "Preprocessor"] = (0.0, 1.0),
+    ):
+        """
+        Initialize a `BaseEstimator` object.
+
+        :param model: The model
+        :param clip_values: Tuple of the form `(min, max)` of floats or `np.ndarray` representing the minimum and
+               maximum values allowed for features. If floats are provided, these will be used as the range of all
+               features. If arrays are provided, each value will be considered the bound for a feature, thus
+               the shape of clip values needs to match the total number of features.
+        :param preprocessing_defences: Preprocessing defence(s) to be applied by the estimator.
+        :param postprocessing_defences: Postprocessing defence(s) to be applied by the estimator.
+        :param preprocessing: Tuple of the form `(subtrahend, divisor)` of floats or `np.ndarray` of values to be
+               used for data preprocessing. The first value will be subtracted from the input and the results will be
+               divided by the second value.
+        """
+        self._model = model
+        self._clip_values = clip_values
+
+        self.preprocessing = self._set_preprocessing(preprocessing)
+        self.preprocessing_defences = self._set_preprocessing_defences(preprocessing_defences)
+        self.postprocessing_defences = self._set_postprocessing_defences(postprocessing_defences)
+        self.preprocessing_operations: List["Preprocessor"] = []
+        BaseEstimator._update_preprocessing_operations(self)
+        BaseEstimator._check_params(self)
+
+    def _update_preprocessing_operations(self):
+        from art.defences.preprocessor.preprocessor import Preprocessor
+
+        self.preprocessing_operations.clear()
+
+        if self.preprocessing_defences is None:
+            pass
+        elif isinstance(self.preprocessing_defences, Preprocessor):
+            self.preprocessing_operations.append(self.preprocessing_defences)
+        else:
+            self.preprocessing_operations += self.preprocessing_defences
+
+        if self.preprocessing is None:
+            pass
+        elif isinstance(self.preprocessing, tuple):
+            from art.preprocessing.standardisation_mean_std.numpy import StandardisationMeanStd
+
+            self.preprocessing_operations.append(
+                StandardisationMeanStd(mean=self.preprocessing[0], std=self.preprocessing[1])
+            )
+        elif isinstance(self.preprocessing, Preprocessor):
+            self.preprocessing_operations.append(self.preprocessing)
+        else:
+            raise ValueError("Preprocessing argument not recognised.")
+
+    @staticmethod
+    def _set_preprocessing(
+        preprocessing: Optional[Union["PREPROCESSING_TYPE", "Preprocessor"]]
+    ) -> Optional["Preprocessor"]:
+        from art.defences.preprocessor.preprocessor import Preprocessor
+
+        if preprocessing is None:
+            return None
+        if isinstance(preprocessing, tuple):
+            from art.preprocessing.standardisation_mean_std.numpy import StandardisationMeanStd
+
+            return StandardisationMeanStd(mean=preprocessing[0], std=preprocessing[1])
+        if isinstance(preprocessing, Preprocessor):
+            return preprocessing
+
+        raise ValueError("Preprocessing argument not recognised.")
+
+    @staticmethod
+    def _set_preprocessing_defences(
+        preprocessing_defences: Optional[Union["Preprocessor", List["Preprocessor"]]]
+    ) -> Optional[List["Preprocessor"]]:
+        from art.defences.preprocessor.preprocessor import Preprocessor
+
+        if isinstance(preprocessing_defences, Preprocessor):
+            return [preprocessing_defences]
+
+        return preprocessing_defences
+
+    @staticmethod
+    def _set_postprocessing_defences(
+        postprocessing_defences: Optional[Union["Postprocessor", List["Postprocessor"]]]
+    ) -> Optional[List["Postprocessor"]]:
+        from art.defences.postprocessor.postprocessor import Postprocessor
+
+        if isinstance(postprocessing_defences, Postprocessor):
+            return [postprocessing_defences]
+
+        return postprocessing_defences
+
+    def set_params(self, **kwargs) -> None:
+        """
+        Take a dictionary of parameters and apply checks before setting them as attributes.
+
+        :param kwargs: A dictionary of attributes.
+        """
+        for key, value in kwargs.items():
+            if key in self.estimator_params:
+                if hasattr(BaseEstimator, key) and isinstance(getattr(BaseEstimator, key), property):
+                    setattr(self, "_" + key, value)
+                else:
+                    if key == "preprocessing":
+                        setattr(self, key, self._set_preprocessing(value))
+                    elif key == "preprocessing_defences":
+                        setattr(self, key, self._set_preprocessing_defences(value))
+                    elif key == "postprocessing_defences":
+                        setattr(self, key, self._set_postprocessing_defences(value))
+                    else:
+                        setattr(self, key, value)
+            else:
+                raise ValueError("Unexpected parameter `{}` found in kwargs.".format(key))
+        self._update_preprocessing_operations()
+        self._check_params()
+
+    def get_params(self) -> Dict[str, Any]:
+        """
+        Get all parameters and their values of this estimator.
+
+        :return: A dictionary of string parameter names to their value.
+        """
+        params = dict()
+        for key in self.estimator_params:
+            params[key] = getattr(self, key)
+        return params
+
+    def _check_params(self) -> None:
+        from art.defences.postprocessor.postprocessor import Postprocessor
+        from art.defences.preprocessor.preprocessor import Preprocessor
+
+        if self._clip_values is not None:
+            if len(self._clip_values) != 2:
+                raise ValueError(
+                    "`clip_values` should be a tuple of 2 floats or arrays containing the allowed data range."
+                )
+            if np.array(self._clip_values[0] >= self._clip_values[1]).any():
+                raise ValueError("Invalid `clip_values`: min >= max.")
+
+            if isinstance(self._clip_values, np.ndarray):
+                self._clip_values = self._clip_values.astype(ART_NUMPY_DTYPE)
+            else:
+                self._clip_values = np.array(self._clip_values, dtype=ART_NUMPY_DTYPE)
+
+        if isinstance(self.preprocessing_operations, list):
+            for preprocess in self.preprocessing_operations:
+                if not isinstance(preprocess, Preprocessor):
+                    raise ValueError(
+                        "All preprocessing defences have to be instance of "
+                        "art.defences.preprocessor.preprocessor.Preprocessor."
+                    )
+        else:
+            raise ValueError(
+                "All preprocessing defences have to be instance of "
+                "art.defences.preprocessor.preprocessor.Preprocessor."
+            )
+
+        if isinstance(self.postprocessing_defences, list):
+            for postproc_defence in self.postprocessing_defences:
+                if not isinstance(postproc_defence, Postprocessor):
+                    raise ValueError(
+                        "All postprocessing defences have to be instance of "
+                        "art.defences.postprocessor.postprocessor.Postprocessor."
+                    )
+        elif self.postprocessing_defences is None:
+            pass
+        else:
+            raise ValueError(
+                "All postprocessing defences have to be instance of "
+                "art.defences.postprocessor.postprocessor.Postprocessor."
+            )
+
+    @abstractmethod
+    def predict(self, x, **kwargs) -> Any:  # lgtm [py/inheritance/incorrect-overridden-signature]
+        """
+        Perform prediction of the estimator for input `x`.
+
+        :param x: Samples.
+        :type x: Format as expected by the `model`
+        :return: Predictions by the model.
+        :rtype: Format as produced by the `model`
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    def fit(self, x, y, **kwargs) -> None:  # lgtm [py/inheritance/incorrect-overridden-signature]
+        """
+        Fit the estimator using the training data `(x, y)`.
+
+        :param x: Training data.
+        :type x: Format as expected by the `model`
+        :param y: Target values.
+        :type y: Format as expected by the `model`
+        """
+        raise NotImplementedError
+
+    @property
+    def model(self):
+        """
+        Return the model.
+
+        :return: The model.
+        """
+        return self._model
+
+    @property
+    @abstractmethod
+    def input_shape(self) -> Tuple[int, ...]:
+        """
+        Return the shape of one input sample.
+
+        :return: Shape of one input sample.
+        """
+        raise NotImplementedError
+
+    @property
+    def clip_values(self) -> Optional["CLIP_VALUES_TYPE"]:
+        """
+        Return the clip values of the input samples.
+
+        :return: Clip values (min, max).
+        """
+        return self._clip_values
+
+    def _apply_preprocessing(self, x, y, fit: bool) -> Tuple[Any, Any]:
+        """
+        Apply all defences and preprocessing operations on the inputs `x` and `y`. This function has to be applied to
+        all raw inputs `x` and `y` provided to the estimator.
+
+        :param x: Samples.
+        :type x: Format as expected by the `model`
+        :param y: Target values.
+        :type y: Format as expected by the `model` or `None`
+        :param fit: `True` if the defences are applied during training.
+        :return: Tuple of `x` and `y` after applying the defences and standardisation.
+        :rtype: Format as expected by the `model`
+        """
+        if self.preprocessing_operations:
+            for preprocess in self.preprocessing_operations:
+                if fit:
+                    if preprocess.apply_fit:
+                        x, y = preprocess(x, y)
+                else:
+                    if preprocess.apply_predict:
+                        x, y = preprocess(x, y)
+
+        return x, y
+
+    def _apply_postprocessing(self, preds, fit: bool) -> np.ndarray:
+        """
+        Apply all postprocessing defences on model predictions.
+
+        :param preds: model output to be post-processed.
+        :type preds: Format as expected by the `model`
+        :param fit: `True` if the defences are applied during training.
+        :return: Post-processed model predictions.
+        """
+        post_preds = preds.copy()
+        if self.postprocessing_defences is not None:
+            for defence in self.postprocessing_defences:
+                if fit:
+                    if defence.apply_fit:
+                        post_preds = defence(post_preds)
+                else:
+                    if defence.apply_predict:
+                        post_preds = defence(post_preds)
+
+        return post_preds
+
+    def __repr__(self):
+        class_name = self.__class__.__name__
+        attributes = {}
+        for k, value in self.__dict__.items():
+            k = k[1:] if k[0] == "_" else k
+            attributes[k] = value
+        attributes = ["{}={}".format(k, v) for k, v in attributes.items()]
+        repr_string = class_name + "(" + ", ".join(attributes) + ")"
+        return repr_string
+
+
+class LossGradientsMixin(ABC):
+    """
+    Mixin abstract base class defining additional functionality for estimators providing loss gradients. An estimator
+    of this type can be combined with white-box attacks. This mixin abstract base class has to be mixed in with
+    class `BaseEstimator`.
+    """
+
+    @abstractmethod
+    def loss_gradient(self, x, y, **kwargs):
+        """
+        Compute the gradient of the loss function w.r.t. `x`.
+
+        :param x: Samples.
+        :type x: Format as expected by the `model`
+        :param y: Target values.
+        :type y: Format as expected by the `model`
+        :return: Loss gradients w.r.t. `x` in the same format as `x`.
+        :rtype: Format as expected by the `model`
+        """
+        raise NotImplementedError
+
+    def _apply_preprocessing_gradient(self, x, gradients, fit=False):
+        """
+        Apply the backward pass to the gradients through all normalization and preprocessing defences that have been
+        applied to `x` and `y` in the forward pass. This function has to be applied to all gradients w.r.t. `x`
+        calculated by the estimator.
+
+        :param x: Features, where first dimension is the number of samples.
+        :type x: Format as expected by the `model`
+        :param gradients: Gradients before backward pass through normalization and preprocessing defences.
+        :type gradients: Format as expected by the `model`
+        :return: Gradients after backward pass through normalization and preprocessing defences.
+        :rtype: Format as expected by the `model`
+        """
+        if self.preprocessing_operations:
+            for preprocess in self.preprocessing_operations[::-1]:
+                if fit:
+                    if preprocess.apply_fit:
+                        gradients = preprocess.estimate_gradient(x, gradients)
+                else:
+                    if preprocess.apply_predict:
+                        gradients = preprocess.estimate_gradient(x, gradients)
+
+        return gradients
+
+
+class NeuralNetworkMixin(ABC):
+    """
+    Mixin abstract base class defining additional functionality required for neural network estimators. This base class
+    has to be mixed in with class `BaseEstimator`.
+    """
+
+    estimator_params = ["channels_first"]
+
+    def __init__(self, channels_first: Optional[bool], **kwargs) -> None:
+        """
+        Initialize a neural network attributes.
+
+        :param channels_first: Set channels first or last.
+        """
+        self._channels_first: Optional[bool] = channels_first
+        super().__init__(**kwargs)
+
+    @abstractmethod
+    def predict(self, x: np.ndarray, batch_size: int = 128, **kwargs):
+        """
+        Perform prediction of the neural network for samples `x`.
+
+        :param x: Input samples.
+        :param batch_size: Batch size.
+        :return: Predictions.
+        :rtype: Format as expected by the `model`
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    def fit(self, x: np.ndarray, y, batch_size: int = 128, nb_epochs: int = 20, **kwargs) -> None:
+        """
+        Fit the model of the estimator on the training data `x` and `y`.
+
+        :param x: Samples of shape (nb_samples, nb_features) or (nb_samples, nb_pixels_1, nb_pixels_2,
+                  nb_channels) or (nb_samples, nb_channels, nb_pixels_1, nb_pixels_2).
+        :param y: Target values.
+        :type y: Format as expected by the `model`
+        :param batch_size: Batch size.
+        :param nb_epochs: Number of training epochs.
+        """
+        raise NotImplementedError
+
+    def fit_generator(self, generator: "DataGenerator", nb_epochs: int = 20, **kwargs) -> None:
+        """
+        Fit the estimator using a `generator` yielding training batches. Implementations can
+        provide framework-specific versions of this function to speed-up computation.
+
+        :param generator: Batch generator providing `(x, y)` for each epoch.
+        :param nb_epochs: Number of training epochs.
+        """
+        from art.data_generators import DataGenerator
+
+        if not isinstance(generator, DataGenerator):
+            raise ValueError(
+                "Expected instance of `DataGenerator` for `fit_generator`, got %s instead." % str(type(generator))
+            )
+
+        for i in range(nb_epochs):
+            for _ in trange(
+                int(generator.size / generator.batch_size), desc="Epoch %i/%i" % (i + 1, nb_epochs)  # type: ignore
+            ):  # type: ignore
+                x, y = generator.get_batch()
+
+                # Apply preprocessing and defences
+                x_preprocessed, y_preprocessed = self._apply_preprocessing(x, y, fit=True)  # type: ignore
+
+                # Fit for current batch
+                self.fit(x_preprocessed, y_preprocessed, nb_epochs=1, batch_size=generator.batch_size, **kwargs)
+
+    @abstractmethod
+    def get_activations(
+        self, x: np.ndarray, layer: Union[int, str], batch_size: int, framework: bool = False
+    ) -> np.ndarray:
+        """
+        Return the output of a specific layer for samples `x` where `layer` is the index of the layer between 0 and
+        `nb_layers - 1 or the name of the layer. The number of layers can be determined by counting the results
+        returned by calling `layer_names`.
+
+        :param x: Samples
+        :param layer: Index or name of the layer.
+        :param batch_size: Batch size.
+        :param framework: If true, return the intermediate tensor representation of the activation.
+        :return: The output of `layer`, where the first dimension is the batch size corresponding to `x`.
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    def compute_loss(self, x: np.ndarray, y: np.ndarray, **kwargs) -> np.ndarray:
+        """
+        Compute the loss of the neural network for samples `x`.
+
+        :param x: Samples of shape (nb_samples, nb_features) or (nb_samples, nb_pixels_1, nb_pixels_2,
+                  nb_channels) or (nb_samples, nb_channels, nb_pixels_1, nb_pixels_2).
+        :param y: Target values (class labels) one-hot-encoded of shape `(nb_samples, nb_classes)` or indices
+                  of shape `(nb_samples,)`.
+        :return: Loss values.
+        :rtype: Format as expected by the `model`
+        """
+        raise NotImplementedError
+
+    @property
+    def channels_first(self) -> Optional[bool]:
+        """
+        :return: Boolean to indicate index of the color channels in the sample `x`.
+        """
+        return self._channels_first
+
+    @property
+    def layer_names(self) -> Optional[List[str]]:
+        """
+        Return the names of the hidden layers in the model, if applicable.
+
+        :return: The names of the hidden layers in the model, input and output layers are ignored.
+
+        .. warning:: `layer_names` tries to infer the internal structure of the model.
+                     This feature comes with no guarantees on the correctness of the result.
+                     The intended order of the layers tries to match their order in the model, but this is not
+                     guaranteed either.
+        """
+        return self._layer_names  # type: ignore
+
+    def __repr__(self):
+        name = self.__class__.__name__
+
+        attributes = {}
+        for k, value in self.__dict__.items():
+            k = k[1:] if k[0] == "_" else k
+            attributes[k] = value
+        attrs = ["{}={}".format(k, v) for k, v in attributes.items()]
+        repr_ = name + "(" + ", ".join(attrs) + ")"
+
+        return repr_
+
+
+class DecisionTreeMixin(ABC):
+    """
+    Mixin abstract base class defining additional functionality for decision-tree-based estimators. This mixin abstract
+    base class has to be mixed in with class `BaseEstimator`.
+    """
+
+    @abstractmethod
+    def get_trees(self) -> List["Tree"]:
+        """
+        Get the decision trees.
+
+        :return: A list of decision trees.
+        """
+        raise NotImplementedError
diff --git a/adversarial-robustness-toolbox/art/estimators/generation/__init__.py b/adversarial-robustness-toolbox/art/estimators/generation/__init__.py
new file mode 100644
index 0000000..b814839
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/estimators/generation/__init__.py
@@ -0,0 +1,6 @@
+"""
+Generator API.
+"""
+from art.estimators.generation.generator import GeneratorMixin
+
+from art.estimators.generation.tensorflow import TensorFlowGenerator
diff --git a/adversarial-robustness-toolbox/art/estimators/generation/generator.py b/adversarial-robustness-toolbox/art/estimators/generation/generator.py
new file mode 100644
index 0000000..6d9dc00
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/estimators/generation/generator.py
@@ -0,0 +1,37 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements mixin abstract base classes defining properties for all generators in ART.
+"""
+import abc
+
+
+class GeneratorMixin(abc.ABC):
+    """
+    Mixin abstract base class defining functionality for generators.
+    """
+
+    @property
+    @abc.abstractmethod
+    def encoding_length(self) -> int:
+        """
+        Returns the length of the encoding size output.
+
+        :return: The length of the encoding size output.
+        """
+        raise NotImplementedError
diff --git a/adversarial-robustness-toolbox/art/estimators/generation/tensorflow.py b/adversarial-robustness-toolbox/art/estimators/generation/tensorflow.py
new file mode 100644
index 0000000..102f4ea
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/estimators/generation/tensorflow.py
@@ -0,0 +1,201 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements the classifier `TensorFlowGenerator` for TensorFlow models.
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+from typing import Any, Dict, List, Optional, Union, Tuple, TYPE_CHECKING
+
+from art.estimators.generation.generator import GeneratorMixin
+from art.estimators.tensorflow import TensorFlowEstimator
+
+if TYPE_CHECKING:
+    # pylint: disable=C0412
+    import numpy as np
+    import tensorflow.compat.v1 as tf
+
+    from art.utils import CLIP_VALUES_TYPE, PREPROCESSING_TYPE
+    from art.defences.preprocessor import Preprocessor
+    from art.defences.postprocessor import Postprocessor
+
+logger = logging.getLogger(__name__)
+
+
+class TensorFlowGenerator(GeneratorMixin, TensorFlowEstimator):  # lgtm [py/missing-call-to-init]
+    """
+    This class implements a GAN with the TensorFlow framework.
+    """
+
+    estimator_params = TensorFlowEstimator.estimator_params + [
+        "input_ph",
+        "loss",
+        "sess",
+        "feed_dict",
+    ]
+
+    def __init__(
+        self,
+        input_ph: "tf.Placeholder",
+        model: "tf.Tensor",
+        loss: Optional["tf.Tensor"] = None,
+        sess: Optional["tf.compat.v1.Session"] = None,
+        channels_first=False,
+        clip_values: Optional["CLIP_VALUES_TYPE"] = None,
+        preprocessing_defences: Union["Preprocessor", List["Preprocessor"], None] = None,
+        postprocessing_defences: Union["Postprocessor", List["Postprocessor"], None] = None,
+        preprocessing: "PREPROCESSING_TYPE" = (0.0, 1.0),
+        feed_dict: Optional[Dict[Any, Any]] = None,
+    ):
+        """
+        Initialization specific to TensorFlow generator implementations.
+
+        :param input_ph: The input placeholder.
+        :param model: TensorFlow model, neural network or other.
+        :param loss: The loss function for which to compute gradients. This parameter is necessary when training the
+                     model and when computing gradients w.r.t. the loss function.
+        :param sess: Computation session.
+        :param channels_first: Set channels first or last.
+        :param clip_values: Tuple of the form `(min, max)` of floats or `np.ndarray` representing the minimum and
+                            maximum values allowed for features. If floats are provided, these will be used as the range
+                            of all features. If arrays are provided, each value will be considered the bound for a
+                            feature, thus the shape of clip values needs to match the total number of features.
+        :param preprocessing_defences: Preprocessing defence(s) to be applied by the classifier.
+        :param postprocessing_defences: Postprocessing defence(s) to be applied by the classifier.
+        :param preprocessing: Tuple of the form `(subtrahend, divisor)` of floats or `np.ndarray` of values to be
+                              used for data preprocessing. The first value will be subtracted from the input. The input
+                              will then be divided by the second one.
+        :param feed_dict: A feed dictionary for the session run evaluating the classifier. This dictionary includes all
+                          additionally required placeholders except the placeholders defined in this class.
+        """
+        import tensorflow.compat.v1 as tf  # lgtm [py/repeated-import]
+
+        super().__init__(
+            model=model,
+            clip_values=clip_values,
+            channels_first=channels_first,
+            preprocessing_defences=preprocessing_defences,
+            postprocessing_defences=postprocessing_defences,
+            preprocessing=preprocessing,
+        )
+
+        self._input_ph = input_ph
+        self._encoding_length = self._input_ph.shape[1]
+        self._loss = loss
+        if self._loss is not None:
+            self._grad = tf.gradients(self._loss, self._input_ph)
+        if feed_dict is None:
+            self._feed_dict = dict()
+        else:
+            self._feed_dict = feed_dict
+
+        # Assign session
+        if sess is None:
+            raise ValueError("A session cannot be None.")
+            # TODO do the same thing for all not None variables
+        self._sess = sess
+
+    @property
+    def input_shape(self) -> Tuple[int, ...]:
+        """
+        Return the shape of one input sample.
+
+        :return: Shape of one input sample.
+        """
+        return self._input_shape  # type: ignore
+
+    @property
+    def input_ph(self) -> "tf.Placeholder":
+        """
+        Return the input placeholder.
+
+        :return: The input placeholder.
+        """
+        return self._input_ph  # type: ignore
+
+    @property
+    def loss(self) -> "tf.Tensor":
+        """
+        Return the loss function.
+
+        :return: The loss function.
+        """
+        return self._loss  # type: ignore
+
+    @property
+    def feed_dict(self) -> Dict[Any, Any]:
+        """
+        Return the feed dictionary for the session run evaluating the classifier.
+
+        :return: The feed dictionary for the session run evaluating the classifier.
+        """
+        return self._feed_dict  # type: ignore
+
+    def predict(self, x: "np.ndarray", batch_size: int = 128, **kwargs) -> "np.ndarray":
+        """
+        Perform projections over a batch of encodings.
+
+        :param x: Encodings.
+        :param batch_size: Batch size.
+        :return: Array of prediction projections of shape `(num_inputs, nb_classes)`.
+        """
+        logging.info("Projecting new sample from z value")
+        feed_dict = {self._input_ph: x}
+        if self._feed_dict is not None:
+            feed_dict.update(self._feed_dict)
+        y = self._sess.run(self._model, feed_dict=feed_dict)
+        return y
+
+    def loss_gradient(self, x, y, training_mode: bool = False, **kwargs) -> "np.ndarray":
+        raise NotImplementedError
+
+    def fit(self, x, y, batch_size=128, nb_epochs=10, **kwargs):
+        """
+        Do nothing.
+        """
+        raise NotImplementedError
+
+    def get_activations(
+        self, x: "np.ndarray", layer: Union[int, str], batch_size: int, framework: bool = False
+    ) -> "np.ndarray":
+        """
+        Do nothing.
+        """
+        raise NotImplementedError
+
+    def compute_loss(self, x: "np.ndarray", y: "np.ndarray", **kwargs) -> "np.ndarray":
+        raise NotImplementedError
+
+    @property
+    def model(self) -> "tf.Tensor":
+        """
+        Returns the generator tensor.
+
+        :return: The generator tensor.
+        """
+        return self._model
+
+    @property
+    def encoding_length(self) -> int:
+        """
+        Returns the length of the encoding size output.
+
+        :return: The length of the encoding size output.
+        """
+        return self._encoding_length
diff --git a/adversarial-robustness-toolbox/art/estimators/keras.py b/adversarial-robustness-toolbox/art/estimators/keras.py
new file mode 100644
index 0000000..4a5a71a
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/estimators/keras.py
@@ -0,0 +1,85 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements the abstract estimator `KerasEstimator` for Keras models.
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+
+import numpy as np
+
+from art.estimators.estimator import (
+    BaseEstimator,
+    NeuralNetworkMixin,
+    LossGradientsMixin,
+)
+
+logger = logging.getLogger(__name__)
+
+
+class KerasEstimator(NeuralNetworkMixin, LossGradientsMixin, BaseEstimator):
+    """
+    Estimator class for Keras models.
+    """
+
+    estimator_params = BaseEstimator.estimator_params + NeuralNetworkMixin.estimator_params
+
+    def __init__(self, **kwargs) -> None:
+        """
+        Estimator class for Keras models.
+        """
+        super().__init__(**kwargs)
+
+    def predict(self, x: np.ndarray, batch_size: int = 128, **kwargs):
+        """
+        Perform prediction of the neural network for samples `x`.
+
+        :param x: Samples of shape (nb_samples, nb_features) or (nb_samples, nb_pixels_1, nb_pixels_2,
+                  nb_channels) or (nb_samples, nb_channels, nb_pixels_1, nb_pixels_2).
+        :param batch_size: Batch size.
+        :return: Predictions.
+        :rtype: Format as expected by the `model`
+        """
+        return NeuralNetworkMixin.predict(self, x, batch_size=batch_size, **kwargs)
+
+    def fit(self, x: np.ndarray, y, batch_size: int = 128, nb_epochs: int = 20, **kwargs) -> None:
+        """
+        Fit the model of the estimator on the training data `x` and `y`.
+
+        :param x: Samples of shape (nb_samples, nb_features) or (nb_samples, nb_pixels_1, nb_pixels_2,
+                  nb_channels) or (nb_samples, nb_channels, nb_pixels_1, nb_pixels_2).
+        :param y: Target values.
+        :type y: Format as expected by the `model`
+        :param batch_size: Batch size.
+        :param nb_epochs: Number of training epochs.
+        """
+        NeuralNetworkMixin.fit(self, x, y, batch_size=batch_size, nb_epochs=nb_epochs, **kwargs)
+
+    def compute_loss(self, x: np.ndarray, y: np.ndarray, **kwargs) -> np.ndarray:
+        """
+        Compute the loss of the neural network for samples `x`.
+
+        :param x: Samples of shape (nb_samples, nb_features) or (nb_samples, nb_pixels_1, nb_pixels_2,
+                  nb_channels) or (nb_samples, nb_channels, nb_pixels_1, nb_pixels_2).
+        :param y: Target values (class labels) one-hot-encoded of shape `(nb_samples, nb_classes)` or indices
+                  of shape `(nb_samples,)`.
+        :return: Loss values.
+        :rtype: Format as expected by the `model`
+        """
+        raise NotImplementedError
diff --git a/adversarial-robustness-toolbox/art/estimators/mxnet.py b/adversarial-robustness-toolbox/art/estimators/mxnet.py
new file mode 100644
index 0000000..322456d
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/estimators/mxnet.py
@@ -0,0 +1,72 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements the abstract estimator `MXEstimator` for MXNet Gluon models.
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+
+import numpy as np
+
+from art.estimators.estimator import (
+    BaseEstimator,
+    LossGradientsMixin,
+    NeuralNetworkMixin,
+)
+
+logger = logging.getLogger(__name__)
+
+
+class MXEstimator(NeuralNetworkMixin, LossGradientsMixin, BaseEstimator):
+    """
+    Estimator for MXNet Gluon models.
+    """
+
+    estimator_params = BaseEstimator.estimator_params + NeuralNetworkMixin.estimator_params
+
+    def __init__(self, **kwargs) -> None:
+        """
+        Estimator class for MXNet Gluon models.
+        """
+        super().__init__(**kwargs)
+
+    def predict(self, x: np.ndarray, batch_size: int = 128, **kwargs):
+        """
+        Perform prediction of the neural network for samples `x`.
+
+        :param x: Samples of shape (nb_samples, nb_features) or (nb_samples, nb_pixels_1, nb_pixels_2,
+                  nb_channels) or (nb_samples, nb_channels, nb_pixels_1, nb_pixels_2).
+        :param batch_size: Batch size.
+        :return: Predictions.
+        :rtype: Format as expected by the `model`
+        """
+        return NeuralNetworkMixin.predict(self, x, batch_size=batch_size, **kwargs)
+
+    def fit(self, x: np.ndarray, y, batch_size: int = 128, nb_epochs: int = 20, **kwargs) -> None:
+        """
+        Fit the model of the estimator on the training data `x` and `y`.
+
+        :param x: Samples of shape (nb_samples, nb_features) or (nb_samples, nb_pixels_1, nb_pixels_2,
+                  nb_channels) or (nb_samples, nb_channels, nb_pixels_1, nb_pixels_2).
+        :param y: Target values.
+        :type y: Format as expected by the `model`
+        :param batch_size: Batch size.
+        :param nb_epochs: Number of training epochs.
+        """
+        NeuralNetworkMixin.fit(self, x, y, batch_size=batch_size, nb_epochs=nb_epochs, **kwargs)
diff --git a/adversarial-robustness-toolbox/art/estimators/object_detection/__init__.py b/adversarial-robustness-toolbox/art/estimators/object_detection/__init__.py
new file mode 100644
index 0000000..aeb5783
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/estimators/object_detection/__init__.py
@@ -0,0 +1,7 @@
+"""
+Module containing estimators for object detection.
+"""
+from art.estimators.object_detection.object_detector import ObjectDetectorMixin
+
+from art.estimators.object_detection.pytorch_faster_rcnn import PyTorchFasterRCNN
+from art.estimators.object_detection.tensorflow_faster_rcnn import TensorFlowFasterRCNN
diff --git a/adversarial-robustness-toolbox/art/estimators/object_detection/object_detector.py b/adversarial-robustness-toolbox/art/estimators/object_detection/object_detector.py
new file mode 100644
index 0000000..aeb9d38
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/estimators/object_detection/object_detector.py
@@ -0,0 +1,39 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements mixin abstract base class for all object detectors in ART.
+"""
+
+from abc import ABC
+
+from art.estimators.estimator import BaseEstimator
+from art.estimators.classification.classifier import LossGradientsMixin
+
+
+class ObjectDetectorMixin(ABC):
+    """
+    Mix-in Base class for ART object detectors.
+    """
+
+
+class ObjectDetector(ObjectDetectorMixin, LossGradientsMixin, BaseEstimator, ABC):
+    """
+    Typing variable definition.
+    """
+
+    pass
diff --git a/adversarial-robustness-toolbox/art/estimators/object_detection/pytorch_faster_rcnn.py b/adversarial-robustness-toolbox/art/estimators/object_detection/pytorch_faster_rcnn.py
new file mode 100644
index 0000000..dcb652f
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/estimators/object_detection/pytorch_faster_rcnn.py
@@ -0,0 +1,336 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements the task specific estimator for Faster R-CNN v3 in PyTorch.
+"""
+import logging
+from typing import List, Dict, Optional, Tuple, Union, TYPE_CHECKING
+
+import numpy as np
+
+from art.estimators.object_detection.object_detector import ObjectDetectorMixin
+from art.estimators.pytorch import PyTorchEstimator
+
+if TYPE_CHECKING:
+    # pylint: disable=C0412
+    import torch
+    import torchvision
+
+    from art.utils import CLIP_VALUES_TYPE, PREPROCESSING_TYPE
+    from art.defences.preprocessor.preprocessor import Preprocessor
+    from art.defences.postprocessor.postprocessor import Postprocessor
+
+logger = logging.getLogger(__name__)
+
+
+class PyTorchFasterRCNN(ObjectDetectorMixin, PyTorchEstimator):
+    """
+    This class implements a model-specific object detector using Faster-RCNN and PyTorch.
+    """
+
+    estimator_params = PyTorchEstimator.estimator_params + ["attack_losses"]
+
+    def __init__(
+        self,
+        model: Optional["torchvision.models.detection.fasterrcnn_resnet50_fpn"] = None,
+        clip_values: Optional["CLIP_VALUES_TYPE"] = None,
+        channels_first: Optional[bool] = None,
+        preprocessing_defences: Union["Preprocessor", List["Preprocessor"], None] = None,
+        postprocessing_defences: Union["Postprocessor", List["Postprocessor"], None] = None,
+        preprocessing: "PREPROCESSING_TYPE" = None,
+        attack_losses: Tuple[str, ...] = ("loss_classifier", "loss_box_reg", "loss_objectness", "loss_rpn_box_reg",),
+        device_type: str = "gpu",
+    ):
+        """
+        Initialization.
+
+        :param model: Faster-RCNN model. The output of the model is `List[Dict[Tensor]]`, one for each input image. The
+                      fields of the Dict are as follows:
+
+                      - boxes (FloatTensor[N, 4]): the predicted boxes in [x1, y1, x2, y2] format, with values \
+                        between 0 and H and 0 and W
+                      - labels (Int64Tensor[N]): the predicted labels for each image
+                      - scores (Tensor[N]): the scores or each prediction
+        :param clip_values: Tuple of the form `(min, max)` of floats or `np.ndarray` representing the minimum and
+               maximum values allowed for features. If floats are provided, these will be used as the range of all
+               features. If arrays are provided, each value will be considered the bound for a feature, thus
+               the shape of clip values needs to match the total number of features.
+        :param channels_first: Set channels first or last.
+        :param preprocessing_defences: Preprocessing defence(s) to be applied by the classifier.
+        :param postprocessing_defences: Postprocessing defence(s) to be applied by the classifier.
+        :param preprocessing: Tuple of the form `(subtrahend, divisor)` of floats or `np.ndarray` of values to be
+               used for data preprocessing. The first value will be subtracted from the input. The input will then
+               be divided by the second one.
+        :param attack_losses: Tuple of any combination of strings of loss components: 'loss_classifier', 'loss_box_reg',
+                              'loss_objectness', and 'loss_rpn_box_reg'.
+        :param device_type: Type of device to be used for model and tensors, if `cpu` run on CPU, if `gpu` run on GPU
+                            if available otherwise run on CPU.
+        """
+        import torch  # lgtm [py/repeated-import]
+
+        super().__init__(
+            model=model,
+            clip_values=clip_values,
+            channels_first=channels_first,
+            preprocessing_defences=preprocessing_defences,
+            postprocessing_defences=postprocessing_defences,
+            preprocessing=preprocessing,
+        )
+
+        self._input_shape = None
+
+        if self.clip_values is not None:
+            if self.clip_values[0] != 0:
+                raise ValueError("This classifier requires un-normalized input images with clip_vales=(0, max_value).")
+            if self.clip_values[1] <= 0:
+                raise ValueError("This classifier requires un-normalized input images with clip_vales=(0, max_value).")
+
+        if preprocessing is not None:
+            raise ValueError("This estimator does not support `preprocessing`.")
+        if self.postprocessing_defences is not None:
+            raise ValueError("This estimator does not support `postprocessing_defences`.")
+
+        if model is None:
+            import torchvision  # lgtm [py/repeated-import]
+
+            self._model = torchvision.models.detection.fasterrcnn_resnet50_fpn(
+                pretrained=True, progress=True, num_classes=91, pretrained_backbone=True
+            )
+        else:
+            self._model = model
+
+        # Set device
+        self._device: torch.device
+        if device_type == "cpu" or not torch.cuda.is_available():
+            self._device = torch.device("cpu")
+        else:
+            cuda_idx = torch.cuda.current_device()
+            self._device = torch.device("cuda:{}".format(cuda_idx))
+
+        self._model.to(self._device)
+        self._model.eval()
+        self.attack_losses: Tuple[str, ...] = attack_losses
+
+    @property
+    def input_shape(self) -> Tuple[int, ...]:
+        """
+        Return the shape of one input sample.
+
+        :return: Shape of one input sample.
+        """
+        return self._input_shape  # type: ignore
+
+    @property
+    def device(self) -> "torch.device":
+        """
+        Get current used device.
+
+        :return: Current used device.
+        """
+        return self._device
+
+    def loss_gradient(
+        self, x: np.ndarray, y: Union[List[Dict[str, np.ndarray]], List[Dict[str, "torch.Tensor"]]], **kwargs
+    ) -> np.ndarray:
+        """
+        Compute the gradient of the loss function w.r.t. `x`.
+
+        :param x: Samples of shape (nb_samples, height, width, nb_channels).
+        :param y: Target values of format `List[Dict[Tensor]]`, one for each input image. The
+                  fields of the Dict are as follows:
+
+                  - boxes (FloatTensor[N, 4]): the predicted boxes in [x1, y1, x2, y2] format, with values \
+                    between 0 and H and 0 and W
+                  - labels (Int64Tensor[N]): the predicted labels for each image
+                  - scores (Tensor[N]): the scores or each prediction.
+        :return: Loss gradients of the same shape as `x`.
+        """
+        import torch  # lgtm [py/repeated-import]
+        import torchvision  # lgtm [py/repeated-import]
+
+        self._model.train()
+
+        # Apply preprocessing
+        if self.all_framework_preprocessing:
+            if isinstance(x, torch.Tensor):
+                raise NotImplementedError
+
+            if y is not None and isinstance(y[0]["boxes"], np.ndarray):
+                y_tensor = list()
+                for i, y_i in enumerate(y):
+                    y_t = dict()
+                    y_t["boxes"] = torch.from_numpy(y_i["boxes"]).type(torch.float).to(self._device)
+                    y_t["labels"] = torch.from_numpy(y_i["labels"]).type(torch.int64).to(self._device)
+                    y_t["scores"] = torch.from_numpy(y_i["scores"]).to(self._device)
+                    y_tensor.append(y_t)
+            else:
+                y_tensor = y
+
+            transform = torchvision.transforms.Compose([torchvision.transforms.ToTensor()])
+            image_tensor_list_grad = list()
+            y_preprocessed = list()
+            inputs_t = list()
+
+            for i in range(x.shape[0]):
+                if self.clip_values is not None:
+                    x_grad = transform(x[i] / self.clip_values[1]).to(self._device)
+                else:
+                    x_grad = transform(x[i]).to(self._device)
+                x_grad.requires_grad = True
+                image_tensor_list_grad.append(x_grad)
+                x_grad_1 = torch.unsqueeze(x_grad, dim=0)
+                x_preprocessed_i, y_preprocessed_i = self._apply_preprocessing(
+                    x_grad_1, y=[y_tensor[i]], fit=False, no_grad=False
+                )
+                x_preprocessed_i = torch.squeeze(x_preprocessed_i)
+                y_preprocessed.append(y_preprocessed_i[0])
+                inputs_t.append(x_preprocessed_i)
+
+        elif isinstance(x, np.ndarray):
+            x_preprocessed, y_preprocessed = self._apply_preprocessing(x, y=y, fit=False, no_grad=True)
+
+            if y_preprocessed is not None and isinstance(y_preprocessed[0]["boxes"], np.ndarray):
+                y_preprocessed_tensor = list()
+                for i, y_i in enumerate(y_preprocessed):
+                    y_preprocessed_t = dict()
+                    y_preprocessed_t["boxes"] = torch.from_numpy(y_i["boxes"]).type(torch.float).to(self._device)
+                    y_preprocessed_t["labels"] = torch.from_numpy(y_i["labels"]).type(torch.int64).to(self._device)
+                    y_preprocessed_t["scores"] = torch.from_numpy(y_i["scores"]).to(self._device)
+                    y_preprocessed_tensor.append(y_preprocessed_t)
+                y_preprocessed = y_preprocessed_tensor
+
+            transform = torchvision.transforms.Compose([torchvision.transforms.ToTensor()])
+            image_tensor_list_grad = list()
+
+            for i in range(x_preprocessed.shape[0]):
+                if self.clip_values is not None:
+                    x_grad = transform(x_preprocessed[i] / self.clip_values[1]).to(self._device)
+                else:
+                    x_grad = transform(x_preprocessed[i]).to(self._device)
+                x_grad.requires_grad = True
+                image_tensor_list_grad.append(x_grad)
+
+            inputs_t = image_tensor_list_grad
+
+        else:
+            raise NotImplementedError("Combination of inputs and preprocessing not supported.")
+
+        if isinstance(y_preprocessed, np.ndarray):
+            labels_t = torch.from_numpy(y_preprocessed).to(self._device)
+        else:
+            labels_t = y_preprocessed
+
+        output = self._model(inputs_t, labels_t)
+
+        # Compute the gradient and return
+        loss = None
+        for loss_name in self.attack_losses:
+            if loss is None:
+                loss = output[loss_name]
+            else:
+                loss = loss + output[loss_name]
+
+        # Clean gradients
+        self._model.zero_grad()
+
+        # Compute gradients
+        loss.backward(retain_graph=True)  # type: ignore
+
+        grad_list = list()
+        if isinstance(x, np.ndarray):
+            for img in image_tensor_list_grad:
+                gradients = img.grad.cpu().numpy().copy()
+                grad_list.append(gradients)
+            grads = np.stack(grad_list, axis=0)
+        else:
+            for img in inputs_t:
+                gradients = img.grad.copy()
+                grad_list.append(gradients)
+            grads = torch.stack(grad_list, dim=0)
+
+        grads = np.transpose(grads, (0, 2, 3, 1))
+
+        if self.clip_values is not None:
+            grads = grads / self.clip_values[1]
+
+        if not self.all_framework_preprocessing:
+            grads = self._apply_preprocessing_gradient(x, grads)
+
+        assert grads.shape == x.shape
+
+        return grads
+
+    def predict(self, x: np.ndarray, batch_size: int = 128, **kwargs) -> List[Dict[str, np.ndarray]]:
+        """
+        Perform prediction for a batch of inputs.
+
+        :param x: Samples of shape (nb_samples, height, width, nb_channels).
+        :param batch_size: Batch size.
+        :return: Predictions of format `List[Dict[str, np.ndarray]]`, one for each input image. The
+                 fields of the Dict are as follows:
+
+                 - boxes [N, 4]: the predicted boxes in [x1, y1, x2, y2] format, with values \
+                   between 0 and H and 0 and W
+                 - labels [N]: the predicted labels for each image
+                 - scores [N]: the scores or each prediction.
+        """
+        import torchvision  # lgtm [py/repeated-import]
+
+        self._model.eval()
+
+        # Apply preprocessing
+        x, _ = self._apply_preprocessing(x, y=None, fit=False)
+
+        transform = torchvision.transforms.Compose([torchvision.transforms.ToTensor()])
+        image_tensor_list: List[np.ndarray] = list()
+
+        if self.clip_values is not None:
+            norm_factor = self.clip_values[1]
+        else:
+            norm_factor = 1.0
+        for i in range(x.shape[0]):
+            image_tensor_list.append(transform(x[i] / norm_factor).to(self._device))
+        predictions = self._model(image_tensor_list)
+
+        for i_prediction, _ in enumerate(predictions):
+            predictions[i_prediction]["boxes"] = predictions[i_prediction]["boxes"].detach().cpu().numpy()
+            predictions[i_prediction]["labels"] = predictions[i_prediction]["labels"].detach().cpu().numpy()
+            predictions[i_prediction]["scores"] = predictions[i_prediction]["scores"].detach().cpu().numpy()
+
+        return predictions
+
+    def fit(self, x: np.ndarray, y, batch_size: int = 128, nb_epochs: int = 20, **kwargs) -> None:
+        raise NotImplementedError
+
+    def get_activations(
+        self, x: np.ndarray, layer: Union[int, str], batch_size: int, framework: bool = False
+    ) -> np.ndarray:
+        raise NotImplementedError
+
+    def compute_loss(self, x: np.ndarray, y: np.ndarray, **kwargs) -> np.ndarray:
+        """
+        Compute the loss of the neural network for samples `x`.
+
+        :param x: Samples of shape (nb_samples, nb_features) or (nb_samples, nb_pixels_1, nb_pixels_2,
+                  nb_channels) or (nb_samples, nb_channels, nb_pixels_1, nb_pixels_2).
+        :param y: Target values (class labels) one-hot-encoded of shape `(nb_samples, nb_classes)` or indices
+                  of shape `(nb_samples,)`.
+        :return: Loss values.
+        :rtype: Format as expected by the `model`
+        """
+        raise NotImplementedError
diff --git a/adversarial-robustness-toolbox/art/estimators/object_detection/tensorflow_faster_rcnn.py b/adversarial-robustness-toolbox/art/estimators/object_detection/tensorflow_faster_rcnn.py
new file mode 100644
index 0000000..7c58678
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/estimators/object_detection/tensorflow_faster_rcnn.py
@@ -0,0 +1,496 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements the task specific estimator for Faster R-CNN in TensorFlow.
+"""
+import logging
+from typing import List, Dict, Optional, Tuple, Union, TYPE_CHECKING
+
+import numpy as np
+
+from art.estimators.object_detection.object_detector import ObjectDetectorMixin
+from art.estimators.tensorflow import TensorFlowEstimator
+from art.utils import get_file
+from art import config
+
+if TYPE_CHECKING:
+    # pylint: disable=C0412
+    import tensorflow.compat.v1 as tf
+    from object_detection.meta_architectures.faster_rcnn_meta_arch import FasterRCNNMetaArch
+    from tensorflow.python.framework.ops import Tensor
+    from tensorflow.python.client.session import Session
+
+    from art.utils import CLIP_VALUES_TYPE, PREPROCESSING_TYPE
+    from art.defences.preprocessor.preprocessor import Preprocessor
+    from art.defences.postprocessor.postprocessor import Postprocessor
+
+logger = logging.getLogger(__name__)
+
+
+class TensorFlowFasterRCNN(ObjectDetectorMixin, TensorFlowEstimator):
+    """
+    This class implements a model-specific object detector using Faster-RCNN and TensorFlow.
+    """
+
+    estimator_params = TensorFlowEstimator.estimator_params + ["images", "sess", "is_training", "attack_losses"]
+
+    def __init__(
+        self,
+        images: "tf.Tensor",
+        model: Optional["FasterRCNNMetaArch"] = None,
+        filename: Optional[str] = None,
+        url: Optional[str] = None,
+        sess: Optional["Session"] = None,
+        is_training: bool = False,
+        clip_values: Optional["CLIP_VALUES_TYPE"] = None,
+        channels_first: bool = False,
+        preprocessing_defences: Union["Preprocessor", List["Preprocessor"], None] = None,
+        postprocessing_defences: Union["Postprocessor", List["Postprocessor"], None] = None,
+        preprocessing: "PREPROCESSING_TYPE" = (0.0, 1.0),
+        attack_losses: Tuple[str, ...] = (
+            "Loss/RPNLoss/localization_loss",
+            "Loss/RPNLoss/objectness_loss",
+            "Loss/BoxClassifierLoss/localization_loss",
+            "Loss/BoxClassifierLoss/classification_loss",
+        ),
+    ):
+        """
+        Initialization of an instance TensorFlowFasterRCNN.
+
+        :param images: Input samples of shape (nb_samples, height, width, nb_channels).
+        :param model: A TensorFlow Faster-RCNN model. The output that can be computed from the model includes a tuple
+                      of (predictions, losses, detections):
+
+                        - predictions: a dictionary holding "raw" prediction tensors.
+                        - losses: a dictionary mapping loss keys (`Loss/RPNLoss/localization_loss`,
+                                  `Loss/RPNLoss/objectness_loss`, `Loss/BoxClassifierLoss/localization_loss`,
+                                  `Loss/BoxClassifierLoss/classification_loss`) to scalar tensors representing
+                                  corresponding loss values.
+                        - detections: a dictionary containing final detection results.
+        :param filename: Filename of the detection model without filename extension.
+        :param url: URL to download archive of detection model including filename extension.
+        :param sess: Computation session.
+        :param is_training: A boolean indicating whether the training version of the computation graph should be
+                            constructed.
+        :param clip_values: Tuple of the form `(min, max)` of floats or `np.ndarray` representing the minimum and
+                            maximum values allowed for input image features. If floats are provided, these will be
+                            used as the range of all features. If arrays are provided, each value will be considered
+                            the bound for a feature, thus the shape of clip values needs to match the total number
+                            of features.
+        :param channels_first: Set channels first or last.
+        :param preprocessing_defences: Preprocessing defence(s) to be applied by the classifier.
+        :param postprocessing_defences: Postprocessing defence(s) to be applied by the classifier.
+        :param preprocessing: Tuple of the form `(subtractor, divider)` of floats or `np.ndarray` of values to be
+                              used for data preprocessing. The first value will be subtracted from the input. The
+                              input will then be divided by the second one.
+        :param attack_losses: Tuple of any combination of strings of the following loss components:
+                              `first_stage_localization_loss`, `first_stage_objectness_loss`,
+                              `second_stage_localization_loss`, `second_stage_classification_loss`.
+        """
+        import tensorflow.compat.v1 as tf  # lgtm [py/repeated-import]
+
+        # Super initialization
+        super().__init__(
+            model=model,
+            clip_values=clip_values,
+            channels_first=channels_first,
+            preprocessing_defences=preprocessing_defences,
+            postprocessing_defences=postprocessing_defences,
+            preprocessing=preprocessing,
+        )
+
+        # Check clip values
+        if self.clip_values is not None:
+            if not np.all(self.clip_values[0] == 0):
+                raise ValueError("This classifier requires normalized input images with clip_vales=(0, 1).")
+            if not np.all(self.clip_values[1] == 1):
+                raise ValueError("This classifier requires normalized input images with clip_vales=(0, 1).")
+
+        # Check preprocessing and postprocessing defences
+        if self.preprocessing_defences is not None:
+            raise ValueError("This estimator does not support `preprocessing_defences`.")
+        if self.postprocessing_defences is not None:
+            raise ValueError("This estimator does not support `postprocessing_defences`.")
+
+        # Create placeholders for groundtruth boxes
+        self._groundtruth_boxes_list: List["tf.Tensor"]
+        self._groundtruth_boxes_list = [
+            tf.placeholder(dtype=tf.float32, shape=(None, 4), name="groundtruth_boxes_{}".format(i))
+            for i in range(images.shape[0])
+        ]
+
+        # Create placeholders for groundtruth classes
+        self._groundtruth_classes_list: List["tf.Tensor"]
+        self._groundtruth_classes_list = [
+            tf.placeholder(dtype=tf.int32, shape=(None,), name="groundtruth_classes_{}".format(i))
+            for i in range(images.shape[0])
+        ]
+
+        # Create placeholders for groundtruth weights
+        self._groundtruth_weights_list: List["tf.Tensor"]
+        self._groundtruth_weights_list = [
+            tf.placeholder(dtype=tf.float32, shape=(None,), name="groundtruth_weights_{}".format(i))
+            for i in range(images.shape[0])
+        ]
+
+        # Load model
+        if model is None:
+            # If model is None, then we need to have parameters filename and url to download, extract and load the
+            # object detection model
+            if filename is None or url is None:
+                filename, url = (
+                    "faster_rcnn_inception_v2_coco_2017_11_08",
+                    "http://download.tensorflow.org/models/object_detection/"
+                    "faster_rcnn_inception_v2_coco_2017_11_08.tar.gz",
+                )
+
+            self._model, self._predictions, self._losses, self._detections = self._load_model(
+                images=images,
+                filename=filename,
+                url=url,
+                obj_detection_model=None,
+                is_training=is_training,
+                groundtruth_boxes_list=self._groundtruth_boxes_list,
+                groundtruth_classes_list=self._groundtruth_classes_list,
+                groundtruth_weights_list=self._groundtruth_weights_list,
+            )
+
+        else:
+            self._model, self._predictions, self._losses, self._detections = self._load_model(
+                images=images,
+                filename=None,
+                url=None,
+                obj_detection_model=model,
+                is_training=is_training,
+                groundtruth_boxes_list=self._groundtruth_boxes_list,
+                groundtruth_classes_list=self._groundtruth_classes_list,
+                groundtruth_weights_list=self._groundtruth_weights_list,
+            )
+
+        # Save new attributes
+        self._input_shape = images.shape.as_list()[1:]
+        self.is_training: bool = is_training
+        self.images: Optional["tf.Tensor"] = images
+        self.attack_losses: Tuple[str, ...] = attack_losses
+
+        # Assign session
+        if sess is None:
+            logger.warning("A session cannot be None, create a new session.")
+            self._sess = tf.Session()
+        else:
+            self._sess = sess
+
+        # Initialize variables
+        self._sess.run(tf.global_variables_initializer())
+        self._sess.run(tf.local_variables_initializer())
+
+    @property
+    def input_shape(self) -> Tuple[int, ...]:
+        """
+        Return the shape of one input sample.
+
+        :return: Shape of one input sample.
+        """
+        return self._input_shape  # type: ignore
+
+    @staticmethod
+    def _load_model(
+        images: "tf.Tensor",
+        filename: Optional[str] = None,
+        url: Optional[str] = None,
+        obj_detection_model: Optional["FasterRCNNMetaArch"] = None,
+        is_training: bool = False,
+        groundtruth_boxes_list: Optional[List["tf.Tensor"]] = None,
+        groundtruth_classes_list: Optional[List["tf.Tensor"]] = None,
+        groundtruth_weights_list: Optional[List["tf.Tensor"]] = None,
+    ) -> Tuple[Dict[str, "tf.Tensor"], ...]:
+        """
+        Download, extract and load a model from a URL if it not already in the cache. The file at indicated by `url`
+        is downloaded to the path ~/.art/data and given the name `filename`. Files in tar, tar.gz, tar.bz, and zip
+        formats will also be extracted. Then the model is loaded, pipelined and its outputs are returned as a tuple
+        of (predictions, losses, detections).
+
+        :param images: Input samples of shape (nb_samples, height, width, nb_channels).
+        :param filename: Name of the file.
+        :param url: Download URL.
+        :param is_training: A boolean indicating whether the training version of the computation graph should be
+                            constructed.
+        :param groundtruth_boxes_list: A list of 2-D tf.float32 tensors of shape [num_boxes, 4] containing
+                                       coordinates of the groundtruth boxes. Groundtruth boxes are provided in
+                                       [y_min, x_min, y_max, x_max] format and also assumed to be normalized and
+                                       clipped relative to the image window with conditions y_min <= y_max and
+                                       x_min <= x_max.
+        :param groundtruth_classes_list: A list of 1-D tf.float32 tensors of shape [num_boxes] containing the class
+                                         targets with the zero index which is assumed to map to the first
+                                         non-background class.
+        :param groundtruth_weights_list: A list of 1-D tf.float32 tensors of shape [num_boxes] containing weights for
+                                         groundtruth boxes.
+        :return: A tuple of (predictions, losses, detections):
+
+                    - predictions: a dictionary holding "raw" prediction tensors.
+                    - losses: a dictionary mapping loss keys (`Loss/RPNLoss/localization_loss`,
+                              `Loss/RPNLoss/objectness_loss`, `Loss/BoxClassifierLoss/localization_loss`,
+                              `Loss/BoxClassifierLoss/classification_loss`) to scalar tensors representing
+                              corresponding loss values.
+                    - detections: a dictionary containing final detection results.
+        """
+        import tensorflow.compat.v1 as tf  # lgtm [py/repeated-import]
+        from object_detection.utils import variables_helper
+
+        if obj_detection_model is None:
+            from object_detection.utils import config_util
+            from object_detection.builders import model_builder
+
+            # If obj_detection_model is None, then we need to have parameters filename and url to download, extract
+            # and load the object detection model
+            if filename is None or url is None:
+                raise ValueError(
+                    "Need input parameters `filename` and `url` to download, "
+                    "extract and load the object detection model."
+                )
+
+            # Download and extract
+            path = get_file(filename=filename, path=config.ART_DATA_PATH, url=url, extract=True)
+
+            # Load model config
+            pipeline_config = path + "/pipeline.config"
+            configs = config_util.get_configs_from_pipeline_file(pipeline_config)
+            configs["model"].faster_rcnn.second_stage_batch_size = configs[
+                "model"
+            ].faster_rcnn.first_stage_max_proposals
+
+            # Load model
+            obj_detection_model = model_builder.build(
+                model_config=configs["model"], is_training=is_training, add_summaries=False
+            )
+
+        # Provide groundtruth
+        if groundtruth_classes_list is not None:
+            groundtruth_classes_list = [
+                tf.one_hot(groundtruth_class, obj_detection_model.num_classes)
+                for groundtruth_class in groundtruth_classes_list
+            ]
+
+        obj_detection_model.provide_groundtruth(
+            groundtruth_boxes_list=groundtruth_boxes_list,
+            groundtruth_classes_list=groundtruth_classes_list,
+            groundtruth_weights_list=groundtruth_weights_list,
+        )
+
+        # Create model pipeline
+        images *= 255.0
+        preprocessed_images, true_image_shapes = obj_detection_model.preprocess(images)
+        predictions = obj_detection_model.predict(preprocessed_images, true_image_shapes)
+        losses = obj_detection_model.loss(predictions, true_image_shapes)
+        detections = obj_detection_model.postprocess(predictions, true_image_shapes)
+
+        # Initialize variables from checkpoint
+        # Get variables to restore
+        variables_to_restore = obj_detection_model.restore_map(
+            fine_tune_checkpoint_type="detection", load_all_detection_checkpoint_vars=True
+        )
+
+        # Get variables from checkpoint
+        fine_tune_checkpoint_path = path + "/model.ckpt"
+        vars_in_ckpt = variables_helper.get_variables_available_in_checkpoint(
+            variables_to_restore, fine_tune_checkpoint_path, include_global_step=False
+        )
+
+        # Initialize from checkpoint
+        tf.train.init_from_checkpoint(fine_tune_checkpoint_path, vars_in_ckpt)
+
+        return obj_detection_model, predictions, losses, detections
+
+    def loss_gradient(self, x: np.ndarray, y: List[Dict[str, np.ndarray]], **kwargs) -> np.ndarray:
+        """
+        Compute the gradient of the loss function w.r.t. `x`.
+
+        :param x: Samples of shape (nb_samples, height, width, nb_channels).
+        :param y: A dictionary of target values. The fields of the dictionary are as follows:
+
+                  - `boxes`: A list of `nb_samples` size of 2-D tf.float32 tensors of shape [num_boxes, 4] containing
+                    coordinates of the groundtruth boxes. Groundtruth boxes are provided in [y_min, x_min, y_max, x_max]
+                    format and also assumed to be normalized as well as clipped relative to the image window with
+                    conditions y_min <= y_max and x_min <= x_max.
+                  - `labels`: A list of `nb_samples` size of 1-D tf.float32 tensors of shape [num_boxes] containing
+                    the class targets with the zero index assumed to map to the first non-background class.
+                  - `scores`: A list of `nb_samples` size of 1-D tf.float32 tensors of shape [num_boxes] containing
+                    weights for groundtruth boxes.
+        :return: Loss gradients of the same shape as `x`.
+        """
+        import tensorflow.compat.v1 as tf  # lgtm [py/repeated-import]
+
+        # Only do loss_gradient if is_training is False
+        if self.is_training:
+            raise NotImplementedError(
+                "This object detector was loaded in training mode and therefore not support loss_gradient."
+            )
+
+        # Apply preprocessing
+        x_preprocessed, _ = self._apply_preprocessing(x, y=None, fit=False)
+
+        # Get the loss gradients graph
+        if not hasattr(self, "_loss_grads"):
+            loss = None
+            for loss_name in self.attack_losses:
+                if loss is None:
+                    loss = self._losses[loss_name]
+                else:
+                    loss = loss + self._losses[loss_name]
+
+            self._loss_grads: tf.Tensor = tf.gradients(loss, self.images)[0]
+
+        # Create feed_dict
+        feed_dict = {self.images: x_preprocessed}
+
+        for (placeholder, value) in zip(self._groundtruth_boxes_list, y):
+            feed_dict[placeholder] = value["boxes"]
+
+        for (placeholder, value) in zip(self._groundtruth_classes_list, y):
+            feed_dict[placeholder] = value["labels"]
+
+        for (placeholder, value) in zip(self._groundtruth_weights_list, y):
+            feed_dict[placeholder] = value["scores"]
+
+        # Compute gradients
+        grads = self._sess.run(self._loss_grads, feed_dict=feed_dict)
+        grads = self._apply_preprocessing_gradient(x, grads)
+        assert grads.shape == x.shape
+
+        return grads
+
+    def predict(
+        self, x: np.ndarray, batch_size: int = 128, standardise_output: bool = False, **kwargs
+    ) -> List[Dict[str, np.ndarray]]:
+        """
+        Perform prediction for a batch of inputs.
+
+        :param x: Samples of shape (nb_samples, height, width, nb_channels).
+        :param batch_size: Batch size.
+        :param standardise_output: True if output should be standardised. Box coordinates will be normalised to [0, 1]
+                                   and label index will be decreased by 1 to adhere to COCO categories.
+        :return: A dictionary containing the following fields:
+
+        :return: Predictions of format `List[Dict[str, np.ndarray]]`, one for each input image. The
+                 fields of the Dict are as follows:
+
+                 - boxes [N, 4]: the predicted boxes in [x1, y1, x2, y2] format, with values \
+                   between 0 and H and 0 and W
+                 - labels [N]: the predicted labels for each image
+                 - scores [N]: the scores or each prediction.
+        """
+        # Only do prediction if is_training is False
+        if self.is_training:
+            raise NotImplementedError(
+                "This object detector was loaded in training mode and therefore not support prediction."
+            )
+
+        # Apply preprocessing
+        x, _ = self._apply_preprocessing(x, y=None, fit=False)
+
+        # Check if batch processing is appropriately set
+        if self.images is not None and self.images.shape[0].value is not None:
+            if x.shape[0] % self.images.shape[0].value != 0:
+                raise ValueError("Number of prediction samples must be a multiple of input size.")
+
+            logger.warning("Reset batch size to input size.")
+            batch_size = self.images.shape[0].value
+
+        # Run prediction with batch processing
+        num_samples = x.shape[0]
+        num_batch = int(np.ceil(num_samples / float(batch_size)))
+        results = list()
+
+        for m in range(num_batch):
+            # Batch indexes
+            begin, end = m * batch_size, min((m + 1) * batch_size, num_samples)
+
+            # Create feed_dict
+            feed_dict = {self.images: x[begin:end]}
+
+            # Run prediction
+            batch_results = self._sess.run(self._detections, feed_dict=feed_dict)
+
+            for i in range(end - begin):
+                d_sample = dict()
+
+                d_sample["boxes"] = batch_results["detection_boxes"][i]
+                d_sample["labels"] = batch_results["detection_classes"][i].astype(np.int32)
+
+                if standardise_output:
+                    height = x.shape[1]
+                    width = x.shape[2]
+
+                    d_sample["boxes"][:, 0] *= height
+                    d_sample["boxes"][:, 1] *= width
+                    d_sample["boxes"][:, 2] *= height
+                    d_sample["boxes"][:, 3] *= width
+
+                    d_sample["boxes"] = d_sample["boxes"][:, [1, 0, 3, 2]]
+
+                    d_sample["labels"] = d_sample["labels"] + 1
+
+                d_sample["scores"] = batch_results["detection_scores"][i]
+
+                results.append(d_sample)
+
+        return results
+
+    @property
+    def input_images(self) -> "tf.Tensor":
+        """
+        Get the `images` attribute.
+
+        :return: The input image tensor.
+        """
+        return self.images
+
+    @property
+    def predictions(self) -> Dict[str, "tf.Tensor"]:
+        """
+        Get the `_predictions` attribute.
+
+        :return: A dictionary holding "raw" prediction tensors.
+        """
+        return self._predictions
+
+    @property
+    def losses(self) -> Dict[str, "tf.Tensor"]:
+        """
+        Get the `_losses` attribute.
+
+        :return: A dictionary mapping loss keys (`Loss/RPNLoss/localization_loss`, `Loss/RPNLoss/objectness_loss`,
+                 `Loss/BoxClassifierLoss/localization_loss`, `Loss/BoxClassifierLoss/classification_loss`) to scalar
+                 tensors representing corresponding loss values.
+        """
+        return self._losses
+
+    @property
+    def detections(self) -> Dict[str, "tf.Tensor"]:
+        """
+        Get the `_detections` attribute.
+
+        :return: A dictionary containing final detection results.
+        """
+        return self._detections
+
+    def fit(self):
+        raise NotImplementedError
+
+    def get_activations(self):
+        raise NotImplementedError
diff --git a/adversarial-robustness-toolbox/art/estimators/poison_mitigation/__init__.py b/adversarial-robustness-toolbox/art/estimators/poison_mitigation/__init__.py
new file mode 100644
index 0000000..ffdfc4a
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/estimators/poison_mitigation/__init__.py
@@ -0,0 +1,5 @@
+"""
+This module implements all poison mitigation models in ART.
+"""
+from art.estimators.poison_mitigation import neural_cleanse
+from art.estimators.poison_mitigation.strip import strip
diff --git a/adversarial-robustness-toolbox/art/estimators/poison_mitigation/neural_cleanse/__init__.py b/adversarial-robustness-toolbox/art/estimators/poison_mitigation/neural_cleanse/__init__.py
new file mode 100644
index 0000000..997b9cf
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/estimators/poison_mitigation/neural_cleanse/__init__.py
@@ -0,0 +1,5 @@
+"""
+Neural cleanse estimators.
+"""
+from art.estimators.poison_mitigation.neural_cleanse.neural_cleanse import NeuralCleanseMixin
+from art.estimators.poison_mitigation.neural_cleanse.keras import KerasNeuralCleanse
diff --git a/adversarial-robustness-toolbox/art/estimators/poison_mitigation/neural_cleanse/keras.py b/adversarial-robustness-toolbox/art/estimators/poison_mitigation/neural_cleanse/keras.py
new file mode 100644
index 0000000..13835d2
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/estimators/poison_mitigation/neural_cleanse/keras.py
@@ -0,0 +1,397 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+Implementation of methods in Neural Cleanse: Identifying and Mitigating Backdoor Attacks in Neural Networks.
+Wang et al. (2019).
+
+| Paper link: https://people.cs.uchicago.edu/~ravenben/publications/pdf/backdoor-sp19.pdf
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+from typing import List, Optional, Tuple, Union, TYPE_CHECKING
+
+import numpy as np
+from tqdm.auto import tqdm
+
+from art.config import ART_NUMPY_DTYPE
+from art.estimators.poison_mitigation.neural_cleanse.neural_cleanse import NeuralCleanseMixin
+from art.estimators.classification.keras import KerasClassifier, KERAS_MODEL_TYPE
+
+if TYPE_CHECKING:
+    from art.defences.preprocessor import Preprocessor
+    from art.defences.postprocessor import Postprocessor
+    from art.utils import CLIP_VALUES_TYPE, PREPROCESSING_TYPE
+
+logger = logging.getLogger(__name__)
+
+
+class KerasNeuralCleanse(NeuralCleanseMixin, KerasClassifier):
+    """
+    Implementation of methods in Neural Cleanse: Identifying and Mitigating Backdoor Attacks in Neural Networks.
+    Wang et al. (2019).
+
+    | Paper link: https://people.cs.uchicago.edu/~ravenben/publications/pdf/backdoor-sp19.pdf
+    """
+
+    estimator_params = KerasClassifier.estimator_params + [
+        "steps",
+        "init_cost",
+        "norm",
+        "learning_rate",
+        "attack_success_threshold",
+        "patience",
+        "early_stop",
+        "early_stop_threshold",
+        "early_stop_patience",
+        "cost_multiplier_up",
+        "cost_multiplier_down",
+        "batch_size",
+    ]
+
+    def __init__(
+        self,
+        model: KERAS_MODEL_TYPE,
+        use_logits: bool = False,
+        channels_first: bool = False,
+        clip_values: Optional["CLIP_VALUES_TYPE"] = None,
+        preprocessing_defences: Union["Preprocessor", List["Preprocessor"], None] = None,
+        postprocessing_defences: Union["Postprocessor", List["Postprocessor"], None] = None,
+        preprocessing: "PREPROCESSING_TYPE" = (0.0, 1.0),
+        input_layer: int = 0,
+        output_layer: int = 0,
+        steps: int = 1000,
+        init_cost: float = 1e-3,
+        norm: Union[int, float] = 2,
+        learning_rate: float = 0.1,
+        attack_success_threshold: float = 0.99,
+        patience: int = 5,
+        early_stop: bool = True,
+        early_stop_threshold: float = 0.99,
+        early_stop_patience: int = 10,
+        cost_multiplier: float = 1.5,
+        batch_size: int = 32,
+    ):
+        """
+        Create a Neural Cleanse classifier.
+
+        :param model: Keras model, neural network or other.
+        :param use_logits: True if the output of the model are logits; false for probabilities or any other type of
+               outputs. Logits output should be favored when possible to ensure attack efficiency.
+        :param channels_first: Set channels first or last.
+        :param clip_values: Tuple of the form `(min, max)` of floats or `np.ndarray` representing the minimum and
+               maximum values allowed for features. If floats are provided, these will be used as the range of all
+               features. If arrays are provided, each value will be considered the bound for a feature, thus
+               the shape of clip values needs to match the total number of features.
+        :param preprocessing_defences: Preprocessing defence(s) to be applied by the classifier.
+        :param postprocessing_defences: Postprocessing defence(s) to be applied by the classifier.
+        :param preprocessing: Tuple of the form `(subtrahend, divisor)` of floats or `np.ndarray` of values to be
+               used for data preprocessing. The first value will be subtracted from the input. The input will then
+               be divided by the second one.
+        :param input_layer: The index of the layer to consider as input for models with multiple input layers. The layer
+                            with this index will be considered for computing gradients. For models with only one input
+                            layer this values is not required.
+        :param output_layer: Which layer to consider as the output when the models has multiple output layers. The layer
+                             with this index will be considered for computing gradients. For models with only one output
+                             layer this values is not required.
+        :param steps: The maximum number of steps to run the Neural Cleanse optimization
+        :param init_cost: The initial value for the cost tensor in the Neural Cleanse optimization
+        :param norm: The norm to use for the Neural Cleanse optimization, can be 1, 2, or np.inf
+        :param learning_rate: The learning rate for the Neural Cleanse optimization
+        :param attack_success_threshold: The threshold at which the generated backdoor is successful enough to stop the
+                                         Neural Cleanse optimization
+        :param patience: How long to wait for changing the cost multiplier in the Neural Cleanse optimization
+        :param early_stop: Whether or not to allow early stopping in the Neural Cleanse optimization
+        :param early_stop_threshold: How close values need to come to max value to start counting early stop
+        :param early_stop_patience: How long to wait to determine early stopping in the Neural Cleanse optimization
+        :param cost_multiplier: How much to change the cost in the Neural Cleanse optimization
+        :param batch_size: The batch size for optimizations in the Neural Cleanse optimization
+        """
+        import keras.backend as K
+        from keras.losses import categorical_crossentropy
+        from keras.metrics import categorical_accuracy
+        from keras.optimizers import Adam
+
+        super().__init__(
+            model=model,
+            use_logits=use_logits,
+            channels_first=channels_first,
+            clip_values=clip_values,
+            preprocessing_defences=preprocessing_defences,
+            postprocessing_defences=postprocessing_defences,
+            preprocessing=preprocessing,
+            input_layer=input_layer,
+            output_layer=output_layer,
+            steps=steps,
+            init_cost=init_cost,
+            norm=norm,
+            learning_rate=learning_rate,
+            attack_success_threshold=attack_success_threshold,
+            early_stop=early_stop,
+            early_stop_threshold=early_stop_threshold,
+            early_stop_patience=early_stop_patience,
+            patience=patience,
+            cost_multiplier=cost_multiplier,
+            batch_size=batch_size,
+        )
+        mask = np.random.uniform(size=super().input_shape)
+        pattern = np.random.uniform(size=super().input_shape)
+        self.epsilon = K.epsilon()
+
+        # Normalize mask between [0, 1]
+        self.mask_tensor_raw = K.variable(mask)
+        # self.mask_tensor = K.expand_dims(K.tanh(self.mask_tensor_raw) / (2 - self.epsilon) + 0.5, axis=0)
+        self.mask_tensor = K.tanh(self.mask_tensor_raw) / (2 - self.epsilon) + 0.5
+
+        # Normalize pattern between [0, 1]
+        self.pattern_tensor_raw = K.variable(pattern)
+        self.pattern_tensor = K.expand_dims(K.tanh(self.pattern_tensor_raw) / (2 - self.epsilon) + 0.5, axis=0)
+
+        reverse_mask_tensor = K.ones_like(self.mask_tensor) - self.mask_tensor
+        input_tensor = K.placeholder(model.input_shape)
+        x_adv_tensor = reverse_mask_tensor * input_tensor + self.mask_tensor * self.pattern_tensor
+
+        output_tensor = self.model(x_adv_tensor)
+        y_true_tensor = K.placeholder(model.outputs[0].shape.as_list())
+
+        self.loss_acc = categorical_accuracy(output_tensor, y_true_tensor)
+        self.loss_ce = categorical_crossentropy(output_tensor, y_true_tensor)
+
+        if self.norm == 1:
+            # TODO: change 3 to dynamically set img_color
+            self.loss_reg = K.sum(K.abs(self.mask_tensor)) / 3
+        elif self.norm == 2:
+            self.loss_reg = K.sqrt(K.sum(K.square(self.mask_tensor)) / 3)
+
+        self.cost = self.init_cost
+        self.cost_tensor = K.variable(self.cost)
+        self.loss_combined = self.loss_ce + self.loss_reg * self.cost_tensor
+        self.opt = Adam(lr=self.learning_rate, beta_1=0.5, beta_2=0.9)
+
+        self.updates = self.opt.get_updates(
+            params=[self.pattern_tensor_raw, self.mask_tensor_raw], loss=self.loss_combined
+        )
+        self.train = K.function(
+            [input_tensor, y_true_tensor],
+            [self.loss_ce, self.loss_reg, self.loss_combined, self.loss_acc],
+            updates=self.updates,
+        )
+
+    @property
+    def input_shape(self) -> Tuple[int, ...]:
+        """
+        Return the shape of one input sample.
+
+        :return: Shape of one input sample.
+        """
+        return self._input_shape  # type: ignore
+
+    def reset(self):
+        """
+        Reset the state of the defense
+        :return:
+        """
+        import keras.backend as K
+
+        self.cost = self.init_cost
+        K.set_value(self.cost_tensor, self.init_cost)
+        K.set_value(self.opt.iterations, 0)
+        for weight in self.opt.weights:
+            K.set_value(weight, np.zeros(K.int_shape(weight)))
+
+    def generate_backdoor(
+        self, x_val: np.ndarray, y_val: np.ndarray, y_target: np.ndarray
+    ) -> Tuple[np.ndarray, np.ndarray]:
+        """
+        Generates a possible backdoor for the model. Returns the pattern and the mask
+        :return: A tuple of the pattern and mask for the model.
+        """
+        import keras.backend as K
+        from keras_preprocessing.image import ImageDataGenerator
+
+        self.reset()
+        datagen = ImageDataGenerator()
+        gen = datagen.flow(x_val, y_val, batch_size=self.batch_size)
+        mask_best = None
+        pattern_best = None
+        reg_best = float("inf")
+        cost_set_counter = 0
+        cost_up_counter = 0
+        cost_down_counter = 0
+        cost_up_flag = False
+        cost_down_flag = False
+        early_stop_counter = 0
+        early_stop_reg_best = reg_best
+        mini_batch_size = len(x_val) // self.batch_size
+        for _ in tqdm(range(self.steps), desc="Generating backdoor for class {}".format(np.argmax(y_target))):
+            loss_reg_list = []
+            loss_acc_list = []
+
+            for _ in range(mini_batch_size):
+                x_batch, _ = gen.next()
+                y_batch = [y_target] * x_batch.shape[0]
+                _, batch_loss_reg, _, batch_loss_acc = self.train([x_batch, y_batch])
+
+                loss_reg_list.extend(list(batch_loss_reg.flatten()))
+                loss_acc_list.extend(list(batch_loss_acc.flatten()))
+
+            avg_loss_reg = np.mean(loss_reg_list)
+            avg_loss_acc = np.mean(loss_acc_list)
+
+            # save best mask/pattern so far
+            if avg_loss_acc >= self.attack_success_threshold and avg_loss_reg < reg_best:
+                mask_best = K.eval(self.mask_tensor)
+                pattern_best = K.eval(self.pattern_tensor)
+                reg_best = avg_loss_reg
+
+            # check early stop
+            if self.early_stop:
+                if reg_best < float("inf"):
+                    if reg_best >= self.early_stop_threshold * early_stop_reg_best:
+                        early_stop_counter += 1
+                    else:
+                        early_stop_counter = 0
+                early_stop_reg_best = min(reg_best, early_stop_reg_best)
+
+                if cost_down_flag and cost_up_flag and early_stop_counter >= self.early_stop_patience:
+                    logger.info("Early stop")
+                    break
+
+            # cost modification
+            if avg_loss_acc >= self.attack_success_threshold:
+                cost_set_counter += 1
+                if cost_set_counter >= self.patience:
+                    self.cost = self.init_cost
+                    K.set_value(self.cost_tensor, self.cost)
+                    cost_up_counter = 0
+                    cost_down_counter = 0
+                    cost_up_flag = False
+                    cost_down_flag = False
+            else:
+                cost_set_counter = 0
+
+            if avg_loss_acc >= self.attack_success_threshold:
+                cost_up_counter += 1
+                cost_down_counter = 0
+            else:
+                cost_up_counter = 0
+                cost_down_counter += 1
+
+            if cost_up_counter >= self.patience:
+                cost_up_counter = 0
+                self.cost *= self.cost_multiplier_up
+                K.set_value(self.cost_tensor, self.cost)
+                cost_up_flag = True
+            elif cost_down_counter >= self.patience:
+                cost_down_counter = 0
+                self.cost /= self.cost_multiplier_down
+                K.set_value(self.cost_tensor, self.cost)
+                cost_down_flag = True
+
+        if mask_best is None:
+            mask_best = K.eval(self.mask_tensor)
+            pattern_best = K.eval(self.pattern_tensor)
+
+        return mask_best, pattern_best
+
+    def _predict_classifier(self, x: np.ndarray) -> np.ndarray:
+        x = x.astype(ART_NUMPY_DTYPE)
+        return KerasClassifier.predict(self, x=x, batch_size=self.batch_size)
+
+    def _fit_classifier(self, x: np.ndarray, y: np.ndarray, batch_size: int, nb_epochs: int, **kwargs) -> None:
+        x = x.astype(ART_NUMPY_DTYPE)
+        return self.fit(x, y, batch_size=batch_size, nb_epochs=nb_epochs, **kwargs)
+
+    def _get_penultimate_layer_activations(self, x: np.ndarray) -> np.ndarray:
+        """
+        Return the output of the second to last layer for input `x`.
+
+        :param x: Input for computing the activations.
+        :return: The output of `layer`, where the first dimension is the batch size corresponding to `x`.
+        """
+        if self.layer_names is not None:
+            penultimate_layer = len(self.layer_names) - 2
+        else:
+            raise ValueError("No layer names found.")
+        return self.get_activations(x, penultimate_layer, batch_size=self.batch_size, framework=False)
+
+    def _prune_neuron_at_index(self, index: int) -> None:
+        """
+        Set the weights (and biases) of a neuron at index in the penultimate layer of the neural network to zero
+
+        :param index: An index of the penultimate layer
+        """
+        if self.layer_names is not None:
+            layer = self._model.layers[len(self.layer_names) - 2]
+        else:
+            raise ValueError("No layer names found.")
+        weights, biases = layer.get_weights()
+        weights[:, index] = np.zeros_like(weights[:, index])
+        biases[index] = 0
+        layer.set_weights([weights, biases])
+
+    def predict(self, x: np.ndarray, batch_size: int = 128) -> np.ndarray:
+        """
+        Perform prediction of the given classifier for a batch of inputs, potentially filtering suspicious input
+
+        :param x: Input data to predict.
+        :param batch_size: Batch size.
+        :return: Array of predictions of shape `(nb_inputs, nb_classes)`.
+        """
+        return NeuralCleanseMixin.predict(self, x, batch_size=batch_size)
+
+    def mitigate(self, x_val: np.ndarray, y_val: np.ndarray, mitigation_types: List[str]) -> None:
+        """
+        Mitigates the effect of poison on a classifier
+
+        :param x_val: Validation data to use to mitigate the effect of poison.
+        :param y_val: Validation labels to use to mitigate the effect of poison.
+        :param mitigation_types: The types of mitigation method, can include 'unlearning', 'pruning', or 'filtering'
+        :return: Tuple of length 2 of the selected class and certified radius.
+        """
+        return NeuralCleanseMixin.mitigate(self, x_val, y_val, mitigation_types)
+
+    def loss_gradient(self, x: np.ndarray, y: np.ndarray, training_mode: bool = False, **kwargs) -> np.ndarray:
+        """
+        Compute the gradient of the loss function w.r.t. `x`.
+
+        :param x: Sample input with shape as expected by the model.
+        :param y: Target values (class labels) one-hot-encoded of shape (nb_samples, nb_classes) or indices of shape
+                  (nb_samples,).
+        :param training_mode: `True` for model set to training mode and `'False` for model set to evaluation mode.
+        :return: Array of gradients of the same shape as `x`.
+        """
+        return self.loss_gradient(x=x, y=y, training_mode=training_mode, **kwargs)
+
+    def class_gradient(
+        self, x: np.ndarray, label: Union[int, List[int], None] = None, training_mode: bool = False, **kwargs
+    ) -> np.ndarray:
+        """
+        Compute per-class derivatives of the given classifier w.r.t. `x` of original classifier.
+
+        :param x: Sample input with shape as expected by the model.
+        :param label: Index of a specific per-class derivative. If an integer is provided, the gradient of that class
+                      output is computed for all samples. If multiple values as provided, the first dimension should
+                      match the batch size of `x`, and each value will be used as target for its corresponding sample in
+                      `x`. If `None`, then gradients for all classes will be computed for each sample.
+        :param training_mode: `True` for model set to training mode and `'False` for model set to evaluation mode.
+        :return: Array of gradients of input features w.r.t. each class in the form
+                 `(batch_size, nb_classes, input_shape)` when computing for all classes, otherwise shape becomes
+                 `(batch_size, 1, input_shape)` when `label` parameter is specified.
+        """
+        return self.class_gradient(x=x, label=label, training_mode=training_mode, **kwargs)
diff --git a/adversarial-robustness-toolbox/art/estimators/poison_mitigation/neural_cleanse/neural_cleanse.py b/adversarial-robustness-toolbox/art/estimators/poison_mitigation/neural_cleanse/neural_cleanse.py
new file mode 100644
index 0000000..6ca5d58
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/estimators/poison_mitigation/neural_cleanse/neural_cleanse.py
@@ -0,0 +1,298 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements Neural Cleanse on a classifier.
+
+| Paper link: https://people.cs.uchicago.edu/~ravenben/publications/pdf/backdoor-sp19.pdf
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+from typing import Union, Tuple, List
+
+import numpy as np
+
+from art.estimators.certification.abstain import AbstainPredictorMixin
+from art.utils import to_categorical
+
+logger = logging.getLogger(__name__)
+
+
+class NeuralCleanseMixin(AbstainPredictorMixin):
+    """
+    Implementation of methods in Neural Cleanse: Identifying and Mitigating Backdoor Attacks in Neural Networks.
+    Wang et al. (2019).
+
+    | Paper link: https://people.cs.uchicago.edu/~ravenben/publications/pdf/backdoor-sp19.pdf
+    """
+
+    def __init__(
+        self,
+        steps: int = 1000,
+        *args,
+        init_cost: float = 1e-3,
+        norm: Union[int, float] = 2,
+        learning_rate: float = 0.1,
+        attack_success_threshold: float = 0.99,
+        patience: int = 5,
+        early_stop: bool = True,
+        early_stop_threshold: float = 0.99,
+        early_stop_patience: int = 10,
+        cost_multiplier: float = 1.5,
+        batch_size: int = 32,
+        **kwargs
+    ) -> None:
+        """
+        Create a neural cleanse wrapper.
+
+        :param steps: The maximum number of steps to run the Neural Cleanse optimization
+        :param init_cost: The initial value for the cost tensor in the Neural Cleanse optimization
+        :param norm: The norm to use for the Neural Cleanse optimization, can be 1, 2, or np.inf
+        :param learning_rate: The learning rate for the Neural Cleanse optimization
+        :param attack_success_threshold: The threshold at which the generated backdoor is successful enough to stop the
+                                         Neural Cleanse optimization
+        :param patience: How long to wait for changing the cost multiplier in the Neural Cleanse optimization
+        :param early_stop: Whether or not to allow early stopping in the Neural Cleanse optimization
+        :param early_stop_threshold: How close values need to come to max value to start counting early stop
+        :param early_stop_patience: How long to wait to determine early stopping in the Neural Cleanse optimization
+        :param cost_multiplier: How much to change the cost in the Neural Cleanse optimization
+        :param batch_size: The batch size for optimizations in the Neural Cleanse optimization
+        """
+        super().__init__(*args, **kwargs)
+        self.steps = steps
+        self.init_cost = init_cost
+        self.norm = norm
+        self.learning_rate = learning_rate
+        self.attack_success_threshold = attack_success_threshold
+        self.patience = patience
+        self.early_stop = early_stop
+        self.early_stop_threshold = early_stop_threshold
+        self.early_stop_patience = early_stop_patience
+        self.cost_multiplier_up = cost_multiplier
+        self.cost_multiplier_down = cost_multiplier ** 1.5
+        self.batch_size = batch_size
+        self.top_indices = []
+        self.activation_threshold = 0
+
+    def _predict_classifier(self, x: np.ndarray) -> np.ndarray:
+        """
+        Perform prediction for a batch of inputs.
+
+        :param x: Input samples.
+        :return: Array of predictions of shape `(nb_inputs, nb_classes)`.
+        """
+        raise NotImplementedError
+
+    def _fit_classifier(self, x: np.ndarray, y: np.ndarray, batch_size: int, nb_epochs: int, **kwargs) -> None:
+        raise NotImplementedError
+
+    def _get_penultimate_layer_activations(self, x: np.ndarray) -> np.ndarray:
+        """
+        Return the output of the second to last layer for input `x`.
+
+        :param x: Input for computing the activations.
+        :return: The output of `layer`, where the first dimension is the batch size corresponding to `x`.
+        """
+        raise NotImplementedError
+
+    def _prune_neuron_at_index(self, index: int) -> None:
+        """
+        Set the weights (and biases) of a neuron at index in the penultimate layer of the neural network to zero
+
+        :param index: An index of the penultimate layer
+        """
+        raise NotImplementedError
+
+    def predict(self, x: np.ndarray, batch_size: int = 128) -> np.ndarray:
+        """
+        Perform prediction of the given classifier for a batch of inputs, potentially filtering suspicious input
+
+        :param x: Input samples.
+        :param batch_size: Batch size.
+        :return: Array of predictions of shape `(nb_inputs, nb_classes)`.
+        """
+        predictions = self._predict_classifier(x)
+
+        if len(self.top_indices) == 0:
+            logger.warning("Filtering mitigation not activated, suspected backdoors may be triggered")
+            return predictions
+
+        all_activations = self._get_penultimate_layer_activations(x)
+        suspected_neuron_activations = all_activations[:, self.top_indices]
+        predictions[np.any(suspected_neuron_activations > self.activation_threshold, axis=1)] = self.abstain()
+
+        return predictions
+
+    def mitigate(self, x_val: np.ndarray, y_val: np.ndarray, mitigation_types: List[str]) -> None:
+        """
+        Mitigates the effect of poison on a classifier
+
+        :param x_val: Validation data to use to mitigate the effect of poison.
+        :param y_val: Validation labels to use to mitigate the effect of poison.
+        :param mitigation_types: The types of mitigation method, can include 'unlearning', 'pruning', or 'filtering'
+        :return: Tuple of length 2 of the selected class and certified radius.
+        """
+        clean_data, backdoor_data, backdoor_labels = self.backdoor_examples(x_val, y_val)
+
+        # If no backdoors detected from outlier detection, do nothing
+        if len(backdoor_data) == 0:
+            logger.info("No backdoor labels were detected")
+            return
+
+        if "pruning" in mitigation_types or "filtering" in mitigation_types:
+            # get activations from penultimate layer from clean and backdoor images
+            clean_activations = self._get_penultimate_layer_activations(clean_data)
+            backdoor_activations = self._get_penultimate_layer_activations(backdoor_data)
+
+            # rank activations descending by difference in backdoor and clean inputs
+            ranked_indices = np.argsort(np.sum(clean_activations - backdoor_activations, axis=0))
+
+        for mitigation_type in mitigation_types:
+            if mitigation_type == "unlearning":
+                # Train one epoch on generated backdoors
+                # This mitigation method works well for Trojan attacks
+
+                self._fit_classifier(backdoor_data, backdoor_labels, batch_size=1, nb_epochs=1)
+
+            elif mitigation_type == "pruning":
+                # zero out activations from highly ranked neurons until backdoor is unresponsive
+                # This mitigation method works well for backdoors.
+
+                backdoor_effective = self.check_backdoor_effective(backdoor_data, backdoor_labels)
+                num_neurons_pruned = 0
+                total_neurons = clean_activations.shape[1]
+
+                # starting from indices of high activation neurons, set weights (and biases) of high activation
+                # neurons to zero, until backdoor ineffective or pruned 30% of neurons
+                logger.info("Pruning model...")
+                while (
+                    backdoor_effective
+                    and num_neurons_pruned < 0.3 * total_neurons
+                    and num_neurons_pruned < len(ranked_indices)
+                ):
+                    self._prune_neuron_at_index(ranked_indices[num_neurons_pruned])
+                    num_neurons_pruned += 1
+                    backdoor_effective = self.check_backdoor_effective(backdoor_data, backdoor_labels)
+                logger.info("Pruning complete. Pruned %d neurons", num_neurons_pruned)
+
+            elif mitigation_type == "filtering":
+                # using top 1% of ranked neurons by activation difference to adv vs. clean inputs
+                # generate a profile of average activation, when above threshold, abstain
+
+                # get indicies of top 1% of ranked neurons
+                num_top = int(np.ceil(len(ranked_indices) * 0.01))
+                self.top_indices = ranked_indices[:num_top]
+
+                # measure average activation for clean images and backdoor images
+                avg_clean_activation = np.average(clean_activations[:, self.top_indices], axis=0)
+                std_clean_activation = np.std(clean_activations[:, self.top_indices], axis=0)
+
+                # if average activation for selected neurons is above a threshold, flag input and abstain
+                # activation over threshold function can be called at predict
+                # TODO: explore different values for threshold
+                self.activation_threshold = avg_clean_activation + 1 * std_clean_activation
+
+            else:
+                raise TypeError("Mitigation type: `" + mitigation_type + "` not supported")
+
+    def check_backdoor_effective(self, backdoor_data: np.ndarray, backdoor_labels: np.ndarray) -> bool:
+        """
+        Check if supposed backdoors are effective against the classifier
+
+        :param backdoor_data: data with the backdoor added
+        :param backdoor_labels: the correct label for the data
+        :return: true if any of the backdoors are effective on the model
+        """
+        backdoor_predictions = self._predict_classifier(backdoor_data)
+        backdoor_effective = np.logical_not(np.all(backdoor_predictions == backdoor_labels, axis=1))
+        return np.any(backdoor_effective)
+
+    def backdoor_examples(self, x_val: np.ndarray, y_val: np.ndarray) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
+        """
+        Generate reverse-engineered backdoored examples using validation data
+        :param x_val: validation data
+        :param y_val: validation labels
+        :return: a tuple containing (clean data, backdoored data, labels)
+        """
+        clean_data = []
+        example_data = []
+        example_labels = []
+        for backdoored_label, mask, pattern in self.outlier_detection(x_val, y_val):
+            data_for_class = np.copy(x_val[np.argmax(y_val, axis=1) == backdoored_label])
+            labels_for_class = np.copy(y_val[np.argmax(y_val, axis=1) == backdoored_label])
+
+            if len(data_for_class) == 0:
+                logger.warning("No validation data exists for infected class: %s", str(backdoored_label))
+
+            clean_data.append(np.copy(data_for_class))
+            data_for_class = (1 - mask) * data_for_class + mask * pattern
+            example_data.append(data_for_class)
+            example_labels.append(labels_for_class)
+
+        # If any backdoor examples were found, stack data into one array
+        if example_data:
+            clean_data = np.vstack(clean_data)
+            example_data = np.vstack(example_data)
+            example_labels = np.vstack(example_labels)
+
+        return clean_data, example_data, example_labels
+
+    def generate_backdoor(
+        self, x_val: np.ndarray, y_val: np.ndarray, y_target: np.ndarray
+    ) -> Tuple[np.ndarray, np.ndarray]:
+        """
+        Generates a possible backdoor for the model. Returns the pattern and the mask
+        :return: A tuple of the pattern and mask for the model.
+        """
+        raise NotImplementedError
+
+    def outlier_detection(self, x_val: np.ndarray, y_val: np.ndarray) -> List[Tuple[int, np.ndarray, np.ndarray]]:
+        """
+        Returns a tuple of suspected of suspected poison labels and their mask and pattern
+        :return: A list of tuples containing the the class index, mask, and pattern for suspected labels
+        """
+        l1_norms = []
+        masks = []
+        patterns = []
+        num_classes = self.nb_classes
+        for class_idx in range(num_classes):
+            # Assuming classes are indexed
+            target_label = to_categorical([class_idx], num_classes).flatten()
+            mask, pattern = self.generate_backdoor(x_val, y_val, target_label)
+            norm = np.sum(np.abs(mask))
+            l1_norms.append(norm)
+            masks.append(mask)
+            patterns.append(pattern)
+
+        # assuming l1 norms would naturally create a normal distribution
+        consistency_constant = 1.4826
+
+        median = np.median(l1_norms)
+        mad = consistency_constant * np.median(np.abs(l1_norms - median))
+        # min_mad = np.abs(np.min(l1_norms) - median) / mad
+        flagged_labels = []
+
+        for class_idx in range(num_classes):
+            anomaly_index = np.abs(l1_norms[class_idx] - median) / mad
+            # Points with anomaly_index > 2 have 95% probability of being an outlier
+            # Backdoor outliers show up as masks with small l1 norms
+            if l1_norms[class_idx] <= median and anomaly_index > 2:
+                logger.warning("Detected potential backdoor in class: %s", str(class_idx))
+                flagged_labels.append(class_idx)
+
+        return [(label, masks[label], patterns[label]) for label in flagged_labels]
diff --git a/adversarial-robustness-toolbox/art/estimators/poison_mitigation/strip/__init__.py b/adversarial-robustness-toolbox/art/estimators/poison_mitigation/strip/__init__.py
new file mode 100644
index 0000000..4ebf7d5
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/estimators/poison_mitigation/strip/__init__.py
@@ -0,0 +1,4 @@
+"""
+STRIP estimators.
+"""
+from art.estimators.poison_mitigation.strip.strip import STRIPMixin
diff --git a/adversarial-robustness-toolbox/art/estimators/poison_mitigation/strip/strip.py b/adversarial-robustness-toolbox/art/estimators/poison_mitigation/strip/strip.py
new file mode 100644
index 0000000..a4d8343
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/estimators/poison_mitigation/strip/strip.py
@@ -0,0 +1,136 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements STRIP: A Defence Against Trojan Attacks on Deep Neural Networks.
+
+| Paper link: https://arxiv.org/abs/1902.06531
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+from typing import Callable, Optional
+
+import numpy as np
+from scipy.stats import entropy, norm
+from tqdm.auto import tqdm
+
+from art.estimators.certification.abstain import AbstainPredictorMixin
+
+logger = logging.getLogger(__name__)
+
+
+class STRIPMixin(AbstainPredictorMixin):
+    """
+    Implementation of STRIP: A Defence Against Trojan Attacks on Deep Neural Networks (Gao et. al. 2020)
+
+    | Paper link: https://arxiv.org/abs/1902.06531
+    """
+
+    def __init__(
+        self,
+        predict_fn: Callable[[np.ndarray], np.ndarray],
+        num_samples: int = 20,
+        false_acceptance_rate: float = 0.01,
+        **kwargs
+    ) -> None:
+        """
+        Create a STRIP defense
+
+        :param predict_fn: The predict function of the original classifier
+        :param num_samples: The number of samples to use to test entropy at inference time
+        :param false_acceptance_rate: The percentage of acceptable false acceptance
+        """
+        super().__init__(**kwargs)
+        self.predict_fn = predict_fn
+        self.num_samples = num_samples
+        self.false_acceptance_rate = false_acceptance_rate
+        self.entropy_threshold: Optional[float] = None
+        self.validation_data: Optional[np.ndarray] = None
+
+    def predict(self, x: np.ndarray) -> np.ndarray:
+        """
+        Perform prediction of the given classifier for a batch of inputs, potentially filtering suspicious input
+
+        :param x: Input samples.
+        :return: Array of predictions of shape `(nb_inputs, nb_classes)`.
+        """
+        raw_predictions = self.predict_fn(x)
+
+        if self.entropy_threshold is None or self.validation_data is None:
+            logger.warning("Mitigation has not been performed. Predictions may be unsafe.")
+            return raw_predictions
+
+        x_val = self.validation_data
+        final_predictions = []
+
+        for i, img in enumerate(x):
+            # Randomly select samples from test set
+            selected_indices = np.random.choice(np.arange(len(x_val)), self.num_samples)
+
+            # Perturb the images by combining them
+            perturbed_images = np.array([combine_images(img, x_val[idx]) for idx in selected_indices])
+
+            # Predict on the perturbed images
+            perturbed_predictions = self.predict_fn(perturbed_images)
+
+            # Calculate normalized entropy
+            normalized_entropy = np.sum(entropy(perturbed_predictions, base=2, axis=0)) / float(self.num_samples)
+
+            # Abstain if entropy is below threshold
+            if normalized_entropy <= self.entropy_threshold:
+                final_predictions.append(self.abstain())
+            else:
+                final_predictions.append(raw_predictions[i])
+
+        return np.array(final_predictions)
+
+    def mitigate(self, x_val: np.ndarray) -> None:
+        """
+        Mitigates the effect of poison on a classifier
+
+        :param x_val: Validation data to use to mitigate the effect of poison.
+        """
+        self.validation_data = x_val
+        entropies = []
+
+        # Find normal entropy distribution
+        for _, img in enumerate(tqdm(x_val)):
+            selected_indices = np.random.choice(np.arange(len(x_val)), self.num_samples)
+            perturbed_images = np.array([combine_images(img, x_val[idx]) for idx in selected_indices])
+            perturbed_predictions = self.predict_fn(perturbed_images)
+            normalized_entropy = np.sum(entropy(perturbed_predictions, base=2, axis=0)) / float(self.num_samples)
+            entropies.append(normalized_entropy)
+
+        mean_entropy, std_entropy = norm.fit(entropies)
+
+        # Set threshold to FAR percentile
+        self.entropy_threshold = norm.ppf(self.false_acceptance_rate, loc=mean_entropy, scale=std_entropy)
+        if self.entropy_threshold < 0:
+            logger.warning("Entropy value is negative. Increase FAR for reasonable performance.")
+
+
+def combine_images(img1: np.ndarray, img2: np.ndarray, alpha=0.5) -> np.ndarray:
+    """
+    Combine two Numpy arrays of the same shape
+
+    :param img1: a Numpy array
+    :param img2: a Numpy array
+    :param alpha: percentage weight for the first image
+    :return: The combined image
+    """
+    return alpha * img1 + (1 - alpha) * img2
diff --git a/adversarial-robustness-toolbox/art/estimators/pytorch.py b/adversarial-robustness-toolbox/art/estimators/pytorch.py
new file mode 100644
index 0000000..a2302d1
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/estimators/pytorch.py
@@ -0,0 +1,320 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements the abstract estimator `PyTorchEstimator` for PyTorch models.
+"""
+import logging
+from typing import TYPE_CHECKING, Any, List, Tuple
+
+import numpy as np
+
+from art.estimators.estimator import BaseEstimator, LossGradientsMixin, NeuralNetworkMixin
+
+if TYPE_CHECKING:
+    import torch
+
+logger = logging.getLogger(__name__)
+
+
+class PyTorchEstimator(NeuralNetworkMixin, LossGradientsMixin, BaseEstimator):
+    """
+    Estimator class for PyTorch models.
+    """
+
+    estimator_params = (
+        BaseEstimator.estimator_params + NeuralNetworkMixin.estimator_params + ["device_type",]
+    )
+
+    def __init__(self, device_type: str = "gpu", **kwargs) -> None:
+        """
+        Estimator class for PyTorch models.
+
+        :param channels_first: Set channels first or last.
+        :param clip_values: Tuple of the form `(min, max)` of floats or `np.ndarray` representing the minimum and
+               maximum values allowed for features. If floats are provided, these will be used as the range of all
+               features. If arrays are provided, each value will be considered the bound for a feature, thus
+               the shape of clip values needs to match the total number of features.
+        :param preprocessing_defences: Preprocessing defence(s) to be applied by the classifier.
+        :param postprocessing_defences: Postprocessing defence(s) to be applied by the classifier.
+        :param preprocessing: Tuple of the form `(subtrahend, divisor)` of floats or `np.ndarray` of values to be
+               used for data preprocessing. The first value will be subtracted from the input. The input will then
+               be divided by the second one.
+        :param device_type: Type of device on which the classifier is run, either `gpu` or `cpu`.
+        """
+        import torch  # lgtm [py/repeated-import]
+
+        preprocessing = kwargs.get("preprocessing")
+        if isinstance(preprocessing, tuple):
+            from art.preprocessing.standardisation_mean_std.pytorch import StandardisationMeanStdPyTorch
+
+            kwargs["preprocessing"] = StandardisationMeanStdPyTorch(mean=preprocessing[0], std=preprocessing[1])
+
+        super().__init__(**kwargs)
+
+        self._device_type = device_type
+
+        # Set device
+        if device_type == "cpu" or not torch.cuda.is_available():
+            self._device = torch.device("cpu")
+        else:
+            cuda_idx = torch.cuda.current_device()
+            self._device = torch.device("cuda:{}".format(cuda_idx))
+
+        PyTorchEstimator._check_params(self)
+
+    @property
+    def device_type(self) -> str:
+        """
+        Return the type of device on which the estimator is run.
+
+        :return: Type of device on which the estimator is run, either `gpu` or `cpu`.
+        """
+        return self._device_type  # type: ignore
+
+    def predict(self, x: np.ndarray, batch_size: int = 128, **kwargs):
+        """
+        Perform prediction of the neural network for samples `x`.
+
+        :param x: Samples of shape (nb_samples, nb_features) or (nb_samples, nb_pixels_1, nb_pixels_2,
+                  nb_channels) or (nb_samples, nb_channels, nb_pixels_1, nb_pixels_2).
+        :param batch_size: Batch size.
+        :return: Predictions.
+        :rtype: Format as expected by the `model`
+        """
+        return NeuralNetworkMixin.predict(self, x, batch_size=batch_size, **kwargs)
+
+    def fit(self, x: np.ndarray, y, batch_size: int = 128, nb_epochs: int = 20, **kwargs) -> None:
+        """
+        Fit the model of the estimator on the training data `x` and `y`.
+
+        :param x: Samples of shape (nb_samples, nb_features) or (nb_samples, nb_pixels_1, nb_pixels_2,
+                  nb_channels) or (nb_samples, nb_channels, nb_pixels_1, nb_pixels_2).
+        :param y: Target values.
+        :type y: Format as expected by the `model`
+        :param batch_size: Batch size.
+        :param nb_epochs: Number of training epochs.
+        """
+        NeuralNetworkMixin.fit(self, x, y, batch_size=batch_size, nb_epochs=nb_epochs, **kwargs)
+
+    def set_params(self, **kwargs) -> None:
+        """
+        Take a dictionary of parameters and apply checks before setting them as attributes.
+
+        :param kwargs: A dictionary of attributes.
+        """
+        super().set_params(**kwargs)
+        self._check_params()
+
+    def _check_params(self) -> None:
+        from art.defences.preprocessor.preprocessor import PreprocessorPyTorch
+
+        super()._check_params()
+        self.all_framework_preprocessing = all(
+            [isinstance(p, PreprocessorPyTorch) for p in self.preprocessing_operations]
+        )
+
+    def _apply_preprocessing(self, x, y, fit: bool = False, no_grad=True) -> Tuple[Any, Any]:
+        """
+        Apply all preprocessing defences of the estimator on the raw inputs `x` and `y`. This function is should
+        only be called from function `_apply_preprocessing`.
+
+        The method overrides art.estimators.estimator::BaseEstimator._apply_preprocessing().
+        It requires all defenses to have a method `forward()`.
+        It converts numpy arrays to PyTorch tensors first, then chains a series of defenses by calling
+        defence.forward() which contains PyTorch operations. At the end, it converts PyTorch tensors
+        back to numpy arrays.
+
+        :param x: Samples.
+        :type x: Format as expected by the `model`
+        :param y: Target values.
+        :type y: Format as expected by the `model`
+        :param fit: `True` if the function is call before fit/training and `False` if the function is called before a
+                    predict operation.
+        :param no_grad: `True` if no gradients required.
+        :type no_grad: bool
+        :return: Tuple of `x` and `y` after applying the defences and standardisation.
+        :rtype: Format as expected by the `model`
+        """
+        import torch  # lgtm [py/repeated-import]
+
+        from art.preprocessing.standardisation_mean_std.numpy import StandardisationMeanStd
+        from art.preprocessing.standardisation_mean_std.pytorch import StandardisationMeanStdPyTorch
+
+        if not self.preprocessing_operations:
+            return x, y
+
+        input_is_tensor = isinstance(x, torch.Tensor)
+
+        if self.all_framework_preprocessing and not (not input_is_tensor and x.dtype == np.object):
+            if not input_is_tensor:
+                # Convert np arrays to torch tensors.
+                x = torch.tensor(x, device=self._device)
+                if y is not None:
+                    y = torch.tensor(y, device=self._device)
+
+            def chain_processes(x, y):
+                for preprocess in self.preprocessing_operations:
+                    if fit:
+                        if preprocess.apply_fit:
+                            x, y = preprocess.forward(x, y)
+                    else:
+                        if preprocess.apply_predict:
+                            x, y = preprocess.forward(x, y)
+                return x, y
+
+            if no_grad:
+                with torch.no_grad():
+                    x, y = chain_processes(x, y)
+            else:
+                x, y = chain_processes(x, y)
+
+            # Convert torch tensors back to np arrays.
+            if not input_is_tensor:
+                x = x.cpu().numpy()
+                if y is not None:
+                    y = y.cpu().numpy()
+
+        elif len(self.preprocessing_operations) == 1 or (
+            len(self.preprocessing_operations) == 2
+            and isinstance(self.preprocessing_operations[-1], (StandardisationMeanStd, StandardisationMeanStdPyTorch))
+        ):
+            # Compatible with non-PyTorch defences if no chaining.
+            for preprocess in self.preprocessing_operations:
+                x, y = preprocess(x, y)
+
+        else:
+            raise NotImplementedError("The current combination of preprocessing types is not supported.")
+
+        return x, y
+
+    def _apply_preprocessing_gradient(self, x, gradients, fit=False):
+        """
+        Apply the backward pass to the gradients through all preprocessing defences that have been applied to `x`
+        and `y` in the forward pass. This function is should only be called from function
+        `_apply_preprocessing_gradient`.
+
+        The method overrides art.estimators.estimator::LossGradientsMixin._apply_preprocessing_gradient().
+        It requires all defenses to have a method estimate_forward().
+        It converts numpy arrays to PyTorch tensors first, then chains a series of defenses by calling
+        defence.estimate_forward() which contains differentiable estimate of the operations. At the end,
+        it converts PyTorch tensors back to numpy arrays.
+
+        :param x: Samples.
+        :type x: Format as expected by the `model`
+        :param gradients: Gradients before backward pass through preprocessing defences.
+        :type gradients: Format as expected by the `model`
+        :param fit: `True` if the gradients are computed during training.
+        :return: Gradients after backward pass through preprocessing defences.
+        :rtype: Format as expected by the `model`
+        """
+        import torch  # lgtm [py/repeated-import]
+
+        from art.preprocessing.standardisation_mean_std.numpy import StandardisationMeanStd
+        from art.preprocessing.standardisation_mean_std.pytorch import StandardisationMeanStdPyTorch
+
+        if not self.preprocessing_operations:
+            return gradients
+
+        input_is_tensor = isinstance(x, torch.Tensor)
+
+        if self.all_framework_preprocessing and not (not input_is_tensor and x.dtype == np.object):
+            # Convert np arrays to torch tensors.
+            x = torch.tensor(x, device=self._device, requires_grad=True)
+            gradients = torch.tensor(gradients, device=self._device)
+            x_orig = x
+
+            for preprocess in self.preprocessing_operations:
+                if fit:
+                    if preprocess.apply_fit:
+                        x = preprocess.estimate_forward(x)
+                else:
+                    if preprocess.apply_predict:
+                        x = preprocess.estimate_forward(x)
+
+            x.backward(gradients)
+
+            # Convert torch tensors back to np arrays.
+            gradients = x_orig.grad.detach().cpu().numpy()
+            if gradients.shape != x_orig.shape:
+                raise ValueError(
+                    "The input shape is {} while the gradient shape is {}".format(x.shape, gradients.shape)
+                )
+
+        elif len(self.preprocessing_operations) == 1 or (
+            len(self.preprocessing_operations) == 2
+            and isinstance(self.preprocessing_operations[-1], (StandardisationMeanStd, StandardisationMeanStdPyTorch))
+        ):
+            # Compatible with non-PyTorch defences if no chaining.
+            for preprocess in self.preprocessing_operations[::-1]:
+                if fit:
+                    if preprocess.apply_fit:
+                        gradients = preprocess.estimate_gradient(x, gradients)
+                else:
+                    if preprocess.apply_predict:
+                        gradients = preprocess.estimate_gradient(x, gradients)
+
+        else:
+            raise NotImplementedError("The current combination of preprocessing types is not supported.")
+
+        return gradients
+
+    def _set_layer(self, train: bool, layerinfo: List["torch.nn.modules.Module"]) -> None:
+        """
+        Set all layers that are an instance of `layerinfo` into training or evaluation mode.
+
+        :param train: False for evaluation mode.
+        :param layerinfo: List of module types.
+        """
+        import torch  # lgtm [py/repeated-import]
+
+        assert all([issubclass(l, torch.nn.modules.Module) for l in layerinfo])
+
+        def set_train(layer, layerinfo=layerinfo):
+            "Set layer into training mode if instance of `layerinfo`."
+            if isinstance(layer, tuple(layerinfo)):
+                layer.train()
+
+        def set_eval(layer, layerinfo=layerinfo):
+            "Set layer into evaluation mode if instance of `layerinfo`."
+            if isinstance(layer, tuple(layerinfo)):
+                layer.eval()
+
+        if train:
+            self._model.apply(set_train)
+        else:
+            self._model.apply(set_eval)
+
+    def set_dropout(self, train: bool) -> None:
+        """
+        Set all dropout layers into train or eval mode.
+
+        :param train: False for evaluation mode.
+        """
+        import torch  # lgtm [py/repeated-import]
+
+        self._set_layer(train=train, layerinfo=[torch.nn.modules.dropout._DropoutNd])
+
+    def set_batchnorm(self, train: bool) -> None:
+        """
+        Set all batch normalization layers into train or eval mode.
+
+        :param train: False for evaluation mode.
+        """
+        import torch  # lgtm [py/repeated-import]
+
+        self._set_layer(train=train, layerinfo=[torch.nn.modules.batchnorm._BatchNorm])
diff --git a/adversarial-robustness-toolbox/art/estimators/regression/__init__.py b/adversarial-robustness-toolbox/art/estimators/regression/__init__.py
new file mode 100644
index 0000000..2808328
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/estimators/regression/__init__.py
@@ -0,0 +1,4 @@
+"""
+This module implements all regressors in ART.
+"""
+from art.estimators.regression.regressor import RegressorMixin
diff --git a/adversarial-robustness-toolbox/art/estimators/regression/regressor.py b/adversarial-robustness-toolbox/art/estimators/regression/regressor.py
new file mode 100644
index 0000000..28603ea
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/estimators/regression/regressor.py
@@ -0,0 +1,28 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements mixin abstract base class for all regressors in ART.
+"""
+
+from abc import ABC
+
+
+class RegressorMixin(ABC):
+    """
+    Mixin abstract Base class for ART regressors.
+    """
diff --git a/adversarial-robustness-toolbox/art/estimators/scikitlearn.py b/adversarial-robustness-toolbox/art/estimators/scikitlearn.py
new file mode 100644
index 0000000..9a51730
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/estimators/scikitlearn.py
@@ -0,0 +1,28 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements the abstract estimator for scikit-learn models.
+"""
+
+from art.estimators.estimator import BaseEstimator
+
+
+class ScikitlearnEstimator(BaseEstimator):
+    """
+    Estimator class for scikit-learn models.
+    """
diff --git a/adversarial-robustness-toolbox/art/estimators/speech_recognition/__init__.py b/adversarial-robustness-toolbox/art/estimators/speech_recognition/__init__.py
new file mode 100644
index 0000000..fc90fc6
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/estimators/speech_recognition/__init__.py
@@ -0,0 +1,7 @@
+"""
+Module containing estimators for speech recognition.
+"""
+from art.estimators.speech_recognition.speech_recognizer import SpeechRecognizerMixin
+
+from art.estimators.speech_recognition.pytorch_deep_speech import PyTorchDeepSpeech
+from art.estimators.speech_recognition.tensorflow_lingvo import TensorFlowLingvoASR
diff --git a/adversarial-robustness-toolbox/art/estimators/speech_recognition/pytorch_deep_speech.py b/adversarial-robustness-toolbox/art/estimators/speech_recognition/pytorch_deep_speech.py
new file mode 100644
index 0000000..703737a
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/estimators/speech_recognition/pytorch_deep_speech.py
@@ -0,0 +1,725 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements the task specific estimator for DeepSpeech, an end-to-end speech recognition in English and
+Mandarin in PyTorch.
+
+| Paper link: https://arxiv.org/abs/1512.02595
+"""
+import logging
+from typing import TYPE_CHECKING, List, Optional, Tuple, Union
+
+import numpy as np
+
+from art import config
+from art.estimators.pytorch import PyTorchEstimator
+from art.estimators.speech_recognition.speech_recognizer import SpeechRecognizerMixin
+from art.utils import get_file
+
+if TYPE_CHECKING:
+    import torch
+    from deepspeech_pytorch.model import DeepSpeech
+
+    from art.defences.postprocessor.postprocessor import Postprocessor
+    from art.defences.preprocessor.preprocessor import Preprocessor
+    from art.utils import CLIP_VALUES_TYPE, PREPROCESSING_TYPE
+
+logger = logging.getLogger(__name__)
+
+
+class PyTorchDeepSpeech(SpeechRecognizerMixin, PyTorchEstimator):
+    """
+    This class implements a model-specific automatic speech recognizer using the end-to-end speech recognizer
+    DeepSpeech and PyTorch.
+
+    | Paper link: https://arxiv.org/abs/1512.02595
+    """
+
+    estimator_params = PyTorchEstimator.estimator_params + ["optimizer", "use_amp", "opt_level", "lm_config", "verbose"]
+
+    def __init__(
+        self,
+        model: Optional["DeepSpeech"] = None,
+        pretrained_model: Optional[str] = None,
+        filename: Optional[str] = None,
+        url: Optional[str] = None,
+        use_half: bool = False,
+        optimizer: Optional["torch.optim.Optimizer"] = None,  # type: ignore
+        use_amp: bool = False,
+        opt_level: str = "O1",
+        decoder_type: str = "greedy",
+        lm_path: str = "",
+        top_paths: int = 1,
+        alpha: float = 0.0,
+        beta: float = 0.0,
+        cutoff_top_n: int = 40,
+        cutoff_prob: float = 1.0,
+        beam_width: int = 10,
+        lm_workers: int = 4,
+        clip_values: Optional["CLIP_VALUES_TYPE"] = None,
+        preprocessing_defences: Union["Preprocessor", List["Preprocessor"], None] = None,
+        postprocessing_defences: Union["Postprocessor", List["Postprocessor"], None] = None,
+        preprocessing: "PREPROCESSING_TYPE" = None,
+        device_type: str = "gpu",
+        verbose: bool = True,
+    ):
+        """
+        Initialization of an instance PyTorchDeepSpeech.
+
+        :param model: DeepSpeech model.
+        :param pretrained_model: The choice of pretrained model if a pretrained model is required. Currently this
+                                 estimator supports 3 different pretrained models consisting of `an4`, `librispeech`
+                                 and `tedlium`.
+        :param filename: Name of the file.
+        :param url: Download URL.
+        :param use_half: Whether to use FP16 for pretrained model.
+        :param optimizer: The optimizer used to train the estimator.
+        :param use_amp: Whether to use the automatic mixed precision tool to enable mixed precision training or
+                        gradient computation, e.g. with loss gradient computation. When set to True, this option is
+                        only triggered if there are GPUs available.
+        :param opt_level: Specify a pure or mixed precision optimization level. Used when use_amp is True. Accepted
+                          values are `O0`, `O1`, `O2`, and `O3`.
+        :param decoder_type: Decoder type. Either `greedy` or `beam`. This parameter is only used when users want
+                             transcription outputs.
+        :param lm_path: Path to an (optional) kenlm language model for use with beam search. This parameter is only
+                        used when users want transcription outputs.
+        :param top_paths: Number of beams to be returned. This parameter is only used when users want transcription
+                          outputs.
+        :param alpha: The weight used for the language model. This parameter is only used when users want transcription
+                      outputs.
+        :param beta: Language model word bonus (all words). This parameter is only used when users want transcription
+                     outputs.
+        :param cutoff_top_n: Cutoff_top_n characters with highest probs in vocabulary will be used in beam search. This
+                             parameter is only used when users want transcription outputs.
+        :param cutoff_prob: Cutoff probability in pruning. This parameter is only used when users want transcription
+                            outputs.
+        :param beam_width: The width of beam to be used. This parameter is only used when users want transcription
+                           outputs.
+        :param lm_workers: Number of language model processes to use. This parameter is only used when users want
+                           transcription outputs.
+        :param clip_values: Tuple of the form `(min, max)` of floats or `np.ndarray` representing the minimum and
+               maximum values allowed for features. If floats are provided, these will be used as the range of all
+               features. If arrays are provided, each value will be considered the bound for a feature, thus
+               the shape of clip values needs to match the total number of features.
+        :param preprocessing_defences: Preprocessing defence(s) to be applied by the estimator.
+        :param postprocessing_defences: Postprocessing defence(s) to be applied by the estimator.
+        :param preprocessing: Tuple of the form `(subtrahend, divisor)` of floats or `np.ndarray` of values to be
+               used for data preprocessing. The first value will be subtracted from the input. The input will then
+               be divided by the second one.
+        :param device_type: Type of device to be used for model and tensors, if `cpu` run on CPU, if `gpu` run on GPU
+                            if available otherwise run on CPU.
+        """
+        import torch  # lgtm [py/repeated-import]
+        from deepspeech_pytorch.configs.inference_config import LMConfig
+        from deepspeech_pytorch.enums import DecoderType
+        from deepspeech_pytorch.utils import load_decoder, load_model
+
+        # Super initialization
+        super().__init__(
+            model=None,
+            clip_values=clip_values,
+            channels_first=None,
+            preprocessing_defences=preprocessing_defences,
+            postprocessing_defences=postprocessing_defences,
+            preprocessing=preprocessing,
+        )
+
+        self.verbose = verbose
+
+        # Check clip values
+        if self.clip_values is not None:
+            if not np.all(self.clip_values[0] == -1):
+                raise ValueError("This estimator requires normalized input audios with clip_vales=(-1, 1).")
+            if not np.all(self.clip_values[1] == 1):
+                raise ValueError("This estimator requires normalized input audios with clip_vales=(-1, 1).")
+
+        # Check postprocessing defences
+        if self.postprocessing_defences is not None:
+            raise ValueError("This estimator does not support `postprocessing_defences`.")
+
+        # Set cpu/gpu device
+        self._device: torch.device
+        if device_type == "cpu" or not torch.cuda.is_available():
+            self._device = torch.device("cpu")
+        else:
+            cuda_idx = torch.cuda.current_device()
+            self._device = torch.device("cuda:{}".format(cuda_idx))
+
+        self._input_shape = None
+
+        # Load model
+        if model is None:
+            if pretrained_model == "an4":
+                filename, url = (
+                    "an4_pretrained_v2.pth",
+                    "https://github.com/SeanNaren/deepspeech.pytorch/releases/download/v2.0/an4_pretrained_v2.pth",
+                )
+
+            elif pretrained_model == "librispeech":
+                filename, url = (
+                    "librispeech_pretrained_v2.pth",
+                    "https://github.com/SeanNaren/deepspeech.pytorch/releases/download/v2.0/"
+                    "librispeech_pretrained_v2.pth",
+                )
+
+            elif pretrained_model == "tedlium":
+                filename, url = (
+                    "ted_pretrained_v2.pth",
+                    "https://github.com/SeanNaren/deepspeech.pytorch/releases/download/v2.0/ted_pretrained_v2.pth",
+                )
+
+            elif pretrained_model is None:
+                # If model is None and no pretrained model is selected, then we need to have parameters filename and
+                # url to download, extract and load the automatic speech recognition model
+                if filename is None or url is None:
+                    filename, url = (
+                        "librispeech_pretrained_v2.pth",
+                        "https://github.com/SeanNaren/deepspeech.pytorch/releases/download/v2.0/"
+                        "librispeech_pretrained_v2.pth",
+                    )
+
+            else:
+                raise ValueError("The input pretrained model %s is not supported." % pretrained_model)
+
+            # Download model
+            model_path = get_file(
+                filename=filename, path=config.ART_DATA_PATH, url=url, extract=False, verbose=self.verbose
+            )
+
+            # Then load model
+            self._model = load_model(device=self._device, model_path=model_path, use_half=use_half)
+
+        else:
+            self._model = model
+
+            # Push model to the corresponding device
+            self._model.to(self._device)
+
+        # Save first version of the optimizer
+        self._optimizer = optimizer
+        self._use_amp = use_amp
+        self._opt_level = opt_level
+
+        # Now create a decoder
+        # Create the language model config first
+        lm_config = LMConfig()
+
+        # Then setup the config
+        if decoder_type == "greedy":
+            lm_config.decoder_type = DecoderType.greedy
+        elif decoder_type == "beam":
+            lm_config.decoder_type = DecoderType.beam
+        else:
+            raise ValueError("Decoder type %s currently not supported." % decoder_type)
+
+        lm_config.lm_path = lm_path
+        lm_config.top_paths = top_paths
+        lm_config.alpha = alpha
+        lm_config.beta = beta
+        lm_config.cutoff_top_n = cutoff_top_n
+        lm_config.cutoff_prob = cutoff_prob
+        lm_config.beam_width = beam_width
+        lm_config.lm_workers = lm_workers
+        self.lm_config = lm_config
+
+        # Create the decoder with the lm config
+        self.decoder = load_decoder(labels=self._model.labels, cfg=lm_config)
+
+        # Setup for AMP use
+        if self._use_amp:
+            from apex import amp
+
+            if self._optimizer is None:
+                logger.warning(
+                    "An optimizer is needed to use the automatic mixed precision tool, but none for provided. "
+                    "A default optimizer is used."
+                )
+
+                # Create the optimizers
+                parameters = self._model.parameters()
+                self._optimizer = torch.optim.SGD(parameters, lr=0.01)
+
+            if self._device.type == "cpu":
+                enabled = False
+            else:
+                enabled = True
+
+            self._model, self._optimizer = amp.initialize(
+                models=self._model, optimizers=self._optimizer, enabled=enabled, opt_level=opt_level, loss_scale=1.0,
+            )
+
+    def predict(
+        self, x: np.ndarray, batch_size: int = 128, **kwargs
+    ) -> Union[Tuple[np.ndarray, np.ndarray], np.ndarray]:
+        """
+        Perform prediction for a batch of inputs.
+
+        :param x: Samples of shape (nb_samples, seq_length). Note that, it is allowable that sequences in the batch
+                  could have different lengths. A possible example of `x` could be:
+                  `x = np.array([np.array([0.1, 0.2, 0.1, 0.4]), np.array([0.3, 0.1])])`.
+        :param batch_size: Batch size.
+        :param transcription_output: Indicate whether the function will produce probability or transcription as
+                                     prediction output. If transcription_output is not available, then probability
+                                     output is returned. Default: True
+        :return: Predicted probability (if transcription_output False) or transcription (default, if
+                 transcription_output is True):
+                 - Probability return is a tuple of (probs, sizes), where `probs` is the probability of characters of
+                 shape (nb_samples, seq_length, nb_classes) and `sizes` is the real sequence length of shape
+                 (nb_samples,).
+                 - Transcription return is a numpy array of characters. A possible example of a transcription return
+                 is `np.array(['SIXTY ONE', 'HELLO'])`.
+        """
+        import torch  # lgtm [py/repeated-import]
+
+        x_in = np.empty(len(x), dtype=object)
+        x_in[:] = list(x)
+
+        # Put the model in the eval mode
+        self._model.eval()
+
+        # Apply preprocessing
+        x_preprocessed, _ = self._apply_preprocessing(x_in, y=None, fit=False)
+
+        # Transform x into the model input space
+        inputs, _, input_rates, _, batch_idx = self._transform_model_input(x=x_preprocessed)
+
+        # Compute real input sizes
+        input_sizes = input_rates.mul_(inputs.size()[-1]).int()
+
+        # Run prediction with batch processing
+        results = []
+        result_output_sizes = np.zeros(x_preprocessed.shape[0], dtype=np.int)
+        num_batch = int(np.ceil(len(x_preprocessed) / float(batch_size)))
+
+        for m in range(num_batch):
+            # Batch indexes
+            begin, end = (
+                m * batch_size,
+                min((m + 1) * batch_size, x_preprocessed.shape[0]),
+            )
+
+            # Call to DeepSpeech model for prediction
+            with torch.no_grad():
+                outputs, output_sizes = self._model(
+                    inputs[begin:end].to(self._device), input_sizes[begin:end].to(self._device)
+                )
+
+            results.append(outputs)
+            result_output_sizes[begin:end] = output_sizes.detach().cpu().numpy()
+
+        # Aggregate results
+        result_outputs = np.zeros(
+            shape=(x_preprocessed.shape[0], result_output_sizes.max(), results[0].shape[-1]),
+            dtype=config.ART_NUMPY_DTYPE,
+        )
+
+        for m in range(num_batch):
+            # Batch indexes
+            begin, end = (
+                m * batch_size,
+                min((m + 1) * batch_size, x_preprocessed.shape[0]),
+            )
+
+            # Overwrite results
+            result_outputs[begin:end, : results[m].shape[1], : results[m].shape[-1]] = results[m].cpu().numpy()
+
+        # Rearrange to the original order
+        result_output_sizes_ = result_output_sizes.copy()
+        result_outputs_ = result_outputs.copy()
+        result_output_sizes[batch_idx] = result_output_sizes_
+        result_outputs[batch_idx] = result_outputs_
+
+        # Check if users want transcription outputs
+        transcription_output = kwargs.get("transcription_output", True)
+
+        if transcription_output is False:
+            return result_outputs, result_output_sizes
+
+        # Now users want transcription outputs
+        # Compute transcription
+        decoded_output, _ = self.decoder.decode(
+            torch.tensor(result_outputs, device=self._device), torch.tensor(result_output_sizes, device=self._device)
+        )
+        decoded_output = [do[0] for do in decoded_output]
+        decoded_output = np.array(decoded_output)
+
+        return decoded_output
+
+    def loss_gradient(self, x: np.ndarray, y: np.ndarray, **kwargs) -> np.ndarray:
+        """
+        Compute the gradient of the loss function w.r.t. `x`.
+
+        :param x: Samples of shape (nb_samples, seq_length). Note that, it is allowable that sequences in the batch
+                  could have different lengths. A possible example of `x` could be:
+                  `x = np.array([np.array([0.1, 0.2, 0.1, 0.4]), np.array([0.3, 0.1])])`.
+        :param y: Target values of shape (nb_samples). Each sample in `y` is a string and it may possess different
+                  lengths. A possible example of `y` could be: `y = np.array(['SIXTY ONE', 'HELLO'])`.
+        :return: Loss gradients of the same shape as `x`.
+        """
+        from warpctc_pytorch import CTCLoss
+
+        x_in = np.empty(len(x), dtype=object)
+        x_in[:] = list(x)
+
+        # Put the model in the training mode, otherwise CUDA can't backpropagate through the model.
+        # However, model uses batch norm layers which need to be frozen
+        self._model.train()
+        self.set_batchnorm(train=False)
+
+        # Apply preprocessing
+        x_preprocessed, y_preprocessed = self._apply_preprocessing(x_in, y, fit=False)
+
+        # Transform data into the model input space
+        inputs, targets, input_rates, target_sizes, _ = self._transform_model_input(
+            x=x_preprocessed, y=y_preprocessed, compute_gradient=True
+        )
+
+        # Compute real input sizes
+        input_sizes = input_rates.mul_(inputs.size()[-1]).int()
+
+        # Call to DeepSpeech model for prediction
+        outputs, output_sizes = self._model(inputs.to(self._device), input_sizes.to(self._device))
+        outputs = outputs.transpose(0, 1)
+        float_outputs = outputs.float()
+
+        # Loss function
+        criterion = CTCLoss()
+        loss = criterion(float_outputs, targets, output_sizes, target_sizes).to(self._device)
+        loss = loss / inputs.size(0)
+
+        # Compute gradients
+        if self._use_amp:
+            from apex import amp
+
+            with amp.scale_loss(loss, self._optimizer) as scaled_loss:
+                scaled_loss.backward()
+
+        else:
+            loss.backward()
+
+        # Get results
+        results_list = list()
+        for i, _ in enumerate(x_preprocessed):
+            results_list.append(x_preprocessed[i].grad.cpu().numpy().copy())
+
+        results = np.array(results_list)
+
+        if results.shape[0] == 1:
+            results_ = np.empty(len(results), dtype=object)
+            results_[:] = list(results)
+            results = results_
+
+        results = self._apply_preprocessing_gradient(x_in, results)
+
+        if x.dtype != np.object:
+            results = np.array([i for i in results], dtype=x.dtype)
+            assert results.shape == x.shape and results.dtype == x.dtype
+
+        # Unfreeze batch norm layers again
+        self.set_batchnorm(train=True)
+        return results
+
+    def fit(self, x: np.ndarray, y: np.ndarray, batch_size: int = 128, nb_epochs: int = 10, **kwargs) -> None:
+        """
+        Fit the estimator on the training set `(x, y)`.
+
+        :param x: Samples of shape (nb_samples, seq_length). Note that, it is allowable that sequences in the batch
+                  could have different lengths. A possible example of `x` could be:
+                  `x = np.array([np.array([0.1, 0.2, 0.1, 0.4]), np.array([0.3, 0.1])])`.
+        :param y: Target values of shape (nb_samples). Each sample in `y` is a string and it may possess different
+                  lengths. A possible example of `y` could be: `y = np.array(['SIXTY ONE', 'HELLO'])`.
+        :param batch_size: Size of batches.
+        :param nb_epochs: Number of epochs to use for training.
+        :param kwargs: Dictionary of framework-specific arguments. This parameter is not currently supported for PyTorch
+               and providing it takes no effect.
+        """
+        import random
+
+        from warpctc_pytorch import CTCLoss
+
+        x_in = np.empty(len(x), dtype=object)
+        x_in[:] = list(x)
+
+        # Put the model in the training mode
+        self._model.train()
+
+        if self._optimizer is None:
+            raise ValueError("An optimizer is required to train the model, but none was provided.")
+
+        # Apply preprocessing
+        x_preprocessed, y_preprocessed = self._apply_preprocessing(x_in, y, fit=True)
+
+        # Train with batch processing
+        num_batch = int(np.ceil(len(x_preprocessed) / float(batch_size)))
+        ind = np.arange(len(x_preprocessed))
+
+        # Loss function
+        criterion = CTCLoss()
+
+        # Start training
+        for _ in range(nb_epochs):
+            # Shuffle the examples
+            random.shuffle(ind)
+
+            # Train for one epoch
+            for m in range(num_batch):
+                # Batch indexes
+                begin, end = (
+                    m * batch_size,
+                    min((m + 1) * batch_size, x_preprocessed.shape[0]),
+                )
+
+                # Extract random batch
+                i_batch = np.empty(len(x_preprocessed[ind[begin:end]]), dtype=object)
+                i_batch[:] = list(x_preprocessed[ind[begin:end]])
+                o_batch = y_preprocessed[ind[begin:end]]
+
+                # Transform data into the model input space
+                inputs, targets, input_rates, target_sizes, _ = self._transform_model_input(
+                    x=i_batch, y=o_batch, compute_gradient=False
+                )
+
+                # Compute real input sizes
+                input_sizes = input_rates.mul_(inputs.size(-1)).int()
+
+                # Zero the parameter gradients
+                self._optimizer.zero_grad()
+
+                # Call to DeepSpeech model for prediction
+                outputs, output_sizes = self._model(inputs.to(self._device), input_sizes.to(self._device))
+                outputs = outputs.transpose(0, 1)
+                float_outputs = outputs.float()
+
+                # Form the loss
+                loss = criterion(float_outputs, targets, output_sizes, target_sizes).to(self._device)
+                loss = loss / inputs.size(0)
+
+                # Actual training
+                if self._use_amp:
+                    from apex import amp
+
+                    with amp.scale_loss(loss, self._optimizer) as scaled_loss:
+                        scaled_loss.backward()
+
+                else:
+                    loss.backward()
+
+                self._optimizer.step()
+
+    def preprocess_transform_model_input(
+        self, x: "torch.Tensor", y: np.ndarray, real_lengths: np.ndarray,
+    ) -> Tuple["torch.Tensor", "torch.Tensor", "torch.Tensor", "torch.Tensor", List]:
+        """
+        Apply preprocessing and then transform the user input space into the model input space. This function is used
+        by the ASR attack to attack into the PyTorchDeepSpeech estimator whose defences are called with the
+        `_apply_preprocessing` function.
+
+        :param x: Samples of shape (nb_samples, seq_length).
+        :param y: Target values of shape (nb_samples). Each sample in `y` is a string and it may possess different
+                  lengths. A possible example of `y` could be: `y = np.array(['SIXTY ONE', 'HELLO'])`.
+        :param real_lengths: Real lengths of original sequences.
+        :return: A tuple of inputs and targets in the model space with the original index
+                 `(inputs, targets, input_percentages, target_sizes, batch_idx)`, where:
+                 - inputs: model inputs of shape (nb_samples, nb_frequencies, seq_length).
+                 - targets: ground truth targets of shape (sum over nb_samples of real seq_lengths).
+                 - input_percentages: percentages of real inputs in inputs.
+                 - target_sizes: list of real seq_lengths.
+                 - batch_idx: original index of inputs.
+        """
+        import torch  # lgtm [py/repeated-import]
+
+        # Apply preprocessing
+        x_batch = []
+        for i, _ in enumerate(x):
+            preprocessed_x_i, _ = self._apply_preprocessing(x=x[i], y=None, no_grad=False)
+            x_batch.append(preprocessed_x_i)
+
+        x = torch.stack(x_batch)
+
+        # Transform the input space
+        inputs, targets, input_rates, target_sizes, batch_idx = self._transform_model_input(
+            x=x, y=y, compute_gradient=False, tensor_input=True, real_lengths=real_lengths,
+        )
+
+        return inputs, targets, input_rates, target_sizes, batch_idx
+
+    def _transform_model_input(
+        self,
+        x: Union[np.ndarray, "torch.Tensor"],
+        y: Optional[np.ndarray] = None,
+        compute_gradient: bool = False,
+        tensor_input: bool = False,
+        real_lengths: Optional[np.ndarray] = None,
+    ) -> Tuple["torch.Tensor", "torch.Tensor", "torch.Tensor", "torch.Tensor", List]:
+        """
+        Transform the user input space into the model input space.
+
+        :param x: Samples of shape (nb_samples, seq_length). Note that, it is allowable that sequences in the batch
+                  could have different lengths. A possible example of `x` could be:
+                  `x = np.ndarray([[0.1, 0.2, 0.1, 0.4], [0.3, 0.1]])`.
+        :param y: Target values of shape (nb_samples). Each sample in `y` is a string and it may possess different
+                  lengths. A possible example of `y` could be: `y = np.array(['SIXTY ONE', 'HELLO'])`.
+        :param compute_gradient: Indicate whether to compute gradients for the input `x`.
+        :param tensor_input: Indicate whether input is tensor.
+        :param real_lengths: Real lengths of original sequences.
+        :return: A tuple of inputs and targets in the model space with the original index
+                 `(inputs, targets, input_percentages, target_sizes, batch_idx)`, where:
+                 - inputs: model inputs of shape (nb_samples, nb_frequencies, seq_length).
+                 - targets: ground truth targets of shape (sum over nb_samples of real seq_lengths).
+                 - input_percentages: percentages of real inputs in inputs.
+                 - target_sizes: list of real seq_lengths.
+                 - batch_idx: original index of inputs.
+        """
+        import torch  # lgtm [py/repeated-import]
+        import torchaudio
+        from deepspeech_pytorch.loader.data_loader import _collate_fn
+
+        # These parameters are needed for the transformation
+        sample_rate = self._model.audio_conf.sample_rate
+        window_size = self._model.audio_conf.window_size
+        window_stride = self._model.audio_conf.window_stride
+
+        n_fft = int(sample_rate * window_size)
+        hop_length = int(sample_rate * window_stride)
+        win_length = n_fft
+
+        window = self._model.audio_conf.window.value
+
+        if window == "hamming":
+            window_fn = torch.hamming_window
+        elif window == "hann":
+            window_fn = torch.hann_window
+        elif window == "blackman":
+            window_fn = torch.blackman_window
+        elif window == "bartlett":
+            window_fn = torch.bartlett_window
+        else:
+            raise NotImplementedError("Spectrogram window %s not supported." % window)
+
+        # Create a transformer to transform between the two spaces
+        transformer = torchaudio.transforms.Spectrogram(
+            n_fft=n_fft, hop_length=hop_length, win_length=win_length, window_fn=window_fn, power=None
+        )
+        transformer.to(self._device)
+
+        # Create a label map
+        label_map = {self._model.labels[i]: i for i in range(len(self._model.labels))}
+
+        # We must process each sequence separately due to the diversity of their length
+        batch = []
+        for i, _ in enumerate(x):
+            # First process the target
+            if y is None:
+                target = []
+            else:
+                target = list(filter(None, [label_map.get(letter) for letter in list(y[i])]))
+
+            # Push the sequence to device
+            if not tensor_input:
+                x[i] = x[i].astype(config.ART_NUMPY_DTYPE)
+                x[i] = torch.tensor(x[i]).to(self._device)
+
+            # Set gradient computation permission
+            if compute_gradient:
+                x[i].requires_grad = True
+
+            # Transform the sequence into the frequency space
+            if tensor_input and real_lengths is not None:
+                transformed_input = transformer(x[i][: real_lengths[i]])
+            else:
+                transformed_input = transformer(x[i])
+
+            spectrogram, _ = torchaudio.functional.magphase(transformed_input)
+            spectrogram = torch.log1p(spectrogram)
+
+            # Normalize data
+            mean = spectrogram.mean()
+            std = spectrogram.std()
+            spectrogram = spectrogram - mean
+            spectrogram = spectrogram / std
+
+            # Then form the batch
+            batch.append((spectrogram, target))
+
+        # We must keep the order of the batch for later use as the following function will change its order
+        batch_idx = sorted(range(len(batch)), key=lambda i: batch[i][0].size(1), reverse=True)
+
+        # The collate function is important to convert input into model space
+        inputs, targets, input_percentages, target_sizes = _collate_fn(batch)
+
+        return inputs, targets, input_percentages, target_sizes, batch_idx
+
+    @property
+    def input_shape(self) -> Tuple[int, ...]:
+        """
+        Return the shape of one input sample.
+
+        :return: Shape of one input sample.
+        """
+        return self._input_shape  # type: ignore
+
+    @property
+    def model(self) -> "DeepSpeech":
+        """
+        Get current model.
+
+        :return: Current model.
+        """
+        return self._model
+
+    @property
+    def device(self) -> "torch.device":
+        """
+        Get current used device.
+
+        :return: Current used device.
+        """
+        return self._device
+
+    @property
+    def use_amp(self) -> bool:
+        """
+        Return a boolean indicating whether to use the automatic mixed precision tool.
+
+        :return: Whether to use the automatic mixed precision tool.
+        """
+        return self._use_amp  # type: ignore
+
+    @property
+    def optimizer(self) -> "torch.optim.Optimizer":
+        """
+        Return the optimizer.
+
+        :return: The optimizer.
+        """
+        return self._optimizer  # type: ignore
+
+    @property
+    def opt_level(self) -> str:
+        """
+        Return a string specifying a pure or mixed precision optimization level.
+
+        :return: A string specifying a pure or mixed precision optimization level. Possible
+                 values are `O0`, `O1`, `O2`, and `O3`.
+        """
+        return self._opt_level  # type: ignore
+
+    def get_activations(
+        self, x: np.ndarray, layer: Union[int, str], batch_size: int, framework: bool = False
+    ) -> np.ndarray:
+        raise NotImplementedError
diff --git a/adversarial-robustness-toolbox/art/estimators/speech_recognition/speech_recognizer.py b/adversarial-robustness-toolbox/art/estimators/speech_recognition/speech_recognizer.py
new file mode 100644
index 0000000..7e97b0f
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/estimators/speech_recognition/speech_recognizer.py
@@ -0,0 +1,28 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements mixin abstract base class for all speech recognizers in ART.
+"""
+
+from abc import ABC
+
+
+class SpeechRecognizerMixin(ABC):
+    """
+    Mix-in Base class for ART speech recognizers.
+    """
diff --git a/adversarial-robustness-toolbox/art/estimators/speech_recognition/tensorflow_lingvo.py b/adversarial-robustness-toolbox/art/estimators/speech_recognition/tensorflow_lingvo.py
new file mode 100644
index 0000000..df9509a
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/estimators/speech_recognition/tensorflow_lingvo.py
@@ -0,0 +1,558 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements task-specific estimators for automatic speech recognition in TensorFlow.
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import os
+import sys
+from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
+
+import numpy as np
+
+from art import config
+from art.estimators.speech_recognition.speech_recognizer import SpeechRecognizerMixin
+from art.estimators.tensorflow import TensorFlowV2Estimator
+from art.utils import get_file, make_directory
+
+if TYPE_CHECKING:
+    from art.utils import CLIP_VALUES_TYPE, PREPROCESSING_TYPE
+    from art.defences.preprocessor.preprocessor import Preprocessor
+    from art.defences.postprocessor.postprocessor import Postprocessor
+
+    from tensorflow.compat.v1 import Tensor
+    from tensorflow.compat.v1 import Session
+
+logger = logging.getLogger(__name__)
+
+
+class TensorFlowLingvoASR(SpeechRecognizerMixin, TensorFlowV2Estimator):
+    """
+    This class implements the task-specific Lingvo ASR model of Qin et al. (2019).
+
+    The estimator uses a pre-trained model provided by Qin et al., which is trained using the Lingvo library and the
+    LibriSpeech dataset.
+
+    | Paper link: http://proceedings.mlr.press/v97/qin19a.html, https://arxiv.org/abs/1902.08295
+
+    .. warning:: In order to calculate loss gradients, this estimator requires a user-patched Lingvo module. A patched
+                 source file for the `lingvo.tasks.asr.decoder` module will be automatically applied. The original
+                 source file can be found in `<PYTHON_SITE_PACKAGES>/lingvo/tasks/asr/decoder.py` and will be patched as
+                 outlined in the following commit diff:
+                 https://github.com/yaq007/lingvo/commit/414e035b2c60372de732c9d67db14d1003be6dd6
+
+    The patched `decoder_patched.py` can be found in `ART_DATA_PATH/lingvo/asr`.
+
+    Note: Run `python -m site` to obtain a list of possible candidates where to find the `<PYTHON_SITE_PACKAGES` folder.
+    """
+
+    # Note: Support for the estimator is pinned to Lingvo version 0.6.4. Some additional source files that are not
+    # provided by pip package need to be downloaded. Those downloads are pinned to the following commit:
+    # https://github.com/tensorflow/lingvo/commit/9961306adf66f7340e27f109f096c9322d4f9636
+    _LINGVO_CFG: Dict[str, Any] = {
+        "path": os.path.join(config.ART_DATA_PATH, "lingvo"),
+        "model_data": {
+            "uri": "http://cseweb.ucsd.edu/~yaq007/ckpt-00908156.data-00000-of-00001",
+            "basename": "ckpt-00908156.data-00000-of-00001",
+        },
+        "model_index": {
+            "uri": (
+                "https://github.com/tensorflow/cleverhans/blob/"
+                "6ef939059172901db582c7702eb803b7171e3db5/examples/adversarial_asr/model/ckpt-00908156.index?raw=true"
+            ),
+            "basename": "ckpt-00908156.index",
+        },
+        "params": {
+            "uri": (
+                "https://raw.githubusercontent.com/tensorflow/lingvo/"
+                "9961306adf66f7340e27f109f096c9322d4f9636/lingvo/tasks/asr/params/librispeech.py"
+            ),
+            "basename": "librispeech.py",
+        },
+        "vocab": {
+            "uri": (
+                "https://raw.githubusercontent.com/tensorflow/lingvo/"
+                "9961306adf66f7340e27f109f096c9322d4f9636/lingvo/tasks/asr/wpm_16k_librispeech.vocab"
+            ),
+            "basename": "wpm_16k_librispeech.vocab",
+        },
+        "decoder": {
+            "uri": (
+                "https://raw.githubusercontent.com/hesseltuinhof/lingvo/qin_patched_decoder/lingvo/tasks/asr/decoder.py"
+            ),
+            "basename": "decoder_patched.py",
+        },
+    }
+
+    estimator_params = TensorFlowV2Estimator.estimator_params + ["random_seed", "sess"]
+
+    def __init__(
+        self,
+        clip_values: Optional["CLIP_VALUES_TYPE"] = None,
+        channels_first: Optional[bool] = None,
+        preprocessing_defences: Union["Preprocessor", List["Preprocessor"], None] = None,
+        postprocessing_defences: Union["Postprocessor", List["Postprocessor"], None] = None,
+        preprocessing: "PREPROCESSING_TYPE" = None,
+        random_seed: Optional[int] = None,
+        sess: Optional["Session"] = None,
+    ):
+        """
+        Initialization.
+
+        :param clip_values: Tuple of the form `(min, max)` of floats or `np.ndarray` representing the minimum and
+               maximum values allowed for features. If floats are provided, these will be used as the range of all
+               features. If arrays are provided, each value will be considered the bound for a feature, thus
+               the shape of clip values needs to match the total number of features.
+        :param channels_first: Set channels first or last.
+        :param preprocessing_defences: Preprocessing defence(s) to be applied by the classifier.
+        :param postprocessing_defences: Postprocessing defence(s) to be applied by the classifier.
+        :param preprocessing: Tuple of the form `(subtrahend, divisor)` of floats or `np.ndarray` of values to be
+                used for data preprocessing. The first value will be subtracted from the input. The input will then
+                be divided by the second one.
+        :param random_seed: Specify a random seed.
+        """
+        import pkg_resources
+
+        import tensorflow.compat.v1 as tf1
+
+        # Super initialization
+        super().__init__(
+            model=None,
+            clip_values=clip_values,
+            channels_first=channels_first,
+            preprocessing_defences=preprocessing_defences,
+            postprocessing_defences=postprocessing_defences,
+            preprocessing=preprocessing,
+        )
+        self.random_seed = random_seed
+        if self.postprocessing_defences is not None:
+            raise ValueError("This estimator does not support `postprocessing_defences`.")
+
+        self._input_shape = None
+
+        # check required TensorFlow version
+        if tf1.__version__ != "2.1.0":
+            raise AssertionError("The Lingvo estimator only supports TensorFlow 2.1.0.")
+
+        # check required Python version
+        if sys.version_info[:2] != (3, 6):
+            raise AssertionError("The Lingvo estimator only supports Python 3.6.")
+
+        # check required Lingvo version
+        if pkg_resources.get_distribution("lingvo").version != "0.6.4":
+            raise AssertionError("The Lingvo estimator only supports Lingvo 0.6.4")
+
+        # disable eager execution as Lingvo uses tensorflow.compat.v1 API
+        tf1.disable_eager_execution()
+
+        # init necessary local Lingvo ASR namespace and flags
+        sys.path.append(self._LINGVO_CFG["path"])
+        tf1.flags.FLAGS(tuple(sys.argv[0]))
+
+        # check and download additional Lingvo ASR params file
+        _ = self._check_and_download_file(
+            self._LINGVO_CFG["params"]["uri"], self._LINGVO_CFG["params"]["basename"], self._LINGVO_CFG["path"], "asr"
+        )
+
+        # placeholders
+        self._x_padded = tf1.placeholder(tf1.float32, shape=[None, None], name="art_x_padded")
+        self._y_target = tf1.placeholder(tf1.string, name="art_y_target")
+        self._mask_frequency = tf1.placeholder(tf1.float32, shape=[None, None, 80], name="art_mask_frequency")
+
+        # init Lingvo computation graph
+        self._sess: "Session" = tf1.Session() if sess is None else sess
+        model, task, cluster = self._load_model()
+        self._model = model
+        self._task = task
+        self._cluster = cluster
+        self._metrics = None
+
+        # add prediction and loss gradient ops to graph
+        self._predict_batch_op = self._predict_batch(self._x_padded, self._y_target, self._mask_frequency)
+        self._loss_gradient_op = self._loss_gradient(self._x_padded, self._y_target, self._mask_frequency)
+
+    @property
+    def input_shape(self) -> Tuple[int, ...]:
+        """
+        Return the shape of one input sample.
+
+        :return: Shape of one input sample.
+        """
+        return self._input_shape  # type: ignore
+
+    @property
+    def sess(self) -> "Session":
+        """
+        Get current TensorFlow session.
+
+        :return: The current TensorFlow session.
+        """
+        return self._sess
+
+    @staticmethod
+    def _check_and_download_file(uri: str, basename: str, *paths: str) -> str:
+        """Check and download the file from given URI."""
+        dir_path = os.path.join(*paths)
+        file_path = os.path.join(dir_path, basename)
+        if not os.path.isdir(dir_path):
+            make_directory(dir_path)
+        if not os.path.isfile(file_path):
+            logger.info("Could not find %s. Downloading it now...", basename)
+            get_file(basename, uri, path=dir_path)
+        return file_path
+
+    def _load_model(self):
+        """
+        Define and instantiate the computation graph.
+        """
+        import tensorflow.compat.v1 as tf1
+        from lingvo import model_registry, model_imports
+        from lingvo.core import cluster_factory
+
+        from asr.librispeech import Librispeech960Wpm
+
+        # check and download patched Lingvo ASR decoder
+        _ = self._check_and_download_file(
+            self._LINGVO_CFG["decoder"]["uri"], self._LINGVO_CFG["decoder"]["basename"], self._LINGVO_CFG["path"], "asr"
+        )
+
+        # monkey-patch the lingvo.asr.decoder.AsrDecoderBase._ComputeMetrics method with patched method according
+        # to Qin et al
+        import lingvo.tasks.asr.decoder as decoder
+        import asr.decoder_patched as decoder_patched
+
+        decoder.AsrDecoderBase._ComputeMetrics = decoder_patched.AsrDecoderBase._ComputeMetrics
+
+        # check and download Lingvo ASR vocab
+        # vocab_path = self._check_and_download_vocab()
+        vocab_path = self._check_and_download_file(
+            self._LINGVO_CFG["vocab"]["uri"], self._LINGVO_CFG["vocab"]["basename"], self._LINGVO_CFG["path"], "asr"
+        )
+
+        # monkey-patch tasks.asr.librispeechLibriSpeech960Wpm class attribute WPM_SYMBOL_TABLE_FILEPATH
+        Librispeech960Wpm.WPM_SYMBOL_TABLE_FILEPATH = vocab_path
+
+        # register model params
+        model_name = "asr.librispeech.Librispeech960Wpm"
+        model_imports.ImportParams(model_name)
+        params = model_registry._ModelRegistryHelper.GetParams(model_name, "Test")
+
+        # set random seed parameter
+        if self.random_seed is not None:
+            params.random_seed = self.random_seed
+
+        # instantiate Lingvo ASR model
+        cluster = cluster_factory.Cluster(params.cluster)
+        with cluster, tf1.device(cluster.GetPlacer()):
+            model = params.Instantiate()
+            task = model.GetTask()
+
+        # load Qin et al. pretrained model
+        _ = self._check_and_download_file(
+            self._LINGVO_CFG["model_data"]["uri"],
+            self._LINGVO_CFG["model_data"]["basename"],
+            self._LINGVO_CFG["path"],
+            "asr",
+            "model",
+        )
+        model_index_path = self._check_and_download_file(
+            self._LINGVO_CFG["model_index"]["uri"],
+            self._LINGVO_CFG["model_index"]["basename"],
+            self._LINGVO_CFG["path"],
+            "asr",
+            "model",
+        )
+        self._sess.run(tf1.global_variables_initializer())
+        saver = tf1.train.Saver([var for var in tf1.global_variables() if var.name.startswith("librispeech")])
+        saver.restore(self._sess, os.path.splitext(model_index_path)[0])
+
+        # set 'enable_asserts'-flag to False (Note: this flag ensures correct GPU support)
+        tf1.flags.FLAGS.enable_asserts = False
+
+        return model, task, cluster
+
+    def _create_decoder_input(self, x: "Tensor", y: "Tensor", mask_frequency: "Tensor") -> "Tensor":
+        """Create decoder input per batch."""
+        import tensorflow.compat.v1 as tf1
+        from lingvo.core.py_utils import NestedMap
+
+        # prepare model input source, i.e. input features
+        # note: paddings have values 0/1, where 1 represents a padded timestep
+        source_features = self._create_log_mel_features(x)
+        source_features *= tf1.expand_dims(mask_frequency, dim=-1)
+        source_paddings = 1.0 - mask_frequency[:, :, 0]
+
+        # prepare model input target, i.e. transcription target
+        target = self._task.input_generator.StringsToIds(y)
+
+        # create decoder input
+        decoder_inputs = NestedMap(
+            {
+                "src": NestedMap({"src_inputs": source_features, "paddings": source_paddings}),
+                "sample_ids": tf1.zeros(tf1.shape(source_features)[0]),
+                "tgt": NestedMap(zip(("ids", "labels", "paddings"), target)),
+            }
+        )
+        decoder_inputs.tgt["weights"] = 1.0 - decoder_inputs.tgt["paddings"]
+        return decoder_inputs
+
+    @staticmethod
+    def _create_log_mel_features(x: "Tensor") -> "Tensor":
+        """Extract Log-Mel features from audio samples of shape (batch_size, max_length)."""
+        from lingvo.core.py_utils import NestedMap
+        import tensorflow.compat.v1 as tf1
+
+        def _create_asr_frontend():
+            """Parameters corresponding to default ASR frontend."""
+            from lingvo.tasks.asr import frontend
+
+            params = frontend.MelAsrFrontend.Params()
+            # default params from Lingvo
+            params.sample_rate = 16000.0
+            params.frame_size_ms = 25.0
+            params.frame_step_ms = 10.0
+            params.num_bins = 80
+            params.lower_edge_hertz = 125.0
+            params.upper_edge_hertz = 7600.0
+            params.preemph = 0.97
+            params.noise_scale = 0.0
+            params.pad_end = False
+            # additional params from Qin et al.
+            params.stack_left_context = 2
+            params.frame_stride = 3
+            return params.Instantiate()
+
+        # init Lingvo ASR frontend
+        mel_frontend = _create_asr_frontend()
+
+        # extract log-mel features
+        log_mel = mel_frontend.FPropDefaultTheta(NestedMap(src_inputs=x, paddings=tf1.zeros_like(x)))
+        features = log_mel.src_inputs
+
+        # reshape features to shape (batch_size, n_frames, n_features, channels) in compliance with Qin et al.
+        features_shape = (tf1.shape(x)[0], -1, 80, features.shape[-1])
+        features = tf1.reshape(features, features_shape)
+        return features
+
+    @staticmethod
+    def _pad_audio_input(x: np.ndarray) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
+        """Apply padding to a batch of audio samples such that it has shape of (batch_size, max_length)."""
+        max_length = max(map(len, x))
+        batch_size = x.shape[0]
+
+        # calculate maximum frequency length
+        assert max_length >= 480, "Maximum length of audio input must be at least 480."
+        frequency_length = [((len(item) // 2 + 1) // 240 * 3) for item in x]
+        max_frequency_length = max(frequency_length)
+
+        x_padded = np.zeros((batch_size, max_length))
+        x_mask = np.zeros((batch_size, max_length), dtype=bool)
+        mask_frequency = np.zeros((batch_size, max_frequency_length, 80))
+
+        for i, x_i in enumerate(x):
+            x_padded[i, : len(x_i)] = x_i
+            x_mask[i, : len(x_i)] = 1
+            mask_frequency[i, : frequency_length[i], :] = 1
+        return x_padded, x_mask, mask_frequency
+
+    def _predict_batch(self, x: "Tensor", y: "Tensor", mask_frequency: "Tensor") -> "Tensor":
+        """Create prediction operation for a batch of padded inputs."""
+        import tensorflow.compat.v1 as tf1
+
+        # create decoder inputs
+        decoder_inputs = self._create_decoder_input(x, y, mask_frequency)
+
+        # call decoder
+        if self._metrics is None:
+            with self._cluster, tf1.device(self._cluster.GetPlacer()):
+                self._metrics = self._task.FPropDefaultTheta(decoder_inputs)
+        predictions = self._task.Decode(decoder_inputs)
+
+        return predictions
+
+    def predict(
+        self, x: np.ndarray, batch_size: int = 128, **kwargs
+    ) -> Union[Tuple[np.ndarray, np.ndarray], np.ndarray]:
+        """
+        Perform batch-wise prediction for given inputs.
+
+        :param x: Samples of shape `(nb_samples)` with values in range `[-32768, 32767]`. Note that it is allowable
+                  that sequences in the batch could have different lengths. A possible example of `x` could be:
+                  `x = np.ndarray([[0.1, 0.2, 0.1, 0.4], [0.3, 0.1]])`.
+        :param batch_size: Size of batches.
+        :return: Array of predicted transcriptions of shape `(nb_samples)`. A possible example of a transcription
+                 return is `np.array(['SIXTY ONE', 'HELLO'])`.
+        """
+        if x[0].ndim != 1:
+            raise ValueError(
+                "The LingvoASR estimator can only be used temporal data of type mono. Please remove any channel"
+                "dimension."
+            )
+        # if inputs have 32-bit floating point wav format, the preprocessing argument is required
+        is_normalized = max(map(max, np.abs(x))) <= 1.0
+        if is_normalized and self.preprocessing is None:
+            raise ValueError(
+                "The LingvoASR estimator requires input values in the range [-32768, 32767] or normalized input values"
+                " with correct preprocessing argument (mean=0, stddev=1/normalization_factor)."
+            )
+
+        nb_samples = x.shape[0]
+        assert nb_samples % batch_size == 0, "Number of samples must be divisible by batch_size"
+
+        # Apply preprocessing
+        x, _ = self._apply_preprocessing(x, y=None, fit=False)
+
+        y = list()
+        nb_batches = int(np.ceil(nb_samples / float(batch_size)))
+        for m in range(nb_batches):
+            # batch indices
+            begin, end = m * batch_size, min((m + 1) * batch_size, nb_samples)
+
+            x_batch_padded, _, mask_frequency = self._pad_audio_input(x[begin:end])
+
+            feed_dict = {
+                self._x_padded: x_batch_padded,
+                self._y_target: np.array(["DUMMY"] * batch_size),
+                self._mask_frequency: mask_frequency,
+            }
+            # run prediction
+            y_batch = self._sess.run(self._predict_batch_op, feed_dict)
+
+            # extract and append transcription result
+            y += y_batch["topk_decoded"][:, 0].tolist()
+
+        y_decoded = [item.decode("utf-8").upper() for item in y]
+        return np.array(y_decoded, dtype=str)
+
+    def _loss_gradient(self, x: "Tensor", y: "Tensor", mask: "Tensor") -> "Tensor":
+        """Define loss gradients computation operation for a batch of padded inputs."""
+        import tensorflow.compat.v1 as tf1
+
+        # create decoder inputs
+        decoder_inputs = self._create_decoder_input(x, y, mask)
+
+        # call decoder
+        if self._metrics is None:
+            with self._cluster, tf1.device(self._cluster.GetPlacer()):
+                self._metrics = self._task.FPropDefaultTheta(decoder_inputs)
+
+        # compute loss gradient
+        loss = tf1.get_collection("per_loss")[0]
+        loss_gradient = tf1.gradients(loss, [x])[0]
+        return loss_gradient
+
+    def loss_gradient(self, x: np.ndarray, y: np.ndarray, batch_mode: bool = False, **kwargs) -> np.ndarray:
+        """
+        Compute the gradient of the loss function w.r.t. `x`.
+
+        :param x: Samples of shape `(nb_samples)`. Note that, it is allowable that sequences in the batch
+                  could have different lengths. A possible example of `x` could be:
+                  `x = np.ndarray([[0.1, 0.2, 0.1, 0.4], [0.3, 0.1]])`.
+        :param y: Target values of shape (nb_samples). Each sample in `y` is a string and it may possess different
+                  lengths. A possible example of `y` could be: `y = np.array(['SIXTY ONE', 'HELLO'])`.
+        :param batch_mode: If `True` calculate gradient per batch or otherwise per sequence.
+        :return: Loss gradients of the same shape as `x`.
+        """
+        # if inputs have 32-bit floating point wav format, the preprocessing argument is required
+        is_normalized = max(map(max, np.abs(x))) <= 1.0
+        if is_normalized and self.preprocessing is None:
+            raise ValueError(
+                "The LingvoASR estimator requires input values in the range [-32768, 32767] or normalized input values"
+                " with correct preprocessing argument (mean=0, stddev=1/normalization_factor)."
+            )
+
+        # Lingvo model works with lower case transcriptions
+        y = np.array([y_i.lower() for y_i in y])
+
+        # Apply preprocessing
+        x_preprocessed, y_preprocessed = self._apply_preprocessing(x, y, fit=False)
+
+        if batch_mode:
+            gradients = self._loss_gradient_per_batch(x_preprocessed, y_preprocessed)
+        else:
+            gradients = self._loss_gradient_per_sequence(x_preprocessed, y_preprocessed)
+
+        # Apply preprocessing gradients
+        gradients = self._apply_preprocessing_gradient(x, gradients)
+        return gradients
+
+    def _loss_gradient_per_batch(self, x: np.ndarray, y: np.ndarray) -> np.ndarray:
+        """
+        Compute the gradient of the loss function w.r.t. `x` per batch.
+        """
+        assert x.shape[0] == y.shape[0], "Number of samples in x and y differ."
+
+        # pad input
+        x_padded, mask, mask_frequency = self._pad_audio_input(x)
+
+        # get loss gradients
+        feed_dict = {
+            self._x_padded: x_padded,
+            self._y_target: y,
+            self._mask_frequency: mask_frequency,
+        }
+        gradients_padded = self._sess.run(self._loss_gradient_op, feed_dict)
+
+        # undo padding, i.e. change gradients shape from (nb_samples, max_length) to (nb_samples)
+        lengths = mask.sum(axis=1)
+        gradients = list()
+        for gradient_padded, length in zip(gradients_padded, lengths):
+            gradient = gradient_padded[:length]
+            gradients.append(gradient)
+
+        # for ragged input, use np.object dtype
+        dtype = np.float32 if x.ndim != 1 else np.object
+        return np.array(gradients, dtype=dtype)
+
+    def _loss_gradient_per_sequence(self, x: np.ndarray, y: np.ndarray) -> np.ndarray:
+        """
+        Compute the gradient of the loss function w.r.t. `x` per sequence.
+        """
+        assert x.shape[0] == y.shape[0], "Number of samples in x and y differ."
+
+        # get frequency masks
+        _, _, mask_frequency = self._pad_audio_input(x)
+
+        # iterate over sequences
+        gradients = list()
+        for x_i, y_i, mask_frequency_i in zip(x, y, mask_frequency):
+            # calculate frequency length for x_i
+            frequency_length = (len(x_i) // 2 + 1) // 240 * 3
+
+            feed_dict = {
+                self._x_padded: np.expand_dims(x_i, 0),
+                self._y_target: np.array([y_i]),
+                self._mask_frequency: np.expand_dims(mask_frequency_i[:frequency_length], 0),
+            }
+            # get loss gradient
+            gradient = self._sess.run(self._loss_gradient_op, feed_dict)
+            gradients.append(np.squeeze(gradient))
+
+        # for ragged input, use np.object dtype
+        dtype = np.float32 if x.ndim != 1 else np.object
+        return np.array(gradients, dtype=dtype)
+
+    def get_activations(
+        self, x: np.ndarray, layer: Union[int, str], batch_size: int, framework: bool = False
+    ) -> np.ndarray:
+        raise NotImplementedError
+
+    def compute_loss(self, x: np.ndarray, y: np.ndarray, **kwargs) -> np.ndarray:
+        raise NotImplementedError
diff --git a/adversarial-robustness-toolbox/art/estimators/tensorflow.py b/adversarial-robustness-toolbox/art/estimators/tensorflow.py
new file mode 100644
index 0000000..398534e
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/estimators/tensorflow.py
@@ -0,0 +1,290 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements the abstract estimators `TensorFlowEstimator` and `TensorFlowV2Estimator` for TensorFlow models.
+"""
+import logging
+from typing import Any, Tuple, TYPE_CHECKING
+
+import numpy as np
+
+from art import config
+from art.estimators.estimator import (
+    BaseEstimator,
+    LossGradientsMixin,
+    NeuralNetworkMixin,
+)
+
+if TYPE_CHECKING:
+    import tensorflow as tf
+
+logger = logging.getLogger(__name__)
+
+
+class TensorFlowEstimator(NeuralNetworkMixin, LossGradientsMixin, BaseEstimator):
+    """
+    Estimator class for TensorFlow models.
+    """
+
+    estimator_params = BaseEstimator.estimator_params + NeuralNetworkMixin.estimator_params
+
+    def __init__(self, **kwargs) -> None:
+        """
+        Estimator class for TensorFlow models.
+        """
+        self._sess: "tf.python.client.session.Session" = None
+        super().__init__(**kwargs)
+
+    def predict(self, x: np.ndarray, batch_size: int = 128, **kwargs):
+        """
+        Perform prediction of the neural network for samples `x`.
+
+        :param x: Samples of shape (nb_samples, nb_features) or (nb_samples, nb_pixels_1, nb_pixels_2,
+                  nb_channels) or (nb_samples, nb_channels, nb_pixels_1, nb_pixels_2).
+        :param batch_size: Batch size.
+        :return: Predictions.
+        :rtype: Format as expected by the `model`
+        """
+        return NeuralNetworkMixin.predict(self, x, batch_size=batch_size, **kwargs)
+
+    def fit(self, x: np.ndarray, y, batch_size: int = 128, nb_epochs: int = 20, **kwargs) -> None:
+        """
+        Fit the model of the estimator on the training data `x` and `y`.
+
+        :param x: Samples of shape (nb_samples, nb_features) or (nb_samples, nb_pixels_1, nb_pixels_2,
+                  nb_channels) or (nb_samples, nb_channels, nb_pixels_1, nb_pixels_2).
+        :param y: Target values.
+        :type y: Format as expected by the `model`
+        :param batch_size: Batch size.
+        :param nb_epochs: Number of training epochs.
+        """
+        NeuralNetworkMixin.fit(self, x, y, batch_size=batch_size, nb_epochs=nb_epochs, **kwargs)
+
+    @property
+    def sess(self) -> "tf.python.client.session.Session":
+        """
+        Get current TensorFlow session.
+
+        :return: The current TensorFlow session.
+        """
+        if self._sess is not None:
+            return self._sess
+
+        raise NotImplementedError("A valid TensorFlow session is not available.")
+
+
+class TensorFlowV2Estimator(NeuralNetworkMixin, LossGradientsMixin, BaseEstimator):
+    """
+    Estimator class for TensorFlow v2 models.
+    """
+
+    estimator_params = BaseEstimator.estimator_params + NeuralNetworkMixin.estimator_params
+
+    def __init__(self, **kwargs):
+        """
+        Estimator class for TensorFlow v2 models.
+        """
+        preprocessing = kwargs.get("preprocessing")
+        if isinstance(preprocessing, tuple):
+            from art.preprocessing.standardisation_mean_std.tensorflow import StandardisationMeanStdTensorFlow
+
+            kwargs["preprocessing"] = StandardisationMeanStdTensorFlow(mean=preprocessing[0], std=preprocessing[1])
+
+        super().__init__(**kwargs)
+        TensorFlowV2Estimator._check_params(self)
+
+    def predict(self, x: np.ndarray, batch_size: int = 128, **kwargs):
+        """
+        Perform prediction of the neural network for samples `x`.
+
+        :param x: Samples of shape (nb_samples, nb_features) or (nb_samples, nb_pixels_1, nb_pixels_2,
+                  nb_channels) or (nb_samples, nb_channels, nb_pixels_1, nb_pixels_2).
+        :param batch_size: Batch size.
+        :return: Predictions.
+        :rtype: Format as expected by the `model`
+        """
+        return NeuralNetworkMixin.predict(self, x, batch_size=batch_size, **kwargs)
+
+    def fit(self, x: np.ndarray, y, batch_size: int = 128, nb_epochs: int = 20, **kwargs) -> None:
+        """
+        Fit the model of the estimator on the training data `x` and `y`.
+
+        :param x: Samples of shape (nb_samples, nb_features) or (nb_samples, nb_pixels_1, nb_pixels_2,
+                  nb_channels) or (nb_samples, nb_channels, nb_pixels_1, nb_pixels_2).
+        :param y: Target values.
+        :type y: Format as expected by the `model`
+        :param batch_size: Batch size.
+        :param nb_epochs: Number of training epochs.
+        """
+        NeuralNetworkMixin.fit(self, x, y, batch_size=batch_size, nb_epochs=nb_epochs, **kwargs)
+
+    def set_params(self, **kwargs) -> None:
+        """
+        Take a dictionary of parameters and apply checks before setting them as attributes.
+
+        :param kwargs: A dictionary of attributes.
+        """
+        super().set_params(**kwargs)
+        self._check_params()
+
+    def _check_params(self) -> None:
+        from art.defences.preprocessor.preprocessor import PreprocessorTensorFlowV2
+
+        super()._check_params()
+        self.all_framework_preprocessing = all(
+            [isinstance(p, PreprocessorTensorFlowV2) for p in self.preprocessing_operations]
+        )
+
+    def _apply_preprocessing(self, x, y, fit: bool = False) -> Tuple[Any, Any]:
+        """
+        Apply all preprocessing defences of the estimator on the raw inputs `x` and `y`. This function is should
+        only be called from function `_apply_preprocessing`.
+
+        The method overrides art.estimators.estimator::BaseEstimator._apply_preprocessing().
+        It requires all defenses to have a method `forward()`.
+        It converts numpy arrays to TensorFlow tensors first, then chains a series of defenses by calling
+        defence.forward() which contains TensorFlow operations. At the end, it converts TensorFlow tensors
+        back to numpy arrays.
+
+        :param x: Samples.
+        :type x: Format as expected by the `model`
+        :param y: Target values.
+        :type y: Format as expected by the `model`
+        :param fit: `True` if the function is call before fit/training and `False` if the function is called before a
+                    predict operation.
+        :return: Tuple of `x` and `y` after applying the defences and standardisation.
+        :rtype: Format as expected by the `model`
+        """
+        import tensorflow as tf  # lgtm [py/repeated-import]
+        from art.preprocessing.standardisation_mean_std.numpy import StandardisationMeanStd
+        from art.preprocessing.standardisation_mean_std.tensorflow import StandardisationMeanStdTensorFlow
+
+        if not self.preprocessing_operations:
+            return x, y
+
+        input_is_tensor = isinstance(x, tf.Tensor)
+
+        if self.all_framework_preprocessing and not (not input_is_tensor and x.dtype == np.object):
+            # Convert np arrays to torch tensors.
+            if not input_is_tensor:
+                x = tf.convert_to_tensor(x)
+                if y is not None:
+                    y = tf.convert_to_tensor(y)
+
+            for preprocess in self.preprocessing_operations:
+                if fit:
+                    if preprocess.apply_fit:
+                        x, y = preprocess.forward(x, y)
+                else:
+                    if preprocess.apply_predict:
+                        x, y = preprocess.forward(x, y)
+
+            # Convert torch tensors back to np arrays.
+            if not input_is_tensor:
+                x = x.numpy()
+                if y is not None:
+                    y = y.numpy()
+
+        elif len(self.preprocessing_operations) == 1 or (
+            len(self.preprocessing_operations) == 2
+            and isinstance(
+                self.preprocessing_operations[-1], (StandardisationMeanStd, StandardisationMeanStdTensorFlow)
+            )
+        ):
+            # Compatible with non-TensorFlow defences if no chaining.
+            for preprocess in self.preprocessing_operations:
+                x, y = preprocess(x, y)
+
+        else:
+            raise NotImplementedError("The current combination of preprocessing types is not supported.")
+
+        return x, y
+
+    def _apply_preprocessing_gradient(self, x, gradients, fit=False):
+        """
+        Apply the backward pass to the gradients through all preprocessing defences that have been applied to `x`
+        and `y` in the forward pass. This function is should only be called from function
+        `_apply_preprocessing_gradient`.
+
+        The method overrides art.estimators.estimator::LossGradientsMixin._apply_preprocessing_gradient().
+        It requires all defenses to have a method estimate_forward().
+        It converts numpy arrays to TensorFlow tensors first, then chains a series of defenses by calling
+        defence.estimate_forward() which contains differentiable estimate of the operations. At the end,
+        it converts TensorFlow tensors back to numpy arrays.
+
+        :param x: Samples.
+        :type x: Format as expected by the `model`
+        :param gradients: Gradients before backward pass through preprocessing defences.
+        :type gradients: Format as expected by the `model`
+        :param fit: `True` if the gradients are computed during training.
+        :return: Gradients after backward pass through preprocessing defences.
+        :rtype: Format as expected by the `model`
+        """
+        import tensorflow as tf  # lgtm [py/repeated-import]
+        from art.preprocessing.standardisation_mean_std.numpy import StandardisationMeanStd
+        from art.preprocessing.standardisation_mean_std.tensorflow import StandardisationMeanStdTensorFlow
+
+        if not self.preprocessing_operations:
+            return gradients
+
+        input_is_tensor = isinstance(x, tf.Tensor)
+
+        if self.all_framework_preprocessing and not (not input_is_tensor and x.dtype == np.object):
+            with tf.GradientTape() as tape:
+                # Convert np arrays to TensorFlow tensors.
+                x = tf.convert_to_tensor(x, dtype=config.ART_NUMPY_DTYPE)
+                tape.watch(x)
+                gradients = tf.convert_to_tensor(gradients, dtype=config.ART_NUMPY_DTYPE)
+                x_orig = x
+
+                for preprocess in self.preprocessing_operations:
+                    if fit:
+                        if preprocess.apply_fit:
+                            x = preprocess.estimate_forward(x)
+                    else:
+                        if preprocess.apply_predict:
+                            x = preprocess.estimate_forward(x)
+
+            x_grad = tape.gradient(target=x, sources=x_orig, output_gradients=gradients)
+
+            # Convert torch tensors back to np arrays.
+            gradients = x_grad.numpy()
+            if gradients.shape != x_orig.shape:
+                raise ValueError(
+                    "The input shape is {} while the gradient shape is {}".format(x.shape, gradients.shape)
+                )
+
+        elif len(self.preprocessing_operations) == 1 or (
+            len(self.preprocessing_operations) == 2
+            and isinstance(
+                self.preprocessing_operations[-1], (StandardisationMeanStd, StandardisationMeanStdTensorFlow)
+            )
+        ):
+            # Compatible with non-TensorFlow defences if no chaining.
+            for preprocess in self.preprocessing_operations[::-1]:
+                if fit:
+                    if preprocess.apply_fit:
+                        gradients = preprocess.estimate_gradient(x, gradients)
+                else:
+                    if preprocess.apply_predict:
+                        gradients = preprocess.estimate_gradient(x, gradients)
+
+        else:
+            raise NotImplementedError("The current combination of preprocessing types is not supported.")
+
+        return gradients
diff --git a/adversarial-robustness-toolbox/art/evaluations/__init__.py b/adversarial-robustness-toolbox/art/evaluations/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/adversarial-robustness-toolbox/art/evaluations/evaluation.py b/adversarial-robustness-toolbox/art/evaluations/evaluation.py
new file mode 100644
index 0000000..8e6fd53
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/evaluations/evaluation.py
@@ -0,0 +1,35 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module contains the abstract base class for evaluations.
+"""
+from abc import ABC, abstractmethod
+from typing import Any
+
+
+class Evaluation(ABC):
+    """
+    This class defines the abstract base class for evaluations.
+    """
+
+    @abstractmethod
+    def evaluate(self, *args, **kwargs) -> Any:
+        """
+        Abstract method running the evaluation.
+        """
+        raise NotImplementedError
diff --git a/adversarial-robustness-toolbox/art/evaluations/security_curve/__init__.py b/adversarial-robustness-toolbox/art/evaluations/security_curve/__init__.py
new file mode 100644
index 0000000..f6fc6f1
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/evaluations/security_curve/__init__.py
@@ -0,0 +1,4 @@
+"""
+This module implements the evaluation of Security Curves.
+"""
+from art.evaluations.security_curve.security_curve import SecurityCurve
diff --git a/adversarial-robustness-toolbox/art/evaluations/security_curve/security_curve.py b/adversarial-robustness-toolbox/art/evaluations/security_curve/security_curve.py
new file mode 100644
index 0000000..cb3765a
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/evaluations/security_curve/security_curve.py
@@ -0,0 +1,182 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements the evaluation of Security Curves.
+
+Examples of Security Curves can be found in Figure 6 of Madry et al., 2017 (https://arxiv.org/abs/1706.06083).
+"""
+from typing import List, Optional, Tuple, TYPE_CHECKING, Union
+
+import numpy as np
+from matplotlib import pyplot as plt
+
+from art.evaluations.evaluation import Evaluation
+from art.attacks.evasion.projected_gradient_descent.projected_gradient_descent import ProjectedGradientDescent
+
+if TYPE_CHECKING:
+    from art.utils import CLASSIFIER_LOSS_GRADIENTS_TYPE
+
+
+class SecurityCurve(Evaluation):
+    """
+    This class implements the evaluation of Security Curves.
+
+    Examples of Security Curves can be found in Figure 6 of Madry et al., 2017 (https://arxiv.org/abs/1706.06083).
+    """
+
+    def __init__(self, eps: Union[int, List[float], List[int]]):
+        """
+        Create an instance of a Security Curve evaluation.
+
+        :param eps: Defines the attack budgets `eps` for Projected Gradient Descent used for evaluation.
+        """
+
+        self.eps = eps
+        self.eps_list: List[float] = list()
+        self.accuracy_adv_list: List[float] = list()
+        self.accuracy: Optional[float] = None
+
+    def evaluate(
+        self,
+        classifier: "CLASSIFIER_LOSS_GRADIENTS_TYPE",
+        x: np.ndarray,
+        y: np.ndarray,
+        **kwargs: Union[str, bool, int, float]
+    ) -> Tuple[List[float], List[float], float]:
+        """
+        Evaluate the Security Curve of a classifier using Projected Gradient Descent.
+
+        :param classifier: A trained classifier that provides loss gradients.
+        :param x: Input data to classifier for evaluation.
+        :param y: True labels for input data `x`.
+        :param kwargs: Keyword arguments for the Projected Gradient Descent attack used for evaluation, except keywords
+                       `classifier` and `eps`.
+        :return: List of evaluated `eps` values, List of adversarial accuracies, and benign accuracy.
+        """
+
+        kwargs.pop("classifier", None)
+        kwargs.pop("eps", None)
+        self.eps_list.clear()
+        self.accuracy_adv_list.clear()
+        self.accuracy = None
+
+        # Check type of eps
+        if isinstance(self.eps, int):
+            eps_increment = (classifier.clip_values[1] - classifier.clip_values[0]) / self.eps
+
+            for i in range(1, self.eps + 1):
+                self.eps_list.append(i * eps_increment)
+
+        else:
+            self.eps_list = self.eps.copy()
+
+        # Determine benign accuracy
+        y_pred = classifier.predict(x=x, y=y)
+        self.accuracy = self._get_accuracy(y=y, y_pred=y_pred)
+
+        # Determine adversarial accuracy for each eps
+        for eps in self.eps_list:
+            attack_pgd = ProjectedGradientDescent(estimator=classifier, eps=eps, **kwargs)
+
+            x_adv = attack_pgd.generate(x=x, y=y)
+
+            y_pred_adv = classifier.predict(x=x_adv, y=y)
+            accuracy_adv = self._get_accuracy(y=y, y_pred=y_pred_adv)
+            self.accuracy_adv_list.append(accuracy_adv)
+
+        # Check gradients for potential obfuscation
+        self._check_gradient(classifier=classifier, x=x, y=y, **kwargs)
+
+        return self.eps_list, self.accuracy_adv_list, self.accuracy
+
+    @property
+    def detected_obfuscating_gradients(self) -> bool:
+        """
+        This property describes if the previous call to method `evaluate` identified potential gradient obfuscation.
+        """
+        return self._detected_obfuscating_gradients
+
+    def _check_gradient(
+        self,
+        classifier: "CLASSIFIER_LOSS_GRADIENTS_TYPE",
+        x: np.ndarray,
+        y: np.ndarray,
+        **kwargs: Union[str, bool, int, float]
+    ) -> None:
+        """
+        Check if potential gradient obfuscation can be detected. Projected Gradient Descent with 100 iterations is run
+        with maximum attack budget `eps` being equal to upper clip value of input data and `eps_step` of
+        `eps / (max_iter / 2)`.
+
+        :param classifier: A trained classifier that provides loss gradients.
+        :param x: Input data to classifier for evaluation.
+        :param y: True labels for input data `x`.
+        :param kwargs: Keyword arguments for the Projected Gradient Descent attack used for evaluation, except keywords
+                       `classifier` and `eps`.
+        """
+        # Define parameters for Projected Gradient Descent
+        max_iter = 100
+        kwargs["max_iter"] = max_iter
+        kwargs["eps"] = float(classifier.clip_values[1])
+        kwargs["eps_step"] = float(classifier.clip_values[1] / (max_iter / 2))
+
+        # Create attack
+        attack_pgd = ProjectedGradientDescent(estimator=classifier, **kwargs)
+
+        # Evaluate accuracy with maximal attack budget
+        x_adv = attack_pgd.generate(x=x, y=y)
+        y_pred_adv = classifier.predict(x=x_adv, y=y)
+        accuracy_adv = self._get_accuracy(y=y, y_pred=y_pred_adv)
+
+        # Decide of obfuscated gradients likely
+        if accuracy_adv > 1 / classifier.nb_classes:
+            self._detected_obfuscating_gradients = True
+        else:
+            self._detected_obfuscating_gradients = False
+
+    def plot(self) -> None:
+        """
+        Plot the Security Curve of adversarial accuracy as function opf attack budget `eps` together with the accuracy
+        on benign samples.
+        """
+        plt.plot(self.eps_list, self.accuracy_adv_list, label="adversarial", marker="o")
+        plt.plot([self.eps_list[0], self.eps_list[-1]], [self.accuracy, self.accuracy], linestyle="--", label="benign")
+        plt.legend()
+        plt.xlabel("Attack budget eps")
+        plt.ylabel("Accuracy")
+        if self.detected_obfuscating_gradients:
+            plt.title("Potential gradient obfuscation detected.")
+        else:
+            plt.title("No gradient obfuscation detected")
+        plt.ylim([0, 1.05])
+        plt.show()
+
+    @staticmethod
+    def _get_accuracy(y: np.ndarray, y_pred: np.ndarray) -> float:
+        """
+        Calculate accuracy of predicted labels.
+
+        :param y: True labels.
+        :param y_pred: Predicted labels.
+        :return: Accuracy.
+        """
+        return np.mean(np.argmax(y, axis=1) == np.argmax(y_pred, axis=1)).item()
+
+    def __repr__(self):
+        repr_ = "{}(eps={})".format(self.__module__ + "." + self.__class__.__name__, self.eps,)
+        return repr_
diff --git a/adversarial-robustness-toolbox/art/exceptions.py b/adversarial-robustness-toolbox/art/exceptions.py
new file mode 100644
index 0000000..00c1857
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/exceptions.py
@@ -0,0 +1,52 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+Module containing ART's exceptions.
+"""
+from typing import List
+
+
+class EstimatorError(TypeError):
+    """
+    Basic exception for errors raised by unexpected estimator types.
+    """
+
+    def __init__(self, this_class, class_expected_list: List[str], classifier_given) -> None:
+        self.this_class = this_class
+        self.class_expected_list = class_expected_list
+        self.classifier_given = classifier_given
+
+        classes_expected_message = ""
+        for idx, class_expected in enumerate(class_expected_list):
+            if idx == 0:
+                classes_expected_message += "{0}".format(class_expected)
+            else:
+                classes_expected_message += " and {0}".format(class_expected)
+
+        self.message = (
+            "{0} requires an estimator derived from {1}, "
+            "the provided classifier is an instance of {2} and is derived from {3}.".format(
+                this_class.__name__,
+                classes_expected_message,
+                type(classifier_given),
+                classifier_given.__class__.__bases__,
+            )
+        )
+
+    def __str__(self) -> str:
+        return self.message
diff --git a/adversarial-robustness-toolbox/art/metrics/__init__.py b/adversarial-robustness-toolbox/art/metrics/__init__.py
new file mode 100644
index 0000000..e77eab7
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/metrics/__init__.py
@@ -0,0 +1,12 @@
+"""
+Module providing metrics and verifications.
+"""
+from art.metrics.metrics import empirical_robustness
+from art.metrics.metrics import loss_sensitivity
+from art.metrics.metrics import clever
+from art.metrics.metrics import clever_u
+from art.metrics.metrics import clever_t
+from art.metrics.metrics import wasserstein_distance
+from art.metrics.verification_decisions_trees import RobustnessVerificationTreeModelsCliqueMethod
+from art.metrics.gradient_check import loss_gradient_check
+from art.metrics.privacy import PDTP
diff --git a/adversarial-robustness-toolbox/art/metrics/gradient_check.py b/adversarial-robustness-toolbox/art/metrics/gradient_check.py
new file mode 100644
index 0000000..25b94cf
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/metrics/gradient_check.py
@@ -0,0 +1,53 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements gradient check functions for estimators
+"""
+from typing import TYPE_CHECKING
+
+import numpy as np
+from tqdm.auto import trange
+
+if TYPE_CHECKING:
+    from art.estimators.estimator import LossGradientsMixin
+
+
+def loss_gradient_check(
+    estimator: "LossGradientsMixin", x: np.ndarray, y: np.ndarray, training_mode: bool = False, **kwargs
+) -> np.ndarray:
+    """
+        Compute the gradient of the loss function w.r.t. `x` and identify points where the gradient is zero, nan, or inf
+
+        :param estimator: The classifier to be analyzed.
+        :param x: Input with shape as expected by the classifier's model.
+        :param y: Target values (class labels) one-hot-encoded of shape (nb_samples, nb_classes) or indices of shape
+                  (nb_samples,).
+        :param training_mode: `True` for model set to training mode and `'False` for model set to evaluation mode.
+        :return: Array of booleans with the shape (len(x), 3). If true means the gradient of the loss w.r.t. the
+                 particular `x` was bad (zero, nan, inf).
+        """
+    assert len(x) == len(y), "x and y must be the same length"
+
+    is_bad = []
+    for i in trange(len(x), desc="Gradient check"):
+        grad = estimator.loss_gradient(x=x[[i]], y=y[[i]], training_mode=training_mode, **kwargs)
+        is_bad.append(
+            [(np.min(grad) == 0 and np.max(grad) == 0), np.any(np.isnan(grad)), np.any(np.isinf(grad)),]
+        )
+
+    return np.array(is_bad, dtype=bool)
diff --git a/adversarial-robustness-toolbox/art/metrics/metrics.py b/adversarial-robustness-toolbox/art/metrics/metrics.py
new file mode 100644
index 0000000..04b923c
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/metrics/metrics.py
@@ -0,0 +1,403 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+Module implementing varying metrics for assessing model robustness. These fall mainly under two categories:
+attack-dependent and attack-independent.
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+from functools import reduce
+import logging
+from typing import Any, Dict, List, Optional, Union, TYPE_CHECKING
+
+import numpy as np
+import numpy.linalg as la
+from scipy.optimize import fmin as scipy_optimizer
+from scipy.stats import weibull_min
+from tqdm.auto import tqdm
+
+from art.config import ART_NUMPY_DTYPE
+from art.attacks.evasion.fast_gradient import FastGradientMethod
+from art.attacks.evasion.hop_skip_jump import HopSkipJump
+from art.utils import random_sphere
+
+if TYPE_CHECKING:
+    from art.attacks.attack import EvasionAttack
+    from art.utils import CLASSIFIER_TYPE, CLASSIFIER_LOSS_GRADIENTS_TYPE, CLASSIFIER_CLASS_LOSS_GRADIENTS_TYPE
+
+logger = logging.getLogger(__name__)
+
+SUPPORTED_METHODS: Dict[str, Dict[str, Any]] = {
+    "fgsm": {
+        "class": FastGradientMethod,
+        "params": {"eps_step": 0.1, "eps_max": 1.0, "clip_min": 0.0, "clip_max": 1.0},
+    },
+    "hsj": {"class": HopSkipJump, "params": {"max_iter": 50, "max_eval": 10000, "init_eval": 100, "init_size": 100,},},
+}
+
+
+def get_crafter(classifier: "CLASSIFIER_TYPE", attack: str, params: Optional[Dict[str, Any]] = None) -> "EvasionAttack":
+    """
+    Create an attack instance to craft adversarial samples.
+
+    :param classifier: A trained model.
+    :param attack: adversarial attack name.
+    :param params: Parameters specific to the adversarial attack.
+    :return: An attack instance.
+    """
+    try:
+        crafter = SUPPORTED_METHODS[attack]["class"](classifier)
+    except Exception:
+        raise NotImplementedError("{} crafting method not supported.".format(attack)) from Exception
+
+    if params:
+        crafter.set_params(**params)
+
+    return crafter
+
+
+def empirical_robustness(
+    classifier: "CLASSIFIER_TYPE", x: np.ndarray, attack_name: str, attack_params: Optional[Dict[str, Any]] = None,
+) -> Union[float, np.ndarray]:
+    """
+    Compute the Empirical Robustness of a classifier object over the sample `x` for a given adversarial crafting
+    method `attack`. This is equivalent to computing the minimal perturbation that the attacker must introduce for a
+    successful attack.
+
+    | Paper link: https://arxiv.org/abs/1511.04599
+
+    :param classifier: A trained model.
+    :param x: Data sample of shape that can be fed into `classifier`.
+    :param attack_name: A string specifying the attack to be used. Currently supported attacks are {`fgsm', `hsj`}
+                        (Fast Gradient Sign Method, Hop Skip Jump).
+    :param attack_params: A dictionary with attack-specific parameters. If the attack has a norm attribute, then it will
+                          be used as the norm for calculating the robustness; otherwise the standard Euclidean distance
+                          is used (norm=2).
+    :return: The average empirical robustness computed on `x`.
+    """
+    crafter = get_crafter(classifier, attack_name, attack_params)
+    crafter.set_params(**{"minimal": True})
+    adv_x = crafter.generate(x)
+
+    # Predict the labels for adversarial examples
+    y = classifier.predict(x)
+    y_pred = classifier.predict(adv_x)
+
+    idxs = np.argmax(y_pred, axis=1) != np.argmax(y, axis=1)
+    if np.sum(idxs) == 0.0:
+        return 0.0
+
+    norm_type = 2
+    if hasattr(crafter, "norm"):
+        norm_type = crafter.norm  # type: ignore
+    perts_norm = la.norm((adv_x - x).reshape(x.shape[0], -1), ord=norm_type, axis=1)
+    perts_norm = perts_norm[idxs]
+
+    return np.mean(perts_norm / la.norm(x[idxs].reshape(np.sum(idxs), -1), ord=norm_type, axis=1))
+
+
+# def nearest_neighbour_dist(classifier, x, x_ref, attack_name, attack_params=None):
+#     """
+#     Compute the (average) nearest neighbour distance between the sets `x` and `x_train`: for each point in `x`,
+#     measure the Euclidean distance to its closest point in `x_train`, then average over all points.
+#
+#     :param classifier: A trained model
+#     :type classifier: :class:`.Classifier`
+#     :param x: Data sample of shape that can be fed into `classifier`
+#     :type x: `np.ndarray`
+#     :param x_ref: Reference data sample to be considered as neighbors
+#     :type x_ref: `np.ndarray`
+#     :param attack_name: adversarial attack name
+#     :type attack_name: `str`
+#     :param attack_params: Parameters specific to the adversarial attack
+#     :type attack_params: `dict`
+#     :return: The average nearest neighbors distance
+#     :rtype: `float`
+#     """
+#     # Craft the adversarial examples
+#     crafter = get_crafter(classifier, attack_name, attack_params)
+#     adv_x = crafter.generate(x, minimal=True)
+#
+#     # Predict the labels for adversarial examples
+#     y = classifier.predict(x)
+#     y_pred = classifier.predict(adv_x)
+#
+#     adv_x_ = adv_x.reshape(adv_x.shape[0], np.prod(adv_x.shape[1:]))
+#     x_ = x_ref.reshape(x_ref.shape[0], np.prod(x_ref.shape[1:]))
+#     dists = la.norm(adv_x_ - x_, axis=1)
+#
+#     # TODO check if following computation is correct ?
+#     dists = np.min(dists, axis=1) / la.norm(x.reshape(x.shape[0], -1), ord=2, axis=1)
+#     idxs = (np.argmax(y_pred, axis=1) != np.argmax(y, axis=1))
+#     avg_nn_dist = np.mean(dists[idxs])
+#
+#     return avg_nn_dist
+
+
+def loss_sensitivity(classifier: "CLASSIFIER_LOSS_GRADIENTS_TYPE", x: np.ndarray, y: np.ndarray) -> np.ndarray:
+    """
+    Local loss sensitivity estimated through the gradients of the prediction at points in `x`.
+
+    | Paper link: https://arxiv.org/abs/1706.05394
+
+    :param classifier: A trained model.
+    :param x: Data sample of shape that can be fed into `classifier`.
+    :param y: Labels for sample `x`, one-hot encoded.
+    :return: The average loss sensitivity of the model.
+    """
+    grads = classifier.loss_gradient(x, y)
+    norm = la.norm(grads.reshape(grads.shape[0], -1), ord=2, axis=1)
+
+    return np.mean(norm)
+
+
+def clever(
+    classifier: "CLASSIFIER_CLASS_LOSS_GRADIENTS_TYPE",
+    x: np.ndarray,
+    nb_batches: int,
+    batch_size: int,
+    radius: float,
+    norm: int,
+    target: Union[int, List[int], None] = None,
+    target_sort: bool = False,
+    c_init: float = 1.0,
+    pool_factor: int = 10,
+) -> Optional[np.ndarray]:
+    """
+    Compute CLEVER score for an untargeted attack.
+
+    | Paper link: https://arxiv.org/abs/1801.10578
+
+    :param classifier: A trained model.
+    :param x: One input sample.
+    :param nb_batches: Number of repetitions of the estimate.
+    :param batch_size: Number of random examples to sample per batch.
+    :param radius: Radius of the maximum perturbation.
+    :param norm: Current support: 1, 2, np.inf.
+    :param target: Class or classes to target. If `None`, targets all classes.
+    :param target_sort: Should the target classes be sorted in prediction order. When `True` and `target` is `None`,
+           sort results.
+    :param c_init: initialization of Weibull distribution.
+    :param pool_factor: The factor to create a pool of random samples with size pool_factor x n_s.
+    :return: CLEVER score.
+    """
+    # Find the predicted class first
+    y_pred = classifier.predict(np.array([x]))
+    pred_class = np.argmax(y_pred, axis=1)[0]
+    if target is None:
+        # Get a list of untargeted classes
+        if target_sort:
+            target_classes = np.argsort(y_pred)[0][:-1]
+        else:
+            target_classes = [i for i in range(classifier.nb_classes) if i != pred_class]
+    elif isinstance(target, (int, np.integer)):
+        target_classes = [target]
+    else:
+        # Assume it's iterable
+        target_classes = target
+    score_list: List[Optional[float]] = []
+    for j in tqdm(target_classes, desc="CLEVER untargeted"):
+        if j == pred_class:
+            score_list.append(None)
+            continue
+        score = clever_t(classifier, x, j, nb_batches, batch_size, radius, norm, c_init, pool_factor)
+        score_list.append(score)
+    return np.array(score_list)
+
+
+def clever_u(
+    classifier: "CLASSIFIER_CLASS_LOSS_GRADIENTS_TYPE",
+    x: np.ndarray,
+    nb_batches: int,
+    batch_size: int,
+    radius: float,
+    norm: int,
+    c_init: float = 1.0,
+    pool_factor: int = 10,
+) -> float:
+    """
+    Compute CLEVER score for an untargeted attack.
+
+    | Paper link: https://arxiv.org/abs/1801.10578
+
+    :param classifier: A trained model.
+    :param x: One input sample.
+    :param nb_batches: Number of repetitions of the estimate.
+    :param batch_size: Number of random examples to sample per batch.
+    :param radius: Radius of the maximum perturbation.
+    :param norm: Current support: 1, 2, np.inf.
+    :param c_init: initialization of Weibull distribution.
+    :param pool_factor: The factor to create a pool of random samples with size pool_factor x n_s.
+    :return: CLEVER score.
+    """
+    # Get a list of untargeted classes
+    y_pred = classifier.predict(np.array([x]))
+    pred_class = np.argmax(y_pred, axis=1)[0]
+    untarget_classes = [i for i in range(classifier.nb_classes) if i != pred_class]
+
+    # Compute CLEVER score for each untargeted class
+    score_list = []
+    for j in tqdm(untarget_classes, desc="CLEVER untargeted"):
+        score = clever_t(classifier, x, j, nb_batches, batch_size, radius, norm, c_init, pool_factor)
+        score_list.append(score)
+
+    return np.min(score_list)
+
+
+def clever_t(
+    classifier: "CLASSIFIER_CLASS_LOSS_GRADIENTS_TYPE",
+    x: np.ndarray,
+    target_class: int,
+    nb_batches: int,
+    batch_size: int,
+    radius: float,
+    norm: int,
+    c_init: float = 1.0,
+    pool_factor: int = 10,
+) -> float:
+    """
+    Compute CLEVER score for a targeted attack.
+
+    | Paper link: https://arxiv.org/abs/1801.10578
+
+    :param classifier: A trained model.
+    :param x: One input sample.
+    :param target_class: Targeted class.
+    :param nb_batches: Number of repetitions of the estimate.
+    :param batch_size: Number of random examples to sample per batch.
+    :param radius: Radius of the maximum perturbation.
+    :param norm: Current support: 1, 2, np.inf.
+    :param c_init: Initialization of Weibull distribution.
+    :param pool_factor: The factor to create a pool of random samples with size pool_factor x n_s.
+    :return: CLEVER score.
+    """
+    # Check if the targeted class is different from the predicted class
+    y_pred = classifier.predict(np.array([x]))
+    pred_class = np.argmax(y_pred, axis=1)[0]
+    if target_class == pred_class:
+        raise ValueError("The targeted class is the predicted class.")
+
+    # Check if pool_factor is smaller than 1
+    if pool_factor < 1:
+        raise ValueError("The `pool_factor` must be larger than 1.")
+
+    # Some auxiliary vars
+    rand_pool_grad_set = []
+    grad_norm_set = []
+    dim = reduce(lambda x_, y: x_ * y, x.shape, 1)
+    shape = [pool_factor * batch_size]
+    shape.extend(x.shape)
+
+    # Generate a pool of samples
+    rand_pool = np.reshape(
+        random_sphere(nb_points=pool_factor * batch_size, nb_dims=dim, radius=radius, norm=norm), shape,
+    )
+    rand_pool += np.repeat(np.array([x]), pool_factor * batch_size, 0)
+    rand_pool = rand_pool.astype(ART_NUMPY_DTYPE)
+    if hasattr(classifier, "clip_values") and classifier.clip_values is not None:
+        np.clip(rand_pool, classifier.clip_values[0], classifier.clip_values[1], out=rand_pool)
+
+    # Change norm since q = p / (p-1)
+    if norm == 1:
+        norm = np.inf
+    elif norm == np.inf:
+        norm = 1
+    elif norm != 2:
+        raise ValueError("Norm {} not supported".format(norm))
+
+    # Compute gradients for all samples in rand_pool
+    for i in range(batch_size):
+        rand_pool_batch = rand_pool[i * pool_factor : (i + 1) * pool_factor]
+
+        # Compute gradients
+        grad_pred_class = classifier.class_gradient(rand_pool_batch, label=pred_class)
+        grad_target_class = classifier.class_gradient(rand_pool_batch, label=target_class)
+
+        if np.isnan(grad_pred_class).any() or np.isnan(grad_target_class).any():
+            raise Exception("The classifier results NaN gradients.")
+
+        grad = grad_pred_class - grad_target_class
+        grad = np.reshape(grad, (pool_factor, -1))
+        grad = np.linalg.norm(grad, ord=norm, axis=1)
+        rand_pool_grad_set.extend(grad)
+
+    rand_pool_grads = np.array(rand_pool_grad_set)
+
+    # Loop over the batches
+    for _ in range(nb_batches):
+        # Random selection of gradients
+        grad_norm = rand_pool_grads[np.random.choice(pool_factor * batch_size, batch_size)]
+        grad_norm = np.max(grad_norm)
+        grad_norm_set.append(grad_norm)
+
+    # Maximum likelihood estimation for max gradient norms
+    [_, loc, _] = weibull_min.fit(-np.array(grad_norm_set), c_init, optimizer=scipy_optimizer)
+
+    # Compute function value
+    values = classifier.predict(np.array([x]))
+    value = values[:, pred_class] - values[:, target_class]
+
+    # Compute scores
+    score = np.min([-value[0] / loc, radius])
+
+    return score
+
+
+def wasserstein_distance(
+    u_values: np.ndarray,
+    v_values: np.ndarray,
+    u_weights: Optional[np.ndarray] = None,
+    v_weights: Optional[np.ndarray] = None,
+) -> np.ndarray:
+    """
+    Compute the first Wasserstein distance between two 1D distributions.
+
+    :param u_values: Values of first distribution with shape (nb_samples, feature_dim_1, ..., feature_dim_n).
+    :param v_values: Values of second distribution with shape (nb_samples, feature_dim_1, ..., feature_dim_n).
+    :param u_weights: Weight for each value. If None, equal weights will be used.
+    :param v_weights: Weight for each value. If None, equal weights will be used.
+    :return: The Wasserstein distance between the two distributions.
+    """
+    import scipy
+
+    assert u_values.shape == v_values.shape
+    if u_weights is not None:
+        assert v_weights is not None
+    if u_weights is None:
+        assert v_weights is None
+    if u_weights is not None and v_weights is not None:
+        assert u_weights.shape == v_weights.shape
+    if u_weights is not None:
+        assert u_values.shape[0] == u_weights.shape[0]
+
+    u_values = u_values.flatten().reshape(u_values.shape[0], -1)
+    v_values = v_values.flatten().reshape(v_values.shape[0], -1)
+
+    if u_weights is not None and v_weights is not None:
+        u_weights = u_weights.flatten().reshape(u_weights.shape[0], -1)
+        v_weights = v_weights.flatten().reshape(v_weights.shape[0], -1)
+
+    w_d = np.zeros(u_values.shape[0])
+
+    for i in range(u_values.shape[0]):
+        if u_weights is None and v_weights is None:
+            w_d[i] = scipy.stats.wasserstein_distance(u_values[i], v_values[i])
+        elif u_weights is not None and v_weights is not None:
+            w_d[i] = scipy.stats.wasserstein_distance(u_values[i], v_values[i], u_weights[i], v_weights[i])
+
+    return w_d
diff --git a/adversarial-robustness-toolbox/art/metrics/privacy/__init__.py b/adversarial-robustness-toolbox/art/metrics/privacy/__init__.py
new file mode 100644
index 0000000..bde9a40
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/metrics/privacy/__init__.py
@@ -0,0 +1,4 @@
+"""
+Module providing metrics and verifications.
+"""
+from art.metrics.privacy.membership_leakage import PDTP
diff --git a/adversarial-robustness-toolbox/art/metrics/privacy/membership_leakage.py b/adversarial-robustness-toolbox/art/metrics/privacy/membership_leakage.py
new file mode 100644
index 0000000..5e24f8f
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/metrics/privacy/membership_leakage.py
@@ -0,0 +1,122 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements membership leakage metrics.
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+from typing import TYPE_CHECKING, Optional
+
+import numpy as np
+import scipy
+
+from art.utils import check_and_transform_label_format, is_probability
+
+if TYPE_CHECKING:
+    from art.estimators.classification.classifier import Classifier
+
+
+def PDTP(
+    target_estimator: "Classifier",
+    extra_estimator: "Classifier",
+    x: np.ndarray,
+    y: np.ndarray,
+    indexes: Optional[np.ndarray] = None,
+    num_iter: Optional[int] = 10,
+) -> np.ndarray:
+    """
+        Compute the pointwise differential training privacy metric for the given classifier and training set.
+        | Paper link: https://arxiv.org/abs/1712.09136
+
+        :param target_estimator: The classifier to be analyzed.
+        :param extra_estimator: Another classifier of the same type as the target classifier, but not yet fit.
+        :param x: The training data of the classifier.
+        :param y: Target values (class labels) of `x`, one-hot-encoded of shape (nb_samples, nb_classes) or indices of
+                  shape (nb_samples,).
+        :param indexes: the subset of indexes of `x` to compute the PDTP metric on. If not supplied, PDTP will be
+                        computed for all samples in `x`.
+        :param num_iter: the number of iterations of PDTP computation to run for each sample. If not supplied,
+                         defaults to 10. The result is the average across iterations.
+        :return: an array containing the average PDTP value for each sample in the training set. The higher the value,
+                 the higher the privacy leakage for that sample.
+    """
+    from art.estimators.classification.pytorch import PyTorchClassifier
+    from art.estimators.classification.tensorflow import TensorFlowV2Classifier
+    from art.estimators.classification.scikitlearn import ScikitlearnClassifier
+
+    supported_classifiers = (PyTorchClassifier, TensorFlowV2Classifier, ScikitlearnClassifier)
+
+    if not isinstance(target_estimator, supported_classifiers) or not isinstance(
+        extra_estimator, supported_classifiers
+    ):
+        raise ValueError("PDTP metric only supports classifiers of type PyTorch, TensorFlowV2 and ScikitLearn.")
+    if target_estimator.input_shape[0] != x.shape[1]:
+        raise ValueError("Shape of x does not match input_shape of classifier")
+    y = check_and_transform_label_format(y, target_estimator.nb_classes)
+    if y.shape[0] != x.shape[0]:
+        raise ValueError("Number of rows in x and y do not match")
+
+    results = []
+
+    for _ in range(num_iter):
+        iter_results = []
+        # get probabilities from original model
+        pred = target_estimator.predict(x)
+        if not is_probability(pred):
+            try:
+                pred = scipy.special.softmax(pred, axis=1)
+            except:
+                raise ValueError("PDTP metric only supports classifiers that output logits or probabilities.")
+        # divide into 100 bins and return center of bin
+        bins = np.array(np.arange(0.0, 1.01, 0.01).round(decimals=2))
+        pred_bin_indexes = np.digitize(pred, bins)
+        pred_bin = bins[pred_bin_indexes] - 0.005
+
+        if not indexes:
+            indexes = range(x.shape[0])
+        for row in indexes:
+            # create new model without sample in training data
+            alt_x = np.delete(x, row, 0)
+            alt_y = np.delete(y, row, 0)
+            try:
+                extra_estimator.reset()
+            except NotImplementedError as e:
+                raise ValueError(
+                    "PDTP metric can only be applied to classifiers that implement the reset method."
+                ) from e
+            extra_estimator.fit(alt_x, alt_y)
+            # get probabilities from new model
+            alt_pred = extra_estimator.predict(x)
+            if not is_probability(alt_pred):
+                alt_pred = scipy.special.softmax(alt_pred, axis=1)
+            # divide into 100 bins and return center of bin
+            alt_pred_bin_indexes = np.digitize(alt_pred, bins)
+            alt_pred_bin = bins[alt_pred_bin_indexes] - 0.005
+            ratio_1 = pred_bin / alt_pred_bin
+            ratio_2 = alt_pred_bin / pred_bin
+            # get max value
+            max_value = max(ratio_1.max(), ratio_2.max())
+            iter_results.append(max_value)
+        results.append(iter_results)
+
+    # get average of iterations for each sample
+    # We now have a list of list, internal lists represent an iteration. We need to transpose and get averages.
+    per_sample = list(map(list, zip(*results)))
+    avg_per_sample = np.array([sum(l) / len(l) for l in per_sample])
+
+    # return leakage per sample
+    return avg_per_sample
diff --git a/adversarial-robustness-toolbox/art/metrics/verification_decisions_trees.py b/adversarial-robustness-toolbox/art/metrics/verification_decisions_trees.py
new file mode 100644
index 0000000..7f15c01
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/metrics/verification_decisions_trees.py
@@ -0,0 +1,451 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements robustness verifications for decision-tree-based models.
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+from typing import Dict, List, Optional, Tuple, Union, TYPE_CHECKING
+
+import numpy as np
+from tqdm.auto import trange
+
+if TYPE_CHECKING:
+    from art.estimators.classification.classifier import ClassifierDecisionTree
+
+logger = logging.getLogger(__name__)
+
+
+class Interval:
+    """
+    Representation of an intervals bound.
+    """
+
+    def __init__(self, lower_bound: float, upper_bound: float) -> None:
+        """
+        An interval of a feature.
+
+        :param lower_bound: The lower boundary of the feature.
+        :param upper_bound: The upper boundary of the feature.
+        """
+        self.lower_bound = lower_bound
+        self.upper_bound = upper_bound
+
+
+class Box:
+    """
+    Representation of a box of intervals bounds.
+    """
+
+    def __init__(self, intervals: Optional[Dict[int, Interval]] = None) -> None:
+        """
+        A box of intervals.
+
+        :param intervals: A dictionary of intervals with features as keys.
+        """
+        if intervals is None:
+            self.intervals = dict()
+        else:
+            self.intervals = intervals
+
+    def intersect_with_box(self, box: "Box") -> None:
+        """
+        Get the intersection of two interval boxes. This function modifies this box instance.
+
+        :param box: Interval box to intersect with this box.
+        """
+        for key, value in box.intervals.items():
+            if key not in self.intervals:
+                self.intervals[key] = value
+            else:
+                lower_bound = max(self.intervals[key].lower_bound, value.lower_bound)
+                upper_bound = min(self.intervals[key].upper_bound, value.upper_bound)
+
+                if lower_bound >= upper_bound:
+                    self.intervals.clear()
+                    break
+
+                self.intervals[key] = Interval(lower_bound, upper_bound)
+
+    def get_intersection(self, box: "Box") -> "Box":
+        """
+        Get the intersection of two interval boxes. This function creates a new box instance.
+
+        :param box: Interval box to intersect with this box.
+        """
+        box_new = Box(intervals=self.intervals.copy())
+
+        for key, value in box.intervals.items():
+            if key not in box_new.intervals:
+                box_new.intervals[key] = value
+            else:
+                lower_bound = max(box_new.intervals[key].lower_bound, value.lower_bound)
+                upper_bound = min(box_new.intervals[key].upper_bound, value.upper_bound)
+
+                if lower_bound >= upper_bound:
+                    box_new.intervals.clear()
+                    return box_new
+
+                box_new.intervals[key] = Interval(lower_bound, upper_bound)
+
+        return box_new
+
+    def __repr__(self):
+        return self.__class__.__name__ + "({})".format(self.intervals)
+
+
+class LeafNode:
+    """
+    Representation of a leaf node of a decision tree.
+    """
+
+    def __init__(
+        self, tree_id: Optional[int], class_label: int, node_id: Optional[int], box: Box, value: float,
+    ) -> None:
+        """
+        Create a leaf node representation.
+
+        :param tree_id: ID of the decision tree.
+        :param class_label: ID of class to which this leaf node is contributing.
+        :param box: A box representing the n_feature-dimensional bounding intervals that reach this leaf node.
+        :param value: Prediction value at this leaf node.
+        """
+        self.tree_id = tree_id
+        self.class_label = class_label
+        self.node_id = node_id
+        self.box = box
+        self.value = value
+
+    def __repr__(self):
+        return self.__class__.__name__ + "({}, {}, {}, {}, {})".format(
+            self.tree_id, self.class_label, self.node_id, self.box, self.value
+        )
+
+
+class Tree:
+    """
+    Representation of a decision tree.
+    """
+
+    def __init__(self, class_id: Optional[int], leaf_nodes: List[LeafNode]) -> None:
+        """
+        Create a decision tree representation.
+
+        :param class_id: ID of the class to which this decision tree contributes.
+        :param leaf_nodes: A list of leaf nodes of this decision tree.
+        """
+        self.class_id = class_id
+        self.leaf_nodes = leaf_nodes
+
+
+class RobustnessVerificationTreeModelsCliqueMethod:
+    """
+    Robustness verification for decision-tree-based models.
+    Following the implementation in https://github.com/chenhongge/treeVerification (MIT License, 9 August 2019)
+
+    | Paper link: https://arxiv.org/abs/1906.03849
+    """
+
+    def __init__(self, classifier: "ClassifierDecisionTree") -> None:
+        """
+        Create robustness verification for a decision-tree-based classifier.
+
+        :param classifier: A trained decision-tree-based classifier.
+        """
+        self._classifier = classifier
+        self._trees = self._classifier.get_trees()
+
+    def verify(
+        self,
+        x: np.ndarray,
+        y: np.ndarray,
+        eps_init: float,
+        norm: int = np.inf,
+        nb_search_steps: int = 10,
+        max_clique: int = 2,
+        max_level: int = 2,
+    ) -> Tuple[float, float]:
+        """
+        Verify the robustness of the classifier on the dataset `(x, y)`.
+
+        :param x: Feature data of shape `(nb_samples, nb_features)`.
+        :param y: Labels, one-vs-rest encoding of shape `(nb_samples, nb_classes)`.
+        :param eps_init: Attack budget for the first search step.
+        :param norm: The norm to apply epsilon.
+        :param nb_search_steps: The number of search steps.
+        :param max_clique: The maximum number of nodes in a clique.
+        :param max_level: The maximum number of clique search levels.
+        :return: A tuple of the average robustness bound and the verification error at `eps`.
+        """
+        self.x: np.ndarray = x
+        self.y: np.ndarray = np.argmax(y, axis=1)
+        self.max_clique: int = max_clique
+        self.max_level: int = max_level
+
+        average_bound: float = 0.0
+        num_initial_successes: int = 0
+        num_samples: int = x.shape[0]
+
+        # pylint: disable=R1702
+        pbar = trange(num_samples, desc="Decision tree verification")
+        for i_sample in pbar:
+
+            eps: float = eps_init
+            robust_log: List[bool] = list()
+            i_robust = None
+            i_not_robust = None
+            eps_robust: float = 0.0
+            eps_not_robust: float = 0.0
+            best_score: Optional[float]
+
+            for i_step in range(nb_search_steps):
+                logger.info("Search step {0:d}: eps = {1:.4g}".format(i_step, eps))
+
+                is_robust = True
+
+                if self._classifier.nb_classes <= 2:
+                    best_score = self._get_best_score(i_sample, eps, norm, target_label=None)
+                    is_robust = (self.y[i_sample] < 0.5 and best_score < 0) or (
+                        self.y[i_sample] > 0.5 and best_score > 0.0
+                    )
+                else:
+                    for i_class in range(self._classifier.nb_classes):
+                        if i_class != self.y[i_sample]:
+                            best_score = self._get_best_score(i_sample, eps, norm, target_label=i_class)
+                            is_robust = is_robust and (best_score > 0.0)
+                            if not is_robust:
+                                break
+
+                robust_log.append(is_robust)
+
+                if is_robust:
+                    if i_step == 0:
+                        num_initial_successes += 1
+                    logger.info("Model is robust at eps = {:.4g}".format(eps))
+                    i_robust = i_step
+                    eps_robust = eps
+                else:
+                    logger.info("Model is not robust at eps = {:.4g}".format(eps))
+                    i_not_robust = i_step
+                    eps_not_robust = eps
+
+                if i_robust is None:
+                    eps /= 2.0
+                else:
+                    if i_not_robust is None:
+                        if eps >= 1.0:
+                            logger.info("Abort binary search because eps increased above 1.0")
+                            break
+                        eps = min(eps * 2.0, 1.0)
+                    else:
+                        eps = (eps_robust + eps_not_robust) / 2.0
+
+            if i_robust is not None:
+                clique_bound = eps_robust
+                average_bound += clique_bound
+            else:
+                logger.info(
+                    "point %s: WARNING! no robust eps found, verification bound is set as 0 !", i_sample,
+                )
+
+        verified_error = 1.0 - num_initial_successes / num_samples
+        average_bound = average_bound / num_samples
+
+        logger.info("The average interval bound is: {:.4g}".format(average_bound))
+        logger.info("The verified error at eps = {0:.4g} is: {1:.4g}".format(eps_init, verified_error))
+
+        return average_bound, verified_error
+
+    def _get_k_partite_clique(
+        self, accessible_leaves: List[List[LeafNode]], label: int, target_label: Optional[int],
+    ) -> Tuple[float, List]:
+        """
+        Find the K partite cliques among the accessible leaf nodes.
+
+        :param accessible_leaves: List of lists of accessible leaf nodes.
+        :param label: The try label of the current sample.
+        :param target_label: The target label.
+        :return: The best score and a list of new cliques.
+        """
+        new_nodes_list = list()
+        best_scores_sum = 0.0
+
+        # pylint: disable=R1702
+        for start_tree in range(0, len(accessible_leaves), self.max_clique):
+            cliques_old: List[Dict[str, Union[Box, float]]] = list()
+            cliques_new: List[Dict[str, Union[Box, float]]] = list()
+
+            # Start searching for cliques
+            for accessible_leaf in accessible_leaves[start_tree]:
+                if (
+                    self._classifier.nb_classes > 2
+                    and target_label is not None
+                    and target_label == accessible_leaf.class_label
+                ):
+                    new_leaf_value = -accessible_leaf.value
+                else:
+                    new_leaf_value = accessible_leaf.value
+                cliques_old.append({"box": accessible_leaf.box, "value": new_leaf_value})
+
+            # Loop over all all trees
+            for i_tree in range(start_tree + 1, min(len(accessible_leaves), start_tree + self.max_clique),):
+                cliques_new.clear()
+                # Loop over all existing cliques
+                for clique in cliques_old:
+                    # Loop over leaf nodes in tree
+                    for accessible_leaf in accessible_leaves[i_tree]:
+                        leaf_box = accessible_leaf.box.get_intersection(clique["box"])  # type: ignore
+                        if leaf_box.intervals:
+                            if (
+                                self._classifier.nb_classes > 2
+                                and target_label is not None
+                                and target_label == accessible_leaf.class_label
+                            ):
+                                new_leaf_value = -accessible_leaf.value
+                            else:
+                                new_leaf_value = accessible_leaf.value
+                            cliques_new.append(
+                                {
+                                    "box": leaf_box,
+                                    "value": new_leaf_value + clique["value"],  # type: ignore
+                                }
+                            )
+
+                cliques_old = cliques_new.copy()
+
+            new_nodes = list()
+            best_score = 0.0
+            for i, clique in enumerate(cliques_old):
+                # Create a new node without tree_id and node_id to represent clique
+                new_nodes.append(
+                    LeafNode(
+                        tree_id=None,
+                        class_label=label,
+                        node_id=None,
+                        box=clique["box"],  # type: ignore
+                        value=clique["value"],  # type: ignore
+                    )
+                )
+
+                if i == 0:
+                    best_score = clique["value"]  # type: ignore
+                else:
+                    if label < 0.5 and self._classifier.nb_classes <= 2:
+                        best_score = max(best_score, clique["value"])  # type: ignore
+                    else:
+                        best_score = min(best_score, clique["value"])  # type: ignore
+
+            new_nodes_list.append(new_nodes)
+            best_scores_sum += best_score
+
+        return best_scores_sum, new_nodes_list
+
+    def _get_best_score(self, i_sample: int, eps: float, norm: int, target_label: Optional[int]) -> float:
+        """
+        Get the list of best scores.
+
+        :param i_sample: Index of training sample in `x`.
+        :param eps: Attack budget epsilon.
+        :param norm: The norm to apply epsilon.
+        :param target_label: The target label.
+        :return: The best scores.
+        """
+        nodes = self._get_accessible_leaves(i_sample, eps, norm, target_label)
+        best_score: float = 0.0
+
+        for i_level in range(self.max_level):
+            if self._classifier.nb_classes > 2 and i_level > 0:
+                target_label = None
+            best_score, nodes = self._get_k_partite_clique(nodes, label=self.y[i_sample], target_label=target_label)
+
+            # Stop if the root node has been reached
+            if len(nodes) <= 1:
+                break
+
+        return best_score
+
+    def _get_distance(self, box: Box, i_sample: int, norm: int) -> float:
+        """
+        Determine the distance between sample and interval box.
+
+        :param box: Interval box.
+        :param i_sample: Index of training sample in `x`.
+        :param norm: The norm to apply epsilon.
+        :return: The distance.
+        """
+        resulting_distance = 0.0
+
+        for feature, interval in box.intervals.items():
+            feature_value = self.x[i_sample, feature]
+
+            if interval.lower_bound < feature_value < interval.upper_bound:
+                distance = 0.0
+            else:
+                difference = max(feature_value - interval.upper_bound, interval.lower_bound - feature_value,)
+                if norm == 0:
+                    distance = 1.0
+                elif norm == np.inf:
+                    distance = difference
+                else:
+                    distance = pow(difference, norm)
+
+            if norm == np.inf:
+                resulting_distance = max(resulting_distance, distance)
+            else:
+                resulting_distance += distance
+
+        if norm not in [0, np.inf]:
+            resulting_distance = pow(resulting_distance, 1.0 / norm)
+
+        return resulting_distance
+
+    def _get_accessible_leaves(
+        self, i_sample: int, eps: float, norm: int, target_label: Optional[int]
+    ) -> List[List[LeafNode]]:
+        """
+        Determine the leaf nodes accessible within the attack budget.
+
+        :param i_sample: Index of training sample in `x`.
+        :param eps: Attack budget epsilon.
+        :param norm: The norm to apply epsilon.
+        :param target_label: The target label.
+        :return: A list of lists of leaf nodes.
+        """
+        accessible_leaves = list()
+
+        for tree in self._trees:
+            if (
+                self._classifier.nb_classes <= 2
+                or target_label is None
+                or tree.class_id in [self.y[i_sample], target_label]
+            ):
+
+                leaves = list()
+
+                for leaf_node in tree.leaf_nodes:
+                    distance = self._get_distance(leaf_node.box, i_sample, norm)
+                    if leaf_node.box and distance <= eps:
+                        leaves.append(leaf_node)
+
+                if not leaves:
+                    raise ValueError("No accessible leaves found.")
+
+                accessible_leaves.append(leaves)
+
+        return accessible_leaves
diff --git a/adversarial-robustness-toolbox/art/preprocessing/__init__.py b/adversarial-robustness-toolbox/art/preprocessing/__init__.py
new file mode 100644
index 0000000..f35b664
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/preprocessing/__init__.py
@@ -0,0 +1,6 @@
+"""
+Module for preprocessing operations.
+"""
+from art.preprocessing.preprocessing import Preprocessor  # lgtm [py/unused-import]
+from art.preprocessing.preprocessing import PreprocessorPyTorch  # lgtm [py/unused-import]
+from art.preprocessing.preprocessing import PreprocessorTensorFlowV2  # lgtm [py/unused-import]
diff --git a/adversarial-robustness-toolbox/art/preprocessing/audio/__init__.py b/adversarial-robustness-toolbox/art/preprocessing/audio/__init__.py
new file mode 100644
index 0000000..4b6f22b
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/preprocessing/audio/__init__.py
@@ -0,0 +1,5 @@
+"""
+This module contains audio preprocessing tools.
+"""
+from art.preprocessing.audio.l_filter.numpy import LFilter
+from art.preprocessing.audio.l_filter.pytorch import LFilterPyTorch
diff --git a/adversarial-robustness-toolbox/art/preprocessing/audio/l_filter/__init__.py b/adversarial-robustness-toolbox/art/preprocessing/audio/l_filter/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/adversarial-robustness-toolbox/art/preprocessing/audio/l_filter/numpy.py b/adversarial-robustness-toolbox/art/preprocessing/audio/l_filter/numpy.py
new file mode 100644
index 0000000..c4df369
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/preprocessing/audio/l_filter/numpy.py
@@ -0,0 +1,132 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements the filter function for audio signals. It provides with an infinite impulse response (IIR) or
+finite impulse response (FIR) filter. This implementation is a wrapper around the `scipy.signal.lfilter` function in
+the `scipy` package.
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+from typing import Optional, Tuple, TYPE_CHECKING
+
+from scipy.signal import lfilter
+import numpy as np
+from tqdm.auto import tqdm
+
+from art.config import ART_NUMPY_DTYPE
+from art.preprocessing.preprocessing import Preprocessor
+
+if TYPE_CHECKING:
+    from art.utils import CLIP_VALUES_TYPE
+
+logger = logging.getLogger(__name__)
+
+
+class LFilter(Preprocessor):
+    """
+    This module implements the filter function for audio signals. It provides with an infinite impulse response (IIR)
+    or finite impulse response (FIR) filter. This implementation is a wrapper around the `scipy.signal.lfilter`
+    function in the `scipy` package.
+    """
+
+    params = ["numerator_coef", "denominator_coef", "axis", "initial_cond", "verbose"]
+
+    def __init__(
+        self,
+        numerator_coef: np.ndarray = np.array([1.0]),
+        denominator_coef: np.ndarray = np.array([1.0]),
+        axis: int = -1,
+        initial_cond: Optional[np.ndarray] = None,
+        clip_values: Optional["CLIP_VALUES_TYPE"] = None,
+        apply_fit: bool = False,
+        apply_predict: bool = True,
+        verbose: bool = False,
+    ):
+        """
+        Create an instance of LFilter.
+
+        :param numerator_coef: The numerator coefficient vector in a 1-D sequence.
+        :param denominator_coef: The denominator coefficient vector in a 1-D sequence. By simply setting the array of
+                                 denominator coefficients to np.array([1.0]), this preprocessor can be used to apply a
+                                 FIR filter.
+        :param axis: The axis of the input data array along which to apply the linear filter. The filter is applied to
+                     each subarray along this axis.
+        :param initial_cond: Initial conditions for the filter delays.
+        :param clip_values: Tuple of the form `(min, max)` representing the minimum and maximum values allowed
+               for features.
+        :param apply_fit: True if applied during fitting/training.
+        :param apply_predict: True if applied during predicting.
+        :param verbose: Show progress bars.
+        """
+        super().__init__(is_fitted=True, apply_fit=apply_fit, apply_predict=apply_predict)
+
+        self.numerator_coef = numerator_coef
+        self.denominator_coef = denominator_coef
+        self.axis = axis
+        self.initial_cond = initial_cond
+        self.clip_values = clip_values
+        self.verbose = verbose
+        self._check_params()
+
+    def __call__(self, x: np.ndarray, y: Optional[np.ndarray] = None) -> Tuple[np.ndarray, Optional[np.ndarray]]:
+        """
+        Apply filter to sample `x`.
+
+        :param x: Samples of shape (nb_samples, seq_length). Note that, it is allowable that sequences in the batch
+                  could have different lengths. A possible example of `x` could be:
+                  `x = np.array([np.array([0.1, 0.2, 0.1, 0.4]), np.array([0.3, 0.1])])`.
+        :param y: Labels of the sample `x`. This function does not affect them in any way.
+        :return: Similar samples.
+        """
+        x_preprocess = x.copy()
+
+        # Filter one input at a time
+        for i, x_preprocess_i in enumerate(tqdm(x_preprocess, desc="Apply audio filter", disable=not self.verbose)):
+            x_preprocess[i] = lfilter(
+                b=self.numerator_coef, a=self.denominator_coef, x=x_preprocess_i, axis=self.axis, zi=self.initial_cond
+            )
+            x_preprocess[i] = x_preprocess[i].astype(ART_NUMPY_DTYPE)
+
+        if self.clip_values is not None:
+            np.clip(x_preprocess, self.clip_values[0], self.clip_values[1], out=x_preprocess)
+
+        return x_preprocess, y
+
+    def _check_params(self) -> None:
+        if not isinstance(self.denominator_coef, np.ndarray) or self.denominator_coef[0] == 0:
+            raise ValueError("The first element of the denominator coefficient vector must be non zero.")
+
+        if self.clip_values is not None:
+            if len(self.clip_values) != 2:
+                raise ValueError("`clip_values` should be a tuple of 2 floats containing the allowed data range.")
+
+            if np.array(self.clip_values[0] >= self.clip_values[1]).any():
+                raise ValueError("Invalid `clip_values`: min >= max.")
+
+        if not isinstance(self.numerator_coef, np.ndarray):
+            raise ValueError("The numerator coefficient vector has to be of type `np.ndarray`.")
+
+        if not isinstance(self.axis, int):
+            raise ValueError("The axis of the input data array has to be of type `int`.")
+
+        if self.initial_cond is not None and not isinstance(self.initial_cond, np.ndarray):
+            raise ValueError("The initial conditions for the filter delays must be of type `np.ndarray`.")
+
+        if not isinstance(self.verbose, bool):
+            raise ValueError("The argument `verbose` has to be of type bool.")
diff --git a/adversarial-robustness-toolbox/art/preprocessing/audio/l_filter/pytorch.py b/adversarial-robustness-toolbox/art/preprocessing/audio/l_filter/pytorch.py
new file mode 100644
index 0000000..05d22f2
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/preprocessing/audio/l_filter/pytorch.py
@@ -0,0 +1,173 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements the filter function for audio signals in PyTorch. It provides with an infinite impulse response
+(IIR) or finite impulse response (FIR) filter. This implementation is a wrapper around the
+`torchaudio.functional.lfilter` function in the `torchaudio` package.
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+from typing import Optional, Tuple, TYPE_CHECKING
+
+import numpy as np
+from tqdm.auto import tqdm
+
+from art.preprocessing.preprocessing import PreprocessorPyTorch
+from art.config import ART_NUMPY_DTYPE
+
+if TYPE_CHECKING:
+    import torch
+    from art.utils import CLIP_VALUES_TYPE
+
+logger = logging.getLogger(__name__)
+
+
+class LFilterPyTorch(PreprocessorPyTorch):
+    """
+    This module implements the filter function for audio signals in PyTorch. It provides with an infinite impulse
+    response (IIR) or finite impulse response (FIR) filter. This implementation is a wrapper around the
+    `torchaudio.functional.lfilter` function in the `torchaudio` package.
+    """
+
+    params = ["numerator_coef", "denominator_coef", "verbose"]
+
+    def __init__(
+        self,
+        numerator_coef: np.ndarray = np.array([1.0]),
+        denominator_coef: np.ndarray = np.array([1.0]),
+        clip_values: Optional["CLIP_VALUES_TYPE"] = None,
+        apply_fit: bool = False,
+        apply_predict: bool = True,
+        verbose: bool = False,
+        device_type: str = "gpu",
+    ) -> None:
+        """
+        Create an instance of LFilterPyTorch.
+
+        :param numerator_coef: The numerator coefficient vector in a 1-D sequence.
+        :param denominator_coef: The denominator coefficient vector in a 1-D sequence. By simply setting the array of
+                                 denominator coefficients to np.array([1.0]), this preprocessor can be used to apply a
+                                 FIR filter.
+        :param clip_values: Tuple of the form `(min, max)` representing the minimum and maximum values allowed
+               for features.
+        :param apply_fit: True if applied during fitting/training.
+        :param apply_predict: True if applied during predicting.
+        :param verbose: Show progress bars.
+        :param device_type: Type of device on which the classifier is run, either `gpu` or `cpu`.
+        """
+        import torch  # lgtm [py/repeated-import]
+
+        super().__init__(is_fitted=True, apply_fit=apply_fit, apply_predict=apply_predict)
+
+        self.numerator_coef = numerator_coef.astype(ART_NUMPY_DTYPE)
+        self.denominator_coef = denominator_coef.astype(ART_NUMPY_DTYPE)
+        self.clip_values = clip_values
+        self.verbose = verbose
+        self._check_params()
+
+        # Set device
+        if device_type == "cpu" or not torch.cuda.is_available():
+            self._device = torch.device("cpu")
+        else:
+            cuda_idx = torch.cuda.current_device()
+            self._device = torch.device("cuda:{}".format(cuda_idx))
+
+    def forward(
+        self, x: "torch.Tensor", y: Optional["torch.Tensor"] = None
+    ) -> Tuple["torch.Tensor", Optional["torch.Tensor"]]:
+        """
+        Apply filter to a single sample `x`.
+
+        :param x: A single audio sample.
+        :param y: Label of the sample `x`. This function does not affect them in any way.
+        :return: Similar sample.
+        """
+        import torch  # lgtm [py/repeated-import]
+        from torchaudio.functional import lfilter
+
+        if int(torch.__version__.split(".")[1]) > 5:
+            x_preprocess = lfilter(
+                b_coeffs=torch.tensor(self.numerator_coef, device=self._device),
+                a_coeffs=torch.tensor(self.denominator_coef, device=self._device),
+                waveform=x,
+                clamp=False,
+            )
+        else:
+            x_preprocess = lfilter(
+                b_coeffs=torch.tensor(self.numerator_coef, device=self._device),
+                a_coeffs=torch.tensor(self.denominator_coef, device=self._device),
+                waveform=x,
+            )
+
+        if self.clip_values is not None:
+            x_preprocess = x_preprocess.clamp(min=self.clip_values[0], max=self.clip_values[1])
+
+        return x_preprocess, y
+
+    def __call__(self, x: np.ndarray, y: Optional[np.ndarray] = None) -> Tuple[np.ndarray, Optional[np.ndarray]]:
+        """
+        Apply filter to sample `x`.
+
+        :param x: Samples of shape (nb_samples, seq_length). Note that, it is allowable that sequences in the batch
+                  could have different lengths. A possible example of `x` could be:
+                  `x = np.array([np.array([0.1, 0.2, 0.1, 0.4]), np.array([0.3, 0.1])])`.
+        :param y: Labels of the sample `x`. This function does not affect them in any way.
+        :return: Similar samples.
+        """
+        import torch  # lgtm [py/repeated-import]
+
+        x_preprocess = x.copy()
+
+        # Filter one input at a time
+        for i, x_preprocess_i in enumerate(tqdm(x_preprocess, desc="Apply audio filter", disable=not self.verbose)):
+            if np.min(x_preprocess_i) < -1.0 or np.max(x_preprocess_i) > 1.0:
+                raise ValueError(
+                    "Audio signals must be normalized to the range `[-1.0, 1.0]` to apply the audio filter function."
+                )
+
+            x_preprocess_i = torch.tensor(x_preprocess_i, device=self._device)
+
+            with torch.no_grad():
+                x_preprocess_i, _ = self.forward(x_preprocess_i)
+
+            x_preprocess[i] = x_preprocess_i.cpu().numpy()
+
+        return x_preprocess, y
+
+    def _check_params(self) -> None:
+        if not isinstance(self.denominator_coef, np.ndarray) or self.denominator_coef[0] == 0:
+            raise ValueError("The first element of the denominator coefficient vector must be non zero.")
+
+        if self.clip_values is not None:
+            if len(self.clip_values) != 2:
+                raise ValueError("`clip_values` should be a tuple of 2 floats containing the allowed data range.")
+
+            if np.array(self.clip_values[0] >= self.clip_values[1]).any():
+                raise ValueError("Invalid `clip_values`: min >= max.")
+
+        if not isinstance(self.numerator_coef, np.ndarray):
+            raise ValueError("The numerator coefficient vector has to be of type `np.ndarray`.")
+
+        if not isinstance(self.verbose, bool):
+            raise ValueError("The argument `verbose` has to be of type bool.")
+
+        if len(self.denominator_coef) != len(self.numerator_coef):
+            raise ValueError(
+                "The denominator coefficient vector and the numerator coefficient vector must have the same length."
+            )
diff --git a/adversarial-robustness-toolbox/art/preprocessing/expectation_over_transformation/__init__.py b/adversarial-robustness-toolbox/art/preprocessing/expectation_over_transformation/__init__.py
new file mode 100644
index 0000000..b773e9b
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/preprocessing/expectation_over_transformation/__init__.py
@@ -0,0 +1,28 @@
+"""
+Module providing expectation over transformations.
+"""
+from art.preprocessing.expectation_over_transformation.image_rotation.tensorflow import EoTImageRotationTensorFlow
+from art.preprocessing.expectation_over_transformation.natural_corruptions.brightness.pytorch import (
+    EoTBrightnessPyTorch,
+)
+from art.preprocessing.expectation_over_transformation.natural_corruptions.brightness.tensorflow import (
+    EoTBrightnessTensorFlow,
+)
+from art.preprocessing.expectation_over_transformation.natural_corruptions.contrast.pytorch import EoTContrastPyTorch
+from art.preprocessing.expectation_over_transformation.natural_corruptions.contrast.tensorflow import (
+    EoTContrastTensorFlow,
+)
+from art.preprocessing.expectation_over_transformation.natural_corruptions.gaussian_noise.pytorch import (
+    EoTGaussianNoisePyTorch,
+)
+from art.preprocessing.expectation_over_transformation.natural_corruptions.gaussian_noise.tensorflow import (
+    EoTGaussianNoiseTensorFlow,
+)
+from art.preprocessing.expectation_over_transformation.natural_corruptions.shot_noise.pytorch import EoTShotNoisePyTorch
+from art.preprocessing.expectation_over_transformation.natural_corruptions.shot_noise.tensorflow import (
+    EoTShotNoiseTensorFlow,
+)
+from art.preprocessing.expectation_over_transformation.natural_corruptions.zoom_blur.pytorch import EoTZoomBlurPyTorch
+from art.preprocessing.expectation_over_transformation.natural_corruptions.zoom_blur.tensorflow import (
+    EoTZoomBlurTensorFlow,
+)
diff --git a/adversarial-robustness-toolbox/art/preprocessing/expectation_over_transformation/image_rotation/__init__.py b/adversarial-robustness-toolbox/art/preprocessing/expectation_over_transformation/image_rotation/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/adversarial-robustness-toolbox/art/preprocessing/expectation_over_transformation/image_rotation/tensorflow.py b/adversarial-robustness-toolbox/art/preprocessing/expectation_over_transformation/image_rotation/tensorflow.py
new file mode 100644
index 0000000..7448173
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/preprocessing/expectation_over_transformation/image_rotation/tensorflow.py
@@ -0,0 +1,113 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements Expectation over Transformation preprocessing for image rotation in TensorFlow.
+"""
+import logging
+from typing import Optional, TYPE_CHECKING, Tuple, Union
+
+import numpy as np
+
+from art.preprocessing.expectation_over_transformation.tensorflow import EoTTensorFlowV2
+
+if TYPE_CHECKING:
+    import tensorflow as tf
+    from art.utils import CLIP_VALUES_TYPE
+
+logger = logging.getLogger(__name__)
+
+
+class EoTImageRotationTensorFlow(EoTTensorFlowV2):
+    """
+    This module implements Expectation over Transformation preprocessing for image rotation in TensorFlow.
+    """
+
+    params = ["nb_samples", "angles", "clip_values", "label_type"]
+
+    label_types = ["classification"]
+
+    def __init__(
+        self,
+        nb_samples: int,
+        clip_values: "CLIP_VALUES_TYPE",
+        angles: Union[float, Tuple[float, float]] = 45.0,
+        label_type: str = "classification",
+        apply_fit: bool = False,
+        apply_predict: bool = True,
+    ) -> None:
+        """
+        Create an instance of EoTImageRotationTensorFlow.
+
+        :param nb_samples: Number of random samples per input sample.
+        :param clip_values: Tuple of the form `(min, max)` representing the minimum and maximum values allowed
+                            for features.
+        :param angles: A positive scalar angle in degrees defining the uniform sampling range from negative to
+                       positive angles_range.
+        :param label_type: String defining the type of labels. Currently supported: `classification`
+        :param apply_fit: True if applied during fitting/training.
+        :param apply_predict: True if applied during predicting.
+        """
+        super().__init__(
+            apply_fit=apply_fit, apply_predict=apply_predict, nb_samples=nb_samples, clip_values=clip_values
+        )
+
+        self.angles = angles
+        self.angles_range = (-angles, angles) if isinstance(angles, (int, float)) else angles
+        self.label_type = label_type
+        self._check_params()
+
+    def _transform(
+        self, x: "tf.Tensor", y: Optional["tf.Tensor"], **kwargs
+    ) -> Tuple["tf.Tensor", Optional["tf.Tensor"]]:
+        """
+        Transformation of an input image and its label by randomly sampled rotation.
+
+        :param x: Input samples.
+        :param y: Label of the samples `x`.
+        :return: Transformed samples and labels.
+        """
+        import tensorflow as tf  # lgtm [py/repeated-import]
+        import tensorflow_addons as tfa
+
+        angles = tf.random.uniform(shape=(), minval=self.angles_range[0], maxval=self.angles_range[1])
+        angles = angles / 360.0 * 2.0 * np.pi
+        x_preprocess = tfa.image.rotate(images=x, angles=angles, interpolation="NEAREST", name=None)
+        x_preprocess = tf.clip_by_value(
+            t=x_preprocess, clip_value_min=-self.clip_values[0], clip_value_max=self.clip_values[1], name=None
+        )
+        return x_preprocess, y
+
+    def _check_params(self) -> None:
+
+        if not (isinstance(self.angles, (int, float)) or isinstance(self.angles, tuple)) or (
+            isinstance(self.angles, tuple)
+            and (
+                len(self.angles) != 2
+                or not isinstance(self.angles[0], (int, float))
+                or not isinstance(self.angles[1], (int, float))
+                or self.angles[0] > self.angles[1]
+            )
+        ):
+            raise ValueError("The range of angles must be a float in the range (0.0, 180.0].")
+
+        if self.label_type not in self.label_types:
+            raise ValueError(
+                "The input for label_type needs to be one of {}, currently receiving `{}`.".format(
+                    self.label_types, self.label_type
+                )
+            )
diff --git a/adversarial-robustness-toolbox/art/preprocessing/expectation_over_transformation/natural_corruptions/__init__.py b/adversarial-robustness-toolbox/art/preprocessing/expectation_over_transformation/natural_corruptions/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/adversarial-robustness-toolbox/art/preprocessing/expectation_over_transformation/natural_corruptions/brightness/__init__.py b/adversarial-robustness-toolbox/art/preprocessing/expectation_over_transformation/natural_corruptions/brightness/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/adversarial-robustness-toolbox/art/preprocessing/expectation_over_transformation/natural_corruptions/brightness/pytorch.py b/adversarial-robustness-toolbox/art/preprocessing/expectation_over_transformation/natural_corruptions/brightness/pytorch.py
new file mode 100644
index 0000000..0ad05b3
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/preprocessing/expectation_over_transformation/natural_corruptions/brightness/pytorch.py
@@ -0,0 +1,92 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2021
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements EoT of changes in brightness by addition of uniformly sampled delta.
+"""
+import logging
+from typing import Tuple, Union, TYPE_CHECKING, Optional
+
+import numpy as np
+
+from art.preprocessing.expectation_over_transformation.pytorch import EoTPyTorch
+
+if TYPE_CHECKING:
+    import torch
+
+logger = logging.getLogger(__name__)
+
+
+class EoTBrightnessPyTorch(EoTPyTorch):
+    """
+    This module implements EoT of changes in brightness by addition of uniformly sampled delta.
+    """
+
+    def __init__(
+        self,
+        nb_samples: int,
+        clip_values: Tuple[float, float],
+        delta: Union[float, Tuple[float, float]],
+        apply_fit: bool = False,
+        apply_predict: bool = True,
+    ) -> None:
+        """
+        Create an instance of EoTBrightnessPyTorch.
+
+        :param nb_samples: Number of random samples per input sample.
+        :param clip_values: Tuple of float representing minimum and maximum values of input `(min, max)`.
+        :param delta: Range to sample the delta (addition) to the pixel values to adjust the brightness. A single float
+            is translated to range [-delta, delta] or a tuple of floats is used to create sampling range
+            [delta[0], delta[1]]. The applied delta is sampled uniformly from this range for each image.
+        :param apply_fit: True if applied during fitting/training.
+        :param apply_predict: True if applied during predicting.
+        """
+        super().__init__(
+            apply_fit=apply_fit, apply_predict=apply_predict, nb_samples=nb_samples, clip_values=clip_values
+        )
+
+        self.delta = delta
+        self.delta_range = (-delta, delta) if isinstance(delta, (int, float)) else delta
+        self._check_params()
+
+    def _transform(
+        self, x: "torch.Tensor", y: Optional["torch.Tensor"], **kwargs
+    ) -> Tuple["torch.Tensor", Optional["torch.Tensor"]]:
+        """
+        Transformation of an image with randomly sampled brightness.
+
+        :param x: Input samples.
+        :param y: Label of the samples `x`.
+        :return: Transformed samples and labels.
+        """
+        import torch  # lgtm [py/repeated-import]
+
+        delta_i = np.random.uniform(low=self.delta_range[0], high=self.delta_range[1])
+        return torch.clamp(x + delta_i, min=self.clip_values[0], max=self.clip_values[1]), y
+
+    def _check_params(self) -> None:
+
+        if not (isinstance(self.delta, (int, float)) or isinstance(self.delta, tuple)) or (
+            isinstance(self.delta, tuple)
+            and (
+                len(self.delta) != 2
+                or not isinstance(self.delta[0], (int, float))
+                or not isinstance(self.delta[1], (int, float))
+                or self.delta[0] > self.delta[1]
+            )
+        ):
+            raise ValueError("The argument `delta` has to be a float or tuple of two float values as (min, max).")
diff --git a/adversarial-robustness-toolbox/art/preprocessing/expectation_over_transformation/natural_corruptions/brightness/tensorflow.py b/adversarial-robustness-toolbox/art/preprocessing/expectation_over_transformation/natural_corruptions/brightness/tensorflow.py
new file mode 100644
index 0000000..0e2f28e
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/preprocessing/expectation_over_transformation/natural_corruptions/brightness/tensorflow.py
@@ -0,0 +1,92 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2021
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements EoT of changes in brightness by addition of uniformly sampled delta.
+"""
+import logging
+from typing import Tuple, Union, TYPE_CHECKING, Optional
+
+import numpy as np
+
+from art.preprocessing.expectation_over_transformation.tensorflow import EoTTensorFlowV2
+
+if TYPE_CHECKING:
+    import tensorflow as tf
+
+logger = logging.getLogger(__name__)
+
+
+class EoTBrightnessTensorFlow(EoTTensorFlowV2):
+    """
+    This module implements EoT of changes in brightness by addition of uniformly sampled delta.
+    """
+
+    def __init__(
+        self,
+        nb_samples: int,
+        clip_values: Tuple[float, float],
+        delta: Union[float, Tuple[float, float]],
+        apply_fit: bool = False,
+        apply_predict: bool = True,
+    ) -> None:
+        """
+        Create an instance of EoTBrightnessTensorFlow.
+
+        :param nb_samples: Number of random samples per input sample.
+        :param clip_values: Tuple of float representing minimum and maximum values of input `(min, max)`.
+        :param delta: Range to sample the delta (addition) to the pixel values to adjust the brightness. A single float
+            is translated to range [-delta, delta] or a tuple of floats is used to create sampling range
+            [delta[0], delta[1]]. The applied delta is sampled uniformly from this range for each image.
+        :param apply_fit: True if applied during fitting/training.
+        :param apply_predict: True if applied during predicting.
+        """
+        super().__init__(
+            apply_fit=apply_fit, apply_predict=apply_predict, nb_samples=nb_samples, clip_values=clip_values
+        )
+
+        self.delta = delta
+        self.delta_range = (-delta, delta) if isinstance(delta, (int, float)) else delta
+        self._check_params()
+
+    def _transform(
+        self, x: "tf.Tensor", y: Optional["tf.Tensor"], **kwargs
+    ) -> Tuple["tf.Tensor", Optional["tf.Tensor"]]:
+        """
+        Transformation of an image with randomly sampled brightness.
+
+        :param x: Input samples.
+        :param y: Label of the samples `x`.
+        :return: Transformed samples and labels.
+        """
+        import tensorflow as tf  # lgtm [py/repeated-import]
+
+        delta_i = np.random.uniform(low=self.delta_range[0], high=self.delta_range[1])
+        return tf.clip_by_value(x + delta_i, clip_value_min=self.clip_values[0], clip_value_max=self.clip_values[1]), y
+
+    def _check_params(self) -> None:
+
+        if not (isinstance(self.delta, (int, float)) or isinstance(self.delta, tuple)) or (
+            isinstance(self.delta, tuple)
+            and (
+                len(self.delta) != 2
+                or not isinstance(self.delta[0], (int, float))
+                or not isinstance(self.delta[1], (int, float))
+                or self.delta[0] > self.delta[1]
+            )
+        ):
+            raise ValueError("The argument `delta` has to be a float or tuple of two float values as (min, max).")
diff --git a/adversarial-robustness-toolbox/art/preprocessing/expectation_over_transformation/natural_corruptions/contrast/__init__.py b/adversarial-robustness-toolbox/art/preprocessing/expectation_over_transformation/natural_corruptions/contrast/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/adversarial-robustness-toolbox/art/preprocessing/expectation_over_transformation/natural_corruptions/contrast/pytorch.py b/adversarial-robustness-toolbox/art/preprocessing/expectation_over_transformation/natural_corruptions/contrast/pytorch.py
new file mode 100644
index 0000000..70fdb84
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/preprocessing/expectation_over_transformation/natural_corruptions/contrast/pytorch.py
@@ -0,0 +1,112 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2021
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements EoT of changes in contrast with uniformly sampled factor.
+"""
+import logging
+from typing import Tuple, Union, TYPE_CHECKING, Optional
+
+import numpy as np
+
+from art.preprocessing.expectation_over_transformation.pytorch import EoTPyTorch
+
+if TYPE_CHECKING:
+    import torch
+
+logger = logging.getLogger(__name__)
+
+
+class EoTContrastPyTorch(EoTPyTorch):
+    """
+    This module implements EoT of changes in contrast with uniformly sampled factor.
+    """
+
+    def __init__(
+        self,
+        nb_samples: int,
+        clip_values: Tuple[float, float],
+        contrast_factor: Union[float, Tuple[float, float]],
+        apply_fit: bool = False,
+        apply_predict: bool = True,
+    ) -> None:
+        """
+        Create an instance of EoTContrastPyTorch.
+
+        :param nb_samples: Number of random samples per input sample.
+        :param clip_values: Tuple of float representing minimum and maximum values of input `(min, max)`.
+        :param contrast_factor: Range to sample the contrast factor adjust the contrast. A single float is translated to
+               range [-delta, delta] or a tuple of floats is used to create sampling range [delta[0], delta[1]]. The
+               applied delta is sampled uniformly from this range for each image.
+        :param apply_fit: True if applied during fitting/training.
+        :param apply_predict: True if applied during predicting.
+        """
+        super().__init__(
+            apply_fit=apply_fit, apply_predict=apply_predict, nb_samples=nb_samples, clip_values=clip_values
+        )
+
+        self.contrast_factor = contrast_factor
+        self.contrast_factor_range = (
+            (0, contrast_factor) if isinstance(contrast_factor, (int, float)) else contrast_factor
+        )
+        self._check_params()
+
+    def _transform(
+        self, x: "torch.Tensor", y: Optional["torch.Tensor"], **kwargs
+    ) -> Tuple["torch.Tensor", Optional["torch.Tensor"]]:
+        """
+        Transformation of an image with randomly sampled contrast.
+
+        :param x: Input samples.
+        :param y: Label of the samples `x`.
+        :return: Transformed samples and labels.
+        """
+        import torch  # lgtm [py/repeated-import]
+
+        contrast_factor_i = np.random.uniform(low=self.contrast_factor_range[0], high=self.contrast_factor_range[1])
+        if x.shape[2] == 3:
+            red, green, blue = x[:, :, 0], x[:, :, 1], x[:, :, 2]
+            x_gray = 0.2989 * red + 0.587 * green + 0.114 * blue
+        elif x.shape[2] == 1:
+            x_gray = x[:, :, 0]
+        else:
+            raise ValueError("Number of color channels is not 1 or 3 in input `x` of format HWC.")
+        mean = torch.mean(x_gray, dim=(-2, -1), keepdim=True)
+
+        return (
+            torch.clamp(
+                contrast_factor_i * x + (1.0 - contrast_factor_i) * mean,
+                min=self.clip_values[0],
+                max=self.clip_values[1],
+            ),
+            y,
+        )
+
+    def _check_params(self) -> None:
+
+        if not (isinstance(self.contrast_factor, (int, float)) or isinstance(self.contrast_factor, tuple)) or (
+            isinstance(self.contrast_factor, tuple)
+            and (
+                len(self.contrast_factor) != 2
+                or not isinstance(self.contrast_factor[0], (int, float))
+                or not isinstance(self.contrast_factor[1], (int, float))
+                or self.contrast_factor[0] > self.contrast_factor[1]
+            )
+        ):
+            raise ValueError(
+                "The argument `contrast_factor` has to be a float or tuple of two float values as (min, max)."
+            )
diff --git a/adversarial-robustness-toolbox/art/preprocessing/expectation_over_transformation/natural_corruptions/contrast/tensorflow.py b/adversarial-robustness-toolbox/art/preprocessing/expectation_over_transformation/natural_corruptions/contrast/tensorflow.py
new file mode 100644
index 0000000..bca028b
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/preprocessing/expectation_over_transformation/natural_corruptions/contrast/tensorflow.py
@@ -0,0 +1,112 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2021
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements EoT of changes in contrast with uniformly sampled factor.
+"""
+import logging
+from typing import Tuple, Union, TYPE_CHECKING, Optional
+
+import numpy as np
+
+from art.preprocessing.expectation_over_transformation.tensorflow import EoTTensorFlowV2
+
+if TYPE_CHECKING:
+    import tensorflow as tf
+
+logger = logging.getLogger(__name__)
+
+
+class EoTContrastTensorFlow(EoTTensorFlowV2):
+    """
+    This module implements EoT of changes in contrast with uniformly sampled factor.
+    """
+
+    def __init__(
+        self,
+        nb_samples: int,
+        clip_values: Tuple[float, float],
+        contrast_factor: Union[float, Tuple[float, float]],
+        apply_fit: bool = False,
+        apply_predict: bool = True,
+    ) -> None:
+        """
+        Create an instance of EoTContrastTensorFlow.
+
+        :param nb_samples: Number of random samples per input sample.
+        :param clip_values: Tuple of float representing minimum and maximum values of input `(min, max)`.
+        :param contrast_factor: Range to sample the contrast factor adjust the contrast. A single float is translated to
+               range [-delta, delta] or a tuple of floats is used to create sampling range [delta[0], delta[1]]. The
+               applied delta is sampled uniformly from this range for each image.
+        :param apply_fit: True if applied during fitting/training.
+        :param apply_predict: True if applied during predicting.
+        """
+        super().__init__(
+            apply_fit=apply_fit, apply_predict=apply_predict, nb_samples=nb_samples, clip_values=clip_values
+        )
+
+        self.contrast_factor = contrast_factor
+        self.contrast_factor_range = (
+            (0, contrast_factor) if isinstance(contrast_factor, (int, float)) else contrast_factor
+        )
+        self._check_params()
+
+    def _transform(
+        self, x: "tf.Tensor", y: Optional["tf.Tensor"], **kwargs
+    ) -> Tuple["tf.Tensor", Optional["tf.Tensor"]]:
+        """
+        Transformation of an image with randomly sampled contrast.
+
+        :param x: Input samples.
+        :param y: Label of the samples `x`.
+        :return: Transformed samples and labels.
+        """
+        import tensorflow as tf  # lgtm [py/repeated-import]
+
+        contrast_factor_i = np.random.uniform(low=self.contrast_factor_range[0], high=self.contrast_factor_range[1])
+        if x.shape[2] == 3:
+            red, green, blue = x[:, :, 0], x[:, :, 1], x[:, :, 2]
+            x_gray = 0.2989 * red + 0.587 * green + 0.114 * blue
+        elif x.shape[2] == 1:
+            x_gray = x[:, :, 0]
+        else:
+            raise ValueError("Number of color channels is not 1 or 3 in input `x` of format HWC.")
+        mean = tf.math.reduce_mean(x_gray, axis=None)
+
+        return (
+            tf.clip_by_value(
+                contrast_factor_i * x + (1.0 - contrast_factor_i) * mean,
+                clip_value_min=self.clip_values[0],
+                clip_value_max=self.clip_values[1],
+            ),
+            y,
+        )
+
+    def _check_params(self) -> None:
+
+        if not (isinstance(self.contrast_factor, (int, float)) or isinstance(self.contrast_factor, tuple)) or (
+            isinstance(self.contrast_factor, tuple)
+            and (
+                len(self.contrast_factor) != 2
+                or not isinstance(self.contrast_factor[0], (int, float))
+                or not isinstance(self.contrast_factor[1], (int, float))
+                or self.contrast_factor[0] > self.contrast_factor[1]
+            )
+        ):
+            raise ValueError(
+                "The argument `contrast_factor` has to be a float or tuple of two float values as (min, max)."
+            )
diff --git a/adversarial-robustness-toolbox/art/preprocessing/expectation_over_transformation/natural_corruptions/gaussian_noise/__init__.py b/adversarial-robustness-toolbox/art/preprocessing/expectation_over_transformation/natural_corruptions/gaussian_noise/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/adversarial-robustness-toolbox/art/preprocessing/expectation_over_transformation/natural_corruptions/gaussian_noise/pytorch.py b/adversarial-robustness-toolbox/art/preprocessing/expectation_over_transformation/natural_corruptions/gaussian_noise/pytorch.py
new file mode 100644
index 0000000..d644bb6
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/preprocessing/expectation_over_transformation/natural_corruptions/gaussian_noise/pytorch.py
@@ -0,0 +1,94 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2021
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements EoT of adding Gaussian noise with uniformly sampled standard deviation.
+"""
+import logging
+from typing import Tuple, Union, TYPE_CHECKING, Optional
+
+import numpy as np
+
+from art.preprocessing.expectation_over_transformation.pytorch import EoTPyTorch
+
+if TYPE_CHECKING:
+    import torch
+
+logger = logging.getLogger(__name__)
+
+
+class EoTGaussianNoisePyTorch(EoTPyTorch):
+    """
+    This module implements EoT of adding Gaussian noise with uniformly sampled standard deviation.
+    """
+
+    def __init__(
+        self,
+        nb_samples: int,
+        clip_values: Tuple[float, float],
+        std: Union[float, Tuple[float, float]],
+        apply_fit: bool = False,
+        apply_predict: bool = True,
+    ) -> None:
+        """
+        Create an instance of EoTBrightnessPyTorch.
+
+        :param nb_samples: Number of random samples per input sample.
+        :param clip_values: Tuple of float representing minimum and maximum values of input `(min, max)`.
+        :param std: Range to sample the standard deviation for the Gaussian distribution. A single float
+                    is translated to range [0, std]. The applied delta is sampled uniformly from this range for each
+                    image.
+        :param apply_fit: True if applied during fitting/training.
+        :param apply_predict: True if applied during predicting.
+        """
+        super().__init__(
+            apply_fit=apply_fit, apply_predict=apply_predict, nb_samples=nb_samples, clip_values=clip_values
+        )
+
+        self.std = std
+        self.std_range = (0.0, std) if isinstance(std, (int, float)) else std
+        self._check_params()
+
+    def _transform(
+        self, x: "torch.Tensor", y: Optional["torch.Tensor"], **kwargs
+    ) -> Tuple["torch.Tensor", Optional["torch.Tensor"]]:
+        """
+        Transformation of an image with randomly sampled Gaussian noise.
+
+        :param x: Input samples.
+        :param y: Label of the samples `x`.
+        :return: Transformed samples and labels.
+        """
+        import torch  # lgtm [py/repeated-import]
+
+        std_i = np.random.uniform(low=self.std_range[0], high=self.std_range[1])
+        delta_i = torch.normal(mean=torch.zeros_like(x), std=torch.ones_like(x) * std_i)
+        return torch.clamp(x + delta_i, min=self.clip_values[0], max=self.clip_values[1]), y
+
+    def _check_params(self) -> None:
+
+        if not (isinstance(self.std, (int, float)) or isinstance(self.std, tuple)) or (
+            isinstance(self.std, tuple)
+            and (
+                len(self.std) != 2
+                or not isinstance(self.std[0], (int, float))
+                or not isinstance(self.std[1], (int, float))
+                or self.std[0] > self.std[1]
+                or self.std[0] < 0.0
+            )
+        ):
+            raise ValueError("The argument `std` has to be a float or tuple of two float values as (min, max).")
diff --git a/adversarial-robustness-toolbox/art/preprocessing/expectation_over_transformation/natural_corruptions/gaussian_noise/tensorflow.py b/adversarial-robustness-toolbox/art/preprocessing/expectation_over_transformation/natural_corruptions/gaussian_noise/tensorflow.py
new file mode 100644
index 0000000..e4a2286
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/preprocessing/expectation_over_transformation/natural_corruptions/gaussian_noise/tensorflow.py
@@ -0,0 +1,94 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2021
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements EoT of adding Gaussian noise with uniformly sampled standard deviation.
+"""
+import logging
+from typing import Tuple, Union, TYPE_CHECKING, Optional
+
+import numpy as np
+
+from art.preprocessing.expectation_over_transformation.tensorflow import EoTTensorFlowV2
+
+if TYPE_CHECKING:
+    import tensorflow as tf
+
+logger = logging.getLogger(__name__)
+
+
+class EoTGaussianNoiseTensorFlow(EoTTensorFlowV2):
+    """
+    This module implements EoT of adding Gaussian noise with uniformly sampled standard deviation.
+    """
+
+    def __init__(
+        self,
+        nb_samples: int,
+        clip_values: Tuple[float, float],
+        std: Union[float, Tuple[float, float]],
+        apply_fit: bool = False,
+        apply_predict: bool = True,
+    ) -> None:
+        """
+        Create an instance of EoTGaussianNoiseTensorFlow.
+
+        :param nb_samples: Number of random samples per input sample.
+        :param clip_values: Tuple of float representing minimum and maximum values of input `(min, max)`.
+        :param std: Range to sample the standard deviation for the Gaussian distribution. A single float
+                    is translated to range [0, std]. The applied delta is sampled uniformly from this range for each
+                    image.
+        :param apply_fit: True if applied during fitting/training.
+        :param apply_predict: True if applied during predicting.
+        """
+        super().__init__(
+            apply_fit=apply_fit, apply_predict=apply_predict, nb_samples=nb_samples, clip_values=clip_values
+        )
+
+        self.std = std
+        self.std_range = (0.0, std) if isinstance(std, (int, float)) else std
+        self._check_params()
+
+    def _transform(
+        self, x: "tf.Tensor", y: Optional["tf.Tensor"], **kwargs
+    ) -> Tuple["tf.Tensor", Optional["tf.Tensor"]]:
+        """
+        Transformation of an image with randomly sampled Gaussian noise.
+
+        :param x: Input samples.
+        :param y: Label of the samples `x`.
+        :return: Transformed samples and labels.
+        """
+        import tensorflow as tf  # lgtm [py/repeated-import]
+
+        std_i = np.random.uniform(low=self.std_range[0], high=self.std_range[1])
+        delta_i = tf.random.normal(shape=x.shape, mean=0.0, stddev=std_i, seed=None)
+        return tf.clip_by_value(x + delta_i, clip_value_min=self.clip_values[0], clip_value_max=self.clip_values[1]), y
+
+    def _check_params(self) -> None:
+
+        if not (isinstance(self.std, (int, float)) or isinstance(self.std, tuple)) or (
+            isinstance(self.std, tuple)
+            and (
+                len(self.std) != 2
+                or not isinstance(self.std[0], (int, float))
+                or not isinstance(self.std[1], (int, float))
+                or self.std[0] > self.std[1]
+                or self.std[0] < 0.0
+            )
+        ):
+            raise ValueError("The argument `std` has to be a float or tuple of two float values as (min, max).")
diff --git a/adversarial-robustness-toolbox/art/preprocessing/expectation_over_transformation/natural_corruptions/shot_noise/__init__.py b/adversarial-robustness-toolbox/art/preprocessing/expectation_over_transformation/natural_corruptions/shot_noise/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/adversarial-robustness-toolbox/art/preprocessing/expectation_over_transformation/natural_corruptions/shot_noise/pytorch.py b/adversarial-robustness-toolbox/art/preprocessing/expectation_over_transformation/natural_corruptions/shot_noise/pytorch.py
new file mode 100644
index 0000000..852ce9a
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/preprocessing/expectation_over_transformation/natural_corruptions/shot_noise/pytorch.py
@@ -0,0 +1,94 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2021
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements EoT of adding shot noise (Poisson) with uniformly sampled rate parameter.
+"""
+import logging
+from typing import Tuple, Union, TYPE_CHECKING, Optional
+
+import numpy as np
+
+from art.preprocessing.expectation_over_transformation.pytorch import EoTPyTorch
+
+if TYPE_CHECKING:
+    import torch
+
+logger = logging.getLogger(__name__)
+
+
+class EoTShotNoisePyTorch(EoTPyTorch):
+    """
+    This module implements EoT of adding shot noise (Poisson) with uniformly sampled rate parameter.
+    """
+
+    def __init__(
+        self,
+        nb_samples: int,
+        clip_values: Tuple[float, float],
+        lam: Union[float, Tuple[float, float]],
+        apply_fit: bool = False,
+        apply_predict: bool = True,
+    ) -> None:
+        """
+        Create an instance of EoTShotNoisePyTorch.
+
+        :param nb_samples: Number of random samples per input sample.
+        :param clip_values: Tuple of float representing minimum and maximum values of input `(min, max)`.
+        :param lam: Range to sample the rate of the Poisson distribution. A single float
+                    is translated to range [0.0, lam] or a tuple of floats is used to create sampling range
+                    [lam[0], lam[1]]. The applied delta is sampled uniformly from this range for each image.
+        :param apply_fit: True if applied during fitting/training.
+        :param apply_predict: True if applied during predicting.
+        """
+        super().__init__(
+            apply_fit=apply_fit, apply_predict=apply_predict, nb_samples=nb_samples, clip_values=clip_values
+        )
+
+        self.lam = lam
+        self.lam_range = (0.0, lam) if isinstance(lam, (int, float)) else lam
+        self._check_params()
+
+    def _transform(
+        self, x: "torch.Tensor", y: Optional["torch.Tensor"], **kwargs
+    ) -> Tuple["torch.Tensor", Optional["torch.Tensor"]]:
+        """
+        Transformation of an image with randomly sampled shot (Poisson) noise.
+
+        :param x: Input samples.
+        :param y: Label of the samples `x`.
+        :return: Transformed samples and labels.
+        """
+        import torch  # lgtm [py/repeated-import]
+
+        lam_i = np.random.uniform(low=self.lam_range[0], high=self.lam_range[1])
+        delta_i = torch.poisson(input=torch.ones_like(x) * lam_i) / lam_i * self.clip_values[1]
+        return torch.clamp(x + delta_i, min=self.clip_values[0], max=self.clip_values[1]), y
+
+    def _check_params(self) -> None:
+
+        if not (isinstance(self.lam, (int, float)) or isinstance(self.lam, tuple)) or (
+            isinstance(self.lam, tuple)
+            and (
+                len(self.lam) != 2
+                or not isinstance(self.lam[0], (int, float))
+                or not isinstance(self.lam[1], (int, float))
+                or self.lam[0] > self.lam[1]
+                or self.lam[0] < 0.0
+            )
+        ):
+            raise ValueError("The argument `lam` has to be a float or tuple of two float values as (min, max).")
diff --git a/adversarial-robustness-toolbox/art/preprocessing/expectation_over_transformation/natural_corruptions/shot_noise/tensorflow.py b/adversarial-robustness-toolbox/art/preprocessing/expectation_over_transformation/natural_corruptions/shot_noise/tensorflow.py
new file mode 100644
index 0000000..3e71921
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/preprocessing/expectation_over_transformation/natural_corruptions/shot_noise/tensorflow.py
@@ -0,0 +1,94 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2021
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements EoT of adding shot noise (Poisson) with uniformly sampled rate parameter.
+"""
+import logging
+from typing import Tuple, Union, TYPE_CHECKING, Optional
+
+import numpy as np
+
+from art.preprocessing.expectation_over_transformation.tensorflow import EoTTensorFlowV2
+
+if TYPE_CHECKING:
+    import tensorflow as tf
+
+logger = logging.getLogger(__name__)
+
+
+class EoTShotNoiseTensorFlow(EoTTensorFlowV2):
+    """
+    This module implements EoT of adding shot noise (Poisson) with uniformly sampled rate parameter.
+    """
+
+    def __init__(
+        self,
+        nb_samples: int,
+        clip_values: Tuple[float, float],
+        lam: Union[float, Tuple[float, float]],
+        apply_fit: bool = False,
+        apply_predict: bool = True,
+    ) -> None:
+        """
+        Create an instance of EoTShotNoiseTensorFlow.
+
+        :param nb_samples: Number of random samples per input sample.
+        :param clip_values: Tuple of float representing minimum and maximum values of input `(min, max)`.
+        :param lam: Range to sample the rate of the Poisson distribution. A single float
+                    is translated to range [0.0, lam] or a tuple of floats is used to create sampling range
+                    [lam[0], lam[1]]. The applied delta is sampled uniformly from this range for each image.
+        :param apply_fit: True if applied during fitting/training.
+        :param apply_predict: True if applied during predicting.
+        """
+        super().__init__(
+            apply_fit=apply_fit, apply_predict=apply_predict, nb_samples=nb_samples, clip_values=clip_values
+        )
+
+        self.lam = lam
+        self.lam_range = (0.0, lam) if isinstance(lam, (int, float)) else lam
+        self._check_params()
+
+    def _transform(
+        self, x: "tf.Tensor", y: Optional["tf.Tensor"], **kwargs
+    ) -> Tuple["tf.Tensor", Optional["tf.Tensor"]]:
+        """
+        Transformation of an image with randomly sampled shot (Poisson) noise.
+
+        :param x: Input samples.
+        :param y: Label of the samples `x`.
+        :return: Transformed samples and labels.
+        """
+        import tensorflow as tf  # lgtm [py/repeated-import]
+
+        lam_i = np.random.uniform(low=self.lam_range[0], high=self.lam_range[1])
+        delta_i = tf.random.poisson(shape=x.shape, lam=lam_i, seed=None) / lam_i * self.clip_values[1]
+        return tf.clip_by_value(x + delta_i, clip_value_min=self.clip_values[0], clip_value_max=self.clip_values[1]), y
+
+    def _check_params(self) -> None:
+
+        if not (isinstance(self.lam, (int, float)) or isinstance(self.lam, tuple)) or (
+            isinstance(self.lam, tuple)
+            and (
+                len(self.lam) != 2
+                or not isinstance(self.lam[0], (int, float))
+                or not isinstance(self.lam[1], (int, float))
+                or self.lam[0] > self.lam[1]
+                or self.lam[0] < 0.0
+            )
+        ):
+            raise ValueError("The argument `lam` has to be a float or tuple of two float values as (min, max).")
diff --git a/adversarial-robustness-toolbox/art/preprocessing/expectation_over_transformation/natural_corruptions/zoom_blur/__init__.py b/adversarial-robustness-toolbox/art/preprocessing/expectation_over_transformation/natural_corruptions/zoom_blur/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/adversarial-robustness-toolbox/art/preprocessing/expectation_over_transformation/natural_corruptions/zoom_blur/pytorch.py b/adversarial-robustness-toolbox/art/preprocessing/expectation_over_transformation/natural_corruptions/zoom_blur/pytorch.py
new file mode 100644
index 0000000..7322f64
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/preprocessing/expectation_over_transformation/natural_corruptions/zoom_blur/pytorch.py
@@ -0,0 +1,113 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2021
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements EoT of zoom blur with uniformly sampled zoom factor.
+"""
+import logging
+from typing import Tuple, Union, TYPE_CHECKING, Optional
+
+import numpy as np
+
+from art.preprocessing.expectation_over_transformation.pytorch import EoTPyTorch
+
+if TYPE_CHECKING:
+    import torch
+
+logger = logging.getLogger(__name__)
+
+
+class EoTZoomBlurPyTorch(EoTPyTorch):
+    """
+    This module implements EoT of zoom blur with uniformly sampled zoom factor.
+    """
+
+    def __init__(
+        self,
+        nb_samples: int,
+        clip_values: Tuple[float, float],
+        zoom: Union[float, Tuple[float, float]],
+        apply_fit: bool = False,
+        apply_predict: bool = True,
+    ) -> None:
+        """
+        Create an instance of EoTZoomBlurPyTorch.
+
+        :param nb_samples: Number of random samples per input sample.
+        :param clip_values: Tuple of float representing minimum and maximum values of input `(min, max)`.
+        :param zoom: Range to sample the zoom factor. A single float is translated to range [1.0, zoom] or a tuple of
+                     floats is used to create sampling range [zoom[0], zoom[1]]. The applied zoom is sampled uniformly
+                     from this range for each image.
+        :param apply_fit: True if applied during fitting/training.
+        :param apply_predict: True if applied during predicting.
+        """
+        super().__init__(
+            apply_fit=apply_fit, apply_predict=apply_predict, nb_samples=nb_samples, clip_values=clip_values
+        )
+
+        self.zoom = zoom
+        self.zoom_range = (1.0, zoom) if isinstance(zoom, (int, float)) else zoom
+        self._check_params()
+
+    def _transform(
+        self, x: "torch.Tensor", y: Optional["torch.Tensor"], **kwargs
+    ) -> Tuple["torch.Tensor", Optional["torch.Tensor"]]:
+        """
+        Transformation of an image with randomly sampled zoom blur.
+
+        :param x: Input samples.
+        :param y: Label of the samples `x`.
+        :return: Transformed samples and labels.
+        """
+        import torch  # lgtm [py/repeated-import]
+        import torchvision
+
+        nb_zooms = 10
+        x_blur = torch.zeros_like(x)
+        max_zoom_i = np.random.uniform(low=self.zoom_range[0], high=self.zoom_range[1])
+        zooms = np.arange(start=1.0, stop=max_zoom_i, step=(max_zoom_i - 1.0) / nb_zooms)
+
+        height = x.shape[0]
+        width = x.shape[1]
+
+        x_chw = x.permute(2, 0, 1)
+
+        for zoom in zooms:
+            size = [int(a * zoom) for a in x.shape[0:2]]
+            x_resized = torchvision.transforms.functional.resize(img=x_chw, size=size, interpolation=2).permute(1, 2, 0)
+
+            trim_top = (x_resized.shape[0] - height) // 2
+            trim_left = (x_resized.shape[0] - width) // 2
+
+            x_blur += x_resized[trim_top : trim_top + height, trim_left : trim_left + width, :]
+
+        x_out = (x + x_blur) / (nb_zooms + 1)
+        return torch.clamp(x_out, min=self.clip_values[0], max=self.clip_values[1]), y
+
+    def _check_params(self) -> None:
+
+        if not (isinstance(self.zoom, (int, float)) or isinstance(self.zoom, tuple)) or (
+            isinstance(self.zoom, tuple)
+            and (
+                len(self.zoom) != 2
+                or not isinstance(self.zoom[0], (int, float))
+                or not isinstance(self.zoom[1], (int, float))
+                or self.zoom[0] > self.zoom[1]
+                or self.zoom[0] < 1.0
+            )
+        ):
+            raise ValueError("The argument `lam` has to be a float or tuple of two float values as (min, max).")
diff --git a/adversarial-robustness-toolbox/art/preprocessing/expectation_over_transformation/natural_corruptions/zoom_blur/tensorflow.py b/adversarial-robustness-toolbox/art/preprocessing/expectation_over_transformation/natural_corruptions/zoom_blur/tensorflow.py
new file mode 100644
index 0000000..cc6da95
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/preprocessing/expectation_over_transformation/natural_corruptions/zoom_blur/tensorflow.py
@@ -0,0 +1,117 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2021
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements EoT of zoom blur with uniformly sampled zoom factor.
+"""
+import logging
+from typing import Tuple, Union, TYPE_CHECKING, Optional
+
+import numpy as np
+
+from art.preprocessing.expectation_over_transformation.tensorflow import EoTTensorFlowV2
+
+if TYPE_CHECKING:
+    import tensorflow as tf
+
+logger = logging.getLogger(__name__)
+
+
+class EoTZoomBlurTensorFlow(EoTTensorFlowV2):
+    """
+    This module implements EoT of zoom blur with uniformly sampled zoom factor.
+    """
+
+    def __init__(
+        self,
+        nb_samples: int,
+        clip_values: Tuple[float, float],
+        zoom: Union[float, Tuple[float, float]],
+        apply_fit: bool = False,
+        apply_predict: bool = True,
+    ) -> None:
+        """
+        Create an instance of EoTZoomBlurTensorFlow.
+
+        :param nb_samples: Number of random samples per input sample.
+        :param clip_values: Tuple of float representing minimum and maximum values of input `(min, max)`.
+        :param zoom: Range to sample the zoom factor. A single float is translated to range [1.0, zoom] or a tuple of
+                     floats is used to create sampling range [zoom[0], zoom[1]]. The applied zoom is sampled uniformly
+                     from this range for each image.
+        :param apply_fit: True if applied during fitting/training.
+        :param apply_predict: True if applied during predicting.
+        """
+        super().__init__(
+            apply_fit=apply_fit, apply_predict=apply_predict, nb_samples=nb_samples, clip_values=clip_values
+        )
+
+        self.zoom = zoom
+        self.zoom_range = (1.0, zoom) if isinstance(zoom, (int, float)) else zoom
+        self._check_params()
+
+    def _transform(
+        self, x: "tf.Tensor", y: Optional["tf.Tensor"], **kwargs
+    ) -> Tuple["tf.Tensor", Optional["tf.Tensor"]]:
+        """
+        Transformation of an image with randomly sampled zoom blur.
+
+        :param x: Input samples.
+        :param y: Label of the samples `x`.
+        :return: Transformed samples and labels.
+        """
+        import tensorflow as tf  # lgtm [py/repeated-import]
+
+        nb_zooms = 10
+        x_blur = tf.zeros_like(x)
+        max_zoom_i = np.random.uniform(low=self.zoom_range[0], high=self.zoom_range[1])
+        zooms = np.arange(start=1.0, stop=max_zoom_i, step=(max_zoom_i - 1.0) / nb_zooms)
+
+        height = x.shape[0]
+        width = x.shape[1]
+
+        for zoom in zooms:
+            size = [int(a * zoom) for a in x.shape[0:2]]
+            x_resized = tf.image.resize(
+                images=x,
+                size=size,
+                method=tf.image.ResizeMethod.BILINEAR,
+                preserve_aspect_ratio=True,
+                antialias=False,
+                name=None,
+            )
+
+            trim_top = (x_resized.shape[0] - height) // 2
+            trim_left = (x_resized.shape[0] - width) // 2
+
+            x_blur += x_resized[trim_top : trim_top + height, trim_left : trim_left + width, :]
+
+        x_out = (x + x_blur) / (nb_zooms + 1)
+        return tf.clip_by_value(x_out, clip_value_min=self.clip_values[0], clip_value_max=self.clip_values[1]), y
+
+    def _check_params(self) -> None:
+
+        if not (isinstance(self.zoom, (int, float)) or isinstance(self.zoom, tuple)) or (
+            isinstance(self.zoom, tuple)
+            and (
+                len(self.zoom) != 2
+                or not isinstance(self.zoom[0], (int, float))
+                or not isinstance(self.zoom[1], (int, float))
+                or self.zoom[0] > self.zoom[1]
+                or self.zoom[0] < 1.0
+            )
+        ):
+            raise ValueError("The argument `lam` has to be a float or tuple of two float values as (min, max).")
diff --git a/adversarial-robustness-toolbox/art/preprocessing/expectation_over_transformation/pytorch.py b/adversarial-robustness-toolbox/art/preprocessing/expectation_over_transformation/pytorch.py
new file mode 100644
index 0000000..da8f4b5
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/preprocessing/expectation_over_transformation/pytorch.py
@@ -0,0 +1,115 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2021
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module defines a base class for EoT in PyTorch.
+"""
+from abc import abstractmethod
+import logging
+from typing import Optional, Tuple, TYPE_CHECKING
+
+from art.preprocessing.preprocessing import PreprocessorPyTorch
+
+if TYPE_CHECKING:
+    import torch
+
+logger = logging.getLogger(__name__)
+
+
+class EoTPyTorch(PreprocessorPyTorch):
+    """
+    This module defines a base class for EoT in PyTorch.
+    """
+
+    def __init__(
+        self, nb_samples: int, clip_values: Tuple[float, float], apply_fit: bool = False, apply_predict: bool = True,
+    ) -> None:
+        """
+        Create an instance of EoTPyTorch.
+
+        :param nb_samples: Number of random samples per input sample.
+        :param clip_values: Tuple of float representing minimum and maximum values of input `(min, max)`.
+        :param apply_fit: True if applied during fitting/training.
+        :param apply_predict: True if applied during predicting.
+        """
+        super().__init__(is_fitted=True, apply_fit=apply_fit, apply_predict=apply_predict)
+
+        self.nb_samples = nb_samples
+        self.clip_values = clip_values
+        EoTPyTorch._check_params(self)
+
+    @abstractmethod
+    def _transform(
+        self, x: "torch.Tensor", y: Optional["torch.Tensor"], **kwargs
+    ) -> Tuple["torch.Tensor", Optional["torch.Tensor"]]:
+        """
+        Internal method implementing the transformation per input sample.
+
+        :param x: Input samples.
+        :param y: Label of the samples `x`.
+        :return: Transformed samples and labels.
+        """
+        raise NotImplementedError
+
+    def forward(
+        self, x: "torch.Tensor", y: Optional["torch.Tensor"] = None
+    ) -> Tuple["torch.Tensor", Optional["torch.Tensor"]]:
+        """
+        Apply transformations to inputs `x` and labels `y`.
+
+        :param x: Input samples.
+        :param y: Label of the samples `x`.
+        :return: Transformed samples and labels.
+        """
+        import torch  # lgtm [py/repeated-import]
+
+        x_preprocess_list = list()
+        y_preprocess_list = list()
+
+        for i_image in range(x.shape[0]):
+            for _ in range(self.nb_samples):
+                x_i = x[i_image]
+                if y is not None:
+                    y_i = y[i_image]
+                else:
+                    y_i = None
+                x_preprocess, y_preprocess_i = self._transform(x_i, y_i)
+                x_preprocess_list.append(x_preprocess)
+
+                if y is not None:
+                    y_preprocess_list.append(y_preprocess_i)
+
+        x_preprocess = torch.stack(x_preprocess_list, dim=0)
+        if y is None:
+            y_preprocess = y
+        else:
+            y_preprocess = torch.stack(y_preprocess_list, dim=0)
+
+        return x_preprocess, y_preprocess
+
+    def _check_params(self) -> None:
+
+        if not isinstance(self.nb_samples, int) or self.nb_samples < 1:
+            raise ValueError("The number of samples needs to be an integer greater than or equal to 1.")
+
+        if not isinstance(self.clip_values, tuple) or (
+            len(self.clip_values) != 2
+            or not isinstance(self.clip_values[0], (int, float))
+            or not isinstance(self.clip_values[1], (int, float))
+            or self.clip_values[0] > self.clip_values[1]
+        ):
+            raise ValueError("The argument `clip_Values` has to be a float or tuple of two float values as (min, max).")
diff --git a/adversarial-robustness-toolbox/art/preprocessing/expectation_over_transformation/tensorflow.py b/adversarial-robustness-toolbox/art/preprocessing/expectation_over_transformation/tensorflow.py
new file mode 100644
index 0000000..f50b3b0
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/preprocessing/expectation_over_transformation/tensorflow.py
@@ -0,0 +1,113 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2021
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module defines a base class for EoT in TensorFlow v2.
+"""
+from abc import abstractmethod
+import logging
+from typing import Optional, Tuple, TYPE_CHECKING
+
+from art.preprocessing.preprocessing import PreprocessorTensorFlowV2
+
+if TYPE_CHECKING:
+    import tensorflow as tf
+
+logger = logging.getLogger(__name__)
+
+
+class EoTTensorFlowV2(PreprocessorTensorFlowV2):
+    """
+    This module defines a base class for EoT in TensorFlow v2.
+    """
+
+    def __init__(
+        self, nb_samples: int, clip_values: Tuple[float, float], apply_fit: bool = False, apply_predict: bool = True,
+    ) -> None:
+        """
+        Create an instance of EoTTensorFlowV2.
+
+        :param nb_samples: Number of random samples per input sample.
+        :param clip_values: Tuple of float representing minimum and maximum values of input `(min, max)`.
+        :param apply_fit: True if applied during fitting/training.
+        :param apply_predict: True if applied during predicting.
+        """
+        super().__init__(is_fitted=True, apply_fit=apply_fit, apply_predict=apply_predict)
+
+        self.nb_samples = nb_samples
+        self.clip_values = clip_values
+        EoTTensorFlowV2._check_params(self)
+
+    @abstractmethod
+    def _transform(
+        self, x: "tf.Tensor", y: Optional["tf.Tensor"], **kwargs
+    ) -> Tuple["tf.Tensor", Optional["tf.Tensor"]]:
+        """
+        Internal method implementing the transformation per input sample.
+
+        :param x: Input samples.
+        :param y: Label of the samples `x`.
+        :return: Transformed samples and labels.
+        """
+        raise NotImplementedError
+
+    def forward(self, x: "tf.Tensor", y: Optional["tf.Tensor"] = None) -> Tuple["tf.Tensor", Optional["tf.Tensor"]]:
+        """
+        Apply transformations to inputs `x` and labels `y`.
+
+        :param x: Input samples.
+        :param y: Label of the sample `x`. This function does not modify `y`.
+        :return: Corrupted samples and labels.
+        """
+        import tensorflow as tf  # lgtm [py/repeated-import]
+
+        x_preprocess_list = list()
+        y_preprocess_list = list()
+
+        for i_image in range(x.shape[0]):
+            for _ in range(self.nb_samples):
+                x_i = x[i_image]
+                if y is not None:
+                    y_i = y[i_image]
+                else:
+                    y_i = None
+                x_preprocess, y_preprocess_i = self._transform(x_i, y_i)
+                x_preprocess_list.append(x_preprocess)
+
+                if y is not None:
+                    y_preprocess_list.append(y_preprocess_i)
+
+        x_preprocess = tf.stack(x_preprocess_list, axis=0)
+        if y is None:
+            y_preprocess = y
+        else:
+            y_preprocess = tf.stack(y_preprocess_list, axis=0)
+
+        return x_preprocess, y_preprocess
+
+    def _check_params(self) -> None:
+
+        if not isinstance(self.nb_samples, int) or self.nb_samples < 1:
+            raise ValueError("The number of samples needs to be an integer greater than or equal to 1.")
+
+        if not isinstance(self.clip_values, tuple) or (
+            len(self.clip_values) != 2
+            or not isinstance(self.clip_values[0], (int, float))
+            or not isinstance(self.clip_values[1], (int, float))
+            or self.clip_values[0] > self.clip_values[1]
+        ):
+            raise ValueError("The argument `clip_Values` has to be a float or tuple of two float values as (min, max).")
diff --git a/adversarial-robustness-toolbox/art/preprocessing/preprocessing.py b/adversarial-robustness-toolbox/art/preprocessing/preprocessing.py
new file mode 100644
index 0000000..d15ded4
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/preprocessing/preprocessing.py
@@ -0,0 +1,23 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module contains the Preprocessor API.
+"""
+from art.defences.preprocessor.preprocessor import Preprocessor  # lgtm [py/unused-import]
+from art.defences.preprocessor.preprocessor import PreprocessorPyTorch  # lgtm [py/unused-import]
+from art.defences.preprocessor.preprocessor import PreprocessorTensorFlowV2  # lgtm [py/unused-import]
diff --git a/adversarial-robustness-toolbox/art/preprocessing/standardisation_mean_std/__init__.py b/adversarial-robustness-toolbox/art/preprocessing/standardisation_mean_std/__init__.py
new file mode 100644
index 0000000..7b8593b
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/preprocessing/standardisation_mean_std/__init__.py
@@ -0,0 +1,6 @@
+"""
+This module contains tool for input standardisation with mean and standard deviation.
+"""
+from art.preprocessing.standardisation_mean_std.numpy import StandardisationMeanStd
+from art.preprocessing.standardisation_mean_std.pytorch import StandardisationMeanStdPyTorch
+from art.preprocessing.standardisation_mean_std.tensorflow import StandardisationMeanStdTensorFlow
diff --git a/adversarial-robustness-toolbox/art/preprocessing/standardisation_mean_std/numpy.py b/adversarial-robustness-toolbox/art/preprocessing/standardisation_mean_std/numpy.py
new file mode 100644
index 0000000..f22c1c4
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/preprocessing/standardisation_mean_std/numpy.py
@@ -0,0 +1,107 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements the standardisation with mean and standard deviation.
+"""
+import logging
+from typing import Optional, Tuple, Union
+
+import numpy as np
+
+from art.config import ART_NUMPY_DTYPE
+from art.preprocessing.preprocessing import Preprocessor
+from art.preprocessing.standardisation_mean_std.utils import broadcastable_mean_std
+
+logger = logging.getLogger(__name__)
+
+
+class StandardisationMeanStd(Preprocessor):
+    """
+    Implement the standardisation with mean and standard deviation.
+    """
+
+    params = ["mean", "std", "apply_fit", "apply_predict"]
+
+    def __init__(
+        self,
+        mean: Union[float, np.ndarray] = 0.0,
+        std: Union[float, np.ndarray] = 1.0,
+        apply_fit: bool = True,
+        apply_predict: bool = True,
+    ):
+        """
+        Create an instance of StandardisationMeanStd.
+
+        :param mean: Mean.
+        :param std: Standard Deviation.
+        """
+        super().__init__(is_fitted=True, apply_fit=apply_fit, apply_predict=apply_predict)
+        self.mean = np.asarray(mean, dtype=ART_NUMPY_DTYPE)
+        self.std = np.asarray(std, dtype=ART_NUMPY_DTYPE)
+        self._check_params()
+
+        # init broadcastable mean and std for lazy loading
+        self._broadcastable_mean = None
+        self._broadcastable_std = None
+
+    def __call__(self, x: np.ndarray, y: Optional[np.ndarray] = None,) -> Tuple[np.ndarray, Optional[np.ndarray]]:
+        """
+        Apply StandardisationMeanStd inputs `x`.
+
+        :param x: Input samples to standardise.
+        :param y: Label data, will not be affected by this preprocessing.
+        :return: Standardise input samples and unmodified labels.
+        """
+        if x.dtype in [np.uint8, np.uint16, np.uint32, np.uint64]:
+            raise TypeError(
+                "The data type of input data `x` is {} and cannot represent negative values. Consider "
+                "changing the data type of the input data `x` to a type that supports negative values e.g. "
+                "np.float32.".format(x.dtype)
+            )
+
+        if self._broadcastable_mean is None:
+            self._broadcastable_mean, self._broadcastable_std = broadcastable_mean_std(x, self.mean, self.std)
+
+        x_norm = x - self._broadcastable_mean
+        x_norm = x_norm / self._broadcastable_std
+        x_norm = x_norm.astype(ART_NUMPY_DTYPE)
+
+        return x_norm, y
+
+    def estimate_gradient(self, x: np.ndarray, gradient: np.ndarray) -> np.ndarray:
+        """
+        Provide an estimate of the gradients of preprocessor for the backward pass. If the preprocessor is not
+        differentiable, this is an estimate of the gradient, most often replacing the computation performed by the
+        preprocessor with the identity function (the default).
+
+        :param x: Input data for which the gradient is estimated. First dimension is the batch size.
+        :param grad: Gradient value so far.
+        :return: The gradient (estimate) of the defence.
+        """
+        _, std = broadcastable_mean_std(x, self.mean, self.std)
+        gradient_back = gradient / std
+
+        return gradient_back
+
+    def _check_params(self) -> None:
+        pass
+
+    def __repr__(self):
+        return "StandardisationMeanStd(mean={}, std={}, apply_fit={}, apply_predict={})".format(
+            self.mean, self.std, self.apply_fit, self.apply_predict
+        )
diff --git a/adversarial-robustness-toolbox/art/preprocessing/standardisation_mean_std/pytorch.py b/adversarial-robustness-toolbox/art/preprocessing/standardisation_mean_std/pytorch.py
new file mode 100644
index 0000000..7a2960c
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/preprocessing/standardisation_mean_std/pytorch.py
@@ -0,0 +1,101 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements the standardisation with mean and standard deviation.
+"""
+import logging
+from typing import TYPE_CHECKING, Optional, Tuple, Union
+
+import numpy as np
+
+from art.config import ART_NUMPY_DTYPE
+from art.preprocessing.preprocessing import PreprocessorPyTorch
+from art.preprocessing.standardisation_mean_std.utils import broadcastable_mean_std
+
+if TYPE_CHECKING:
+    import torch
+
+logger = logging.getLogger(__name__)
+
+
+class StandardisationMeanStdPyTorch(PreprocessorPyTorch):
+    """
+    Implement the standardisation with mean and standard deviation.
+    """
+
+    params = ["mean", "std", "apply_fit", "apply_predict"]
+
+    def __init__(
+        self,
+        mean: Union[float, np.ndarray] = 0.0,
+        std: Union[float, np.ndarray] = 1.0,
+        apply_fit: bool = True,
+        apply_predict: bool = True,
+        device_type: str = "gpu",
+    ):
+        """
+        Create an instance of StandardisationMeanStdPyTorch.
+
+        :param mean: Mean.
+        :param std: Standard Deviation.
+        """
+        import torch  # lgtm [py/repeated-import]
+
+        super().__init__(is_fitted=True, apply_fit=apply_fit, apply_predict=apply_predict)
+        self.mean = np.asarray(mean, dtype=ART_NUMPY_DTYPE)
+        self.std = np.asarray(std, dtype=ART_NUMPY_DTYPE)
+        self._check_params()
+
+        # init broadcastable mean and std for lazy loading
+        self._broadcastable_mean = None
+        self._broadcastable_std = None
+
+        # Set device
+        if device_type == "cpu" or not torch.cuda.is_available():
+            self._device = torch.device("cpu")
+        else:
+            cuda_idx = torch.cuda.current_device()
+            self._device = torch.device("cuda:{}".format(cuda_idx))
+
+    def forward(
+        self, x: "torch.Tensor", y: Optional["torch.Tensor"] = None
+    ) -> Tuple["torch.Tensor", Optional["torch.Tensor"]]:
+        """
+        Apply standardisation with mean and standard deviation to input `x`.
+
+        :param x: Input samples to standardise.
+        :param y: Label data, will not be affected by this preprocessing.
+        :return: Standardised input samples and unmodified labels.
+        """
+        import torch  # lgtm [py/repeated-import]
+
+        if self._broadcastable_mean is None:
+            self._broadcastable_mean, self._broadcastable_std = broadcastable_mean_std(x, self.mean, self.std)
+
+        x_norm = x - torch.tensor(self._broadcastable_mean, device=self._device, dtype=torch.float32)
+        x_norm = x_norm / torch.tensor(self._broadcastable_std, device=self._device, dtype=torch.float32)
+
+        return x_norm, y
+
+    def _check_params(self) -> None:
+        pass
+
+    def __repr__(self):
+        return "StandardisationMeanStdPyTorch(mean={}, std={}, apply_fit={}, apply_predict={}, device={})".format(
+            self.mean, self.std, self.apply_fit, self.apply_predict, self._device
+        )
diff --git a/adversarial-robustness-toolbox/art/preprocessing/standardisation_mean_std/tensorflow.py b/adversarial-robustness-toolbox/art/preprocessing/standardisation_mean_std/tensorflow.py
new file mode 100644
index 0000000..c8d65d2
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/preprocessing/standardisation_mean_std/tensorflow.py
@@ -0,0 +1,90 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements the standardisation with mean and standard deviation.
+"""
+import logging
+from typing import TYPE_CHECKING, Optional, Tuple, Union
+
+import numpy as np
+
+from art.config import ART_NUMPY_DTYPE
+from art.preprocessing.preprocessing import PreprocessorTensorFlowV2
+from art.preprocessing.standardisation_mean_std.utils import broadcastable_mean_std
+
+if TYPE_CHECKING:
+    import tensorflow as tf
+
+logger = logging.getLogger(__name__)
+
+
+class StandardisationMeanStdTensorFlow(PreprocessorTensorFlowV2):
+    """
+    Implement the standardisation with mean and standard deviation.
+    """
+
+    params = ["mean", "std", "apply_fit", "apply_predict"]
+
+    def __init__(
+        self,
+        mean: Union[float, np.ndarray] = 0.0,
+        std: Union[float, np.ndarray] = 1.0,
+        apply_fit: bool = True,
+        apply_predict: bool = True,
+    ):
+        """
+        Create an instance of StandardisationMeanStdTensorFlow.
+
+        :param mean: Mean.
+        :param std: Standard Deviation.
+        """
+        super().__init__(is_fitted=True, apply_fit=apply_fit, apply_predict=apply_predict)
+        self.mean = np.asarray(mean, dtype=ART_NUMPY_DTYPE)
+        self.std = np.asarray(std, dtype=ART_NUMPY_DTYPE)
+        self._check_params()
+
+        # init broadcastable mean and std for lazy loading
+        self._broadcastable_mean = None
+        self._broadcastable_std = None
+
+    def forward(self, x: "tf.Tensor", y: Optional["tf.Tensor"] = None) -> Tuple["tf.Tensor", Optional["tf.Tensor"]]:
+        """
+        Apply standardisation with mean and standard deviation to input `x`.
+
+        :param x: Input samples to standardise.
+        :param y: Label data, will not be affected by this preprocessing.
+        :return: Standardised input samples and unmodified labels.
+        """
+        import tensorflow as tf  # lgtm [py/repeated-import]
+
+        if self._broadcastable_mean is None:
+            self._broadcastable_mean, self._broadcastable_std = broadcastable_mean_std(x, self.mean, self.std)
+
+        x_norm = x - self._broadcastable_mean
+        x_norm = x_norm / self._broadcastable_std
+        x_norm = tf.cast(x_norm, dtype=ART_NUMPY_DTYPE)
+
+        return x_norm, y
+
+    def _check_params(self) -> None:
+        pass
+
+    def __repr__(self):
+        return "StandardisationMeanStdTensorFlow(mean={}, std={}, apply_fit={}, apply_predict={})".format(
+            self.mean, self.std, self.apply_fit, self.apply_predict
+        )
diff --git a/adversarial-robustness-toolbox/art/preprocessing/standardisation_mean_std/utils.py b/adversarial-robustness-toolbox/art/preprocessing/standardisation_mean_std/utils.py
new file mode 100644
index 0000000..153b841
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/preprocessing/standardisation_mean_std/utils.py
@@ -0,0 +1,48 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2021
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements utilities for standardisation with mean and standard deviation.
+"""
+
+from typing import Tuple
+
+import numpy as np
+
+
+def broadcastable_mean_std(x: np.ndarray, mean: np.ndarray, std: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
+    """
+    Ensure that the mean and standard deviation are broadcastable with respect to input `x`.
+
+    :param x: Input samples to standardise.
+    :param mean: Mean.
+    :param std: Standard Deviation.
+    """
+    if mean.shape != std.shape:
+        raise ValueError("The shape of mean and the standard deviation must be identical.")
+
+    # catch non-broadcastable input, when mean and std are vectors
+    if mean.ndim == 1 and mean.shape[0] > 1 and mean.shape[0] != x.shape[-1]:
+        # allow input shapes NC* (batch) and C* (non-batch)
+        channel_idx = 1 if x.shape[1] == mean.shape[0] else 0
+        broadcastable_shape = [1] * x.ndim
+        broadcastable_shape[channel_idx] = mean.shape[0]
+
+        # expand mean and std to new shape
+        mean = mean.reshape(broadcastable_shape)
+        std = std.reshape(broadcastable_shape)
+    return mean, std
diff --git a/adversarial-robustness-toolbox/art/utils.py b/adversarial-robustness-toolbox/art/utils.py
new file mode 100644
index 0000000..4f0cf82
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/utils.py
@@ -0,0 +1,1285 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+Module providing convenience functions.
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+from functools import wraps
+from inspect import signature
+import logging
+import math
+import os
+import shutil
+import sys
+import tarfile
+from typing import Callable, List, Optional, Tuple, Union, TYPE_CHECKING
+import warnings
+import zipfile
+
+import numpy as np
+from scipy.special import gammainc
+import six
+from tqdm.auto import tqdm
+
+from art import config
+
+if TYPE_CHECKING:
+    import torch
+
+logger = logging.getLogger(__name__)
+
+
+# ------------------------------------------------------------------------------------------------- CONSTANTS AND TYPES
+
+
+DATASET_TYPE = Tuple[Tuple[np.ndarray, np.ndarray], Tuple[np.ndarray, np.ndarray], float, float]
+CLIP_VALUES_TYPE = Tuple[Union[int, float, np.ndarray], Union[int, float, np.ndarray]]
+
+if TYPE_CHECKING:
+    # pylint: disable=R0401
+    from art.defences.preprocessor.preprocessor import Preprocessor
+
+    PREPROCESSING_TYPE = Optional[
+        Union[
+            Tuple[Union[int, float, np.ndarray], Union[int, float, np.ndarray]], Preprocessor, Tuple[Preprocessor, ...]
+        ]
+    ]
+
+    from art.estimators.classification.classifier import (
+        Classifier,
+        ClassifierLossGradients,
+        ClassifierClassLossGradients,
+        ClassifierNeuralNetwork,
+        ClassifierDecisionTree,
+    )
+    from art.estimators.classification.blackbox import BlackBoxClassifier
+    from art.estimators.classification.catboost import CatBoostARTClassifier
+    from art.estimators.classification.detector_classifier import DetectorClassifier
+    from art.estimators.classification.ensemble import EnsembleClassifier
+    from art.estimators.classification.GPy import GPyGaussianProcessClassifier
+    from art.estimators.classification.keras import KerasClassifier
+    from art.estimators.classification.lightgbm import LightGBMClassifier
+    from art.estimators.classification.mxnet import MXClassifier
+    from art.estimators.classification.pytorch import PyTorchClassifier
+    from art.estimators.classification.scikitlearn import (
+        ScikitlearnClassifier,
+        ScikitlearnDecisionTreeClassifier,
+        ScikitlearnDecisionTreeRegressor,
+        ScikitlearnExtraTreeClassifier,
+        ScikitlearnAdaBoostClassifier,
+        ScikitlearnBaggingClassifier,
+        ScikitlearnExtraTreesClassifier,
+        ScikitlearnGradientBoostingClassifier,
+        ScikitlearnRandomForestClassifier,
+        ScikitlearnLogisticRegression,
+        ScikitlearnSVC,
+    )
+    from art.estimators.classification.tensorflow import TensorFlowClassifier, TensorFlowV2Classifier
+    from art.estimators.classification.xgboost import XGBoostClassifier
+
+    from art.estimators.object_detection.object_detector import ObjectDetector
+    from art.estimators.object_detection.pytorch_faster_rcnn import PyTorchFasterRCNN
+    from art.estimators.object_detection.tensorflow_faster_rcnn import TensorFlowFasterRCNN
+
+    from art.estimators.speech_recognition.pytorch_deep_speech import PyTorchDeepSpeech
+    from art.estimators.speech_recognition.tensorflow_lingvo import TensorFlowLingvoASR
+
+    CLASSIFIER_LOSS_GRADIENTS_TYPE = Union[
+        ClassifierLossGradients,
+        EnsembleClassifier,
+        GPyGaussianProcessClassifier,
+        KerasClassifier,
+        MXClassifier,
+        PyTorchClassifier,
+        ScikitlearnLogisticRegression,
+        ScikitlearnSVC,
+        TensorFlowClassifier,
+        TensorFlowV2Classifier,
+    ]
+
+    CLASSIFIER_CLASS_LOSS_GRADIENTS_TYPE = Union[
+        ClassifierClassLossGradients,
+        EnsembleClassifier,
+        GPyGaussianProcessClassifier,
+        KerasClassifier,
+        MXClassifier,
+        PyTorchClassifier,
+        ScikitlearnLogisticRegression,
+        ScikitlearnSVC,
+        TensorFlowClassifier,
+        TensorFlowV2Classifier,
+    ]
+
+    CLASSIFIER_NEURALNETWORK_TYPE = Union[
+        ClassifierNeuralNetwork,
+        DetectorClassifier,
+        EnsembleClassifier,
+        KerasClassifier,
+        MXClassifier,
+        PyTorchClassifier,
+        TensorFlowClassifier,
+        TensorFlowV2Classifier,
+    ]
+
+    CLASSIFIER_DECISION_TREE_TYPE = Union[
+        ClassifierDecisionTree,
+        LightGBMClassifier,
+        ScikitlearnDecisionTreeClassifier,
+        ScikitlearnDecisionTreeRegressor,
+        ScikitlearnExtraTreesClassifier,
+        ScikitlearnGradientBoostingClassifier,
+        ScikitlearnRandomForestClassifier,
+        XGBoostClassifier,
+    ]
+
+    CLASSIFIER_TYPE = Union[
+        Classifier,
+        BlackBoxClassifier,
+        CatBoostARTClassifier,
+        DetectorClassifier,
+        EnsembleClassifier,
+        GPyGaussianProcessClassifier,
+        KerasClassifier,
+        LightGBMClassifier,
+        MXClassifier,
+        PyTorchClassifier,
+        ScikitlearnClassifier,
+        ScikitlearnDecisionTreeClassifier,
+        ScikitlearnDecisionTreeRegressor,
+        ScikitlearnExtraTreeClassifier,
+        ScikitlearnAdaBoostClassifier,
+        ScikitlearnBaggingClassifier,
+        ScikitlearnExtraTreesClassifier,
+        ScikitlearnGradientBoostingClassifier,
+        ScikitlearnRandomForestClassifier,
+        ScikitlearnLogisticRegression,
+        ScikitlearnSVC,
+        TensorFlowClassifier,
+        TensorFlowV2Classifier,
+        XGBoostClassifier,
+        CLASSIFIER_NEURALNETWORK_TYPE,
+    ]
+
+    OBJECT_DETECTOR_TYPE = Union[
+        ObjectDetector, PyTorchFasterRCNN, TensorFlowFasterRCNN,
+    ]
+
+    SPEECH_RECOGNIZER_TYPE = Union[
+        PyTorchDeepSpeech, TensorFlowLingvoASR,
+    ]
+
+# --------------------------------------------------------------------------------------------------------- DEPRECATION
+
+
+class _Deprecated:
+    """
+    Create Deprecated() singleton object.
+    """
+
+    _instance = None
+
+    def __new__(cls):
+        if _Deprecated._instance is None:
+            _Deprecated._instance = object.__new__(cls)
+        return _Deprecated._instance
+
+
+Deprecated = _Deprecated()
+
+
+def deprecated(end_version: str, *, reason: str = "", replaced_by: str = "") -> Callable:
+    """
+    Deprecate a function or method and raise a `DeprecationWarning`.
+
+    The `@deprecated` decorator is used to deprecate functions and methods. Several cases are supported. For example
+    one can use it to deprecate a function that has become redundant or rename a function. The following code examples
+    provide different use cases of how to use decorator.
+
+    .. code-block:: python
+
+      @deprecated("0.1.5", replaced_by="sum")
+      def simple_addition(a, b):
+          return a + b
+
+    :param end_version: Release version of removal.
+    :param reason: Additional deprecation reason.
+    :param replaced_by: Function that replaces deprecated function.
+    """
+
+    def decorator(function):
+        reason_msg = "\n" + reason if reason else reason
+        replaced_msg = f" It will be replaced by '{replaced_by}'." if replaced_by else replaced_by
+        deprecated_msg = (
+            f"Function '{function.__name__}' is deprecated and will be removed in future release {end_version}."
+        )
+
+        @wraps(function)
+        def wrapper(*args, **kwargs):
+            warnings.simplefilter("always", category=DeprecationWarning)
+            warnings.warn(
+                deprecated_msg + replaced_msg + reason_msg, category=DeprecationWarning, stacklevel=2,
+            )
+            warnings.simplefilter("default", category=DeprecationWarning)
+            return function(*args, **kwargs)
+
+        return wrapper
+
+    return decorator
+
+
+def deprecated_keyword_arg(identifier: str, end_version: str, *, reason: str = "", replaced_by: str = "") -> Callable:
+    """
+    Deprecate a keyword argument and raise a `DeprecationWarning`.
+
+    The `@deprecated_keyword_arg` decorator is used to deprecate keyword arguments. The deprecated keyword argument must
+    default to `Deprecated`. Several use cases are supported. For example one can use it to to rename a keyword
+    identifier. The following code examples provide different use cases of how to use the decorator.
+
+    .. code-block:: python
+
+      @deprecated_keyword_arg("print", "1.1.0", replaced_by="verbose")
+      def simple_addition(a, b, print=Deprecated, verbose=False):
+          if verbose:
+              print(a + b)
+          return a + b
+
+      @deprecated_keyword_arg("verbose", "1.1.0")
+      def simple_addition(a, b, verbose=Deprecated):
+          return a + b
+
+    :param identifier: Keyword identifier.
+    :param end_version: Release version of removal.
+    :param reason: Additional deprecation reason.
+    :param replaced_by: Function that replaces deprecated function.
+    """
+
+    def decorator(function):
+        reason_msg = "\n" + reason if reason else reason
+        replaced_msg = f" It will be replaced by '{replaced_by}'." if replaced_by else replaced_by
+        deprecated_msg = (
+            f"Keyword argument '{identifier}' in '{function.__name__}' is deprecated and will be removed in"
+            f" future release {end_version}."
+        )
+
+        @wraps(function)
+        def wrapper(*args, **kwargs):
+            params = signature(function).bind(*args, **kwargs)
+            params.apply_defaults()
+
+            if params.signature.parameters[identifier].default is not Deprecated:
+                raise ValueError("Deprecated keyword argument must default to the Decorator singleton.")
+            if replaced_by != "" and replaced_by not in params.arguments:
+                raise ValueError("Deprecated keyword replacement not found in function signature.")
+
+            if params.arguments[identifier] is not Deprecated:
+                warnings.simplefilter("always", category=DeprecationWarning)
+                warnings.warn(deprecated_msg + replaced_msg + reason_msg, category=DeprecationWarning, stacklevel=2)
+                warnings.simplefilter("default", category=DeprecationWarning)
+            return function(*args, **kwargs)
+
+        return wrapper
+
+    return decorator
+
+
+# ----------------------------------------------------------------------------------------------------- MATH OPERATIONS
+
+
+def projection(values: np.ndarray, eps: Union[int, float, np.ndarray], norm_p: Union[int, float, str]) -> np.ndarray:
+    """
+    Project `values` on the L_p norm ball of size `eps`.
+
+    :param values: Array of perturbations to clip.
+    :param eps: Maximum norm allowed.
+    :param norm_p: L_p norm to use for clipping. Only 1, 2, `np.Inf` and "inf" supported for now.
+    :return: Values of `values` after projection.
+    """
+    # Pick a small scalar to avoid division by 0
+    tol = 10e-8
+    values_tmp = values.reshape((values.shape[0], -1))
+
+    if norm_p == 2:
+        if isinstance(eps, np.ndarray):
+            raise NotImplementedError("The parameter `eps` of type `np.ndarray` is not supported to use with norm 2.")
+
+        values_tmp = values_tmp * np.expand_dims(
+            np.minimum(1.0, eps / (np.linalg.norm(values_tmp, axis=1) + tol)), axis=1
+        )
+
+    elif norm_p == 1:
+        if isinstance(eps, np.ndarray):
+            raise NotImplementedError("The parameter `eps` of type `np.ndarray` is not supported to use with norm 1.")
+
+        values_tmp = values_tmp * np.expand_dims(
+            np.minimum(1.0, eps / (np.linalg.norm(values_tmp, axis=1, ord=1) + tol)), axis=1,
+        )
+
+    elif norm_p in [np.inf, "inf"]:
+        if isinstance(eps, np.ndarray):
+            eps = eps * np.ones_like(values)
+            eps = eps.reshape([eps.shape[0], -1])
+
+        values_tmp = np.sign(values_tmp) * np.minimum(abs(values_tmp), eps)
+
+    else:
+        raise NotImplementedError(
+            'Values of `norm_p` different from 1, 2, `np.inf` and "inf" are currently not ' "supported."
+        )
+
+    values = values_tmp.reshape(values.shape)
+
+    return values
+
+
+def random_sphere(
+    nb_points: int, nb_dims: int, radius: Union[int, float, np.ndarray], norm: Union[int, float, str],
+) -> np.ndarray:
+    """
+    Generate randomly `m x n`-dimension points with radius `radius` and centered around 0.
+
+    :param nb_points: Number of random data points.
+    :param nb_dims: Dimensionality of the sphere.
+    :param radius: Radius of the sphere.
+    :param norm: Current support: 1, 2, np.inf, "inf".
+    :return: The generated random sphere.
+    """
+    if norm == 1:
+        if isinstance(radius, np.ndarray):
+            raise NotImplementedError(
+                "The parameter `radius` of type `np.ndarray` is not supported to use with norm 1."
+            )
+
+        a_tmp = np.zeros(shape=(nb_points, nb_dims + 1))
+        a_tmp[:, -1] = np.sqrt(np.random.uniform(0, radius ** 2, nb_points))
+
+        for i in range(nb_points):
+            a_tmp[i, 1:-1] = np.sort(np.random.uniform(0, a_tmp[i, -1], nb_dims - 1))
+
+        res = (a_tmp[:, 1:] - a_tmp[:, :-1]) * np.random.choice([-1, 1], (nb_points, nb_dims))
+
+    elif norm == 2:
+        if isinstance(radius, np.ndarray):
+            raise NotImplementedError(
+                "The parameter `radius` of type `np.ndarray` is not supported to use with norm 2."
+            )
+
+        a_tmp = np.random.randn(nb_points, nb_dims)
+        s_2 = np.sum(a_tmp ** 2, axis=1)
+        base = gammainc(nb_dims / 2.0, s_2 / 2.0) ** (1 / nb_dims) * radius / np.sqrt(s_2)
+        res = a_tmp * (np.tile(base, (nb_dims, 1))).T
+
+    elif norm in [np.inf, "inf"]:
+        if isinstance(radius, np.ndarray):
+            radius = radius * np.ones(shape=(nb_points, nb_dims))
+
+        res = np.random.uniform(-radius, radius, (nb_points, nb_dims))
+
+    else:
+        raise NotImplementedError("Norm {} not supported".format(norm))
+
+    return res
+
+
+def original_to_tanh(
+    x_original: np.ndarray,
+    clip_min: Union[float, np.ndarray],
+    clip_max: Union[float, np.ndarray],
+    tanh_smoother: float = 0.999999,
+) -> np.ndarray:
+    """
+    Transform input from original to tanh space.
+
+    :param x_original: An array with the input to be transformed.
+    :param clip_min: Minimum clipping value.
+    :param clip_max: Maximum clipping value.
+    :param tanh_smoother: Scalar for multiplying arguments of arctanh to avoid division by zero.
+    :return: An array holding the transformed input.
+    """
+    x_tanh = np.clip(x_original, clip_min, clip_max)
+    x_tanh = (x_tanh - clip_min) / (clip_max - clip_min)
+    x_tanh = np.arctanh(((x_tanh * 2) - 1) * tanh_smoother)
+    return x_tanh
+
+
+def tanh_to_original(
+    x_tanh: np.ndarray, clip_min: Union[float, np.ndarray], clip_max: Union[float, np.ndarray],
+) -> np.ndarray:
+    """
+    Transform input from tanh to original space.
+
+    :param x_tanh: An array with the input to be transformed.
+    :param clip_min: Minimum clipping value.
+    :param clip_max: Maximum clipping value.
+    :return: An array holding the transformed input.
+    """
+    return (np.tanh(x_tanh) + 1.0) / 2.0 * (clip_max - clip_min) + clip_min
+
+
+# --------------------------------------------------------------------------------------------------- LABELS OPERATIONS
+
+
+def to_categorical(labels: Union[np.ndarray, List[float]], nb_classes: Optional[int] = None) -> np.ndarray:
+    """
+    Convert an array of labels to binary class matrix.
+
+    :param labels: An array of integer labels of shape `(nb_samples,)`.
+    :param nb_classes: The number of classes (possible labels).
+    :return: A binary matrix representation of `y` in the shape `(nb_samples, nb_classes)`.
+    """
+    labels = np.array(labels, dtype=np.int32)
+    if nb_classes is None:
+        nb_classes = np.max(labels) + 1
+    categorical = np.zeros((labels.shape[0], nb_classes), dtype=np.float32)
+    categorical[np.arange(labels.shape[0]), np.squeeze(labels)] = 1
+    return categorical
+
+
+def float_to_categorical(labels: np.ndarray, nb_classes: Optional[int] = None):
+    """
+    Convert an array of floating point labels to binary class matrix.
+
+    :param labels: An array of integer labels of shape `(nb_samples,)`
+    :param nb_classes: The number of classes (possible labels)
+    :return: A binary matrix representation of `labels` in the shape `(nb_samples, nb_classes)`
+    :rtype: `np.ndarray`
+    """
+    labels = np.array(labels)
+    unique = np.unique(labels)
+    unique.sort()
+    indexes = [np.where(unique == value)[0] for value in labels]
+    if nb_classes is None:
+        nb_classes = len(unique) + 1
+    categorical = np.zeros((labels.shape[0], nb_classes), dtype=np.float32)
+    categorical[np.arange(labels.shape[0]), np.squeeze(indexes)] = 1
+    return categorical
+
+
+def floats_to_one_hot(labels: np.ndarray):
+    """
+    Convert a 2D-array of floating point labels to binary class matrix.
+
+    :param labels: A 2D-array of floating point labels of shape `(nb_samples, nb_classes)`
+    :return: A binary matrix representation of `labels` in the shape `(nb_samples, nb_classes)`
+    :rtype: `np.ndarray`
+    """
+    labels = np.array(labels)
+    for feature in labels.T:
+        unique = np.unique(feature)
+        unique.sort()
+        for index, value in enumerate(unique):
+            feature[feature == value] = index
+    return labels.astype(np.float32)
+
+
+def check_and_transform_label_format(
+    labels: np.ndarray, nb_classes: Optional[int] = None, return_one_hot: bool = True
+) -> np.ndarray:
+    """
+    Check label format and transform to one-hot-encoded labels if necessary
+
+    :param labels: An array of integer labels of shape `(nb_samples,)`, `(nb_samples, 1)` or `(nb_samples, nb_classes)`.
+    :param nb_classes: The number of classes.
+    :param return_one_hot: True if returning one-hot encoded labels, False if returning index labels.
+    :return: Labels with shape `(nb_samples, nb_classes)` (one-hot) or `(nb_samples,)` (index).
+    """
+    if labels is not None:
+        if len(labels.shape) == 2 and labels.shape[1] > 1:
+            if not return_one_hot:
+                labels = np.argmax(labels, axis=1)
+        elif len(labels.shape) == 2 and labels.shape[1] == 1:
+            labels = np.squeeze(labels)
+            if return_one_hot:
+                labels = to_categorical(labels, nb_classes)
+        elif len(labels.shape) == 1:
+            if return_one_hot:
+                labels = to_categorical(labels, nb_classes)
+        else:
+            raise ValueError(
+                "Shape of labels not recognised."
+                "Please provide labels in shape (nb_samples,) or (nb_samples, nb_classes)"
+            )
+
+    return labels
+
+
+def random_targets(labels: np.ndarray, nb_classes: int) -> np.ndarray:
+    """
+    Given a set of correct labels, randomly changes some correct labels to target labels different from the original
+    ones. These can be one-hot encoded or integers.
+
+    :param labels: The correct labels.
+    :param nb_classes: The number of classes for this model.
+    :return: An array holding the randomly-selected target classes, one-hot encoded.
+    """
+    if len(labels.shape) > 1:
+        labels = np.argmax(labels, axis=1)
+
+    result = np.zeros(labels.shape)
+
+    for class_ind in range(nb_classes):
+        other_classes = list(range(nb_classes))
+        other_classes.remove(class_ind)
+        in_cl = labels == class_ind
+        result[in_cl] = np.random.choice(other_classes)
+
+    return to_categorical(result, nb_classes)
+
+
+def least_likely_class(x: np.ndarray, classifier: "CLASSIFIER_TYPE") -> np.ndarray:
+    """
+    Compute the least likely class predictions for sample `x`. This strategy for choosing attack targets was used in
+    (Kurakin et al., 2016).
+
+    | Paper link: https://arxiv.org/abs/1607.02533
+
+    :param x: A data sample of shape accepted by `classifier`.
+    :param classifier: The classifier used for computing predictions.
+    :return: Least-likely class predicted by `classifier` for sample `x` in one-hot encoding.
+    """
+    return to_categorical(np.argmin(classifier.predict(x), axis=1), nb_classes=classifier.nb_classes)
+
+
+def second_most_likely_class(x: np.ndarray, classifier: "CLASSIFIER_TYPE") -> np.ndarray:
+    """
+    Compute the second most likely class predictions for sample `x`. This strategy can be used for choosing target
+    labels for an attack to improve its chances to succeed.
+
+    :param x: A data sample of shape accepted by `classifier`.
+    :param classifier: The classifier used for computing predictions.
+    :return: Second most likely class predicted by `classifier` for sample `x` in one-hot encoding.
+    """
+    return to_categorical(np.argpartition(classifier.predict(x), -2, axis=1)[:, -2], nb_classes=classifier.nb_classes,)
+
+
+def get_label_conf(y_vec: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
+    """
+    Returns the confidence and the label of the most probable class given a vector of class confidences
+
+    :param y_vec: Vector of class confidences, no. of instances as first dimension.
+    :return: Confidences and labels.
+    """
+    assert len(y_vec.shape) == 2
+
+    confs, labels = np.amax(y_vec, axis=1), np.argmax(y_vec, axis=1)
+    return confs, labels
+
+
+def get_labels_np_array(preds: np.ndarray) -> np.ndarray:
+    """
+    Returns the label of the most probable class given a array of class confidences.
+
+    :param preds: Array of class confidences, nb of instances as first dimension.
+    :return: Labels.
+    """
+    preds_max = np.amax(preds, axis=1, keepdims=True)
+    y = preds == preds_max
+    y = y.astype(np.uint8)
+    return y
+
+
+def compute_success_array(
+    classifier: "CLASSIFIER_TYPE",
+    x_clean: np.ndarray,
+    labels: np.ndarray,
+    x_adv: np.ndarray,
+    targeted: bool = False,
+    batch_size: int = 1,
+) -> float:
+    """
+    Compute the success rate of an attack based on clean samples, adversarial samples and targets or correct labels.
+
+    :param classifier: Classifier used for prediction.
+    :param x_clean: Original clean samples.
+    :param labels: Correct labels of `x_clean` if the attack is untargeted, or target labels of the attack otherwise.
+    :param x_adv: Adversarial samples to be evaluated.
+    :param targeted: `True` if the attack is targeted. In that case, `labels` are treated as target classes instead of
+           correct labels of the clean samples.
+    :param batch_size: Batch size.
+    :return: Percentage of successful adversarial samples.
+    """
+    adv_preds = np.argmax(classifier.predict(x_adv, batch_size=batch_size), axis=1)
+    if targeted:
+        attack_success = adv_preds == np.argmax(labels, axis=1)
+    else:
+        preds = np.argmax(classifier.predict(x_clean, batch_size=batch_size), axis=1)
+        attack_success = adv_preds != preds
+
+    return attack_success
+
+
+def compute_success(
+    classifier: "CLASSIFIER_TYPE",
+    x_clean: np.ndarray,
+    labels: np.ndarray,
+    x_adv: np.ndarray,
+    targeted: bool = False,
+    batch_size: int = 1,
+) -> float:
+    """
+    Compute the success rate of an attack based on clean samples, adversarial samples and targets or correct labels.
+
+    :param classifier: Classifier used for prediction.
+    :param x_clean: Original clean samples.
+    :param labels: Correct labels of `x_clean` if the attack is untargeted, or target labels of the attack otherwise.
+    :param x_adv: Adversarial samples to be evaluated.
+    :param targeted: `True` if the attack is targeted. In that case, `labels` are treated as target classes instead of
+           correct labels of the clean samples.
+    :param batch_size: Batch size.
+    :return: Percentage of successful adversarial samples.
+    """
+    attack_success = compute_success_array(classifier, x_clean, labels, x_adv, targeted, batch_size)
+    return np.sum(attack_success) / x_adv.shape[0]
+
+
+def compute_accuracy(preds: np.ndarray, labels: np.ndarray, abstain: bool = True) -> Tuple[np.ndarray, int]:
+    """
+    Compute the accuracy rate and coverage rate of predictions
+    In the case where predictions are abstained, those samples are ignored.
+
+    :param preds: Predictions.
+    :param labels: Correct labels of `x`.
+    :param abstain: True if ignore abstained prediction, False if count them as incorrect.
+    :return: Tuple of accuracy rate and coverage rate.
+    """
+    has_pred = np.sum(preds, axis=1)
+    idx_pred = np.where(has_pred)[0]
+    labels = np.argmax(labels[idx_pred], axis=1)
+    num_correct = np.sum(np.argmax(preds[idx_pred], axis=1) == labels)
+    coverage_rate = len(idx_pred) / preds.shape[0]
+
+    if abstain:
+        acc_rate = num_correct / preds[idx_pred].shape[0]
+    else:
+        acc_rate = num_correct / preds.shape[0]
+
+    return acc_rate, coverage_rate
+
+
+# -------------------------------------------------------------------------------------------------- DATASET OPERATIONS
+
+
+def load_cifar10(raw: bool = False,) -> DATASET_TYPE:
+    """
+    Loads CIFAR10 dataset from config.CIFAR10_PATH or downloads it if necessary.
+
+    :param raw: `True` if no preprocessing should be applied to the data. Otherwise, data is normalized to 1.
+    :return: `(x_train, y_train), (x_test, y_test), min, max`
+    """
+
+    def load_batch(fpath: str) -> Tuple[np.ndarray, np.ndarray]:
+        """
+        Utility function for loading CIFAR batches, as written in Keras.
+
+        :param fpath: Full path to the batch file.
+        :return: `(data, labels)`
+        """
+        with open(fpath, "rb") as file_:
+            if sys.version_info < (3,):
+                content = six.moves.cPickle.load(file_)
+            else:
+                content = six.moves.cPickle.load(file_, encoding="bytes")
+                content_decoded = {}
+                for key, value in content.items():
+                    content_decoded[key.decode("utf8")] = value
+                content = content_decoded
+        data = content["data"]
+        labels = content["labels"]
+
+        data = data.reshape(data.shape[0], 3, 32, 32)
+        return data, labels
+
+    path = get_file(
+        "cifar-10-batches-py",
+        extract=True,
+        path=config.ART_DATA_PATH,
+        url="http://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz",
+    )
+
+    num_train_samples = 50000
+
+    x_train = np.zeros((num_train_samples, 3, 32, 32), dtype=np.uint8)
+    y_train = np.zeros((num_train_samples,), dtype=np.uint8)
+
+    for i in range(1, 6):
+        fpath = os.path.join(path, "data_batch_" + str(i))
+        data, labels = load_batch(fpath)
+        x_train[(i - 1) * 10000 : i * 10000, :, :, :] = data
+        y_train[(i - 1) * 10000 : i * 10000] = labels
+
+    fpath = os.path.join(path, "test_batch")
+    x_test, y_test = load_batch(fpath)
+    y_train = np.reshape(y_train, (len(y_train), 1))
+    y_test = np.reshape(y_test, (len(y_test), 1))
+
+    # Set channels last
+    x_train = x_train.transpose((0, 2, 3, 1))
+    x_test = x_test.transpose((0, 2, 3, 1))
+
+    min_, max_ = 0.0, 255.0
+    if not raw:
+        min_, max_ = 0.0, 1.0
+        x_train, y_train = preprocess(x_train, y_train, clip_values=(0, 255))
+        x_test, y_test = preprocess(x_test, y_test, clip_values=(0, 255))
+
+    return (x_train, y_train), (x_test, y_test), min_, max_
+
+
+def load_mnist(raw: bool = False,) -> DATASET_TYPE:
+    """
+    Loads MNIST dataset from `config.ART_DATA_PATH` or downloads it if necessary.
+
+    :param raw: `True` if no preprocessing should be applied to the data. Otherwise, data is normalized to 1.
+    :return: `(x_train, y_train), (x_test, y_test), min, max`.
+    """
+    path = get_file("mnist.npz", path=config.ART_DATA_PATH, url="https://s3.amazonaws.com/img-datasets/mnist.npz",)
+
+    dict_mnist = np.load(path)
+    x_train = dict_mnist["x_train"]
+    y_train = dict_mnist["y_train"]
+    x_test = dict_mnist["x_test"]
+    y_test = dict_mnist["y_test"]
+    dict_mnist.close()
+
+    # Add channel axis
+    min_, max_ = 0.0, 255.0
+    if not raw:
+        min_, max_ = 0.0, 1.0
+        x_train = np.expand_dims(x_train, axis=3)
+        x_test = np.expand_dims(x_test, axis=3)
+        x_train, y_train = preprocess(x_train, y_train)
+        x_test, y_test = preprocess(x_test, y_test)
+
+    return (x_train, y_train), (x_test, y_test), min_, max_
+
+
+def load_stl() -> DATASET_TYPE:
+    """
+    Loads the STL-10 dataset from `config.ART_DATA_PATH` or downloads it if necessary.
+
+    :return: `(x_train, y_train), (x_test, y_test), min, max`.
+    """
+    min_, max_ = 0.0, 1.0
+
+    # Download and extract data if needed
+
+    path = get_file(
+        "stl10_binary",
+        path=config.ART_DATA_PATH,
+        extract=True,
+        url="https://ai.stanford.edu/~acoates/stl10/stl10_binary.tar.gz",
+    )
+
+    with open(os.path.join(path, "train_X.bin"), "rb") as f_numpy:
+        x_train = np.fromfile(f_numpy, dtype=np.uint8)
+        x_train = np.reshape(x_train, (-1, 3, 96, 96))
+
+    with open(os.path.join(path, "test_X.bin"), "rb") as f_numpy:
+        x_test = np.fromfile(f_numpy, dtype=np.uint8)
+        x_test = np.reshape(x_test, (-1, 3, 96, 96))
+
+    # Set channel last
+    x_train = x_train.transpose((0, 2, 3, 1))
+    x_test = x_test.transpose((0, 2, 3, 1))
+
+    with open(os.path.join(path, "train_y.bin"), "rb") as f_numpy:
+        y_train = np.fromfile(f_numpy, dtype=np.uint8)
+        y_train -= 1
+
+    with open(os.path.join(path, "test_y.bin"), "rb") as f_numpy:
+        y_test = np.fromfile(f_numpy, dtype=np.uint8)
+        y_test -= 1
+
+    x_train, y_train = preprocess(x_train, y_train)
+    x_test, y_test = preprocess(x_test, y_test)
+
+    return (x_train, y_train), (x_test, y_test), min_, max_
+
+
+def load_iris(raw: bool = False, test_set: float = 0.3) -> DATASET_TYPE:
+    """
+    Loads the UCI Iris dataset from `config.ART_DATA_PATH` or downloads it if necessary.
+
+    :param raw: `True` if no preprocessing should be applied to the data. Otherwise, data is normalized to 1.
+    :param test_set: Proportion of the data to use as validation split. The value should be between 0 and 1.
+    :return: Entire dataset and labels.
+    """
+    # Download data if needed
+    path = get_file(
+        "iris.data",
+        path=config.ART_DATA_PATH,
+        extract=False,
+        url="https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data",
+    )
+
+    data = np.loadtxt(path, delimiter=",", usecols=(0, 1, 2, 3), dtype=config.ART_NUMPY_DTYPE)
+    labels = np.loadtxt(path, delimiter=",", usecols=4, dtype=str)
+
+    # Preprocess
+    if not raw:
+        label_map = {"Iris-setosa": 0, "Iris-versicolor": 1, "Iris-virginica": 2}
+        labels = np.array([label_map[labels[i]] for i in range(labels.size)], dtype=np.int32)
+        data, labels = preprocess(data, labels, nb_classes=3)
+    min_, max_ = np.amin(data), np.amax(data)
+
+    # Split training and test sets
+    split_index = int((1 - test_set) * len(data) / 3)
+    x_train = np.vstack((data[:split_index], data[50 : 50 + split_index], data[100 : 100 + split_index]))
+    y_train = np.vstack((labels[:split_index], labels[50 : 50 + split_index], labels[100 : 100 + split_index],))
+
+    if split_index >= 49:
+        x_test, y_test = None, None
+    else:
+
+        x_test = np.vstack((data[split_index:50], data[50 + split_index : 100], data[100 + split_index :],))
+        y_test = np.vstack((labels[split_index:50], labels[50 + split_index : 100], labels[100 + split_index :],))
+        assert len(x_train) + len(x_test) == 150
+
+        # Shuffle test set
+        random_indices = np.random.permutation(len(y_test))
+        x_test, y_test = x_test[random_indices], y_test[random_indices]
+
+    # Shuffle training set
+    random_indices = np.random.permutation(len(y_train))
+    x_train, y_train = x_train[random_indices], y_train[random_indices]
+
+    return (x_train, y_train), (x_test, y_test), min_, max_
+
+
+def load_nursery(raw: bool = False, test_set: float = 0.2, transform_social: bool = False) -> DATASET_TYPE:
+    """
+    Loads the UCI Nursery dataset from `config.ART_DATA_PATH` or downloads it if necessary.
+
+    :param raw: `True` if no preprocessing should be applied to the data. Otherwise, categorical data is one-hot
+                encoded and data is scaled using sklearn's StandardScaler.
+    :param test_set: Proportion of the data to use as validation split. The value should be between 0 and 1.
+    :param transform_social: If `True`, transforms the social feature to be binary for the purpose of attribute
+                             inference. This is done by assigning the original value 'problematic' the new value 1, and
+                             the other original values are assigned the new value 0.
+    :return: Entire dataset and labels.
+    """
+    import pandas as pd
+    import sklearn.model_selection
+    import sklearn.preprocessing
+
+    # Download data if needed
+    path = get_file(
+        "nursery.data",
+        path=config.ART_DATA_PATH,
+        extract=False,
+        url="https://archive.ics.uci.edu/ml/machine-learning-databases/nursery/nursery.data",
+    )
+
+    # load data
+    features = ["parents", "has_nurs", "form", "children", "housing", "finance", "social", "health", "label"]
+    categorical_features = ["parents", "has_nurs", "form", "housing", "finance", "social", "health"]
+    data = pd.read_csv(path, sep=",", names=features, engine="python")
+    # remove rows with missing label or too sparse label
+    data = data.dropna(subset=["label"])
+    data.drop(data.loc[data["label"] == "recommend"].index, axis=0, inplace=True)
+
+    # fill missing values
+    data["children"] = data["children"].fillna(0)
+
+    for col in ["parents", "has_nurs", "form", "housing", "finance", "social", "health"]:
+        data[col] = data[col].fillna("other")
+
+    # make categorical label
+    def modify_label(value):  # 5 classes
+        if value == "not_recom":
+            return 0
+        if value == "very_recom":
+            return 1
+        if value == "priority":
+            return 2
+        if value == "spec_prior":
+            return 3
+        raise Exception("Bad label value: %s" % value)
+
+    data["label"] = data["label"].apply(modify_label)
+    data["children"] = data["children"].apply(lambda x: 4 if x == "more" else x)
+
+    if transform_social:
+
+        def modify_social(value):
+            if value == "problematic":
+                return 1
+            return 0
+
+        data["social"] = data["social"].apply(modify_social)
+        categorical_features.remove("social")
+
+    if not raw:
+        # one-hot-encode categorical features
+        features_to_remove = []
+        for feature in categorical_features:
+            all_values = data.loc[:, feature]
+            values = list(all_values.unique())
+            data[feature] = pd.Categorical(data.loc[:, feature], categories=values, ordered=False)
+            one_hot_vector = pd.get_dummies(data[feature], prefix=feature)
+            data = pd.concat([data, one_hot_vector], axis=1)
+            features_to_remove.append(feature)
+        data = data.drop(features_to_remove, axis=1)
+
+        # normalize data
+        label = data.loc[:, "label"]
+        features = data.drop(["label"], axis=1)
+        scaler = sklearn.preprocessing.StandardScaler()
+        scaler.fit(features)
+        scaled_features = pd.DataFrame(scaler.transform(features), columns=features.columns)
+
+        data = pd.concat([label, scaled_features], axis=1, join="inner")
+
+    features = data.drop(["label"], axis=1)
+    min_, max_ = np.amin(features.to_numpy()), np.amax(features.to_numpy())
+
+    # Split training and test sets
+    stratified = sklearn.model_selection.StratifiedShuffleSplit(n_splits=1, test_size=test_set, random_state=18)
+    for train_set, test_set in stratified.split(data, data["label"]):
+        train = data.iloc[train_set]
+        test = data.iloc[test_set]
+    x_train = train.drop(["label"], axis=1).to_numpy()
+    y_train = train.loc[:, "label"].to_numpy()
+    x_test = test.drop(["label"], axis=1).to_numpy()
+    y_test = test.loc[:, "label"].to_numpy()
+
+    return (x_train, y_train), (x_test, y_test), min_, max_
+
+
+def load_dataset(name: str,) -> DATASET_TYPE:
+    """
+    Loads or downloads the dataset corresponding to `name`. Options are: `mnist`, `cifar10` and `stl10`.
+
+    :param name: Name of the dataset.
+    :return: The dataset separated in training and test sets as `(x_train, y_train), (x_test, y_test), min, max`.
+    :raises NotImplementedError: If the dataset is unknown.
+    """
+    if "mnist" in name:
+        return load_mnist()
+    if "cifar10" in name:
+        return load_cifar10()
+    if "stl10" in name:
+        return load_stl()
+    if "iris" in name:
+        return load_iris()
+    if "nursery" in name:
+        return load_nursery()
+
+    raise NotImplementedError("There is no loader for dataset '{}'.".format(name))
+
+
+def _extract(full_path: str, path: str) -> bool:
+    archive: Union[zipfile.ZipFile, tarfile.TarFile]
+    if full_path.endswith("tar"):
+        if tarfile.is_tarfile(full_path):
+            archive = tarfile.open(full_path, "r:")
+    elif full_path.endswith("tar.gz"):
+        if tarfile.is_tarfile(full_path):
+            archive = tarfile.open(full_path, "r:gz")
+    elif full_path.endswith("zip"):
+        if zipfile.is_zipfile(full_path):
+            archive = zipfile.ZipFile(full_path)
+        else:
+            return False
+    else:
+        return False
+
+    try:
+        archive.extractall(path)
+    except (tarfile.TarError, RuntimeError, KeyboardInterrupt):
+        if os.path.exists(path):
+            if os.path.isfile(path):
+                os.remove(path)
+            else:
+                shutil.rmtree(path)
+        raise
+    return True
+
+
+def get_file(filename: str, url: str, path: Optional[str] = None, extract: bool = False, verbose: bool = False) -> str:
+    """
+    Downloads a file from a URL if it not already in the cache. The file at indicated by `url` is downloaded to the
+    path `path` (default is ~/.art/data). and given the name `filename`. Files in tar, tar.gz, tar.bz, and zip formats
+    can also be extracted. This is a simplified version of the function with the same name in Keras.
+
+    :param filename: Name of the file.
+    :param url: Download URL.
+    :param path: Folder to store the download. If not specified, `~/.art/data` is used instead.
+    :param extract: If true, tries to extract the archive.
+    :param verbose: If true, print download progress bar.
+    :return: Path to the downloaded file.
+    """
+    if path is None:
+        path_ = os.path.expanduser(config.ART_DATA_PATH)
+    else:
+        path_ = os.path.expanduser(path)
+    if not os.access(path_, os.W_OK):
+        path_ = os.path.join("/tmp", ".art")
+
+    if not os.path.exists(path_):
+        os.makedirs(path_)
+
+    if extract:
+        extract_path = os.path.join(path_, filename)
+        full_path = extract_path + ".tar.gz"
+    else:
+        full_path = os.path.join(path_, filename)
+
+    # Determine if dataset needs downloading
+    download = not os.path.exists(full_path)
+
+    if download:
+        logger.info("Downloading data from %s", url)
+        error_msg = "URL fetch failure on {}: {} -- {}"
+        try:
+            try:
+                from six.moves.urllib.error import HTTPError, URLError
+                from six.moves.urllib.request import urlretrieve
+
+                # The following two lines should prevent occasionally occurring
+                # [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed (_ssl.c:847)
+                import ssl
+
+                ssl._create_default_https_context = ssl._create_unverified_context
+
+                if verbose:
+                    with tqdm() as t_bar:
+                        last_block = [0]
+
+                        def progress_bar(blocks: int = 1, block_size: int = 1, total_size: Optional[int] = None):
+                            """
+                            :param blocks: Number of blocks transferred so far [default: 1].
+                            :param block_size: Size of each block (in tqdm units) [default: 1].
+                            :param total_size: Total size (in tqdm units). If [default: None] or -1, remains unchanged.
+                            """
+                            if total_size not in (None, -1):
+                                t_bar.total = total_size
+                            displayed = t_bar.update((blocks - last_block[0]) * block_size)
+                            last_block[0] = blocks
+                            return displayed
+
+                        urlretrieve(url, full_path, reporthook=progress_bar)
+                else:
+                    urlretrieve(url, full_path)
+
+            except HTTPError as exception:
+                raise Exception(error_msg.format(url, exception.code, exception.msg)) from HTTPError  # type: ignore
+            except URLError as exception:
+                raise Exception(error_msg.format(url, exception.errno, exception.reason)) from HTTPError
+        except (Exception, KeyboardInterrupt):
+            if os.path.exists(full_path):
+                os.remove(full_path)
+            raise
+
+    if extract:
+        if not os.path.exists(extract_path):
+            _extract(full_path, path_)
+        return extract_path
+
+    return full_path
+
+
+def make_directory(dir_path: str) -> None:
+    """
+    Creates the specified tree of directories if needed.
+
+    :param dir_path: Folder or file path.
+    """
+    if not os.path.exists(dir_path):
+        os.makedirs(dir_path)
+
+
+def clip_and_round(x: np.ndarray, clip_values: Optional["CLIP_VALUES_TYPE"], round_samples: float) -> np.ndarray:
+    """
+    Rounds the input to the correct level of granularity.
+    Useful to ensure data passed to classifier can be represented
+    in the correct domain, e.g., [0, 255] integers verses [0,1]
+    or [0, 255] floating points.
+
+    :param x: Sample input with shape as expected by the model.
+    :param clip_values: Tuple of the form `(min, max)` representing the minimum and maximum values allowed
+           for features, or `None` if no clipping should be performed.
+    :param round_samples: The resolution of the input domain to round the data to, e.g., 1.0, or 1/255. Set to 0 to
+           disable.
+    """
+    if round_samples == 0.0:
+        return x
+    if clip_values is not None:
+        np.clip(x, clip_values[0], clip_values[1], out=x)
+    x = np.around(x / round_samples) * round_samples
+    return x
+
+
+def preprocess(
+    x: np.ndarray, y: np.ndarray, nb_classes: int = 10, clip_values: Optional["CLIP_VALUES_TYPE"] = None,
+) -> Tuple[np.ndarray, np.ndarray]:
+    """
+    Scales `x` to [0, 1] and converts `y` to class categorical confidences.
+
+    :param x: Data instances.
+    :param y: Labels.
+    :param nb_classes: Number of classes in dataset.
+    :param clip_values: Original data range allowed value for features, either one respective scalar or one value per
+           feature.
+    :return: Rescaled values of `x`, `y`.
+    """
+    if clip_values is None:
+        min_, max_ = np.amin(x), np.amax(x)
+    else:
+        min_, max_ = clip_values
+
+    normalized_x = (x - min_) / (max_ - min_)
+    categorical_y = to_categorical(y, nb_classes)
+
+    return normalized_x, categorical_y
+
+
+def segment_by_class(data: Union[np.ndarray, List[int]], classes: np.ndarray, num_classes: int) -> List[np.ndarray]:
+    """
+    Returns segmented data according to specified features.
+
+    :param data: Data to be segmented.
+    :param classes: Classes used to segment data, e.g., segment according to predicted label or to `y_train` or other
+                    array of one hot encodings the same length as data.
+    :param num_classes: How many features.
+    :return: Segmented data according to specified features.
+    """
+    by_class: List[List[int]] = [[] for _ in range(num_classes)]
+    for indx, feature in enumerate(classes):
+        if num_classes > 2:
+            assigned = np.argmax(feature)
+        else:
+            assigned = int(feature)
+        by_class[assigned].append(data[indx])
+
+    return [np.asarray(i) for i in by_class]
+
+
+def performance_diff(
+    model1: "CLASSIFIER_TYPE",
+    model2: "CLASSIFIER_TYPE",
+    test_data: np.ndarray,
+    test_labels: np.ndarray,
+    perf_function: Union[str, Callable] = "accuracy",
+    **kwargs,
+) -> float:
+    """
+    Calculates the difference in performance between two models on the test_data with a performance function.
+
+    Note: For multi-label classification, f1 scores will use 'micro' averaging unless otherwise specified.
+
+    :param model1: A trained ART classifier.
+    :param model2: Another trained ART classifier.
+    :param test_data: The data to test both model's performance.
+    :param test_labels: The labels to the testing data.
+    :param perf_function: The performance metric to be used. One of ['accuracy', 'f1'] or a callable function
+           `(true_labels, model_labels[, kwargs]) -> float`.
+    :param kwargs: Arguments to add to performance function.
+    :return: The difference in performance performance(model1) - performance(model2).
+    :raises `ValueError`: If an unsupported performance function is requested.
+    """
+    from sklearn.metrics import accuracy_score
+    from sklearn.metrics import f1_score
+
+    model1_labels = model1.predict(test_data)
+    model2_labels = model2.predict(test_data)
+
+    if perf_function == "accuracy":
+        model1_acc = accuracy_score(test_labels, model1_labels, **kwargs)
+        model2_acc = accuracy_score(test_labels, model2_labels, **kwargs)
+        return model1_acc - model2_acc
+
+    if perf_function == "f1":
+        n_classes = test_labels.shape[1]
+        if n_classes > 2 and "average" not in kwargs:
+            kwargs["average"] = "micro"
+        model1_f1 = f1_score(test_labels, model1_labels, **kwargs)
+        model2_f1 = f1_score(test_labels, model2_labels, **kwargs)
+        return model1_f1 - model2_f1
+
+    if callable(perf_function):
+        return perf_function(test_labels, model1_labels, **kwargs) - perf_function(test_labels, model2_labels, **kwargs)
+
+    raise ValueError("Performance function '{}' not supported".format(str(perf_function)))
+
+
+def is_probability(vector: np.ndarray) -> bool:
+    """
+    Check if an 1D-array is a probability vector.
+
+    :param vector: An 1D-array.
+    :return: True if it is a probability vector.
+    """
+    is_sum_1 = math.isclose(np.sum(vector), 1.0, rel_tol=1e-03)
+    is_smaller_1 = np.amax(vector) <= 1.0
+    is_larger_0 = np.amin(vector) >= 0.0
+
+    return is_sum_1 and is_smaller_1 and is_larger_0
+
+
+def pad_sequence_input(x: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
+    """
+    Apply padding to a batch of 1-dimensional samples such that it has shape of (batch_size, max_length).
+
+    :param x: A batch of 1-dimensional input data, e.g. `np.array([np.array([1,2,3]), np.array([4,5,6,7])])`.
+    :return: The padded input batch and its corresponding mask.
+    """
+    max_length = max(map(len, x))
+    batch_size = x.shape[0]
+
+    # note: use dtype of inner elements
+    x_padded = np.zeros((batch_size, max_length), dtype=x[0].dtype)
+    x_mask = np.zeros((batch_size, max_length), dtype=bool)
+
+    for i, x_i in enumerate(x):
+        x_padded[i, : len(x_i)] = x_i
+        x_mask[i, : len(x_i)] = 1
+    return x_padded, x_mask
+
+
+# -------------------------------------------------------------------------------------------------------- CUDA SUPPORT
+
+
+def to_cuda(x: "torch.Tensor") -> "torch.Tensor":
+    """
+    Move the tensor from the CPU to the GPU if a GPU is available.
+
+    :param x: CPU Tensor to move to GPU if available.
+    :return: The CPU Tensor moved to a GPU Tensor.
+    """
+    from torch.cuda import is_available
+
+    use_cuda = is_available()
+    if use_cuda:
+        x = x.cuda()
+    return x
+
+
+def from_cuda(x: "torch.Tensor") -> "torch.Tensor":
+    """
+    Move the tensor from the GPU to the CPU if a GPU is available.
+
+    :param x: GPU Tensor to move to CPU if available.
+    :return: The GPU Tensor moved to a CPU Tensor.
+    """
+    from torch.cuda import is_available
+
+    use_cuda = is_available()
+    if use_cuda:
+        x = x.cpu()
+    return x
diff --git a/adversarial-robustness-toolbox/art/visualization.py b/adversarial-robustness-toolbox/art/visualization.py
new file mode 100644
index 0000000..9edcf08
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/visualization.py
@@ -0,0 +1,164 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+Module providing visualization functions.
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import os.path
+from typing import List, Optional, TYPE_CHECKING
+
+import numpy as np
+from PIL import Image
+
+from art import config
+
+if TYPE_CHECKING:
+    import matplotlib
+
+logger = logging.getLogger(__name__)
+
+
+def create_sprite(images: np.ndarray) -> np.ndarray:
+    """
+    Creates a sprite of provided images.
+
+    :param images: Images to construct the sprite.
+    :return: An image array containing the sprite.
+    """
+    shape = np.shape(images)
+
+    if len(shape) < 3 or len(shape) > 4:
+        raise ValueError("Images provided for sprite have wrong dimensions " + str(len(shape)))
+
+    if len(shape) == 3:
+        # Check to see if it's MNIST type of images and add axis to show image is gray-scale
+        images = np.expand_dims(images, axis=3)
+        shape = np.shape(images)
+
+    # Change black and white images to RGB
+    if shape[3] == 1:
+        images = convert_to_rgb(images)
+
+    n = int(np.ceil(np.sqrt(images.shape[0])))
+    padding = ((0, n ** 2 - images.shape[0]), (0, 0), (0, 0)) + ((0, 0),) * (images.ndim - 3)
+    images = np.pad(images, padding, mode="constant", constant_values=0)
+
+    # Tile the individual thumbnails into an image
+    images = images.reshape((n, n) + images.shape[1:]).transpose((0, 2, 1, 3) + tuple(range(4, images.ndim + 1)))
+    images = images.reshape((n * images.shape[1], n * images.shape[3]) + images.shape[4:])
+    sprite = (images * 255).astype(np.uint8)
+
+    return sprite
+
+
+def convert_to_rgb(images: np.ndarray) -> np.ndarray:
+    """
+    Converts grayscale images to RGB. It changes NxHxWx1 to a NxHxWx3 array, where N is the number of figures,
+    H is the high and W the width.
+
+    :param images: Grayscale images of shape (NxHxWx1).
+    :return: Images in RGB format of shape (NxHxWx3).
+    """
+    dims = np.shape(images)
+    if not ((len(dims) == 4 and dims[-1] == 1) or len(dims) == 3):
+        raise ValueError("Unexpected shape for grayscale images:" + str(dims))
+
+    if dims[-1] == 1:
+        # Squeeze channel axis if it exists
+        rgb_images = np.squeeze(images, axis=-1)
+    else:
+        rgb_images = images
+    rgb_images = np.stack((rgb_images,) * 3, axis=-1)
+
+    return rgb_images
+
+
+def save_image(image_array: np.ndarray, f_name: str) -> None:
+    """
+    Saves image into a file inside `ART_DATA_PATH` with the name `f_name`.
+
+    :param image_array: Image to be saved.
+    :param f_name: File name containing extension e.g., my_img.jpg, my_img.png, my_images/my_img.png.
+    """
+    file_name = os.path.join(config.ART_DATA_PATH, f_name)
+    folder = os.path.split(file_name)[0]
+    if not os.path.exists(folder):
+        os.makedirs(folder)
+
+    image = Image.fromarray(image_array)
+    image.save(file_name)
+    logger.info("Image saved to %s.", file_name)
+
+
+def plot_3d(
+    points: np.ndarray, labels: List[int], colors: Optional[List[str]] = None, save: bool = True, f_name: str = "",
+) -> "matplotlib.figure.Figure":
+    """
+    Generates a 3-D plot in of the provided points where the labels define the color that will be used to color each
+    data point. Concretely, the color of points[i] is defined by colors(labels[i]). Thus, there should be as many labels
+     as colors.
+
+    :param points: arrays with 3-D coordinates of the plots to be plotted.
+    :param labels: array of integers that determines the color used in the plot for the data point.
+        Need to start from 0 and be sequential from there on.
+    :param colors: Optional argument to specify colors to be used in the plot. If provided, this array should contain
+    as many colors as labels.
+    :param save:  When set to True, saves image into a file inside `ART_DATA_PATH` with the name `f_name`.
+    :param f_name: Name used to save the file when save is set to True.
+    :return: A figure object.
+    """
+    try:
+        # Disable warnings of unused import because all imports in this block are required
+        # pylint: disable=W0611
+        import matplotlib  # lgtm [py/repeated-import]
+        import matplotlib.pyplot as plt  # lgtm [py/repeated-import]
+        from mpl_toolkits import mplot3d
+
+        if colors is None:
+            colors = []
+            for i in range(len(np.unique(labels))):
+                colors.append("C" + str(i))
+        else:
+            if len(colors) != len(np.unique(labels)):
+                raise ValueError("The amount of provided colors should match the number of labels in the 3pd plot.")
+
+        fig = plt.figure()
+        axis = plt.axes(projection="3d")
+
+        for i, coord in enumerate(points):
+            try:
+                color_point = labels[i]
+                axis.scatter3D(coord[0], coord[1], coord[2], color=colors[color_point])
+            except IndexError:
+                raise ValueError(
+                    "Labels outside the range. Should start from zero and be sequential there after"
+                ) from IndexError
+        if save:
+            file_name = os.path.realpath(os.path.join(config.ART_DATA_PATH, f_name))
+            folder = os.path.split(file_name)[0]
+
+            if not os.path.exists(folder):
+                os.makedirs(folder)
+            fig.savefig(file_name, bbox_inches="tight")
+            logger.info("3d-plot saved to %s.", file_name)
+
+        return fig
+    except ImportError:
+        logger.warning("matplotlib not installed. For this reason, cluster visualization was not displayed.")
diff --git a/adversarial-robustness-toolbox/art/wrappers/__init__.py b/adversarial-robustness-toolbox/art/wrappers/__init__.py
new file mode 100644
index 0000000..32cba4d
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/wrappers/__init__.py
@@ -0,0 +1,7 @@
+"""
+Module providing wrappers for :class:`.Classifier` instances to simulate different capacities and behaviours, like
+black-box gradient estimation.
+"""
+from art.wrappers.wrapper import ClassifierWrapper
+from art.wrappers.expectation import ExpectationOverTransformations
+from art.wrappers.query_efficient_bb import QueryEfficientBBGradientEstimation
diff --git a/adversarial-robustness-toolbox/art/wrappers/expectation.py b/adversarial-robustness-toolbox/art/wrappers/expectation.py
new file mode 100644
index 0000000..867806f
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/wrappers/expectation.py
@@ -0,0 +1,190 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module implements the Expectation Over Transformation applied to classifier predictions and gradients.
+
+| Paper link: https://arxiv.org/abs/1707.07397
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+from typing import List, Optional, Union, Tuple, TYPE_CHECKING
+
+import numpy as np
+
+from art.wrappers.wrapper import ClassifierWrapper
+from art.estimators.classification.classifier import ClassifierClassLossGradients
+
+if TYPE_CHECKING:
+    from art.utils import CLASSIFIER_CLASS_LOSS_GRADIENTS_TYPE
+
+logger = logging.getLogger(__name__)
+
+
+class ExpectationOverTransformations(ClassifierWrapper, ClassifierClassLossGradients):
+    """
+    Implementation of Expectation Over Transformations applied to classifier predictions and gradients, as introduced
+    in Athalye et al. (2017).
+
+    | Paper link: https://arxiv.org/abs/1707.07397
+    """
+
+    def __init__(self, classifier: "CLASSIFIER_CLASS_LOSS_GRADIENTS_TYPE", sample_size: int, transformation) -> None:
+        """
+        Create an expectation over transformations wrapper.
+
+        :param classifier: The Classifier we want to wrap the functionality for the purpose of an attack.
+        :param sample_size: Number of transformations to sample.
+        :param transformation: An iterator over transformations.
+        :type transformation: :class:`.Classifier`
+        """
+        super().__init__(classifier)
+        self.sample_size = sample_size
+        self.transformation = transformation
+        self._predict = self.classifier.predict
+
+    @property
+    def input_shape(self) -> Tuple[int, ...]:
+        """
+        Return the shape of one input sample.
+
+        :return: Shape of one input sample.
+        """
+        return self._input_shape  # type: ignore
+
+    def predict(self, x: np.ndarray, batch_size: int = 128, **kwargs) -> np.ndarray:
+        """
+        Perform prediction of the given classifier for a batch of inputs, taking an expectation over transformations.
+
+        :param x: Input samples.
+        :param batch_size: Size of batches.
+        :return: Array of predictions of shape `(nb_inputs, nb_classes)`.
+        """
+        logger.info("Applying expectation over transformations.")
+        prediction = self._predict(next(self.transformation())(x), **{"batch_size": batch_size})
+        for _ in range(self.sample_size - 1):
+            prediction += self._predict(next(self.transformation())(x), **{"batch_size": batch_size})
+        return prediction / self.sample_size
+
+    def fit(self, x: np.ndarray, y: np.ndarray, batch_size: int = 128, nb_epochs: int = 20, **kwargs) -> None:
+        """
+        Fit the classifier using the training data `(x, y)`.
+
+        :param x: Features in array of shape (nb_samples, nb_features) or (nb_samples, nb_pixels_1, nb_pixels_2,
+                  nb_channels) or (nb_samples, nb_channels, nb_pixels_1, nb_pixels_2).
+        :param y: Target values (class labels in classification) in array of shape (nb_samples, nb_classes) in
+                  one-hot encoding format.
+        :param batch_size: Size of batches.
+        :param nb_epochs: Number of epochs to use for training.
+        :param kwargs: Dictionary of framework-specific arguments.
+        """
+        raise NotImplementedError
+
+    def loss_gradient(self, x: np.ndarray, y: np.ndarray, training_mode: bool = False, **kwargs) -> np.ndarray:
+        """
+        Compute the gradient of the given classifier's loss function w.r.t. `x`, taking an expectation
+        over transformations.
+
+        :param x: Sample input with shape as expected by the model.
+        :param y: Correct labels, one-hot encoded.
+        :return: Array of gradients of the same shape as `x`.
+        """
+        logger.info("Applying expectation over transformations.")
+        loss_gradient = self.classifier.loss_gradient(
+            x=next(self.transformation())(x), y=y, training_mode=training_mode, **kwargs
+        )
+        for _ in range(self.sample_size - 1):
+            loss_gradient += self.classifier.loss_gradient(
+                x=next(self.transformation())(x), y=y, training_mode=training_mode, **kwargs
+            )
+        return loss_gradient / self.sample_size
+
+    def class_gradient(
+        self, x: np.ndarray, label: Union[int, List[int], None] = None, training_mode: bool = False, **kwargs
+    ) -> np.ndarray:
+        """
+        Compute per-class derivatives of the given classifier w.r.t. `x`, taking an expectation over transformations.
+
+        :param x: Sample input with shape as expected by the model.
+        :param label: Index of a specific per-class derivative. If an integer is provided, the gradient of that class
+                      output is computed for all samples. If multiple values as provided, the first dimension should
+                      match the batch size of `x`, and each value will be used as target for its corresponding sample in
+                      `x`. If `None`, then gradients for all classes will be computed for each sample.
+        :param training_mode: `True` for model set to training mode and `'False` for model set to evaluation mode.
+        :return: Array of gradients of input features w.r.t. each class in the form
+                 `(batch_size, nb_classes, input_shape)` when computing for all classes, otherwise shape becomes
+                 `(batch_size, 1, input_shape)` when `label` parameter is specified.
+        """
+        logger.info("Apply Expectation over Transformations.")
+        class_gradient = self.classifier.class_gradient(
+            x=next(self.transformation())(x), label=label, training_mode=training_mode, *kwargs
+        )
+
+        for _ in range(self.sample_size - 1):
+            class_gradient += self.classifier.class_gradient(
+                x=next(self.transformation())(x), label=label, training_mode=training_mode, *kwargs
+            )
+
+        return class_gradient / self.sample_size
+
+    @property
+    def layer_names(self) -> List[str]:
+        """
+        Return the hidden layers in the model, if applicable.
+
+        :return: The hidden layers in the model, input and output layers excluded.
+        :rtype: `list`
+
+        .. warning:: `layer_names` tries to infer the internal structure of the model.
+                     This feature comes with no guarantees on the correctness of the result.
+                     The intended order of the layers tries to match their order in the model, but this is not
+                     guaranteed either.
+        """
+        raise NotImplementedError
+
+    def get_activations(self, x: np.ndarray, layer: Union[int, str], batch_size: int) -> np.ndarray:
+        """
+        Return the output of the specified layer for input `x`. `layer` is specified by layer index (between 0 and
+        `nb_layers - 1`) or by name. The number of layers can be determined by counting the results returned by
+        calling `layer_names`.
+
+        :param x: Input for computing the activations.
+        :param layer: Layer for computing the activations.
+        :param batch_size: Size of batches.
+        :return: The output of `layer`, where the first dimension is the batch size corresponding to `x`.
+        """
+        raise NotImplementedError
+
+    @property
+    def nb_classes(self) -> int:
+        """
+        Return the number of output classes.
+
+        :return: Number of classes in the data.
+        """
+        return self._nb_classes
+
+    def save(self, filename: str, path: Optional[str] = None) -> None:
+        """
+        Save a model to file specific to the backend framework.
+
+        :param filename: Name of the file where to save the model.
+        :param path: Path of the directory where to save the model. If no path is specified, the model will be stored in
+                     the default data location of ART at `ART_DATA_PATH`.
+        """
+        raise NotImplementedError
diff --git a/adversarial-robustness-toolbox/art/wrappers/query_efficient_bb.py b/adversarial-robustness-toolbox/art/wrappers/query_efficient_bb.py
new file mode 100644
index 0000000..d3b52ec
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/wrappers/query_efficient_bb.py
@@ -0,0 +1,195 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+Provides black-box gradient estimation using NES.
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+from typing import List, Optional, Tuple, Union, TYPE_CHECKING
+
+import numpy as np
+from scipy.stats import entropy
+
+from art.estimators.classification.classifier import ClassifierClassLossGradients
+from art.utils import clip_and_round
+from art.wrappers.wrapper import ClassifierWrapper
+
+if TYPE_CHECKING:
+    from art.utils import CLASSIFIER_CLASS_LOSS_GRADIENTS_TYPE
+
+logger = logging.getLogger(__name__)
+
+
+class QueryEfficientBBGradientEstimation(ClassifierWrapper, ClassifierClassLossGradients):
+    """
+    Implementation of Query-Efficient Black-box Adversarial Examples. The attack approximates the gradient by
+    maximizing the loss function over samples drawn from random Gaussian noise around the input.
+
+    | Paper link: https://arxiv.org/abs/1712.07113
+    """
+
+    attack_params = ["num_basis", "sigma", "round_samples"]
+
+    def __init__(
+        self,
+        classifier: "CLASSIFIER_CLASS_LOSS_GRADIENTS_TYPE",
+        num_basis: int,
+        sigma: float,
+        round_samples: float = 0.0,
+    ) -> None:
+        """
+        :param classifier: An instance of a `Classifier` whose loss_gradient is being approximated.
+        :param num_basis:  The number of samples to draw to approximate the gradient.
+        :param sigma: Scaling on the Gaussian noise N(0,1).
+        :param round_samples: The resolution of the input domain to round the data to, e.g., 1.0, or 1/255. Set to 0 to
+                              disable.
+        """
+        super().__init__(classifier)
+        # self.predict refers to predict of classifier
+        # pylint: disable=E0203
+        self._predict = self.classifier.predict
+        self.num_basis = num_basis
+        self.sigma = sigma
+        self.round_samples = round_samples
+        self._nb_classes = self.classifier.nb_classes
+
+    @property
+    def input_shape(self) -> Tuple[int, ...]:
+        """
+        Return the shape of one input sample.
+
+        :return: Shape of one input sample.
+        """
+        return self._input_shape  # type: ignore
+
+    def predict(self, x: np.ndarray, batch_size: int = 128, **kwargs) -> np.ndarray:
+        """
+        Perform prediction of the classifier for input `x`.
+
+        :param x: Features in array of shape (nb_samples, nb_features) or (nb_samples, nb_pixels_1, nb_pixels_2,
+                  nb_channels) or (nb_samples, nb_channels, nb_pixels_1, nb_pixels_2).
+        :param batch_size: Size of batches.
+        :return: Array of predictions of shape `(nb_inputs, nb_classes)`.
+        """
+        return self._wrap_predict(x, batch_size=batch_size)
+
+    def fit(self, x: np.ndarray, y: np.ndarray, **kwargs) -> None:
+        """
+        Fit the classifier using the training data `(x, y)`.
+
+        :param x: Features in array of shape (nb_samples, nb_features) or (nb_samples, nb_pixels_1, nb_pixels_2,
+                  nb_channels) or (nb_samples, nb_channels, nb_pixels_1, nb_pixels_2).
+        :param y: Target values (class labels in classification) in array of shape (nb_samples, nb_classes) in
+                  one-hot encoding format.
+        :param kwargs: Dictionary of framework-specific arguments.
+        """
+        raise NotImplementedError
+
+    def _generate_samples(self, x: np.ndarray, epsilon_map: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
+        """
+        Generate samples around the current image.
+
+        :param x: Sample input with shape as expected by the model.
+        :param epsilon_map: Samples drawn from search space.
+        :return: Two arrays of new input samples to approximate gradient.
+        """
+        minus = clip_and_round(
+            np.repeat(x, self.num_basis, axis=0) - epsilon_map, self.clip_values, self.round_samples,
+        )
+        plus = clip_and_round(np.repeat(x, self.num_basis, axis=0) + epsilon_map, self.clip_values, self.round_samples,)
+        return minus, plus
+
+    def class_gradient(self, x: np.ndarray, label: Union[int, List[int], None] = None, **kwargs) -> np.ndarray:
+        """
+        Compute per-class derivatives w.r.t. `x`.
+
+        :param x: Input with shape as expected by the classifier's model.
+        :param label: Index of a specific per-class derivative. If an integer is provided, the gradient of that class
+                      output is computed for all samples. If multiple values as provided, the first dimension should
+                      match the batch size of `x`, and each value will be used as target for its corresponding sample in
+                      `x`. If `None`, then gradients for all classes will be computed for each sample.
+        :return: Array of gradients of input features w.r.t. each class in the form
+                 `(batch_size, nb_classes, input_shape)` when computing for all classes, otherwise shape becomes
+                 `(batch_size, 1, input_shape)` when `label` parameter is specified.
+        """
+        raise NotImplementedError
+
+    def loss_gradient(self, x: np.ndarray, y: np.ndarray, **kwargs) -> np.ndarray:
+        """
+        Compute the gradient of the loss function w.r.t. `x`.
+
+        :param x: Sample input with shape as expected by the model.
+        :param y: Correct labels, one-vs-rest encoding.
+        :return: Array of gradients of the same shape as `x`.
+        """
+        epsilon_map = self.sigma * np.random.normal(size=([self.num_basis] + list(self.input_shape)))
+        grads = []
+        for i in range(len(x)):
+            minus, plus = self._generate_samples(x[i : i + 1], epsilon_map)
+
+            # Vectorized; small tests weren't faster
+            # ent_vec = np.vectorize(lambda p: entropy(y[i], p), signature='(n)->()')
+            # new_y_minus = ent_vec(self.predict(minus))
+            # new_y_plus = ent_vec(self.predict(plus))
+            # Vanilla
+            new_y_minus = np.array([entropy(y[i], p) for p in self.predict(minus)])
+            new_y_plus = np.array([entropy(y[i], p) for p in self.predict(plus)])
+            query_efficient_grad = 2 * np.mean(
+                np.multiply(
+                    epsilon_map.reshape(self.num_basis, -1),
+                    (new_y_plus - new_y_minus).reshape(self.num_basis, -1) / (2 * self.sigma),
+                ).reshape([-1] + list(self.input_shape)),
+                axis=0,
+            )
+            grads.append(query_efficient_grad)
+        grads = self._apply_preprocessing_gradient(x, np.array(grads))
+        return grads
+
+    def _wrap_predict(self, x: np.ndarray, batch_size: int = 128) -> np.ndarray:
+        """
+        Perform prediction for a batch of inputs. Rounds results first.
+
+        :param x: Input samples.
+        :param batch_size: Size of batches.
+        :return: Array of predictions of shape `(nb_inputs, nb_classes)`.
+        """
+        return self._predict(clip_and_round(x, self.clip_values, self.round_samples), **{"batch_size": batch_size})
+
+    def get_activations(self, x: np.ndarray, layer: Union[int, str], batch_size: int) -> np.ndarray:
+        """
+        Return the output of the specified layer for input `x`. `layer` is specified by layer index (between 0 and
+        `nb_layers - 1`) or by name. The number of layers can be determined by counting the results returned by
+        calling `layer_names`.
+
+        :param x: Input for computing the activations.
+        :param layer: Layer for computing the activations.
+        :param batch_size: Size of batches.
+        :return: The output of `layer`, where the first dimension is the batch size corresponding to `x`.
+        """
+        raise NotImplementedError
+
+    def save(self, filename: str, path: Optional[str] = None) -> None:
+        """
+        Save a model to file specific to the backend framework.
+
+        :param filename: Name of the file where to save the model.
+        :param path: Path of the directory where to save the model. If no path is specified, the model will be stored in
+                     the default data location of ART at `ART_DATA_PATH`.
+        """
+        raise NotImplementedError
diff --git a/adversarial-robustness-toolbox/art/wrappers/wrapper.py b/adversarial-robustness-toolbox/art/wrappers/wrapper.py
new file mode 100644
index 0000000..48b828d
--- /dev/null
+++ b/adversarial-robustness-toolbox/art/wrappers/wrapper.py
@@ -0,0 +1,52 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+Wrapper class for any classifier. Subclass of the ClassifierWrapper can override the behavior of key functions, such as
+loss_gradient, to facilitate new attacks.
+"""
+
+
+class ClassifierWrapper:
+    """
+    Wrapper class for any classifier instance.
+    """
+
+    attack_params = ["classifier"]
+
+    def __init__(self, classifier) -> None:
+        """
+        Initialize a :class:`.ClassifierWrapper` object.
+
+        :param classifier: The Classifier we want to wrap the functionality for the purpose of an attack.
+        """
+        self.classifier = classifier
+
+    def __getattr__(self, attr):
+        """
+        A generic grab-bag for the classifier instance. This makes the wrapped class look like a subclass.
+        """
+        return getattr(self.classifier, attr)
+
+    def __setattr__(self, attr, value):
+        """
+        A generic grab-bag for the classifier instance. This makes the wrapped class look like a subclass.
+        """
+        if attr == "classifier":
+            object.__setattr__(self, attr, value)
+        else:
+            setattr(self.classifier, attr, value)
diff --git a/adversarial-robustness-toolbox/codecov.yml b/adversarial-robustness-toolbox/codecov.yml
new file mode 100644
index 0000000..8eb4490
--- /dev/null
+++ b/adversarial-robustness-toolbox/codecov.yml
@@ -0,0 +1,52 @@
+codecov:
+  bot: "codecov-io"
+  max_report_age: 24
+  disable_default_path_fixes: no
+  require_ci_to_pass: yes
+  notify:
+    after_n_builds: 2
+    wait_for_ci: yes
+
+coverage:
+  precision: 2
+  round: down
+  range: "70...100"
+
+  status:
+    project: yes
+    patch: yes
+    changes: no
+
+  status:
+    project:
+      default:
+        # basic
+        target: 50
+        threshold: 5
+        base: auto
+        flags: null
+        paths: null
+        # advanced
+        branches: null
+        if_not_found: success
+        if_ci_failed: error
+        informational: False
+        only_pulls: false
+
+    patch:
+      default:
+        target: auto
+        threshold: 95
+
+parsers:
+  gcov:
+    branch_detection:
+      conditional: yes
+      loop: yes
+      method: no
+      macro: no
+
+comment:
+  layout: "reach,diff,flags,tree"
+  behavior: default
+  require_changes: no
diff --git a/adversarial-robustness-toolbox/conftest.py b/adversarial-robustness-toolbox/conftest.py
new file mode 100644
index 0000000..286bc28
--- /dev/null
+++ b/adversarial-robustness-toolbox/conftest.py
@@ -0,0 +1,834 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+import importlib
+import json
+import logging
+import os
+import shutil
+import tempfile
+import warnings
+
+import numpy as np
+import pytest
+import requests
+
+from art.data_generators import KerasDataGenerator, MXDataGenerator, PyTorchDataGenerator, TensorFlowDataGenerator
+from art.defences.preprocessor import FeatureSqueezing, JpegCompression, SpatialSmoothing
+from art.estimators.classification import KerasClassifier
+from tests.utils import (
+    ARTTestFixtureNotImplemented,
+    get_attack_classifier_pt,
+    get_image_classifier_kr,
+    get_image_classifier_kr_functional,
+    get_image_classifier_kr_tf,
+    get_image_classifier_kr_tf_functional,
+    get_image_classifier_kr_tf_with_wildcard,
+    get_image_classifier_mxnet_custom_ini,
+    get_image_classifier_pt,
+    get_image_classifier_tf,
+    get_tabular_classifier_kr,
+    get_tabular_classifier_pt,
+    get_tabular_classifier_scikit_list,
+    get_tabular_classifier_tf,
+    load_dataset,
+    master_seed,
+)
+
+logger = logging.getLogger(__name__)
+
+deep_learning_frameworks = ["keras", "tensorflow1", "tensorflow2", "tensorflow2v1", "pytorch", "kerastf", "mxnet"]
+non_deep_learning_frameworks = ["scikitlearn"]
+
+art_supported_frameworks = []
+art_supported_frameworks.extend(deep_learning_frameworks)
+art_supported_frameworks.extend(non_deep_learning_frameworks)
+
+master_seed(1234)
+
+
+def get_default_framework():
+    import tensorflow as tf
+
+    if tf.__version__[0] == "2":
+        default_framework = "tensorflow2"
+    else:
+        default_framework = "tensorflow1"
+
+    return default_framework
+
+
+def pytest_addoption(parser):
+    parser.addoption(
+        "--framework",
+        action="store",
+        default=get_default_framework(),
+        help="ART tests allow you to specify which framework to use. The default framework used is `tensorflow`. "
+        "Other options available are {0}".format(art_supported_frameworks),
+    )
+    parser.addoption(
+        "--skip_travis",
+        action="store",
+        default=False,
+        help="Whether tests annotated with the decorator skip_travis should be skipped or not",
+    )
+
+
+@pytest.fixture
+def image_dl_estimator_defended(framework):
+    def _image_dl_estimator_defended(one_classifier=False, **kwargs):
+        sess = None
+        classifier = None
+
+        clip_values = (0, 1)
+        fs = FeatureSqueezing(bit_depth=2, clip_values=clip_values)
+
+        defenses = []
+        if kwargs.get("defenses") is None:
+            defenses.append(fs)
+        else:
+            if "FeatureSqueezing" in kwargs.get("defenses"):
+                defenses.append(fs)
+            if "JpegCompression" in kwargs.get("defenses"):
+                defenses.append(JpegCompression(clip_values=clip_values, apply_predict=True))
+            if "SpatialSmoothing" in kwargs.get("defenses"):
+                defenses.append(SpatialSmoothing())
+            del kwargs["defenses"]
+
+        if framework == "keras":
+            kr_classifier = get_image_classifier_kr(**kwargs)
+            # Get the ready-trained Keras model
+
+            classifier = KerasClassifier(
+                model=kr_classifier._model, clip_values=(0, 1), preprocessing_defences=defenses
+            )
+
+        if framework == "kerastf":
+            kr_tf_classifier = get_image_classifier_kr_tf(**kwargs)
+            classifier = KerasClassifier(
+                model=kr_tf_classifier._model, clip_values=(0, 1), preprocessing_defences=defenses
+            )
+
+        if classifier is None:
+            raise ARTTestFixtureNotImplemented(
+                "no defended image estimator", image_dl_estimator_defended.__name__, framework, {"defenses": defenses}
+            )
+        return classifier, sess
+
+    return _image_dl_estimator_defended
+
+
+@pytest.fixture(scope="function")
+def image_dl_estimator_for_attack(framework, image_dl_estimator, image_dl_estimator_defended):
+    def _image_dl_estimator_for_attack(attack, defended=False, **kwargs):
+        if defended:
+            potential_classifier, _ = image_dl_estimator_defended(**kwargs)
+        else:
+            potential_classifier, _ = image_dl_estimator(**kwargs)
+
+        classifier_list = [potential_classifier]
+        classifier_tested = [
+            potential_classifier
+            for potential_classifier in classifier_list
+            if all(t in type(potential_classifier).__mro__ for t in attack._estimator_requirements)
+        ]
+
+        if len(classifier_tested) == 0:
+            raise ARTTestFixtureNotImplemented(
+                "no estimator available", image_dl_estimator_for_attack.__name__, framework, {"attack": attack}
+            )
+        return classifier_tested[0]
+
+    return _image_dl_estimator_for_attack
+
+
+@pytest.fixture
+def estimator_for_attack(framework):
+    # TODO DO NOT USE THIS FIXTURE this needs to be refactored into image_dl_estimator_for_attack
+    def _get_attack_classifier_list(**kwargs):
+        if framework == "pytorch":
+            return get_attack_classifier_pt(**kwargs)
+
+        raise ARTTestFixtureNotImplemented("no estimator available", image_dl_estimator_for_attack.__name__, framework)
+
+    return _get_attack_classifier_list
+
+
+@pytest.fixture(autouse=True)
+def setup_tear_down_framework(framework):
+    # Ran before each test
+    if framework == "tensorflow1" or framework == "tensorflow2":
+        import tensorflow as tf
+
+        if tf.__version__[0] != "2":
+            tf.reset_default_graph()
+
+    if framework == "tensorflow2v1":
+        import tensorflow.compat.v1 as tf1
+
+        tf1.reset_default_graph()
+    yield True
+
+    # Ran after each test
+    if framework == "keras":
+        import keras
+
+        keras.backend.clear_session()
+
+
+@pytest.fixture
+def image_iterator(framework, get_default_mnist_subset, default_batch_size):
+    (x_train_mnist, y_train_mnist), (_, _) = get_default_mnist_subset
+
+    def _get_image_iterator():
+        if framework == "keras" or framework == "kerastf":
+            from keras.preprocessing.image import ImageDataGenerator
+
+            keras_gen = ImageDataGenerator(
+                width_shift_range=0.075,
+                height_shift_range=0.075,
+                rotation_range=12,
+                shear_range=0.075,
+                zoom_range=0.05,
+                fill_mode="constant",
+                cval=0,
+            )
+            return keras_gen.flow(x_train_mnist, y_train_mnist, batch_size=default_batch_size)
+
+        if framework == "tensorflow1":
+            import tensorflow as tf
+
+            x_tensor = tf.convert_to_tensor(x_train_mnist.reshape(10, 100, 28, 28, 1))
+            y_tensor = tf.convert_to_tensor(y_train_mnist.reshape(10, 100, 10))
+            dataset = tf.data.Dataset.from_tensor_slices((x_tensor, y_tensor))
+            return dataset.make_initializable_iterator()
+
+        if framework == "pytorch":
+            import torch
+
+            # Create tensors from data
+            x_train_tens = torch.from_numpy(x_train_mnist)
+            x_train_tens = x_train_tens.float()
+            y_train_tens = torch.from_numpy(y_train_mnist)
+            dataset = torch.utils.data.TensorDataset(x_train_tens, y_train_tens)
+            return torch.utils.data.DataLoader(dataset=dataset, batch_size=default_batch_size, shuffle=True)
+
+        if framework == "mxnet":
+            from mxnet import gluon
+
+            dataset = gluon.data.dataset.ArrayDataset(x_train_mnist, y_train_mnist)
+            return gluon.data.DataLoader(dataset, batch_size=5, shuffle=True)
+
+        raise ARTTestFixtureNotImplemented("no image test iterator available", image_iterator.__name__, framework)
+
+    return _get_image_iterator
+
+
+@pytest.fixture
+def image_data_generator(framework, get_default_mnist_subset, image_iterator, default_batch_size):
+    def _image_data_generator(**kwargs):
+        (x_train_mnist, y_train_mnist), (_, _) = get_default_mnist_subset
+
+        image_it = image_iterator()
+
+        data_generator = None
+        if framework == "keras" or framework == "kerastf":
+            data_generator = KerasDataGenerator(
+                iterator=image_it, size=x_train_mnist.shape[0], batch_size=default_batch_size,
+            )
+
+        if framework == "tensorflow1":
+            data_generator = TensorFlowDataGenerator(
+                sess=kwargs["sess"],
+                iterator=image_it,
+                iterator_type="initializable",
+                iterator_arg={},
+                size=x_train_mnist.shape[0],
+                batch_size=default_batch_size,
+            )
+
+        if framework == "pytorch":
+            data_generator = PyTorchDataGenerator(
+                iterator=image_it, size=x_train_mnist.shape[0], batch_size=default_batch_size
+            )
+
+        if framework == "mxnet":
+            data_generator = MXDataGenerator(
+                iterator=image_it, size=x_train_mnist.shape[0], batch_size=default_batch_size
+            )
+
+        if data_generator is None:
+            raise ARTTestFixtureNotImplemented(
+                "framework {0} does not current have any data generator implemented",
+                image_data_generator.__name__,
+                framework,
+            )
+
+        return data_generator
+
+    return _image_data_generator
+
+
+@pytest.fixture
+def store_expected_values(request):
+    """
+    Stores expected values to be retrieved by the expected_values fixture
+    Note1: Numpy arrays MUST be converted to list before being stored as json
+    Note2: It's possible to store both a framework independent and framework specific value. If both are stored the
+    framework specific value will be used
+    :param request:
+    :return:
+    """
+
+    def _store_expected_values(values_to_store, framework=""):
+
+        framework_name = framework
+        if framework_name is not "":
+            framework_name = "_" + framework_name
+
+        file_name = request.node.location[0].split("/")[-1][:-3] + ".json"
+
+        try:
+            with open(
+                os.path.join(os.path.dirname(__file__), os.path.dirname(request.node.location[0]), file_name), "r"
+            ) as f:
+                expected_values = json.load(f)
+        except FileNotFoundError:
+            expected_values = {}
+
+        test_name = request.node.name + framework_name
+        expected_values[test_name] = values_to_store
+
+        with open(
+            os.path.join(os.path.dirname(__file__), os.path.dirname(request.node.location[0]), file_name), "w"
+        ) as f:
+            json.dump(expected_values, f, indent=4)
+
+    return _store_expected_values
+
+
+@pytest.fixture
+def expected_values(framework, request):
+    """
+    Retrieves the expected values that were stored using the store_expected_values fixture
+    :param request:
+    :return:
+    """
+
+    file_name = request.node.location[0].split("/")[-1][:-3] + ".json"
+
+    framework_name = framework
+    if framework_name is not "":
+        framework_name = "_" + framework_name
+
+    def _expected_values():
+        with open(
+            os.path.join(os.path.dirname(__file__), os.path.dirname(request.node.location[0]), file_name), "r"
+        ) as f:
+            expected_values = json.load(f)
+
+            # searching first for any framework specific expected value
+            framework_specific_values = request.node.name + framework_name
+            if framework_specific_values in expected_values:
+                return expected_values[framework_specific_values]
+            elif request.node.name in expected_values:
+                return expected_values[request.node.name]
+            else:
+                raise ARTTestFixtureNotImplemented(
+                    "Couldn't find any expected values for test {0}".format(request.node.name),
+                    expected_values.__name__,
+                    framework_name,
+                )
+
+    return _expected_values
+
+
+@pytest.fixture(scope="session")
+def get_image_classifier_mx_model():
+    import mxnet  # lgtm [py/import-and-import-from]
+
+    # TODO needs to be made parameterizable once Mxnet allows multiple identical models to be created in one session
+    from_logits = True
+
+    class Model(mxnet.gluon.nn.Block):
+        def __init__(self, **kwargs):
+            super(Model, self).__init__(**kwargs)
+            self.model = mxnet.gluon.nn.Sequential()
+            self.model.add(
+                mxnet.gluon.nn.Conv2D(channels=1, kernel_size=7, activation="relu",),
+                mxnet.gluon.nn.MaxPool2D(pool_size=4, strides=4),
+                mxnet.gluon.nn.Flatten(),
+                mxnet.gluon.nn.Dense(10, activation=None,),
+            )
+
+        def forward(self, x):
+            y = self.model(x)
+            if from_logits:
+                return y
+
+            return y.softmax()
+
+    model = Model()
+    custom_init = get_image_classifier_mxnet_custom_ini()
+    model.initialize(init=custom_init)
+    return model
+
+
+@pytest.fixture
+def get_image_classifier_mx_instance(get_image_classifier_mx_model, mnist_shape):
+    import mxnet  # lgtm [py/import-and-import-from]
+    from art.estimators.classification import MXClassifier
+
+    model = get_image_classifier_mx_model
+
+    def _get_image_classifier_mx_instance(from_logits=True):
+        if from_logits is False:
+            # due to the fact that only 1 instance of get_image_classifier_mx_model can be created in one session
+            # this will be resolved once Mxnet allows for 2 models with identical weights to be created in 1 session
+            raise ARTTestFixtureNotImplemented(
+                "Currently only supporting Mxnet classifier with from_logit set to True",
+                get_image_classifier_mx_instance.__name__,
+                framework,
+            )
+
+        loss = mxnet.gluon.loss.SoftmaxCrossEntropyLoss(from_logits=from_logits)
+        trainer = mxnet.gluon.Trainer(model.collect_params(), "sgd", {"learning_rate": 0.1})
+
+        # Get classifier
+        mxc = MXClassifier(
+            model=model,
+            loss=loss,
+            input_shape=mnist_shape,
+            # input_shape=(28, 28, 1),
+            nb_classes=10,
+            optimizer=trainer,
+            ctx=None,
+            channels_first=True,
+            clip_values=(0, 1),
+            preprocessing_defences=None,
+            postprocessing_defences=None,
+            preprocessing=(0.0, 1.0),
+        )
+
+        return mxc
+
+    return _get_image_classifier_mx_instance
+
+
+@pytest.fixture
+def supported_losses_types(framework):
+    def supported_losses_types():
+        if framework == "keras":
+            return ["label", "function_losses", "function_backend"]
+        if framework == "kerastf":
+            # if loss_type is not "label" and loss_name not in ["categorical_hinge", "kullback_leibler_divergence"]:
+            return ["label", "function", "class"]
+
+        raise ARTTestFixtureNotImplemented(
+            "Could not find supported_losses_types", supported_losses_types.__name__, framework
+        )
+
+    return supported_losses_types
+
+
+@pytest.fixture
+def supported_losses_logit(framework):
+    def _supported_losses_logit():
+        if framework == "keras":
+            return ["categorical_crossentropy_function_backend", "sparse_categorical_crossentropy_function_backend"]
+        if framework == "kerastf":
+            # if loss_type is not "label" and loss_name not in ["categorical_hinge", "kullback_leibler_divergence"]:
+            return [
+                "categorical_crossentropy_function",
+                "categorical_crossentropy_class",
+                "sparse_categorical_crossentropy_function",
+                "sparse_categorical_crossentropy_class",
+            ]
+        raise ARTTestFixtureNotImplemented(
+            "Could not find  supported_losses_logit", supported_losses_logit.__name__, framework
+        )
+
+    return _supported_losses_logit
+
+
+@pytest.fixture
+def supported_losses_proba(framework):
+    def _supported_losses_proba():
+        if framework == "keras":
+            return [
+                "categorical_hinge_function_losses",
+                "categorical_crossentropy_label",
+                "categorical_crossentropy_function_losses",
+                "categorical_crossentropy_function_backend",
+                "sparse_categorical_crossentropy_label",
+                "sparse_categorical_crossentropy_function_losses",
+                "sparse_categorical_crossentropy_function_backend",
+                "kullback_leibler_divergence_function_losses",
+            ]
+        if framework == "kerastf":
+            return [
+                "categorical_hinge_function",
+                "categorical_hinge_class",
+                "categorical_crossentropy_label",
+                "categorical_crossentropy_function",
+                "categorical_crossentropy_class",
+                "sparse_categorical_crossentropy_label",
+                "sparse_categorical_crossentropy_function",
+                "sparse_categorical_crossentropy_class",
+                "kullback_leibler_divergence_function",
+                "kullback_leibler_divergence_class",
+            ]
+
+        raise ARTTestFixtureNotImplemented(
+            "Could not find supported_losses_proba", supported_losses_proba.__name__, framework
+        )
+
+    return _supported_losses_proba
+
+
+@pytest.fixture
+def image_dl_estimator(framework, get_image_classifier_mx_instance):
+    def _image_dl_estimator(functional=False, **kwargs):
+        sess = None
+        wildcard = False
+        classifier = None
+
+        if kwargs.get("wildcard") is not None:
+            if kwargs.get("wildcard") is True:
+                wildcard = True
+            del kwargs["wildcard"]
+
+        if framework == "keras":
+            if wildcard is False and functional is False:
+                if functional:
+                    classifier = get_image_classifier_kr_functional(**kwargs)
+                else:
+                    try:
+                        classifier = get_image_classifier_kr(**kwargs)
+                    except NotImplementedError:
+                        raise ARTTestFixtureNotImplemented(
+                            "This combination of loss function options is currently not supported.",
+                            image_dl_estimator.__name__,
+                            framework,
+                        )
+        if framework == "tensorflow1" or framework == "tensorflow2":
+            if wildcard is False and functional is False:
+                classifier, sess = get_image_classifier_tf(**kwargs)
+                return classifier, sess
+        if framework == "pytorch":
+            if wildcard is False and functional is False:
+                classifier = get_image_classifier_pt(**kwargs)
+        if framework == "kerastf":
+            if wildcard:
+                classifier = get_image_classifier_kr_tf_with_wildcard(**kwargs)
+            else:
+                if functional:
+                    classifier = get_image_classifier_kr_tf_functional(**kwargs)
+                else:
+                    classifier = get_image_classifier_kr_tf(**kwargs)
+
+        if framework == "mxnet":
+            if wildcard is False and functional is False:
+                classifier = get_image_classifier_mx_instance(**kwargs)
+
+        if classifier is None:
+            raise ARTTestFixtureNotImplemented(
+                "no test deep learning estimator available", image_dl_estimator.__name__, framework
+            )
+
+        return classifier, sess
+
+    return _image_dl_estimator
+
+
+@pytest.fixture
+def art_warning(request):
+    def _art_warning(exception):
+        if type(exception) is ARTTestFixtureNotImplemented:
+            if request.node.get_closest_marker("framework_agnostic"):
+                if not request.node.get_closest_marker("parametrize"):
+                    raise Exception(
+                        "This test has marker framework_agnostic decorator which means it will only be ran "
+                        "once. However the ART test exception was thrown, hence it is never run fully. "
+                    )
+            elif (
+                request.node.get_closest_marker("only_with_platform")
+                and len(request.node.get_closest_marker("only_with_platform").args) == 1
+            ):
+                raise Exception(
+                    "This test has marker only_with_platform decorator which means it will only be ran "
+                    "once. However the ARTTestFixtureNotImplemented exception was thrown, hence it is "
+                    "never run fully. "
+                )
+
+            # NotImplementedErrors are raised in ART whenever a test model does not exist for a specific
+            # model/framework combination. By catching there here, we can provide a report at the end of each
+            # pytest run list all models requiring to be implemented.
+            warnings.warn(UserWarning(exception))
+        else:
+            raise exception
+
+    return _art_warning
+
+
+@pytest.fixture
+def decision_tree_estimator(framework):
+    def _decision_tree_estimator(clipped=True):
+        if framework == "scikitlearn":
+            return get_tabular_classifier_scikit_list(clipped=clipped, model_list_names=["decisionTreeClassifier"])[0]
+
+        raise ARTTestFixtureNotImplemented(
+            "no test decision_tree_classifier available", decision_tree_estimator.__name__, framework
+        )
+
+    return _decision_tree_estimator
+
+
+@pytest.fixture
+def tabular_dl_estimator(framework):
+    def _tabular_dl_estimator(clipped=True):
+        classifier = None
+        if framework == "keras":
+            if clipped:
+                classifier = get_tabular_classifier_kr()
+            else:
+                kr_classifier = get_tabular_classifier_kr()
+                classifier = KerasClassifier(model=kr_classifier.model, use_logits=False, channels_first=True)
+
+        if framework == "tensorflow1" or framework == "tensorflow2":
+            if clipped:
+                classifier, _ = get_tabular_classifier_tf()
+
+        if framework == "pytorch":
+            if clipped:
+                classifier = get_tabular_classifier_pt()
+
+        if classifier is None:
+            raise ARTTestFixtureNotImplemented(
+                "no deep learning tabular estimator available", tabular_dl_estimator.__name__, framework
+            )
+        return classifier
+
+    return _tabular_dl_estimator
+
+
+@pytest.fixture(scope="function")
+def create_test_image(create_test_dir):
+    test_dir = create_test_dir
+    # Download one ImageNet pic for tests
+    url = "http://farm1.static.flickr.com/163/381342603_81db58bea4.jpg"
+    result = requests.get(url, stream=True)
+    if result.status_code == 200:
+        image = result.raw.read()
+        f = open(os.path.join(test_dir, "test.jpg"), "wb")
+        f.write(image)
+        f.close()
+
+    yield os.path.join(test_dir, "test.jpg")
+
+
+@pytest.fixture(scope="session")
+def framework(request):
+    ml_framework = request.config.getoption("--framework")
+    if ml_framework == "tensorflow":
+        import tensorflow as tf
+
+        if tf.__version__[0] == "2":
+            ml_framework = "tensorflow2"
+        else:
+            ml_framework = "tensorflow1"
+
+    if ml_framework not in art_supported_frameworks:
+        raise Exception(
+            "framework value {0} is unsupported. Please use one of these valid values: {1}".format(
+                ml_framework, " ".join(art_supported_frameworks)
+            )
+        )
+    # if utils_test.is_valid_framework(framework):
+    #     raise Exception("The framework specified was incorrect. Valid options available
+    #     are {0}".format(art_supported_frameworks))
+    return ml_framework
+
+
+@pytest.fixture(scope="session")
+def default_batch_size():
+    yield 16
+
+
+@pytest.fixture(scope="session")
+def load_iris_dataset():
+    logging.info("Loading Iris dataset")
+    (x_train_iris, y_train_iris), (x_test_iris, y_test_iris), _, _ = load_dataset("iris")
+
+    yield (x_train_iris, y_train_iris), (x_test_iris, y_test_iris)
+
+
+@pytest.fixture(scope="function")
+def get_iris_dataset(load_iris_dataset, framework):
+    (x_train_iris, y_train_iris), (x_test_iris, y_test_iris) = load_iris_dataset
+
+    x_train_iris_original = x_train_iris.copy()
+    y_train_iris_original = y_train_iris.copy()
+    x_test_iris_original = x_test_iris.copy()
+    y_test_iris_original = y_test_iris.copy()
+
+    yield (x_train_iris, y_train_iris), (x_test_iris, y_test_iris)
+
+    np.testing.assert_array_almost_equal(x_train_iris_original, x_train_iris, decimal=3)
+    np.testing.assert_array_almost_equal(y_train_iris_original, y_train_iris, decimal=3)
+    np.testing.assert_array_almost_equal(x_test_iris_original, x_test_iris, decimal=3)
+    np.testing.assert_array_almost_equal(y_test_iris_original, y_test_iris, decimal=3)
+
+
+@pytest.fixture(scope="session")
+def default_dataset_subset_sizes():
+    n_train = 1000
+    n_test = 100
+    yield n_train, n_test
+
+
+@pytest.fixture()
+def mnist_shape(framework):
+    if framework == "pytorch" or framework == "mxnet":
+        return (1, 28, 28)
+    else:
+        return (28, 28, 1)
+
+
+@pytest.fixture()
+def get_default_mnist_subset(get_mnist_dataset, default_dataset_subset_sizes, mnist_shape):
+    (x_train_mnist, y_train_mnist), (x_test_mnist, y_test_mnist) = get_mnist_dataset
+    n_train, n_test = default_dataset_subset_sizes
+
+    x_train_mnist = np.reshape(x_train_mnist, (x_train_mnist.shape[0],) + mnist_shape).astype(np.float32)
+    x_test_mnist = np.reshape(x_test_mnist, (x_test_mnist.shape[0],) + mnist_shape).astype(np.float32)
+
+    yield (x_train_mnist[:n_train], y_train_mnist[:n_train]), (x_test_mnist[:n_test], y_test_mnist[:n_test])
+
+
+@pytest.fixture(scope="session")
+def load_mnist_dataset():
+    logging.info("Loading mnist")
+    (x_train_mnist, y_train_mnist), (x_test_mnist, y_test_mnist), _, _ = load_dataset("mnist")
+    yield (x_train_mnist, y_train_mnist), (x_test_mnist, y_test_mnist)
+
+
+@pytest.fixture(scope="function")
+def create_test_dir():
+    test_dir = tempfile.mkdtemp()
+    yield test_dir
+    shutil.rmtree(test_dir)
+
+
+@pytest.fixture(scope="function")
+def get_mnist_dataset(load_mnist_dataset, mnist_shape):
+    (x_train_mnist, y_train_mnist), (x_test_mnist, y_test_mnist) = load_mnist_dataset
+
+    x_train_mnist = np.reshape(x_train_mnist, (x_train_mnist.shape[0],) + mnist_shape).astype(np.float32)
+    x_test_mnist = np.reshape(x_test_mnist, (x_test_mnist.shape[0],) + mnist_shape).astype(np.float32)
+
+    x_train_mnist_original = x_train_mnist.copy()
+    y_train_mnist_original = y_train_mnist.copy()
+    x_test_mnist_original = x_test_mnist.copy()
+    y_test_mnist_original = y_test_mnist.copy()
+
+    yield (x_train_mnist, y_train_mnist), (x_test_mnist, y_test_mnist)
+
+    # Check that the test data has not been modified, only catches changes in attack.generate if self has been used
+    np.testing.assert_array_almost_equal(x_train_mnist_original, x_train_mnist, decimal=3)
+    np.testing.assert_array_almost_equal(y_train_mnist_original, y_train_mnist, decimal=3)
+    np.testing.assert_array_almost_equal(x_test_mnist_original, x_test_mnist, decimal=3)
+    np.testing.assert_array_almost_equal(y_test_mnist_original, y_test_mnist, decimal=3)
+
+
+# ART test fixture to skip test for specific framework values
+# eg: @pytest.mark.only_with_platform("tensorflow")
+@pytest.fixture(autouse=True)
+def only_with_platform(request, framework):
+    if request.node.get_closest_marker("only_with_platform"):
+        if framework not in request.node.get_closest_marker("only_with_platform").args:
+            pytest.skip("skipped on this platform: {}".format(framework))
+
+
+# ART test fixture to skip test for specific framework values
+# eg: @pytest.mark.skip_framework("tensorflow", "keras", "pytorch", "scikitlearn",
+# "mxnet", "kerastf", "non_dl_frameworks", "dl_frameworks")
+@pytest.fixture(autouse=True)
+def skip_by_framework(request, framework):
+    if request.node.get_closest_marker("skip_framework"):
+        framework_to_skip_list = list(request.node.get_closest_marker("skip_framework").args)
+        if "dl_frameworks" in framework_to_skip_list:
+            framework_to_skip_list.extend(deep_learning_frameworks)
+
+        if "non_dl_frameworks" in framework_to_skip_list:
+            framework_to_skip_list.extend(non_deep_learning_frameworks)
+
+        if "tensorflow" in framework_to_skip_list:
+            framework_to_skip_list.append("tensorflow1")
+            framework_to_skip_list.append("tensorflow2")
+            framework_to_skip_list.append("tensorflow2v1")
+
+        if framework in framework_to_skip_list:
+            pytest.skip("skipped on this platform: {}".format(framework))
+
+
+@pytest.fixture(autouse=True)
+def skip_travis(request):
+    """
+    Skips a test marked with this decorator if the command line argument skip_travis is set to true
+    :param request:
+    :return:
+    """
+    if request.node.get_closest_marker("skip_travis") and request.config.getoption("--skip_travis"):
+        pytest.skip("skipped due to skip_travis being set to {}".format(skip_travis))
+
+
+@pytest.fixture
+def make_customer_record():
+    def _make_customer_record(name):
+        return {"name": name, "orders": []}
+
+    return _make_customer_record
+
+
+@pytest.fixture(autouse=True)
+def framework_agnostic(request, framework):
+    if request.node.get_closest_marker("framework_agnostic"):
+        if framework != get_default_framework():
+            pytest.skip("framework agnostic test skipped for framework : {}".format(framework))
+
+
+# ART test fixture to skip test for specific required modules
+# eg: @pytest.mark.skip_module("deepspeech_pytorch", "apex.amp", "object_detection")
+@pytest.fixture(autouse=True)
+def skip_by_module(request):
+    if request.node.get_closest_marker("skip_module"):
+        modules_from_args = request.node.get_closest_marker("skip_module").args
+
+        # separate possible parent modules and test them first
+        modules_parents = [m.split(".", 1)[0] for m in modules_from_args]
+
+        # merge with modules including submodules (Note: sort ensures that parent comes first)
+        modules_full = sorted(set(modules_parents).union(modules_from_args))
+
+        for module in modules_full:
+            if module in modules_full:
+                module_spec = importlib.util.find_spec(module)
+                module_found = module_spec is not None
+
+                if not module_found:
+                    pytest.skip(f"Test skipped because package {module} not available.")
diff --git a/adversarial-robustness-toolbox/docs/Makefile b/adversarial-robustness-toolbox/docs/Makefile
new file mode 100644
index 0000000..c9305f8
--- /dev/null
+++ b/adversarial-robustness-toolbox/docs/Makefile
@@ -0,0 +1,20 @@
+# Minimal makefile for Sphinx documentation
+#
+
+# You can set these variables from the command line.
+SPHINXOPTS    =
+SPHINXBUILD   = sphinx-build
+SPHINXPROJ    = adversarial-robustness-toolbox
+SOURCEDIR     = .
+BUILDDIR      = _build
+
+# Put it first so that "make" without argument is like "make help".
+help:
+	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+
+.PHONY: help Makefile
+
+# Catch-all target: route all unknown targets to Sphinx using the new
+# "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
+%: Makefile
+	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
\ No newline at end of file
diff --git a/adversarial-robustness-toolbox/docs/conf.py b/adversarial-robustness-toolbox/docs/conf.py
new file mode 100644
index 0000000..04e75de
--- /dev/null
+++ b/adversarial-robustness-toolbox/docs/conf.py
@@ -0,0 +1,174 @@
+# -*- coding: utf-8 -*-
+#
+# Configuration file for the Sphinx documentation builder.
+#
+# This file does only contain a selection of the most common options. For a
+# full list see the documentation:
+# http://www.sphinx-doc.org/en/stable/config
+
+# -- Path setup --------------------------------------------------------------
+
+# If extensions (or modules to document with autodoc) are in another directory,
+# add these directories to sys.path here. If the directory is relative to the
+# documentation root, use os.path.abspath to make it absolute, like shown here.
+import os
+import sys
+sys.path.insert(0, os.path.abspath('..'))
+
+import art
+
+# -- Project information -----------------------------------------------------
+
+project = 'Adversarial Robustness Toolbox'
+copyright = '2018, The Adversarial Robustness Toolbox (ART) Authors'
+author = 'Maria-Irina Nicolae'
+
+# The short X.Y version
+version = '1.6'
+# The full version, including alpha/beta/rc tags
+release = '1.6.0'
+
+
+# -- General configuration ---------------------------------------------------
+
+# If your documentation needs a minimal Sphinx version, state it here.
+#
+# needs_sphinx = '1.0'
+
+# Add any Sphinx extension module names here, as strings. They can be
+# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
+# ones.
+extensions = [
+    'sphinx.ext.viewcode',
+    'sphinx.ext.autodoc'
+]
+
+# Add any paths that contain templates here, relative to this directory.
+templates_path = ['_templates']
+
+# The suffix(es) of source filenames.
+# You can specify multiple suffix as a list of string:
+#
+# source_suffix = ['.rst', '.md']
+source_suffix = '.rst'
+
+# The master toctree document.
+master_doc = 'index'
+
+# The language for content autogenerated by Sphinx. Refer to documentation
+# for a list of supported languages.
+#
+# This is also used if you do content translation via gettext catalogs.
+# Usually you set "language" from the command line for these cases.
+language = None
+
+# List of patterns, relative to source directory, that match files and
+# directories to ignore when looking for source files.
+# This pattern also affects html_static_path and html_extra_path .
+exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store']
+
+# The name of the Pygments (syntax highlighting) style to use.
+pygments_style = 'sphinx'
+
+
+# -- Options for HTML output -------------------------------------------------
+
+# The theme to use for HTML and HTML Help pages.  See the documentation for
+# a list of builtin themes.
+#
+# html_theme = 'alabaster'
+
+if os.environ.get('READTHEDOCS') != 'True':
+    try:
+        import sphinx_rtd_theme
+    except ImportError:
+        pass  # assume we have sphinx >= 1.3
+    else:
+        html_theme_path = [sphinx_rtd_theme.get_html_theme_path()]
+    html_theme = 'sphinx_rtd_theme'
+
+# Theme options are theme-specific and customize the look and feel of a theme
+# further.  For a list of options available for each theme, see the
+# documentation.
+#
+# html_theme_options = {}
+
+# Add any paths that contain custom static files (such as style sheets) here,
+# relative to this directory. They are copied after the builtin static files,
+# so a file named "default.css" will overwrite the builtin "default.css".
+html_static_path = []
+
+# Custom sidebar templates, must be a dictionary that maps document names
+# to template names.
+#
+# The default sidebars (for documents that don't match any pattern) are
+# defined by theme itself.  Builtin themes are using these templates by
+# default: ``['localtoc.html', 'relations.html', 'sourcelink.html',
+# 'searchbox.html']``.
+#
+# html_sidebars = {}
+
+
+# -- Options for HTMLHelp output ---------------------------------------------
+
+# Output file base name for HTML help builder.
+htmlhelp_basename = 'adversarial-robustness-toolboxdoc'
+
+
+# -- Options for LaTeX output ------------------------------------------------
+
+latex_elements = {
+    # The paper size ('letterpaper' or 'a4paper').
+    #
+    # 'papersize': 'letterpaper',
+
+    # The font size ('10pt', '11pt' or '12pt').
+    #
+    # 'pointsize': '10pt',
+
+    # Additional stuff for the LaTeX preamble.
+    #
+    # 'preamble': '',
+
+    # Latex figure (float) alignment
+    #
+    # 'figure_align': 'htbp',
+}
+
+# Grouping the document tree into LaTeX files. List of tuples
+# (source start file, target name, title,
+#  author, documentclass [howto, manual, or own class]).
+latex_documents = [
+    (master_doc, 'adversarial-robustness-toolbox.tex', 'adversarial-robustness-toolbox Documentation',
+     'Maria-Irina Nicolae', 'manual'),
+]
+
+
+# -- Options for manual page output ------------------------------------------
+
+# One entry per manual page. List of tuples
+# (source start file, name, description, authors, manual section).
+man_pages = [
+    (master_doc, 'adversarial-robustness-toolbox', 'adversarial-robustness-toolbox Documentation',
+     [author], 1)
+]
+
+
+# -- Options for Texinfo output ----------------------------------------------
+
+# Grouping the document tree into Texinfo files. List of tuples
+# (source start file, target name, title, author,
+#  dir menu entry, description, category)
+texinfo_documents = [
+    (master_doc, 'adversarial-robustness-toolbox', 'adversarial-robustness-toolbox Documentation',
+     author, 'adversarial-robustness-toolbox', 'One line description of project.',
+     'Miscellaneous'),
+]
+
+
+# -- Extension configuration -------------------------------------------------
+
+extensions = [
+    "sphinx.ext.autodoc",
+    "sphinx_autodoc_annotation",
+]
diff --git a/adversarial-robustness-toolbox/docs/guide/examples.rst b/adversarial-robustness-toolbox/docs/guide/examples.rst
new file mode 100644
index 0000000..1cfea8c
--- /dev/null
+++ b/adversarial-robustness-toolbox/docs/guide/examples.rst
@@ -0,0 +1,7 @@
+Examples
+========
+
+Get Started examples of ART can be found in directory `examples` on `GitHub`_.
+
+
+.. _GitHub: https://github.com/Trusted-AI/adversarial-robustness-toolbox/blob/main/examples/README.md
diff --git a/adversarial-robustness-toolbox/docs/guide/notebooks.rst b/adversarial-robustness-toolbox/docs/guide/notebooks.rst
new file mode 100644
index 0000000..e667019
--- /dev/null
+++ b/adversarial-robustness-toolbox/docs/guide/notebooks.rst
@@ -0,0 +1,7 @@
+Notebooks
+=========
+
+Jupyter notebooks with detailed case studies using ART can be found in directory `notebooks` on `GitHub`_.
+
+
+.. _GitHub: https://github.com/Trusted-AI/adversarial-robustness-toolbox/blob/main/notebooks/README.md
\ No newline at end of file
diff --git a/adversarial-robustness-toolbox/docs/guide/setup.rst b/adversarial-robustness-toolbox/docs/guide/setup.rst
new file mode 100644
index 0000000..d0e2191
--- /dev/null
+++ b/adversarial-robustness-toolbox/docs/guide/setup.rst
@@ -0,0 +1,6 @@
+Setup
+=====
+
+Information on installing the Adversarial Robustness Toolbox can be found on `GitHub`_.
+
+.. _GitHub: https://github.com/Trusted-AI/adversarial-robustness-toolbox/wiki/Get-Started#setup
\ No newline at end of file
diff --git a/adversarial-robustness-toolbox/docs/images/adversarial_threats_art.png b/adversarial-robustness-toolbox/docs/images/adversarial_threats_art.png
new file mode 100644
index 0000000..90374ba
Binary files /dev/null and b/adversarial-robustness-toolbox/docs/images/adversarial_threats_art.png differ
diff --git a/adversarial-robustness-toolbox/docs/images/adversarial_threats_attacker.png b/adversarial-robustness-toolbox/docs/images/adversarial_threats_attacker.png
new file mode 100644
index 0000000..d68eb5d
Binary files /dev/null and b/adversarial-robustness-toolbox/docs/images/adversarial_threats_attacker.png differ
diff --git a/adversarial-robustness-toolbox/docs/images/art_lfai.png b/adversarial-robustness-toolbox/docs/images/art_lfai.png
new file mode 100644
index 0000000..142a2e4
Binary files /dev/null and b/adversarial-robustness-toolbox/docs/images/art_lfai.png differ
diff --git a/adversarial-robustness-toolbox/docs/images/art_logo.png b/adversarial-robustness-toolbox/docs/images/art_logo.png
new file mode 100644
index 0000000..a7769e0
Binary files /dev/null and b/adversarial-robustness-toolbox/docs/images/art_logo.png differ
diff --git a/adversarial-robustness-toolbox/docs/images/art_logo_3d_1.png b/adversarial-robustness-toolbox/docs/images/art_logo_3d_1.png
new file mode 100644
index 0000000..beec17d
Binary files /dev/null and b/adversarial-robustness-toolbox/docs/images/art_logo_3d_1.png differ
diff --git a/adversarial-robustness-toolbox/docs/index.rst b/adversarial-robustness-toolbox/docs/index.rst
new file mode 100644
index 0000000..bb0b32c
--- /dev/null
+++ b/adversarial-robustness-toolbox/docs/index.rst
@@ -0,0 +1,112 @@
+.. adversarial-robustness-toolbox documentation master file, created by
+   sphinx-quickstart on Fri Mar 23 17:02:19 2018.
+   You can adapt this file completely to your liking, but it should at least
+   contain the root `toctree` directive.
+
+Welcome to the Adversarial Robustness Toolbox
+=============================================
+
+.. image:: ./images/art_lfai.png
+  :width: 400
+  :alt: ART Logo
+  :align: center
+
+Adversarial Robustness Toolbox (ART) is a Python library for Machine Learning Security. ART provides tools that enable
+developers and researchers to evaluate, defend, certify and verify Machine Learning models and applications against
+the adversarial threats of Evasion, Poisoning, Extraction, and Inference. ART supports all popular machine learning
+frameworks (TensorFlow, Keras, PyTorch, MXNet, scikit-learn, XGBoost, LightGBM, CatBoost, GPy, etc.), all data types
+(images, tables, audio, video, etc.) and machine learning tasks (classification, object detection, generation,
+certification, etc.).
+
+.. image:: ./images/adversarial_threats_attacker.png
+  :width: 400
+  :alt: ART Logo
+  :align: center
+
+.. image:: ./images/adversarial_threats_art.png
+  :width: 400
+  :alt: ART Logo
+  :align: center
+
+The code of ART is on `GitHub`_ and the Wiki contains overviews of implemented `attacks`_, `defences`_ and `metrics`_.
+
+The library is under continuous development. Feedback, bug reports and contributions are very welcome!
+
+Supported Machine Learning Libraries
+------------------------------------
+
+* TensorFlow (v1 and v2) (https://www.tensorflow.org)
+* Keras (https://www.keras.io)
+* PyTorch (https://www.pytorch.org)
+* MXNet (https://mxnet.apache.org)
+* Scikit-learn (https://www.scikit-learn.org)
+* XGBoost (https://www.xgboost.ai)
+* LightGBM (https://lightgbm.readthedocs.io)
+* CatBoost (https://www.catboost.ai)
+* GPy (https://sheffieldml.github.io/GPy/)
+
+.. toctree::
+   :maxdepth: 2
+   :caption: User guide
+
+   guide/setup
+   guide/examples
+   guide/notebooks
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Modules
+
+   modules/attacks
+   modules/attacks/evasion
+   modules/attacks/extraction
+   modules/attacks/inference/attribute_inference
+   modules/attacks/inference/membership_inference
+   modules/attacks/inference/model_inversion
+   modules/attacks/inference/reconstruction
+   modules/attacks/poisoning
+   modules/defences
+   modules/defences/detector_evasion
+   modules/defences/detector_evasion_subsetscanning
+   modules/defences/detector_poisoning
+   modules/defences/postprocessor
+   modules/defences/preprocessor
+   modules/defences/trainer
+   modules/defences/transformer_evasion
+   modules/defences/transformer_poisoning
+   modules/estimators
+   modules/estimators/certification
+   modules/estimators/certification_randomized_smoothing
+   modules/estimators/classification
+   modules/estimators/classification_scikitlearn
+   modules/estimators/encoding
+   modules/estimators/generation
+   modules/estimators/object_detection
+   modules/estimators/poison_mitigation_neural_cleanse
+   modules/estimators/poison_mitigation_strip
+   modules/estimators/regression
+   modules/estimators/speech_recognition
+   modules/evaluations
+   modules/metrics
+   modules/preprocessing
+   modules/preprocessing/audio
+   modules/preprocessing/expectation_over_transformation
+   modules/preprocessing/standardisation_mean_std
+   modules/wrappers
+   modules/data_generators
+   modules/exceptions
+   modules/utils
+   modules/tests/utils
+
+
+Indices and Tables
+==================
+
+* :ref:`genindex`
+* :ref:`modindex`
+* :ref:`search`
+
+.. _GitHub: https://github.com/Trusted-AI/adversarial-robustness-toolbox
+.. _attacks: https://github.com/Trusted-AI/adversarial-robustness-toolbox/wiki/ART-Attacks
+.. _defences: https://github.com/Trusted-AI/adversarial-robustness-toolbox/wiki/ART-Defences
+.. _metrics: https://github.com/Trusted-AI/adversarial-robustness-toolbox/wiki/ART-Metrics
diff --git a/adversarial-robustness-toolbox/docs/make.bat b/adversarial-robustness-toolbox/docs/make.bat
new file mode 100644
index 0000000..4778cdb
--- /dev/null
+++ b/adversarial-robustness-toolbox/docs/make.bat
@@ -0,0 +1,36 @@
+@ECHO OFF
+
+pushd %~dp0
+
+REM Command file for Sphinx documentation
+
+if "%SPHINXBUILD%" == "" (
+	set SPHINXBUILD=sphinx-build
+)
+set SOURCEDIR=.
+set BUILDDIR=_build
+set SPHINXPROJ=adversarial-robustness-toolbox
+
+if "%1" == "" goto help
+
+%SPHINXBUILD% >NUL 2>NUL
+if errorlevel 9009 (
+	echo.
+	echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
+	echo.installed, then set the SPHINXBUILD environment variable to point
+	echo.to the full path of the 'sphinx-build' executable. Alternatively you
+	echo.may add the Sphinx directory to PATH.
+	echo.
+	echo.If you don't have Sphinx installed, grab it from
+	echo.http://sphinx-doc.org/
+	exit /b 1
+)
+
+%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS%
+goto end
+
+:help
+%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS%
+
+:end
+popd
diff --git a/adversarial-robustness-toolbox/docs/modules/attacks.rst b/adversarial-robustness-toolbox/docs/modules/attacks.rst
new file mode 100644
index 0000000..066aff8
--- /dev/null
+++ b/adversarial-robustness-toolbox/docs/modules/attacks.rst
@@ -0,0 +1,37 @@
+:mod:`art.attacks`
+==================
+.. automodule:: art.attacks
+
+Base Class Attacks
+------------------
+.. autoclass:: Attack
+   :members:
+
+Base Class Evasion Attacks
+--------------------------
+.. autoclass:: EvasionAttack
+   :members:
+
+Base Class Poisoning Attacks
+----------------------------
+.. autoclass:: PoisoningAttack
+.. autoclass:: PoisoningAttackBlackBox
+.. autoclass:: PoisoningAttackWhiteBox
+.. autoclass:: PoisoningAttackTransformer
+   :members:
+
+Base Class Extraction Attacks
+-----------------------------
+.. autoclass:: ExtractionAttack
+   :members:
+
+Base Class Inference Attacks
+----------------------------
+.. autoclass:: InferenceAttack
+.. autoclass:: AttributeInferenceAttack
+   :members:
+
+Base Class Reconstruction Attacks
+---------------------------------
+.. autoclass:: ReconstructionAttack
+   :members:
diff --git a/adversarial-robustness-toolbox/docs/modules/attacks/evasion.rst b/adversarial-robustness-toolbox/docs/modules/attacks/evasion.rst
new file mode 100644
index 0000000..ef48e5b
--- /dev/null
+++ b/adversarial-robustness-toolbox/docs/modules/attacks/evasion.rst
@@ -0,0 +1,255 @@
+:mod:`art.attacks.evasion`
+==========================
+.. automodule:: art.attacks.evasion
+
+Adversarial Patch
+-----------------
+.. autoclass:: AdversarialPatch
+   :members:
+   :special-members:
+
+Adversarial Patch - Numpy
+-------------------------
+.. autoclass:: AdversarialPatchNumpy
+   :members:
+   :special-members:
+
+Adversarial Patch - PyTorch
+---------------------------
+.. autoclass:: AdversarialPatchPyTorch
+   :members:
+   :special-members:
+
+Adversarial Patch - TensorFlowV2
+--------------------------------
+.. autoclass:: AdversarialPatchTensorFlowV2
+   :members:
+   :special-members:
+
+Auto Attack
+-----------
+.. autoclass:: AutoAttack
+   :members:
+   :special-members:
+
+Auto Projected Gradient Descent (Auto-PGD)
+------------------------------------------
+.. autoclass:: AutoProjectedGradientDescent
+   :members:
+   :special-members:
+
+Boundary Attack / Decision-Based Attack
+---------------------------------------
+.. autoclass:: BoundaryAttack
+   :members:
+   :special-members:
+
+Brendel and Bethge Attack
+-------------------------
+.. autoclass:: BrendelBethgeAttack
+   :members:
+   :special-members:
+
+Carlini and Wagner L_2 Attack
+-----------------------------
+.. autoclass:: CarliniL2Method
+   :members:
+   :special-members:
+
+Carlini and Wagner L_inf Attack
+-------------------------------
+.. autoclass:: CarliniLInfMethod
+   :members:
+   :special-members:
+
+Carlini and Wagner ASR Attack
+-----------------------------
+.. autoclass:: CarliniWagnerASR
+   :members:
+   :special-members:
+
+Decision Tree Attack
+--------------------
+.. autoclass:: DecisionTreeAttack
+   :members:
+   :special-members:
+
+DeepFool
+--------
+.. autoclass:: DeepFool
+   :members:
+   :special-members:
+
+DPatch
+------
+.. autoclass:: DPatch
+   :members:
+   :special-members:
+
+RobustDPatch
+------------
+.. autoclass:: RobustDPatch
+   :members:
+   :special-members:
+
+Elastic Net Attack
+------------------
+.. autoclass:: ElasticNet
+   :members:
+   :special-members:
+
+Fast Gradient Method (FGM)
+--------------------------
+.. autoclass:: FastGradientMethod
+   :members:
+   :special-members:
+
+Feature Adversaries
+-------------------
+.. autoclass:: FeatureAdversaries
+   :members:
+   :special-members:
+
+Frame Saliency Attack
+---------------------
+.. autoclass:: FrameSaliencyAttack
+   :members:
+   :special-members:
+
+High Confidence Low Uncertainty Attack
+--------------------------------------
+.. autoclass:: HighConfidenceLowUncertainty
+   :members:
+   :special-members:
+
+HopSkipJump Attack
+------------------
+.. autoclass:: HopSkipJump
+   :members:
+   :special-members:
+
+Imperceptible ASR Attack
+------------------------
+.. autoclass:: ImperceptibleASR
+   :members:
+   :special-members:
+
+Imperceptible ASR Attack - PyTorch
+----------------------------------
+.. autoclass:: ImperceptibleASRPyTorch
+   :members:
+   :special-members:
+
+Basic Iterative Method (BIM)
+----------------------------
+.. autoclass:: BasicIterativeMethod
+   :members:
+   :special-members:
+
+Projected Gradient Descent (PGD)
+--------------------------------
+.. autoclass:: ProjectedGradientDescent
+   :members:
+   :special-members:
+
+Projected Gradient Descent (PGD) - Numpy
+----------------------------------------
+.. autoclass:: ProjectedGradientDescentNumpy
+   :members:
+   :special-members:
+
+Projected Gradient Descent (PGD) - PyTorch
+------------------------------------------
+.. autoclass:: ProjectedGradientDescentPyTorch
+   :members:
+   :special-members:
+
+Projected Gradient Descent (PGD) - TensorFlowV2
+-----------------------------------------------
+.. autoclass:: ProjectedGradientDescentTensorFlowV2
+   :members:
+   :special-members:
+
+NewtonFool
+----------
+.. autoclass:: NewtonFool
+   :members:
+   :special-members:
+
+PixelAttack
+-----------
+.. autoclass:: PixelAttack
+   :members:
+   :special-members:
+
+ThresholdAttack
+---------------
+.. autoclass:: ThresholdAttack
+   :members:
+   :special-members:
+
+Jacobian Saliency Map Attack (JSMA)
+-----------------------------------
+.. autoclass:: SaliencyMapMethod
+   :members:
+   :special-members:
+
+Shadow Attack
+-------------
+.. autoclass:: ShadowAttack
+   :members:
+   :special-members:
+
+ShapeShifter Attack
+-------------------
+.. autoclass:: ShapeShifter
+   :members:
+   :special-members:
+
+Simple Black-box Adversarial Attack
+-----------------------------------
+.. autoclass:: SimBA
+   :members:
+   :special-members:
+
+Spatial Transformations Attack
+------------------------------
+.. autoclass:: SpatialTransformation
+   :members:
+   :special-members:
+
+Square Attack
+-------------
+.. autoclass:: SquareAttack
+   :members:
+   :special-members:
+
+Targeted Universal Perturbation Attack
+--------------------------------------
+.. autoclass:: TargetedUniversalPerturbation
+   :members:
+   :special-members:
+
+Universal Perturbation Attack
+-----------------------------
+.. autoclass:: UniversalPerturbation
+   :members:
+   :special-members:
+
+Virtual Adversarial Method
+--------------------------
+.. autoclass:: VirtualAdversarialMethod
+   :members:
+   :special-members:
+
+Wasserstein Attack
+------------------
+.. autoclass:: Wasserstein
+   :members:
+   :special-members:
+
+Zeroth-Order Optimization (ZOO) Attack
+--------------------------------------
+.. autoclass:: ZooAttack
+   :members:
+   :special-members:
diff --git a/adversarial-robustness-toolbox/docs/modules/attacks/extraction.rst b/adversarial-robustness-toolbox/docs/modules/attacks/extraction.rst
new file mode 100644
index 0000000..5c49579
--- /dev/null
+++ b/adversarial-robustness-toolbox/docs/modules/attacks/extraction.rst
@@ -0,0 +1,21 @@
+:mod:`art.attacks.extraction`
+=============================
+.. automodule:: art.attacks.extraction
+
+Copycat CNN
+-----------
+.. autoclass:: CopycatCNN
+   :members:
+   :special-members:
+
+Functionally Equivalent Extraction
+----------------------------------
+.. autoclass:: FunctionallyEquivalentExtraction
+   :members:
+   :special-members:
+
+Knockoff Nets
+-------------
+.. autoclass:: KnockoffNets
+   :members:
+   :special-members:
diff --git a/adversarial-robustness-toolbox/docs/modules/attacks/inference/attribute_inference.rst b/adversarial-robustness-toolbox/docs/modules/attacks/inference/attribute_inference.rst
new file mode 100644
index 0000000..fd54192
--- /dev/null
+++ b/adversarial-robustness-toolbox/docs/modules/attacks/inference/attribute_inference.rst
@@ -0,0 +1,27 @@
+:mod:`art.attacks.inference.attribute_inference`
+================================================
+.. automodule:: art.attacks.inference.attribute_inference
+
+Attribute Inference Baseline
+----------------------------
+.. autoclass:: AttributeInferenceBaseline
+   :members:
+   :special-members:
+
+Attribute Inference Black-Box
+-----------------------------
+.. autoclass:: AttributeInferenceBlackBox
+   :members:
+   :special-members:
+
+Attribute Inference White-Box Lifestyle Decision-Tree
+-----------------------------------------------------
+.. autoclass:: AttributeInferenceWhiteBoxLifestyleDecisionTree
+   :members:
+   :special-members:
+
+Attribute Inference White-Box Decision-Tree
+-------------------------------------------
+.. autoclass:: AttributeInferenceWhiteBoxDecisionTree
+   :members:
+   :special-members:
diff --git a/adversarial-robustness-toolbox/docs/modules/attacks/inference/membership_inference.rst b/adversarial-robustness-toolbox/docs/modules/attacks/inference/membership_inference.rst
new file mode 100644
index 0000000..029d664
--- /dev/null
+++ b/adversarial-robustness-toolbox/docs/modules/attacks/inference/membership_inference.rst
@@ -0,0 +1,27 @@
+:mod:`art.attacks.inference.membership_inference`
+=================================================
+.. automodule:: art.attacks.inference.membership_inference
+
+Membership Inference Black-Box
+------------------------------
+.. autoclass:: MembershipInferenceBlackBox
+   :members:
+   :special-members:
+
+Membership Inference Black-Box Rule-Based
+-----------------------------------------
+.. autoclass:: MembershipInferenceBlackBoxRuleBased
+   :members:
+   :special-members:
+
+Membership Inference Label-Only - Decision Boundary
+---------------------------------------------------
+.. autoclass:: LabelOnlyDecisionBoundary
+   :members:
+   :special-members:
+
+Membership Inference Label-Only - Gap Attack
+--------------------------------------------
+.. autoclass:: LabelOnlyGapAttack
+   :members:
+   :special-members:
diff --git a/adversarial-robustness-toolbox/docs/modules/attacks/inference/model_inversion.rst b/adversarial-robustness-toolbox/docs/modules/attacks/inference/model_inversion.rst
new file mode 100644
index 0000000..217e80b
--- /dev/null
+++ b/adversarial-robustness-toolbox/docs/modules/attacks/inference/model_inversion.rst
@@ -0,0 +1,9 @@
+:mod:`art.attacks.inference.model_inversion`
+============================================
+.. automodule:: art.attacks.inference.model_inversion
+
+Model Inversion MIFace
+----------------------
+.. autoclass:: MIFace
+   :members:
+   :special-members:
diff --git a/adversarial-robustness-toolbox/docs/modules/attacks/inference/reconstruction.rst b/adversarial-robustness-toolbox/docs/modules/attacks/inference/reconstruction.rst
new file mode 100644
index 0000000..9705426
--- /dev/null
+++ b/adversarial-robustness-toolbox/docs/modules/attacks/inference/reconstruction.rst
@@ -0,0 +1,9 @@
+:mod:`art.attacks.inference.reconstruction`
+===========================================
+.. automodule:: art.attacks.inference.reconstruction
+
+Database Reconstruction
+-----------------------
+.. autoclass:: DatabaseReconstruction
+   :members:
+   :special-members:
diff --git a/adversarial-robustness-toolbox/docs/modules/attacks/poisoning.rst b/adversarial-robustness-toolbox/docs/modules/attacks/poisoning.rst
new file mode 100644
index 0000000..6484e8c
--- /dev/null
+++ b/adversarial-robustness-toolbox/docs/modules/attacks/poisoning.rst
@@ -0,0 +1,39 @@
+:mod:`art.attacks.poisoning`
+============================
+.. automodule:: art.attacks.poisoning
+
+Adversarial Embedding Attack
+----------------------------
+.. autoclass:: PoisoningAttackAdversarialEmbedding
+   :members:
+   :special-members:
+
+Backdoor Poisoning Attack
+-------------------------
+.. autoclass:: PoisoningAttackBackdoor
+   :members:
+   :special-members:
+
+Bullseye Polytope Attack
+---------------------------
+.. autoclass:: BullseyePolytopeAttackPyTorch
+   :members:
+   :special-members:
+
+Clean Label Backdoor Attack
+---------------------------
+.. autoclass:: PoisoningAttackCleanLabelBackdoor
+   :members:
+   :special-members:
+
+Feature Collision Attack
+------------------------
+.. autoclass:: FeatureCollisionAttack
+   :members:
+   :special-members:
+
+Poisoning SVM Attack
+--------------------
+.. autoclass:: PoisoningAttackSVM
+   :members:
+   :special-members:
diff --git a/adversarial-robustness-toolbox/docs/modules/data_generators.rst b/adversarial-robustness-toolbox/docs/modules/data_generators.rst
new file mode 100644
index 0000000..1c37577
--- /dev/null
+++ b/adversarial-robustness-toolbox/docs/modules/data_generators.rst
@@ -0,0 +1,26 @@
+:mod:`art.data_generators`
+==========================
+.. automodule:: art.data_generators
+
+Base Class
+----------
+.. autoclass:: DataGenerator
+   :members:
+
+
+Framework-Specific Data Generators
+----------------------------------
+.. autoclass:: KerasDataGenerator
+   :members:
+
+.. autoclass:: MXDataGenerator
+   :members:
+
+.. autoclass:: PyTorchDataGenerator
+   :members:
+
+.. autoclass:: TensorFlowDataGenerator
+   :members:
+
+.. autoclass:: TensorFlowV2DataGenerator
+   :members:
diff --git a/adversarial-robustness-toolbox/docs/modules/defences.rst b/adversarial-robustness-toolbox/docs/modules/defences.rst
new file mode 100644
index 0000000..044a9d1
--- /dev/null
+++ b/adversarial-robustness-toolbox/docs/modules/defences.rst
@@ -0,0 +1,3 @@
+:mod:`art.defences`
+===================
+.. automodule:: art.defences
diff --git a/adversarial-robustness-toolbox/docs/modules/defences/detector_evasion.rst b/adversarial-robustness-toolbox/docs/modules/defences/detector_evasion.rst
new file mode 100644
index 0000000..35e6d95
--- /dev/null
+++ b/adversarial-robustness-toolbox/docs/modules/defences/detector_evasion.rst
@@ -0,0 +1,13 @@
+:mod:`art.defences.detector.evasion`
+====================================
+.. automodule:: art.defences.detector.evasion
+
+Binary Input Detector
+---------------------
+.. autoclass:: BinaryInputDetector
+   :members:
+
+Binary Activation Detector
+--------------------------
+.. autoclass:: BinaryActivationDetector
+   :members:
diff --git a/adversarial-robustness-toolbox/docs/modules/defences/detector_evasion_subsetscanning.rst b/adversarial-robustness-toolbox/docs/modules/defences/detector_evasion_subsetscanning.rst
new file mode 100644
index 0000000..37fbf59
--- /dev/null
+++ b/adversarial-robustness-toolbox/docs/modules/defences/detector_evasion_subsetscanning.rst
@@ -0,0 +1,8 @@
+:mod:`art.defences.detector.evasion.subsetscanning`
+===================================================
+.. automodule:: art.defences.detector.evasion.subsetscanning
+
+Subset Scanning Detector
+------------------------
+.. autoclass:: SubsetScanningDetector
+   :members:
diff --git a/adversarial-robustness-toolbox/docs/modules/defences/detector_poisoning.rst b/adversarial-robustness-toolbox/docs/modules/defences/detector_poisoning.rst
new file mode 100644
index 0000000..3bd5fe4
--- /dev/null
+++ b/adversarial-robustness-toolbox/docs/modules/defences/detector_poisoning.rst
@@ -0,0 +1,28 @@
+:mod:`art.defences.detector.poison`
+===================================
+.. automodule:: art.defences.detector.poison
+
+Base Class
+----------
+.. autoclass:: PoisonFilteringDefence
+   :members:
+
+Activation Defence
+------------------
+.. autoclass:: ActivationDefence
+   :members:
+
+Data Provenance Defense
+-----------------------
+.. autoclass:: ProvenanceDefense
+   :members:
+
+Reject on Negative Impact (RONI) Defense
+----------------------------------------
+.. autoclass:: RONIDefense
+   :members:
+
+Spectral Signature Defense
+--------------------------
+.. autoclass:: SpectralSignatureDefense
+   :members:
diff --git a/adversarial-robustness-toolbox/docs/modules/defences/postprocessor.rst b/adversarial-robustness-toolbox/docs/modules/defences/postprocessor.rst
new file mode 100644
index 0000000..ecfa7c5
--- /dev/null
+++ b/adversarial-robustness-toolbox/docs/modules/defences/postprocessor.rst
@@ -0,0 +1,39 @@
+:mod:`art.defences.postprocessor`
+=================================
+.. automodule:: art.defences.postprocessor
+
+Base Class Postprocessor
+------------------------
+.. autoclass:: Postprocessor
+   :members:
+   :special-members:
+
+Class Labels
+------------
+.. autoclass:: ClassLabels
+   :members:
+   :special-members:
+
+Gaussian Noise
+--------------
+.. autoclass:: GaussianNoise
+   :members:
+   :special-members:
+
+High Confidence
+---------------
+.. autoclass:: HighConfidence
+   :members:
+   :special-members:
+
+Reverse Sigmoid
+---------------
+.. autoclass:: ReverseSigmoid
+   :members:
+   :special-members:
+
+Rounded
+-------
+.. autoclass:: Rounded
+   :members:
+   :special-members:
diff --git a/adversarial-robustness-toolbox/docs/modules/defences/preprocessor.rst b/adversarial-robustness-toolbox/docs/modules/defences/preprocessor.rst
new file mode 100644
index 0000000..181d640
--- /dev/null
+++ b/adversarial-robustness-toolbox/docs/modules/defences/preprocessor.rst
@@ -0,0 +1,99 @@
+:mod:`art.defences.preprocessor`
+================================
+.. automodule:: art.defences.preprocessor
+
+Base Class Preprocessor
+-----------------------
+.. autoclass:: Preprocessor
+   :members:
+   :special-members:
+
+Feature Squeezing
+-----------------
+.. autoclass:: FeatureSqueezing
+   :members:
+   :special-members:
+
+Gaussian Data Augmentation
+--------------------------
+.. autoclass:: GaussianAugmentation
+   :members:
+   :special-members:
+
+InverseGAN
+----------
+.. autoclass:: InverseGAN
+   :members:
+   :special-members:
+
+DefenseGAN
+----------
+.. autoclass:: DefenseGAN
+   :members:
+   :special-members:
+
+JPEG Compression
+----------------
+.. autoclass:: JpegCompression
+   :members:
+   :special-members:
+
+Label Smoothing
+---------------
+.. autoclass:: LabelSmoothing
+   :members:
+   :special-members:
+
+Mp3 Compression
+---------------
+.. autoclass:: Mp3Compression
+   :members:
+   :special-members:
+
+PixelDefend
+-----------
+.. autoclass:: PixelDefend
+   :members:
+   :special-members:
+
+Resample
+--------
+.. autoclass:: Resample
+   :members:
+   :special-members:
+
+Spatial Smoothing
+-----------------
+.. autoclass:: SpatialSmoothing
+   :members:
+   :special-members:
+
+Spatial Smoothing - PyTorch
+---------------------------
+.. autoclass:: SpatialSmoothingPyTorch
+   :members:
+   :special-members:
+
+Spatial Smoothing - TensorFlow v2
+---------------------------------
+.. autoclass:: SpatialSmoothingTensorFlowV2
+   :members:
+   :special-members:
+
+Thermometer Encoding
+--------------------
+.. autoclass:: ThermometerEncoding
+   :members:
+   :special-members:
+
+Total Variance Minimization
+---------------------------
+.. autoclass:: TotalVarMin
+   :members:
+   :special-members:
+
+Video Compression
+-----------------
+.. autoclass:: VideoCompression
+   :members:
+   :special-members:
diff --git a/adversarial-robustness-toolbox/docs/modules/defences/trainer.rst b/adversarial-robustness-toolbox/docs/modules/defences/trainer.rst
new file mode 100644
index 0000000..7f42f65
--- /dev/null
+++ b/adversarial-robustness-toolbox/docs/modules/defences/trainer.rst
@@ -0,0 +1,33 @@
+:mod:`art.defences.trainer`
+===========================
+.. automodule:: art.defences.trainer
+
+Base Class Trainer
+------------------
+.. autoclass:: Trainer
+   :members:
+   :special-members:
+
+Adversarial Training
+--------------------
+.. autoclass:: AdversarialTrainer
+   :members:
+   :special-members:
+
+Adversarial Training Madry PGD
+------------------------------
+.. autoclass:: AdversarialTrainerMadryPGD
+   :members:
+   :special-members:
+
+Base Class Adversarial Training Fast is Better than Free
+--------------------------------------------------------
+.. autoclass:: AdversarialTrainerFBF
+   :members:
+   :special-members:
+
+Adversarial Training Fast is Better than Free - PyTorch
+-------------------------------------------------------
+.. autoclass:: AdversarialTrainerFBFPyTorch
+   :members:
+   :special-members:
diff --git a/adversarial-robustness-toolbox/docs/modules/defences/transformer_evasion.rst b/adversarial-robustness-toolbox/docs/modules/defences/transformer_evasion.rst
new file mode 100644
index 0000000..e7d96a4
--- /dev/null
+++ b/adversarial-robustness-toolbox/docs/modules/defences/transformer_evasion.rst
@@ -0,0 +1,9 @@
+:mod:`art.defences.transformer.evasion`
+=======================================
+.. automodule:: art.defences.transformer.evasion
+
+Defensive Distillation
+----------------------
+.. autoclass:: DefensiveDistillation
+   :members:
+   :special-members:
diff --git a/adversarial-robustness-toolbox/docs/modules/defences/transformer_poisoning.rst b/adversarial-robustness-toolbox/docs/modules/defences/transformer_poisoning.rst
new file mode 100644
index 0000000..9ed25ac
--- /dev/null
+++ b/adversarial-robustness-toolbox/docs/modules/defences/transformer_poisoning.rst
@@ -0,0 +1,15 @@
+:mod:`art.defences.transformer.poisoning`
+=========================================
+.. automodule:: art.defences.transformer.poisoning
+
+Neural Cleanse
+--------------
+.. autoclass:: NeuralCleanse
+   :members:
+   :special-members:
+
+STRIP
+--------------
+.. autoclass:: STRIP
+   :members:
+   :special-members:
diff --git a/adversarial-robustness-toolbox/docs/modules/estimators.rst b/adversarial-robustness-toolbox/docs/modules/estimators.rst
new file mode 100644
index 0000000..c0708c4
--- /dev/null
+++ b/adversarial-robustness-toolbox/docs/modules/estimators.rst
@@ -0,0 +1,65 @@
+:mod:`art.estimators`
+=====================
+.. automodule:: art.estimators
+
+Base Class Estimator
+--------------------
+.. autoclass:: BaseEstimator
+   :members:
+
+Mixin Base Class Loss Gradients
+-------------------------------
+.. autoclass:: LossGradientsMixin
+   :members:
+
+Mixin Base Class Neural Networks
+--------------------------------
+.. autoclass:: NeuralNetworkMixin
+   :members:
+
+Mixin Base Class Decision Trees
+-------------------------------
+.. autoclass:: DecisionTreeMixin
+   :members:
+
+Base Class KerasEstimator
+-------------------------
+.. autoclass:: KerasEstimator
+   :members:
+   :special-members: __init__
+   :inherited-members:
+
+Base Class MXEstimator
+----------------------
+.. autoclass:: MXEstimator
+   :members:
+   :special-members: __init__
+   :inherited-members:
+
+Base Class PyTorchEstimator
+---------------------------
+.. autoclass:: PyTorchEstimator
+   :members:
+   :special-members: __init__
+   :inherited-members:
+
+Base Class ScikitlearnEstimator
+-------------------------------
+.. autoclass:: ScikitlearnEstimator
+   :members:
+   :special-members: __init__
+   :inherited-members:
+
+Base Class TensorFlowEstimator
+------------------------------
+.. autoclass:: TensorFlowEstimator
+   :members:
+   :special-members: __init__
+   :inherited-members:
+
+Base Class TensorFlowV2Estimator
+--------------------------------
+.. autoclass:: TensorFlowV2Estimator
+   :members:
+   :special-members: __init__
+   :inherited-members:
diff --git a/adversarial-robustness-toolbox/docs/modules/estimators/certification.rst b/adversarial-robustness-toolbox/docs/modules/estimators/certification.rst
new file mode 100644
index 0000000..755c81a
--- /dev/null
+++ b/adversarial-robustness-toolbox/docs/modules/estimators/certification.rst
@@ -0,0 +1,3 @@
+:mod:`art.estimators.certification`
+===================================
+.. automodule:: art.estimators.certification
diff --git a/adversarial-robustness-toolbox/docs/modules/estimators/certification_randomized_smoothing.rst b/adversarial-robustness-toolbox/docs/modules/estimators/certification_randomized_smoothing.rst
new file mode 100644
index 0000000..27102f8
--- /dev/null
+++ b/adversarial-robustness-toolbox/docs/modules/estimators/certification_randomized_smoothing.rst
@@ -0,0 +1,22 @@
+:mod:`art.estimators.certification.randomized_smoothing`
+========================================================
+.. automodule:: art.estimators.certification.randomized_smoothing
+
+Mixin Base Class Randomized Smoothing
+-------------------------------------
+.. autoclass:: RandomizedSmoothingMixin
+   :members:
+
+PyTorch Randomized Smoothing Classifier
+---------------------------------------
+.. autoclass:: PyTorchRandomizedSmoothing
+   :members:
+   :special-members: __init__
+   :inherited-members:
+
+TensorFlow V2 Randomized Smoothing Classifier
+---------------------------------------------
+.. autoclass:: TensorFlowV2RandomizedSmoothing
+   :members:
+   :special-members: __init__
+   :inherited-members:
diff --git a/adversarial-robustness-toolbox/docs/modules/estimators/classification.rst b/adversarial-robustness-toolbox/docs/modules/estimators/classification.rst
new file mode 100644
index 0000000..81a3c5e
--- /dev/null
+++ b/adversarial-robustness-toolbox/docs/modules/estimators/classification.rst
@@ -0,0 +1,80 @@
+:mod:`art.estimators.classification`
+====================================
+.. automodule:: art.estimators.classification
+
+Mixin Base Class Classifier
+---------------------------
+.. autoclass:: ClassifierMixin
+   :members:
+
+Mixin Base Class Class Gradients
+--------------------------------
+.. autoclass:: ClassGradientsMixin
+   :members:
+
+BlackBox Classifier
+-------------------
+.. autoclass:: BlackBoxClassifier
+   :members:
+   :special-members: __init__
+   :inherited-members:
+
+BlackBox Classifier NeuralNetwork
+---------------------------------
+.. autoclass:: BlackBoxClassifierNeuralNetwork
+   :members:
+   :special-members: __init__
+   :inherited-members:
+
+Keras Classifier
+----------------
+.. autoclass:: KerasClassifier
+   :members:
+   :special-members: __init__
+   :inherited-members:
+
+MXNet Classifier
+----------------
+.. autoclass:: MXClassifier
+   :members:
+   :special-members: __init__
+   :inherited-members:
+
+PyTorch Classifier
+------------------
+.. autoclass:: PyTorchClassifier
+   :members:
+   :special-members: __init__
+   :inherited-members:
+
+TensorFlow Classifier
+---------------------
+.. autoclass:: TensorFlowClassifier
+   :members:
+   :special-members: __init__
+   :inherited-members:
+
+TensorFlow v2 Classifier
+------------------------
+.. autoclass:: TensorFlowV2Classifier
+   :members:
+   :special-members: __init__
+   :inherited-members:
+
+Ensemble Classifier
+-------------------
+.. autoclass:: EnsembleClassifier
+   :members:
+   :special-members: __init__
+   :inherited-members:
+
+Scikit-learn Classifier Classifier
+----------------------------------
+.. autofunction:: SklearnClassifier
+
+GPy Gaussian Process Classifier
+-------------------------------
+.. autoclass:: GPyGaussianProcessClassifier
+   :members:
+   :special-members: __init__
+   :inherited-members:
diff --git a/adversarial-robustness-toolbox/docs/modules/estimators/classification_scikitlearn.rst b/adversarial-robustness-toolbox/docs/modules/estimators/classification_scikitlearn.rst
new file mode 100644
index 0000000..252c413
--- /dev/null
+++ b/adversarial-robustness-toolbox/docs/modules/estimators/classification_scikitlearn.rst
@@ -0,0 +1,71 @@
+:mod:`art.estimators.classification.scikitlearn`
+================================================
+.. automodule:: art.estimators.classification.scikitlearn
+
+Base Class Scikit-learn
+-----------------------
+.. autoclass:: ScikitlearnClassifier
+   :members:
+
+Scikit-learn DecisionTreeClassifier Classifier
+----------------------------------------------
+.. autoclass:: ScikitlearnDecisionTreeClassifier
+   :members:
+   :special-members: __init__
+   :inherited-members:
+
+Scikit-learn ExtraTreeClassifier Classifier
+-------------------------------------------
+.. autoclass:: ScikitlearnExtraTreeClassifier
+   :members:
+   :special-members: __init__
+   :inherited-members:
+
+Scikit-learn AdaBoostClassifier Classifier
+------------------------------------------
+.. autoclass:: ScikitlearnAdaBoostClassifier
+   :members:
+   :special-members: __init__
+   :inherited-members:
+
+Scikit-learn BaggingClassifier Classifier
+-----------------------------------------
+.. autoclass:: ScikitlearnBaggingClassifier
+   :members:
+   :special-members: __init__
+   :inherited-members:
+
+Scikit-learn ExtraTreesClassifier Classifier
+--------------------------------------------
+.. autoclass:: ScikitlearnExtraTreesClassifier
+   :members:
+   :special-members: __init__
+   :inherited-members:
+
+Scikit-learn GradientBoostingClassifier Classifier
+--------------------------------------------------
+.. autoclass:: ScikitlearnGradientBoostingClassifier
+   :members:
+   :special-members: __init__
+   :inherited-members:
+
+Scikit-learn RandomForestClassifier Classifier
+----------------------------------------------
+.. autoclass:: ScikitlearnRandomForestClassifier
+   :members:
+   :special-members: __init__
+   :inherited-members:
+
+Scikit-learn LogisticRegression Classifier
+------------------------------------------
+.. autoclass:: ScikitlearnLogisticRegression
+   :members:
+   :special-members: __init__
+   :inherited-members:
+
+Scikit-learn SVC Classifier
+---------------------------
+.. autoclass:: ScikitlearnSVC
+   :members:
+   :special-members: __init__
+   :inherited-members:
diff --git a/adversarial-robustness-toolbox/docs/modules/estimators/encoding.rst b/adversarial-robustness-toolbox/docs/modules/estimators/encoding.rst
new file mode 100644
index 0000000..a3a74a9
--- /dev/null
+++ b/adversarial-robustness-toolbox/docs/modules/estimators/encoding.rst
@@ -0,0 +1,15 @@
+:mod:`art.estimators.encoding`
+==============================
+.. automodule:: art.estimators.encoding
+
+Mixin Base Class Encoder
+------------------------
+.. autoclass:: EncoderMixin
+   :members:
+
+TensorFlow Encoder
+-------------------
+.. autoclass:: TensorFlowEncoder
+   :members:
+   :special-members: __init__
+   :inherited-members:
diff --git a/adversarial-robustness-toolbox/docs/modules/estimators/generation.rst b/adversarial-robustness-toolbox/docs/modules/estimators/generation.rst
new file mode 100644
index 0000000..b9f411f
--- /dev/null
+++ b/adversarial-robustness-toolbox/docs/modules/estimators/generation.rst
@@ -0,0 +1,15 @@
+:mod:`art.estimators.generation`
+================================
+.. automodule:: art.estimators.generation
+
+Mixin Base Class Generator
+--------------------------
+.. autoclass:: GeneratorMixin
+   :members:
+
+TensorFlow Generator
+--------------------
+.. autoclass:: TensorFlowGenerator
+   :members:
+   :special-members: __init__
+   :inherited-members:
diff --git a/adversarial-robustness-toolbox/docs/modules/estimators/object_detection.rst b/adversarial-robustness-toolbox/docs/modules/estimators/object_detection.rst
new file mode 100644
index 0000000..ac0d758
--- /dev/null
+++ b/adversarial-robustness-toolbox/docs/modules/estimators/object_detection.rst
@@ -0,0 +1,24 @@
+:mod:`art.estimators.object_detection`
+======================================
+.. automodule:: art.estimators.object_detection
+
+Mixin Base Class Object Detector
+--------------------------------
+.. autoclass:: ObjectDetectorMixin
+   :members:
+   :special-members: __init__
+   :inherited-members:
+
+Object Detector PyTorch Faster-RCNN
+-----------------------------------
+.. autoclass:: PyTorchFasterRCNN
+   :members:
+   :special-members: __init__
+   :inherited-members:
+
+Object Detector TensorFlow Faster-RCNN
+--------------------------------------
+.. autoclass:: TensorFlowFasterRCNN
+   :members:
+   :special-members: __init__
+   :inherited-members:
diff --git a/adversarial-robustness-toolbox/docs/modules/estimators/poison_mitigation_neural_cleanse.rst b/adversarial-robustness-toolbox/docs/modules/estimators/poison_mitigation_neural_cleanse.rst
new file mode 100644
index 0000000..42080d8
--- /dev/null
+++ b/adversarial-robustness-toolbox/docs/modules/estimators/poison_mitigation_neural_cleanse.rst
@@ -0,0 +1,10 @@
+:mod:`art.estimators.poison_mitigation.neural_cleanse`
+======================================================
+.. automodule:: art.estimators.poison_mitigation.neural_cleanse
+
+Keras Neural Cleanse Classifier
+-------------------------------
+.. autoclass:: KerasNeuralCleanse
+   :members:
+   :special-members: __init__
+   :inherited-members:
diff --git a/adversarial-robustness-toolbox/docs/modules/estimators/poison_mitigation_strip.rst b/adversarial-robustness-toolbox/docs/modules/estimators/poison_mitigation_strip.rst
new file mode 100644
index 0000000..e3850d1
--- /dev/null
+++ b/adversarial-robustness-toolbox/docs/modules/estimators/poison_mitigation_strip.rst
@@ -0,0 +1,10 @@
+:mod:`art.estimators.poison_mitigation.strip`
+=============================================
+.. automodule:: art.estimators.poison_mitigation.strip
+
+Mixin Base Class STRIP
+----------------------
+.. autoclass:: STRIPMixin
+   :members:
+   :special-members: __init__
+   :inherited-members:
diff --git a/adversarial-robustness-toolbox/docs/modules/estimators/regression.rst b/adversarial-robustness-toolbox/docs/modules/estimators/regression.rst
new file mode 100644
index 0000000..34577ae
--- /dev/null
+++ b/adversarial-robustness-toolbox/docs/modules/estimators/regression.rst
@@ -0,0 +1,10 @@
+:mod:`art.estimators.regression`
+================================
+.. automodule:: art.estimators.regression
+
+Mixin Base Class Regressor
+--------------------------
+.. autoclass:: RegressorMixin
+   :members:
+   :special-members: __init__
+   :inherited-members:
diff --git a/adversarial-robustness-toolbox/docs/modules/estimators/speech_recognition.rst b/adversarial-robustness-toolbox/docs/modules/estimators/speech_recognition.rst
new file mode 100644
index 0000000..969b958
--- /dev/null
+++ b/adversarial-robustness-toolbox/docs/modules/estimators/speech_recognition.rst
@@ -0,0 +1,24 @@
+:mod:`art.estimators.speech_recognition`
+========================================
+.. automodule:: art.estimators.speech_recognition
+
+Mixin Base Class Speech Recognizer
+----------------------------------
+.. autoclass:: SpeechRecognizerMixin
+   :members:
+   :special-members: __init__
+   :inherited-members:
+
+Speech Recognizer Deep Speech - PyTorch
+---------------------------------------
+.. autoclass:: PyTorchDeepSpeech
+   :members:
+   :special-members: __init__
+   :inherited-members:
+
+Speech Recognizer Lingvo ASR - TensorFlow
+-----------------------------------------
+.. autoclass:: TensorFlowLingvoASR
+   :members:
+   :special-members: __init__
+   :inherited-members:
diff --git a/adversarial-robustness-toolbox/docs/modules/evaluations.rst b/adversarial-robustness-toolbox/docs/modules/evaluations.rst
new file mode 100644
index 0000000..a13f1be
--- /dev/null
+++ b/adversarial-robustness-toolbox/docs/modules/evaluations.rst
@@ -0,0 +1,3 @@
+:mod:`art.evaluations`
+======================
+.. automodule:: art.evaluations
diff --git a/adversarial-robustness-toolbox/docs/modules/exceptions.rst b/adversarial-robustness-toolbox/docs/modules/exceptions.rst
new file mode 100644
index 0000000..2ccaa75
--- /dev/null
+++ b/adversarial-robustness-toolbox/docs/modules/exceptions.rst
@@ -0,0 +1,7 @@
+:mod:`art.exceptions`
+=====================
+.. automodule:: art.exceptions
+
+EstimatorError
+---------------
+.. autoexception:: EstimatorError
diff --git a/adversarial-robustness-toolbox/docs/modules/metrics.rst b/adversarial-robustness-toolbox/docs/modules/metrics.rst
new file mode 100644
index 0000000..ca45aed
--- /dev/null
+++ b/adversarial-robustness-toolbox/docs/modules/metrics.rst
@@ -0,0 +1,30 @@
+:mod:`art.metrics`
+==================
+.. automodule:: art.metrics
+
+Clique Method Robustness Verification
+-------------------------------------
+.. autoclass:: RobustnessVerificationTreeModelsCliqueMethod
+   :members:
+   :special-members:
+
+Loss Sensitivity
+----------------
+.. autofunction:: loss_sensitivity
+
+Empirical Robustness
+--------------------
+.. autofunction:: empirical_robustness
+
+CLEVER
+------
+.. autofunction:: clever_u
+.. autofunction:: clever_t
+
+Wasserstein Distance
+--------------------
+.. autofunction:: wasserstein_distance
+
+Pointwise Differential Training Privacy
+---------------------------------------
+.. autofunction:: PDTP
diff --git a/adversarial-robustness-toolbox/docs/modules/preprocessing.rst b/adversarial-robustness-toolbox/docs/modules/preprocessing.rst
new file mode 100644
index 0000000..3fea2d6
--- /dev/null
+++ b/adversarial-robustness-toolbox/docs/modules/preprocessing.rst
@@ -0,0 +1,3 @@
+:mod:`art.preprocessing`
+========================
+.. automodule:: art.preprocessing
diff --git a/adversarial-robustness-toolbox/docs/modules/preprocessing/audio.rst b/adversarial-robustness-toolbox/docs/modules/preprocessing/audio.rst
new file mode 100644
index 0000000..be35104
--- /dev/null
+++ b/adversarial-robustness-toolbox/docs/modules/preprocessing/audio.rst
@@ -0,0 +1,15 @@
+:mod:`art.preprocessing.audio`
+==============================
+.. automodule:: art.preprocessing.audio
+
+L-Filter
+--------
+.. autoclass:: LFilter
+   :members:
+   :special-members:
+
+LFilter - PyTorch
+-----------------
+.. autoclass:: LFilterPyTorch
+   :members:
+   :special-members:
diff --git a/adversarial-robustness-toolbox/docs/modules/preprocessing/expectation_over_transformation.rst b/adversarial-robustness-toolbox/docs/modules/preprocessing/expectation_over_transformation.rst
new file mode 100644
index 0000000..b185d69
--- /dev/null
+++ b/adversarial-robustness-toolbox/docs/modules/preprocessing/expectation_over_transformation.rst
@@ -0,0 +1,69 @@
+:mod:`art.preprocessing.expectation_over_transformation`
+========================================================
+.. automodule:: art.preprocessing.expectation_over_transformation
+
+EOT Image Rotation - TensorFlow V2
+----------------------------------
+.. autoclass:: EoTImageRotationTensorFlow
+   :members:
+   :special-members:
+
+EOT Brightness - PyTorch
+------------------------
+.. autoclass:: EoTBrightnessPyTorch
+   :members:
+   :special-members:
+
+EOT Brightness - TensorFlow V2
+----------------------------------
+.. autoclass:: EoTBrightnessTensorFlow
+   :members:
+   :special-members:
+
+EOT Contrast - PyTorch
+----------------------
+.. autoclass:: EoTContrastPyTorch
+   :members:
+   :special-members:
+
+EOT Contrast - TensorFlow V2
+----------------------------
+.. autoclass:: EoTContrastTensorFlow
+   :members:
+   :special-members:
+
+EOT Gaussian Noise - PyTorch
+----------------------------
+.. autoclass:: EoTGaussianNoisePyTorch
+   :members:
+   :special-members:
+
+EOT Gaussian Noise - TensorFlow V2
+----------------------------------
+.. autoclass:: EoTGaussianNoiseTensorFlow
+   :members:
+   :special-members:
+
+EOT Shot Noise - PyTorch
+------------------------
+.. autoclass:: EoTShotNoisePyTorch
+   :members:
+   :special-members:
+
+EOT Shot Noise - TensorFlow V2
+------------------------------
+.. autoclass:: EoTShotNoiseTensorFlow
+   :members:
+   :special-members:
+
+EOT Zoom Blur - PyTorch
+-----------------------
+.. autoclass:: EoTZoomBlurPyTorch
+   :members:
+   :special-members:
+
+EOT Zoom Blur - TensorFlow V2
+----------------------------------
+.. autoclass:: EoTZoomBlurTensorFlow
+   :members:
+   :special-members:
diff --git a/adversarial-robustness-toolbox/docs/modules/preprocessing/standardisation_mean_std.rst b/adversarial-robustness-toolbox/docs/modules/preprocessing/standardisation_mean_std.rst
new file mode 100644
index 0000000..593934e
--- /dev/null
+++ b/adversarial-robustness-toolbox/docs/modules/preprocessing/standardisation_mean_std.rst
@@ -0,0 +1,21 @@
+:mod:`art.preprocessing.standardisation_mean_std`
+=================================================
+.. automodule:: art.preprocessing.standardisation_mean_std
+
+Standardisation Mean and Std
+----------------------------
+.. autoclass:: StandardisationMeanStd
+   :members:
+   :special-members:
+
+Standardisation Mean and Std - PyTorch
+--------------------------------------
+.. autoclass:: StandardisationMeanStdPyTorch
+   :members:
+   :special-members:
+
+Standardisation Mean and Std - TensorFlow V2
+--------------------------------------------
+.. autoclass:: StandardisationMeanStdTensorFlow
+   :members:
+   :special-members:
diff --git a/adversarial-robustness-toolbox/docs/modules/tests/utils.rst b/adversarial-robustness-toolbox/docs/modules/tests/utils.rst
new file mode 100644
index 0000000..8cd821e
--- /dev/null
+++ b/adversarial-robustness-toolbox/docs/modules/tests/utils.rst
@@ -0,0 +1,44 @@
+:mod:`tests.utils`
+===================
+.. automodule:: tests.utils
+
+Test Base Classes
+-----------------
+.. autoclass:: TestBase
+.. autoclass:: ExpectedValue
+
+Trained Models for Unittests, MNIST
+-----------------------------------
+.. autofunction:: get_image_classifier_tf
+.. autofunction:: get_image_classifier_tf_v1
+.. autofunction:: get_image_classifier_tf_v2
+.. autofunction:: get_image_classifier_kr
+.. autofunction:: get_image_classifier_kr_tf
+.. autofunction:: get_image_classifier_kr_functional
+.. autofunction:: get_image_classifier_kr_tf_functional
+.. autofunction:: get_image_classifier_kr_tf_with_wildcard
+.. autofunction:: get_image_classifier_kr_tf_binary
+.. autofunction:: get_image_classifier_pt
+.. autofunction:: get_classifier_bb
+.. autofunction:: get_image_classifier_mxnet_custom_ini
+.. autofunction:: get_gan_inverse_gan_ft
+
+.. autofunction:: get_attack_classifier_pt
+
+.. autofunction:: check_adverse_example_x
+.. autofunction:: check_adverse_predicted_sample_y
+.. autofunction:: is_valid_framework
+
+
+Trained Models for Unittests, Iris
+----------------------------------
+.. autofunction:: get_tabular_classifier_tf
+.. autofunction:: get_tabular_classifier_tf_v1
+.. autofunction:: get_tabular_classifier_tf_v2
+.. autofunction:: get_tabular_classifier_scikit_list
+.. autofunction:: get_tabular_classifier_kr
+.. autofunction:: get_tabular_classifier_pt
+
+Random Number Generators
+------------------------
+.. autofunction:: master_seed
diff --git a/adversarial-robustness-toolbox/docs/modules/utils.rst b/adversarial-robustness-toolbox/docs/modules/utils.rst
new file mode 100644
index 0000000..0f5a08d
--- /dev/null
+++ b/adversarial-robustness-toolbox/docs/modules/utils.rst
@@ -0,0 +1,40 @@
+:mod:`art.utils`
+================
+.. automodule:: art.utils
+
+Deprecation Operations
+----------------------
+.. autofunction:: deprecated
+.. autofunction:: deprecated_keyword_arg
+
+Math Operations
+---------------
+.. autofunction:: projection
+.. autofunction:: random_sphere
+.. autofunction:: original_to_tanh
+.. autofunction:: tanh_to_original
+
+Label Operations
+----------------
+.. autofunction:: to_categorical
+.. autofunction:: float_to_categorical
+.. autofunction:: check_and_transform_label_format
+.. autofunction:: random_targets
+.. autofunction:: least_likely_class
+.. autofunction:: second_most_likely_class
+.. autofunction:: get_label_conf
+.. autofunction:: get_labels_np_array
+.. autofunction:: compute_success_array
+.. autofunction:: compute_success
+.. autofunction:: compute_accuracy
+
+Dataset Operations
+------------------
+.. autofunction:: load_dataset
+.. autofunction:: get_file
+.. autofunction:: make_directory
+.. autofunction:: clip_and_round
+.. autofunction:: preprocess
+.. autofunction:: segment_by_class
+.. autofunction:: performance_diff
+.. autofunction:: is_probability
diff --git a/adversarial-robustness-toolbox/docs/modules/wrappers.rst b/adversarial-robustness-toolbox/docs/modules/wrappers.rst
new file mode 100644
index 0000000..abcab32
--- /dev/null
+++ b/adversarial-robustness-toolbox/docs/modules/wrappers.rst
@@ -0,0 +1,18 @@
+:mod:`art.wrappers`
+===================
+.. automodule:: art.wrappers
+
+Base Class Wrapper
+------------------
+.. autoclass:: ClassifierWrapper
+   :members:
+
+Expectation over Transformations
+--------------------------------
+.. autoclass:: ExpectationOverTransformations
+   :members:
+
+Query-Efficient Black-Box Attack
+--------------------------------
+.. autoclass:: QueryEfficientBBGradientEstimation
+   :members:
diff --git a/adversarial-robustness-toolbox/examples/README.md b/adversarial-robustness-toolbox/examples/README.md
new file mode 100644
index 0000000..75a4b4e
--- /dev/null
+++ b/adversarial-robustness-toolbox/examples/README.md
@@ -0,0 +1,59 @@
+# Adversarial Robustness Toolbox examples
+
+## Get Started with ART
+These examples train a small model on the MNIST dataset and creates adversarial examples using the Fast Gradient Sign 
+Method. Here we use the ART classifier to train the model, it would also be possible to provide a pretrained model to 
+the ART classifier. The parameters are chosen for reduced computational requirements of the script and not optimised 
+for accuracy.
+
+
+### TensorFlow
+[get_started_tensorflow.py](get_started_tensorflow.py) demonstrates a simple example of using ART with TensorFlow v1.x. 
+
+### Keras
+[get_started_keras.py](get_started_keras.py) demonstrates a simple example of using ART with Keras. 
+
+### PyTorch
+[get_started_pytorch.py](get_started_pytorch.py) demonstrates a simple example of using ART with PyTorch. 
+
+### MXNet
+[get_started_mxnet.py](get_started_mxnet.py) demonstrates a simple example of using ART with MXNet. 
+
+### Scikit-learn
+[get_started_scikit_learn.py](get_started_scikit_learn.py) demonstrates a simple example of using ART with Scikit-learn. 
+This example uses the support vector machine SVC, but any other classifier of Scikit-learn can be used as well.
+
+### XGBoost
+[get_started_xgboost.py](get_started_xgboost.py) demonstrates a simple example of using ART with XGBoost.
+Because gradient boosted tree classifier do not provide gradients, the adversarial examples are created with the 
+black-box method Zeroth Order Optimization.
+
+### InverseGAN
+[get_started_inverse_gan.py](get_started_inverse_gan.py) demonstrates a simple example of using 
+InverseGAN and Defense ART with TensorFlow v1.x.
+
+### LightGBM
+[get_started_lightgbm.py](get_started_lightgbm.py) demonstrates a simple example of using ART with LightGBM.
+Because gradient boosted tree classifier do not provide gradients, the adversarial examples are created with the 
+black-box method Zeroth Order Optimization.
+
+
+## Applications
+
+[adversarial_training_cifar10.py](adversarial_training_cifar10.py) trains a convolutional neural network on the CIFAR-10 
+dataset, then generates adversarial images using the DeepFool attack and retrains the network on the training set 
+augmented with the adversarial images.
+
+[adversarial_training_data_augmentation.py](adversarial_training_data_augmentation.py) shows how to use ART and Keras 
+to perform adversarial training using data generators for CIFAR-10.
+
+[mnist_cnn_fgsm.py](mnist_cnn_fgsm.py) trains a convolutional neural network on MNIST, then crafts FGSM attack examples 
+on it.
+
+[mnist_poison_detection.py](mnist_poison_detection.py) generates a backdoor for MNIST dataset, then trains a 
+convolutional neural network on the poisoned dataset and runs activation defence to find poison.
+
+[mnist_transferability.py](mnist_transferability.py) trains a convolutional neural network on the MNIST dataset using 
+the Keras backend, then generates adversarial images using DeepFool and uses them to attack a convolutional neural 
+network trained on MNIST using TensorFlow. This is to show how to perform a black-box attack: the attack never has
+access to the parameters of the TensorFlow model.
diff --git a/adversarial-robustness-toolbox/examples/adversarial_training_FBF.py b/adversarial-robustness-toolbox/examples/adversarial_training_FBF.py
new file mode 100644
index 0000000..e6c9278
--- /dev/null
+++ b/adversarial-robustness-toolbox/examples/adversarial_training_FBF.py
@@ -0,0 +1,232 @@
+"""
+This is an example of how to use ART for adversarial training of a model with Fast is better than free protocol
+"""
+import math
+from PIL import Image
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torchvision.transforms as transforms
+from torch.utils.data import TensorDataset, Dataset, DataLoader
+
+from art.classifiers import PyTorchClassifier
+from art.data_generators import PyTorchDataGenerator
+from art.defences.trainer import AdversarialTrainerFBFPyTorch
+from art.utils import load_cifar10
+from art.attacks.evasion import ProjectedGradientDescent
+
+"""
+For this example we choose the PreActResNet model as used in the paper (https://openreview.net/forum?id=BJx040EFvH)
+The code for the model architecture has been adopted from
+https://github.com/anonymous-sushi-armadillo/fast_is_better_than_free_CIFAR10/blob/master/preact_resnet.py
+"""
+
+
+class PreActBlock(nn.Module):
+    """Pre-activation version of the BasicBlock."""
+
+    expansion = 1
+
+    def __init__(self, in_planes, planes, stride=1):
+        super(PreActBlock, self).__init__()
+        self.bn1 = nn.BatchNorm2d(in_planes)
+        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
+        self.bn2 = nn.BatchNorm2d(planes)
+        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
+
+        if stride != 1 or in_planes != self.expansion * planes:
+            self.shortcut = nn.Sequential(
+                nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False)
+            )
+
+    def forward(self, x):
+        out = F.relu(self.bn1(x))
+        shortcut = self.shortcut(x) if hasattr(self, "shortcut") else x
+        out = self.conv1(out)
+        out = self.conv2(F.relu(self.bn2(out)))
+        out += shortcut
+        return out
+
+
+class PreActBottleneck(nn.Module):
+    """Pre-activation version of the original Bottleneck module."""
+
+    expansion = 4
+
+    def __init__(self, in_planes, planes, stride=1):
+        super(PreActBottleneck, self).__init__()
+        self.bn1 = nn.BatchNorm2d(in_planes)
+        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False)
+        self.bn2 = nn.BatchNorm2d(planes)
+        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
+        self.bn3 = nn.BatchNorm2d(planes)
+        self.conv3 = nn.Conv2d(planes, self.expansion * planes, kernel_size=1, bias=False)
+
+        if stride != 1 or in_planes != self.expansion * planes:
+            self.shortcut = nn.Sequential(
+                nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False)
+            )
+
+    def forward(self, x):
+        out = F.relu(self.bn1(x))
+        shortcut = self.shortcut(out) if hasattr(self, "shortcut") else x
+        out = self.conv1(out)
+        out = self.conv2(F.relu(self.bn2(out)))
+        out = self.conv3(F.relu(self.bn3(out)))
+        out += shortcut
+        return out
+
+
+class PreActResNet(nn.Module):
+    def __init__(self, block, num_blocks, num_classes=10):
+        super(PreActResNet, self).__init__()
+        self.in_planes = 64
+
+        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
+        self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1)
+        self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)
+        self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)
+        self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)
+        self.linear = nn.Linear(512 * block.expansion, num_classes)
+
+    def _make_layer(self, block, planes, num_blocks, stride):
+        strides = [stride] + [1] * (num_blocks - 1)
+        layers = []
+        for stride in strides:
+            layers.append(block(self.in_planes, planes, stride))
+            self.in_planes = planes * block.expansion
+        return nn.Sequential(*layers)
+
+    def forward(self, x):
+        out = self.conv1(x)
+        out = self.layer1(out)
+        out = self.layer2(out)
+        out = self.layer3(out)
+        out = self.layer4(out)
+        out = F.avg_pool2d(out, 4)
+        out = out.view(out.size(0), -1)
+        out = self.linear(out)
+        return out
+
+
+def PreActResNet18():
+    return PreActResNet(PreActBlock, [2, 2, 2, 2])
+
+
+def initialize_weights(module):
+    if isinstance(module, nn.Conv2d):
+        n = module.kernel_size[0] * module.kernel_size[1] * module.out_channels
+        module.weight.data.normal_(0, math.sqrt(2.0 / n))
+        if module.bias is not None:
+            module.bias.data.zero_()
+    elif isinstance(module, nn.BatchNorm2d):
+        module.weight.data.fill_(1)
+        module.bias.data.zero_()
+    elif isinstance(module, nn.Linear):
+        module.bias.data.zero_()
+
+
+class CIFAR10_dataset(Dataset):
+    def __init__(self, data, targets, transform=None):
+        self.data = data
+        self.targets = torch.LongTensor(targets)
+        self.transform = transform
+
+    def __getitem__(self, index):
+        x = Image.fromarray(((self.data[index] * 255).round()).astype(np.uint8).transpose(1, 2, 0))
+        x = self.transform(x)
+        y = self.targets[index]
+        return x, y
+
+    def __len__(self):
+        return len(self.data)
+
+
+# Step 1: Load the CIFAR10 dataset
+(x_train, y_train), (x_test, y_test), min_pixel_value, max_pixel_value = load_cifar10()
+
+cifar_mu = np.ones((3, 32, 32))
+cifar_mu[0, :, :] = 0.4914
+cifar_mu[1, :, :] = 0.4822
+cifar_mu[2, :, :] = 0.4465
+
+# (0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))
+
+cifar_std = np.ones((3, 32, 32))
+cifar_std[0, :, :] = 0.2471
+cifar_std[1, :, :] = 0.2435
+cifar_std[2, :, :] = 0.2616
+
+x_train = x_train.transpose(0, 3, 1, 2).astype("float32")
+x_test = x_test.transpose(0, 3, 1, 2).astype("float32")
+
+transform = transforms.Compose(
+    [transforms.RandomCrop(32, padding=4), transforms.RandomHorizontalFlip(), transforms.ToTensor()]
+)
+
+dataset = CIFAR10_dataset(x_train, y_train, transform=transform)
+dataloader = DataLoader(dataset, batch_size=128, shuffle=True)
+
+# Step 2: create the PyTorch model
+model = PreActResNet18()
+# For running on GPU replace the model with the
+# model = PreActResNet18().cuda()
+
+model.apply(initialize_weights)
+model.train()
+
+opt = torch.optim.SGD(model.parameters(), lr=0.21, momentum=0.9, weight_decay=5e-4)
+
+# if you have apex installed, the following line should be uncommented for faster processing
+# import apex.amp as amp
+# model, opt = amp.initialize(model, opt, opt_level="O2", loss_scale=1.0, master_weights=False)
+
+criterion = nn.CrossEntropyLoss()
+# Step 3: Create the ART classifier
+
+classifier = PyTorchClassifier(
+    model=model,
+    clip_values=(0.0, 1.0),
+    preprocessing=(cifar_mu, cifar_std),
+    loss=criterion,
+    optimizer=opt,
+    input_shape=(3, 32, 32),
+    nb_classes=10,
+)
+
+attack = ProjectedGradientDescent(
+    classifier,
+    norm=np.inf,
+    eps=8.0 / 255.0,
+    eps_step=2.0 / 255.0,
+    max_iter=40,
+    targeted=False,
+    num_random_init=5,
+    batch_size=32,
+)
+
+# Step 4: Create the trainer object - AdversarialTrainerFBFPyTorch
+# if you have apex installed, change use_amp to True
+epsilon = 8.0 / 255.0
+trainer = AdversarialTrainerFBFPyTorch(classifier, eps=epsilon, use_amp=False)
+
+# Build a Keras image augmentation object and wrap it in ART
+art_datagen = PyTorchDataGenerator(iterator=dataloader, size=x_train.shape[0], batch_size=128)
+
+# Step 5: fit the trainer
+trainer.fit_generator(art_datagen, nb_epochs=30)
+
+x_test_pred = np.argmax(classifier.predict(x_test), axis=1)
+print(
+    "Accuracy on benign test samples after adversarial training: %.2f%%"
+    % (np.sum(x_test_pred == np.argmax(y_test, axis=1)) / x_test.shape[0] * 100)
+)
+
+x_test_attack = attack.generate(x_test)
+x_test_attack_pred = np.argmax(classifier.predict(x_test_attack), axis=1)
+print(
+    "Accuracy on original PGD adversarial samples after adversarial training: %.2f%%"
+    % (np.sum(x_test_attack_pred == np.argmax(y_test, axis=1)) / x_test.shape[0] * 100)
+)
diff --git a/adversarial-robustness-toolbox/examples/adversarial_training_cifar10.py b/adversarial-robustness-toolbox/examples/adversarial_training_cifar10.py
new file mode 100644
index 0000000..078c023
--- /dev/null
+++ b/adversarial-robustness-toolbox/examples/adversarial_training_cifar10.py
@@ -0,0 +1,88 @@
+# -*- coding: utf-8 -*-
+"""
+Trains a convolutional neural network on the CIFAR-10 dataset, then generated adversarial images using the
+DeepFool attack and retrains the network on the training set augmented with the adversarial images.
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+
+from keras.models import Sequential
+from keras.layers import Dense, Flatten, Conv2D, MaxPooling2D, Activation, Dropout
+import numpy as np
+
+from art.attacks.evasion import DeepFool
+from art.estimators.classification import KerasClassifier
+from art.utils import load_dataset
+
+# Configure a logger to capture ART outputs; these are printed in console and the level of detail is set to INFO
+logger = logging.getLogger()
+logger.setLevel(logging.INFO)
+handler = logging.StreamHandler()
+formatter = logging.Formatter("[%(levelname)s] %(message)s")
+handler.setFormatter(formatter)
+logger.addHandler(handler)
+
+# Read CIFAR10 dataset
+(x_train, y_train), (x_test, y_test), min_, max_ = load_dataset(str("cifar10"))
+x_train, y_train = x_train[:5000], y_train[:5000]
+x_test, y_test = x_test[:500], y_test[:500]
+im_shape = x_train[0].shape
+
+# Create Keras convolutional neural network - basic architecture from Keras examples
+# Source here: https://github.com/keras-team/keras/blob/master/examples/cifar10_cnn.py
+model = Sequential()
+model.add(Conv2D(32, (3, 3), padding="same", input_shape=x_train.shape[1:]))
+model.add(Activation("relu"))
+model.add(Conv2D(32, (3, 3)))
+model.add(Activation("relu"))
+model.add(MaxPooling2D(pool_size=(2, 2)))
+model.add(Dropout(0.25))
+
+model.add(Conv2D(64, (3, 3), padding="same"))
+model.add(Activation("relu"))
+model.add(Conv2D(64, (3, 3)))
+model.add(Activation("relu"))
+model.add(MaxPooling2D(pool_size=(2, 2)))
+model.add(Dropout(0.25))
+
+model.add(Flatten())
+model.add(Dense(512))
+model.add(Activation("relu"))
+model.add(Dropout(0.5))
+model.add(Dense(10))
+model.add(Activation("softmax"))
+
+model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])
+
+# Create classifier wrapper
+classifier = KerasClassifier(model=model, clip_values=(min_, max_))
+classifier.fit(x_train, y_train, nb_epochs=10, batch_size=128)
+
+# Craft adversarial samples with DeepFool
+logger.info("Create DeepFool attack")
+adv_crafter = DeepFool(classifier)
+logger.info("Craft attack on training examples")
+x_train_adv = adv_crafter.generate(x_train)
+logger.info("Craft attack test examples")
+x_test_adv = adv_crafter.generate(x_test)
+
+# Evaluate the classifier on the adversarial samples
+preds = np.argmax(classifier.predict(x_test_adv), axis=1)
+acc = np.sum(preds == np.argmax(y_test, axis=1)) / y_test.shape[0]
+logger.info("Classifier before adversarial training")
+logger.info("Accuracy on adversarial samples: %.2f%%", (acc * 100))
+
+# Data augmentation: expand the training set with the adversarial samples
+x_train = np.append(x_train, x_train_adv, axis=0)
+y_train = np.append(y_train, y_train, axis=0)
+
+# Retrain the CNN on the extended dataset
+model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])
+classifier.fit(x_train, y_train, nb_epochs=10, batch_size=128)
+
+# Evaluate the adversarially trained classifier on the test set
+preds = np.argmax(classifier.predict(x_test_adv), axis=1)
+acc = np.sum(preds == np.argmax(y_test, axis=1)) / y_test.shape[0]
+logger.info("Classifier with adversarial training")
+logger.info("Accuracy on adversarial samples: %.2f%%", (acc * 100))
diff --git a/adversarial-robustness-toolbox/examples/adversarial_training_data_augmentation.py b/adversarial-robustness-toolbox/examples/adversarial_training_data_augmentation.py
new file mode 100644
index 0000000..d85062e
--- /dev/null
+++ b/adversarial-robustness-toolbox/examples/adversarial_training_data_augmentation.py
@@ -0,0 +1,90 @@
+"""
+This is an example of how to use ART and Keras to perform adversarial training using data generators for CIFAR10
+"""
+import keras
+import numpy as np
+from keras.layers import Conv2D, Dense, Flatten, MaxPooling2D, Input, BatchNormalization
+from keras.models import Model
+from keras.preprocessing.image import ImageDataGenerator
+from keras.regularizers import l2
+
+from art.attacks.evasion import ProjectedGradientDescent
+from art.estimators.classification import KerasClassifier
+from art.data_generators import KerasDataGenerator
+from art.defences.trainer import AdversarialTrainer
+from art.utils import load_cifar10
+
+
+# Example LeNet classifier architecture with Keras & ART
+# To obtain good performance in adversarial training on CIFAR-10, use a larger architecture
+def build_model(input_shape=(32, 32, 3), nb_classes=10):
+    img_input = Input(shape=input_shape)
+    conv2d_1 = Conv2D(
+        6,
+        (5, 5),
+        padding="valid",
+        kernel_regularizer=l2(0.0001),
+        activation="relu",
+        kernel_initializer="he_normal",
+        input_shape=input_shape,
+    )(img_input)
+    conv2d_1_bn = BatchNormalization()(conv2d_1)
+    conv2d_1_pool = MaxPooling2D((2, 2), strides=(2, 2))(conv2d_1_bn)
+    conv2d_2 = Conv2D(16, (5, 5), padding="valid", activation="relu", kernel_initializer="he_normal")(conv2d_1_pool)
+    conv2d_2_pool = MaxPooling2D((2, 2), strides=(2, 2))(conv2d_2)
+    flatten_1 = Flatten()(conv2d_2_pool)
+    dense_1 = Dense(120, activation="relu", kernel_initializer="he_normal")(flatten_1)
+    dense_2 = Dense(84, activation="relu", kernel_initializer="he_normal")(dense_1)
+    img_output = Dense(nb_classes, activation="softmax", kernel_initializer="he_normal")(dense_2)
+    model = Model(img_input, img_output)
+
+    model.compile(
+        loss=keras.losses.categorical_crossentropy, optimizer=keras.optimizers.Adam(lr=0.01), metrics=["accuracy"]
+    )
+
+    return model
+
+
+# Load data and normalize
+(x_train, y_train), (x_test, y_test), min_, max_ = load_cifar10()
+
+# Build a Keras image augmentation object and wrap it in ART
+batch_size = 50
+datagen = ImageDataGenerator(
+    horizontal_flip=True, width_shift_range=0.125, height_shift_range=0.125, fill_mode="constant", cval=0.0
+)
+datagen.fit(x_train)
+art_datagen = KerasDataGenerator(
+    datagen.flow(x=x_train, y=y_train, batch_size=batch_size, shuffle=True),
+    size=x_train.shape[0],
+    batch_size=batch_size,
+)
+
+# Create a toy Keras CNN architecture & wrap it under ART interface
+classifier = KerasClassifier(build_model(), clip_values=(0, 1), use_logits=False)
+
+# Create attack for adversarial trainer; here, we use 2 attacks, both crafting adv examples on the target model
+pgd = ProjectedGradientDescent(classifier, eps=8 / 255, eps_step=2 / 255, max_iter=10, num_random_init=1)
+
+# Create some adversarial samples for evaluation
+x_test_pgd = pgd.generate(x_test)
+
+# Create adversarial trainer and perform adversarial training
+adv_trainer = AdversarialTrainer(classifier, attacks=pgd, ratio=1.0)
+adv_trainer.fit_generator(art_datagen, nb_epochs=83)
+
+# Evaluate the adversarially trained model on clean test set
+labels_true = np.argmax(y_test, axis=1)
+labels_test = np.argmax(classifier.predict(x_test), axis=1)
+print("Accuracy test set: %.2f%%" % (np.sum(labels_test == labels_true) / x_test.shape[0] * 100))
+
+# Evaluate the adversarially trained model on original adversarial samples
+labels_pgd = np.argmax(classifier.predict(x_test_pgd), axis=1)
+print(
+    "Accuracy on original PGD adversarial samples: %.2f%%" % (np.sum(labels_pgd == labels_true) / x_test.shape[0] * 100)
+)
+
+# Evaluate the adversarially trained model on fresh adversarial samples produced on the adversarially trained model
+x_test_pgd = pgd.generate(x_test)
+labels_pgd = np.argmax(classifier.predict(x_test_pgd), axis=1)
+print("Accuracy on new PGD adversarial samples: %.2f%%" % (np.sum(labels_pgd == labels_true) / x_test.shape[0] * 100))
diff --git a/adversarial-robustness-toolbox/examples/application_object_detection.py b/adversarial-robustness-toolbox/examples/application_object_detection.py
new file mode 100644
index 0000000..ed1252d
--- /dev/null
+++ b/adversarial-robustness-toolbox/examples/application_object_detection.py
@@ -0,0 +1,209 @@
+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+
+from art.estimators.object_detection import PyTorchFasterRCNN
+from art.attacks.evasion import FastGradientMethod
+
+COCO_INSTANCE_CATEGORY_NAMES = [
+    "__background__",
+    "person",
+    "bicycle",
+    "car",
+    "motorcycle",
+    "airplane",
+    "bus",
+    "train",
+    "truck",
+    "boat",
+    "traffic light",
+    "fire hydrant",
+    "N/A",
+    "stop sign",
+    "parking meter",
+    "bench",
+    "bird",
+    "cat",
+    "dog",
+    "horse",
+    "sheep",
+    "cow",
+    "elephant",
+    "bear",
+    "zebra",
+    "giraffe",
+    "N/A",
+    "backpack",
+    "umbrella",
+    "N/A",
+    "N/A",
+    "handbag",
+    "tie",
+    "suitcase",
+    "frisbee",
+    "skis",
+    "snowboard",
+    "sports ball",
+    "kite",
+    "baseball bat",
+    "baseball glove",
+    "skateboard",
+    "surfboard",
+    "tennis racket",
+    "bottle",
+    "N/A",
+    "wine glass",
+    "cup",
+    "fork",
+    "knife",
+    "spoon",
+    "bowl",
+    "banana",
+    "apple",
+    "sandwich",
+    "orange",
+    "broccoli",
+    "carrot",
+    "hot dog",
+    "pizza",
+    "donut",
+    "cake",
+    "chair",
+    "couch",
+    "potted plant",
+    "bed",
+    "N/A",
+    "dining table",
+    "N/A",
+    "N/A",
+    "toilet",
+    "N/A",
+    "tv",
+    "laptop",
+    "mouse",
+    "remote",
+    "keyboard",
+    "cell phone",
+    "microwave",
+    "oven",
+    "toaster",
+    "sink",
+    "refrigerator",
+    "N/A",
+    "book",
+    "clock",
+    "vase",
+    "scissors",
+    "teddy bear",
+    "hair drier",
+    "toothbrush",
+]
+
+
+def extract_predictions(predictions_):
+    # Get the predicted class
+    predictions_class = [COCO_INSTANCE_CATEGORY_NAMES[i] for i in list(predictions_["labels"])]
+    print("\npredicted classes:", predictions_class)
+
+    # Get the predicted bounding boxes
+    predictions_boxes = [[(i[0], i[1]), (i[2], i[3])] for i in list(predictions_["boxes"])]
+
+    # Get the predicted prediction score
+    predictions_score = list(predictions_["scores"])
+    print("predicted score:", predictions_score)
+
+    # Get a list of index with score greater than threshold
+    threshold = 0.5
+    predictions_t = [predictions_score.index(x) for x in predictions_score if x > threshold][-1]
+
+    predictions_boxes = predictions_boxes[: predictions_t + 1]
+    predictions_class = predictions_class[: predictions_t + 1]
+
+    return predictions_class, predictions_boxes, predictions_class
+
+
+def plot_image_with_boxes(img, boxes, pred_cls):
+    text_size = 5
+    text_th = 5
+    rect_th = 6
+
+    for i in range(len(boxes)):
+        # Draw Rectangle with the coordinates
+        cv2.rectangle(img, boxes[i][0], boxes[i][1], color=(0, 255, 0), thickness=rect_th)
+
+        # Write the prediction class
+        cv2.putText(img, pred_cls[i], boxes[i][0], cv2.FONT_HERSHEY_SIMPLEX, text_size, (0, 255, 0), thickness=text_th)
+
+    plt.axis("off")
+    plt.imshow(img.astype(np.uint8), interpolation="nearest")
+    plt.show()
+
+
+def main():
+    # Create ART object detector
+    frcnn = PyTorchFasterRCNN(
+        clip_values=(0, 255), attack_losses=["loss_classifier", "loss_box_reg", "loss_objectness", "loss_rpn_box_reg"]
+    )
+
+    # Load image 1
+    image_0 = cv2.imread("./10best-cars-group-cropped-1542126037.jpg")
+    image_0 = cv2.cvtColor(image_0, cv2.COLOR_BGR2RGB)  # Convert to RGB
+    print("image_0.shape:", image_0.shape)
+
+    # Load image 2
+    image_1 = cv2.imread("./banner-diverse-group-of-people-2.jpg")
+    image_1 = cv2.cvtColor(image_1, cv2.COLOR_BGR2RGB)  # Convert to RGB
+    image_1 = cv2.resize(image_1, dsize=(image_0.shape[1], image_0.shape[0]), interpolation=cv2.INTER_CUBIC)
+    print("image_1.shape:", image_1.shape)
+
+    # Stack images
+    image = np.stack([image_0, image_1], axis=0).astype(np.float32)
+    print("image.shape:", image.shape)
+
+    for i in range(image.shape[0]):
+        plt.axis("off")
+        plt.title("image {}".format(i))
+        plt.imshow(image[i].astype(np.uint8), interpolation="nearest")
+        plt.show()
+
+    # Make prediction on benign samples
+    predictions = frcnn.predict(x=image)
+
+    for i in range(image.shape[0]):
+        print("\nPredictions image {}:".format(i))
+
+        # Process predictions
+        predictions_class, predictions_boxes, predictions_class = extract_predictions(predictions[i])
+
+        # Plot predictions
+        plot_image_with_boxes(img=image[i].copy(), boxes=predictions_boxes, pred_cls=predictions_class)
+
+    # Create and run attack
+    eps = 32
+    attack = FastGradientMethod(estimator=frcnn, eps=eps)
+    image_adv = attack.generate(x=image, y=None)
+
+    print("\nThe attack budget eps is {}".format(eps))
+    print("The resulting maximal difference in pixel values is {}.".format(np.amax(np.abs(image - image_adv))))
+    assert np.amax(np.abs(image - image_adv)) == eps
+
+    for i in range(image_adv.shape[0]):
+        plt.axis("off")
+        plt.title("image_adv {}".format(i))
+        plt.imshow(image_adv[i].astype(np.uint8), interpolation="nearest")
+        plt.show()
+
+    predictions_adv = frcnn.predict(x=image_adv)
+
+    for i in range(image.shape[0]):
+        print("\nPredictions adversarial image {}:".format(i))
+
+        # Process predictions
+        predictions_adv_class, predictions_adv_boxes, predictions_adv_class = extract_predictions(predictions_adv[i])
+
+        # Plot predictions
+        plot_image_with_boxes(img=image_adv[i].copy(), boxes=predictions_adv_boxes, pred_cls=predictions_adv_class)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/adversarial-robustness-toolbox/examples/get_started_fasterrcnn.py b/adversarial-robustness-toolbox/examples/get_started_fasterrcnn.py
new file mode 100644
index 0000000..3821208
--- /dev/null
+++ b/adversarial-robustness-toolbox/examples/get_started_fasterrcnn.py
@@ -0,0 +1,282 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+import os
+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+import torch
+import torchvision
+import argparse
+import json
+import yaml
+import pprint
+
+from art.estimators.object_detection import PyTorchFasterRCNN
+from art.attacks.evasion import RobustDPatch
+
+
+COCO_INSTANCE_CATEGORY_NAMES = [
+    "__background__",
+    "person",
+    "bicycle",
+    "car",
+    "motorcycle",
+    "airplane",
+    "bus",
+    "train",
+    "truck",
+    "boat",
+    "traffic light",
+    "fire hydrant",
+    "N/A",
+    "stop sign",
+    "parking meter",
+    "bench",
+    "bird",
+    "cat",
+    "dog",
+    "horse",
+    "sheep",
+    "cow",
+    "elephant",
+    "bear",
+    "zebra",
+    "giraffe",
+    "N/A",
+    "backpack",
+    "umbrella",
+    "N/A",
+    "N/A",
+    "handbag",
+    "tie",
+    "suitcase",
+    "frisbee",
+    "skis",
+    "snowboard",
+    "sports ball",
+    "kite",
+    "baseball bat",
+    "baseball glove",
+    "skateboard",
+    "surfboard",
+    "tennis racket",
+    "bottle",
+    "N/A",
+    "wine glass",
+    "cup",
+    "fork",
+    "knife",
+    "spoon",
+    "bowl",
+    "banana",
+    "apple",
+    "sandwich",
+    "orange",
+    "broccoli",
+    "carrot",
+    "hot dog",
+    "pizza",
+    "donut",
+    "cake",
+    "chair",
+    "couch",
+    "potted plant",
+    "bed",
+    "N/A",
+    "dining table",
+    "N/A",
+    "N/A",
+    "toilet",
+    "N/A",
+    "tv",
+    "laptop",
+    "mouse",
+    "remote",
+    "keyboard",
+    "cell phone",
+    "microwave",
+    "oven",
+    "toaster",
+    "sink",
+    "refrigerator",
+    "N/A",
+    "book",
+    "clock",
+    "vase",
+    "scissors",
+    "teddy bear",
+    "hair drier",
+    "toothbrush",
+]
+
+
+def extract_predictions(predictions_):
+
+    # for key, item in predictions[0].items():
+    #     print(key, item)
+
+    # Get the predicted class
+    predictions_class = [COCO_INSTANCE_CATEGORY_NAMES[i] for i in list(predictions_["labels"])]
+    print("\npredicted classes:", predictions_class)
+
+    # Get the predicted bounding boxes
+    predictions_boxes = [[(i[0], i[1]), (i[2], i[3])] for i in list(predictions_["boxes"])]
+
+    # Get the predicted prediction score
+    predictions_score = list(predictions_["scores"])
+    print("predicted score:", predictions_score)
+
+    # Get a list of index with score greater than threshold
+    threshold = 0.5
+    predictions_t = [predictions_score.index(x) for x in predictions_score if x > threshold][-1]
+
+    predictions_boxes = predictions_boxes[: predictions_t + 1]
+    predictions_class = predictions_class[: predictions_t + 1]
+
+    return predictions_class, predictions_boxes, predictions_class
+
+
+def plot_image_with_boxes(img, boxes, pred_cls):
+    text_size = 5
+    text_th = 5
+    rect_th = 6
+
+    for i in range(len(boxes)):
+        # Draw Rectangle with the coordinates
+        cv2.rectangle(img, boxes[i][0], boxes[i][1], color=(0, 255, 0), thickness=rect_th)
+
+        # Write the prediction class
+        cv2.putText(img, pred_cls[i], boxes[i][0], cv2.FONT_HERSHEY_SIMPLEX, text_size, (0, 255, 0), thickness=text_th)
+
+    plt.axis("off")
+    plt.imshow(img.astype(np.uint8), interpolation="nearest")
+    plt.show()
+
+
+def get_loss(frcnn, x, y):
+    frcnn._model.train()
+    transform = torchvision.transforms.Compose([torchvision.transforms.ToTensor()])
+    image_tensor_list = list()
+
+    for i in range(x.shape[0]):
+        if frcnn.clip_values is not None:
+            img = transform(x[i] / frcnn.clip_values[1]).to(frcnn._device)
+        else:
+            img = transform(x[i]).to(frcnn._device)
+        image_tensor_list.append(img)
+
+    loss = frcnn._model(image_tensor_list, y)
+    for loss_type in ["loss_classifier", "loss_box_reg", "loss_objectness", "loss_rpn_box_reg"]:
+        loss[loss_type] = loss[loss_type].cpu().detach().numpy().item()
+    return loss
+
+
+def append_loss_history(loss_history, output):
+    for loss in ["loss_classifier", "loss_box_reg", "loss_objectness", "loss_rpn_box_reg"]:
+        loss_history[loss] += [output[loss]]
+    return loss_history
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--config", required=False, default=None, help="Path of config yaml file")
+    cmdline = parser.parse_args()
+
+    if cmdline.config and os.path.exists(cmdline.config):
+        with open(cmdline.config, "r") as cf:
+            config = yaml.safe_load(cf.read())
+    else:
+        config = {
+            "attack_losses": ["loss_classifier", "loss_box_reg", "loss_objectness", "loss_rpn_box_reg"],
+            "cuda_visible_devices": "1",
+            "patch_shape": [450, 450, 3],
+            "patch_location": [600, 750],
+            "crop_range": [0, 0],
+            "brightness_range": [1.0, 1.0],
+            "rotation_weights": [1, 0, 0, 0],
+            "sample_size": 1,
+            "learning_rate": 1.0,
+            "max_iter": 5000,
+            "batch_size": 1,
+            "image_file": "banner-diverse-group-of-people-2.jpg",
+            "resume": False,
+            "path": "xp/",
+        }
+
+    pp = pprint.PrettyPrinter(indent=4)
+    pp.pprint(config)
+
+    if config["cuda_visible_devices"] is None:
+        device_type = "cpu"
+    else:
+        device_type = "gpu"
+        os.environ["CUDA_VISIBLE_DEVICES"] = config["cuda_visible_devices"]
+
+    frcnn = PyTorchFasterRCNN(
+        clip_values=(0, 255), channels_first=False, attack_losses=config["attack_losses"], device_type=device_type
+    )
+
+    image_1 = cv2.imread(config["image_file"])
+    image_1 = cv2.cvtColor(image_1, cv2.COLOR_BGR2RGB)  # Convert to RGB
+    image_1 = cv2.resize(image_1, dsize=(image_1.shape[1], image_1.shape[0]), interpolation=cv2.INTER_CUBIC)
+
+    image = np.stack([image_1], axis=0).astype(np.float32)
+
+    attack = RobustDPatch(
+        frcnn,
+        patch_shape=config["patch_shape"],
+        patch_location=config["patch_location"],
+        crop_range=config["crop_range"],
+        brightness_range=config["brightness_range"],
+        rotation_weights=config["rotation_weights"],
+        sample_size=config["sample_size"],
+        learning_rate=config["learning_rate"],
+        max_iter=1,
+        batch_size=config["batch_size"],
+    )
+
+    x = image.copy()
+
+    y = frcnn.predict(x=x)
+    for i, y_i in enumerate(y):
+        y[i]["boxes"] = torch.from_numpy(y_i["boxes"]).type(torch.float).to(frcnn._device)
+        y[i]["labels"] = torch.from_numpy(y_i["labels"]).type(torch.int64).to(frcnn._device)
+        y[i]["scores"] = torch.from_numpy(y_i["scores"]).to(frcnn._device)
+
+    if config["resume"]:
+        patch = np.load(os.path.join(config["path"], "patch.npy"))
+        attack._patch = patch
+
+        with open(os.path.join(config["path"], "loss_history.json"), "r") as file:
+            loss_history = json.load(file)
+    else:
+        loss_history = {"loss_classifier": [], "loss_box_reg": [], "loss_objectness": [], "loss_rpn_box_reg": []}
+
+    for i in range(config["max_iter"]):
+        print("Iteration:", i)
+        patch = attack.generate(x)
+        x_patch = attack.apply_patch(x)
+
+        loss = get_loss(frcnn, x_patch, y)
+        print(loss)
+        loss_history = append_loss_history(loss_history, loss)
+
+        with open(os.path.join(config["path"], "loss_history.json"), "w") as file:
+            file.write(json.dumps(loss_history))
+
+        np.save(os.path.join(config["path"], "patch"), attack._patch)
diff --git a/adversarial-robustness-toolbox/examples/get_started_inverse_gan.py b/adversarial-robustness-toolbox/examples/get_started_inverse_gan.py
new file mode 100644
index 0000000..56a5bde
--- /dev/null
+++ b/adversarial-robustness-toolbox/examples/get_started_inverse_gan.py
@@ -0,0 +1,152 @@
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+
+import numpy as np
+import tensorflow as tf
+
+from art.estimators.classification import TensorFlowClassifier
+from art.defences.preprocessor.inverse_gan import InverseGAN
+from art.estimators.encoding.tensorflow import TensorFlowEncoder
+from art.estimators.generation.tensorflow import TensorFlowGenerator
+from art.utils import load_mnist
+from art.attacks.evasion import FastGradientMethod
+
+from examples.inverse_gan_author_utils import EncoderReconstructor, GeneratorReconstructor
+
+logging.root.setLevel(logging.NOTSET)
+logging.basicConfig(level=logging.NOTSET)
+logger = logging.getLogger(__name__)
+
+logger.setLevel(logging.INFO)
+
+config = tf.ConfigProto()
+config.gpu_options.allow_growth = True
+sess = tf.Session(config=config)
+
+
+def create_ts1_art_mnist_classifier(min_pixel_value, max_pixel_value):
+    input_ph = tf.placeholder(tf.float32, shape=[None, 28, 28, 1])
+    labels_ph = tf.placeholder(tf.int32, shape=[None, 10])
+
+    x = tf.layers.conv2d(input_ph, filters=4, kernel_size=5, activation=tf.nn.relu)
+    x = tf.layers.max_pooling2d(x, 2, 2)
+    x = tf.layers.conv2d(x, filters=10, kernel_size=5, activation=tf.nn.relu)
+    x = tf.layers.max_pooling2d(x, 2, 2)
+    x = tf.contrib.layers.flatten(x)
+    x = tf.layers.dense(x, 100, activation=tf.nn.relu)
+    logits = tf.layers.dense(x, 10)
+
+    loss = tf.reduce_mean(tf.losses.softmax_cross_entropy(logits=logits, onehot_labels=labels_ph))
+    optimizer = tf.train.AdamOptimizer(learning_rate=0.01)
+    train = optimizer.minimize(loss)
+
+    sess.run(tf.global_variables_initializer())
+
+    classifier = TensorFlowClassifier(
+        clip_values=(min_pixel_value, max_pixel_value),
+        input_ph=input_ph,
+        output=logits,
+        labels_ph=labels_ph,
+        train=train,
+        loss=loss,
+        learning=None,
+        sess=sess,
+        preprocessing_defences=[],
+    )
+
+    return classifier
+
+
+def create_ts1_encoder_model(batch_size):
+    encoder_reconstructor = EncoderReconstructor(batch_size)
+
+    unmodified_z_tensor, images_tensor = encoder_reconstructor.generate_z_extrapolated_k()
+
+    encoder = TensorFlowEncoder(input_ph=images_tensor, model=unmodified_z_tensor, sess=sess,)
+
+    return encoder
+
+
+def create_ts1_generator_model(batch_size):
+    generator = GeneratorReconstructor(batch_size)
+
+    generator.sess.run(generator.init_opt)
+
+    generator = TensorFlowGenerator(
+        input_ph=generator.z_general_placeholder, model=generator.z_hats_recs, sess=generator.sess,
+    )
+
+    return generator
+
+
+def get_accuracy(y_pred, y):
+    accuracy = np.sum(np.argmax(y_pred, axis=1) == np.argmax(y, axis=1)) / len(y)
+    return round(accuracy * 100, 2)
+
+
+def main():
+    # SETTING UP DEFENCE GAN TRAINED MODELS
+    # * Clone the defence gan gitrepo https://github.com/yogeshbalaji/InvGAN
+    # * Follow the setup instructions and copy the following:
+    #   * data/ to adversarial-robustness-toolbox/defence_gan/data/
+    #   * output/gans/mnist to adversarial-robustness-toolbox/defence_gan/output/gans/mnist
+    #   * output/gans_inv_nottrain/mnist to adversarial-robustness-toolbox/defence_gan/output/gans_inv_nottrain/mnist
+
+    # STEP 0
+    logging.info("Loading a Dataset")
+    (_, _), (x_test_original, y_test_original), min_pixel_value, max_pixel_value = load_mnist()
+
+    # TODO remove before PR request
+    # batch_size = x_test_original.shape[0]
+    batch_size = 1000
+
+    (x_test, y_test) = (x_test_original[:batch_size], y_test_original[:batch_size])
+
+    # STEP 1
+    logging.info("Creating a TS1 Mnist Classifier")
+    classifier = create_ts1_art_mnist_classifier(min_pixel_value, max_pixel_value)
+    classifier.fit(x_test, y_test, batch_size=batch_size, nb_epochs=3)
+
+    # Code to load the original defense_gan paper mnist classifier to reproduce paper results
+    # classifier_paper = create_defense_gan_paper_mnist_art_classifier()
+
+    # STEP 2
+    logging.info("Evaluate the ART classifier on non adversarial examples")
+    predictions = classifier.predict(x_test)
+    accuracy_non_adv = get_accuracy(predictions, y_test)
+
+    # STEP 3
+    logging.info("Generate adversarial examples")
+    attack = FastGradientMethod(classifier, eps=0.2)
+    x_test_adv = attack.generate(x=x_test)
+
+    # STEP 4
+    logging.info("Evaluate the classifier on the adversarial examples")
+    predictions = classifier.predict(x_test_adv)
+    accuracy_adv = get_accuracy(predictions, y_test)
+
+    # STEP 5
+    logging.info("Create DefenceGAN")
+    encoder = create_ts1_encoder_model(batch_size)
+    generator = create_ts1_generator_model(batch_size)
+
+    inverse_gan = InverseGAN(sess=generator._sess, gan=generator, inverse_gan=encoder)
+    # defense_gan = DefenseGAN(sess=generator.sess,
+    #                          generator=generator)
+
+    logging.info("Generating Defended Samples")
+    x_test_defended = inverse_gan(x_test_adv, maxiter=1)
+
+    # STEP 6
+    logging.info("Evaluate the classifier on the defended examples")
+    predictions = classifier.predict(x_test_defended)
+    accuracy_defended = get_accuracy(predictions, y_test)
+
+    logger.info("Accuracy on non adversarial examples: {}%".format(accuracy_non_adv))
+    logger.info("Accuracy on adversarial examples: {}%".format(accuracy_adv))
+    logger.info("Accuracy on defended examples: {}%".format(accuracy_defended))
+
+
+if __name__ == "__main__":
+    main()
diff --git a/adversarial-robustness-toolbox/examples/get_started_keras.py b/adversarial-robustness-toolbox/examples/get_started_keras.py
new file mode 100644
index 0000000..2155ed3
--- /dev/null
+++ b/adversarial-robustness-toolbox/examples/get_started_keras.py
@@ -0,0 +1,57 @@
+"""
+The script demonstrates a simple example of using ART with Keras. The example train a small model on the MNIST dataset
+and creates adversarial examples using the Fast Gradient Sign Method. Here we use the ART classifier to train the model,
+it would also be possible to provide a pretrained model to the ART classifier.
+The parameters are chosen for reduced computational requirements of the script and not optimised for accuracy.
+"""
+import keras
+from keras.models import Sequential
+from keras.layers import Dense, Flatten, Conv2D, MaxPooling2D
+import numpy as np
+
+from art.attacks.evasion import FastGradientMethod
+from art.estimators.classification import KerasClassifier
+from art.utils import load_mnist
+
+# Step 1: Load the MNIST dataset
+
+(x_train, y_train), (x_test, y_test), min_pixel_value, max_pixel_value = load_mnist()
+
+# Step 2: Create the model
+
+model = Sequential()
+model.add(Conv2D(filters=4, kernel_size=(5, 5), strides=1, activation="relu", input_shape=(28, 28, 1)))
+model.add(MaxPooling2D(pool_size=(2, 2)))
+model.add(Conv2D(filters=10, kernel_size=(5, 5), strides=1, activation="relu", input_shape=(23, 23, 4)))
+model.add(MaxPooling2D(pool_size=(2, 2)))
+model.add(Flatten())
+model.add(Dense(100, activation="relu"))
+model.add(Dense(10, activation="softmax"))
+
+model.compile(
+    loss=keras.losses.categorical_crossentropy, optimizer=keras.optimizers.Adam(lr=0.01), metrics=["accuracy"]
+)
+
+# Step 3: Create the ART classifier
+
+classifier = KerasClassifier(model=model, clip_values=(min_pixel_value, max_pixel_value), use_logits=False)
+
+# Step 4: Train the ART classifier
+
+classifier.fit(x_train, y_train, batch_size=64, nb_epochs=3)
+
+# Step 5: Evaluate the ART classifier on benign test examples
+
+predictions = classifier.predict(x_test)
+accuracy = np.sum(np.argmax(predictions, axis=1) == np.argmax(y_test, axis=1)) / len(y_test)
+print("Accuracy on benign test examples: {}%".format(accuracy * 100))
+
+# Step 6: Generate adversarial test examples
+attack = FastGradientMethod(estimator=classifier, eps=0.2)
+x_test_adv = attack.generate(x=x_test)
+
+# Step 7: Evaluate the ART classifier on adversarial test examples
+
+predictions = classifier.predict(x_test_adv)
+accuracy = np.sum(np.argmax(predictions, axis=1) == np.argmax(y_test, axis=1)) / len(y_test)
+print("Accuracy on adversarial test examples: {}%".format(accuracy * 100))
diff --git a/adversarial-robustness-toolbox/examples/get_started_lightgbm.py b/adversarial-robustness-toolbox/examples/get_started_lightgbm.py
new file mode 100644
index 0000000..507d98a
--- /dev/null
+++ b/adversarial-robustness-toolbox/examples/get_started_lightgbm.py
@@ -0,0 +1,71 @@
+"""
+The script demonstrates a simple example of using ART with LightGBM. The example train a small model on the MNIST
+dataset and creates adversarial examples using the Fast Gradient Sign Method. Here we use the ART classifier to train
+the model, it would also be possible to provide a pretrained model to the ART classifier.
+The parameters are chosen for reduced computational requirements of the script and not optimised for accuracy.
+"""
+import lightgbm as lgb
+import numpy as np
+
+from art.attacks.evasion import ZooAttack
+from art.estimators.classification import LightGBMClassifier
+from art.utils import load_mnist
+
+# Step 1: Load the MNIST dataset
+
+(x_train, y_train), (x_test, y_test), min_pixel_value, max_pixel_value = load_mnist()
+
+# Step 1a: Flatten dataset
+
+x_test = x_test[0:5]
+y_test = y_test[0:5]
+
+nb_samples_train = x_train.shape[0]
+nb_samples_test = x_test.shape[0]
+x_train = x_train.reshape((nb_samples_train, 28 * 28))
+x_test = x_test.reshape((nb_samples_test, 28 * 28))
+
+# Step 2: Create the model
+
+params = {"objective": "multiclass", "metric": "multi_logloss", "num_class": 10}
+train_set = lgb.Dataset(x_train, label=np.argmax(y_train, axis=1))
+test_set = lgb.Dataset(x_test, label=np.argmax(y_test, axis=1))
+model = lgb.train(params=params, train_set=train_set, num_boost_round=100, valid_sets=[test_set])
+
+# Step 3: Create the ART classifier
+
+classifier = LightGBMClassifier(model=model, clip_values=(min_pixel_value, max_pixel_value))
+
+# Step 4: Train the ART classifier
+
+# The model has already been trained in step 2
+
+# Step 5: Evaluate the ART classifier on benign test examples
+
+predictions = classifier.predict(x_test)
+accuracy = np.sum(np.argmax(predictions, axis=1) == np.argmax(y_test, axis=1)) / len(y_test)
+print("Accuracy on benign test examples: {}%".format(accuracy * 100))
+
+# Step 6: Generate adversarial test examples
+attack = ZooAttack(
+    classifier=classifier,
+    confidence=0.5,
+    targeted=False,
+    learning_rate=1e-1,
+    max_iter=200,
+    binary_search_steps=100,
+    initial_const=1e-1,
+    abort_early=True,
+    use_resize=False,
+    use_importance=False,
+    nb_parallel=250,
+    batch_size=1,
+    variable_h=0.01,
+)
+x_test_adv = attack.generate(x=x_test)
+
+# Step 7: Evaluate the ART classifier on adversarial test examples
+
+predictions = classifier.predict(x_test_adv)
+accuracy = np.sum(np.argmax(predictions, axis=1) == np.argmax(y_test, axis=1)) / len(y_test)
+print("Accuracy on adversarial test examples: {}%".format(accuracy * 100))
diff --git a/adversarial-robustness-toolbox/examples/get_started_mxnet.py b/adversarial-robustness-toolbox/examples/get_started_mxnet.py
new file mode 100644
index 0000000..34f5892
--- /dev/null
+++ b/adversarial-robustness-toolbox/examples/get_started_mxnet.py
@@ -0,0 +1,73 @@
+"""
+The script demonstrates a simple example of using ART with MXNet. The example train a small model on the MNIST dataset
+and creates adversarial examples using the Fast Gradient Sign Method. Here we use the ART classifier to train the model,
+it would also be possible to provide a pretrained model to the ART classifier.
+The parameters are chosen for reduced computational requirements of the script and not optimised for accuracy.
+"""
+import mxnet
+from mxnet.gluon.nn import Conv2D, MaxPool2D, Flatten, Dense
+import numpy as np
+
+from art.attacks.evasion import FastGradientMethod
+from art.estimators.classification import MXClassifier
+from art.utils import load_mnist
+
+# Step 1: Load the MNIST dataset
+
+(x_train, y_train), (x_test, y_test), min_pixel_value, max_pixel_value = load_mnist()
+
+# Step 1a: Swap axes to MXNet's NCHW format
+
+x_train = np.transpose(x_train, (0, 3, 1, 2))
+x_test = np.transpose(x_test, (0, 3, 1, 2))
+
+# Step 2: Create the model
+
+model = mxnet.gluon.nn.Sequential()
+with model.name_scope():
+    model.add(Conv2D(channels=4, kernel_size=5, activation="relu"))
+    model.add(MaxPool2D(pool_size=2, strides=1))
+    model.add(Conv2D(channels=10, kernel_size=5, activation="relu"))
+    model.add(MaxPool2D(pool_size=2, strides=1))
+    model.add(Flatten())
+    model.add(Dense(100, activation="relu"))
+    model.add(Dense(10))
+    model.initialize()
+
+loss = mxnet.gluon.loss.SoftmaxCrossEntropyLoss()
+trainer = mxnet.gluon.Trainer(model.collect_params(), "adam", {"learning_rate": 0.01})
+
+# Step 3: Create the ART classifier
+
+classifier = MXClassifier(
+    model=model,
+    clip_values=(min_pixel_value, max_pixel_value),
+    loss=loss,
+    input_shape=(28, 28, 1),
+    nb_classes=10,
+    optimizer=trainer,
+    ctx=None,
+    channels_first=True,
+    preprocessing_defences=None,
+    preprocessing=(0.0, 1.0),
+)
+
+# Step 4: Train the ART classifier
+
+classifier.fit(x_train, y_train, batch_size=64, nb_epochs=3)
+
+# Step 5: Evaluate the ART classifier on benign test examples
+
+predictions = classifier.predict(x_test)
+accuracy = np.sum(np.argmax(predictions, axis=1) == np.argmax(y_test, axis=1)) / len(y_test)
+print("Accuracy on benign test examples: {}%".format(accuracy * 100))
+
+# Step 6: Generate adversarial test examples
+attack = FastGradientMethod(estimator=classifier, eps=0.2)
+x_test_adv = attack.generate(x=x_test)
+
+# Step 7: Evaluate the ART classifier on adversarial test examples
+
+predictions = classifier.predict(x_test_adv)
+accuracy = np.sum(np.argmax(predictions, axis=1) == np.argmax(y_test, axis=1)) / len(y_test)
+print("Accuracy on adversarial test examples: {}%".format(accuracy * 100))
diff --git a/adversarial-robustness-toolbox/examples/get_started_pytorch.py b/adversarial-robustness-toolbox/examples/get_started_pytorch.py
new file mode 100644
index 0000000..ef461ab
--- /dev/null
+++ b/adversarial-robustness-toolbox/examples/get_started_pytorch.py
@@ -0,0 +1,86 @@
+"""
+The script demonstrates a simple example of using ART with PyTorch. The example train a small model on the MNIST dataset
+and creates adversarial examples using the Fast Gradient Sign Method. Here we use the ART classifier to train the model,
+it would also be possible to provide a pretrained model to the ART classifier.
+The parameters are chosen for reduced computational requirements of the script and not optimised for accuracy.
+"""
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+import numpy as np
+
+from art.attacks.evasion import FastGradientMethod
+from art.estimators.classification import PyTorchClassifier
+from art.utils import load_mnist
+
+
+# Step 0: Define the neural network model, return logits instead of activation in forward method
+
+
+class Net(nn.Module):
+    def __init__(self):
+        super(Net, self).__init__()
+        self.conv_1 = nn.Conv2d(in_channels=1, out_channels=4, kernel_size=5, stride=1)
+        self.conv_2 = nn.Conv2d(in_channels=4, out_channels=10, kernel_size=5, stride=1)
+        self.fc_1 = nn.Linear(in_features=4 * 4 * 10, out_features=100)
+        self.fc_2 = nn.Linear(in_features=100, out_features=10)
+
+    def forward(self, x):
+        x = F.relu(self.conv_1(x))
+        x = F.max_pool2d(x, 2, 2)
+        x = F.relu(self.conv_2(x))
+        x = F.max_pool2d(x, 2, 2)
+        x = x.view(-1, 4 * 4 * 10)
+        x = F.relu(self.fc_1(x))
+        x = self.fc_2(x)
+        return x
+
+
+# Step 1: Load the MNIST dataset
+
+(x_train, y_train), (x_test, y_test), min_pixel_value, max_pixel_value = load_mnist()
+
+# Step 1a: Swap axes to PyTorch's NCHW format
+
+x_train = np.transpose(x_train, (0, 3, 1, 2)).astype(np.float32)
+x_test = np.transpose(x_test, (0, 3, 1, 2)).astype(np.float32)
+
+# Step 2: Create the model
+
+model = Net()
+
+# Step 2a: Define the loss function and the optimizer
+
+criterion = nn.CrossEntropyLoss()
+optimizer = optim.Adam(model.parameters(), lr=0.01)
+
+# Step 3: Create the ART classifier
+
+classifier = PyTorchClassifier(
+    model=model,
+    clip_values=(min_pixel_value, max_pixel_value),
+    loss=criterion,
+    optimizer=optimizer,
+    input_shape=(1, 28, 28),
+    nb_classes=10,
+)
+
+# Step 4: Train the ART classifier
+
+classifier.fit(x_train, y_train, batch_size=64, nb_epochs=3)
+
+# Step 5: Evaluate the ART classifier on benign test examples
+
+predictions = classifier.predict(x_test)
+accuracy = np.sum(np.argmax(predictions, axis=1) == np.argmax(y_test, axis=1)) / len(y_test)
+print("Accuracy on benign test examples: {}%".format(accuracy * 100))
+
+# Step 6: Generate adversarial test examples
+attack = FastGradientMethod(estimator=classifier, eps=0.2)
+x_test_adv = attack.generate(x=x_test)
+
+# Step 7: Evaluate the ART classifier on adversarial test examples
+
+predictions = classifier.predict(x_test_adv)
+accuracy = np.sum(np.argmax(predictions, axis=1) == np.argmax(y_test, axis=1)) / len(y_test)
+print("Accuracy on adversarial test examples: {}%".format(accuracy * 100))
diff --git a/adversarial-robustness-toolbox/examples/get_started_scikit_learn.py b/adversarial-robustness-toolbox/examples/get_started_scikit_learn.py
new file mode 100644
index 0000000..5ca87e8
--- /dev/null
+++ b/adversarial-robustness-toolbox/examples/get_started_scikit_learn.py
@@ -0,0 +1,51 @@
+"""
+The script demonstrates a simple example of using ART with scikit-learn. The example train a small model on the MNIST
+dataset and creates adversarial examples using the Fast Gradient Sign Method. Here we use the ART classifier to train
+the model, it would also be possible to provide a pretrained model to the ART classifier.
+The parameters are chosen for reduced computational requirements of the script and not optimised for accuracy.
+"""
+from sklearn.svm import SVC
+import numpy as np
+
+from art.attacks.evasion import FastGradientMethod
+from art.estimators.classification import SklearnClassifier
+from art.utils import load_mnist
+
+# Step 1: Load the MNIST dataset
+
+(x_train, y_train), (x_test, y_test), min_pixel_value, max_pixel_value = load_mnist()
+
+# Step 1a: Flatten dataset
+
+nb_samples_train = x_train.shape[0]
+nb_samples_test = x_test.shape[0]
+x_train = x_train.reshape((nb_samples_train, 28 * 28))
+x_test = x_test.reshape((nb_samples_test, 28 * 28))
+
+# Step 2: Create the model
+
+model = SVC(C=1.0, kernel="rbf")
+
+# Step 3: Create the ART classifier
+
+classifier = SklearnClassifier(model=model, clip_values=(min_pixel_value, max_pixel_value))
+
+# Step 4: Train the ART classifier
+
+classifier.fit(x_train, y_train)
+
+# Step 5: Evaluate the ART classifier on benign test examples
+
+predictions = classifier.predict(x_test)
+accuracy = np.sum(np.argmax(predictions, axis=1) == np.argmax(y_test, axis=1)) / len(y_test)
+print("Accuracy on benign test examples: {}%".format(accuracy * 100))
+
+# Step 6: Generate adversarial test examples
+attack = FastGradientMethod(estimator=classifier, eps=0.2)
+x_test_adv = attack.generate(x=x_test)
+
+# Step 7: Evaluate the ART classifier on adversarial test examples
+
+predictions = classifier.predict(x_test_adv)
+accuracy = np.sum(np.argmax(predictions, axis=1) == np.argmax(y_test, axis=1)) / len(y_test)
+print("Accuracy on adversarial test examples: {}%".format(accuracy * 100))
diff --git a/adversarial-robustness-toolbox/examples/get_started_tensorflow.py b/adversarial-robustness-toolbox/examples/get_started_tensorflow.py
new file mode 100644
index 0000000..263b716
--- /dev/null
+++ b/adversarial-robustness-toolbox/examples/get_started_tensorflow.py
@@ -0,0 +1,69 @@
+"""
+The script demonstrates a simple example of using ART with TensorFlow v1.x. The example train a small model on the MNIST
+dataset and creates adversarial examples using the Fast Gradient Sign Method. Here we use the ART classifier to train
+the model, it would also be possible to provide a pretrained model to the ART classifier.
+The parameters are chosen for reduced computational requirements of the script and not optimised for accuracy.
+"""
+import tensorflow.compat.v1 as tf
+import numpy as np
+
+from art.attacks.evasion import FastGradientMethod
+from art.estimators.classification import TensorFlowClassifier
+from art.utils import load_mnist
+
+# Step 1: Load the MNIST dataset
+
+(x_train, y_train), (x_test, y_test), min_pixel_value, max_pixel_value = load_mnist()
+
+# Step 2: Create the model
+
+input_ph = tf.placeholder(tf.float32, shape=[None, 28, 28, 1])
+labels_ph = tf.placeholder(tf.int32, shape=[None, 10])
+
+x = tf.layers.conv2d(input_ph, filters=4, kernel_size=5, activation=tf.nn.relu)
+x = tf.layers.max_pooling2d(x, 2, 2)
+x = tf.layers.conv2d(x, filters=10, kernel_size=5, activation=tf.nn.relu)
+x = tf.layers.max_pooling2d(x, 2, 2)
+x = tf.layers.flatten(x)
+x = tf.layers.dense(x, 100, activation=tf.nn.relu)
+logits = tf.layers.dense(x, 10)
+
+loss = tf.reduce_mean(tf.losses.softmax_cross_entropy(logits=logits, onehot_labels=labels_ph))
+optimizer = tf.train.AdamOptimizer(learning_rate=0.01)
+train = optimizer.minimize(loss)
+sess = tf.Session()
+sess.run(tf.global_variables_initializer())
+
+# Step 3: Create the ART classifier
+
+classifier = TensorFlowClassifier(
+    clip_values=(min_pixel_value, max_pixel_value),
+    input_ph=input_ph,
+    output=logits,
+    labels_ph=labels_ph,
+    train=train,
+    loss=loss,
+    learning=None,
+    sess=sess,
+    preprocessing_defences=[],
+)
+
+# Step 4: Train the ART classifier
+
+classifier.fit(x_train, y_train, batch_size=64, nb_epochs=3)
+
+# Step 5: Evaluate the ART classifier on benign test examples
+
+predictions = classifier.predict(x_test)
+accuracy = np.sum(np.argmax(predictions, axis=1) == np.argmax(y_test, axis=1)) / len(y_test)
+print("Accuracy on benign test examples: {}%".format(accuracy * 100))
+
+# Step 6: Generate adversarial test examples
+attack = FastGradientMethod(estimator=classifier, eps=0.2)
+x_test_adv = attack.generate(x=x_test)
+
+# Step 7: Evaluate the ART classifier on adversarial test examples
+
+predictions = classifier.predict(x_test_adv)
+accuracy = np.sum(np.argmax(predictions, axis=1) == np.argmax(y_test, axis=1)) / len(y_test)
+print("Accuracy on adversarial test examples: {}%".format(accuracy * 100))
diff --git a/adversarial-robustness-toolbox/examples/get_started_tensorflow_v2.py b/adversarial-robustness-toolbox/examples/get_started_tensorflow_v2.py
new file mode 100644
index 0000000..acf3191
--- /dev/null
+++ b/adversarial-robustness-toolbox/examples/get_started_tensorflow_v2.py
@@ -0,0 +1,98 @@
+"""
+The script demonstrates a simple example of using ART with TensorFlow v1.x. The example train a small model on the MNIST
+dataset and creates adversarial examples using the Fast Gradient Sign Method. Here we use the ART classifier to train
+the model, it would also be possible to provide a pretrained model to the ART classifier.
+The parameters are chosen for reduced computational requirements of the script and not optimised for accuracy.
+"""
+import numpy as np
+
+from art.attacks.evasion import FastGradientMethod
+from art.estimators.classification import TensorFlowV2Classifier
+from art.utils import load_mnist
+
+# Step 1: Load the MNIST dataset
+
+(x_train, y_train), (x_test, y_test), min_pixel_value, max_pixel_value = load_mnist()
+
+# Step 2: Create the model
+
+import tensorflow as tf
+from tensorflow.keras import Model
+from tensorflow.keras.layers import Dense, Flatten, Conv2D, MaxPool2D
+
+
+class TensorFlowModel(Model):
+    """
+    Standard TensorFlow model for unit testing.
+    """
+
+    def __init__(self):
+        super(TensorFlowModel, self).__init__()
+        self.conv1 = Conv2D(filters=4, kernel_size=5, activation="relu")
+        self.conv2 = Conv2D(filters=10, kernel_size=5, activation="relu")
+        self.maxpool = MaxPool2D(pool_size=(2, 2), strides=(2, 2), padding="valid", data_format=None)
+        self.flatten = Flatten()
+        self.dense1 = Dense(100, activation="relu")
+        self.logits = Dense(10, activation="linear")
+
+    def call(self, x):
+        """
+        Call function to evaluate the model.
+
+        :param x: Input to the model
+        :return: Prediction of the model
+        """
+        x = self.conv1(x)
+        x = self.maxpool(x)
+        x = self.conv2(x)
+        x = self.maxpool(x)
+        x = self.flatten(x)
+        x = self.dense1(x)
+        x = self.logits(x)
+        return x
+
+
+optimizer = tf.keras.optimizers.Adam(learning_rate=0.01)
+
+
+def train_step(model, images, labels):
+    with tf.GradientTape() as tape:
+        predictions = model(images, training=True)
+        loss = loss_object(labels, predictions)
+    gradients = tape.gradient(loss, model.trainable_variables)
+    optimizer.apply_gradients(zip(gradients, model.trainable_variables))
+
+
+model = TensorFlowModel()
+loss_object = tf.keras.losses.CategoricalCrossentropy(from_logits=True)
+
+# Step 3: Create the ART classifier
+
+classifier = TensorFlowV2Classifier(
+    model=model,
+    loss_object=loss_object,
+    train_step=train_step,
+    nb_classes=10,
+    input_shape=(28, 28, 1),
+    clip_values=(0, 1),
+)
+
+# Step 4: Train the ART classifier
+
+classifier.fit(x_train, y_train, batch_size=64, nb_epochs=3)
+
+# Step 5: Evaluate the ART classifier on benign test examples
+
+predictions = classifier.predict(x_test)
+accuracy = np.sum(np.argmax(predictions, axis=1) == np.argmax(y_test, axis=1)) / len(y_test)
+print("Accuracy on benign test examples: {}%".format(accuracy * 100))
+
+# Step 6: Generate adversarial test examples
+attack = FastGradientMethod(estimator=classifier, eps=0.2)
+x_test_adv = attack.generate(x=x_test)
+
+# Step 7: Evaluate the ART classifier on adversarial test examples
+
+predictions = classifier.predict(x_test_adv)
+accuracy = np.sum(np.argmax(predictions, axis=1) == np.argmax(y_test, axis=1)) / len(y_test)
+print("Accuracy on adversarial test examples: {}%".format(accuracy * 100))
diff --git a/adversarial-robustness-toolbox/examples/get_started_xgboost.py b/adversarial-robustness-toolbox/examples/get_started_xgboost.py
new file mode 100644
index 0000000..f6930eb
--- /dev/null
+++ b/adversarial-robustness-toolbox/examples/get_started_xgboost.py
@@ -0,0 +1,74 @@
+"""
+The script demonstrates a simple example of using ART with XGBoost. The example train a small model on the MNIST dataset
+and creates adversarial examples using the Zeroth Order Optimization attack. Here we provide a pretrained model to the
+ART classifier.
+The parameters are chosen for reduced computational requirements of the script and not optimised for accuracy.
+"""
+import xgboost as xgb
+import numpy as np
+
+from art.attacks.evasion import ZooAttack
+from art.estimators.classification import XGBoostClassifier
+from art.utils import load_mnist
+
+# Step 1: Load the MNIST dataset
+
+(x_train, y_train), (x_test, y_test), min_pixel_value, max_pixel_value = load_mnist()
+
+# Step 1a: Flatten dataset
+
+x_test = x_test[0:5]
+y_test = y_test[0:5]
+
+nb_samples_train = x_train.shape[0]
+nb_samples_test = x_test.shape[0]
+x_train = x_train.reshape((nb_samples_train, 28 * 28))
+x_test = x_test.reshape((nb_samples_test, 28 * 28))
+
+# Step 2: Create the model
+
+params = {"objective": "multi:softprob", "metric": "accuracy", "num_class": 10}
+dtrain = xgb.DMatrix(x_train, label=np.argmax(y_train, axis=1))
+dtest = xgb.DMatrix(x_test, label=np.argmax(y_test, axis=1))
+evals = [(dtest, "test"), (dtrain, "train")]
+model = xgb.train(params=params, dtrain=dtrain, num_boost_round=2, evals=evals)
+
+# Step 3: Create the ART classifier
+
+classifier = XGBoostClassifier(
+    model=model, clip_values=(min_pixel_value, max_pixel_value), nb_features=28 * 28, nb_classes=10
+)
+
+# Step 4: Train the ART classifier
+
+# The model has already been trained in step 2
+
+# Step 5: Evaluate the ART classifier on benign test examples
+
+predictions = classifier.predict(x_test)
+accuracy = np.sum(np.argmax(predictions, axis=1) == np.argmax(y_test, axis=1)) / len(y_test)
+print("Accuracy on benign test examples: {}%".format(accuracy * 100))
+
+# Step 6: Generate adversarial test examples
+attack = ZooAttack(
+    classifier=classifier,
+    confidence=0.0,
+    targeted=False,
+    learning_rate=1e-1,
+    max_iter=200,
+    binary_search_steps=10,
+    initial_const=1e-3,
+    abort_early=True,
+    use_resize=False,
+    use_importance=False,
+    nb_parallel=5,
+    batch_size=1,
+    variable_h=0.01,
+)
+x_test_adv = attack.generate(x=x_test, y=y_test)
+
+# Step 7: Evaluate the ART classifier on adversarial test examples
+
+predictions = classifier.predict(x_test_adv)
+accuracy = np.sum(np.argmax(predictions, axis=1) == np.argmax(y_test, axis=1)) / len(y_test)
+print("Accuracy on adversarial test examples: {}%".format(accuracy * 100))
diff --git a/adversarial-robustness-toolbox/examples/inverse_gan_author_utils.py b/adversarial-robustness-toolbox/examples/inverse_gan_author_utils.py
new file mode 100644
index 0000000..2738dbf
--- /dev/null
+++ b/adversarial-robustness-toolbox/examples/inverse_gan_author_utils.py
@@ -0,0 +1,1904 @@
+# Copyright 2018 The Defense-GAN Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import yaml
+import os
+
+import numpy as np
+import tensorflow as tf
+from tensorflow.contrib import slim
+
+# Code borrowed as a demo from the original [InverseGan Repo](https://github.com/yogeshbalaji/InvGAN)
+# For any questions related to this code base please contact the original authors
+
+inverse_gan_models_dir = "../defence_gan/"
+path_locations = {}
+
+path_locations["GENERATOR_INIT_PATH"] = inverse_gan_models_dir + "output/gans/mnist"
+path_locations["BPDA_ENCODER_CP_PATH"] = inverse_gan_models_dir + "output/gans_inv_notrain/mnist"
+path_locations["output_dir"] = inverse_gan_models_dir + "output"
+path_locations["data"] = inverse_gan_models_dir + "/data/"
+
+# Code to load the original defense_gan paper mnist classifier to reproduce paper results
+# Note: model_a is a cleverhans model
+# from utils.network_builder_art import model_a
+
+# def _load_defense_gan_paper_classifier():
+#
+#     config = tf.ConfigProto()
+#     config.gpu_options.allow_growth = True
+#     model_sess = tf.Session(config=config)
+#
+#     x_shape = [28,28,1]
+#     classes = 10
+#     with tf.variable_scope(tf.get_variable_scope(), reuse=tf.AUTO_REUSE):
+#         bb_model = model_a(
+#             input_shape=[None] + x_shape, nb_classes=classes,
+#         )
+#
+#     ### From blackbox_art.prep_bbox
+#     model = bb_model
+#
+#     images_tensor = tf.placeholder(tf.float32, shape=[None] + x_shape)
+#     labels_tensor = tf.placeholder(tf.float32, shape=(None, classes))
+#
+#     used_vars = model.get_params()
+#     pred_train = model.get_logits(images_tensor, dropout=True)
+#     pred_eval = model.get_logits(images_tensor)
+#
+#     path = tf.train.latest_checkpoint('./utils/resources/tmpMnistModel/mnist')
+#     saver = tf.train.Saver(var_list=used_vars)
+#     saver.restore(model_sess, path)
+#     print('[+] BB model loaded successfully ...')
+#
+#     return model, model_sess, images_tensor, labels_tensor, pred_train, pred_eval
+#
+#
+# def create_defense_gan_paper_mnist_art_classifier():
+#     model, model_sess, images_tensor, labels_tensor, pred_train, pred_eval = _load_defense_gan_paper_classifier()
+#
+#     classifier = TFClassifier(
+#         # clip_values=(min_pixel_value, max_pixel_value),
+#         input_ph=images_tensor,
+#         output=pred_eval,
+#         labels_ph=labels_tensor,
+#         # train=train,
+#         # loss=loss,
+#         # learning=None,
+#         sess=model_sess,
+#         preprocessing_defences=[]
+#     )
+#
+#     return classifier
+
+###################
+
+IMSAVE_TRANSFORM_DICT = {
+    "mnist": lambda x: x.reshape((len(x), 28, 28)),
+    "f-mnist": lambda x: x.reshape((len(x), 28, 28)),
+    "cifar-10": lambda x: (x.reshape((len(x), 32, 32, 3)) + 1) / 2.0,
+    "celeba": lambda x: (x.reshape((len(x), 64, 64, 3)) + 1) / 2.0,
+}
+
+INPUT_TRANSFORM_DICT = {
+    "mnist": lambda x: tf.cast(x, tf.float32) / 255.0,
+    "f-mnist": lambda x: tf.cast(x, tf.float32) / 255.0,
+    "cifar-10": lambda x: tf.cast(x, tf.float32) / 255.0 * 2.0 - 1.0,
+    "celeba": lambda x: tf.cast(x, tf.float32) / 255.0 * 2.0 - 1.0,
+}
+
+
+def model_a(nb_filters=64, nb_classes=10, input_shape=(None, 28, 28, 1)):
+    layers = [
+        Conv2D(nb_filters, (5, 5), (1, 1), "SAME", use_bias=True),
+        ReLU(),
+        Conv2D(nb_filters, (5, 5), (2, 2), "VALID", use_bias=True),
+        ReLU(),
+        Flatten(),
+        Dropout(0.25),
+        Linear(128),
+        ReLU(),
+        Dropout(0.5),
+        Linear(nb_classes),
+        Softmax(),
+    ]
+
+    model = DefenseMLP(layers, input_shape, feature_layer="ReLU7")
+    return model
+
+
+def generator_loss(loss_func, fake):
+    fake_loss = 0
+
+    if loss_func.__contains__("wgan"):
+        fake_loss = -tf.reduce_mean(fake)
+
+    if loss_func == "dcgan":
+        fake_loss = tf.losses.sigmoid_cross_entropy(fake, tf.ones_like(fake), reduction=Reduction.MEAN,)
+
+    if loss_func == "hingegan":
+        fake_loss = -tf.reduce_mean(fake)
+
+    return fake_loss
+
+
+def discriminator_loss(loss_func, real, fake):
+    real_loss = 0
+    fake_loss = 0
+
+    if loss_func.__contains__("wgan"):
+        real_loss = -tf.reduce_mean(real)
+        fake_loss = tf.reduce_mean(fake)
+
+    if loss_func == "dcgan":
+        real_loss = tf.losses.sigmoid_cross_entropy(tf.ones_like(real), real, reduction=Reduction.MEAN,)
+        fake_loss = tf.losses.sigmoid_cross_entropy(tf.zeros_like(fake), fake, reduction=Reduction.MEAN,)
+
+    if loss_func == "hingegan":
+        real_loss = tf.reduce_mean(relu(1 - real))
+        fake_loss = tf.reduce_mean(relu(1 + fake))
+
+    if loss_func == "ragan":
+        real_loss = tf.reduce_mean(tf.nn.softplus(-(real - tf.reduce_mean(fake))))
+        fake_loss = tf.reduce_mean(tf.nn.softplus(fake - tf.reduce_mean(real)))
+
+    loss = real_loss + fake_loss
+
+    return loss
+
+
+class DummySummaryWriter(object):
+    def write(self, *args, **arg_dicts):
+        pass
+
+    def add_summary(self, summary_str, counter):
+        pass
+
+
+def make_dir(dir_path):
+    if not os.path.exists(dir_path):
+        os.makedirs(dir_path)
+        print("[+] Created the directory: {}".format(dir_path))
+
+
+ensure_dir = make_dir
+
+
+def mnist_generator(z, is_training=True):
+    net_dim = 64
+    use_sn = False
+    with tf.variable_scope("Generator", reuse=tf.AUTO_REUSE):
+        output = linear(z, 4 * 4 * 4 * net_dim, sn=use_sn, name="linear")
+        output = batch_norm(output, is_training=is_training, name="bn_linear")
+        output = tf.nn.relu(output)
+        output = tf.reshape(output, [-1, 4, 4, 4 * net_dim])
+
+        # deconv-bn-relu
+        output = deconv2d(output, 2 * net_dim, 5, 2, sn=use_sn, name="deconv_0")
+        output = batch_norm(output, is_training=is_training, name="bn_0")
+        output = tf.nn.relu(output)
+
+        output = output[:, :7, :7, :]
+
+        output = deconv2d(output, net_dim, 5, 2, sn=use_sn, name="deconv_1")
+        output = batch_norm(output, is_training=is_training, name="bn_1")
+        output = tf.nn.relu(output)
+
+        output = deconv2d(output, 1, 5, 2, sn=use_sn, name="deconv_2")
+        output = tf.sigmoid(output)
+
+        return output
+
+
+def mnist_discriminator(x, update_collection=None, is_training=False):
+    net_dim = 64
+    use_sn = True
+    with tf.variable_scope("Discriminator", reuse=tf.AUTO_REUSE):
+        # block 1
+        x = conv2d(x, net_dim, 5, 2, sn=use_sn, update_collection=update_collection, name="conv0")
+        x = lrelu(x)
+        # block 2
+        x = conv2d(x, 2 * net_dim, 5, 2, sn=use_sn, update_collection=update_collection, name="conv1")
+        x = lrelu(x)
+        # block 3
+        x = conv2d(x, 4 * net_dim, 5, 2, sn=use_sn, update_collection=update_collection, name="conv2")
+        x = lrelu(x)
+        # output
+        x = tf.reshape(x, [-1, 4 * 4 * 4 * net_dim])
+        x = linear(x, 1, sn=use_sn, update_collection=update_collection, name="linear")
+        return tf.reshape(x, [-1])
+
+
+def mnist_encoder(x, is_training=False, use_bn=False, net_dim=64, latent_dim=128):
+    with tf.variable_scope("Encoder", reuse=tf.AUTO_REUSE):
+        x = conv2d(x, net_dim, 5, 2, name="conv0")
+        if use_bn:
+            x = batch_norm(x, is_training=is_training, name="bn0")
+        x = tf.nn.relu(x)
+
+        x = conv2d(x, 2 * net_dim, 5, 2, name="conv1")
+        if use_bn:
+            x = batch_norm(x, is_training=is_training, name="bn1")
+        x = tf.nn.relu(x)
+
+        x = conv2d(x, 4 * net_dim, 5, 2, name="conv2")
+        if use_bn:
+            x = batch_norm(x, is_training=is_training, name="bn2")
+        x = tf.nn.relu(x)
+
+        x = tf.reshape(x, [-1, 4 * 4 * 4 * net_dim])
+        x = linear(x, 2 * latent_dim, name="linear")
+
+        return x[:, :latent_dim], x[:, latent_dim:]
+
+
+GENERATOR_DICT = {"mnist": [mnist_generator, mnist_generator]}
+DISCRIMINATOR_DICT = {"mnist": [mnist_discriminator, mnist_discriminator]}
+ENCODER_DICT = {"mnist": [mnist_encoder, mnist_encoder]}
+
+
+class Dataset(object):
+    """The abstract class for handling datasets.
+
+    Attributes:
+        name: Name of the dataset.
+        data_dir: The directory where the dataset resides.
+    """
+
+    def __init__(self, name, data_dir=path_locations["data"]):
+        """The datasaet default constructor.
+
+            Args:
+                name: A string, name of the dataset.
+                data_dir (optional): The path of the datasets on disk.
+        """
+
+        self.data_dir = os.path.join(data_dir, name)
+        self.name = name
+        self.images = None
+        self.labels = None
+
+    def __len__(self):
+        """Gives the number of images in the dataset.
+
+        Returns:
+            Number of images in the dataset.
+        """
+
+        return len(self.images)
+
+    def load(self, split, lazy=True, randomize=True):
+        """ Abstract function specific to each dataset."""
+        pass
+
+
+class Mnist(Dataset):
+    """Implements the Dataset class to handle MNIST.
+
+    Attributes:
+        y_dim: The dimension of label vectors (number of classes).
+        split_data: A dictionary of
+            {
+                'train': Images of np.ndarray, Int array of labels, and int
+                array of ids.
+                'val': Images of np.ndarray, Int array of labels, and int
+                array of ids.
+                'test': Images of np.ndarray, Int array of labels, and int
+                array of ids.
+            }
+    """
+
+    def __init__(self):
+        super(Mnist, self).__init__("mnist")
+        self.y_dim = 10
+        self.split_data = {}
+
+    def load(self, split="train", lazy=True, randomize=True):
+        """Implements the load function.
+
+        Args:
+            split: Dataset split, can be [train|dev|test], default: train.
+            lazy: Not used for MNIST.
+
+        Returns:
+             Images of np.ndarray, Int array of labels, and int array of ids.
+
+        Raises:
+            ValueError: If split is not one of [train|val|test].
+        """
+
+        if split in self.split_data.keys():
+            return self.split_data[split]
+
+        data_dir = self.data_dir
+
+        fd = open(os.path.join(data_dir, "train-images-idx3-ubyte"))
+        loaded = np.fromfile(file=fd, dtype=np.uint8)
+        train_images = loaded[16:].reshape((60000, 28, 28, 1)).astype(np.float)
+
+        fd = open(os.path.join(data_dir, "train-labels-idx1-ubyte"))
+        loaded = np.fromfile(file=fd, dtype=np.uint8)
+        train_labels = loaded[8:].reshape((60000)).astype(np.float)
+
+        fd = open(os.path.join(data_dir, "t10k-images-idx3-ubyte"))
+        loaded = np.fromfile(file=fd, dtype=np.uint8)
+        test_images = loaded[16:].reshape((10000, 28, 28, 1)).astype(np.float)
+
+        fd = open(os.path.join(data_dir, "t10k-labels-idx1-ubyte"))
+        loaded = np.fromfile(file=fd, dtype=np.uint8)
+        test_labels = loaded[8:].reshape((10000)).astype(np.float)
+
+        train_labels = np.asarray(train_labels)
+        test_labels = np.asarray(test_labels)
+        if split == "train":
+            images = train_images[:50000]
+            labels = train_labels[:50000]
+        elif split == "val":
+            images = train_images[50000:60000]
+            labels = train_labels[50000:60000]
+        elif split == "test":
+            images = test_images
+            labels = test_labels
+
+        if randomize:
+            rng_state = np.random.get_state()
+            np.random.shuffle(images)
+            np.random.set_state(rng_state)
+            np.random.shuffle(labels)
+        images = np.reshape(images, [-1, 28, 28, 1])
+        self.split_data[split] = [images, labels]
+        self.images = images
+        self.labels = labels
+
+        return images, labels
+
+
+def create_generator(dataset_name, split, batch_size, randomize, attribute=None):
+    """Creates a batch generator for the dataset.
+
+    Args:
+        dataset_name: `str`. The name of the dataset.
+        split: `str`. The split of data. It can be `train`, `val`, or `test`.
+        batch_size: An integer. The batch size.
+        randomize: `bool`. Whether to randomize the order of images before
+            batching.
+        attribute (optional): For cele
+
+    Returns:
+        image_batch: A Python generator for the images.
+        label_batch: A Python generator for the labels.
+    """
+
+    if dataset_name.lower() == "mnist":
+        ds = Mnist()
+    else:
+        raise ValueError("Dataset {} is not supported.".format(dataset_name))
+
+    ds.load(split=split, randomize=randomize)
+
+    def get_gen():
+        for i in range(0, len(ds) - batch_size, batch_size):
+            image_batch, label_batch = ds.images[i : i + batch_size], ds.labels[i : i + batch_size]
+            yield image_batch, label_batch
+
+    return get_gen
+
+
+def get_generators(dataset_name, batch_size, randomize=True, attribute="gender"):
+    """Creates batch generators for datasets.
+
+    Args:
+        dataset_name: A `string`. Name of the dataset.
+        batch_size: An `integer`. The size of each batch.
+        randomize: A `boolean`.
+        attribute: A `string`. If the dataset name is `celeba`, this will
+         indicate the attribute name that labels should be returned for.
+
+    Returns:
+        Training, validation, and test dataset generators which are the
+            return values of `create_generator`.
+    """
+    splits = ["train", "val", "test"]
+    gens = []
+    for i in range(3):
+        if i > 0:
+            randomize = False
+        gens.append(create_generator(dataset_name, splits[i], batch_size, randomize, attribute=attribute))
+
+    return gens
+
+
+def get_encoder_fn(dataset_name, use_resblock=False):
+    if use_resblock:
+        return ENCODER_DICT[dataset_name][1]
+    else:
+        return ENCODER_DICT[dataset_name][0]
+
+
+def get_discriminator_fn(dataset_name, use_resblock=False, use_label=False):
+    if use_resblock:
+        return DISCRIMINATOR_DICT[dataset_name][1]
+    else:
+        return DISCRIMINATOR_DICT[dataset_name][0]
+
+
+def get_generator_fn(dataset_name, use_resblock=False):
+    if use_resblock:
+        return GENERATOR_DICT[dataset_name][1]
+    else:
+        return GENERATOR_DICT[dataset_name][0]
+
+
+def gan_from_config(batch_size, test_mode):
+    cfg = {
+        "TYPE": "inv",
+        "MODE": "hingegan",
+        "BATCH_SIZE": batch_size,
+        "USE_BN": True,
+        "USE_RESBLOCK": False,
+        "LATENT_DIM": 128,
+        "GRADIENT_PENALTY_LAMBDA": 10.0,
+        "OUTPUT_DIR": "output",
+        "NET_DIM": 64,
+        "TRAIN_ITERS": 20000,
+        "DISC_LAMBDA": 0.0,
+        "TV_LAMBDA": 0.0,
+        "ATTRIBUTE": None,
+        "TEST_BATCH_SIZE": 20,
+        "NUM_GPUS": 1,
+        "INPUT_TRANSFORM_TYPE": 0,
+        "ENCODER_LR": 0.0002,
+        "GENERATOR_LR": 0.0001,
+        "DISCRIMINATOR_LR": 0.0004,
+        "DISCRIMINATOR_REC_LR": 0.0004,
+        "USE_ENCODER_INIT": True,
+        "ENCODER_LOSS_TYPE": "margin",
+        "REC_LOSS_SCALE": 100.0,
+        "REC_DISC_LOSS_SCALE": 1.0,
+        "LATENT_REG_LOSS_SCALE": 0.5,
+        "REC_MARGIN": 0.02,
+        "ENC_DISC_TRAIN_ITER": 0,
+        "ENC_TRAIN_ITER": 1,
+        "DISC_TRAIN_ITER": 1,
+        "GENERATOR_INIT_PATH": path_locations["GENERATOR_INIT_PATH"],
+        "ENCODER_INIT_PATH": "none",
+        "ENC_DISC_LR": 1e-05,
+        "NO_TRAINING_IMAGES": True,
+        "GEN_SAMPLES_DISC_LOSS_SCALE": 1.0,
+        "LATENTS_TO_Z_LOSS_SCALE": 1.0,
+        "REC_CYCLED_LOSS_SCALE": 100.0,
+        "GEN_SAMPLES_FAKING_LOSS_SCALE": 1.0,
+        "DATASET_NAME": "mnist",
+        "ARCH_TYPE": "mnist",
+        "REC_ITERS": 200,
+        "REC_LR": 0.01,
+        "REC_RR": 1,
+        "IMAGE_DIM": [28, 28, 1],
+        "INPUR_TRANSFORM_TYPE": 1,
+        "BPDA_ENCODER_CP_PATH": path_locations["BPDA_ENCODER_CP_PATH"],
+        "BPDA_GENERATOR_INIT_PATH": path_locations["GENERATOR_INIT_PATH"],
+        "cfg_path": "experiments/cfgs/gans_inv_notrain/mnist.yml",
+    }
+
+    # from config.py
+    if cfg["TYPE"] == "v2":
+        gan = DefenseGANv2(get_generator_fn(cfg["DATASET_NAME"], cfg["USE_RESBLOCK"]), cfg=cfg, test_mode=test_mode,)
+    elif cfg["TYPE"] == "inv":
+        gan = InvertorDefenseGAN(
+            get_generator_fn(cfg["DATASET_NAME"], cfg["USE_RESBLOCK"]), cfg=cfg, test_mode=test_mode,
+        )
+
+    return gan
+
+
+class AbstractModel(object):
+    @property
+    def default_properties(self):
+        return []
+
+    def __init__(self, test_mode=False, verbose=True, cfg=None, **args):
+        """The abstract model that the other models_art extend.
+
+        Args:
+            default_properties: The attributes of an experiment, read from a
+            config file
+            test_mode: If in the test mode, computation graph for loss will
+            not be constructed, config will be saved in the output directory
+            verbose: If true, prints debug information
+            cfg: Config dictionary
+            args: The rest of the arguments which can become object attributes
+        """
+
+        # Set attributes either from FLAGS or **args.
+        self.cfg = cfg
+
+        # Active session parameter.
+        self.active_sess = None
+
+        self.tensorboard_log = True
+
+        # Object attributes.
+        default_properties = self.default_properties
+
+        default_properties.extend(["tensorboard_log", "output_dir", "num_gpus"])
+        self.initialized = False
+        self.verbose = verbose
+        self.output_dir = path_locations["output_dir"]
+
+        local_vals = locals()
+        args.update(local_vals)
+        for attr in default_properties:
+            if attr in args.keys():
+                self._set_attr(attr, args[attr])
+            else:
+                self._set_attr(attr, None)
+
+        # Runtime attributes.
+        self.saver = None
+        self.global_step = tf.train.get_or_create_global_step()
+        self.global_step_inc = tf.assign(self.global_step, tf.add(self.global_step, 1))
+
+        # Phase: 1 train 0 test.
+        self.is_training = tf.placeholder(dtype=tf.bool)
+        self.is_training_enc = tf.placeholder(dtype=tf.bool)
+        self.save_vars = {}
+        self.save_var_prefixes = []
+        self.dataset = None
+        self.test_mode = test_mode
+
+        self._set_checkpoint_dir()
+        self._build()  # lgtm [py/init-calls-subclass]
+        self._gather_variables()  # lgtm [py/init-calls-subclass]
+        if not test_mode:
+            self._save_cfg_in_ckpt()
+            self._loss()  # lgtm [py/init-calls-subclass]
+            self._optimizers()  # lgtm [py/init-calls-subclass]
+
+        # summary writer
+        self.merged_summary_op = tf.summary.merge_all()
+        self._initialize_summary_writer()
+
+    def _load_dataset(self):
+        pass
+
+    def _build(self):
+        pass
+
+    def _loss(self):
+        pass
+
+    def _optimizers(self):
+        pass
+
+    def _gather_variables(self):
+        pass
+
+    def test(self, input):
+        pass
+
+    def train(self):
+        pass
+
+    def _verbose_print(self, message):
+        """Handy verbose print function"""
+        if self.verbose:
+            print(message)
+
+    def _save_cfg_in_ckpt(self):
+        """Saves the configuration in the experiment's output directory."""
+        final_cfg = {}
+        if hasattr(self, "cfg"):
+            for k in self.cfg.keys():
+                if hasattr(self, k.lower()):
+                    if getattr(self, k.lower()) is not None:
+                        final_cfg[k] = getattr(self, k.lower())
+            if not self.test_mode:
+                with open(os.path.join(self.checkpoint_dir, "cfg.yml"), "w") as f:
+                    yaml.dump(final_cfg, f)
+
+    def _set_attr(self, attr_name, val):
+        """Sets an object attribute from FLAGS if it exists, if not it
+        prints out an error. Note that FLAGS is set from config and command
+        line inputs.
+
+
+        Args:
+            attr_name: The name of the field.
+            val: The value, if None it will set it from tf.apps.flags.FLAGS
+        """
+
+        FLAGS = tf.app.flags.FLAGS
+
+        if val is None:
+            if hasattr(FLAGS, attr_name):
+                val = getattr(FLAGS, attr_name)
+            elif hasattr(self, "cfg"):
+                if attr_name.upper() in self.cfg.keys():
+                    val = self.cfg[attr_name.upper()]
+                elif attr_name.lower() in self.cfg.keys():
+                    val = self.cfg[attr_name.lower()]
+        if val is None and self.verbose:
+            print("[-] {}.{} is not set.".format(type(self).__name__, attr_name))
+
+        setattr(self, attr_name, val)
+        if self.verbose:
+            print("[#] {}.{} is set to {}.".format(type(self).__name__, attr_name, val))
+
+    def get_learning_rate(
+        self,
+        init_lr=None,
+        decay_epoch=None,
+        decay_mult=None,
+        iters_per_epoch=None,
+        decay_iter=None,
+        global_step=None,
+        decay_lr=True,
+    ):
+        """Prepares the learning rate.
+
+        Args:
+            init_lr: The initial learning rate
+            decay_epoch: The epoch of decay
+            decay_mult: The decay factor
+            iters_per_epoch: Number of iterations per epoch
+            decay_iter: The iteration of decay [either this or decay_epoch
+            should be set]
+            global_step:
+            decay_lr:
+
+        Returns:
+            `tf.Tensor` of the learning rate.
+        """
+        if init_lr is None:
+            init_lr = self.learning_rate
+        if global_step is None:
+            global_step = self.global_step
+
+        if decay_epoch:
+            assert iters_per_epoch
+
+            # if iters_per_epoch is None:
+            #     iters_per_epoch = self.iters_per_epoch
+        else:
+            assert decay_iter
+
+        if decay_lr:
+            if decay_epoch:
+                decay_iter = decay_epoch * iters_per_epoch
+            return tf.train.exponential_decay(init_lr, global_step, decay_iter, decay_mult, staircase=True)
+        else:
+            return tf.constant(self.learning_rate)
+
+    def _set_checkpoint_dir(self):
+        """Sets the directory containing snapshots of the model."""
+
+        self.cfg_file = self.cfg["cfg_path"]
+        if "cfg.yml" in self.cfg_file:
+            ckpt_dir = os.path.dirname(self.cfg_file)
+
+        else:
+            ckpt_dir = os.path.join(
+                path_locations["output_dir"],
+                self.cfg_file.replace("experiments/cfgs/", "").replace("cfg.yml", "").replace(".yml", ""),
+            )
+            # ckpt_dir = os.path.join(self.output_dir,
+            #                         self.cfg_file.replace('experiments/cfgs/',
+            #                                               '').replace(
+            #                             'cfg.yml', '').replace(
+            #                             '.yml', ''))
+            if not self.test_mode:
+                postfix = ""
+                ignore_list = ["dataset", "cfg_file", "batch_size"]
+                if hasattr(self, "cfg"):
+                    if self.cfg is not None:
+                        for prop in self.default_properties:
+                            if prop in ignore_list:
+                                continue
+
+                            if prop.upper() in self.cfg.keys():
+                                self_val = getattr(self, prop)
+                                if self_val is not None:
+                                    if getattr(self, prop) != self.cfg[prop.upper()]:
+                                        postfix += "-{}={}".format(prop, self_val).replace(".", "_")
+
+                ckpt_dir += postfix
+            ensure_dir(ckpt_dir)
+
+        self.checkpoint_dir = ckpt_dir
+        self.debug_dir = self.checkpoint_dir.replace("output", "debug")
+        self.encoder_checkpoint_dir = os.path.join(self.checkpoint_dir, "encoding")
+        self.encoder_debug_dir = os.path.join(self.debug_dir, "encoding")
+        ensure_dir(self.debug_dir)
+        ensure_dir(self.encoder_checkpoint_dir)
+        ensure_dir(self.encoder_debug_dir)
+
+    def _initialize_summary_writer(self):
+        # Setup the summary writer.
+        if not self.tensorboard_log:
+            self.summary_writer = DummySummaryWriter()
+        else:
+            sum_dir = os.path.join(self.checkpoint_dir, "tb_logs")
+            if not os.path.exists(sum_dir):
+                os.makedirs(sum_dir)
+
+            self.summary_writer = tf.summary.FileWriter(sum_dir, graph=tf.get_default_graph())
+
+    def _initialize_saver(self, prefixes=None, force=False, max_to_keep=5):
+        """Initializes the saver object.
+
+        Args:
+            prefixes: The prefixes that the saver should take care of.
+            force (optional): Even if saver is set, reconstruct the saver
+                object.
+            max_to_keep (optional):
+        """
+        if self.saver is not None and not force:
+            return
+        else:
+            if prefixes is None or not (type(prefixes) != list or type(prefixes) != tuple):
+                raise ValueError("Prefix of variables that needs saving are not defined")
+
+            prefixes_str = ""
+            for pref in prefixes:
+                prefixes_str = prefixes_str + pref + " "
+
+            print("[#] Initializing it with variable prefixes: {}".format(prefixes_str))
+            saved_vars = []
+            for pref in prefixes:
+                saved_vars.extend(slim.get_variables(pref))
+
+            self.saver = tf.train.Saver(saved_vars, max_to_keep=max_to_keep)
+
+    def set_session(self, sess):
+        """"""
+        if self.active_sess is None:
+            self.active_sess = sess
+        else:
+            raise EnvironmentError("Session is already set.")
+
+    @property
+    def sess(self):
+        if self.active_sess is None:
+            config = tf.ConfigProto()
+            config.gpu_options.allow_growth = True
+            self.active_sess = tf.Session(config=config)
+
+        return self.active_sess
+
+    def close_session(self):
+        if self.active_sess:
+            self.active_sess.close()
+
+    def load(self, checkpoint_dir=None, prefixes=None, saver=None):
+        """Loads the saved weights to the model from the checkpoint directory
+
+        Args:
+            checkpoint_dir: The path to saved models_art
+        """
+        if prefixes is None:
+            prefixes = self.save_var_prefixes
+        if self.saver is None:
+            print("[!] Saver is not initialized")
+            self._initialize_saver(prefixes=prefixes)
+
+        if saver is None:
+            saver = self.saver
+
+        if checkpoint_dir is None:
+            checkpoint_dir = self.checkpoint_dir
+
+        if not os.path.isdir(checkpoint_dir):
+            try:
+                saver.restore(self.sess, checkpoint_dir)
+            except Exception as e:
+                print(" [!] Failed to find a checkpoint at {}".format(checkpoint_dir))
+        else:
+            print(" [-] Reading checkpoints... {} ".format(checkpoint_dir))
+
+            ckpt = tf.train.get_checkpoint_state(checkpoint_dir)
+            if ckpt and ckpt.model_checkpoint_path:
+                ckpt_name = os.path.basename(ckpt.model_checkpoint_path)
+                saver.restore(self.sess, os.path.join(checkpoint_dir, ckpt_name))
+            else:
+                print(" [!] Failed to find a checkpoint " "within directory {}".format(checkpoint_dir))
+                return False
+
+        print(" [*] Checkpoint is read successfully from {}".format(checkpoint_dir))
+
+        return True
+
+    def add_save_vars(self, prefixes):
+        """Prepares the list of variables that should be saved based on
+        their name prefix.
+
+        Args:
+            prefixes: Variable name prefixes to find and save.
+        """
+
+        for pre in prefixes:
+            pre_vars = slim.get_variables(pre)
+            self.save_vars.update(pre_vars)
+
+        var_list = ""
+        for var in self.save_vars:
+            var_list = var_list + var.name + " "
+
+        print("Saving these variables: {}".format(var_list))
+
+    def input_pl_transform(self):
+        self.real_data = self.input_transform(self.real_data_pl)
+        self.real_data_test = self.input_transform(self.real_data_test_pl)
+
+    def initialize_uninitialized(self,):
+        """Only initializes the variables of a TensorFlow session that were not
+        already initialized.
+        """
+        # List all global variables.
+        sess = self.sess
+        global_vars = tf.global_variables()
+
+        # Find initialized status for all variables.
+        is_var_init = [tf.is_variable_initialized(var) for var in global_vars]
+        is_initialized = sess.run(is_var_init)
+
+        # List all variables that were not previously initialized.
+        not_initialized_vars = [var for (var, init) in zip(global_vars, is_initialized) if not init]
+        for v in not_initialized_vars:
+            print("[!] not init: {}".format(v.name))
+        # Initialize all uninitialized variables found, if any.
+        if len(not_initialized_vars):
+            sess.run(tf.variables_initializer(not_initialized_vars))
+
+    def save(self, prefixes=None, global_step=None, checkpoint_dir=None):
+        if global_step is None:
+            global_step = self.global_step
+        if checkpoint_dir is None:
+            checkpoint_dir = self._set_checkpoint_dir
+
+        ensure_dir(checkpoint_dir)
+        self._initialize_saver(prefixes)
+        self.saver.save(self.sess, os.path.join(checkpoint_dir, self.model_save_name), global_step=global_step)
+        print("Saved at iter {} to {}".format(self.sess.run(global_step), checkpoint_dir))
+
+    def initialize(self, dir):
+        self.load(dir)
+        self.initialized = True
+
+    def input_transform(self, images):
+        return INPUT_TRANSFORM_DICT[self.dataset_name](images)
+
+    def imsave_transform(self, images):
+        return IMSAVE_TRANSFORM_DICT[self.dataset_name](images)
+
+
+class DefenseGANv2(AbstractModel):
+    @property
+    def default_properties(self):
+        return [
+            "dataset_name",
+            "batch_size",
+            "use_bn",
+            "use_resblock",
+            "test_batch_size",
+            "train_iters",
+            "latent_dim",
+            "net_dim",
+            "input_transform_type",
+            "debug",
+            "rec_iters",
+            "image_dim",
+            "rec_rr",
+            "rec_lr",
+            "test_again",
+            "loss_type",
+            "attribute",
+            "encoder_loss_type",
+            "encoder_lr",
+            "discriminator_lr",
+            "generator_lr",
+            "discriminator_rec_lr",
+            "rec_margin",
+            "rec_loss_scale",
+            "rec_disc_loss_scale",
+            "latent_reg_loss_scale",
+            "generator_init_path",
+            "encoder_init_path",
+            "enc_train_iter",
+            "disc_train_iter",
+            "enc_disc_lr",
+        ]
+
+    def __init__(
+        self,
+        generator_fn,
+        encoder_fn=None,
+        classifier_fn=None,
+        discriminator_fn=None,
+        generator_var_prefix="Generator",
+        classifier_var_prefix="Classifier",
+        discriminator_var_prefix="Discriminator",
+        encoder_var_prefix="Encoder",
+        cfg=None,
+        test_mode=False,
+        verbose=True,
+        **args
+    ):
+        self.dataset_name = None  # Name of the datsaet.
+        self.batch_size = 32  # Batch size for training the GAN.
+        self.use_bn = True  # Use batchnorm in the discriminator and generator.
+        self.use_resblock = False  # Use resblocks in DefenseGAN.
+        self.test_batch_size = 20  # Batch size for test time.
+        self.mode = "wgan-gp"  # The mode of training the GAN (default: gp-wgan).
+        self.gradient_penalty_lambda = 10.0  # Gradient penalty scale.
+        self.train_iters = 200000  # Number of training iterations.
+        self.critic_iters = 5  # Critic iterations per training step.
+        self.latent_dim = None  # The dimension of the latent vectors.
+        self.net_dim = None  # The complexity of network per layer.
+        self.input_transform_type = 0  # The normalization used for the inputs.
+        self.debug = False  # Debug info will be printed.
+        self.rec_iters = 200  # Number of reconstruction iterations.
+        self.image_dim = [None, None, None]  # [height, width, number of channels] of the output image.
+        self.rec_rr = 10  # Number of random restarts for the reconstruction
+        self.encoder_loss_type = "margin"  # Loss used for encoding
+
+        self.rec_lr = 10.0  # The reconstruction learning rate.
+        self.test_again = False  # If true, do not use the cached info for test phase.
+        self.attribute = "gender"
+
+        self.rec_loss_scale = 100.0
+        self.rec_disc_loss_scale = 1.0
+        self.latent_reg_loss_scale = 1.0
+        self.rec_margin = 0.05
+        self.generator_init_path = None
+        self.encoder_init_path = None
+        self.enc_disc_train_iter = 0
+        self.enc_train_iter = 1
+        self.disc_train_iter = 1
+
+        self.encoder_lr = 2e-4
+        self.enc_disc_lr = 1e-5
+        self.discriminator_rec_lr = 4e-4
+
+        # Should be implemented in the child classes.
+        self.discriminator_fn = discriminator_fn
+        self.generator_fn = generator_fn
+        self.classifier_fn = classifier_fn
+        self.encoder_fn = encoder_fn
+        self.train_data_gen = None
+        self.generator_var_prefix = generator_var_prefix
+        self.classifier_var_prefix = classifier_var_prefix
+        self.discriminator_var_prefix = discriminator_var_prefix
+        self.encoder_var_prefix = encoder_var_prefix
+
+        self.gen_samples_faking_loss_scale = 1.0
+        self.latents_to_z_loss_scale = 1.0
+        self.rec_cycled_loss_scale = 1.0
+        self.gen_samples_disc_loss_scale = 1.0
+        self.no_training_images = False
+
+        self.model_save_name = "GAN.model"
+
+        # calls _build() and _loss()
+        # generator_vars and encoder_vars are created
+        super(DefenseGANv2, self).__init__(test_mode=test_mode, verbose=verbose, cfg=cfg, **args)
+        self.save_var_prefixes = ["Encoder", "Discriminator"]
+        self._load_dataset()
+
+        # create a method that only loads generator and encoding
+        g_saver = tf.train.Saver(var_list=self.generator_vars)
+        self.load_generator = lambda ckpt_path=None: self.load(checkpoint_dir=ckpt_path, saver=g_saver)
+
+        d_saver = tf.train.Saver(var_list=self.discriminator_vars)
+        self.load_discriminator = lambda ckpt_path=None: self.load(checkpoint_dir=ckpt_path, saver=d_saver)
+
+        e_saver = tf.train.Saver(var_list=self.encoder_vars)
+        self.load_encoder = lambda ckpt_path=None: self.load(checkpoint_dir=ckpt_path, saver=e_saver)
+
+    def _load_dataset(self):
+        """Loads the dataset."""
+        self.train_data_gen, self.dev_gen, _ = get_generators(self.dataset_name, self.batch_size,)
+        self.train_gen_test, self.dev_gen_test, self.test_gen_test = get_generators(
+            self.dataset_name, self.test_batch_size, randomize=False,
+        )
+
+    def _build(self):
+        """Builds the computation graph."""
+
+        assert (self.batch_size % self.rec_rr) == 0, "Batch size should be divisable by random restart"
+
+        self.discriminator_training = tf.placeholder(tf.bool)
+        self.encoder_training = tf.placeholder(tf.bool)
+
+        if self.discriminator_fn is None:
+            self.discriminator_fn = get_discriminator_fn(self.dataset_name, use_resblock=True,)
+
+        if self.encoder_fn is None:
+            self.encoder_fn = get_encoder_fn(self.dataset_name, use_resblock=True,)
+
+        self.test_batch_size = self.batch_size
+
+        # Defining batch_size in input placeholders is inevitable at least
+        # for now, because the z vectors are TensorFlow variables.
+        self.real_data_pl = tf.placeholder(tf.float32, shape=[self.batch_size] + self.image_dim,)
+        self.real_data_test_pl = tf.placeholder(tf.float32, shape=[self.test_batch_size] + self.image_dim,)
+
+        self.random_z = tf.constant(np.random.randn(self.batch_size, self.latent_dim), tf.float32,)
+
+        self.input_pl_transform()
+
+        self.encoder_latent_before = self.encoder_fn(self.real_data, is_training=self.encoder_training)[0]
+        self.encoder_latent = self.encoder_latent_before
+
+        tf.summary.histogram("Encoder latents", self.encoder_latent)
+
+        self.enc_reconstruction = self.generator_fn(self.encoder_latent, is_training=False)
+        tf.summary.image("Real data", self.real_data, max_outputs=20)
+        tf.summary.image("Encoder reconstruction", self.enc_reconstruction, max_outputs=20)
+
+        self.x_hat_sample = self.generator_fn(self.random_z, is_training=False)
+
+        if self.discriminator_fn is not None:
+            self.disc_real = self.discriminator_fn(self.real_data, is_training=self.discriminator_training,)
+            tf.summary.histogram("disc/real", tf.nn.sigmoid(self.disc_real))
+
+            self.disc_enc_rec = self.discriminator_fn(self.enc_reconstruction, is_training=self.discriminator_training,)
+            tf.summary.histogram("disc/enc_rec", tf.nn.sigmoid(self.disc_enc_rec))
+
+    def _loss(self):
+        """Builds the loss part of the graph.."""
+        # Loss terms
+
+        raw_reconstruction_error = slim.flatten(
+            tf.reduce_mean(tf.abs(self.enc_reconstruction - self.real_data), axis=1,)
+        )
+        tf.summary.histogram("raw reconstruction error", raw_reconstruction_error)
+
+        img_rec_loss = self.rec_loss_scale * tf.reduce_mean(tf.nn.relu(raw_reconstruction_error - self.rec_margin))
+        tf.summary.scalar("losses/margin_rec", img_rec_loss)
+
+        self.enc_rec_faking_loss = generator_loss("dcgan", self.disc_enc_rec,)
+
+        self.enc_rec_disc_loss = self.rec_disc_loss_scale * discriminator_loss(
+            "dcgan", self.disc_real, self.disc_enc_rec,
+        )
+
+        tf.summary.scalar("losses/enc_recon_faking_disc", self.enc_rec_faking_loss)
+
+        self.latent_reg_loss = self.latent_reg_loss_scale * tf.reduce_mean(tf.square(self.encoder_latent_before))
+        tf.summary.scalar("losses/latent_reg", self.latent_reg_loss)
+
+        self.enc_cost = img_rec_loss + self.rec_disc_loss_scale * self.enc_rec_faking_loss + self.latent_reg_loss
+        self.discriminator_loss = self.enc_rec_disc_loss
+        tf.summary.scalar("losses/encoder_loss", self.enc_cost)
+        tf.summary.scalar("losses/discriminator_loss", self.enc_rec_disc_loss)
+
+    def _gather_variables(self):
+        self.generator_vars = slim.get_variables(self.generator_var_prefix)
+        self.encoder_vars = slim.get_variables(self.encoder_var_prefix)
+
+        self.discriminator_vars = slim.get_variables(self.discriminator_var_prefix) if self.discriminator_fn else []
+
+    def _optimizers(self):
+        # define optimizer op
+        self.disc_train_op = tf.train.AdamOptimizer(learning_rate=self.discriminator_rec_lr, beta1=0.5).minimize(
+            self.discriminator_loss, var_list=self.discriminator_vars
+        )
+
+        self.encoder_recon_train_op = tf.train.AdamOptimizer(learning_rate=self.encoder_lr, beta1=0.5,).minimize(
+            self.enc_cost, var_list=self.encoder_vars
+        )
+        #
+        self.encoder_disc_fooling_train_op = tf.train.AdamOptimizer(
+            learning_rate=self.enc_disc_lr, beta1=0.5,
+        ).minimize(self.enc_rec_faking_loss + self.latent_reg_loss, var_list=self.encoder_vars,)
+
+    def _inf_train_gen(self):
+        """A generator function for input training data."""
+        while True:
+            for images, targets in self.train_data_gen():
+                yield images
+
+    def train(self, gan_init_path=None):
+        sess = self.sess
+        self.initialize_uninitialized()
+        self.save_var_prefixes = ["Encoder", "Discriminator"]
+
+        data_generator = self._inf_train_gen()
+
+        could_load = self.load_generator(self.generator_init_path)
+
+        if could_load:
+            print("[*] Generator loaded.")
+        else:
+            raise ValueError("Generator could not be loaded")
+
+        cur_iter = self.sess.run(self.global_step)
+        max_train_iters = self.train_iters
+        step_inc = self.global_step_inc
+        global_step = self.global_step
+        ckpt_dir = self.checkpoint_dir
+
+        # sanity check for the generator
+        samples = self.sess.run(
+            self.x_hat_sample, feed_dict={self.encoder_training: False, self.discriminator_training: False},
+        )
+        self.save_image(samples, "sanity_check.png")
+
+        for iteration in range(cur_iter, max_train_iters):
+            _data = data_generator.next()
+
+            # Discriminator update
+            for _ in range(self.disc_train_iter):
+                _ = sess.run(
+                    [self.disc_train_op],
+                    feed_dict={
+                        self.real_data_pl: _data,
+                        self.encoder_training: False,
+                        self.discriminator_training: True,
+                    },
+                )
+
+            # Encoder update
+            for _ in range(self.enc_train_iter):
+                loss, _ = sess.run(
+                    [self.enc_cost, self.encoder_recon_train_op],
+                    feed_dict={
+                        self.real_data_pl: _data,
+                        self.encoder_training: True,
+                        self.discriminator_training: False,
+                    },
+                )
+
+            for _ in range(self.enc_disc_train_iter):
+                # Encoder trying to fool the discriminator
+                sess.run(
+                    self.encoder_disc_fooling_train_op,
+                    feed_dict={
+                        self.real_data_pl: _data,
+                        self.encoder_training: True,
+                        self.discriminator_training: False,
+                    },
+                )
+
+            self.sess.run(step_inc)
+
+            if iteration % 100 == 1:
+                summaries = sess.run(
+                    self.merged_summary_op,
+                    feed_dict={
+                        self.real_data_pl: _data,
+                        self.encoder_training: False,
+                        self.discriminator_training: False,
+                    },
+                )
+                self.summary_writer.add_summary(
+                    summaries, global_step=iteration,
+                )
+
+            if iteration % 1000 == 999:
+                x_hat, x = sess.run(
+                    [self.enc_reconstruction, self.real_data],
+                    feed_dict={
+                        self.real_data_pl: _data,
+                        self.encoder_training: False,
+                        self.discriminator_training: False,
+                    },
+                )
+                self.save_image(x_hat, "x_hat_{}.png".format(iteration))
+                self.save_image(x, "x_{}.png".format(iteration))
+                self.save(checkpoint_dir=ckpt_dir, global_step=global_step)
+
+        self.save(checkpoint_dir=ckpt_dir, global_step=global_step)
+
+    def autoencode(self, images, batch_size=None):
+        """ Creates op for autoencoding images.
+        reconstruct method without GD
+        """
+        images.set_shape((batch_size, images.shape[1], images.shape[2], images.shape[3]))
+        z_hat = self.encoder_fn(images, is_training=False)[0]
+        recons = self.generator_fn(z_hat, is_training=False)
+        return recons
+
+    # def test_batch(self):
+    #     """Tests the image batch generator."""
+    #     output_dir = os.path.join(self.debug_dir, 'test_batch')
+    #     ensure_dir(output_dir)
+    #
+    #     img, target = self.train_data_gen().next()
+    #     img = img.reshape([self.batch_size] + self.image_dim)
+    #     save_images_files(img / 255.0, output_dir=output_dir, labels=target)
+
+    def load_model(self):
+        could_load_generator = self.load_generator(ckpt_path=self.generator_init_path)
+
+        if self.encoder_init_path == "none":
+            print("[*] Loading default encoding")
+            could_load_encoder = self.load_encoder(ckpt_path=self.checkpoint_dir)
+
+        else:
+            print("[*] Loading encoding from {}".format(self.encoder_init_path))
+            could_load_encoder = self.load_encoder(ckpt_path=self.encoder_init_path)
+        assert could_load_generator and could_load_encoder
+        self.initialized = True
+
+
+def discriminator_loss(loss_func, real, fake):
+    real_loss = 0
+    fake_loss = 0
+
+    if loss_func.__contains__("wgan"):
+        real_loss = -tf.reduce_mean(real)
+        fake_loss = tf.reduce_mean(fake)
+
+    if loss_func == "dcgan":
+        real_loss = tf.losses.sigmoid_cross_entropy(tf.ones_like(real), real, reduction=Reduction.MEAN,)
+        fake_loss = tf.losses.sigmoid_cross_entropy(tf.zeros_like(fake), fake, reduction=Reduction.MEAN,)
+
+    if loss_func == "hingegan":
+        real_loss = tf.reduce_mean(relu(1 - real))
+        fake_loss = tf.reduce_mean(relu(1 + fake))
+
+    if loss_func == "ragan":
+        real_loss = tf.reduce_mean(tf.nn.softplus(-(real - tf.reduce_mean(fake))))
+        fake_loss = tf.reduce_mean(tf.nn.softplus(fake - tf.reduce_mean(real)))
+
+    loss = real_loss + fake_loss
+
+    return loss
+
+
+class InvertorDefenseGAN(DefenseGANv2):
+    @property
+    def default_properties(self):
+        super_properties = super(InvertorDefenseGAN, self).default_properties
+        super_properties.extend(
+            [
+                "gen_samples_disc_loss_scale",
+                "latents_to_z_loss_scale",
+                "rec_cycled_loss_scale",
+                "no_training_images",
+                "gen_samples_faking_loss_scale",
+            ]
+        )
+
+        return super_properties
+
+    def _build(self):
+        # Build v2
+        super(InvertorDefenseGAN, self)._build()
+
+        # Sample random z
+        self.z_samples = tf.random_normal([self.batch_size // 2, self.latent_dim])
+
+        # Generate the zs
+        self.generator_samples = self.generator_fn(self.z_samples, is_training=False,)
+        tf.summary.image(
+            "generator_samples", self.generator_samples, max_outputs=10,
+        )
+
+        # Pass the generated samples through the encoding
+        self.generator_samples_latents = self.encoder_fn(self.generator_samples, is_training=self.encoder_training,)[0]
+
+        # Cycle the generated images through the encoding
+        self.cycled_back_generator = self.generator_fn(self.generator_samples_latents, is_training=False,)
+        tf.summary.image(
+            "cycled_generator_samples", self.cycled_back_generator, max_outputs=10,
+        )
+
+        # Pass all the fake examples through the discriminator
+        with tf.variable_scope("Discriminator_gen"):
+            self.gen_cycled_disc = self.discriminator_fn(
+                self.cycled_back_generator, is_training=self.discriminator_training,
+            )
+            self.gen_samples_disc = self.discriminator_fn(
+                self.generator_samples, is_training=self.discriminator_training,
+            )
+
+        tf.summary.histogram(
+            "sample disc", tf.nn.sigmoid(self.gen_samples_disc),
+        )
+        tf.summary.histogram(
+            "cycled disc", tf.nn.sigmoid(self.gen_cycled_disc),
+        )
+
+    def _loss(self):
+        # All v2 losses
+        if self.no_training_images:
+            self.enc_cost = 0
+            self.discriminator_loss = 0
+        else:
+            super(InvertorDefenseGAN, self)._loss()
+
+        # Fake samples should fool the discriminator
+        self.gen_samples_faking_loss = self.gen_samples_faking_loss_scale * generator_loss(
+            "dcgan", self.gen_cycled_disc,
+        )
+
+        # The latents of the encoded samples should be close to the zs
+        self.latents_to_sample_zs = self.latents_to_z_loss_scale * tf.losses.mean_squared_error(
+            self.z_samples, self.generator_samples_latents, reduction=Reduction.MEAN,
+        )
+        tf.summary.scalar(
+            "losses/latents to zs loss", self.latents_to_sample_zs,
+        )
+
+        # The cycled back reconstructions
+        raw_cycled_reconstruction_error = slim.flatten(
+            tf.reduce_mean(tf.abs(self.cycled_back_generator - self.generator_samples), axis=1,)
+        )
+        tf.summary.histogram(
+            "raw cycled reconstruction error", raw_cycled_reconstruction_error,
+        )
+
+        self.cycled_reconstruction_loss = self.rec_cycled_loss_scale * tf.reduce_mean(
+            tf.nn.relu(raw_cycled_reconstruction_error - self.rec_margin)
+        )
+        tf.summary.scalar("losses/cycled_margin_rec", self.cycled_reconstruction_loss)
+
+        self.enc_cost += self.cycled_reconstruction_loss + self.gen_samples_faking_loss + self.latents_to_sample_zs
+
+        # Discriminator loss
+        self.gen_samples_disc_loss = self.gen_samples_disc_loss_scale * discriminator_loss(
+            "dcgan", self.gen_samples_disc, self.gen_cycled_disc,
+        )
+        tf.summary.scalar(
+            "losses/gen_samples_disc_loss", self.gen_samples_disc_loss,
+        )
+        tf.summary.scalar(
+            "losses/gen_samples_faking_loss", self.gen_samples_faking_loss,
+        )
+        self.discriminator_loss += self.gen_samples_disc_loss
+
+    def _optimizers(self):
+        # define optimizer op
+        # variables for saving and loading (e.g. batchnorm moving average)
+
+        self.disc_train_op = tf.train.AdamOptimizer(learning_rate=self.discriminator_rec_lr, beta1=0.5).minimize(
+            self.discriminator_loss, var_list=self.discriminator_vars
+        )
+
+        self.encoder_recon_train_op = tf.train.AdamOptimizer(learning_rate=self.encoder_lr, beta1=0.5,).minimize(
+            self.enc_cost, var_list=self.encoder_vars
+        )
+
+        if not self.no_training_images:
+            self.encoder_disc_fooling_train_op = tf.train.AdamOptimizer(
+                learning_rate=self.enc_disc_lr, beta1=0.5,
+            ).minimize(self.enc_rec_faking_loss + self.latent_reg_loss, var_list=self.encoder_vars,)
+
+    def _gather_variables(self):
+        self.generator_vars = slim.get_variables(self.generator_var_prefix)
+        self.encoder_vars = slim.get_variables(self.encoder_var_prefix)
+
+        if self.no_training_images:
+            self.discriminator_vars = slim.get_variables("Discriminator_gen")
+        else:
+            self.discriminator_vars = slim.get_variables(self.discriminator_var_prefix) if self.discriminator_fn else []
+
+
+class EncoderReconstructor(object):
+    def __init__(self, batch_size):
+
+        gan = gan_from_config(batch_size, True)
+
+        gan.load_model()
+        self.batch_size = gan.batch_size
+        self.latent_dim = gan.latent_dim
+
+        image_dim = gan.image_dim
+        rec_rr = gan.rec_rr  # # Number of random restarts for the reconstruction
+
+        self.sess = gan.sess
+        self.rec_iters = gan.rec_iters
+
+        x_shape = [self.batch_size] + image_dim
+        timg = tf.Variable(np.zeros(x_shape), dtype=tf.float32, name="timg")
+
+        timg_tiled_rr = tf.reshape(timg, [x_shape[0], np.prod(x_shape[1:])])
+        timg_tiled_rr = tf.tile(timg_tiled_rr, [1, rec_rr])
+        timg_tiled_rr = tf.reshape(timg_tiled_rr, [x_shape[0] * rec_rr] + x_shape[1:])
+
+        if isinstance(gan, InvertorDefenseGAN):
+            # DefenseGAN++
+            self.z_init = gan.encoder_fn(timg_tiled_rr, is_training=False)[0]
+        else:
+            # DefenseGAN
+            self.z_init = tf.Variable(
+                np.random.normal(size=(self.batch_size * rec_rr, self.latent_dim)),
+                collections=[tf.GraphKeys.LOCAL_VARIABLES],
+                trainable=False,
+                dtype=tf.float32,
+                name="z_init_rec",
+            )
+
+        modifier_k = tf.Variable(np.zeros([self.batch_size, self.latent_dim]), dtype=tf.float32, name="modifier_k")
+
+        z_init = tf.Variable(np.zeros([self.batch_size, self.latent_dim]), dtype=tf.float32, name="z_init")
+        z_init_reshaped = z_init
+
+        self.z_hats_recs = gan.generator_fn(z_init_reshaped + modifier_k, is_training=False)
+
+        start_vars = set(x.name for x in tf.global_variables())
+
+        end_vars = tf.global_variables()
+        new_vars = [x for x in end_vars if x.name not in start_vars]
+
+        # TODO I don't think we need the assign and timg variables anymore
+        self.assign_timg = tf.placeholder(tf.float32, x_shape, name="assign_timg")
+        self.z_init_input_placeholder = tf.placeholder(
+            tf.float32, shape=[self.batch_size, self.latent_dim], name="z_init_input_placeholder"
+        )
+        self.modifier_placeholder = tf.placeholder(
+            tf.float32, shape=[self.batch_size, self.latent_dim], name="z_modifier_placeholder"
+        )
+
+        self.setup = tf.assign(timg, self.assign_timg)
+        self.setup_z_init = tf.assign(z_init, self.z_init_input_placeholder)
+        self.setup_modifier_k = tf.assign(modifier_k, self.modifier_placeholder)
+
+        # original self.init_opt = tf.variables_initializer(var_list=[modifier] + new_vars)
+        self.init_opt = tf.variables_initializer(var_list=[] + new_vars)
+
+        print("Reconstruction module initialzied...\n")
+
+    def generate_z_extrapolated_k(self):
+        # config = tf.ConfigProto()
+        # config.gpu_options.allow_growth = True
+
+        x_shape = [28, 28, 1]
+        classes = 10  # lgtm [py/unused-local-variable]
+
+        # TODO use as TS1Encoder Input
+        images_tensor = tf.placeholder(tf.float32, shape=[None] + x_shape)
+
+        images = images_tensor
+        batch_size = self.batch_size
+        latent_dim = self.latent_dim
+
+        x_shape = images.get_shape().as_list()
+        x_shape[0] = batch_size
+
+        def recon_wrap(im, b):
+            unmodified_z = self.generate_z_batch(im, b)
+            return np.array(unmodified_z, dtype=np.float32)
+
+        unmodified_z = tf.py_func(recon_wrap, [images, batch_size], [tf.float32])
+
+        unmodified_z_reshaped = tf.reshape(unmodified_z, [batch_size, latent_dim])
+
+        unmodified_z_tensor = tf.stop_gradient(unmodified_z_reshaped)
+        return unmodified_z_tensor, images_tensor
+
+    def generate_z_batch(self, images, batch_size):
+        # images and batch_size are treated as numpy
+
+        self.sess.run(self.init_opt)
+        self.sess.run(self.setup, feed_dict={self.assign_timg: images})
+
+        for _ in range(self.rec_iters):
+            unmodified_z = self.sess.run([self.z_init])
+
+        return unmodified_z
+
+
+class GeneratorReconstructor(object):
+    def __init__(self, batch_size):
+
+        gan = gan_from_config(batch_size, True)
+        gan.load_model()
+
+        self.batch_size = gan.batch_size
+
+        self.latent_dim = gan.latent_dim
+
+        image_dim = gan.image_dim
+        rec_rr = gan.rec_rr  # # Number of random restarts for the reconstruction
+
+        self.sess = gan.sess
+        self.rec_iters = gan.rec_iters
+
+        x_shape = [self.batch_size] + image_dim
+
+        self.image_adverse_placeholder = tf.placeholder(
+            tf.float32, shape=[self.batch_size, 28, 28, 1], name="image_adverse_placeholder_1"
+        )
+
+        self.z_general_placeholder = tf.placeholder(
+            tf.float32, shape=[self.batch_size, self.latent_dim], name="z_general_placeholder"
+        )
+
+        # TODO basically this can be removed since we're not using rec_rr
+        self.timg_tiled_rr = tf.reshape(self.image_adverse_placeholder, [x_shape[0], np.prod(x_shape[1:])])
+        self.timg_tiled_rr = tf.tile(self.timg_tiled_rr, [1, rec_rr])
+        self.timg_tiled_rr = tf.reshape(self.timg_tiled_rr, [x_shape[0] * rec_rr] + x_shape[1:])
+
+        # TODO this is where the difference between Invert and Defence Gan happens -
+        # in the case of just defenceGan, the encoder is ignored and Z is randomly initialised
+
+        if isinstance(gan, InvertorDefenseGAN):
+            # DefenseGAN++
+            self.z_init = gan.encoder_fn(self.timg_tiled_rr, is_training=False)[0]
+        else:
+            # DefenseGAN
+            self.z_init = tf.Variable(
+                np.random.normal(size=(self.batch_size * rec_rr, self.latent_dim)),
+                collections=[tf.GraphKeys.LOCAL_VARIABLES],
+                trainable=False,
+                dtype=tf.float32,
+                name="z_init_rec",
+            )
+
+        self.z_hats_recs = gan.generator_fn(self.z_general_placeholder, is_training=False)
+
+        num_dim = len(self.z_hats_recs.get_shape())
+
+        self.axes = list(range(1, num_dim))
+
+        image_rec_loss = tf.reduce_mean(tf.square(self.z_hats_recs - self.timg_tiled_rr), axis=self.axes)
+
+        self.rec_loss = tf.reduce_sum(image_rec_loss)
+
+        # # Setup the adam optimizer and keep track of variables we're creating
+        start_vars = set(x.name for x in tf.global_variables())
+
+        end_vars = tf.global_variables()
+        new_vars = [x for x in end_vars if x.name not in start_vars]
+
+        # TODO I don't think we need the assign and timg variables anymore
+
+        # original self.init_opt = tf.variables_initializer(var_list=[modifier] + new_vars)
+        self.init_opt = tf.variables_initializer(var_list=[] + new_vars)
+
+        print("Reconstruction module initialized...\n")
+
+
+# tflib.layers.py
+
+
+# tf.truncated_normal_initializer(stddev=0.02)
+from tensorflow.python.ops.losses.losses_impl import Reduction
+
+weight_init = tf.contrib.layers.xavier_initializer()
+rng = np.random.RandomState([2016, 6, 1])
+
+
+def conv2d(x, out_channels, kernel=3, stride=1, sn=False, update_collection=None, name="conv2d"):
+    with tf.variable_scope(name):
+        w = tf.get_variable("w", [kernel, kernel, x.get_shape()[-1], out_channels], initializer=weight_init)
+
+        if sn:
+            w = spectral_norm(w, update_collection=update_collection)
+
+        conv = tf.nn.conv2d(x, w, strides=[1, stride, stride, 1], padding="SAME")
+
+        bias = tf.get_variable("biases", [out_channels], initializer=tf.zeros_initializer())
+        conv = tf.nn.bias_add(conv, bias)
+
+        return conv
+
+
+def deconv2d(x, out_channels, kernel=4, stride=2, sn=False, update_collection=None, name="deconv2d"):
+    with tf.variable_scope(name):
+        x_shape = x.get_shape().as_list()
+        output_shape = [x_shape[0], x_shape[1] * stride, x_shape[2] * stride, out_channels]
+
+        w = tf.get_variable("w", [kernel, kernel, out_channels, x_shape[-1]], initializer=weight_init)
+
+        if sn:
+            w = spectral_norm(w, update_collection=update_collection)
+
+        deconv = tf.nn.conv2d_transpose(x, w, output_shape=output_shape, strides=[1, stride, stride, 1], padding="SAME")
+
+        bias = tf.get_variable("biases", [out_channels], initializer=tf.zeros_initializer())
+        deconv = tf.nn.bias_add(deconv, bias)
+        deconv.shape.assert_is_compatible_with(output_shape)
+
+        return deconv
+
+
+def linear(x, out_features, sn=False, update_collection=None, name="linear"):
+    with tf.variable_scope(name):
+        x_shape = x.get_shape().as_list()
+        assert len(x_shape) == 2
+
+        matrix = tf.get_variable("W", [x_shape[1], out_features], tf.float32, initializer=weight_init)
+
+        if sn:
+            matrix = spectral_norm(matrix, update_collection=update_collection)
+
+        bias = tf.get_variable("bias", [out_features], initializer=tf.zeros_initializer())
+        out = tf.matmul(x, matrix) + bias
+        return out
+
+
+def embedding(labels, number_classes, embedding_size, update_collection=None, name="snembedding"):
+    with tf.variable_scope(name):
+        embedding_map = tf.get_variable(
+            name="embedding_map",
+            shape=[number_classes, embedding_size],
+            initializer=tf.contrib.layers.xavier_initializer(),
+        )
+
+        embedding_map_bar_transpose = spectral_norm(tf.transpose(embedding_map), update_collection=update_collection)
+        embedding_map_bar = tf.transpose(embedding_map_bar_transpose)
+
+        return tf.nn.embedding_lookup(embedding_map_bar, labels)
+
+
+##################################################################################
+# Activation function
+##################################################################################
+
+
+def lrelu(x, alpha=0.2):
+    return tf.nn.leaky_relu(x, alpha)
+
+
+def relu(x):
+    return tf.nn.relu(x)
+
+
+def tanh(x):
+    return tf.tanh(x)
+
+
+##################################################################################
+# Sampling
+##################################################################################
+
+
+def global_sum_pooling(x):
+    gsp = tf.reduce_sum(x, axis=[1, 2])
+    return gsp
+
+
+def up_sample(x):
+    _, h, w, _ = x.get_shape().as_list()
+    x = tf.image.resize_nearest_neighbor(x, [h * 2, w * 2])
+    return x
+
+
+def down_sample(x):
+    x = tf.nn.avg_pool(x, [1, 2, 2, 1], [1, 2, 2, 1], "VALID")
+    return x
+
+
+##################################################################################
+# Normalization
+##################################################################################
+
+
+def batch_norm(x, is_training=True, name="batch_norm"):
+    return tf.contrib.layers.batch_norm(
+        x,
+        decay=0.9,
+        epsilon=1e-05,
+        center=True,
+        scale=True,
+        is_training=is_training,
+        scope=name,
+        updates_collections=None,
+    )
+
+
+def condition_batch_norm(x, z, is_training=True, scope="batch_norm"):
+    """
+    Hierarchical Embedding (without class-conditioning).
+    Input latent vector z is linearly projected to produce per-sample gain and bias for batchnorm
+
+    Note: Each instance has (2 x len(z) x n_feature) parameters
+    """
+    with tf.variable_scope(scope):
+        _, _, _, c = x.get_shape().as_list()
+        decay = 0.9
+        epsilon = 1e-05
+
+        test_mean = tf.get_variable(
+            "pop_mean", shape=[c], dtype=tf.float32, initializer=tf.constant_initializer(0.0), trainable=False
+        )
+        test_var = tf.get_variable(
+            "pop_var", shape=[c], dtype=tf.float32, initializer=tf.constant_initializer(1.0), trainable=False
+        )
+
+        beta = linear(z, c, name="beta")
+        gamma = linear(z, c, name="gamma")
+
+        beta = tf.reshape(beta, shape=[-1, 1, 1, c])
+        gamma = tf.reshape(gamma, shape=[-1, 1, 1, c])
+
+        if is_training:
+            batch_mean, batch_var = tf.nn.moments(x, [0, 1, 2])
+            ema_mean = tf.assign(test_mean, test_mean * decay + batch_mean * (1 - decay))
+            ema_var = tf.assign(test_var, test_var * decay + batch_var * (1 - decay))
+
+            with tf.control_dependencies([ema_mean, ema_var]):
+                return tf.nn.batch_normalization(x, batch_mean, batch_var, beta, gamma, epsilon)
+        else:
+            return tf.nn.batch_normalization(x, test_mean, test_var, beta, gamma, epsilon)
+
+
+class ConditionalBatchNorm(object):
+    """Conditional BatchNorm.
+    For each  class, it has a specific gamma and beta as normalization variable.
+
+    Note: Each batch norm has (2 x n_class x n_feature) parameters
+    """
+
+    def __init__(self, num_categories, name="conditional_batch_norm", decay_rate=0.999, center=True, scale=True):
+        with tf.variable_scope(name):
+            self.name = name
+            self.num_categories = num_categories
+            self.center = center
+            self.scale = scale
+            self.decay_rate = decay_rate
+
+    def __call__(self, inputs, labels, is_training=True):
+        inputs = tf.convert_to_tensor(inputs)
+        inputs_shape = inputs.get_shape()
+        params_shape = inputs_shape[-1:]
+        # axis = [0, 1, 2]
+        axis = range(0, len(inputs_shape) - 1)
+        shape = tf.TensorShape([self.num_categories]).concatenate(params_shape)
+        # moving_shape = tf.TensorShape([1, 1, 1]).concatenate(params_shape)
+        moving_shape = tf.TensorShape((len(inputs_shape) - 1) * [1]).concatenate(params_shape)
+
+        with tf.variable_scope(self.name):
+            self.gamma = tf.get_variable("gamma", shape, initializer=tf.ones_initializer())
+            self.beta = tf.get_variable("beta", shape, initializer=tf.zeros_initializer())
+            self.moving_mean = tf.get_variable(
+                "mean", moving_shape, initializer=tf.zeros_initializer(), trainable=False
+            )
+            self.moving_var = tf.get_variable("var", moving_shape, initializer=tf.ones_initializer(), trainable=False)
+
+            beta = tf.gather(self.beta, labels)
+            gamma = tf.gather(self.gamma, labels)
+
+            for _ in range(len(inputs_shape) - len(shape)):
+                beta = tf.expand_dims(beta, 1)
+                gamma = tf.expand_dims(gamma, 1)
+
+            decay = self.decay_rate
+            variance_epsilon = 1e-5
+            if is_training:
+                mean, variance = tf.nn.moments(inputs, axis, keepdims=True)
+                update_mean = tf.assign(self.moving_mean, self.moving_mean * decay + mean * (1 - decay))
+                update_var = tf.assign(self.moving_var, self.moving_var * decay + variance * (1 - decay))
+                # tf.add_to_collection(tf.GraphKeys.UPDATE_OPS, update_mean)
+                # tf.add_to_collection(tf.GraphKeys.UPDATE_OPS, update_var)
+                with tf.control_dependencies([update_mean, update_var]):
+                    outputs = tf.nn.batch_normalization(inputs, mean, variance, beta, gamma, variance_epsilon)
+            else:
+                outputs = tf.nn.batch_normalization(
+                    inputs, self.moving_mean, self.moving_var, beta, gamma, variance_epsilon
+                )
+            outputs.set_shape(inputs_shape)
+            return outputs
+
+
+def _l2normalize(v, eps=1e-12):
+    return v / (tf.reduce_sum(v ** 2) ** 0.5 + eps)
+
+
+def spectral_norm(w, num_iters=1, update_collection=None):
+    """
+    https://github.com/taki0112/BigGAN-Tensorflow/blob/master/ops.py
+    """
+    w_shape = w.shape.as_list()
+    w = tf.reshape(w, [-1, w_shape[-1]])
+
+    u = tf.get_variable("u", [1, w_shape[-1]], initializer=tf.truncated_normal_initializer(), trainable=False)
+
+    u_hat = u
+    v_hat = None
+    for _ in range(num_iters):
+        v_ = tf.matmul(u_hat, w, transpose_b=True)
+        v_hat = _l2normalize(v_)
+
+        u_ = tf.matmul(v_hat, w)
+        u_hat = _l2normalize(u_)
+
+    sigma = tf.squeeze(tf.matmul(tf.matmul(v_hat, w), u_hat, transpose_b=True))
+    w_norm = w / sigma
+
+    if update_collection is None:
+        with tf.control_dependencies([u.assign(u_hat)]):
+            w_norm = tf.reshape(w_norm, w_shape)
+    elif update_collection == "NO_OPS":
+        w_norm = tf.reshape(w_norm, w_shape)
+    else:
+        raise NotImplementedError
+
+    return w_norm
+
+
+##################################################################################
+# Residual Blockes
+##################################################################################
+
+
+def resblock_up(x, out_channels, is_training=True, sn=False, update_collection=None, name="resblock_up"):
+    with tf.variable_scope(name):
+        x_0 = x
+        # block 1
+        x = tf.nn.relu(batch_norm(x, is_training=is_training, name="bn1"))
+        x = up_sample(x)
+        x = conv2d(x, out_channels, 3, 1, sn=sn, update_collection=update_collection, name="conv1")
+
+        # block 2
+        x = tf.nn.relu(batch_norm(x, is_training=is_training, name="bn2"))
+        x = conv2d(x, out_channels, 3, 1, sn=sn, update_collection=update_collection, name="conv2")
+
+        # skip connection
+        x_0 = up_sample(x_0)
+        x_0 = conv2d(x_0, out_channels, 1, 1, sn=sn, update_collection=update_collection, name="conv3")
+
+        return x_0 + x
+
+
+def resblock_down(x, out_channels, sn=False, update_collection=None, downsample=True, name="resblock_down"):
+    with tf.variable_scope(name):
+        input_channels = x.shape.as_list()[-1]
+        x_0 = x
+        x = tf.nn.relu(x)
+        x = conv2d(x, out_channels, 3, 1, sn=sn, update_collection=update_collection, name="sn_conv1")
+        x = tf.nn.relu(x)
+        x = conv2d(x, out_channels, 3, 1, sn=sn, update_collection=update_collection, name="sn_conv2")
+
+        if downsample:
+            x = down_sample(x)
+        if downsample or input_channels != out_channels:
+            x_0 = conv2d(x_0, out_channels, 1, 1, sn=sn, update_collection=update_collection, name="sn_conv3")
+            if downsample:
+                x_0 = down_sample(x_0)
+
+        return x_0 + x
+
+
+def inblock(x, out_channels, sn=False, update_collection=None, name="inblock"):
+    with tf.variable_scope(name):
+        x_0 = x
+        x = conv2d(x, out_channels, 3, 1, sn=sn, update_collection=update_collection, name="sn_conv1")
+        x = tf.nn.relu(x)
+        x = conv2d(x, out_channels, 3, 1, sn=sn, update_collection=update_collection, name="sn_conv2")
+
+        x = down_sample(x)
+        x_0 = down_sample(x_0)
+        x_0 = conv2d(x_0, out_channels, 1, 1, sn=sn, update_collection=update_collection, name="sn_conv3")
+
+        return x_0 + x
+
+
+##################################################################################
+# Loss Functions
+##################################################################################
+
+
+def encoder_gan_loss(loss_func, fake):
+    fake_loss = 0
+
+    if loss_func.__contains__("wgan"):
+        fake_loss = -tf.reduce_mean(fake)
+
+    if loss_func == "dcgan":
+        fake_loss = tf.reduce_mean(tf.nn.softplus(-fake))
+
+    if loss_func == "hingegan":
+        fake_loss = -tf.reduce_mean(fake)
+
+    return fake_loss
diff --git a/adversarial-robustness-toolbox/examples/mnist_cnn_features_level_fgsm.py b/adversarial-robustness-toolbox/examples/mnist_cnn_features_level_fgsm.py
new file mode 100644
index 0000000..e315b9a
--- /dev/null
+++ b/adversarial-robustness-toolbox/examples/mnist_cnn_features_level_fgsm.py
@@ -0,0 +1,72 @@
+# -*- coding: utf-8 -*-
+"""Trains a convolutional neural network on the MNIST dataset, then attacks one of the hidden layers with the FGSM
+attack."""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import sys
+from os.path import abspath
+
+sys.path.append(abspath("."))
+
+from keras.models import Model, Sequential
+from keras.layers import Input, Dense, Flatten, Conv2D, MaxPooling2D, Dropout
+import numpy as np
+
+from art.attacks.evasion import FastGradientMethod
+from art.estimators.classification import KerasClassifier
+from art.utils import load_dataset
+
+# Read MNIST dataset
+(x_train, y_train), (x_test, y_test), min_, max_ = load_dataset(str("mnist"))
+
+# Create Keras convolutional neural network - basic architecture from Keras examples
+# Source here: https://github.com/keras-team/keras/blob/master/examples/mnist_cnn.py
+model = Sequential()
+model.add(Conv2D(32, kernel_size=(3, 3), activation="relu", input_shape=x_train.shape[1:]))
+model.add(Conv2D(64, (3, 3), activation="relu"))
+model.add(MaxPooling2D(pool_size=(2, 2)))
+model.add(Dropout(0.25))
+model.add(Flatten())
+model.add(Dense(128, activation="relu"))
+model.add(Dropout(0.5))
+model.add(Dense(10, activation="softmax"))
+
+model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])
+
+classifier = KerasClassifier(model=model, clip_values=(min_, max_))
+classifier.fit(x_train, y_train, nb_epochs=5, batch_size=128)
+
+# Attack one of the inner layers, instead of the input one. In this example
+# we are going to attack the second convolutional layer. To this aim, we need to
+# split the network in 2 sub-nets, in order to have has input layer of the
+# second network, the layer we want to attack
+
+HL_model = Model(inputs=model.input, outputs=model.layers[2].output)
+
+DL_input = Input(model.layers[3].input_shape[1:])
+DL_model = DL_input
+for layer in model.layers[3:]:
+    DL_model = layer(DL_model)
+DL_model = Model(inputs=DL_input, outputs=DL_model)
+
+classifier = KerasClassifier(model=DL_model)
+
+# Now we need to create the dataset for the DL_model, since the original one is
+# suited only for the "model" network (and thus for the HL_model). Note that it
+# is not needed to change the labels
+x_test_inner = HL_model.predict(x_test)
+
+# Evaluate the classifier on the test set
+preds = np.argmax(classifier.predict(x_test_inner), axis=1)
+acc = np.sum(preds == np.argmax(y_test, axis=1)) / y_test.shape[0]
+print("\nTest accuracy: %.2f%%" % (acc * 100))
+
+# Craft adversarial samples with FGSM
+epsilon = 0.1  # Maximum perturbation
+adv_crafter = FastGradientMethod(classifier, eps=epsilon)
+x_test_adv = adv_crafter.generate(x=x_test_inner)
+
+# Evaluate the classifier on the adversarial examples
+preds = np.argmax(classifier.predict(x_test_adv), axis=1)
+acc = np.sum(preds == np.argmax(y_test, axis=1)) / y_test.shape[0]
+print("\nTest accuracy on adversarial sample: %.2f%%" % (acc * 100))
diff --git a/adversarial-robustness-toolbox/examples/mnist_cnn_fgsm.py b/adversarial-robustness-toolbox/examples/mnist_cnn_fgsm.py
new file mode 100644
index 0000000..e3ba4f7
--- /dev/null
+++ b/adversarial-robustness-toolbox/examples/mnist_cnn_fgsm.py
@@ -0,0 +1,46 @@
+# -*- coding: utf-8 -*-
+"""Trains a convolutional neural network on the MNIST dataset, then attacks it with the FGSM attack."""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+from keras.models import Sequential
+from keras.layers import Dense, Flatten, Conv2D, MaxPooling2D, Dropout
+import numpy as np
+
+from art.attacks.evasion import FastGradientMethod
+from art.estimators.classification import KerasClassifier
+from art.utils import load_dataset
+
+# Read MNIST dataset
+(x_train, y_train), (x_test, y_test), min_, max_ = load_dataset(str("mnist"))
+
+# Create Keras convolutional neural network - basic architecture from Keras examples
+# Source here: https://github.com/keras-team/keras/blob/master/examples/mnist_cnn.py
+model = Sequential()
+model.add(Conv2D(32, kernel_size=(3, 3), activation="relu", input_shape=x_train.shape[1:]))
+model.add(Conv2D(64, (3, 3), activation="relu"))
+model.add(MaxPooling2D(pool_size=(2, 2)))
+model.add(Dropout(0.25))
+model.add(Flatten())
+model.add(Dense(128, activation="relu"))
+model.add(Dropout(0.5))
+model.add(Dense(10, activation="softmax"))
+
+model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])
+
+classifier = KerasClassifier(model=model, clip_values=(min_, max_))
+classifier.fit(x_train, y_train, nb_epochs=5, batch_size=128)
+
+# Evaluate the classifier on the test set
+preds = np.argmax(classifier.predict(x_test), axis=1)
+acc = np.sum(preds == np.argmax(y_test, axis=1)) / y_test.shape[0]
+print("\nTest accuracy: %.2f%%" % (acc * 100))
+
+# Craft adversarial samples with FGSM
+epsilon = 0.1  # Maximum perturbation
+adv_crafter = FastGradientMethod(classifier, eps=epsilon)
+x_test_adv = adv_crafter.generate(x=x_test)
+
+# Evaluate the classifier on the adversarial examples
+preds = np.argmax(classifier.predict(x_test_adv), axis=1)
+acc = np.sum(preds == np.argmax(y_test, axis=1)) / y_test.shape[0]
+print("\nTest accuracy on adversarial sample: %.2f%%" % (acc * 100))
diff --git a/adversarial-robustness-toolbox/examples/mnist_poison_detection.py b/adversarial-robustness-toolbox/examples/mnist_poison_detection.py
new file mode 100644
index 0000000..3cef7bc
--- /dev/null
+++ b/adversarial-robustness-toolbox/examples/mnist_poison_detection.py
@@ -0,0 +1,200 @@
+# -*- coding: utf-8 -*-
+"""Generates a backdoor for MNIST dataset, then trains a convolutional neural network on the poisoned dataset,
+ and runs activation defence to find poison."""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import pprint
+import json
+
+from keras.models import Sequential
+from keras.layers import Dense, Flatten, Conv2D, MaxPooling2D, Dropout
+import numpy as np
+
+from art.attacks.poisoning.perturbations.image_perturbations import add_pattern_bd, add_single_bd
+from art.estimators.classification import KerasClassifier
+from art.utils import load_mnist, preprocess
+from art.defences.detector.poison import ActivationDefence
+
+
+def main():
+    # Read MNIST dataset (x_raw contains the original images):
+    (x_raw, y_raw), (x_raw_test, y_raw_test), min_, max_ = load_mnist(raw=True)
+
+    n_train = np.shape(x_raw)[0]
+    num_selection = 5000
+    random_selection_indices = np.random.choice(n_train, num_selection)
+    x_raw = x_raw[random_selection_indices]
+    y_raw = y_raw[random_selection_indices]
+
+    # Poison training data
+    perc_poison = 0.33
+    (is_poison_train, x_poisoned_raw, y_poisoned_raw) = generate_backdoor(x_raw, y_raw, perc_poison)
+    x_train, y_train = preprocess(x_poisoned_raw, y_poisoned_raw)
+    # Add channel axis:
+    x_train = np.expand_dims(x_train, axis=3)
+
+    # Poison test data
+    (is_poison_test, x_poisoned_raw_test, y_poisoned_raw_test) = generate_backdoor(x_raw_test, y_raw_test, perc_poison)
+    x_test, y_test = preprocess(x_poisoned_raw_test, y_poisoned_raw_test)
+    # Add channel axis:
+    x_test = np.expand_dims(x_test, axis=3)
+
+    # Shuffle training data so poison is not together
+    n_train = np.shape(y_train)[0]
+    shuffled_indices = np.arange(n_train)
+    np.random.shuffle(shuffled_indices)
+    x_train = x_train[shuffled_indices]
+    y_train = y_train[shuffled_indices]
+    is_poison_train = is_poison_train[shuffled_indices]
+
+    # Create Keras convolutional neural network - basic architecture from Keras examples
+    # Source here: https://github.com/keras-team/keras/blob/master/examples/mnist_cnn.py
+    model = Sequential()
+    model.add(Conv2D(32, kernel_size=(3, 3), activation="relu", input_shape=x_train.shape[1:]))
+    model.add(Conv2D(64, (3, 3), activation="relu"))
+    model.add(MaxPooling2D(pool_size=(2, 2)))
+    model.add(Dropout(0.25))
+    model.add(Flatten())
+    model.add(Dense(128, activation="relu"))
+    model.add(Dropout(0.5))
+    model.add(Dense(10, activation="softmax"))
+
+    model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])
+
+    classifier = KerasClassifier(model=model, clip_values=(min_, max_))
+
+    classifier.fit(x_train, y_train, nb_epochs=30, batch_size=128)
+
+    # Evaluate the classifier on the test set
+    preds = np.argmax(classifier.predict(x_test), axis=1)
+    acc = np.sum(preds == np.argmax(y_test, axis=1)) / y_test.shape[0]
+    print("\nTest accuracy: %.2f%%" % (acc * 100))
+
+    # Evaluate the classifier on poisonous data
+    preds = np.argmax(classifier.predict(x_test[is_poison_test]), axis=1)
+    acc = np.sum(preds == np.argmax(y_test[is_poison_test], axis=1)) / y_test[is_poison_test].shape[0]
+    print("\nPoisonous test set accuracy (i.e. effectiveness of poison): %.2f%%" % (acc * 100))
+
+    # Evaluate the classifier on clean data
+    preds = np.argmax(classifier.predict(x_test[is_poison_test == 0]), axis=1)
+    acc = np.sum(preds == np.argmax(y_test[is_poison_test == 0], axis=1)) / y_test[is_poison_test == 0].shape[0]
+    print("\nClean test set accuracy: %.2f%%" % (acc * 100))
+
+    # Calling poisoning defence:
+    defence = ActivationDefence(classifier, x_train, y_train)
+
+    # End-to-end method:
+    print("------------------- Results using size metric -------------------")
+    print(defence.get_params())
+    defence.detect_poison(nb_clusters=2, nb_dims=10, reduce="PCA")
+
+    # Evaluate method when ground truth is known:
+    is_clean = is_poison_train == 0
+    confusion_matrix = defence.evaluate_defence(is_clean)
+    print("Evaluation defence results for size-based metric: ")
+    jsonObject = json.loads(confusion_matrix)
+    for label in jsonObject:
+        print(label)
+        pprint.pprint(jsonObject[label])
+
+    # Visualize clusters:
+    print("Visualize clusters")
+    sprites_by_class = defence.visualize_clusters(x_train, "mnist_poison_demo")
+    # Show plots for clusters of class 5
+    n_class = 5
+    try:
+        import matplotlib.pyplot as plt
+
+        plt.imshow(sprites_by_class[n_class][0])
+        plt.title("Class " + str(n_class) + " cluster: 0")
+        plt.show()
+        plt.imshow(sprites_by_class[n_class][1])
+        plt.title("Class " + str(n_class) + " cluster: 1")
+        plt.show()
+    except ImportError:
+        print("matplotlib not installed. For this reason, cluster visualization was not displayed")
+
+    # Try again using distance analysis this time:
+    print("------------------- Results using distance metric -------------------")
+    print(defence.get_params())
+    defence.detect_poison(nb_clusters=2, nb_dims=10, reduce="PCA", cluster_analysis="distance")
+    confusion_matrix = defence.evaluate_defence(is_clean)
+    print("Evaluation defence results for distance-based metric: ")
+    jsonObject = json.loads(confusion_matrix)
+    for label in jsonObject:
+        print(label)
+        pprint.pprint(jsonObject[label])
+
+    # Other ways to invoke the defence:
+    kwargs = {"nb_clusters": 2, "nb_dims": 10, "reduce": "PCA"}
+    defence.cluster_activations(**kwargs)
+
+    kwargs = {"cluster_analysis": "distance"}
+    defence.analyze_clusters(**kwargs)
+    defence.evaluate_defence(is_clean)
+
+    kwargs = {"cluster_analysis": "smaller"}
+    defence.analyze_clusters(**kwargs)
+    defence.evaluate_defence(is_clean)
+
+    print("done :) ")
+
+
+def generate_backdoor(
+    x_clean, y_clean, percent_poison, backdoor_type="pattern", sources=np.arange(10), targets=(np.arange(10) + 1) % 10
+):
+    """
+    Creates a backdoor in MNIST images by adding a pattern or pixel to the image and changing the label to a targeted
+    class. Default parameters poison each digit so that it gets classified to the next digit.
+
+    :param x_clean: Original raw data
+    :type x_clean: `np.ndarray`
+    :param y_clean: Original labels
+    :type y_clean:`np.ndarray`
+    :param percent_poison: After poisoning, the target class should contain this percentage of poison
+    :type percent_poison: `float`
+    :param backdoor_type: Backdoor type can be `pixel` or `pattern`.
+    :type backdoor_type: `str`
+    :param sources: Array that holds the source classes for each backdoor. Poison is
+    generating by taking images from the source class, adding the backdoor trigger, and labeling as the target class.
+    Poisonous images from sources[i] will be labeled as targets[i].
+    :type sources: `np.ndarray`
+    :param targets: This array holds the target classes for each backdoor. Poisonous images from sources[i] will be
+                    labeled as targets[i].
+    :type targets: `np.ndarray`
+    :return: Returns is_poison, which is a boolean array indicating which points are poisonous, x_poison, which
+    contains all of the data both legitimate and poisoned, and y_poison, which contains all of the labels
+    both legitimate and poisoned.
+    :rtype: `tuple`
+    """
+
+    max_val = np.max(x_clean)
+
+    x_poison = np.copy(x_clean)
+    y_poison = np.copy(y_clean)
+    is_poison = np.zeros(np.shape(y_poison))
+
+    for i, (src, tgt) in enumerate(zip(sources, targets)):
+        n_points_in_tgt = np.size(np.where(y_clean == tgt))
+        num_poison = round((percent_poison * n_points_in_tgt) / (1 - percent_poison))
+        src_imgs = x_clean[y_clean == src]
+
+        n_points_in_src = np.shape(src_imgs)[0]
+        indices_to_be_poisoned = np.random.choice(n_points_in_src, num_poison)
+
+        imgs_to_be_poisoned = np.copy(src_imgs[indices_to_be_poisoned])
+        if backdoor_type == "pattern":
+            imgs_to_be_poisoned = add_pattern_bd(x=imgs_to_be_poisoned, pixel_value=max_val)
+        elif backdoor_type == "pixel":
+            imgs_to_be_poisoned = add_single_bd(imgs_to_be_poisoned, pixel_value=max_val)
+        x_poison = np.append(x_poison, imgs_to_be_poisoned, axis=0)
+        y_poison = np.append(y_poison, np.ones(num_poison) * tgt, axis=0)
+        is_poison = np.append(is_poison, np.ones(num_poison))
+
+    is_poison = is_poison != 0
+
+    return is_poison, x_poison, y_poison
+
+
+if __name__ == "__main__":
+    main()
diff --git a/adversarial-robustness-toolbox/examples/mnist_transferability.py b/adversarial-robustness-toolbox/examples/mnist_transferability.py
new file mode 100644
index 0000000..76b75a7
--- /dev/null
+++ b/adversarial-robustness-toolbox/examples/mnist_transferability.py
@@ -0,0 +1,85 @@
+# -*- coding: utf-8 -*-
+"""Trains a CNN on the MNIST dataset using the Keras backend, then generates adversarial images using DeepFool
+and uses them to attack a CNN trained on MNIST using TensorFlow. This is to show how to perform a
+black-box attack: the attack never has access to the parameters of the TensorFlow model.
+"""
+from __future__ import absolute_import, division, print_function
+
+import keras
+import keras.backend as k
+from keras.models import Sequential
+from keras.layers import Dense, Flatten, Conv2D, MaxPooling2D
+import numpy as np
+import tensorflow as tf
+
+from art.attacks.evasion import DeepFool
+from art.estimators.classification import KerasClassifier, TensorFlowClassifier
+from art.utils import load_mnist
+
+
+def cnn_mnist_tf(input_shape):
+    labels_tf = tf.placeholder(tf.float32, [None, 10])
+    inputs_tf = tf.placeholder(tf.float32, [None] + list(input_shape))
+
+    # Define the TensorFlow graph
+    conv = tf.layers.conv2d(inputs_tf, 4, 5, activation=tf.nn.relu)
+    conv = tf.layers.max_pooling2d(conv, 2, 2)
+    fc = tf.contrib.layers.flatten(conv)
+
+    # Logits layer
+    logits = tf.layers.dense(fc, 10)
+
+    # Train operator
+    loss = tf.reduce_mean(tf.losses.softmax_cross_entropy(logits=logits, onehot_labels=labels_tf))
+    optimizer = tf.train.AdamOptimizer(learning_rate=0.01)
+    train_tf = optimizer.minimize(loss)
+
+    sess = tf.Session()
+    sess.run(tf.global_variables_initializer())
+
+    classifier = TensorFlowClassifier(
+        clip_values=(0, 1), input_ph=inputs_tf, output=logits, loss=loss, train=train_tf, labels_ph=labels_tf, sess=sess
+    )
+    return classifier
+
+
+def cnn_mnist_k(input_shape):
+    # Create simple CNN
+    model = Sequential()
+    model.add(Conv2D(4, kernel_size=(5, 5), activation="relu", input_shape=input_shape))
+    model.add(MaxPooling2D(pool_size=(2, 2)))
+    model.add(Flatten())
+    model.add(Dense(10, activation="softmax"))
+
+    model.compile(
+        loss=keras.losses.categorical_crossentropy, optimizer=keras.optimizers.Adam(lr=0.01), metrics=["accuracy"]
+    )
+
+    classifier = KerasClassifier(model=model, clip_values=(0, 1))
+    return classifier
+
+
+# Get session
+session = tf.Session()
+k.set_session(session)
+
+# Read MNIST dataset
+(x_train, y_train), (x_test, y_test), min_, max_ = load_mnist()
+
+# Construct and train a convolutional neural network on MNIST using Keras
+source = cnn_mnist_k(x_train.shape[1:])
+source.fit(x_train, y_train, nb_epochs=5, batch_size=128)
+
+# Craft adversarial samples with DeepFool
+adv_crafter = DeepFool(source)
+x_train_adv = adv_crafter.generate(x_train)
+x_test_adv = adv_crafter.generate(x_test)
+
+# Construct and train a convolutional neural network
+target = cnn_mnist_tf(x_train.shape[1:])
+target.fit(x_train, y_train, nb_epochs=5, batch_size=128)
+
+# Evaluate the CNN on the adversarial samples
+preds = target.predict(x_test_adv)
+acc = np.sum(np.equal(np.argmax(preds, axis=1), np.argmax(y_test, axis=1))) / y_test.shape[0]
+print("\nAccuracy on adversarial samples: %.2f%%" % (acc * 100))
diff --git a/adversarial-robustness-toolbox/notebooks/README.md b/adversarial-robustness-toolbox/notebooks/README.md
new file mode 100644
index 0000000..333cb1d
--- /dev/null
+++ b/adversarial-robustness-toolbox/notebooks/README.md
@@ -0,0 +1,199 @@
+# Adversarial Robustness Toolbox notebooks
+
+## Video Action Recognition
+
+[adversarial_action_recognition.ipynb](adversarial_action_recognition.ipynb) [[on nbviewer](https://nbviewer.jupyter.org/github/Trusted-AI/adversarial-robustness-toolbox/blob/main/notebooks/adversarial_action_recognition.ipynb)]
+shows how to create an adversarial attack on a video action recognition classification task with ART. Experiments in this notebook show how to modify a video sample by employing a Fast Gradient Method attack so that the modified video sample get mis-classified.
+
+<p align="center">
+  <img src="../utils/data/images/basketball.gif?raw=true" width="200" title="benign_basketball_sample">
+  <img src="../utils/data/images/adversarial_basketball.gif?raw=true" width="200" title="adversarial_basketball_sample">
+</p>
+
+
+## Audio
+
+[adversarial_audio_examples.ipynb](adversarial_audio_examples.ipynb) [[on nbviewer](https://nbviewer.jupyter.org/github/Trusted-AI/adversarial-robustness-toolbox/blob/main/notebooks/adversarial_audio_examples.ipynb)]
+shows how to create adversarial examples of audio data with ART. Experiments in this notebook show how the waveform of a spoken digit of the AudioMNIST dataset can be modified with almost imperceptible changes so that the waveform gets mis-classified as different digit.
+
+<p align="center">
+  <img src="../utils/data/images/adversarial_audio_waveform.png?raw=true" width="200" title="adversarial_audio_waveform">
+</p>
+
+## Adversarial training
+
+[adversarial_retraining.ipynb](adversarial_retraining.ipynb) [[on nbviewer](https://nbviewer.jupyter.org/github/Trusted-AI/adversarial-robustness-toolbox/blob/main/notebooks/adversarial_retraining.ipynb)]
+shows how to load and evaluate the MNIST and CIFAR-10 models synthesized and adversarially trained by 
+[Sinn et al., 2019](https://drive.google.com/uc?export=download&id=1XmUSqU7qCYigVqgEKvoT2p__Fy-Dq9Cx).
+
+[adversarial_training_mnist.ipynb](adversarial_training_mnist.ipynb) [[on nbviewer](https://nbviewer.jupyter.org/github/Trusted-AI/adversarial-robustness-toolbox/blob/main/notebooks/adversarial_training_mnist.ipynb)]
+demonstrates adversarial training of a neural network to harden the model against adversarial samples using the MNIST 
+dataset.
+
+<p align="center">
+  <img src="../utils/data/images/adversarial_training.png?raw=true" width="200" title="adversarial_training">
+</p>
+
+## TensorFlow v2
+
+[art-for-tensorflow-v2-callable.ipynb](art-for-tensorflow-v2-callable.ipynb) [[on nbviewer](https://nbviewer.jupyter.org/github/Trusted-AI/adversarial-robustness-toolbox/blob/main/notebooks/art-for-tensorflow-v2-callable.ipynb)]
+show how to use ART with TensorFlow v2 in eager execution mode with a model in form of a callable class or python 
+function.
+
+[art-for-tensorflow-v2-keras.ipynb](art-for-tensorflow-v2-keras.ipynb) [[on nbviewer](https://nbviewer.jupyter.org/github/Trusted-AI/adversarial-robustness-toolbox/blob/main/notebooks/art-for-tensorflow-v2-keras.ipynb)]
+demonstrates ART with TensorFlow v2 using tensorflow.keras without eager execution.
+
+## Attacks
+
+[attack_adversarial_patch.ipynb](adversarial_patch/attack_adversarial_patch.ipynb) [[on nbviewer](https://nbviewer.jupyter.org/github/Trusted-AI/adversarial-robustness-toolbox/blob/main/notebooks/attack_adversarial_patch.ipynb)]
+shows how to use ART to create real-world adversarial patches that fool real-world object detection and classification 
+models.
+
+<p align="center">
+  <img src="../utils/data/images/adversarial_patch.png?raw=true" width="200" title="adversarial_patch">
+</p>
+
+[attack_decision_based_boundary.ipynb](attack_decision_based_boundary.ipynb) [[on nbviewer](https://nbviewer.jupyter.org/github/Trusted-AI/adversarial-robustness-toolbox/blob/main/notebooks/attack_decision_based_boundary.ipynb)]
+demonstrates Decision-Based Adversarial Attack (Boundary) attack. This is a black-box attack which only requires class 
+predictions.
+
+[attack_decision_tree.ipynb](attack_decision_tree.ipynb) [[on nbviewer](https://nbviewer.jupyter.org/github/Trusted-AI/adversarial-robustness-toolbox/blob/main/notebooks/attack_decision_tree.ipynb)]
+shows how to compute adversarial examples on decision trees ([Papernot et al., 2016](https://arxiv.org/abs/1605.07277)).
+It traversing the structure of a decision tree classifier to create adversarial examples can be computed without 
+explicit gradients.
+
+[attack_defence_imagenet.ipynb](attack_defence_imagenet.ipynb) [[on nbviewer](https://nbviewer.jupyter.org/github/Trusted-AI/adversarial-robustness-toolbox/blob/main/notebooks/attack_defence_imagenet.ipynb)]
+explains the basic workflow of using ART with defences and attacks on an neural network classifier for ImageNet.
+
+[attack_hopskipjump.ipynb](attack_hopskipjump.ipynb) [[on nbviewer](https://nbviewer.jupyter.org/github/Trusted-AI/adversarial-robustness-toolbox/blob/main/notebooks/attack_hopskipjump.ipynb)]
+demonstrates the HopSkipJumpAttack. This is a black-box attack that only requires class predictions. It is an advanced 
+version of the Boundary attack.
+
+## Classifiers
+
+[classifier_blackbox.ipynb](classifier_blackbox.ipynb) [[on nbviewer](https://nbviewer.jupyter.org/github/Trusted-AI/adversarial-robustness-toolbox/blob/main/notebooks/classifier_blackbox.ipynb)] demonstrates BlackBoxClassifier, the most general and
+versatile classifier of ART requiring only a single predict function definition without any additional assumptions or 
+requirements. The notebook shows how use BlackBoxClassifier to attack a remote, deployed model (in this case on IBM
+Watson Machine Learning, https://cloud.ibm.com) using the HopSkiJump attack.
+
+[classifier_blackbox_tesseract.ipynb](classifier_blackbox_tesseract.ipynb) [[on nbviewer](https://nbviewer.jupyter.org/github/Trusted-AI/adversarial-robustness-toolbox/blob/main/notebooks/classifier_blackbox_tesseract.ipynb)]
+demonstrates a black-box attack on Tesseract OCR. It uses BlackBoxClassifier and HopSkipJump attack to change the image 
+of one word into the image of another word and shows how to apply pre-processing defences.
+
+<p align="center">
+  <img src="../utils/data/tesseract/assent_benign.png?raw=true" width="200" title="assent_benign">
+  <img src="../utils/data/tesseract/assent_adversarial.png?raw=true" width="200" title="assent_adversarial">
+</p>
+
+[classifier_catboost.ipynb](classifier_catboost.ipynb) [[on nbviewer](https://nbviewer.jupyter.org/github/Trusted-AI/adversarial-robustness-toolbox/blob/main/notebooks/classifier_catboost.ipynb)]
+shows how to use ART with CatBoost models. It demonstrates and analyzes Zeroth Order Optimisation attacks using the Iris
+and MNIST datasets.
+
+[classifier_gpy_gaussian_process.ipynb](classifier_gpy_gaussian_process.ipynb) [[on nbviewer](https://nbviewer.jupyter.org/github/Trusted-AI/adversarial-robustness-toolbox/blob/main/notebooks/classifier_gpy_gaussian_process.ipynb)]
+shows how to create adversarial examples for the Gaussian Process classifier of GPy. It crafts adversarial examples with
+the HighConfidenceLowUncertainty (HCLU) attack ([Grosse et al., 2018](https://arxiv.org/abs/1812.02606)), specifically 
+targeting Gaussian Process classifiers, and compares it to Projected Gradient Descent (PGD) 
+([Madry et al., 2017](https://arxiv.org/abs/1706.06083)).
+
+<p align="center">
+  <img src="../utils/data/images/gaussian_process_hclu.png?raw=true" width="200" title="gaussian_process_hclu">
+</p>
+
+[classifier_lightgbm.ipynb](classifier_lightgbm.ipynb) [[on nbviewer](https://nbviewer.jupyter.org/github/Trusted-AI/adversarial-robustness-toolbox/blob/main/notebooks/classifier_lightgbm.ipynb)]
+shows how to use ART with LightGBM models. It demonstrates and analyzes Zeroth Order Optimisation attacks using the Iris
+and MNIST datasets.
+
+[classifier_scikitlearn_AdaBoostClassifier.ipynb](classifier_scikitlearn_AdaBoostClassifier.ipynb) [[on nbviewer](https://nbviewer.jupyter.org/github/Trusted-AI/adversarial-robustness-toolbox/blob/main/notebooks/classifier_scikitlearn_AdaBoostClassifier.ipynb)]
+shows how to use ART with Scikit-learn AdaBoostClassifier. It demonstrates and analyzes Zeroth Order Optimisation 
+attacks using the Iris and MNIST datasets.
+
+[classifier_scikitlearn_BaggingClassifier.ipynb](classifier_scikitlearn_BaggingClassifier.ipynb) [[on nbviewer](https://nbviewer.jupyter.org/github/Trusted-AI/adversarial-robustness-toolbox/blob/main/notebooks/classifier_scikitlearn_BaggingClassifier.ipynb)]
+shows how to use ART with Scikit-learn BaggingClassifier. It demonstrates and analyzes Zeroth Order Optimisation attacks
+using the Iris and MNIST datasets.
+
+[classifier_scikitlearn_DecisionTreeClassifier.ipynb](classifier_scikitlearn_DecisionTreeClassifier.ipynb) [[on nbviewer](https://nbviewer.jupyter.org/github/Trusted-AI/adversarial-robustness-toolbox/blob/main/notebooks/classifier_scikitlearn_DecisionTreeClassifier.ipynb)]
+shows how to use ART with Scikit-learn DecisionTreeClassifier. It demonstrates and analyzes Zeroth Order Optimisation 
+attacks using the Iris and MNIST datasets.
+
+[classifier_scikitlearn_ExtraTreesClassifier.ipynb](classifier_scikitlearn_ExtraTreesClassifier.ipynb) [[on nbviewer](https://nbviewer.jupyter.org/github/Trusted-AI/adversarial-robustness-toolbox/blob/main/notebooks/classifier_scikitlearn_ExtraTreesClassifier.ipynb)]
+shows how to use ART with Scikit-learn ExtraTreesClassifier. It demonstrates and analyzes Zeroth Order Optimisation
+attacks using the Iris and MNIST datasets.
+
+[classifier_scikitlearn_GradientBoostingClassifier.ipynb](classifier_scikitlearn_GradientBoostingClassifier.ipynb) [[on nbviewer](https://nbviewer.jupyter.org/github/Trusted-AI/adversarial-robustness-toolbox/blob/main/notebooks/classifier_scikitlearn_GradientBoostingClassifier.ipynb)]
+shows how to use ART with Scikit-learn GradientBoostingClassifier. It demonstrates and analyzes Zeroth Order 
+Optimisation attacks using the Iris and MNIST datasets.
+
+[classifier_scikitlearn_LogisticRegression.ipynb](classifier_scikitlearn_LogisticRegression.ipynb) [[on nbviewer](https://nbviewer.jupyter.org/github/Trusted-AI/adversarial-robustness-toolbox/blob/main/notebooks/classifier_scikitlearn_LogisticRegression.ipynb)]
+shows how to use ART with Scikit-learn LogisticRegression. It demonstrates and analyzes Projected Gradient Descent 
+attacks using the MNIST dataset.
+
+[classifier_scikitlearn_pipeline_pca_cv_svc.ipynb](classifier_scikitlearn_pipeline_pca_cv_svc.ipynb) [[on nbviewer](https://nbviewer.jupyter.org/github/Trusted-AI/adversarial-robustness-toolbox/blob/main/notebooks/classifier_scikitlearn_pipeline_pca_cv_svc.ipynb)]
+contains an example of generating adversarial examples using a black-box attack against a scikit-learn pipeline 
+consisting of principal component analysis (PCA), cross validation (CV) and a support vector machine classifier (SVC), 
+but any other valid pipeline would work too. The pipeline is optimised using grid search with cross validation. The 
+adversarial samples are created with black-box HopSkipJump attack. The training data is MNIST, because of its intuitive 
+visualisation, but any other dataset including tabular data would be suitable too.
+
+[classifier_scikitlearn_RandomForestClassifier.ipynb](classifier_scikitlearn_RandomForestClassifier.ipynb) [[on nbviewer](https://nbviewer.jupyter.org/github/Trusted-AI/adversarial-robustness-toolbox/blob/main/notebooks/classifier_scikitlearn_RandomForestClassifier.ipynb)]
+shows how to use ART with Scikit-learn RandomForestClassifier. It demonstrates and analyzes Zeroth Order Optimisation
+attacks using the Iris and MNIST datasets.
+
+[classifier_scikitlearn_SVC_LinearSVC.ipynb](classifier_scikitlearn_SVC_LinearSVC.ipynb) [[on nbviewer](https://nbviewer.jupyter.org/github/Trusted-AI/adversarial-robustness-toolbox/blob/main/notebooks/classifier_scikitlearn_SVC_LinearSVC.ipynb)]
+shows how to use ART with Scikit-learn SVC and LinearSVC support vector machines. It demonstrates and analyzes Projected 
+Gradient Descent attacks using the Iris and MNIST dataset for binary and multiclass classification for linear and 
+radial-basis-function kernels.
+
+<p align="center">
+  <img src="../utils/data/images/svc.png?raw=true" width="200" title="svc">
+</p>
+
+[classifier_xgboost.ipynb](classifier_xgboost.ipynb) [[on nbviewer](https://nbviewer.jupyter.org/github/Trusted-AI/adversarial-robustness-toolbox/blob/main/notebooks/classifier_xgboost.ipynb)]
+shows how to use ART with XGBoost models. It demonstrates and analyzes Zeroth Order Optimisation attacks using the Iris 
+and MNIST datasets.
+
+## Detectors
+
+[detection_adversarial_samples_cifar10.ipynb](detection_adversarial_samples_cifar10.ipynb) [[on nbviewer](https://nbviewer.jupyter.org/github/Trusted-AI/adversarial-robustness-toolbox/blob/main/notebooks/detection_adversarial_samples_cifar10.ipynb)]
+demonstrates the detection of adversarial examples using ART. The classifier model is a neural network of a ResNet 
+architecture in Keras for the CIFAR-10 dataset.
+
+## Model stealing / model theft / model extraction
+
+[model-stealing-demo.ipynb](model-stealing-demo.ipynb) [[on nbviewer](https://nbviewer.jupyter.org/github/Trusted-AI/adversarial-robustness-toolbox/blob/main/notebooks/model-stealing-demo.ipynb)] demonstrates model stealing attacks and a reverse sigmoid defense against them.
+
+## Poisoning
+
+[poisoning_attack_svm.ipynb](poisoning_attack_svm.ipynb) [[on nbviewer](https://nbviewer.jupyter.org/github/Trusted-AI/adversarial-robustness-toolbox/blob/main/notebooks/poisoning_attack_svm.ipynb)]
+demonstrates a poisoning attack on a Support Vector Machine.
+
+<p align="center">
+  <img src="../utils/data/images/svm_poly.gif?raw=true" width="200" title="svm_poly">
+</p>
+
+[poisoning_defense_activation_clustering.ipynb](poisoning_defense_activation_clustering.ipynb) [[on nbviewer](https://nbviewer.jupyter.org/github/Trusted-AI/adversarial-robustness-toolbox/blob/main/notebooks/poisoning_defense_activation_clustering.ipynb)]
+demonstrates the generation and detection of backdoors in neural networks via Activation Clustering.
+
+<p align="center">
+  <img src="../utils/data/images/poisoning.png?raw=true" width="200" title="poisoning">
+</p>
+
+[poisoning_defense_neural_cleanse.ipynb](poisoning_defense_neural_cleanse.ipynb) [[on nbviewer](https://nbviewer.jupyter.org/github/Trusted-AI/adversarial-robustness-toolbox/blob/main/notebooks/poisoning_defense_neural_cleanse.ipynb)]
+demonstrates a defense against poisoning attacks that generation the suspected backdoor and applies runtime mitigation methods on the classifier.
+
+[poisoning_defence_strip.ipynb](poisoning_defence_strip.ipynb) [[on nbviewer](https://nbviewer.jupyter.org/github/Trusted-AI/adversarial-robustness-toolbox/blob/main/notebooks/poisoning_defence_strip.ipynb)]
+demonstrates a defense against input-agnostic backdoor attacks that filters suspicious inputs at runtime.
+
+## Certification and Verification
+
+[output_randomized_smoothing_mnist.ipynb](output_randomized_smoothing_mnist.ipynb) [[on nbviewer](https://nbviewer.jupyter.org/github/Trusted-AI/adversarial-robustness-toolbox/blob/main/notebooks/output_randomized_smoothing_mnist.ipynb)]
+shows how to achieve certified adversarial robustness for neural networks via Randomized Smoothing.
+
+[robustness_verification_clique_method_tree_ensembles_gradient_boosted_decision_trees_classifiers.ipynb](robustness_verification_clique_method_tree_ensembles_gradient_boosted_decision_trees_classifiers.ipynb) [[on nbviewer](https://nbviewer.jupyter.org/github/Trusted-AI/adversarial-robustness-toolbox/blob/main/notebooks/robustness_verification_clique_method_tree_ensembles_gradient_boosted_decision_trees_classifiers.ipynb)]
+demonstrates the verification of adversarial robustness in decision tree ensemble classifiers 
+(Gradient Boosted Decision Trees, Random Forests, etc.) using XGBoost, LightGBM and Scikit-learn.
+
+
+## MNIST
+
+[fabric_for_deep_learning_adversarial_samples_fashion_mnist.ipynb](fabric_for_deep_learning_adversarial_samples_fashion_mnist.ipynb) [[on nbviewer](https://nbviewer.jupyter.org/github/Trusted-AI/adversarial-robustness-toolbox/blob/main/notebooks/fabric_for_deep_learning_adversarial_samples_fashion_mnist.ipynb)]
+shows how to use ART with deep learning models trained with the Fabric for Deep Learning (FfDL).
diff --git a/adversarial-robustness-toolbox/notebooks/adaptive_defence_evaluations/evaluation_12_EMPIR.ipynb b/adversarial-robustness-toolbox/notebooks/adaptive_defence_evaluations/evaluation_12_EMPIR.ipynb
new file mode 100644
index 0000000..71532fc
--- /dev/null
+++ b/adversarial-robustness-toolbox/notebooks/adaptive_defence_evaluations/evaluation_12_EMPIR.ipynb
@@ -0,0 +1,654 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Evaluation 12 - EMPIR\n",
+    "This notebook implements the evaluation of [Tramer, Carlini, Brendel and Madry (2020)](https://arxiv.org/abs/2002.08347) using ART and focuses on section 12 evaluating \"EMPIR: Ensembles of Mixed Precision Deep Networks for Increased Robustness against Adversarial Attacks\".\n",
+    "\n",
+    "This notebook uses code from [Sen et al. (2020)](https://openreview.net/forum?id=HJem3yHKwH) at : https://github.com/sancharisen/EMPIR\n",
+    "\n",
+    "Before running this notebook you need to download the CIFAR-10 EMPIR models from https://github.com/sancharisen/EMPIR. into the local directory containing this notebook and save the 3 models into directories named `./CIFARconv/Model1`, `./CIFARconv/Model2`, and `./CIFARconv/Model3`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using TensorFlow backend.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\n",
+    "import sys\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "\n",
+    "import tensorflow as tf\n",
+    "tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)\n",
+    "import keras\n",
+    "from keras.datasets import cifar10\n",
+    "from keras.utils import np_utils\n",
+    "import numpy as np\n",
+    "\n",
+    "from art.estimators.classification import TensorFlowClassifier\n",
+    "from art.attacks.evasion import ProjectedGradientDescent"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%bash\n",
+    "if ! [[ -d \"./EMPIR\" ]]\n",
+    "then\n",
+    "    git clone git@github.com:sancharisen/EMPIR.git\n",
+    "fi\n",
+    "touch ./EMPIR/__init__.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sys.path.append(\"./\")\n",
+    "sys.path.append(\"./EMPIR\")\n",
+    "from EMPIR.cleverhans.utils_tf import model_eval_ensemble"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sess = tf.Session()\n",
+    "keras.backend.set_session(sess)\n",
+    "tf.set_random_seed(1234)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# CIFAR10-specific dimensions\n",
+    "img_rows = 32\n",
+    "img_cols = 32\n",
+    "channels = 3\n",
+    "nb_classes = 10\n",
+    "\n",
+    "# Model specifications\n",
+    "nb_filters = 32\n",
+    "batch_size = 128\n",
+    "nb_samples = 10000\n",
+    "\n",
+    "abits=2\n",
+    "wbits=4\n",
+    "\n",
+    "abits2=2\n",
+    "wbits2=2\n",
+    "\n",
+    "model_path1 = './CIFARconv/Model1'\n",
+    "model_path2 = './CIFARconv/Model2'\n",
+    "model_path3 = './CIFARconv/Model3'\n",
+    "\n",
+    "# Scaling input to softmax\n",
+    "INIT_T = 1.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "X_train shape: (50000, 32, 32, 3)\n",
+      "50000 train samples\n",
+      "10000 test samples\n"
+     ]
+    }
+   ],
+   "source": [
+    "def data_cifar10():\n",
+    "    \"\"\"\n",
+    "    Preprocess CIFAR10 dataset\n",
+    "    :return:\n",
+    "    \"\"\"\n",
+    "\n",
+    "    # These values are specific to CIFAR10\n",
+    "    img_rows = 32\n",
+    "    img_cols = 32\n",
+    "    nb_classes = 10\n",
+    "\n",
+    "    # the data, shuffled and split between train and test sets\n",
+    "    (X_train, y_train), (X_test, y_test) = cifar10.load_data()\n",
+    "\n",
+    "    X_train = X_train.reshape(X_train.shape[0], img_rows, img_cols, 3)\n",
+    "    X_test = X_test.reshape(X_test.shape[0], img_rows, img_cols, 3)\n",
+    "\n",
+    "    X_train = X_train.astype('float32')\n",
+    "    X_test = X_test.astype('float32')\n",
+    "    \n",
+    "    X_train /= 255\n",
+    "    X_test /= 255\n",
+    "\n",
+    "    print('X_train shape:', X_train.shape)\n",
+    "    print(X_train.shape[0], 'train samples')\n",
+    "    print(X_test.shape[0], 'test samples')\n",
+    "\n",
+    "    # convert class vectors to binary class matrices\n",
+    "    Y_train = np_utils.to_categorical(y_train, nb_classes)\n",
+    "    Y_test = np_utils.to_categorical(y_test, nb_classes)\n",
+    "    return X_train, Y_train, X_test, Y_test\n",
+    "\n",
+    "# Get CIFAR10 test data\n",
+    "X_train, Y_train, X_test, Y_test = data_cifar10()\n",
+    "\n",
+    "assert Y_train.shape[1] == 10.\n",
+    "label_smooth = .1\n",
+    "Y_train = Y_train.clip(label_smooth / 9., 1. - label_smooth)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create placegolders\n",
+    "x = tf.placeholder(tf.float32, shape=(None, img_rows, img_cols, channels))\n",
+    "y = tf.placeholder(tf.float32, shape=(None, 10))\n",
+    "phase = tf.placeholder(tf.bool, name=\"phase\")\n",
+    "logits_scalar = tf.placeholder_with_default(INIT_T, shape=(), name=\"logits_temperature\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%capture\n",
+    "from EMPIR.cleverhans_tutorials.tutorial_models import make_ensemble_three_cifar_cnn\n",
+    "model = make_ensemble_three_cifar_cnn(phase, logits_scalar, 'lp1_', 'lp2_', 'fp_', wbits, abits, wbits2,\n",
+    "                                      abits2, input_shape=(None, img_rows, img_cols, channels),\n",
+    "                                      nb_filters=nb_filters) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "%%capture\n",
+    "preds_index = model.ensemble_call(x, reuse=False)\n",
+    "preds_one_hot = tf.one_hot(preds_index, depth=nb_classes, on_value=None, off_value=None, axis=None,\n",
+    "                           dtype=None, name=None)\n",
+    "preds_prob = model.get_probs(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test accuracy on legitimate test examples: 0.7256\n"
+     ]
+    }
+   ],
+   "source": [
+    "variables = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)\n",
+    "stored_variables = ['lp_conv1_init/k', 'lp_conv2_init/k', 'lp_conv3_init/k', 'lp_ip1init/W',\n",
+    "                    'lp_logits_init/W']\n",
+    "variable_dict = dict(zip(stored_variables, variables[:5]))\n",
+    "\n",
+    "# Restore the first set of variables from model_path1\n",
+    "saver = tf.train.Saver(variable_dict)\n",
+    "saver.restore(sess, tf.train.latest_checkpoint(model_path1))\n",
+    "\n",
+    "# Restore the second set of variables from model_path2\n",
+    "variable_dict = dict(zip(stored_variables, variables[5:10]))\n",
+    "saver2 = tf.train.Saver(variable_dict)\n",
+    "saver2.restore(sess, tf.train.latest_checkpoint(model_path2))\n",
+    "stored_variables = ['fp_conv1_init/k', 'fp_conv2_init/k', 'fp_conv3_init/k', 'fp_ip1init/W',\n",
+    "                    'fp_logits_init/W']\n",
+    "variable_dict = dict(zip(stored_variables, variables[10:]))\n",
+    "saver3 = tf.train.Saver(variable_dict)\n",
+    "saver3.restore(sess, tf.train.latest_checkpoint(model_path3))\n",
+    "\n",
+    "# Evaluate the accuracy of the CIFAR10 model on legitimate test examples\n",
+    "eval_params = {'batch_size': batch_size}\n",
+    "accuracy = model_eval_ensemble(sess, x, y, preds_index, X_test, Y_test, phase=phase, args=eval_params)\n",
+    "print('Test accuracy on legitimate test examples: {0}'.format(accuracy))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_accuracy(X, Y, batch_size, predictions):\n",
+    "    \n",
+    "    sum_correct = 0\n",
+    "    sum_samples = 0\n",
+    "\n",
+    "    with sess.as_default():\n",
+    "\n",
+    "        nb_batches = int(X.shape[0] / batch_size)\n",
+    "\n",
+    "        for i_batch in range(nb_batches):\n",
+    "        \n",
+    "            i_start = i_batch * batch_size\n",
+    "            i_end = i_start + batch_size\n",
+    "        \n",
+    "            if i_end <= X.shape[0]:\n",
+    "        \n",
+    "                feed_dict = {x: X[i_start:i_end],\n",
+    "                             phase: False}\n",
+    "\n",
+    "                y_pred = sess.run(predictions, feed_dict=feed_dict)\n",
+    "                \n",
+    "                sum_correct += np.sum(np.argmax(Y[i_start:i_end], axis=1) == np.argmax(y_pred, axis=1))\n",
+    "                sum_samples += batch_size\n",
+    "\n",
+    "    accuracy = sum_correct / sum_samples\n",
+    "    \n",
+    "    return accuracy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The accuracy on benign test samples: 72.57%\n"
+     ]
+    }
+   ],
+   "source": [
+    "accuracy_test_benign = get_accuracy(X=X_test, Y=Y_test, batch_size=batch_size, predictions=preds_one_hot)\n",
+    "print('The accuracy on benign test samples: {0:.2f}%'.format(accuracy_test_benign *100))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is in agreement with the Unperturbed Accuracy of 72.56% reported by Sen et al. (2020)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# EMPIR Evaluation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loss = tf.reduce_mean(tf.keras.losses.categorical_crossentropy(y_true=y, y_pred=preds_prob, from_logits=False,\n",
+    "                                                               label_smoothing=0))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "feed_dict = {phase: False}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "classifier_empir = TensorFlowClassifier(input_ph=x,\n",
+    "                                        output=preds_prob,\n",
+    "                                        labels_ph=y,\n",
+    "                                        train=None,\n",
+    "                                        loss=loss,\n",
+    "                                        learning=phase,\n",
+    "                                        sess=sess,\n",
+    "                                        channels_first=False,\n",
+    "                                        clip_values=(0, 1),\n",
+    "                                        preprocessing=(0, 1),\n",
+    "                                        feed_dict=feed_dict)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "attack_empir = ProjectedGradientDescent(classifier=classifier_empir,\n",
+    "                                        norm=np.inf,\n",
+    "                                        eps=0.1,\n",
+    "                                        eps_step=0.01,\n",
+    "                                        max_iter=40,\n",
+    "                                        targeted=False,\n",
+    "                                        num_random_init=1,\n",
+    "                                        batch_size=batch_size)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_test_adv = attack_empir.generate(X_test[:nb_samples], Y_test[:nb_samples])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy on adversarial test examples created by ART using EMPIR's loss: 11.57%\n"
+     ]
+    }
+   ],
+   "source": [
+    "accuracy_test_adv = get_accuracy(X=X_test_adv, Y=Y_test, batch_size=batch_size, predictions=preds_one_hot)\n",
+    "print('Accuracy on adversarial test examples created by ART using EMPIR\\'s loss: '\n",
+    "      '{0:.2f}%'.format(accuracy_test_adv * 100))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Accuracy on adversarial test examples created by ART using EMPIR's loss is in agreement with the accuracy of 13.55% reported by Sen et al. (2020)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 12.3 Final Robustness Evaluation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%capture\n",
+    "feed_dict = {x: X_test[0:12], phase: False}\n",
+    "\n",
+    "x_1 = x\n",
+    "for layer in model.layers1:\n",
+    "    x_1 = layer.fprop(x_1, reuse=False)\n",
+    "    assert x_1 is not None\n",
+    "preds_prob_1 = x_1\n",
+    "    \n",
+    "x_2 = x\n",
+    "for layer in model.layers2:\n",
+    "    x_2 = layer.fprop(x_2, reuse=False)\n",
+    "    assert x_2 is not None\n",
+    "preds_prob_2 = x_2\n",
+    "    \n",
+    "x_3 = x\n",
+    "for layer in model.layers3:\n",
+    "    x_3 = layer.fprop(x_3, reuse=False)\n",
+    "    assert x_3 is not None\n",
+    "preds_prob_3 = x_3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%capture\n",
+    "preds_prob_new = (preds_prob_1 + preds_prob_2 + preds_prob_3) / 3\n",
+    "loss_new = tf.keras.losses.categorical_crossentropy(y_true=y, y_pred=preds_prob_new, from_logits=False,\n",
+    "                                                    label_smoothing=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "feed_dict = {phase: False}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "classifier_eval = TensorFlowClassifier(input_ph=x,\n",
+    "                                       output=preds_prob_new,\n",
+    "                                       labels_ph=y,\n",
+    "                                       train=None,\n",
+    "                                       loss=loss_new,\n",
+    "                                       learning=phase,\n",
+    "                                       sess=sess,\n",
+    "                                       channels_first=False,\n",
+    "                                       clip_values=(0, 1),\n",
+    "                                       preprocessing=(0, 1),\n",
+    "                                       feed_dict=feed_dict)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "attack_eval = ProjectedGradientDescent(classifier=classifier_eval,\n",
+    "                                       norm=np.inf,\n",
+    "                                       eps=0.031,\n",
+    "                                       eps_step=0.0078,\n",
+    "                                       max_iter=100,\n",
+    "                                       targeted=False,\n",
+    "                                       num_random_init=1,\n",
+    "                                       batch_size=batch_size)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_test_adv_final = attack_eval.generate(X_test[:nb_samples], Y_test[:nb_samples])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy on adversarial test examples created by ART using the loss by Tramer et al. (2020): 1.41%.\n"
+     ]
+    }
+   ],
+   "source": [
+    "accuracy_test_adv_final = get_accuracy(X=X_test_adv_final, Y=Y_test, batch_size=batch_size,\n",
+    "                                       predictions=preds_one_hot)\n",
+    "print('Accuracy on adversarial test examples created by ART using the loss by Tramer et al. (2020): '\n",
+    "      '{0:.2f}%.'.format(accuracy_test_adv_final * 100))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is in agreement with the accuracy of 1.5% reported by Tramer et al. (2020)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exercises for the reader (from Tramèr et al.)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "1. We only construct a very simple loss function that reduces model accuracy to 1.5%. Can a stronger (probably consistent) loss function reduce the accuracy to 0%? At ε = 4/255?\n",
+    "2. Try to attack each of the models $f_{i}$ individually. Are they all similarly robust, or is one harder to attack than the others? (If the robustness of the models differ, how might a more consistent loss function look to attack the full defense)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model 1 - Accuracy on benign test samples: 64.55%.\n",
+      "Model 2 - Accuracy on benign test samples: 61.80%.\n",
+      "Model 3 - Accuracy on benign test samples: 74.54%.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Get accuracy on benging test samples for each model separately\n",
+    "\n",
+    "accuracy_test_benign_1 = get_accuracy(X=X_test, Y=Y_test, batch_size=batch_size, predictions=preds_prob_1)\n",
+    "print('Model 1 - Accuracy on benign test samples: {0:.2f}%.'.format(accuracy_test_benign_1 * 100))\n",
+    "accuracy_test_benign_2 = get_accuracy(X=X_test, Y=Y_test, batch_size=batch_size, predictions=preds_prob_2)\n",
+    "print('Model 2 - Accuracy on benign test samples: {0:.2f}%.'.format(accuracy_test_benign_2 * 100))\n",
+    "accuracy_test_benign_3 = get_accuracy(X=X_test, Y=Y_test, batch_size=batch_size, predictions=preds_prob_3)\n",
+    "print('Model 3 - Accuracy on benign test samples: {0:.2f}%.'.format(accuracy_test_benign_3 * 100))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model 1 - Accuracy on adversarial test examples: 0.98%.\n",
+      "Model 2 - Accuracy on adversarial test examples: 1.06%.\n",
+      "Model 3 - Accuracy on adversarial test examples: 0.02%.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Get accuracy on adversarial test examples for each model separately\n",
+    "\n",
+    "for i_pred, preds_prob_i in enumerate([preds_prob_1, preds_prob_2, preds_prob_3]):\n",
+    "    \n",
+    "    loss_i = tf.keras.losses.categorical_crossentropy(y_true=y, y_pred=preds_prob_i, from_logits=False,\n",
+    "                                                      label_smoothing=0)\n",
+    "\n",
+    "    classifier_eval_i = TensorFlowClassifier(input_ph=x,\n",
+    "                                             output=preds_prob_i,\n",
+    "                                             labels_ph=y,\n",
+    "                                             train=None,\n",
+    "                                             loss=loss_i,\n",
+    "                                             learning=phase,\n",
+    "                                             sess=sess,\n",
+    "                                             channels_first=False,\n",
+    "                                             clip_values=(0, 1),\n",
+    "                                             preprocessing=(0, 1),\n",
+    "                                             feed_dict=feed_dict)\n",
+    "\n",
+    "    attack_eval_i = ProjectedGradientDescent(classifier=classifier_eval_i,\n",
+    "                                             norm=np.inf,\n",
+    "                                             eps=0.031,\n",
+    "                                             eps_step=0.0078,\n",
+    "                                             max_iter=100,\n",
+    "                                             targeted=False,\n",
+    "                                             num_random_init=1,\n",
+    "                                             batch_size=batch_size)\n",
+    "\n",
+    "    X_test_adv_i = attack_eval_i.generate(X_test[:nb_samples], Y_test[:nb_samples])\n",
+    "    \n",
+    "    accuracy_test_adv_i = get_accuracy(X=X_test_adv_i, Y=Y_test, batch_size=batch_size,\n",
+    "                                       predictions=preds_prob_i)\n",
+    "    print('Model {0} - Accuracy on adversarial test examples: {1:.2f}%.'.format(i_pred + 1, \n",
+    "                                                                               accuracy_test_adv_i * 100))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/adversarial-robustness-toolbox/notebooks/adversarial_action_recognition.ipynb b/adversarial-robustness-toolbox/notebooks/adversarial_action_recognition.ipynb
new file mode 100644
index 0000000..f92e9d0
--- /dev/null
+++ b/adversarial-robustness-toolbox/notebooks/adversarial_action_recognition.ipynb
@@ -0,0 +1,527 @@
+{
+ "cells": [
+  {
+   "attachments": {
+    "basketball.gif": {
+     "image/gif": "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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Adversarial Action Recognition Attack\n",
+    "\n",
+    "This notebook demonstrates how to use the ART library to conduct an adversarial attack on video action recognition.\n",
+    "\n",
+    "---\n",
+    "\n",
+    "First, we are going to show how to use [MXNet](https://mxnet.apache.org/) and [GluonCV](https://gluon-cv.mxnet.io/) for video action recognition. MXNet provides a set of pretrained models for this classification task. In our demonstration we use the pretrained `i3d_resnet50_v1_ucf101` model, which is based on [Carreira and Zisserman '18](https://arxiv.org/abs/1705.07750).\n",
+    "\n",
+    "In the following we are going to use a video clip of a **basketball** action taken from the [UCF101](https://www.crcv.ucf.edu/data/UCF101.php) data set. Namely, we will show how to correctly classify the following short video clip.\n",
+    "\n",
+    "![basketball.gif](attachment:basketball.gif)\n",
+    "\n",
+    "Let's walk through some initial work steps ensuring that the notebook will work smoothly. We will\n",
+    "\n",
+    "1. set up a small configuration cell,\n",
+    "2. define some helpful Python functions,\n",
+    "3. download the basketball video sample and load the pretrained action recognition model,\n",
+    "4. and show that the model correctly classifies the video action as playing basketball.\n",
+    "\n",
+    "\n",
+    "## Load Model and Basketball Sample"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import tempfile\n",
+    "\n",
+    "import decord\n",
+    "from gluoncv import utils\n",
+    "from gluoncv.data.transforms import video\n",
+    "from gluoncv.model_zoo import get_model\n",
+    "from gluoncv.utils.filesystem import try_import_decord\n",
+    "import imageio\n",
+    "from matplotlib.image import imsave\n",
+    "import matplotlib.pyplot as plt\n",
+    "import mxnet as mx\n",
+    "from mxnet import gluon, nd, image\n",
+    "from mxnet.gluon.data.vision import transforms\n",
+    "import numpy as np\n",
+    "\n",
+    "from art.attacks.evasion import FastGradientMethod, FrameSaliencyAttack\n",
+    "from art import config\n",
+    "from art.defences.preprocessor import VideoCompression\n",
+    "from art.estimators.classification import MXClassifier\n",
+    "\n",
+    "# set global variables\n",
+    "PRETRAINED_MODEL_NAME = 'i3d_resnet50_v1_ucf101'\n",
+    "VIDEO_SAMPLE_URI = 'https://github.com/bryanyzhu/tiny-ucf101/raw/master/v_Basketball_g01_c01.avi'\n",
+    "\n",
+    "# set seed\n",
+    "np.random.seed(123)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def predict_top_k(video_input, model, k=5, verbose=True):\n",
+    "    \"\"\"Return top-k class indices.\"\"\"\n",
+    "    pred = model(nd.array(video_input))\n",
+    "    classes = model.classes    \n",
+    "    ind = nd.topk(pred, k=k)[0].astype('int')\n",
+    "    if verbose:\n",
+    "        msg = \"The video sample clip is classified to be\"\n",
+    "        for i in range(k):\n",
+    "            msg += f\"\\n\\t[{classes[ind[i].asscalar()]}], with probability {nd.softmax(pred)[0][ind[i]].asscalar():.3f}.\"\n",
+    "        print(msg)\n",
+    "    return ind\n",
+    "\n",
+    "\n",
+    "def sample_to_gif(sample, output=\"sample.gif\", path=config.ART_DATA_PATH, postprocess=None):\n",
+    "    \"\"\"Convert a numpy video sample of shape 3xFramesxMxN into GIF.\"\"\"\n",
+    "    frame_count = sample.shape[1]\n",
+    "    output_path = os.path.join(path, output)\n",
+    "    with tempfile.TemporaryDirectory() as tmpdir, imageio.get_writer(output_path, mode='I') as writer:\n",
+    "        for frame in range(frame_count):\n",
+    "            file_path = os.path.join(tmpdir, f\"{frame}.png\")\n",
+    "            imsave(file_path, np.transpose(sample[:,frame,:,:], (1,2,0)))\n",
+    "            writer.append_data(imageio.imread(file_path))\n",
+    "    return output_path"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "`/home/hessel/.art/data/v_Basketball_g01_c01.avi` has been downloaded and preprocessed.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# download video sample\n",
+    "decord = try_import_decord()\n",
+    "video_fname = utils.download(VIDEO_SAMPLE_URI, path=config.ART_DATA_PATH);\n",
+    "video_reader = decord.VideoReader(video_fname)\n",
+    "frame_id_list = range(0, 64, 2)\n",
+    "video_data = video_reader.get_batch(frame_id_list).asnumpy()\n",
+    "video_sample_lst = [video_data[vid, :, :, :] for vid, _ in enumerate(frame_id_list)]\n",
+    "\n",
+    "# preprocess benign video sample\n",
+    "transform_fn = video.VideoGroupValTransform(size=224, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
+    "sample = np.stack(transform_fn(video_sample_lst),  axis=0)\n",
+    "sample = sample.reshape((-1,) + (32, 3, 224, 224))\n",
+    "sample = np.transpose(sample, (0, 2, 1, 3, 4))\n",
+    "print(f\"`{video_fname}` has been downloaded and preprocessed.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "`i3d_resnet50_v1_ucf101` model was successfully loaded.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# load pretrained model\n",
+    "model = get_model(PRETRAINED_MODEL_NAME, nclass=101, pretrained=True)\n",
+    "print(f\"`{PRETRAINED_MODEL_NAME}` model was successfully loaded.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The video sample clip is classified to be\n",
+      "\t[Basketball], with probability 0.725.\n",
+      "\t[TennisSwing], with probability 0.212.\n",
+      "\t[VolleyballSpiking], with probability 0.036.\n",
+      "\t[SoccerJuggling], with probability 0.012.\n",
+      "\t[TableTennisShot], with probability 0.007.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# evaluate model on basketball video sample\n",
+    "_ = predict_top_k(sample, model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We observe that for the given video sample, the model correctly classified it as playing **basketball**.\n",
+    "\n",
+    "## Create Adversarial Attack\n",
+    "\n",
+    "We are now ready to employ the ART library and craft an adversarial attack via the Fast Gradient Method. The attack will corrupt the video sample in such a way that it will be misclassified. We will also show how to convert the adversarial example into a GIF as shown below.\n",
+    "\n",
+    "![AdversarialBasketball](../utils/data/images/adversarial_basketball_8-255.gif \"adversarial basketball\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# preprocess adversarial video sample input\n",
+    "transform_fn_unnormalized = video.VideoGroupValTransform(size=224, mean=[0, 0, 0], std=[1, 1, 1])\n",
+    "adv_sample_input = np.stack(transform_fn_unnormalized(video_sample_lst),  axis=0)\n",
+    "adv_sample_input = adv_sample_input.reshape((-1,) + (32, 3, 224, 224))\n",
+    "adv_sample_input = np.transpose(adv_sample_input, (0, 2, 1, 3, 4))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# wrap model in a ART classifier\n",
+    "model_wrapper = gluon.nn.Sequential()\n",
+    "with model_wrapper.name_scope():\n",
+    "    model_wrapper.add(model)\n",
+    "\n",
+    "# prepare mean and std arrays for ART classifier preprocessing\n",
+    "mean = np.array([0.485, 0.456, 0.406] * (32 * 224 * 224)).reshape((3, 32, 224, 224), order='F')\n",
+    "std = np.array([0.229, 0.224, 0.225] * (32 * 224 * 224)).reshape((3, 32, 224, 224), order='F')\n",
+    "\n",
+    "classifier_art = MXClassifier(\n",
+    "    model=model_wrapper,\n",
+    "    loss=gluon.loss.SoftmaxCrossEntropyLoss(),\n",
+    "    input_shape=(3, 32, 224, 224),\n",
+    "    nb_classes=101,\n",
+    "    preprocessing=(mean, std),\n",
+    "    clip_values=(0, 1),\n",
+    "    channels_first=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The video sample clip is classified by the ART classifier to be\n",
+      "\t[Basketball], with probability 0.725.\n",
+      "\t[TennisSwing], with probability 0.212.\n",
+      "\t[VolleyballSpiking], with probability 0.036.\n",
+      "\t[SoccerJuggling], with probability 0.012.\n",
+      "\t[TableTennisShot], with probability 0.007.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# verify that ART classifier predictions are consistent with original model:\n",
+    "pred = nd.array(classifier_art.predict(adv_sample_input))\n",
+    "ind = nd.topk(pred, k=5)[0].astype('int')\n",
+    "\n",
+    "msg = \"The video sample clip is classified by the ART classifier to be\"\n",
+    "for i in range(len(ind)):\n",
+    "    msg += f\"\\n\\t[{model.classes[ind[i].asscalar()]}], with probability {nd.softmax(pred)[0][ind[i]].asscalar():.3f}.\"\n",
+    "print(msg)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# craft adversarial attack with FGM\n",
+    "epsilon = 8/255\n",
+    "fgm = FastGradientMethod(\n",
+    "    classifier_art,\n",
+    "    eps=epsilon,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 3min 7s, sys: 1.77 s, total: 3min 9s\n",
+      "Wall time: 2min 48s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "adv_sample = fgm.generate(\n",
+    "    x=adv_sample_input\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The video sample clip is classified to be\n",
+      "\t[ThrowDiscus], with probability 0.266.\n",
+      "\t[Hammering], with probability 0.244.\n",
+      "\t[TennisSwing], with probability 0.155.\n",
+      "\t[HulaHoop], with probability 0.082.\n",
+      "\t[JavelinThrow], with probability 0.055.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# print results\n",
+    "_ = predict_top_k((adv_sample-mean)/std, model)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "`/home/hessel/.art/data/adversarial_basketball.gif` has been successfully created.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# now we save the adversarial example to gif:\n",
+    "adversarial_gif = sample_to_gif(np.squeeze(adv_sample), \"adversarial_basketball.gif\")\n",
+    "print(f\"`{adversarial_gif}` has been successfully created.\")"
+   ]
+  },
+  {
+   "attachments": {
+    "adversarial_basketball_sparse.gif": {
+     "image/gif": "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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create Sparse Adversarial Attack\n",
+    "\n",
+    "Using the Frame Saliency Attack, we finally show how to create a sparse adversarial example. The final result is shown in the GIF below. As we will see, only one frame needs to be perturbed to achieve a misclassification.\n",
+    "\n",
+    "![adversarial_basketball_sparse.gif](attachment:adversarial_basketball_sparse.gif)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# We create a Frame Saliency Attack. Note: we specify here the frame axis, which is 2.\n",
+    "fsa = FrameSaliencyAttack(\n",
+    "    classifier_art,\n",
+    "    fgm,\n",
+    "    \"iterative_saliency\",\n",
+    "    frame_index = 2\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 6min 48s, sys: 3.1 s, total: 6min 52s\n",
+      "Wall time: 6min 6s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "adv_sample_sparse = fsa.generate(\n",
+    "    x=adv_sample_input\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The video sample clip is classified to be\n",
+      "\t[TennisSwing], with probability 0.497.\n",
+      "\t[Basketball], with probability 0.425.\n",
+      "\t[VolleyballSpiking], with probability 0.040.\n",
+      "\t[SoccerJuggling], with probability 0.014.\n",
+      "\t[TableTennisShot], with probability 0.009.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# We print the resulting predictions:\n",
+    "_ = predict_top_k((adv_sample_sparse-mean)/std, model)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "`/home/hessel/.art/data/adversarial_basketball_sparse.gif` has been successfully created.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Again, we can save the adversarial example to gif:\n",
+    "adversarial_sparse_gif = sample_to_gif(np.squeeze(adv_sample_sparse), \"adversarial_basketball_sparse.gif\")\n",
+    "print(f\"`{adversarial_sparse_gif}` has been successfully created.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of perturbed frames: 1\n"
+     ]
+    }
+   ],
+   "source": [
+    "# And now count the number of perturbed frames:\n",
+    "x_diff = adv_sample_sparse - adv_sample_input\n",
+    "x_diff = np.swapaxes(x_diff, 1, 2)\n",
+    "x_diff = np.reshape(x_diff, x_diff.shape[:2] + (np.prod(x_diff.shape[2:]), ))\n",
+    "x_diff_norm = np.sign(np.round(np.linalg.norm(x_diff, axis=-1), decimals=4))\n",
+    "print(f\"Number of perturbed frames: {int(np.sum(x_diff_norm))}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Apply H.264 compression defense\n",
+    "\n",
+    "Next we are going to apply a simple input preprocessing defense, namely `VideoCompression`. Ideally, we want this defense to result in correct predictions when applied both to the original and the adversarial video input."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The video sample clip is classified to be\n",
+      "\t[Basketball], with probability 0.516.\n",
+      "\t[TennisSwing], with probability 0.443.\n",
+      "\t[VolleyballSpiking], with probability 0.018.\n",
+      "\t[TableTennisShot], with probability 0.010.\n",
+      "\t[SoccerJuggling], with probability 0.005.\n",
+      "The video sample clip is classified to be\n",
+      "\t[Basketball], with probability 0.697.\n",
+      "\t[TennisSwing], with probability 0.242.\n",
+      "\t[VolleyballSpiking], with probability 0.026.\n",
+      "\t[SoccerJuggling], with probability 0.011.\n",
+      "\t[TableTennisShot], with probability 0.009.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# initialize VideoCompression defense\n",
+    "video_compression = VideoCompression(video_format=\"avi\", constant_rate_factor=30, channels_first=True)\n",
+    "\n",
+    "# apply defense to original input\n",
+    "adv_sample_input_compressed = video_compression(adv_sample_input * 255)[0] / 255\n",
+    "\n",
+    "# apply defense to sparse adversarial sample\n",
+    "adv_sample_sparse_compressed = video_compression(adv_sample_sparse * 255)[0] / 255\n",
+    "\n",
+    "# print the resulting predictions on compressed original input\n",
+    "_ = predict_top_k((adv_sample_input_compressed-mean)/std, model)\n",
+    "\n",
+    "# print the resulting predictions on sparse adversarial sample\n",
+    "_ = predict_top_k((adv_sample_sparse_compressed-mean)/std, model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Conclusion\n",
+    "In this notebook we have demonstrated how we can apply the ART library to a video action recognition task. By employing a pretrained MXNet model and loading it via ART's `MXNetClassifier` we can easily plug in several off the shelf attacks like Fast Gradient Method."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/adversarial-robustness-toolbox/notebooks/adversarial_audio_examples.ipynb b/adversarial-robustness-toolbox/notebooks/adversarial_audio_examples.ipynb
new file mode 100644
index 0000000..4d5dd0d
--- /dev/null
+++ b/adversarial-robustness-toolbox/notebooks/adversarial_audio_examples.ipynb
@@ -0,0 +1,1242 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Adversarial Audio Examples\n",
+    "\n",
+    "This notebook demonstrates how to use the ART library to create adversarial audio examples.\n",
+    "\n",
+    "---\n",
+    "\n",
+    "## Preliminaries\n",
+    "\n",
+    "Before diving into the different steps necessary, we walk through some initial work steps ensuring that the notebook will work smoothly. We will \n",
+    "\n",
+    "1. set up a small configuration cell,\n",
+    "2. check if the test data and pretrained model are available or otherwise download them\n",
+    "3. define some necessary Python classes to handle the data.\n",
+    "\n",
+    "**Important note:** This notebook requires `torch==1.4.0`, `torchvision==0.5.0` and `torchaudio==0.4.0`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "import glob\n",
+    "import os\n",
+    "\n",
+    "import IPython.display as ipd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import torchaudio\n",
+    "\n",
+    "\n",
+    "\n",
+    "from art.attacks.evasion import ProjectedGradientDescent\n",
+    "from art.estimators.classification import PyTorchClassifier\n",
+    "from art import config\n",
+    "from art.defences.preprocessor import Mp3Compression\n",
+    "from art.utils import get_file\n",
+    "\n",
+    "OUTPUT_SIZE = 8000\n",
+    "ORIGINAL_SAMPLING_RATE = 48000\n",
+    "DOWNSAMPLED_SAMPLING_RATE = 8000\n",
+    "\n",
+    "# set global variables\n",
+    "AUDIO_DATA_PATH = os.path.join(config.ART_DATA_PATH, \"audiomnist/test\")\n",
+    "AUDIO_MODEL_PATH = os.path.join(config.ART_DATA_PATH, \"adversarial_audio_model.pt\")\n",
+    "\n",
+    "# set seed\n",
+    "np.random.seed(123)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# download AudioMNIST data and pretrained model\n",
+    "get_file('adversarial_audio_model.pt', 'https://www.dropbox.com/s/o7nmahozshz2k3i/model_raw_audio_state_dict_202002260446.pt?dl=1')\n",
+    "get_file('audiomnist.tar.gz', 'https://api.github.com/repos/soerenab/AudioMNIST/tarball');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%bash -s \"$AUDIO_DATA_PATH\" \"$ART_DATA_PATH\"\n",
+    "mkdir -p $1\n",
+    "tar -xf $2/audiomnist.tar.gz \\\n",
+    "    -C $1 \\\n",
+    "    --strip-components=2 */data \\\n",
+    "    --exclude=**/*/{01,02,03,04,05,06,07,08,09,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# dataloader and preprocessing classes.\n",
+    "class AudioMNISTDataset(torch.utils.data.Dataset):\n",
+    "    \"\"\"Dataset object for the AudioMNIST data set.\"\"\"\n",
+    "    def __init__(self, root_dir, transform=None, verbose=False):\n",
+    "        self.root_dir = root_dir\n",
+    "        self.audio_list = sorted(glob.glob(f\"{root_dir}/*/*.wav\"))\n",
+    "        self.transform = transform\n",
+    "        self.verbose = verbose\n",
+    "\n",
+    "    def __len__(self):\n",
+    "        return len(self.audio_list)\n",
+    "\n",
+    "    def __getitem__(self, idx):\n",
+    "        audio_fn = self.audio_list[idx]\n",
+    "        if self.verbose:\n",
+    "            print(f\"Loading audio file {audio_fn}\")\n",
+    "        waveform, sample_rate = torchaudio.load_wav(audio_fn)\n",
+    "        if self.transform:\n",
+    "            waveform = self.transform(waveform)\n",
+    "        sample = {\n",
+    "            'input': waveform,\n",
+    "            'digit': int(os.path.basename(audio_fn).split(\"_\")[0])\n",
+    "        }\n",
+    "        return sample\n",
+    "\n",
+    "\n",
+    "class PreprocessRaw(object):\n",
+    "    \"\"\"Transform audio waveform of given shape.\"\"\"\n",
+    "    def __init__(self, size_out=OUTPUT_SIZE, orig_freq=ORIGINAL_SAMPLING_RATE,\n",
+    "                 new_freq=DOWNSAMPLED_SAMPLING_RATE):\n",
+    "        self.size_out = size_out\n",
+    "        self.orig_freq = orig_freq\n",
+    "        self.new_freq = new_freq\n",
+    "\n",
+    "    def __call__(self, waveform):\n",
+    "        transformed_waveform = _ZeroPadWaveform(self.size_out)(\n",
+    "            _ResampleWaveform(self.orig_freq, self.new_freq)(waveform)\n",
+    "        )\n",
+    "        return transformed_waveform\n",
+    "\n",
+    "\n",
+    "class _ResampleWaveform(object):\n",
+    "    \"\"\"Resample signal frequency.\"\"\"\n",
+    "    def __init__(self, orig_freq, new_freq):\n",
+    "        self.orig_freq = orig_freq\n",
+    "        self.new_freq = new_freq\n",
+    "\n",
+    "    def __call__(self, waveform):\n",
+    "        return self._resample_waveform(waveform)\n",
+    "\n",
+    "    def _resample_waveform(self, waveform):\n",
+    "        resampled_waveform = torchaudio.transforms.Resample(\n",
+    "            orig_freq=self.orig_freq,\n",
+    "            new_freq=self.new_freq,\n",
+    "        )(waveform)\n",
+    "        return resampled_waveform\n",
+    "\n",
+    "\n",
+    "class _ZeroPadWaveform(object):\n",
+    "    \"\"\"Apply zero-padding to waveform.\n",
+    "\n",
+    "    Return a zero-padded waveform of desired output size. The waveform is\n",
+    "    positioned randomly.\n",
+    "    \"\"\"\n",
+    "    def __init__(self, size_out):\n",
+    "        self.size_out = size_out\n",
+    "\n",
+    "    def __call__(self, waveform):\n",
+    "        return self._zero_pad_waveform(waveform)\n",
+    "\n",
+    "    def _zero_pad_waveform(self, waveform):\n",
+    "        padding_total = self.size_out - waveform.shape[-1]\n",
+    "        padding_left = np.random.randint(padding_total + 1)\n",
+    "        padding_right = padding_total - padding_left\n",
+    "        padded_waveform = torch.nn.ConstantPad1d(\n",
+    "            (padding_left, padding_right),\n",
+    "            0\n",
+    "        )(waveform)\n",
+    "        return padded_waveform"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# RawAudioCNN model class\n",
+    "class RawAudioCNN(nn.Module):\n",
+    "    \"\"\"Adaption of AudioNet (arXiv:1807.03418).\"\"\"\n",
+    "    def __init__(self):\n",
+    "        super().__init__()\n",
+    "        # 1 x 8000\n",
+    "        self.conv1 = nn.Sequential(\n",
+    "            nn.Conv1d(1, 100, kernel_size=3, stride=1, padding=2),\n",
+    "            nn.BatchNorm1d(100),\n",
+    "            nn.ReLU(),\n",
+    "            nn.MaxPool1d(3, stride=2))\n",
+    "        # 32 x 4000\n",
+    "        self.conv2 = nn.Sequential(\n",
+    "            nn.Conv1d(100, 64, kernel_size=3, stride=1, padding=1),\n",
+    "            nn.BatchNorm1d(64),\n",
+    "            nn.ReLU(),\n",
+    "            nn.MaxPool1d(2, stride=2))\n",
+    "        # 64 x 2000\n",
+    "        self.conv3 = nn.Sequential(\n",
+    "            nn.Conv1d(64, 128, kernel_size=3, stride=1, padding=1),\n",
+    "            nn.BatchNorm1d(128),\n",
+    "            nn.ReLU(),\n",
+    "            nn.MaxPool1d(2, stride=2))\n",
+    "        # 128 x 1000\n",
+    "        self.conv4 = nn.Sequential(\n",
+    "            nn.Conv1d(128, 128, kernel_size=3, stride=1, padding=1),\n",
+    "            nn.BatchNorm1d(128),\n",
+    "            nn.ReLU(),\n",
+    "            nn.MaxPool1d(2, stride=2))\n",
+    "        # 128 x 500\n",
+    "        self.conv5 = nn.Sequential(\n",
+    "            nn.Conv1d(128, 128, kernel_size=3, stride=1, padding=1),\n",
+    "            nn.BatchNorm1d(128),\n",
+    "            nn.ReLU(),\n",
+    "            nn.MaxPool1d(2, stride=2))\n",
+    "        # 128 x 250\n",
+    "        self.conv6 = nn.Sequential(\n",
+    "            nn.Conv1d(128, 128, kernel_size=3, stride=1, padding=1),\n",
+    "            nn.BatchNorm1d(128),\n",
+    "            nn.ReLU(),\n",
+    "            nn.MaxPool1d(2, stride=2))\n",
+    "        # 128 x 125\n",
+    "        self.conv7 = nn.Sequential(\n",
+    "            nn.Conv1d(128, 64, kernel_size=3, stride=1, padding=1),\n",
+    "            nn.BatchNorm1d(64),\n",
+    "            nn.ReLU(),\n",
+    "            nn.MaxPool1d(2, stride=2))\n",
+    "        # 64 x 62\n",
+    "        self.conv8 = nn.Sequential(\n",
+    "            nn.Conv1d(64, 32, kernel_size=3, stride=1, padding=0),\n",
+    "            nn.BatchNorm1d(32),\n",
+    "            nn.ReLU(),\n",
+    "            # maybe replace pool with dropout here\n",
+    "            nn.MaxPool1d(2, stride=2))\n",
+    "\n",
+    "        # 32 x 30\n",
+    "        self.fc = nn.Linear(32 * 30, 10)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        x = self.conv1(x)\n",
+    "        x = self.conv2(x)\n",
+    "        x = self.conv3(x)\n",
+    "        x = self.conv4(x)\n",
+    "        x = self.conv5(x)\n",
+    "        x = self.conv6(x)\n",
+    "        x = self.conv7(x)\n",
+    "        x = self.conv8(x)\n",
+    "        x = x.view(x.shape[0], 32 * 30)\n",
+    "        x = self.fc(x)\n",
+    "        return x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def display_waveform(waveform, title=\"\", sr=8000):\n",
+    "    \"\"\"Display waveform plot and audio play UI.\"\"\"\n",
+    "    plt.figure()\n",
+    "    plt.title(title)\n",
+    "    plt.plot(waveform)\n",
+    "    ipd.display(ipd.Audio(waveform, rate=sr))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load Model and Test Data\n",
+    "\n",
+    "In the following section we are going to load the pretrained model that we downloaded in the previous section. Let's also load the test data set from which we will generate adversarial examples."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load AudioMNIST test set\n",
+    "audiomnist_test = AudioMNISTDataset(\n",
+    "    root_dir=AUDIO_DATA_PATH,\n",
+    "    transform=PreprocessRaw(),\n",
+    ")\n",
+    "\n",
+    "# load pretrained model\n",
+    "model = RawAudioCNN()\n",
+    "model.load_state_dict(\n",
+    "    torch.load(AUDIO_MODEL_PATH, map_location=\"cpu\")\n",
+    ")\n",
+    "model.eval();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create Adversarial Examples\n",
+    "\n",
+    "After loading the test set and model, we are ready to employ the ART library. We will first load a sample, which here will have label 1. The classification model correctly classifies it as such. We will then use ART and perform a Projected Gradient Descent attack. The attack will corrupt the spoken audio and will be misclassified as 9. However, there is almost no hearable difference in the original audio file and the adversarial audio file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# wrap model in a ART classifier\n",
+    "classifier_art = PyTorchClassifier(\n",
+    "    model=model,\n",
+    "    loss=torch.nn.CrossEntropyLoss(),\n",
+    "    optimizer=None,\n",
+    "    input_shape=[1, DOWNSAMPLED_SAMPLING_RATE],\n",
+    "    nb_classes=10,\n",
+    "    clip_values=(-2**15, 2**15 - 1)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Original prediction (ground truth):\t1 (1)\n",
+      "Adversarial prediction:\t\t\t9\n"
+     ]
+    }
+   ],
+   "source": [
+    "# load a test sample\n",
+    "sample = audiomnist_test[3559]\n",
+    "\n",
+    "waveform = sample['input']\n",
+    "label = sample['digit']\n",
+    "\n",
+    "# craft adversarial example with PGD\n",
+    "epsilon = .2\n",
+    "pgd = ProjectedGradientDescent(classifier_art, eps=epsilon)\n",
+    "adv_waveform = pgd.generate(\n",
+    "    x=torch.unsqueeze(waveform, 0).numpy()\n",
+    ")\n",
+    "\n",
+    "# evaluate the classifier on the adversarial example\n",
+    "with torch.no_grad():\n",
+    "    _, pred = torch.max(model(torch.unsqueeze(waveform, 0)), 1)\n",
+    "    _, pred_adv = torch.max(model(torch.from_numpy(adv_waveform)), 1)\n",
+    "\n",
+    "# print results\n",
+    "print(f\"Original prediction (ground truth):\\t{pred.tolist()[0]} ({label})\")\n",
+    "print(f\"Adversarial prediction:\\t\\t\\t{pred_adv.tolist()[0]}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We observe that for the given test sample, the model correctly classified it as **1**. Applying PGD, we can create an adversarial example that is now classified as **9**.\n",
+    "\n",
+    "Now we can qualitatively explore the result."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "                <audio  controls=\"controls\" >\n",
+       "                    <source src=\"data:audio/wav;base64,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\" type=\"audio/wav\" />\n",
+       "                    Your browser does not support the audio element.\n",
+       "                </audio>\n",
+       "              "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.Audio object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# display adversarial example\n",
+    "display_waveform(adv_waveform[0,0,:], title=f\"Adversarial Audio Example (classified as {pred_adv.tolist()[0]} instead of {pred.tolist()[0]})\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "                <audio  controls=\"controls\" >\n",
+       "                    <source src=\"data:audio/wav;base64,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\" type=\"audio/wav\" />\n",
+       "                    Your browser does not support the audio element.\n",
+       "                </audio>\n",
+       "              "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.Audio object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# display original example\n",
+    "display_waveform(waveform.numpy()[0,:], title=f\"Original Audio Example (correctly classified as {pred.tolist()[0]})\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's create a second adversarial example."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Original prediction (ground truth):\t5 (5)\n",
+      "Adversarial prediction:\t\t\t6\n"
+     ]
+    }
+   ],
+   "source": [
+    "# load a test sample\n",
+    "sample = audiomnist_test[3773]\n",
+    "\n",
+    "waveform = sample['input']\n",
+    "label = sample['digit']\n",
+    "\n",
+    "# craft adversarial example with PGD\n",
+    "epsilon = 0.5\n",
+    "pgd = ProjectedGradientDescent(classifier_art, eps=epsilon)\n",
+    "adv_waveform = pgd.generate(\n",
+    "    x=torch.unsqueeze(waveform, 0).numpy()\n",
+    ")\n",
+    "\n",
+    "# evaluate the classifier on the adversarial example\n",
+    "with torch.no_grad():\n",
+    "    _, pred = torch.max(model(torch.unsqueeze(waveform, 0)), 1)\n",
+    "    _, pred_adv = torch.max(model(torch.from_numpy(adv_waveform)), 1)\n",
+    "\n",
+    "# print results\n",
+    "print(f\"Original prediction (ground truth):\\t{pred.tolist()[0]} ({label})\")\n",
+    "print(f\"Adversarial prediction:\\t\\t\\t{pred_adv.tolist()[0]}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we observe that for the given test sample, the model correctly classified it as **5**. Applying PGD, we can create an adversarial example that is now classified as **6**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "                <audio  controls=\"controls\" >\n",
+       "                    <source src=\"data:audio/wav;base64,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\" type=\"audio/wav\" />\n",
+       "                    Your browser does not support the audio element.\n",
+       "                </audio>\n",
+       "              "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.Audio object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# display adversarial example\n",
+    "display_waveform(adv_waveform[0,0,:], title=f\"Adversarial Audio Example (classified as {pred_adv.tolist()[0]} instead of {pred.tolist()[0]})\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "                <audio  controls=\"controls\" >\n",
+       "                    <source src=\"data:audio/wav;base64,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\" type=\"audio/wav\" />\n",
+       "                    Your browser does not support the audio element.\n",
+       "                </audio>\n",
+       "              "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.Audio object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# display original example\n",
+    "display_waveform(waveform.numpy()[0,:], title=f\"Original Audio Example (correctly classified as {pred.tolist()[0]})\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We present a final third example. For this example observe that the model correctly classifies it as **8**, but the adversarial example is classified as **3**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Original prediction (ground truth):\t8 (8)\n",
+      "Adversarial prediction:\t\t\t3\n"
+     ]
+    }
+   ],
+   "source": [
+    "# load a test sample\n",
+    "sample = audiomnist_test[5905]\n",
+    "\n",
+    "waveform = sample['input']\n",
+    "label = sample['digit']\n",
+    "\n",
+    "# craft adversarial example with PGD\n",
+    "epsilon = 0.5\n",
+    "pgd = ProjectedGradientDescent(classifier_art, eps=epsilon)\n",
+    "adv_waveform = pgd.generate(\n",
+    "    x=torch.unsqueeze(waveform, 0).numpy()\n",
+    ")\n",
+    "\n",
+    "# evaluate the classifier on the adversarial example\n",
+    "with torch.no_grad():\n",
+    "    _, pred = torch.max(model(torch.unsqueeze(waveform, 0)), 1)\n",
+    "    _, pred_adv = torch.max(model(torch.from_numpy(adv_waveform)), 1)\n",
+    "\n",
+    "# print results\n",
+    "print(f\"Original prediction (ground truth):\\t{pred.tolist()[0]} ({label})\")\n",
+    "print(f\"Adversarial prediction:\\t\\t\\t{pred_adv.tolist()[0]}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "                <audio  controls=\"controls\" >\n",
+       "                    <source src=\"data:audio/wav;base64,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\" type=\"audio/wav\" />\n",
+       "                    Your browser does not support the audio element.\n",
+       "                </audio>\n",
+       "              "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.Audio object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# display adversarial example\n",
+    "display_waveform(adv_waveform[0,0,:], title=f\"Adversarial Audio Example (classified as {pred_adv.tolist()[0]} instead of {pred.tolist()[0]})\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "                <audio  controls=\"controls\" >\n",
+       "                    <source src=\"data:audio/wav;base64,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\" type=\"audio/wav\" />\n",
+       "                    Your browser does not support the audio element.\n",
+       "                </audio>\n",
+       "              "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.Audio object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# display original example\n",
+    "display_waveform(waveform.numpy()[0,:], title=f\"Original Audio Example (correctly classified as {pred.tolist()[0]})\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Apply MP3 compression defense\n",
+    "\n",
+    "Next we are going to apply a simple input preprocessing defense, namely `Mp3Compression`. Ideally, we want this defense to result in correct predictions when applied both to the original and the adversarial audio waveforms."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "MP3 compression: 100%|██████████| 1/1 [00:00<00:00,  6.28it/s]\n",
+      "MP3 compression: 100%|██████████| 1/1 [00:00<00:00,  6.62it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Original prediction with MP3 compression (ground truth):\t8 (8)\n",
+      "Adversarial prediction with MP3 compression:\t\t\t8\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# initialize Mp3Compression defense\n",
+    "mp3_compression = Mp3Compression(sample_rate=DOWNSAMPLED_SAMPLING_RATE, channels_first=True)\n",
+    "\n",
+    "# apply defense to original input\n",
+    "waveform_mp3 = mp3_compression(torch.unsqueeze(waveform, 0).numpy())[0]\n",
+    "\n",
+    "\n",
+    "# apply defense to adversarial sample\n",
+    "adv_waveform_mp3 = mp3_compression(adv_waveform)[0]\n",
+    "\n",
+    "# evaluate the classifier on the adversarial example\n",
+    "with torch.no_grad():\n",
+    "    _, pred_mp3 = torch.max(model(torch.Tensor(waveform_mp3)), 1)\n",
+    "    _, pred_adv_mp3 = torch.max(model(torch.Tensor(adv_waveform_mp3)), 1)\n",
+    "\n",
+    "# print results\n",
+    "print(f\"Original prediction with MP3 compression (ground truth):\\t{pred_mp3.tolist()[0]} ({label})\")\n",
+    "print(f\"Adversarial prediction with MP3 compression:\\t\\t\\t{pred_adv_mp3.tolist()[0]}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Apply adaptive whitebox attack to defeat MP3 compression defense"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# wrap model and MP3 defense in a ART classifier\n",
+    "classifier_art_def = PyTorchClassifier(\n",
+    "    model=model,\n",
+    "    loss=torch.nn.CrossEntropyLoss(),\n",
+    "    optimizer=None,\n",
+    "    input_shape=[1, DOWNSAMPLED_SAMPLING_RATE],\n",
+    "    nb_classes=10,\n",
+    "    clip_values=(-2**15, 2**15 - 1),\n",
+    "    preprocessing_defences=[mp3_compression],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "MP3 compression: 100%|██████████| 1/1 [00:00<00:00,  6.22it/s]\n",
+      "MP3 compression: 100%|██████████| 1/1 [00:00<00:00,  6.76it/s]\n",
+      "MP3 compression: 100%|██████████| 1/1 [00:00<00:00,  6.77it/s]\n",
+      "MP3 compression: 100%|██████████| 1/1 [00:00<00:00,  6.85it/s]\n",
+      "MP3 compression: 100%|██████████| 1/1 [00:00<00:00,  6.85it/s]\n",
+      "MP3 compression: 100%|██████████| 1/1 [00:00<00:00,  6.68it/s]\n",
+      "MP3 compression: 100%|██████████| 1/1 [00:00<00:00,  6.80it/s]\n",
+      "MP3 compression: 100%|██████████| 1/1 [00:00<00:00,  6.74it/s]\n",
+      "MP3 compression: 100%|██████████| 1/1 [00:00<00:00,  6.65it/s]\n",
+      "MP3 compression: 100%|██████████| 1/1 [00:00<00:00,  6.42it/s]\n",
+      "MP3 compression: 100%|██████████| 1/1 [00:00<00:00,  6.26it/s]\n",
+      "MP3 compression: 100%|██████████| 1/1 [00:00<00:00,  6.58it/s]\n",
+      "MP3 compression: 100%|██████████| 1/1 [00:00<00:00,  6.26it/s]\n",
+      "MP3 compression: 100%|██████████| 1/1 [00:00<00:00,  6.15it/s]\n",
+      "MP3 compression: 100%|██████████| 1/1 [00:00<00:00,  6.83it/s]\n",
+      "MP3 compression: 100%|██████████| 1/1 [00:00<00:00,  6.60it/s]\n",
+      "MP3 compression: 100%|██████████| 1/1 [00:00<00:00,  6.05it/s]\n",
+      "MP3 compression: 100%|██████████| 1/1 [00:00<00:00,  6.44it/s]\n",
+      "MP3 compression: 100%|██████████| 1/1 [00:00<00:00,  6.43it/s]\n",
+      "MP3 compression: 100%|██████████| 1/1 [00:00<00:00,  6.51it/s]\n",
+      "MP3 compression: 100%|██████████| 1/1 [00:00<00:00,  6.20it/s]\n",
+      "MP3 compression: 100%|██████████| 1/1 [00:00<00:00,  6.48it/s]\n",
+      "MP3 compression: 100%|██████████| 1/1 [00:00<00:00,  6.32it/s]\n",
+      "MP3 compression: 100%|██████████| 1/1 [00:00<00:00,  6.24it/s]\n",
+      "MP3 compression: 100%|██████████| 1/1 [00:00<00:00,  6.41it/s]\n",
+      "MP3 compression: 100%|██████████| 1/1 [00:00<00:00,  6.83it/s]\n",
+      "MP3 compression: 100%|██████████| 1/1 [00:00<00:00,  7.00it/s]\n",
+      "MP3 compression: 100%|██████████| 1/1 [00:00<00:00,  6.90it/s]\n",
+      "MP3 compression: 100%|██████████| 1/1 [00:00<00:00,  6.59it/s]\n",
+      "MP3 compression: 100%|██████████| 1/1 [00:00<00:00,  6.52it/s]\n",
+      "MP3 compression: 100%|██████████| 1/1 [00:00<00:00,  7.10it/s]\n",
+      "MP3 compression: 100%|██████████| 1/1 [00:00<00:00,  6.20it/s]\n",
+      "MP3 compression: 100%|██████████| 1/1 [00:00<00:00,  5.68it/s]\n",
+      "MP3 compression: 100%|██████████| 1/1 [00:00<00:00,  6.32it/s]\n",
+      "MP3 compression: 100%|██████████| 1/1 [00:00<00:00,  6.60it/s]\n",
+      "MP3 compression: 100%|██████████| 1/1 [00:00<00:00,  6.95it/s]\n",
+      "MP3 compression: 100%|██████████| 1/1 [00:00<00:00,  6.64it/s]\n",
+      "MP3 compression: 100%|██████████| 1/1 [00:00<00:00,  6.80it/s]\n",
+      "MP3 compression: 100%|██████████| 1/1 [00:00<00:00,  6.79it/s]\n",
+      "MP3 compression: 100%|██████████| 1/1 [00:00<00:00,  6.59it/s]\n",
+      "MP3 compression: 100%|██████████| 1/1 [00:00<00:00,  6.96it/s]\n",
+      "MP3 compression: 100%|██████████| 1/1 [00:00<00:00,  6.82it/s]\n",
+      "MP3 compression: 100%|██████████| 1/1 [00:00<00:00,  7.00it/s]\n",
+      "MP3 compression: 100%|██████████| 1/1 [00:00<00:00,  6.98it/s]\n",
+      "MP3 compression: 100%|██████████| 1/1 [00:00<00:00,  6.93it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Original prediction with adaptive classifier (ground truth):\t8 (8)\n",
+      "Adversarial prediction with adaptive classifier:\t\t3\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# craft adversarial example with PGD\n",
+    "epsilon = 0.5\n",
+    "pgd = ProjectedGradientDescent(classifier_art_def, eps=epsilon, eps_step=0.1, max_iter=40)\n",
+    "adv_waveform_def = pgd.generate(\n",
+    "    x=torch.unsqueeze(waveform, 0).numpy()\n",
+    ")\n",
+    "\n",
+    "pred_def = np.argmax(classifier_art_def.predict(torch.unsqueeze(waveform, 1).numpy()), axis=1)[0]\n",
+    "pred_adv_def = np.argmax(classifier_art_def.predict(adv_waveform_def), axis=1)[0]\n",
+    "\n",
+    "# print results\n",
+    "print(f\"Original prediction with adaptive classifier (ground truth):\\t{pred_def} ({label})\")\n",
+    "print(f\"Adversarial prediction with adaptive classifier:\\t\\t{pred_adv_def}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Conclusion\n",
+    "\n",
+    "In this notebook we have demonstrated how we can apply the ART library to audio data. By providing a pretrained PyTorch model and loading it via ART's `PyTorchClassifier` we can easily plug in several off the shelf attacks like Projected Gradient Descent.\n",
+    "\n",
+    "Furthermore, we have demonstrated how to apply the `Mp3Compression` defense and demonstrated how to circumvent it with an adaptive whitebox attack.\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Reproduce CNN\n",
+    "\n",
+    "Our goal is to make it as easy as possible to reproduce or modify our modified AudioMNIST classifier, which we provided as a pretrained fixture in the notebook. Therefore, we provide in the following the original code that we used for training the AudioMNIST classifier.\n",
+    "\n",
+    "**Training script `train.py`**\n",
+    "\n",
+    "```python\n",
+    "#!/usr/bin/env python\n",
+    "\n",
+    "\"\"\"Train a simple AudioNet-like classifier for AudioMNIST.\"\"\"\n",
+    "\n",
+    "import logging\n",
+    "import time\n",
+    "\n",
+    "import torch\n",
+    "\n",
+    "from dataloader import AudioMNISTDataset, PreprocessRaw\n",
+    "from model import RawAudioCNN\n",
+    "\n",
+    "# set global variables\n",
+    "AUDIO_DATA_TRAIN_ROOT = \"data/audiomnist/train\"\n",
+    "AUDIO_DATA_TEST_ROOT = \"data/audiomnist/test\"\n",
+    "\n",
+    "\n",
+    "def _is_cuda_available():\n",
+    "    return torch.cuda.is_available()\n",
+    "\n",
+    "\n",
+    "def _get_device():\n",
+    "    return torch.device(\"cuda\" if _is_cuda_available() else \"cpu\")\n",
+    "\n",
+    "\n",
+    "def main():\n",
+    "    # Step 0: parse args and init logger\n",
+    "    logging.basicConfig(level=logging.INFO)\n",
+    "\n",
+    "    generator_params = {\n",
+    "        'batch_size': 64,\n",
+    "        'shuffle': True,\n",
+    "        'num_workers': 6\n",
+    "    }\n",
+    "\n",
+    "    # Step 1: load data set\n",
+    "    train_data = AudioMNISTDataset(\n",
+    "        root_dir=AUDIO_DATA_TRAIN_ROOT,\n",
+    "        transform=PreprocessRaw(),\n",
+    "    )\n",
+    "    test_data = AudioMNISTDataset(\n",
+    "        root_dir=AUDIO_DATA_TEST_ROOT,\n",
+    "        transform=PreprocessRaw(),\n",
+    "    )\n",
+    "\n",
+    "    train_generator = torch.utils.data.DataLoader(\n",
+    "        train_data,\n",
+    "        **generator_params,\n",
+    "    )\n",
+    "    test_generator = torch.utils.data.DataLoader(\n",
+    "        test_data,\n",
+    "        **generator_params,\n",
+    "    )\n",
+    "\n",
+    "    # Step 2: prepare training\n",
+    "    device = _get_device()\n",
+    "    logging.info(device)\n",
+    "\n",
+    "    model = RawAudioCNN()\n",
+    "    if _is_cuda_available():\n",
+    "        model.to(device)\n",
+    "\n",
+    "    criterion = torch.nn.CrossEntropyLoss()\n",
+    "    optimizer = torch.optim.SGD(model.parameters(), lr=0.001, momentum=0.9)\n",
+    "\n",
+    "    # Step 3: train\n",
+    "    n_epochs = 60\n",
+    "    for epoch in range(n_epochs):\n",
+    "        # training loss\n",
+    "        training_loss = 0.0\n",
+    "        # validation loss\n",
+    "        validation_loss = 0\n",
+    "        # accuracy\n",
+    "        correct = 0\n",
+    "        total = 0\n",
+    "\n",
+    "        model.train()\n",
+    "        for batch_idx, batch_data in enumerate(train_generator):\n",
+    "            inputs = batch_data['input']\n",
+    "            labels = batch_data['digit']\n",
+    "            if _is_cuda_available():\n",
+    "                inputs = inputs.to(device)\n",
+    "                labels = labels.to(device)\n",
+    "            # Model computations\n",
+    "            optimizer.zero_grad()\n",
+    "            # forward + backward + optimize\n",
+    "            outputs = model(inputs)\n",
+    "            loss = criterion(outputs, labels)\n",
+    "            loss.backward()\n",
+    "            optimizer.step()\n",
+    "            # sum training loss\n",
+    "            training_loss += loss.item()\n",
+    "        model.eval()\n",
+    "        with torch.no_grad():\n",
+    "            for batch_idx, batch_data in enumerate(test_generator):\n",
+    "                inputs = batch_data['input']\n",
+    "                labels = batch_data['digit']\n",
+    "                if _is_cuda_available():\n",
+    "                    inputs = inputs.to(device)\n",
+    "                    labels = labels.to(device)\n",
+    "                outputs = model(inputs)\n",
+    "                loss = criterion(outputs, labels)\n",
+    "                # sum validation loss\n",
+    "                validation_loss += loss.item()\n",
+    "                # calculate validation accuracy\n",
+    "                predictions = torch.max(outputs.data, 1)[1]\n",
+    "                total += labels.size(0)\n",
+    "                correct += (predictions == labels).sum().item()\n",
+    "\n",
+    "        # calculate final metrics\n",
+    "        validation_loss /= len(test_generator)\n",
+    "        training_loss /= len(train_generator)\n",
+    "        accuracy = 100 * correct / total\n",
+    "        logging.info(f\"[{epoch+1}] train-loss: {training_loss:.3f}\"\n",
+    "                     f\"\\tval-loss: {validation_loss:.3f}\"\n",
+    "                     f\"\\taccuracy: {accuracy:.2f}\")\n",
+    "    logging.info(\"Finished Training\")\n",
+    "\n",
+    "    # Step 4: save model\n",
+    "    torch.save(\n",
+    "        model,\n",
+    "        f\"model/model_raw_audio_{time.strftime('%Y%m%d%H%M')}.pt\"\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "if __name__ == \"__main__\":\n",
+    "    main()\n",
+    "```\n",
+    "\n",
+    "---\n",
+    "\n",
+    "**Dataloader module `dataloader.py`:**\n",
+    "\n",
+    "```python\n",
+    "import glob\n",
+    "import os\n",
+    "\n",
+    "import numpy as np\n",
+    "import torch\n",
+    "import torchaudio\n",
+    "\n",
+    "\n",
+    "OUTPUT_SIZE = 8000\n",
+    "ORIGINAL_SAMPLING_RATE = 48000\n",
+    "DOWNSAMPLED_SAMPLING_RATE = 8000\n",
+    "\n",
+    "\n",
+    "class AudioMNISTDataset(torch.utils.data.Dataset):\n",
+    "    \"\"\"Dataset object for the AudioMNIST data set.\"\"\"\n",
+    "    def __init__(self, root_dir, transform=None, verbose=False):\n",
+    "        self.root_dir = root_dir\n",
+    "        self.audio_list = glob.glob(f\"{root_dir}/*/*.wav\")\n",
+    "        self.transform = transform\n",
+    "        self.verbose = verbose\n",
+    "\n",
+    "    def __len__(self):\n",
+    "        return len(self.audio_list)\n",
+    "\n",
+    "    def __getitem__(self, idx):\n",
+    "        audio_fn = self.audio_list[idx]\n",
+    "        if self.verbose:\n",
+    "            print(f\"Loading audio file {audio_fn}\")\n",
+    "        waveform, sample_rate = torchaudio.load_wav(audio_fn)\n",
+    "        if self.transform:\n",
+    "            waveform = self.transform(waveform)\n",
+    "        sample = {\n",
+    "            'input': waveform,\n",
+    "            'digit': int(os.path.basename(audio_fn).split(\"_\")[0])\n",
+    "        }\n",
+    "        return sample\n",
+    "\n",
+    "\n",
+    "class PreprocessRaw(object):\n",
+    "    \"\"\"Transform audio waveform of given shape.\"\"\"\n",
+    "    def __init__(self, size_out=OUTPUT_SIZE, orig_freq=ORIGINAL_SAMPLING_RATE,\n",
+    "                 new_freq=DOWNSAMPLED_SAMPLING_RATE):\n",
+    "        self.size_out = size_out\n",
+    "        self.orig_freq = orig_freq\n",
+    "        self.new_freq = new_freq\n",
+    "\n",
+    "    def __call__(self, waveform):\n",
+    "        transformed_waveform = _ZeroPadWaveform(self.size_out)(\n",
+    "            _ResampleWaveform(self.orig_freq, self.new_freq)(waveform)\n",
+    "        )\n",
+    "        return transformed_waveform\n",
+    "\n",
+    "\n",
+    "class _ResampleWaveform(object):\n",
+    "    \"\"\"Resample signal frequency.\"\"\"\n",
+    "    def __init__(self, orig_freq, new_freq):\n",
+    "        self.orig_freq = orig_freq\n",
+    "        self.new_freq = new_freq\n",
+    "\n",
+    "    def __call__(self, waveform):\n",
+    "        return self._resample_waveform(waveform)\n",
+    "\n",
+    "    def _resample_waveform(self, waveform):\n",
+    "        resampled_waveform = torchaudio.transforms.Resample(\n",
+    "            orig_freq=self.orig_freq,\n",
+    "            new_freq=self.new_freq,\n",
+    "        )(waveform)\n",
+    "        return resampled_waveform\n",
+    "\n",
+    "\n",
+    "class _ZeroPadWaveform(object):\n",
+    "    \"\"\"Apply zero-padding to waveform.\n",
+    "\n",
+    "    Return a zero-padded waveform of desired output size. The waveform is\n",
+    "    positioned randomly.\n",
+    "    \"\"\"\n",
+    "    def __init__(self, size_out):\n",
+    "        self.size_out = size_out\n",
+    "\n",
+    "    def __call__(self, waveform):\n",
+    "        return self._zero_pad_waveform(waveform)\n",
+    "\n",
+    "    def _zero_pad_waveform(self, waveform):\n",
+    "        padding_total = self.size_out - waveform.shape[-1]\n",
+    "        padding_left = np.random.randint(padding_total + 1)\n",
+    "        padding_right = padding_total - padding_left\n",
+    "        padded_waveform = torch.nn.ConstantPad1d(\n",
+    "            (padding_left, padding_right),\n",
+    "            0\n",
+    "        )(waveform)\n",
+    "        return padded_waveform\n",
+    "```\n",
+    "\n",
+    "---\n",
+    "\n",
+    "**Model module `model.py`:**\n",
+    "\n",
+    "```python\n",
+    "import torch.nn as nn\n",
+    "\n",
+    "\n",
+    "class RawAudioCNN(nn.Module):\n",
+    "    \"\"\"Adaption of AudioNet (arXiv:1807.03418).\"\"\"\n",
+    "    def __init__(self):\n",
+    "        super().__init__()\n",
+    "        # 1 x 8000\n",
+    "        self.conv1 = nn.Sequential(\n",
+    "            nn.Conv1d(1, 100, kernel_size=3, stride=1, padding=2),\n",
+    "            nn.BatchNorm1d(100),\n",
+    "            nn.ReLU(),\n",
+    "            nn.MaxPool1d(3, stride=2))\n",
+    "        # 32 x 4000\n",
+    "        self.conv2 = nn.Sequential(\n",
+    "            nn.Conv1d(100, 64, kernel_size=3, stride=1, padding=1),\n",
+    "            nn.BatchNorm1d(64),\n",
+    "            nn.ReLU(),\n",
+    "            nn.MaxPool1d(2, stride=2))\n",
+    "        # 64 x 2000\n",
+    "        self.conv3 = nn.Sequential(\n",
+    "            nn.Conv1d(64, 128, kernel_size=3, stride=1, padding=1),\n",
+    "            nn.BatchNorm1d(128),\n",
+    "            nn.ReLU(),\n",
+    "            nn.MaxPool1d(2, stride=2))\n",
+    "        # 128 x 1000\n",
+    "        self.conv4 = nn.Sequential(\n",
+    "            nn.Conv1d(128, 128, kernel_size=3, stride=1, padding=1),\n",
+    "            nn.BatchNorm1d(128),\n",
+    "            nn.ReLU(),\n",
+    "            nn.MaxPool1d(2, stride=2))\n",
+    "        # 128 x 500\n",
+    "        self.conv5 = nn.Sequential(\n",
+    "            nn.Conv1d(128, 128, kernel_size=3, stride=1, padding=1),\n",
+    "            nn.BatchNorm1d(128),\n",
+    "            nn.ReLU(),\n",
+    "            nn.MaxPool1d(2, stride=2))\n",
+    "        # 128 x 250\n",
+    "        self.conv6 = nn.Sequential(\n",
+    "            nn.Conv1d(128, 128, kernel_size=3, stride=1, padding=1),\n",
+    "            nn.BatchNorm1d(128),\n",
+    "            nn.ReLU(),\n",
+    "            nn.MaxPool1d(2, stride=2))\n",
+    "        # 128 x 125\n",
+    "        self.conv7 = nn.Sequential(\n",
+    "            nn.Conv1d(128, 64, kernel_size=3, stride=1, padding=1),\n",
+    "            nn.BatchNorm1d(64),\n",
+    "            nn.ReLU(),\n",
+    "            nn.MaxPool1d(2, stride=2))\n",
+    "        # 64 x 62\n",
+    "        self.conv8 = nn.Sequential(\n",
+    "            nn.Conv1d(64, 32, kernel_size=3, stride=1, padding=0),\n",
+    "            nn.BatchNorm1d(32),\n",
+    "            nn.ReLU(),\n",
+    "            # maybe replace pool with dropout here\n",
+    "            nn.MaxPool1d(2, stride=2))\n",
+    "\n",
+    "        # 32 x 30\n",
+    "        self.fc = nn.Linear(32 * 30, 10)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        x = self.conv1(x)\n",
+    "        x = self.conv2(x)\n",
+    "        x = self.conv3(x)\n",
+    "        x = self.conv4(x)\n",
+    "        x = self.conv5(x)\n",
+    "        x = self.conv6(x)\n",
+    "        x = self.conv7(x)\n",
+    "        x = self.conv8(x)\n",
+    "        x = x.view(x.shape[0], 32 * 30)\n",
+    "        x = self.fc(x)\n",
+    "        return x\n",
+    "```\n",
+    "\n",
+    "---"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/adversarial-robustness-toolbox/notebooks/adversarial_patch/attack_adversarial_patch.ipynb b/adversarial-robustness-toolbox/notebooks/adversarial_patch/attack_adversarial_patch.ipynb
new file mode 100644
index 0000000..4d51749
--- /dev/null
+++ b/adversarial-robustness-toolbox/notebooks/adversarial_patch/attack_adversarial_patch.ipynb
@@ -0,0 +1,381 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# ART - Adversarial Patch - TensorFlow v1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "\n",
+    "import random\n",
+    "import numpy as np\n",
+    "from matplotlib import pyplot as plt\n",
+    "plt.rcParams['figure.figsize'] = [10, 10]\n",
+    "import imagenet_stubs\n",
+    "from imagenet_stubs.imagenet_2012_labels import name_to_label\n",
+    "\n",
+    "import tensorflow as tf\n",
+    "sess = tf.InteractiveSession()\n",
+    "from tensorflow.keras.applications.resnet50 import preprocess_input, decode_predictions\n",
+    "from tensorflow.keras.preprocessing import image\n",
+    "\n",
+    "from art.estimators.classification import TensorFlowClassifier\n",
+    "from art.attacks.evasion import AdversarialPatch"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Settings"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "target_name = 'toaster'\n",
+    "image_shape = (224, 224, 3)\n",
+    "clip_values = (0, 255)\n",
+    "nb_classes = 1000\n",
+    "batch_size = 16\n",
+    "scale_min = 0.4\n",
+    "scale_max = 1.0\n",
+    "rotation_max = 22.5\n",
+    "learning_rate = 5000.\n",
+    "max_iter = 500"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Model definition"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From /home/beat/codes/anaconda3/envs/TF1_15/lib/python3.6/site-packages/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "If using Keras pass *_constraint arguments to layers.\n",
+      "WARNING:tensorflow:From <ipython-input-3-bf2dd6c9f1b3>:5: softmax_cross_entropy_with_logits (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "\n",
+      "Future major versions of TensorFlow will allow gradients to flow\n",
+      "into the labels input on backprop by default.\n",
+      "\n",
+      "See `tf.nn.softmax_cross_entropy_with_logits_v2`.\n",
+      "\n",
+      "WARNING:tensorflow:From /home/beat/codes/anaconda3/envs/TF1_15/lib/python3.6/site-packages/art/estimators/classification/tensorflow.py:399: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%capture\n",
+    "_image_input = tf.keras.Input(shape=image_shape)\n",
+    "_target_ys = tf.placeholder(tf.float32, shape=(None, nb_classes))\n",
+    "model = tf.keras.applications.resnet50.ResNet50(input_tensor=_image_input, weights='imagenet')\n",
+    "_logits = model.outputs[0].op.inputs[0]\n",
+    "target_loss = tf.nn.softmax_cross_entropy_with_logits(labels=_target_ys, logits=_logits)\n",
+    "\n",
+    "mean_b = 103.939\n",
+    "mean_g = 116.779\n",
+    "mean_r = 123.680\n",
+    "\n",
+    "tfc = TensorFlowClassifier(clip_values=clip_values, input_ph=_image_input, labels_ph=_target_ys,\n",
+    "                           output=_logits, sess=sess, loss=target_loss,\n",
+    "                           preprocessing=([mean_b, mean_g, mean_r], 1))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Imagenet training images"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "images_list = list()\n",
+    "\n",
+    "for image_path in imagenet_stubs.get_image_paths():\n",
+    "    im = image.load_img(image_path, target_size=(224, 224))\n",
+    "    im = image.img_to_array(im)\n",
+    "    im = im[:, :, ::-1].astype(np.float32) # RGB to BGR\n",
+    "    im = np.expand_dims(im, axis=0)\n",
+    "    images_list.append(im)\n",
+    "\n",
+    "images = np.vstack(images_list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def bgr_to_rgb(x):\n",
+    "    return x[:, :, ::-1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Adversarial patch generation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "ap = AdversarialPatch(classifier=tfc, rotation_max=rotation_max, scale_min=scale_min, scale_max=scale_max,\n",
+    "                      learning_rate=learning_rate, max_iter=max_iter, batch_size=batch_size)\n",
+    "\n",
+    "label = name_to_label(target_name)\n",
+    "y_one_hot = np.zeros(nb_classes)\n",
+    "y_one_hot[label] = 1.0\n",
+    "y_target = np.tile(y_one_hot, (images.shape[0], 1))\n",
+    "\n",
+    "patch, patch_mask = ap.generate(x=images, y=y_target)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f55258e5550>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.imshow((bgr_to_rgb(patch) * patch_mask).astype(np.uint8))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Evaluation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "patched_images = ap.apply_patch(images, scale=0.5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def predict_model(classifier, image):\n",
+    "    plt.imshow(bgr_to_rgb(image.astype(np.uint8)))\n",
+    "    plt.show()\n",
+    "    \n",
+    "    image = np.copy(image)\n",
+    "    image = np.expand_dims(image, axis=0)\n",
+    "    \n",
+    "    prediction = classifier.predict(image)\n",
+    "    \n",
+    "    top = 5\n",
+    "    prediction_decode = decode_predictions(prediction, top=top)[0]\n",
+    "    print('Predictions:')\n",
+    "    \n",
+    "    lengths = list()\n",
+    "    for i in range(top):\n",
+    "        lengths.append(len(prediction_decode[i][1]))\n",
+    "    max_length = max(lengths)\n",
+    "    \n",
+    "    for i in range(top):\n",
+    "        name = prediction_decode[i][1]\n",
+    "        name = name.ljust(max_length, \" \")\n",
+    "        probability = prediction_decode[i][2]\n",
+    "        output_str = \"{} {:.2f}\".format(name, probability)\n",
+    "        print(output_str)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Predictions:\n",
+      "toaster    38.39\n",
+      "bagel      28.03\n",
+      "piggy_bank 17.30\n",
+      "bakery     15.63\n",
+      "pretzel    14.71\n"
+     ]
+    }
+   ],
+   "source": [
+    "predict_model(tfc, patched_images[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Predictions:\n",
+      "toaster          24.77\n",
+      "beagle           17.96\n",
+      "Walker_hound     13.95\n",
+      "English_foxhound 12.82\n",
+      "piggy_bank       12.43\n"
+     ]
+    }
+   ],
+   "source": [
+    "predict_model(tfc, patched_images[1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Predictions:\n",
+      "toaster          30.13\n",
+      "piggy_bank       13.52\n",
+      "pencil_sharpener 10.79\n",
+      "radio            9.54\n",
+      "teapot           9.39\n"
+     ]
+    }
+   ],
+   "source": [
+    "predict_model(tfc, patched_images[2])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/adversarial-robustness-toolbox/notebooks/adversarial_patch/attack_adversarial_patch_TensorFlowV2.ipynb b/adversarial-robustness-toolbox/notebooks/adversarial_patch/attack_adversarial_patch_TensorFlowV2.ipynb
new file mode 100644
index 0000000..c8ecf1a
--- /dev/null
+++ b/adversarial-robustness-toolbox/notebooks/adversarial_patch/attack_adversarial_patch_TensorFlowV2.ipynb
@@ -0,0 +1,364 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# ART - Adversarial Patch - TensorFlow v2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "\n",
+    "import random\n",
+    "import numpy as np\n",
+    "from matplotlib import pyplot as plt\n",
+    "plt.rcParams['figure.figsize'] = [10, 10]\n",
+    "import imagenet_stubs\n",
+    "from imagenet_stubs.imagenet_2012_labels import name_to_label\n",
+    "\n",
+    "import tensorflow as tf\n",
+    "from tensorflow.keras.applications.resnet50 import preprocess_input, decode_predictions\n",
+    "from tensorflow.keras.preprocessing import image\n",
+    "\n",
+    "from art.estimators.classification import TensorFlowV2Classifier, EnsembleClassifier\n",
+    "from art.attacks.evasion import AdversarialPatch"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Settings"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "target_name = 'toaster'\n",
+    "image_shape = (224, 224, 3)\n",
+    "clip_values = (0, 255)\n",
+    "nb_classes  =1000\n",
+    "batch_size = 16\n",
+    "scale_min = 0.4\n",
+    "scale_max = 1.0\n",
+    "rotation_max = 22.5\n",
+    "learning_rate = 5000.\n",
+    "max_iter = 500"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Model definition"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = tf.keras.applications.resnet50.ResNet50(weights=\"imagenet\")\n",
+    "\n",
+    "mean_b = 103.939\n",
+    "mean_g = 116.779\n",
+    "mean_r = 123.680\n",
+    "\n",
+    "tfc = TensorFlowV2Classifier(model=model, loss_object=None, train_step=None, nb_classes=nb_classes,\n",
+    "                             input_shape=image_shape, clip_values=clip_values, \n",
+    "                             preprocessing=([mean_b, mean_g, mean_r], 1))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Imagenet training images"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "images_list = list()\n",
+    "\n",
+    "for image_path in imagenet_stubs.get_image_paths():\n",
+    "    im = image.load_img(image_path, target_size=(224, 224))\n",
+    "    im = image.img_to_array(im)\n",
+    "    im = im[:, :, ::-1].astype(np.float32) # RGB to BGR\n",
+    "    im = np.expand_dims(im, axis=0)\n",
+    "    images_list.append(im)\n",
+    "\n",
+    "images = np.vstack(images_list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def bgr_to_rgb(x):\n",
+    "    return x[:, :, ::-1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Adversarial patch generation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Adversarial Patch TensorFlow v2: 100%|██████████| 500/500 [09:33<00:00,  1.15s/it]\n"
+     ]
+    }
+   ],
+   "source": [
+    "ap = AdversarialPatch(classifier=tfc, rotation_max=rotation_max, scale_min=scale_min, scale_max=scale_max,\n",
+    "                      learning_rate=learning_rate, max_iter=max_iter, batch_size=batch_size,\n",
+    "                      patch_shape=(224, 224, 3))\n",
+    "\n",
+    "label = name_to_label(target_name)\n",
+    "y_one_hot = np.zeros(nb_classes)\n",
+    "y_one_hot[label] = 1.0\n",
+    "y_target = np.tile(y_one_hot, (images.shape[0], 1))\n",
+    "\n",
+    "patch, patch_mask = ap.generate(x=images, y=y_target)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f9ee007e810>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.imshow((bgr_to_rgb(patch) * patch_mask).astype(np.uint8))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Evaluation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "patched_images = ap.apply_patch(images, scale=0.5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def predict_model(classifier, image):\n",
+    "    plt.imshow(bgr_to_rgb(image.astype(np.uint8)))\n",
+    "    plt.show()\n",
+    "    \n",
+    "    image = np.copy(image)\n",
+    "    image = np.expand_dims(image, axis=0)\n",
+    "    \n",
+    "    prediction = classifier.predict(image)\n",
+    "    \n",
+    "    top = 5\n",
+    "    prediction_decode = decode_predictions(prediction, top=top)[0]\n",
+    "    print('Predictions:')\n",
+    "    \n",
+    "    lengths = list()\n",
+    "    for i in range(top):\n",
+    "        lengths.append(len(prediction_decode[i][1]))\n",
+    "    max_length = max(lengths)\n",
+    "    \n",
+    "    for i in range(top):\n",
+    "        name = prediction_decode[i][1]\n",
+    "        name = name.ljust(max_length, \" \")\n",
+    "        probability = prediction_decode[i][2]\n",
+    "        output_str = \"{} {:.2f}\".format(name, probability)\n",
+    "        print(output_str)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Predictions:\n",
+      "toaster    1.00\n",
+      "bagel      0.00\n",
+      "Crock_Pot  0.00\n",
+      "coffee_mug 0.00\n",
+      "eggnog     0.00\n"
+     ]
+    }
+   ],
+   "source": [
+    "predict_model(tfc, patched_images[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Predictions:\n",
+      "toaster        1.00\n",
+      "sewing_machine 0.00\n",
+      "space_heater   0.00\n",
+      "dishwasher     0.00\n",
+      "beagle         0.00\n"
+     ]
+    }
+   ],
+   "source": [
+    "predict_model(tfc, patched_images[1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Predictions:\n",
+      "toaster      0.88\n",
+      "centipede    0.11\n",
+      "Crock_Pot    0.00\n",
+      "space_heater 0.00\n",
+      "cup          0.00\n"
+     ]
+    }
+   ],
+   "source": [
+    "predict_model(tfc, patched_images[2])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/adversarial-robustness-toolbox/notebooks/adversarial_retraining.ipynb b/adversarial-robustness-toolbox/notebooks/adversarial_retraining.ipynb
new file mode 100644
index 0000000..d8c98e2
--- /dev/null
+++ b/adversarial-robustness-toolbox/notebooks/adversarial_retraining.ipynb
@@ -0,0 +1,327 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Introduction\n",
+    "\n",
+    "This notebook shows how to load and evaluate the MNIST and CIFAR-10 models synthesized and trained as described in the following paper:\n",
+    "\n",
+    "M.Sinn, M.Wistuba, B.Buesser, M.-I.Nicolae, M.N.Tran: **Evolutionary Search for Adversarially Robust Neural Network** *ICLR SafeML Workshop 2019 (arXiv link to the paper will be added shortly)*.\n",
+    "\n",
+    "The models were saved in `.h5` using Python 3.6, TensorFlow 1.11.0, Keras 2.2.4."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using TensorFlow backend.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "from keras.datasets import mnist, cifar10\n",
+    "from keras.models import load_model\n",
+    "from keras.utils.np_utils import to_categorical\n",
+    "import numpy as np\n",
+    "\n",
+    "from art import config\n",
+    "from art.estimators.classification import KerasClassifier\n",
+    "from art.attacks.evasion import ProjectedGradientDescent\n",
+    "from art.utils import get_file"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## MNIST\n",
+    "\n",
+    "Three different MNIST models are available. Use the following URLs to access them:\n",
+    "- `mnist_ratio=0.h5`: trained on 100% benign samples (https://www.dropbox.com/s/bv1xwjaf1ov4u7y/mnist_ratio%3D0.h5?dl=1)\n",
+    "- `mnist_ratio=0.5.h5`: trained on 50% benign and 50% adversarial samples (https://www.dropbox.com/s/0skvoxjd6klvti3/mnist_ratio%3D0.5.h5?dl=1)\n",
+    "- `mnist_ratio=1.h5`: trained on 100% adversarial samples (https://www.dropbox.com/s/oa2kowq7kgaxh1o/mnist_ratio%3D1.h5?dl=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloading data from https://s3.amazonaws.com/img-datasets/mnist.npz\n",
+      "11493376/11490434 [==============================] - 0s 0us/step\n"
+     ]
+    }
+   ],
+   "source": [
+    "(X_train, y_train), (X_test, y_test) = mnist.load_data()\n",
+    "X_train = X_train.reshape(X_train.shape[0], 28, 28, 1).astype('float32') / 255\n",
+    "X_test = X_test.reshape(X_test.shape[0], 28, 28, 1).astype('float32') / 255\n",
+    "y_train = to_categorical(y_train, 10)\n",
+    "y_test = to_categorical(y_test, 10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "E.g. load the model trained on 50% benign and 50% adversarial samples:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path = get_file('mnist_ratio=0.5.h5',extract=False, path=config.ART_DATA_PATH,\n",
+    "                url='https://www.dropbox.com/s/0skvoxjd6klvti3/mnist_ratio%3D0.5.h5?dl=1')\n",
+    "model = load_model(path)\n",
+    "classifier = KerasClassifier(model=model, use_logits=False, clip_values=[0,1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Assess accuracy on first `n` benign test samples:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy on first 10000 benign test samples: 0.995100\n"
+     ]
+    }
+   ],
+   "source": [
+    "n = 10000\n",
+    "y_pred = classifier.predict(X_test[:n])\n",
+    "accuracy = np.mean(np.argmax(y_pred, axis=1) == np.argmax(y_test[:n], axis=1))\n",
+    "print(\"Accuracy on first %i benign test samples: %f\" % (n, accuracy))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define adversarial attack:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "attack = ProjectedGradientDescent(classifier, eps=0.3, eps_step=0.01, max_iter=40, targeted=False, \n",
+    "                                  num_random_init=True) "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Assess accuracy on first `n` adversarial test samples:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy on first 10 adversarial test samples: 0.900000\n"
+     ]
+    }
+   ],
+   "source": [
+    "n = 10\n",
+    "X_test_adv = attack.generate(X_test[:n], y=y_test[:n])\n",
+    "y_adv_pred = classifier.predict(X_test_adv)\n",
+    "accuracy = np.mean(np.argmax(y_adv_pred, axis=1) == np.argmax(y_test[:n], axis=1))\n",
+    "print(\"Accuracy on first %i adversarial test samples: %f\" % (n, accuracy))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## CIFAR-10\n",
+    "\n",
+    "Similarly to MNIST, three different CIFAR-10 models are available at the following URLs:\n",
+    "- `cifar-10_ratio=0.h5`: trained on 100% benign samples (https://www.dropbox.com/s/hbvua7ynhvara12/cifar-10_ratio%3D0.h5?dl=1)\n",
+    "- `cifar-10_ratio=0.5.h5`: trained on 50% benign and 50% adversarial samples (https://www.dropbox.com/s/96yv0r2gqzockmw/cifar-10_ratio%3D0.5.h5?dl=1)\n",
+    "- `cifar-10_ratio=1.h5`: trained on 100% adversarial samples (https://www.dropbox.com/s/7btc2sq7syf68at/cifar-10_ratio%3D1.h5?dl=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "(X_train, y_train), (X_test, y_test) = cifar10.load_data()\n",
+    "X_train = X_train.reshape(X_train.shape[0], 32, 32, 3).astype('float32')\n",
+    "X_test = X_test.reshape(X_test.shape[0], 32, 32, 3).astype('float32')\n",
+    "y_train = to_categorical(y_train, 10)\n",
+    "y_test = to_categorical(y_test, 10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "E.g. load the model trained on 50% benign and 50% adversarial samples:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path = get_file('cifar-10_ratio=0.5.h5',extract=False, path=config.ART_DATA_PATH,\n",
+    "                url='https://www.dropbox.com/s/96yv0r2gqzockmw/cifar-10_ratio%3D0.5.h5?dl=1')\n",
+    "model = load_model(path)\n",
+    "classifier = KerasClassifier(model=model, use_logits=False, clip_values=[0,255])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Assess accuracy on first `n` benign test samples:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy on first 100 benign test samples: 0.940000\n"
+     ]
+    }
+   ],
+   "source": [
+    "n = 100\n",
+    "y_pred = classifier.predict(X_test[:n])\n",
+    "accuracy = np.mean(np.argmax(y_pred, axis=1) == np.argmax(y_test[:n], axis=1))\n",
+    "print(\"Accuracy on first %i benign test samples: %f\" % (n, accuracy))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define adversarial attack:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "attack = ProjectedGradientDescent(classifier, eps=8, eps_step=2, max_iter=10, targeted=False, \n",
+    "                                  num_random_init=True) "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Assess accuracy on first `n` adversarial test samples:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy on first 100 adversarial test samples: 0.470000\n"
+     ]
+    }
+   ],
+   "source": [
+    "n = 100\n",
+    "X_test_adv = attack.generate(X_test[:n], y=y_test[:n])\n",
+    "y_adv_pred = classifier.predict(X_test_adv)\n",
+    "accuracy = np.mean(np.argmax(y_adv_pred, axis=1) == np.argmax(y_test[:n], axis=1))\n",
+    "print(\"Accuracy on first %i adversarial test samples: %f\" % (n, accuracy))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/adversarial-robustness-toolbox/notebooks/adversarial_training_mnist.ipynb b/adversarial-robustness-toolbox/notebooks/adversarial_training_mnist.ipynb
new file mode 100644
index 0000000..213a694
--- /dev/null
+++ b/adversarial-robustness-toolbox/notebooks/adversarial_training_mnist.ipynb
@@ -0,0 +1,499 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<table style=\"border: none\" align=\"center\">\n",
+    "   <tr style=\"border: none\">\n",
+    "      <th style=\"border: none\"><font face=\"verdana\" size=\"4\" color=\"black\"><b>  Demonstrate adversarial training using ART  </b></font></font></th>\n",
+    "   </tr> \n",
+    "</table>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this notebook we demonstrate adversarial training using ART on the MNIST dataset.\n",
+    "\n",
+    "\n",
+    "## Contents\n",
+    "\n",
+    "1.\t[Load prereqs and data](#prereqs)\n",
+    "2.  [Train and evaluate a baseline classifier](#classifier)\n",
+    "3.  [Adversarially train a robust classifier](#adv_training)\n",
+    "4.\t[Evaluate the robust classifier](#evaluation)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"prereqs\"></a>\n",
+    "## 1. Load prereqs and data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using TensorFlow backend.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "from keras.models import load_model\n",
+    "\n",
+    "from art import config\n",
+    "from art.utils import load_dataset, get_file\n",
+    "from art.estimators.classification import KerasClassifier\n",
+    "from art.attacks.evasion import FastGradientMethod\n",
+    "from art.attacks.evasion import BasicIterativeMethod\n",
+    "from art.defences.trainer import AdversarialTrainer\n",
+    "\n",
+    "import numpy as np\n",
+    "\n",
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "(x_train, y_train), (x_test, y_test), min_, max_ = load_dataset('mnist')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"classifier\"></a>\n",
+    "## 2. Train and evaluate a baseline classifier"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load the classifier model:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path = get_file('mnist_cnn_original.h5', extract=False, path=config.ART_DATA_PATH,\n",
+    "                url='https://www.dropbox.com/s/p2nyzne9chcerid/mnist_cnn_original.h5?dl=1')\n",
+    "classifier_model = load_model(path)\n",
+    "classifier = KerasClassifier(clip_values=(min_, max_), model=classifier_model, use_logits=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: \"sequential_1\"\n",
+      "_________________________________________________________________\n",
+      "Layer (type)                 Output Shape              Param #   \n",
+      "=================================================================\n",
+      "conv2d_1 (Conv2D)            (None, 26, 26, 32)        320       \n",
+      "_________________________________________________________________\n",
+      "max_pooling2d_1 (MaxPooling2 (None, 13, 13, 32)        0         \n",
+      "_________________________________________________________________\n",
+      "conv2d_2 (Conv2D)            (None, 11, 11, 64)        18496     \n",
+      "_________________________________________________________________\n",
+      "max_pooling2d_2 (MaxPooling2 (None, 5, 5, 64)          0         \n",
+      "_________________________________________________________________\n",
+      "flatten_1 (Flatten)          (None, 1600)              0         \n",
+      "_________________________________________________________________\n",
+      "dense_1 (Dense)              (None, 128)               204928    \n",
+      "_________________________________________________________________\n",
+      "dense_2 (Dense)              (None, 10)                1290      \n",
+      "=================================================================\n",
+      "Total params: 225,034\n",
+      "Trainable params: 225,034\n",
+      "Non-trainable params: 0\n",
+      "_________________________________________________________________\n"
+     ]
+    }
+   ],
+   "source": [
+    "classifier_model.summary()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Evaluate the classifier performance on the first 100 original test samples:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Original test data (first 100 images):\n",
+      "Correctly classified: 100\n",
+      "Incorrectly classified: 0\n"
+     ]
+    }
+   ],
+   "source": [
+    "x_test_pred = np.argmax(classifier.predict(x_test[:100]), axis=1)\n",
+    "nb_correct_pred = np.sum(x_test_pred == np.argmax(y_test[:100], axis=1))\n",
+    "\n",
+    "print(\"Original test data (first 100 images):\")\n",
+    "print(\"Correctly classified: {}\".format(nb_correct_pred))\n",
+    "print(\"Incorrectly classified: {}\".format(100-nb_correct_pred))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Generate some adversarial samples:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "attacker = FastGradientMethod(classifier, eps=0.5)\n",
+    "x_test_adv = attacker.generate(x_test[:100])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And evaluate performance on those:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial test data (first 100 images):\n",
+      "Correctly classified: 21\n",
+      "Incorrectly classified: 79\n"
+     ]
+    }
+   ],
+   "source": [
+    "x_test_adv_pred = np.argmax(classifier.predict(x_test_adv), axis=1)\n",
+    "nb_correct_adv_pred = np.sum(x_test_adv_pred == np.argmax(y_test[:100], axis=1))\n",
+    "\n",
+    "print(\"Adversarial test data (first 100 images):\")\n",
+    "print(\"Correctly classified: {}\".format(nb_correct_adv_pred))\n",
+    "print(\"Incorrectly classified: {}\".format(100-nb_correct_adv_pred))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"adv_training\"></a>\n",
+    "## 3. Adversarially train a robust classifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path = get_file('mnist_cnn_robust.h5', extract=False, path=config.ART_DATA_PATH,\n",
+    "                url='https://www.dropbox.com/s/yutsncaniiy5uy8/mnist_cnn_robust.h5?dl=1')\n",
+    "robust_classifier_model = load_model(path)\n",
+    "robust_classifier = KerasClassifier(clip_values=(min_, max_), model=robust_classifier_model, use_logits=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note: the robust classifier has the same architecture as above, except the first dense layer has **1024** instead of **128** units. (This was recommend by Madry et al. (2017), *Towards Deep Learning Models Resistant to Adversarial Attacks*)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: \"sequential_2\"\n",
+      "_________________________________________________________________\n",
+      "Layer (type)                 Output Shape              Param #   \n",
+      "=================================================================\n",
+      "conv2d_3 (Conv2D)            (None, 26, 26, 32)        320       \n",
+      "_________________________________________________________________\n",
+      "max_pooling2d_3 (MaxPooling2 (None, 13, 13, 32)        0         \n",
+      "_________________________________________________________________\n",
+      "conv2d_4 (Conv2D)            (None, 11, 11, 64)        18496     \n",
+      "_________________________________________________________________\n",
+      "max_pooling2d_4 (MaxPooling2 (None, 5, 5, 64)          0         \n",
+      "_________________________________________________________________\n",
+      "flatten_2 (Flatten)          (None, 1600)              0         \n",
+      "_________________________________________________________________\n",
+      "dense_3 (Dense)              (None, 1024)              1639424   \n",
+      "_________________________________________________________________\n",
+      "dense_4 (Dense)              (None, 10)                10250     \n",
+      "=================================================================\n",
+      "Total params: 1,668,490\n",
+      "Trainable params: 1,668,490\n",
+      "Non-trainable params: 0\n",
+      "_________________________________________________________________\n"
+     ]
+    }
+   ],
+   "source": [
+    "robust_classifier_model.summary()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Also as recommended by Madry et al., we use BIM/PGD attacks during adversarial training:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "attacks = BasicIterativeMethod(robust_classifier, eps=0.3, eps_step=0.01, max_iter=40)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Perform adversarial training:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# We had performed this before, starting with a randomly intialized model.\n",
+    "# Adversarial training takes about 80 minutes on an NVIDIA V100.\n",
+    "# The resulting model is the one loaded from mnist_cnn_robust.h5 above.\n",
+    "\n",
+    "# Here is the command we had used for the Adversarial Training\n",
+    "\n",
+    "# trainer = AdversarialTrainer(robust_classifier, attacks, ratio=1.0)\n",
+    "# trainer.fit(x_train, y_train, nb_epochs=83, batch_size=50)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"evaluation\"></a>\n",
+    "## 4. Evaluate the robust classifier"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Evaluate the robust classifier's performance on the original test data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Original test data (first 100 images):\n",
+      "Correctly classified: 99\n",
+      "Incorrectly classified: 1\n"
+     ]
+    }
+   ],
+   "source": [
+    "x_test_robust_pred = np.argmax(robust_classifier.predict(x_test[:100]), axis=1)\n",
+    "nb_correct_robust_pred = np.sum(x_test_robust_pred == np.argmax(y_test[:100], axis=1))\n",
+    "\n",
+    "print(\"Original test data (first 100 images):\")\n",
+    "print(\"Correctly classified: {}\".format(nb_correct_robust_pred))\n",
+    "print(\"Incorrectly classified: {}\".format(100-nb_correct_robust_pred))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Evaluate the robust classifier's performance on the adversarial test data (**white-box** setting):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "attacker_robust = FastGradientMethod(robust_classifier, eps=0.5)\n",
+    "x_test_adv_robust = attacker_robust.generate(x_test[:100])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial test data (first 100 images):\n",
+      "Correctly classified: 79\n",
+      "Incorrectly classified: 21\n"
+     ]
+    }
+   ],
+   "source": [
+    "x_test_adv_robust_pred = np.argmax(robust_classifier.predict(x_test_adv_robust), axis=1)\n",
+    "nb_correct_adv_robust_pred = np.sum(x_test_adv_robust_pred == np.argmax(y_test[:100], axis=1))\n",
+    "\n",
+    "print(\"Adversarial test data (first 100 images):\")\n",
+    "print(\"Correctly classified: {}\".format(nb_correct_adv_robust_pred))\n",
+    "print(\"Incorrectly classified: {}\".format(100-nb_correct_adv_robust_pred))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Compare the performance of the original and the robust classifier over a range of `eps` values:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "eps_range = [0.01, 0.02, 0.03, 0.04, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]\n",
+    "nb_correct_original = []\n",
+    "nb_correct_robust = []\n",
+    "\n",
+    "for eps in eps_range:\n",
+    "    attacker.set_params(**{'eps': eps})\n",
+    "    attacker_robust.set_params(**{'eps': eps})\n",
+    "    x_test_adv = attacker.generate(x_test[:100])\n",
+    "    x_test_adv_robust = attacker_robust.generate(x_test[:100])\n",
+    "    \n",
+    "    x_test_adv_pred = np.argmax(classifier.predict(x_test_adv), axis=1)\n",
+    "    nb_correct_original += [np.sum(x_test_adv_pred == np.argmax(y_test[:100], axis=1))]\n",
+    "    \n",
+    "    x_test_adv_robust_pred = np.argmax(robust_classifier.predict(x_test_adv_robust), axis=1)\n",
+    "    nb_correct_robust += [np.sum(x_test_adv_robust_pred == np.argmax(y_test[:100], axis=1))]\n",
+    "\n",
+    "eps_range = [0] + eps_range\n",
+    "nb_correct_original = [nb_correct_pred] + nb_correct_original\n",
+    "nb_correct_robust = [nb_correct_robust_pred] + nb_correct_robust"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEGCAYAAACKB4k+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nO3debxU8//A8de7bvu+lxaRFiTRDRFKiUj2rF+SvSzhS+SH7ETWL5GtrJFd1vQV+VIqiiJU2ve0aF/u+/fH+0wz93aX6d47c+be+34+HvOYOcvMec+pe97zWc7nI6qKc845B1Aq7ACcc86lDk8KzjnndvKk4JxzbidPCs4553bypOCcc26ntLADKIjatWtr06ZNww7DOeeKlClTpqxU1TrZbSvSSaFp06ZMnjw57DCcc65IEZF5OW3z6iPnnHM7eVJwzjm3kycF55xzO3lScM45t1ORbmh24cjIyGDa7D/Yvm6D/6pw+ZIBpFWtxIHNWlCqlP8vSiUJSwoi8iLQA1iuqq2DdTWBN4GmwFygl6quFhEBHgdOADYCvVX1x0TF5gpm3pLFTJCNXHcQbPG/Z5cP5TLg0TkbqbRwPi2aNA07HBcjkX/Sw4Hjs6y7GRirqs2BscEyQHegefC4DBiawLhcAS1buYJ7G6snBJdvW0rBvY2VhcuW8tznH7F9x/awQ3KBhP1Zq+o3wN9ZVp8MjAhejwBOiVn/spoJQHURaZCo2AD++gu2bAE2b4ahQ+HPPxN5uGIlbXsGS8uGHYUr6paWhSql0hg/Yxrv/O/rsMNxgWT/1qunqksAgue6wfqGwIKY/RYG63YhIpeJyGQRmbxixYp8BbFtGxx/PFzQ+kc2798O+vaFgw6CpUvz9XkljQA7JOwoXFG3Q6AUQr3qNZk6d1bY4bhAqlQAZHeJyXb2H1Udpqrpqppep062d2nnqUypHXzQ/h5enXUoq+as4aNzXidjyKNQv77tsGFDvj7XObf7SpcqxZZtW8MOwwWSnRSWRaqFguflwfqFQOOY/RoBixMWhQitFn7JjtN6cUuP6fR84xxu/ONS2/bdd9C0KbzxBvisdCXHfS/CJXcX/r55kXSYtSDv/WKNmwyNTiic42fnivvg7uejy0PfhnrdoPKRsGqNPc9ZmLjju1Alu0vqh8CFwAPB8wcx668SkZHAocDaSDVTQpQqBZ98QvmKFRmhUOUq+Oknq1YqU7cuNG8O554L778PTz8NtWolLBSXAMM/giGvwuyFULUynNoJ7r8KqlfJ+T0D+8T/+buzb1H0zMDo623b4fpHYcJLcGALW7d+fDhxuaRIWElBRN4AvgdaishCEbkYSwbHisifwLHBMsAnwBxgFvAc0DdRce1UsWIQJzzyCIwZA2XKAPvsA+PHw333wXvvQbNmMGBA9H2//57w0FwBDHkVBjwJD10La7+2i9m8JXBsP9i6Lfv3bPeeLzlatgo2b4H99y74Z/l5LhIS2fvoHFVtoKplVLWRqr6gqqtUtYuqNg+e/w72VVXtp6rNVPUAVU3q0KflykHp0rBkCfzf/0GGlIZbboFJk+C002CvvWzHZ56B1q1tvUs969bDHc/CkzfC8YdDmTRouge89YAlhlc/sf0GPQtn3ATn3wZVj4bho23d+bdFP+vl0bBnD6jVxapSmp4EX06Mvj+y79zFVgU0YjQ0ORFqd4F7X4h+zg/TocNFUL0TNDgOrnow5+SU1d9r4aI7YY/joUZnOOWG7Pd7YDg0OxmqHAX7nQnvfRXdNmsBHH0ZVDvaYjvrFluvCtcNgbrH2rY2Z8P0oLG39yD4v6fhj3nQ8nRbV70zHHOFvY6t8tqyFf79mH33et2s6mnTZtsWqeZ6cDjUP86+i0t5fkdzjE8/hXvvtRLD7beDHHggvPhidIezzoL774dzzoEff4SqVcMLNpV0arHrul6roe8K2ChwQvNdt/deZY+VpeGMZrtuv3IFnLUaFpSBxnFeRL/7GTZvhdM6Z15fuSJ0PxzGTIQ+J9u6D76GUQ/Cy3fahe3BEdH9f50DfR+Ez56EQ/aHgU/BouXk6tup8Ps78Md8OORCOO0Y2Hcv+7Xx6PWQvi8sXA7dr4GnR0H/c/P+Pv+63WKf8ZY9fzct+/2aNYLxz0P9WjDqS0tYs96HBrXhtqHQ7VD46hlLRpN/s/d8MQG++Qn+eBeqVYaZc3etXmuxpx17r56w5itIy+ZyMeBJa1+Y+rol4XP/D+563qrrAJaugr/XwbyPICMj7+/sQpcqvY9SwkUX2fV+0CA4+eRseqjWqAGvv243OfTt6w3RqWblGqhdPfuLV4Patj2iQxs4pZO1L1Uon3nft8fCSUdCx7ZQtgzcdYXVM+bmjkvtcw5sAQc2h2l/2Pp2+8JhB1hMTfeAy0+Dr+O4WX/JSvj0O3jmFqhR1S64R7fLft8zu8Iedey7nNUNmjexEgrY++YthcUroHw5+06R9f9stGSgagmsQe2844qlCs+9B4/eADWrQZVKMPAiGPlFdJ9SAndeDuXK7nqeXUrykkIMEXj1VTjkEKs9at3aSg/t21seWL0a2hx6BGmDBllR4rjj4F//Cjvs8I37I+dtFTX37bV35L493lICWEJYucbqrrMmhiUrbfvOz62X8+csXpF5e8XyUKta7seuH9MZoWJ5WL/JXv8xzxpqJ/8GGzdbbO32zfu7LFgKNataQsjLy6PhkdetKgvs2JEEOPgauO0ZK73UqAo3nGelpWPaw1W9oN+DMH8pnNoZHr7WGubjtWK1fad250fXqcKOmBJBnRqWjFyR4SWFLEqVgv79rXaoc2do1crW33UXtGsHp5wCGTcPhE6doHHjXD/LJVmHNlCuDLz7Veb1GzbZr+4uh0TX5fbLv0Ftq+qJ2LQZVq3NX0xXPgCtmsKf78K6r+G+fvGVMBvXt2qXNf/kvt+8JXDpvfCfm2DVWFgzDlo3i97lU782PPd/sPgzeHagVYtF2gOuORumvGpVRH/Mg4de2b3vVrs6VChn718zzh5rv87cOymvEpZLOZ4UcrDvvjBqFFQJqlmvvRZuvRU+/hieeKo0fPaZJQaAZ5/1YTJSQbXKVo1z9UPw2XfWnXLuYjhzADSqC/+Ks2//GV3go/FWh791mzVe57eq8J8NULWStQnMnGt9/uPRoLa1g/R9AFavs+/yTTbVThs22YW3TlAKeulDmD47un3Ul7Bwmb2uUcX2LV0KJs2AidPtcytVsF/zpXfzclCqFFx6Klz3CCwPRrRZtBw+/373PselFE8KcWrbFu6+29oabroJfpwRFIlXr7Zs0bat3dPg7QzhuulCuK+v9YipejQc2tuqgsYOtXrteOzfzHownT0QGhxvdeV1a8b//lgP94fXP7OeQZfeY3X+8XrlLqv7b3WG9RJ67I1d99lvb6sS6tDHev/8MguOODC6fdIMOweVj4Se18PjN8BeDWHdBounRuegl1U1+Hc+qkIfvBr2aQyHXWTnu2tf+D3H6X9dESBahC9i6enpOnlyUnuvsmoVHHigdUQaMiRYuWgR9OkDX3wB3bpZj6WG2Q7dVCxMmTKF9BzaPIul9RutS+af79oF1RWayVNg1C+T2LpjG49cfHXY4ZQYIjJFVdOz2+Ylhd1Uqxb88AM8/HDMyoYNrTrpqafsxrf0dNi4MbQYXSH46BtrRN2wyUodBzSz3kPOFXOeFPJhjz2sanbWLOt8tG4dtqJvX5g2DR57bOcd06xfH2qsLp8++NpuGtvjePhzAYy8zxtNXYngSaEAfvjBxs07/HCYMydY2by51S0BvP02tGhh/Vpd0fL8bdHeNGOHQsumYUfkXFJ4UiiAc8+Fzz+HxYvtXoavsvSEZJ99rL7phBPg8su91OCcS3meFAqoSxcrMdSrZ23MY8bEbGzb1sZJuvFGeO45a6H+3/9Ci9U55/LiSaEQ7LMPfP+93fTWsWOWjeXLw+DB8PXX1l11/vxQYnTOuXh4Uigk1arBQw9BhQqwdq3VFv0dO0P1kUfCjBlw9tm2PHKkNUo751wK8aSQAJMmwfDhNobSb7/FbKhQwXqwbN0KAwdaQ8T998OOHWGF6pxzmXhSSICuXWHcOGtXPuww+OSTLDuULWsNEaecYsnhqKOsf6sL1/CPoOPFYUeRWWRug0SJnVpz02Y46TqbX+HMAfDap9CtX+KO7VKSJ4UE6dDBSgzNmkGPHjb6aia1a8Obb8Jrr8Gvv8JBB8HyPMbsd3lrehJUOMIudvWPs4vq+pBvJOx0GTz/frgx5GT9eNi7kb1+e6zNtLZqrM01cV53+OKpcONzSedJIYEaN7YbnC+/PDp2XiYi1q/1l1/g0Uehbl1bv2FDMsMsfj56xC52U1+Dn36H+18KO6KiYd4Sm1gnu/kodpdXiRZZnhQSrFIlGDoUGjWyv5Nbb81m8p5GjeCSS+z1t99C06bWEO0Kpn5tOO4wmBozX8Pa9XDB7VCnqw0Ed8/zmWcEU4WrB1sVSqvTYewP0W2xU3JC5mk5N2+x17W62NSb7S+wX923PgXjp8JVg630ctWD2cf67VQ4vI+9t/GJVpWV1ep10KO/xV6js72OjIAK9p69g2k59+pp1T+Q85ScEJ1a845nbca0N7+wOF94f9fqtJlz4di+UPMYaHkavBXT/7r3ILjyfjjhGqjUEb5K7phkrvD4JDtJNGOGjYDx8svwwQdw8MHZ7FSvnvVxPecceP99G3m1Zs2kx7o7vup02S7r3up1LEP7nkmFjZv55IRrdtk+vPdJjOh9ErVWruHtM27aZfvQK8/grbO60WjBUhY2rp+/wBYus3kUjmkfXXf1YEsMcz6wORK6XWXDVF98im2fOMOGzl45Ft79L5x2I/z1oc0slpsRo+1zF3xsczpM/cPmGri3H/xvGpx/AlxySvbvnb/UpukcNhDO6GpzTS9Ytut+GRlw0Uk25/SOHdDnLks27w+xMZqueRgmjbC7r5estDmeIecpOWPdeTkIMGshvHq3rYtNTBs2wbH94K7L4dMn4OdZ1t6w/942qizYaLCfPA6jH4t/HmqXcrykkERt2ti9ayJ2P8OoUdns1Ly51Tndey+8+65N//bZZ0mPtUg75d/2a7nxiTbk9Z2X2/odO+DNMTZ/cJVKNsDdDefBKzE9AerWsPmTy6TZMNct94SPv837mGXSLMnMWmDzMrfbN/5ZzF77FLoeAuccb59Tqzq0bbnrfrWqw+ldbGa3KpXg1j6Zp/YsJTaXwqbNlugiF+ucpuTcHaPHQ9MGcFFPq146uBWcfoy1Q0ScfDQc0dbmWfDZ1oosLykkWeQm51NPhV694MEHbX6GTNLSrFfSCSfYiHvTp8Pxx4cSbzw6jxuW47ZNFcvnun1V7eq5bs9XKeH9h6HrofD1FJtIfuUam5R+5Rr7Bbtng+i+ezaARSuiyw3rZh74bs8GdjHNy79OtF/3Zw+02dLO726lhDJx/IktWAbNGuW938bNcN0Q+Ox7WB3MyPbPBkt2lSrAm/fDw6/AxXfbnApDrrNZ33KaknN3zFtik/JU7xRdt31H5omLcpvi1BUZXlIIQb16Nk7SwIFwwAG57BjJINddZ8uffurDZOyOo9tB7x429DXY9JFl0uwCFzF/KTSsE11etDzzREnzl8IewfZKFezCHLF0VfR1mTS44zL4dRR89yKM/tbmToa8R1dtXA9mL8z7+wx51SawmTjcpvb8JkimkXiP6wBjnoYln1kyuPQeW5/blJzxalwPjj44Ou3mmnHWmD80tn3CR5EtDjwphKRcOash6t49jx3Ll7fqCFW44w67M/rmm2HLlqTEWeT1PxfGTISpv9t57HUs3Pq0/cKetwQeec3q+yOWr4YnRto0laO+hN/mwglH2La2LWDkF7Zt8q+Zq06+mmyznu3YYdNvlkmz4wHUqwlzFuUc43nd4csfrOF2+3ZYtcbizeqfDVChvJV6/l4Ldz4X3bZsFXz4tdX9lytr039GptfMaUrO3dHjSPhjPrzysX3/bdttVrff/tq9z3Epz5NCyBYutOSwfXseO4rA2LFw8cVW59S+vQ+TEY86NeCCE+Hu5235yRvtF//eJ0PHS+Dc46FPz+j+h+4Pf863Xjq3Pg1vP2h1+QB3X2G/6Gt0tt4658ZU6S1dCWfcZFNS7num/ao+P8j4155jCaRGZ7jmoV1jbFLfGmiHvAo1u0Db82BaNnN+9z/X2gtqd7XpL4/vEN2WoTDkNZv/oeYxVnX29M22LacpOXdHlUrwxX8sKe5xvN0DMuBJ2LJ19z7HpTyfjjNk779v7QvvvmvPcRk92rqw/v23JYZ9901ojFmVuOk4XcL4dJzh8Ok4U1iPHnaT29O7M5JBjx7W+Dx4MLRqZet8+k/nXCHwpBCytDS74/nLL+H3bKqRc1S7to3VLQIzZ8Kee8Izz2RuJHXOud3kSSEFXHIJlCljdz7nS+XK1lPpyiutG+vixYUan3Ou5PCkkALq1bMbmPNdA9Sokc0L+p//2GQ+rVv7MBnOuXzxpJAihg+HYTnfw5W3UqWgXz+YOhVatIAJEwortF0okJaR527O5SotAzLw6s5U40khRUTu+/nzzwI2C7RoYYPqPRgMvDZhQqEPk6EVy3PBkgxPDC7f0jLggqUZrNy2BUUpLaXDDskFfJiLFBLpnvrddzYfQ76lpUWHP77/fvjwQ7jiCnj4YRu2tYAObNacM3+azBVLylLK72J1+ZCBsnLbVqbNn8Pf6/9h/8Z7hR2SC3hSSCFdu0LVqtY9tUBJIdabb8Jtt8GQITBmjA3RevjhBfrIsmXL0rZFSx54+zXWbtwAXgXg8kEVRISalatyfudjww7HBfzmtRRzzTXw7LOwYEF0zp1C8c03cOGFMH++Dbx01FEF/sjV6/9hztLFbN7qd7W6/KlQrizN6jekWqU4R5R1hSK3m9c8KaSY336D/fazWp+bby7kD//nHyuG/PvfNi7Ppk1QoUIhH8Q5l+r8juYiZN99oXNnq+Up9HxdpQoMGGAJYdkym7vhwQd96kTn3E6eFFLQM89EJ+NJmLQ0OOwwK44cfbTdUr1ggd8R7VwJF0pDs4hcB1yCtVD+AlwENABGAjWBH4F/qWqJrKxu0SIJB6lVy6Z+e/11u7/h2GOhbFm7g650aXjqKesf26xZ9NG0qY357ZwrtpKeFESkIXANsJ+qbhKRt4CzgROAR1V1pIg8A1wM5HfghyLvp59sTKQ33rDrcUKIwHnn2aQOP/4IK1ZE5wCYNs0SxoYN0f2bNYNZs+z1E09Ym0Rs0qhaNUGBOueSJawuqWlABRHZBlQElgDHAOcG20cAgyjBSaFePUsMzzwDD2UzBH+hqlnT+sPGGjbMukEtXw6zZ1syiK1aeuONXe+aPukkuycCbMiNatWiCaNuXZ+Zy7ndsXkz/PUXrFsHhx5q6y6/3Iay6dTJLg4JkPSkoKqLRORhYD6wCfgCmAKsUdXIVDMLgWxnARGRy4DLAJo0aZL4gEOyxx52I9uLL8Jdd4XUSUjEslO9erve2/D99/afdc4cSxizZ0P9YD5lVbjlFli/Prp/5cpw9dVw3322/YUXrDqqWTMbOzzNb5lxJdDatbBokXU5BLufaPRo+3tauND+VmJL6GXK2By+Bx+csJDCqD6qAZwM7AWsAUYB2U1KmW2Lp6oOA4aBdUlNUJgpoV8/q/Z/803o3TvsaLJRtaqNztq2beb1IrByJcydGy1lzJ5tA/UBrFoFl14a3b9MGUsQt9wCF11ko7xee+2ux7vsMmv7mD07+/6611xj05XOmAGDBu26fcAASE+HKVPggQd23X7HHRbjt9/C44/vuv2BB+wPdMyY7AeqeuwxaNgQPvrIuo9l9eyzViobNQreemvX7S+/bNn/5ZftM7J66y07t88+ax0DYpUvD6+8Yq8ff9y+Q6zq1eG556LfY8qUzNsbNLAqQbBzN2NGdFuZMjZvx+232/Knn8LWrVCjhn2fyKN8+V1jLulUYelS+8EkYrNpvf22/R+ePdv+FsqVs7a8UqWsV+DWrVYSiJSymzePft5//pPwkMP4edYV+EtVVwCIyLvA4UB1EUkLSguNgBI//vNRR9kPiKefTtGkkJty5aBlS3tkVbMmzJsX/cOIPGrWtO1bt8Kvv+76vtWr7Xnz5uy3r1tnzxs3Zr/9n3+iz9ltj7SfrF2b/fZNm+x5zZrst0du4lu1KvvtkTlXV6zIfntGMJjUsmXZb49YsmTX7bFFyYULd91eu3b09YIFu26PbTuaNy/z9q1b7TtHDBgAv/yS+f2dOtlNkWCTQK1dG00WNWrAIYfA2Wfb9nHjbLiVSFKpVi3allUUbd8ezHtdGiZOtKQf+/9640b7N61b125E+v57u9iffjrss4+9zsiwpDB4cNjfJvk3r4nIocCLQHus+mg4MBk4CngnpqH5Z1XNdT6y4njzWlbvvGN/X7172/8Z50K3YIElttWrbUrYv/+GOnXgtNNse58+Vhce2b56NfTsCa+9ZtsrV86chERsLpCnnrJf1t27W6KITSpHHGFjv2zZAu+9t2tMbdrYL6j16636JauDD7ZufWvWZD9A5KGHwl57WQk3aykM7PiNG1spduTIzCXgefOsfa1dO3jpJfsusR0wmjWD88+30lqKyO3mtTDaFCaKyNtYt9PtwE9YddDHwEgRuSdY90KyY0tFp58edgTOZdG4sT1y8uKLu67LiBlS94svoskiklQOOsi2bdliv4Lmzo1u27EDbr3VksI//9jkI1ndd58lhRUrst/+5JOWFObPz3778OGWFP74I/vt77xj3/mXX+CGG+wC36yZJYJevaKl3PPPt+FkivAvOB/moghYu9b+z55/vt1e4FyJoRrtsFClilXVRBpdY9WpY38cW7da54es6tWzEsfmzZZwsmrQwEonGzda4siqYUM7/oYNlrgiSaCISqmSgtt9CxbYdMzbttmwRc6VGCJ2MY5IS7NG75yULZv79vLlc99esWLu2ytVKpTh51NZnmUcEblWRKqKeUFEfhSRbskIzpnWra3ReejQzKVw55wrbPFUfPVR1XVAN6AONiRFNv35XCL17Wul4s8/DzsS51xxFk9SiNyGegLwkqpOi1nnkuTUU62r89O59sdyzrmCiScpTBGRL7Ck8LmIVAG8EiPJypa1+702bbK2BeecS4R4GpovBtoCc1R1o4jUwqqQXJINGlSke7o554qAPJOCqmaIyDJgPxHx3kohiiSEJUusd52PKuCcK2x5XuRF5EHgLOBXIDJFlwLfJDAul4Pp0+0+nxdfhH/9K+xonHPFTTy//E8BWqrqlkQH4/K2//52I+XTT3tScM4VvnhqqOcAZRIdiItPZJiYCRNsXhznnCtM8SSFjcBUEXlWRJ6IPBIdmMvZhRfajZfePdU5V9jiqT76MHi4FFG9us2i+cYbNgR+xYphR+ScKy7iGhBPRMoCkenkf1fVlOgpX1IGxMvO/Pk2Vtiee4YdiXOuqCnQgHgi0gmbM3kudidzYxG5UFW991GIivFMpM65EMXTpjAE6KaqR6vqUcBxwKOJDcvFY9EiOPHE7OcEcc65/IgnKZRR1d8jC6r6B94bKSXUrg2TJiVl2lbnXAkRT1KYHAyZ3Sl4PAdMyfNdLuHKlYNLLrE53rObF8Q553ZXPEnhSmAGcA1wLXZn8xWJDMrF7/LLrcF52LCwI3HOFQc+HWcx0LMnTJxopYVy5cKOxjmX6vLV+0hE3lLVXiLyCzbWUSaq2qYQY3QF8O9/w3ff2fS1nhSccwWRW5fUa4PnHskIxOXfUUfZwznnCirHNgVVXRK87Kuq82IfQN/khOfitXWr3eE8c2bYkTjnirJ4GpqPzWZd98IOxBXM+vXQpw886neQOOcKIMekICJXBu0JrUTk55jHX8AvyQvRxaNmTTjnHHj1VVi7NuxonHNFVW4lhdeBk4APgufIo52qnpeE2Nxu6tcPNm6El18OOxLnXFGVW5vCWlWdCzwO/B3TnrBNRA5NVoAufu3awSGH2JDaRbinsXMuRPG0KQwF1scsbwjWuRTUt6/N5bx0adiROOeKoniSgmjMHW6qmkF88zC4EJx/vs3j3KBB2JE454qiuKbjFJFrRKRM8LgWm6LTpaDSpW3KzvXrvcHZObf74kkKVwCHA4uAhcChwGWJDMoVzNq10LgxPPJI2JE454qaPJOCqi5X1bNVta6q1lPVc1V1eTKCc/lTrRocfrgNkrctJebIc84VFbmNfXSTqg4WkSfJfuyjaxIamSuQvn2hRw947z3o1SvsaJxzRUVuDca/Bc8+DGkRdPzxsNde1j3Vk4JzLl45JgVV/Sh4HpG8cFxhKV0arrgCBgyAWbNgn33Cjsg5VxTkVn30EdlUG0Woas+EROQKzSWXQJcunhCcc/HLrfro4eD5NKA+8GqwfA4wN4ExuUJSs6Y9nHMuXrlVH30NICJ3q2rsaP0ficg3CY/MFYpNm6zRuWNHuPjisKNxzqW6eO5TqCMie0cWRGQvoE5BDioi1UXkbRGZKSK/iUgHEakpImNE5M/guUZBjuFM+fLw8882pLaPh+Scy0s8SeE6YJyIjBORccBXQP8CHvdx4DNVbQUciPV0uhkYq6rNgbHBsisgERs9dcYM+MbLd865PIjG8fNRRMoBrYLFmaq6Jd8HFKkKTAP2jh1TSUR+Bzqp6hIRaQCMU9WWuX1Wenq6Tp7sPWbzsnEjNGwI3brBm2+GHY1zLmwiMkVV07PblmdJQUQqAjcCV6nqNKCJiBRk3ua9gRXASyLyk4g8LyKVgHqRKUCD57o5xHOZiEwWkckrVqwoQBglR8WKcNFF8O67sGRJ3vs750queKqPXgK2Ah2C5YXAPQU4ZhpwMDBUVQ/ChuKOu6pIVYeparqqptepU6CmjRLlyivhqqusOsk553IST1JopqqDgW0AqroJKMilZSGwUFUnBstvY0liWVBtRPDs4ysVoubNrbG5fv2wI3HOpbJ4ksJWEalAcCObiDQD8t2moKpLgQUiEmkv6AL8CnwIXBisuxCbBtQVIlX473/h++/DjsQ5l6rimSznDuAzoLGIvAYcAfQu4HGvBl4TkbLY3AwXYTdmCYgAABh1SURBVAnqLRG5GJgPnFnAY7gsMjKsbaF5c/jyy7Cjcc6lolyTgogIMBO7q/kwrNroWlVdWZCDqupUILuW7y4F+VyXu8h4SAMHwsyZ0KpV3u9xzpUsuVYfBV1G31fVVar6saqOLmhCcOG6+GIoUwaG+izbzrlsxNOmMEFE2ic8EpcUdevCmWfC8OE2ZadzzsWKJyl0xhLDbBH5WUR+EZGfEx2YS5x+/ezehZkzw47EOZdq4mlo7p7wKFxSdegA8+dbNZJzzsWKZ47meUAt4GSgJ1ArWOeKKBFLCNu3w99/hx2Ncy6VxDPMxe3ACCwx1MaGp/i/RAfmEksVDj4Yrr467Eicc6kknjaFc4D2qnqHqt6BdU09L7FhuUQTgWOOgVGjYLnfO+6cC8STFOYC5WOWywGzExKNS6orr4Rt2+CFF8KOxDmXKuJJCluAGSIyXEReAqYD60XkCRF5IrHhuURq2dLmcH7mGdixI+xonHOpIJ7eR+8Fj4hxiQnFhaFfPzjtNPj8czjhhLCjcc6FLc+koKojkhGIC8dJJ1lC6No17Eicc6kgnuojV4ylpdmMbKVKwdatYUfjnAubJwUHwBtv2Oipq1aFHYlzLkzx3KewyxDW2a1zRdv++8OiRTaCqnOu5IqnpHBLnOtcEdamDVxzDTz3HPzwQ9jROOfCkmNDs4h0B04AGmbpeloV2J7owFzyDRoEI0fa/Qs//GDzLzjnSpbcSgqLgcnAZmBKzOND4LjEh+aSrWpVeOQR+OknGD8+7Gicc2HIsaSgqtOAaSLyHrBBVXcAiEhp7K5mVwyddRYccIC1MTjnSp542hS+ACrELFcAfIbfYkokmhDmzAk3Fudc8sWTFMqr6s45uoLXFRMXkksF771nXVS//TbsSJxzyRRPUtggIgdHFkSkHbApcSG5VNCtGzRqFB00zzlXMsSTFPoDo0RkvIiMB94ErkpsWC5slSrB44/D9Onw5JNhR+OcSxZR1bx3EikDtAQEmKmqKfHbMT09XSdPnhx2GMWWKvToAd98Y/M5N2wYdkTOucIgIlNUNT27bfHc0VwRGABcq6q/AE1FpEchx+hSkAg88QSULQuee50rGeIZOvsl7P6EDsHyQmAUMDpRQbnU0awZzJ9v1UnOueIvnjaFZqo6GNgGoKqbsGokV0JUqmRVSR9+CFu2hB2Ncy6R4kkKW0WkAqAAItIMm43NlSATJ8LJJ9sdz8654iuepHAH8BnQWEReA8YCNyU0KpdyDjvMZmi7+26YNy/saJxziZJrUhARAWYCpwG9gTeAdFUdl/DIXMp57DFrfO7fP+xInHOJkmtSUOuv+r6qrlLVj1V1tKquTFJsLsU0bgy33w7vvw+ffBJ2NM65RIin+miCiLRPeCSuSLjuOpvPOY7bW5xzRVA8XVI7A5eLyDxgA9bzSFW1TUIjcympbFkYMybsKJxziRJPUuie8ChckbN1qw2DceqpsM8+YUfjnCssuSYFESkFfKyqrZMUjysiVq2ynkj//a+1L4jfueJcsZBXQ3MGNtFOkyTF44qIBg3grrvgs89smG3nXPEQT0NzA2CGiIwVkQ8jj0QH5lLfVVdBmzbWRXXDhrCjcc4VhnjaFO5MeBSuSEpLg6efho4drSrpgQfCjsg5V1B5JgVV/VpE6gGRbqk/qOryxIblioojjoB774XOncOOxDlXGOIZOrsX8ANwJtALmCgiZxT0wCJSWkR+EpHRwfJeIjJRRP4UkTdFpGxBj+GSY+BA6NAh7/2cc6kvnjaFW4H2qnqhql4AHALcVgjHvhb4LWb5QeBRVW0OrAYuLoRjuCTZuNGm7hw5MuxInHMFEU9SKJWlumhVnO/LkYg0Ak4Eng+WBTgGeDvYZQRwSkGO4ZKrXDmbiOe662Dt2rCjcc7lVzwX989E5HMR6S0ivYGPgU8LeNzHsJFWM4LlWsAaVd0eLC8Esp38UUQuE5HJIjJ5xYoVBQzDFZbSpWHoUFi2DO64I+xonHP5lWdSUNUbgWeBNsCBwDBVzffQ2cFUnstVdUrs6uwOnUM8w1Q1XVXT69Spk98wXAKkp8Pll8OTT8LUqWFH45zLjxyTgojsIyJHAKjqu6p6vapeB6wKJtrJryOAniIyFxiJVRs9BlQXkUhvqEbA4gIcw4Xk3nuhZk3497/DjsQ5lx+5lRQeA/7JZv3GYFu+qOotqtpIVZsCZwP/VdXzgK+ASK+mC4EP8nsMF56aNa2x+fnnw47EOZcfuSWFpqr6c9aVqjoZaJqAWAYA14vILKyN4YUEHMMlQZcu0LSpDa+9dWvY0TjndkduN6+Vz2VbhcI4eDCD27jg9Rysu6srBjIyoGdPm5hn6NCwo3HOxSu3ksIkEbk060oRuRiYks3+zu1UqhQ0bw7PPguTJoUdjXMuXqI5TKEVDG3xHrCVaBJIB8oCp6rq0qREmIv09HSdPHly2GG4HKxbB61aQcOGMGGCdVt1zoVPRKaoanp223IsKajqMlU9HBsQb27wuFNVO6RCQnCpr2pVGDLEbmobNizsaJxz8cixpFAUeEkh9alaw/OyZfDLL1at5JwLV24lhXiGznYu30RgxAgrNXhCcC71+Z+pS7jGjaFaNdi2DebPDzsa51xuPCm4pDn5ZOjRA7Zvz3tf51w4PCm4pLn0UmtXePLJsCNxzuXEk4JLmlNOge7dbRTVxT6ylXMpyZOCSxoRKyVs3Qo33BB2NM657HhScEnVrBncfDP89husXx92NM65rDwpuKQbONBuaKtcOexInHNZeVJwSVe2LKSlwerV8MUXYUfjnIvlScGF5vrr4dRT/d4F51KJJwUXmkGDbBiM/v3DjsQ5F+FJwYVmzz3httvgvffgk0/CjsY5B54UXMhuuAFatoSrr4ZNm8KOxjnnScGFqmxZeOop2G8/+Ce7GcGdc0nlo6S60HXpYg+waTx9NFXnwuN/fi5lrFkDBx0Eb7xhDdDOueTzpOBSxrp1ULEinHsunH02rFoVdkTOlTyeFFzKaNIExo+He++1HkmtW3uvJOeSzZOCSylpaTYMxg8/QO3a8NhjXpXkXDJ5Q7NLSW3b2vhI//xjo6suWADz5kHHjmFH5lzx5iUFl7LKlbPSAsDtt8NRR8GAAbBlS7hxOVeceVJwRcITT8All8DgwZCeDlOnhh2Rc8WTJwVXJFSpAsOGwejRsGIFHHKIvXbOFS5PCq5IOfFEmD7d5ns+8khb5w3RzhUeTwquyKld24bGqFbN2hc6doRnnvHk4Fxh8KTgirT166FSJbjySujeHRYtCjsi54o2TwquSKtVCz7/3EoO33wDBxwAI0eGHZVzRZcnBVfkiUDfvjBtmg3Dff/9sG1b2FE5VzR5UnDFRvPmNkzGZ59BmTKwdq3PAe3c7vKk4IqVtDRo0MBeDx4Mxx0HV1xhbQ/Oubx5UnDF1m23wY032v0NBx4I//tf2BE5l/o8Kbhiq3x5Ky18/bV1Vz3ySHjppbCjci61eVJwxd6RR1oj9FVXQdeuts7vaXAue0kfJVVEGgMvA/WBDGCYqj4uIjWBN4GmwFygl6quTnZ8rniqUsXGTwKb8vPkk+GII6x6qXTpcGNTtR5UAC+8AL//DrNn2yRDrVvbXdzdu4cboys5whg6eztwg6r+KCJVgCkiMgboDYxV1QdE5GbgZmBACPG5Ym7zZqtauuUW+OgjGDEC9tknOcceOxZ++cUu+rNnw6xZsN9+8P77tv2ee2DxYth7b6hRw2IDSwrbt9vzgQdC+/b22GuvaEJxrjAkPSmo6hJgSfD6HxH5DWgInAx0CnYbAYzDk4JLgIoV4a23bC7ofv3sIjtkCFx+ecEvsDNnws8/28U+cuEvWzbaNfbOO63bbNWq0KyZzRtx+OHR9//wA9SsGS297NgBGzfa6xUrbH6J//wnOnx4zZpWAjrvPEt2q1dHe185lx+iIVauikhT4BugNTBfVavHbFutqjWyec9lwGUATZo0aTdv3rzkBOuKpYULoU8f+O03mDHDLta5WbnS9o1c8GfPhmXL4MsvLaGcdx68/rrtW6+elUAOOACGDrV1c+bYMWrVyn8C2rrVBgWcNMkeffpYYhkzBrp1g4YNbXjxSGniiCNsKBDnIkRkiqqmZ7strKQgIpWBr4F7VfVdEVkTT1KIlZ6erpMnT050qK6YU7Xk0LixXXA/+sjaICLVO7Nnwyuv2LoBA6xHE0CpUrDnnvaL/8MPoUIFSxjbtln1T+XKyf0e8+fDu+/ajHWTJsEff9j6H3+Egw6Cb7+FiRMtURx8cPLjc6kjt6QQynScIlIGeAd4TVXfDVYvE5EGqrpERBoAy8OIzZU8IpYQAJ57znopRVSoYBf4lSstKVxwAXTubIlgzz2taijWvvsmL+6smjSB/v2jy2vWwJQp1lgNNkbUPffY61KlLNb0dHj2WZvlLrbB25VcSS8piIhgbQZ/q2r/mPUPAatiGpprqupNuX2WlxRcYdu+3S6eVapY1U/9+nYBLS6WLbOSRKQ0sWgR/PSTbbvgAqtCi1Q7tW9vjeBpPpN7sZNS1Uci0hEYD/yCdUkFGAhMBN4CmgDzgTNV9e/cPsuTgnOF59FH4eOPLWGsXWvrDj0UJkyw12PGWOlon32KV6IsCrZvt6rJSDtSy5aZS4W7K6Wqj1T1WyCnQmqXZMbinIu67jp7ZGRYW8qkSdFSQkYGnH669X6qVg3atbOSRI8eNsmRKzwZGVZdWbeuLZ9+ug3yGOmFVrUqXHRR4o7vBUPnXCalSkGLFvaIELGG6ki106RJ8MgjNhptx46WLM45J3Ovp8hFzeVu8WIrjUXO6+TJUKcO/PmnbW/Rwtq82re389u8eWJLaqF2SS0orz5yLjybN9v9EtWqWU+n006DX3+NDiHSuLFNk3rCCfYrd9s227ckW77cLvo//ggDB9rF/dJL4fnnrVTWpk00qfbpk7iG/5SqPnLOFQ/ly9sD7Nfs9Ok2RPmPP0ZLFHvsYdtHj4azzrL9Yhuy09N37cFV3IwfD48/budj/nxbJwLnnms92/r3t8TQpk30fIbJSwrOuYSbORPeeSdaRbJ4sa2fM8eG6vjvf626pH1760Jb1BLFxo3Wiyu2eu35520wxg8+gOuvz5wMDzrIeriFxUsKzrlQtWoFt94aXV682O6haNrUlt96y+6XALtn4sADrefT44+n3r0T27bZ+FXVqtn9KlOnWolnxw7bvsceduEvU8aWe/a0ARiLCi8pOOdCpwp//ZX5l/bmzdHusGeeafdQxCaIgw6CV1+11z172p3nsTp2jCaaLl1g6dLM27t1s264AB06wLp1mbefemr0Zr82beyin5FhcW7ZAjfdBA8+CJs2wX33RavDIlVmqcxLCs65lCZi9et77w29etm62N+rrVrt+p699oq+btbMShixmjSJvm7RwgYPjNWoUfR1y5awYUPm7bEX9333tYQANpR5+/bRgQwrVIC77875uxU1XlJwzrkSJreSgt+X6JxzbidPCs4553bypOCcc24nTwrOOed28qTgnHNuJ08KzjnndvKk4JxzbidPCs4553Yq0jevicgKYF4+314bWFmI4RR1fj4y8/MR5ecis+JwPvZU1TrZbSjSSaEgRGRyTnf0lUR+PjLz8xHl5yKz4n4+vPrIOefcTp4UnHPO7VSSk8KwsANIMX4+MvPzEeXnIrNifT5KbJuCc865XZXkkoJzzrksPCk455zbqdgnBRE5XkR+F5FZInJzNtvLicibwfaJItI0+VEmTxzn43oR+VVEfhaRsSKyZxhxJkNe5yJmvzNEREWk2HZDhPjOh4j0Cv5/zBCR15MdYzLF8bfSRES+EpGfgr+XE8KIs9CparF9AKWB2cDeQFlgGrBfln36As8Er88G3gw77pDPR2egYvD6yuJ6PuI5F8F+VYBvgAlAethxh/x/oznwE1AjWK4bdtwhn49hwJXB6/2AuWHHXRiP4l5SOASYpapzVHUrMBI4Ocs+JwMjgtdvA11EYqcHL1byPB+q+pWqbgwWJwCNKJ7i+b8BcDcwGNiczOBCEM/5uBR4SlVXA6jq8iTHmEzxnA8FqgavqwGLkxhfwhT3pNAQWBCzvDBYl+0+qrodWAvUSkp0yRfP+Yh1MfBpQiMKT57nQkQOAhqr6uhkBhaSeP5vtABaiMj/RGSCiByftOiSL57zMQg4X0QWAp8AVycntMRKCzuABMvuF3/WPrjx7FNcxP1dReR8IB04OqERhSfXcyEipYBHgd7JCihk8fzfSMOqkDphJcjxItJaVdckOLYwxHM+zgGGq+oQEekAvBKcj4zEh5c4xb2ksBBoHLPciF2LeDv3EZE0rBj4d1KiS754zgci0hW4FeipqluSFFuy5XUuqgCtgXEiMhc4DPiwGDc2x/u38oGqblPVv4DfsSRRHMVzPi4G3gJQ1e+B8thgeUVacU8Kk4DmIrKXiJTFGpI/zLLPh8CFweszgP9q0HJUDOV5PoIqk2exhFCc64xzPRequlZVa6tqU1VtirWv9FTVyeGEm3Dx/K28j3VEQERqY9VJc5IaZfLEcz7mA10ARGRfLCmsSGqUCVCsk0LQRnAV8DnwG/CWqs4QkbtEpGew2wtALRGZBVwP5Ng1saiL83w8BFQGRonIVBHJ+odQLMR5LkqMOM/H58AqEfkV+Aq4UVVXhRNxYsV5Pm4ALhWRacAbQO/i8IPSh7lwzjm3U7EuKTjnnNs9nhScc87t5EnBOefcTp4UnHPO7eRJwTnn3E6eFFzKEJFTg9FIW8Wsayoi58Ysty3IaJQiMjfoY7+77+stInvk97i7eaz+IlIxZnl9nO87RURuL+RYvhSRGoX5mS61eVJwqeQc4FvsRqGIpsC5McttgTCGKO4NZJsURKR0IR+rP1Axz712dRPwdCHH8go2krArITwpuJQgIpWBI7ChA2KTwgPAkcGNdAOAu4CzguWzROQQEfkuGNP+OxFpGXxeaRF5WER+Cca6vzrL8SqIyGcicmmW9aVFZLiITA/ee52InIGNA/VacNwKQYnjdhH5FjhTRJoFnzdFRMZHSjvBZz0RxDYn+CxEpJSIPB3MSzBaRD4Rm7fhGiz5fCUiX8XEda+ITAsGoquXzflrAWxR1ZXBch0ReUdEJgWPI4L1g0TkFRH5r4j8Gfn+ItJARL4Jvt90ETky+OgPsWTtSoqwx+72hz9UFeB84IXg9XfAwcHrTsDomP16A/+JWa4KpAWvuwLvBK+vBN6J2VYzeJ6LlT6+BC7IJo52wJiY5erB8zhi5lMIPuemmOWxQPPg9aHYcCkAw4FR2A+w/bDhmMGGVPkkWF8fWA2cEfPZtWM+W4GTgteDgf/LJu6LgCExy68DHYPXTYDfgteDsLkBKmDj9CzAktANwK3BPqWBKjGf9SdQK+z/I/5IzqO4j5Lqio5zgMeC1yOD5R/jeF81YISINMcunmWC9V2xyZO2A6hq7CCHHwCDVfW1bD5vDrC3iDwJfAx8kcux34SdpZzDsaFBItvKxez3vtrImb/G/MrvCIwK1i+NLRVkYysQGb57CnBsNvs0IPO4O12B/WLiqSoiVYLXH6jqJmBTcNxDsLF+XhSRMkG8U2M+azmWOIrlkBYuM08KLnQiUgs4BmgtIor9UlURuSmOt98NfKWqp4pNpTou8rHkPAT6/4DuIvK6qmbaR1VXi8iBwHFAP6AX0CeHz9kQPJcC1qhq2xz2ix1pVrI8x2NbTJw7yP7vdhOWICNKAR2Ci3/04JYksp4XVdVvROQo4ERsCOiHVPXlYHv54PNdCeBtCi4VnAG8rKp7qo1K2hj4C/s1/Q82jHVE1uVqwKLgde+Y9V8AV4gNh46I1IzZdjv2q3eXRtmgZ1IpVX0HuA04OIfj7qSq64C/ROTM4DMkSCy5+RY4PWhbqIdVk+X0HePxG7BPzPIX2IBuBDHFJqyTRaR8kIw7AZPE5uJerqrPYYNEHhz5Llj11tzdjMcVUZ4UXCo4B3gvy7p3sF5HPwPbg0bW67DROfeLNDRjdez3i8j/sBJGxPPY0MY/i41ieW7mj6c/UF5EBmdZ3xCbQ2Eq1h5wS7B+OPBMpKE5m+9wHnBxcKwZZD+1Z9bvtxCYjg1VPhGb9Q9s7t9P86hSyuob4CCJ1hddA6QHjey/AlfE7PsDVjU2AbhbVRdjyWGqiPwEnA48HuzbDpgQqYZzxZ+PkupcSESksqquD36x/wAcoapLC/B5jwMfqeqXuewzCFivqg/vxmd+qKpj8xuXK1q8TcG58IwWkepAWewXe74TQuA+rOdTYZruCaFk8ZKCc865nbxNwTnn3E6eFJxzzu3kScE559xOnhScc87t5EnBOefcTv8PIeBCbYYr2BIAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "ax.plot(np.array(eps_range), np.array(nb_correct_original), 'b--', label='Original classifier')\n",
+    "ax.plot(np.array(eps_range), np.array(nb_correct_robust), 'r--', label='Robust classifier')\n",
+    "\n",
+    "legend = ax.legend(loc='upper center', shadow=True, fontsize='large')\n",
+    "legend.get_frame().set_facecolor('#00FFCC')\n",
+    "\n",
+    "plt.xlabel('Attack strength (eps)')\n",
+    "plt.ylabel('Correct predictions')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/adversarial-robustness-toolbox/notebooks/art-for-tensorflow-v2-callable.ipynb b/adversarial-robustness-toolbox/notebooks/art-for-tensorflow-v2-callable.ipynb
new file mode 100644
index 0000000..4c6f284
--- /dev/null
+++ b/adversarial-robustness-toolbox/notebooks/art-for-tensorflow-v2-callable.ipynb
@@ -0,0 +1,964 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# ART for TensorFlow v2 - Callable Class/Function"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This notebook demonstrates applying ART with TensorFlow v2 using callable classes or functions to define models. The code follows and extends the examples on www.tensorflow.org."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "import os\n",
+    "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' \n",
+    "import tensorflow as tf\n",
+    "from tensorflow.keras.layers import Dense, Flatten, Conv2D\n",
+    "from tensorflow.keras import Model\n",
+    "import numpy as np\n",
+    "from matplotlib import pyplot as plt\n",
+    "\n",
+    "from art.estimators.classification import TensorFlowV2Classifier\n",
+    "from art.attacks.evasion import FastGradientMethod, CarliniLInfMethod"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if tf.__version__[0] != '2':\n",
+    "    raise ImportError('This notebook requires TensorFlow v2.')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Load MNIST dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()\n",
+    "x_train, x_test = x_train / 255.0, x_test / 255.0\n",
+    "\n",
+    "x_train = x_train.astype(np.float32)\n",
+    "x_test = x_test.astype(np.float32)\n",
+    "\n",
+    "x_test = x_test[0:100]\n",
+    "y_test = y_test[0:100]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Add a dimension for color channel"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_train = x_train[..., tf.newaxis]\n",
+    "x_test = x_test[..., tf.newaxis]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create loss object and optimizer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loss_object = tf.keras.losses.SparseCategoricalCrossentropy()\n",
+    "optimizer = tf.keras.optimizers.Adam()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define metrics for training and testing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_loss = tf.keras.metrics.Mean(name='train_loss')\n",
+    "train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='train_accuracy')\n",
+    "\n",
+    "test_loss = tf.keras.metrics.Mean(name='test_loss')\n",
+    "test_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='test_accuracy')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# TensorFlow with callable class"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create a custom model class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class KerasModel(Model):\n",
+    "    def __init__(self):\n",
+    "        super(KerasModel, self).__init__()\n",
+    "        self.conv1 = Conv2D(filters=3, kernel_size=3, activation='relu')\n",
+    "        self.flatten = Flatten()\n",
+    "        self.dense1 = Dense(10, activation='softmax')\n",
+    "\n",
+    "    def call(self, x):\n",
+    "        x = self.conv1(x)\n",
+    "        x = self.flatten(x)\n",
+    "        x = self.dense1(x)\n",
+    "        return x"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create callable model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = KerasModel()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create input pipelines for training and testing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_ds = tf.data.Dataset.from_tensor_slices((x_train, y_train)).shuffle(10000).batch(32)\n",
+    "test_ds = tf.data.Dataset.from_tensor_slices((x_test, y_test)).batch(32)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define the training step."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "@tf.function\n",
+    "def train_step(images, labels):\n",
+    "    with tf.GradientTape() as tape:\n",
+    "        predictions = model(images)\n",
+    "        loss = loss_object(labels, predictions)\n",
+    "    gradients = tape.gradient(loss, model.trainable_variables)\n",
+    "    optimizer.apply_gradients(zip(gradients, model.trainable_variables))\n",
+    "\n",
+    "    train_loss(loss)\n",
+    "    train_accuracy(labels, predictions)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define the testing step."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "@tf.function\n",
+    "def test_step(images, labels):\n",
+    "    predictions = model(images)\n",
+    "    t_loss = loss_object(labels, predictions)\n",
+    "\n",
+    "    test_loss(t_loss)\n",
+    "    test_accuracy(labels, predictions)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Fit the model on training data and collect metrics for training and testing."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1, Loss: 0.30, Accuracy: 91.59, Test Loss: 0.08, Test Accuracy: 97.00\n",
+      "Epoch 2, Loss: 0.23, Accuracy: 93.57, Test Loss: 0.07, Test Accuracy: 97.50\n",
+      "Epoch 3, Loss: 0.20, Accuracy: 94.46, Test Loss: 0.06, Test Accuracy: 98.00\n"
+     ]
+    }
+   ],
+   "source": [
+    "epochs = 3\n",
+    "\n",
+    "for epoch in range(epochs):\n",
+    "    for images, labels in train_ds:\n",
+    "        train_step(images, labels)\n",
+    "\n",
+    "    for test_images, test_labels in test_ds:\n",
+    "        test_step(test_images, test_labels)\n",
+    "\n",
+    "    template = 'Epoch {}, Loss: {:4.2f}, Accuracy: {:4.2f}, Test Loss: {:4.2f}, Test Accuracy: {:4.2f}'\n",
+    "    print(template.format(epoch + 1,\n",
+    "                          train_loss.result(),\n",
+    "                          train_accuracy.result() * 100,\n",
+    "                          test_loss.result(),\n",
+    "                          test_accuracy.result() * 100))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Evaluate model accuracy on test data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy on test data: 99.00%\n"
+     ]
+    }
+   ],
+   "source": [
+    "y_test_pred = np.argmax(model(x_test), axis=1)\n",
+    "accuracy_test = np.sum(y_test_pred == y_test) / y_test.shape[0]\n",
+    "print('Accuracy on test data: {:4.2f}%'.format(accuracy_test * 100))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create a ART TensorFlow v2 classifier for the TensorFlow custom model class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "classifier = TensorFlowV2Classifier(model=model, nb_classes=10, input_shape=(28, 28, 1), loss_object=loss_object, \n",
+    "                                    clip_values=(0, 1), channels_first=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Fast Gradient Sign Method attack"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create a ART Fast Gradient Sign Method attack."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "attack_fgsm = FastGradientMethod(estimator=classifier)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Generate adversarial test data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "x_test_adv = attack_fgsm.generate(x_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Evaluate accuracy on adversarial test data and calculate average perturbation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy on adversarial test data: 1.00%\n",
+      "Average perturbation: 0.17\n"
+     ]
+    }
+   ],
+   "source": [
+    "y_test_pred = np.argmax(model(x_test_adv), axis=1)\n",
+    "accuracy_test_adv = np.sum(y_test_pred == y_test) / y_test.shape[0]\n",
+    "perturbation = np.mean(np.abs((x_test_adv - x_test)))\n",
+    "print('Accuracy on adversarial test data: {:4.2f}%'.format(accuracy_test_adv * 100))\n",
+    "print('Average perturbation: {:4.2f}'.format(perturbation))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Visualise the first adversarial test sample."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.matshow(x_test_adv[0, :, :, 0])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Carlini&Wagner Infinity-norm attack"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create a ART Carlini&Wagner Infinity-norm attack."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "attack_cw = CarliniLInfMethod(classifier=classifier, eps=0.3, max_iter=100, learning_rate=0.01)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Generate adversarial test data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C&W L_inf: 100%|██████████| 1/1 [00:08<00:00,  8.29s/it]\n"
+     ]
+    }
+   ],
+   "source": [
+    "x_test_adv = attack_cw.generate(x_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Evaluate accuracy on adversarial test data and calculate average perturbation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy on adversarial test data: 23.00%\n",
+      "Average perturbation: 0.02\n"
+     ]
+    }
+   ],
+   "source": [
+    "y_test_pred = np.argmax(model(x_test_adv), axis=1)\n",
+    "accuracy_test_adv = np.sum(y_test_pred == y_test) / y_test.shape[0]\n",
+    "perturbation = np.mean(np.abs((x_test_adv - x_test)))\n",
+    "print('Accuracy on adversarial test data: {:4.2f}%'.format(accuracy_test_adv * 100))\n",
+    "print('Average perturbation: {:4.2f}'.format(perturbation))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Visualise the first adversarial test sample."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.matshow(x_test_adv[0, :, :, 0])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# TensorFlow with custom function"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Reshape dataset in feature vectors because the model in this example requires feature vectors."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_train = x_train.reshape((60000, 784))\n",
+    "x_test = x_test.reshape((100, 784))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create input pipelines for training and testing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_ds = tf.data.Dataset.from_tensor_slices((x_train, y_train)).shuffle(10000).batch(32)\n",
+    "test_ds = tf.data.Dataset.from_tensor_slices((x_test, y_test)).batch(32)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create variables and keep track of them"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "W = tf.Variable(initial_value=tf.random.normal(shape=(784, 10)), name=\"W\")\n",
+    "b = tf.Variable(tf.zeros(shape=(10)), name=\"b\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define a function representing the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "@tf.function\n",
+    "def forward(x):\n",
+    "    x = tf.matmul(x, W) + b\n",
+    "    denominator = tf.expand_dims(tf.reduce_sum(tf.exp(x), axis=1), axis=1)\n",
+    "    softmax = (1.0 / denominator) * tf.exp(x)\n",
+    "    return softmax"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define the training step."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "@tf.function\n",
+    "def train_step(images, labels):\n",
+    "    with tf.GradientTape() as tape:\n",
+    "        predictions = forward(images)\n",
+    "        loss = loss_object(labels, predictions)\n",
+    "    gradients = tape.gradient(loss, [W, b])\n",
+    "    optimizer.apply_gradients(zip(gradients, [W, b]))\n",
+    "\n",
+    "    train_loss(loss)\n",
+    "    train_accuracy(labels, predictions)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define the testing step."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "@tf.function\n",
+    "def test_step(images, labels):\n",
+    "    predictions = forward(images)\n",
+    "    t_loss = loss_object(labels, predictions)\n",
+    "\n",
+    "    test_loss(t_loss)\n",
+    "    test_accuracy(labels, predictions)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Fit the model on training data and collect metrics for training and testing."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1, Loss: 0.55, Accuracy: 89.07, Test Loss: 0.17, Test Accuracy: 95.00\n",
+      "Epoch 2, Loss: 0.59, Accuracy: 88.21, Test Loss: 0.21, Test Accuracy: 94.60\n",
+      "Epoch 3, Loss: 0.59, Accuracy: 88.01, Test Loss: 0.22, Test Accuracy: 94.33\n"
+     ]
+    }
+   ],
+   "source": [
+    "epochs = 3\n",
+    "\n",
+    "for epoch in range(epochs):\n",
+    "    for images, labels in train_ds:\n",
+    "        train_step(images, labels)\n",
+    "\n",
+    "    for test_images, test_labels in test_ds:\n",
+    "        test_step(test_images, test_labels)\n",
+    "\n",
+    "    template = 'Epoch {}, Loss: {:4.2f}, Accuracy: {:4.2f}, Test Loss: {:4.2f}, Test Accuracy: {:4.2f}'\n",
+    "    print(template.format(epoch + 1,\n",
+    "                          train_loss.result(),\n",
+    "                          train_accuracy.result() * 100,\n",
+    "                          test_loss.result(),\n",
+    "                          test_accuracy.result() * 100))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Evaluate model accuracy on test data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy on test data: 93.00%\n"
+     ]
+    }
+   ],
+   "source": [
+    "y_test_pred = np.argmax(forward(x_test), axis=1)\n",
+    "accuracy_test = np.sum(y_test_pred == y_test) / y_test.shape[0]\n",
+    "print('Accuracy on test data: {:4.2f}%'.format(accuracy_test * 100))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create a ART TensorFlow v2 classifier for the TensorFlow custom model function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "classifier = TensorFlowV2Classifier(model=forward, nb_classes=10, input_shape=(28, 28, 1),\n",
+    "                                    loss_object=loss_object, clip_values=(0, 1), channels_first=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Fast Gradient Sign Method attack"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create a ART Fast Gradient Sign Method attack."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "attack_fgsm = FastGradientMethod(estimator=classifier)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Generate adversarial test data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "x_test_adv = attack_fgsm.generate(x_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Evaluate accuracy on adversarial test data and calculate average perturbation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy on adversarial test data: 8.00%\n",
+      "Average perturbation: 0.16\n"
+     ]
+    }
+   ],
+   "source": [
+    "y_test_pred = np.argmax(forward(x_test_adv), axis=1)\n",
+    "accuracy_test_adv = np.sum(y_test_pred == y_test) / y_test.shape[0]\n",
+    "perturbation = np.mean(np.abs((x_test_adv - x_test)))\n",
+    "print('Accuracy on adversarial test data: {:4.2f}%'.format(accuracy_test_adv * 100))\n",
+    "print('Average perturbation: {:4.2f}'.format(perturbation))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Visualise the first adversarial test sample."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.matshow(x_test_adv[0, :].reshape((28, 28)))\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Carlini&Wagner Infinity-norm attack"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create a ART Carlini&Wagner Infinity-norm attack."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "attack_cw = CarliniLInfMethod(classifier=classifier, eps=0.3, max_iter=100, learning_rate=0.01)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Generate adversarial test data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%capture\n",
+    "x_test_adv = attack_cw.generate(x_test);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Evaluate accuracy on adversarial test data and calculate average perturbation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy on adversarial test data: 33.00%\n",
+      "Average perturbation: 0.007617\n"
+     ]
+    }
+   ],
+   "source": [
+    "y_test_pred = np.argmax(forward(x_test_adv), axis=1)\n",
+    "accuracy_test_adv = np.sum(y_test_pred == y_test) / y_test.shape[0]\n",
+    "perturbation = np.mean(np.abs((x_test_adv - x_test)))\n",
+    "print('Accuracy on adversarial test data: {:4.2f}%'.format(accuracy_test_adv * 100))\n",
+    "print('Average perturbation: {:4.6f}'.format(perturbation))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Visualise the first adversarial test sample."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.matshow(x_test_adv[0, :].reshape((28, 28)))\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/adversarial-robustness-toolbox/notebooks/art-for-tensorflow-v2-keras.ipynb b/adversarial-robustness-toolbox/notebooks/art-for-tensorflow-v2-keras.ipynb
new file mode 100644
index 0000000..d060b4c
--- /dev/null
+++ b/adversarial-robustness-toolbox/notebooks/art-for-tensorflow-v2-keras.ipynb
@@ -0,0 +1,399 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# ART for TensorFlow v2 - Keras API"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This notebook demonstrate applying ART with the new TensorFlow v2 using the Keras API. The code follows and extends the examples on www.tensorflow.org."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "import tensorflow as tf\n",
+    "tf.compat.v1.disable_eager_execution()\n",
+    "import numpy as np\n",
+    "from matplotlib import pyplot as plt\n",
+    "\n",
+    "from art.estimators.classification import KerasClassifier\n",
+    "from art.attacks.evasion import FastGradientMethod, CarliniLInfMethod"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if tf.__version__[0] != '2':\n",
+    "    raise ImportError('This notebook requires TensorFlow v2.')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Load MNIST dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()\n",
+    "x_train, x_test = x_train / 255.0, x_test / 255.0\n",
+    "\n",
+    "x_test = x_test[0:100]\n",
+    "y_test = y_test[0:100]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# TensorFlow with Keras API"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create a model using Keras API. Here we use the Keras Sequential model and add a sequence of layers. Afterwards the model is compiles with optimizer, loss function and metrics."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = tf.keras.models.Sequential([\n",
+    "    tf.keras.layers.InputLayer(input_shape=(28, 28)),\n",
+    "    tf.keras.layers.Flatten(),\n",
+    "    tf.keras.layers.Dense(128, activation='relu'),\n",
+    "    tf.keras.layers.Dropout(0.2),\n",
+    "    tf.keras.layers.Dense(10, activation='softmax')\n",
+    "])\n",
+    "\n",
+    "model.compile(optimizer='adam',\n",
+    "              loss='sparse_categorical_crossentropy',\n",
+    "              metrics=['accuracy']);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Fit the model on training data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train on 60000 samples\n",
+      "Epoch 1/3\n",
+      "60000/60000 [==============================] - 3s 46us/sample - loss: 0.2968 - accuracy: 0.9131\n",
+      "Epoch 2/3\n",
+      "60000/60000 [==============================] - 3s 46us/sample - loss: 0.1435 - accuracy: 0.9575\n",
+      "Epoch 3/3\n",
+      "60000/60000 [==============================] - 3s 46us/sample - loss: 0.1102 - accuracy: 0.9664\n"
+     ]
+    }
+   ],
+   "source": [
+    "model.fit(x_train, y_train, epochs=3);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Evaluate model accuracy on test data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy on test data: 100.00%\n"
+     ]
+    }
+   ],
+   "source": [
+    "loss_test, accuracy_test = model.evaluate(x_test, y_test)\n",
+    "print('Accuracy on test data: {:4.2f}%'.format(accuracy_test * 100))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create a ART Keras classifier for the TensorFlow Keras model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "classifier = KerasClassifier(model=model, clip_values=(0, 1))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Fast Gradient Sign Method attack"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create a ART Fast Gradient Sign Method attack."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "attack_fgsm = FastGradientMethod(estimator=classifier, eps=0.3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Generate adversarial test data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_test_adv = attack_fgsm.generate(x_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Evaluate accuracy on adversarial test data and calculate average perturbation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy on adversarial test data: 0.00%\n",
+      "Average perturbation: 0.18\n"
+     ]
+    }
+   ],
+   "source": [
+    "loss_test, accuracy_test = model.evaluate(x_test_adv, y_test)\n",
+    "perturbation = np.mean(np.abs((x_test_adv - x_test)))\n",
+    "print('Accuracy on adversarial test data: {:4.2f}%'.format(accuracy_test * 100))\n",
+    "print('Average perturbation: {:4.2f}'.format(perturbation))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Visualise the first adversarial test sample."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQEAAAECCAYAAAD+eGJTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAQB0lEQVR4nO3dfYxc5XXH8d/BODYs0NhssQ1Z10AdKQ5VTLICUreRK6uB4j8AtVS1KuqmUU0rUIFGtAhVAqWqhKqAoVXr1tRuTARUSLy2Rg2uFYVWKW5sYoHNUsyLAeOVjdmAXyqMvZz+sZdkY888d3bu3Jfd8/1IaHfvmZl79s74x515nn2uubsAxHVK3Q0AqBchAARHCADBEQJAcIQAEBwhAARXSwiY2RVm9r9m9qqZ3VZHDylmttvMXjSz7Wa2tQH9rDez/Wa2Y9y22Wa2ycx2ZV9nNay/O83snewYbjezK2vsb8DMvmdmQ2a208xuyrY34hgm+qvkGFrV8wTMbJqkVyT9uqQ9kn4oaYW7v1RpIwlmtlvSoLsfqLsXSTKzr0g6LOkBd78o2/bXkkbc/a4sSGe5+583qL87JR1292/V0dN4ZjZP0jx3f97MzpS0TdLVkn5fDTiGif5+WxUcwzrOBC6R9Kq7v+7uH0n6F0lX1dDHpOHuz0oaOWHzVZI2ZN9v0NiLphZt+msMdx929+ez7w9JGpJ0nhpyDBP9VaKOEDhP0tvjft6jCn/hDrmkZ8xsm5mtqruZNua4+7A09iKSdE7N/bRyo5m9kL1dqO3tynhmtkDSxZK2qIHH8IT+pAqOYR0hYC22NW3u8hJ3/6Kk35B0Q3a6i4lZI+lCSYslDUu6u952JDM7Q9Kjkm5294N193OiFv1VcgzrCIE9kgbG/fwZSXtr6KMtd9+bfd0v6XGNvYVpmn3Ze8lP3lPur7mfn+Hu+9x91N0/lnS/aj6GZjZdY//AHnT3x7LNjTmGrfqr6hjWEQI/lLTQzM43s09J+h1JT9XQR0tm1pd9OCMz65P0VUk70veqxVOSVmbfr5T0ZI29nOSTf1yZa1TjMTQzk7RO0pC73zOu1Ihj2K6/qo5h5aMDkpQNddwraZqk9e7+V5U30YaZXaCx//tL0qmSHqq7PzN7WNJSSf2S9km6Q9ITkh6RNF/SW5KudfdaPpxr099SjZ3GuqTdkq7/5P13Df39iqT/lPSipI+zzbdr7H137ccw0d8KVXAMawkBAM3BjEEgOEIACI4QAIIjBIDgCAEguFpDoMFTciXRX1FN7q/JvUnV9lf3mUCjnwjRX1FN7q/JvUkV9ld3CACoWaHJQmZ2haT7NDbz75/c/a7U7T9lM3ym+n7y8zEd1XTN6Hr/vTZ6dt/P/Hz8wyM6dWZfm1ufbNp7Ryb0+EVNtL88ef1PVK+f314ev1bHrujz18vj1+rYFfn9jx4e0fEPj7T64z2d2u2DZouD/J3GLQ5iZk+lFgeZqT5dasu63WXp3l/+5UL3//R3/rvUxy9bXv91K/v4FX3+yj5+RX7/lzaublsr8naAxUGAKaBICEyGxUEA5Oj67YA6XBwkG+pYJUkzdXqB3QEoQ5EzgY4WB3H3te4+6O6DTfoQEMCYIiHQ6MVBAHSm67cD7n7czG6U9F39dHGQnUWaef+6kj+dL/j4ecp+/LLl9V/38a17/3mafvzaKfKZgNz9aUlP96gXADVgxiAQHCEABEcIAMERAkBwhAAQHCEABFfpdQf6+gd80fJbKtvfRDV1HLdTk73/6Io+f6n7b/HNOugjLf+UmDMBIDhCAAiOEACCIwSA4AgBIDhCAAiOEACCK/SnxL1WdLXWouPgk30cfbL3j3pwJgAERwgAwRECQHCEABAcIQAERwgAwRECQHCNmidQdN32PPy9PcpU9PVV5jyX0Y3Pta1xJgAERwgAwRECQHCEABAcIQAERwgAwRECQHCVzhOY9t6R5Fhq0XHSMtdt70Tdj496Tdbnp1AImNluSYckjUo67u6DvWgKQHV6cSbwa+5+oAePA6AGfCYABFc0BFzSM2a2zcxW9aIhANUq+nZgibvvNbNzJG0ys5fd/dnxN8jCYZUkzdTpBXcHoNcKnQm4+97s635Jj0u6pMVt1rr7oLsPTteMIrsDUIKuQ8DM+szszE++l/RVSTt61RiAahR5OzBH0uNm9snjPOTu/96TrkpSdJy+7scve/91j3PXvd5D3fuvS9ch4O6vS/pCD3sBUAOGCIHgCAEgOEIACI4QAIIjBIDgCAEguEZdd6DpTjk9Pe35zT9dnKw/9PXVyfof/cVNyfpknwdQFPMwysGZABAcIQAERwgAwRECQHCEABAcIQAERwgAwZm7V7azvv4BX7T8lrb1useB88Zx3132UbJ+4cD+XrYzYQcO9yXrh4dmJeu7fm9Nof0vfOCPC92/bH17LVmf8zc/qKiT1sqcR/DSxtU6cuDtlgeAMwEgOEIACI4QAIIjBIDgCAEgOEIACI4QAIKrdJ7AWTbbL7Vlle2vanbx55N1/9HOQvd/95KzkvUPvvJhsn7+3PTFo1/f25+sX3BuuRefPno8vbzF8Hs/l6wvmPNesf3/7bxk/bQn/qfQ4xeVN48gNc9mi2/WQR9hngCAkxECQHCEABAcIQAERwgAwRECQHCEABDclLruQJFx1F748UXpcfxP/yh9/7x5BP059+//x3Q9zy/q7WT9lfWDyfpn/2Brof2fllO/IKe+59H0PIudX34wWb9y6LeS9dGc/dct9fof3fhc21rumYCZrTez/Wa2Y9y22Wa2ycx2ZV/Tq1UAaKxO3g58W9IVJ2y7TdJmd18oaXP2M4BJKDcE3P1ZSSMnbL5K0obs+w2Sru5xXwAq0u0Hg3PcfViSsq/n9K4lAFUq/YNBM1slaZUkzVT6gp4AqtftmcA+M5snSdnXtsvsuvtadx9098HpmtHl7gCUpdsQeErSyuz7lZKe7E07AKqWu56AmT0saamkfkn7JN0h6QlJj0iaL+ktSde6+4kfHp6k7vUE6p5H0HRlrnvfibzjf8rMmcn6R/+W/mjqjZfT6wWcuWtasj73vmLXJajz+KauO5D7mYC7r2hTmrqrgwCBMG0YCI4QAIIjBIDgCAEgOEIACI4QAIKbUusJFFV0HkHT5yHUPQ+gqDceWJisv7zoO8n65z/43WR95pb0ehBlK/P1Nc2PtK1xJgAERwgAwRECQHCEABAcIQAERwgAwRECQHCVzhMYPbtP7y9vP9ZZ9zh6UZO9/7LlHZ/jy76UrA+c3XYBq4585jfT13UoW9mvj9KuOwBgaiMEgOAIASA4QgAIjhAAgiMEgOAIASC4UOsJFB2nbfp6A01fL+CVNZck6+edfyBZPy3n8S8/d/EEO5pcirw+WE8AQFuEABAcIQAERwgAwRECQHCEABAcIQAEV+k8gWnvHUmOddY9jp73+HX+PfhUsOBJT9ZPvfVYsr7n+wPJ+ny9PeGeqlT289vt6zP3TMDM1pvZfjPbMW7bnWb2jpltz/67squ9A6hdJ28Hvi3pihbbV7v74uy/p3vbFoCq5IaAuz8raaSCXgDUoMgHgzea2QvZ24VZPesIQKW6DYE1ki6UtFjSsKS7293QzFaZ2VYz23pMR7vcHYCydBUC7r7P3Ufd/WNJ90tq++dh7r7W3QfdfXC6ZnTbJ4CSdBUCZjZv3I/XSNrR7rYAmi13noCZPSxpqaR+M9sj6Q5JS81ssSSXtFvS9SX2+BOTfRx9qq8XkPf7vXrvZcn63GPTk/X53/zBhHvqpbLnseQpsl5F6roDuSHg7itabF6Xdz8AkwPThoHgCAEgOEIACI4QAIIjBIDgCAEguFDXHUAxeePUo0u/mKxf8EvvJOt7/yO9XsAZej1Zr3seRdH9lz3PoB3OBIDgCAEgOEIACI4QAIIjBIDgCAEgOEIACM7c02vB99JZNtsvtWVd37/uceCimr6eQNH+jj6zIFn/8f+dlqzPvXooWZ/sz39RRZ6fLb5ZB33EWtU4EwCCIwSA4AgBIDhCAAiOEACCIwSA4AgBILhJtZ5AkXXXm6Dp/eV55Z+/lKy/cVF6Jfq3jh9O1q+97tYJ9zSVsJ4AgFoQAkBwhAAQHCEABEcIAMERAkBwhAAQ3KSaJzDZx9nrljcO/drdlyXrb1z+D4X2f/m6P0vWz1J1a1vgp3LPBMxswMy+Z2ZDZrbTzG7Kts82s01mtiv7Oqv8dgH0WidvB45L+oa7f07SZZJuMLNFkm6TtNndF0ranP0MYJLJDQF3H3b357PvD0kaknSepKskbchutkHS1WU1CaA8E/pg0MwWSLpY0hZJc9x9WBoLCknn9Lo5AOXrOATM7AxJj0q62d0PTuB+q8xsq5ltPaaj3fQIoEQdhYCZTddYADzo7o9lm/eZ2bysPk/S/lb3dfe17j7o7oPTNaMXPQPooU5GB0zSOklD7n7PuNJTklZm36+U9GTv2wNQtk7mCSyRdJ2kF81se7btdkl3SXrEzL4u6S1J1+Y90OjZfXp/OWP9dXnzm+lj/+qKNYUe/7PfX5msz3qdeQBF5M2T6XY9gtwQcPf/ktTyogWSur+SCIBGYNowEBwhAARHCADBEQJAcIQAEBwhAARn7tWN3Z5ls/1Saz+qyHoBxRya324kd8xLN/x9ocf/2lu/mqzvvexQss7zW5+XNq7WkQNvt3yBcCYABEcIAMERAkBwhAAQHCEABEcIAMERAkBwk+q6A3XL+3vtusfBv7biu6U+PvMAytXtegCdmOZH2tY4EwCCIwSA4AgBIDhCAAiOEACCIwSA4AgBILhK5wk0/boDZY7T9sL+JaPJ+q2zXyt1/6/ee1my3r+t1N1PeUWvK5C6/+jG59rWOBMAgiMEgOAIASA4QgAIjhAAgiMEgOAIASC43OsOmNmApAckzZX0saS17n6fmd0p6Q8lvZvd9HZ3fzr1WHnXHchT1vXZe7X/sl1609ZkfeiDucn6ps/9a6H9X37u4mS97uMz2RVdryJ1/y2+WQd9pOV1BzqZLHRc0jfc/XkzO1PSNjPblNVWu/u3OngMAA2VGwLuPixpOPv+kJkNSTqv7MYAVGNCnwmY2QJJF0vakm260cxeMLP1Zjarx70BqEDHIWBmZ0h6VNLN7n5Q0hpJF0parLEzhbvb3G+VmW01s63HdLQHLQPopY5CwMymaywAHnT3xyTJ3fe5+6i7fyzpfkmXtLqvu69190F3H5yuGb3qG0CP5IaAmZmkdZKG3P2ecdvnjbvZNZJ29L49AGXrZHRgiaTrJL1oZtuzbbdLWmFmiyW5pN2Sri+lQwClyp0n0Et9/QO+aPktbetFx/nrHqcue56C//IX0vW/HEnWX9t5brK+8E+2JOtTXZl/z9/J/fOUNU+AGYNAcIQAEBwhAARHCADBEQJAcIQAEBwhAARX6TyBvPUEio7zN/26AUBKmfMMmCcAoC1CAAiOEACCIwSA4AgBIDhCAAiOEACCq3SegJm9K+nNcZv6JR2orIGJo79imtxfk3uTet/fL7j7z7cqVBoCJ+3cbKu7D9bWQA76K6bJ/TW5N6na/ng7AARHCADB1R0Ca2vefx76K6bJ/TW5N6nC/mr9TABA/eo+EwBQM0IACI4QAIIjBIDgCAEguP8HYccW/xF2T1AAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 288x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.matshow(x_test_adv[0])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Carlini&Wagner Infinity-norm attack"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create a ART Carlini&Wagner Infinity-norm attack."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "attack_cw = CarliniLInfMethod(classifier=classifier, eps=0.3, max_iter=100, learning_rate=0.01)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Generate adversarial test data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C&W L_inf: 100%|██████████| 1/1 [00:04<00:00,  4.23s/it]\n"
+     ]
+    }
+   ],
+   "source": [
+    "x_test_adv = attack_cw.generate(x_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Evaluate accuracy on adversarial test data and calculate average perturbation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy on adversarial test data: 10.00%\n",
+      "Average perturbation: 0.03\n"
+     ]
+    }
+   ],
+   "source": [
+    "loss_test, accuracy_test = model.evaluate(x_test_adv, y_test)\n",
+    "perturbation = np.mean(np.abs((x_test_adv - x_test)))\n",
+    "print('Accuracy on adversarial test data: {:4.2f}%'.format(accuracy_test * 100))\n",
+    "print('Average perturbation: {:4.2f}'.format(perturbation))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Visualise the first adversarial test sample."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.matshow(x_test_adv[0, :, :])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/adversarial-robustness-toolbox/notebooks/art_evaluations/security_curve.ipynb b/adversarial-robustness-toolbox/notebooks/art_evaluations/security_curve.ipynb
new file mode 100644
index 0000000..7f09cc7
--- /dev/null
+++ b/adversarial-robustness-toolbox/notebooks/art_evaluations/security_curve.ipynb
@@ -0,0 +1,379 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Robustness Evaluation with Security Curves\n",
+    "\n",
+    "This notebook demonstrates how to use the ART evaluations module for generating Security Curves. Security Curves have been widely used in the literature to determine and compare the robustness of machine learning models against adversarial examples [1, 2], as well as to identify shortcomings of defences - gradient masking in particular - which could be exploited by an adaptive adversary [3]. Essentially, Security Curves convey the performance of a given model under increasing attack perturbation budgets - commonly in terms of an l_p norm.\n",
+    "\n",
+    "[1] A. Madry et al.: Towards Machine Learning Models Resistant to Adversarial Attacks. https://arxiv.org/abs/1706.06083\n",
+    "<br>\n",
+    "[2] Y. Dong et al.: Benchmarking Adversarial Robustness on Image Classification. https://arxiv.org/abs/1912.11852\n",
+    "<br>\n",
+    "[3] N. Carlini et al.: On Evaluating Adversarial Robustness. https://arxiv.org/abs/1902.06705"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using TensorFlow backend.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "from keras.models import load_model\n",
+    "\n",
+    "from robustness.datasets import CIFAR\n",
+    "from robustness.model_utils import make_and_restore_model\n",
+    "\n",
+    "import numpy as np\n",
+    "\n",
+    "from art.config import ART_DATA_PATH\n",
+    "from art.utils import load_dataset, get_file\n",
+    "from art.evaluations.security_curve import SecurityCurve\n",
+    "\n",
+    "from art.estimators.classification import PyTorchClassifier, KerasClassifier"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Evaluate robust classifier trained with adversarial training"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load CIFAR-10 dataset\n",
+    "(x_train_cifar, y_train_cifar), (x_test_cifar, y_test_cifar), min_cifar, max_cifar = load_dataset(\"cifar10\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Change format frm NHWC to NCHW for PyTorch\n",
+    "x_train_cifar = np.transpose(x_train_cifar, (0, 3, 1, 2)).astype(np.float32)\n",
+    "x_test_cifar = np.transpose(x_test_cifar, (0, 3, 1, 2)).astype(np.float32)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define argument for PGD attack used in evaluation, except `classifier` and `eps`\n",
+    "kwargs_pgd = {\"norm\": \"inf\",\n",
+    "              \"eps_step\": 1/255,\n",
+    "              \"max_iter\": 100,\n",
+    "              \"targeted\": False,\n",
+    "              \"num_random_init\": 0,\n",
+    "              \"batch_size\": 128,\n",
+    "              \"random_eps\": False,\n",
+    "              \"verbose\": False,\n",
+    "             }"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define attack budgets for evaluation as a list of floats\n",
+    "eps = [i / 255 for i in range(1, 32, 2)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create an instance of a Security Curve evaluation\n",
+    "sc = SecurityCurve(eps=eps)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "=> loading checkpoint '/tmp/cifar_linf_8.pt'\n",
+      "=> loaded checkpoint '/tmp/cifar_linf_8.pt' (epoch 153)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Load a robust PyTorch model using the package `robustness` (https://github.com/MadryLab/robustness)\n",
+    "path_to_cifar10_data = \"/tmp/data/\"\n",
+    "resume_path = \"/tmp/cifar_linf_8.pt\"\n",
+    "\n",
+    "ds = CIFAR(path_to_cifar10_data)\n",
+    "m, _ = make_and_restore_model(arch='resnet50',\n",
+    "                              dataset=ds,\n",
+    "                              resume_path=resume_path,\n",
+    "                             )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define mean and standard-deviation for preprocessing of the PyTorch model\n",
+    "mean=np.asarray([0.4914, 0.4822, 0.4465]).reshape(3, 1, 1).astype(np.float32)\n",
+    "std=np.asarray([0.2023,  0.1994, 0.2010]).reshape(3, 1, 1).astype(np.float32)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create an ART estimator for the PyTorch classification model\n",
+    "robust_classifier_cifar = PyTorchClassifier(model=m.model, \n",
+    "                                            clip_values=(min_cifar, max_cifar),\n",
+    "                                            loss=torch.nn.CrossEntropyLoss(),\n",
+    "                                            input_shape=(3, 32, 32),\n",
+    "                                            nb_classes=10,\n",
+    "                                            preprocessing=(mean, std))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Evaluate Security Curve for robust PyTorch classifier on CIFAR-10 on the first 100 samples\n",
+    "eps_list, accuracy_adv_list, accuracy = sc.evaluate(classifier=robust_classifier_cifar,\n",
+    "                                                    x=x_train_cifar[0:100],\n",
+    "                                                    y=y_train_cifar[0:100],\n",
+    "                                                    **kwargs_pgd)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEWCAYAAABrDZDcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nO3deXhU5fXA8e/JHiALENaETWQHIcqmoIJVARdA1Cpq1brgRtXaalFbt9pq1f60VixuuG+44S5VAa2Cssi+r0LCFgIJhCSQhPP7497ESTJJBpLJZGbO53nmyd3vee9M5sy9773vK6qKMcaY8BUR6ACMMcYEliUCY4wJc5YIjDEmzFkiMMaYMGeJwBhjwpwlAmOMCXOWCEy9EZH7ROQ1d7i9iOSJSGQDiGuziJxezfwbRGSnG2/z+oytinhOFpE19bSvao9NQyQiHUVERSQq0LEEC0sEQcz9J90pIo09pl0jIrMDGJZPVHWLqjZR1ZLabktEZovINXURl5dtRwP/B5zpxpvtj/3UEIOKyLGl46r6P1XtVt9xVKcuv3w9fzCY+mGJIPhFAbfU907D6NdWKyAOWBHoQIzxF0sEwe9R4I8ikuxtpoicJCLzRSTX/XtSVRsSkeNFZJGI7BeRd0TkbRF50J03TEQyRORPIrIDeFFEmorIJyKSJSJ73eE0j+11EpFv3O19CaR4zCv3C1JEkkTkBRHZLiKZIvJg6WUjEblSRL4Tkcfc/WwSkVHuvL8BJwNPuZdunqqibKNFZIWI5LhnED0qLDJARFa6239RROJEpCtQegkmR0Rmevvl63lGIiLHumXOFZHdIvK2x3K9RORLEdnjnsnd5U4fKCJz3di2i8hTIhLjzvvWXX2JW76LSt8Lj+32cGPIccs42mPeSyIyWUQ+dd+HH0WkczWfgd+IyM8iki0id1eYFyEik0Rkgzt/mog0c2eXxpnjxnmiu85VIrLKPa4zRKRDdcdDREYCdwEXudtZ4i5b3ecj0v1s7BaRjcDZVZXPVEFV7RWkL2AzcDrwPvCgO+0aYLY73AzYC/wG58xhvDve3Mu2YoCfcc4uooFxwCGP7Q4DioF/ALFAPNAcOB9oBCQA7wDTPbY5F+eySixwCrAfeM2d1xFQIModnw48AzQGWgLzgOvceVcCRcC1QCRwA7ANEHf+bOCaao5TV+AAcIZbtjuA9UCMx3FcDrRzj9n3HuWuGGe58Yr7B94E7sb5kRUHDHWnJwDbgT+40xOAQe68E4DB7nvUEVgF3OqxfQWO9RgfBmS4w9FuWe5y38PT3OPczZ3/ErAHGOhu/3XgrSqOU08gz32vYt33rhg43Z1/K/ADkObOfwZ4s5rjMtaNrYe77z8Dc3w4Hvfhfk48tlXd5+N6YLXH+zerYiz2quG7JNAB2KsWb94viaA3kAu0oHwi+A0wr8I6c4ErvWzrFCAT98vVnfYd5RPBISCumnj6AXvd4fbul0hjj/lv4CUR4Fx+OQjEeyw7HpjlDl8JrPeY18hdt7U7PpvqE8FfgGke4xFuWYd5HMfrPeafBWyoGKe38Yr7B14BngXSKsQwHljk4/t6K/CBx3h1ieBkYAcQ4TH/TeA+d/gl4PkKZVtdxX7vwSNJ4HzpHuKXRLAK+JXH/DY4Cbo0gVU8Lp8DV1c47vlAh+qOBxUSgQ+fj5kV3r8zK8Zir+pf4XKdN6Sp6nIR+QSYhPPPWqotzq98Tz8DqV420xbIVPc/ybW1wjJZqlpYOiIijYDHgZFAU3dygnvK3hYnKRyosO92XvbdAeeX7XYRKZ0WUWH/O0oHVDXfXa6Jl215U+44qOphEdlK+ePgua+f3XWOxh3AX4F5IrIX+KeqTsUp9wZvK7iXoP4P6I+T5KKAhT7ury2wVVUPe0yr+B7v8BjOp+rj1haP46CqB0TEs3K8A/CBiHjuqwTni9qbDsC/ROSfHtPEja3K41HFdqr7fJSLm8qfeVMDqyMIHffiXDrx/ALYhvNP5Kk9zq/hirYDqeLxn0blL+2KTdX+AeiGc0qfiHNWAc4/+3agqXjc0eTu25utOL/4UlQ12X0lqmqvKpavqKYmdMsdB7eM7Sh/HDzL2t5dx5vSxNbIY1rrskBUd6jqtaraFrgOeFqcO362AlVdm/8PzqWNLu5xvAvnGPpiG9BORDz/l6t6j2uyHY/j4CZ6z9tltwKjPN6jZFWNU9VMvL8HW3Eu33guH6+qc6j+eFTcVk2fj3JxU/XnzFTBEkGIUNX1wNvAzR6TPwO6isglIhIlIhfhXAf+xMsm5uL8upvoLjsG57pydRKAApwKwmY4yag0np+BBcD9IhIjIkOBc6uIfTvwX+CfIpLoVkp2FpFTfSg6wE7gmGrmTwPOFpFfiXM76B9wvljmeCxzk4ikueW4C+dYeos1C+dL9jK3kvIqPL7QRORC+aXCfC/Ol1oJzjFvLSK3ikisiCSIyCB3uQRgH5AnIt1x6kB8Ld+POMnpDhGJFpFhOMf5rWqOR1XeBc4RkaFuZfUDlP+OmAL8rbTCV0RauJ8TgCzgcIU4pwB3ikgvd/kkEbnQnVfd8dgJdCxNbj58PqYBN7vvX1OcM2NzBCwRhJYHcK7rAqDOPe/n4HzxZeNctjhHVXdXXFFVD+FUEF8N5ACX4fyzHqxmf0/gVBrvxqlE/KLC/EuAQTiVlffiXD+vyuU4lZ0rcb5A38W5Bu2LfwEXuHemPFlxpqquwSnPv91YzwXOdctc6g2cL5uN7uvBavZ3LXA7zjHtRfmEMgD4UUTygI+AW1R1k6rux6msPhfnUs06YLi7zh9xjtV+4DkqJ6H7gJfdu4J+XaFsh4DRwCi3bE8Dl6vq6mri90pVVwA34RyL7TjvQ4bHIv9yy/RfEdmP854PctfNB/4GfO/GOVhVP8C5ueAtEdmHUyE/yl2+uuPxjvs3W0R+coer+3w8B8wAlgA/4dw8YY5A6V0XxlQiIj8CU1T1xUDHYozxHzsjMGVE5FQRae1eGroCOI7Kv/KNMSHG7hoynrrhXG9tgnNHxwXu9VljTAizS0PGGBPm7NKQMcaEuaC7NJSSkqIdO3YMdBjGGBNUFi5cuFtVW3ibF3SJoGPHjixYsCDQYRhjTFARkSqfuLZLQ8YYE+YsERhjTJizRGCMMWHOEoExxoQ5SwTGGBPmLBEYY0yY81siEJGpIrJLRJZXMV9E5EkRWS8iS0XkeH/FYowxpmr+PCN4CafnqqqMArq4rwk4nXMYY4ypZ35LBKr6LU479FUZA7yijh+AZBHxtf15Y4wxdSSQdQSplO9nNAPvfekiIhNEZIGILMjKyqqX4IwxJlwEMhF465PVa1OoqvqsqvZX1f4tWnhtKsMYY8xRCmQiyKB8h9NpVN1huDHGGD8JZCL4CLjcvXtoMJBrnaAYY0z981vroyLyJjAMSBGRDJzOy6MBVHUK8BlwFrAeyAd+669YjDHGVM1viUBVx9cwX4Gb/LV/Y4wxvrEni40xJsxZIjDGmDBnicAYY8Jc0HVVeTSmL8rk0Rlr+Gf+XcRERtC+WSNSmsQ6M3uNhYHXwqF8eP3Cyiv3uwTSL4UD2TDt8srzB1wFvc+H3Ax4/7rK80+aCN1Gwe518PGtleef8kfoPBy2L4Uv7qw8/1f3QPtBsOVH+PqByvNHPgRtjoMNs+DbxyrPP/cJSOkCaz6HOU9Vnj/uGUhKg+Xvwfyplef/+hVo3BwWvQ6L36g8/9J3IKYRzHsOVkyvPP+3nzp/v38S1s4oPy86Di57zxn+5hHY+E35+Y2awkWvOcNf3Qdb55efn9gWzn/OGf58EuxYVn5+884w+kln+KObIXtD+fmt+8Coh53h966FfRXuXm43AE6/zxl++zLI31t+/jGnwql3OMOvnQ9FheXndx0BQ252hl88m0rss2efPTiyz15pmepYyCeC6YsyufP9ZRQUlUAMHCo5zMbdBwB+SQbGGBPGxLl5J3j0799fj6Tz+iEPzyQzp6DS9NTkeL6fdFpdhmaMMQ2WiCxU1f7e5oV8HcE2L0mguunGGBNuQj4RtE2O9zpdgQc+Xsmu/YVe5xtjTLgI+URw+4huxEdHlpsWGxXBwI5NeXnuZk55ZBYPfbaKPQcOBSZAY4wJsJCvLB6b7rRs/eiMNWzLKaBtcjy3j+jG2PRUNu0+wJNfr+PZ/23ktR9+5sohHbn25GNIbhQT4KiNMab+hHxlsS/W79rP41+t49Ol20mIjeKqoZ24+uROJMZF1+l+jDEmUKqrLLZE4GH1jn08/uVaZqzYSVJ8NBNOOYYrTupIk9iQP3EyxoQ4SwRHaHlmLo9/uZavV++iWeMYrjvlGC4/sSPxMZE1r2yMMQ2QJYKjtGjLXh7/ah3frs0ipUksNwzrzKWD2hMXHVn2tHLFegdjjGmILBHU0vzNe3j8y7XM2ZBNq8RYhh6bwqfLtlNYdLhsmfjoSB4a18eSgTGmQQrrB8rqwoCOzXjj2sG8ee1g2jdrxHs/ZZZLAgAFRSU8OmNNgCI0xpijZ4ngCJzYuTnTrjuxyvmZOQV8uzaLnHx7JsEYEzzsdpgjJCKkJsd7bb8I4PKp8wBo36wRfdKS6JuWRJ/UZHqnJpJgt6MaYxogSwRH4fYR3X5p0dQVHx3JX87pQYfmjVmSkcOyjFwWb8nh06XbARCBY1Iac1xaMn1Sk+jbLomebZLK7kSyymdjTKBYIjgK1T2tDDDk2JSyZbPzDrIsM5elGc5rzobdfLAoE4AIga6tEkiMi2LR1hyKSpyK+8ycAu58f1m5fRljjL/YXUMBsHNfIUszclmWkcPSzFy+XZvFYS9vgzWVbYypK9XdNWRnBAHQKjGOM3rGcUbPVgB0muS91yFrKtsYUx/srqEGoKqmslsmWg9qxhj/s0TQAHhrKhsgr7CY5Zm5AYjIGBNOLBE0AGPTU3loXB9Sk+MRnLqBO8/qTnKjGC5+9gfmbsgOdIjGmBBmlcUN2PbcAi5/YR4/78nn3+PTGdGrdaBDMsYEKWtiIki1SYpn2nUn0qttIje8tpBp87cGOiRjTAiyRNDANW0cw+vXDGJolxbc8d5SpnyzIdAhGWNCjCWCINAoJornL+/PuX3b8vDnq/n7Z6sItkt6xpiGy54jCBIxURH866J+NG0UzbPfbmTPgUM8PK4PUZGWy40xtWOJIIhERAj3j+5Fs8YxPPHVOnLyi3jqknTivNx6aowxvrKfk0FGRLj19K78dUwvvl69k8unzmNfYVGgwzLGBDG/JgIRGSkia0RkvYhM8jK/vYjMEpFFIrJURM7yZzyh5DcnduRfF6ezaMteLn7mB7L2Hwx0SMaYIOW3RCAikcBkYBTQExgvIj0rLPZnYJqqpgMXA0/7K55QNLpvW164YgCbdh/ggilz2JKdH+iQjDFByJ9nBAOB9aq6UVUPAW8BYyoso0CiO5wEbPNjPCHplK4teOPaQeQWFHH+lDms2r4v0CEZY4KMPxNBKuD5BFSGO83TfcBlIpIBfAb8ztuGRGSCiCwQkQVZWVn+iDWopbdvyjvXnUikCBc9M5f5m/cEOiRjTBDxZyIQL9Mq3vw+HnhJVdOAs4BXRaRSTKr6rKr2V9X+LVq08EOowa9LqwTeveFEUprEctnzP/K3T1cy5OGZdJr0KUMensl0tzMcY4ypyJ+JIANo5zGeRuVLP1cD0wBUdS4QB6Rgjkpa00a8c/2JtEyI5bn/bSIzpwDllx7PLBkYY7zxZyKYD3QRkU4iEoNTGfxRhWW2AL8CEJEeOInArv3UQvMmsRR76e6soKiER2esCUBExpiGzm+JQFWLgYnADGAVzt1BK0TkAREZ7S72B+BaEVkCvAlcqdZ2Qq3tyC30Ot16PDPGeOPXJ4tV9TOcSmDPafd4DK8EhvgzhnDUNjmeTC9f+lX1hGaMCW/2ZHEI8tbjWUykcPuIbgGKyBjTkFlbQyFobLpzl+6jM9awLaeAyAihSVwUI3tbxzbGmMosEYSosempZQnhu3W7ueyFH5n6/SZuHHZsgCMzxjQ0dmkoDAztksIZPVsxeeZ6du3zXpFsjAlflgjCxN1n9eBQyWEesVtIjTEVWCIIEx1TGnPV0E68uzCDJVtzAh2OMaYBsUQQRiYOP5aUJrE88MlK6+rSGFPGEkEYSYiL5o4R3Vj4814+WmINvRpjHJYIwswFJ6TRJzWJhz9fTf6h4kCHY4xpACwRhJmICOGec3uyPbeQZ77ZGOhwjDENgCWCMDSgYzPO7duWKd9s8NoUhTEmvFgiCFOTRnVHBB76bFWgQzHGBJglgjCVmhzPdad05pOl25m3yXo0MyacWSIIY9ef2pk2SXE88MkKDnvpw8AYEx4sEYSx+JhIJo3qzvLMfby7MCPQ4RhjAsQSQZgb3bctJ3RoyiMzVrO/sCjQ4RhjAsASQZgTEe49tye78w7x1Kz1gQ7HGBMAlggMx6Ulc+EJaUz9bhObdx8IdDjGmHpmicAAcPvIbsRERvA3u53UmLBjicAA0DIhjomndeHLlTv5bt3uQIdjjKlHlghMmauGdqR9s0Y88MkKiksOBzocY0w9sURgysRGRXL32T1YuzOPN+ZtCXQ4xph6YonAlHNmz1ac1Lk5//flWnLyDwU6HGNMPbBEYMoRcVon3VdQxBNfrQt0OMaYemCJwFTSvXUilwxqz6s//MzanfsDHY4xxs8sERivbjujG41jIvmrdWtpTMizRGC8atY4ht+f0ZX/rdvN16t2BTocY4wfWSIwVbpscAeObdmEBz9dyaFiu53UmFBlicBUKToygr+c05PN2fm8NGdToMMxxviJJQJTrVO7tuC07i3599frydp/MNDhGGP8wBKBqdGfz+5B3sFiTn10Fp0mfcqQh2cyfVFmoMMyxtSRGhOBiEwUkab1EYxpmJZm5BIRIeQfKkGBzJwC7nx/mSUDY0KEL2cErYH5IjJNREaKiPi6cXf5NSKyXkQmVbHMr0VkpYisEJE3fN22qT+PzlhDSYWuLAuKSnh0xpoARWSMqUs1JgJV/TPQBXgBuBJYJyJ/F5HO1a0nIpHAZGAU0BMYLyI9KyzTBbgTGKKqvYBbj6YQxr+25RQc0XRjTHDxqY5AnSeKdrivYqAp8K6IPFLNagOB9aq6UVUPAW8BYyoscy0wWVX3uvuxG9YboLbJ8Uc03RgTXHypI7hZRBYCjwDfA31U9QbgBOD8alZNBbZ6jGe40zx1BbqKyPci8oOIjDyi6E29uH1EN+KjI8tNi4oQbh/RLUARGWPqUpQPy6QA41T1Z8+JqnpYRM6pZj1vdQkV2yqIwrnsNAxIA/4nIr1VNafchkQmABMA2rdv70PIpi6NTXfy96Mz1rAtp4DY6AhKSpRBxzQLcGTGmLrgy6Whz4A9pSMikiAigwBUtbp+DTOAdh7jacA2L8t8qKpFqroJWIOTGMpR1WdVtb+q9m/RooUPIZu6NjY9le8nncamh8/my9+fikQI//h8daDDMsbUAV8SwX+API/xA+60mswHuohIJxGJAS4GPqqwzHRgOICIpOBcKtrow7ZNALVr1ogJJx/D9MXbWPjz3kCHY4ypJV8SgahH85OqehgfLimpajEwEZgBrAKmqeoKEXlAREa7i80AskVkJTALuF1Vs4+0EKb+3TCsM60SY3ng4xUcPmytkxoTzHxJBBvdCuNo93ULPv5qV9XPVLWrqnZW1b+50+5R1Y/cYVXV21S1p6r2UdW3jr4opj41jo3iTyO7syQjlw/swTJjgpovieB64CQgE+ea/iDcilsT3sb2S6Vfu2T+8cVqDhwsDnQ4xpij5MsDZbtU9WJVbamqrVT1Ervf3wBERAj3ntuTXfsP8vTs9YEOxxhzlGq81i8iccDVQC8grnS6ql7lx7hMkEhv35Rx6ak8979NXNS/Pe2bNwp0SMaYI+TLpaFXcdobGgF8g3MbqHVka8rcMbI7kSL8/bPq7iY2xjRUviSCY1X1L8ABVX0ZOBvo49+wTDBpnRTHTcM788WKHczZsDvQ4RhjjpAviaDI/ZsjIr2BJKCj3yIyQemak48hNTmeBz5eWamlUmNMw+ZLInjW7Y/gzzgPhK0E/uHXqEzQiYuO5O6ze7B6x37emr8l0OEYY45AtYlARCKAfaq6V1W/VdVj3LuHnqmn+EwQGdW7NQM7NeOf/11LbkFRzSsYYxqEahOB+xTxxHqKxQQ5Eed20r35h3jy63WBDscY4yNfLg19KSJ/FJF2ItKs9OX3yExQ6tU2iYsHtOPlOZtZvyuv5hWMMQHnSyK4CrgJ+BZY6L4W+DMoE9z+cKbTf8HfPl0Z6FCMMT7w5cniTl5ex9RHcCY4pTSJ5eZfdWHWmixmrbaH0I1p6Hx5svhyb9NV9ZW6D8eEiitO6sgb87bw109XMrRLCtGRPvWKaowJAF/+Owd4vE4G7gNGV7eCMTFREfz57B5szDrAK3N/rnkFY0zA+NKvwO88x0UkCafZCWOqdVr3lpzStQVPfLWWsf3a0rxJbKBDMsZ4cTTn6/l46U7SmIpEhHvO6UH+oRL+78u1gQ7HGFMFX+oIPuaXTucjgJ7ANH8GZULHsS0T+M3gDrwydzOXDe5AjzaJgQ7JGFNBjYkAeMxjuBj4WVUz/BSPCUG/P70rHy7O5IGPV/LGtYMQkUCHZIzx4MuloS3Aj6r6jap+j9PHcEe/RmVCSlKjaG47oytzN2YzY8WOQIdjjKnAl0TwDnDYY7zEnWaMz8YPbE+3Vgn87bNVFBaVBDocY4wHXxJBlKoeKh1xh2P8F5IJRVGREdxzbk+27inghe82BTocY4wHXxJBloiUPTcgImMA633EHLEhx6ZwZs9WTJ61np37CgMdjjHG5UsiuB64S0S2iMgW4E/Adf4Ny4Squ8/uQXGJ8sgXawIdijHG5UtbQxtUdTDObaO9VPUkVV3v/9BMKOrQvDFXDe3Eez9lsHhrTqDDMcbgQyIQkb+LSLKq5qnqfhFpKiIP1kdwJjRNPO1YUprEcv/HK1C1bi2NCTRfLg2NUtWyn26quhc4y38hmVDXJDaKO0Z2Y9GWHI7/65d0mvQpQx6eyfRFmYEOzZiw5EsiiBSRskZiRCQesEZjTK1EiyACe/OLUCAzp4A7319mycCYAPAlEbwGfC0iV4vI1cCXwMv+DcuEuse+XEvFq0IFRSU8OsMqkY2pb760PvqIiCwFTgcE+ALo4O/ATGjbllNwRNONMf7ja+ujO3CeLj4f+BWwym8RmbDQNjn+iKYbY/ynykQgIl1F5B4RWQU8BWwFRFWHq+pT9RahCUm3j3D6Na7o7ONaByAaY8JbdWcEq3F+/Z+rqkNV9d847QwZU2tj01N5aFwfUpPjEaBNUhypyXG89sMWlmXkBjo8Y8JKdXUE5wMXA7NE5AvgLZw6AmPqxNj0VMamp5aN79pfyLin5/Dbl+bx3g0n0aF54wBGZ0z4qPKMQFU/UNWLgO7AbOD3QCsR+Y+InOnLxkVkpIisEZH1IjKpmuUuEBEVkf5HGL8JIS0T4nj5qoEUH1aumDqP7LyDgQ7JmLDgSxMTB1T1dVU9B0gDFgNVfqmXEpFIYDIwCqd5ivEi0tPLcgnAzcCPRxi7CUGdWzThhSsGsD23kKteXkD+oeJAh2RMyDuiPotVdY+qPqOqp/mw+EBgvapudJuufgsY42W5vwKPANYcpQHghA5N+ff4dJZl5DDxjUUUlxyueSVjzFE7ms7rfZWKc6dRqQx3WhkRSQfaqeon1W1IRCaIyAIRWZCVlVX3kZoG58xerfnr2N7MXL2LP09fbm0SGeNHvvRZfLS8VSyX/TeLSATwOHBlTRtS1WeBZwH69+9v3whh4tJBHdiRW8i/Z66ndVIct57eNdAhGROS/JkIMoB2HuNpwDaP8QSgNzDb7cy8NfCRiIxW1QV+jMsEkdvO6Mr23EKe+GodrRPjuHhg+0CHZEzI8WcimA90EZFOQCbOraiXlM5U1VwgpXRcRGYDf7QkYDyJCA+N60PW/oPcPX05LRJi+VWPVoEOy5iQ4rc6AlUtBiYCM3CapJimqitE5AHPri+NqUl0ZARPX3o8PdskctMbP7Foy95Ah2RMSJFgq4Tr37+/LlhgJw3hKGv/Qc7/zxzyDhbz3g0n0SnFHjgzxlcislBVvT6r5c+7hoypUy0SYnn5qoEAXDF1Hln77YEzY+qCJQITVDqlNGbqlQPI2n+Qq16az4GD9sCZMbVlicAEnX7tkpl8aTort+/jxtd/osgeODOmViwRmKB0WvdW/G1sb75Zm8Wd7y+zB86MqQV/3j5qjF9dPLA9O/Y5zxi0SYrjD2d2C3RIxgQlSwQmqN3yqy5lTx9vzy1k7oZstuUU0DY5nttHdCvXzLUxxjtLBCaoiQgPju3N0owc3l2YUTY9M6eAO99fBmDJwJgaWB2BCXpRkRHszS+qNL2gqIRHZ6wJQETGBBdLBCYk7Mj13or5tpyCeo7EmOBjicCEhLbJ8V6nt0mOq+dIjAk+lghMSLh9RDfioyMrTU9pHGMPnRlTA0sEJiSMTU/loXF9SE2OR4DU5DjG9mvL8m37OP8/c9i6Jz/QIRrTYFmjcyakfbs2i4lv/ERkhDD50uM5qXNKzSsZE4Ks0TkTtk7p2oIPJw6leZNYfvPCPF6du9meQjamAksEJuR1SmnMBzeexLCuLfjLhyu464PlHCq29omMKWWJwISFhLhonr28PzcO68yb87Zw6fM/sDvPmrE2BiwRmDASGSHcMbI7T45PZ1lmLmOe+p7lmbmBDsuYgLNEYMLO6L5teff6kzisygVT5vDJ0m2BDsmYgLJEYMJS79QkPpo4lN5tk5j4xiIem7GGw4etEtmEJ0sEJmy1SIjl9WsHcfGAdjw1az0TXl3A/sLKbRYZE+osEZiwFhsVyUPj+nD/6F7MWpPFuKfnsHn3gUCHZUy9skRgwp6IcMVJHXn1qoFk5R1kzOTv+W7d7kCHZUy9sSeLjfGwJTufa19ZwLpd+xnTty3zNu9hW06hdXRjgp49WWyMj9o3b8R7N55EzzaJfLB4G5k5hSi/dHQzfVFmoEM0ps5ZIjCmgiaxUezJP1RpunV0Y0KVJQJjvNieYx3dmPBhicAYL6yjG/nBZG8AABl8SURBVBNOLBEY40VVHd30aJ0YgGiM8S9LBMZ44a2jmyGdm/H16l289P2mQIdnTJ2KCnQAxjRUY9NTy90uWnJYueG1hdz/yUpaJcYxqk+bAEZnTN2xMwJjfBQZITw5Pp3j2zfllrcXM2/TnkCHZEydsERgzBGIi47k+cv7k9Y0nmtens/anfsDHZIxtebXRCAiI0VkjYisF5FJXubfJiIrRWSpiHwtIh38GY8xdaFp4xhe/u1AYqMjuWLqPLbn2i2lJrj5LRGISCQwGRgF9ATGi0jPCostAvqr6nHAu8Aj/orHmLrUrlkjXvrtAPYXFnPl1PnkFlirpSZ4+fOMYCCwXlU3quoh4C1gjOcCqjpLVfPd0R+AND/GY0yd6tU2iSmXncCGrDyue3UBB4tLAh2SMUfFn4kgFdjqMZ7hTqvK1cDn3maIyAQRWSAiC7KysuowRGNqZ2iXFB698Dh+2LiHP0xbYp3bmKDkz9tHxcs0r/8lInIZ0B841dt8VX0WeBac1kfrKkBj6sJ56Wns3HeQhz9fTevEOP58TsUroMY0bP5MBBlAO4/xNKBS57AicjpwN3Cqqh70YzzG+M11pxzDjtxCnv9uE62T4rjm5GMCHZIxPvNnIpgPdBGRTkAmcDFwiecCIpIOPAOMVNVdfozFGL8SEf5yTk927ivkwU9X0TIxjtF92wY6LGN84rdEoKrFIjIRmAFEAlNVdYWIPAAsUNWPgEeBJsA7IgKwRVVHH+m+ioqKyMjIoLDQe4uRpu7ExcWRlpZGdHR0oENpcCIjhMcv6kd23jz+OG0JKU1iOKlzSqDDMqZGIdFD2aZNm0hISKB58+a4CcX4gaqSnZ3N/v376dSpU6DDabBy84u4YMocduQW8s4NJ9LdGqozDUDI91BWWFhoSaAeiAjNmze3M68aJDWK5qWrBtIo1nngLNP6MDANXEgkAsCSQD2x4+yb1OR4XvrtQPIPlnDl1Hnk5tsDZ6bhCplEYExD06NNIs9cfgKbsw9w7SsLKCyyB85MwxSWiWD6okyGPDyTTpM+ZcjDM+utQ/KXXnqJiRMn1su+anLWWWeRk5NT7TIdO3Zk9+7d9RRRaDqpcwr//HU/5m3ew23TFlNiD5yZBijs+iOYviiTO99fRoH76ywzp4A7318GUK7t+YasuLiYqKije+tUFVXls88+q+OoTFVG923LLve20v2F89iYlce2nELaJsdz+4huQfO5M6Er5BLB/R+vYOW2fVXOX7Qlh0Mlh8tNKygq4Y53l/LmvC1e1+nZNpF7z+1V477Hjh3L1q1bKSws5JZbbmHChAm8+OKLPPTQQ7Rp04auXbsSGxtLbm4uffv2ZePGjURERJCfn0+3bt3YuHEjW7Zs4aabbiIrK4tGjRrx3HPP0b17d6688kqaNWvGokWLOP744xk9ejS33HIL4Fy3//bbbxERxowZw969eykqKuLBBx9kzJgxbN68mVGjRjF8+HDmzp3L9OnTOfXUU1mwYAEpKSle4zZ165qTj+F/67L4Zu0vZ1jB+CPEhKaQSwQ1qZgEapp+JKZOnUqzZs0oKChgwIABnH322dx7770sXLiQpKQkhg8fTnp6OklJSfTt25dvvvmG4cOH8/HHHzNixAiio6OZMGECU6ZMoUuXLvz444/ceOONzJw5E4C1a9fy1VdfERkZybnnnsvkyZMZMmQIeXl5xMU5nap/8MEHJCYmsnv3bgYPHszo0c5jGWvWrOHFF1/k6aefrjHu888/n+bNm9f6eJjy1u3KqzStoKiER2essURgAirkEkFNv9yHPDzT6+18qcnxvH3dibXa95NPPskHH3wAwNatW3n11VcZNmwYLVq0AOCiiy5i7dq1ZcNvv/02w4cP56233uLGG28kLy+POXPmcOGFF5Zt8+DBX1rduPDCC4mMdDpUHzJkCLfddhuXXnop48aNIy0tjaKiIu666y6+/fZbIiIiyMzMZOfOnQB06NCBwYMH+xT3unXrLBH4wfYc77fdbrPbS02AhV1l8e0juhEfHVluWnx0JLeP6Far7c6ePZuvvvqKuXPnsmTJEtLT0+nevXuVt1uOHj2azz//nD179rBw4UJOO+00Dh8+THJyMosXLy57rVq1qmydxo0blw1PmjSJ559/noKCAgYPHszq1at5/fXXycrKYuHChSxevJhWrVqV3fPvuW5NcdtzAv7RNjne6/TYqAi27sn3Os+Y+hB2iWBseioPjetDanI8gnMm8NC4PrU+Nc/NzaVp06Y0atSI1atX88MPP1BQUMDs2bPJzs6mqKiId955p2z5Jk2aMHDgQG655RbOOeccIiMjSUxMpFOnTmXLqSpLlizxur8NGzbQp08f/vSnP9G/f39Wr15Nbm4uLVu2JDo6mlmzZvHzzz8fVdzGP7z9CImKEEpUOePxb5g8az2Himt/idKYIxVyl4Z8MTY9tc6vyY4cOZIpU6Zw3HHH0a1bNwYPHkybNm247777OPHEE2nTpg3HH388JSW/3Et+0UUXceGFFzJ79uyyaa+//jo33HADDz74IEVFRVx88cX07du30v6eeOIJZs2aRWRkJD179mTUqFHs37+fc889l/79+9OvXz+6d+9+VHEb/yj9zD06Yw3bcgrK7hoa2KkZD3y8kkdnrOH9nzL469je1kaRqVch0dbQqlWr6NGjR4AiCj92vP1j1upd3PPRcrbuKeC89FTuOqsHLRJiAx2WCREh39aQMaFgePeW/PfWU5k4/Fg+WbqN0/45m1fnbraH0IzfWSIwpgGJj4nkjyO68cWtp9AnNYm/fLiCcU9/z7KM3ECHZkKYJQJjGqDOLZrw+jWD+NfF/diWW8iYyd9x74fL2VdojdeZumeJwJgGSkQY0y+Vr/9wKr8Z3IFXf/iZ0x77hg8XZxJsdXumYQvLu4aMCSaJcdHcP6Y3F5zQjrunL+OWtxYzbcFWHhjTm2UZuZXuQrKnlM2RskRgTJDok5bEBzcO4Y15W3jki9Wc+X/fICIUu5XJ1naROVp2aaiObN68md69e9d6OwsWLODmm2+ug4hMKIqMEH4zuAMz/zCMmKjIsiRQqrTtImOOhJ0RNDD9+/enf3+vt/oaU6ZFQmyVHd2Unhkcl5ZEn9QkurVOIDrSfvOZqoVmInjx7MrTeo2FgdfCoXx4/cLK8/tdAumXwoFsmHZ5+Xm//dSn3RYXF3PFFVewaNEiunbtyiuvvMKqVau47bbbyMvLIyUlhZdeeok2bdowbNgwBg0axKxZs8jJyeGFF17g5JNPZvbs2Tz22GN88sknZGVlcckll5Cdnc2AAQP44osvWLhwIXl5eYwaNYqhQ4cyZ84cUlNT+fDDD4mP996WjQlNbZPjvTagGBsVwadLt5U1qx4TFUHPNolliaFvu2Q6t2hCZETldrCmL8q0OocwZD8T6tCaNWuYMGECS5cuJTExkcmTJ/O73/2Od999l4ULF3LVVVdx9913ly1fXFzMvHnzeOKJJ7j//vsrbe/+++/ntNNO46effuK8885jy5Zf+ktYt24dN910EytWrCA5OZn33nuvXspoGo6qGlD8x/nHseTeM5n9x2E8OT6dK07sQGxUBO8tzOD2d5dy5uPf0vveGVw4ZQ5//WQlHy7OZGNWHh8szODO95eRmVOA8suZRX314GcCJzTPCKr7BR/TqPr5jZv7fAZQUbt27RgyZAgAl112GX//+99Zvnw5Z5xxBgAlJSW0adOmbPlx48YBcMIJJ7B58+ZK2/vuu+/KmoceOXIkTZs2LZvXqVMn+vXrV+36JrRV1XZR6fSOKY3pmNKY0X3bAnD4sLJxdx5LM3JZmpHLssxcXv/xZ174zmnoToCKN6VafwnhITQTQYBUbHI6ISGBXr16MXfuXK/Lx8Y67chERkZSXFxcaX5194qXrlu6fkGBtWkfjo6kAcWICOHYlgkc2zKBccenAVBccph1u/JYlpHLHe8t9bpeZk4Bj81YQ5+0JPqmJdMqMbbK5tVNcLJLQ3Voy5YtZV/6b775JoMHDyYrK6tsWlFREStWrPB5e0OHDmXatGkA/Pe//2Xv3r11H7QJa1GREfRok8ivB7QjtYr+EqIihP98s4HrXl3I4Ie+ZuDfv+bql+bzxFdrmbV6F7vzDnpdzwQPOyOoQz169ODll1/muuuuo0uXLvzud79jxIgR3HzzzeTm5lJcXMytt95Kr141938McO+99zJ+/HjefvttTj31VNq0aUNCQgJ5eZW7PDSmtm4f0Y07319GgcfdSPHRkTw0rg8je7dm5fZ9LN2aw9LMXJZl5DJzzS5KT1rbJsXRJy2J49KSyyqlkxvFWOVzkLBmqBuwgwcPEhkZSVRUFHPnzuWGG25g8eLFgQ4rZI+3ObK7hg4cLGbFtn0szcgpq3PYtPtA2fxmjaPJzS+ixOMrJi46gofHHWfJIACqa4bazggasC1btvDrX/+aw4cPExMTw3PPPRfokEyIO5I6h8axUQzs1IyBnZqVTcvNL2L5Nqcy+l9fry2XBAAKiw7z+7cXM3nWelolxtEyMZbWiXG0SoyjVWIsLd3hFk1iiYnyfuXazjLqniWCBqxLly4sWrQo0GEY47OkRtEMOTaFIcem8MgXq70uozitq+7cX8iPGw+wc19hpSekAZo3jqFlYhytE2PdpBHH9twCPly0jUMlzp1O1qxG3QiZRKCqdidDPQi2S4kmcKp64C01OZ4pvzmhbPzwYWVP/iF27itk176D7NxXyM59B9m5v5Bd+wrZsa+Q5dv2sTvvIN4+fgVFJdz+7hI+XbadVomxtEpwzzCS4srGkxtFV/n9YGcYIZII4uLiyM7Opnnz5pYM/EhVyc7OJi4uLtChmCBQVeXz7SO6lVsuIkJIaRJLSpNYerWtenvFJYfpcvfnlZ51ACgqUbbuyWfB5j3sza/cZ0NMZAQt3TOL1u4lqVaJcWzde4B35meG/RlGSCSCtLQ0MjIyyMrKCnQoIS8uLo60tLRAh2GCQE0PvB2pqMiIas8yvrj1FAAKi0rI2u9xZrGv0D27OMiO3EJW7djHN2sPknew8rM74Jxh3PHeUmat2eVckkpwE0hSHK0SnCQSV+GJbk/BeIYREncNGWPCw/RFmVXe4nqkX7Z5B4vpc+8Mr2cYAO2bNWLHvkIOFR+uNC8pPtq57JQYR8uEuLLhzdkHeOPHLRz0WOdo46trAbtrSERGAv8CIoHnVfXhCvNjgVeAE4Bs4CJV3ezPmIwxwasuzzKaxEZVe4bx7R3DUVX2FRSzY1+he4ZRyK6ysw3njGPDrt3s2n/Qa4U3OGcYf3pvKT9szHbvjvolcbRMjKV541ivDQB68vdZht/OCEQkElgLnAFkAPOB8aq60mOZG4HjVPV6EbkYOE9VL6puu3ZGYIypK3V1hnH4sJJ94BAD//ZVlWcYLRJivVZ4R0YILZrEetw+61Z4JzlJY8W2XJ78eh2FRbU7ywjUGcFAYL2qbnSDeAsYA6z0WGYMcJ87/C7wlIiIBtv1KmNMUKqrM4yICKFFQmy1ZxjfTzqNopLD7M47WFZ3satcPcbBaiu8PdV1Y4D+TASpwFaP8QxgUFXLqGqxiOQCzYHdnguJyARggjuaJyIVu2BKqbhOkLPyNFyhVBaw8pSzGTjvzqPfeUR8YrOoxBYdEPnlaTjVw1v3Zf0sd+7bcxSbTIlpfWwHbzO2A3Ln+oVHsC2v2wH/JgJvF70q/tL3ZRlU9Vng2Sp3JLKgqlOeYGTlabhCqSxg5WnoRGTBwe3rOvp7P/5sfTQDaOcxngZsq2oZEYkCkoCjyZrGGGOOkj8TwXygi4h0EpEY4GLgowrLfARc4Q5fAMy0+gFjjKlffrs05F7znwjMwLl9dKqqrhCRB4AFqvoR8ALwqoisxzkTuPgod1flZaMgZeVpuEKpLGDlaejqpTxB90CZMcaYumU9lBljTJizRGCMMWGuwScCERkpImtEZL2ITPIyP1ZE3nbn/ygiHT3m3elOXyMiI+ozbm+OtiwicoaILBSRZe7f0+o7dm9q896489uLSJ6I/LG+Yq5OLT9rx4nIXBFZ4b5PAW+itRaft2gRedktxyoRqcWd9XXDh7KcIiI/iUixiFxQYd4VIrLOfV1Rcd1AONryiEg/j8/ZUhGptiUGn6lqg33hVDJvAI4BYoAlQM8Ky9wITHGHLwbedod7usvHAp3c7UQGaVnSgbbucG8gM5jfG4/57wHvAH8M5vLg3HSxFOjrjjcP5GetDspzCfCWO9wI5zmrjg28LB2B43DaLrvAY3ozYKP7t6k73DQI3puqytMV6OIOt8V5riy5tjE19DOCsmYqVPUQUNpMhacxwMvu8LvAr8TplGAMzof5oKpuAta72wuUoy6Lqi5S1dJnMFYAcW6DfYFUm/cGERmL80+5op7irUltynMmsFRVlwCoaraqlhBYtSmPAo3dZ3vigUPAvvoJ26say6Kqm1V1KVCxqdARwJequkdV9wJfAiPrI+hqHHV5VHWtqq5zh7cBu4AWtQ2ooScCb81UVGxco1wzFUBpMxW+rFufalMWT+cDi1T1oJ/i9NVRl0dEGgN/Au6vhzh9VZv3pyugIjLDPZ2/ox7irUltyvMucADn1+YW4DFVDeSDnrX5X25o3wNQRzGJyECcM4oNtQ2ooXdMU5tmKnxqvqIe1brJDRHpBfwD5xdooNWmPPcDj6tqnjScHuVqU54oYCgwAMgHvhanpcev6zbEI1Kb8gwESnAuPTQF/iciX6nbgGQA1OZ/uaF9D0AdxCQibYBXgStUtXKHCUeooZ8R1KaZCl/WrU+1anJDRNKAD4DLVbXWvwDqQG3KMwh4REQ2A7cCd7kPHwZSbT9r36jqblXNBz4Djvd7xNWrTXkuAb5Q1SJV3QV8DwSy/Z7a/C83tO8BqGVMIpIIfAr8WVV/qJOIAllp4kOlShTOdeRO/FKp0qvCMjdRvsJrmjvci/KVxRsJbGVxbcqS7C5/fqDfk7ooT4Vl7qNhVBbX5v1pCvyEU7EaBXwFnB3E5fkT8CLOL9fGOE3HH9eQy+Kx7EtUrize5L5HTd3hZg39vammPDHA18CtdRpTIA+IjwftLJwObjYAd7vTHgBGu8NxOHeerAfmAcd4rHu3u94aYFSwlgX4M84128Uer5bBWp4K27iPBpAI6uCzdhlOxfdy4JFAl6WWn7cm7vQVOEng9iAoywCcX9oHcHo7XOGx7lVuGdcDvw10WWpTHvdzVlThu6BfbeOxJiaMMSbMNfQ6AmOMMX5micAYY8KcJQJjjAlzlgiMMSbMWSIwxpgwZ4nANGgicp6IqIh095jWUUQu8RjvJyJn1WIfm0UkpYZl8o52++76s0XkqB7KEpFhInJSbfZvTHUsEZiGbjzwHeW7Me2I8/RrqX4492WHqmGAJQLjN5YITIMlIk2AIcDVlE8EDwMni8hiEfkTzoM4F7njF4nIQBGZIyKL3L/d3O1Fishjbjv7S0XkdxX2Fy8iX4jItVXE80+3UbmvRaSFO63sl76IpLjNZpRu6y13P2/jtOJZup2rRWStu+5zIvKUO72FiLwnIvPd1xC3j4Drgd+75Tu5QkyNRWSqu/wiERnjTr9SRD50y7NGRO71WP5TEVkiIsvrrD17E9QaeqNzJryNxWnzZq2I7BGR41X1J2ASztPI5wCIyE6gv6pOdMcTgVNUtVhETgf+jtNq6wScx/rT3XnNPPbVBKc54FdU9RUvsTQGflLVP4jIPcC9QHXtI90A5KvqcSJyHE4TFIhIW+AvOG0R7Qdm4jQxAPAvnMb4vhOR9sAMVe0hIlOAPFV9zMt+7gZmqupVIpIMzBORr9x5A3H6r8gH5ovIp0AHYJuqnu3Gk1RNGUyYsERgGrLxwBPu8Fvu+E8+rJcEvCwiXXBadYx2p5+O07ZOMYCWb1r5Q5ymIV6vYpuHgbfd4deA92uI4RTgSXc/S0VkqTt9IE4DdaWNCb6D04x1aXw9PVpkTRSRhBr2cyYwWn7p5S0OaO8Of6mq2e5+3sdpIfUz4DER+Qfwiar+r4btmzBgicA0SCLSHDgN6C0iitOrk/rY1v9fgVmqep57aWV26Wapurnf74FRIvKG+tbuSukyxfxyibVi95TetlNdu9sRwImqWlBuheqb6hacxgjXVFhnkJf9q3t2dQJOncpDIvJfVX2guh2Y0Gd1BKahugDnMk0HVe2oqu1wWo4cinNJxfOXcsXxJCDTHb7SY/p/gevdJpepcGnoHpzGvZ6uIp4INyZwKqq/c4c3Ayd4xFzqW+BSdz+9cbodBKdxt1NFpKkbx/kV4iu73CQi/aoon6cZwO9Eynp+S/eYd4aINBOReJzLbN+7l6byVfU14DEC31y2aQAsEZiGajxO/wue3sP5El4KFLsVnr8HZuFcUlnsVn4+gvNr93ucM4lSz+P0uLVURJZQ/s4jcPpGiBORR7zEcwDoJSILcc5USn9FPwbcICJzAM9bUP8DNHEvCd2BkwBQ1UycOosfcZqrXonTMxjAzUB/t4J5JU4lMcDHwHneKotxzn6i3TItd8dLfYfTecli4D1VXQD0walHWIxTv/Cgl7KaMGOtjxpTz0SkiTq9s0XhJLupqlox6dV2H1fiUYFuTHXsjMCY+nef+4t8Oc7lrukBjseEOTsjMMaYMGdnBMYYE+YsERhjTJizRGCMMWHOEoExxoQ5SwTGGBPm/h8ADduBCqFMrQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the Security Curve for the evaluation of the robust PyTorch classifier on CIFAR-10\n",
+    "sc.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Check if the robust PyTorch classifier is potentially obfuscating loss gradients\n",
+    "sc.detected_obfuscating_gradients"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Robust Classifier with Gradient Obfuscation by Vanishing Gradients"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load MNIST dataset\n",
+    "(x_train_mnist, y_train_mnist), (x_test_mnist, y_test_mnist), min_mnist, max_mnist = load_dataset('mnist')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load a robust Keras model with potential gradient obfuscation caused by vanishing gradients.\n",
+    "path = get_file('mnist_cnn_robust.h5', extract=False, path=ART_DATA_PATH,\n",
+    "                url='https://www.dropbox.com/s/yutsncaniiy5uy8/mnist_cnn_robust.h5?dl=1')\n",
+    "robust_classifier_model = load_model(path)\n",
+    "robust_classifier_mnist = KerasClassifier(clip_values=(min_mnist, max_mnist),\n",
+    "                                          model=robust_classifier_model,\n",
+    "                                          use_logits=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define argument for PGD attack used in evaluation, except `classifier` and `eps`\n",
+    "kwargs_pgd = {\"norm\": \"inf\",\n",
+    "              \"eps_step\": 1/255,\n",
+    "              \"max_iter\": 100,\n",
+    "              \"targeted\": False,\n",
+    "              \"num_random_init\": 0,\n",
+    "              \"batch_size\": 128,\n",
+    "              \"random_eps\": False,\n",
+    "              \"verbose\": False,\n",
+    "             }"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define attack budgets for evaluation as number of evaluations equally spaced between minimal and \n",
+    "# maximal `eps`.\n",
+    "eps = 64"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create an instance of a Security Curve evaluation\n",
+    "sc = SecurityCurve(eps=eps)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Evaluate Security Curve for robust PyTorch classifier on CIFAR-10 on the first 100 samples\n",
+    "eps_list, accuracy_adv_list, accuracy = sc.evaluate(classifier=robust_classifier_mnist,\n",
+    "                                                    x=x_train_mnist[0:100],\n",
+    "                                                    y=y_train_mnist[0:100],\n",
+    "                                                    **kwargs_pgd)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the Security Curve for the evaluation of the robust PyTorch classifier on CIFAR-10\n",
+    "sc.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Check if the robust PyTorch classifier is potentially obfuscating loss gradients\n",
+    "sc.detected_obfuscating_gradients"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.4.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/adversarial-robustness-toolbox/notebooks/asr_deepspeech_examples.ipynb b/adversarial-robustness-toolbox/notebooks/asr_deepspeech_examples.ipynb
new file mode 100644
index 0000000..60b8cb5
--- /dev/null
+++ b/adversarial-robustness-toolbox/notebooks/asr_deepspeech_examples.ipynb
@@ -0,0 +1,653 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# ASR DeepSpeech Examples\n",
+    "\n",
+    "This notebook demonstrates ART's DeepSpeech estimator and the Imperceptible ASR attack.\n",
+    "\n",
+    "---\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Preliminaries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "import torch\n",
+    "import numpy as np\n",
+    "import IPython.display as ipd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from deepspeech_pytorch.loader.data_loader import load_audio\n",
+    "\n",
+    "from art.estimators.speech_recognition.pytorch_deep_speech import PyTorchDeepSpeech\n",
+    "from art.attacks.evasion.imperceptible_asr.imperceptible_asr_pytorch import ImperceptibleASRPyTorch\n",
+    "from art import config\n",
+    "from art.utils import get_file\n",
+    "\n",
+    "\n",
+    "# Set seed\n",
+    "np.random.seed(1234)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Audio Data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.1 Download Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/home/minhtn/.art/data/deepspeech_audio\n",
+      "Skipping url: http://www.openslr.org/resources/12/train-clean-100.tar.gz\n",
+      "Skipping url: http://www.openslr.org/resources/12/train-clean-360.tar.gz\n",
+      "Skipping url: http://www.openslr.org/resources/12/train-other-500.tar.gz\n",
+      "Sorting manifests...\n",
+      "Pruning manifests between 1 and 15 seconds\n",
+      "0it [00:00, ?it/s]\n",
+      "\n",
+      "\n",
+      "Skipping url: http://www.openslr.org/resources/12/dev-clean.tar.gz\n",
+      "Skipping url: http://www.openslr.org/resources/12/dev-other.tar.gz\n",
+      "Sorting manifests...\n",
+      "0it [00:00, ?it/s]\n",
+      "\n",
+      "\n",
+      "100% [..................................................] 346663984 / 346663984Unpacking test-clean.tar.gz...\n",
+      "Converting flac files to wav and extracting transcripts...\n",
+      "129it [00:28,  4.61it/s]\n",
+      "Finished http://www.openslr.org/resources/12/test-clean.tar.gz\n",
+      "Sorting manifests...\n",
+      "100%|████████████████████████████████████| 2620/2620 [00:00<00:00, 61634.93it/s]\n",
+      "\n",
+      "\n",
+      "Skipping url: http://www.openslr.org/resources/12/test-other.tar.gz\n",
+      "Sorting manifests...\n",
+      "0it [00:00, ?it/s]\n",
+      "\n",
+      "\n",
+      "/home/minhtn/ibm/projects/adversarial-robustness-toolbox/notebooks\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Prepare to download data\n",
+    "data_dir = os.path.join(config.ART_DATA_PATH, \"deepspeech_audio\")\n",
+    "current_dir = %pwd\n",
+    "\n",
+    "if not os.path.exists(data_dir):\n",
+    "    os.makedirs(data_dir)\n",
+    "\n",
+    "# Download audio data\n",
+    "get_file('librispeech.py', 'https://raw.githubusercontent.com/SeanNaren/deepspeech.pytorch/master/data/librispeech.py', path=data_dir)\n",
+    "\n",
+    "%cd $data_dir\n",
+    "!python librispeech.py --files-to-use test-clean.tar.gz\n",
+    "%cd $current_dir"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.2 Create Model and Data Utilities"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a DeepSpeech estimator\n",
+    "speech_recognizer = PyTorchDeepSpeech(pretrained_model=\"librispeech\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def display_waveform(waveform, title=\"\", sample_rate=16000):\n",
+    "    \"\"\"\n",
+    "    Display waveform plot and audio play UI.\n",
+    "    \"\"\"\n",
+    "    plt.figure()\n",
+    "    plt.title(title)\n",
+    "    plt.plot(waveform)\n",
+    "    ipd.display(ipd.Audio(waveform, rate=sample_rate))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "labels_map = dict([(speech_recognizer.model.labels[i], i) for i in range(len(speech_recognizer.model.labels))])\n",
+    "def parse_transcript(path):\n",
+    "    with open(path, 'r', encoding='utf8') as f:\n",
+    "        transcript = f.read().replace('\\n', '')\n",
+    "    result = list(filter(None, [labels_map.get(x) for x in list(transcript)]))\n",
+    "    return transcript, result"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.3 Play with Some Audios"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Encoded label:  [9, 6, 28, 9, 16, 17, 6, 5, 28, 21, 9, 6, 19, 6, 28, 24, 16, 22, 13, 5, 28, 3, 6, 28, 20, 21, 6, 24, 28, 7, 16, 19, 28, 5, 10, 15, 15, 6, 19, 28, 21, 22, 19, 15, 10, 17, 20, 28, 2, 15, 5, 28, 4, 2, 19, 19, 16, 21, 20, 28, 2, 15, 5, 28, 3, 19, 22, 10, 20, 6, 5, 28, 17, 16, 21, 2, 21, 16, 6, 20, 28, 2, 15, 5, 28, 7, 2, 21, 28, 14, 22, 21, 21, 16, 15, 28, 17, 10, 6, 4, 6, 20, 28, 21, 16, 28, 3, 6, 28, 13, 2, 5, 13, 6, 5, 28, 16, 22, 21, 28, 10, 15, 28, 21, 9, 10, 4, 12, 28, 17, 6, 17, 17, 6, 19, 6, 5, 28, 7, 13, 16, 22, 19, 28, 7, 2, 21, 21, 6, 15, 6, 5, 28, 20, 2, 22, 4, 6]\n",
+      "Groundtrue label:  HE HOPED THERE WOULD BE STEW FOR DINNER TURNIPS AND CARROTS AND BRUISED POTATOES AND FAT MUTTON PIECES TO BE LADLED OUT IN THICK PEPPERED FLOUR FATTENED SAUCE\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "                <audio  controls=\"controls\" >\n",
+       "                    <source src=\"data:audio/wav;base64,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\" type=\"audio/wav\" />\n",
+       "                    Your browser does not support the audio element.\n",
+       "                </audio>\n",
+       "              "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.Audio object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# A long audio sample\n",
+    "x1 = load_audio(os.path.join(data_dir, \"LibriSpeech_dataset/test_clean/wav/1089-134686-0000.wav\"))\n",
+    "label1, encoded_label1 = parse_transcript(os.path.join(data_dir, \"LibriSpeech_dataset/test_clean/txt/1089-134686-0000.txt\"))\n",
+    "print(\"Encoded label: \", encoded_label1)\n",
+    "print(\"Groundtrue label: \", label1)\n",
+    "display_waveform(x1, title=\"Long Sample\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Encoded label:  [21, 9, 6, 28, 22, 15, 10, 23, 6, 19, 20, 10, 21, 26]\n",
+      "Groundtrue label:  THE UNIVERSITY\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "                <audio  controls=\"controls\" >\n",
+       "                    <source src=\"data:audio/wav;base64,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\" type=\"audio/wav\" />\n",
+       "                    Your browser does not support the audio element.\n",
+       "                </audio>\n",
+       "              "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.Audio object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# A short audio sample\n",
+    "x2 = load_audio(os.path.join(data_dir, \"LibriSpeech_dataset/test_clean/wav/1089-134691-0003.wav\"))\n",
+    "label2, encoded_label2 = parse_transcript(os.path.join(data_dir, \"LibriSpeech_dataset/test_clean/txt/1089-134691-0003.txt\"))\n",
+    "print(\"Encoded label: \", encoded_label2)\n",
+    "print(\"Groundtrue label: \", label2)\n",
+    "display_waveform(x2, title=\"Short Sample\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Encoded label:  [2, 8, 2, 10, 15, 28, 2, 8, 2, 10, 15]\n",
+      "Groundtrue label:  AGAIN AGAIN\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "                <audio  controls=\"controls\" >\n",
+       "                    <source src=\"data:audio/wav;base64,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\" type=\"audio/wav\" />\n",
+       "                    Your browser does not support the audio element.\n",
+       "                </audio>\n",
+       "              "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.Audio object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Another short audio sample\n",
+    "x3 = load_audio(os.path.join(data_dir, \"LibriSpeech_dataset/test_clean/wav/1089-134691-0018.wav\"))\n",
+    "label3, encoded_label3 = parse_transcript(os.path.join(data_dir, \"LibriSpeech_dataset/test_clean/txt/1089-134691-0018.txt\"))\n",
+    "print(\"Encoded label: \", encoded_label3)\n",
+    "print(\"Groundtrue label: \", label3)\n",
+    "display_waveform(x3, title=\"Short Sample\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. The Estimator Performance"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.1 Get Transcription Outputs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Groundtrue label:  HE HOPED THERE WOULD BE STEW FOR DINNER TURNIPS AND CARROTS AND BRUISED POTATOES AND FAT MUTTON PIECES TO BE LADLED OUT IN THICK PEPPERED FLOUR FATTENED SAUCE\n",
+      "Predicted  label:  HE HOPED THERE WOULD BE STEW FOR DINNER TURNIPS AND CARROTS AND BRUISED POTATOES AND FAT MUTTON PIECES TO BE LADLED OUT IN THICK PEPPERD FLOUR FAT AND SAUCE\n"
+     ]
+    }
+   ],
+   "source": [
+    "pred1 = speech_recognizer.predict(np.array([x1]), transcription_output=True)\n",
+    "print(\"Groundtruth label: \", label1)\n",
+    "print(\"Predicted   label: \", pred1[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Groundtrue label:  THE UNIVERSITY\n",
+      "Predicted  label:  THE UNIVERSITY\n"
+     ]
+    }
+   ],
+   "source": [
+    "pred2 = speech_recognizer.predict(np.array([x2]), transcription_output=True)\n",
+    "print(\"Groundtruth label: \", label2)\n",
+    "print(\"Predicted   label: \", pred2[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Groundtrue label:  AGAIN AGAIN\n",
+      "Predicted  label:  AGAIN AGAIN\n"
+     ]
+    }
+   ],
+   "source": [
+    "pred3 = speech_recognizer.predict(np.array([x3]), transcription_output=True)\n",
+    "print(\"Groundtruth label: \", label3)\n",
+    "print(\"Predicted   label: \", pred3[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Predicted  labels:  ['HE HOPED THERE WOULD BE STEW FOR DINNER TURNIPS AND CARROTS AND BRUISED POTATOES AND FAT MUTTON PIECES TO BE LADLED OUT IN THICK PEPPERD FLOUR FAT AND SAUCE'\n",
+      " 'THE UNIVERSITY' 'AGAIN AGAIN']\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = np.array([x1, x2, x3])\n",
+    "pred_all = speech_recognizer.predict(x, transcription_output=True)\n",
+    "print(\"Predicted  labels: \", pred_all)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4. Imperceptible ASR Attack"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Selected optimization level O1:  Insert automatic casts around PyTorch functions and Tensor methods.\n",
+      "\n",
+      "Defaults for this optimization level are:\n",
+      "enabled                : True\n",
+      "opt_level              : O1\n",
+      "cast_model_type        : None\n",
+      "patch_torch_functions  : True\n",
+      "keep_batchnorm_fp32    : None\n",
+      "master_weights         : None\n",
+      "loss_scale             : dynamic\n",
+      "Processing user overrides (additional kwargs that are not None)...\n",
+      "After processing overrides, optimization options are:\n",
+      "enabled                : True\n",
+      "opt_level              : O1\n",
+      "cast_model_type        : None\n",
+      "patch_torch_functions  : True\n",
+      "keep_batchnorm_fp32    : None\n",
+      "master_weights         : None\n",
+      "loss_scale             : 1.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "global_max_length = int(np.max([len(x2), len(x3)]))\n",
+    "\n",
+    "# Define an Imperceptible ASR attack\n",
+    "asr_attack = ImperceptibleASRPyTorch(\n",
+    "    estimator=speech_recognizer,\n",
+    "    initial_eps=0.0005,\n",
+    "    max_iter_1st_stage=400,\n",
+    "    max_iter_2nd_stage=100,\n",
+    "    learning_rate_1st_stage=0.000001,\n",
+    "    learning_rate_2nd_stage=0.000001,\n",
+    "    optimizer_1st_stage=torch.optim.SGD,\n",
+    "    optimizer_2nd_stage=torch.optim.SGD,\n",
+    "    global_max_length=global_max_length,\n",
+    "    initial_rescale=1.0,\n",
+    "    rescale_factor=0.8,\n",
+    "    num_iter_adjust_rescale=20,\n",
+    "    initial_alpha=0.01,\n",
+    "    increase_factor_alpha=1.2,\n",
+    "    num_iter_increase_alpha=20,\n",
+    "    decrease_factor_alpha=0.8,\n",
+    "    num_iter_decrease_alpha=20,\n",
+    "    batch_size=2,\n",
+    "    use_amp=True,\n",
+    "    opt_level=\"O1\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Stage 1, step 0 has loss:  tensor([78.0668], device='cuda:0', grad_fn=<DivBackward0>)\n",
+      "Stage 1, step 5 has loss:  tensor([75.0406], device='cuda:0', grad_fn=<DivBackward0>)\n",
+      "Stage 1, step 10 has loss:  tensor([72.4438], device='cuda:0', grad_fn=<DivBackward0>)\n",
+      "Stage 1, step 15 has loss:  tensor([70.3462], device='cuda:0', grad_fn=<DivBackward0>)\n",
+      "Stage 1, step 20 has loss:  tensor([68.7566], device='cuda:0', grad_fn=<DivBackward0>)\n",
+      "Stage 1, step 25 has loss:  tensor([67.2724], device='cuda:0', grad_fn=<DivBackward0>)\n",
+      "Stage 1, step 30 has loss:  tensor([65.8015], device='cuda:0', grad_fn=<DivBackward0>)\n",
+      "Stage 1, step 35 has loss:  tensor([64.3956], device='cuda:0', grad_fn=<DivBackward0>)\n",
+      "Stage 1, step 40 has loss:  tensor([63.0640], device='cuda:0', grad_fn=<DivBackward0>)\n",
+      "Stage 1, step 45 has loss:  tensor([61.7141], device='cuda:0', grad_fn=<DivBackward0>)\n",
+      "Stage 1, step 50 has loss:  tensor([60.3800], device='cuda:0', grad_fn=<DivBackward0>)\n",
+      "Stage 1, step 55 has loss:  tensor([59.0455], device='cuda:0', grad_fn=<DivBackward0>)\n",
+      "Stage 1, step 60 has loss:  tensor([57.8292], device='cuda:0', grad_fn=<DivBackward0>)\n",
+      "Stage 1, step 65 has loss:  tensor([56.4391], device='cuda:0', grad_fn=<DivBackward0>)\n",
+      "Stage 1, step 70 has loss:  tensor([55.0872], device='cuda:0', grad_fn=<DivBackward0>)\n",
+      "Stage 1, step 75 has loss:  tensor([53.6269], device='cuda:0', grad_fn=<DivBackward0>)\n",
+      "Stage 1, step 80 has loss:  tensor([52.1382], device='cuda:0', grad_fn=<DivBackward0>)\n",
+      "Stage 1, step 85 has loss:  tensor([50.6888], device='cuda:0', grad_fn=<DivBackward0>)\n",
+      "Stage 1, step 90 has loss:  tensor([49.2503], device='cuda:0', grad_fn=<DivBackward0>)\n",
+      "Stage 1, step 95 has loss:  tensor([47.8770], device='cuda:0', grad_fn=<DivBackward0>)\n",
+      "Stage 1, step 100 has loss:  tensor([46.5206], device='cuda:0', grad_fn=<DivBackward0>)\n",
+      "Stage 1, step 105 has loss:  tensor([45.2272], device='cuda:0', grad_fn=<DivBackward0>)\n",
+      "Stage 1, step 110 has loss:  tensor([43.9854], device='cuda:0', grad_fn=<DivBackward0>)\n",
+      "Stage 1, step 115 has loss:  tensor([42.7628], device='cuda:0', grad_fn=<DivBackward0>)\n",
+      "Stage 1, step 120 has loss:  tensor([41.5179], device='cuda:0', grad_fn=<DivBackward0>)\n",
+      "Stage 1, step 125 has loss:  tensor([40.2884], device='cuda:0', grad_fn=<DivBackward0>)\n",
+      "Stage 1, step 130 has loss:  tensor([39.0639], device='cuda:0', grad_fn=<DivBackward0>)\n",
+      "Stage 1, step 135 has loss:  tensor([37.8573], device='cuda:0', grad_fn=<DivBackward0>)\n",
+      "Stage 1, step 140 has loss:  tensor([36.6473], device='cuda:0', grad_fn=<DivBackward0>)\n",
+      "Stage 1, step 145 has loss:  tensor([35.4803], device='cuda:0', grad_fn=<DivBackward0>)\n",
+      "Stage 1, step 150 has loss:  tensor([34.2981], device='cuda:0', grad_fn=<DivBackward0>)\n",
+      "Stage 1, step 155 has loss:  tensor([33.1324], device='cuda:0', grad_fn=<DivBackward0>)\n",
+      "Stage 1, step 160 has loss:  tensor([32.0204], device='cuda:0', grad_fn=<DivBackward0>)\n",
+      "Stage 1, step 165 has loss:  tensor([30.9088], device='cuda:0', grad_fn=<DivBackward0>)\n",
+      "Stage 1, step 170 has loss:  tensor([29.7555], device='cuda:0', grad_fn=<DivBackward0>)\n",
+      "Stage 1, step 175 has loss:  tensor([28.6488], device='cuda:0', grad_fn=<DivBackward0>)\n",
+      "Stage 1, step 180 has loss:  tensor([27.5450], device='cuda:0', grad_fn=<DivBackward0>)\n",
+      "Stage 1, step 185 has loss:  tensor([26.4578], device='cuda:0', grad_fn=<DivBackward0>)\n",
+      "Stage 1, step 190 has loss:  tensor([25.3506], device='cuda:0', grad_fn=<DivBackward0>)\n",
+      "Stage 1, step 195 has loss:  tensor([24.3297], device='cuda:0', grad_fn=<DivBackward0>)\n",
+      "Stage 1, step 200 has loss:  tensor([23.2563], device='cuda:0', grad_fn=<DivBackward0>)\n",
+      "Stage 1, step 205 has loss:  tensor([22.2379], device='cuda:0', grad_fn=<DivBackward0>)\n",
+      "Stage 1, step 210 has loss:  tensor([21.2252], device='cuda:0', grad_fn=<DivBackward0>)\n",
+      "Stage 1, step 215 has loss:  tensor([20.2773], device='cuda:0', grad_fn=<DivBackward0>)\n",
+      "Stage 1, step 220 has loss:  tensor([19.3601], device='cuda:0', grad_fn=<DivBackward0>)\n",
+      "Stage 1, step 225 has loss:  tensor([18.3961], device='cuda:0', grad_fn=<DivBackward0>)\n",
+      "Stage 1, step 230 has loss:  tensor([17.5150], device='cuda:0', grad_fn=<DivBackward0>)\n",
+      "Stage 1, step 235 has loss:  tensor([16.6183], device='cuda:0', grad_fn=<DivBackward0>)\n",
+      "Stage 1, step 240 has loss:  tensor([15.7800], device='cuda:0', grad_fn=<DivBackward0>)\n",
+      "Stage 1, step 245 has loss:  tensor([14.9663], device='cuda:0', grad_fn=<DivBackward0>)\n",
+      "Stage 1, step 250 has loss:  tensor([14.1992], device='cuda:0', grad_fn=<DivBackward0>)\n",
+      "Stage 1, step 255 has loss:  tensor([13.4178], device='cuda:0', grad_fn=<DivBackward0>)\n",
+      "Stage 1, step 260 has loss:  tensor([12.7053], device='cuda:0', grad_fn=<DivBackward0>)\n",
+      "Stage 1, step 265 has loss:  tensor([12.0614], device='cuda:0', grad_fn=<DivBackward0>)\n",
+      "Stage 1, step 270 has loss:  tensor([11.4076], device='cuda:0', grad_fn=<DivBackward0>)\n",
+      "Stage 1, step 275 has loss:  tensor([10.8047], device='cuda:0', grad_fn=<DivBackward0>)\n",
+      "Stage 1, step 280 has loss:  tensor([10.2175], device='cuda:0', grad_fn=<DivBackward0>)\n",
+      "Stage 1, step 285 has loss:  tensor([9.6791], device='cuda:0', grad_fn=<DivBackward0>)\n",
+      "Stage 1, step 290 has loss:  tensor([9.1661], device='cuda:0', grad_fn=<DivBackward0>)\n",
+      "Stage 1, step 295 has loss:  tensor([8.6720], device='cuda:0', grad_fn=<DivBackward0>)\n",
+      "Stage 1, step 300 has loss:  tensor([8.1893], device='cuda:0', grad_fn=<DivBackward0>)\n",
+      "Stage 1, step 305 has loss:  tensor([21.2783], device='cuda:0', grad_fn=<DivBackward0>)\n",
+      "Stage 1, step 310 has loss:  tensor([20.1049], device='cuda:0', grad_fn=<DivBackward0>)\n",
+      "Stage 1, step 315 has loss:  tensor([19.0227], device='cuda:0', grad_fn=<DivBackward0>)\n",
+      "Stage 1, step 320 has loss:  tensor([18.1159], device='cuda:0', grad_fn=<DivBackward0>)\n",
+      "Stage 1, step 325 has loss:  tensor([17.2428], device='cuda:0', grad_fn=<DivBackward0>)\n",
+      "Stage 1, step 330 has loss:  tensor([16.3554], device='cuda:0', grad_fn=<DivBackward0>)\n",
+      "Stage 1, step 335 has loss:  tensor([15.6353], device='cuda:0', grad_fn=<DivBackward0>)\n",
+      "Stage 1, step 340 has loss:  tensor([14.8845], device='cuda:0', grad_fn=<DivBackward0>)\n",
+      "Stage 1, step 345 has loss:  tensor([14.2237], device='cuda:0', grad_fn=<DivBackward0>)\n",
+      "Stage 1, step 350 has loss:  tensor([13.5317], device='cuda:0', grad_fn=<DivBackward0>)\n",
+      "Stage 1, step 355 has loss:  tensor([12.9087], device='cuda:0', grad_fn=<DivBackward0>)\n",
+      "Stage 1, step 360 has loss:  tensor([12.2849], device='cuda:0', grad_fn=<DivBackward0>)\n",
+      "Stage 1, step 365 has loss:  tensor([11.6713], device='cuda:0', grad_fn=<DivBackward0>)\n",
+      "Stage 1, step 370 has loss:  tensor([11.0370], device='cuda:0', grad_fn=<DivBackward0>)\n",
+      "Stage 1, step 375 has loss:  tensor([10.4525], device='cuda:0', grad_fn=<DivBackward0>)\n",
+      "Stage 1, step 380 has loss:  tensor([9.8739], device='cuda:0', grad_fn=<DivBackward0>)\n",
+      "Stage 1, step 385 has loss:  tensor([9.3079], device='cuda:0', grad_fn=<DivBackward0>)\n",
+      "Stage 1, step 390 has loss:  tensor([8.7574], device='cuda:0', grad_fn=<DivBackward0>)\n",
+      "Stage 1, step 395 has loss:  tensor([8.2437], device='cuda:0', grad_fn=<DivBackward0>)\n",
+      "Stage 2, step 0 has loss:  tensor(5.5476, device='cuda:0', grad_fn=<MeanBackward0>)\n",
+      "Stage 2, step 5 has loss:  tensor(4.2969, device='cuda:0', grad_fn=<MeanBackward0>)\n",
+      "Stage 2, step 10 has loss:  tensor(3.4117, device='cuda:0', grad_fn=<MeanBackward0>)\n",
+      "Stage 2, step 15 has loss:  tensor(2.5284, device='cuda:0', grad_fn=<MeanBackward0>)\n",
+      "Stage 2, step 20 has loss:  tensor(1.8743, device='cuda:0', grad_fn=<MeanBackward0>)\n",
+      "Stage 2, step 25 has loss:  tensor(1.4079, device='cuda:0', grad_fn=<MeanBackward0>)\n",
+      "Stage 2, step 30 has loss:  tensor(1.5555, device='cuda:0', grad_fn=<MeanBackward0>)\n",
+      "Stage 2, step 35 has loss:  tensor(1.1920, device='cuda:0', grad_fn=<MeanBackward0>)\n",
+      "Stage 2, step 40 has loss:  tensor(1.0302, device='cuda:0', grad_fn=<MeanBackward0>)\n",
+      "Stage 2, step 45 has loss:  tensor(1.1060, device='cuda:0', grad_fn=<MeanBackward0>)\n",
+      "Stage 2, step 50 has loss:  tensor(1.0339, device='cuda:0', grad_fn=<MeanBackward0>)\n",
+      "Stage 2, step 55 has loss:  tensor(0.9974, device='cuda:0', grad_fn=<MeanBackward0>)\n",
+      "Stage 2, step 60 has loss:  tensor(0.9719, device='cuda:0', grad_fn=<MeanBackward0>)\n",
+      "Stage 2, step 65 has loss:  tensor(1.0845, device='cuda:0', grad_fn=<MeanBackward0>)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Stage 2, step 70 has loss:  tensor(1.0606, device='cuda:0', grad_fn=<MeanBackward0>)\n",
+      "Stage 2, step 75 has loss:  tensor(1.0354, device='cuda:0', grad_fn=<MeanBackward0>)\n",
+      "Stage 2, step 80 has loss:  tensor(1.0122, device='cuda:0', grad_fn=<MeanBackward0>)\n",
+      "Stage 2, step 85 has loss:  tensor(1.1424, device='cuda:0', grad_fn=<MeanBackward0>)\n",
+      "Stage 2, step 90 has loss:  tensor(1.1174, device='cuda:0', grad_fn=<MeanBackward0>)\n",
+      "Stage 2, step 95 has loss:  tensor(1.0900, device='cuda:0', grad_fn=<MeanBackward0>)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Target labels\n",
+    "y = np.array(['THE UNIVERSAL', 'GAIN GAIN'])\n",
+    "\n",
+    "# Generate adversarial examples\n",
+    "x_adv = asr_attack.generate(np.array([x2, x3]), y, batch_size=2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "adv_transcriptions = speech_recognizer.predict(x_adv, batch_size=2, transcription_output=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Groundtrue  transcriptions:  ['THE UNIVERSITY' 'AGAIN AGAIN']\n",
+      "Targeted    transcriptions:  ['THE UNIVERSAL' 'GAIN GAIN']\n",
+      "Adversarial transcriptions:  ['THE UNIVERSAL' 'GAIN GAIN']\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Groundtruth transcriptions: \", np.array([label2, label3]))\n",
+    "print(\"Target      transcriptions: \", y)\n",
+    "print(\"Adversarial transcriptions: \", adv_transcriptions)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "display_waveform(x_adv[0][:len(x2)], title=\"THE UNIVERSITY is attacked to THE UNIVERSAL\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "display_waveform(x_adv[1][:len(x3)], title=\"AGAIN AGAIN is attacked to GAIN GAIN\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.4.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/adversarial-robustness-toolbox/notebooks/attack_attribute_inference.ipynb b/adversarial-robustness-toolbox/notebooks/attack_attribute_inference.ipynb
new file mode 100644
index 0000000..662e392
--- /dev/null
+++ b/adversarial-robustness-toolbox/notebooks/attack_attribute_inference.ipynb
@@ -0,0 +1,343 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Running attribute inference attacks on the Nursery data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this tutorial we will show how to run both black-box and white-box inference attacks. This will be demonstrated on the Nursery dataset (original dataset can be found here: https://archive.ics.uci.edu/ml/datasets/nursery). "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Preliminaries\n",
+    "In order to mount a successful attribute inference attack, the attacked feature must be categorical, and with a relatively small number of possible values (preferably binary, but should at least be less then the number of label classes).\n",
+    "\n",
+    "In the case of the nursery dataset, the sensitive feature we want to infer is the 'social' feature. In the original dataset this is a categorical feature with 3 possible values. To make the attack more successful, we reduced this to two possible feature values by assigning the original value 'problematic' the new value 1, and the other original values were assigned the new value 0.\n",
+    "\n",
+    "We have also already preprocessed the dataset such that all categorical features are one-hot encoded, and the data was scaled using sklearn's StandardScaler."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import sys\n",
+    "sys.path.insert(0, os.path.abspath('..'))\n",
+    "\n",
+    "from art.utils import load_nursery\n",
+    "\n",
+    "(x_train, y_train), (x_test, y_test), _, _ = load_nursery(test_set=0.8, transform_social=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Train decision tree model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Base model accuracy:  0.9552339604438013\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "from art.estimators.classification.scikitlearn import ScikitlearnDecisionTreeClassifier\n",
+    "\n",
+    "model = DecisionTreeClassifier()\n",
+    "model.fit(x_train, y_train)\n",
+    "art_classifier = ScikitlearnDecisionTreeClassifier(model)\n",
+    "\n",
+    "print('Base model accuracy: ', model.score(x_test, y_test))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Attack\n",
+    "### Black-box attack\n",
+    "The black-box attack basically trains an additional classifier (called the attack model) to predict the attacked feature's value from the remaining n-1 features as well as the original (attacked) model's predictions.\n",
+    "#### Train attack model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from art.attacks.inference.attribute_inference import AttributeInferenceBlackBox\n",
+    "\n",
+    "attack_feature = 1  # social\n",
+    "\n",
+    "# only attacked feature\n",
+    "x_train_feature = x_train[:, attack_feature].copy().reshape(-1, 1)\n",
+    "# training data without attacked feature\n",
+    "x_train_for_attack = np.delete(x_train, attack_feature, 1)\n",
+    "\n",
+    "bb_attack = AttributeInferenceBlackBox(art_classifier, attack_feature=attack_feature)\n",
+    "\n",
+    "# get original model's predictions\n",
+    "x_train_predictions = np.array([np.argmax(arr) for arr in art_classifier.predict(x_train)]).reshape(-1,1)\n",
+    "\n",
+    "# train attack model\n",
+    "bb_attack.fit(x_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Infer sensitive feature and check accuracy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.6981860285604014\n"
+     ]
+    }
+   ],
+   "source": [
+    "# get inferred values\n",
+    "values = [-0.70718864, 1.41404987]\n",
+    "inferred_train_bb = bb_attack.infer(x_train_for_attack, x_train_predictions, values=values)\n",
+    "# check accuracy\n",
+    "train_acc = np.sum(inferred_train_bb == np.around(x_train_feature, decimals=8).reshape(1,-1)) / len(inferred_train_bb)\n",
+    "print(train_acc)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This means that for 70% of the training set, the attacked feature is inferred correctly using this attack."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Whitebox attacks\n",
+    "These two attacks do not train any additional model, they simply use additional information coded within the attacked decision tree model to compute the probability of each value of the attacked feature and outputs the value with the highest probability.\n",
+    "### First attack"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.6522578155152451\n"
+     ]
+    }
+   ],
+   "source": [
+    "from art.attacks.inference.attribute_inference import AttributeInferenceWhiteBoxLifestyleDecisionTree\n",
+    "\n",
+    "wb_attack = AttributeInferenceWhiteBoxLifestyleDecisionTree(art_classifier, attack_feature=attack_feature)\n",
+    "\n",
+    "priors = [3465 / 5183, 1718 / 5183]\n",
+    "\n",
+    "# get inferred values\n",
+    "inferred_train_wb1 = wb_attack.infer(x_train_for_attack, x_train_predictions, values=values, priors=priors)\n",
+    "\n",
+    "# check accuracy\n",
+    "train_acc = np.sum(inferred_train_wb1 == np.around(x_train_feature, decimals=8).reshape(1,-1)) / len(inferred_train_wb1)\n",
+    "print(train_acc)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Second attack"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.713624083365496\n"
+     ]
+    }
+   ],
+   "source": [
+    "from art.attacks.inference.attribute_inference import AttributeInferenceWhiteBoxDecisionTree\n",
+    "\n",
+    "wb2_attack = AttributeInferenceWhiteBoxDecisionTree(art_classifier, attack_feature=attack_feature)\n",
+    "\n",
+    "# get inferred values\n",
+    "inferred_train_wb2 = wb2_attack.infer(x_train_for_attack, x_train_predictions, values=values, priors=priors)\n",
+    "\n",
+    "# check accuracy\n",
+    "train_acc = np.sum(inferred_train_wb2 == np.around(x_train_feature, decimals=8).reshape(1,-1)) / len(inferred_train_wb2)\n",
+    "print(train_acc)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The white-box attacks are able to correctly infer the attacked feature value in 65% and 71% of the training set respectively. \n",
+    "\n",
+    "Now let's check the precision and recall:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(0.654054054054054, 0.14421930870083433)\n",
+      "(0.3892857142857143, 0.1299165673420739)\n",
+      "(0.6644067796610169, 0.23361144219308702)\n"
+     ]
+    }
+   ],
+   "source": [
+    "def calc_precision_recall(predicted, actual, positive_value=1):\n",
+    "    score = 0  # both predicted and actual are positive\n",
+    "    num_positive_predicted = 0  # predicted positive\n",
+    "    num_positive_actual = 0  # actual positive\n",
+    "    for i in range(len(predicted)):\n",
+    "        if predicted[i] == positive_value:\n",
+    "            num_positive_predicted += 1\n",
+    "        if actual[i] == positive_value:\n",
+    "            num_positive_actual += 1\n",
+    "        if predicted[i] == actual[i]:\n",
+    "            if predicted[i] == positive_value:\n",
+    "                score += 1\n",
+    "    \n",
+    "    if num_positive_predicted == 0:\n",
+    "        precision = 1\n",
+    "    else:\n",
+    "        precision = score / num_positive_predicted  # the fraction of predicted “Yes” responses that are correct\n",
+    "    if num_positive_actual == 0:\n",
+    "        recall = 1\n",
+    "    else:\n",
+    "        recall = score / num_positive_actual  # the fraction of “Yes” responses that are predicted correctly\n",
+    "\n",
+    "    return precision, recall\n",
+    "    \n",
+    "# black-box\n",
+    "print(calc_precision_recall(inferred_train_bb, np.around(x_train_feature, decimals=8), positive_value=1.41404987))\n",
+    "# white-box 1\n",
+    "print(calc_precision_recall(inferred_train_wb1, np.around(x_train_feature, decimals=8), positive_value=1.41404987))\n",
+    "# white-box 2\n",
+    "print(calc_precision_recall(inferred_train_wb2, np.around(x_train_feature, decimals=8), positive_value=1.41404987))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To verify the significance of these results, we now run a baseline attack that uses only the remaining features to try to predict the value of the attacked feature, with no use of the model itself."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.6761868004631416\n"
+     ]
+    }
+   ],
+   "source": [
+    "from art.attacks.inference.attribute_inference import AttributeInferenceBaseline\n",
+    "\n",
+    "baseline_attack = AttributeInferenceBaseline(attack_feature=attack_feature)\n",
+    "\n",
+    "# train attack model\n",
+    "baseline_attack.fit(x_test)\n",
+    "# infer values\n",
+    "inferred_train_baseline = baseline_attack.infer(x_train_for_attack, values=values)\n",
+    "# check accuracy\n",
+    "baseline_train_acc = np.sum(inferred_train_baseline == np.around(x_train_feature, decimals=8).reshape(1,-1)) / len(inferred_train_baseline)\n",
+    "print(baseline_train_acc)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can see that both the black-box attack and the second white-box attack do slightly better than the baseline."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/adversarial-robustness-toolbox/notebooks/attack_database_reconstruction.ipynb b/adversarial-robustness-toolbox/notebooks/attack_database_reconstruction.ipynb
new file mode 100644
index 0000000..81b4a1f
--- /dev/null
+++ b/adversarial-robustness-toolbox/notebooks/attack_database_reconstruction.ipynb
@@ -0,0 +1,395 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Running database reconstruction attacks on the Iris dataset"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this tutorial we will show how to run a database reconstruction attack on the Iris dataset and evaluate its effectiveness against models trained non-privately (i.e., naively with scikit-learn) and models trained with differential privacy guarantees."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Preliminaries"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The database reconstruction attack takes a trained machine learning model `model`, which has been trained by a training dataset of `n` examples.  Then, using `n-1` examples of the training dataset (i.e., with the target row removed), we seek to reconstruct the `n`th example of the dataset by using `model`.\n",
+    "\n",
+    "In this example, we train a Gaussian Naive Bayes classifier (`model`) with the training dataset, then remove a single row from that dataset, and seek to reconstruct that row using `model`. For typical examples, this attack is successful up to machine precision.\n",
+    "\n",
+    "We then show that launching the same attack on a ML model trained with differential privacy guarantees provides protection for the traning dataset, and prevents learning the target row with precision."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example usage"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First, we load the data of interest and split into train/test subsets. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn import datasets\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import numpy as np\n",
+    "\n",
+    "dataset = datasets.load_iris()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(dataset.data, dataset.target, test_size=0.2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Train model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can now train a Gaussian naive Bayes classifier using the full training dataset. This is the model that will be used to attack the training dataset later."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sklearn.naive_bayes as naive_bayes\n",
+    "from art.estimators.classification.scikitlearn import ScikitlearnGaussianNB\n",
+    "\n",
+    "model1 = naive_bayes.GaussianNB().fit(x_train, y_train)\n",
+    "non_private_art = ScikitlearnGaussianNB(model1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model accuracy (on the test dataset): 0.9666666666666667\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Model accuracy (on the test dataset): {}\".format(model1.score(x_test, y_test)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Launch and evaluate attack"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We now select a row from the training dataset that we will remove. This is the **target row** which the attack will seek to reconstruct. The attacker will have access to `x_public` and `y_public`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "target_row = int(np.random.random() * x_train.shape[0])\n",
+    "\n",
+    "x_public = np.delete(x_train, target_row, axis=0)\n",
+    "y_public = np.delete(y_train, target_row, axis=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can now launch the attack, and seek to infer the value of the target row. This is typically completed in less than a second."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from art.attacks.inference.reconstruction import DatabaseReconstruction\n",
+    "\n",
+    "dbrecon = DatabaseReconstruction(non_private_art)\n",
+    "\n",
+    "x, y = dbrecon.reconstruct(x_public, y_public)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can evaluate the accuracy of the attack using root-mean-square error (RMSE), showing a high level of accuracy in the inferred value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Inference RMSE: 5.789723287688911e-08\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Inference RMSE: {}\".format(\n",
+    "    np.sqrt(((x_train[target_row] - x) ** 2).sum() / x_train.shape[1])))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can confirm that the attack also inferred the correct label `y`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.argmax(y) == y_train[target_row]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Attacking a model trained with differential privacy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can mitigate against this attack by training the public ML model with differential privacy.  We will use [diffprivlib](https://github.com/Trusted-AI/differential-privacy-library) to train a differentially private Guassian naive Bayes classifier. We can mitigate against any loss in accuracy of the model by choosing an `epsilon` value appropriate to our needs."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Train the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.7"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from diffprivlib import models\n",
+    "\n",
+    "model2 = models.GaussianNB(bounds=([4.3, 2.0, 1.1, 0.1], [7.9, 4.4, 6.9, 2.5]), epsilon=3).fit(x_train, y_train)\n",
+    "private_art = ScikitlearnGaussianNB(model2)\n",
+    "\n",
+    "model2.score(x_test, y_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Launch and evaluate attack"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We then launch the same attack as before. In this case, the attack may take a number of seconds to return a result."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dbrecon = DatabaseReconstruction(private_art)\n",
+    "\n",
+    "x_dp, y_dp = dbrecon.reconstruct(x_public, y_public)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this case, the RMSE shows our attack has not been as successful"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Inference RMSE (with differential privacy): 2.2594246979517965\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Inference RMSE (with differential privacy): {}\".format(\n",
+    "    np.sqrt(((x_train[target_row] - x_dp) ** 2).sum() / x_train.shape[1])))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is confirmed by inspecting the inferred value and the true value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([[4.80000094, 3.00000298, 1.39999864, 0.30000296]]),\n",
+       " array([6.4, 2.7, 5.3, 1.9]))"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_dp, x_train[target_row]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In fact, the attack may not even be able to correctly infer the target label."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0, 2)"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.argmax(y_dp), y_train[target_row]"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/adversarial-robustness-toolbox/notebooks/attack_decision_based_boundary.ipynb b/adversarial-robustness-toolbox/notebooks/attack_decision_based_boundary.ipynb
new file mode 100644
index 0000000..6379eae
--- /dev/null
+++ b/adversarial-robustness-toolbox/notebooks/attack_decision_based_boundary.ipynb
@@ -0,0 +1,1451 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# ART Boundary Attack"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting git+https://github.com/nottombrown/imagenet_stubs\n",
+      "  Cloning https://github.com/nottombrown/imagenet_stubs to /tmp/pip-req-build-8hrhmevf\n",
+      "  Running command git clone -q https://github.com/nottombrown/imagenet_stubs /tmp/pip-req-build-8hrhmevf\n",
+      "Requirement already satisfied (use --upgrade to upgrade): imagenet-stubs==0.0.7 from git+https://github.com/nottombrown/imagenet_stubs in /home/beat/codes/anaconda3/envs/py37_tf220/lib/python3.7/site-packages\n",
+      "Building wheels for collected packages: imagenet-stubs\n",
+      "  Building wheel for imagenet-stubs (setup.py) ... \u001b[?25ldone\n",
+      "\u001b[?25h  Created wheel for imagenet-stubs: filename=imagenet_stubs-0.0.7-py3-none-any.whl size=794838 sha256=261c8f0cd1dead411c47c0a6717a53fb34d9eaa8256348a03d41d9cd4a83566c\n",
+      "  Stored in directory: /tmp/pip-ephem-wheel-cache-xasqstx3/wheels/33/0f/5a/c83688c23a05eb9e88527a8944da56dbe007c86f534b0c1dad\n",
+      "Successfully built imagenet-stubs\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using TensorFlow backend.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from __future__ import absolute_import, division, print_function, unicode_literals\n",
+    "\n",
+    "import sys\n",
+    "!{sys.executable} -m pip install git+https://github.com/nottombrown/imagenet_stubs\n",
+    "sys.path.append(\"..\")\n",
+    "\n",
+    "%matplotlib inline\n",
+    "\n",
+    "import imagenet_stubs\n",
+    "import numpy as np\n",
+    "import keras\n",
+    "from keras.preprocessing import image\n",
+    "from keras.applications.resnet50 import ResNet50, preprocess_input\n",
+    "from keras.layers import Dense, Flatten\n",
+    "from keras.models import Model\n",
+    "import keras.backend as k\n",
+    "from matplotlib import pyplot as plt\n",
+    "from IPython.display import clear_output\n",
+    "\n",
+    "from art.estimators.classification import KerasClassifier\n",
+    "from art.attacks.evasion import BoundaryAttack\n",
+    "from art.utils import to_categorical"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Model Definition"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mean_imagenet = np.zeros([224, 224, 3])\n",
+    "mean_imagenet[...,0].fill(103.939)\n",
+    "mean_imagenet[...,1].fill(116.779)\n",
+    "mean_imagenet[...,2].fill(123.68)\n",
+    "model = ResNet50(weights='imagenet')\n",
+    "classifier = KerasClassifier(clip_values=(0, 255), model=model, preprocessing=(mean_imagenet, 1))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Get Target and Init Images"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Target image is:  105\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Init image is:  866\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQEAAAD8CAYAAAB3lxGOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nOy9eZAl2XXe9zv33sx8W71au3qb3mbHPgSxDBaOSRoAAZKQTVmyCMty2AqbUihIWzYtS5Rt2Q6Z+sOWZclWiDQpiJZobmaYpESRAEgAJEEAg8EymL17pqf3pbq6a6+3ZeZd/MfNzPdqgCEpgROaCPaN6K6qV/nyZd6895zvfOc7pySEwN1xd9wdf3KH+jd9AXfH3XF3/Jsdd43A3XF3/Akfd43A3XF3/Akfd43A3XF3/Akfd43A3XF3/Akfd43A3XF3/Akfr5kREJEPi8iLIvKyiPyN1+pz7o674+741oa8FjoBEdHAS8AHgevAV4CPhRBe+GP/sLvj7rg7vqXxWiGBdwEvhxAuhhAK4BeBf+c1+qy74+64O76FYV6j8x4Hrs38fB1496sd3JubCyurRwFBRICITgRAAgSJP0n1GiD1NwhSHV+/GM8Q/w/E0wVegXhC8/bmHc3B00+JPx+4Bpk5QUBQBFx1rFT3MPs5QgiOgEOhUVqq90BZ5qRpSgjxHkQJBFCiZj4zTC+lukVh9gPiiwJx7mbmaPZ2IuKT6muY3u43TMp0Dv/AMTv/s3M4c6rwTQ73wR/8uOpLjUhDqL5vHsn0LCFMn+k33GP16uy5fI1yvwHtSvNyM59y8PtvNgfSPIPZdUYz9/Hf9PcHP296f7OTEw6sFb7hh+lL3/y5TD8q4AMEH/AhVPNQ7454lovnz22EEA694hSvmRH4ZmvowC2KyA8BPwTQnZvjv/87P42zYJIAKkdJhisNqDFIDhLQoYNIAsqiRFAqAhk9O1XVw3i1MGe62OTA8fVwOEBNn44EwFc3VU2Xmj5UrTW2TPDiEXEoH1Da4fyILFsh+Bxnd3juuRc4vJKQpW129nLe8NAjlFbozvUIXjCmwCRCalI66Twi4L0l4KprDBitUEqhRB+4H6XjPSQqzkl9P9776n4VpXeEECjykhACEgK2+T0EmYJCkYBCmrnRIoRqTqeLvZo/FefglQu/PlaF2TmvjLsoQgh47ymdwzmH9+AJ2NJTOIv3Hk+gLF28Dw/OxfcrFZ9f/Yi8Ay8K7300sALeg3PxnksXCMEdvO7q+owxaAGlBa0FY1QDj0UEreNcG6XRWuL9ikJV992sjWqetNbTe1fTOfXeVs9DcDZMDZVM5+vgmo3ndTMvKXTzuab6qip/ATAqc4oysLc/YjguQUx17niuP/M9777CNxmvlRG4DpyY+fke4ObsASGEnwJ+CmBx5VAYT/ZJWz1G+ShOqIpWTvnaqwsuuGjeNBilgDI+VB+99ewGqIdGXrHRay9QLUx/0MK7xmpXC3b2wYiP5wo0hsa6AhcM3uvoxdmlk2qCh0CBTgyaDKPhxo3zrBw6zMadbewDZ9jd87TbbQiKoDVaGUajEUKCCpCmaXNPIgFRGkHwfupNQ+XORARPvRnre5TKs/pmI/nZjV8vRIRQnXO6UeM5FQGvFChdTUGIz0WFau6lMpwceJ8mEHzA+spQNc8mXr9zDus91tq4AUQRvFSbQfAegkhzvSFEIyEiBOdB6QiWqi0bPBA0QeJzEEI0lgJBhcqYVteBR1WPNTUJ2iiMgDbVi742NookiVukMcAKUm1Aqs2vU9Qr5i0a2enmDDMbvfHor3BU3xw9gJo5TgiIKDSCUtFoaVGEymCk3uC8j4ZNe5wPFdqR5nq/2XitjMBXgAdE5AxwA/hB4D94tYO9d6ADZTmuFkuCc6CUryylwft4HyIhLqDKykavFUdqplC88RLVAqReQDMPqzqygYsVKKe2wvX76+PrsENEcNUGwztsGCOhQwjC9uZV7vgJh1fvQSU98olFecvtmzcIaoO9vW1eOHeW23eukabH+NP/7sdAGQgJ+ciRtbr4kCPaYF2Brja2SGg8TAjRU8eLCQQXvY6fMXi1p61HbQC8ryG3a7xpCHFTunqzVp9T36sK9eavzo8AGoJHo/A2NNfSGC1CdQ3MLPbKcHqPtb4xBqGC4SF4bIjXaX00LN4FfHWuGMlEFCHETeqD4IkGQ0RXRhxAUTlxHL5aEB6I16iqzZEaQWuFNkJi4jwGNzWUWgkqeIwS0kyjlEKLwleGQqp12KyrEO+zXkO1ka7nst6UjtCEcd+4JutzxHmRmRBJBUA8ingd2qgYTkrAJB0kdyhJSNOCnf0xZVnyh43XxAiEEKyI/DDwKUAD/ySE8PyrHe98Ab7NcOhJM2F3eBnvNAvzR1BKgzjAg2/HS9YF3vkKcukGDvsq1hKRJvjwIvh6YUz3O8H7ZtJD5cmiBxMO8qVT2KZIEDEENB6HD57gE0TaWLFImNBK21y+coEL55/kuz/4w5R+yO/+zi/zhd/7bXQyIE17tNqGza3LPPP07zEarrO6eoQ3vuG9nDn9IGXpEBtQAkoXM/BSYyoYDSCqXnS+goUKpz1KufoZoJiaM2sjHC1shNciUsFoGoMRN5ptjE09amg7Gwokegp3JUwhcVKFBmXwzXlFFC4EnIsG3M0YBkfkTJyL/IoL1bU2RqTaTL56LiK46rkHZfDVz454PzP2pmJeIlpQWhPNpEeJQyNoJXHza4U2kGiFwle7It6fUZoQYgjSztIGHVmrqjU3w2EQGQutBe9l5tqnxjG+5JEAHsUrEUT8PjThqgZCE4LEjZ8kGpPE9aBUFV7hyEvLZJKztz9iNMmxtXPwr44C4LVDAoQQfhP4zT/asVCWljTpsLd3hzvb6xidMjc3TwgtRNUbdmrnI0cUF4mvvZ9U3uoVJ282ewgHiBjBN9a5/r1qmLRqgSvwEiqyLgHRBG1wPm4gI31ssBVUC4wnAy5dOIdnSFEMyFoaY8YcPd5CJGNvz7O/N2JhAU6fOs7W1nVePP8UJ07ez631Lp1Oj/n+AhaNpmg8qQh4Hb/XhANhSpDQbMRZqO+YLi7nQrURY5zswizUrhHB1BhMY+faCPgDRkC8RiqDM+UPfIMiYizumgUoorHeURYOG6Zez1mPGIlfBXwQrKsNxJS7oTKAEgSRyNnUfIeX6A1RoaJq6/v24GNYYqpwhpn58T7gnY58jhO8+GpTScMHaCWEEI0s3iEqGhSIiEWMbjY5RH5CZMYAvAIJzBKHKvjq/mqnNQPbZziY+v1aqHgLQ6p1heYA8TFEQhiNRoxGI6wPKJ1RWluhu1fbfa+hEfhXGVoZ8vEew8EGC4td2PR059sU5ZBWpvDBIGi0dviQg09jjFxtd0+o4OpB4mp289ejgVaqjj8PxsAhWIKPkFdEE4IFiSGL0oLzKnoh8XgVsDZB9ASjYDQYc+3aWc6ee4q5fpfxaINsfomvPfkEabLNcF9hZIFuZ54LL19ifmkZpR2ECbaYkOc5Ipq5/jzOWlxwqPo6vccoHy27AnyMa5MkoSwi6WVVQZqmB+453pNQ2rIi4kJlFDyls828eHcwXGrmq9oUytdoAEQ8NEhM4ZVHVZ4zr4yMiETCj1AZVIdDsAHyopy5Pl3F86oJUWyNErxviLnQbJJoDCLJXj0nNCIBlEZXD1nVqE8J2tXrIQZRrnSU3mNURTaKBRRZmqIUjdeueb0GCRmN8x7nLEHFzRtCaNDarAE2xlCWkbOKDkQO/L5Zc41tqg2vOvBzfC0ihyRJ0FqTaBXvtzK+BEe8Vc1kMmk+3waa+fuGjNLMeF0YgXwy4blnPsv8Ypfnz13GK02aPUK/u4ItFa1OQlk6rFeEkGDUwcl0LlpPo2YWsaqILTeFrQc2hgugZwyFD/jg8coTKm+odcC5EmM0iEJpA5LgROK1qASdjdG+ZDS8w+//7j9nb/siw9EOrXbK808/zYnjR1heWGRtfZvxUJFP9jBJ9CJznTZXrl5iOC65euUmh1fuj5duSlA5QoYtXYx1bQATPX6w0SB57xkWBcFXqIWSEEYVgaUObOpQbZ4iL3EhxuTW2mY+6vfAdBPEyaWZW5Ep7J8dNVQV8Q3SqBn6+NlT4tB6KMtp1gKidxUJ+GoDeK/j+0M0us01iolxvGpBfY2iG67GNRupSvOpeilofIQ0+BDwWiEqUHqLCtFIGXQMuVLdoBlVwfA63NJaNRu+KIrmnmoDUGdd6vBzisJcNFgSSWdp3LI6cFy8dw/4KXKRQJpmiAhGpiGZmskEhSr0KC20Wi1KO6b6yIiaqJDsq4zXhRHIspTh6CZBpdy4+RxLK6e5fuUqKixxzz3LOOeqiUoqGFhU8D9Ot5LI1obZOJYa5iumMX7A13BMZo+p3iMgSlBaN+moNGuT52OSLKGwOaJBlEElpnpwJbubO6wsZpT5HpcuXaTfX2A0mXDi+BGc22f99hrz/VW2tm6hJSOfWIqi4OqVNYzukqUF2ni++uQXOHPfg6wc7sfUo3VxQQWP9R5n44LSKqBc3GRaGfJJ9DiiPM7bBspCtUArXiyEQF6lCK2f9TagQkAqriCpEGw853RxitToiCpNWsFqAuLie7yvMxIB22gCVANpoxevgq5qQXsBURqIGQIvVUiiTGUg4nHGpBWpV4d/8eyayAepEHPkU8NehRzE1wnR0HgUSjwBjRNPIgIV+x+q42bJ5NpIEiQy8UGaTR2YxtsiQlBUIctBTuVgSDCbqqxj+pjG1Fo1IZBSikQrEm2oyeH4OgfO4TxV2CgkSYYyBWV5kGt4tZQ5vE6MgBDY3b3C9s6YPN/BFadZvmeVVmpQ2hFEIRg8hgiNNAEfFyTxmagDaoF4VqlIozq91UxEvUhUDaemD6wMIM7hg8V7i3Ypnc4cpfWYFJT2eEpCyPAeCue5tXaV8Y6jGBfs7u7S6nbIJ5ZPfeKXyFoGlGE0mQCwvbdNYjp0W0tsbeyQpIrhZMyXvvx79LrzPPP8Uzzw4N8CAkorrPVolcTN5mKay4fAeDxmd2efxcUllKQEBGddxVoHKGcyHBWycUEaTx1xaJ3unPIOEMDEBaiDIKGeq+hcm7RXc/bKKDTZlwomV+IVqHLhMyjMo5swWBqVTvTESuuKwH0FByGCaF2FAxx41nXmYNbY1cYJIIjG1fdBwLn6yjWmMgz1JvM+oOuYnZnNLBEpRiNRnUumE9EcF2RKEs4iT6ZznGjTGJxoXON6dW6qMwBIUkOqp6juleFafZ/1M7U24IKPVy6RI/DCVHf2KuN1YQTyfEw52sHjyKRLv32ct73p7Vxfu8lTz36OUycfJEnmWDq0gihLcN0p7AwBVbPTegp5lFKgBCemSh8dtMyiVONZaiLHe4+xws7eFQITJsMR96zej0ibxAQuXz1LXgxYPXKU+f4ZytyzMbjFlatf4fmvPo4Jjl57iWGe47xif/8Ot9dK8mBY21xjrrXCqTOrrF1fZ2tnhzLPkZGgk4yiHHD1+hpz/cNonbG1ucs432B5+RC2gs97+1uM8jErK0us3bqBEkM6aUFwEfLiD2y2qUdUuFATfglBCUpHD3ogPq3Wl3NxcVpmvE6oNAgSc9XeqZk5VzFfH2iQlvbgdTj4PKgyGWZ6nQBBm8gJVJtFVWnAoAQfpqFEaSNUDjpmFOrrnSW9XOURfQj4ikB0weMIaJXENRHzBUhwGFRjLBQeVbHv9TkVAbzDl9NsR1Bxo4dKnMSM5/dhui5rNDa7HpXE2L4hTwvbvLcKWkEgyzLSNEWIWR2lYvq6XvfReOiG3wEorGd/b1ClijUhqqoISjWZkm82XhdGADxZ2mNzaw9lEu679zQhBF588UXSnsIkjqztSVOF8wohRYjeOi7iivyorSXTuDbUfIDSB62pCE4qUhEBFRchXpNmGePxiLl+l939fVYWelhbsrTQ5872iLLM8d7T68wxCIr9wR6DwR0yFRgMUybekrQczz9znkfe8ggXb94kuDbjHNLhkMWVRXa39tkrRrTbc4hOcQWUZcnm5ibb29vkeY7WOj50X6EaAkmSMBju8cwzz/CGh98Uiblmn8gUZqspvNVGRdFViBmCqDj0KGMarweVkQgQ8DMep4agCmTKFShRU5Wh6MorqqmH11WIUR+vFCJmel5pEreVzDWy+ASFrxlyX2Vq6iup3luGEO9U6rw5jW7C+zhPoQn1JG7aEDdhQOGDBYnybV8hStGR9JzG2VN2f5aka/QK9e/kFZmaME1rGmOa15RSiFYkRh+A6DXfEELAqOkmN6YKAQ6I3ep7jNxL/WzqcFlrHT9bknh/lXN7JYfzyvG6MAJJmjAqNul2+7Tbx3npwmXubE8Y5iVH7n2IhZXTKN3GBo1KKia2SDC6hXV5ZEKtwqoCrwyQ4YNC6RCjxZBVBqEmFCP0y3RJCB4VBKPBlyWudZt8N7A41+HpJz/NI2//98ilB62SnZsvom3ADrtkR+cAONk+TTFZo7vQYf3qHq1EWJ5PubO9gVrMuFXeoGSbbjtBbM7GzRHzi4u0Wi0mLcfR430G+yO6vZTBsMe73/V9jCZDnnr2czz45g+SVaIcoxQbN6+xtvYSrbbw5S9+llPH7sEcvZ9SEgIep0uykCKTnMH2Jnf2buFVwXA4YGHxCEeOn6TsHsKGlEAbpw1aPEY7lMvREihFsW2W8NYxr0vmB9skkuH8PibNmlBCtKq8uSeEuBk9VJkTQfkRTmdYNA6NF4V4h+BJlOCCxiOUqoUEhw0+bt0ZSXRM9VU4H8BHIjNqPxIaat1HTYQODlxBWhsObaLEWDxOJ4zLkjIEvM6QKtvhRWEFgjHkKqdbhTR1WFFWWZDgfYWCBDWDHAXws5ybd5GFUgqjIrMPYEw0LkYJ1kbZuxZQiULraLC8F5wSvA8IFiWR7AwCoQpZkiSJpG6ISKQO9ZRSuCpV6b3DqIDzMSQgHJzXV47XhRFQEtje2MeHMdoMONVapnALdHopb3rje0hbXUo/jg/d93Gpw4cE6z0qVZSAU4IkgoQQN7aqpLaNBLiy6FojIfqN0uVoUYhKYzpLNNqvMt46z9Nf/D3W1s7y8vmf47Hv/ADjYo2nvvS7dJIOx0+ss3SkT7t7HGuWefbcFr4osOKZn9dYl3OoP8+k1OzcGrHYO8xoNKLX7zEY5dy6tY42nm97xxspRo5uZ55ut8X8gmf9zmUuXXyJX//1X+cdG4aPfOBD3Fpb4+jhZf75v/x55voJ48k+Dzz0Nu459UZI+hjtKENOIh3STptf/cVf4Jc+/lM8fN8p2p0Ou7u7bOzs89E/9zF+9IPvI7l1mcXbn2Vsx2hREBKy3jGK8Q6GAcqN0TqlDB2unPp+xkffzJqZxwaJyWrRjYcJIYCpEYFqYu9RUDgXKnIuipAQjxHF2PsGsXgKlKZCO6opdoEqMRFAV8glqLhcy0oY5IipQBUsOnhEPC0sWkVkoMSiRJF7jQRHVyV4BOujoAdvaQVLhqLlFUkplOKaa53V/kOdIQFf8yrENKbIVGyWZVmUIRtDqg0hTI+tUUKSJM3xdbgFNIjAOYe1FmttE+L6YAluGr7ZvIhkZvUsnIvh0Xx/jr3BPt45EIVWugp3XufhgPewtzvi0MoROvN9TpzucmPtCg+/6TFa2Ry7+7sUbsTCwhJlkUeRildRnUbAGI0XIW1FOOWcrVJIlUTVxTywrrEjtSV3ZFkSH5S3ZGmKLz0mlFy+8CLbu2usrKxw6nifFy88x/Gjh1i7dofrl1/g+p017r3/vTz4xg/wn/zFv8Rvf+KX2Vlfo9tLGI+F4aBgPCxYWlxh4/Y6q4eX2N7ZZDKZ0O/3WT08z/b2NvvbI2wpvPf0Ozh58gyXLm7wmc98mqNHj/G+97yP+X6P3S1hPNqmKMbcXFtnMpnwlrd9iKzbjXF0tdhU4TFG0e2nZC2YjAeYpE1ZgA4eO87pvvhZsud+h1X7MjIXxTSYPpg3w+5tGKzDZAuCoixbvLTm6L+94NaD3xkXWl2gU2kGmvkUqvg7LjdLhgTXwH4lVUpQqSqtV22CmtHnoJ6DmZ/DK+BwoqKB0HWa0SskeASJCBAIUqfqhDTxKJPiQ52itCgcSQqLHUXLKBLlCMUEq9NGIh18LdaJ3tyiUFVWhLoOQ8DMZD5ivUFyAMIfgONN5iFKmOVA4VY8riYHvfcxVKqJTldlXSoBUGISRJtGG4DE599KE0rnScRQuigLfd0Tg7YMHF09QZK1KMcl12+ex4U+w8Eeu3sbjPIBk6IkTVsYo0jygpZJSLSgjcH7EpUmtExFRnkhywwigb0C8iKKbVAB6wPWO1KtWbt9i2x1lcy0YqrHTeL5GbO1fo3N3ducOPYWPveZ32A4ucb2xj5z7R6+3OHSS1e4fXufI/c8zNb2Ov/WdzzGrWuX+frjn4+ahlIY7OccW23TbrfZ2d5iZ7jH/PxqJIzEMR4P2d8fEFzK+vptnn/+Bba2Ct73HR+h3UnoZC1aqdBqwcvnn2fj9i2UDozzgk6nze7+NqUfMb+yik7atBNHqjS9rI2zOSbV5CHglSZzlsR7Bme/yImNJxGjIoumATsk397GiKDFgVhC4TF+gr78HHSXSd7w3dVCq1KBjfbCxfotH3DUCjhQJkV5IHiUBGbqHgkz2RpxlqD11AiIR+oCnppsq+F9pQMRV2JEI0rha4FYSGIdR735g0eJQaHoqJxWqnA2YPC0WhEhdjNDLxOUeMRF8tD7KqSp6wz8lNfwFbSPdRqhkam7EBqx1CyDH+P2g/xAVKlOuY5ZAzGr1qxRgcy87pxvdARpmpIkyYEqRK0Eo6DdzjAupkB9PiGUHl7vOgHvPYleZDzax6Sa+088xnNnX+QTv/lLJB3HO97xAfKx5tr1yywvz6NDgUsnzGWLKBSIwbsJqQsNq5vZARBQJsaPUTUOpRe8E0pfspBY5lTOcG+HTjvj6qWLPHP2CX7/E7+BK4ccPrzMC8/+HpdeSrB2wkJ/mezYUa5dvcSlW1dYObrPE5/6WYaT87jCkw+FQe4QZckW5viub3sfF146z2TsOLx6jOVjC2xtDul2U7KWYpKnzPWWyCeB82cvkNuSb3/7e3n4wYdxzlGMNnj66af4mY//JLbI0apNK2nT76U89cyXmLicldVT9JePEsoWW0lKZ+xRahktc0xsSXvBoEICt8fIqGAjH2BLGOkuqSrA5QQNRiyUnuCE/aQFxoF1tPsrjEexjNagsN5FXfxsVVrwkZzzEnsiiEaFHBVs1OoH37DTSpnGgBAE0hQrMccvxGIlnUx1/9ZXvAMKWxGIokxkygngFbaSECttkGAa792QeUUsJyY4DNDJBKMNmVZ46wmiUMHgQgISC27qzTwr/01S3cD1Ro/vHSIGbTRJErkZmzuMUZR19krqqljBVhWddfl1MYmiI2OmpOkBJED11R00EiGEpjjIGBOzOa6EYEmqjI0nFkoZNVsS943jddFoNITA7Y11itxiS89XvvIVjh05waPvfIynvvo0xWjIwlyKChNur11mOB4wv9hiYofsDffJnceJwkY6Ba8MXunoYSpLrhAkuKixDj4WjWhIE4Utx2jlyFrCGx66H4Lm4Yfeiisci8stdnc32d7e595TJ1lfv4kxKcePnmC4t8vXv/A51q++zJ0bVxntD7j33vuZuJzNvXW2d3ZYWlllkgfWb++wv79P1tIESkbjAZNJiegE7z2Li8v02j2+/uTTfM8HPshHP/pRzpw5xKd/6zdIjeaxxx5Di5Bqw+rKIa68fJEnvvB5nn3qa4x2NmklRLIsNaT9Nk4LbRF6oqH02DRhpxyiRmPaE+jk+7RtTttD5iC1grYecY7eaMTcIKc3sXTaGaIVWmLhjITozYwCLaH5J4HK4ws6eLSKDLcxBp1kJM2/BE2V1pWACxHWGwIGh8aRKkg1tBJFS4f4szhSLEkoowEIkfGvy4ulDjO0iUy/MtM8OobSR4JNNARfRpIxeFyINfuFj0jgAHPPVPHnwzSV51yJtQXORSm2STRJamJPAmbkxlC5Ht8Qibb0lIVr/o3zCePxmOFwSJ7nlGWJtbbhBcqybPoP1Ahhmhp0lN41xwYbZYLeT2s4pmnK1zkSQBRBD8idxo8XefQ9b+H93/Fe+ot9NjcsK3OLmMTyyV/5Zzxw7/08fvMy95w5RFHAO97xnTz44KME8VjrUNJikjsmUmKS2ppDZJg01pZEGW7O3/u7P85b3xy97nd/9/t54guf5eih4/xXf+1/5rc+8S/odwdsD66ycvQw41HOpQuX2dnZQbUrKDwS6AujiQLnmOt57tzYxg875OMxg6VtFuZXedPb7+PajXNI6FPmBYNiiIjG5i2KYgJhjNaL9OeWGQ7WGU9u8Y/+/k9w+dJL+NJSTMZ8+fHP4Sw89h0f5uGH3kRp4YtPfJGPfuAxjp00zM1NGGyv0W11yQ+3aGUpw7yg4ydoFbDKMB7u8Vv97+DX1jq8fG3EaOcG470c72BCiQGcgvZih162yHKvz6mHe7zzLffzcD92T1K6dUCNqDRNOjMxGaUPTCYTbgwCw9IRVNp0GzDiSIwwn7qK2S4ZWyHVCq3AaMGIwbu68YkDrRrI62shkp9EVScxotnLA6UXcoQJScOki6RReGRLktSRGU+WeDpVrYMtcpyfNgHRxiMzhVVWdDRkPsqMXSXWgrqRCqQVyeeKsqlonKYTXWM4aqWmE9Wk9ESk6ncRDVhwAZyjtNP0aUzLVt+KjlWfPvYK8H6KSurUZVk6ShfrX9Ah9htQyYE6kVeO14UR0FpTFtBqZTz44BsZ7A34zGc/wZF7lrnv9LfTbh/ma19/nF67y9LCCc6cOcrnvvBZ7j3zAP1Oj6wSYuRBQVCxMMRbbBnwoSTRGbZO3UisCdjf32VlscvttasQHL/xa/8v129c5Mb8Et/z4T/DrbUHOPvMNjpzrK2vE/Ds7OwzGE4wwVCWY1qmTREM2mccOrxCMcnZH2xjRJHqlHKyz75XXL35MgtLCYkk7O/vEwIURY6iRTGZsLDYoSgKyiLqyv+bv/6jlM6SqIzJZEB/vkOn02Z54Sif/OQn+YWf/xX+xo/9GLvbd/iFn/u/+Zs2uOoAACAASURBVGt//ccox3t8/nOf5cMf+F6WunNRSqs1TnkUFuU0WhI+8t/+r2TliB00yfgO//DH/weeeeILqKSLtQXaBP7y3/n7vO3Rx/DWcXihg/UeP95BtMYoH6XVSqEQvHeIiQs1uJxO1qab9XBp4OadbdCxKYn3Hh2EUE5IOwajhRAUBhtje+8QryLP523TqKOJu2caogh134Mo0c0Sg3hifYVU1YNwQM2niUpEnMd6osbfBaybMvuiwBIrFUOlgrS20ky4mg+gSvVFuW+s7ZhmEpq6CwlNkRVM9QahUTjFf15XgsmqGUhNGk7LkKEoYkk5elpe7ewUXdTHWl/1Xqi0EoS6kYz9A8uJXxdGQBDarQW+/e3v5sKFS+yNNjly7DDPPnuLVnqc4/cc4V/8+i/zoe/6U5x/8SYLS/MoScjSORYWVhAVEC1on+KCR6mAIiXPS+7cuc3Ro91mckNw4AMri4sYFdjcuIXgGRlPMRrg51J+6Rd/iitXL9Ey8JY3vpX9vcfpzbU4/8LLBFEMd/ZZWOww35ujs3iU7e2bjEcFRT6m1RaMSmilGXs760xaI1qtFkYnYCO52WqleA87WznKGHZ3d/nIRz7Cb33qM/R6PXZ2duh2epSFq9R7sLW1hS01x47fw+qqQWl46OF7uXL1Ip/6xK/z5jc/wtLiMXLrSNspJoFMBWRS4icFBYIVhZURQXsyrVmcX2Z1scchDUoHWt0FQlFyZL5Lb6mFD8JgMECyhE6WRnFQBUlrNKCYws64SR0hKBbahmFL4XCUIZDb+DvxVZytYnjRSQQrQlnWmg4BYv+BKH6acgFlrUNGY21lJILHShR6aQWhkgpB3FgxdKk6AYUKOmNjiTHTikUlCuUC3ldingo9hlBOFXpVQZFIoFQKpRypmumrIFKRgaph8Zs1XocZ4pq+ACIBqWv+lUKp6UZ1lXHyHkrvqn4WYabVmmsMQBP2+ND0iCAc7CL1B7EC/9pGQEROAP8MOFJ9wk+FEP6BiPyPwH8G3KkO/Zsh9hZ41WFdSautub1xkWMnM3YHBeOJ4oPf9Wf5/Bef4OrVm7zn3d/Hk898hYWljDvb21iXc+PWDV46/xwmfYFWq8P8whmWVrqIKfj6U+doZQucfuBBRuU09SLGUHpLILCxs0e3O0c+GVHYQYRRQbj68pO8fOFlOv15zr3wHFmSortLzB/2rCwd59qlHTKluXD9Amf0GFcW7O92OHr0OE9ceJylxcM8/OC3sb3/NEp5drcUk6Flrj/h1KF7OXv2RfI858Q997Kz5XjrW9/Ol7/0LCvL93D12kssH15Aqw5FsY91KRcvbjApR9x3f5tjJ0/y0ksX+YmP/5889v73sLOzxe/+zqd59pln2Nrf4NT9/xO91jwdrfBjj+4oOkmbrWTCxA/ol1u0dAs1GeCyNt/1wHGOfHqLD95vGYWC69vw1pUWmYwZDy1JyNnzA4QW7VY23fwqQmRvNcFbRDyiTMWuK4rJkJOLbcaFZ+xgvShxypAHYWuiSE0MA1phgrgJiZKqzVnA+Vhy7Hys5QhoRBSuEuloCc2adiFQ2MgJOdFVyBCHViBB4cVS+hCRCAbr4oaOxFt9IstkUqEEmemn6Ot0ckzLKU3TZkwpRWYUWZYR6nLuZuMriqJoNmhtKIzM9GQU39QRGKPIc4Uomr6MIcSCtyRJYvxf2OmGDwcNAEDxKohfVaTuq41vBQlY4EdDCE+KyBzwNRH57ep3/3sI4e/+UU8kCpQuKf0GN2+NUeFBHrzvQa7euEJeDtjYUBSTLrc31nn+pfMcW13l/e//INevrdNtG1546Vnuu+9+Hjj0CFt7a5i0YH/vNm9855soxeCqUleLj6SUSbGh4M997M+zvbHGtcuXePqpLxJEk08cQfbo9+bAGIZ2h24rZbC/jWkF7mxeRzPH9tZOtNL5FsEpxqXn+efP0l9YxqP5+tPPcfJeT69nGA8D+9sjVo8Y8I48z0lTw8qhRVaW5isCyNPpZDz66KOsb95iuG9pd1Jurt1BqYxjRw7R6rR58cI5tnd3OXn8FLdv36bbn8fabe677wzji7foZpq5pMVc0mW7mFCIMLZjxE8oRwMK02fkW3SVY1+ltBaO8o43P8Sjx/cZJnMcHc5RJG1KeqhMEco9QtujxwWtVKGNxvsasgeQ2Ek51h0JkbZTtLOEkAc67QTtFXdGDm3alKVjkpdVKXNAJ4qEMtYOVORdUDH3H8Vu0TjEfoEVhnYlWkcZrkIocDGm9qpSjEZXKCE2DEnxESKrmDoUb5AgOFdWbb5i0U0oPKWdoPU0zz8Lt02ImZHYf6AS9xia45RSlGXlwYnsfe2569EyUxQlIoQsxu2xnd7UCDSFXlpVCsNpxqA2Agd1FVUNR6OtIN6rTAuQXm38axuBEMIasFZ9vy8iZ4mtxv/Vz+WFtOsYjEuM6jPJN3niiV/l9tptVlfuRSnI2iXD/Ab9lsEVA46v3sOh+UU27zzH5vqT7Kxfop2mrG+f58atC3zvh/8qyvfJhwO6LY23nlS1CEbhvaXE88g7PkIrCXzp85/kqS9/jqW5PqPJPms7ExKZI89LDh0+TD4esL6zg9ag6LC4uML+ZEDIYXe3Ta87z/5gHRfGnD78MAB5McK4Y1w5f4P5fkp/vs1gr0DCJqk29Np99raG3Hvv/Zx94UVKt8Oo2GLttiE1C+AL9vYtSapZWOzQ76fsbuwTvOLQ/FGGe+vM33eM5569zHsffT9vfvNbWTnS5etPP0m/t8RE71O4AmfbKKWj/BSNdwVKLIYhc+Uyfb2FTq7hijYZsOo2CH6fUZogSc5CYcmyVYLbR0giQBdH3U8wFutExWXpKmY8QGkcJpRol6OD51gnMHFjEi9sigJlGDtXefSM1MeMgfYe7SwSAkYbXLBALAgKAi44ShUbkSgU3gVCpZojOIxMe+rpUENwoaxqRRQKtImpZBFcPkGqfn124sglQbmpQCiGHLWij1iQEzxppXsq0NiiUuR5S6hgvHOOvCwbjUPsgxhf10nN9mvSGBlVPQ+niMGGWNOgQ4FSBpMkoE1lFMHauohIGg2CqRvAxDrNRm6dVCTqq40/Fk5ARE4D3wY8AbwP+GER+Y+ArxLRwvYf+H4FW5tDkkTR6WiWFlfY2R5g3YSd3Tvs7Y655/hJrl0PnDixyuZgzM/+/D9gPBnS7x3i0NJDJJ0+//hn/w9Magiqw/Khczzy1kVMZ4WxTJC2ZuwMLZPgiiHjwQ7nnvwc5577CrfWLtBttSMxZ4Td7T06nYITJ4+Rqoxep02318Y6TWK6tDoZw1EftWcYDHYYDoe0uymtdIGtO1uVNfb4Fk1nnqKc0M0MK8uLVb8/uHb9Ig89fD+Hjy6wtr6B8wFsycadXbqdBcaDEc7uMRk5cBaVCoUb4jGkepVPf+qrdLoJ7U6Hf/zxj2PSEgkdhvsT9veG1A1aQ1XlludjlMT+dN3WHAOt0JnB+qg0c1Wsr5MEl2XgA0lISFODVlkkpDw4B+12N6oUdYjZgWrxOhdi8w+XkCQtgi5wzpGVI3xZ0k9SRsUY64S2Mbi8ZKiEYV05KApUl0BV+Vg1TwkNH5CgK+SBcwi6aiZrINgD4lhfiZKcRB5Iqmajuq6qE8E5hS0KbDEhSwy2FMqyaIhJW+nvG2IveLQIiYnSZL9vSZJJJViayoNFhElRVChCNfLfifjYEZlpwZuqRUgS24dJJYSKrwVECpIkIcuS5rqCs7GWIYBPdBXqppUhqWoXqmKlP4ATBP4YjICI9ID/D/irIYQ9EfkJ4G8TH9PfBv434C9+k/c1f3cgSTWhSNG6hZ0Iz7x8luA1vV4fpS1Jarm9cYl212GyCZgWY2dp9Y5T2hb9pUXyfMzb3vJ+HnjgTYjq89Zvew8heMpygk1UbAypDM7nKJMzGW3w/JNfZDTcYX97k73xPlnWZbI3RimY63cIlNzZ2ObQoWW01mxtjxgONun2U9K0g7WxLfh4nDMeerxv4axjb2+f/nyXLGmxurrKzu4GJ06fYmdnh6efPYvRMWf+4e/9KN///d/Pr/3ar3L5+ou0ez2OHj3KxsY5Dh05yq1bX48NVwZjylyxtNLlnmNHKIqCnc1tiqKg1U7Y3NzGO83+3h4f/f6Pcvb5s+xs7zMclKRpGrvgSMDbaesvrePCabVaDEVwvqQooVaytRLDJFcNrE2Mr1pmOYbDMdZ6er0OSgV0YqK4qbCMRyUhQCvrIKmuGoPEBqR5USK+oNcSnBWUFoLO0LagDLEApvBC6WPRj68Yc6N0UyQUQqCUEJWOsW656Q4sIk06UYWqcUklYxYljYFwxN58Aqg0hmjeWrxSlby2aiAK+IpAFFE47ypPHLs2i0jsTegP+tmptqCSNQPiIpKxsWU2seY/fhsqxaFWHrFVzYuelriniaYMNhpGX8ZuR+JiJ2QEqWshqFvs17xFHdLYV4QOB8e3ZAREJCEagJ8LIfxKNQHrM7//aeBffrP3hpm/O5BlJqRJh+GgoCwtttQsLy9RFAXdbsLcvGaS77F6eJ5WS7h4fYujqw/x7//p/xhjUn78f/nPMUbxt/67f0K3t4JSHYLX2DAiMxrvE5RkiGoRZAJuzNXr57h06QKnTh5nfnGF9b1NJpMdji4exeiEdrvFaG/E8soc7Y5hbW2LTrtPt9smqJJW1mF15QQ7e+vcWb/N4sIyIoq1tTV6nS7d1hzdbheCoFXGzRu3abc6FLkj6WrSpMOL5y5w5cpPsL5+k/n5RTrtNs5qwHDp4hVOnTrEaBgYSIFIQqe9wLmz52m3U3q9efrzbUajEauHjrO9/WX6CxnvefT9/OCf/Q/5K3/lL/Py+at4V3VeouoOROzrU5YWdEqWZQyqXoaJ0RQustq6gq7WWnzisDbHaMVgMGI4GKMTQ2ktogKdTovCWQajEeNRrNMockurnWJMNDbeqZgH956uTghaYgYg0XRKzcRB7hW5g7Er8UTo3wrxerXS+IoRz42OXaElRC1/oCL6ZiUxVYkwdWFOZQDDzDES6w3qMt/o8zVBysawRM2BxlbCohCigbHeAh6D1OC7KYoKVUVgbVBrBAEx5veVdLhuli/EgrcEgeo662am2ghFCaruhOIcSkNW986Q2GlZAhgPsV/LTFepKkth7WuTHRDg48DZEMLfm3n9aMUXAPwA8Nwfei4VmFtQLJsVhgNHUQ45fmKB3d1dNjd26KuMw0cWyCeBbmuZwy3h2gtP8+Nf/1EOH1mh3xmzsHwPTz3/VXZGGyyuHOfNb/4QRSmYjsakCaJSCAEjwvrWLo9//RnypMvXXriA0QWrh5YBT7GnGI9LlM/Y2BjSnVNMCkuv1+HS5euMJxMeevgMV69d4r774NChQywtzLN2c4PRcEI0yB68Y3vnNqNRh83tPcDiym1OnTnN5uYmaMfp++7h0Ooioy/dZjjaoShH7O7us7KywGg0YrhfcOLEg5w8eYJPf+aTrKyscP6li3TbPZZX+jgb2NlO+Kf/9Gf47n/7u7hy+SpK2pikTZq2ptVlEnvn58UELQ5XlkyKgp3JiH4oUUZjXYEyCUpgY3ODZz7zafoLc5yZM7SOL+CcY2vzNkUZMEkGQTEpHNaVjCZFRAxFlOB6B8gEO7KISlHKUBQOV4LWBkMsjPHeMfJVRWgQygBOPKkBLZ4khZaycTOLZ1LG3og9baLyUyzB+ogoQtXsRE0VcvHPvwWUjTJeUSq2Ga97GVbkW2EUCUkMLbQmS3Wj3gOPD7H6kUrvEJXRGoWQ6IBOk0oZqWO5dYVGiklOURQUkwmT0TiSqZZKmRg7YSVGYbQCZ8kSRZoYsiwhS3SlHiw4ceI4u7u7rN/ZbAhIl7YaRNdRCeI9yg5JM01mNCap7rFaA7Z8DYwAMfb/C8CzIvJU9drfBD4mIo8QDeNl4C/9YSdKUsPCQp+93QmTyYSlhXkunL+IMSkrS6vk5ZDhwNJpHeKlczcI1rE/2mX18D3k5YhOp8exI0e5dv1x9sdb3HvmQ4z3LnHk8DFMv0cx2SOEIbaEna2bvHj2ad77zvdx5gdO8TM//RMM9m4x2t9gMBhQjmOX2Hy4Q57n7Gwr3vLWN7K/N2R5ZcLOzg5p0mFpYZ7gLc6V7O5tsbu3RararB5e4NixoyRJyvMvfp12Z4H8Vk4IJe10EYUm0Sn7uwNcafnql7/C0tISzuf0ugtsbw1oZS1yGfOxH/xP+Uc/+Q/Z2V3n3Y++nW53kapqnwvnb3L06GFWVpY4dOgQa2vX2N8f8uCDD8YYPMuAg+muuqCltGP6WcK1S5dp376NshalUry3gCEfDzE9xc7uNp2jp3n+xXMkxZB7TpxBV11udJKQ5zkEqTr+SNXaTKLKzxMbhQSPtwWutFWlH/EPlvjoWa3TjOvajspzJTqQSeyunFWQNihwYlAmYIoSRRTpBB3ottuEEBiO80jcqdglOkL6QCKQmaTppuRdiXMlqtpQUfyj8AHSbuwpGRwEB0aZ6tqk0hFED1v/RSKtQSem6UYc6v6gIiStpOlL0NQblK4KA+J9tdKExECqEvpdQ7fdoj/Xo51FQ5QlhoWlZfrtBBUcu/uDKA1WscFuCIHxJKYiE28RqXtnVDL5qvrQvno08C1lBz5PjXcOjj/S3xo4cC4f2NocsL62g7dRikrI8E4xGg0YDHcR+kyGe5RlydagYPXwEaxSaFKyzLC/uwni2Lx9k68Unne9y8Gwy8LKGR5+8KG4+FSbc4Ocq88/RXeuj4w97/z2R/n8738KLwnLiyus5xvRSivH4lKPO7e3ufjyDZaW52i1Dd1yjtEwJzEtbt64zeKSi63QfUHSaiOqYDLZQdQcJ08d4/q1O5w+fZqtrduUg4TN9R2GkzEf+cj3RQGQ9WxubnPn9i7Jao9QKiY4fuSHf5RnnzzHiZOHeeObHuDq1as8/8KLLK8s0Gl3uHFjn82NPcbjMT/wAz9Ar9fns5/5ApPJhFYr6v1DCGgjVZ5dwHm+/MTnOXX6GEtHj3Hx0ktc/vSn+FNpCi723FcBXj5/jievbPPQw1FS/fjjX+CNp89w8qTBmJh2stZWC1kzrVONca53FkNseR3wlNY1VXlaRQ/oReGcx3lD4WPLsxAC4h3ddkqKx/iCxJWIjl4+SKDE06Zkbq6DlkBwvuq8m6CdJXchIh6lUNWfEFNBoUKJlBGKuzLHKI0yVblwEoVUscioxGihlSWxvDxrRb2CCxTWEnwMMYxRJEqDju3LxGh0Yip4PlU0pi3VdAny1mJ1WQmlohHod1O0QKICnQwy5ekmgX43w7cT0kRQUrLU72DUEUaTPNYcWEtelOzs7OGrGgbxHogEblG4qlmJgED5aiICXieKQWsDly+us7J8mFtrN2i1NYdWl5if69Hrd9nb77K3O+DalWsszB/izKkFjh5ZoD+3TLAK5zdxLvDCU9c5euwEk23hK5//JKXbJe2u0MkO0eusMs6F0k0Y7N1me+sWo50CpRT93gIXbr1Ip5PS7aUUuaPb63FodZGdzcPsD0bsDW6TdUqOHj3Bi2evQkhIkjYSFKPhmLc/8ghlWbKxeYOs1eLll1+i1+sxGhYszBm6rQVO33cvJ04e5fLlywRbMtc1BD/H2XPP8ug7H+VLj3+NycTyoQ98kJ/+v36SY8cPMRrvcv78eSZ57JMwyfdJEs2P/MiP8OK58/zO7/42/+V/8V9TFAV/4c//EC+dP8fp06eZm5vDEyhsjhKD90JZ5nzxi5/k8ccD73rTG7h26w5cuYA6qRvGOXhQYnnXO95Ce26On//5/4cXbmzw3re9qyHDvLdVnlqYTCZ4X3tFwaQaZQPFpEB0Sl6WjKv23InSFM6Dh9J5Shf7DygdxTjBe0SpWE8hHq2ENDNocSSJZ6lT/fUn20JTe1cYjieUOcy3E4YFGKLI5v9n7s2CbLvOOs/f2mvt6czn5Jx556vZkiUPEgYbPAHGpnBBQ9EFFRUEHd1Nm47gofuhKqjo6ojuqICq7i6IggiapgobcAMuCmxsYwwekTxIsoYr6UpXuld3yjnzzNMe116rH/a5V4YoufsBd+i8ZOZDnojcedZa3/q+///3L4oM4ZRIukKXjkYlBNWKjxS3XHoO0oDjCEyeI8nxPEm9UkUtN4lTTZqmpJkh8NRCjecgF+M8I7ySiiRAOLeUi4vF55RWYOUU1JX32rjRlvJwhUWaBIVhdanO1koDRYGrSgqTtWKRYl1KtVXDp10LKKwh1wbHUeT5KlGSEscxCL/kdZqSWpymOVK6JSrfef0h4RtiE7DWEoQCL7BsbW0xnfVoNUN0EbG7M0BKQbtVY2V5E51LPJWSzYeM0xTPrZCk8zIHQDnMkphCQHOtyd7NXTbCE1jpUygPjML1A2Y7OzQaDeJkwo2b1xhPjlld71CCMAvirEu11iCOY3q9CdVqgygCN4DtG1ewBXRa68zmIw72u7Q7dW5c3+YDH/gA33oq4tUrNzl37i729w/IY8POjV2KokwsPn/HKe688w4OD/dZXmswHO+ztbXOzs4OQegRRRHPPf80WmuuXOsiHZ95lFGrVej1D/H9gJMnT7K9vY3Wmh/54R9lb28HXSTUauu023WUKvnzpSjFYmxJqbEUTKY9gsDn4PAmyq3i+IqSsOuAUhjjMJ8Ouf7c0wwmU+5ZW+cH732IVrOzAFpasiwpJwVZOaYyJsNaiVIuhc6xxhBWFLkVxKnGal1yFLVAf1tmhBCGil9eQ4xjEQ7kacI8yjCui/Q9MgOeKiWzlaB0XDrSpTB5iQWzAmM8hDTESUGu7YILsOAtUt7B7cLObKWD5y0chkUBi4CUgrIZ50qJK1WZ/KtKnDuUM/y/jRlfjA2lQKBu0QEW7f7yGnL7KoYsx3midCw6C8ZC2Sw0SAcC38N1ysQjKQXavjZdcJzF+xZlRoPNNZ5yKFH0ORXfIXCrZIVDEL5GMh5NZqXwyZHfncbg3+dLSkmz7SOVYTyI8FTIeDzG9RyiKEIph3rDY2OjycHhiIODLoiMMCjY2GgyPErwA5d6Q6G8glrDxZElOWZ0NAANw+EYP+hQb7RpNdoMBj3yLMPzNI2GRxRnFFbgW8vychscgeeFOHJAvV6hUgmYJwecOn2CTmuLD/zgP+IT//H/ZjobIITg8HCf/YNdzp6/i2Z7meV2hyQC320wHo8JwyrvfMcjfOvJZwiCgMl0iFBbzGYzBoMBjfoyrvJZWlphfWOLNE3pD3dpN1exaGbzEbVahbXVU/zSL/0P/M1Xv8aPf/gnuOvOe/jKo59k//A6b7r33XSWQl64+DzPPvvsQo+gUcoj8CtgLEtLVVZXV+nu3eCV4yl3z2awUl3IXXOs9egPjrhwOMYN63zfvffhb6zh+WpBtBF/y0/vSQdflU0sYUtab7vVxjgZaWYWtlhLUdxqxkHguxRFji8FVqTkhaQociSCWqh46fpl3nTffaALpAwWJ2dpFkKbEhvmiHLCUJSBo5k2jOdzYqPK01Y6sLD1lpqDsiwu6UJl1VMGn5asAW2KhZiqVOmV93iL1t8G8XQcMOUIzoqyaik7+eXI79shXrfi24C/RU1SSkGhweZIAfValdAteYSlOam8+tziCxSmZDDAa/0Lx0qyTKNtDsLgL1KnVCFLqrKU6LygUauQF2XD9jspBsV3mh/+//XyAmXvvr/NaDghcGtUKxWUa0HkVMI6vh9yeHDMZDIvy0/ZploLcBzDbDpibb3DaNBjbXmj3DRciRUzljttokmBFgWnTp8n1wpjPXZubmOMYXN1i1xnvPTS87Q6q1hccmYURVbKXq2gWq1yeHhIkRV4XoArod2psLKyTui3SPMhl1+5jiVHeorAazEeTwnDkNCt8eCD9/LCxaeQwiOJU+6880F+8zd/EyM0H/7J9zEaD3BVQCVo47o+cRyTJBl3njuPF1R58aXniaIZ7XaTwWBEvVJlMpngewrPl9RqFRr1KltbWzzyyI/wJ3/2Hyi0Aybk0ktXWV5pIR2X3tGMetNh40xM4NfQkyNse4uHe3v8XN1yf7sU1xRG8hvqHJfbp7lw8WXefscD/IP/6hc409mg1xsQBCUpqVoNaLVrzOZjhv0enc4ySrkoWQHroE1OkpQTh+l0ymg8x6+EBEEAlPp/6VhcYRBuhc/95adI05grr77Mu3/gvWxunaJaX0Lg4VjwPYUrLNVKACZB+R5WQJZbjscp/UlElBpyVS3TlsRChgfIvJzWSAcCJUvnIqB1VvodhKAwoJE4OkMtxDbGGOI0R0hVGpkWxqa/5e1Xr2VXmG87/QsrSo8Dr20AAEaXf7fCohzDiaUaFDnKZLQaAdXAL5+RdMrpwCLEtXyj0pwkhCBfCM5ujTcBijxbyI3LPMd5nDGdJWhdNmE/+P7vedpa+/a/u/7eEJWAQBBHZdmlzRw/qNAfdGk2q0zHAw4j8FQDJXyyuE/haM6f3eTqtZdQniHNpjTaLbb3BjgYVtfaLC+fwhhDbc1ld/+QaW6ohjWG3S7alI2h0K+wtrJO9+CIVrOFlIphbul1c3IFrhR0u13A4PkSncFkOCLTY1xPMhUJlhErq20Kk5AVGb3jPq7y2Nm9yX/z8x/hyScew1WWMHRZWmpSqbpcfPE5/uTP/hBERq0WEMc5s/kYKV2CwGNv/yZaJ9Qqa8xmE6bTKQIPV9XwKxVWQ48H33wvr1y+iOc7bG9vc+XyTb75zUusbgSkiSWeJ+UoqShQ0rv9oU6SmIpfJctiqlIRhiHWzrEWlHAwQoDRvPjSC2gtWF9fp9Pp0Gw22dnZo15vIISg3W6zu3eDJJ5RqwfUGyGO8IjmBY4QFHl5igbKxXcV1Zpf8hwdg2MtjrA4GJ5+4nFUrcH+7nXWN5a5dPFp/MDhHd/7A3B0yJvufxsUZfPNdxUF5Zi3RGvL0khDaSJCuTjSXwSUWowo1kPIDAAAIABJREFURTKedEtV4UJvn+U5Tnk1R2AxYsGcsOWJn2u9aLKBo9xyo8DBEWIhWBK3Jb7WKRfmLfLwbSzYLRekLeuDWyex7yow5WRCWIOSDmbR5Q89/zafUH8bzPRWCpQ1r2UeOo4Cp8xS0LcyJSil00JIpFMi7JUUGFMGtrze6w1BFjKFZnBY6vAP+jlKefg0GBzCIAvZG83YH/eorUm8ZYtfnbHeUbz1wXPce89ZOv4GRc8lzSISUXBzMAFdZSvYIJn6nNm8A0drxv1dXGFZ7qzQ6Swzz4ccj3cIWi6Hw2NSqwk9nySKKFIB2iedOyQzQeC1mc6GaKuZTi2V2hoH3WN64xkqkFQrTWpBm/FgxKg/AuPx1IVnubqzxzgyHPZHjPYPuNHf5ZkrzzOPY/auHjOdS+orZ9A4VHyPeD5mdWsV7YLOMrI44/TWOSQSz80YDQ44ceIEV6+/Sq4NcSTRhAxmM5I8ZXdvSJQUTKN5eWJkRWlqERnGaJK5YjBIWGss4zaWmeUlU1A5CdqZAAli5pKOBEIGzOc9hvvXsUWKklMcOaIoJqRxQe+4i6NSsAqd+XztsScATZJGZMWMvCiI04JUl7FujiNKjqEDrifQNmWUOjz5rb+g1hBcfPFZZvOYdn2ZiqpQRBleURA4hoorCANVOhbLvGLyPCfXEKcSR1ZQbghOjHRmBF5GiKAiPCpuhr+IUtcmJ3UsswKi3MU4dYSolDJbYXCIcGQbK9uosIEXOFjPI3dDjO9QOAnS1TjKxQgPqR1y6WA8F+G6+MKhaiwNY5BK4/mWKpa6cFGOwjo+rvTxlUsrCHHSDA8WhjUflI8Vbpk7WBiMTXGQCF1eRZTjLhyI5ZUCUhyTI02OXQiQrHDI8gLlqTL/QRqU8l53/b0hKgGpPO59qE2n3WL35iG9vTnd3phcCJQW1L0GNpuhBxknqicwnQkv33iR1aU208GIydwS6ZTQb+OQU6vkFCbiOG+yuXoa6RvG4yH7u1cWHe1S0x2EJTtvNp9Qq7YYT7r4YUCr1WDQHzPIB+R5Kb211nLu3B0LCaZgb7fLZJyQkzEa7BP6AWmas7ZyCq0Ng8GI5556lofe8iDRrE+epxxry4fOv5lf+Il/ym93Z3zF/RLEOdG1fYQSjGTC+uYJ8nFMbi1JbDl54gztpZAtr1aOwaRkNDrie9/xHj77mc8zm3dprzTQRZP5bEqzWaVaDUniHLtI73GcUocvpIdBMBxPCKKM7f4VltIEu1XFmBkYiSNcOkst1goPHQa88MJLvOfDHuNJj0/86e/SbFfY2tpgZ/c67dYKo0HKe9/9o/zZJ/+I555/lre9/f+iMAlHoznjYYLrllkF66tNTJHjSYV0wFeSetDg5YuP88KlC4xGI5QsG6Nh6LOyusynP/27rK+vMhwOefCBN2NsmbeoHUWaOeTaMhrHaK1RwuLIEh+utcbBELgSITKaISgZLEJSXbTJyTSMhjGCOcpdtA8dSd2vMtcRSVqi6AQejrG4UmOsQRgX1yoKUqzVCAP1VJKLAiMFggozK9GeR0iOZyCVgqkDjhW4TrRAm+VIVxJUAipBeU1yvTIV21jK0Fnl4Ik6STxdVAACS0GWL0ROWYKlQLmWq1ev0uv1OLF5J35Qo1Zt3kaMKeUQzWevu/7eEJtAYQryBJ575kXiaU4QdhCuV84/85w4TqmGCs81eKogc6DV7OBJyYnNVY5GCdHhIbNBl63NNRpVl/2Dm4yme7z5nreiXMNg2CUIKsRxidPKdU6ex7Q7DTy3VNflWjOdz4iiBNdzb6faaJ2zv79PrV6h2Wjzvh98P+9657v5rd/6LbZvXi/LUSOYpzm1motAs9xZQlqNyTP0ggWHE9A96vLrv/EbXH71MokjSbMUNzO4Sw3G0wmdVodkHiN9l1a7yaVLl1DhKZw4xRqHvb09tk6sMxj0uZUkvNRZ4/LL2wjhcP6OB+h1h/jKZSaS11BWlLHVcTYs77MrHvMoQyqfkjwlyfMCRxRMJkOOu33uf8f3cjAacOXKZZ547Ots716lNa8wGB5SrVa4fPlVblw9Zn39HH/z2KNE84X/QDhs7+zwxOMXeNObHuCOc2fxfFWKhWwpzJEGdm5cx/MMSnpMZhHVqiBKE77++Nd59NFHF0Yaw/rGCs1WjV/7tf+DSqXCD33ow0jVwBpFlKRI16FSD5BSkGUaIRWB7+OqEitfdZ1b+alki7LZWEPgllcCqUAIhbFg9BxpJaEr0bkgzQqELL0RRrkIKTHWQboe2uQIAgqblzkotgxvEVKUgJC8bDhKV6K4BcFdAFAp5dZCLKS9eYrru6WjUXmkOi/hJQtrcjlJKHsXSimSJAMc0jSi1++xt7fNzs4OtvC4596HAIfJZIZdbDZxOnrd9feGuA5oXXDtyoCjnZwsVwxmM1AuoRdS8QXSk4SNFoWy1NplaXmwOyTXHofDIf3REZ1WjXpdYU1Grl1Cv0Krrnjp0vMYWyClpNvtk2cFnufhe6W3fTKe4wiXg/0enlfBdV3a7TYbGxtkWYYxZSe8Xq8jHbc8qYIay8vrNJtLOBbq1RoUhjAM6faOGA/6TKdjmq0aritZWVtmZWUFZRVf+NIX+cP/9B959MnHSQuDEQ6yUaHebrG+ss76yjrtRptOe5nDox3uuvts2ShyXOI4Zn19jZMnT/LZv/gzHJnTbDa4dnWHpc4qjUaNo6MjhCijs/M8f81YYwyu8llf38D3AwK/QqfTKYEVi56265bcPOkYmo0qN25eptc/4MJzT/LMM09zdLTHjRvXuXL5Kjeu9phPLK32GhdfvMBwOETgcfXVXfb2jri5fZWjoyM21tapVCroLMVovYh9F+zv73Pp0iVeuPh0qX0XgtFoRJYlRPGcF156ASkFTz31OGtrSzjS8I1vPkqtHqBtOe2ZTCaEfslT9BxLIKFZcVluVujUAuq+pO47iEKgsxyd5Qht8IQkVB5LjRaeKxHaYPKCYiFLLkNTSs8AjsCgwRHkhQDlYhTERlMIReYIkkWikrQuUqa4TKjrCTJPAcjiCNdo0DnGOEgnpBK2cJCLkBRBmqYYnaOLrMzNoHQUlglKJTbMWbgCy6lPOXkJgoBr165x+fJlBsNj/KC8LmSZRilFYTT7+9d59crrq/ffEJWAEA7Cq1GoDETIcjMk8B26x7usnD7Djf1jrh0dseQ6zLpjHM+loM4zl67T6igeOHue2TQnmlnSxLLa6TAYzTjY3SaohCjlcOrUKTY2trjw/AvEcbaYAUM0nxMEBa3GGsN+RFoMWV5eJUkSKtWAVqt1m/661Fnh4OiIfn9ItdLAVRWOhoeoLjzy4IN0Gi32jg44HPbZOezjzCR1E9Pb2S259qKC0BHDiaba6XCm6JDGEb6Q1LszgrCFjCWJo3jpm08StEKqDZfRWFNogZAJo2nO9m6XjY2TtDtNTm6d4wuf/xL1RoCj6gx7XYSQVMI2UpWNrlJCXHr/O+1VklgzmU0Z2TH9eQ/TCrBk5Uloy80sOZjgN0L8muFocJPVTUMuVhmPx0gpefXqLpWwRlh1OBpdYhLHOK5DrOdcuXSV3/vob/Ked/04z194ig996EcAW0arU5AkCY8/8wxf+/rfcHVnB8cJEFLRaFWpNers7NzEDxQH3V2+8c3HyPKEj3/84/z3H/kIUkrGvRF33XVPqe23t5KQwcMhCMq0IkxCUQgcfGK5ECABOs8Qosx0dJwSfKpLwQHWGqKiUZbepuzuByqisJrASorRlEzP0PkYnQqS2BJHN5gOZogMikQjxBCTz9Bpxi//q9/h2vGMq4cRtlAoI8l1TmYtxSzCb3sEIWTzWTnfl4IszkmKYsEVhCCs4fk1Cl0CXLM8wXHg6Pgmx91Dbt68znQ6ZTAY0u1f5+ln26SZ5uyZuzk83OGjH/s/2du/Qb3xBu8JYKFwB7jNhDRJqIVVfN9gV0Im0z7LnSrLyw3EOOfk6iaj9Jhhr4erMlwnQHkh0oWj/SG1SkgSTzh3x3mmMWxuNAEHbCnEuPuOe4nTFKkE169fL+9V1qKLjGarQZyVJ8F0PKJWqzEYDMoTJ/Sp1Wqsra2SZUkZN+W6OLp8iAcHB0jhcGNnm6BWZXNrFa9WReeazuoGh3v7FDbh7OoSG+dPMc5SJjd6SGGpVULevHYWVEhw6gTPfOVlVjordKMBcVRuWK4Hrlsj9BWn7j/P448/yf5el+0bR7z5wXs5ONyh0VzGFhnLy6t0jyflo7VlVQClmWR3d5/ZbMbJrTZuKplFi3m2fM0hJ4XHoD/GMTlhodk/PCCLIgbHC3xWQ9Nq1anX64RVwWFvwurqKsr1+YOPf5TJKEM6hul0ytvf9ghZppkkc1rNJgBffvQrvHL5EgfdLoPJFHTE8kq7NNtkCe12k/Fwwnw+p9fr8cUvfpFarcZnPvNZ2u02H26cApMReJI4zaFYhHoasah+ysovyQVZlhGZMqJLSrEAzhocJSmKW9jwgsJYdGFRSHI9I09TJvOIQf8m2Iz5POJgp4sjCoyJypyCTIDXo06rTPnRGa7UJBlsbp7gY7/xb3jkvT/K5vIGu2NwLXhugW/LfIX5bEiIwmqLH1QZ9QdoI6hWlwgrAVkWMZlEJGl5shuTIYUhSSd8/RtfZTjqMh6PsbaUShtj2Nvfplotn3O1FvDq1ZeRSjObv8GR49KxnDzdwYoCaRU14xEnU9zApeJVSJKEVj1gEhccD/vMzIS3vu08k+Exs8mUCy+8RBAEVAKfShCQZDFXrl6mWm9z6uRZ5vOYohC4qkKt6tFsliOew8N9sixhNivn+nt720jPod3o4LkB49EUhOXcuTMLLlzJ0i+KUpbZqFVoqQo5hsNen4PBADfwqTVbYCxHBz3cMGASRdgwpO4UrGx2GEVDolwzTyYsLy9jXMGN/V0EPpefe5bDWZ+OV95xHRGiVCmmOXXibt7y0MPU63VevnSd0WjEdDpfyG4Nh4eHrK+uMB5P6Pf7WLMgzij3dhmZpikbGxskyZgkc6lWS1yYNSW73xqHNNH0j4eIIuNE02c+yxgf96iHp1FuhUFvwL33nCvn/9OINC7BFsdHQ971zrv53Of+miSLqTWbWEcyizJAEGcFgedx3D/g1WuXGU5G+EGVeBIxn5X6DscRTCaTUhobRZw6dYbxYMTx8fFtLNfa6mqJZCsKtC4whUUYiSoKcCxFkWOFJE4sOofYany3FNCYosBzJUme3RYEFZbbTkWZx0yGN3nqW0+SxjlZluC5BcYUOFQA8N0AacB6YP0WZGEZxqIssZUEzWV+4qd/lv/9X/yP7G1f5yP/069yOJqjHIWUCU4hcExKHI3I/RqVICCOpoymfdpLaxTFjCQtiKLZQvnokxcFgoIknXK4f42nn3mCwpRKUiEE9VYDRIfRaMILLzzHwcEB73zXw6yvr7C3f5N2Z/l1198bYhNQSuHuZfRGPTqn6nz/z/7X1Kslmiub9rn37rvY2zvgsce/xg//6A+hRYtv/fVHeftb3kdn5RSf+4PfxncN7tISCsE81yRGs1pNODzaQcmQbrfPxvrJktxiyk1gfWWV0WjE0tIycRzRWWoRzVOMsYzHE3zfx3ddpqNxyQUMAxoNw3H3kCD0WFrqUF1Z4/j4kKDaJEki1tbWEMIiPUmzWUUpj3Qesb62hetlUA/Rg5i1ahPPjrlvZYujyZBnr11BWZeVpTUCRzEkwzc5vaMhxliU8nB0n73tL/P+H/wBPE+xtrZCt7fPje19ZqMEpMONnV3iWQR4OIso8CxbxJybHMdxODo6oqMSWmt3Io5H5Lr09klKiW3gSVZXl8lCj/F4zNg6VOw6O71dGo0Grhty+ZXrhHWP0XgAeKyvtglD6A8Okf4AX62wfPIc3VlKux3gSkWsFUme8MrVl+j2tylyi9AsrlzZbYqxMaU5KS8Muzt71GoNao0WJ0+c5oMf/CAvX3uVamcVbSyu65PnKcIaAs8jmZZze8cppcDa5ng4oDUScB0PnZYaACGgUq1TGEGaFORFRmEGPPrVL+EriSvKSBTXugyHxyx1aiAUOstQrsHqHOIAKw2FLbDK430f/GFONZp89Fd+hXuWq2TFhN/8pX/CL/36x3jpuEdNBXihJFABkV/h+rVLTMcTHnroQaTjgnU4PNxHOGGpZEwzvLqkoEC50Kq0UeocnfYGxuQMhsdMoyFpnjAba+679wGG4xGeD7/7u79DnMypVqvsbndff/1995f4//sr04Z6EfOPf+oDfOvKZZq10ywvn+HhB9/Pk09+Fe24/KN/+hMMpimuqvL9b/9h5vuXuPct72Vl/Szy8tfJnBl//OgRFZuyvrXChSs7ZIM+9bTBic07mc/HTKZVOp0Os9kUx3GoVGqsr2ySZFm5yN0Q1fDx/ZD5ZA6FYTSf4Thl08zzAg4PjlHSJwxDPM+DWkB2bDDRDAkc7e9Ra9VoLrdprLboH3YReY7IcnTgsLd3xOjgmInTZ9mtMhlNmBz3SY0haATcefd5plc125MuIoMwNPh+QJZCt3fI+9//Q5w/fzej0QTHsdRqDQSKRrPK/mGfStXFCyrk8Wsk2tdSaGA+n5JlBffes8Hf3LjJqTjB2axhzC1xikXJgkqowFMErQ5JZKmHHtYpfQs3b5Qmr1s6hLX1DlJZzp0/wYXnnyDNRqjKWc7fcR9hrVHaWK1DnubMp0N6g2O63SOCoELF9VGBj9ZlkOd4MiIIAjzPo9lsc3R4TJ4XRFFEe7nNlVdf4dKrR9z34MNlXH1WZgdUAo84z8pTdhFGK5TFiAJVFAhnAe8UGiV9HCnxfAcrDHleECcRpoCnnvgmonBKOXEe4/s+Wmvq1dZC/quRymCtxpEGnaeYJEIGFaZacP/ZLf79v/qXtGyG0C6khhPNJsPdG6wsr5PNSnm2LiCLNdE8wXEEr1y+xNmzd3P12hUc6aKUoVJr0mhWictEWQTldUcIiZIhu/tHZDrH80LCoE6jUqXZbLK6uoo2mpdfuSVqUjQabWD8n11/b4jpgHAcVsKY7717gxUJpzqnuGPrHN39m+BYHnjoQYajCV/4/Jf40z/6JM898QQnzt7JMBc02pv83I+9Hz85IncMystpNQruuucE9UaIcDRZPscPPGq1CtvbN3j++ed55pmniOMUrTU7O3vMZynGwGw24+a168xmMyaTGVmaE8cZ43FMq9UiDCvMZuUHxvN8sjwhrIcUpUmVWqOK9By0Tsh1yvFxD6W80mE4njMdJjjaA+GR1gOu9/tkUiECj4nNeOy5J/E9yUZnCeWUuLFOp0MY+szmfR772pf41pNPccf5exgOR4yGU3Z3dzk63iMIAgTl2LDdbpfPdqFUM0YjpaBer3PvvXcxHk3AcUgSSvagQ7mwbQkKQRhOntxid3eXLMvQZo70ZAk7mcd4nodSinqtw9HhgOlsxPbuK/T7Rxjr0O9NaLXbCCWwjuW42wUh2d49ZGVtFS/0MMbg+yGOoygKS380RBtbVmC+z2w6JwgCao0qtVqF4+4Rn/urv1yEb1j2DvY57HUphGQaJyRZTpob0lwvvtfootwEw2qAH3oozyUIQypVD0daCh2RpJMSvqo03f3rkGlEXhDKMhzFFgYlXZI0o3DAyhKSmuiCaIFAywrNqbPn+OP/8DvIbAZOKaIKZQ3PKn7/d36LZU8iCgNFynjQJ44ifN+nVg/odg+4fPkyjgOD4TE7u1dJ0jlCvIYXL6whN2VuwcrKCrVqg62tk8xnKXt7h+Q65elnnuTii89x4uQ6rqtoNOqAWeQ4vM76eyN4B8LQte/wNSuNFqLRQTc22VpZYzYdcrN3yNkT52iEDf76mSc5SEbEk5zljQ6BL3nvww9z4wufJ9dzKt/zVsazCXE+pV1vcGLlFPuDIy48cxmHECnL0rFer3J4dECtWmd1dR2tNQfH+9QbVSaDKZ4XMBqNEEIS+h6ZLptNlUptMc+u88yzF/nUJz/Nx37313nx5UucOXeGZrPJ0fHegr3n4Hv1RSiJRCBxZAHWRRQGRxSsnltl0OvT2zuiKFKEb2ksNfAK2N/dZ339HDduXANZ0m+Xl1Y5ffoMe3t79PtDHMchCMoos1uR2Mq1hGGVo8MBjimzAJeXV+l3h1gyOmuCZrNJ0ZtxYV7wUzXDL3Zi3nUyKh3/qcMnwnfyL77wOGNh2VpucXOS4uqYt77tfra3bxLHGek8J7dlB/uuO97Cyuo6uki4ufcqSgbc/+B7eOQdP8Q8mXDi9Covv7LD937P+7BFzi9+5EN4UpMlmtXWJnEcMZtP0DpD25JroHWp3gxCn9lsRqvVIPBLWfVq5z5+/Kd/mqP+EC+s86b730aW5khHwOJ/ZQXlSE84BFKi3FuJPgJf+TiiwAssX/qrz6AzyyNve4QvffHzpHh4RUY86+OHCo1CGMXRwR6nz51lNMtpL29x+dLzNBseq62AMGzyc//4Z7jxwgu8fPFbnLn7LL4vufLVryCiDOn5DM2UWTTl9Lt/mp3DV1heXuXk1h0k6YQ//8wf0263ePjtP4jjKrqDA86cfhOV6gqCABU2SdKcOJmSzMcM+ns88+SXmc9GTOYjsjxfJFhFuG4Jk8l0jnDKEXG13mR3p8+Nl/vfHe+AEOIGMKWMjNfW2rcLITrAJ4AzlHShn/5OxGFTaP7hh/873v++H8FfqeHVHIpJgkkLclGgpwk2h3f86AfoqpRnn3iJx7/2GPOjqzxy9xl+5t0fgyzjX37sl9md5bTaq1y++BK99gAtBTo3RPMJreYyKytrrK52cKRgOBwyGPQIQp/QlyTRhDCsUBSWpaWlxYnn02q1GE3GTKdTEJIgqFBoS61W45FHHuHHfuzHqDbqLC112Dvc5eMf/336ox6VIMdVVRw8POVDEZEY8F0fm6UEhUsrrBOHc3Rsyju71syynEqjzupak8PDClIKvIrL0tIq9913H64Hw+EApRS1WoXhMCeJLdZmGGupVVtUq1Xm47IXcCvPPklzWo0lDvb3qOV1mESYSlCaYoxASIu1DqYQZKlFK5hO5jSbSyyFG+ztb+OoDOUWVDtL7B8fEIRQr4cUGmq11u0riCslK0ttdp67xjee+Arvee+HKIqC44Njtk6d5PJLz+OrgHmcMptMyPKk1EPY0vnY6XRKu3JajsRmsxmNZjmibAYxjz36VaTn8/3veT8HBwesr22hs5yi0AtQp0TYUvevrUOepXhSIYQkLxwcCooo57773kw8j8pkX11Q3TiNW0zY7R2wtdzmofsf5st/9UWW1zeI4pi3vPX7WF07x8HODqvLFcJ8wAf+wYeY9/p87VOfZO3uU8SOX0aC6RjPtWS5oR6AG2q+9rUvcu8Dp3jl0nNYA0fH+yByEJobN68RVCps71zlqade5M7zb+ahh96NyBN0YZlMI/rdI5678OQiPi8jzmYUBuIsJXQVvd4x1lpW1laZRzOUUgyGPfzQf901/PfVE3ivtbb3bT//c+BL1tpfFUL888XP/+z1fnlTC77nvY/Q/vC7uXQ8IEki/A0XRylCqwg9l/5kxJlmk3ZvxMPv+xF+qf9PuHY0xG9vkPkOuh3wA59oMe7ucdVL6a1vEJqY6/sxeVaWxePZmNzkHPa6xPGcai1AO5qkAMf3mXTnVJoKkxZkekahYqQ2RJEhK1IcG+A6AbVqnYsvP8eNV68wGec88vCdBKHH5uY6H/+DT1D116ivbzKbJeR5Spz1UPUqxqkgdEna1cahPx6RpGOEn5ImObPxnHmUE/o+aZbywguXyPIUoUuo5mQ44MXnX+Ds2fNccl9lGs3L1Noix2IocglGcv3qVRr1JsbmQJkaVNgcqRSDQUancwKZ9Kg0KjSXAqJ0hBIWW4AQGkcYbFFgnBCHKlW/TZJepdXYwFpLLzqg3rGc8Jfw3ICV9SWSuGAwHpOnilwmxNGEZ579Eo8/+QR+tcapExukSY80HRKqJYyukBUFMzukubzJ9rUXaLVqFJScPQ+F1BUcISmqgsTkjAeGYgonH1A88+ST3P3ggzz99Ne59743k6chFo/CK+lDpnBouHUwgn5xlUb1FBkuVueENkPZOcnokPT4kDzKefK553BdF0TIpee/zF2nTqBjwc1XruNJja9yTGrxi4g/+f1f49TJVTwbgw7ZrNb52O/8azp1sNLljjvvQcdTjsIKcREzdRPqKoS4QVsMWW2+jfmy5qmLTzCbJrTqJ/C9OuP5hN48Y/OO72NF1EnzhFf2x6xsOHjKp7A9Xr36JFbPSRLwwhbSbdFswM7ey6QmIBeK1dV1Uq3JtU9hHCqVxu3AlO/mJvB3X/8QeM/i+98Dvsp32ASq1QqH//bfcuPjHye4527G0scrwAjNqFpGjtdkSD6cEgQ+rwYtfv6f/SKbaxv8xR/+Kd5wSBZI7h45jGsnEVtrdKMJs5cu02y3mM4OyjLTGObzOcp10HnKbJJTrYX4btlJr/oBlVoJ4GiFDQrq9G4M0MqUYRA2x5GC/b3rPPTgXeTzCZ/69Cf488/8EZ1Og85SC+kpoiRib++Idn0V6bisdNqMRiNWNxYCo6MuvnJwpUX4Pg6G3cEejnAxjkMUa7KswIoC1/WJojlYQZYOGAxGXLz4UmkvdQRZlrC01CmFJEmClJLV1VXarQ67u4e3462llGRZwvHxMa4rWQ4Uy6dOsrP7Cqz4FDpCKshz8BoOG5vL1FSIT85o2sXMluge7FOvV/G8JQbdhE6nRZylHO1N2Nja4ODgAOWCNT7zKOGxR5/gobe+laWVFbJ0xnMXLvDVL32Z2bDH5vIy3YMymGW5KXjXz/4iQSVgfa3Nv/s3/5pW28XIPvO0IAirtKshQhtypfjGxWd5x7s+gFep8Gef+hN+3ASsLd+FH9ZBFThGo1yH7avfwJM5w6zOyl0ncWxM6Br+8lOfZNjbQ4mERsWnXm+SFTHy+GG0AAAgAElEQVQH2zdJdq5yx8YyxDMoJOkkYqkKopjjBRUuvvQ4mycauBScWN7iZ/7LD/Erv/w/sxkqmgpSBdVGyJyMIvMIC4eKW+EonzH3BCQRn/nUJ3AqNU7feZ7lThtPesTRDOl61MIGypG02ieRysMLJW5YodAJN/evUjg5qdE0l1s0OnUODrtsnXoTe4djZpMDTm6dYjgc0u/3aTabOI5DHMfkafy6i/XvYxOwwF+LMrP5txco8bVbxGFr7YEQYvXv/tK35w60fZ+nCs2se8B8NobKMo4uwCkYeZp5nNJQNVqOQghL0qzz8ypAuIaL21fY/9ZzSCnZyjSRUhxOY44OB6TTnMLGVBt1VJqXsVC5plHtMLMlpZYCZuMZURThuT5KaCqNACF8JtMI13VLnp4UKGUoRCkFLYpikUOv2dxcZ3mlyWg0oOZXWFvpMOz3mM0muK7PcDjGQaBcB+n4pc3UKjCW6XjC7u42nufhuSFCKmazGY50EJSIqDAs59PfnmtnjFkk6lqm02lpG13YWIMgIE3Lpmfge7cZd2VGwpQszShGIGozpPQRwmAsuA4EgUQ5AmFL3s5w2CMNPDw6JJHAFAkQ4UjL8dEIKS133XUXvd52CXHpDzh//k7u2NzCs4r+3iHtWgOnSNjbvkyvt4PJyww+XMnh4JiNtfPgQK8/4IH776NW9zAmZxKlCC+kVquhk5j+oMfKyhLKC3n0bx5DeT7kgnd/3w8Q+B5pPsMLK1jjkqUpuzduEs973PnAewhVhK9ydm9e5nD3EqHnk+qUytoS1jHkxYytjQ4mA2VzskKTGYvNYlxZIApLPM3wKlWEdGi3OvzEf/FTfPHPP0rLzXBNQjSNSY4Dbl58jjxNidMC1xQ4aUbhzEmLHAFUwworJ07SbrZ49dpV0AXWFoT1GtkkoVJfox56ZHkZWIotR7uTcU7gtTh/foXHvvZFcpuQJSl5lLDS7jBkXqot/ZBOe5kgCLh+/Tr1SpVKpQL852/kfx+bwDuttfuLhf4FIcTL/19+6dtzB0I/tJ90HQoBdp5QjA4RSqBtRpJEpT9ajPCyBE8IJm5aJtEGITfnYx7fu07geLh5hpA+0XhAY3mVzsMneOrZr5QLUC089UA8jyi0LqWYUbyAQgocJM16A1LJYDArQzZMjvLKUZIucpSSBNUaWA/PrVCtVpEKsqx0G87nc7rHfUCwsrLGeDAj8Eos1nw6Q8kSXllGxwgCL2B5aYlub8DJk8sYC3keE2cJgaovTnGXOJ7fFioZY1C+h04Svp1tDyygHdxWCQK3hUIsNogoipAS5nFMtVajsFOklFhbkGUFSikatQqq1uRg2mU8mlNBI6UiyzJOnlpnZ3uXpaUl2p0Gr1x5ppThihDfDdi+cZPe9UNOnbuDGzu7HB/cYDC8yaWLT5MlY86cvotrr16j3mxw3B1z8cKr7PYG7O0f8tlP/ymbyx5xkhFHmmbVIwx9esMhzUq9PAQmmniaYExEo9Hiy5//PPc/9FbO3Hke31XM5wlWW2qVGoFnUGaGz5yLF57kyW9+ldCrksQzmq36awBQrcnTBE9LYl2AkhRoBGlJDLIOftjA9UNyND/5kz/JxRcvcOGxr9CsVsh1jpQWMZ/y+Gc/S4FABXV0IbFRRMV3EH5AYl1qNYd2u1PyK/oDKp5LWHHZ3b1Gq7NFPOshxYxq6FOkCsfm5Llla+NOoskAX1matTaOEXTqFfqH2+i4TxxHJSk7zYnjmE57mWoQEgQVlPou9gSstfuLr8dCiE8CjwBHt/IHhBAbwPF3eo+kyLl0cFSmRxgLeVJ+tqULsUW4Cist2BgcgWstvd6AU+vrnKi12ZkMy2C4uADpIT1JJx4yFpIH7n8LV165TBzHFHlO6JfjuluJsa7y0XmB75cP68bVAe1mndE4orASpyYoRM7q2jqtdpXZNCKZGRxTwfca3H3fm3jxxReYzaYMBlOm01kJgEw0Z06uUGiH3nG3hE5kBkGOckode1jx2N05JMsjTpzYZDIdIJVi89QK+/v7SOvQ9OulVXaRoVi+HIooJsszWu02s9kEx7k1QxYcH3UpigLPq9zOtpPCQRflqMj3faoqp3VijasXLpDcWY7rpAQpwXMcilxz5eVX8F1Dq10jm825983LLLXb7GwfsLYZ0m57CJFRdTcYDHrEyTH12jJJbAjrHo0lxSnVYBod8cQ3b2KtZW2pyqh7jVbFEM3H3HvqTdx1z2leubzLfXee5uUrLzKc+1jt4SlLEo05Osy45+xZXnnuOm3/BFneK/+fhWY+n/EX/+mjfOZPPkpQa/LwOz/ImbN3cfrkSb78uU9i7JSV+iZP1FsYmyL9gDhNaLUauFJTpCk6L8hSjVf1iZIp434PT7g0a8sUOsSvNUiEoNI4TSWo8vBD9/Pv/7f/FV/kNCpNpFQYGWCEQ5JZJKACn/DcHXzkv/0FHv+rz/D4Vz5NnmsGoxn9LGF25TrCVbQbLRxrGfe6tOo1uvvXydMIY4blGNZucdfD78H1Fefvu4/97X26hz02TjxIqy25ee1FXn3xefIsIWivsrS0VFKtwoD+oIusS7I8otr4LsmGhRBVwFkEklaBHwb+F+DTwM8Bv7r4+uff6X18T2G1g5AhVuZUHXCMgxAuttPAiBKoiNsg04ZqoZiO5zibkhOtLep+m5gCJ0qp1FpsnjlB98ZlrDFEs5iVlTXi+bR0qaWlxlot0neSJCEvDCZN0KY8AabzmPWtTRyh0GbM0mobP6zxzIVnaNYbpFqQ65Qg8HAU7O7v0qjVbws5XOWjqgHb2zdZW1sjTaMS/W0LXBmUWX1CcHBwRJZleH5Ar1d+sK2AZtYmzy1JuhB3iNIJ+M53vZOXXnqJ4WBEUViUI5lPpri+W1YMQhBF0W3EldYazy2rBN8P0Hq2aCRqlAQ3mrESQF7oRS5BidA0WuNLSa1e4fSpDXYHI2TFQypBb3hEqx2ystpmMo6Qjksytzi2yumtE3R7R0xGQ7bW76LfOyYrIpJ0djs0M46n1CoBjvCwVnP+jnO8ePkCs5GhXg3LfD+hsbZgrbPEzvEennDZv7nPHafO8eq1PYzo4ft14rggDDxyM6XdrHLcu8FXPvd7uKrGxvpJzp5dAxFSc5ZLyKijieJxqeLUgmie4CpBrVbjsN9jpDXZPAaTozzBJOn/P8y9Z7AlZ32v+3TuXjnsHGfPzN6TR5qRNKOcRiiDBAITBMaA8TnGcjkc2bec5GMwNuc4YZeTwBgjMtggIUABZUsaSaMZTc57ds4rh87d7/2wZJ2qY3Pq3HvurdL7ZVWtD/2hq///7jf8ngfHDQmq80iKQfXIKWJfQpTnyRkqqhIgOxKmpiEJ8CJBKEcYpgJyQHNliT/9zH9F8ZusVVZpxxINL6bWrBG1awyPraO8WkaVJEI/RGp5JMwkge9SLc93XhxyiUJ5C7WGx/ZtO8kXc7TaVWYWzvDk00cYHepFUzrPZLXa4V2apsH8wiyFQgee6/gecwsrP638/o+/BHqB7795IEUFviGEeFySpAPAdyRJ+gQwC7zvf3WRRCrNnivfgaImmJqfpra2hBFLZLLdOIaBEBEqUKnXsBQFOZYJ/I5kzir0kTBymF5AajCDmk6QyfZQYhpZjSmVKiQME98L8b3wTUZ+Z+7shxG6aSGrnXPokRAkkymyGQ3fbRP6IUFUJZsyaJQbpK0EmWQaR4rx/BaKAYKIMPKpNaukcync5RKO2+xkxkXAwmILWZFImSnMRJJGtY7vx0RBjOe4b2X9bacNdI7NNqutjoSTCFVVCIIYTZM4d+4Mvu8iyXT2uuUOiFWKJboL3bRaDeJIYBgd/+C/rR0Ab/1KkowkyfT1pWnrOo4DstqRdShKZ5/XcdqUSyXShSKVSoV22+HySzczNTWD49j0dOksLS3RqHkUC/1kUzk2btxIV7GPY8cPsXHjejJGhrmVyU7s1QvIFztw1zh0iGUJ3VDoGy5w6OTrBJ5HwkwAMrKkMzLaTbveol6qIwudwA2oNWu4a20kWaClYvK5AkknplItse+mKxgfH+Ds2WNMnZunWq7TLDU50yyzadMWUj0ZHNbwgzq+qCCEwcziHIV0lqYDi6VpllZnaYctNFfGSCYwE9abFSLQFLA0k7GuFI2VFqdefoa73nUd588doVvTCN0WkqLh2DZRQqdcq1LIpdEdjeWlVbqLKdaqJTxZoWaHCF1BTxg0nQZtu0V/dy9r9Qb1ehNJlUhnUyzPLRFFEZlii6nzJ0hYvchRSMYQnD3xAu3GNIWMQUJLMLM0R8K0UC2Pnp4+FKXzHIlYIo4lUsn8W4Gy/2j8HzUBIcQF4KL/4P8ysO9/9zq5dJavfenPkEI4/MYpPv7rv4dLzAO/dz93X7sHzTBwY7jiqptx2y5uaHDs2KvsvnSciW1jRD5Iss6X//EfOD87zde//S0uu2Ef01PnmDl8HM/ovIkVuSPYFHGM9Ob+eeh3FvoMowPdaJchrjvcue8qdk2MYWghju3TagacXq7z+HP/ShDLVGsr5Hu70eUURHLndJuiMThQJJ1OEUUBktKRlc7PLXfgF1EFIXW0UIqsEUsSQsQ0nTaSHJFMGaTTaVTFJJlM0/ZqbxZ6C6fVRlYi2nYL0zDIZQudaY3rEXghtUqdSPhvHRE2TZM4pgOliHnr/4GBARqNBuXVFiv1Va7vKRLgIeQYSerYcZKWyrqhXs607c7UqpBnZWkWS9fYvHEPk+cWIfYYHE6i6Q7ZrE21dYJq6wSDo30EbsBr5w4godFqtdEUmXhlBUUOyaQtElqCxbkVDMMidFwaFQ+pbxw0BcMwuXBskVwmjaIKkkWNvKyRbrksrFVoWRkGenpZq9Y6wZmuFK8dfZ1ycwBdVugu9LF+nQl6xIpdwdHOcWTtFGuzEqWVBqEfsW37RiIJVkvzdPXK2G2PQ2/MMjycpSZV8OZ1ujIFDDkknzdIZboJybBt6zZWjTMkfJd03CIrOouyLT1JqVwGTaG+2kCNY0TkEJtN6qbB9EqZZlPD1BOIRAsjZYLcEbQWurpw3DqF7hSLC21Aw2m76GqahFWEtqA9d4ILy49y9tm/wpR18pku0jUZIzYxQ4eLd+VwXRc37iN0Q9puCyWGpKVhOy3C0Gd8Uy8Hlhf/w/p7W2QHvMCDOO7sD6cMPB0CLyS0XRKaQdv3+e1P/yHJTB4R1AlihVKlCbFEKpEgljthEEVVufXmW1g3PsYzx9+gtN9EPnqSZMrqrJ6rMv6bCGnf9zF1jSDy0TSV0PVQBQjfZfvWcQYKBfIJHdv2kOMYEXjMTM2iKiah6OxDhwHk0jmcloeqaDhtj1q1RaNWJ4hCxjduodpuIkkqmmqQNnRqjSqyrBAGAal0HlVXcJw26zeOkEp18ggHDx6mVqtgJk2shEk6nSWKIhKJBMViDtNIEMfgv+nL03X9zeSceBMdDl1dXayuVjr31/Pemh6U1ioMDPbRCiNou6w6LaSuHCLuqL6EADmWsFsOmWwBn7Czg4CgXm8zeX6KWEAmkyKKXBAasqpQWaq8hcByXZdsMsHKUg1ZSJ2kZNRCJkDTDKJYYmh0Pc1mG8ky8VouYeRitz0atSqKrFKvxwgpJj+cx3dsZE0llc7SlgVrKzaZlInnOhAGmBmVpcUS/X09WIbFWrWOmdKYXy0jZJ3ycoRvCzL5BJaR4OixkxQKXWRyaVQ5ja5LJJMGKDGhDalMgZbr4EgO6Xyept1E0dO8duQAo7k09WaNSBKgCQw9SdMVZJMJqvUaoRdiGBYtxyeUHCJJe2tHxwld5FimXm+SzuYI/AA/sJFCl4SpkUwm8cKAZNrsiGSVCFlVKFfXMJMJnJJPobtAKAkG1vVy/NQ5imYGJwbNMmlVO/dXS+qUy2VajTZxHOEHAf8Dhv7vx9uiCfh+zPs+/nvUmg0q9RoSOknd4DN/9Bc88OnPYmYSmKk0cjpPrGZoLZxmdnGFOIae/iKRmmJgZIS/+Zdv8rF33c2ekR5O+h5bBkbZde/7efb5Z6jXXbp7u3j/+9/fIQf19tGqVTly+A2+8bV/YuO6froKOUZ7B2g1atRWljheWuDgsTNU6g6SrOEleth71TtIp9NUS1X6BteRTRvkM51VZlXRsVsOXtwBVZw8fppUOgtRjKrLDBSy9HWnaNltao0WdbuK7GnkCxnWKiVqTaVDm0UjjmWq9QbFYjfNZp1isRuimHw+jyJrnDx+tuMEVKU3KbOC3bt3cfjwYTzXZXlpCV3r4L9lWcb3fTRNp1RZxA9cDAlKSoKutSlKf/cA8pHv8G+q7NHhjTz1ua/QTuXQ0il8oWPhg6Rw/vxZTNNElnVURQcp5uChF/F9n/7+QUzdQtM0RCzzyCOPMLZ+FF3vJC41XeKNw6/x5a/+DW4U0A7a6MksXUNJqjUbDZ2EkaIlOudB8slupubmSaoygQuBUNC6UrRbAUG1TsZMMTY4AVpAvqhhWDGNagUrmSAUEmltiIXlMr4XoSck9FxMKJrkehI06zWarSpry2sk0wmK6V4URed919zKqfk11mprJBMy9bVF+nuSFJIJHL9MreVRSOWoOjKt0CIt+0hOjCpHqMKjYKQQsYos6zTsFkgaOUWipTRxlJBEmCGOPCqlFTQ1gd9so8kybtsnnekhDmqEcUSuYOLaNWIXah74QYwaZIirIQiX89OzuF5MwYbV1RBdS9JqLTMwMIQkSWwcnSCMIsq1Krqso8sa8B8nCd8WTUBBxmkJ7JaEEifICgffc1BUlUxvH109RcyEgTDzOH7EhmGNXD6FJDwsRSKfL9A/MMTS6grf+fpX2POpj9HdqnF5Xz/7p06TSCSoVEps3LiR06dPIoTER+79MF35DPMzF+guFujKZbl872UkopjJaZ+ps8cZ7esGJcn45gl2XryLIzMVrrzhZu666w6OnzjSSSKaBglDR9MMVFWn1UgQxh2UdDKdoNGsoesmnucQBg4jI6Mk00lOnT5DsFTCC0Nc12V4rI9yeY2hoRGcZkyr6RC8+dVi23ZHye04tNttfC9GVZW3tgWjKKK/v59bb72V48ePvyWp6Ew71DdZ9OItZeCRI0cYLWb56x88xTZ/jQ3Tk+yI445XR+pg1n/8/UeJBka47d3v7rAWI4PXDr5MtVpneGg927auf1NLBsODWzFMhfXr1yELA1UGX4L7xjaSTFrIkozSkY1zxd49rJamePqZx8FQ8QIfPQnxmgBFwXN8Ql3DMBM4TrXz9ZPJw0qNVttBV9JoVp2w6dFyAk6dPsHu3bvJZTOcPvsG2aSFlrTIppJUGotkkxapjES1EdFudbIEnu9hWKDJEq1aG8cOSKWzPPLPjzLRl+Y///rvMDKyHlURvDT1BG0t4nRlkp4i9Pb0Y6kpkmYRKVoh9B0k2SDAQU8kqdQDUnqyY2kOIjRdIwgiuvq7CRIKcUWAH2BYnWRo1rCwWy0iP0ZTTbqKfahGSKu1hmUlCVsecSAhySFWSu4sZrcFhlYE4ZNO5alXbPLJPkRUx1A1UtkccQR+0CZlJVhcWcQL3+ZQEUHIbR8Y4/R0k9kzIdkGDF08RMldI3H4KHMnTnIopVHvyXBROkUj3cVRd6WTuddMWqbKG2szZP0WFcNg/1IFgU3Cn8MRYee0nBtgaCblWh3L0jlx6hC7t60j1sukhyT6e1TytNAVl7a1SnIsQ8pI8vMT23CikFp5kcu6+7lyKIc3exREHVkN0KwCWQkUWafiythBTE716cum8JODRJJO5LWIgoirN3VhpUwmV8tU6lU2bO7BqbWQIw01dClkkqzML7KuJ8lMcxUZi4W1Fbr6+8iZaY7sP0ygQ7KYxWxEOA0HTdcYHdZ4/7uuJFGd5qMfeRcFETCqaDzw94/QzqZw5Ahdc4l8hde/+CUqU4dZiBMMFQaQSss01haJ38zyy0FE7DisLtU4NdtE68qz+5JLOL9yjh/9y1Moss4z3vP89u/+KgnT5Pips3zjRz/EtwNGegb5L792HzOlBZ566hWWJ2fYODbM7bdfj5HT2H/wKPtfOcj42JWMfWAj3/v+V5hqT6IIjdHhBM3qEpLqIkdgN8tIQUh/XhAFdbTeNIVlQTQ7SbhxM1G1hKLFhFKDlQvPUV/tRi0M44QNTHsRS8/hOgatJoRilYJu4YYRkZWkJJUwQgnd08n3FllaWMSp+nzrr77APfe8j7vveCeGlUFRTe788L1oyGRFgm49iWbJmKaOUFS2BDZSw2FFjmlMH+Xphx4ko2qEsY2lCTJBhKREVFuLWIVevNDE99oUFIOwuUomoZAZ3clCaYVkQkavu+SyCUqtJbRUAdsrEvoNorZDb3c/jTjEJyaIYnJJmdASZFSXLlMF0aLbVMhYCroqMTmzwOqSRzqdIXZzVNruT62/t0UTSLoe+144zrZawKsLTQ415pk+MMP1I6MMkQC/gbroc3SpyoxusCE8QfqijRSWm5BN8xvLb7A1VFnNyBhhG/tTP8sLkcZx4XGR3surzgJ6l4WZSfDgX3yenp4uPM/jX19+njDIsWHDFQyn8xhqDttIE0cBg70mkhfyncMXuOn2u5k5P0vW7ONvH/oe1foCd37oXnYrOax0GlOSSKsexTQMZtPYkUasJOlp11D0kJKRo6tvmMTGS1iaW6Mhx3SPbkO3Ypq2hutHDGTzNGtljO4ChZTFRG+BuXoAuISxR7m8xrqJftpND7dtk0gJNo72cu2le9m7+TISZopEdz+P3P9L3J3MsC4UZOSYNXy6KRJ5IZ7m8ddf/ApaMont+yzQzQZ/keuUgHhIEEsxsSrhxxHbd0+QwOCpp57ka9/8LmO963jvz9+N59lMTc9x/x9/HrveZnx0mNuuu5r56WlWF1b5xKc+xcDEekaTCt2b+jm9ssxrf/0F0rKMrOugmTx9+Dzd3f04mZvA/HXCVIgvF7HWaVx/UzemKlN3SiiGzPyKQJUtdCWkIGY4cegA2ol/5LpP/AWOMkir7aG2T9Mun2fbtjEefexhhkcuRimsp79njYFej9B6D1UvQC5PMnf6ZbqDNIW+Qa667hr+2x/8PulUkYWVFTZfPEZZb/L5z/wpa9PzBG2HwW3biTSIZYk40iEWGGqMY3voIouUbZNREljDY6xNV3jmJz8kkdTI9RYxKzFDoxOEIiZS2tRaTU5P7Se+6DISqoLSbJIgxe033syhwwcp2zXKToiR2kHl3DT5tA9qgFVI4wQuScvEDyWKxUGc1ACxcEkPDLDoTqOnMthLOq8en0aWV5FVhVALWKotICIfRf/pGrK3RRNI6SrG0RP0tx2uVzW6nTVGu3JklxcwtAI1VcPOp6kYEk1FQlEKxLUmYaOJms+yIRasr5QwHFBaNhkzybpkluVKHWlsALvhcctd95BP5HnPh+/l8muu4Jab9vGOm2/jqn038fz+F9m7aTMZWcWL0zzwx7/MsYPHMTCpKx7rLhrj5nfdzSPf/i6m0aK/z8S1beLARzV0btt3J35QQU7GnYU6TcZIJdh7zUexMjp6dwFIUNAC1EglVnUiRSIKGsia0aHWtquEcUAzDPjL++9H93w2yAHTkc5S06ONiakoTIxvZmWlhESb3Tsm+C//+aP8/oNf4j2/+EtkUxsYTuv0+Q26AqnjEdAs/CgkqWpUXYezK4vo6QzpZA+/+qefIzPzOsZzX0EtP4ssBR21eODQW+iiLRnIikYQhcR2gGoqhEoWoTfBEISqoNl2ufDs43QPdFGSVdpWN2uOy/beUUa37cAOD1Op1QndiMFMitvuuIPnX3mdM+fnuXb31Rw5JTDSGmerJlGyh9cnq+RTFoGUI5mzSCrLtFtV1OIIUw2HdZfdSq11ildfeJKrrrkH4cGB4yfIpmPm1lz6NtxAqbrGyqFTnYRjXxdqY5W1aoXF+ZMkZAMCn958H4GQ+NXffQA9hi2bNrFx/TDfeOgbfOG//zmf//znaFSbfP4r38GPInRJkJJCQlnGj1UCKULEMQuNiOF0h1seqGlePXGO7RMbsZKCkZE+LtmzlbGJrazr20CrUuFb0md47wMPoFh54lAhdB1abpsrrr0TJ2hjWR168OJ8DdeLaLZXeeHpR1E0heVKgzjSmJ4uUegeYWrqPK4Tc/bY6wRxgJXuo1pr4UcdxoHnv6ksl2Ag30Mn7Pvvx9uiCQR2C201JOnbJOKQgpxCblZQA4/lpMXsQIHz410c7o1QVZ0TXoLxtYCmLMgrMvZqhTjwUDWLNCZxM2DQDLhM03j4wmkuv+NGDuw/iBErpPsKHJs9xw/u/yE3XnIl1+y7gdGN61ASGkLVmD92hii2sTIJSqttukaSPPfcI0wVjzJzbj/btwwQ+J2FJdQIw9AJw4hGs006V+SS3dv40df/nhuuu5Q9t7wDRQLXq6FbOeSgiRGoRLLMcmmF9aNjoJsEyKxOC85NnmV82zaEE5JNpNjR28XI6EVccGKkZArLb9Is1ZD0FJdsn+CSwW6SsslqweWR+gH0Hz/OaFoh2xR4UYCmWwSeR5QUyFGEHsm0pYBYCtADweLaPG51mUEilECAClIMSuTj1Ju0iYGO5VaXBa/96yvUPZ21ikMQtol1Qa7Yw6DbYjhpcXa+hFBM0oZGLJLYjWbHpCtJBKqKkTBJayGGU0Ftlxka6Wb3tQaOL/jh4VlmAhtj3QhrJZ9YMvBsj0typxjcWOT1mbM09AE81aN/112c+eGfc0pUSKX60KMVRnrH6ekeomZkuWhvjuNPfY1GY4HVNQV/4Q2GugyuufUyfvjjx9k8vo6Z6aM8e+h5pFSOnKbw8cz7uHLPXs6eWORP/vjTnJk+hxfpaAkdfIEaehB3fIMoCoEbIwKXRDLHgRefYs+le7n6+us5c+YUjz7yQw4enWXHxWO8eOoYqpklJYpsGRrhjquv4/gLz6F1b2Rq1WXXtg30dKf59d/8LY3MGN8AACAASURBVBqNJh96z110F/v49o9fwcx3kU8VuOf9P0/eVKnZLdotDxHJDA2N8+KLL4Lv0WdAs7HGokhjpouoegJZUVEME/lNWWyj0aCT6v/3420BFblIlcV30waBpFCPDCpGHkN2EGbAl7dP8Hp3ilZOwtJCbFelJDwmpqr898/8HbfuupwfFhOMNh0mUxbpyEXEKg01jaKEPKcX6fm5e3j+8BFee/lVPvLuu7nrlnewND3Noy+8yMNPPsmOLZtZN9SFkTHwmm1WKg1ct0qzskSf0cvYQBfXXj3O1t39jA5vxlK7+dw/PcEnf/MBTp84y1NffpD77ruP8T172NabZu9lFyNJMbfdcgs33/PzkBvGkEOamoQcxiRMi1f3/yvNZpOf/YX78IXCcL4bQ1YJgoCelEbcqDCa1+m6dBcLNZfAt3DtkEJOISs73DY2wcs/ep6Z5WXsfX1MssZHPYP+cyap1TKKJfjVuTkWEkmShorlKdQDwfXb+pg5O0mc7aV3xxUM1+e4O1vnJukYCV0QufBoOMaFgT20hrZRarb48SM/olVpgOqx65JL2bPnWmbLS1yYmaTVDtk5spegtkjeaLJ5Rz+SnuJsO6Y3sLnj9ruYbwY8feoM2Av0qDXSikItTLHrsuuIX38EOZWEdosTiwrfes3lQs/PUDBCRqRz/Nq+WfKWzssvL/P8ymaeeuMoG7VXue6a+3n8iS9TbRxhfP0lNJfK3P3e9/F4dSd5SWKX+xzv6znKV3+wn4dqJl994D6OnXoNq5AkoQgMI82yp/D3395PvtCPkUuQz2eRFku8cvoFeoc3EYcGv/8rH+KGK3bg+DJ2bhOZTIq8LoPvEOkKyYVnePDB71JxLd79wZ/FxOPYcpMHf3IEUZjg8LM/QfYa/MZ//TnMhM9nH/gcYqnNrstvx5UsvvjpT9KTN3jPRz7GwePnSSoKppXlL7/yEA999UusLBfo6tWpTZ7ijQPPQcJCNUwu3XUxf/gHv4vTanL73e/FzBbpX7cFDZ1GrY5uqERyx+htaDoffOe7uf9X7n37Ckl9WaZkpvAjiTiXpmzHpI0EdS3iWEZiVnUZ1TN88rp38/2nX8ZvTdL0HVrNOiKG47JMbKh4xLRkgSQkWl5MpEZYXoONo4MoPTnK9TJP/eQxcoqgK5tnanGOgaFB/LbHwsoKwjPIqxnsepuEHHHlpXuhEtJTTJDVDbrz/SxMzeA3TzM+1EvUDlFljR07dvB3X3iQ686c5tLxDfSm0wysH+X1535AIlvkkrt/GS+WEWGEkrCIg5igZeP6MclcgYFiD6YToUkyvYUUnl/B7Othvm2TjmIsLULyqpw4dZJGV5rhhErUDsmtrVGrNzHmBzDm21zYNMquK3ZjnDxM+fxRdEUlimWEMEGXQITcfuWVnMtnOCvnqed7SBYSuM3TxKFOFHoICUzFIgx9phbPc+rYBdora0iqRRM4ce4MQ4OjWFYCu1Qmig2mpC62XnE5Lz/8IBvGJWRFISmF9EkR7VNHEK2QvkYN4VQZ77EIbRev1eCpb/8zP3vtbjynxrmVGrWVWbpVg3nFwG3UGOgP8F2Z8/WYy2+4mef//Otc1qviLl1gebXEYPcG9GCNlYV5+nNpkrqD5tfIaQpd3hKb0ir9uo6HTFwz2Da8g7PV00hWkumz01x0+TUM9RZBT+IGMeVSi7i6gutBfXWVDaPj/OhHj3LJ1t4OO8D3sRI7UBMFfGLqrQqlySe54bJN2F4erbxM2mwxYajce8UmHvzOy1w/3M3aTJnpJx5mavEk+uISWXOAkVyBlZZNdmCIo2dO8on7foOlT/8B9bUVZCli82gff/Rff4O//bsfcWJ6kd7xcW4byqBoFqliF7fcdhWRt8aQpXHRtp1UtBRpUyBsn6ylIUTEyIb1aGZH4nP00Bs/tf7eFk2gaWi86Psk44ioVsdSu5guB8xnZOadFdRMEafZQJeTGH6M6biESDjNWoeQe+nVrFRLhLPnsGMTEWr4moqrqQReTLm8xsD4ej7y0Y/whb/6c545+irpRJIVt8nC8gIFI0VPoYdcXx4qOmld4h17rqEgybRZJJNVMSQFYUuU5uc4f/oUTvYyRBiQ0ExWG23OXJim2mpx+213sHlimOHxEb7ydY9zcwts9R1iXyMdOzhCkDNN7AsLdE9sJJ1KkTR0jDAgIWuEdhtVA48IWc2w/9Xj7Lh4E4lkzMaxTGeO7oTkPInLuwqszyZpDl2EVkrwsLyZyye2ctt4H//6xbPEaxLCsPBR0ESMpMhctetiLlo/wDMLLmeSfRjLs7hroCkWsqBDGfIiTh8/QWP9ZuYW54jCjsXXVyJKtTrzC0sMDA/he2/GmBs1iuvH6dt+Bd/53le55spLGCzmyLVXCU6sYPkue1J5QttmlzHOcsVhWDU4aq8RLdVJuBWEn+PUycdpBTqysUxSj6ktT/L0M2+w/foPMrjreiaGvsG1V+7hhYMtHn/5xxRC2JYa4FjzPAtrKzz6+KNsumEb8uxrjHCew+cDzjFAvhcWJ0t0jaRJ5Md57qWX0OyA+bVnmNi6hcMnJgkigfAlTF3H0jM4lSqt9DKTnsGBAwfQ6ydpOxkuuvUX2Lz3Jhx3gaULZ0hjE1ZrKH7EUHc3RthCrrW4QcuzlC5jKoL+ywc4fOgAY806g8k05dBmOChz5WUXY4ch33r4R/itkM/94WdZmjrPE9//AWlFRcpn+b9+6+NUygFe7ODWy7RrNvOrJVp+BTvwefLJp/mdT32clxdXUFpTPPq955jYsJXTZyeZO98glmU2b9pO0kz91Pp7WzQBrX+IjZ/6GNPHTqFICY7OL1P2q6zpDmHpDKFTYtW0+LN/+DOCUpNq5KO0bQ6eOc4H73gfM6fOcy4I0Irr6As9/HaTekZmrVlFhAlWDh4iv7zK4OAw67ZuYXj9CG6zjZ1b5eqbb0JHxnbq7LlmLwYpfvGuu3nymw+he3VazV4uufgSpDjg3OI0z7zaolbbiFOIuUF2SRsmX359PwVTpTI7R3b3TqTujXzpiw/jKnku6bmIyceewHFr9PUMMnHnPiS7SX+7TVcqga6ELM2fY2umm9CNkYQgFhEt20G0TBRVo7xsk8+pqLKCr8m46DiSwoZ8FwNywELksOPKy/jBXZ/k9bVFps6+zkLvRbSma6SxUYVM2irg1uucnpxFLjfY+PT32NOzHaldIi0WCEZaaCJEDSAf+dzYPYFW2MG5+gHWcKhpHsVGxPqdExRHMlRq4DQUYrvK3tLzXHM24u7xCutG1qM1lpBZQk7XkGIHRY8gMpF0E2lqmbQQuG2b7VkNe6aEHPrsi0a4Yd/VtDIqL1SXmF6tMz1/lvPLUxz5xj/RNTpOom+QrVt34iWKvPLan1NfaaHoOQqJLGVfxnMzHP7q/fzmviE+sCXJh75+nqUdP8N9N1zMwece5qUvv8aN73ovw7uvQrg27VbI6OhO3jh0klZgY0oGC00fQzZwZZPlisPdm0bI16Yp6i7qVZ9kyTZYePj7XJtq0b9UYvVsk2ee3U+53KJUr2HHAk1WkQQEaieW6bsecWzixiqSBrGoc+Sxf+T8C2kmH/8SScelVWnxZz95gg/d815+8abbYHmZZM928Fq480/TWlwlNbITI5WmUYVjr5/k2p/9AMNDEzzx1GNUZs7Sk1DJGdBXTNPqSbPUXEbVDZ575lHuvPNnfmr9vS2aQCaR4qkDR5mZWWFmtclq3EQ1QiQtJJvLI8IIIzAYsJI09ZDYNcg1GijlBn4c0mNAs2YjrVuHUVshnTQY3jLO1Mw5FmZa1Bs1tu+5jGppjXdefSNt36Wq1pg8O4fkd7bGcsk8E0PjvH7yJKFqsvuqq/juP/4NH7jn/Vh6mrOnT9O97loaqo0+NIYdL9FqC/oSCepOhBRGtGbm2BFHLLZCDpxbpZgzaJSfZfu738POnZfSPTjMvNPEarscPXaYO2+7hR07d3Lw4EHqjRaqbnYMwr5EHKkEskNGSGR1iXwySexmCAyNKBS03ZBY07BkjXS7idr0aNQWeO25F5h58lGc6jyppIHhN5EjBccPkGVYLK0x+fIh7l1eoZjfTLa/D99rErPaiW/rYDVsbts6QT00uO+afTxz4iXOdvUiqj69o+vRDB1j9QITUYnNE0n+042D5NoHScVLWM4KeDroEq7kEysSsWIQ44MUIssQBz5WQkMVAs13CKSIVOsCUSAT1+Hqri5uuniEpU1X8MhxhQuVNoNpmSNqmtfOlXn86R9jJrvwCkmEaZGNQiS5i0agcPu2Lq5a302tYbPt8uuRTIWrr7qCFx/7NpXI59XXX+Kmd1xHuruXudVzTD/9FKZuoCoRURSjKTFC8vHDGFM1Wb++DyMjKPRvZ3qtQey06Y5tvv+1rzN5fh53YYE1WSLWTULNwpETHbC3LGgFEqErMNUMrhLRjgWyFKCHFm0/wnCqNJYPUMiZ9I+OoGsJ7rrqMrotk5emTnHw1RfZd/V1fPOLX0X2I7Zdq1D3YirlBl//6jcggve87z3YqkGuf5TelMXk7NOcOvcoMQGbdo5QrdcYX9fPc088+lPr723RBGanp4k8gaQnidIWfQkVTZEglpiav4CiRkT4zEoXsN02FV/nplBhX7KHpJBQBvop+DFtZGQzQ6O8wtFn9iMpoJopxjaO0jPUw+E3DvJHv/N7NDyblufjBH/LHbfdiYglvvythzh47BSaqiErIatrVXKZQWRNECpg5TbwlX85jaOtY2m5Tm93gqChIXXJbB3czcrkGSQpyfKxU6ycmSHwa9TrCfo2FHni9Vd4+KUIS9gsWwqFRhtpfhFFN/nsZ/4bwg/4mRtvRqDgBxGGZhG5MapmI8sa68YGyBaSDA8WqAcBCBV1soQUREihTwqVoBWQkWUmDz1N2JxFNWNiISFiFREpCEUhROKb3/wGlG0+nDV51fP4wI23s3jYQ69Nvum3h6QfUT9zAjk9xr7BQS7rv4MDExcT94xw8uwh3JVTvHO9xMimdUz0GCjV/eixhyoEhFLHtyXHGIqGUHQkxUTECpIIiGIfCJFjGd8PUXwbOZaQTYgCn4ykopcPE1WnGE118+Erb+bw4jzPP/wtin0TKGaSYxdmGc4XMYw0ac0kFYQETQlJ07h5m8xgUuXVWZcnDh2jONHkC3//J5QrLoMDY8zPT/PiCwcwEknC0KfVqGClcxSUDLV6BS1qY/tNdEMnjFzm61XGlHWoLYv+eIXS1Cwvvvwyx09O4kWCrJmipaqEcofw6yDhhh4CD1UxkTQFTzh4QUCsSBgiIBQWkSrhSAFxLLFSczlUPculWyKe/vY/cO+9H+QTv/QJ4r4BHvr6t9m7dRMNt8YLX/0qklCQQti8dYJHvvddTh89wOJahXXbt5MasHA8hyiUiYXP5o1jnD9/nhuuu4bvfPsHP7X+3hZNwBMhY9fsRkEirctkNKi3bRquh2yAYZmQMHnplefRRAwZifvTvegnjqGUbHIbNhMZEouXXYTrBG9m6wOKssxCs8RKQuEfHvseQpP5mQd+FQCfkJnZaZ478gJdVppACPrHe0leqHHPnbdixTJdZoGS28+Lh6ZwwgTZ/gnmV5fp6clTWjtNYIcoVpoBY5zEyEbGb/pFFs8/RmlmElMSLK3Mc/2N1/Erv34/spBQJR8vVEjHAZ/97d+kqiikYoXpk8dBiwgUF8swCV0PWZVIhCqOYXJqbo1i06GvmEJP50glMyxMrbFWXyCf1olUmbLfYseZgxycOUJSC3HiGE3W8FSNUDHQJAlFURnV8rz3HXsZftcOlJGdTLYcllptukIdSQ8IJUHLUpmT29TUJuUDZ7BaLRbckL3jk+xinrGBMvnWebQ4gHYESgiqBkoS1xcggyECCGVwbKS4ifAbxEIQxwIhGUSALECOOvyepnCRVQlFSBS9CF+u4nlz6KuT3Jjoxxtah3n1TlRL4+De7Vx/0528+vpxvvP1f2Zs3U52XLWJEb3EjYUq+49f4O+OxpSL43hrSxjVEtXpFaLYR40FK8uLjA2tp1FtoKcUmtVVevK9jPb08fLcAkqskukdItM3QHK1zLp6P6dfPMo3Hz+Eqso0tJh53UQVMotBRDv00SRISQqK38aSOiQiJe6YoRUdTBmIJDRZw5d9fCERAr5hEMUqkiw4O7nI0vFv8cjXvsUvXL+DbP8g07LGk0dP4zgOzXCFWPYwiSkMjlKzj/Lk84dJauC2Z9n83p0MjceEoUwylWaleoAtO4Yp1Y+y++r1HD115D+sv7dFE0gkElh5FQ0FWWjMO220Yop6pYWnB/hKTCQcxrYOowuZJVMhPj9NpdKDsD02mBYvr87zg6Ucva5NpKmUdUHebiHHBnKtQqzKRGpMbWUOXVaI5Jg4JVNplDl56g3W9w5y4IU21/ZuYGmuSk+XSULXODVr0IwnSPUkKNtLpNMZegqDpEYUbNFCyBlKjQqKNoLjZdB61hF7LuX5eXIpBYSC5gsMqUZVMZFjhUBAybWJdAVDUzh96gSSGqGnNCLHRtY0hBQRuiquJDhx7Dxnz57EMmV8VUKVBR/bcRF5AjJmzNTsYZymy8z3GxgB2FECT4WM4hI5LWJJYAqDOIywKzY9m0wem3qJuadf4MruIcZ0iSAEVREoQtAyJI60Kkx7GikionqF+vIZbol91ktz5JQKWu8orqQj5M4BKBwfWZTQtQiiFFHkEToCJYoQwkWRXXwhEJqBHzmocgc1L8cmji6T8gI8ByTNwA9iAs3HUwWpOCBpm6xMm6wNnCfVY/Hp++7lsSM2o1su4baPZChmNpKjjndkP6Fa4/DZFS40uqknPbxmg6JVIFPQcZsBKSXF/NocdrNE6HV4C5qhMnXmAjlVZWTXZdjLK8RdI9z13o9w/ZnvcfxrD3F6Ftr5LTS8FrHagtgm9jU0IizdQIQRkpDQIvAUlUAx8UIVXRegRPi0kHWJZmRhhA10RSaJStKrISFwsXCMbnQjYFNeYaBSZvH4MYpXXcbSuTMkMYjTLiMXjbFuuIchXadvbB9NJaZHz5OKVFKFmMuv2UUYK0hyiGfP4ctlwtilZ2jjT62//9dNQJKkTXTcAv821gMPADngk/yPyNJvCyF+/L+6Vttrc2TuGBEdgk4cQ1TqiCKNrIbk+4SOh5NUqCtgCo3/tG4bd960j92DFjs+9XG+Ia2gLr5KM5DxVBMp1hGhgqsuEZIGWyBLHce735TRIxMn5ROmTPThInURUqkvYo5tIXfbJRjmAOVmAsv16B/JMXthjZ7xflzX5vz50ySzNfbcvIlcLoeIdYYLPdSmF+ge2ozan2dp5mHaiSTprZsIJIWklySjxniySx2TS277GJXlVXqlGkr7LP2yiefKNCKVUNIxYhXbaGBKIe3KKlldwRMashuhyCFBd4Jy7NDUZcTm9UyenUTUygSWRODZaDEQKChoqLKEkH0syePpdA9TIs1vhSXu6J5jqJCkGmqIlooaeggBySDGnVogHEriF1IMpXu5Tnaol1ocCdOk5W62qAnSuTahqBF6NSShE6GDLyHiJlYQ44qOLUfxJFAziMhHkxQMX0JIEj4qUqBgyhqxDIocEcshgewiYhkjljvJwWCRLrGMOBuRiXdT1qv09g7ByhpjPX00e0YIpp/loqLP/mWDn9g9zEoKYblFzR+gaS+QUFXWj63HtFSsVjeq74PjsyyZ5IpZPnj79Wwe387uDcPc8gufZZ29zHuWn+E7P3ycWdtE7TZpuHPUNZVYhMhCw1UtcgGIIEQmRsMlNiS0WEKPwLUU3LiNLkKSsUIkFAqBh42KHkjIUoyjmGSEyqYo4LrCLBN9CmkrxRdOC9a6thK/NMkH3jVKIxEjSxZ5JYMsoKXYqEpMLoqhuUZTSGyqj5HyZvFkQRwqKHEPhiigGCl06f+HKLEQ4gxw8ZsNQQEWgO8DHwP+Qgjxp/+713J9j9mFeaDziUgsEFInCWepBoaqEUUhth0gayoyFbqSe/n5e34ZOUyS7OrnxivewY//8gCWpCG8Np68REWOCUWAFtqEBAglINXU+MQ7P8gvfvTn2HX9VSSGR7nipvcRR01SmkptLuT1h7+L7IGuFtl7069hWwoXX7OVyf3HGOgZ5FR1ll/61K1cvW0CU/e45wMXsf+V8zR1D2eliBMEXH/Lh9l18G+55vknOBA0Gbv7nThnZtBNi+GBdWzIqjRW5mDzXoZuvIuZR49goaFELkGrgpAFwgmI5IBs0qC05hEID12XiHyBLrkYSohGROwEFFXBbD1CxB5COAhZxdFShMJAimUSRh67HZIPSiy++GP+5DWbT39gD+ePL6E5If3JDgFZihTasseWi8e5dOc7CE4cpj9awKyZVO0GbT/ElzX2zyww2i/YvDGFhk9HXxSDEnQEIkIhFSkEQsZDJQokJMnCiyQCNejEq2WdOGGgkERVaoSSjtASCElCikD2VETcRlJlolDHmpknXqkQHuvicO8Gnj8xRSh0hlPPclexyXVdGr97toxx3bXs0fKkRYyfzmMtnGDVV+gtDmAoKj1BhTgMkHyZUTfi/R+6hy07NtLX1U2Y1fnau4c5/+w0D/39V5hJ9+DqbfCa2KSIhYweK2SCEEl4hJpPjIyEQqykkfyIhO/QrbYZkiGZkDHR6dM7IR9Vlkl4EYEcEUswpCnomk22y6JtZzm+6vNCvc6zNiiVk6CqfEDaxkDGoJ4TOI7TMSsLG0mGwAvQkgUEKlPOJJEpEXgRiaSG1uVTq68QBiE9cuH/+ybwP419wKQQYubf3Hf/T4YSSxitjkDz33h6qiqDgMi28VUVoYBiqAg/RFg6tmfj0CRSTRKqTle6h5GRIVKSgYKgGq4ia4JEJJMKMqRNmUjY6NYAoa5T93zWFwa58vpb0YpZJk8tcHj/G8zPlTFUGZHScIRBspCjYTscOXiQTDbPydkLJBI6rVqdWrOF7spUS3O49SX6kwNMzVfZePF2ZuZO8HFZgjcO8OALr+E+8wqnHvvu/83cm8Vall73fb9v2sOZ7rnzrbm6qqt6qh7cA8XBFCVO0ejIggHbkSLIFmI7hhI7SALHD44fnIdAkGP4JUZiI0gQC0oU0bFFSZZIkZREstktsgd2V7O7q6ura647nnPPtKdvysO+3WQMtpJAIdAbuLj37HvPcPf+1vrW+q+1/n/StM/P/+Iv8fnf+yIXH3mAYj6mPz3kmfEtbteBSadPkyeopkYsDaibGUU5pzICLwVlCGCg0gnzeUmmFQ2a5l21HR9QSqGRCK8QMbbnnUOjmKWBqW4ofYef+83nuGDgbzz1EEbvgXNAS2teTybIa9dRkx0a5pTVhDK0Ii22CQTRZ1w0zFmGREE0GG8xdkKMiuBTXJJB2sfHhEQFfDPHVQUDV4CMhBhwUtDIPlEsUUffajyGtuuzch4jIjFYQqhRagrW0jsUfOTRp+ksbfDFb73Mp044Lvp9prNDFitbPPfqZS6cf5y90R6xv4+5d4vNrVNcee15eitDvMqpXU2WChLr+O3f/RxvvHWGn/vFv87Ju+/w5S//Fvdu7lMqwdRWyGgRKBIkJoAOglpIYippQkrXN2TOkbiacwPDxmqHYRJYF9AhYqqGbtWqXTdSM8k7LW1et8MkiwxNjUtht7PMdGMIM1g/3CWWc6aN5o+/+jyf/Ss/wXg6Z14pMt1hfbCFtyWNn+NNHx8hZksksiKUNZ4GbyONV5ikS13X72t//385gb8C/Pr3PP5lIcQvAN8C/vM/TYIMIE9zPvrgh1peNEnLz39Eh+VweAFJN0WmmizLmEnP6sSwaEbEpI8MkqV8ifVORqgsQgRWDCQhkBUSasFpb7h0/Az/4sWXePalV/j1/+M3yEaWr33lj0g2U268cBk/DvQ6mrKnsZUkUxkbx9eZvvE22gv2pnv4xGOLOYvZgvFsyrC/gYiwMVxhdrfm7NYm2/fuUBYN1WjCY2dPonb3+aPf/SpbwrKze5df/dVfYbh1kjtfP2D/pW/xk8td/urWgF9//QqjFPZtSr4omOgSIywhePrDJcaLVilZAr6JCCfQOqUuIw+de5jVnuAPn71KiiePGt0fMgoRpwRagrcWmSnwFhcjZci5VlXcG02wSQkpYD1DbRA3dwmjF8hNwUjNSKWkEQJNJEgDYoDINN5sEVf7WBdR0RFGd1vi0jogezlkXbK8j53t4ogIlUDRrjyZakwClhJru8QQSGKNbhoaH0h0hvUCKSDg8YyQok8MGf1rb3LpoQ/zrC05sX+F48vwjd0JgzMf5tTBdR44dYbrpAwGGc5bZGMJdsrqyhpSrXD5zdfYuHSMThQ8cukBfuZnfpbtm1cIz36O27tT/GCT0XgXiISoIETyGNEOFJq6Iymo6ak+W/WUk1pxpt/nvoFHygUQyG0PHSJSRoLKCdFQ6ZTLecasN6R/3znupTVbVJjxPtWx0yw6KxyLOZ3ZmDIsCKKHvXud0UzT33qQvCMRXiC8IzGe5WWLJeB85HBu6PZXqee7TCd7dLtDrEsxSR8Xf4DiI0KIBPgLwN8/OvXPgH9Ey2f0j4B/DPz17/O898RH8m4HuQiM9/cgRHpZTjGbMy8LHv7Ik+wuxkiv0KJBGkue5qhgqXZmdDcGjIylv55xXA1Q+h6feCTnZy49zO/+1pf52ms1f5KdYfrtu+S5ZuvkCr3z58iiJl8UfOnVyxS3C6TLUEPF0rzhqdmCj6wv8dDWJi/+yZc5medgJ6w0E2bVnEkx4mSzwfFFH7t6nCTX7NuKq4cLNhceESWbg2V2jp/kC1eucOWtWzzw5y7x8z/zn/L6y6+wMh7zQAwYNHes4+XXrjOKgjz0ODfzPLOast7NWM8E9x1b59jaCjt3bpNJjfAOEzxL5QLhLaZwLKpIvLUg8QX/4LHT9EuPcoF/Wnn+9Z0RfmmLTEnG/ZR//uQnBxBzEAAAIABJREFUeYKIzBd0xIDD0Q7T2VUqldLRgAtkTcmDSY0s7uI6fRYsIVyHkAqaWKO7hmrm8dOS0eXX6B7X6CynKSt290tmokO2sszwrdfJOuAHGa6sWFQps/Q0Z3/kb2HLCtc4Dva+Ra+6hxF7dAmYBsaNQAiLKSu0GnLzULI3Djx2ukPKnO2kw+bt1ykO9vmvnjjPhe23+NYLV/nW4DyTquBcv0GW90AnqNkcpQR7wrBgSB42WBYVhYmcaASnn3iYmCg+/7l/y9MH3+Zv/3f/M6azTMQS9RI2BozpkHiL8B6fZwTredyOONGXnOkbNrUgcQ7ihN2lTZokJaunrBYzbqiEvYsPs7dxmhXVIQyhZ1agCTTRMz885FYhKAen6NSGFd3gXEERBGXdoRpf5627I8RuzaA74djGOv2lhLHYpdvTKC042JtT1g2ql3LwZsbG5iaFDRzemraTjvUtMP4H5wSAHwdejDHuALz7/cjQ/znw29/vSd8rPmK0is/9wReAlu1WR0GmUoge8eTDNMWcu9O7NLGEEHGNJykdP/Wpz4JSqOAxqeGL3/w9ntis+fQv/gT57Bs8/VhDdV3xQj1j9bFTdLsD8AmL0jDa22Fl9A6X8kjhFH40Iqbw9MDwy5/8GGftnF70nFgO3AsTUhZsiQLpGhJtGN9+GXffCcxZQaYFvU7CcBmaWUMmDE1Vs5caZKb57FMPc+LjH+XJ5ePcf75CvPMG9y9mrAvFjuqwv3SJvowI76m9JxcO6SoGxRT5zgHmmmfDe2gcGoHyljxWRNu0bdMqxUfQscSJlJ4VgKCvErJUMlOC0ARi9Lz63Fc4tbSKTCOTxZRRteBOcciDJ7v4qkDKDrWUsL7BiFWmIaPfFaRii8Gwi0gcaZ5wqr9J/faLyNG36cWKTIKkwKUakfbJzjxF4i1ZrtFLOWV9D6m7pIOz7C5SQiNRQXPs2FNQ7DLbucIslBgJwo+JBIJURBEQKHYPAze7ilPLA1IxoyQwjJJw84B9Cq5qQ3L2JBfu24T7etzcm7GlezCZsvnQOoNDy2oGJ46vUC3mrGxs0hksM64lAzfjgSTlO89+g3JpgPUSEz22rtCphtAQCGhfkVYTtlLFp052GCpPHiY0NmINFEnKtc3T+P4S/XKCufs6O1FwuLLC2zKyXY5pJhWDtGQ2m9BNE66/9hY7pWNqUlZURkcEvAnU+ZCN4XGWu5s8+sQJgg1ceek13t57h6RrGJ7qkifrZDLh7NYmnU6PO8UrPPLIx4kq4623vs3J+4Y0tUCoAVFk/MG/+uIPzAn8Vb4nFXhXdOTo4V8ELv8/vYCMkChNQ0AqhY+iXdS0TQ87+3e4sdjFdiGKQBoUqz7h3uQuPq3QZaCb92n8gh/+8FnOnNTooub00LL1ZcHoxnUOTJ+weZHYX8Ie7HH31Zf42WfOsnflDS6d2ODBp86gcs/FqBHhFt2wQPuGi+Uh92uNjjWBGbmv6dnANw5TypDQDxIdFYn1bA36bNdTlje3eP3y6wx/5H6u7N/k0pnHOLi5zSuv3uJYHokHt7hbz8mEoesF/eBRbgYxEgNHuEgksx4dHSpEWqJ0eXS9Ijq2Y76BiA+CKCSenAmGSrTS3iE1+Fi2YbjWZA1M15d4UUY6yRLrG2tIE1mZ7ZC462Qy4mJFJYac+PAnOf/oj+K6ayyHBVIuUVsPviA0NWJs6caITx1NfovOyoC6vAU6pzPYpBDHSTeewWpJhUd0lsj7XWK2RnNnByUCDs0iziBfo7OUUtcTqmKfdDJGqhYDiTgG3ZZO+9W7cwo35NFjnpHxmGaHfhJ5qapQf+4ScqvP3uJ6q4h8qsOWV/hiQNMveGx9i8lQcjDeZm5Tkl4H2fH0BynrszHrhzf5F197k3TtJFHM8NIRCWjfIJREBMdJ0fDwmub8kmaFAgloAVolTKXmno3s9tdxKyfYKwpm+1P2yxmzOzNuxwmdEFhiiduLWyBBhMiNcUMZBFVoOLQFUgWCCPgw4lZyj2GyRGfNc3JzhScfO8+sbHj59be4ezjh+tU9VpcGPHHpQRLhEHaB9wfcuL7Lwd47zMdTvE04c9+TKB3e1/7+rOIjHeAzwN/8ntO/IoR4gjYduP7v/O77Hk4KCqWJPiC9QsWADxEfPW5Sckr1MeUhK2gyo4mZ4MLSGZ7O11GLgnnaZWmS8M5/9vc5LOb88b+8zic/+iD90YukvYf46Yf7vLPv6C4c/372Es88sUx5aoU1rchOniMXc7L5TUQZaRJoshSGHerKccLfglrR6C4zt0zp+9xOjnN55QEeX90i8w7ZWcIn+7z5ylWW+yd58aVX+dAPPck//md/h4dPnOKKu8bIHzJ69S1OdZcppmPyjubjj1+i4w1N6emaIauiZM2X5KQ0OsH2+uR5jkoM2iR0VtYwaUba7ZF2+pg0wyQZaZojUCgJIIgCEpNx6qtfgl//lxhqfAdE6dl4+qdY3ryPxs4Zdh0njCMfX8dev04GaOHJreSdF7/D2tSQd5bYCQEhBHhHKiwxNLioybGkq8dYxHV2a0kyOIleksxFTja7iwgNc6uopKSbdTHek9oDelQoGkI0NMqRNZpGSkQxws8PSc8/xmy0hzjcJhGHaFHz9IU+z74+ZaxW+MpL2zzy1P0sJRVL/T6pPk9hlhnVC5qqYjItyfuamEXu7sw5wwZv33mbz//ON4EVHp/e5T/++Z9k0LO89Dv/Cw9ceIT/4XOf50auOGktJZYQBVJkdOuaDVXzxFrOxzoS4S02NC1uFQNNhDXf0BDQvS0KH9GyYJ7U7B97iOJgSphbsswg8py7dkq6tIIUCYXzrD9xGjc9RNclh/M5VQW+ClRuQRlKpgtNPzh27k1oKk8dJCbtgkgoK8vB4YIbd/4EnGVzbcgnfvyA+9f7bPXPkvRL5oWi01nG++9PKAJ/dt2BAlj9d879h/9fXyePkY90MnKd0M87HN86QZ6k5HnK6VMnUPE88cKjrClNgsDKCnFoMa/cQH4io+4rhlLxr//7L3FzOsdJOHlDUNydceH+0/yDH/sZXnv7GjuvP89fXDsgr+5BT3EYKmoJpeoCkiREdFxQzAyMDZVN+WZY5TBbY1x56gPLzZnh2MeeYZQaquDQwiNiQAbLD3/4SV758ks8tLzEa3/0Ozzz4KPsvHGdn/rZn0af7PG13/wiy6JDgqSXSNaOrZH0VlmoPivDVU6nlnOqZllnoLqEjkKoVijFek8VAlFAUTVMjgRW3WxOHLc32LsKoSA4j4qwmB5gdKCKjuAiUSqKa3cRo5Ra7nLquCetZjz/7Ff44UtdEIdQgdESU4w5ZsfkhzvE4PFCICTI6IgqUkWBjJYQa7TqE1FI4QnRo2QgcyM60aHQEDVKWCQRLyS1atDBI3lXXq4gRZF2PPT73K4mLK0uE1xBOLiDFpY0V+hmQmgO6Z+4n/l8l/vPHme+f8hy2qU/9ZjYqjg3sUGgmBYlWics5JAPZfvw1MN8/fVDTh16Lk5usth1fPrBS/zvX/oar2wHOksrzHxBLRISoOsjDxjB4xtLnDUFsa7wnS5lCPSakgQosyH38ppxtsR2zBhNRgwzS7UYoeUy/c0eSifUWpDmfQZJjy2Ro4WmCZHBUg9bFHhn2b59wDvX7rJnFzQqR3pFFsFaQ9mA9y1zdIyRTAXq6PGiTSEzk3CwH/jKF57jwoXz5H3DShowJkMISSLf39Q/EB2DpwcD/tuPfQxvHdE22LrBu4LqYEJx7W1EiCTyqFogJYl36MUhQWqoa5J+ggiWlQsfot9UdH3FCVdw996YpH6Dsfs8j59aIb2YkB00oLqQdvHBkw2GqKDRomR2eEh3VjK565kfWOp8g98YXGT94qc52N4DdriX17x45SafPHGOpC7pNCXHhOCBPGG2fYu/8OhJNjZXmBY9PnT6oxQHJUunt6An+cwv/Sf4uiFTCQkCFQOV85RRMq9KlJ0wtp7icIKgxO6VrZ5AjHg8EJBEtNYYIikRTUR6R4yBiMVbi/EB7QIDd4iSFq8jiQUjUqYHN6hHM6zZYWktpW4KXn9zxCceXiVGEAkELRntj5j7QzI9I4kVlYxIleJiQCmNFhItAupIuSiEgBYBgifRGUbNkEKT0obMMTqEjCglIbbNNVFAFiVWGTLnIRq8Ugy8xTeHJMMhs7s3yZRDiBkrK0vc3L1D58wS/Y6FTPD2eMZgE4Z1wzCTTDuBJIc8Vbi6QzeHmUn4CGNOnV/n+mTGhVRzQjRc3dnnBb/KF1/ZJ2QSFQRoT5QJ0tZ06jmfuW+J5TgF65gtr1HYQGbaVumZh518g+sDjVvbYBQMeTzCY9I+K0sDyHJGRcHkYML8YBshFG+WRXsPpUT5SDQaVM7rl69RVg3kHayXpKaHdJa5dyiT4FxF0zS4qmSwvEoQkhA9CIF3AS0id29PsfVtPvbnn+Rw721srDh3/gKjncn72t8HwgnoLKdqAvpwymBRsdGUxGBxwRJkQBAJjadRIGVCkqb0M7jh9qAck4eUzGhefPN5Tjf7XFjuUG5bNnoDBtUe337j2/z+V29h+oJjA8+yjETnOVg5x+bJDnH/Nreff4vKw/p9T/L0x/qYvAPr57nQ2SDoCe4RibU5wkaM0xTVnOrZV9h+9mXud477g0NvKSIVobmBziDs7TNQiuTtHTQSowJCeqIUmFSjQkPfVYjgwBc40XbZ0YD2Eu8OW7rw4AghIINrNQagFScVAQUoYVECgm/Ld1o0qOg45pcYesWoTlB9IEiSZMRmf8Hq4iZpLdFqwt/8a4qk8cRkQB0sxc6YPFF85+rzfOITDyIq6JBgdUMMkRgDmYto+W5PyIwoLTFaOjEH27QDMtKhhSMJkegdQQSic5jag4AgPLIBi8TR4GgFUwcBCIoiGszGWSa7O3SD5IEtxbFhzh2/x/mHzvPsK9cIx55ibXWddP8mqzHhmi0xOqKqwACD9XN+cu8lvvTqLp+7cQdz6ROcFjOauiA/e4L/+te/ihl2kU2FwuKs5pI74LHNPheOL7HmJ+wMlvnO8DQ766s8ff44r7z6HWalwqQauTzgIO9RNw7lI+/cvIW1gmgFe9uvMW8KXIjomKK1JlpNJRtC8CgRQRqESZDK4ILFe4sqajLVp2kg9w0xaenhekYxxeISzcJFRNOWKxvtwUikH9Mw4M5eyb/53T/mxHrKfefvY9bd56UXXnt/+/tBG/j/m8Osr/Ghv/t38W9cZfbCK1TffB6hBV4qgrakUZJFQQ4gBJXyzJ3ARQ21xdHSincOdtjqOFasJPMLyjBn16eojdNcOPkod8d3eOhUwaC8TSI0u2c/ysb952nuvsTk2m3Whhe5kp9jMHkeVXma3e9w7vAAHeeERGEaQ4ahMcskxz9LVy1TKccgkTTK43ODawwd1SM4S500iLoEaakFuFgi6kAMjmJWAAXBeYJv5cgUDUmwpDYCAmEK9JFuogCUEm1bNRFHBBkRMiKEJxBRaLTXSANBRvRIk/uUzBuaYAlSIMua8XSbJTkhnQ2g7yGH0DE4qxC+5uLpFaxL6K9nxOkEFbpgDWmnQeBBBpIYwEZQKUpI0BmNl/jGoIImmhLlS/A1PjQE5wg+IhHUXn8XAI01uQ2QVmjfyrVHuYLwkY6ILISizocYGpb8hKVhjuif5PDOGJNtMVdDsv4qg2LCIlHU0z1kcJgkpxKBECMfPrjDH6w/ztWiR3PVcPbsCklW8cJrr9NLu8ytQ8sOWZZwUCmeWfU8mGrSumA+MNzIlvmOWWNqNEljuddf507puLC+xmp/wfj1Vyj2ZyAzdmuJVBk906FSKTHPMSIhOElJpE5rMgspER8CLoLwHqEgqgoZPbK2pJ3ltiFJR6TQeNsghCYqTR0ddWzItUTGiDISR6SjE1zlCdJjgNFeYHJwhVu3bvOxT32GL/zes9/X/j4QTqBBsn3ucdTpC4wfu8BOckDn8re4rx5jYt12nFnIiwpCIFcdjJiyt5dBc8iCB0m0Zm96SAgKEyqupQnT1YsMN84iVhyjO9+iE++wKvsIXYBJ2DDfArePOLzJiUHJieO3eHxxm9ngNFH06Pa6+N6Y0lWkaZdo1lE6pSckMe6hsxF9q4nekThHKBzOzonegW/oxgoRPZFWGixIifHgtSNoRx4zIBCFx8QEETVeR3zm0UECbet0RCBkxAsAj4iRVAhiEEgEkjZVmhhFr24I1AgZ0fmCaOYE2SEJA4yrqKsFSsKJPsTVlCCHCMB5iTAGmWfIviSVEUKDIm01DpM5SEkiFCDwonVOhBpcg7AO7R14SyAQS42MERFbnEDQSnbLGBDRIYAYIjJKEBpfrSCUQ+pA4zwxFQjRoKeCRKa41FHJQF8nbJ5bwb58hfvWz3PYl+yGKVzY5HCxx8oiZVpkjJucH5te5RPxgF9LNumePM3xheRgcUAyqHl7POPfvlyxSBSDpIZ5xRNbPd4Y7XLupCA2C2wCspEMY+BYJ2UUIrkyLMeaWwdXWb3Y4dqdhtfueEy6TllYpEzAS5yE5U6fxlmiFLg0EEKg49uuTgBJQDqHVopoI4nMscHjBNSi1ZQIMUc0NdEIRKZhUZGEiA2WoBRaJxhpcFVFoQzGOGIUiGCoRUQFyf5OQ7G9eF/7+0A4ARUW3P3GP6G4NeHBS/8e2z/2X3L2xyuu/dN/yKkr3+CuFEwHW0wfvcBOKFmbTnlo7xqjJmH62usMJgVi7vj0p/88y2LO4WCZ7bs3WUobhr2r9NM+p06uocMSlZgShEA0nvL6HczOmGgbVo9v4JIcslWMLZFuhIyRQiwjpEcUdyHs40ixMuC0xU8zshCREqQQSCnJiAgDmICUAiEVQrSXOUZPiBlRBBAO5QxeBqy01EG03WU4xBGAFqVFxLaUJHzAEN9LB2Js26x9dDRHOnNLtUXWCh8tXkDmtuhIINQc1mMsHj9YQfQyzPEDmuEAEUo0YMqAcAFflcQwaVl1CThpQQSCzd5TPBLxaAEfSaBLvtsq3uoRRkRskKJ1ZERFjK0z81IRosDTchdELbF6QlZnmCBp6oYYI2reYKJgmmSIWJMcThgeP4Y5/SCZbnh5Ebm1c5NTj5+h8BMkGwi/ySgekMkRF2WJyiz/ameH6bmzXH5rm9n1OSvFgut3O3zpD6+wl4JRCaaCJVtySUaKxrNWdSm9QOqEoV0wWkzIkgZV95nfhWjXmMkDnnvpFrOoWFpZJVGSpZ5nOitoHMxsg40KqTVCRKgLOllK42Ur3wZ4W5MkSSuM6xyedtePwtM0TYuz6ATpBd4LvHekTmNtq2KUdRNEAyHUGCGIIeAai1KmTR+1pHENEclX/vir72t/HwgnUJYVz33t2+y8dpM4X6NzMkIWePRDT+GnO2zpDqVMqETK6tIaxwZw36PPsNrLmTaWq3/4Bfp14MSJZRazkqv7b/DQqXWGMpKoXUK5ICVBI2i0xiwNkS6ghQAZkBJUJ6V2ERkbHAItBUJ6TPBE4TBGIpwlREAFRAaeSBKa92S/ACL/93psjODf8/wNMYqW1zs6fKiJHpSOLT98DEgsIgp80KjY8L1s0O6914wg33UHgng0bxGNBmlQ0qBEIAkGaSAisSgCgrIsWQhPKGbUk4AIJSFGVBMhHE1aCoukVXEX8kiLIGikELw7jBYC+BgQQmExR409Eo8kCtkSwYR4NA/SqiJLAYqIjg5DQMUAtsI1NbHcRzcRI1peEuFaiTOhBVFpDhvH2nAFlodw9xUmi5psuMXOZM7W2jkm0xHzeoQSjn5VcjGHnVnBaOMSea+LA7qH9zhRTXjuZuRmCrXJyI+iqEEa6THn0bMDxrpGGkEnNggJlZTcG03YOH8fSTNh2EuwbzgmtUAlkf7qKrWr29w/TUg7Aq01ed4ly7usra2xs7PHO9euE2ML7HrvCUiUFCh1NK9hfbuhSPmedJxMNDJVNEWDIBJF2/QlpcTaGqUUQqhWaVuD1poQ4pGAbUbpPc4HyuYHPzvwZzpCYemVq1z68MOsa426+k3mccFidA+T95FpTlItSK6/xdbSFs4FXt7rsJdEOt6yMjBsv/YCsbiFbfY4f6zDFvskLiWqhmAgFa0Yh9KBKNubRVQ0dtE2pvg2lJOhJM8zhMxQQtD1CrQGpcAGggNEQKoI3hJ1K/AQw5F1xLahJ4QARxWNdw050kDQQKR14f5op3cI5yE6HBbvIjFo0iNy0KgUQkpEkoBQRClQ0rRW0r4ZAF62EQbRQfSo6Ei1ItZANKgQaKYTDqspnbWSpJLIWLWGj0YoBd4TpWrD9CiPPm/AIhAI1NGuH5VEREkUAh8kIRw190hDGycIpG5LWlKBFK2TM1JAWSKsxdsaSod24EJsHUx7mckT8A0spgv2S4vUOWp9DaSi2d2lYxJWz9zHlakkdQEtGuZxzCmpOe0iJ33gxnCD27bLybphuLHFpNzmia7gO+MZd5MUvCETAd8suP94h2UsvTSjTGp6gCzhXq/Ln4wO2TELHuwKEBVJ1+DrAqcHrAuFLWs6vQ699QFCOjpZAtGyNOizvrbBbFFw+fI9VCqxhcD7SNM0eO9BKaROMMq0ady7m8nRmrF1hVKKgKcJbfzkZECKiLWOXKQI0QLHxpuj53qcax20MYaqLt5rNPt+xwfCCWTTGX/JzxEL8GFG/6EPw2wMK13GdsH8cMI6nnOyRDdvUQQQI8eaMYyfu8H+0hBjHE+fivR8hvBTpKxakKm2NB1D5RvSKBB1jVDghACdknRTogUtMqI0+FiTiDvgwPucRnXAOqT1R7tgINhIZjsQHbVuUN8TCRCPPHgMRGcB3guWgwgoX4OwRGlR4Wj3lYF2DCwSpUcrh8AR0AR862y8hKZp3yJGYmgVcGTwECKKiLARDMTW96DKLquJpl9HqhCwTUOpNbPUYKSHAEGo9jMfGblXkog+Ot8CeDKCV3Xru0QLTiopECJipCSRgXaWOICdQPAQFL7yeBfwPlI7hUMxlykL0aXB0AjNvg1MqLBjz8FiSk2guVfz6UcyznQ8ppwjJhCznFd++yuc2FinqWoee+QZ7sbAhZ5k985lfuPNG7wymfE//ehZDg62+fx8lRfPbdGb3uKwOIvOHD/+E89w4c4tXvr6gnHaJYrIk/MRz5zIeHhZks5rut6jHMQE7uVD/sks51ajye/cQX7zOch7oFP+/A9/nCJa1rI+vl7gmpIQS5xrCE3Fvd0DvvyVd0hMTpakzA8nOOeo1JAk0RjTOssYI3Vt0VpiTBvGO+eOlIMEcd6me1IZPBEfI6DxwaOEonEBcRQZtI4lIo+igbIsSfMMYRukMu9rfx8IJzCrK37/2a8TckPIe9z39ghVHKKKbTrFBB0NBMfCz9ESUt9QA7ESpL0abVZ468bbfHLJI7wlKI2LFi0CQnmCD+AhRg22QeeaEBwqbVVqRZKA1xAj2teMkmMkztMTNblqcEgkGikaIi0SjxdEDIoa4cJ7aYCKR0F6pDUG0ebuQghcaJBBEYQnCotsEqKARnpwERVASIjSgw+EdwP+KAH/Xu4tpQQjgHa6ra4stYeOAGvB+7aVNcoUk+ZQBkTS6jEUARYyIcgCHwOyXVoQIcQ2tYnCAQIhDFLq1uh1hpCaIBVBCByaICQL7/FHXzE4om89kG0EwXmcczSN5eCwpLEwnhfcnY2ZNlBYOGig6sCKAJlokl6Xi6f7xExQxH00kRhheXOFFaNJpnvcmgSElawyJxnt8W92DM+/doX7VxKaxTK/df2APzKKur5Bb36PK0mkR8WWDNwYzTl0kmACWXB8eDXjkdUhMXXsrG9ysFdzJiQcKsk3S9hjCSkTjLYsbxzHqwwfBFmSUs1Kbo+vUyymNGVBXdfM5wW1dRxOFkjTo6otTWporEbrDOc9mczQOsG5ojX0GGkahxBtWhhj+z2EgJGaeHR9hRBtGTg4hJItk7FrkFpBjMjQ/k30nhCgaRqiAGMMrvnBDhD9mY9dF/hfr0/J0kivd8hH7o350MYqfTtGL6ZokRFi29eOUsimZDvvoo9vEJfXuHz1dT790YcQixdBSIL0yOhAKJCORGlUbNtepdHEEBHCURczhPQkEjASYQQ0kfjYX6YsS6orX2WtvIIUpo0qqPBaglBYX+GFJ7HNd/+R4BGxvdjRBwj2u6mACAhqolcIBRDBJgQCNvUoqcB6jNBtOG/De7k0QIyC8D1S5Pi2RKi1hkTg6kgVQBhFU3uiBylkO1tAZPnkBsVkgd3dx+kEbSRRQFS63SWiblFqGQnRYYRuHUAQQKAQOR6Fi60W36FtmYFqF9lfNBRVzbxw7OxNKEpLvYD59D2KArKszaryHJZWDavdHsfSDo8N1nDaQxEpq4bFomS6fZtFr4vrRnRosZDhmWOYsibMAzEVWGXolTvoxR63Z8v4ecMvfvoYX3rzKuUTn8Hf2eenf+gpvnn5ddbPnuSRgxv0d0fcOKxYGEUaajZtyVmVYOuCe/1ldh+4yNcXb7O245jiuSEk62sbnFoe0hkYso0edVXQLBa89Z3L+BoOql3ube/jAijTMkE7FxBRYUQgBsmirNBJj4XzGCOYLxbUTUOappTFvE0daXs/3t0w3o0sm2AJMSCPojAtBda3G4L39mh2xLa4VAyAwvu2QuC9R1qHxLRr5n2OD4QTaLThD1cSsmpBMqt4vhzzU9MJ/8FwiY1iyiwpwCtkZamShLHQNE8/ifqhH+XZV1/nr31Kk41ewCqFCTU6NliVgUzxMsGEANKC8QQpET4gY0oiArV1OFkQqwmYyJXlHyd79BeYmz6rH//bbH/xV9DvfIP+9Aa6DjQChK4RsQEyQv3+c9rxe/hVIlALSTd2qBqHIyKDJkpPEiLCO4Qw1NYTVQ8VNV7WrS8QgiAgEltyTiWJtW3R+RoSn5KECCHQTC19Wlrv7jCgizmqgu2dfaRsW1WtMWyr04ylIiq3kfLqAAAgAElEQVSofGS/lNgAk8WMvf0dqnJOU9SMd44MGVBJa8RZB5ZXBiSdDllngExzvO7i+xDCAFcscPmUIk5pWuiDqQUVIPEQ84ClIbHQ7ZYo4dGmh0NgFNRFilNdvFVIOWOgIzuvvEZBzcULFzhfVNjZLQ5Cwu0y5cntK/y9X/4wf/Da23xRXsTd3OeHjq3y6qtfZ5zC9p09Pj6ac3B3xBu7Yy7JhAe7hjWpKFPJ/vopvqF7DLqn2UtG3Fo3HDt2jA0hGGYp2AXTquL1lx3j2YKymhOKCbZuqOQQJYZoc2RKIqIN+BCoQtslGaNH65JurrCVQiqom5IQXQvwhkhwLSgoRGu8Ibo2KqMtsYajRfQu0NreFIlGIGOLA/hUEH1ECIVWLbhorcUYRSfL3nedfiCcgCHSSUryEDBZn73hgDdcw60m0K0CtYkQA8tCUISGw8EJrAksJtvce+cyHTlD1DNcZ6lF3lEI3UHGCGbQ1rJDRZQBZ1q2GhpASVTS5u9BLbGIgvSHf45oDFZKdvQyj3zil6iXj3Pr65/j/uYGnXoB0TO1jkGvj43Vd0tnQkAU7xl/jG2wzREoI7TCO4ORGik9NlQYIqppKwLO16hUE6JAKYOMBUjRRgEh4qNCCIl3AmWWiEgawAVP49s8krQtNymVcsdqFjIjYulKhTEJh1XB5ZsFv3swZ1ZD7WFRtaTBUYFOYWltSJ6v0NnqcPGRFbSWdI5aguuqpC4XjA52KEdTFottxlPwoa2E5H3odsF04KGLm6QmI0ky+lkXQiQ4x/zwkKpYUC8KvvXsFSwg3iUa6cBqBmbDoYVEKUgi9ILHVZ47N3bY6C3h4yF7rsPt2Zy/9NmLfPPaDr/2wgHy0kMsN1NuXzvk4JRGpAUMN9h+e49q4qCEB9dSTjYFvQT20g470VAnXdS8ZqWR9E9usLLSR5mM6/d2KMsJvqy5efMQG1OU1iRpF5km9FlCK0GqJApBZRt8ABsiNnhCcARf4+oZWIkLfcIR8m+tPVon8SgFiN8tsYZI8I5wBLJGAVEelWSlBC9aGr4gjlJVgQ1lS3MWAtaHdg1JSXDuT40EPhCCpFrp6IXh7OYaW8MuB/Uhw6rk8abiv0gky2WFNJLLDz7Bh37pb/HWVz7PN25eI5m8yV/+0GnSssTrSIrHyR5CWpT2IHMaqUjCBGQC9HBZgkgzgh5i1AqEGRQjih/5ZQ7PfIz9qFERVhLDzWtvs6skmyeOs57lnJ3vsP/C77Pztd/k3OE72DqQJlWbrx1VBcSRtw4hwFFeF46uv8Gzt3wK3XjM7IBZIpknK4x9hztRI8sDHuwETFnwnXsz3iwF43GkWoBtwMU2ohBAtwd592hXXl8lyzJiuoZKJT4KnJfcrAXPXb3NXlBkS0tIFdi7egPKgo8C60MY9mDQk2ysHiPLMkyWEI2gtoG6CezujSiKisl8zmwM3oKKsJxBbqCXwplV6GWarjGsDHooCYloQa53d0JnKyQOKQKJcBgR0UrQTxVGGboSCBYMIDSkHaJMKWYJb+wsuOkUo/E2n3z0AU6HHUrdo8cCjOLvvZrya6/cQw+XiAnsHFZ08x4PXFB89qd+lDP1hPWvvsatyzcIWpMbx4qHntSM1pa4trzFHxaKrccv8saLbxPUhJXhKrvbE3wQCB9IVY7IJUFatI+E0KeSEh2m+NgCfK0jFBA80fv3Sn1ESeMDzgeaev7eeefce5jAu2tGyCMHcJQiDOo5p1ZXSZXExDYVjEIwaixzZ5lZx0IIkBIp236MdytULfVnaFWlU832zt4HV5BUETlzep2NfkaqPD4b4gYr7NzZppANQw2VDfzIP/xveDnt8+r/9ivMDyY8c+w4YubQsiLGgA0pLniEDITE4HVKKgBXHRW9LVJ0iEkH3V0FcRwOrkOjGekee50V8saRxhJDZD5rWL1wgs/92v/II+cu4H/sF9j81GkunTzB/q/+RzRW4l3rBHi3RBhiW2OPEenb3fHoflI08Me3r2NHcKEHBxK+8OackYE7GWRz+DufOccKihdvzXilP2Tz1JClXs5KZtgcpORakoqIqBa4osCWJeO9XfYWnsnkDqM5R6NGMNLgOwmdwQbWe5AabVI2hgM+fmaF/kBQN3PqYsGVm3eoa5iXUDWts5EC+oOENM0ZLq9x8fwS/TShkyrWTCCPnjTUdOsxaagxviSpS2SE1LfUgwBSgskBrdqyZqfb/izbHoeZkISiwiWKEk8sHcVoTIPgYFfzlW/XvBzh0YeH/J9ff5O/cT+QFtQ4vAysyWU6IjDymmP9PjKU7B/O6K+sMp+9yTNsMCsP2RZQasdS0zKpNdIR4wLKKX29iZg7VkXk9mRK6VOSmDJPPCp4cumxLtIoiRcZSkoSLaiaDoQ251FHQK6j7aiWR+ApMeCbGiFa/sem+S6O9C7g910coHUAxhi63S5PD9dhOkfWDXkURxOlgW5qaLopCym4OZ+yqBua2E4MwruO5QhMDIE/bav/QDiB4doa9t4uZx75BOO64cQoUDvL/lBxbXWN/4u59w6y7Lrv/D4n3PRy5+6ZnpyQBsAQBAGQAgMYRUGksuzy2ivV2ruWrd21tlZa2a7a9T9yyVvlkleyqkTZq7QKXFFaSqTEYIoEA4hEZGAGmJy6p3O//G46wX/cBsSlSFm2Si6eqq73+vTtW/d13/O75/f7fcN24FiZEfzkO06z9n98nN31FtnoEvMzS2Rll1rYwhQBKuwiRa/qj44SAiYQVKwsdAOoIeuH2Fw8iTl1P/r188z0NpFWIz/xs5y54wQX3/drlCpiYyJ5+huvI1/+BraoM3CLxKZkU4UMzvw4Nw9+nM7LnyMvC3biBSZhk4GNaZUTDvhtauWAnzsfceeRed7X7qKd479eXaZjU4JoTLq1y4ceOM611Uvc9s6HeWdnmo3H/5S5YIKeZPzwW5Y4Hs/R6/cZTXZIt3Oe3exRZFDmVdajqW7mpQ4kETQWBIduX0I128iggZEdvvDaWS77glY9QRSegXV013p8fn2dZQE1Ba06HIih2QF9KGahGTDnHG1nichwosBJ8LaLKAN8LjGiQCmDBOoBSC0gBlcDdEVJqKCIssoxcgsuBBtS5AIyjyfk6iRknPfoTTRF6vATTznO8cBMBy7swNRdR7ndjHl4WdPuZPyD1UV+/eQNGpljPWjzhyuCTdXEp2N6SciudcQ25r+vTfHA7gDvI87GAVpDvQAZKgZSowPoFBmtaIiKWtzo9WgtJIR5i8IJRAB1obFCU0Q1styBAyEciAE2yxD1mJkyJx3mGJ9gqKOUQ2tRpZwOUmEYiwJVQq4zGiIkyy0yjnFmhHYORI2CAFGO2Rfl/EgzpzPqMrsNS7c3ULFmplRoBCUFk94u3RFcHsPZpEnZmualieWa3cIQIX2LwFpsOMGoAlVE33H9/Y2CgBDiN4BHgU3v/V17c9NUvgOHqcRDfsx73xVVOPs3wIepJCV/wnv//F93/vFwCJFk9fo1VJGiYokOJMnMDM/cezf33/M2Hn3H91CULTh2ij9w29xZTHHuBtzYzVhqZyA07VkFQQMVaFQUIoIEZECt9FjRpAyaaNvGpTGNkSMUKRQDhId6qVl79nnGX3qI5JF/yrH3vot/9I/P8PqFF/jKFz+HGF/ERodYcg2Cl54lvfgYcZ6RNxf52MUG9957imk54Le//io//I6DnIgM43s/wq889XXe/46Qdn8XtX4BW0KqBXUFbw1CrgDDlescakvuWJ6FYcagUceNhhAuMb18gM5eQS/eHZDmGel4zObqDYq8gg6MRqAmsNzzTO/eot26RaAgmFpkabLJbthgxwaoMIAwZ74R8sCBWQoVU+ZVAWqxCYdEyn7Xp1YMKTSkVnBxADspPLC/whYEYU6tHhFOz+GQGCvIU4kpBWXp6K/nZIXhBWPIdkvyIkOYFAv0gwLbqrFvPGFfWNUO2knMguzg6jvIeoCyMWMlyAONNYL52oQXslU+/XrO/3yhRttlHNQrHLvsePe84kfvWiZJL6HbUwzx/EQccaYV8uBbK2TpQDZZcH06LVFt17UEHB2bExJzvb7MFy+use8DpwgCOLE8x8qNL1I4jxUWrTW1elRBtl2GUJrOdBuZzHDi1HHO7KvzL3/l9zEmIJQeWc9xeAIkpRlSeocpLLrwCK2IsoTSp0SBQZmC1IeUAdSyLX5mKWG2dCRxmx0ZsZpv8ngA0esjGqGsMBcO3F4BdSYRHF8I+WcLdUJT8szaNl8TSzw9HHFxssE4iZAuwhlNICd/uyAA/BbwvwO/801zPw980Xv/i0KIn9/7/l9QaQ6e2Pt6gEp49IG/7uSmLKjFdTZvbTDdqDNbD5nft8Ty8eP88H/1kxw6eJzAKEpnOXj8KEkyw/7amNsOnWQQNbgc1UhNRmfcg9IiTQF+gAsU0nqQGcY5rAtwLkFOzeKnFnjxySf5/ntOcECNKYe3yBwcyeGrX/q3DGsbcOIgv/Rrv81LX30OY2AglvmJd7yd3U//n+wzI7oKdmlw4MAUB1hnauciH3r3GbIwYjOTfO3FF9mVETc78/SF5KFHbqcxe5TcWr76yd+nXzgGBmw4zSfPrpC+vM2P36c40oALVwas7D5PfwIl1RYz1lVpo1GHI/vrhLU6YVynPT2DDmNCmxOUOaUtcA4u3RqzKwJS54kpUQ52nGelP+STGwMmVGmDAI4AR6fg1FLIh5anmConTGtHuVBjNmmgxjsoXyIdpKOMG+trpBYyCzs7kGdVZ5O9+ocJYV8rwLRaJIFgsV6ytnCcIz/633Dpt/43jmY3ET7FkqPNACdKPAbrSpQskQZEKWhqTd0Z7piJyIqCtoNbmaEfdXhss8fW42fpypgGklbZ55+8/510n3+SRTVCFZJ6XAO3Q5EZ6gqccQSBJvKGgIzneyNeGlqapcOWQ5LkIGWRVUxMV4ngAlghmNu/H4XA2IIDC1PUVcmzzz+LsxprJJm05P0+zXoLYyujW2ts1Y2SYESJcQH4EhEGpEVJfdxnX01wZnmWlh/QaNbZ3trlxd2SiZKMdJ04jpDKVfR0q3ClYqwKJsWInZWcqXydQ9NN3rHvAFvXbxA368wnUzwxGlA6CEXtzRrD/+cg4L3/qhDi8LdMfxR499773wa+vBcEPgr8jq8qjk8JITrfojv4V4ZzHlc6+mVJqUM+9qu/y+Hl5Uo4IYzJ0pxQeApvSIKADy8fZmlynu3zL3P+apcvBh1GoeZELaQVaNpBjWY0hZQK6T1J24KQhD7kuBrQqGUMxSZ/tgLcHyLcNvMRTLykbRz7j0d09kf82n/4JGfOfB9njn4/417B7/7mH1O/eo63rl/jkNJVfzuMSTcv0mg1WPRDXly5RblwDBHv54fee5itzPMn5x/nzgMHeerJF0hba5hRj33NiEuDATsORisrtOY63Ha6w+LRNs3+OrGAu9owexya9ZCkFqPjACc1Vods24iR1QyN4Nr1bQaTlG3r2RyM2M1zupnFOYlotaDWoG4LMCmEDUbpiELUMapL0AiQKK5MBK+PUj55vqBrJB+Za3NMjknKPoHOsNahhaTIHVkG3T7kvuosRCG0pyGJoV2PUEoRiSZ1t8MkFHinaKZjLgzG7EYtRlGJTkeIEkwEpZzgAIPHK4EQEDvw1pMZTzuI+PCJ/XxkRrIvSfnPP3eDRaORQcINn1Kr1Zgvxjywr0Z97TyjrIdMZkiEpRxukXVvQDGHzaDIwbQkXsNEwYWyoNess9Ld5PSROcajLu1Gg8EopVlvE0cJJY7SOMZlTqwVs1MtVJny0pNnubyxiqaNFZ6szAi0YLK7S6NWxwpfQau9wJJTUqC1JreyIlNZzweWpjkVC+plxoaq8/iVnWrNRQ0KJ4lwCFfVzaSv6OKhChmZnDJImKiSL/VK6pMhH6gL3nW8yfTNIeGk5HwmGIV+T65d/e2CwHcYC28sbO/9mhBifm9+P3Dzm45b2Zv7jkEg8oJZmzK71KE5M8Url6+wdPd9bOsY6UqE9jhfUFN1tla2eevKY6xPJuxbCjizFNG8POQPX7U8T/VkK4EebzyUQjoU7MqYQsT82wf7vPNAh1tZwOIimKxLLBwu10gpSUOHePB9/OLnn+UjH/3H3Dd/P+tbA/7oTz7PP/2xf8nZ5z9Fr4hZiOvMDgxTw0t8+YWMP7sEHTHD9+wbcUfd8uq5q7z3xJBiNGLhyBzWTHjooRlqNkV7hbMJhRzxrg+0EEVKrHJKGdMdlXyiO83NZMTlsad7pZKTzm1BCbjKrBkf6DddmjySvLQ4rar3MmLfoUNo4zDeUzqPyj2ucBS5q1yBEkvYmidEYfOS7aYntrAsHNncbfzU119kGviN99fZPxqzozT4AO8himB5n32Taaj3oMUVrCjHOfBMyD2ItEesYFPHBAr6T3yaOExZETAbgLMS7x3SgpMKIwWhD5DeUOIpQ4fOSmaLnG+8tMPnVsecPr6IubzOmbuOUgaS5y5c4ntPH+Qj9xzg7HNXyOwso0t9DtZWmWk3ieOY8ysTdo4cwk3NcWt1HSvgmslY0ZbpQHHu5ZdYqJ1hbeMGUa1GU9cpraJeb2LtmE4zxOYpq2ubXDy7Q3doKa3DiZBWbYiKNY6CyNXwCoZ5SklAQFC1jL2lbgTCleAc+/IhHzy4wL5Icm5jl/ObI2yjhWvN4es1Ni6t0RYRvuY46D2pUTSlJyIHMSYhQGcOKQM2ZIOCiJuDdd5+TvHQcpP7lyzB+YCXbYNXJj12kr8DG7K/Znw7C6K/cgXf7DswoxQ/dXAOF3jGvQ1u/NL/wrXtW9z5nndR278MSlFqxdpGnxvnrzLdaaCKgNrcfqTY5qF72hy4M+ZW2iVLDVnh2ckMJYpyAoy3SHWLiVXUggkldTrTy+ysXUfcU/nfeaFwOiCTI86d3aGWHGRhfhlFzvSMYvFgTKw6LB96CyvnL5O5CWFmyRPLf/bu28kX7yGJmxxPn0ZMbrF8cppwskUQCKZ2L1JzI7zytEpf9fYVtAWUBvIYructXlnd4ukN+PwYCgn9uIWTU7i6BhFgdIU3F0qCNXtPh0q7QIUV3iLxjuW5GbT3NGs1tnt9jHeMhMOWJdbllU6gKQhMQBAoUlsihSGyOWZo+eKTL3INuCQ0T+4GvKeeIGSKKatCIF5WJiiy+mdLW7UNhVCgRIVR1J7CxkQuxzrPQCWUkyHq4vPIckwRRZR5jhIhYq+jI/YYjNqKqjeuPIFziCDh4qDgQs9y6vQh6gePs3lrnfsXE7LxmLMGmjXN4uwMv3T9ZZ7aWOM48AvvDrl2a5cjLYk9cjcH736Q565c46nXr+KCJiPZRqqUhlaUrmB1Y5vO/DQ6krSTKuApN8HlIzb7YzY2txiOSyaZp5QRVgpaJiQzIygttaSJKCSFLavr95LSlAhvUAIKIyiF5ZQ3fO+pw0ynfb5ydpOzJdQ7syTlkNZwRJINEHMttkuHSyXrOidO6pRpQUMaYi2QKiAqJdIYposC7xSTWHN+HCJXhzy0LHn7UkC5kbKrAtZ88a1L8M3xtwkCG29s84UQS8Dm3vwKcOCbjlsGbn3rL3+z78BdofIP9/uYSFA4T63fY+Njv8w3/q8/4tjDj7DhBC8UGb//hW9QrvZ4u1vhjuk2r19eZ9jdYjjnKMMGS9Oa2XpATSXEIkZqQeg9Qs6jRYjw0BQdhLfMzLeZbsLIKMrY0ZI5hdBYAVs3djBqmi//+Rf4kfd9P/10zJGD+/n6l6+yMNvC2pxQ5HgBuZJ8T7BGujvEOWi57p746CZGRQhRp2FHNI0jExVFwQHaQ2KBGC6LNj/3lT5jJdmxjkEzwkrwXiFlBRDywqK9rGikSLAWAYg9Pr/wYL1n/4HDFUDFOfKiAg45b7E+3+OoGxymYknmGf3xAKsEgZeMvSALwJYxIxUSRJI/fKVH7S3HORNfQqsq7w/UXqSvOE9vbjQtHuM9XnjYCwzSCxAeJ0M2NzY40Y4qRqSryFHeWQIpsd7gA7Unm7ZnbQhETlHIiJtecfLILI8ea/MzZ69zMIRFOUIGY2ptyR233022tcVTW0NelyGlK8gzx9o6HLhdciVuEHpDsjzNVruNFR1ckVNTEu+hWXc0Oh2cqBHHMDvVIk9HXLn8OqPJmMEoJS8iCqdBaaSsdBKxAuM9NnNIa6oUQEmUFWTGIJ2hrjyFgVKHKDvme48fYDofUuBY1xD6AJ+XaJdzuCl59G338utPP4+ZaTM7qnFlt89uUaCasxjjiMuMjjYYX+0EQmfRNsXkdQah4UomSFYd7zlVMilzCt3kpZEl+zsIAp8C/j7wi3uvf/pN8z8thPg4VUGw/9fVAwB2PHwpnKUVaAJZMpMIbGnQtzI+/quf4oIznBc50pX88EMP427mzPoeR+OUqSNT/Pa1lE+cu8Xu3o1jgFhUN6iXkDgQFZ+Vf3iH4YdOzjAZ97i+C/0iImzPISY30GJMPYflEzFfe+ISx4/cza6UtPfNcSzssHJU8bWv/B4zgx1q9YRBOEEUjmXXQ4seOCpijQzw2rO1PcD5MdF8wiQfk0gYxC2CPEVawT+/FvLHN0ds0CdvNGnPHyLvpgR2SIml4TIksgICeU+aDlGq8hrw3uP2kIh+T+WndDkXrpyjRCDrMUoLfJbTCGNMllagpVKAD/FCMiksUdSgcJ72XItxv4txOYtlxltsxmHAtzW/+NQl/tUJOLFYI6CEsqxszfbAkPbNdLMSGqk0DgSagnKPUGVtiW4lrOYpyU6Xw80IjyEioBA5oZf43KAVWKHAKbSBzTTgcm55cWOV/+7Dp2kPNnl0/jCr/VucffY6bzsMP/6WU8y5lGvPPs0/u7vOS3lA3WqmoozlU4qr4X6eWOlyajFnf01wz6EZxrJOb2cX7zT1ziyddhMfQrm+yc44pbu9zjDPuHJzmzhpEERzNFsdlJA4X4If4nxBoQNmy1lE4SnKjJQxPrdEPqyIPxKMcUQ+pR1E/MP9MeNRyjNrPcZxgJxuE++OaZqUZqTQccwTjz/N92mo+W2SNiRzTSal4FcvbbKuEhbrbbLRVlU/CTqMKQgSRziQ+MjS1TWeTx1z6xNOzybc1spZOTfmE99h/f1NW4R/QFUEnBVCrAD/am/x/6EQ4h8AN4Af3Tv8M1TtwUtULcKf/H86/5Zx/OKNm7Sre4pYQyeC2HQpw1k6p47R9CM+et9tvOP2O/iz33oCkoBGO6ATW37s7WdYONDlGxt9xqVgYlMGkyF54SkUREPHhBplIHHFJiES64MK2lGmmInBWyAOEGlJsfoqNSzzh1vkQcF6f5dhd8hzL3+OfPMCnWKIEAYdVE+7UodMpMYiUKZAesNkXDLXbqAR+HyIBnQBiR3gogYXTcC/u9llU7RAZTTzIfXBKspJMqdAhqQ4hJCkxR67TCdvIswqeHS18ISvyEZeWJq1BhvbXXyZgYSaDslMinUe50CKEITDkqNqUxRpRUndXdumIxxzac6//shp2uUWNVGy4xp8/pnrXLgOh5bqCD8i1AK3J7hh5V+iGCv5MIHwEoemlBlGV5faMBNqgeVab4f7Gk1CMyEIQJUlUVidQ3uwZg8Rbyq05Su7BSte4UdgGkt87IkLXF1Z523HF0mKnLjR5MPH9/Hq5cvUlw7SOnuFn37wYTY3dkizi9SE41Ye4W2IEBG5zUiEp7AZKoLpqSWmpmYoioxufxNfDBgMKienYWmYnlsGoFGr0Qzre9RfSenBeoOwFhmFCO0RhcXnkmIPEdgIQ3bSDJlEzA56vOfIEktZjz9f3UBGTWJraNgCYo0gphUq7GTAyWOHeaghCGyG0xnT+YiJCfhSDaaPLdG9ucVUu8XYZOSjLk5LUuNphxLjQnKT4yPFuR5Mx55jTc/9keMT30Fh7G/aHfhPv8OP3vttjvXAf/s3Oe8bI6zXCPfNsTnoURQZPstxGUibcajtOHZ4kVbe5S13HkeYMTuZIliqszoZsjuxuGyXe2da3HagDWhCHM6NMFJiyZFFxMh4Chy3+QaNSCAOnuTg7CrkKXGgETFMnCAKYO76RR49eR9PfPX3mDm8n8WZFjezFYZbz3C6mbJwtUsUC4QXDK2nURqQCiM1Uhik9zQ0IDK8cISV2XJFjlExz6WKX3muS6oaCDtBYlBSIm2BEiXIAO3jqgDoHMZUWPMwjve24FVjT4jqUezYw6J76LRnCFTETr9HYUpcafFK4AOJgT2KKngEpQUdSqyz1F1EsxxxPIb7wjVqaoJHsCwsd7495HPPFiitEUKRlzlBBUwDIaiEw/YATL76u2A9I11V/ylgThZcMY4tK7BKUDMGaarAIQwMcYQCIg3kjlBYjJbc9JqdieHMFGwPSv5kpeQocKwVMVq1bPQmzK/fZHJrgxe2h+yfTWAyRGZw+VbJzF1T9MceKyK6wwnt2ZBhKfBRwtxik0CF5EVJOhoQu5ILu336w5IwVtTChLhZxzlTsTWVw9sKw4+p2Kg1H9IzOYXJUbZECU0UKUpryMc5UaiZuJJHTh3l9jxjuxTIJEThUKUhEYYiqkxGA2upFyWtyXalJqwD2oHG+5JAO/7L9xxiZnqB1cTw/LVtZg4t4QY9trYHDMMade8xOmBcOvrOsI3g+sSzUJOcaQO73379fXcgBpsN/osf+AEmRcnUwhxxpFCBAiWhP0B7S7N+krXBDjNCcvLgMbo7F2nXoNlu8DtPvMhXNqqOgKSCnzepPpzYe1VAGMBpBX//nS1mhttc2YYjR9qk2lPLdtBSU0jP/RPDzkvPkowsr5y7wDNCgs354OYW9y+GzB5UUDgMglIIhHJ0ypTApgxiKAQEHrQyFL6S1Bbek4Xz/OxTG3xplLGWQORGdKjSl6Fq4UUCjAmkxRclUlb9jUBXtVbh/mO5MS/e2IerCiYqEvLU4CwkzRbTzTqxlHQ3tuhlGU6CFFXRzjZWMv8AACAASURBVPuQIB9XXHNjOB0a7m0VPHI0JmKbQFfIxFYlwMQPvn+B9StrBIFEKoH3VFoMApTfkx0TYKXFe0vgBEIL4tQTO3hm+k6aH/5BPnz0GPqPfp2ds88xW+aMEokuLNQjFvYvQBKxcvMmUVaiTcj2JOP03XdwUOzwm599jFU1wwc7O5ysbzPep6gtLnBr7TpHZucZ9AynjzSZrl0n0YqDM4uY0TqZm+bE0X3oWgTC4gPPoaOH6Ha7dGKHUpKrOymXr29Tm1vE6QznBJEKidgD5+iKcm2FRzhRFVOlQqgQUYwR1lCUHmUM3pektsQrw7Qpeajd4KApubR6i1WV0K9FtGemOXHvbXBbDLvbpE+f5UfCiFML06jxJrbwZEIgett8bdxhvlbnaLGDv3adZQt6X4PnXrrOoQU4ON1gvTdCOMEmE2ZDRSwbvJ56zm8WmJHn/SdCuPrti4PfFUFgOBxx7vwV0smYxvUbzB9dZGZumnoYI2WIkZ5a0oDJBAtka6/TkIpWUnBYjnnfu29n43LGZzc9IysZOwG5QGYSUxO0igArDVaVnF7K0Dqn7gdYAV0TMi4lMzmoqEQ5jVWKduZ4Z9hAZUN2BDS84t1zbbwdkEmPEZJASCJrGASSVEjiUjIRVXCIlUKmGYGm8k1QgkmZ8soIyppiWlpSKchEBDKnXvaqBSojCrvHAZcFe+IDe9hy+ZdY871YIPF7pCUIvWPY28RLh0sUu6MhERFJI8ZlOXiB8DlKCRCWSIA0ntBH3Hdylh86GnNKdRFKMDEF9bogLzxlu05Y7lLTIKttBABKg/MesSd3UtUpwEkwoWBcRCz5FF1Ccc8jqLvfTz/d4WBnEabmcYObpE7SCWKmO03oTMNkRD9U1Lwm8ZrIQz2StNodRnaDZGqWI+UOUWRJGm1K4ZC1gI1bq4QpLGUlOIMuwCrBJIdSQ8iE3k7GVDzLcOs6m1HEMIPmNGzubLO5mZIRMiNhBCgdYlWIVQFCVmSgZhSR55XrkgN0ENA3ORiLMhXpSgiPLEu0FOShpO0UDx86wNrZl9jUEOPRJ04xc/QAg7bkZr7OjBxx/+3zHL21Qie27BYpcWGplRNcG/7i6z0O0mP+HRFWVburRqg5fkxz47LhwFuajPMR49SjYghKS5D1mY3n2Bml9IcjjPt2TbtqfFcEgSDUrOxsMBiMya+mFM89RZQExPUaSa1JohTToeCnfvCd1BNNNDOHQbEWlUzVNQ/mV3j4sIFpAxayEq52oYxgaCEtIbXgMnjfDMQBdKdu41DnPDODqyR0SAOQKIwo0MbiFThX8OAe5LoiYlTvYyvxeHxgKLxiPrdI7zASYl/tRoQpsWFMrkoSpwkLwf843M+kltO0Ac5LTDJA+wxXaAwBuixpC4eTliE9vJlDK1GpFDuHlp5ASYy1lMLjVaUvULfVayZqiHolYV1aQSQcZZYyGA2qY6XEujoOaBtJrxGwr7fD31uE/+lURo7HizZROosgo/QTZOlQo4KxcpU2vqlyd1GT5EagCAlFRqmrzkdNQ96DMPNQS8ks+LZkqUgZffIT1IZDLpcxQeckqc1oGIGWFg6ehGaN9PqzHLo5Zqxg1IpYT0cc9RrV2c8d7jw/9XCNe1cgbCzyzKvXWNuF20+f5GbvAg++ZYE0miCdRwU5WEXfZOgiY3WzoEw8yz7n6iTm+Sdf5/7bjvDySpetiaDIAhooNnsFQRDjHMShxjuHE5JJYWh0AkaTIQZDLYwweUE6HsIgReoUJcbIsoHzMVJLDk/W+L6TJ2nnY162ljQOGN8W0zqZc0teJusW1M1+yiGsdEtEbQrTG9IkJmOM0CByeDqAz4ctprcl71noUCt20VmP5nRCd2zI3ZiTNXjVwPxA4wOFCHP2uwyvHH2lGWYR8O2LAt8VQWBmeoYH7ruf/mDCYDCg19um0W4xM7tIq7NMu9lkulFjrRjxma99jblsgN/aZGGuSe7q/MwzGZ8bwHbSJNEaaQuORxCVGXfXE/YfP8DKjWuMrOKxy46PLAveMrlGWXquEjBQkjkDgzgmcH9J73zjieffEHN4Q2Tzm/QDhA3IvUBKg5VgpcJ6RdiMcYMBbQWv6jl+82vrPMtrxEGIDcZkzjPnmkRCUboeo9CQOxh5iBzUkchkF2k1xlQLLotCBAKHJbYGZQVeCiZKYIQgNg7rDFEUIQKNMQU6qVML6oyLCVlZoLQgiCW5y/j46Q4PNmPEaIvNdYM2JbJMGUVViqLZkwuU0LaasQkotSSMQ2KboqVDUDAxArt4kqUP/RDXvvBpmt3XGLdCilISiAlu6MgPH+PZo++h3HeI9/zBz9P54pc5JAw2hEjCbnad9a5hbGDDHUGZCfOjMR85Djs72zzfc3zv6Ta3lTcQIqYbee557wPc1be8+vizvP8dx5GyIAo9JTmFTrCrllouONoYMVsP6QYhV66sc/uhA1z2G1y7tY0NHV6GNKMQ7SOckiRJjPeWdDJkMhzRaLcIw5Dz515DqYAgjhDOUBQFLVGjG5fkah7nQNPDS6hJwT+5907crVVeudHjhqhzdN8Stz94hItrVwhaNcKmQqarmKmQC2KHopwQjfvQ6NAPG4juEK0zvu82xWcuD/jlGwmfvdzjPznZ5K1TFkPG/UfaPP9qH3XbLIfbORs3U5wo94RGJhzoNJATw/nNvz134O90OGOYnm7TmZrCso+vf/1xtrY2mZld5AMf+hBFbmjVEj77x/+Of//5L/PR6ZK7j9xGf/cmMnb82LuPkrxwjS/cHFIAIRBM4OjBOm404pnnzzMuHKmD22OYO9ag0YzIZUZcSBIRgYQCx3eWYxR/GRy++dpFtqcnWO0AtLcExlZ6fUGIygu+sLbDpwEbS/ZpXdmBBYK2VThjkLoJoqAhCgoBAypjkXBg0TEIVUl/qTLF5Y5Ia7xwVbcPhfca6SVSQlqkGOdAhJWllQdvJTKQhDpAOg9FypIvOF3PqHU3K6abq5wNicyeiAsoUXkDlsIzKUt0XOfW9pC5+ZC2MxQl+CjASVg6sgx3HkX87goz1rIzTolViDceqWDrqcc4ungHG9uOS+df5s7mDBe9Y2s84FaquPi16/T7oFOwW9t8/7vmmV6YpjEd0tcdrnUtczKglncZWEFDK5pBzLlrF7j9tmniMKvUnnyKCC1WKHqlwOqYfZ2Y7tY6m0GLODAEFDSTmHFmKYUjUIJYa4rS4oXGS8G4P2Q87HL/fW9h//4DXLh0mZsra0jpyIpKEUgIQR1VkdUKhy1KSimq+2A8gn7IzfUeAyROBOzsDojGY5Zm5zFJgFKKdlOSdhpsJpr8xqVKbx2JV5ooihg7z7vuPcRFdnlxtWC9SPnslSF3PjRFM+8i0j5hAL1+yb4WKGUQrtrt4j1CW6ZixWBSfsc7+7siCFhn2d1exytN6Szzi20GfUleDLjrzuNsbfd4/rlv8NmvfJ72Qoe1lessxNssTdfpLLS5O93h9vsWefRIj82JYTIoyEdQiDEmhkg5hkWVw35oWTIbZIxFjNIKMUoR4xzeUNc23/4a39B+++b3leyXR7qKM2+9RNmqe5+mldUUwFevl6zXGrT2ugVJVMd7QehyfKhABkhTufSGQpFJjZWWxOUMc4tVFqk1zSBAeklpDGMtMKIqWikrER5KUSLVnvAEBq2DysjVCdJ8gnGOpqpUi3/kcIQb7+DDSvYrQkBco9ACEVpCqnljAygEebiL84ZCSy5tDmk2KhpybhypdFDswGd/j2bRw1nopTA1pUh1RBlE2GsXuPmxX2A7Thi9foFnBhO6Nua17fxNItOSgtsXZ/l7j1qOHwigyBG9CTN+jTyQLM02EVvbFDKklnu611eoS0+7HeDLAZqi8oQAlKng0hNnaIYJRWFQcY3CFBw+OEN/fJXCB2TWVp4SeyAfZ2FnZ4tAeG67/QRHjxwkLw1rqyuUeYEOBc5aCmcwpiArQTSbiHRIU0n6IQTjMXd32mzu9LlRQl9HOOMwMsaHnoXZNuuTCePBmCnRwFCQp5J+QYXJhkraXQnysEmjHPADP/h+Xv5f/z1RErJSFHRtzLxUeCyLCwlba31oRURhxSxVsuoiaZfSDkNWh///wob/X4+ytHz9yW+QpimjyZCf+xc/wwsvvADA5z/9e1y8eJmvfOUxQjkgqTl+4COPcLi3w9q5l7gZjfnjx/q8Dqy3NXO6RawVSzOCoMg40i65a3mJYpwytjEbIVyVuxzNS95153FeOnueQlVqu/Kb1IH2AN97Vyj2DDi+1VjEoQooo7DS18eRaUOuAkyoUcWE1XieEZscsQWLueGh00e5tXGDbl6QR9OEtqTmu+QRlCohsZK50qNEwjCJaApP7jwZlmGRUTqL1AplIfASIQwlAoOohFK9xInKEcmWhhSH9BIHSK1oDEd8z8kFfvZoDuOS0hQYbbG6YJwVCBkR9wElyJRDW0vDSiatGeomYzuZ4xee2OAfvXOZB5MJLTyjeocL6wVr567y2asRW4OcV4H0lZQhYMhpMQCuIAVMpEAHEbnNqdc7/OvjAw4euYtR17J1/RqnioJkrc+kFCwsHkPGEe7addQoRWrJpm7y7Feukmg4c0TT8QkmTQnCyqxE2jrKhexvZRA4PqMWuLR+iU5rP8aHzO3fxynlee7F11BG4oViK89ASZK4xf6l/WjlmZ+bZZxO+Iu/+BK73QFCxdUuSwqiKKiYgbpEFSlxHDKcjKn7CR9cXOCD+/fzu994hlta4Y0jcI4xElubkBpDq9NgXBYkdc1w1GOhBpthxlqZMoMjUB1cUn2O2ck5+r0XOHJima3zNzk6XePZs2uEB2aZjsbsq2fM7xNEocV1JOORw9rKuk5jqYmCwV+z0r8rgoCQkjNn3srW1hY7Ozvccfs9vHbuElevXmU8nHDp9fMEztGKG3hXsJ4aimGBTgQiCnj72w+w/totntw2rLKLA2q3YLkFm17yzM0rkMPGpHrQ//hJuPfOhKtXrrEzgmzPu13abxctJd+G+vDmnPKgpMHLACc8iXWENsfKkq36DJ+93iestVkoLbNNR3drizTNUIEixqGEQukaTT8idGOEFwxcgjEeGaRIV7Ubna88B4UU5NYSIgmoiDtGFggExgWEWgESZyqZKgLwssL6g6QNnFmcxhcX0T5CoSu4r4QoCQlFRKhLhBDEQZ18PMDKlKwfE8qUUjYZBvDLX13hiRZ0QohMlyyFazlcaCb0htBttmjGBZmIyazk0HxEs93g6dcv0wkSAmEQked9C03eW+vx3OVXeeWC49iyZmu7QS0Q6FrIY+MRUufMmoSOGNC3jlZL8+ij97C7s81CqOl2r9OZ65CSUlMhBG2urxe0Zk/QW7nCY9e36GYDpqRB65BLly9z5Mh+JAZfQmokhZfEOuDEscMMB7s0kgb7lvbz+OOP0x+MQWqst1WrUEqMqRR/g0gxSQ3eBsT1KWaLgtsO7Gd1bZ1tEsYioi4cARO0yhhsN1lcqmFKCKOEkc/xcYX58EmLPC3IywJR8xSE1ArIvKSRjnjw4Qf4zGaffr8PzYir3S7Th6eQJiXqhHRaCWnWx2oovaBUVfdIe0v9O+e53y1BQHD06FHm5uYwpePokVO8+5EPcOT6Dbo7u2xv7NCsNTHDbeZmZnj51gZy7DgmmjzcafLQdM7x/Uc49toluhMYjWGYgdcQmIDdNGecggrggTnJqTmHJ0fEU5gyxztdod6+Kef/j/rx3oMXlcrvniCkEHs2WSGEwlH6Ai8ilC9JJGQ4PnUt5Qs3ctKohaJg3Rl8mlP6mEDFBLbEeIn3EctmyNuPThHqgN98aRsbxkgEkfJ7LD2DcZBJBVZQlJUKbairrZ+QFicSwCCFRgqJ8QVSCJwSKKGxzrMcwT7hMIAPJGEQoISqTFSFovCGzI4r1N8kRSqHjCQxNepmSERlK74TRjw2zPEKHrBwZCqknxdsveGo4xVxCWWokdJxwPY5Pj3DRevQwqJcjgvhHbUMHFxacQwVnBsaNnNYjDTTUwGfXlnHWctHDi9wtKaQzSk6ZU6j6FPkObuDCfNJTOotBo00ClNrcME7tkdNrt+yPD/sMzYV4KYz1WY0vs7U9DR33303Tz95rmq7qpDZmUV622vMzMzQaLR4+eVXee6FV/BCorVGhxHOGaTUe65NYIsSIS3GaiLnOb24QIzj4u6QzIfEXqNsicUS2pSdtTGT2jQ9N8SFAedWV+kszBDXA8LWIYrdAqFGCFngdQuR9wjCFuEg5+FH3srFq7u89sWnmGAYF5beoEvclMwcWGCyPSCpBZi0rNIPAaGuwFj1WPGm2MO3jO+KILC+vsanPv0JWvUWcVyj3mryrve/j43Nbf6Hn/3nBLUQ5w2NdosrV68zJR2nF46x9eJFVtqCP3+uz2YHwsPzLNRDDi0pFpsJdQENOUDpFqEUlCok1wGzfhcvMj78ntPU5FexozGuBpb0zbxfij1s/N54U6vNV4tfKajXasT7Ff3VnMAUWJHxb67Ac7uwW0KqLDqYoqkcuc6JbZNUeDphCBNDmoQkWZ8z2vEj90REdNEl1N+6nz99eZXXgllCVeKcBQOR9wy9oHSeIlA4WzkVK+kRWpDomLLIKgcT9jwLpMRLjzUlzkmO12F/fwvRjuhFnhCIjUIHIITE5RaCFpnLsTInGitC22Ar2aFW5ARyTFvAlmkz1AUTOeEuWTJ39C40lzBpRscU2LwgcWAkhNrw6OmT3Hz1PI906lCrMTPOONmBd0dbbGt48GDMtq/z1c0Jv7vS46ffe4TYbrM/gXM3PLuLtvKjtJWNWSK3WZKaQRhSa3ga2uJLyRMzJ3hJNHk2TDl3dgtv5skCz8y04rmvP87p02do1C1/9B/+nMHA0GxP09SCeqsJ1rG8vI9PffozlKVHBTFC1/F4nFIY78jTCbU4QTpPnhfkQYe63cT5AGVz3jk3zfWXn0O7mN/4wbfyykvP8JmzBefqsyy2F5g7VWNla4t+b8ja2iYnD97JcC1lR6R8fPUmH52pMy88HW/YlqDbBfXdGgfKCd+4ucszZ69iQ82wLJiJIla3c+anWty8ucONoeZgI6Le0uBSZCChdCgF7dp3eRCwzrO+eos1ewsvJGdfO8ehY0dJszHXblyjnUSURcHG7hqzM9OMVm4yX45oRoD3HL79AOe3U/70mU3mqD5UG5gXcCiCWu0WNQ2rQ1h18LY2/OjdAS/vPMGLF1MeWq4UhjVvtP5489W5v5oKiD1yktYapyCa3kc83iIKLWfHGV8v2gyDhON+h8VQEpZjdCiJxwGOAmMnBKEitpY7mgEfPBiTmB65rLQCDssBj8zA1uYOaRyTO4GVIdJKlPSVhZgpkRJCpQh0uGcCJii8x/qKgyf33I+sNWjrUUIRKQhtgfQl5AZEhEUxcWOU8DSIMHnVBpSRouEDVK4JlAA/xpeqErnQDlWOiKRhpYBb/R0S4SH3yNYCKsjpdXvUTIaewEjEvHqrJG7AqdjxtgNNTsQZxajqYMRlxqIoONOJ+fI67E62uG/G8v6D8+ys3eLaxi7yjiXsaJdINP5v5t40xtL0uu/7Pdu73L32pbfq7pnp6dlnONxJ0RI5siQqlixISowEimU5gBwDRgwLMRRbCBwgMQIbyAdHcOBYVhbYgRArcmgpFExJFEVxnY2zcdg903tXd9d26+7v8mz58BYpwiYtw5YAHqBQqFv1XqAu7nvuec75n/+PRV9xNL4PC5AmwS9qgtK8+GDGg1OnYbkD/hAnE2JRkCVdjCx47aVX2DrVo7CBpZVTeAGt3KClZzDo8+WvvohShtp5ggepNXme4YJFKUHSbqhPOI9KU2qpCHWbno703ISDYUJVwHpS8ai4z8Vn1ji/A7/4W7tsZ2eJsY1JDJcvn2Nl+S7tVJGnKeNZCad7jKSntCeQGWWJNmJMihAFqSu4dGaTa6M9RNTgDCJTzMoKZTTzmHL/8IDttiZJFDYGogQlBNk339TfIb4nkkCepSwtrTCfLogx8qu/8k/4a3/9v0IIKBdzOkZRLOYElfDWtRsURxU/diqiFKgufPhsi/MXBuz+/hC/CFQ1zCPsR1AOigksXNMPWErhzADWc4Vs95kwpZAJCEiQTe3ESSL4tvv/W9AH/igJQMAXBc6mqFiirKdjEjJtWBCpI8RQI1VzrotS0pKSqBJKb3nUjfnBndOsiWPS0FQX3iiUXfCBTag6y/zerSMmRrIQLZrP7UCNZSGbTb5ECRLXWGGVuuHSRfRJXzPgg21UfQ7QzTEmUQ3qrC0CJokEKTCkpDKAPVlRdhFfR4qqaIQCdQ+jU4xNMAKkKNDGUXsaJBwlP/zEaa588R1GQaHnjuWW4ozz/MCTGW996RV2TnX50BMXeaR9QFcV9IGbTiGlZm4qWpXlNAsu9eHwYAZrLZZFzWZHc1w7KgRZIonTks7OY9TWMfnGAbGy0IZ5dLxzf4paT+mlilQrhk6RA0omtPMOIkYCgvX1DUzSAaNIWikSj3UlN27dJUs7tNsdEIq83UXqhuCTJBprK7RSLCZjPBHlZlS0aVd7XO5W3B05UtXlY49toKohIdc8sab4r39giy9fe5vf/L8iDxYlP/rjH2VlqY1u1ZgYiDoltHrU8zEiGogFUcwwIaFUCVJ16S0esMKYG8FSVJHeehvqKfMKdHAYs4SoBWnWAluj6hKZQHSRTP4H2ov9aUe/1+OTP/ICRml8DHil+N/+8S/zxT/8Aht5Gz+vCKVj7mFl+wKdsyXDacFat8u1exPufH1CP4X/4qMX0VmLVGparR5eKVreoJOSECu0yUimBWtMSHTJj7//+1ks/iXHoyGxpzFFiZPy3+wHnHyXJ/CHEJpzuHOONHrc4oAkNFCYC23B1sEhvRyCHrBwHqUgJjlV37FWRDbThGQ8468+0yL3u3gEddAkHkwQWGmJCj7RP+JD72tTkHI8j3z6zQNuerifJFhaaKExUSCEw+Gog21KfxEIOGQIROebfYrQmFqQKXSm8EIiS0/pSqqkJJPdZu9aKXwS0UmOMCkmS1lMRmgyxGiPVEhaEgKaRKYEHBHPiin58xcyfvtzjn2O+L5Oxfc//xx+9zW2Tcn2Jx/i5Rff5UP6Fu36mLmCqUhwIfLP3iz52Lmcy0sp/XLBTz+8ymtX95lfNKxmU77vrOTeOKPXXudwdogRkbsv36acebxOIVXMqoJZuszMtRg4zVKnhZeBKD3CLPDSkPVy1k4PWN9apdfqE63i3fu3eO3t64yGx3R1zsr6Q5w+tYFJGn5AVVRUtcNaQVF6nIP6BF/vYkCGOR0Fp3LDxy6d48E3bvHCR57kseWSORonErKq5oW1kg+dXeKvJKf41d98ja/++ud59CcfZ9fOqLyEqHjKL3hMSvpRE0pHx1R4vYa3jtontHev8oOPrCHTnNHLr/K+j19mQx6zd/0qnWBoM8cEyGKNjRal1Lck4jJ+l9k33yNJYDQa8fW3X2dp0CdvtdkfHvP221fwtaeVdRAxcHRwyNKZM6g0peOH5HWkrw2zIWSnn+ezN+7x6qeuMQAGwPkE+gZO5XC6D20NcwuHBhIJD50eML/zO0xuH7C0AYtS0JbfVSbwR9Thb4sYIzpCrJtP4ijB5y2KtGKmNFnhIdd4WyFDgS9mSG94dKvD5e01UjFsTEFEhCSldI12QGiodCPPHVRzltycLaGRz57ipSLyufsjklmGdAoDOOPw0iPiCcwSTziBWcQTnLgWEnXSzbTBE4PCSI1QkGSKhDauiCiRImVBGSOTokBMFiTOMpUzthSo4JAGfJlRuzmZ8Wz0O5Rlxe7eAZ98boP58mM8PH+JZLjP088+y/KW4MGdd1EOPDUByC20qUmyjGff/yybD75Kpwahc/pa0Y0wORxzahXOdSTVceTOu7cx2jKeK8Y37pH1UkZVxdZqQooBl3Nc1JSjIcsrHQw1aT1rEkEKzz7zFA8O7uOEp6wqpkdjbt68ycw5tGmhdEq330eoBh8fo6OuK6SQaKmIrj7Zwmw+GJxzSGPI7ZhTZy4wtZrntzIurwSICxCWTEp8TAhpSgwzNsoX+bmPP8eF4YK3Ks/BbEZMWoymQz7QdixnCcZahOkiq4SipUjilKgSvE14/vn3Mni+xT959SVELwUkuXTImSdNLUZEhCvJ0ox52ZCHjJTICN/TPQEhBPPpgslkRtCSm9fugA3oJKda6nDnxnU0gZ1gSa1nKAc4eR/faTMbwV8+/SY/tVHzyd+G+x6uRPiUy8lEznbYpqw0YX8PUZUoofnBpQkPbweyNPLWIWyuCIyWTJAIGqdbQqMXqFE4LQgy0qo8SYDQNOghJuArvJB4ERrXnaApK5g6B6lEtAzGG6JMUF4zrjSfe/c+hxs97PZFVjJBjykr/j6RiD3Zz0/qBKECI+VItEAHzym5y5kufDxv81e+MuRuL8fLlPZcInSH1MFCJeAC1joWMiOVmsRXVGqOUoJ2rSmTHCNL1KBg6hzRplQLxzzRTNPAch1pjw8ZYOlnkHmodWPU8mEs//0F+OxoSm9ti1YnR9sZVVXxxpFFjfe4WO7hAzz57EUWSUWgiynPsNE+Bm2pKkgqqARM3DkeK4+4Epd5Z14TKs8tWXHbNvqjR08NUHbE9hLsTSx7doUwGvK5Ag5GlswJ3n+xjXDHeD2jHc/gRyWtVspUKKJZg3rIztZplAAdJfUsUDJnb3hEXXkyY0iSjLzdJk09LRxKCObe4bWhLi3Rg0gkwnqcqxA0mLQyLrMTbvCQ6VDu1jz+gVWMuINFocnRsUCkDlW3yZ0hZptkYZefHgRq6/hf25donT3PxXrIB979LMYVBJPgTYqOCVEpvBlgreWhRcWN195Cv+c50gRax3u03TH7LqDahsTXiDSn8CWpspgYiVEBjfPxd4vviSSQd3p84P0fxS0mvPziV2k7jbeQZG5dNwAAIABJREFUmZRqdIj2c86c3+KBrQgokqUN3OwueRIpI/za7T49bfjln1ilI+cM0rIpq2tJz9+gkBmUzex7lEZcLenmCi59hA8d/C7heB8pAmlsmkG+mQjiIxjvkaE5FkcpsKLpyIvQuOXEVpeFniJss67cSxvFYADMYoqKNRdkweVTGfN5wt5iwZ99fJnnlg3Hi2vcGXreOAbhodsfsNzpcDovSFNLEiZ0BGQxEiJUPsX4wEpwbBjYnxuCihy6KcfC4E1GdFOsBRMztpngLFiVUFcZJhGsmYJTkwNGOzntA0cuob1U0c6rRiQ1WOLe/oj21ipW5Lw57zAMgavzCdevl4yPJ/Qyx6MbC668vkBo+PCTZ2nVlns39rh47iHyLOXs0hG/+qmvsbaUcWlrmWpyj0EPMtnBhZwbi5LD2REXNm6z0lF8WM2o6eCs5KN+yPUW/H9vgXivY+ABlRID7BR7TJOEpTpjKY+sdQwL5hgJfbkglQVXjoY8Pl1wymj2RQ/TsvjoGA6HzIsF+0dDRscTAoJut49KDMYYWq0WEkHhLMILqtpjKwch4n2gXMyx1qKNxNfNZqFWFR9/9DSDyVXqMCdLzhF9REWLTXoIoRBugZdjYtZ07VPRpgo1MoX/VEyJt19D+mOiiTjRQckWsxgJmYc4w/tAIQJuqUXHHcGXPs/P7SyT1AuG0mJTgT0hXklpUTGipMekNGxMToCm3yX+2CTwXcAjfw/4j2gYK9eAn40xjk5syd8Grpxc/uUY48//8WlAUNQV71y9wnA2QaSKQKCSAecKkpYhRk9pa6I3LCZTukvbTF1JpaCXzrn15ozfePMupg3dDmykOYN8mUEnJ+sv03aRRGZMlGSFMRd8zfTmNTbWl9k72qd2AqkiCEPEnugGmhJfRhrU90lPQIimGoi60Tj42JDEm+qhGc9JIE9b7LQlP/tUn4e2lrh3UDEe7bLWWoCt2U4lnTSh2mxz90HN8HDE7Qcj7CZ02xnd7hIdK6hlhdSOLHhUbBaNfmi7S31jyh3Vwko4W1tCaTlONSptYzwYA76V4qY1q3VBUsLO6Q18mNPrZCwWhl7exmWBe5mkcAmjo8jdY8X+tQOOjuHuYbNaW9OQhtpGkmnBR599lg8+UlC5ivWkwNQaeQC3rr/LbAHXOpB1V4iuYvfGPdIWPHL5Ycaj29y7OUQNBKdOD2jLOU460gD4CklKpuDs5oDluyNsa0BqZ+RSMLI5o2JEOziWYySrIjprJN/9XFGEhBpB3u6wmBT0ukuMbYKJGaPRCOsbz8XpdApAkiSYLEWrBK01gqaZKKTE+0hp3bcYgVVVIKJHiYhCfKuwNgJWkhb7797l4Z0OiRwj3QQV20hrUZRIHVmEZqnLxICxkmhSQqwYFIcnZoo1C5MQfIoQGTI3eCNRwpL6xnSm1JYkLujOpiytdqmrgqAjOip8CA3lDcEJiaxZMSc0I+3/kCTAdwaPfAb4xRijE0L8j8Av0jAHAK7FGJ/5d3jeP4oYuHt/l3du3aByFQiNSjVCe0bjMb0sw1rPSquP85G9uWUUCkw7Q+Up79kSPNfL+Ee/XTKbQ70PhoI2u3Q6LUQmSMo5oXBUEd7Xqfnps3DqYcXnfvcO/bzxeQ+hIIYG7ihCJKhmP17GpozUXhBC8+paQdPEDHNwHfAzXIA6mhObLUnHzXmslfDUZoZzu5zKDA+fipS2YjqHThkYRChtTXelhVptMXcVb+96hscllSx5fHMT08pRpmRDTLAGfAu+T2n8IudfDBfMAjy9vMa5fsLn7pVkRrDcDtxaePZHUygDy8Am4GdHqHPnyDLFva7k5nCGWuR85uY97owdxVxAZSFAS8BOFx7tw1or5/KS4cAn/MHVQ6b379GzE85ttsnkAUm+RH1pg5085c3Xb/PufcjlnFqUnL64xNHomOvXr9NxniefEHiZ4pjB3BGEQkqQViC0Id3o4U2LZx83HNUtuhtniPsPeOml+6yf3WYnHqC0Reumh7PebTM9nDOsLcporCvZPxxipaQKM/LUUNaN0epkPsPWniTLMTpBSdMIgU6+UqUweYvZoiTP2tTjMdZavLVIQIqIsxVSiKbxVpbEusNhAdya8bH3Z7S1BOsw0TWQ1QhBZ3gXCVKThXCi4AwnNs1Qx4hMOkhSIgaVZrjoYTokzKuGwZDnmDShpSUzPycxHahB1AKdZSi/ODGcbW6rb064BHyLlP2d4o9NAt8JPBJj/Fff9uOXgZ/8d73fv1O4quArX/giUmlU0sIdTyjKirX1dZKYkKgOsRLE4KkWC2qpmcdj+qbLWneNyV4gizW//pcexZQTWkJjI4SwQE099+uIDYaqrmnrLsWw5uy6YGl1g+fOH3L9ZkGlA6m1CJGeUHn0yYjQApIYJKVu8mkqFK0QUF5BhHpaoRq+CUfTORo4lbb4539ug3UOMCsDxrHFqcO7TEpJO13FzkomsibREm0dSb3AJxANPH06QcUAMiFwROkih/uOt+43v++sZWx1HO/b1FzaXOXv35zytcmYe/s1f/f7z5KLwLhcsHruKe6WlttRcf2tm1xykQ8uV4x2r/G/vyx51bdgsWBbBR7a6PPs2WXWEkE/m6BMQmIFujwi6URcqThrJ7wUe0wErC0HHo4lKpbM0h6T0OG1B2OSdsLDD3VZ3r7Iqv0ay1tn+fotz3seh832jFBJvFNkoUQAhYAWiloEZEugEo/vtWA65tFOwv/8a1f5j3/2Izz9vkf5hPsq9+8cMtGRta2Eja5h+GDOK3fXGe8NObw3ZufJMYfW8fb1Mc89coEvfOYzXJnD+tYmZVk2E528hVYGnWYY0+C7Y4yUixnV0ZypkEhtIDT7FzEGVKIophOklAgUSguCdTzjhryxn/B6G/YdVL9d8sENwXuf6eDjgjTmKCFQqkDVEExGrQsyIiKmjI0i6hRp27RqB9rjkgX10RH1pED7nEk5aUa7kzlaSUrvUN115jUUC087SozTSAExaBQOLUAYhXd8Wz/gOx8K/iR6An+Jhkn4zTgvhHgVmAB/O8b4+e900bdzB7QUTIXFBo8Pkc3ldbq9FuUJJ8/bEmkSagcVHi0CyfIKToAPjgVw5euHvH3nkEEflluGU8sbKB1ZSQRLgyUSqZB4TAL64gbGWvI856MfeZ5Z9XmU0A36OYbGKpvG6z892StQKqMSAe89KkjirITKMR02LPhEGEIdCXZBz8Cg02XNT0lFxXx/jPRzjqqSxcijleTmvZrN022sm2JUbAi2AUJQGGtJY6DSjlRBR8PqRoted8DxdMLRcMY3jkrW2oKV1oLnM8d7n36ErZblyV7JZFhQRsOv/sbLTNrw1hG8N4OLj65hpSKIgqoM9LbWufPWdX70yQHP9x2SI/JQkdRQumYzUprGZqzUkWMLc2HoD7rs7e6zc2bAxGleee2QWE/QteDwwYTWCqTqKs8+DSM/xtVjOqZHXFikTrAqJxAQPhC0R5c1UxpxzOa5sxzPhsTJnE4sGQf4tc+9ycVLP0KuZyy3PV61yFWb124cMJFdXnr7Jksucmm7zbzb4t1jy7hQrK9tsbO1zLu7Fc4GvItI1dB+fTxRgUZHIhNiDIxnE5YSSSvPmM5KgovUZYGPjhAdWgiUPIGJ+GZ34PJKj1fv7nNdCCZGc28WeHtaEFzBBz5wGkKNwhGdw8gM5wIh09S2RkaBOVlJNgi0BIsjWguTMWkVuO0M61ETo8MEg4onBjKlRcoUoQSJyQjWkWhw/psuVBEpA0GCFuI/rCfwbwshxN+iqcj+6clD94GzMcYjIcR7gH8hhHg8xjj516/9du5AO0/j1vYqLjYuLpNhjUsNo/GITk/QabfomYThdI5XAhE9b+8dc2lzhSRXdAYlz79/nV/67X1mgMHS5y5GQKcLg/yAzELXpGAWbBB4pg1PnjvkK7fG3LgHoY4kEWqaXfpUZUjZZrS/i4xQVCWuagRKrmp8DL0AGQ11GjA6ggNbWta6bTY6A6bVkCIqVkyJPB4zzFuMjy0TUXN1qrFHc86sNG9Eb1NMEEQaabIMYJXE+IAS4OOC1cSythpxqyk3hoq9gwWLmeM/+8A6uqNRSwPU4XXqENkb1Xy9hFtlMxj6gYdbPDzwfOHakPER9BV89Z3r7PThwkDTcyOqk5JRxkY2XQpIIujQ7CZUCpSd0p7W7Dy1xZXbD/j6nUjegoeW4eFMEy8mLHcVaZRgKgbLmuceVU2H3Qra7QSRgKsVWgUqJ9AiYhW01paoMsX8/oKeBRk8ndWUK7sTPv3ZV/nzKxnJ8pwObV6/ueCW2OIrDw45lUWeXG+zLCV73rEZYCJzuoN1Tm1tMQ+WoiiwvkF+a2UghoZeLRXeW6qqQhvBJ174OP/np34L6wSu9kgiUguUbKo+k2YUdYFGkAjBVnfA77gpRd4mSsmpnXW2U7hy5V2euKzZbHt8dYBMMqLQaCQLF5AqBaUQtkbbBTpoSA2ucLiqIs48SsKeD2xGgxeeOgiMko3F/KJAJZIkgcKVJKZFjHOkkVR1IJcCfwKIae7VP4UkIIT4z2kahh8/cRgmxljRCMiIMb4shLgGPAK89G97Luc9cx9BKmaT5pMsuDmD5T7bGytIW3B4NObeeME8Qh0yttKKTtaHYsairEi6Gf/3zzxGLgW2gKk1LOoJ48WQuxMoYo2zBZeXL7F/6xskaWRlOWFwkDApa2yswdLQiJTCRokblSSTZisvOoOfVwilENpg8LSkZe4y2l1PJ3VkHdDzjPX+eeZHI3yrDWVB3TLUZonWwZRfvgYPzBwbO+zNF7zQ6rGZSIyo8Kop2IJqXsQ8AkpSGUVQnpaz6ABeCi5sRzZ3tnjznuPqfMAyU3bvLsgdLLUSLu0E/va5J/nSW7dptzLO6DFfvzrkx//iJ7j+2ktkxyNODVcJoSBxcyYSUCkWQaTGi2ZWGUQkKFDe4UyL4zsLdrqw1HYsn+uxc7EDocSEI4y2EC2hgpnV3L7naTEizj0HFJhuzv2jkra0tBJP1ziEN0xUZKnTZVpJ4tSxJgI1MJPwwxcUX7pa8YU/eJsf/cvvp1t9hd3C8ql35vTbc17YHnAhndGSc0QFG66HmlvWz53mlTde5anHPsj+/PXmza414aQCUFHhrMMGj3U1vU6bp595jnkx+9ZSmZaSKCJehqYvpAxl7VBakxUzdlZX6XYzypYmI7Ai4Z+9/i5Lbc37n3iaX/7813mkbXnhsVU2Woc4UxBMh8zmYBRljCiV4UVEDTJm4wnGa6r9ChkEZYw8bKBzep1oBLbSpMIwHx5hjo+JYUooPTZtMw6GDdN4QiIFQYDJwVlBCPGEUPEnmASEED9E0wj8WIxx8W2PrwHDGKMXQlygIRNf/+OeTyvNaGo5nkypCsdf/9n/kuOjIddvvYsNJQeHU2bHU6bW49OEVHuW1zdYW1mluDbhxl5Bujem427TMrDcTVnqdtjoGFqtlPLsOoUOSBXpzQ1i51mimdM38LH3P8rN6UtEY8AWIMGGSJANUaflJYWDUYCDLMUKj0o152tByztsAq22pp3UqABdnbBbVdQqsFwMkWGCam3SMQPcgzH7Cg5ETkvn1OUxzguUMEhV4TQEFDo20t2OC9SmIfwKFfFSEn2GCxIrK15+9T5v7sLTP7DCaFGStjf5xAsvcPzG78P8AO494FldsJxJQt5h4/lHWOxeJ/cjUgHrwrGoS3KhSZUA75DRIwQYAokDryVWSowIeJ0ysQtCNyVIRSIKRvfHbJ1awkfNzDq6rRVcMSNbOgP1A6LVdNqGdnvOzJcgDPsPKrZXM2KmMXi8TBHllMl4gj12rCpFXUHWkjzRU1z8s+/j197c483r93jvSodERH7sR8+z6izriwlHh56Z0YSB5LO393n5yLF89jzzgzn3syW21jeYTqc45/D2m47NDcexqiqWlwasrPbZf3CflaUBMQikaOxTRapxsVFe2sohE9MkEbdgs5UyCR4dBS0Dvq7xrR7TxPD7b19hXUVmooX/2iF/8fu6wJTKzWgLkN6TSokVS6h2G9lJ0Mcjiun0ROnY3BfrBqKqqaNgUVtmxYQM0LliUZaECLaGg6NDBhvggkCqFJNIymKBFPIECHPSiPz3SQLfBTzyi0AKfOZESffNUeD3Af+dEOKbkOqfjzF+F7fzP4oYInsPjglILjx8mcViTl2MsOWY/ckD3LwgU4L19S1Mp0W0Q+5M5rx18zYXksB0MuWJxy7yP/y/17gFCCqWdEXegqUMBoMaoTW9XDGIU7pGs6Iczw/gXnXEcFzgVQ/kBCEUTjVruhGJCoLKB4604WU75509S3+l5C/0uqwJTTih6FoPONC1xbkZi2JCHlKQGhdKjBUkK+u0zANynZDg6GkYpIFE1WhlEEqRKBrMGCCdR6mA1M3ZDpGAbBSPaTB0csvpFXh0VRAKwXh6j2tf/jSM71GPAn/4NjxyZonLF5Z4q4i8fDCj3r/FqZVl0jBEYcmURxaemIBOBVI0/4dAIZREBEf0DRmoEpZZCaNZRVloUtli0I3EaoEWjkyAlIrKB0YHE5KYEGQNSUSFBW0hqLXmQVXhTcLE1eQ6IlTA1JIcuHHzmJeO4KmH4UwOy/0VfufGXXaPZnx+POGpH9hhWe6Dn9PSHtMKhPU+2alLfPHWPp8a3eCe19ijMZtr67x75w7bZ08TXI1SzfnbuYYjUNee7a3NZkGosiytDzgcjQkhYGQzYgshUNqKTCQkWiOUJlLTN4rNdsaN4yNUBEJC5TxZFklFxHvFdV+ycJBmHW7drjl9rkuqZohgCfUck2TM4iqt3gbFZB83HuOKCozA+khiNHqQIIIlIWFSzikWNWmrSzQCozNCGVBO0sla+Djn9dfnPPywIMsTpBYEL06SgPr3TwLfBTzyK9/lb38d+PU/7jn/9fAEHtvpstHN2OnW/Px5jW0J5Poy6wlkvqIaj7lrC37vpbv83uAiLDtOnV5nJwTu3bvF24fH/DePwJkMWimwRpOmpGKGpsjTpulytIzP27h0Sq8dUPo8/uq4gWl6CFHRrj1aRIIXHMvAVQ1fGI9Ry+dYWZvQHrSYuREBS7ssGIkWG9Tg4AtS8sa9+zyuYe0353igYsGi3+WD1TGn13Z42t/n557ps5oL6lzhfB9iAFGjqBAqxQpFpEL7Euk0KijqrMIjUb7NUrXgfZfg9DHMd2ta+QpnO9fR+yNu7MHdkeSJ1cDFlRFv3S35G28ERq0+f201o9vJMcCnrs956tEe/a6jHRY07SNJ4QNCe6Tw3NiDexO4cghflJpV4Bc+sc2g3oVWjsoDi2BRiaClNXExQghBOV+wuTSj7cFNJS7tUkVHVZRc2oAQJjjdZjd47t+uObN+ivXzmo+s3cLV8M/fgL//jcA43eWps6c5/dAGr7z+Nr/wuwV/44kOfryPNuB3HuMzl57lK8eO18Y1pr9Nv5hy9eoVTm2f4fTSgL39XZ597gmufOMqQkZm05JBv8+gv8xoPAQf6HRzPv2bn8amKaluo6WhneVMRUm/lUHpGYgu43KM0p5LKy1OU/APbx9xbGt6OrKSK7RPGEnFcSi4oJZZoPiVwwM6ueSnih5tNWXtfIvUW3AFSz/2Aq+8cY2H2luEo9dQVhDRbDx+nqKVMLlxyFwfURaBbdNlHizj4z2SFKhABUHuRqy0G4jt+z64QaQGNSPECMIhkCfAmu8c3xOKwQup49X3WrQagXoAXz8iOlgIyxu2RTHXzPcXfEPmfC12UL0B+cVNdv0EWVSE1gBpLf/wLvxWOWC/1+XspMdqZ4uz2ZyH+rDka/qq4lSq6AbHaiUJ1QKnj/jghVMIFjgDukqZ5zUtOyb6Dk5KTkfLT/dyinBI6FmktNyezXj5bIdLD2Z4IRlLgdSRc2LOnoaJgWGaEK1jI9S0/T0ecx7sAJkGbMeBiqSuoBumgMfrHCETCl1TqUC3SCl1HxMg1pF5aFNFSxpK2gVo0WJ4f8F7Lu8R5zN8tsUb79xnvQdnNzI+tVfyr/a3eXcuqNnjkdWcTy8sr+23GVePMwpvoWYRZXq06DLCcOwlx1PH1EuOfOSd8YhFWeKl4XQ24s+c16xmjkm3xfqpCxS7Q8LeLn0XobTUXrIfAnVVg2/KU680pZgRdMQVIGij6jlZPscnj6O3PGuLb9BWp7iRX+LvfOk6e7NAHVI+tHOKi8trJKrF1yYlT13oka9L4mabntZUYsRseItzW49zY2DYHQcWLmept0YYVvT7PW5ZeOXVtyjLkhgjp09v41zgeHSI1IrKW25fvUs0OZlao7MSUTJBxg7LtcAuFtRVxLabo1FqPWd6hs7wHge14Lzv0K0kWgpmMiLrinb0YGA0G4OE/TJQr67RdQdUZkyZZ3g1YPnOO1yY3yXuH6GqxgikyhXkluLoXWZhgy6aehGYqRneu4YXMU0Q0lNrz0IYlF6hXc+J9R6JaURMUUocmjoISBJg+h3vv++JJDCygr/1GpSVYB4jwyQgokREw0xHeuUR78kd7zvXYW098LXlBNlT7ORneW55jS/fvM27u/v80k9c4m/aEl3NGQ3vsm/fYnwAf/gOXF805d3mElQY6vuWf/Dnelg551f/8C5/96ceRc/3KVtTTKUIqk+hAzOh+eJRRTlIeHI+Qxaa20aT0OGRcYe2nJOVM/Ks6ahv0GFFAvkANbpLV8GagzPdZf6nH035R7+7SzaOhH3BYn2NIA1JqJB+ii09wjfEH28ErK/jp/cQVJi+plMGOmhkkI0nvSt45vGMr9ZrvPgg8DtfPUYJaA8hUyW3q5xwNCKRnkW7xYu7E/6T1oj3LOX0AviNSxzXgc/v7vL1YU0MCdpDV1cgI9LkbCYJspXTn475hafPMmCX1mIfGRLc/B3SXHPEBi9eXXC3qrl+v2K7C598rI0UkiLWeFGhbNNy8dZQiAWJgSxZZvvZgu0r9/lfbvWYX9nlh57K+TvPZwwXil6vx60H19D1dc5vdPmpn13iuCpZm0+oixa7C8HXveTtrqWnp5xb3WZ0a0RZH6A7Y37rlevYlwUrrRRpNJcffRxoPC0HgwFFVfLqq69ia4dOE3qDJVY7S9TMCBiEFAyNpYg1eZZiJ4doDd5L8u1t3nztNZSCYy2o0zZtnSCqiFtMUElk4sZMvMYmfbpuzI6e0xI5SjqK2uKNZXLtGrKuWYyOSLuCWqVsnH+EoprSH2wyK1dQwrN1OuPowS5L/Q7VvMAGj1KGpObEzeqIqBNKd7LSokGpSPQ1OoJyfzpo8j+xqILiGmuEFJyRHBkNPiB8pNaKpG3obSS0okUdD8nOPcTOuQ3aVeRoPKU2KaWDV1+7Qr62RNJaJtu5SNskdFzNJ9OcqvLoyiKmB0g83adLXF4jWgMeXrtLVo1Ag3AZxMZBWISaxEjWW4qr90akjy7hnKNbBx7t5vT9lGEiGG6tUspIVnq6sqZfz0nqyE4no5XlPMeEDz26jHf7PH7hFKoqqOua+dSh0oxARVt60iTDYJAp5DoynSxQPpIkhtpakiSjWlh8NEQkKQFfl3z5zRt8bg+qtTUWY8vEZLg60jEJSZISguecrxkFy6l+n4v1hDsHC7585x73AwwTzTxdIkGTCEmIBYkUKO9ZFoFQznhu3bJupngHheow3LO0Wo6ekaTas3dzSpnAh55d4eHNs/TL66jo8B6iVJAL2rGNG1qiaBgNtR8yvKa48yBhMi75qz/zk9R3vsD07n1WPCy7ERcfHzTv6NoyeTCidnDUHvDiHXjzqOKtyZi905LLm2fIWjlKCZQQWGtJOxnCGrJMsb6+TpoZppM5aZqzd7DP4eEhwUfSVgN67Xa7IANl5TFKI0Ug1DW5SRG1Q5lA6Twpik7SYn8OmTKk1pF6R3CBKmisSSlj3UwBAmSuotOBkAlULfClRemMSCCUU5QNmBipfMSnoNsZxcGE1CtW+gPSes7o4C7L0hPmM0INQiTY2hOFxBLwMUNg0QISDNIHtKRBxRuBC/A9vUWI0tikhQ2haV2I+qQhlrCydYrLGznh+AY9odnKU452dlhUJYtRyd7BGKcEWU9z/b7j9avH3FLHbJyNrLdarGwGNlPHetvQTWBtsIRhSkaJzzPydp+/8CPfjzp4ETSYOqfUY7LgyKxE4HiqI3i03eat4TFr223O2chKqJikFYvVdR7snEV4R3JvxIXTkXypJKqEXr5BJSPvL97hctyjTjK8K1As2D67TkvOKWNEKk2GgKCJAZwvEVGgMORKI3xACok/wVs5D0XVMBIyAWUQdLa3WVtSDG2XmpS0k7PuarwQFLXlfDFhaWMZPx/y0Nll6kGX4fX7lO2cJO+QhkDHRIRdYHyFic2I8kLiOX9+mfdvDqmOj5mJFvujhKtvzXjikkT3PEZN+Nh7wCRtQuIx5V2sW6AjpEpRWuj0lpkPJ0gswUW8gjQR/MaXjvn8vuNvfuxx2qMrlIshaysZeUuDtDAe4WqF9RmHkwwvFC8OJf/0ypj9mDIO8OjqMv1+l5gZgqCRApuMdmpZaXfJ04pWq0WwDZhlPB5z994uVVkjtEIpRZ63UcrgvMUBSkCwFXmUpDFBpwlH5ZAoBQrIC0trDhmK5U4LGT0uOoII1CFSeImQKZYKEz39Nsg4IyQWom4adgZiNSbWERPASomMgeH9W8weTJk5T6ELuqnFFo52KihtbPoBSLwRlCGwKKCX9WC+hxaSJIhGkSyaUaENgZDkNJDwfzO+J5KAJ+DDEUFqvExIg27MQfo9ljqaeTFn0VsmcbtslyM+++6Qf/nlV3gwOWZZW/7xCx/i3VeG/NAnL/PT3nIcanbvHSKP7vLmu21ePzrmzgRqB6sDOAyS+Tjw//xEGz/7Bv/HF474mQ89yurRN4jZMcjGI0CLDswmdGREKc9ga4PDWGFWl/gH79zgsR/+Mzx8us/Rg3uERcGprSUHfc/vAAAgAElEQVSuXb3NW1cPSDJ4IG8RZvDM+5bYmE+Yjms++thFFqPb5GIf7VqYGLEiNAaiQiKVB2eQdYpXBV45hJeomBPiGJ2ACAGjl5qZfpCM6kNUZnhP2mIs9kl0wczuE0SHMy3Lx9+7xEoJ3s64Hla4PdhC2RmXV6EqC87lBTtdQyuVZElksysJWlInKXkrZdBu4Q/3+YPrcDBfsAgLPvBkypmlHLuYU2lNLRsz1HZp8XXNLNOkOuKEBxFY3B0SEkHV9pzv7zC5f8Tdd6ecXc/5b59pc671NtjASg7jUY9JLTisLN+4At558t6cD3/4Pbz4pVv8xkHJmwpcErEh5XY9w771Ci984EMkWR/X08wnDxjgsaHi/PnzGGOYTueMRhOu37xFYlKMMXT6g0ZhJyURCGVzFHXBY+sSIwSucBS1Z+I8vVTQqgrmw0O6q/BLj+zw9166Qd3JCeWMrlWYKMlMYOGPqJMMFw0//NQ23dkt5gNQY41MDKZtCBTgoaoamlS3LegpjxSKxcIiOWTtIx+DTp96NCIrj3FxSOL30WVAhTZLFdR+F2EatkY0zdJTUCeO2AFa+nucO+BjJEhF0Jraa7K8T2d1Caclg26H9UGHHEu8d5ftFnzti28wn5Vk/Q0qOyXKNq32Jl998UVWSljZkGxffAZ1apOB2uQxOaV0ilDW7D24yZHNEaVHuCNkqsg4sRvXJypAAQ6BjxXO++ZaAYf3D3GdnNfv3uD5n/hhOo+cohYzkusloZpz48E9ymwJKw5RKE6fWubyxjrL7QnaWow0qDAjkRW+qlE6R0rIoqQO7qRYc2iREHWGUBllnKNVQowZQrcowwwfPUejOXVVs5jDQysZmS/I7xzxnnNttlY73Nw/4MrRlMdXBOthn6gg+pqXXj7ieloi5nM+dnmJR5ZTtoylKyuEEMhgIZZEDXXqcFpQFDWInEc++DhLxxNOr7U5vvoKDx5USAPdfp/79wumVcWTFxIkilgJhGl8D0OQCJnhfeChJ5/itS+/TDfC1laPh7aXEdUhxQJqC9NScG1fcXh0TF1Bd2UTF2rO7mi+duMqL96fsshW0Jknq0tyF+nZwGK6aOb72mJSSSU1Skvayz2KoqDX6/Hg4JDDw8NG/y8E+v9n7k1jNMvu877fWe76rvXWXr13z3RzZshZyBkuEkVKiiQ7FE1ZlqIolmB5UWInFvQlDpBEQGBBMYIAiW1AcQwogG0oSBTYAgHZihbIEcV1SM6QnBmyu2em966qrr3e9e5nyYfbIRiJIyJyIPB8qrfq1luFW3X+95z/eZ7nF4XfBMl476mqCmscTV2itUZpRVnkxCoiSlLCvCYWltjnHEnPK8fw8xcnuLrCmlZjXUtFowMmwhLbnA1T0nHQbYOgicIEpR1lY7Dz7LE6VID0KOGRxlDP5yitkWGDzuD6m9dBBZxfHZIkgiTugzCY3QNocjrakyTgR0vMJzmVsXjr6WgIVGsgcnX5jvPvu6II4AUq7FDUBpRiY3WF3BuSUDLqdRAy5HBe0jSCQQqjIKUfZpwIh0xH3DzOCFSPjY0zbL+5y6dfcdzbvslgY8ATQ8OF9ZihFAxTePrJLQIdkQpD3FjCdJ2f/aFruL3r+MfHLjYICKQC1SC1JM8aLAH0A968Ncdp2AgMeX1ErkO8k3S6A3LnmO4azGOp7cnJmODieWZ2j8plHL/lufK0ID8tsR2I+zWR0GjrCbTGS4n0Gq9CaunB9WikoKgdtrCczKYYm4PxxAqSANaGio+urPDW4YKXzq7QD3ZI5IL1jYinzvfoUBI1FqM6oDMmBkrZpekHZKVjkIZ0RI4SOd7CY4wyImx7AqEwNK5BUtJdCTDSkB2/jZA9dqdzNs8E+NKzEsdoXWKoUU4RiABFhTeCrBHYwLO7mzNtrrN6NmZjpFECODomU3B4GrJTWG5OLa+9NeajT3S4ejbkeGY5PZphzlzg99445FhoaiOwVcN65bkaQKcb8sr+CdvHJ6SpI6oNPu6ThJ60l1DmJffvP2Q8nVDXdTvBlUI/ZgjIx9paawxWtl1+1aaLkSQJkVCURYOSATQlkbWcuNYv/+mb+wwi2J0V2CCmUZ4cg5EBUQ0vCPipFySNLyiuPEc82cH6Sfuk0TGiimjqCudaAHUggMYTRZ66hqCKqJoMWQrycg/RlQgCOmGIL1pKEY/jz1na4uThdWIVIKxEJ6rNmfQWtKY1w/3J8V1RBFZHA0rTsLK2wYVzZ5k3x4y6Hc6sbnLw6JgTp/jGzVtcFQdc3Ej4+fMZv3xfksUxQaR55d4bfOL5Z7n96j2uPvsszy73uXeyzWwx5u72Ea982vKogQmwRCvJrYB//Zcj4mzOH15f8P6rFxHFKVJqahpC4RAuQHhLkkoqWdMNQr7/agze0HvhGrc2hhzeHDPmLl54wqiPSxumUYBWiruHBdu/+yqXPnyWeNfTXV5lPQm5S8oX3sx5/sUersiwuSbPC7wrcUjiTkkQ1IziIUGiSQKBCjzdVIPttow5VxB4C7VF90pELBHFQ7r9Vm2oqeh4hW88IpQom9OLFb/4Y0vszRYcH5QIb9FSYoIGjMNoqF2NdtCpA7STUM/Qos1T0Ievs15nREnA7PIPkghLfnib43vbRN2Ip5a7lJMFZeXI44K08ogE1ldDljoDLl4ZQjXD5xnjg5oDq7n3ek1nHR5lIdvRBV7bP+SXPnEGmR+ykznuTCfEgx7//It3uButo/C8aA75e+8/x7qbMQwKXunOGI8L3nj5S2xduUY+vs/q8oBZtqAYz8mrmsOTY7x7bNZ5vBX4prD+sYjmmXe9iyzLMEVGVVtOpgum+QTtG6wRuCghsJahglivcunikC/oHZoThcBTVTOuyIqPL8P3XLJ8+OwTrPYU2i9wxnL81h1yX9LVghpQYUQ9KZE+BtmgsbiiVaRKVTPqQxMFxLllN/dMLXDkCF2FCYA4xiiHVoJEBVSLA84urSMlHB0eIMOQpmkIQtlalt9hfFcUAesscX/AxpnzLIoc1Q0YjUZEMqSo4LXbt9k/POKWLtg9u87T0QGpH2GNoixnnByNueG+Ag6aN7/OWeG5djYgHG1y9fKLjF/M2XUJi1oy2bnPvumyKGtidQ9nc9TSgKxphftG2faf3jTULsAojw1qxOOz10gJaik4TQecphsYn7G5scrGIGZv+xFRr6bWChMmjCiQCnxRcWV1wI4GkS3wJmBWaA62xyRBSZAOWF0b0dVt1qAQjshNca7BeouyHoEhEFWr4kPjvWwR3EhMqEiXh+QPFhS0MErTOOIgQYkGfE2AB2vpNMdc6sK7hMDHKXU9Rj7mNCIh1AFNKTjJPE0Yc4LCupo6HNGcWrzVNNYz2f4Sos44OwxItMYpT4NFpCCFIukaRmvLEJUgatyioc4L6mbBvVM4PIGjeU3w1JOcGQhu3Trk3vXbPL0Ed2/MOLHwxim4KMZVDbtBhF0ccD4J+PH3dliabxM6CAewEWgGTcPDRc7GmbPcuvcWndGIo1mDzysyWyOFBgVeSJRSrTvVe7SAQAd478nyBfsnR/iioFiUzCvDcG1Ik8/wosTVBThHGAY8PNrjre0xeVIyzBwD2nj7X/zYGZ5b0pwxC4KkpGkc+1VNfzFlo2lYhAoaiRWSfJa36kLvKC2kocRJcNISJTGuLJEsOHEbTFzG1Y1L3Lh5j50xDEaeYehIQ0Wioac0ISUubBWYQkPVlIShxDqH+FMCBb4risB4MmPj/GWyvEYLwdbqOp0oRjaKr752k+OsAKfYdprrdZePqwOSbE5kUpwOeea59zG9f4uyH+GM5Fxi+eRnM4r+Q9gyPP/8ec50NP2eYGV5QKm6GCsIsx1Ub5OPbF4hu3W9bQh6gbYe4Q0ycpjlNpAh1o+TtxWUKKwTaJVS2IZFfsK9hcOqgKXVPmc3R5hxxrmo7cdevdSld3KPeOkSi/EDlBphvOapi1eIxAG19uhmjrYVjYlwMiAwJaVweBljvECI4FuquWxfNxYVSjA1/TQkE4rAW2StCIlo6gXycRSa8AKcx7T54LjGIwNDhALlqBvBrAHjJGUVsIi63J5avjGu8FZwtzilGDdkFooKfvqS4oKyhI1DKY80DjuH7lbMSneIklOsqRDzDCng1rEhm1pmM/iXt2FrSfPiyoB/9KVbbISC/szzkTNwZSniX71V8cW6db4NewInQ7QK+QtPhLx4+Syb4gF21v49jIN4XLKu+txQIXQEOoWH+ztMSkfH2jYdWimM80gpSZKk7QVYS9M0OG9pqpp7dxaIYY/FYkZgW8xbUVSUiwXdMGxRcFbgAkFeH+CUYVhoPpA6Xjwb8qFr61wZKgZSEURLVPuPaEJN4BzGNFQelLFgFUq0v7zwjgaPjxOqugAsMhCUpcMXFl3CZ+8d8P0vXibbuY9cWeEzu8ckYcRqtWAr9gw9rHYGrMicNAXnHVEEgW6PhxEBTkig+Lbz77uiCOi4Q19aqrBgfWsTjSRbFPz2pz/FOHN0ZEqUKu7KmH+2E/D+keRvXgn5tVdv8zWZ8mtH2yyXNT+68RQPd+/TVCnPfuwFfKqQszlf+8MvsjuG46a9DRWtovgf/rBmkJ1wkBuCcoFdkigb4F2FFxWqAwJJU7fhF4oGLySVAJ8V+OMFuZcMzp3Fqy5ra1d49Fuf5MnLA7728i5PxvD1KbgUuhKWTE1WOJyYk8YNn/36m7z/Wp+onrfGfRGgVIPQnkZFKANGt0BR7fVjUW+DsyVSSLy3oBSdpkQee4qiQocJgTLgcwKlaGpP5QNc1ebPn5YB5tQzH3Q4PAkovOTwdMqdh3PyHGpfcWlUEUQZQZTQKyo6ccDFyFKcX+W0KBF+wUtblnM9SSeOiNZ7YNutSZ5lLKaHnM663NqZkY9puXtPL7MvFa+dHrDTd8T9mNdnU3rhKp+vKv7O08uU9YRPnc75TNhnEUgak1HLmDPVhJ+4kPJMWnL/9k22+7CuQqZSIoKIuHJE8zn9pfN85stf4ke+9wf51Ke+xM7iEcJVZGjSNCWQEoH8JlhWStkeCRYlq6urnNlc5+VXr+MCicMRKkka9om6kqacQxBSKdCx5SeSOf/plSFPri5ztqsZhB4Cz979W+RhwgJF4ARytiBxjiaEaRjgbUMXhzPNY1txjeukZF6yRg8WUzwBtW99Ab9xq+SFOMA+uEMZwTDM+Mn3rfD3X52x3E34q5cHfF/HcNSk5JVBioYzmwO0NuAMTW1b/0pTvfP8+/OY5N9pCAH14pDNc6sEieFkUrG9u8e9oz08KwzjgE4cMCkKJvOC3crxxAXFM6uCW8cVpx1PHsDR9Zs8tdllNZRkX/kcgYCzQ8XP/PCzXNfL3HJdXtuvuVN2OZqUxOmXGcgZZSdgZlJUPaeSbWadADAQG40PGgyefqlQjaOJNPuN5eHBKZ97+av80AdXSKKUTjBkZbDEnp1SBJp8YZgCtR+B2SU4HpPGYK3DGcdhDid1wNn+Kt5FVMIjRYb1DaUK6PkaJVt3YuAkOAPC4C14YbFA49tIo2K+YOwcPRHjxIw4gvmRpzKSylfgwVvNdJYRBgH3Hxzx6R3YM1DHimF/k9VzA850FOr0Ld575RK9qma1nqNFw6iqmakFakkSpxHdTYsIAiAkL6ZYITG5pzqqOTiGN09n5DUMl9eIAsH167fZT2LuW0GvqFnkM+okpp9P2ewYbu/P+OwRLCJBGM0YOpgKSWk87z7X54NnBH3V5V624PRowZWldtsVrqzTDTskBzNOdu9x1JzneG/Cc+96mnnpyMYHWNoGYKBDhBDUdauek1LirWN1dZV3P/009+7doW80E1mjAo30EmNd222XraU7NArpLX/5iSsMvCVKt5n6NfYzzWI+ZhQrdF3QVbCowUdQWklgoGMbTiPxTTszBrwK8EFAFMT4xYLIhxRZm2R0clTyjQX8kKmhI5iOuizN51ytCpYUGOUZ9hXhbI8o7NAdjojSAhkpsnKOkp4wCpDe4+tvLxSC75Yi4C2rl6/QWRqyKBsm0xnXv36XSAwIOimdGLaWO7z6jUN8POBXZ/BPzJxPnEt5Y5qxXW6SNGP+znuHfP6NfT7nFjSrV7jYi7i5c4PTl+9z4Ql4amj4z4YLUnMdt7xPpgzN0gXOupjd3fuMB1sk5hG6iKkpaWRr+ew2EaVvyENBIzRFGjO78TrepBzcOGL7Wo/1cM6GP+bwuGJWRgw2LvFHb9xCA30zJT+sEbJmLgUWRZeGpe4qvg5RoiFWDzDEIDReGgLVoGxOgMBKTeYbAhW2qcbe4CpQWlE5qGVIllUEJSQnJygFcwtTmzBTPQ5Vwmu54tG85o23H/Kh91ziZLLP06MZP5rCemQZre4Rir02Xrwn2Tm8xXQOa8shztfsprCxaghiQdyLwdVgaihKysUSu1WftxeS37+9g9OSrre81OnR15bt0xM+4wZMXUqtZhRRj30fUZgcaXOo4HeIkCl0hERZhZGWnIq0NJyaZV6bNWz0A4phl0G+TSEaOs4Szx8w2fxeXis892uDM4Kbt+/z733kWcR1S7J0FbI9rADjHUq30FVMQyBgpdvl0uYanUAiXE0Z5IRhlyjQRFKQ5ROcbXDWEJPS9RqtC0p/QF/XNFkX2ezTMZauaHHu3dEaLgNr5jhfIZzDEFFJQVh7qtISJg11YBnnI4ZlyIg5ucvwSuLxvDyP+eRtxeXLI7LqiNR7JnXCZ04qotEWKQecD0M2RisUnQWKDt24JPcRTkpSqdqUaS8JhcDZmneianxXFAGtBKurm9y7d488z3n5K9cRUZcokuggpmwMb97dwZYzXLXgU+UV9s1dnhhE/DfvXebI7LG1eQE7rfgrz8JPCcXbvua1m3d4eQw/8MwlZF7y6puf41cfesbAsoJ/9uNL9A4esB5KShxps6DxgLZ4WmNfHoD0Jc6ANBDomKyUmKhk49oGf2W4wY1XdhErE4bvsUyHHb7w+dd57ulrnD24xV//wBZ65zYzYhpZslR4GqUJJSytQV7uUIsB3neRRhB4CGVB7cHVGisc0jV0LbRe5ZCFjNlxEtnEZHnFxHlKp/jyRHA8qTnOK45n8MEnU/bu76OBZ1c6/PiVi/ziD64Q2keoJYPxQ2pR4XxJXLQ5ezPhmFtHdxhw5nLE6opE0MOpHDkJaWrNwb7k7aOG00PDSQZ/VJ9wY3qCjWEoFMNOzIFx/N7OCQYYdRNWhKM7OSYMHYNuTCETXnuUMYkkPTxJWZELzVEM3hcIq/GqSy1mfHJvwW/twA8Y+KHz8OkJPBh1uTqwnBdDfulLd/icX+ZYFDxpcsankk/+3iu88MJV7j64xakPOZ3NMc7jSoGzLVcgSWM2zmwyy2b0myF146jrmtrmBL0uOopxomzBrt7hXEtb0jqgrlvpdy0WpNJQCo3oDrFiwaNFRpRu4aqM2Lm2J2MrVAlGBjhlqEuBFDHT2SlKD/HECGEpVIlT8Ey9xwc/tMbns4JfPhoSH0xo4pI9kaJmJ/zwqOA/+gvvY3UlRBxPWC4qXANr50YU81MaH+B0B93vY63Fz7N3nn9/HpP8Ow2lFDvbjzC1ZTqeUdUQd0Mk4vG+xtNYMKYmDEMmScTvP/R87MkeZ8KGjQrKkwMe2QFDFWCahk15wKUX+mzkXb78pddZWY+4evU5lp/THM5rxnfusmoKlAXq1r21CFIiO8MIAUYhbbuEaqSG0KIbj6hLOsqzf/+YL732gJ3DnHrR5ynn6D3axk5rIh1hastf/9AzDMf30B4cJaGHIkxoUIQdQEdMxjAfT1nvgm1AqhSRQxoATuOspfAhi8qykD0OFh65ts4DK0GEqGHIw/0T0JL5VkyxPiCKElZsyas3XmPz4gaXtzb53rBC3L/O2qUOhYfGGVyRIbB44cmkBQlRpLg67KC7IYSO0oMxAVml8CeOo+MTDuawaECHEhOljImYywmhTnBRwGFTk/mQWerJLBwWNS+MEjQCKz33dk6YMeaYgL80bJg4uNd0UDIg9hWVUnij6ZYVKxE8cnAoAqYrMeb8GZaXCyY3HpClMeP+MmIdOrLhpKyYLRasrw+oKseFCxc4OLzDTDuUkDhvQQisbyPrzp8/Q9MUpGkKUnB8Om69IcMe4ePwEO89xrSR397ZllBMS5ySEpyKUHWNEg5XG0JbYoxGJILaiTYmTgA6JKSmEgrjDLH1RK6mrmAyq+j2QpyxeBURC09X17hmj6d7Ax7WIb1gxHI9o6MSnJB85KWrnO1KykWJqAVhtWBaD8gLg3SSftKlCEKSKMYtFpTu22sE4M/OHfj7wH8MHD2+7L/23v/O46/9V8DfonUr/KL3/ve/08+QUtI0Bu8kb791BxV0scYThhJTWZy11EVJEAQYHIQ5n30I79qyPBU3FJM2MjwvC4p+ivFTOtqgypwXOnD2A11uPFjw1c+8xpUXrvD+rTXO/8BLLB5+ikEf0IpZZVlIRdiAdw7ReEILFkFmBEiFwBAK0MJw53O3uDuG43SFaXHC0org0kLyzNYGb1/fZbo7IRkeE3mJ1yDtY+UWTQsxcSBcQCdM6UYBTTNFBoLaepQYkU0zCluzXUqaTo+xTHkYLzPrxliVsmMySmNpaotMVjHWMhrEeGL2jo+5+q4n6YYOVdXYYY/F/BYbS3A6z8h9iJcQuQbhINIQbyiCKKIb9UDU0ExxmWJcdJmUBZNpzulOi7keLQlWpWdmO9zYKTicS5Ah0jimviGzjrn1NLbGeLBI3h7PMN6QN+19KIXESs/fe3bE23rEL//hAVhPaCpUFBIby2VtuDxY5ovjnEoKvnQ056C4zY8+dZGPf/8T2CTh048mLG1ssVWMyXspk6NT1oRjNOyzv3/E5uoWs9kxeWmwWUldOyIdsL6+ThRFzMo5WZGjplP2D08Z9vpEYUSaxgjbYrySKKKqS6QM8NbSuDaZSAHGNTgBQgRUlSVsIArCllhkWi+MFWC0B9kG4xoEynu0c4RBl2xS0QBp6DEWfAWuC3kCS8WMLTFESs8lY0hSy85JxWY3pGvm7N3eZrU/4MH2nPuTms1+wJnlPqbIiJIBWI8sFn8qi1B8K3zzHYrAR2j5E7/+x4rAwnv/P/yxa58GfgN4P7AF/Fvgqvf+nbsSwLmtNf8Tf/Gj/NtPfYHGSQaDAUGgMbZmPp2RzWcI77EixgrNqDnif/3oWXqLPbKs5mxnwMFRzacmBd97rUO3ypBRShMoznhF0CsxXjKdGxaN4nha8MY2vFHBpYsjPvLuyzypG05Pj1kRJwRWop3DNzV55RFCIrVibg1SBQRKgw2xTnHb13z+7RnLT1yic3FA78Iyb37jhPs3F/y19TmrnNBRhgZwZZ8zYsZUCF47DHj2mcvc27nDOFrlSwclo54lCD1F7zLzXLAtPHUcQ5ritObgdEKUxKSdhH4n5NK5M2T5nKwpiJIYXTje+/RFjLPsns45OKqQtmKUam5//TX2Hzzi714MOKtqzsgTuqs9wLbR6nmNdYpZI7l77Ng/qihmcCaFla4iihVhp8vR6RgVd3m90PzOjTFvW9gbBFgvwFgikaBUiK8XzFxNVrVBLWnYwzhDaUrivoSiptPA90kYK0UWSg6Mp5SKd8uK96306DQNB05xO/N8fV5wGAQYX7NiJBUODaz3R/z0z/8Iu7vbPLiz4HNv3KOWMz7wgfcwioasrK7TG3b4wz/6LHlW4zx0Ox3yPGM07NPpRNy+fZvJfEHSXWJttIxFg2iBI1leoiTgLIEKCKuSJ8OG//4anLMZVdIm/HoVYkWIsxVeCpyV9OqSkLbxXdnHJ7wCagteS2onKGyH/dOK/UnFe69JdONIYs1MhNSyIFp4jk0fogAdFswmHkGX731pFXN0i/q44Te+CuI8XLv0NB/a7JF2LKV2+GCV0M2ZH9xgoCLkvzj4ivf+xT8+//5M3IE/ZfwY8H88Dhy9J4S4/bggvPynfZPz8OnPfo7cQBB3SKMAU5d4KyiKDI8FodDeEZiCH17SDGcPHjuvNB3huTiwnBzBozzkWihR+Rzd7zKLegylJQ0cztQUBgbDLu9fXyHbLth7dMDnZqe8GSdMFw0f3HRcPb9FU81xxqAjzbwRnJaPj+2Kgq2RQhRjIuCahuGlIYvAkSQDdv2Qxh2yuhZzZ2+HrQshMjcIEdOh5GEvpPBdnM54c/eEzzxo+OrsEbOkz6bssTZYZad0yCRhJvosKOlK2Bx0eWlzFe8tIgpaKs68oKNiPIKjwwm+aPidnV10GGNlyDzLSQKJWRlg1p4gSC/wb258mhfPLxNf7OKaGbG12Nqwt+izmOQsTgruH0G0EtPf7LIaVgSmQlHTq0+xBo6mC/5g13NDwkmiqWpwzhNGXZw1+HJG7ARGhHjpCWVAVc+ROGI8QwORgRS42V/B6CWa4iGkIdoYluOAabbgwErerEsmDfgwRnpDImAiNVXgkD4gBHYPJixvnOf05AEisngCrt+4xUeffT/ZJOf2vbcZj8eEQczyaIXDw0O63S5CCG7evEVRVYRRQpwmQLsydY+TeIIgwD8WCDV1O8FrL2hMmwIdWLASQlMT2BorobTtZK/ClAyJkwEzE2JlxKmZk5QVA9Hi7CUz0n5Ck8HMdliO8zYGvXBETlDhWZKWpio5yBzOQT+eI8oQWzmkhr/6cx/mG/OS73n3c9j7NyDROOEJkpT6ZIy3mnx5Ezj4tvPv36Un8AtCiL9GmyT8n3vvx8AZWhjJ/zN2Hn/uT4xv5Q70uh16QYyKuhCGaDxCaRrnqU3Tnnd7zaDIuTgI+blnlpH5IxpS0EscZKdsdhyigqOZ5OJGyubaEkcm5BvTKc+nIWkiEXWN0orEK6JmzifOJpizZxlXhl/7w32qAK4M4WLQYTabEHvPUMfc3p/whYewtQTv6kE6grmC3EFqILQ5aWk5+kbFb955hSqEdz15jo5I8A4F8GYAACAASURBVHVJAHijSeSCX38Il7dgc6j4py8f8Rbg11d57n3vYV55bhtHb10iAkswtVw7d5ZBJ4aqZLkXk1U1k7KksGDzgjqr2Ts8xQhPqg2zvEaIOYNBjyw7wTnL9t4jllfPIpygfvKD/MFiwme/cpO/9dQmg+kRzczw5Z0pdWbpO1gfCtYunGO+WFA3U4QwNB4GHoSGdGmFW28fcRRHZF4xNBLpJXXhIGjQ2iFsl9A6KlHTUKJU23eRQDdvydG9juJE9hGuoVEJkSlYrisarXngAx4VDQ8bSSPBihL/+EnqAkPiOzSmZGEXnE7GnN3aYGVzyGi9w95+TTFtmE8yvvLmq+TK0On32DqzwWQyYWlpQBBEGOfIywYdRAjV+gm89+AMWrYpQt63KLJssSCOI6xtKKxvn/w2x9cSEWukqwkqcDGIAGrZ5a4bsFd79uc1X3twyMwKdrzj6Rpe6JS8uBmz3AEdNowGijv7JeLMkK45pe8EtgnwQUnWKlRwtUAGnrgrcHVNmQu8j9g4M2Lr7gPUfEo91JhQ4J1AyZJiMUY1Gn3pGvDa/69F4J8Cv0K7kPwV4H+khZB8O23it91vfCt3YNDteKsDVJIwHAwY7+9QN5BZgVABlVAoIv7280v8wJpl8/ABx+EWN8c1b5Y15370EzTXv8B/8dwBWZgxF/ALf1Ryt3OJUeD471YUy0LRGa5Ck1NkBVlTES0gDiEVIb/y00M0C0QB4fw+S5Gmyhw+b0gFdBS8a6XDs8tdRD2j9u3yrnaaQNbIjibMZqzPSvxwg+2v7vCTz0gi0TD3EMWe3At+5vyAuiiZRDHv/hsfo+863L97l0MXkAeeIKx59skLGOkRTUNVa4yF3Ib87ss3sLYhCjWjQcTm5jq5rlsXXZ4jZIwQBb0opJ7tEaYRIuwThR3GB9skWnLDQKUUq53n+Z/uz+hyhpUm5y+tLkjPBkyl5N7dbcZfuUXiYek8dFI4dQP+zbjijcOSR/6IedrBFgYFFKpBiBqEx3uohSLWjiXlkTpg4RRuVlAqiQ8dLz5xnsXRhNdOZ3w8v8tKDNeuLRMlIxYy4B+/cchbiwYbtCg67yTeBXhXUwiPlZpOMycSUFea7Z1jOuHbXLx8gUEcYTpdFq7ic1//Kv1+ykpvA6U9wln6/S6z2YJH23ttaGfSAyAIFE3Tpvpq5ZAyIsuy1lxkfYsQMzVewswLTNQj1XDsFb+7qLg/idgfe149XXDagBMLSrGApOVUymhA5mp01OHA5pyagMN7OT9xoYuUNUsKto3lN189IYrg2QuraFEhGk02WVA1jg6Ko8ry2iPov1sysALX7XDw9utc2VrCFvcJYsmkiUh7S8jdN3DFhCweUZ3+CfTHv1sR8N5/c10hhPhfgN9+/HIHOPctl54FHn2n92uaBqGiFgrpG/KiQoYpVV6gtEQ5UFXG95xbZ2nva5RIJkdzHp5UZFee5JXc8r7nf5jtV34daQxNuszcVth6zr1MouQSUkpmizlVnmNMjYgT6kiTFwt06FnOJqQOCglUhkZUCC1xVUFHQlpBaipcrVGRRDbQC0Baw2kG1A2r/Q4feEKxtHmBz7z2Nk2iKMo53UBSNQ2BlwiXMZVddrtnuDfZ5yS3pFHKuPbEieeZyxcROqbyljjpMS8XPNo7IM/LdvmpFcM0YWPYpaM9x+MDDvYf4KUg1FtUVUmVTdEIptOMKCwI5IxZMSeMAzaHHbyvWWrmnAaavNNHqwEn49sc7u0yW8CZs0uM4iHOWDIWHM1P2c1qvnBSciBDsrBH4CTejMmocIEiEBDSElyNVzR2gbHQBBFOtKSdnhDUjeCzbz5EAmWk+NAHzkAl2H/4gPtjuGdg14KXIY23aBw4h3MCR4CkxtZQdGLqrCRBI3WPhwdTLl2OGIYDstiRNxWpTlFRQpIk1E2O954wCjk4PGSxKNFtOgdIgVQBzlQQSYwxNE2DUoper8f0dEoURXhfo4KQuq7IG8vB6YSvdNb4J19acBxA5jpMk2WaWCBlhK7H0OTgYBRaOrYmmjQY5WkQrK0uUy4OCFMJSrHajbBKcv+04ov39+loaALFwDm6AKFm5gQiSTGVQCSS/lLIWAmQCYWb0m16xEGMqyxmcto23TsdzN7hO86/Pyt3YNN7v/f45Y8D33j88b8G/nchxD+kbQw+CXz5O72fdQ4dxMRBSJFnLYzROpIkQgqQZc6yyEnHj+g5OJYDdvfHFEtDDjX81m/+n7y+eYbjtYv0jOfo7glreK7aY34773Hr9pjdJmNRFVxb0yS+dWDOTEESKoxtYZM4CB5jyJ1UGGNREZiMFuYQxRhKohho2r1fWLXmG2EtYZXzsRcuc+fRDlf6hgenhvf0E/yiIE4ChPW8snSJg/4mezomLwrSfh9TKjYSz/PPXSNGcZrlBP0OxYnhazffRAcKUdckwpMkEaXJeePmHkv9AXt7B8y9hFCj8orKK0wh6CUpeEOZNRhpKKxh4SwxirqusbpEbqxRVRV7OP7VtOF9wwHv24Se1Rw1nv3M89beKeMSZrakSjpYL4mcJA0kHoMRUDeexiqcl6hY4zQgPbHQBFa0WofEkxpL4EJMonFYUul59eZDbswlD8pW0l0ApQgxCBqn8MI+Nvk1eBkjCNCuocpDdJTgTMn+4Qm9bsgsq4nDiEgHxHGIcTWDzoAsnzMc9nGu9QIsFjmd3oC6bnB4tFStBBtBnuf0wggrBCsrKxwdHWGMIQ6jtrPvLCJqG9RBEvIHe4KHITSdDlkOkSvRQuKMIxLBN/siTeVahaBzuMZxblnx3Iamc+pZCEtRWPoKGpXiRgHbx2MMMAmXcE3FQDiOsoIm7JAYyKc5vhcR9zTL3RFl2RZJCFAOaEq8afAioNPp4ew7c4n/rNyB7xdCPE+71L8P/G0A7/11IcS/BG7QypP+7nc6GWh/iEIHHUxVoxHMG0tTZISBQHjYrGf8lx+9yOjkAaUacecIduKA46Fk3ivZ2tzkR37iZ/lv/9H/TKQDLl6+zIfOHrG5qHkqn3DCEocnh6QW2EiwVuCLgjRshW8hgsL3WESepbykCNsObiIlpmmwiWCsPAchrI06SGWQTYXzGhMoat8Ajp50ZG/eZi1QfHjLsb1vKH1ItrpGJmPeWr/GF8MuB7OCnkqYLio2Vjs8cXGEDeccZUekwSpfe+UNanJSmbAURYynE4xx1E7w8Oi0PWnwiqP5FC17CKlxTtDUU4wOUWFMlllkJ0JqTxSEhNmUxjsOTh0VIQ90zRMnc7QpyYIQde2DXBee1x/cxb71Fo0/wkcBmRhhE0llDUvKMCpz5HxOuNxhuhwxnlUoI5FeUGMRZY2QkLmQUhUo35p8AhcADcbXxCWc9zU//8IKz3UF/8Kt87tfvk2bjd8QmBohEjAGK2hl3AI8NcJLvH58fFsWNAq2D7a51r3K//XpV/nwi08jtMUd1PSjDXxZ06StXHjn0T5ZVhDGHRprEVrhhUEqibOWUIWsnVnnwvoaxgm+8eZNTFXjrcAbD8rhVUSJ5GieU4eCZy89yT+/dYCRWRun1kg8jiiUGKvp9LrUvqIpahonOIljfmmQ87NPS3rFDrcceCK0qJgWjoCSDVviz4z48sMFr01O+MQgResSLSMSHxH5jHRJsHJ2mcV8QaQbRKhJu2sYCwk51BMm3tBJlxjYnEln8GcvAv9fuAOPr/8HwD/4Tu/7/xqPRRmS1gFVmwatW+agrR1Xt0ZsxY5YK/Yyz8G4pI4cpVsgdBc1GjCbZ3ziQ+/m9qMHfPXrX0VtXeJ+ndIXU8b5KfYEnn8GIllTWIeKQ2rlkC4gaCShmfEYD0d7CqzBaaQ3xGHAuTM156+cpx8LioNHxEYT4Ml0hbC0BhEMI9WHpiJVlkt9z8m8Yg9NnvR4fe050r1X2ZgXRD7lyRfeS+jmDGPFW3OJ73TYe7SHdJ5B3EEnksOjGaaCed5gnEKpFIklkhYhLHGkyedzhPcgarxp0D5AhhFjYwi1InSWojJYGRHbEmlynIuYT2Y00pDENU+Nj7nw9Hu5ES1z5yhjlB+hmwovc1IriZXB1TU6UJwbLXPbGMpJhrctuu3xTgBtWo5igiJvgw1QJiBuejRqjI09Z4zk3z97jg8MGob1lP40Y6MpmUDrk9fQNAVKyFZb4Vt3pxAgMDgZYjAo23ITKwePjvZZ64zYWl/j6PSIeTOimjV0I43rRmxv7+LRCCHBO5RSNMagtMSYBiUUKytrnD9/nsnBPvuHJxweHpKEEepx0xApcUpiaoexHiEV7+nXjBLYBVyUULguSIs0U1YSRX8YYZ1nZWuFplJs1zWfOBOyacdMNTQuIaoVDZIyFAhTEWnBreOce7mj7Hd5/vIaL11Y5nc+/XU8CqdCVs+n4Eu0F+ggpBKG3Jb0icEZ3OQALwOCbo/j/YcE5y684/T7rlAMIlq/c90YEt0eA6IDaqG5uDjhY5uXGZzuU4iUvdzSuIY0syRhgJyn6Cxk//4ucus8QdUg0ke8fnhKYkJe+tBLPIgWXBmdYgJHmO9RxZZMSzbmAVFpkImi1orESSoEmppAGvAGpSF1NR9+YkB35xbTrEG29aE9c3YwzMBVYLohD7sRJ2KJ7Srkf3vrmBNp+Ys/9H2E0nPvtT9iZeMi+8UO672Si8OExbzi1Bh8JjjZ3qXOc8KuJKsss72McVaipAYEMQ0OQaMUc90l0JqibCirBh1AoLuYqsYAEouvC5wImViL0aClpaojnNcEds7CBoRIqtzxyqMD7jev0u2l9DoBeZES6wSUJQsccVlzafUCvUBSRZJPvnGHqQQCELa9F8LSbg80BKZB2TY41QvI1BTlISkVernLK07w+m3Buzeu8qsPbpEhWkOPkzjvkap9MHhcm3TUCrnxApRt74OVAc55tLdMJjPiNOK0sWxuXGByeJ2CkiyKWtqva/CmJNQKZaExNVoojJcIpUFonFTsPdjmeLrgeP+AjgywMoUowvkKW1kq5wmU5sBbxp0+75nd5+xGn5OpZRCH1M0R5UIQ2w618Dw6PSXVgjDxxBcH/NS85ko6Jp7CtOpRhXOcalekiQuxaBq1xNcXR0w2IjpFysefOCWJjnhqtWS8V7AUw6jTYTLPGC6dg8qhZY2wc4gvQjmnrqaI3gZUEhYFrz5avPP0+05ioT+PESeRv3bxKkhPXkypFwsIEgIkf2Ol5j+41CU+ecBuFvDmdoVTQxI95ekLS+hBzN/8yj4bH/447niPeGmJz37hK/zMT/+HON/wyU9+kulpwcbWKh986V383Chn+cGbuPyI87/wk3z9zj2KG/d47/6YpTSA2lHZkAaN9RA1Gb5p+XzOLVHImGmkmCaKJi+5OYfP74zZO66ZVZBFgIDIwUs/9nGuvftZPvO5z9AZdrFGstLTDNfXiDtdmsWcQMccTzPu3b4OtsZ4yWTaYOuChQtJ1eP0GyUpHp9IaC+gmOGEo/EwXZQ4C7UTWGmQyrd6AmuQTpBEMaYw7RFYGmC9R5AitaCXxFCXBKKi9hVBGDPsj7i8ssywmPA9Bze42gkZ+Yr/5LrjC7mhQmCVxyAex7NrnHB4+X8z92ZBlt33fd/nv5ztrr1PT8+CmcEMgMEAIEBCBBeJoriYokjFIrXQshLLsWRbTuw4dh4iO3Ie8hK5kqgSW1ISV2RLLC0B7YikitooSqQIgCtAEOsAGMzaM9P73e/Z/lseTgOiyqSUSFUqnqfbp+6c0z3n/H//3/JdPNrFSBKMbIwuAo2Qx58cHkUTMJIIvGneU2iszzyN5t9rRwjhdR3A184HHEJqCE2mQDAEEZBScHJtmY/80Id57LN/hMoydgcD5nmJtRZbu0ZbME6an/2h/TKghCTVEf3FPldv3qalNSoE4t4yRVkjXQ2hxCcdlBB8SO7w/WfX+a6VnMeTM3xy8wYLb36ITz95kXxccvPmLnkNSWghipquspw8mfHLCwUraYfbo5K9qUMIRRUcJgSUS7iVdHn0xj6VjjkVLB+4r8WPblgmZc24bhEj6GQC263J+osgI2wALwWRjUgosbMdnDdE/bvYvLHD8smT/KPPX+eXX5z8xcBCfxWHCKBFo+nuEXgCoAkhcGG1RTTZIVGag+JQSMIULC5HrCeGVEArTvjCU0/zpjvWuO/8vYxyz5VXLvHOd38Pi/0lJvWEK6MpVz/7Zdbecp733/GdqNEWLwwM8ZnTnLvzLOHffYw4BHywCGNpSYUPgZGMqdp9pjV41+XJS9f5+j6MujAfwo6HrSyiFj2SVooNOVHiMa7m4Qfv5/LWLVAtROgyHd7gkUfeha0LNjc3cV4zGt3Gm0aX0HpJVVXM5wUhNEgUFWnAU7oKlMI7gTGWurIUpiZ3NdaLQ01ERXAWgkeKgFQRnV4f7yXOVTjAlxNCcMRB4loxw/EYrGGhqylqx6yYYIIiUvAdb30jJ7+2Q3ewRywNIxQTmg525AuUCHgvkGhC8ARnCATc4Y79J8+3UUIAQCpcCATpMYBIQR76YjSgMJDqG4KAF4QgDwNAc40QDlMPAkJCEOFQOEVSljXPPPscDz78Jj79mT9gMBgRdEqSJGTtjLqum53fB0RwBOOIYk0SRSz3e9w62EVLhfGOdqdFWRaIoOh0OkymFd57pJTs546tWnB5HjjDLudEzbOvXkN5xc7mLnUNUrQRxAQtWJxb3tvtcbpjuVUIRlOL1hElCbEt6QXLC50un7i2z66B93Qt7zl3irecGJMPC/bHntaCQNRT2u0+uc4a57pgEQR8EHipIJ9TlwYrwU6HRFmbWbzClhwA33xM+Gc5Fv+VHQEOdew8xjginaBUhKsNJzJPWuRIBMMcRBSh8fS7gSypqfIRvWyBe+85T6vXI2l3+Wvf9368g0SlHF05ghYV/UTxYz/wYX71cy/yzz/xFb4qT/Pxjz/HH/7uM+xODEMXcIbGeyCVSO8QwbOXrPCHw4Sf+8qAGwsn+MIYvlbDk1O4pmDWkkgknVgSS8OClKjS0e32mewf8MxTT7K4uIjJa+69+zTOS2bTglRFbO3uExAU+YTptGIwKjA1xFqStrv02ynWGwwWHSu8M5RVzjifsZ/njOsah0KogPE1zldo2TRTg4MsadHvL2K9x4pG/tsiGuy6KQi+wNG4DY0Lw9wEUCk42D4Y8sk/fozrIWMrWeLrus+N3OFwTU0dmvm3E+DRCGIkEQ6LpWpINqFhYjaKugHpBdKCCgolooYp6BSvbfxBNKQcIcLhWxEQMiCVPzznCSE0MN7XXHalIITmHso3ngOXr16h3e8yngyJtUIjGnJOCFgfsO6w66M1kVbEUqBxXLj3HHXdcFSyLGM2m9Fqp0ghiOP48G+yBOcZOc2NSrA514xevMHdUuNv7HHx4iZVCbFLUbnDBU8dS96yCh9ZaVMIxd5oijrUrBAKtLNg4TM7Ay7XcKKj+Mk3bvDmE5LFMGNaKUY51HVNf6ENMhDHGdJFKKvQLhADLgrUkwLvY0LcZjTeI+svc2mimerOt1x/3xblQJKmYaPXIhAhlUJJULpFGGzz22+3HJlXDH3EJ18RaO9IrOPtb1vmuBsTpo5/znm+FFJOLPVYO36K33j0Y7zzHW/l7BvOo2rNbz76m+zuH7C+vsbf/okf52vPfp0vPPFF5KjkgQtnuXB+gze7XY7Oc8rZFl/etMxVyvZwzIu3C0yrTYGjGyzWQZ12CcHR8YaxtBwxAZyHVosCxxsefIC3v/Nt/P6LL3P8+AmK0S3OnbsDE9oMbm1zMC3YPxgT6gkex9beCFtBsI6k3SVOY1b6GZOpZXs0REaa+WRKPcuxBJz0iGCJgkB5MHhM8CgdCGiCUIQAbR0RqopIerwxOGAYNeO3TilwrYqyymmplChbobQOFTwdqfA4Qqx46I5VxpvXeeL6qJFj903m5rRsyFyioVjL0IygrDpkSjn1p5BjIngkEAgIxJ9K752QgG8kz2Wz0L6xJAAgyMPS4FD1FJqpgWyu04lT7jh6jFevvMrK0VXavYwPvufdjA/2ufTKNS5fv0FubfN/IyVKaISzdJKIs6dOsLa2ClT89mNfJXiNs5a1lT6xTnFW4bwntxO894TasSEly92En70j4Pb2WF1ImXYy3v37ewyzZSJbkCY5hRT068BX3neCo7ubbMs+g+0xEyWpRcxiWbLf6fB4FTOa1fzghXXuaM9Y6NaIqkaXM8b5AiKRrCxp6qpAakXIWsQiQhiHVzU29igL0WyAIUW1lpjpNs+UXX7hSzd52qRcunrtm5YD3zaZQFlXBGepixKQBFvRpkK4mtqAC7p5AYBWDGQaIzU2SJY6KZtXrjAejyEY2klKkIL98T5VXaCyDCskm1vbKFeSj7Y5vr7Ahbc+wuMXL/Mrv/041/QGjxdtfvGL+/zGxREfe3GHP9g25HGKsyVt4dEiIotSet6z4C2tEFiIE2ayTZ2tMLeakw/czfH7zzGwFp+mXHz1ZR44f461pS6mtAxGI3Z3d3DOECUpw8Ec70SzK6XJYf0r2R+MGYwGFJXhYH/MfFaBd+hgyIRtZttKUyuFlCmtuEs/KBbxLAXDMobIFkSxokZRxC3qqEPbQtcYXJTiXKAVpURIXFWjhQShsNbiHZiyZkpg6Z67SddWsa7Z+UOksOEwPT9s/Hnh8MI1PwTQQR76STbP2Euw8jBz0EAsMcLhZWhs0IVqnntQEDRapRCaup/DckBK2diLkxxmDOD4k139vvsuoLRgZ38P6wQ7O3scWV3ju976CJ12hlYCIRt5d4THuQZBGMcx48kQpZrfw9aOXq+HlJpgHVU+Rx3eu9nBA5WK2SkdOztbLCwuEruclfkeP7gRsT4/IBKSEZpkGnhrBK18D5PCdDIGQAdPbGvKaIGrZczXbwz44Yfu4P4451jSBKtybpnlEMoRcRgBOQGLiiKEtPhQEqSDSOCxiNmU3HjiVDDd2Waq1/jjy1vs0zAav9XxbREEhAAdJSACWjfdYWEKUmfxPhxKYXPYrRdkKWgKjHQUGt60scA77ljn+q094qRNrDTBK5wRHGzuIlONDzVtJZhO53zxS1/l5PFTvPnhBzl56hh33HUX/9t/+H2+Mgyc/5GfoIo1PtHEqYJgGy6DO0xFnSP1hoSAFoqoBuIMg0bEmnMX7mb51Ekef+Z59re3WMwiUpmwd3OECrC5s4cSgVgYtvcmBJkRqZiAhDhBaompKgqrCZElnw7wdYGSHi9sY1aiI2KpSHREojXtUNEzOUfLCWeqKWfrKefMlKNmQqcYE3uDkxFGapIg6ArV7KJeE6zCGokzFm9qtAqN/r2WJEqzv3uA8Y4Hz55sGno6og4CIRXCBaQTry/0JkZLCBEKyWu5gBCNNj4KiMDLpjSRkSQIjw8lQoTXU34pJbU1r2cLjUNQOFRM9SjZzP2tBSkF3kNZlgxHe2gNpg7MZgW3bm6zvb1Lt9NiY32VfreFIlDXFXhPFOsmAMymTCYTKmeZT+e0Wh2CF5RFRawjJAJjKpxzeGPRQlIET6kiLo5BkBNpj3TwX5xf5T/b0PTLGdol3JvAP3zLMTIBQ6fIDVRIEg9tPDdEixdvDHj7qSXuWvRkHYUIEnkww0xLnIVEQTfzWDMnSSKgKX3wTUnkbY2rSrSr0FmEmZeE2vLFzTFP7pbkcdqgIb/V+vt2KAfSNA0rC110aNiCIU7oVBPutVN+9sGYxMK4znhmz1IHw1FqvvOkoAhQhoSIii2d8X8c/1E+94efZ3//Nu/78AdZWOjxxOefoLfY5bmvP0kriXnXh34Aj6QcTnn+2ed493vfwyuvvMyHPvjXuXLlCr/2iV/hWJlQDccUxlLFETpuY4MnMc2YxQlBplv4oqDvLe9YgtXzJ0gfusBeepy9zX2K+Yx7HjrL+sIa41HBeDbl+q1bjAZ7lLXlYDBEiRgpIoTSSNnUv8ZZirxmNBqRB0Nal2gl8UGSdlYJlSGrCx6OD3jweJt7NrqcP9JFeke39MyMpbbghSCWTZrthOTiuOaF/Rn/6rqn0C2Ur0lVQp4XWClBRwRjUdKTpA10VgaJFBGtrMPp9YwvP3uRSQFKxjhf01jexeggCZS4Jt8/3Ln967U+ogn08rBf4X1ACFCHZUEVPFHUmH14DzpWOPta/f/aPhXwPqCUxJkYT4lqfGsJVhCJwPGjizz8HW/iD/7oMeaF5cLZs7g6Z2O5zTve9W5euXKVS1evcvP2FmVl2Dh6lGI2J441i4uLvHr1MlUVEyUZWku0hEwptI4ZTieUriRRDVlKxik1cE9d8q8esCy6MVI1imtWCOreIrNkmXtOreGmW1x/6gp+qgjWUaeasgzYOMYc63Pn+irLtSP4feZmipgV9A6nJia0WD8KZbCIVg8lJCoIQm0PG6geW0/x1mBSgZomjCpBev6N/K3fvcqWWGBeO8rJNtf2xt++0wEOA1EQzWdHU2NHgqZ2A0A0SD08kYf97Cg3a8mlzT3u7wRCz/HPfvqf8N3v+m4effRRHn/887zjbW9h5Gec6R6nKh1vf9c7eOKxz/OffO/3sT2acP6++/nkb32KfDZnrdfj/gv3EklFL+mxLy1WVdSRxuBwtkZGCbV3lNYxnU9Z77X4yfsXeeTCeW4rwe7SAr/z+HOcWFrnvvP3kLUFZTUnd4Yb+zt0+j3m4yEHsxm113Qjj1AerwXeOIT3hCA5GA3BzFGyQ9ACoSQtYCHfo+9rLhzp8o/e9kbWZU6UHzDefAWAsoISMEQgFJGrSF0gcvBwJrlzfYHH9kuujHYZxX2qMidOE4K32OAQWoCTeNdkY1YEJBG29GzvTdg4dpTZq1sIGl17H4BDPwPRPCJEEMQIqtebfM3jFYejzRACBF7/vkMQaYk1HqkF6rXJgAhsbBxlsN/g36uqIkliqqoiiJIkaVSMI92Mcp23FHVFCIETxze4dvUWN2/e5Ny5GNOYYgAAIABJREFUs5hQ4Qmsra1x7cYm3lhWlhfpdDoYY0hbLVCawXBOr50CTYNaaElhDUkQJFmM9ArhHJHUjQydd+zrlIHJWZDgLOQC+pGmNRywkI4Yv7JNe32N7tICUUsgS8OBn9FaWsDGKWsnUoQdUwSQoyEZBuehjFOcr7GVpQoKp1tInSKwTZNVCYQIBAx4g3QQoj7VfET7xFke26mZx11cbamdw4i/BGz4r+T4JsmIF815RZMKCiEQNCYcVsD/eXnAY7dKZjbi3gAPv6HDT20s8K4feDdvePt38Hd/7D/lc5/5A/Z1Sf+Bh0EKDoYjqkGBrgTKCh647142r9/gWnGFF154gQt3nsUbzamNYxTDCVbD3M3REmKtqYICIUhigTCO0yfW+M6lKZtRYC9dRneOsbGa019cYF4WLMou82LC1Ws38SqhnE+Z5oY8r8niBCc8SaSbFFUIhEiYj8cEV6LjiMhBSFrIYEjqGe9crPjeB+7gnpWM7ug60/1hA2ARTa88BMiDxDam1zgkxjtSAZHzpPMZ/+CedV64fINfGdYUccLMlnR7HQaTKVLGIFXTPdcaeA0fEZiXglNnjnDj9jbTvIHaggfRlGxKAAJ0UERALfkT1WYOe4UEAqFB3gFIgSMQeY1UHoIniiOMqbjrrjOHAaNLHKVcv36ToqhIkoisJciLGhU1GH2tFCqOGUxybm9vc/b0KQ62dxnNSi5eusTDD5zj6Wee4+BgyMHBAYsLfZaXV5psRymch73hiHa3hwRcaNyLlVIoD6PZYVM0SGId4VzduDqFwDRusV3kHM06qFASyYAvIJVtxmWJkAa/PaDXSYj6gdS16EUtglToKGFkLbO6pt/p4PcN8hC6kAdNUddsLGX4KBClHYKOCNbipUUqh8dTmzlS0qBdXYbqj7itYn7vpS0mpoOzJdbV1CH6lsvv2yIICNHoDEoLwnm8D/ggKSz4UJMoSRw1ZhCJhs/7dX7p5h678SJoxWUz5rZf54cGFUSC9V6fT//eH/DEE09w5eWr/OIv/Twi0jz3zNf473/6Z9i6cYsb127wmcc+x0/+1N/n0Ucf5Z6H3sjzt24xHB0wP+GZ2TFaW473u+yOaubGk0QlidLYsmJdQG864ppb55WFcwz0Atef3uRkr8/yeod0oc3g9gziNr3FFbZu3ebW7gGjaYGOG7pqnPYpigLvKsbTKbMiJ9ZNimycYFUZjs52+Wv3rfPBB9/A0eHLVMOr5Bc9N6uIsY0pvGKeN6lzFRzKW7TwRMKTxBFKRSgtaHlBWwUu2Bs8tCH43nuW+O++fIuXXUQ5TVlJMmauphYB6TTCClSk8SZHBs0kxFhXc+HCeZ567hLSGGJJY5yh4mYBB/AqUOIaxx8XEEod4v4D/hD4I7Ug+MYjr5kGOFZW+kymAxaWFMdPHkeqEudr+suCfj/lrvvuZjiYAhLrK158dg9TgSRCCIf1nhA0t2/vEOqCj/zQB/jf/+3HyGvHE08+QzDQShVHlpc4ffoU49mMwXhE7hwHg9uNKEqUooTFKZiWNa04JZExp+66k/vuu49Pffx3cF4TxQpXT9EqEMqcX90s2T62yCOLy2TDW6QrK0y9oHvsBPlwHz8aYIWn0DBxIFVE7A1eglk8z51vuI+Dl/+YrB8xmijyueH0aQ3pErgZQbfQNB4CUh9aRYkprizwJqBVl7KqmO5u8UTvAr/+2ZtsxwvMbc54Nsd5hXLfujH4bREEAgFt1etz54wAWrFdN+MjbSqyJMaJiNRl/O5YIK2jY4couUinhDOnT2KMoaNirLC4vISBoUTw1z/8d7EUWDvg5/7n/4lp7TBa8I//3t8H0+AT7j5zit/75O8gQ0p7LaXCkgnJeGxwAaT0jaqcD6RRzEjUjI8s8fHeGWbP32RtFe49e5rh8AVanT62DFwdjEmilGsvXWK4f5s8pHgdobOUtNNh69VrJFJgXM2gntCOU7q1xMqYPBb8DbfHP/6J+7B7U2aXL/PqQc2+jRh6xY1JQHiB9qKZFEiDlAkLkcGGQCkTCtEmEoEYi7Q5wnoGmQbpMeVt/u15zdO2z79+ZcCzvstakjGvPXuqIvKGqJLgOlgsWhTsj1OOrh3h7pNHeeXyTQLN4vey6Q9ICeJQyk56DapqbNQ0TRNPAGR4K4migLAlysc4UXL/Q0fR0QZ5nmPMGKQjbcVs788Zm5L11S4nj7XIx3Nsp0d2ZQ+tPXVeEXzSiI0E2DqY4kXE0xdf5af/yY/zW7/577l4PWalJ7n37jOcPn2SV65cI2u3WNYRt55/kSSKCFpQC4tE4XJHGsV00xarq6uMhiM+/rH/hzhrIZWnNBVBKqzQaDHhKbFGfW2bFZfw4NoRvrI7wLdTzvgThFBzJC3ozufUFkoNqbOUBnwrxdy+iPO79ExBtLpCt1uAi6gTDy3BrHWE3va8CbrtBIdB+IqinGKNpB2vcHNzj41jqzx1x1v4hU98hWHcpRA1o7EBq/C++jNHAN8WQQChKBkjZIoOh57tUcQoZNTpIsHcpu1zUiJqUzE2BdnpFTIhmg72rQnf+b7vp59luOCxucTJms49XVaLDO9TIp/iZ5Zzx++gd2SNhaVFPvrvfpms02V3Z4eD8YQXb12j1LDYWWIpSujoGGeanSFoSRICiRckQnLs9FnuO/8Aa2fP8tjjX0Womus3L/LA+QvMZjk7eze4eWMPJRwHBwdYE5EKRZpk4BXz/RFSxezNhgjp6NQRSbvDTOT81B2Bn3jbG4jchO0nv0o5h4MCnpkqCiI8grYLxMogZE6kNBpFK9RMpSTGcSRUnPAVxgCJaBqPSpPk0FaSqqU5kIbTrYp/8fBJ/vMv32QnBIIUZC7CiQQXeYRstAfbacZ8PufWrU3W1tZ49cqNhlXnQYhmhi+8II4z2lmH/rJhY+NO9vb2KYrAYL9gMp2ycSyh3ZYsLS0hhOTlF19Bp6BkAGc42L1NN0vJy4Kjx5c4spwxn88YDXNmO8OmvJjBhbvv4+UXr2IoQVqkAoKnrgI7u1vYasp73/EIH/7hH+OJLz9FHMdIoXn1xm3ysmY2GLO1vYdOW1SmyVDA4yNB1uqwsryMc46t3T1GB4MGvObc64jBuq5BBXLRpeNnvKqO8HNXJ3x0ZczbFkpCq8Nsf5Pd/R0+s+8YOOhp6NfgWgEdwUpdcqKzwHNXJlResn1tnzd+91s48cDd6K1r1KMhS9MdvAYrFIWdNlJvs5pWp83clOzu7CEuPMxHL13jE09+kXl0hLyuyOdTVK0hBIIX1OLb3IHIe4/1DqkjZNAI53BRhou6zL1iOYD0jk6SYqjp9Rbo9I6wN9mHUNNNNaePHUGkkmpaIZCoKOL0qbs5uXaaX/zXv46yFSvdiGI04YWXLtJbWeLtjzzCV7/6JCLAzt4uwkMvbeECWKmZWds0y6xFC6i9QeoYFWu6a6ssrG5wY/M24LGmQAaL84LhaE5/cYUsGRFcoHYOJROSb5hDm8oyLCZY05hgyDjD1FOWmPMPvvvttEc3uL0zZ5hr9gvL1gwKGxOUQCgaYTsvUUojtUR4jRA1i4DUGU5GXENjfaAKglhAT3iOUzBzkNaCMoXMTFk3hru7McPSUwuNRoCKcMGCN0RaEILAOYdzsuHV20Z1WQiJjhKca6C8EoVSEatLXb7+9EWK3DeWaXiOrHU4trGK0HO6bTg42OP73v9udg6u4kxNmU/pZjGREvRbHUxeE4RlNpkyn9c8ePc5nK3pLHe4fX1GwKIjSVl6lBA4B3GsccYynVR88StP88aH7md1qc2la7fRUYZAcuv2DvP5HB0nzCuDiGIUilRBYWqEEBRlSaQ1g8GAOIlflx9/7X313uK9RchGFt9rySxb4Pl8wEOxZNkWxL1lji0scvy+hP3KMy1KViqH6FhSZekriZhOWVvfwKBZd4LVTkSxfYN6MqDf7ULmsbduIjXo2ZTIg4rBV4b50BG3Uz7xygGfvlwyba9QlILSOqz1SOFwwRDimnZXMhp+8/X3bREEQjgUdFTNi5Z4Se0UqISDvOJEBHWAfjejdiWndIuXrm5xYCtkJjm1sMz6ch/SjNTHREoxnk4JuSNeiPi+D7yPa6++zGR/E1s1og8HkwE3rl3hfe98N9e2bvKFL3yBRCiCMehWh5ExRIBUqplhu4DSChNqnLGsHDtOLSOuXL1FrxNR5mPW19eYTuekrR57+wcsLrS5fHkbJROiKEGEgJWeylgCkqqYkamIOERIEWjbAR96w0lak21GNzeZzjQ3x5KtSjC2EOuU4EqQNbHUJFI1vYVYErmISOZEPiGXbTZFxGeu7VKVHiEilrXl/kXNkTUPUUKoa3Q7ECykoeR7jm9w7eJttsUCTgqCtzjviIQgFu5wXKcpiuLQ1ReEbFJ87wwCUJECV1LMHVeuFKigyTKBEI5jJ5aJYgFhznQ0ZD7eZ2FhAeED+XxCr7dCXdYsLnTBWQ72x1hryZbbbKyvMZtWzGYFnXbK2nKLwW7dLMTQWI270OgCKpngqamN5/kXXqLb73HPnXfy/EvXgRTjPJXxJFkLoRVKusMg6tFKIl2zyydJQr/XayjHxrxuYgq+8SFQDagqxjRjXjcnaM1vXavo3r2CsmOyLGLqAh2bsyFqwvIyOp806Mlg0KYi7YJ1Q1KhWFaC6ZU9RBSjWyku5NS9ReJIY11JGjRSQlCCwU5N1MngxAV+91PXmHfOUMg5syrHuWZigHQEURO04a77z3Hr2svfdP39RX0HHgXuPvzKAjAKITx4qEp8EXjtbl8KIfzUn3cPSYCQ4ZuhL8EnSCEQceBTl7ZZvH+ZZTtiue2Zuoy/58f8nYtjytYiug7c/z2PsLi+yqJJsGlEaWf0Ok0giSLFvW++l4fecIrq4BY//29+jmmwtHXC5uZ1rl6+QozkIz/yo6gk4bNfe4zaB2TaYTQeQcvjBbTilNPtLsXkgPMX7mN9aYU//Mzn6K2s48KY06fuodtZZH84Y/dgyPb2PvV8j/H+mN7iOjbUzGVCXh5QmorxYEZPpHjvkBG8t+v5Zx/6IEfcbZ597CnqosWVasbOKKWIevgIWrakJWuWWpI7e5pW5FCtQBwkWa3IA/yLFwJf3N/HpTCMU+Zo1tMW75D7WBkxDyWrao5uKVLjqbKEgbV8MBywfHaZX7iU82onoe0rKiJMiNFmROEjBJI4aTMaTlle7DKdF7jgcd6zutZidW2Jha5GyEDWdfT6GcZUaB0T6YzxeExdG5xLySeGfKzYb10i1JYyr5jPK9pZRq/d481vPs8zz32de+86y6Url6Gl8POclaMrPPX4S8zzNrOpQ8oWXjQWW1krosxnOB9Qccz2YMrnvvAkl19dYTiYsNjX7Ozt45FYFwjec2S5Swie6XhCFHfQxrO2vMDq6irXrl1rEIqiUb1OkgwhJM45pGxQinPvQWuiUFPYmi/Hy7zy5D7fe0Twt91zdLIusncUV89o57vUTpBKjZMeqwJF6UilbMBgyrMYKUQwCCcw45JoPGZazGm1O0xEQjUr8c5Qn3uI33x+lz++uMVWawlTHTCnxDmFNAFXO0ISIaOUwsw52PvLlQO/DPw88NHXToQQPvINQeJ/Acbf8P3LIYQH/z9c9xuOgHcCJRWCQ4qqb1Bkz03h1SqiH8W0Q4npLbK8fYs3deHKeMhyBj/+jjfRq6bs7T1HSFJIFKrbJk67pMGjZCCECrTjnW97hK1P/E4TZKwnBrT3fOzXfpWz5+7hne95Kx3ZQasIpKKoLc4GjAzM2xlnLtyN7qbc3t1BpzF1nSM7mizLyPOceV6xu7uPEIHhYE4St6lMjdBgggQfKCdTlLcgJVkck7mK//L738sxOWN4+SaVa3FQBG6HjJs+YjI16FDxniOaY/2YfidiLdNoUSMTiZ1VqLpGd1f57P4uwzSiFQVk7QilIYsCd7U9dyZRM35EYoVDB0EdNCQJndmEc/0+x9PAFd+gIT0CQ0BFCRy+/HXtMXFMkkSMplOQsLqWcubOoxxZXcCHCXiDyBTD6T7BC7rdHpPBHkncQHdbmUbiyGczpnPH4uIas7Km3+9TmYBxki988Svcdc9ZXnrpZZaXl9gv9jh6fJVYWRYWFpjnFcI3Lki1A61VY7cVAnEc42xAxYJpPmdrT3J8fZ1XL71Et90mShKmRY0HinlOqjVZFBPpBOlnHF1dQkUa3aQ54A6nGvKw9yEU3gekVCgVMK7ESIVWKbn1yGyJP9oe8pE7l9H5AdJL/OIKUyeRSuAPeR7QWMw5LxCyASEZB5EUWGMaNGU5wUddisox290jRF2i9Tv56FNbPLlbMkh6uLJGGkuJRzmNqS3BSUxtsbamNgZT1X/xIPBn+Q6IhuXxI8C7/v8t+v/oLigVgXFIEagwxNITvOOmiPjS7Zz7z3aRs11c2iaNIz5yrMV4reb8vae5e3iD7NZLZPc8DEYyK0p2d3JkVFCEmqWFZWw1RIkKZgWpBbwk8p4kCNACK+DGy5e4fvMlPvQDf5NZkWOBygQIAuMcrxzsU3YCbzyzzu39Wzgt6CSBE8dPYWrHYDBkb7+kKCqiGAgRQiqss8RphqxrTAG+CLTTGOscynjWheH8aszwuVeZ7E05mAp2bGArtPnarETohHURuHtRspqWSFliXUJtDTZ3SNvYll0LKxTsUgTwtUP5lFh5VDnn3lNLHI0j6nxEnSYk7QrnNMIDdQ0CMlHwwEaHp3YjaidAChSO2muUEvi6Ifl4bzlxcgMRWVQkOH5qjSwFF2ZsrPcYjQ4wUuHqDCk1u9tzOt2EyWRKHHWYzSdkWUZbNT2N/kKX8mCARzAcTJnPSrSOAYGtGg3Au8+dZjlrUcxzDtoTjq4vcOXqLlI05AUpI7yzeCcQSJCgtMA5w3A6485Tx/jAB9/P6tIin33sC6jRlGleEGpBWZcoHZPnJa0kot1KqMqaXqfFYDBA6ogQwNpm9CGlfP1zJjw1liBauKBQwExqQmuVj1+f8H13rLLsh6i6jY06KGUwtiaVGm9KhNJ4CSp4IhcQBEoR8LimB2MrNB2mwxELnS71wjG+MHE8vu8R2RLGFNTOI+sYIoG1DolCakFuBlgViGM4c2qVi09/c83fv2xP4LuAnRDCpW84d1oI8TQNeflnQgiP/XkXCQRsqNFa4IXCe4VxDiUEs/Yan9o6QNaGf3oUlvwew3bGQ/UYG0VsP/8Sm7dvsvvoL/HfXPJsuTbLrTY/+P6HqCNH0ruTH/7xv8XKSotqMueVZ59lQSdonxKUwUuD9QaBgFhjneU3fuPXECqmth6nYuwhOuzYap+3fMdbOH3mJHujp1lcXmI+2GZxYZngPevHT3LlxtcaUctQEUQMUpN1NNN5TsvWbI1qZLyIp6JXDXjw2Cr/63/9X3H7Ez9Pvj/k9lzzZdPlpb0BYwQjHUjqgjRxdHsZ0js0YF2JUI0zsY4dkYLfH84JMiG1TYk1iS1tb2l52OjBymJEVy3DbIYLEbX0dNycWEAeCZTPec/RLr++mbObdIm1J/XmUIEoEESj9IBwrG9krB49gU4Es2JEr9+hnaXM53NEkAwGY5ZXjmBNIMsyJtMBWTshLw44frpPVVqcbZOlHaaTHKkd7VZG6Sq8tcRRYHt/k3Nnz7N9+ybTgyEjFXP3mfOU5R5Xr15ndaXN+tGTvPjSKyjpqa1tRmHCNJkeDazRBsdTz13kyo3rrK8s8iMf+gDPPvs8O9v7XHxls1F3rmuOrC9yfHWBg51tjh47RpJscO3GzcPsIsH5Ztb+Gp9BSglFhE4D1hpiEaFxoGfkOvAbZZtPv+Q4EyJ+5gFDuv8qxIpsaRGtNURdautQkaIylo6WOG8o65qWinC2ws017XaEW7uXn31ulxcv7TNNu4zjLkVeYL2lEJ4q8gjjiSMo6oLOYoe7zqzTWYpJk4i6HnzL9feXDQI/SmM79tqxBZwMIRwIId4EfEIIcSGE8B+pGXyj+YiSErzBakVQAlkrgo/xWiCDoY4yPntzyN851edYMUZLQ51CYSVTZ1kI0POBk3lAx4F6NGa9G3jy5S/x9Ze/wkc/+R84sbHM0YU24+m8eYDOUocaK4HDh2oBiyQoqFyN180ujo5RAo4trLDWXsRNCha7HdrdFkvxBvN8iiAiLxw6EsSRZHBQIjVEScx0OsUFy6SsIOQgElyQrKiUn/yBD5AUe+wNhxQVbNfw1f0RYxUR2UBbeRLnWKDxs9NphpkZsghCFEAopJMoZRGypBA1RjZINy083gRkgN5il/ZSi46Z4+YVc9dGRXOiADqAU4rEOBbqglgaQiywSOKgAI/SEhE0ztYIETC2ZGEpQSWgs4yyyBkPB5w8dpLN7Rs4DMPRNlEUkc8NiwtrDEf7KBU1MODSMByM6C+UtGSfxSPL7OzvYUzJ+mofKSzLC8tcevkiUmhaqWZl/Ti7e1Mm05qFhQWQMcc2VnnhpYvY2jW6BVoiQjiEKzsIAeE1eV6hlaUstymqmu9445s4OBhwMCnZORiAgtWVPsPxiOm8IJtMORgMG1RjCNiyyRZeawi2sgwhBPO5QAWHDAZCSaQifBDYKqeMMm6LFq61wm9d2+Rdp4+zkikOBge0M48zNb1Wgp85EiHJD8VXgnXsF2OQgkR0uaiWeeyZTb7i15i1E4KpEaHEKcncxXiTE8saHzRFPiPJWhgsR+7YYH90C+kzGvbWNz/+wixCIYQGPgw8+tq5EEIVQjg4/PwUcBm465v9+xDCvwkhPBxCeLiptRxOegyNYEWDDAPtagpjGXUXuKYXGMcx1ghirRC+opUEYuUxWvFjb1riu/oFq66kyPfpa8eRlS75aMIzz73Mp//4a7jaEiHxNJx06UJT+wbfyEE7iZGBWfDMMaDVoTqG48SpO2j1+ly+cYu41WZrb5c4ilBKECcpu/sHlNWMMp8zGowRMtDptSnKCucMg7JGhIJEVLSk4L1vfwvf+ab72fnaF5j5wJbXbBnN1ZnnmmuMPK0QqABHepLFbgsdK9JWhG61UK02Ku0QkhZGZ9y53CHoQBVZQirQISEJYANMKo9OY7yZNWPFIIlUI5feMHYDCYG0ysmoiFQ4ZOxJlBAYY/D+sER2jXxZt9smTgRSenwwpGnMwcGQ9fVjnDhxR/PihYheb5GqcqTxIsL3cD4lzlosH1lkXk2pKoMxjqoqWFvrs7rSp99usbK82Eh9W8t4krM/GDMrSlo9hdQFR9ZabBzto4U4pCBHCDTON8xCKQSK5nMUJUxnBUUZ+L8f/Ti3trfZOHGcM2dOvS5ckqQKFSUkWYuirLhx6zYe2VBbpEApRRRFOGvp9/vURQ5Rs7i0AqipgcprAh3qGpzQ7BUVnzcL/I/P5/z7K1PC8l04uYQUCfP9A/xoRDUYUE/GmPEECktrZZX02FHKk3fwL5/a5A9nMaXQlEVFURnqusZYj0chioAcuUZIJ26alz4IinpOqxsjokAUx99yLf9lMoH3AC+FEG6+dkIIsQoMQghOCHGGxnfgyp93IUGj8+9rBQacGBG0IBAR+xaeOXXk+Kefv84bj6/yD9cNR/yM1RTWMoG3M6xSbOgBH75L8M77lnns8k2MT9CbN1kqM4qoTyUsUWjchY20BB+wQWC8xAaP8J7SxdTURIdy5FIFgjfI4Fk+uc7F2zd49dZNluwKS0sLrC52sUEzGA2ZVVO0SpkMp0xHU9LFBfZGe5RliTNgzJhExbSCol0c8N/+zbdy+1P/F8WVS1yf97g4lzy1N8UnLVwdGHtD33vuX1K888waMlR0NzZgNqIqZwQVNZTYoKis5IO6YK2GzazduN2YkiRAvwOr585QuDEd1WJsZ/TljP24YefGThALSRw5klBxOo7ZLStqHSGdINFtoMTXgrTVYT6dUxaO0WiGSmriRLGc9kmjLvnEMBgOmBWWNO3RzjJarRZXrtzAO4G1nk4rpbPSx4eKEyfXKYc1USsF57FlTkscwYaYfM/QijKSJGM0nrI12qSqc0QU0+5rWh2HEDl1FYgiTWkb3kTAkWlNhMI6jwkFEYosVgTnuL495GOf+iyrq89y5dKLlLnnzKl1bt3YZVrVBCSbL16l1clwSKRqmqJ5nhNFEQsLPcp8irMGlwQyJ5AorJJU2jcq1URgQVQ5zta8lCxhki6vzmt+++kD1HyGnw25Z2OBbhbRijXtJGUyrzmYl9y4MqEKglnLMI6Oo2qHGQ+wyoOMMXXzO2FyfOzQC32msxktrdEiBR9Iak9/sY0SmstX9//iQeCb+Q6EEH4J+Bv86VIA4B3A/yCEsDTW5D8VQvjWxcjr95CNpLRTRF6DdgQsEoGzAiUFwlVMsozP7ta8+WDM+x5IEU4jXUAphwqSUkDlHTGSejilbCW868F72Xpyi7IKtKOMCkPQkhhP4Qy1UA2X3wuMd+SA9hDZQxV8F3B42r0W/azN9ZcukXlNHAQnjm6w0FXc2C4pyxKEQyKZTuf/L3Nv9mNrlp55/dbwzXuO+cSZ82TmyaHmKrtcbZcHXDI2IJoLjKWWoFEDAvUNEojmDrjgD+irluAKbGiQ2mrZLbXAYLDb1WW7sF1jjidP5hkiTsx7/OZvDVx8kWWrqew2TSPlvor4zo694+zYa+13ve/z/B6CUAGColgTxSGdV0jj0OGIznTcv7FP+cM/prh6Rmla8rVmsWnJheyDM1uHVWv2teJLBzPuDgNUpmi9QqIJhcWIDoslsgnSacL8BXfHe7woLDpSpOEGvYZ7t0ckCYQMWDzfkIYS3XqskFjxMdRDYEW/Ic/SlGjteuinFEip6TqLknGf4dfUgMa7/lw8GAxYLJa4LkfKiCzLqJuSIOjouhZjHFtbW3jvyfMldbuAOkWpgOfnzxmFA3QSsbW1Bd7w7KMjEjVEqQjjDR2O4WSIFRtikTJfaEIlGQ8nvPve29y9PeXZ0QJE71OQAsKYrqbvAAAgAElEQVRQgQFjeuiGwOK9AycwTnJ0cs4yz7GtY29nyL07d3jr7Q/QWcqzZ8+JkpCm63MK69YgpSTUmsFgwGg04vL0FKUUtfRIK3sUuJDgm77wdj0lqe40OhzgixU7UctGjDgZjnHZDDc44MQ4wsISlpZES4wIyeUQMxjSlA2+7ZhVS5TRLMYhJoTIeAR9Q9HYFjLHRbiiNg2xyhgOh1xdXXEwu0mUdmgR8pTNP/8m8Am5A3jv//qPufabwG/+sx7zn7wJIdEmwWFpVYkXEuFDJB4nayQhuBAvC5AV/6maMS0Tvha3xPYKEzpqkRF1Hh0rLpSHVHAwFPy0/JCvPKj5vQv4Xy7hmDGd7dhcbzKx7agULLxCWAm6xEpo6U8BnbKEFnZiSTCSHBdrQpnx+cNDbHPBWu2Bq8gXF8RhzOV6zbouUUECzhHqhKbJySJPGziayjJoC/7Nr36e7r13aRcllc544iKO8Jz4FUFYkrQdj8MBvzpt+MlkQ6bX+LhFuRQRxJRygiBAGEHdPgfgKhT8F59R/J23zvitDcgu4ZdnFf/eqylhd8J0ekil+5jszQAmVvcut8gQOonuevffLJJkbYvRCU1gcT4nk4patSzLhlAl2M5x584Oy/VjHt6+y2MvaNqOvemUfL5mNI65OL8CK0mDAa1f07YGjWJ7a4enz58ShIowEdApAu8xdYfpHGVlyPWG8ViQJAGiqhnGGuEOkFIxnK45Prvk+z94j/k6Z7ZzlySvsKsWWwsGwzHCeWpb02lBQIwXHiMEQkJAr26s5znbswkPX32T86srGlruZNscWYEOFV712NsQiXSQRoIs1NSVYWMtOlQkDdhAI2VvcPM27BFk3oPSKG/x7YYuSViQ4myJ3vScBklv2W6BpicyAA7nHNYUKCewImCdRXStIXEhQRfSti2VWeHoEMoS+Bi5cTSbnCjz6HDJ3k3D4YN7vPfkXfLmilr/C84i/Bd9CyMBaoMkRFiFJEO6fkQor5s7XgqcDPHCs9OW/JdvrRgaxxuzAf/hQ8OOKZh2cOThMg1IadmroKpqrNXYMODcVOSxICRAoziqW0zbz8IJNCiPEjE4gbNdn1VmIAg8X/3q1/i//vhPwQkm0wFBCKPRGNM5jk/PkB7KsuTq6qrHaok/p+o0TUNpLVG3w4E749/5uTf4V17OePLbJ9SNZuU1T9cL5kagPbRlhHMJ/0G34MtvPiR0c2zUEtghKpuBMySqQDRzRFfS+JSN1Xgr2QpL/v0vTvlVI/j8LGWiK7KBxyw7jh/9IVnsSGRIhwXtEFyr/q4XiFYwShOcW/Z/HN83Fo3UtHWOcAKJQIcZ7z95wt5+xvHRBQ/uPODo9IzGGi7yBdYrhqO0R3hNYwbDiHxT8vz4CG8Nw2zAnTt3OHrxAh1WGBty6/A2xnrmi/OeENQ1nLzICXSEM4a6PKNta7a3dxEiQWB55cEB7z96zP72BLm1w/yyIi9XGN9DPoUSCFEiUFjfA0iUBK099+8dsjMb8uijH9B2lijK+OZbH5BkIa3wCBf2zARp8EKDHCNEhvA1aX8ZL+W1hJjr8alDa42UCvcXpgnGmP574WhqjxAdghbrc6QI8S5ESo1UBqUdziT9B2GQ42yCc47l+gwlIIoDjG2RurfZHxwO+dznPkOr5yQaklijhOHF8Xd5/cFtPvroI77+ja/zp//rP/yx6+9TsQkIPLNJxtW8RssQ6/tZtKBPisFLXL8e+9xCIVgPJ6w7yXJVMnyn4rUd+Or2lLmCTZRg6hKdDpjriou849QEbGTLZVezO0hwOMp1gyAAJbHOovAYWxHqBE+H8Q4ImIwGTKYpH777lDge4Z0FZzEdrNYboihivcrRUiKFJgjAet9rzK2lNR3OWJQfchBZfv7hTczZMzoPRWO4yjcspWAZSLyKmW1atpKUrx/CyH/IIGgJAwWTz4P14JZIW/a6cAleWZQLMQrEek0iFFpqko0hGYY0uWG1eEYoLOFIIro+Tdhz3Y+hn31LaXFArEWfaHQN8dRo6s7ivUBaD9KgtWR7b5tNfsp0e4RpPN5Cbkp2D/a5vFiytbXFZDLh7OwM7z1Rqrhz95DzszkHe3vY1rA12mY5v0KrkMXiiqKsybIMHWnadsWbn32V5WKBEIK6NSRhQjLICPIN2SglCiS3bxyyXGwYjgLSKGS+VByfnKF0P4mwpr2mFonrHm8v/tndO+D73/kTZrMZUZgwXxYASAzC9zZlZH9U9UBjLFZquk6ACLFWgDaInq7yI3/Bx4teXlORPq4M+uv9hEXpvqEqhEaKAGP7o5UQAik0nbV42yGcwMsNzhmstQRxTFm0jMYxw0lCEDruvrLF9r7koq453NulWK/YrBaMsohJmhI4x43p9ieuv0/FJgCOndmYfNPQWg9eoWTQE2hEz6TrKSPX99Zhv6iCgEYO+d1NxTeXjnebhp1pxEu7I4wqeVbBIp1wGdY8qhynteVSWhpXMxgM2JvuUtQVeVMhpQXrEAqSVNA2jrYBKS17+9sMBgl13TKZxX3cl/d9piFQ1hXr9ZrBYNQHVgpFIBWdczRNz6bDe3zo+NKDHQ5CT30yZ2UMq9bSdYqldZjBhLaouWUqfnJvyKsPpowGi+sRHqjJAeQrqC/BFFgtcYFCOUMsapyX3BgNcGVLFaY8u6opKocIDGlgifX1OEgYlNT9mZ+e2d9dk36VoFfKXcM/pJS9ZVkrrGtBWbyzaK3J8xylI54+Oeb05IpsPKQwJcZZojQhTEOKpmDnYIvHjx+TxRlaS27c2EeiePbsmPWqJg5Sjtcn3L9/n6Y74epqwd7eHjpMubw6I4wkSikODvew1iGl5ZWHhygV8N67T8DF3Ltzk/V6zv6dA67m5z3wyIAVFmFV33Py/f9R+H5Rfuc7P8BbRVEagkCxWRdEGkKCHjHuXB/e6l0PtgkcOhYUZYdXGusc/i8IiDTiGlbae2CUlkghe4Oc7XDegovwGLwXOOeBsO/NeIO8rrqcV1jf4mihVThZ4rwhCFK8gyTNmK+O+YVf+pdp3JIbt8es1kds74w4OX2OsJ7F1ZzXXn6Dy5NLdif7bI/3PnH1fSo2AS01sQ549d5dnp9csCwqLB6HxziHNQ7hekqsBpxq2TYh1rbUoWCth0RO8r8var6+PeWBHvL97iO+/fwUoSaUZk1VQ00AgYBRjA1CUiuRKiCaKuq6RhlwMkAIT5TEjGYhy8uWQZogdcCNg5ssViviVDGdZazXLSqMKMqWwWjMi+MTpNR9EIQQNHXdu9W0JlCSpC34tZ96iDv6IZfPzzluQq6qDtNp3pQ1TbtgewR/469ssZsUxJng+ewL7H3trxK99JOsv/n3iOoXIFf90aVzhKa38zpfkwTgoowHsaSwG1Y+wRUFkeywgaSWCal2VKYiU4JOGKS/LmNRvfhIgu1M/4UUSC/70WDT0RrbG4voXWq7k1vEiWcdnlOZhjbwpOmQcr1mmO1wdrmkbkqqquDmjT26rkNrSeAVF2dXKO043JtxuTwDD48ePWI4HLJ/MKFpV0jhmU52ubpakKYxk+mQsswp8iWLo4YgiAl0hBdQ1Uu83BCmGZ/70iEPm5A/+Mc/xHmJbxTd9e/9cYaJB/K6RglJs94QhxW37+/QVA1XVw1106F0DymQXrA9TrhzO6XrVui45Wy9QAlHK6M+58F7WmcYRtmfG4y6fgz9sbjIOUfXzVEyxbsAISRCdngqUL3XRXpN3dUI0YNlg8jTNTFKC4Zb8PWf/zLGLFHBHjsHkvVGst7MaduG1YdHvP7KQ148OyaUGfm6Jt+U1HXNN7/9yeHgnwrasLGOMAyp6oIbBzvXWn+Hcxb3I1RWX157Y2ms62f7nUU4Qew8ifcEvmU7jek2SxZNS5ko1iKlkil553DCkQaaUCp8ZyhNTS0NVjqiIGQymTCexOiwxfia8Szhpfv7jIYxZVlibEscaQ4Pt7m8vKRtGjZ5yWazwdgeYd11Xc/UEwJnbS8vdX35mTWGLWVwqytoGupOUpsAhOZXXjrgr726zb/95iHjsMCrnMYIbn/+60Q7L5NvHLLJkU3ex4uJAI3v6T0yRMgYhGJDx6oqcU1LJErSoUUKR6BTqsZjXdUz9bgurv5JvL+H1jm8lPhrSDAItJAY6/BSEMUp+bpgs8gJVUScJRT1Bqcszlmm4wlV2xCnCdZBnGS0xtJZR5QkbMoCHWpu375JGCm++IWfQGvN9myL27dvE8cxcdxPIsqyJEki1uslURRwcXGGc47JeAclMqSUTLZiHrx6m/2DA2ZbW9y8u8dsR/HVrz4kSwydaZDKEQYSfW3nFmiUDnH0f5vGdPzSL/0Sn331JndvzRhlIdI7nPUEUjEdjSlXBcM0Y/9gF3/NR/RO/OgxvPcIrX604OV1JQX9kaCXN0uEVqAdXrW9YtU7rLm2KPsG5xsEIbgM7z3xwBIPGn72G59j90ZENobWluTlhtOzC6zRdK1ChynLVUFeN+zsHfTP6w03DncZztJPXH+fikrAekfVFESRwtma7cmYi8WSxnmckAQywFsH12KV1isaZF+itpYwTDDGM1SacaIpNwvaViJ8RhimNGWBlwKhexqkanXfPVYg4xDblQQOxoMEPe3ADXEyI04TwjZmNImou5okiZiMIgaDhDRIqUvB+dkp1vRlY9dZrBHoQOIsNE1zLS/1KCFJTIlYbTB5iW97NLa/NqncjVqmYY3uNsQTiUpiRHRI8eh7uHmJmhyQbE4QxQpc/+bEeQj4uKjHdQkbU5ONBmAsvotxvsQ7Q+AESnikAodHC9FzCen3Ae897jotqDa9ictLgTcOUDj/cdmrEUKw3lR877tvU5t77B/GDEYZxjasVwW3Dm5gfcd6vSYMNSrQ1I2lqkuCIGEwGlKXDYvVkrOzM84vKu7dfYnd/R1OT09RIkQrwXIzZzoL+rDVKGW5umC2NaFrDEEQURRrBuOIdBxyvjhDiIDVpmKTv2A4zHjw8i7Pnz+iKjtM55FKk4YZZdVbuQEcfeYD3vIPfvt3+M/+o3+Nx09P+YNvfYezi5LVoiQINNbB+eWGTQ2jyZjKSxABwrvrwNiPicoa77v+CHDdF/i4SWyMQYgA5ypQPQlSyRAlI7xTQG/4iaKItrZoleCAr/9LrxAkjrI75XKZ8eLoiPFwjFYJeEVdOfJNy7OLZzyNjrh985CLxZLtUYbWkGUJxn/KoSJRFtKmBc2qIZEpW6MRt/dv8MGTpyyqNa21tMYifYjUAWljMEHXR2I7S+FaOhXxUjZgdzLjcjPnfGkQcoTXK9abinQ4pO5ytraGhMJRrjd0nSJuU37iCw8RzZybN2/w7ouPaNuGw5uHHL84Z2c0Ymc25b0Pjtk93CEKOlxXMJjus1qes1qsmE6nHB296IM3JcRZivOC7rxDOYgCjbeGn7/fUV1cUeWalVU01hCFMEwMwwCEqGgUuEozMAprCuLRHHX5J3BqMYsPweYIB95rnNY426GDAecvNgynM7aShKrc4Lwhkopi0yEsFE1OMgggGdN6T+gEXnY9/pve3SgEdB6ebGpMOgSh0FJRWjDCoIVGW9CBYl20XL644snZGX/j3/0GL925w3sfvkucJpRtgwotdVf3Z/PWMBxO8UJSNwYlY3Scsj+akmRDsJIPPnjEB0/eZ29vjzjpoaxFuabrGsaTUT/7Xp6xtT0jDiPKesXujZjRdMJitSYdZ8wXG9bL4poHkLC8OuJf/ZWf4mrZ8uLoit3dG3Sd4Ld++/d6bYpTqEDTdQ0CxfnZFX/rv/pv+av/xjcYTbd5+93vsr27w3Q25uLyktJr1pcb8k7gbI9QR4C7thZHUXStrHSEYXztuqx/5D6UUhLojM6ukT6kaw3xcEDTlOjAUlcWrVKKvOGNL46499Iut+6+xuNnH1HmFQZD3VZEWcp6UzGbWrJszGwyJtCgk1dx3uK8Zr1ec2t7j4OdA05OjvmnwIY/HccBHSheeniL6d6QvNtQlmuqOmdna4ywBiUsUajxAoz1KG9AeJquo2jrvoRyObNEE2mPkVAIQS0NnavwUhDGEdtbUz73xdd448uvcfOVA0aZ5t6tbV6cPOalz99CZDVZlqBkQFW3TLYmBEqDD+haaJqOOOyPDeDYbDZ0dYMQ6hq0IUkGA6Io6qsA12cJKO/w1vLGBLqqppaatXEIGfTKLgVC9hFbAkkoQkKVEYsVrfM0FmxTYb25ninTS7E6j0ZzVSnc6A6TW/tUQiK0ZjaO2Y4M22lKXYIQCukdbaf6CYPUCBn2YMBrCzf0+YLzymJ0CEoinKezf9791koh8dStxYmQooaiaCjzktFgTBAE1/kAgjAM+ukOgLMkUV/mH794Qdd1FFVDbSxBJrh97yYvvXofL+Ho5Bnz1TnZKAGlWW8aNkXN3t4BxhjKOkcIwcXFnNPTc4wxFEXBYBijI08UQ5hIhIB3332Xopzz8sMDyuKEs/P3+fKX7zOZBL0Ls6v7gNeuQSpP6TR//x98k3/0h9+jsbB3Y5v1ZkPVNmTDBKEEL07PrgNpakzX/ei16ZuoHq01bVsDH48LJah+wtU0FVpFOCsYDCZ44+m6vvOP6IgSx42bI17/zC6T7Y6r5VOydIQXMdYIOtMQhJ7JNAHRIjAs55ccP3vKRx+8h/KOpsipNjlxFPW0bu956613Pnn9/f+1sP/f3Kw1NKZmvDskTGJOHi1ofUWUZhzu77PYrFiuC6QIscLRKNC2N4yEQULblFgp2RqF1OUVja3JTUcTeHTXIqWgLFbEQcDZxRP2b+7y0hu3uH3jgKqpEUnIi/UxAYrpboT3EUW9gECggiFFUbDZbODScPvGS1hrWS7X1FVLGIYUm5xik+MQRMkAYy153geVyGsCpgIeZmC7FoOgtP0UQQlNqAVagHYCaQVYh/Ue5UtsvUZJhaxKZNd750MJqnMI2TP3z3PLGz/7dXjxR+itfURbUpwfkZQ1XRdDPKDzHVESkQQaY8BLB+o6TVj00wD/sc/ACozq37y9XrL/rIiDEGsakiRjtapwKJRKqEvPYKjYLDckw2GvDciGrJcneA/7+/ucnlxycHDA+fklaRZTNw1dl5MkGUKF1HYNtmNRzNk72CfPSy4vL9mZ7TIYpv1xxfepRCpS4BLCSCGdQuLYmo4o65bdnRlgmIwyLo4XHB7epOocVbFiPI1Js4BssIuUoPUZL14UBIHq9fbWIn1EW/dRauNJyPn5GfPlBh1ErPMVSI0OYlpnEdKhpMK5PhnKOQNKY60hCALquv7zo4DvewRpFNGagu2dXkE5yLZ5+rTGWce9l3Z46eUbxKnlzr1d8mLF+ZXj9OwYoRV37tzi6uKMzWrN7RuHDAcBygfYDgZpxt7+NtZ7wiSj3qwoyxxsfyQ5OLwBvP9j19+nYhNoK0NZ1sjIkx6EjEXKk3c+YkffYDRIaEvJZH+HDk1Zt2yqhkgJhIwoigopE2qneOVwn2LxjLpzNK3A2xY1EKSq46VXb3D75Sk//eWHHD35iI9On3FRNyRhTOihWwUk2Yzj+XuMolsUvqRzOflqm2SoqGrDVhhjG49xLW0LpydXeC3Ji+q6CaSoqor5YkVjut7N5sDj2JltcWvXM3/maHMDFjopybRmFBi8aumE6+nBoqP0FmsjBsUKhcJbh8gNsdd0OESs8dahdMrLtyacv/MPiSuHHEdI48hbRWNTuqYkdHAc7PNH75zy17ZB+BikBSUQ7jrKqyn76kJFLJzGJkOEa3HG0nlJYgTLNifNAsJRQHfZEcgMYRT/5+//GT/1M6/hZUS5KhiEMYvLkjSa4LFs5jlb4xFttUHQooKAzuSEScLl6pyD7BZO9/2KG7f3KPOa4WRINhxSNyVCOfJ8TWsVSZJgfMhmdU6WxownU5qqJQREAGESc3W14GSzYjQYowKFthVRELLeLPvX0W842A8Joyl3792grCzHz85YzyucrfoQHA2LdcsmX/RYOOF6CTINore4gIPOWrQWBEoyGmQ9jNT3DWLnLdb0OPQvfOELxHHM2z/8Nr/8K7/Qg0+l5jf+x7/P3oHkG7/0V9i/HRME/Tjz7MWCyWzK1jgmLzcILSmrnIP9Q85OzrEdfPToQ7anE2bTCWm0hUoiOgvGeYLwFsbCW++8z517d/Cq/MT196nYBLzrQzilhiDVxOOI+68/QDUhJ2+dsL29zWq1IS+WjCdTDie7XJVLrjYVnTek0gCeyELVASpmPFLYDhrZcutglwcv32bePuOdH36Hh/fvE2eak+9/D4MiRiMKz2w6oxjv0WwC0niLIBuRzqeUhSUMMgIdoXXIZJJw/GLJYJhRtm0fDaUUne0/M53omYmN933jzXnSOMEE4KxEOQW2QwQCbzoCaelwNA6k8Iy9BWtxMqVtNR0alEQ3C5QHF8C6bYm1wJQlabIkLi7Jhi8j2ktMuSH0gqIVxPTr/fFqydtn4JF4HeJdH/zKx1nBvq8ErHd0HlSgoWn7sZYzZL6fhaswIBkOrqEaFu8ceV5zNV+zfzihzSvm6ytENibSimyQopRgky/QWpPGMaeXFwRBQBCGSC3Iy3O8NGTZkK6zxImiqSu6xhBHY5brnMFgRtOuibOUNu8YjkJMVzC/qgjkEIlHR5KqLGlqkIQURUOaKaT0bPIFWZKwtT1ltc5R2rC3P+Ji3hBnEa+8/FW+9fvfoaha7r9yj2w44x/9/h9jKksgHM5YvvjaZ7hz9wZ5veLRs0dcXM5BZLR1jVKKzWbD3t4e9roSrKq2j0wPYTQeMJ/POTgc8cUvfQZnFb/+63+Xf+vXfpEwzdnateiwY71qiIIJg8GIOAx5//H3iNIRQgUE1x8y+7vbaKsIJgFaCUbDhM36HBNI1kVFEg8JwhjfSe4/eLmvYuWnPIHIS0/nQup1RdsWTEcZua9YNGcEtxuuihMu2xrrUi6ePycyV3zpJ19j5gzr5YKJ0zw9OqKTLZ0VFE1DPOsR0SNxwMp8xPmiZedwxg+fHvFstebWzQPu7mzRNQZ8j/te0zcR23rJdDYlzlLkvGVdXtEpgzYtibP4LiQYjIgGC1TbUNUa19hrVVmLlpbAe3LRW4F36yVvxBtGpia3HRUhlYyvY74tNZK21SjbayOs1T03UDg2uiN0NVlrqbqY3HqwgmhkMB3IWmHWFi8T5LCiXHhs6XFWU8YDbFXgZM3vP6/JgwQvt2mChtTHJHVDGySsaAmsp3UBL/Q2jAJC6xE2ZO4sUrcU3hE7xSyK0KZDKI+R/Zvf+QxTZcg2gFASD7bwTUNnLJuNRSlFGAUILajbhjBKSAeT/oxs58jWE8URKuj/ve4qhsMBwSSjKmr2dhPathcVlasN3giqWpKmW7S2bwIShH2VuNnQNA17eymbIkc2MYkIydIJOtS8uJhfi8M0Asd0mFAXFaY64ud++pCPnh3ztZ+4Sds4zj5UPH7uqWuFVZoX+WPifMn+/oCfunfI1WXKd9+qqI5zTBMgW08WghzHnJy3HDyYMhj2yT9SNdy+tcXJ8Q9IBh2/8T/8OuHEcPiyJk12GGYJ77/1Nl5Z0p0OAsU7j9/CZppI1wwnEYfpHmernGcXcx7O7mKp+Pb3/5QHL/8KY73iw6fPuXv3Lsv1Cp3GLH1LV/TViO0+OXP0U7EJCClQumU8HJLna7rKkEYp6UHGIliitjQ/9/WXqPOaJ08/RIuYH373T7hx6x7n56f4bMTP/OIX8W7N2ju6zPHy7TEfvvuco5MrfuEbbzDb0ijh6Ka76DDCdYJlXpAkSR9jpXq6YRQrkngIwGq1YWxCLAHee3YOtrG+I88tz44v2awbJsOY1eoSJ0Ocd9juWlJsLEhNIAUamA0GmCalEjm1FlTeIVqIIo2UHbFv+056klBYjdK9g06vClrrWDZQ1SGhh0BC1TqsAh04RtUK3zi+/7Tm5WiJaqG0Hi8uWYuAp+KA75klqQIzHEH3jNYdoLJTvDVkFdDCqY/5gyuPGYyQbkMoLNIrtBW0bUMYSbZ3d9iUK7zo8d4OzaZagqopGoMVhiB0aB0zGoUU5YYo6jfkclVjvGN/b4erxRyBYjbYoawLlvmKKipJopjpaExRFBjRY7zatqWrO3a39imLBWmWkKVRv+DrCq36Ssxai9Yho9GE5TIniTO8EWxcRZ2XDMYDVvmaIAoRWjHIhrw4OmM4GoF1HF+dUTjPn7z9FoHSbB0MCUaOqm6I45idnSFtvWFZ5izP1kRJysN7Q3a/epP9g1u89c77PD16zIPP3OPh175IEsN6vcR28IU3bxBGEtf9GrZreO3Bbe7e3cf7FbLVnC8X3Lxzj65ruDw7I9CaMQMyEbM7HrOYz/mTD99BqoByvuHUKm4c7PP5zz3k8uqYDxfPqRNFpT0+izjPVzTWY1pLqjNmk9Enrr9PxSYADiU7ys2axdWKYHuHeKDxWA5v7LGcX3F6/phhmjEc94EKr795By8Ur71xF1M5inZDIxyNkgz2ZlRxSlV2tM4RaEesJVVeEDhJvihw1rO9s0OSRrSN4eLFJdPplDxfMRnN2Gw2CCEJggjvexFMUWzYTcdczS+pyo4gCAmCPlXY2Z66qwNN17geb3XdFJIOIqV68ZC09NYR6JTpeQCdo9UD1tmY783XfO94w/604fUAPjt2aGGRDiyW3DhcaxgEAqE8chywVgFShSSjEZfLFamUYB1SaS5NwrdPNyy9IvDgA4HQGbaKe1GLhwhP20ClUt5ZdJhJREQD3uCQSOuw1hGEAi8k88WmN3SJ3ssxnIQMJxHT7YzT8zV5nhOLhCRVKNUfN+qqZTAY9aWp88RhiNYBZ2crhrMJUgjquiRLJG1bMxiktE3DeLjF1dWCJMkwzjMcz2jqDWEYEoY96WcyHPdzeWA6GqOUYrCXMl+tcc6TZDG+8ZRl+SODj9aSi8tzmq5mIAZUTcn27g5GFrND87EAACAASURBVMhAMRqPMMISDyVCDCiKgsXylOFwSN21bO/uMZlMSK1muBPy4bO3WVVzPvOV11HKU9VL9if7SJlydb6krK+QQcA4OSQM4OHr96jbnChMOX1xxmuvvE7Xej54ekIaRRg8UZzw8M59nh09RRpPkGUszueMogQvoGhbtrd3aV1NMJ6xujzHWsvFxQWdsyA0NJ7Kbniwe/cTV9+nZBMQOGfpWkcSxYyyAXW9JktTXNcxGMbU5YY0U+hgzCbvG1ZtVbG1vcemgDt3dpEnZ8jRiC7Q/OmfvsNq0bG3H1HVOeWmZXtrSlutCXRMnGV0oqKpO9quZjgZ0LnuehTZz3STJKVYVVSVvfYKdDhvSNMUd7akrR1q3MtELQIhxY+cY1JoHAYh+yz5EJDW4X0fe4YHqw2ua9BGc6T2+Ls/+Ij/7bljE0B00fKNEAYvh9yfRDhbgXCsraHzEDSSAIdRgm4UURQdWXzBWRkxTSJwNc+7kPfahO/OrzAyxV9n1EuR4KMQbVOsqWhdi0MwryXPcwfTAOklrTEYBNI6tNDEccSmyLmcr3o4q7WEYcT9l7bofEndCobDMZ1rqDY1YS1YLxcMBilRlNI0DUmSUBUFOlA4Y5lfXSF0QNtWZIMU0xiyQYizlsFgABaiKMFZeH58wngwxlnDdLLNaDilqiqypJ/P9+nFNaEI+1Gy8n8+uvOeoigYjUbcunmD46MTsiSlrQxhoOgCSRAoppOUJMm4uLjAekcU9sGrcaLZ27uLEOJHTAXtPUmScXJyRNf1v+97jz5gOp0wGKb88Ifvsru9g3Oe09MXfO7gIfXyHMSYZyfPaNua3e0ZP/PTv8hmvmJ1dsLWcEzebJhtbZMNRlwcPWUyGFKpkHm+pus6lE5Jsoznpy94+NLP8M1vf4sqFb3fwVi2pzOW6xUeRVFU3N67yb3DW5+4+v4yUJFb9LjxfXrD2X/jvf/bQogZPVrsLvAE+FXv/eKaQPy3gV8BSuCve+//7J/2HN5JutYRBJYkAsSaKJDYzlEuJU3bYZ0jCOcMBgN2dqecug6lJINMsHLnLHNHaELE1i2++/b7LC4ML79ynzc+s0NdXZBlQ/K8ZHdvRhRmzJcLsihja3ebp8+fEY77FNo7t/Z5/uIYEcbkm4LMRty8eZNo3fQmk6Kg6zxFXuOs5NlHRxgETdcRxb1FuUej9VbWHnPVJyiFxhJYTWYSxkYiRN+PuFBT/ubvPOZZImGwRdY2CF3y92xEchZz/3jNy6OYYQxL41m2lmdlT9gNNoafkCX7TcPZBZwW8Dvv5Hxo4N2spXEl3icIaSlMiWACIiNIQRU7qO45bZ3zvpvxW+9e0WS3CW1H2wjmba+5cK1BCc+tW7f4wTvv0Is3Fd57jC2Y7OzgZI4VijDJUFYxGkhWizXjrR2MMXSuYzgeEEhNGExpmo48L3n9lQesq45xlhLHATu7W1xdnTMYDFiv12gtCaOIsqzZO9jtpcSrkvWmZjgccHm14slmQZqmgGN7Z4uqLnsHpHYEQYAUMB1P2JpOkFIyP7kkUQFBFJJFMW3bsjObgpJsDyVPTs4JQsnBzj5t3XFxdslstsvbb7/NV77yFTaLOW++dBvftvzw+IrjF+dUZUcYRnzhjc+xWufXY9NbvP/4A7ZnI6bbO1ycL2lcw7O3/oy7t+9yY/eQNBrx3rsfcHp+QphIpPR89Wd/mt/7P/6Y4vE51WbJm28eslifYmqQImCwPaV2OXdu73C5PMFFBuev49SF4urqgqZtkTLg4OCA7Z1d/uCPvvmJ6+8vIxYywH/ivX8N+CrwN4UQrwP/OfC73vuXgd+9/h7gl+mxYi/Tg0T/zj/rCaSENE1RMmKQzUjiMaPhlK716EBwevqCg5s3ETIhCIeYtgDRu7CCIMCZgiByWK346OSU9x8/5+DGjN3DmNkw5ubhIc55rIMwDvCyJU0CuqLh0Q/eY3U+J6AvoUMZU1UVk/GQKAxI0oAnzz/i4uKiT5yJE7yDtm3JBglJkvY6cd2LhWzbXRty5I805NZC5zxOdkhnQDmEdiTWI2TMc6NYREAY9bFfPicSjjyR1BKCMMbWLdZIhEuQXcKidTyrWt7JHd9dwmmyjTt8BfHqIfd/8bMkD+9xbj1eWAbUJMKQRsBoi4tkgjUNpemgqhhYeL9NeKfRqOEI7TpqZ/rZNmCdRilJnhcURUHTtbSN6TX3bU2ShBjTAo4sy8jzkrqraJ3l7PySsmqomoqmaTi7PKcsawQaQf/pG0cB1vZx4Kenp9RlTbEpwIHWITqQDAYpUsHJ6XPCLEQGgsV60TMKu5aqqanbhrwoMQha40gHE8J4wGAyBq24nF+xKXLKusI4S9u2nJ6fUzUNHZYXpyeMsxh8i6fhydPHzJdXjKcjlss5B4f7TGdDNqs5bVHw5IMPOLl8jhcO6xpMW+BsydMn77O4vGCxvOLBy3d5+bX7rNdzrq7OWc4rHrzyKls7W8TDmA+fPUZFkuV6yc07N2mc5Tf+p/+ZR0+eUHQt2wd7WA+vvfkGP/Wlr/DZNz9H4zo2qxXlasN8tcQiyC/WpCpCWkhEwCQdsr+7R6wCzo5ekKbxJ66/vwxZ6ISeIoz3fiOEeAc4BP51euwYwH8H/B7wt66v//e+z2z6IyHERAhxcP04P34TUB5rLUk0pmkdx0+fkaUpd+7cRsgOI3bx3lBXngUV46FDBZLxbu+RHgxTrq6uGCZjHj1/n2yoeO31Q9AFL46eMtmaIYQmiSOqKmcwGFBWK1wnKTYlN27cYHk+5/DwAFMZRtmArm4YZynNsiaOQ2ojQCrCMMSYNW1bE0SSUPWbgJe9fVSLXlIK9Dpw3WOgys5SKocUILRFRJak8jQy5oUDHykC29tJa3FNABaave2I14cpg8UZpbFIHxAJRRgINh3kHfzmM0Px+IqtQUN0mLJaPePkomYSBSS2IbItWzph7OGiNsyTjJuN57JdM6pLuhX846OcxeSAWCtsnVM7Q2s7fGVwMkQKwyYvqesOqSVOKASOLIU4gizbRmtBnq8JZADS9x4B3fSvWVtirCXNsh7Z5nul3WazoTGWKIpYzOes1z2HII57IMl8Pmd7e/u68bdhd3frmqjTN1139rYo84TBYNAj3qRGaU2+WrHJz0jTlGzUUBUlXiqcVOg4QmqNVIrZ7owwjPFCIAKNDgP2DnYpu4ZB28dkN1WF8TVvvvE6y9UFN27tYrwhGWTcmE3YnU3BCZqy4ujsEQeHM7yPiNIYIQ2PHr3DZ197jbYtcVUCFq6WCz5cfYipPev3S27dusXyYkWT18xm25z6BZWo6QgxtqKpSp49+YholLC9v8/yqEUTIGXAZLCDqhTTUUa7qUlURJJlnJ6dc//2fW7ff5VvfesP//k3gb94uw4h+QLwx8Dexwvbe38ihNi9vtsh8Pwv/NjR9bVP3gSEYphNee+994gCyZ07t8B5NuUcoSqm2ymXFxdIGSBkR9O0WNFRuQ4vYu7fepViteLi4pyHn7/LcJRxdbmgKWF/HBIGCVXVYLuO0UxibIUOFYaAO3fuYeqGmcyYkvDRxYIwChmMBkgvmc83VLkEZ/FOkBcFUZIiBZzPz9hJpmgt6bwD60jTEfPlusdFByFKCkoFT5YVm90YLUGakEh0CFeTD8d8/3yBQDH0lhaLVRGR8dRNQlec8uBzL5EuBzx6vkZYSxYpxl7yOpqyU/zXx5Y/shrKDZ991zEJEjwBu0pTiiFtEvFG+ZyvPdgmFAEPNhWyLvBmQ5Ft8aIN+TMX0O3u0ZkNbecoqo4UQSc6Gi2I45BnR6c4AUJ5lPDEkefLX3qFQRayKWqCMEQJz2qzQTvNeDQiHSeUZc10tou3UBUFkqqfGEhL1TjGk57VIIQgTQdoHXJ5Oe9BJFHIfD7He4sxHToM8EKyypdY48myjNa0vDg9YTrZojOOtmsYjvrU4/F4zPPjR2TpgJs3b7JerTCmrzxAMpttc3x8jAOGownvnpwjQkE2nuCqEu0Vg/GAyWxI61uscsTTAc8vVwwnY0a15vTsiM4YdBIRTjOcc0yHI5aXV9g2YhhPefzhCWGkCFzLh09PuHX7kJ/80tco1h2PHn/Iux884eDGNs/Pz9namjIcSaKo15tIKXnve9/n4MYez85OiAYJW8MxbV2xOL/ssfNVRxk7bNNxdXnOeDJhd/8GZxfnPP3wac9v/P+6CQghBvT8wP/Ye7/+uPP94+76Y679P4aUfzF3IB2EnLy4YG9nxvZOhlQWbz1d17I1nbJeb5BSMpsNabsSB0RRRCgVtuqTWpquZLo9JT+5oMwLtIjQ0QivCjySOI5JwoAotixWS0bDCZfzAocnG2UkDkxjWF+tOHi4xWrzfzP3Zr/Wbel512/Mvl/92v33fac/1bjK2OWyXI4FxMiKuEoQXCGEULgLf0Ek7rniiptYQggQKAI5gKUgblCiOE6CCbarynWqTvP1u139mn0zxhxczH2OC3ARhIU4S9ra0txb+/uW5hpjjvd9n+f3ZMRBTBRF5Jkk9AaoY920eHZEHMfsigPKHdx1uu/Rj1FdwjRQg6+PXpkoy+GhrDBr6LAwTRu/M2io6AxFKhuafkCLCSEQUiKkQtmC07OEZBIiqwOuY2LWCowe37WwZMNZHPCrRc/N24KDCQvHw9QabbqI3iAxTfp8y2+8G/BXv/8xTrsnqitocppSUY6v+FEqMEcmZt+ie0ktNRYmZq9odIflmozHMzZ5iTYGlDdIzk4XhMEQ2DoeTymKlEbVGJYgDEfs9jtsx8R3PaTsKYsGz/VRXY3sFU1dE0Zj2jYf8gnKkuloitaaOBgWU5iEPDzcUbcN8/mMw3FHmMxQrcb0TNq64cmTJ6zuVwS+Sycf3XtiCPUYPCgLLMOkyivoBZZhIzQ4tsPzTz+nf/RSit4gWM7ZbtdsdsehqagN0kOGEILvff9XwXLI8xwjCnm9WRGUAa3TUKmOXna4kc1sOiEKEmI/YLve8+rNHRfPnpCVObaWWE5Aryx+9pMvuL/fsN6uWJ4tEJ7mV77/yxRZTpYeEb3CC0I6NZCIqq7m7OyM65vXjKfTAWZTFozHU+zAoewa3nv2jMunT8jLYpBdz+ZsVlvs9C+JHBdC2I8bwH+ltf57j5cfvjzmCyHOgNXj9Wvg51uRl8D/Jf9Ia/27wO8CTGaBtk2BbRt0XUfoWUihkE2N4w4a9jD0yfOcOAnBbMl2A5ZRSE1RFXS6xvIXTGYn3F+/xXMTirzEsjtUs0c2LQaKKLZRaNDuIwrMpK5LbMvn4WHF5ckFz198yun5GV3R0VQK27AxhcayDNzARlYdX+51vdaDB0cbiB6KfEiF6cXAFzAQKNtn1/RQN4M81rAwW4VpgkRQS43GpNNyQE26JtICU5d8851LLBpMS+OhsGwTKXuk1WNZYFLy/XnEi7cdm97HtcDSHZ5lYrSCsdzx4bjhr//GLxPMHbp6Q61anOOOhjG3xozff/EpnbHAqCsQirZT2FgoVdPpHt82sF2HWtYI0wDTwrV7njw9o+9ziqLA8Vz8KKEsc6zH+3h+fs4x3dO2LX1bUpUNZV4xGQ8lFMZg43WchL7vsW136PaH4aMjr+Hm5oYwHOzHTVMT+BGykViGjed62MJmv93jmBayaRGmQa86OtEjVctisSDf15ieQ5FmSNmTJBFhmJCmB55dPUUbgsMhJQoT3m5W2KZDlqaDtj+KUAZEUUjd9ly/vefk7JQXrz/HtG282MP2EkTbsTvm0Gpevbwm9HyulqfUTcfJ2Tn7tGS1viM0bb7xjY/oMPjhz77AcQ1m51PC2MZzDdYPDygFnh8BcCy3TMIxwcjjYXvPu/MPeP+dd9mu1mjhcnlxwv1mQ14WjKYTPvvkp0RRRN8rvv2tb3Hc7Hn36Tuorv9/vwk8dvv/U+CnWuv/+Od+9PvAvwv8R4/f/4efu/4fCCH+LvDrwPH/rh8w7Ag9vtvz7OoKwzLJiwOu49Irg/22pCyGJ5TjeLSVonI0kRvS5EPzaZtumExmNLpgcZZQlT6jIERNGzaHFj+wKKuMcRJjOwG6UnhiSlFcI03J/GTCT37yktn0lOZYcHlyjlYWsh1oRnmeU5SDB912TLJdTttILNvFsqHrOtBDoKoyDXoNhmVCXdMbFplpkZc1/2Ct+VcnCq2OCFtzdAS9EuTFEOttGhJMQScVpoZ/Ux75Vz74AaR3mJfPMF7+Ma7X41vGgLZSgAm/FWp++68t+SJV3NVQ1QJDdPzgl6YsggCzPuL70BwPGM0NbVMgjznPZ/8S/8n/9BPW0QwtSoToqBqJqWqarqNUDXE843Q0Y71PMVwBxjB++8GvfYzvSRzPJs2PiNpCGBVB4BBPQ9Jjw+efrqnKmsViQdEeOT095fr6GqeyqIuSpmk4P3NomwH75Tg24/GYusnZH1Lquub05IT71YpACyxrAG6KvsY1LfLDFt8NGIUhgesBgvV2T3ksmM5nCCHY3m9wfIesLDCERVFVHIuSMPSZz6e0XU22Hwxiq/WexeUUVRk8Obkcph9uzdnTMZv9hj/4J/+Qq/NnbFZrQBGPQrA7bBFwEc6YFYrN+g7DcNjepQid0XWSND9g+xGX77/PxEv44v4VQRzRBkMwrKkbLGFi1R1p2TKdzOmNZqAKmYrr/TWe52FOXX74yY949+odjnXDfn9kcyzIkRihR/3mmsvTcxxhoiyTf/aH/5Tl/IS+UTTVXyKQFPhN4N8BfiyE+NPHa3/7cfH/N0KIvwm8Af6tx5/9jwzjwS8YRoT/3r/oH5BSEicBVZ0NtJfHD/hhnxF6Hl07nARAYhkmph2wubtmHMX0PZieS1lKtEpRfUk88SgPB9ouQ7YurdUiTJNOSYymRbYGb1+/5fxygrBa7u9vsXyX+82W9/w5ta5Js5zF+JRaDfr5qihJU81s/CWU0yDPCgL0Y9NKo/s/B01q/eek2V5rEAafZvA7YxuvlSgTMgaQh2UaKCUH6KfSJL3DB1HCv/9th3yb4odTTMfG8Ex6MZQDQpkETgi2YByaGLTMp4Kblw25E2OMQkbmW+pGYieXmI2FYSiaoiMscg4d/KMff0baWdiGRaMbmr6nUxpHGBSyRho9vhfhOxEvr18je4nhOaA0k/kY1axxbJOqUfheiOyHzMWx7WAaMBlPQR/JsoLFbEyaHplMxmSHEsd0SBIf0zbYbI447nAq66WDZcPl5QU3Nzd0XcdoNELKQeTj+yFNucdxHHzPoa5bTAYhkJQK33H5+ONvIqXk1ZvXuI7PsdywPLmg73seNutBq9CU2K7F7e0t5+fnzBZLlITDYYVDyOX5Ca/evCSITA75mrTY8+ydK5qsJi1rvMTANgRVX1McU+5uUqq8I/RMikNJnjWM5iDpqWQD0uPm+o6tv2U8CYmjEKOA+WKM61ogJK22aHr47MUL3n82oahz7NEJQplUXYvuYTRJBi5mVXDx5IIizcCB2/0ar2nIywLHcQYqlO2QlsWQkfiXnA78Y/7iOh/gt/+C39fA3/oX/d2ff5mWiRfEGK5N1Q5vMA4TkmRMmWfM5jOyvMRyI3ZliVN3OG5AOBnR6xbHsdlstjSZga0DmrKi66CubbzARNPyjY/e4+bmjq7RxFFAXhzQYsiHN0REm22p84qVq1i6T+jrgjLb4VgGo7nD/V5g4uOZE4TXEYU91w8pVTSCXiCUGnoCfYehNX03YDyFIR+xUg6f7mD3sY0tBWbr43lHpu2e79qa7cEgZhBj/M0PFb90KTl7x8bJn+OIkP2PXxIzIMKVNjBHAuwOy7KQ9lDaKMfl4l2DmprKaHHsMwIA1dF6Lb0E1ezYC8iMGf/rpqc+eYKwLewspep6GobaE8DuTS5nCfnmmqavMQhBNXixpPPfYviQ9xauNaM6dHieyekowHE0VREQRC55XiK04vb6lpPTc1w/Ym9WeJGDcEwyJem0wjIEjerwTJeqruG4x3A1h2qN68RUsmdTVkRdS+IYtD24foBt+aiqw7dMLNvEcQKE4fDi/i22mSCVQTI+5+72AUMokshhNAoJw4g8L/EcHyFMosBnf9ggpY0/sbhJ3+DMbB7uC9pa4jpLDNciqx7wQ5tkNEZ2LQYhSdRRWyleJDjsK2ptYHsxD7d7whgul0vqvOPp1RPSQ4dZQVGVzBdj0oc1fmDjuhama+J4kpHl8JCVuH6Eun1gspjTCwNhQpXmWEbHKIhRRUueVmA5TNsQvIRDqjEMSdvWaKUwjAqtej786P1fuP7EsGb//31NZq7+rb/2BKl6kmRMkTdMxmOyw5Fnz55wd3dH3ZSUdY0W8HB/y6/+6i8jDEWvO1x3ULit7naMx2OSJME0YLVaESUBdTmYbVCaJAgo8wy0RjU9WVbwwXvv08mazWbDbDHl7d0tJycnGLqjq3t6EfHq9Y7r1ysuT074+P33USj+9Ec/ZJtrXMfncDiglH7MgdN0vUI27WCeMQwMBJmV8Pc/SrmyD0gBfSq40yP+6y8ObKMZ59mBf+39E37z/RrfOGCZHgqbY1ZjCBfbjYfcPDQYDaAGf705IL9MQ1MLj15Db4Jra7xeIGSPoqTLjny6X/IHrx74XZ4wni6p6oa2lRgddG1L1TbkbcokdLlYzvmjLz6lD0KoS777/XOWpxGeb1EVGdPxBM+xyfOU0ShGoYZgk7SkbhuiMEA2LaenS5qqZX880CNoZUfdNnRdizbVIxZcDzJgIZiNJvSyJU+PvPP0CQBt29FjUBQFVSWo6pTJZEQYeNhacNwcmCUzAjtE6KFEi2IP2Us+eX7LfD7B9WzC0OXm5gbXDxiNJmjT4nA8DlAYB8oyJy8qyqbF9T1Wq9Wjb2CBJRy22y2TyRjXh/V2ReAmrFb3TOYTDGMYeZqGg+vEbNclx2PKKIkZjxP2+y1eFAxjVNsG0TMej6jrkiD0ULIhigJGoxFCaPaHLZ4b0D+eNotjgWpaItcnsDwiPyQejThmOavVin06nJAuLi549eoVvu8/pj6VLJZL/u5/9rP/TWv9vf/z+vtakIV6Dff3D9i2y3q9ZT6fo7UmiAOev/icFy9e8OTqGW0DZSEZj8eP0IaWsqip65abmzviOEZrzXa3xrJMnjy9QPUmjmcwmjjYvubuYUXd9ETRlPEkJkksqubAcZsyH5/gWC5JktC0FZ1SX82jR4mPaXk4XsLN3QrZ1vSqG5yKyWDOME0TDY+23EFb/1V5IECakn/66kDqzal7gS80lmfTORbjsuK7YUD5csCU2YbG0hJZtpj9QPz98qX6DqEHf4IhBiirfsRoGxowe3pzgLCKroP2SFqm1KXmH+/h7+UhTnJK0zTYhsY2FFnT0AqTtKkw3R438jG9CDoXte8Qds+zd5f4Xs848XENh6YoMXTPOI6YjMeP9tkG0/YIgghDmBRFyYsXrzgcjoNkWg8BoVlWoIWFbfkslqdMFwnCkMTjGNvx6bFwfQ9MKOuGspYcswOmozm7GPPk6Tl1U1I3HdvDkcX5KWmVok1JmHicX5xS5A35sWYyClBdhWdbFGnBZLKgLjrSY8HxkDObzRFiGINOp1PaVpKlBfmx4MmTS7quYbfZslptcOyA/S7nzes7+g66dkCHZYeC467g5HRGUaa8evUapcB1Qg6HekCe+T2mKTg5OaHuWg5pjuwFVSPZbVOquqNtFHU91O+hHz3iydVXZZHvhViWg+w0TafI85qqaohHE5LJmPFsOoynfQ9hWCyXp8znc/LDL44h+3qcBOa+/s4PEj7++GP6vqcoMixTYFkGljAoy5o0r7l+s+X8/ALX6VgsZ5Rl/ojyqojjGLRGSoltDzUWDCnDeZGyWCyoq5a2kLx9e8NsMsUVmsDz8F2Pk9kFn/7sC3ot2dcFtmsjMOhyxWgcc79a8eJ5gyF8nl6e8vQs4uFuxafXB66urvjixavh5onhQ6ENc9Cz6x73MRFWyIK+7/nXQ4u/9W7EVD6wtyMKmTDxBE5Q4juSWVjRSYmjIvaZwjBdtBhKGyE6hO74alcwhtggYQ5RbsI4oMwhINNuTUoElWqws4ytjPm3r88xwwSrESi7J6tSZFtS1hZZWaFVwcWVi59M+emLe3xsYgc+/K0Fy6UzBIUIj9/89R+w29zRtCV52VCUNcEo5uZhxWg8w7IkJoKXL18ym0zpuo7F8pT1bguWQVmWmLZN29WME5fLi/dYr7cUWcpiOcU2LSzLZb1eI0SP1A1F2TJKpvTp4Mnw/UEkRK+oqgrZNRi9Zjodo9phgw7DkDzNBiioMNluDihtUdWSrlPE45hjtsMLHSxPkx8alDZwXH/o+1QZnu8gu54qb1FyEIW5gU1VF7hez27TMBqNsCwHJQ0QHZdXS14+X5Ede9q25uJJjNYKLXpaqVgsFtytHgjDcMDUdR2nyylKtVRlzocfvEtVVbiuy939A47j4VsOZVEQuT6T8RgTDaaB4zlIKdkWJYfDgShKsCyLMhs25el0imw7/vv/8tOv70nAsk1GY5+yTBmPEwQ9nudgOwYIhefbWEbP+cUcw1S0TUVVlKhO0lQ1quu5eXvL1dUVURTx8PBA17QIDWW9JfAjuhY2qwNeZBPFJrLPaDrJw+qI543JqiOz0wgthuCT3fZIllXUrSbPc+aLEZ5vYRjQdC1lWTKfz4miAMexiEOfvh+ktPDYPHyc+3/1PrVF5cb8cF/xR5tugJK2OQuOmOU9tiywe0mbSXQLhxq0EaCwhyP+Y5LNMF4zBkah4aAZ6MZGXwywU+3S9wFtAzovscqaFQv+4cHDG8+wZUPbKYq2Jq8rGgVlkaG7EhNF4Mbc3T1Q1BmTc5erj+dcnQ+U3+loyXQ85Wef/oQ3N28GXHYPYZyANplPpsiuJggCsixjPp2R5yW27T4i2RW91CTxGCEEcTTC0AYmmsB1mE3HGEJR1QVdV2MY0MkGtvieJQAAIABJREFU3QuiZETXD09F33fpVcPD/RvWq1uaukQIk/niDKlMqq7HDlwq3aCMIXU6KzOmp1PCxOfps3P8wGQ6S5hOxliGie5g9bCnV9A1w9N4HI9wTQ/f8TAMgyw74nkOfQ/TyZIkmTKdjfECnx5N4Cf0SrDfZfQ9hL6L5ziIXmPQc9hlWFgcdkc826NtJI7jEMfhY+qxO8SsYTEeTZiOxoyTEbPJlMloim257A5HmnbQc2R5yeGY03YSRwtoOro8p68aejn0WmTbsN1uf+H6+1psAlK2XF6e0cmauh5GR1JK6qrlkA0SXWH0LOYRi3nEyckC0xTkWYbsOhzbhl5w8/aW9HBkOp7guu7ArrcgTwt8J6KpFZZlcHIx5+xigQYsx+F2tWY8HZFXh2G0h4GSgiJv8f2QrChYre9RfY3r2hRVThAEtG07pN32iouLMyzbwDAf7cOPx/PBZKOQStEqB08IHmyf33u9QxITKMAoUIZGHxVyb5LnLn1p0WqXpodOG2gGIdJg3jFQvY3sTZRkyD4EDKOiMVyq3qHrbeqmxZIduuz4/Qf4L942IDvqrqeiJ22OSFq0aaCFpNcNvu8ia4vyWBPYMFraJOcxx8MtdVajWsGzp+8CkqzISEZj6rZhs9kgpWQ8ijlbTLGFi2MHOHaIwMK2Anw3JtvnGMKmKGqOu5ymbLEJ8HybIDTpZI1qFXVR4rkQBTamYaMUTEdjmqYkmjiU1R4/sBhPYuIwYJIMIaS77Z40L5C94Jg3tO2ABrf9AMO1cQKXeOqjRM35kwW3d68xTYHQ0NSacTyjqzuU0jRVTZ7XKCUIvBjPcVnMp3SyYb/Zk2UFRS4IIg9NN/hJRibLkxnHQ4ltmASB8/hgg7LcYWmLru4wlMAWNp7jEPkBqJ44jum6jq5V3N+vWK12ZPmWq4tTJqMI0xIEnscomZBmJXf3a1a7FKkteuHhOy6TZEQchLiWyWI84mw+Y5bEXJ4ufuH6+1qUA9Olr3/n33iGYQzqsiId/OJxEtF1Hfv9lqurpwgNt7e3OGIYFQkhBqvkkyccDge6rkFqiePYOK41SFNbzfG4J0kigiDg1ZtbpFScn58TeDZ3t9dUZcMonHJ9fY8QgiScs3rYYpg+oe9xdTUj8DXXtxV/8sefE4YJV4sFsR/Q9po3t7d8+zvf5XAs+IM//GcMfGeDpm4xDINWycGCaho4ljOEkwrBX+8P/I0PppzbB0xDYuJj9ha2pYaoM9sd1G1iqPUtu8e0jK8SbYQGeokhJKYBRS/IAb8HMy84mj6fdAm/91nGT8cf0bsOss0pmwpLWuz1hlEUs73dQG8SjCOS8Yj7F6/53ve+ibIzwvnQ1Hz61Gc2+oDbh3s0NVLU2G7I6mHL6cWcw+FA4IUcd3tc18YJfWSrcGwP/ZjLcPtwP7gDqxzf99kd9pyenBFGDtfX18zmY1zHIIk8bNNi87BhlMxQSjEeT1lvd5i2w/XmOZN4gaw1dILlaIZlmxwOOxRD7dx0kuOxYrE4Ia9y1ts1lmvheiaLyYTscERoQeKPeLhdY7sBGof1Q04Qe8Om2A9wlCIvOaYH/upv/xX2+y2HLOXl87shjtySnJ3PUP0QTSdbgew6+l4yHY1xnACtQEk9sBQ6A6U6doc9vejBANMxsW2TVjbM53O6rsO2baaTCaGn6TqFlortdk/TNDy5eoZhW1RN/fiZfsPp6SnZcU8YhsRhhG3bFFmGwRCIa5mC//zv/OgvLAe+FjyBrlOkx8G515QVdZWzPHlKURSAQRBErFcbTGNYREmYUJb1UAdKyZvX1zx75wkPDzcYhqCqKupm2NyeXXzEJ5/8GaZ1TtvVTKcL7u9WNLWiLo/EoxA/9Ng9VLRSDze00lRlQRx7SN2wP+4YTc7p1ZH5bMQxrXE8F9f38ISma2pur99yfnmFZRnUVYfxGEjx5RFea02pa3ph4WiNUB3/qOjpnh/4G+/GfBTsEKLC1JpeQu+C1UgwTRBiMLiYJkgDhMAwTYTuv8q500JjWCELE6qmodA2f7g2+L1XKz5zzxmbLlZdUNDh0FFnBebYHRqyrSAJE/xwgH58/PEF86WDmcxYLBMmQcQme8uLl18ghMaPHaq6o2lLHNdHoTEdk061JJMxXV09EngFoe+y3x9xXBut5EAoMgf34GwypleSze6IFgauE9C0OZYt2G12WJZDV0nCyMMyh/6N7GB5+oQqrYYmWWDz2fMvyLKU995/B9lLNrsts/kS1+soqwzhguFA09X44Zi8KDFtj8jzqY81fd/TVDWebxFFEUkSgqE5HA7U1UAVmk7e4eHunk61HHdHttst8/kJeXWga6d4vkeW77k4e5/9bsNq/ZaiMWjbFtu2uX67pcwV716dMR7Pabp2WOyeTZqnoMCwbKqiZTqdkqYHbq7viEIT2XY0VcXFxQVKKc6vzgfrsWNQ1EeC0KLXNVoL0jRHyp66HPIxLWEwmUzwkq85WUh2Ctlp2kaSRB7jSYLsGpq6JIon5FmBa3scdkeiKKJpOqpy4P2XRQ7a4Pr6mvv1LWdnpyyWM27vb/B9n/Vux2gyJwinw/FdCFzXJU9TFstB5pokCd2oxfV98vrIcrRguZzTNoqLy1Nev/qc+/s3/NI3foW20WRZxWq1YfmtGYFjE0ceveywDEFbV5iWg2kYKNU/yoqHqqs1LegafMumF1CMZvyTLOPwsx1/+5c94qbGE9BZA4jE6jX0Em0MCDbVaTSDuq5TCkyNwKRHDLHbbUFVWaRuzOfK5e/89Jr+9BnxaIboO7TUKIaSxrNhXxVYpsP56ZTICxGRw+5ww8fffMbyfMy22aNpOR7WpHmF43hI1VGkR8q2JkwCOtnx8tUbRqN48BTIlqdPnrA/btDKQClNWVQYRodj+4ReQKslruUim+HUlhWKKPDp9SCYKlOJoS2m4xnpPsexfYq0ocwLbNvGsizqugJH05Uti7MlSvSstwcMexiX1t2ay/NzsixDyY4g8pFKUzU1XjxBd5ooHHO4f00cxAhhkhUVWtnsds1wgpOS9JgSBAGObZGmFfv9lige8ezZM5JkRFRFTEZjnr/4lOXJDNtWKNViCIf7uw2LxZLVdocXJISxS68lqu+4uLhgu92S5iWuNcSpKSUxseAxk8DzPAyrospSTEvQdYNP4ub29WCbznMcx2E5n9MDVmuzXq+5W+04Pz8nnJ9gWRZaa16/ufmF6+9rUQ4Eia3/yu+cEwc277/3jM36AcuyiOIxb9/c8eTJJU1bgaxJswOqDUniMV3XDTNfz2Y8HlE0BzrVMpkOiKl4FPHq8y1aD2WDa1s0bc6Ty1Mm0zHr1Y7d7jCYkRyH09MlD+kGt4ZJMEWYguv1a3wvwjF8hDR4e/2A4y158/qGk5MRv/bxR/zzP/0Rb+/umZ2cI3ubwz6jbSVp3tBJiUKjBZi9gd2baAtMb4hCU2rIJTjrtnzsW/zG6ZTfTBoCUREoibDFcGwETCEeR4NDlFgNSNOhsqY0SvCzFP67mx0bK6GyElQ0wgsUhihZnpzz6vUti9GYTz75KZPlGX5cESYhaIvtwz3f/e4HaFoOhx3LszmmpWi6CqENLNPHDySz8Yi+F9zcrambhvnZgkrCbrcjjEOKYodpmrjYTCYz8kOBgUnbDo0227VAaLIyG2LGvYCqrQlCC6k6DDEYfZJkTF0VjJIJYRzRti0PuzvKKmWcTGirjsCP2O4zGqWwHJtjluKGAZZpsN1uOZ0t0arHsDrqpmO+XGBZDkJp+rYjsH3K7RFDWFRVQ8/AKdzu8uGkECa0XU3dFI9ZiD7zxQn39w+cnp9S18NpNB6N6LpB+7DaXePYAdPZOT/55BPmiwlxEuJYIZ7nUR3XtE3PyckpdSW5u14NoapakBYpQgwMjflyguc7FGpPEgYEroPnWnRNheN7NE01MDjMwUuyXq/pm54oiijKkigaTsujx/9bVTb8/f/21de3HHBdZ8B3ZweOx+PQne172kaS5yU3N3dMZwn73Yquazjsa7KiIj+mPHnyhKIpSIuc+ckY1Q/Glc1uTVVVNF3L2cklu+1zDGFxeXmJUg3X19e4ToDAJPAj7u5vaNuaxlJ84/Qp5b7EMAyi2KNtOprOYhpPHsdSCVpfgzEIXEajhO0xpcxywvHyq1xC0zSRSg1yYN3jdpLc8jDQ2FqipCToDXzD4mV8ztu24s3LB8YJXM7gzAbbtNHGgLgCgdmbKC1RFmjDoMPiT352z5t7+KNO8OMPLwlOrogajZ2+5VBqiENiVdDKA7WMOJmMGT89J93+kCiJ2RwKFu/MqPSRJHCJxBLXjVFdSuCF7LKCUIBtm6TZAdsKCNyAeBRxe/OWs2cf03ZbnE5SlCVFURBaJlEUMJlGVEVNJwfgiGEJVN/gK5MgsDANTbnfYRlTxuMJZZljO+YgrHEiPH9AdR+LHaZn8fSdD7ClhsTmk59+wXZzJFnM8EIfJVJMt2e72+AGNvt0y3y6IA4Tivs7uk6R5we07HANC91KdK+QaiBAOW6A6ffEyZztJqUsa0yrZzwe0zQVJ8szHMdFYPLJJ5/w3nvvcH4xYb9VdBJ818c2x2RpzjF9yZMnT3h4eAAT8mxHFCx45zKhLFr2+z1t0w9iIDXkajqmgzY0lgWy68i6Dh1YROGIIktBKybTMaYpSLMtli2YxmPaqubp1ZJVWuH5PtIdXKzeyOf56xdEUcRsMv+F6+9rsQmormMUJ4SOT5q2xGMD2UFVSxzXxXYN9ocDkohwtMR2BTg9WX3gYXuP5YcgoFMttw8rTFwuT895/fYFi9mcquxIDxmea5LuM06WZ5TFCiceRkS9ErjemKyoeO/qQ67vbxjFHsI0qVaglGA+idjsb0nGBlLeEHgdq5sHPpu8pWhcxuNz1vtbogjmCxepBC+eZ2ihH5/gNqXdYRnV8KYbgWNaKEtQGYJINZimyVt/zn/Ygrky+Y4JT0cRM99kbElGYsB6NVrzWZbzpmi4reB55WHMPELtcelFeKrGm3h8ujcQvSB0Jfv0OVfvzbh5u+Gbv/FtbDvnzducD5KE7X7Fu08+YHV/i/JNbKumkw3CNLH9iKnlEHgmtw/XPHv2Lr02GY00VVEThVOMuuQk8TkWJX4wx/QT+mOG4Tvc73cE/ohCrjC6GkdZJN4S27EoypxVekdsexzqI31lYBiCtpG89877FHlO1xUcDjuiUYSpNU6j2DUm7XGH7dkEMw/Tr6m1pKVAtA5eaxJ4Ccq3kcIkk0e00ZEet5wsTvHcYXxZ1SWz5RSjN8gOObKpadIG1eckYch8EjCdJtiOhakqtqsbkmTMu1cnRIGibzJe/nQHlk2PIpokeF6A58bs93tGUYxnO1zf3DGaBbhBxaEw8QObrKrp6PjgW09Y3+05HgriqUFRVPTKYbdpaFXKKAnY9HvG44RedWzWR/quJY5GTKIx1S5jOpnQNA1zeu7f3nJ6foIWgu0h43Q+wzAc0PYvXH9fi3JgtvD19/7lGfPJgsMhxXJqPM/DdSJ2+zVB4FBVDUl0wnq9wRQWyui4OjtlfbfCG09Y39+TjE1Ozi/oKoltQl7luOaSh4cblFJcnF1yOG6ZTONBc9/1pI+W0SCIqcoG+o6ryyWql2weViTBCXlaDBHYI5eqKijqhtCf87Offk5xMDi/vEJJc/Cxuznf/7Uf8A/+5z/i0y9eY5r2YG/tWwTOVyBS4KucPsP4csQ3iKO0fkwctiqQYPQdpu4xhYchhgy9ZBFStQ1BOCIKR4N+wGrBKlFGwbe/8xF36wOb7WFIUQ7lwNo3HLquIgymHNMd43GCUh1Nk+HZFmHkM05GAKy3G8bT4QmyO2xoZc7V0/eGhF8UqusJnJhXL16ilGKxPOPtZovlCIqmJXQsfNNnMTulq480Mufs7IwXr1b4noempW86DLOnExonCKmrCl21WIaNY5t4gc8uHWbzkedDryk6E0drTNdiX6YIS1F1LR09Ey/CUhbbY8rqeOBkucQ0DVRX0CvFdLLEtXzu7m/RWmELg6ZoicMxru3g2wZaWKANsixDo0jTA2EYk8STwZB0zHACi6oqoXORRsUxS+mUie0HZNmRKq8wjYC71R3f/PaHvHzzBX4Y41jGMF6uO66uLpBdhWoEu92RMPRI05Sy6Dg5OcNxBV3XUVbpAEGdJnRNTRQHtFWJ7nv6frCrh76PFDbHfIgfq5qK5eKMtu1BGTw8bPnhH2ZfX7EQDDnqh8PhqxrHNO1HOmyERuF5Dk3TMpvNmM8mHI47yrIkSRIi3x8WtusgDAPbsWjaFst1oBdUdU4QuhRVjuPZWI6BFoqm6XAcj9E4pmlKHNciGXmsd+tHL0CP53kEQYBhGFRtw2g6GaKeVcnyZEIv5BAU2XVAT37s+LMff8poFGNZLjDoICzrzzeALwm4Xwp/vtyIvxICAcI0qIVF6wa0fkIVJhxdn73jUQQx7smScDHFiTxOz2ZAPWwOkxHChE8+/RGahiBwKYqCNGuGFJ+mwrTAsjVdV+G4Bn5gsVhMsB0DxzF5WN2y2T7QdS1ZfiCKfWzHZHm25JDukb2kqIc6+fr6De+++y5hNPAeDustZZrjBDEP90NzcL/a8uTyGU3d8eLNW+wwJBgnQ4ryaEpdD7Ja27KGTr3saNphYTXtkPfYtsO41bAGJWYcx3iew3I+xRQGlmEQuIP607ZNQs9lPpsR+QHpscI0bVzPYzabcfewwnbcgWXgupieQzhKwFRYgUHZ5KTVkV22R6II4gAv9CibmtuHe8qm5pjmrDdb3rx9RZY2tA3YlsvD7T1COfheQhiGTJIJb17dMp9eYGjzkV/R0rYtn3/+KWmaczxm9H1PryWe7yAMTVWnSFU/xpkLsizjsD+SlxXrzY66k9iOR1115EXNw3rHISs55jVp2RCNp1i+z3x5gh8GXF0+/YVr72uxCah+wDV/GSCxWCy+YsPbtonj2EwmE8Iw+EqLP51OKcsS6AdkeBwyn89ZrVY03ZDg4icR0HN1dcGHH76P61n4oYfsFavNmiCMHoGTNUHoEEU+tufgui5S9ViOx/XtAJI8OR/05VVVUVQlGDA/GfPBx0959eoNYTgQfaui58Xz64E+0xt0XYPWmrYZFvjPf30lJnq80UIIFPqrr0E34GBgYWA+hmxo/MDCcTXvfnTF/HzMOrvHDMAJQm4fHkCY+OGIzSOvv647tuuST3/2hv0uR+DQtvUwhZENhgnCGHINpJTEcUzTDGaWOI65ubnBsC2kUj9nSkkJ44CT0wV1UyJli22ZfOeb32AcRRR5icCmrhvWDxuyLBtGfkrTqhbZK3oNWg9/Mw4TXHcgPn+56dZ1TdMMmPT5fEnVDItiSNTS+L5PWZacn5xiYQxNQK3pe0knG1CKpqrx/QFSonrJ8+efD3FqlkEYB+yyPZPZjKxIWV7MqZoCJSTxKGJ5OsPxbLwwICszbNcmSmJc30MLsBybIA4whI/rDJqWwAtxLI+26jnudywWCxzDJ4mm9N0AUek7Tdd1uI7DdjN4KgzDII5jknjM+fkZdZOjVEer5BBOa7s0sqOuGsqyJgrHFFVH0/YYposwHNbbI1lR4HoBTSvx/JBOSSzXYbP7xYrBr0VPwDAsJpMJ6KGGtV1JXbf0PbSNJozG7LZH/HCEsCVN2zOZjTms97iBj2EbGKJHtRrLcgflVhyQFjm6Fmx2a0xL4EcWrexpFcyWFxyOBzSKP/vJ53znlz7GD6FuBG/ePnB+fomhBZ4naZojz1/dYpoWbSMHaedomLuOZg3f+O6cLz77KaaImM8nlGXJZrVjceqTpYK8EINuHPV/ePJ/ySUQQgyQkMfrljXcFqs3hiw7BQjohaRHYweCql2R1g3f+/Vf4Y/++f8ySJY7yenZAiEUYeTS6w27/S0ffHjFi9dviJII3XTcvHnAcuE73/k29w+3KGUj6MnzBlkPqTXCNFHSQkmDLK3xepu2U3i+QCtNW0n6QONHPnmesbxYsl3tsK2Q+Sig2bfUYcTD+oFRnFCpGsu1uFyOuXvYsl3luLbJKPRJxiMe7tc0TYM2Ba7jYBsmk8mEtCyYT6dUeYHuBev0gOnElHXF9rgmrTJsAaMgJm1KXr9+jWkYTKYzsl2GIXtapbDGMbHrkqcZruUQxz5llROPQ9zIoNXQihQrtEjCgP0xY7c7Mp6MGEUh2WNKkmwUUZQQJQmu71McU1ar1/TaZDY7wRSaPF/huhau9mnqnJPTgOtXX+D7Mb7j0mib0+kMqSpE29OWFn5g8fKLGwxhEYQetuWTZy1pmhEEPsf8yOnpknA0wnMcOqlZb/YEnk8rNZvNFsdzodekacq77z3j9u4NgR8NLlFP/eL19//t8v5/9tL9EAzR9z3HdI9t23ieh+cFGIaDkj2j6Yz9YUtZFkOGXaeIRgl5VmJaBofjBkNbxGGCbX95fLQwzSEQo2kleZXTo1BKU9ctqu8oioJn77yHRtF2FVIqlqdnFPkwgdBaYzoGtiO4urpkPp8znc45pCmrzQbTklw9WfLRx8/Iiz1aK+I4xvcdLi/O8DxnMHo8vr5c+MAjjnxgJP58qdD3PV3XIVU9PA20RmKgcOmFx+FQ4vs+Ty8v+OzzT5jNfCyrQ4qUtFjTm5L1dsN6s8JxTdJsi9Q1o7E3GHmmJ+jeoG0lnhtQ1x2b9YH0WIG2uX/YoXsL2Qn+5I//jCyv2G5SdG+hlcHd3QOWYeNaLl2rOOQZP/zxj0imE+7u7nj16hVPT05wDHOgAKuGtMpQWnLY73jv6pxpGHK6XOBGQ4agaRh0dYMlDFzfwzRNTMtCCwYMeV1TtQ09ms1mh9I913e3eF4A2sAwLBzLxXVdDMOgqCtOFksuzy+RskTJFtk2jMcJUWhhGT0mg4aj7hqcwESYgzlIIYiiBMMyadqO9W5LXQ+nkPFsSt223NzcoPrBw/Ds3fdYLE4wDQvLshiPx7iuC0iElpimIIlD6DVVXjGKRuy3B477I12lqfOW/FhQ5y1FUVNXkl72GFiEQYzvB4yiMbLp6eqOLC347LMvEMKkabqvThFnZydMxyOuLs95/IiR5UfqKuMXI0G/JpuAlArPG8gnpmGzXu3Ybna0jSSKIg6HlOu3twR+xHK5pFMSLaCqW5pOUlUF4/EYx3LRnQDVM5lMiMMEw4Q0TR+NPg53qzWtkhRVjVSaMI6/irT68oncNh1V06KUZrPbDn7s05OvatesKGiahiAIuH94i9aa0/MJ3/rOwD7ouo7JNCY9lpydnmA5CqG7r8xEfd9/dfz/ckOAP+8JGIbxKHrpUbp/JBUN8tEvj8i/9r1fGRpqTYnvWgS+wfxkjLY0nVI4ro/jeWw2K/739s4mxo2zDMDPO3/2zHh2be96N7tOmp8SkQapgghFRaAcgfQSyqmn9oDEpZXgwCGol94QSHBAICQQlQpC9AKIXpBABQkuFApK07Qhm26z+dusk3U2/p2xPTMfh5lNV+kuTZSWsbXzSNaMP8/h+fR6Xn/z/fhrNBocPXqEYDjALpa4s9FjcXEvq9cbRJECpQE6xaJDGMKhg5/EsadBmZhGAbvoYVs2M5VZbly7gcQ6h/YfRENjbe0mG+02c4t7aXV8/GBEtxtwbeUKt281KZfLhHGEU3LxPI84Tv5GzjQ01lZXGcXJUGq1XKFarVIsWrhuMiW83W7jeR7VahVd0/B9n/4goNcPWL50Cd2wiFD4/iCZjy8GtT3zSfJQMZevrgBQX5jFNE103aRcmWbP/CyOXWDaK1Ms2KAJYTwiDENCBd1OH0TDMCw2NlqEUYxuWlQqFaIoxA96uCUbwzBwPZdbjS5+P2Y0HKKLwaCv6LYCdANct8DQD1BhhKHF9DptjM0dgtPpxEE/wNQsNE2nVq3hFGyINHRlMgwiiqaLho5lFRgMRun/JE4ly58dO3l8DUPW1tYQXadYLNJsNolG4d0dsw1z51t9LEYHqjVHnTi5gGU6DPsRpiW4rgOApod0/T4bt3vYjosipOiU8MMeruXRvdNmuqIzNzNFsFFk+coKphlRqZXpDEZ4VpGiW0jGZcMQ15tmfX2ddrvL/vohqpUp2nfWmasl6+GDoU95qs6l5atYug1xgGNb2I6J53kMhyGtVgvHKRIEAQXxGI46ySSNXkCvrdFYu0PgR1SmqzSbt/jqUye5sPQOf/7b8t1f/s3+gM1kYKSdYlHaaojjOBlVCJOeeMOASsXFMDSOf+5xmsESsR4S6jC1p0bRLdK8uopbmqLnD7HEZH56GrtQYGXlMqWZGstXVqiXHuHiO8vU6h5GIZl/oFREwbIYDSO6rR71xf20Wi1CFVJ/ZIFms4ldsGh1mxw58hiiNG6vXk76bYollhvX6fp9olAn7kYc2LuANowQy+Xi8hJetUR930zSyRuHRINkiqtgYk/rKHRKmkkMDLWk/mEwQESwvRKdVpuS5RBqsLTyHp0O1MoOmpncvI8/+ikajZu0gx6hlrSeCnaRzp0B9dkFvHJMY72HP/BR8ZADi3Nc/M8l9i4epotioHoohoy6A2oz8zQaLfx+RD8ICMMRcTSgNjOLjk44jIjTLceCICAIezjmDO2WTzQKmV8o0+l0cByHURQwNW0TqxF+TzE/t49WZ43WbcUwGGBaOs1GshWeaBGPHf0EG602umbgB/1kcxc/TjosDQM/6GLZFlE0QjdA1yWJnySPknbZS5fi95L1E76PazuUHIf52hw/+s5r244OjEUSEJFbQA9Yz9rlIZhlsv1h8usw6f7w8dZhv1LqA8sJxyIJAIjIG9tlqUlh0v1h8usw6f6QTR3Gok8gJycnO/IkkJOzyxmnJPDTrAUekkn3h8mvw6T7QwZ1GJs+gZycnGwYp5ZATk5OBmSeBETkyyJyQUTeFZHTWfvcLyKyIiJvicgZEXkjLauKyJ9E5GJ6rGTtuRXdWApsAAACtElEQVQReUlEborIuS1l2zpLwg/TuJwVkWPZmd913c7/RRG5nsbhjIg8ueWzb6f+F0TkS9lYv4+I7BORv4jIeRF5W0S+kZZnG4PNSStZvAAdWAYOARbwJnA0S6cHcF8BZu8p+x5wOj0/DXw3a897/E4Ax4BzH+ZMsp/kH0i2oHsCeH1M/V8EvrXNtUfT71MBOJh+z/SM/ReAY+m5ByylnpnGIOuWwHHgXaXUe0qpIfAKcCpjp4fhFPByev4y8JUMXT6AUuqvwO17indyPgX8QiX8HSinW9Bnxg7+O3EKeEUpNVBKXSLZIPf4xyZ3Hyilbiil/p2ed4DzQJ2MY5B1EqgDV7e8v5aWTQIK+KOI/EtEvp6Wzat0G/b0OJeZ3f2zk/Mkxeb5tLn80pZHsLH2F5EDwGeA18k4Blknge3WNk3KcMXnlVLHgJPAcyJyImuhj5hJic1PgEeBTwM3gO+n5WPrLyIl4DfAN5VS7f916TZlH3kdsk4C14B9W97vBVYzcnkglFKr6fEm8DuSpmZjs7mWHm9mZ3jf7OQ8EbFRSjWUUpFSKgZ+xvtN/rH0FxGTJAH8Sin127Q40xhknQT+CRwWkYMiYgFPA69m7PShiIgrIt7mOfBF4ByJ+7PpZc8Cv8/G8IHYyflV4Jm0h/oJoLXZZB0n7nlGfookDpD4Py0iBRE5CBwG/vH/9tuKJGvJfw6cV0r9YMtH2cYgy97SLT2gSyS9ty9k7XOfzodIep7fBN7e9AZmgNeAi+mxmrXrPd6/Jmkyj0h+Zb62kzNJU/THaVzeAj47pv6/TP3OpjfNwpbrX0j9LwAnx8D/CyTN+bPAmfT1ZNYxyGcM5uTscrJ+HMjJycmYPAnk5Oxy8iSQk7PLyZNATs4uJ08COTm7nDwJ5OTscvIkkJOzy8mTQE7OLue/Ib1KD7f8U/gAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "target_image_name = 'koala.jpg'\n",
+    "init_image_name = 'tractor.jpg'\n",
+    "for image_path in imagenet_stubs.get_image_paths():\n",
+    "    if image_path.endswith(target_image_name):\n",
+    "        target_image = image.load_img(image_path, target_size=(224, 224))\n",
+    "        target_image = image.img_to_array(target_image)\n",
+    "    if image_path.endswith(init_image_name):\n",
+    "        init_image = image.load_img(image_path, target_size=(224, 224))\n",
+    "        init_image = image.img_to_array(init_image)\n",
+    "\n",
+    "print(\"Target image is: \", np.argmax(classifier.predict(np.array([target_image[..., ::-1]]))[0]))\n",
+    "plt.imshow(target_image.astype(np.uint))\n",
+    "plt.show()\n",
+    "print(\"Init image is: \", np.argmax(classifier.predict(np.array([init_image[..., ::-1]]))[0]))\n",
+    "plt.imshow(init_image.astype(np.uint))\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Boundary Untargeted Attack"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Boundary attack: 100%|██████████| 1/1 [00:00<00:00, 64.39it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 0. L2 error 42823.133 and class label 852.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQEAAAD8CAYAAAB3lxGOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nOzbR1BW377u+y85J8koKAqIgqKiYkIUcwCzomJOmHNARTEnMAJGDJgTZkUMgIoZUAREEFEySBRRRPB3Grez77373zh1zqq1qrafqtmYb416x+w8T41RY04FEeGvv/76n0vx3/0Af/3117/X3xL466//4f6WwF9//Q/3twT++ut/uL8l8Ndf/8P9LYG//vof7l9WAgoKCn0VFBQ+KCgofFRQUFjxr5rnr7/++j+j8K94T0BBQUEJSAN6ATnAK2C0iKT8X5/sr7/++j/yr1oJtAc+isgnEakBzgGD/kVz/fXXX/8HlP9F/1sfyP4v9zmAyz8+RD1FaajfnLIfmWjW0+Wr1KCba4Tuz0w0TRtTW1pBeT2htk4digSNxqr8+lWEsuZPFIuaoKtXTHaVGcaWlVQnCbpaPyiz1KI6rwqjH99Q19JDLFX5UquIbpUOGmrZfFfWpKjmO4pFTaln+g2z0hy+mjRDTTKoSP9B4+Y2KBRW8r3IBBVTUPmeyM8/WoiqBpU6tegpKJKRa0QzLUUURIGselmUqQuWvzWpU1amulaLMh1FbAvy+KTeEBMDZb59zMDY9Be/v/0ip1ILYw0zNBU+k6dthU5VAXXmJtTLKqC6gRbm1Q34pqJIXXYSYt2SnIoMzPPV+KlaTaVWNY4/bBCrHD4mN8TcXFAz/EBZpgJoNearpGNapodiCyXUvxqgaFRO0Zcf1Kc++XVFVOmbY6WcSnp+PeoZKlNtoEXtx++o12pj3OI7mpV15Kvp8/PPV3TKG1FQr5IGXz7zxdYe888F6Jer8V3BjHzdJP5oK2Gvb01pmQLqtR9QMTRG7dcf3pf/QslEC3KVUFUtQeWrCpV6Ruj8zOSnvTXqBZX8rtFF8fsXjIxryTRogkZpIU1N/5CabIUFP6hWq8LEQoPqT2UUNLXFQP0rlfEFlGubot1EG8WiT9QWNMdQJZ3fGjqIxi+Uv6lRpFuF/DHGorQGHavvFGnWUF76G/lmhabZN9CppmFpYxKyk7CzaEXtz++oNsikPNcUI8tyqr98R6vclg+tlDHLyyPP4jcaSVoYNPzD9x+aqKqroJ1di0Z9ZRKztGig9JOvWnnU6phgqJWP0XsQKz1+G2mjlaHJV5VC9HTNyCMN3RILiqzyME6t46ulMnV59lha1VChWYFhfi3fjX+S/7USRSVjWpSVUdtYh3SF+ijk5qBuWoNKiRq66pUU/NTFTF2Xz4rVaOhqoKsgGBbl8tZUkZaVuhSpqmH+u5icn41Q1PmCpoKQ+eFnsYgY///y9y8oAACF/+a3/9e+Q0FBYTowHaC+oiVL1WDU3rM8bJeCw/I33OwTSajhdYZvaErpmkZ8W3mPrIe/UTy6hG93z+Edbcn6vo4kHbZgPp1RcUxg6ddcNhj3w+PTGVa3+UqW3xsqCioJ1X/GpUlbOFs0A5Py/dhPeU7znNmMLn9G+CEjOoQeZl3L+2xYsIkvTfRpYNGa08uM6K7Xmi8nYnD840Gg9iOi1R/SoucfjNc9R79sJZ+S9dhUfJNex7pyyL8/za0tWdVvCisdRxFsY8kg7yu8b+VFblQQQ2oKeO36nu1uhvzoPoNgowPMSFnOSIuJbC7eRcT0OEy/1GDvOZdFShdptmA/bjrD2NUwG/+Pp+hQvYioiCgUjIVf68dw78gZrh98wL39f5gwKocegVEkDSnhzk89VJ8t5mC0L8eLdvM+0pLJl0qpmTydNOttjI5/yJGOw2hT2oWogYG0PTweFa1DbFjWhqNq+kyK1mWp2hBK7HpTvGgSa0x8WLNtP4ucLjDiYW9mWNjhc2sCs7yWkFhkScE3H+7uGYzZnzPk78si0OM6gwoNud/agWl93MiN74Ta2G64n7qBY6vGzKt3jUt3LNn01Iii/Gv02HaVdouNiXu7CvWcChTsu3Gj11wq6z9lYzMN7jVZzsgO4dQ1d8aqRQSOPy+j3GMLk7YsoXPoYz7dMWWffS16mtMJ290dg5GhuO/5TcldJTyrL/HgWR6x9wPZEL2CGZF3GPn7IzamHakxa0nU6EkcbO5CvYJtqPTcT0XXOO6/0aRgaSxjTxXR5N0UwgakIBPu0ft4BQFxTZl1YQK7Ot9iuIIKPQ9VMLN1GV3MppIa84gNG+No+buEgVFWXB0+me6axzkbNIcf47wZ5WZLS4OrRBY2pPSqPR0b9aCt6XrWtrzEd4XFyM7TnFofRocHP/mSVMLAl4dRCWrL6Va6nMrYTuDRjbTrksK5yaX0LXJArygT7Y8VbI9JJSk8nxSj+yi9/omtkgMd2ryi8v4fZun054JWGae6HqOD5TwY/PPLfxfWf1UJ5ACW/+W+AZD3XweIyCHgEIBio1YyvkUackQf33pHadA9kD0XvQnvNYjodH+mtMmg5mohbdt85FD4UA5eUmOsSScM5wVR6m5GvR+JLDJeRGGUHUHbV3PrQwdyzvlRPtSMG15z6BrZjc6/P+G9MZrrK29TEBzC3RsTmBmSwmrDQyQkhzDI6xCXtL/T8Y8fEQbZbLF9TdTSA5ipFvN7hiGXVpnTaXspw6yaMU9pISN/1zHozV2yll2mZZgwK+grbV6e4/HSJkwfoUzhxXKuJNhQvHsDpZmHqH52kFVm7+i4fh/n80M5M6AP3p+vULUylsC7z2hYY4WKiz++v7fhaByEdwddxr+OJTvzLm87J9PB7BGP2/qi/sWfnIQcileO4qDDUzoHmqDRfx8t7/biYWglT4+3ooP6SHT0R6HVuJzxnQNwNCmhZRcYP/srprXXmNhegW1duvP1gxmXdwYzvq0P3XOduD8+mrwBzdg12pkmJ48ybbQ5m/Z3Y4JpLcmtbViT4sW41qXkVKQxMakCM0NLHFc8p2TvUbZdaE/YMkdebjrEl3FzqbdRk979/7Bu6HpqrZcQvT6eqSN2UG/AYYZa/uJnf30eJjQk6tFCzqfmUDRpN7UzNfl4y4l38bPwGrgAxxODWTxShYrhPiiMGcDAUzO5k1bN5DECnVbS0z0Fj8zVZOmn0SmthHehR7mV4oRWsxKmrV7Lms8bUe87hG0z0vBdt5UXAaeZttmAi6dP4Wt/gtQqDT5GBWIa0pV5VTpMfWNHWMAnpqdYsmH8Tdb8Dmb07dZ4Rnjw/XwKRXv06eLUirHJ6+lurEHyyS9MTgrh95CLDO39ABn0koCNRrw6cRU5d4VD5R6cMOrI4fJOBLuW4nFtNNOCL9CpxJHmqiUcjzPC5PxY+p5ZiHZqPpV5Plz4bo5r2g4u2C/lfZQ9L1x2UbDxEG3Tp3HjkSJx/n84n/MOFWMLnh9vy/aZSUQf9mHIkToKL02iX4wJvVzSGHRiOmphV1FzNUWtxJUNfb+y9J/SKiL/1y/+n3L5BFgDqsBbwOGfxuvYGYlOtLHkh7nLkZ2+kjXtirxqt1Bu5/qKm3+m3ByXI/61S8TG95u81SgQt3cfpfOye7LNvVoONfaVYReNxOZmYzn3IEieD1gm6ZemiFL/iWK/rkY0D12Qhn7NxUdxrpy5fUU2ta6Udr1bSweXH7JzR6GUuNhIoGtf6db4ueSO2yg9B/+Rd6c8pZVRrNx3N5VBD4eIy5qWUqqcL/PqpcuHA1tk+qlg+VPtKzHDtki5d4iMUbGWhX0d5aGtr6yw2SUHDbdJUaCxJGUtk9MfyqVtqJZcSUmS5rNGypQOo2XTq07SwL++6M/fIgc26Mq18YPl/u84Wd0lXYpOv5bCacHifilchq6KkYeH3EWvfo08cMyTW8b7ZI/nUYmdOEhOxUeJc+tGkjR5rQS8/iTfrAwlLktX5un6yOxhYWLo5SOdb26RMQ09ZcWO4ZJqHy+n6ibLuOFP5crc1/JldSsJ0n4oQz/Ey8nURFENmSONbm0R+8cmsvHUdPkxVUWGH3OQ9J9H5GCfEEkv1pMd49/LnBevJTtwmETrL5e4AHOpantT9I6/kR2rdGWVRX253ddfPNwqpWfAA3Gcd1+eR9aXaGtn+TYnTbY+qJUuj87LQf0AMfldJH4jImX0wvYyOfO1jKyLF6+gSHny3lPOFaWJ4oK7cjklR371/SDGGkvk/kwlsRw3Q44M0RSjU65iH6MonQKHy90h6hIXESX3HT0lfIyujL/8SxJf1Em50QEJ3n9DLj1+ItrVK0U/sZe0HLNOetg0EAfzTtLqahcp6NpKehfZiUGusexds1dSG9lJmGsLWdNtsjyvGynq5hlyMvGw1D1eLjUv+slui+vyqeEx2XnFT3y/BsizX7niHR4nccZTZJCXnkS89pYh0aflWtwSGRb3W5b+dpHJij1kY9ZxiXawFc3gJhJtPV9MLf/Ihmn9pO3uEvGKtJZB8adF730TUbO7Kcv1W8ihP1flT/YuUTHtLRP8b8nijEo52G++fPkaJqrHKsUkZ78Yp6pLqMkzSThWKBq1StJ//E0ZOMpPerNGEv16it9Lfck5vFMWfNksd+3eiMXi6QK8/u/y9y9ZCYhIrYKCwhzgLqAEHBWR5H8ab6f5g05hsTx/WoWdaj3aWqvi2GovJjqHiLk6js6LHqFTeIrXz/phnP6AA79uMMJqF/tVX3J0xlrmzfbm/PYQNHbaY7zoB5nnB+B48iqP71awvGY8haax5O7+hObhz3iMvoHvQEfaDcmhqWMjzGwHkJp9l963jxFcWopjj0MMXtCYIwN38sn1IGk2E6hdvZ8LBcUkn+9IwzGvMNF24eXNHqCrQnT2ZWJbrGHDwYu8uJvNnMjBRGzIxG/ULCzHpmPVXoV3dzrirzCdLk9rmFn0ltiVLTE+fIdTGmXMWXeG0yab6fuyHbNL1nFz32KiQh2IG9OLqCGXWaY0Ct0+w6mIF/oFzuNwm0aULWnNPN8GvFpeTa8N13laZIei5RgmrVTExtKfC/GtaX9yBlcT7BnS7xkHg1LJqt7IxQ1vKLBLY9izrez9dIlt3Z8Qud+R5+k7eXPSlL15euwyU8O3zozVCn+4Fp3BipBcrI4o8SJOiQuZdqR7lPPuhT+dMp2QtdYkDe/PmvnWrBl4gjt+A9BO8WKHy3YeeyRxZVQW94d6kWaei1eVLw8N1vOi2RmiH8QRFOXMm9IgVJOyuBuxn+Hl66n22UlRz900en2cM7e3YrjkFMcKEiiovMGzxat5YuLOsY5jsciayrtYNWZfecJQpXjqsp2xycth05lbxPn/pFClLW47x2Kg70rD/f1QDUmj/4piTLwmYNTAga1tg9BsG8Ju9ec0L/jBtajhOHvMpIm6F4U3a9gWXIzHwkn8aruQhFoltGqUmZS1B03VYXx7v4Su0YPolfIM0++9UTh+mUXPzYi/rIvt6DR6tb9FVFgC01Q2sClYm86O6WTHfuP9j5P0HPqORyXObMt9QVG/EYyqvwb1OZPQjl9PjmYiG8ZX0vnaLZz8Chh/PIAN2z+x2MiXUdUB/LjVi2VuLuxe/5gJ2SuZOOAiCQqd0ZyXTyeXYkyPLuZHSQLDwuqIPFxAarcMgvy60UZzNgH2jahaXwmB/xzYf/ulo91IWroclN9zasW2farkmF+UVkePyd4GNWI1zll2/Rgo+neuSrfdnhIf1ESeLR4gZYrTZU+TTtL4/Uex3vJGerr6y4GRBpLwqaEERrYXgweKsig9RFybvpdhz41kgn2ADH6cKW03tZWuK+tJUscpstp4lfzw7SeLikpl7oeHsnlisfwojpG+Cqulf36k3BzUVNR/T5AkyxwxnfNYttpbilZCQxnxsEgaut8UF7aKa9FPWdCtozxa+16ym4yUYyNvyJ42C8UkMEGezd0gTnq7xN1/slwuNpfIuMPyKileopf7yfHO7STHS1EMzSxkV7fF0mpHtBS30RfPC+OkQfRNqTVYK6+uWUrs2yvSvtkj0TSukEjl4VIRP0TWafyQc0dmyT29eTJ4SowETtsq2q81xO+iufzp6C+HfpwVj6z3smqjiRS015MLQ+5L/Z3zxTCqQor62smR3ImyYPJFedXfTNoP05Ztu5uJ9sAgqRg1V+57rpXBWzWkwMpfRqmniL3PNmnZ87g4ebaWnaO9pLvnZrmT/F1KrerJPVcnmRb2TL55bJB2GtOkr/JU6ZzVS6x2bpInoQ9k+pUsGVz8Xvoe2CaFIWfkwfcAKXArkGLnLdLTWUt0Bs2TF20CpW/Yc4lOVJHJ6UUy8IiuzLfZKcQ8l7zNbrLwjpW0Kk+Vrb0cxXlMD4k16ywOe49Jm1028qDIUmKf6cuN1GS5fGywjG+yW3bMsJIeph6ycZ+B3O7ZXYblIwfjhkrhgllyIuas7LEcKe3amUrvjwfEuf0tKVpYJGGf6mTl0JYSmm0vz6fFyvFxIXJnwhmpvqcrO6ovycUVbcXL/52cqsqQ9t8jRatbsJyJnC7JBQ9krXaF9D4yTe6dLRQlM3M5meYsX4ZnitKML6LjYigPitvKwFfe0tLFRXb5XZV6w8bLgjsGcmFjO2nwLlBepY8U7QZrZLeLkXiVLZFPjSeLl1KeJK40EUe755Lc2kV2DciRss+h0vnpEdG4ZSn2rfuLw4CXstrssgy+YSwN63WTzgtq5Zz3TRnr1UR2/54qJ1NSxb4oS6KDFsmCj0dlSe7Hf1wJ/EveE/jf5dRUTYKfZ1JTsoSMcGXySn7ycWIc26pmMNWxN9ULh9KnSwU2ennoG97EIaYdRwar8PirG43e9iS9+TcstvtSHZ1EzetZTGl9gTQDI/pHNWJgq708fRHHiMlPaBTWhk22CYSHmpCu6co49VqmZbiyN8+BBKtiAiZfRmV2Lz6rqvN2WjFJplv5vKU/8cOeEf7Uj3MXApn4exJOmzRRsh1O6JFw1MLW4RaTSbfT+5DtGWR0uM6e4I0sOdeDcz5FRA8aw2cnI4JHh6I84CKHqwbhfegls9T3snF4PZR1FnG4txNr1uvw5bIlfS47MDRjM87+v2hxRZfTrxV49cmVNntCMX3ZHIOqaWxjFX6bd1GUYMbs6Bh+aKygaeFo/C9swGmTDefmXMQhcA11fQ9QFNKKhcdyeTi7AzFffzBXt5pN4yKxDc8gpuIJARNecOdsG8qua3BeMZLJrcxp030YZ7WHo+CSyVSHaSgVNEbZzIzHTX+yeIkRq9yHk3OlF4F/0om9rMyFPccZNnwi2ef8eL7rEYHDd6BxqAtHQmJJDcxk1aItLD/rRD2FNjh8OM/40UtYe0Bwb/yOpwa+aKTWkJFUn5lJuwj06MLYblEstnYlofsWSmNWoReaQpPpVhzUsePu7TJSrj9gxpTmFL58RLNSfRQiPLCb1Ji2Xpt4rLSUSWPScFoxE73kAOrfdmHO0hgUDHJwC01B0f4CbosacufHdxzHLSJTazNrbLphbDWMpNhZ3Gn3is2Z5rR8/pKkRYkc0unB9bv+uPtcpGlMANN+3KZpZDzfewRi/Rp2zAsn3mcJenYh9MoyZ7JJLzoavsVOL5xKoxf0+uOFfdADKhybs4VGlH1QpospBNWEouR0lHzNQ5Q11aTvwkqO7C3j+qMMij6s56SzMWv7pfD50CI2vpuCYcMyojMa8qnJLB5dBdE4SXlUNHW93hLUaSCaE89i0W4yRj3W0e/oWXreaU6DQ7Pot76UZ80Wsk2tMaOSy+NEpO3/N3//ESVg1Lq1qOUtJKvFCjre0KD1wJcYqIzBeHgyMYstmXQijsGbblNzzIJbyzpR7K3NgeqBuKo6YdvDjlOejYh1nYfTc3280wej/9OPAQcqcF9bhEO5ChM3apDkkM7s0kEMehnBWLNgmp84g1PGTTZZN2X+cKi3eSwzDIzYvdaJMbaJaM+vz/gABar292BozAN2LvlD27JrbEjUwP7uBmzDDJm/vIoBnKfPotHUN/tGG8sheK5swo2JIRxeYEP/Q9egtzsOn1vQasl8Alzd8EpowOTL5+jcwI7vC4oZ5dMNr1UqvLmow0i/vTS8DJdXdmN861CUf8zm6ZaJBDWto2jGFt6qnceifAW2acFYj0jGpN14OmSp8WjXArIH1WHW2pCIr+uIOmPCiND1BL4roXaNNwurDzB3bSOSJjagwe95TLfK4G6vZPK/b+dzZRJL7vzibhstFtdOpOhWD7aqBdDR8SuVx4eQen86C0ZGkNTciB0vI7C7sow2WwLYb1/MiBx9/AefZkxBbx5s3sCjRdUkpC+iVr+CeclzKNMey9URF0m+YIHBis588AjG6r4mf9YUkGTQn0NNVQjRLqKu02b294tg20I98m5UkN9akw7hS1jjVE72+FTmrVCD1Ll4pg7l6cwEyu50ZVFCBcemT6GizzkO7g3Bw1uZ9A227FXtzDwPX8ouKLM9Pg+PEXUsvv4Ix0WjOdduO5PmnePcXEWuGClS9v0uKrmreesdiYeHPSvOudD9uhARt45Z3sZs3zeaV14u7DHVotjaCKszuvgfKOP3mabom1eyssU+lu5YRzetvbgYlTHz0wG+zW6C2tEt5C6Kp4OpPQW3gvE7cocw02h+mR+hgeoU8h5OJ+fEeWZt9+CwT29WZ3Zk4+m1dIkdRtjghlhMyiNROZ6P1gqk2KZzfbMiPW1qsanSwHlzBXfnziRugTn7bf4QGbiYpiOfod23K43XxdHsxAqCYpQJ6T+PGa3Gc0lzIibr6/HwwAxeOCT8tyXwb98KiAgWek0lrW2V3B3aWNKH+Er2N0tRNVaWmMYFErEgTM60d5ExPc3EzeSXvFt4T3rP3iHBsStk3XFD+W00Tdx0h4jRalfZ1WaTdI4eKosfdpVVAeqy5PAh2W9zQJZMUJWq7W9E9d52+eS7S6zefJTxngPlbDiirJMl6yISJU+hRvS2OMvslCtiPmmBeC/cJ5X1fORbTBdJOJIrq6zNZP2GaxLj5iLPhhrIdvWFkn4hUDLsdsqOx+PEdpGNdB57Vx6NnC79O+eI8QJzOaQwRSYfHibbTSwl+d55mbnwuEx5PFgemcdLwOsrssdxs4yJmin6mYrytrpKCuqKJTF5rrzSVJKC7A1CKwMJNTsoCUdHydGdKvKsJEb0b6TKh3hH2dbWT5LPNJfiy0PkVQMXqQhyluOjLsil11tE3+2xNLHME+NyWylKCpJ2/RNk5uQlUvbws2icTJddyfNkxNvrEqj0Sdpn7pbXKubS98s2SfYZInEhe6WRwmwpmuUkLuO2ydMpT2T82Frx8qwvTxMeSmy/PaJ7z1aCmjSX68WLxaahl9T6hMgtw2iZ/ueD+I3SkRvf3GVnnzqZNGSVBLjqysCiVLlT/UU+TAkQx4kWYvjZRp6tHS3+O9rIya+Hpfmt83LL8rwU+zYSo5+uEv9us0w0nC0Z2p7SomahtNE6IkYrwsTVdL24dkuQMrVlkpw2WUJ1J4jdiWOyVGuYWL+Kk9UB/SS4KECUOlvJnfdzpKdTc/FTLZDfVxMkvPid9D7eTtJvKsqJfcVyfOkEadI0WPqPayjRzsPFc1uBPBgeKMktu8nbw35y9thisW1hIAHXT4vdtTZi9PWp7NqoKFnDbaTl8Uh5WNdEHu0/ImdGvhW3F1fF7mOObPhpIUtrx8vTE0aysPFqsfQ/I40olHbLXkhQUrooFb6S5V9vy7ucdLEeUiyfN5yRc8YnxOvwMRneqkLuqHyR1QnXpcDfTRROjpIxsWdkjY2hVLubi/KaydJd5aKs0ouWczHnxbblJ+keOVFSjy+UZxfeyf12MbLLrINo2E6VS5u6yxetyTL320JpeURVfJq+l+irsf+4Hfi3F4CI0MbKUe5VhMuEXnXSQEFDosuOib7lfanfr6HMs0qT1NbZ0iZ2iiR0cZBuhXFi/f2K+FdFS/OQHeIyzl4O5QaKw+fFYqS2Q050yBOPh+flxPc5YltiJdpBvnJ++jbxXd5IMpX8JNEyQD5dPiZqsZlyv4WKLNUylqAJB6SP0VhxXYHM7GYsFw0iJDNLU9Y6rZBEgxTxSn0rVR+8JHnnCJn4x1DWpzeVnN22kuDfUbznJslY7VC5vjNNgnoNlEKb9jLWfKScmPhKmh4fJqNbOEtoUmfpF6gk6y2V5G2plkztcUkGvfaWsIXHxEMjQDpPDBW9y4Hy2tBPMsOayJSLltI7KEvKfhVLVNeVcljfTIwuDxPfW48kuUGMqNXFyLzm+vK45Vt5V3tRbg6IkGNTiuWtcpGM2NdQqtJTpX43Q/lW4SnWAcbyLGu//PxzUT71Pi7h24Mkb2W6xG29JuOM3cXZZ63ktvCRTad6ylfnXmIxwVnGj28v8VZ3Zcq1ReJ+sJf0+HxF7E/HiF/JetGp91MqawOl8M8b8WndW3Z7NpVorQnSabWBLG9qK4ov14rdrCfiUTJIhlm/FsPTM6U2op9Y7bgkCiHq8r4nsvy+q4wMzRK7KSrSZNAbGW5pK8kLo6V9w9byflaE9KncKJXOPrLoem/R8rsku6u1pe3gdpIaPFNO3syQiO3DJXpntXgvN5Lsg1bifXqjuLknymvlJFG5fUkefH0qAY3XSo53J1mtpC4fd5wW98XOkmFyUvQW9ZGw5WnSpWu6vF+vL2NPakmTX0ny6kia+LjlSMy0SDnSzlHedRsvIQMmiOOM1tJd1UzCtB9KV2NluRY0RUYm+smrpyGibh0kNyOypVdjO3GZeFr6VL0RpdlxkrQlWPY1bi5Ojyols2KFZE+ylJQdDjIoa6/on1SVvG9I+6VDpcDSUo71nSB6T1xlsYqnJC1xklFG2jI7wleeJluK68z2csFmn6zfNFby9inJjJ63ZafNIKkcMlyG+9XJllkfpHXTRNm23058PmjIjw/hklO0V/panpXkU31kccIvCenxUDbM/Sw/nJ7/Ywn8R3xFmKYFq75nYnv2PQ6ae+hl4IzNsMG8m+PFgvQZGLZexaD0Nihvvsvmxo7cfyKsj25B4qE5PL3izqWSPO7iTfTnvbRo/xwf51k8T11HZ29bdBWfouxwgqIBNZS2vsYJTwMOKihRdKptsoEAACAASURBVPcXqeNuM/ezEm1PZZPrmM7qiDDef0mnouEYtoXM49UKbR5tiCL78EfCU+YTOPQSKxqoM2W5FePTPdnaMQeNBao86fGSK+nuPDqmh+rM9Ux8/JZHhoFMGevE81+f+DxqNQv7ZpE+6zk/l/ng/LQNLcve8faACV3MSni/+CVZHWdSt+A0j9p6EXSuhhsfXjHrxx46h9viX3oQi/vfGT5uFZmZ4/hWYMln+5OcsZrNXcvJ2ESUEb5qNQsDwkjUUqFn8xPscpnEy2JDxh8zIGXEb66MM8bFRJtzJ+Yxu/Qq3Vvk8/HJQfxnd8TUeyWb6megq7qYu62msyumESpHLBl6dwax9bTIjejP1tSeJKrdIkvDk8hTXYmumM4Gn1o0CuwwOrWL12O2sX6kKwOim2Jp3Z6B/SxIeGhBj7kq9I/5SW3OGXzaCW6H8nGpOs6loI4cUmtBQOEE9nWwIjnuE6bB31DovZcO1xJRm32CsZY+9NoXhVfEVlYr6rHx02kUWq4ix/kgE2JH0/WgEiPOXuLPCQfilM1p3ySEB53rsT+/L40tspgyNAIfRXe27JiKWYwKeid9+em1lFlzrYmcv4Tc7ZcZGZvBhOV1dPjqhE3aRbIGbGGP5SImzlYi9londjkpcdp/AdY2I3AOfAqnani4zZeiycNIdFBl8tQKxqY7s3+0DZuOJZHXsAEL1F/wqONM3nV9xI2te9EtHkQj3zoaWkUR2PQjxXuf0rVKgSUGa4iNCGRNe2+ab8zj7Yi3bDWJo1kDL9aMacjkBcu5U3GQVm6CnmYojzr2pCDHlD+8Ye7TOJrN24fr+X7cm3yVBi5BtH1bQW6LQmbMs+BCx75cvxnPzcMD+NXjMSOx+Mf8/UeUQMPs7/SJNkA7/TkT5p8icGdb2n+4Q6P4SOoF3cfxWw96bi6huuMuTp3bh49SPfac60SYVh33zliyfGpfjMLjsanzZmHGcz492ArKhxh8oh9HpqXimHWPhLJ9OB3JoYOeEvpbq7icasq4Sw3oX/86vUaGYMAQvFL9yKtdTfxgHwJ7jmLq3KU4LFjO4rYHGBQ8mOwzFUS5K9B9xS/aTWxA4PpqZiUNIHZUL268V6dft13kvvfjfnUWExMbM35WR4qbGNP6/hoKt1zlZognvU5eZPFCG87O0cXlpQ211sYc+DSeGXUTGfS7C5NPK/Hz3T18Fxbz0fMOce4m5FcX86fFHGZYrCBu3Ai2fF9OcHlrnLr3IHRBEs2+ZzDdyJdJwzpREpeKhXUGwz16oLmtM7PWl9P+hi0f+t/l0VETTt94j+ekGsbNnMglqyU0/PyN+r5DqJfviVrMGtpVLoNBiXz5PZglLRYSnRFMWsph7i9pSuSysUxV96BmWipeq5oxvaUVEUNf0mtgOWdGPaJhsCHT4jpz9ZYZrU3zqHMeztJt5hCaxnrnHSRHr+GOQ3fM2tbybd5sptXeZJhOFvXOjOLHMA3atVFmhIUxCxsoMM5iKwcP2qMXaovtzq9M9xzLfS89lm92xmubM6PfGHKjTovorSoEPrbn0C+hqss+eqt7k/PHgfHBnXA3iiXDdhfbn9wi7WYpt2bZ4TfdHafLCVivH4H+HVOyOu/i29Wv6Ov3YuJjD1IeNqXXsccsLMxgUVkCfa57sG6vOoExicwNiSR++GsK9Waw86k5BqW72PjBk+dfLPjY9QjttArJ2LKDdkW72e6szSBnNbJdJ5O9LZyWjkJdeBqaLZ7S5N46BoR7cT37PKH9UzD5o86VXzu55+GL49JW2OjPoPXHaK4O1WaQ5gKml49g2fnnrLvZgl9K8fyx3seO+ul4J03iYeU2Xo7zYveyBQxv/xrHMdXsXnAErVd9Gb2jjHb68ymI1qXLFK9/zN9/RAlkKOvy+ccPCgb64uEegV6nQbSdoYbjri/MvLGerw03Y2f6Ae85txiqoM+kSym4vi/BumEfrM8V8rvQg3uDS1gRt5U5VxyIU43ixZPO9LdU5uycLygauJB2eR2vWt2gk/5Lsutt4mLgY5r1uI6LaTjKGWUYdzBi2b3ZzP3ckyl302ib2p+Nmxez9Ycpkd9uMmDfdJSq8njeaSDjdjRGXfpjE+5Ll7rF5P0opKfHMlZrr2T43GLu11vByxNryPfYz5bm8bx9+46zEUcIf5pPnoo6PkUmHNS4xrLyU/RZWEbFnYeMsZ9P+Ov6bLrUlCzbtmQ3iSBItwNhF+bj1UuL/MTPWJw250G31iSNMUflRCMMLXN45dSD9W+HE+H4Cf8pq9m4aQUp0VvQff6QzGYZ1O6q5l1FIZ8tQwmOPIdrlC+Ph+kzYuUYFCcWsCP7Nfo/G7OvfwY2LUbyY9NOnDZW8PLicg78sGL084n0GPiJZ26LGXZxN8p1jUgKv4WLqQub9o7hSPeuFO75gHmDFoy0CabMZwJftE+j1iiNMy9TeHPaBM8fHti4+6Ow7ytOizezdJ8BH1wzyE7UJqL/Ah5aprPXuw82j89yafZhPu+w4nD+BZoMVcS88Vyqsrsye2ZXtN59xt/PiR1ZF3jV6gbfN05EPXEbSTeyuXVwGKHNZxDmcZy4fDNmdFxFxKNTxIf2467DAC4dv03DvfY8mryNT/2fMLfaGR+3Vdx23kJvYw9ufs/j8JtUbLuX8ysTrjrbY3ruMUP6rqB6cH1m3TZnXegcZl38iPrY79x0z2NH5DT859cjedl3LvjYYbyqjIv2STx4G0FOSgQKClaULZ5Gs5x9TD24j5tzbjAhcyuzDdaSejgVLdtQIuZqMWtvMzw3K9N76zda3+vIj289OLUgn9o1OQztnYT6/EK2K3kQvuEiUQcrcc9twUNz4enDWL60SmS12Qcygtqz/cRpfneoQ61FAsUHx9FJt5ifPfXotnkVhTml/5i//4zTAUV1OTI7jcfja5lZP5FPHTpya9YIGiitJCzuLSFTUul05CSaQztjcfob9d5collSOwxDJmOnvZ3rspA77m4UdFfA7Y47wR1zmWQcj/nmXMbumURE61WwpIrMhYuw9d1E94RSAlLymfpDi6aDnVnm3Z3f+/pRVTeEdUpD6fn7GCaGQZzptJ3CUwqMyvzAgeIwNnWtpY9VFMvPPcShtIrgJv5YJPUh2Pkmxg/NmDpjDI4l3dgc0IrN20vwH1NDq3ab2bT3IW7ju9I97hk6F29i/XAz85Zb8j7iI0bdWlEd4YPxtzG43LjH2c9RJF+/TfjvEKZtOM/PUH2sN4zDso8G9dIK+XnrGqumNUIt9iUBr3ayNTiS4NUvWHctjCzbgZTfVSTM0YyMNyPYGjuW+PAhdLrSmQN9tcivy6Jy8maaNfjAq06BDK9tiHn+BSo/9KXq9zAOqsZQX8WJ1MBPvOgXwa/dxZyI+oKmVytyrrzlgd81Pg7QYujpcbhc2s3gjcVk5Gix/cATLiiZsXX7CprlKBCm9Qj/sU2Y3/4uIe12s7dWkbrg27ju6MSSfd0Y6PGByckzebW4PfdHrCfqTSI9o8KpSemGjdpyIi22olrXg1FpS4iqPoeqTi1jNT8xu+Uf2kSFo2bvhd6REjxmPiI67Q2iGcnrgSroXPPEeXocbarcGDMhiC+GlixpqItPQE9anhmB84f9DCzdR8tdN7mptZbDlTk0uaJKcORgei3dzatlPqzcmU6qY0fWf1mE847H/Exog+7POn5MvI+DmQtDcy8RUrgZxZkvea9tT1W/6bTbvoUm13LQ1UxmfrQxpYceINqzeHjUA6fGgSy4tAjnuPqc77ucue1S2OQ1DocP5YzlMC66o2kUqUtJihYBjiG49V+Nq2E3PId78Pp4EC+zF3D29ySMvdbin3GPN4o5KA89ypgDlTyb5sruPz70ikqg0vU705K9aVbnxcrQVsSWZjHj2mo8dnlyqfkxPqjYYDre4L89HfhXfTvwv+W7TTN+62/jXpM/3J31mUVu7Tl6OYX9J7TYoTuGOO80frdJRGlnO6z7j2DoLRtSGniinHSYWxcuor/2EHtnfab6xkeKQmZi9LMr/i2C6Viow5a7CuRNWsfcy0cZsOEg8liV3ct70Hu+Dh45BvR4fJjJLi94pt2Dbtu0ic7sj98cR7Jr5lFj0ITsEAWSDdyZsm0jj+fso++xVB5ON6ZXfVsmVC8hcf8rbK4XsGPpDe6NdGGwVypLDwUxd2AyZge00L3/htf1vmBrmIvNHT/WjvPD3MyFAwdqOXVLm3c/Z1P8LIIJFRMI1g6kvnMhHWqGc+m8B02eWvCyzUTuKLlztcV+2id9Zc+8Q8zo5kf/tF106q/I6rercb+2iUnNzzOouo5VSkuJdl7FqmarqQwNIL46FuOpl2g4fwzTmq0hKaUlhvMzUW08iv09r6Hrl8qBkk48/FyJr94wLm82Q617NXPsi9ldvYRZX08yYPlOjuZ85+iihZiuS2HF88W0zvpC4ZQHHNYyZ8/NNRzocIbC2PpcflNN6yo/luxqx7AZvxlUmIVO+B1sWyzCcZgeeybNoHj8CabH3qH6lgNfMqpJflPF6ZbraK14mfXx/WixbQB3T8zAeOcCNH6eo3MbFwK+l3AyWZcZV6cyySuB0ojBDPszn43tulFllIBim76Y7PnNpP3naDviJOGGFlyPb0Gnxq/4E3EHDe1A5s3y4LBTJScPeeJaVMLzL9PROtWKl19UqIttgN8mV/anWpKU1ARF/XDGqLvRdcYcZnYbToDKOJafaEqX0hJqItzYFBVFm1a9+HoulOTuPRloV8RolVP0X6THol9dKIu4xKLCvXxutYnd+/vTdVI1LfZn8KGwGzN6LMO9fyxDnm9l+dVhjKkp4GT1DdotPMHRvHeU39tA4wn1SJhwkqadXbBwXEp/5+1YX1yOlX0M9ntvkV9Qj5NSQmzrElLGfSdUMQzT0z4cv9CQfIstbN9/jumv9nP20jlqmpqQUNDmnwP47z4ZEBHqm2pKQlWAdM7vKfPdasR64U+huJX09rUV81trJVpZUV6fDZXpV7Rl7s8W8j18jdR/fFh6fdKRzPPzZVOvW7Jxo6Yc2awnZvdrpGvhPFl3dpG4JWrIt4tJsiXXUMwrdkq0gqmk3OsrPQsnyNDEUMlPeyrrS0+LUnWZvA9pI8cWTxa/nGLxKPgtSq0y5fTow7LmbH0ZF6Ygq/adFlX/CZL45YuofsqUphGRojE8U/ZELZf5E5bL00gdsWwVI21ja0V9uIZU6c+Qn34H5FnX06Lvvl3G576QyYtdJELHSjTMb4jasydyvcUj6WyFbN/ZQtINTojd55OSp71bmjxXlfuGo+SI4yiJ+nVOjCx7yfrRHeWkmqa43/CXxD6KkhPQTeI/DpHpvjOlM5nSJuCYOC65IgNy70nL6lppFnZdyvSRoO450iJ/n4w4rSVvcnfItsT9Eq62Ufz0V4l93z1Sd+eCGDxYKMfqx8p8oywpznOSVYuQ4j/eonDMTEoMv4vN2ZbiEJwl6wNeS++iRlISbCQa4xbKzJ6RMuriHWmlnygKWf7icC5brFLby4khF2XtByV50alazu6cJVZKvcT31WBpv/OrvO72WYrDfkryo+Fy2um7rPaeIqUdv4qP33VZ6ecqNSr9JEA/WEq1p4qi/wW53rGtKGrYydoOuyUx+5msNQuTx+Mny82znSXE10d2LFogNbZ10lzJR3SyN0pBnpFMLzgle68lSHLHWHnb9ZU4uHaRAW9jZXDXvnL6XbiMLTws7mnTJHGepUTn3RYP76eyZ9xrGbk3UubMHiwfQ+wl9Pgo+XCrt4w2miTxcYj6zO3i2D9a7sdZi/PBAaKTqCbfTqZKi5bFotX0rTRWU5Q7D/vJNl9V6XqjXIZoT5dDN37JeKu2sjNzkpz8elZeX7gtIc7tRPNjlGwpNJd8p4nSvnC94PlHrsx6IC6Dl0kz5U8yZ+obGeOnIa0Ca+RO0WV5m1srO+1ey/WJ8WKcZCUfhj2XN1PK5XXrITJtyTx5ZpciMeuVZUIre+lSbCzFk/pLWayx3B+fI0/s7STL7Y6s/fD9P/t0IM9IhZLpBZh69GNhVgKhAWsZaN2StTbjsYq7hPrZerRfaUBeajzL9hnyNiUNnYZuTJ9zEYWYnsxobkCI3Q3WzNpESsRi1hgtROH3Qw7MLML7Vz/sC/swOD+JK4et+LLcHL0BV2hRtp4pxjo8qN+UORtzCZ5eRFjuF/JGfOeIclc+vhgIHW8Q5PSQqFUr+BNzjt1esYS+qGBvi4t0fV9N5uI2eDS047zOI74d0cO/wwe+u6nSYJ4XrXvM4rfbNwI9BhCZNYd9YbtotTeCxaZOGOnmElHgyfn7z5j1YhyPGuTwbEZ9vJsY0rVXY/bkPia8wXoizLYR1GMVlxtdY/YbfRJcb1BqV59dDwKZei6X+Skb8Cy/x61BLVjXfApma3owvHQERft2M//LUsYfruFk3Qsmv1vJac+jJFaepcStO232pHNtdz4ZcxvzplsfyjsXElHRGOVPU1j+zo7uOlOYaq2ESc0rLOsrMqhJJq6PdJjW+ieTenRF6+d7zvQ9yM1j/lTZurPPIonMmRmEZbSj35duHJxcSeL5c7z7GUpsz/nYz2pL+aanKJtEoFvphKvhILzebCTTdTZ5+xeTvHsl/pfb0LjHMCYZ17KruSdb+vzApWEz+o6cw/kPDXheVEn4hDwskj8Q+uoK6XpPuZb/msP3WzLkkAut3/Xg2rKdnL+nxt4R2rS+3wTHT0cYq3+MFc0K+dkmlttDJuB02RXP/tpYrP1G1/37aKdTy7z0CxQbnKRyehRWHg9Jq1nO3kdeND+xmpapDozacol+vmfpHR9DvSzhkPoBrjrOoUtMMWrdexDc/TMtdKP4sfkJ3Uuf4jXxEcZLRpL1diAW8e8ZH2XI4osn2LlgHwM+ZfPggQLeLlfQX+tESMN63NIoZn2NOh1ymxDfRpVTO0I5Un8ws/Nf4P26PiaXV1ISloji1r3YhTXnlvUAZp5+yI4r3TnyeT8tV/bimpIDl8O86TZTjav7PSmq+cWwR+NZHuKD5sEmvNI79o/5+48ogZalVXTcch+dG3M4uugtTdq0434nPyJVr1LZeAnOJUEYtdlDxc4pdLN/SXhsGcNmjmPNuo4siJ2E+f2RJG1aRaRaf2asuUXXvu3YUXOaB1t701PxFkm6PaiZMReTr/bkN84mIfc+rRWqSUkr58bzQEzjP3Om83ImFp/AYu0DrLedx7YiHgvPTqQ23UGfuu70cdpLL1cf3n+JYvHXZpTlraanZ3faO3xiops7R+pt4WK7tTRY1Jy9iVl88s7kmutArClld34J9as7MbvJH/qUtERv1DN08geg3Gw/L5tl0i+wnBC3LDqM/cwjvTT6j9iFddxGRv14S2fj17hlGWCbocLolY7M/6NGedfRHJcrdOw4nttWj5jVYgM9dpWzrn8Jkbc3cvDXBcyDvGke5gK91+B+8ijPK+OxdPem3ulgbhqUMy/0LP2sNeg6+hijsvLx6KKN00o7WjkZ8HD2efxaerMjbgfbrt5iTn5v1tzxRWtQBH7ZPRgQtRq3U8m0OFHGtANOvEqqxH3ONEa3Ek772pKdkse9Ec3onK/Bo5OwxC4Hx6hz3DPwZEPANXpvmE19thBRfySRi9oy5OsyQqs643q3ipUOP0iPPM81m1ouaSdzMHUHm22f4KqwhnpD16LjsB6Hba0Idv+FU9NiApYup3pGU+JOVtND7xGpVW48d6smZtJ7Yj9ponx2MdnTqxm8MIYHoyNIO2XDhEUjaFiwjxW3mpJu9ormHWyp2G7Nin6X+H5+P3pR4Vx2VuDemwEYbW5OpKonm6qMqG92C5/iXA5Fvcfu4nAsSnXppNGBS/seYHMvma3DHThYlsxNzako7Myka2NNHq+vpPB1JG5vT7Nt9Qf21vry53kXlANaMuh4DuUl/QntfZhGbo9ZX9aQ66592NFsMb5r16DotptsuxkcWZaMz7MFDPSPxWHmdvJK/ahZtBS9VSb8SPDBwcCR3Ycn4pB8lP1OvZkec4brsXnsvwgDTa/RKnIJfXd2+8f8/UeUwDtjXdaWRaLs5UNMSASvS5bT9MkWBlh2IenNSt7r7ubPiceU91Xn8+0geijdYNyQIUyxr8F0QQK973dlYe4Z6h85y9wtB4nJmYr3qWbM3HSe7tX3GNIzkd+HrRjn9oTcF+YoR6kT//4NahbrWB3+gMZNvWl68hLTHUehFfSC16P7U6baiCbTOnBl4W6OZj5krs8MtC9boDrzJD3iHJm31IjrEed5ZBJE3qMwCiduI+WgImq74rg55To75y0jX6cjEYmazLK4hWXpK1YlvmR1zRAi1YbxXOMxZ9ofQ3/1KQLPv0K16XLqTttQW1lKpzaNuXdNHaP9dpy/FkPPXcN5Fz+ctFsnmdyhLc7u4XhtTSC/9jrvayNJfdCGyb3T0Z81naPdD9DiYTl9G35k751hnPfeQvM6X3qvqSJw1Tbmbo1k3rCNHCm7gOrz32gGXEUWfaVxzi8M/hdzbxoV4vf9/b8q0iCkgUSDEtE8EYUoqShD5jmzyBwSSSjSYChDpgplKJQhUpSkSamIUAkNNBelQdf94Pd98Hvw/fzvdf/ue/3Xd691rXXWvq5znr322ue8r7P3oN88kPSj+lYTemlv0JX0xVjqAKUrGshaZ0Bf5xGccTDl1MIwNO8mESYvQaLfaZJbRmN/oBz5Vc+4YprAFdcM1nS8pmJhKTNelXFjkwg98+dSemkAUuX3SLDbSe/YMHr+jeL7g0oGPNrFl5GiWN+wJFdRlAr3+4yy/8K37jMsnqiDtkwapT98SD2ykfTxnuzWGs43q+XUJppxfr8mHst+MaTjDSGPulkotQaJbVrsSJ1IZut8vn97iGObP0v31zDAvYXq88eRuKPDqrCrTNygx82V4xhmUM093Z9ErLbDMa6Eul1jCd91ETPlTL65/maSqAVOT5Vws9JgT/hDCm02k7M5BZeWa1TM24xRyHvmeKoSMTofxcQjGO8vYmzLDFRT7flkksTtOfeRm+TJ5ExDLqfbonmoidoSdyqyepO18iz37H8h6/cOH42zRKv8xG6dFxNldKi+o0SJrCTaxebM2elMeu1rgvzmYPx6PquKx3PpnQx7Wg1ZuDaf52NkGKE5lpPDi7AduIniYxvYcGc57xJUyN5fS47CtX/k7z9CHegpZiz0bc3E7sYWJDrO0mpoiN+WLozSe9Bn+E/URrzg0oNOTvgq0phhxUSR+bwZoU9r5HIyJqdyud9knBQm8feFA5XPijB8a8QWrbPs/FHCkeLjHNt6moxWERIl7uKu8Quz9X44utXQ50gGdfk+mAxYyo1sS0a5KiApZsLVL22UHZHjRIc7Tv2cKZFM5O3V/pxzWkeDsgOlZ6vQ0uzB+CHjyf1WxI0aeR7rtJN1ZwF9vdYTO6yRU499aep/EvMiW7Zfqea4jytPslcj3T2PiD3jcHK8QrSkK+ef9+DjuieckRjOvVv2XHedgYl3Bu4jdOnfHs31RzpofUxFUSmRme+UmS3nRe8mFZx6ZXKSHMQT/1B4yJz9y7ahv9aDBN1GIvfVImZvz/gx43my7xtHWu8y0MSOxa+3sWDSYF40ziX/xQD2ld3G66IEj5MDUA0sxkh+Al/+evLI5i1mOw5R+KGAS2N0MP+Ywtabo+iZroFufS6LxRYya/BcVtu/Y/wSXUY15CJxIQTDaXOJEDuKltJ55M7500/xC1OnhHL9zn2M1nZw8IcHmZdO0bK1CZO3LtSvG8zi+JnU3FHgwqF6/qrU8eKIGQ9GreVR5DYKct9Q1GDG30FTcNj3kxWWLtRNfo+rbit2yxfxoPQ+D/qeRFfjE47F+8jaKMKmuYvx77+Pb25/Wf5xEql7xvBJ4zJjxq5A7IE5L9aVsMtoGOvM1zPkujitakEEHNhL8qMe7FewQLHkBvf6TeNX3Bcc1qUxf305MrHtZIeu4oPDEQzNe6FT1sLdaeFI57mh128ImwIqKD/SiXhMKEY+Lazu1MLr1k+8Yiv4rGTEoU+RjAzqYGhFHsqPttNgfoinzXvpc6SL4w/2MrwyH0sJuLbjJn+fWHNptwkHn+/hqY8DQbu2IHT/xP5rPeN+XCXIZSl+ZfpEbMnGeJIkz8Py0JDyx6jtBPqzxbAOs6XkZAUWWtuQNunPuDPjCCsQZcHwJWRcq/rPVQdEBlYz+Os+fnjWsqIumIk7C/DUXMDBh5d4eekxJ48soywjhaSfvgy/NhuT1GRGDNDDIewRyyyiKEw/iL/Jd7Jy1iP/YzQZ5VvQH7GH+Yk36WmWwBm9Yt4GJlB6cgljxq3kWKk3dt+jSbe6zYjI09ztr8rC6eOxkXNk+2A5Wt+1odiwklkS6/D1/k6DaR8WNLdQc+orVxybUb+1AeF9KCtSlBC/MQetIeYMcB9BS89n+FwMZuC5N1zKAkWrjZi/VkNrbgiX+w+l46sWKyp2smDnZVpSfLDfWE/0pv4U6AtEpY0jV1+fR74CyzMLmJ9vg9/+xWwKkibzw2R8vN0Ir+nNrtQUDv8OIMu7g9G9DNjWeAiHVaYIjZfpYa9EaGcpN5ObyLLWYPefOIRsaxbdGYe7kQmXSxpZG3ediO+W2KlXMiJwFY9+S1OxzoMe+5Yx7tsGtpecZEVMD9Q3aBM16jBy/afzIfAq18eL0aSRy1azYmRHPOa1/HqMBzXwIzwf47J1+JhFUKb1iZepVrhk9kNGNZCIY/JUG9TS3RJOeVQofReW0Rw7m90DW3Ga8AqtgZ2MWPyec++6uGZczahgU0THmWAfEcATUUN6yrbQp68Kt445EXVrCW+eDEQ9dgEyNePJkb9Br+Fu1LotptjnGOZZS8ipNGW95lUW7VjD5/DxeG7J4+Livji3RLPccT79qhwYrxzJYJUdNOtWM2HnZUaoOCLvMAv9I9ksL3RlRO86Bkk2/QnVdgAAIABJREFUcMC6CuNN79E/25ePGfNYaHGME1aiSJr9xMFLnuG2ZTwp+UtOeDRBQ9TZ5zMMF43lrPjbiaNYEc73ZXEY85isI5Z05izinp4lfY7psnviMNZKf2TWS0W8XsdzfEoYw0/84Y/qaNSz85mtdwtzzy+4npUl4m4Jt9IWo55ugrh9AEbyfzjWls24v8ewVdlKy977hJzzwr/xB4Fzr5LcvZsPvU8gmu+Mmv0pCl/u4JP5VZYOreG7gRJcq/q3/P2PtwMiIiJDREREnomIiLwXERF5JyIisvlf/gMiIiIVIiIib/712P/v1hosJov12l3cPKPLYpmhHBB9QOK1XUzevJHNYjUMGDUDn0X5zJ99hIBWCaSa95DvvpGpz0Vo635CzyHGTG2upTZwIbV/XrFjtS6rPpnT7X2ZRN8VFPU0YPIpc86mXEDulyJew5yZv2wAzwx+g9ZZYj1aED82GvfBccQuesKMh31YUzKLs/NSWOaQhXL/GRy600zfNnl2v87il68TqzMLUL58k7WbehDUdZBQ91oWLlXEeGI169uNkT2qjHVCCy23PSnTMMS9s5Mjw+Yy88tQgroG4L2rlhH+C7jzfArXRwrYjQrgwoPjWMcfw6yznSXrt2M24ioFjq34ek6EIU2MW/4Sy8TH3Eu2RFt2MvlJjlQaO9CW24J7+i/yZprx58M+PpR/wn3tFgaduoP67yt8m6fEbC7gMqiG7/eqODYvG9vNB3hfpYhMshF9nFvx+nMM5VvneXBRk5Lblcw2b0R8nDtZ4ZK86LpDkVoXQ6fvJOKvB5Mvz+Rg0S8shxRh9LiBbQfWMS5hLqWXdahs9mbl1XBEpS4SpJiMpq4yUmFuOLzczJr6QSzLKUd261a6/8zm3cjDZOwYxYbnEaRvEidv0nquz5LiY94wogMHs77VharKbt6eyMJTfTRux0M4me+Mb7cFye9beBu3BVuvvZg9qaah3g67a7+JGvsEm1f25Iw+jlxdNR6HXNi45Snt2tP4MESHkVt9mbdwOXIOG5jnosjqpcFY9iqlY6U/zzK30aYTymSRuRy7dIRzb9OovniRpt4b2T1KB4/p5WzOXkb/WAWyZO8y0mcmsuGbSXrTG6OfMpRGFhM414Lu4MVsWadH/5aRbLfU56HLbjQsr4G2CYbB1UxXamRZkz1vW+6R+zKCK+/K2XgiFAP1AHxf+PBYJQKHG7sY6HEI1UeeWFV1c6ZmPk8vN/HmRwWzcioITDXlzO0ibjX04df1M0id7+TRotts8P5OwlRDZizcTtTwj1iWWDJ//TeK9i/+ZwD/p7IeoAQY/Wssw3/1GRgJHAB2/B+tpTFMqHh+TpAY1i5ojqwV/FLUBQ0rI+Fj3k5Bpddjwa6lWdjZ1FuYvipOiH4WJih6nRS0JUYK4/WOCQe6/gja4qlCvPoUQe3XCeHi0FfC7utDhStDvwrBt7sFWakq4W7KISFA975gkTJGOKpRI2y/NkyIts4S1vROElaYewmRp/4IruMSBRuLYmHL03DBP8ZRqI88K+z++UmoD5IWdh+xFaSadAS7u9OF5qOLhcsJkkKwa7SAVG/hR3OiUJuXIMT6VQn9zioJbl92CBHbPgurr5cKvuobhbhubyH6eKMwNqlNeOEUKByL2yvEHVoo7A7cK5QMFhNmmhoJMRJLhHIlTaFRJ16I7x0iTPlVK0ROshAavOWEPbLFQpNBmqAzqluwO5YivD01U9jQp1bY3DBN6HTQEsorI4XYNgPBfaufMPDuNqHeX0zQaJ4pZMifFgoebRF23V0uSFlOFTzWKQj2I/oKAyYrC4W1sYKUhIEw6q+9MN5mgOA8sI8w5No94e3gJqG+xlIQnddbGDH4m1D17o2wdu5soX5jpXDizUThUuMu4cPqc0Jw9myhr3aOMHTZfeGaY4Cw5Mpc4efzJ0Jjx1/hhcJ3ATM/Ieuxu3BlTbLwbdYs4VLFJaFM2V/QEssVNBO/C+lLDwuZdzcJblNvC4qpEcLFj7HC2PD+Qp3zVeGwto/Qbl8guKq4CAp9VgkGKXbCn+FOwvjEWYJZnK8Q+zJQWHlEVPhgP0Wo7VwrKBT1FJzTa4TG1x1CrO8XIXT+T8Frn5nw/KGmMHHiTcEg64yw8oaeULNNQ5iumS7I768RFu4cJwz9KCs0nNQXIqz3CXmKE4U3TY+FJuu3wqyNFkLIBAXhZqOJ0PwEYfJZV6GuvknYVRks9LN8LLjHRwteZUHCyFEWQu9xd4WdXyUEUdc0QUE+Xph87pWgYTpGeDV6juA5Kl64s2iEMGNqnNAjo1gIDz0lFDckCeIXS4XJpn+FeQ+jBNWhtkLuJDnh9xZjoXrPIGHIvHDB8P5FYaXlQ0HldKagp+8kpDy4K9y9LSf0vm0syC0bJuz7s0+Yf26SkPQmTFBW1hAqGkyEgvQgwSJOS3DU8RTapmgKdu8cheXmmcJT0RBBfFC7gKmlEKby7v+9RCgIQpUgCLn/GrcA7/mvUuP/xzbqTwVdm3WJq7Xi1eMcBs92widSg4EKEYz2GYHmnma6d+2l9GEnekFZjDkbgnleNgZ+gQyrXE2cUy5hDxrI1Y/Fd2sdUaLKfJI14oaaOdajJ6O5fTH6Qy14pTGYxiFliLSL0zhbmTvFsujKeeDxuQ/Fiw7zblIjtzeo8nHdOwp/faEudgf+H704/qkVxVx/XunLcrjeldfhRlhNkWPWqqcMjB7Ci/1K+KpGcCthM4NGj0dpeTeTlKspvTKAvHZlCjNNMLvhRvrtZt6Vl2Nzp4ayOmeCd2TQo0cXj3WG4jXrPmFyx4jt7ImatTUpgxexwaqNWSaDkesUwWDjUy5W7GZ1oRtt643pf1ub4bYaeHaXs/jDMXrd2M3D8QoUaaUg/acDz96fGLnLkdhGYwxvZOCwOJD8V2843i2Kz4NR1JnJ8Wztb2Zfu8FWT22meKngOF6eGS7dPK0YTaZRAGc8vXkS/oiRaqL8XNyHOXFSSJQvoWzwSipf70Z1cn88Liyj5HUyNhJiJOp10mNvKSGfT9Lf8Q9pUu7IuR/n0DQH2saJMU1pHFEDHxJWU0L5NB+Sfz9ip2wWQTmpfF0vSQ+xSbz58YPq3kZIf/Dm6f3xfL20kg3l4WSFmLF8/BXGDHuG5pJV2NwOxFTJEPHnRxmm7UFgYwJK5z1wnhGDxIRFWIiE0dXexjW3hWycNpLyMcUE79LngIIYfY8MYeQuKfLM4phRfY8XhslM2tOfistpWH8PI1P3Dy5/JDiovZQViy5SueMeboNlCMqTwfTNT9L6yOKUn0CU/Dtkh19G5NYDyhqfs/qBDM/S99O7YCB7a7eSt7yDjaomiBea8NlRm6bPhrx6UEVHwFRUKyYy96Q5RieT6fl2M2GzNvDQTB7dPHd8D0fhNXoLayZUsi3kI2/yHJgzpgqHGYH8NRlLvaUs2rVOiIiFMmuyJGnp9+BEAoar4rhy+hltvj948nYWGzeb89NpFY8qNYgcfO4f+ft/cjAoIiKiBqQCOsA2YDnQDOQA2wVBaPj/mi8jry/Yft7CBRSQ85Nln4UkT7+u5UtaFOHPQ3CXKkfHZzAH/EKQDc7l3pcOzoco8WGIK+GGFjS1n6V3RTCxvVawZdVEjp5bS9c2Bb6vOMbiiR2kREnxQu0DkTeMKPmry9OgJqb2b8T873DKI8uo0bam8/RGShWsqVFdRfOHgwhtvYizW4bt9Am0j9XnZEcND/5k4PxrNfOTmxhWUonn/SLWlb1Hf0YBcwZl01rnSeuQlVy6eYbomsGsHKfFqCxdhu4dyLdv4tTkmDG1701Cf1ZQl3KYy9o/aZe8wD11LyQM5nDsow9pzW9IuTqbnqMGMMvyILfWGuGpqs4yvwuovltFr7U5GMkNZeirySgPX4q07BvUz1syyMYcw0ElaG+XQ7lHDV1vLqHnacVS7Ghq742xZyWtC3SZkzydNtNunMMHYrqsmUVbbhFYG0XnGSnClEqYkXYQ24mzeJ91nVYDP7QP1eLy8zPO2ReIicpl1q0xnNtRz50YSYJyR2NnCXN8FdH/2p8jzmOw3zeHK6NC6bpyn+lxs7Eu+obBqXzU5OdxY7Qy8/WTcXQfS9HB0dyShgvbLJn26h6ZRufxe7EIg4IIjvfwI/zyN/oqRdN0bT57to7mhPd5rA7MYKawluo+Y1EtDyQ3N5MJL8poOODEmu5TDE+ajl6JMwEF+qzPUOC5pSJfjuiwfXMnYncf0nrvDrVLRzPI8CqStzOIOp9Pzx7zWbb+GeWZ2lzov5XQeeocPKHPPRcrRq5v4ENuKlteu3PvXBiejtsJu+7LoPmrkM6vwqR4NAY+oQQWr+OIQwStyet5claKn25JzHhehdpUebISdpG7M5nyO6t5J/MFaemRlEX74DTHkvluNix+OBGt+yl0zilhy/gvRO57wuNFebz2j0BmayU3X/xgpepsCk1mEfNpC8czr6K5P4Ko87254SzNX98o1o7vQtpcEv0FYszPHMfkhaex9Q5AvdSLmecK8X4qjuXUq6R/f/BvDwb/ryVCERGR3kAMsEUQhGbgDKABGABV/EN5QxERkTUiIiI5IiIiOf3EGhgQAC6qr/nZX4T4k2eZHxvM8tJXBHa95cGS+2h0pTJw0B/Eesqzy/oOh9yreRq+mN2SI7mSUMDpt93YvB1NYNFftI92c22+BT+2lmF7oIGuDcWI7pDB9rU0ruqmLNrWSJzhFrZqeSNaOAjtiioe/apj9rB+hP/JRG/GDraFv2BF+j481B5hnttAtWx/1g9+xTpPCSQTf7H2aCYnanawfFgMb1v7Ivn3Jc1de2h+3I1T6wNueDqwu7OI5vFWvMpLIKWnHXI6RRTeDOPRcR80GgqoSQ8kpEcxBjs+MaT/WOzr97Gi73VSDxzDpfQMYu834qRtyDrD7yioqXDGJQ7Rolk43fnD9tum7FVyIXulCCfTvKkwn8gGyfeESC/loibI5F+iSi2emE1HkDx6nak7QnksJsuohZ/wXT2CVmEEBeHduKxJYXHRDmpLxMgKziZsjSra03tzd/4fhtuKsSZtCvvfbmZsdjePxGfRPmogYflpbNpYTd9sN34v0ETVv4HlP6eSEv+AJAmB7dIh6ERtYsT5IJaPXscHx1NUTUvHs+M9MlopGL28hc2VWny2y6EQOpuBrotQWebNk5Lt1BWFYHT8E70fzqX6YgZqlql808pihVw//lo7UdaUxd1bx/B2G4LufUvOtt+luzGLvgb2/Di1F/WbXTg8mMBzGT16r3vM5PvTsByXxr6VXjSdliM9aTiVUlM5d1WXU/EaBC7xx9lmHT1vT6N4+l4iKqYwxXgHqQ1h9Ag6Rn+r5VzXecmcb9kcVc1joL8JMoUbWTX/IjKqE7kQ8pq0m5kcOJTNtSB7bCfL8cJBgwtnD9MQUsvGiDZOSv7ip8Vv1k+9iK3lD3r3FSUhyYTE5y8x6TyGb8ZOfDcMJaLoMeYukhRFiyP5O4DjgwKYEO6KQtQH+t+rw1q7ng/v7PmzwBajiGQOL7zHOHsL3u9fz3KdLCx3C0zU88PE1Jo4y3Z8Ft4g/VkTrUrxeH449I8M/1+pAyIiIj3/FQCuCYIQCyAIwo//9j4MuP/v5gr/re+AsZGqoFQehrvSbIY39+Tt2xKC39bQLTOP4DUvUSj7wY7rMxjaHo7fhmdo1ldxLlGKNwnSuCdE0La8g+CyE4iK1DPsxnGqq20wL9nA0vye3PlyBM+qDG52XKZZ0GFA0mIeFZZh+GMNb/vJ42W5megoe+77DMRvTwWbv+7FTdeHWocyEkRa2fFGhcr05SxeqELBkjB+1JfyKXo04ZfFWPl9Ip7DPpKdMw/PnNmkCa1kK3bQq7qYoOcWuJmpkzM6k+o/64mxlcLzlzL9xGO4VhPMHpFg2nzO0Lx1KtfqJKiuXM80j2k0H7VB0Xsn/XuaMK3pF5H5V5kyRcDhyU4yy7T4O1Wb/u8CMR03iquyu7HILONhg0C/IB9MD1ty3vY9h0p3I5OaxNPHk3hx9CbDmkII65Smx73BXBwWgE7UWTKEQSxvtCJ0vTF1oU0YKqvyW8eENNEMUj0Xs36NJ30tLnD6sBx3Z0vSdeQXWnKyiIbOxkdTIHGAGCZnz3KkWJUne0vxLJrL9sRn+F2px3nFC/LaN9C1+jX9hD6Yi6eS8z2JxxlJzJ0xjlSZdDqkPnH1bz+yVJ9SttqZOyFtNHXU4fnalqTKHGqrK9k0OJKqpwVsMJvFEYdi5FNX4hOTANK5CEUrMNMPYrpvIsfPZOJlsBndE2sY1vibpQs6kOv/h+lLDvCnJYSYM/qobNMk4/M2+vvPIN5iGnbGc5m25QUJRz6hOl6e02IDOPNjCscLfnMiZB+uczL4LSvJha8+vMiP5VB1T3ZeCqB2vwGNSWH0d2nn/h01JhX/psBDgTGf5jB+iD223oFMEK6QX1jG+iNPWXxzLwbJ/ZCV3cQZz+W0NZhTtFqfjHvXOH18IibSKRRcKaCqTZO/wxsJ6ifH54Pn0RGv58jETgxTlpK44iinFROoShEhIvAYx8P3cDpmPY6rfVhdPIK3R/Own1qH1PtjbBVO8bnfFEZbbuaz/HqmRf7mWpAPLmH/2ADs/0odEAEuAu8FQQj8b36l//bZTODt/26tzw1fqYz/iujpXxiq7ueR5R0qfify9O0vyk7Op1I1BPRyCT/szzhraaYsscBsxV8mPYyDqF0kZMsQ2+rAwLXn8W4+jOFeQxRuL+Jk1xr2BE9jQEUgHxnDpEW3WRQdz/Y1v3lv1ofFg4ZRvC6DuDU+eKVGM/SJA3cmObP0gi27tT4i6RyA8ZlwWr0WQUA1cu9l+S1jBfbtyDknMsSxkMXPVDg38xIe/Q5w/6AlvoF56Cw1YsGqy3y5fRoVz2k8O5GDqfg6Yp9M49aYfpSKNOD9QY77TrNZNTSGbauD2NjiydErV7jwtphjD3szW0WGsnV72bm/H9vDqhALV8G6PpOTQx8ywyAafdlSzH178N0xGHHzZ7iMu8TYNn+khViKTCKwMk5mk7YiS2P+oOPxlS97XVk0TJI3q/fRdLSSnWOq0E+JZ43ud4pPHqDbpQ6n880UmrSheHwoGq/3MeeMD6GuPVE2T+BqfRKTinS4nHGRmdrqeOhfpL1iGwbzb/J34WPkXeS526BN4beXSLc+ZuPYSkZETuPqmmY67nuzUcOAZ4MjURfxJGbrLkY8jEdGX5OsvCD0dv3k2n1n8iqLudyQTnePmexsnE69oTszNq8lQnkHIW4rONhrKZ2qRuxOiOC04XsKVBS5b17K4eRlVLhacf+iGsZnJVANGMPt5cv4G2+C9Z1y9DVtMN9igWH6e6LLL/BpxRsMXt6g0S6a5dtt6DugjJrZ5+lsj+ePdgVTV0uSllnB+OwwOg2+I5okT05dF/eHmqKsqI7Q6y+90w3wjN/Ao2+HiCML99TJVA04xBwlP3J8g8j//pAB90cR7K9CxMdYknZfYtdYcZ7kfOaeUhWem7upcE3CrtcdOnYsRmHNEiJjruOispGB2RcIr77N9oJOPppswWXIUwZ828hSeyU2HjzB+nWx7HUeR6DKDQLv56H2vpmx9j24sUhAf7MHtyoUmfCujaG7ZRDfbElV9zWcM73+meX/6ZmAiIiIBfACKAS6/+X2ABbwX1sBAfgCrBUE4d8LlP+y4SOGCUPEK3kZq8iytpmM8TXhZ9UhxgSGYp1YgaVmPWuPrSZc7D3Hm67RY8sL9hzzI2/uJ55mPMY89yi5fU9SnVZL1eltmNvdxubSGTKzonDbHIvkCjVan1xhjcJ8dp4KIevAHZJlz2C/uJEz+nq0rVOlKqQci8RZ5I33J16kHq1acRLCVCgvn4fzj/04PR+Gc8dkknS3cui2Ns0HfjNigglNm2ppqjUmv/wxkcFxnJllhtykcxSf3sjAFeoMdrnM8bu6uBwO5KxOA9mxNfySjOXPj5mEfnnMMW8XXlzxY9vgyXTc0+RCHvw4GMjjF09JffWFlCdz0ZMtI1PEFYOkp3ReFKdu3l/mbHxG2JHXNHgoMrfHT17vfs02iUx6JOwhbKYIEaH+9NrjQVXCWqJ0O1Btv0XY73aU9K+S72zB19A8llsuY7q+B+lrzxL44Tqfd27j/Nv9hFnP5uiUT0TYPEM5qZod5Zoo7brOhU8maMbEscJFBZH0ZxR7BbI9ugczR3czxjibIilf8gefRCOzCxcPeUZF1ZA85hbDx8WTmlTODj1t1P6EMVPlOreX26Lt5sBN0TIWSy3ncmwnbQuUKYmo5KmWClg1ctxzB3b6Q1ny6whJwiyOG8YQ2S8ZVas4YiLHYidqwefWGIpPPyFcso3sv53cNpvHCu/NGFvBm7QICpemknTVBAcRCUK6ixmabkfo2ed801zN0osnmPa+mvtGFXwyK2OBSxhDhk7A6McOju9bzUWTFkps2jjnnUukC4yZ3EmwqxO9r7fi7xdJ2Vgtuv5o8kT8KvePF7O36RLy5ZORHG/Fc6mhZLzXxqJiKZn3ncj7EEfpy6uMvzSC4Fv7kLppS/vnsZyoUSB4+l0Uf8mwYdtlFvYKYIpYDOEnZ6D0JJElI22xDWhn3tR8TstaY159g0/jbeg7J5scAw0UnFcxvcCX6k+rcZSsoGlvMUcDn2D/rRefL33kxPxIGoM+4WV9nQGlF/9zqw2rDdMTKjxecMe9kPkBN9CU6qbnIzsKFZSZsHMP9+wmMXu1GLpTcpmvu4QmyfM0qUayz9yOlr9S6Ed9IyfqD/OlehFr84Q+QRfZpGnHwvYudq2uZUKKDU1GflSOzSQjt4trP3tw6O00PH70QTjQiIafEhsO2lEQPBnTl8tYOj+AKSkW1KZLc25DNm+vbGRL7EY8bS9R800C6Q5vBhgu5ZCyK5eUVci6Xc4H6VpkfW8hqdFASGE983br4WVSROmueGKHvkTGvzcRVdG0Rgyn3m0wg9sl2NpTj+W/Y2ifsZLEHQZUBdzGv/Q5WkuiqRKNQm6lFTHG+Xzw2sueRx3MGdaHyhl6TKi7yYSmUdx0XUjC53JmxvpSqp/D+RY9ijY7IuqvTkm8PIvdRhE7cjC9BrkyPM2MmAWnqWgdRR/DYKqa6ikKekTmwmpObHDGVLqVgM39uR17ilCJOmyMZ9Gl5YLI6RqmNV2g7eM0bKUekhukT/ClPRxuekpN5HFMmi7R7JyNau+e7H/4kkOFFgROf024XyclsmFo6KximuJvXs21Q7yPMvN+VxFzLZv2Q4+5lS7KMtNUViQ5sLShjCfxO5Esv8WTp51E7l2GU19TvjjKY96hyM4eMdRpdjHV0IsQ2dMcfZZCjIoF+YGFdO5YharZEW70scEx2pXBU8bhb7mGgRvtmT30PcGfjRmnncdpNQvSpNswS3qP3xkvuhprueMUitwRcbZ8qKA9UxXZmr8M1w8nfsEjdDwfMObUYd5nHkBjxTSel+mzf5A+nh9baVp8lLBL46gumEDw0ziWSBpTd3soovkHUbNJRTnrM6LPRSkiEvvmZRQZ7+PT1zhe5S3Ey8yceT26CVd/yGXXJ3z6psHuI06MD93EaWkPEvTNSb+0il5q4ugWeLPZ9ABPVB7wJqALpXVXyfxgjdF5bzIXbUVx5VgOl4ezvyOO0YlLyEjppvipFE3DNnLlvDgFpt8JXlRK67sqHkr/+z8G/yPuDghfv6OjI/D6byLnEl8zbt0QtrQfpHSkGHujjnK6eiGDIk3ot2E2V/5GU9ZPlvb5Fnwd2MSmpT5MNInGrS0czX13KV5RRvLkeeQnOLF/vRhhiao0n+qk/cFhxnuVI7luIRVLm7A9MRW/LY1MJBGxgXXsGfSGH3oNyLQ8YcrPr8yOcMRqUg6PricTsvs0w9ZsIdX2FGozN5Kn4EFxwFySNc+jHfiLngfXsUc5k9DlcwjpMwhTiTUsqBNl63QHRKeJ00dKgRHJHzisqo3QezyLJvQioMGIXS45aGRFUa1+ijHLGvGTNqU08TMO97J4JdGPdXnd1PjMJqnrPGt3p7JRzQyzOzsI2zCJPak7uTVkNApd1txTTcI8JwSjTVlsqpcn1v0Zv6YmMHb/CUxqRNEzjOSzeQJvJy1l0pYjjEnwRvKgEXbxXdh8OYrKqu38/irD0Jl9CDHayqrnSbj9OszoQ72QyY6mUeY9VtOT+RJnSLT+YrZ73+Zh0nS8v8WgNLaA9wU2fKx5Qs03GzTlgziy/hwfX5URtHcbzbdD+d6znewubzRuJ1OxZhXLpk7h96ijjJ+qRtP1Cxwacphxyls54aPHqqWh7GqxA9FqvrRJUbNrKDsKNpP80Zaex+tIe2/BlOTTfDG4g1VEI4krA/jdzx8TbXOuXB1I9sXd9KmRYE1ffQ69u8vI65aMzF6GTrIE/fvtZNI7Wc75B3I1IpHRfaJQerEGSfHh5NZ8pto1Et0uFTKSF+Mvk8DLEx+5nW5O+JUSsnKOofMyAuOKeyTu+MmwX9O51CLKvW36DHpuR5LNHdbOvUmbdzcXk7NQ6bAnUcSMR5um0xkoRnDsEQ67fmXqH2OyqpaiOGkGc2pv4dYSiZXXKjY5V/ErbwBTywTSqooJzG3AZ7o7S26VI6XkgnbASt49qcTT9xjtO/6w4fYjwnPfMeCxDpIBF5mq5kr0jBQcs49zPqQ/yqFLqXUoIm3zLKQeLsJTSf4f+fuPyASGDRAV2vpXIntoOD4inQxaeJatojYscDrAfC9p3qebIZRt44BSI6F/uym0MePLth0Ezd9J6qdc9lqO5WfgUkZdNKeqbAg6sZkcfnuYqY2upJrG4z9zCtF64ym7F0p58CLU85SZnBJA4burtP04j9G9BQSvMeTW7kxG5Wkgky1Gd94Z4oeMwWi9CF3vP5Fu6UZyywcGzNXGybSKtTt0eDkmh/jGPiw0HIFHvTxHlJZyU3wlgwI/YTuR9wSQAAAgAElEQVS2jKGDXfAY8IqRw/sybdEhvos/w6lwLl+7fjHerQcy5OByoyfzJ+3A5WgrOofHUuDRgES/MuaqbUZcsQBnE12WacjSMqaGgLG2/Eq7imzEbSxjRaiJe8/hB66UrtFgZKUkdaLq/PltSbj4NOQM1Ej78ZKQvGw+TbzOmYy3/DKQ5sXCaFb0Vabg3R72L8xg3m4LNrQNw1tWgrp5pqzfuAbzWa+J2tnBjmxd3BR6MjEhi8uf19C8VxeFV+oMXyvDoI/fePL9Ada9tHmyNZDLhkp8v2jGfbXvvJKvZuv8N+jnGDKxWpYfAa/x5CLf45bRr3k1MntT2bhgDv03LKPn6SsoFeYSPXoMyt9GEpM1iWLlOC48Xo2GVwwpQaGM+P2H9BH+PNAOwyttEnr9dtAys5YX5zxIU7GhquALLc+XcteqnuhxtegNzOPj05U0nAtnleFbXrilUXx7E6d6ppOu2IKFRR5bT4rSsiuPQRFyLInNxcpDnD5fg5lUNouSZ944u87iTlIih/TKqWypBiMYltnMzxNxaP7Qwe98Ie07FpBfHMSaH/kMdPrBl+H5nNywkFWmpugne1D8JBiFbfd51zsL+bgiNqb3Je36ZcZ/8qKHdh82zNxJ6wgfLuy5ztGTmaSd7QIFCabZ2XPuyyoW9w7l9MhaVBvcuTxjMcveGnJHv5Tbl2oZFq3Oo8Xm1A8v5dGDFWw0mcA1u2noLqmg98bdxGjaUqiRw9XxZxBP9eK7qBVnYkz+czMBSVkFvjiI8WDTLdw7B1HQX47CopU4TTCl8ZYfYZ9TcE9yZPAYKzQTxBjstJIHV/wJ0XRlvs1Eug06UFCbQIfuAe5/PEucT2/+FH6k8Ho2t0xq8TzhTd+Z53hf2Q+z405w4wSOzUl4J9RjaR9Bvok47wb5cslkLGPQRf7pJr5cCEViw1ASV2dSddSCxC96dE79imFIBPUvy7gT3p8tZtvx3jKNr9PkGTvJE9U3L2levxv1I25squjAYlIPvi66SuBoY54lpBCn+QTFdfVI54iim6rJuGvhRDU6s018Icq/L5K29QBZJ78yaNs+XAx7objsPQee76fz7D1iFigwPWE7iZr65JZ8xHlBPcpHn1L7K5b5ZnmcyS9jzNAm/KMvc0D6FVkePzDIG8NZraUUiQfyfNFemvvFIyPaH5G2ZowOVmGmEUb3yC5eXhuKdVMIyzMvsFxqNd0Hw0kT2YNoqDsDP/qheiULvUN70U2YzLrD6pye9InGLWo07yth+b00zsycjv/9BfRQrsP2aRqNC0q5fN0XmSc7OeMajcmctUxfVEe/tDEsMNpFkF4+x0ML8dyTQrOEHvOzupmnqIz8iU2olDRQOcGft6d2c3vvX/ZfOs1S8T8oFWQR8HgXJZfK0X41nEyDVZzUPMxDN3cWvm/EJOYK0oaT6ViqgbVLBCOuTUHp1BvuGp+A6hc4WMYw+oYPxg/vIGqfyvVWX6bc0CPmYio36mWYe/8leQ8L0OiRiduq/fz9+43hGVEw+A8GLR48NTHE3nQ6A+Y2Y6VhwfbifHZ8i6alRZb0khpMTp1j+t0vHB6ygzCVKUQUerJhsyrd4rm4PjCjru9UdjsKtDf85vvnjdi+KOPD1ExmnM2lI80HrSX7UJQroGiWEmuT4/n95S9vKvchf+8GWtU5zFSvQbx0JFO6Z/F+VB43H31j353pbDzehWzlZ3Z/c+Juz22oHzzA4eND6Hw4hTwZY8Q3d9EjLhzrg3//kb//iCDQQ+I3+1Rq8V+qhrWqNDIurvjn3ObWimnE93vBX797mHV/50HCW4acd8P37mg2jZPmiUQWf68EkV3ayfq9vVBotyTBSotdppG46TShUelMjdFrjmYYorIpCYuoaHYPDcfC4Co7UwIIUtVkwdzP7HkxAjeJ6Tw43IWN6jFyXY7hYaWDh5MTU/K3U1dszqmvSVyxnozo+rHYj05ntOt5bg+fQJ7IA5xUe/PjXQee0ywxWG2GUfNZCt9cxHvEQnjijKaDKcm7dqFyYA7yVnPYWfkAb+ltfOzOR+TLWB5uuMRTpwAGDWvh75cjrNqcyCLzVcyd+Zq73+uI7tHAc/k05D6PxEiykpkv36ByeiLb9HIo+VTCr1+/8Hm7nzFJZTR59EThwz1ujZ7ErHI/gkaWEGYRQo+OHSgsSMbxjgI7Lq/Ay8YZ/3xr9pmvRezdJiTa47F/EkKS4yGMg4JYpeTN0NLvjPWXQC7jMZ7vD+CxW5ZZvVsZtqU/2+min08ibzoKaFtehvKwm+go2LBIYxdqn/2wSYF3p1eyYeAkpifUY2+2EsPfq1hk/RKX5gIaC33QH3iRCTVHeNFkwo9jeShU/MY46ik3V/sSMPUbw4aIcFN8HydtFND88oHmURHcdB/M8djvTA/pjapOO8rK1dybOxaxAAWsW1r57uvLlT/RWLeC2e+pfOpTj3TzMIIcx6IzohxplXqsn73ATz6EXfGLiBRX5tH8Kha0zsZQmEJj/UISjt/EOGs/LutP81yuHJE3DymzTcHrTTyDV7gTNyuLz3sUUF72GZeb7/B5NJNJna743nmG3qRVKKsdoXP2KUxKNYkzieF0xh48zVdwMXwlrwdeIjLSAwNPR0Kmt6I/qy+fbTdTfCEJpTvB3Kguo3GcL0mRUhwosuTrhfektI/i6Zt55B4fheizm0zLNKWnTQ7PNAexeNh73A/co1llBsZ7OxDpPMpr8atMWzqKUUvVUD3uygs1XaInF/wzf/8/sv6P1iqixRX9JIQ9oVgtMmB8r5+YSKoRLteX66+/c1K5P5GzlOhVM4SR4S50uNmTmZyHUG1AeVEYYnm9kL3YRnJ6B5MSDPGcKU1ApiHb0lbxYs0+xlhNxmZkf4Z7qVJ4IJFXffcwffYrHKa/4uWyR2jOGI5j1WGGi7fzIc8V6e8hFO+vJ/TwKbQPvKXqtx3+4ZrcP1fDb7cx3KhMY5+8FQ89R7JUmEvU9nVsC5hInzx1pNM+U/tEmV0zJYm/b4NWRBeXHefQqred8Jga0spzWGf5ifHxUYw086NOfAv52XcwCWmhPWAPb5P28/7aDfrmpWPmcoE11n1QL84hVvUvIRf8CZC3o2m3E7G6+izWzKDAqx+3irR4FbKO7P0bUc59j9TQbE7sNaR4kzk/1WNxiBmImbkGuOhxOm8Ok8d+ov/GB3QZTcBR3ghtseFEj62je147m5YGov3pAW9XBLDwZx9awiTYFXCOzx9/4bdfHD/V3px2ec72iUuwc08gzX8HF9+tQK9+LPd/LqVM6hR237I5ddaBSSKKePOXN9qKnFgdzr3FGqRVjuXMpQZqJUvIePOT/L8ZxE3w5oJHDvROYuaiRfyIWcX0own0N5CneHYOcaelSQlu4dysl1heScbyz3k+nVHhz8c4rMcMpypdBCe/QM6cyEaifSdtD+0w219Me9oWmsZVssZsLoLUc+J7SSLyoD/tae9oefeSUR5ejC5exPHNPxiS+JYVbz6xtnAua6a74bB6OrYLI7iq+J3riR1Ydnjx5rssDX/imXvajVafCM6PmMOUlb2pfanOobePUPP5wsyWuYzJtcfVWJWwC5GY9jXg630HfovPQiu3kRrPZCQ/NVAy+DIKFpE0pi7mTXg5g8ck4ra4kdaABORVv6NlvIKkzOHsKxyCdpId80RLiNS7iuc2UZ7sSKRgUSrrnHOJ2xKHbUkbysOL+d2cytzgNlasmsnzD6649E1gXKRA7PaZtFy9yq3qf8/ff0QmINZVh5zMb1QLbxIkVsrqhI8M/GXJ8B4SnD0WhK6wi8nVcXj/dGXMy1/ER4qxasIBfsQpERymzklTJ3RcTXkw7Dt7xe9wZYY2r4O+0TM6kXCbBBa4BVL0wYuiqZ9QP63NwJct+NmdJ0D6DtsWLyBoxQyWly+hImEAp5Q0qRlRzcAyJ6wLpLmg6MbKUeu51X8QY7PGsXGkFTLh/uQE7GFh8E0uVE7FXGkm/u/2MOndMnqkRTFIZTWbxxby4uJmAj/sRcy9jvDPO9ni08FIr2tEhKYzYG8KWA1j4kcL7hm30LsgnacWe9h19jybZ6QwJO8jd195on1uMEtbL5H4V5YNXpVEjwzij/YIpMv78GhKCmvC5zEnIZKd9yzZV6vHh9z1PDZXBjVJXDX7Mt8sj0AMWeS5mPPDFOjsuo9pbB0vdFxR933Gh+rJLOyu4XvxWj66PEJxnS7+mRvo8dgQ3aVX+DVvHKEvo7AuyKLzchE38nxZHG9OoNpt+jY2IGt7mXw/bxa0vOeaWAmmlxcgtlWG7Hvp3DxkxsvzVUhrNXDTUoIakVwOZ1wg23YB039XoFwaxdRLGzCxmoLhZHVURqkSaL+e9oirVNRNo1+0gHd/RQaqTUbL+AR1LzNoNy7jl48a2qZRxGw+SofWWq7rHuBY9U6MvjVSdEaVPfdeoDRGjYfNE+lxsTdnnHfx9e0rzIsCmPlrN0ULX7BTvZ6FLbORnnAJI9k2bN1TOO8mUPI0GPWWKlbY6vL11SmedWkhczyGyNZ8ROfW88JBm6vrSyhQPUP57A34ng8gabcjMTFjWWSjRWH1bBKO1LFC4yWVj8+xUjBgqGkT7vJW3P98G8fSw/QavprSW84ES8shNuc0ni2ZJFbkYFuyjp+f1Wk89JDHWzZxp/cJlHSGYObhh8vwT0h09kPYdATHiRe4PVkHpfWWnPhVwsdtJig5nkR/V39kr16k3SuWCbv2UDH+OWnbdhPs+5TVu7z/kb//iCAg0lzBzoHKZN8qxS3WENt3Uxjl/AM3U2vkU98TNPMcNvE70ZYoRHZYOeGmpUQ3qhF+3YBnQ/YytqWLoUsmccX3I+GdvrT1eYvYU2VcZnsxWvYKUpxl7ihxZLtXMrMmFR2RuxxTX0h96VziRMbiemg/P8bEcye9hXUVe5k47RIJZjK8M/iKhctT9pfVEzVZCZOJmTiMWMaI/Y/IDT9HYOwizkun8rEhjW/jjJD6JkJuzCnuT9LE16Qv0xXdsKk+zY8lEykxTKb7Zidv563CdmYl+zLNWDphKtk3ZmIwKQSZvooMnB6LicYpmh208Hk8hD9nk5B+M4CTXfv5ZBqAx8XHXO9pyFWpQmzd9Zg4rASp4Sr4eG6g9WUmKfc8uK91jAHr3Tl+P4N3PcO5+8gJzRPTEatVpW5UJd9fh+KbUUDA6i4mfu2JcYAXA7+o8917C5bf3Ej9cRUV50peLT/F+uhQrmqNwFe1k5zCKTzKuMMx8QqCb0YR+X0TC0bnoz3fFn/7ldjc0GXRcRUKZU8yJkKfztJNvPUbgdPkz7yytuPBKdCXTaBBpzeSGm3Ejk5hjGQvXEsPMm2dBlVHIVTWnw73l3THf2Bm3B8mfnEmstyX64UzST4xEe9V8jj+cEbfPJMO42LW3RdnjVcBkfWX6CP+hax3txgfmskNjUPkRGcwuq6DKOcgds/VZ49nFLMyRblt5ceZk938VdXigb4JZ77rcm+aHY89xhIb4s673/vZbxXF2gmnmVl/F80bS4iu60OlmAVtSpUE93uFcEdAYV8CzY8+U2rZQefDlTiHZGFRdg+7pluck3Rnr3IwjvX70crdTHjqEGav1UP9jC/rT1wgI86PS1/iKeu7i+CVF/DbeoCMySpUn7PGeL0/NrP96Fn6nSyHqVgdyftfzL1pVAjc1/f/KUopFYqkNGlAKikUSYRMGTNlLCQJZUqmBnMyjxkyJfMYEUmKDGWqyJAhQiVUGqTa/xf39eJZz3p+9/q/uO+1rrPWWeecvc7e7757nX3OPvtLQvvXRFh58Ez1AMZm5nT6MZvIV1NRb78V3dC1RO37juGxAF7rm+C/URFz/URMntwkM2chvYe25vQGH4Zuuvef8fdveB1QsbSSij16ZNTtxMPYG70xGwnLLkU/vwu77y0h49crmrzvze8T9wj2Xssp5wz61k1FZlcyVG8e++83oyzsMx0GKFPftDG7th1ndEIScWbllLW2wyNnDgHB5dzbISzcrYK6ew0Dh5rQ6HRznitZoO55ij1/uhCd9J6/o9M5tvA0BZKO9vt7dLrXD/8F2+mSf576vZH0mF3D+86hBGbu4dQrBSZ/3snDzrvJiZyMtlk9g+IVUIstI+atGw7jt7J55hEeqSvR8WQ0E3ffIyLwM1FJPdALLWDM6iqmzh/K2Xk2WBSmELp/HMqpTVj2yox3j4NJ67mFRwZfqFDW5VT1ROr9l7P5jj1N41LxXWTMVJlKWFdF9lpfQCm7P9fevSJtxgJUel7BM+k9Pd4uY4W3Cifn5HPT9wKb96hTXqHL1LFl+PzyQU1FhRI9d4I1igke95eDR1+SezaP2wtmYeNkQueBFuxXNCQ70pje/Z/RdslTTFSf4lXViocN4+kyy5JlHv7Up7nT/Kw2FeaPUarIpsuDC+R1L+f5lEG0GTAD/2HZ3LLrT/bb3Whpn8NxSiSDZ6Thcs+L5TvW4zJ0GEa91vCja1uiDR1JL//LO8W7PLa2oaG5Ip2Un+K58Bk7lsSzpL4dtTNfkDZ6BMnN7rF01HpmdgziaEZvSsMT+Kq+n3mBK2l+OojH4R95sqkdqkddWEkNUX73MTtfg/2qMtS1V9C59Xf2hY3FYP18fh8eR0b9NyZ22cKzNHXczuhzeGUSHYtKWXD8ICretxjWZwXnB7ty7Oh6NLac5e6reDwdj+JmepKGu9Sw8ayk0mYU3Tu70L1LJvf7teRiihNHBz7jZ7+HTNDtQMuBL1BzeYTbjXBOBPTliUMAhzuMxeNiDokd0tE+2Ya+Zkdxfz6T5FVfcJmZTJONUwl10uGoQiZXDVzRODSMky170uP4XHo0d6M8UY+8TjXM+5rN1NYl6Gwdwuu6Pf9Fyz7Djad7P+Jzfimmc6b8e1mJbbVMRM3ijZRsDJHduVny4quuGCZPkHFLh0hoQIxcnmYjep83ypmZr+SUor+0i0+R3rHu4u4yQz6eTxD/E1XyYlg3sVd/KAd+1YnCLztZeSlP3pRqi3VghGz1biTKPX/I/p6hcvZohZx9/ENGO3aXXgP7iWW+iZTkd5IJ3Ypl8+178ru5u7hvXSWv3j8QNr2RAt1OolBfKOpT10hPi9Oy/fgG0S47Km/zzWXJ899i1mu1XFipKwNyVsml7hcluXmV3J81Xwbs3COD8vJl0Pr7UhH8Wd7ol0pS2QTp22CwKCYfkLUrD8sMF1cZVKMna5Xbisk8X1mu9ULKHc+Jz4QO0lfrkIwe81ZKPofI98fZkm9nJIeHLxUdRX15NWesDBoyQRrOS5YFG1pJ20ZLpMG8Oim/+lm2q22Ssjk3ZLT1ODmk00U+3b8ty96YyKEXcdL4dKjUJkbIFB9zGT/SXubNipTXjW/Jm9mF0mZkmih9cRMf1Wz5elxZlHcMl4ovYRK+e7zsP6Yqh8aPlwuzbsuz7FT5o2Mni04fkOwZ2bIqs0i2OkZL4qyx8tjPQNpfPCctv3WWbRV+8v3BEDnRcZS8Hx0qhUZGssJsg/xx1ZUrgY9kUN1IWZiyRizyjslaeyfpcFpNLtnPlMSL02VndTPZWPJDgnYelddVG6Tn+8/ySu+lJA1oKkn9F8rt/qfl0qpVMlnTRZa26CK1XsNlSJu2smbofMnLeivbk0eKf4W23Fh8W7yG3pKhIdWSODJAXOJeyNYOljKkrJ+0abBBipsNFYWNL0XZe79ore0j1+w1xSnylgQX2cicDtqyNN5TzqcelPWRj6T8grPMvH1KVIOURFl7kFwwHC9vI87IoH7fRKNjqpQq7JHTYZqy4Pcn8bu/SJatrpAZj7aLxbGvEpiUJD53qsQwOVHuPDSS8NPhkqBzWfo9vC/1buYyZK6ynP6QI3Y3G8nWUCSo8z1RSZ4mJwe/kWFKj+Ra21Gy+KWCdNZ9IiumfhSNkaPERj1Q7GzXSMuUgRL1JkrMk7rK+A6r5Mf7e5JlNFHmxsSJesQcaf6ju5xflyNrDxv9u1mJO+krS8a713JfL0z6GNhIRkGNvHiySF5t6isxCe4S4Rcnv8qeylglHYl17C1p33JlX9sY+f32uOwa2VmWd3wmOxOGidaLD1K9VkP058ySj88+y9qPnyTFdIo07P5Qjs3sL+rjbERnwQbZnaoqnkG5kqLxWsb8jBb/MW8k6cgdsfV/IU3O1MsgB0X5nBggiRGXZf/VSkmoipJXSt/l83IfiW/9WnxdLOT153FSZrhYnBK+yyn/akkekyBqXt1lxmoP6XToscSbXRb7NypSpdtfBjnVypiTY2XCQDMp6uchrQwLpM0nN3F1txKrBe1k330lqcmolMS7t6RbWJHEH4+Seys1JXzCZZk3PFa+ZSyS01rrxDdnqmxsNkiqHm4V2+VVcmFulCjbIKY3Psn6N/6i9UlP/nyaKms4LEM/1ElXz7sypcxOMq1Hy6v6LvLtzlnp2mGn2J98Kqqhn2XGaU15M15HDBxspGGwpzy+1V6MTxyRTZsd5WhQlswZUSTzX2qIc7in7HlrKakbzskjA2+5/veM5HbxEJOcLbK8p4K42P2SUp11opZiIp32PJEq/WRJeXFaztV9lX1FkTIlSVGmhTeQpNQD0v6Ig6w4f0r8J+6TnHFP5WvDfXIhuJP4XlQRhRU/JNz9qEQZzJRKRzvRv/JNjgz1kqi0z9Jdd5kcGxUmOq3nSW/XhrLV8aAUZr4WwxB72dG1l1g9ypZl3QKk76ds6dotSqrtj8iAeYXiv0NbLuy5KJXXTGTXjVvS30xFvqw7L2NjrkrN80kSqdRVkl87S9jSATKxKlTW1baR299PyZm5ZyVmSbG0tq4Vt8ftJKvNAeml0FAqHtSJ8trRsqPcSMz69ZdWRmOlPu+DtOi8RnbcbCB9R8eItoKljHRqKxPW6kn5vZ9yXb+ntHUok9gbEbIw7o9kjugqt85qyYWdZhITEyhu7ffI5nHbJTAvQqbqmEsPazvJW6IuB6M+y/sJNeJy7Zdk9l0sQTr1otbNVTTnTBfdhh9E0+uGXNszWp75vZb3dlPlfO16mfAtXHLXXZKRu0qlT4K7pMekyeHsOrnicO8/OoF/RTigoGAqzQ4O5ebiTqj6mHIgvQndK23IO/uMk+n1jF2wnnW7D6Kk9gL/jQF4TkjinepHrr+sIK1pAZf6jCDm7AROpLwkuMoPdTsnTKycuWw5ilCPX/TY50fR/WP8MDZkk18iTinufE7/SppBY4zVzWimNBkl53o6rd7Hl7cnWWseQJTbZDS79iB8ni/HE2Op2pbBhPlRjGoVRtSANixY+JDrZu/waruSS1vusyUviIICb3yfG6BcFU3vPs5U3lxH5Wgd9IMH8XzqBzZfzCEz+QOtfeOof7YdD6nB0/oJp89V8Ne1Ne/sFDljf5MGj0p55afNJFdfFDeo8b2dKqWNVnGqeTMyjexI7LkRf3cDGkyK5pVWG44EmZIde58/f3eyyL2IyHo3Buyu433acha1c6dHdUtGjFbjTWIapT7HKS2rY2J1R+pSdtE+fzEG7z5heT2Ga4E3GVqShnWLWJqZeLM3uwk+4Q4Mnx7L0aR9hJ/ZQUnxJYzPJSBLWzBu4xO2dlvIT/v2yLzL6BxIRIbtZnrqG74/6snB8LNsuO3LoQW/UHsUx8kZkdhMa0hTgzrWmCqzcVlLFiQE8GFzBQ9POnKlS0s+5dphkL2PVPsLvPloxpYn77lq+x2FhzWoVCfT/9AAMtzXU2Vix0rFWg7NdqMibQg5q8Mp7BzG3BOWLAxJJGBgHtm7PxN3rS3ueXoUdjRB/3Y0M05NZnFgD57nRZFq58Qriw98v65H5DMvQs4Y0dxpNykWG1AZuphDqm2paj2Nb5V+pB0dy3SNDOqzQ+i9ZwCeERUcj84kuKo/Pfr60ykukEcVewhqmkr49zTK523k4aUYrm07y7RlDxm6Q4FjrYI5POwqMlidbu3Oc7ewHWWe3mgtC6GaAzS0K2BTSS5DJvVm3oMNmCaMwn7vYD50LKNiWCsydz5g79XH9N/Qhk75QaQH32Jc3h6851cz444jAx0rue1ag1O2FW9jo+nzdxeu3b3YYnsNo9OHGbOv7N8bDih0NBOX0h/SqJun2GWslMen3KVnxEcZNXCmuFhWyPbvmtL1nIFE/pope07ck2d1K2TS/BQJ+9xNXtzbLhH3LKV4VnN5sfKUzPQYJLnx9dJ91wPR+VIrKyY2keLIp2Jo+0nUK/eJYmcDSU94JFMHVMvtlTqibbRAGi0tliUNXsl8q3li+OiePNmqIQ+TO4nL3q5SUhkmrtcV5YL+ablTWSFF7edJcV9zeembJnVzW8ml69FSpZYum2uC5YT9X1n4+7L4Gj8WLaNAyR/fWDbuPCHp/g/klZu+DDgUJ3NGr5Aux9fJcbsyyTqhKlefZktmXzvpEGcps8YoSMxMcwlYFC3O/daK2oAQae6aLzsb+Mqor2Wia6csucG9xOzFKbFw9hDtvx9ExeqbZOWHiUFAggzSsRL1Hg/Fo0BRDrU/J29X7ZNWRjfFe4Wv3JhzVA62jJbshVvENeep2CXYiuYga0lxTZZHC3/KDMdbohf0Xn6WJIv7szjxD+slvw1NxMDKQzzP3pHHLTpKhn28RGV5S4uXA2TxvWJpWrBGhkQ8Et1TebLe+rcs7pEhh0NyxLXqknx/tFdG/naVKXdfyLqyYrmcqCHJKy2lb1ytDO80WmL2PhXveHNpE5Mm8XvqRIOOcmHYXNGpfyKxS97L375PpUffMfKr9o7krtotjT1eSGLeUPmwqFwUvxyUtufj5OqFHNk/MkWazLgqNbl7pbTwmtSfnCFmfZuIc/BY8fB4I96LoyRQ9614DmkgZa+tZcn6x7Jhe6pMDjsmy/30JbvJGAkaGCErLxVJpcVBMRy4VG6f/yX2tQ/l9vEOoj9qtQxws5BnjcbL+vpZkvN0jFx9cUsczDxkYsYoMc6MrogAACAASURBVFBSF8+hbeVNA0+pXLRb1Hpoy5WbM6Q0vEYaPrCQA92/S0X1PLE/Nk2qJ8WJY9xrOdxVXRKiV0iHl51FfWArOfG9gzw9c0LS+jWScMcusnDhbnFa20Sm3dIVrbcu4n+xtzTX+ysK9eOlptU+eXIjSLadt5Kvmm0kWu+5TNZuKZ81J4rpqOYyPzlS2s3YJL6vQ+XFuRrZ+7WH2B9YKjorNf7dJwFN245i26Ezn45X8tEokCMW33DRXkW8zi7iynXZ1MSElNbD6JGtj8+C7sT306KTUQo2v40YeuIQFcvcWXM/mOp5STw382F5bhjBQYHsnVTP4WNh7Ggxnkb1Kgw1NMH1tQsrnjozc3caT7YY8aTxer61NuBjq2lMnORM5JXHqJfvRWGcDdN79Sd0rAc7t1vh8n0gd8z8qbq3noTiYjoXH8LC6Q4H9BfxekojQkbU0c2uJZvVu/Hh91SUv4cz9+1xmuedYdSpvSTXHiX3dyqBjmXsat+BsrE6fCtcwkqNk4RM18Wt/3QevHGBUdspnj6KzGPL6aFSRtXkfZSeaYb2pwhettrImT87WT3sLKfPv2ZC295sW+1L39UTyO5lR+e3L6h+94F5J7uwYlwFx96VEzF2H28dfKhtkYKdxwh89kLV3Hec03jHzLFtaHolkMSR+dhfecjFlWPZM/4jvdJuMdhpOp0iL7Pw5BuaOb3E5qUmQbc+UXUzCuWPC9k8yRjVDiM5qPWAjkVT2eP+mx6lXZmLNnEFf7nWW4P2YakE7m7Mg5uOFD2xZmZqexRT39Gv/XAajTlJtxed2Go3mKDuN6iMUCDbdzwb7TRpqZNJYpENM58683LQXqKbaDDubTraWV95lZ9Bg/SjDP39HqcJwzg5N43eu925d2oZXTe3Zk/BAao2B7JjhiKr9g8g1LkR5sN3Uhukx6gXH3lbmcbPwCbMDrOkakxnfrR8j0/xJVZpm/LUui13Ykdge60vL5zbMO7vPJ7dg7xd6/DL2scjGyMWW0fiqm7P09hoOt1azHP1bzhNOEF17nWifHWpmRHC0ZPd8f0dQd7AFDY1ScZg02S2v+5Gn5Q8yitK+Fakjm1YPc6jKrgUlcye0Y2YvsqQd9rbWBh5lGveWagpJ/F7rAvXlwuXWrkxLtAdv5xPLHm4lpRRhTwrT+GRmwee6he4mX0d2tsxrWshR/7GUFt7h+rtjzl+q553UzU52Pg6R762ZH9W5r+35LhW3W8yMmuJO6ZIwxu9GRegysFpS4gx3M++LZ/4PUGbQu8dVDR7zLlxK/BctZ3dyvGEtrChw2Zj5ukrcGGCcKkwhza3c3m14A8lfh9J23+Gn5vDuWw2lcnFa6nTcqduxUw6Fn3G5XQmz2pXMND8Dz0WdmSl9jVavRlHx1f+KKYk4W20lrb5FWS796NHny/83aHApiRFLh9TQH9THsU9j1G6xosjn9fTwuAXey7cp09KN5Y/SqDhimoe+YymepYS+Z828inuEZO3rOfXmYEkaobS7r0tmi9s6dN2H8mZL9C+ehQ/ly0YvbvBZ1tDBo/RwnNVOvtP3GRcaT7uS0oJ8dInb5UpT7ouotR1NnNHjuHkPmuGzC6knbkvsb36cjBwLYWlUWRcMudNm7Ps+KJKSMti1Aw3E3DyBJdCJpF+JwL/VXZ81P/BnZ6tKYyPxSJ1Ox8e9yO87h7DdbuSvO0CPed24LPFJhzGT2enTU/05lfyuEKDP6lDqVV+R4pGOX8KWjJ/pBfHv2qS8dCN1NTbjOpXReoiS8xrPnDyZRG/87fTNs2Wl9rr2LCsBd1qwwh7u5vVmZsJf3yBF0WP+ba3ltaPBpDrNosHTxII/5ZD6q5ylm1djDFzuTFUgT5fh/BjVR0XcsbhcF/44hvEoL9N6FoxgC2eVzjhkMW1qByKptozpt0c+nwcQV6aNUcLN9PugjObjc6zfcl87l51QFUlAIXGRwmeWIxD6Q18kjej2PUXp5Z/4+oLb0Jf3yG4/zwCLvrxN+g4+ksH8td7JOF+n/iU5UtdC2duGu2i88VoLpq60V7xJnXbe2GDN7NdmmJ+fBbvB6ZjeeQ4dyK10c6G5G4b2doljRZK3zFdP5OQdSP4Ua7PsN2NKCz6TFW+M37Hl/PBczkxeY35+OIYWReXcP2+OrGmB2lRNYlbj1fw1PMoTyaYMXRBJS5TlTlhFoLa0VK0dfvxZ1Zr0hpspJWLOqYVuzmQ/oPhLn5Y13oj55NB7f+Nv39HnkC1Mupr7nHw0jKUArOYZvuZ6acmUBXQgRWjDrL4xkRsfdaTW3+BYf5WvH1UQMBhISipHv+eAdi2VcEl7B0ZG+bR4Zg6BhsXo7ExDqvL6YSfSKCl8yd2fslheMEYRtml8uPXbMb2Oc0Gx1i+FqbwY+IoHuxpz5y3y6h55MP68cvwuPYUp1/BXLysybLJmXSw92WGNOTcEFV22x9hx7kI9E5No0X6BEb67+DIDE3KC/4y+hBMbF5CvrEZD8c+x75yAYHKrTid1BCHn5fp/COI8OCX+GZ35FNBDAfORWEx24bKAbGovnjGR6sq5mSmEVM8k+Z6Z3gdUs2z+zOZ9ucEVhdWU+a4gOvTe6Fr8gEXrUyKLxTj9CKc6Rb2rHk9mKbBgfxuXsKx9gXc/3WXmIu7uRtsRI9xjZhf8YPyVY2xvTuRh8uL2TB5BdUXf3Il8DxZsy8z5+Ad7q07xaZx7linL6BBdQVK9QOYXzOWNgv+0L9sAs3cNAnUbQNv7/Dd8wkBW68yeM8KqnqG0PSbBZe7v2Vpljtrx71GOa0/S0MvMv+qCRcXz+fFgKVc0RzA71FeTB42nSfHXPl5QI0sU2cm92+Kh+oeTHoNoWrVLdZ2P03JYAviOjzE4utLfLcI1w91oMyiD+MCQohssJDMwXfYOHMRWh9/sUTNgNdOxqRMakaSjzv6WysxPLGNkJCfxDWcjkHdS56tfITq/VOMsLbk0JsG5Bz9y1nNTM4+dGXy1JnY3rkOp1vjW7KLGUrZFCyah0vQe5rrtIN3P4gve8e4jGq6uU5Cy3g4Bl+VKZsagtfju8yb6UHdgjRCD75hiEM4GwaUoKG3hX0H9qNw9imlw4S3iukkPKlgz9RAYmpGUrL7PCo/FuN7filfDn1Bq1MWcYrtCT6hwpDvQzj/cRVO3daisLE7g1vcIvf6MEYarcKrKJkeEU7oRHph21CbQQfj+Dv8E3ccbxJ5pw1/dGbw9/pOlno35ZeqKkapRrRu+OA/4u9fcRL4/E6dkRUL2Ns3mbfKSowzGU+inzMn41szzsiSi1FwfdIrdpyrpf7uCU77+bA/7xYL9g7h+rh0tLt85Uu3vtSYDsX37TqanPnK4k6bsBlgSM7aNfRMnIFNYgpbez1gmEI7qv0q8DhyEaf3YZicCeXb0jo+L1tFVT9zJvu502roL2YfHcTxO9eYP0QRU51krm++w1trT27bdOWWfiJLQxdhrluHhkMjzmkUUeyawKaXT9iWch779BreP5zNK5uvxJ9bREavpxw2Oc/u8CgmDrtJp0lX+Wv7mo4u62kW44rfSX+cK38zvHkf2rm/Z6bSDeanWnHf7gbH5/ekZspM3kfW4rHrFTEJdVT7X2SRw0aa3RtFz/BmWHxS4nU/d7IaJuJ/bjnzGs/n77FVrHJSYtFo+Kt+GIecEKa6GDOxuBvNn3Wnc+B+XDODefHCC8OQyyiNH0tMzy0o1dixN6+A514VXJpXQfvoPXz4Ycv+J9rc5jn1D3zobBHEtpExVLk9Z4j3Ffy6ePC9Yh0+Ly7R+6MD/l2rqbn7isY/NmBlkUVw3QU+7Mygafp29C5mUt6lNTtaG3FjRCpBLTK44Lqfw5MSmfR8MaFWuix/f4gHN64xvuMTvGz1aDLsFq927maTuwVni8ewoslfpuf95HBLW8Y+XMylRhVE7vxOUPMq3mToYnPYiuShrvxonk7ooBI2h2dx1e8lroXvUKvcR4tJ1pgfSUf3nCnKN3bTqGk85yYHkz70EYZ103gz0pqZ26wod1hM5NtdqHUpwm9qNCs8SrBWUcUr/zYHGr+mU693VA39Q2hdT0ZOraXnou1sOtKRe08icOxlztC2Y+h9J4bzih34pbGG6ifaTDYLQqXnHzRazSe28gv75+hw0NuMyFadsIj9wv2PaWQ5bOP5iunMtPKgZXo9o+w307Q2Du+FWxh9bTQHIrfhenktJi2CSHJviLdJMTsWt8chQpWlw97go92IzxWPCZ7TgphOycRP38hfB2Pg1f8Tf/8ThUY/KCgoZP1DNJLxj6yZgoLCDQUFhTf/jE3/Oxs6Gj+5HtmVg13DSE2uQev5BRq3L2JudAg3DrbjtXsBDVv142teISGO08mxzGPRmVNsHVqIU5UTdx3j+ZirR0OPVAZPOUm2TxV+CwIIW/yTPw0O081rNKphLfj5uQlPpjVj/v4+WJjd4Oa2JhQq6jNcxZquV4N5mjOfDjeGYmA6iDXtv1IZncuPrZmMahpD5AsTtszphr34szo+gtKhASiEbiK3QQXOjWYRf7IPScHGHN7zm2n6Dwg7GU3rr11xTCtjV+E3+vVNpNvx47Se5smk4QEczlAlrOkJFs9vxHfPLcxdmsGyPgl8a1TGryI7/n48wACTuZx9PoaGzqFsyxnFskToYH2ZK80G0Ch7GNYfrlG4PBBPrRgO9y+n5b7O/Fr+nP4GIzhiNgNV26tkajfg78ez/G73hYv9LlKYdQDFu88IHvOE+a09mHx2NntW9mPz5y08uzse8ZiMeqQepxS2Ul00iy299zHpWUe+T9rAvi2vsd+wmYgdiuj8aYFRnh9HFRrTbc5jfDMMudvClK6LY5HDAyhVCWBCdCSK3zOZ+f0AbvOvYZ59mFG5auxfm0Njh6m4m5WzO/4de7v700rzPKkbLWg/Kop1t7QZvL2K30ee0GvHI55PnMnY9vqs3z4W/z/fuWz+ml8j0ul8v5TwpO4czzzA+pB9qOq9pOXXnfSt00IrWJ3x+pFobvck8fN3erfM5Mhae779deG1xmOKD91j0/408qPv82z7IPQKlTF89QMVl8s8Lj7IHOUrDNP/ytgX2+lom0Rzm3xqd2zhkm0Es2ymo1NnxrpOiri2rqRdVCQ7/NLodrOMST9jKU3YxZ7DP9n9IADHKBN620XQZFMExYV55FQOZKq1CRuGOLN/ryXXj43Eo5cDE2q68bR4D7nnv9MzvjFJqdO4kfaFsV2NWL+ghCM13tzWX0b6+hmoocbkfjHY6uaSfmwEIRdGU7fdkmz9z4zclk6igxPzm3hRcFWdDsNq8W5oxiqVT/8Rf/9T4YCriNj+H5cOwUCSiJgBSf+s/2Nr8EuNUX4jydVx5Na97cR47uH5h6ss67eY6k1fmKSUTLyRE597PSDmviL7V25nyCglkh5nUTq9Lyd7j+bT0ROcizcgr0Vzdiovwqf7Eo7ezGaTC2Q2f8ClFrc4F7WYtJ6u3Ni0gHQFZe7dOE6FfxyzAiqxrBnEspGLKOpnT/8fD7FNreDTvZX0XRKA+wNj7NN7kDW9NcuVr+L1+Ap5P8wxeubNgjODqfEsYOfRLSxXWI+VUxLuV58TkqHMlIhJFG/dh53BNgyDPFh58jpfYo+CZxeyy7K5v7IJOrlNsd3VFb3dMfyYdYAzLaM5e+Y4/Zt5cOhUJe8fPWNrviYlNsb02ppJWVV3FBf6Edl3MOrnlXBv+Zs9E/ehXZLDmtgQJp1pjfqtdhis6EdJowSOrD1PxqBcErxnkXqhDetf92Oozg76Hk4hvWA5GxKHoWTcnXJrd9ar3MWqXzjRFzXpFPCQA8kdsW26iTwnV15dbo1GXB0vz65kcM45Dnb6g0+uGrExven99yuBUc8wiYrApUcc42JzWdDnOm1+HcTUeSyPNjXk47wSQvpM5sOsGXgd7MnNDWN4Y9+FD6qZVO3ZT89xr9C4e5gB5x8R3r+Q3IQZ9FtzjajwMTjd9KbO7A1ZXf4w3UaXJgWb6J7XiqTbT9Do8Izcv184+/wEUZX5nDocxi7n7mhsrcRKw5hL/vMoXNKUy24aPB38hN89wkjqWcGUjyeQh8sYfGkK8aN/kOF/F/MPcQwKreLFrZnc0RrMg0MlvM8tIsylGL+qbyyL7sbKX5sYN90P00l/iSvtR/ifs3zyb4HHyJUsL9RH3d6AXrt1mW+swNqkafi+n8UVK19a/XUmqEcpSwvnc1Cphj+La7CYNZWWAXc50/IbC/pfwuJTLXf3N8d1Zin9By7FLP01f123M19pCMMdK7nSdyLLtW8T89KF/pN/8uiPCrXzjnPmWAnuK3/hr+jFaY0O+Fe3xbf2LN/2WNK58RIUkuxYqG3wv+4E/u82FDj8z/wwMOy/21xoXse1Dp1IO7eFkvp2OB1rwybXrkS8usORduU41B6g782RRET/JlrpAEued+WejxEh0wKY3EKHCfO+cLJzOi1HdcRq6Rne/F3MgxpLPiXkstrsKMfPzuVU3y58bRLBubA3PCqawN4We0lpGM0ku2A+Ld1N9o0vuDf2Y2r6K1TtM3i7YDhJWxRwaNiMYB8PukbcZHb/YTQetpQLk1xRrLnMlWs3SWhfRcbYrfSMeEzPhksJUAOdg4l86twe99+PUNf042ETTV5mPydqzTLuMIF288owSMrh0K8MTvaei6ZtB7KcNHCITiU17glOrqoMuruQ+7uOY7oukryMVKzTazAOGEudaRS6c97xx+cOao2O0ar8AJ+az6XLtzzc2rzDeMJa1uyHkU9y+DFiApnuDyl9P5SIlpfYdHo4d9Yq0vVnC5K8DHFbeRW9M1PIH/gWb/ObLH26FrW6MkzXnsV50nPmqu1F8koYvmwevR5O4Mwga+ZrXSE70QjVmwG8tSrl1uo09mvdwj9lLbWaNeQd1sBmeQeCGwymz9FfdP7yjBvbvpJpDMN1FqKT0InlkYfZFufL7owAQnuk8W2GAQ3aOPNy8UmyzS258nw+baZ2ZaJKY3oH9iKiSxNOpKkwwNiXkQdVCfDP5tX22eTfCKTjst84Z3xnhLPQx3k00ZuFDepTMF3SnsglizljVY929BLu55QzYMwk+lTGMnCgJnVfjZm5bxJ9q8fz5yKon/Hm2stAJjj5wsrB5J20J6nwCZOzZ6KyYhCFywdy+qw98i6VozcTuV6ujNKtYwxea4eRVPDnfQQmfd9gYpHEnlGV7PrWim37Islcto8Xv7/xOUhwb9YTp6pv5GZXYZb9nQst/DlkG8muZpb0SWrMzt2/uLG3O99W9KOvdzmjD7RiY4oZjX160eiUJjbz/uAYrEtx1nEW1YWQYa3OhXmfeV+sxv4OZ3n4ujW+fbdwe8R1Oqusw2xRAU3jP7P55yvstiT+R/z9TzgBARIVFBQyFRQUZvwjayn/FBf9Z2zxfyv9n7wDar9f4uLcAQ+3EaxWzkftuB13+k6hx9JZTDFSYX+aJrpeeTgNC2BS1XkOeD2ihdcFDq89QJv+yhjH5xHk+YEJzbvxx2YkvuFKaCRq8nHOV1opqWG4cAUhx97x69AtWjV5g0fEMAo+bmeL9UKiC/wZ6p/Jh+vxGPsZ0LLZBj43zKClVj4Zem2JWDCL2yGB/Cw1Jy/PgY5hjix63p9Dff1Yp1TO8P3W1P1yJH9Td4K1OvDkmgaf3YIpG7+Q5ivvM+PnLSYaVdC2xxoaNFCmo18cDkW9CLXfxRSl3iy+84x+Bfc5evMp162/UO45nJZd9mHaaipv+jRn8+YmuG3sxeZtV9jyui2n9CO5mX4eZdP7BNU0JoabTPZqQeXeRWh1Pc2jPvNxqnjDzvZbWLjajUUP4LCnImYOsVy1bouquSFTE4NIduvEkJM3Oe1izxD/rjg9q2DR5nyKWtcQV1/Eg+QwWgzpSMUuLQ5F9OLx1ba03xpIm5idZEkca9+84EGNHbkvLrC+YgDPNDfjdagL7ZxyUF9+mA9vHvDpdwkv0yqY3reWlOSmmHvkEnPMi9h+u+l01Rbt1ivQmDaV81vhx/sCMk83YmDlbVY7qDB5xyc8phQypqicxCHebCyyZmafgXxrMIeU+yNwKl5PF/uWhEf85FTWdg5yHddTyzjq15ft4ek80X1IwW8H/nrEMTgmn4ol9sjvj6z51oX4ZV1ps1mPpX4pLCGDD546nC4+QmLyN/b27YjBunrMTmUyJiWFHpoPUe1ex7OATexVKsTnZRsOdbtAI4u1eP/5wrWcNwTONeFWSQZWWptoNK8c6+M6jFhhyqL0j1i2XE96gDXKnn/4nHqLXp9jSfCwYk7JR64OXMGsLtu4dGoOqTarKV61DtMVB2g8diqPOzYk7rE772J6o/X0EEcDbzBz1Ve++1+l758VDK//yeb7Edy92YBpg9RRsu2G6818LuTvImTCSZIVZjH7ZRXtTl4lx2EPVj69/ledQHcRsQMGAP4KCgo9//8oiUi0iNiLiL1BI1MujsjBJmws9zc6kmt9HjcvBW5evUmWhhZBO7bRs2NLuk+cxe0Z3+jsoUuEfhgLK/W5VxbHmYrrBD4rpE1LWDighkb1hvQ41I/QkGPUKbvxLXcbcdqvmJQYhcHA3bwaNp91nsd4/PQpzx8a4rwpEzP34Zw/mMHl/G5sPrOLjXNWodXsDZnHZqBVaskwvyB8W7fE6sgABq0q5c+8paz48Zq0r21puXsMS02H8ndKAlaP8hle1YrBDoVEewjLbzgx9+kRlqcmMO/KSU6HxNIqdCQ7lQvRtF/Cy5BrzD97mDAdYy6uLCD0Uw5dZt1mmk4b7FV86bbOhcxnfzn2pwOdnHVZGVJCsdlP7ihmMkTlCPeZiW3nHkz1rcUx5BivewSwrpkNK8cH8tJ6Ak0nhdJy6mx+9NFgZePnjEt/QcDTXTh3s2CuQQkJg9qQsLqCr04JKKSUsPeyLacjllF8r5gOY5uSYuzFulpVPkwPI924IS+u/+LwjwcEnn3O1IjlNM9LZHGMJpHnLAme/gDHfiMpbPuTm+cCQMeT2z2MOa0zHcsv7zh3xYOz01Vo3KyAbenh1HUOxWTKA9ZU2tF3hBYR+xphVvSBUONB5Je1ZW9GEGprYxkSsYD2l1vR3cQM4yOFhL0Zx+Vof9Tf2DP693v6tytjZGcvngTPZ84PYxrXmnKqwwIuVPjwbpgiLwaMZdgaLRJSVrFsXztODH5MQIcFbM/3oJdSEqcOmjHp6BJivHuwea4VD9N3sGhnNKOK0tG7cowxVqk8j15BWd1cVL41xr2pD1U7vKi0tuD3hUHot43HuegW7fO38EP/D1aXh3CgzU3GRvnS+H0MvxI8SbRZwvcBFaSU/MIq5SrXk/dzOKQZ6slbWTD7KU8sFrG42QsWNTbGJ9sRTH8yWmURAWsn82udF21ZQ9oJS2xMz9HpawJt/v4k1jub6Snb2XQoBl2F1sxXv8x8zzlsGPGevKJsDiwrp8JwLWes1Ah28f5vwfg/1vmHjJT/uoZs9Y+sFfDqv9PraNRRGv8MlrDni0TBYoRUdDSTI1pHZV/6d3nr/EdqLRLkvvkjcat+IfMmNRKtM0tFxzJHbhWZyrdX66XP6wZy53BvCXceIx2NV8mVFedkikWpGH7uL/ev7BDnSENR1YyR1c+rZFlmmUxqYi2KQaEy/7ahlNXdk9mrLsuG3G9ydpOHdL1kIC2+VcqQUXoSGxkmm1ZoyNkvnWTboVJZF9ZOPvpaitPYcVJkdkhGrjgvQ/XGSYte7WXouVnSVLVCMv6OEL2JI8Qi87Ko2ftKRtIjqVRtJEfi3UTZq04ilY2lgfU+6RXfTwxUSqX1ik9yvvipzNBcIvOm64tXdq34N86TQ+16SdHsCFn6fJo0WTpJbq8pktk/NMUlqUoaV10Q+7SvsqPZUTnjYCGPQ+7IyZnjZF1uUyl3HCRubx1kh+0iaZ/eTbL2R0g3E1XJ6uol+y0Oiv/8G9JoeoR0fJAlTyqfiLHLUOmgaSl6O5CRL31lb221DCqOkNlGNqJuMkEK76ZLd/MdUro+Xp6uSRX3kiHiNKS/5K6eK3U/1otO6AWZMtRJIgqNpHlBrMw/pCJVjiay7uIPMXreXjrO0JCt3QxlsrqxFM9oJXaT9MXk/TLZtUhZBuZsl93rH0ub8hmy+8MyyQ27LSyrk7XXq0UxtI0M63xNmnw4IRHnnMVEabqYahaIR+NhUnDjk5y45y5lZtkyKXazNJkQJKppb6S8JlayVtfJnYHLRGvKGclpayg2Q90kaFkzWV6QI1UfVKVxdqh8PLhWarXixGnBbPm58qKMvz9MyjWrJTtoqGzfmi5D7TylZOwNWfV1tEwqypOczT4yQddFej/cIkwLkiuRe2XrtvNieaGXfNluLydKPaR5+2CJLx0sl7vly+Dnq0X7wl+ZOKyBVGk9Ed9ZNaLfcbd8nqwom/uOk6zYYjky9pmo5QyUtBZb5ZhGhrwtqJHmESUyp1pdlMqvyfDQPbJrbKE0z1OTpYNcZF/GeXl6/LmM/pgvCuePS0T5Y4k+s0XmTX8v9WbpEtS+VvSO35W4uDES6lYu/bIqJWpDhgzLKZO95kcka6nP/84HIv4r/aDJ/zG/B7gDkUDwP/JgYMN/Z8fSSlEuPQ2WwWMr5b65p2xV8Jaz+p3k7ZJnYmmzQ9Yf15Sdtnfk2Tg7ubCzSM53eCS+L49I9scz8nbHQlF3+yV3NjWS91erZXxmrXw3UhHDkkdyXbGVjE5/J/MUv8rgqMYS2/ivzHj4VsL1LsqzUDNZGjJMju0cIC8fhciAuQUy8EO9vP2pL79+lMuAGy3lsKKlHDB4KYmbH0hExWoZ8fujjE7vLFGFv6TFXy0J+XhfDGcVS9ybKKnubCsNBw+Qw3sTRTd2g7QIuSJ5xx9LbckVGRjZUPIrL0rd8oHSdUyYDFj7QRxX/ZXrlw9Ji+0npUFmnLSpOikKwS+kfNcWOZTWQKKHVCIuCwAAIABJREFU3pN7+51krntPcbSxlMt3C6XhuI7y/tIQaTv6jrz7MkeSk92l5fqjcsnoloQtLJCSyOYytWqvDB93XV5GPJAte7bI5gpnaT3olUS3NJRbQdZSrV8k6orxUhdcJ1l1P+XS1yBpvXeQFDa/JJ/adBRfo0Ny176JHPpjI81Vk2TB/hviUl8pz8ZFydE+CdLD8q3sWHJD7Cv6y7u1njLV0UZsh0dLrFOlxKiOkMZ5T8V/aHOZVlQvrQ+pS/Wp77JgYENJ12wsihHvJbffLNEvT5cE9UAhdraUn54opw+4SvlBDznR4I+M6bta6v4+Fq/Wg8R+/T6x3bVC0t3+yJVTkfIsurt4qRbL+uqL0v5qhUwKcpJ72/LEOW6TrPjiLwNC20vTxEQ5W7pOstoNlK2tZ0ua/y05tHOWmAVPkt8je0qkna7Yh5qJx64uUjx+s9wpz5HodadkjJ+znDk2RvS1jGS5l4ckXjKW+xtXSmLqH+lx9rbcP6wntds7yJiS+zJKN0eumr+S22nt5ermMLE87S2V8+9I09Av0ud4tFgdPikVpfuk829lqbA4Id7fw2TipkYSlNJNVNt+lZ9q1qLs00AuNHgp30eEitnJjuJ9IlyKv8VKbq2qXHjpKidVUmVB95byItNMzCtHSPHdnRL7rUKOL1srRaPdJDDdVc6nRcoh+zUys+CurOxmLNOUjsvv2D5yq7JS9J91kNUqJRI25qt08ouRio6x/ztpwwoKCibA+X+WDYHjIrJaQUGhOXAKaAPkA54i8uM/2ems31oSyoaRfM2P0GMLafZ3P5cbzMaphQZ9l63GL0CF6YoOBGVnkf06h8oHzTG3PcqWpQG4rbEiev91mqdqM9JnBd9OjqPU15yVTj7k/DBCpUUpjdZ78GhdIS1XhXMzp4qdniYkR6lz/v0ELJUasC1Dj/anvzA3YS07qkfgc7sRCnpajA3bTGVmAi0XDaKh5y8uFbTg077pDN6kR06eCuO1txI9byG6atOYo+2E9ZwdxN9aB60e0q53By79fIjblPH47DnHOKPTrGvdjZKrc5lpU8XCOWuoeD6PVXHqODp84dmKtfxauh5LMaJg2V1cHyxFoY0e+Ro7KFg2gg1tYfceN6Zst6Bikz59fg7gbJUlaw9vIDWgmqg8bQ4en0+ya3uCGukwc4sq969lkXcjg317F3LKYyrhUfdwtp9HfL+bzGnrT1HARgwtq/hquIlpl4ZRdLkXeYr1WDypoml/Q1q+Lme7Qi1ha/ZzwdeHsjhlTp/2oLNlGklNrnJK+yR7XNoSrWLMJQtNDj4K5Kq3NYPzfagf5URCvhUfxr5k184cvjT7yVsPXfI1tBh0U4XRNb0wTK3m+pU5VGkuYWXoWh7kfydyyXtOX4+ig28p1gqbaHRlIuYHPPDLsiK4dhJtt4dg+koVT/WnhPUbwsXdo9k57QPZ7Qay1b4rlWZv+BWbjU6/zqzpMouHmRdYnfSazwdvsKyvFqZB/Ykfdp8tN2xZNXwD9bcvo5Peke+aT3ApvUT2mAY4aevSbucYfr414duGWpyuLGGkYRlqIx/jefsxJyqbUB1vxoopK9AdvhqH87twShvCQIsaWrVpzPGaQO58v8o2kyIarM/BfL4Sj6YMJ7pdGtuSq7Gf9RKTj65Ehdyk8aI0crXdeaW+kHdqq/m09wBmu9QID9vI4B5/uWNlgFX9ZYa8Ho7GXw9E5RLnGnWj9FUmDgFPcEmIYXpFNu18N9C1vh/ZNp0pmVXLXfMiVvSYjK6uKTeX72bQFit0rSb9e8lHtHRay5X++tzOeIGt0XvOpDTHpm8orvMM2evRGu/zO+lyaDAWB3vzM9aIvaOrCTKaxW3TamK/ujPZYT+TSzeS9+0s+9q1IrK6P96PU1AyHsU2x9H0GQUbInuxz2MeIbW78EqJ5tj4Uxi/u0diqzmEDXSk3ckNZHh4kPXkC78+9UMzyZvbkskzjfOUDbbHuWYmyWfciMn9Q3jqHAx3bMbxRyyd7UvZ4+LPiMzdFFwdQ4lGCha2JzmVdISZU6t5e3ckjVU6cLq2KROcDqA7SYkfb1+ypP1itiV8YU67OJaGb2fCuQn4+odwufkephSkML54NYFBazHZNY1vi3vSZFRTJvrEE/WnhF4Xr+Jxsg9RA9+iqyI8fHyYgg+meBT9YUPxIh42dqRRTj+sz9wjp913eqccQLGsnuPtBrAy0hqrGdUoG89kzdNb3BhwCPdO7RngOIZ4R08ubsmgW/cI2hrOJKjvIx7smE+kz0HUY3+jd2c4Jnzmt8INvuYqsW/5aRy+7iDRxYXg+BHMMRhCj/UL2fS2ntaro0lRseXiewc+rzvGgpXKqPwOIf/SZg4dDUF3UxV2Y1ZzeNYEZvuMQC92Pk0cAjG6ZI5zmzFcrXhKQbwCPyqFud9d0V2nRK+mCji3HI/ZnC/k5prx4PkASjtvw0p9C4/1ezH6nCs9tiSxq0k1k5TisP7ViMDMAzRR6kZ8gRcje14iSd2N1pcmcHBJHU+dwLBqCeZ5iSzt95o7Od1p7OPC4iOp7FEYwg3Tu5RNeMC7jVr0qJiNjaspg8MreVjynGZq31jSw5wbz8spLnKjud4N8oq9mLVAn+dncmk3uAePo+x403Qszw0s8V2ogqOXOScmvMfq1hRa+5ax4E0MaipFbDNYhpp3Id6Zpuz7FctFi0ZYODjiukWdNbMUaXtoKF4ed1l/vwj/lw7Ms1Vm9jEXTJ0P8XacFaXn3Tip3gynz8MwN1vPpE59GR0RyNil+/F9OJUeY3/gYqpBo56J/96/Aw00m3N1mT+WB+9S4x/GsS4deVvmwMMcPfZ1DKHO2pjk9FH03NkANbNezDYfRGPNY5zfO53BKo347viVsX22kPqwDXuVPWnw7D2Z89fQ+lVrUgpX8N6qloqme7HuP5B1ZdNQ6VOFgtomskY7cTzWnsxt9rhuuIKlThaGmqmMOBKEa0I4OrF30Au6StnIP+R/nMTOKQ94rD6Cxp3a4Bn8gzrFBrzXtUen4zZOnTvE1V96nK2Yy/ppzTnrspGODj2xHLGekC/G+LXSY/vFZG549WTH8sXk6Tbi3oI77L4VxqkDJXwr6E3zhxEYZhWTs66GNga9mDjyC+HhIaQt2UKxTx8yx5xm4CNTdBwPcy1DiSmDdlPql8jurHgM9pUT2K2e2nXmGIwbxJs1KjzYd4BBffLw6NSWJ6sVaJh/jwHvGnJ6hPDRpDl9Q6bjEGiAR81x7P624dW9UD516szX/4+5P40KAWrjfuFfkkQRhQaKVIYoChVlJiGZQ4bImERE5jFzlMwimaJJojIl0qCkZApFgyGR0mSo5Dof3ud91/nw3O9Z56znrOf+sD/svda1v/322tf/2vv6L1Zg8swvHDd7QrXeDi4FNGLP/Gk06necO3vzSDA1R8F/Nbu2BTA3ZgMjJm1h3yELxsybROXQCgZ3P41+nD/OS98QP7U5BqHxNMuI4kV6Nr+nhLOtlTGtw90ZvbOIJzGLMP5ljVd9OV/UfjB4jjvGBTtQbdKZMX3PsO3jJ44usWfI/nrUVyvQYj+MuamCcZN5HDf2pXrRJ7Z8t+dmeTKPFroSlmCIt+1xcjcoseKYBld7rqHtu9N0PTMJ+6LetBv0m9kDTuNnpcPKsA48PnSVVK8tBB1/Qk5JKEO2OROmY49Jz0/EfRzAD4c/7F/ZgkvOw1F4nsnEeZ+pPRvDlGIDxlQ7cmtCf3b+UmN42jCu1j/lT1oQ9tYfCXdrTM99CjwNP8u1VxfZ6XiQyj6daBswm8BpMVh1m0xWuzkoBtcx+5YfX2zdmPrFl9cXNCm4UYj5ozGMORpPh+IiNKpqyLYvJuSCJnWufbl4xZSUy8dYOmgntp028OK1cK/3WvoPfE4v2zC+LF5I7wUdyKypo/msUSzMPkns0EXA/7xM+F/xd6CuaRUZms2xjPLGymUxJtYnsFw7BEW7QqxWKlNyS52GBg16uheRMPsRDx8Y0f/oLa5oTcTHv5Y5qZMh/iWeR9fzIdOBCfs28/nuOYZsVWCEy0/s1vcn8vFpDi3XJXdKGl0dSkhLr6HlRFXqz/fHq+V0jOsfccymFNfdejgdPIKOz0I8Iv1I85tOcto08ts/pNhvAIPTq/jg2pJgg84Ef9Xns3Mu5anj6TqoF+5hYRg282Hh+Lds1trFs2HJNJ1WRYqqJrs/hbH0ug8X/33H+Vcrjqj8I2u50Lv3K9JMHrAgpguOcaVMUA3hsttZzo14ySsbAyZYNmZI2jqmzW1Ep/Ru/JvanVSdIYQ/Xs0Jg54sF22sHq5ifPUjwgpP4Pc8k4sjhjJi502stZYz9O9P3nQppvJNJVe6hzL2xHkmb7XCoTqGVTkGmEf8Iv/NPECB631bsSLtOC2qVtJ86Cy2T3vBC6dGeA8ZQS/rlei6xGO7YwEWSr24eiAVu63diK8woMb0EHf3e2C31ZnBK6r4wRGcWg2kdr0pXWe/5XTRc8rcHzDF7RSv95bi9taLvt3GE1H2jqdP7Xi2JRmHcTUkj05D20YTL99QDLsv4IPPV5Z0raVL06Zseu9DoP0URuW9pE1CN5R/FvF9uQIL/R7j0+QmGsNfoTrbiw3KazmSeJWoJwtIOzCZd8d+cT7qF6Yvd5NR7AdBCoQfHs/28qXU1uUwePgooqcO53mRGRd+z2Dmt8fc77wY4/6vWNlpDTMrttIwIJGcRBtWty7EffQF5nzV4v2lFQTZbGKoen+0Sj/zLc6ViqEt6dLpFw27Kvm6K5Lxy6O4GzALXYs3FF2BSIU0PKrqiJ2hxO7FJ3A5W0vYIyUuN6SCty4z1MHkHvReFMCJMgNaRS/FKaGaz0viUAgfRKfaCVweXErF7VR0Ww7gaFRbdo0ezJU/HjyYfx2FFU7oPEtipKc5ax+/5dbfQKI+5zBu0Tya73D7zwD+7+4lICL0MOwlB1uPkMmFx2Rgr/bytGSE7L6XIY3ca+RTsZdcWBItNWaTZPzDHXLlYrFUDVss+QkNorMsR+6t6Sl6apWy+a+iHN7+V456d5DZmXaS+S1TwucpiJGrgtSM/yBdpzUSnzkXZV3zI9I+d4dkLTWUlbr2EmR0XeKel8mhdS3lwhE/yckNFl/3U9I1L1lmB46Te6G60ki/UnaqfpLpQX9l6KUAuXy6RA7+NpbKUFt5MnCrjLIdIOETT0rqzitidXexJPvlisKhrtK8/LPElS4Ta11Fif4RLaojWsvOjKaSFbNX+txtK93rH0meorIcW28lrrW6EvOlWjwMO4tOZ3PZvy1OFNsZydrnjyW++IDcCjSRgb9TJSnyifwz95YpWWOk9O4cWaS7Qd7bTBXFGUjZ8QB5UXVBvNIRb89KiZttIK/0/MT7bSOZUlstd4PGycqYpTKh/rx4q56Vv4aPZE/Ec/Gf9l2+qH6VT+l7ZeK6NCluv0YCuiPNWl2TDJ9WUjLPSvpZW4jr6BYS8zNTmudnychuhyVg0WL5c6G/5NVcEV+jAInr/UP63ciTA2Vlcvn1YdmnfE2CtufL0k7rpdGgxzLQr1qG9vwudq7+smX2Wemn91hWvLwpryMnSa7fcFm6UFl0Oi0WHbWmMm7EFimtHyq3GirEw32bKFZHiX7bVPk1pFAajLbLguWe8qNxpQx+XS5R045IbeMR4vlHVT6seSsdfsRK5/kD5XMPc7HosV9q774X3TE7paS8RtxClKTsrqtURrWS7ZrX5J1JgLzZnyzjHFbJ4WNb5c3selkxaJ9cWTxR/E8tklEqh6T1PBeZNWG4nHe/KCXW9+WMZoLs++Uk2v7mEqlqIA/iQ+VZi3pR+nZNQq3sxO3JBlmQOFcWz7SXtbe2y3bzJPlVWSMF+u+kbePtsq+wl7ysTpA0hZeicnOSPH5XKq/ebZcKw16icHy1PFvYRMbYLBSnnYnytNpNRn7ykFWvXsnaqxVyeOtpORZhI/fGpkl5xwAJ9FQXl4m6MqB+oDg0FEnnvbekdcEn6aXRXTIq3kvOYVVx8JwhXXbp/Edh8L/iJtCg8pYTOx4ycs0hHskQLl2Yz4A+W+nt/wnNgbnUToyhh6ESlY555GsaknXxMd1zzSlUDkH/8yVWTW7PylmNuJXyG0WDp2zo0AkX0ph5bxV7Zudxrm0KQ0t3833Se4xD9nBbVY9Ry0L46pLMo8OjWe6rx5iyPAZ2CiDzshHbsobRfdI59n05Qni/Hay6vJautS95dsuURcN9yEh5Rp+4UexX74aJVjGX74+hJsCKbelX6V3kQX62Oo81pxObYknre2HU74sgbqsPkyfcJfjCJk7s0GdwgDUf7zQmVHMmH1Y/Yfy2SJJGqWHauJwvDz2461iIxsgQDJYnMaTeG/vwD6QO+EWXZevJ3HOIMQ8KUHmig6PRQOJ+PeR3QlssO2SgPrKKL5V/CVn+neV97TkVMZmDG52wrTzMgLsGlLcIoNPscTjEDeVqoS3BHXyJ+fqPNrttqCtLoGiABTY/YjlxO4mkmEE8XDWC1L7JlO9YhKbbVW6kO3JmeQ79j5qj9O0JGroT+HWoGX4Z6Vw5+Aiztu+wGuFGUmtjHhTuoEk3TSZbbOWM8zBanjvCo0ahBH2bxMl9XTCa0IfmWs/o/aoLm65NJX35KQwuvKHb0glsrn9OxsotGB75xa7BnzB82pshg4JRWhGF2cJ1NP/nxpxqLVyaWlKav44WqbfpOGkTY3eb4XFhLgseGPApy42xO57QJ/sJw80vcuJiJUObL2B35AXWOKaweWwqs3s4s3GoPl9iQMUjhL26wej+zSU1uh6HqxGMyU/j5PT1xDORJ/ZKdFq4l8BV4+n75CxPSr9zeuxhrBr+oJd9Gr/vrtzxTWHY4O8c2mvCO08ViDfEqF0SGWE3KRmylpD3l1lwJIln5RkUd4vifspUih+vpO3n3xQWHGahzW3mfC5gRVke8bbaXFu/EWPND+wxzeOYcwpG57eys+1l1rTQp9JzMQcf2aLeuJIrOwIYa3SZhfdaY/6jE6d/htBqRikNX5KZV6jEv6an/yN//xXCYKMmuhJ4SpVPG6Nok/mG/W/KSO6xC6vJTfhzKQXXJYpYKhtRXXydq3UeFB26T8i8uygY5GFgVsyPX5nURFXQa5sJA14voyzlJlvuvEe5YjddzZSxMuuFxcNcyuO74W1ykU9fb+AWpkiTpzE4HZvH2It3GBnzj+6LvBh1OhPt7e1xOlnI7lN3mXavK31maOHhrMdw2yaYrrbHYd5TPDq8xPG8KSHZSZyauwi1KT0x27eHG7ONsFx9FI3wrayz8yH6rhIpLyezxeYNMba2NPM4z7ieqznbQYvjZn84e/4Mn8bmYH5xI6bt1hDb+i0/LapZtV2diBgd9h1sj9aWUDbmZeCjaUrnn5a4pPrytcSEP11TWbpnPZY/lhB7/AKqXeKJt7hDwpb2dM5vQVGFHZ3ef2Pa0788tP3LrMiTnHS/yL16H57f9mBx77lcam6H0UtTBt1XwOlqKxaGbmFWwwO+tNjLv5a5HFg9msDQExiHWuKns4ruVo/per6SU4l51AZHs1hFlWHNGuO3eQODQsah4L2Qkv4KDMupYOeMCqo2ZROlmcHWS6/Y7jUN83oPlvd7j3k7FQpSxxMa05oRc0ay5vBrZrTehLn6EMz9e+B85wWdP+hxK/U5AzR/YFNYRg8fBcadysd1QCqz/wyinUsvro5UYnPzqeycr4WpXwukw0sKJkxgaqM15BaYE/ncnEbGXujkL+X470TaXShm2cc2eKxrg0ltN36s/kzat1pa7/mF7ZJhuKbeRu3ISWZVNVCf+Zje4w0Z2SeOE42duJM8kCGn3HjbcgHVg+sYu9CcOTFzKRyoQTvXJALnJ7IjO4t3hUOREc2xm7WfLen+bG3/nv06inzqqs6O0fPw+XuPVlJGYMdenNu6CO7VsXWgD0baGahnvWT4v9ec7ViOU5ovbspNqci8yItoNaJCQmk4/5wspwhqzkTRz2o7trPLGRa7EfczcVS9a8BbtQtd6pfyvf1LVv48w2zbfHZVDOPygiuo3fr73+tF2EH5C0cO/IBpM/CM+8OYf/7k+buyaZ8DYmBK2JgY+nRowusDYXzL9WXm6WyuaoRg66yLSs5gyK9ntONKxu18SFbbd7hNOECXd6qYjOqORe0C/r1vROCL3tR8DGXpjR20LJiD7m1rGk0wI2DPSNbZTKLX3GDa/c0k/7gVTvo2XMiZygz/IFTs7/NgZz4Gx1ry4uk2SHjCJ71GJN1QQ3uZBR7OY/nT+hjeSR0wCpzA2ptnMbG6hH9qFns/K3CtKoPe2y4xbNpUBmXuwOBcJ55/jCC9KpLxh7bil5vMiycHqV3fD+dJc5Drtug32sV78280WXCIOTNWsSEriN6zTPm57DmmG05zKOUlTk0XcUrhOyrjHZillccW85ek6PbGwN2LiPx9eMQ68C5/GI1tjdHLrSSjYzKmS36za0MSjZS2cSPKi4+XvLHYlIS9+lWU+8TxZs0Urqd34tr8LrxNHYnBFFUsx9vwdUUc6m2bsLDFUHwdbfFwqCUyYTdjNlugolBIHvX0HtODDaqNiTjyjDYL22GsNA5f30y2e88l+boX2l90UEycyIbb/2i5UgO95vM4vPcmxU1b4XvLkf1abdFXysOquj9ab0eRveEgJs97cyL0Am9a/KHq0wNaj7yP+mpnvFwHsGdABql5Bymtz8M/1YNWPz8yQSeLhJG6DDu1lD4RCsQ/y0K1ejEPHrQgcMYRDOLK2VUqDLz1lAV3/qETMon8QGc8VMfSbuhq6o8XoWF4nlwjXRRbT6Lrs57sv/kaz7i3ZFx3p//klwxfNQyLDxU4WCwlwTuQKd5PsfaZyJFmESztl84UFWeObllLxW9VnoR8wCbTjYxdJoSMnMC7Ht+5evIwNtt+k+6TQJ63D31bVhB6P42Dai24sU+flt5HiLlsQ8feIawZkIHx6Ub02LyN7WedsDU7gPuSclJSwpkScId/V18y9L4fLR/MY7nWaTzu26O3OBv3B+eZMGYVjksvce6tC1lrvhP8cMZ/BvB/tx4gInTpoCs9F14Vw36dZerhFHEcYiObLmiI+cSXMtzoh7DxhVxvHiFnVedIqz1FUha9SEJTf0iMRZR4GqRKTN8O0lFngTQqzpJBdu2lYlugOJrckgXeaTLZo0wWfgwVdYV8+Rq+TgL1/kp0Uxtx6zlH5us+Ee1+n+VJ1ldZ2z9dNF+9kPQV/eVXTVtRUD0so0dsFLXUatGNCBN37SvyVr2/6PgMlVpPQ3mSO0acMobK1J9Wkj38kaydVy9Htw2WXx99JENnkPgtjZKlvxqk5MRosfE9K5pddSTURFVim2yUqZnOUv3uhZwvuyB5bQaLe7PrkqGiIhkKWaLc7LjEtukpmx/Fy6sPjjL0Zwt5j4nkGLYRTR0H6d5wW5ouay3JyV9kQ1CtNBo6WLKD5siZxGsyac0POagxXH7WOEmn4MGS8bNIzvXOk4xH8+TE01Hi/+yUeIdsloFTR8uDFWfl0T5vmTujjez26S9PUnrIhtGdxaQiTebc7iFv3PVk5K3Tcu9bnhzOaSobpg+RR92HyogFVqLnt0NqXKpl9BhDSYrWltvvsmR+7HqZ/0pNbE1dJSTrogQ6dJN9P12l1aFPonM0TjI//JDfQ3bLz+M9pDZESxRNQsTlmZIkfDSWL8s+iPOQ2bLJo68MuL1L8uqPybkl1fLs0HK5orhPbjlUyB4PA/l8O1c+RI4Xhe5Hxd/hkmyO9pbHO0/LkPgD0jemn8QnBsrQMQulzYZ9olV3Q56MvyPBg79IWckM+XVkjCjcqxQLVWUxziiXvPLLcjFWW2yP5MqGtmOlT3W8aEy0lsw5WTLO9Lckls8Xvacu8sAgVk4a2Yq5iZks/Ogoo88dllFe7aWLcbr0iNophge9RV23lXgGOUvWQGPR6tVNCuL6S8vgZfLx02lJCfou99WdJUtruIQ0nyTGz13l/hhrMfeqkJHTmonW4anS/uM3eX5FQ9o+y5ZnY17IqvBOUv2tp5Tdvidmk/bIxU/P5f2BljLb6LeopL+Q90/ny9uXp6TT1vGSWOMvk6qKxUYtSpxjhsj6/vdk3d5h0qrohXQNPypr/7URE60wOXXP6D9qAv8VJUJllcbofT6AWmJzgi77cEXhIdF2WowvaMu7X/vY/KotbollHHv6CaN5q+hlEor/ChOOuqjSxW8yL7/toiTeheM13dHPmYVb8F9GHXHiqephJne5zKJT1xiz9jvmvj1w+NCRkFPGeHZX5Jq6Na6nlbn9fA3H8rox5qM6jc+0xyV8FlMzhqNUaULhiZaUnJtA7owsvCoXEfF8CSNyCtGjKY47NpH8vIHmM2r59GQ1vU2rCBurzPuL2ZQZjWJ9cQuW9b/LebvxhI6/zkL3k3R+epPGiuV8WfOJkPOjiRq2mMPpfahf/41PwX4UywS+Tu/A3D3q9N45m5DNGoz3+c6bwWp47jvCl3Qjbr4v5cHGULR+9eRrm0d8mjaMFaciiGqvxPqUyTjHqpESn8Qo710s75eKzZzVRI+sZvfq3rRScOPoRmVMryhwU7s9PpbFlFbZ0fb4OQb+MabmzRYKFT+zx16bLdGJlMzPxGaeGqN3taXBAJpVdwOTQ3Q89pzP/dqxOtmARt0nUTRZDb+UVhR+KkXVN4tZ+5TYoOuP3Zqm3NHNQDfJjOCIhZyN+ch+3xYsOGSDp6Iu6hf28WZxG+y/vMJr63QuVmTyrPE++kzy4WlYEgXbr6D0fjW+7lMYee8WQ1aO5XNIBddQ4JLKbZJupOF5V53tF2pxy37PsR0+vCotZc/jD4z33cOjmTXMHpZMpVUFxos7MD/+E66zPrDo5FIS97zHV+EcGaMqWRO8GL2vwUxOSGLlyu3UP7jGLOfHxE/Kpm7ULzo2S+Oy9Uc0Dv24AAAgAElEQVQixu7l8Pxa8qYW0sh1KtdCZ9I1s4StFWMZ9ukI6gOvs0jjLLE/Fdgdtp116SuY2Pcs9r+88Pr+grdvbRnbPYPO1e9I1jrOnA4qeJr+Q+nqDZJrTvLVrDGJc/yZduoRHwbE0UnxK42//qTJh/E8WxpI1ovd5Lc6wCyTUM6+24rhtGzuv73Nr3ZJRM4Mo+WIPE6dCMbC/Qvvr3kyfE80YycrYZewAFjzP+XvvyIdqFBTwWReV8JWf2HddRWW1mzjxkpviv9NYVV0FNnb5tNtzUYi7z3kZrgTVuNHsTf8ChYu+ZxcFYK1khOjp7ylTdYXlCZ142e3loQNVmFt0hkSJzzD1Gk/A4qVKQ4dQ023A1jrRlFrf49NpwcxaXsq8Z9WMiNyIbP0CzC8eIA/BjroNi7Bc8UILKOrOPllPjOLYhm03Ja0eS+oXyLUN97FvoYXBA/ypXJtGB8UAwnx8cYnzpFB5Y0xvxGF7+cWeLi0Jy1dlT+ODVi0PMsxmwP0OtKdxaYebB+dyAKvj6yoOkuE0xOuNHHkrWoHlB/EMlk9hCte9bzevIteAU8I1nbArcVNzty4jeHSM9x6aIfG5K3opupxfH8BtUk7KGncla4FbVl14xD9e67kbtUGsrNd4HgB3zr/YMvv+ay/O5z6YRfIWpBKetONuG3oStXjbQT4fSBprxKPfy6m63FdWl7QpWj5atKzr/Nznwn7MvsRVhLI/MLGFL+0pl/764wO/EHQkH38Wa5EyKG9NGy1YUBgD2Kqf9K03SiejHbh558ZxCnt5uXs96Tfa8u5sEAmOjRipGsMsT4NXIx4zYwxPVFr1Idm/fXoVVrP+CGrudZ0FzWqh3gRPptXPTYS+mMnaTdjmFeqg2+gAqa3XOih+5ILl/pzfeg3Lq8uZ5DDUraGHqCutTpu9yxpEeeIuV0BfYbvx7JsM553VGlo4kF4zQ80r6QyL8WIZjvO0Eqlgm7Jtzk5tgb9LjP4/NwT56BuLHhdSRuD6awLjuVnWAQDry+imWYmZV9qidRIIj+rPW29ZuNccp2M7FV8Vijm9RQrDl+dh36iOWXW49jlO4WWcxIY/2Aah04uYp3tFC6FfOHg6kJG6KdRJ/u4a9zA6kWvSb1WQ/iT6VTkzedPv0T0n1xCo3gwZumuLHjtSse0CXx3u42O+wZKz5axYZM3xUmadEvfyNBWRtgF2nDudyydCxSZG1vA+qivjLkxh0P7LTl5/vh/5O+/4hBoVfabH1tT8N3oSsSvJSQr98WjmTFlpX/xqLzI8fSB9Ovyk13hwaSHd0V5ZR5qdy/wcHEmbwtfUmF4mlkWmmxblcLgDuMotSviW9o9Nt61YsGRHYwfZMC6r+OI2nCJNnPWEt2tjvfOI1G+ZEZM3iHM1BfxLu4QXx2GM+V9GW2yQ6k1GY3u6jakjK6j3doqXpilcVv7Ea+0ltGo/Do/h/+l5YtDLHHqwtdP3kz5vILLChY02+TCl7nqdLW+T1VaABuazMAx7SWbHhnTLicO0/XbmbKnBc/aPMOhbyRrg09wNf81P4K/4a30nXlLGqG7t4qfJwIo2faGK0FZtLdIxVftCfYrLbn2LZ/ujXxQv7IcQ+udPNrdBWPXfM5E/yA6J52lCTGM/WWHZqduVH6xI3nzUHaqBdOk80hueszi6whFhs5N4rvfSgZt3846m3X035NF7X5l9L3f0H3rQ2Kv7qWRSzmeRYsY8NKF2BntcLlwjuD8B3zVCiXJU5nSt+8pvLWOJZYT8O1Wjk37HGb9WkRoRV80O36k6djdjI58hKZ/a4bEVeD+r4K2Hu/pubYUz0f9qT3gSs/Pdti/b4b/qFgu6HTDRCGRtG9pFPkYoF5vyGqV5ox2a8abjQ/otX0QKQo/KNBOYIKCF54XfXhY5cmuZhEM0R7HuQPu+Jg0oL+6HsXPYTjVKqKypwOtn60m1LkFzPdj6qPfKH99zunv15i4fSZb6jey8+8gEmas4aKnL81zfyMdv5JZZMKo4MZc/V1Omz6DeNC1Bk3zOYyfOpnpLh25ONmZP1MWkr/oGgYn/WgctgIvzVjmVftQMW4oA5MnUal5k/2Kj9H4uoh9jZ/T5qcxlbXpxPo5Me/rMGJGq5Lz1YVWowbRvlE2X7tEcq/0BYlJGrQfsAf3DeHcVe/E6uaKPIxajfWbR8S+DkGtIIoZXQbST77iOHcbbT4OZVC5Elk3tOnVEEjpjlZEdIrEa3oDD08cYL+GBhrxHdEN+/8D4P9uPUBEaGHYXRo1/iy6Ww5LZ62+8qc8T36sXiqdAjZIgeIK+ThwhyhZX5Ty+/fk3d7x4lW+RgY3/S1m+2aKu2aNJPy4KUdvDBe/QX2ldOUXsZmWKNcHnBSSKiXDa6P8XndXklyfyG3fVBlfvVHiDn6UCTZrxfrsZzmkf1Ou/zsh+/NKZPbDdnK08RG52GK73NnvKgcufZV3H/RFZftbeZrxUWYk9JHK/BZScTlWWrv8kcHT70lK9TF5vW2vxKQHiGmDg8TFHZedNxbLj/0pktkuVTwWaIhD9XtpNc5JOr/NlNbtS+XtrERJ1FKQwSldpC7/r9Sfd5Gw7r3EJ/KRGPy2ksxjA+W2aoismxok25MniV3xZim9FCXaP4tFzXyfxB3xlgSlORJttVHaHFkp3a6vlw3hU8Ww9zZ52P6ZfLmlIvXaS0XFU1n+JjeTujOGsu+wijy5aSSnBmvIx9RsORt3TLza75VSjWJ5bxMvnWMmSatmM+XNkRfSLvOdGPsoSI+pQyXhjKr0/jtWXGZpScmAw/LKYaNcmfVM/lyeLN2XTpd3kxfLxFbbJdFspDyYHiSDNzaI48A2svrTBjHc21my780R52WzZEfKLJkxQE2q8lLlbp9rsvrpa3l1tqUsUVOUhOMrpfLVWGHiTmmZeEq+VQfJ2ZsP5M67cinX/y2XdxyXG1tcZK+jsdh4XhDbwUZSrBIsXc1myqEHUXL7oKqE9bwr6vfNJbKfjdQcuSZTEtKkcMdF6bPRUdrceiQeXc3EMuGnpCaFS+O/jeVmSI68Sv0rBTktZLS/peSofJGnr1fJwsVh8qmJsYx9US9PdjjJg/Px8mi7m4T69JTdObtE0+CT9B56TX44TJd2jVMkO+STTDDIFL1x06RyYh/p7Gci+31zxU8rU2o1xkmXoFw52NlbOhuViI+9hqypCpIhezKkz7J6aVg2TlZ2QXp+LJJNPw3F0ytErtz9Leu8deV3aqgsdN0s85tOEre37jJoYIqMcCkV72XecrNPe+mU1kgqkxfK20HtJLuLury/UScrx+tL9MNvkl96V2IVL4nlXwXp2ralOE3tI9YmVf/dmkB9wxf+qPZk2eklzBj1gh4ZR/lsGEZkrxi+XQmiIlEHxW2/qVu9jqGjp5BxJ5jCJY9ou8WROfHtiCyezgj9X2geu0zXSmv0Ng7i8awmOP/qxqO3pjgO3UHpgT4s+5HLFts5tP9uxM8ed2m74icTe02iYtl0RmcOIWWCEh9vbMHm/msaUrZgFaBC3eW/5Jg+5OfZNNSsutPEwRHnd7PJHDaCqvw6Xi4wwmvbFkiYTOObo1mYUkKBdxZNAx0ZcOAgad1UabbRDNe+lnj9G4vTcw02WO3hbouHTFsLyoO86d9am3nL8vhw1gA7+UxZhD6je3swwrUjMyZOJ3RfLnfv3MbXP5dy06ds0jYhsMVrllvlkx/5lYBlqYS5/8GhcyesJ9VwwCCUGyMyGR1tScqrJtjeqOS6SQnHN99B2U2X9ptrePolgoIxa/gwcw/7Xy/iy+hVBA4II9MgjaY97Wk8fx9NZ3Zh0IulHNKeyb7LeRzct59G9qqsfLSXwtNN0DH+Q2TCOE60v8DyAH+Sm37Ad/VXjo1ugeqHUuw3PMShYCKGvwfx+UcSze/GMmHxd3poK/A1fBu3tEpYV9SN0UeDiba8zJwNjanY9pxp+1xwafeAKfe/Mc14JmlFrzFdFkJHjVx6XY3HJvEP5gP9qEgx4KF/FYs7KdFwzAfXC2vZe/gmqTfKaH25gJJaPXY5HkW/rAqlG5P5vfsDKjKSvApLmngvY//qMEpylrFEaRs7+19FfcVhYu/bsqVuAKadp9FJbz5z7fqTlpPK2IxFeLWu5shBR/SMeqL0+A4rC/MJmFxAUPhiovdqc7VZDJH2mwk0Ps+P++Y4uq/hwZ9jDF3elBsWa1gxVIHUKz5c31XG3Ctv2WsQR3KnECKNm5JotwirE4sIOTgFDVNPqlqpMDV7KrlqI5imVofBXSP+RZxDoWgwO46XEOmkyPAOaczf5MTO9495aqaN3byt7Fm8nH+tl+DysQuaflPwnNyL+YOsWd/bjUfr/uf8/Ve8E9Bsqysn3dyYdSQINRMd+mglULwmi1/L3Ij6pcaqtAgMH07iTIc/LB3vzHj7qahOnMfnT+1JGGTKu5DXbCxJ5ubz76y/N5s95xdz3voWz5yC2eH/hIiM5gy0/cV+43AixhRwvfVZtrY/x7qwdJqkKLGzoASHrrpYrtPnYaYab8oaox1bid9aE9ovz2N9wjUe/GnL9MeRaJ80xvpXIbUXY+j9YArzfpqzVs+RHRsqsMkfw9hDMQwa+4ENb/7RJ2oY3RpPxuf+bajcjUeSDodmOLLxbD+2+5cy5UkCLn+68rdbFabr7Zjw4Cx5Q4PoM7wj2hZduHKpitDW27kb6M/lnQ44jtenvlQfwxapWPWsY1zkQYwn32Hto1+cTFfn2/SmNFZWI3v8aB5824Ob1nEsF2Rzq7iMNXOLKNt9kDOBbSl/a8CCYmtS5n3F/JA+sRNSMFC4wRiPNFZZVOIdbYFFcxeyIkLQ6LIZNc13XM3y4c3X1UzRGcaNiwVkuS1hVo4R2w9E4qHgyspHCXzSymfikFKmahuydnxrVtv0xzvzIpc11XEw746d/XWctL6xMuMk+kUeWIVq0rR/N+KG5PCiXQPhsXfQWPOYCy/HI5YHGOl0jJDsoXy2XI7nhU8suN6amffuYPTiMzcCLtJ7S3tSI9ohyvdx3v+BydOXkF/ygmLNNwzNv0JO9XUGfWhNbIsSjNXvcco3gi8LO1GWv5i7mz2JfzSZk93UOZb+Af1dnflhZs6qhgNs1HbkyoXbuDqbE/T5HRZT7zFyhTPOT37wNespddlDSDpzDZWgrYSG3KTI1Iw1/m1QzNyJ/ti36DnF4BBeSpRGMoXlYaRVu/CmUVeelIezwH85Y80P4vc7h6eGZ5lR/hjrw9WU9onm2bc3ODQEcdG3PTc+bmB0yhAmMpsnzRxQiR7CqTgd9q+qpyRlDstOj+WL4UBUSw1xy13FqDgtOtxUQd99FPGv3nHDvoBjbp8577yKt776vK7syK7gY/+9NmS6au3k8Zye4uVWI7nbP8v5fsMk++5CuT5giChpHRWPVYclPvKwtNIbIsdbmEi00li5dNxBuk17Kf3t6uRBsJVE3jEV9zOWMlExTBbl6UjKtVPSYYiGvP5tKWdad5bIK83k79jlMqquqdxI7yjWRltkoHW2NGnqIh869ZKpJq/FOWmZLO4yVHw89wrbfkjEOgv5bZMnv4M7i03zgTIwTknMJ02RHFMDMQ49LD7btWXw/B9S1cZI8resEbPH2dK1V7iE5HQXv5lN5MnRY1KwJ0+2ne0kvzZckh0Z8ZJuUi55p+0lo9hGHhWpiEuNl7R/lSCxl/5Km75K4nB8jDwNCpciw1gxjfWUUWHWotJGUb6fnC0phb9lxp1cqaj0lDnpDpLQp7EYZefJ39deUnLITcqLrWRqgbHkjFgsAw/4yNWSERL4LlbcjxlK0xN95Y7lIhnx2kjSz+6VsY1j5VVelXy4uUfCD6vJce1tUnd9k0Qn20vW8hh5HrxV9I7cF50zjqLSYYUUvQyUeM9u8upDI1lnNl0WnfguBT5BkmSZIZZK2fJgzGY5EJQml3Y9EZf5+TJgUaqEfwgTnXuV8uziRKnW6Cf+mcpSUbRKWvjlSJ1db1Fa3VIO3vgiV0ZWyjMXPQnq00/cAuql7Z0Hor/xgVya3UTqNDxlaNQ9yeiTJXPeH5dNeday8ludvF55T/59N5SI9X/FK01fOpXai/LdcfL+ra7YWKjJkXOjJDK3XIKOrJWDD/rIqO/Ocsfpn9z8MFNudegkOU3Hi3VtvnwKeSE9FDdK3w3XxDP+lSxt6SvhTbfK3HbPpKA2V6ybbxKPKcOli+JhWZylKVZfL0v6GDcp/ZMryuvHylqj1pI3eLY4RMwR5y5t5MLPcpnmoipXmwZIYZcEsWxuIB0zHeWdppnMmtZDPEmXyW7lUmm3UBrsJ8nbYltJ854mX8y6i6HrHVnU1lnKgj6Ia+0+eVjvJGlDW4qneWexz9aUv1aDpP2FrzL2T6p4KnYSg+Kj0vfYfYk41lZ27t4iG7otk60fh8mfj0HiuOuKlF17LZNCG4l3xzh51eXXf3c60NBBF3rk8KinCx7xA3DQ+Mqwqg8My05krvclTlp1p+2Gp2iGRbMqN4Te74Nx9L9G4+O/sHJz4ledAe6eOSh0qSbIdQRDJ3vTrslnHCJdqE0/gnF0LgrXDHj36jE9hh5g80A7ZlimMP7QKRp5NeHlN3VcTXXoO94D3cgfuD4soJtvV66M6sqUzD44DtjDmMY+PN+ZTa+cpzQ6Pwr/SW/xnziYU9cciZwdiOvcQuKPbqKjQRNOnVPBzzwS24M6jJmhyMh3ocTb/6T10A60tehMYJ8i5i7YRu8DcXRc0xGtgH/YHfhA2v5pDFP4iEKxPXLqIDOj75O+uBut1phj462OofJydig84M/HIlqYqdE38AD+g1M4l9CasLvtGTCpO86Px+J/zpKW90zIyZ9F2cxFFF7PYnTfEs66Z+NvVs9anVM8a2vBLJ8oMl7uwqT5Rxwfp+CRZE9FcmfSex5A6d9wViouJfxfE9oMbMZgjyqy2zvwqaQAx8cjsNO5i+mxZdRXVNF7gBPHXm8k+fosAlw6Yan6kRGKEzlu0Z9uqpu5dXUBR8o7kq4dypm4LM46v+cruvReqEzHAe85f+4lSTs7U7RpJsEHv2DRaRzPWqXzznETk0auZ2auD12vHeft7GQ04q+wJ6Ka6bd/EnB6LTefq9PBIorLk87j/tqW7VnzMW+RyahLH8F3OTamU9nSwY7Nm34QfMcW/ZFN+HU5C5uf/Wgwm0iZmS4DZ5gxpWUaWRXTMDczpihQFYX1btjn7uFPmQqJpuo4/SiiVZ01ijV/CZnRmdMWfjxruYq6qC+cbrKVO6fPY7E2h+OGZgTqvyJ6ZADSopJnNSOYcv0ecY/vk7Zfl6Do1yhlp/NlbzbeN2IpXGjLKANPfJY44v/MlGuNq3m/4Al7m80j4Xg065UWsf2vG0Oa7mbFnGE4VeUyNQvCZ5RydHMaPUaVs8w3i5OTW9H80wa2zNuDxeu56E+8j8vqKgbH96OsdzJd4/5zw+//x9UBBQWFLv/Da+D/O6oUFBRWKCgobFVQUPj8f1of/X+1148/Vfz+qkUf/cfoL+5O4z6TONn1DwHqUym7qcpF00WEHFvPp6PduOaQxcCZzYhQNiFaU4WbHZLpkqvHxpPR6Ll342BSIisPLqSk7TRWBu0g9819xlz5wFc7JyyNp9N8bg6927iiNKgfRz460fGKLVbuB7lxYzDF9yyYNziKQWcs0dOy48+tq6yd/Aqxa8T4ND+0dHP4ljGc2vBYjh58QYTWNZ7sGkOHGfqMUz3B85EpJJSY8z3HnKtxh9C+eh+NF03RO9SIprZdKNcuwvjuBsY1rOTbwXa8m5nB7zejOazaC9dUN8wCrVFvUY2unTqT300m8uNRspRGMSltNpZjj3NgvhrqEy7xvOUYdtUmYhI2jUeNo2i3J47KIGvu5ReieOwk86/4skfzEkmJY9EwyGe3XS6f9Zcwj1qq2q5nyRI4vcScKv1anu8/T8sn82g7UY9zbZPZpJiDn/VO7Hf35O3kGs68+YfT7DDG+2xk1tq77Fx+lq3t3FEZ9ZfEyWOwODOSe632ozXoC0+iK7CbZUiTv7NopvuGtMW7eLbQmz0L26BPHL7bV3J8+UniUlfwo99qFHtW07jXE5T9pmD8pD27ZjRna/smzJMNaJwYzK34AgpO6tFu5XiadHpHwJZmWGnF8S08DN3Ns3BY3I4u5c8xmXSEk8cSqZ/7mY7OQeTbdkH3hyF6Dj6c3O3PtAg1lvgEk6Rtxv0viQwObE+/X1fZt6UB20PtOFLvj9bhWCrn/2X9lbesPXULT7M5bH85m6B+tvx4WIvWrSU81hxHxsif2GzsQRflM6C9hhbBnQh8ehqNndbUDeiLru8cHPPOY+A1koZfHTncWQGFM695WfebqlsvUA1zwG/wHOwP9OX70ADUlEyIr41BrJxQ7/2FzpcjWf+yPzeNO5G08BWKhYL3mH3M9V+Gb3MHPncZwENXTUzRJv25EoXjTXkaEIftgM945VQwyz0QndZFJD2NZF1Od4JKXXG1eMitBf+55fj/kus8oAiUAPr8jz6D/3fim3RqIzMLNkmQep1cfPJQzkXXis2QaaJ/NVUs3DSlfckK8el6VUzem8vHN/flbGCVbP3nL5a7lsiyyRdEUcVaWgR7S5b2GzFp8kaOWq4SnaJr4u3mJd7Lf8vUF0GS7qYnKedtZO0hA2mh902O1XcQveRImfDbXVYEjJCwfm3EK2ehlFjOlhFdT0rZ8CRJm/hCPloMEK+t0bLHMUAGHB0h1m9uSktXB3H5bSDv+9nKoXBlORcYLbfvlEtxYbjMn35DTh6ZK8pHNKSZX6Esb+UkqkorJC2vWDSW7BKFjE6i5uUvoz9vlgILe3GKN5JGBx+LQnqldLXrI5rRv6TX3bZSPqpBqkoWyrnbi2Rb4HuZqNVDyj+tlx3tFojjlTWiseWtOF94KGkrp8iuhosyMi5J1GzsJe5xtSQcfy5mu+/Lg01rJEZznbz46yLtEt2l9nqMnD6kJzk/1UVJyV4ef/8pU3p8kAavQKn9NVf+bDwopieWiGv392JqUyqdN92SiuN+MtKlSJZrREmHi1bikNxPAkI+yWMfVdl0w1AG9rsvX46ukIaNXcUt8qAsfrlc+k7qLndu9pUt6zqLTr9BcsfTXjIumYvp6ggJqBkt53Svy4E5z2SjdoPs0fko2WZ/JEV5kASNTBKtoGYSf2CDaF77LepVSyRof7Bk2LiKUtQ0cX41QtoVdZRmBwbLweHPpG6ivqirt5FzXe/K981HJCh9rGQ0qhWFH1ckYEO6sHqPnPCoEeXMKEkuKJaiZC9Rs54lHueXyu1FpXLmb7h83/5b/gZ9EwVXbTmw8px8qu0rRx7YSdsOUWJUqCWv1/eUeOVf4pIbJC9N3sqtrCppKHQU++/dpfj+Q0loc0l2D1aW00wVz+sRomY8W8bNSZaSlvNkwSYzeRq1TbZl3pKGKVula9BSSTO5L9cM9suQkt5SGXFOFj4bLTWNSqSH/0Z5uvK5XNHqLqGP70n8G3NZbLBaum2aK8t/q8iVvekSNa5WqhYdlR8PfsvW1gUyv7iZuLk1k5pvPaSNSXdxu/pdXiXoyCWNGKnTsZK+g5qIxaWhstnURrafL/t/PR0YBrwXkSIFBYX/28H/vpRR5PeT8j4ZdFY+St+8GeyJMcHh3HTuT0zE3qCW7o89WDXjIf62afTPrUJlnDPGPWfD8gp8FjZjadIcovq15tW40fRTaM24tc2IX9TA1aC1DPkTw4C3VSRuu07pprVoHGpFYPs4alOdeRr3ntnB5pQcGoXt4FWYqpYw/Xs2he6PKVENImjZOfaevUqNUSy3V2QQv82FBX2OE71eiU6vYjlcs5M26+v4rdybdhOXc+pBCyKTRlM/6yk3Ju9kc1A+amnn0Z6xGtUlZmz+fInkzKHUD7dnzSJbliS2QjOtiFP/LAhracAMmzWonOuCSsouJvuHkftuBCcC+nNVhPmhHiw6c5kNwYfp1rsvLzwb8+bdOe5UpLF+dwRVN5wY+3IKfY07U5BnSd6odnTqO4DUssHsyPrC96SPxD67z666G2wtmka8Xh7nDUu4OPEpgRk6VOUVs9r+Bknf/3A60oXO4RpMDanEaJ4KRi3ncdqyEf9aVXLLZC3bxRp1hVEMKKwgVu8s9xsSeLYsHAcVT1RHaOBT852SEH9chvjT8cETjDMtWf/6/1MDH7npJBv6JlNbNoC5Rf+obH6BAt06VK8+xy8xjyn2s9jSMZ64Ik2mdNjEmI3JzL93FI38vwTEHeNOthd7O25HY1YdZypX8fZvT0p63uF8RgQXrTux9tECmswKp8rkDg5JhziyCUK2LmdQy2ieDZtDVexaphxWR3GkP+tCljE+tDuRO+6wKwX2vlzB5eYVeFzfw60X37BZsBc1XWWuz71NeNMh1Nb9I/FYL/y7reTyAAf67+yPyvU+9H92h3YHn5FesRDPe6eYs6uaZzsnsWV/HZqrK5l+fg6RT2tQfLacxR1UuDxmE0XnLdkbcI6L07vTY5IujX06kKyrg17kTr699mHwV1VKIjTx1a5iZMIAxOIEIbGlxHQ+icrACaRs1mN7rBcdT23B46UlAQrncJlXxudv/YnZ24p6z9sEVMdRuOoM6y5c/Y/8/a96LDQNuPx/mrsrKCg8V1BQCPq/siADaNtEhZ7F7anYFMwuDz30f75m07KLFM84Q6tqR1bF2OKStZ9fLrHc1dFl8tJquvuHEVoVyqBMXU7XmLHJ3giHyM5E5VowMkCDG0cbaHX+PpM0k9l64QdLtlVhUtQJvcRUxO4qsU0f8MjsGu/mdMa920cKjLfxc1kVW/Ka8ve9Oh7+yxmsuYTCP9N46PaVRy1suePcneI36pyfvg+rRVF4hJ3l1p1a7Ozqqfa0IF6lMx1qDrKyeCKy/TqJnmPJ2WrGpSglcs9k09wlmicLyxio3YfE5b3JMX1QA8oAACAASURBVBjJsRfzuGToyPRXlph45zNQoT2qs+fx3fQEPiaXKc72oKhOiwFaXlyXYq6u16a19RYUt2xgeEkIByoGkXq2kntnzPFd05hfDnYUHn/EwvebmHsoB5/cZM5l5vHweShVRkPI0ThL/yWCvd0epg7yxOlVFU0tE1FplszUhMXs3qrMDr295OZfQs6rk3iqDosOvjRXK+Js3xSuTtXga1AN0eU65LjPYn3eR3bui+Dt5qn8OtgTnc8qONV5MDBPjWmrw9lxagQKLb2Z1tWT7b+uYe1bzOfqE6z5EUzbQ+94fG8nq+ZEk2GfjHbFUT50WMvDpre4/XcZo402cf6dGg9GVbBsuD5tn7oSpHCSeG1VcvzNmW9RxWWrMSxcvp8piaNZd/4mC/s3Z2RsZ9wtbKn8YoizzwoUlUIYF5fKvVfbaduqDSZ737BtUinpjVx4m5CLepM6bjgrsGvgaroNUOZxbQ5L206k6vJWYvcWke03g2bLPnAaJ865t6DNV1/2PgjDzrI7hjEd6DrsCk4PhhOwpClRwW749Z/LJUsltFUjcK9OJFM9imXP6rHrb8uhUTPpv2wxE+yv0HV/LSqNpqK6/i2eI5dzq5c1+zLS6DX3A10jm3DVNp5hG85TXRNC1nEnKueVYvSujBd67nSZXU+0/ijeXL+JBypElxrR4WcwbfZdYufYBawcv4SI38OpGDiIycbXiG+T+B/5+1/hRdgEGAeE/4+l40BnoBfwBTjwH+L+f+YjNFdnAgZMSm9MK9NQjCoSUV4bS5H2ZqymenLa1o5XBSOZ97wR3YPvoaKgTxuratzN76M+ewZu9Ql8yi5mfLd16NT9pu//wdybRYUAtfHev6QSpQmVRkM0KJQpUwkpElKhiJAMhYyZ53kmYzKLkqkiFGWKogghIUqp0CA0UM93c761zsV5z3fxnbPWe7HXXmuvtffd71n7Gf89slne2gj/D4qcr45Ev08ATju9sbLNpMi6FA+b92wfbsq3E/a49VhEYHwPdgUPpWLWHrpPtGCffS9G37pEs+T+OIdfJmVrc+I9jlG24x87J48jMqExXbQesmhOIEr3TxBlEElKT3sum/gyoLg7ups7cSnhPa+iA1G+6I7zptFEH1AgrzgAtdm/mfNwA/ufKxOt9A6LTxew0jpE35MzmB/xixVD1/Irw58ZihrYvdzMl16rSRjfkdRVM9G4352hwR85Xb+cgK4HMUv9TvNTZ2k81oChqX2oipzJJIVO7DZaTSdtZZY3ieBJwmeebU2h8QNt7sdrMd14Nh87lzLsaCI3RizidX4dHsbRRNi3Ry34G2sLshi/NJslC37h2GQ4Rx748ltjFpVnrMm4nIbxWAtq+n5gV5v1LLNaxo/3VdQkNSPlRF/ExYl1r8x5a5nBokHtWaily4wx+xh+bCPf918h58UGYqod6LjLk4nWXQkf/43iO5f59bcLhrov2F7nw5gvg9jRaiEhRi/YMwN6bXnG8tYNPHC9y4VW/Xjvdwen8E/cWWtJ5Sw3in078Ld/BkXGHZnz/C5NJq8ioFtHfv5I4+jseXh+G82xmXZolD6hvHUyex5vI21of96tT2b0hQUEKJaTG7KCW7v9+W6YxMbvzQl8+4cMJQucQvLYNdMZwwPT2bG+AP/Yn7jaX2H2pHhG/mlEx9LXOO7ToLnXGw5UKpF59CTBUaX0aRfIwPNaDPTLI1fhF7/sP7KoRSTh+nt5NMWdaXf0CSi4yNUgc0YNNWbo8fbUn8qjss9cAoc/onvNekw+byNoxlR6nxpIixM7WHXMFYuPC7ELU+Xg8SAK55zl0uEariSOZpGqEyNmrObCo0O0LM6jTeZTkoKWotLcmYg5jagIDKJ6Uvp/ZPj/hDvgCmSKSAnA/7v/D9DDgfj/1SUROQIcAWjZoYO06vMejUHt6V+1C63XtsRV5JM8ZD+JcQOJP1KE39ok1C6EYWJVT+ReP0ZM1eBkLfT9eIjD471pvngg+0IO8kO/KbMVerB75CHGLuhB2Od8kk2/MfPvMswy9nP/Tww9Qp9S0rwWzVx3Om8cy/CIbjhYuRHo6Ym33TCM8/YSPWkrSU2v8+TNPRINo/nQ7Dg7tXwZ8nM6NyLVaOo7C88EA/y+XmKzlQ45pjc4q9CN+NZNaNT3ETGDNbk8bRznNLvR/Ic197PCUFpXQvqXzqw/N4hh+72wVi8l7FoDmQbL2TdZg05DhzF/mi5zxozgvfMExC6RPT3X8jPABMOuQv3vMYw2nkaz244ohBaQv9cBO7WFtHugRNF9Ta6Yfmd42FpGV4eiq7GLyG2fuXx5Aj9z6xk1LxHfQDWqMqOYvWEbJ4/24v7DLnjEuPFRfxnrUlYydN4zBh7uQF3rXpyxG0b35+Gcdb9D2Kyz/FpXQ7FzBe3SVnA8PgSDiEJ6FO1HR6klDuVj6fHNmHETDYm41YXLlkdJ3tiF7alBfLt1F2N9XQ7bldH5lwHd3m1B7WAg6+xV8I/twt2ZjzFQOcPdt+o0nfqbbdfa8LP/Tqw6j2P+IENSvtdi/nUalU/rMCwOhg+qnJu1DuvLBrTqkM2cO7Xot6gjycqTZqui8Pg0iymJPhglWRB6XIUu249wstUftoREMGj0JR623Mn9NqeZnR5HXN0abG1KqfhgRd6NcCzaH2PC3tM0fWHA1Y4vGZI2Ar3lCvxe8pga43pCOnRi8r9uNN2qAytMWL0tj7Dwt7z51JeEHUbk63qj/KSE5MBrrPSwZG6LCrrEbmHXQPBe60HTpqsZm/ie59u8sTTwZfDQRDb5lOFsoszXQatIOW6G26py3BRmMzq9FOudB+iuDl2PpnK5sAXrrsYSV7gAJ+dZ+C12JXPCezQKdCg1zuC9cgtMbczICDiIb/dsNjS6wNtDq9jX9BbHrReT/M8GsP6/ZgTG8T+5AgoKCvryPyTIgFHAq/+vB36X5OPqbEyPxP2Yd1vO4uk6HL5wntUTj/BswhXWHzXDMeYIkzte4GPTAzgWXOB9bAfK2wdjZxDPgrnhqO69in1WKlsn1eG1fgFrZ8TT7sJGHt25gJZNGIdKztO3lxcj94RS+3Ek5X8qaaF1mmsFXXFsfJWaO1rMmTYIrYtbSGmWjNLTGjrmveee3jdCP9pRp3uTG19tWHF8Ci8nZxPyrpBdBaNptCqU33f/MPdXU6KzO3El8zMtRu2hmaMZ/Z4cINdJgRHul/n63Raj0i5EtHrE4P0BdJg6hQ/W+/jQIEQUnaaqOIaWrYOoa74Ui8Ym1IW9wC1THdVN6tg+LeJHs4cc7GZARN8o7qd1ZNyaOG6de4fyuvMU7ziKwg1VMh8u5dGkO6yvNqHx3OMEaUcR0UOXpuatCQ7cx9MbIajmHifUcgxNO6qx6E5nDpaaMmF8Prolr0lsa8rkUWPoaKaAuZ8fG58lM/OmIpHJFWjXrqZkP2Tvm0riJ0M+17Qh98wRqr5/5nC3vXR3Psi/2c5Mb5ZC447qxNfPoMWkSegu6knBvj1Mnd8P34o2DNhsgPvYyTTcWcDb2m508i5As9tZOvj1pqfpXypbfeRpzFpsNPxxergF3eZZ5Flc5ObOZPpULMHDXRWjbj9ZMvwfodM3EPkjB4vJWZyL+Yy3W3uW3hpA5Zz73KmrYrDlMY4Y38Lf1ZJ7nw8zqW4yJ1SjyHn3io8vW9I6qZTxY57wpNNimjV+SWNRwkXvEG4jOpNqWkOA4wgsg0dxwO82W+2bca+tBcvzvYlbUUPbgjBqHllyNDCNvX/m47n7Hi30hZO319PLoYDfZuqM6rmUSpMvTH+mTa1dEcprgkn6GIdW5hZyz61lpP8r9m9yRNnvHiMLnmBi34S3rZZS+KeAzY6WbH7Yhy2denH+zWkMn65gWb41HYeV8tzUk4cRQ9nUoQxz/RjSXndiWr8qPH8O4+TmQSxadIoVJy9iZx7G/K+XaWYwnlLDYf8ZwP+fWYGmwA9A4386Ow28BF4AsfwPJaL/3TI06Sxc+ia7XveUGCMH6dHykRy3iha7tV9l1eVocXH8IhPNqqXisp4sqP8gim+QCd33yYgbNtJ23kBpbPtYMkY4SPLOKlGNHiTFL3+JjfdwmZIaLRYZNqIzd77c79BTnh10FquJN2Wyz3lZ3M1S9BziZEHZJ7FsGCHWNVOl9yYVeWnXSbo3VRQt1ZlSFjxFLFWuSJtUJVGasFr052VKwBtXMWpTKRX1RhKmnyfDsufL2+uhUrTGX/565Ul2RZrc29pO8jTi5arPZBkd2kNGTzgk3gphErcsQapGV0m023Jxfh0vao3XS3img4Ttd5GhBXulavsqOfXlmNic6Suax1tJrOIVsWriL/a5xTLO6YKMtLaVudUzZKrfdll0b7Ck/7ghXTRSRNW1m6z9slbc11TKnH0t5VPqQ7miMlLiPwSL1dt+ojrunujobRev9o+kR+/j0uB1TWosmsh4xTx5q9JP9mw7IkN0fsqt2PlyMmmZ+L9qKbub2IqTl7Oofm8ix+IXSve+2yTs0EyxW/JPrIJPSuNZu2Tx1uXSU72NhA6IlZ67fGRwRZisGR8ori9fSH2fIKndECVaPm1kVf8B0mv4C1Ea7iLjnl6XzfnjZFiGhsw4FiMHVu6R8x7R8nfSZQlYs0aiBg0T3Rw9aacTJdPLqkTdJl8uRpiJ4RNTKU3NFf21QVIQ8ku0GzWTxW7fZPvj7lK2s4mU/VWUj73KpLdimFyr+yqvVI7LPJtusqV8pPhYForaqhJR7+Ypo9rlidM6PZncaL10nXNIdOeclcQLueLUo7G0Tx0grx2GSr8vRfKk5SlZ0qJQvIvDxPy1n7Q7Ml6cSo5JaHmSHIxIkmsXimR/0DEZ5jxcDI9+kd96CyRtQWPZ/2KUtI9+J/k/bsmkhwul2YdySbv/XvQqZoqOV7H4NNOTiUWTRNXruGTUJMv01XvE990zuWOQKn06K8pV/0nS8vw/+fPPVQo+BIqn9Ui5GBAqSgWfZHjwQLFP+SxnFk+TpC+TRG3Fbdlte0xuOTyRlt8KJefkS4kzTpVdT+ykqjRI1A0uS2xKlVQqJEvO7Nz/OwpE/6eWvqK+qFjHyegDz2R+kJZczVSX+yMHiU/sItGy95eAsqGyekGMvOkaJpXKDmKn1ENG1RySPulu8qpPvSTaXhPbSUfk6G8L2eajKqeD8+X4EAuxen9OHHPGStSiefJmQ7V0275a6gseSFmCnfwN/yHj+7aRcJ/PUjCmWNap/ZQNXp3l8aoaGRW1WZQ3DpSYCW+k2xNleXz+oXyJ9BNzh8tSe/iSdDLuIHUafyQk3lMSPNvK0QvOcrr2hjRoNcjE1h+k3Z0Cqb5oKX236UhXg0byNO6x/J2ZKL3Ni+XqaT3R/NBJ/NY7iOGSafKpLFS0O/vIoDmrpWthnjhlrZFLJ4JlzM0AUXgaJnbv4qSqwVCyffbL/eUTxTHMUuzGGsup8HJpveWxTA1cJGkDbkhQTa00e2UkOmneUtL6kiz+0FqGupeI943h0uj6IJkccFQcRnaW+eMCJLiuUGr87GTf2nMy98du+RraRPyCO4um+1zJT1KSLu3SZNqMcFGcVSC+3hvk/bitEuUVLs8VC+XKt1Xys2WVfFI1ko+vFKRqe5SML7KQPR2vyT+rKhm6X12WXrwq9zaclojRPaRUcbp4VKaIQ8B0SdC+Ld5H/eXxkkayz7a/tP4YK7rLisRpsp/sPm8oGyu/yvUHe8RxZncZfr5M1rzbLPMfFMujoUFSO7Ol9PdtKR1HHxCvaZqyvbmfnAmsEq2IIZKlclbc/M6K6jw1Seu5Tl5H9hAL73KJ7ZUv9TNmy8xGvaSzwRBxDuwjb33bi+WhzmLv3F9GNNaQrEnK0vNSpLROeiGZ11RF2fy0nLu5S3JP75WmLwIlZMV62Xa6WLIWq8miMc+lnb+zfHFykNbJ7STmR42YdRglEZfbS656W1mo/0OSB4XI20hz6Vr1QM53/SyBUY2kWv+qHB3VICePhMjQqc3kcbKrPE7zk46r3ojJrAdyudZK7IKviNr6r1K6dZqkZPuKyskjMsautyiM3CbmLyNl+47OsupYjdh8OiJWs0ukxUAFCRtQJUonP0nwbmWJ9Rosagl5ctm5VpS3NRHDsJaisGaHTI1sJ22zfMXRPP2/u2Lwh/o31mj1YKS2Dyt+v+LQrSOM/9CaOZW98W8cxamOLtyLfM643droEkvCz1rcWnfgxZAoJr0N5V/vAezdW8KdM6PJ+AS39VzJHOWBjs5ulnZRQLGuPfe/jGZR9woOv3nAkXWhKDUuRaH8Hb/CzSncMRLN9LkYXVjKyqPHuVF6iTd/nRjnlYn6uiD6bu7I9dwIvts/w0/Bl843TVGYW0fSMmW6yBFmm0+iS3kZ/7Y44d4nmJWyBffKdgw9H8+6hdr8mDkZxweN8LKrIjG0Ge43CzmOM2pTJuJ+dDZlr7fQ1F6HIydL+BPdwIjJzeldsZSbO4sJnnyah2usCX6kxscET86eeUq/Qaa4zHzCTo/lLN2yE8fCsdj2O4tLwyw0N66i7eIFnLcZgm5wBuNnpVH42Zjm68+jf3AYtQpJ1Chn0r64OcYBJXheeYtP0GjSHnzE/p8Lz8rac2uPFYUyDb2oPcxQUiff4j6GZlYsTzlJ/TMnOs1/QHFIEdkNI1h/Ro2co6dYnJHIrHWOHO9gi8q6Yi6qPcbxR1eiWh4h8ndzdt+bSrL/dhZOM0ar7QRSR0cRU3qXxIVP2Wt9hw0fx9DTzIINrYpA25Kdm1tw7aEGUVr98d3TmJ1JhsSk7KZP9QqWLvxAWstntP8WT2JKKWkNz5iTVMOv3l50j3RHxWo987+b0/XJZ+4XtkBzVBMa/e3HkqM/SVf6w9PcM7TPiiWxSV+iMm9jkehKjM0gbh4Ko2/VVYzyJqEwwIcRQ4w58iaV8n7ORCo8YWkHBxpMh/PspTKrNa/jUjad91O68GhxDl9TI3F7rcxW07/0X7+TUSW9afPlLFf0n5IRM54BC2NZv6SQ77eNsHpWwNDd+ZTcd2FFjj2njcZSrBpPvusVjpi+oo2ZHw52b6j9Z4XBikk4r7LF6akfRusvY2g0iDrzfXQ/egw/z+a49FchsNEidplEs2bbAWpqUnj8r4J+iROJCDxPrFU7gvq7MmP8cFQ3/K/5+6+YJ2BdbUpP9av0+N6XuX9ycO6lRXH9fCaVj0VF04D273rj98qbNkPH8O9ne5oPOcMm7Q+MfDcL85yFlBlMo950HbF+2rw+uYeM6yq8sW+g5sY5nNbs42TCXlRjV+G/OgC5MIYj+4zp28oDhSWtmGsygyG9P9L4ajj1u9/RUP+MkPt7iA38zq9SRdoXjsdxxUx017RE3agTL1sUUtOimHwDXRJzXYlc7oT7jdk4fPahZOJzNmm+pGiILx/3nGKQag5aQzvzbMItXl94SkThS06fa41LmSpeBXkop50hoWYBLSPzcBywA7d+YJ3SHZvpFixL7opq7EoeJsdgOskH+4djWP19O81ePueFchJ/JqbQL2Uhp5Y6YpPkR0Avc2ZazudxiR8/3XNIKlTi3Vp1Hi37wq6QIkpsFjDOzothg1+h1jIYm9wwHnXK5KdKGgWNdGlo0ggFu2oOGvag/ekj6O3rSqeKNkwyb8eK0d+Y6P6XuYv68NZvC3MydlD9PoKZwRsYczIIQ4f+BFzbQvRDB54/0iLQzJPBSz2J32lDix4zCDieh84dS161P0TPvj3IM+1L0ZnbtFfxpz6mjG8qO2i37Dx3vxwk3qUIq6ohHLd04PqPi1w+5kavBkU6XSngb4EXJ6tt2Hc6FSfXBmyedKWwcjIPittiq9aN9omveXZRSKpazvmahexf/ZaG6XEozs+kOrCUl9Gj2P+qOYqWNlx09eJil6vc7dwXz0tziQs/ikrqFWIXRdJX8zerhzdnWOQDVv0LJ2dSIgfObWHrbnUGDpjKG8XV7LPdyPrwm/QNsuHgmzqK7zYn2MeUafq3+K0pVP26hnJAKh5fFxOsWkrZY3P6hz8jpclozi7qwdRLC1Fe50+7rc2p2zCZcf2Wwj9TCpPbsfqNIdltpjP6jiktv9+Af7N4+vg7cdHKHLHoyOn8BPRKvNAY0pa4mZPoMU6Rxvb2LL5czhXj32TdmsnO0Ei2iwavjp7mY0sL+h0d+x/5+6/oIlQ2UpQ5DS/RcRpPvnkWSgtes/BdDcFBGRwY24OMIzdoPnwGSf3W0+JhN1b6evBaWZtte53IVPiO0e+l7FiaxvXg0egvKMJ+1lcaQjew0iWGlozGWu0JZu2/ka5ZwSgPJ34e+MwAnTd4qPxjt8cunKc05ZXrTHLVGnPRJoQikybsNt7NtjY/ePn+C1nqo9DeWUH5rZsc+hNPm8RWPH+qynIDRcokhv4XPXCeX4//2i7YLJ2I/UINtFPDKW/anrPG98jb2JjlDYHc6TgVnel3eZfegoTdP7E6eptlYSoo3X+Fiell9nRehJ7tItZV+nBkQiKVMyJZ4jyExc1+se1ESwZ22MTG831Q/+tOwLp03NqdZZLKKs7f+ce5uIHMHOzAjroCljd6S65XY26lP6Z17W2OXi5FZ/l06PGeMVM/Ytj9Pd1MvVn8awDq7Y1JC67FrbqcaLfXnH0zgc+l87jftzWP9j/AzH8KA3sXE15hwC6/QywoHcqATe9JXJ/LovDznFvwhdw8G8ZMs+dsjhVeg2dzoN1tjN/85GejJpz9NReT3x4cc/3DsKAsNmX1J8f3EN9dlqPjcBo30yNEJxhy9YoF9es6knxUmZ12imQ/MMB3pQcLoveT7dGenVOs2dh8Cb3FnOu7NxKZcxADm224jXrEHs1XJBSEo36gN6HzlbjaOB+z7OmU1iSiUHGWsE+3iOi9hKLhJdhVt8Hj2A0Cutxh4A5XBg54Tm7eaQy7l7Mk5A4NL85grRVEjbsri9UceVOrS4KFAjNTYzlnv4WTmWHEuFaz1rgcnVMR9HjahIQjUeB1AmW7pxjPF5oHDqc7D5g37wCd2p4jqIMWrScLn/aWM+dJFac6z0D3xG581DX4t3gg32ekcuJiPpcNbfj52ZbqvXOpvruSlS7PeKLTi1+FRhi9mo7K80t8m5XPnvbnGH26hsCcjnieKmfQ8+FcfOzPvbPPeV8WR+uXWqxqWYN313gKZ60k9fQYYletYsiHVf+904b1/tiwfakb1fOyyHpvxl/nLQTeO4CWxy1G7wrB/sZO8sw9+FabypDsqcw5VMbl5I342u/Bsud+Uhdn4xJoAkG1HHu9m5E/qpl+aRfuG57wu8aO56rdUW25kuEn3uNt64lm7A4umr4lQPMzpm8c2OBTw6RIQ1IHmRKzOIPin62YtluHdXc+ccAxj/LWG0h/2ZY83QP0iZ5P/uEk+rfJw+1VFd8+TeSgfgkWVd1pb1yN1rIZlKl4kxDYDL/WvgQ/PEe9YwO63+bRWPMf9/61odtQX6I1c2ijm4JNkwfc/7qCe8ONmDdgFulPSsh/aEjvBycpNjPh8unrhDWfSYzxVw6MWYHvqEqqSsZSM+EV6UcHQ4+rLNhixdD5xhjEzkMpLojV3k/IUDjLlMWb2LBnBf1t8vhcVkaSVhRbhuey7EQLKjv/Irs4Co/jnTDqXEtilxR8Q6azw2EHbwpzWX3sIgEPN1LeXoe3364xf+oc2r9Io7PPF9TckvE9k4Ka42xWjqwnL14FG+t9FOfeZuqdTFyPtGCewm00J+qzMXcI0eZRNO5zgS+/NnD/TC5+pU4UzqvkjedUVEzX0sU3jx9LJpLTIpEPU77S5MRyFNYsZLr9KBwyt/Nr1imcMs6yNzCGaed02fdIk0Pxvwg8YsvrTa8Z7GfF9RwlunmPptujSnwuTmCUhxE9XFyZm/WSE2EXaTtCiYFjC8lb7kDe1saYmvdhX10V5dNu0KTvby43mOGsGMigldV8KnJgw4lsNib4ULi3Fr3NH5hsWYvquH/Yr2/Do8mBfIvoTtstfxlh+5bfT26gtbmShT+TMIk2Yeig72zJ68yTs/1oOlyP5a1/0dx/Cg8PD6LkzHo+p+ZxSc+TKKf72C9vjN7lLDLnmzJ9dCiz3S7xWWcxBQ8sGGa/n3jHF7TU9+Xe2650ij3HtxIFbNSrGTi1iMMLe3FnuTKZRVVsa/qKFmb5VDsso76dJWGGp7hxzZ6muYHs/67IC7ve/5G//4qYgFLjnzgpdsJ45A7uXK+jV80Fnn7cQKqxM11VxqL4rhYHHSP6Wc3iz7Va8harMMbNCc1lBXQcacIwc2MWKHfCw72Kij6TOFG7hh7XAjB93orjd3ax/5sD85qn0t2yC2fULNm+WYdU7Ztc1H7Ou46JnNhbw+5F1zjarwkTC5oy7msbDkV8oM2X66x9eIiuZ9bxyfsyObdzcBg7jNi33XlpPp461VCOetcSYd7A4EXm5OSUotDLleWrLlBdfpKUJ4sZc3UPngErWK0UzvZmqrw7GMyl8q1MUzvM0NiJHGjjzEq1vbh878MB7QGkxbtj5JfJdI8GZlQLz9xvcndpJloXrdnybyWTb8djfWkwvk2zaO7cnomOQ/G4PZg9XSbS7dsO3jTdjVmsK1nPv+NXvZvn4w+T/jmNM4e+ojQ1Bb26x1Qs684Nf2X6p/6hbadbDF0/Fg3dShybLeTf5U/8cB/DNPca6tsVkWxQAiNK6L2hFc2O2PLvYB4f3Aahd6qaRqU3MfwWQE52W/b4fmZEs04s+BpBuJ8LLgd8yZlbjucHwbY1bDmRQMSSC6QXjiTotSb9Ygw4aqmLtmYC+2q247c1gJH+Y6ktakmA7WE0lLXxvRXNyk7vSdjfkdapsbw604sZR3dy/gfE1WhzPcOJg6d3Yf76NnPnDufBiTisoyZj7dYK9aj+zMuPoFZ24KmoR92L3qz+60ab3bN5s+4su/5spK3iH0q3hrBfu5LKYF9mreuLd2fXCwAAIABJREFUaek1tPc3peOrMaTIVp71D0PxvSdhHR05EG7GK8X2/Ngeil9WAYvL55ER05KBPavJmH2BLu/0mNL1GL72tex+X8U70zA29ivDaU4ayqsLOJ0yk0krzmFh05Q2ubH8zR3OtR0KmCYU4XxZh34J33D21iRIK4iz6asI1zhO5ws7uTCgGL2bXTH9V0na8BlYHi5kb8AxbHP78WGOGydijUm/Yor13ZkcM2xCGzdHxptNJvJXBdsvWtMrIZZXL0b/R/7+K9yBVkbWsnJvDiaN26E8LJzt/S5hGAlfK/pyUL2GA8eDsa2chc8GR7ZsOUG/gM38WbCD+DXeZL/YSMH0u/h9tuKS3h9OX5nEyY0BGHpvptWLR2gGnGLmhq1YqvSks2MzZvaArgtsyF0SzAjPbMbu+ENpv0Z450TgfGou3c2+sFr+sqXhN8mj3rHHuTchN8tgfQQDslO40/8vnpuV0dWNpfiIBX/9S9hRbkHfuWeYYJBGlP5pCpJKyJuriVHAAfZu6kH52c9samzDxpXf6ew1ktHj3Qloe4iu746j/ekcN3RWMdVQj7X1yxmY40BtUg/ye4/Ca4k7D3I682HoO471r+LHmsU07TifbUZHWLKrBTNCDFinEY3L760sXGiLYcZBTgxfxq4rL8i5OpgD9WG0MqwnMWQTHX01OCkedOlqRMjVesq7K6H1zRM/RQ+avflD8Kt40tSuoTVxCiF/jbnUrxfru5txIbsHJa23YeK9BVpPJDkgBdXBXXFanICZuTefr3jxNfEup2yboB93gRu5peyK8GF+IztSzoXS0uYXj39Hk3d7Bjkzg0jOmMTTICemaWSyvEUyu58vo/V1PZZlj2bW8BLG1W8ir3srrvQGj3GD+ds/icgrO+karEPgSQtK3/Vnzh5PDPbAdbNlLHP6QXhTfZ72f8Y5r7boVpfx+e0OntUN5cCRIRwocWb+9RGMu9sG2zElZL26RCPtg+Sax9ClzV6SDUNweSgYD8jkQdPZbO7xm423FuN7KoCXqgdxS3TBFn3UPdLonK5KVGxbgn+a4/fiCcn7tfHtOYdPGp9xNOmFo3oEj629ycz0ocP5IjJybqGj6s6mu52wsN7Nwye5/J0bzfSwR0wJu4ryfkv26K4i1+0TLW93Y2fNGKYsV+Rc2XjGvP1At8T3rG3xjez1wYzUnYtCQwHLa0ahEf0Nm6UryVrRloAvWry1ncyebYq4P9/NnrrpLOUodo1sKdz4niP2TqgX7sXsyQgarRv636tK/Ksyj9DaGhRLJkD7fUwbv5ae2ao08UvGeO5zBnYspMx0BZ7HvLnStITpNSb00cxnycMONI+5Q7qVGV39t6F/uhCHiBt0qHxDu7L7RA9VZqyVKeFW9xhdupK6kW+ZftWQ1V5zSFzah52lLfBN30nF8AwMb+9lWhc93iwcR9KdJyyIU6KxVxRf1pzBYIYL9x/U8GJKMj7Xo/gzYya9FHVpejqdp3YRrB41jISXU/mkcQePJipsPHGHmhPPOJetxuXHvcgtzyfxWi77PpRRvmYmm3/P4d6ZfsQvWoql/QV014+gsiaWOeGfyJ9xlO3afXg8qAzf3s6U2H9iybdwcqzXMmnqY9I7TMPx0k9cI7yISyjnzf4OuA4KwdrFhiS39WiH/2H9l8YM3fKEfkE9CJ29kg7r1uG15CjKzV5y89IyXt81IunFfTKMz7Lcdh/TLK/wdZIrR7WbMtzxGQoze/HhhzHO59U50KUjUUufcjvSkDljx5Cl94YZXVoxRXMa4VVzMYtW4an5Mq5kFXMtK4UnvzZjc2EuT3/ro7gsC+PPnzj6OYtSPydujPCiesgKXpo4MsYhgyuLnqMcf5Pff/aTUr2XXgXtGOKkx+zULB6XpNJ8TxjxP8dzY8E9DOceJuBbHSPS9xHnMpgbLezQtt9KtaYaR2dkYDg/GAPlDXRpeM/T7gN5evkj/9p0419iB9Zs2MnsbBXcB95l5RIHCj93ZPw7M1bebYetWzqX3DxI+XyQf9fVaFyqQIyiA4MaGrg6Yj2lJ3oz8txIAgt/cuDUE3bE+GG6wp/vTjfw9jbkp8ZZBogRx/5FU/gLxt1vS27OK87czyDi4iqeqduhnqBIwqiTGLrHcHNVW86qZrM06xs6J/zJqSimaPUG1J9N5p+LGsEJDvy9cxBXNXUWDixnmG8Gy+yWcSb8LfVn3jFmbSHh9qtZ32oI7ScnsapJB/Ssn3JrciQNX73ZE9yDs0sSaZQyjQ9Fr5jk+poOKTXEeP5nQdL/ipiAkZEuE1Z+weFzGq/V2tNncm86fbqAWUBXlBUKeXG5LQlj/emT85BvqcV0u/iQvb0a87PkD8rqA1n7NQuT313o2leHc6f/EHL8IYX+v3hrd42eXYrY9KUJbU4EUmc8ksu726MbeZAL/cZTfM4fLx9Fri5rSsCEDnQeGIFd+Sgmqyax8Vk3yu7U43iwnJ9G44h1U0Y/9CpfrunwZ+t2dObf4O6FU9wKWcChIW2JVajg2ZhkypdMo6qnJ4uC3LnRpIqFofv4ceMrd7VGcOLJDfK/B1Bed4DJO3SoDrlN8VHoPKMex+ehtCizJ9lgFVNSi8ipPES6rT6zwwwxzg+lhVlL+iWNocO8QVw7NIAZ9toUN6tnycJY6n/0JDWpD3UNZzg5uJLVzvZsVbvNnLz+xDZZgotRKdHdVNizVoEf/Y0wyUxAf+oucp+uJV/pB6FF01jSKIaWFws4HvmGP00XE7o6gOBm3wkoiadR91p8tIcxxzmU6kHL2aok9A92oquXC+bP49FcM5hP7y/SVS2IXktc2VrWh1Efm5PeOpzOrqMRbV2MpB/jF46hMsKA59Pf0WLRRcr2JPGx2xgSE6zpPOo+g77q4JJewPqhh+hZ/hOfqfpEuy0k/KQGx1c2MHjUYfTPN3Crpo4+4k+GQh2LAl6xpGoana9tw+VWBT8sxvBbfyQfsx9gXe+FZ+wyfGa58Km8AvdsBaZNSCKnNJ9Cbz9O2nTja6MlzJsbgPP1VKoLHuNRn8CfI2/4NvgF+/aXcGjtdS5GvkBpgwW6h6cyONqRnENphHR5zg7XJAoKZ5F8eAbjVlSQvfkQq1rH0D3kFlMuPafzlNPMGBLMeM07TIvsx7+Preh3/Ru65gPIjM7A0VKbBZ3U6djCjO82O5n+043jinm0PPOZIX1GUnS0iiVvq8iw8CIzahranSP5/PMiuc0uox/6CxM7A7p8H0vJ6BHM2/yDRjdeYZFWwJTfPdk/KgZn+0o6Bo2j2ZlfjK2e+B/5+68wAvKukJjaFWR/fkmP+o9EL9zCWr3u1IzRYP+ulUyvjeSJRwpvNzyl35dPcMaS3h3GEBO4ha9b22K0MYElen1wSVtBg+d5HG4U8jXpDx4nerI9bSoR8oJQDz20Ho+l5oAHsT+7kOkwkzKDeHoNnoRWTjULZ8TgoqBKu25hlLcSbs/RYPDXMGrc3bD3ruae/XV665lQM2EsB/otoOtyR/IfBFIdc4/XPV/xvPM1XIr9cb17i7aX3uEeYs3e9z3ZN9WREJfTeLctZmJKAOfefcdiozmlEzZwLdECxb3jUY7dSXXxfprbPCNx6wW26YDSGE+CrZ6QaeVLeUY6GZsmovq0Fd9dd/D+cAYmQ09hZT0G/+dvme4/hxHrD/KpVTfOaFiQM2ozxw9fYOVddxz1q3g/ygWbVoqEleiwVUWPP67DqLkUyfSadRyoTKe/ri99UqO5dz6BAX10aHwymITVhXyafwcVgzJOanxh1aSD9Iw6iInRQGxiVPm4cAIdp8Sz2TmVZ272tE25QzufaJJv7eJ5fU/0Ml4SfkKTnJHn6fPYgiWbt7IkZQjDX3gwYUsph+suc884kaHpv9B3XEj8nv20OrAGBVsnXnRrxTMmE7WiIwNstfDZvYR7Q85j0XCHTc2raLHnImott/J80R1Oxp/Eta/g2XEoy7MT0Iz5hkmMEg/Ui6j7vIO41dlEuAZQ/2QMG33HceitOoadB3L3bhINcSXsfFPIH11D9j87xrVFHRiUb0Sm9ini361l04OvPF83EqP8QUx6aI1LxD/cw1by+Pdc7C1reBSTypZIbWwGz6ezdQo+uU/xsRiAS/+V7FZcTNynXuwONWGZxmJ2v2iD6tsqMr5cp5WaDwkf8shxakpy6HYu9a3HxPw9Ll77yendnq2evZgUW4iatilxB9M4u/8PUVFTUFu5hAKTk4SMGkL2nxUUD8qk38bvdLuRhbr2eOpmPuDRohgWRY3G5FoftuS6ca77Olj5nk76b/83AP4XVAwqtjGQkxfei/lrVbEvXSERWa/ETP+1zLaaKj0+rZL8P0vkge456be8vxQNyBB7n5mSeWCDHD5nJQUuXvIu+5BY5A6VK8k+8sjnt6z6vkRWuBRLX8Od8iX2nKz4qyZ507tIT8c62ROiJSWZt6XRvXrJyk2TkKYzxGqFnrzVTZAJjrFidXuXTGhzVVZ9UZKgpx/k1yEXmev8Vyp9rovmikxRzDKTvpday8ddtpIX9FpubzOW6TOjJe27i4xOK5dXA5+IS1Ku1Dspy6+KHXLDdIb0H79G5igOkPx3ldLYa4ZEHTeTviMOS8WXC3Lc6aH8XO0pfQI8xWyVlczobyCz+tVJYtB7qahIFIMPofKy+1MpUJopW9b2kyCNXNmyw0IMbpwQ7a7lcqyFn6hm9ha9tKXiqfBQIvSnyIDM5hJm/U0GbhwiVodmy1jH76L9/JGUnR4nJfWb5Xb8ZTlRfEvOd1KRCpMP0ldHQ3yvbJF29z/KX6Od8lJDUbzi20oH240yc1xL0TtXJG93WkufhBKZcmeOVEa/l2lZ/SRono40NR4vt0MviPpVFfnS/qGMTC6ToU5qErjokSSdvyLe05fJ2Jy2kqmxTL7HB8jOWyFirPBWQlWHiUpbBbFSGiAt1ryQ9K4+ojk6UO5+qpZlEVayvraDxA9+IXVxWeK7OVJi+n8QKgxlqOIrsZn8XZat8ZW0lTOl9aSP4mBTL+Y9XSROSV9m72okZdXaorThpmg0Pix9b20SW4PL8txokKS/eSnbL3iIxVd1uR7nIH1m/RDbzuWyPOqfnHDYItmNA+SbdoK8aWQnKzRK5fvIOJnzyURid7eWLb8MZHt3M3nnoybxUxfIFYV3Mv9KmfQYXiN3PmVIiP4vGeYWJ7OVWsuPdRvlpWc7OXZCTx6095C1X8xkzbEEKTM6LhGZ5rJ4x2Dpp6EvO2a5y4dJynJdw1CuaGbJGcMoSTQdJV83m4pJyFjRqmoqBZZektLGXTZ1MJDHf7IlsY2J5FaMk+WTh8k2h3tyv9ctWXP0qASHOUmLCncZFX1RxuwvlrybL+Tn0l0ScOCKqD7cLfvX/WcZsv+Kn4Dpbw0apa6kWu0TUaPOkd7yGBNXPyL5hidDzEo4OaUtNgY7CLDxwb5Sk2cK6RwbpIuG60kq9Ltz+4A9hYdtqavpxAG/bPwbr2TBtCZM+vqEoBVnudD9LOufF3LbcgMzr35kuG4+iXevc8j3N2+nGDFbI4vb64spyvnI0UxTyhos8Z4WQslyZ642bcHxdu9Y8k+wmBNH37jm/FQ/zQlle6YYbiVqz1gU3BoYvSGCCQaRuGTFYrNxE+EW/ijMWspCg6us2fqB5ClnWNx7LGsdhzOqwyVMe+hysdtrtq5J4OXEOyi7LuB7hB1Fp5Yz4Ox60mdq4TlkKrZLFzHfIoGDq/fSp9sC3mfW4T0hA+dpWXRa14RrWf7kK8+mS4kpTRISqL2byNL2GyiLUGF108dceXuSMxU7GRhyj4Avb9jjFcto20yeB5wh/bQK+0xjcKuN4lCHMVzYl0CK3hCU5nsSM92Suy1KKJ0VyfrGfTHYuo2XDd05/nQp/iZT+DJqGaZbOnGzvo7tli4s6D2Dj+3akLXjGm/+ZTJHZyUR/u24s0URuw9nWeYSjFfnJAxGaTN20Tk6rv2A4cQPXHxqTnTRKbpaqmH2qjmL+tbx45o5e2smc+1FOcodF1JjbcNSUaZ5fDsWVx1nqcJ1fuz9TMB3B6btvEft9hVM9jnKDt1V+DtqU/U7heOjdlJ+sBIV36/UnSmnvPIu+0Of8+BqERFDI3iqpo/i3lbUZ29CJpjTbNRR/MbPo+jjAMpLRxHQI4F5bWpJj32FyqaxRGV5Ybq5hAZHC7StLbk6r5KyFzMI9bPApOgFzfy/MzzyMrbtHFjXajWOE7+w+skFJre9w57MdkxI9sHZ3Zdo+4eYac6k9ZYsVn7/weFmR2ji05QXoda46huQ0P8fTXWbYmicwL4BZ3n75zcjZ6SzrJ8XiveGodi8LXZr5lKQ/JkNr/two0cNi9bd5YnDVA73u8hAt0raZgvfB28laYkjo7+PYWXcNKr9Jv9H/v4rsgNaCi0l9Jgbk3/5sjPam3epQXTYUc/TWSrUmB+lS0kHtApassdmHie7fGHgBD0qr3rQZdkzHp3bSNy80VSUNCNrSxwxZ1VJHqENjXW5dUsBn3ltOfNBm/zEU3gP2sMwg95M11rLjHUxLHw9GKXoYDZ+uk1Z7GZY8YDYYQaMfLiPcwUVuH9TZnDjWXxw+IJxehL9ZDYHnKJQL6xmqMYOMt4cpMp0FvPWpRBfWUHbvc6YliRw8YEm41u8JbvdAWpaP+bxI3/WPl6CZ2AG4zT9aLbpAy8X9+TAlk1kXlvNxOtfqO2uxcc9B/j3ewO99h+lcnQ1B/9eZsefYCbumEyXIy7sSFrBKbXDvH8Uh3+nS+jfdePb7BKMV7zAJj+eDbE5bB7+izKdcrR0m6DdxRGznpNZubg3OTdfkB+phUq+Hy2DJ7J+1Tp69u/GvmFL8Dp2hcB4BTQVglCInUufjx15X1vH+XE6PIgwwvyIExrD9/I4aCdLR9Sy1eQp258dovq2Cj+1K4k7tJtJN+5h/caLUlVHup1ZwP3QTtwvs6JlXnOu3Z3G2xef2fzyCUunf8Bs2RdWm8RxXv8pfxdFUtRzIu29lAlt3ptws2R8KszxGhFKi6wockeW0ebEWX6ductpyzUYP35Iuo4y7hdqiG+UzKykVTQJWETymEe8WzSGFZ6buH7WEGPvhRg/6cv1iatZkNmElcfa8Mh8K2uGHqRteAO6PfWYfdyf24+O03h9GbVHh2DTfD5Pq0YSEvKLdB1DFkc4UvbyDeEOSTQUvMB93wf+dM1jc3cz8sMWo/CvGy28rViz8T1R5U+pWdCNCZNOsDsxghADR862m06meRotYl1RDLlCk8oMNoVYcFFjO4bX45g++AmT9rdmTX1nvobVsL9hO5v3XaeP6niUX09lWcU4vmsqMj3dA6XEv9Aii0Z1/Ql6soQ1wY+xS/Xmg5sCuw8q8aF+Ll57C2k2ujlVh5LY3FODA1ajuHZ7AnHRHgw4rkPjFNX/3pHjqLQXj8bFcnX/Kdm+31ZCenjIjphPEpNzWFoaJMn+H91kv9JpMev4WGx1DWRrSG8pbzZUdFOjxWNevOTe1ZUBO/XkYv0w+RTWRpr/9hK1tUtl4oCrEt5tp3Q+9V54/VsSSu/IkEb/JOWnsihYzRKr6mfyrWS3tPZqIbP3dpdpkxXkVkShnGjuLk4/NMUyu1CeVB+VIJdouV9UIP/ar5X6+Ztk6/Vi+dV5r3R4eU7qLnrJHN9qKVs/S1x/W8qCI4GSoGcsOgtbyLFN+RL4SlMsL7hJbd5wMa7sKuuuB0nm/kApfLFROjazFDUHS0lzsZb+19uJhdZcKe5WJSkBr+RKc2dZuqZUmm4/L39+PpWwm4/FPOKrrFRwFw2HYlEqUJDhUw1EoWu0aJVNkuYuQXJ44UyJ1T8ohyZGy/YTHeWURr0sPFAr7celSa1CusS33yGODl1kcI6RTDExkrnRn8Tn1S85O7tUHrxpLbV+6+RJ6lY5O7xGaiJ3StPkMonqXy9rvCqleZeRsrlQW0oGNRf7NDNJONNbTqeXiEmPI/L7gJHc962SX53txO71YKmvqZaf6WbivdZc5t8PlHs1tpLLYwkLaidvjmwT667q0l85UzKOm8qKl96SPWOjdNN/JJqP8+RAfIXcUUmSMOWJ0tqytTwf91YuZ84T9+OzxDypu/Ta+1ZMls6SebM+ivlQJbGa9UQ0P36UndOaSlCfL3LhoJ8sGBghWhszJC2grQRMi5Xia0ekJmm41F6wlLdKXwSNc3J9paVs81cX9awj0qNksWSphwsGpVL06aScy1KRfdciZMe7afJtl4oszboljfPHSblblWgPmi1Kb63lhuJZmah9U04mTRL7x37S7WdH2Xdnkdy82F/61sSI//lCMeg2Sj6NbZCbGkaSd/Ch+JTVyEv137Lz5i6pePhJXtl3ErURs8Wx3F6+9j0uX8IdpGrFOxn9fZl8vuglTiF68rfdZhlnliNKqaskZFCgdG5ZL3fO7ZG2B8fI5gN+sl1HQ/7sWyerZ34Unyxd2XrrjbQ/kyEK/6ylkctS0Z7tLoleRnLtmPt/dxehhXYnuZ3ZTkw9tsoXu3sSeixGBlw3lZ4vPklr395y5EO2PP1ZLjaBe2RZxTmp//VK3pS6Svfl1bK+f1t5NaSJbDhmJPHvD0nNo3ypqHaU5TfKRX90iuSMUZJZqgtlfVq+HDSaIOa9WsvuMldRdKiQIab7pFm2suwwOiT+b+7K3+p5MvLeIwkv+CIj/P9K1OIqWbVvp0Q2/iU1OxJl3eE2UrftvXzrECD9Cl/L8CPD5JBmE0m7aiQn04dIxYO5Mvb6KRmmUyYtjL6LyZhSqT6HJKz4J1telsjIj+cks9Fw+disWNr10JeCLanysthBnCePk5sD68R9mYYMGLZF3EKDxSS3Xv6GR4jmwe6y+7255N2zkE+R7nIscaAY7YuSzJ+DxbJrnDhau8iQp6vla7MyGTgkUWa0iZP3UV8k5dBAydOrEt3PZ2Tbq1PipXBeLs19KEFzLSV9nqtkqnaTiXn+En96mrh+6CqGqdViW+4jl4Y4yYz28TKzdrgcu54iRna+krtGWYafOyQ/uuZI94NvpUnMWTG0cpMTl+1lmcIjsd2hJte+eUvu7VRpMJ4n+z+Ml0LHIum5rE7eL+4pn05dlOTc7tIk+JJoX86SdPWmcn3hRYke9EG02w4V/dooGT1unqzfNEauvOoi38rjZeGQP/Jvjb18Xrdd1hu5yje/i1L+fKV0aTdfVv+ykCOWnnLKU01itvtLXaNQqe2qJEnmrrLT10+G5Q2WOWbbZXTbUbJWcbKoNjaTbJc4+bFwnCzs20aOaKtJyqqxsipurnT20JBte2/KRa0JEjaji9Qah0mjolFipP3/MPfmUSF339/3K6JoIipKGiUNKEMlY6ZIGYtoMGTIVAlFmVKikCEu85AMEVFKpGgwVShDg5JQGlSkiKZz//G97vX81v18r/v5rede91rfvdZZ55z9OWv/997nnM8++73fiSknTorUa0miMC1X3IrVEjeOHRWTu5iKwCt3xeYtGiJ91QjRIWCzSFjrIbrv+SJO2BiKYIM/or3FRoQdPyM0c3+K2uaDQlJdQYzdIy/O9fMUY3/XiNkzDolP7mVCKXWZ2LtinLB0+yZGp20UWxvWiJWj5cSD9q5CIyBZ9LjQVXQ08xS2f/qK2sFHxbl6H6Gs1VG82v9RHLt7Txg7DxIhll/EgMXxYrDiIrGseLEI6WouPuwNEPFb08UDv3si4aSlWLHypBjtWvef/U+gRekNwsWI7jM3Ii40YzgukgWjZlI5zgpHrzhODXHkcHAyWc/3oD4vHL0e24mRsmBCz47oyVaz6dJq2rpsZ1jrW9QGHEDKopGURVdR2BBIeFALT8YOp8dLbUZ3uEPqygls//6e1bJlNF4pY3hWPLYRQ1B3mEfqOSnaH8fhMlQf7RunKBxpQOqLAVzo/JLbxY951uZPr5QgIhyUKZi4jpRJFzhcbMDpvFIkNxjx7uRXLplL4BdfxOu63wwdlIDLbjO0Q7+x1DaMK97VuL78js66bTwS3Ul/kc3ClRYoHpSgKnIh5jfXcDh1E4MvRXBn0DTiMkJZ+HgLDlYnma2owO0PffAKlCBvthmNtReZd2wxWxT7kn/lA0pNIZwvbcSlWZHYmpfIRvajb/B3XFeps27oGCLrI4mrKObYGxOk9HTwz9XB9NYwRt/fTlS6PYax75iidA19a0OmBPVEyiKZH+H1TPI3QOr2cQa6reDg1YNYP3ZEpeEpbkO1yejehH0PeQLeK7CrSoGcSkmiP4TjvjiVSY/7M+BKHy7MCCPz+geWDplK4KJ1vItdhUOlO/b373C3414iR50henEVqZpf+bl6KL1fzsQssA/FL3vgseQ3Zs8uY/HOmJflVWhrnMXl0CW++xnya78+B/vosLPZgqLLQ5Hqv4uba/TwH3ON9OwH1Oq4oivXnyFJf1GxsAK/8NE4VkXQbOrLBdsYzhVPxveDIQ/6OeA28Q6JihPxXB3P2Rcbqf/zHUVXNebV7WTTRlvMLwzjePV4tKTCCQjexud0ZR6bR1K1Mp2WBUMJ+Z6B/KNC7EUB14v8WHr+LZl2XRkR2EL/Al+2ON7irf9ZQvt58TpyN0Wl29B3GkrTwGZWjdGhIWk9L3vsRWfQRtadU+L6qB3sv2BDRbcrqOyO4Mmd9zQqFiPVfy85nR7y/rcxakatPNNzoc7AnPd31ajrF4HWoSysXpizaF0LlmMtqZKXx948mMIx62iovvuP+PtvOYG/CUOrJSQk3vwXnaKEhESShIRE0d9997/1EhISEockJCSK/yYbNf3/st/2rQ/yJkvYUDWf9TpTaXDexEJ1YzIShjPPajAjzbpjOHoP3eXikd5lSUxFBsevfmSazya89sfw2CCdDwPusSU4kHdt9lyuOc30lj5EaO4hqkshofrGvHy1h0i/UtrCu3GmNpq5B0148kCKleOn83XlDIZ3r8a0VpnvIw0p8Jint9OlAAAgAElEQVTIcPcC/LybONH+gW2rXdm7PInPigdYJueC361LDLYOIz50LBdrkrFuOs/OqfNQ0YpDv0KZ/K1m3NLdSfDrY3y0u4KvuQ3SIzK4ODCRPuFdKcpsYGrtZGbKLqGHdxqGew1In+tFw7omNgY9onTNIKbdqqPTQn3OmNmR/6oE218bkM1po0yzgX2WZ3gzqx7PofmM9Ekiv8sf7luf4/YdDxK+D6ZUDh6uuM2rASXczb3BiQWZrNnwgrZrXbnmZMX0/aWMcajg7PH7xFXPw8HlGlL3PPk87AcfS3ax/uo3wgbrUtvpIEN0PzH7eCYXHVdS/lWVK72eMGb2bdb9Fc2qose095nCMvcuyOlYcPGgMfc0u5A//QZXkysxaB6Ble4wLi/+xY+zr3DIzma/gSTKUqGE315NsudXgjfcoLPRETTyRhJTtJ+DcWW4vR2J8fdMZPP8setoxLVp+nR+u4h+Yy4zfcVrosckEuT5F8PeaFA3wor5U0rR27Oc/m1prNo6l2O/7Bmq4kyBTQ8mTV3ARFV3pAxWsaG+iQyvBUh170rtORVcOthgefgAN1NvEzPVnOibgow4L5Jd/2Kcx0D2Rp3FS2Sh52GCYmIQWy91ItKlnRctLkxx3IlvoBILX/xgWKUibw6/IknCg113xjKwTouu1kqY95yG99hY7P12cLq/JvY17fRfsYWIkWpsLNhFtk5XOmxcy5QFDby6MZjm+rE4VPdEbYYmmcXrubZ0KJaB+zkdswTXw9I0GuzmrE4MbvMNmXw3hw3aJ7iSN4CttsvwfTSP55+GUr1gJ2d/u7OzfQmFuXOYO/g7+3psZIOR9f+ZEwDOAf+rFV8gWQjRD0j+ew7/4hzs93dbxr+IR/+3Uv9VhmeBIZiOWMKKk5OpKPpCWUp/Omh8pnDVdnp/KmDShUG8MvLnfXAVnh5rqEmPxXjqHfz6DsA5KpZKtcN83POGUXHr6W81htIRJsisdKe7zyY+qUnyS7Mf/T5GM8ElCy92odDYwkn9nVRrfCT2znPirZ/j9zARqQ3DGDDxErdkrvGoYiF51zuxL7Avz199xKNCgZXO3ViR5sG4T0+IGDeDqXE5aH65g9+U+bz3LUVp+Bsibj/icGYVA1/oYbm0C3HP6nmVV8XYdF00JLeSfkiK5Z0Osaw6gDxZfaY9Gsp216P4N0/j5rdvyH6S48ZPJ47qtXJ9uyEOoZeQi0rijeJMyodocbSsgbmlfjS9cuZT3mdsfd/i7j+GnEkncTBI5+fQBG62nGGB6V/suLif5m2bGfjhN5d/jMDz+id+HnentKMmPl0aiTuYwR6zbiT1z+eyZQnXphygl2MkU993I2fqXu6ehdjtWpy8kMgozUjurnIku+98ulpswnPFHBws8vhV3pOox25Ilxmzf3dHUq9uYLTWWdqOhNPxrDSrpozB4vwzbhuY49BpENetD9EypY5hJgnUuBXT7BmC5LGvfH8ShMqzOdhvXss88xXUSK1jYiPcer2CavssVBPOk55cygD9mYw3zCFrwUuKjZdwR6EnLcIW59GedHnpz8+uE2kMrsE/ezSG65dzO1YB9fmqqOcfwO5pEBHfjdi6yhTZPVfQqvbllYo8mcpxlCw6gnnHHpyeP4BZ3TTYZe8NDgWM//EGo6ZPLP/px/fbtlQevkDfinnETozDS+U1Ha7kMLnWi0ZNCayvHqHXejfuGHmx4J0K06dqIyP7gzllZvib11GcbEC5/k+i2uy40203qjFnyD1VyBKls5Q+aubVtlmYHq7goO0iBrnnsKPXTOJu/Oao/FWyVS+z4pYXczfdwj5FH48XrcR0+UN7Wz8i6+6w69tF6uVDSe2hwIRF2Qx85cmItO70H6hDh4B/3ov/W05ACJEG1P0v6unA+b/H54EZ/0UfIf4lT4FuEhISvf939hUH1PPw0CvGbriAxqk//Kyr5s81Bb5uWY7t2p9EmnXEziwd046LeVh5iq5re1N5MYjB7fr0tXuGUp0Gcjn3OJ4/C32PaN4/MqPm52K2PCmnx/1grIpWUDtJE/kprfT86MXtK5PYu8yQz0HevPBNYsh2UyaOGo1i2kEiJhfiEXsaqZLH1PfIYsSXNzTWrMTmeAVlRUqEv4+h+6VpZAzrxIJJwxj4ZyIX3I7y/JIJ6sVJLI9/xb2AQsJ2fKEufCsd/PviFH2A7LuN9DJqY/IpM/SV5Umx8mKwSQO2Br25tmcYlzL2c6c+gkXRFziTfpWExSrkqhkiZWlOSekrTvzS4/XnJl6smoFD8THSkpehvXcNahW+2IZU42oeQtdx7ejceIpvyTbu7m3E01we98uHmByTSed7/XgmvRjJuTOItpfE7MxUZCoeoLwhhjeJ2rz5UY+SYgm5ro70mTePtLOLUEisxGrPDvzvlJNpt58rfc6j327CxYAYOq++i992A2bc+UaRXwGFdjYMbpmCrPpoXk6NpuuTnvSXcGfqYCXKjkEn9QS8FJy48+IUM8+lcM1yPl5b3xC2KQzJ9mDOb3XFRVuGt9NT+Hl+BgGBhbwIt8a/owYNTwW35fIxcLiGhcdKQuZdJ73vLM7baHOiTxmBzotoEVNwWreb8N62FGxW5RbH6HbgAe6pfZG1W0p6v/0YaB/g56RsdjYep0N8IeOt7nJ0w2vixCyMD6YwfM5cZqlsw35FOg/mDmbDk3jcTxjikZtAgJQ17XfjyD+syoUOfekzcgRyQ3tzcK8C94csJCYlgwiTZAI6mLH2qyG3uk/n19v39FL/TNj9MThGxTHLtCdf18YRP/wF+ns3E1uwnEzJ9Vh5/OTBmM1EdepOgFF/Qjx9CRkmRa2HHYMaNdh23IEGpcnkZheQd1me66MUCPKMo8Q/hMiM7ah87YLlGHn2Dn6N5RVvVnRI4d3zHD4MlMZTdS/H3PXYqHnu/8wJ/IOoiL8JRf/ulf/WqwGf/8u6sr91/yj17bIoOZuzapAxGz/34b33JLyfGDMqT5mTWVP5prCAkBtm7C06zvgDmfT6eg4n04HE20QRI2lI04hIDA+r0LzQCo1FNqwcZUMXp2jmWBShqNGJB1bjeGqxkupPLcTONMe9ZCb6lTfQ6KdJfclWRNJsVtucxuZRN7b5TuVHJzM2Xv9M2xkY4DSenylp6C/sQs54O5rGDOHy9cf8jFUiKzKXz5du8vuyBScGviGsZSN9jvUjrDyD2kOtzMwspSF2M60THhFtMYp1CtfQe7+Cjc+n0/79KkHa9hR37kbN+640/AlF9tIoLgz8yOT0ncw5NhfXikxsX1hQ9q4NJb1jNG84S/HYy8g/aiDRqpbTbR25neqLtEsvOrgMIPHMWyLmD6b9+gbkc4bSejyMnYcDWRKfg+VGHborzmSqVCsrNJI59yiHQQcDmG3SndULwhmaYM7cS0Eccd3A/B+b+Cbnjuenoxx9p4XHuhbslWuJDFDA03kSMi1HGW6iRHmPJl6sP8FIGX3K50/lxYQijifP406WM3tH+tKx93eihg1nzbw8nE9ps6fPSYwtdZEfNZ1J8+bg/bYNh9St/Kh7RYX+Oc5+lGPsSC9kPiYxZVsFjs3O7LTK5+K9CrboFxGn+ZvQN2Mo0ElGK/o4d0bmMbxsEh8clzHX9TZuk+fh/H03l/YUk1ETz8xOYZT4/mLC6Te4LJuHhlwT2otHE39vAIN8ypHfMx3Hip50adtF0+IqNnZzJsDDgKqyo9SHVqOjtIg+XVeyP6cbHWrz2L4rkl1LNrN4yhY6DalH/9MlauVsuBBznQm683nzcj+32sejd6qCqG4WXCwsweFLMs8qrUhdH4fahK+c8HAiK96R/de9KfcbiFRLV6bPOkbBITVW7txJ3jdHhpv34+Xnjgwf5Ulr8xtC7Q/x5NdHqnqkceJXG8MOfGeawnHax/5FptlCSv8oUGlbyNZDLxmQdpKN7hk8TN5Ms3QNvgO0mVw9Bir+eR/+v5FA9O9KEP2/HiNISEgs41/XBZRle7BpoCoRUXswyrBl9qO9xBZvJfl8R0KPB3Ip24ejr6w5ZL6A+2k76KrdheUhDynQ6oO7w2PUvxmxTy8Cyw4x1MQqs/i6E1q7jLlb3cDBEmXMH08m8MYjVFT6Y70LZg70ojwxmpLu+rQ2LOfyz31UPq6g/VA0+W1eHDeCxgX36d2wjf5bzvLc6glrV9VhZJPD4Q82vFPfR2FLPpcSrtGwW4YouUxOe4Zhk3ubCxtOsL7YlZ9eBRgckWZLzQ4M/Nw5b9WXnBMXsNnwG5U+UYzwPIdM50mEeJsQ9TiAuOuHmPnEkY/Pc/H7lUdwuAkrH6dxZYAR4wY+JDTIi8k7WtnnFURgzlvO/NHELyCBmqbDTNSoY8acArKPTOB9UDofnkoyxaOQkpX+ODg+YpaEE6+6fsIlQZl1owdyyH4dsiE7OOL+jCyzfF4mDmfw67Wc/+sAeisvcKtJl4IjbqzbrMPMC4eZUd+dWT4L+PQsHC/f1YyQ1mFv9XRG924m48N9VLLdWat7gmijSkJmr0SvewHav7uwRLeaI6cnY9uiwjNfI/JVpZnuEoKbVDr309dxuyiOt4F90S5IJ3emAkd6/GKhQiife+bSeCIVz88TWDP3Csd/Z3JQ9Tyd82poKXdjWb8aVGus0WrrzoDtklwqGkV92APmOt7F0kqHbndsSRdTibkhg6t2O68O/cW9WfeIaixm8KxbaGhUYu73Fu2uDgTovWWdwlBSLvZC2c+LPV8eYyOTTPdWPfr5NTOuvxWLJVvpVHwV6afVvB+WTqGMN3e0D7Loy1XKF+xE6+dh7p3yplv5L1QLsulScweXsx0oaw1gqcMucmVn4eosQ0eTPrxNtEFvaRmmrYs4+aED17KKeRHwnV7bP7NX35DE94588+vFZJP5DJt7ltCusQxXmcwYy1fceprJoFHmpGRrMLXClvfDJvDcJQyZp2fZUWjNC7VCXHI90fhVSOL8QSjXpDGl41n8fKqpH+7yz4j9b8fyQRN481/mhfzNJAz0Bgr/Hh8HHP/dun9qqgqDRMj5bPE6sllYub8X8rHvxfR6DXF14Rehcl1V3N5xRhwYliNSjywUqWnzhOdjazH3+jbR+dQKMaWxXZzb7SgeLpYQ+nPvC7PUViF38Yc4Eakrznw8K0qUV4ubD7+I3aH2Yu6CPkLxVIWQVXMWAYPrhNH2NyJa/at4qLhJvN29WyRV6AvjjT7C0UZXuDXlCuf8ShGV30WE2s4Xe7ueE4uurBctG5vEDc9DQvers5jwdZOo3qQjlhpqij/buokzvaWE2pIB4lOLinhZriy+9rQVvzw2i5AiPyG1Zpuwqr8ltvcZLH5U+ojVi8+LotkN4kB9oRgzTE9IrrMXi65dEs/0TYSf6WCh0p4ubv9OE8ovtEXdhHJxKfuKOJSzQiif1hAOE1zFhPxNwm5UoQj28RcDls4S58srxFqXNvFC8qxo2vJCLN3+lzhpP06cmmMg5i97KBwM1MUUrToxrrSPKC+0E4rSA8SKtvmiU1IPca3DMzH9j79QTQgV5lkjRdIOTfFip5xY52Ek5khfEjdtdwv/LjuE2coh4tzmQjFRKkgMPD1XxPfrJH5IyQq35Gfi1fNKMT/hgIgsNxO6lp2FtIGZaC2bKMpm3BPbereLTPPJovVrtZDSihV/DVwu/FYtEHWtdkJ9k4bYJD9aBMffFGNUPoh9uy0ExZ1Eqc1M4WE1Qyxd2yZsp5sK06ir4svtWeLp6HOCtlzxKOK02PMoRdgFfhBxz7uIO2rl4oyRnPh5X1LYf4sVLjaTxB4LSfE2wUfc1vkinC8tENWqs4VffyUR3sVC/NVpt3iidkLcuZYkWn6GiHm270Tfndbi8fhTwrnzE7FSp1j4qoWI0DovcSOySGgerRTnq1YJTb/pYky/WtHxTZ1Y7bxEyFpViYMWc4RRxiCxfaC1WP9dXnTWiha6ZZNEibGkOBMjJ8wWvReD9LJF7/epYsgubfFdTV7ofHki7rZViz7Fz8XBy5/FaQNlMeyYrihbul+4NKaKu7V5ouHxPpHqdkR0mHJfFOVbCJlds8S7tkAhE/VESCjYivYLreJtS7h43ydI2AT3FbpTtUXN+hqx0mqTUJfpLKJHbBK/+9uLzVol4qBt6f+VEGEs8D9Tk1yBW/9F7/J3lMAcqBf/Tx2CfyvSDR/RXpLCsz5FvBughbnydcoMHekVHY3/bkl2XXyP7XFfPO5Y8qVXId0zZmB9viu3x8/EX/UDhZE5PLexIXiZH/HX2ji4oDcnjXeQXl9GeftVRsh/YPTm/shP2sKf/n94uEuXfnY70c3w4UViJjJzfqF81ZTOc34wsMqSF7M6ET9OELDVD4u/TtN96XR6JXehzVKVdRHFOITrsTrkJLHGUXjUPeL1UgNqCl2RPX8Fi8tGrKjbxcHCVo48SeNQQzYb3+zi+cy7yGyro3RAEvad7DCcq8bVQAmOPuvE9oogvq2QZtDSTuSYnGNpB3/sytaTMqCIiteH8HvuRD+zE7RmpTPrtAV55Srskz/Dy27BqCj2YlzdE2p0mshovsLFK6PxezGJ6Y2rWZmcTcwVL3ZW1dG8uoWiCC+G5z7G1mc199puMnPKM3zk5lDx+yGZVzYQKJ+CTOhaltwsA4fFvBtYxNrtLhj/+sjLy15k2f7hZc9bXDqWTaeDhVj4T6KDjwmHWvozcIErWa/fkqZjyi6VlYx/nsbkb2bYRS8lQGUQF0/IMHaNNT22vEX7dRBe6m+wrr9A3kxXbqtNo5/+MornTiNx4wHGBt7Hs20oc+5O4uiuVNwnL6bnuwr+LP7FDN2rOJlf5+XFffQYu49x9bmw7yq+rbvppJhH1SULupyWxmzIfTx+5LNpRwUHE17jYhLBa/sHLC89RJuXJr7jKlGJHMvbiDsEXpFiZ68tvDa+RPa4lRzLmgayV1n7+BBJN90pdgplbns2N3ZUsag0hXXBCix585r1UfOJr88h7JEPZ06HMrDYnijl2bTmu7J9tzmVYSVUxVZiuO8TC7yn45UXz22ZEF60XiepbCn1vvFkZpzn58QKCg4o8UR5C1+Pd2LDRg0mfhyC8koJVnYewr5vNrQ+z+fS3nwatqhRGveUDzYH6D5mKX3Ncvjh5cbmDXNY3OkcMnJ1zOk9jpujevH+9ziWTOnOsYW3cXYI/kf8/beeDUtISFwGxgI9gSpgG3ATuAr0BT4B9kKIOol/VSQN51/RhF/AIiFE9v/Ofo9BsmLauFBq17ugrPKGeMMMEiy+cFr/GhHebiyse02kVDlh+mdpDjAja9chbM2UuejbyL3kKoJSG4k805uWxlNIeEtyRfoMh4NGI1Wvy2Nrazy+OvPFaQw7/xyiIdaKMucgBtZ4cODcONofynLqqjrz/dXQbDDGd982FPO/sb72FCd++rI5qgKtX+eoMlbnj/15TkyOZHDLEfYEO9NTP4FH1Y5ofHfnSZM8E/4spfDGUG6MzkNx5hj6DHrFi4XWFLdt5UOHh8xWTKfvDgny+3hhiDMRGx4TPK4P1eeGMCwmF8lu0bz5rsLZ7DL2manSNbmMMMVqrjXlcPPzEE5e/IHV/j2U2JQRPvYBHw/dovL9Ns68l2eL3lnmLjFiwJy99L36g8Eh8SyZ8pqpPf7iYoM/08dZYqTjydYXSWT092bTqh7EzVDHZbYi5xoX4+7hgdH1p+Q7x3JEegsFeuXklw3j+MNqOk/qj6rbdI4njqBsYh2qTsEE2gXSf1szq7b244GNKc7WPVHutZ50y2WcCfkAid78ut+BgVujKT1wj6ZYIwLL3pDqI0ta9ArWrJ/AOdd5TE+PoSoigTe7btIgt5fRPo7k7Ypk2Fo/5qk2sqViLetLBhLbup+8ukSkX3WmtmMUzaeGYnNKi8SqJNTTBxJ5v5JZD8ay2K4/8eZN6Gt9J8e9GwGfr6O1VBCu7MKzm5I4Gl3FYvxaKs7UoutRxbneR2ns9Ju7GtqEPrtCp8orDMnVxVvHnEvnm5Gs1kN+9ANenOrExb8Eg7oIdvcdztuvGnRnCLc8rzBjTT6pzo0kPv7M6XcezJ4/krFvpXnm8JAOG/dw8LMg+85dij2zWTYjDe9d91hiCBNu3aPlSQVZixYzdZYXFrKWPC+TxjI/muOvT7PxqASuqZfQPytNtqQv2lf381Z3Ad/3R2D2uyvSZgoUOrZSvfwwI86fZpyeBdVJcQgpNfr9SMMm5Al1yS2oynfCZd1pipss//+TigghHP/h0/h/s1YAq/47dv+n9Mz/zSqvauaqe/NRZhrqc35wRCqVaVsHETIzgxAlVy4e38Ph4fpss2rjiFcrNw2zSPw6jMD3SshdUWNa9F4W6keh3eUKcTGS9Lh1HitZVYYsvE+FWj6l643pK5dB3aQouqktJXbOQky93lBWYYDTiluklHTnaScf/opbSvX4O0ibHmNBsT1r+qtgctKTR48kCC4+hHrrMOS3R5N4S5Kzmtsp6lnGPfO5DF/4CfWtlaRp+vLdcz919duYZVVGaDcHvLWGkrtyCXWG3bi3fxs2yRL0SfyBieMQvMO3Euyfz6nNE7CTUqVwbzonh8uSk/SO2oNaXEpSw69Kg8T2Gch0NuX3X5mcGeVOatNSSp0rud88gIsVuzCbNhOzpoWsbo4m+J0beQXNdFUawakFfUixPcf8BZl8WPqU/cET+Bj8mFkOa5mwJweTNXJ0lOvJt+IGRjb5MODUETLTvpHdvomz0pcJc5dlW+BYbp5cwPPfNsy38mGbexJdhqpCrxscjrXArVoBi2H6RFSuYlHOAga5LeC+1yv6Rbxh8mhF1rql8cldk3LZCJYeukm/MA/sdyVQF6bP6weqHJ4eRlXjNvK6pLC64iRi0BHGL+xK88JzrKmZw4Gm0dxv0yJm4BS8bUazeEMgO9obMftgwo9nsXx+XsX29ZuJTlXgoI0ibctqOX3yOUd0pHj6lzHhtSb07qJFoVslq9rL0M+VRtvxMxP95Jhgn0RhmzpeupU4HddHrqoBx42NZB8S7Le8Rx/rSIwWzmDvI3POfivmcvkfFM/LskisJMPlDn19TVFMcUNS3hbdZk2SG6bSOuARt1TckHNejLrbE97u92GYrjuaYgCeHwqQqQsiX3IfatOfcaL5DKYTUpkck0/RqWdcdvNC/r43tgbvEXmC6DmjCD4wlF0FJngEdKbjlw5U1DaSUVDIMqlwxm2u5Ll1H8YEKGK9tTd2500IXh6M/IZyRq3QYe8WFwo6grLMZIqb/j3+/iNeDFZL9qG11zoGeUgiM0qNst27WeXowfKvRgzqspnfu8bx1d8StWWRnOncj1sHXrJ7+k8Mxm2heagGVYO/sSjKCavWJqKUxzDE8z5HtVPQePech9kPuPAhkPxQBbrZpPH1ayIGyisZ2ektl7vW8zbYnB8jLdinupk43WEsWPGcu6sLaHwEPxtO865BkZANWby1+kD0JmeildeTMtSTYC1vJqcFkJXvjVev7eyLfMBIz758dD/OmaLLmCpkI+fxlEtPP5PR/yWxvQfTuU2OzueNiNqkwymb65zo4cFcvVdE5jmR/8GUpJRHyLx4zPK8NNKOd2TjDVPMp/xCqXoHu/zr6dDaERmT8RyrmoWuaX9CjUMofTOF5DZZYrPq2OGeS/xZbWzfy7L2xXNSCj5S0b6V9xmXOTh8FH2+dMJ2fjvdjpvTMWMNLWPB2fQprgfdaL1cSkLRQm790uOhrCQFJ0pZMieFpZtBaUUwN52kCf+2DUsTF9R3WRIRY8qxX3LUn8sn7oscnZx6UfetGy9Mwvm0tBPao9woejgHLftxHPXXonOgDtV9wwkqN+PhtQH06zWPzYdW8T2wnLIz+SxofcCYnQMJHLuBy/uvkLZ6ANI7vrC/a0eWly/FO/I9bsVDaTaSoe55GXOMnjDnihPbLsUxX2ozQxp70CWsH4sGXSY2xYrsKR0wzxmBsVomhbsm0nH9NRq9iqlf2J1jXetYMbknd1Ut2WAE37sdIyWvjB1fd5KquQ+nlzWoS1QgVyjBlhffqUKNp0/mcOMvGXxH3CD+akdG9bDHpfgHXT028KXOmZ/n4znruQS98C5c3n4K9YtWyBoP5KRNLOUPDrCyzo4O+0ZiM0aP41bSjNy0lU9F/qy+XUBSxXbmN17mQfI3rn525uLFcCbEyuCv8YVBy8cTdH0vIz10+LSkitdGPhiqj+HEkrdUpr/F9nwIqatSWBrWTNjc3xzofJj+s1voGTGQGe2TadmQSYH9VGpmP/1H/P1HZBFKGnYQlZb7sKmVw3p/FEPHhaBiIsGHuZK4+HiyuasnD4M7kz/mMHvj/mK9nTueZb7ILV2Lt5QNH35pIs4YUHPuNye2XWRm4kpcJXfR6asqG3T6k+QUwv4vVVSkqnFqhw1lt5vRXmfNa0UTRoc/JGfdVZTyzrIzfQkPH24kqrssTcVqmA30RaTlU2W8ha+fxlM6P4VjXiN4u3MlCneeo650gWs9v6Hoe4GwGSOpGvkF/z0P0Dy8mIZPhbR30kZdJQzDC5KYRFRxZ+xodD/q42lyhNK7pbwv30XUwvkE/srCJ2cBm3J/s7NwIyOkY1g0wpk+usO56b+ZjMbnuKX8pm15R2bdqSJJ4yTbo6UwSdpDS2wqtX3d6bW9ieUBozFsCWHveEcku1qzt7cUcma5DNrdSPfa1SyqGMf4vDS+pJ1Hym44UUaa1By/StSEhUzwzmeP1TXcLBV5tTuTXr/30ighULluzNCHkWgekSAz3Ii7cQp8++mGUmIt1lYl9KlxZuaPKfxZrsZUjc10/n0fJa1HVG/Vw/ZJAMNvauFi78nGib2YFOPD2md+SMmaM7j3d1r6qZFjEMGLQ18x+WKPlO0DRpZb8fVdLBdmdMSndBc+v3sQlDCD4m3a9H2jzeTLg/Dus4sUSTcMDIbhd9qEAv0gNs4di+2tCGK/X2RpWIXlPGgAACAASURBVDyH7wUwwSCSO6pTmdm/msPJ+znxahTHtiegEF3Jt0J/6m9pMz7cBOvzTugmxlB4Zj/f+xdyYdQZ5ngq4jl9FX0K17Lkgjuv1h1l2E1Hkk0aKN5XRExWOM9cg0jbsAVDRzkmO1fy7cECKqf60UP1NF3fveTL1Uhu/y5COziFIQ412J6KJkZjC3VN+7BmOk0NjXwefpyhnddzJGAjXRMncC4mg+n6nekvF4lORR1LK/vzsaQHcTIOjNNZzaDLM9m1tSff08+z+KIze8MHcHTaXH5lvcUwNZffvZPQGZ3Ire2zSbXex+gOizld94oz5l9YI9P7314H/iOcQA8DQ9FV+w/9pyWwctkY1m5oovs6CUJm2xFfcozYuP2MHP6EL5vVmX1vGj8HPkPdcQ6fFjgS7hbOUn2IWnADxcczmRmsh87scgq2nCPv+hkmbuxM3MqnHOg9l5prFfwYbcfvEBs2bazhWIEMLeZB2PQuJ+VuR1qWhXDdwoLiPhGsD53BmusqHLHeSUJhDe3pQwh/WkrkxWpqL3VkwzcNflYnMEGqN82vVnDmoybTpFZx4mMC36tnMHH0ZOacUic1cwQj851IfnaIbI+TzEtoZc7HwdgNzsVReTf3Ox5CYUkun+dm088phwktbyiYE8aBHRtIcBlH/QUbIrs5EJoxgFlqs1CY6oWE1EcCY8bjvO8h3VcHc8hkBzvUtMiSfU/zgyjerJjLkLo1nJ49ncCAfsz9NYh6j8Esv7mNdx3q6Fr3gFMxn7k3YSHxC92oaPTllr0DWW4ZdJFK449MHK6DT3AqqYa1w8uYHjkV/YzhKOpNwbykA3UlOvzKu80m01AG3LEgd+Z3KuSVGDNoLObzIthqYYTZlmAUXc5yx1uBludL0B55ith139lwT5/vZ4tICkrgqHVXAgdd4cA6DUKmbsU4+y029f0weLCKiw0azJKdR6a3BHVvDJhmGsMSj1AO9/2FiUQ8+cmfGNcngsefHyAat1Jil8AOqzDS1p3jQkotSydkYOJ+jYezTfmmfIAz6v0w7/WMiJJnjNi/AF3zWaz5tRu2riUuIw1e1xE24Re/djdwb/AFVLfuoNclJQq4S5ZZMF+0NrHg00G+XCxmXJgTd3sexsEgiF/Fpxm2rTML3vgwdN48DHtEMWPMSS73+EZzh29kTzrFwPVH6bXcjJ7rNSk2espHKwPWq/zmRdJnguzV0Pg8GmulQNQfZHLy0Eiun3xC+uJ4ag+70N0gkYnN/bCa94Vp7/eRe/gBpZkHWDH0Gk5B5VhI1DIxzwi97sF0ClNCSDjRM/4JVTc3cDK0kfsZD8jt5cCp3WNIWhbyn5tKbGQiLSLl9cUtqQnirdwZoVFdJj7v/Sb6mXmL/L9cxJ9aVaGxx1B88Y4TQyr9RU1lrVjx1VzM8ToqKoeMEv20g4ThaBcR1RAg2sptRYfZrmK7qZcoty0XZfleQst1vRgW3UPMLj8nHuRdFgOih4ughHkiyVFRLI60ET/WRgglW0sxVzdD5GRNEJ/di0XAtbsiKSFVDE4zF7fkisX7QWFina6hsL5jKZT0ssXG6XeFXclw0fYlRyic+CCyuv4S/a5XiOFXpol2lSJx6cg3UeU1UixKiBcSo5+LbWsiheUwGeHqvUtU/HgqqgLeicyT9SLLf5jomTReKO3OFmomIWJ5bKIYnXlLWOvki1FnpcRfR1yFddYS0cf2o8idqyc6vD0tkk+GC/ubD0TK2FxxsDhajLCYK7SWtoshDhJixp5OwrY5RkyNeykurR0lbttriXP9l4igjOui4ctL4RKcKwx7WQnF1I3C2Ctb6LtoiBV0EEqNfwntmZki/nymsMm6JXyPqovny9eJgrqxgssqYofvY+H+wVjMO9NZdD38Umze7CB+L28R7gG+Im94Z7ElXEL4dP4hrukUidX3kkRFfX8xWKJEtHR5Jbw3fxB29uli0MEdQutGT9F6MkMkfg0Wp1v3ilSnfuLE1GciPWK58FT2F79yOwm3bSGiY9VgET9ETsxUPSh0slLFhQZzEVX/VXharRGLo1eLnbk5YspTW5HrVC06PNYQdVF14puPqdBbs1JMlH8lGiffFoGzV4nr96zEtZFbxNihG0TUvPFi+OrZwu90qrh8r0y4vtkorm0tErXXd4rNWd/ExivrhM/q7qLJM0F8eTNFHF/wVTSeVxQqiuZiyOIcsdThtsiQ2SquOmiK89+DRccJd0Vu/EpRq1giPrZrCp+5z8SYjjoiTmaiMFl+UzzP9BKfe/mK4NoS8WNQmli9SUW0h/UUVZvmiojlCI2khUJ5hbSw+JYjbF6sES4yn4XWz4tiRvB94VqjKfxbFYRZ3hThZ6MjLjbeFBEbz4kRj51F2hM9kWwnJ6r8Zwrb44bi1ygpsfPHIZH8Q1Uk+D8T7TPSRLrxFWG1JkCse9hTjB5XIYo3KvxnZxEWvPmDUp4uGa4jcMx6yLIsN8zi+5GbtpZucq1InzvOnPcKnLQvhe/j8Piwj4wiLya7Cq71V+JtazCSZVUkDanjs/QmhjaG8Gb4OjxyA5h9awuh/XszeUsrXbWdmPVGHu12CZYu3s+Qbk1cWX6Bj30v8ftEJwZM+En8hSnMnpSKdYAVayboUX7fBvdPttjLXaeuy3WkNLpwf+JpnJMU0Qn1ofOxL3gmnKZXdy1sAxyYV/0Ap8BmBvefwImqF9gMGs/w3hqMKbOhJy782GCBZGYYJRt68ehSBUUDPrDC8y1Gu0ZwyHEGWolvqZmmhLf0M0weeWL5agqpD5pINwsjY1ICHcbmYPniJB89bnJKOZ+bjdHozZtJgd8qjAZXUbYogatJc1is48z8zZvY/M2N0+1+1C76wGCTSNYnd2N7cB4nnm/gUpIC67f84dekKNbXmZFx5zP2J6M4OrWNYVuqcbaW56POF6ZeKmTxbk0W3s8k7V0TjhOl0Th+H8fycSgHpJCcZ8EEu5+sGHaCxRMvM91zBWoFkzi6aiF+271YJmvIPffumJ49R+lKCZ7qnGLesmquvhrPptPGlHjb0aQpjUriH+auPIWm92U23zLnfMcIkjwqMcvWJk/tDSdKhpN5rw8LNOR4JXUfrxEfqX9hyMupsmz/KkfGgCYur5HlZ/lbtp86hcdxDaxs1fC+MwZFo7Fc61zCqbQ2yurO8DByJLE2bjxz1mJk3zycMldiYPOHpQYXKDWp4J2GB9OG9eWUYziJfRN5Hj2ZTj1HkjbEjPM9Kzgof4E+kRMgVZG4i0p4qQ8jsXsUKUorOXxCYD5UipXZhcRLGzNC9xDnXmTy0D0aWW9Z+paU0zdnKG6qkvwZ/5nzZ+6ypXwM+Su+c1kyEeP+Wih2mcAQ1a1MGuyHw1BlHHcoMOLNe/RGnaDFcwUhPjXc9vXl7eJ44nV3Uiq9l4rNH6ncbkyQwwnCbwXRrXUEz7ePo3DAcTqoqv4j/v4jrgOmcgpik/xCDrW4ME+MI3/jHUznFdBoeYrrmfO46V1FhaoTZ56mM2WRBv4yO3lmV8fx9XWU/Mjh65ObOJs286fUmt+hvYmrfIhjdH92vlqEln0gE8+7I9PtIDm3ntI30pzIlDgyEvQxOVFMwE8zNFN7Yf7DnCKJ63ilnmef4XgC8nojsjsQoHiT0lBLrtruZkTSUaJ+naa2cwkvdAoYKp1M14ybaHabQe2hd4w8+4m6S195PG0dlX1MuLoHrswVfHxkRc28arqHLGdyYAKPL25G89hdFH5nkz9kPDZrjrO/Tp6vASeoPtZE5pZgzFeWM7xfV7b5fuSKlQQf1ANYZBBGdtUCkhcGs2xoEqu8DtLmtAod941kbfqJr1YrtdsTOW4wAOM2Kw67JSPVPJsnv2dwXdceCX1fZsqZ8kJtPFEjU3ne/pPFz68TqHiJru3dsdObxHvNHBpbSxgSvBezXT4YBoRg51fG2oDPrBvchQN7c3EKUKPXxNck61dwUKeGsdfOM6boDp0Ni/CsqsLlmg9/nBZy23wQ6+2yUJfcQd3aRlRDEtm31p6eVks4evcYPiE9uF/2B1GVQfOFgdwcew7jpc68kr+GVaQf+68Khv2ewKQh/nhfM2X6Vw0ums6n19KJOOyMJdHOldntc+ha9xPdNzJcTgxhhsMvVK98xPdnC7U6TWwevYKrp15joWmMgXktiWsjOFoehOomR9asf8/+4J5Mim4i6XIM23Y3MnPaOB40aeDx4gIxxjk4rV/D74p8LINukqe3nwnyKTRNlEGxVo/lKvH8LPWgYKwHhhajmb4ok3nTtiApvQKJM6GsPybPqiJBQ5k50rZOdHVqoiFkDD8mX8Ttz0/sZ+mT69hGxw1ypCcoo6TnSF1hLLNe90ZnZyGtb+xoTLpKgNNlPG0GsfBZJSeVbhF6M5mPhTtJP6zH3GWDOfvoOKqa14k4a8soaT92STbx2kWdh0OfEmg1mLmKvwmu7MyAuAv/uXUHGpoUSD07hoGaHVGRaaPxqBPfx6wh7vVPXF/PZl3xOEKuzMI1dAvpVYHMOBSN7JBt1L2vx/X7MlZdd0Np4DBi9XbQUGrIQPdyYlNO49KtCNtZr7ja+T53x0ehs64D3ZZksG+9IdmnLUn6sZuTQ1uZ/bWR29KdufStDr85V9l1+zemvXw45W/GiDO/SbjghNXIL0wZrIhl5zDmHFdi+mB/Lp7V5M2VaFbtzqCnmS67OxQzZFw5G/f+Ilz5E4aDlrNp1HsCirWpH2jPuuIjBMbFEu4cg75cHCYzqpG710IHHweWZRuwd85z1D3usG1tA5Jr3lL15TEbjk+h4eI9bMpjcPnai8fiGCrTn3Bt8C66++pzI02P6JYsht37TKKzJgv8LTi52YWx25eg+rEfl+ceosVpMudeOuIVcxTjpn48rgWFMTPovcGUew9VOPxZBrs139E7pYV69CSSYpI57X+Y/omzSdhyjSND5Nhbo8STBR8o8bJEQ9KITsbNrFzog7bUVnZWhPPRrhLpy72x9RH0XNhK0c89HEjzJvDOMYZ1MWVnijZzXA5jamSG1OzOLOyoAnbP2FzehPHJp7wYroundxA9HrnSr1tHjF0GIe2dhe7pLxRPXMopenBtbleWqSxj5sh5rD2kz/1V3Zkx5C67hi9mvL8bNz4MIHSaN3sPRmMa44RTfCUjKwypDapnRoMcJ07fRULyHhs61NJLUh3/DWlsG7ceCYOfuDvv5K3xLA5ZDePnbm2au8uy8mwKi1QG09N4AhM9Z9D3tyqrV1Qx9UIJ+k/+sGW2PJndltJRZKHWLQEDpURGrUhhsIUy7lcDGbBjMfnTNjI24hZdTO5g6XoYnzdTsHweRNxFJYKqW6mtXsLMi4oQ+o31416h+2MatwbaY3n2E0anu/N1RX9k1NMI3VdNu2YWESvrCNf+ixMrpfH/mk6JxRked3yHcpcf/NljwenANfhHdmZ27CF+1kwgJXkWj2WqKJ48Hbjwb/H3H3ESMOylICSG72Gq0gfCxk/k2ltdUs408rZTFbcfZDOppJRc7dV09zdH+aAiEpMK+WMSSsvbd4TNKmXRk3okntcwf6EetXIljLV3Z/egar54vKJuWyJhG0qJetyTntsH4arkxNJRhcwOqOOzUw9+r4jhVVIYKkMnkWcXT+F+VWYe7YDHks3Y6Efy7pg0Dc6J1LrUYnjTmbFq91ho8g4vhUd86naTvpNl2bV8HSeGredzmBTP62r5WTKIsPxIrh70ZZluZ7SWDabDX37cWB7G8asbuaSnxdK677yPtmHdjzqq5eJo6SjF9G0jeHA1B63paZir2+KR/QE+PGBZ2VSUDnfnRbIktbGh2G224qH3KqRbx5O5zJrguUYkNvxi9P0SQvNSccszZfzwaAYN20n5kXXotcmSbBaN114LLnt1o6BbBx5ctiHw8xKOnC1CSVaOvm7DGR0wm3sfUjjk5krpYWtcPZrJ01Zg1arz6H+PJiZgJzd+3GPzTneSfRLQVM3io9p4LLRLqfpST93LWk6PdSWqbgr1KZtZGrSDs2vbWb6vlWF9l3Gy6SjPw8rJeZJLxsLf+B1JoKaTFi0KB5DM2I1r2lSyRt5lxatzuG7uT8/rAt3xN6lYsIT0mBy458NVpcMktLUS9E2Voo0d6d7HEfce8/F7YsSY+wFckJvN9LsN+I+q5I9HJ04VuGI7u4rZIffp1OhP81obNCbL4COTz82BgzALDWNy9R4irK2Zq2KK0+1eFP2yxnC6Kt3v2POxxAL/qmrmZxji49uFkaY3iVErpvuY60ge0WTccis+ydewv/dsJk7eheuEifz2DGJ7kQnhy5aTrpXJPmszsh16kGrfkyfPH5FmuYKqTXa8GTUWy3gHisoHYO9rR2TwUya5SBB19gee8gko5BQS9dAQs+K+5DS343awgksKucQmryN88kKyKl+S/H49PmF72CepRUyOAjdKDvC8eD1Tji4is7Af93eGYuh0FLmdfrQF1/3nFiR9J63L2DIlSm1noRt2lKeWUTi4HebAdHXqyo9hFLyGDcelmDoblgcs4mh4EB+7NFCovBhTqfGYRf8gNM2JhRpRfFQ+gNKhjhQv8eKJrCQvPOSpLUplwMSnHDdPoOnsSAq2XCXdeC8HpGrY2n8UxmtP0PAknxTPWai/Mifn7iNe5HVk071n9Pqcycx+X7CUnUb65PM8m90Bj0sDOeIgTZ+3R8iddpfF+8dyR/Ia/4O5N40KOer+vj9FkShD0USlARVRSBlKFIlMGYqQqSIypTKUzFMISYYSSoZIhhClQSVNSElKVIaURKPKeV5c172e67mf+3r+9/1fz4trr3XW2mev8zvvvnvts397769j0R4m532kdXodpeYO/N3lyUnDsST1rMD7VwPLEwvZXqlAlt9ivK+e489Xex4tqSdSbTW+Zy9ibvMLef1B/NZ/QqFUd+KnZXHr/mZabqyC4ZMocrrASKtSinSWsSPrEhOO/eXYrlRSvCew8LIXm4PkkNwxGH2riaTHVLBgfyphDc6M9HXkSEEx223jCZF4SbVENsMffeJ3xhh2aX5nRd8ERt4xoj0hGd9T1XxTiCLB3w+ZBY945e+ETcQf1q0J5qL/IDwlOzHUyo0pN53YvPkLnfy241HYTk3Rco7stWH9ZgNOdTUi6Hd3vm4uJsnNmaquadxd1oumkxV0GDyX7X+20r38Nznnb2CmGsuI56epe+VO51n2nPE1IiB6OpW3S1lcH8qmvGVEbXzBjcnBWNx9wduflihn/MJjuDN178rpeiWfEfNec9HzBAoVGei/cCbMoJaAq+l4z2jBOdOXbhMDicpVoM/dKhQDAzF2GMjPk8PpsbqCBq0BfKrLQbfZgjvfBuJevxUVH0k86s7hr98T7YMWaEp5kp79jexGT/pHd+KmtBJST/V4c+IBUv2moHqihjPpCWwcaEFWh/4MjVpGe7+PHGt+Sh+dYbzfnklw0E4Ou/UjqEkB5cRwYjQ6IWOmy5HaTnQzvs27Nhfe9JuJmepj8ipWcOnsaT47yDA/8TuyLr58/TuWl2YX6bDhPAkvW9FJtSRdMwSZ+DBi41I4VPSDGsM4ep43x1avgYx+T1nw+ysj9zkwavITSnyj/y3+/iOeA0q1OZhfzORDsT9BHQeyOX0hfXeWMixtJfPk3xMjPqIg0jGc8R6vQmfejZxE/Kw/jFaYxsG0rzyZeYy5DrtpCpejv3sfLNyGcmjKI1qUdhEZ+gGXIC2uZ+phLWrRkPvJVfejrDmgwo/8ybQ6S0P6VE6NWExPjSxipsfSVlbDsp1v6JYegmv3zZQbDqd0ajwOW2JI6tSKh15/1D69ZFEnVV5NDyTp1QW8dn9hVb46bz2m4tu9iQqDUu5OWo1T1z2MiWhm/vlczCtCMZlRgtH+dlY80uPn2nw67HNnnkdPnoZpkpTci06DvtFbdxsjZu9mYMg3vKJWoLsmipu3CgkoyWXWbn9ufQ7k9vm5tBZdxu/yEKS+FfDcbzRe4i37y+fQGH6DiOvtfD53FekIY753OYJvWxIuG8sIltnGvIflSCs1oRm/kpeOEXh/t8B6hBny/vEot9tzrDKDE3ucuPJjLY/DX/N40Wp+ntrO40NjqVP6QGTSZLQ3lGEjU0bqwtEY1/bir1QSiwe9YcGjQFSXl6C9RILwPV9oeapKDyV/VMZ248BTD4JP/qVC5xD3HU3pYVHAQ8ONyMyUpipDmhOHLvJb4jdesnu5e3IJG7vMop/aQKxf92feRHUGeX0mfGogvl+UMZeuo3SDA8qDZGlYb8L+fefQmKjN8dhZ/Bj7neIB/sg6bCBWax/XbVTZkprJ7cGnKDZ5zOFlsXiv7MHjVXm8Wt0d6xkp9LVIRmW7GbefuqNyyoEipw8MKO+J/Mme2IVNYUiUNLuEJ9LbD7Fd7xLmQ0PQr6mg/6kG1nlNplEMJKN1C7VtebzI+s5Z40IuTVjKye+qHLLuRWO9OdI6+fSp+sDDIl0GK/4iwqQKz7JCRjtuol/neD5fnIrTlLkk7tpEu9ENVLe/R/V6IxVT9VHY2wddVVfmVS9j7+uxzNd9hk5ZDpm1RhzaHMvXFf3IXRbOJLVvZDg/puBUVx7Msibg729CvvxmQfuHf4u//wgnUN9bjnETRjF5YAPVjV1IXWJEj7MnmfVAmQOl2uwvbwaVtfy0MeSEzxJs9Fbysy2aoC2+GNk9xFE9maozn/Gf2Y/Agxd41y7D5dyutG3sjVzkFDZ+L4FuWezbfYH5lkFMar6IcXApZ1z7oXzFCE05QyQeK6A+6ic7l06jy6LjjEstY2hENIoa/Tl2vhZLw45YxFbSJn0auXttbNu6jj316swOyeOegR8qPh6c21TN9K2ybO5yiIU+BRi4NsCDe3Q878li683U1e7CY/ljsk0uMTLiKiYT7ZH4dAX6qBGzLg/XbUaoOE9g3LgWxhfH0+Vrdwb9LuCe40QU2r3oedWffXu3UKRtyd70ZobGx1K8UYOV9/txQuc6QWMSieq0gp4/dnI73Zv56h9pWHqEmzEzWX+mihWvZJEa9J1HrRLY2L6iNKSQF4aKOCTb03WOEiZLwzj1TQnrA6tYsUeLp10/8adcnY61j1Hr7sTN7LMEpPoydPYk+JCEjkoISvoF5ClZk94/gB7bujGkpzOONzsjO+4iUcNscNwwG+9zuRhbvkTSwZviA1bEXw6hpHoNZtu12DlNHgkzSdYmT+XBz9f0cFRnh48B9Yt0cH/9l0kfhlP8RJLMT7EUjZfFw6mUpsR6JhQdY96yCmIuB9I1zJbHdvWUn9RiQNJQpq66zFGns8x46QInlqDZuYHAUEOkGoMYJv+Cnb0D+TtuKddH3GZMvBcWWR85pvAT+UlTCJrylHWRgXgvjCHacyCuJka8/jyHPX0EhbE/qXGaiJZ8Mi5a+dTqxDIk4DCyUpd4at4LrbyZ9J+WgcW5NG60hNFq/RCPo5MYHHGWv4vlUXsRgar5Lm5XrqLscw0at8egb9AZxYly/Gw3Rbd0I0N3b2DqixcMOKNK9z0FTA7YSeMtXdb9LONMbSnmB+XZqJrA0+ItjOjQj8vKWvh27cbTrteorelLxFkHTFwtOGjXiy8m5sheOE7GZk0MfU3+Lf7+I5xAXdfOpDXKU1BaxQr5vsSMbEf56zDah9sQGhjKwqnafFe7j9fmDPrbzeHF+zI8r+1HuvYvp1ZdQWGCK9ov91KzuyNnj4xF7u59HLUNiKqbxMxtNwka9YmUsnt8lnHk63wFjJ+3MrHuPt3mNLI8rpFdj9LovkcJ/+EfSd1hx9GQySgktZBWXs7z1Ai+HNjPx90JtN+LJDPvHNJX1BjYZMmcPt3Q1DeiPPQyfSVW0vYgjoytS6mOzUOkbCHBUp15smMpeJeLf0UmnVrMiVzqRoNUGc4iCOtjHZm3IRrliExKrg6h7G4CG0+Oo8Ofdpou+RFWcoa5x8LwVl/A502+eKa7MEgthbjQAWzv64pKvzlcMpfC4EotLTnrUfbLoik2GbPX3ekxRR+f2drknVdjl0cBLtUnKSjdSp+pzmRItuIx+gEnNOsY9mM7d3q78TYnnCKNRlbMTMNbsQNjfSyZOSaMV1zh85x7dPr0gvfel+jR/IolIcF8Kcrh3Z9UFvqZ8eioCooBQZgd/IXu53vsUjhEhUsdXTRPs3OlDCtLJOlzqRnHeZH0jjrNTd/PuN/tw5jxWXR5vJQe5c7MC+9MX5UtdPqai67ENxrSLnNUPMV+UkfmXppL5uQ6Yoe8Q3rzIGJ75dNdexBqCtJUe+iQrXyBHP3tfA1Tp8OFBZQ6Z5A9MplXJX44PbEk8YAur7J7Mc1uD8dzpWmd6seR+CNYVPwhXiuL8LfPGKl6hKDp7QQFTCClTwcUumjTN7OVpZkFWMg0oOzeQrjTGXoGpLH/wQ6cVyiiP8+B01JlrL7YFclOP4mbtoJ9NpJsrL6FftfLfBuRwbz6M+z2tic+PpWqhcW4n+mD9tVntKTPJapDE+EuG7DqHU+EhTJeDmaEmObTUSaAc76j+LP2KKt2PWLe0DLerxmGysqROCvc51u0JN/1HDBVPEi/LlNo8Z/NvHFfyD8TwQCfuXyqm8vQm8/YHDkS7XmhyASbYCwzgE9H/tf4+4/ICRi2N9P98zCMbj7AZOpbbv45RrHvKXKa9qPwIYnbG2LJX2XP46j5jGpyIWCgGRcmSbLkSRxVhmrsKS6hZfoIIo9a4XPDFaleIXxe3ZHIjVs5XrMee29tDC4+4/orXypmt2OvaUu0nwqLrY4jJXOSi0PPsbPLTDJ2hGHcupsAs4+8XJXHi/gv6PtacHi/Ip7bb6PYaR5mmpLIXPJgYrknj7ZPY5ivKc3RF6k900Yf1yEYPj7GCbX73LZ5w6rnsZg/nMHk4zbURgjmJwSjNhja9+/iw7E0FM3yeRCgxMf0RuwKXEmIMuDDGHtU/c8ycrAj2gf2mjrlugAAIABJREFUkqu6DqtmFczWFNIh7wM79suSv7mU4zJ29PkwkqS+Ztx9fxwtu7f83HaM5KyNyKoM4+lLe9RmTebypx0oukxATeYxR9Z+IUC+hWntdagp57I2sB7nQcnkHz7C7n4eHN77jUUf5mBVnM8nhTNoj8tlW4bg2IVQFLNmcO/OcjJUMjFu/cJ42Wg8Gzag+7wrQfEGJDuqM86tgbDUGr7120+B82GmW/Yhxl2JXcclWfbYjKd1M1CsqKHd7hY7uyfTN7CGpZbWFOotYsbRfMok9hBUpUOOjSq2G67iKDkHyQ/X2TJTjTgXBe6YjSOu0I2UHVl0GnqDo36S6Et+orf9bp4wF/+Qfji9X4HPqmSq0y+S+ugaBvEb6ZzWhIqnA3Epd8jcasiXsyPIKTZmj/NAMl1sWGbvSeh6Y1zirLGuVOLR0JO82v6NwOOqZO9PYnG5CZo/XhM7WwnT3A3MPXGQ+uJSfr3IYsSEIpTfLSF0vRvR9xZwbYUd57faEqfZiNKCa4yq90T3eQX6AYN5WmBJ15GZ3Kp0JUfvOq5V6mwqUKD3jTjUwhTx6/WbLD1tnl1LQ7YsDfPqXQwIqqDifRse6s+J769FSdYLftuZM+7dXqLvDefOLmP8hnXh+UMdbtVv4YphGhoZl5hZXsaIIg8SP7nyYPcB3C8H/Vv8/Uf8HTCUGCrKy9YzeXEOllcNmPzBlg4frvJ30S00BzuidEOH+V5Pme7uRXpkAu+jOvFm/ylOn27DYOwhXo48w7XhqkTp7uRlTBbF+hOR6qDFUP0qAjpa0tN4HS+D28k1KuKoygWs8x6j/dOWXtvVCOq/gV8DdlA5/CQXpzfzc+pS+kx5wMaehzhyKJgXMWmoFr1hxnQH5ubm0nkjtLXI86b4Gl/GG8FgZyQCF+OULMvCQ1Xc8FpM8NYa2j58opPtMNbKdeNezA/mb37JUT1dbvWZzl0Vdc4kB/PG8R6v3teiFzyR8LJcHrTvRW7aHRptl/DTSYuoRYvo/XENWZ3MmPq6kr+2C8j5pMfxqZs4rdnO4e1b2VVtzZRTM5ncy5rlEzez+llHjGWfsXftHg5cHI+0vzIfzb9iczuMTz9eozP8Hs5zH2BlbkGQRh92/FKjp2M90rarKKx1oiXXheLSdPZeX4KE9QjGv//IfslnSGkFEe3gzpon59g5ZTDB75PYuCWUQdb96SYXQ+aG3nh0n05AjjU+azoT3Lc7JV8l2RQ9HUOJkdSlVNHwrAY5hSXMlIgn01SBMLMxlIYWsLtlOg4vJ1Ppr8GOpS+42lmPDq9PcV5eifr9nRGT65FYnkNG1USGmbxiklEio0O16O+egKfSMmw8N3DAag7yxmE0x+fx3d6R4IkN9Pc+wJfq+8ilJ1JpeJpFH0qJkxjHyXfz8Xtgw+ejd7nS/yBW21N5YmFIztFlTOu7ijcHPiHXp4Li5V9JbK+ixPgN9+/G8FDlBiue/GJo2g5S3Mo5HtmLLjl5RE47yC77aHaOERTFHkMUqpAlWvHYrcTrtYnsTEsnSnovRz49ourvMUYZTuN+pTNTFOp42nM4Fw5lcHO7NvlVA1l34SiqFjMYq6mHpnkUTf5dOdL2HKOHpwgbLEGopzNbz8Vw3T2LQ2lqhFXNx8d1K/5Shtxb+IvKzApqJW5TnZvJrCmNFCgvYUCQB0ubR/I6o+Y/t3ege4fB4kXX51hZzGWm6XUW3FcmMqw7t//qU5dXSOD+tVSEexI2JQZ7r4f87bqQye4m7DnVheiEMC5+vYRuJ3PMU4ZhMfAAc/QqCXi4kJDvFgQPkMbpejKySdV00UzAXC4ezRpLeja3Yi5/lfKMOIy+KaE1Lop+iyXJWX0e9SWtxBW58eNMJLW3B2K8HppWHCR5QzOqySdoeWaPhHswuqazGFXSE/+iZ9h5GFHQepY1F4ZgZNqI9IsQljY+xqqlG6e7JqJ/8RkzItYwOcONe8d3Y2h4mOKiY7R2bKdPVUecemwkwuo0Rt736DxiK86bw5h22obi88+5+r4Zz4xkXCO1OVjcjnHZFSYHrcfvmQthYzszoNiELmevUdd1Go8iThJ7OZeUG37EdPdA83YrukmLmHv5AJ4rJZjW8pD1DxTZUf0HnbAAPp4MwzFuKo4Pt2HjJEHutIWUzTrKqgkKNCweh8HZZ7j4DyZHsZzF0zrxpX0nd/q/ZqejATM2J7Hg8QDCKr3YtnMT83T+cnroUmwsS0lM1CQoaA86p+tImArBiy/R+4Aft2/Mobn4K44TNenZz5wVETe5pCDHu+cOfH+aj9bD29j5mFA/tCfaaVLopKoy0mkVR0z98bJ0QSLwLmZut7k9rpJuOuc4VCSH71hJhuaGMWL2CyY+/EvYhFNotz0mbvo5NDZG0jDkFmaKUiS3wDCZ18z4o8CSxkrMlhVjsnYtV4Lq2L3gGj9O+VDzq4W3p5J51u0HBWavSXndxsK/XoypfY6pmQS3HDfy98ZzLEKiybV5wr1KOT6kt1Lev568P+XYD4N9O8wY86KC5K4/kH2rzqbVwykpPs871VuM8Z2B46F29P5o0L1HHTfMZmGlsZqXX5/ycWIgX2NjOVvQm7rvJmwMTGPk3/OsuxPCi/q1nLi8At0V8/E4Gs3cbnmE5fakepAlfa4bUPloCL5V/WjKvcf8L7ZcMXOgMVyRQW1rKClooUrrBgrdA/9znYCqoY6oenuS3wPsCPx2nZ6umvQb9Z6TASt5cuAcvx5pE9X4h93LQrm3JIgk24eYN1nzc9wVGst6EBQwi8nKidz6asvKw715o9cfiaga1i4fwIN+d/j+s46PGe4ozUglcmUZAValNL2pZ/6gUs6OdiDtjQyOXhNR1e/MsQ3DSR+lTJB3JNEzHJid85nLbZfZc18Ol/pifoz/zbHYeaRvO4B3Ygazw/tQc8WQBUs0+T5chktuv8nfupyt5S40hvdmzacHzHqyHZeJL1mjasX53uPwDHrDyotDkJ+aj8W+u/T9egDjnBKOujXRR30cLwd7sslVGf3uh9k8uIxt6XFkfonhRpchREjoMO+aGoc/faTEbCk2LomMby/F4c1vrhf4Ej54MRI5R5m9ajz6/eI5bGfEzmkJvLp8gRcaVnyW+sWNnDrUsrZy7IcKHn4ZBHhewr/LBKyu3sX9VBQLOx/mZeZe1vf1wkrqFfbH83k9OZymsiO4zk+kyLAILWsbtg9qx2llJSc7fWZ96xqiHObyd5Qp54dXsnqWPYPDZZB7MJ3Hxq5MLX3MRbuhdFM9CR2N+ZbQmT6rl1JfOJfIilgM1Ldh1vUpz7tPQfvsFLL80glZFMTf+3eZObWedYM1ML7viPrc/VxKM8L9XWc2pAbhYxfIrlAJaitG4zFmLhdTluLXPplEk89YDLbjY9UbRqXn8XJ1d6ICMqiKjyYs2BedVX9wkXrD7aGm5C6ZSMCDdoqehbBTZRAxH6rYdGYtZoOWs9K0kvFzH2AZ04jb7Q48Sy9ns2woca49kfj8nbPKKXhZHefMXQuG//2OdE1XutltwvnqQ8KGDiJqTWd6xLSyevsfDr90Zti1m2R0+sm25nZqzWNI7G/JonlDOWigRfvBmXSdNIYPFY5EFj3HzvUHqYvh0Zka1pxcj2XbWmZXLyA59hNOt6twmbId7f4yPJ5Yz7GUXsysOk9CiisLZd4TMOQpibpx9MivodE5mq49fuK0w/O/10AEhAJV/D/nCx4C3gKvgFtA93+ZQ9gE5P1znf7faSDqrjdQHPR+KczO7hEL97eJNbETRMdZssJhWn/xw0FB5B7QFitTf4urtYHi8DAFoWl3R3Scd1AU7NwmwneWCumDg4WDfbvomL1GuGTYiED17uL0w2gx6csKIXPpq5BO2i48JjwSP5siREHeBfH4o54wM20SXxzTxbe4SWLizBIxSS5bFKxMFgnrbUXPSUPF2IImIWWvIIZ2iRRt044K6aHhInTqXiGxslxUe5wXUVPPCjvDj0KqtUAMbc8QQQ6nxHGTOhExb48Y7KwvXj/IF6vezxcvwtpFummNWCy1S/z2KRN/9L6IhmR3MUH5g9i+ebWw0+ggxhkoi9lj7EVlZyORNG6XSHuxX0Tau4kUHT1hPmatGL2+l8gcMEi0nzwufh6uF9fXPBWFS8aLqtdrhVHeLfHpTncxwVpWSGdfEhHVm0VTdLjY6/RHnMkuEZXHY4WygoPYNXGQ2BnkJrZaHRBf828JjYwEkaj6Ssw/4S4atDqLP+064tL8W0Lf4KVQLuwuVi+2F7LX/EX61xPC48MFodLtgujloSE8nquI3KsfhNr3EBE8eaB4bSAjBg5vEA7l9WL1/noRat5LtNifEcn3zolWk1eibJWsGBVvKzJuyokAjR+iW2SjkJq2TSwZ1E8EjvUSprN3iKlx80Smf2eRtyRHLBq/RXTI8hc3BviJiz+VhPvF96J/y1Kh3BAqGmQcxe+uzSIgpFLYxc4Wx59eFJ2eDBOTdKOFfK2xiH6vJ4ojRoovigbi9851Qi5/uEjJdhSpkS/EloH2oskqV3Sz+y2SwlpE/bqLouKbtNhvrS98m2TFoO/LRGLhDeFaoi9ODu4r7IeeE+bJq0Sv+gFC7k282FpzVGyxeSHky7qLw2U+4tLNkSLXOUJcdfwjjI67ibHGgUIhUF4U/C0Qlwb1FUtvzBKyg1VF44Kuwu/7KvHLL0EsevpJ1FhKisyfF0UKG4R/32ViZ5+totvp+eJT34+i6m+6GGDZLjKnegvla49Ez0OPxL7aseKpdrBYlXpPKBSfF1qnr4m1gYjsQaaim2qe6Du3n1jYs4NoXpko3h1ZLCLX3xNXnBeKxrxmEWeeKqpUu4vK7Woi4qj7v20g+i8jAQkJiXFAPf/gEjD4p80aSBBCtElISBz4pzPxkpCQ0ADu/o9z/7syUF9XBH9YT8+qRGwmrCFOQppnK4ezuf8prhf/wqRCmWuXLKgwrqP3KlnUL9ZS8GYItfNHEz74GVmfAqkqD6fqti6vrt3kpvwOGg20Kd/Zi36OE5jlEs+hlJMYyN5jfrg0d+SH8DdlBTFNs1EgHM/yEsJdD5E5vYV9nwpx++DGUzsbRuT24+OnPyz4043j848xOKYLA9aUMzanO1nLXmHzbQL1IhmvX750tjPk/GlDsksCiN2YxaaV4zk7eRh9807QV9+A0U43kZExxeTBKyI6zcO/vwHZZ9ZxvdtKho2exAKzwRRoTWVB13lY3u/Doh0GyGrX8mzuA9aWaZB6qz+HtZw4YvaUXx3fcFTaFb8oH0rNlbF2+8ymCdPZ/ewyexed48tPeRbZL2Ku6xIipBdzOfgRineMEBoF/MgczhWDkeT8baTnm2i2fUzjlpkdtlor2TjDkr/K9xm28Stz7ofTePk4xiHruB/izQAhSOtuxrLdszmS4sMX1UpW5K3gQ29NNqUmkDR1CaS4s/J4PKuPN9BRcRxP1LewS7OWTaZWzD1bjEd8fybtKsRH5iFuR6dQo/GHvCX3Sa6Ooc5pL9P7dWTZmCdI5iuzU2MXsmO9CHJ9wfTfJ5A8MJA9JwZwcH4cuj5BxB9sY1TCe2wX6DNqsTS+bzty5/dDrn3KpXfSXJx3dmWVoxzZPmrcbnvNpHnZ7NKbgt72dALiCzjtEkCgQhBzYi2weemIr/jOaZM6Hv34iPqmUJ6WKeHcfzFGFU48DbjCsbUx9J24gtyiWYTtNGXLuzHMOVTCJ6ux/Lg/nF49Eil4HkNNmym7rDZQs+A9+oqtGG1LZf5bH1TOTCYgoIYv6boIuT0sD4tlbJM6zcfbyAs/zQ2rMOJCZvO5aj/aFYF0ebYJqdvnGXT+Mp0TDqO73RePzml83/2eRXtecexyCGYVlXzUPE9RTDA1rqvpuacn383raW5OQSNgDYfmu7DV+BxjHUzZHPgD+bEZ/73eASFE8j/B/a+2R/+yzQDs/09A/z/Lj7ZqrtoW0qNSneFpxljd6Ua582vMjsoz82w2SYr5zB/5F6cgX7IrAmhYm8HXu1VU1ZdR7fGF6gXPeBSjj/LKjmR4p5GTFoualycvI1rxmvMUtcBX3D1ZwL45lzF9tYQ5fSso69mKfqAst5dqMH7QbAYcHkH2ZlMigvvicn8SlhlfuBD9HOP7cTwf6k9kTxm2+A7kuOdqJDWmcEjyCkfVJlGj2cC0jQ9odCgncfw3JLd85a+iN+l7xpFw3h/zDX6sbejFcCkJMofcQME7Cp9tXWmOXUSWlhwHz2egNVYT59UpuL8QTNk4mPh1khw7MZqNV3zZO7uNcsmLHNf+i47SUwwKPJlU/pDtD26g/rwTr8s+U3vZi6UPjxGsPo49YZO4lJXIoUU3GFM4kL5SDziv6UePXj8Zcj6PwIAaPk0IonlGMdP0C6nwT6DqSjtSo/OIMJiEsepcbJbPQtJzFUp1t9Dep4D8rtno9CtF5tQ6nKfNZqlOESXesDUtkUOxe/m1PxCT3y20Nr3D/FwH1KabE7CwEhVTd9wL2uBTMjHRhQSk+TBDah2pW/YyS6WJZWescNMZz8QmT17LuBM/24MNJZc4MzGT0hs/uBt1keLg8dRc3Mtj/acoZZ7j75p+fOm4gHOz61FbNB2tecns7v0LqbsmqKiWsN/Hjz9RipQkzOdWx5X0mvqTuyWO7Jfuy1rbNMJG3sdgz3qOHPbGXXIYKZu283KpFYXv5jHG8SYmzVKMj+3J8r6j0PdVpHa1PXMfSjPvgBJTIhTY97eIcycKaOjRk5zl/vQoS2D5ome8ebKXs8PtcTWq4oDVBn70rmO4zmwkX6gyu988Ymcq0fTDDluDYqQK7+H97iCHrzTiafsFjaRVmPQLZ37gNi6rVfLdpwedre7ilHSJMSKPiEPnKS1LpnP5MGZOP0XII20URz1jjd8y7FY0YRJVj4LlASZMXk6X6D1YDV2GT90Kxmp0wlhTlZIkU2Iv/Hsuwv8/6gSWAlf/Za8pISGRC/wCtgkhUv5XH/0r74CUVGeO3D7Knqpi3oe4Mmt4Cxd01+Gr94OdPczZYaJCw4WhLDbcRrBWLU+MVLCM02GC/yLyR0vikfYYEnex7KwBNUMmoRa7jLjczjzI0UfVSJD0phcqyYsYNeQz+2ZUoN0hnMsNKSzpO5iMA778bZFkQLcm8qr3EDHHFs1RJcRZZfLqsT3Kb6340n8iRh12cWq/HV8cZlDqrsn16yo4eHckUTePK/F9aTgxGH20+BD/ihef9/Nox0hc9b3YcMaMCcPAMH00xg672LUhnvT3bajU/GJjbDwTa5QZ6uDBoEHLOTI7nhuKu3j2axHVQ7uxyfEVueOyUO0UwIVuV/D+WsqlC2FU53iwvuQFe6a0sybvFMNSNnHtziN6Fc3g8xtr/D6646ExGZWDt0g5LUl63gl8MvrybMdOfr22YmGf7aiMuMzYyCjUo4vx6thMXLo8xoZnif16h7lndpKv30aonATy9u6g/Zaufivwee9KUq8gvJ/asLYgnM5XVvHTwJ5sn1KGfN/Bj5B62ndUMiX7Nd8PfqFHWhQTddLwWn2eo5cS6Fm7mWJndcb9CmZDSzJjdAsZ/MOPBQ+iCXOqJPWpC4t2r8NkUDMmG5OwueCEojjNZL1Wnl7UwmDbXdoKS9Dt9IDJHZT4ciYYiXsvSQ1WYOqNE2R1OkBSeSAzhAZ2l3+QsDKRY1Kj6D3yOybPW9gxehQJUQ2YDH5DqsoMzC7W0Xe3Hx1Cy3CbZoDDDi00379iRGojIzeFsKZ+AsVNJsxdn0MD4RxYtIi7+07xecgWUsbr0SuhlW0vGyl4fxa5oBF8yqzgUVITtarrGSlfz+RQBwLzI7BXUaH7NA0Wzqpk57v9ZOXF0H+3FF9Kh+Mz2ZdOu4q4Ofsi1SvP0VP1EjsypEk9s4SKjd7s3HOcg1rauP6dgfqGV5zs8YsJL9eyafUKPvu2kRy2n19BchhkGbDlx2ZG5h2l6dYWigO38v3kN6JebaeLbSMbz24AZP/XCP7vcA78i30r/8gJ/I9nRSeg1z91Y/7BRCT3X92vI6srnH4+FZtOe4vrP91ElWoHYRZRLea4nBGjzrwUds0uwtRttDDzihZdzVvEnA4WYq3bSDGtZ6IIDxwknmlpiKN6k0Vxfx2x7FGd0HDZIWyODBQOHc6KZ11chH7hXWF4dIfof71NfDSMEgcsxohuRbfEC+/Hwqs6Vtxfcl2Mj+sqZG/Ki2cnvEXAxVRx5XZH4fwoXAz1yBfFnb6IZvMfwnD2L5EU9UWc9rQQ5hlfxSm7V6JDWLlYd/WykJXcKtbNihMPT8sId98JIvNGsegwXVGsP/xSXD4xUOTbfBXr/P6IzMu1oujOVLEvqUb4+28UVkNaxeHmWuFc3V0c1dMW6jOGiLkjbgrpyDixOOm3GGHwXHTIniNWejwVw7ami9p7Q8T3ZS1C9YGFaFsfJbzzLosg/7Fi1csvYkVAjdgz7ZbQe3FRBMhWCgPfSNFWeViYKI4XFjk1YsOlASL+4k9RPsRIxNx2FJv3e4nAhRdFsnW5uD+5t8j2ixVLs+PEXtnT4lVztWgYKSceak0TXU1HCMdNJsL+QL4I2rJV9E47JdYndBC5Z9TEbLt64TCyUUz7WC0CP18Uq+/9EPNuFIq73/aLAaMqxXC5THGzW4zYe+K7COwzTtzSuS4oNBCOOQdEwuJUYTdPXYRoHBbZcYvFGrdssVR5n1C57ShsVQ+KJ28sRZ9Tk4T7orvi0Y+fwsLFUixakiNeab8TyuXbhcHzYcLEtEFE1z8TI8amiF32z8XH5ADhktNJzKZR6EypEN3ktEWEez+xaVOI+LlmkrhgvVn8sugn5t+zEqFJ8kKjt6GoDN4mVg3TFnW/I0WE7w0hX7BFqOSWitVeu8T0v9Hi9Lkh4iCrhCKHhdxNP2EwxVrMqHUWmzrpitOFh0XjlS6iB6GiYHSOcKjSFe4uv8RLj+nCpuWw0NzSV3y1nSXadLaJTQUFomy8oui7bazolFkpDkfaiqkjrcQeyzbRPKdF6DdcEMoSbuKBX4qwGGUrWt5dFOfGzRBli1eK5evWiXTd/iKxf5wwerdTdDcwFLkzMkXxVyUx5US6MPm7RHgO9BeLO8uKCX3qxKWoJUL1R2cxo+9Q8en0QrE3q+Df5gT+25GAhITEYmAqMOGfE4YRQrQALf/UsyUkJEoAXeD/c+R4Z5mO9B8wkKHO32jILuWGggQrJyjR+LmId7s8aXl6iWe3W2kaqEiR8hRmuUXQ1WU4M5erc/XMSk57ZrA/exrnugRjb5/NxY9nkdA5S+XybZz8cxaPmjTGB09GUs6fbcbz+DpqIcHVutRrXaB21kSK/VtxvNmbFCsT8qLaqB2ojJRtBr3/XmD5Bw8sej7Gp6aWXOsfFI3OIijbhsqSl8yqvo6KucBydBG5Np1ZMdsW+XcVXNF2o9/1BwRPCWTcwxXsaN+E9lQVJsbvwrrFAovC54TvDiEw15TMH7rkfx/ASU8HGk+78bNei/YDc2hO2sZm/91omn/m2+5pnDz0hOkHvVjRYsQjCUvWtntxUyuXGxWLWHy5jXjj7gwar4KWzkRG68wgaL8F2x1nIzFmJDa2EhRYq9H7/nW056vwoMsp2HmCj/t30jS1A1tPe1F4/iBdl3mR8SmXXn0b2KuQwrR1dlh10ERvfgOT30SzcNNnSuPOEfbbEJmVJ/kaUU6aqR6my+XpU7KMzf4gfcuX5+EOXNuyhUPLjjJ9nQQ/41rReuLO9C4HGB8/A8vLNkg+lGb21QXcyOvAe4u1rHRJRttAnk/lj5FZIcmCH1/w+tCLFQY/WCcpQ8HhXlRNVWRXaBor15qiM+4ehTeG8N5zCm/HDaQmwBOl6CHs6jydr/MmUVd9jtfm8/CRH85Eg0do2WdSNn4VR3arU2coxYeB2vRZuobC/Q8x073HI9fVZLd9ZO2iF7zxnkXoxmS8dZxpi9/DUefH2N+fxyg9B47udqN62jlWtK7CrqSVuL0jOPL8EuEOPRlZcZWOzrEc6W0P39ewKvsO+/vJc22ADj8m/6YoZSjZQ7fTsb8012tnM0WlleYuPxgskc1ax0CWvZzFQokajLJXsW/wC/YukOOxohxqp5+jb7UOlwmjMPuQyZArmby/KbifMAvnkM08YTVxai+QG/gcdaknVFk9YM9uEy5LXOLtEwW0Y7I4vP7vv8Xff6tiUEJCYjLgBdgJIRr/xa4oISHR4Z96f/7BTFz6X93X0K+FylZPSuo3YtH6gd+tWbi0H0da/zrvCrrg0TIZw9AHbNNbzP7nLrg2fGWC/zdOKwxk+KxAKup6k2eizIBZlUxcHYZ+8gjOJdZwc9QhskvXMrwwl0mes+nQuQeTi0pwX7CdCymxbDLyYmSvG7xZvJOl1xRYc0uRi6En8Xc8R/+xnkQ7WvCuugubTM8RvVCO2kxv8t1SKDqwkqEbT9LZL4/Bk8rYM0udL1Y2WI6KodO94eim7OOjew8OxHdktmwyxtGWTC2Zzbsl3zlovo+STcdYONmEXgussfzkxYOkdejE9sB35Uh+e09j+rKTbDs5H119RXoEVbJS+DBpYzeGvbyLpc4ctHeUcuJKPWMaiujyqQ96mXlMCw1nw94cVtoo8m30T7Bex4DoV9yRzWGu+QScLpvyakkuKrX9ePMmjxMTztN9pQ/VS91h2EPie1hwvEIBhfOpnHI2IEHjM3MmvcNbdw7NcZG0LbmE29oNdNn/HJvPLmwZUso57xZenhvA6zeJNHZawocpW/GxL2F3yzMsNEPZeyWeiDBb9Ip9aMxZw95pg+nfpSN3Jlzk8GhHJF5o8SEyhhmVP6iUHITMVIHu6CxOr7nCpabhtIwZzvvoBt7kvuHa7Ek4Dmug5eJx1EPvIF4/pMeTnfS5GYJb34F0lSsgNrUGM+cHTO19muy30tyY05ERz+dQ//Q2ERdKsJmhyrRCJ45KmrL4RjZY9vazAAAgAElEQVSTPtzEYWwDXRSHIGGagmVfYzI+b+bzhk+4tZTQa6khrh/b0A85isre97R8DGLj/iLav+fRHuzFgxlWtK7+SPRmE6Z2HkvZ2RSMphjwuuNyCjbtY06yARNWtbOxsiORf32orbzDxy0NLOpiQULCePbP2kHi8354Op0h4pQ2JuvnIrn6CI7Pl5GbFsSsyvXUm3nT89oQRimPwi/PCBk7d7LVJ+OVk0Pp2BRudxBIBV9no9RZ6nQuMU/fgc0tSQwLvc+YMWNYK2vIBJkyfn/P/7f4+y8jgX8lHpGQkKjgH8QjPvwj9I//B9cIGUIIV2AcsFNCQqINaAdchRD/M5vx/0skP3ckUTMb3SIblLTGojfAj2c51/ggf4YPGyL5buKL+fdHzJC7gXFmK34PvUgPDseRXoSHGLMiYT4r4pTIyCjCrDaVVfs/kngkBv0b95i1aBotq+5xpfYLFs8bifnwGO8xe7jevSON9vEMMtZl4cyBrMxSYpWzLy2eeZz0v0/qyIH8+mmN1K196Pa3ocuxmfgnDeAwRkS/taZLnibSD+9TKbefqwNCCHrXnWEe5zjrkEnvl6f4kH+TgKpWIqY68l56PhtKwvG4r4pecxqZ4e0c/9uPqOQYntTncz5yF2+PP2RL9+vU1bzkZPFoQm0LafKL42k/DTSWOxPl8IvOLcOQznBlxm9pTOaeY+n0ZCJ/jOTmIVkGbx2Hd9NiCvV30PHrCv4c9ebKp3703FTIwGkxjNhcg9T2rbQHHWL58+N0b9Jksd0Kwh7oMnfkM/aNdWT7LE82JNbjMv4u0ZMtaJFw483bXvjURiFpux/CZzK5ahRruqRzYON7Cs+nsuz5Rj5fjWScqh6hXRyJqNBiS8UOFu4uY0K3HazqtJ1lnazpl6+Ga/MTfulI01zqxRmrlbi/fsMMVT2ePHXH4ZUN2ef9OOUwlqu+owjZtxrbxcNwW5WLfEgx5VcS+HzkPulp+pB2H4mOHxn2ejH+amFkxY8ifL0pgYZVpBQlsOBuLy4XbuTRiUqW3vRC7agDP652xXbiSya4WfL0fg8+fK9DVWki5rmR5Gx4whtHbyTXK5E7WJeN1hMZMruenwOPcOqeISGPD7DhlQT+axvQq6lnycZVPNdQ4tK4rUQ3pbBcXp2vsQsY8/IGddVy7Alup6ytGpkNtwhK88In1JK63V+xbD5F7MdnlB6YSlLbYO4PPoTt1zc0fIzhybF3DGg6gfLcQaifG8G052NYP9AORYUlDPHYSdWZPyi+ssNwfC4yH5zo0vEkq5zSiYuVpErZgFnGjRgrH8Vo2irktlaj4TGL979teT1nPrLHSzh0Mvn/5g3/P3UC/4Z45Py/ORsN/PvG5X8j0l2kMbRZTvBCN4Z8WsLl3Ud5Z1TCY3tbNvsX8aVrBIWJ0ih1OkfN/Ouss+9ARvN1VhwIJrO6FM9NxnS7tQm7wxPpMy2YhpupmFds4N35N0yQOINbjTZTbdNpOteAq9pzziq9Y1DMRDbf0uWq1mJeBJ6l5nUZW3bp08mnN91CrzG5Yg67Rkzhhk8D3wr7YDQqCb+uYXyvvkFr0k4WqGfxMy2du4sHcCL9D98PK/FqwnUGavjxyDqM7s+u0em1GqPHTeewqzx73ruh61BJr0PunB8Zg5W8H5PSg/h5sC9Ts8fiG2HBzW1naE/8SOH71SwrmU1bzWJuP1tK8+9eXE/1oECpBuFizrcRynQeNRilnYeJiInF+c9DwirH07fiHSrfv7HpRA++2FozdKsCF14v4H1eGCoOzzkwxg317HxGtZ7Fec0N6nvnMXO6ApuUj1Fgpsq6HHVSawaQ1SGLunkvMZv0kqX74vn0I4jpITHUP1hHWKotbYrDML3jRM9eu9HzbKKlZwBL+zjSvKcPFopvuYIN1hvqUOYm23LkWGNnyQFLN2KawH+BO0mrA7D7+pmin+rcefeUdTKDuDDWmhrpW6wLMiHw/Ft2Z/wi45I9Stq3KVhphFOXeTRr5ZJh9oTA8PM8GZ6BhOpuxtucRzNxFhJvn9DScy9nW2wZaN8fhyepqCYVIlN5mnvZlRQaDmedvR0xV0twC/zCUMs9WAzywiF2CK9m6TFsgSn2YU8ZNdKaiMTxFH9dTWhVJ6xu72XUsAX87jsFTVs7zkX95PXkJhreh6BnW0//XVE0pHbA++gz7LYtxPT5daa8eU5Z0XIyVwdTaljPia7qDB4VwBLN8bwzkse/UZdgrTKsOp8j8voMHK8cIkHjG/IhsigZuhG2byEnmgqZ1vsYbzbMpH6PI6NWbGHp/Xi2W4+m9k4vbn4cj/dMO6rVncm9PIv5Wb8IybJkqPpyjp33xn7HKbaEz6O15Qp9NhxkZMO9f4u//4iKQXU9I/HiYS2J6n6ERzvx4vwi1l24xfHWobicXEzCiRS+FvzG3suCLl+q6KB9jLVtvRjSnIrDwlb+Pm2mytCb8JEvqDi3hTbJcuxW3uZ4jA4j690pLcrm7X0f/JPfcPplJc9jZPG5qc6NU6u4oyrHehkvLP6e42asN5+GGVEo+R7/AbYs7ajM0eZcFi1dx8MsbY5c0uLO5yvM8w0g5pAPJ+s/MS4qFe28wdiUjuD33H1M2H+JifIyKHVUoaPpI9zPX6FNaSnHpZfRnLSXIxPz6Bj5AfMVuyl4NBX5xAVs+JFCdYEHbzQNOWDnxPxl40hTTKHatIyfHWM4rX+GkvEFDDn2mXzxhd8reyGrrc7lmVfp/suXESIIjT9GjHk0kfthtSj9KWH83FyWaYcw//I57kY+JsbiK0tU0pnq/ofu5zsj96KGl4e1sSyvICfqDH1TJtHk/o6a4J7IBugyqX8hl2ePZ96oBmSU+1IRWo/x9d+cfFDLlCfjKPLoz935Uxjje4QtlvcwUf3Nwj9Z3E/Vx127ktvSpliElnO2MJ4Fei4sz7rHwZXpPIl0QmpWM4d2tXEx8icp5UM55zyLyjtqODmlMf3SWWzFPvKWGXLf2IsmFQd8Lr1gWMNNKmb05ElEJxrNX9C181jU9gyh4lYcajMzKbLO5p2uG2ZRj5kQ68OZuWtoClzLVDMYVb6RInlFko+70TLIhIfLY9jnF0poUn88p2igllDLCPNJyFrKE9RfERXr9+zZ1MCGb59RdqkjO/MssRsH03ttJusX1OEX3cavjV7cda3BvJsOPyY4s3vpJ3zrhzHq9kwu759L+rLVXD2Sz53fu/BsWUKnifnkW0jS9Kw7Uo/m8af8CG7RN/lZ3JU7yXXcHxhA2p0PKP/xQ6qXNTsGlmMpU8ja8fCruZlzJ43Z+GUp0953Yu/IIhZ/lqL0RH92/T6IokU9+TuGM1JajrUrfLiT3cYTs8vUu5rSVfcOdV63iHLM5J1BwH/uZCH5ys8k+8Uzc/hdNCo2EPIlnTXVCfj0HcvD4x7sSA5m0DANTO7uYFpgEIPVBiLX9wRrQiPIn3OKukNe7Fvii6SCN7JPTFC9M5Mz6gP4Gv+GFbtkiDs4g+o51sxK02D20xpy+h1mi4wxTqZurDjvwZjb58kfG8yNLnU4Hp3NiO9dqLwzANPUv7yMO4HCx9W4TtxK6apf3JD6wqPiHhQ46KBj6cvRJy9INEnGeakDl2PaSBn7maVhMHztQMKKmtiveYVifyccPq7GdeYeYkPvsuO6Nvdzl2M7oo2529YTuaQbz37NxcJsIT3yKnlef5gPo6r4O24+CWO7UvbpChMve9Jn2hsGVbjTL3gRvg72vEjeRpvDG+a1aTKrMACjnDjOHjHE5eEQipze8HnqG0bo9uLMtTTsvq3gocYyunUq5OGR+2zp/oXan01I79zLwsgaWs22sfp8Pp2SeqKZN5sJ1i3cDvVhiYop9fWCvikXiDTM4c+CHJQLVlNc/hLVkO64SrmRX9qNhNxUEv0ncm5pIH6RT7lvt5MpqT3Js1nKvttB7Mu6yd7r8STNy6Hq6AdCjKegE7iPoJEOxOYPxHvVcc6ucMJ0626GzNzGo/56DJQqpXL/Is54F/Kqyy76daijbKwq1sPMCZ09n+lXBXK9pTB65U5aUTBDrzzH6bE9L819kct8QVzv8v+LuTePCvF9270/ZShDSGiiKBlCmkPxbTBlpiRJRINCMmUeQubMmSVDhjJFkqkyJIqMFVFJk7E0KtT5/vE8e73vu/bz23utvd/17t/x53Wf93X/dRzXfV3ntY6DTlpeaPf/gOmw0Vz6Ow4F0xwGreiAjqItFS3qUK2fwLsdEcxd1I0LV7S4UaVLnloXhlReR/X1QPaXjMFm4Td2vasnsnAHAZE7GLw1iDNqv+hzyZF39ZeJTn3F25eb+JEYTIs+fcjapsixQc5UTdrIu2g9Yj5dptuMnvTf9A17b3/2d7rGthplHOfU8ufmWWI3feRm0/38yc1lkO4nvte/RX/ieiovN6ZHqwt4DbyDs/omtp9JYEWjecxzvM3HBz4szFPBrqiY6K4tab3xE7Q8wPlMW87ng4JnGs7FT+jdrgw3lSc4HWv5rwn4fzpzQETo2thAdjvNlaTiAdJ4pa64dEqU73vXidUhYylM+i6LCt+Kr8cD4fxmGRQUIVcG7JQ+RZXS+ZqvzLcZJr9npsiiwhqJWLFD7H6vloySxZJZ2VVequvK9yv/SOUbBbF/1U4iXM0lJeUf0Q4qlH5Kg8W6ZxO5+LqZjI6JlvzyQBm2ukEKp42RCx9vi7ttgjyaMUpMOSz7vlfKqv2B4tY9SeZcCZOIC7OkrecOaaLYVjoczZF9HRtJ17N/ZGHcQsnu9UgSvHzl47yJwhxj2avYUVYaBst3Vx8pOPVbqqcXiZlDsQxXvycLd2aIVvtceTpkoTT6fkJmhjWVQvdNouWbLe8Op0mC7ytRsogX24DTsrffRZnifEG+z4kUu2VHZPqtMdKqYqek2piLw6+u0lk3WUbrnJU9h8Ml0XyAPAxoIV0+/hI6O4v2/EIJqLsva2YUyf7BbtJq7WBRalMvERveisGKUVLu/VS+9TKT989/iNHyceJ6/5HsidgnUbPeiefiN/LUR1HaPqiVDK/1Et4pT2a5+MlQ1zeiqXhKVj4fJcef2kqroi9yODdBOqf9lbo/jnLp9F75cnSQfCneK6/id8jt3+aiNzZB7rgelR7fTohCh38kq7pYPPu/l1T9K+J6REnSmw+TVLMXEmjVRjKeXhDNxzHy3mKNaKl+FoVrY6TF6eViFDpWfiQ/lvbaR6TlwbZSaT5VZpvmytjdhnL+wEJZ3nq4NAvMkLi9ppK5O0S2nXaUId2zpfavstgOdRe3/fmi+M5MvlVek3vT7OT+4VxRLfwptSqmcuJXMznS6ZYoOUbKNLPzcjxznwTejxQPFXvp7GAuFof/isvopvIwolZ63xwkgabFEnrtqbRZWS0xlaFirBUulyubyL0Jp6STVmup9UkUl/2DxD3voSiVWMnPlEtyDXNptU9Pzu3ZIk16dRAXfV+JH18ogQ2DJeZ+lmQFnBC3je/k5OF7MvH+CVFtWS8m93bIrJhC2aVySUICLGTc+7/i4tle/mQFye29X8S3MFuuTZwqGiMq5G34LUltkSARa8rlfIOOTE9dJA6Jm+RZVO2/bBH+HxcAEcGgF2L5upWMuvZFvkYmiUOypdgMWC5Tn/yUzf2+ie7I96Kf1lGUu/SQ4J9TZZNunCR1S5aMBz0kv7G3GDfbKpULWsvuvv3kwdjF0v15V0m3WSf3rfPl3eLxMjhsiTxsWCYqQ55K/MQNMrTjbDEYdk96ffkpP7w7yN2kN5J6PVEOaK2W9a+mS0SNtvxz7LEcdb4pfQo7i2qNi+wuHycHjE1kZddDcn+dn3x6c0sGVeXI6MsaMmHAEjG+miQdpxlIp0OZsr9NncQ+Oic9Bi+T6hRjUWz6WfKy/aV4UktZl/9VctRey6yF/8i2Fy6yrKGVrDo6U0paLpSSVgayyry19OjRX5ossxb/7cGyZtAh0f7qKoUztskWO2VZPcBTWmsFykm7TfJ6so3EBSyWA1fXStXgbdLswTwZm99BGrtUiLvKPJn9M0BiFKuldk6gNBzyFdU9N8R7fbJEzl0vbtcWS/+s8WI0NEwqMjLlUEqJ5L6zkiG+PWTUi6Gypu8BOZx0XB6FRsqg4e6y6qmbJH0eIO1ahcrXZUdkZ7PFol2uIcVNU2TotSBJrB8uNbt05HRjJ6meWCg9A75LVHqIVBj3kHanHonv4myxdTIV/WFX5feHSnHf4CW3Da5J2/W3RSv6kESolso97U8yLMlDvi53EVePWnl2ZKgcfRgolt8cpNfr6ZKuVyrNr4+UvSkeMkVxnTz2VxTnNc4yath2KTGZIV2jl8u04HoZXPROMofNlvhmFqLm3k8e+bQS3z+PJCH3nlzs1UKChsSK35s38m6StqjfXSH1qoulqqRYbs5eKl9Nn8vmmyYyJrGrnEvIFfOd32V+QzvRGDFYbocMkKKYR3KqxTGZpGEilyz2S4/sF6LgNV8+xDSXq3vjRKHLZ7E6uF48SyvFbu0++RHqI4v/TBHn8aOlKs1dxjWxlrvLf0u74hSxH7JVWjY7L6tnFYq+kq4k9hkvKz/clfFfesiR4BAZed5GOuR6yKroWnmt8VvG73wlL6+9kpyzDaLkNVhO9H8l7VodF+/KTvLm7iC5qj9bzAaqSb2TvdT/2CiRt53Fo+C+mLc4Lbnpk/69RcBQRUuq1/4QB2M/mdRdRyqr4sXm4xPRXDZH7nRbK+bHU+XQMXfxDJkob0d1ktwBDXLVSUHutE4W++Z9xWLuKym5Mlws1ntJeVY7OdZogNw5ryhvTMOk8NdFaa69R2x75MoSjTWy9sR4UXMeJdr9suXgjPmiG/FSVPqGyYNBHUTF3Fj8u7aVhD9/ZU2fKGkaOF28NBaI3oROsi91gEQeSJM3rw2kYcwD2W+VIb9iNKX/l7GyOjVLWu41lSulyPXVK2Vc7mFpPWa8TDQdIcd+3RGvi8FisylMvgfniLPdcDmY8lH2bO0u8TEdZF9AsfzJOSGvHp4R/WgrWf/FT8pStsjARuFisHiV5DoZiY/7MGnhlS4+A8rFoFRJFO2NZEqdnnTZ/0CmbKwVIs6KuelJUd/6WA511ZNL29/LHrPLciz2idR83yM6EXEyLf2AlH6cI03iQqWkbKf8XdBCvGOHif+AxqJp/lou3qmRTb7F0iVPUc5nPJaZc7pK9fZIiWaLtEitkoPD38qu4lIJPnNErGqni2qRnbSzbiHrftfL5O+3pHHtBNmkpC5jr2yS5YNPSZsDtdLC9ZkcLQwRu/WBsqmXkoy8d0zielTLobZD5dz4CjF/aijf3gRKs1u2supVtPhurZWkF1ul9QJ1uXCvp6itsJCdBTul9FG1rIoOlvymHpJroSLv84dL9LiOMmB8vXhcjhfnQV+kWfBeCfE5IhkdW0nvF76S1qNIfrZdKiOyK2RodZQUrY6QKC1jyVD6IjWfDaX14kWScUVPdmUbyc7KJLkUlCe+uzpI33sDpP1wTdF0+Cu6rYxlVfBqUctZIbEXtOT5k+1itK6HdD95SYK+WEu48hdp6PBJZhY8lybNz4mqYm/pvyVYpimlSGZ6rYyvjpK4HZGirLtDcvTy5cvocJnYCEnb1FaePN0tgcFGMkSzmajvGiIbnnhKI68OEu1xRjTP7JGe7fPk0aYKORnxWKzn5slx9xcSm3hCTs6pl9Ijg2VFg5nkatuKQlaSKD36Im8SLohLpos8V78mc5udkdud/0gX83ZiV1IlbhHPxWdUj3/vBKKG6j+4ZCRyblJPxjj9ZEW1Asml9/jbcTNdLr7EeLk/ScmGlMU0Y+ubTQwaosPWzT9Q/7KI0wp7+PxDlVsdJ9BtWyemhUVQWPCa2XF/URvrw8Rf3TlntpV389rhNvk4/rs/cElfl8mB1ZQm6HLxpBKLvY05HexNTMFmvse4MTzzJN0Mn3K60T6srBrI+3qJkPVd0Es7y6bhalS92sj9wVpUmpQxINuUW5aPudjUnl2P3lGXkkJ0U0/843RY1EYdv6FJHC0aRdHafliv6Ea/ng+ZE7kItbFRhD1fyfKnbiTM+Ex2TjSdXyYwIG0ssRZxdCwcy1VVf7R1Csi7pEHd4xl4Z9vwc5cpqlseo5SqR4uumSh+DiS9UxQhSyKxXtKH6hB7cntsR7t9IP3amfDPrHd0dlnIwrk1DLQO5khCAhMCsgl69IAjkxMwGDmayIV/2LdjNFaNHzHhxTg6x2mjb+CJxudSQnw7siWtF2fXptKkSRIrAwZxP/wIdRaJ6CYUMPbPMXbl3mDF0wDab67iWJetXFCIYkprSyIachmbmMuFj3f57pzKhOSjqHvq8NbqGmm1z9BJquPQu1m8fPmSCvOF6DQ4salqPi4JfXApfknviAr6+V3mfNU+XM8sICl8LE7VvxjSbj5bPvdlTFUZM6e5sCz+MSGtO/J+mxZGleH4JEzEJG4JQ66toptVEENzfmG0/jXX6t/iorYeje4O/LXewfl3P7jzTJcilVKcHU6xuscHoh/YsnqnPb3PnmGQxUWG2xgROEuXjF4n2XG2D9F/X2MaeRevoSa4PsukifE31G2jGb/Ria2vJ7F/+Q7i3/QmZfNEEm4ZYHJlMVVbDxIw6g3rpxZxZe01zDyMmeSRTNe3t+lfFcBqsrkR4I3qHA+G6g5D/9tTbhRMo2TDSW72uIXu+wj2jbnK0rnfuJthR5MjJhTUhuHxtyk9PFx5H+jK5w3xtFj5gwUGs1kzWolVbjq8uOGMDSp8is+kceWuf8m/fwsRUFJsheXkc5xoZUHs2nXENnbHtE0BVz2N2Lv4K9abcvkZ5sVew3Pk2B9GVWsfn4vtMRRzgiMOMb7JDvrkbaZHfRBVq1SYezkHs3Cw7pPGKQdLgmZbMvedG9ebhPDAKIkHdT50NPFh/sFYNJOn8ca4OTFhV0gMdcRG4xBph97wubE6kdbnmaM+mZG+Z/jUX5mBDzUxffeM6NaVHJ28hqXP9jDk+EPKZr/h0UUNUuMjqZ06FteUvXjEXaDJi4UUJmYwY1s9Y7xek3GqmKaFEVwaWEio8nFi1i3gTnQ5wbn17EvrS8r8TdzQyqCylz+vjo8j48oowlv5oriqkE2t71Nn+BNfZwcqyutR+/ydtXPPE/AshKyZg6hYe5D0X60xG+KI3p8cTJdP5tiTt+Q1us6vd64cb3UbRf/2pDW3I+rnQ7Y/K6RQmrBN+QCl6SGE2qSRdnsye8Pbk+00jRt2t/BXy+P0vmfcrl/KxWJjhnRoi+GdHpT/cSPy2U+Spx6g4EYr1j93J/nOZ5o7OjHQOJ5w218Evkhm5v4AZqrvJS+3O0NjP5O1ZRmPOt2kda4a+rZvUJ2bRHOVFijo/CY/6SDWy4050rInMSqHyXgfwqMrPbCqNefSmTxm/a3nw6xy5tW0o/n7GOxrZ7L1QDwaWUcxGrmJAo8S5q0s5+B2C5Kqd+M36ClV23dhems5RV4mrNjoRCvTufROGcDtAf60WKTI+A/1VK2ezyIVVW5/u46dl3D1ay+i67QwdrrE0C5PeTj5M8MXdsRBewhXxzvw5dtkXuQ3Jlp7ERonF2NidZTfZ8wJ1QtjaI4+wxd5MyZ2Cfv7eLI4eAVT56nydKYaI278pH3cRPoc0OS6wR4ird+h3KUnM5ZPpjByNveNS+ncPhbNG/mol3fh1sV9xKgHoGVsyD5nI8aHF1Po9g/Nd+bxz8Re1Bd3ZmGMJwf0NtJksQuDEvuRVmZG643HsF21jmULDWnZtACH/iFo53/iT3vlf8m/f4sWoWljc4ka3AqVqcPI+bae4KQxaPdvy2fN2eibFqCbUEn8i+t0DumMn1Y2GRcduWL6CY3V7hxKnYPpkFDM9lVy+30yxqF+zPIago7RYBpf9+LqBV9Knb1Ia+hA6KKeJCvVsKHfKka3KqdZ0B9yFY0Y5B+K0bR4kqdeoMeUKbiOOoh2eHv8IlbzycmDbKP7xD06RcX8Nzw/+gl3wzSy9ueTOvUvtrHHsPQy5EduNGPCptG9yUKeW/oxuNthGofs512hOnf61hLfuDntGwKZOf0EtLjEdrWJhL6ZiHqkIxZRiXROWU90i3ac8u9OszAD6n5/pswznRvlWVTs8kbfbDtvt+xnzYnOqDR2YnWTOHL9z9PFbzqBX5JI236fJpc7s2pXM3IHDuf6hQ8UrlNk47swdHvOI7PTJlr/Osq+89oM9uvH5aimLFyhwfzsAdyZo8TotwVc8tpNYEIrIrRNiE48w5/afvxdm0xF2XTMssdiqN6ebgq7adphM5Pn/CBmiAN7Dq1h+pNbpOVnEdLuD2+uq2OzKY/OpcvxXeiC/7t/yJxymtPm+qTPbIaV92sO3c/EIUKdYUObYXJYi77/KOP7NJ/BZ+/TPc8D85c5PF+nwpftsdwZfIeG6FK6r1lFWpATqRm/+LLyK+7rNHGMOUT1mDjGq+bzNWgzd3TaM2lHPO63q3DXfkrUm8P0N2rL8XhbVv/w5GBhAcsch7PmqR6a5alMjTvNnkWdiD91hrS2Okxf6EfEvD5E6CrzKDeQZSctONv8LDEm41haOZg4c0tOdDnC7CcqjGvyjuizC7m15Dv17cZQ2fQGyWOnUNy2nNNOFfS+Ysrp09sxNZtMyG8lBv6aQZnOFUKrx/D0bQ+GTWlM5vsx5E4ayY/SBnb49uRm/1L2rPKiUekKorJUUT7/jbHFRTwZ14KWbjspyX7IwKeaRBtepO3ABWxcHorNknd8PL6YCff1ebjuKz5lM9mqUoFh4+F4a7VmQPBhPv+uY9z+atz1w/99W4S/e39hf2QB+v2/UzB6OUE1V9mxvQmTwjoTuF6VrIfrUbj0lKf7tqGctI+VB2y4nbGO4+bTOd85l2LL8fiVFlF3/BKn2vzG8OIsjsY9pn7IRP7GfqbfhpnE7hnEiv25XNZ+yqKqpjiObaVZItkAACAASURBVMHozetwlERcQqajMtGPPyW6nPcfxarkA+wZ84cfIR9o3dWEkQ2+XBy0jusd31HVLYxjhn6k7rmOQccPtLK4xdnEFDLGtGHgsTom/vbE6NB6HmglEPs5ip7LfWjefTNyczAeow8Tu3M8v9d3xNgqgrHLrUhe0IvuE3wZX26OvfY/fMhKYEXvHjzfl0F9l6F4/FzOr1HtWNdjJitnrORPVRElAbOYbeJCf73GpFt9p+0HbcKvh1Dc/TeKAR1pPsKSnIr19FZVJ2xBJD/uBqJao8jSYAO8rPQZ08GaAy7qzG3anxTb10zqPIN+I+eQdc2D3vuFi97LiIiJRzX1BVfuRtO8qR4Los8Sn9MMnRkuzNzTng2mbzDff4KcDU8weeBKtyXHMPeeydGuNezuqofZyF9MdXxO+dtouk8cyqt+llSYtOFLr0J0zd/QKqGagytmY7UlgOdvvtH5cxgnE+KpNj1Io/cbUVzyhhmhIyma/gGfujp+3t/LlSEfCeq5gmur5hEX2olS/RCmKnag1LcVd4wDOBaijW+sBk09FZgUo0T3I22wnfKGLq9PknwylVXBGsx+nMPrqbfZsTGLCcfdqLm8HPXc49w/5UJ5VVMODh6K7vatLBAP9A+OoO2kEaze6smlyXsJmKrCskPNmOjuimfpNVYrvudCwxEyT/3AUSGBR+0+MeH8FB4t1SBAtx1RQfl8uKFJn4HqlB3dxtylPZlRG8GYmnIcDN6jrfKCYkcPPv95TdZgNW46TKTPejOqUj6hUF3Ax6P+PC7zYcvQGPK39MPlb1Mi554iyOotQ9Kv8X2yBhoZlzHuCm611cxOGEfzRS/Zp3iXsGYzaPt5IU630ymyy8N2Xc9/yb9/CxEoym3E0fxhHC9VxvxeHiG277mS+g4ny7EcXrSWM46+xF/RZIVtMkX33On/YTj3T16h+mgJl2fe5Izmbvx+5xGg15nUt81Ra2GMfkVThm24RWivy5jeUkS9zSXGnP1I3oPLlCQ7YrvvBysbjOjZ0JbdZWqoa8wiXacZ/Yt30CdkJmad1nN8SgArukxh3DVjtHbPoMznCp4ZrUnxXYKu4xQuzX5P6N9FuLR5QrTiXqLnbuP89zKsJozlg35fQiIW8ngJ9FX3ZHfTZPYv2kf5/PVU7O5E+ewMhrWuJ8fHgSE3/PEKv0GRThyhPk2w6/0WQws7mgweT+BCC6pqa7n3rJIVZ1UpW+jNmx+tsdtszvCNvhSlmrPvahnnnqvwtO1xGhxzKO61ha0e+TipvOW9SSPe1L4m1aMp81+fxf/BRFzVu9FS7S9ayhqEVumw5XUf3Da2wSzeg9YFVew+bI2e7xyK50zE0/0D43ZugFVNyejTgfJxZ7Acv5arRbHMe6DOzcxAqtVfM2HLebTeX6Z/fAeWHdpA8x/erLz5lsgzz9G6FM/r+PXEeu2i9bVazr+dj+qm+cji/qTrvKPg9zH+ZPeiY1odK2dnM8/iJSdUx7N9e0uuN73N7RVBnFkygVn2ExjTcx1fyrex1jMY008jUVS6wuCIseQ/HsP1By15lDKf+z2cOafnif6q/mjr5uC9WIf4r5/YOMyTa6nJBPfWZ/jcWvo7R3Ci8i+5uQ5oa2tR52+C48RTxNQd53heIb1nfqF84na+9a6gjcYceu18Q9T70fRq3hWdrbps9swi5egRspY50dBYeJvnRsSWP3TvHMj4F8vp6TOdBxHr6CY7OT1pBVqu/tTsa8p+swraarXDQceRpRnOWE6oY4x9EeFdgtlybAUq54rxPneD0dWm9E5fTr/rKYwydURjTguavF+OyrR7PHF14uGCGsz8wpDe32ixw4z1b9phcSSEK/aniXu+gaZup7lWtZAOrePZ9HjGv+Tfv8V2oEPfpvLcqwX926fhnauK2s9R1F7fzkvdAPatTCBT6z0zRu4jKOAgj9b2oMJpAX+1emCm3IBGu/s82mXDp4QDbF7TgtV9xjG6zTRsL7/Cb9RjpuCCxeqNzN4/h5sDd/PXtg9tLfLp+TuSDTonOPbzGrOsXGgU1IXRiu4oTa3gnEIJUwc943ytCp++hqHgk8J9+uOrsoU3ayexP9uaxrXn2fRgASFq21Dtp0jlPFeyvWrw2NuL8hG9CPPXYInDV87qltHo+3FE04tDfu3ZPz+M36P8ade/OUu6NEXjT09ch2QQZ+RBmEYjng05SNsp+QSn62P9uCXdej0mLWY4eTcfYmjwi0Xbh7LM+jIDCyvR/HwP1Ta5LLtzkOm7nnGi9igu0efIVCvDq9QUow2FDFvXitC7DxjnZ8u34+7MtmrDzjmJqLqtZKmNGdPd7fAuXsqyQ435eKkLjhe70/j1PA7kN/Dg6z+E9e7O8TvP6OvxgOBRqpwyzyLH5ih+PV/ynA0Y6fzA4NgANK0Pou4/n8yV+ug86sO4G9VYbbGn6JIBEaeF83qrUHf6S6j5eMyOKfPY3hfDd084O+8IHae1o36FLT71NuybdZuzB+tZXfeVsuSHLPY+TEu3+axRMeJC97bcab6MdU8caH6klGklC2n8w4yoHiPJLC1k0hpNxjoH8rRRKY1HJmJ6tzUGg9J4V+zJNbPF3BpvxIzubpw4fJGRVVdY0ymT0WMjSSz4QvlIU17ma1EQGkX4+t20P5iFwQRf7txVotNfQ5qovOfxy/EM6hfDr5PT+OB+CJeHRixxOYbuEyVeHrflh2cU5ukmBJtm8fFmJPPdgphrb0phqzXoa98jc996LPopUb5xP50NpzJ+VUcOZzVDU6WCMetacHdEIQvnf2PFjx+EfzyO46FJvLzjy8Bh5mzVbMvuj4UovGqPfS8trnqPJWFrHj8fXuXLjjCWdjBk6+0wFvn+ZVqUKi+3l1Kmewm/TgUMDnTEd/5wOi1N//dNJdb81gdHH0t064v4lt6BctUi9uS8wOlAO6qaeqA08iahC+4yfpMa1k86s+SdJlnq3eilN49VBwqZXW7FqKHVpG6+yNJ1xxm4bDUDtF150aieW1m9eWs1g2dNR9C1zoRZAVXkqoSTFVODyYZ7HJg7mOQqffJdXnJ4mAXbluowNrCA3UuNebE8kPvnc6m725zp5mdxPzOIlApNWiXORzdkPOUNbxkyXYXwGec5tXoZkWO6EHswHbdtTpRu1KaVzWhKNEcza14Wa0ouUaMZQOSzLCZe+0ZZKwcO3zvNvYN+BHd4xJfNRXywCedxbhnqBaNI1liMVHih+VmJJk6JOJ53ZL1jAIZfremhuoChCq0IauNK+HBbet3Q40TcAkznfuXHmPakv7zL5QM/Oee+nE7/OPNgezKFP3Ox0DhAyElXDn5XwGJSJWO6hHC1/AAjf63mfM5HKp//Q6zOTH5mvOZblT05rZLR2r6UOUcMCe64CqW4dQy2XYaN9XROGQfivSSOZ503Mc3cEx/1/bSc3AS37LP8iTjJ4Jh5lBSW4NtElRCNCjzOB/PP2RoIvs6fxXNwtGvJ7iorlEo6ovmggcpxt2j4oEF6WiJvNGGr0kny3UtxvtEVz40KaHbTpOBZNpduJrBhx2aqzT/jsDiQ3bp32D06DuOecdT0rSZh0Q/yrbrTa6sXbWYVMz0uAzvLNAqmB9D2/gj27m5H9SENUrxm47O6B9Yh2zj7oRlBenvpf8iWIQpdMVPSIDjembbbmmG+aCAtKiLQ0SjHNNKaW63cKItT45/qO4wxfs7hsk4Efy1l+Yzu9DjqwvLsVkzMXU/PJsrUDmiF2uiLhLbohpaxHQe+WjNgohWJHbZxqcaOsRyk58dxlGnspb9ZR2ZlPeHX8ON8Pr0a5W+a7DtxAmryWXjWm7N7lhPmnceTnOG82zePbqcP02PnDTRepDD7cCBlU1KpSR7IeWdN8v/+ZuftJD7vGMPiG9fZkpiGxdXuQPp/yb9/i+1AfrNKEs7bY2Yxkrn1n6h4eZQnZx1J3OJOclQB6m27s/6tkGFbSMAgLdQG+pJnU4P/yXTSpxjRr81SwtXUuZr0iq5TvhMa0Q2d+sv82pSPcuRJXl42521WIb0fj2B+cj1n62JQLVPn9/rmrIjuQWunblheO4Tu0N+sbfuCF8qdSY8/QPjzfOaMvo1H55HUNnHj0ONZJPqY4td8CPH2Nyk62ZjE9NX86HqYAu9AuriWMKtFEuN/zGZQezdMPAZzf4Y9bqHlHPIIQ6fzEC73a884fWOu6HWkj4kPhTFNSBidx+LskaSNu4ZhpALuA/aRk72WBIVU/Dy86KTdHbXve1ihEMDYmyrMqY/hwdzTBDoM4INyCWvjlrE/fw2W+3ty8747uw81weK5FeM+dOCaz2OMFuRQWWpI+wQHsvUKWXuqgGiHbcxaNI6idycwG3GVC0Fr6bD6NoOc2lO68yoqmr2w/ec2rZ3/4PHqIpnnftJ4wwccnlxkbMl7nPrMIWXVLQp+WKDiMALNofUsmZvNhIOdsFx5FYM6JdZ9N8YyzRbPqQ+YabwO3fBuxE5phufRVDavqKR2jyq62U70LFOkb4/+pDiZcm72Emoq7/ElMpm8azb0M7Vm59vRzOl8lohvvWmXNhqNK50YfK2et68a0IhPpHuXFozmJlHm9lw/ZcKld/a0+fyZ7SfrGbmmGAO/Mmr8HqL1JoNJC4xw796Rwx7HyFX3YpnmJIKUJnDMKR9P7bPs5j7LPN7wbU8PWjZzIFU/nJ9Nn9KT76xqtIXSIFfc7ylwNSKP9ykaLJiqRcmQR/x8UkSdizMmv05iN8MCj8z5rHI+hL7dJOIqXjFwzQMOJNkwPmcoZ03b0ubhKq7OLuL2oQ3cXtKMbj1cWDL1G4VnF+B62ITQAckMSnmJ19upuB9RZIrKCyyq5/Ps4Evm5KjQ7kAf1hXas2qLHSqDNnJigDej7Vqz3tuGkZrd6eOlwCrHPqS4JmJTf5zOa9v9awL+n74oJCKo9VWTpLZ3ZKX6TJm16YCMr7wqBtt7y71+I8S/v7o0rP4u4bdN5GL3u3KSM6Jm+0QaRfcTV6McGZbaQgx9X4jHb1XJ9E+TcxY/JNN3kFQpf5frl0/IoCRvuTigm5yrXyoDW4yQqwcjpY2ttzT2/irNV/YUn7NL5IHtFVma9lHO5l2U05opErJBT74sPiG3lDzFcoijWPXdKKtXx0ivBzUyU/Wj+Mypls5LNkiGd0vJ12grIffPy/b9NnKn2RJ5PzVGtJOVxGjCeDk09Ib8KYmW/IVp8qXzYWlYaiqBv+NEM2mYhK2fJw5B1WIy9qh8uaAtt/L7y73OY+ViZa387NhfXvdJlCiTQlF/XyQhmovlaW8XaarmLUEvbeXitDtiOS1JTMclyomJ1XLptL601R4oJ9R1xey5mfyTFSfRRWoSvHinZL3ylkqf+6LdY6QMO6wsNZ8PScrAxbKn9yx5v6Cl6BYlifG8TbLQsEEitk6QLmemSo/DDyRJ9arkjj4sMQ8fytPfo+VwfDdJuXZSQht2S6cNQyQszUx8O0+XwTNeyXTPLPlyZLcs8Dwlvws/ydmu4yR0ygvRG35d7n5PlN3dPknh9KaiGOkiFdEPpduR1+Kx+asEX0iQVfYTJWnnYDnZqEKOmutK8OXzMrlEWfrd+SbrQrtKbvF7WaX7VlLVXsqLGQ3SYt4O+aTyWi4ETpaaJkOlKsRVksYGSWOVWdLRVkG6Hz8meo+TJFffSIZnxEqsfzs5peQpM/aOFJM/Z+Thqf4SpjJLvKZfkVdXDaTDGzfRuZkoj7MHiH38Z+mU4yaDr3rIyZs5MqD5TPk901buG/aVdbXtRamzo1ysTpeZNy2lzxhH+T70oMxJuyWf+h6QL2lthfOLpfbsKTHVVJfBI1Pk/vArknVzgRy4XCh5Hspy0FBdUnv7CosmS+Cbu/JizQZp9MJOJkXHy4tnA2R6tLUsdxwlLh7VYvtFTUbHRskX9zkSdSZZwt6ni1mLYVJubCiGXTPEulmepOl5yLyEBzLGFrF4PVDsP/mIjrmXpGbFyXOXPOl4bJWMLmovfVf+lPzJL/7X3Yb//4CWmqkk3DLEYOVncp7Usrkgiw+pI/nd0YvA2yasVe+F3xgfXJb2pMikEq3706jJ6U5IYm9eeD7BdcY0Ug6pAxWs63KSVM9GoHgK1bXOjPa7gUPRJIYE9aCxYwPDTlUwT7eBlMV3WDOxhllht+jT1IS8tKZs0Xdl4udFFNSWk/nrOW/GLCfd/RZZ0zqyJOYkBiWORE58gmVZBUZrO2G6Kwib0AhCZBV/3cZQeL2I8vvzUGt5mIEr0gmy6k2v/RPZU3uZIzPcCb+4nn42n1j+eQLRyW8JCvxB/t9jTFHJ57xKCWuWRjF+7SM6j7jNC8vbtKhexkGbJ/S705J7L9O4/rYJ55yOMe2zIrMOzaTDhd84hZ+lzax8dvXRYe84N35Pns6O4k3scerGvUdF/MysJP6nFfEpg0gqK2G59hk8W+9BrygdPaMTbLuggt0mMxacyOKWRldGxapiHhFAzO1NfNL9h76lsUxuFkXR3AJiK22p0Uqk7oIzlT1+c3GkCZr7lPlmf4vLA46x4p/2FNQrsKr1TObfCGLhbl26GLwk3DaPNy16ov3oAZeP17F4VCdu3DzNg+nxDFDoTYG2F2Waa5jeZBQHo1NZ0CuWoMXlrL42lSqXdbzdXsTjjk/JHD4Yzf0H8TafjGHDXULrNLnd1o+Kd5P4+zCUX6OSWJ1vS5vPiiglx+B+bDuD137mwMYTtDwayNNLSjxZtYGMR9nsGTGJLyPOcdCyMyssbLB9bkbggkvUbhvOWM9N+JXN5bpHKpPUK9l0x4I9k3bydUEE/wyoxKO9HtMDgrhvMAqniSWYTezGx6+tMRg9jZeXAnjoU47Z8SIe1blxblY5vecrEl1owMvmbZh2czMRI5Xp76PMYbsW1OQmotKkGJ3OZQRoOqCftId5Q9cywuQ8/haTMW05jrjsAs443cDi2xwCpi1m5ZLzNFvuxqNZ+tz/swbF4jf4VL1ll482ZuU6jI4o5UtNPo4fmrKwTWMWR5fhL69ZbTXhfy18REFBIZz/sBH7Kv+35fhawBv49p9ly0Uk7j+fLQNm8h+mIgEi8q9tTv8TjVtoy4gu4/BLdeeV1kAUZ00l5E4J41M/ED1iH3rJ7Ugw3U5Ey8lc2+BBqUo36o5Mps3TYj4cWMML/0iON8pm9KrZ+LY5TVaFGjP3LaZu6AU2Hp5Dawdj3Jct5JOBEvYaeXwdPROFcgcuTLHGtNF+Og4bxKgwbbCeg+OkH2gbqmBQo4ZhwRx8lo0jtqMZZ5fsZefVYpRuKmC7IpW2IbOxKLLEHzt+pBpRticTI7MZfLXNZvJwdxYZuhFedAK3ixexnx7OdzN9zFQPYl+9gb5bv/OyqIAIQ29qFysT8biIbn8qKOrkifeTBVQpBHHlZiY2S5dR1P48Xz8msLTcmvMHSxmTooniKjtGqJ9D56gJgwt/MdbzNXN9XtEieiOTWr5m6rkYvN/sxvbSIgY1+o7bsTiCfleQ9GMox238OPrThT6BHcizUOSkZne81w6g4YABT2ON0flrRtGXDQx/U4huSB2/F43lT1EZ4zrmEFNnxZ9JQ3ihUUO3R9144recrE2TWTbeGMOiG5Sfns40j2GoD83g2zYtbngHY6C/gnZFR6lLjmGG8iRC9Q4zpHgaZ/5pRj9rJRIXGBMV054ul0fx1b6YzAVdUIovxdywE1WL96Lzqxf7HjXCed8kdOMOY7JRgeZxEfxt/pgzraEu2BSHPc941d4FsViHVD7l6/DRuN5QI3jkB75mpeNvNxK7Vi/pMWwQHltfE5GWwMyDMzi62ZqoRgMYvceCNVNXcXfkYoy+JOOa0JMLcZ8Ivbedu2XTWTHrFtHe7bHy6ED3ETe469WdoHpVHhyL51xdK8bfhbQHQ3ndZxurd7fB6nsEhiMa8eXnEOKW90Tve09m7cihKDqQ7uuVsH/kyOJMKyp+TcOyVxK79WLZHNieKT87Ur/JlSrXB/T9fB/9LRVoKuyksLgRa0oUyG5nQPn3RQSpKaAyw5NWTZ8zP1afrzNraL2hA32XWxPwQoHhDzbxwmAoSoPHYe8/AO1UQ8YMbcTWz9P/l8NHBgGm/L/DR9YCi/6LWkPgJf/hOtQFyAEa/c++0QVD2Vf8jwxbPFouRVVJwSkXed5XV/barJMSS2u5cuOt6K7sJHlV68W9tZPEGsTJh70B4mq0TMInfJc3/YrlhX6tGB+2lAF/n4hX1E5ZFugjrvnrpE3vjzKtUZ3ULzwndoNLJc+qn1ieeSPVqm4yMN9duj08LeP3vJHqvd7yMDtP4hyTROuZgWxr0SCmIxPlkuMeCVZqLjaZV6XFmWeytUhZAof7yoSrETJ+e7Kcilstfs82yJ02/tJ5R3eZv3KIKOS5yrqSJ2LgoCOXd3+SstF9pGubjmLSoVou2SfI6tcT5PQzbVnUYr/ssjovN3MLxa1bvCSo5UpjVX25/dZB3m1Xksi0ThI9ZJ8MPNtNvAsyZODHQzLu8lLZOnurpB9Ulq8208XitKIMD78jiX6pcuJLqnQYdU5MSyMlLmiInLS1FhevhXKtS55UKXaWJm+XifUZfbHIRFwmTZXMe+Mko91Isdp+VpoOcRKt3o3kRvNYUV/XWxTjNollWqGkXFgg8We3i9qiClmtkyGVEfoSVJEkj/e0FfNxfWXzkc/S48g7eZqfINOUv0rrk/mybWIvWbS9TqaXnZZf2e2FgNPSKdZaTt/2EsvKoWK+uUom2X6Q+sux0k99lARrPJbdP9ZIJ9/tomu+T4Zfai5683Pk6/NFonW7kWwIayuvIg7Kw0knZdpVdcl+PF4iHX/J3NP7JCp9hoTUlUrbrlEycXdPWRGzVCqPvJWkvS3F+PZH2Ti5UB4e7i8FV7pIXGm89FL+JJ1iHeXMqymyyKuLRE15LztabJXZ+n1ldG9FsdxQJD3eVopL1VNRO+EstrqfpL3xRqm+YiVfG1nKAcOd8qlkp2zoO1su2elIzig/Ub4/UHSVHaV0kK74di2TPQ0XpEmek9jbzRPFuBXyp1EHyXxwUJzsNsluN2OZYrBT3ne+KR9e1Mvs3rXi219BZjsaSZGRovxtGycje++RAUHPZWmHXKntMlX81p6QYYuM5fSzD3LLtassmj9PbqUuEKM+xZIxPVOOhNdKgvNwiY/WkMtT2kv91ZdybGCE9G38Szo1eSfh7dbJ/MhX/+tGo/Jf5A78DzAWOCf/YTiap6Cg8AGwBFL+Ry+V9symed5U/IO/sm3rRsYtGcXrigOoup0j71dbmv+NYEy/F9TMj0H38R/M7hZwePhz/L815U+GKy0MoqkcpsigeYU8sT2CfArDiE4MfO/O+sPXaPMindCdNthbl+H/ayfBU1czMTeHhqaGjHdOZ4hje6pf9cb9YTJ7atTw8fzCRu82bLx5nEjn1bzWmsvP8Gs0fjKHh5+3Mn67PleTqvAJcyMn3IHbC9tzKH0bU3aaMW9MPUYPjrPAPIgNoans3O9AzJcyWvzQJTswgVOOYYRrbmPjwxr2RlYR6vqDpnOaY7XfjmEL9nAz7Tmb1//Es66IEYE57DulwOoDxvwND2fUo3VsNf/MmO+WNPJP48qceswb2TCz6AEHmm9nXvVDtvwq51WtOX/i1OgXtY6jpq7YzW6O6smJfPwaz/pyd9JePeVOy9/EZ4azJvskUdMU8dMP572xAR+zH2Ejjth5L8Tydh3KxTu5XzWcq4YzeBb+HN81S3havAjd13msuNWUI7HXMZE1vBr6hPuvxnFx+jb+WdeXrPBt7H++hPdaf9H+MZOo0ATqhnUn424NkUphcLkb7x56MmaMFh4nj7Co0R/6d+2N/9v52JhtxnbeC/p5K3Mq9T1RjQ5jNnEUypuEHktUUZw9mybFjTjyqC9KbpUcSExGIWgB+jcccZ4ejn+pE98mhbFrUiAXcrrTqlE0f05cZ32yOwfUW+GvNJAlm2ahfMser7l1uBkHMHjnV5zdAkkwqkA1aTdz1rSmy4c+rDt6mW9hLWk4M41Hp69i57aCA9dDiH2wDe1PT3nZ9hML9caSNqmSX+XPeKKWT5S1Ap9N0zlXcAofozQOfu9C3yEJtBxgTsiQ31TqabPL5ix39AW7Gg8aVaczzU2d4s5uWF/bzP4TfVkdc4btJzMYZTSI6zMmkO07kG+/NVC2VsL5eDs2FKwnedtrUu00ef83BJ1GHeiZd4yQExH01dDndeo40jbdpE1DIh9bniHrmda/5N//TndgjoKCwisFBYVwBQUF1f8c0+Y/bMb/Gwr/c+y/g4KCgo+CgsJTBQWFpy1zW/OgMIDs0SPQaSY0zzIgLL6e6+nfGea6gJvyCafbrenX9Ds/Ws7CK0yNl86BBNolcfxZGllzSsn45cpZK38+75mIp/0Wwk2SaPM7iG5pKxjb3oif/o15eyAfn53q5HrdZdEpC4K0l9IlZyFPrGy42DEc+3R/RnXL4O76BF53fcNsDzUSj4Ryd7ozO4qtWdy8EocXZhzV6IOh1lCsikZzdHEJmyviGXD8APFnP+D+UIUT5yfzN0+L07uT0evai5pel9gyZxF/3TuQp6LPuC3LsZs6jn5GxzFsOR39Al/+vszgpPUuVkXbMP8C3Fbzx+f9MJYFHubW2wDmxdyjz90+rDq0h3OxKaRalFKy8w8Jt5pxptqPSdo2eJvW8+BnGE1/zibT2YFdnjOJ2jaZlucKeBKgSa1pK/IHWmK2TodPOfNYpT4Zy55RBOx4zCXLm+SHDOC5QQAOkfuIOLQE1UfuNP6tg8PMJF5fdWZMv71EqX1FJdQa3aEXKNk9F6O7flxUn032zru01PejenJbUse2ZEb2dvTaZzLeMoqtib1R73eEed4XUdn+nqvhulS1+sjhj39496QxfVq2pkP0FeaEzufRyHEcdCthmoxg7cOfnJloQI15DUUKgVzsFVYzPwAAIABJREFU0IqVI/pwZ+lW3ipvoK/DWIpMhjOh1ScGp/fF9th7knwdGTBpBrXynGtau1EOace2Xr+Z0aOWig3lLAlPJWjILSwXV/NYuT1uMVUE3phGpNFCVunfRzGmJaNlD64Or4hcnkjrTC+2dzjF7akurPMooq4lbO4+ncp/0tj8/SlzUtqxq70VipNeY2M3mxT1TKb/fkqLyabEz1NjzaWeTAh+yshuqrz9s5dLNY6sb9oD67BabN6EcmjiGR4fusb7yD6YaW2lqPt43Juk077nAlLPTqenuj0F/c/h7GDBzzoNNKqVCNuwmtDMKdicT2dS9XXupWmzuFKBp3+TGX4mnucdC4jpZUaylToL9nznx/o/WK6J/P9cBA4A+oAxUAKE/jdu/xe1/+Whg4gcFhFzETGvbFPNoODb3P/Zne6zW2PS+TFDIiq40X8L925NoM39Z4QsO0J/0zEU97/FBP9ikpob4NSokgHNDVFoXM6CY93J0D5Dt+sxDNush0lSPuMub2K5228ORbVFt1cgCVPPM8dJjfETFDj/eQRG/V5ib9kBvbB4wtsHclStFe5XU3jo0BQ5E453em+crnwk+eA6Hmo2Z6CmIb77nMk2nkmlRhELzX15Ok2HYREz6JR1gflXnQjRLMB2+VXMVLfTT20Sb3/1oXuMK0Of/MTuyDU66Ewm4+0cXsT+oWLzCO753cO2uT7vb14m2nkizSYpcChCn4uHUtmW4UeU23M2Tk7ix6guNLMcygjvzVhXteOR83UW53ZFb0NbNtivxnlzM1y6tGD3ZFccRuaiu+Egrpk9mLfkDMpDo3HcrM/OTpNJXPSYFtej8a5IYUZjX5LnD+SV4l+S8zrCsdNsHmnEJ+ufTCrajN7yrmz+CHFv72IxdAMhDQpYLUhl2IbevM/MJ3HoAsx3bce55DITrgXztHgal0v2IEkdUT5TxD23UjK3mxFeloStcRUPTRSIWtWSpsd3Edh6AmP9dRmt0Y7pu4YxcZMd7u92oP+xJ+o/hL4lg1jsF4WyyRLuhJej99AFj/hrWN7Zy/BYN56FD+Xm18NsnmvCFr9ZfHhwn4YDZax45k2JiRNuz1uiYDmYG3pFJJ4azqLJUSxp5MDRkrmMGngW5WmzmZXykqmDg5lyOwQLlW2UffnD8jZdqHvRnVF9H+M92p3OGo3p/GArn7bu4am7GUW97hFw4jc5ARZYDd3F49DfrFGtZGigHiYdutBa2YMDK1pxcpoxhesC8bf/y/a6i2Sdvo127538EF8m5lznSztF3s4YxpyfSzgS3oZdba/Szu8lhw7mUXvaB8vGZ2nW7xKVf/9wLnsXSTOfcGh9f5YpRqBpXk5AeR75x9Zz2KYvn0fqsTQRlrUtYl+ZGhFfYrHakUhJVSp9mt1iw6FgltX890cB/1siICJfRKReRBqAI/zHLz/8x8rf6f9R2hEo/p9O+EubSxPHERXUn4IhQ9Cc3ZHUJiFYjT/H2IBvfD53jSXXzjNtQi6RH7Ox/94N25fH6ff6Hr81lrM2bxPqE2LRdF/LwIczKfI7Rtmjv2wsvMD8fHtefypDZVAcFgFt6bbrC2ZawXy/pcA838vMb3jJqe8H2ddwnG15npg23YhZdSxLOo1npl8hOscyufpyC9tU+qCn78nILA/IH8zkN87U+ySxyEvp/2LuzcJCgLq4319RSMnUIEqZUoaIJkNUhlLmKIRQmTKTqcicKGRMIYVkyJQiQyiSlCEaSGRoIJSKlFrn4vsuznOe857ne76L77zral/sZ9/91l57/ddei6ToVG6+8CSjwYmAX385OW4ZG7Rm0urpe3a+H0yfzVdx8n9Ixt1xfAjdzFfHnzTW+MXr7/nEz1vFkimFZF8vwDzhF02GmrJ/qS2Rl6wJd6qgfshh2n9bR0b5IUrGmfDTdDtT6+poUAD1nU5UjHxMq4g3uGk059emWPQyrfi+MwjrF9uJGzyTV2Oi6Jd+lpbP0jDVWEWKnicBecXsqNfkbsgYQux9MVDW4Z7vcIpfzMbfaw9FOjt51CGV+wNHsk1VhfhtOaj/HMmo8BB6x99i4BkvPj38QHxFOROsr+P/9i2qIdX8dVrIkl8WxLvN5cqiD1Rk3eOjSg79Kr+zR2UWK6L+sa1HCkaxh3DTziTs+0y6q+1g1KqfJHcsxk/9FmsNzXh1JYcWSqmEtJrF1DNb0EhToCw8EOfRA7jy3YP0QiOe/krCP+Q8Lc6osVN3HRciNlKw/xJL/fJIn62KRidzhqw15baekBOcwNrKbpTa55G78iJ1czN45aCE6YV7qAckYhroRdLzfQRbGqGx+ggZtoG09Eykf2kI4wb84GOgB3fuzOCSTyy92k9F60wQ3TZOQidmJW3CPNkQu5+S2ixOLS3h0sqlbMg3oM+mIRyK7Yj2s1AuL4vgg393ynO3YBI+klntn5O0fCpaHrY0ndWS+afm8fuVEdMlgd+5H/CYfYaNr814p+fF544DcF7ThSmf44jrn0Dz4nbcXdKLd70GMUB3MvZ5Ojyz/8LfXztIumnPTOfxVHy3J2fObfxfDGDp41O0Ux/CrCk7/iN+/0sS4f9z0KiCgkI7ESn+n+tlgIWIuCooKPQAzvxPp6AD3AG6ikj9/9f5PZpoS5Xnc1qc+EC8/UEORd+h8klXHGO+UKGzjZ6PjPn79TbhWa60TS4nJfkPn98up0lcK1LCnpCt3Yw/HXuQN3AgL8yaMeyPNYoXBrNnfSDlUSfRZRRRJsMYXbiWF681UUztQfi4fG5atkfxghGe9Wo0Pn2b/d79mbFtMkpPv3DZPJT5EzW4tdQO0/sv6bWzMS2T42i2L5O/f5cyJv4slsUTWOKTysBRgTQJW8fDg0Ws079Kjx+J+HT6hkYLKywP2OFXEMO7R+u5VlHLmqDd6PwMI+9aOQv0dBh6pBOJuT7kqeQwa3IYM1OKcHoUyeCjyez7vA6/m4G0rrzMZFUHlu8wwebTBu6oO7Ch0hWF58+5Mb4XDZv1mVOrwdElNox1V0ffpZ59w1Sh4AiuxUl0a7GSqbM8cGqqz4NlNRjVqPLgTxJHHedwKOswCW5LYftZNrx9S1mTbuSUutO9nQ+x12bQdnwWs66e5uVJF7bNDsX5chbJE2IIP67O3cH7WDyiIxpvtuBdGc8yzWcsfNIby/pa/E/1pOPujiSc9uZtxj1ix0wibJIboVYnmLf3Nx08WjC2Uw2FOZYUa5WTOug46u5u6PuokT/Qild3GpNYdJZl5xV4eGI3RmvDiAyeyPXZw8hSUkU7/hRpO7Noo1OJWcR1HE070rfBF9vfXfDX0GTfGRWWWczi024r2pQ3UPRpKdc8qpi8uw/vLy5D19qfh35a2N83pfVKO3r13IH944e8ZiceHwZSOWcCEVvyObejmFNH5xNpqEje4M68vKXA4sivTNV/yZGL+8l51p1lqRux8V9B5aUibB5uxX7ULhTr4rkV+JM7b05SPO4Ra3qtptWFV7x1fkPE3jhyapuzyMSO1cWNiYvRxDo2lanF/dCf8oAVngl4+upjOSiQYTpjmG+wmFZjwtjfP4BPFx/Q8GsjDicTGdJSjQNTU8mfPIyMWZv4PEARLdWBzF2ynZmKVTSumoFHzWaaFRzluct4wgbt+99WB6L5HyF/Hf/jpp8DRAFZ/I/R5FeBdv+3/ev5H6pAHuDwv1IspK1hKObZf2XJ5oFifv+TxIRvkHinFKmqtJPVNm+k/ZKR8mnrW9Huc1m8E+7Ku/wYCT/iK066ubK0yS+55ZAguk3SpOOrqZI6QVEKW7+X+E3rxOxmiJQ9HyZ723aWS48ey5HSQvkeqSKRtmFioD5Ndt37K0dWjJCE6Z+laVd7eWITKJFf70uoxgE5kfRSSiyayCvjFPmeclk89LdK7we5UueuL/+UrojdI1tZbtNZDrV6JqeM1eV6dJBMiHgiXaxfy+rqf1JtYinjzPpKk/ojYttIX54tWiRbjhjI9CUVknxLUdqlFsnZAz2k3Y5HUqraW+qfWUh1WTtpOWyqHLhlLX7psfIu5IpcXuktzXUc5VnkfWm8116udLaQwYlHZNrRj5J1/KUsOVcnqn3fyMXbhmLgXCF7fvQUnR9dpG92Dxm6wlPGHE0Vs8kn5HrpHVnbqVAOvbeRvl0aJCquXHxKL8unOy/EeWuSDGhaJMXHV0iXIANJLjeSm6/dxKR1otzML5IrdqmyfvUeaXzaRhT3a8qklChpcShN+v3+I0uvrRVPmx9y0e6U7O0cLtoW4ZJxVUPW2Q2SzWcWyKEFwVJo3Uo+PguRv1/85Wb6H3GyLZYzwRWi1fiQaLyvE/tlTeWJrbXkNGsqtaYPpM9CP4nd30G2X60Sn9c+4jXovHTKfChdtyFn2oaL86QDoqRfLRYO0+Thza0ybNkH2d1qhexzMJXIxlvEo36MTA4/ItfWbhernHGiXFQvhp0CZMir+/Ko5VRZlvRPzt4ql7mjbKR56Al5ukBLDB51lF/tT8jnvoFiP7pWUlU6y8RTg0S1nZUszJsvhQf+yMgrmyVrX4icmHJD2h+3lRHZehJoXS2W8QWiecheCvq+kOLKfPGz7S5MuiU/V6iIQrsdsj7PWpI3OMsJr1Fy/l+obPd2kjUP1KV22gtZfcJA1t25IP2OXZJBv8NlWpmmbB0wRnSmHZXJri4yLWqSuKzNl+iIk3Ll1BXZNf2SXPaPFrfm3vI1yV5svybJwo620k5nqsT2+yRVdx+LWhMVOadnKt1HHJLsDYek2f2Rcreg+r+7vZhCe0U51fmtfP+nIPOibSQm7Lw8+lEqGTOqZNXDaZLhmymmn4fKzs0q8lq3SjSiJkhb1VyprfCS+GwzaVuoIRnXC6TJhWNSpmYsWWk35dxZH1l0ZoO89T8o7td7S/NTZ+V62mTZutBS4n80lyWud8TA9K4UHG6QfxvbSduG5zI5dKAkT+wto/uUSrf41rIxu4Wcaa8tOxbGinrwCDk48bJ4fzwjzub+MufbHBlfM0L+po2X5RtURH1jR4lKGSVxw1rK0JlXRc9nlMxo0VwO3/4gSkrXxOBpO3HYdVLe73koPi4ZortITQ59nikJYw/K49NVMk5hpgSY9JT7+b5SOWqfdPE6Ly6btaV05RS52OycuP3uJY/zV8uXHR3l45rR4hk7SYL0kqRymLKcOYnUhQwWx0p3GeCgJfteb5Qmb8tkZ7MfEmRuKw1Kb+Rv4V/x6tBfXjjXyIKlntIrxkSyg+Nk+/zT8vaApVzfuEkMlXtI90Mecmr3CvkYf07GNxsrKqe0JWvVEdk94Y80rVGRcfd3y47nFbJV010eO+2SyW8VxUJ1p5xM6CQf8kzEccc/CX8aJFqv70hC8jHJ/jJc1IekiuWwVvL60SDpE5EhH+MXyNBQM2mVGy370ptIxIimcvPAMWnUrodsC0A0hrWVYadsZex7X2kTFSzHlumKiV68HB7YWSb5LpFQ5SfyO8xc1k3WkJQDN6RR7npRy7sg3eu2ysX+HWXsh3qJ3WQqRZeVZdek62IX5CsL01TkZ/YdMe2kLm1XDJbAjxfk0eEs8Yn4IYlt8iWx21XRP7BIFO8qy6QnOjLJoZcMSHaQeYlmstX5oeyqnyrVC8aK7vpDop5rIBk1pfKxb1OZ7KQhvcJfS+G8NtKi12YpSzaTP1nD5OXnlWJ79Y84TRopn745y12/WeKfny2xOWtF9/w3CUjtIkVexaKlGCYx35qI098HovajhwzQviSbdFsJ33Sl4LyvTPP8ITf+npEOrRRk20xlWVn9Si5qbZCCl7dFbYqttJ/WS+JKbWX5wtFycGqe/Eh4LoUnymXYvANyskWcTJu0XGp9/4ni86D/7vZiqkqtuNLrNA22Hqh8TCa+YTsz0l+QptyFnWMCyJ1ozkMrfVRS4tA4O4G+3pspTZpEbc+rGLt0pOfJDegebc74fxrct+9BUMsr1A2zYYi3Krl5lYR8t0D301nKrgWTZbmZ0+qDGP+hLYtcLzG/xAabYR9Zbj2cUW2GMmV/EJFrs1lS1pQZGj9w19jJjb+veHOrLeZRH+g5fyYz7xfS4Vg5Wn+FpW1fcD3vAMYvrnJtZxWNlkdw28SVvie34teimqXbNTmwcRkpfwLo/3gDKz4VUx4ZhUOAEyuKr3J72zomd/hAdptNKMwzwk0pnLB7w/F4NISHX3uyMEqTo93mYr93JzFNfOk8WIcb8WmcP/iSrtnrkd6O6FS+Z9tqX3RqnmEU94LAurkEhl3hzMF2HFj4kZzR+YxMjMPjfTbvMgdhF9ud5baXWe0xhyjtcLSnHOdDgQm7tn/jYNUqqgIy6N2zFb7fL/JkRQce6U+n0RwfRq4LRLl8HPqLB7FmcjnXD2YzqSAQ/bf9afR6DL9KduE5syeHJi4jxfYGQbrFPJz5Ba0FMTwpTONfmRkJg/MJHpZKk8gHaDywx2bRPKrKs0mdGc6QLlO4dd6ArMgdKEs02kt8OPb0EZtuNGbY53zm+PYmc3kZ81JLaRWyjKwdEegZzsbr1kFK9z3D5fQODEZ3Ze7xX8y95MmmPftxNPTgq1zm5fQK+jtYcSBtBn9utWXE8WR+NvqN6aseTCmfzYuwHUy4MIz+2hqYnBtBUONczqd/YKlyDituZDD4fDJ1Aebc+XqEpdqO7Ah5QP7bu3zzNiTy6QKO2EYzeW1fLCce5KvmBJo2nsr634vofNyBQXFPmatuTNGv3ji/n0Os01lSJhdh0qsjM6vLuHRLjTdTvqIaPZbaDcoE1e3GNLUzfTJfMVg/CO2RK6jX6I2rkgIqjf/xYEQ63RTm0sEoHmWNcM7qlLKy9STC3V+i+DOO4Rkvie19Dvfmx2k09B2RUeEM07L5j/z9V5QNt+tpJPGDHHm3T5vReSMx2LqdxeZJGM4+TumRiXwxhO1mLqi/iaJZyGdWVxnivaOInuVr8bL7xPaqKbTt40fN6u0E6K9H4/BVdmc2ULTPkdDDQ3ALvUt9YQMmugWsqwuldScLDqanU+P2GPeyzQT8UGPJsv4s3nQWnaETOfbpNx/H6dLjVzrN8qoIdjjOV2cFEj78QdzeUJwTh5VfGTF/N/I5vBDD1Om8WXKcuVe6slHfg39Xi3EMPk3rBcOx6vKGhqSLVCRMRW/JPV63sePFfJjXJx63VaNZPcsIpYijPHPqTJl9FBWaF9j3J5aiLtlUWAXRrNKVnpveEGxZzaPmgfh9aUrITw1MX69gu64SMfft8R3rTXHJeYZtmcfyftF0VPhFzIJCtGyu4rjGhuNp4/F7GEpc52nUNVgy3taUFl1WMmxJR7osGcG8pnFELjDFTKMnjxtnUh95HNWmSvzY15+ejMXkwwjeOlaSnaeFT1UUKzz0UPd2YWLyMb4+/swUhxGk5ewmxno7WiN3sXuMHxazvpF1yZWUM6W4HJxC6dPrDMhJQE29gdYPu7N+twOn+5hx8utH9ox6wh2HbnRtrIzySDtMmq7j8Oq2GH2bzppXgs6LVB7c/45Pjy60dFKhT+l6WlYpUX46gU5NY1ma64fR0SS6jbdjYttXdDCdS9H5UBQvtSXOSZFNBilcfjudwMhoXquf4lGXbjRs7samvnacmngF52clNFl+He0UV27VfeBnozesW6pM2JxNvLZcyLQtwcyWEFT/FLP8rzNrcrbxrFE5616a0sLgIysKH/GlbxXtRhbgPmgn0RsuYbjgEU6z59N94hWSlWfRM3UvlRUhNIvvTcCd50wMvsmVDE9GKa5jgHksvftbk9nIEfcZO7Dvv5J+ll70ddFg2tsY1ic958TFPqzRreZM6XeOKNQRddqA52ELCC9agft5S4z3naHASBn9M9Hsfh7MoX6FDL7uT8b8EkZfSuHt+pVUl9n8934lVqysIPGiNuNvn+S3Zg0V8T6EGY/G6mg4Na/XU9WtGsUdd2iel8u6higeD9pO1JheJMY+JmT2RrrpeLNP8zNx39NJnT2eWV5N2Kw4iWVW+5jW+Qd5FdWUzRiCuuUB+th+ZdvkOOa9UKWydWP6/xrDXK+5KCc+Y9/tHYTWFDNppTfZjVOYKlt5PewiyZazuZr/hx9TmrJOx5OcAl1q8nIIOvmSwrIEPri1ZarSXb55aLF0QjUtuyxiVEdr+na9RIfMBBzjVUhW1cbWcCjromYw8PljHvbUJHnfLNbuN2XHTQ88RmijmD2ftpV3MRmyipEWBhx034dDjjkdp5ty7p0b1+02krYgkTukUPjNgiHqPli0PEfnu9bUz07GOb45BSMsUdtzhpA7BmRcUyW78WOaf3Nhh+0ORnltYPaVi4zf8IumC8Eq9zbH3qyll1EdLXps5GrzgQy/u4u8FiXcbDiBTukdHnY4TdGEIhg6gu/tAvHoM5ZVk6/idPkwA4tvsH3aET5WqyPn7fi+ZyBvFocx+WUvCv0+0qRUBy3dbvjELmWVXTLdP40is9MsHLy8CO5xiBsb44i5s5+2Fo9w3dmOB5WaKB4PwmBRJTbHTuDi1hX7RyUsmjQb3T8+3L+UxD+VAJbGZGKytBlql4Yz+9MlVsdM5ecCc/rcsqHpCwUW9FvG54uRhHzWI+a8Oou6JdLW5DKbD9+gl60Sw1o0YOc7kxZXwrG/24KLfxfSoZUizgtTmNd0G3qFMZzQGceIlZXMz/1Hkw7fyJ7hibXdESoSchlw/TVVrQup9tjP6tlmfB/9hvzeFxmUvBCdsX9wb17FT+toTNOacX1LIolRP/ijfoFdZzzpedqCUcc+UZwRg6dhMYt/dGflkn60VAhivmY9CxRLWXq6KZ3mKtOgMoGwqQWMTnxEF58hXBv3lT4RW1EtK2HpiuUceHKMD+trMXIxJvSyA13c4lCp/kj5rAh2HylhvH4/qq2P8aj1FjoFGVE98z/w938W9/93+6OmwCWHXnQ4upPCNUvpesqW+IhM2gyyZGUfQ7T37eT+zBO4NnVk2pFvDOp8m5Hu81npmsiuE+GY+rfh0+Dm2MZUcv/KHN6P9WWVwwmazbrPwFh/7PSjOdP7GelNy5jnYMRnoxOoDm1CysGrWP/pzwbXS/idzyfNzA+dfesJbvYPm9On0PtexXkNG+prwzDTLsO8Jo67yqnoDVyH1916ol7dYmprJ86l7Ma77i+Pf38lbEQYle6z0TQN56N1OEa97Vn8VoseNU3QbDsc37x5bEgXNq1+SWGLVM5/n86tySEUaKfwWGsFeh838rmJLyd2DGCi9Q6atgpi0bRnvMr6R7uiBzQv/YmK3ma6HfWh7IINPfu9Jf/IXUy6a+G7byU31TTYkPKHMl11+lbv4qzGCJ4lJ9Fh7A06zJiL31pP1N27cHbGCta+XcfwjTeIP9mHjg4pqFn2ZcTpSIoWW/NIrxlXI+bwfdBw+l6+xKYYJbxORpD+1IHPXy+wwusLazcsZHrlUSpXhFP8tyUjlPM5U29BWKssHjQTDBrbU/ahM7pqx2g8JB8fy9EMaPaITzcms9DVkT4d57CwvT+V96eR4HeZZ9YLGGDSi+5Bu1hguoUeQfMI3PcW6wv29Dq4l8SiJO5Obsv0kHOEcJ/hOj+5MagV3mq2/L6vxErFXwTfuEmrknfEjXKh5s037v29xvnfxzC4VExNXSxtti/i4/puLC80oyrvL/4OKrT3O0pAx3YstR3FTPV/LN29gvYOp2lUW8aqDTo0Cb3Bi3596O+mgoZ3LQsGVGJ3zY0qexN+Wa9mQk0/fo7J4nazGMpSH6Pw9zBLWMyGAwms3eqIl+VFnKIsUAvoyhA9TZJ6TMHZPJVlrz0puzUPpTIPuo+cz0Hln2QcUKNwwwasRh5m3vWp6Kjdw664O/d9F9E27CnZN9oxK/M8GyzcCGy/kRZbZvKgXwleRqHs31HP8sQq+lwZwcTgQnKijlJ59jg+HctZ5Dv9PwP4/3dSUEQw1W4nY5U+iXvkF3nzeb5cn2Ur/SIDZU6v15KfpSRV2WnSof0A2VZtJPlJX+VWrydyLlZZdIPqxN36peTOqpe077OkMHeJJATUyifz5TIksFKONOyU8V8mS8myoXL7s70k32wiuxr5ib1bsLzP0pHrixOkbOdnccnxF79LHUQxS1My65Uk521XORm7Xvz9gsXDzloKX7eTnyZxorrvixyo9xH1sgfSy36IvD3ZVM596C/x8yZLzd5KKU17LvPVg0SrSX/59kJbMj6nyoAAW1GKuixHP0WIpd53OX1/jGT5homGQ1cZZPpSrpXayMToIOkYPlguVV2TXtcmik50hfh1OiU9fu+Qi2sCxOD8SplQeFqmex6WHOc/0t/6t/RS05WBx6fJYbu/EjbeQxI+GUvHoQpSVe0gKXPvS+iIDpJ1pa+8jTGTRkbxUqP3VZSXG8vs8gI54v9eTMO05PQ+P2nQspGPE3IlNaRCtny8LpZt+sk410XSeWC2XOaEfNHxlFyDg7Ju7XEpvmcsmkqqssfVXeZuy5JjJ8/IsxWNJEGjvyzdHy3uZUly6EkXKVj1TQo9FktRYw9RvjlIDvzTkY2znsnURm+l6MY0+awRJibrIuX49MmyrccTqVg2U3712yjDX28R3+TmklkYIwuD/OXvljvyY+BqMUl+KUzcL8fGREr8QwWZuTxZTDR2yOnJf6TcvK+YTimQ9rFbxc69VKLThsnW75NE8ZOiVK1sIXlbN0jikCQxTGol3T8+laanw2WM32j58nSVOHvpSbvQDaLU87PMLbwlJdnpUjrRSkx7eMtPi8/i639OJvyokNDbf+TS1gGis/K+tDa6Jlo2xWJarSYPYxzkdvVPmbrcTTxXNZMLOVelp6OLHDudLoNOG4vmut+SUHZK3BuViOPjLvJGKUpMW44Vm9GZcu/nLqka4yvFxnOl6eHtctz5pvjfTRdVU1NZ+eip3FkbK0olW+W00RWJXeosqb3spE3FCzmp/0xKvlpLddNkmd9yvwyaMl1+aofS2kdYAAAgAElEQVTIwnPt5MyP67IqN0m6nKiVjTe/yja9F+IxoYsUaNyVXt/i/7sTg89atWG4rzbt7yyi4Fs7+j/phrF1HJtNatneYyuHhmkw31iLDN8L6HjkkfhnI1ddFVFLSWL5AhWOX8xFMTyF7+UHqdH5jY/yTjJeDuCWTzoR81wY9+MXkyZ4c/vYHq6qR3D+5jbeHLVC89gXvGrs2JXuyNbK+6hPreLc69t8a/Yb65QDZNTW0eFWHaau8/Eb6cI243V8sEnC75Q3ik+bYvEmneeXNqC5biI/Z15mxMc0qlfuZWvf33RoFMXpvK60L1GheU4/7vnE4XVPFf9je/jQ+TID/JbRZ+xgbs63IvDjfEamlXDlzCw8V1jj8jyJk+7FPJuUSn3jXXSNeMqwEH8mL15NQNeXBPQaSNiU9uzpexBCPWh+PwDObiZ8dyemnG+JU3gI4ZEVdNKz4NzmBkoyH/Ev3p8RN3Vx6d+WkjH3yfmQwCovZbwnDmLRrzacuv6X409jSN+XxaxaRQr6BlK0cTPW9T1wjIMeK0pZZaLKzjaNqDLKodmEAZguVCSr9VbOZk1D840ClQY2HPRsRLcjTrwxS+P2ylF4Z+kT/akABflB0y7dubVVBcNRvzF+FcjMUVVo9QolddFXtH9WIKaDeZvoRJ9yS7zz69j9vpJMw804ZB3iRxszjNR+YfOikPpRU6jo1he3Q01wTD7Ct03bKB/ngcvkqUw2OUD2njhm1mSQPGc6H/0PsObVUt4ZtCRQ6wXTWyjS5m0ws9qOQeNVKa22DWHq9Y0cLnKjrX4Y+Z4lGIaf41+FBVuTHpLa8JV/2oUU2i1lt8UHKiekc+1MFzKTPuFmr8TgiXWUKrWn/aaBGOR9IHegJV+XXmVypRUBnRahPW0fgVNyWO1myI3We4mc8JCI90dZ6/CExIzpWFpfoyijG2u3NedB7l1GVeei5evLzWfHWPUgm66Rc0k1usGB4f+osk1m7ixXUg2XkuPrjqbjSdJzYpjgNZYb4ZrYuKeye3A+dVW11Jl9Yd/szUxf+5yLXRoR8zviPwP4/3cUICJ0MFaXcb3bSMsMHbHOc5fXr61lVN8ZsqFFrCT8fSRnf4fL0FZ7Zf6oodK+i5O0/txW/r66J+GLXMXl9wVZ/89R4gvny+euK6Qw957s8kmVoYFm0sXyjihcRw5PtBT/mA/ifGWQ1B1NlxnbY+XW03A5YaQmT6tCxaFETf6NyxbVlnOlWYaBDMj0Fv0gY7He7indhxmKeustkjbol2QHnpd+c/Uk7b6XhCUay2SDAqlUqpLGSxSkmeJ88T06QnxHFciiM05yq4OH7PbvK8HXdkhO3z3S5a+iLCmolEuh2+RflL5036IlIc5pUjM5T+5s8JLxGlaStamNTNq9T2z29JaT9vuktK5O9PoOlAyvBWIWaSJ7eobK6pUJMsnqvmSpNkiX9JNy8tB2OVy+XALzGslv1XlisR5prqEnN/z0xKzPPnHrZCXBbvdF5UOR+PWtlv5rB0tIq2Txuxkk3ywj5Kn2Bhl9cpycvHNWXismyyn9/dLK4Z9oFtrLBwctcbd5Kf2ih0jr2DcyTVdBgqc1knFdNsuP02tk0IKRMsNouJxwbyI+g5+LQr8oyTszR7rlBImpv5EUtVWVzWmnZIqOsoz/ukK+JAbIztXZEhgwRowOrpdxNa2k1Yvb8qWFoSyYYS89E1TEceIkCd3rLEUHXoue7TBJaDxIHhWVyvsJrmL3ulgWJVvIzxv10nvBd8nfvkEa1HtLtwEK0rZ9cxk5uavYNU6RiumrpEkLDynZrSJPXPfKZu8XsupdvbyxuizKF7rKWx89aXi9TYIfNZe+YxuL1olZoln/WKInmMiM+y1lhu1yOflmmTS4poiDawvxHvtFtHZriumkO/L6Zo30nhwgGx9/lebGXlKyxFL2/jgjBUq58nJ6mMSk7hdl14Hy1ChbvNqmiMqKMVIzPEw6r74uOYP3idOJ5tJ1fjM5cjJBluv3Euv7Y8VeZZXstVov+tmxopDfTTweBYiGZaVEdLooSn02yNbASAlJuCumfUvkyt4aaTd8jmgEXpBus1ylc6dEUfWOlMUXW0n8iz4yb8BqUY4cI8cv/JK0X6Ml53prabq/s4zdl/Tf3VSkVR9FCUo7y3X9juxR/8COQfdQr3hJTfF9/pmncc1lHWa/dnLN4wmx6oYoDtJhTu1TLipo8z7lB2fzfxD4yJUtoQvRlWUoPnSnNrIVaT1KqTfJ5cKybdRdLqKniSk5iQUsPrua7fuceVo/Cq1D+5maeYuit/8IHrWG0dVeXI6NZt4vH9zazOOrpRtvO0bS0+sW+0+p8aSXCbatjZnkmsXec19IbNSXnud82ebylrBF63DcmkKLKHV26D4ludd0ar37c2WtB60+TcV6XStUXjswb5ERf/vEceXfMlb7uHA6P5F9qz5wt+8GLr16wfNWlhgne9F86giMF2yizb0XLNC/z7K1t3mj64EbezBsiOFSuR9NWh1gjWFTmkztQFuT4ygPaoyLejG2R+Hu+ySa7h+C6qb1NIu5gmPrLsxRPULwLi18Hhdwa0Ev+m0fQVm9N/6u+lhe1aB6VlscJi/g7RtPrE0P82/iKcqGLOKj8woMPnUmdsFJ/q4+h3f3dLy3xlL3/SCXpngx+OQ1NP9uQfdOJpeLxpBSoMfOuwWYvnfn9fBzWBh35/PgF6T0scKyVomcxmcw99dAIaUJ2jX2mJgs4+m2Shx1NuPer4IDtQ4EmYXSV7eWoPvP+ZTyFa0nexlgkstey3PYvKxjQOsKLpYX4Ng7EmkSSoOXOg8+ORD19x4LzAdgNMkRrbiBRG9dy8iKMdx8aor5oJ4EG/bg5jUjepX0455FE6qbHcHKXYl7rcbQp4UN0/qEYdviMsXN9tN2wwbsLlYxMVyJUoPbRHn/xtB0MJ+tw8h+bcvBPUNxvrUIRXcLHkTnMXx7axSNPvFCtyUPgi5Q03IjZWVDMXf9zY21+hiOUsQzbR1btbtxJGs6/Tq1pPNWO4ovPedwoxgm7BtHwIsP9DDpxdDH3UhPrmdzW2vaPwzhTHgTTl02xnmsF8825JClHYuD3kZOJehS8CUcky/jWTLXmXZP9mF38DhzD7my5aUZu1o95qX3Ubwrsv975w78K1DHf8o4nkyvomHNCKxSv6E3/QIJ+6dwxvEoqhk1/HofRvWFzgyIiadj4iZeXYzi98pATAf+pPKmB5rKvlwvL8LHQJ1VNfu4aqJAoKYZJ2fGExFlxm+Hl3hUHCX+wga2vzJjESf5XpdJspULgZdvkJe0nzcpH9GvXY7Cqv1cHuBGapdKekek4KrgyM3kwZz9+IvMg6sxV7zC2F7ncDxowB/V5bRRSeDYpAgOze7P9w5fSboVSG1eNq4TulHrdZD3uq3YmmWIo4UjukczGTDSnENz3vF2hBFz0gLw3KlFTU8b9Nt1RNLyqGm3n5t5tiSf+MasgUnUWjYnTc2WK7EeTBpQz4+fi5lTdYSUmw2cyXjLtmGW/DNTo2SbMGZkLAezQxkWNJ+S96k4K/ag/OJ2VrlPpIvCQGbuSmfbylhUlzfjkuF3GgVPpaFPPI4tfAnRd6bVgbkE/dOmPnAad6qKeXB7HMt6RNCkbAiz6gJoahxFRQ8nRnlu4/2yYP42SiSx3Im5bqFMaWRG9p3VmH1dw5hbW1g+2pOmYdnYnKwkWwPsn7fiX2cdrgR4UhazAHsrHbrGRhA/5xhmJTE4pA3C0Cee3eeLyWiuj+YmV8ZppDB6ZS3eQyPZ8DCRH5rr+RZTwruxebQ8cYLV7wJxv5KBX2R7zD5Pw8dZj+fvl3K+5Dp1x95gnqOF7ugfHO37gNA/5jS4LWdd05ZEa5zn48RUznXsxYo0cyS3D3OfVGGkuJyB+t5c0ypjRIdrrOx8kA/DndEY3pLGHV4SoGXGyDURNA1N5ExvA/QmapGRdoYFmz3pVHge68UTSX1xkOlv9PlrEMjWx+EYLm/LbF81Ll78i/phT/z6nWfhz0yulJaybWBP7G7aUHVqGcYRdeS/7sjoz70ZdSOJso22uKWP4kmLvrDLHqM119Doa87Ld06MsFTkxNtlvPEXVEYKpq4b+Z7lwbnrBpTfjWFAnQKNig9x+/xBVia8IniF2n/k779CItRoUGZ1ZiceXO+BXX0VeWWf8Jlgy+ZFo7DspMNR40LUVl0gv+AeD54ewvtyFG6X4nm9KZq37Qfx+kE0X14+YUVkIht7rML4jCJhWjb4tYjgzXMdMtubs/NaGpEuHXhVdI1pqhp4b9emc95+bprO4muL3Szv+5fhh4vQ+JXDmtI+yBFn2jzsgPLtKnpVPMH4UC1nz73n+OadLPyWiuGgDJ7GZnDPKp3ml6PIUS1kf7UyNnHvmNlalWNFU/hXdppgt10YvLTkeGYJtQqb2B90juIOd3h+JorR0YlctzXhRLumZF2yQav2EI9fTudAMz1+Ta1meMNlnoZoULLsIk5tt+Gv+gcjiwbmD1bgc5scnr/TYXNGZ5qHT+bxznw6fWvC7rJ4Ko968LOTM0POjMQpdh6jzILZ/66Mr1FBvH46kz/TjTnQPJOzjpbkB/wizs6aiPI8/mX2xWXUb7xSXtNveC7HlO7xqVULprjN5V9dLlZ9ojg16AwaP6dg9b45hr6WGPmpEO06njkDR9Lp2ylexRpyNb+a9OclKOqksnNtOn3XazB8bQZR39zQazoL5+ibtPv5m7PH4+mYZYLdTAv0F0/FxaKQG1/eM+zlXPqFFBLeYzyj/7SnxfRVqG4chdOMraia+6M6cwlHO/xj5ulkJl7egt3D5XhYP2JpVS1j1SYRu7Oa8gGt4dcnksdH8u5sCxRq26KwagnWCScY97U3Tw0cOaFymae3VLAxb0LpirtsHniP7s9OkTVyDCOftGNBTzuuRfzAanoig/aNpL7Ahfp3lrx6e5RT6SEs2xvBnEvP+NLkGTojdmP35yd3LZNIV3qC0rxklB1PcORJMR+0jKiL28HP34d446nPJvuB9HLqQ9DwQB7PGkZwp9eYlY7Hcror2iGvsLavQuQtB8d/5FTaWkZvMKTFTE/qatqy1CUGKyUNflXkknW1lKCFyXTdfpushbfovmIQ5XUevFjuRgcrA7Ia+ePuvou3H1rzp1l7tP4Df/8VkUB1k0oOus7gnaoLs7brsnfhForCbTHf7o2DZioBv6P4HdCf4jlGLO/8C5fMh8Qdf4PvAC1uXFzHI7+naP4JR3HNJgzDVuGUkcvZM3fp3/Eu1gm3adl4G/3PLaRxcT4xBpaExbgxwvE+E3bkUzTYhlFrN9DZ/yy7098wYsA0urrPxmLLSQ5PuMzy8mUc+a5Ce8N6Wj/3oa37AUqqdlKa2QfP1E34P9uPYQdj7KedJfSVFUfyH3Fzek/09kTQ3PMBD6K0uHoinGtBdhwoncDgIG0+ZB4gdbAml2+F4zQ0nbmfj2DZdSflo11p+q6QgH63UOkUzYmMBfSLnsHVYCjpHczO1qtZo2pL/ks/tm7XxW2FKYVTLZmfv4GJh1agqabDpnGHMRzxlnmNX7Hte1PGJJeT8i+HWz3iuT+lHPXaFlwq6MXWEZfYNi+JaycuM7K5Ol5K86he2Qj1p3fRyHyPoutKlPtpMVh5E+a2xUwb0IwpDt8ICarhjmUXurrrsrC+gcXKlzl3owDdwqlU6DVm3MezrHVvRPrP38QeGIRppCPGw7xZEdGL5JHLWXrsDe3d+9NX4w8aH93ouD8Qs70JHAgLxu1yOr9TzfnwdR5DO01jQIgtveMtSOvYh76/l7Ah2Jhhg6+grXoOdxYyf1cn1Mz/ovdmBldXPGBSbBWmmyM5sGwJJk4tiFs9A4dLS7n6cQIB+amUnD3LjYMdyLiwnp6exSTZfaNIcxCjs6JoND+HJg+CCXjhzZf+n8kaA25qK5CeV3BO0kANdaxCb/PFWJleu1axWcuK+ZOGc777dDS/LebbtE0cSzBj2YKWaFzfQ5l5Bj1yg0j4ncCe/JZcXulH8ybzuHrgBfUTXjDjcRgqG1VQ/WfJR6vzzEvsTpaPLwkfZ3DB5RFm987ROiGUPtGuzAycS5C+JVZ6HlhZjqflnWyMniVyPbw/WaXFHM/4wZpILzT+jKVJiB77ggZgrr+EkId1zM/XQb31Zp4oPPiP/P1XOAF1le48jm/P/WpTetzVJa/UlXKFTMbahfAmbyNqo75yS2cKFRs/M/NiH/pPTGbjtwV4XljCJr0MQhIDaXlvKIm+h9BZdIzPds1xmNSWJ0uT+PTQlaEeZzH95sDBxHeYbX9I8QsXmqfksjq0Bz2No1n9ZjU328ejWD+DY2qnSLofiMLBnfQ4n8/wT7NQ3mXFjbS1zOrzlcm/R9OviyoX7z5nw8wGXgQtQNGnnsat33C/bSt8uifR/IkqZUaBtAwYQHXMA/xnLaLNiVqeD/yDwjthpMYm/nir8+RAJRd6hmE14gmub1dh1uErD8884IyWCUPVQwk1L2fbAFteX2tN65zn2NnkYn/eltVKEzE+Es/htHLOntdE23EX2rt+MWnZIN5ZmXA3MYIfdkYEd7fnsuoncrNb8nWoI1rhN/mpeBC/xLl8PzSCsC/1JPgq0bjlH0pe2PHZ4gm7JqTTp+09EuNyuXhqCWpn6zlXU8/1LeYczvpH6JcVWPZ3RKv1UmK8T3KlbzMciucS2LCNDbNreKCZTcVgQ3a0OUzeMi3MUjtjPOoOMXVjmR/sSey1buSPvo517SNend+ExpAgppuP4ej7Gp6nuGC93g2zBiOGaTVjQ3gKqfU7WaP2gYgjq1k5KZiYCwvJuxrDmgtduDY6hXa/B7Cr53KSMoM4XZvI5+c9eZDfkrinWlw8EYiDYTs+j9Xlvt0gwmZN5reqLqX905gW4064RxSBOqPYGBbHzGGXeBF3joDSFNpPcOXg22Zs939C0zVTMdjxhDsWKrxTqWDtLANM8lujo+SGa9Qp/EYWYNOigB3PLNEwrCXpdi1/J+7iwqnfLIpbhufarRRWRfC9yzJsXryganwl4+tPovvHALMIZXQ8x/JxSzumPcngskIwJzx2EHM2h+ZbnclJykJ94AgaNUljZ+UjnmR50cw9mrMfyzhzSwM3hxkU34shr2IUnza+xE/nGJfeDGbwAm2+hKuwu9qGAXf24uXShD/mCf+Rv/+K50Bt6TdMV+7GO0MJiyIDjCNnY3axBpdcKyaILsZxzkTo3yE4YTE3FYuYdyeZR2f/0nFGDGmRV1CZNIl/hUO5fuQiowr8+Rg/GCuPaJb0MyNp5ye67u3Igj1T6WtcwrnfU/lk0I6254bT/Vob7Ns3o/8VfxptekSQsQ3Tj1uRvteMR+nV/CjZjOlbZYg+RqN1Dljf6cw3HRMs3Orov+oeprd1+Np1KT+iUxk14hxucTN4OCoBz6Bi6oauolHHORh+9KBbdRGOlb8oG3gYu+LxjE7bzqEhvwge2IHUoJZs9zfjYng3PmYewKWbE4alHpR2y6Wn5lfU2umh1m8eTndyGBXXjA/z71HzeQNtR2ZjHH2D0vaVVCxTwNxjMNuXNmbk5H0MFXNuDM5nadZX5rgcQGWJEUo+cbj1WMKy+U6Eh0Wz41caw81aciToHN99Qig9PJvnqZ/Y/PUVrzroUTE0m7rBFpQO/EPqFweqVlVRoOjJq/S2LHHZzppTJSi5jqKLxxo0btWi6VPEB/3X/H5wlRG9TMnovgTPCxdRXOjBBwnl9S1LHg+8gg0W7Nnyj05FEdjOmYvvlj0kfCvhScU3Zp4cREC2JlUth+G34zSvOl2k7vEE5kf0xd9oFpkGL2k1r5qkl1pEH9nIQX8NzksWZ774cmhcGV8HtqOFpzaKJaMJvBDFlOg2NFryktqKFrg3zsb5jQ8bVjpRsyuO0Ke5fKqxxi3gDj+aPeO5dOR2xBB+G7wnTPUJg4I6cefGJ7r7/eNI3U46zgxgrfMtXi4ZRMvWzvSf0J4KVTvGr/Rnozzmr6sdWX6NSX81jWeRu3k02QtzOcivWx94nabDNqnCKV0f8/4+3HqfxonWQ9E6l4lu6iEU92xjx9fHLGlQ5c/3GLYZ+dN+eS6jVS6SyQ0y07/zJ2Apx5Jzqdz2E3uXwfRvNJb+E/NRLdzIAd9xjEo/zfdpL1BuOoSPvedg7Hubf1teEbm5PV3nNdC6qcV/5O+/Qh3Q7dNLboQ3Yn7GYDa5ziZx+l0exbQium1rPvtMwf/zSdrnNCb/wmUU3+/A2TmP9as/0bHChRHX7ElP74tGsB+rppbypvtGzPYponzekCGrW9Hw8S9d+rhi8fAYbzMv8Uh3D5c8z+J27xreWdaMvl9M5s8yVq+y4JlTbyb7neDq6L4cq4ey5I44aCyjvI0XlRcL6aUVwbrhT9iy9jlpeiO4mPiK6HkHsF6exfxvY5hepsJHk2XENhpHi6J1BNxtTrc3scRlX6e7vg6+DcU8zHVm6SVbal5GYfHjChPXabDI0YS9DuswsrJg6Lw1PKw/wqz8RrQvScI8fQC1Ma0ZqDCVNK1t/F2Xi+H9e6gsL2d0h6nUZyxiV9ME7E92osm0LL7FPGNA72om9LBgxKK21LQ/zzejUop3d6PZwvs4VlkQsTmE+39TaecXhkHHSvbY1DL/y3Len0ule3k1TsdyiTcYSrM2n2j0tjUmQ++zZP0KLqcaofz3I4PbBVPf7ga165vTvTCWhOGH+R75hLitoYzdAVq+GfSr92HOhPcMWXeP+12SCHSbw/CWT6kJPkOLZH9aeH+jhacD5b31GB2ryRC7XN40XopeoTkl85041vsoqtUryP54ldmuzgS3MGeHznbGeTqwys6KbmMH43fmLrtrxzDgwHgciy9g8PwYwzWXoK0fwpYOGcy7upVxsVFMcNzC9pNqlPi9I+fkOly3+8LawdQV7sI5sga/vDP03jkfrw61nDFuYNGVEPRrdnJZz5kQV1ty5SfX7QcyeHNPdh0q5POYrmT37IDpOi9q110i2/w4v78N5cbKMCq6T+OPpwJmLbcS2iKKKwXD6NlyESaPdxO9cwetS9vhazaQjUvvc+7qRVoOdeHT73+smv6X5GlNmXX9GxMCOvM8OoGBGwMJuneVzJdbqf4QxoV0T+aHH+Dtl2jau/Vnp9ti7LRTKNmwkuJl1ZyICcNp2iJMb8XhZ9gKsx6TcUw8TKdXxiiUh/JYdv7vtRz/P2E9OxnKFI9L/Dp1iMR2S/DpNILOjdO4OnkiI3eVc6NRAXXq3dniH80Q38vM+VzM+K5TGJL9lAc9bXmyMIX1Bsv5PWEu9cMOY/B1Dy3u7MIltTXyLZ5eTwbz+Hw3TPafoEK5lvfxalhsVaDpy2mo967E4tJ82vRci7FyD94p2ZMYspnk71a8qG3LZ0szCoZH4x28jI0PflL3KxGPaCPWd9lK5+YrCd2vxZIrrTi+7Dp7XU05YT+JfdefMqZiDcGdHvP69lIOJOqTGtmAp+Yrao3ecbYoj60+1gTsTKN+sDbrJs8gcsFszNtnkLnECW2X/sT1z6N46Eme5FtT0cWCPJt5KJmWY89oBhSr0af3UkzXLsNpwWHa2/Xi4LR62l7uxIufHcmccAzdpR5UH/7GkEGqlBY+xOXXV9xHrOaqJLJo82NUwrahVrqU+N7fyZ/hRf6vJ3hcfILT1jf8exiB+WQ3buTqYRXYiRPOjZm0PZVQ3XJuzG/E+T3FFFsoMaegNTFpQ7nzq4GGFDV8b3fi45XltOlYxJU2WayvySbi9F1WOxcwcHkeqnY7MOuXy88B12m11gmvxbmEmFejsnA18xeBbcR7Nu0ZTebpB0y/ks6Ks8l87VKLoU17pv5aR7l3CWXTbNl5SYkxs8/yJjqFGzE7GNLoBg6Zn7h3aic9Zx/nHm+xjQ3ATeM+3mYJtD/bhWc/yyhJbI9Hc1XWPF7AwLIE/pWZozFUj3tuW1gUaIWL8UJGq3jwIrCBP4fd0FjhTEPeXHKuhdL3aC+Ojy2kR1g1Ft47Mdbsyo9W7UnOfsHPvTmkmjbnp9pxwp8PQrshkh8ac2m0diLP/45jzJYXKL+7wRjTYfx1D6F0ynUiinM4PjDt/2LuzaOBDLu275+iDKGSoUFJo6nB0ERIFJWUCg00SoMGlUbNA82aByQKlQbRqMEQDSqpFCWhgRBNSirt74/neb91f++67+951vt8a333/vO49rmvv469rvM8r30c5EwsYXRaBanjNTBuPJLWHp3wbqWN54gSlE2PM/CGNZua1aH8cSNHTU6QkrWY3spzMF+3nrndOpDXby4nzA4RGHuI3we24Ffowrhz1xmh3x7TR66UzqhF59dhTjtl0WuD/f/ZANG/8B04CXT5z5SmwGcR6fGfCkR5/IegCMBdEZnxX72j0e8KJgxeyrXE24QOTKbSzoWwy2q0qr7DI1mH57X3KOZGsfSOAYNr9/OlthlWMQMJaBbHvHY9SA6eRNv2a/CacITT+k+4ln2eD/VWVA7XZ9nHNDb8WkX18OaUhGgQVfyYrY+C0V3bAWsnQ2K8dvBlTU+2di4hYulO8not5su9L/TaHkZrlRk0HXWcSZuDUagO49fK+bS+H0Trr1eZYnKJs+k+XIobhsbPGXQdZ0+uhh3RW/tgkHmFY2dyuJJtSM7pTEx1f9Fq00m0znng/jqfgBxHRr2vwrvvLr5Nb8dE/wGUGXnw4FsjrsQNwnRPAK4f7rC11zzKeutiOLg/81oW4HZ6MpH7hFcrdmCsd4fokSepbR9Mp57XCXB/RlJeIZOMFIn7PoqiN1Np22kEmuJI3mZdrNp9wNHsDZuGPuazsw7TZobyNKsctfWvKBp+n/VzPjFk2VkmD9lO0UVTxjmVcL1LEjofq2l7ygu9G3vYfVGdD+bxLKnI4NPKLSRrxmOcNpWzbnZ83XeL/tdnU92nAQYFmsQJpqoAACAASURBVBx1jmHGFSOa7/dm1QN99unHcFmy8R1wk3ffuxLbpYRpjc1xOuTHsxvf0IwIwHazCvOjHXhteZbmTVX5tHgBynN7sH7+cDa6LuDLQwPevt+A1YZUpmzQ52jDGD5P2MA+63S+DEzB+vVq8qvvEecXzoDCAEzzS7mzLxiPl3VMiDhC2d06fnvG09WghJsTWxLhJeRPUKfJwo5ofu1K98w+6L11Zvh4Ny7bNiDNO4xWGnm80Bfa9Lfm78MjtP1zksk/P3JnryVZ8WNJH1rCmAN1bD7uQ23XF2SOGkLGCz3KnPfxUeExsx4+4++aclJDXTEIV6H39Xw2TOqO/Zj29NO7TbtF+4n+fJIGUQ5oON7Ebuos3rWqwO9lEr5hU9jot5DcywP41GoaH79sxTvgKn7v00iYdp/hTlb49l7GxYVxjFAuJ+zIO+ynTaTilhMuC+OZ/vAol/+8IWx6B150uAIb/jn//jtnAkeBvUD0/wJExPMfmsR24Ms/5BeKSI//Rt3/O759aYb+AFXWK2ymd0gS9Y1nMmLFHV4U7KLihgWdv4bxfmhDqjzOUpJ+hA8d9Olq5M2W2asYxRAK7y7mWa/HNLY7j6/xQQ5mBrLw1CCmTrOi9pMB9aUH+PIhnJMdtjNgSjCb8y9wbelhnK2VeWA6h8CyThx+0wytkMe8627PzvAVbE81I/DSQpoNN2HV3Bk0d/xMVZkSt8ufMHpnU54HXMSHP/RdkkT+WU1Me3nQo9tXxtdu4UXwT9ZPPU1Y/l/u7w1FJXMs45q95tA+DToPvoPG6Ay0DWwJczYk0yKKP+NGs/2WN6amFhSc2kCCSTSZRQbMHPYVv6edqTOy53JQFVOj97Ko4C0W75X4VH2cToP70ackh/u+vdheOZcWJTNw1fvGsrlpXF97huAhN7j0I4zx0zP5Vm1Bz6PdCXyrxDK3EIIXnuZcUxucwi2Zm9+WX0tPMTqwHebXTem8wR3f+4VorEkk7K8jmiv+4Gg9lgLFHzTun8G93wdp2+4HzeZuQt4F0n97PO/m+/L+fjy+ZvEsXjoJvcYv2KuVTs2YHFo30WRF5QnWBtgxO/UQfYZ0J3XCFKJG/MRZQ5/F64QR04xIWrqK5xvOcSApluGKlkS9nk+G8zcuOhnSYs5lCtZGMWHJZX7vHk9o6W2UdI9SUdOLBpaT+Nr4NRNXpZEeaYPa5rko6x7DZk09yrN8+X6zI80ODMQsLZJfh7YhW9L49LsPF7SO8TC+jGD7LeTN7cja5gsYN7QlKiN02V5SwJFXWrzzDMf98H12XWvN5bj9KPyu4tmedPLyK1AyaEnKmObscJiHfmQsRb0DGRfXk78Ty7HM6kBO+gneZPnwxe0u43fNYu3nXfi1ac30xArq0i5RHvQCl30ezK/RZ8+SeJztRpNb58TDBVfw3NKfkGP3sDPbgOu5KbxO3Ev2oJksbpzJxLnuPLUeQfX5JPyatmDZ1TGo9rjPzCufKGzfmFPlsSwJKaBuUDEaQ4pwaL4JL+eX/5J//+XtgIikA9X/7JmCgoIC4MF/SJD9H4ea4ge2qqeRobSZ1HcXUUgcSeiKg4zp/YPeV34TfGg4C5eM40KgE06BHRmxQovO9/X5FHiSC67ONHqdxQalmSx9FcZu1x1YhB/g3Vx92vhlEhWbTdMJW9Bt2JRlezvh9M6QI1lL0J+tzviwaaROmITpqhq+dpvN+C7daWHsy/PO9/nW6jZZ3woJclvLri21LCsvpDCujr4/ssnoX83EO1XEDtGjZO8jGttPYOm758ybE0Lv/FEktIWO384zZ1EY7Wya0M7kKUXGHSgJ+o63blvGaL3E4/NHOnX2QPWsIm7nxqK5vgHNxtsRMfQLdX5zaLGyJ4vtzrP62Sxa9R1J6gc7vCf8wTfmJA180xnw9hRuljcY1TKEaFMl8reXMmtdAUv2nCHxzB7qT4xm6LYelPyqJs+khIvzixn/6AsX+q6nx+m3dPK25du8brjlR3AvcRuzAk7Q7n0Niy9UsjDJAGfVAbx030m53Q9m++ih2zQWmaVN+IQbjGq6gcETKrGt8OObWz3HAsqZYZbB6sLZFFW1IrdJBAqNe7A66zOXr3biW2YRASOH0kj5CsW6+0nprk2KaiSZ+kG0LN+AVVx7Gr1uyjS1O6xcpYaYH6Xycgj1K39T0LEjYz4a0MrbnDNbVfkwcid+HbUIauHJg5+7ubZpHX00LjJ3ehA9bPJQvJnHw4hkKtqHs6XLLO700OSCazDn5/hTODuNU7uWMOqMCbVffrCy4AbLu4wjJcqejcbLWX+zjtcq33D7sYdneZ8w1O3Lr1/XeNE+jq6fF/C6b1cG7xzL6zdzibqqT9aafBq0HEjjrvlYfHHhtucsVPzfsLFsI/0MB3HbZzo9Kg3QOj6F6/Vt6GKZxLA/jfj2xgc73xuMaeCI3Y7vqH9oycax8HKbLxVPx3Mx7h69Yq5gnLScyfHdKHQNZKXDI3S9+tLr80Ie/vHhj90Z7jZyRS7fp4lrLM4eZhSP9CDSqBuXdFPJ7T2d2X+u8extFh+9vuAaG/Uv+fc/vR3oD5SLSME/YO0VFBQeAV+BIBG59V8VqVBV4+Dd1+i5PMNpYhXHnizj6spSBqgl8H3KMNrOmMLGnKO4Ga6lrPcZ9Nen8ljO8PpgBUmLh+G0rBCdd6to4biQsYlT8brehOT1UDKvOTlrPlHr15+5I0vQzflK0+mPCP0N9v1v831vI25UzeB0XAYd79zm65kOLPDdxNt5rqxp7M+kICvcDlygfWQc9/p8IjlfCa9lCxh+RB/L2Vlw/x551/pi2nwwaAbR9uluwq65ceawMi4j0+nazIWGswfgpW/OzqNCnV4THp/7Q5tPt/gyfhaqxSks1DnM58eBDPFcwakm/qhtH0trE1VUtWNItogj//Bt3n9z5cz5j9h1PIXZUV382l/B/uxlei7NY8Z6Jb5uW8m+BidIKlOnSdpqpqht5mGKG+Ob/0Bdsz++kdE8qs8l7/VG6rR/om2vxY6jQRxalUl8vBKbPLqx/HY8dSnDabm0NyebTsB2RSnd0iaz/EsgZav/YmF8nCN2qTSMP8etgF+EKA7iwJKFFHU8w4ZNpfyYk0CLuYtRcFvP2vkLqGxfg07pIS66TsPneWPiemhRHDWbxOG9iDdS5uWjtth0Gkal2m8uP53OnUz49HgwdZdN6alYxofkX2wPdWbazJ80zn7L5tUXGbkkGQ0JIKdjLeZRe1ht0pq2667y6GkYz7sfIm3odB61P85INV9eP3VgQqcLPBx3Cb+ZSrS0Wct5rXYE92jIn+2xPGk2DzUDX5IU26P0o4ZNHd1ZnjGCYo2j9I7cyM21KZy4VUHH8HJWxFwjLDWUQee6ozO7LQtnHee7zzxGOJxi0oZGnDe1IPn+JwZdVKOl+UNy22mzf8RC/tyZzKnsLYS83MOF0SVsX/mXyHpvTFQaofj2O+0Cz2DZ/Tv6jnaovl5HqcsTXt26hI7VSww6T2PRvAlYjdUh70sCKhP1cVjlRJv3N8nuFU/GtHh+tbvOMedoWr/XJacsjbP9f7LA2o9uDdJR8KhlwZMCzOYu5oHhJE7NKP+X/Puf/icwlv/nV0AZ0FZEegILgFgFBQWNf7bwH81HPlaoMvTnMswMltFTLhEx+gSjUnQx+HaElMVhXPK/TrjpJzrdiud4vCsj8s/SpYE59aWb+Bb5nk4hp5ileIuM0lzu7U4hMKQl8fc7MT3sGgej1vLwTTOsjyizot0grr6ZyPl3CgyuGIflpUXsLjHjqU8Iu5aPp2x/FLv6FjH6TzjFM4qY2SuB+x/7MdkUYrMCyXw+gb2PFmF+qoL67qG42EXhbvECh7ETCbWcxuBPTly70x+Fou1s3LeL5GW67DK9id3t3pxZ3oSKm0+JXDWLxlUqJPjBlknteOL5mQH6Hfh77CxvCSJtizPdTpQzQb2E+mNNqVx5n8J5ddjVz6FFi1kE7omj15ECTLwyGNtyAfXROYQddKblCU+GN4nCZIUVJjnauEUZcCDMmSibFMovnua+Q3tK9+dw/v1xIoaPxzT1BgcehfJ+qTNV92bhnRSB9voyPmfvw8nkF6UDR7NsTSadSmup67GH7ufV8cvbwLeIJZTY9Ud3qhexdi/hhCE7K8sZYPKSPVZveehWz6LfhmQVVdHp5mGKrufgmPmaLU9HceqyIU7Ps5n7+TwO8oVRHULwiR2FzZYCfLyiOHghldJ9dRjZXsZLPY/3XmVsXbscw/6uLPzSiHL/V2j6ZGC/thmP1D9gpt2VH0u3YmOzFOXfHuzw+sO7DSMYOSqRhq9TWVV+kxmPlzPm1R/8HJsS1GIgK9pEkqwbyagLp7C9t5PTW4cy0GwDyb0GMui9GzWqfej05zyVWQvwjbhLTOFz8ranYT99AX9jfnDr9FUOt/Zk9h8FPp0azaL6NBo1a8jKZmb0Vh1Lnbo/e3o+YXPEa9r9fcrL5k8xi1/Fgcy2fFp4mjFa+zl2ZgzXckdy3SOB3FNbWTbxBZdb/cEloCdHujRivcp5xt48yqY3w/m0pR8OTXWJu6TOypAVbFjoicXJZ+g2ek984AfybP+yesJ2guwU8Dn/ni43z5G1agea697g3l2V9Jtt6Gtykk7ub/8ZDf8j/jtTfoAB/+BF+J+YIlAOtPl/WZcKWP5X9ZVaNxEX2wPS8HOuqLb4KkP2DpYfudlS0myk3Op/UFJtDSThlKn8CjshjV4ekqW/fWWryR0Z1KqJrFGbLC2et5SJvdfJy/oh4vV4lhSeUJJGz2NFI7CzDPiVIa6DW0u8g4HceLhProWHi67JdAnqmiNpGq2kScoMWaZTIK8TX8uArQflQcxYWbvgsHz78kBuXPMRbeuRMjfLUKKOtJe1ZyfKlWoXMX0aI40r78ophVAZsTpYfl0PFFOlKnl7sYN8/BgoQ+sfS7fHy+X3HiepWnRGnGwcJbE6RuK3tZX05J9y8PdT+T3bSHrFbJCpLqZSEjVJ+prFyZa78XL1YhuJc/aRMi1tcZ/iKIGvHORZlbd86NpaVJOSxKN5E7nQKF027nIRRdUQqXIyl21vx4lJrymSdNxcVLcgOYuPyeDCHfJKuUIely4T61pvGVd8SozXuIll7kDJNN4vXfalSdiMDlK8fpjU7OknDuf6Cn4a4qnhIIVOY2RE8UspGPVWLmUPlbmbkTXPDku7Yh+pypgrLpbNZGnEEwkP7yeNrWbI5Dm3ZW2YpmxUiJI+lQNkV2WSuF04Iduqfks7VSOx0p0rtRHKMvjEIsmqWypTR42R4m6/JdpRWyb3j5PeE7PlaNtv4jGsXtbbOEhK6S9Z6JMhl5vUiY/OLNlyJ0PcEh7I7I8t5PaabVJq81UmRg6R04POiNXwGFE6OkBS/36XEF8n6XvulRjP6SIZw+7I8bUW0t7ZRRRWHBbVUXel7nu0TN9+V/o1CJcQ9ZPi/WCYJNzKFZ0bm2Tl2QCxtbkkS//elcqwMHHtPEEqq3zExa9cCsOcJbabg3hHdJUshRB5VOEsoxo4yimXbGmTfUw2NTsoaZPcZOwtRylOsZYr559KbaCLjFGKlYrRY+S+oZJUFLQTk3u1MvqSiTh1TJWCoS1lZadosT2iJjO/DpcProWinqck81JdpL4QOfpFT+KLEyTTyUl23YoVbT1nibHeIX8OKMmUpC3SYc57OWqsIn9VbCTPvEDMZn6XhxlJMqOVpzhemSBmY6zlYQ81adVLUQbPui4PcpL/Z1OE/7vvwH9izsAyEbH7B0wbqBaRegUFBUPgFmAmIv/0TOF/RauurWTS3ik8uZnBpbln+dqtNRaBuaQdvEDm21kMve9AzHg9nij3wHFnBJcmPqHthXpULDpyMecY50au47RpGhMbX+dw4UlUWseh9nIPs88MxOb3GW6t6kNR9E7MNWajM+kN4yZ1ZM+tODxT7jL1RCiFG+LA1Ar3ppsJ7/+dwnvNUGruzpC4+7yZWERRw8tMMFQjoMEIrgyM5PSlt0Sssmbi13tUaJVT8iyBI8aXsfPczOqwz8R6NmDD7c8EsQC77QuYHzqXM6c/slChDevqj1Pg588tlTj2anem49scJmqdxinJn8p4O4zOLWTdzFqU+3lyoHYBFQWKNIquoKrqFy22PaX732i+DfvE5B6NaddGhfXOavRJWU3l5ygW23Zlt90lbFwX80phPi+f3mRCA2NsfubRZ9N27F8eYNKeJUzv5U4rDyfib5RxuMFv5vz2xnZWG84uLKPw8QcKN6zj9wo/pta50qPwN2ojetHLS4tBtz6SNSga9aZr6HQvhhVXhlK3OZ3qj1vQTziOy01FDrcfS5NmQdxv8JiLdfNo3E8D24tj2H97CUN/dmbF2iX01rbk4v06wpNV2LmmC4rqhRTMKKPnziIcN2vSp8Nd1Nw24Gq6GWeN0fzYtBf/a+nkKefSuf4+jbJbcLVfDG+6HSJhkg9X7PcyMb8HCT+7EHutD+5r87hSmklw435sxQWXszM5sHkQjbpW4lDpxo35mUyc/pG29iP5cDQZI00dFn1WZN+NAPJ+lBL/LJBhU15QssGPTR8O4vnmDakjO1K0ewzn+8WgXdiQyaXhPMqr4onxMnTjSnl/exKLftcTX/qZ0W/0SA9QJ8JSCec7nTn1E+psvjKv0T3mHL+DUuUNjCf+5s8XS84tDEXXdyFF4Sf5WlbOLxsnkkfOR61ImaU1ifgMuk9fhSusbz8apdApuI8YRuPoEwTHOXDiXCGdevrgOESB4HQ1OvRw5tDKiVx3D4eJTbky/QrN/lyjV8OP3D1wlDVBWv/f+Q78J34UmPG/5Y4CnvEfzsTZgOt/S1mogYbssw6Qv9tVJa7fcnEpmymWZcfF7f0s6TvljzxdWinPjj4VnScrZOi6e2K6oYEMP7NFjEwaS5O8TuKXGyULh74Sx30DxXvMcFnecrK0XHZaWm1LEIX5XvJ8xHgxuflSKvvryNiGh8VmYTM5an1HPigskHUF2bLo+3hZu/my7H/1Tq65bRP1jNcSYX1fCkv2SVxendw4NlgUpr2QHx8yxLeDuqTbL5IF/V5Kp9oMCZzZWoKvJkunwbbScpq7hKbukej2SqKYlCPX148RR18zUTRaIEFPxkn44e3yPSNSlBw/irXOUBnQ2E1mV3+Rv0ofxeHhU/kxqa083N1DOuyIFe+6TNmr1VhcXqyVvbu+S9vjDeSuTWu5/idC5mYkiOHjZ/L2TLk0PRYoFaYFEhI0Sp76J4tOpwGSmWkkOh7X5VCBpVTPjpVZ6lbSPmqytEu8LKpzh8jLtDi5sClIZjcvk2VhM2TidA95/OamHNw8USoiR0p65gK51NFELs0pkY2dt0uRWoFkxZZLo59HJftcmVx89lYm/2wun290kWE96iVrwUhJSGoszkHPZN/MveL2CDl3/rucXFgs6r5GUmb5RrTV1kjlmmOSGqwi6huSJfKzs9ycqCdKu7Xl+a320mu6howOGC5n479It1xr0QuJk3tzV8jHEiPpN6VCgg/Vyx63FLnx/IRsc4yTCYouEjmlhQwIspVm1t0l5WSa9Os3R1prO8lzl5uyqPamtO59QtSjO0nK3hLxK38jX9PjZMoFe3nfKEjOPn0uz6bMF9uw01JmaiiLct9I0X5PGb/QSzbu6SdZl87JtPLPEtZzjDzOcZe2m2xE032Q6N1tLkrhDUXXwlieJEyTilmN5eraxvJc86AMuXtCxiy/JrVJu6Vq3RB5tnmwqDnXytq3fuLXp7+UdrUWVSVjWWFnIkVZKvJ7Y5jseTxaasOtpOqKrbwaMUiWWcyTLM3+Ms5KQ6oDNsi0yf3lhZOWnGqhL/01v0vD85Mk9biZdPngI5GqdXKxMlM0W1eKa39b6VrjLIZrrotm8QPpbZQjOp5Oss3qgUz2uCFXW6X/e/sO6HRSkcte18W+YKmsmh8gt5pVi47SDqlXcpeu6v7yJuybOJakStfsO6L2/Zq0ddaR3xqP5K5+pryoOiWbvl6VgS+vyV/dENnj5y83Pk2XzOz1ElcwV7Je9ZH92nNl7rDWsrK/qVTPTpJjKaXS84ueGNqflyW1xnLzYBPpcM9NXja3k1WxNdLd6rLs6lUpIwcayOLVbrLbJlSauIXLtxeIYkZPiVOcLK7+36TByCeSHb5K6iwWi6tJoeyM/SBzVk+V/PDNsvXncml9JU123vGXB5Z9pHr9PBkxXUXa3I2RFQm14hrZXYa8VpAZC07LZ6bKgNfzxbXBa3FNXS7JWn7SonlLWdKwUH5/dZMVizXF+bqSTDGvkKjw4zJ+YbDk3HQUFZUSmX7WQXYldZJu7zpKU6O/0qDrMukztb0czjwtzz1MxcE/RdrE+ch4/Vtyr7G1tM9/I9vmb5WfZy7JkDb95fB0ZQlWD5SzRx5JkYOP+B5Ul8YWE2ReyTk51VpF+oVNkI+X30iyjoVYXUiR8a9vSn3sBxnoekUut+kruRrDpOsmSxnj+1Duv54sY5Z7S5++jvLEb4SY3kmRKL2n4uhlIB2j1OV2o0NSb3VEWmxTlYnj9ovj8ZsyfJyTVJxYJqWdj8rSm+tlQVSgvLn8QrR0b0rClO+ytLWTPExpK2EtFsu4q2sk/cR4+ayZIu7en8SkYLDYGF2Vcb9PiUJpomiUlcmGrl0l8+FnGT/GWxo1LZKdX1Kl2POjBE/5LUabkL2pzWRrVIJMe/pG9o74JIGX/opHVJ2ceDFQ9Nu+kOubM2XgwL/itGGMfFKulqFJe4SGc0TNwFYyLyGh+3XFf1Gu5I4/Iyq9eojW/RqZYeonhjv7y/gFVpKcli2LFziK/iITsVnkLl5vpoipXqgcOaoid/fvEt+AafJsv4boxJlKYOIqUfkeINY9t4m2pocYPtwumfemyYCOcfJXZ5qMbeAoC45ayGZPFWmyf4CsHd5EtLqnS5ztdMlYdFoGvsmXYxmbpcxZWWyU46WyUFV+m9yVj1OqxSgmS8JTWonLo3S5tqC1dOg//d9bXqy2QpkPPpMJ167D7dsqnN924cnw6dSHTCVEpSl+t/K5c6g36Yn25HffyWzFF1TplzNrRkf+nnrDsHB3GhftY2HaVE7rD+B5Dx0ibhiT8Ekflz5X+Wu6mlWbrYkdsZ/bfz/Q17kBe1tpU5clVCVPIDZBB4uHdyhpFsSH6M90bLuGSevDWH92NzM2XcVr9RBMxjak5M4QKiY7YrloFVtO3cMqUZ8pPz4x50QkjwsXEp/ViE9PDLh69zFhVWcpN0hhZS81LrqN5e3zxzSZnsaon4cY/PkRB8bv53PMXvaffM9e46EcsRuCpv0Num5ewfLNA2njeonwy3epnbqEJd/1uaARQV91DVLbtqfOVNBR6cP2HBMyL3gRoL6dSfe1sCxcyULHb3T4thm/k3U8DI7j8++ZrDSeSMD883iXJ5BzaxUf1DvSqtl4JgZl8WbaPtRWHOd6vgoNFDdx3ncA1rE+9Hm1mJ4aUxi9+jvjklUIcmiBUU0ZVwYO4XOGB8+GJ9NmyCq+LAllzcICBqY1R7P4NeYhM6iwbEvPHo8ZuegbjQ0vcuNPCyKVBrJQax7P6yew9Y0KT2/Woi/9UExN4oVhDovn6fDAeiV5vl1JW1vL5cwYvs+cyzPHY7SOO0D8hY2UVdkyoXUznOPaM0XvB8td97Lc3Q31v0bMfJ9OR6sOTHPrQPqC/mz3nMId/Bj2cj3eo/syQuM1vWJ2UT9nNSFjvvA3VYMgex0iQ9YyIbU/7olerBwez911h+j+vhfxNwIZPDKAMbUezC4xZLdNOEOWW1CjrEnUqYHUGbfAKuoV/hjg1v4kv5Kes2SQJ7dO+nBxZBz9Ys+R/zkPTZtN1NW+4WdwCT83xtPfKhx3w7VE7DXjkelR/BafoFjrLv3zH3Fn3GmOLS6hsmcWv+zG0Hz7K+IOriLCUwnrB+9QaGFLcFxbbu8ZgWFDL5a0bcoz9X3obC/k1d46hmpvojx1HZtnruJ8b3PONF/LPce31NZaML/rg3/Jv3+LAaL6mq+8jHlAztjlJPTMZ88DB0Z2HETu5uEEDa8hNsUG3YeW/FhuwtMoYzq1vs3S6lgUg4cS+aOKNvMciT7RiDHb2rPWpT0rlK6jbvYMzxRNajMLqN8VyHu79rwYF0Oz2Pa0fDiSMx3VWBHhy40DVdxu8Yxfah+Y9CCfzZ034DPnCCnLPqP+vC2elg1pOTyQXAsd9BJHoGkSxOABV3H+M4J1ZZ5s62CJbo4HbhPO4xluxbQh7emS7kr+rsaM0/2KYvpJ5JIzTZ3L6bmwL29q1ek5YBetiq/QPeYMvU+OJ7VhJeuVYgmd/JBpBh6cmR/OkSE7cen4BrUOYBBjT7d2H3nyJoiI9Yv5o9mJ9iYTsW2fz5aG9lzKOony/BOUFM0gcbgDPzrvpHziNm41W8HdUVPYf3s0J88mkpJxnWEOIcR555Fr5EsvmUnnCdOx8z9Co0e/6Gwdiqm6IdfMc7mWGkOT6KX8TDzBnnutmRuxmuJBD5hi+YHD778TNrEDuVrKmH9/z4K9B6FhOirnjtC0Jp8+7qNQLNrHCK29jGrSjT3ryth1/S+K+m1Rnr8M3UJIWJCM1TcH1PQ2EXfpDR0/TGHeM39OnM8gtEE+wxtr4KWsjW3nXPIevaXhoiXo2p1jwhN/shcU8PJLEtknAhgZOIT4C63YdEwJuWqMfkEfpu/2JHtWNO1+BDAtOheZ5oH9Sn/G2f3FYuFWLgaOJ2P6b4pK/tJctZBR7Qdh4zoIzT7P2RkWhmapD4/vu/Do9U36Dr7IgbLfRL4J4JxtBW27vCZ//kzabe3Jw2e+PFXrSPz0fmxJ+MnAzw1ZsDKV4nVpzDxsxdiaZQxu3Z7QaFMWLytlU/kSrgzQYODZ1rSdchnN0GCar33BbbdB3PuVQG7ObgZMiMLJaj1mHYP5EWnJZNsvRAAAIABJREFUualLGVTZhOfhi6n64oXPfhOOt0xmVG4PLMuW0t//JMNWvmPehZ50ud6NcQmhFHnt4HgLcx5nL2XBxccUqCcz1uUFc/4VAf//3gqICBrqHWWndXOJD3sk6SOmSZugEjHOyBQVsxNyLc9Lkh5+k40tIuTsm2jZXaUlW2/NF+NUU0mITZHYj3GyZ3aprH8XJ/1fpcn2yb3k+LiT0vvVF6nz+yWnks6Ku/Ynse/8XX4vtBDz82pSqblJkp5+Ef+3kXLU+7106jRKxqd8l8YK5uJjqC4/XvUQ34ht8jf1ncRcbCcHrRNl79fuEjFpkTR23SNualry93KyVGZZyGfDbpK18oS8Hxgi8zsskRbF0+SG61pZOa2rVE5OEo/V9vIq54w8f9dd8l7dFG33oeJycLjoKUeK0ehUCW2+WfrFdpeAitPiZaMt9gfz5Pybq1KZ7Sbf6i2ld+hlyWi5R+IWnxPj5u9k/lMFOVIYJ753IuVWdZokhR6QguRgaZq0Vb7EfBe9dTrSUfeZWNpmi4G/meyZUy4dfUfJGq2fMjXeQGrsbcXlwDuJVw6RmXuGycB+I8QktU4CGt+TSasnyMjKpvK87qZ4N1orXdwrxcJ8uIQ2vS+Vnk+la14LOfSyl4xW2CIZXXJkSNB2STl/W8403ifOaulyosNSmednLR2qbeWu4zvZ2PmQ1GhdlD1Hjourpb24BI6XSVrh8mWqjhw7fkzaPFOTOZ+bS6dzqRJuZSAzJ1yV6WHtxaDzb5kU2FP2O+tK025JMnq1ldR0DZbQnUekUfwNudZsttgohUl987ZyrtpIgqfdlcNx6+T+vnyxjLsmLa93lzaJ36RI8YtMXPZDLt4oEd1sHel5J00GGHwT1SdKkhX/Qgz26cmhIE2p0z4o5+65Ss3HQ2LVN1Lunlkp1n1zJa1njpz0HCe0L5foJnaifa+zRM+3kPn3G4v1+1vSKapE1LOeim3fhTKjap34NyoW7VGzxOH6WrH1KJK+SrPEs3WuVBbES937zlLe3Vd+GpiL+aQiuXtsrtx2cJd5bXzF0cdPTm+tlGqLxWKSHS21YZXS6v4kKfNNl2WXFcTQ5rXs+Ppb9owaLKc+KsvagEIZrnJB9uUPl6Gt9cT39HCJ+tBJIlV3y4Wc27Lxc3/Z8OGDmGS+kjODPsmvMS5id3PYv7fGYMvW6rIg9in2hzJIzG3JMcc/9D+9HMcETZqZHsd+ejQ6064xA2Vywtbz0sUK5aOuBBZ3xbn1ASZsqWZr19EMcD1E1eQ7zHNP53XdK5JvRjMnPpuRQ2ZRE53FovKZzHTsg1GbRJZHzeBmcBUX/57ir/cufLq35lTpIYLFi5o0WwYX67Du8VeOP+7M6aXPaWAzglaFxnSfsoVX9+F+0xkornlE38Wb6bSvgOd2TsQ57CNm0gh2r4njcL8rVJkl8G77eH7ohlHdUYfCadnseV3LsG83eLx7H++nxqEVMQhV63UohoRxzncDDcu7cbpbe9bdMAScKLnTnohmufh06UtRsC8Gbkn42qfzUK07Kyd8wPl9BM7WbuyPTeZii7PsS/3CrRG/abq4gsZKL9gbYoXnxVVUfL6JkfJ0hth4cPu1JTOzs1j42JtQsyvcu6XEjnm6bCh0o9qnFXO6GdG7+CRbt3ZD22wgtj0XsarNVLbeu8vokXGsrndCxzKU6DZVmDcfhpd/J4JG+eFWvItmp1biMuslTwb2IfrGVMqv/eXC2RoOtHuMdbfXvB5dwnhuUJa4hv79kln7tCGHFzen+Z2+GAxahZHNdv7enYha4W0+XlFh5/Z6lpYfRlOliniLMXQzU8TMNpfGgztxu1EIp/J+4HixBxd79eLwvBDyEhXwvtAO88yRDFUJ58WOj5S+reLRiCquvJjH0sO6+D0ZhtPSG+TNS+WxyV7qD3egXVJzjAcux6B9EhHlH1Gx3sTT009Zozafue9N+NY4mNWGc+i04ySmXapY8f0eOs/GEm03k09JKRgMceGv2gg6bFPGLcmJ2B0n2L6qgpDgdgxRHUxlejEPttdxtKYvBTqJTDmsz94Xv/iS/RiHyB8sm5FBSo+eKFZ/xL1BGZpVqoRzlAyTRAqXtyZ4020WfVvDSpNhvJ7tgH50Np1VYgmNf8gDm3SsHviRYfGS87qpuD2twdc6nsZuV3nfQ4WTfd3QqLtE68WN/n0diPSUG5DvkEWrI9F4K8SRXFjB6pQa2v1eyMff0YRO+kn9iSBcRi3H65EqMWMV+GnylnYxUezTSeVOxkcUs8/T5MsxVC+fpSRZmaZLnPkySxHXe6Yoj4tlWufRlGePo+eB8dyfnsq5hLPsa/GMEJ+V3OnnTlB3C3bMTWeKcTAFyV3p+DoL29uDebdjDp2/R1Bzdzn1n6ox2luGamRblJcqE3dSH7P5F7ian86R9HCMbLwZENiHr3FDGLT2HXpLmnFg8C4u9cjmWvNPFM89R58ENTYX1nBFaSDbpymxsJMvTwyriKkvITXhPJZeeqi2ecbAOBd0Px3AKKCe1GJznAc/5G6iN7OmJfLyWCyvSzzI/17Hg2Zm7DbWoKmWJZtuuHB7/U28DzqgvVKDJPNUNvdwp7SnItpPIzC+vRuvvZ5YmVkxuJUjKTo7yJ/YDM2Je6mrvc+IOT/Yfd6TN1sUGfj2MpGZHSj5tZKpFm2o+JVLaOJX0uyOce+ZGrccXlJjq8qs0l60Wx+DybgfDB77kcWKJty8mszORUs5t1uRgXeD+GvjRpnLQx5cbIpJrwwqmjwjqM98tOzXEKu9npi/+oyZcYou3nk072qDXqQlqq/P0vu0E8vGjaPN0Ef0HVrM3Ka36FA0AZWRI8nVaYJzxFgWn7iMs1U891a4M8qwGWtOm5M8ZzMeJmO59fg2sYt+ohS/kxsml1gcMAZrOU/nNfno9dLi1JR1XPo8mbcDXrPTNJgf8xcyUjea9RPbcSTUHdOZhlQ8LkFNp46hzdviWPUDrWgLUps0YMzG7lSqJDJsoj835jqTu7yYnprnKMv2wumMJ1MmJ/H0xxlKViewd9Q+/thY0VPzJv03zqONcQ43avfRUM+FdTP8YYkDa6p3UnZ4HBtyvnKtYDoKW1cz8cFoGk2Zz1FdQyr76pFTHUXyiZucsWqB8ofZbBlgTaX9VxbqLiXTcBzvjikz3L8L+3p15phrMAfmjWHn51bs7rWSD6Uf/yX//i2aQFGxIm8szhN2ajtvXvZnv3IGFSMW43ulhopEDY42bou/4VymZtnyzmgwCo8HUdYum0engkho74/ilV68XfkYnzVzaOK0gialXakztuX7rVksa3iSoTOLCTq1F/frp1G4VUnOt4do+a8m58h1+hp3Zr3GCbrfNMKqogXbjxUTufAuSqOnMHKfNWOnX6HPywc4Xi3i8eE8Eg8GsORoP/ruPkDXJd/J67iMl3o+9J5cxOnvNiwNHU7BzVjyuhcyfoQ1px5Wckj/MKs2HaBZmieTm30i9bsSy9sqof+2huHPenA7Koaal2t4GW7OqZ29uOJjTMSRj0x+0Y0Ga9OoGOdAiG4KL1Sd8a5ZQ33xMKIePWLFu+Y036FOTEwH8ptMg+vh3NvmS1j7+YSeMiXB5ir7zwUwVL8TyX9eUvNqEovW+TG7xXGUfp3npH0lqrb6nEiKImDvK/b+eUOzPeWsU2/DQ3U43fQqx61e0MBzDuse3aOiygxd58E8MqrDYq4ZB/XmsTaiPxeyFjFVw5RtPUbS7E4Ga5UWM3e7Ow8uRtLsUSGHjJXo+EiXfvM9OdOtDI+JabxrPpmsPdN5p+ZHn2l6WA5qiZHud45dWUZ132WUfv1Dl4ARNAkyZ2BgLZfTL7Ir4hjzLN+hlhuF+zs/0saUo6cyHNO9BvTMvMdwWz9yb3Ths4M3s4se88NuJXoZO/h93YMuhsaYOyxnrd4Q5moGsM7iIveP3uTDj0dsNP7AjghlLnlN4a3CeNa8VWBb0hP0VEKptdDl6ggd3tjPQHnhUmyGjeN6iQONm7fncAs7gp1VKB0WgL5BFv4Db3C0MJ7CnIZ8GD2cDkFTKIzpwWzrLgxrFsKtUf6M/n6WqNZf6LtvI/FRLbkfNI2erYtQbruIHREvmWHcggEtBvH3+Tsu31KmTac27PgwAQPlP3SPqaUuso4XwfqU71yLnncyVo+e8a3DQGLNf9BgXSq9T9zl0MbXmI5qgs3iKC61DeLj3P2kjtT5l/z7t2gCimrCxWxDeiRboKZowUa7Bawyvs3yRi25NOs661x92fw4hk89sqnp85IlpY2YnJXL4/jnVA71YeDnniirz2G/dxcsDjRn6KN5FEc25Pr3I5xwqebFnu5ssyzlVeJXlil+JyvqG96lm9HyH8aLS1toMWw2r8yXc/Jea7a+sGH86PvsjXzKgqFrSQpWY2SzNK7d683MnU9Y5hhGh0l/yDS7zIVYK7Y9GMaPoEyUB05l3O6/zL59EsUT5/hiZkh/26FEtj/LmQUpnLu3iuO3OhGu0oikmR/Z1yWSPs+acztSA7MOr3DolsZshw4Uzz2NdZex9Bq6nbzVjTALTOCBKOHX9xa3gtN426Uhg3sYUZLWg7zuEVzy70fy5icMrnLjifZJLn17jkLRaa7VfEZH24Iqrf0UfNBijE4T8uem0L7ZW4xMXrOg7gARG2OJaqlCjf0YzvZcje6+s1xstxvXkgeYd0vioFMyv862ZOmPJlx50QGfXatZIz95MHo2+qN207TyOiNnhuGsr8CkokHMNm9B/bx8OicKhwx/MkWxHyFey2hhmIptEzPGqUby7LARpSfXctLgHjP3xNL+xQHsVBy45OCCX4ou10qG8MjvGEpaC2gbrcumqQoEX2rA8LfKHDbYyPBAX3pld6Zw6jBa/SxFy3Av2evaofHUHfcdqqRHTePa+Pds2piAZkgF1Wu2ceFPEd9/DsfhvQ/jPBZRue4+w2cp4JnQgV2n9bg4xINufhd5PqEdi9VseVJgyszqRthsc+LFm9bM3DWPbpW1qL8ZwIF1z7HTesf5RovQMHiAf6efXM0YhJFXc1Q0ujJkeBzpXwPwMFfneLoFVumfiNbsjHn+TeyWevG4N3S8spTyVo9YOakE9XI/1vR+Rr6xEWd/mDNFW4Hbhm+I3FtFXrczdC5bz63YVJrM3MjJnR3w7jsHg7y/jLb4Qd48NQYtN2fqnkRmX3fHOfYDKx/YklL7jPHd7lLa8TDNF/7m1Vo/up1Ro5o9/5R//xZXhF+bN8QnsYg+swYSqTWICZ1/cXmqA3VPLOnwYz1tXM+wdOl0/McbMjYmmuyez6np481y/4FcuqyHa2cf3EcUY6W3EOUEA9wW9GHYp6f8XvIQv8UptNJ04X3yLx5tqGTZjXasjBvNu6Bqvqe/I9F4JE2TR2L4M4XSaj1GfdjN1mUzMcs+S0LhJ54vq2ZFSgAdYvxp2nkXrf6U0iq4N+ctohm+yYzXh2bRNyKYL6uO8E6lmHlPp9HggAPxQ7YzJlSPi720GfxeDR91J3pk23Kv3zvyfdfjPfYOVywb8evbYs7aTyTYupSbSd48SXGku6Ux1pnlnHt4nr991tGzbg41G9dx8WQpZcss6KZ1la7mAXx3imX21EwytLZxKO4hXS524clmD65GeZB6wJcRC1WZ0ceP2JJvdBq9hRmJwRjdPk5uzRiMAu+xsHkuWmu/sqTFPvxTBrFL35ykTeswmqRP7qlZXFg/ipjvqlyyP0iW/S6mfq/CJm0WjXNOMrhRPU+mqHFubjk+IybRr7g77/OyMXBsSH9zDS7axVC76y3rirUJ39mZB/2mEnHzK5OGlqJd1pBDStcwrx7K4+Ed2PKhBpehXzGctYq76wYQnlBG3XZVHq6yIzgjl+Fbk8lQSuBYxB+80jXp+E0XAx9//K/7Uv/ShyL7BnRW82LawC+8i/9E4V0zku478mT0UGoP9ebjsANsC0ygm9cXzH+aI299udqhnADtzYStSEYjL5T+RYcJjZnIhVADep3RoX3qNL7vUcEvOoTQiXYcuJnIq4tRuDTdhHZJGw4OeUi25zAmTvtCVsk5LMwCeOrakSWKsRhG1NLr51VOK+VidMWJeU2vYLVuMhmnWxD5y5nqXzNZW7mUOV5f8RjrzchN6Xi6TufC3O28iLyGX8gnuoQYo9X8IEFplRwbPZkXbXSZ0PkSlr3f0dY+hTW7XxERZMnLC5G4P87j3a+u1IRs5EhiGxaFtOZn3jr2TK/C/VY0/som6Hrt+pf8+7doAqbqDXAveUzNq3z6pXdEqZsd2ha7adS2OQnenzjzyJr95t3Zna2ETaumaL/7QLPeqiS328SN/XvomXCQ7d8msyVYH3XzbIa93cCm92HcUqthddloQiJ2MUxpH3/yInGwacqFyfDx2ANm6sUS8uMkZw4fp/Oju9y6v5HFbRty+3M0WQsekXhFDZ3lY3GfFcPn9GHsWreCnx/bYadxhMWjltMgLAX1kxUk376N/aNBZF4yY9HV66Rd+0TfnQe4ozEf07yv7NVaRwu/QALHVGHx0QKFASp02zCS+haxbD9tTfSv/rR88pM8mwHE2WexqWUinQ5OYtL7JvSzncTklYM4oq5KcakST0vHUnAihytb1BjxK4UbTpq0WV3BhMWHaTjAg45OS/irvYRjhQFsKqjE6MxfJOMSq56rsLeHEW/dQylqmMHir+M5ZGKHit02yr9vpPxdK7pdGIrVynNc2FHCrR92HP2zFHtLI+bcOUT1nTto5oyj/EpXntlk0Km/Oi8iLaj7uJrjHltZNK+KZn93cDW1Ed1rRpFxxowulVEkz/3A2Ia76HremSNdU1G23kBVUGfm+D8hL+IXz/+25KHGPHp6XCEmpAc6fmpcNy5n95P2eHob4FV8gIuJlqxU+4DJbAsmDT7J/b+mdPIKYIDHLnbcDsMuZwwPBo+hza7P7MuJICHaFn9nFdY9+0GiTxqehbtJvWLFsRqhOK0D/o8XkGc/BOUjenTwdKCh/WFcFw2mxYYTpNTlM2fxNrQfNGeS6z66Dq7kmt9QDJbup7L2OYPmDiLMOIdq9SgULw3m6LwBbHFR5FbDHOLN1ElqPhun3BIefjAk9Nh7zKZ0pvP7HArnPaREr5zQEX5cKftKV9tSxjfJZuORN4RdH8gvLT0a9rzC49fjWR8/GY/ll2kUtItzo8JwXqeD++KuuFu8RTHXgg0Hq5nid4OETz1oZb4R1wIfuicaU+d1i66n7Lgx6AWOrzwYu2oB72JcuTPrEufvBPxL/v1bbAee1X1iW9ZmtOIsUbibjUK3NbRTWon329GcbvKSrA39KVs0gwuxLZnY5SlVGXOI2HaGAs1RHA+0xFvlMt8O5VP65SH7HKdROqOImPegeHwNX/4UsOG4OSf96nBPimCQZzI9Yw+QXL2J8KeB7B4zip7ZdbxsU0jyfQOUrx/mhaone54Kx7opMmx7Ha+PHsFwxCj+nDhETb4CqbP7kxCvwcy4+RjFldG9bif152ZySbMRb5XvYuKwlwYt4mkjYWh9vMCB1x4Mil7KKLX+dGhiw8eKGIprw4g5NYsg56PM7n6P9e1+M0DrDtdP9yZ8zmw6+68i/HgJU5J3crm4H41OdKWuKpKbm1piuWEX9x2+otgmA62YT8QpHia4ahz/F3NvHhTy//V/P9qLFElpQalsIUUlyp5KqKxZs5VKkT37GtllyRbZt0JCQooWWyG7oogSSqQiLc79x/W577lm7uv7u6/7uu/5zffMnHm95syZOX+d57zPOe/X85S91qIqfBbXF88gucdwdk96S5lmEscGeVK2cRM252p4UhvBuTM5ND1QRPEeXXJT4OceHdILQmlYY0bQoIl4dash9b4jHeea0rljN84WrWRfp0I6xPck6WQvzjQ9hO9tH7KsImhy1Is2oZ+YN/ghlUvSMV82ieFdutAvQpGhf79iu8aH+mBlni8uIzu6mDnq28kzKSdh7ihMXHP4PNQVu2MVdMvwovdZXzQDKkmbdwz9mHMYbX5NpM8gUp9Uc/XSUmr85xL24DkdXsxGbbUO94O2oVY/Cpc8J7pVJHDU5hEjB1xnvoIJpsWDeau2n6BvZTjOvsbi5w6cvX4OV9diMhq+J/2lHiNW2JBw1Jjhf+ZQ97kTivMVudpqFgO3V9N7zAyO1dXgbqqJWr0BYwYsxVNpDZbPYeyDaB6088W44jK/BkezKegBqi2daWHUjc1l72hVMwhFk02MbDQRzd5LMAzeykrXj9xJKCHmXiyvTEYiOrcZucYKLZuWnHLyxmDyPNZ/PMjDqTOZOFUTl8Jkdq50pGfcK3p4PKbOxZN7Wk2YPX89Xken0EtHuGeeytKsJaxtGYT7YXcClk+iU8QVll1YRsShKkIbl9Kr4juDu3Rm5ZJMOP9f59+/xYhQUa2djOqcTHWgMTZND2J02x2bXROZNeYJRcqKdO8ymvTvzxjWfTCfrmezpfce2sSVcenmd46qniNn1yyGTn1FiL8LhU3T2ThrKQfNmpN1S5sjUkGCSzVLvs5l//xBlO4sY0B4IWUKjpRZnuROcBDRE7LQ25NNtf1Iarub0dboFllj/Ln8fSGz/YfjVrSUbq0XIitPkDM7mpV6E1hxLAOvn8/YGzaRbd69mFx3kIcOY2l++isXtJy5e6YUOxtlqkfPptinOZ81+/JEvJmgo8WFcgdumdvTtelAVp05TFetYgq2lqN/qDW30g9TnnaG2QF76Ty6A60uJ3J842Z+D7Gjd1wpE4Ku8DhGnbDtN9FVUWL8gCcM1ymmp/so/vwxJDo0Hz/neax/c5I1B7vw830uDW8MZZvWbF7GBqAR2Zbpj7U49+A8jZf8wdVzGCVfrrE/RJWo7wa0WF9BzvrrTDnWgKMtwtm1uyUab/rT3+42wS89eWPtTZviWBQNdBmm5s6snV1pv28tn2+MZ3lGI05+rqG2woairm14cDSN5569MP1izp7RCnRb3psi7TCaHO/LgNSXPNmthmbjBpz130ls6Doe7znEALW3VBk048uzkXSx0eV9eChTrarpk+fHD38PmozZzqiWrrx7uB6ttCTCi6vpG2mBldJbyjV6MundPS717kOzX8m8/DMJ+8bj2PP0CRVdJ9AjczqtTB3YftyUdrNUcX+yA7NTYzgXXEdvG1vKmnXnqKMTRp4esLwvXnujaKDYgWXNUxivfo33eSYMdp2PdcAerrpFoPfuE3lmMXT+1oRHZ6K5+PoBgz7HM/XUJOIWOvL+cwiVI/X42q6aZ9cEIwN3PI4V0VXvC+9mL+Heowg2FQ/irPERRj58zrnep7mV7UP2+9esaHqIlCE9Wb9zBYqhZwifX4dnkR2TctrjczuAxhcecP+7Gku2bGZK4WIsL2/H46Y/804V823tWJKGNCPU3oFq6948GrCKbVnnaLu1778v0WjXRqpiHR5AYBtFNobfxuSWGi+9k7HrP4CDQeZ8yB3GuoP7CBivwNS0wcTuXcv6msa0sL3NpWvOfDq0iS7Oxzm0sALpZsFY69dcs1XEo5snN4YcZY5bHr1/r+HoliQuNPNjvsp+tncIZldcMpei7DneNZDX7e6zrO1aPKrKSQmN5XJtEOveWaA16QoJBe04cDCQS037kKIUQ5BHDvunv0db4z1uLvtoqd+CafXGBJ94x+iAXviGGOMW15/UDyWkZy8n5PBBLJxL+bpjP1bt99NkyXUGOpWy/d4upn7TZ5prKsMNdpL1sgvlf8+h0GIEehYHURt1nLa+yjwwm8qjTcf51SKOkpQsXvTrxU/XnoT1Wc1xA110VJx5270Bf+PTMLvhgWPRTLyG6hLjVUdgbg3BO5LoWweGg29wq1casWKOw7D5mJz25o19N96cbYvt2LY0aBvBJo1BqLrVoDSlOQrpJqgMK6JZUGOGDWvKfa8qpsdqsfOgD1zrT5/qd8TLcizvw7jndmxJqac8opzZY4y5dGEHswt6UNR+F/Mar2VvfDZv6m7itVaFQ6EN2PDiGg8tduKd9Jk4rdOo5bzEwHcdZtuHonIum+oVvzizegzXW+sS3+IyKy/d5KXRWPxXneWXcxvicvpisn06E/4aEVlwCYPMUKyG7KPUsJJRB/fTO2Y5F5amMaxtJ0arWpJx6xeTnxZzzs4JnyY+nKiPpeeDYyzzf8bkujIcj3VjWo0/Bl+iCAlNoUVFCX+21nNHcT+2W/U4UR1N+2MjyPuciKKPAgf93lGROoTLxvpM0fVh1PPjVBX3wURlFJHOS9EOecKQr5ZEt8mnVXU696p6cue6Ke45mRiJIhXHTtPt52hcFR0pfLsLn/LFaG0q5c7QWlpHbaHk6AyaFETgmGiNVdJ59PS1KdCJInRuOEd/PkOvcCtJadepe7QDh/09aXIzh5+Jw4noOpwn2QeRuAOcGdIZ/WOHKZ1ZSbfJJ1FfM+bfFwTUVJvJrfIjfN4bh2+MLu+qfAlOriRV5S1htUZ8yDvADLe7zD36h6sNu9C3WTyH7iny3MmfTo03Me/Oe1Z7pNFIZw+ZJmMxqnOmQ+kDys7mYN/MHbvFlzhg8YIpuyx5NK45YzL64Bg+Hpc9rrSYZUCGyUOWDntBjsNoms8aQv58Q9KUVmF4sg2et68RuPogJ8/0pEXndFYofiFi4gVuj+rNcNMLFJp7M+nTb149WEWMz2zu+k0huqMeBh1i8DV5idNqFQbkz8TOdgMP0o1wbNOYoMhiZo4OY+uzenod6Yda8FySH6VyZFwSvzIT2DSwkiDzq4R1LuKTaiwVjxYz3WYGIWetcCwyoeHb5VT/ribAqiGdps6iNvoUvxZe4mrlUzJKLpL/yIcVIbNYoFWC3fHR3G04m0mN/nK2zWGaNFrN35fvmGqRwdfaVTzwrSZV7zL+9h1Zds2GVm91sG3jiZeKYL1xOaO3pLD5xmUsgp9grHuLZoMecG31WaYv7oOymwEC4kl5AAAgAElEQVRm923ZVdSd/u8mcS52KhP6/uDlmndsSvzNn7iR9G5WjPnpIurWdyZfI4wOUxNobp7AueVVWL7pQrNns7itP49LS/qhVWCHlaYq9fdvsWxxPTrjnvHoQyWBg8N5WjON08r3cdtugE+fGBat8eBhlQZyPgdL3/vc/HSYe71a4NV0IJ6NzGnd8w72fd+Sotqb5HmdUPGz4uL8Hnx81RO1LVuIP3yYqZ1y+fluKb6jxvHdYixaMY/wvmpK5td42pk35FOP9qie+UCdkbBR4zjaM3W5MnwSncb0o8Rfl8zqSKKNw7nhNJJTRzwYbW1DzZNKomrv0+LASf6eXIGm1VMuXFnNzy5PqX54BYNl3zD48YPFB/pg566Nz73tHO3xgISPgxjo9Yc+n034mONBav0FFBo50ejcQra1sGDyS8H36yCuVi2kkXlDNO8UYvrHghMTGpDasCdKTlY4jtZjzksvbjv1otlzKxpu345h5lhubp5DrEo39Da1+vcFAdOOVtK25zYG6FQRsiuO/oqzuLftHG2jUgjN0+VIh+lY//ZB98QRll29zqqlX1GsySLngCZqxS1xHufLsq/eTGvSG7p8JsD/I3/aBZN4/DjD9/zlUYA6Dc+YknbSj8dzHVnzqRKjwmRM9d3RdhxIUq0pK4e+5LHGXd682YRzXg0hua1QUt1Ei+gn1GfosGdPIEfrlrA0bT+2viH8+nOVm/V/uJvyifpRJVxpasOhyqukL31Iwrt+zB08n2nNL+O3/zVqU77x6PInesbmkNZxJ5eqgnhT0Y/YBqX8/qjJl6ALTFjSAOUHa2l9ZyoXB+1H8WccMU/WscezjiVWoeyKHcH1Ufv4e/wsN27dZZ/PMD60jeX6nCBuDo6lIPUMI+f4cEX+sh1lurYvonHALV59O0L8qR5En47C68lg0pVScLh3k2Mmqzk7sR2f318l0XUPsz/uQut5FcFTWjDb7TJjPcZw6MVIpg9S4rzBLrIiZ/NzsiqpCim0ahTHkoQxbFUeS6eQC7SI7EarbZWsKp7KRZ0kNl5Zy/jGDRk8+BBT89swJ7GQc+rKNK+fTOb1XAKWBtPnijPt01vheFaVxh3c2PZnIu9iq+FbATEG2zG3X4vP9gNsW2jP0NqPVL56wzALM5QzvjDOfhnPVN2IKNzOnTQl3mT1oKmpCukfuvKtLIusq66kdTXDeVotK10fUF79F/ffGaQ8f8mLqbuwahlHpmE+GiOXcW7da/aUviVRqSsFN/tz6HISpxroof3tA+HNfOk2t4Ye85vQ9NImlnT4xdXNfTmpXUXbrkdptWwBC04ZcWdAJEmnG9Fs7EC6DxlD392V3Ninh1H2JQwv65CzyJo/3f+ivO8gX8u78LujBh0/ryA5rwy9HkdQHSk8cTLD9+glrnsvpCj7Jx7bbWmlksqAdQ2oLu5CsnsMF11bY9FAE7M7T/ji+w4H7Ta4JibhFfGBSsUP6F3qxYCP3XAtsMV1+geUex9hc3wwTzcGsju2IyWfD/77gkBnPT1pN1WX+TEltJtzmbHeu7gRvJpDh9vSC3XuVehRMm0vr65dwGzdCf5oXORqp+7YNjVG/4AqT0oLeJk5mn1RaWitq6LMqJKOKcbY9vfnyiofjL1nss1JnTXJuzGqm8pFmy5Ury0m5kc93e+m0SDfEdPEQlb+EU5d8CM/7QlOtg6cqldiyFNlOm7cSNDdJgQnW/Kq50I62A5jcc14rB3V+FB9hmaxlsRfuc/mxr0ZnnaURZptMHrXiVdmlUw0LiPksgtBazLRzN/J8j4jybh/hx8rO7HC8C5Twp6ieGocqwL24ZQ0mRnNDvDq8z4GF/hiFjCbtRW25HqHkX94Ko3CQjBb6Ynp8jAalKjyoP9BPo7uiX7r/iQuuMOz8J80OHwQ5T+ORLsXcvn6XMr8/ZntaMifCg9a7anCfIs2+RrveTI7hVsLGrNxoS/f23cjM9uT/CbXeZN/k8GPH9CvsifhLnvIdx9Go+NKnBinTt6GDF6amHPOoy36IwdzaJEOLSdPJn2eEjMCFxIYcw4fu4bsbr+QeYq6VJxLYWpDHcK9LfgSP4uja5xJ+rUB7ZLLBGuPQL3bMnaY1qC8ozNvTTvR0N+N0JbBXNyrz1iFxfzO64X6KltSX8bQ43hTNvUxwLBpLx5P9yHv8QkKOy4if6QZ1qVLqd4wkOc7q7HZGkW61w3CvD1pENAM3Yy3bHP157t1MVhe4Ea/+dy/PYNZmRXcOw+RTXpgctsPpYGn2Tgqk4Y/hC+L+7BpmzIXvxaSaJ5FwuBT/C5XZl3UQJZWBuBSrM/I6eokhUTQMPkHndutY2fHMJakWbLRciDH2ugzZbYDqlPVUZ1TyflW+/l76Cg7c6L5vrYRCjM2snVZFS8WW9GwoJAE11Q6/7RhReUKRuo8J9ExEA2jIE5/a8qcIRdRC2nAyhMDGPyjCBMNVbxn9WD72md8iLzOAC9XcgynM2pHGz5m9+Oo1St8dMtRKc2jKm0ciw7VcDbDnFlZ+ZR8b/I/AwEFBYUW/AfdeHPgL7BfRCIUFBR0gDP8B/XYe2CUiHz/h4E4AhgE/AImicij/1UMHT0LMfU35U5ZB2YeX8CZvcbs982j3et5zDTsw80+o7he0JvRoyeRc/wj06fPJXfiFXKv1PC11XdGGhVjeKaQwMFrufvCFOUeLWh6tIC5R29jcHEswz1cUJszAsU1bdAbXk6u0y5sHtxgvrUd5Zf/4laszrQIU6asdyY4OxqcV9Hi7DuOn+7AhbDDODvM5syXObjF9Kf2x2uWW6zhi4cOiUUWfOqRh92YAm48HU6jeZWEjlHmRuRmztkr0b3dG3KLbmOh60erPeZMjd7J6XGVOI2v46XqaTbvM+Xj0gPETffj1I1A/NSMiNZRxt26HO9+HaHBVao3pqFX3IKIt2dI0+qI5tVZlIYYYyPzSH0bRzvTGN6GZJB0bCZnkuypHOTLrbok9pl+JO73JmKfaxFi9YlVKja83jGB1yF/2TroHBaTb9CjeTc+Ju1lzfI93GomeLrGUqo9k0b+d9l2YhomBS0oX5TJflNFJg7qSGzSQL6rGmFjsoMO5SEsDxjH2oJ+uAzcTLHpWC5VXcI/KJX8wb1Rth1BF/8N9Nh/h+Va17hueZxeU9/SW7Oa826bcN6wkPF7xzHJOJVq5y9sXdGb2ZUlVPlMIt19Girmd4k1XI5PrRubHXfS/3cA82zfUBHeFKeTIXTf4c/VXusId23EnJV76P/TlMJRvamdsZqIkK0orS1Dv2cKiXkBrLfZw2v701xqbMOwvS3pW/+EwoFRLDyTRfdeDnh0HYP2snkU+/Xl4oNtLJ/ajYDm/VGJs2fQwW2MLi9jWaRw5PUunD8UsV0riFaPG6PVZAAXzSyJarSJ3qm+bPmmTeP1/chs+o2KeUbERU3BSxbR6mQtDur3mEccMd2tMPBYxEfrat7ZVbHNI5STBneZX9yQ9P75vIo8yF3T3fxo04rB6Xsp21iOpb8X29tuJenaOnQya/m0+RfKGufpqjyFFD09bIy12JTwmeZPg7nfRY8zW/eSnXkEq9AwrrZaSO3eVIru+vHre/z/+O1AHTBXRB4pKCg0Ah4qKCjcACYBN0UkXEFBIRQIBRYCboDFP2oP7Pnn/Jfyt/YHiRq+fH+6inbO8/BcPY6Bm+ZyNsILe78oIlwcOXL3MqY5ybR55sjSNzfwDT9Lxoo5fHrrycRTm7CyH4Fjsxd87tUNxY7J1JXe4v78Pwzcs5Yl6wxJLhmN58XnKI8q48c5bxzyAli9qwZPp8UYWKSQljEHm5sHyNzxm6IbV2gWO4f1Dlc48rUdB6Yacnx9GNnHHBkx7hkDRq5k1nZtNBKzqWrfDw1bX1SzuxPmeZPyCwF8ObWIPhYRLHg0kcuLlem9xJvx8z9QvDmL9OfH2bH9K73Kozmm15AHa5/RJ7iOn19riHEpIeVHex5O/4WhgjFN6kdyrUUMlQ5v6a7oxbdnR1jR/CSGP47RZ8hqXmpWMOPLKb74DuZdEz9clw3D/IUGs0714JqaIcNvNedXnSsN6vToWb+HkPMdabJjGh/u5JKUYMDt9+8IXXmH243qOHe+HL+7B1jUvAH79c3R/NqVGVEdie8zhCvLOhPdwQvHiRas9SlnTqfb+G/qgVvzNzTPjeF2xGBaNpjL6jvaVLs9JMTZmLna7Rhx/yl3G11kw5xrrC06QE7ILT7cM+bhhGPs1vOk6xNlajruRbU2n5kRmowrWsbd3s14G9aUwI9bcR63mciBHYjXUOJzrjsJ+xRZdH4kbtfHcayqF1XBM7hxvZKvn97gGzqHHwvsMTfYQaZeEzQW3+LJ6IZcaqtHbp4ni9Qm8c45nZ8th2Ce58f+Dw05nz+U9qMnM2X2CX7dX8GA9xeJ73gFnVdzefB8DR3ctxE5cg1e0/5yuLYnuQMvUHEnlLJ7S/kkmxnt1YO6fvpElU3l9/sqBpp1Z+NUF46V9aaFbgBd32vycetJwo//ZFBqayyKi+mcv4PfPZuxYGsHql8YYD3CnazbcZw0rSE67wqmUz1Z9kOHIVkFlCetxbrTS4rj0iieeY+a0ouYFI4lMGcUaTs38jshDKpdeO0QjtOuA0zYncmMtE28Oh5Hn19voaSedpfUmVlgw6zem3gSF/9fJ+D/22e/wEXAmf/YMmTwj80AyPnnvg8Y85/8/y+/f6Vd2luK48srUjrUV0q7nZbIKc6iNO+3TB2vIPd2Ilk9lsunoP4S9uONjHHtJDr9lsqobFsxd2siRerqsm/vFmmcZCDNXR3konKMFL6bJZX7CkTD6Krs3vpOJt5/LJf6+su+VntkZkGK3Ot4SHSnrROT/SflWGMNqdS2kRXtqyTIxVUWfd4pXdZlScvcBxJ/ylycMtdLu5Yl8v5gU7ncUEXU3+nKt65jhZ9HpahvjeyuKxG9ZmXy1OidWKeYyM4zyfJp3B854TRIfq5cIH03d5R1XhXyLqxW9E3aSXjWNglZc1IqglfInQlBsjW3v4z+9Vo+eq6XOUFLJTFnsbR47y7D5ziLadY8mXQ0WRoZmol7vovMTHorY8Yckcnf50mdd6IUhXwVw6TGor7WUAIHZIn1Nw2Z2/KthNXukxlpheI8Nl12Pp4s5058Fp2mTaRZsYo8tCqSYp+HUrhwkwz+MEMelB2UV7/iZHd5aym0PCcvh48Q40R9qbOIFvdfb8TbYZt4Zu2R7L47xKhxipxc/UkWbZwqrwL7yjbjA1JScFP2LM8Vq99vRft0gSxclC6z22mJU1qtPB69QLTra6Wx5njpZJYqg5ZoSe7RhrJhd75MvB4hVTUVsrHPSOk4oJ9YeyCrh08Ty36ZEtm2u/RwzZCvCxxk8K5IOdXgiPht/yp737WSvRavZZlGoTT67CaDfx0Xt+cb5crbEnnQf43c2VUuy191kBtXRsiH+rZyOtRS7OffE4UnTyVA96T4n3kor7b0l9VXe0vwltvSI2yf+I35Ix4/M+TdzkL5rh8lnZf4i+V0I9mwKUCiFS5I3snf0nizufiOPiRe5g/k95MqcWl0TE5UBckn09XSP6Gj3DbfL6MWzpIHLbMkYXZjafZnnbQ0vS17GypI/ul1EnzFXkZWuEnDllHiprpbNG4/kEunCsUvMEDyzmiJks1QWZk/TZ6snyAhV7TFIHu+fOyTI62ru0v0qyw5echW+gTliXvyDFGvmyGek9dJ9K8wWfNol5hHaMiwPQXyakF/6RG8Xp4rTxIb5+fCmUrpGeku3u2eSPRXfdl+U/n/H2ahfwhHrYH7gL6IFP8DJMXA//lCwQj4z/zGhf/Y/qWUvSlh5kdFhnqtI2b7Oia2zsJbwsDLGb2zb8mdXs8qkzm8D72JccoZals+4v6wL1hUnKWVy21MHaPYcXw4i2+248/deFS6PGaY+T22Nn9I2cA2uHkVEq/5iKjZmZhOPUwXTw/+jLrAnAcraJK6hGP7K7Ef0oKHKxQw/xtMpyvm9Hq0Cvsd2cRr9mRFUDpTkh0Ze7qeLVkO6O7qic5mL9L27SbWdSENPA+j7vuYl+GKDA925PD0BO7n6dFszgys1cqZ/vAVKxc402d5D45dPYxq30pu2Zei5bYCw+N+LNxoz3v7BLyfjufYrJ0MCFPmbJkNntbbafu5FO1G57FvsoKGwRZkG8ZwZks4345M5MP7YHJut2b2znhWJ0Qyb747nRZOZ/P+GySF3WbzIHv8XsxEe+N9Pi1cygJbC+bO/kHtDmHGtlVMqR4Nirux+nqD5Nl7CMj24OCL97z7EMW+TTfZ30eZwMAIGo9LZlu3I7hPbIC1SiW+GROIHX8T1W5D0biTRN23Eqyqa8hJ+4Nm+nlOHthHqP4WumsacKWlOg7xhriMEDaPbY1H7mVOJNfypKM92ZZgN3AYf1oVsrvjBepuZ3PkeymWIyKI/LGX7Kuj0FyvQN06M9Zmt+ZG2CLMPx3G/vUP8stC2bPxDDWp8eQfcketxhGb+ZUoXdHg/kxb+sfd5FvRRjS/f+d544H4hY5j2MudvC90pd+kJ6TYV+CZlsE0484YDFXlm9FBggd+p49bJs2sMgkJ/k7XZ74cuNuL0xpeRFZ/p85xMEnRDqxUXsKFXBNqO45l/5uRcPMY+7zCGZbRjvrAePTCwskbb08T5wJGD3HgXjtlDiYOofhqZ5KO/qZEaxpXdupxs6qI2V/1aTMwmZBHq/Ed7kD2fA+yTDozPcuUiz7JqE+PY8QzFZp6qBP37i+vflzkan17rK8Wczv8K24W6pRMs8Ci2zBCp8XwJ/sminZPUNJw4t3YA3TiM/2ux/Ik1Olf5t9/GwQUFBQ0gXNAiIj8/F+5/he2/1vj4T/vHSivr6Cdfw8CnhwltmMkM5fmkuy0i0aWa2jbcCjzOnpg/DeaF3Z7aDWkFY+blxE+/woPN/gSnuGPyqbpeNXXc+ZkX3bcLaarRU/Mh6zl4dAL+Dq1p1P7EAru/8CkRwx/NtwgPGE5KYVl/DF6QdixaWjd/oFRfC6OiwuZc8KOpkGzafRIjR7W09A1yebOr2+k/ormx6RZ9N+/iYOv5jEtTpl54dq8f7+en2lODJy7grfympA+8ZRcWUTxsfV8epOGfuFnqtIPsqbdGZ6MnEjrujv0fnWIvupxnHcbyawBX2kYe5KIR+34u7uEheYW3Ntgybc4Jf5mO1KqZk50gRrB2rrMMVZE0WQ1xs8vkGEXjKLaJob4mFDYrACH7HDOHTrKnX66PK84Sd3QEN57vaF562pGqC/E4XoMCz0CsFv1hRe6ffBqPZFVfkdIWG/KBZso/PzW0tnsPLo72+HYPI6X7cfz1C2Cr6PPcf9oIBdd3Bn6ZxL1SSfYb6dJcsQtsg3XMnLmOc4n6PDwaRDTbP2J8t3Bp/TxWIZ9wCUvjCNvE/nW5DpDXmnSo08R6x7cQqVTOU3XdEIxeBmaY/yx0j/C3wH3uB87ifD6KPx+9Sc+/y7N1OJZWrOe6JrjDJ6QR9/PY3CwG43R0zOoJ82kl68W1vqneHv1BnvCZxA+/B6fLN8w4ucRZruUkZmRyHXrwww+sRcNuyC2evXF7csCnIsP0z+xHZ5fR+Gv9gO3RhaMdXzG2au6KN2eyv577pzY34Z0z2pKtDbievg6yxQvkbnFg4TsWNQGzWP+YzNWtrHgSwd9rvavY7v5GhSuD+FXlh23jmygk1YPTjcfiM/PvZRGz+PZ9QJGzupEafIw9i1y4fms1SiWduDaKRsmz3jBgE3HubPDl5eL4LFzKYc8k2mln8GEdkHE9b9G9uuR6O7aykrVoxRp9SV3ni/Nf80nfek1QiqbkBTXBuMnnXiReZ/vD08yztOc2OMrefChjkZtXdmRf/xfZ+x/swRQAa4Bc/6rz3z+P5YDmipWcvv+aVnc3kcofys7TgwR/xaZErOosZS8c5bGre7I/fc/JeBAgpjFjZHnthGyLemM/Erxkdo5jyR2YBfpGlAvenYfxWXBDbk1t16M200Wm975kt/1gihmeEqW9ylpml4vlmtD5PLaQrmV0kNSA5fL5IybMrzjQRlk6isn1RRkbFQbqdd2k/L746X8dJ68njZfHFtvkxHlmZI0q51szzIVraAIOR3VWvp7fZDZVlska8MSCYnWl1CdgTI1yk8aaMfLN+UBYpSiJx1/WsuS33Xyufk1KTlcIu1rt8rxCUvk8BdLCfBuKCt63ZMlPwbLpB6r5PbxoTJJ65S0ntxJJrVXljm+1yTG/4mcHFUsc/cHi2Gv9tK5wELOGmSK9ffv8vjDWHnacai43wiQ1VbuUpq5VPbfUpQLTkuk8o+XnE+tkke338k2n1+SerhQ1u/fIEd2LJKuf/pJomqSlCdEi9neM9Ju/A5R6dJamjnuFCevcik6O12czg4U8wsFMsRJVzIadJLQhc/l2ajmMtDhsIStnib94s1Fu1ei3PGeL2Fr2ouGZpxsKOoi3hm6onNru9T+9pRhkwbKuZxHcuFLc1lw+orsSdsr7dbflm8+YyT2T6TM+PlXpqyeJX9mFkpDfWspKG0ij3N2yDvNczLR0FA6XNKXrh+OStnwsdIgUFGaLG4hUdd/yLrSEvGeUSGXVo2TpgHVojjOUJ7VzpecIwGy8PEBMf1YJNFnBkuB60UZp3FcspVXyoAlxdI2savkajjI4BFKcsDul1ga75QrP4IlZ3Zredu1t+wes1DynGfItmHbZA2bJcDstSQ7fpRdvzaKyboqaZZsKoO/Zklk9Hxp1+SEXB66UrK3FInumJsSkLlavNVnyULX9XJOpZEsaKsrB3VniFF+a/nY/qGMOWQsCYmnpf0yNxn/YLaMjvKSww3aypvN3cTbMl36rX8qITbaMqB2krgv7i43CoPkdtk92RFWKzraV8XSKEJ87WzF2HSOFHXtJ0vrFomHqaasXnpN4nObyY3tO+TwuiZyuVxD6n//kaXeyOmBpfJYcZG8LK+QY15N/ufMQv90+4/wH/sEQv6TfRPw7T81BnVEZIGCgoI7EMR/TAfsgR0iYve/imGhoCotPjxFc0YhAzKe0sLoECd7BTHjTD5N1n/H62ZzbE0+cDt7NHG97qK5UZtc4y4YntQj4KAvSn9XcKrwIH+9oO/jdqS5pDDwUzi5PiNo9nEKM16FsiDYkTHbTzOz5yva9ClEb7wrOarOYHeDgBAd7uzxZ/I0W3Zn/+VE7DfuWkzmxvA8pvkacXPDFe6s0iN96E+ULk/keOhCWs6yRqVBNatj3jBQ/yGljb+xL3k8H17d55OSBROXJGC1JhdNj424GH2g4fgujN9gj51CAH1fRjDProyn4YW0nniZblG29G95APuZWzEfFEijTx8JSHBj4Zl5FNs9ovT3Joo+uXKx3Shcji3mVFB/3nZZiYr5KHoanWLBos2o7RlMceANBphMx648m7NeDvypGULkG32Ctqhg4N4PswtTaLiuG9V72lMltnypbUon5Qt499dnz9NA7COesHzhbL43Wsf6ijLqjRy4MOwCexT8mTP3Ierb7vD3RTyqLtqYtJhF2MXdKKfP53JxKLe9d7FiyzUO7prGzUePuXZ6Hg4yGYvyGVjqZeIyfh2Lm+YTqZCGVWFjet5qhrrTO/5kmKDk0ouFwVd54DKOgwrfsT7sSo5ua447XiZ4vT6P1rSkzOoId0eXccLiGp0Uj7CuySKGNB9ETOheJuo/pt+vKvS0p/HotAZDUtK43MqYXYujSNR5we8B86h4tguNwPv8fuHAnqidnFvSDq3wAgbhRmhaLD4rv1L5oBl3Ry9l0E1nuvxw58yWH0wzqqdvkh2/F07nlk0wCX9cMHT/TrvXalit9WPXgihcwqpQrVOn1cFHPM5rinKPsxgW9uaMtSZxnT/wsnQ5Ft8P8bY+F63lK6m5uBZ/TwuycrTQbH+eJ+reZOePxelwQ4buc2Wp8wZ0znaj8kAZF2YpEN//BjbVMVicOM22Cckc3l9DH921dLE5heKuYuzbFPFicRRKhuN4fsYDB3tNXD8PQT9/GeFHejDkxwHm943j6UK3/3I68N8pB3oCE4B+CgoK2f/oICAccFZQUHjDfzQKw//xTwDygbfAASDw/ynAl7b1vFI9xRF6EOzxhT+WXdn9dgDRD035dP4qW5XD2NLTF/WO+cwJdWGLtQ4ldSnsOjmegaqneGBgjWb7LNbJQrJyb5Ll4YPDx92kz9JG8co1cgIzCVr7mC66O7H8G07q9Fu09FKiyE6JqPkl2PxRZ9yrJZitHs4U+w20N0xnaMFbSlo5Mtt7EaWrG9Lg5EM+1CxjXpUn1coxZDWNoLjHZe4e7IWlkQo6l0eTOvEOy2Yk4pQwhHWGM6jtVkCDFzlkL1Rj0WZjboUYUvZ2D1+iH3N+mg4Zz0+i1x5M3XsRM8ITk/Io3jR/hJrLG0bkxvLYvIL7n0Zw/lkGia90WLyxH+kFfck8tpTAvCG0VNVmgmkHXnQdyQqvQwxvcZa7Kwu5kzwZpQ2vcTJzxctvKv5HEviaoc3Qoymk3iqn/tMEhoftY2GZPQdz3/NTYT9eZ78TYjeK8x7beKrhi1bCdoa2dCCw3Q7O/TLl1qGvDB8wDJW3zqx59oNZ/qFk/bBlSlUEy/VcUfMyIHi9O43iJ5HSJJDSQaOxdIlC5UY6cxXUeb8qjwX19QTv7cqlmKbUr+6H2VVL3hu9x6lqLY3MBjJ0XC5xJ9qD91WcpY6zET949mQ/XXooc2vQfNI9VpBn1ZSNsZqY6F5Fxq/E0jOaHhnTcbe8wLnd34iumodLxkiuqeayeE0dP52cUA7IwNnAm6zQe/QrCePwn0g26NXRPiGd1F+/6BF3lvlukzG+c40BXppsv7OeDGUtmsfkMT3zEb42B4hInY/mrElMUFag1/NxDO8SREmKL0N3ZLN50iXmmKRTNbyAvvpptO2wjvT1E7Aeb6fzW9kAACAASURBVMm6A3fwd+uHt9Exuhmf5IeKEz0fJnLfyphHG/qyrqE/+osecH1ZBrZFw3hqo8LAy5dJO+9M14o0AkqeoLDPjotpIbhFPuXvyyBmmJ9l3qyplMa2oYe6GU2HjiRiSjc2nrpCUcJQXqTfpXuSCa8i/Zi0dCKRSUeo6/WvU/2/s5U4XUQURKSziHT5RxNE5JuI9BcRi3/Osn/8RURmiIiZiHQSkX/NdfyPVEgTjO6FUPcAnP2WMkpVi7i7JcibVP4a+rKy0Qi694zCLv0MLdZYcf5lAvGDUqm36cSYBX7cv3sVcQ6j111fLq+5zuA5hnw1y2f90yOE7dRj1OPf9InXxsq6AaNvVtL54QWy+umhrvOGjcN1uV3rTN9Wmxlg+hzLdwUUB1yg76sxXPSZwF6UaVSixZLf76gM64n+vHWsdTqBoYkbD+P3MtHnOZqPYOkDVS5lRzDJ7zG+TUfxfaA2eTyl+EV30nLt6PDzJgNWVdHFO5pbxlZ8LWnEw/YLuWVkRDvDe0zVaEnDDh8IvJrIj9fDSFR9SZcv+sQHbSHe7C39ol5yu7M3SVbr8Lt4iMINz7j604IpfzfxRWkyTz8+5kXoGrplP2V7aV9qd9bRdmsepyoXYLR4A7p7x9H2zyQiw1RI8plM+a8WnEpWo+MOd6KzNfBPquDOkhSebJjJ53vx+ExVJ/nELQrtG1NQUUuzBpmULNQg6mUWf4MquOA0HNvXuVTf1aQmcifOw85TZlLLsPYh9A8pp7veUgqu/qRwgC+xW+eQt/83mcrXWDXOlIzgfB7/yGFu+2K2iiY7ne3YPTOZ3+H3KR11mclZI/idtJNeejtxvd6ZkatWk3XhPv3bLqRf1iI8o1NoOn496W3CeODuz7TMNfQp3E36qrkoXNBEfVYWTedMY/669Yw914X4RY3wV8mg7t5tdgRocLfNET5kj6BFzW30bT141lmB+Q7b+HFGDf/xqij2LGOyugr1x6eR276GtOWTqYnczAhtfcweBhKUEkLZGmMqBsdy07EBj97cRbPUlSJXHXrE5OAS0oT9K0v4nmtE7szD5OmeIe2BA5fcYkk6/ZHx3QZRN2IP01ru4Zp3FdXJ3bn5sJbs6o8EDfpBTMtqzi45yOYZX+gaXMAhx50kTzzLyu5LeTt5Jf6bW3Nv8DEsOl4ipWlrjNR00I+y4vH+fELHZXDg1BJerD5AXth++tVs4LXqATbHTvmfg8D/DlH8VUl6sBYvF51iSrOZXNF8TqLfKKYlTGfT1DXU6BawSO0Um0e0YEyvKbRwCGHIzK9sPXCWI7+bkHnJgs11P9E8kcqKbAXWXKpn8/chtO9ZzbtVK5n4YS2Xyuuw7P6SzmbP6exzBP0Jg+lcYETw6k2caLMJVTMjOtxTIfilKq4vO9MmbjPtx7TnfHwrfj9dwpvC4WSM6Ezk6HQiKKSx9TL+/PRn7Hgl7hqb8XtGE4blH+bBjR8M0E1kT0Q1xrHJfLmzhL7+jdAID+f+3klcHn2e0yYqVG+IZW3iIp6PusBnB116Bh7HO3Y12psmUxqvjalaHj22a7Dp6zuW3XjNOP+uBBT3x+rFY077FuLhN4zs669Yp3+dFbZZlC5ezKT343DIXkfbnEhUJvbkptU3XJP6E6USxcHb06h1y6etfyoVDQZyaeQz6sMe4nJ3OH/aqvB6gBbjPJ6hfng7k5KjmPPqM8svrSXU+TGFsVco3TuWOXoD2Z78GOOmbuyfnU3rMT25VqNIdpMwyhqZoJj5l0+tf2BSPIrGv6vQ9TZnXEk+q3yXoZehh2T6Eec1iVfKLZndYyaXq9yx0zhBjJ8lBj99qV7Ui8P2c1iffYs24y4zLmkuRsrNMNvWl0b1yozMf0zMkYdsWfWK+1VdsDboSXaZNqUf3rEzzhYVtygeGxgTsLgPBbFbeeX0ntULLOnd8hiKJWfImdSZ37t/4rT0LO92puB4Ox3rom8o7zNn5aFeRBlbopWwH7Mu3zH0DKP7sT3cSDbkZdBVyiwPkL5vHUNSD2MYfpbPOWPZlD+ZFLOPPIrYRa3PArpp9SN+eSRN1ZMIj6ujdnUuhnpe/K4wo5/eHD623saOntGMLljAAoc5mJ86T+Cmo7iVJlPXXQmFsL9kTzpFeLkDh/r9ZfjddSRmbMf9Zx+cO6XhPT2dCwqWVLlmE916Ee4ebdj16htr4n5jVhKCtUcoak1bYdmkNQmnnIkx68ko50x2l4wlvl3+v8y/f4vfhvXbmEl1XXPM7v3Evs0P1saY0KxRAt8SGxORMJxoqng8swiPI0cZ4fCS8shv1MwbTvHWq+ytvkjghSiyjK1ZWdGUDvbqPNTrxqwJHUg1saWjxSiULgaT+1CBDztuM+uRJyWx1kwZMYqsb6lMW/KS2TEJ2Bu25OLcIxR8mYRuRw9Untznl+YdqlLPMbelG21yA/n9wYYHYb84vr0PN3nB36XnGWfyjGAzP97fv4mx9yGMW30kbnQAYWXTuOjxkLlhpxh8XoHrnnZE/7Un36UZn0YvI2VvBYuidPlStZSKvYasrf7Jr6LNGC6IpU77MTZ2hxgVvpPvLeaDYSLrnuxk1/4BjKzXYuHDi3zNuUmiqTmFZi9YvPwEbxYN4Mi2e4xRcGG4YTA3d97i2tHZKPU7Q2l4AEk7G9Lg1XJe9Vdg6j5fnE8OIb8mm43pgzlz+STbOt+hV2I+rbWC0TO5ilb0GmzGp+H5YyUaHpHMCDdijOZKtsU9w7nhQNJ1a9Ds9ZCFT1tj2vQx6088RW/COGZM78PoBvb4F77lTXQKlw3m0vKtHm8bj0Zr5Qq27WhP4qfdPDjelgZSToRnJEYXWzHrsTVFWw7TyqqMsg3PWDOzlgsBH/Cx/8yCSCMKPzclZNQvBpw7zmTFO6xJVcIw+SsKNzsQYtKB713moJFzhOJJVdTfGY7WsFAsvZrQercw91QkCl2Hkf1SDZeXnvj12kea9WU+2Jui0rmSqCuv0CxLYraBKXN6KXAwciyxb9YQtugTQ/1GEJiYwMvzKeysLsdmdzdOv1Qgp3MgK0a2QduzgtId29kSepjA9PZsnN8Kuw4huPWLwqKVGusbBtJAwQzFU9o8jUnGyDOaFpPakpc+jPt7bTm/4yHm1oNQn2dFrek7DszLZovLHVJ0tXjetJ6Nc5LYU9+f4yM6sKDVRr736M6DYx/J6/uC2Ua23J81lauhj1h5/QpuK7bS5vAyGsxNIKpPPe0Vc3Befp0U2/7knrnKs33e/7NdhP87tHl7dXkx1kvWGyyW93l9ZdDcaGn3wEMejrshFS5KkumXJyN/TpSbg5dK7y29RNl7hWh7N5Smhufkm6unxC5eI+b7y+XIxjuSNPC5bLT9Jq0e+0p5qwYy2eCsuPvbySjFVAnq81Ga+qbLsEAfGbKqjbQ/d0Iad28gIWdbynsXRwm8ayDbDSLFI8xWDvZdJAE3a+XP4/kSZ3dUyhq3ljKNVNmkNF3sum4RN8Mc8ZtlKzNntpGOLYKl3e11kj1vhwyc4iupKrnysEOiJHarloHG5ZKbrCi+ZwZLi+5XpFGXNtK5729x//JC8lYgPvNWyLcNoySzTaUYJNrK1Jma4qSVLxdCHCXGro2s7XJetvQ2F7OaYbLWokIsHUulcbcDcnJ0L0meNVLqikvkbeRKOVRbK1P+1sndIiWx7NpXesbqitHiI3JmQ66sPzhBPozvKjV71svgqo2iHj9F3LMmSSfdqXI1ZLa8UOopbT4Xi3oDdTG610v2jM+UqEPusvrNCllVZieODrlydF9HOXzLUjIznaTF9ImiNH+ONDM+LAt2Fkj/Vttlyf498i7jrMxVdZLfIWukzYNAcRnYTVIVnSTF5718yZ0grmPGiW1RY3lk9ERWvcqT1I/H5GfQPTFtpCSqkQ6iVDFPpn6pkfaPDcR8hYcouJVJkmpHScx6J9MjbGTnAF/JPBEo+YEjxGyEhsz3dpF55Y8luuVnCX+yVPK+q0pO3kcZbmYpVZuuyNHND6RiRGvRN7gn9VkPpXHKYYk490dMBgTKvC8tpEfmFXmtVS+q+9tK9KErkmS3VOK9h0jnquGSEtZGFF0nyfmP3jLUTUkqfzjJ/fgpUmakJq8//haLtcOl00JvWT3BWAaMjZOEzxvk3ICLMmvJddF6XSgTK3vLw7gpEqgQIxp1AVJw21bKQr/J88FTJNnsqTQy9JftlwfJtjs6Mn9OsERnDZFK26lS2VZPeldflM2bFssFTT25MeyNZOuoyFPVGeI5RkF2Wy6XxR9sxCUwSfwqkfvmf6Rvi4PSzrmPPGrYWnTmI5lBhXJ8gqOk/RogV36riPWs89Lh0KR/712Eph3byb5FHcWmVbJYXh0niUnf5daRYZJU+UVmljnJt4cjxW5fkJwMuimj5jeUtXOsZNTFo+JwwlHmHrSUfe1bSGGJi+TtVRCXfTtkx4sgWTAyT9I1Non6helyb5K63BuwV9KydcR9rYKMdHSTkM3LJTihq/y+eVb63B8sJ7vbi9fhZVJx4pWE5c+REd2Pyfj6KNlh3kiiLbpIh5gV0nJmlowo/SbFF0aJ/sleMna+rTzX6iwqN4rE1zdaFpAj35uXy5teG+SAa395pddF9LoXyngvLYluZS09rqiKcos0Obasg8wu3ylr9z6UA1qdRflmjVTdbyL7LB+J4ff7YrR4sRzuPUuc2SBVDg2kn9sB+dKor1x3N5Sew5dKZp2r/A6NlvWXTEUnWlGsptvIrkM58vj9K+movUyKzU9L75gOcqq+n2xa3ECWKvQQrdMWUrEyUnSqhsjFO8VyylFNfKavFr+ap9LHtZ1Yfw2XDVHNpfG1NzI81kccvn6WdctsxXevuxyZ7yRb1pRKtsEXsTgZI7+/dJaRBf1lbrmfPO+SIZv5JL39VsihTxtkYoGOnEk7JA7TroruruOiZt9QHCLi5Jz6WPneYpEc2FErvYy6yfKfGtJ2QRO5cmCHOKp4y60rOqKGhaybVSJ104eI1XxfWR1vLCev1kuqtbM0XhgqkUovZaiGs8wefV4aXVKW2ccy5XPFZzlwwUieLw+TbRV10nughZy9+1MWbdaRYIOjcm1ythhWdJK13X/JyRZdZXHbLOlpWy1KO/6P9t48HMuub/T/XEpSCkkhSlGIqBSFJpVkiJI0K2me51E0z0g00ShNQqWipEElSogkKaEiKqTIUNb+4+757ed4fvf97nfvd79b9/H4HIfjPK91nX981vG91tf3XNe6zuUvqhrpi9P3isXuETOFrU60aBF6QrxXPSc8bleJ9BIZYVurLDY5FokDj3RFpGGyaJs9ThgUS8SIAS7iZfdLYsGOr6LbrbkiZG+laLzBTPSfkiF2jJYXRmlOQt6gh8gzbCs0J8mJ/C5vxJJYOXHUZo9Qznkgwq0/iwNn74sIyxTxzdheeKaliPKMWOGQOle8mfZFpAc/FurXDwmHvf1Ej5pkMWDmPKEcNkYsl3YXG/d9EkFW0SJs307x/Pkoof3GULhYdBZ6g9uJyy+7CJW9x4Wj9gBh2StUNJ99SjgtsBfzT2aJKxui/zIJ/BaPFyutzWe3jSCz12ZElhdWuw+zTTjj0kOX75260e14ALXLmiCrdAnMp9Cod1++VXfj9JxLJHexRXORhMfnI6gMqSTL1Ajbr1PZ0jKQt2c/Ud1mIT16PCfwTiS6VX05rdIHG2UzjvVWwEPpFlMdbVkbfYEZkVNRCo3l4eUJ9Bj9iJ1+nmxv04vAlCLu5V+k4sJRNr0oI8kqCaVl1izvvpqK1J8cFdMx2noM0zeK9Cy9yueKcoaUT8Zi1hCyXqXSVqsjZ8b3pS51Fxs73qTNk/18MZ9P4tB13HwfzSJ/C/ZtzqYurx0u4/M4fuYsMo8jGd5oL/eXpjHjtRLbj7vSf8A9egXvQd0xmh4ZzVjc5Cq1g5NQTzVDOvklw3q6Utz4MVulQsg9co7X8mMwe7SdvYdacPCcNfrxkcjf6E9Wv/dYrDCn7s0MjMJeMVbJibeyPWmUUYinQ1uGrGjKUSk5hjv25/DWbnQbb06L2Yo4afpz2HMzdrUOPBzfCPN5llTePIxHv498GllEzczl7LcbyZtelXzZN4vuU89irujK6+uvGTezDs9NE9EoCeGSbie2Zd2hxxQFjhi+ocvCTkjlJHFVqZqcQZ+JybWi8Zl8irwSaJxhgM2F02yutObo6RVoDxAcur4UccKHLS9WsfwQLF5VyaCfDlhfuM8tkwJOqc3GoKMrn3INKbwQxBjPibi9suBikh/Lpuhj8zMcMfAenssfUmIwmJ2tn3M44xz39rdhuv8m+rs/ZJ9mOfK7xjA7N5/b7o+Y8KgDA7sZ0MRrM4MtLRjZ0pC8VcdYITeNNW5V/JheQ2XbWyRoH8Zm9HAC3X2Z32ITvrfDqB5yGiONZbi/WUVajgoDB0UyrXQ2BzWUqTIoYWzPj2zoEoDKrO4kHJ5KwgIjIrOD+BHbmZPHNtJm2Du6Dr1MaUIdIdO1sBlkS5b2fg5mvMQ4YSp2noLjgU7YJD5iYttnvDxtSP43JRL2P6Gl2kSkK9+xZVAEaT5N/nL8/RZJQO6dIgfKldiusp4rTn3ZNvItcoYaKH4pov+KdAJbvWd5ki5H1/ozdIoH2q+ceXxgFK+/lfE64A0mhl3R1nIgyzyX5DFG6OtvxyJ0D1lRctx/+40OkQac8vBktYspZq9l6TGnB8N6r2PHivu8in/Jc8+LpD3tiK5VGz6saolXq3fcV+jGR20dBq5RpbHcYFbaheGmVIThsjkk5hYTb7yQfsdaU2gXwTSramy+n6CRViJDTeM56ueOQvx0csP68OrgImZUumK24xgKyqu5vR+efVpL/JVKHJM9UfsgT6rfNBbP3M4a7yc0Dy/ivo4iOwrsqJAeycXxS5mc9ZiONsPx979NhGwSW4c3YuPPApqqx5Nc2gLJ2BW8mbsYqalPaZydRXDj/Sy+fpoT99aw/qw7VdMGsKbVF/RVnflgH8W0kQtQmrmJrK5N2Nh6JMqrJjCo12pufFlNZaoZl2e0IS3ci9rlH8mY2I29PRPY5V5A7KpihsxdTqduT3l51on7xtZkJSexrOtUZJwOM//BYA56/ERX4R7nU2VIXDmcXjnrKFiSh4PuEzq4pzC3SQfU5o0gyeE+rjtHsaP4I9P7xdP4+QZujjTkenUE64qVMa++xMFHtXy7foRrBl9QD/mGSmtfumZuxr7JbHZcUuXk/H6o9bpNqMU9YvOnMljmMQptnmPitJoBTaZxcXousqIOF42vpK4+h0ziTZ7q+9MiKYCFKT7INtWj4rg7388a8PRQZ2pCP7CzfQfGzvAkqSieL+af2HrKnZOnr7KoMpBHL+6SHqnFfY32rNm4k4/fgrC815ajd3UYv+wzpVvNkPLwxcz2EDGnTuJk9IHWFqe5E9+CWA8bloWnM7tQDVPvKbQIaMMpk1jWtejNrQWbeDF1IvF2bny51Z7CAYM4sTWZnONOWPevwOa5FfqD2pNo8AZDpS/MCf2E8feTPH9fQF/fl3QeZcI5m2kcyrjBMovGSL48YFWL/lj7mPF1WzyNxr1kmtMzmPHn4++3mBjU6CwtLrafwxtpK9YszCZjRVNyA1ZyO6gJoR+t2FFaQXkHKWILJrM8IB3pJfkE6fjisf4l61xG8H77UYp7jMbE6R7+DzfQPdGGW1kB9Go9mIozFZTHqdIhdAJT1F/RUl6aR5v2MSdsF+9mzcFtnCYuN0LY/OAd/uN7svxcNc83ejJ10Rc0Zh8nc8gaTNLGMLSfMXN1LrG9TxXHuylRKPuKpqfS2PBjA4Xb0ziVPomo0f0pDLDE/PkqLsc8p2UnCV7mO+mufoBG5W4EXGhGZMd+6E1/QNPd7ckb/5W72nGE2Sui186J84pH6DvOmIP7ujGsthGaB6tx6+7PuA1KaG60Yei2q9z2ziJNdQVu7hfpf2E+Y37exnH1HfR9dxOfJUPbKnVe2l3BqbUse51P499tDE2UpnB5xwJGTuzHltCh+MftxX6OGXcHBGFkF8KADVoEjpnC1ZE1mP0oJu7KdmaE3mLqxxe0vz4U6WFtKPObTbu4GrqIhzwbPgcDnckYGNuhbjSVaT7H6dpnHUdq8hmVW8KjXPjczxSleeosfDeYwpkDOHjkJkm2b5h88xyVrhm87rsMcSuX/McT8Dt1imsnK9lZk4xv2wtYqiZxZudFPuwzZf/3Zris6MF7xfFUBmxhZ0Az1L4b8ryFISOW6HPv0VwqvjjTUsaULhNOkPt9GxtvOaE9vj+Hjz7jp9ohRujWYtPjBPlpC3BMGcSi+5+Rn1vBkXhDDDrc45SyASMD+1CycB4/ThVwb+lnmkfd5KvhEj74tMJWuQSllQdI37eRSisPjCprsa3TIMA0A7egrSSemIeTszOTTK8wQGMCxhvc8R+Tha/Scj7/dMNSdR5V4U0w9rLneFM15Mu/0UKixcaMCPpaRnOv1xB0d15gT2IotTvtGdvKmYMbC8hZnMbVmAm837ueyZ3W0TvhDZXRfZCftYOEb7E4dAvhk/Jh7h3zo09CDCl2H5hgAUr7Tenp0JV+fRQ4tqARJpbB+DlHsslQ5/94sdB/OzW5dXh3PMSXguO8VfmOR+UUyl1kmP99FQ+fJeOpsoWvp7Som1NMxeJWKDg6EZVmzsFzY5lsPR3Z7S5YJ3/i693tTJAyZ9WmXB71MsZ6+SHM8lvjGV/KfddSSpbf5fSRXFrMOsmw0GzqrrzhJiHo9rdiEMcxVW7FGaMhaAzZzK2cECKNRjBDKxB/i9bk1PYgJ3sYm6sO4+ffiGu+N5E/VcOw4hrku69GPfgVrffEc6VvFVlJ7gyodse74hW91A+i8DwW7S1ZLAkOYc31XchPHkHa0EmEtPdjnPUchrj7cPSCA5aNC6mlnA7TdxHctzGTon7Qzi0f870PKUuvQKOZLueHPCKw3VPSDLTptX4cex8UoTJBnfbBrziWKMe8ZaaoffxCs6Uz2SC7nzlrgpgQo0TuUT9afOjO5E+qzDIazvdn7+iU9p6d4XexalrAy2cPSOl0EZVD2qz0fM/MpmFUmlpysM8MtjwIw+ZmNuuW2eBvbcaNHV9Jm3yJ80v3ITvpHbvf6aNXFoq9psByYUccVn3GKtaOQ/Z3mddyI84GjRhulItOzHEsTr6l85uW2JvLIBtwFF/PWlQ75JB1ZzVTRsVjMugSs+OziTG6zrx3XbieZUy+TDT9olaREn0a32eltE7K5GHEaTq/1GTJ9POYnJJFPdof0+uNyFikwJrwfnQwCWe1Rw9atbuDqmcH1HSN8O7mia13BH0cNMm3d0IpZhw/ZUxwOPacyb32M+/uArK0X1I1YzLpp5pzb+JgPkYdR2p3B5YrzCVujzUxUg/od8uYlZ4F3H/9mJdj4zi98TrPdmti3GYOyzr5YV0UzM8z0bi09OOMYRAPZPdwOC6exZbT0B49g7kLM1DdpYbDzhacs43mVWJHQoPfMEVdihGPtiLTui9qZT+Iy1lCra8ZUvY7GLUmCOsfs3EevYIwpRvc29wXOX9XPn/czoZGc/liMBNvh/kE/QynelYdT22LiJhsind5MOWOHQm57P6X4++3uB34od6C71FjST4ai1TdF4olCjxL8MfTrR0Vq8ei2WMWrhGOmCUE4WgRi2eEFDnp1cjMWorZ29codK/licpO7ihe5GiP3Zj4mdB3y1T6zMlF9600c9t60bPdeZIePiKwGWSsmUu/th2R1VCkyyJrTg5ZwuM10pQEKeMf+pTnZck0rtTjSdhPzLybkn6hKUHHt5A3ciyKWfkkrTfFectGusqv58qs+XiM2k/KnhQOee1m6ZMiHtl3x2y2PB7ZZ1D9qU/kSk8Wjt3BZNdBWP7IYMb7i8x594h925dQZ9CSyX1WE3jZnhcu0tgeTWBdzQQi229hbqEmVgZjSdNQwirMgUtJE9AuHMbqT9dJXCbwThnDZesQ0qfsZsIgHwpmKeE8ypXi3ueI6PgYnRRlLv804H1ECAkug/jcQZqPBTP5sjiNnSn3kfKKx22WIo2XvGPKlGo0OidwdKweSjXlRJjc5Mm886z94EWHQlWOlA7jY/uj5KUfQCnDmIsynRiTG4LXhHEsKlegd60pH0ufcm3jHTLDBxEXPheVYnee3rrBsohzrEpaw8CX17A3s6BRajMOJZvinWlD3bpFdPUqJX+KISZaH3lYpIe+fyY5jkmYOlaS3UkFw85qhD3vh1lbR7INEtDo44i0gyV+rVqgL3OFeJVW6ERbcXjjPTrF70PvymuiD6jTqFEX9mbtwS9zMSvipyIdF8jhvHSuOumxdVQf9NOtuH4sh4vPR1KjnAU/rtLRXZUFBrvY/+gUfZvmM3SoFJG5I1iko4VzuQF6ed2ImjmHZxW76TohmD2bDhL97RsZx9xx2XSXs/4FHHx+l11BO2jW/DFaUZNxfxHP481n2FU5jkePTnLVYDQPij7T9ZEmjSJdGP1Yh80T9XG5IUvc+uVoP7pJmU5Hxk05hN1ML7YpGfFFX5fK97osdrzOis0q5B97yebxN0k84MT0t1UskdOnU9Zh1k1NZbGSDzvmfydEaQdy8Te4NhVcquXYRus/HX+/RSXQKr8c28GLGdFuILq599llsJX1vTNpZtiVN2t3EnHGg28DYvj8bANTr3RCT9OOV5nx6FyoISAyGjm3ISxpL8dBvUI89VNxDjzHz+vNqLG5wUENdTq0kSdl5Hk2hCpyQreKHReG8/nAEzZYCswJ5/2Gm4xcP4yKzq+5qKnBqQGQZGrJ+dz1jP+kxtDvAfy4koXv85W4Zsdw9P5kzo/K4PTdGpwWP0BLuTW1TWOpbjobqZC+1Mgfx3R/AoYXFEjNLKRb16nskq0ib3AH+slI8WHFVRznyhBy9gktVs5EW3UDn25+prHvGCzuReDZbAF5fPs4fwAAHZpJREFUizJxcNPifY4fOYZBGC6cicJgGewGPOBMVk8UvM4hHxTHgIoSYroc422UM5V2IUT6OWLbeSqLL5XSiRGc1BpHmGo7TK3c+HzNnhr7ftxyrGXBOX8mKIWySb8GpdDvdEjpg9yZ4wws8KcqtwTt7DJOha9m/KAshufe5O6grRxzCEJ5qyWd5p3FOO4Ee6OqWJLfC/FSny3n6rhusp5tzb7R5P1pts74yZ5Bu4nfsovDKScY7bOaGsMLrGk1jbq7nqw6m8bIW3nkpuTQ4X0+sZ1SGKV1ikBpCbVrxqA+YivGGrPIfmVCy+ZrOBM5GomTF9fcjOnxPZRCpwnMVLnNio8D0S4disv1xcgb6ePklkTkEmkmOB/B7MMWapZcxNc4hoQps6kLMiPtVRW+x4Zg0zqPlab+LP+SwpG+2dxVdaHbFR3a2usyfWw0ZZ/qGNj/Aa2/RfHQRppnE55QO9oQnaRbNO0dS21UGF0GD+B29QcmqQWhpPuUI455tGy+nzs5AyjMHolGQC6zKy0JCD9Cl5vSLO3zDo9ZhyhQMGC351k0faox877ErcWf+aAXxbJVDly3LiarnxpSMm0pb++OXNoYphuqoeHvgPGPvlRLBvL6khcHXfPZoN6LEVN9UB54latq+UjUv/PZ+xIvGx1B9vsVPs71ocWqkWTsdMOv9sRfjr/fIgn87K5A76LJ3G1bwtdDrxDr76LWqRBFiyvM3duLOYmZmF/siMOgAHoueUR62AM+GX5jS9lyVKTV8U1shdOexsRO7E1IpQVPOh0joUsqhpb7OKF0FCs9K3opu9O/Y292D8okSGcjY9PKqfRdTPq6S1xZFE3LpblsNVTn7m0HgiKDmF19hzbDFjJX/zGHZATRdh0omS/Dxa4Z2EwvQeHhQp63kmZzwR1mGcYRfKYCn44bWV/gjbpRJfNC39Dz51Pab/fmx8Tx2B6qZkZQe+ab/CBoWgaTUs7yNbMJYV2vcSr4HWkrUzH4lMi1zQO4HtaMpqsNePbDifip/ugeaEp8+7fojtrF2HkS3krekvN+J8FfpJCZsI+s0Vkk7Vei49o+yO+uYN3wR6w20SDlpeDOHQ9KFC8zZfQWHiZeo6W9EeZfrXmbEcPpCA+mrv3KaY0Eun64xhX1/kRrNeFqnBqvpKfT5vMLunQNpqPePvwDZ+HwtBIHxfaEbFfi1f5etPOW5epXY9Z5XeZF0ELKR7qjo7OZtYVWTAw5xshmW2kSUYBm2VoWJfthnm/Oycd1fDh8EYc9NwgxGIfz48PIDeyGb7olCDeE5lKqXsTSdIAzZY9bku1+iAGuh+lgtYI3Fh+w2G7BtPPnSDAMpdT2EduURlK9eQ2rQ57TOkGBhAgTWlqHYXHNlLntR7EjuS9vlU6yzjEH9dhmTLubylb7J0jntOXA98W0jZPDzWQeb2rH0WjCJVITi5htUodZexmWD+3BgY/PKE0op/XCdC5vCsCyspTj8bqMfWUAXa+Q4FPO83fGuA/yZbe0IQNzn6IX0p4mt4NZHpdIRHY5amEdeL8siKrR8cx4VMH3G2OJWhVN6jUZMtT7ohx9merQrmC1hOqMKwSJY8w4vpyVTx3wdJZwo0YBH88zyIxyJK5zK0qfbOaEdB7rlKYzcLUzC4sa8XZ9PwYldqXR1fOMG9+FPqqfcPlqz+POe4kdHsfjfn+9IWm9rxEQQqCqoCJcb1YLZePBYkrIHvEy0VB02tdV5HosFcGp0sLRf6PQXnVLfP14Utzvmy4qbh8UKbdqRfKOb8J64CSh/MNCHHGdI9LzuwuZ/bPFteu3RXXLLWJR3HixZv1K4XthldC/kyrOlrwX9sU14uTVgcL1xENxzMVOTDXoIpzeXhcWzkXCpn1b4XHwoGixfpSonJQthuolCIu7leKcdqCoqjkvdn2zFKEmiUL23lkReVFDpGXWitOrRogn5kVi5tDvIqkoRsx5oyQs76SKzZqTxBMFabFEpbEwMS4Ww0dPF3PC14vdOl7CQ6pKDPq5X7yokBUO0/uLVoVXxbNXiPyT28SR3K/ip7GTUNowTIzfs0lcuqMmFjbXFb2lNolm6sdF2sUw8cR7rFjptU20395GdK+sFnPko0VUjrTwCT4mvixAuB1dJpY27yZmD3AT1/qfEJ0mdRcVh8eIr5WDRYcPd0V5npt4fayVKIhyEYvjJ4mB/ZqKJMvPQv7ufnFzsLPYl/hCrHtvKt4XNBdulvtFYY8PwrnJO1E38ZpQdV8t3g23FTccjwiVg+fFnVFHRMbbNGE/JUXcPW4t0iLbi9MpPuLd7cbCVHm6MBreRVTcHyHmNssWqk+ShJ2etxh320D4r8sVvWK7iB5z/MW9cjtxya9K9Dr7WKyL6iaa9Q8UC3y+ileSSaLVvk/i6axtosTsq5gY1kjM0N0m+phIRM73UyK4S6CoCQwXaxPbimnrlooJzS+Iuv7uwmtqS9FyipE44HpH+D5/I5YEW4uuy7YIg/WbhPbn7WL3+yoRqeIrmju8E2WK08QF/8GiR6a9mN14lui9dbi4oPdN1Pa5JX7KjBW2qz8Ik9ZPRE+7c8JY11Lc2tJKJI0xFAdHfBTLIhRFuXSO2Dw+TQRPShfyk0cJlU0pYshDdWGY3k5sWfpVXCuZJEZ3txVv2zcVdUMPioEbz4mSIcNFgM5dYSK/S5wabyk63N0h2m/fKL5tWCYq0iWibPB6cdx2u+h747owUAwVm4vbiSX9PgkjrZPiZFJncWnhfDHXc6Bop3dVROsdE+Oa1Yjydd1E4lAV0Sh+rGi9IEHMuKErBjVvJwLfFIkjmz8KvSi533udwCe1Gtqst+FTnCpFx4dy1j0cdSkZUsfnMzl5CZsDW7HHTAuFjEyejxvNzptHsQksI6TpdzrUziUisRRji2n0n+3FMcs2XNk6idcus1BdmUrYE0X0rf04cGoRVTdrsSluzJfeTXjrdx6Npo2R0vYiz3UMGVvC2dnxO0v2WtFm9ikeb0ij0coo2q5riuGli8heuMKqlTfYuLKIbYO+MqtXDcMjzlPcZwm6yv0Jd75H7Ld05J6fJeO2N+avQ5gb1RznWfJ07GNJ1+quyM9QQdvqOiP62iB/rAd35Y4g1301Mzq/Y66GJfJfnjErNpFNC6uoUdLBTvMoo7csYM84by7NbIpU3lqco58iP8KRnW9qafo+G1O3sQQsvMALFVV6n7vMw1EDqNibw6Hos4xX0UN+TglfRk4gdLg3wz6P58V3GeSNw+ljGkPjJifoFDuB6WOyyE8/w9OBxijNuYdxUikGW4PRV1lM39I8Opd5c/12KA/bv6Dv1AMYq08izaGGziYvaVSlzbnmA0mfa0vfOAnn197gRK82iKcT2Ce3gh1RXyiqXY5k90DGt/VGd5sD+cqa+K7ow46Hl/Dx3cIrK18unbuLRnwPWkb3ZmsHHxLXJLDg43qOvwpkwvg+uO6IoEb7PKc8QkmNrGRs8gwu257jnVwr8oJjMLybivRaNcY8+sjg1S4Yubeiz7iTPGlqgdK8y+T5zse3URVW7eVoUV6GygV59Hxm8jp3PcdSXektI1jyaDGbSut421cPBftEHp5JJq/8NN0GdqDdsFMkz//ErOpdBO2GuFwFNlUFEhUYg3fFAxZ30mD7PAmF++8yeocDPdcoEvPjCk/Ti9lUeJ3tpyczxMuKJpN2saj2C3kRzQmIC6EsfRtieBimRwejGe6D8kdPPDNV2OS5EsfD2ryuPM2QbqNwkz+Mw+OTKBjF8CNAgbhhzdGxkdDcrQ06ujJkyszmo+Q8w8v06Kf7CafdimgE2lI9ZwaG6tdpNNwauPin4++3+IpQq7GqCPDKpberNLOrN9Jv3kTODMviVvhgbgYpcc1mIvdVgjmu5cqC8ON0Vq6m5ZBdqG025eD2OzTRGcCh1KFo6D6j6TApjqdGseXwHAa8i2C43zJ+qKvwOPMnG0dUsHrsGqqqven47AMaMh5YqzbDa7UKUc+vsK1qHQF7lmH9KRiXy1249foLz97FclAtgDmf1ZF71YraBy3YHXqMTC1ZjrpY8DLuCcN8irmjH8b8CSPodVGGbdOUWNz3A7GbuhGhq0yVShlvB4yh6+DDrFjtyovyGYz47M7bOwmotN3F4OPJxBavpMlcLeaf24K46cu0WHfSEj/QIm4ci8dtpSB6E4M0irFtOon+h4wpC1MgMnU6ZwO9sTH1wnZaMN2VlVlg1p8M21m4D5Xw0jsfn2sGDLs8nRGpXYg7Ho7T52xq1ZQo2edOvGsed1s8IfyaDQrdD3K05X2kt0zhTtl31pWFs2O2GkOj7xI83QyvXRlE316AzCE5Ygd05qHJIpKtguj2tpD9Ft0IVlzB/JbefC/LYuAdFcbYLeHyi228LnzK062XMTw4jgFS15CVakXAJBjkWcRoQ3361MpyI9uGZpWClIF7kO/Rk5y5lSifXk7lOxOEXCzrNJMYnunHYD1p3naQ58DTboSdWcC9W22IDhSkbSqlUCuI2x/NYGAPtFd9Y+11N15kSXDbo8gVrxDWXslnWeUTDr5aiV6TBzTJ6obHDwXaFz5n7/XbTNU/QnKAC1ftluB3egVTrqxDPmYrQSMusL+zP2UPqjhxaz4mzaPJ131LJ4cq9oXAAIcfZO4PJKNZAQcuNaKr7yt8ag/T63EM52t7s+/6Xeo6yzHA/DGfjaRRs23BqvJgIk164qnYD688I6J0vzJt7W489QLpYFLMmROLaTntJ5crpDmTUMpZn2LsjCZj5z2UMuWLtLsZhbXHLRxqrxJm1YQ+eQep63yJIYfDWTn1KHHVU1kYncecpWEYpE/hzaAYPotrjOn18/fdd6BpF31Rt8yTMa6amPZsxPGWXqxve4HOu5L5uTKf/i9fsXX0ZvoGHeHIwNlcyW6HnVcV/UxVUcuZR9jxh+xoEkBLcy2OrPNmRLo6o9z2UeakTEhoInYlsWi3vsGEhWHovJYjdVUk0r2bM8i2ltMf+hGtsZ+wo43ZoLkCFW1d7ktP5IVHf/Yb96W6IJXe8nJIDbrC5b2t6bbaic8+bzgY5YD/t7NYXbzEuiO51Okacf8gHC4yoLzVcZ53cyBOL4tNDqqUl/ejnexYjHs3Zcfr54xSqWKU/j6WmHXH6rA5iz525HYTGVq368PqFTZozAyg3O4cfXcPxdd1LP5xlpQP/YLW2J94dL9Ocfkc7JT7YjW+Nbume5DdNIMZc1TR1Y7BYH4UZ9a25nziZz4ec+DBi/f457VE9/QGuhZl8cPFjEM/diORjiepZ1M2XJzBDMMVrNNwpcWbfL51Dmf6w+VIhm9DN2k21jV72DG+DzXyGejcPUJQozJyHzRhZvcZPDJ8xf30VEa1+UzKLGXGjp2F12d/vOctxEY6h/u5vbik7MOjG0850qKa6nNrOXCzH8Efyqi5co7Jj9yx+7kA3wP2rOp6itrCa6hlHyco6AXbiteweo8b81Ik7HqtQKfALF6MaUeb7NusXtSFLn2/0mrNOcpfX6bjoM9MCV7IvMjGJBuac2bwa1Y5vURZdS7Xy6/RNusFH1Ts8Yh1xryTDnm7I5GXcUflsB2Zso/oYJRIoL4He/0nMCysnNIYOUboqlEzJxmfWB8+uCnyo3QvpcvUmRXXnYlm+2n/VB77sLeMu9IHr4w0Sia9YeOcr3xsJcU2D3WmdUthUr8UNArOIv9kHiOli7i4uwnyvq0oXHmIWa6NkXHT4pVcBa2zXvN00mhmrj3JyYFSnFF/SFPD7ai2SKPndj3aFxRyS10K5Tc7uJxeTFpiHneVOhNydRdeaRtxGNSb0NAwTpbvxOJ+JuaJsbzUKafF9heczV9I4IB+2E7w5bxr9f/xI8f/22n8DZrpbEf23WG8tq3k6kY/PqoGE/j9KnoTK7hzaw6afra4HUvEJcKMhWXn2DzNjeXXJiF1wpTCkqekdPLAYdoCAg6NIFx/Hx0sVRhkp46bogFmL5zRTHKlecgHPnmkEZvzjSGqn7H/5k+I3DhC0iMYHGXFcBk9lvS2YehjL+zd9rIyKRLFDXN5sDYCeR87AmXTeF+2mejHiazr/gKt8YoYWFdj9GMcbuGvSdh5mLOfzFGyHclAFRNkN1RwPvwSLUq0CR6wmCzzqVwvPUHZvCfsvxbLGf1BBKr4MEnGCFIMqfTUxySuhHl7jfgqZcOj1yMIyrvE2GXdmbcqjynPEzC78Rb1FR1pbfmeOrkdjO2Wxp0f2Sy8ex9VS20WvHTHMN2Ifivvoehmg5WOET/7O9HpZlt0bv3AYvMEXON7MMmhFWOytGjRbAsaa8tRTLyFolkkJ4YZcnmBQFPxAkrbOiHlcw2LsZm4yvci8IsTef3astHMj00ekxEWCUiOn2XhJy8cJ1iRM+AsXUYHcc1gL0o3L5OsP43I2Y2JsftKBh1pVD6EdU2uUv1Dh92eXty+vAEfPUueaHkSPw/a3BzOpMfBNF6xj4tGPziQcppr4htDIw15ZLWNkAw/kst+ktT0BF1zhtFh+lqizw/myMpM7r+Tp1a+O5P6HuPpbW08hn1hU5uLHG58jpquxcSouXPr5ykehprSNrQPw0bGoRe7B7fzSti89uPZ/KUkpY7B/el6Pq7vypaevnxLnERRgDl9HI+hlmSBw96HtM7VYqjOBnrljsEx05ZI584siBhE7vCD+Kyajv67KCo362H/OoZeV80ZnK2N+bLm5HQfwfKP5iRZbSTGtwWTSjvw9XsWqmahDMzNYEHqfjRFCpM8L2OkfQEDu87Uleoiu0/QNlyW97oDGfLwE1vuhzH/wwra6dZRMjmMUf08CK4LoY/PbHKzQ7jXyQzFqxmc/3GRwOnB5O2z5mVAOr3cznKekX8+/v4fj/c/RbvNGyqS85G9tZqj7dSJ3iGhi0kp0hNzmdRjIUXDK/ER++g6ehZF5YMZUvCU/vlDMDhbR2x2F241KeN0yAkMLhgRcM4Si7kaXPXoSJj8Am59DGKybzrWl2R56XSTwdKdMb+gT0XKXrS2XYOShzgYDadA8pR99s0Jn1pOTkdZ9t8v4mvRSRz6q3Pi+nXKJ7VCbX8yadvqKJzlzMhDXbleupvgyvfkxLSheZUdrQ3D2JoVSq/MEgosVtF2z3fOaViioGNNXKAUOkrTmLulL5ojIa1VGjFp55FprsGEL5VUbutKD88ysvuXUOJWwBopG3Sr5NmQeZKSJvcZqGLOcykpPEq+c9BmLAPnvMWzNJlaQyPWuxWi3OE4k1bAiTd70QrpR92MYia/qCBpxx2m9nagjbsiNi2H09rrDoVbsknMLCEv5R6JOu9YvOU0X0rkOVvxDP+YLWzOK0An9RAesovpnKpFku0ZYoduorVKOOPyZrL3sR+ndyUwdH8dHusvcMTEh4UKeYxTXYqBggzzJXcZmtmXKOcSVKKvskmqiI6uCZibjiN0URkL1W0ZvKuA3Ld+nAt25qHdWi4p6dPzw3girs6kh5c96TlFjKlqDt9k2azTl4Gmu9iUps2o4kf4zU2li7kFh8IHETZmPmb+BRh/fcN6uw9sTvEj1TIX3/0JiKfDsX78kYsZTXALa0/e/DcsfRbF5oC5tDS/yO4x9uTHjCTGUbDvp+CUYxEtX64jsqsDoVNLyb61iuD1/Ul1+ckZuyEsu7qZG21caOZuyo133+mnmcvj5zlcvKREycNYvB57YBq4iVsrexNp5cGSnFSMrhbww6gzO28U8GTOF3btSWZoqDRqzpbsaL+Izvubs97ICP/LvTjy+iHzNFswWf8DoZdH887pCBNCVjHaK5hMyykcT4V4FU0+xdSxuMsQLpz+gXtKPm5lsYQmmNA41pvkAxaYNp6JuvcrunXR4tILGVY+esfGkoi/HH+/RRKQ+ightW49Uc7dePzAmSYTavE6tIjM21Zs7DyHipI1DDbujlVSb8yu5OEysYLVcr052rMDlh6WbLpvx6y4IBy3x2N56RkvXSIZULsS71e3CY1dwGHXZnwvmsU2lSpaOTpio6jL4GIPbC/dorXxKMbv/oz1j2wibuxi8cslPPBVQ/NOX56lr6fLTUNOTLpC2JlCsk7u4MESNT4PNGLlkwiUF42mW10AU8954N35A57XlRgzaSbtP4yiUWsHRlf2YcHpk5wct5LtMzYRMdGFrTV7yb/qxk79j1hH72TtgQIqCqdi2vYlne7E0/ZKDvbf97MvuQOOn4uQSb7JSfc8tPf15nRvW3pMecWc6mWEJb4nI+klzePteG7WkqdG+4jdvZqgQ+E80/TnnOpK4ixs+Gl6jZ77LNCXSqPNss9oThmD47xptJjaCNV7TSm1NyVfWZa55VMpPn6ZMzFmDO4OgY43sL6pQVZZOY9H36alynZ2qpzlYqI2V77N4ufkfBorHmJZUCcuzuiA6eUxfFMtoM3WZLokfuLFJH0SqjbQ89AqjDsLQm8OQll2P6Wzw1l9wZ630ZPR19VHR3oNvSu+4ebsQPJOW/YcOU1WiAWf+wVy+doJ2pduJaeTBqP8U7nevQzp1JMk5r7gw3pnYmQy6FisxrOKe1zo6oLOQT/81zfmsdtYsrP7k7PMngTfDdxVfMDqU+Esb6zFvbcutCwawAa9UCIHTuPpyEJ+6vnR7f5TDvi9YMGmkQS9vodNp9XYKjmyS2MIPa2fIgm8xQKJO8ey1OnYxZxuzTxQMj5C51c19CxQ58wdd3Rvq7LCyp9ve5sz6/1DzPxH0aN0IXsGXMDj61QGem8gpOkD8kv0iHUei5OMLs9CzRhiCKlDM+nIe1p9C8ZkbxXnelxkd8FSnreMxcllCIeOXOaQaUcyk+/g2qyKljML6aRpj6zKSrTWX8R6S3PGREmxxcyehBdXuNhkAra93nNP7wPv3M9i/V6GWE7+6fj7LeYEJBLJR6AC+FTfLv8FWvP39oe/fx/+7v7w39uHDkII5X9t/C2SAIBEIkn606ee/E34u/vD378Pf3d/qJ8+/BYrBhtooIH6oyEJNNDAvzm/UxI4XN8C/0X+7v7w9+/D390f6qEPv82cQAMNNFA//E6VQAMNNFAP1HsSkEgk1hKJJEsikbz6tafh3wKJRJIrkUjSf23LlvSrrZVEIomRSCTZv46K9e35z0gkkqMSiaRYIpE8+6e2P3WW/IHfr7ikSSSSnvVn/v+5/pm/l0Qief8vW+T9473Vv/yzJBLJsPqx/p9IJBINiURyWyKRZEokkgyJRLLwV3v9xqA+f0IMNAJeA52AJsBToGt9/7T5P+meC7T+l7adwKpf56uAHfXt+S9+/YGewLP/lTN/bCgbxR9bzfcBEn9Tfy9g2Z9c2/XX50kG6Pjrc9aonv1VgZ6/zlsAL3951msM6rsSMAFeCSFyhBA1wFnAoZ6d/is48McOzvw6Otajy/8PIUQcUPIvzX/l7ACcFH+QAChIJBLV/zemf85f+P8VDsBZIUS1EOINf2yQ+x/ujv3fjRCiUAiR/Ov8K5AJtKOeY1DfSaAd8PafXr/71fZ3QAA3JBLJE4lE8o+HObcVQhTCHwEH/oPHufw2/JXz3yk2836Vy0f/6Rbst/aXSCSaQA8gkXqOQX0nAcmftP1dvq4wF0L0BIYDcyUSSf/6Fvq/zN8lNgcALaA7UAjs+dX+2/pLJBI5IAxYJIQo/48u/ZO2/+t9qO8k8A7Q+KfX6kBBPbn8byGEKPh1LAYi+KPULPpHufbrWFx/hv9p/sr5bxEbIUSREOKnEKIOCOR/lvy/pb9EIpHmjwQQIoQI/9VcrzGo7yTwGOgskUg6SiSSJsBY4HI9O/0vkUgkzSUSSYt/nANWwDP+cHf9dZkrcKl+DP+3+Cvny8DkXzPUfYAv/yhZfyf+5R55JH/EAf7wHyuRSGQkEklHoDPw6P+13z8jkUgkwBEgUwjh/U9v1W8M6nO29J9mQF/yx+zt2vr2+U86d+KPmeenQMY/vAElIBbI/nVsVd+u/+J9hj9K5lr++C8z7a+c+aMUDfgVl3Sg12/qH/zLL+3XoFH9p+vX/vLPAob/Bv4W/FHOpwGpv/5s6jsGDSsGG2jg35z6vh1ooIEG6pmGJNBAA//mNCSBBhr4N6chCTTQwL85DUmggQb+zWlIAg008G9OQxJooIF/cxqSQAMN/JvzPwAlXf5g3nL9eAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Boundary attack: 100%|██████████| 1/1 [01:58<00:00, 118.30s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 200. L2 error 22607.926 and class label 852.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Boundary attack: 100%|██████████| 1/1 [01:57<00:00, 117.37s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 400. L2 error 19856.832 and class label 852.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Boundary attack: 100%|██████████| 1/1 [01:57<00:00, 117.56s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 600. L2 error 18425.56 and class label 852.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Boundary attack: 100%|██████████| 1/1 [01:54<00:00, 114.37s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 800. L2 error 17192.477 and class label 852.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Boundary attack: 100%|██████████| 1/1 [01:55<00:00, 115.16s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 1000. L2 error 16510.979 and class label 852.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Boundary attack: 100%|██████████| 1/1 [01:53<00:00, 113.19s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 1200. L2 error 15601.822 and class label 852.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Boundary attack: 100%|██████████| 1/1 [01:52<00:00, 112.96s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 1400. L2 error 14864.178 and class label 852.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Boundary attack: 100%|██████████| 1/1 [01:54<00:00, 114.81s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 1600. L2 error 13942.241 and class label 852.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Boundary attack: 100%|██████████| 1/1 [01:55<00:00, 115.90s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 1800. L2 error 12897.614 and class label 852.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Boundary attack: 100%|██████████| 1/1 [01:56<00:00, 116.97s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 2000. L2 error 11487.73 and class label 852.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Boundary attack: 100%|██████████| 1/1 [01:50<00:00, 110.39s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 2200. L2 error 10311.714 and class label 852.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Boundary attack: 100%|██████████| 1/1 [01:51<00:00, 111.93s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 2400. L2 error 9183.943 and class label 852.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Boundary attack: 100%|██████████| 1/1 [01:52<00:00, 112.77s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 2600. L2 error 8077.276 and class label 852.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Boundary attack: 100%|██████████| 1/1 [01:52<00:00, 112.29s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 2800. L2 error 7018.702 and class label 852.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Boundary attack: 100%|██████████| 1/1 [01:50<00:00, 110.68s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 3000. L2 error 5974.582 and class label 852.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Boundary attack: 100%|██████████| 1/1 [01:53<00:00, 113.46s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 3200. L2 error 5375.803 and class label 852.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Boundary attack: 100%|██████████| 1/1 [01:50<00:00, 110.99s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 3400. L2 error 4734.8096 and class label 852.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Boundary attack: 100%|██████████| 1/1 [01:50<00:00, 110.84s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 3600. L2 error 4254.806 and class label 852.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Boundary attack: 100%|██████████| 1/1 [01:52<00:00, 112.57s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 3800. L2 error 3860.585 and class label 852.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQEAAAD8CAYAAAB3lxGOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nOy7Sc5lybbn9bN6l6f4CnePG3FfvmzkDFIwAiR6dGEC2WIAjIURIJr0GEOKJiAgk6tHVO5fcYpdbytp+G08Ui+Q0COUIV3/SUc6+8jOtrWX2frLltnaopTCN77xjb9d5H9sA77xjW/8x+WbCHzjG3/jfBOBb3zjb5xvIvCNb/yN800EvvGNv3G+icA3vvE3zu8mAkKI/1wI8b8JIf6dEOK/+b36+cY3vvHPQ/wedQJCCAX878B/BvwE/Fvgvyql/C//v3f2jW9845/F77US+E+Af1dK+T9LKR7474D/4nfq6xvf+MY/A/073fd74Md/dP0T8J/+VmMhRFFaQSoUARkFOSFlQRRNKgGUQCVBoiA1FFEgQpEaUkRIhRQgUiIDUgiiUIgSoWiQEVEkGRAiI4EsBUSJ0BmSIIuMVIIcClJLSKAoJFGgSIrIgIBSEFJRsuDrHTO5SIoCkQvIgiyKrAQlJpQwFAIlA6pAAZCIkslCIilQChmBogCCLAoSAyWRZEGkAuKvfStBSRIpCrlIpCwokUmpkJEgMqLIr6aqTCkgk0AISS4JoQUUSU5/fR5ZEFlQyEgryFEiC1ASAk1SIFKmiIz668KxSChKUhJQvvpHigTIr7YXQSmC8tWJX8c5SaBQpIScQUpEKYgivo7nV5PJBRSZkiELjS6JpAqlGMgJIQRCZCgCUEgCEYUUCYUg5ILS6uv8EOmrrQhEFl99QkEVAXwdiyRB8PW6FL7aJBSZjBaFr24SCApFKBAZlSWFQi6Z8lefKyAhUAWE/HpfEyGKr76lgBR/dReaYiIiaAoJIcXXOSkEkCgZlICiBCJKEglRFIj01dICUikSIGUmZ4HMmSy/zqtSJFJ9dbOSkBJQ8lsp5fk/jL/fSwTEP/Hb/yPvEEL8G+Df/PU7+jtFMyUCsBlNey0sRqMSlOoBtV4AiRQGESNFdMR2pZWKeZ1xtaL1BScVF3EmNzN2mrHnE35YURkaceDurhTxwEM9sw4SWRXm2SBPI3k+kR8C4rrR2MTuH0Dt1F1NNUYmHRHLRE1DkZpiA0sC96FieZmpzwEzPLBZj98ETRkwD4Y0dMSYsc3MvTMc74ocJdmCFIpF33Fe8RALt2PDNETOf5bovwSG7iNO/YQSFjUWVF3wq0bVgZkjUkKVJ7ZSeEg1r66nVu9YkQiipTBRvEIcLXm0qDSj7QlT7txLj/ALJxG5dM8I7mQ8D6rlVq3IJWNzi2gn1F0Q6w6bEpuMkB8J5YWqlQgOqPXCEC26afh7s3IJgUYmrr6hwTIV2BnpDy3hPpKOB7T0NB7mGWyzM9NQZKJrHPs1EXzCakVUjnpbMawsbUXYMs540gbhGHjaPnA9XgmvLfboqQZPoKV1PftJod8+M+kDD92FNwtu04hbjW1WspRsRSG+s+SfJsShkF8kzgay6dhEolo83fcd8y8z6imyXj4Q1cBDliwdbByo7hv2I9gt4veZrS50d4N3hrPoWFxBiDtF9JhtYBYQtaYEgesPwETazNeIrJ6p5hu5bLT5gWs34YQlroo+BYxIXErCPgj2a+Z0NgxTRZ0WfIgo1+AiSCl4T4LjyUI9cPkx/8M/Fay/VzrwE/Dnf3T9A/DLP25QSvlvSyn/+utHEH/2rOMT0w3SvWPKR4Iv+KeaQ3ynhIJWO0FFvGvBvlHNhZLgeDqSaLkeOl6yIjRX6vtEpyX7q8coTzgWRn/HWU03DdyujjmszHtEnwT92nLOBbPAwRb2tUWkibY3cM+kdseMhZwqVrXiDwLXZEQO6JcJawx6emZbr4gMxu+U6FhWSf5whccbw0NLffeUw8z4aEiHRFUutP5MME9cXE+OE7WG6aeF3XrIP1GNZ7Z74C4L43zCy8y6FQ4lo/NITAEbHnk1hToM2MOBmHvaKMnrgTo47CIxBxB9IS3vvJcnculAShYEKX8hK41ZFdcUOPuWs5T4J4m/Fzj0rLLCNxsxNFQxUjpLbg2LuTBkcG1Gmjvj8JHGaN50TWgcd++wLoHU5BVUpwhZI65/FX29s7ZnHjbHYdsoaeRYGZo6k6JAp3c258k8IOTKuSS2/SO6+YR1sBaBShF7XLDbjjQtuxxI7Sv2bSGmQvPhHT9o1BeojeSoBCFGbEyo5Pl03XFVIb0IZJEke0bHwPNSkeQBd1PEXPAbPOh3lNq4V5azSygd2I41etvY5pll6zgOPdFI9mpnUxvxHlmqyDhOjCGgVAWpRR8tcppJExA1wjUc4gIqUZLhfvSkoChTpjSCWDK3upAOAj+2VAWGm6JkT64UuZHoZiOkQlQOXTTj5cr6q/vNYP29RODfAv9KCPEvhRAW+C+B/+E3WysBxuLKRNMeOLg7p36lonD4WSBdjT7V5ObEowFV7kTVs/YeFwtDmilpobzsiC7CrBlKxWAcpkto55BDQR6eyaNlrxTGK7py5JAF1X1lShvBDqhhIZaMSJneeZb3BZ8m5NuBSQRUXYEuHC4L8WJpG5i8RC4rk3+jEgq1wLneSXVDipHtXVM1msP7wr63jKkgt5GtLNyLBnOnKV8QOSFyopIKZxSDajBes6uVtrRYkxFu5JQE9gjvynPSNVusadRK9aRYjcBXF2QW3NMO1Y5rG9iAIZOWxKwdjb+j5AvpvKNFzZNokY2lGEFHzRJXNntALgO6OhDuCcMKo6LRdwILf7qA+uyQ4QDCsC2RP02C18bzQk/YGtr9SmtfmWRN//0jq1oQm0KnQnCC1QtM/EDKntd+4IDDX3re1EzKFY8mEQ6SSghGvROiIBZB/3xnL2+YS8OTLJRbw2HJFFshjUPUPdNa8M2dJHrCZ4O0hWBq5IvkS9dgq8IUCk4VfDEsV0N/1OR+p3WZEjtGbhi9s4sRIwXx9kRYOsqHZ9Q2875HqreIXu9EtzHplr6TDOxUnzzN3nCPhWSu1K+FKu5k8YjdZrJXyHVBVIJiDyjrYbuwyYVdZaKoqK4zqtekRvAcM/FZEmaBPRbUNrFUNdloTOjY10RZMkI6eNq5d4bzB/hoPxKM/83w+11EoJQSgf8a+B+B/xX470sp//Nv/iFliiyEViPmhW0BuXp03TNzY12fCHZGhoWrVbSdQfuFh0lwrSN6PHMKBqNaFJkPx8BBbXTp79hjYl83UhEouSGk5CkFwtPAclLsz4KowTtoF8PW1KS1wjSKQXcIEUmmxbhMXQqPvcTKluUQWezOumQQR/JTzWMlWVWD/LjwJlqOZUClFukEt/cesTa4fsOsf4fZWkQ8kKNhCZK5saz+jjBnti3hg8Fo8KqwxMIsJ7q1pbGOUTpOocHMG6/zwp/zyisR9XlH+hrzqtj8iHGJvEZe54HluOCrCalBmsRR7HTniuYGrguMeyAOgag1k1jZimC8TwipUedMPllCtsTqibkVoFZem8imGvSyoxvDh/MDL0lwnF+I2wXFiK9aeq85zG/U84LCMB9BcEXRUkVFZd/oR4HOkutjQ3qYcKniQUTuuaWZCxWJgynYtSGWnjIm6j3QI/mLj3ij8L7D47j5d4QX1LlnnzLhsJOK4i4iLu/cABvv6EWQgW5R3L1CCUGcWtJ+Yl3u+LiRUsVeG95Gjc8VpZtJ/UK/LlR9oMyG4GqyS/gvFSavyLrCCnj7DHPZEDYhg2DLGqk/klxhQFKFhNUShCFnx6nqOccWNxoeM2g9U7RCloaiaq7rV6Gta0n7k0PahNodcjuS64HWtPTiA/MlYO4V7i3yJmYucQL326eAv8sR4f9XpBCldYI6HpizwTx4pm3AxO/x8TP2lInmERknqqXAnmm04dXs1PyZTYzE3fPJZsQuuT0KzD2Q653p2lHpEdFDnI+gPFpmDmtmtR0xr+jOMKSEvtU8HC+8b5qD7oiLZ/8ukC8W/I42Ce8lbZeYtwO9iEQZKe1Gftc0LjHHGml2/F7RPC3M3lIPkfWD4jxBSmeGp3fUzxFb/4mwfEHqlmIa4voZdayRi6ZWEYqBfeDWnLC+oal+ZT59JPiIjW+48UBtBK/pAfgHipS0laQyBy7rCHqniRrpK0RI+EojcyA0EntXIHeELOguIa8tlQi8l41KKGJx7BJSvVHmB+QPb5z+L7gbQdsI1GzZ2Mk80TRvjPt3pPyGiREtWpQ7MG8e/bTTvC5snWNl5hgk0rfULvDqFX2euTUf+D688lNrEQOcWsM0K6rnxFoi5b0n12+IzeByZDEZUxT6XLDDCe8Nu3oHD5wiAoUuDqU3CDWbjXyaFG/+SJYjqlrIokOvBSNWYteyTgt1qyg77GRaG3EG0gTDpuk7SacNN+0JY0CZE0wzqe/wcUN9EOQrHJaJNmte64rAjjKRuDcYAl1/Yr+DrF5pFsMoC7IO1IPl4hK9N+wqI5KlyiuhPDB1AyjNxy3zpiVp9dRdonFHckiA4L5M9O6JbQ/4ZuNTSQyDZz+eECJj9E5tJO+/LP9TKeVf/4fx94cQAWNlwSiefcNnY9Hqhu1a5oukyZldF1K9oG+OT4+JXRfehhOiX7BfEuW8sd8KB3tiEndOmyE0EuUDXkVULRgHTfOxQgfPPAdUUfThyDuGSr0Q1QeKeEfvHhLIB4EMEr9rSkz8i9xxF5KBjKgWEoXWWfYh42UixwbzMaDeF2zT0HlFVoqb2qmGBSVb3h8j/bIzVwptFPFF4KTECcfNJGom8tbRiMCkzhg/E1pBzCu1yjA5rFkZSo2KibPcuTiJSwpiR2PvjK4iE5HeordEIVJkgRwIpQeVyEdBvgp013BKV+6bo6kM3kDZN7qws9mW+LTSDomovu6ub7eGKDX2dAOvUJxwu2D1F1IlqfMJtGAsK4fKMTcL8XUle3jSHXtaUZWlXzd+dIJOPpDjjvATtqkInaCgad4nbq4l7Jame2fZK45Csm+C3c2UxnC6RCpT8RI8T1qycEDVC3lbGdUTtni82HjoPPfpA2UfsE81+m1kOx+J15W2eMKnI+qycpCZnYZp37FHyyYzXY4sNwn1QisKpsA1PyP2iVQnhHToHZo8EKqeWSZU8FRNx5wG2tFStzVXc6dBMUXHxyyBji/Lz6gHiMsZES0u78R6xjUWN2eSDxhTodqJYXxmZ+eThKVaKLeEzw6HJ+gzpV3Z7pJiPM9YfHQMjcbJV8zSsCjQcmbbyj8pAn+IsuGSFHLRzDlTxxvBVuRtoBd32jTiRObsE/q7lusFhhKp5Eozr+SqwW+a56IZmhntLIPwBL8jcqaNsCwNx2OHv4HIjrwKHgjsdeBJDNTF8eewUkrASEf97IizZU4JmQsf9IF/LxJXt4EdUeaZ/lC43yzORWSWWKV5WgL60dIRuIjM2yzQi8LZlkVLTq+WsDvERRP2CqMF5vjMWo+wF5ysCXWBrqOyLzRPCykOHPZC3k5Uh8h+OPJdCbRCM9YWkSr22lHEjWsRUHZq4dBbZnE9qy6YY8BUEiEjUTSYbcNUkg9VZDnVmBqmNbEtd6LdAM2SDoSb5RaOsAiW1SKYEHomXyTrDNt2ZbawNYKcW+6HxJYHyiLZ7wL9RWJwiHPNLTsWURiD47Vo6oNgK4Kmn8n2QIgb+b1QjYXpURPjStUEVtWhkmReI43ccL7C7BXhqeL9sJH1I9dYkfuR3W8ssaNWI49KIXThEmrqekCWDe9HQpGU+8BBQFtH8m3GVhXXY+KeVzCePUdMXJitoDz1HJ87goPbVmgeB1wfEF7izEyFBC0gCto94VQgTHc61aGea96DpL87Egq7jbwvhcs8cTqCftdI5xBqIsoVl+Dp0nAREtFUTHpmePuO1L6iTp5JefwYWc8N1N9zt5/IbaDKG+6w4AosVUQykWPArYb11NL5zB7q34y/P4QIpAJtXRFYEOLIQa3stx6dH7jUBlECwyLwKdLrE+1bRRILRRiqskEovD0lWAuu0kSjkcfM3UqutUMhcNOGQvEUEsI5XkPHOu+s6URovubUNAL7LBjvBkLiuFtcDVet0N2ECgsH0SPVz7iosR80yTuSUlRl432qKG+eOXxk9x4rDAsrq81gHRwUddvS9jUmVRTtGOcJoY7wOLPKinbb0MuIKI7bHfpd4hvAeIyCVDZeS40pUGzioSTSfcK1GXUoqLlivS34euWTfUeqhm3UrK5CtALad/RqKLrwMhZWvxKSAbFxPhfKJnglUvEzZcq0m+MuJTEkqtYhTWavJK1InDiyzTckJ5QYaNeWVGV40hTeCE8ru4FuKri84kpPOY4EKYiroSpX/KZZxUKLQ7QVq5lppgN9q+m2yEOcsWGl9EeuqiUWTZsEh3com0HYQC9HsrD0Hvp6ZS2K+2HjvAra+YBbFd81PVXsOB09NYFcYA5HKtcjckBcLVXtOFFIasEpg0tf6zZu74HVgRKGPETmcEI2Hh1qAmeidSxKsh4tohyoa8U6K6Z5pqlhUSf2tSJ9p+g+rrjvNGP4xOnccLhlGt9QimNWkZdHzUPcGKaZdHxkPX4hXT8QXnbmJRLqhnpvOJZ32nil2QXLwbAi8QVKMZRTwOyJ4nbUJTBhMSr9Zvz9IURAiMTgboRsCIeRbjphesVd3vjkH3G9Jz12nL/svJoLl2dFjWQRG7FMGNMh7kCsWccAVYsv32FUImdNjJ4pWM5K8POYMW4lEanLRnSfUYNjjjv6/cB1NbiyE54TyQXynPGxYITmWZ246YFIjVo9x/md5WHGCs9RVXg7swnLZH9CSUVVX6ljQ6ssW1zYBkHUA17UlPyCVYVTmIleoH2LHUZSVTHXOwuGZ1OzmYyzGV9mXsZnDnMhHFZ24eknuBCRrcDNinhzGDaMkhRfEMKg5Mou4Ow9+2ho1gOlCPSmiWlC3DQf9xFUQ3qp0W3BtT17eca2G1u7YtCgHXdzwC8ZaTRb98xbyjyKyPMKOMjTCylb5GUkHzT55YF+CuRQWM9nsrqTN835qMm+w2cB4hmVE2OJsIwgOkKZKKPj0nm2cEAdDbK8YuRMpSxSR37td9xZ0oqJXRui1WRdsz4cOLETXyTgWd07GzO/hJ06bbxmi5U929GxuQ2qgbGpoNeou2P3PfYmkNMJ9gKXNw7sPL7UlKagpkKfFh7qEyVMrPlHpklylDPNvuD2kZIdH5sLYo5sy4U+/YreI/HFEeeAeL3wFF+xa2HjylRfKYdIJ1vk+xu31PHUK+IvG+reIN2Vk1JUHFDBMEwrtzRh28gcPfpasG3GGYEzhThkqn74WqxmLqizR5Sn346/P8KegBK2dN8pxv0Rc/mVok/ENPOpiqxBEtsCW4WtM+vWYfdIOgy06wPTccQOsO0Spx0pWlI7sIWMqQoqe6qlAbEya4MIsMszdVpoHybyqmBzTApUlZFRUQmIuTA1klMa8bmBrNnXjM0FYQSTyFR6o5QnmtXzLjaskFStRqiBrdTEuCPngpICVTRz3fDDPnDPNUtreFoTb3pDNZHjdOL1MGGiQ+wrQVeUeePwrElJMN8MJ7OyiR84mn/gNktyhtwV+jVzPxnkKEFupN3RdIW0eWSEBsdNFJStSKuHp4qyeJ74xL36CzEd0ezo2SFs+lqMFY6Ep3fKlEnxmeN5ZDE78oumipLc7UgtsLFmrXf2K6RzjXst0DXEKcK/Mmx/+RVpDWZVHPqdt4ND+IS7V2igZGiyJMmBq5U8d4r7F0+qNXKVGAyxmyHWNFqRlWYSihxG6lwzA7bM+EPFYcqUfWP8XmGu4EKFTSsTAuwjx7RxdRPZP9OWKzFb6mBYKk1cr9RKs3eFGAQNnjY+spY3Vm1gS4hPD8jlCyXVhMnQtTtDUIh25eH496ifrux6xwdL7gvp3hDVKx9MQXvJXTj6s+SuFhgP7FvAWo0yC3n/WtnqoqIcG+7rjaY7EKeAyxsmdKxVJvmFozjxyoQ0B8rhRru3pDUSOjAXSKVCnzTZF/wPb7i/WKww3P39D7wxqEQpT2eEX7C3QtXCPlvmP+0cPmsmnlFcsG1iUyvtvcOfdvKiCP5r2WSrFMkGfLZUYQUn8PpAE3YImkUnXLcwvYmvE686IMrOLiWlbAirCYuAFDlohSiecGyJ64J7OKGvN66xh26huiSELvRGMCYJumNbN/paInxFeZ4RvySEbBhVwXzK8HnHK3BKUafAoDXO12AH5rmnrib2xpJ1xt46VHUlTApDhvYTIkzseYTYU+oVHTpE3NmTQJ0WtDugfUW4z3i3c1IVOSVcCrwfM2VxNFLQ6sxl26jXI9vTSOOOLFuNvgys7Y7NNdoPKGcY58LT6Qe8fyGWhUU3nMfCtfHY1ZFPO3ns4HHBDBllDPtUYcrIXisqbwmHiLp7qI88VXfyJEj2QIl3ZtkRt4GugWk7Ej8I9KvAiCuNkeTlmZsacEmxao2rbsgs0bNg0YJDjujUkFTGyweWvCI/Luj3glOSKRisDfitQKVo/EZJzyS5scU7bd8yDQeexIXZaZIriF1iOkf0d3QfWeePtP4L99TwZ79xPVfYy0ISgk03CLPQFolIAm8V65Joypm92QnrEdncqbVnnwIIiWwtfgp0teU+boBFnneq0LLmjE4roiRyrFDnSLxDTmf08RW5OGKVOU6Cm6gxZaQ4DY0nqJ5qCZTZEc2IDpKt1BgnaLtCfB+Z4I+7MZhQ8P6IDYnSVMS50DlofoGtBKrqM89uh1Gik0JYSb9ErPIcjonvTGDXDWW2KL0z2zPzfqKbE3dfY/JCTgvlUlN1FiM1WUMyGaMtyRXimujaFqcaRBXZbUeYWqg0yyyZisSGDf2eyH3Bd3/i1p9g1wQXUPnIvkSGamCOifu/qKjFgs07aghQt3QiEbLiJhqMPxHViFgcBxFpfAs+0q2JQ9lJc49B0NrCeXkhaegOglJa7JZxZUBUO02TOagW8TazNzM6B6y23OzMrjauvqDT8evpQE7cg8Qky3xM2FuBOSG2L6x24rQqjkuD7Gq26KjPkXH+kTlqTv4JlxvufUBFjTkm5ChxhwV1qXgKj8jZ01UbGcPROrJaSYvES0k/K36+Ke7bibtIbHWLTZKQJbddk/SB6mXB7jtZdgQKt6eEMZJgdk7qTo4auxa0fCBZyWw1r7Xg3m/45ley20m/SnKsCbNF6UQyGzlH6mlheNQUObCUleb49yx+hrOnVYHnmOhjoOiVuVxpRc1WV5hxAe1Qp5ovpyNu9swfeio6DmdLWgzrrriohlEmgonsImK3lVr8RMgeKQN7rclVj2wLBk8kYJ/g/MlTk9hWx1Gs4BqMUzT6TBcjlorO7fxphlJX9CKwWoM0d1pbUWxH+36gekkU77EkRK6QaGqdeM4VKxtBVL8Zf38IERA5IdUNv7RkN1OpEy9nz0ZLNIUiPe9VphILbB1btLx9KEzLiX068bn0PPnAY1vjOaPyhIkXrmGkYmPMYDvL5DaYMvahsC4bFZItbjT3wrFyrENEHT0HazmfJOfuTr53HMY7H+QT2grKsaMbJE18J+8rzlSoS0GKmYigcRVPl8Lp55UXo9lTIe8FvzdsxlFnx/crrFxRXrM2hUquBBnI3iCpWPUGWRIqzdxbqM6cxsL0+gHlJrwAZyx+7lHLRpglD0fF8RLxPyTqqDiqHuMrYvNA5QJddrA6vN+oW4OtPN2hMJgBmSw6P1EfHHdmtE4Yr1FNRWgzVRl4KQtNeqcKPbrPePmErwvb7euSfmwjeyXQFHjoMLeNZ/mAjCtHMnOdEFmgH95Ry0gnG0TJqIeCMAXpf4UQKdWOFhODV+AbjiIityMr3xFmSVQto73gnKP1ErEGmqGjTg0sC02nMNJTVxnbWvZJo0uhbmvcTVCkQdWerfwEXuPmzI+Hll/Ezltf2GVDVWCcKtofA/6UuEeDHGb628KQCuE98SonohmQD4bUfn2pyK4WVVdULDgNB9twypGFD9icKO2I+nllf/7I7DR6yvjpxCIN3XFgFAq77SzpmZEr2/5M0y/seuFn9UBZEyG0qLihd8tNbsgSiCJR60gyn1hL4mQDvpO4IvnF3XGrpDv9tgj8IdIBKXXp65atHtALbNGSlcfWlp4j6X7nVgsqD8r1iHxj8R1NtWOmjSgdJdek7oacoOkkYVGU1lJCBTFQy8wsPSIWpPH455bDr4pbtRHHSD7U4DfaLZMPPSVPbC2090KKj6TnC88vgl+0QTwGat+R3waK6zDBs1mPyDWFr7tkoesw1wX5qebBTrx9+Xp0NzxoxFVQm4K8LygtyJUAf8Q2O9MkaCT4ZiO4J7r3dzZh6dtAWQSv8kjL9PX4spNwL+g6QCkoadhDjQkr+UHBo8T/H9CplUlJallY90J7amE2SOkI6hdaZQg5UVbLlHYaU2jCM0szkLPEIFlzplEr016hpaMvI1k5ZnUgyCtNsCxhpf07xb5J+l93toNB64i/K046s54U8eXIrAVP54nXfcH6A7EMEB/5aFZeDwvi1eCoKTmwfSdg3zgMR6b6jtAdz3HgPfWErcapd2Q2kAzFLThfM9qN5D5S1p+xhxY1K/oycskK2Wm4KlKdiVbQtor8JaCSoX6W3D0Iv1GFAJVj3DTG7tg14o4av0BoCmY0YBwp3rDngrg0rHWkmS2p8pSzYXqNUCK1dFASE5ba7NS7YdQB3TqqGIiiZ98WnIsso6BrC3GS2Ied66VDfKh4TC+8X1r6KrAEQc6Jh7riGjbq6szKjSoL5qipXUQPiqkoOr2TZUuMG3ve/rjpQFGJRk0EJ/DiRC4OdXacHUxCoC08zprmSRLWd0TSf32VVlOUg86zqBVFzXysuVPYW0WUG7Qj8bDw3k/UIlC3MKk/wY87RTfkqMm94SQCdcjI7u/w6oyILfoKujF0jwPcOr7ogssP5KGFOePbQtUUfK2Qu6SNK0K1kCU/xDtt3SBvkfFLRag065p5+jXSlwzrmVY3VGiM7VBcuB4DR+XZP66USVLfP4PqUGLlbfO8xwjyztGuNBLEdaNWkrLV7KmBWSH0jHu2JJMtZm0AACAASURBVGeo//1MrRpma+lcjeGEOzWEKaHLQvIrlYHLYBiWgs4Nh0+FtNdsh4XNeCrfM3pNFoUhN5iToCmeS2jQMUDtcbllrqF5bBmvgcNguZ8MZekRoyFVkXdgXDJGTTRu5FptqKUiNp6ufE9m4UaivLa0NhHLyNZV1G8ZtprVLPSnI82omKWjkoFcS5rmCZUF8/eC1HTIfidYR5t2nvMT3T2x15Kr+khHhblJqgPoKiLuO1pI1nxE6MzwqijTQNkDUiiEKEghSE1hPBpWIdlLJqavx5lF17Sx5X5RCLEjfWFSoN0j/leDqCzZJZyQ7M7jbCQky3pMxAgdkavyJALqaWE2GqUjuyysbmPYzjy4ma69cLEtTw/gO4MxHbmvWXykDgIxvvNxCrQpIRzEmOAhUuE560Sx2//r+8J/CBGgCD5rhfxsqVQGOaKXiHqDcnllUQ2DdFxGh80NVu7gJGYMxBIZo4DyzFrv6F1hP1iyUeTNskQovgIluTYHXn2irL/yQWTm+E7OlspHhtpQbI2ff8XlF0JOCCrK1DNPmWJmpJNoMeKWgd0F/mVusA787Gkryb30uC0TzMrnSSDEG9lt+E3SjD2hgWsOzNWKyF94NQdulWW67wSt0Z8L7xmWyaGKxOcPbEoQYkOfTwhRODl4p8GETDFHlrJT54RrPEIErJPcX0eOnxVD+Q7BQl+vsCdUu+LvNTEnxrgS88Q9P3OsHYf6e2I9M84CxEpMM/b6zFXdKV3B5p7HAOZmuYbEg1p5zUeCq2iz5nhf0PPKw/DIUM2Ye2SNM7vRiP1ANgm3J4QrbNLTfv6B4gJatYz5lSexEpUndzOj/h6rDNl4pmrjsEV0KkxvG6OQDHdJ1AK2F67DTFV53G2kn0euFOxkmdfIm5q5mR25bgj1ShKCWh9Zt4qwC8ypRV8bOrvBMRCOnowj5sL9cGQOFmwg3go2WLY7VMeEjgorJGX9Qjg3HM0DU66YoyDXgct4I+tIO23UQbI5TSqWPSeimIib5iE/8ZodTzdNnhP7zxo9GOoUCXPhkB1ue+ciBXkrPI+KNzNTbo5NKtoU4MlQiUx2LanAi84co6KSHWVIBOW5lZY2CvTJ/Gb4/SHSAa1Veeozw/aErq6UQTM5izA7IjVUSlPGQlAXVBLIYyEtmhwE6REOtwCV5KGG4V7xXu244jCzQhiNqy9cQkVfFeZrxTEL9u5GME/05cr7anF+IwhL6RQiFlKrOWwF1S1cL5qDs6xbJgmPdAWZa6p5I7Q1GhDTjq0ybzniqoY8bNRPj7jhlVf9ESlmGiWIs0O2F7bdUOmdIEEtks0ankXF25p5bBJvTaJ/M2Qr2cKCqEHpM0GBSXeqqUd3V943Cz7Q2QZfR+IukcphRWDZJmpT2FyPcCvqJaE/CcIvks5k7ulEcxgxMjLdn9B2JGgN+44KCXd4Zo8X+i2xakudNb5ognLkcscb0HFD65Z0lITRw9IixRVdOYR2pCyofWQPiQMRn2BQmSY5hKxp5J1ADSaTpYc10quWX2pNryJ5WNmqlpPPXH/YcD86mi4yxBa1r1QfLHyeGXSNNSO10Fyt4HEH6RzD5YH48Vfii6D9UEgvGn8CcW0o1qNzpg8LQ3ekyQGzWt77gfOkuTuow4ev83C9IVuHSAspW8SzgS+egEcdIc49mwuIUWJlpJUVwcParDgKZTc0Hzbu04lunClVzd2PVMcfiOKdWAIy7ohFUivHhOThtODFiWm+YUOh7Gf2j5nj7Nn8QiMMt9BwbgLzpGjtzlA3yLhzCAWjM8NeCHWEpcHn+Q+cDpTMHA8EeaGsiZzBrZaDPHJcNsqwsn4acZ3CnyT6/kAnnzicDoi3lllVTDHxZdEEHXgYT+TRIXVk7+6IsUUkj7oVip157za2uSflN+4+I5UhlQ55bJHjxjHC4w5xzwzhicbB5iXVQ4Xygk4osB4vGnxcCDnj5ZH37QD1B5ysqRqDuLxxixohvwCJXU8c0wN5znRo9u1AmZ/xj5lGeraSaL8LvAmDXDXtB8+SCukk6KJBLAPVciXvkTV5xs5QHxJPvWBWBa0Kjd45BU+z7cgED7ritHvkHWJbE94h1oWtFMp3G1XOpBlyWsnKk4sAW5CqQvmNXhlGp3AlsQmLcQP74Yq10BSFVi27OJE+jxxDhS4SeypEfaBZNrpxZUwR9V1glhFUQ68tUhbWPPBqW2Y7Ef2G2it80/ArEWkHuG0YUSgRprxhfhL0B8fbHuh8ROjI9ctAbo/kXOPnD4TRoFDcU8NdTLjuneNn+MElxEskqYpqK5RG8WFfoS7sjw61TkgDk914nmomHUn7zv/N3Jv8bLedd1rXane/n+5tvu4cn3Nsx3accsppCwJCBQJEyAAmSMwQE4ZIMGOEhJgh/oBCDEtiUiUhhUExiZIila6cznHv+LRf8zZPu/u9V8PgsyADh0JJSvKWHmk3Wvfs/j1r3fda12/OjzTTmaWMmN4STU6sBvSrmQnDkGxQc4INBhU9dewYK8HeCEQS8WIhCY5Ud4xvIkZGut3M5DtyaVn8G5ahI50naCOL8XTZQjWPhDHHnkaKqBFOYdYT1d0CxjHNBV3QbEKkmCYSPTB42PkL8zRw0YpmDkAk70Gu//pU/6kQARDItKUOBXpXMyQJUp45dz39aqYqLWr/HHHZoNoaoSX9dKDrJHFzZOsH5FiwdI52NpzUEYoDzhtEu2FJR2oVWfyaoHaoIsXYHjsbTF2wiyPLeiKZToCgx7FvS8Lck517qlaxbHrmR9BqyyBhPif4pMX7NX70wJ5rNWEuCW4eCZMkT2qsDFhfQBhITpJFfsJsFFpO1Hqmqlt25wTXpbg40b8KXOsZ4wOnXmLXM6q1nMNMXDKC86z8NeBZvxHYR0VDiag6XF+yjDC5SC8dKk9YvKZzM3GJbNIZMQv0mDO7W5Jpoll2tFKQbnqCjmBH0rlEeWjEjJ8EpY+0LuHKzITKstorQue4UjMuC6jlFaW+ZXIDKh2Y9rBtGjpzgzZgVwU8OnzU9HLEiIkhV7yfVMg6wVMh0pQLC34UKFuyOefYKqVPdySyoawC4d1AbxqwEL1gQZOKK8Jl4YY9tXyg0xmuz0n0hG3BbxeOStKOGYod0p5w4i2g5nxbsNMC0UWS5wVnExl9RmN7AjekpKgRKpsjjeBSTZwaRegzToD2AukdUmrm0LE2C+dKoUZLOQz03hGyK7TMaaaUIW7p9ZHYKp5iEMnChi23IWI7Rcy3pKPCqIwxU7RTRYgaP2hUjFgv8SanPCYYlaKWyFFEXl5p4lrhyDEyZZVrnJRv98qEa6agUd1fP+P/qRABIWAcJNNo6e87Cu9hLbnFkdgVj0tkZT4iqANqOVOvG4gCESdkWONWCVfbgNIQE0esDWq0xPSCv+pZFsVgN3TiEZt3FMeJyQlmGZhP0KcLlfKMyrImMBmFfLrHbSxZHDneSngUhOvuLVuuk7xTaqJOqdIztorYDXRrj1cSHzVBLLwJLSEIQpSYsaTUJeebDWGlGI3inE40fWScIipdU0yacrviHBKik8TBY8WamOxQQhJNIDErhquFaAN7OzKVBbI5UT5UJOpMlQkmC6PPSCaNGB1ZfYWSV8gloUoNJB3F5gGnBEtyQI9r6ouhutSIMWNMZjrrEGrHYEbSUpBJ2NuAPhuaoqBEcNdH0nOCKUom2ZJWYNMSm6eclSeqCZc/JS5nBquIiaROUvoI3g28DCNxf6Ke3vbOr/2CLzSyGxnpkARifSQZoA0WPozMVnDbJ4yiY9Eebw6EGh6SLS7bUIUTG+HfFhCfFMxdRZIvhHpgDBqxK4g7h2/PDPc9h0MkWsXlpYQpw27OCJFwLR4RSJ5kgn4emU8l1ltyc8InKWIVCJkiZC3NZWR4mnDuNqTzc8RgEKuRlfasG4FbUtLdFpGdqI4KEHyYCnKZcuoHDiR0xlMPLeXzyPqoCYmldntaO1EyMqqCxveMeuFULljV4IqEbK1I7ieWI6zimS7zZIMmRMU0Rj5Qe2Ki6My/hrMDQoh3hBC/JYT4jhDiW0KI//rH7/97IcRLIcSf/vj36/+qWMFH3GRxdkSjGW3P0EWkTGj2M1kY6HtFryK22tAvGlkq3CaAWJjjljvl0XUAPPURtIgwGOLD2y2obpyBlOk44WRka58gBsjJ6eac+VKwmRcaq9BGU7yxhG7hGCT22GONxD1MjOuBECZezQv4hXFRuLAwTAYrBXl6QN56pqSgwjNYidMd+VXGyUvCwx20gn6KhC7BJymLVWTiDefdQiYv+F6RVBOzq9GnE+twYFsoatGzzBPLfsKoa1T2gtg0yCQnGEfrC44admEmxdMZARtPe3xLXlom0IskJJE5VNSPhkTV2OctsvAs64ZkGYkF7GwgHT6jGiVHUlrV4l3JPDbUXcckS6o6IyQ9QhS4PGOKnnk84YVkq6FsZuJwT9I4yjlQyEhYJpISdrea1C+kOqPLFWOX8ZDcYueJaa0gLTgSSceKMbfk3YpsW2LerLh3NcY5au2IcYtaeqyLCN9w2QV6EbjYgeLVglEjY7Nl8hFdCpa7EXO0YC2pdRSFYOgjuhpJwxHlbljPiscpwYuFB6/Z6oUkHvHzTL/A6nRETZpZXKCzuCuFGhYSJaH6DJkPuMlyNJqxfDvr2YYHfOoon2qE2CDEiqFNycLIkyIgdjllljBeah7zheXsqYucJW4YxdsC4LVPqcUFIVLyeUGMa3xvCTFhSReWGDntC/ZUKBpMBRftCXHkyfSvASoihHgKPI0x/rEQogK+AfwnwH8GtDHG/+n/bywpVCyUoMsEsk1RusUmkk6nxDGlrhyXocdOGUVwDNnA7CTaaNKYEOaaXb7nfgAnHEqXmDKyPGbE5J6VNOwXgV4txCHhVg98GkvKoWX0GTKfsJeaUUdUdiJmEv9wRVFOtPNEkCP1nNDkAoFD9BCkRZYBKzes9oa5PhOmETcpRK0YdA3NntLNiLymnxbqMNHbGpW10BiEnei9xK49dl8xZI5pnojFiurcESIQVrB5K4JSnykNdEtJlCOmsUy7jkKBPgrO65Jts3C0lpoTWhRcWJh7T240TnnsEuFJhn/jGIuJ9LgjFB7pPCufcCr3hP4F2g0k8cLFBOokJfMjJxPJ+gQvZy6uIhY9evEYUyHLBXsoaIeWmQW19lz30OvAqDbs2pGHG03WjwQ/MQRBaSNTlyMyi/AXjM+YhKSYErr0iCuLt8xHJvZLQCeCxSUo1VN2wCrixsCEIR0Twm1gOW5I1GtCkdJ21+TmNf048zS1nEb1FoG7LMhQEK4d4QhVphm7Han+GJ9KlgC0C5WEN0GQrUqccYQocN1IlYFvIrO8orzac35VUqSSNo4ks8dQYPLApR9Ypykn3ZB3G9z2jJcly4OgSh3EntxG2taSmIrWH4jeoqJiuJ3hLJHU6GSBy0CeZdj1Gd8bRuHQ/VusXNdLhjyidEWCAt3RNJGdCzzEAlMK0i7SxJ98duBvPBOIMb6OMf7xj+8b3mLEnv9NYgnAebCpRhWeFYrJZGTJhEpOtI8VuXQINTIag4iKRGWYqJjcSHQNL1vBs0zifYbrBlQ3Meo3IG4YpkjlK3Sj0W3CfSJgbhimFc9sj7yk9PKEf3qiVBr9KKjyey5Nz8Yk6Fwy2gorI4XP+CAXaDNjT4ax7zjTcZZnDBKRCZIl51liCXXCUkSOk6fQkuM6oRtbllPK5A1+NAQhSR7XeAW1m8mMY9NM9GVEF46JN8hzSoJiHST94LHpTCBSpw1C54w+cjaRahlZ8hGBx08VrtsSxwVtK26WCmbDRMCfZqYwU50UpZFY42EeaBmZGk02vqLMW84mEKTjNFpe9wVFk+KmitOVxqQdIk5QOub5gnwIHMWe7GohE3AzVjzOK2JdsUpvOW5ntpPjiayZvWTr3sMtiuxqBNkzU2DnGRMqZPnIykgYM1yqGScNlSZIQZGBrKBRAs4bGp9ALRmSNe5xZJ3ukbsKN5TU4yNOJAgt6CbLGBOi1qRlSZ8s+EagvaF1LaM50L4ocNMWnnq8SDmFnCq3JOnMVTuQNA5hIfodosp4JkbGuwxZejbTwJVfkT7bMdmBszFku4VZ1OSJIETFqv8C9rBQpwPDStLUcA5XLEUE3ZLHhKt3HBmW7SBJXCCSsHSeJHMMec903jL2Dn0pWbyk7StaG6jcltE1DFFznjxFyDisDCuxvOUO2uGvz7+/ixahEOI94HeAnwP+G+C/AC7AvwT+2xjj8f9rvFQqEhVZ4VCyYloCYmjxusTIkdmViGLgWaPZY7HFzDHTiMeZd/OFg1bIPtJnitIPBFkyaUs4H5DaMJeO67PhWEkyV3GxPTYMGFcgRU8vCmx5QUyWZSoR9IgpMCY5UlwQOkN0GSQluXlJd6zI1hGbHDkcDFJn7Gg4SyjGnLiLjAeLyTpGCWW/Qi97zkqzs45Hd4VNBdFEFndExIIUT+8UdTJzjJachXheKNCcVgEjZkyoCaFltDnX3cRjjMT0OaZ7iUZxWSekThB9IPqaq/iaOxVRfY299cyngvw6Ii4zF9+CtKzmkdMYWaUpPukZ3A22kHhxIlWSRgZWjzmXpcXewnjYkq4eka7g6XLNm/wRO+SgHf3cs14rAjl5/Cr25hO+8O6/x+ZzB/7wT/+E99/9Nbo3e/7io0/RySO1+xxPvrTh9/7Zn5BkR2wUpE+eEfY/QucJD59oEqXQMjDGBaky0mlBpJp5Wgj1SHlJcSZnWd5ALGjrQGYF2UUyyQ4jSjrdkbaWUAWGruYqNG+3oW8UyX5iMZamBNMaSilp0wVxViSpZ2o8ZCmr8cJwVbKcZ1SpMYeJLrHYQRKzDO8X1mng2PXcaEkzB5QoEKbimJ5YjwsmWB7KCd0q8kWxxIgvJIKZxc0UU8VcGWzTMttIWjviOXIxJVUQdBr0sPBMRA7ecdaKMinJhp52s0buDwwk7Ohp0hWpHzF+xb58gxQ57vSTW4R/axEQQpTAbwP/Y4zxnwohboFH3voM/A+8XTL8lz9h3P/rOwC/uC5WXPozYrMjjSfEuGVSZ4pxTYgzbdYTlhUxG6n6EZ0ltBdPTHOeqBN3i+d5gLtwiykeUDM06TVWSEL7yLzT6PMM0ZImI9NSI9MGmTyBNxfETSBcBL2PbNcW0U3k1vOgM9RBspgTZdRMc6C3kbKKCDRDWCAmpM1MlJHotths5rS0mFAxR4OVDSwawQCrK0JvKFYXlO84rkpuP83x4YGj0LjgUGicMNxuBvpO4tMbithyGBSlMXTLiFeBYgnYUOLyjhbHVWE4dxLtW8KzCvlaMuUpV3vF2R4RzrEtc47NmSQrOGvwY4IULYoEaxKGYUMRX2IywagnICUNgSHL8OMZ61O6yWOUZrWpyYRmGgxXX3mOdYJFtwxtyZf+wZco24bd9lf4sx/+Ls+TWy5LDztNqnLK5zPf+vSHZOKau08/5vDdN1QRnj254qX8IcOfwrkeIAN115Kla7SccV5w8Z7c50xqIPOBLhagJoysuJpO7EVGkg1M0pHPW3q1x2NRxjEQ2JkSO1leT3vsAl7l7JKMMV6YQsDMCeBQes2UHlB5Tnrq6WaJ0wXGjVSFYAFiJjlJWIWGiWt0d0JMCm8LTNpw7moS2YBaUFwzTEfKtGARR0a2qBZiCrv8yLkrcCIjX45MVpKamT7WxGbk9tYx3kcKXfCZnjHlis2jpk33lL3neGupL9cgHhkDxFKweIEbLYk+U0TJvp1/ogj8rcxHhBAG+CfAP44x/lOAGOPdX/n+vwC/+ZPGxhj/EfCPALRRcYwL7wEv5wtFAM+FpYShO2GkZB0iXd4hm5GuSN46BJmFQpw4LZaY15yOR6Z8wg0JSnqy/hEvMtZyRdf3xFiw0JEqRTY23M0S2z/ic4F/mLmqNIO9wj802LXk4C3m0rEoD4lBNRWuOpG0huEsyWRAkzCsSxw5kSM2zDglMbNkiRM7o+mniHqS4+eZqW3I1YTqBPNSopcWXUhOU02hzjRDSRAt1cpyPK9ReUuMCxcTSOaBaYykpobpSKe3WDuTTCN9+h7Tw0fUTw3nuaS4F0S7oJeCuDliBkGsLI+Dx68qwiWSpi0iFciuYFrP9PHAehyZreAkHSIkxCCxoUXIkoUMZwLVItm8m9C3mks582zzgnfffYcbJenUgD084wtXTzlUI59/seHbfx45ykhSa3o8v1Z+Af35QC6+xHI/cXNb8DsfHrl+subc5diQcRIdpANysYggKOfIsezJ1ArtEpZVi5sKpq4nDQdMqTnNKZ32iBiYXcYwz6R5y3pd0t9f8KNCqYqzHtgUjtzXyHJBXjwunZF9gnA9YdNhlaUbO1YbT/dwptOGdSLp/Ikk1cyTY5gjgjXKHFBLjlo6lCjpsgNohx0CtZnxJNBH5nKiSh2XeOR6zpH2hF8V+NTz2BqoBky34IwgM1ua5YIeW+qN5eFekwbB/XYiawTTw57jTSA9VFg1sZkS9vJTbGkYZ48UO6rzA1npeOwib325fvL1NxYBIYQA/lfgOzHG//mvvH8aY3z948f/FPiLf1Ws4ASDDJxzENbRnCQOgThqVBXJVII4e8Y0crORjIeMWg0YMiZ1pq0Et+cjaS6Z/UCF5TF3pOkK9+bMYibmS4F6fsLu15yWyLJ24AoW9cCuuaZVjv0EKtzjzee4nF6ykoaL2LwFdYSZU7wgFot8OlM8rJDzgNMF9LDyLzlpjd0BoyHPYDEJcT6BSeiaM1ubI/OUbN6TjSsaBiq5QYqFTI3MCWz1SDeALBRuvLDTnsclkB4dclXQiYJ5OLJNCgY30IsMZySsGtply+3DnqgM5/SM1EAjOc8lVxxofWBSgRUKYyyhuIbuwHmjyVtD5XouQBkkQbxto2a1wh81rp353NNbuvZE2zV88fm/iQ2aJ1crdr/8cxy7gR/+yzf82n/8JS7nHGMcobnhO3/5B3z9q18h+cLnWOQjaZvhRE/7zZRN/cijb/jZp5/n9P4j3/vkI57faIrtV7DbwDvljs+/+4Lf+70fcnj9u+i24pCOPP2lnyH9nVd8LDu8NGTRMUweMRvGzcJw0WRGoK3HTjOH+4HSWOZk5tpONJ2nbxa0dPhTZJGaMOZUU4sTkdDVjMmFXEM7K0yekC4l9+qR3RzoZSQVASkh6EBKxeAcYfJ4MRLEc5LhxNlkmGkhZp7b2nOYYcgN+pCy1wlXSY3r7pm8YnIC22UMIlKOHcN4wNQBlT/nfPiM22TFWZ3I25JzMmJkirxYXDpzcBLdLIgiRTxObOwKUXRcbEkYHS5AlwH9X5PLf4vuwL8F/HPgm/D/yMx/B/znwN/n7XLgI+C/+iui8JNjaRHXScaoA2WbEhLFtBzJbUnbD4zWIq0iDoZMt8hU014SdNJSa8UgJOvR8SoL2EFz5eCV9LDK2UYYncctPU7nbLygRZBfNYRXhjKDey+xU0Be5cjHjr6K2EvArwIxbojNQJJ6jLtCpPewCDoToZOMyYISCroMUSq0cCzC4i8dyswkNiVMkdwsLC5hVpGyniAa/JITzh6vZ1LlOJs1cmywMuJWJel8Zm4UYgPuXKJeNG9R5VZwHFeIXUtx8ayFhlLy2RRJjca3A1FX3M6egx7pjCLtDct2Rpwl2RJYVuAvKYIRpSucX1iCwm4XfJ9BjNzYmWPSIoaU67Lkde+JjeaDq6d8+Ze+Sryy6F5x9eIZ9U1J++YO+84HjLPigxj56PU3+eT7Z9Szkv/wl/8d3N0bvidHJAbVNJy94kluOIWET//gW1Sfv+HxRy8xmxWmhJ/76vt844d/yeGjhuP9n/Hlz/3bsJXcf/f/4Pu/t1BvzgxRM58X2M00VOzCgX1reCKuuMwTNt3TAmbRTLmgCJZgIMkEQ+tQTcBdG+K9wJsF+3zN8GnL+gqMz8Cc6SL4x4iQkbwQdMcEceOYCk18WbAOJ2IRmFvDUih835GvBG5S1G3GpEdCyOhuR+wZjIbFKtxjj18LVhfBQEotPK0JoB2DTNBqQQ4eGwraZULVHjsL8lkyF54MyXypOImRvI5kYcW5PHD7puS1PrOzlofZULqeaKCbw08vWUgpGWtVo0ygI7CxM3fzlkyf6BILaiLvDE4oMp/jRU9GyUl3VH5hYWLBUEaBYsN+mNndSNR0ZjpkmJWiac6QOeKyo0pG9g6SpGIdGpZUcniwVMkFpQL0cKHA1h1jNGRK4U4py82JpJH0c0q+6Zjmd7HjS/roMCojjZ50ssgkMrkU6kdiuyaIhWkCaTPS8szpIHlaJfRDgsoupE4S04rxfELJGy68xj4V0OSEZWZMBOu+4CyOmLwgtiOTKKmTgS6pSR4GMJoimfCpJm0CnU64hAO5DihqWh2QS4HxGru8ZtQFUU/EDrQIhOQZqXikVTO1SsjcxKvEcl2uSew16/XMh9+YefZrX+Zr71QcJ5BDYPWVZ+QPsLm25NM7qJ3AlQV195p/8a3vktUrvnyd0ldfYPjwY14lni/xhOKJom8GPjnckR1LPhxe8+KdDeOV4Revf5bKL9hC8Vvf/Yjf/4M/5N/4pa/wcPeab93/iMOPXvIlU9FPHZ+cH5GXAXl7zdD1JB1IEwi+YdiusG2LD5abZWK/kvgYUF1gCivSNBK7GpW/ok8MxTAhXII3OWFzwVqYukjhBfZ4TScewS6o8JQzF9LMULuFx8WyyzRJPNJdMkTe0mqFXlJ6NCJNUA3Y4gERM/pyZne/Yc+CKRtcn0NWI8JLbsw1Yerp48LowCwLU1QUTyI8SkZZ4+IjTyl5vUxwpalHxZI5dCdoJkcwJboYyAZPGzxyKfHyAstPJgv9VIiAlCKqcoNuWpbnnnxfkOqW0dXMboJcEUcFukf4islH6ji89eCr1gROeLHGetj4gZO5oVOvIO7Ii5lz03MTJXMRaQ6edfSMyTVSpozJZ7jJAbw4+gAAIABJREFUkk+ReReoHyPH9xf8y1sqd2QMoGtP7yD1UOiEk8uo25kpc5h6pG0joVvzXFd49xn76xUxzKh5jRrfoNwNAVjiQKnOxHdK4l2PkAZURHSW/Y9tphbnGAtB0qTEOmNp9mhtkKonEymnIUEnloWWKenJGkMWNRdpMLnHnXtKrXFkNN6zzR29mnCzBW1IxEIvIzd95A7LOjPcdmdeRoFJwHsNRUJ3sZS7a27sQPniOe+//w+pUs/58Jr8y7fYlznvrBVvkpYvvftFLq9y6t0rZL3DnCMfykf6h5n2R9+j0xtyW7M8PLD9Bx9QD5pMZLwaJvz+B9w+e4c2tqTLltaPxEFga/jg9gXmZ94jfHLCJUd+9Bcfo65vyU4N/9v//o/57NNPiGqGm5n0bEj7gVOw5FLgpUBmHjc6Zlch6LldeVqf0rctLiRgPTZ64mpHebpwqReyribQI8bAaEowLemYg1hIXMJoJJNY2CyG9InnZT8SxohIBGm4YWkXMnWgTRRKjqRTwuQgWQXGo8HmC7MU1L2hz0fCqDClRPVb5uQOQw5dQrA9VmYokXERb0g7B1XK0AXkpsT4E66PJF7SxBWCMwmSwgQmZRjGQKUEbpEMMqCjZwju774w+Hd1qShwCqptwJ/ebncM5Qppz6TVjrS74HxK0ClOnzFliW8Vy+1EJSLjIcPT4sWK11Kg5R4RLEL0DF2gjIJ5BZcTyPU1p+4lle64pI+oY030HenOM9zVHNOZ9PsRb48ok/BiVXKOPe7Yk9eRsRrZPUTuy0g6L7i+oPKRnjMHv2YsBUY6tk3PSSzgE/KsoSk0+XTmEBLifkZMFVd64GQkSnqkTgl+YtSKWViM7Qm2J8oUMQamNDDICVuNtC1YrbDFhmToGNeC4vEtoMSbnD6+Nc80K0W1pFzyW+z4KbMylDIh6IxWp+TjBbO0vNlAdIFx0CwSdpeWS11TcOF6/Tm+9u9/jWIZCPOOLr1j//olK14QjpHOpUQT8Vcf8oPHO8YPD8z9gTSLmM012eYZgx958/AhV0nKk80VIbvjez8cEHbmS89/EZ19xA/vSj5XFkwyYbsdyPwNRzx8+2Pyesc0eZ5//VfYbo785Q87lrTkyXvPOPWO8OaBWe0xqma76jj6gnRWiElS5D0m9ORDzeLPhHZgR05fpLRq4MpE7FTwiTygG43TFoHDVz2ehkQqXLcQMk+XT9hlQzo3cFNw9/BARJF7sFEzu3tMXNEhsS5wbRRjnJirazIe8e8I6i4S2oQYAiGWLHEin0b6YobRMFcLcgGdw2nYk/SGeb1FbwLDYeBW5ywXyxgzMCnNPBKLjkrdcAn3VFHSOoX1jiY6NpQEBbN10P7k/PupODvgReRJ3xHbLSL1BDowC3ISmMcLxyHjpANat+g+xe0jYjNwfYJpv9CNC97kyLkj6oFlcPggUMZgR8lQBy69Ixsi8+UeO1bMXYsYM6Sa0Eozngus8Ngw4FQApdC14LPDK04Xg6gF3teEO0mIPWVnmVRCIibanUNvavJnH5HPAX/fMmWwjGumq5q9TIltyyh24EfMolFbeLQzwgZqF8mnM+NQMQWFPklGpVAnh7QLsRakq5zSeAqvEakipCtC1zLqFcNjj/QBtzjOIkJ9oeCCV5KPwxF9fsDuLEoNjG5DEsEOd9hpYV/mzJcEL0rmFzWr2/fxT255L31G10U+/wu/gPJf5ObmFynrguMnJR9/7zXlC8XrqLGbC3/26ttcvnFEPha8+eRPkfWAySq+rp7x7GvPWOsFM1eY7bs0MhDmlA9WgkJu+dhfuFt6Hu++xaff/XP6k6DMLJengfT2ii989RlPbgXvbX6eTZx4+OTM/Tc/xumPaF6/oji/4un7X+Jrv/zrbG9XHCeLDAElLujpQjM6JgGz3TM7g9eaMQtc5jXJuLCfSz6cPiaWsFE5o27RrSUdS9IuJckiW7MlmRbyJCFTeyYfaB8OrPME4zN6AV30GJuiswtC5SRZwjB4hmVNziMXkcFdZN9taGbJaSXx/owtDGBRpyPLDLOoUXlD1qSks8CtAoQ94qDJ6pmHpOcQFyZjMUwkyYAMEcSRTFnGYMhIWNYFXlsOhUNLQz6u/tr8+6lYDhit4ibZsJ/OECzaDizmipt14ND2yGZAo+lygy4GlK/x5xpTnFjaQIqHfIGuQKzWZJe3vfZ7OWDykbztOIoCfCDZWmY9k18UZp5BRmavmW9GNtHQDLdM8ZFcT8imxlcz3X6k3hnK4e1GEo2j7Up2ieeNdqgoEOcSF0ZsXiK4kAGdlXg14Q8FUk9Ek2FkhpgeiXWNjBOinVA6EmKNSXu6c05W7uncU+AMQJglVdWSjoqjtth5YeMcjVbMqqJ0F6QIjLMibNaEsCeZLV5KGrdF7x6pTzmXzJM0jnYOXBUaomdfpZR3J4ryK/TTPdVzeP8Lv8Ltr/4C4puv+Zlf/Q0m/Yb55Uu++v7XOLuWLvX81m/+Jvos0V8U1MUOd3Dcv3pgUys+//7PI8OGb3//B3TB8cUXlr/39d9AcEJPM7/1R3/EvDvwwfu/zN1H30P1a772S1/hWL6huTzhfVfwlZ+94TTOhOUNrz4e4Crj4fQjXu8z3tvc4g+vePW4p9hZ/slv/5/o77ack4+YzzvM6jPWc4qtP6B9uCO5Tph7R9NNaNWwjBZzFYivLHXtOHQjfpOgDivs1RnONS4e2LiEQwysEohjxFvFaZ5Iby3pUWNCZLhZ0E3BuZ1IhQOdMoSBIiRENREXSHNNkJZ+OVGYlFgt9KeI0wLrNxTJmVMQrFrHtMrfGqGOsNYrxnlGpQudnEkCSJPghSaJDu0TummmEgPjDIvWlCGltQ3VlLCkln5eyPMAfcp5ufz01gSUUjG5ViQPFX3Sc+UFr7Yz+o3H6AxfjbhhRTad8bVCtBHqW5y/Jw05/dTyfAncP08wswBpaBvBTkzMqUINisDMogJp72nUlhjPyHRHNkb0KhLnA2N0KGDY3hIfjngv2U7XnOSniNIgxjUyRkIUKHlikjnlMuCLiiBmct0zH1IWLUnTFq8y5GnFvBmIi2WJRxK3Zc7u2Mgt3alnLWeOMuKtxvUlyZOW+piiguTBzNRKUoaeg7cMWaAYNQuWcdR82XTcz56z9CRBs1WGx2tP+eCZ0x1tcMhioOgVk2nx3iLiQmEELRuW8YD0JcJEPv+kZKtumT5f8fXnv0rylWvCxx03n/97tC+/wzG0EEeeJhvuP575ne/8gMvjJ3yw3XGzeUG0P+D7Pyh4+iTnax/UPOxynrx4TsfA7vIzrJ9kPNlEXj46vn38EV/ICi7DEevfo1gnfPLyFVkGhzBys/lZpD6z21S8+Ysf8fz9NaUVfKYmQvc+724qRtny7T//Bn/4+7/Lt//kn/O8fIJbIFxLlrsTVjY8mjXZq8C5GrmeA/vZ8kRdOIcnqOo140lSK5htjtMzCwHTKXqzpp5GWjGTC0+4koyHnlRHRCaYTwnKeBKTMwwSIz1d0pAka5KDYxIdT2zKZ+mEcZI4pQR6TC7xzYROLX61kN4luHKmw5Mu10x2wveSm+TCoygohMKpidJ4mnEGnyK6hNFmSHchxIFUWVzIcFGwqi5cxshKwrlMCMeBNLPE4QOK5BMO/U+xIamVIjoyrmrFTMvcVUweRBZQ8m2//Lk2HBZLnmjGpSXPA30fUXnOInLS/kyS5Dz6jiK/ZpmP2Mkyi4T2RcfuPDH0JZiJoRmpvOKSKcqheMuz1wmxX2ivcspRsncp6+mOoKCrDKtD4PDEEh8iwjv0lUIWgbRNcHuNvTWM+xOLXpOpM322kMxbJnsm6TW60xS3GW1zRJsSM739J49eoVRPtXjadM16GdnLmSmp0YPmOul4fQmktwnjmBHOb8hsBFkyZQ1Xl4z+WhBfT6S54dwryjCgyhV92KHmBpdBnI7IrGZo77HekpaeNk+IB4dIa37myRf51f/o16kqBbYkoyCpZvQQOd59RBAdf/HpHfP9Z6T5B0T7l9zvR562NZ/NJ95fP+PCDVdbTTq1hM894Yvvv0NTSdJDzW6nUFPkftrz7rtfRd+kDJee0JzYBMEffdxwsy2J3ZF4k1C+zngY7thtKrbPd1gyXp0tuY3crK64v7ziD//8X9C+Cfxw+phm/oihXeE//H3im5lezARdsr0V6FNCM3qU2jOQkFnLkgncMf4YaFuRHFvGIiDWV8hXCyp94OItq7hwykokkC89drrhvO6Jx5aABqvZ1IGlmZmEIXcT3UZCviG+mViJERLHMGnkrPEMTNuEeFGsQ4qqR46nmrBtoYFNWrB0e6g8vRVUkyAVb4vRWmrseOI05WxfRKZXM50ZEKOkNgVDmFh8oLYLUZZ0qseXFbbpUXNF7w4/vSKgpYq74gWP2UtUk1LimIJnTHKqomF+TPAxoquUfvDs0oE+aMwoKKMiDzWflkeMyCm9o/UDwWbEumY8OrZhYJ9OlCxkxnLYR5ApW29IN2cuXcGsEkqxZy8D1aUg2RmaLqDihVEW5ONA3EgW5zArgf7UEzcZJ1uTHB5Yrl6QtyfMODKGjFT2DHMgfZGyuJ70jaZbwWqcGeQN3l2YzIILKbXxtDcO+YkhsQlzB16O2EqgrUDoiWJZMYgJe5KMoqDJ9oAC67jxKxSO/dgge0FdbxHTmYuEm1hzP45YO+NQZPWIajYMIoPtG54mf5/VV2+p5jX/7i9+nc/uz9Qvfo71uiCJ9/zmD/8vfuP5f8CfPX6DP/rt76Iqx1qt+OPzN/nZJTDersjzLyKbB4ITbOsryg8+4HN+S/Zly8+b9zh9sEa9/JStege3ytH9iZfdK4a85llpePhk4G6x1GnDpsjw0nMrDB/ae/Sba7Tt+LPvv+HJi2uGIecqfkr15Ipvvxx490kgyJH7jzv6u44/+M4/4+4732UfLFk4cepAVinlkhAmR1XBQ3PhudjwUM7UnaMpIt4tJEFjsKil5Lw546JBPAry1HNpW3K1wpsLSVIxKM/SG0TSkERQsmCae5YhJSsGUp9zuvJvaVYSwgzb4BiW7C3R6d4xZClmcCwIfC6J84LUmtw6+uDJ5xv6pcGvPOmykHlBO2/IxSNCrmlCh5IZ5dAxiy1LfSKMKaICux8YxZpEXJB5RF4qDvxkEfgpKQyCD68x0ZNuai4iJRWawhm6TjImBiMiY+94Yg0qZrhhjbfP8FVGnySsdcCdPfs6w845U1Ow3L9EjHf0DBRuIW0FpynFAsIujOGRc7sl2ECaz/SJpJI1Xgju/ZHBX2jHgpUfaXhOKg1xEExjRRQpzZRTNx2phfj4hjHpWIRBBcm8i5grQ33yLDoj/ELAuZoxs6RDTyoEt/YKky9Yb6lPkpAm9NayMZ7r1FGMO6aHhWHKeLMH5xTH1BHVRD56dNDks+ZRtDyeI9YWyFRzvpyZ444qWfh0ORLyif+buTcLtW1L7/t+Y8y+Xf1auz1nn/beW7e/VaVSyWpjSXYsKcIkTkQIJARC3gx5iiEEjCHgh0AgD4GQQN4ckpfgIIixJCxLVSVU/e3vPf3Z3dp7r3bONftu5OFUQElUJsES1HyZczyM8fZ9c3x8/+/3LyuLJmjYEFBWCc00I89HmCchLvf4zZ9/jyfFjMEo4CqJOL35gvQsQc5hOT9DL3OKbEf/xqROcqb5gJflNU+u55TzjvY6p/Y6nMMe+wf7DO9MOO77ZF1K+skzjJ6P+dohvZMJu0mOaCz2Q53T0znuYcPJLdh7w6ezFOXlmk/XNsXGQZU3ZDLhnfv3qOfXGFbGJqx58kKn1p4R5xAtG7774kNeNhGmPGHvzb9NP3AoiiMsETBIauquJu23JHmOpnusqpiWkrin0WQ1XWySVJJSF2TeGmte0aYmlh+R1jq64ZD7BqbvUZUVWqnwSx2/UVRtSBdnOKKHE6aMO0FVZuhrByedouoKUZW0U43OrQkvBGXjo1TGrq4whwatrjGwPGaFTbrSCJI+qtniNjm9TkMke6S1h1YuWRcahV/StDUYOcpUNCrCzBxKpcjLhnbUgbvDEYJ415LJ5qfG389EEpCqYyc1rLWF2s3ZkxFRWVKbG3RNYyoL6r6HWcNNVtBor/z+NO05ia2x02ryjY3Vt6m7hOS1HcdBgWsHGDMXr2tp5AzR61PXLZ09QVeSOnDJemv0osHcFIRRiRk3ZC7cdXVcc8BQdOR6i++es4gTepYOrYvRV+hhh5kl2AJ0V71ClDcWGlvsax2z9Vl0HWbRsvsRaE7MLrGITUVkQUmL1gg0Ysq1xbTpeJjWaEbHTfdK4OEFGtO2hKDAjHNU0tA0BWg2TScQZYXdVXTeHmk1wtY8zJ6Nq29oGkU4dKh0mExrHHpM3WOieyOMtYYXHDIW9/iVX3wTd3yCO1pxubXpdU9YzG/4ZPkJ8apkla9ZLTeEE5N40qHMx6yTHTR7yJ3GzWZDjoaTWvh7B3RphZbH7Nsu7VBjcuch6aZBZKcE0Rm3gil3HxwRjE+IxhrLTYu21Wmf5CzdJdmwQjQRljJJuo5Fb0zVFrhGTre65PH3rjk8ueHW7iHJusJre9w+fIPj/QPe/+Yb6MYNx+/fYu/ujFtv3CJ844SR6TNqeoQDD0RFbumoooZygJmb1Mpjzy3RIgchIQsVSsV0Rg+rbhiGLiJfke1aikIh6xZj2FB0QzzRIc0BlbXDqY+4Ug5paOKRwt4FehAwHAxQNw36QJLaFa1ZUGYhuqlRRQ0jy2MpCyIVYzEhdS2aakzNmCpRpNYKG43UHdAXILcNlmdRNgZLIcmnirhJcBrJ4U4n2ZhYu5ob2SccuAjzp/QH+ZkpB4SSwsRBEROimS29siYnp9DBHmp0tUW3LbC1jmYww9hs0NoOvbOIzR62saYOHcJyzW6nEbgWiSpo8x6lqXC9knqh0boNnV8j1i77o5LIhPpmQjepqVcCZUo8c4PsClzNZXPTsa/rr5yCE5dKK2isCssxKDeCodWjsjrMjcVORRhOS9sNwbjBEg67uuBAlyzKCikUjqmzSX0wMlyrI0treraixqNrXdpRS7Pa4MgZnbFEzw0GgU5WQKlaVJ2jjTy83CAxStq0oehsZoFLLrfohk9xbUEvpdpEtD2TQReSNiX2TPD1d/8W9t6YpLP5lf0DeodT1mnB4vKSXptRWg4jfcy3Pv4um0cZUf+U285tCu2GrHuAuPqMweEBF58/4hlbxHCfd7y3CY2WdJjxG2/8HVxXo//uIfqLlsxPGNcNxtEDhkHDSte4efySILQwzI4uVbjthFP9Ghn3oGiJt09whwa7oIc4W/Gy1egvcw4OBCsjxLOW2NUD/InCd/pg+px7OXtnEf/iiz9jFDfs0pSPdisuF4+wH+14uXuB2ikMPUezLTJDo6lb6Dq8uqVUBoZocduOrQ9aOUGfFVTLjtrcYXUGTWsiO5dOrdEPPeyLmEZIfCsk3uXo/ZY4tREkuK2k7cAINHaxRFMtjttS9STWZkJjLJBdR5OCkgNKraZvZeRNQK2ZGO2ayhqxn69YO1OGu0sWcoBqOzpTgWqwhIcVbOmWPp3YIAyLzu4QpUcpHfR8gfL0V8izdsemufnZFQspJI10sBsFVkpi9ki1mE4OMZoKe1uSGS7CqQiMhqrZ4OkaS8NgMwQruqBuTdQiRhd9pBFRFDp24JIWCYHXI69SrBEkCfipTaI3bLqK/LqH160phcVYb1kkKaauY2kGwi4xwzFX9TVSN2n9FLWT+H2LqmhxVMdObrFUwM7cUSsNo6jQzA1O5ZJZArvVyXsF9DzUpmTXasheQ1db5LsG2zJolaLsBJ27xsp1+oFGtivQ7Sm623HdCGxTI9XOMcuQWimcOw6mOKS4LggNuP/WfabNIR+ff8pgVPLD5RnNwGfPt0i1CHNj0ZdT3jp6H9ONaGrFyH9I05+j5SuqXcmXsU6vd8k82uH2tzw+SDlR96kHKdu5xvEbt9mYLVUSc6kkslEcZ1v8SU5n+rz74BbiUGKpgrCxCN7ekK9nNNaaa55z8VmP8UOL/uCAqZ2yfLpldHyLnVhiX5l0zTmJY9LNO9xihGtoRPsH3H4aYR0OEMGKYZvRmRMCK8doDS7P1ij9hoSMpGwRnkfdL2lfBvhuzS+Nf5svyj+ievIUs9TJjFtMzAvsVUM+0VGZR9vXCNCRroGZXCHEaxT5S8KsRXUWnvAp6fCTgly01J6P9qRkZ/fwxA5tUyC1jiRvUV2Ga46p2jW21pFtdJSQuKKmam1kBXm3pY5tpArRgxsMPcLcumxLA4GkT0uMg5mnbDQL186ZdxOoolcod1HQaQa52iIXDkbQoes9MhVRGy6hzGjiLVooKTWTSDvFzP4a8GJ/lY+QmjJ7NlUhkJXCNzQqX6OLWjpV0/oVZi4xghYrCqn0BCwLVbs4eoPsDIy0QI4nLOrnjKsJF8MIL5nSiBiZFLQStL6DTFIsDbLyhFK/QLUZhj5DZBk1Ke1MEGxMwkZnbaWYpk3eFRgJFPaUWZcSqxxhKNrURfQqlIB+7ZBUEYkRICwbfaVo3ZRWvbLI9pRip3RMv6CtNUReYFqCnVJYsqIWEk0qevg0eUHZtZTjAWo4wRGCO/sjRDlhbw9eu/0687ji4K13SObP2NYxRjbh4W2Hq6VimX+JOnNIsg/54sWSYO+Qcd2g7T3gP/pP/wPON2uMfMMu0dDTjlKd8+l1gRVXRNEnbIoOd2ggYpPJaw/wUsHTj/8Qs/8V7EHGqm358Z99hmEp3ph9wGtvBwhvgCoGvPFgwIMH+5RRhRIWne9Rhx7y2Z8zHPpsjRmB6hMcQ/6s4AePPuXerI/SBwRjlzgQPP7DHzA2PKqxZPj2BPVxS3W7R/X8lJ47IE81lB9jLwZE1ZwnxTX7wQhf6Liuy0fzD1k9W9B6Op88e8r191Yk2WP6+wZy08fxCirXRNZrJtqI86uarSMY5mu2smYQuiAkWZfTmTlFbWJdmehGRR7qaFmOVblkdYcrFHLPIb+JqZoJmhXhmB1dJ2lsDbnVqXpbpLDxVUO7nZCIOW5nUwQFtQjoNTVSr+hysDydavMKtdeTJYmpsLR90rWC3g1haZBKSeN4aIsNI8tkqStGqiK1JHXaIGxolUHreHhlTldqCJGxy3+GZwc0Q1OB1IlkRd8dU2zW6KqPN6zYJi1aLcntjgPdpHQMnDimtX3iqmFwNCR9URAZGqFaURsGbQWTYUWy1dlpQ8bFklyb0HKNlAJleATZGjmxKdc6WVVg+gaV5lKkCbJr6XQHzbKRpSTUd6xGJuNVTt7aGE1Bp4UoM2Jnl7ibEFHXZEFNbbVY6ZDaypG1QLU6vp6SJ6ANHGRTkhoGDw/e4MX6KaPJIcXpls0O3E5w8vZ97v/yPv6tQ35B/yrvf/CAlTFmIJ+xuiyQZonpzSiyALd3hdrq6I6FNCSreYyrS8rejBs1x+0OsPsr7CtFVPkM9xU//NFzjpsQJ0j48SJFbtecZs+ZWEMufvApz9od/WCfjWjpdTaltsJzbJgq3h28y+efXqCbBfNnL/H3d5y8+XX0axPNrpkcvEb/9pR9Oaa0rukZARLF+PA22Tal8Y5JmiXt6pTZcMBa+ezkGUG5T3J1SoUBXcFFWzCojrGlQaF9TGlo6HFNa5gMOsFNa3AWLxiaPYK+yywvMHz5SpmZDblc/jlme5unX/5LusxktzhllW14vj4lcDy6THJ68xw9y+l6OnUmEZMQb7Eicnr0asmuVVj9LfrSx5MNqV2jSpdORIhKvvpZLPtEZspIg7lQ+K1G4udMFx1re4ZjSpJ0ju1IGsDf9bGEJPFBz1OytsXxQSQ+hV5DzyPcbkgNhVY2aOaIxqtfqVJTEymgVTVNnjN1AqKipaHB1ToaraPSLTQyXGWSdBZC29FmHo0o8OhIOvUzXA60kl1gY2gV2U7guB12l5PqktYtMWsPozNZ2RrDdMeFdBhJRTsWzNcxqidxuoTGcBmvhySjkmidEhsp9t6azdkRbXvNYKgRb0dY3o65svCqENNoKfQE5UgsZVJJxbjpEYsO0e7QJWwMHfO0JrYOaf1L0q5FWh3D1GKX16i6QYQCWYPp9PHrmKYd0Tk5iQ5N1EcTFR46y0GPwBqz/9Y7GI8mfOXfepcX3/2cZ0+/wJzc4t/+u7/L0e13MPo+d4OGJK1wojmnjUbYpqQLg/mXn7G/H5IsDYZHFqfXEXrZYt+dsr3ZchCvqJIIp11SboYY44pJV2FkewxGitKLSB6VPN9m7Gsle4Pb/Omf/DPapGZ065gHt/qcPYtI7JjBssfB20OWUc36bIXZlOS15N3jgKu2z/VjxcP9hrx3wtGsQESSbrAhjn2skw49v8tiVTMMawx2+O6awnaR2wrNMtFyl7rLCK0DNnaGrVqcVYu0Ipq8Qxe3iC5v2K5XZEvBrEtY9Rz0iU7d1rTzFO7eIqslA/eab3/+fU5fpPTH36W+ueCpuYVWJ80jtpaOL7ZcJRla+MrqTTNLdKujMFeUvRZjE2GIAOVHtCsNzzOoyNglFqHM0F2drXQhT3D0gsqBTdcQmiOKcsO4MLGMDpEL8mGOF01o2ohW04iNhgM3pZ56tFclpvKouhJl5Cjl0Gwbak2h5SZZ3yEkIykUpq2/AuhKi1pkSEuxcWMq04NckrY1ruPitCZ5oij6Ej+ryazXaLQv0ROHdizg5i8HCvxMJAEkkICqfTyjZadNMIIB2DdY3T38tzseViGGcnlUnWNnCb5+SL7+AkPuo91paB/lVE1J8ZaBb98lf/mUQO5RzBe0Yk7q62grDTeMKUYR+rMhZrwiC0y6rodVZFRpxXDosCkLXD1jVbmYRUnbKVzfI3POcCoL2cAOlyqIcAoLL2jYFjZ15xHqFWqg4cQLNnLIcZPBzx9QVgbTvVv84t43+ebbh7xx3OP3n37Jg/33aeIBgX+P4cMpd4J9bLUmXy2dsLTFAAAgAElEQVQ5vzBwqwrrYEIbbdiomrOzLwlkwFXbsjtvSC2LeGRxrzSJ5gUXz1bIr2aUO8HZasXdcEg5tTCLDNGWqOsNnfDx7o7YfvtbLAcB96w+s1Tg3LqDdD/g8ZefQXHF0dsfUNtLVvOasfE6K/1LeiOQseLjsqCv97kduniDKaESLFYajr9mWh5RDQ2meY9tdMo4GJEULsJYkmU6/heSi3f7BPaW6hPY2+th9G/QXQ+5NnHshk2RovdXPHlyidxGVO2WLkn48V7FKA2xlEE47BHJJcsXiklvj+ZFyo+//8esnuygZ5FtbpBmQ4HP7PgDHmYx8fwT9FRHSUVVbOksDS9TuGuBpvoUhxbby0t6Wp/IhUrGZLXEsRtsOoq0oy8KitIgnZQMKgtjqxFPb6hFR9761G1GFyyQVy49KeicjmWiYdodUe7QPU1gfIJdn9NWPdrhjm4FgS/IComlZTS1waaycC2bslritjpFbaN3JoZmkGYleq+BuEGKHkW+RVcJbtgj25SkusDcrZAqQMgar6z4aajRn4lyQEqpTGuAO87YiwVzb4DTG/C7b/w8n3Utv/K1rxJtz+H4FtW2x4MTxYcfX9JvTykzl3cfvsOqviZRgqqDapzxL//nb2P3dJwatp++ZHHxlGyko4oMazhAPy3IQwOhOtpKsDequYorGs9C25pkomagK3LdQ+oxepqhpE6jxuiipBQNo0QwHwZo1gItGeKMGzbKJEgEh9+8x+98/XfQhvv88lt3MCobh2ucWHGNR9ul6NlzpHaXq2bJ1hzhbnLM7EM+3sBgnnN2dMRv/cYHrM8Shv2SWnr4VwonDPhuvmHalWi+R1fnqPGM5Xe+QDgnxOIlSm+xmxhlucxeO2Q/3ecsfYF+0WA99FGR5DsffsqHnzzCdzrmn39Crzel7VtM05pPVmt+7quHpIbPoLHYmzlcxDp1veG9u/f5+PEnuPh89W8es/pEZ3AQEgQB+dEQT4wx6pypDpg6YlBghTPSwQxvrdDrFl3bUa5tbvInmGUPsgs2bcO6NrF2Fb2wZNOFJOqMKnapos9JG4Or6wg/1DAMC3HZsBuOGJcBWa9AH7vc1kO4VeElJp/96EOi0mdvr+P3/8c/YBl/yHwp6WTM2LZQvskirkEW2Ekf38pI9h20ixaMgrrS8LFJVU61pxNeV5RtjWOalDUIT2HmBrseyLSmcXXYNYR+yXYzRJdbNL2Prlo0vSYpAhx9QWMP8dsY5ZqU1g61tDC6ip1h0asEVasoBja9bczaUjh5gO3ldE1LW0O238dZmWj6lrYQlEFNL3KprQyvlWRdQGOm2KWkFAVZ30HfSqpm99dTDgghXgA7oAUapdTXhBBD4H8BTnhFF/p3/5XEYQme53LUL3hWG1jDBvWi5J8fnfOrx29g5SmDtz7g4vqK2/2aMoL7t0YE3jEeIaQNs4nA1ypE7WKYFY+OPuKed4+823H14BbrP6y5c1CTfNRRJxWrvRJtvqHbA03c4jq7gsaiVQLsGH9gkJ/q2DKl8wRlI+h6Nj1bJ93WNJnN9E5IlgW89d7XuTMaIqXOZ4bLV8Yed977Jb5xe4+2ttDKNes4Zdq5nF4/5savKdcJlVvBzSfs6yMm/SXaVca14zPwM3Z3xrw/8AhFwLLXsl7GqKpPHmiY82uqqwUvb6eElUW52BCe5WiyI6gvqY467hkhp0pgrlPapxE3xYorP+Th3gF+1/CFtcazNQ78HevWJuxA74/wnBZ76vCVQwtNjfCbDY3lklvvEdTPedGUzI0xrx/fRW8nzJeCk4dHHA47lmuJXVvYg4KJP6Q19jkKr1CLCZFmcD+6QGlHXPsd/qUgtxv68jb5oGJ9McQ1tixPl6hbE5YbySRYMdk9oD055/T6A+TuMYtnC4pNx+BwRHTHxb+OGLx7m0lu480yPO0Wql6R7SLcap8L65offbGkuG8iPpEMwpKwsdjZJctFi6kX1E1IYxqsK4VdRkipkzU6ptPCoKbcCPZudjAwqCqDIq5oNRM71Yj9HWZi4Os1cXxI22bossQ312RuD6NpqXzBYGXh99dcpoCTExQGxrpGmA47S0NLS3rCpDA16jLHroGBwMn7NGObqMiwELS9CftlwcprqHODtpcTNoJ85mBuc9pOEtnWKzx/VmJIG6NrXxnN/hS90F9VOfBrSqnlX1j/A+CPlFL/WAjxD36y/s9/2mZTd4iVSxyOeOve63x59RHt2Gb5eEFz8ut8cnHBw1YQlDrVw5jkUU6s7fAmgmlvANsh7kiSRH2UKzkxXiPcnxM3grfe/Qon8SWh+m3mN89Je+fI9oa7k31OpYu7KUnDGmNl0hkW3WaNoQJqbLpeTiMblGnTWBneBrKmpBppNH5IPAt5fXTE7NbX+flf+jm8IOW3yzH9Wz7Z5SW3loorcc18W6KZFWmbcHZxzbOm4rW3DzE/bGhGAfWtIWns0XrPUHXI3bvvcX7znMOTN/ne5z9mmuxxsb3AnZXUFyl9w8C5Z9M1fZKlQps2HAd9PusknVHQPFpy1itRQrJY5BzuBbgPRjy8uiTwMi5Eyh37kM3Z5/jjQ8KV4PlXAk6ODnH7IZ//+cfUWkRrRbw3G/G0iJnP/xQhpsTrDHd7hbOno4yAaV6ylAXdqqEdDbBvJN1QohUpo/CaXZdR9l3CRqMqexSjDKvIqO0CbdtHzDKCJmebVlT2DtHt0KKQu72KK00jshbYsY2ZVVTxQ+7dd7mWcy6Tju7iKeHhDMfXaY2GqDOJ9BQrbeG6ZTnM0b+84otv/wnr9ZosbOi0jF2sYbY2rp+hdoeYek1RxXQe7G/GRM6SNO0xpGS5BdG1JHKEE9dYToupNyyckkkaUsYBnS6IzZpavyTUPDY7MK2A0K2oLk10zWNhXiMrgeMcoUWnpOWI3qghjTSGzprM8YmTFMuz0aRHWxbEgUQkHTLaoms6ldbSFkt20sGRGYljo+1sUi2hpzbkusHObJjECWWYI26ZaKuKYtvSKuunokb/KpDjL4Cv/cUkIIT4EvhVpdT8J05Ff6yUeu2nnSFNU33j3/tdRsqj5/d5+HO/jKsVXLyMUMkNtj6lMlaQ7pNezfFOPLKLpzR6izKmeOaQtD1H2CbWWmP6tosMjnByxdXLLcN7t8i7lNXqGSdfecDHv/8dUj3ij//gD5G1JLq6AtcjzBpEqIOpkeY5fVOh+5KVf4fwKqE2XLwHLl+7/U3+nZ//BsGdN1Eo8ijh6NinMzKWpzecnq5Zli/ZfXTDm9/4JvP1HOPkCF+U1Dsotwo1WjK09lGxpBq77JsmoWbTOGvi4h6+lrJpDApK8uUFblsihM7N9RXu0R5fuz1lt/+Q+mxJY9xw9sUzRD1BiYyB7bHRJIYYYPtf4IkZ2WSf/OmcvZFBXdzlWfKI4vyGvgtf1Kdkjzc8iyI0bcYePrOg5Dx6zF7ScVrWLB2f3/t7v422ySjEmMhJ+IXhHi+3C9Z6zp5zyPQ1l+HcIxIl9tGMTrn0zQo9aGHRcU2JLQwIfdSypcoXnM8/IS51ri4tTmbXGN2QSjNQgcNmkWHHGiun5FhPoGewKcAuAwI9Y1HayNELZOojBjMsBePCYX1jcq494Xt/8B2M4ZTp6zY//LNH1JdzotMz1mqLkRsYuokfZmTSoI23GIZJ7mlUqcRLGmpV07kBlkgpuz6OU5HEOqbQscKcaGUiexsGrka5dhAVcGgidhl6qdi6BrPaYavXlNUGUQwYmAXSqUhUgJVpJKLDNxq2uwQ7MLHbhlhqGF1JUMPOctB1i66IUZ1GqWykl2IUFkat0yiTzliDUOhBSLOuqJ0RR41ioa0YmoIrs6Tb6XRZ/dfWHVDAPxdCKOC//wlKfPZ/wUV/kgim/89N/zffAaGhzlp+4W9NuZyPqLMly0WPkEvyfkW3Kfje46e8/3aAnC94fLrjG9Mh17TcrG6Y9Rrc8D7rJ0tiW3LzecZvvqUzLwXJ4prbXzmmXCxotx2u1Jm+P6G53mOvekQiN4x/8Rtkz8+Z+zG20iiThmao0aQ+vT0PLxPU91x+7fVvkBzc5le/+iZZ/xbs6fSfr0nciJvUwEt2LC8vWX82RxzrhO/3eXazYKT56CrDb33EQ0X3pcd1kWANZzStIiXFlhPM0GfbGizbKwKzwG5zRKkT+hWXNxmH+xMc4yuc7eZsVI98vUNbL4nKHJ0hpBGXTYthV0x2ay4CSa+eUR53cPOEkTXBCz1S74Jh7HCp2fizMW9ODljsbXkjqsmyiG9//5w+O67WguF4xmDQoG1jkucFg4f7+CJj/eKCJ9s1Uegxcj16asfNqcdov0N2faw4pu+1bDqJsWlBRTjdANVldIuOfL1i69So3EBFW0TpcPrpks5ccnQwI84MktPnmLZiNe8zPPSIfIMDqbiwUxq5w8sb7rbvciqWrHY1+0GNHh5j2y8IkoqjB69zuviIH/+zFK/nYJiQHo/YO2tJLImyKparnC7QcIshmr+hn0iyxqTQK4zOxu5aqg4sVVBswVQl1uEOI2kwPI1QaqxvOoZTSbFO0OKUVumInk5/m1I2LaUpsVyNPJdksqRpAjRiNqbAKBoS02AykWRZSaJboGp6QOGAVmtUzQ6rlijNotNKqHUqWzEIJElREytFX4Nm94q3aRURF02HpXVc1zYODU7XcfNTAvivIgn8DaXU5U8C/Q+EEF/8f9n0F30HXLevrtRj/vyHU37j3/gmjZdzFSmWFzPshzrxy6c80AMGosb7xvs4m5THZcpJm/JEE/zR917wtf2C2NbQxTEPHhhcLArKokD2JdlO0oaHBEFF8bRAPh5w+6sm73z9Htuk4ODBhOeTdxg++THnV2c0rsKbX7PyNCb5kEwbc+erX+HX/s6/iVHbjHWLOmswX1yQNgXeqkVTL7lIOk7TJRjQzFMOD3p8JgV7t2ecjDRWVY33QqBGMebigHvDEDlxubj+hNVqzapNSO2SUHa0ymJ5dY7VTNDqGQdBiOlbLJIf4Jo9np6eE1oFpv06vuNStXPifcX1D77PYHof3dvHOjBRZUq9y9ifjMm2PR5/8Zi8Mnhjf0I1GDJ094iXKTLTKEaXlGrHV11Fq44YDQoae4NaeVjxjM3mkiN/TPPSxPJu0SVLCqASfeKxjuf6bC8M+m9LNpcecr1FujMWUcr0vgPRkHQ952ydsT8q2T1vKcSGusiRIxtXN5F5yicXjzCvdfqzIzrZYN3ryP0p7UYxN5bMNI8mDrgyrik3FtbIZc8XDJ03yK2O9tkdmuyG/dsPefjabc4//IxvffqnnF1vqdWSE8+h1a6pI0AFYOik5oY6N9CxwIwxyj6OvqExHVplM9F1znNBYAh2eUWv9ZFGikhnWP6c1TJDyhCrjqjzGlsJyr7BsNTZbAuMpoepR0SOhiY7tI3NqBNoomQbaJipRSYydE1ipgqh+zRdThZYmJlN7W2pVU4Q9yirNW2mc6VpWJrCrhzUsAWnYpwICiR23VAZOqrYoac6meHxyhTs//38lXYHhBD/kFcks/+E/x/lwHA8U/ff/VXe/8ab5FZJ+fELjoZvMdgfcPFsiak2vPHWm/yLJ1tW6eec9ANm+YT1sUeVbrFak8DL+eJix+RwyPWjCGuXszVvkP6Itx68Q6MUmfNKzipWHa2n8+u/9h6B5/L05YespUaSb9HXNmfinFFl4OkhX3vrb6KcDettwcxLSBYBVbNkpI051a95drF7VbrcXOMuFHenAWt7jBi4MI/xDg+4f6I4/XJFpGyGVCSuomeaaInFSpXYgwHdzSVnN3Nc6y7DaY+meM7M3+cmWdEKk9Dt8TQ+52RygDt8nXh9Q+ikXD8+xxoeIgQcOSFrtcLeM7E3kmUHJ75JtLuk1WquNcm0GVM2sGojRrbO08dX2FIyvDPFsT2kdsQPH/0x+eIxF+cldyYTPj2d8/brx+z1e2S6QdYGTA2FE5ostqDrW7KxZLC0sA2fcGwjKshNxazv0p3rmOM55VXMkh2ZHNCUj8jmDrftA256Zzw5BzcTZEIRWh7L8yuiuiRbXWE5CU0msNwHDO4JZk4fa+LgD45oVpB3JbtwycH2mOD+HmZhsEWgLs8x+xa2m/F/fOtj5k+/T7zZEH1yStwvCNMe6yqhpsUY3MIrX5BXLnXb4nc2+V6HdtVS2Dp9FZP3bMoyI0ggMYdIGWNZOrUrUFdQH5iMTiu6I8FmIVFWwl4RsPMF7CSYW1Ru4lsNqeuQZR2+rWGWIVGo0a2vGCuPXViSVxWB8snMnG4tULOKg8Km7GCtGezJlnniMVQliYxQeoh0Qox4S9F1+LOKMrYQmYbRlki7ZFP85YrBf10HIg+QSqndT75/E/hHwP8O/IfAP/7J+5/+q84JB30++OaIVdzw2tbj4+gu8+YjRPoOyTDAyAo+WV0xMTu84jUulpdE4QV6MaJbLsn9Ea/ZU5K7Exanz5l6AvPhO+zdvODH0SUv1hmzkx5T4aMf9hHVHGfQMT97Tmz7KH2MY5pM5JAou+Y3j36d2l3y/Hsbut2CrNFotY7llcRIVmRiyTI6wwnv4fgpoeuSpw6np1c0jcXbAw3vtQd8zKc44x2nhUFh9tHFGavCxmumVLaPETR0y4qmCsj3avbst7i++hzRO2CxzpHtF6SFjWMe0jgeFha7s4gufYwuYjTPZdvm9PWI/f09jnsVxdqmuKowQsnBckfgWTyNPOoiZToKcZaSzVigL1tKz0QNfBxviNwpjKoiy9e8Ke/yHW1LWKZUpsUvvvYBrq6I5y2pXJO2Kc39Y+6mJreOUl5cOkyfQNLfMD4cUz6tmNwxuTlLMdYxea0TrZYsSovB85TNYYW91qg6xY33GXU2YCJalkWDZu0Yty35yYCZ6fH4XOGZD4jY0WaCLBnizB5yen6GFUWMRh7OsIdxDVfRgmhl8FpgMc4MPrZS/OuU82ZDsb0gjxKyhcHNgUDsFEWbwJGNNm+o2yVFZmLrBYXm0GkJehrQDjScfENTQZgKrk0HoQksS5Er6FcTunqDH+rEpx27kaTZgb1vU551lG1LJy0kDgYtW6PBMgVFWqM1LjsKDG2JWYI7slhfNRyolgvTxVuntBYIS+KWHjvdoNkWjERFbircMEIsLSzpUZQFdVMyUDZVz6C4ruhUiylL8r6GvR7zyh3wL4njf52bgBDiLvC//WSpA/9EKfVfCSFGwP8K3AJOgb+nlFr/tHOOT+6r/+z3/iHZ4JoXF4oyKPEXJS+vlgyHh0RFRNmtuXPnAVfPFrgD2GQFY72laD1erBbcmYSMW4+LVYIQLaO9PW7ducvm4jnGtKJZzdhzdjwVM3bJJ3z9zns0bURe95jeccmzmqGQyL5HXx5yrrZMREVleOhXz7mWLm52gTU8IYliqtWO2/dDDGPAs7OPeGn0CZeCQhfYbUY+NRimkmw04oQ+hmXzg0ff4t4bb7E30FjuGuqRjbjcMZV9ouyGLLdYq2vyMmdbt/RUx9d6fYreV4nNC5ylYhPUpGdXHO+PySqFa5u0RxOsrUvPL2gqi+ryS2ojZ9PskZ0VaHuXmGqGymrCmcNIDHAGNU/ma1Yv10zuvE2yfYztufQmM+Y3C4rNhqas+OJHc+68brNhSx+frrI4zVbcG99lcn/GcvGS+HxLP5ii2x3D40OsKsMTHsLqiOSYOr/k5bamf31O03dB01DbLUUMq02FNZQcn5RUC4l0JqwrRZbUKDOj54fUwsJVySsa8rBFSIdxHXCeaeyl51j3ZwSJTWHC3i2Py2twnBs+voj58qNHmHkBes55dsnT754ykCs2jc3UqLiuOmSneOAEbGtJbLW0YUF1E2IXGY6tETkVk6RmO/Qx0yGdXNEqoLIo2GH6DnbWghxhyi1mqpP0c+Kmxo9McvfVeLlWCizbJUlzXFMSeyZaXqF3DladYToBSVfSla+0KUpagKByFWxbFDk9zUTImq4SVIGFmwk2QcJIeUS7hiYEr6jIqh69UU3UmIRxhTlwuVkufnZnB0bDQ/Uf/xd/H/V8ya7ZB9+jFSlXqxf0MwtjBvr+jA+Ovo5oRvSPch5/5xHx8iWbTczJ1w5pPutRTa5Jdy4Xl0/I0zmTI59eHVLf6zO/Kli9uOSdva8gbhXki5yqCUg3VzyYukz3+zy4PeB7N4LZuCZfK2xAzwzCvscygOUXj5DRkEx7iab2qfSC/b5H038DMztD2mOaqiVREcZa4eybjIc95Dbl0y83HH7NJL12ybY1k6HBpV9wxz4mOrtgfZ2wi+f8jd/7Lf6H/+a/45vvjbAtj4P7b3Hxw+dIr49rmTSmgdHGrNsKbXDM7UGPOg3QJxqqzBiPdOKLS5xpwHc++pSTwSGODLHrGa33lGVq8XCm8bwtMFY6Tf4lzWifgdzDKRO68YQiyuntHXB2/hEvv3/DzfoplaXYu/0ufZZkMueNb/xteBHz6OIlHz3+kja6ZGhP+cZv/QqTUqcd5FyfQqU5FHKLvV3iHvSojBAWBo52QxUIWjNAypyxeYBTZ+ziBRvLxxxZmJGkqBTzq4TgzYrsWYxr1NwkGYPKZvjePVRpsImWpHmDZwKxpHJtrKnG5tkV4aCiuwz4p3/yT3BKm6TbIKqU96YHWPeO+fd//Vf49vmPkfOavF7x9r23MfsmSZ3x4+cppDk/dzTls0Lnf/qv/1saLafetejKJDVgpBoupYPtaDROg1h67PUll82WaSqoBz3yJsGPMwq7TxomyKxBNIoudDAig7psESYErceKln7bYpg5Wi1YSguTFF0X+EVHbo8pu5hi7OEsI7KqQbj72EnFWJTcaD3Kdo7UJVYX0Kka26hpa9i1xc/u7IDr6xjrHrPXA27Sihefx+hhRK8ecBgqpvu3MJWBCnbkZyvaL8Dbqwkevs3++XOq1KEcRVxsz+HGYDLpEOGUKNVJ+2Oe/OgZUZaSnD7n5M5DbK+lebJD6S0HDyfIoxPmnz1jYo6587Bi8TjhmVbS6yS99RXkNiPh8WmtcXys4T8dkBx77A8qPP2Yi6unTEcmF4vn7OlDTlyN+A5sPl1zs1jzlXffoZekrJ/lmJ7EczIitujRCCNeUrYFu0HNZH/M+cdnzCwfdfcXGcc1opS0mqRZvuTk7i+g7Y2Zf/k5szCkvn7JSp8R4BJ0Ni9XSyZ9h+suxs/g3v23kCsXpc+Rt2tIDcxOIPszbpUm1fKS6PZdRLdPr8spK9CNjlVjYEUvmVYhs1+b8uPPjhgEBj/+/Dtse4r+eMLFZ5f4hyZD4TGIRgT3YHr4Jvqg4VIoqmc1BAEz44qF7WIHt3EHKe35kkJKJrVDalpYjkcq+pRti78fsH90yDBYoK0dLLtEmT0Ofq5Cv0xp399nE+2YrDwusuds19e4us2e71AOhmQpDPwVebVj86yl5w6JCklbfMnPf/A++6bP3ZM3SfIt/r7Ny+uXtKsG+7wklTW2K1ioms33V1wtVuiajqk7lLdq3u8N6P7u7/DsUc638g9559YdvEbjyacfskw3lGmLqzy6Xstmlb3qy+9JWq2jrGwqq0NqgqbrCDudTrrIq5TJ24dYmktl2rz88y+x9/qYck69FmyGFtq2B6ZLWc6pwj5VUtF1FlocYUqH2mlpyy3C8Vlr0GtjcmxsU9K1HoW5oCxrsDzIir80/n4mbgJHt++rv/+P/kvaNKGu9rA6HfcwZL24obH4P5l7k1jdsjQ969l9v/ffN6e/555zm4gbXUZWZlVWZlG4EQaBZAkETBgYwQCBBENGKCQkZApKhcUMS8CARgbLplHZlnEWrqxyZlZkF3Gju/1p//P33f533zJIhEpWpoyskpVLWoO9pG9Ntp5PS9/61vvSLbaIik5adjluq6RaiGT2AANT3HCXbVG+ilBKi49XF4TRlPIZNN8XWCUKy6sLHhyc8t0vn/Pk0KNX7DMN5iSDiKY+JK4DTg2LtMhoNzrsIo3Si8nDmIdSB9uG6zpg/Ow5vcYBqgON/fdZ+msWtzMW9TP2iiGdgyPKHCayhBjlCGGC99YR9wuRRSGx240pdwnpkYV5E1G7C9zdI+x7Jpsi57zR5Q8+u+TXH55Tpju2fkFRrVBrg+l4iZWFXMoTTty3GBMSITEwXHqux/LyNavOliH7rP2Ms4NDUn9NpMu0j/axEo3ryVdYrS66EFA3DBbXBVZXpG24iPOa2F1gOCaXFyInSsxVJfKg4aF0TNbjNZ98+ZLVqx1lu2Z4vE+bBnmVUO8VSFv3//1/IyRJw9uppFpA2D7kgdnhzSagGSeszAixv48uBpixQkOsCPyAfc3iGgO5yAg1CVeUiSUD1U/Q3A269wihCtEqiUV1g2aeYi5Fkr0UJ1jyrA7ZzjLsLKU9uM+z588IRlPqUuHQec3++w+4/f1PGVsxHU3D+9oHtFWD6eSary5eYuY9vvWwS8c94q//g7/NE7sFbk0ZtYnlOWYfZm8MCOdIDY33H7yNZx0Q+yt+8NUF81Lg/XsSv/O7/zvOmUVflWk+fpdi7dPu9RE2JTEbfvbZazS7jVL10OUtneM2+vkp/+r7f5n/5L//zxn/+BPsaPJz6fJCpnJFrHRNXIBSV7iKwE5UkZMCg5qwZVIuEwpXwaHGTyKkSKZSoV00EZtL7pKf6xaGq1/sRSh99NFH/+yp/8fG7/zVv/rRvuxwuSyotyGGJzFmR/p6i2eWqLqHZ4vEZsEuViA1cfUmUbqgSkpWVwGRW7Ci5LhrUlYJOvD9F59hBDVHgx6+XvG+tMfNbkJ+v8kiXeNGJbrUwpQ1ZEdGTCSsdYzcbTILYkQ/JoimvIglxJHMbi4QCBtuBYkBY/pag/Z+AzVo0cpddFnibpsyXV0TVwGfXNyQLW452D+kyGXW0wkXqzeYm4y2aZMZBsphg0OvQ7nxkZsuq5svEE0DRTrCT+/YyAKvpp/RpeTNZEWnc0iuCBw/bvDW6RDccNsAACAASURBVBlSKaFkCakWImLR63+INyxxnSEHZoekrDEPPZZiQFlLaHKJ0d3nxOyzCBIEP0MJTfIih0rElhXixRTB7CIVJaYa0KveonJKVss1qZMwHUd0lAPsUwutKFluJPTNBtuSKGsVIzFondjI8ik9FzZzBftQonl6QtM4RlViLHkAYsLNLMRwa6p4SZBmJJ0KY12xqzK64o5QlSjrms+vX2Lq0HA8hAsdw0lBlEmFHMtUMHUbsZEhixqJI9BIUjbJkvVGxry5QtU9Xq+/RK1WtBoHNGqB9eKOlg/53Y5A1Hjrw1N8p+D6p5eIaoYp6Jx9eEhSKgRXBU6mU8kCb9ld5pGPK7oY+zmTxYp7Z0+o7UNuv/op733jPRrDI+zQ5eGTQ6plTaPh0FYOsfv3GO9i/HqKECrUtc159xwlSHnYbvOVeIt8c01kG5RCTd0ISUQRvVIpJYOkyqkrSNIBhVYirEtqoUJoeRRZgpTLpIaAVOpsahU3DChkgdjXgHz80Ucf/Tf/OH+/EhqDmVDwyWyJP50z2V0RRktO5nOMI+hqArICq9jCjCtQa3q2xrx+jlroqEKbJ+84nGstzK2PErucrXpclm+wShFjoLJaxTx/fsd1dkH7ZIg2ucENRtiDFrVW4rQklJWE1K3YHndZBU3s2QZhqyDoTXr6lkwLuNIukTyT7d2WXdzizfaWYLHh6F2brGXAYIs91Enrkm4VoKUzyjdXzJdLitUIoUwYnh0RSyaFbrG7jkn8BvNozI3QZD3Z0uk7UKtkRkDixhw3RU69AV8IY2ZFRL2KyIwdtrLH5VfXdDSDSt7hdG3kTUUzrzg9eJtCC7jSoTJqghcVx6mM7XuopUR2l3CZLyHfkdQb/KKm0gLKdsg0MXEO9hD0imHHRuufUbkTDNUm8DREwaV3fwCDkHS5YpyL9BxQnziEukiw18A8sShVD6k/xyh1Wqdwv3mIaC6Ihgn7Ro1az0gTle79mCwcMorOQZNx5yWzPKSKQi5riVwsUXoLHh+fUYg7Mm8G91SmkU1+NkXN59yMY2Rd514iU0oJdx+/YButCDcvacR/jzfZK6J4TBQ5pFKbNSKRVfDmbspXqYbfKAnTNeNnOevXBU5DR0rA2YnIokUgSnx1N2NVPSVTtzwtZ0xSiWoQcjuTef18ibL5Ajv7hK+/d05vm3KuZujSBmle4mkFXrfm7cGI04Mt22c/ILv5MfLlH3H56R/x2T/8H3n6f/8teh98E9c22CgN6ngLdYpx28SpZNS4QssqylqgVERkVlhyTCBnxEKJGoi4K5Ui05DNErGs0OuKiShR5B6ilP9S/n4lagJiJtLpK9hih9ZQRem10KwtfbNJnLbIVJl7Qk7cOGYQVmhHGo/9XyNXtiiiTRL6yGVKNLCpJxGLMkXH4Pj8AY5k8lr5lEFqUqhTLq832DubxtAj2NVUwQi76lF6e2z9HQ0b3EaF6N3jurhC3uQ0vD6xKtFfOlx9tuTo/RMmZkp/12AkCTDL0Foxt9chaVXQaR8SRgtc/YxMHCFaGVsh40g65Wopc+8wZbXJ8N59xHEusIo37BUSzv0u+V0bp2Ej5lOqScXOkzkanqElFheHt4yWMV/TH5OPJ6hRwK0UUVl92lZC9eiMdXnJ+EplqJ0yLH0+223RxTnxxkJqmhz0KqrM4eYuZF/S0AcPeTr7Ex7KQ14vAywSxGRL0DXRRBtF3DGKVTa5zDsfPELsLMiAXLqP1ajpN3UWr2ZEUoHaD9BKFQqB2Mg5Hr7PJkyQidkpOVXS46jqQCNl5TQwpGuWQYesuCLpu1RTgZgt0Uog1Wa0yib1wKS4aTMOZxw6DqMXW8p8i97wWH+iIeQ5uZIQTefkdUyjaNN8eJ/57Q9Y/uQKyXVoZCqfffIxEi79J+/zrmGwSBp8++ge/+fTv8WqiPlAO0TrBlz/8IZMtuj0ZcI8IzV1Ln/8KT/66R3er9+n2/MQw4K86/H56wmvP/2E77zziHAjMZrc8eT8EO2wS6fe47P/47tI8R/RH/4WwTUs9ru45Y6gNulWPhcNBTmY8PJLCf28w3L0mr/yL/z7/M2n/zHXdyHUEVURUyxsRKvC1lOqqkAJCvy6wyJJ0FzIN038aE1gQUvX2PguhlOw3S3odB3WMx3Z3lJufwl//0xp/yUjrRLKdUYaLliENV17iL7t4dYFkze3VJdLRtsdsuojKh71VkeQttxoGZMiIl1t2VhtGnGI1p1z9lint9fA89+QINNT+5S9LUXngHIe0+moOOkhVTRnv3vAvF7i7yYUfkW9nhAu7pAmCQ/1DpWWEmwkbAo8IeHgVKa+eYMU7nN65tHRehRhidc7xz55BycvedvxeNDcw1RWJG2BL797wW665fLuexjugmQ7YanNMeMdm/KaLBmyW89wqNHlMwx3hyg28RoaRlhxIc3QHhp0B0P8dEEm6oxlWA1l9GiBfzPHX2dUk88pIoPoyxV+csnn4w2lWSJphwTmMS3LYZWpxG5FI1fJezkkBarcYdx0ONHPCcoli1aLYu2zkgq2E4n5T79iE7xCyUTqfZe94j6H92SOxIIwEKiOXbqySKOyyCUwj2I6tks49tE8C2vhYQYxIk1Udcv8tsacL+hvHmEtSoZNg2aVsFEL1NLh6L1Der1zGu0WJ419cq/Dh0dDimYX2XBxTIWy8Ig0n22jjXegUKh3vHm2ICh8YnPB5fiO0kzZ7rYsOyGds68hnD1gl0b83fmCZqPC8nJE3cYK+hj3XLRKJ9UTTlSYJgp3qx3h1ZIbAmxhxqPhKcusCbXNOwOJ6+klBFDJBtgaZtdEbA+QpiavFq95uzunsA4xmxKJOEOVDboMKbQ1s9xA3LRQbJvukz53Rkhl50iaQ/adv8BGV0jcFnnLI27mBFmJHJSoVQ26gSZt8dQ2eTRA8n5uWuLUGcs8RBATdsEOvZaJtzFG9w4l+RWXHM+LCsUb4kga52KDq4uP+Wwx4mYOjx92yfcsvMYAfaOxMlNctSBQJM6qhJYYM6901ndbxL1jLLtBLFkY6gmN3rc46XvUDUizHh41j60nmGLj5y2fog3eIXq+T3y3xYsKbm/WCPEbJvUzAl/Hjw6ZRRvSXcVnb2Z8tVuxWM7wx3/As9sdI/8zNi/v+Ox7zzCqMarRRo5foe/vMfc8tMUc378jWob4tcjtxQveLNb0cXArmXqxQ19PMNSKcCmwDcfMX6/oWxaIGcm+w6Bs8mj/FDWvOX78GFkU8CKJU+c+26Ii1n1C0QJ9j5vlDPdBk7yQ2GQr9gsVpa5Jgje8yO8wNk3ieUSyb6JtDabiFlXfUkpLRvkrbpWKPUPl7ZNTSrmDfm5x9H6bAyXnWG9zqO5TPdji7JaIlYZhFzwoaxS3wnaXiI4E9IniEqnVJvFDVpKObg1oJRF1S2NvL0UQLULnDcP3KtyySXPRxukLuKd79Cydx6cHCIgElxXtMmS53mA/aCG29jDaB3gluFabh57AstlGFzzefuuU5E5g+VnCF7evOJQstJ7Mbi7TrDe0wop2qLC6vWBxseb5558gUrK9ecoX/orCs1gYJUtNx+oUtHt7LKdjltcLxDzH1y55p455cuaw8i0M2ii9BqMNGHWBHCYMGhqSNeLL+TUbr4c+3uFsZ5h6QT5ZEFkLPvzafcpMxZQXhHmbXaFynFssrr6kXsz4D//1f4u9VgsjKkj9GllwqMSctTAgoEEpitRqRhguaRh3aOWSqJDwhQojsslSk1oXiCWdUjbx55CYyi/l71eiMPhf/M7vfvTt3/4t3v7WAxQ1pTfYZ3h6ygN9wErxaBMidHTSekm7lBjpCv21TlWHSCgo7QGGIyAtdzjyHgyGsHyFrNYobsaLywnHRzpRpNFXXFbZVyCptO7prG6vWcxXSKaG1zZIkxCh1eDs/ilxuYU6RCwLiiTg1fqK8OpTNmWLSAmoFxFJ+HObqKvJJcn2lrsfP+e2LNmMN2Svfsg8jsiyCz59uuYnn/2AwfFjHMPHWZs8eOeMVbhkdHXJWqxZ73K6povbyrjetnAdnfVsRW0aXN2tMetDijjFVTu8uHjKfJrQiGZkosP90wGVGODnJfFkwSPHg65IJIikkoYepdh6A3uoUjgZ47GPEBQIsoBe6chljVlX2Fqfea7RFyQsUrIgQrUGcKAhvRpTSjq1GuKLMnUZ4nh9FmmCYerIdp9gpzFoamTmkmHRZSmDSAkdk+1qRrQNyLYVedMAMWExElGVGYmeUMQCNbCcRCRGSqk4lM1Tqt2S/IMM6VWNP8nxtIDcC7DqgnHho2cGbtvgQu5xIJRM5DGrzefcPfuSbFNRFDKG2uZy9zO+Wi3YvLnhg3t9RuUhcXDHy8kl1mqOpneI5yGGAfUu4/zkjD9+esfkJ1+wtg0OhkPcxoa5n9IfNtHKBq6Z09r3WFzPaLpdaksjm6dwl6D3bIq8RjAGuJHILk8IWgLvfvAB33rwAe+cDvj2tz8k2ob8RLnA2Eq0qghRHzAzTUZvntNNM9BSRGpKFZpRxFJWkAyNRlFTayrRZg85X2DWKmIt4YolqalgBhWOkNKgQVyKlEX6q1sYFFSRw3tHFIWDeNCl3T6mq1lkZkGzWOBoHcRpipdp1N0m5nRDXe1YDiQ2uxgxkzEaGk7LQHJFqp8uUe0hyoFHHuvseQ5anpNEPquDNQr7rBdzVp+EbJIVZRmxTG/xJ0sawz0+efqc5VylzMF6naHra+Q9Hdc26RhfJ1svOD44pug1WV5O+eHH32O1/IQXX2wpHp8izATiixc8CxOcaclqWrAzQ6xU4nr9I5bPK9y9Dc8vfoZY7NMdHHDedOk0Orx6vsbWXVIvode22OvYCFVNY09l8IFGed6j7JYopkdUPmeUaHTct+kVJUXngLbd5ugbD3hRilQvcsSdTq1L1JXAeHRJMp2QXJT0pBmVqpKxQq13yILDItTplw06zoSokaA0XDJZQV8+xRhBUtpkeU26zXEihSJQsUqFTmUz7LcR4pJ39wuSuGKYPWQjj2Adc9AJUdczHKPDoWYiHhzRzjI2u4oqtbncqUg6CIaIHyc03JL0NiK/LImj14RyRfBSRTVqGm9XKF0dsxiwy6Bp72EXKsQW98IS2W1gtI+RZgZm7ZC0HTquyXFLQtQH5InOecNltcuZrL5PkezINAWT7s877kSVXDS5EyPm2w3j+ReErog5tLieveHF9yaEkxxyl3snGg3bZvlqzpvJhPAuQ6lc4raI0anYb9kcP7yPWE3BS8kcgVNHoBdv8Loyj759QGPQ4dHpIYeSzexmS1U4yMaCBx8+RGq0CeWUzFeodzLSVkMQdOw4QlpVzAWdatVAbN5RyiZZ3SIkZuFVKL4OkslEzbnpNtBS95fy9ytxEvi93/trH93/xgeIwYpm0aA2TdZ3IctpSjZQ8GcryrBArEXu5j6xOGYjrHBEGcFSKZcRelETRgWKKaDZa15vwFiHzMZztGqAsT/AKXQQNez6KzZ3G2a7lA0+HaNNsl6x2VVshJi3zg+Ri5JO0+T1aMwyU9C3EderlLx8AYrO/c7XqYod0/kFubRDjkwOOgPC6Atq2aJKKvxtzUhO2O0yWmUFTajCmk1wx63RYnudkpg5hVqxzIc0OhWKJ3K3fo4mNwnTHENsU2sLMEXKoI2nLFAzH0dT8OTHFP2EfjMkXzjkeU2lrdC3Aqt8ivJYp6hMzPEtN0rO5Psvybwu6XpBvz1gsr4mrB1OMdnM1liCRq1mWMkA5TBg9Dynr8qMVJnBg3NSpUALcjQFYruLbeuoakmnp3Gn1siaRUBBUQOVgny9pjZy4uQY20jRWzZRp8shMa6isdttcXOZTs8j8/Zx4xBJW5PvdLRkztyF1HDIdxVdJWG729BcFpSRznZbYORt4lZGr6rwDQEp2zF3Iupiw6sfPmU0nWJkJtJAYit4iNefsUsKGt1DqioiFCVGl0tU0ePs0Qm2mLHnCtxsuzTrGNWW+eKLFwQrhUbtEsnQ6Mnc67UwZJP5bocr6lxtv0D0W8j9lOH+kM2kpNsUiGUDRzJotFKEcMiaDWrWQi/3USOZ282GuBToejqe2uOnX7zG1Yb0HIkgNvnB8y8Ro1tEJcGVSnZKjqTG7HKVXKuh1EmsHZUEuhhRCjViadBKDaRiR+ZmyLlJiU9mhRAVv/Ak8CtxO5CXKcJ4ysdXI/L1DzGMx7S9FUXqIP/oFsoD3KZFY2hxdKBRFW3kzCI0C9zSQOxILLczxGRFtjpiFCwILlaUgyb3dY9rfEx6TEWT2r9mdCmxCl20Xh+jvuViPOPBaZurMKS1heVqQ3g+5ft/d4ulyzQSA63b5cNH7xJPVeRGm5c/+yFO8wi5KNFb55wOPF4Ea+x6QCUp+KM7DjsC8q7kM0tiE2+RpyF7egun66F8+opPBYOvGyJxtebe+xLVRZv73zoiCIdIecGb0ZriIOG43aKfPeFv3v4hQ6mmziS6lszT6R9hLQ55dRhyUL8mVR5R+hX11zacbA7ZTGMcJ8I4bTAYLbj3nXNe+THyPZfIasPK5/5Jm89fv8Rsn1FYOaLWQzAz7l5O8Pwuo6ZIgzbr6ysGZUXRbaA1h8iLW1RziCfnzAud/WRDKbWIxDZf3SxQ66dkfYOB6KKLGakuI2khqm9SHfdhpPPOk5yrpUzDKgnvMlKnzS5S0ewJC8tBy1Ki2RKnLBCbTayGwHpbE5dzbCSitoy83LB74GC/LJkaAV3hiC8/ecpyPsXRCtQ9k2bSZn19jdI/4WEpciEveSi9w2CPn7sfLZ8jKSLTLEJSznmyr2Af/kUUccnl/H/lgWdycGyiDvdQ/IhVZNEsY6TogO1uhKo8QO6URAuf5598yUpeU24e4dQ3zM66zD++Y2WLdPcC8lmC/hsdxp/MONBt1HTEzLY4PNcJ/i+PuTjj7/93f8y3/71/FzXQSbcKDVVmkdloloAcrhHKBMkWsa2cwhewDIUg83DSAEnUqCQNW89ZbxRkJ6C/0tlhEfKLOwZ/JZKAJmn89AfPGUpDMs9hLryiCB16jSWvXy4xuxLzVUV3kfPlqEtXc2ju5XRWT1iLt+j7HbqYrHo94uBTssqi1gPais5XskJaR4TzGxarCdXtErkoqKyM9PKabR3Q3TtBrVS6uzvGzhJXPWYzkjk920fezJjrJevCx1bPOT03WC7GhJhYxTWO2yZRBoQCnAwdFl9eYroivt5ilY9ZZjlicIdVmIiuRNUbEIQqt8sd3/zOPTa7KS37Aef9Y3xTQdo3EP7BU26sDpZeoq1kRsqGqljyyDmmjF6wCxwC2eZ+55x64CFkFlIiMsx89J7D85XE40aTbGFSBTfsJA2n0ec2ydg7MJBvfHIp4OTdIVUoYrkD0Obk0QGCMKa0mzi7PutkwSP9hHmRIe1qpMM2lSYiUtDpt9nmKWx07Hs1cdJClRLGP1oTJBuKKEPxPSbthIa0pmib9Bo96nBDvbxP1Ako54coxQ3zZISTy9yGMeXcR/eOOZZzruMxrrIjU3MuApH7ahdRkpC6Dqtnn2K9yWn3XZS1DmWOrA75yeols2sfN9GoNjniscbav8G6FxFNPLY9mfdbe2wMEbXrII1fcNp7F6uo6PY65OWCtxrnRGrIVZJiaAWi0eTOKvn6dkLUdqm8CsGI2cYLdgdz5FXGycEpi5FCJURUkkz7fskXz0oejQs47aKmAo3KJm1rXLy8ZWMIpMkaSxJxOWJdzXlsrPGkU/jOQ3y9pmEoFD0DYbuiECVkM2MeOXiNGCFKCCQH3ciZ7wTaRUKueqzzJVK+Y46B3Mtwi5LAlBCsAOa/mL9fiSSQ5innh/dIHZcoGXEUHrPmhs+fxRzd6+C5A5Jiih81OK104ipnvMwxnCmrSKE7temfFoi+QeF3UMsp9qDJaDTBOWvR+jzn88U1xTrkZjFGkDTqYsLdeoR7cI6TvyIzP6TV/XU2wVPifE3R3EOZ5WzyDmKacZeFKP5PWR50aDebWMIFKQ5KsaWTFLyUl5zkhyhFg2UxhUJiug5R8gkIBlWrR1fMWSwcTs5LNL1Db79Dv93lxefPuB4/QnZA/JMlfqOHpKWcdI559ocf46xdNvsRgvgMqeWwXK94R9ApDzxajgWhzkYbsQo8somPc19l97mMNhiyKBSEekxXUOnWFevXAovNLYftmmVYUU4TrKyG2KC555MpItVuQpS9ZBNImPIj7MgiLr5gPa5p9C1SSaOUHBxdQOqWVIKJmapMwleM4zE5EUruIjQ34DuYXZl0DKt0idjVkEYvyEWTsi6Ikh1t4wjzkYrw/BWpuE+7q7NQe/QvZMRcQGz7LKYGm/KShIdoV6/RLId5saEOCuTZls1SpmFYqPMdz7c/w25rXMhwJsosxAknew+40GPKcomRuuhejpZN8a0jOt0McVRRCAmroGI0XrCa7Uj0JavC4H4lEM8llk9OadQ1hpZjKqfo1hXxyqXRKigLh1rZcrezOe5IPP1yhl3V+HIB1ZC+mNJRG+yimE3Woy9tCaoOrfoa17sizWsWokGr2PCW+y7pq4S5sULbxOhKA7G5oZipGPUGtTCZyw1IfLJKxDMsNCFhubJR9CV5LePVBdt5E72fUm98ttEvR/1XoibwX/3u7330/q99QOTn6BSE4YyuO8S2c6ppzmx+Rz/QCMMNd3d3bC7HNC2NVamh+CJiFJMDkzef449TUBMuPgkx6gDJ2KN4U7DdXTO68dmkl0TzOWVZ0TryyHZt9hs6+cakLi8QNisi/YBkPUY3FDqOyeFBQak6RPmaZLHlJrkm3QQE80sufTC1kMbhCbqqIXk2e8MWuWqSzC6oizbuuw/4bfch6eCQbiqzCN9w/+QtLnYXnDx4wtc/+G1ub8ZMrz4nVisM1eItdY+bTUCq19zmKUoBzT0P2zph36rZ9Ldsn5aE1DS8iucvXiJ3DUTHJXgRE0shbl9HlZYcxiILCeapwP4jg28N36UKBdSijdXJKPOQSlfZSBFVXfP8+8/ZZhr6/pBscYUfCzx0LdqtAbbVIqktVCFBlQqK1CLcZVRWzCwQ0QKZZCsiuwZO24amQhEpyJGCXN5iZhlJoNDJI1Ijp6c0sRwbf7WmDCTsZovROCdR1+wpLlVVU8keXkdG15ukyQS5slETiZqQyShGcXS2lU9Gg32x5pMf/QlVJZKWdwhSzdHeHmtBQU4dhoBt9bCdHleXEb7hU9zotPYGXN9Oca0aPdE4Ot7nu99/Q5Gq3H8o46gSTS2joR8y1AakHQHFzdFribvnPo6RYbtQSiWV1OOh10QXLapyhqxZrMuY8WgJroRrShztyUi7BNeWMcwGrjjgz//Gb3J+dEa09fnJ7g2/8cGvQxwym22oVRMnSKiMmo1kYeQClZoiZRay6pNGFhkLNEegVnKqwqFTrPGLHL9uoMkWRRn86tYEylrk9eXnNPYe4NFGCkOiak7lx8iNLl7VpspLZK9LVXyBJljEc49OFjDLJ7DfZPuTGXfylnNVI75OeBXO6IwLpOX3MA9OCHyDm+SCA9HmQpexx3dciT4P0Xm1Emg5Ax7v9bh0B5ypDn68xQ8UZGOEyDu0+yBsC6ZpgRtvOWo3+aHuI79K2L5/iBHNefTNb/L8FlxPRO7VLMYpncYt+71jJMvhcehy4/999LTF6sWnPPhX/jLBjyc8PdzQ0xrUToc//O4/4i99+8+xbOiky4zReMx+a5+oztG0A5RSYLNXw7VA/71T7m4SwniNJ9+nyqGja0hPVNbTBNWWUK7bJHsawyjH1nXueyIzPJaLmELZcKiekx7PucpvqF9GXJQL7EONzcstnUEf3TBY5mN2uwcUwxKrDKA9580koy8PEauMxlZHLXeM9AlbIUO0S+q+gLuG0NGI5TV+0OKdExffFwkkAaGlUSQVOBWamPKZX/FIL1hOdsjdlMCPuNEXJGoHowjx5QBr0sLMFJK2y+yzL8mzOXd6wOZZTBjGlNqKVZAjphFdtyK+6tA6PcAzNOTQQHMWKEKH0LZRxBBFmNNZtFCsFUnPQ1tOaLVOKLOcn01WPLv8lG88eYdsdkdylJACcr+iIeps/YgwEhgMZMz3DvjqxqfvPKAKX/DwSCEoEmYzHzlxKbwJstgjESQsrcRyt6xfyUylkkLUqaMatVVyYmWsjuHzqUCzcPFS0IQYO/UJxZStKyIaFo24pM4i4gTy4xxCg3ZuUCs2eeHjRSmJJLAxFBQhp+Us2G69X8rfP/UVoSAIDwVB+ORPTV8QhP9IEISPBEEY/an1f+mftFdaZsjWAKtOUZ0ObvuIfsvEOOljWSW6llFoAqV/gbUzEAOR8dWn3M1CrtMQMaqpmiot75DlfMX3lktODRlsn5tlwHI6R/FyHg46zHs+liuRHArYux6KWtPw7tFv5TzL17zndvEFAUM+w+uClnV589MvKVKfWKppdgrKdo9t54Sv7b/Lh998TG8uocQa83nNoFejtgpOdZeD+y6a9IjTd75Dp3nC253HiI++RVcYMNvv4VyarNSai6drvlws0QUdTJtNPcOvVARjw+nwMQ/2DjloysS7FZIV0Y2OOPXOcJUSqita++d09jx0SyHehVgrqPchucnRhzb2psTXCzBykPocagLnH7zN3lkXq+nR1Xpkz10SycITInbFhmUj5CoeMUekKqdcVilGBOF2x/alhOE0qe0Mo5GytqfcWRXOaEhn28bqv417LfEiXTCdC6x2DoE/5cXLiOefV8T5jvBuSzwXUT6pGS0z3I3LzFfQhAh7u6UqNDRxn4FbI7YV7KsOupezrgrSyZK6d0rW/QaPmu+x3+xT7cugXfFy/V3UuEO+tjnpqzTyDSLQsUQa6oBy2ECMblFaLnm7y+k775Pk0LhYQGJTHuVcxhq2OKMOK7bBlkl1hfZVhT7XkVZrQnHK1ldxjZTrmUDlNNGEDZv5x9QDue+dFAAAIABJREFUkavLBEcUsYw+dh9EZcBqO0amoBAnXPzsObmeovVVcqeBr8qktynjjU9R64irHW+91cVqGSztt9gIOuUWOlEPYZ3jBz6VVeHIXayJTb2uiDoKib5CVHP8WkUXapIyISocYh90JfvlLP9ZPCUWBEECRsA3gb8CBHVd/5f/f+O97qD+l//5f4dW08eWD3k2foUtbbmNIvxJQFPMKffaiJLAIBEJI4di6CP4XQRpRaU6KErJ6nLLPCjpqjVyF/ZMk4tdQYGIlU3pGgPeFDuS24Ld5oJI1qEDXZo0eiVCp0Fv2YaWB0VAr3fO/PVTypaGM+igCyvK+C1m13M6pwVyYbGcXvFk8JhqT0eNpmTmGagLSsVCmivYosir+S2Hj44JljFlveZqtmO0vuEt1cM5PSGei2zLGMOC5vA9DCfhzFKxbBtVSlgKClVkseOad3kIwyY/+NFXdNw1DRmkh7+JtLhjHO5Q5YpyabEsJpwfWLSjDutOxeBJH+1ORTBlhqlPXJZEts16tabj1Exqh9uXL6jf7JhFKV3PZR1PeHjYYxVqzAuZt+93EQcB1qaNfSgR5TKq4mCkIkay4IXb4WfPf8R2XlFebHCMDAmVoz2bRHPZ6hn37B0djogsjU7SIlBDJMti9uIZWqeLoNaIhUss2iS7OaFesTfbcWPmHLZ1JouSMFqhKAPs7Rs2agMpSlhna1bVgud/5x+y9W/Ya7toioCamqzMCiWsqOMrPvzw24hHFvUl+I7F+POfMewM+OPgBb34iLf3V9w7+gv813/7fyH2n/Gb7/4Gs5cKamdBe/iY95sKua5SBG28rxs8/4OvOD45oaxrqqZB8CplE31Jt/+bCNYNAgaCKeOmMct5hmcNGF1/inSoc6i8TdDLkVY6UTymJe5TVFfcIvJ8teO09etc9fb4G//Zv0Y/s9kqBkKwAaPGLGXSBGovQM+aRBSUpgfZGCFsU6c7SkqkRkoduzSNBrP11S98Svxn1Sz054HXdV1f/dMEqwLU5TWvs5pit6GyJ9yFc3JD4PS8R/f4IcNaxd3YxGKLTEmwQg/bytEaAQ0RvKrEGxxx+pbBg7f7VDZcCylNa48Du8nJk68Tmi6rqY9jC6jOACO9QskypN0OTehxLH1A++gRttlH1jvo0oT6oE8/f4AbgRme4ikBfbODHTkU8xGC5/JKyymVLurZGfuNkmWl01Md0uqGke5zcP4W8eUWy22SqgLtuKKVqPj5ls+f/Zhe16IlCwwdgUZyQT5bYJsW27DCz1pIeUQlbVBClafL16xGr3jvrTMy+wm1MySJfbR+l1O3BysNrzlD1QqibZOslSFMbyhutki6yO1iyzx2uSgU4lcZXbNFFDdQ1AyPI+yzIc1uibing+1Su/uoTZmTnk9eJYyeX2PqKsuFiG0qtKucwt5xp/WQf7KleVUze/EUW0yQ7D7D/T7zSUxUJwzWO4RtD+dY4Z5twVnGvm6SZDOSTpdSWlGHBS+qT/Dnz4jE57TiEX6zQa80cUQDNRI4NQdI9YLMy2k2VBZCgbZusbjTEbcpy/mSlZ/SUw6gXRPOblnsRsxSH73RQZxK2EcOZjnAePuEXalxP+0RFRO+uFvzavYz0ukL6sxFcx5x9uiU3zz7GnZeMi9VxqmALZc4ixrb0tAkhU7Poywr8nSH6nsQpwjZgDbHtE2HL7IMUd+idgvijcp2JSO6YG0KijKifX6C20owyg4EErVvctqqiNc3qGuBRbDDCiPqMociJthFVLpKvTUJ4gBV21JEd0jLBk09RvJKpNrCMz1EKWYeBL+Uvz+rJPBvAv/zn/r+DwRBeCoIwn8rCELznxxekzZV0mLCj8d3TEY1zuYIqQwoyZB7kIkW6t6KUrpFsRWUA42q2cHRWkhChGy3sPQpseKiyhF7xgkPen26Q4N7D86pUxlRvKbfkgmsiOGBjWUf4wU1S1ek7HoohATlCuehjrp3hNbsosYd4vMN2rrkcvEVAiruaUmiCFwLEq7togopry9+xNXVHXFlUl+IhIuaKHcQr32W0wm167Mo1thpA6HjcmC3MLX72Nxjto1RXRk96ZBEMQsqbu8+xtRqKk2m2+rSllTy5j6a1GRReFDH9Acak7imut5x++mIzLDAztnUCvluSlyumK0ilpVNVPmkiw0/+Rt/nf/hf/trfPL7v8/1zQuePb1jXKyRTY/WsUi2y9FaR+RhgqG32IgLWk2bTvchB2aTQ9tlqmzYuy/SKCXqWqVqHaIaEjyW2ExnyKlEpW8R05CsW9DbP0RMDCz5gKzICdKURFXJ1JRNU+f4+B7vnXVxmvfR93SOvIfoZUkx8dhWKoV/zcjzuVwHFLXDxItw3R61bnBT5XRqkVE8pquVDJ7U/NrDh9h7B2THJboR0Qn32K0E7LJNtLum9DeMrmKy4JK3d02ajYAwyrjfaPGW+oTPblao2RCv9wCvfM6JCiNVYN+18NMWhp9w2ykpawNb6GO0miS7GrsxpHXYpDo74PggQXAzcnvObPSaJ7ZDo/+EdRRgfP0UIc1Jlhtix+HVeox9HZKoPYLuhnKYk6KQVDqPrT1aHR00jZUWYNQZrmqRmw7iLiB2ZXKxJFU9Kk1E7mUsgwyxVmj3I9KJhqgOaDaXv5S+PwsHIhW4A96u63oqCEKfn8ua1sB/Cgzruv63f0Hc/2c+Ytn2h//iX/xz9E8esfR99kSPhVXhzWKmsUox3DDMDQrRRTBy7KCHv/eaxB8yjHdkdUliCJhRk5pLotrFVjWuk4CDysBXEpazCZNFgavWLGcrWkOL7dWCmVrS0GX6J++w1xygHzvUdzckqoJrGMShxPZiQWbXCHVB67RFmQi8fnHDYPiQNP0Sz+hTKArGNqU+s4njDC/eY4lP29O5evGCvY5Es/1bpFwSBtAR4dnHLwj0mLKsOHVPiV2LOlvTOuyC2OfAdPlLX3+HL9U3LF4W5OE1ivIeRHOW5OwfHyCOU7x7HuJuQRiuMKyvYXqgVtdcjxbkyoBML+isEn748hXv/3NfY363Y3f793jyzr9BfaLQK01qvYFjqxSBxj96+SdIX33KRi8Jtx1+451DGgOVjdLh4UHFfAP773fJRhquZhKVIlVxzWxl8+LlK9LVkqxQQUjwHZlBo0MfmU11Q1c4YHPY5WE5Yed2MQKbqiPSLIcIeYgvrIhvcoyewpSE8CZHTETUds7L6zENpaThpaw3NpYpQd4gc+948TRGL2NuP/6f/h/m3qPXlnU7z3sq56qZw5or73TO2Sfee3l5aV0FW5BhSnAQYBhQw23/BTfddscw7J5/gRs2LcBwEEWREHlF3nRy2HmvvGaes3KucoMiQBg8dkMScEbnA77CV71n4B1V4xsvd37CO0cOvfEhyW++5aWVklynxGHE4wdDfvL4PyQ8hm1ZYGcCbePz55/9IZ8Y7/M7f++n/A9/9Me8+OaCj985YCgdkgFSE7BeB5weuEy9GZvNPfKw4ujox6RZguy0CGHB9S7gvDeg3x3SNCaaI7DKtpQlHB54fPtNiMAWQ9Wo9BRB8Ni9uKAoc05+9jEvvniJNTgj2KyY2R/T/49/n//6H/4EoVGQewJyE7CPc0RRwywlxFZEtWv8UkMtdTRlRZ63pKYAHZVqmzMVKuaFRpP/u5sx+PvAp23bLgD+av3XoP9PwP/+Nx366+Yjw/G0HZ78LbweaJKFYamc7HxWs5SR1EORPmCvX1GVPqe4qJqBaJzTEWQUz2LheQy7PtWtQBu9Q5YUJHZJ16+5kgOCVkcq+/SnKVmR0u8NWYcy+izjRGqRZAe3Z1BLMVJrEcQWaXhP2x9gm2d0TkO8/oy6vEMtdKKmj9ENsToRJ9Epi8xHbASkfsNoOyQkJnMSniQSaVPT17psNyqF8AqjMZFJWUYV8kOPmXSGp8FcUvmR4bDpuOxeqxyeJVimy2fxF3z7acl8cYPbdGjjL7mNrjk/7zHLCiRbos4rto2Cpx+T2DustGAtqqwKg4krIUQ112/vebF/Rfetx+RowIn8hHj5FdPeT0nxcI59Xr6usUSHI0VgMetSXfnYnRTdTyk7DSgVd8sc0de4vYgYVxH3UYTSOaZuPSRpRU+tuZVc+nbOaNRFUA7YawU6MofljKxnIi9K9psGYbYFISOLJEIydmVAX3Wp7Io6gqHjoAwCgiQjcTMODz3k0qVUagaTLc8vCw7skk51xOzgM1ZvclLDwSPl/ss1yk87LI+6TBnxp+l3dLWaBQa77j3N5gFtkCLZPislItmN2HQqNpsb0m/uGNgWx5ZEdvWKqjsi3/vI5Y771z75B+CqGvvrgnEvZn635oOJy5ttzPtHAsbggN0tONM9u9uSezXj/cMhxa5CDK6we1OOj88pwrcsyzG31tesV13OwhhJsfAsMOcjPK/g+tmvSawW1VWo1itayQErRwpNstZHPCoRWwXpMiM2EipDJzFzRqnAcg+GobESx5hKQpT/zR2D/zbKgX/CXysF/rXZyF/FPwa+/v99Qy2gP9ZQmy6GppO3MPdUzN4xU92B6hJvcMiZ84S94ZEoLWo4JBQr8vER55WCOR9gmw5G54iT92xG0oCi1MnFjAeCgmP0GE9G9FQ4zmv6I4tRz8EejblJU7qii2qN6JRQiylJkTN1Dzif2Dx992+jnPXxzAHFUGJ6UvI73gGqqhEXNRfLnM/fvGDxecudkyPGEmYiUnsuN7cScqUy1FLKeMXZsURbw6Bb8O//+D3ohxwczDjsuuSNgJfqZGLFq6VAHPv84g8/42KxQCznvPqjP2WRPGO+n7P+dMEfLH5JFepUUpeSkEsxpn6xRqgL1ImJnkN8d025e8sX3/0LhhZI0XOy2zWrm4DXmxt21/coRkAWqcSBwj6KSBSNntWjUSSsUuGbdMPiyseptghs8OoI9bJlmVl0hJJuvUWuK6zmgKKSOB0K5JJNWgzIWSIvtiA6dBWPYTRFEG7xezYvlwG3ygItVLCiisO0j5AmtFONethSyjH5ZklwL1KnNvtoQ7ctGWyXlCsJTxHZNSm/vf+OSOkxfeCiRBGDUYn7HoxokYOQdbzgiSXjpw5dc0S1r6nFC2qrpdd/SH6XIrNCjk10+5h21NJPYrZBzWYnMx0WVNOE058eUB54JNsVi6qiFHKyJmNkCTRyl0jLwf6Iqlawpy1Fa7KVRYa6SXsVUPnfYR0ekkUafvaGUjuG+EvEvcRHU5VEbWnjGvFe5lLyyQyJSDew1QBnt6ASNVQhxaSDXUuUmkYWWiSrlthqERyZPCuQG5UtYIglbZ4jbiLS8vuvEv+bmo+YwD8A/qu/tv3fCoLwMX9ZDlz8v579jSFKLat1QVrcYAU2Qi5g2yYiJZXTw3Jc5OsCqXeFzUNcvWHpF8SmgJNmXFsReqLQcS3sMqZVT9i6a061M47sI755/YaDU4vGrbGs38PBx6pq/vyrJV0KPvz4CGM65rHWcq3IGK9WZK2Jrrb8xW9eUnZixFBCbSWs8Zo//2LLqXzMdSxSaV9jiEOGhk3H7DKtZdpRQr6xCT0fexDy/PMVllRyev4hv3mxIZunhIcmweeXqIMOv/3159hDj/HsAUH3ls0v79mpBX/6B9/QmgqPTx4gdh8y+amJPp2hzWqWL78kuYHPrCv+I7lHbzbm04uA2L3ibfSQ0VVKK6X4wS27yuXu869h7eEdSPx29cfk2YD/9B//I1yjpc4U2nnKoRNwFy0QFzbDjssHRz/hi81n9JKWcGzTDVP2G5t9kaP1S3puxW6dUioSUtxiejrDBzM290s6YUC4uEbVR6RGwnb5loviFkMdIe4SQm1FtIbDZsy1+JJa6zAfDJmIe5QaksAm83d4x+/QOUrZiD7TyOKu8THLnJtWR3W7HLUFw6Yh3q/Z7DVkQcD3BeRIIe2pWGMPvSrYxF0OBi/hPuCiFyDuO9jjgnVhk9s1ojBi+tDi4qvfEO9LlNZC2Bp88PdPEFqf3r5DmZrQfoXEIQ/GBmE9xnMHbJuaietQCBrXt18jah1OBiWrUEXtjjkSJHZ6wvLyAXI3QejlBNcm0vElknaEOjPZL2o0JePnP/k97qqW5LM5ha2xe/mvSLY9GrNCpSLPdXpNxFaREQWXdhOiSkPKboVYC9BK1K3I1GrYRzG1McaQVMqw4fs+Df4bKYG2bZO2bftt2/p/be+/bNv2g7ZtP2zb9j/5K2PS/68oyoTt9SuW3y15dfUVunFFeHHL8iLg7eUbwqSk0e/Y8JTjGpZNhdhPsQQfXvkcRy6W2FBrClEMaZniuDricQNrnQP3BKMHw2GP2VGP8YMxpq1x+vgd3HpI4ffxQvDTmv6tzV03xx7YhFcNUf2a5KqiCVeEjUDxVYAjy6SCSGRfMipGyIMBx06fI8diUWjkZpfxgw5FXCP7KZqrEWURV68+p9rdox8IyGOZpBHoFR6Hjz8gyVu2aUt6l/H1F99x+eVvCWwZXekwmQ4hvkN/+CH9ocnEVDjkCa7mcvnlF/zRr/+MF35Jx40JxC6b+w1z1UdRNL6L1kTtnJXisrq44vIuJJAEHs2m5IKIfHZKVw5I7rc8f32FsE0Q5JhFeEM69PmR1yNTdjSLO5Sgw3Q64PyRi102mNstTbdPUzbIho1RFJRGglspRKII3hQ/DsmqGiNqsQoTKa25f/uGV//8G66+WXMtNZStwCJtsBSdqhyh+jl9s8VpZOoyQ7cjngg1uTtAbIeoVclTyWbcliyLjKyZEewr7qNXOFKMeu0Thwa+JjO1E9rdnqFsI8UdNPOCKpTxbFi+jkmvXiO+bPn4Rx+gaCrWyQnUIQunJn0c4SsZedQhbJd05JxHTgdb3OPXNVOp4eqmoVQydp0I0/w7qDuJkRgT6GNsVadIAnb7AlFxefpYJ3ZV7p+/oukU5JkBYkXXbDHeP6MxeyhCQeXNKUcdKinm2XcXGGZKE0SokoVkxWxbFVNQ0ZoCy5CJ7R3mOsOoZLqlyQCBXZST1n2QVyRyRN2q38vfD2KeQFlDsx8h2w254/LLLwKexxuqaosTZgTP3/J2mdHdbvhys0KJY6ywpdM8RHsyIc1rzP4EvdLJy4Zt0VBLIQemgjRrGA862OYTjMIjlzV26phdojE0PsZ7YPHu0x7y4wFr1WTbDxkGPYrURhmucMxThn2BSpaIqlu+dn3ixkZLdnTyEm/sctRRYWLiK0v64xinTMgrgfxFwbVYcdDY2H2VTXrHJikpoivEZ99SVA0vVi+5erti/NFj8hfPef3pnrgKIVE57p5xcHZCtxZhekqnjekKM7z+gE/+0UccPX1CeDTkz7/5Na9/8VuEtGYm24RCwHTlMTroc5AfoOUOs5++j+Q5WO//mB998vs8+p1zFHuAVJjsbJlmpNE1jkiDMU27QLEUyosA6/CEH/34H+C6A7bqDWRXNLqI2tG5jirKlcJMgFKqWRYqp7VDz54hCgnb8CWa46NuFcIowd/HpMsdm/0Vt9lr1ukrzHmKljQMByqd8AV+HbMvbN5uV0huC7LKMLEo6zGTZoytBhwcfcR37QVhHWEFEUH6nDINkbKGxLH4NoDUvEcKZL580RLsV3S7fYbaDEHt09dyKsVkENZ8eXlDR6vxS5WoNln5txQrBUuWeK90cCuPRMuQ5zKroEA7OOL44RH2w/cYjnXen5U0+w6LO58lv6LzpIvGgCjOCISEbOmz6d4wqAMaWebMkTgyH1KmG47Uhr1moUbQrUzOLJ+2bJASlfd4wMXbgLs3r6nKAtHuIccRcglCK+E3AmITUFchYlFi2QXpfkXQWVFHAoWowdmWouoiBC19df69/P0g2oZpW/zVL6l6NkrR4/ThAfkyJAtjoodD2qiDGG0o7JjGshkfTFALkaZIkY469IUeblZzO9SwOz2G9Y66axF9LeJOZMp2T7cEhkPszRGtscf6qcb66oJJeYrd7bCfx5z1ZkRozI9jHi50dvWW1H9B60oMyjtOvA9508A3y5xHsx6/++D3EKw1r5/XdPp3hN0ZydpniA5SSe+4It761FyQBw49rc98u2Ki2iRqy7PqGQ+Txyy0HcWvAvZhiBBtGR17dByPh4curd5yVdl0Ozb9tGFVhAg9l1pVOdaeovdUmtLmV9/csnp9z0//87/Dg0+OCOWco2LKJ7//GDUyeacv47/771HLe4qiJK0GHM8G3GwuaWi5X/oElyvaYknzvGHQW+N0zwnTLYHv4s4shFSmrsZcL2oeH4Kkv0OppayEmONYZd5ZIkYeuDVjbcz1X+RcvXlJGj9D63v89uo7CEMOLAWm72PtEn519efMGJFmc477j0i1Fe7EwsJG6/Zp9vd8dZ/hVBW106XnmSzjPUfDKaurkKy6IqWP1+/zJv4UMVrwO39nSlG2yPdz9tKe6n7P0QdrhuOatX1GRy6Z39wx/OQx1Z9c8Xmxol+I9OUCx/o5gfVHdKod1+Yhf2+UsGRAKjXIwZLC7zI7fo9dtENDx1d13vsoIk6G7PZrmm3O3WGJkeeYtY7ykUkpPOT+zQarn6GoElsxJa1MhqrDrMm4ok9fXXFz68Oja/bFAd+mK37zf/wh++wldm2S60vSuoNaFBTmnhP3lFUcYJYiSaqwT00aL6ZqVZR+gkwP5bokrhzqaoUvNN+L3w9DCbQtrndCR5jgORVfv/qOzaaimg6opAIlvMQVFFLP4B3FBRq6rk/HmSCGGyrRZG+DuBVQqxrL7qOvupyeOEw1Ha/bQ9KP6EY9Bmc7ZN3Cbm26K4ebtuHCFFD6HiPTwUyveac3RJoohNs9RQJzocsyOeWrKMGpNT46sKl7Em+Tr/nlN18T5lty36RaGdTzks2LPXeLr3CEDh33iCh1aGKFPM8gveF1nRE2IsK1QXi3411DZno8YNJXCMSGo7MRR2OPm1YgnOeI9VvUbcBdc0R3kGELPo1Sg9uiZC7Ve2NOTgw6xzblOqbX9gluFmhjgSP7E0J1xTIuKcNrFLXgTG+wTmoWyYrqbYY5VzkGzF5FIQ45etdBfXhOX2u5cgMarWRw21L7KhM9YzjrEy8LKknGqjKEtk+i2vSTQ7Z1iWxuMUuBZPuGuDth0+TcRzdMmNKzapzYQSkvaCKTjvIh255Bkw95fnfDohTYvAgItxGb9YZ0VyNbJes6ZVUGpHuRONI50gyOT/pYeg87krhdPUdmwzxpMe/GGLnDtr5AnLfI5yeUMbSHNgNbwShUtqWCnNX0DjrIAiiWQLo5pG1LDo5mDPoTrJs186xP3cSMJInZ6Ji+K9B2C6T1muskoxn1EO5V1GVCd/AA0XDplB6mboB0ROH3kOYhxaAgWAnE6xhDAFdu8dwWX0vpmD554qCNe+S8w7t1j3z3BfO7FyiVRlDlyL5GUaoIVcVhY1HWl6SVQiG2yEZLPqnp1CBFEbIv0iRzlLZCLO5QuhqG+P2tAD+IJOCaLh9MdLxTG6vs8ODxKb2fTHgg9ND0kuNHjzh/cMaZYJEMTARBJdlNkJQS1zwi21RUuxrPU5HsAm0R04igpDVhDp1lRltdU1kRt8gcNA0WGu55hWWbWK8WtKx4ub6m3Oe0dwGbiwtSOWXY6aPvctbRGvvNksgoELOYVrYR9yGpbzEZiuiuRZ3uKVe3XC4+59mnX/Fnv3qObPn0uxJJ+DWIEZKt4kYQCwoFsD5astjvMHOR7a4GKcQINGhFDLVhp+2J8iFZWiIpO/b7hlTy0JcFliGiHjWooUJHHKL3LJ7fbli9TDkSOqwlkdbZIdfvIAtdqt4JtuiCNyO58hH8At9seLYN0LMl9euQqrrCUqG4uebbV2se5k+Q24oXw5yjrkUuHeJUPktHwNy9we4IdMqcWMyJxC2ukLC4ivku3DOdTFGCNa3Xkl3t2cdfMp4MkHoG/XDC4cchdfGM6Ks73tTfkAYbvGJDm4ckixxWJW68oboNOJmekhQbPv3iX3L33f/Fy82esJNTeTO+Dt4SLkvU53PE1uZeVv/SlUeaonQ1jvpjbuOC223IS/+OpNE5b0yadsOs7TBUbIyJid7LCQ0NebVFSnrksolr6Lze5rhahSYpjN0eVVpROQLYCmqWIh4M2QtQxHOC6QZjIKFLHhIBWrDBNBL6zQHRuYTRG6NOhhxOTLqiTDc3SK0BlRVzMJrSyinSsODyTkOw+vT2G7yBS9WraXoReadlFdTE9x5OlqHkDZQNaiFRNxqObFPoAmYuU8kyouKSlDGZVn8vfz+IckAWZJ4He7ybHO3EpG1BiMH90RR9JyNS860/53fP36XrKKyiHcV0iGmMaYUdzliE4AS9SZHkCaXtUxewawTKSiRxRFxxSCPaTIScjVEjCza9YMJTcUt5cAryBrGnMx+EmK8laHPcKuPZIsAhpa+ZhGmIWwo0CLThGsM54pFcofnwOvyczVzi6u5zFHLyUufhE5M/+p8/Jy0CynpEp84QHIdMTOlvJH78/hn7uYmnZ1wsr1juf0Nz4RH9nsab+y2aN8dsf0ZRLNm7MtK1wsG5iGSEdL1zsvQ1jWKRiQWWkVBGHlZT8Cp/RdG4jN5uWR6sOO89xP7Ju6x3b9jcaeTiPa3a59kv39J5+i7Pn39LbmjkxZr+6SnPvlsjGDXNWKdt72naGGHbsJqGDO/3FE6KK+kUnkxaFKhVSr5L2RcFu/iOpkiYtS4vLu95uXzGdOLCY52z+gRBcKhFmdqVePZ2wYdnE5yPUxJJZy+k5OGC9HlD1L1Cu1YwOjJGZrLrTGCxQZhqJK9Cwttfo/5S5Zp7XG9A77zgLoATHmFbOy7XKZOeQlykxPOMJ4ef8Eq4xE48rsRrPjl/iDGz+fXrV5iDGaeaha0ZfHrxOeJkijIqcU8OuHvxJW7dQHdAX/MYuR7P5gHNuEtnY9GEIWlX5/wAouuGsNthu1qgmzKy0UULFfw0JUq+Q16NWQ58IuktivKQb5+VnNioqwBlAAAgAElEQVQFve4ZV7vXqE3GA/2A21LlZfYFolQQqB3KZUxXFUh1BTHSSA4c9BDETMCXPSxJRKAiUHxaelBXWHpFnci49ppN3iUocyD5G/n7QSiBkhKvY3LYtRBNk9HsAF1zuUnWFJHMdp3w8wObyJFplIShNUPcrtkGN8TBAswOtrUl2+/oiBGladI5qChacGY5VWxRSjKFDkrcECIxnLT4UkVuxzRGhKocENYbvJVK2eRgulyuTJrFDn3s4Y16ZGqFfy2wQCNY1ywXrwksH8utmIg9pk1CZYno0RaVksW3VxRKRS2cItUi22xNez+n2qT0pwL+qsY5gKiqKDY7xsYD3BOX25drjtOWQ6mHbrxFVArqTKE6D6jzkGrbIu0DPLGHYJu4/ZqON6B/CFJXxLFs2rqP0DW5vYBn1TUYFsnYpWpr1rd74rpgMDJQqpAHH40JpwN0+wRBaOmcaYyFPic7lzTSkGuFE2UEZUsolTRSnzTZYJykKEaBvxFIk7eI8gCrPMbqGxRji7J/gGV5hG8XVLcil2LB5fyOWBUoRgWnvR7XccSzZzeE+xhB67JaZ1R2y+hKoiOlZJc7vnv7FwS//Yxq2xDf6xSTglcv9/zzm1/z8s0lWm9MfCGxCGLqumWRStQDg6jnknsShhFy267peE9pK53lvUylaoSxgzIVkMuSzKxohxNcXUESBEaZghzr2KbLfV7z3P+GVbbH7FpM+l2sSic8Tul1O9imxLLb58skRQ9hOHIpGgPPHaINukR9lVZpEJyEpjQ4s44RUVguI6LCIJSg33g0NykrTeXeWfIIEcNRqGQRQUm5ry3UJKKmw1G6Q0h9QMYkIpRS6iSgKT1kKUKMI5KqSyrpJIKGKbVMVed7+ftBJAGhbbH7D+DdPlrbo5kr6KMCcy8hmi69kUhiiWz3b1lsHPI4RR4NGZopct2l2SVkyZ4yKPj2+SuudldcvUkprYpkVzG1DerWR/XfUBkZh3uNZCeh0ydhgLVpuMtfEKQ5fhXz6iZEFEIm6iMOf/wh08GQ7qzhp+894NwUmdoN2qjmcNqjvrKRRIfWGdI/6dFZ5uzKCd1Mxp/vadKYZn5P1rlDrYcIuoMiJLy5kSi0kjC6pXElFFlDa0UcNUfsVKyVhmwrsL1bIZom80xCf9vgiz0EyedWzEllEV3cUe9L8gi2kUwbCaRFxvrmHkHIsTSZQWvj9mo6rUPiNTTSjHIFu7yANuJ9UeHALdHGNcmLGqU12eR7LvMVmulwph1TkhNFDaKckMcbRKuLmPQJL3ICtUaenFCMM7yhyrl0xrlkQ1rSqhKxWiF2SoxrcO0hph3Szw/xLZlpV6Wve2xfXzG/f0mZGWj6HrGvMs6OifYV3UmXOyFiq6+4efML7p6v2VdbOlmLti/4s//ln7LsauAZiM4CT9GY6dAsG7rWOe7gI4xGYxjCAw9a3rAUY5rqHkd3MI5tkrRC9feE1wmdQkTtdYi5w3rwLqP+IbLfQe955LJKx2moCwcjaNlGKYJvU89XBNUST9ZQ5RrjQKPWQ+KmobttMGOX/TagKvdos3fRlgX+5jOW7PD9jHK65RUCx3mA9qJDo/wIWWnRzYZKN9DVlNru0gxbtmFFZuqUSk1W1NholJKJS4oUthiahi2VdLQ9VdySFRlb4u/l7wcxWei/++//x/9m4E6oog2XNwFnjyu66YxdJmG9d8ggGbBXPDTrkOmBCbqOOWo56ByiWiZ5KeFLIlsxZCdtub6LifYX7J77xK2IFVa0bo9SnTGLKu4dFduVMASfONqjd1J2ewUvqcgrm0a4R40N3j1UWWQ2ZiYQLxO+uF9R6QXtes/R1KNs+pT+Dcs64OiwT1Md0ndm7LMljdKh1V3sREAaG5imRe2oHOgPGR45iB2NbpmjDRTqbExwveFmv+c2lmjit7h2y76piJQTmlxAqnKMYUatmhSLFGW35y5pOFJUfMkgWBSYTknaG5MlEYNsj+IFGM2AdSDx9OkxtaUyvi94ezOnp2+I9BWqcEqZKwS3NX23QOpVFLKGKR8grgJ6jcZq3BBd3iM/6aGqAmYfPB8yq8FKTeIyRFJERnFJfh9wI8uElUrbnaPvYtyRx3mkUE8M9GpBpX2EKkeMh4eY5zqFr5MJDdGrPUFVcr28ICHmcnGH3YOOeYSg6uz2K9RC4MnkId9cvWJ8PkJOXEqh4dtf/zN+dnCA1XcJqi1Wz0VsFfLrgJac99//HZyjltvrgGCTcihZHMxsktCk2qccPpoS+xH/6//2Ke989A7dgcIj9YznF9+C0jKwx+RNgyHuUQ+GzByH2nFIC5+ZqiAvU97ma4SOSLkcIjkbyqXI7i5HUfe0nka13tDoIoezIb/+7Feoscbx5JxXt9dUVUpbx2yFkCgp+PPv/hnFTUspXVHFBnWR0hEKcqGim5T4uc5EqYmxEIwU/Jp8WFAoEroPYqcmyzS6Qo3YaKBVlNnfPG34B6EE0shHLRMi85yTpwf0x58wenjIu++fMrhKiSwBvUzw3JDYj7HjipE1oqBB6IjYTkiPAZbUMlIdZpKApnQpnZywSgnJyHdzfLYkUo6oJGybFQkFHalLlbt0LB/UDppX0pcEGgLmVcvRWKLoRORqwvRMJwtkmr5B1J1SizKjcRdDNSmvapp2j/Ok5ufvnPJhd8Lxxyruu0/QxwNm3iHnoUcrlJgMORcm5HFFvPdQVQidmHVW0wQrksRiVdeIqy4WV+RFQlS2rO9y9vu3VEJJpAsIVsXlXYQhyIjDJVHdpRv6nHk1eQ0T+QlREtGRQtKbBjUfslIl1LNjalOk2Q1YBZco7Q1Vf8HzfUw2L1i9XZLs5kyfDil6EjIK6vEU70Jn2xasrgPajoEdq1Riy6qro88t3kot8shB1FVyp0JNDGRLwtUnWEcSapWjnWh02XJuz+ibDcq1xtNjh5FjoBwPsZSYM+uMsTmh66qstilXr9cIYcPaFxFkA9/M0ASfq2evEKwAt2sze+QgKyZvdm8hczGqLmJgo3g6qdqQyktu4wzLlFhEGo3ZJSqm1GzAbGgbi6tAoRjtaMuvCZsQ3UtwZZXgzQJV3mGoIopzRKmrhGKPZhUzKCdc3O744+YCM9fhC2jdZ8xzg741YTyTEfHo5BrOxOPk4Iz71YqXyz1CtWRbBTi6jil4mIlBT7GQTIH8LiCp7rD3GoKXcXCgEtsClVixt7r0nIz7QkLRCzJFRrFUrMDALmXKSU5SZQhSTo6Gr9Qoe/N7+ftBJAE0Ce+Td1AeqDwdTylDgWfXey7XGVnPQbBzMEYsUOhXDrlRkAUlTbWlvo0p85ZaXhG0MsFWo6kEgtsQO1MQ9zuacUCbCbThlldZgBU7CKsxuSrRWhJ4BnLUgSRA2ScEaofBOzO84YSmvUYNOri6QJaMaQ5r+oMx1dsG4S4grwV6UkasxQROg2w48N5TjA+GPBZMhCqmpmSnFTDTUbsythiQKTKdp+9wag04c0TCywVym6FFC/BEsjuFeDRnL+iYQotnQMctEV+W7LY+K2pCv6WhSxAEKFEPTZRQspKrLCOpVBKzz7jXw/Rm5LmKIt7geR4nPZ2pe8Y7fZXTeoDZGRKuQrwsw+g69C0Zb5bjB7cU1GTbBHl7Sezu6IYKnpCRorEwVCRT5Om6x+qw5FTssR6IoOvYRYFtjfAOT3F7PpvG4PDkCFU9Rp1J+G6E4jvsiPnqZk+WJHSliL0qEZ80aJ09gzMVbzJh2M3ZLm+orYzN7YIvn/+WYf8BQ3PKNk/ZtBlWY9CqDQ4DOu6Ky+SOpRPg9wzM3V8a1Z77CjevbmlHO9bRDega+7IgriW2V2v6rko3t0nWPcytR80MvXtK77CDkkoMrAY1zLFrF4MFwsGUvQva0yHe7RHZruRNs8RQpzwtZXbVJeusR6pUxFOB6jphffmGr774nDaKSFwTM9qT3r0kuE3QGpVbv+FMEMiKAs+UcVqLfiQQzzMKDmhSiWpQEpkCGqACLglEEaGi0WQ2Qt7HkmUUySSWakRbQzrefS9+P4i/AwOzy3v9KeuTEaetzNUu5MHRlKosWP3qiulHE5IywsblzXbNY01nx5pONUXmGpQRUVohbVoQWs7f9ZBUj+T2GrkHl9cBHcNASyWUVue1+C29eoCwK0jElEITyQcN9sBgt63x5BO6pkK1SdhWjxGclCo4o5R+QScfgtfBsDa8SXasL3O6yoSBYjKd6IRhBasaxTJJVZ2zR6dYhUY6sejuYxrXwxRTlkXDbp2wSueo9yWdn7yPdX3L3HuC7N9TNjLhqmSg6kj2Hq1zQGvELIcZ/a6CsQjZWxJ5L+ep55K6Fbq6oLJyessGeepQpnM6gz5UFTdezSTS2dxdE9s6VTFna9q4/pIX+wrj/BAn3JEJCopqk7ca2rDk5FGfZBFzlTpMxS5Zv4upaNRvYrIHKfeGSUWMI+dkocsUm6TMkGodH58Hox471cLtVXjGhqPbR5ROg6+lCIGNFGw5P9e4+HXN6/0OKS5R7UN+c79HrkWydI1pjsmDhouXl3zyk5/xfnHMZ5f/gkW6Jwe0uuDpz/4+phFylId89voWqSo4efA+0WqF8/6YSvd45YtUI52jVZ9VnXF9d4EnD5DqJYfdAb98u4B+jHikYUxFRFPi6WmP5csphZFSWjmKniFXS54tYsTNcyZnP6KjL3n7tGBQdjktFO6jhNnhkBuxQrl6BYMI/2JP//EjLjcR9y8+Rz8YMYkbrnchg3ePEbwDeLmlE6t4PzuhtCKipUPlbWlSaGwVRV1gJyrCLkFKagJphhpItGlCPovRfY1GbmmIqFQFXdbRChnF6lPEMt83c/wHoQSSImMjRVifv+WP/+JLFn7Ajf+GSpKZfTxgL4nohoOQb+n2RNpQ4uBewOzGRJWNX+kMByqL2kfMMvaXEvPNPYZWY2YD3FBkfReyv91S5vc4GxluV0hdFavI6e4k2ouEYhcjVxAFG9b7G8J9wqDX0JP2HEkLDu0zpqMx5Sbmcqmi+AMeTA6pwiV2J+Haj1jHCV55SbqKWSYaMg0bBGDO3Lsnd+G+zjjqKfQ6LbI1pVA1nLsApXVwgojdukAaBORlB+ukZD7yaK0NzQsdyy+w5yVxE9PsXyK9mbNlTqh0KKuW9Rx8wSRoMjYrmZvVho7S4x2zQVA1GNv0kpqke8jzb77hu13G5YsdxW7Hszc++WbOLlcwQhFzX7HZNqgPjzEUF6dROK5S4iuD9nDFwbKkmwVokYJqZMRaSpuHaGpLdSIzfOwgq1P0fQdXNCEc4g8SzFzhLOvTOYKOp5HnN9wGMSPVRuz2YRkQqi3VNmRfldze3hPvL/npg2N+9+mHDD884kc//dv86Hf/Cx48+g9oOjP+zz/5BZef37C+hsb5hLQRUNOQwhbpHjiI73Wo7RVVIGCbOkePp9hii1c1eMdDNsmGOhIRaw1RNZlWGeg5179dobca0nWXdjOjbkGWTNr7HWbXJE4kLpcTPh4fciAcMZdbytuKz6qEB47G+NygjXVmp49h0iORV4wnBmdih3TQEpIRqikXyxV130AcO+zuG1zBpW8KyJVCJbak1LRzi1LVkRqNbNzQlRdIKtQo9HYKjZshVTFtZCBvVfx1QKS0yElCW35/s9APQgmIiomZPMATF1Tv2ygvFIrukGZ3jZ/2kb0tumKS3wxoJgmxFWHsBJ6/rqhD6JY77u8yJkqJKijcaT73jo+xccm1gLzakLQu4l5nqVgIXkpBQft2j+0IiImPPZB5UeY8EDsI4kvSnUSub4mDAF22cLRDlPICtalQhl06wYr9ENRyiTsaYib33M4j7JMjrtwJZRSjyB6hZpHf3mGfzxCUMa3jMjN7lGlDlFySz7+i7Zqkw4zgska2h8zI8Vc1gb2hzHs8bRr2m4h7QUTuCLyIRCp5R2fectF8jdZ26acrBh+6hM/eUroDznoGG2XBo0fvsW1NDqKU7KZCjBLUcUL/QkUZDBhM+1T7Dpv9LWKwwfemDFoP77yHKT7kdOiiyh7toYOgltwvr8lcFV3vkyUQBjqDUUOxH2F5OVXjUXV0hru3bFIN2cyRFA+pctGtPZ3JkPV3GcgWZnmP1JkSuyf8LPe5SK6oLp7zf3/2KY7QZZ5eEpVjDs6P+L1/8nOmxs+5Wr6mE8HkvWPuPv8KcZuQJNco2xv+5O0LhO097/3D/4yPTn5Gm6w5e3oAYYX1xY7l/Q7H7TAyDnj/Jw8od2vevPoUtxnx7uGP+Ve//KcIfs2PP3yAqR/Syne478IWDbEpeXJ4wLfNmg+FFYLm8dCd8GX0Ci0zUK2KJMvZLV9wL9j8fDdhvggYnL3LVH6De3LC/rsrwvmWg57HO7O/xTp9TlurtLsF9Tqm7dxyszuinfhkzZ5m1yK5AmlrYgQFSc9HSQdIdYdyEbN0VA6UG1pZJYgV9CCjGmi4y4pEqRA0i8lWItHu0Fvl380twn9bIbcijrri2k6RQgP70ZSxtyJTxuysFW1PR+z1sQ4rdElDrX3WLiixipSsEO9i8trmXrb49e458utb3i3HVFaNEbwhWtq4pkmtKwRyQFXFVJKGKHQJShtzkCFPHd4xKzZdHyFU0GVwNh7FWif6XCMjRYp0OqrOB66LYR1zqrrI0RC5uWUreTycPqG9V2luE7pqF4OGqr7GciYoKwV1HdIsX5JcpzwXXtKWKtP3pzj5DGM+YBetcdU9i3SP3Gk4TCQ0LWUuB1iugiy+JrwPyYNn6PMNVZgQJw3pNwv02qf6l9cgt8hmCkaAWetE6RZVM2nFmNFUZDpVcNQugzbCWu9ZXF6yS+fYHNN6U+ptwmIb8PrNF4TFhlWyo9heY1kj8tijGb5LLWnYYp/eZEA32FAYGZENYn2I4hZ0zC1bw0JVRHRB51BqmI0qalln7uvoGghiza7zkOZEoDubEho1urpgW9c4woigKDGkQyRueOJ2eVK9h2iLdJddDqYp8tWCqaExOKhw/Jx9kdAaDrnU8Oy3f8Bm8ZrIge6ziHAjIkoiTq8PdYThZCRSxMQc4kl9KFzyYYPaqbGEhtSP2CFyt5GozD4nzjl6FwJlzqjUWN6fsVl9yqbdItFDjEvWS5VSrDl98Ls8kPqU/hqlZzLp6LTyKcGbFfHdlheLe0qlx2rxFVUdY40mpJ6H2lEobIdyd0O431Jua4RBhh+YoIdYRoskdRDze1QhRurOkEuRsnQJXJdSKMhFk3at0w5thHj0l/Zkxh6pOiSSDr+Xvx9EEmjFEsuxMccdBq5JGF+zWMTcJ18z6w0w9wbC+hYj89Gymrwa4gwG1EIJaKydnEqEcnWN6Q/YInOzaaEr0Zpj0uIO/9tvsPZrxrnCTm+xeypm2yIpOluewG1Ns9ax5gmaV2BjU9sBZ48O6f7dlt6Rzdk7H6GoGqs4ocl3pIFOUYdcXcq8+uot3zz7BUm0RLTXLBdLskbGyCaso4o0vqHv9ynlHpgx72U9alEjuFMpyfGVOV3JoihDbKsmXyyIqh3psqG3b2ikOWkrIy2v0fICoYD/h7k3+bVlz+68PtH3sft99j79uf3r8mXnzLQtG0NSoijRVBWCAiYUQkJC8AcwA4SQmDJCYoAoJgUMQAWFy8gUZaezcWY683X3vtuec+5p9tl9E30fweA9C+PKxJYspFxSKCK+sfSbrW+siLXW9xeIz+hEzzHrKdP5HXk1xZ+kCKsN6ZWJINespwJFMMXoqgjtLgNrn7QwWAtdHv/aV1FR6WktpCpjWMfY9jHdrkRLOsZPa9JlhrdRmIVz1EGFUeY4xiPcXUSUh8R7x+iGiN0ICOISK8pJP2lYhBusqEEWVILM4no7wFD6GKLMrmUTSjkdyWdQ2QhribhKWUVtvj46Ie03WHHCukxpP3mf+6NTPE/E2nqcvFtSKz3+z+kSUp11muMVS+71T3H7Bvmhzmapc/viJcxLliON4ahNcyQQdCukiwW+CLs3Cbo6JIplDEtE2A0pGwsKjyRRmV+84nY2ZTfL8eI1xrhFZbvILQtBF3gmjnk220PY79IdPURRujwY17SNPSytzav1BbWh8mKxQ9RsxmaLm55AufEY7FRuqxylOyLPXnP10Rt+dnHObhYxujeiykq01MVMOkhOgCsqVIWM2qwQHJOdXmDrHq1MYV1uMN6GQEGe5MRuTpnG2Ooaq4pJVKiLBHn3yweI/lKfA4Ig/LfAvwQsmqZ5/0usC/yPwClfiIf8G03TbAVBEID/CvgbfNGn+Hebpvn5/9f6RVVy+zJhVV6yFiOGrQMs1+Xd6BGDMCV2BFTRJBcddNOATcB2HdKW2jT7LW52t+TpEkMTGXQtUsNg9lKkHVgoioHrlrjjmkzU6O9CMk2iFLdcNQK9KiPxj4i2b6jUEXXLo5prbPMNXr6jpT3CatbE1pJVlNGuZFb5ik0tMlaXGMcjHgwcxuqAzfwZk0AnmLwiSds82Dtj7Ze49zKEpM2i3NCr9yCt+TTaMrzXhtggutghKA6562EEBwjFDaKjkkYFy+1rtopIvT2AYkJVL4mzhwjKiv3GJQh3eEJGd7bkB0Od94XfhpZDWPrU1QZF7xBJCctwDHlMXiRojsjeXhdpY/Dhb+wR7G5ofJ1A+ipf3QuZUuMWDbEY4bX6iM0KsxxiNhp3Yo4/eYP+TZtWqtIrN8x2ArIVYgkHbPINpdPjvXyMb+tUakDTrjGQoHZoe1OcjsWq5VKkPl15SFre8aj1VU46PebTmHrzPfTWHoIf8nf++X+bhycP2N7tiGsdubLomglPOgcwWZFIA9z2CWIJh77EnnWP+eGWOyHnXbdCmzmk9zye/mjK6tkl9sGA45HLO90efjnFlsYYdkwg+aR1wd7XHvHh6SPyxyPq11tmwYDtvQ2954dc20v64hEMJVqjr7Jsi3RTGVwHzVKYzE44GB9yJOgMPI39KkLRLYoDDXHdoJ5XnBh7ZH2NkRahRyVX5xWXrz7DONVo5N8kUHWeLV8QqRVqbSDoDabnsdrLkec6TiOw0QSyBVRugSPqlDsFzZCIlRq1tmkUn0SugYpWYJBJPpUdwC/RGv3LZgL/HfDX/xz2HwP/uGmah8A//vIevtAcfPjl8e8D//VftHgeF7wuP0HKbfzEQVRANjUGD2z8LMaxVMzqHRy/5m7nU1UaURbgJ1fk3g1VWJA1BYHgMAmu2T1fU9dvmf/0E2Zv3yKkc4pSY/425Ke7JetZSjCP6Bc1rv8EQ6p4pJ4gujFq3EPVD8nMBiXrYDJHrnTwLR7oBpnnsfNCrDQnykRSD6yowhwIdMcntCudk713EPdKVrsVhpmTXsW4TcT+WYftdUhSzBmbOcrblDhpcAd9emLJwBOIt5cUmYEQN+QmrCZT7rwIMbtmm/gEC5E6mzMQF/j1HIUKLc1IBZNi0iDoF6SbZ5SbLTP1FlWrMFo7HKGgMLeclzLYbUw5ZdA18Kpz2suK19452+SCTVQhliVyZjNq2ug7me3zhHTzBkUIeTBUaKOgP91QaSo3mUlLCDDWOmJUYFgamm6x6UiIloiWd9HkCs3zKdUv3sJmImDlFT0zJy7gnvkex4+hbbpYhLxzfMzwrIWrpmiJQCAGZIZPpGxZZz5v6hylbVHf7/K+09APRWqpBLPg6MNH9O0+R3sG97snvC1zpHXI7fVrJCoyOyC8WrClxC5NUqdkbPd43BngKyXvv/OvUI5sZLGPvn8f1XZZfpLws+nHpDsNX5cRi4qvPfgWZn/4RUYm+zzfltxNcv7nf/iP+OGnH3OziynkAzJNo9nF5D2DpAxJHY1Mz5mZXXxRwzs2aA+HaPSxXIdg6+H94SuatEEsfNJtSCLIlL4ARYXRajBklSjT6AoZSVFhdCvSygfFpha3NJ5FsZUoI9CoURoXV/4rioo0TfM9YPPn4H8V+HtfXv894G/+Gfy/b76wPwbaf0538J8mgarASzo0nsdJJdHf66MqKourivHxmM1CZxHfcqGESFuDrF3SzdoIZY4aihw4FS3rjFZwR7wLkDoN8nxH90BBUG1iUSC+8kiZ0+Q5bhNRbwyUlsZK3aApFatOSrMCS5BQ0kvCVAG9pLN/gKqdoeYVU6EiPovomB22xobO2CAvJZL+KTdPl1y9DZFORGyrw6E5AklAtQTSUuRWE0h3MkkjgHlIkGkkexVmLKOPLKz9LzQL9UymaFzK1GK0rBAk6IUZq9kWPctR2zat1pKFKrNYR6xzA9HwKcczDCUmlXZUU4UguyN7quMLUK8GCNMCK+2jJTN0P0CqBkyuP2U4H1CcOhzN3uBs75i8nJEsbXKnJu+7iF0om4ZVWVPMBWoFHj82WZsuzGS6So2mWti9kkKJsTUJU1hTWSmaFtHIPnroIHVMDN3BbQvsCHCKCFk6Rm4VrJcrmlhHa48wH53w3le+htBS2Lv/mCixuXvzFHsTEV1m9DER7jKa5Rxj/YxXTz8i1nboRLyeJNRbgY6zz64qcAeP6Ld2XK9q+m0L1QloiQMefOtD+kJFaG/YoyCrH6C0Gr714Ddptx6yC/rcflbyZhHgLRLCrUkmWEwv73jx8hmT159T5gv0TUq0Ebi+VFivcn7wk2f8we//Ib/3Bz/ihz/+PnZ+AUKDQkXeLJg1Jd1ohN0UfCBrmLKEEdRIdsQ2LEDVWeVbzvUFhlqT7EcIckkkSghljaiJRF5Gbuxo7afEShsrHLJTY1Stg1I06L5G0sRoeyqNqxFZJlXPYO4Nfmn8/VWqA3t/Kh3WNM1UEIThl/gBcPNn/G6/xH6pzJjTavG3fusDxJnKoltRZDZBeUVWbfnDP0npNisC8YjuCApBJr1NsKSH2EYHpZogdgz011u0okOvA29WMSvRZ/qRSF97w7Jtocc7/HZCr054Kz9Bbm4xl11GRU62TbnsdbABT65Q2l16QUZazHn18wuS/ZhBV0KMPQbWB0RuxHoe420AACAASURBVMP2A3w/BCvi0djmdnTA/OIpjusgdFR2P91SSAbbRY4zrPBeFUwOX3F0NiacxbQGY/R2Sm72SZZbrn96S26n7B/KeHmMLmpIkY+3Nsgrj6xl4mQSTXpHttTxipTjMkETbG7SAuvaICgSPv5kS7fzkuP9eyiPIgY9j2HrEYt4huMN6YoDlksZ21rgjAdEvkh2J5PqT4hkkXA34fHJA5LlM9rpV4kPQ+KWz56rEskpxdririjp6CZCUFDKAWKvTb1MiXLIlCG9yqelllR1TlvtMVEKDtoWYr7BU3QUvYWxN6O8lhlbFum9CC2wUP0Aqf0+cl1wMBpze7Pms8//IaI1YiHPePe9b/KDZz/GaTtogs7i+RI/8vj0T36OIKp8+O4DonpDqs9oDYcE4nO07iHvHZm05xumVQ91nvA4rVFa32SrhfgnffZthd+fXmK//w6ZF7GbTsmHD/D8LeeTzznuP6auA27LFeJdQ6y1cC9z7spXnLXeJbMLXt5e8Cb5Gbt0g7OL+XFScSP/B7ROVGrhiPnkGaMunBwNudUUvnr4Hmq6Iil8pqtT3OicHIs/+f5HBBcRgu3Sv26TyBnGPQF/KpPlOZWm4ngyfibRsEFvVzhTjUgrMeocJGjHLvI2QBAgD1KKfobrJvi/WGz4/5cSofALsH+qSPln9x3odLuokkLUWaJnCaKkIyaQKTmVJXF33aJ3AG11jFcUdPYdhKLEHZQkdxLh5yF3wopni6cQi6z8DfHtDaVrojQyZd2m1WrIVyJbSURIbzg5GIGvE+glmZYhWTVKBfa6YduS2fkTctoIpk/+ViOxDNSBhKZIWE9sPM/BqnekQ4tZkWPPE863Lrvolq4rMvigTfVS5KZTUa4VRuMciR7XYoqiw0NjgLbekNQrnK6K+M4BE8Ehvb6jyqfMrkIGuUks3uEI+/SEKW6hcKE37OkpPUqKJiUqLlFbKoWYMc5DGlElK/pcpRmP2xHSdEjl7rA1lVtdxt/tiIOa6dMJ6VjhwZ6NtVexrIYIwReNRDt8Bk++hrAKqfIe/ZZNZfogF2TlHYWiE0kx2d4Ku+mTZymFE6PHLpG6YCX6uPIj3H5Gso4400TWfoYtxdTFAR03x6vPMPcL8tyhzlQER0XIXQZZQ9hpIZcC3V1FOWxTE/A8XBNffE6lHCA/26G916MzOGO+Fen3PuE7oyfI9x6AsaBORgh+AqVBozUkrBhJp2heQrxXkhwNeOP52FWIY7j80dMblp6PPjSoJYui49DPJ1ws3/LOgxPcROCjSuGbD76FtJ3z6sUV//vqNfcOVbztSzZ9i+rVU5TrLValojkJT1pnHCsluqeQNw2XmyV6LFE5IqepzNvlU5xMxqrhpNtlUUrcXt+RBT6CpuGWEb7moMsN6ZWEVSkgC9SlQlDHGG4XjIR82SZRE6pSQJMaDKOgNEKiKCU0NHr3C9jIxGsTyH5hwP5VqgPzP03zvzwvvsRvgaM/43fIF5uT/L9ZoWn+m6Zpvtk0zTd1s8Wbuy3bqc/Ki4ksDdk6xc1kJFXk0dcP6Z4dUJsFulQQ3N6x8u+Y7STOo5yn/iVCVmHHBoK7h4xB78kZjTaiuzcifeUTqS72QKHqtEn8iKvPt8itnDoySAUdp67Z1htmVU0dTFD3BtRCSSVtMcslSZkS7dp4F28JX92yjD7BFyechRKm7xN0TcJlgDexqEPYt44Qoi0noUirBV6ukCsz+oGILYTsZhuikzGJobHLJGRtjLUDx+zQ7ERU1+bWqZEVkyCeUWBwbdlIHbirFTJBZmEWRAqogYZddEmSe5DlaLJBNyqQo5qQOxYR5AVEP36FKEoU0TmRWxB657zYXRMuSo66EV13n35bxtn5iOuASLcomop25lF6GXd3EzYhuIpJuCqophXuZEm6qrCMiqYbc2bV2IJDFG6psxSlUAhnGtgljTxEaClUKwFzlSAqColUkepbrI3OmSghdmQkY0Rv74y2U3P7csLsfEm10Xh+e8nDPKZ8oGPaNwiDHv1Tm7/zt/85xEEXXfYJoxT7nsVkERNMPI4EBTvVyI5dHM1ClSTi2qBUFcp4yPNPb/EzyO0echQQkDB7cc1PPpvhrjq4nUPcUZ93jg/YVXc0ZouqdchhS0BOXPwopo5zvvpOh69/5xH9YxNNbfONJ4ekVk2qD8kUCyPdI5FkNFOhiQpCP6foH1CPJVr7BrYMweIcMSoRqwY5ERDkAl0XKSkoVZC0EuleRuVYBEmFIh5jn9VYQoLdZNSSRl0N8HyolQI1zDDudKq8A798dOCvlAn8r8C/A/yXX57/wZ/B/yNBEP4Hvtig1PuLFIerPCa/eYN0f8g0yAk+ekOwfIHZha5kk7ckDiSXxWpJ3pEQPUgnW968+Zj08xm2EfJyXTHbTeh0v05evSW3DmCxZhL2efRtl47sITYHFPqOrD5EFBKUuYTvSrRjg+ufn3N41Kc2S2qpg7wSGVVrYr+HbwpkQU5LCNmJBtqtjzLcw/ef87/Fn/Hwg/sc+Qnvf/shk/mG9TIhjs4pDnqQFYTLiNIx2Nt7l00c0jtWyRcW8YsNjdxnX0q5FErk/ghTzTFvLIqioRcpiO0jAvEGwgizrlDFim0GIytk4oFgVhiChCDYcJSyzU4ZVj67IibOI+qmT73c8f1nf4zlauwX36E+PCV4ekWVppTGgIMPLV69fEDv7I778QOaWqeOcrIkxFEy1kFD3LiM9ALVtlHKBV0R1Npg5QgM+hLPrzTu9VO8qIUzkpDQUdoypalTJjWntcydkFPnEUpvgNDeUa9czowbFusDIn1JVVnEZDz0XP6PF7/L60nMycAgC0JiOWJ8FZK8GzE/r+kLfVrNnFrrs5pP8G8vUIQ+f+u73+bzVUrTXYOxzy6uUR7dY0wP8TcsOkFB5nSZX62YljGvz1dk4i2jQ5cfv4lxxIb3Hw5R6g7nic/1D36X9s5FOB0Ti1fcXH9CIjks1xvspEBqPP7l3zlFsBveC1T+2oNvE2gJrhfglGO2uxLhYEqYTznuPGHP1Dm331Bna6Z3Bp2RidkekR763Pk1a8nHKnsk/YhUDCm2LlIRYB/XBLGNNJFxqoJMEAiltxg3FWW6h2Xs2CkFLTHFFG3qUESWKjaNiK5PUUyH5S+ZJv7Llgj/PvA7QF8QhFvgP/ky+P8nQRD+PeAa+Ne/dP9dvigPvuGLEuG/+xeuLwksJJVmOkOySqaXFQf7DfNkS/u3foNiEvMq3dJPW4jLFwj+gG0aU05KXoprRpuKp5slYriiED9HVXoMQoFiKOBcrYjcb+AVOzQWuH6PrF7SPX5Izo5qmxPaIqN7LSZTj2Npx1IvMLZ9YrtB7ybklcqBreNNVjh2SSbmlOmEbVIz0iNUf8FM6YBmUYkLilzGECzsdk0dCAiDPTTFQakd9M1TXl23ePJIQOs8oDI8IkOmu9dQeRJmpVMbNqVgo7QuidcJRS1iViaGmbDINFxVIfItJN1HKWSSpsYsfbRKwAhuKFoqtXBI332Cna/4w/MFq6spw8N9rvIf8bC9R+voMdW8iyyu+dEP5+wbKsN6iKB51EmX1S5gz24z93N0RcC2dmhCGyONaWXH5NUEfRSS5i5CmNItQ+Yzk8OBTCXIdEsD4zqn2VO5lmP8XYapFQiyQNmUpGFNZASouU5jN5RqA2KFmOg8Ez/i85sLVMEgUCyEJmIYd3iezVHOn9Iev08Sb1GGBrZZcOmDe3JE1pZ4KwaYLQn7yRnCWQuxaThVLbJ+B7XaQxoLXLya0TEVwnLHg/t7eJcBN69jjNUUwe4SlSF+EiK+SRDNhk9WE07DBZ60RKxP2YRTyuyCdqvPo4N3qD0YtvokJwWnR/fxfJ2fR/8X7WLCfksi20RsX+94dE8l3dtn9XrJqjY4yTQCPaEQJrTDDrPZp6w2Ii0rp/RKbBRC00eMKzYzkN2aqs5pigLJTWkyBa+yMCWIikPUcEOsiARyhWimOK0CeVeTJiVZ/MuT/r8UCTRN82/9kkff/QW+DfAf/mXW/VPLiwKNHen1mo7xHoH5ms8ShZPMoLm8IdUM5Ks73kgmw70Dpm8u+fj857S7Z4wXKVPRw7+7ZWjeJ1xKuOMBiysfadtBPIww7BvSwCSYbmnGFXumjZl79AYtRlZDviw4F310ocdL5SWH1n3sQ5N622GZ7ZCNkLIpKbWAxZsU94HG1otBrJB4h5Gm8GZ1wdj+gLNvP+FSWZKLAj/5+A84O+nRG4HkKzhVxa4aY5c5tqciiytis0O2jNg7NGl8k3ZUs2e22N69oVE8TKuDjcoyMJGbJQeiwm2R4lQ1RmOiKiv8ysA2XITdllAfIm6nGLLMvS58/tmMhf8zPv3hzznsHPBrj79K+Wsied3BsrpUtUA3fMmOI+omoykS6NwhLG2elUveeyzjhRqPE5jYDY2zoxWqrJyKtnRKVU+4WTekssOojCmJEKUzslxk027oxBH9vMZQZeTMonEy6uga2R2xl9jMKBhVEU05YFgbhPEbnv+TCe52xFKbUN3NEJUEHINv/vbXUFtnpHWMNRwjLWKGZ13uzlNK5ceMB19HLxyark477rM/uMfjgwHLXEHuSyQLg83NH+O6Nq+iDtYqwbEjnhcBjVTTf3JKsBF5er1i9scrhu8VXF3qEF3x8UZh6yec3tsy9Uu+ftTnGx/+Or0Di25rgEsXlAjFcDhzDMr936KQakRFoyWobLyIxi0wGgOnbTCU9xjISy5yn8ZLkcqcfJYjpBmSHVMoLqpTY64aFEGiNEcU4gZbV8jqANYWlZEjCD0y3UNQIrSqodBqummGJDTEYY1etcgMCYkIkl8cf78SswOCKLL7+C3SO0dc3jyj9a5EvbBZelN+7/c/ZliL/O3f/hfYrTzeLM5pAlD0kM3qx3y22KJ5JveOn/DOuw/ZZRGWIPLJ3YbTgz7C4THxOsNyTDrjA3bLLb2vjlBVBa91xHibkZwqjG6m5MKKRNxDzxL8iYSmJMiaB1KXoohAyimGJdMXd+zvP6TCoPZnvJ0M6blDEmnD937wlv6wjVwo1F0VW+igih0aP+fZy4/Zvz9g7HwFZeSxlSTSqwuC0mKZNtyXY5ZyzWD/EDlb8NFlgKXBMFfQ90283CTLh5yKMRs3R1Q8krs24r6DEqTIHYVSFDE67/B3/81/kS4tsu4Cay6Q7Bqurl+Q2ylfb84w7ifIsknbF7grHWwjgVxm71RHY8jurEDMFuz8fQQxwhspzK42iOohN3qflq4grH0av8fBsYPerui1HkKUIBlLmo6Cm9jktkC2TJEEnSLe0Vht7DBiJ0o07VfsB12MvEszcLlYXPK7P/oet1dztMM28krlbPAu3ZFOLMS0R8eYcpvxvQ4d7YDmuxXSncaHYsZsLtDedTj6yrdwaxn7dJ+oyrlOFmwKjdZTgdzKeNg55W16y+M9j61xwPf+0TOEnsW3u4f86I9+TNXrUSzu2O3byJ9tOTjQGL7/DU4H7/M73/51ItFid/NTlMMjBM/nuGXwdisSG0tupyrXosj+WZdhekhrL2Uy7FP+ZIqlRAQ3Kd0mR6oqDNcgymTyqyl3ScZkETJTY0wJkrJGFlOKpYBhOQTSGs3fYQJJEiFVQ3ASNDQKdYkYdWmSAKGfUa8FSkWCskJpqSysEjFRsaPml/wW/BUhgSzNaGyT6e0EJ2sRXmo0wUsExeSo6fCtd58QazK3wWe0/REb/xIhs9gGEeIs4RvfekAj2ry9fcNykZMs55x950NCReJEgBfBglhIyfxb9PGYRIioQ4WeuWXmL/CWLkrPx6g0dDNkaVi4ighSHyXMKI2Uosl4NQloKwbLtsyondCSdKbolGmBMt/gj4/p5jGTty1KeUGnd5+q2FHlMoJQoRkFaq2Shz8iLY9pWjk4OkmyQRNqXr6J8YWGwVAnXx/TacX4pkcU9/ELCSXZxzQtQkNkP42Qioe8sTeclJD1VBTb5l87/eu43xggF0PsfZHtCxCbhvtjjdtuyfKq5ifSW36zNWRSZXinCqfZiDSaIzs96o1KLvoolU28FnhnlBLs7+HtEsTIotJ2aFnDSi1w60dUp2sOhzZ+aSPJczSOaMqSYJ3SKCGNYXMsObzVHNqdDOKSYCxiSxnWW5dp36WScortDDmKCXcdsvqaZx89oy13GUsisn6CE7xgnbU5NvYx1AZR8BAWEWnT5cz8CqoDw1MboZTp9kyuVmtqN2V2lWLZMZeLlMIo2QxkhHXJtliQTULUdkNH7DAzFBaNjzl5g1R3+HZfg2+f8e6vfYuOpFB2B0zrBaKccHR6xFLvUix0Jk2Gpau8tR4iX83QjyS2n/vk+xGdckwvFvGOTM5O3qFlpayZsQ5CxmfQalw+/v4tew8+oCX7fP9WpdYNSEBzMjBEJC3F9ERKqyFwRdpBB78V05IbNn7B/lAg8QPWDxvE120UfU1SO0jNDso9VDFiICVMWvIXe4X/AvuVIIE8S/jp3U8YGANa9wPChcMmKOg6Ik5/xCaIiD2Nm4uGwsnZFiVj12aY1iSjFrmk0B5r/Mn3JkBBp3VIuW7QP/DY3hYMDR3FX3M+vSP0GvJU5lu/vs9ue4fTa9FtFLpll1n/mhbvUk4vEYYFsmGwy9cUQQ8l0ugZHXI2fOe9D3kxK8mCLZ0zC/WtxF5L43xzSXj/iMOnS9JCw3Zj7qp9rNsNtWbR3j/g5nZCbzxmqBUsijaaHCMXIZuPK+bOjnq6IJkpdHsuD379Kzz/0Q8g25EJBpamYqoiY0FAcPqYTYxLm+6+iWPrmH2Dxx8c0hqf0ccl7peMFiVvL+ekrRbytkQ1L0jm73Pj+HzjvT2yUGQnN6RVRrWesVjHnHRPKA4W9KSMS47oZ5cEuxQ1ckjvBIKeQCuy2NfnpKqLPzfAzPCKAaO2R1QbWGEP6cCnWGes9ZTB1iNwFCxdpkptlnmAOqrRsw1COUSQTZJkR8ef8OkChCDFOFtj9/dxejGhc48TMaFn2ciSwaif4oUFRkfFerTHYN4j7pl8/0/+CRf1EW3LJos2CEJNMfPQ/YZMXFAtjsn3a5yJyfDU5u4jF+M0xpjmnPTfI9fvMT5+wCCQEO6LDOzHhGrF4ulTwvGA5MZj60TogwKrbeGFJf10gyJuidwWarmH2u9Qd3e8qTcEdwluULLVtmiayYsLj/yjiOHjmHXWJ+k5jHYRyqCHcJuRuQqGaJOoE9Rtm0LyEIo2oh2jRRl5ndHsXHxzgawqeHFFtNemdVtS1QJSbaIeBjS3NbmQYwo5s52JZTW/dIrwV4IE6rrg+ifX+P1bNp+7VI5Dx9hn18QkyRvCvSH76YLvfuMRn09njOd9LrbPiSwJa7UhOT5ic/ucnuBR9QxUIeLtZMXQfxertWRm1UyefopmF1i5gdZrmL/Z5/0DA6Gno/oVk5aOcGmRKtcc9Fvcej6qNkWquwgbiW5iErkJPUXk7fMd+609hKMEfaWitE0iDIT7IQebms0TF8N3UfU1WuGR5TJmkhFmOxB3FMKQ17GCzueEkc3VqzXh2wu21ZSkVGk/bjHybQ6H7/LPfvB1Ut0inlaISoB02qKbOjhSBmkbbWRyejBgkcn0ahDcNlJbQOyImItjeg9W7L1tcfGqog5UYlnAEy9x365ZfnACdcFI1yjMJ0zOX9M5sKjsFOelhDtqkz5IiG5gv3WP6CDCCUVEBISuRmGZqKFBvzfH6ur4mUiuuLgpyMMpy+IQzcwQDZEsr8nCirB3jqwc0il0IlFEkjVEq8AoGrz5jJu7G1bzHb2HBc3E4mu/9R2Mjs3r5I4HrUO2zQQ73UOW+0Rql0Gsg+vhyzKbz31atYU2PycubZoqYCnFPLH6+Ic97ruPyLOAsdah9cjFsyPMewP0QiL7Z1K+vfWZpzmjWGOWGhTxiuPeIRN5zfC3/wbl6o509A7das5PXl9TvQ45G+3zWaKiFqDuVJ5ePkMsF7if38c5drH6LRJNZRG5/P3z13Smd4zdPj9/9YJV+4JHD48ohQNEWyAJZKpsCZaOswVVaFjHLq6TUwESA1J7gaUuiQoFRS+IVwaONEPVVeT9msWmpLPuEbZq5FWO1K2orZC6OwB/9wvj71eCBJpGwRwLGNWAbbWi1e4x8W75oHyHok4hzLmIQesqZOGWbL9GT1uoQY7w+F06tk8rGvBp/Ao57ZD4EbNBxD3nnLs9i/TNU8ygRk4NAnvHIB3QGh4R2zX+jcTgOGEQxKyPHdw7nY10hV4Y6NUh+aiiMGbUno0zU0hbNg9rj1txzn7dJ25U3HTNJg+JFiqyvaOuDebRDbJcYlZdstgmcxri+Y66fUQjbdEVje1LMPprvHiLH6xJjT6EOfl5gXJfotpW7LXOyEhpnIhreY/jXYrbLyjkA+q2id21EUqDfSUhE7scuF3YFGR1TmFkzK/GdE4DnB/ukasTkrDDsFmi9Y7ZfvYRx+P75NUSpQ99TWYzkzHiBVK/S6LYWPMSFYcwn+DKxxSnLQZeRIREllYoFMShQdzRUUuLWkmRwzZbx8BZhEhmgbkr8V2Ffu6R+z0ayybQJbpsSFQJohmNZaONjkjcNt/+VovN3Ed6v4Wzp7MoPAZ2RSSEaKLFeKixVBP0lzW39QuscETQzSnjFWG1o+h2MLtD7j7/COs6pPnN9xiNepTzW8J5yWzYpzZSquyYigl+L+VY6bLOYnppiGw4lNaWu7c36FZNHJWk4RXT3RLX2EMbbnj3nUe8Pr9FbGQKS0XbGWTZis0u4fjeE0xrzOXd7xF9dsrF/JzJ7R+SNClvlzPK0/v8Ne27iG6KrUpIuzmTTYKg7NAUKMMOjR2y8zJEV0UIYnK9BaTU7KGkG2TnALGeIt+Xie4OqasZWWkhygaRsqFpHBhBvirRDZl6/ee7/v8f+5UgAbmpIA0JdjWUAYFxwNlpj8nmLYKZMtRkrLpkcXGOsvJIzZJ2bbEptygDnTI5pNYLsI+JblMkc8mIh7y+inEzBSHIyQSLublk3+hTFyWr/DXaLqOlukTzfRY9hUfrCWXLp9i4eJ0axVgwnLfIemdk9jUtzURNbsldm8qL2K00Do9r0sBCKBTGpsA8ahgZO5zsmCbZkRKCWrAQSqykpLsfMv+8RpFqUvma2actXD3kSmzQ2KHqMkZbIK9Dyl1O+6hEzrssn8h8y6uwOx3kVYJnJywlkXarRS5GjB0FQfBQ7H1k0cATXSyrQmutSW+ukKQ5UbHFOmtQwgdEwwYVkevY4+BeFz24oLQchnpO62BIVko0YoPEirpl4lRtTEWgwkcxXFrdFoNlwsbyafpt7ErFGV6TBRaZ3MHeWghKgWRYRIVPXpjonYTZzkQXU46zGctdiyY1kB0TPYGjcYcxx+yK5wy+dsJRv0ugWrhVQcd+wOazktY3RlwXc0bRAZ54Qbye8Cy4pZzAQ8tFWm5B0rhdndMRTHxDp9hesw0rJEtFP9inL/o08hhLKcjPJ6hCC0G9QKRFedilWvu42OSdLmupIUh92oM2Y8VllbwlDipkW+NovAeKjP3Rx6TGEZlaIx90mPOCm4VPxzlCqDK6lcPqhw1RdUuhSpx/9hn/S7/F31S/i/1Y4FUec3sjoukGdgKL1oqmEOnWfKGwJFvUZY2saqhRRmY3KN5bEqGho8usjQlFoqLtMhIjo4415NJD6Ll0Mpu5XiFLCvCL+4Z/JSTH//P/7L/4T9Vuj6xuMIcCY9Pk6U/XRO4Of1ehRCGi1qUItzhOhzCMGLoOTSbQLRvkhzb9ps3m/CntfQulMAj2ExBU8mxNmskI8ZqksomqAFMo8eYqHUeiFtsMug3rjyfs6hy1M2Cey9hZQ2NIJFGJW4O3EhCbhlB2EbUuj9T7uGObXTKBSsa2dRy9S6eBPfMehapyszxnl0g8sDW4rxG88RD7B5wqKavokixriMW3JLGAEoUIcUHpQHuoIest7IMn7J1a5PUhHd1AcjRMySLec5DKDoqqoFoxbiKhBXuUroi5b2O0RyiNwiKdczTQWVwmZOWOuqwZ6Dpu+4zUjHhk9rjf7pFVOULRJW1W1HaFmEdkixRz5+Med9mtaoxJivpeHyETcQuHgWEjdCv6RR86EqWlU21NHMNmHQdENsiaDYaJ6hTIBtRRB8ksOE4FUn+fQkvpjHPEyIW9hjo0efA794hXNV8/eY/Oow9oNgKjtoFgacSDDeluR/k849n0Y/z5G7aVwfbtFnkZIeouL3ZTvjo4QsvAVvcYPVBZbwXEfoFChWsbVJJAnsHi7YJYucHbOeRaSlg1iH6AwQOkPESzFWarkvXtlJubGwJd4iiR0SWbn9x8wvLyFfcanRfiFabpcCQGTO9WFMsLOpXL2f6Y+82Ywb6C5+fMlTXqWqSOQJlt6Z1UnDofME0VXi7ecPmzzwnVmm6hEqGSVDW2XlI2GcgpUmRQ7Nco64gcF1nukeY5VSNhiDLFoKHvCURSgi6K+IpBUoJiJ2S1SZPGv1By/FciEyilkiwysUcJ8c2AhWWh70vkKwG9HeLvKq6lP+JR+z0WXFKtGjayRdOrebUW+I6iUnf7ZPsaatzD3AuJr6FUZ5zZPV5k1yRCRVuqqWizmif0u7dkvovfLEgVnbxeM2APZaWhthekFx6ScR/LDGlqE/nAp5PsUdVTnKpG2PfJnALr+pBlfYfaKDRqRV2LbPMJwa3A2Gix9RqumoBqLcA7A5RS5bLj0pEG+K/OMWKbBhG76/I2rLGynF3QxTB08osF08ERI8PG6tZUaovYD5D7Bqa+wRJk+pXORjDxzQCLmsQTkVcx2X6JJB3y+vItY+uAf3Cb0AwreplAWn7E++UHDL75iMbUaV+V1N2Cnt2D/I72XYflUYmXdXBKlZGTUOgp7aXOeKTSmBax5oHRQnNtOpnJKqwR9Ih4JtIa2tRKhNQ01EmOLEWUdUOhSGilzlRpXdfZYAAAIABJREFUEAcRA1GAtCFuQbWTkMwKyy/5yneG+KLNoNgRHni8erPA7vVI/Q6dxSU/U1K6HRVaY2Y/mBGWKeWpBYuIk8MHXORrai/j7OiI+Y3PotnxnfhdKmeFHkd8f31NNwm4mes8+fCQI7oEVkova1G7JU2yI6or1t6cfemO1UhhvBLZree8qlacNGfEdc5h3OXzOGC7qBGVC87zCv/iNanSZik+pdeK0U4GPFEf8IlT0fFkNHPF4JsPEFORQnQ4NwJkL2P6cYjYTuh6MqmVIJYymgxBLiKKDkLhUSkZxjohUySQctLYwx5CtRUQGgMpM7lTlrQ0Dd93kMIUQQ9xE5kkW+H9kvj7lSABqVFo6yJFKFLKU4qshyRqtAUXMdswUVSsVZuitcH3U9TU5c3VFVI15D1rwHQVo6sXmO5DvOWUNg0HBw5R0mErJFR1St2uCRKVkRgi9GR0RSWVuzjpnOXrmp5ikaCjWB4f7jRmI4fCLVnGUwqtwsm7ZFrGWNJoGGLsgX4nEFkw1AXM2OdqvmHoWqw2BaorEzcDBmc1u+0N8fWWMt1QHckIZUSx86g8F8uFfHXFtrBx44b7p11eltfM0gGZkzNOjmnKa5ZdF10VME0FO6qpq2MyJWRa7JD8Ndb99ymChHFHx3dL6qJGm09Z+AWb5Dl6X6S+7aE+2lFXRyB1KMWQCIG6XXE6jlhOC8r1gLmwYDJRGdSQdXO0RKc4OEXvJ3hmiq3JNNIBZlMgSy1kN8MpZBq5JBMyRKVCoYUsKHjLDUpXRnR1tFRG8CVsbcI0bjPxVJxeAaSksYXS8lEGbfz1PkIK86yCOKA1sFCfh0y3dwiPhoy3Ca5gs4rW+OTc/8o9qhgaMSS8TRDLDeXhIdbkBS82dwhui/wsYhvB9eJzrNETpnc/oJQyhHRI7NzSBH3Ugc7KV8jzK7L5LWURct7sMbJ6RFaA/+mUXZhjijecyGeY7wdk2xXB1Q11v0MWbXjjx+yZK3KjTSbb7Ak2R20ByfH55jve/83cm/xakl93fp+Y54g73/umzJdjZVZWsVgsSiLZEjVwIbXt7rZbNmxvPK36PzAangDvDHvZgA2tbANe2YANNKw2Wm5ZoiyRFFkkq5iVWTm++b0735jnwYtiw4KatNpqA+bZxYk4ZxXfbxyc+J3zpe0+5Gvf+i2WVYwXqEzDkk/HW6rtM/KqB3VGzpS28OlUULMcKhdDVoksnbjL0QQTOXQoez5JKKNrHY3T0oUVVmxRmwKoK+TGpkhh0VYMjnU4/dmnhX4hSKBrWgT/lFKTqCKNoG0ZeAHX4pb9XEESBbZ1hPtGJ3mQstcz6N4mVOUlT40NXiCSikNE/4LJE48uf0TmJvBpy5V0hhH3EKuQTBZZtBKCkdK7LLE/lFDFh1jLN4z6dxC7mqYzeNWreHDrkCjRKbQRY1Jac0s2Nyg5Rp40XL3x6SydfpehVS03rczx2CNSDbIy51aZYzcdW0ljzzokbQ2UVQjKjng+x6yPce58Dgl4H8yoL3xyOec6M+gJA5qlTh1seLn7FOnoXR5OLbRBHzKDalShxTuOqz6557Ibx7Tamv3SZG4UuFmBaom83vnoecjFOicMMmZqQLFUODzsMxsI6N0e+csLXkZzFi8iNHXAyIlR5Nu8c5Bz2Buy5z5i1zNwCxXb7uHLEUWqcGwnXLYTLKUkVHaImoO/PGDcv0IqNAJxTiHpFFpF1vaxrxJyfYPjaHSSwazQUbyK9UsFbrfUnUSwkzCTmNWbV7TqlKK8Jr2MGd1qSG8dkmRnaBsVY+rw/Wc/xhB9fmXykGevLrEPBhS7FqM0uHgd8ObkDb/7rd/gg/GXEfdlXr2O+PTVD5FZ837c8fiDr3EUj9hZG97OI2LpE77Vex+rLjklQhC2qGrDi/ML6ucl2+aGVhbxVIXTUiX6/MccDDTuHPSYDh0yqUO0Cn77GzOai4CHX37Mh792hwE9gkzk7/5b/zID599kGAicrjbY4Q3TyZSX5TXFD65Z+GcY6NgilEFKQ0WjuoiCiGpuyTsRIY1xdZWoS9E6k1ErYtVwSokRCpRdSyMniKGFa95Gba+IGpna7ggrj593ZPAXggQkoaFqBvTylNywyYUN5H0mnU3VSNxNK3aySFOlKHONoAsRLZFRL2JdmjiCySIp8NoGeX6AH/yIGyzsWU35NKBQPBDA7FLqskHPFSS9oy9OMFsf35VR7jTYYZ9YLxiKDtWJhTJQuVNHNEYLcQ9N71NNdsQXEpbao0gi+vfGFKcC5l6OLexTbH/MKKg5KWKmis3k1pBayZC6KUoe8nDyiKfdlNX5DtPvUXoptTTBkDKGg5SNtCbNdQJ/TeKZ7N2fUckF53XDoyagUhScrMbsOQSbAlO8zUAoqWMXYegSJwWIU5KgpCy2lNkleZSz1x9RbW6w9zz0gcQiLhHXF2RhgHQZUXodqg/NyGHkhhzP9hkZt0h7N0yLGZrq0sgFw9KmHWjE7phes0HqGnpzj1ozSI01tujgWyZa7JBrGao+pugukbwJdtDQyUMspeRGyTDLiPZ4gNKk1P6GxBIoS5VU2WBsfG4NLP5IkFH0CSNlR7FKSfodXhRyZ7qPKR1RKjK/fvwun++2jG4ZzFNYLCWcKEfeqWzbc7KTkLkIXs/DiVritsF/85ZaCrA1BbNrGVcH0MlkQor9WchOsNk2NX9j6nCqxswkD1c94EDU6I8GCB+VrC7gVfKGv/vv/C6FuCQ8u0bqrqgOC46nNn0vw9LGZKuSYTShLFueFzl1paGbtwntlCqMaWSRjSGgbiUiIUNUS2RXxts2WFSsNZk8rVEEkYQUO4PSixBjjTNK+rVCU6Z0oozc1nSGQVskhIZBJasYWUdZ/4KvHK9EmXQgU8s6ZjqnSUySXo7k1zBzCLctadWhqQLZm5x8EuG5E8RtA+6a07MFlpKzqlwMaYk02We6NshefEJmGUyaisCv2FYiil4jaCZxk7KT5xiMsPp3aZcTfGmN0tXUd/uIbUqPDHdksNTGpLpJe7klnvcZOAtaXcM2LKTrBHFooAYRin5Kb6LRU0ymkcxpbqNrOUmSMxnUhLJLazZM900mWU5u3OZl8JI60ukEBeQRYVrTLCOsvQxXnpEFJaUJQnqO2B5gKQvkUsXfZQi9CoozPE0mlwreqDnToGOlJDidwIkRcvI0otsskGYW4XiEF4yQtD73TZWw2WLrOZP7Q4Q8ZnBwyJE1oLEFcs2l9cES99k1JkYvxSp6DEY1gXePLDtjVkisBjKhmiM6Mr1GJ3YjlDihMXoou5LGXuJZPcLNFkVWKJWQJHQxsx21u09QXWKZR1T+HOFVyYUR4roTzjZnPH+ZMxlb+E8/YSOPsG/ZPHpsss7gxXdP+MpXf4Wjmy3/5PQtu88/wzKn3PnA40t773AzhMsLH78OMGj5rd/s8UrtMVm5WLaBX/VJ4xo9qhn2PeKu4fJ6SYVPZqyIa4UDfZ/9d028cIRYb5noJj8+fQNhTrEv03/U8eGrW9RliCDJqJXC9z9J2ctcZFHnaOXRCjYjpWYXd9iOQWX+hCbr0FOTSO0zEh/wPIm5nTdc1wlyz6MtW4abkp0KCSLVtEK7cbGTjF1tEnktWlDTNDBRZdK6QvBEqrBAUkRMfEo5p9u2NHqPumtQMp/y5+BP+GLe5/9fk1S5m457FEGLmXUIowq/MThAZWHmKK1EIzpU4Zq+VrLYDBAnHd1WwJ3IFGuTwvQRxRbHTKB3jLZrmKsXKKlCr3DIjJiiDpFoQTMZ9u4zPbQ53HtEs404un1AahkQqjx4cohhezhxTOQomHmOKoqItseuqBCiHaEUM8vHCK6GrnZoYkkeK3RxyBtNYJgHVOuOxJBYTEoebTSujBBp3SGQIIg96naF4qdEo3ss/+yPufDAXEa8vsnQ3ZYqmBN1faz+DA+dkeli7CnI5Y7m3hGW3uNR2ePgyEV0DOSoBEtHEHWKTuXt+pxsnpI++5ittMQwHEbvHdGsSzrB4sFEJLPuonLOXesB0bSPK/SQzCX96RG10KenL2lKA0W+j9G8QDL26VktiSpRlDatCvlCp98ZiIOKXZchaRGWOGV9mtP3ZFLNR5mrBLcF7ERhLjto52tETebs7JrrMqVa+lxvzjDujXAzieenAc6tmOn2kOfROfJFjGTrjI8mHL+n8fQzH89v+OT7f87jX/s6UZnxcPiEp913sXMLyTDZBjXuUKTnHmIkMoYUEJUS1kjgzSfn5Lsl+x+8T9fAkXuXLF/jF0u2WUyZS9zKHQ4/uoPq2sSVz/JVymxkkssQbQouti8AnWy54XInYtXXXHzynNeXT7HUiv/sP/3POR7YaLdd2qKHtEl5K17zD3/vH/Ov/c5vEE8L/vTHP+F73/kT3v54jVkFrGSQJRU3bQgkHbNryY0CI7Vo1ZpOUqgaUHsVed5iRwWBBmJjo9QpdmsQmwXEErlbI6UNDR66npCF9cdd1331L+PvF6ISEGuZLO0jCmvmTo2TOXizHH8Z0wpDhFwn00JsZUbu+hSJyDS/ohX2qbICwVmhRTqdUqAwRpZjCqemfZtQVDad0hJ1FV4noRgmmWawxGfU3uKWAeeKSn86QFuY6HdzVMNC0ypK0UIpFXJPo5M7qkrBKreokoE9dWiXK3SnR7/RmKPi9kROdwpHWsWVKDC+f8TNs3P6jcpmIDMTeogHOXI1xT+9ZCH0UZ2CYZJiPHiE3OZo/TnF+RWtW1LKR6hySabHFM8jLscJhiIQ+RX3JAd9pHA5cLhrCvi1zrTfclYHmLKFPa3oX2QUyYZib0pbNkiuwQPvNknt48kio+MH9AcV/vorjG9LNIlCqt9gyi5lpjPopbS6RttYSFXCMu5hCApa1lLbMolUYVYiTtgS3X9Dvp7gqglNu4efhJhuQemWsDCJ7A43LVnEKsrqU6KZQ3pq8CfP/3ecyZdw6yWdX5IudJLdGlVaY0cDljdXDN+RWfoZelujpgJRNqMMIxJ9zPC9fXbBFa+vtjw9ecaomiKaCrob4EjvcDQcsUljKqmiRmZdwMV3fsjrAhxRYpqauIOCS96yfHXDwcSldSseiQOyI5fXyYqHvRFtE5OmOaFcUvszWiIk+R6OuaWtPT6YtLw+M7m4fo08FpmQY1sqV7rBYeUgVC2DocA4Mvnau/ukSorwtmW9C7g5T5CaGr10keuanluz1SYY0g1qBYZiomg2V3rH/SrgCp0mjpBKhUJUQJTpGhm5MUm6hk4ykTQFOUyRzAzB0GgwgPnPxN8vBgmINWIRo5BhyhKKKJKUNakxwUlipCpFyyuavowUbrF7h8iiTqjVqIWAVQlImkyj6myULXpmEm1EemgkPYWiSLC1gihuGUUqQZTh9VOKg4rn1ZqBY3J63XCgisTqHvZYZmA2aHVJkJnYaUlV7KPVGybDPmGo0+Ur9P4xSpywFiUcXaZtau6MJJZhzH15gKFWCF5FMtQZegVdY9CF9wmVt0yPOrpGJPcnXFYC+lHA490BG2qsjwya3YLWEzG2G7ygYCWGdIpGdx1zbNssXpuU12viY5niyVcYOgVh42BXLagd1k7B2uuj2yqj+ZJhch/LC+i6HKFX4uoGdtmRdj3U/Tl+2Kc3bCDVafNDvP2KYOMzCO6wbRMUxae/59EFczb+hKwfMNi4VG5CMyqQoz6eElPYEvoux9dlxDSlyQWstGChzSDZYA401hudF79/QzrqGFb3mLUZ21EfMyrZZicMNBttM8Lct5H2Mn708YZKaujfusc6esv6U53nb14wFj9HuJSQpx6Oo2OMXXoLAXkU8Dps+dV3RbRuQSccosQfI5djxvGSzd4eD2uJQSqRzTdM9D49yyLYPEe+d8A463OtichSxORmQG7d0IzuYHkruu2CVn/Dp394hqr57AYumt3HEiUmhsR7T4b4gcaDBw3FaQj7K4J6yqQzyGdTgjLh4YdfZuknVN2OrDDZlRFUBrghdeGxihpo16iGRCx4SGGB1s3RPJXrrENPAyrXQjAa8qihnxRUPZnGbil3KnKYU3YR7kwginX6TUIr5D9vfugXgwQ6uUU2AsgFjK5HKoU45YiyrqgFA8eMqcoRShpQ60eMooyoVlFdAUEsuGgMlCxFSkp0qyJOC7xJj95giH6yZNEDuZDpipZrCyaFRqxJnD57ihCMeX3H4N++e4Q7FIi6CfK25UA65LTdcTSEwOjoKRv8UmEX5UyMGYkl0bgiajlkXIv4fYFJ0XE6SXA+HTFRVCpzR3/8FezlDcbekHDXIes7xto+I/19Vq+/x8gUuLhec0+cIA9dbm8mOPdWRP4Q3ThkpZ+RP32J3puSySJxekVY9YkCn0hr2V/9KV73NRZzCcOd4agLnMpA0BIcr8djZw/1/m3i+SVO9ZhCS3H0I9IUpoZIJQ0xPAvbs5DaCrUMyA4kpDCmb41Qo5hSUlAnOl4nsh4a1EZCsxYxBin+vMNpCnZ6wFS5RyWWXEUXHKUyr4caD+s+r7U56fqGhgs+f24ikxKM1oylKZ+3GSef3qA5MsXVObff/Rt8/vbb+IrNE/8OB3smH3RjRlrGs7JjTU63+iF/9PwZ37x9jFylfH30DqXdEBsb3v3wkNcvY8b5K34YX2CKfDFf4oD6ZkM9DfjGrV+jtCLa0EO3Am6CCnsJ9771m8xfvya4WhHvz3hkOszVKxY/6ritLwnlC9bPllzpNoeDmkyuyebX3DYbEq9mZFgMv/lvsK/WNE3GT7bPUVY2j/+WRSdojPOYi9MYWRF4rJpsjY4jMUXJExyjY2saGFmGbGaUSkcidihVjqhq5FJJGY7plQHMWqI8YxT2SaWcQnRQ8i15YqD1BORCRGxlspuWThIIpYbGkYHmZ+LvrySBnyM88l8CfwsogTfAv991nS8IwjHwHHjx0/Dvdl339/5qFpCIZB1jr0BIS6q8TxhESBYEdY82qbB7Hb7doixDqqIm9DQGdUoTZFiOgYvN0ipB8bFEGT+o2dpbJpM99MynKTTkQUtRRciWQL0wKXtwcb7iG5NfJsxsvLpHxIYjvcdZFkAaUmV97HxEbWSMFZ2izKjcJZ2mYRc1zZ5Ci463NpHNiqMlhBMXQzQpZZNRrmDuOYSRiEhK3WzRipZkIfJgb0a69fjqo5hy0zIRGqp3DMzLCrdvsQvXxJ6GPr3LRNiRHThUhxph2LK/7fCnM6a2wptP/4iN6GH/6ob7mY2kS+SXW1q9IR1otL7EkTdG9nR6mzHspQSVSqYbaG2DJo0wbZEu6Gjv3SYLI1CHtGqN1PYYjzoWYULU7eh5MhIOre2yytcIexbBRQxjl0yX6NKEWz2dTSohnbS8jV6wtlqai5ywqpiHSxSpZrkK0a0WzTXY10oWyzWyc0iqRSRmy1EjYtUxP3oaEq1F3v3yHdpP/5R5GDO6O+NL9gDHvguzFv1bX6a7eMNs1zHUHmI9FvmD71/zTtsxtGbsRJ/FSYO3FyLZ7yPP16yrhpV9ivBWRe6pXB9YTJwh7r6Ga4+4CmKSQqBRY3q2zqrZkW0aAqXDZIFZjFGMMU3wCWfLgCBtOX7o8tHDIdV5xfC2jaLdoicV9PQhwk6i7u+zd+eKLisIdgaV2PDiOqEydNK4Q0kLkAqKZEzd22D7Q4xBge/n1D0VNYFdkzJIO9wUfL3CpKRLc9rWwhByukwh1qEzJKSwj1lDh0+8+xf7O/DfAv8A+O//gu8PgL/fdV0tCMJ/Afx94D/86b03Xdd9+Z8j7//NAY2AHKgIZUNrJExo2YzGuOlr2lJl1JMomwLxeoxkKyjVGYeNhF9blFKHTUfm+GiZCUxQ+xHSskWqXG7UBLNsqKUacWMyHBSksoBk14xFh6Q3JKjnzKsAw6/RLJloW+MqBq59m2SvIAojHtU6uQp9eZ9r7YaxbFGIDXqtoUoF+tQiyAzaJmcixIhJijZtaQ2NYOvgHAYMapHTlUSqDfDsAs8+JjiYci+9pLjrIN90hMOQZ8GGjAnuocweGn5vgTWRwSwZ+/fwDkOILSgOOBqqrOqaqspIfvgG3n2IMIeNLmFJKv0kRNIN6lXJQoL+fZkjq49xmWKJFq1W0egyYibRaSNSb8EDveGmA6vUSYdberKCrbcMqhnLNMSuCjIho5CgXTloWkcaNry+uKInSXx+/hK1e0Q9SLm6VPCUhJeLV8xuP2ZSN7x8e47quTzYu0W0fAq9B8jdFkfWGKQKXnOPsRdwGXgY4hWt3iN9M2dVShw4OWNnSPngLpWY8YE+xLg5oa40FpWEHb/hzuGEPfcuQ2eE7imUy5JSX2GL76PXDb4iknktZqWwNV2OOgEnHyBJMkWu0LDjaHrIaXZFfTHHMB38qsZNbYZaxep1wwvrFXrW4+hozMXZhsLbMhNctl3L8L5GTZ9eFjO69S6YCuxc6i6As4LT7TmO2WDIDqLRopQiepXRYUA7ptGWNO0ITQ/p4pqq7DEqd+R5gWgb5JlM3RSIZkFdqDRqQ952qIwp6hSlrCFsEZU1oarhpTq6CWlU/fVIoOu6b//0C/8Xff/4L1x+F/jX/9+A/i9bS4ciBXS1SK0dcK6fcCd1qVSLNAG/KhArDWG6o6ihWwlEVDhGiC6NqeI5oqoijwPUlcqiNBgPW+osRCg1BLFBEVvUQ+h2FsU6pj+WiZUhw21MGsY04Y84me4xOFC4de9r7HZnzIuaO3KKIdk8b1WkKmdsJ3SlRS302NNaak1DKgSEVcZIEdgeiSTxhL5hUokeeiOh9rZspR1t1APRZE+XEVWVZpSwJxsY8jushYz6WGboT/ib799jZ4QkcQZVzsg74tFxw8mpgtsWwJrTVYJCQjy7wy+XLuWkQ0slxH5NXcRMb48Rc5eR/YWstXNksS97yPoQualwbpUkkkZZjfCkkHKWIwkF/cAlUxPETUNdFAxM8HONrO0Q7RZDG7CNdKImRfYbEm3J5jxCzBVSZcNJFHO5KhD4UyZLjaqQeK1JiI5BuN5wen2GO5riCQbfeXaJtz/kZLPjlufRCiLbqyXapMV98i3aTz9mu2mY7wLq4RX1uGVk3kfwA06fXmAdupyvW95UKcubBbEM2O+z+yxHO7J5FQf0Mw/PmpBIS0o95GJzybCeEgcVztji7h0NxxcYzizmWcugN2E5Fzm7+AHJm4rJgyGGXqAoJZ+ffJv5QoO85HA0Yy+tObhXEz6v8W+uyUybe+ITNK/Fzh3OpJTrZMfxGxFv5DCWNHj4Lm+e9fnl/Q2+rPLL4q/wk++dsi0ukW2olIRJPsJHI2626KKJNPDZ7ByacYwcd3R2gBiDEzkkRYk8EyjmFXW/Bb9BlFQKV0C0JRAKsk6hNX2Ifjb+/r/oCfwHfKFJ+E/tjiAIPwJC4D/uuu5PflbQX9QdEAGz75FUJVmxwkqnRM2WHTF9oSQrBrRZi1Yn2I5MpIrICMRJj16zJBl2VIWKd2EQqh1aBbGmQAl5pSKLBqVXcXijsCbEGCgsKwetWbMat0TbGs1skewOWxyyMVo22wbPvmATWtjjijrQSQ2f4ZVBPu6Iw4RuLdCS4iot1uiIqC7R8wqzvEGQbyNWAkv1illR4Go9ClOir4FbdqwnFX3VJlYSiGyKTYTYlzDGEY2hUb4SmBweMCxj6rGOdlTyTXfISvS5ye5wV9liuZD1C3pdnz1PRi5q0taj8RrcYxF/J+IUGmKvQ/FEgqCgXOcYQwdB0clTic7YEhQRg3iCKAhkbUW90HFGCkmtUMsKSiMgLTcsxwJRJaPrKatrn36kkPbgxc2G4WgPbfwA6+wfYWgT0syjqAournIs/RpBOeJobOMXLWlzzfZ1zO1fegfkAuMixbRNTtZzVE2h3O5YvnjBJjfIwylXn/4JzdGIrDvlfGCjrTq+/utf4+jJO+R+hlUVrB/c5cWzMya6Qub1Mc0tL/70nJGjc2GIzCOV2Vct3tNu8/t/8scczr5EIe2wVYtoG7PwN4ytS/JCYUnF7b09rqSasnjBi7mEVFxRLBquNt9lJo95qMwIlCWd95ClfkJZBNRZQa8pkYQRjV7iSDrb5Tn+bIAhCWzJ6Zsix+UV226fPNogCCa60zDYlEjyiKTOWTclbeYjaJDnBkeSyqWUo3YTGiJUZBi0VMuI/sgiWTTUQouSbWiwsQWfNrCRFWj8hlwu0UWBn10H/AuSgCAI/xFQA//DT103wK2u6zaCIHwE/C+CIDzpui78y7Fd1/0e8HsAmqJ3bSiRiA5Dt0buYjauwH3J4YW/j2POkQ0ZbQOR7aHoOXUWUYoia03BDEUSuSFtFcqmRvMayjrB6PYw2gDlqKOpYrZJjGH0qJOcbrBAbUY01ynOcEBkCjjblnn6HMGUGPVNLp9eMfzar6G/LLD3O5p4SmGLCIb8RXd2lyHZOW41IShvqDQJS+pYZz2sScN6+4J94Q6VssDIRghGSysG5GWNmxg0dYSgeNSmzEjfpyxydqWLbqdMDkU23ZYutbDHFtZLncBLORhKlIqJeLdFtsbsDXO6a9g5BrcwWNhbRopDvmkolQTtKEaMHfJMQlSnWNMN6a5EGop49TVmdQc3MwnjlvpQoZ+Ar/WpdQkrjRGChlXVgebiv0qws4CtJrO92LCyV+hnM9aRzmQs0l/EfCcZMT0e0Fop4balGHlM7tbcGf4S3ibhJLzAdCvyvZJWKBnIA5ZDhU/OXvF4co+z7TV+DfvEtMs3aP0hk/4ARSsxix4f9r+O3ssx7n2JPJvTEw9Q9hIO4pZl16DIE3raFyPd9x9+nSS7IInnTJ0J9bMdW3nAh7/6EIIOoRYpF5c4R3dZhzuyVUZo3uAEfVZay8Ba8pNlyOblJ5RXKgfv7DG86Li/P0ZxSg4uZKLztxw0LZe6jqW7vIxe8ehwny5tKQ7HLD95zlDVce6JvN5dMc2g965N647Q/YJudcV8LoI4Reh85DZDkg2OFxmmAAAgAElEQVTkVEESQWky4lJHRKH11xiOghq2VEAj9snyHWnXR/cy4sjCFCOCxEHSJKo8RURFbQvk0OPnKZL+tUlAEIR/ly8aht/66YZhuq4r+KnMSdd1HwuC8AZ4CPzg/zGZWFHWHlbvlKhwkfUCMRJZzEwOpDW70MLVEkKrQ13nNEaH7DVMxZJ15CB6JcIuQTvoUFcqjRBi5h6pssFzHMIo5vZOZW5rpE1Lnel4YYY7lQhljaqpkd6eczNOyWqZ9tNn3OwP0fOStyff55U04+isxvJ0qiZHq220TKE1OlzZIQ92sLaob0s0NAheRn7R4eh98uw1eqVR9a5p9SGlVFJmArOqIeskzoOIyURElA0UTAo9ofLBEgY4jk2SxLRJQ307Qk4s/NpAbiS0/gESO2oKLPk2YlWz9gTU5ABR2SHpFo4jo/geLTFVEtM6MlI9wnZF/CQE6w7UKgunRDuBuky56IH2umGTBLSGQj2/JnMbasHl7Sefcn59jj5QOXZuoTQTdrMC6c1bzsqG5z95wbVcMdqo7O/rmE8GfHCm8Pr6nJNn/yvZ0SOsA4W+KzJLb/F6XqGqG+7qKpfbkj88/zZ3vvIYEo3yxMecHPJ2ccnDD29RlwnmvsRjy2a19x7PL3/A3uAubb5k8YeX7LTP0KwxFzc/4ejhE740ep/vn1+xWJyzPn/L3fd11idTBu9cgyBjzUzU2kF2AtLPYxr9hiT02TYLhq+XrPML4q7GKwwS9y7O6gXv3brN2+0zNnFK5+5x8f4JhrBPQooYWHz77Bndcw3/LOYrf/u3GeYrpu8+oVE8akklvrpBSGYkXcRJ8gfclR0+PvkJ4+SatVvSs/tk847CEpB1m6LN0OQUqbWxLJ/Akqh3KYKlIqkaZhmRqyJdLtIkNuJIQAxkVCQko0YIK2LVQRSgqX5eHfDXJAFBEH6HLxqBv951XfoX/GNg23VdIwjCXb5QJn77V+VrEYm6c6aSg0NGuZFpPYNqtSUpLEwlJwPqVkYXNbo6Qao05rqEqIns5hGCekCTX4ImUaORGQmIGkG2pCzhRJSxmoq9dMiJF5JXBsUqRRBiilBEEe9xRzQQZhUGHdHFijiCSTGksDJ+IJ5x+x48snusbJtbYktZRkRnW1pFovSgSWq81oY+mBasiNEDD2UqkfgdzW7H/i2L0CzYigZIEuPAZbC+JLBs8iLGuakZ3ra4SSPsCkzHJxcstpnIWBzTtQZRu6KOlhjTHravEB9lsBJxZZNBr2aX6vSVjrrzCOySXtuhxg5b2WSvLOgaF6FR6Zcxm85Fa2N8q0K9jjiPZfb6S+LQQW/m/PjE555tYJoaLz77c6puRp7Z5B+N6TLwv/OSKGu5/MOn1PPP+NZvf4UozagDmfMfvSUvFMIfnRC0EvpVzvH7j7j40QmaOuXdr5qcPH1NuOzRemDoQ5Qi4z3VJpQkonLLsaiSBC2rMuHuvXv8eB6Sbc+4dbyH1055/vZ7PP7Vx1TzHvuCyoG9j5CknGsxtxyB8b0nfFwVLE+uSIIThgMb9+gALRWRVQVFMlE/CHEXOVdPX9LtFoTeEKXV6ISAxBUZblvqDxyKKmScK5wVKd+8VpCXLvLdBMNRmY0ratdhMisxnSGdFhKeSQyNiERKmO9ENlVNePE/o96/z0HUYPQN0v9Toxh3GKlEdyXS4SAXPpUYM1NL1lkfhDVVo7C3rQkHFk0pIIgaolqibSVya0uTmhi7lEwU0dSEuBMYjocYUUatd9T5z9s1/M8hQ/ZT4ZHvAO8IgnD5U7GRfwA4wB8IgvBjQRD+m58+/k3gU0EQPgH+J+DvdV338/ca/VMrO7RugHxTUiIjdiWtYmLaLokSESsislwiKyW5V9BoCpVk0d80aFWJq9pIkxCjvY2oywipAaKLY6e0iomn9RkcqVR1j7dijd5oqBV4Vk6lmNAHscoIZxGisseuKohFA0F2OCl8/PVrVsFPSG9qwhT6RcY8OaVUFZLLhFw8ZCDCg7ThRi643PYJNR9bEsDMqbYL2qGEY8csVzWJL7A5SxA2MW3vLRsJVBWGZYHQC7laX6EGKxZXC5bKjvgqw19WrE5fUa/+jJ5xTRLW2K/WFIqH9/oGW9eo4yXxNkXttnRFyza+pvITwijG6A0ZNTFql6ELS5y8o7Um6BGI5ogqWHG6rek2Ca/Oc15fPuXzVwKiZPFpnvF284pFOyCOdtiiSBydEpk5P/w/3lJqOu9MBcThkAv/kNQticoA6ZnJ9Y/e8E+WL+hajTdXp6zWPtLBEcE0pUxNKsvgMnrNVz/8ZW4fSFy/+ZyrN3NWwQnXFydId2dYfYsH79l4WxtPH3BsgdYUfPL5H7IItuwPZ/zSB/e4d3dC2K7IxYq0WrBqK66u3jJ0VWbTA/THFpypDDYi+rSGJucyvKH4bM36JCNtVeR9EyEP0a1L9g2dh8Zdjt7pIXsTcsFA2LNQdjcsjSXVtCD0G+p8ieEpONWAyeDLJG0MJzDe07hetLS5xPbtkuxtwS6QOPnJK9IyI6muuTPssIov3tmGlEzYImJipgq72EW0dsi1iCkatLaCmFYUdYWUBxSlitIquAioboE8NrHLFqmycESDuK6RRAXCjhr952P8F2F2QJbVbmb1SUQwpCXb4D7SaEnZ6LhBg+qKBKmIJNXUqUCtmYzrOQg9Nr01RBayZ5CvdghmhyJ2VLqKlHTQOUiqjxxWiOMeZVQiNQW1PEQtcw5mHTfo3DUmlIMGUaowNzo7IcLQTA6G71NKG/KLkLWoceCYpELGh9/4KobrsMlW9MQaenvoxYiPjgZsydiICvG3/wjJvsurzYJh0eF8eAsx6RhKKu7sDj1Z4bPoDQeGwsQT+fymwsgtAjaUcsOeAEFaoSY6cXnG9NYHnEsqtr7jjm6j5gJzQWa3XGFOdGY9m8rvcSR05AcuN+evOH54QOkP2Hs8pk1ULsMLsqhE6oYIZYCUy2jjhrPtGdug5OPvbXj0dQPjTGWnmbiIaKMGPdb5fHPF/YNDBCVE9UY8++OPedA/RHtnQtu1bBcxtxwXY0/j4qxlVb7hoXsLcyTzg88/pdtBZqn8yv0psTfh9Y9/yKA45h999x8yEGuul+cIpsyXpvfR7864vPyMuV8R70TqImDgDsiNhkcji9/8nd+lIieOIjxHp5r3eVv9kFnaRxl7mJqDrxekW59nH3/CnfcOOHDuUGsCkqyQJxv0tOK/+6P/EU1SSTeX/HsPv4Z5XLHHY85zDaG5IV6C+6HI9642PBFVPt3FPP32M7754bvQH+MlN5x0KcuLHCe95m/+7X+FXV1i2A9pTl8xeuiRrEMWnc6r3Zzb0RJj9Ih3P3Cp6wE/yGL+q//kv2bvToX/aUSm69ilTK3pCGJL0y4xBirdVZ/k0MddQqCBRkFWgys5iGVMiommyUSdgqqECGVJJdxGLCukakPhWBBuf3FnB7quZUeMHHtsZ33aIsYLS5ZUNH0oA5G832CsVCqnwM5atJHFTa5Tlj3uuhWhVJMNFUZRQ9p3EXOfHiqRnuPVCrnaR5ZzZBwy3UItGrBlPt+k3JvI7HIFozHZG4nMQ43+/gDZAxZLiqqiG40Y2zKGpjAwCj6fv0D9eMGLK4F3jid47jUz94Br7wO2bYsqSUTWPmV2yUdfeswaCFcLPnhyRK4oaJsdq+QU6UokezJgW3psljGuuiGnYGIP6MUSN8sbLp2CqSGh9VQ+VA3iRc7V5QmqM6JWaswaVps1ZqLTyJf8QGhxT+6QTIfc+DJTfKJFnyjMaGyPePsSQSl4sQzR0yWPta9SpDZ1uuJ4b0B0ukbTC7LFgtrQmHmHZJ5Lt/qEbenS20lgS/zKbxyzumhwJRlZV3i2PkXIcqobCUHb4+qTgMmdhutrk/XnC2rD4cgxud6do+0iLsuA88vf5yq9ZlVIiJKGvElJH5qkacXEuIv/2XNO5A3v3T1ipM1Yxec0eo3mlbTnBXEYk8lw+MTk/vYd/BuDg72E1JrBD/+M6f1bDH/nX+LVzQWRGRL7GoMqoihVRGmJk6gs5jccM6AYy9TtBOeOyGS9Yn6+wJ4NiFqXY2HAotghXO2QzRhZGiBlMkHaYtoOjh0jT/ZIywZhDIa/wx/v8AuJaKpind4wkDsGpsO12rDaXeDlNRNjjNOzyD73UZwWV5NZdwpKtAbNoedrrK4brOGWo2s4d0R6TYlvgpZplKVMTZ9Bp5DEW2xVRoxVcl1AlecoQkMliUhGSfPPtOe/sF8IEhA6ASFW6WsBO1/FSjcEjoaVpIihiufqpPOEUgpRYoVQqkhbnbpaoHQG8zxh2thEToOSy6jXNdbeHlG2RfEVEq9FtQL8bYmkKUiFT6QqqKLMWBtysUv54KMOyTxgcfoMy7lLl22oYpukFVnOr6GNGL5jkl7uiO0B9UCjrgfMpgJlXGLIHpfOBf7zmrR0eXLX5atffcKbJGFxI+I4S/p2SnBxRTAvkNxDllOL/M6QaROyuvgBfW1K2ezYvW64Yok9bnm+3vFR/h7WV475zv/2MYMnE4hjjq0PSIS3vLxYMxvaDDuV9eYFkmthGHeRBiKzbY9KjwkMh2cXT5HXMst2R6dvGcYmE8Wi6u1zzRtEccSo1DltE7JmQ72yufY/4ze/8a+ykOGWI/Pd5YZuZBN1Mo/rPd5ewMtFwO3da4rePtLLgJv7Q2arGm2QsHc8ITUr+vcyjgZPSF6suH7+HPfvvM/lScYj633eHvX5qNzDfRDQrUdkecGoL3J7NqV3fJ+l5PPRtYIwPOJo4NLf9amdjOiVSiaW6J5OpVoo8xv00YCBGrFtJfLtCwaHI7R2hicvse73WS0uaYu3nL/JyNKUA1fEdDoun2fE/hl/R/4GNgNWucixbiLd11ic5Fi+jKcLdFVDJ6rcmR5ytX3J0XCAPjhgW75kW5Z82drnKnaxtj7KXkGj30ZbFsRyycnlObHcY91W9Jwd25cZytEtRm6fe709ToolbS6yjXwmncXaNeiyji0yqtygNAJr0cGKUvJ2gBr75I6IK3SUWkDVTpBbKFuJRu9oJYeSmLa0mSoiTd79s9LgP7VfCBJohIbO0Nm2JUnuI7YiNTWl1OL2FOZlhTpU0AowpRLShvU6RpIsutbA00xOvZxBqbB0c4yqIp5fIst9QrfEa3TyLiPvemhSjTbYQ70Mwe0IzC1qsofcWjhNwnh2B3/TsmkMHlDz0heJpYQ9RaQ9M0n3VAI55ijbx+7p9N6/j7/6c4oK+tGU8voMb3SAbN4lXslU4Yb8qqKsXuOLU37r6x8hmQEVIhgx9tUWbeT+X8y9SawteXbu9Yu+j903Z5/+9k32XWU1drlsy7jhCUs8PRggEHNmjJghMQUxeAMGCAaMkB4ChPywhP1elZuqclZmZXebzHvPvefc0+9+79jR9wzST5SEE1AhWfVNImJFTNen/1qx1vfRGh7w+KsTjI3IGRWWZHJXHWGOLjB6ba6uIpwDByEa461vcdT5jEbYYmtkIp+uOQ01vsqveK21yzsPFwy0Ak/Y5urliiujRHNVlq+WVLcCBsIdzqQNh0oPqx9DPKK1LfF3G+gHS9a1wo3DEfJ1xToNqJ5NWb3fZff2NltZh+uXZ/zt9U+4XIAdn3DWdEE3sTQJR/AYF2OsVRdHrJn5cPF/POds/ZzadHHKgq+/SDlwEmopo6hVOoMSpCHlZs721i0uzr7kOih4oNi8/eb7HBlL8iLEaN4glJ9gGQallVF1NrTSCqtscSHNYXnF6507SOIKpAbh8iWRc4oqOiwfH3G9WTA+e0bDaKK1l8QbkVuqymf5Gnerg6PG0NbYTUpWVUmSamxMCfE6QNzbJosS+ls1kmjjXfksKLgt6US+RphlXM4L3r7xNaGgkigVDa+Hb87xJxaeVmN6C6ZNCTm4wXcPHrJ0Y9Qyw49KojzEKES0UmMqxIiCSVGnSKqO6ihsMh+5sEgBsxGQpRJuXKLoBWplEaUxillThtAwbLzQp4vC3PIZRxqCIHxr/v2/Ngb/MSCIIHcXVKaBpAB6jempOPKAhd+nMFM2VUgYiMxsmbkmo2gGhhXQkKZ4ZQXLGDVYoXsafjsl0WRcScKqC7ywoFodotcCRi3iTy9RyEh1mdoT0d0xYVIjrhTWUUkcvESoVZx2A9mZUIUmqyTC2Wlzs/193uw84PDGLp1392lF5yxemXixzqvcw3nzIYejH7JrCVyvfkywfoZoKmiv3WPfDDjzLojjM0Rpw6Hg0ttxyMqIF8dHiGFObOSU3gTNrXEOoHvjfVJ9ibYZEzyfYpsmjnnBKO4TzGP0sua4bKHNfDYvpjS2mrySBzx7VrCypqiOizFLSc8veSU9Y9gb0NZFuuoQr1rzy0/PmHPN+Cqjb1jYO7dR1SZrVeHGnddpNUYse0Py86dEi2uyxYLID9GqDCn6mtniGD+MSa4nGE2J4KvHzCZTPnv6kj/7yV9z+tO/4m+PjvnkqyXl1RpJNXG1KWGskcxe0TQLGuUe9kpH6Q15HLxg+/4ukuDw1Y+/JDd0rK7P8KBNY0dib+82727fwmjolMcp4xcxQk/k9kah222y3oyZFQJ+pLA+KVlfn3P6xQUn8xmzeUA9TnHkNnuSS14ILM2UtlhjGxVp4yZ1KpPqMUHDxi0cWomDaKWs51PsZslh16SOUoJEpl5keOkavxWR6jW6WPB8opLLElLdYmLWXE4rantOlRmcrD2Ec5k7qspa3rBfOixnFTNxgbKWSXKJtNnA6dYUSwHRzug0PcTEwM4dkDwaTRFZN2hUKr5WEGUxiRXRFgOCvI2GxCb2UVoVsZnSNptotUhlfTsJ/EacBJRSwApzJusNdqXhdIYshGsqGfQsIB3btMyYSEiw5hVqKjExU+R1i1RIMfciTE9FiXPsToV1YeH1cjbxmkowsZWEtfESJVZRGwl1aYNtsbNSCLsl2UZEkxQqpabKZmy9vk+n6HAV+rSnBllPpXXLZmf3Jpk/p0mTTR7QPqo562jcPtzi+XzD/a0W2627mM2IWZWxvBIR7CFiI6YbDOi/3SMSZey6SxVM+ehvvqSoa0JC8q8uqVSP9s13efjOLYb5AdfBivlHf4HZH6C2HiDeEFl6HqoyQu/J6PKQk/OQOspwDg+5d/Me33ntXSyzxfFOSLxaUcpLzrsZ2srkR9/9HqtFwlfphlFdYG3plEnBxecRwexvSFWZ3ustfvgHP+Lz4yvW56ecP5/Qe3CA6xzw23d3eHSy4kJcUS4X7L93m2efluhui4Nuly//9VfErs74/DnucJ/7d/Y4eXXBnZsdHo5l0tEB4viEjz99RUO22HJ0VvOQj+cyRTVh2+1w2DI4v4Tvf/gWe73f5tmzE+zmA772jvB/McWLX/HYD5loOo1U59133+L56QWGqqKOS7JZzlljTjsoOfr0I74UQvKrkNtqSdupGS9ibvx2Sjo1GMkpTfOQLxrHxGuJWt0gOE3SsYjWqXjhzTEjl9Qs6UtQOR0+efEVQtjFGl5g+V1EMWCn9S6LqyOaN5soeQc3Cmj2HPS2hX/2jKOn12zb7xM3ImabiHvNCidUCSmppSuysKKsJVwK0tBjU4hYgk1WVsz9gtoVECMJ01DIvAhdC9jICk0L0lDHmAn4KmjVBq0qqRSJfKlhugU5PmmnQhrXFN+Sf78RJJALMFto6GZKLLYJ5TlaDmZa4+syeZmi+DlmrZJVFSkSoqqiygV2mrOeFuBUrCpwxhIoBVYpYEgDwlpBdReIWRPLPUc8G2AYMXlesDYrVF/BaK2x7zbQT3V8ecTZZkJeWIhaE/1+jOrBTm0hnYZMswlj/wplcB9TC+gqOoZi8mazieX2Satz9nc+5OzLI0oSri8C7r7+BkWa8gqQTkMCWeKe1SeUXuFFX3Oj2uOJ/ZJJ0OKf7XSJhQlhO8SbLVg14Do8416U0SxKTiSFO4MMNd6mDI7R5ZzunT6KN6NZFayUJX5uoR+dYdy+jxdJNKtzokLk1bWMFEu0rYqkTkhikTixudgc8/rr7yEmElo0YXY0ZfrFKW13gHErY3N2TfqawvlPX5KnLbajhMdBSvf2NvJA4ufPPyJZdil3Rcz1HEt06Aol08sZcSGgqT6Dm+9gSRIn1td4TzcIss+iI1AVBYmWYy4MvMWC55Och4rL0/0trq+vsFYuM31DN3RYrn3QLLThkDsTgcrIWJ9fc/ijHsxLdMng6zxEmlYshTOeRyrT2dcI5ZijmcnNoYVsWKzKnMoUsKwhcV0jrNcEukLtgW2pHOsbWolOKxoRiWMQhtROjakF7Ncyx9EXmH6L5GaDdhhhKBWvN9sYbYHweoLsbCG3Ey6uc9IiRXUV5tO/wtno8J7F14tTdv0W9v0Ott5Dq2Jiy2QhlIhRTaXLRFmMFKQIRk21vkKoO/jVGFFrQlkjaDnBtEJyIozawKekJchsFAM7LchbGnWSEmUFeioT1zW/9irxPwbEusKWHARVphCWVJlA3WjR2XyTMEanRrUU4olIUSeIdopcgK0qLMoCMZRQahG17CA7FbItMItcPDdB2SyIw5KGFJBVLWJVANFCNFPamYBoQeXeJzl9hirvouc+kuQS55cEaYdmnuPKKWezjKt8hbsjY1sd+sOYut6iLQm8DDfspC3afZl+cYuJtyb3rxmvdA63C55+dcKelWLHe4xu3UDJFoxFn8HcY5lUXNcvEDwXtZUQnSxJ04JocMp0NUbSHB4c3GMam+RXpzzc30OqdZaVQGsg0FjtUxUGZa7jDhfMP1viJ1eQQ7uesQmvGN26yS8//d+QnDcwlBpH0fhqnnKntjhJUyabJW/VIa2uylo+QNHWuG5KplzSG+h8fpaiffEVjU3Jq2gM6pLX7w0JT8+oG1uM1ytePJ9yf6BjDN+h0T+j37OYtQweVAbhYsxVekKSSSiOSk7OJIyplRLXuIF+PqVqaNT5hnYmUvabFN6Ci0WOf/EIffsQs5bI8hwtyuk2++z96BZxs8ZNQ7q5i7xVkBVt3ilKrsozkiuXMHmKVSzAz9ju9EjLnCzOUec1+SBFXMc0pbtY2wf4xyfM12BKNS1KqnlKv7/i1Vjlpp4yWV5Bb5t2q8OXRYBwnGFOA7KbAo1tiX25wcurAHoOhtVCSSy2ZJ/z0sKZeVSixhMz4f1zl5PlEtO+ZPDmjxA7b3D7rdf5+ie/QCPF12sazQrxuiCpaiqlg9heoE5D1B2LahORiTpqFCM5Lkbu4xsVzbTDHB9b8FmIBloWkkopSiwhmDVSpVCFv+YW4T8GyhqEyiMQethZShwU1E2fs17rGzZcV8RlhJqVpGUHVYDSycjzGCuWEQyJnD51PcYXBljhnKZYocxjpjTQlBLP3SBettFlnyxWqGSJ0KqRFI9ybTPXLZJKoqH0aZk+RTWiv7XNRfo5Vi5za+s+ri6yXm6IdBEll8icMScvAxaBS7j1iulU4H7vPpOXS+R4m/f/YJvw2U9584FLQ3MohAFFMON6tsEZKIyp0NWKi2jDD/7khzy88Q7zcsr/9D/8NeLnZ/gth+/ffo9+t8uP+vv889kjPn/1iooO+9aMBweHtG7t8ejkjKI5Z/oyYjDS6Q36ZEbJk48+oTGU4MkUe9Dn8aM/p22/zeJ+CwGRqB1STq55qFk8W84oPg05Hp9y76GKP9Zo7HS4f/hdrA9mhLOE7OKcwd4+Qjfl5MmSr9MNXeOITuawc9il1XHwNlfEaoEv5Nyo9vkiWvKd0X38PGR8mpPN57x28ybnYc3NRknWcnglxcyCJW82DCzzDkEekl95SLbGb33wXaQDkVm6on76zSjm9SzmQVPlII0pB6+RCD7qakkgTgg6Odm5yuX0CZV/irGqURWTXn9GZbyOqMm8qgM6nk7c1CgEHWf/LufnRxyM+iR6QBaYLBoJ6rJGb6U8S0FJFdza5onvYfhTspt9VsEL7K87qMM9ErdELtokYkJiRmimTnHukzZj6tphEnocJiO23t/ld1IBr9uh2+4y6Rf0d/v8MocOMplR4qUanU5OsmlghBVF3KJo+hQXAoKQ03cc8kYG85pA/GYIaK3M0SWdulWgL3xEaYBdayz0AKEs2Ep1zr4l/34jSEAUBFZmBalHam+hZNc4tYE//cY6TJcC0qWMJdTYtoAQVBRSSqgLZFKK1pBoBhvGhcCuOoVFj0V/RS4aCNGMRtCgiAQiW0SubFR3hVDYCJlFhsSWaUCUoW4njEufMO1iRBuM81PswUN22zrWJqA7Mpn7PofKTeymQ5R5nPRjbuh7DHYVKt+lVE3C62McUtSsj6G+hb+6gptrtmSDp6dXPLtc0Rzn9HYPubwo+e7N1xDSIaoC9bXIfktjNhjxnVu3ycKUyWzJzd4t7jjfpzOouV547A/30WKJReDT2sCs3OdOsyRHx5/7xNo1cR4iXQc4w+9QuWOSc5Ww/4re0ZK66/KTjxK6UYRgDbjnGPwvp58QTFc47/wOYWNF6Wf89VefoKcLWvvf4Zfn5zT2Eu46B0TxU0RRRp+OkMtf8v5rJrfeuMVXPz+g0is2WyfMnr5g1G/y4198jFlZZFrJoHRIByJvdPewRzKro3MKx6ZTRZwXMtvrF9z5/vd4lZ1QTWvmQx9TOcA+G6DcGaPLDnI7ZXa1Qu0rxLmH5auIhUwJFCcL1v6Yyw3o44i5avLBqI2n1tzZ61CVBeFsgUybQizRkwsO9jIWuo0kh9RCQaWqqPKcVDDJwoKiLilmILcLho0Nm+kuWVQzzFySooWZW2yqlLbho8kWY63JhaCgDTSUj27QrBLWwtd07YKqbpIPVNY6dPyIzJwgWgKOGVPVAlkhIpcpJBJyY03qqWhItENY6DmlajLVoVy2sawUp1SJhYACgyRPYCZg6pBtpkw1sPI2lbLBE8VvqwZ+M0igQkSJZVzNQpyMmTVapLWHIuiUYkAhCDRkWBiLCFoAACAASURBVCodtHJFYoMbaUhJidmswXMJmxlKJJGqBZkmoCQSCSGG0GQ+KJAnbVI5IapKhFRHqzNKMeDerkPeWmCX+3ihxG5dIOor7P0+zy597jYK5muPCwqCi12aFlydndHRh1j7h7wV9UmriE2c09ltU4UJiu2yPH1FeZxhdNpUuUgx76OMBDSjRb9T8OjTrzFe05E6Mk/XF/zhO28yyWaU04J2ewe3NAlXHQw3JC8yvgyfM+rH+HVCVs1QZ0PkrRHKcsG6I7HMAoaiwIm+JngSIpJw5Z2zXFf8SfclcljRv38DLVtxHvtowQKjNPDlHDtYIw32eevdezRdm7+annKrsLguzthTb+H42ySzR4iLAicVOJuekZsybpaR9dYkSsFMUdlRDlhL/5JoqtEWuuRcUgUporchP6yQni/Jty3EeBd7O2Gv3YSbb/F74QVRvc3kcoVj3mFWJ7x9+D0a+wrzaEV6nrG0r3BjGc2GhwcVotggFzt0o5Kqdc7plchiUZCaAce/PGWlLXGaKqrQoN0fIeug5BlhaEClsvZUWo0OM1sk+9JkLWSkzGBpIrsprUDkpTRDyAycTYWnxMzVinTcwt2SCa6WpJ0GRpaS5gppaiCKKtoyYWTnpGuBZtambj9hJW0of76B/QGKGLFaSNSOhmYKDFSX2U99Qj3HygRGksTGU1lYKaaqgisixjVTpSBLYVAXzMOCjijhxRW5rGEoGkUh0rZUKESS1KQSFfRshakuWLdt2Hh82y7xbwQJCHJNKaQs7QRVFnG8NYJgsZEDlLKJYxTUYYRuqNS5juFUeLqMUcjIdYZfbjDCiAZNNksZKblGzzpkUoFbgHFVk+sZpicQdVoYeUVZzKgQ8c4WxFeHSB0ouhFhFKMGLo+vfkEnVlmLGnanye7uLnbmsdWyOIlvsppdUExldrZtCh1elDHJ8wva7prZ6Yb15oJNVJK+OiPPCy7ymqHawLFlhOUx2ztN1DimK6jkvsrJq5ckeYKqiOx8eIvwfMIlS96+9wHhdUQ5D6h7t+grV0Qtl2UmUnk+6+wVx0/OcO++xSTxocxILY+ReYBJiPtag+uNw4f9DenzDedbIuHmnOpnfVa7z9lM4caNN9FX50RJzOPrL/GOl3wSRCSZyVsP3uJE9JD9gH/2H7zPP//v/ldePbrk3oGNtpI4WV7SrWSuJmuufvwvCM83uOo29Sjjg/vv8uc/+4gw/YryRZ/1JKMXCvzuf7xFuUyZrkvm4QbXy/CLFFOSqO0TBqlFsRohbw+57/S5ShNULyL1EpJwxoW0R7OtsKdMkEWXpXBAT76gKkS+unjJjDHa0TVb2zscvLZFurKx73TIPY/5aoy0PEXbtlCiCQ4jorsu1nORcuOjWTrXiUooVwymOet3XbQjhU2gQG1iWRV1LPA00tkfifQHTWaNBEPuI/gp41dP2Oo3cN0aUU552H+T2H/Gyc0pZtNgIAawd4hKyHWkcfrqBVfpFxhrCUWvCQSQ9Rg1VUnMFDXWyfUONVO0soVAgtVTibyAhqKS+Sqp7qEmbVJ1ibiRQVfYYc1lX2FTmsi1jB3XxIT/YP79RswJUAiYpoSUaOihQqw4ZPYGSaix9IgsrUiVioglIjXmxMH0UzIjIkgUhLRG3IDUUKGKSDs1RT9HMRLSpkNuGsh1QNX1KNIINZqwkWQsycUr+2jE5G6GjUqwNLhONzgNnbElko/AuONiGguqRsFFKDAJpvjRhnbmE/RSDE3GGsh4csEs6nJjy2G3/QDLKtkJJQYjl1FeI21ybm5JbN35PbZuPaQ5eICXpmzdNjCWCnPPoBhnNFsz3N093EUbMXSRO21cV2MRXhCe5LR7TXL/OS8mn2LmFvPSYive4NkJN6w27zQ/RA8kysjgeFEhTjK+2IB1T+SGNOTBnQ9497fusufcp1WmrJ4dcXZxjZcEZJWIXhnIsomUXHBxMSGfrZmfLLmYVOwdHPCgKfLCT4iFJQ3VwBxsk50/I/vqBDFIqJgTPU145U3Yt4ek6Q02lcat3QGd79zDxcVtNJEli/fffBt5b4BQgdWWEOf73Nx5nZHVp1UtiIUU09CwtzW6fRt1p0W3n6HGM8aly3iUs4nP8E2Nl+enHPsh9lLkIjHQRJP55Qq/kbJZrinlmp6qo1kKmDpM2+zIJv1SId7A2MiRmyHDVYQUuFy0BXpfyqw3El1rm+GoBXOBXOqiW59zeT5l5Z0yTEvMtY8expj9AX5LosYgjk3WLFgPHfpCg7BO8M5rRHWNXIns7CQswxQxGpLrIrUgkPgigijjKDHVpotUmpRFgKaArUb4gxh5PEWVTJZxRi34VEoDyYAkaDIUBTR5xrme0R5HCBuFIo0I7Opb0+83ggRqKoaVgZMmZE6KUIKuGEhiTZnpiFKEVGlsyypVKVDJCaXSRJlXtKwKQVMxxTZesaHnmJiJgzbRIepQ5it8MWTlqiSLClmUyY0mbioQzz3cJMKwAtSFjzaV6NkRRR3R0Ufc2x2x3dpCmORMY5co3CE3CsKNwJa2zaUacv15iig0cecKXTVGCJa8WKTMSpvlo5ioL+FdlIxea9PYkmn2b6EfhGzwsF0wm0PGM5HPXv0C1fuKx2efMZ46uHXF997eoUrG7A4UnNaAk8dHHDMmG79gFpikccJj/5I7B23WhohyXeBvaZznIdPqCahDBMXCN454/OyaRVZyHF0TvNxwEvtkS5/G6DahKHEwlzHEnFYZ8Eff/13uvLlPqGaI6TWyHLNKQuaLrxifrjktRAaJSqR3oWHh9G3mXkpUKISDLhfjmMZbFsL5En/5nPaOzBu7t9BHHcrzc4bWPQb7NwmkhHJ1jpgX7Nzv0W7ucOc1CVvvoe4bCNIWy0lKXWcc1DfY7rbY7nVo5E1Sa4+wiEifKlw/ytEvQ8ReTvDZRzxO5xTrGbG3QrB9WoFLQ00RRIU4qJgvb1LMPGJ3zaJTkAc+tEyG0iFR2WO9FdMzFXad25xYPr3mOUEWsZ6IzLoJlhESZjtAQLXpsNgJ2O41YKjRvCViLW3cVUZLLWg6Au7VC5rSAFlvcZwFbDwTWVrjVzGLj1+yTl9Q1gbLRKLvimSJTFV1ELUaXVhiiQF1BJGRIFzqIInockwtCwRGhRAWlHWO3fR4WdYkioUa1HiOROknNFMBJfg2/6HflHJArHkVVAh1hVQ61GzIJIuq1sksHysbsVTP2fguomZRl0u0NMY3DZJ5DC2BTZbj+jVXzYJhURLpOZkEthJDVCKUGnWuUiQeVqRx1XIw+hq+uSBLWjTMHtKox2biseWOaCsDBLHm+vyE3B7wtjPEGbSIi4j33tpGtmTWV68ghlN/TCJeY05VajTsnouj2FytCr6ebhiUDX7+F3NumDHqa++jhiVDp8npx5c8K+Zo6YpjMkbKDf7on/xTfvnTv2Q+2qVp+hyFZ/THXbrigF57GzlfkSYdIn+CLSR88N1/m7PjJ5A6jLZsjo4ukJFRJRfff0nkmUwXCVF6yU+PFvzpn/4Bk0KjVWR8dv6EvVsfMDoU+bPPvmC3k+F2tvnx5x/xu3dvIPzJv8ff/d0R9z/s8vsfbvPxx89YNwu+9/AP8a8fEZ5U2A9vglnyT9/8dzmfL7GaTZ6Wn3L0rz7F3dvh7bvvoFwF7N0Wubd9SK4M8YolLaPkd+58lz//mx9jJjNE5R1krojaOzg3StZTFUVccvjGAankI4ZD5DrDGXRxLzRutGJCtcGX4wtU6ZpfnMQ8evGvSbUKUVJpyQHutoa5lKgPYtJY5WhzRjZZMGrvIDkdUkOjkg0au9vcEMApO5ys56jyDrUYI4kVb2xtM5k4YAXM4iWCJqEUFVtRTpWVeHbCa0/7LAcxtqqiZS6x6rBgSStqYelL1mmLe3ckwtJCU1UMN8Vx91iJET+bPaXcKBjZBsGoOY8SFMUgUHzkyiUrvxGMXaAhBzGKnODrLdxiQzdX2Gg6Yl5QqRFCoYIsowUS60KHoEZo+iSaSG0YMPuHy4HfCBIQawlXjpB0mVVQoCsy+SahQUko1njqmMrcwZhcYegVjm4T1iV6ZTCVYxAiREFiTkZDq/BcizpcgNRFCA20ZkKySlFlMPQGMy1HwkSQDXrFPRI9wdUcqM7IEotuy6C5b7BYmwy1HVxXoL1fc/T0S+YXa1Tpgp3f/2P8DTT1CL3fIXwu8tJb0Xn4gGxyiVeNeZUp3DAqKh1u7g9J84zPnhwRiAHV4gypjBlfWnR3bO6VIm0nZLPwSLMui5Mjxrs3GQnbNLZ7SBcl88tPSLoNrr445k9/8HukRY/N9BVl2iFIjtDtHnqus9fWWCQmF/UEu9uimixJjHeRh0tOSfmO08SL3+Tm/hl3Gy0uizVJuWTmVzw5T3nzzjaPjTneacbd13d5vdtlvBERa53fud/jbLok0xKEW23qRkSv1UDq7NG3+5ydfI3rNSi2UwajHncObzOunnGxWhJ4a9rbAoVUsPnCIulfoVSwzCXevVPT7j3g6TzixCt5IIucuxLv7nUJN0MK64KF1MRbniN3h8xKDdQau9J4HiwxYwtNPcAXIu56Mau7LdZSQafRZHEWsBYsInEKcY7bSlHbA+RyRjeRuN5k+KVKpJVoc5HcsOiaBVfpmraror0UUauSwLpAjics2tusMwXXMkgzCcWeEws+rn+TjePS02GzzAjtAGNtcrGOeH/0DqVakIo5ZlCS91N6noUrd7ksz9CQqWMZscyQ5BIqDcEukCoY+zGNqiZxaqII3GqBmogECMh5AZWKEpqkcYojiKD49Isevhwhxip5rpM6Evy6PQFBEP57QRCmgiA8/pXYfy4IwuXfC4p8LgjCH//Ku/9MEIQXgiA8EwTh3/r/QgJVVaGqLqnSx1AFoMDJM9BMktImKU16Xkxs2yxlhZPSY1rEzLIpwkCjNS8RExO128YTarKzFelcQU4yyjSkLhRKdUhudylCgbyUcOOQKv+KIDkjJmHo6CjaPq6okzk2SeRi2Rl5Y0EltSizXYwbDta+hIaKMt6gd0yEGTSjiNN6jiaK35w6FJvCOWC773KSLBnt73KjrTI4MPh6vKCIp4j2NoPOTT7c3+HhOw+5073Jo0hgmT/m1k3ovtWk0+yy07foqAXtA5eq79ANBMTZAuXOAXZ7m9VKQXULVEWlyDtoXZl9d0DpdlHXAW1Hw9i9i2CccBS8oNu+zbOpgnf5CFG6zfFiSZXW7G216IkHuGmFpInYikGv1eD9O2/S6g3p32/T7ORI1ZAdJCbjGCVMmcwk1HhEv5Jxdk2q6YLm79jcbb2JobmUI4PDtzsMxCErsSDoL7FKmFYzArtA3+5TmCbrZUiZ5jxoizRDkRfegj19QD4PQVhg5zcYKTZKuQeXMWEBcuZTyQnp8XP86hVy7rN9o0fUtkhPStpygyzLyZKIz579mPCna+JKZKmWlJnHerFDZgtceyGCn9OXVcatNVU5RkxzOm4DWg0WyExik4HyGnFl4yQy9tAmW48J2JDpbfzVHqt8g6tWCIMYrRdi6i2CcoZqCSSDJu1bN+gc6hhVCy2RECyBa++EKhGR7JqiH9JwTHJVRUsk5KlHVOkYchNfFlAjkChJcpHQ6OIIJqVsUsgloRijiw6enbBOFTKlxmlvEMqCkhwl/YdtyeHX9x0A+K/ruv4vfzUgCMID4N8HHgIj4C8FQbhT1/W3/KH8BpIokZclhV4hqgHqSkZs6GRKghNIFFHFVBWw9Ioi1tBznbRKSSowJj0UaUqUxaRKjJVraHZJnlcUVsFotMOguUf54oq1nLEQShQUYjEmbzgsJiHiImOdPceTJCQZ6ijBlFVkYjprhaCagL2gTiPsSkOS7+OHOeJAZdEMOPvomL5kYrzVpJ6ERGLIdqfLF//yEwrHYrJzyaB3A1OR+KCXk99bsp3fQWgk1HNYqCpTy+fDh/uk6zXHpxNG+3u4akSW5KzmGk5tYNrblEbOa67FL3/yPzPwRGbCJWnvITIVZfQEPTD5i3aO5W0Qh29z9WRD/+aQLSNneOs28ekpHz36iFlScf/wPXYZMtaeY9Y1UbPmR3d/wAU5URqSySZxFLMMKrx4hWQNefHFT/j05RNWcsrR6pRm22BfMLnq9ZgeeTQHb3D28yOy7pSRAJ/8q79FEF16WyZuuiE/Ttn/zg9pJAvUUkK1c1KrSdyGSNXZsdp4whq5EMiCCc/VAGm2RacD8cs1RV9HN2yCizGffH0KLz9m4dT4R3P01hKncsjXS4qRTDxJcUcmaqvBbQGq/Q6ZUaHEJYEtYzcnfPnRNf7pinW05mLynEapMQMahkwSyuinAg1HwhQKSv+YQ/UerZFCvpRIhiDotziKr7HUjB21iaINCeI1ZqNDflrw6Bef4GgqDT1HLDw+f/yCbaNJo+xQdXLWkys0QaVOI9JQpCUkrDIVtZmxybdw0hl1BoYcs9FdGkUAokKmKFwJHqQZhttBVGKqeEVDElHUhKwSWY1FckNFs0XS/Nsbg7+W78D/A/4d4H/8e8HRE0EQXgAf8I082beioGBTlOSJioqG7jQI5yGqqyLoJb22wOWqIphL2DJUkoSiaxBkFMUZhdjHkmuYh0RCTKYM+eBmg+GbB7xhvQFDhfKDiFLTCb01r549JagToiig7shMOy6yVZNKJsN0jtHoMpAqAs0gkW02K49+2eZyXSIkKqv6FI4l2pcDJk5F+8ZDnI5DkVxxurpgv7GDKBi8/tuv8fnZjG5vxGBoM/YkssM16nWHrJlwfhzS9jPU+31k1+STzz0Ufclt+yGb6xP+ZhXxWzffo7MRMaQzlmMPagHJP6dkm3F9SWL06OQFlTfBL2rMzi49ySVZL7g70rhqFZTnMY+qa4ZpG7Masjd6m3tRSnvnkHB5xKH+BsIP/pA8fIbuFXQ6MR9u/5B5dsn01YR+e5siyfj4xeecn59Aq81rWg85v+JlnnO8OiV/XOMtL1GqJt1RTimPGB8fk16LmI0MeTZEubtHHWxYzi8Ijr9kmSXcf/AjRtIU7fA9WsEZcXLKaKvB6bxNoLsI16fEVcamvaAUasRszdWigXQ1RqsXfD0pOUsSBu05TXaprQlLs6CjWpxcXVD3OuxZLquyT6yr3MhjhI5NcmWRHaYMJJkv04hClSlWKpkNo67NoDbxByXVy5JULcmrDFHLSHIPbz5E6HcIzi4wnBR/7CKhkm/ZBMkSYVMi6xvsRoPW6/fRL6fIkkSZVrhbFX/36CkPggaaOUQOSnLBJMpk7HZK7FW0DIXQryiUNZVVEGsyncDBFiKKUiZHQmKJmknQ1xG8gmqRYsoVgW1D7FLrMwRNRtRByVPyxODbaOD/T0/gPxEE4T/kGyXh/7Su6xWwzTdmJP8GF38f+7/hV30HBAFEy8SMAlBk1oaH1a3w1gm10aeab1DygroJYd6AfEEdK9BVaHomub3CEDLsyCatcr4zajL63e+zXzbQujFqs0cki2w7JcN33mX1ztvEkoNNjh96zC+v+OyzT1FLgThr0S72qKsAxYWmaFAJXTbHGa1be6i6x/xrgzSFThrjxBm9ocFiFqLGCccXZwSnK/aKAvPWLm+WImGZsTyLiDoChztdrpkxf/5z2N7mqpMzPKp5cPs1FtPPuJpC+p6EFA5ohguKzEd5fYe0lFmNP2ZX2iZr1xydPOXN9gBB85md++iii1i7rLIQpz3l5PIlN4wesmgiHsDyxyK+PObWvkPD3OB2O4TTj/Felsjv7TI9/RmW20aymtRnIY/lFxjdnNOTXyJKC4K8Rg9WaJnJ7dEeezsHjO0evc0rkumaTc9hWWr88Ts3uDqPSJWQ13du84iX+Bchs4EK5YZG1UQNYja7HaInV4z2HB7NdPr+p6ThTeL2Bikd0tNUeuIKr69zWs8p5zmKYFNfVAT2nIX5GO95jV0uOURAUxVKeUkUKpiSzrr0sRsH5LXJSbBmLShUk4TlTo9BrmIPfbjaId0WkKZnGKdwuXmB5O4hX78i23kTNy04N1eEcUjadJAmCWIWU3QM7GzJ1AB5HeH0DQSx4Djr814HNkLBuqpwqBn1H7CIE4LSx27aNNImw+WU9usP2Kxj8kpH7E1ojQXWSw3JyVA9jVxOcSKJTS6AXuLbIUptsXFDxBQ6m5pMVxGylLTMUGyLTS2iTjwWCohVga7a1AgkqUylfLvU569LAv8N8F8A9d9f/yu+MSH5h5aW/0ERw1/1HRBFsZZKmbi20PQlg1pmGii0qMBYEyYiZdWjKmOqYoVdy0S2iJkkxKTIkcjUbfAf/dEfcirMyKff1IOiLJCOaw7VgouqQyReczFzMA9F+uKa5mmL1Q2VjtPlWZzBo7/FdzWm4in2usUy07g9yonCBVpPY3V9gqgItGcFW+98SBS+JEgHTE9fcrq4RN+BH/72D2l2Lnh1vCRfifhCRbCYkCYFk6OaZfuYTGsy2t3HLjS6dgP37h3OLs/Yvn+bN7o/4MtHf4UoN/igdw8/e8HJ32kE9TU7WoNNFHFjuMfjv/7fOa4SLo+m3GveZ/BbQ5JaY7V+TmdjM5ELxp//Gbs7byI3V+itOWkiM5ldEaUz/PQ5g50umeOQT76g5+ySlT6mmCMPtxHXFcevHuHs3OLF8xk/++Iz3v/eW/zx7z9EiGuK2Sn7b7zH68q7/OLkZ5TTBaKmkEs2/ddS1OQ2j5+d8YN33uFv4iekOzbfG3SpI4fJ6pzTacL2TsX4yQXOdoNkrLLULrgvjdjTExJlzdQREBYjmoaLlnzFWI/oxiFFpfH0eEb58hnP1mO+132fE73mVpbxRBEpyog7Rg9rp41sb7iuFXZKhYt1SbE5Iel9n2S5RnEX3Fe7fFwZnCYpz47HfL+1zULLUIov6bVvs6P2UF4W+GuPr8sLRvKIMvCoxCUPzTvEVkaapmSSSlmHvJrlbDt7xJcRQk/FtFPCWsNsp6jGfTprA/2fdFkJFX/5L36CIxss1yGV3EFVFqheTVYHNCoFXwclF6nSgkzUEIQKVzEhaFILPqVR0lpW5K2EaFkgNlVSWUfzUlxHZKlFDHOHmbxGkSu+rSvwa5FAXdeTf3MvCMJ/C/zZ3z9eALu/8ukOfKuq0f8FUaAWA2oxIp+abNQ2pf2KZCPApImkWwg9j2rqU+00qIMIxROoJA3JFiiyjH29S2Ql7Dpt8iJBXYsEvQhBg0lTxUgNmkmDCTXyqc80WbNsi/BCppYv6Wc5R63blM+egqbRb22xMyi5vErJDYsbnQ6P/Rl+qpI1I7bUa5qZxcX5c+rCx0RnV+6y54ecTjT6sspVGKBUPXb3tpie+bTFgIO9t7B1geNojmYkVIqEM33BF5sIe3FFlpS4RgPPW/Dl6pzedp+3Rruk9QFPLx6hKEdE+QHWqMW6TBA3EateTLXYsG0adHsPcMweu57NeCMwi0scz+FinbBdapjv7FJ9XrPJxtzsvo6ewTxSsHZNzKhENBscf/Rz5NYAW77DyPH5qKxQ1YzRoM+DtkUe+fzk2Ryj+ZyDzm36XodAc9npNFgLG2R/GynR2Bl06Gg3+KMf9pDimn6zx6WaoYQpH97S0VSIkRBmCrHjMbANxnORvUMHNir2uYHYWxDHa2K5gRbNmTfXBE+6LF7GyJGKtIjJHqy5oXVY5wKbrx6hpBXitoMpCdjWLSypxTL6GMNRWEka+x0docpRdImgfYZzLWDoCm7Xotc3uDwZ01VvousSnqIhNmWUwzYPThNMRWU+2ec4fsyZ4nPL+oC8mbGri0RThZviAFEUyLVtQnnBsCUxv9lEpcMqPiUsV3SMBkolEq5SksKnp23hhwusookiF6SphGcU/J/MvVmvJld2pvfEPEd883e+M5/MkwOTSRZZrElVGhrdupAAA4bhG/sP2Ab8I1wGun+DAQP2nWHIl41uo9FtQRakkmoQiyyyyGQyhzOfbx5iniN8UTJgG1WWYAgG113s2Nhx9byIvdfa6/WqnFawsLyMIKsBh3BRoWk7KmVAvYso7JQcg6ZfoaUKqblDlDwytaFeVMw7UCky1uZ31wn8fyoWEgRh8n95/E+A/zNz8K+B/0wQBE0QhDN+4zvw839ovbZu0HwHVSmxjZzMuaIrmjS6gj4qKNoIdStSDizUxCdVbSChUiv6Tc3e5ICnAx19KhC+2qFqCet8h7kq2e5ymGfE1YbLaI89c42gJBh6S5pV1HLDRnHZ7SKK6RtE00IWJO7FW642O0pRQSrvuY4CVEZ0vRxim1+8XPB29xX+/ZT7uM/gdMjg6BGXWkuMwFUc0YQJynaFsKnpegOefueQvlgz3QSIV3N2q5LF3Q2ffXXH436f2SpB237Mw/Ef8PDh+/QcGf8u5S++/jNezC9ZxT7Rrst0dY9pvMPEOaXrnFEoAkrl499U9DWTPL4ldR7SO3qKdxqwCa8YIuI8fAdxvmYlLLD7Y4wmIDyyqVxgkXP+bMI7+31KM6PvRjiySNg9I0oihKTLXbDDjSvS0T7qYMAehwRbFUPus6/sYUUuP/mrS6avviYx1pw+VblpbpmnF8gnHa4LhVB+RZSLcKxxeHaCMVCxnxgs/Yrbuxz5YMHVPCYsA8pBxKzdoWcZUiRiFw3OxmP8TpflqzfcRyus3gNM74AWB6v1ETULe3jI2gdfbmgSiaq8xdt/l71xB3PvAJINltdBbw2Edoz5WMOXCtb5jKL1yNo+99KMsvZRuUXtmAhXIkVh88lSotRmDNUTHoR9yvYVZSbTajHL5IJEbynqEvegQVjnrEOZ49Kk3lYIkUy3AtWo2NyuWTU5oqziCymV1RA3EkkbIVkhZDoJElFHYxE1VKGLFgYojkiRVSj1lINOTBZotImKVNekcYkagqpU5HWO3E1wtBovgdyTfid//xhr8v8Z+GfAQBCEW+C/Af6ZIAgf8Jtf/UvgvwRo2/YLQRD+F+BLfmNP9l//iFcupgAAIABJREFUQ5kBAAGBXE1wSoFAyzGlDtEu47Bj8ra0ENw74uWA4aYl6Kjofo0+0Ah3DbN6yD93h2xCk+g84DTPieIzPKvkSpeYHMjcvPkZaWfI4ZFE67hI85TPoiXnz44YmGsOlxPe9MFrnzLu58x+teIuNRk2EebBAFeZkIYtnGe8b5yzHji8/uwvWckVz548Y0/IqfefIDkaf/7vf8ah1fLw6IyffHrP6CMNc/oFbafDxec+2uAYwwNHfYjY+tzfJWTpnNFGwXoId/IZlvsLmqWJofXZzl8i+yMeP++x+ewTXl2tiHsGP/roQ8YP93h1cUP49WtW6zVyssJ+9Qn9p3+Kuv6URJNJX2s0bc3zR8+RC4/r4Ct++PiPGTwwuFlv6NUCJ+8d8r/+T3/FpsgQsy09W0XVLDzB4WJ3S4IEasOx+pTAMlh/NSUKKq7jO84fwOHxGY5tsPrp1zx7MCCXl/iXEn92v0BNRY48ladPCuyxzN3NIXuPzjC8liSUOP/eQ7L1kqePPWLDQXRShFCjUSrC7QJ1G1OXCsKwILQ7uLcCrdSiGjL2BDqOha4bOHqfxBtyHv6Eq23BcD/E1k6QvAzZOCG9mXP84DGHYctaTiDcIqkln19FdNUhPcFhP+/jk2JUNUU8YjmoSCqHh4KGeDpm9uufc/P1ayYnPc6e/4hgccnlVYg7WLMpSh50BiBV5H5A1UhMhC63/lumXsjpfUUyMQjzlmd5yt4HB/zwV9/i3795SzoOcW8FMndH1nQwooxMaCnVFrVYkjUt6Brr3ETTBGRVpMgLJKci7w8wUwHZbyhlATvvMrcL1KZGrfbwiyWNImDVJuXvcCT9x2QH/vPfMvw//L/M/1fAv/qH1v2/h4BY6Ii1gFILVFWOWrukeUxrp/SmKjvLZ5dr6Jsu2nhFmWjYfQGhKLiQljwYdli8hBe5jN275/SdDwku18RqQmLUtDORtfWKQ/eHNI7KSM0p85hlUNBEMwR3D+YrFrMNTl9AWNyRaCJPKDA6Y968/ArhtcynxzvU1iPpu3TEktnLCuldl+LqDcnXIu/oCYr3HhlgH9oo85Lht75DrMVEbyboeo22LNk2cxxV5bk5Zv/Db1PmK9paQ0hiimuHbX5JVeaUVUVVbfnrl/+Oet/h0ZN98pmIKDSk8yVyCYZWMPuy4fj9FVnnhGIWsiee8rNf/YzUUHm/8yG+tkXILnHlB7SqSFStcNMWXYwpW5v6bM2LNxt6/TFFodAfDemcqDQLDUWUOex4nJ9JZB0J7bVMqVpk4ZyLKZw+NOlXHm+bW86cR9yKMtezX+N1bEaH79PXNb7++oKT/WNmpYpWvibfaVi9AaK/g6BE8FzcJsAXFPI8oqMHCELB7mSPbrKgqA3G65LuUGNRtQjmDi2BSeeAqABlkKP6CkkuYPR0OkWD3Cm5n9qcD3es3RJZaUm0lHwpcpO3PDQlOnKK4J6QuCLLQcljryTNdmRKjrTq0xkIlLuSxLimZ3fpnw24rnR68zl6BZbQMI8MxqnGa93nuIW4P6IvtLy0djiiyTiVaOjgUGKbHrMqp9mlWL0Jq6hBuIT4UEaeQ5mUaEKK5AoMGolNPMAUGlLWdIcQFSrdWibWFLbrGkdqya0AtWxQLZWkzOmLGkXRIVFjdLHG6jxhLV6D/9vp+0bcHRCEFhmZWpGxS4mqbMlbhVWTcTwTEKQaufqNP0ElxayKPh2joJtLPD7SkFYZq9UNciEiqBWJuaAJUvSsoa67mJlLri+Ig4JN9jVYAkNZxVhVpLHMVb1Bvo+wrB12zyVY+dBR6XVd5orC57sbIjGn96Cl1nJm8ga97THIBJwHLjtjgmE8J6gr2uM93jUbakXhpKxIIhFzvaT8SkSWLom+vmWxNZAsiUi14FnD3Sril3cVV/4lkRyy9jc0jcVG0ZF7D5gVCdM3OzqjDgPXRJoUHOxbZGLEm1+/pNt/ysGz9yn9GLE2mJYJt/GaWi0ptte0eU4eVghSw9MfDHD7Mo1oozx22UwgluC75x/QN2uM0mLxd1f4RY5yPCF/85Yjo4dzqPHpMuTLX27I2g3fJkYQLKQoYL+vc/Xpz1glMb50hdPYfG/4LkfWI7pNTdu2fHF1x/rulud7Nr1Zw/LLkGxaYOQpXc2kqiqyjopb2xh9m13yAKEe4foGC9mhLUXkXsEboSLp1Oi7Dq45oG/EqGKLfS9SCBvqMKRehuyma5qswdgr2CFQ2UPErIfZ1ihmwZ6n8bqZMbIkaiOiJ/aRfRsjldAATbHxJA8jgEQGtehQWzYHtslmcU/egVnRIlgyI61EFWN0T6StHFStQTJlHCNhqZlcXO7wtSlVvGKWJ4xTC1Pq8lqNkHo5ijam3drUpoaiJqSKQCkLBI3LUE5R2xK5FdhsazRJIqgbWi1DbFoid04Tl6idllz8jQV8XmXISopdqmiJzXY7h8j+nfxJP/7xj/9/g/13xX/7L//lj9ElqtZAsC1ycUu3jggNC00rSfSSzJMhVBGanK4Q4lgTClfHPjihzDPitmK+SXEmoNHnIopwHIPjXcvfbC4omwJX8sgKE2W7Ie5oKH0DqZAoa2iLJRc5RPUFE2/CuWUTBUPqxZyBYyGNDliKKvfLOUrQ8P7j98AUuI2nnGg6l8VrHNfhuPMt/GzHbJPS/84jGmGM2JephAy9Mni9yCjkO9oITKdPsfEpuw55dMszsYstj3mxfUFULHnYHxFICd8/n1DKLS9++prVnU9x+zX/+i9/zXI1x2tH/Mkfv8d4/4jh+RHhbYrnVexPTnn95Wu0tibQNepNxLrecH50RoGPJ5rc34EsycxWW3abnI++/wPKl59xV4aouoJYZtyHW7bRgqOTpxwMunx08i2cgyNu8wotTFEtCyGDvbHCyemQqnTR+wbvfv9dhq7BJqsphRS3NoiFikRtca0ullywWmccPz1glyvM7go0YcO2VHjYlZC8mkayyG+n5KWPWMiopodnGziGzU9/8rd4XpfeQKGKKnaKgdBOmSUOtVBx9OBbKO4+mbSiCUp0RycmJvMVhs0NuiYhy+cskxsUFUoxYNCccuTI7IorXPGMqaWSrWcI7QNWzZrcX3A4eMD27uc8+OD3QEhxEgW6CqJzhll30RwQYw1Z7WN4MXutRWa2qEIfU++jpSIdWyOU5nz1H/6a6et77CxDalt6qURuqLRtCYZKXSZUYorQbWlKHcsSsEnxlZxOoCP2bCgUKizsUqAOMoZaRqFAGFq0RYnezVDbHkmcQZlNf/zjH//3/0/+vhF3B6gbzFxEqSPCov6NL7vQRYwTlqqEFJmIuojpBggVxLWGwxRbeoK63qDkOWbVR/Jk0nLLoW+RWS1vxzcETY8fDUas1zljscTrOHx1e89jHJKDLfnbBLHXMrvWUFYryuE+84FJp+rweL/CVx4QqXdYsktZBuzne4iHChfzCDOLeSB2kcOWfVWm2zEIZ3e8uNjw5P0Rp7WEPNpw9eUauZlw4jn0BhLicIhZdjCykLI3RN8u8BuDX76+QjiMqDYJ/b7LbSJQbH0yLNq0g6f8ml3Tp/V6vNs9QzNDxlLEVX1HXKr88q9ekS1rDt63mb39OUK3ZreD0wOFdJXSXm8RLA1zabARA158/pfohk7bmRCuSx4eN1jvjjG/eIvJkKasOJSGTMcjOpLC68u3vLldsr/fwYxDQs9FE9Zc7LbMmwlPn56RJr8iXudM7Hs0a0zHW5OnNvZzhb78lEi8I1Yt1EnMwcuCbFeTvY7QzkoWepdeJHIbFrQ9C124ZzYRmMgq+sxAqWrcIuZuZ7A3hkFPQBvq6MEhprHiYusipGuobDQzR3ISzHBIXPoIusLwRuHXTU5udXE7Akmasq8O6QuQFgZb75paf0ix2Wc5zhk1NXGjEMVfYmsGC1UnSHcMHn+fNJpzoJosvBCvkdhlN3QEiwkagmYjBAn5TUvaEfAFlcPSp7H2KDSV0JZYbSPu5IxU1ailiqaOqRULPYTcNTDmOYJpI7cN242EYiqkZUxWd7D1kFDvY0lLikgBPWHTrahEgaWpUtQCspvTVA7iSqbuNwylksXvwO8bIQKiAI0Q0+gqpShjlS1WnRO5JYpmULYZvaSgEF2aRgUzQLfHlHmE1q1pjSGVk7O6vEWrde7yv0Wr36e3siiymHX3HZrBklURU6Q7XEklWMUIXk0pa7iJT2wVyLqDIRko/gZjKBNmIUrTsNx6POhsuVtEdFw4cA5YL9e0xSuudzHvnP0BeZmwuKwxHI3z3oCJ1nArljy0z9mJAW1XYdUrse+3VPEEtfRJD7pE6Q2nyYBdfMOpLHP55i3bXYYlKXSVGLP/Lnlf4FSt2K3GnLrP8MqYoitQlxLbCrR0yWpp01FyZMlknHsE0j3+WxlzKHEuW3wlzSlqk40k4m+/YnbxCmWX8en1FZl2z4ePHzCtR8i3JeqjIZ1hDj2Tu7+5Qdgfo45qwtsEL1NIdiXdwwmnuwVVe8Sbq6+5CafsUzByDeQzG+W+S+cdk/bJM06WKl/Lb7GFHeL4HZ6NMsKVjf6hipJt2J3pGLcy6sEVRtOlnilUuxC3NDioLigrFQ6miH2LKPLwozlFPmSvKJkvGvb6NZvKpHVS/DTC9tZUjYNWZchiD0m/J1q0SMOYx+lDGv0V7qxFNVRyd0DSabj+uxXd4JyVv8Q5jOhNdXrfO+JQ7vBpfM0ubamma5ayykgcUR34yIKE5FiUQc2oM0YWQNFkYiehYAC2i5KqnEkCqVzQdyVCI6EfO+zsAxTZRagzSqvCLByEtCBSLBo9o24aiqwGoYti7SibNQgeo6pm13qM7Q2l4aJLPtqkhzmbExgOSZ3Qr3Oq6IBSuWfnmORJipT8E1uT/1NHI7Y0jsYy1OjZa3ZCn9LbYt3rqFpJqjes2xqhbXFrKCIV0W4wNImNaHMolySyQteQ2VuPkfYFNv0U4T6nN9GRl1sSlsyqHr1lyOH7h5huyG2acjryaNMEWbxhXtTIVYOKgZBOmTUJ2i6mEzSsM5MDp0dP2DEvM1RNwOoeUQ8zFmmBfNGQj1z2bYXbxiDRG3Yfv+In3OF+eMi+EtCUfd6IDa4YcJflTIoWvRD4vH6BrXUI1T7PH5/x+foNSaPQHzq0zZJt0FJvdpjSQ56ZLavz5xS/+pQwAUvKufxJSFi/oaOO+b0/esjN529Z1V0++MOG1Urgy5nPU2WPu6HGJ1/8AiUQ2Qo6w9Mj/ovv/McMBw/5t3/xb9k3Ohz+8yPu/7sFr36+Zu/bKkfSEe6xjmYc8KfvnHO1mkOlMixLGveIm/tr3lOHxOM+91144pqMVBP/Qcmeo9OZlDT7LqNf71ErIq52RzCbII4HtFWIFp8x0ZbssowoFbiJXtIRzxBTn1WypcsRN6cOVpZxMLVR7JQ3X6/Q64C1d8xRV2KeGRxb8NVsh6aUpKJN69dIxY7R0CVQOkyFLT1hyDa6QC6gVFyUvkGT3TC90cjrY9yDikyJCXaPMY0t8zih9lWOnjzl8WzFv7n7in31MXmvprUNgk1Dm0Ek+0i5Qs82mJsSYlDTOhGraIFuHpDsFIRRQroVkXOHedcnWG2xp1NIbdo6IxUFsDWadguxgBTtY/ciyiylDmrsnkDqa+xEAblas5R7aFFM4aWYVxumlse+nbDY1SRll8ZMyEoLyRTZ3xXMOx3YrX4rf9+Ig0EkgTKMEAcJktrDIyaNRFBdKkuCbIIkN9hNTliptEJGoGQURYIWNFwUDtl1g9rJWHdixIMTxNzAcm22r2JexHPCdsLz0TGFGBG+/ildy2J0C7UQURsDqmSfNk6QpR6JkDP/aonS6AhGl8o5IW965LuU175JNZ2jJ3tYnWc8aJ+ThBEzV+VidsvrIEKvFgTmiMTTqJwSqR5RzVzmlUn3+CHoCumypg23PNkfcXryIf7Vgq3/lnRxz9O9E/Z6IvOPf041bSDa0VNtJE9kZcps377knpCrZMp1mNJx+zwaPsM+K7gWEj6NBHbNDSfuCZODd9lzNfLnRxhnY7TpkPRuy82biKUUsltfcnWxpC1lfvo3n/PxdgsTyHopShFw05txm2xI0nuibgNrn7/5/CucscV3zib88F98hPtHH/B4csCJ6KEIJmsZnj09oDEUjhob1xog7/VpOzo98YC0FNHzHSPLwhi2nB3t4bcx5bZgEPTQ2WIKNv3UoznPGWZbhsuCfnJPKoIcz5BHE5paJitUnGHIqmtwrCScnpiMpYSEGtE75tJfUmCBlrEIlmgjm6HUUOy1rHdblNrGjwr04CtUXWITTtG9OYXdIKQpfT1Bv9MpC4mxPELtFojBik51Qp5WbGQftzxC6VakHQNKkdj3kesWXegyWt5TxxGdNyG+mfHUEBExqfMuq8xFEEN0QKws6o2KG4AuWIj2lLau8OSAWjdI8i6t5FNIc1yxi7RbkWcJteMQWh5tsmMd2GiVR6qVyHKBLug4usqi3WPU/e0CAN8QEWjrlrwSMSuZndiSKB2aXKNsU9JGR+1fYXtd6kpFUpfUbovc6WFqsFMrsnbHvZNTr2yKPKCMt6htwWaT4Y/WWOsNngMrXiNbBUs94HIn0A4VLKOHXyYojY5kn2F7ER31BGMgks9TcquPqtkcHw5J7nzy5ZawmiMfBsiNiWEbbMU5RltxZBu8b3bRhz3SL+4Ik4oqbVh98jG31YY4WPPgYZ+9gc3oGBYrg9VawjZUNv4G/XbJzy9+zevrnyHuIpTxM95udJb+lk/eThEVCU8a8fDRQ370zjPeGzzlkD6aazI8NzGme0T3CYLwJdMvfVZNxd74IaOzHl4TMdkboo8lCnPMs4dnPIp7pIsDUuUNB7rGgf2M8sagp+2jyQW6JDAU17z61RKh57D9LMIZjfmjP/x9XOmcT4qA+1AifvuSbeCTB9A5NhhZPeRaRdrOWbUN9tLHsuFRZx+5dGjHIxol4apIyJ2E2bXE6EEHBj7JWKaIZXSloBp3cEqBsSlhjhVWnoMXpBi2iSvsqNsMO0sINw1tek26rDHSU0but9BjiXW5wO0e0ppDJHWMFHjssoy0adDKgtFJSa3GqGy4Kz3CtoTrFXGlkUgGpDXXdcy6WtKYFabeoa8/YtmqSElOta6pwoZNf4mxaRFeJwi2iNnt0qxT5KLB3zcRsoi7icfBzCIzHBR1S6HNWQshxUCkLAElQHZhpwtIqUaTu0RJQ10I6I2ImJU0SsNR7TGrKmLRpFY1iqUEgojg9dCrAFEvEaQGWWkpez5xJaG5Eeva+J38fSO2A9QiYr+LnCiUdYqmLbBKhVgyUbINvuTRqhK2JdOPHVa7gKlWUKIzmG+Jaw3jpI9wbKLd18w+36BMYkxXpMM5vcewDDN00WBfncPwKXV2w26tEgkvka0hebsmzWYEW5s/+mBIkLjs5lsulAJdWvHycob79BTtcstAew9pGfP6/t9hjDSsOMPWT+j0hqRnAyxNZvJYZ/jnAcrBmK+chse9c4rdkvlySbc+ZPzMpSgD7uY55d8uGPT28PZslKbhk49/yYvpz+g/+S7f++AxRx/8C6JIwb+aoXZVimLHUi4wHypsRJm76le46+8iOhv8uxV79kPe/+MJ8a5mVn6JcJnjfGTQNB1m1y843jvjxRcvOHn3GXV5j3zRYRndU8ZdhHrJg49Mdj8t+cnffozZf4LYbsm/EjEeamSbgv6JyXXY4ikWBGtOnz9HX25ZlBWHzjHqvo1QR/hLlWynobo5stxHdCWKLKWXBsiVTTFfsViGZO2Ukb2PZ76He3FJeW7STAtaOycKTEyrZNaK7O9aAvuWcO5jWh6O+Qh/dUOWZYTXW/63L77iO9//fTa3t0hOwlP1OZoSYXZliumGQHEYxjWyKqBpPZrlmlRUCF/ECPmUifacwZ/+gOXHnzOR3ifr52hlzbrc4eyfsjf0yJWYA6lHnEiITye4tzV2oHGR3+PKIfrO44FmEystYaXRrBt+FS15ss0Ret8mTQUyb8SvkwViGDNsbLadhlqKaKoGrZFpxIxCqVDUgroxIPUZGDpRVrEWCx6IsDIg0UoQa9pEQ7FycuOIvdUNG0w2VoZemGQyGIVMVf0Tlw3/U4dEQ7VuSaSMXC2oc5HkaB9J9UG2ENYiiDliFiH0dnglSP4WQ5LIRl18NaFcxdS7AM/Q0J0diRKQRnvUzZLplUBf2qGIazh4QpNFhLGKWNyw/soh9xWkPhybQ/pHLrvSomhyAlXg3DLYO5zw4OyH2GVBbIlM07es5jkbApKtj1PYRMqcLA7pLFLSzRZ/c8HWE8gEgf5CQ4ky7L5CdJdwlXxGcPGSU+0hoiBxemozeeoRCn18WWB4eEj/9/8Ee6QRKD2K+Yq/+fIT1mbN62DL6lrAbsc42Dw6HPK43KesFdw9l2o4QvEkDp6OcYyW2d2W67CAqUvXcBmPeojGlrojEBCQxB3WRsHd4pL59C9wglvkWGS/HSB0XR44JX/wvE9txEyTNVe3IR//m5+irD8hzHbMwg2DuiF9cowexgiLgl2wpsoOMbomoucTiBKiEhL7V+yrHo4HVC3KkUORN6S5jn//Me62oRqdULYW1wcd6tagVlq+DFy07TX3/Xtk9zF3VxcUjkiqbSlNAU8P+dXFBdnsAqVKME41DvRDqnzDLFIpkohkpSJqOfEgR6xcVoFA05HI8x1zeUucSqSdjE0YY2cTYq/kIJGQxYLjtsH2A0w351DWIJ9j7iLStKAZZejdLaNjFzWv6Ncxd4qDnxi0YglFjNcqvP70lnXnLa4lILcVwyKmmZaEu4Cm9rEzD4oaXaiIuwWCqCPJCkFWkVsCcWhQtANS0WNe1pQFsNDo+SbVIEWsNJTtDVMvpyJCiQuMtqSzCEmSLW2s/07+vhEiUAOWGtJmOUYhIBoKpX9LKhh0pB2W2SImDZJZsWlyqtMKKchAkJiXJZ7qYVYBorghLjKkw0PqpcUyfUMSa3REiXml0eQa7SufdVIy/yKgNsZ0lCmZn1DuJBJJoU5z3lx8xusX9zjygGPrKbK0h6gnBDsRK63JXA/TyXn0+PvsH75L5Ti06QG1ZxMMKuqioWr3WRQFF/dvEEc9kiwimous/Zgqq+h6Q+7mrziVC17WEcuXX5OvE4TVLe8NxpzbG0RBo3z5Jdv7koeBjDfdElxfMG9DfvHr/51f/O0XxPkK1ephqwlVo1BclExflVxFCcOBwdn738U5ywjkGLES6CkGZnPIwNWQSwOcKUUoIggF7pHF1BTIIoXu0SFnkyPOTz/EOf0+3YGCtgvpOAqO3eOillmHG3ruELHfQQtyitEJM0GiCLpkVcB05dEPRMosRctlPP2I+1omzTxSqYui9Ig1mSaeszP6+JsVdbpC1yI+yGXa2CXApVO1NKMOvdU+wZs7bKVHGQnU8h26K6LIIzJRxHBVPNlmOO9R9o9JRRU7FQmjHt2uhiTHVBuboidTxzP0rUO+s1gLEVQRRgTZSmH6yCBJY77sVojXCxKjh+/EBEZEc9zQPXFQDB9PDZGziouFTdsqnHz0iLgaUOoCQbUlu01A3EcUC4ZigXBXERQRoTtCaCp8qcGxbVRZJm4NvExASmTkpYGctDSlhlM4SIVAvL9Dk3a0ZUDCGKunYYgyvqihSTpVIFIjI8pDNLdGrxrKvMfGKumVEmr9Wy/zAt+Q7YDYihR1CbVIaUqkZYFomKg7mHcstErCG+5oriVyw8KKcwq5wy5dIGUR86FG0UxY3NwwckWGaYu3b6Hlx8y1FUaaUs9h17g0H3aRvpQ5+d4JVVqgH0ps7q/JGoXJ8ZiiynioOUz1BJcdU6XL+lXBuopwLAFR1zndO+Huyx2+tcS8TckOXSRhhqg0tNM+HcchNToYWQ9r1JJ89hkv1YZsr8AWjtGDjNqQqeKQv/bvCDYhzw9/j3mxoJc/ZO227Etn7NYzdnrL6sUL3qSX7OsTik3Nqn7Ft771jNCRyROFaDNDOBvzgbiH+IOC1HKYvXzFwhSQ4q9wGwntqU2V7ghXN+j7Ej1ZQ50GGIc6r8IZD5884Wa+Za/qMjYNAqHg/qLgtvOC1Zcaj88eIB0UWPc1m/COx/1z8qaDUAb4zDg2T3l2fsAnX35K50FGJzcYqT6JbSLrOm25IN6eETc+lhBjiynxWqLJcwxDRPLnZI5K0trYpUWW7hBdm3HYIgk76nXItR6TXy9Q+gXdoYue2KQVrIvX/Oj775FfPyatIxahyeHffUVz0Gc9DtHzFCEoMKxjzp2GN1uBVl6wo0HvOrR3Mq7awb+UGT1ZgfKAaP1L7M0Qv6ujRy9J7ju41RHz9RtSu8tydYGpPEXZhEjHOixzrmcRx+/0qRYR1lGXrLlBK6cYbQwjj+OjLrra435xye3OoKxTklSjagVUJSbvSlDKNI2IqESIokvmpqglMLfhIEfY5ijhgjCVKN0KwWgQ7nQGbsK2zmhSjVYy8UyBQFriKjaJ6qI2V2S7387fN0MEaCk9Fzv2qVoBV4eoTbGVllQwSKUELVEpWxG5MomaHEmMWW00hMahM5HQXy3pPvBQohU5NeVcQRw2lOGS2ppg6hNOdR09VWjOW6LlmkLNaf0Iny5nI4G8ijAuE25djVzJuCl1XKY0RxVHeY+2zelvFO7WIf2DitaUifsmfUWl9bukWsRlWpAuQzRvyZMDl1zT+bQXsq/o1M2Ig3eGJKaCfLlAqn1++Pj3iJIF8y9eIg+GVNWOZFcxs0xOH014OH5MYW15OH3AZz/5jMKY068H4IicjHvs7tac/tFHPBl8l9JNiD8N+cXrC8Lqlif7H7G3922qX7/i/qXPohUQtANSa87dJ2ve1Cn8h5DgyMI9fY+HE5MHBxOqXcjF9A3L+0+Qy3eZr7ZcF2v+00fPuGteMb3fcJaGSB2DodslXef4WkTYvuDI1KmFmMBPaQsZx1IZrGE61q8GAAAgAElEQVS2t0eqBfTMHrtFgJ7XpLsQR66p4obakmhjhXq3oymXVMYeSjRlO0gRBAuSA/Y3S6aWjBsLRGlIi4vhehzvHnLpf0FgDjjVUtxTg9Ydk7YSmujRMQuiToojlczinKDZYW5MosLh4LSL55XcJIfIxwbUBref/jmPJl2aTKAvHDPwOtyZ9wzbNTfTkqNuzqeJi/piyuiwz+7NHXgt+tkzzFZF0QVuvgh5PDpinsXo3iPEq1/xJq0YCju0ekCbliiWjlgGNGKKYPcxk5Qk1lBQiKQaV05wIoFAMGm7AsmlgKZYSGpOpRVYsQOFQmv7xLUHeY2Sp4jn4PsKjeRSZCC6MkLiAsFv5e8bIgISXcEnKZ4w1K65bTL6RUNlq1RRQU8piQORWjzBVa8obZskzGgTFc2Jyd7Atd4wCgZIXkEYmFTVGkN0OBw+wrPW5MmU67KBe4mRpSLYOUoOs2bCqV6wamLqSxXtzGN6s+ChPmahpaSzmMXKZ72OeHC+T6BtaduYxfWaaT1grN5QNo9Ypjt6WZfjY5OHo31KW6WsBQQnxrpqURYiexONXb6lvUp+414rniLaBZN4QNHJ2EVzZtMFy3KHJngcJgUfqx+jRDHVSZdZ/JY6qThWT3HVIV+/uMERZK7/8hr19J68t8fVyzfcfbxhMhogqhX+my/RjhyOh4+IFjvudl+wfpNg6TFpEtCcG3htxuqLX2MoEvfLFEdaMfW3KNkejqEyPtnnwx98QFubxOE++z84QhU7TL/6GMU1oe6y3WvxxJgn4z/B1CKKScUnH8c4K5EgmTNaHTMvZGTjGt3c0ZqHJKZI8mqBlpSkYc1C2tCZeRQHCeL1HUtny2A6QbJbQueCKjeY3kncqAkfSY/YNQKBtKXbK3C0H5HsVkyv3iC8k/FsLXDb9CnGAWVoUBoFYV2SKSpC0GIrLXOtYPtmSiCt+NaD5/izDeMPjjirPiAuX2HqIm9vrlFGIr32lMbcYCoas6uAB6M+oRaSrHW6xQDHkSFv+fz+FftjB9XIafa6iFuX07Rhdf4etqaT+BKbIEVMZjSVT6XXiKGJmFTQqrRCgylLDCybYpsTtzqxUSGj0kMCtyDKdIStj1DuKDWBtHWw44JCaVBMlfpORW1F6s4ScBH9DeTfcN+BSqipK4mcS3KlRskNksZFIUcQtviZQGOCeLQgftuhSFSkvkqdCJSVjCuZrBMRZRiSrgpa2+DUGLGtBTI/Qg48ikmLdZlx/IFNU+W0bUuayLS8Jq7GNJ5KbYbc3l1zUp0RCRuOm1OqbUGeBkReTuYHtIaKqUzod0Zsi7eY5R56V0K0OniGwmA0Qgun3Pkdzg/Omb7eMDIn3IULhi2UFzfM6pYTR6X76BFjUeWFdo/X03BKB0GEeCNho5ImNbEiUnRMBlJNU3bIywJlElHc3FKvllymCWcfPuNmnrC63aH1G0S5RW5zrHdN5q8XLP01j1sQsoa7aMPhckdiT/jo/JgwLGiyBa9mOfWpydNTkYHzhzhXb8gnfQ6eaDidQ2pLwRymfNt9wuerG1TNx7Z+U/CTH8U8LkV26gS0JUh9pE1O71FGISdUikTShthlxlKJcSMD0YjopQv8eMMsXmKUBp6jkfdzSgXkw5rOWqKUc4q7C+x2jD9KaI0tnipzn6cYWoYRNvjCGV4xxdFiLp0uzxci805I3gyRmimb5hF1NcCJfVxZZaoaJEmD7sqISUASeQjPWsaqRC3GdF2J6NIh7wkY/Rm69BCnG7NsRBojRe6qhGsFw5kgDyp8KSaoxxx6JbJRE69FLFnDDkoK2+JaXCJpLVkukbcSHQ+isksVS5hdFdlOiPyc1qgxYwch90ljnUJwib2QcaqzjgtCK6AMRESjQVM7ZFqGIEkoWYFsqgxklTSSwCnItwpmo5CJOo3Ukv/2buPAN+RgUGhBLMfYokRUKWiOStf4jSWWJ/axHIHWrfESlUquUIUMJypQrB2C1tDkAWM1ZFfptKZCbTX4w5ZRX8eUZPSBhODn1AOdXRHj1yr2ssbwQsqFzOvYZ/pqiUCXjvIdRD3GNG3sPZ+DD132Do751t5j0kZGsDRsL0Mw7nC8PealTLzUmNgik+Ep80bgq6lGPF2g+Sb5OMfopXimj6S2XBYlx3FDU0kkX3/N7eKvSWdbZtWWbbhC2pY80PeoyozsxCON1jx1LDrNOYbawdiTGHW63Ny/pZ64hFaBlZXc3exQOwpysk/3fYHB6BkT4wjPGvOe/V3m24LXV1vEdUH/aJ+zswm6N8JwerQHj7AskY6k0dMneIcSJxOND8772JLN9/YGdNsxj+szDEHgSNaR5xsksU+hHCKvaxrjCE9wuLqD2ClR1ZrjUsHaREzUgrq4Jky2FHGMsIwpNlP8+5hdkdAtD5lKGvHl9jc9DaYB21nEupizKl8SliLbLiwKBccoaQU4FEL6okpjmcjlikorMZZ9HgwVwk6FUY1QnAR1NuYo32HmGZnWoZ/t4WUhWpbQnzY41QRZEykWEbeix/x1QqKKLCSV6fU9WWkTbkuWK4/+Zoxkg3pvohU1l59/zXxjkuk6ethgbzJM54hicAt+yqYWCZMV7sDAzXN6/hovK1C9hLzaocgq1UwkjFVsQSdd2xRJRSlouFFOKTbIkcBGDTFLlVoxaCULNVTRJR8lr2maAq1uQSrRShOxk5OJfZz+Brm2EOuUIrEwe53fyd8/pqnI/wj8R8Cibdvnfz/2Z8CTv5/SAXZt237w912JXwAv//7dT9u2/a/+4W8AnTX5fYvRitShRVzElEpIUO2T1wG9tCFvodfE7FqdzGqoG/CSgkIXiRUZY72llUwk/57aOGGtXlDK36fxZkz0CTdBTDlPGI93pOikqcnhww5FqZC1Fds05eBJwUn3MbLcIb/T6BoiwonLIl1gtjWD3hhhtSC6lDh/prHbb3h+OOZWVFgH98i47Hdrgu5j5sEX0MJnn15x9NGIjnjAR55Fz5EoKHizKHFfD0i8t4zEI6x4StgXSBdrWkVm+vInNOoDPstXSPcvkb0jzjtjbsIts+sNej3k9OiQTXlJoHs83++zd/QB87+zuVy8Yqnsc7fz6RUrdL9mxSue/OmQJ/JH3Egp8dUFF9uAMF3zew+eshgPeXMz4zSscPqPaZOA/Q8Oud/e83Z+j9P/HntnNnvdfYLpW+SBSk9x2fgyYR7iHJ3Sz8X/g7n3+NUuy9K8fvt4f87rr7+fi4iMjIzIzPLVplpAC5B6BhJ0iQkSEmLMmD+BMUJIIEYgBM0ECYkBA5qmizZl0me4z9x7v3tfb453+2wGmS0VTQZVIiWUa3S09j5r9jxae6299oPKa56ki79IGd7e8PXdHeatwnzakq5j9ssVZ0PD6lnK+yxnJ7b0QU/jpphLnXyU0+wc0u2OXk+I2ZH0d5TyFanYEWUnTtGcQbORaYYzNVHvLVrrPVXukVlgSYNhA7ZfUq1dZh/EzNqGLDhhiRt2Yokna6r8R0T9CktL2P/Zz/C//YrH1zbTSUVbfoTevqe2W7LVmheveob5Dfv9O6pkxDg841lTc9c7DKOMpZxTd3uS6pZyu8fLM+aOwf7R5SRadoXO2CsoG58+MmnPdEgPOEcDSyn0QafUQqxmz06M8bIdB88mrm0a1TMuFWmgEFKjbzU6Z0C0DnmoQdnSznLKpY4zWiGaMzpZI4uOUbxkaEbfiL+/TibwXwP/5l92KKX+XaXU95RS3wP+AfA//qXlr//F2l+HAH4RcCDcKrRIkYsIpUna4YQyLYL+QByGdJZFpXJSZdARok49SnkoT0c5PQ4mwh6wq4547LLrNmijc/RkiahNNkPGyG8w2FDuJI+FTpeCGXs0c0G2yvDnKdOdxY9L+NnynoP2nvuHNd3+kRffeYk9isgOR95IgfutGSejp3vs+Lo8kBcr8p/vqLUnTp7CqXa4WKjTgdoJ8Q8WqSgR7oBSHq45p9zeoS8OnPKE1fEN23qgaGJ+dkrJDiXxJKLfHDgu7+lCuP72gjPnFfPFFdrtFMvd44cXaGLKJ88m2PlA/+7/pJnYDImBtc54cT3mrl2SmhmDfcEfXv4x6nzOMxmi2SGfzgNezm4ZBzHX5YlRnLDfGJhXAa9+b4anz2h6jQtzijooMmvM+TjjyvRx9ADNkdwsRlyYHpp+h9fndPGeUDvx9OigFgeGJKb8qUEaJ4h0T2Pp7L7jMLQhShnkVYi5gcP+OfnQsq+fc5QZp9qkPu84OilfpyNs8YCzhbpzsWpBVJbolo+zt+jGAqkrai3HetqhlTladGRjdeznTzR1zttBUcQVcvtA69s4U5uqvaZgQWgJpjdndFqLFmRs9mMuT2sYeobPMzrrF52E4cmkiTXOyg7Xn7DWF3iqou1s5CojLmJME3a3Gaas2KgjR6shdALUpUHnCDbS5ZUdIQ8SwQTD1jlEFqZToxkNUik0v6Eypvia5NRauIPPxmrQ9ha5rXHUBI6p0+klsdBJJgZDUeB70Bc+ZTaQuzl6INCtMfpQ/38nAaXUPwR+5XvFQggB/DvAf/vXAvs32KAJjn1IUEJgtTTdGs3ymJct9VxD5iatPwbbxMFhJCwIBVbTUuaSodXR6oLc6uncmtOhxbFDTn3D+UmhK0lxSGm2Pp5xhhQ6yVWNGPXUmUDqLlasI48xP6EhORS4u4hmVfKjxxPvUsX93Yq55qBpF9gyZVQmPP3FA5WTo5aC8aHheGljSRfVNfRNyz6OGcYuf/h8jJ7EDFWJ1dg0boUxqjFFSN37vBQO6arkLPb5juczixbslgXjdIxUPrPkO3x68Xf4zicfcfM3PiGenPHsJmGRuBRPj5y0R4qiJSpd/uQg+PlP/xFbPcUbj5k+W/CZ/YKXZxMcptT6nvlg0y4Uv/Xbr7j4W3/E3/o3vo/pn/PUXlNoa84+83hmWmTaDenjEWF2DPOALhm4yTradspwe87H52dMZue/0Hych6g6pLJ6jEeXIynjPkBvTQo35ZC4DINETUvi8IiVl3SyYWaCczRoixVuOxBIB21Y47UtTaNB6WMrHbPZcbIHCmXQW4qughafo5FzF+WcSfDPZ7xgQIQLOrvFUAFJruMx4dwTnLcjwiLm9NzlZmgwtieunnsYfcUPjytEOXBQCmNTY7Q/onH2UIxYRScit+ftQ0Hom5SNDnQUe4Hwv6a3DNLSZDdRxGcNW1/Hra9Z1hahPaVxnshLD1XXmJZgVkuMM4dEq7G0LbkjCQ4lZWviGhXe5AIpJ+h6jmtIrhnInR1mAM1Iw+tD7NKgKmeoTNE6JdXTiEYa1LqBiBUkOwI5R1cOR71h0H7102Lw6xcG/zawUkp9+Zd8z4UQf84v+hH/iVLqf/+rggih0PwD+yrG1Eqmlstg9ywriZ21OKpFa3S8qqIweiyl0+oSUwPR9qi5ICtcjLAjqm2WXc8s7DDbmHu9pHlrcPlByZM8YJQN6PCwknwwCqlvT1Tygnd+w3w7MKpL5n/wkkwV2OuY6ssTqXnE+brk2W+fYT1WJM++TWSdSC7/VQpZYdomgd7w3a1BMz8yG8/ZPfZMZxrmxSvevr2neOqgrii6BkNJ2rsWXyt/MSDUWRTrJbE356nfs5A6+mc3nLlw9X3FB7f/GnZQ83988edU+jvqdYHoNNpmSuw2+MMl7s2H7M9jXv/3/wvaweXT3iZ4VXGyYP1iyidJzN//2wY/+cFXrEVP/PIcZWg8Gzz+yU+/4iYcE+Q/pFgV6PaR7eU7dj/cM/nokmDxAedJCW99tEVIoQ5YO4+GHUEbMgk+IPffkdy1CAbKmYm2FrTWHdKP8F8PJH3B7s2R/PyWYvsTTm93NKrFNQZcy+IgPsSzj5xCQdULpBAkquKYKXa6Ylw3WO9aRPuK8Y1OZkpKadO9rfC1iuPlK0xLslMLorMnlNVyfOdjXczx/DG78kDrvcXEw6kFm74nxkTtTWSbU74+oT4d89Gz30MOJbvVGWl9hh28x/EvOa7usV9ckb9N6SowRYVFy1pWeKbHpWy5ywvyZkZTKc5fuQR7j67rmAav6M2OqpljBIraXHJaFZxkh20OzLIGwzFI4546dRiVOXbdclQNezdGFy56laO1Hm60p+hGSKdG6yoczaYqQhacaA0HKxgoaosq0+nnDj1gbk8cw2++LPTrFgb/mP97FvAE3Cilvg/8x8B/I4SIfjXwxX8ohPjnQoh/jtKpEoFhlvSlS9NJjpnBuIoxm4YyjKjqBjF1sAadUXfAawRhb+NcJsgsITmW9I8TWiW4Ycz+vmerVWC0VGbDKRdMa4l6ZjMVU3p1ie1qWCLmsyRHLFNk0YBXY5w6jMrGvXFxYp0PXQ/HHijTK5KbVwyawWD+Pvqnc57pgsnG4Ch1/OdjxCZG37vMBhO9V9RFx0UwZjKy0QbFzjniiIGscpn4F8w9nXutYpOl3KUrtEHy4lJxlta8zqEqfL5WP+cHP6pxZUC1W/LM8fidj17x/Magt0y2XsDFOKDfr1FMmPR79MUNuzpDSxXffmkQaBn70uT52ZjzyXdZpBN2/+yJvzi+Q+gNG3WHO/oW5vhTrEnI9l1LOR64jcYEg0K+3eHYDeNjB5VD4+WolU7tBjS1QVCPMNwF0hUMpzW1llL2OdVdQX33ls1mzT7JkJvXVM2WywtFIsasiKmqCuwNWRKyaxwGUWM8BJy8HrHPCDuwLjTq2UBs3iMPNhgjEr1iPBJY8hLRbGGd4xprRDvB6aZYC4/AEjibhkPVssLGHCKCwse0OqRV0y1yes1gg6T3PW50jyZ18LYn3PorlD1gmxqGNqPL9+j1l8ykYuXNUfuSZ90VaEs2QceFGeDVExZnF2BfET2v6aTHsGkYlj1GuWcndhRfZbxpHzALk6GacNANHiOJ5SkGB/aTlr02oMwYuhNGvKUeair3AFqAOSnRHZ1eNyDyCJTJPulJhQvHlqJpMa9txsmA0T9iTuY41a9XE/iVJoQwgH8L+O/+hU8p1Sildr/8/lPga+DDX/W/Uuq/UEr9jlLqd5SmsPZnCD8gbHWkWzMPDRz7SKzbSG1NTIPsKko94mk+kA8JUlTUhxLykqM1RYgTqT9wtFqKs4hTt8U82ASazsyo6Qq4PEIXalwkEYemxuhX/Oy1gfIstEnLEM94jA5EuWBI4fazaz781z/mo997ibAfOJRbEt+lPJ642neoj59jv9S51s5wQ5PF75hkdcCRjH4KInMReU9KQRfBh/UNjhxT6jv2esqhr5iGJbNX5xw2PlWxJGtilHOGtjoi6ZBPAs18S+ZUjJXGzk2wwzPOX3zMBxffQoj3fPnzn5GuVnj5WyavXjBokvIuhdDF9S2eTI9sk/HlqiXRK7pFS/C7n/H87Ia/89kr/G6KMygWtoXUYkaJzbSFBSeccsWytDnqPm/9FE8KHHmF8BTr5YGlzBjygsqz2HiCQo7J0h4hffJ0xbZryHfvqdYKq+7Qt/Du81vEuGbmZhimhVZbNO0B0R2JlUltpow8l8rR0E89aSPoHisO4QzHSbkw9xiVIj5NKMc5D2cjdnrH150GWoo9JJhOilEXpD08VnClh6yNDZ2tYysb36iY7HuyWqGXAU1v8+X+wMgzcW6umJ5/zLC5RX/YYQqbettxVzhkqmcQD8wuYK0VHN9rOEsD29giohN+UyHUE2+riE6kqKomH9dsLI0sLbivFdsfvWOIOxyV0kmNSXkJqU+Y91w4kmLa0Y1zDARtFhBqFjd5jCgKrKFCNjPMYc90U9Bke/pyhG50HHXwmwGWkvbdPWZtoo4FXvDNSf+vcxz4u8DPlVIPf4kYZsBeKSWFEC/4he7A678qkCZ03EvQnyC7thmWIb1y8U1Bbw340qaSv5BnujH3bNYzsDYUnYYR25giJ9dhXPWcnjrEyCEsl5wSn6Hf0mUjjkBLw32mKMyGZ9oDF+YLOj0lHpX8Phe0Vc3Irjisa7LQYhLf4lQpXz2NmG0CipFCKxx0ryAMeyiPhIcPuPybY3apxDM9otWMn75waL0pfjngGUt259e84Jzsiwf2p4wkVNwuFnz5xZbk2TkXCay3Ja6pkMmOh2GLcnouvz3l7WrLxSLhX5l8n3+UPdG6BkF34O3pC87tmFPl84e/9fd4s1thrB/59tnHNIsx33l1S+qm3H4UIytBd6h5yr5GKy1ORoY8OvzhBwF9oKF1V1x/q6e0f8Z2VeC4IfMupEokqzpkGDQCrWCxP7LVJMXzKfrPnxiNIa3myKsD2ZNFvx4YCkHnOfihSZ5myPWRUGt4c9DRggcedHAvej7MKzIZcb8pcKcWfTZCWCuK9zseaxM1eUtznDEZAnRN4m5CShSzqkZWJk+xyTQc2BoVdWkyvFlhrisurzzYuTxFe9zaIG4U/k2LvxvonzYkkwHh9xidw8oeMXYCrqIzHrIH+ubA6qdgfnqBLSRzuaeZGWxLnROP2LXiXXXgxfwFFwcLI9T4YHTOTkjeyZ6usThvR9jHltLtiFxJqfeszSPPxIgNR5ws4PpjxcX/fMs+f01p2UBJTkGbSho3oj3U6FIhKo9BLzGCGnOQ7MuOyrTxkWCv6UyTR89lfqxZBSd0keC6A7HesRxs6jikSwdc90iq/RoDRL/UHfgT4CMhxIMQ4j/45dLf5/9ZEPwj4IdCiB8A/wPwHymlvlkE7ZemDwInKzhLbIJaw/NNrMUSwzQocpu26Zl4DiqAp6mG4bXY9i2D5aG1UPsT3MajGJv4TkLR76gNn/De5Fh7JIFBxgWxpjGvfcb6HlU6tFVOUwT0ToeeuBjPrzg+5bStQ7dUYGU0rUXZPrCzBmgEq+Yetdti2R1t9Bwme9alw9ibUw0eWTBhoQ1YIx09W7KcazjlgJMUdBeC0bVH3R1Yfr2mqBqanYt+d8b5J7f4Fw1lnaJnLsnFR8T7hJezBdFO8RfyJ9ibJVEiUOGYkTWm6R26uCR0x/zuqzN++/u/x+i3r3h5a/N4OBLPFxhvdGIZ8KR/TmhGaF6EVJLF9ZSmmzFOFgizZ7pYM07nXJyFTKMCnZ5pOEdGB2IDEith60Giz9DfZxzMA7tDRzcUzHcVnd6gYgsrCkmtFLnp6dZ7HkKdL7Ke8rCl3xqI2mO1b9mLA7o6YU19cqWje2/I6o7LoKSxO7Jiii4bRFtT1h6r6hGlmzRRTXZRE8mAdFgiOmjLB65EzmGQBI82nhdyacaA4phoHEpJJW26MECZNqJaokcRXgrBhcvCdThehbhtTz/kPD3eUz+N+HE7RbckkZIYac9MmFwMR0znSDqcWKbdLwqz9sAoS1GujbJ7NKlYKEFYFKwrhUxb2sFiVtuUdostB7zrHmk7tEnLC8NCkzXKUwTZCbtu0AcJRo5Gx1nZ0KdQSIWpDbT7AbfzIRcYdBzMGL+wqfQ99TLgnWFQNTr9Y43r9GiVjpd9M9T/Ot2BP1ZKnSulTKXUlVLqv/yl/99XSv3n/9Lef6CU+kQp9V2l1G8ppf6nvyo+wCA0ijakFQNJu0M7xZQHA7Ijmt2jdwZZ19NpLdQG2TDg6PdETchYtpilhSkb2lTQ6Ed8L6AxHbo5aKOaSrPo2hN6aDK9brD7GVosOUY2pneOIx2Yj6h3e77eHmhXLps+Y9g1dLcaz0Iff9ox6nQcwyLdGChp0CubIk0xdisObxWR21KM3uILyY2hoc/mjCMIkpztV9UvBkmGHr1NqMyAT35/zMdXIfKFYjr28SYLlH7LeBJwM9exPr3GWfj0hmS7XREYJupdRtNmmEFAEFpc9j2e1xAKkzwaSG7OUJngTEp2P9mSBQIFXNQTpsmM0bXHSF9w1gr2/dcYRcF52DAtXT5YBDzzPXR3hvj+BZGwGOPgjAWGvcdyBEbXI4sCpVqEzHC37/hZ19ClJtfGkbFfYa82SC3g3bFgslwz0XPUleIoUibDBrfu0E4Ny9Zi3sNLB/qHMUEzcLQumekextpnv79gCCSt3TJNE+Jmjywz7N6jVo90wxSRVsQvL+jaBDu+Jg9y6hFUg46mOwjdYXYKEV7LobhjvdsT9HMYejTHIGki2osFs3xDYc6xnCnJ5Jb8aknsHck8n7TREYbGgROxsimFhlW7POU1b90tSeai6h5ze8Jra2q34kHpbE2XfrVDaAHV6cRr0XBuO2hFz8w7Bz0lVB1PlkHXg9Ua9OOAo+4iWgPp9jSeYvB0Grdlbg60nYdpSdrAxopG2JqDpZcY2pEk8yitDM23iEcmg11QCINx5lP+v7QIhVLfXDX8/8scx1LXtzPUMDDUIbW7x105hEhedw2qFwxaxszWyLuGSjORVYB0BFZ1wI9NCnTaYcBrNTqjo9dGuNeShYyRyudZ0tGFE+wyZbBmDNUW37wmuphjmi2NOf/Fu3YVvAwctKsE2/L4+NzgqV1wO3HJaTC7jrF/gax82rBAFzm153FTWshFgtAMDDI2aokrP+BYHxjuNPzmjr1v4ZgDb9/tmXgdu6sZHz4JZG2wP/yITTnHH5nkaoWNx5vdFrk7Ii2dROVsWh8M2O6e+N4Hn1EvDMR+RpAYnD+fwz7n8+Ytz4wJp/GY1//bP8ZyXf7oo0/IypR7bvjD6y2HQUOGLd/mFswRiAw3sXjcCRxdstNTos5m4Y/Jy5xurqGvPAbWdGWFY0g26pyxylgaBU71QKuNoH/Hlz/tqeIKs6/Jf5jR1285hA6OnaBXA6EIybwDsuzQ3BuUkgxeS/PVgSGBUe2w0gpKCXXxGroXJHpBbWt4ncS8nvCSMbnhMQ5yuqKg0ufIfE2rclw8isHBPrMwH3c4VxG7pca40NEvKuzO5KRqTMdF7w6UhkVYC7764ZKmO6LsBb/7nTOC6IqfL58wbYvRTKIde4TM+WKfMnguv/XRJ1SnhjKwGDmKNq0QjcGnn31MFkqcxxI5Fzy90RhajcuPzkj/yT/k4eGOT//tf48f/uqlMCsAACAASURBVOjP+U//s/+KrNpiDT1DOEIcTgyui34KcI2Gg9MhOh1hZeilogsSPriUbDJBvfZwzQKJSeQOPAgHo9kxsSTbIWRkQbErUO4CzzvQ54o0q/5UKfU7/zL+fiOuDQ8YiNpG1DrKCUiMmKPTsA5b9HDA0Cp6w6USBjQO2lRniEt6UTCYc0o03M5A1wYCOtrBw+ga3KcQ0xFEMqM6Sc7qnrzqsYMey9YJRgW2nqLJEOPS5eOXAb93fcO9kDi1jiV1vlortuqB+8PnaI7iIrQogo7B3nJp+xhaTNj5FG6PmxecnBqniTizb9H6jljWGO4B7SzBaw3SMiUa6WRnLotdyON5w3rR8BYD4TZonsar59d4HYw8nbKtQa94CF+iCofHny6p9ikPu0eG9z2344LkRYw7SORLg49Nn4+enfOtkcMf/M2XfPzimszp6aKIT8wDtTHhgzhAz2P6ZsbBTXlfpaS9wnZb1OnIrCqJApNHZ4MYHERdg/WWrNbBs9hlBsr5Ae+GB6zHjnvZsD5u2W8MKqVo3xcc35wwz1rMD59zFYTEyUBXZBzNFtNo8TuL3tSQ4Xs4CXrf42zI2YU1xjhgNq8JZtdM23u6PqMrNzTWnuppTtoC3T3pxkA7E/TGGr+Ica2Y1NdxwwFZHRkGH70qETOD6tWJrokZeWParuVgzChmZ6S5Q0uJurpFjkza8YHh3KY3O4QtGMUO6rDgqLt03jmBmhB0Cg49clXRd4o8cxgGCCJFIZ+QTx5tVOH1IZG9oqm/xtU60tjiB+aB3PaptYJOlNhC4NUavZ7ia4IpiiDac9J8NAq8QWPoNJxowbkpeXwaaJcdenDECHMMtySXDV55JGwluzZAS2vaNkQ3Bjxd5yg80t78Rvz9RpCApgakXRGWA2bacjR8emXCacR0qJFxgl67NK3JgR51ctEPAi8MEUJS1Dq52WIVsFYWrtehJ5JhSNkWA27kUB8Mqi7Hnk+J1i26HbF7bKkeNWzPxrrfkacT3pcpXjqgueAsruiDBvFGsdu1rJYt/+s7HT3t8LUJVV/g5AaTsY+pTbHNPeEgaNsjjsiZIBHoTCYjRmceFx84zO2BKI8wlYkxHtDyc5Im4Ox7HzKejxCWwyEzseYxL22XxdRk6tzyu2ceeAPFqCdexOzbHq2tuTttcXDJDItgN0JeTrHCC4zGZjS64urVM+KRxfl1AuOSJtAp+kuuwjH7+Tv0xxpzMiA20PcZ4sJAjUfk+5LhwaLrt/QHi64dMEY9Vr+n7hy6TUj4uqSqv6LPLKqvv2L1+IQrN4g57CrI8hX2MYKRh7U5Y+TNCa0WdYxpXYNOvKc4ROyCDl3b89iEnNya/jhQdxHWtmevj3CMEXHh0OcBkxc57n6JNGIWvqTc5gRyTv3C43AyMJTGU2eQn3yixscVFyR6S1uGlCOHOy2m7Gq8/meYecTH1z2yPWMx31FlHUYxxxo8nuqUixchgyExLBN/GDgsoAssDMMjtSZoc4FmNsSOjm8M7L0Rg3gGCejDc94/VuRNTKTZdPojoTfhD/oFDoqz/hJ9CEgqjT0GfubjGzqrrmIvEvRkjS9BxCm2O8VLdYpdR9lKLNOhKCQ7PSRrdI5ZQ+m3VP0ZmILeNyidlNL3qWXJrKwxXPGN+PuNmCJkUMR5TDMaSI5gV3u6SczUbjlqHzC9X/He6xlEiqeNaO0M4QywH7CNDCu4RmpLulPCfNpD1/C0a7GiCf7rmvsXLS/GCcfXB8TvTpBTm0qvGcY21liSbhXJq4Cndz/DzHw++LvfppKSKP0aNOietzibBC9RaOmSbDmiSRQXo4jc0fEOJ5yZYFktCLY6By3GOUjqqKM7OAh3hp3/mN1pTK6PECMLs9cwpcFln1KMQibRCJ8Kse9JXh55/y5h45R8z/htvmzeU1eKz771LT5sXd78fMM+rZFxg2h6uvstVwuTXR1xZd4i29cYTsTIGZEPcOUavOkixgub4jTgk1M4grB12DkWTa3R51u05ojnz5FdRa6VWNaazrwkX93RXU+56C2UNCjn94j3DW/MhnCTg3mP4UIuNG67c6Kq5frlhKfdgqY8Eugalh3hzxSVMGnkHpn7NINO5JwwH3SK6JKRqhit5hTakqLzSLUA2NEeHrHcD/GGJd7jifLGJRwmPLJhRshT+h5D6gR9iTEy8O8b8taiGt2QmYLjQTDuVlSrDuF8TWxN0KTBpQdlF3I9D9jscvSuZKxt2e8ilveSt+8d7M7CH/8ZY/c5+s/ekB0Tak3jZrKnKWaYsmbdHij9kgtziTBcRvWYVWzB1ymfrx5xs5Rgcsv0cszc/HuocsfrlwafGDZfEmM6GZrKOfQmlmFhKg0aH9n21E6DW6eUNgyBwOktjnsDPZLIQhIoCzW2GOoBw0zpgxyr9hmyFqsNMZyBrm8I9BuO3yBG+BuRCWAIDrqgkSfE2MZTN7yydGxXx3nX08Ua4zgk1C0Ms0FoGvocNKemkgNduaPMS3rRUp50nooa5UrCumY/3SD6lLTesb/yEGlHuzpiqY6p8NgfDALjwFBktGcVGDC5qkmclv0aTv2Ibl3w0GSsDymR1nDMS2pl0Z8yWr/DunIpWwNd6yjZEYw6Tr6LzA3m8zF2fMfOvGTmDrywn/HiHM5iFyPyqGZTjEnMuDmhgppeH6H8c2ZJzPX0guLFiA8nl0wmLmPvHHEREo0MJrOeSqvg1GAaBzTDInB25POC3WHEvsyoG7itbLTE54OJwtA8ZhOH/VyjqVe05YiJXjCyWmZuR2KUVGWLphTX52PGpo7S7il8nXl64rF64p2KEauWatgzby3054KhHHOoPHzZ0ZdL2mFN121xtJrOgc0xoR3bDDMTfxwxOSmy64LrzsW0I3RDcq5WNINg374hjnrsOkWWik6WkGjY6ZJGG9NYKb2vsKsthmVSdDpKt3DTMd3tArW0MMop/lhjFzxg0jOzLVatThl+jWllWJGGrAO2D1tkY5BeXlPMQSQhx3MPEceMxoLz6xzXXdE8FXR2jq+P8IaaqD/SGx5uNKDShr5YY/+zPcHWxR9iumlHnEHbV3Rdy1N1ILMFejOghxU5gkm54VTXmO6A11eYvYkjEmpDR5o1eZljRQ2aOaJ2bVSkKKRi8E9chidMAmLV0/Y1Wl4S1T6tZWH3EUkaoQU9Q1whvYFU8zmqwzfC7zeCBLRB0gYSbVjQ1GsKT5GXHtVxTD+ymWng64LOgMEcGA463X6g1ho0w0Q3eubOiJFT0PcnDEthl/DO6BArRV/6lJbFJ6EJEo5RhZ8L7LMaLwzILY9BH2Orc5LbBevPS9q7J7qkpx+WbNOOk5dBBneve0qzYvCeyAqBcRTc/XjJ+tQyDDWmHlEWJjEFfbwndVOOxYCb5+zNiKcwpmxjnHYCvcnCdnl2GuiXBm7jMP0OnE5H7KBnsrhmbge4FwbRAOvtkmf6R9y+/A6h6eL4Iw7jj2gLxUHzyVoTb/Uee1bQb8bsHv+C07xCygo9L7CRqHzMpKwwopr8+CW1N+BoJoPhAtd48471Rcup2iFtndK4wAk1ml6DdoN5uGfuK/oiYH/csf1yy5Du8E0D8WIGsUenYsKxTkOC3tcsoglC7wica0Rh8DoYYb1tEOGR5gtFOeqpLIepBenI4XPlgGNiJgOme2Qg4f654sqs2EuP4Y1EGxmobUbbebiGxspvqJ56mlKheQXiaDMvbcaqJN8INndfsnqdcdgXnPY1LDuMSFL0Ov7DA6ULfRVzLk3aLsP3EsLMxe8HTv6CTarYec+x3I69zEk3R45mz0q8g9SGiaSTI3aioV929GKLZrvc3kyZnC1YJB52b4MhcbSW5RtFKQTpsEW45xxET3pdo+ctdV2giynFMMXQWuxUElSSs0whHj1y36OrFG3RYGoOfa1hOS2qOGEVkrXqMPWQYSOx1wrRChK3/0b8/UYcB4ZBMN5kbJwBXXfp2wxHN5lMO7ylZN8HaLqDZaWoPsebG6yfJjhmhdWVZO1ALxt0AQWSiXlG55T4VQ3aFN006A+KPy8li+sHjqsO59zhrLsksxR+MqFVD4xTndIq8aXFUX+J4xy4CQPScUxChxQdan6JLU0u25LBzDlmGxz7gkbv2cuaa61ELQ3eRyZpdWCcHLFEhC5LEr3labmjTHLOzCmtnmKVIw7xE1oFbR5QLTM8FZIFRwIdzi58LOPbbOKeUfSe4qzke2efYYc2bdUQjwKKTcl++RPMzuHHDxovn6VMvTOs0RXb9oHz7gIVHdHbK0zn5xRZQ3bwiK8NEitCcw+8/fGKLi7xXvts7ALrGFGnT2jXCW3esRJHhl7yePdEWln4sc+tE5F4PkvR4+mCxbrk5uKaVsJx6PGcNc7oAhODxJ4hggTagnlaYthn+E4MrwRWHbI79ChnQ5sbfOqGDLSYxpF79yX17i1hf8nD3GTsKuxAI21W9H2IKwqE62EONU4kqI5HysFHt0wejZJINER+xbXzMatxxmEric9DRmMdT7M45Sce7R3ZP/UxNMVeXTF5snAnJbYT0B4UQRTxyo9Yc8exhZW+YipXfOvhHFt9QuXfM9InnE86Bn9AyAmGfCBlSy4hjDuMDDZajt50lATgVDwNK0zDo5EZF9Jm/R4iU4I54qTtCQYojg6GaiCLWUUdmqUj8p7BsxBlhGgbrLFPZe3QDZOylUTekZOaE4uKYZaipS4y++aawG9GJmAJUjtkWhUEtkQ0FcmoZ7PMyDKdSJnY/RrLjbC7Ee2ji69SqjqnMAOGawPdBsOPCANBpjRyW5GPYiq/JdtllKZCBJLyOBC4EcZRcixWDNyzvvsCV/XoZwmjFzaxH2LNW9qThmc43GgxlxfPCKc3VG6JaSq+2ua8PriYqwWGAQEnnGPKV08N666kenzLWItxNwmHIaOZdWA1uOMASzNxJ2DpFjsq6i5Bd2PKMwNZSKTr0j1NePpS8lRlpN0ezdiROxqh5jOMWr77vQXPLwM0y2DyzOHm7Izby5DkYiBTBcZoTVZ6GKsZItvx9gkeTk/kxwm14yAMk/4gKbdrmvuYyfMFmgyR/Rbn7YbP9R13wmf/7guq7R3b1+9ZPhyoTkd8rSDKJaHvE4g5fmAhWx07mKFZLlpsY7oRUR+RLwVatsdKFFOjIOCSdhKRWhbmwsDuFLOxxbG5pzlkzJCI6ZpGeRwOOaehRhUvkOURv7UZh98mzY/40RhdM+hGHY1VkXYGRd1RugnbkYdmlTxjoLMCwjjC+o7Ht7Qpgyjo9nuGasfWUGixTrz5gD46Yl+04BXkTomca1jKogxcbncn0A9Uu4ZSa7k+niF/1pJFEbcXOvUQo6wefQgYy1vCM4v0NCUcR3w3ucLMRjy8/xwnFeiDg42kOz1hqZiu9uh9jYOuo9qe3NEozAKnGv2yWyZpZUAdlIgmx6lyPE9gVjlxn9EbPXbf4yWK0eAi+4ZS69F7KMOGprtCRiGO9huuRYi0KD2BNm05VWP0TudNNfD8hUX9BewtgWcn2GWFH2uoDvbGhKDLoNCxlx5VVRAHFafSIHJ3nNqA8W7JML3B7h4QOWhej2sZuKbiqGlolsV5a9H5Don6FoXW0aRTyu4JzXpCiTGp9HEck7c/TnHilFradPoX9OOXjM0dXXFJ0IbItuVzoRi3B4wm5ii3lCsT1/zFEEufZxx9l4Ql3bOQ/arBcg1OFEzdGVWgYe4PjCY2B/1E97InOQykIkLPoHR07DBH7XVMYbAGjPkHxNqe+n6P53dodog7tunWFehHUAovP6M0C5YyJDItmvMSozeY1zpfWRlzsyWudPJsT1rsyXPBrruDwkX0P+Bu2RLq52hjnYnjkFx/RLja4/oGzlgwrhIyr0WNLoj1jqMvEFlD7H2M+Bst67ueaGtRlieGJMARDR/XBrnxnNrbofkm+mzF1BGcygTHqlneX9K3OU3oEW16tFkBZYddN6TN54xOFq0yqKclZhFiaicWZc9wNaAfISp7tg24Fy7pSaINPWN7hCYMgk82dD8cWHuSoT4x8Uak3j3Ozyuc0YTC13i58HlWWxwjk3GbItySqpUQwTMv5M7KGfmSRMs5dGe80o+U9ox1XyLNjFPXMbuyST+vkGcul88tnp7uMS9couGMrd7xZpNSCwetq7HbilbBYGrowsc+tZTCQFkVnmWiXIve1PFqHUdqlLlGS8DeanFsQWsqircRhmai2WOcskJaYA7nqGKNsGpKYqD5lfD7jSABw+h4ZjkUx3M610IMO2rl8OXxwDxJ8KIUtb7BPFbspcXWcRnpBzRloqYNYduxig2GpsPHQx4yJqrjFNsMeUagaSijIWgTWqFBcUZyfkIrJPJixCSGO95QbjpM+w2+fcHcqTC6luLuc1bTV1iaQk8FtxONr1KL76Y9+zPJqD7yp/d/Qng25drwyYWkVAWe9jGezEhrRecolDIZmo67vEJ7tyIUE9yxhTe47MI1fujhOAXtwcBu7iAYMLoxTW+TiZg67RkcDWFUPB6/pjgkmK8OnBc5rTNHsxSBmXMThFTFDePFCWoD08/5ei0ZmTmVXPFn//QRYeucawG+6bLsHL7e/QTLiTBTgUHLdXvJanfE1c755NUzyi6lncIr7wM698Tef4UTN6jcI9WOhOKSviix7DGJUVOYU9LhkeC9h68k9XnPjZTIbcByPyCCS7TwiaaeEsxSlD1lMRuhuvfIAzjalvdKxxvrtKcjo+ZIxRTtuUT1EuuZhddqqFqjdXTe9D3uXGK97jFuY8qtj9s8kEqHm9gi3e8Y9o84L58RrW/pzg/YWoPl3eJHGoP/is3kyNPjia510GaS3J+xlhv2TxvG01cklyHGX/xjcjPBZUpLTV4LvHnBui95HlooV2Bkj0wMQVE49M0Db39i04U7RhcfM/dfMJaCvZazf1TodUp/ZtGsBoJIwSFAOh1pZxAY78kHF1FoJEONOilSaYPT4U8OlKnOtI15lEd0kaB3KU1oMBsM9r2GK7c0uo/ua3hiRF458A3dgd8IEkA4dCJAJSdmymYZuYzfdVQTjcb5v5h7k9/bsiy/67PPPn17+/vr3u81EfEiMqKyslIuqDQWLmTLEpiBGTFDMGKAGSAx4U9ghOQREoiBLSEhMUAggRjgARamjMtVmZVVEZkRr/u99+tuf+/p+7MZRJSUyBmuHDDILR3ts9dZe6/Rd521u/UFbRMim4rUapCe5NxKWL03Meoew+05alCdFIP0wDboe0VuwLTuOLgVWR4y2RecghYMwVy/IdlaeNca3TBgpRLLmtE0W9rTAzLwuLtvqGTC4qylPrxjJGHVZjTVE5yriC9XNySPHb4yufroGkuFFHKP2xmskwNS7BDPnnN1F1NLn6EwuBmOLLWW3IgItSMH0SOUQfsuYm/nmHaNaEpM/4wuK3jnZFzdn6jdj+n1gaGr6eYR2WNDYaYss4pKn2PPdDxHIncGVXbHqNuSPEQ4BTDqeXZtU793OGgWVvweaUvKRjB57uMEEPY+ebJg9kTDrWe0szuUXiHSG/SlZFS+IK1S3JGLDKdkroN5e0fX1wyZwfzMZe+ZSNVz8x6meo5RxKwHH1E7BE8vyEMTbRUzX/q8TXUsZpzbR455QJg3bNoDlnIYpoI6b5gMLW4TkO9y0itB37a0m4BppfEQDnw+TbEdn9waGD/qGJ3Drj0yva+RTyXt/Qg7hFNjs+4z4m3Lj57ERIZN7IMejvlUa6h6l/Vxx1AJ/GmAGa6Js57n8il3RUNnSVq1R9wKSn2KMeiYfU00umaUWBhmzctmTtEXmHmDMXpC488Q3Rap6UxfzEnvE3KhIbUDe32OqDP87IjyFM6+x51GdElKbcaUhsmInnbhwcpG02J6qyc1HTTzW3Kc/OhgVYKHJzGT1CTdSaJIoJIDiTXHDCM81dHHBQevwXB1zE7wfQeHfyvWBKAlGHoWRs0qKymzmOosxFUV9X1KrQ3Qp5h6RKsJWCmmQcyZYRGqCLseYwQWnVWDrWNqAW6rYbU9mAO2lqFsj+Npz3YtyTufXs6x24L2UJCaBll6Tzac+FB3vMm/IZ/kGPUBpTl4puQ+KxlLKF2JdkzR4ha90BF+SmHGDOo9bilJ/AGMgdJzSB5P/Jl5S5MY5FbDCyXJ3CnKyLntdGRVINMJ5rLi8e0H0uMthbzD2ayoHh/5+E3HSW9pkr9g3/7fPFYmp1ffEDoJY22gyCvy8pH27jXbTUo83NOlc/5iqDilJxItZ33ckw6SnbxFFPdMQ58XLz/l439rzti3adIey3nKD35vxvxyhvyRhje/Zl4HiOqcvlMos2QcjrHNijZzsJwTqRlxu9dZaYpCGOhrC4XBfGJR+BUHR1FMPhB659jBwFWmGEsXXS2whhjfSKjTASMv2GtjRtcz9PmUOV+guya2ZlB5GnkE1SlE1zI+uvI4jvdodcfhkNDKHCfoCJqOiZ3y8XyE5hkMW0VuxgzHCKkyMvscafoU6RLdFbSdw1g1pMME1bqUmc4h7OnFmsA8I4tmrL0dZ6GJfRBUJgwGONWIwDHpbRO5O3EYlzwom8TMELWHZlvgVHTll0xDRXJvcHfaMThzfKPk61dviJM3ZA8dm+cKWTUoA/aNTy80em1gtG5oNEnZG+jsCaVHZ0cEPYhSUbuSzpLUBCw2NoOh4Y07ysSnNsd0bo526tmYBa1bsaRjOFYYzfenG/6tiASk0jm7lmzqEedui5E/5/bUM4x/h0Bb0yiNuheIqOCsnNCEOR+ygEkQk2hjsqZHSslwMvBOCd2g0RuS1DVwMh1TK0nLE4MaMf+kJd2VMM84vPGorwYu44bFpcW75JxnrYO3uKByauSYb/eh38aYk5rJ9N8kTu6wjIw/f/2O8GyM3brETQN+QKtVJLueqZT4FwFD1bPoR2TZWx7jHm3XkAceL/NnCPeRtnMQRcm8hpfmjLTIUUZAEbbMnv6QZTaQ7XqO/ppFaDOcNhCEONmBvpsQ6IqVFlN+nZPLLa0uUdk/o898bpwTlryiUCtWbzY4swVRGPFv/7t/SLJtaIsC67mOdvOChaOjjVviU48d94ztMcXzJ/SiROUufVXRdBZf2z1RV9Jlksm043w0pUovsK8LGLco2fPhTcKHYgv5ipH2gnJ2ojk+JzwvaLqA9PEB89Uj4888Gt+mLiTy9kvU5AlPxpd4veD145bIH3M6vacdcqzzGV1m8+FtRmNJRlKCs2SXNFh5Q9bo6HUPz1Oa/QJDWzM3Q8aGQNfnhIeUbzbfYMk39MYLFsMM/cLFVz1Vv2Q0usV8nNOJFsORhO9W5NGU2XPB84+mlGHPatfQvbxF6yccf75mfOlTFQnDyMRWCzp3S5NPuW7G5H1CkRUUjsFIO5B3PuprCK4tMqnzP/4f/ytff/WAjwVNhmlqGKIn1Qxq3edCDNxnHa6lU4uCujdpLAezTKjqOa4sEIbHhiNmEuAPFa0raZ0Sq9SRy5xh29FNHHadiWGNkbaE/Ld4OiB0jbaM8Kot03TCq2XNMm8o8hJdQNo6jPU9j/kYMwRx9FjoPTL00XOF65ccdwLdTOjznmakGA8mcWdjhwWiUgTS5+ifYHeBDAekrtN+fEV2WFM3Po9lT7jt2TysMOsZP7jy4bLh9v2R3K7xtyHv8y/ZiVv8okNqgqa5oT1/Sf0+5zh9IJADz3/8I4yD5MMmwR7mTI0jxb5AN01KvcEfQt6Wf4Rx1PGvrrFVwjdJSa4XjGOT8GyEERqM84zYl5yLkhAXZbmIc8WkEDwQsjAWCHuHbpisrjU8/xF5G/ILfYwTjHg+iRCextf3Lq4jefHRNdfzS2hcPLWnaAOMouHpTCKamuGhY2LZrJ0C29MwHzXm1guUFEjrwMltucoWlPUDcRoTODry/CMWVk6xFgg9oPJPaO2RhXOGFD6OETEZ+zRdjmF6SHWgH49Yn3Ws8gh7eg+nDd3Ta7STSes7bNUtzj+fcvEyQu1SNK/AimuORUZnV6yqmuvJhODsjjYJEIcJ0bSnXDkMb9b0Vx1tmlAbOteXJlrSMO5bltGc5UdL4mNDm205NtcEXsWJV7wrdVT9F0TBDLvIiIIRoWNRJzpHOj6pIibnJr9oK9SqoypO7PUZ88zmk1HCMHYY69dkXcc6XpEIF8tqCLqaNveYXM7Zm2sWzhPyZiDfRpjZlroWtIFAFxV7HJZ+S98dyaTNedFxmAzoxRTbrDHihmI+ZhEXbOocacO0FxRRz3FjYIgMMdiYrUedSeyLB7LjjNCJafIDJzF8L/5+K5wAtGhWiZ7PsJ8K7OOBUwRLJyEZPM4L0FYhZ0LD0ASH6ROapKU63NP0kmrw8aqMBI1Od/E6idZkCDdhiC1yzcIxY6ztC4rJibPVEj7fY6UNgx/S9Rqju5bN5R1j+a+hzCPvRM6T4zlbY4MhApwzeP3Hj/imzb3W8OPnM0rrDH0b0Zglvm0STCbcv44JmoHQGuH/jkI0F8h6w0gPuBpZrG4ObEYe/bYj3j5Q7BV1/57RkxcE1pZ74fDUeMqhn/K0q7iZ+Uy7FN8bc6wElm5jFiseRIPdpljJmIU7w3TO2F4e+JtPl1R1zG2h2P7sDezfMr08IxgFdDLDPR6xnSV7Z8A1NIQrKcsp+9UHllqPXoVooWQyD3F0yDPYNj3+ULEpY1Q5xT5Aax950CvmTYLeD7SnMdmwpx2dE7QPzNWYMojwLyRu7nDbFdSZjd3subp2KQ876F24mCIfW1r9Ld2pptrZOBOPD0hOuo917GkGGwMDIRVf+E9QcUMjXzJhy270wHAaML0O3bLp+5bcdKi/amiMAvsHV3S/3OOc6wz9OVW3xty1WPrA6qXGQnNxTicmdUQWzjCaKalWIGwoU4MijXk9EzhihNwnHAwdw50y622e2y6DGRLoKaY2x7+QqO7A/HTNKd9jOhJbGxM0a+bLz+jMDl8X1BcVY4py1QAAIABJREFU/VcXaP57Jr1LXtWEAeyLCLtrUJrDWlmo447BBN1wQUsRTcmqzXAGl85MaEuJts3RFkuCwqMLW+JDx3gwqYSH0eQUlo2UW4R2Cdz9WvT9JrwDT4B/BJwBA/DfKKX+gRBiwrepxZ4BN8C/r5Q6fpeB+B8AfxcogP9IKfWn/yobfaehyQZtZnCUJs65j3+r0NXv0qkBVyi05xn7XcNyMEm6HbYVMUibSLQkh45sJgh0SZ5rVG1CpVmYsUY3BftUE0c6o3TLNtbg43dozRk/rHd0KgSjZeRNqTOHWj5gSMUhVrx5+Cd8fPEpnu6wfxXzyXOfui/w+nO+2v6SXRPywrpHnD1HtS3dqeSJ5xMnOY/lPf5PTfTIJlQjqqrgtFmRjdYE20/xFgX/5O2/YP44xVooDN2jixTqlxWjH4GebsnOL3kW+Gwfe7RNjriec0wbfM+GnY8/MzGUSW/nSH/KxzJA+TmUU9pkSz3a03z8B4zEOZPRC4rhnmzqUaU6bR+T7c+xixwjsAiev8D8ILGcPWVk8UlZ8SpRFHZCabZMbhpmzyN++fYNh27F5/O/juOayLIm6y9wonv6vU/p9rj+km37SHjwyW93GOElXS6Y5j15EzENd5SXDgfNpL7tCZc5bT6i+/OW0nlHEysKkVDGPcWQsqlb/K3i0zONQ/ZLmmczHLnlXFhEwqIxP3AYFlSqoD1sOaoSvX7g9p2Fq+CoHxmrnnJ9gyx6ujObfWDwtNLYVi3jK5v4bsz7228wr3zKlYk6Cmwsfnj9BapM0ZpbisMUw+gYzV3aQTK4CXr4El2XrLevmcolvV2xq+4o1ilcvWAsQmJjjPFkyhMnod25HL/6E3QZUzUv2JsPmLJHCZ++TCmMCFPkOE5JVziEw4F2MCj8M9z0A32gowZBm9iUk44mBy894pomh7hEejMKdUKrWpRhI6qUoThjGH3/OYHfZGGwA/5zpdQPgJ8Af18I8TnwXwD/WCn1CfCPv2sD/Dt8m1bsE+A/Bv7rv8qApMfXLSwBvdszaWcslwFy7jF56jJ4BkXvMR6PuXcqHDHGKHNOQlF1I6q9jrvTSZngdBJz0JkYHU0Q0LUWhdszP2pkwqQWBsWj4sqZ0dDgHSOqrufP85jdzQ2ldSCtY4wMhBfRnUq225y7pOCPDn/OzwtFa7zl2p5xFQksp8d6n9I+KAwKPsQVP+3/lPXhAc/xGTLB/fEd7SihxmN7nLJff+AQryj+ZMVOHRnbF3xuS3DOOUx9+oOiuRzoi4HN6RG8hHZao29qXCtkFIUsz0sCLULrW0xh01od41Byl5pkXYOVWjh6iHWI8AyTQT8ws1yk8nAvCrTCYCYEkysHY2SyRNGdZ0wtn+Q+57EfKKYJo42FOpnEs4LXrx/pTzl2eoHyKiyRoc0+YTHJyeUUw1f4EgxtxEy7Qp8NJJZBESvs8Yly3FGYDTd1xPGUI9oBb1BoWYvoGvZGxqJsOQmTomgwuwxVZQRmi3WWUK0s2sEkvW8Ic4PdY0DRtVTqGVar6JsaeZeyeNwTzq950+x4t4mZ6ToEGsk2oy0aOtVytlgyciWVVfLwNiYZB7A6MYx9VvWGm9tbMlWRmilR5CBcg+fPfbRQIQRUbc37vODY7SjFlNn4CjE2yZJzImGS9h2NpaidA0HyFtuNaJlSzy3ax56iW6K6LdQtunOG5mboxgSjB0VHPjHRgoHS92BvM3QlYzlFr0dMswivlTiGQowHHA22jYneeQx6BU5HMfVx3YQ+q4isA4y/f2HwN8ks9PiXf3KlVMq3DEOXwN8D/uF3av8Q+Pe+e/97wD9S35Z/BoyEEOf/KhutJkgMHaG12AcQU53MmjJSNY6tWGiKszAlai0CqSHthtQ5cNVKjCLh6iON2Eowtnv0+oCpDErNoElyzLZF9g5HswES5k5Lnvachop7LeDQPULXk1c9vXYiLzqqxEPLBVE88PrxK27KIzORME9mXLgWw3DFMA2pcpsPJ426HNCLji4r6LdvOW9e4p505P2G3e0t+SAYnyRWkvJCnmiyDfuHns+fX/O7v/v7vPjxpzjuBU5Z8sNAp5wfydc1W33DkHZozROM4Bnja5vB3pDLgZlu0LQRbeRRziNs26G+WhAeevJ6xOAqupnFx+Oerk6w0oCR2SGyPfIees+mO1PUB4emTFkfTmgFZNLF0m/Zn7a4a4PwYoTjNDhHncD06cQlhanjhWNEYmJYJce6J5fvKTSNaxWiqiP7ShFaPufGp9R2iTqYWJmOYVXEj2uaVOKVDloes9J9muMdcgRftmMc/4GZnpKKjsnyGq+VeJZGdumgnDGL4DnHsUP4yQpbr5GehX8GM1yKUPGzLGEj7uliA7na86rUcHOPy/CCiSOQSFR9pGpmNLst9cRhpu+ZPzcZjIBTGWMdBnh7C/UB4fkIb4pVO1xGHkbZ05gGRyGZdgUXFWhWS/0LCAsXtdDx6xLrsCZSSzbBCHl6zyjz8Q9j4jML0zoy9jIieqgbsjuHRn+kjxI6YaBtBhrNR9c6tKjBynOOaguNzklVqImLzDvkXjJInbbsqOuYWVPQdgayKMnjKdZ4RhsEyOP/T3cHvqMZ+zHw/wBLpdTjXzoKIcTiO7VL4PZXut19J3v8vnFl21KkJ5pgzkSGTAadohGIpQNpgxg5iMbjMB5wiitaVkzwiWsHw4opykfGlsGD8midHnVqSJWL6aX0uWSwA4SusDSD8jTClT13d6/45GJOO4/YHbec2XNqeozNmt7TEXqPkXtcfXJFmoLyp8zGGvbR5CgyvvoXR5roAfPOofoiYpR3fH3n88RfEsdrdEfyi5HP1eFEwDnbywbzpLFvJdfXX6DNdJ7Nf8L7Y8v+/ddU/nOCyTOqY0U+TvDzlmaocVG8P+3xrAm2OafMAsayJ5+PmLMmvYd2VqEPA12x5fysYwglefkRCyekURGy7NAXJtp2wPvXf4BvWrjJmmHoiVnz+MuGThzJomegBma0rAcL48OBx2WC153QLn3KYoLoHriIFFlmcOrX6JsEGevYz67xqyN/sjtQ5SZPX+w5dBPcXOFOzuiOG5qLHcleoqoxOytntF3DcsDddGjRZxjblCu9pR9dUd584PJpSLir2V+4nMc5IQ9IOSeuW6IkZkDRVhm27BmLKZpsOP+sZXXo0R81/voPI74pc6ZOwaB0krGBfHzOTB1xlwLVvMdaSoKdy2H9BlvzkWXCj0Y/4Tg9Mh0F+M0cWQ5YiUmv1RzvM1b374n6EWNXkW1GHD6qMNMZpa1h6zuktPnk8y/48tUNh2iFZ54jAkUpakpafl8b+HkOsWVitS2NdqBzdAJ9INu7eF5D33cMbc0pqRlJHS2aU1QZ9qwj2feI6kDVmkhNERs9009Cdq8GbP+ENWg0nQ5qQB01zLMjaebA95wU+I3PCQghfL6lHPvPlFK/nuj8O9VfI/uXcpj9Ku9ANfQYzQgz6YiHE3nnUD+t0ewx5WRCSoA0HXTV4NcH5m6KtAJMP0cfJMZpyiAEIyPDCMGU4PUJop+DLXD6Bpk21FFPPt5gCLC2A0Z4hlHpGF2Kagxkd8YvVYRqB/CmKKcl2PmkmaTTBeFgUxwVj+/ekqRHxMZjNr8g6aCZ2DxbzAmChOnZlOA8YC4U56MnDGcF/esDj9qW7mHLZnfEzAbiVx9oig1VH1HKjqY9oRkDRrIgflBsVwVtMGbiOJwXHcXuBknKnZCoY0czjug+n4DjssYnXUmOIuAweJROhXX5hOrCZH6RYSpoPnZp9ind+gOyMWjFmJE459Noyng5ZbGwOB9Jgh/+mE+fP2X6bIo2HwjObBaDxliPufKnhPMztD7lYnaJZurUckQwtBgXHmHVMZmUyP4Fp9TAXGw4iQoVjdi8K3n84w8spwYfG2d82YDaF/g/0PHsBM04sFIxylKUzydoCOrGYHFrchAuZnxBWcXfLvLmGsUgUCKkyGz2+ZqdUeNa15wZDVrok/cRL3INJTJ0WTMTE94Ub7lxCvKbgvpOZ1d6TKSONnvB+OkUKUwe3v4px8eEMqvIxMB9c0J4FupKYF+HhNEIIWLsYaBrDJpkRzProVlxOGlc3us0XYESBafkQNOXjPYTVlmPpGD66R+i5j2uMmmUhux1PEzMwsGZNOStRJcGfp3jRwZy4jGcYppuTu4I/LpABXMsJ2KofNxB0j7cMx+bKGdJX8zw0FH+ESOoiIWLHILvBexv5ASEEMZ3DuC/V0r9Je/g+i/D/O/qzXfyO+DJr3S/Ah7+Ja/wK7wDurQYqhiz89jpPvaw4rzpmedHzooTkW9hGSOuzJbc1ilz8B2FPnjYmk8eNSRyTlHPkccxh35Gg0Pb5/gY1F6FJR3MysaMBaXTkRoDc3eJdx6gtzOcSDINV/zwvGTqFizHDWPpEExKXrgGTqbx5mdv+ca8Ix81vHzhc331gquRS7Tr2KuK1kxoIovA8RhhMMiGh3DGYX1k/XXMzc9O3Lke9ss51dOI3dDS5zmYj+jSJKg63DNFP06wlzbn0YwibhmXAbqreLvJeR9vEG9WJMYUMQQsnQXjcGBqt4TLOSd5pDqtyVUDyYlJntMaNWf+mJfWJd1QsD3uSQ4tjg/J2YjdxKUZIvKDgExhHjLCQqLpJdrrCv3RoxxdcOYITLvGKzUMs6B1Gs6nP+aLFxrhoafDJ/p0iRfMuR415L1P2c240BTd8cgpnXBqBDOxo4s6RtcXpBcj/J1Pky7x9JKlB3YraIeWvip49CyUfqJXOcmyJREWfuiijQ0YQWzkJCLFtUI06TDuBxpnjld3JIecjePg7C+p9XPunZxZWCJOIbtMZ9NLpqqg6zPmSUKyfeCFHtJoDu7a4fC6IMsPzOiwLJNCagg7pJQxqzREO+q0lk9W6Ty83jIIQWdJvm6/xMptRl2IN7YIOwt7ZjIxTO7SmCFyaFsDc4DehL62qDWbU1fQZgauqREFGm0w0AweZZnRSh3LrDDeZfiaQvYFddMgXY382FKYAyl7EqtmMm6wZY/EQ3MKJnnFsr//Xnz/JrsDAvjvgF8opf6rX/n0vwD/IfBfflf/z78i/0+FEP8D8AdA/JfThu+1oXq2Wkd+PBKWGscXLc3tBCFt8lFOvt8RaD1tJRk3UxhSjgeHsZ/RGxn1q4hZ9J44sUiNDktTVJaLb7SUhot7GBi8lrzUmdk9bixpXI2Hn77i+g8+Zlg4qHTFw2LJuBG8qzq8XY6RaWyUwWN5S9TWdAOYP/fRPjqn6CWw4p9uUi6uXzDpO8KDiXGto6oj75KaRf6E+OEXCE/jxU9+yE8MC5KKZOrTNwMymjE1fLrAYC0ystk1U1Hz1L7G/x2NX1ZH5CqnXFoMg81fe/KC2iuxti2W1UCxxRouaMsZ/lmFuYHLj55QvWtJi4EkvcPyFmhc4eqw21u8fHHB8nrBO/0pdvYA45RTqqFlkry7x2HM4zrHURWL+RxP06itJVqUUiUvmE97UCW31Y5s8xaVDhyXGpEeodZ7ns2uGM4l8WrH9TykLlN2+z1/tn/HLq9w+o5h8pJJFvB0mfH+4Zw33VsKMUIcQixyuqmGOEriwoNTQeKXZH3LKDQI64YsFQyejh5Ihjubj6cOj8JFawW3Kxs16ZlXF6inDaPUZ+WWxOaBtglQFzMuXrfY44TIe4rUFnxTJFThFPWQ8OGJIlI63g8i4qJCS3w+lIq5m1C9Ac3fET6d4K836M+uCPsPXKtrUldg+4L20abwn5DpCa7d4BgzUr2myhqyWhHFG6r8axwPYs3BPSR0JtRxh2baCFui9QPbIUfVBoaKqYTEtVoUPVqnsQod9L7Htlv6ocaIBsxmzNg5sst6NknAzCjwVMypH1EcS4bZAnbrX4u/3yQS+BvAfwD8LSHEz757/u534P87QohXwN/5rg3wv/Et4chr4L8F/pO/ysCgQCYllqoIjXtEIimCCrWs8fdHpvmRxhvjBwLN3dFfaXhORqfVtIXNdNKhuguwPLREwx8UWl3S5g15XFJRYpYVZgBx71BedqRBgPAThvUDQzjmfSUZ9QNJkzPKe8rMJ8eiSjpEEnI8CcbhgrM/VIx8RVvnmE7BcrJAWh2u94TV8MhxH7M/9lx7I/rDisVH15xLgybLiUcZtWlR1BlFUiD8c0qtodwplpWDe0opVUAl4d5VjNIZudVSNilF1nCjDkRKUJxbiLhnYs25aXfU7QjigbGs0DaX6Kol2BtMwueMnp9zNTa4L15x0x5IVMxrMYH2kWIcYcQ1XaoYZg2X4+fo+hPuHit24jXvaguhxpSU1I8Zw/Sepuo5GQXe+54q7ujChG32hjR9xemXJzbVK4ytQPNDbK0hFw2bREcUNkGcM/bH2LmF/nGMwMMaeXwinuAZW6qLkFNa4ww2k2HKEzPDxSFOTIbaw9eP+NYlRZfgdQOXbwcMbc+tGMhUS/mwJmKDLVp0dUQ89OS6olvVuKmGfp/T7GqylxZXbg2ez4EEXeR0u7ccs57J/oTUe9LKxo5K1DhBy3TAwIwSZG8SVQpz5MIdVIXF3p1i6S5qHNDxiJ02pHWG5tec+y5WbnPSLmjNLf/8/hUP64LmvkfkFZH1BKtZYkU69tjFGnR0rUXpFo1R4+oGQkj6SYmoS0rGeKmilxKrzqmTjq7u6JqWx9xH8yyEHhPPbLTOR7Q1oatzKZvvxd9fGQkopf4vfv08H+Bv/xp9Bfz9v2rc/0+RPa1js41z2rlg1vUMGwuMBhX5HEc2UjzS3FWUfsc4NsE/4VcthbBx8hBHz6njAP1KI9k19FqHEUn8bYuaTamzHY7Z44qeXHpYu5r9xzOMJGDqrhjbt+xvbV5cXPB22OH6HdIqSXcnFBr6SOJ6GtXxJeP5wGF4YHea4IU2pp1wPLyjHmyC+oypVrF7jBm9/AJbGmxEwv7+Lc/7M+xyxdBcgdBI/Zq46rG7kq6qKMYhZ6cjeddxPBoslhWLtc3jvmbn93wxHhOjODUJmpuSKZfQ0il4jzeRPKATFik3ckqZHDgXOqfmRF14DIczQrsg3jmMl494/pjNhzVNLfE7CIanYBX4Tsykt3C8CxYTnfCFTrbV8ZoJ1dcP3FRfM76wWTw1cW9CGn1Dn57x880trXbP+PEnHD5JmMYe/+fNV3Q9pP09drrA++QjlvM5mh1il1AZgnVb4LeCJgso37+ldiU3bcfs8YG7mY8aFUzckqKyaLYLLDdm+jgwtAnZHMwqRFU21tyl9N+jAo+PPkypz3XqYcTSeuBwkSGrKYfNDeHJQ//DlA/2GeHua0ZPrmmJaQ8HtkFFsojoH7fM6oqTTBHNcwZ5YDw+5/6bgnohgAE3H2G0FkrZFPY7Ks3mZfpDXl0PaHd7bvqUy7hgn8YEX1zjRil3xw193OKcUnqjxdI6solNFZf0aYuULRQO6dTFiFOujIBVNTBYgnrdYQYjtOSevovQ9SNpbeA+BW1nYfQVJ0bk+yPzsuewCUi0gaCHgzQp4t/yuwN0A96mQHeO5LlLr2J6c0WRjmjEiFl8oncaLMPg0FXghzj2BG+7ord0SiqOvo9bP7DbQev3zM0xZXWgGNks44Z1N8WwBlIGlq0CR3Hz4QPH5JGn6hLn/AWelfNm+4BrerxbPzCyW46VRE/2NMrmpJ2jnFfUNwLCKYVb0dJwXZ1jTQeaoeW+vMfVXNTIZNX9OT/9+Vs4hmR5y/3xDb8fLbGjKeqxJ3AU0g8xm4jj/s84KxXV2VMC1WMogbYbk3VHZsEE40JweL9mrM1wJyHp7SOVp+FZI+ZyTqRpHEjJmxJP19HnV9TNCW+4wvFKnBc50g85FR35qse4WuEYC6x6jiW+5l38ALpGIAXS1rH1c4z9A+viCdNxSbXJ8D/6hKfZI3HxmlReEswmKDNAuV/yyfXv8Cz6PRJlci0G7gz4N774FFcfcT8k8PpAOwVh2GQiR4Ubkg8e5ebIEMRcX1yxOFNkh4o/efVThtDm0izYlxG36Z7PLJPSrXid1ry83FF2kq674JTtuLrMyFY9/uiMeHtPPpYExRhCjdi8wNh1tOMtv/fjl5Cu2aknjDSBOLPZ399gaTCMP2JY/TF+ZXP92Y8p1AP+8Yp6mrA+SNqv3jH1BfUmpc09HDNEmILoxRmPr98QjDVO12us+pGfv9pyv/4z6pnJOl5xcWix/saUC/ED9s9MHuL/HdFJ+mub9q3EHhp0z0GzBcemx+kySnfEiYYwUGTHCl0KhjjDkgsKfcDb+wwLDWMnUHpNKkfoUcmQXJCetfRZihmdUWV7/HlPuvL5vnwCvxW3CFuhc2hj1pmDnlpUpaAefUp4GrCOCbHsaYqGo6pwO4s+U/iNou1DzNpAWRI9Lek6SV0ZhEeXevOAXkRETUKiKc576LMBzTA5xDG50kBNaHSLffGImcRoSYnueJieQ6jVDHWDldf0KmNyPkG5K1QdIf0Wq1c8kwbz2OW1k/KY1+wtyWKo2Z4MxIcC46ZjaU3RhgPj5Yn4IPjqQeP1h5jV9EgvO7Su4JT9BfNnJo0/Zq+dWFU9cOQu2+KhI8MKV55zFTzB9iWHsgQ0TLMnquDh4p7H5A6/brD8DqWtGDjw6YspU72isASjfIF+spmrBcvrEU22QAsbTNdg9fQJ41PAaHqGZl2i1qCTckxtTD2jz+5RU53jcMPj4PFQTDgqna43GKUdU+djZDBwCA2UrvMXvUArt2SDyQ6DMjYYnnrQXmHx7Z6/pa6pbYfFyGBpu2TDW4qm4eZ4QO8HhqVP1ljobo3bKhI9IrlrmZUGb1afohlz1CCR+UDQahhKEt8WuHJGGYfUncMTs2WWDIyshloYpKpib1vMjzqZY4HdIBoHqRks/A8YNNj9CunuaJsGyh0ya3GPHbLI8fs5L1+EBBc9zbVCRop5XDIzCmRaojcOdbWk7ErmtYa3dUiRHPv3jGVAcRqoNJ1jGNMvBepGcOmfaK05sdZQbAR25VMOHjQ6WT0Q9wM9HrVh0Z63WKMasy8YfINWVYjCp3cNoial3Tj41p4hOaL6AafJGfkluirALr8Xf78VkYAlOhzdRNMC+jaliAy0hw+UgYU9FDR6gH+S6HOLSoOII4fYwrDOCJyMNjbBu+N2oWEIjaYD0Z7R9jFOMaI4O9IPCzqlYVcaxWChwhSr9bGrHau0Q8tu+NEPl1AI+jDDbyLyRGIPGzp9Tt9U9KsRQafIFjrXU5OyvyYL/inVLwzuTIPFbM7GymgPgvGs4fF1THnKMJwDeqczff6HeN2e7nCishX79gF9PuV3zIChWPJhtkd+uWLfzen0Ex/bJn/kg/W2Qzt/z+9GP0aFDk56JBlVXAwvMD+Hz9Sc5nmLMbj0TkqzCxlNC376PmW60BnFA5WWopcuq+mBfdGx2eSc25e49lsWq4if+hrj9x8ofUEuFNWrA7nQ6G/vGVlzmijG0EY02i8YeWc8/PIdvW/zoUpwzQnLS51wtaPWTJaWRXH5Oa9+9j9xbod0PMFLTsizlsEF69GjjAaehCHOyeNN9xVTTA5FS2tDeH5Gk4LfBtw/bvDMJWFzj91OmEw7zvITvX9BU5WEE5vj4GGGcPWRThVPuNpvkXlB77oc2VMdLJ5++gXFq68peod8fiDsB/r0GnuUcVzvKRwHo3URjcAqJiyESWyZXMx9vnxYsfrqS+LzPZ+IiIW15NXXMdZfe8Y3yS37wxRvoqFpDsebNXftK1zX4K//zb/NkA+0lwvah9c4ro1xn+PdO+RtwuAI9o2H5m6xhx7ds2jzLTLSEf2Rvgar0ZkoSTFo1P2Uw15DCzXqQsdSBalfYyQme78iihPivEUaIU7WkekFndIotwukdqD/Hvz9VkQCjZBURkFFTWvCuS/xZzD4Eu1MkhmK++cDmnLIRycOpsHYNIiC12QkcIpxmxJ/l9CUNb4rMEcdluxRmsDc+BgK9OAILQjfwT56YKzQLmfog6BKXP70Q4m1sEnzATmysYKSega2FxL7LV0YoM4Lrs0X2IuYWvs55TuL2l3Tl3uSt9+g1gNJ/o796oh1TBg8QaN8jOmSq/Ib4nqCMc3ReoVd+egfavZAu7zD/kXKox+AfqBte+IkQ9sV5OnA5GiTm3dwu8MNBqJuTiEL8tzCbixcMUWrC9Qq4zPHRFoto0hR7vdUhcmxyolXMcYpo91lzJsDjsqJohm1U3BGA2HAaPwZnpPTCgdTtpS7E7vylurLkq7pCHWdeZbQyQHf3mLfxJRFT1m33FiC+nqKN736lnxl9GP6bkHlXZA/ucQqxmRFxo25QhLgDScKP0dEDnma0O8rwqRnssvo+pxMHmhGI6p4hRjOmHw+5a4N+TobU0sXWY4x8FGtRrCrKLMrjG3BMAj0hUVVexjFGbnd0r4rsEwHz1C4VUFVjDHEHqU1uAVYdxE7e839TmdwTErviHFpgghYLkPGk6cM05bYd+kjOHN10nVOqo8YtDcYliStwQk0Wg900eNULf4PDK4bj2wUMTDl/QCGGnBfgI8Nsxq78TBGDroFUvMITgNyb2OmEVrksx4U6QjybYUYWgxDI/BbbK2j848o/URgSiyjRWvOGFpJa42Qewfr4GFoNWPD/F78/VY4AaMfUAqW5hqhNWxXBSJuecxOnBoXs1K4KmAtLZYfLKJojmaEFN5zJpFPMutI5YzMdZiaBYfiRLJTtFg09oBpd9Rajt9JQn+NXggsVdPtWk43Hb0XMYxK7jYxtz9/x8JeEJ825JZk0k3QWpPLeEZ/yli9Lfnm9hv2e8X9K49uYZE+euh6h3095c7aUM8liTbQfmZyyhST6Jxpp5EZBheXB4bMQI8rvNGc8fWMvBV4ty6TJ1d89nzE9Ucfc6m7DNNz5tcLzq6vKayca2vK9CxC+nNk1ONeOpizjo21pZc7Ck9iB1M608ZyR9i2RmiBGGus6oqjUdBULn2mqIIJueHTWlOOAAAgAElEQVSTHh5Q2v/b3pvEypKlCZnfMTs2m7m5u7n7nd8UERmRkVlFVWWRBQLVEig2Re9YNQskNiDBgkUhNrWlpe5FS62WutVINEKwoVvUBgmEEGwYKqHIOcY33nevX59tnu2weC+rQkk+kdXVcF8o7ie5zPy4Lb6j3+2//znm9xyTfjFiHLqYSqNoHWrtKfG2pmp8bH2G+16INyyx+hGdcPn16AGj3mVyMmcWQuCe4TY77J1JtovpqxzXGujuWdxf6MykjvJTdE9wMZkxUhZXsU0X9KgY4mMTZ9wzVHuyQODaNjMNuv0KzB7PgENtEegwPc/x24rWHwhciREIDvOeabVmzYF8L9m1Cr1JqT2TcIB9U6F5KYEY2Ds5Zm3gFgZCTemDOa3XMpYLdr5J1rxAe2YgntVYbY9vTuHhCfpwRGjboI4ZJgm2m2BuE/xc51QKxppCRCHRfoxlXnAajTgfHtHc63FSh54dk1qws1OGnYfl+ugl5EVFvrMwNMlgZQigNSyE2UGywXQMBCBPJHOnxdlrIErqWsJGgmFCY9A7IwYB9wwLc7hCMxJKs8egRW+LN95/b8VwoEOn9yDRTokLDWsC0rGx3BYrKzA9MAaPob1kP7Mx1ed8Lh9yli6J12O8w45SLIj6kv14QHUWjqrxZMQ2TzFnI0hqsn2PDDRkW5I4IZZmoM0MsusdlWMTjQ2uk+eojyPm83cpmwJxbOKJnmVdMopsxlrKZDMl/jhHNhu6zGE0cdAsE7OHs3yCpSyKIGOIJV4wIe073DzHmM4JihlqWJK4Jj07ijxD9BrX/Te5d2jRvIDx1OC9P/3LfPdFSqd5PLqXclBzLjOd40mH37t0ccTg+ph7xURf0lr3cdcdRALRSrxYRx98jIcG5n7G6fsuTw8Z1Y929HMbTdXo/Q2afcrm8GOKwuCKltN6g+vBg/Gvsn63RpYPkZ92VHrAqTNGTgwKWp4nOY75Ls4vT5iGEbvHa0b+Q5zOwPEkW8NAiohz+xpV7enKOWEwY5TcsLreIc90pCrIlyvqusEa+3gWhOMLdu0O1dns2xnd/SXyI5/kXYNpUZBZCmEInNrHPj0Qbi54mj8j9M+42jRYvU0s1xz2V0zSIwx9jHlh0lRPqa5DYl3Q6iH1tGSrPWKvnvCuX9JUDbFqeCA6RD1Bnh7Q2gNR+OuYTo2x+j76YkxAS6lBbIwpDxbSSrHGZ7x8GdMYS46/eQ97fELx7DF7pcBYUTQREpNDp9OqT9GFA56gWeaYmodrlCS5TUdJNViIyiSKBDJuaJVO48Vow5zgpmNLC5Q4jYeoGrSHOd1NRN1tGSITLTd4GlbMxYhuC3mgQyFJTY23eqFRTQ2YtYXWHPBCl3uxRetlZGtFMjgsOhs1FAyeBUNIe21x7OsU1TmZtaKKArr8gL72GJwBYfdUY4cmfYHCQxQNQpi47+kMlyG5K7BFDq0g8QtMW9Kmx7C+Yq31eM4au+6xLzRkNiVNl3gHHU2cI9ueuBSoew7i8YCet4hQQ1M6TwrJyaygsnK6qw5mDfa6QjuRaMfv42YTJicmqQgJi5CzyT2adMsyKQiFoHl3hOuaLFcZub2jcweGzQs2NzN2jc7pmY5UI3rbZmVuOB1sni97jD92nypR4JtMs5y60Zm2kt00oS0jxnqGuG8xMudIo+B0MNiXU+ygoKh70iHlQnsfV+2RowuOgxwj8PnAm1IeCpqjG4KNxqHRaU1QmJjOQFgMxAU0l1fUrsaQZHTjkNSwKBudsFriWpDnHs1Fw+U6xV5L9CAgLSTh+D6/u3pKUWTM7TkJDfYcRluD+MUKIyg56Y/Joxb/0sN9H7TLlK3Z0Z6XOOnAJ9ZzFgMkh5ZoviNvHKyy58HgEc97DnFJval5NDqhndeMs4QnnUe06unOn+L2R+xGz3A2E0KzYOYZdE6L3jpsNIu9M2C0Pb055r3jb3CddVjGnub6gPJBdBHyesr4qGLhpEijQOt9LGuMd8/gpWYxyJQzH1RqgVwwKnvKvMMNOoY6orFbKlljajEzLWTdNDSDzlQbYcuMfGITPN6xCUDWDYN3Thqs8SYabrOgY0M07VkdGoRj4GQGaZtQ2z5Bs0MrHWjHvKkWeCuGA6CokgPKzcj6nuehg36j0XYOzbxFVwOr0KBVEtkmbE3FXr2gylMsvabPdMJuQozGsVGhKw2xj2krm9GgI8cZfVmibXp6U2MUlrSNgW0WjNYKo7eR8YbRYkpr+vzo2SU7/YrtekRV11gF1I7Cr2ICb4qWDnTrlIMW0/kVfbNl//SA3R6wUkGYKZrWJ6h93EcuXz/9kPfdr+GOe7K1wAseEp2M6GXMzcpmdL8imZZoXYlnBvT6kk2eodZLcvY8vfw9DuVTHgZnjMyMvl4hpYPuviRSB+QG5KARmmvULEV3KlZmgyVrhjim6nSSXcbIWnESBvTdGNfpSZWHeqmwtSP62YTxTGdiFiStRTQYpMuWhZch61O8IHj1lGbponUlluFRuyP2y4pECeympPFC+qxhw0AZbxAjwTUWh6bFelYxKw3c+wZKnyH0lqzdkH72KTPdI70q6RwNKQNWlc2NaTNqTcaapDcdGnnD5ZOEfF8xepKx22lsVIgz9jEa8Lw1h+rAUFnMZh43rU4Qz5kEAjeF0HChrDDb+zxIJGpWojKBoaWYSYR3PCWoQvaNJC18JsWU4LLF1DO2mxW1XrO5WuG1n1DeJExqB+2pCQyIyQ5nGuGfnhKvbTjqKK2cPpngDD6nIiDVZsxsh7E/p9BtRK9IdxppndO2KUd1QK25CNGhVwODTLEGjRcK+huNztS41ykqJ0LbgNyYWIcpSRnTDxrX5YRgMoAtUJQMUcDESujEMU3gU73t+w4oAZ5tk8kx5yolGzpiU8dQB46dY/aqIchiGulhdx1zrWdocmrPYruUpNklmj9jP2rI9Ajl7pCpBGmgLMlm5aN6D+XllAeL4rrmaDyQ7yxKBYvWo32UsboSWH3L+8aU7KmB977Hyb0pnHlohUPjFhjFmnJc8kwT3LciVKu4zFq6yONEzrkaVnQKLt6ZE9k+lWVyNBuTZzWj+TH3uMB5WCPEEbLMOf5Fm0JNaY2Ul32Le/M57Uub4LTkyj7mXnhGurXxL1KuVx9hfuNbWG3H1xyDbSpobJdtscKvTQpHEI0C8kLQZQWxZiBkx6HqOepdDh5k3xtoJxlW4RBWn9Ed3SeqTCaWQT46p3+xwgY++f6ek3dG5NKhL1vqwOXiaEG8SdFHEWJbs7kqCP0tmeFirloOHlw1W5orh/vnEZ9857uo9oRQ1lS/8Iiob1jetFSD4MitSG5skqMF35qdsFtkXC8NqvJzHh1dUI4V02jKv/lOQ+t8QidDqmdLMv2Yd3+h41jWdFlHJ8c0toGDj9NeMQp9tmXNzBcs6xzbSTnTT2g1hbITNsLlXSthOczQtVPy6z06n5E2JjeehtVKgvd1mtKjZ0KcdIS/OsH5ccL++vvo9z1Gp79EnyXUmsmL+jHG/I8xG2vUn/fYvyB4MB6xcuckloXlKbrKZCeukGLCqT8jMBWN65CvW1xpMNRT1saWLpfIKMV3Z+RJxSrYITjFkC25tqGWIeddTjJRuDPBTZty1IDTmexSm76dEXQDuR4RpCWFoeiaHMcMqbQ3rTX8llQCUghqd0pjplzHJVaZsW4GJkcRVVbQ5waVDrLUaUVPXO3R1ASrszk1SjRbopcpVCZtX9BvStpO4Fo2+ZBBB8otGIwezVpiS4HybbroGMdR7MyeYm1g6QWFMHheQmpmpMkl+/qGfpjQHOsUMmLfa6TVgCx7SJbs6j3zyYiziYdld5w7M07ckIvGZNXWnM8HZpOQxTfe4+I4YrgowBqR9c952V6x63NGdYGV1cxKgz5uuOpqCunyznGEcCPuLxrOjRF2FBA3B8LpjkwMmG6IM/4BmpeyscGwDNpkQn3U4z3oyEqBbi+Q4Z7LyqLpC8SopFumtM0TdtUJAy6dr/O4+CHD8xuy0iGLV7jRnn2+Y3kzQJ+zWT0hvZLIwSR9WdFqJvZJwZUSqLWiO4uYhlPaySm+EfH8Bx9ztRf4rElDi3z9kqbtwdih26/2k3SLjF87+gC1OMIWLsrcUvYjTNfBGE9I1gtO39E4w8cJbIKLMSezJUU8Z+mEmLaH6zWMLYtioRHMF3R2gR7UWINHHaZY+ZjB35DrDfc2J8hVy0af8cDVsLwd/gReOCNuBo/Twz2m9or+0qBcdvRWRXWo8R5fU/kWV6pjt7Kw0pbSk4xmFcXWZOQ0VFZOO/do9Jp20DGpmHQ5XQJWMDBvHPxdjff1Ob5+hCgasDQMr2YIJeedg6YNlN2MojuAVePXc3Rnw6B0zM5Atj2VC5nmki87TrcZe3Mgx2A8zemdHsP2Cfol+WiHlvr4dY8+vkF2P3uRUXhLkkCjCVoq3GyBq044nYMfKFbrBD0rOG4PbHOHMjjQpxYaisPQEefP2bUWNi6J1jGpeihyhnyEJnLibIXsPCbTEsPU6ZMxum7QqY7VZQPage5Ip9mssDSNzgAvHCjtnGytUUYFlqvjDIrOeYejWmfT94yjEaOiROskR6bNxBtxZBjsJwrt5B66kqxCCyfW0MuIAwaeAexduoPBD/efsP84oU4PjERMIGbciJ4Xu+ccdnu8aYEVBehNzREOnEQM3jGBseAkqGlLky5u2K4ybg4L+pdjZC5RWcVObUjWCflOp9R1rE2PsCNG+YrxRw7FZUOCIhvmpJvPMdsV7jDlvJJclop+/TF2W3JT5xzaAu3wHbLDd7kyBH5wRWGWJPOUvI9p4jFnNZw/HDEyDgxux4fRQyZHPY/uh1R6w2fbEUNncG6NsULB/P7XOTKOyI4cMitEDzrU0CN0B8s06CVUakOkx5RBzXBdYQY1drXH0xRBcMyJ72BZiqZJyNca9WjAUjsMOeYksnDK9wi0AXfVc7XZU1WShQ9PDIE/NajMlmeZhxm7TERFZ9pMzIbKfULjLnCTl3RnPZUQ2FpOKo+x8oAuzumdjNJpeUcYON6cr3UuXi6odj6xtscoC1y1J1gcI6KGxcRDvixpDxaZmWGuKpz7HWkwQQUlQ26gqoSX1gZ5KLH3NX45QVUWWGus2sbapPiWwDcG2lJx1qyo5YilOUNdRsSdZF0KRFLRNzWxHWBc+WjBDZlnkz3vcA33jfffW5EEtEFjkbvUy5TRNKfbCIpkg4pMEqdnOVsw8zR8S9DKFmEc47Q6B3PCKoGqtZCHATvMsXQbd6TjiQn+2KJ3W0rl0bUtwipp6gDTtdH8DCve0+SAN6XZ9HSHHpEoPM3EMBT9ekqsP6B2dI71DZ+Xz7jXjum1ARXAbiRYJ5Iu7bkcQh62Ora7YrT4BoHVMzoz2KWKlAnZdcLW2WF7ivv1BfoHHzLx7jEbdJbqBY/qkvkYjqYzJrVJv9xyMCSx8Zj8ZUl1qHBVT9ZPkEnPSW9imgX6qKMWJl5YoXzJWa3hlC5hPaOPMw7lNZuNYmYY1FOJEzp0xg1l/p9wZwFXhwPp4Qn72MENB4zxiJdNz3azpV8+5TKf8nw7pv7eQLUWVNUNwWWI9yIlnfSYj95j2cTUtctxLxjZGxbmmPEk4BcvPmT+7QecP1CM5TFd4tEUOfs2Qct0NHFgfHDwBsmuzbnMNKZdydiZc3k5w8039NqEfS+oC5/g3MOfudi2zVEJlu1zdDKndnqGxqAs98gXDlrwnM1U8M7Zt5jef0S8z4m3Oco8UJ3rDHZI1JrcdCH9IDhRLZGhaJSOtrzGX0RoVzV2L9F6CYVBXUiG0iUaHmE7FUtvgvBippNH6EQkfUO+zbin+VhfP8Y8dpCtSdI0xJGkST+nqABng+p0hk1PIC1cr8W2dXAsvAXIiY5p10wXJWUd0hk97emepoR4N6Kze3IHZLSCNsd017SBZOKZREqQ2CmaimkXA3Zu4NkVFnN21Zt+KvSWJAFd08ntHdaJRt5OqKWN5QnMfEySLRgf9sikYL+VaK6CtKavXEZXI3yv5JAZ5GZPUU6RrYVKE5IgoY9LvBzqBiaaQZtpHLcVJTa6fkSuxvipiWgqlNnBsUXXDWjDhrSDNP0POGWG5Vr86Cpj0o9ojALxNKHcN5RLgakEw/mYc3NO7OosXwjyzY/QBp1+7uGNW07da7Tpgf5yYL3J8Ec2C1+nVAbX8xWqMWjD+xjDlCwM2SYb9ki09RNSOQW5Ju236EFHNlR04TG7SBA6U86qiDrfkRoHyuuGyrHw2hHmqc79D2ZYoYt0FXutZzRYjOYh8xsJ+kOqXtFuTcpdShs0nNUj2sCk7wTm9Zg0flX6muSE559hzyZM2w8JHpp47/kc2zXF888pspJOGzAKneKFIFYe65uIyJO4ekplOKRhRmJDcWUj7Q6r3XE6mvDR9EAjegJnwBwkS2OKYba4Zw3W7JxwVhBUA55nopwJGRNWmsYmlLiGwNs3DKsjiq0AZVC6HmeTAL84oEcGp7JF3JswWBGzGVjPdswdi0yUjKxLahP2WkV54jLxA/YXOtFJR1rGaGxpoxDPifCON1gPz+lPbLLaQTMLkpWgP20oIp+FIVgMLpXZMH4R0s4NVKxTdRlheqD0fQIjwOzH3CQhKrA56JK9ZVLHFlpR0A02RW1R5hqHqqSpcuSuQV4FtO6C/rhDXvkcxIzu2REwwtDHWK0Gh5jCdzAHE61xCQqLQimKqsPRN5jem9cBeiuSgOh6gnHI0ZlNMOkpgp7DeMFRE3Oqp/SGTzfqmPWKMltSOS2FcUM921N2IW4Y4x7mmFZLGaRITaIPPr2wkE2MLgdaPaPJYzK7RU8P6MUVQ6toaxNsi0qYdCsb3ArXqtGGFWlXstMrXtYlE+3AdveSzecbXppLjEaggpThaxZn41OMUMMzIrThQGiGOHqHG1ygOzOSdI5enqI+DJh4Z7gbk4XI0R2L49XXSM0KN1OcTmf4SY3ZmxjlnqrRmIkC+0yntVsOjcW0GBDbNc4ypZEFsqkxpoI2s2l1SXxwOMwqnqYDeeYR6ibdqmN3IynKPYqM5v0TooWFX0+R7+cwP2awDLbhitGDER/MZpz9osME6MY7NKVojDO6oWHsN4yMFENEzNsxveegSYnA45kF7kijkTtGzpZ1tCBy5/T5iKzX6KqKcFIh1wOyHXPjlhyLOWmjqK4MAs/jg2iBxinfnI6ZqAKtCukKG6FDt2zAzBGGx8gp6LWc1NJRVoF/JjGNEve4J+kyeqFjLTOkMSfa+LxndIj2Atcbsbn5EZVawzgg3Zl4m0eIYopnKMwfaLwoBNJaoZsXLFRGySeYbsQ9r2Eod0SOxajQad0eu7si2OZ0wiB8Z8qVVDhug3o8RTNKPKkT2y6rquDTj3+PIs8x8j2TxsaIB+y8pGx7Qkegix6lNXSRom3HHJ1aDEZOHwwkcc/iZsAatxwNBd7IIHAySk/hdCl9NGU/6AxeQmRK9Nqi8FyC5pwmcjGzNz8DeCueDgxSkbQ1/RaaTsMJambpN9lOL0E1NP0OKQ2eJC2T+Yj4psWYDfR7Sdj1XLYORlDSqxQjH+G5FXlTMtgDVS/odxWZcvCCCi9vcJRgLQWSmMIt0EsDT29oNIMqlqQTF9sZMLwJw4sfM/ffg5OIeL9B1zecqXOSICO0R3jynOL5Hk0rIAx5/1u/zDvaGf0sY/gsZh/6uDc3HN55AEOG67as6wPcVHi+xnW1o70K+IF+SdIdYZYpF9/6Nc7mOvvMxKkVmkoJQwuZvaSdHqNbPY8HibrKOXEF89E5h+sVz72ab4sbZOFS+U+Yjs7pnRx3ewG7Z5RWTxw9xEqe0zUeM6PGdQzyHkQraDwH/SYgmozRZUQZlGxVQvSeSftEo3X2NNrXcTJAb8hkw9FijtY1HAqTqG343ScvqOqBaKgJ9R492LPf23wQz7jwer6f7jHGEeNmT55D4za4aLjHIVGpkXkLVJPTridgCeTiiuBiTFnmzI51iqXJyS/mGE/v0f6SoBlM5GGHaiI8uyXfS5pkiuUNaINNMzngXDRUlkuRbSm6lkk/ZjoeU+Y97jHU5ecYPKCZ6Zi2jVrreMFDnJNX/w+QqJZFZ3L+zW/jVB2lv6cZfPrjNVV6xjwawZnEHUKyDsrzlpnsOWx18nHKvD5iSG74aP+Cf/2vHuOHcClb2thHyQZ1AV3iIZRAt0v6ymdsZrzcB3j+EUPl4k8f89y2ECuNqAsxSGiGDln31H1AOzSoEKzBZ59XnMwlUdfT9U8RtU71roIf/Oz7762oBBAw0gYsvcN3eoKdTxXekB8K2rRl2p0xbCrcuUGWVbzTerSqI/X3PK+hUjlFU6PnMwx7R9kKvK7DywZaM8AebDSzxUogtVx600TYLi4eolT0rk5ueLRpjdvVUFi0wqC9GYjrMR/1Sz770WOkqKgySZruCKWN1R2jZRVqWhHrPVozha4lFwdUpXF1MuCUA09uBtLrx4jaZ6VrlLLj2SHm+Ucxth6gn4OqbC73H3Pwt/SqgNbhZCHRxy3OqUcdT1gfHdFrU/aGwQVgzUxaFmzbASEtLpqWIsmwJwnGPqRZ2ix/OEZPlzjeOdIsOY1r/MzBswWZaaAnE/zWpjcku0+gIqaQDrNZgz9U+NqYyV7DMySb1GZdboiVR709plIu/k1Iox/hj0qqesVpb2DpGcoZU8tLdgdBdFGTnn3O9WzP2SgkGm1JmobOCjkNTjCGGT4drXBImx1pldAvMuqypuoFusyoWfPuzEKbGujlhO5ba7xcMbrJkbWOqSxq02EnFSMTZAPpsCf7tGK3UxwMk77qGayGMrTI+pYwtCAHqilds8TdFQQ1mN01vl0zyRq2pYsmBXXS0Ws+w0Qw6GeIkw51FSI0ydBbTPqOuLARysJlTHhVYj64QHQhVX6DL+b8wuSP8yfvP2BTQrvfEWgavpUgchi6V+sJmINJJxVd43BslfgqQzYdQ3LMaBMyDiSJc0Xu5rTmiELz0OuBdt/gJA1se2YzwdO0wtZ6AuWTAfJm/Mbb761IAvqg0aoZle2hypbd2CfsCrJWogeSWj3Djx7iFw3W6YTDN/aMmx5/8OjGa6alQPUVtb5kNOhMjhWONqecGAz6QBtleIFDIQRt25KYEntVkMqcqXuKuRuYqh7drSjnkNkNDAZanqOFG6SmqGcN7YmBadRcliWd52OMJNLuMDtJpI7wH1Y4VkgWr/hsU8NNwUsqNFenxcaNV0wsMKYuohfYg0nZGRjC4vho4KFxj4r38SwbFfcYuc2EU4Z1jzPx+Fo25kjuaaRPaZucDz7C2OCIhvGkwW1GrEufy51DqjQyeSB8EOOmAVgxrvqAbZWiZyn9i4LxQ0GvQ7LdER9+xCK8RKU1j5cvOTzzSYYRrt5hVwPNqeBYDpBWDPkKy33MNTmX5pKuiNF0SRA9pIsi/L4mUzk87ZG9R7GyyLeC/jJnuHeFvZY04YyveT2qaWj6jsPFiHE05mhwOSHG3VrU4xGnVkCtAkTT82Lp4BYWmlHjXgZMRINyOzqtoyxaZH2DMazZ9A31YsJ+qEm9FDFq0bsDk5FG2IwJOwvDuE+eeVzWJVVfoCWSejRlO58y+JJlekTpeEyqHLPu2RoNnrmhzzb0dY6uXTAfGzTKgsmKvDnGHF0xlGB7gj5M6RvBiT1gfvgB4cmCi2+ecm+0YDB6rFBHqZq6nzFtY8qJRR26jDsXV3QoWVLXLlorUUOKoa+QooPMxOg0Os2i6mrOZUx9ruhPCqxswJo1XO0qjo2cOIbdacLUsbAP/8Vav7/PWzEcUCgC1WGWPZvJAruoSaTLI21LcnCItSMC9yW66FmUkOx6yCNKrcN1OvJRR5AFVI0iuBcxrAU75xonntJYCSqV1CR0E41gp+gHndwWGLVB3K3Qp4oih8GXmK1EygxNQl3BD34v5/1HJeP73yC8ueJqETFPwDHvcX+ecVhe8NHyh9x/10a+lCT959RjgbNcMjo9YtpUMNFo64HDJqXXbCJVMfdSbqqWvBxx6mk0w5TFr02ZpSXBzRNenr2Hvx843C8IDi7xcEXct8S7GtMLaROLp3rDiR2QOQMdAflpwbB3cbuCjVSs1wOLTufMiJnXgstnK67FR6hriZy4dJ8qSl3jJsqoNpLs04rAXeG99wCaklKsOOQHzMxnyAqeDy3TiUY2BGyXGeay5Gqicf+oIZEWquhIRYZyFpz4E+SooUkzyrKmPR4xV6A/MThMJePDhqVxBkPC6Mwhz2uaPGWBxTUzynONafyM9f4Ka1MS6h7SVRijlzjJEbNHPnFRoxUT7HGCFrrU8TGmucJCR2Xw0mk5W1dctwYz54ydnxDNBtRSUI0b9E1NaCu8eMRaS2jKnLOuwQh6/NlLBu5xCAy4acinNXk2Ro4H5KFFdXtKbA5pin5zhv+1jFRFNEWDynPiVcO3LhKyl6/WRBCOQPUTkgcDQ6bhVSWJKSkKC3+Y0nQFouoo6g7vqKZuTLQ0pgg0dFWQVid0oy2zaoEhE/q+BV+y3ztESYPwXJJjyVAKLK/FaTvSSFHsdGq9RwzeG+8/8Wo1sNtFCLHmVWG2uW2XPwIzvtz+8OXvw5fdH/7b9uG+Umr+041vRRIAEEJ8Ryn1q7ft8f+VL7s/fPn78GX3h9vpw1sxJ3DHHXfcHndJ4I47vuK8TUng/7htgT8iX3Z/+PL34cvuD7fQh7dmTuCOO+64Hd6mSuCOO+64BW49CQgh/pwQ4mMhxGdCiN+6bZ+fFyHEUyHE919vy/ad121TIcQ/F0J8+vo4uW3PLyKE+LtCiJUQ4gdfaPuZzuIV/+vruHxPCPErt2f++64/y/+3hRAvf2qLvJ989rde+38shPizt2P9BwghLoQQ/1II8WMhxA+FEH/9dfvtxkApdWsvQAc+Bx4BJvBd4MPbdPpDuD8FZj/V9j8Bv/X6/LeAvy4CT0cAAAKWSURBVHPbnj/l9+vArwA/+K85A38e+Ke82oLuTwD/7i31/23gb/6Maz98/X2ygIevv2f6LfufAL/y+jwAPnnteasxuO1K4NvAZ0qpx0qpBvhHwG/estMfhd8E/t7r878H/IVbdPkvUEr9a2D3U81vcv5N4P9Wr/i3wPgnW9HfFm/wfxO/CfwjpVStlHrCqw1yv/3fTO7nQCl1rZT6j6/PU+DHwBm3HIPbTgJnwIsvvL983fZlQAH/TAjxH4QQf+V125F6vQ376+Pi1ux+ft7k/GWKzV97XS7/3S8Mwd5qfyHEA+CXgX/HLcfgtpPAz9rt+MvyuOJPKaV+BfgN4K8KIX79toX+f+bLEpv/HXgH+CXgGvifX7e/tf5CCB/4x8DfUEq9ebWP/059uO0kcAlcfOH9OXB1Sy5/KJRSV6+PK+D/5VWpefOTcu31cXV7hj83b3L+UsRGKXWjlOqVUgPwf/IHJf9b6S+EMHiVAP6BUur/ed18qzG47STwu8B7QoiHQggT+IvA79yy038VIYQnhAh+cg78GV4t2fA7wF96fdlfAv7J7Rj+oXiT8+8A/+PrGeo/AcQ/KVnfJn5qjPw/8AdLZ/wO8BeFEJYQ4iHwHvDv/3v7fREhhAD+L+DHSqn/5Qsf3W4MbnO29AszoJ/wavb2b9+2z8/p/IhXM8/fBX74E28gAv4F8Onr4/S2XX/K+x/yqmRuefVX5i+/yZlXpej/9jou3wd+9S31//uv/b73+qY5+cL1f/u1/8fAb7wF/n+aV+X894D/9Pr15287Bne/GLzjjq84tz0cuOOOO26ZuyRwxx1fce6SwB13fMW5SwJ33PEV5y4J3HHHV5y7JHDHHV9x7pLAHXd8xblLAnfc8RXnPwMEHu16st2zlAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "attack = BoundaryAttack(estimator=classifier, targeted=False, max_iter=0, delta=0.001, epsilon=0.001)\n",
+    "iter_step = 200\n",
+    "x_adv = None\n",
+    "for i in range(20):\n",
+    "    x_adv = attack.generate(x=np.array([target_image[..., ::-1]]), x_adv_init=x_adv)\n",
+    "\n",
+    "    #clear_output()\n",
+    "    print(\"Adversarial image at step %d.\" % (i * iter_step), \"L2 error\", \n",
+    "          np.linalg.norm(np.reshape(x_adv[0] - target_image[..., ::-1], [-1])),\n",
+    "          \"and class label %d.\" % np.argmax(classifier.predict(x_adv)[0]))\n",
+    "    plt.imshow(x_adv[0][..., ::-1].astype(np.uint))\n",
+    "    plt.show(block=False)\n",
+    "    \n",
+    "    if hasattr(attack, 'curr_delta') and hasattr(attack, 'curr_epsilon'):\n",
+    "        attack.max_iter = iter_step \n",
+    "        attack.delta = attack.curr_delta\n",
+    "        attack.epsilon = attack.curr_epsilon\n",
+    "    else:\n",
+    "        break"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "# Boundary Targeted Attack"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Boundary attack: 100%|██████████| 1/1 [00:00<00:00, 1498.50it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 0. L2 error 44400.055 and class label 866.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Boundary attack: 100%|██████████| 1/1 [01:59<00:00, 119.28s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 200. L2 error 10340.596 and class label 866.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Boundary attack: 100%|██████████| 1/1 [01:55<00:00, 115.81s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 400. L2 error 7954.148 and class label 866.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Boundary attack: 100%|██████████| 1/1 [01:54<00:00, 114.77s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 600. L2 error 6624.0684 and class label 866.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Boundary attack: 100%|██████████| 1/1 [02:06<00:00, 126.04s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 800. L2 error 5803.8657 and class label 866.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Boundary attack: 100%|██████████| 1/1 [02:03<00:00, 123.83s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 1000. L2 error 5038.742 and class label 866.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Boundary attack: 100%|██████████| 1/1 [02:08<00:00, 128.52s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 1200. L2 error 4460.5894 and class label 866.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Boundary attack: 100%|██████████| 1/1 [02:13<00:00, 133.93s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 1400. L2 error 3998.8152 and class label 866.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Boundary attack: 100%|██████████| 1/1 [02:11<00:00, 131.81s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 1600. L2 error 3682.3438 and class label 866.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Boundary attack: 100%|██████████| 1/1 [02:05<00:00, 125.42s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 1800. L2 error 3387.0066 and class label 866.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Boundary attack: 100%|██████████| 1/1 [02:15<00:00, 135.02s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 2000. L2 error 3120.6455 and class label 866.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQEAAAD8CAYAAAB3lxGOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nOy8W64sXZKg9dm6+TUi9j7n/JlZFxhFqRkAQuKNZxhAPzEAxsIIEDNgDIhHQKAWEqKqOyv//+y9I8Iv62487CypgEqkVnV2p5Tnewl5aIUvX+Zu5ma2zEJUlR/84Ad/vpj/0Bfwgx/84D8sP4zAD37wZ84PI/CDH/yZ88MI/OAHf+b8MAI/+MGfOT+MwA9+8GfOH80IiMh/LiL/m4j8KxH5b/5Y8/zgBz/45yF/jDoBEbHA/w78Z8DfAv8j8F+q6v/y73yyH/zgB/8s/liewL8A/pWq/h+qmoH/Dvgv/khz/eAHP/hn4P5I5/0r4P/6R8d/C/wnf2iwiKiIQaygrYMI0hU1ClhUOwYBBTFKV4OgqAG0owoiBukKFnoHMSAduoIAIoqKQDeoGMRUTBeaA1MFpYMKQgcBUUezDduUbgREMc3QpCNiUQSvnYqANjDgFKoF6Q5o9G4wKCJKB1QUo4IIKIJ2EOkIhkbHGEPvioigKFahIyAdxSHo7xcHKGDlc51VUQVE6ICVjoqj989zKg0wmC506YgKAmD7769DsRiqduw/OIaf4qYLmA4q5vONIUrX3wtVzae8PiVMl/Y51hjoBiMN5XOt2E/5ohZMR7pigS4GOkBDDahaTO80+Yd75rCt0TEYGpjfy+r3QuhdEASV38tVP3+HKFYNTYDWMGJQVTqCiOAUlE61v19HV8QAdKRb1HZUBdHPFXRA1KAGjPD5nGIxrtFwmK6/F1tHRegNxHQUAwpOQLuhUbG/Xyfy+/vRDWhHrNC1fR5bRTCgDUUwyuddtArVoEYwdBqC+/31qQFtgtgO1SBG6HSMABh6a7+o6k//b/37YxkB+Se++3/EHSLyL4F/+Q/DJ7NgQqWdHjGNMHhKyPAcULeh1lMl49uI6ZXaFe0DYc48pfM1TfSlsp+RPExcUB4FljFjvGWvkfkcsMFT3RN7OJ7uikgGTlBBTSAMSq8B155gbpiWMdZSRSihEIoD2zFmIIYH4RzxAodkmg78BEQ5ifKCNzuHMSyukTdHn4XwmjkOxdUb3kTyXXGm4b6NcFyQ/HcMLx79WIBGNoVcC7cF+t6IavFfFswvlr4ehL3zcSs4/0L9iNhxJbTKWTKsAVczNXcIjdVOnGfDtopcBUonMnMzUNNB9VAPT38B+4T5FcI71BdwJvD2vSKXhh06vVts6/j7ikyWFCaG54mdD3y23MWDSXzpmVIXJJzY8sI+7Yy+cP8wTKK0uZMfih9mFtM4VRDt1NuE/dmwLbCeFerI89vG5Rh5ElEjcCvcfisUH0jLSf/4BiEySMR7IXXBp0Rrr3juxBfBdMsSO0+zInnHOc/iCkMb+CVEhvPXNPfGYheyMxy7wJfMl5Zos6N/n5AJtnqCWzDviZffKM9cmU+hr45937gNnnaM7KOwPh2nZMKSIV2RWyA+P7jZRi0DYQjEBOknz/q7k+MvhXAIvYyUsjH2QveKj6/oq2fa34l9Jph3Wh/wtlO1o36iPjMmVMwi7GlkbLAujlyePHb+z39KWf9Y4cDfAv/RPzr+a+Bf/+MBqvrfqurfqOrfiDG0/qT0Bn4j+5FTP+AQxD4pMpPigom/IeaN0iOhe/QvCnoqf6k3PrRwtMbsAy4lVGaurwmbbrSzYzVw2M7pO8dx5fy14cutouvBeh0wxjLYSh2E4BuJK9U+KM5x9J2UMyEsOC/QG4M7mKpF84Hayou1hApbO4kSKNYwNo9cOyUozVTamUm/vcJ9xR8H+jzQvvBFCzk+sHynzRPladmGiigksdzCNz5qhfYTQUfk2XjKg93D+aL44liTwfnI+vzAt4NpPBD3gfMj7hJwOzytI4tQZ8sggFOmI2GLcAwzrnfW8dOLYh6o2fI0ibZd+Dk12mCpp7D87ieGlrhlT/EHUk6CvnHpngGl+MK1HXB17MPC2QwfstJkw5uDqp3b14x+DUg3rONMHB3PccbPcA4/4T461Z583QqyFo7hYLp3jmKgGNbaMZunG4iiyHtHl5NvOJg7/bzQsNAX1Dyx4pjrwLo3ovEEefBrHRkAzMwvk/JqArPf8ZMQSkM3kPnOX++d91Mo9wr5wB4R279gDoN72XjsHZ7CsxTizwfXOnFEoYvnOu/oVZjGwIRHfxJaLEi/8XQz+3KBL0/KfNDyRn016H0nbhs+PsA3JhlI9YYMH/h+wOFx9oOeZkQqA47NVWqLhPXTM419ZpwGXFXOGNnOf+q9/MkfKzHo+EwM/qfA3/GZGPyvVPV//qfGGxEdXkfamXFdUb9QeyMksFPm2GEaRk51LK6TTgtzIu+F2V0pPCnesLaJWiN19rTRYbaGuoiOhnAoNQmilnG4UTSTS+bVFN7Hig6Gb++Jt/kFDQd6esbUeJHA23SQumdcLPNH5m00LJLp9YqYSEqGroGFTu+Rc5jw0rAl0UfPpVZ+KQ07NogD1lTsOHA+lG+SeXpLdgZXHaM2thyYpp3WV6bJwcc7+fIrevw3+GLQqdPHiR4NZjzhHOkmofaFfmxMo2AHSMngYqW7QHedcMIzJNbFcN5X+rXA2Zn0wBRP/Uvl+PsZ63bk8KxjIrsXpHU0VIxErF/gl8oeOsY4vtrAdxex90heZjQJ3cI03ejpDZ8qZpogG5IofWrUQ1iGCs8Og+XIlm9D5eEFaSdnesUsDyR6tCRe7MrTnczekqiYccK9n+RQibqgpmDU8zo6to8dG4TirpgcscbSZ8G+dcprZ1Vlj4Ix0FvFaad6RadKL46xdEy/kVykpoaqZ7lZalRsylgSsQ6UeeB2PjnGG91GQg+YmpCQSbFTrg7z7Az2G+qg9ndC8hxysl6uaLVsOWGrIrZhusO5hk+FY3ohHyPL+luCCu9YLk3RMUBZ2Pp3VjMwquO9R/7CJt6uK7wnju75lXVEW9icZ8lPjjJgeyQ3/idV/Zv/j/79O7cAgKpW4L8G/gfgfwX++z9kAOAzVPRPhdwx5kaTBkTOi2f3r9iLoYVMkEw7E900jKuIrbBknHXM0XL0D/ISGFNl+HjitdNi/4yli6G7SLcHZ3mgNAbpVGsIp+K/Vw4f0Hyy3huugnEjT9sIp/B6zsxvmXvvmK1ghomUdjR1VomMtrL4g9CuXDP001LnQk7CPY4YK7QumNqp1ZHPhPXCm+uoLqw+0VJjGwSz7hh7pU+Fj49IWhomnYzjxLx4ymTpx4LTyLjP5FAx6mg0qvOcrXLsnzF6vQSkCn5qxEkJwPEQRvPOnAtmuLK5iURlv3dew4kRj7kEtvpK3TKnfWKj8MyW+HPh/lJZvWPolV+M0qMjBcdQLN4WJkDiv8YVxzYF9l4R90SHk7oVhv7A5w6roC+dHk7ea2WoHu+/Mg0b7lnw9sDNF8pSac7So9IuXygfmXP0+NNyqRFXIWDob0/SOKOyUnrE2ETyhWEvFJ7QKjwCS4k0YymjpbuRqY94VYLpiC48SsGXTNCB1STyuSM0DgdxvVGIXHBYZ3HywRwDeniyz6TREerK1+yZzAL2d5T6zugMRgxhHdH9JG+B2XfmMROc55sziA7ItSNExsvviHkkxgt2mHGvSpSD7hPD7cZUMlXvXLrjvt/Ij0rzymjg2Qp78pANHcNsO3X4j/+gvv5RPIF/W0SMTqIM3nBYQ9VKaDfaXBmOyDksGNlYR+EZPTI07Btkv+C04Ens1jNrQHNHQyaWzjAG3NHwrnAYR9PPBNprmNnPguknbhKOwTHnxLONiDdMh6NfM/YcOcXAcjK2zl4ujOaB2Spp9bgD8ktgaI0YP5NltifGHsgUdGy4aMmmQ3YMcyWqwURLszBZIfdAl52QBgwFt1r8DnnItCDUGHBDx54XIt+xFczNEO43drljZsu1z1QOTndBafSWmdaB8djQ5cLxAZI2/KVQjpVIAe8YuuUQw+wj+fD08YBquFThGCq4mdBWhvhOWSBRaM2ip8W8ztj9ILhI8l8pHCwnlDyQh8pUHIdprP5k8Y1THVnBFIsPI/fnnWvw5Gxpo0eKA3si40k9hHkx9PprqA/svLPVF0J9oGogvXKd7/y8zayvO/Me+EUsIRVkuEF/0tuBWwIpGWQ19M3iyoEyUfF0m5C6sZoBnYVQF96q8uV2Es8L+UyYOSLaGYrjqcptFXLzSEzY20j97qi/OSkRxhhZ+0RzM0dPhEFpe6R3Q3sptHjhcjl5xG9M5QT7IDBxys7lsfBcO1IOmhtweSC6gr062m8V+xqZ7iPde4RIbFe8PqmXzFQbe5+4hcbHeWEM38nLiskn3jhiamhv+EmJv+i/P0/g3xYjymkXPkaw0cAYPrOhRbHG8TIrTWbuaWHOkbkPNGm8psJYhKtxGAtHb+S1MidHCAOcB8dQieaCcSuYTrgG3pKhT40WLGccCUdmaytDSkwc6AVcrWhLiNtZoqfXBi3Tt0AK3wgpkMzMopmYErduGE+DzxOxZ6QMXNOF1zqyiEMG4dwtIXWMKEKjWKXxRHPHLgV/dWxiebteKHrB9s4oEXNCHB68MHH5OjMmw3FLuPWFyw6/OMOHCVhjmMyJ9Yb+nomHxT13nDmo15HSr5zTSdfCsBwsXZmW+2c2+2ZZ6oj/6oh+xlqh9IK0D8ww0PdXxvmKYljXyNALeRrY8o12bCwpw6WRJ2VyBdENhplaA/cTqrf0fsUkoe0HYQgczZAuDd/6p8GcFIfl4gWaUNoTaUJvDnt80GTBqmEYnvxsPF//8sAchu8hMQ2fnkb2vyC+s/iFYBNuEHryGP8gfLthx4qMBW87/jqTg2PsjncDU49kVbqcLNeMayPdCsdoGXon7Y6yK9kWcjPkOfPTw/HTRyGcwlMshIJkR4oJexlRD2Wfsbay3weuw0Yt73gmNo2E6omvG55MdFe6q9AL3jeG6LiMK/UBDkvrAyQLvDGshj6CdTNzdGSxTGGnFEf9OVHeQffyuSuyz9jH9If179+fqv9hugFflbB5jB/QvXDWDUlCGZTvv2uYYWfskd05jje4ycB+PSi687vSeNkrvo5c94R1iWYK4gd8HWgZHDumd3IFbZUlXhm7MPuKXgZ8CbR5Ju0jLWWOUtn9iteRPAmTjkhSnC5M5p1zyYT2zrgpg79gx0pyhj4dmNWRQ+EpShwre6xIrAyzA3dhUsvSGuICbp5x3hB7xVbAeF71jV4svc30ZSI5hyuNDy98PEY277G5Y9TRvWOpH9BP2H+hnSNSdtzaSDrzc+vUGcanpYeGaxPcAmlbeG8HxigUwT87qon2O4Ga0L3hU+ccbtzNgyB30rnjY8D3gdIrvlfmq0V8RstPlGMlxIMsiWO4gAqpdfLN058Ttijl0tiMp+WKN2DryrlmgntgcmKPgdo8SR1yzjhNtOKYg6WmRu8jW1z4KSaeh6Jpwm8ejRPZT9jDcFwvbJPSk8PMnctREDXw/sYpEIZOEMVsDSeNt24hbyxOyXZkbZl4VI58YJpCdOgQYD4Zf2UIeiHtD9Qafmvh3ReuryuoEN8Lfdo+t1yrYTTK0ncumzC7kbZ76uRJyRMK4AR9/AY/DzB0nPXEkBirpT8j5/pgvHSeQ6HqzxQHw9jZi2U+LcEo0pWqAdkTQWb8EHhZ4axX2mlYviUOsX9Q//4kjADNwK0Rls7ed4LxON9x152+db6MEf9zwIRAxzOYjbdXIcSFcrvQwgtv84oxbzwGy0dV7GKZ3UTXxLxmlIXWPb0qfoRt/oVsC3ufiO+K/avGNHWsm1DjcJeZyX1gAoxp45E6AGX5mVpXXmNg9Cu/1FeSHnwUYW4H3RjMXXDFMayV3QjeDZ/75bmRh51dlRYG7N4Z7hFq5zp+JZXEEAU9lV993UjVYRGaOprM1DnhwxvDVtHZ4I6DPAzkAsbc0G7pVqnjTDsM3T65mgF3VpI8qIfDBs9QGt0e+HEk66+5SsNNkak3/JLJvSJhxdrOZDd+PVyp4eTaJ3wfufeJ7itdM2X/oOeZff3OcB6k0Gn6E5SRr+OGU4++O5Kv1GGnxc7LfDAOAVcdzhSWe2D/WOl54GuxRNeRYknmnU0m0lFJfUCDEHtnnN/5uAZs9Oj6QIdCyidfz4UFj317kNXRgsO+TRxzYnWGaidGTcix4/yIeEG9ZWyd0QXqC/AWeXDDGEOQCftlYDKCGkerM2lT6vhEq8E8n0ym8ZNM/DZWkj/RWyRnRwqG3pQu6+du1xwJ45OyC0v1xKYYmZDHSB3fcZpZJEJWXL6SZqHiuP0i9PMLr6HRZ6GRyeKZjzu9jPQT6iwohee8UuwTw8jeLcPQGS6B9pwhnH9Q/f40jACdsGX2CDcv2DyzBEN4Cr2/8lENp4P8fkKxRDNgUuP0nfB8IOlJaEqrV1pwXBDc28lZ7tSLcI/CubwxXIRLKjTnECwqXzH1A1yHv1fOsWHGTo8NdxdaudBiY0uB8dLxS4JxprqNLRQeeOx8Z3GdbpRoGkqnu0y1wnZ2JlupEpm8MqhDt8ZwGT6LU9SRh4GuQnk+UH+hq+EUz0dtzKLk58BLTdgycDWBCct+9YRs6SgtnAQfuPiMtTc4I75bWnd8NSMMgSsB4zyVxloSct64lY5rFXt/400aXQeYR9p0YVgB/1kfcbxn3t8UGX7irTRO53DxYN4C3w5LD46pVsbSeGjEzFcMB6bt3E9hNE/m24QukQGPHyfiGaglsc0HReEwCbl10rjyGCKDKKE1ws0yU/CDw+nAEhyLE47jFf+9YqKjpAs9X/HjyH1+srnG6IW5GaKttJYYzStntrjUGcZGtCspPj4Lx8qMcQ4ZHOWY4WbQ+GBVg5Un8ftJCpHh3BmrZ9525v0v8eNPmMuKA37nGqvJ+NnTW8dMnSFZatgwR6e2kXQMvN87o7/zcJ2xCo2T5+XEN8P9/YbbVnxwtC+ddC4MUyf6iOONhGU1FV6+0U7B/cVIOyrVdZyJ5LyyxEavvyb7J6Nmuh6I3fHpJMRvf1D7/jQSg9aqMxZvGrHMLL6ibabzRlpfGJ+G7j/ow4XRVvoGhUCxiZtkPkaPqV+QdKe1wuQtx0Xxb6AtML4mslb63WN9J/kbY4nky8iLfqe+r8RpQw4Hk8PICuk7pVmG18IZX7hcPzj/fmFyiadmpsUhYtgOi+kjJrzjW+A0mblZzrkz5YUaM8FmungKHZxisyNJg9qYMeyzZz4bST5rB+3UcTkQW0DEM8jOQcDPFSMjcd+Z6kL6Frkky9Z2nBH8+UL3J6dpzNFSlk6thssQye2FsRaeZ6Ji0bHgSie8fKHvjeyEEGGwCbl0Wi3k6LHOoIejjBtqLK01Fl1hKpw79FGZj0CvC+OYeMiBdoNvnqoWCWB9ZqqV2jLROaY+EKviL4LZK3bq7MVhuqE2D7aiQ2ZqjvpMjKunHjdsf7JYQwyZpBkzz+izkQRGOqcYTJmQxTPEHaOVJ4Gbe7JNA2tUHgriOt0FxtORW2WaCrPMPFvmOjW2MhDLwKwHuxMWIJjO5gOrFuwJMgvbs2HKArpTv0R8upCzodWC9xash37n9RL4N334zJvMleM5YC6Z9W5Iaqg+E7riTYCzE5bAcWaojnLrhOg5SsGazrAIA4Z7S3hf6QT6JlymgfM8OMzAqykc/Rtl/IWeR15InGJJx/GnmxhEFemGs3eMPVHtiNmofcQ8njR9MCew6YndJs614ptjbIGIR+RE+js1H9jJoljWLaHhZBwPtAnXx0S3wiqWwWaKFmS7E/ONA8XpSLk2SnUk/Zk8T7QX4dgDYiLyS6fNlrQYGOXT9Y4zplTseKcmQzGKC5bSBbN5mh6wFHofyN1T7ciYDakX3NgZRMgBvHbcaKA7bOi0I1BqBomMl0J1cHUJqhDYmIyiYWPuwnNQfL+QxbKNJ72Xz0w7hvq0+ObI95mqkRIEOy58cSPfFk9dR/rHnRROfG5UZ3hmC7sn1AF7udJjR4El3FjSyqv5CVs9aYvYaWHMlttaQf6eZ0mM2fObCl4Pri5De+KeOy0LJy+4uiBOseZEPxaO5En3lV5Hlqr4saFRuXwYtB3gDaINuZ4cQ6VfhUcwNO8o0YCP9HGh65XXq8WEnfncaHNitytjVzb/whodyX3lVWde24A/HbSIHytjn/heGy1N5ByY1NCXJzOWZTxJcSC2xvxdGLqwNcP3EmkiDLc7FaW0iSbCLAvzdGHoO5SdOl94OysvrZDLRL5bOoXxaajBImsnpAlnFzYdeN4WvutJni374mkPR3t54PzEODhkTHzPFWdHho8rvY10gdwy0XVe3MHRDL0eLNvCS9t5DAlX4x9Uvz8JI2BUKYPFtgVnLUU756pABYQ+Bu5cCNbRXneGu+DcA2Mj+IVhu1Dnk4Ub0zxTxsg5XBlk4bRXcpp5Mz9hXjtpjLhnY+rK8jKhxcE8crYTl4QwK4N9pbfEBcctZ8RmtmGlc9CjZzwU3IWSKuJBd4e7KEYdZlNGDL4X6J56eo5uIXRcKmTtSPgsLtL1Bc0TmgxnzoitaPUEhRwWJARiPVAVnnGkH4kaA4d2kjPsz86YD9xw4J6GkMCHQLBC9iPGQa4759KpW2OPkeI670F5v2f8MePXAc3QLx0b35hmy4c2vveFGD/wrTEPgokFawux/kK9PPHTxLIfxMXyS/U0J3BxiIGHTyxfVzYPIV8Y14V5SYRQKL2SmkHHQDYbNmy0qfMrf+ejFjwHsyoMjlwXjBf2FrC1Mlwr6QR7gKNxORT6C5OpVPcgZofoSJKGrxUl0edCMAeldfRovLtIHCy975jFk7VTw4GaCWmRu488c4GnZ5uVuk8M9kB6INrK9wzBFniuuMnxTIpdlbENaIiY+p2N73xcJkwZeW0d9pXjqRRTEb9yc548KUUSrWbieKXGRjCCnDtGbrycylcvXF2Bw5CvnSSGfixcxOCr56md/nC82JGnGsbqGWog24W5A7eTZ230NrP//3QI/EmEA1ZEh+FCTU/ETDgTOavDDI2hLwze8r6O2HPHn5liG0ZX+tiwh2VedtzdsQ+V6BbWkikKdoBjFEzrkA2rUbbuce1AzcjAztYMa5qpL9DLQDo3xCTsIEhqyAw1DXSbCReh/c7gZmVA6NXThkI64cUbkjbOOOGWA9kCyRhMKIg6Jgu7AzGVvjdCEcQISdtn45A6vPHUlpgHZTcN6StSKrel8JECkiJiJrpknBjUdGY7cdaMiMe2A7UBciXbxqiW3DtjMBxV0dnypQzk9CSyMgwHPn32CphsOUrnVjJvxjJfG+Nj5B4iZjbUQ3BFaNOV1k+WybHnO/PxQpkbWhtelZyEMnw2aF3LZyOMs5atdhZVNg8pCy9TRSp8uMaX5ngOM+14w14XxvfOY2q85JW7F37thf0tkoDJnnBRUlrQU6jOMNcHz58MIX0jtCe6C+Ey8swVLcKwVuJDsaultg3sSjgbyQ28IrT8oE6Zw1xZT+WcN1pyzFZRHRDj0fFB2wJ2ArMvHEvm+oyc04gfhFpAI6ixzPWgDSMhZcpvKvyd4RgXbH/S3IAJI8PxpP+Fo/ytIk4I84D2Jy2OOF8IO9x9ZFw8U63s+0C+WHxT+gmgTFj6AFuxGP/EpBuzeRIXsALBXCjvgrqD7h1x3/50wwEVsHXDhkC3Jx2LvYIRi44dg8LbdyYgm0KwA74mhj4xV4sNAV06dXAssdDHQpw6NVYusWL2lfFL5dxHfK7YAZajkJ3g0sgmTyTCyBurN8w6U2PDjRNSLSuwJMPl7mhByCpsKdC1w7bCorwdDilCt5WCgTFihgzWEVJirxm/R9YPJdgLDCvGdIx9YdDG6EFdYnDCkCwuX/C2Yb96qn7upeMM3mQkFIypUEZ0rkzN443HzB1XLF0uvHRLpCODEFsDPH5XDm1ss6H4A20jPRTs0UBPtHe+i+JulRw757zj1WGeAyYr2Q2o2fiijUeKhPTCdhM4IouxxFlooxBCgjay2UjLnZQiY8mcFyXtA24S6jFzSmdKQt4tvZ7M7pV6QHGOsQvVDKjJvPPO8dMAA6TLyP0+0X1EphM7Zeryjfne8Mc7Lp00hC1/pw9CcyemfVbavfqEHQfslGDxLNMdMzwoc8WbK8NpyYvylStrmBlORzUDNoJ5XjA5kNNEC99ZU2OeJgYpVBJmT2SJGDnxLSGtc16gPyyMF37d7ky28ldhpNSD6FfC3+6Um6cOnq6FbjxXEskZnqPBmld0cxQRzjXiOrj2wmBneuikwZFGRWpDdKLLSbLKqILPM+37A2siR6lMxv9B/fuT8ATEiY4WsoGbsex0fHakBi9T5TRKyFeyCjpvFOcpu2esDfGG4ArRVXQHgsEaA0eiMLF8he3hPtspjwNvrrQ5frbgSiWmla+jUPdOoVGXCXN/wwyNXIXeRnQ0EJVOwv/KYt8M0jJt6IznwrFmgmZStOhgMTVjBmHKyr1YnGt0LKbOGHeAaeQkeJkxnPRgkB7QUCjlUyZjNbSxM5eF1HdMKNT1Qn8knC3UHFDXsd4iuUG74br9rHA0f08Xg88KXigdjHyWkDo7cAuGj/ZEozCHzuXsHEYp3jNTSE1AB54m4aoye2H3V8Z9ws4H2Z7kaeC2NwgD8b6RLxYZw2fVW52hnrjmGDQRpwW7ZUK3dGNobcReBmzc6bXRxoj0LxRfWV3jozbC5himiGudR7eINbjRcuwH0zywlMQuE9IPSllgOGlekNK4msZDLF/2V9ya+J6F0Tnc+WR3QhHL6+L5SJXZKb01khn4EjvvVpjk5IwL/lLwW0cvA25stCL058loX9nOThx3Jl9w6UKkorcBEwv6+Cx8CkbpxX8+N9WTXjzzm+UYPdlGbq0Sw8hMocRGNBVphmZX3K1xue98Lys1bLy6yGYCfhtR07BLp2+WohuOCcZK0ZluMj0ql1DI5oLWg5tO/FJPtOU/XU+ABrk4enY8i8PEieYCqHKvN/Y+8tES1INeJi5nQWj4MdBNI4S/ZjwmWFbY4TIawjijg0zIF6cAACAASURBVCfVz6aeoSfMC5iXB0FHprGQnx1vCsfHE68PDjtT7r+j0UlWaK1jx4iWjrlVLIH+TOjQicbio8IEkqGeEz0oNjVchtwcCc/NTLTi0LJQe0GypeKYAsh0UE2gSUO00OyElcDVWerVIEentztGJ0o0tPOgm4KPhmY/E4DDaTCXiUkOov1Axsav1LBky4Cnz4FBhKkIMkWGZefxeOdqKsEOZBz7Esi3mU6jqsG2CXEGj4evV6wazNlw6wfHaahiuGbP80h0NcRbYHhMTO+NcDSMFi5qKJLx5YY+K90XjrURf4rIWmm8c5iKu65EK+yy03Xj8azY3eFfTg5jOCcBdcwqHMeOuzqswpY6xTjm2hm60ErFx4kpNt7Pr1wLRDn4vimT4TO8unmCG7FD4uM4sc5wps8e/eHpqC5hh52nHXFfwW0np1iO0rjvV9ITTBt41p1+U77QcUWptiFmwuyCdYn+ZeAokHNn6BP7WBh7w9VIlUjk5Ooa8bXhzZMUhFMUArRukLJT7m88RZhXZebCu7/hfeYcI70dHJtD1og1gTopkYFL31nbybw47GpZQmaKwj52Ljr/QfX7kzACgjCLgkKrhTYkcjsIk0XazpfDsOjMOBS6FJJ0Zsl4u2HLzMfzd9z7hvbO6Ee+Pwq7nhgLBMtvquG8ZFoRzveB1O98VEOfO+UwZN95DBOS3jA6MUydZQ90Kzhr0A6tWJo9Pj/PgMwju4w8bYRmUTlZu8V7j04Gkw0ijsOCuTW8ngzBI64CjpovhKzgIto8uUDfHth8Eum4wzHPDjc7shr6KuhzxBQhL4Ih0IgkkwiPzr1nhqz4MfE2TMRhImpCtxPXK20p2PTK+R0ma3H1gtOdhU7MDbdnbBeqKmGuaGlIK7Rn471c6CZRXKCbja9U6v0OztLzO34Tmt1oVKobGdtIyleWMnE4y5UAfaLmxhwD1SknI2KVaJ4M9cJA43pOqCmEKdGjxxwT7XT0aeMMA9autP3TuOg04+PGW/hGNQ/C9Rs6RE7716y3jWx+ouhAnjvHuPO6PdGixD5zwRFcZ7KC1Eo8Jtqt0nXAV+Vmof7OsruZvhi0TKytoz1zhoY1Bt0Le62IOmaz431mXhpeO+Op/GotXG5CfnnwKi/chxmqZZsOvqQH8dEZH5W8BdoB3ht6CxhbETMyykhqhV02Zv8L/u3gSJ7bDupe4EuknzOrm7nkwjVDUk9RKCbDzwNx63gsfbNsyx9W9T+NcMCKinpwjekqmE1wwWKflnOe6OZJ7QqHZ/AV44UWO6dTbK3MzsLoqLUTT2G2DnGVqp5+C7T3ih8qqXgufqMdgd0J9M9/n+m9sIijzhmjA2euiBYCn1uXh3HcKtxDxxrFlhuFHXph8OBmJX54qu9cjRCrobkCyTJ4j44nPXq8Qg2WfnjgSRsCtIRYj0kGvRSkWGq9IvWD5htePteS28DVZly8cNgDHQzLmTn6CzKclDEzzA7zMBylYKqluZmhbDQdkPBZcNQU6hxpu2fyBVsNaa1MT8dpG9onrJ5ksSzGgFEKK2764Lrf+LCeMiYMgvGF9U04XSVqwrnARUZiOunXimTQeCWNyuQGSnrDmEDgpNlXMkI5Gxdd2N3PuGkkpJNqoGWlLhOXs3KijOPIsQe+fjn5ee9cgqEeCZ0NUSq9Tkz62Rmae4fiiE4xYSCcif410T4cxTWmesNJwhRFryPYk8ej4P0r/llQX7BOkWMhf83UtwdjmCiXBVsK+QnjGD+b1lpg7EobEvMR2daZUw/Gw2PGAX0K1kXsVNFHI0/fyPWJv3R4c/jQ+VguyJ65zJG6z7TeMNZgxw5WqDWTMZhXx/p4UBixJfzfzL3Lr2zped73++7rVrVr730ufU73aVLNqyxRVCwlMiI7goM4AeJJRkYyCBBkkmFus4wCBJkF+R8yyCTIzYAgRAiUxHFsS45lUSIpWqLYbDa7+3Sfc/beVbWu3z2DYoIMSAuSZYAFFLDwVa0Pqwr1PrXey/M8eAw6eVZZELlizcoWHH3JbMJB3di7lk0b2rRwt/gfmw78dICAFJU6IG2mLyuTdVQUyB3GP2BaTfQCGVaiLqA0Qhfq1tElwSQF+taQ/cRe7AiMEAZ8WWiawHYqtGaHNgul9qhGMS8rCE3eJvROkcaKaC0yRaJq6A4b6ZgR2RFrgZSxrlJdJc4OZTaa5BhrpiORB0meWlSzYiKkXAjGInPBWcG6VYRVsGWMMfgakbFD2DNtFoyto9kU2WYyiap69LagS6Ji0O9I5ruA8xJf9+AeLhJkm0DaFhMkRQJWoIls2sMksbWQi0OjiGphXxXTzpGToEstwc/UrqC2iBeChorPmlYH3L7jWALtFOGqR0bNvM2IK9DnHrWbmc8tgw3MfeUQBCZbHsqKzIoOyWoU1UrcrDingEqZ2EIH+GCgWdBLi9UGIVeChM72zHPBDB7hb/BupI6CpniSDhhZWGJHu6+MvjI0lTw3JBZE1RdgaxbKZmlkJVtNMgqVFC4H1GBY7z12MLBJsqjYunA1KI5zRXa3eHVGe82aA33sKcaz5EAvM2XtwRi2fI+2mbQ31NcKt+8Qm8DLhS4b6mBZt0TrHSkfod0jbcIIj8qFo5fsQk8cZkTv2N9n0i5Sa+IcWqzyiKwoRROUoC8LKMXRZPQ6sFOZkB2EjN6fqGtP8iNy0IBjCxatPM0SOMtMKeWnuCaAgGGixoqUki6DXDfUdodRDVkKECu2tuhqUDkzxBYlAuvjlbZq5DjTy8KY3jAEhYkjmhaxWdqdxJFJqSMKzzaNtK3BbSuqlcS5oA+FDGhXEHFGHIFioE/oFCiygSKooUGKSvKatVZsVXgpCF5RjSessEUIQlG2DFqxLA4jLSoodOMoArQtlx52btlsRzNZQl5QcUN4kPGEsg4/SGTW5A8DzJZgLK0605UeY0A4Q6kBsGjrcSTmkpCzwxhBsVfQeRIBJTrWvSaGHqsshZFVZpw3qKuKzIWSJR2FYq5YRUNKMF8dmCoEEZG9w82PEBT80lOuBKHdsZssD1ukyoJuBnQHZ9OTo6X1klIX9npB7CN7PLoazFCoC3RtSxKwJsiqIXQzvk9sNbDs7ukmiWsq2fQIYdnMDqE01WesdCwhIfyMEj1WKDadUL7lsY0U4TnsBWoyNDqwSUl9M6KMRI8rqzkjg0ZIw3hWQALxGrEtGCStgSUHWpuxwZJNvjAC9YTqwYuePh3onYOtXMa3hwZTInk+0w4rSi1I12HLxBqOlFLI2SF0ZR3O+CwwrwqnOpOLZooNeX8mzgLn4aoGXB0Zoya5TF8drdOs6cy6BeTTyhlDlhHZ7olzYi6CxIqWIK3F7nY/Mfr+3CAghHghhPjfhRDfEUJ8WwjxH/5o/T8XQnwshPjGj57/5p8OAZXdZBlcInUdSSU0hkYpYvHktdBYRRALQVdIEMoCMsDJIdyCTYr59Ihies4lUHaF7ApLXknGMWZBVhGTNe5q4FwTUSdyOlCNpBwtLmRW7xDVEEKBLAhHBUYj1YoWmeRWRIkgEkYLggAjJDVUmjahm4JFoqzDGIPMASEmKAEpV9hmBJ66QvOja5A6UFTCIol5oGpHzgbvJ9QoKboQB4CIiBF0T02KrhwQjaSESpEbNSrGUrkZeqzYsNEi/ISiUnUkppnsNW3aMEKQVMdwUFAS/qiQWuOR+INkVQnhE21zjbs/cxsKTkryapBphBZCluyKQK0joS+068CDK+RoiGdJnx4QbUQ0iVEYZqex44HgBkLNeJO5uskUN9HKM7ZYbApIb3hsG9qzoBwNsyws60xyZ5xtyUmTS2X1laoS177HtwbECSUHmqTRg+SMohSHGTO7emKSPa0oBNeTaIj7FhaHvVrYNk9wAp8GysNAU67x44lVKDSCaY6UfWJYDZmM8WBjg0orsjwwDyO1D2S9h3Eh3whk11PWDikyoQQWq9n7J4ylI26Cg9nT+YyImbBfaK811m+o7GjvniKuBfEgOWlHtJK+WuYj+DVR1gmnO64Oknp8YDcqQh5IWyW7HTud6EWCH+kv2lP+yfH3500HhBDPgGe11n8shNgBvwv8W8DfAqZa63/1Z9irSgZs4wlRYA1sQaJqwToQeSDzgKAiokb3sGyJLiu23RUpP9DLgWKhmkpeCymstPaAWDd8SnRdz7mO9FUxScu+1aznxK0oPLQBfSpkdVFtLVYhlkRRlmIkMiYyClkErU54KShC4lREUMhBIjF4kdBCY2pg3rWYh0CpjgHD2q/EVdCISlAeKQ1shqQSVV2h/B2WgreOvYtMVHISmKxBW5AzIWnaqBEqs4lMX6GkwqINTWfZ8ko/9NhXksCCcx0PZaUVmlISqU2UY0PbD4SQkNcTzakS6uUzHaogJIHsHyPyiNpWzrWnaQMlPEZxh1OOByK1s2R/x5Ptmnl4QIUWceVIZ0/KEpELcu+Ii6K5KsyzwO0X/NTShgswdzYzbxWjMnYsnExC1sc04vVFa3DbkcSZNhlSCZihAwQ5b2glKFsDOaEG2LaNVgysNpOipNeacS3o3jOsGalhWXpyr3BVEtKMZKD2E957ZLD02XGyloGZLa00gyOeC9UKkodWVlIX6Z1imx1iM+ibwrbO1KXDdJqlCjqmi2ZE1YzS0tSMaBJu9jS25yw9/dawyEo1FdNX4kOm5AFtBbFbMKtmkwICqLgiegmhxyRPaBI577B+YttLTNXUUChRI7tIKg1lOjE4i2ejNlfIpeLj8S82Hai1vqy1/uMfHY9cZMTe/vPut5MeUTRFXub6DQlDBK8pjKi+JciWimDVGSwUVdhvI2LVKFERccTcFcrmcaaBNCO0RDWGmgu7pCitYydn5Bvoi+A+G4rds0gJ+YqaLWYD5S63Vyo2FBzGRLSNRJOxRaLrhXxY15YkFMl2DGVABkmlYk8CXTOGjbNNRBlwdaBkicmaFCTFgqwWUV4jhSZajSiZ89yQJ4EompwyiQm5GR6RWPtKVwoDlVlfUfsWgcJSaZcbwnEm20SRinNI1FLxybN5jZg0j6zAXL2me7zSjZLZC8gaXQVVXlpUy/meWgzL7obOSCab6LoHoo0crUSaQPfg6IolNBtCXMBy+iyTpCR1gJU0x0qnIoPpGFTCx1sOXYVs0NJwKpFBX1HjNbQGl6DGz2iRxAKbLBjdMLmOLAxbHZjyithu8EWR80VunFniyhXn3LCVQpcrSY/IXSLHSJEta3dDbBWVmdXdE2PDaka0EJSiUUWj3IaNkaQ3bjighEUODcMm2PeGYCtys8gzNAZ8r5BogjR0h4rMK7tUSbLFpAPn6pHbQo0KHQxrpwk+oacnTEESdCXJxPrgWQ4t1SZmfcY+GKSqmJRwcsUIR8UgUkC5hIuCnRyJTyvuaAh+RsRCMYnaC1SRXLnMaCWiamyYSXb6ibH3F1IYFEJ8Hvg/gZ8H/hPg3wPOwD8C/tNa68M/7XwpRBUo3K6S5h6jAjkHkm5xeiWXilSaPB+IdqXqGbk2dC2ULWGcoKw7ij2xloquHUJXZMrUXJC9pomJdOguldmtxW8NQleMm5kjqBiR0pGkZ4iVsxa47Ei6ULNH1haVC0gIpaJMxhYIRpJSphOSKgpbU1DTDZKRlkSixYsN0xnSnMEIZGNp40xcDaHLJAQdmmoE4pgIUqOagFgEm06YbLFVM1sNMQCK3khE9CxmjxMntuQw+4kaJWnSCPUYq47kkggpY61CmYQUT9nsyj4JTiHRhHihvoZAtQ2byohzi6cgDoXOHMjriKwJIQ+szR03tUGlhik3RH1kULe4NnM8vkHrHbsnNyh7i+oNX/7S5zD2MR/+3v/GV7/yV/jepx/z4fe/TdhLnsprhref8Hv/62/R7iTaWV7s3+a7H/8hLLAOF2q0NJbbeWHsYKdaZmkJ40SWG2IQuGXH/rBxHD1Fa/RWCfmKOrzBTYp9MzAbwZw9dlZYJfAlkHcCUwVD1JzWDU1FHSrN2VCKYRkig4L7VBBZ0ewNW1zZr5ZgEirviFtFqRNNFtxpg+0E7drhhxPutCcfFuYHBwREXSm2oxEzyQzEyaGEp80VrSammx2ySOpyolMCL3e0pbDOmdooRFtoKMxnge7i5S7FaBoE0RhqrCgtyM1MMw8Eo5BpIudETT2R+Z9Pd0AIMQB/B/gva63/oxDiKfCGi8/Af8ElZfj3f8x5/5/vgIBfuhUNs4hs2iJTRRtDChtCJGqrwEtklihZiFlRmkAVCnlQ9A+JYDN6K6zK0NuWUCeCcezijjQmdHvEqGvOsiLEAkVeCDtzpKYWqwKiV2xngayeUPfslGfOCScUgyu8SQVZFM4qFm/QtwXuKrgZkTVSZbpiWLMiGE8RgiZqBik4UZGiELVCxw0RB+gXVHYsMeFKJiuHqJa+jkziYrcRc6FtLUu2aCZq0tQiQUmEBJskEPA2QRIciuNkPFI21LBi1HBhD4aVEARGtTRSM3WFLibwEPVMF1pOeqGohi725AZUryBsSCU5TIZXw0Y6B5QJKDTXj14gpEEYxeHxczSCWa900vH1r/4cr6eNL/781/id//k3ePzez1C6E249cC4Nn/viNR+9/x0aOfDBt77By+mOJkiePL3h0/e/z/wmse0jNjhEPbFQGaQhu43CNf4s2IvCuU3IuVCfSJrskRnS2OOv4JAWJt/gg+NgEhsLqrUUv+Br5GAt1VkecuGwWfK6EYyj6pEiKm2jmY4ClQvFdpg4o5RCGcO0elzTsSszSfR4PZLycDHKsZ5G7FiuAs3rQpUXg5dsIm6qrEogTKKWARFW/KARQbKLmVor2fU4N7LVW/AjNHuiXBjkhi071llSzEbJkmxnOhq2qEgxUVuP9LfoqxWyI80TjejJbWZ5+PHpwD8TCAghDPDrwG/WWv/rH/P654Ffr7X+/D99H1klCnlTSQ8Z2WjkdqHYRipWV9YkkbJQmh6zaYL0uGtHe9yYUgAjKX1HdxKkOpLagTZG1p2lewjMvcZ4idEg04rCcY4LUhV2g+a8ZPqimU2kZMMOwylWqoa9CoyhIBpDSRrEhskSKwTRFFTI5GJIAzgVqac9uW6opkOawNImmjeC1FxosQWHCxshO4orSHZQzwi1YYNheSSpq0YHTy6ZxhkYE+gelSdW02KyR+uOxMQQHxHcK85R0dFSnUVsR4xsIUbOSiH1hpKOvjisaNjSiLi6EIP86hnaSEyGFDXdjQav8HWiNj3N+YpOnyi3hvvXM7J2vPXuM07iA9o3z3HvvcV7X3pOqzvkTmJTx3s/+w7rlHg2POU3fvv/oIsCea25bg48evpFlN3zsH6Xugbq+YH/7jf/Pi+eDRyU5KMffMZ5fUU6wk1TuFf2Mn2YRkR/8aAwy4TPexrpafQGwpCFJ5oDMk0kWTCbYs4bew4EeSIWaGqLtys1CJwMGPWYUz3S2p5VFfal4uf/d3hno1jAe3bDwMk7UjrTWEHJCWEsxidK6Ui9oEsL59CSrKHTgaoTaix0e8ddHZH+EclvtDqzNitiUVAU2kiyUoiyYfUNy7TiFKRmplqDPRukLKzCYmvkps+80o5dXImTYb5NXG+WMFbkPqJweFHoR8moKzaMbDdXxLvTXywICCEE8N8A97XW/+j/t/6s1vryR8f/MfArtdZ/+0/Zq+4sjNmA6RDhhNKCJDQiKWwOF/GEncEQKfEiBJqFurwvJZRoqU0gRoPCY7eG7WajNBY7ghOJUhpKOWGdwK8dAYlLG5u8TCq2bkAE8DYhlo7GnohGkXJEpuHC78+RRdxg5BFchQmyEqjeklZFe6MZj5f8SyBRFSwCIQpkQ+nBToWleBq5Q3WFTYHPM423pBoxsSLtFXOIyGFBhCtKnNhpTewUhEgUCaIGfakPjFnQScEcd/Q7w+gXCJE2V0on0ANsbzxJ7LnuE3VrSTqhQ2SVCy2grMEKxafZIOJMXwcSFdvD1Au+ar7EZ8sPmVPkb/yNfx0fCm/fvuDpu19iWj7h/OrIl37mq5yrp5ctZX/i/d9/4PqR4dlbX2ApiT5EvFbc6w35keSH86cMV3v+6O/9Q76XP+Kd8AxzU1HLxu3j57jHK9/7J0c++/CbnD/LiF7x4uYFf/z6DzEYXDxy1haZoNeZExqbI82q8DeakCQcPU3X0+mRZVMwCLrFMXYWezxR28AqFKboi1pTKLhhRxQz8qGnPZyYxw7dKprVczSKxmfsYBnzRL8M0GWWksAUBt0jNs/iV+hbTE00oWWumagrgoCWDQJHFJ6yJloRWPeXicl+lixxw6QBdCWIgRt5YqwNKUfMLvP4FPnskaR/bTmpQtdVRNFssiDkjCoCITVkh1oLSSV8+PHcgX8WG7JfBf5d4JtCiG/8aO0/A/4dIcQvckkHPgD+gz9tIykgRInpNAgPqoclIPqCCxpvDSZW2mUh0aGqx1Nx9fKD0sXRKk/WLdl7UpuoOnBQgu31QNVHgtCYVbE01zCeSEPgekucqqJUi8qCWgObBLEJjNiYU0bJQk+L1yNRGDaxJ7sRtVQSDtkFVGnIoyK1G3m99Nl9rtgi8J0kGUedV1SxpGlEobCiJcuNOGVSd6kkV1EhWcJNQB8nXLND+ETIZ7Tb4+NC3ApGFVRoL7yGpeKtopgO3xrkfCTW4WKqoT2b6bGp4l9rlJTsrj1ncY0JG65f8bpFbJqarljjGQ6C/SRYrEN3AjFdahNvt8/4ZPmAehrY/8xb3N9tvHhyy8N8Yjffc9V21EPP1uw4h4lHbcu3vv2KT8+BYCpvfbnh/Ccvka5hnU903cZnFB5FgwsRc7Pjr3e/xqfrK97ZG8LO8e5XvsD3vv0x/TOw8XP81X/58/Ry4Pf/4T+gC5Z2EKjS0eqF4ypxQJM9sVpiSWifkUVh+spUHmjjE2pf8OFI2Te4GJF9oCyVnRE/skTMrLRsp5HDTeF09UDOFoXiymde1QYrjyjXoxag27PpjSFaBrVRk2FioauK2u6pBbRvmO2CMYZmqtA6wiGQjwWbAeVR0lE7jfoo0MhCPwzM40oxCZla1royFMlkCqVaTkJRTjOyaVEBso+YnJFDxokdm4jYOSIajZeBLif8T4i/PzcI1Fr/L3685+Bv/Fn3KhKqLkQPThlimBBI9FJI7YqQilQVbVUXccsqAcVSIzuvqHLFDzu2KaCUR80NrVJsNSDCPbU4GiGYr84MoUFiKVPFJ0syDU3wRFvIWVOjABKby+xjBznhbUBkqMVhSJQcKeaKXR6ZthbVBbRd0Jthaxesv+bGTKy2IFLCycBSNIkRBoNYGja5UGTBtppHM9xLQ0wJ7QLJ94guU9MMNuNSx5rPaCmp0RCjY18n5knRCk3uGswp4euEOQyYc6K4TD85lJkZowCpKU3FHweyPDFYg4oF0sIVmaM401jDskrKVcFMDdN84vnVO2x2ZXjxmIf3Pc//8pf52XeectVV3pwrN2815E9f8+a646bd8+xK0cYD85yI4VMO+cCjd14QPp7xwfPy9UdcP3uM2joeXfW8v36L6/QWSnnqs4EnSfP0K+/yaLfDCsOn7Rs+/IHnV/+1X+Oz7534u3/wh5zvT7z7uWecVAMP77MtlqYJjNnhyxvEQVOzhFgQrnAuETe3ZBaEKuitkOZI0YE2PAd3JlVBsBuue4Ib37BNPeuqsekERkOfWLxCt4Fu0fjbjF8zezboBMUacmrJp5Z+f0LYTC3q0tptdwRdiWnANWdKzqQ3gAVEQdOS+kz/+sRyozmeLCUKHh064sMZ87giHm6Yho2bJXGPRNvIMHTMR9jvJuJoKU5T68WEt1s8Z9mx9y3ZRWIBlh8ffz8lY8OqCjNQzUpTy8WJNUhEZ6hrompNLBpiReMvXqzSopXDiglfDVJUtpwZlII2Mp97OrMQ3RUlrAgPTrRsJPY1cFY7hv7EuCiurOVOePZbZTbg8sXFV8uW0c5Y3RDH5VLZzw5VPLk6kpT0uhBkIE4KriN2lajGIU6CLD2p6zBjRZiZYjUlR8gGKw2meGZ5i8kjaYCweAYlWN2B9nRi1RYVN6SrJNvipKKu04V+agV1Xoh1uDjVbjPJ3tJLEGXiKC26rphqCAmK21D+wNCdmMcrbBPYto39oVIWSZsd2UDYBE5WXpXC9bMeY655evt5fvkvfwHsgfluph968ia4fVtxLIJ/6Z2vskTFp8sdj94VxI8E/V7x3Q8+4s3DggwL8urAcg68+/gJjx4/ZqwPjNtK/CRx9Z5mfTA0jyXLZ4ZRvOLp9bu8eOsWPVhKr+iPhm/94Dt0pSXJD/kf/tv/ieMnb9iEJzhP91DZm4ZgImMO2Nqw6EhVmsyKCwofNFpIcty4sj3tVeLVWWJMQFSLjJmpCkReaGVHyIbBeYRsmX2kaQprhFsdmM0NU1qwy44yvEJJgwuedVXInWNbenbmAXxDKRO+v8IS0FtCVMvcFdSUifuEmTsap/G5Z58/ZRwaWBpyPKOtYxCWKb6hbQVrukXkSo0PLKLnoAOpqUg0i5KYWClig21A2JEuWJadwoTEOP947sA/L1fiP+OjYsoZuQpy6ZHtSi4FOVayllxtkcUYgisQCjvjWGRAIpkxtDZiT4bkYAmFXkJrEpuq1KWSJRysAL3RbBvn0iDyRhQCNyiWJDmMmegKVhkQPT5FfBrBQFkjMguUMWRhaGoltZbsA15XxLlhaDRyzsy6wZxWct8QlgGZz2RjkVWTmoJ8GIguE/3CYFtkOlOcwaWFhkLMAuE9yyCR0RHwDKrDbzOLrYgEJhQChuwOKLsgsuX2umNcZmqUTK1iVxITCh3DhQkZLS4fCdlxfbUShUCHnmma0VKymg3bZ8SjHW/uDKqtFPY8+5nn/Cu/+stYDpSm8tlxZPKveHJ7TdLv0Y73vIwzUoOezvyTv1dI4mOeNVdYO9D09tJu+/ANWMvbbz9nFZHp4Yx//cDjL34ddYjEH3zCo+4df1n3WAAAIABJREFUvme/x6F7FzvAuLxELNccdrf4Av/CL7zHTnW8/Niw9prnz2/4YLqjvz8TjCDZBl8d7Qq5UzRuJc8ZHQaW3nHrF+ZhxZ0b4k1kfpkYOkuVlhoSpay05RHmKsCaaHXhITyiP7whY6iLoKqG+7wSlzP94wOzP7HPPSKvjLq5dCHmgO0KU2lwrWcTiiGf8eIW222MooXtBJ2jJEm0BWM6mvWBujPoGKk64FxLzROrTFThOKHo7ARBMmEv4i0JoraIs2awK5uyaKXxw8KhNIQkqMmyhvgTo++nAgREhZAbBl1oSmFJGtlEQonsU8vUSKL0uCSwO40/C7LVEBKmS9StYXYRF6CqG+btHgyQD3R1YS2J6ARrqVxZh5GeoB3rJDFEQl7QuqUkRdYFlz2uCLbawKgoYqE1ilmAjeniziM0QiSaLVPawBY2aqO5iZHRWvwWEcUjs6FWSy2R3UkSRSWXSttYlLjYhJe2Uu8ryhyQzZk8r3RKIPUDsjSccqKplqwLQlW6nSQvmSAzGw5rAst9ZBO32HqmWSulCoSMSGfYtGIYF8Y97NG8NgE5RqiCtq3kVdJ0haAPHGKPfNyiauF4/JSvfe1v8dbjpwzNu3y6fYDqMz/8qPCzN8/54auPaEVlff9P2AbFY99wPv6QOmTO/S1fevcLvFNX3v/u97nLhrcOz/jheKS1Ay49Qd9o7qcj+fXEJw8f8RH3vGNecHN9MY991nyB5knFd4bbruWDY+IHH7zkm+f3YTb8cFsJUfKzX/gXebrr+MF25s23fx9105LihhoHOnNiqROCzJ2AVmjEoZIeEvKgEKlwGjOPROU1LcNbK+aYyaLl1C703LPMGdkWZKiIJtAlyelKo1ZP6zTeVVQJHB5gfKKwp4EcF2wBn67ZuSPSKVyo5NFjdxvRVth7OBuMj2j1QD4ktmOmHATdqji6FSl7VEroCt0UGYdrnnQBGQJ1c0zyxGELuGx53bQ0c2YOBuscMydsZ1HzglGS6SfgwE9HOiBUVUrQacksNEJU2DyqgSwbshdYvYI2pByQ/ookN1zW+GblHX/gVX/CFUvOGeEri2roMyS1kNIVtp9IOWA3xdxJtK8oNSDLSjEQTKGJ6iIRvpOsCQ7BsuqI9xf5MjFHxNN6sSBjQ5wqtdW4tSM3nugFVm/EatFSkEPCtoqUNbkEnNDkDI+F4Xxbia8zWTtqWqlCIHWA0lBZGGhZsqdqQ62Bg2h4zUrvBclUKI6aMwJFuiro2OFiIcmFHB3KejYLNSpkFajsoRfsg8DXjjklGgGxicil4Z1n17yZR547x6P3fokv/sqX+aNvfcrf/Ju/Rm72jH/8fT739S8z13u2Gf72f/+3uc23mN0Vh6fw+h7K/fe5eX7N9dXnOLw48Ht/8H3WrfL4Scdf+5VfYtMdWwp84+/+HaZY+Su/8GW+850/RssDv/JLv0hMG6/OZ27efsTn6ufpH0Ve6pHt/ddUDNPo8dNrzPVTXuePYQ00jeF/+a3f5OGb3+VUz1gaxEPgtNvz+FowLAa/F8S7Bx7SgNzuMUpRqiBohdUGGVeUyogUoF4zBo8R0OiOpB6Q+QovRkTJ5L6SU+H2PLDakW5oWe5vMN0n+NLiiuKsR8QgcW8c1QVyHXBlZpYWWduLqehWWHeBmwfFcp0ZfEQsO+47ie1nxH3FNY8hrZcJWZPQsVLWSM2F7bbjqgpC1AQ5sa8t2a+k64F0OiFUQxaF6Hdc1Yv60sKbn14qsRKiymeW67vKVFvWeMYMF6utrRjEANVbxBY5mMIsEgZBFfIS9KYha0k3R86twTHR6ccclxP7LNm0QDeFJRRklpTWw3IR1zBmxzGd6apiFQkRKuaqQa4WETaQCu8ydYFHjeS8BaJSmKbgZ4NoPPvNcDpkuqNmlrCXiTkpOqdISSLlRlvAJ8OkJbYv9EtmqR2BDZEiWVu0ydAl9BlyqeS9RK+SxgvOLrPfFF5HUjU0QpNFYrCZHArHejEmSVVR7Ayq0MQe4QeCuadkhW43NrNHLIE+wloMtt1QMvHk0Qv2e0czvMW7X/t53n78mOXo+eLXv8IPPrxDPpzYzhLxLDPeO/7427/Lpy9f8uILT3j+6AXnMTDefURzuOHLn+85uxe8uLkC5ehoaZ/2VCepY+IP3nyDv/T8F5jHEzJMiN01r948kN6s9H3l81/7BZY08kg/53vf/G2+8Is/R0iSevJw0+KqQjYD3/mdP+Qf/MGv88Hv/hap3BCwPN8pxqMi31jW8BnlkwLWEFqBVGf8aeMmaeY+o4pAGqhTIjaZ6htcv0euE1UFQtcTo2SYZ0JRyFYRl0rTCHq9MsXmAtryRNoL/NSxqwtVXL7bXCJKGFqTcGgeskGnmSQEgyh4pRFK4IVi7yXVWWobUEdLLZnabUjbM5bKLk8s2wFXj+RgkG0gNj12FWQ/Ew47ZEj02wpWEKIiSUs1E2SNrJLN/3jfgZ8KEBBC1E60+GqxTKzKss+VUQPZ0wDJSkgVJxTTrUX6i2gnu4IJlugqfjNI/GUuv9VI0dI1M2EWaK2YN9BNQMZIij2tq5AcijNRSLIQFDIlZkyrkFJeTDK2hJcOxyVYr03lVbQMLMypgpS43Q4/RYQs4BW1AaUyel1J9CSRucEzSWivNeVNZBOa6BJXiyM04qJjaDUpKuTe46bILBSNAJ8CQitk1RhZyalShSULjx4ycTSUmpBDoc4NVklyKhhlqCIgjKXUhVoyLrWkHCj9noEjvj7m6Ysn/NVf+zd4cTiwNT1677CdQr/yfHL3ivWs+WR+iTtPhC5w/Ogl65RQQ6acDN17LcN2jXir5Xqu1P6ar33tKzwsnl2759HzjpNXjB/e8eKrT7gahkvbbj1wr0c+/uMPub464JfKKiqP+5aHhzcc1DXmmWPo36apE0ULnLhiGk/89rf/Pqej4YOPv81nL0/EJ2fefPdb9LXHrSvza09tem4fwfHeM+vELjcU50h+Qcw72u5MzlfMpiDURDtHlqbHRpAshHZPDZGmjKzlwL4WUhfIXl8Gv5pKj+PNMqIPhfpKolqHzJIUC4OthLKyAXa3R5WNfLbom4k89Vw/Ddx9lNhfFRbbkydDNUDSiHhk5yTSSh5Wg9oCvaispkfbB/xqqb4l2sKzxnIsiaJH/GbofUFwIF0tJKlw0TOO609vYVAAqa3gF9amRy0bvgOxVZSsiGwQqcEUiGJBv/IkV6itYssWXCUGBzKgRY/pKylCLJ6t7ikiU+JI0RpRIDc7bJiwUbDgkYPAjBWvEylIlAbkjjKdyMIhBFy5is8dMVe82dAhI6zhphGMISHyiNBgN4U3GzsHITSEOiBFReaNewYUiVIUsrF0WeHDxHmIEBU7VTmXjN5DiZXJZVzVrBWkga70TDJTS8KoliInaoY4DigbabbCoGCrG3M1FJXJIqBSR00LjVBswhGMRxmHWSPP3vp5hucHnrz7lJ8Zel4dR27eesQuX7E+rPzOd77JX/vaL/ORfcnLP/oDunOge3LDD/7kyON3HzNOgnc+19EZeLm94sv65+i+dMWT5in6uucXv/QVqincTYVnB8tf+vLXqDnyan3/MmQzDPhP/4RnzbscW8sXd6D2b7HJV7Ta0b7VsH468Y0P/xE/u3+bUSeyvGPYdTx6/AW++DZ88Su3lDRxvLvj/54sd99/yeIf6Oue1c98+mC4MposK7VUxHRP9ZncSTYn6O4DLt2xa/acVWXXe7wt6BMoKkt1LNpw20rGKRICVCLtFlg7SX8vaB9VullyNzjsWumvJeVhYmFPbBrs7p7ubmW73uFPCbM6pFhYXu6wJjBFze4kqXjeoBn0jAyanCxnGTG64qXleDB0cuFEy1VV5N1G++D5pCqcjLQrbF6wOUe3XwkRxFhJ7U82H/mpAAF+9A98g+R+SVTtKH4DAakKkks4It6Yi1/ALBm0hbQi2kQ5OtKw0PhrcllYVcI1AhMkbGfcLlNXRQyC6sD4SBo0o2+IXSSdJMVI+qayNZEyFcq6kcUAdaQzimXbUa9HmjmxzT2NhNEFDpMl64heMp3o0e3K5iXjmHAm0gwrzWpYFaSDp50M6jixFU2gv4BLApMDowKDIJ0NWgpEq4jTjsGsLAgWBFZFZJBsZsVKjUmG2C5QMqvUbA8GDgk5CSrQxgHrJrw1LEFjY8C2PT73BLkRH+3oPvcWX//6v4qRnl2onOIrUp1Q9ZYmBR7ySvj+R9zPGSst60PF7guvTt9jHh37L7zg7njEyLfp1CO+/Ln3kFFx6K7J8YidB57fPOH6xVN6Fbn/bOV6uqFWz/TRkWfP3mN73LITmXjy3H3yQ4Q8kU6Ku1OieaL5uXd/jo8//iOM3NOh+XB8xW7L3JuBxgj+6Pe/Q9GCR8/eZdhZfvgtydEesSewaHza8C6QKci9wvkdKXtql4mLwjjNvBTWnSQfFbJPnGLLnhW6nqom4usVbgztBlZ0HF1C3xeKllStQWiYPVIX7rLAmJZiKip5tjsQyqDNRO8sqQoa07CqBdVJzFRZhojWiWHxBJdo/x/m3izWti09zPrGHGP2c65+92ef/ty26lbVrSo3csqJDTEPiUJiEhKEBBIICYlXJPKCBDz5lTeIxBtCChKgIAVFWEaxsRXb1bh861bd5vT7nN3v1c9+zDEGD7uQArgsUBxU62Wt+TDmw5LmP+Y//v//PgQ2MSRC0/cBQe8jlg2trAhVjF+3rOOAiRMoI/DriDKwDNOEtdlCFxDUHXWcEfQxP6tR4OeCLHSbkHgspEHIjsCrMfRARBDGOKdARHhVgaxqatthqwoTg7eKMbIiEzmNvMHLWpRO0Z3ENJZIebSFxPN8FApjFJ3wCERI5wRRM8J4Lb5rKHVHXwhSz6GEwY8rVO6zRaBGFemiAyLGdNjcZ9gKtmEBLkZbHxMbTCtB9kgU2vSoyhE4R+8S9ELSu4qlVbTyViVWECAbeatRj4YIoXAD6HHYElxgML1D0SObHl0maCeRnibQAUGsicsAWw1uZxuGAbaIMZ4j8DKsX1B3Ar8dEHqGg/ceU5UDkBV3Hh0yu7PPtz/4GpNsCKpnM1wx90peffKS7/7xd3nx8jXl2yvOco9h6lMnhk1VYYTHzVpCf01ZGoqlRKUZe3s+82VFPdaIXFCrHKYjrsUFzeVrNtueNO4Y3N9j9mhKo3yqa0csluibitK1CG/L3OQkUYr013SuZZKUhGHClb7msy//kKma4HZz8pEkT+8wvvcx+f2HPL53F1eE7L33PtPJMbOv/QrJkzHBMEQSMPR6vOK2KjDocuTG3laQCg8/bQldhAs03nxIKm+oPUO8ucE1DuHHNK3GuJhtqxgIh0sGFFYTbDtEoIiiDpGFjFaC3lh81xL7BZHaxY8k/XmHC9QtIck6OjWgqRSFLenDlr4KsQGIYkY5GdN2g1uhjQ3xE0MtDIopfRZRTgSx62n8lLT1aENN5lKsLIhkSBv01MoQNQWh97PT/p+TMwHPhaRor8AlEqUtvvbQvqU3IZHpaINb4YiQMdq2SAWBERReiuxaohhiZdlicU0EqqazhkEPTSQYmAQVFMwrRagkjbMMO80qivCDkL4x2LBBeAZ/7eM8h3EKoRps5JMYj20p8ZQgt9BMamTpUTe3O660EEeWxoQYbfBukxxQI4RdI53DugDUrRxDSUWvHZFvkMLQ+RlmW2PyCK8CgUX4DbpVBInGLyVC+digw/UpXgS66TAmx/dWyFEEJkU3K3Rn8HqLTS1+7eGJiCg0bOKcv/ztb5FMj4j8nt3dxzy4P6XqAl68umQviFCBRfUpv/3d36PYPmU4HIPYp1tcYeWtsTmNY16+/AFXRczwMODjd9+jEo6kE/zav/ZvkMuWyeE+/bqiGkgm8Q6x9AiCA3y2PJufkKQRPR5V1TEmpFivMF5GJVr6boGLHabx6coVi+sLtBuwP9ujj1bIZp/j/QCdZ9yP7lJHmqpY0LcD/vj3/jHGH1OrORd//JzzxVO2r694rW6wiy3jBLTy6KxBbWLEoIVVQpMqPM/QE+KqjlB2BMZSo8jp6QwQCmwc0RQ9kd8iO4ufQ9UKrE1wXUk/UuQ2oBcblB1QCkO+6amdRxwZShkgza1iL/RiTKRBa7rOI9Ye7cCRVAlh1nHdaoSvUJUkkiG9q2ltSmrWNKbDxgmZrthEEZmuWPsJdB7poKMuOmKh0FFPq0NST1Bufo7PBDwcvmqw1kM3Ah17GA1CewxczzqDtG0wCIaiYy0sfedwmSQVt8x1XMWm7lDqtsEmwMcLHNZP8ZuObeATmJAwTCjKCl94FIMIaxsa05E2McZ6tNbQZDEUJRE9Tni3bkRtSGJHpxRb2zOqJMvKokINNoSopfcSvKaGIMbpFs95eH2JDQxC+1h6DDl+1qIbQaQMrQXrxShpSZVP05VYI2+nyswQoRq0J7H7KbZdo+oElXv4gxFpWbEIPCb+uxzcGTFO7/DixY+JJTyfXyJWK6TyaaOYWvocRSn3H36dBh9PJrz36CGd0uS1ZVpd8Oaix9qeyGbkM0NZBphmyt5Ox7Plgl/8+td4/sMbtOgQJiWrr0nUI+QoZa82zN4/ojcV4WiGloqd4WNauyRSsKkd6/IT9nePONgPuCwyvPaGw3SfdXKOV/v0Xsuwr7m4KclmB8jUEUw1tj8mMgoR9ET6CJULTNMTRzecLzs6WrpSserPSe4G1GdnhP2E7EnCL7uvcjL8kvJ3z9HjIeu6JbWWPIJsnLCQGXHuGEx99NrQ6Z5sIljlPuEioq3WNH2AyzpM7xMXG7SDwESouER5GWtbY7UmmDj81tFoTaqhxYDvIxMYm4bLJCLpwbQGJS2+t0HKAGtSgqFHvXB41kJYoFcRkYwoVX1Lf44qRBfi7BV4CaFNaIYSUzl6XbFJBE42JKWP2Hoo7dFkPv7SIAPw9M+aHPg5CQIWgfUEViqE7JA6ADq8LKB0BlX4OL+l9wJKc9tu62ca5/nUbUGsLZacIBL4WhP7KVta/DpDR5Ygt+ilgdAg2xWjMKbVAa4sCaKE3tbIxFH2CbFb0fYbVKAwvsEVoCJHLHyMdVjXEpuI2jnCtKJrFaHvqEmQVY0vQ7SwMAScgm2LViHaCkTi4bdbMuex0g419elNT1wabGWorCOPJYW1DK2hCEf4w0OmocDb32fgWUbTER89+oiVX3BnvMfZeUF/fYU42ucrdx/w+JP3WasNT5rnPP1kSaVq8EL8JuPu3bv8xl/7tzj58ofIzLJaSoLkgm3RQKqQreP1F2/wswDTR0x2ptyZPMBuDfQFP/rRFe+/v8/Lk5LzZU9ydJeD/RF7o5ggzvDWM8xdxzgdcNlBIs+J/YyLJsHvVgg/ZVus8dN9RvGWqBjzg1d/RBhMOTryCb0RqhnT+G/ZnL7m/uOcmB0e3ItYVUsmODbVFn80oG01ejvkQt9QlK84zO7h43MYjXmaW8ovbui14JPTlyzO3lDnHjaLOAx3UUmDcBZPZWSuwCY9et1QC0mQCIT2kKcVRd5CLBBlhdKgrKbxfFLhWMueXI1Ybntc4KHahqQN6XuQKgHb4FSDrzx83XFtfIQOCQJDE7XYsqcREU0jyfyefq2RpoF+Qi4k535DH0ZMq4DWpnjbJW7myLcJjYZGdlB0aO0xthGmAC07mlwjK0kgoW81IhHIzhJ4CZg/my70c5EOKCGd8iN612BDD1/7JKZnFQoiA71MIF5BMSaSjtYPiftrWhswCg4I+ysWDQTWp94zhJ2glR3hwrDyYwYCbNfjpz3bFlQo8fFxRY0Whja2iMZHBI6AGGs1VoRY1aIqiRI1Ng2wpYc31ERFTG0C+qAE1aP6GFtVP3UoG6JYoY3BdQEydHRKE1gJ0tJ7htBG3DnY4UUN+8pnWRQ0rcPFcPfJY959+DH54Zhvz2b80td+BZ3VUFsuTjT5wQhpCyJ5h419zaQJWSvBUIYsV1eEcU4TxdxcnjK7v8fpNuCgL+lDj1Gd8/rmJf2wIyXmRz/6jL4wrMst3gC++KM/oe0b0nFMHwdMzJCzsznD+1Nwkq9+eMz5n1wjc80Xry4ZRJYH9+/R4RgHMfnRIY/2Dtk5zkCPUSrEGwrsQUT8JYhxi1/nrNQFdhvRhGu0p1BhTPHlG/p4yLbb3laC6Al8B8uWXpRImbA1gpKaQCS0mw1REpJECVFrcX7GKG1x8YSTs+c0xQ2fvX6OFZrzLzasiivM5pzlJsE2C6xb0G4jnFvgBgOadU8UKrRbIZyi6wPSQXdLfeotpvXY2ltbEVgyk7CWHUE+wGsrbOnwXI03DijLAcreELoBfVqQVIoqm2CrK3yV4HpN4Pd4NqB2HknfUuUZmC2uiQlkT9nG+G5NnuSUfo+3kXhKowkh3+JXUAcGUSdoryImxnYt1vNIgxTdbWitR+p5GBlQiwrTuJ/fdMAIS6agUwJde8i+Qw8kmQupDKS2ojMZcRJQdj00l0TZPbq2oreakhC3JxDWwialsBuk1RRk5H5HIT1y7SirKc7f4mxFLwUuVdgUWMdEQ+gbaKKK1MZ0RUFYB4hMUpuMwFiSxCJrKNuC4e5d6lXL1rulHyWRoW8cQaDQSYpf1LQDibOSoAdtDCoKSeUx+W7Lg3d+ieTqlA8/+iaff/qGT//0hxwd5fyd3/wbHD3+gFGyw0N/y5Xv6LeCkfYp9Qp1UtI3J9jsEkzI4K5Pd7ngpCkIs5xiMydpRiQK6pc1O3sd0oUkQUUfOrwi4sBInm7W6PkakY05Hu3yB3/wT5G65fDOQwb7KTfnr7EIRrOQB0f36GzJySX4Q0MnNe/d22Fl1gRpgu8Z9uIxe1GG8zuaRlPpmkd3I4zIyE4bikGLNR1tZpHLBKEV3nBEtlpSlob06C5SlcSrltWzFW4nYQuI3nDT+6zmXxIuoI8C9nOwapfVqmd2X+JSRY5hWQe8vfgBL06fw7Xlqqypb+ZsZU2z2bJwAfuDhjeNoG97olTR+0NabclkRBtusc2MuBNotcR0PtQt6xBc6gj6kLRx9Dhap0lTidzW9F2NVh59ErDjh/RyTk9G2XX4pc9WwdhbsRxIooVk5UFSBlQywDclCxXh6Q2xlUgPGk+TWEUx8HEedGvH0Ck6v8OUPaEVFCYl1SW1p/GkwBmLtv5tudwUEDh659E6iVYNpgngZwwT/1wEAYFHVdek3Drg/ZkHzT2i6SWenZGqIZO8oRtOSU8XbJdjZrOc+dbD6wWTgwmr5ye0VUD4wQ7vyh3evNnSeg3N1hFJSxd6TOOCVetR97dwiDaBYOER6xBkzaCK2PYVnQAjfPKZpNn2jDNBU5boNqVLNW0eYPQKKQxxI5GyYysGjLwN2z5Errf4wiPpBFu/I767y9SM8O/v8iv3v8EH3/yAaf6Qp6+/x4eTPcqJI9/JuPf4DsH4mDzwcNs112HJaikZ5ZbNcsNYCp5enNJHmuOq5lSv6C8tYnGB293FExq3vsYbQ1nXVOvn3IuO8PMpzUKhWVNub0AIonTAF2cFaXTD/YN7uL5hfPeI48N9nr1+wbr2eHg0wm0V18/XjB+8iyqfYwLB/FVHLUrGw4DQluw8eoDY+pSxJGVMvxaEg57FuifPCzZqBJUHEXR1iQ1yTOAzjSXlPGG4PyBbLNn4MWr4BP/rG9zyhstNx1l1TR8u4KrgtbcmXEUU1QNG4RvufOURz5ol+52gCwdsdMs/+d0fsf78x1RpgjIlq9UVd3YO+co3HvFmUVO8fYHvLwmrmKbWiEoThJoi0KTCYfoFvhKEnk/jPNI0p3E9aauxfUPjYrqwRnrQb8AoiZYJcaJpNz4XpcDzfRLX4hCIIKVVK6qlQo0zttLgJ4K68MG1MLQMti3bMqL1BLFvsW2AiBsGNkJvSvKBhy1rvD5mKgVXqSKuKsquZxLkdKaiEeB7Ft14qDCizDVyFaO6Gu1yZF/ws6DjPxfpgCeEizzBIEsgMjR6zGh8zF/61Ses5pZ3vvFN+lIw3g0wXsrONOZ7p58jV5JQRjwMh+how5vOEUqLVwt+74/+EGskQX3GWfuWxZfXeH6IVR6xS1h3G4RXE9oAIXqMF9BVGtF3+KME2yqchiDZ0IUJ/bIkDiMoBXKWYswKraa4bUNvaoQ/IEgstrPIIGH2tXf4D3/9NxG7Ix4dPiQPRmTmmsvBFnMe0BmLvNniRmO2dcEoaClWJYtmg71ecG0qpBrzq3/pO7y4KtgLJkRRyaLasDcbMD95ShUeMg0UVdUQDHJOXr5gsDfl8uk56c7wFkQhfO4fvUcf1XT9OXoV03c+dQpffv5jLn78E3rb8pMvXqBkyMPdu2ypuLx4zd07HzEZhnDskTR7yFizWLcc5UO+uHzB4Z0j3js64vP1Kd8evEc29vH3dgisII0Fw2ifIijIkEzjOzTDLX6dsY0MXd3QdR31fEG/jujkipuyYeBXUHuooWJda7brnvXqCu9qyUs0QTtHq0OGLTQh5GGIVCmtbxjMYo5399DWQ/U9P/r8R0y8CTq3/KN/+I+5eX7Cxi1oqpIpOYxb1ss1Ms4RRYu1DWYY0jcd4z6ltFtUIKjbAUGwpmWAsBWJJ2g8SWgdYQ+diPD9BEtBkG5ZbUMgRLYCY7ekSYZuN0hPIrIE22uaVtNbiQhC4m6LJxVFFuBvBJmxWAK2ez2h9gnqANssiZVk6WlCz6cLPGgUsq9xaISIkF5IaQs8DH4f0dMSCo868HDSxxV/dtvwv/CbgBDiFbAFDNA7574lhJgA/xC4zy1d6N/884jDDhgevEMkWm6aOZFT+N4FP3w+4KO77yJcw/2vPOCkmHNvmFJebZnZEe9/cAc7HeEvWhq7T9af49qQcJrw+dNPOb57hC7ucN/7Ov+s/W1idUB9dYUI1mycj6gEnXLItEcWLb5LQDloOjzX0SWWpsgJNxqVKDzX0iugdxibcXgY4yY73PngMVm55Z48AAAgAElEQVQ2JB6nFDXs3R1z5+F7vHv8hJlJWMxPbz15acvqd9cE05rLy57WNSTFGXEwZSEbuoVPm5eUsxz/JmD/wUOcP+JwtuHN9WsGMmbkh1SnC55tNLFZsKg3BMDg4R6aGrU9ZbCf4DzJqILClMybn5A1E84Ky53JkHwSML8sobNUqkF7I5I0Zu9whzz2mY2mTI4OkG5FYQTDlYf/YJ/t2Rl+J8myEV/x7xEO9xh1gm/uv0cS7hBlPWrRkO4n+O4uwZFleBkTj2a4PMDrY1ya4p0uGE2h7ATB7v4tcfm6o5p1iFcDNqFmLFoC6XGQj8lHEUX2gJ2rc85XFW23Zrw7JYhrwjhl5/AQXzuiLGAw3qM8vaYTPn4QUV1ec/l6gy836EGNW0lSPKp0iSstgQdJWdNl0DQRYtsR2AmtLehCD9sHpKKiFCN8UyFjga8stgppGo3ILF5ZgTFsLcSkCByyq0mdYisVoS1ZBx7SOHzT0BYSIo+8E3TjgFpNcWaO1/f0SUSPoqy3ZCvY2pawc1S5orU9aZ/RVDWm74lbTT0A0UkQDV3bIHKBtT7OtPgio3OSVG8o+p+92f9FpQO/5py7+eeu/z7wO8653xJC/P2fXv8nP2txEod0VpHsSr71+GO++91n1H5Mf1HzG798j9cvF0j/mrSu6YKOqqkx85Knm0/YfzBEs4vsFOMooYgMw/GU4fQRm1Ly0ePH3OgVv/jNf5Wn81c0nuLmVc5+Ijkzl6he4lZbIpOyyGtcbciDW67bMNK4XqKFpTENfhBjpALfYExOHB4wvnPIw+P7fPPjj9hN7+JHJWM1Y3FxzXjpYaZXOD3nfNtRvj7nulqzuF5x//B9wj5DjjQiHBHWjm1+Q+Tf48negKvwisl4h598/gPiiUOfvuHae0y8I2hkiLeniC4F/SgH4zNMUtiPsOsxdXnJcORRRR7r5Yo9N0GPICsict9nUa3ZG1s+LwoODu+z2pbE0z2Gx7schRP+t1ef4U7WBIcpT/KMZtuzfPoTrOxYlUsOtpKDg338bMZye8OezFi4NUXhmO0mRGnAsKmx2xFR6lP1WzJfUJKh3QI/bejWPsJvsE1PJSp6ucWe1SzshqG3S73JEIFGrwt0UKBVw9GxIEr22F573MRbwkuPdNgRBAY9TVlvzpBRS+vHnJ28IXUhP6i/5Eff+0Oa6pLecyhb04cVqosoW4WIQ6rQMNzGqLCiVIbMLeiTlMlWs+hbCgmkDaNe0LYBxdYyyRtM2+NtfToVEdAT+wIhIJMBbtTQdxE0Ede6JHM+NtGIuiefeZSbmBaHXd5Sq+I4I15Ltmxo5QgXaOg1oQjpgwahYNBMqGhwYUQUbBCEjJqevjNUYkQv1nhbh0g1fZTg1Q3Oh8LLiU3D7dH1//PzF4EcfwV8658PAkKIL4C/4pw7/6mp6J865979WfeQnnS/9lf/Onm+Q3Iv4e7db3GUWL548ZbW9fjCJ3cgQsn8fMl4usvrp5eMI0N8MKWJLf6bijaRtyjp4/vMsgBnAm5O33Dn4WOW6yt0uSKeTfjxn37C9vqSP/ij3yMlgLbiJmrxKm6R0drRR5JceLT5Lqad3xpe4oTR7C5PPvgaf+9v/3WO4wm+uHX/7R5PcC87zu2K+ckXvL1ccr2u+PDdd6nfbDk4GKFrQTGtiD1DsfCYzPa5Mi3HhzH9KiCJErrYgzrCNBXeoMOdGm6KlyRSYOIJ6s1T1tMJDx5/wH6UYJVg1Zzz/E9PmKkx/thhupzV5oZ8f8iirzkYx/TLlFKt2BuMiErHJ+UJ63PByI2Zb79gWZ6wef6Wehaj1JBpMOaL15+QxCP01ZxuuMPf/c2/Qtv41CpAGMfdnYyzixW1XPPR8VfQ4wzfBhi5Yte/QzRSDKYxmzc+VfgGv5qh/YokD8mblhenp5w+v6CTHr1psa0kmQwx9RV9FGF0RaJT1sUalfrks4Cb1y0u7smiBM9kdO2C0d4eqgAGDTa8y+bsDauu5fc/+x0imzOZZrz6/kvK7VuWr17xdrsklIYoSQjkAa09Z7OsCYMRHXOUChnaHo3CGkNIxMJqQr+nMYY48KmFICs0Ss0oKelFR2YMznmIZETv5lTKZ9ymrFSD7EAICL0ez0LbGxQCkYzQzQLjB4TKUjUWQUAYtuhe0UuIogC3SECuCZ1kG6R4ckFgHU3rIBIknaJyHiqwJMZQ6wBJQj0q8asW3Ugc5l9adcAB/6sQwgH/tXPuHwB7/ydx+KeBYPf/vuj/4h3wFPPlmm99/Tvo0CILzav5lr6FblMjBh2fvlnwzY+/RdWeYOaOyfGMSmxYPT9h52if9IN32Tz7nFTlPP+Tp2Tf/jZyXbAolzxuDduuY3O94RcePmH++B7T+7t8fvGU9uSGfPcR3nnJlVqgRAWVRQaKrd+w5ytsvM8oC3n//V9g/95dHr1zh8PDXfZdxNm8IV6ec2UCmmLOxduXLC4X9Eqwuz9jfXpKFo4xMQjXMJ0c0m19TPol4wkEbU5XCSZRiMiHOHPNdtWxM+hoadjYLWl8gO1e4s8SrP8h6voKPzWIsuU6NdRzD7U7Ytutefb6FR+M3+PSdaxevWYwmuArwcIt2WEfKzuKGI76Q4x3ys6Ox9Hh+5xvpqiv/ypX8ws++/RzbrihrgIe7Q8oopBGJGzOHcFxRO4Jlhcv+H7tMcoG7A6/QdWeERaacG+H5GZIeTjHV7tsz1sYV4ilR+9f4NUOfZnwZtPQSYNuDbqbo5c116qlnXc8nO4y38wpN1c4N2NrK+7HQ7zzHY4mGa1eclZGxP6C/YcT0k3IuquIEbhMk+3dI7p+wXuTh3z3y5/w6vQZe7FHFOWMml3OREFmA+ZrQ6ZO0LInyA1RvaQVksgEbEVMY7dMidnoiqGnkI1HnyhIPFjElMOewfoGQ0SU+hQmAu2RigYnBH4ZsxQrnBkghWagHOtKEfsSO6iotY/sbm1PKR6lkQhhMWNBVIQI6+MlmsRIPFHSZY52a5DGYKWjtR5OCZxWaCy+A+scRWCRXYse9ASFRFqF8CWt/rOPBv8igsCvOOfOfvqg/7YQ4vP/N4t+Giz+AUAaZW69dry4fs03fv072K2GhaFUmoMw4rrcUHWadbHg2x//BmcnP6SMW2ZBwnM/4Huffp+vtGvq2mPjlzy4P6Bvr+hzn6BPqEXOUTZHHewwnxfU1zV3d3f5+OEHfB695sN7H3MmSgaf/Ijzs3PaWYm9kYSDHRo/Yjaa8dGTI/7mX/3XafZDphYGbcI2c0htsSpELN5gqjWiaOgECGOIrODaDBgdG8bxMRyuMZsQaovuFKNoxu5OwMunl9xww47q6C4jlF+wrBvUmUedxRg094fvogOPa3cNA8Wrl3MuTMFOMiOOU15f3pCZmNNNQeKtuLuzg+538Leai6jm3dkBm37D9Sms+4aPjne5cCMylWDSgB3rsYlWNPOOsRcR7mT4K831xRYxVIi85E3reN9FmLQjbkasPUMrA7zpOXE3Jg08/Kpgkeao0qfZbGlsSzQPaYcDJpVgPn/Jop2DrrksS1r9ktCM2CaatAtZXy/5cfMCs8w5vD+iqnxGs4DUxrTrc5ZMWVUt+4Mb3t54WBbEecGj/XvYSKK6hAvvLSZoyEeH/LV/ZY9PP/2S1z/6EU+3LwmeF6Tv+vhvJuyNbljXNUJ5NFrRCUmaScqlxhcVwxEURYPOJGWZAhbZS1S9ZphoShtghcOPe6paMlY9ZSjwPY1XSlpZI4IU1QCuo2wj+qAGERBXHlppGk8xCKG1GVq2BLYiNdA24KaK9CqjThQCTdn2xB64HmzoE5sO7UJs0uAaDyEFoY2pjIcItoi1xPkROizw8OD/D7KQEOI/AwrgP+D/Qzow2tlz733tL/Px0QfEjxWf/On3eOfxx6S94PKsoKDl27/0mD/8w7dsl2v2H0lm+R1YGa5qR36/hpuAeXXDNIpZXGhku+BKRtRNwTfe+xpRGFAHC1wYUy0s97M7fPM77+G2Pl9cPGOy2/D2bUdh1rQ3mmGisJnm41/4Dm1l6esO3y/JG8e6gid7Oedvt7x6c4kUcLq6pCkWxIcTZunO7Uw5EhsrdmdjLhYd0WbD+towHvr4d4ZslguEkHQSYhtyevKM2SBiODqmaa8Jk4BuWSOiFN/POC3eMB5NObITVn7BKDR88cWG0e4INfQZ5Rn99Q06HBJKwWK75s5oTMMWeS5YxY5QdTSMqOxb9l3I65cF2SDgztF95uGSvA355NUrmpsFPzl/wZ7YZd027OyMGd+LSfIR1aYhCjPicYp3USMPFdtGE5XgeyGP9yecXc+Jn9wjDx3WOfpFy9vVkvB0SbGb0Z73NOaCg50pfbXi5aszjJfR6kv8ZEjXXjGfS25uLvHjiMQfE40cd4/usywaJpMd7h8OeSU60mVJ3QpSO+HB19+h8zeISnN13RPnPhqP7/3B/8izVxdsL19xdnmNMxqxCYmSDXUPoYhohi12ZfGNh6d8TCvxkxprY2q/RLUDfFnjdz2lBOssUeShjcO3CdpWiCDGb0B5FYWGmQpYOYlJHEY4YqvwNjVtoDCBJiyhBwwZkWww1mBQqFzgGh8vqPHqEXbS0zeaSKe03RKRhdArXFdjQ4NXOqJcUBmDqUJCzyKkpSfExBJEhVv9S0gHhBAp4Dnntj/9/RvAfwH8z8C/C/zWT7//0Z93n8FgyOG7h8yrjuGiI1X7vP3ilMlegj6MGV1nnLw4Y+JBfmfM+UYjmyuUf/tnby7G7N2PGX5+yGb1GYN7DxHM2Eskr394Tr0+Z3j3mGT4gLiGdVbQDXtOT79kIHN20h3KdsHR0ZD+TDH8lXcx12vONqcsb9aMy4g8GVBfrWAY0rx+w+9f9Nw5zBkNcirvjN0o4tOfrEm7nOBAcf/wCZ+9/JSsnmHmgvp6Ti1b4jyjmMJeN2DoGs6rS+7c+ZDFzSn3HhxzdfGCmBVnV+f4IsJjy0ge0ZmUzlpWyxWhVVjZsPRndPYNcMAwT9mbhrzaOJrNgmYQsCtTPN3TtJJVsiE3CVpqXNqhX5VcBR5bueYovMfb9pTH3OEUw97+fT47uSDxjoh3HYfTr6Cjls1ZTbPc0LmG/s6AvJfkuxmnb66RU0NtAwYHMS/Xc7I7Uy5fnHAlGsJowmLxllVxha096jOPaNVjhaDrNwgd4e8N8Z7e0M5SdD9iFPnMjseogSYUe5SFoYoaTq6e8c7d9zgt3yCvLFkUcXCYc3kjcOVrrhYB0zim9QO2YsHiStBdXjG/vGR5NsdvAo5jy8tljJ8scQ6ckVSiIdkImiShqxtwHt6gp1+HJKIj8RLauIK6pxcRARqhfHSrwRvQsbmtOpT1bUtOJPBjhektnTbktUepAnTjyLA4L8ZqTZIoFr0kDwyyGCBFQUVI19fEpqEuPAbxitVaEreOLi0wwS2OPrQSZ4aYtkJFltr0oENkLtC1wTcSPwa3bRC+T/8zOgX+RdOBPeB/upURoYD/zjn3T4QQ3wX+eyHEvw+cAH/nz7uJMY7Hk0dUs4jq/IZpKulHjuvXG7zpKfM+pP6i5+DOQ7blW3aCHq9JSRlxqTTbL9/SXqxxeYRo4fLVCz6aHpEN9tg78tk6Q0LIxdsVc+Vxvbnind1HnL29ZjtZUbcth7lHZ+BwtkPhNgSZZDraZ1xC2S/ply85WZXM+l1c5kiUIJmkVOGI5Q9rmn7N4Xtfw7vSLG800ntOtfKQd9YETc1oFPLy/Jr44Yh9b4Jxb4lnCQ/KQ5pqQSwDOhboJuCyWrOxPTttz8HxXabj91iZDr8/xDmfan1KOJlh5jXp7gF7Bwm2bTi5qInzCWFZsKq2nEUFzTzAnV0xeeceNzen+O6Ae4Fk9M4jLq4vKK4VQRSQSMnZ9Yp816NsLMOj99hzNX9y8l2EDDHGY5I2LFu4XLW8NzqjSx9xs+6pC41F4HRHtAjI0haxEkwl9NlD7GaJTH2SbYqzjonw6JWhDzvqq5SV33I/9qmPR5RhzI5UrM4lfXjF4eBDukHPAy1Ydx6NNPSNz/sPvkW37ij7gpcLxXRoubAfsOcd86p7xmBpWV5d8elnJzR1w8HoEVncc/XmBxR1xUC1aKuwNiVQS3yp6AMwUYDYdAhbIXWOkS1F4Bhpg1c7SCJK3eG1KenIYtaSgIbeKeoMLCEDXdNXkiZ3rMoBO37NuvNQSpNYSR1LOlUTVREb4RG1msZqjPTwHXhhj+l7SgQEAZ1LEWZFn3qE+AQqokLTNB7kS+LKozUC0fsEqaavErywARx1a1BWYfqfTRv+uWgWmu4euL/97/2nOLGknqdM0oLVekt9sWX00KdzEw4OJty//5i2Vgz3hpw8O2V7MadSBcOR4HZ4z3Lxquf86vvEBqw6YDiNyMKem5uQi+oZv/z4XTb4VIsVgYuwRYM3GTA9HPFkb4cXzQWZSskjS1lBYAP8VFKvJd3FUxbrkKU/53h4jzQ0+B10IsekpxwO3+XKbXG65uZ6zRF7pHd2WLaO+Q++y+AX7uDZCL1cE3gBvWpRsyndyzmXuoSN5uvf/Cr/zf/w3/Kdu0+I7mYc3n2f0+dvKLuOcTZEmRDUgq1W5OMh4+wI11iioSQ1G8gzrs8W+JOEL7/7Y+4eHEMumA1z5vMNseiQwx2qskBbj83NG8a7ObEbMvIdNtqj8edE0yecvPkuX/zvz1ivzkmSHYbHI4IoZX5T8Wvf/BZNv+Dk5C2ff/GUm/OaR3f3+fBb72OymoEas3qzJIhH3LRzGjyOsLg8ZTRvuBQ/9UCqAU1uSeI9tKzp5w2TOGYbTBHeitXykjoO8IlZvDVoLilURJbschgLhruKxacrynyNDHbQ6wWBclTSpzMVmU25udnw+7//vxAnlmJlyJs5D48OyGYz/u3f+Ft8/+0P6a5WtLbk6P33mZoBrX3L28ayvarYPZxxheS//M//K3ph8cKarIrpVYtGoYRGRAFaaCLn0W1imrBC6BQhPIzUZH0BmcJZR98aOt9H1g6rJIERyMjDSIPoWoyFoFc0gYdpLISS1GhKF6PSkqhOqVWDrcEJQRaGbL0a1QmcN8DXa5STFFKRmpZqEOJaD9o/20r8c9E2HAcxB6lluP8+n49XXH6xIN5RjIgJo2MezHJsAI0MaCJNe3HOThxx8K37vH57gm9ibP+SL082JH3Avewhncow/SWBNrx4U6G7My42p7zeO+QozHlbXHEYDEhmPjvvHXL26or1fUFoLNfXBUUa4xpN6ErC3seIjjkb/HTMTEjG0xSjPfK05k3dYG3AyfwKqzwSOWAyE7x9u2b4bMvRwxHmyRHbmxv2oxEr37CRPbJuSU3CJp4TxpJ9b0rVGQYqZeejX2TdnNEuDefrJX1v+PCdA7I850+/9OhjWK2XiKVj9GDEMFAUVxW99SiWJZmX8OGDd3CzEdxc0vQeqT9FhqeEmWU6PeTy7C3h8BGzLMQPUhpaci/CLHOq7WccpUcM/saQ+fMbqqTn8x+ekVrNbJhxeXGG8BT7h/dY6i3xpOf4yX1aKxC9o970jCYxhQ0YEBE1NSo9pmkLLnSJ3Z0xFB1WBQxVhuglaRCT3Nuh6zSzpqU82OedwRMSaVgGNzwZ1OgmZVWFvGif0XoJN+cjJvdzhrMp6kLQ+hGN2DBaako1om3XDDrDVz98j6PxmK9/5asUqwuGoeT8+hQretqzU0Q2IGp8ur7k2etTLjctUSrJc0XgpjzIff7e3/13uH71xzw7mfPk198hagRPbz7j+RcrqqZkKHwqERK6CtEFDHOfyDputE/lB7CRBJ4DqxgEHkXquLf/iG2qSc2Mi6fPkc0WLwITC3qr8UeGfhtghgbZNSAjKq2xRqEESK+l7CKkVfSZg3JFlnmsdYBqOnTmUNuGXjp+1nb/c/EmcPfuQ/cf/ce/RWsLjA1JwwRfWdqi4brtySYFM46JdEc2mbC2PtPdgEEzoJpU6Oqaq1c+bE+5qeecn27o1wvUIKYKEs5fnTDNd3l7/iX3d6eIZEIpNHmxJL17zPz8gkc7x2ivYjzboxc1UntUTcFI5gR5zqIqOD1/yuHdXeIqZPLgGKcqbj694enFGfnehJ29I9y8REpNrRK0u+Se2aPa2UXtahZfXrHoGsbxkDAxqJMa+XBGkKZoLTg4fI9/9r3f4Ze/+QtsX6/pVc2ivSKnQjc+23XJxjUM4/dpzBlR7RNnWwbDPT6vSgavFhw/PuKyvWa2/wC3bTHKI7r/gIHXsXh5ysH9GW2jEJOEzfmGxFNMByl6s0QlhlaGLBvDYNiwurCMd6ekwQGvq1Oef/8HLJcFcSe4/+5XsWmP8WoGNiV0lkW3ZV17KOYM9PvE8hndaMxOfMB8e4UyIdt8wyQ7xoSOQblkVaTYICBQPVW7Jdn0mMxh4xgZpNTrgoH0SUcR0kuxXkPT94ShwtgYz/bcDCX/B3NvFrJblp/3/dae5/3O7zcPZ6hTdaqqq3qSumUrTqyQRA7WRXQRg+MEO7e5CPgqdw0GX0hxAoJgSDAYEhKCA4lDTAZbkpHUbkk9VFdXnaozD9/4zvvd757nXJQSlNCVwSikFmw2e8Fa7Jvn4b8Waz0/43bD603AkZajDY+4eTxjcfGIyuo4qwVn7+zxB7/zQ2oiNMPhl775EF2XuFwVPP3JRzhH+9ybHjI+OeK/+a//c877R4i9MUm0w5RNkr6EuNyQdzJaq/LBdMJo9Dbr/obvf/aMIA645z7gt/7+32O4N8ZCZfTwALOUcM0+UVGSB5d8+mjN4NjEaR3WqsTR+Tl3x2/xnb/85/jNv/tb3P7O7yA5KXKu0KYVlqbQdQ11I1FoDVJtIXUVhWSiVTs6RaKWBGZbU2LQ1i2N2mEIFUFBXoNi1bSFQVPlP7cSkL/3ve/9/yD7/2P723/7N75n+VNmV7dUwqBtF2TJlvm2ZpR16MYEZyKo0ZibLbZrIu1KNLWiXGxZJx16FhH3FQxZQVYKijxiPpuhZYK+3cPQJbyDMWXaIno1uzCikVRMWzCRVfTDEXFS4WYmMhbF9Ru2ty2xljEvc0jX3O5iRBKSGRK10PAcG921GOgaAgMzq8l6HcvXVzQ1fPboGUsZ3n2vR3pbUoU7bl9dooklhuexROWeN2I8PGc1nzMyPW6DC4z2C4R3HK4Ris1Pf/YCVWnYLkJUxeTIbxiOT+mfOZjKGboEnWiRhcH0nTvIvstB38a7e0qXrXC9Kau4QZUK5NplLBkYvkVRXRBe7RgqGiUZ21TgGTLXs4DD3oS0rL4IDPEGeJgs44isrbjepYztPXqjPmVsUGUJhQjoaypqYeLpLoZnYI8MOtGnwuLwzpDBnUPGzilNGzMpDRK7ZrlaIpPTrAN0T/8C5Gr0WGY5w3RNrchkpkn44pa+79A0Kbq8o8lGCGlDFWUMGKIYMkN0Uk2gVRLGwYDtKiaJAsKfXeOdmDx/9oJ9ycboS5ApXGYZqqSzXmxxVXj/6x+SxDXzF0t2tmCcq9z71h2kXGV19Qq9cejclp4FhiohT1swTZafP+Hb97+B1bN5vHzN23t3eee9t1FLeHj0FqliMCwKnNMh79z7OlfFhsUsJ+0i9qoJ59Me8SLn9K1Tyt0Nu6sEkXXktkotatpGUMgKRltTODIKCkpRUUoKEhWOpULeUMgyrlpQoqDrDbpiUKFBWeLUUNDefu973/tP/8/6+0osBzrRsJt9TNb10CoJ/a0B/b7KQKnYGzs0UkGauRhKhb8pMc/EF+EivsCf7FHMVmimwezpM1xdQ/UG5Mk17RqMCQTKls3rFe74GFlxqZZz6ijg7nunWJ2PODYJLlNUW0Oa6iyuN6jOmLjaciTJqMUOWwywtynVPZ/ddUJf2rDu5ciFj3t0SjK7xJZ6JFVEiaC0EoLNLV3ZsLwzpdm1tHOJe+/do0xT2tym3RZshyuKyiLzU2J1xf3pOalokV2FPKqxXYcPv3bCzR9/xq6WUUyfOLF572DA54vHHA8UblOFycERjTQDSeHO4TnRekP7JKYoZEwr4ci0yPKWts2RnYrXLxrU4ZjWTVlmc1RvwqDqyNcB3z7os44qHLdiqLo0ak3ZVeyCFcUu4mzfonQqpDRCtztqrcRvTkmiCtdqkI/26ZKIDAPtoGLaadgSBHHIRPRpDEHQRShbHRkJu+uh+BHBOkViTFDNGGYl0tjCqArSdEPvfEguAmh1tlUfuQzwC4PGrtmlGZbVITc6suLw+euX9FWJ7uYT8ixjJ73m7szCkBtCJ8PLfTJZIXj5ku2DAaWR8SaROX5xge4MEMcSo7ggViNkq6W04elNyd2zLXISsRUDpBJsf0y+1ajnO4KDayoVfuXwIZGxY89JyMIUkaw59Wp65gjT1vkRc2b/3fepKh1fyvnJ+hKRH+I1H/Dn/qO/wf/yj31KI8KRBJ0j0y0bDE1GKmpqpUPaZqiKQWfk5CqMEsEsr5FUn64NKQobw0rQcpWtWkHdoLYymdfB7ufr7ythAkISWNaQw/4EdzxmOnSwhMHwawMWMRR5Sd+qGJpj2kmJZKrsPewhNS62HiCAXZXT3x9Qb5fUNw2ForH/QY/+4D7Li09xhj1EFZNGtxBLjO8aLDcl0e6a83yPQ3XETReSFTX+xEGSFRQb8kgwOjqEUKL0L9jNXjHuH+N7A8JVhlyG9N0W3+6xTK64uu042vOZE7J/+B7rzZba8gi7GSfneyyWK5T9U9LlkrsnPgPNZhFsGPUmmI2g6DmMC428u6UpMtpSYe/4ASKfUsyf0XYb/PGET95cI3UlkhsxHPdQ25bq3GUblkThE/Ztl5XbJ6pquu0t9XiCo9hMDoYYuYpdviFMTB5MBjy+arijNoSKjKP6XK4zOqvG10aoVU52U5LaDv/Cd/8SdXJJKukotsakhmQyon61Jm0ipMrFpEGWKirTYjg+x6saar0hzmvURiGUazptit4FrG3BSTEAACAASURBVJsM/9JgLgV0QqYRCoY5J7u9Jhrb5MmQqtWp7Y482eEoFnkn8PMF+d4eddkiNymN3kEiKMwWo5W599673H72nI+e/ZSxYeP48OMnl/gTn73+Ho4xpHfoc3jq8vEnn5J3Ch+6HnJP5rNXHxOnCr2BwygXeM2QP/j4t3n9/BP6znvcPXlAtsyQ98e8+GzOq4++zwfv32UjhchP1+zt3+Ht4VsMHZcXz/8ht7uaA+stNs0Wtx1xkE3ISx9rkJLMdaSy4Plizf3xLddPA/7qr/9V/t5qTvz8MVnYYnsyYdLRdQK3E7SWiahrECZy1TJTWvSuoapTLDw6JaRBkDQCtWowqEhlHVH+/HsD8BVJGy6zknXWsV6+JAli/GmPpq9RzUramxldsqMuWxRVQXUtRo5J18nIImEXKV8c17Q7qq5C6x3hPhww6nVEywqliHFljbjIUSyHII/pTQxs5witqdhz99lsFtyUK3aLK6qiowxz6lpGVDJSv6BdJ7RWhqto3Ml10jdbErllMh0z6Y/ZRnAy8Di2HjA6tNBP73LXvYdebzj14fLRxzSJ4OnHL5DzHqak0+o1jmkhfIeBaGBdIPkaquIyHcUYdg/DVWnLlCxKkScFmueyCne0Ikc7jHFHB2SmyatPH1MUMsUspV3v2FKwSWq63UtiWcVQ9zhcK/RckypJKbwKZWRwMDKIez2GpkyvkZi4Bnld0ZkdyBrkOYt4w6vrS5L1UwwFPPOYg+kU1/DIej5yuqA/tZGxGGoahmGiWH0m+1NEaxBU0KY5KimWbTMwDbIsxqwNfNNGsVROej2MSYtm+nSSw/jeO7juKT3fZXw25NjbY298j7rXQ8sTqnZAW0KDBaVFVjpcVxVvbiO2RUkSb1hePkLVS2ZZSp3aHPXfwuksQrVlGdyiZS2y1SMrTFav5zB26LIGaZFzfzyhWjZcr1+y3oSsowzJsnj/zjF53GGPY06METfbjFoRpGXFtPTpjH38kY41NLl88Zj9/QGlonJgaUQdjHoeZ2cmbr+i2fTQrSm6u8/UOWbbNLwbpRyqR9z94FvkrYEiFIqdgmglDCCSfEgzNLlDbyo0qUKTaupaIOT2i/h0VUHkglYIJEklVRTaRkVU6pfq7ythAkVV4riCwfiEjpKby2uWj655dn1D72jA6LCP17hUVktYSYSlShe0yLWDmUkk2zlsG4aTEUq5Qypcht4eb7/3Ls6gj9bv4/dkCrXicP8MaWLSRCbXwY7RuwN6BxphUtLKFU8uP2MX37KafYS61yJvt2Tmht1szYv5pzxPJVLlmk8//ZgVCbe31zx58ox/8rM/JmpfMTIl2G04PT8j8DR2VcHV4zlEIUqrElxccnv9BE+xsD2Z+XpFZQ/oqoRBpqNlM17uaqxOxTJHOIM7CKnh3ZMP2fcyfun+EUlho+UjbN/ASCRka8IiLEiFwq4K6Ic2onG4Ws84bSTUuuJGnnN9tcLICuply+FoSFZb1Lsr6i28lg1WFyG3uxbbFRxYZ8iqQ88552jsM/B8TH/N+MDFLASFSJDVHF0aIeUek5GJ1tMRdYotGiQFNOMSoxJ0osSRT9jKWxqlYt/wqeMMLU5x7ziIXs3YsOhLJQNfYd87ZH/aJ1MFcR1RtreIKuRMO+POgwccvut+kdLcphgDA8dRmXo9zo/PCOuKWVBy82bBUBpiqyZlvsGexNRKTFfEfPLZK56/WPH5T5+zqd5QVwseffoEb3hM0BTkqk7rFOgHUz7bBryYvyJcxbS14OzY5KD/LnkjYa9LZKOh6nKyCiSv5mCyTxzVvCmXFI3Ghi230QV+umF2M6OoIx6efYusEpR5idu2hHsW2lTm0atPSbnmr/07/y7y6RlNoaOrEqYwUISMU+bIJuwkjVyYSChINdS2j63LKElNndcUNshdR2fLqGaHajWo4svhI1+JjcHf/I3f/N5f/O6vcXd/n+nRCZZ/l5MHE05Oz2lDSPMCeWKRzBs6uUE1BYoUIcs1pQljc4DhGizSiKltM9UttqOWZlHRSjK78gbT76MpNefeiKjMKYotk8kh65tbrp7OGWg1nuuSlhKjqsfp1+/SXhbE0YpCKRCFwZvgivnzJ8iSwnVSIzYxbSOR1zJQsIs6fvrjxyx3T5g9LtlcvaQutyQ3Sz6dv+L7P/2Idz44x+9U3FaiPzpiu0v58UdPaeMVF9stiq7QSDIjVUU2h8zCG+gMZquXeL0zFhdzjJHF89cLttkFSVyTVwkPH+6hKCZaklI3Mu5EwTQOSEwD0+toNiW22UOS+3R6yPV6QbqeI1f6F0Gs2Q77EJz+gKr8gpFnekM2i1sy08BVfZZhTR1UqFZDredQ9Tg6MqnLHewfo8k50U6lr+6RZzt60gTNzCkaHeG0NLVgt1yyC+dIox66nfL6WqUWFU3douc5XWBxmVwi1l8k5fS0Y0akaFpNbqWEtylOW9FoFvqgIF8qSD0VIgnHaxk4e2S7F8xfzQivf4KQIM8aLFMmfBlwtdtw8fyC77zzFgNH8MlqweUnbwialL39AU3eIKgpdhWnZ2M+/8NbXr2+ZUCL4e4jyw1lVnLy7oRKL9EjF2vPY5uW9HsH2HpCGO0o1kts7W0cdYnqHlJ2OooeUOQm3/j2Xb759Xe48+4e3/ngz7MNtzx7/pq+0L5IlO4NEG2f1epTmryi06ApM3IZlEalbjO0UqXrmWRNjlY1VLKEoEJIOpIQKGpHVbVU+Rf8A83SqMr6q7sxqCgSH7x9ROf1UXoRTiJhWxrBboFqSgy7Kek6pX9vgtOV5NuMrrdHFs6wGoetkaDLJXpn0potSRrihh2t5JHIAYpsokUVt4sUv9/hygazYsX2NqeIK9JC46baYBcmnuHx2ZNHyGc1te2winROcgfNVrG0Ac1xzfrFjO/+a++Tyw2z20tW81uS2sTQG+7vnbBdyZReShfPuZZcNFK61MXX4ZOfPWYyPuDtB/eIH32Ga9Z883BA5brokx63P33C0cMjbmQJtxM4yFS1RNfUDMZDlLfvUWTQl2Ykm5payTl7+y6itnELQej3mHo6N3WImZQoxoRWKhkKh2fLDQ8f2KRBgu1PCdbXtF3MuO/gOxaxJrEfC5ZpDYcqjRSjjV3UeEMh2zhVS+7voPZQZA/KHeGNgqsf0eQBt53MybsuVV4xVGziQrBc5himTtUUiM6g7w2QXZdNVVBuNSZuRpNoNG1GYVisRE7fnhBxi5l3qHZNEA3p6gqzsTj0SjJHxSp0giBGyDV+btMOEyTZQSwDHP+MUvwBeq1R1RpWz6HnTEmPS/LLNft3B6w2F6wbGWIJeW+MV1toi4ys6pDynM4SvFkq5MmCIs+xbYjS11x+ItAOTrmTlvjSCX7/gtlmy+XtNb18h376C9ibAM2ZMj4scJIPCMIQzTFZbFPuHoJagebV/OLhKaXT5325R5kdsnj1lDu/cIxTNzx8b8zvPbvHKPgZizqi1Fw0paOVU4xGQrFygiDDNWVyqUOuOpRKoXMKshacvCOVTPRWR6gRMl++HPhKmIBQVF4Gt5w3OZXoEeshqycRsm4T2BVSfclE7yjnNau4YCfrWG3OXmPQTkC5sYj6YJolyUKFnkRbVwjTICoidG3C6FSj7OlIecFqmRO8jIn3K5Rna9z3T8mTNcv5Bt/tc/TeO2i1z9FAIXgSEukC2ha9DLi+SWl6ByRFh6N5PC9fsGuh1RMGgwm3aYgxFATrSyrfw9nlzPIOSwugSVgtXxGvbxFqxcPBHTZmH+QKO9rhDkwOvnHC68st7+s9QiVl6HgUZYoh7lDOVpiuSis1HNw/wEpybnYpoqnZXcfk+gxfmdCZHeWjnOnUZat2pEXEKixYXD9GN2uMXGIgyxRdQZfBthsTuTuslc3cDnHtfdQ6Z5nF+K5EVA+wexVSW9J2MslMo3dekjKgGxvEXYxe2biagpS3CM2griXyLsVWfcxNibdvwlBBIGNVNVksEVUpTX2Ji06h+liZCkSkdUlemMjCwghikjBB2bO4fhkRTsdMo4K6EhwYKmVpsM62uJLMNgypRYtFTZevWbY5WmWjtTnrzYIgD2kqhXFvyDLs0EYFwVWOLZt8+/0HvKpXjEY+myCnVBIm4pDPLreoaYd10CeIQxrrgLNGIoi3zLcpw57K+vMMu3RpDJd51ZEPG/zAomgURJdhuya21FKWDX6W40lH0NREeUQVBtzfe4D23RH//T/4IS8eh+y5EWgjmrQlp8VxW7ZNgV0UkEPUGNRSS99qyRqLWpFpqgjZMijaArt2CUSNXufUmkonubRt9qX6+0qYQFs2XBQRv/vbf8QAMNwhmlKBNaQILtDMAaNen9o/5cST6PV7lLZJ4PbwaplQ3VA8jWkUBcfpyBYxrxeXOK2B5qnU61va5gA7drmYxWTBHPoxbmugn50TbWJG/iGqJyH8gs3mGcn2mH/6+59ijSSmVYfmu5yff4hnRKjKnJvbV9Suh74TnBxpWNqU+Sqm7yg0bZ9VXNCXVuzSFds8IAwlJLlkIo3oTSzWyzX/8/MV9x86FPqIDx98k2SVcufoHfp7c7adyvbiFnlvhDudMPJP+f0f/xOG+gHhZsnxoc/vf/KEQ9tkFoSM9RjpwRHzT1fsiTFnp2OirEVUGk7rsfQT7h58kzauMM9UTEKGusxEeLxaPmPIANGBLFu0VLzchfR2EmldorkqRlqhyC6u4mO8L7HMVXwk1Dqm142RdA+rWzOfb2nqgHURIkyLM1uwGY/R9YimcWgMB9dsOCl0ivs+VSSziUDd5BReixVtyO0CH4cwDrl9s2Pju5wVOYpzCDczrj2XXlmS+x2lSDFcDyQVpYLR3gF/9P3/iWy3JJElBtoU1265TQKiRuf81KfeJfTPz9FHBndSmXazI1IbxFaicGQ+ePeEVhpBpbFYXHHHVzjt+5SDIQdWgm2EtPEJA2tAElxiTV24ybiZR7TKC9JlhHvoUM5fUY5sNk93KK5CO9S5Wc6wRkfEqwZddOhlRlvNGE1MojJjfbPh7/8nv8Wv/83v4dQKmWxiZDVOA2QmhdYgSzZdExJZNmrSoLQxQtfRc5WqckhEguEoyKgUqczQqNhGXxYz+hUxAcO2ePyDx5z2NKrGp0oKcqlFVCHRvEAznlNfHRAbz7kWOqPDEf3pHqNxwlTR0If7eHaN8BI2tzlRVNCUOoUlEe5KmsogmW+4mj8jLQR2XVDHCrsyR1dWHA/eQpZM8jKhuwmwvH0MJ2L/nSPMm5a2N6CtQvrjMZY7YlUPyH70YzIyKmOKlWYoypAPDqd8un1BvxJIXkwn9WjCGENOKYwGExvJHYAmePPiE37x63+ZZdExtFWOh1M6KgYnLS8/3qIWoFsNWbGjmsc4usP93h1mUYCKxLwyePfdY2zZpk4FrmtTLRqkvk6nLNGm91HCW+qgoBUy9zyTN+sdJ7JHeF1Q7SlY033qpEJOpmhqwJEzYrtJkfbAaSpWq5y3jsdk8gXZckLPtckUHUlXMawSrVQwpRJJ65C6DY3SELRXLGcrJClHq0+43nko+YbYMnDtmqKCSDdhvMPsdpSzLYqt8aZcohcqLzcVB8LBsQVSYCP6MqM0Jywk9ocBsqMhqX0uLp+hxxb7RxWmfMS6axDyltnFJdvLp2Smjp+XtMREQc7a0DCillguONk7gLbCjWUkyWBwpiE3CuOxTJnnuK2OLHXcGHPMokXrnRBLHYf6AGlX0DlD6mxGWVqkWk02T5keH1CFOUFckHYxtjcijVy0bEjPVck0mbFVYFo2r3cxQR2hlzojvcOaqizTCM8oGd055p29Y3J5RaiW6G5DtspoNJW230MkMWpbktUd3Vpg2wpSp1LnFY1RoWlQ5R11VlMZ0DNCwkxB8gXN9ufr7ythAkWWcvZgjC7rcBPQ2D6i3bB9vsI66uN7x6jhLW1jY9+5gxGtKRYBkdShThycOETSD5F3FqK+oFVqVEVnF9cMJYvcLXn9+CWkgu3mU7atR7XbcJXl3NcNrkcvOD34FhNJ4+nTgmbYkCUbulin8Dzk6JbtUiNtnzH13mFU+TyVNGpknG7LFhupWiAfDqkvG2YUyHHN81efkTUZcpNgtRPYM1CqG/JNn/HgfdSjHr98/5zf/h9/wOrDLV1ck79WcXKTtBWcHJ/xR59/wj33jFW8IdGW2KXghVjyQNKx9GOgoz1SyJ9s0SY6TRiST32a6yt65oSODVLX0JkjTNMkyW9ZxwEit1CiiKJUqfU1cZ4xy2cYfZtmEbELAuo6xRf3kIZ9ZlmF29k4wxZZ6rA6C0fJSHwHq/HZ1VvUZcKr9S290EFyNcy0plAzPE1CFhqrrMVtM67TBQPpGEVv2fgZI9fnPUtjfVkzeuihWzalmSOr1+xaj/5YYbbOWcUpegUBMX3PZ7sJuXndoowXSGmCowjiLOP5zS29tmGZaYixwi4ruOMfEhkhQbagL9scCYVrW2HcH+CjU+hbhOWSbLZcripsbcY6SSmkGl1JaeYCvR/Tm3iorYJGnyraoRgR+1OH3sjmRrUItgvu6h75uqEUayboFL0Yzz9Hbgqa7Y7c+OIymFbWtJJMkCiomYeUDSBY4X7zQy6CLXKsUG5zdENCTj1kKkrNoKtaDGFSdjlZ1FAZLWZPI9lK6G2O1GrIokXKW3ZOh5xqUEh8WarIV8IEJEWmVHyMsERIPuY6pBtLiKFPFctcxQvMTkIztmx/8APkqOPggz1ku6CIKzQRoNkJoixYKg6HvZLXFy/Y7ztsxZRSVMTrmOXLG+ZVRpetaaWW/qBPoauYyYDbmxV9tUHJc6QbiU0acPzWmD3JJuoN2d/Bs1dPyaQXpHJCm1cslktqv0VuDjCG++jGKWenFoYmiIMeVf2c3aXG5IP3kU/2aXcJ8iplGa6567g8/9FzHhxN+Ou//le4evMZWbOljnb4UxfFMUjCHQdC4Sq4ZdSauL1TjnoDJr094kZiefucjgK/GPFJ/oJ74gEYPtdBi1xsMIY6paTTqjqaVKCWCeLggO88+JBsuyBSDojXT5C2Fjkdn3WvOKvf4aNHf8j0dI++t8ejNxt6s5z+yUM8y0CpctQqY9eWIGk0HcTGkrLQuCk6hv198rJhrOUouoquF0QbhY2jcKqlZIDq9SFLMTWfk5MOERiITsUbpDSezNXtG/rYOPv75ElGWRsM903kG5/ES6huIqgUFLlgvV4jxTZmFmPsj1A0qJcaloAQCS+u2TucoBsFlTAZaOfs+wdoskoz25CaLSIN8IYjnm9v2bcVUm2J23ubH378fe6+f8Jo6NKhkDUbJsoBmmtSk2ENXTx5xE8fPeHeJkPZs+mLBPP0XRQjhtdTsq7EFmMuX85p4wL1jsSksShNkJIUQ+1xbNmIQ4vf+Dv/FvGu4A8fv2C3lPlXfvVfZfkDwc9+9GPKLkDTdJoyA0sj7xpkXaJNBF1lkeUZktAphI4kciwLqtpDtCGaU1NV2pfq7ythAqKTWLyZ452NcDxYr3xkUaDba3TFpKttrFqnFhkrO8DtCbTGYnVToVhbLMlAbd4QFjuMfo9gMeFNHBGGC4z2Fap/H0e0LO0WV+ooo4RloGEmCVgOi+yKvW6AMrqD7ymo/gBp6KC2cFOVTG0LrBZrMKBqG8Q2487DB1w8ekVwE8F3ZLalxAd2j1fTiIPhAUZWs1284fgbLc7RAwYDi3Kq8PTlj0lNnTd5yp//xW+zutqQrX+MomrkkcZvf/zP+KV//Rt8OBiQtw0vVztOT4/J5Jjxgcai2JJWGV1YMZy+RZS8QBISD3onFMECs2fx0DwmvlIwDg7ZLxOiXEX0LVzVYHo4QdQ5mmlSzVUs9wj3uObq6QVxbHAze4M/vc/i2RXGh3foEbBEZz+7JpqUWJkDeoYQGqEnkLQeTr5F6UI4yNGe7jA1D6lWUE0bQxLkdYurh4gSAkVBXixpG5m6tRioJWEUchuHtLlFW9e43QGzZkv/4gIzbSk8A4IBjVxhljCwHWbrl6yTLXm6pVd8znJW82r1OUOhkyi3DH0buzZxdBkvrdCVIfawhlgj3SXI4z22cYKcCmrXQFYMrF1KO9inW2dsyjkXj1/wzt1Tit0FuCqF0SNRLI68ITu1g6RB6+eMA4eom+FrU/KpTqfv6DUGG7FEbnwWyyvMvT0wQrTcwrMVkqDhhpB7A5dYr7FjlULe4dsDhkJwa3SMTMGtblKaJUatUGktIpVptAahStRBi2tJ6E3KqrJQ6g60hLKVSDuJtg5xOomkKlD0Lz8S9M99WEgI8UAI8dM/9eyEEP++EOJ7QojrP9X/l/7v5sqKknFn0YgYf+wytBsGekp/cp+BaWMoLZYek6YhI93FxufzZ7csdq/ZzbbIckvRfBFHvXo047NHn7EnmcRZzKzLKOoXRIaKPBBEqYzcHGP0VfKkIVvlHA+OSbwDmrhmMvBQvRRL0ShlF7kNuH11Qat2GFWDVzcMVI06Vjn62l0OvvaA8euISS2xnS+ZKsfoZoEwHA72Bsj37/P1b/8ysjHi7OxreN+9w3uDBzhSB0pM5sa8fPGIZRNhuAZaa5DuJMLNgniZcP/DY+4cj9nz77ErlhhdS8+yMYx9pAOochllOkQ/tDCHA4oE8ha2vYQsec2uMOmkFK0E11BovAxrOsJy73E4jdg/PaSsTXayRIqM6nncxEtCKyaPQ+YSJMmWaBVhbC1KQsrKwNEr5NxgUJSkkaDtHPxEpued0uxr5K1Ps/iERTJHEjHFyx2bbYV2neOHMnrfoCxCyrBh02qIREfpSrLdlijdMg4a0uEIbdgjbWU8kRNnIbebBN1ucdxj9ib3uXt6H12c4Y9M2k5n9voRFBmBVuFpNr2hht5z0R0L05jgezZVtgEnR+8f8v55H5FmpFKEqvc58lXS/oi6WmPurinshEWTEcQlPglanBDHKYUIidM1z56tOT4bkguVV88D/MSkeBkg1zl2/xRT6eF4A+bJLVGl0tYKb15copcZd9xzGpGg1zJZc4NdthSbFaLY8Z27H3I4mdD6I4ZlhV4VdOjIwkOXZMgtDFmQqibbwkRrBYpUQG6BolDbJnqtIno2siVROF+uv3/uSqDruifAh39iCDJwDfy3wF8H/uOu6/7D/6dzKbKEYbdElUovqgjjApSKKvucNxczxmKPpNciNJlCyaiZ079/jOXukYUhyyAlbuZIrzKeWivMwkeMPA4NnzJOSdM5mfBw7An3VImNuMEP+6Tlmq51SeYh1qAitwe8uNLY36sp1RBfm/ImyBFqzlBzGXs+a9VDK1eoxoRSidF6Sw7u/goHPZ9ZNccRoK76FM6Ou3ceUg0FP3n9Yx5oPmmw5M7JiCDWiNs3FJuAvXtTnPt3ef46oNlr+eZf/JC9YwVd7DE46VHQcREHeBrMXm44eDjFlAVPiueMXksY9pCB58Jty2sHyixnGz/FynWKpMPRAoq+TG/YpypCNGHSbjK0rqLR9qmrmL1zD+fgmyw/fUNYbzhop5ycPSQPA0bqAZm6pEwi0nSD1HZQtNidB3LAtusjXI0qq6iDPp9d/oDeTctn5SVnzRC5SnDcCMPzaPKaXK1Ru2OoEg5tlTdlg207LIPnHHrnuEIjFwalKTgpVizLI8ZmgSbrHIuAdBXR1SVILpEFXTHgoC9YZyaue83nnwcQB3Qzh/vDKTtHRWwjXq1esQmf82/+2l+j7PZIopqDgcNPn/yQ4dTg4qNXlI6EeB1x9vA7/MP/4ncJ64JvKBJG7wFCXiEyi9xVifIZ2uKIUc+nDpYkao/zOxLbUEOKQuZFQC91sZyCJLMZ3bN4Z2VSVBUjf8KmfE1ryNDTwThitdthBCsyqc86meH4Pv/oo9/jF965z7vf/mU+/af/JaPWQZczAk+jSAuMTpApLV1b4Cg5VWOSiRrVMDGzHNEqtHpJurNAqMiS/CXUgT+75cCvAC+6rnvzJ1Fj/6+aUAVhWaEUgrKKCLdrwqrCl2pOTw/paoEsVMquRZMUpM4mj2I0aYspCXS1pBIK8VDnfv2A/TsWi01GuirA3mM8PqfzVG5fveBKzPCcMQN1xyarMbKIsFLwrQHe+Yij845uC2XlUuUuYy3GHj9ApDq5LmOZgsQYM7RT3sxl7N59rKagzGruT94mVU1mF08xPJ8wv8bZ7nFmH7Fz11hFyey6o1M1RKNzEWq8/Oiab3zwLUbDjtZQkMuO5UWO9iDEqHS0qsJWTYqqZE9W+fjx5+zbKt+YnlMlGanWUKUVfs+m30ngh+SbCaX0Gkc/RKo7rj5/hf81j77r0czWlI1G0eVEss3RwGJeZSitjLk3Rul0KinE2AhK06GnQasqqLYOmyUBKpN3PYIsRTUEslrQEx7rLEJSEpSi5PHukizMKb425mQ84upizqkmiBqB0+mopxqaYiPMipFrE12FDMw7hFlAJaVkRYq1NCgVlza8wOj3kcyEBEFtqsiay8gVdI3OqmnQtBRTa5hftxTrgs0uo9NcjF/WcJ4JPtsULNMSapmkiSmKnGPphFCLOOlPyIQEdoa+M0iiBXPlNS+urnH7UxzjgG5Q4e3dpdw8oW+UJLsGxVgj/DGtEdG3zunyiqHRcrXKcOjImxqpHXBqSyiyQ1mFtPWGtDEowpyuuWE6MmnDipQp/qCP5FlI3ZDd1RLiBdPeL/A8KGgkjzBpaWqN0o7xckE6kDByma4uyDWDqtRwJRlRtCiaSSg3WCWkbYup1KhJ/f+5CfwV4L/6U9//nhDi3wZ+BPzN/ysE2Rc/IYEa0kQmj5YL6rKgPxqSvUpx7DEMA5RUUCYmtAWYI3zTYtzzEIlKqEoouofNljox2RodSj9nOhxhnNiMIosX20vkZolnS2R5hWRaGLZPNtapdoIjuSPZ5aSp4O23D7HbDpWEdHVEa0jUb1Li9DWjr72DQUS0q2mvQkZve1TtkJfXofvSfQAAIABJREFUH7FaTLj77kPWYclQ1tHFmDxd0ckw/6hmOloytHV2dYK3f0opWtrYIVrlTAcD8iJn1XY4lcz6dYjxziljz0CNVBZdgLZv4KQmsmxR2wad6IhuIrpxwMWqwdo/p0sU9r2Sp1c69vUlq4HBvq0jsoalyPntf/CPaeOE4bnK3btvs9X2sTyVw+kYc+gwCyI8VeJGj3G3AcW+jB2McEctTe0h11vKa43RiU0uSxi5SeXIWLpLkrSE12uSuuNkOKVZFcwJmB64dF1NZ5lkSUC320Pqy1CbOGWGdraPvY7BcwiuEkaK4OV6Szuf4Z4bLMs18kqmP5VJZQMNh6yoqR0YGh1hkxE+W6KQM7GHjO+oaJ6EVwxYzB+TqS1WGtO6PcKowpZlrts1YVDRl8YYyjWy56JoMWeTE370/BlyC9awo0wLvIGE2kR4xjG2kGmdCq1vk6HhaR5tltHvakKhYjkKWs+h3+sjlTaqmZMGOa4vM7buc3W9RTqecrtZ0qU244HE9ZNHjM4eIqSIuu6QJh79awvVbDjxXAwpp1BydM1BbjQwWpotCLcjbfeQ5QLZKLBjidWgxdwIdDQyo8Csdih9k27ewpfYwJ8Fi1ADfg34D/6k6+8Cf4svoCR/C/g7wN/4OeP+d/iIaTl0YU3/7VMO9wYYnUFRNjDeEFfpF6gwe4yp9lH2TSbyFnXjEmgVTi0wfEhEg92DzabEUybEex7WJmH9ouRNvGUu56S5harbtG1G3zdBgjJuiQ9U3LcOmOgSendIs9lQJy3xWKbTJIqrG7YIakNDWsyRUpVYbBjcnTLbZRhdAK1Hoa75fPY5riaxk28RwRxjOOUPv/8H3Du/hzacsFxmyIXDvt/wyfOPKDfwsprjzqecuWPi+g0MHfY5ZPX6MW+9+zVu6hRVZCyetAy0gpX6gviFTXHPpV+72KVCaUG7esHBvo8jqxyO9/jjl6/QyzFX2YZ89oQf/86P+At/4V9iV8jcXv8z1MZEVgpct0fXyJgIRvohP12+ZHkRMO8lXP5hwdfPG/TuCKMf4TdjGpHS1C6a3FEZKZJI2FUSsllwvn/OnrqliTqyvKaeLyntfUxPkGcpI/0YWU8pO5Vkm6K0JqJIGA51toWM7qrYIuOBPyE70Ki2CTvdQjZqkiijbWqKIqBsdxiVjdYzMWuHN0PBeTPlehDTVeCgMdRa4v6AsZlzGads1mtmj17ywde/QetpSGmE0QMpHvPs2Q85vXPIiXbC78XPiGOdf9k5ZxfPUEqDi/CGtllw4g3pG33KxQI5ENz75XO2t7dsNYOm2pHstkxP7mBbHo2WYloCKJltKzQbcr3FqEvuvj1lOV+Q2UOUzOF3f+8f8d1/8d/g1fXPaAyPkX/Mm+tb7vzKr9LGNZrm06gVXSmhUCOUjLQQ6PKaItYwlYK50aLmNpXaYKglbaqBrVLsii9gBV/S/iwqgV8FftJ13Rzgf3v/idD/M+B/+HmD/jR8ZDieducPvkVjGLimR5R2bPM5dt1nb3SAYqzQ4hFrKcOUcoTjgyUwFA27lnCHLs2m5jpK8fyS1u0wbgrCVMdWc3ZOi70xsP09LqIto1GPaLOgEzrOvgOBhCcrVK7JoSxxM5fZZjvchcrxeZ9Pd0Pe7lukosS2NNS64PXzDL2TsEce4TwGrcHExNVMFrsEparx3COyPObsfJ8y2nLzIqLf22fVKYim4MA6IzjSeO/gkOzyFvW0z7ftHm/erFm0KneFw7PVjOLJSz7+X5l7k5/dsjS767f36fvz9l//3bj3RpcRkZmVWQ0ulY0tyxQgGjFBYoDkGWP+DWb8A0yBAfIYbArK1WefERkR9+Ztv/u1b/+evj+HQRjJMplGyFUo1nBvac/W0n4ePc9a9zm2obEbV7z59Y7vny/w33YMWkzhz/DEgONAp/Q83CeoExXSnkje4zUGr754xfV2z/O7G46Pp/xeOOX11YrFxx+RFTV1u2UdVxxHLZ7jcDRrKQtQdYsySXm32GFFM8JxxqLzqYRG8bCiUkLGYxdFhbLoGc8MbguJniscvz+j1gxca0zYCIIjh3J/T5YaDOuEtaiYTkM8U+FQ1GhFydw0uc1ywqYFx6XSHbxdjW6Brjk4WEQyQ2QJQ95RRzX0Fo/HJ0TXMXZjooYNyc09L9cu9qJkcuhYWhbH2ikJW8y9ZENKGXV4IezzHXdxxFk05c1FwvpNwvjUYqNUFFWE2riYlUrXK7x5iFCeSoLOJA4h2bTsNw/Mx0f0VY4mLRobhjZGczzuDzHFYYv1+H0oS6qViRc6vDcOsBQTke3JHJV9e8Rtcos2WBiuglDP8QdBEt/TKgqO1ZMfanxHI0oBZYaiHGhqge729JZEJAq6otB5LW2sI9UWsy6pDYXUVCD/zULwt7FK/F/xr5UC/yps5P/GfwH86v/tAVURHIVwMkooqxSjbzg5nTM7OsXSfbZ9SP3+QDhWGEwLUoVGNfFLC/HoMUKcYPkKxxdzwtmEJ+6Y4+kMw84phUlHwJOFQ3gScjoOsCctZ5dzAmeMF4yovB2oOl4m6S2FftjRdgqKMeXCOOcf/eCHjGYhI2mhxCpH2jGnlxaGEOzXa5r4wNvrO66u10RRi+FK1F4gQkldVehVj9QdcrVDGY9RrAfifOB7//h3eFy3jOoSdzHF2WvEa5OJLTHSG/pux1/+73/CV6/vKJt3PP/Vj3n7/B23z1/x+s01X/7kb1B1iaZLru4jHt4VbN6leL6Pq/jUroLbD9Sqwq9++ue4fY5sfw3xA882OXerZ8S7Neeai7kPGKstDwao1pzg5Iy6U2ldh31RsdlFWEVNFzW8ExXJJiJudKxxj9cP6FqFas9Y1xqBOkMbnZLTYyY123xJE0SM+wJFO2bIazQvITkkRGnKLi4Z8opcWgxZidHPqdqAJq9gl0MVw7Zh/+sHqniHukoI1ICejvh+yTreYrcto9DmrohpEwvf9giCCVGi0BwGFoFF76QEixlbb4vMLKRWcnzqEZcCZVRj2Aan1il512ClErXZU1QNyI7e0vjw0SWfnEyZVhqpUNCKnl2xw5c6Vig4NBmziwnTbkQsbda7PUW0o5NTmqFke9ezOCqQumCTG0i3YJnUNKuER0L5ZhlORLRlxe3tSxAxN6uO3pZku5p+PKGSA9owMMgVvZDo2oA61NRLA0VI8magi3Q6WVFLh5iBoeuQ/5Ze3b9r+IgN/BPgv/nXjv87IcT3+aYcePtv3P1GtI3gLslp4h5bGmgyQgwTRBXjLTTkIaDctai2jZ1W+IFCmjVgWPTFgXcPPdNQgtQ48RSE3pE5GiczC5nkvLp+C/acR77N5WRMMahk0TVRuqTeNvzB6WfMLsbM+3Mm9oq13TPpK86kzV88+xlN2NFsG6aqpExafvzmHSN9Tlom0A4MrsJosDAWZ4zFmFztaJuK5H6JY5ncPzuQ6C1nswXr23sOaUGQl3zxFQTjgK/frumUlKOnHyKFwpe/WjLIhP/pf/znhPM5j3wP5/FjJh9PcNQpk8Uj7g5vMbYpX7+649//8EO+//SUH7/8azzh8XD4kg/9D3EPG37eRYRxy/MXXzAKAtRgxb/8/E9oSpf/7L/9pywmOn2ak2odyk7QtBGhyDjR5kw+e8qzZ1c8yIijlcLm8g4ltvDqHjFyCVwX+dCSexWKqTAOdM6PfeJDxt12iy5iHPEYs1hz93nFc+uWQ6FhtAZyk5PtUnTDI6oeUO0LqqEimuuEdUzqGSirlOl7JzjVBJGnhFOL23WLyFtWHQzjnoV5xJvNS250nWGbsAhHiK6gUDqUXiAnDSPdp04SFprF3YsEp16iKwnjeUidplQ3G4SYYk+n/GL3c2LRYNgKpbD54e98iLxzOcg9TVtxvV3zKJxghQtEWPGd6RGHqMfzXc7PVO5+/UB8ltLoAUPaMdFt5JGBGqUI0VEcaUQPLlndM2R7pguHLJVk/ZxMW/GHf/CHbA8xX13/Auf4KdnXt1hNRe6byDijtnxgi2YO9LlLM2S4akdj1ajdgGdC1jU0pUprbLEKj87sUGpB/3cxMTgMQw5M/o2z//r/6ztdW3B/f4OraSzjiMvgnDh5YAgb0koycyyctKAWU2aOSdO4CHdD0lSo+5rF5AjVrHDihn7S06zADXV6YG2PME4Eip4QegGYKmFkkJQVYiGIOkkuWha4YG5YtTbpnc50qpJmS7bLDfXGhSajtDTKLEJxTNquoG0UPAWsmQ92yCOhUOsKliYYzae8eyM47Hdcqz16EfHqpuV43mATMBwLwrJnMncx7IB3N3vsyiPNH9i8eMnN7Yp2YjOx9rgXT0DP+ezsEcsYHOuCossoSslPX79G/ec6T77/EeOFS7KL0GxImwg5OaP4xRXeYNK4Ab863CN/3DHYPt/59CmTKkXxP6W3jhi2z3mVpIjmQD2o3HWv8ccjFK8lXqUog8WTboZAwzQV8krHFjnKdExjgdI2xCuB00/IkxxbadCwebe6Qcgczz0jr2tOpc0vDp9T3a6wrQnnR3O00ORttuFiaqAtXdojjT5JuK9VTuIKa+xh5ApXWoU5NTFERjV38e9UdnXCeXjEz796BmUJbUyyWiJMk6wHUUDcS/zJDBoT5W6Dmyq0I7jL9uwfvukxfP/4CUUWcWReksv/DdH1zBdnaHlGNT9w81XJDz6bkFdzknrPpDvD7wXFIabVdDzVpw5KZKCi9hZeMCJT74kl6PucwBqhGz1J2lJevURtZziPAsptjz4yOQ+OSLMDzl7hZaYxXnxE3JW8vntOX4ItcgpjiinWVKqCi0ekFfQ1CBu6RsPsOrKsRddVCjVlnGlUVkNdC/pv+ypx3w14uU7qlfjDhK/vbkAITq0RVX/gxf6A73bMLJu7Q0O6uWFyOqM3JSfdnMGSjDWbxIG+atibG+zewFQtxu1A4J5TOxmKaWIYKmaj04UKU+sRozYh7D2c0Yzdfk0XHVD0nqZS4DJEHFIUbUBBUrUVlWxoKwVbdKhVz/jpMWeTxyz7N2waj3GQ0Q0WlZRku7cksuIHE4+vtrds4gY2Boq7g1uD0lyQPzxQS433Pj7j6vVr0nd3XK2vMTSV0Pc4PT5Fcx0M/QzH0QhdH2l0fH/2B0RvXvPFizt+9Vc/pzEc3vtkzLFt8/NlwezCwhEZlvRJgoHH3/2M5P/omf/Bd3n06BFOP8dwFrhdz/3DHeveIPAiotcFnSxQ9XM2Sczv/uA7vJc9Zfv2wD7bMB+doE8qRp1K2qZ0bYNcdSA8OivGUXRGFwbb1xCvY+wjl+yrhIQtXmux0Xa8vLlHf7mnXUQ8/uyI8OAynh+zizV8U6F6naKNBWPbhHKHWg+Yoc9JOuO+fMCcLcjffYmUAWWZEh8KLHNCGz1HlD6VfMBMBspDymaffbOu+52/TyEOWDOHO63jiW6xTh7Yrx5wHZdsWqEbI9b3N4h1T/DYJSvmnI0VCjnikfWcZw97fn92RDs6ITBDvFqSPJqTfP5LvriqceWEycWYNqmo24gBA7HeUgbHjJUOY2IzNxXunXfYsmCqHJP2W8o2wFYMGtMktwvMOEefHHH/4oYv/uZv6DSJoimIcs9Q6IhBJY0b1FFPY3YktYbX1VRDTy9VBAOigdI06LUOrwno9A1R/pv59+0QAdFzv36LXS7ovZIPTi+5275jyDOscUPfhfR5xSGK8AKDs08vuYxnZHqGMQ6xvYBUT3HuS2Soc9xO2DU5RRUxwacxWmLHYSZdAkMjNzrmwe8j7q+IMxNr7FHvM078U8yjhCTKcac6baZSygi7H9AsF3c8QuYd222KHT7iw0/OUaXgavUC33OokpbdquEkgGa1wfMdZG3wdvsKXRwR+jFZtsE1ZoyCjmb9lmp0gXK/YbPJSR7u+FrZM310iisNxrMxrjWmyho0+8A79QwzqbG6EAwdef6IM2Fz66u8+PJz3vwK/sF/+p/zvYsT0CM0+yP+3t/30Kuazy5H/EdP/4i7YoOrqfhKhz8PeYhSykYl2i8pNlvaeIvoDYrwC84VjWdLmx6dxcmYqPRoNY/iPmU272kVH1NV0YRCopSMAxsOGk1WcHbyiH95+y+4+8trRJOhbVIenv0FV4nJxVmPdj6hbxSe/ekzqpHNd95rGJ/Z1JnECX0ad8Q4VVjnguH5HSv3HuwJsigZ9A5zckH14hW1W1H3KoHSctdUkK+xLp8wrlv0bcRNWjJdtbQf7ZCug6FZaErN7bDlw/Pv8af/4p/xbqh4mr5HMcm4PDrF8hx62SD0DdIc81TMeXtygbe94q62eN9/xEht2NqCOq6ZXXwIdUWql2RvSppewSGhLFvG02P6dsT1ZkkwragSiaF11FuF3ZlgrNRgdfRBzuHrisAayPE4rJ/z53/yC5LdO8Z0pKWO1QjKsCLMIVZUKErUTqFXDTJTxU8joqGm1FUcVdJaFmIXM+glifLbewLfCo9BBsnsaMbRZYAZqjz/4isaYsIwYF+4NGpOKS0M02AuLjgdTO6tCtMzkX2G6Q1MqwZ90TE0W2q1ITRNxqMZ0zOBN18QqjZZWaFfuniOizfS6HQwtJja7PA8F39ssU0szj/6jMGxWR7WiExg9gZ5vmd7df/NEBMnWF5I9PCWr372K3a3LVrkIvyIItrxto45tDe0uodnShzfIy0iijRFL2u6asnDQ0d0OLD66opipDF+egSnl/hNy6lUOD2xUMuOXZaQ9woyA3NfQtuTDxlm3WBJgadoPD5eEJ49YvAaimVCPcoYaoXjeUc4mmKEA4c3LQ/RnmMZMpcOoWtzv16TDXtkt0Ef1Uh9xJKSybHHyHpE51zSLjXCwWGb3eIqA65qMfbPiKoKbJUyESS2jTUxUUqTujnQtyNoHR4OGZ7a8/DqLUVxizY/ZnIWUZgeWg2yqHn0/hwhPGK7YH27ZbNMeB1HZDcVudNRIVjZFnnrYK9qtL6hrVY8GR0xe3LJtJ0gm579Zk9e3ZMOBgtLRdNNIna49YhmoVI8GDgjD9M/wjiM2O1qood7jh99iK9NUE99fCPDGkL0YEAPJ5hRi9n4LKM9lBWnT9/HdI5wFI3b2EBPBFMnZNx1dHqBXwe4/ojgsY3vOhyHc5xKMh4lOO4It5BMDMFYBDQjmLg6iQiwhEl6MJl6OUKVfPrI4+rNa4q711iVyqHW6GRNoUjMQicX36Qmq7pOZ2p0XYqfFuwFqJ2kHhSSkURJcjRXUCkF4W+xG4dvyU/AdixO3/+Ypi7oypjLj2a45gLzyEDflJw+foKlulR5Qx/m1PopR+aWtrcxxzOSMkfVNLI+RxghfdOiezpD3LMLJFa0R5Y5o0mAKmoGLcdQwVCOEeqBclOwUgfSNqJpO3KjYL19oGtrRqGFGHrqKEOzDEbuBUkeY2RLWlGRqRXHs5CkURjWgia/YvdnOlftFcF0wvjyA7SyQuxy7LCh7Q6k0THuxQNFUeGaGsRT8mGgSWO6VlIdLwhFTecOqE1D3XVsaotZb2AOIxo6VMtAeANuY/N2aJEHiTmbcdi+ZfzmE7SJRkuJrRvc38/IxSvE2KT1XJoJ7F4XjKVAyUwK/4AubJr6K1QpKCwNJVpx96Xgj/7h98nzDq3V8WwgUBG6S1pGjJqYQrSQFMhUBUMlV3tW27f0FHzv5JR3r75CHdlsnt/ReTrO9JRAzmjUjtOJwZs0pt9C9abHCkeUVoF2aFCnLdX1HEvtoY3QVYtnmzvqXUTeKFx8ajKfBBTjnvurdzTFlmGzp1Yaosqgb1J8SxB6AZPjgbvbL5juz2jkLfPJjPN9jmEIrGFgZNhM64HSP2GrwlmkoI1zMi2hdP6Q9PBrlLFF1Rc8PZ2h+ir75xu6qctZU9FqA0pnk9RLNGNAtU9Qho6u1dmJgqlhMIsl2Uin71Wsk4pzy8HKdZZ2zmltsRli3lvM2AySSBmTZy11X9CaGiLLoNGQqkUm9liDRVe0DAMIvUDtdHKlx3clVQpu1dMwkNUNfa2AUaLoGt/EJf8/8a0QAaFKlrfPaNQQ051iBxZmb1H5U9wuJ4piro09T0+/R18VFNoe25nhtgFN7TFYGSOlRy8mdFqHqyjfDAapK0SkE6kNlhPimzp9VNAPOqYmWLwXo8bHNLFBGgjctuZwJ/C7irgfqNKSq9UtY8NCHYeUy5jEekuhz5kFJkPvc2nt6PuUt9st3m7Jy3cv2eYZZlXRexO++Gf/C0VZ4eoGRm7TqQYX84w2nTJ55JJjUhUtt81rli+XpI7KaLtj2ysYKuQe9KmK3r8kPvwO3XSLwZys1vFlzaYbmO0Ssg8N/PKS+1XOqHhN2V2i3lc0BFw+PqUZPiSP7rhv9sjbANSML1684HT6HtHbr+jD96iLiskkIHv5ikzvGAcuXWKQdXcgddRewiZiIwbGY+g6Qa5UKHZFsjJQ2pb4Pqds1gzSYr3a8PVmjV/YKO8foQ89+ApdfMAUGjernI8/OUYcDTR6wX5ZE497inu4NxPs/oR2rmIpLcbk++yFg1B39HHJm9WaZ3/9CyJth+qccqx0PMge92iCqSvkmwJXmfI8/Rp1FfDx2Yeshz1u5JIVOyYn7zG1zli9fYP9NKTrJB+dfMjPb25gpnA+8TBGU/ZvXrBRBUelwrkzZhT47NOc4/dOiaw96+KAGWiEuUSpampXJbk6kHo95swl7BSKqGefLhlaC0XdU77LmH80Yr9ZMZv7CCXAXW6prJLzqc91kvH27SukM8AmxnZ7ytSiHyrMxkRTNIa2RajfxKM72jfR6mmtIFSdujIRdMghY/AFSqeSt38HW4R/mxjqjpnqYds6s6oidGxUp0S/ztltW0Rv8EQ7ZjArjnyHeeGTJHt21Z6Nccuo77E0k0YF2atENNTVPbnQKDQYmhkjz0FqBq2h0fU6faVS3xeUqoPse0T/jS3ZoK5YxipDY6A4MZ1a47kmYXBOZw3klYk11Ow2MdWyRJMO49EjcFuivkAUOqaa0iuC69VzzCbFNSXZEJFvt7R9SxyrBIOG3iWMLRtHWkTrDmckCKVGVuts1ZI81BBdS5cdIM8ZgphaOhi7PWrQMzQdvjIh9o+oCp986JiMBFVi0UY5vjYn365Zbt8QTEw4dchUl1Xxmsg0MMc+uTLDP/l9LLXG9TW0ygRlwqkR4I8uyfoddiewXJvCMGjaDFdo5I2C2po4dOiRy0Sv0JQe0w8IggWqotF1Onaqc992lNEtQ9Wxvn6BMWgkQ8vR3OHhZsvdm1v2NzvE2KTISjqrZdeZrMycd9cv+fzPf030+heI5ZZtLhmalq9+9gVfXD1jc/0cp9VYryOelQOhAm1VY3QBvbznyXREVRmsywMz75TSU8h2KZrpYhgCZzKlTWsU16d3KvS0xB2HGHbApBujexInytnWGU2h4LkT+tLFM1rm7RRnOkVrVCpDJyfDKE2Ox2NsVWOWduhmgGo26JqDpuiUUsNbnODUKbvdK4aDjWEbmKFBXua0jUKRNDimi23pBE5Lk0saUWP00A+CUlRotgKmQZMKyrijqUHwjY+CqWWYXUynGlgqyIrfGkYK3xIR6AUY0yMuz8aEoykbaVE3Fs004+ypS89AJ1WKNxH7KqWtBxzFJWsjjunZZBVvdzu2+3u2y4jlfkeSVMiqx9UrRnXLwejpywxNQKAkDJ3OoNvEuzXV0KO099zJnjTW2a1fI9sdgzvnk9OnLB5/zNQt+M7pd7ENnbQsaS0NfzaQZw5OW3Cs2vgyIGJDvlOosNk9v+dNB0lW4MU14fwSuw1Q+gNZHdP2AVnaMnQqtlfjiAWFLujqCL9uSbOCZJOgpR694lFHGlXRkLgt7WGLrhkkYYpXtwSmgyGneNKgkZJ8tycyejpD4soAxx0TOieME4+ZdYxZtzSVhhjv0bMU9CNmcsI+W+Meq6yLhH12jeEKgmBEU8UcVrckfUDu3COLPWrboVYhnShoBw99MBjrNco0YBpI8Er0uUeoVUjPoxY1i+Axhg6PL8/Q8x7NmmGODfIm5fr1W/YVNHlHqKv4toPmTTAXp8RGQ14VbOIl+3RFtnlH3cF6q/CnP/lLsqpiapn4zoRZq2B5OZWckOsjTqYBnZxjKg2B7rOq9rTbmo1SURUDR/YU6Zpkyw33xZbhoIK0uc92zBdjpq5GFhcMfgFNw3Shc1Ub1KZFdUhQtSll5fH6+g27fsmmf2DSaHS1A0qNKFxKpaQrDyywmR+FHPD5+uEZ+2zHcrvB0jXydcZdumEwEy68I6pBkuQevTmg09HKHKm2IDtyetqiodckta8gCx+RlySyoDFasl5giZyqt2hMnUE3fiv/vhXlgK7r3MQ3NMuGsmn4waOPaWY2ND0Xo/cwTgSH7RLr6ISF62JMBZXVMlccOjnCyCJ2h55svUVRD2xuatqhQkaSzq55/Mlj/OsR8bGDWrTM6AlkTz33MVYSVU/ZZhYOLdq4QgwLuI85DzR2ZgRpRrxuWS5/iRIqUH3j5iMdg1J/x3LrcjwdcfTZKc58wtWzvyDeOmCv8XSHQVEQNJRNyvHoDN1zMBUL33NJu4Gqg343cLv5nLpVqZQDInGgCVFMD8qCuMvx9h4LKg6VwkPVsKg2WIaDRk2yekBqBgehYGQ1/aKlja4xPINYlpzNpyhbk9ujnPrztzR6j243WGmMNCYM2wP1OODItDCKnNk45LAsOUwzSq2ligXBbIbRFdilgWx17lWHE12SJj1VcsBwXHZ6jvL6gLBnfBReUI02qIuAaDsQU7Irco4nY4KyRvngHN8fcbMC/Wbg6/Vr3LsH7u2Gtf0YS7lFwePi/DHZYcPDcIdWKMwfP+XrQ8zp+IRcbLn5fMmP3v2aH/zuY8ZGQ6SWqOE5tblhuLqj4YQffhgiS59q+QJvkNSJJOhdTnSfxMl572RMfLfk6zef80dPv4NuNowvHvPFT58TTsecn31TYty6d+A95bMmGxkkAAAgAElEQVT3Qw7biHoA0ykQooHRhC6qsAaXZZ6h2Fuy5Zr2OMCXBftszyHS+XR+zKuf/BVtOgJlz/12hZzYrKqWizylKDxe5wnJfYwqYOgUOlTmnUqhdBTCQCkzsFXMskJLIZE1buDQljltL1A6A/Qa4g7LEdgUbH4L/74VIpDGMcVeMD09YtyOaMdT/L4mswLyJmaVSKSu4g2CRE8JnDGjUiUzdLo6pe4knq0jpucIkbFMVzgRbDWJ0Az6QkPoHVqWU0qTtWHj9RVNm2BYknVnovgFqigQ1QRXLpHjAHMx4C4FaX2NUDOm4Rm7IcWeBMiT9/Gaa/JiRj3s6WOVTdBz8dinM36P9Ksd4ycBZqcT6SuUyKWNBwZFYTpZIH3B/TrjA9ul8TR+ud6hmD1im1MqCrXak0Y7prrB3rVx9J51kqMYBsag4smWbpgT9QO+BpozphuWPDUsHrQaA43xOGR1v+RKCublCabeEoiO6HJCkKZsViXLQXIyysn1HMqSOmtR+gZVWiweXeJbGpgNayrcDtYyo0wjxosfMqZkL0CTB8TkGDVVCKpz6tMHomVBNgx44RwFCcNrjLij1juUSmd27LHqodg/YFsmqR4xtxzyI4+J8Jk3Ba1/xJuba/o1zM8V8uuGyWLBgEWSdry7+ppHH57hXTa0Kx8SeOgLBkqmTsP1WxUt8ynUnLKp6IwDpV1zKCW4LWo3MBQFItMRvsFdkVGnCpbes1NLLhuFtW/y/M0znp4d482O8Pw5lkhB6uAV2InO7ddbxGGLPaSk+45BmlhByDRwUBxJst8iZhMWioI8U9iXCQ/rexz3jKZ2GcolST7FUSIU2dMkEX38FksT1HWB7C2E35FnA61m0YmSVjExSig7ldYvmZY6WZugDypar1IaLdLqUYxvfs55/Xc0Nvy3Bd3QuDiZUXsBIyvABmI7R7uLEReX1F7FuaWTtQN2YnBdZ0ztlqoKSLucslZYqCaqWLIqQ467lld5wWSqkSUdtZ6zJ2aSO2znHd9RXOIiwMkHynrAVm2KwwYnsJF9QqqrWMGISdCTHA6I8RyjrxjqFZrScByM6N9+yZdEWM4JljVmvU2ZCJdOjTidXLD/nQl8+RxVJsTbDmFneNMRXTClz2rMY4/poCDeO2Y45OTJhrZW2XQdytAyNSFURrSVTqhopFWCpg1EeYwVSaQbkMyvOGZB0akcy5RDc8IqP9DKkjTS6NOBcXiOaiuMZMc73SGc2gxliKo0GPqYUjFY6BOyt6+p7CmjfoTqJFTSZl2nzKVFUpY4vaTvSwLVpepawrBCaAIlH0g9lyHpSY2ORuxx2gDVK7AjFc9xMB9SKi3E9jT0oUDF4UYdUAV0ckq5fsNdG9OFNTJraJ0x1klAJSwuZw6HbcWyhbDpeHtzxcP2GRdPppRewbv8lhoFpztwfjplfdOi2hOur1aowkafTtHaFllrNFnDKtrgCI0Xh5rZ8UBsWLS7gqS4wzcvqNu/5ma55AlHaDOP05NLFN1lqluI8Ql1/hrr6N9jY8SEa5d6VuGKCz5Pn1HGFUqtYacqo4sFRZQxSAVTM/A1k8+blPlacrP9CSktfltxOGxw046sfmCYWuwOEvvRCYMmKQwFhM4ge/pUI+tLxkNK6Qq0uqMTPjYdshoo9Ja2lDSqQdPnDLpPkaVYLaiKQ2qnEP1m/n0rRMB1Qk4+/YBRM6EJoDzUTMczlOOW1c+vsb8zYvUmZzJ1WHHLI+OCspJYWgvqlMCuoY9JspCmyBh7F1xqK27imuDE5nC/JDRGNBMF64XKm1GBWzXk45qhVDDSnLjo0EYWTWNihTAKB0IsQlYYxkDhttyuSoKZxWaQjOyOLBK0q3cMukD1FsSOgVWfktVQFwlHZwEUHtbMQgQegSFJBp0Alb5NyUXLs7+8wrMbzj/4lJvrWz5yKpbLmET0tHrBSOa0QmHaPkIaDS09le2Q1QXuQee2KZmeCQ6Y6FVOrli08ZrgwmLTjZiMVHQOJGqHrLfcZRHmJGT7fEdc9ZRNwr2ICS+njFOXtpV0lotWe0h9Rz6+REnGxG+v8T9yiIcO17bZvl1RByGjoSXNB1QPbNNmGDTWdYlHSBuMGcYL8tE7FvEEx97Rqu/R9hVWrdJ1Nut8hx8cE65rrl8VOL5gNIp49rMNWdmhKQLNFSSRxcPNFb/zye8y//CInz7/EdvbFZ3e0Sc+f/xP/kO0YODSi/nZs3ekdcR3359jph39yEAdGvouJxAWvaNSR0uEMmWia+znDTP7gi9Wv8JsC8b+Z5w4GpmTMptNOZ4KsqpCdGukDCiqiHazJd1lMD9j0RsszAuCFlaTAUY9E8djPajcrx5whpZuv+Ls8hH51Vu+fnnF+4tL0qBjFBXIj6acjCdstnvGXc/idEbd7PCylqJrMAAMlbKHTgPRWRhqSSliBimoBguKDENT0ZFUQkGSIzSdpPbxzBy1U2n+rvwE/jZQNx3Z/Z5dElP0PaNTj2aj4esVJ4/P0bUxq2kEvsSWJ6z7CEW10OUEJ25JwwD3oLDNlnj6QKrsqQ4pGA5KabAbAJlx/+KBxWmIEgfUZs48dqET5G6KofR0dzl5p7HKI+zS4pdFjmc5ZGnFBkkXTtHMliCGbNAZ9wEpO/Z9h1MnuIeelTInq++YDC5JNiA1l8Q18TJYxykXi4+p+iWaF+L3McVRRBV3NA81kzbkevtTyi6k2zWchCAch1aqqKKl6luCQ0+URdjnY14dYs7GPfnOJpjaKEZAkZXEXcpo76LM3pJvHazgA6qiwhJjvLpAkSuuVItnL39MmWkERoc2LPj6q+ccXVziqTa21VIkLrIcMR9puO8dYTgKbqVRKh07RbIYYlICvMBm1Sl0uwphazgS7BCE5qGKBmtYMDh76saiN1o0eYaRShzjwP6LjPtc4c36Bf7FMSoDV0nNUtPQ1Jjh0NHu7tHVgKeXT/jo9/4RM3oORcbx0WfUfcXNzWt+8lc/Qjk74Ydjm8tE4dZw0FXYai2eCDGnHslPU653e87DMfNHT0iLAifomXVTSu2AVgsCxySgZTue8UialLucqoTO7ph3CophY2kDb1YJjm7gr3NSReXkZE6yrxhV99w/9FzlLzHOjpjbI9K6wLs4pdgP1MJCUxTCE4lTwWu95YNYsq22jGSLaz7iITsg+zlBeCDf53QTKHcKou8p+wHR9NRjBaVUqcoeW1QUvWAYBmTX481t8m1Gpxr4IiZuJGbrAr85eOBbIQJSAf/JGWmSM89VdFunsuD2psSf7JlsWtr6gJgGWNhsC3BFwzr5NYIGGWvc5i2u4yH6mCrpuNtXmFpNOtTk2gPKi57DiWRSTTEr0AKdqyZG7xrCZsDST4jUA85IZfxqy65uaZueF7evCcMZI9+jNBKKrUJjq+hKQaMMKLUkiBXUpuF2H+EcGbi7gdrRcR2fcqjg6jXG+4+xpmcoeoSfTiidlsPDlsNrGM01kHCTvkXRTuirFbWu8NBUHPeSpuq5TlZoZk0rPbJBUCz3qGXDj68NxrbDF67F+7bOTZ/iKgapvuHVXvC79t9jGBd47QmbeEnjVaDMmB2/4uqNzfHpJRN63rx5Tls0tGLLECX41oTHH79H4IKraMgTj+LQ8ma3JojH+Ocqba9jKwVN3HHmSXRNJzvyIVKQpoaWabSyIZDQOu9RjHvmh4qw79lIFVUz+ex7Jo3ScvF4Qhxt+OVPf86XXz7Dt2w2+QEZqkzsM37vj/6YT57+Ab+4eUU0lXz/O5/w6vOveZm0rK5zoiSmv3/Jz69v+Md//B/w3fOnHDYV5yfniK6huyu5rzuO5+8xCn2+9w++R39X8cv1jzgyBPrpBzz8n19yKFI++MFTJidnHGJBb+ywA5v0INA+OKcsVWw6Qt3m+D2Hh6SnqWJC12O9z3hxtcPQIuqRglOcMP3UYPxwjONZRH3Gz/9sxQ+PTR5fPObV1yt+8GTBm9s7XFmzwuW2+BHVMKfcPpC2A2g2XZ3jqQNJp2L4A/VGpa96RK1jtAVCU7FUDcPq6GtBVHSoqoNt9KRS4FqQFSkkv4V////S/TdDSIUGnaDyaTwb61Rl6MALRnSDQeGpHB2NQIxpOhgKGyNP0VuDroBsqWPT0R/e8Orz18TZNWeuQW/5tEpBXHZo4zkzxaQsEjqvQ3QJodUz8kdotobXZtjeiGbX03Uq63KJyGBQxuz2Ma0RoYUTFsePmJ+fcLmY44QOJ4FP0XQ09oLH3z2jjl4x9DnYMatsxWHoEKMRdd4iX6cs91sekh3pssJopzz+wSWNG5CaOdXg4J3bcJDou5ioaqj6Bk03ORpqylxjG6/Q85z+0JKQIsgodg8oxZZlE8MuQtt2vE06wtxiNcRMy4ZOS7BGPiejGTPfwWhd1I3KbnvHq3iHO3I4ciasV2sK0XBVZezuVxzaJdt2QK0Fsew5ff8c6xwu9Tl6OFANYB2PUauBRGvRm4yR1dPXHabmcGw6tPYlnhBcHlp8TLZDwGiqgd7je8fonoXW5yyXa8q4QAwaWZ1jBQpX1wmPLzxOz09oqwcWXcRIqCzLLV3oceTb1EqOqB7oljrKMPCjv/wrrp7H6LpG1WtI3aPUPezJNwNgY9ejXBXkJxldJBCVxsmowTU9RprJIek4bHTyQ4KmjQm7U+zMoyg6elGS73seXj2nK0qCRqdrIrLNDoTk6SOXwfbZiQHVaDmOpuCFFGWHXGvcLF+husesH+44CsYE4xFHCwOlMXFbSVNq9AcDRa+p1BpPpgxJT1GCU8KwlQzEUJc05oCcqEinoRpUdnlPoQ8YjWBAg66AStJGJV5j/Vb+fStEQEFQKw2qrDkJBbe3BfHhgWVyx+lszKCZ7FKHvh1oZM4olIjxE2LxTdrttrlBeRg4vALF9hjSgG1rYZQJqlmhJznLt89wGhcE6E5FFYQM+gQUlTiyqWVOen9LFK0xRpIn4n1aPWcchBw9mnHkHXEmPRqzh23JXbRF6Ss2O8G+XvLFi1f89f/6BSU+Ax3VfYJ0JhjDN/kBbdWzmvRYVYh0POTI51q2bG4rjDYjjwcMdUl5F2NPShxDx9nucft74ECk16jtjlRWrFYvaYoNu22Ccf2CZHhAxnsOV/eIpkE17hlRks0HdFVw0Dt6dWA2qTgem2Sbik0vePzdjwnjjrlZM3Q6zhODTy8u0HoFtQ7RyhZ3aWBUGSqCCQ5Gt8O2AzZmR39QMYaSfSJpPAVTMdHKirg2SQaVdtLiKgYTu2NsQTIbg+My8zWkOuApGuYEpLpgrZqUMmD6ye+jTSW7PKNKJB9/9CHu4lOyvIeLEfrFGZZ1xu3r13QN7Da3yId75qNTpgsLRmPKtODLd39GpOREUUojJG3f4XoCo97QVFvuh1tC8YTpSKOuQR5CWh2UJqGSDVfbn3ATbxGaRruwGC5tXHHKkFgk+pi7PuRn2zHbyZzF5AP044DTEYRPPsBfTHn28y8ZpgN3zQ2ybdBMhVfiAVl2CM+j3Q50o5hD8pKf/c1Lrn61ZJfbmMExdQfSMPEVha7qCMwpwuqRWo2uDKi2Buj4JSi7BnIfoTWYbQNljypLjKYgSw2kLzAF6O2/o72YEOJ/AP4TYDUMw6f/6mwM/M/AI74xD/kvh2HYi2/shv974D8GcuCfDsPws3/b+3VbM/z0HV8UObyWPD2dkWk2l2OXUtWw3Z7QcBH+DBnvSPuWbbrC1A2q7gP+L+be5NeWLDvv++0dfXfi9Of2993X5MvMyqrMKlYVyWKJIkCLFCwJhATIgAEDhiHAA9sTj+yZPfTM/4LkiWnDMGALMgzJpGU2xapiNdlnvpcvX3P7c08ffb89yJRBSJUyAYpALSAQESs29mx92LG/b3/LsD9i3kI5MQi0HVowYXnX4OIQdjqaOePoVy3iTUnPnHC7qhFygydzok6itQmLrUerK0SvI39Zs+nfYpYJoh9QWy3NpiQxfByZsCs3tBj0O4t2pGEFr/Mr/hG3t694+vwFK02nbTIOrF9Fs9bM+qdYmx1F1yICibatWG4j3tgfE1cty5tzHN1igcDTTYrKIG7W1I7kdnOFflvRmhCnNdZAYfs1n21eMtFLnhcK5/yOT4nofJ03hydkvWPKaMHx5gH0Kqq1QzeEOu7x9OoSZ9jjTH+NVl7hTzSIE5b9Df5wxKjyyCqfk17GtmkpZEeRzLEmJwizYl1baK9u8UKF27fxTA+iO1pnRN9vKSxJme84jAOSRFHNNAqnIOo0+nmBblRU0iKzPDq/YC8NSMOI7+wdc+JqvJgX/NlmyNnU44OLc/7b/+zvsTd7g9sXV2jLmDbWqewVk/2HrK826K7EfBiytwvpRj3GQ4/N8kNAx4s97EmD0yU8uf6Uz9+bE/Y8PDRm+pCAG9zBHng1y5uU3WbNcPoN7k2+xfT0EcvLS7Z2i1d6BL37rLeKZriHdxBinv8GdeIgkZijMdV8Reb67HljeHTAqXWIcwWdHzDsH6Cyju7iM2aDHqEYknlrjGyfF5unbBZzIs/lUe3QN7/Bn/3kR3Q7nciw6VRLnc/RPZe4a6gtiy7PGAU2WZYjUeQqx5cdKQ7UDY3mopsRJB5d6hGZCquVwC/uTPyXXQn8Y+Bv/2u5/xr4A6XUI+APvnyHLzwHH315/ad8YTz6b426qJhnGdrQwQl1NNtgYh5yejhDw+bA3Uf2XFS0Jmp2FO0cJ2pY7m5ouxX1ViPKE6SoibYNL967gOUL5i8+YvHZmqC9Jdoqorzm5YefcHt+S5olVGVG2voM2wnTiYFvVZBVuPsWlaGRZhW201LIAt3OcHRFvLgiqjfclTfM1wsircb0higVMNjbJ3g85f43HmCFY7abS0pDkhVXYFuMjD3KsiIT0FcOspM0mxDPvIc9AE2GRNs5uqpwtQ6hfOJlTlSviBcbmqIgv8uIV+C1CXXlodU5ne9TyAo9l+zyjGyZosqYSih0kWMYBo4GRRODZuA1EW25IPJtLrdbGqsj2jpslzsWi0v6vZS7RtLzxlhDg2TVslvm9DzBXi/EdQak1ZK2ypgjkWFHV2Sc51tULXFLH+nFWF6AyAUH0mMvd8E0oHXpyTHjnWAgx9QGhEWDvT8mCB/gj01OXw8pPJcw6GGqMQUdjSi426R0uqQRCn04Zub1ESKgtw1pxwlJccvr9/scj8aM9qZMTmzWO8VSpnzy2SUdC1zRMPcr8kaA7pGsdwyDPsPHAzQTHn/vt7HefEht+YjXj8C7x4+uFzz59I6bqkMrJuR3Jm+fTbCOAja54ik3pHLHB5+l/P4//l95/4f/kqhZY45Mevs2RllhTkMqS2IZAXEd07UWqVjTCp+jfZdTb4AxOGVeX/PJk6dIqyHPExono1EmXVXh1wZamiMNjW2mvnAPMqH2LETTIe0a35foWUnVupiOxCBDaRW6/VdsTa6U+iMhxL1/Lf17wG99+fxPgH8J/Fdf5v8HpZQCfiiE6Ash9pVSN181f9GU1K1CXKdMH83oWTNyUfCkkDw+PGZxvsLuGmpd0LQeUpd0RGjCI85respkf7bPD378p/QNF33PJs9qht4eW32H2Bp0l1dUqqbnSFwXylIDK8Q2O2pRQ3aDISxCoUiVoMtf0FlDhoNj6p2DdDYkUQy2hW6MGCwa/NEx/bxFhDEXcYRxFRE+GCBqi/BUR114dIVJmm8wBwss4dOWLbLfsbrM0EwLJhqn3oSbTzbcui22NqK9uiF1TnHbGLNo6IoEXVbUpo0mNcpSJ5SSXNwgdjpCVbh2g0oKIr0g0ipGqcfk5XPa/XtYZkvsKdTWQ18VFKcDxFZyd/UeoTmiM2qi+mPSHeTGHoeawjRLNA3ETsNsY3bLnNHka/Qbg3YkaaKYUiisbUZGCe4ET/RQWU2v9ij8DMe3aZSOaBKkXiI6C21msk4SRrpOtMzQR0N8LaHNHYJ7GYfbB/za1zb8+OmHBNqEuGkoP/oQy+jTaTA8sVl9XpHLFdHtS5ZXn+ONUtIUdpHCUx3O6AgjnTMxZ2jOjnfXz/H8PkF4jG9Ivvfm63SVS5PoDO19WvuYnhHwvbd/F/NsyvVdwza9wC0alsIjMzQamZE8+4jdbIdlCoazGV1kI1ROm+t8sGz5yXsf8sGn73L8MufBXsHs7/+XGGKD4Zpsz2+IdwvavoFDyf7xIwqjoc5+yqKGOt5iWZLPLxYk2wytsEB0tAosodCLhtZsaG0HrzOJtAhDgWwUqvXY2AKvMqnLhnpootqGXKuw0gZTl8SVy1cJBf4q7MDsXxW2UupGCDH9Mn8IXPyFcZdf5r4SBMIw5K2/+RZ6m2LUIa0vGHY9tKsrfvjxz3CmB+i5Ti/QUHUP17pB9idMNHDXJrE34sX1Z+yPD9gGOZt3n3HTrFksaiZkNI6LVoAR+KyvS04Dg83yBaJ4g8DI6VoH17dI4wLTDOgPgGZM5pk8u/4Yp/YJrBLDGzIb2gx3Ci08JC5WrFY6wdExp0nCU/05k8ak7/nMn1tU9Rx77hD1AoxVyrz+jEezB+h5hDMc0JMmk0nAxcuPOL85p2hm7Ok7Ln0HI3Gp9Ft2tDSFpAp8/DalkoK6rckXAfpI0XoCX7+jmLsopROrlPzmOdrkNyiVJDNrXMtAbSPSWlKFPVa7HIFiv/+ILtqh6hyzN6S8TdkaBVFjIRYbjDMTyw6o+iZDPaBO7lg1BlKAc2Cgr03koEYYY9xoh21AFk7J2guE7qCLDFEoMtdE8238WqBig2GYo+k9JkVMUwl0u/fF+QnjhAdn1zTNr9CNRsR3O/70j/+Q/sBGFjrf/xvf58//xUdfiLYGAas0Ib7d8aOnHxDYDb/+zRNuGzDFhkC6ZKoimGj8jvVN3kufYZo98sU5pt6n1w9Y1hbu6Ax7PORHN88xT+9jtH2KzQ2GM+SmTLi6+pig1/9ChZd0fJo/ZXzg8Xz1nOvLa777+q+R0fLe+ad8fvcebF5w2xmoOfCPNHrhAZ7rc7d9Rjhw+c79Q2KlGPYnqK5Aem9yOcu5XL5kl8CTz35C190hWsFIamwaDdOFquhohMLOBXkbo2PTuTlFLfB0SVkJOgHCbDFLg1ZAlfq4VkzS+njGhvQr6u+vgyL8RfrEf+MQ01/sOzAYjhgZFhtVoawF9WZKpFbE0mB8+JA43mL5Do4MyfsJrjdBKy2cVuN2tKFeJBhdzuX5n3KRhqw2r7CyDN+cEvh96rKjtQVttELrGm4uPd4+OKCsSu6Kgv2hQWs6uJ5GbXRs1yaX8Zpd4vJoqFPKlldpzv50TCIMDmbQOBriWYZ3ZFCmMZuloI/BzcWS+KRg3Je8yHq4SuCYGVYDueYSNxsoG8ajPWo9pcxNTgZvon1tQHD1LvE2J98pMnVBlTS0BtiVQa9bkXgCmVq4UmdLTF1LRq1BuvBJw5qDXUlRD5mGB1TDJTIv6Umd0uxRVi2mXRPWc+6WC64uMgYji9H9Y5qVRLvsMLuc1ioQRcekd0TuANEtQe6T2RVOZmP0cjLPxk4EbRdjlg6ak1OHNuvdHf1OoekBwhFUtU1XJ1RXDXbfRA5mX5yKzAM6CywxYq2vsXYmnlZBpdNMDxglF3jRPV45r3jxpE+XpCzTLZ/++FMYNKSNyeumiRjt0U7nOAv45oM3+db97/Bk/gF+NyMyt5Q9HbFOSaslez2LbSbw9m3iLmSxFTRun9ysePniFZ/fbHE0RdirIOxTRSXry3PGB3uITcY6/oTTk7eYB4LVBx/yyecr9s8ETz9f4nSvKOJbtHQNroUdZ4z3H3I662FkIUXRoPKKeqdIbYWvHxLpa5y1RiJLHEbsnepcRVc8va7olEILBE0hsZuWOqkoO8nQddhVJY7Q0SzJrgY3NKiLGC2uEUZDXulYXoYUBm6laFuJpuWkhs9XcYR/FRCY/6tl/pc243df5i+B478w7gi4/jdQ4S/0HTg6OlOr8wuurxL6JxajsCRsRjRc0IiQk/4xctKxelZgNx3LPEYULV6okS4U28+vQVgstgFD36TNQ/Q9mzqLqPUR692GB3tnxJsAJhu2t7d8LCTvvPk6ch7R1SXWdsyyWNPZEWarCGyLpI0oBGySAE/pPNtuUVnNtSexru4YDsd40w5p6DieYvEiwWot9F3Lmyd7qJtzyqymLQRNrWF1Ctfpk68y8jZj2Peo9I7WFGiWgfDPcLKMVJ9j5gaRYyK3l8RKku0M2sylJwVSgMWGrpqQ+DkaHTOnTybWpHmC1HXEqqHWakRpwGaJ1jXcXiwRmk1WdfQ8g+U2pdfcgi6Z2X129y3mz2NUGpGOBWPNJ2JLqptUVY3ZbVG1zYEW8iq7o2c5mKKj3EGLgzEdYmsjVG+HuZki3Q7NFwwGY6rKQlQZiQFeXJN6I8xtzLC2EH5DbdoUrUYvL6kCnyqoebK2uI7ex+8sUn2PP//s/+H7b/8tTDvDlgJ3mOMMTf6jf/9vUUUtNWtcWaLsgOy8oXs+p3ywT2+bMjsaki/aLwxSRgOKVKesWp5fPKVahZiuSys7Ktacf7qkqu8YBDMeTwLU/QHrZcBinXBm+1hHQz6/WNDnhGa9IbRbHuw7HJuHvPdnimBi8M43DhFrm6K3JdVcisbDDBr8QUjbLUiuErTBBKcIGI43lLbG3cUVgezYFh5CS8gsE7Mu0UyBUbqURYtvaKSyQ6oKRwqoHdqiQQ8lVV6hNyZt2mIFiiTcIhsNwxIY7VfKBP5KIPC/A/8x8N99ef/f/kL+vxBC/D7wq8Du37YfANDUFfmyxA40oo1OWrwi+fQO776Pfttiv6HQlj3QWtI8xSs0WrXgow9fchGt6CP46DZivf2McXRAZTdYmaCsRyg952T/mKwyCKeCtPN57cBil5jcpgkDe0JTZNzmn1D1HPalR9FvkFrILLGJ0oauSlmbDb3aBiMlnysOj33m2ZrLnyWMHt5H+IrT/YdceM/Jlgt++LzEdOuCFfAAACAASURBVPsYzRYRN1QeHB+csCq3zAY+nS3YyQ1NNEWf5XiyIxxPaeUFh0895r2UXidp/QGqaWhqG1lsKC0HfQCqMKiThDprcfwv7Kb63YxSz/F2LUshGBsDkrikaZ/zZH6JdEIeHpwyUCHnd5/ipRp3wuHBo2OatMIND3js5ehBgy9drq8uMbSULOkx6jcI38XzJEkccy/0KKuGogM3CMjSjPIl5AcJpmfRmWBbNnotaDDRgwjRmKimphl6+GWJP2zIdIESPqJridKC1h6Sb0x+9vEPuP78gqF+RBEm1DcbnMbA7Lecv9xitAO0oc+Ra3O9rtluXhG1PX77d36dj88rYq1EezCgFRbG0Cayjjk7CChUiyZ0ilXBx3HO/Lrkdvc+7shis1AYzR1vvvEtnHSP+e2Cf/b8PfTBkEPPIl6XbD5YcV02GFXMZ8/+mEBI/sE//NtkbUXvrWP+3jffJG0kdVRSDlbU3YShpvH+9RVHszHmxODukzVNZVKqa4LRGTNtyrL+gom4Wm/QDB2pW7hCkbkGsqxxtJROuJRdQaN0ArsmT0HkMYIBZbXCa1wGNiS1QbbRcF0HU2YsC9CkxVexA39ZivB/5ItNwLEQ4hL4b74s/v9ZCPGPgHPgH345/P/gC3rwGV9QhP/J/9/8rZTMRUaxqXCMDXfnMYEjUHnNw7Meiw1oYomUFrY0admyuWm42CSIdsvLVHD5/COM0YBKr0F12J7PJI1IcpPpIKRmSyU8rMxE+ibje1PIcjohSNuCtO0ziyJuZUF7K1H9MU6o6Ns69Uyn6XS09RWprqjalvmdR7pY07qS9O4ag4rJ9Ay/HmB4OQLQKZhrBqOJxPNDpGhpbwuujQ3ToxMsaeHumciupX/QQ1srFtYejByau5RkW+C5AZ6xYqVLlHBp7Jz6uo8+qpE7gdWDpgnRyRHSY0iLppkMXUkTlOheyo+fXLCbv2A0O+Znzwre+c4M7fg+WRKxvEz44NMf43c2vl/RpS26ETIvX7Lnjkh7M4wugtaisy1Eb4wXdJS7OXLmYCqbNs2p0g2ydfH0GUaR4FsdLQ1C69C1GiPXKUcOVlJhRim6H7LUJFrmIsQG1/MJW4fc3HI7v+Oz558i0x5dX2A0Nk7PJ3p6zoefXDDqT9GtAjt2ML/+Jtmf/RGmN8WqdXZ3FkNycvmArjtg4rk4w1P6Q5fGCAnLmM9uLmgth8M8QZ8dolcd58kF5fkSa+Kz/HyOZebMX81pHEn54kN+OvRQVcR994Dt8pzy1Q3Dh0e88WBIXRWEkzFs7ziafZNdvebPX/6cu5VFcOiQb3bE2TkP28f01JAfXPyc2dBl4I1pioSL1YpB1rJ5eYeoLHRdUbU5RufiStADj1Z1lFmOqAXC1MhiHWXbWLJFdhs6YVAqg7hKaQ2H0CjIm5YKC7usKGclJL+4/v6y7MB/+BWffvsXjFXAf/6Xmff/iyonMiKsLkH29mmSHeuywkksrGhHE0fsZIOd6bSzA9ptwr/4+Z8wtiAqS8qnMdEu5+HIZLtJOT58iGOnZLmLli9ItSE91SNtlkhTImoJ6Y4HD0+p0wJhgTmHpKjY5gXHoz71GJrOQ0u36OaQUjYsc43d3ZrxmcuL6zWBqHD6IUcn+1y9d4k0FG+9dsbibkJOwvt/9MfkwwF2PUAFFcrYEukKy2hItAZH36f2IN1I+sonFQ19x6dnjLiVNwz9EE3PsFwXZy1RPTAzjWLSEjgmUQVpa2FHKa3uUxm3lJmLchzqJuXg+B7X8zueP/05P/7pDzgY3uc3v/U1zi9r7P4JhhgzPVxy+2pF0++oa4lumphag1UIXhbnHJpTbHmG4y4okopsscV/KFBNgBUN2dVzRF2R1A6WcDGGKa0JyBJtK9B6OVFs0Dc7tDj+wvlp7DDYdLiGhtUkZOaEqkrRdYlDy/XlJ5RbibJXyE8yylnBvt8y+O5j2lmfTJc03og2bhhbOnuBwULLOTk9puhqdO0+j74d87WjI7zBjFJYVHqPLH3J/KXHMPB41S4omwZDKVbWFvc64uydx7y8u2Jxt+RaXWNaFdouIVEZ2WVOuSy56c3ZawVn9/f43re/izet8faP0RRokwe0ocVr/a8xdAdsVYOKBJntIWhwqWlNwZsPe6g6pJEWu8tXoKUk6CzLl8hW0FKg2zZNqahcE6ORWLsWZVpoZkNPr2g7gdF11LVAV0OMYMmWHUlnoGsVcdlShwJzBwLFqDJZ/FVWAn/d0eo6248vObs3JbuNOAlCknXNVVzy3/9P/4RDb8rf+J3f4Ly7Jn3yOXnUYCUdy/mSy5ev0IN73HvrhNf2v4UdVER1xPM/f87g9IjTg8eoeI09digTi9Ja0vceUbkubbxEC4aI2MSwNxRSx410MB1kmdMrSoTqWG3vKBCYbcfAUbx69gmP9t6kMzSSXcVyvmU88hBNxtNPLuiaGi10KTE5sG2CUcAmythcXLJ/cIZ7MMMpdNx0zdVlRtXXuIolUvUQw4LhQ5e9+JCb6CleZFNUGpo+wBApQ1HQTAZUywghIpQm0Q5DivUK6VlMRlN605BvfP8/wNp0FHWOadjolcbN04/5pKlIr27wv5syNafYpqC1PIZ2iKyWeMEUz1dYgzO0mw9JrI7B+lO6/iO68xvMxy11vY8+Kom1Ba5lMQr2sAsPMRKEnaCsLdQAuo1JKWwCkVP6DUqfEjcVZtNSzCy0pKQxfaaJ4EY3KTOT/+UP/ikffvCUA3XCvLpjcPSA1x4HbIyEVBtyeLjPyWTILLiHEmsGGXStZBPdoXWCb7/9NvZeQNv6rCuNdFliuRkXawNXTth7WHO7qnkw/Aapl/N/vvv7zHo23dd/jZ+/+8/ZE2PO1SVm2rLcJtyf2jw++Tr7w6/z3b/7beokp0wviEwX3xRIKdjWNe2yQjkDlhcl66zG2j+h198HUbNYbOkqm7u7AvNBizIEh5OQXVlxnkXYJTw9n+NsPJZtSi0CrKjA0iTeqiLWTFpZMzYNNm2LUh2YkGaCqdVxSUwYC4xA0UsgaTRUB0YmKJSH0Sso06/iBn5JQKDJK0z6LG9y0mrJOp+xoaSXJZwODnn0xluk4R7JB5dkeveF44ofkb9QWMLn6K0T7Kbi+d0zsp9dcL1b8fU3vo/m5DR6zWIdM9X6XFxesN8L6cYto1pRdy7NIiLrCprcx7Vq7ryaeqew7ls0qcZlvmGcuJRORVfeUksBVYhsNUqtoZUtsSopyhIrrYl2Kyrl0CyWnI1dpD5EMwtcQxDdGVQGbJ6/wDs5xhY1hVkyqBOWWITrOy42Ef3BkPtHOaunLnmb0pUOZb0kGEiaSYihHDpbcGhKlLBAVpj7D5C64utff5NvvPFthg89xsU+H3z0J7RVRM/vkaG4i1YUVsujp4ekR4Jpz6R/2pFebBn1htitjkFKku7YJiHvHGpwOEHbRfzc6ug2BU2X04qAQ7WjrRy60KcdphiWzW3iMeqbiKRD36sRpU3dBzQN2a6QYoRedqg8RZgWTZGibB8pQVYxBiGd3eO9F+8zcT1c08JvDliTMbZqxv0RrtGjkR3NQrEJBfuP3sB7YTM+mhCHIWQOteqhZRFxWpFhIJYrYqMkbRVZW1OvXnIdrelPB6jOQnYFYgNxeEPYltjDGY8fnvKNd76OMkIaqbGOGlqRMRl+DadbU/Ygyhv6PKScznkWZQwDDzfakuDQ6Rqe7tK3c6b+DNuXbKMdi7uc09BmfHjAH/5ff8Lrrz/gtVHIj158TDhWLHcppmwRrU2OhjA0ukIwr1sGvk6XKlK9wzY6bjsBBuTCwCw78g6ElRO2kk2pY5kZTWWgaxp/ba3J/11EWcf8/IM/ZBoc05955GlLXq4R/QG6NyDJtqiPX3Cze5dB/z6iUVy3NoPDNaYYYLU597whH/3wh4g+7O/16Nw1rRqwTWNCd4h5s8QpSl7UF/Ci5TuvvUNULRjqFp41ALtmt3HoGy0ZGeGzGmPWQ1vVlP2IoAhIz0YUL3N6j46QZkpb95n6BkHRkPkO14uE8SCkFC2beIcTHFN3MfEiJOwK+qdjrufXnA3OkHXCugiYKJNkrWiyDR9nDdGLZ8jXT5j2TB7v3+NJ/il1YxAECjlO6aItljgiGJlMSosoVdiTE8Kpx8A45Gv33sCeTPBUAIdH9JKHuLsLdH0PN4ypKpuLeMUgec6vOH02EXS6Te3V3HUbllcbRCdZ9EIMfU3WnaJsjTK7RZodXm6SyJc8sE+xghEpKYW4RcpDAlNhOR262yCagG0mcFVGqXk03Q5bWsioQ+glEQEDa43RBGzqBt/quFEJcTpn8ew5epbRmS7V0OC6LxkwwR0NcDUHv6kJrBg5niBFg+maTN4eYbseT//sJ1SHBvSmsCmI24TqoiTraxSbCFeGaGJCoc15ODplJ3PqRUWUpvgnZ5hWxf3+a7SqYDw7QDfuYdgpzy/OaYuPyNZrXlpL3rj/Bo1scboLkuQpdekxKCL601NKw8Hu7TCqHW5bs2xisj2fvMo4f7VE+3RL79GY+Trh5PQA2XZUjUa+ttBlzdBs6RINR7ZoNkR1juso6q6lzBSm0mk7HUWNbRuIuKXwBGVh0DMaGs0kkYLAktRFhqbZNM0vubNQW8PFcsdNk2O/MJBhjzOzxevvc7u5pqhs9u93nJ38OovbK1J9S7ddsdztvtC1vznmh/FTWgPMLqBtWp4+/YB7wzep25K5grv5MzTNwMRk0a942W6ZTc7oGYLGNNDyLYt0S23aeKrkMk5xlxWnZxPSTQ6TnOHVDHorvKZDaRqdpVGJO7RwhJ04TGYNdIrpWiezfDQaytTHcHZc5Qbe9RrXMSm7OcVTiZIlS7PiYnnD7UfvE+cOUm3Z5jEj1+fB4zdwj13cVOe20tDVnIN7E1TjcaCXVAeHeIDzYEA/LhHOBCvoMww08AZINycMR8zCe3zuv+DlXUNXXjPR97l+ueKdx4KDwz5dWaIFLtHdGtGvqa0ps6RA9cesixpHWyLCESemjVnmuKGN9AeklUY4MQiSEE23SZVktDJpCh3d7HAFaKaOa5jUuKB3uGYFmiSMGozWIbc6atugtnVuonM+eHZBaypEqlF4Hb/1/d9Ea1OSpOH44BG73RpzPMUUx3SDgtaNsO8GzN0182fPaMSUKtmRvDjnZpsTjBLGk4d4fZPx6UPSKsPvOfT0A2LD5jsvh/S/3ZGXFm27oqkraDrqpuYivuP+qU9WGfgzDzONaA4fUG5rfvbhP+MuKXjj+Fvk+StK6dLvHRC/94zcvqSoPF4/yJmfnCCVzfl1zR9Fc7qLj9g3jvnJxz8AKdmfDFDmkCLNKVmR7RR2TyE9gzwv2XVgYKNrBZqyqFwNLW9pigJpmXQ1FGg4tU3tJqR5h6XZuAV0skXpfbJix2ygEa1/cf39UoAAAkzZYeY++AkjFy6SW0bJnG2tqGWL/eSKg3v3KHZrzJ3EK3uUIw13PKRvd9x//Jh/fvV/c7VZMfY04lRSnqTYreL86Wc4smJVG0xlQXY1o/9dFxF17IIUrRMYWoZj7eMzp7AsfDWhzlJ2aY3mOwykwaK/whIBXdbQqoKhglJNuK52VFmKkVaMNMEnUmO4y8j6Dq2dsF64yH7Kkg6ZKRJpcTyIebJeYRQGXdXS5BXKa9FKQVFkmKcDlKnxVv81op7AyeYQ7+MJm55b400P8CYGTaczER7L1GBvoOEbJhO3R2Io0sxCDnzC03vITxxOslMW8pLaXuKPDli9fE6X7xgOj9BcMANBV7WkmxuqmYPv9rD0EbpTUBsrguoA/SSkTAoMVeIPErJkgOObFIMSTR+jvAjciiZwkTsXq6lxgNiWdLWDVDVlYqN1CcJy0ZoYrbVp2orQsfFDE0edkg8axoZDGFokmw59KFCbHUHfhp5Pbc8pM8H1Zxt2+zn18gvKMm4b9LLicH+PZ8v3MFOD/aGJptvElNTLNUvZw3DGjP2WCymYahOcscbHTzd0UYI1mEA8p72quHPvMFY5K0Px8tkCv+fSs01OH/8K+ucvSCrJRH9E7CxZJzfM1yvefHyfac/j8+dP2D55yqvbFc8++ClasiDuF/zaqKC9f4yWSYJZj3lSUsTXWAXUrsCKOyKzQJMgpIMSBUWhY6qSdmuRWx29QEdEklrPaS0TUWYIpWFIG9VlVMKitRz0fIdwLDa5wV+HWOjfWchWoboVZdZRUjHAZ3j8Gh9drhnVLd6gR9vvc/nsM+I8QeoGTgBLMh75OkU54MV5gr9rKB2LppAEriQ7f07em2LaJsmqoMkTNr2QaZURzSMSDMocQl0n8/u4MmGFjlFEbNdLvL7PQHewRhaqcDA3EnYJsVUTRxW+WzN96GOnY1ZZhK7P2K0+YjSZ0dMn1NWGSedjOzEXmUlTmIz2DKKrS577HpvrlK4q0YyaXSBwzA6jqbHNAXc3G8Z7dzSj+7iejnlRoU1gaCp2GwOlK7aly4EjwNaYDQI628eeDdBCEErDx2ERJ2RPnrBMTbTkEkdTGNUQ8pqiq1nIiKFVkJU2g8YmEgKrF2LVOoZekCdryDq80MSQNSrXsQcDdNuniVvstqXQWpzaY1JtaBqFbHp0ixS3r5FVQLZi3Qvok5AUEhXUdAsBLjSVh6eW2EnA3ugeb/XOeJKeMxqdMhzq+KUkHhacdCGrTDCVPvH2El97xM3yM7LbO+7OJVVXMpmNqdJXWNYZn9ytGIYjys0lV9Ec1z9F2xU0tkGoSZA5222HqG/YZAl25jO1DIyTPVIBnWnRFQPSOKYpGzTd5vVZyDrK6LQITTM4O52g9Q949ac/xzmSmEIQOj1uLm9otALLsrANRam3aNqC+XqFSjM+vdiiWT/h9/7+v8fUGpEuN9wZ+zimCWVFgUFdVxiuja7lyNKlcjva2sJpUurSJlEtnioRBvhdQ+Z1yFajkIIwh53eYDQRSkkULa1ov7L+filAAK1D1S5JHfN4ZrI2WtT779E4JnkrCT8dc3RYE9ce49Zlp9UMBmN0pch3Lr29Af2kYDOZ8FikLHXFti7IGsn6OsLTv2jMbPkN5W7JrevS//wD9t9+CNoebc8lfnnFrlexF/YRWcjo0McLG9osZr6WiNsS2+3IJibeXOG88Sv0qpZkfUUSwej+HrZhMxjto/VGLKuO5pM1z4o5h2bIXhkyt18Q4KMCh+3NHW6841ZkaLsGv+6os5rc6KPXBbPjY6Qckxs6k1Rh2gPUSGItC8KxhtHkmN6OqjCxbj3ktMVZSlpbcYeGrfdQMmHkPmBzmHH/8gWp9RpdGXEa6HRFS9j38fpjdmWOLWsSvcMkh2LHou2hbTTO7pnsGlgmGr17I7S6xcVA9gSemhEaJsJ1icSSteWiZ4AGnWHRtQ3txuZyDKNYoS98hK0wVYdua+RJja9q8v4EaaWwEvzm7/0u8ud/wuPBEVYwYpmtmMkR+mDKxLlj20ouzy9499MnOGkLU53NzQ7DchlEHa9u57zTf4Bt77DCY7x7+6x3OZ4oUY7E1Xy60mBdzZGphSkkUaTAT4ijCs0yOB30uJQ6Z/fW3L6Y8/n5Fbvijv3ZEb5oCQZD3n3/B+iZycPXGq79JXuihyklmoyJdgv2RyP2ZyFKHDAODvlRfYPQfkZ6U7DOFBeffsTtu1P8b34PaXc8/eQjdnqDnxkstRqndWgzA121CLtDxB2NW9K60NYNXdNi6zp5qaF1FYGEpmdRryuUGKDMlGHbsLV9hKmg6YD6F5bfLwUItErSFvAwnLAuNlh7CrGZULcFZtEjde64vKqYHR6xMhLUrqZYW1/o09USu39Cbzple/4JrhGiiQg/conylOmwZbuOqYqS3NQRaCR6zmpXsX+RcLtd0H+jorI2BKVH0/bQrJJoeY5ah9RVSxB0KAfcqUOTmEzvt+ihSV1XBHOLbFjTxQmFaug0SXqzonV29CY6xsojbjs2xg1yzyBNTVrLxBhYlHPQ6HAcFzcrqDzQjYa+NkATcHWxZP+1Al3amAOXthDoJxqeKOjSId0A9K1OYpn4hk/qbNnvefimRBtItvMF9U2H6XWs1glBs6FzDWqzY+KfcHDvAGccIOMGIVuyYkBQr1FpiduCI3M8T0euJULAzBS0wRe/DrLpEJ5PXFaYzgp9N6FTKV0rMPWWLC/AsvAfFLQLh66qSXoNmtuhGhvLiincKUIVjCvIIo2x5uHdE7x+fR81EJRFRy363F49w9s1FKmPxZy4bjg6uUe92PLk3Z+yw+VwKlhtW8J7Z8y3nyDpMTmR3K4S2mqFkd6n8BsUivPzJfnqjiSvefvNtwkci3DYEDaCxoRok+FmO26KBtV0iDDksOfS7AqWdcKq6TBjQdXVPN1csnma0Z3tMLYG85srlKpp58+x+VWO9o659/iIl9cJC10x1DUevnOftM1J/Y7CbdnsdJaXa0TT0uFhKEnZLxg0NWbRsVMVqdCQhUXXFijZgG2xFgKvKWgMHRqddG0g3BbqCLdoSVudTmRAh27oXwEBvyQgAB2TwZCuqmijisAXJKOMsuyj2ohW2eSLnJEfMVc5RmmR7j6icz0mgYfIrlitTYZuyHW2xbThbBLwWWOhNwlNnFOjYagGx+qh5QLh1BShRqHt0Eudnqmhay1tnWB2NhN3QClTyrBjYll02JSWyVR41FWFo1sYqqabhtR5Sa63JC9qjEMLWw/wmpa2MtCGJtmiYpWsaG8F09Ml6k5iFQWG2eHasKpLOkNgFB2ePWVDwXp5w1jEVBfHpAdnMFCE4QD9fIM8CpB1DitBVS/IjBNOuwE7WWKyoiwd3EzD7EacVy9YXN8wFhWtHOMBbinQRxaqddDyjrwrCV2Pev2cTdFSGg4aO1ZKElykZCisgc2qbhjZSwxjgtXr02lrdGOIoAVR00gLzdhSFyNqy8bSSqLYQNgWo1KyosWpFFGtWCkNL4jINjWGNMldj7SKcSqHwcmIdKuAAmO1AN9mkWeoxS3+achUhgRmn8ZOSC3FN2d9Ujxyp8G7aHlvFPE1r8+r21t++v5TwpMZcpjQVR3bZwmDA58svUS/q2itI0rdI9nptEFATzQ8Wz4lq4do0ZIkMzgUB1j3rnjyJytS2RHsasb+GK03YSVuyJev6FSfurnj9mbNNPBIWoHTFUwOBwwD8KuatwYOzXjCO7/7W/hxgdMouvWSKs9Y5XMEipW2ZWh6VJUibQ3MrqKUgpFUVIGgKDzMoqIpOwRfbPxVRkZgdVRJjigbbKVTqY5CgrQlTtVDawvyX2aKUACL5go9Br3WSJsFpGByR1J3+K1iN+w4v1OEexPqbsEqK7BEx6XouHm/gFpRWDaHho0K+9xkLVZbURQJemiQZTV267NxMiCkf75i7xsT1geS+GrD/skeZlvSNibSMrCGDmPrPoWaU7Zj3HZFXudIevSCijy9wRYmUVawy8DsCgb3W7KNQaZ2VLVLbddoiUEviHlojrgRMUbaEuUJ/b09HKUjdhFnhwYX2oZlVlJ0HUba0FYVeq/lvU9f8SDLOOt/Bz+pqSY+jhIcmR3LqqEzp4SBw5Il42GP2NQIzJKUlNu7V8RxTnKzI5Ip1XKNP3Dpc0xvOqHzDeKo4nx+iSyX9IMJhugIQhN/cEDg2jyaHdLqJro9QLd8FJDGGqJ1EENBVtUMrnsM9jaskhBZGhijEk1C0tTEpYVFTnHYYRQmtp5TJBpNqTCaHTJwSdMYShutakhVyfWLDZ1+yd2Lio3scdbXmDQGHzQXWI2FEC5Pnv8h6bXBvXtv8+rlC7yBTp1GbKyY9OMV/3T3Od/7O/+A/5e5N/v5Ncvuuz5772cefvPvHc881NhVXe3utjuOEzdx7FiGIARYWFwwXeVfsMhFK0RCiBuEuIIbhITEXRAXCBEFktik3R2Xq6u7q07Vmd9z3vk3P/O4NxfHAWO6sCEGet09W9p7SY+01rOf71rr+/3Vv/ZLWErxenPFF598Tnm64+vyIYPjb3D/oxmXqyXb00tU3/K1b/0ldtsNuvHptwuyKuPJ9pws27H4QUmgUwJ3Ri0VZ5zjD1e8d3iAuX/Eur6kTaf8+vsjzss13z58wG//a79B5hXk54Lf/p1/i6nnUC63nNKwvjhhtH9EfXLN6fmG65cVbR7jhztsnbHsQjwhODeamW5JHQcr1XQaJB2O9mlMT0nGwIrZ1Vt8OUA3GUunQZuAeCjpEoP0W2T7c14i1EbSVgHKaakCjdcZmj4Ap2auDOXOZxTmiEFFmm6oanCbFiUCurTFm3oYp6LeSto9m832Fe1lhghjLs9XRARv6Jr7AseAI1rMLKJRe3gXFUO7wiQR7p6PyTzCuY3du6hK4MkRut/RZSNmByP6dUUhR/S6pDnwGOQBHjZkLaNQ87QtsFZr0jrDiUL29l1KPUfmS9w45Ci4xWPxGbuqJOkVs0GEF9qodsmMOWmTYjwXiojrHt4eenR2T7pZMa0HhEc2XhmxlQYhB8SjCO2C40larYlEiNxklMqwLTS7Xcnm6pTZYcxmZxHPbuLdGFAnGa1yyOoXLC/WREojo5bBYIhbKm68vU8w8Mi1Rxi7GBNifIWKDJNe4XYedV7ixzWBblh3HmEEXagRnY02EMY2y6bBs94Qd3ZhTZkFhL2F7RUkxRzCBRqFWZ9Rm4BNc0m+a2mkRo1jZqsr+mSGGgZ0i5ZduMbbg4m6y927a3QBb/3l91i8TjlZW4xji2t3izYa53rNI6NR+prsKmYcjQjvjSjOAS/hy3rNWE2ZBB5S+ThdQ7c8J28zkjKn2VS8G7/DWXTJ+5HE713kTUkcTvHiGbuLV1w83vAv/5u/Q/JyTaVXVOdLPmo2DO+NoN9gF2OMKLGyY6qJTW4Zwu2C2r/LQNu8nq+JPmsR9Y429KjzAOE3BG6N3ylMpOlyG1UJtNPjmIjOlJReg5uAQkG1wyMgNwXxTKB2HoESmF1JFCr4LAAAIABJREFUIAV16cA/L7PQ/9tmjEaKkpoBQatJmxTLqxiuLHa2YmxbFMalaKDaJoRSIYkos4bZUJCtC6p1i9gryPo9vOoAO0o5WZzQ2RppS7qtQHk2hhzhbFDRHk20w3iCSTXBCw27ssMOM5zRhN4Y9jMbEylmtosi5OKsQQwVUddTiSFOVaN9hVt29AOLHQ2+cOCdu+in17gIcCVpukSUApUHdDfhZnXMwa7kWFxzWVyRpDX+4QjSErdyyVY5iyAl6n3SJCEMHNJ1gbz5NjpP8QLJ1VVNOO7prZquH9BsK/yxRZNfYHqJ2++Q6ZoXZyc0UYqfzhjd26DCK/ZHQ0QB3XrNwXyAe1OhcblxNMRz9hnKiF54eE1EHVpkwsUPd4xMTG0cLN2zGL3CSfawOrC8Aa7bE1UZK21jq4LGcgi2LbNthJjWqLZAZRH+qEb0OcoMUP6WovIJsLjcbdAq47rrcJ2GFycXyCDiWE+4WJ1jHluMDkPeHx+Rej4/Wv4++9/+NqYx/ODjjzl5/BTsI+5+6y73949xVEPSbLl4lTCJSmb3bzD17tNYCWYywLzukOWK8qBjmvhMpw5XmxVl16A2Lk6Xs79/yM0H9/ma/Ta7NmPUuzx58ZRu1yH2cmb+bfL3Apa7nnogMFcRX2ZfMA/ATwWXjc3+XofXuWS8YpyEbOsS47m4KkSXPeHWR88MyJq41TSiwzaKNtWU0ib2eipbkyMZS4uu2eIrjzap8aRHY/ckjiFqDAPVUqUagUWlwKlssmlIvVljegdofmb8iTfzPv//mpKOmYU+RhhwQAQBed1hOS1+MiCJemZXBbt9jWhsmkKw5zjs6hTlKXSX0rYB9tgmNhWuu09jBKvVOdFQkeQCu7eRskb2HZvDgA9GB8w/vM8ta87FRcrDe7ewbUnf1HzjW1+n6DsC6SG1TaF6gi7DOAKVKnaWZNhWJMZmHEgkHq7UMJFcP1nSOi1Jb/D6EJOsuZYVY+Ni+ZJ0B121IBOCwC9xOoktJT/56TmyLkl3PZ9fXeJTYNVQWhZ2MMQbSax6wp13j9jla/ZvTRn4LuMgYDicMohm9J2h0QovFohG8Cpdkr845ctnr9k1OXcDh7c+uEtT2OwNaiwnIN7zscqScH6fcTRA+5rIzBjFmg37DCYFeusQxA6h01PWHqvQwXMUbufiJII0zgnSEOYuXdGBLpCepK98fCuj6CyMUAyUjaGhkC2NcqkSzfPzBdv1Je0q4/xiDVbF8Z27PH39Y/Q25t37t3j64jW77QraijuzA2Z33+bV6SUrp+Cnv/d9HvziBwSt5Padu3y+fIy19JndHXO+2DIKxow9CxXaXGUWkzpl+HDC0x88Yttteefeh4SBgNDQX2q2XUXd9nSd5mB0yMF8iDsYc75bUa5KpscSkxuKuuDxyQVStchGs9xtqF5lPH/1D7l+vmCUFPwn/8Xfhv27OKok0kPwC076NX/v7/53/Na/+jdIvIwfPPqcT/7xH/HjLx+jPZdxq6HuybRHOyoICgddh3hyQ4+gC2xkU2NKgbIklicpckEve4TQWJ2kj3qCzqIMe7yVpJWK0DJcNe3Hxphv/en4+/m4CdBROQ1WPadxVliLDmu4RegBfaEYsmM5FriFh7ITjBtxWeeEkaCqGvxyRjepsHKLPN6ncDz0NiczDullhetKirpgJAW7ic/NNOa8WTC9PiC7cQe9vCD8wGVsW2zHAY3RxMYFo7Bcn4FVIcxtlLrkclHj7U9p6whHLVCOg+igsxR9XrHYU0xykF2GF0ZsswqTWTi3fbzSxn3QI5pbrK4uoAwomwjDmnv7YyodYHRLlJxyFB2w69b0aY3jVZTbCmXVPP6ioREbTLOhCO7iP3CZH+3hVgI1HnHx6hQRRnTDiEF6zZW2OH4wY7y2meqWo2BGPlKMu4LbN/ew3Jh8brCVYeg2VLXEUTYbX6BEQruZU1k7qo2i8n3krCcqtkgxJOpL1l6EM7Qp0EyaDShDVwXoVqCsLbkVYAtBr6HYZohIkAsLXl6QeIo//L2PGc8VTuOzc3ZEYsirxxeIxmFkeZxentJZIUSX6FRQ64bSTwh0Qz2wuHX7XaLLjOfJgk8fnRJNRuAZinXIJIoYTqcchJrFecqxD9dexcn3f0qyK1GWJMtapFZQlZxtV8yjkKpvGcU3yfcMm6TB9TbYTc91ucI/DciHhnXeMfBmtE6J7bUcFVsu9gPMT3smomd+Y8BatoTbgtmtAcapKTOHfqH4q794F9dtGDYx18+XXD97jdO40HhUQUnd9EyEoK7fgNm5WCPdgFL1IHxau8eUFm5nI5oGocDvffJBhqwMKrPpIk3c+eSDki4zNK39lfH3c5EElARLO9Rqhdi5qKig6COGrY8aZVz3CpseLPC0T6q3hL6NxkY5A7RJ0VlHoXtcWtxrh3VX4lcNIvQpW/BQpLSw7ilEQuYpqlVKKVKO3zumtkrMbIz0JiR1iuveonQ7BmaAG0U0lxtWhzOO3s6plYtjSrr+NoO2pmpK7H5E2rtM8hzVWLjdMVYb481SbszBCjxsx6JrarJlzmF8h4RzPFmzqh3GcUE0uQ2DS6RzxLo0eBcD3H1NkvTYjUbrhn5xzXDosm46yuAFo+aYD3/Zo9s5xL7LfHYX7ddYmc2WdzkeJ9S1RRJBPPWpFKgqw7kpMVOLpgArEYipRBPSmJZ63DIpZojBBtet8GSElh3xoKZsO0Q+xLghNQ2+q2lWCRM9Q3s2cgGVv6ByB3iWx9qCYSPx057ueEa5bnn92SuuN5d0gY1tW8Ryn5VJmZSKRb9miE+fddT3JF4Z0KzP2J6njB++y4W8pv30kn/w/DF3nocstwva/ZC2dXh4HJH1Em/akb7+jHvf+kUqNpxegedqMmOjsp5grBDhmJEK2SVXHAY30XHAxaPnjAZ3uTGxKJZn5N2YamwzbEPUOGRlFqR5i5/B2Q//kKKxcWLNrdsB/uSAqbgkOhKYoc34OKa/zjCzS67Pl4TOIfu3YxavLO7/yrcpl0u6sxdMb09ZeQ6N1TDfbslqC1fZKFlRZBI5UPiVQdctvQWiyvGloZKKPGhRxiArieNnhLlLE9Y4wsVXDVXdIHuJmRg2Gw1f0S/0c5EEDIJChsSdoVMZOzViv27ZdmBMTeQYSu3j2i3LpGTY+TTjBrEr38g3dyG6Ebh+QZG2uH3PBEke1oT2HLte40x68sKiEg2tE1DqhheLnN3mf2a1vc9fefBtLH+Av6mxbsbYns2eG1FmFaFbk0085k2P648RTYrlzvDdjhGHdN4WRMB8W7GYzajrDLwWx3LZnN5Cej5lu2M8sJB5jXt3j72pxerzhrZZUetL9K138XXH3vgmt759wPlFSfztllfLEn2+5VmVMe0XbNyOorGo64JqkPI4/T6/dLmPnjgYV8NUo2qbbl8xsS4J53cYmJZkfYQeOdhpzWS4T9b15JWNcipGoc3APmBtw0BIeqvHsTtUP0KaHEYuYiWpOpvAgVVkYWcLGqURlUSKkIs6od1IJvOITHfI2mW7Koj1mPVwQZmkFOst/etLFqOKYGiwtwV9mfPFo49Juh6lFEf35jz74jll3vJLaUx88w5DoRi9Nef69JpukfODs1Me/eEn6IfH7Mshe6Mjjm5JRl6ENQi5uGjZ+DuSiwvWZY3r2YRCobOcXSD55Y++SVsFrHfnvBffpGxzmhp++7d+jUef/JhHpzmBO+Dgvs3ixTmPn3/JjdGQ3Tbj1dkTfAV4JcNgzma1JY0F2pwyDSS/892/ge8rEm3x2RdPkb7LX/lr38aIHrNM2dbn1CsfJ3AI5wO4aLHqBruqSJSD07loYbgUGaOhINt2GOfNlzxWsLEaAhGjVcFYSHbFG/EePJClDWlPFdhkXUOoOyot8XNDKAWb/6dJ4CuER/5j4G/yBml4Bvy7xpjtH9OSPwK+/OPtf2CM+Vt/lg+JJMy3bMMOJxszcDeUtU01bIi7kKbpCLySpjIYArRT05sA2VQ0RqOdFqlTug6GjmbVBGDnuJVF6V2BI2ANhhLXsWmKLdHIp8ovSf197vgjispCbMAdWoyakCByaXJF5XXYWPSRwa98atfBtcZI28FTHgbDSA/oZoam95n3iioE4Q3ZliuOv36DJEuZOXP0DmSxwYtg27V84+CYYh6xyQ5I0iWxOyW4mbNN97kzyMlYsm9ZrGTNR92MrjlmNl0jmpYXqwShBEE84fHJS4aftwxuPUAchygV45Rr1mWNj00dTZnd7mlFjzfYx9U1jdMxCEa4/Yq5t0cyHzLvetquJ1AxhdVjWS7NlcckKllpzdCtWWxj7O4KEwSooY8uIjqdELqSzgrpa4W9luRuxnbRI5ovKNOS3UWL6Hq+uDhhv4pIrmsKT7IXDeksQbZbEQcF7VWJVbXsDcbsKosXP/g+Jnd55+v3+cn6hPTVS24f3WN7J+buvX0sx+Mb7z2kynd8vk74lQfvYsU1F//TY7gR82A+JEslT5cr7oqAG4cPWF21sD6hrjOenCyobM3xrQMWuwrXmvBgtMdLa0F1uaarDE7csWpTkuU1Ti1YFxbHx3MQDZloyV6ccbU6Y/yNdxgfTsjzBnXk8va9bxENBd4kQFQrNtZ9jp07FPoclbRkRnBx+ozKrRmWkrYzuFZL1rkMtYDCR7g1vRXhdxmpMASWjXEKVNrTVoI9IUgHLVltoSgQ0qIuKiJbkTQSz7NwSsMuEl/VMPjnugn8l8B/BvxXf2Lt7wO/a4zphBD/EfC7vNEcAHhmjPnoz3Hu/2a9NuQeqN5DDAq22iOwJX4u6VSO03vUuY0X9Lh9T+cHNKVL6PdYfYMMXFoNbS+5Tqcop0Y0Nqqz2CYtkdWyCQ1u61K4Ln5T4ykQOx9/3lJkV7TXe5zFAw63kE0KdOsyJ2RKS2ti4l6zihoO2w4bhZX2lB60oiKPx/jKxZt1JIsA18loWhdX7OF0Cfv+DZTfU7AmlHts647YEcyOInRwwM00payOqXtBWjSUqycM/IBBZTMc34JWIB0PV9RYesyGBV9bHhH9MSj45fUFSis+uXrMW/5HqEFHqX3G4hqJwVcJZmMRKZ/Bh4q9zT3S4pxGHeBEksI+AF1hOQMKBYGsCY2FMCtq36PsHWbeAbK8Rkc2VhXS9xJnZUBWFL6LXhckXIIK2KZrTN7iBC7nhaC+tDk/WxEcBozjA87XL2i05DtvfZ2nFy/YZ4w0Oxr7kAdRSJ/dxr/jIOue9kyhVcYqvaStLwmH+1j7Ax46b2OWgr0bd1hWNWlms68dXi/PGQ5ucLB/k71xzMxxeFQ+YWIagv0Ar9phXIPxQ+rQMO16mkGHqwRuJjntPKS95p35Aaevt2TLp1jMYP0axx/TrCSlesX1l2vU0PDO0GJRdHSOYBIoxiJG+RmuNvSxx3AUMHIDrtQewm3Jtwl/lCx4y48JBh2ZXSILzUZKxl5HikMoM3ahw6DooLHoybFCw7z0KYyg6SysoCDPDI0yxD1UtYV2amzHwrE01q5FO5JGlASeQqK/Mv7+zCTws4RHjDH/4594/APgX/+/E/T/Jx+WxjUtVtGT4uFj4wQdNTnkit7T0DT0ncbpLKqmpe87+r6mwiHY1VieJO0lgdzhBYY2aTFezViNWBcJInSoBpJ5mbGtY7LrDt/pyHPBs+IcuZgQtld00Xvcs0OWqqJYt4hBzmhYUuLi4lJFLTtpMYuOmFoKXAsr6cnFOXH0NjJco619bOExUAWJnBL0hqUNoeWR1wWTocNUKnolqYVHd8Nink3ZqZ6AkjvHN5Cq5bLRWFcFt9/eo6kaDFuGSYgZDnl1dk2WPiOazvkX3z6iUB2xP0KLGrnz8EIH++gO7ugQu8jJHYUsavxuiAksDu7cZW161GqK7zow3seVKcXCIY1yhm1A62gUAXnf4eoKy53iRNcU2ZTcZMh6h9XeYJEviSh41UNVXFBUKeenawahwu0MO5Mhoormsmdzec5gGKOclh+8PuGB1jy+POPDtz/gNFvwLKswoeIg8HlyfoGMcpKLDc9frJjuFHJvjCcMnzw6Z+6PEJ894uLRCbnaUeaCX+i/yfUoZzAc8eSzL9jcu4cw+0grx1iaZxePCQdDHNXgeVMurZhDNWIy8SgKOJxnfJn2XH32CfnlhvmNGOnuWPZXrJ8/ZbPdECQnBLO/ROBmvHVwh+X3L0levCIbtUx+/ZhNfRvTNewmK/R2S3vpEo8fIv0pD7/mc/5xwoPBPmvnmu9+5wN++MkLgusFCA9DxaLRhJ0iEyXaSIQFC9fBbQu6VtB2DlEfgVsge0VSFFg2eI0kazvGxrCzwDMC2TgsJbiV4qtKhH8RmMC/xxtNwn9md4UQnwAJ8LeNMb/3szb9Sd0BIaDuAhxHY0yD8HtK5cBaI50eGQYoR6ItmzIvcbMY29vQBi7xrqSKNJudhS0N/cgjz1Js3yH3W/ymwW8dhPZoxJa1tgmHKS0hjqvplkva2EcmBdIdo0drytTmunjFwDtijk+bOejJlnYT8FIbjsOeyq9p64bQAVEZWicga1KUZSN3NXIi6FXEyOqw7Yy9bctOR4zjMQZFHSdYnk+3UyDHNDLD9S3KS0nvGqzeYj8yxM4RhSXRQU1XlNjGxl47+N/yYXNILRX21GPoHmA3W4zdox5GtLLDacdUdkPg+RwQ0dgRlXWOtBQXwmFUdRS+JNEFrhngtAMmgw1ZL1C+giJgVkIRBlTGI09rTBUh2orlZocyPm6w4fmzj7k1u0W0H7J8uSARAZF7iVA3ePn0FDEOCCIPb+BTLcFWWzbLhPuDBxR9TJluWWYXJLuCMA/o1guej8DG5ToTrM8XTCYTNtWGqLrixTOHX//2LzDfv0Oe72iCCXWV8ejzzwkOLHpqVBdz3komZxc837T0MuHdw4c0h+/y4tN/hDOfM9A2xwchy+Ql9blFO7CpO4OXNcTTY27FNc+erLDCmua0pFpd8Pr8hENXcP8dRdxZaNHTJgXYhlU+ZtPZTISNG3dUuzkXxSXmy4LJNyVi2aE8GPgRr4Mdqm1Q/oBRm1JFNeXOUFmKKPZRdUHu+YxMhS0Vq8zDalt05EBS02ibgdNSlzatZzPsDAhBZDesW4WjDZZoqR2HUBuk01H/7Bzwz5cEhBD/Pm84i/7rP166AG4ZY1ZCiG8C/60Q4n1jTPKn9/5J3QHbkobIZrmsGDuGoo1Q5ICPdBWt0ehc0usYz8touoxcObiVRSc6Bo3GUQ6JbggrQemG1LqHdYCwCnppI9oOexdQOBW5kcR1jumH9I4mEhGNrugLzenJEm66yD2P1bNzrLffIpiWuNkefpxzxzumjzP68y2er0irnsAO0L5DwQ7PGqMtqJRgvi1Jxhqvt9j4hmkDiZTousbPItZeg6cgudrRew7hZkA82WHpIWlzDU5IKnuUbXAtg7YH+FMwfkvgjJC+x9hrYOcghUIGU7yxTdvVaBXgCIGLpkh9nBshw7rF0hpfSqhqOkswRJJYEVbW4CvDYhDCziAbgbZm9NGb97f1WpqzilInOL4hqRLy6y03giHrVjBuYsq05nVW8/bdPdgM2ZoV8cwwP4i5sfcQy2kw6RZj5SRLTV1rjm9HzNMZn/7wNQ/uHrLtU67Fmocy5GR9xu3ZIfnigsQHS8R87YMDTHPEYHLAlgox3OPmxOL5Sc94HDJSPeUiZKFWfPP2A9a7BTMvoYtjtl1BLXbceu9dhIioRUkXFkytIzL7Gn25wxm11K3B1prrleLsfENVnrBZ7vjGrQc8TSpufudtxoOGPG/ZXmSUfkZxXXOwl/H81WsOH0wpegtfNdimo53F7ByL8uSEzcxjMtQMZEjillhXAkc4bDIP39MMq4bStbBdi0Hb0NchuV0wrAtqzwZZY/sSTcsuV4Rhx7hr6VqfznR0AlTcY2egHBtEg9VGlEIA9V9sEhBC/Nu8AQx/7Y8ZhjHG1P/MkzHmYyHEM+At4A//r87SRiC3GaGr6AMLV6+RfUwc9aR9RV3EGKmxTYVpfYSucTofRxeslaS2A+KwpcltaqvB6212qoTOIJVAdIpWlNjSxapgIKHpwDt0qFXE8nVOdCNg3F+QjyKeX5RMFwfEzjWrE4NKYywrYHwwIl8/o7N97kwn9AR4VotWLVVeEWAhRiuqbErp1NhWRb1pUHpE5A1IvAZV+pRiR6drxIVPE3bsjYZ0Xo7st3i7np17hiOHGOHi2JAFDT0h0gjq1xqx5+LJS4RtYdlj1J0Kp7VI/JB22zLzJKly8YYVDT6WCCjMDmm7hOktukFPbGuavKSOB+xlgqrPed0Kgm7DWmmuUhvLLlktV7gDQbNx+P2f/Ij26hTH3+PtD+c4wrApoVhs2I2uuf7BU0wXkngN4a0p7w0iVje/xunTH/P7n37K3PMI90e48ginP+NCFpRPtkjbpg9rvviDL7j5nVtQOaQ7yd445NOLZ+yFDzBOzY13faK7D5l1Hp8/PufgwKFuV3z/91ecVa+Z2yGPnq+4e/eYD8ff4CfXj7isXrF5vCS4PcVWmv3JDXZ1xzyyMNM5+rxgvbvmNH9JKBPkxibrdm/o5LNLvG6Aox2qeJ9b7094eHqP86cv+eibH2BRkBqL2Lc40S1PXu748ul/z/r8gt/8l/4V+qzl/s05Zmsz22V8mjSE56/RoeDp7gkiq/n05BW7Yo1PR11JomEEdY3dGXJLMPAgKx0c15D04NcWZaEYuZpMaKpK0/QRym6x+xYaC6eWlEispqbqIgZWAfIvmE9ACPGbvAECf9UYU/yJ9TmwNsb0Qoh7vFEmfv5nn2hQvkQ0EtFFhE7GRZeR1ALVO8Qioa56LGljREbZh2hpaOMQq2qwS6hrG1v1KCPIlGZsedSRwIQ9e9uG0kT0fkdVS+oWxNAn3aSooMWEPsVFQrzn07QrdDBho87ZJR1H7+ac64xsVTLZ3ubgwSH7uNSyQKYKLR3SoMGPBHUrkV2I0K/wgwh51XMQT+nDjMaxsROJEStC28XSAXWb03s+sisRdQxpjxmk+GJA3cfYbUYhHbylg9KCbpwzuDXCWkPm+Vi+R3W1w3d8ClsxTGvabsCK6g0n3UpT1SDsLVHj0Vg5tl2hcx9HQi8cEBntKGKblKRtzsl5QSAGmLjkapGxOT3HJ2R0OGZ1coWbNCSHL3lrcYvG6/j8+ceIJuLTH3/J1eklv/HXv4PZ1ujlgnVl8Xz9GV9+fIGlM6rRkPfCd3ly9ghv4PKOdZefFl9grjwcM8a6vaa5Fjy4d0BdtzQlHAw8droh3xUc9nukLy5Y5B3TmyOG9iEvT37AW998wPDkgGggCZwBNTkrf0lUO7y7/zUenf8E0UiefP6CYrbkcH6PSwO36RBBx3QyJspSPv/Hn3JdvmbGlL6rmYh9xLhmJyU3yx1JkpANdrzOXLbrFnMZ4ERLPCdGTi3aneRoFhBEIdebV9hKU5yG2IMVC9lhxhf8+NlzhsExcZBze36H/+H5c7BtWm0TllC0Eqv1yE2LW9RUfsN46JIVOYFxoW0ZCkWCw9DK2HZjLDuj6ySxdtlKgZAVqvPobcnA6qmMwdaK6isaBf48JcKfJTzyu4AL/H0hBPzvpcC/CvwdIUTHm9aEv2WM+Qpms/+DEypd4ooRdbhD5BKZj3B1hhPVVJWm8y0aBHYjkVGP29eUjYdWLqmdoI3HKKmpogjTVWyERLQtUSLZGYvcKwkrTVcHOHaJyBoyu8UrRihaQqtjpGyE9NGexLd92gZOHl9ghR6d0zGZtYg0IwlsgmVP5VeYwGG/3yMY+WRnOeI6p1UhdtsinCHLfku8tXGDlLa32eoajEbLFs+2GJTX9OGQOLmk9VxSy2A2BcvlgsnQopQJWT1lHHrkLTQvdzhDl27p4sw1gV+RbOw3cmBDF8kr7Kzn2jpGtoKx7GkdEArczZj1pMeuAzAJgeOS1BG6N1yd5ii5pa9SLnYNyRcpLj3abVgmCX15RrN+RRrNuNkJNrpD2IJnn215cHvI4aik0S657uCGpl6UvDxds7pe86K45M7eHuflJTc3exzPhqTSRzkCKV021hUffv07vD59xPnz12S+xcVljdXvOL79HvutZDEfYdkt2oa9UYTX1LzY/ZCfFjv+naO7HE8zmiLnKr9GVpJi02CslPV2zcFAYE/GOEXNxWXGjf2Gt0KPzNqjXD5jqV5R7Vqo4EjEmMhhkjo0c4PXHjMOIckvEHZP31tY+Y5im1MFDdO65lpu2R/fYBxI9gZTgrSCzRXMH7CtzpgODskvE5av1uh2w9l5yu3JmNPZCreeY4oK0XoUosISDY2GThoipWhkh6pajDZYUUdTCDy3wTI9TaMYyi10LnVUUVQ2QriYVhK5LWUAJgfL1tSNw1d1C/2czA4oE0QOQhj6qieIhhRBz+C8RJqOxsQ0sqa3OobaI1Ga3irR1hw/z+m1BRj2vIbO1sSVYiMMKT1COZigRPWKpvKIu5za9yk7B2EqpuGQTuyY3LmFLW2sqmS76/DyLfODe4zjG9Rmx4uiYu6/AdDOnIJfefhNwn2f1fmO/dEUOehx+z2mt99FexmjwuIf/dOP2d8P2V0n7HY1x0dTQhVg9gV3b97F7TIWL14xsOeM9vf56YunDEeCRbZlPPSZSI8yWdIaxW6zYnL8gGjiIcoh/qzEKhyaleZLccG+E3G8PydbwOA4AO1x/uSMhw9uYc08wsGQ3lNsPntGGcdo4ZPsEkZOQFtAsnzNZr3lh9ePeX/vLrQ2wUTimTFVaGOvND++fsrDe+/hZEu6YMrjFx/z4OiQydERqtJsuxWWe8zIUZQ7wYurz3nr9rvYseGH/8vHMFLE1poHR3+ZeujwxZenjNSWj3/vM06LE7pdht0MOH7nLoPjEdfPTtiaLecXJX6RMx4fYHq4/+CIX/yVv4JlLJpdjhVaNKZkeb4j9Fu8oz0mzphZ15dVAAAgAElEQVTnS4E4P+Hl6jHv3f06RA7CKQmJqQNNcXnO3/tv/oC5Lll3r/nN9z9kctMiOLhDcZlhdwI5bElCycuPX3Ln3jGvfvyCf/jkc/6F734X2RfISiC6jMvTS9K64tf+5m/hNSFVANnuioPxkLZoaPuOk50mWJwSPbjJN/duk/kRT8SWv/Mf/qd4a4nbZPRBQFUJSrfC7SWdsfH9AlFppIa6C1Eqpw8jhlnKyvfotMGrNDJssUpJayJ61TAQmka3tMpFGY+03v48zw5ohqkmc6C1HOo0Z9a1bGwHv+9prR6MhSUsEtVSWB2OjKEoqaI3TShlq+kKgecqrmWBMANa4aDsAmdp0/sWXqPoYoGuDJ1VsJ/XnOsGP/AZdoak07zvTRi/LQmTA3QwpWgjXHNBbElK3+LIP2LUrigvn/Dix1vSSrF8a4/iquWDrxncg32izRXCt4n8juVuxb2Hdxm3FVGeEu+NkHZEs7uiLGwK7eNYC3baIm4KKHsGG4P2FNXU5aefSUKvIrZGTKMRyrVJyy2vnl0hPA+7bLGthmwj2Daa2g0QVy2v05Jw4rJ0PORliZeOCXY5567F9WWK2+Wki4RnZs3h0V3c6RGmgHf3PiQrL5Eq5PLLGifMuHEvIpvt0bxsKLIrChlydLPjPe99kjyltntGYsb58z8iGFasqn2mTsLjq1eo2Yy9Tc/zV9eEi4zJ6COuvGc0lxN2J0/ZNh0nz5+gTU/WwEFcEQqFte04iA+4+L1zartnfzLgaHqLq+QUQnBDi9wE1LsXdDU8eOcmk/0pr89O8cI7aNUx378iGB1yaMacLE7YMyNaLJZFhXveorIrJqMzPnl8xt1pjLwfYbdD5oMxWeTx6suXOEWIN22ZvvcN2s0LyrYkCjtEUhLHIesyRQ0iSjNHTbf4RmA6TZW0VK0GGSGcEp1XDPQJ5pbA6xrWbkeR58xmIwbGwzgpmSOQpcaVFWNPsU1d4r6nqCWdtIitFsvk6Mgh7nOWBpxOILsOLQyuEBRIpNNhTEMbWPQGRFZh3OqrcEHU9773vf9PA/5n2d/93n/wPVyDai1aR+Gonm1usKVH3wvqwRt034iOWmls5eLokiAQBFVPI1qsrsdRPX3nMBj4IBSWNGgrQNOR24amLTByCH2JXcXU+z6RsRFRwFE85GZwk8JcvvlSAHl9RuM1LC8XmE2M1XecZteowCbpNRYKu7Vx+o7jgymbckuSb7g621E7MbfuH+PZewRBw2Er6OqSZb8i25T4wqF3U6auQ2ELNlfXaNeh6VOuViuurnqqdMHJk6dM92Ki+YQf/eTkzQzEKmcSxfRJw5NFSasahsrw5CpHpyW19ogPIoYmptpWlK7HxfqMq37J6tUJ+SKjtTp0XjOc+lRBRn3h4g47FvkLFssL0sTlycmP+OiXv06eBkyDgC9Ofoo98WiXp7j2AWl/xeWzJYuzFNorXpynmLJlmu3ow4jpxGfUe6h9h1k4wdMZJ/k5h/u36OqScBJSmIYb4UP07RG/8MGHjOyIeLjHnf0R3/nrv8qr/JJxrXj4wQP2DucMxodMAh8n8NDpjljZSDFFtC5S9XjhALnZgKwZ+DaRDlj2ij03YFNmJBfPKDcZJ19+yWiesN44vH71iO11xb/xG98kcX2MjLHbltQoelJoQZcFVaXIAH8QQQOZstg7POLV4oK0WDCL5zjeiK21IfJucNO1WXQblrLg0ReP0buaoPGpxor2aovvSKLDKT/+4gUvlxfEnaazArQrEH2NDiyaosbqLGy7otYeLj1Z44GuSX0HVfQMbE2jFU1lwNfYlUJog659vA6GukdoRa7Nxfe+973//E/H38/FTaAXhkJbKK+nqWwsWmRs0+QpBCFt3qGxcJv6jfhE1KEtQ9N5GOXiuTUlJdIMMRK2bYKLi9YOdZbhjWBSK8Q4okodGjxcv6Go/TfgiU6xxrcRYste+xaXL85wjgRCT6iSlmbdU4dL9uQBESn10jC7FTKbjZnM5ry8fkFaZgROSProFd7hIXv4hJ2gpeTZ40dQuEin5Rfe/zp9A41VYm0d0pFDIHr82V0ev/ineG3AZr3gOFR0zoj3f2HGYXBIWuccHileXb1k6N3k2TLFFTYP4wGvN1tempTT69f0t2/zVnibkW24lDW7qsXbLNFDi81nG0ZRxM3DI9b1U+LJAY7nvWnPenvEj55e0+1i3Kbl1q2bhNOOblfz/PI19+5+xMP5Tcaly+tlwR/u/gG1OaN6LTk4fovXgabPBZ6vOOs9gmyHKFuS6Qnnn1SUTx9Tby2Ctzy2VyvksGUi5mztjCzw+eh4xLOXO25MPZ5sV1S42N//Cd/91e/y5fhzym3Fg1+8x9mPnqIzGJc9vYZNv2P/5iG6zygbOJjNKWKXZJeyuUqJLZtDy/DiuqBZbTk/WbC3r/HmcPpsh4wcdCcZTAVOs8/RuCM3S5S08cSKVdqRVi4zLeiqhHCoGK0O2IQLympHFMwxUlDuWppDQdPXuAnI6IrMjglR+C96lqZnkzZIp+XuxufmW/uMHZfLZsYubbG1ou8EtqUxaUcuJKap8H2PrLbw6xAxTeiXEuPl1LX7ZjR+MGCbt4iwR3QOgamw45as92nKllC0bHnzu/xV9nORBCQG1TX0loVycvLG4CQGHUGft/hxA4XNOPAQJZzHPe21Q2A67FZj9zV9L0lbwcBv3wS/0+J3DVZkYaWQKIFpKmZ2RiYVrpLoNqSfZ1S1R7+U+HOLZf2K1s446kasD1yCLxPauiYUhsmBxyyccSEr9o9vE1LjBor1pmC6N6KoXe5/7YCb99/F8Qz/5EePaZuC9w72EXs22SbhZN0xnkDsTPhfmXuTH0uSO8/vY2a+u7/9vYgXe0ZkZuVWWcWq4iqKTfZMz7C1YAQNBGjmor9AN510EzBXCTpqIAE6CBIwgE5qNTCNaQ17nSZZLLL2rMrKJSJjf/H293zfTIdkCwSG7BZ6JIA/wAE3c4fdfl+4m30XSYppFYjI4umzjynSnMAAYbrMvRYP3QHl/h3ir45ZzmuulmPeevw1ovWSgXIZJ5ckQQNtlqSThPl5wbvvvYGyKiaXOZ1GE1FnrCZL4vOScLxgo/sQYdsY4hY31yOi0UtuDw+wLma0tE2wu8dzx2NMzkbzFpmRs9uw+fLJE5KbGcNHQ4p8ieW1SF4YXE6eYpgdjNTj4GiL8WTCYn5K9VmILkPM3pD19IzTszW3729S4jFbznBtwXJdQMenfzjg5vqcjmEyS895+zsPOP9oyo+TX/C10e8S5TX92/uYS5P+7hZ22KC/A58/iyjNGkPZWGaFNAQ36ZLtLMWtGyAywnXGrFxxPT4hub7GyE8wzEM6YUju2IiiJKOmpQxEr4ZC4yiTdZVgVi6pFWNXirxWBP4A08u4cSeI4wBzXXH2csIdo0cWXFJEinW8xDIHZLJDzITV+ZpMJORTzcV6SteHqDiimMYsGzZLNUKHS1SeUwiFmZdI08GrUvJckPoZjhBIlmRrjZAOTqVJzRxlSIRY4WmPNJV4QBUErMISaWX4oiCXksTReLL+d9IO/P9elZZoIZGJQrckKgdhG+jYQXkplvBJWzmXSU5TuYgLE6FydFWxlCYN30LGDpavMMqIXBo4S5vQrwgixVKWyLzELy1mysA0FKVVEdgF2VS+Zhq2UlZll47q0n2jiRn26KU1480lntuhu7XD23fv8epsxaBlMZcwr0x2b5bsHe1DovF7bQZbO8SUNFYedTJlz96grG2CYIPGRoeOCDDbOVWueP/zL8hHCTQyTs9OyITBm4Mjepu7bDYHhIXBlz/5cxyvx+7mXfaNnJurK8yNAVs7HvHzh8xHx1xWmt39He4Pd3jj8D36D11WryquryZkyYjcNhCm4NGdt0lpcHN+hrHhs3v3FsenIc9fTbhejAhnNd94+02+/533+OrsBdE65Pr4iuGBz0Zjm+zwPq+OnxCWHap1zMF773D9pwu8bYst2+PDj96nZ7scv3+M92ibzeYmRTimefQWD3tLjna6HN+c89XNB5jnQzrNFtaLiuP55ySEaK/B7pbm9Gev+J1vfh+rt8nl6hMeL7vcjMY8H19yfbbmZnmGMdzDNkOOjg6Yf3GBvwGxMokXKadijr00eXX1MZ+fniLSJdINuNXcYjKJeXzPpdwy4Trl2PXYVE3iOmM9t6nclEZeYJseF7NXBLLBeplQbbaw1IrxzZgizqhCD2PXoFEXOJ02G9UhluthBgJhBFjCpSm2mKQvOXu14utvDkl+MUKJjMHDNlVYkKiCPMkpk5BGCZFRoaWJzJfkDZM0zjFSydoJaRUWRi0RdoVpG2RlQaNQ5EogrRI3q4hNMEKFKCxUI6LOPBIFhJqq9dq49dfVbwUIIDS1rvGCBklZUNYKQ6c4QFkKcmlAFeNqQZil2JjUtSK3c5y0Il+b1NUamZkklk3bgXULmllFHggCWVLnPSpiWoWgsFZksgUywRQmoa3pNnfxVktCo0S+8sj2bhCTmGbVwGx3MAvFxTgkjS4oEgO1CV2/jTSb7AwcbqYrgrbPNTWP+9tMzy9JCpOnxjFvifcoo3MaeZ8TPcdfWjy6ZVFZDpP8OUHdJDVAPFsw+FaPuXAxM5fxaoy9aBDHS56mL/ArhTSmGLVPuWySJTcsAsmh06EMLPYmJaYYEY1cplFKy3Uot/t4X9ZcxidU8ZwojTD6Q+yspGpUuLnDR5ef8q133qUMC6xmzs3iJdOLYwbBDkkj4OJqRhr1GEXPSKOQXI6RQLP2+Oa73+ODH/9rZkaD3uYmeTTG3nVwMs1arYiuM+7vhwRv7OA0urTSGV9+tKbdgSxcYlRLRkuPrhWjc8GzVU3P9vls5yvcZA0jjV/GmIbgdBpReRVb3h2W1hK/zJnPpmwd3kVaCYXymTJGJzXz+JrLmzHT0Rk6FQTFK9I3CqyGoMzbuCpBdQLavqZqC6oprMJrdtstzuYxt/o2oiGxJxVqs6Ber6HhIesm16szlJNyJ95HqtexX83Kod0PCBcCY8dlu2lynZwhVIemf8Unzy7BczE3Nzh5+hGdxoAjvQF3liSlIFMtaiulkq9jx720otYWtpC0qoIlNQIJsaJMMhodE1vkjBOJkC6euUYVHqYuMNyEdGbQaWiSLIVeG3P9b5F2/5/67QABDV1DoMoFlTYp7BKZQuoKekIQGQnuSpM3XISOCVOTQJdkmQZLUhQltijRnotRFURRRlV3qcmRZkaYBUh7ioh61E6GLRsY2sRE4Aw6rz38lucYQRPXqFktZ9RzRRU4+LFmXlQ00iWrIsFxPFTXwW8br1l/LcHZccHu1i69vkNlbbIMl8yXU+p0zcZum+PnX+IFAfG+wX7rNtgXXF8u8NYh9Tjm6iJmLRUi0BxfhGhzhSmumM4mRNLh9vAxZTlHLVJE/zZSexQOeIM93mouiVKbdJ7DpsPF6RnRVZMyquhsDWApaN9T/PQPrmk5A2SVYRRrptmcvXiTWEypJzf4nsAZbuAlNUYiiRGcTeY0Ox6t65QvTj6kq23SZEm81BztNDifXNIetHmxmuPOnmMntznY3sH1JO5BE1c32D3ocrPKGD89Y9P4jFmZIPWS65Giq0LaysYMBJnukscl2pnT3xhSrqfMx0tObk7pNW5TFGMKYdJsCHZ7fe5sD2noLl5l0NjrErkxG5mHJ7cJZymWLnkxrgitBDV2GB7sEE4SPGEjVufUewHz8QLPOqDVGbIYH2OVNcvSQDkQrpc0hU3cF+izHsWgwiwsBl3FeqaYUSA9jahrdrsOlbVBmq1pdXs0mzBPBGnVhPSGTtElNeCT5YrHecFPvzqneXTD9mafHfMWd95+g0//4ifUpYlZlzg1hBbI2mFFRF24KFWgzRSkhVYg4pKFMpBaYLlrrNymEiUmHkmeYsucqNQIKXDDBZHTgnzxa9vvtwIElNTkpkTYgiot0JWg9lzsZkqaKNKwoqo15qqg1baZy5giUdSWTW5B31CEqYOqMxK7QvRd6qmJISQxBq2yZmlpdL5CKCikAbKkVGvy0EALmCYJVVfgBgE9JwBbodwBWl2hpGSw/Sa10jhRzlWZ0bc6LIuI+VXBKLwgmZZMY4vtrSXrlUWoLR7/7ve4OXtJ63aLu02H3Opwk19Qnc1ptBKuzq/RfpvKmvHD3/kBu8M3yasZf/wv/oAvJxkiqLn3791nZ9umf+v3+F/+x3+OtYCumSOSXR7e/xZ0AxZfXZC3DW5OL2nf3aZpNSi6mq8+eknQ9JinIVtbLj/68x/x5t4O7naPunYJg5ryyuL2xgEXlyPKH3/Cp+NL7t/a4uTVmKODR+y/9w5mW9BYbrO8ekYz2GfrawZPjm+o1gsiq83QbLP95iNsPyPUmiJd0eOQlqM5P025//g+ejlhVgYk0Yg7b3yPyXLMQLk0dzfJr055eXHMnTtb7Ml7hHZNuorB6vD73/6PWQQ5hD1G6zVmoQnjS96zf4Dct5CGiaEy6lAR1iuILSb6jMnVMVN9RfuiRNvQ9uFoc4vYhIUAmVmU7SamI3jz8ZB/+elTNof7mI6HzFwuowvKysGrYy6JmJ5dcP/hI0ajgtiIGdy0OCtfsWX1iJRBr9tnMfFQZolhG3RakuoakkZJvHYoiznfenhI594h/+DoG2wGNXbvFpXrc++NR/zsp+/jFg7SKogosGyLWpUoakw7wYg1dmYTe5JaG+gKSjPByQwILYrKorJThFqR1RVOIckKm6awWFghRvnrfwXgtwQEtJbIXJJUJaXy2Cxz4rIg8wQ6r1HFL5WGoiDPQXgudR0j69fy2FVtIl1BUWg0Ae1xjTYXxKIDTKl1gLYsmo5LIdfEwkZnOUPlI5yIqr2Nofsk6xhZJqzFmnjgEIxStnYDnI09KqHYUxYXqzMOmpvYtWbD3eTJ9efc276Fv+PilAPysmIcT1DriGDjm0ixgy5qwlVEt7vHy/mXTM8m2E81+4cHjI5f0N++h+U0afUcLs4rHBvaj5p8+853mF8fE19M0Js5j995h5at+CKfMLxzj1qOWVUSo9lm+eySu4e7TCWI7AXxc0W5vGC68NnaGuIX+yTrj7k59whMQdex+PD6mPY8Zd12eWvQ5I//5AMW0QT78bsMdgaYXsHHP/+UenZF59YD3v/sK+4cDfHKfbJwTVS7BM2M2s5o7/R5+9YOnx4/xRzeIysrrnROa7/Dn//LP6bpdjG7GS3dxNxUvLN/h81OwM+fvcIWNlutDZYnGdEg4t3hPvNRgrEKeWY+o/voFo2WwY63TxWHlGmbmRXjvIjY2O1wg02WJ0QyZR1NMC4F4U1MNl8QbHXZ9RV1YeC1uxQLizMVMogmCPqIuiKPPLrKIGmUTPNrSisnOdbgSiaiYD3IaNwAIsbTNV4VULVKZO1zoivuCAU6pybGlAozs6irNQio6xZuv2QyTUhxUZnJRr9HnsZsOpKLrGCZTZBVgKwz1omm7fFavl0IXC1IK586TygDgRPCQmXoUtMoJLls4OmU3F8iS0mmPNw6JfI1nq4p6oyeA6viN5AE+C0BgVrXxNRgByi9YhYINA4uBXlqUMqcUjjUFagYNsuaUSqoLIElJGVtUOY2nh2T5zGGK1iVAY41x6gMhJvQXuWoQmKUNaVfUzoxpdXDzWu6lcDRFZ7jkaUJorNBGSdklMzXLdzsFDv1KR/fJbC6XK9fMmgcse+UvPmNd2AR4ygLw7KJqim+7zELY54/+5DGdo8yMbBaJmmW4vrbNPYEX3z8F9TjN6m8Lvm6pqkbJGfnyBIObt+jkhGJsaLZ73NhFNjzEa3W65jsfhKisww6NvF5iqFtOr2A0lyh55vcTGYsoykvX1zTaIRYgYFnSd549DWW02uicE6RujQdRRrXWG6NVCb79x7yeKPBi09P6O84XHzh0H1LIpwe09E1ThZRTypeTY65SUK27BI53cMQkkxMcbbeZfb5Bd7sBnlvh+J6SjTJMMw1dGxG0xmVY9MwFaFV4tsNtnd3qLuaIr3L6eqEbdUiNwSPv/GQWFk0pU0V5ozWIX0zprnTYtAc4MYlcqNJaIAar4hkSDZLKSYVT28+ZrE8o2c2aFs+fmOfzc0YV9hEzoo0WaGrTZpHBtW0osxyVp5CXM9RroEvNJlZkMkSeePgOyuUp/BCi7XqYHdKkmWJKHJ22iYNZRGrhGHgMS5rSiUwIh9Lp1jSJw2WlE8mmNxGLjVhGzyliJOIvOMRPw+RXkF3VVJhEosKJ6tx/Zr1XFGZCbUvKZYlDjWaGtNyqQxFWS3IMoO6MjDzBoWVUdgCVWgyA1ypiacCpQx+q7MIEQZVbQIpviHJcoUya5KpgeUkiFzgSclUxhiuzVrXWEKSpgo8m8o18Oo1svSpyoLlWpD6K/TcoUaQOSnb2CS9mmSqULaAuIm1jFhulshFythY8cjzMDdd0njF6stXzAxwvTauY9I52CZdnyN2bjGcVcwu5xwnkl7QQloOV4tLNuWSNAq4HL1kNT9lt32fn3xxRVtNiRoxA3WbZidgOg1puAfMnJyB7nJcpjw/P8ZKCwov5uEb32Y2ecF8bdB7eIhrJ+TzguHwFvNwgVJ3qM4UN0jGq4jZ88+w+gPsomS+ElRuzX6vQ6eQmIc9WJs0tjY4+egvsc0O88kVX94cIx2f+WzBoXGXybFAKvjqs0/48vwc4+Nz1s1d/uk3/xHjaIIVlfzgP//H/B///H9jtDzh6OAOn15EzH/0U+qhZjvO+dM//COi+hjbv0U0nvLW/a/x05/8KWfjc7KrhMC4IvUV3/37fw93XFM4JSMR4i5jStdh0w9IjIImLnNZcW/g4ao263FI6QpCPUUtcpaznHRDY1cKtzaxDzq0ywvWo4yb1ZhwNSeKYlp+zTtvPmApC3pbmxi5xWQ8wWaCY+dMJpKGDtjue/RlzXVZs4VLLlMSyyZeX9HbGGIbG3z5+Qkbh4qEDMftcfnsCw4OB+z7PZRfoeseeU+jT79ChQFRN6CtPPK+iRFanPsB92+ZuIcDfLOirCW5rnj1+VNOr0+xlzUpEiEcWqIgNdvMixWFUlha4K1yMssnF2tsYaOLhCJvYKGAjDK1SZoJ7dIAbeKQM0srcDSmXyMKQfjrU8h+O0BA6AphGrTjmNJrY9UF2apGiZpEK7AlKRIjFWTCwpQpbmygVI5MSmRHIUMXlRf4PY0uKgInoM4Sam3QquFKgFp4aF9gRxl1XVGpNvkkId+CW45BpAVOPCOeV9QK5LiFvL/CurNHVhrUmU8xfUUtEmZljJz6tPpLamGwgc9sscR1HFy7g3vYI1vPGboZ/q0HVM+fM5cJR8ND9GETL19jtDfJklccLFwMK+BsfEXba1L4OW21wegqomu1KG0fm5KLp8fo7ZJ+c5txfcHlScq2YZLVFXeCNq+k4t6jBsXYYByNqByYTq7oK4eTVyN2tjcQWUF/8JDsvmZ+ecr1asSr0Utkv0ZFMUWe0DZMVqJDNZuTXb1kNlZYixGHB336mzukpFxPbmg1BdHAZrcV8OTDj2koF9NxKFsJfp0zDlYEaogtJnRbHnnWZvNgn75w6QQJJ5HJo7t/j5X9nJdPP0IGHZJC8fVHhzi5TV0uSWobhENnZ00860BtYrUD6iInbYaYtsYMV9Rjn0n0JdPz56goIplpurcdptc3MNiintQU2qBv9bhcXrNutfFLi7ZlYBpdZFdiKEGUR7DVY2ORcm57pFNJXK25vdNlqFosihFppZmvUzrLMeeiRKldWpmDSqakgUPWNFgg2HAdHHuEsjdpDnu8Gl1yuKFp7L+LPb/GMm1ynbMuC7SomQQVO3LFOm6iZUKjViRmimVL8pUPeUItXaSrKVID7axoYpD7AU5ZEKcp0haEJhSRi+sLcqFgpWkGBWS/xV8CWoBVVSSuSWGtKHMTIRSlKzHRmAXEhUntS/Q6AkMjtY1QJaFy0FOBtEsMUsqJoDSaOIag7GcUNxKzrbjlWcyuahxRU2iJm8UsW0t6dgM5n0CniW3kqDQgtlY0jQOCvQabd95ATCYotcDv+mi1wc1nn7I13CLN16xf1tx97zGXsxDT2CYrNDOV4rwS5GKF0XUZjTM6e1vIzKQ/vEU6GpGYcOA4vIgDsrAkmX/C3FQsnl5z+9Y2w5bPd+60WRs1QbKJ3gt5+Zd/QnsxgMcrwmgJi5RLYTHY+xrnpcSOX9Iw7nOaXzCNLnC8PnZlcDKdkZQpe4Mek9k5QtW07COS8ZzN4R5VHNNaFpSl4qQK+e7Xv890/IS/+slT4vmUuiyZqJqTyRK7jJGiwkgEzcIltgSFVuRzQbyhGDZ2uV5/wdeP/iHz1SVGPqHT7dPZ7VBHKdX4gv3Wf4a4DcYXpzg3IVdVzIODN3E9xaoyaHe2KVog5gZRnNAcWHTqO+RHKXk8Z8O1EEnJdVyxFBlymqGtS4J0yfj8U5IQJCGF3KH0gJYitx0MPUXLHOPaoa4MorKgdWCwHJUYa5OB0sykjblYkQkP39tGWxne/JSb2MOWOwSpi1dV9HxNniuS3KSRtXE6mjqTGP4GrtPAjQW1nWMZTUbz5wStbRy95Iuna9wgpydd4l7G6KMJZeKQKY0pFbOioi5rDEeQZwa1kqhEUjslduGQiNfkNq1qjEqwUpp6WQMZzdpiJcHJoMagykMwawzPYB0F/HbzBIDc1DTwScoV5AXaVzRCQWJokmaNmaWITFEpSFRFXgoMYdKuBdrQRDqlMBRYFlUSUacO/jog04pkFTFOFbVyIElJhI9EYBkdUhnT7mxhOh5RpjArSbfVZqexRZ6vGH16QhD0kEOTXqtJFSU0Hr/LZu6yXB+TBwYvv3hCmChKa85qPaI32OONb/f4xQczrhYpt5wl088tFlbG3v3bSB+GzjYnn78gWs54xZR8qXl45y0ePLrDk1/8iKe9u2z7La7yOUPZoa3GM28AACAASURBVDAc3OEA2bNJjpcsLgt63Zpb7zzk1fWUjq6p+wc8ORuRBZqqsFkVNVfhKcU6Rqclf/blKf/oh9/GXFmMgjWv/mrKnbcHNPweP/nsA4atHlvtTT44/kvefvgmv+v1+IsPPufugy3uv3efz3/xV8iy5Dvff5uTL85ZrlYcHbSo8Xj76w+5HEfI0oRpxk+e/iUbrS3eefcu1c2UnXv7POreZSF9rqsLrLjLd+6/yZ/97M/Qacja8QmTlNJpU9qS8nKJ0++wuT3As33yaI4YRezuHxBWMYfdA8o6YXz8BcfTMenFgs+f/BXz8rUrlU4kQ6NH1zaJJgX5zoJX11dcXE55d3AbRQPV0jRil7IpUT2TjuExWdQEA0nTnxFHJnJ3h7TMiWZrsnnEwjfQhYcnLEhS6CeU8ZKVbrDv2+jKIs5TVFVjehl22qYUlxzsbWKlXaxVhOvVuG0XI4dP3v+AbDGiqU3qOCETLUy5hkJhI8hzg7RhkJcZWrw21amCDEtKypXClxWRmWOWLqFKcPMWpR+howQwcBNBXbvU1uI3Coh+S0BAo5SHkcwRtYG2FHZYIGxNWbbxwyWxVaMqiSlr6lJTCwV1RS1iHGGR1wJRGmiVYxoBsCIpFaZtoZSLUQnWckahFdKyqI0GyqiwN2wUmjxxaBolWVnQaTRQWxKWFsOei1m2OTg64vLkOaOLc9ZVTPPxt5nrGjFP2RrucvXqKfNjwb2Ht1hcrDiJYZFrug0Xg4DOQU54WTN9dsO6vuHsOsaqYy4nUw529xCNCjoZtbVmhIt7+RK2d2l4XZrbHczQ4adPXlCeLzhZhPzT3/shZ1lOuNbUZczV3KQRGCzSkt3hHn5V8enJFTuNPp+fLTjwM4y+oswmOPfvcbAIyN7aousfshq/ohqbPJvekDhTHu29yXwVsxqveOvBbfbfPUSvMxwvoPPgiPloRJhXqI0WaeYz2B6yvX0XL1jw8U8/JtUGDXuXwdGA23fucrIUzE+mfHSe0dhrIIQLN3OC3Qi3eJ2t8M1vPcRubHN68RlFWeC2O5hpjNcxaZg5qe5j7MeEStAQPlG2ou3bhEGbxSdfYdclqmrRbEwoVBPciJZnQSNAFBXVtGQ+TjHiJaGjaUuoM0XhKOKbMaKMWHRMOnJNqAE5oG2k2MWMLyybTGZEiymj8ZQ721181WBcpuxmYCAwkoTppkTIjH2GPIte4U06FOsMcxnSOtSUos2eaxGUJnEq0ZZCRA52nZCIjFqaFHWK7WpUZhL6BUGu0WVOmWkiy0aJEpFKRGWCUbGqbHSZ48mU0lBkOqQAbNvAySvmnqZJSBSZ/KaNQfm3tacQ4n8WQtwIIT77lbn/RghxIYT46JfXf/grz/5rIcRzIcRTIcQP/99hgImUK2bSQUqwrZrShrKo6XoxZQkiL/CDlLQlkbVEiox+UWAVkrLy8CpFLBWObaJ1BpmiahnUFBRVjKigUjbdWmGpEUo30FFFfq6ZXa3otwfI2kO2CxINRqjx6LKeS2xHUUcZIrDIDAcvE8ySCW3RgKLmOknJQwvfX1LEOfGmievUNJVAn1zSPLqH3dvn8f6bHL94SlEb7HW22dza4/F773Hn7hFvvnGH8Dpl9Pyaw+3bdPa3aGx4eJ0uRt6j0bHw93bJhwHJYo01aHF/0H59puz0cAYGhlyzMejS77Qo2j3qaklre8hb91tUZki+mtHfeQTPYyajM6rSJnHmpHj075rcOtym0+7hFRkitQg2t3jzd75DI+nT7N2mWQZ4iY1YCNJkSqPQlOEVOvEpGxVef4tiR3C48TZvvTGk3/FwN49493Abp2lTiIrFuCSKF5xfXXK5XNDt3KLRMZmsJ1iM2Ny9Rb6asFjlGJ0dVJpSyZqsu8QtbbqOS5RNua4TrhZTFDA/P+UqHENZ0WnsQlUQlwa1EEwSl/Xpgj/6yx+z+vIl6/o1Cy/PUpAGolqSrHssRyZGZRKtNFlmE9kGRa/JQvewljFVWDM8aGO5glRn4MWQ5oxXkKcmUaSRhYVXNFiXFUrVzIKMSMVczDUmm7SHPhu3DymMCmFJTL9islozcSw85WAbJXY3I68V6Ji0NDEwKD0Dbdi00wKlA2wtMVWMNGusbk3gVhS1TSgd8kLh2y6ZnbJqljTLCmEbSPM37Aryd88dAPjvtdb/7a9OCCEeAv8EeARsA/+XEOINrfVvNjgDEBV5amBXObWwgBK7kKS+QVoV1NJC5ikJNkapoC5xgoKrtUFP1BRkFMKn1UqI0pKmbRGFNptGSXN/yNDymMQV29mKWQwiK2lYIQsnw104NAODk4uvqF0bf11jZCGJdpFOhOlazGdjUgn2PGOn3SBtdnB1gN8pGc8LLl9+gZ157O/dRhsSJzPJ7JQPnrxExis6FxO2gj5yN+XA26XTkjSMB8TeNaX2CGcZRRZy+OA+en7NJ8+/4s7hXZy6Tau0mKSXTHRA7R+zc+kz+N53+eCDn1FYkjKOSRs+KtFs2B7dbsHl+QSQbA52uTo+Jbh1F8eLePD2I06fj/n8s5+yMiS3+lvYy5p1KyHPNmn6Dv9Bs8NkWrHK1jiZIDy/IClWhIlNv9fl+ekH/OSDp7irki+ta4RZ8wNvh/FJTVm16MpbnC0+J+zAg9kO/+pf/QGsEg4ebZNGAelyzr0HX6MIE2xTEPkp/ayNR4bOTA4O2lTCpZosKNKcSTrDXLfZalssipJ1MsdQQ1Y3T7i6vubVeEQkC9T0hFoJnCCgMG9oW13G65hAXNLYsTiot9FZC0PPKGsTr2UiC4dXL57w5MmXjFXERXhG6bSpijFDdjGlQWpLzEEPOXvObDTnaOuApjIJ3q2wZjmbhcU4ztj0EuSqgb9nkmmbfbHHyrnhRx88paFXOBbkZpNf/OQD9pob1HmFMfG5Wp7RqSVRkWOZCrHSGKVHqmNUlTM2NXICKrAIUTjpHCl8TF+jS4lcZqxshTB8/DzED0zWVUwQe8RFQm1bhMqibxSMsr+jvdivyx34G+o/Af7FLw1Hj4UQz4FvAj/+m0GgBm1QWDVtWZBkBZlrIHJBXdUYtQIsRGlQ+iWmUOSpIjBq0tqnpMTSESquUJkib2TcfmOfR/du87B/l/ZBm8lsDB2L7KTk5asTFkbB6GqB7irUKsP1LHydEXuSze0GrU6HZZqRq4J6FdNvLsmFwXSZMJ2NMRRsDzYow4L9/R02Gh7F2uCTJ09o7/RxvU2+/90Dnr2YsTV02LZrLmnQ2ixJJ5q8EzI5WeIZAa2+Ry5Cvjg5oQ4r7h/cZzld8urTE9565y38ToNJLbGWkmO3wn5yw6oTMTQMEDYto0UqxmT1itBr0cwDyvk1A28PayiYvRoxG2fowRTXHnD73lvE+YpH+0dcHd9we6/DO3vfYBGNqOOU/pFkb+sQ+3LKuo5pDDaozwt+/sUvuHx5xUC0MbdNOqSMT8ccX19T+E1m0QnOOiS1HO6pFq9efsKkCGn4HVafKRq9BYXOKMIVX336gtru8nvfusNarNjc26ftKFarkEHHJzLb+HbFcmUzdUZYGZSyBZc1ubNktrRZ3dSUz4/J0hlWqmhvSrxK8KoIcMycaT6iW/ap3CGeGKPNEqvTwfcrCr+HG8+JRZtxKXBLF32tqVSJRRex0aBBzDIqWKwiSttDT1Kq7gptHJFphZWEZM4ugX3JOvZwGtcsjAO8OKSqYxpGk3dvH/Di+VNWtUU7gbqWfPLhj9gYPMD6ZotCWOighNRiWRUEsiaqY9pVTS0kaenSMDMylaC0SS0N8tohjzNMr8IU0MkEWZnhu5DrAh11SFSEISxyXRMsS4q/wUDs32VP4L8UQvwXvHYS/q+01nNgh9dhJH9d57+c+7f7/ldyB0CAo0EWRLWHqAOseE1u5DgYpGaKISvMEupKUpDSUpLcqalXCkMoloagLU1k2+BosMvv//D7uA2DRurR6biUaoiSDfa+2ebbP3hMuZJUDR9nNuaTmxEfPfmUxc0My4LaaCOyisDwCNQmx+UXnMwcDu/22M0XRC9CitmK6RSaWxbD0iacxyRrzc3qgtUyw3zXoucdcrjpU2j46gbMVowc7pI7c/Ivrmi3O5yqC3QOt954zOSDE9biKY3+kKXewMhXzOSEnfYhzSzk/1ytCSoDNTQYnV9xcLRFmLqcjJ7R0RA8vEN1ccXMDnh+fM0P37rHyfoCq93ixWmKdbZkf1vSLiSDRpvr82PW8wQ9POLVV+/T9ht0uj7hNMSSp0R+h49//jNu7+2QZzFFOEd3FFvuATsPPJZXa6RnkU3WrK0LVjri7Xe/Rza6INUme3ce4WaXnJ1N6aRrrDgn0D5RmrLV9Hm+mCG33sBdnBOWMVlk4Vcr8oFBt+ciG01M/wKmAWEUsWKM7bcwFznJ5edEy2tuZIXONE6jialtLusUVWsSLegVFZO5S0suSBILpRa4E5dkfwMvXJK4NndM+LD2KeMJl/GETqdNFqcYSEINulgRFWuGXZ/VTFFlC0o3pJdKipWHEYSQdfBMjShyglijzYgqL6lx6HWGrK0pQVNQETPsN1k+a+Pf7VFcLgikjZqWpMLF0hWkNkJUlGaFUxrkMkFJA10qjBTWZokpZ6hCYuaSXGuEC5KYtVHTLQ1yNSXPfJpWyqLVIFuvMQINN//fgsD/APwzXjsV/DPgv+N1CIn4Ne/+Wgz61dwBIZX2sxxDmuSyQpsJaVmhhE9dpFgaakwS06K3SlgaArdhU85T1mQo6dBzbP7xf/T3ORkvcNI1Ao98mnHdFGyUBk6saR5UhIS0GnvsqJqJr7GbDR7sDpheXVLdFOgc6nzNPGgwzSNuqTXdtsK0JS+++Dml3Ie04t6jI9ZpRr2seX5zyvnxS4Jej+994x9iOSkvLs5fy4rDCdNjl8JICW9WtG9iVJlT+BZxVXEw2OGot83FYsYbw0Ps4W1eff4cW845HBwxu1nzofqU8KZiM28TJUv23zjg3/zrP+XDxYhR1ObufY/bd94hynPG18fs776JTmP+8Ef/O8OHd1FxjRmtkXlBWVzzV1cZrpOyuelT1xXjJzW9vkdh2dTWAL/Z5GZyxYRzhrc2uDyZ8m/e/3O++d53+Na377C+llyPzth/8F0eGAYffvoTkuIaR5Z4ZoXYuUszgOefvc+to28zH/0ZzcBj+91DqmnOJBlTTWZIW3Hx7DPaGKSZYjZdsvk4wE00bhfiVYqlLIauoG5q0olETy9YY/Lq6prLk+e8HL3i9x68TagSunbOYrrGsCSdYMj9zS2WpUPHT1huZKQjh4lT0Z3bpI0J1kWb4u59TPdzwkCz+GpE698fkMiMeHXFcDjAjXfJGgvWmcEkekqQdgkCQZEZbL/dwKpcloXHzAiJVz5NmVPt21wvMoZ2AHXKRkdSrn1EP6W3c5vv/E6TxLX5X//kjyjjEMvM8YqSvIDETrGUTZobKKEw64xMeGTVglzZaG2gRYE2YJqWCBNEbmM2JV5ZsMg1yqhoBynLVQOrqDAdyH6ziPDvBgJa69Ff3wsh/ifgD385PAf2fuXVXeDyb1tPaChsm5IKTY3OBI4hqERF7SikKXFiA8yMuKhoCoMoLIg8H7tOsEWG09pkOcvZ3XQZTU0qIyGQPpPLHH0oSP0atXaxCoPYnXI5z8hEzSJpUychXtNDu11u6mvc1GJvbvLg7j30xSXrOsPbMmiIbcLpiLpoosoW0lWsLz9ndp3R7Fj0BiZurZitEnqmydXpGaWs6G40mIVzGq0OR90BrjQZr2Jyu00dXZNvdrl68ZIrYfLAfxvV9nl5mbFZLInjmEfBLRp7Dp/9/CmpC1GxZiMYIPwh7uLnTBePePpyxt6ezZ27X8dxm9Sda9brEudihuEELKOUXqNJr38L4+JnRNc5D7/5Dsv2Nro4Y8/ZZFHGOHbBhy8+w/ebbPg9lDS5Lj8jcBS9/Q02+neR1pzL81O+Wpzw+OiAYOM2dpjT3OhyGed4zTXrOKfZu4vXb/H13/8WG0mXvj8gLtasyhT1YJ8DobDXK0ILQtbsDQ0SDJrOEWUsqZSm8hLSTLIc3yCERZ5mIEJurs8IxYxNpaksgRf6zNAcXz2H0GOvl1Eqi8GmSVIUdLoDwnTGPGrhtEKiWRO/60I+wlAuDg0QFk0/4ORyjtxNEJamUBHD1iZGZKB3YqygQbk2meefcP2swUHzEXbPRHkQTwviLYWvm3TaPmI5wRBNlm6PfmOCMbeZpBNubTicziTX4wSd14QYOIBn2pRZRGUAbguzmJKZNplI6ZoOqzLFzV2y3EH4NQ2twIKskAyygtUqQ9vgmDArHXw7QTcUVqbQqiL+DRqiv/V04Nc2rRBbvzL8T4G/Pjn4A+CfCCFsIcQhr3MH3v/b1tNCUJQCwyjJ6xKlBAUmuVaU2kDkmhoDGRc4nsGi9JGVhqTGrGqcfsDWTpe4lbG8SWnKnPFFRL7IuAgvuX4FPpBqgduMydOUGzdE1x5lWhJWU67mIbE4Q2RLBHNGccz5sx9zM1uwpkRHPoVn0HYcEn/M6c1XhMdfcXkxowwygp1D7u49JE+fkcVrytmS3MsI+i5JqQm827y1f+s1cERjVsmItDphcn7Oj3/2M7YODxBVztPzTxg+uMU7776No0oKkfHZL37Ms/c/Y3w+wW3AMptjHuxgeyuqbou2LjHcG2ahwUIGnKYjttySvbduUwmb8fIa21jTH3hMypBUGjh7m3x+kyCOQ+Koz818xdbhXXb3d9FK0Oo7FKbAa5ZEizZZXjA5XeKYgjcsn7pT47Zb6Lxkr5fRaB9RmBU//eh9Js8WNOwORwdDnKymXJh02jZlvuByssJ0NJ2OZPdwSLczpN0/JLuKiEWT8iYml2PibIbQMeW8TYmDJQVJsiYPLfaCbdanp4ilomn79D0L81BR54Keu0N/JyBvNwnLgryQRLbBRrtH1z1g25OkRY7pGfSqAOnWNLo2RZ4QtiI6tUcrEMQLk3UYMvEqilYTrBTP6TJZRVTmDVvBEa7VZGkuWGqwZzarKKGWmqqtaHYd5o4g1zEtx8WoDSo/wZQWtVIYsiRd1NiGiWUahEGJncdI2ybVGWY9I3FcGkis6v9m7k16bUmyK73PzPvu+Glv/+7r40VGkxGRbJJiscsSNJMmmgj6BRKg31FDDTWU5ipAqKEEQaJYFCFSTGYXfbz+3f7e0x/v3c3NNAgC4oBZLBAcxB6am/tsLWwzX2uviqZuCFqbVikcq6ctazpa7Dpn0LTslMZLNE6Q0ucOkdvStwKzbKkDhRG/Her/3NyBPxNCfMr3rf5b4L8BMMZ8JYT4t8DXfB9P9t/9k38GAIRBBj1KaWRnYZyOAEPT9siBoWvBS1rENkb3Gb4pqT0PKWt0J3i0f8i4dbndLNivh/iHEVZV8G7t83Qw5eevfsVQwMG9MfvPPibfNSyuK46eKoL0gKL1sbzvOD09hTpjuQlYWzs8u8Y+HrNvNOtyTujXnHz2MUeFz8sv/gZVl9x/9iF7047EHJPOWv76y4LaveXH+x9zuXxFHD1hpQuMcMl/vsE/UTR2QHoasyo6Cp0wqDo22xXWVDAaTsjfXbOVEZ1oWTU5x3HCeH/Grt/x7//y1zzbT/j45F8x2O+4mW+5u17w9tUl0fA5N7st//onj3lrTdHnGaW9wu46Hh38GM/ueXt2yc8+/hT/xwdsvr4lfVJzun/En/+v59R8jdW7HFozZOkTjRSr9ZKCBuMf8fB3T+jLjpe7BfWmRuoz+vCQ4MEJP+GIN999zU/fD5G55s033/LzpmDahASBg/JniPckk9riYLrHwD5ADGxOjmKaasvk/s8I25yii1Fdjd1XLBYCabX0mwY1aDkIp7hxgwk12q8Rjs+Dw5DBKMX2A6ZPNXmxQJoMq9thHTxk07icTgOKtx2PHx+yCTzW8yVO0rJFkJ913J8O8Q1YBax6g1UZuuH3nYgrWixinDhluXjOzbs36NE+T3/8O+ibF2zmEXl8icoEQz3EqgXyagmxz7Bw+MYoHsuK3aZjazXUouLYPcE7PuQP/vj3+V/+3b/F7zSegiL0qFRN0oe0TQd9jzIGR4Y4sibvLbxhQLdr6QNweotKgC81dqcpQ9BFSzjVhKVL7wkyYai3igku23+ugcgY81//I8v/439g/78B/s0/Cfx/UEIbRFfTtA6+ZSOlpuo9pGVjTIlREloX4WZkrcPQFWxUhTv0QHW0C4t1XBNUmrfNlvE7wXsfPIH8ijIMmPY5Zp1QrHd4pmDgOrhTQSUclquvseQBvR6Sbc8oREXdVvjrDXeWzR+e+pQPj1i/+yXi2qMovmVUOnRtT3Jq0c5v2cwtbkct+gtD1Lf8aPYhvYxIN0O29Q1/dO9PqC3BG29DIwcEnsZaKTzL4f5+yuzJE2y1Zn2bYC1CqllCmK/paXHWHd+YC6rbjjDv+fjJB1SWAtmyMiE7eYtWCaJ2Sb0IO7Z4cVbguz0Xy+dIW3Ly/j2Gap9Ve8Xe4yPMxEe1Hn4Qo4VPdxNxtbxD1AsG3imr8oZP7p8yHR7x1ZWmDr7DNhumYUjVQ7a8IYl6drst107Ee/FDlLXibPuWw/0fMe9eky22aGkIjw+4f2/Au2pBcntAr+Y0Tco8esHBLqVs9xBuR4JFE4REnUeWaaRVMh3aLMwVvp0gK4OweyrhMDA2oh2iZ5rGnbApW7zAQ8kMz2m4UyF7liTRErfvKTqXMm0oo5rQUmy9lnzT0UUdSXKFasaEwxHjwGPYWrxZdETHivZdiR2kZPEcqSCdjKnmOeSwXp5Rb3wiJ+fusmQ0TnHUitC6h+3fo+QV9VAyXJasS0FsKQaJRSkG1PUKy3JJxykRPaqPiLsa7TYIJeh6h77X2G5P7dv4RUumDNprIRMMpU3uGezMx5YVlh1RezlJ77MyJXQBbWMonA7ZagLfo+h+ezT5P+s48C9dQhhs4WNJm8YC3doI26YLWlTp4GmXxoVWO0QIFthEvs2kjXi8/wFNfsNVv2GhLDwj2ZZ3BF6LEAG7ckgXHn5/sdT4LC4UfQ9RNES0hnLrk82/Y5oMGPsHxNKiqGo2js1wPOY27dm+fI1Zhfj7M3QHC31D6leoK4tmEjN6fMJ78QOE63Fop6jEZeHD1jWsspxF3PHl5op+vmB+e8XyZouFS+J5JMMEtSn4+t0lL9dXZDJHPr+gURcU6zHJccSDFl5/9Rb3wOG9A5exgNmHIVrtePvtLUeHDg8+e59lFrLPlNyvmM+XdJ1it1TMO4s7vWZ5tub08CmTdACNoRunbLOKTXHOn370I7LasO3P+PbujJU1IxgNUMsFR+0xT06f8e3bOzaX3yCsGM/bI/RsbvsadwLPL1+xqbYslmeMpMWzR+/x+GBGYlXMVytWr79ke/4d+/FT5ruC+S9XNEVPFxuy3qPaGOJ1z3AkkLahHip2rJl1h3hxiqsVtBVR6pLrAKvf8qgTJEGMiffpV0uaMmQnDe6uwZQ5tmVTDgWRsQndlFgNKW8qrM7gVFvuFne4g/v0dcUkFjRmgBjn6ElDmW3J7I4m7rBNTKk6jHb5ceJxtZ7j+h6OKylUx/54xLISFOaAXDqs/WtGzkNmckwUT9mWEqUyGgT3Vh1eMkVLKK8UmZI445pWCkojUVog0wozsGi1wbQ2XpjiJOArAZaL7AWyt8mSHIyFFAURLbXdMUpn1IXAOIp4qGlGmtAWFL9tyig/ENmwRnyfpCs8jNdibEMnvg9VcCyJEQ7DzFD1mmriINcNtpUwSAWhmFOfHiJXKxZlwewwQexcPv/5O94/HuHbFV98c04faQ5kw9yZUtydM3hwwoPZlCC9oyhT1OYdq2JJk2send7H9WM29YJtuSAOE/b3ZzT1Fbt1R9P3fPbHn3FXbzl/u2Kzbrma30EI5uEjys2WOFuSfPYYyxFUS8N922aZHrM8/zkTL2WZulD7WE0BhwGukhzuDTEefHtzTrjzODod4V0fELx3gsjn/Oov/g57+oCWaz7/H35OPR5wfzTkdz99xloMefDeW7Z/+4IkPmH6k2Oy5TXXqwp9c8G6s7FiB0s25FrgIsFdEcoBb69eIbXPz/7sY/72y88J/SH1q2+56u5zW6yoIpcP7z9ldnjK6TihjGLkr/+CRTVmKAKun19wOEmZDH5Gu7ihi+HJyY+QUcnZV2vWZocKE0zhM5/PGewluMcuX8zX/OnJAVZQU11ntOMZzm3BdBzQuTMsR5EvX6A2Q5SVMpgIEk/iqApn0EMwwvM03fINXriPxxyWBe7ggMH7KYe25mUecJlf4x9YmOAe6+db7s9sbkZDjrOWm5tzAv+QycEpbWxYbHJMVWJmIW5W8W7R8+TJPShc8nbHIDpBhjtms0M6vcO5qqh8C3+cMh66BBpUGWGSEpTi/tCm2ZuAtqBoKVLNNDOIMuft5jtCz6NbtyjpIG2NCDRdIem9DmlrEirqqoSxwFI9tdoR+RrVBMS6pVUuHi1GS7p2hJ2UGC8A1VJf2kwdRRl6JCOb3XX+j+LvB0ECAhBGIh2XtO1YtxpPOFiiQweaoOsohKJPA5yNQtgO/tAj2DRYsxRvkTNyhtyuzmlswcgfobyKl1XFgAM+fO8Bd+slLSCObercMFWGMqtodhLp9+QrxarsOBxFhF2MiUse7d8n1zXJpibTPRgPo0sGRw+5eL3GDiRu4LHaWQRWw9HkgLurBVW+In50n3HqMlyF/B83f82ePyUZhNybvk+elDyKU2wtMXs+TVNQbJes7jr0MCXIc/xUUlsdu/6WyJ3gdB1VB5OigsBn/N4E0Tj48RFnWYHZ3PLy+Vu2+S2t25CvAqpeYseGyeSA+XlBU+fkg4jx1gLZ8uu//H9wwhRneMi2uySIQ368/5SbL/9PjHtMLWoe7B+wszuUbHnx/GteSM14dIIVx/RFS6EU6bJnxZbTBwd8mwksseG23HLqyYP4pQAAIABJREFUHFEPcsalhfV4yuFkDE5MuwE9CXnaZexylwHy+xHxXYHqGu7qjmnn0aqAppzhJTmmMvh9gh8HlPWKYHBCemCxFwp0fIR0SxaLQ1Ryg10r/MZl48b4ewXujYO9Dmh8STiKuU4S0mpDfZpy2A5JpOCGGLcoMNYRibxlqDTOvTHD2zuulze4bkWzqumTlP3DAWfzjLHUtOMZQm2wNFh1iUhrRsOEalMjIpesaSlcC79fMBnO0HZAMgl4fb1DlgpdtTSuj28MshA0lsARClH6mNgmy1uiscBtAoxqcS1DZWz2A8FtZ6HpUdpGo3G8ll3ZkPQdmSeRjs22N3h9her/hVOJ/6XLGAtbSOgaamkhhUS7NcK4mFpCr3AdTW4abC3pjEvouTRJQNAHqFijjIXnhLT0bKqOcLsmGo5R8zPKex/gFzPcmWHV98RiwtWyoBM7tI6ouh1hKdg/nMJ8x+1sw5gp3W5D7DucCwsrd1h1NlVX8TSFq11J9w7m3Rt+79HHFCZgkb3DkQHW3phUuqgLze7IZbQ6QLkusTDsUMhuRt50NDrCXLzDmozJAnBVRHN3zqus5rPNmEqX7L/3I6xSsy8cksNjZntTMq0Z0FLOHPwyY/32lqZrKNScgfZIh0Ne3+2oiwUiPWAWj8nGDbdfbRnqhtfLS67O3/DmJke57yh+8SX/6e89w68KLvqW4cP7RH6CI/b51cUXHN8fMrNnXNZfYEcujl5i7UbMBi6NbfjN3/0KfzejGUQczA4J3EO2A4fQ2/LpswTLuc/dPMf1LYbTAA5T/L7F2z+iDW9Y7wo8BmjLxilqWtPTJ0OsrqWRlzilT1c7rBNNdteyWt6RBha2FTFvAg4HOW0VUVc1VDtGqU9Vt1RxQbG1EdrF6nsGAmw5xF02FKMx+7ZD3m7Qe2Oa6O/YupJH3ZbcHbExPVazZiwD5tmSrPPJ1yC7NYPZ4PtsjKClDQxxEROOXRzhEEd7iLJhJCIK5w6IuRcJim1IHTj4a483bk0kbAQCGwfP71BFS+s6qE4SDBTUmrBVbIaSfp1QRDl62GKtHSJLMc8dEuEhvIZdKwkCiR11+PMUGe1wqhDfd3DljkxKxEby2wJJfxAkYAuFth2UbyFyhXY83Laglz0WIU4EZWNjlRaNrAmMhdOUBOEYl5rUdblWBSrwCF2LqGsQjs3i9o5YpYxWN9zctuj1CHEPJpNDDiPDqlgyCXx0F5OJa6rlLZaMUdca51iz1ZKL+YpgVWEPJMPahYNHhE2L8VOKkxZ7d8B6W5At1wSJz/iTAeYSLMvi+c032JuI9z9+wqKVBJlLp94x1D2bG0U6TShTyfaXr3H2Nd5gxu9+8If86t1LbNmh9YBNlyFWN6xyxcBOGIYRp47HItuw3e641Yr5vMTKlsj9J3z4R6f85q+e4wU+g3tPoYXvvnrFLEop7gd8/Vefsytr8kXB42cn/MnJf8Hok/v833/+l3j7e3w6OuX2/N9xffWGRtscjk54EA4ZHgwZH/8OZ7kgqWHsTdBhy+ruax4/ekbndohmR5o+QkoN1Q366DH70QRX7nM9/ysaP8WvDFUD9smQ2A3x4oDrTlAX39Jcarbhhio7ZlEu6HeGvWGI57bYQpBEBl05/OL1GY5lkTg+kRxQOgeodk0mrsg7zduyYETPaJPjmpAktThXPae7nuK0JVp5pE6D7iSu07O5WrBdGe7tpXT9ij3bos/WZPKYLvG5N7zHrdpQXp8xCkrqdcTJoc256TiuI1auYLOtOLI8xKihcUMcNya/rekHsK5qRji4pU3rurh6x7tWkpsVJYpxJunjIabaYreSrPQYCEWjwSltemtLIiyKlYU7cNjhITcWXlzRaXCCGlkLGlxat6BTklgIBBlFY1NZPv0oh9t/HH8/iItBZSSYEHY90gNbKTrfoKRBCouycpHSYAca23EoTE8f+BTM2ZUtK7dHFQ1OOadG4u65ON4eQqdc3865ui0Qh2OePgowi4qzt79gZrmoTYjqNb4vqBObuk0xoUBFPa+fr1kaONwOGE1tYmvGJtJU8x1Xqw1t3pNMJxw696hZo2rFfLfj5pdzVNcjfEXZtezKkraOcSOoypKnk0fYSpE3hrI0jNop+58c0i0aymXG64sz9p4e0tUBt9tz+nZDV7s8OB2zN4pB+ry7XPHt9Q3Pv3xDudnheJLJ+0/BFOxuOsrymmr+iiA5wpsdEU98RvePOHnwIcYTqHLDcjcnXq14Lm958/prtquSLz//grfzc1QvKR0Lz+tQ7FiZBcvFEtcNYZnz3edfwczh4f0HfPL+T/nsw8ccjg84Ht9nOBPYgebgvU+IooROx2R9wf7skMT3kNaAhzOHqPMJdYaHw/7QRuQJYuThN0NwlnjU7M3WuFJR+5q+tdlVLUJuv58HiU0ooe9bLKdg0LdEJuLh4R6pEzBqO4zjYgUxbjwkqde82Z0z2E7QUlML63shWmOzXpyxaXo8PWDztmJnt6TDmKGb4OmKXuxIkxH9ZE2b7BF5Fk0gSBrI6oaBP8FpPTy7Ye0LdLUmDzPqQYsoS7ZIhGshpIX0ajp7Quo3bLYGb9Czslz0NkdgGAkHr+uwOxfld9QdNI5LV7QkrkF2imGhiKOGro7xe0GbWShGtFZDLzoCWaOjAhWNaXxD6OwI9T8m5v2+fhAkINCESmNChSkEka5xGh+rE0i7QlglUkFNT6ktCJrvhSOrhLpTiCyjDVrs5D79eoXT9iT1htaucWcAFklRkd+0rIMMp4bXbY090WA8mpWN3Gb4nodlHbPnBeSioL26Qu9b9PGU0dRBrXvstub8zQLnns80HjF9MGF3s2NzGDDyJ0we3cM5SlhlG5rWgl7w7vJr7l6vuOszZvdPGTw+5fGTPZp2i3ZzLKZcnL9iubviu6/+hpu/fkldZ+wnAcszOO/m/OpXC8qTAOnbPPjZ+/z+R7/HTz/+jMh2OQ33mPkDgmTA9e4dd9sVu3lDICue7rnce/AY27LxvBHD+BFNOOTZ8fvY00P8jcLNeobDkIOHD+jnLQcHIUNryLpykNua8qu3DCcp55sVyYnkgz/9CUfJY+qsovaG3K0LIktgKZuudrBnEfthTF41VO2SjC2zaUAQxHhDwZuhQ+m3KOmyakOM4xEFPpKeOugZNy5GONh9SoeD7gLsQBMLCRb0QUczmLBsJFbikm1LSrcl6QWj6SnTdIqybLxthaxWKFsSWym7bsi5Mvhli9dY9EJipROUd4TZrPCGgpXaMrY0neuyVhl3ly3bhYMsM8L+CNtRZHVJ29YYbK69NYv2EmdbkOcw7WvytmczLwkEREnEpNZUfct4UHKQTnGtlksFbucR5Qbd5TS2oDMD2qAh8jSl1+OWMROvJ24r7ESAdLA6qPoGhIs0NaXQDF1BrDc40sM3kk4NqTuHfltieYpeWLRl+1vx94M4DgBUUY9VCECicGg8C68WOLWhnUjUFmRhkUqJcR3KpiTUPcbzyBYSGafMxhHf7mxef3tLeM/BCQVT6wATSarewlYdoWwI0/sUNznCajgPBFPpIaSh0CV21/Lkw8dEJ4YqW1GuoK53nG1vcT0bU3sMDk6onmt+vfklyCWbOubxYMDJ/T0GqQQ75mDvCFu77PsRbxrN48EUPJvV6po0HpK7Cff2Ha5urunKgnuze8jTPfRmyNd/+w2rYMd7oykffvoJJx/8jLre8d0X5wyPIoplhkkk0fiAg9ilvFizF91j3+qY37Y8/P3HfBS9z3VR8PL1S2gdjk72CLxjXpz/X5wEx7zdfcMfpT8hjzVzVXC7uuVucctgFnI0ecKLs29YnH9D6ExoesPBuwvuHz9lsakYPpqR7b5FJgNUfsvwZIB1ZlHZJcdPZoy9KY2VITsXJSVJpLFFwCN/gA41o66g9GturzKs9pydGKCDHi/w8UqLXinssYaNQ24rxsIhnwZ0xRpXGuqrLY+sMU7kU6db+kXPl9+84O3ZDQ/f+wntzmHTXeE/eEpkRbjFkAtzxSQwOOU5m6HDkR1QSZtWdMxfv2NRGfxBz4ePH3O5uWXY+SROg0kNMgLLM4zvjxFl971xrSnw0iNmWUXXOdSORpU7ZO3insxItw21UKx1xu31O5LggHb2HmHZQ1xz/eqKs807bB0T0xN1NWVUoAob41k4lcBJIKsNxooYmpambzC+IN4IirrAsRP6UrMLDEZOEX2J1XdEqqMJNEY72JlF7gSI6rePHP9BdAISQZcrAtdGWT293WOLGs9IatvHUZLABAhHovoOt1II4VESsi5c7lRFrWr6TcnTo/sUgceNaqg3Pdd9h75bUOU7ZJ8j9EPSYEtBgcm2LPJLzro1lU4YD6bMJgcYI9gbg6dt4s/2ePbxB/zJZ5+RuHtsZEW3ndPqc1SxQ5kBo8c2pqy42HT0zpC7Vc4vbjt8FfFW5bCGYLpHPE5ZtBXPzz9nsz7n3tEe7myKGxniZI+o3CPQHfs/3ufh8SO68Qxtp+y2FZd/dwcxzM/mFOc7YqdmfGKzfzqjGrcYv2Zip2zVlnubIZNhTBiVZKVkubkEnZB4W9I0wRsoasdwXq5Yr3fUxmaxuiQv1wjlEwqfJB6ROkOO7u/x6e89wgqnfL58y7vVKz7/2/+NdrPlZnnN3fktnnVE/GgfNw9Zyo6iu8LqJb7WhJ5P0A2wMyidEs9MuFwa2m2EsUF3PW28YL2oGexg7Pl4rsehPsCdOaSmZrPL6C5zYtWzrXrmS0Xr7ZBGsLpISPsJl7c1L86vGMcx4wcu97wR7BTOqCX2L5l0Ase+w3MFonR5u7ujTyRZtuM62iL9OY3lsKoEszpk6g5JCo8+8TAsEbZPvD9gPPneer7DQza3eFFKPAyZHIek6Yy6r5iVOXgFsljDukBozfXyG9y2oB1J1v0BgVD4O4vGLmgMdCImbzXGNdiiQ0UWbduS2IpANchCY6sBqkwpbJCWi1YN0qnBhMhkRVdV9PTkvqbXUDcuSivoGyxj/Qfw9wOoHoHxFG0vmWBRmQCvEjQCQg19p9gqGDgaQptdC42lqcIC07gY7SNMSdG2rETJg2djpllCXUvq5QqZOnR6A6FNzwXFFm6Wb1l6Na5uEEZgewFrk8Oy4utfnvPt5wu81OZJ/IBYDVhcS1RoGA499MBBS5+Tp0Pevz8k3YGxhkR+y25XcrDnMhNbdtst67MNw0NYr85Y3a3YLXLUrkE6IS8u7yCT+F3I+fN3mOqSyolJU59jN2GURcyvvmH16gw9yHDtESZbI/01r/7mN/zqL/+C25trTq2Au8albhp8XC5vN5ztLhnvHfHTH/+Uo+QDOp3jOD7OaEYU+jydPMC1PEzb0ax32C7UwmG3W3Cnz5gMPQ73xzy+f8qnTz8mGIUM85a9dEJshrztSgLLY+/oR0wnI5SpKSc24rJFy5iNXSN9C8eu2YgVYmjoyoJtfcfBNGEoBI5nWOqcHo2uey6cirL2QOwjadm2PhsEIwkD15ArhdV6uO6CxqnYBneEQUcfZUSxYTyVtHKAY1LcTw5pXEWzVdAn34uN8oh8HVMpn9jaw2zmVOdLwnObem7Ity2DckMhbNZ9wW7isny3pmtj7DKkzVvicMrsZIpfdBSNi9X1tHVGY2mkEyL8ACeBi3UHfkQa+PgTn1hNyESJvdUcRAWNceiiCEc7aKui8zsCK6HzFNpA20hc2yVoJLVRNImgEjlVu6HzoegqCCq04xDIHFlaRK6PJzzoDWXl01HR+RbaWIhx8lvx94M4Dlho+ga6sCVzeuwNEMZUZU6SVgSdQfsu86rHDWtcaVFuK5QDtbPEbTTSj+jdLSyHBNaY+NRj5ivU3LC7WNLsB5h354zfe8RVfsvR7FMidlgDh9evl6y9nJPDQ+JkSJpH1CanzgsusmuijUVnbdCdwz4B49mPeV19hywturqijY7p2zle8BTfjuiUIIkdluaCvaNDLi43xN6Su1IyGnQ4QYLXuOSh4frlS7blFR/93h9QqWuC3lCIkCfjET/vXjEtQ16+/op5vsIdeeSZx+hFzZMHx7iOR5pXvBCasNwSDkZ89uxjupniy3//LQeTFVdrSRgJ7p08QS4y6vkVow+fMCxbVssd0aMRu4sNn5w84+piQyxPedo/ZjG9YvkbTXa94n//6gXvffCEh8dTln3Gutzwyb3fp3FK2vUVZVZzenjMSRry/Bd/h7wO8ZMMt4+xpWGYeKj+jj7cQ5eGxslo+wJThKQ6Q+Ub4klIs7K4DZbsWzvebFzuD0uWnc9O1LSmRqqW+bZD2h6pN6awDX1Xc1ZqPjj9mOB0gt1tudM9w1WGZ2I6bZgvNlTGMJYD3D2DuCkRox2qSQgO79P98pcMxjGeXVE4KfHAp7wpUKnNZKJYZzdou8YrI37ZvsQZJ6jbjMNPItarCCuMiWrI6gVje0B+p9hzLRbLjL1xgLqzSfZifBUymsz4pvyKQq9RRY0IAkSuMJZFravvtR+uRMqO1tMIaSNzaHMI2wF9WBJ0LipWyApEKGjr7013uqlwjEVlBLa7xhMRuvXpdA1t8Fvx94MggV5KQCAqgacEcuRSlgZLROyaFlfYCHtFIEaEmWJlgystqtLg2A1CQtuCEUNiu0PtbqlUD3KMUBXDsUCECQ8Ge7SOw71kzLo/o7MEt9c+2os5HYBpArbzK/z9faqsxGoN3gKWcokaDHh4HFJvdvT5itTxSd2ErbnhUBoW4xHeNmOXFdy0W2bjE9774B5d63N9uSaJI05Sj/RwgFGCxaomahXvf/QeSrzHN7/5goezIVXW4o573oQFTx5+yKefPuPHt1dcrzJ++c3fIMWczprhH+4RtA63dcsnHz3g/Q+fITvJb959w9l3X3OdrRgcJ/zZHz3kbH7D/MtfUpuAySjk8sWCcv6C9ZXm9t2vGNlThk+e8PDJ+zw+HnCZrbj6dsV3Z9/R9j03+ob+zy9x/rP/kvlFzutX7/jgj68xzYSDgxkVJdvba5rtiOQwJiVnZXrqXYlxJ8hMEhExQCGURm0rqm2NbpdkXodfTOhViTRb4rplF/sEK5+V2dFqgbI7POngWYJ5fYdbdWRJjHNT48c2jt2ztuYkOqEs56TjCbPoASJsyZUi8RWB2cOZeEh6bsYbnDbC6hQnYUg36Eh3DpI9Rk7AF78+J44tpsMJljPmwXTApdEIXOIwYZiFvHYvefvNiOOjmvVqgXEHjKMBLoKWgnYrGB6mrOqM8ZFk/SZj9aMtySRn7+0B1c6lFjusVmOh6ZQmRpKpgEJXhE7JrrQIZETsaZaBhfELrA66oMBuHZxW07o9XTWgTAy2cLB1x9Bx2DohJRUIg08Dyvut+PtBkABGYyNwMbS2RVP0uAJCt6HvI9quInIEfVNQ+B22cOh7g4XE7S163VMWHp5Tkg4kVhPgOQU7N/v+PZWSXzS81QuSJ4IzNSY+TMgqRW82xN4JVT5CcweThH5xieUnOIHNpnhJu+joshXeew+p5h1GjFipr6jXE7S7pPEtzI3g4tDw4fgZPx4/IB9ViDIiaDUjtwTHoTMHBNpwO9+RzGIia4jvtMhcsH8/4Xz5gnlmMFdzhJpiux7fvPp/qa87xnuH3F6d0aYBk6rC8xKeN1/iWTGXbwu4uyGcHnLzzVe8O3/LnjUhuG35jm/ZHz4m+OkD+nclv3jxt+SrHjsMme0V+OIepsj5+vU7XHnGeRax30nOrjaEJ0N06PLh5H2effIRfhyQrQz/6md/hJv4nP38DVa2RDQeeuZTb+/4T/71J2zvJCcDw7vdDZFdIJRGpjHrqsKlxEodZnbKpp4y/+5LmuA1dpXSypLOkvTzEmN1BO9K7KmDqXu2sUXZ+uSdT59oMBIz6FncrZns3Wd8GtI+P2Nr25w0A/QYtrnEUpA7AmNtaFYVdTLF9R3Wec5s6fMunKNvBPGzfZblFQPvPe493qPdtGx1RrLccSsCHvkTOFBcXSk28oqD+BQ1sFgZiTdMCTCEoc0Xv/6ax8d79MOEQeKTVnCrQ5wHY6STcHs2566QLLdXWLIhkDGOgkzV2I753hauXG51wpSKxuwQkctQNOxKG9uO8HcNlrCRfo4pQ9wwp8XFdwRV5aCiFruOcExNY2za0MbV2W+F3w/iTgAjkI5FGwq6ssdVHkHb0wgHWk0YWWxw6LyAXklaNL0yKKtHSkXjSTq/5v7UQXSGFkXrHLB/kBBZKRsfggG0zpJQJLS7AtU55FqSa4ei2uFYkkx05HdrcmUjew8hJXE0pPJslvs+TW3jT3pk0DIZnNL1HcZPmJ2GxA9cTkxMOBSsmit2VzXjxGbtz7FGU6pdTluc8+rVd7xa3iBViTUZcpicUPmScbzPyD8gmUywwh9xdOiRDHvyqzWFhMK9pfZTxLriwBmwWr0jv8t4+dU5WsM3r+Z8dfYFVd6xzVdsnZbDTx5S72Z8sfqS7t2C61xwu2jRlkSpHvwDZrHHZDKm1At83TF98oCnv/NnPH1/zL1nhzz+4JRnn/0BKSkP4hFP3n8fx/fprjqiNKIseuLQJfIDxv6A9bJg2tg4lcus12x7Qee3dHmBynIWiyXlqmXVnqGqz1Eyoy1h067I+4qVvaGLWjAdtz6UrSLTLVbfQH2Lc7Ulzl1Es6ArErrkiJVRuCuQwZABC/rRjqZtSJwebBdd21iZQycifM/FXRR4tsSeWTR2h136xItDhE5JqUidIW1c05U1yysPP4qphPleeegGRPsH2HaJ7gWutqFrCYIpOx0xOR0zzzuSZc+wD2E4wAsmaA1HnsbpNJ3dMLJjfDFG15pq5GB5Aif2aS2Xta3xpGbtWzSOTV0bitrGdg1u3SGlQCSardIIW6PkgMjuaHMXE/o4a0Ejajo5RugKlKC1f+CdgJAGQY/VxPQ6Q/klTeegtEYOW+zcQgdQVh04IH0X7dWIjaSIDIOgxdWK1aKkiwRTYibTmHbRIt2WEzugbNZsA828zxgENqvNO5wuxC4ETaxo2x0T/4juSGPlJdgZ9eQp97qK4eiQgnfcbtdEw1MmXc65d0L61Oby5TXS7wndkPTRAVmvuF5IYvGO0Y9GfH0VIK2OtAlppg5lbti3wJQW2xcXPB/6NNuX1GrEnB6/jNkL5txiMdg6GM/m4cmI8XjM5u4lwQz8w4QXLy4xsUaGDS09ZWoYWSmO33Ea3efkyX1s7TP7xJC8/Zivz99wna3xyTkZTfD3Zmh9gN3dUNWSg64lTO9xunXoZj2n9z4kOrFQ5X1+f3rE2+oCNx0zloqyd1CVIG0T3FHHpi2YuAGBZ9FvG9qjhm7bI/aPiXcXrHMPIW6JU/AZsdu8o20NnR3iWksq4dE1Na7S6FWMYElTprTdjkXo4ngWZmkTBAN2+2s2N3DSKJRX4/aSoHLZ2XOE8Bk4UyhcLM8gIo0WJZ3t4tsSx+6RjU3vhER1hqkNadOyGeZ4s0uOxYcsSomRHfXOReuefF9jSkgOUkyviP2E5WZH2zus71bE94dEUtEULfG+ixuM6bsVt43GKta0niIe2JSNw7KSRAOXQy1QfUFftxjHps8KrAhuC42HhUuMZ1V0RUvuuNhtTyA0vgmpvYasg1HtoGyfWFhsTU3cejROhRM56ChGlDWdarCcCEGLqFp+m5n4P2aoyP8E/OfAnTHmo79f+5+BZ3+/ZQhsjDGf/v1U4m+A7/7+2d8YY/7bf5IFjMBYLlplWHYCVo3qbey2ImxgM7IY1B26d3F6j3XtQigRUYVdW2xKlzDRqE7hZAW7MMQsLrAti84Z0Fs94SRGbTTNZUOb9rSWzaHdU09jpoOQRPRcypywj/jo6SfUKErbZTqbIS4158WOiRzjDxxqLyR7lZF4kmePThgfPyKpKl5vbxH1gMlDzajc4/PXazyz5eXzl5x+9IRkdMDJaobxbJTdsyw2VOsdmbFIhIV91aOGBS++m+OOYpY3L8mDgLfncPP2JaqzmM6m3FUZq+tbTg+GPH54j+XNFmqfMBnw8E/u8faVx00+R1spb17cororRC5oL3Z88PHv8OhHI4yRvLu8Y/X8hquu5tHJMc5xysuuZv9izr1hSiUdTo5m/Gr1gt08JwpHTMY2s/h3eff2ijTu6OJDVJNRVC2DwQRLWHS3OW5g4Q0NcphiXylujeH2omNrvcFbd8SZRXXUcvP2HAewM5tsFGGcNZeWj10tKVYb8CVesWUXj5l4Lu2iYDLyiAYHLMqcGIvOM6jmlFZ8iXRtGmuHrTRuOyEqLYyo8P0HNMElbm3T4mC7sFMV3cbCayS+kXy3+JaPrGO6JGbgHdHYS1RWclPO2SxrPpgOwHcxqwjhtownQ/qrjmroEtAjVEarU7xZRHmeUew8bD/kdtHg+oaN6aHsaIyL1w+x0pYw02ipKeoAX7T0Ts+o99ioEoVk2CkIbWgFpVfQdS6RFVJKsPuGGo2QDpKauDVkTQd1i+wNxq0xjYt0LHSQQLP+55EA/0jugDHmv/oHJPHfA9t/sP+VMebT/4jv/v8cYAy66bGlD6aAymBESCdj1tyR1hayNd//SlHgmR5dOtjCoU4UQW1jhCaMXerOIXAa1HyI/8AQCZuVrXByiOyU0G657CR7lU0zVfT+AbHsWNyt4Ejijx3KzYLN6w1dGuE0IWInOTq5x115webylroVHLo+jduzqkP2iwVb3VCuCuJJQLZ2cdYK8VFCc7Glalp05mKZHgX4aYRb7/j6fMX9B1OqpsNZr+jLWwb7x9yoir6SSMvGrhuu5yuO/SGPPnnA41FE07RkT3zsxnC3rTBtxiz9kL00JLu4QDcuubKp+549Z8C3dxe4akCaav7wT36HOgD5dsnL6oJ4esSDwOFeGHCxLkn9ls0djJ8e83RvhCprRG+RygBPFrjeDN3aPD3Yo/MlrQLV91jNmELXTHBxiemkRbDTbG0gNrQvMyLX4PgNWeDMBS3iAAAgAElEQVRzQ0NQZ3RF9r0l2JL0ukBUAamtyIoMXW2RI5d5HzPolphij35bIoKWZdcQZIY2KeH/Y+7Nen5ZsjOvX0w5539653dPZ5+xBle5bZfkBrotZDUfAC5Q911LCMQ113wQhMQtLSQQF3wBpBZIQDdWtd1ln6oz7HP23u/8n3LOjIwILraRDFThRhbSucuMzFhxtR7FiljP83SOhZmoo4xx31EdHBfrjL6yxKpnZyPcyY6oW/M03LFaLWh2V1xuBHfdbxBFABwn8pQmLHlRFtyEGp4GYjcxTTXP+4ywzFC7lihtceQEYTHnEhkJDtPAtPdsVII/CFAf2txXzhHyiqOLuUoH/F4jY8vFRymzlIxxjG1avLF47ciUohkHpI7JwsiULwnHCa08SnkaPxOHllnNzGPAS0DEdMqgYgd+QqLRkcZ7gUoKZjngut8OAP9GIPD/5jsghBDAfwj86f+XpP9/xFGCEk8dApkUdFYgjEXIETlHjOPAahEhOo9eeLbHGcLMEMMKxzSBUh5PIO89D6Hm/JmjnlPcLIiagtkHen2D02tSd2AoTlHzSCYsU5RxM94S7gXnx8DjyYRdBJqww313ZCxintVryHOyLqFNamz1yMOuJR3veXe+wswKudyg/MQykkyrBWvX0MyGf/izT1EKJtmSZBCcJ19ekss9/Tzy2ZjxS7Fj8dEL1hfXJIvfsOu3bIrP2Ld/wWcfnVCefc4f/tEL8mJDvb3hrBoJaYzsd8SqRJeOqqu4OWx5d3fDrA2nP/8FqxyOuwZZWuR+wkcRpat5zCL+6Cd/SlgO5BZubv418feOYRi4+Pw1VyojDIHtUJEmK9JLS184TobAvFlTrta0YWY1Dox9Qi2eUPlAVynyyyO7fYBoRZy1HG4aFsucSU3Ix45Du0UkI2/3jni1oB8dXnaoUUBWQx3zoDtCG8jkgjP5SJ1GCP9IyHPs1BNXB6Z8yTTFLBYOMysuogu+j29JfEOnFJOXNFFMkuXkg6eNKk6njMNjy7qYsC5jaU4RcmAaU06vLe644yEZGWrNRifMaaDfR+wSS16NRDGM+5G6nNkIDUkCvSfpDfm6ZF0UbMOA0efcbRWuMPRpzFALqlYSzZoojVFTzOro2YWAW0jSWhN7BVIwGIeVAh2WyGEgWkqaIUfaicIGhA/MXmNQaDXSz6AmR4gkOgh8ZBGzwMuYtZ15ki1puqCffrvk8N/1TOAfAvchhN/8jbHXQog/AyrgPw8h/PO/LUgIgjozmHimqQLoAmUHdO6RGMI48VhZktTAqEhNhnJ7jmOBVx5URCQ7qgZytyTYgXjrEHHEmGp6dyQzCapIGdoWqROOjweyy1PSpMeGkW675zxLuJEP/Oz5L+hlSbZ/y7s3d/gHj7bf8PMv/hBxccN6/xr1o+d8+qbjwbUk5xJ979kuZtJ4QxKNiCZm5QpeXaf8uv+Sx+8mIhz3g6ak4i6tSBcV/Zzxldf0b37FOr/gqX6HOf0Jn9KSDoaXr/4tPv7xTzHLlG/e/wXhz3d084EgPEaNrPU54nzm85dr5OpT/qf/9Z9jIs3FRx+zTyGTEZ99dkW+zjg7K/nNX/4FzqScFBOnJyVdr3j77i2pMaROcttYVomgqUfu9vd8/unfo0wmEDGpXmCnGhkLxqYmizU+syQKptNzoqoiymF3qCjHkso0mFtH4hRPwTB+d4teJfjdE+27ipBNzCxZR0ca78EE5GNPUxrUt452I0nvHznMkvwRuoWnlANn+pQWQdmDNBXv38ycX26I1xrZDiz0hj70LKQhEg4ZG7paIIjQyUxeLJCHLcIp8hNNXxluvvmGs9NTLq5e0UUVS+/ZS82ke4rCcOhn0naBuu/pfY7ZTRyyHjNBpiXapLTVEfc6wWCIpSVsYiY9s5QF87AlbU8pJXy7rLi533OQnrNI00yaQfdoLzmQUTqDdxXtBC83jsPBoYJnSlNCPNKNEz46wY811kfkwjItBd4ZSArkfmTKEmJmJjuRyxOE+d3cgb/r7cA/Af7Z33i/BV6GEP4A+M+A/1oIsfhtE4UQ/4kQ4l8IIf6FDJ6yUXgtWVhNPndYoTGDIoyGOU3JUkU6TXQSlDt8IIiYQNVZtLRMs8f3A102IlPBQUrqecQftvR1Qyw993czmcuRXUaUgkqXCJFzonMmV/JYGUK6RDyCf/oWrTVnp89YX0kyt0LEDi0/5fpakp+ecvrxKdHaoAaNPz3hVb4htTPhIWGYFW2qeJT3rPRzXn6U87bfY0MgbDTpjWaVPeOZNuhmzxvb891Tx9BJXrwIGLnDFj0qirjfPxLeV6SPK+rhO+J4zeefXfPsYoO6iLGdQoYFj/cPVDqwfZooVIT45mvEuGW5LBEmw+nAsrzmerHBnL7mm1+/5/377xmGD1LV6YsTXm2uOVERvao4Twr0KmaIMuqmZdhaRHaCa94z+BxbtQSlUJucRSKRLibgcG7BGHr6ydK7lrfVI7p54FDP7Pwb+rknLjxxmTGJlgermIeRSaccXUA+9GQbT2k81o+0o8WmErtrqYMmTiYWZk2VaLzNWJxI5sbhqppllEAych7lmAk0GXrcY5eghpHtLlBkCnE2MCvDfQX10ODTgjF4zl9mJFOE6ye6/h3lFDBTSYzjePdAf+JxaYVfFEyNR9aBw9bRRveIsMNWMcs0JV2ckF8tSCZJNaY4lfIgOw7a4b52DIcDUabZDQ1itigPR6NY9CNad8xzjEkt71xgrwRBOJaDZRwzlFyjhiN+TpDa0AnH6AzSTmRNTyhzjOtxYkk3aaaop5X/P4CAEEID/wHw3/yfYyGEMYSw/evnfwl8DXz+2+aHEP7LEMIvQgi/kNJQSUm8L5g3ikaDiSyDcMioJ5kmml4y5YJIKdJCYJVCiIDPA3NoGKPALA1DaHDCYw4TfnykSxKUlzw9joippS13sB7ZnKyx1tLXR959O3zo35dHFtNMzy2DcMx1ylVxyZ/+7N/hi5+95NB0PLYPPDWG+duKEMGz5+ekScIqntDxKSFZI82WInFkWmFEDvqRm3HgxUZRnCmWcUGT1gj9hFWOeTY8K3/EqAeqsUdUE3aKebr9HjcLxl5y7/bY3FNefUESwafrn/Ls5e/zydXnkDt+83jDtv6GoTFcvfoYK0/46in7cEefa+4Gw3H/xP3hLZHuSVzNi1enPPvsFX/0D/4IERakkeHsxYKYDWJOGJUiObZEwSLnie5SMceSfIyJuMcCyRgxdCPTvSeKjhxshhCP3A8D4fhIvX9Edz1vD3fY5IH8rqDtRoKNyOYlYjuguz3WZAz7ic7mTFHEcZoZR8fBzhg1MnrHmOVkw0BrJYhAWluGeCZ0KXPSM2jN7mnCuxi/KVCpIiSKerC4tsVHgpCNDNPIMGyIh4E5FyycIUyW1Bu+f3PDYhlR5Iar1ZrYKoZ5IiHjabvHPbUIAqmDIlswZoEhOqA6R+2XTOcJjX9C2IlpL2gGx/FY0R4Sxqpj72futne83Ve4DpxLcHnKaDSXSIL0uC5mrRzRpDA2oJVkWBuqKMaoFqUsPoqQckTEFd4p4l5ghaVyIOsBOVmYH6EciCKPOv5u7sDfpRz4R8BfhRDe/Q1gOAN2IQQnhPiYD74D3/xtgYQI5OeKaS/Jqpxp3WJ2GkHLIAPlpPEE6oOB1YybU9JVoA0TunL4TGN8RCc9RQO5FwyZJEzA01v61ZJ1VBE3CdHxFSGvmbCsPrnEuBxddvxedME4L8lOFxzDRAjnXOUnxER8a4+Y0LM8WVNVULiKaRnohiWb1czv/fFPaR57XFHw7HjLOL8kuIFusoxJzKVasbgaqb9/4u13b6guFOcXpzzVW67WEZefrrH/qqE5OWc+CA47i2pzrl7+jPd3j3wSG5598QXd9sCxusPaiK93NwQbUCeB3//0Z+yHifHbjp//pKY42/By7VkVL3n56ooxNuR//hW/+s2OMA/YtqBMc5799EfM00jiNYvf+2Oe9l9y93BDWh6Q+SXOtsQLw/ahQqQpznYgIibtiOcJXSTc7vckiwwvJ4aHAps8EIWUpK7pxCOdnXB5oL17wj32tOuWTSYJSSB0I20iKbmgN45waPH1kXcmIaHHpxoRK8YqoZoGzozGrmeCD+BmRBowi4jt/R7lwM0VFxcnhL5iDhN9Y8n1ksX1JXV1JAwOIs+x7SimkhC3LI6excsl903M4HumhzsWMiIsY9LBYRJB+3TDV25FIXO++fV7Xv34E3zbM14ZLhfPqfdf41qBWMasEfT5Bj91yDqmWFr+8s9+xatXX+AOKdQVxeY1LvkfaYVkKVq6RhJEzEGMCJ8wiA/U+i54xGzASRgsETFTvEB0DRmKLhZYrxHBwOxQEUQ+YBcWPaS4wVJIRX0XIP3drsT/Jtbk/4wPhqJfCCHeCSH+o7/+9I/5v5YCAH8C/CshxC+B/xb4T0MIu79tjUDgRAlSP1JddsR2xCcTvSxJdURtNGhJkA5xcLTtiD+MlA+gh4APEX0duBwmpJhRhx5Z9wyzIJQnnDtDNgWy/IqQj9jMEcuCQVimMNEFiThL0T8653g/sX8/IvY3TGzZyRtuHy1Orflu/5b57o6x+iBttVlFjJFn/xAwyzNS7VEna7JVQZZntLKBztMicD7QeY9+vkFVO6rjHXI4sq8te5FxfbFhbktq9YYs8qSXGfkq4kcvruiM5nb7hrqvmZqZaGMxsqbctKzVyOnFNZ9ev+Dv/4PXvP7oD1nYNX1lcIliH7aUWvCYGrL1BctsTW800aoky0c2589IzhTxEpLNJRfpc1ZRQXxsuUo3HAZBcZaRphpdTSyTilj0tK3i/lHho4l0m0MUyDOBdo7KCZx7Yq809APN7QExzIg0YfQ9bV8idg4XzSy8xycRcgexBxsb9Nwzdj3drUUOEXHoUQqK0mNmydx0HGeFo8fdzyz1jI9q3JTiZcEUNuR7QbwEKWdk3bEYIup5QrgRM0SEhaMtPIvLK04XZ5jjI731FCRs28D2256m33M8nhGvoFSWKPac5YogPY1vkMea+rs9q2lFN1pyNyJrgbAdPkjiouH7uqEkITeSq9MA2hNfKM7Xz9FTRxcLXCKIY4F1KdZ7fBC0AYxKkFZgIo/0H0j2ad+CSOlyjxEW2QWMmLBpwFnJLAKyTYCMRCVMLiIsCvi7sAhDCP8khHAVQjAhhOchhP/qr8f/aQjhv/i//fvfhRB+GkL4/RDCH4YQ/oe/LT7w4eS8T4lPU84OksW8RsmUFIVqZkI8sJESoTRKBrSUVFJx1JJJaNQsCWpmlhBGSZXYDyhqLStGvDGYcMqcVeTFNWosmOaJ6h0UWWCZnrC+eIkYEr778lvs0BDGmHgBrzdrPrlIkEqwGjaohWQ/tZQscMmMf0gYw5ab9pExTHSHPdXjLUpJij5jfSYRU419aLG6pjQ5Ki6RIubyxz/jp198xunVGfnHJaevSmJ/zZA8Y3V2yUl8TXbesw6W/vuMxLbsmpnJPqHMirh4ST86dKqZY8O4jrlYCUw2QGh4PDwQjj1GxujmgeeLhOL1c1anE4mQHO73jNUDZ/kFUgxcRxknP3lFXiw4+8kl52XOOmvJTUyUXrM2hnGvcTuo545J3BMD8+ZLpv3IpAcuspKoashPVpjhyKGZMJNhnDVq7iiw0B0YjGR41xNHFv8w4sUBFTmeLw6kZAwRpNaAaQgChOlpHydU75iTmIWZaBNJMiiS9YqVv0QbT540xMGxjyYma/CyZik3CKexJmFsItxKI/2G5E6QZAlltEJOgjQOH2TGVWBzUqLDmuK6x7oSYxb0sWHIFMdxQsfwLgz05h1N4rDeclc/UE0wyZ42Tzg6xfh+xOan3O7veHcciVRJFmbWS4tfZtAbkl4Suo6QzMh4xGhFLhU2DNggcD5G9Q6BZshAMqKOAd8LnDGAIMyOYMBkGQszYm2LLzxx5NF9g/qdrUI/kI5BpCIvJN2Q0BcxhImo/uDYEqWKfExp/IiME8JgcElDMQhEJql7Q+Q81gtsCHihmWzCgpF4eYb1gUIEykVAJZ8y+wbdOeqzwNo4brYd12vBu5sb7H3P+rMN1+YZ8rTmrmrgOsUOkssl6NOPEXGLTyx5k6ITz/y6JFCTYwnxGS+SkvEkcBSWj85y2qYhjTISf2SzOEc0HfsQozeCdGNoh3MuFo77+/f87LNL/vhHn+Ndyle2oTw8sj2sKHNLZmsmH3G6mugfC0Tx4bbBLxZIP5CYEj/NDAFePFuxNKf8b7/8M77xB1aLNef5gvx0w6t1jg05nplFfomSli8fv+OTVcYNE1Fj6dMVz8xz9MnIFFVIq1kj2Rnw6YAPCes0on9smO2evYlo6FBvHvmq2zMftjQHwX33JVpousaQLSVjDZKCSkr02zvSlx+Bs5TnknqM0HVHNV0TZzesZ1DZkeGNwV0OcD9SZTmrssBJxRwk6qBp1i3JMeI4T8iznJvuyNIH5qog9zPRUvP+fssczZwkMUNsiKqaQYFZRtw//IbLz6/4g/CH7N3IiXA8K5bEFz3j+5QQKz779IJqPLKRM998D2ZyXD07oaAjYUERPKNMSMqZeWk54SPeDTvOzhyX6zNcO7P67Av+93/5P/P2V9/wb//7/zGbH7/G/PeeUY8wpcS5IAyCMUuQ2hOakqCOSCTW12ilqYceM870ZUHRSLqkQU4zoyg4iQ60U4ToJWNxQm7uqRPNZCOyPKIfA/DbhUV+INwBeLQWHWYiqcgLhzCefE7wXmBdizOKua9xxUCcBqZE0XaaVA/sp4FZBXY6MBswZcuQwEHuSVWGMJI2Luh9ze5pj1ktyI893ViRxgHpYzarT3jx8U/5+eYZjXzHfszYKMV+OzJv9/zV9xPpQtD5mIVOaMvAPBTYaE8anxH5cwpTEZ2O5InkWVghnCJ1GasoYn32jIXw6GiGkwVr/RHKnbJIG2ziSUTJ1GjEJiLOC14MA94IOlsT5piw6LE6YVfv2Lbfshtbhpua2ZwipoIQWtq15zoq+ezzF5w9v+Lf+3f/hI9PrxirlPLsJfNQI7REniSQRegQUcyC5GCpwsDzQVImJetRIrOebh5Y9wU6i5FiwKw9MzVheOTdoQd/x9Pdlurh11AdqJctx+PXPLUt7/wtSV6SJSmb0wgdRyRzAu4eNWcUcoVpt9iniUnUSFujE4FWggvzDF1e0Scx43lMNRiU8WitsK1lsDN9dUfZeoo2Qi4ExAH1ZIkGw5QqjDB0w8jKSmQpSBjxTnC+hG525H1LtNxw3Dc0beDVq1e4B4VtPKtNh24MPouRauZ406OGEk9C+XLChBGFIVEZD4nm6AeUjpB+QRZ1VDPIqsM2HknEkLasRWC5KDh8LbC5IXYL/OzQyQkikgw2gOjRqYDG0emGPEikjpBSMRtPphwmKEQ7U0uLmxUiFZikAQzz0qMWARUaOqkQB4cI0BBw6vx3pt8PYicQXOAs+cDhT8M1bldDmhGXAn2wVKkgGaALCuoZp8BEAoFnmgIgCbMj0YJeeOI6ZlEo0lRxmGuW6oz90x3n5WvWixNyMfGNTJDHmk22oo4h7raYfM17N7A9pHy8ytloOExHQtQix4avv7No+cFdN0tS7GSJ/RIdj8TJTGVhTFNc2yIXlmAFiZL4Un9Qth3OSLegryNEdSBNl0whIXM9zY+vOU06qiahiR9Q0xmliviCiQc8P774Eb/61dfECRT2kv3ugUXs8Y8HouslOj2h285kP1YsspzjAbIMzi5+zqKKCNkjt2FJaiHMHyOjI1INPDYWJTTRlLP3llxHhAvoxJHOtMwuYZ5TXGdJs4S4mRnGhjo68OgmprmhaycOh3+NtA5Z5CzVgbh+ojpfoNJLIuuwVsHVA9IVRPMWVprGacQwMww16RxTTzGjOiLsgDsYynzBNjyw9gWQoW3FOrpg5wMLWdIWgqAX+GHPWgw0QZCqU4bDezprGTdLnlyP8RFWSUqrmPcJ26hitDPP65bzTcn3DyMm9+hxQF9sYIhptCOXEqlWNIuakM10/YLxkBGptzS8wlQTS+3oTMSJPpLrBLqOYojwyRmi2ZLohnf3He+eWU7SE84/P8G3ltN5QblccXjY4bKIxFq8y4ifPkjmjaFhVAnS1/hsTTpUpJmisQEzakJokGmCdTPztKZWlqQbCG7GBU2UOVpnOHtqORYJKt/R/3bvkR8GCABU7REfr4iXTxi/JEsNwVbszi7YTN/TRQnLnWDSEjsNjO5Dh6BUisQpdKxxwcHk0PHEoQ88dp6lWaPnW+KTBcf6W0T5GlMYliZG767JZof3NYaMt9uv2L+BP/r5ZxQvz9g+SMyqRR5i0tUpz5YF02h5f3/H+ctXvIwSJgfBW46mIyeifxhY6omxzCingd1SYzoDA2R2oC0FUTegT3LEtID0gB8SPnaCrhGcjYIvnr2iutS4WnObanRdMvqKL15/wmJ/xY3/lq4dmLOB4niGbRxD4jmNci5sz9y0JKNk/ZmhrXPyjxx9/ZyrfYdNPCt/S6kX7JUii1ua1lHVjzSxZxpOsR5aL0n9Cf38gGu2dH5FMQda4bBujWjuae2AHw4c+8CZizhEHadk9FcFcbGivW/p3Y7YXLE69STDRzxOlr611Lsjs5rILi323uBMijYOxYfzoTkNhPlAV5ccleCFhxnF0fUkzYQ8u2S2DT77inq/xukLdNYCPXITI4Vk1YFannFsH+iqiDm6RS4Nl14T+4Kn7IlTc8rnqwve/Oo3iFITrSVNiLj76isuixVP8UT0qFn+eIkdHwnjzJRu0HlLrqBPBNG24ahjQr9kNRQ0BvI846tff8PTzT3+Ycunm48oXp/xJ+Xf48lalHIEYUmMYLILxgl8EQhZwB479JSgI49PY/yuYswjXOeQ0iB0w5zHiARCl1GKljbVhFFgNoLxZiZuBKfJwJxIvE7I5j3978i9H0Q54BGcFI6rqMW4BJ0HymkkSnOeh5HJrJE2oj+b0cZBrImCJ45KYumwkcRrhbceYWZaKZgclKnEiprGCmJniM0SUVXsvt4SSUmePhB0ih8Fx3kmHgQf/cRwdr7B1UdcesDvJLMTVDdf8vb+lof+lrq/hYf3fLt/pGktgzmSDmBbOD0DUUSU9okZxzqCVmlINeNJycn5gpPTjCADaTaReMNSbeiCQq8guczI44w4pGziNa8uX7E+NySnmsXVFS+uX3IpTlk3Be0cc1M/8VjfceY1Q/LAfl7gSkHDzF17YKU0TYjQl0dOLwUnxYxVG77vH7BdhVAzWW6I7DOi7YIhqlChZn2MUcMTcZrTDjFW1BynAd1qTPEG1+6wQ06fPEMEw654YiEHuqgn9Bk+1axjT9EppD0w6vQDScZIhJtIVwLnE2KVIqMRTUNDwM8anwYWGGZnWC5rqLY8Jp7JbBh0QKxyrNozlQ3Br1gUgag5cJ5A2/VUQZCMgaicsCGgSAnhO/ZTy2HXEo0ZbtqiDxLMiPQTmZbEhWaSCtFMrM4X7KME3y3YRTvmp5lIJfj5g0uSmhO6xFH1lsbtuTtWjMWExLHuPU23pekdUe+okxmhHJ2IWboVQR9p/BNNFxF0iXQ7fGwRNkHvJcZ7pOrptUV2A0YFtOqY5Yh0MzpfImrBtHesO0vXS1b7mVbP1NuJkIMlosmXTHpDUIHDnP/O/PtBgAABdv0lE6cIHzNMOdFGYJoEO6UsZE+ZJkRtzjxJ4lkwJTDajnbUiNQTuhFbCIzQBO+QScBKg3CedSI4bG8xKxAKdGJouhm9WGHCgcTMnPSSLHuF4IR3x3uOj4/4Y0uItmznjlEkHNqJw6wIreNgIZcxQU0MO0GIBcU6YT4ONGNLMJ5BCGzdUhqLTmeScYARlC3ItECJEmkyovOYRIDJrolzzUjEdb5kjF+wKjdcZCV5fs1w3LO+8Jw+/5xrG/P89IwX10tEF2HjAVn3yOkNh6qD9MD4AINtMMkB+ZDRuRiRlMhYI4ec3neMaYqYBMVmi1pZFqYArWnyAy7aEbYxWWIxomdsj9jpgbDVHJoO071nfPyWTZmwGl8gQ0xSGbz8nk4F2myJ1Clp/oxozGAxkas1hIrDMZC7GTtW0AmGLCFVjqFpWOYw5TPdCHMNGaAqyTqdkMFTMyD3gvVtgj/GrMaUxFn2tqLUmlifob2gnjV5JzCPDW++2/L4/Z5227GzA0MZ45MF9eNESLZM/UzbBVYixxqLESXLlaK0W9wgEcFTOJip6JqRwQn0VDJuv2M/CERwJFGKLwx93JE7xYKI+LQg9yUkE6vDkSo/0DjHGKfE9Ax9hUtK1l4hZcUgZzyGNJKYyONm0JEgcinxFHAe2rrFh4QS6OMJlTYcpMQoCxKstdgTwfDYMk+CpK+J7O/uE/hBlANKQjduMfoadXxCr2PCqFERrLPAzm+I84rOR5goQDPhgscpixMwdwqReKImRvqeIhG0g8bPisEpHnYdMjKMb3c8P19ixhaTzKR6ybQ5IUazX84U+4H5VBPnEZacKW1Z5AmXxTNUNNLvK05MgrvOKKMCM1UcQgbRSLiTiNMOHxTHqMc1MUn7hHU9FTE5kilZMw0dci1Ytpfc0WMGge+3aG+Zd/eMUUqpJZXqOb8OuF3B5qOJNIw8+XNSX/DTn2jez4Zx1ROPGwYjefv4QNRI6rdPXF2/oFif4TKo7PeUN5/TJw1mmTPVguB36MhirAU3wXnBl39xD+k9x53mq36isDmR31Ese3Y3Gp3dYsaRX+13NO8mLk9y0hPJmUgY2ieSRUqafY4xgdQ+Mro1h2SLucjQS8ulueAmLVDHiKvxDFE2XC1PeXpaIZ8NzIcjk3Msi1NsbyDu+XgR85f3ljTuUNNE3EJdnLF0HXOsOPZbymRF7wxqsSCMHZ10iEqiekHsBfP5CI3g1SeXqDlj376lzC7ZmgXP+okptDQ3A9+aiZNUovcGw4Rart7gWEIAACAASURBVEjlxLss5eRSIDrJ0U0wn+GqB/qbb7n6+AXTsyv6Nw1qkbDKJ/pRoJZnPL6raNmTC+DVCUWx4hg86bxnSDQP9x2tH9G5BlnjY4ufJCaf8YOnk4pcC9qlQaoe4RRTHpNogdKCUTqqg0SMmhxwWU/+uKG5qlE7gQiCzULTZRW2NngMMPzW/PtBgIAQgvWpoei/ZawN+cohwyU6tfS1JFWSxCS0psL6GH3uaO4TZjMhdIKeeqyIUcrSm5yVbSCziHpiUUrE5MkvU/yY4nAEkzBPmj6A3H/PqBeU3XOKRUIkQLYOI0aiWlOc5VylMU/ElAX4UZApw377wC6KWPojx2TBUnl6P3P8/p4hzTHzkelkjRhKyqJH9IErXTNtEh6OgjoZ8AeBVQcGsyBdJAyLCX0/U/s3hOoCNzvmRJILRXPnyaVBmhkjDD/+xXO27ytsEagEnEwDcp2g14qd6cgHUBn4NqXOjnTVIwu7IKiA6BQLDcNWYnKLocIkI9qc0vuauPVEccfYwljfst9uEUCderp7SbqqkeKMLDthCjPxnUa7J+pwzpkWqPw1RW+Z657v/cCqP+I2hk19giwcol4zTwqVWnIx0WlN/7BlLAzkI9FcoO8kN9EDwhoipfBGsys1z5whiXJC0pK1hmHyGNHS1B1IQxRnDOmRflWQjR2zX/Li9TlmF2GEI6lO2B8K9KWhEHAnDVJ5kt0tcciYssDQV6xXZwRlSHTNNEkKk/IuOJbZhPUxsZQ0bUear5hPxg/kpO6M7NwR6phnqcSdbzgLJW8f3/Dl8XueJ4bQn0IpaaodQiQkrQU50/uMk8TRDIEpXiLahlpLRD0wyUASzXjlKFrNgQ9aATKVBDZ04QZv4Sh36LsEdSqZDpYqi0naiDRUVGHmd+0FfiAgAJt4jRWG5sqhFAi/ZT9HnJeCcVwSlGROJ6Iuoe4V2XLk2E2EaSIyMSZYeiUQ04Q1EjMHrPLYPiHQY+4TsrOJcc5Y5jPRoaTtG8p4g54j5qsFLgl0umBKalyjKaaeNJzzeNjydphZMRHJGIInM5LI54h+wUWk6fsa10AnYpL2QGNjYtcxmT1RV7JbtDilWY0N6nSDFg3xYNFTYM4k0jtUqxg2Gt2ckmeSDkHa72myc04Ky0NcsJFPaJEgVM3FyyVu6D4w2i40l0KQxNdkt/dwJWi3krCYSdoDuqu4fzeyuS5RMqKNFHUCZ7ueqszpjEPe9Bxdz+7LHf11BL3ldvseka/YRBHXecT8ImKcFyTXOUuZIVWCLyeG9BVFniKTmWyWmMUFNrRc1SN5fEEzD0TLAt833HlFoRRRV1IuJ9JM8c2fzzAkyMjgNxGemr5VLJYWv1sgihGZp/jBM8YzWmww044xj/BjTZmc0DcTqr2jeHVJ+LpH/vycqXZ8u33ixcWCx13DcLakPXxFsS+o4jWny5JjcPTDjMsSRme5uH7JZhUYu5Kq3KCGmmruSPuBSm1o7Y6DC1znGclpQUaLbw1teCTynxGLPVMq6d/UHEuFjhqO/8vA1WefkFwput4zjw2m0+RJy4NLWClBHzLmsIdxZBE00+wROiGEmcE6QpeySwccMcFO6EkyhQfiuCTSEc1wJCrBh4m59CxmRdsHbOywUwrY35p/PwgQMFohQ0/wCWfZwHEYyIoCM8bMdLj+wJxGbFRKlYARml03sxIRgzTY0KECqEiRTRGDFeAFIgORDnDIeKThk1oSxZLBK4qTnkJkrFcZQqbU8sjb2mD2B4qLHOUDLl9z++sD8jmoaiKsJCKKWHQd7uolx+2exH7Ht2979HmBCRHxHGP9SLY2SAfkkl8/9PxkFVHXD9xXHbV/T0bOaqEQUcK6mmiTFtF7yqCp25HbaCAPBfl8QvN0ZO88mei59QW7uy95tcxwPsUHTbbMeBYZNAvUUFO+OmXqa9LVklYODGHEsKAzTzz+5nuMNci15WR9xndNxHy7Y0gPiClCITm5WtEMR1xn+IOf/n2idc5Ut1xvXtOV76FbEfo9u9GRSYE8/YL1wtOMmkHzYXva3JJlGUN3hGjBMh44Dg0nxvPqxwndW4kMAZdukPXMxz//nLvjge7+wFkas3NLLjO4f/+WQ9KhKTh9HOgvO6JxYjVeMV2WbBrLdhJ4V3EUj5TmAndoEMsYWdeclTn1X028HVouTq4wxY699+AyTs4FdaLYNBOLVUG9HzD2EbOM8S8/5nDf83S448X1Oa5c8t1f/ZIrkzJfTBTKMYSWzga6PuU8CxRmRDNQVQnqxDLsjxAtSd1nfPTSc/GLJX4LKy+5u/kGqw/cCUMsNS4M5KpnthFz1NGPYEkJyxnx5FHGIbKe1Csmb/BaMvmOTWyY5g4rK/KsZB7bD30v/UhINGQDbkwxaWD8HSZEPwgQ8EIiREqa9hwGhWTBvrZkoqafIEug6BWVVBS6JcwpWRsYM4NJZ1xl6IMnzDNTCIigwEw4L4iOCcp/0OF7FAWmnVhvNE/9TJwVpMWaetHT72qGRtNWDd3gcXi0/ZrVsw3xuzOMtxxvWi5+r6DHUf/yS7QJKCU4f/2CVaLZth0DCj3UdHomEzFFKDimA499g6yfENGacxuQycA2L1g+1HyVPKFuPTIvuAiCVo6oduZIxF78GtusuZIZQT8iXcTH0Zpvbh+53Cg2S7B4ChwqSTnmmurJgAzkqiExMdEC6njB4law8wcaObOyBcJes9jssd2SZyLCnUgSu+bh8J77Y4F6bVnEZ8x5jwuaftmxTr5gzB3trWd9HNnnMS+MpbIxcWGJZ7itRuJJ4cKB+xF+trAU6hQtBd1gyOuGavBEZoMSgjZ8ixotF6uIvV8wTQNJ2jDPAc5j0uaDZXmdQs6A55IkG0llYKskpXfsQ0sWMsQ0shhW9LsbVukXTDjcKuH9+ydePyuZRIKSEfHkUeGcMmupHz3BejKTkCSaOV6AFahswN7u2N8qlpFgpXN6cWSjCpZ5gc5SdLJhvdmzO8QkmUNHMdHVnrxdMhnNde4Y3gnmK8/UlERIkjiiHRTKrYj0Ees7JqcZgsQrC7NCJjPx1EFfopSgsJZdrKhtQpw6nJzIpMHPCulGktIwHS1WbIg40J2sycORLI5wtcUWBdD+1vz7QYCAAGIdGHvFOkm5lyPpqHEHzb4fmY1Gno74SnGMA9MkkGcCkcyInccE8CrDjTXRemZswREj3UiEok1jJD1TZamVZGoT0tDg1BMPT4bz+DlNvafyjmMbuHI7fGTRQlOSE9zIYAXF6hoO0OcB4fe0w8B2sWDhOuqDgpUjtDN9s2YSLXI68Da651V2SuhypBhZYajVhE0mzO0T25Bjtp7/g7k3+fkm2fK7PhEZkfPwG5/pHapu1a2ue5t2D7jBxhZCwvICb8yKHYKVF5gFEhv+BFZIXiGBWBgJiY2RYOGFZbMwSBg1dLfVvn3HqnqH533G3/zLOWNg8VZLLbjlvpJZ3JBSmXEyMmIVJ87Jc873O55uGTcWsX6NlxPTALo/MCxD5sGZYxMw7g1dLmlsTCEVjYKwGRi7AaKSQ77D+4lwZwgLwzjW2DrFLSWH5/ccPSzzl6zWR87BjKvEs69nyPnASr+iX8DQl8z8wHRTcP7mFirHzC0YFyPL+IdE845oW7P1klN4pjs8U1988jFJZVpT9luCbqAdG8w+4HtphQ/W1LOc83wg3LTs350JCo0eOvanPWKMKRZrWudZlA0HsUcfDEoLxP2JqatJg5KLuMTseuz1M4/vM4rFjGSybJoGk1RUOuU8PH78ibtaMRQnrqIZD3VEtbS0yqKzmGg/p1g46rkhPRZs7QOmV0SJRNUFCYaN8KRZzDBbonqN8REqFrTDBUHtQQ1Mzch8hHMaMI8U/SEhDiyLaEkdQ2QFjzvL+qZn5ILbpx2Znnh7L7C9xbieSCq0naiTiqI7MWlP3km6MqGfJmJtMAZcFrNoJ7aAtRnVEHJI98heQaRpRsjLnqGxmFGQhEemQdPvFdFFgal/+U9B+DVRAoGAKpjRVAG666g7TeDPtFNLFoyUuSCwKVFg0dOCojwh+5T23HMcC3RiEJNBipxTYyiDDuMdwxDig5ZkqphER1944lhgHt6Rl3NOpxEXPdHeT9xUl2Ruxs49sZylaD2nKQzHxiP7Lc3gib8oCX3E8LTnm+0dyz6jkAOH+w8cZwtEbUhryJIXrJcFzUHwwjzx4LfowWDqM62cEyQG4wKEEKhpgy9WBH6NbU+4FopVhXwZMDtrzlPP7uhJg5pk9Rlx+4zMFcFc4FqJ6hq+OX3A1g5nYg67XyCjiM4VzD1s1SN3//jMSqZ8/+Kaq39nTR7PiYYIv0gJraYajwx5xenoyAaH/OJ3+OzwwIfLzwiDtzyMlgv9Atu9oTVLei1Ya03x2ZfIMUKJM6kecDLibrNj298jHgyL5TXEH2PnqezImojBRTyYHvSCJIT5wXPqOoyZsfge1HcJX/3RT8mvC6ZTz9mP5LOc/rzDqJCjH8mecorLGCt7+rHDqo5EK0zQEemMpwrMfiDzJefck7QB29sP/LRt6WzIpU5ZvnjB4pBi1x5XL8nnI/XBsP5+xTsOfFInjF3M57M1KhuJp4CjLEnKI5ufbzBTyI1awfItSTQnLR0+EGi559xnuHlKNyjW9HR7h0ifcXnGk/H8H//4H/HV/R0+nJisxKQOycDgBBMRpC3GekoUU+gwgWHblYjcYd1AbAwnI4goCPTAFGWwq2l0gssmbJyipwgre6zqsZ1DIrHfsf9+LZSAlA43d2RbRxeHVMGZcdCwuiQdakwwUJuUKD4T2Y7AgtceMWYUseShmZC6JxgkQRBxtiEJMZozZ+GJ2aOEx54nmjEjKQWT7MlljHae1MZ86HoW7Ve0O8/6MqMqA+YkPJhHBuEorjL42ZYfnX+BLCL6s8TkA2t/welQU7kn9mPAdfaC5rrFHRqiXUf/4pp4c2ZIJrA5td+yP8T43Z4vP3vJsYlx9xvalUMkCYQh1+kVm6BFaEdsC26uQB0EQzWQfnLB2w9PzOOSWRDzHCasxpqDsBQhuOAVs0nTTTCkluiccp1F3PzeJ8wDyQu5oJ42hKEktQHB0qJVhZxGqnTCu5iZqDnPcnLdIPiSlRmgP2L0NbpzeLXjZ4Hj88OIu6hhF3BOUvJg4BnP99MZ7ZXFXy1ILYSLmK4OCM2ZMBuZRStcJPAmYbIRYZxThD3pNKe+OqKXFWqowRkQMdYKjBDcO0PVhsSvMk7ekAQDXnvSsWTXzkg5oYUkfuxxUQALzVWecPjJlrL4AlIo8UznDn8wdOWAHyJmO8N26Jh56CfBK53hRoWlQUwtn8y+x7PtKI4xoh15JwWv1jOqC0fgS8q8ZAwlwaQJi4r2bDH3Z6rA4sKEq2XG/WHHl/qSc5Rwkopgmoi9IR1Lnk1GsBhwgUO0Pb1MQCoGTohmhvQt2fWJ8zmklBFtP+AWHmmhcwnF2CAqR64021HgZUQQn7BNgCsUQS2x9rstgV+LZCFrBGpaoYucxAUfCzFcyio6oPSA8hGzfk/SxVw6SStzrNQkocPlERciIEpmTInCqoZgGujij8AfVaRRMsKakMwltMHPKSLLKBxl6fBHh+g6FiqiTiKu/vUXdAfL7rTlYAaCaY6QEE4p9+e3bE8dcnei+v4nDGnFLrhn5Mjd3lFcXvAoG7Z3PVFQkL/+PnOZ4wJDNlzx6vMlWRWz9nPaPOHHb97y/ic/5U8+/AumoaO5f+K2uedZdEzjgn2vcBcZUbGievGXiJIEJwqybEZ8MPS3LYiBxXLNZ59+wSKc88lnX6BeJZRRxI9+8XPOP24pghyCjCbMMaOjjG9QyQqrBWKaMWsv2UjJ89ZxHAXtlCN9wNX6mnlZUvYR2mrO5p7n8MzufcPs3NNLi2piRGjxu5Hj84nLC02yvmY2XzPLA1ZVQY7islpiQ83wqEjCORUVVniqpCBZpzRdwFPTMe1rlmYkERFeKDo65nFJ0gRcjilrmxM9WiYEUTADGzFYieIJ5yVnDz50HLsd+kHQnQPIBPnlJenlGqxjVNCf73BBQLdvGTV0wwGVzcmDltuwJ/MNNoy4ayRvbjdkk+Bw3vG0lsxzmGrQviJZvMTVltzDOol5emiRCkYch0TRmYmdkcyqOVOyJMwTvB9xMmEcrhGiJYlPiF0PKDARc2MopWfSCVLUqMjDOSXzjpEaGTiKMaENJ2TgqNMc3TkeT4ZQBNippj06xtKQAU1pCNUvRfkDfjXegVd8hBu/Ahzw33jv/54QYsFHaLFPgTfAf+C933+LQPz3gL8FtMB/7L3/w3/ZGlIKVHYAO+OYWK58iVKPuOkzwuOZDQFFf2CnCxatZ+Y0Q70l8SltA8cEBtcQecvUhxgNyjrMCEdjyOOPplFvCvyu5yEYPtYBjAP57BNa34KOiZygaxTFrGW/eebYv+Pq8zV5t6Lvdlxe3JDLhlFndD/7mnHoSdcj0XRJUl3SHM/oLCU7jfzBz9/xyewzZvMQpyXt/TPdkOP6lJPoeRVc8UeP/zcEEHcZ085gY4U5GHqVI4cdMloz89DsHU/ygSzR6HEiUjEu7cnmCaGqCM5gco2NEm6c4XhTsEtqrsYD4fcT5oPgOvoE1TyzeQW52CMOCaqLiKqephy5IUL2CU/qxAjkZYe59xxmE2Uq2T16ysUFt88P/GK35a9/+btMLqDzO9LQ48WecWPRSc5aVXTljsf+iBANtVvhzeZj7nreQhxi9JZiKqhVyVEd0PMRuzF0J8+md+TrgsPWkAbP1HdnOhkRMrAnoVAC6SRGjuSVxpst01iRJk8MG837s2OIGr6RP6MYPmHvRsLukXaboHxEGJfoPCUNoR565rOQbPmCP/rwNd/nB9jxwP/V3eKikt/K5gy65XAaiBcKXQdchCXjSwj0A6FeYYs5cnfg8GmCjo9sdvf0g+IHlwoXXRD5I+vf/h0qk/C23POzn94RNk+EQcKTduguhLSn7BxtNGDyiNrUzKznFMcEk2SaFNK0xLGmDhRyGihJGJIzvgkYCkfSC6beoQKDuUmRux4/Zeh0RPV7vgtl8FexBAzwn3vvfwj8VeDvCiF+E/gvgH/ivf8C+Cff9gH+PT7Cin0B/B3gv/6LFnAeWpvT2j1LmWEzTaCu8KJDzBJWUYASIaU/cxozLpUniGJ2ztLHmpm2iP5j5qGUIcKPyAGUgcjF9JPDGYkd9ijtaE8HQq05n0a2m5ay7zk8vaNvj6jdmePO0yJIRcDpIeLp8MT7N4Z3P77l6FrCzLC4VlxeZETpl7g51M0e21mef3rgZ1/9lLs3z7Ruz7bfc/fVI4egBvfAoX+LObxh2z3x5k9+Rr9pKVY50fdfUKx+A9KMaOionETrgV1zwIQHxsji0pRAam6uChbJJyhd0qCYryIWwUQwTrwbR4LNEZGXzIqYmcyYvbxGBZCuVygkKk7ZOfCznOV6Rdw7PFcYA5f+c2TRMXylaExPai2ijjBG8bD7OQ/bD8xFimciyWLm4Q1KJah2SSdzciWYAstxZinGgh0Ss2pIkxFaz2AHmr7h8FQxuhhnGxZBR+oLju0RET0QG4m5PzL2j6j+kkM3ECWOLT1d8AaZn5HliOaE6zTDfE0gezZDzmA8Mh2xjHzVvuE4/SmRM0TVjHCaCPWRwYzINCSPYTg17M8TemiQuz3kHaf7DW/efuDw9pYgLrmav2BJxEJXpNLwOISk7wPaztLTEckz3SqF5sTQr8iDin7/yKkJiCtFO0HmI7zqSG2J221oTc7RduhJEDGiziFDNDG4mLYJSK2GEIQNsKEhthFjHtAZg4wtg7BQeHqX48IQNWoSahACawXyOBDJEaN7rJsIy381ZKH7PzvJvfdnPjIMvQD+NvD3vx3294F//9vnvw389/5j+2fATAhx/S9fw2L7I/NIg52YTSFRMLEuCvQ8JCoUebgmDCKiy5hJe0zeMyU5QegYZEblAmSSfKRnDhKwCq8tlCM6HxAekjxGlprBW/Z5izYBLt6zkRA2H+geD7T2gak9Ek+aXhQ8vvsF4xtJGgxQJmRNgfzgiFVBvTM8/OnXhCdPGnjMtGfSDXHguI5goGP/9oE0CElcypGMfsw43DU8ff0Nn/+V3+Kv/v5f4/WLl8zFgkjseZ3csOfIAwOjhnGCRb5gVRTEQUiqFEPq8ZQw5Cy85SANfRhy+fI1aawZ4gV9/Ux8Sglawe50Js0UY+AQxhOee8rYECF5PjQc7Ih//gmbNCKIntm3B85iRzscKGzKOT4xFiPz6II8Dhk5oyuHGfa4eE9gAw6yJ6ssaXLDqY/Idmvmixtep9fkk0W9V8RWErtLemM52h7NxBg7dueO4byljGK6NqdWI40bUW1IMatwZUIwWgodotsYu4/IOw8+wIgjhdLcJCVxqDE97O5voT4SNhXHR4Wvd5w7R6ojVF8xm0Mk5gz+gsf2md6fqDeOSc+Jyop+52l3Lc9GcJd5VNLjCZgnK4hXRPOUjYD6DGN3wrqSNE0IdUI6nBkVpCYgNAlyFxNGHe3eEOSKNOpoVUEYCnwIeE/nJGM5EJwyoqKHUdD1jv2xwI+Gsoda91QnsFOAcAODEQTPGVHnwEQ0YchgImLrmStHuRuZWo+wZ9aB5vD/FyvxtyQkvwf8n8Cl9/7+zxSFEOLPUAteAO//3Ge338ruv2te6y1KWva941WRMWUDaZjQiyWF/oqwvua4OhGcYqSteS4E6hHWvSfpBr7xI0Om6ZqBLJEYaxmqCH/skF4yjTnEkmk6EjWCQXnObx4oytfENsTEAypfIKzF1AIpJdaEGJ5Zv7hm7AfGwvB59QpJgN1sePPPH7k7tSTF4mMxkuiIbUEQhRyNZlks0ZNjSGYgNJ0Osfsz2oTEry755LNPSE3Fvj1zmFqi7g2L8iW2nogPIfWxpikNeRPxk3rLfCWJkpCBDL0b8OmIlCfGLqPve8zeEK6fuJy9ZIjPLOYvqIoH4kAwnTrSm4L6TcDF9WtSHVIWFnY19e7MYZz48PhIElnkqwJOnnqo6XPJ8e1XyFCjZYgtAla9oiqWPPWWcujY76EqRoRLCF3Em29+hA8aTleXLF3NwhfEix/gnh5pTE0RNdi7E4tcstt5TGko44o6DrBfbUEoXq+XPJ5H1KWGQ8n3RMEke6KuQH/6COPIKELybUqne4YPLb4UpOEF+ecVmz96gw8vePXFmqAbOUnPy2UCPuTx6R3hMWa48hg7sYxeIKYAL77hdy8/VufdLC/RLwuWFxkvgoBkVNxnOY3rGE4HHr95R1G1PHefEWw3XETf52h2SAShNMznK7If/jZv/vCfUgQJ2cVr3CrkcFCo9MT8OqT7YBnHhDjv0BjOpqDNesJzTFQYpk7iFhYjoD0IlGo4pgmqjpB2T4Bm53ckQwbmQCgkjVQkU0AjNWkW4iWYbuTZnCHNvitN4FdXAkKIHPgHwH/mvT99dP1/+dBfIvO/ZL6/w0d3gTBUdLuOUGo26R0z+RswSKqZI21eMc47hm6PGCZ6a1hOms6v2WU1nbRkTUwia4bYcDwqksTCYSRMI1QHbjZhO4GPSpwYEX5iv3f85d8oGc89t2bgZhLMs4KHsWaVzamx3MgVLhiZJPg2oYs6ZF9zu3niJO/JZnPSZUgYjYzzlJEF86khuFmiOg91wypZcQhGht2E2N/irCVca4RZ0FsPSjC6mpfDKx4ee2bhyLnIONgDcbMjFldkUYzbjQRBRVc8MdzMKFDkekmiPaGFpmjYHo+o0IHRhHFNXF6Sq5FRzSimE/1nc7LK8tSeqU4jx2DN6nsgnmoUnjy/II0sy+svmJp7bNvzJHYkLiKaafrAMF+8Zus8V8UakR2odoJaw8pN6BgOK+iHG77wOfXTji7tiIqc4ZMB++OeH/3sluXNJasbz92bBtGFrGNPtjnwjRA42+LLklLWOGmYxIn3uWBpI47+SHKMGKqCaoJ25rFTwETAaCxdN1GsNFQp4TBRtRlnLfHtCE8tbTqishS17QievuJYfEG4sBxMxay5wqk9RS75490edVLMQ0cXj+ydYF0dCNqJMF5wV37ATTO0iCiziKxq6caGMRS0+5jl6ImkJbhYsw9qqrueavmSMQY3hvzO9/5N/tdv/hFhUmO6gJYEGyjKCBoXwuBJxonUd9hJ45Qn7DVdAlYMpC7GzXpoEkLTYcmQw8gisYzOoRMBNiAZalABOss5NjmOX85A9CtFB4QQ+lsF8D947/+nb8WPf2bmf3t/+lZ+C7z6c5+/BO7+P1rhz/EORIHCa0FQeQYfotsWp2r6vaVNPzCMI3mQo6MSbETQxchsILMwtCGHteG5nQhaT6l6pj7CMTK1A5MUmKkncIKxqWm8wIYhvZeYaE30KmV+zlgsljQRBN0cnYfMlxm1lxgUbpXShRO722du3+2xKiRNrrhYfMHrKsd1LWo6UeqGfD6jrDKsTQinOTqaUe8Hdh/ueNwf8QvNRfk9gqPm/nFH3e8ZTxPHfmC+llwvZiST5CLLCdI1aVaSxTPWNxe8nZ4x+zPNh3v0MJKtCorXM5aLC6L8giOX3A1H6oeW7XOLoEUMDVHQEyU5r9KCdx9axObMOEV8T3o637PQC2bJCwIT0Y4TJpyIw4o6azlMDcMsROoV+rRkkgmlVvSHDYGIieaCvEwoCok1hkW65kVZsAoCmGe4LMROLdvNwGlzx11zYB7kqENKehlxlYyQxQxFSlZJkj6gGibyIOTUOJ7OFoXlTEjlQ1rfI/uJowFnYnxYM8wNtovIg5HCJsQyRt1UPOuOMXRkEoIsQXUdTw/PjFOJJWSIa/abjpvhETlMHM6S3HX4bU08ebanI93xyOBGbDwnGAPitGHeS/rhiDMCH1Q8nnvU+xq2CeOlpbu9Qw49eTsSEjBzioUuCFLJ3iqCrEOoEatSPA4vW0R6gK5A0CC0wynHIfIEakTGRYsVRgAAIABJREFUEfU0MnUTPp/wgaM95wgcbZzi05bROM42YfI1HCRt3dOKgDqznAZLpL8LUuRXiw4I4L8Dfuy9/6/+3Kv/BfiPgP/y2/v//Ofk/6kQ4n8E/gpw/DO34buaFxBMA7UMWbYJtHueDnOWnz6jH3NUfsA8KQ77BqU1WhlC0eBXKdZW6PNAUKygPuKkRw8tPvP0bYxwI2GjCBVY9/HkxTlE5dnefsPV7JL4yzN3X33g08+XLC5Dxm7gdNwj+4Bjl/B0d8syLTlvNmSrAnPsSWWEsS0/aT+g1BWfp5ec6pRrNee4P9OUA8cwpHv7HhUafviXfsgq+W2ymWK76xnmmi9JMEoTuAu6cMArQVyU9CJipWOaqSc7H1CFwUU5P/i0pBaWFzpHzxSNkRS+QwyOXEtevs5JwjnHh2faR0HNnpVJUcuUZozRdc3rbMF4mTBzDr1Lic0dQxbwvH0kFSFJUHL//isqqSgXFeaznMTBajR0c4mdzZjOE63b8nj7TLrK6LuOQXbMMMzWr8iNoMWw7EIGeg71ntv7b9jtDwjToi8zsrjAWXjstrhuR2MMYuxpggExBvRRR7wz+N7ykPXw5NC/k9E+e6LYsAhDOJ1plKZSHcfZAtefOTbP+KTg1SFkdpGy33pkNHEaJkRacqnBHA2n8SVXaUf4uuC0aTBBitpNtPOAynsWOqe2GmyMPb7F5Z9SpwH7U053NSM7b8isYXdqWAnNtJjz4kXG9tkwLU/szj3mZcK1KTjoI4WUmF1PfP9AW7fIIcHqljFSzGzAsXU0siMUGtcOiEihFLShwjKRJTGD9whvGZwisgOdGplMjxMhQg0Y0aFihewtQkAqewYfIXyD8f8KvAPAXwf+Q+DfFUL88bfX3/p28/9NIcTPgb/5bR/gH/KRcOQXwH8L/Cd/0QLOO+LIEe084fREQ4XMa7atZeehPy1xecDrTHJVKqw09ENGPVpkrkiznEUsOLuEQ63pxoSsDgitR3qNqxxtKoiWAXGQEgyWhZGcxMCYnchUiQwmTvuInR/pDic2NiJwBhtkBEPG6XHH9VXFdbVgefUpZ20JgolAJURlRIBBBQO30wHjWpZWoO56rj+piKuCvJ3wiWGUlirp6Psto83xqsT0E5GvuQksD4cjcQpHa1miaZ3n7pxwHDpCHzK3IfahoYgVepjo9h6tHcLnVGdDdOhIRUA1q7kuLkiXBX6ecD5+zX2zJZs51sYjspSHoqfJIrrunlm+QIcKZyWdERy9wpuMZV0iWsXX0cQHs6Otn2inFmcCDlONFCeO2zOH48/5+du3TMc9T+eJiYjIDmzuBrq6htueuZn49JMvCQNDV5YUfcrFtWKMSrx5YtrGLEJJMpvhNxE2j5EzQXj06KJG9Yovb67x0cioFGFhmTUBeX/NmhP1MBDriFUTo39DMliLmo34ASorOJz2+PMzxbIgqjrMTtP3juYco5uJsdgyBSNiLLBuIgpGXBrRmhCnAka/pbInZm1EUMbc9hMX7Alfp0R5xBZFIC2uVWT2iLqPiZKYPqnwIiCrLX/y/p+yuduSxB8BQ/MuZFAjQRggpGcYCiwRup7wO4FCI4yhbgOc1OSjJQoMQTyg+hnVIECMSCmYjxo6EBoCHXIWGj9JIimYwl9eQQi/Givx/84v9/MB/sYvGe+Bv/sXzfv/bo/PI4vinge3IGZL5j1Fo3DC048jxqYcYkmynziPZ1Y6JlGOzkpmxyP10BM6DUWP9pa99KxPht3co9uUaOxxXQxTx5SE+MDRHzSH5ciV8qRXMb7ZEdprRp8S9xPDQqBOz8jZE71TyOsZuY8RaMIx5rRyfO5zJq84vYOx+4b5Z3+ZOrcc3m75vd/8bWQk2Z3vaKeB2UPAZTVDi0/ppzOnyDH5PfEkGfsBOSToWCLuaoTVnK8ykjDE7M50+xZxM8PGOT6pabcOHQlqKaGXBNGAri5w7Zls8jy5AY1FXBaYp4Y0W1KqiHZwhLMOWUPgOsx5ZBw11yrmFGfUGCILWXhiEa4Ysgk5zEjcIw8f9jxHDlFNrHXJslzTnVvc9gmXGg5PNeJyggtHFnX89I/PdOEG8zygsoKguuQ3v/gCMY+wNsK+hOjrhPXZ8dU5xwz3PE8TV3mDWSjGO8vJBJgmYTVfIGdrImHR03u6JiDQCY0+w9VAOE2UW8MUHshmAXYbEiwUo0+Ypjt+Gmnat29xZcIP54+I4RW1GYmSkuLFSHfyyM2EG0uqVYhPRsbAYpYb7Pma/aho6oTICJR4QCRXrLSh069pfnaLrF5QzS2HTrBNGp72DendxHitWFefk8U79os97W3Fdt8wRXvKCEZGul4T2wkvErL4xHkSNGUATcRkJ5g8MyRN37GPNE7EFMbgVc9JpmSRxvaaozxQTSHehJytQ7mJIE9wfYI0R9x3JA7/WqQNe+9BOvZCovcj/rJF3Pb4KqLLFWHjgZpyqOn1guVqhahh5o6EjKisYloUTO4JjGVqIPIBe62ZgHCaaKISLUeMzkD07NuY4+Edmybm+LTl5vMrTkQk/YZyERHWt3z9o4g0E6S5ZvAbnr6Zc/I7uGk5kbG4l9wFiuWnCeUsZ7LX3I/fkHVrbpY3/Pj2J9w3NcH9kVqOxKnik+p7fPHyay6yS4KpIxtD6itNYx95O0Ys2gS9jIn6GvHo6KoOyoRqrNhNO0R9Jlkk/PjxlrlZIi8CXnQGZhn75kgah5g+wr1KKXxAOFmSYs1FZYik4slA1DZMhwxfxYRU5NWO97/YoZYTsZKoKsWYlFP7CPNX+OiJqFrx+XzF4fiebtvTxwFDKIh1hpzf83l8zesfXJGmGVkfIxvB7/4b38eof43AeG5vtwRrQXSs6aaR3jfc1SGiPRBdOD4VEfsXv0/07ol/fvsLVkLShiFR0BHfzCn6PeF2y0/GLUVREOs9XZAipxqZFPTtEm+/oe01h8bxKr4iZMBlJ3RQIpWn+u0fQH1GFzOyJKcuHO2Pd8TpBTLX7DaOz2ewjF/QtD3zuiMYC57agXR3ZPAPzIYKkxSsB4/KCla54n19ohoCCrWmXzTc/uGOr7/633idvaT7F47kpiS/uGasS1793m+R/sN/QHOQHEKJFBofeoyLCMeWwQpCkTCOCtwBp0CbjFPaoYQhDypE6BjTjKBpiHuoB0GWnwhdyD6fUHWLjTKwGQE1k8zQesHQPf7S/fdrkTbs8fSTRR5nGLshPtfEQnA0knDvCc2ISEOGcMFUPCI3W8JQM6UhibaMcYQaI3Q/kUqBzaELZnilCfuWLgad7Bm1IWJi8ifiwiLTBNMq7j/0nHc9sh+x2vPhEVqTojKHGScOp5qsfUk4E8BA+zZhPUK2SBDnAf/ccLx/YnzyaGYU1tKoI+5pxA1HlJb4QdPvTmy7O+42NfvhlvrUY6sjbj8gT5f8QEjKWUprPMwLfNGis5RCKIgumVc36KJkfz/ByWDtAdHfsxOCvmlJ04hugMGD7gKW+YJWSVwwsj0ptnJgsAKSC7pPFCJMcWnD6RxQ6J6oVTBlHJ937PYdu0bT9g/IQdM8nHnc7+h2EUc50R2OzEVIuGm5FGvC119QqQB6Df2GB7Pj0LX09ycOfktYKtYy4pRVJC5ijuJz7yEb8JNEcsHpfM+OHQs5MCYDa2XIfMFBn+lSyZNVKCkR1hFHHrdPIArQU0+hTjRpjN4JslnLwJaw8BRRQTgbKWVC4gOkmGFaSWct6SQQqxCz3mDdDL9v6MeCy0iR156DnbC7CW+OtM9nFuH36BOHnsfoJMSGE0Z7wsZzmjqarkO0iuPDLdQVoxx59/ATkv1bokSBDJBK82QEvbBkgGgn8glMbwmlJpgURnjwHVWVopsQEQjcVGAn6NOObjxjniUijOgzicg9snF4MbAYwa1nKHrC9cA0Vri+xbrvKh/6NbEEhBVMUhDMNkzHGYM6Y4kRSNI4pukmwm5CR56yXmIrgykh3Ef4CKa9RfoeFjntBqrBcqqeCHyKUwlqMPQipZh6zqZHiQWyMxB2iGVJvxP86M0t//bNp4ihIlrPEI1FJnu2hx7/UjIkB3SrcLkjiWckPiFSMXVy4sN4jzgp8kISdyl/2vRcTAnPhyce7J7qoFGqYv37l9AEPB5qTGtZvDrim5y4innpJvrR8OGrr8mCiHOgscJgzJk8lYTyNeKvvSQaRsb8TKUUl0WKKdas1i84tzuSYCDKcw6PisTFfFPXzIVglBk6aDEPIEXLm6bFhhuq8ApzgtiHPPiRyUjKoaU+d1jTMQ0nDm8Ns6ggmqfE/Yi9McyOFXfH95zUSNtMVKuKl7s7XDQnu3QYFXBRzvmTf/YHSB+y6OaoWcCbyVG5lKdxJJ1lXMWKU/AD9HnD43BHoixbN5Dnc1osB/PMcdui+jM2X5KlB4zKmKmCcBIsVjHnbYSYehrm3MwSTOE/chqaEFGvaJtHfASXRcyuAxUa0kQxDI5AWXR0yf7xx7RWES9mtNv3RNcxMpW8kmuWNxc8P/4Bf/KzZy71D3ldraGYsfvwzMtFztNmg5CKwEaIVvEwHnjaHZllMb/1O/8WlQwJVleoZw9dy6OumY4jWmloCmQ+MfU9OnBYHaGkJBYtk1TYoUfECUq3CJHgAok+CZTXNCtL6Hr8MEMHLXJVEewNtexwhwNlEHA+pMwY6Zea7vwdSQL8mlgCBkNUK1w9MFQDCk0QKMJJ4MeRWGvG87ehE844IUj3MM5HvLOoWccoGrLzRB4KhiLANRkOSbDxSGVQvWMyAegQz0SrWqSJ8HWPCkfkMPLTZk+4iBHdmeOwRZqE2eeWcloxtwVxWzAL5ix7g0o1tdkwBA73KNk+bdk9NRwejpzeCR73imejcdMlQbHg5stXyHMGbUqbGOLCMfiIZgpg32Fjy8Ouo/Md531Ds9vRt4ZShPRnaBYnurcHmu6MFSE6jLFa06UeFT1QxDcon7NsQl7NC7JZiGkzdpueeL+n3TkOUcM+yYhdTf8+Iu41vpyIS884g2BWsApXiErixpEmmzPVR+7PTzS7DUZYRLemjy3CKWRzpm73dO/uOYiMfdvgm4A4mFP0JS/nr3jxyYqLeclCVcyiGw5TQB4dieORbdCQiwNyljJYgx97fJ3T+YD4dCDoHWLoOPmEWeLQcYkSIyfT0xWKYZxw0tDZlOj0QOAEZrQs7JwmntD5wCwSuE4xOUVPhshypl6hC0NtZmi5J2gC4rEjHjW2//AxTKwsKpGowCP714QvvqTuNUYpsrgmXHuaQaL6gO5sSIQhM4ay88zmAauyYJWnXC7XXJQan+fE64jp5HDLgXGSuMohBhAqJMjmBIyYtKOTCThPb0B6S+ZgGg25iXA4TDgi+pHWX2H6nmFKMeJMVnxEEwpVySHVxGagkyFqNMTTd1UO/JooASkUIvH4oEDLnnHQmEFQTJqtPsEJyiJCe08aLMm1xOUPiFrRZxLroJ40UgliOjJvySJBZGEqPe6k6CeLiz4iFKWmR5LhhUEpj7cRbTTjsTlx9/OfEScDYygZ1ROHTkHVIGyPn555fNrx3J+YfMPwDMGUMdUtSayZxTk1MZVuCOQdsZK8jj0XywI1SerxAb2WiKPlw1FytdRkSjLZnqC3vLqZ8+X1ay5fviTPBUU2cnXzCen6gmrjKBMo5i/44WxBFq7QgWTuJPY2R5yecViaWKKUYEFCWU7MC81zFXCSj3SHjqjdsBk18iqingwqiAjSjIW+plCOJk/w2x7LQPM0YtqQ3FtIQlzkGeQ9QRixXn7CYnbB6/iai/KK0Ch8mDNaGFzCFBwYywgxFNhUIGxOogcCNxL1l8g+ZexmuFHSng5M6kjFDJecmOoHjmrJ7FVKr0KS5ESnPboP0HGGEB+puZpgQEYQy0uil1fkq5KDGblvT1Smp2ZgV80Z7cDQD1wFFula6vMD9J55OXJyKX0cI4xg1NCGJbv6iZ23NEPD0Qe8KDWLpOVV4YmURKkMd4iJzi3nsebcD1g/IcuQ5XJF6wYGD0kVUhQR4XjByWzRR8d4nshaySy1OGOIo4ZeBohGcPZX+HPMlHvaRGEXFWE+YzOl5OuITluiUWJkSOBzkqmF3LIUA+IppR4EQnrGTJCIiSkdUGaHH0OMm33n/vv1cAckEBzp+oiEC4QeiFzMKWmo9DXmoqc4aVzoULFAN5quj7EzgXPAADPZ8taNtAjiwBGGml0/chPCk4GZgKArcKHAuj3RJJkCjx0EKrAoZxDDxJt3NWPpqNI5stRcpy942L2jT0LyKifrHWnimTZbtp0lxDLJI1asUGXOMvY0OqbaGVz4zFNfMrkDu13Di5cXxMSUFzXeGs6nHZOfoWKJMFAGFXmUs35Z8jL9jO6woUbwooopPYzTkoiRXkcol9IvJrLnkG59Rjdz1KFHxR0mEnjbUCzBlku+rx27xQ+ZnjYc+3siQkQNYlbRjvD+m68J05Fz45iFHlNVfCZXHF474mmGHSVBCVW6Ijn3jPmcze5HBBcv+Px1SlR+5M87+IEwCoiCgkGOLKMSXyRMRqDWDm1jlnnI8fmZqR3IxcTzKeD26ZY8mnMYRhb9kr22KFXzbBrifKDZJFy8injQNckQcGDiJtYEmWK2jz8yA00ZD/IWoTX1eYBB4G+/pksrPr8UTMZwpxV+16PHK9yxZPR7LsfXxOWeOhS8fwevXudMTpF0Cd1c8bJaUzPH7zU/fLFiqyWp05jhjl0aE/uE1fz/ae9cQm3b0oP8jed8r+fe+5x9HvdVVaZSmsQUoQgoaapJp7SXlmkIdhS0YSOSTroK2hBEUAxEEYOgYjqCIoIto1HyrtTr3rq3zmO/12u+53jY2KfMpaxjKiZxn8vdHyzmXGPNxjcYa/7rH2ONOUbBdnfg+nc+5L0vfYkH2Rfor5+hTITMs1cXzFePqOtv4iZNGyNxWhDaBmULhBwZ6InpyDArUKMnGQLGK2ozoFXEbiaGAaa8wzpNEA2dFOSF4mb0ZHFCDyNqnJMknl4kRBFRWtLEicXjjstn3/v+eyOCQAyefaepckvs9+RGMNjAzEv0YWByHV0RCQF0f8PBzOmTFJf16JuW2gtcFlCTobKScpuwyQ8UlGzqmtB75Exzo1pYg3wB/qhD7TW5SBnNAULB0Flqq3i0+Qhmc5aLY/ZxwKsZ3p/d9n+zGdvtQKITompw+0hXpLxbpbTTt9F+zpAkbEVDnOY8DBGllxy/9QPExDHM4FFT8tHBYe0xuZWMFyPJ7IjH8weMrqftz2kbw+6wR/d7dvMT2iLy1mzFIAM2W3GoL5nXCUNiWMiCNncIZZm1koCGosLVl2STpCtvV1ROFyX25hF1YXBbS1v0pFtNmnqOFmtOaKmsJZMLFsuKB2+VlN0Rh8FxM2rGzZZeSWJomRUntAZa7/AdHHY9SQr1OMPra9yxQyUFNjaMyrOvM5iNpEGSvCOYneUEqajDV0mlxqmATBJCrqjXEvvMInrNQ7Wgn/cwDRT6CBNHZDswvBjJH+RUlaBuRs6Ux4bIfFbR155dO6DmKcnYs3k5MJsL5ruSWBT4JCKLHZ4jdvke3aTkhUYUE+d9wSK1yAcKLi9YnjqESrjaJMhFwnJMOPQCUywYxh1NueOt5WMquabKF1zhWRQDZ5eO46xk8Clzk3Lcw2H2kOWsxajIhCdZaVrnqVpF1B7XVYz1gK8Sgg20g0UJKIJjgySZOVQs0Z2kyzLUfgAmtAAZFWNaUnYd+wCT0Zi+YygKfBPYbjS85i/CN6M7EDWrZQoqIMmJWUrRHpiMZ1q3KFHgEo0IlsYlCB1wU4tqC3ppsS6SbRNCJagHx17d0A8a5xoGpYjSMMgUM0LcS5QRmGtLohzBCNy1YPIpmY0UAb5yuObq4n2+MR5I93tiAmFXsC4eUyeCPjbU0x4ZYGkj5V7z/P2edFsgeod9DkWxIlG3+8e/uyo5PcqJnWQdcq7sU+YPT1ker8ClmEfQq4EX3RV+Fthf7OkOFwyZZto0fNC95CvPd2TLgtQbmvGStbtm11uanaMWjjTtmSeKcx0ZJ8d43mCmFd5V6GRicg2t09jsmFInrPMWmwU2h/p2sMk6xPGSoSpZFR2dLhAHGJUmz09YRkmW1+TJgGVHLHr6qaXrLuiHjt2hQQw1u3HLtazZn0vGaWTqA+YwkeV7zGYgJANMC9pFRDLx1d9+n65LkXWGKPewfMJb+yWT7zn2x2RHp8hCE9UDmmGLcw3Gg88UIg5cNpFWr5kNNWMX0N2BOhqcjyyLGcVRhqwVuT1lcrCJHe3gaKcaJW5IpGMc9oTuCc4fM19vSaJlrvdMWnLRWOTNNcbWtJs9l3ULZkC5Hp0sWExrrhvNicnJ35NoA8yPcVLg3UA9SrANTTOwDjnzMsc7iRhvB+q0GwnHBqENQ4wYmWLGgN8OSAOq3bGvwGYahCSkCYNuibYhPOwReSROc5rS41PH4TjHjp5cLmBlUY1Ao0Hkr73/3ohMABlpt56YeeZW0E09MZtIJ8nQzphx+xz21WW8nWjS1BSVY3u9QWGh3dD6DrlXJLGj9zBJjQwTpTc0pSa0PSF1mNYQtEE5iUsksmkAQdE0+LVkHAILteKq9hzta2afeRf3wlKu18S8Yz6AnjRj7DGpY6DCG0fVjHSzU67acx5XNamdM//sjGrKWD58izZteStbsXq64qRYsEtGlpOFZE6YTshdoA5bmve3yDKidcYqVuQ/+ieYPRuYv/M5Pry84u3sAeakIoY1MmtYTaDOCi7mG7I5RJ1w0zvsYiA2A1E8RjRblukRfrvjcjpnPDQwFJhm5OHRjDrRVEFxSCvqwzVRnxDlOc1Vgp4luOQCbSxZtqBrHL3NSfqWbHiBNQn180gQFcPumkv/nKOzHXWZ4i4stT3wZH5EfrNgcoKoIpNv0KPka83Ig6Mjnr73kPzQ8fxloLv8iPydgnePfxAxVMT9B4yDpa/g4qOGMC5ITwsSHDHmZHnF6PaUsxPi/obTdU5oJ1oduDloJj9QPUiIiWM8kmQqwzaSh4sC0Rac+QtczJBJR64Hhv0cux6Y2RlCHQg7SbfIKXrNBx9dsigPFIv3UJmm6nqebQNPyyvm88eIWjJVDatKMWYGEytmC0dqjvDRISMsVUaWB4Y+RaoB4Q3DtkPLCSMVQVminwjLGWoUhOQY29yufpTLOX27J8tShn5k2WVsfcDLBrGX2KWg7yORAqlrbC2YhMPPE/JmfM2uA29IJhCJjC6SjLDxW4oxMHpF7BVh2yG0pvETq6cDUgV642j2moPShKEjlg7ZR6wOaAd+XlElHVIIjJ8IoWFgJAwBnwRMtExpIHPHTMOIKyKd94yTRvkUrMb7Dn8m6DYd+clAdawYRrjcNfSJ51nfsemgTzqWdsXi0QNM1fP06AiRHbEKKeY6o5wp0kzz9skXWD05ATXjpre0U8qLTUPRX9OGEa8iWSsZouR6s6ebLInpmPrAw3eOWS3PWK5niGVGJjbEKnJCYDSebbeh2ArERpDXAjNPEOOKPkSm+BzVj7TdnlYp3AiHoYNkj5AD0o+UD3sOfke3v4GuY+jOaTcjHRO7uuXycsJv9xz6iBlTOFxwdKTIh5LL7gqjJMfpwOzBjIcmwyZvI5/XHNoN5WioY8qkR2RygfYNdu6Ri5aq7nj8+Tl2sWarc/pk5Eo7ijDHsCL6A10/EaUlqz3L4BhDTZJ0rJKSkChUMbBOClIjSfMjpmzO/O2S7jTDuwtmlCSuIBkqohWYzlCtGjqb4HQkk2uGtGf3bU8z9iTtFtkIrnuNmDrCdU2MliyH3eBwmSYZak5WC7ZhYOIMN5shYofpCsJ2pB9HxHGJySK5sUQBNSO7uePJ06eIUCETzxg8OrWYOGLHjBAtIAiLQLIZiIxMakduDGWrMPGaWQajdORCMcoRMU0sTc+80Ei/vl2AYmEoGklvC6yTJEOLk6+fJ/BGBAHpXy0AKTPm3hLkgqglTZWgrAIp2daOm70hJi3aeUaZEYfI9sTjuwROLL1q2AyW6EaaTjDkE20YERiyEMlzTToIwqjInabfvcRIAbJArwQhlbj1juuba25iYEhqMJbMWWz1gJMioRcCFRSZLvA7hUkNtijo0gQpNNm8IOQrDguLz3o6ZkzBUixaynGJHALdy3Mu3v8q+5uOG1sylxk7FB80kRdXV4gxZbHQjLMHlGnFJpuI2SnrxFDrnq61uGHi/BKuekkXt9wEbvuKQWJfnqHkOeKqRKFxPsc7w16MdM2WzJ4wHSTfPN9xQ8rYLuj2imfPGw51w7gfEec9z7Y97vqS4A5c769Rh8jOB6LT1LtAnY3MdYmWin6uGILl0dOnmFmFTyr6RnLoHWpXYwKcZj9AenREdViiSCifRBbVKWKUOBNRQZHmBY0Bm9wQpSUGz6J1uEWNXVVUX1wh8xOCUZSHA5nI2B8O6EFg5xbkEnUteCoFwiv6YU8adtisJ4kpZuHpzwJhKohTjk2hnCTBbMmLhCZ1yODInKE4MrThChMEnU4YDzXDVtIJySj32MUa21kW2vFBjLg4kTuPEIGjImVnS0ZZgo8M2x73/AzfvyBdLQiTRfmUVO+RRWTMJsqoML5DX8CUK6ZRUeaC4BQuOdDLjLrPMTJjKsFXEVtaxoPgEBX+0OBDgj3sGFMQZoLEEVOL4vUrC70R3YGoFX3umQmFTxu2y0DWzbBTQ0gKvK6xXhC7FjcJgsuZ0gkVPf5qYNx31GIgxhSzDoidw8Ul7C/RiaCfMsZEQasw1hHkgf4QyY0kZAVy3zKkmhB6VhtJW0lyLehaT4yWxFg2cc+uHvjsgzVxd0NUERMyvv1Nx+One3KzxnlN4j3Fk4a4KTBzy1xJbD6nvg6kaUuoG7KTjLfCmtoGpNvjLz3RNOQ41o+eMDQT37o+UM4kw4OEVQdn7UfwIz9M0Y+Y/rZ/F6THuhZVHWONJaSRLJOIwxJjHfsQNas2AAARv0lEQVTqjH1YolXP3ERKIensko82z8lczywpaTcj8rhnHx2Pi479XjANPef9BZ1KmY8l37r+ENkEnj5ymAj28ZLLoSbqGfkIUzGSaM0kLE0tKfI1x+9BwhnJk4xMZtjFMZPqCZNksFfstym5EsS9Z2EVl6Pi0LdYDLqruHp+Q7CBLDpuMihuFMfzJSJLiIllKy1Df0rRORK9oPYOszuwewDmcaD7wHLy1hrnRp6/f0aRt6iiZ3uWsxRLRD8yGMV4GVBry6lY8uKj3wX5A6Q249qcYVqNsAo57bnpoJwZytKjZIIen3KkG+Y/umYZHjGmjvZqS3zyLu88eYery6+j9xPTumW6MqyqlEE6GjlSVIqbqSaahK5LGcztw0M+dUSdUVQDQTlclzHctER9hZKSWTNwSKB1LTOR0B4mxNQiV3MMw+2Kw+GAa2fIPiBCzTSboaLDd2/4rsSRiJ0Eg4G1PeJ6G1A6QxuFiZJwFXBFgReC1HQM/R4agwsTQiU43RGmEhqPHG4ICKzeoIKgsRn60MKQEG3AjQNMoIRgLyU67ijSSO8Ei75kM90wMwZXD0zqgE88Z6klvdhQxhuuzxrqLnJ1aOm7gLEZfv4UI+fITHG970gnh60CkgVutSRZzYmHS84nwYNCs6wKbL7mKx9eYeYNXdEiRUmVgRYj3262VLlhXidolXItl5ikRZzdUAdNtqrBzfCJJ+OY9nDGWBqcTVi3M8ZlwN8sQEdOvaOeIk4MmJCxzHOaEbJpSRAVqn9Be1mwyjTzqsRXPV/7hmDqM3zaIMQCXWnmNid/+ADfjyilyUxBqBVu94zueMIdMh5lgUOVI3YHZO1YPVrTKkneDgzzHX0XsE1guxNkicJHGJxgO+/IQ8SNCeQTrWmYHysEOXvWyN01hYaYSdJDRVATuZkwRxlWGkwS2I0JygROi4K69vSmp+CIWvekmWNaZYg6ISY7XJkzxIHECqakhx20SrGuHiHjDuLEbD9D2oHlqUFi+Vxm+ah/xLwo0Ggatcfvr5mFYwYC/qBwn0lQrsGaHu1KmlkGFx3LeaQdN4Q6YfHWmpurlkmBGHu8kuDBOomPDqUStsNjivUL8DvCPEF2M7xvaI7AbUEWM+quo5Qr2qxhzCKyHhBe4uQcmQZcEEiXkreBevBQeXjNNmRvRHeAAGWlWRWKrXRkTMzlxDLu8UPNwQrSKJgWB/TUQ5Zjy44kSJpJojCYGAmuQ5kEWwJJoJ9lZF0glx5pDZMX4BOmYBiMRdNDE7BqjkxGtqqHxLLrBE3v6cYWvx2Yrm6/GM+uezYbRZv0FD7ldJHwmUcz3v6RpyyLY1a5x5YBkpQyM1Rzy2G/R3UbrscWGTbIckY8ruhjxWKe4oaCsTYkpUCflKjsiCwThIueZGpIkwOZhbzb4geFWS0IoQLfczqD0rR0Ara1Zjg33Kg9/c4QMjhONMGsyEREbXsOcYvQnlX5iKOkIJ8rQqlZGEEeZ7j5moqKH/7825y884hFeYSfwVGrWWhLrgqyeWQuNWWRIAuY0pzZvkSplGetIWlappAwNT3XwTGNA86e0PcH+m3kZi4QuYR8Qvk5bdXjzgV74Th+oni4nPE4O2L55LOYPGdmZizIiXl+u3yb3xEyjVQZqRxpZ8eMmcWYQOhK1jonAtuqICsNZTInXT+gWhjWRcMi0ZT7lGGyZD4j3wlSHVFCskggReIzTZ1P1H6FblpClyD9DDlXpNpxrVqOcs/kNHvbstUSNWUsxxnWFZiwwk6RpG+xM8FoMrb7il8Zv8bufIeqU8oixy4SotEkNtCqgMoEU9GTzbd4oXDdnFk/kVcGjSMcQGhYHiIqJIhij5YOs5tITMoq3K6qjRZoOgyKm7YlinD7I/ka3ohMQCpBl5SMbkC34DOLakYuZjllCWos6ESLGiV1ppHBsdlnTMmBpMlwSjHOB9AB20VE63FFigqBNhOE1lHZhtA6hE04Mobd2OOERM8t27bFFyWVamn3gkBOLDJK6TnbXLJ48oR5Pudi+wKXv0S2jzBpT7aIJCdvs/3aNbv6wGmx4OHjH2FdZdgk5cxtOB0Fu+sds2KFp2VQPVfPeqZa0KUKNTVMref8xUjqO+bHjs//4Gd5Z1XS1Rnb2JAsYJ28y67reHcvkAtLfS64aGrEMuP0aMX5/pxhEtg+QTQ3xNTQVHN0vyEEwbO8ZTUtyE9OyM9qDk+u8PuRwhg27pS17ejrhtXjgkX9gPm7W65raPuOo+odSDuib4nmMSEJHA8BZSaqtwOLydIoxeaq5VtnN0zDS5LoeDg74RALrscrZpTkq4AbErJkovENXdcxS06xD0ZWYUHTa4YrgdPXKK/BKrIs0i8KuvYCq1fE6FivUpLaoqzGTC3NMOKokOmOl+MFonXMRklMbgNPOY34NuVlD6k5MJUJJwtDaDNm85w6HUiMxG/X7Kce4waOjlZkyjGqitGd0TcVX/r8D7FtarIQGA8d+ngkDZ713JGvBUNRMleG67VB3Sxxm5HuWKJEzRAbbn7tgn/V/A4SRcucqnlGjBkTHh01MokUTUSh6a0nSwK7QaFpaKPgxFv6aLmY15T7gUbmBC0JsSUOS85dT6YypmbEIPDGkWYpaUwY/PDarcnfjEwgQj4aYjZij2fkdo5KIoqIn3LqfMJ4jXWeJCiSpic1Gm0Dk9mya3sONx1m5/Fu4CAyhiBIa0XeCEye0OSOkGqy2DHhMIVFC0VsA2EakVtP0xqqIieLO7JuQIwGN7SIm3M+urmkOb6m7xVKbOl85GSYg94is8ji5Clm8QBhFNa1xHiNiYorNJvLa3bjt/BNR3NjGBrHZnNGuHqOO0g4TvFpzYv9jusXmjSTxGCYHy+YrzKKVuEOAhlPGFVKy0S79NiyJUktu3rAqIo021FNG8xqSfAGFz0ul7R54EgvyGTC1FjqNKIOS1IPM1uyzG84d8+ZdM3mo54zv2E6CPLJYt0xMh0QtWVLTdie0fVbPhw7ar1BbQ1NMmOrPWFxIE4NNyFACs3e0w0bsn2Ds7dZVmECeIn0BdXslHfLnBgbkl4RbiKxvICQsShGkjSCLBkFNL5jXaSEoiDuAvlSI48tXibE1JKbSLIM6GcFnRGMrWU/BbZtTXe9RxM4dgOjh9ZFpkEhkpEL46h3iqRxNHFEK0vjBapvcAeDMTmqShljQ8mB1UlJuUhJlMM6TS6XSJkj+mMGFxkXkdzmZGJOXM9IpoSwDxTHE3/q8Rf54QdfoDETZnpJuyhx9YDJT6jmmmGyjH1ONw+EpsEwoY1BtQGrK4JR9KZF9I5oUmRvCV2HxtCHSLmWMKYsFEgFo14hp5HW9CBff6u/EUFAEIlWouuCsdX4NlIjmY8ps3WPrhtEmPBjigsZndYk9UDVnRCTgkE4FklgO3Osy5yl7FC9wClPKAR2EqhtpDSBwQkOOuC6kYjCTY4sSRFJIPaRbVvTZimynGimkYMWfNj2OD2hgiWrJS/ef8mjB0v6kxNUK+hFYG5zykSTNefILNKfJcizGrF5iXzgEG5OvXCsHjh0KHBa0wyCQXvKsWZWzfjC52bMHg/oC8nL3YGmD6RSUYmBUY3krsX2YIIhFwnZ6Tuk0xGLWJAvNEFmNEnGoXGockK6LTEWyGYgnQwqVbTuBfbqmo1/QSkLzvYjm67DXUWuNlvcVUs2NAw5bFfHxBWMhaFbGEwr6fVAQwNiQPWCbx4cU79hrQJWJIjZu8xVw0U9cHa9JY2SUHbMd9AeWmCGzQy9V2RLT13WZPIJvXUoM8eJNcW6oh1BWShSQdIFpt4zpDUhJijVcWE6srZlbDKkKvBhw3CjmJRGKUnMAuebBtFLrBaEYSRogy4LHmbH2FQy9hn6yiMIjO0Oa6E0OcU4IERKSAHTkRwKRh8ZyGgv9mg/MJ+VFIVEW0FSnhBFQ+k7wiDRImJiT8wUs6M5tqqo/FOefDbhh37sT5IYgUsEXLaEx5a4cwwhEgdHSBIEnqSQXKnAKB193qKKQJCeKB1Fn4HWjEz4IBChZ60amm1gijsu/ciIpvRbOllRaAH+9U8RvhHdAULEuprdImPu97fPWutTGntJ9lGBHkvGo0u0y1B1T3JcMbV7zveBrWnQUTLFjLmaMQ4aPRsprae/inR9JLUjWkj2o0KnkjBZpGwQLrA6KdjUPSIzGG1Q2t9OTokdmUg4+9olb727Itt4OtZU7/XMvvGA45MjVGY59IbDszOOjgvcMuPm7JqLmz1eaU5OMzJdcSLXnLcdw7ORl81LSrkgtI5ERGzjUIuK3FQ8XAmGIeXq+op5kbNpdpTO0qca1TgmEzlrJJnLGLI91veItEWkJXKMFEbRiIldGLF1z75zEBqKdU6YHM3lNR9+3ZGZnvZEomNA6JHd7kC76+iuI/ZRSmmOmA473M0BJxtCVqH2DWM6kPUFEo16+YyLNMXIwIed5vhK0xwtKLKvstg9YnukeGgMZ80BZRIWBtCKffMReZpwrDUyNUzGIJOeJEsookNlJ/hvX6LNirltuPrgGa6pWeVfQA0FSjTcOMPnppzBCoz4kN2UkpgVJnVkU387XTmdaH93QiQWqoL56YxprJhvb7hiz8m4RMcd4WHPpp84tJqlu6RuSuxqwWECu1B0fWDimnTq2JuJmFgOuw0hX7FrIFzcUC4q1vlDiqVnv79iqz0XveNYL1CxZj1k+KRBnH4BV7/E6g34CvItvpWocofUEcGMsPDE2uEbEHONKiX+YkSUAwcpmQaNkbdbmWdeEEvL4EeuvKcwliHt0L3G6RYqy+pGwSTQcwnN954rIG5XA7tbhBCX3K6KfnXXLn8Ijvhk+8Mnvw6fdH/4463D2zHG4+8ufCOCAIAQ4ldjjD921x7/r3zS/eGTX4dPuj/cTR3eiDGBe+655+64DwL33PMp500KAv/4rgX+kHzS/eGTX4dPuj/cQR3emDGBe+655254kzKBe+655w648yAghPgLQoivCiG+IYT42bv2+X4RQnxLCPGbr7Zl+9VXZSshxH8UQnz91XF5154fRwjxC0KICyHEb32s7Hs6i1v+wat2+Q0hxBfvzvx/u34v/58XQjz/ri3yvvPZ337l/1UhxJ+/G+vfQwjxVAjxn4UQXxFC/LYQ4m+8Kr/bNogx3tkLUMA3gfcAC/w68IW7dPoDuH8LOPqusr8L/Oyr858F/s5de36X308AXwR+6/dzBn4K+PfcbkH348CvvKH+Pw/8re9x7RdefZ8S4N1X3zN1x/6nwBdfnVfA11553mkb3HUm8CXgGzHG92OMI/BLwJfv2OkPw5eBX3x1/ovAX7xDl/+DGON/AW6+q/h1zl8G/lm85b8Ci+9sRX9XvMb/dXwZ+KUY4xBj/IDbDXK/9Mcm930QY3wZY/yfr84PwFeAx9xxG9x1EHgMfPtj75+9KvskEIH/IIT4H0KIv/qq7EF8tQ37q+PJndl9/7zO+ZPUNn/9Vbr8Cx/rgr3R/kKId4AfBX6FO26Duw4C32u340/K3xV/Jsb4ReAngb8mhPiJuxb6I+aT0jb/CPgM8KeBl8Dfe1X+xvoLIUrgXwN/M8a4/79d+j3K/sjrcNdB4Bnw9GPvnwAv7sjlD0SM8cWr4wXwb7lNNc+/k669Ol7cneH3zeucPxFtE2M8jzH6GGMA/gm/l/K/kf5CCMNtAPgXMcZ/86r4TtvgroPAfwc+J4R4VwhhgZ8GfvmOnX5fhBCFEKL6zjnw54Df4tb9Z15d9jPAv7sbwz8Qr3P+ZeAvvxqh/nFg952U9U3iu/rIf4nbdoBb/58WQiRCiHeBzwH/7f+338cRQgjgnwJfiTH+/Y99dLdtcJejpR8bAf0at6O3P3fXPt+n83vcjjz/OvDb3/EG1sB/Ar7+6ri6a9fv8v6X3KbME7e/Mn/ldc7cpqL/8FW7/CbwY2+o/z9/5fcbr26a049d/3Ov/L8K/OQb4P9nuU3nfwP4tVevn7rrNrifMXjPPZ9y7ro7cM8999wx90Hgnns+5dwHgXvu+ZRzHwTuuedTzn0QuOeeTzn3QeCeez7l3AeBe+75lHMfBO6551PO/wJKvfz/jtJw8wAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Boundary attack: 100%|██████████| 1/1 [02:11<00:00, 131.75s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 2200. L2 error 2912.2336 and class label 866.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Boundary attack: 100%|██████████| 1/1 [01:53<00:00, 113.47s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 2400. L2 error 2741.6028 and class label 866.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQEAAAD8CAYAAAB3lxGOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nOy8W84sybKg9Zlf45KZ/7+q9kWoYRZHMACExBvPMIB+YgCMhREgZsAYEI+AQC0kxKHFObtqrf/PzLj4zYyHrI0O9Cmk1undXdJe31tEeoa7m4eZm5ubh5gZ3/nOd/56cf+uG/Cd73zn3y3fjcB3vvNXzncj8J3v/JXz3Qh85zt/5Xw3At/5zl85343Ad77zV85fzAiIyH8qIv+LiPwLEfmv/lL1fOc73/mnIX+JPAER8cD/CvwnwN8C/z3wn5vZ//RvvLLvfOc7/yT+Up7Afwj8CzP738ysAv8N8J/9her6zne+808g/IWe+8+A/+MfXP8t8B/9WmERMcEhDjDDADHDHGAOwxBADHCGmsPx+v3PnoyYA1PECzoAD05B7c91GIJgJhgOXMeZoF6QAYbhTDAUERALDDdwCuZ43RswMMQ5zISAMnBgAzx4gyGAeWBg+monYuif+4rgRDADMxBRMMHEEO/QYYCAGM549VwUM4+IYmoggrw69epnVwyHyUseXgw1j5khXl7tE4dTh4oiJq+2OMUQVBUvHlV9PReQX6Su8me5u9eYiIGCOMPUgehL9sIvzwYTAfV4BgavdjlD/ix7DOGX/ol7PU8UFQM8YsqrZYKJw6uiOIRXPxRFcL/IhV8GCOQXucovMnfIS+6qiAiYobzKBnuN+fAG5hC1VxkUMY+5XwYIAwMVebXfvWZOUwUcEhTVgENf42aKOflF5K9xQQ3vBFQYKO4lUEx+GXf95d11gjJe184Qeb1bhuD09W45b9BfchEZqDn860VhOIPhXvUO94s8FOdeI6qqP5nZ7/+/+veXMgLyj9z7f607ROSfA//8z9dXIj0qWiZInegiNRX8PaHhxMxTQyNaxHcAxxgOdy2U5vjRIsN3yjnYU+TilOcppLmSfOJpjelwOEmo35Dm2P0FiR3rFSeKeSFFweqVER8sdsF00J0Rs2eMjjRP8B1zKz18kJoHFroWwvC8e8+wk8O/4eWg4Em+UktCHKS3xtEM6ReyHLRNCQL9FuH8gdj/Jf7msH0hdWULJ6Mrl1nxO+wuEd4m+Ca4ZcftjccqpOXC+FOB9cJUO7s7IEQcA3t6WAbJEjYEGQVWhzbHsJmLC7S6Y+tJe8zIuyKfwuVLwx4RnQRP4P4chElhGtAVIxG2jEuOkoT42YnLjozEo7+M1y10mgYYjczKfTm5xMbHR2B2yrgY/dPwPnNxjT1EtA/6PDNtjmdW1tqxlti+HMxH5qBirmPXyOXvFQuB/Xpi2zuijZwKEeN0QiwDsytOdsrNEBWmOjj6G94OfBZmGUTLfNiJ67/DySdT8DQi5fC498qXURjRo0dCFsdjP5Bphs/O2++NejZ8EWRdeOwbcwrIU9ivxuUzs8tBykocC7ZE6nHn5ge1ZLKPnMPR3oTLR+H8fcDvgE3U/mDqHZ2MdN5o18j0+KSHCW8PVGdyanTraLwyPk986pgL1BEI2slToo+NfeN//8eU9S+1HPhb4D/4B9f/PvAv/2EBM/uvzexvzOxvnHMUKQwd+PSkSaTpE18cGg6qrRS7EuuPtHHSfcWb0n5Q4ub5o5/5qQ2OEQjZk3rFbGL9oRHLjTIqfginU0pSSr9Qfoj8eDFsOrleHeYck4MmiZgLxkpPT47gEW3U7UD8Qlheljn5O6l7Rq24cPAeBHPCU3ceI1FMiUfAcqNGj/lGa4PxLaPbQioN7o1hV67d0Hbg7StjDegOpwOngsrM6n/PPozOj0zi8Efn0I09CudbIHfPfAZkLqzbV0QfSGoQN+YwI9dA2IUzB8qAM0QuzoMZUz+QZtSQEBWWVAGDa6JUx2GDfmQ+bWDTa5aO396xIVw6FLlj9UHoO4tzJBWqFG4UxtVRQmCMxB5mWtyZXaEOY/pdo37JWIMpLZxReCwXkgxGfsOVwcHB22HoPDhiJ949Y4B1mIcQDod42MTwHwapcguKBKXVKyIBcVcGO06FSwus28DwxHznpsJSDRcWnqnylgNv8YHLQmrQT4+kD/5wNr5VoZWGHifuXnHyhXgXwnqwb0LZHEc3jp8ffGmeoyjOZa6x498dS5rIwWM/gu+K8c4WM8e6wg+VNh2YVtqbYvtGOQ/C8USiEnOitQWJO5PtBEtIeDDKgrlCGo7DQ9WDcHF0cZx4YoqIeo5S2Q//q8r6lwoMBl6Bwf8Y+D95BQb/CzP7H/+x8k7E4iVAVUwUjQtiA396JOzUIuS40BxkJ1jJWDwprZH8grFRMSa3INroUajJ4U4h+BObBDuEUSAMzzTNHK2hA26u8jkLkgaXj8o2/4DlJ/70+HZwZeIzVRqROAXm/eARA8lV3PHGWE7KaThdSHZibVDmmRAH8TwZOXKxzkdx+NTREYhm2BQod+PmTqp5yuqxM3KTxqMtTHmnj0xYA+7+jXb7I2P7e3L3WFRkTozTI9OBG4FmA7OJvivrRZHkGM0hW6N7R/RGaMbTd+YE5/PC+LHgnsKFQm0e+YPn/DvBTxXOwOQqdnlDT8NNHfTEp5n+dTDiQGLmSxD+1I2wPSnzgpwOXZUoV2h38mnInKAYfXYU37HTsYSO2wvDZwqO9yBszsAKR13x8ye+zQwbfOmRz9SZglFNcSkTj0qVhsqVEZRxGj9cAs/PA03GkHfieIL3aBLcJ+gNLmbsbeAIKJUwhB4Mmyqte5Zm6HgDd3COhpOZeR2MFgnn+XLBu2NbLlz3T3q+UFxhkcQYnRA6Z2nozRMeA2e/x4JSwjfSHulTZY0rZp69VWL3NAZRFRGHG426Xmn7xDT/HTOOO+7lcUYFnTnHTpLI4oyn67xp51wX2vak6sQiHpFB8Y6gO2UkYhkUxv9gZn/zr+jfv3ELAJhZB/5L4L8D/mfgv/01A/BnQhFkKDFdsX4yeqHdHGf6QnqLSK5EGnIWqj3oc0W1I7mSQmIaE8M29uhZ6mC6bwQd1ENp6vAFzBU0VrZ2IBnmeDACxDpIn4OWHDYq07NhKliYuRMINfLlXImfhXszOIwQZ87+wJ/CdSgXKtd4snDjosZ4OHqCejrue8ahdAVfB9U87VnxTnl4h8nKhQZNuaeAWw4iE2NqHB+FsQjhebLGxJSEtgTkWPHuYNoXCh3Bv5bGGWrvtIchYzAWz4yDVWizw3WhVkdKTy5nx08LT59QjP3ZuE3lFdNYJoqbaffG6Q/cp7J12L8NtltjShekNn5qEe2eHhMJTw6dxRzh/BO+R56r52gFlw4aB3o2HA8Cii0eu0EPJx+tkSUg/kaKG/4IeH+S5oxeBhqBwxjrO/oxOF0gnonQDzgaCU//fNCXicwF9ECsM9xgeipDNvw4sdMxn0ozj6WA+kDWSGpwMQEWilbQwWwzV3fQS8dZ54iesqwcdK5m+OyAO5cWGUeguZMSjcWuvPWIHzdc/AmTD67mSD4SXETLSXk6Ju9wrpCC4xIzTiKydEQL0+X/orTEs6yEkAi3RvOVEjvTbWLWg9YP1uI468q2NyxFogVaPzlqwmoC9SRRWv5XQgH/D38RT+BfFxFvyRlTglMdVYzo37BYcfvOWN6x/smyCNvpCa7i74kzJWbXsd45xbO6CKp016kNUoiE0ojSOFx6BYEwVp/Z6yBZwaLjjIFLO3n6GcNYjoStlVAzdw8xnWSDvSacVHyr1CUQN6hvgen0NH0FwcIoBAsonToZaYcaAgxHzJXqDbd7hkEIjjEy5p+ENpF9YUyeuDvUNdri0CMQouLbhaN9gBh+Dlz3lc09kCmzDI/2kzLNNFXojfk2kY6dkRaeT15u/zLQOrNLwXxkFkdpxrwO+h1qKrgB0/CcQXFpQnRlaQ/GDH1UinPoHuE9E5+FSU/O5Z3uK8vW0R45gzEROFCWWMnWKAY9gpyJnCfu24NLyJRhhBDQ4hm54qfCOGBK0O1HvO04f/CwC7kfCIbWN9a08VEyl/cn8sxsGHEA4YK6O1YLKc6Ubsh7QO8JaRuJSCUxUsH6TvKB5DzeAk91rO8FfVzY20GcBm6Ab4GnwOXaGWcGDtw10X4W9Hed0WA+CqnPkCeetZDjy7Ot2uBS6XXlet241z8yjTvOTrxM7HZy2xeOa2OcHRcdlECJjXydKT813NpZNkdPK6E/qWmBeqDTYLZB1Yk5D+7PmSk92deJ2CvJhLMCVfHXTv3Gvz1P4F8XEaWGlS0Y2gM+zYTa6XXgbeGaKl5XzpaZj0puKyN0figddwhLDIg3ThXq3Ek9kcVBKRyT8EzvtGnC+QZL5Kke8qC5QK+e6SjsdkH2g1lOWAxnhdoqWQtzdTRV1AZSHCP9nqknmsvMOErfWVVwxSE60bRiPbK2lVkzST3mGvUU0uaIfuCBPnnMNtwAN1dImTo823yh+oVQlCwFOYXD3XlLmct1YlHj81oZ0428n3w4z2d2iCTmVJGcKV8rR3Ok42QKlXqbGSOzx41gg2uu5MPw885oyrjNvMmM/H6ixJk4g2kl9E+CTOjzip9voIl56ixt0C+Rh74x9o3rs8IqVC/M/jXz4me0CVs1NCesfiFUpe0nMUWOqow0UDWYOzF03IDZC4ow6gNtoOIJ28aQGTEjx42Hj7z/oTBOxz1X8nKgdnL6b4hGlnglzpUwCe1MOP+N6XcXbDEsngTXSTkjEkgM7s4RKVjzVFdY0sCNRPeDNsNEo+6ZVht1NMpIWDbetsjbpyBVOLzHuUFonjF2mBUJDq0TMRntW+Yt3dFykmxm74WsgfH+xFtn+JURlChKCAHfhOs04x9GtIy2gfXE6A/m7LEFgszkYpw4pqnRAX6qjA/FzoELARsZ91h/Vf9+E0bAHISmyDOQfMCOjaM/8KdDpsbHT8pYHqRe2WOg7IObyzwvhSGFe+1ciuIsMN872Q9qGrjk8WpIUSatyABVZYgx7zciMGVlrA6pEVlmjiNzjpOzGGW5kFykR8+iE7FAsJVof6IsHdd2pnsh5SsuFYZXRj7RS6QGo2qn50HpJ2EYfo7gV9DIokp0gl8XnHlGNRwD9ZE1fUINNBK2LhzRIc34dEY5J3afyL3g8GiKLPpEhsH5FdkzjCf5Oqht4k/WaEnIT6VnZdYLY048zsxDjBRAiuAfJ61X7O89vnb0KdAcLV35iJ84+XgFq/ZI7JFBRfZCuiniB93e6T3j+k6l0tyKs8GJMC4J94g4FL3CaRGtjpjBc0FvSnI7XhtHA1VH757WFtzo0AMpGkqls7CXzFttPM+KHBPz06PHSp8ioXjKJfOIjVE8usDtOBHn4ec7RZS4yGv2b+CD5+uYoRcm4AiJ6+iM1inlQMwzujHEYXkjvQvBv2GPb4iHr+J4hoO3JaNN2B+FvjxxIzBkkFwjt4Pl7pCwMLYAUTk0kYdBcvT7H3BpxtKJ9xNPV1irIs+NPe/I2nlMDdOdMxrzLBwiTGfAh9f2pesetoq1hTAH3hfHqQvtMZjeBye/7vH/JowAw+HfO9Ob4+DAh0hCSHNhbJ1bPMhfIzDjNDLFwtcF4rnCutLlwnNecNy5Z/haGn4K5DlhTcm3g0GijQAtELXzWD8YDB6aac+E+2eVEIwUMuYS4T2R5YHFTuhPPmqn42jLN0ZfuG6Zi5v4pu/UsfPZHVN/xRLCXQkqxLdKcUKShOGQNmjxQTFhv0zIpsTnjogyr19o+2DqAXvCj+9PxpgwUbw5XFjQbCS5k7aGJk8oJz171AwJGTWheodNCT0cuMKNCd8rVU5ciaiLL4OUGnlRivsd6wRxGcwIcmkUGj5kvBMmV/hjWOm5826v/2+89qLFVXrZGWPlmL8Sjk6foNsPWM9c50rsoE/PHkHzJ6MY1+XJFBVfHSonfguc20orM2994pRX3of4ByVO7EejaUYJqCkx3flcwZUMlx0LMGznvU2sKsjHgUige0f6SGxrY3LCHgN+DCgbSTzeDcwG0xikMNFWgW87m1sYyRPjgr0FJo1oCMjI1F2RsKGaGfvBxXd+lIU/DaGlCrfCOBfKJNA9uImWE2W6M1+2V0zCFkpRLCb0mTjDV4IczH5gpePblW111B743afH7e9c3UBnh3cdrY783OCYaMfAokeDcC4JdQ8iE3c8KRrpfcaOCVnOX1W/34YRwPCfleeuTAPSOTFPgrRXlPc+PGd2tG0Hcew2YUVpU0P2HbFCqgo9QZi5Af7e2B8b4yrsp6csG2kezGdFs8c5Y/g3YttR6YSflLoIfhFsKO5boPVM78LZZ+a5E64n+IDmgz3vfIZAyA+WqJgJR+yoVywYXYTnE3I21FdmhFADHDCtCV86jIi6hAxDnw9snikoqo6vfbDSGPeJyzCSJi44JBj1LSEtI+Lo7pWcMiXwcsPqgR8ecNx8QOaJi3mcOCqDaTzx5cJSQA6w+wcfY1A1oDEQw4qflT4PxDmez8bXLeLcF37SDs6YtRJK4kud8AGWfpKrcowdZCG6jdie3E9PdifTJcLlIPcFwkQ5IrUMtvnEO+Ok0K4NzZ5HNJJTYhHSxRO1ElMiZ88SjUWMVr8QHoKvjlpnek9YzNxT4YidOSsZh7pOa43JrpTqmGsnpUaRhbPt9BCxseJzxCWh9wyrY5SDi3nE7rRvOyWeLO0gDM96FtL2AzHeyFNGVPlZOrN24mrYAJsKvhtnPpHd0WTiPCP3J3j/5OEbSQraD/q6M1lku6+kZyY6YfwA47ywZPhMHZYHPQiTVOzyO0qF+MeJZgWigTuoRySWjoR/jyIP8lmoo+H8gZQn4fjyq9r32wgMOm8xOaLB0QJzrCgr0nfKspA3gfzkDFeWVGgPB+ZQGUzB2ILg7Ya0O60VcsyUWciPgUiCpeFHY98yMVeaXIn9ZKwri33D7itnfBJGwBaBvkL9ChpxN6WeV5bLB+3rGy6eHK0w3QQ3EscQpCR8fuB7omgliGOsStgyVQdz6DQ8wqB6z6zCYYbrndVFHiky18oxDC+DOAuueo4+4b0Q5aS5CNEIRM6ysfSF8vuT6+54asUPcHbBucquyqSGTZ5TlZs0ui5EjGcriEL3DsTI7wt8C9Sp4kpgiSf2Sqdja0IUcGemTU+ig7NAdpmU4dEhiCOeHiSRws7mCn14cnN0SYzZCNpJrtKqQgr48cpXSLNhxYjBqBJf8ikeESBXFjz7o7JMCa1XxHbmYGgc7Kr4HBjnoJpjEjhMCC0w5shSC6KdXYTZFbbrxGXvlCE015A54o/IMGGJT5K7slvhloxHz9QuXFAeaqze8ChbmFgppKG0PDG2htYEstOujVQT5xkYakwxIEmw+uDtmvhT9+ShkF95FzZ3lgcUg+YHE0YIEdkHrJnzaPgR2ZfGYpHjKHgPfhKyV561M2ejEvHF4XKg7SfdIqsMDvmCxa+Mkbhop4inleMfDQz+NoyAd+bNY6EDMPkITejeoeNE1DMBPRq+X9iXjXzeUNcRaZR04sdKLzthilAdSU6OEFmqYNmx1sA3U67Anhy9D4arhJCwzRGTUXzBRkaoYBGSYcNIKsy9ceQZiZW6K1MMaE+0eiJrQ05DYgan6NEYPuK84ifF3RMDxQeP184xOu5NiJ+GJUf3ickZ9fC4eafVjBsFE888r9S6k81TFpi1UaugXXBXofiJ/BgMPxjOCHunMZOs08SRgqImjMmYcIwWyMOj14OtQdoLx+zJfUZJNN1ZxTO5zr7ckOc3RkvEJTCqEENETdnaV+J8Q/rOdXZ8fquYLOQsLLWz+47zicdozKMjLlDdglNIeVDriY53uh3kkClOeW8n2yXQNuViSguVZp7VeZpPnHby7jNPGUDFWiZTOfyCtMBlqRxnIxmMqVH2C5N26iTMvdLkjdkqw3f2LrheGIvj2iY+o+JOz23qmEQ2v/G+BfZboT/ecFS8OubU+OxCDydJZy5ZeXx2xi0Ra2BtM3sE1/+O4RbGJcJ2MM+B7Tkh7mT4zqKBngfeRewJIQpP7yCC2x8ENzOG4XpE3k9sm1jiwcjC9mjEZSJ+CuWiaKmE4OhqLCKcCFZmZgwfnjyz4YtR6m94d8CpMSaHsRKbp3flXIVeG0rEZuFgJXlDv5zEPZJkJ1rFJBOeKz0dTOMHpnxBYqNNX1hH4PCJc2S+9TeYGy2fuB18H1yXFekXyInaK646piiktGJ+cMFzK5WeOp/TRLWdcQq5dWDBquFFoCSYHMN5wmksfiL3hlejH5FCYGRj9MqwgSQPzVPDjd5ntAxsq6hv6JmZBric0cmx64ZpYm8Ruw/6GThl0GehbMpanrjLTj8MfxphjsRsEBYIytkHPSrjMEptFOk8c+f41pEWiUuEIvTFsPKV7IStN372mVp+wrXBPA0cg+ROjvGNM29Ms+NSKyVPfB4BvIPV07txxEJ+u3AGz9IzcVpJa8ey0lXpBpYd+JOYDswbP7qdz2g4LcziUR8ZYUHy4PCeEDo5DVo17DBkKJciNLkRw8C7O1UFaR7FSMUgFDR1knRUPdqVj1jpzuH7jp8jNgY6FxwR6YOPUKnnTj8C+xWsZHLe0NCpwDf1zCjpvBAyfJ4VfXO4Di4fqP+Zk5841olRPbex48tCfSoWG8FNLDpRLmBaGG2n5RltRupKrAfevXMtjevFcwknVjrtJmwjoqdjjRFvE9UJbImrW6jmcEOINaC2sJjRfug8mzDGTPXx1/Xv35qm//9gQNZAso0hhrOBPQ1LwqwTqy3098TmZ/TDUabO4SIlJ1obpOvBl4+I5m/sx2AaEXc/6WLYbFioSPogEzj6GzU0yA49nrTxjXQ28vuM5CttH9TtjpdKPTf21cEpuL4RFoGijCUi8iAuBb82hi9cNbJooRE45hMTT1OPE3Cxkon0t0xZEtYNOZXQN1Q3xJQSHM4m2jDUd5p16BnbA+vasDBoQSgjgOaXC6iOUlf2x0LIGeeU3hQ5T+r4xI1GRln0dWClR+GmibCdiCwkOWGD2zUyl4rEzNIakxmzPLjuF45gNKfso9HqwNKMlfRLqm1h+VS89/j5wnw2zJSnX/ncOuvZ8BOICsexcK1GmCtlNyZ7ZSSOKZL9wUdaiTR69AStHHlnqg7jjWuM8Oi0zwk3Kut84saNimGlkT939KJUWwnBoAbE3jAX0GPFCbQW0VygVg5TRBbOlrnpDftUPN8Y2TE/My0PvJ7ooZhCGxNBlCAd7yujZvri8NsAtxI1o9Foz8Tdv7GcwlQjVwZ7AtxO00goleoOao7kxyBcInquoAd+BTc13JlJPFAcn48HxwVuZqSfd1p2YEY7Db2fJKdM88FdFaGgLTO0E9OddtmZ2kRwF3KtZMm/qn+/CSMA4NtBHBHxDnMeSYpTx5gVVOHbVyYE9YVZE64OQp+4qSNKxpKnMxHrE/MnZR70PljKiSuJ/KVTH4nITlqMfChVHLFFDnmi90GSO2uMzD4xdiBErBiL96TmWR+OHpU+PHsNeDV0vxLnyGfxjC6oQi8O715pnt45Qu3U2gifB5dnJM0ZrzMWFR9/JInhJSNSWL2yWMC3lUjF/8HT+2AyxasRpEOoRD8wAvOlcx0Q1YhhEHtCeePNAt2gT45HGzCMeHqqGCUbjQ0biREMtxV892hrfFNl3IRRoc9Pssv0EnFVGTGBO7hQ2ffO6BfKW6DvhdThcfX0FJhcwxk8XGVYo2nFt05Zjb4lJHvckeleyBV086BPkrwhp6PFwFoDJWVMC3f3ZP/xDZeU/ZK5Py+UtKGpwAXK/APxq5KOn4m9YgSe4RMQNJ5oq4yw8SV3Qlpwk9JXx5QfuLBR585kV6ahlFl404U4LUyHMWzCVYeeM615hq209WAujTkGshXMN+Kz0cJg1gcTA8bgeVHG4XF+5V2exNj4Y8hoPThZ8X8a6JvRndCArvDmT0oKfEuGc2/op9BDoC2NtQ2kf8HJhZ4aJcERE6EJ1ifInTIbESG0BJ87SSvFBpP+uqr/JmICzosl71EGl5B5SiEeC4ccvE3COYykN7oN9LojJhyPmRmQZK8DI1rp1ZAsOBNcHwyNLO9w3hPEztg7QVdk3jAJqB/0beHHK7StUkzQPCP7z8gE2h1OE2cSYvM0GssbtLsj2Mu1vjCzp4FJe2UCJkNVcd64mudeDZcMGwE3MiIHPgzO6kgxQTvReYJhuDBQ9YxeyT0wVljrQueAWCk503Yj0Hnl0QouDvyAobc/n3ql8zNOPNIc3XfECaYJN4yQE6s3vvUTX4VlHUw7HAIaPFMejKejWuCUE+ccSeCMC9OZCXGnhkpbZm7PV4LT8/mAKcDkUW3YucA4iQOSU8o0wbOQfaAScMMTrgvjfBJVOMOOuC8MV0nBcZ6VUD1Trmht7M4hePIlUO+NsAamoRwj4u1BGxkCjDigdd4SPPDcthuyNh7FyCHhz409CMMJt9XxuQ/m4Gn1pK+J323Gz0PIUuk2w2rMu9Emh58H1jz+eeLTjaPC6R5k6aT6zu4r9hbxpaK7I6SGR3E9U6WSa3qdEnwaj3Shc7A2pU2RWQr1VKo11CfEFtxkzOeD+1jQWHiTwl0c8YhYADcJWmDogSeTk3HYzLATiieng8YCvvKlzfw8DkzbbzcmYApFHdodjwHhnDEfwIRnnyk+sNkOWpBjJhcPoeBmT7fBHH5POAN+WrFNuORAyhcsBGp3DE5y7eTbIL5vOLkyrx02wy2d5/2JcHD6hdo+KAhFPDiw1BA19FqQAeVZMD84vZFNXsdLm+EPR5/AiuCbR71nF8fqJ7Qn6DPdFbw6TnNMs6Ku0NOEakWAGmc6jsVl+o+CbI3GJ90lyh4YtSOukYfDrEMfhOrQdeIydmrcGcHzTsCZZ/KKm2ccjqgG146/Fc794IvrpOApIpTZ0ZZEY7xczSgIHh8iY56IgBRw6ZOzelr03Grmc1TGCIxLJu8T092T7gqusZjSUifrgh2NMQ2OrNjvDiw7xvj5tSy7JZp3FDawSrlvxBrwa6VIpC4RT+ISAvuz0VdwCPupDCANIWoGVcIxMRXh2/PGVHW4r+wAACAASURBVAcnG4+nEZNQ4qC/CTFMDNf43BpJhLMpUw4snzOHFfxaqGlGroI/dp7mqKoc9Up9durIbK2guXF1IOJQX3AS4ZmADlfP2YTWIOpMzY0sg0kKdQhNDtbQaT8OYjyoyXOKYLNDGjjZaO0bB5F5MSabebgrcx6U1Qi9UPeAXw6yZOqkPMlM+mCmMi2DafGsqZMPeObOVZdf1b/fhicgzibnOMzhpCE54FrHhQS9EzUhbsKHDw7i6+sdaqxBeO4zI4LqA5dW0qGcvhCcoW4lLsbbTyd//z6Q0xHPzJgLPQVcaVAWQi4QEradDElcckGOwKdXYnaMI6HTgKNBcvgWcQv0Heyy458JdZU5RlTAyqBLZBLHHkHCQdo9Pc44Peg+YkXI4aQDwwWkGHhFuiGzI7TXdqZzcDwutMsnbp8Ji2Kj00fEtBA85DHxpDB1D7cZhtKHQdtQL2R7fYyiyI1xPFjT65RfGxsuBI6uBOdwVACm6CknnGMwYkZ6xrET3jP6OLhEx3EOjpCYfAFd0X7inaOv+tppKBEbFcuRpXaOoBz9ZJkTeOHoCd8bce6Mc0XcJ7ndOO0JyaEjIM0xYgd/MskPnF2BRo6VXgJilZq/MLWfsdsXtN/p5++Zl2/4ekFL50gnnsZbGYwpsY8rMT7Q3onrwvbtQPyESwNXBJkOkp95fiZsbS9Pc5uZZXDo+fomRHeoDrobRAfRD7pfkCUh5UmpE1d3YrPjFCM9Zw51r1R0Ku918PAzS2qUI2FrQnzF1NHPjozA7Av3Ycib51Y798Nj8+C6B04/wXtBHpGLc9R+gAvUAFFPivfMNVGkMhvUNlNWZWzP3+4WofNi4jPmGssK7sPBEmAz+jRj7kntoCWQRYmTMY5X7j90Fh+wCXQI+zlYSNil0o6ArYI9BJ+N1mCdTvoeKSJgEAh0G2RAYwXia5a1V36/usAYjlmVLTiSG7T+hvUn5jurUzQK7QgM37nERKnKCIZVIYdIXw54BoIIIzncGenyxInH/AD5xQhcBF+h1BXjAwx8Elw0ep/IFEJZOHNHUKbWOPWCpQKxk2eH7Z7eK71HjJVgGw4HKCYOFcdIDVc9wTXcEPpqTHvgqRXHhJeTQuaCZ/hGswW/3PnyeOMRHUdWvDSidNIjUePO2ZWQPNkC/ezY28BOIffMMwVmItXuiE1EfTLSD1RVXFPyWDjiz7gws/aTGpVeOj2vfKmVjy7kNdFL4nJ9pYlPQbBN6emXo8DVM8vAe6U1kOZeyzifiKXSf+iMXRkNFlYandDB3SbMHeyPQvJfkNoZ0gnekHOhXxV7frxy8G9X9Oj0w1guhaoTXh3TgJFP1q3wvFx++RZGJkyBugtRC3kd2GYc+Z02HqQZ3NNhoXPkN6xX1tTR+0w3JaWOrQLd0UajOYddHZf9Se+BPCZKmpCx0TqgDsLO0MjUBs2tmB5cc6LGwKQbX5/9t2sERMTwE2KBSTaOHKELEmd8eyI5Y7UTrNK7oMEhSZCxkA6lRMG/e/r2jSl9gfak1BWVgzxV+sPh4kySgxEyQWb2+kDU838z9ya/km3Zed9v96eLuE3my3xdvqbIIotVJIvFxpIt0KBlCDDkiQaGYQ8MGJ546G7mkQHDM/8F8sgGPDFkDQSBsCFAbihLhlhsi0VW81jde6zXZOa9cSPiNLtdHkTaIKAiaIkiUAEEEDiBsweB2Pus9a2vKXUjBEWKIN1AiCvFBsJNJN5VLI5oBVZNCIVmhZwMRl0qlE0ynQLTa+Ls0T7iilBEyDYgrdB5S9waWPOKoz6wSERJj4QzYxVm7+hmQ+5BVKLRY9OCEYMWjX4Ky0MiFE1KN0h/j9SGqQoTAm5tVO1QQSFtI7qG3RRSBCsWtKG2yN4I29SzxcbUAltOtABmi1QUXmlWMfQu4q86jrEwpkbtAqYZ1jWipoqZO8wuEg893ZA5d4Vd1OhqWdRFp7FrHSenaKbRb44TEZOF4iudgpQtZigwezptLrhHE3bWcSoK31V0nlj8DEdHR6LoBdMgqR47NbZYmYIhR09rkVoVRmuqirTkL4h+8GyuodvAUFfsYNlOFekcdq0XRada2O87zkdo/Y5kF0wq5KLpqqV0hRwzvRZqdkhw5PWM943tCtSdwfUjkhRNRTocyhrmWumKp5b5Io00lt7OlCxsKLrsKWGjjYHdndB6QUxjiwZlElRDk1f6mtJQprBZkDYwSiaXDpUU9uaIPU/E9oB4BxKIxaBcZEhwbgVpP/oQ+LHABNAKPSWQhLWBXiq2FdR6IiiH0YKi4lMHCLo0QuopcSZez7hqqA8LvQls2z1BNKGu6DrS1oD1hl2uCMNFBbY90PeOrl00BjmB7TSYhOsESRl1p9Fo2qCwWwNncCL43OEFSAYp4JQncfEN0L5QiiFVqGJoKV1K383TKYUWTW86Vl3QHmQohK1nNYF+6Yk6YuN2sU9rC8aNxE6gadqdoJfL6R/8PQGH64HOUXOlmBFrM1I3Ko1QHEYrjBsoY0FRMLon7zq2zeKMo5pIVuCywewuzjNNGj2FpnaU0l/0FWPHpqG1iHYe4i1KIK0T7RaS8+xWy7aA9wbtBswE98HSEgylUdnYuQ09Fa5Vw+DQVxffg/3QEV3jXAy4QLxq5EGTcmYNZ8azJ4RGDR1iR9a+R5xFFYenZy6CpIhy4ZWfXoU2sPcRbYWwA3saGExmFqh3G9oI/TIzmwXdDFUsp1OjmAT6JaxnTHMEp9iKYmcUtjpyELS6UJ3pG6V1dPWKzuygwG0fMbse0xJVjnTdGdSCGgKOSmwH1gJGGWpTbH4lRY95oTnJiohhTZbYnUmr0GfDlWootRLRtKDwdOxbR2qRLVf0U8Vp64gqo7prWhFShcbCUAXdGn74S+AJKKWeKaX+N6XUHymlvq6U+k9eXf+vlFJ/opT63Vfvv/nnLibCdALbXbT3jQbN4rVmrY2yVoKBbDaKM0hTqLpgVEHmjmZWgvasyy2t75lLpewEpngpjTvhoDRVEkEMet9xbpWsCpInmlWoZJg2w1w8DUXKXLj9JxBv8KxQL3xwKa+MJzvIFpTWtJJRfQaTUWJQoUdbh88FmNkquLYQc8SScLHStUQeNoypZNdwzVLanqI84IhxxWwNMY2kK00V3NrA9ugS6GSHthoRsGZDiSZJYBpH9JaxWUOe8UVRVabIyhw1Y4LeW7J4uulCvGmnhnFQsMQrQ3SZVoVgH9HdLdykhnKGXBp9nMl9I7fMEEGvkTw6aB13OiPZUR8sY1mRXUMFxao1mzeEeSTrniJCxbB/VMgy05mFvhhMSbQNrrSmj0I+aWbT2FImdwcm46E5WhbSttEcXMdwsXArJ4LbY6on9IoNRyyV7iQM+shMx+guh1ZOge3KY7LDTZFaE8XD1iY47tjJNWVLJKlY3TieFurQ2BeDQvBbJdAjesPlM3l6wPhIbI+R+UR57NB2gLzDqUrdCskLV+WKWDrKHNjbniEKuiXUsOAni94WbLYMp6e4wZL3laMOaO3wRrMdFXUrFDniCex2wMOBflMUOuI5UYxnGCsuV8R78lgI57+EEaFS6g3gDRH5baXUDvgt4G8B/y5wFpH/9v//WloME9bNUBTGO9aiUa1hbMWqnswRg0YVoDOkpeGUhnG46PlNh4R2QfKjUFNEhx2tFMiZ4AbO9kRXLJuyjIMinyu7Vjh1/y8Sb1GqIkGjUkMZS9ZycZ+VhhZHL5Ho2ytTEIWmoaNQladJQSuDM5nFBbpZSGLw1lDsSl0MXgvJZkRZTBqABel21O2BQSsWq9nbwmYMaQOXQfeB1hZK1lgCVhVW1RgtsMLSWYLyF+HI3jEcAiklrPccamE0ilRXZNLosyO4iTU2uDnhDgotmc0YRg0tK3R3S20zbls5MTEOiZx3mHbAmY4HldBdoKQj13nHFo4XfsLOoU6Qkr5YkV0PkAxqiqxzwE0z9eQJslFcx05nTqtFu4xeGpsVHLf08gL6xlpuUO2eUALZFJTvcVIoNYGysAWySfgB1i3RyUTUlaaFno41R0yXGbaCsopl7alBXf43smJkR54ibVvQyePxnI1hlERUK6b3cAJxhpgbE5kUKkNvqGdPiQZ7rVi2FdkC/WBZq9DZGd0UqjpmZwgtUzyEFBl9zywJmzoWBBcqXQ/zy4sS1KDY+g0bFVl5VBJ03TDeQr2wGlunaDVAWSiTIjRHS5maHXqXKDnQ4sogimgaqu/QC6Ry+pfbDojIxyLy268+n7jYiL31L7gaTq+YGohaLkaiLeFlw2RHbUecHclKX8pQVWCoOFXZrUd0bBhdYU5wgloTvh/R6ojRgnaWpjJdcujJMOiEPlS8NhdFop9I1uO0RYnGJ43SQq4FnXpMaijX0GYmOeizwVWPyxU998RmEGuxDEjRVMCdLYmCaZmtQtUNYyaqMrjmoCnoG814pNxjjSNpg6mwrgE5g2sKpQqxzqjkec0pylDplHAtlU32yN6hRWNDxqdb2h0UqRhtOG8N1yqprpTcoR8CoxhkfKB/tDHOkIuiaI1FQEOplWW9xyvDtr9iHISjz4RuRqzi7C3WVYa5Z1AB0QXlLNV6yl0jeU3bNRg9/aHha2NvHjOYRi1PmAZFSQOmOQ5KCH5HY0JsT1cbOX2GbYYsishKk8Diu8uoVwKnulFkINlG1gVXBT1rujpw5sKLGKtG3IwahCyFbHrW/hF1dGiVqGEmVc/sTpgSqWIRpdF2w9ZKCgvXpce3QBk83baxHyyr05gSMLPC9cK2U1xydgz9TkAd6RqUGiBNnFSFdUNyx1Atq7ektcHxEdsGCo2kxvE+su08IrDamf5oMUbjcyRIIlRLs4pWGyEUXItM9ki7LvgHz9YWyILYTHEaJ5pJJUqvoCr8ulD86c/cff9SgEGl1HvA/wn8LPCfA/8hcAS+CvwXInL/59wvBke3qyxzz6ALpWaa7vF+JbaGshaWPdUlGmds8TinyAl6V8h5AnMkaY1VPZWMj5cDhsEw1MS2n8jpTCiBugSqbRifSLnQmmDFkyTR01iBHk8OQskJJR22KowTcm4on7FAboYmBV+hdReRkzrtELMxKUjVUu2KNgETofYVjMXnSIueGiq1Cd45xDbaA2QjWA96VSSTcPrVE0KpC1ilPZPWUCOrGjHqTEYTdpc5tCwWzR7nNmJrSGoY73B+w+kbziaxS42jNMJSMQP4tCG6o4yF8uCpaNTYCG6PlIUomalcsQ6fcatGiD2b71jljqvwFGsa94cXGDdw/XiP9U/xo+fZ599hPzzmu7/9v/PFn/gVPvjkY77/g6+zjYY3zJ7xrdf57X/wDwk3Fm807+ze4xsf/QE6Vdp4RYkJZT27ZeHsG7e246wd8TRDiLTO0MWRaSyc1kjVCnVuZH2D6l/gV8fODZyc0CThFocyUFskXWtsEqaiOW1gTUL3gl7DhercVSZdOYlGUQhDzxo3+q0jG6FTni2BZ8O1xIO22F4TUk/dnbCHK9JuI501rVaUKijn8bKQTaBtA002RiUYs7FeDZhmaMsRZxVZDQxVOK4ZHTy6u7TF20NDukaZK8YpOq2IdsDFgtiL+Uk/j8y9grhQW0OVnsrylzMdUEpNwP8B/Dci8neVUk+BF5fdx3/NpWX4j37EfX86d+CXHqvAJpUzBqsVykHODSuZEjQ+QcOhdaUUi/QR0Q4zwXiszLpgK2RtLuBbW1C6Y2gj61zouyPa7Jm1QTG/OjkLRGito9MR1WviCZRUsurY6cQshSCa3msO8RKaEbxnFXvx8n9Q5GFGiqfTCaMGUrxUM8oJOml24niwDSONZkGnQpORZldcDVR1sf2uxmHqyGgWZlXBNHIUXHex7UY2qHJpj1TBNIXGYlgpI7QsDLlj8xtNRmyeUd6T8XRmYV0EpyaCMcy+4kvDZKH6Fb95TnajdQG/dBcZ7BTQccFqxW7ruQtn8pKxttAUPHryNk4FqtZcv/aMzijumZlsx8998YvcnxPvf+nn+K2/9+vcvPse2p9x7ZojjnfeGfnogz9mMiPf/dbv8IMXd4yiefONGz764+8TX2ycukpXe8ScWCmMeJoWig7UWTEZmH1CVkE/9bgYIQk5OdKgucmZVRTbNjGOiVYyptOkZSOXwu3oqKbj1DLT4igUEpriFpxoXF9ZjhrVKs5O1HbGaY+1jjUmjAuM+gyyZ7Fncuwv/BSVsG5AhoY6VKwValHkUOiOikRGd0JlpNQV9crbsssRGtANeHtm4xbSjLITxax0suJ0T1w7Wlhoi6F1ZzrxrK3jldkhZX1E2CUUFjmfsGpC9ZXz4Ue3A3+h8BGllAP+Z+B/FJG/CyAin/6p7/874O//qHtF5G8DfxtAaS0vEfyuwlZAK3KxBC3EAn1ppGYQE6nOoZtBisXvA+FFYW6ZqhV6GOhPiqTO6P6Kfk4s04K1lc171FkY3EaVgq2eU8x4NH5XOc+KcQFlC0Up9qI4V03THdoW7mNFeajak9qGUwo9e0pf6VZNEkUaA96u6Dhiq8HoHtVH7k2lmy8goi5CbZ7QNpp0JGPBDGg5o3XEyonjI0uLPWHJOFdAG/qUyLaDstCsIpSCtT1Rb4R8jbQ7SrU0a9D1CtcOaB2wSbGRmG3DDoYug9Oass24vaMmxzZHbL/RF0ueDWFXCQ2WZaUEjTlc0/qNm7HnfqsgO956+xkP6Tu409uM7z7h2bNbbDfxTu8xteO9997mtfPKkzDwB289Jm8ztrc8GgzPXvsp/P4a/YWL7uDnp1/he//rP+L26XTJ8XED8dEMD47RbxzMiOhKiidk35MXh7ULhzbQJ7A+wktN9IJSI85HWimsm2ZjY9KOHBdKgS4HGg2DZTtfSFLiK6vxJKe4ETjOe4yttIcNa4Am7G3jvt2y5hNeN3QTtGmsm6JJQ9Ez2Y05jxcNi1qpWyO0RjATB3uPSo84mZVRYM4NTEGJwuZKtYYtKCzXpGVjY8B2B+pgcMuMS8JiOmyFnZk5OEcIkbp2nHeNqWXaBqpzSFhpVNx9ZrUelR/Ypt2fvY//AsCgAv574E5E/tM/df0NEfn41ef/DPgrIvLv/Tlryd7DMdsL8l0XlGlI9eArvlZa1qTeY0zCxICSBYXDWEOlYsRRbCEXg9cZNsv2qGIwuASmNpyeSPWe4IUtT+QCnSQWZZC8YscBuwoxVPTsCXajeSHWis0DxSSmWlnNDtNm8ELZBKstMhnqZjF9JW4RVRROO5qu+HbZ/EVbWq/p5sxMxqkBO2iQzNoyNjkwGRsbpdsRU4NuQcUJ3TZGZ0i+QBKqaf9fZWRLpTRPpxux9oTQc6oXmnWXGsVb3KSJpxUtO4ahIdnRasUqRWlHrDYYp7Gq8pIBSQtD9mSxmMmw9cIXhs/x0fEjUmn8G3/z36SdNa8/fYs33/0C88OH3D+/53Pvf4nVrIz06KsTH3z9jt2kePb2+zxkRV8zVRsOPsMnhR8+3DHudvzhb/5ffP/0Ma/JY4ZbjawzT56+R/faxh//0Qs+/sE3uPtwQ0+e92/e5Nsvv4URzcCBYzMgjskIx6LpzIZehXUXsNqQD5Ew9Iw6Ma+gRoNLls1b3OlEC4WozasYugzJYoaRaiNyHxiuZ+aTx3cKl+CsG31U2NGwyEy/jbQuESs0p5iUo9RETRvGBYpu9DIR80J0Cl0zznSgPFUKNa8ELWRnaLoyJM2SK76OFH0Zbe/bkaMZaTpjx8bjQ+T+WvAPgZMu7HoLqnKuBkVB64LT0GqPWirFVUr60TyBv0gl8NeA/wD4mlLqd19d+y+Bf18p9Qtc2oHvAf/xn7eQUlxmp53DqEixAeICXcEtjuwVAgwpk+loZEQpeqlsXmNXg9URMR6JheKhTY0b0aTjcLGg1pa8GTbb084bZb+wa3AWBU1jCKhcWdHoJCgKsTVMaYwtsNoFrRRntaeZlVor2r4SroiDI1Qf6XJ3CUBpBVc0y9AQ74hLQ7eGxEiznlAHmsqUpVHC5UcQVVDVsd4U7PlM5/eo6oltxoWOLWfqJrigUZvF+0hLIMqhnCd2GnVayEUIVBKZ2A+4pEn34NSI20WO+hpXEy4kNgmwOFQcyXnFX3n8vSF3Hv8oIIeNcK486t/k49OfUOaB27evOb9YeePJUw6HAze3H7O/CrT2NmkaOG8nHg3C73/zM14eVlZleet6z/atj7F9YM73GJ95XhPXRdHlBdfv+dW3v8jz9SOeBku+eo/3vvge3/3aJ+yfwMv1Tf76X/0Jghn4+j/9x/RN0e0Ed+rR3crpBFo7HAutBqqqDLWSizDtFOf1jHd7Sg+1nEjTgEkJ2yfqZpjCZZKjK2wEyrpyM1UOXaSUAd00fWwcqsJ3K+IG1KJofmJtG1MKGLdQouUcNiaxJNMj1hE2xdatGGPYrQrlHXEfkSMoKUzmkjW5jT3m+YrTcDUMpHkBW8nSM9OY7MaqGqV5DnjycSUEg9kc85wIxtF1GWN7NtkoDwU3Olbk8jD4M/bfv/AhICL/iB+dOfjr/9xrqYuLkJSGNR26nlECemnIUC6BobphLKhaXpEtAmdbGNeCQkjDRDpntCm0dWCUSklAPmJ8jy5Qp5fsYgfqkvW2bA1MwDVBzELBo4sgqlK6xrg5VNHkrmKSJmuHZyHXgmKgK5GZQNALWjVUDaz6jEm37O09xQqkilOKLIpkKmJ7bLJsYUUrCBiutsZcNbVraDKmdCilaHJGI5jgSTVfjDHRtE3jJZFXgzEeM1rSuZFZMDeOfs6XtKZk8XphbhdPxeYN5exR9ohzjq4J521hL42jOuONoSyKdJ0Ic+B4fODp1bvULrF79oTDBw+88wuf52feeZ29g0+XzJMnO9ZP74lbzzje8uxKGMdbzqeNevqIqT3iyRuvUT86s2wzh+cfcvPGE/rc8XS64funbzLpWzpXMDeOpzdv8e7PvMsw7BnF8Vn/Kd992PhX/8av8eJ7B377a1/n9NmBN99+xqIsRf2Auji6kInGUZcF9dijiZSjoKbCXBu9CagacZ3QYkVt+YIvxKfgThRzQdfFP6bbHtjmjocS8Pl4IV1PGzl2hEEIi2HdJ9paGG1C6VemOCogdx3BH6haaOoyAk5hoEql1IFmz1A1+SAQKjoLdetp+0x4OBKvhNNhQKridugpa0T1lW4eOYbI7Zy5s5rQN3QXWO4N43CiZU/RlijCUAr9JsyuI2SP6iLoAvOP3n8/JrRhLcoN4BNGCTYXWrSoXlMTaCuk5iHLxRveVEz14DucOlJUQFomAb1ymC5yOvYEG6n9hE4LEh3OGXKFqUZW2+N3kXg0jM5wFzauz7AoUJJR2mLFsnQJax3tlBCrsHgoF8lnVtAbyHWlJovaV3QC5wPqqMmy0PoBvxnEntHekWqmpZ6gK4bKWd/SlwfKXsgp01dH8h39aeMcDCGu6OAowVwsxk+R3F1MN9taELpLCHJcqP6KHjAts4gBnVCmUbJDzIowcm1WjstEN2TitjBMChMVtnZkqymbYTCR51Vx/WRHN1xxe/s5fvkr72D6W06fzfRXIy0Znj7x3GXhrzz7PHOzfLy+5NFbFn4gDLeNb3zwMS/uV2w7Y/aPmOeVZ6+9zs31Las58XBeqJ82prc0+ezobhTnO8O9esGbt+/w3tNH2H1H6mB/dHztBx8wZEc0H/F3/oe/w/mT5ySViXbFHg3XLhBDIqaMMp7aKsV5Cmfs5qhNMMojeWby13R95C4qAoVkLD5m5mrRLBgJiOvo9BmvRu6yYnIbsRiudeLUXbGlgosBGe5Q1tLHzBIF1Q2kLdB3Z9SqqAi183ib0GfBa8uxL6gZWijYOmEtJCbG+Anb1KOWkdbusGbAacOW7wkWVh7hc0bkzKwnbvVK7A2qWKJR+KQobqatIybM+GTYRnPJr8w/Wkr8l5VK/M/3Uhc2XU3gcgemXlJn46VnmrLGWKiu0RoMxrHYgmNhMYHgCuPRcueEmDWT1Vz5ylGBWwXRlhASojWeylkCqhRiq+jxQlfdP1Si1Rgv6Dqx1kSShORLayAIWntKMwxUcrDkeSMHi44DXQgQH1jpUHMl9dDWCcx8AeuUZTMNt440l9lypDOWQR6ovSPEiCuQVENaZh01NllS0PTiqWkFhCyg1kse42YHvFsQgZvrwLYkpFSW3hJInDdDTwaroDq6OpNUx7Tb0ICRibhs6KbRfUTrBlc9z+97/HWl6p6n7z7lV//1X6CTPdlZPn554nz6mEePntL0Yzp54JO2UFXFzg986x8XpH7M00/22N3EMFhytJw//Axxjjd+9ilRFY6fviSdDrz5E79MHjfKJx+xH97mYD/k9el1+kFxOt0h557rq8esyvCLP/MunR35+BNL23W8aZ7w/cMLxrtE6laq6SgE/NZIvaHrV/K60S0TdQrsloUlRGzaUa8X7j8TwqDQyuFzQkzClR16n7FbI5iZ++UJ9foliIftwv2/bwv1eKS/vSGlE33xmFqYlaW3QsnbBWeontBtJKl0qrLFPVd95Q6DTSsmQBV9AYxDT388oCePrg3CPZYB0xaqGKRznIxj145ULCsG7xdaUiSrcVujp5GcQ2NJu5WraDlrhTTHWtqfuf1+PA4BgawcwYArig2PtYpEYhLPIkLVGVeFbtSsJ6FNjro2gk3YxRJ9Y4gNMbcs66dI0NhyRWBhlkTTsDXYm0CziWoc7WwuefIyI9ohYqgNbM0oGo2LtXmMmaEzLLViyyXDropBOU9IhWoqW8k0Z9jTSN7Q8oJWIMVSjMUkYZo1YgqpCX1/ySdUpWBdI62Nse6o40Y7JbqhoV2iVceqMkNx1A5MyAxBiOdEr2DD07tIPSXWcs3Eht0KjYJWleJ6iiqMKbLsoGuwmAIbVBqDb6TNY6wmD46bOqDf2qFSZXl4wc996d/h7f2b2N3r3J8/xobGn3xc+Omnr/HD5y8IWvjBB99m7oWnbc/p8CGq0nJVXwAAIABJREFUK9zvbvnC2z9Nlch3PvgmL6Ph7cdv8MPjmb7vmPRTzK7n+fmO7e7E888+5CM58I5/xvWNQxfFG9ObdDtL3PfcesN3T5HzN/+E33/4DiyOHywn1qb54ue/xOvTFd9bTnzwR7+P3XmEQp739O2B1Z4p9hKE0nWVZjLcCWqncBUOy8KjZjnYAfdkoz8Wqhm47zf69oKyge4aKhdSSNwUz8NkUVskeMPmG1YnuoNl3lnssqPmM04JKV4zDGewiq5BXlb8AM1W0rVFnQ2khMuFtmssx0q7VvSb5hg2VJqwuqHLyrgkjt2eRz5ji7BtioeauJkjvlruOovfGnM2BNvxoGb64Ckl4axiyT96+/2YtANKLAZvFKtzF0eWmC6e6s6TZov3C3hHThkrV+S20QtEl3g9XfHpcGAQT84GWkVEocVRu42S93h3pFXBZkUMgk0ajEeTyD00EYZkiK3gtWFTjal6iorE9EpbPgvySBOKYiHRzZrNdUxRs7lIbRqv6itiiCO3Rhc0VRx129BGY1HsxHK+bbR7QZme1haqMhi3ITGA2fB4tlLAOKiZSQ8c9JldgtWDviSPopWljIIqgVCF0hZKHei7hdkLzAZrA2pbUXthVwxLC8xSL7RSndBV8+zNN3jx8MBrIfD6+1/hp/6Vn+Jb3/iMf/tv/Q2aCdx9+zu8/+WfZa73zOfC3/uf/j6P6kS4vuHmNc3z+0Z5/hE3745cX7/L9Rs3/N43fsC6bTx5bcevfuWXKX5kLZmv/sZvsNSFv/rlL/OHX/smQe34pV/5CoWV+8M901uPeZ/36G8an+qZ7XufIdZwOEXiw0vc46fcxY+QrbILjv/lH/w6n3zr28S0QAvoh8jx0cDjQdElSwua5biwVkedT4ymkrBEe8lBNCnhfUVKQcsVh1TwquLtSDEPuG1gdhEnK20w5NS4WgeKWfHDyPYQ0OElRfb0rXBUK2qnCYcOuo1URzqJnI1FV3cRqm2VtC/sj451qOxyo0TPabCEbqaeKkN4imwrmyuILagml8lREfJ1YKpQqqOomcF0lCXSbgPlYUZri6DIZWCoGyIdszz8+LYDCkV9rBnvFalqaouozqAa5KgvfOh1oC2Fa99zqiuDKlSrsUp4bjKK3cVSzClc2bDdDefTmX0ybDqC0VS50HeVa7QMXq/Q76jLiUEFFkkoEcoALnakvGKkQ6aGzIqrMbAeKtk3gvFEpdFtwemJdaiEWRGLYXKwZYXXnpYVSs/s/MXlqBjNYdfoH+ol9besVAStLiM/uU7IZphjgsmgY6UvioPbGFbD6gRKIyjIwTEg1Fg4Krlk5zGhzZENzZhHVLFkZqLx+LLx0vfIlhmzZgFCZ9EqYxB+8p3X8bsnvP2l99hdP+JLP9njjOG7n36GWzJf/4e/hTyG5aXFbsJ3Pvlj3gpP6dY3MXHjoW7Iy4HH/YEffmb5xZ/4PJgR7zTVjygFvgrXP33NV25/BdlWfubnfwrjBr7z/Adsh8jUK173O75fvs3V8XN8+M3f5Z2f+RJFa/ay0j17Ay+a9/vP8Xtf/QN+4zd/nc9+53eI7hatO17fBe6B296xrge25xW6Rho8uIVSI7p6pFvpmr/wLtbMrBVqCZgrQ6gLnTRKiGyxR5cVJ4KzE/Gu0I0O183k1hPPGqWOlJ0mP2SMXLIw4oNn1RsqWpxZcUFhNrDlgSgaZ0GSZhsURXlirah9YAgRPV/hGxTzgOkC1VgG2ZjbNV4dgMAYE5vtca3QNsVy3SHK0r94wARLy4aiLJaF5DRWEq88Y/7Z/fdjUQloJZ3qyMph6owYha+OTRVECh1Qg6IlsNqyXntMXujOYIOgxVI6RYmWWjOtKnAG6Jn6M2mxiIY1Cb4rl5l9snQGtPbQZqpVSLW0VqjSCJOiFkMWsKnQxCMtoztHr+AgiqltnKsBpQnDSF0qTRdaatAFPAWTIlUcRSmu1MZiLd0olIOQ9CV9+LpOnHzDbBUxnlQNrt9QWyQaj1URyYoiDuM0ToHURqsabEaNjbJ6Ws7o60Y7OYxYoBGUpgioSaPWTFYZXxy6ZvJ+pItnRD3htfef8td+7d/ind0j8jBgJ4cfFepF4uPnPySvhg/vn6MfVuq4cvj4U5ZDQQ2CPgvjeyMhToQ3d0xrw3WP+eKXv8jL5chVeI2b1z1HZZi/88D7P3nNcLXnnA7sy46XOvLDD77LbnfFukSqdlxPI8eXd0x2z/B6YD+8RmsL9O5y6N6f+Ce/909ZD4Zvf/oHPP/kJeVm5vn3vsZQRnScyS82spt47TXHcV5ZcyVYi+iemlbMvMOPR8g9px5MW+lTYjEToTY0ic116FKx7UzmmkELSSekaBQgV0IfFfcxYXqBg0YPGp01qWh2obC29RIdP0wMshBPPXL9gFsHwnXi/LEw7CubH1EnQwtAsUg6M00FbOBhNthU8aayao93Ky1pWhlIrfB0DJxzo3QzqSi6teC4YvMF3CUte17Sj3ElIFC6it4yZRyRdUMNhbYaRAlFLFIdlkKrBXOMiBJM55ib0DtHOjokbDjdo2ymZU3UiXXZkWvF1QUdLNLUxc7LZGwVViJ2cOi1Xkr6xYKDUgKyFKoSUI7RCov0JNPwKuFmDT7w2MI9DU0k2YqPmmYTwUdidGQGqkmIJA4yoFrBFovpLpz1RYSXIWNSpvNwqg03QhRB94ItGmqPjJldtqwGtrThbI+yK1k05mFE2cykhJAhkjkrQRlYSZB7ZFkZikXoKF0CLH51vPnki9y8/YRHbz3m/f6K58sDj/sdfd0TD4mvfu33+LUv/zLff/kRH/3u1xjWxvT4io++eeD22TVbtDx7d4/vKp+kAz+t3+Dq3R3X4U30VeAX3/0lGAovT4VnQ8f+r3+RqoWXD9+nVwPs9sQPv87j/hkPk+cnryx2umX1L+ltoHvacfr0yFe/8bt84eptzq4w6x9y21/z1tO3GN+1fD4/IsuZ4/OX/JPV8PL7n/Kw3tHJhCqZjw/CjVIkqzANWjpQV4j9BaPp5kZQd0xqYNGePmyUvsBiGVtjxrK5a2685lQrUtvl6bzOnLNiOHb01w29wTYoXKn4oaOf7yhc0XqDs2fsvJLCRGyFYd2T2wyf7XB+Zc0j0xEKhYfgGGSmiiZuI8lnvBVqCaw7jZOZlZ5RBD2u9IfEpy3Tu4ydDVsRovPY60bKDb1UxP24A4NKobWhs4bjXFHKUktGq4JqjewToSri4EAL9uTwg6G1AqYgs6GEmalckSWxBCHsM+asUbHQjRnTLDVpmhKcaMTBLD11WJGzRawnKEXsV5pAjY2qPKIWBgtL7dC7gtsKKQWcGM5uxZ4HcAuybPSqww2R46rgDFjBjif20TFrTdpnupPDbjOpWmZ6JttIUjCiLp5yulFmw4AnjVBjx2Ai52LZlEHpyCCWTUe0svhNU8IZBczNc54tbhexs6PmgpcR5xea8ayAqRU/7ci5YzaJ9voV9q2nfPkrv4ZXld2LgZfpwBguYS6hFF6Ule07zzmUiMOz3gt2X/j04SPOB8PVO59jefGCbnybnX7Cs598HxM9T6Zrij3g7jvefe0NxqdPmELl009P3Bwnqt44v7jn8aO3ET+yp5BOkbtPP0KpM+3ecXcohNc0P/+5L/DhRx/QbY+Y8Hx4/Jhd0jw4i3WZ7371D5BgePLG2wxXA/oPFQ/2jJpXbpomc6JaSFTaZAg2YMoGe00tgleBJWvWqWJOBm0h5Q5XInowWBMpL1a4cdgGg9IcrcYfFNVA8YYpW+ZlQyzMkml6j3aCVEWK+sJP6VbGzVFVYfAdR73SB/BzI05gXKE7b6RBE1qjDRf3Y/Dkk8IeGs0nvFhc3rjPPY9o6Apm8ay+crXbc4wPyHxpjXMYcZvl8qf8Z18/Fs5CglBonFqkUxnl0yWAhIDzPUp5mg7IfMadM5gN2TLWNpgtLczs7I7ZHdHXESMDZbGYVOk7Q1k1FINqAmKIGozqUUqw2w1JJTQrq2zU1V9CUQVUt2KC5VwUtiv4h41aDftWafvAbgsc7Amao4pQrJBWh7zKTkAKelNQGq0Y1J1C1MKcNbEKok9sKuCjQ3uHDddobVGjYZGGXi4ocm6Cb/mSarx2bGKxtdCXHhMqfe4h7sA59GDJ60izmdB5JCzU2CBeETS8+fmfYj7uUGrl7Xef8tpbT/nXfv7nud3foMzKaTpwlCPf//r3+M2v/t9877sfMv/JZ3x63dj5kdIVHtKRiuH/Ye7NYnVL0/uu3zuseX3jnqdzTp1zqrqquqsnd7fttIMHcBIhQSI5xgIpAinihmskIq6sSEThjkhIEQJuEEIkQiACSBFRMEls2o7t7uqu6u6azrzPHr69v3nN6x242IVkkNsCYUOvm+9bF+979zzrfZ73+f9/i6VHiBllUVMVjlBFTI8ClrM1zahE5pKWnGBvzIW/obm5ZL1pmeSO/NEJBw+P6aWmXUPAkua6oO5bDGtWJiXNEghmGNsTJy06yrnorvnw4+8wCQ4Q+zn5JGKQ3Wfv4dcZnJ3x+N5D7EZx+IX3GB0ccPzVnyF9PEaNBiA0GZ5k7cB4BgyQNw2J6aga0NoQ9Ckutoj5kIANhYag2NK2FrTGdi3exqx7eXezEqRU3hCXHSK6mzwkCUkrsLYh6FsyWaDlHiqGft5CECHQ1MYgZUpVKwpZ0ccWV4fIQKOrnCof4mxI2yeoVhMllkY6tB3Q5op6ApGyVDJm0FuasCMnofcFodY0YYvFE5QV4Z9wEvjp6AkI4RUDnKjwsUW0msxJ6tBivULZDhuE6P5OEuusxWhH0glalaK7BjUQxEChQNcSoy29bcl6aFLFsE0QuqDwCq0VVQ1DetaBItEDbNPQpQYhLXpzN4ftfYgVDTrVRJ2lbEKU9CQKTGagExgjsE7hvCXX0FiJtRovLNIZpBxj5YbQCIwI0NQ4pZBS01pDpHuU504cVDn6WCOMQHzOWfQGVGwJOgVW4ZVBuRwzBLNtET5CUKPHMZKYslghPXfjxHFP2IOXAXGoKOOMb//8txjlx4SR42z/EWdv7VKUEc+eXLOXhMigJfZj/tHv/Q7L7Y/ZyfYRaodmfYvRFt1BFAScP/8el9uQyUHIO2+/TSsVA2P4pX/x1xkEPZOzQ7qqwyaOHXVAkgm8OCLRGz65ekk2SPAeqtYyIqZd3dCLEVvVYpoFNgTfSpqiYL1+jfE7HEzHmGiLaPc5PQoxccpZeo8+chTra3BTfvef/k/00YiOBa/+4BlXy4+Zv7plJufY6w3j3NKGAd4Z7DolTBtsmdMPBN62oBJcbdC+JcTRERPpDmnBCImIJXUjCFWL7D1h5ml6BS7EdB12ACkadIVqUopAka+3NDoilj2lDNAESNejggi0RdiWrpWEPqBPBGEZkI5a5r1BqgBXSfIwoutbWgIyU1LS4oIBI9NSpoqk6djIEG09eWLY9oIAiQk6rAsIvaOt/vS1A39qjwCSoMRYR9MFEBkqA8JYBliKWBE1PR7BAMuSnqBTmNwTCUuvYoK+ZGscWoX03qARiCSAICGoazZhQKgDFCltVaORlEmI8IZaLMn7DFkFNM5iwghRl4SiIwwFtfF4B3HqMDqktIZRG7DpDVL0hAJM2NPL9A45Li3Qg4gIVAH+7gZEqpbe56isoWsFsfB0RtMFkkA7Qgna1nQOpBAYP8SrAqTET1NMWxO3IV2kiEYDsqhmoyW7wSkHJ3vsJIc8e/4JOu148foKsSnQgaIONEZl3MtiHp19hU5KwnjAozcf0AlBKnvG2wvO5z29g7ifkR62NGWGcEPGg47ni1u++fWv8fT9G3rd49qcqLlFByek0wHTQnDw1iledISDIVZIDuMzWrsmShxlpWj677FzdMLhYcJ1MSAyS47TKbfZDNOmOFcw6C3XN1tG+0eITDKZCnh+QoQmDhyWI4KBRzSWRC+5uOiBjqLSlN2M8CzCXVwRMGb8Zsob5qs8Sz/B/PYlzTBl1XgS74kCGExT1iInlh41ctgqpu17krGkTWOyVcBNWyFsgE0srRFkZYf0kkAqwqRBiZzCdDjrUEOLsoq2twy2gkqC6AUiTxj1HbdRRu4t7bZBBJ7A3yHcrMjR2lIUIbG1iLig3kYoGdHHNaE0NKpFKI2v7nwe0zaknkjM2tG3PWagkNSIRYjpFML0dIkgLO9w9nQ/STnwU5IEPAIvFZ0HrS3eaYTrcXlAZw2UAh97jFe05vNrscSiSCmbgkErsSIjCmokHXGYUtsGtUnpYofKJGLbYpQl7EtSJbEupG9qXJygbUk78PSVJPIeKwpkIHGRxbYSGXjiTmIt9FTkDGh9SxJZqk7jQ4FxA3RdIESIjBQ27LG9p686TB5hO4kIJapbk/mItWmRI4W0PWHl6GtH7TWZVvTSkRtHkSp0/jbDxKAPDxhrGI93+PIb77JMGs5GB1xdbahmcwanBzy895j7P/wRjWh4t/2MTz5cU/gSgoCoGXDv/hl/8S//Bi9+/AE6syxWEpW+plpZ1DSEa8/5s2cEwwi/1AwnE473z5CNx4sNP/jkinfePeb8dcFl3TI8OeJkf4e9aU6UpVBmNAns5DvcWEmhL0h9zpWNCKoNIh7SzAvU6IBpUhDcjvjek++SJmOOj0PQY1Sb8PL2mpfXr3j4aEIeTXhwNmbd1Owqx6qsUYMhRd+hipwru2a7ecFJekwUhtwLd3iS9Syf3WIrywevn7C8ek6VKlyecpQOCAcNyji8zMho8H2L2Uha3xFIhzKS7qakCSpkqnDbDjrJwDsKpYiloFCWXOa0mw6XOuKyIemCOxGSSpB6iQgbtEsIWsPcaWQg0CrEJAbX9rRCY3qNVj26MkSmwIkRw1Az8xYVh0yKnEZohFlip56Jidn2jk62UAgq4xgRYLeS3ntkCrUxREFI37VIAU4GZFLSuvaPjb+finJACuUjmdLLAhFrbOXJBWwDhTYCwhAdbLFdTigkvU5I7YyqD9iLT5DumtvaEdqQck+RWUcvO/TKsQlChuquQSgjS2k9Wnuk1LhNh488nQBnAsLA4W2Aor1TKyqLMJBQ4/IY1gKbdyRtQmkiRLRBePA+xjd3DUWFhVRheoO0EUpJrOgIncAGYAKHMBH3z0Y82YScpQGLuqDeWHQIZ194kwePv87kZMo3dvb59pf/HG1+Z4gxe9kzOhgiTE2odinFNdMqZKFgHAaUizkqTWmDjPnlK3bfPONq6dlTNb3SDPuEF7Pn+IEkFooffPAJvqhYFSUyU3z8e79P1XVMdlOaWDMVI2ZXM+LjYyIvePtL97h8/xqVdzx/eUUew73HDzA9jNOEwcE+j47O2Ls/wdU5gQ7wmSc8zAjPQQ1bRJmzkgtkG1DLAqskPkoon1zQaU3dG7AhoW7QWiJvG/qgRRJS9JKSipCUslqTxylhlJD0HhvE7CQdJs95cX4OqznfP3+G8B2zz+Ys17c0xRXbbUq3XWH1jG4dAiv6bIiqBcSezhZoAaaJiAc9ToAyBt/F1N4iceAFiQqpvEOlOYFd01UgvUUNQtoug35NomL6sCRsFH02wVS36GCA6Vqy0GD6ACsViW0p0yHCFdhKEgeeotMEoiZP87vg9RGiNyAjbFqjvaURPZQBaAdWoZ3HeEMSpri+pXSOkRB0OsaKhq6xP73lgMcRRHf+777RRHiawJKqiBZPLFocKWGU0nYdspwTDM6QoqRyPVaEhPuSwDjijaagJsBSEhALT6EMY+cpiiEmKvGuIsAj0gA7bGEVE+QW2wX4pESqHLOtiWuNGKRUXYxoLVlqSY1k29YMpwOaKqKSFYFviRKJqwxBGODjFGu3dKECrfGNuSPoJopEPmR02vLmOz9Pdn3Jl77ydT794Ak/+v6HHD0c8eu/9uscvPOYnXSXx1RchxYKR2YEXbulelrgOccne/QuZngypL/YctUuCUZT6mJOohxR6Cmfb5hMJcqkyHALaU6ySBg2lk/bBW65wES7nExyvvPP/hekaTg+foPJ6ZjF+Qu80MQ7KY/vndD1JVdLCyOPCSwPHxyx7dfEKsZFgp3hlINgBIGl3raUzvNw7wivYtLbkiIF01cEiSCsAvpeoqY5arGgKFvSowMGvqQsl6w+meMOQ7ZeIQ0sWsFm+Qn+xqPCiOlIIoJd5uuOR/dSZKYYCFi1mvPX3+fjqxcENzDfbtis1hRyS7XeUiKZ5jXz+m7uIkpjCCZ455FW08sV0k8IGocJNxgTICtLFUuCtCXoU7K+pxfqjoY99uiyxvcglKOLBQdKsfRLbJhS1Q3Kh3TKk7oN25EkWcDSC+gjehei+4ZSpPhmi1YQBAGdrkidog4VG+WwhWekoY4NtvDotKOqEqLEU+MR3hFKSWM8IlJ0piHQDm0CKqkQuqJrQsD+sfH3U5EEpBDUTUtAhKXB78RE5T2C8QwldsmiCaOkw4/HFBe3lPMxu/sjFmtPbgXJyRH1Z6+om4ToC/u8GWuen28IfUO1LoiMo80UI9WwtYamC1FtSx951FzeEVxlTV4FlErSSYcKNMFA0ZcdwxTqosLZmG1m6Ica31eE3tL0gsh6iiBjpFc0TiK3BVp4gi6gUYbkwRExI6KTI37hrS/z9pffZG/8Jp+++APe2XlAN5IMdibcf3xMNtxl6BT9fM1NUrO4skwTWKwLciN4cvuSOnHcbyou5S3y6pCuuEZMdjHU2O0NfiSo2pZ68QGneh852sNtFZvVgnV1TUtGnIz58csNeXxNtPeIru/YObvH3tkpr148ZVXAW/czklKzeLplcu8RbfUMEQrmL3oav2A0jpB03H98D19K2mGAsClu48nGnk1VEoeCUudQVQRRRt1WSBVBFjBOHcVswOQwIdoWtComGj9i8DNrqusZN61jZmZ09oZ+VnAl10TbmGV5yiB9zcMvPOBZs2Cn11RxStEa/sff/iGbTz6iiROUK9gsbjg+OuJr33jMxaJh++pj4rDBVZpWVAR1jwot7UATeknXbFEYEgWFc0RJiMMTVBZDTUVOFzSEMZhNjwwFdSdIIwlVxEw6CAVZX2KJ8EFEp7e0hYZgSClqkkzRlxFabxGpQ24dTS9xnWSUGOpaE+iGzAR0ZUk21NhtQ9Sk6MCyTkOitqYpLaNIY52gdopAWExrUSqlSDsoIlJf0nY52lU/0U/gp6QcED5WEOsR4dBSNhnT/Qf8/J9/g+2N5O2f/Rnstme6O6AMQs7GI777/EeI9g7E8Ea6Q5POua4k2jhwht/5zvv0xhN355wXFyye36BUjBMBYxFy02+AFmUlkRT0AZiNQLgGOUxw1mN6RRQUdDrBliXDKKZvBME4wfYVJhxjihrnLSLICCKHtQYdJOx+9R3+rV/+NdKDAQ/3HzCIpmRiwW28xSw0TS2QqwXRZMy6vgNzbNctq2JJf3vL0pWEesyf+8Vf5cX5kp14TBKVrKsNewcjrl+8oI33mGhF0TXkoxGvPn1Cur/D1dPXDHYzGuMYSMXZ8WNc6umb19TrkI4IE0s++fGHvPzghzhX8oMnr0hEzFt7J6zpuLp9whvHXyIdJohTycTso4Ke+bblYLrDp+fPOD095s3jI55srvjK5G0GOwoxHpH4mCRzjOUBTV4w9AmjfJ86qUhMRiUdTWuo+y3NfI6rBnRmxaIuScIOX4EehxRVSVHAanaDnC95SoM2Vzh/RmYVTSCYJCGeMXVUsz+K2T07wnaCwPZ89OmHjMUUM+j5B3/vH3L99AVLu6CrGyYuQu3AerEkDFNc2eKko88kND3DLqYIK0KlqOoEFZY4NwK5IfGSWkvi3hH2nlalhGGCpSJLC27WEegI3XgcBYNoQN1tUDpARyHW9pQGwCLlgMRt0M6zHsborSPpLUJHFCNLJFJ04fFtSapD1vJzynSgsB1EtkMEHY0NiIkobQlakbQxrWxIvKGIA5RX2Lr5sykHhBDPgS13Zw3jvf+GEGIK/D3gAXfuQv/an+Q47IF8/00SWbNotkQ+QHDBh59lvHf8JZxvOX7vPheXt9yb7lLOC/JwzBffPMZlY6JVSyv3GFQz0CGJDfl09yOOTg6xzSH3rOF3239MpHdpFzdoedexd3WMUoY2MIjGEqgUG0iUsWAdIu6wxYDYtdhcgzG0gLWexKfs7YaY6ZQH777LaJAjRgl1ozh5MOT+g/f4yv03mJqc9e0rUhUSJxtW37klnPbcXrc01pBtLomCKYugw68kNl5R7uaEi4i9x2/hRMzejufV6pIdLclFxObVivNlRSBuWDY1ynnsg45WFgyqjsFRjvIho3ZD3TbM208ZtftclD2HgwPyXPPspkT0hlY11MmUcTxj/3SXONKMphmjk58j8Gsa3zHeRkQPDri9vCDoFHmU8sV7Z6SDQybe86XjRyThiDSziFXP8ChB+PtE9z3RUpCkI9QgRnsgyOF8wWjikF6QHRyy3SrCWUU9beDlkCroGbqKQMTsJTGDxxGrnVP2r665+qyk0luG0ylp0hFEKbv3dlGNYDhRJMMT6vM5fSzRhKyvltw8XePUGpPWyJUkd5YiWaG3kEiPalr61OE6gaw7vBljqPBe0nUxiW+p9YCgLyEUKOkJuhTX15hUopsS2XhKPD0xWoJoCmKh6GSItht6fUc3DkVD22pEaIgbiZlKSpNBs0W0NS4KcZGkriqiCuq+Z2jcHQWKhrxNqfoObwxx39OmCu8keEtpS8RA4o2g6UoSG9GEKXldUPBnPzH4y9772z/y/jeAf+y9/9tCiL/x+fu/95MWp2mMURF+HPDVt3+e3/vdj2nDkIubll/9+WNeP70zV8i2Lf24ZlOV+KstT1Y/YP+NMUt/TGgFA5VQYxnt7DLaeUTdSr7y8G1m/Rbxs56ni9fc6pqbV5r9KGdmbsCHyLIicQnrvMS3EAaexicMAosPJM5r6q7DBhlS3pFyC59xHJ4yOrnHg8dnfPO9r3A7bZRNAAAgAElEQVSUnBKkNQM1YjlfsFMoXHZDb1bMZj3rZsZtMefmuubR6RtEPYiJQcRTkgrmw1tC3uCLB0Nu4zl72R4/+vT7ZBl0r55xEzwiGAnqKMbvBqQLhRlm9D5kMhzi/RZR5dSrK3amY8oo5KZ5zaHdxUw64iJmGMGi2XKaez6qCs5OHrDeNgx2j8kOdriX7fBPXv4YebkhGmU82s2pNz23H32CS3rm9YzpJuTk5IQ4HbIo5hznA27Mlnrt2N/NMUnEbl8htkO0jihdQaojbBtRsSBIG9ryziym9AW9dDRZgTtv2bhLhmIfUyXIyGPnW0zaIXzByRFEyRnbW8siKYkuJem4QV4b3O6A1eICpKfSmtn5FYkI+Z79lPf/4HepqmtaL1B9hQlLQhNTtpo+AptAUsTIuKCzkMVbTJAyKB0rUWG0R4SSFIG1iqaWjKOategRlaLVEd61xCrAOEEkNW7aQhdgasXCWCJCbGRQXcdgEFKUAUYq3KpFeYGOE5JKUlFhmeDiksjduVsTSJLAENcT2qhCWgmyQjlN2hmcFZRigHAr3MYiY4sKJU1rkL6njIekfUdlqj82/v40LMefA9/4o0lACPEx8Eve+8vPSUX/q/f+Cz9pDyWV//P/0r/CcLRDdJLz6P7XOYo1T5+/pNIQCUiMQCWe1eWK0WCPl+dX5N6Tv7GLjR3iZUkX3bn+Tu4dMU1SrNfc3pxz/8GbzBc3mHpDMhnyw+++z3Zzy2//b79F4kOoS+aZQ9XNnXlEZ7ChJNeaJtoFe3uHRlMDsr0T3v7iV/k3/sq/yslgQiI8jfNMjnOai45FueL29cdczZdcrgree+uLbM6X7J+NkSWsJz2x6SjWMXt7O1yZnvvHMa4MSMOMJrCILsP2FSrt8DPHbPOKQeDwwQT3+gmb0YhH73yJXTWhDzrK+pyn3z9nmu4icwttxnp7Q3Y0Zmsq9nen9DeCOijZy2Mik/Dh4hnLa8cuQ262n3KzPmfz5JJqLyNVQ3biIT969T6DaIi5WrMdH/DX/uovU9WSWgUEoudsvMv59Zw22PDuvS+ipiNkH+DYspPukw4C8knM9iqgCl4imz0IWqIwILQt5xcXXH16RRU4fO8xDoaTnHq7wqYBxhUM25xVUaBCwWg/ZfaiRiSObJiimpiu2ZDu7ZBUQO7xyR6r62tWTcVv/+C3CFXCzmjA8x+8oNicM3/ylMtmReIdURyRJMc07YxtUaFFTivXSK0ZeIvzAqxC+YiVbImFx2CQWtGKiKyuCfyIWtdY74itwUlPGA7p/IY61IyalIIW693dLMjnLM3OCqRQyCSj69a4QBILTWUMzkSk9FjtaJUiDDRyE+FVSWhDqiRA+ru+U9N5dCQJemhNiIoNmbmzq5Mio0m3BJW7Ixd792d2O+CB/1kI4YH/5HMr8YP/w3H480Sw/39d9Ee5A0JqytWSb3/rV6hlQ2A6zq9XWGvoVgUq1Xzyes6Xv/ZNVtVrOg+T411qt+X841ccnu6SfvEtqo8+IQ0HPPnBE4Y/801sUbFcrnjruGPbNmxvbvm5Nx8z/8J9pmafj159THu+Jj39AlxuWXCDkw2y16AVtWuZDiVOnjHNQh6/+03und7jjXcfcHK4w7HMuVjUBNs5cwvV9parV09ZzhbUoWL/8JDV+TlhMEIECqKe6eQAVyha85KdHY/2OUUVsZso4nhE56+oZ57huAPRsTQFw+SQvnyCOjxBynfRixlhDHa7YaUc1VqhDnfYtHOev3jNo/23mNke/eoVo2xEoA0zV3OkjnGypg4Ep4Mz7PwVk2nMzvE77Kx2CL/2i1wtL/n4hx9xa5e0Vc7jvQlFNiQyIcvXPdHpDuPIMX/9iu9Wt4zTEYej9yjrOWkBw50JYZFT5GsCtUN12WMHJb4N8XJG11r8TcZ62UPgsc5gb5a4omZGx7ObjnuTfcpVx3x7TRhOqNuC04N9NhcRx3s5fblltogJgzn7D/dI6oRt35LpDoaaWJ1wcPWEt48f8IcffcTT8884UJJ0d4Ds9rm5KgmAYg2mek0TWqLQItstvQsIRUSLoO4rpn7AVq7IfEiMZy0kMvaITUSTgq5WGJug4zsOpHcgRYsQkqCLWIkCaWOk86QqoGk7Qhnjhw1tpwj6NVhF6jSllkiv8BNPWCg6G6C0JDcS4WvqTGDWFt8oiDxd6xBS0jcRUnSfC+wsSwGR89hsQ9pIlJSgBX3/x5cEfxpJ4Nve+4vPA/0fCSE++r+z6I9yB7I497cbyWcvP+Grv/IriKamFwLjW06imPmmpLaWsljw7Z/7C7x48j5VZNhTKatww3c/fJ932prl546+9x6MaJoFNnFEeULVZ+yNEzQH3M42VLctD473+cr9d3mav+Tdh1/lvKu5/OB7XN/c0oQF4cojgl06MiajCe+99YC/8qt/mX4/Y8cZBiZmlQjoelph6davqOsCip7OaVTrSSPJjcx5eAST6Bh1VNKtAupa0bWKcXzIbqZ58eyKddUjpx3iKkAFNdWmJSwkTabBdJwevYUKFa/8CjUMePpkRugLdgdT0mjC9cWS0GRclFvC2YL7p7uY+SFh2zHvCt7eO6GyG2ZXntKveOvojOAwZRgN8AOFcCGbcEn9vGfsc+KJRIwa5ssOHzuC3PKq9bwjFDIQZO2YbWPpUbidGVE/YhAKdFWwDYb4jaBxBWvbozcBfjRm11guZi+5qteIbsOsaWnMS6RMKceOrA5YX6z5tH5Ou0o5uTeiaxWDvZRMhXTVDavNmKJumA5nXNx4nF6TpA2PDk4xuSSsNJW/pFSWncEhf+lXDvjxD5/z4oM/5Ic3z0jPS9KHIYP5hGgwY9M0SCkpuxSlBElk6MqWAMdoaNlstphE4auA2nt0LAn6ChWvMTLECdBRh7GKkYdCeiLvaWtNHxlUkOB7gXWOptd0ISjvETVEytC4iDwB62K8NKi6JTSWrgY3CcjWIU0UYaWmqwoS4cCCRZAqKEyEHlTYShAGgkhEWOsxUYss7noSMm7xSPj/wllICPGb3EmV/m3+H5QD4/0D//aXfolvPn4XeRLy4/e/yztvfY2gh5vZkq2wfOtbD/nd775ie7Ph8H7A/uAQW0oWVcfwxGFXitv1jEmWsr1tsMWGW0K6ruZr732RMEhoghUuCOi3lrPRMV/+1lcISsGHNx9zuGd48bKn6uc0S8cwjeijhm/+wi/Qbz1t1SHDlpFpabaaBwcZl6/XvHh2gQwEr9Zz2u2cweGUncEhItTIIEJpzeh4xM1VTVqWrG47RsOQ8GzM+naJ5e5YqITi6uI5kzRgZ3qfbXPJOInY3Pb4cYByI27r10wmU471Liu7YZw6PvrRkvHRDmocsJcPqWaXoEcECuZNzRv5hK1eIl8LlpEgiSyNS6n710z9gIvLBYMs5uTojEVYEfchP376GfViyY9fPWc/2mGxLTk42mF0P2E42qfabAlkTj4aIG5K9Jliu+kIHAQq5s3JLpe3twwe3yMNAsDQzmtebVfEl7dUByOa85a6n3G6s09lrnnxdIbXCXW9JMwy6mbO7UKzup0RxJIkGpKPFCdHD1i1LdPxlPtHe7wyLWFZ0dSGgd3h3tffohdbhGlZznrkQGJtwu9/57/h6aevqW8uOL+6wvkG20bk1Ky9I9UJfVzg1tw5UkUO3eTopEL1AUVSQ5MTpR1B07PFE3p/R9K2oP2QptkQpAmq8QSioTaSXCtqCyYVOCFJLfi2w+qYXjcErcdYj1UxiTP0ziHVHQnbGk0YW1yVEY576sYSek3XVag0wncCWoPNemQJUaywwtFUiliCUz0OjY9CkC1282egHRBCZID03m8///8XgL8J/APg3wT+9ue///2ftM8wH3H6hRPmpSFfQh5PefLknIOdmP4gYrpIePZkztQ4hsdTrlYGUd8SJSBszfJ2zNHpHl11QLX8hPTkMdrvc5IFvPzwBc1ySXQcsrt7SlzBJq0xsef17GN2/JDd+Ii6XnB2EmCuY4Zvv0W3mLNYv6a4XTBsUhI9pLreINKQ9evX/PNZxd5hzmgypFFzdnXKJ1cvGJiccBhy//QhP/r0x+RuD65gdbtkow3pOKPJJHmXklJzU804On2H1eyc07MTrq5eMPQbbi4vWaoc7wp28xOMd/TGsbjdEMkA7yrW2Q7WnqPsPaZRzHiUUG4c3WZJmWXsiwhPS7dSbOKCoUowdPhY4ZY1C0IKW3Gm9risrjmTZ7x2hsnhQ1YXv0MS7RPthHzl8UPqQUd9WdItbjBVx+itlBBJvDfi6sVrgj3oNgnjs4Bnt3PGx7tcvXoJpkXnY9azc1abG0wf0L6aIRcWqQOe+qeEJiLaG2Cez9HjCMyAaRJxcn/C02GJEic0dUeT9jxffsbb97/I1eYCeSPIdcDByYCrucNVV8wXCaMkpNeald1QXTi6xS3z2Q3z2YKkCdjL4HyREoYr+h6UldRdR+QU/TjAFwb6hD6rcVWGpCByMSbrkVVPLySRBAWYxt7V3mLDIBC0TU9rLTJWyFRA5+mEJ7aCxkfUnWXo7xiWvvXkkWTdhWSBRDYDcrFmaRVSdqS+p6sVUbRkWwVkDfSZR4Ye01coGRDIGFs5RKzpXUdnQY2gqToSG+IjgWoMzomfMCr0/74cOAD+uzsYERr4r7z3/1AI8fvA3xdC/HXgJfDrf9Im1jke7TykPgyprm/YGRxgteXy1Ro5WrFyIdUnPQcnb7DdXjIJG5QZEFVDVoGj+uQVzcUaO0pQneDy+We8t3vKZHREdZTRGMtYR1y/WiOk4Hqz4PH0ATfPr6kmS5w9YC+F2ikOdyYYuyRNNG5wSL71bJsFYfCK14s1u/4Infb0XpGNR3ThiNsPG/pmzfFXvoifGW5uSrx/QrNWBCc3lF3O/jTmxatXjB+OmIQDvLtmtJuQlafYZkkeRPR+Td+EzNYLNsow6XqOj8+YDh+xwnCgHUIKyvkV0fEhft6QHh2zdxjgRMvsuiJLd9F1TdFsucJT3wjU6w35Fw65en1BNDnkTAe0j+5zfXVJsZCIRCBVyuxmzmSi6BvL6PgL7O62vH/+z1FSYNaCaWa5bQyzpmeyVLSnI7pFTV10lCZByJJiOyAdSszGMZLA8AFdsUbliqAeEjQtU5vS6Aqpe6pbyU3UcxoGVEcTujhn3FtWK8PWz9gffgmfe3IrqXrPVnpMKXnnwddpNz2lr3mydEwzza19xJE+5aJ9ymAds5zf8P2Pn9BUDSfjewzTmqurD9hWFZmTeBPibUwkt4Qx9KEnCEKMs2ArVBOjZIuRkrgB2RpMIulKS0BGmCr6rkO7ngGKKgaPJsUgWkUXOLzJGVPfjQGHjsw7tpHH6IqkTll2AmU7jG5xTmA0BErQ1ZZGS4xXYHOkrehiT+ADpA2oVI9tA+xgi9yGmK5ByoA47XBVhI4MnTTY2qFciPhJtQA/JcNCu/tH/tf++r+PsresFiPGaU1ZF7SzDYNDjdETDo/3eXD2JnUv2NkZ8tln52zmN3Riw3ScU7Q9cWS4+czwcv4+kQEVHpDupYxDyeLKc1V8yjfeeotWZWwWt6QiplvXhLsp45Md3t4/4JPymixKmcT2zqDDa4JUUq0Eze0TlouYbXjL/ugeaezQFfhoiEuuORq+wU1X4VzNfLHkVByTHe+yqlpmH7zP9GsnaJlQLwtyoaiDlmQ8Zfvilnm9wjWe9778Ff6z//a/5BffeEx4Mube/UecP72gsj2jZIAmQqglVReSjEbs5rtYA8kwIDQFYT7i6vKaaDjg0+99yP3DE+xQszdOWMwqEt2jpodslnOs16yW5+zuZIRuxDAFHe5S6zXR9E1evf49fvzPnrJaX5OmY8Yne6ggYHXT8is/+w1as+bJi1d88vGnLBcVb+zt8N63vkyfVgyTMatPC8LxgGV7Q20lJ4FExDlpVXDbtaANYzGiHXmieAerS+ytZZxFlOEUK1ds5jPqJET5hM1FR2uW1EqQDo84TSTDg4DrD65ohz2Bn1DXa1Rk6YSm7QsyUha3Nd/5p/8DQeaoVj1xu+btkwPSnR1+4y/9Vd5/+T7mekvlNzx8521yJtT9S87Lju2i4ejgkBuh+Y/+5n+MlRYhG5IuRvieXmuggyTA0yLR+G1CE5RgMxQSIyyZavAKJIa+E/RBgOw9RnhiF+ATAzrEly1WOFIvKZGI3uJ0SOJqapFA1JB1Ea3s6XuPBLIophIdvhbIMES6jtBJCueJRYcdxNjG47r2p1c7EMcJe7FkfPRVPlvPWXy2JB1JBiYkzE54YzLGZ9BrSUPJ5WXBQaI5/eobPL88J7A5sXnGZxcLNAH3B/do4gG+vSIxHU9e17TVnNfbS05OT9hPNavqhjgckO8nHLxzj6snN8z3FmRBxGpeUUUBdD2hWxN1Ib10rO2aIAsZy4idaYI3IcOk4aJq8b3m9WaB945IDtnZhYuXK/JPlhy9NcU8OqZYLZhGU1zQcONCVNuT2YQmBpmknFRjausYpwn7X/s5Vtsb6m3A68UCDHz5584I0pQfPQ2wiabcLpBrx+SNCVEgaRYbehdSrjuEMrzz8G3cwQA1W1L1EWkW49UVWW4Zj4+Zv7wkHD5gMkxIw4TStiQ6x9w6yuoDTvMzRn9xyuzVNU1s+fi7r1Aq5jAbcnV7gXQBpyf32NQLsp0xZ48e0HrwQrK57pgcSioTM/JDgnqLSA5p2y2bCtT+hBEGRECeJAgTkCY50UlG5y1Ra6j3D9jPH5GEhhUb9LDGdGNWreRp+YwtCd3lmJ17u7ATIueedjygc7cwb+n1HnW/IatrvvTOe5zuT3jvi+9Qra/Io5DXs1cI0WGuL3FJRlIJClNwc3HJ5bwhGmgGiSILx4xCzb/+G3+N2Ys/5Mn5jDffe4RwAa/Of8CnHxdU9ZZExrQyIKQFk7IzDqCVLLyibu+UiD7UKOsZxAEb2XBv9036oUC5KbMnnyKMJI46nNDgLIwsvve4KCSoe0yoqCqD0xrNHUuyMBbpND5zuLIhz2Hda6LWIDPwTUff/+SP/U/FSeDevYf+3/l3/xa9qbBEpGlGKD12WzPvW+Jpycg9IMURT8cUnWN3mpC5jDprMN2M28sIM79kWS64OF/Qb5cEg4RKx9y8vGQ/n/Ls4glnR0Pi/ICVahls16THpyyWV9wfnEDQMx7uYHWPNIayKRnonHw8YVavOX/6hNOHhySVZvLGCV53zH94w5OLS8YHY3aPDzGLmtD2tMmQxt1yj126g33CUcns6YJFUzHOJkSxx11t0Sd7ZKMBtrUc7b/D73z/t/j2V77J8mKBp2dVX5FJQ1lLyu2WwhZM4i9SuUvCNiXLNwwm+zzdFCQvFhw9OuWmm7F3dB9fVEgdkt57QKwFt89ecvJgRF8p7DhlOSsY+4DpMKWrKlRQ0UYJm8aTDmvKS8v4YIckOuTl8iWffP99NsuO3FtOHr+LCwVWbBmEQxLXc1UX1LVHhysG5hGBfY0dD9nPD1itbhEipo5WTAf3MJknK5es1jkiVGjd0ThLuGmRmYUgRaqcTbNmKBTZKCcUIXhP4WviPMZ0Cq0860Qir9a8WC840TXhzgPOP3rJ4tXHdIHj1MGjt4/5nd/+Q4xbEuZDvvWld0kTzev5lo+/9wPywz0eH95n994Zf/+//i94sHdItLfLptqSioxuoumvbikqSaJD3ts/ZGfvTZbDgu/86Amr4poHw7f5u//5f8ro4IhISw6/cEpkHMN4TN07quUlH/xoxvRoQCoilkpw8ugRj3ff4Fv/8r/A3/m7f4eX/+S3kHGFdApXQhqAMp5GOXqvkIRI39ChCVx3ByYJIOw9VoY450F5VCCQfUunBVI7bBvgzB9vNKp+8zd/8/+HsP8/P//B3/oPfzMbHnAxv8F3GtMvKasFr1eSXdshw10GU0XvBIsYRnEAXUsYeuqbJZtSILcL6tGdG6+WdwF8cXVJ2juG6ZQw0eSnB3Rrgxi2FOuKHkWSKw5kRHy8S7O1RK1Gupji+pLiylEGLfN+i9huuCw36PWKJnRYHZMnOdkkYxgonIrIWkebK66fvaDtNB9/8DEzKXjn3THzG7C3Ky6fX6D9Nflwyo3TvD0+YH98n8v5DbtxzuXiJdoHjMMxq+0SoSP+8PufEivPdrVC64TjqWd8cMbB6YT/nbk3ibUtS++8fmv3/d6nP7e/r4/3IiOdkc60nbZwNVTRGkoM6ITkKhDMGMEIBmCpBkiUERMQEkg1QEglUULFwKoCqcpIRs5M2+nMdMSLjIjX3ubde09/zj677xm8NLJQpmlkpFiTpb2ktSZb31/6vvWt/09Xj9BkCdF0SKrKwUf3kZ2AycgjOD2jTrdY3pjNvkIS7+k7Q93A7LmU2Q3h3R5fsqm7kKhUcAyTd7M7xt6IvGnQLIsg6NGTXeZRTCklXO4yht4Bw6FHldlk0YZKiuhZFmql0pNcdM/ECywQA8pOZ/R4xOB4yoF/n7wKGXUGqaGw2d5Am8N6j+kqpA0ous8uS3DyLa2qkasm27d39Fyfsk0wpZC6HiHEjjKqCdoBwpToYZIoYOTgHvVZLfbs0y3RZ3d4Zwavv/iCgWZhuhJyo/IuTtEx2SxDTLXl6ccfU4UVN5dzMkujXxo8+OZjRKYwv3qD0RgIT+DpYMkyxqShNQzWzz/nG09+Ad+z+Xx5wdOD+3zt2YeoLTw9ekStWDhViX/i8fTBN3mXL9jMS9I6ZNL0OZ+OyLYFZ0+OSPZLwrsEkQpqV6JtaooO6lbF6ApyG5ROQa6gRCDJNYapQF29d3dSGjIhkKUWQ7UpCwWprjDbhhLufuu3fuu//b/G31ciHUC0RMvPyIWPFjdoTwf4hsVEjRkOxrRaS17ISHKGH7Xok4ZkJ2MMG/zehHYdouo264vXOKpK5brUeYuyzVElQWzvmL9Z0+sfodsWcbgh2614+OF9XCzUA4f5RYLhSyhjl8XdGsMOyLI1U0yKfYyhBfjhnuq0x265JVD3JF5Ns3cJju5RLa+wRY+0ichkDcwtq+iG5m3J6qIPMRTrhrOvHdMWJWWpUYc5K39JUug0RkSq7bk/eUBBhxpolElN3/L5+tfPuPvjL9ihoms2+53FN09GfL74gqPBCbexxPjwkFiZIZWCR6f32c0XlPOYKpXo3IIjyyIva8quoFUbrl62yLZDbVesywWa4+GVgmK75BePDtmmHX7Q4nsWnVRRdjnZbke2Tzg5sqnNFoo9qB2GAq44J4szAr2iPThBLvckHZiTkoHwcYXELksIWpnMVAirBHUvUCXoiR51b0+0TVAYsM1muGWDPNAw6oQkLRid90jZIaGyq/t01QpXMsFoiOIU03sPYFE1iy/eXjLQJerZTyiTPQuueXSjYmodiVoRFDZ5rrC4esPq8ZDMSIkSh7s3F8huD+VAwtzkRN6We+oRiS7xYl5x7yhFi3fsVJ+mBt/uUUQmxSZiF93Ryh1/+d4HpF3C2AlJwz2kK47sjsDuo9s6P6zX3P5P36NrZLym5o+3N8jFZ7j1U3797/x77H/3H9GICNus6UyDNgLLFHRZRamBHlfIvLeOE6rMIBbM6w5FckBKyWsFyyhRc4PITBFSTdPKlGYNP7tr+KshAkIIdCNg6o9xhiNOeh4aFoN7fdZxQVF2BE7DwJjQqDWyrXNwGNBWOroeos4EG0r8UUCxX5HNSzqhMf3gkN7kMbvL5wQji7aK2NztkDOJ4X2DXZRztU85r1qO7CHzbktZ5vRGBrViMnEk8g1Mz6ZUoUbmXbFaXjAdnOAEDtGmRK020OswjYB5esfNu4bjQ59ZHnJ4+Ix1uKewHWI2nHzriNvlAmVwj2S55v6RR9+yWWy2BKMpatmhBAZ2Y5GWM+o0pVJ1Dk4fo5QHFHdfgrRjMDnhh69vUEnpnJD+yEZUHcEDn13UEH75BWPPYu/2SNKSdn3NZjzBVX3GB0PcXMNvLlk3Bg+mNm/mS84swb4BU3O5WacII8PR+phFRrRqyByHX/u1v0od35F1CrJrMJUg7Q+oLlfsy+T9jQ01ulZSyy6j/hi/FtR6SpoKJEkhoqazDjHyDWGVYr9zuZHXUMp0uoquLYhWS7K+QRoegqxTOjJVHKEZBrQyTrGmGIwpuw6tjWiMGnYypSmwJIVHTz9g/votz1/8gIHl4vuCH7y6xO0POOod4VkO/fGQ0ZHNj3/8KUku+HDkIvkWL14/J0oEvYP3omiIPm+e/y5vPv8U1/waz04fkmxT9PGYF6/WvPij7/LtD++zbvdIF1um43v0J8/oaRZv3vxDZjc1p48escvXOPqEaeGT1y6OXRLvBSLMeHEz5/EkYPFiz9/6jX+Lvztbsnv9GfW+QnUa9pVARqA1gkrX0bsWZJOuLNmIDqVt6dQKs7SplIiugqqrkBKBJjoK/c/3E/5KuA2XWcmm7Fit31ImCc7YR/RkymVFvVwjkpCmqFEVDdk1GRoWldQgiYIyUYklBckqaanQvEMGT0b0BhJpVKM0MZZskBQ5hmkTVRn+gY1n30MtKybOmN12w7JYs1+9I80bym2D1HTItYrcK2nWCZ2e4CoWj1ud5HZLKTqmByMG7pgwajkdBRy49xkdGphn93nUu49UhhxbDVeffUK1qXn5yXPMwkYXglov6GkuiuMxlhq4y1BsHUUEDHoxht3DCARNFVIkJeaoxPR8FuuMWqQYRxl+75zUNHn9/BV1Afl1RDffE4uUTVRTbi9IFQNNO2C6lXBdjTrPiL0cqadxMNCpvAF9BdwGRoFOU+TIWgHoEHXMkozL+TvS3StcTeCah0wPJ/imQ2oOkdMNw5GLIXT6uoLj6ChmwGA6RqCwbWrKokaWImxNZ2BqNPketVWxbAPdljn2+phTCaF4dJLN9MFjnOCUwdBncD7ixB0ymp4jrD4iDcmbgLZrqTuVJrGJKpfbquDdfMM2ydkVW+7efoKkVXhkEfAAACAASURBVNykEWVicDZ4hi8bxHrFZjNHz2p0a0hea2xnS/TARpQNYpXx8GhEuYLb1TW7bUwYpeDq/ML5CWEssAYl59qYu9sYVJl93RF0AY0ypT8wsHyTm8uXHEx7dJLCgWaxE9Af+pyfuNheRZPqyHqAOZgw8k9Yd4L7eczYPOT4w29RNRZyq9KkGpTvHasK2aXNc7SuQ20rdAUkSyBqgdrWVFqJ1EqIWtAIGUlWKWQNcoWuMn5u/H0lRKBoSjxbYTI8Bipur+64e37D5d073FEP/3CAo3pUdktRCOIa2lWDKjSUUmK/XNBsOgbDHma5pSkset6Ix88e4wUBSt/FsDVSq+Lw+Bxl4iAVMndhzOhJj+BAY5cUtEhcXHzBNrllefMcNahokx2JtiVeb7m4+YI3u5qsvOaz55+yyjMWixlfvnrF733yfbL8Ct9UYbVhcnbK3u2Iyozrz2c0aUonNFbvrljO3tCXfRQHZqsZsduj6wp6aMjljNtdhS3eB5zXf4QQLffvfciBl/Prj89Iaw89DzBsDT2uUc0+m6QkFBJhs8BLHeTWYba7YYKEIQpmyorL6zVKkiEWBaNhQFlblPENZahz2yisryNukxbD0xjbZyiyguceczLt0XMDVCeid+RjNxqlVCHLCYrUp8lMBkMPxdfp8gpLAhWBYqxRkOhEhqMekSoJhV4RKD5ynGLEOdpZD1mHQHPoGzXuUOfYOeZoOCQ1W+JuQ9XMUaqQe9Yp9x99wPFjF02Rcbsac+xi93VG/YCz8Sn7qmO1yri8njMSEwLNoio3yL2EWkRUdcLzl+94cbvm008+Z5/dUpVzfvLZS3r9CWFTUigWrVlgHQ55vlzy6u4N0XJLJ+DRmcVx7ymJAC3NkLWOuiuoCgk5KDkYjYnjmjfZnEJWWYgdt/UbvChmfvWOsoz58PG3yWqVpmmxS4hGHtpY8Pnlc8puzt/6m78Jp8c0pY5QJQzDQVNM7CZHUyHs9PePheoOkcnUpo2OilY01I2gMlTk9qd0KKNGMjtk6Wf7C8JXpDD4n/+d3/6tv/ZP/Yvcmx4xOj7D7t3n7OGEw5MzRAppkSP3TPJFTqO2qIZAEyVCqSgNCKwxtq8zT/eM3T4Hpk3sNXTbjgyNtLyh7w2RlZb7Xp8kTYjTkOH4hPhuxuvXcwZyi+075JVMH5+zjx/S3JZkuw25UtHUgsvlBe8uXyCjMN9DFUfURU1RyxRdRhp3/OhHz9nuX7F8mbC9uSArI5LbJZ9eveT3P/ljnn3tGY7ocEXHeHLGLkv58R+9pI23XCYbNAWQZXqqiay5vNveIhqDxeoCq3/I/G6J4Zi8vJixL69J9x1plfLRsymSZKOlBUUt4x/ImOYRqaZgmgptXGBqPorwafSUy9WafDlDESqq01JmMfaBjOMPKRqBbEkY3pjt7I7cUrD1HutdTRuXGEZLpZQ0rcX5kUVVFUjDQzRRECcyQ33MrogJVB9FKqlqDWF1lFVHutgQZksk30e1Uq7fydRaQtPJKHmJtDO5CC8Quw5JkelLRwQUaJIgUfdE2xxfKmhUDckrKNcCxZYgU7ACQc/oE4dvWF7M2L37IR0teazgmBK7yz23sw2XF1f88pOHDD2JzxYrLv7kkj0Jg8mQqqiQS0Fa5JycHPDF997x+uYdfUnC9ifQNWRxxoOPjihFjVVaOGOLMC8I+idYcso2CykXO2z9Ppa6Q9ePaFQFSQmpSpNvfechX/+FZzz94JDvfPNX2ew2vHr9Fk/WId6h9EcIucfd/DldmUPb0tQ5WSfQUGgokRG0gU5V5nRFRSvJyKKkag1a0aIpMnXV0VQyUGOqCkXVfHULg6oseHJ+jNbzkd0EJ5ewXZ19PEPRTQbykGKV4TyeYHU1xbqk6g1p91uMBmJjj97lGJ1Bo8lU5QY9F9SySatvkdERRcl6luAMBJpss6mWiFlFGlfUtcxNGWJXNoHp8+WLT9CPKyrVYyMrTBobw5Bw9RHiAFYXc37lr39ELbXczRYs5tfkjYkl1zw5PmG100mdgmq/ZCMMujpDaAZ9Teb5jz5hdDTlo2f3+dFPPqXnNHzjdEBjmxjDEe++eMnZE50buSXoZAJNomze/9DxaIJWZVSZSk/LSXYNhhRx/4OndJVNT0jc9QLGnslttcUOG1RzQK2UOFi8WG7wH5pkUcLI6fFmU1JHGcPAYxI4xIpgnDdsUpB8na6L0ccu3X5LpTeYTUlq5VSJh+bpqG3CbmFhGVMkETLTNM6eqLRlybEwWNeCdZxhyiZFVyOjYPWGBFVC0tVkK4VRUNJEBh0ZuaSyVmP6Rp9MWqEUCmmvpM48RNthyz4DL6eyFMzCYZ2kSAp4jYPwUjTZoc33+MF9CvF7yJ1C21r4A4dRf0JKRnUVMj2dslpcsV7I1HGHmIwwGxN1lUAhUSkJaArXa0jyFW1aIyxBGL6lWqjo0zPuxxm+cojrz5itQy5mV/hFinL0bYJ0QeQE+Cctvfgj7qItpmqwCkvundZoWYnn1Dw4uAeGy0ftgDw7YvPFKx4/O8KpCx5/MOH3nz9kuPmEbRNTqDaa1NKoKaKU0bWW7TrH0QWF3CHaFlGrGGpGroKeKZSajFopdKKikjV+HpH0KyECQtG42S44aXIk0Se2d6xfJgjFJrISujZiorXktzVZXpF0JmabMFZ12r6J/M4mDGQMuyZdgDANumZNo6okeYylj+iNLBprhtJkLO5Sdm9y5oc7pDdbvGfnpNGC9d2GXS/g5OkHGJXPYaCyexkTlg2FK6HXEW/vcjr/kKqRsTqXN81bYiGQlRinf8h1EuJ6Opv1Fc3QQ92ULMsGVUqQ2pS73Rui5BZZrnk8eshS81HMHC2q8QKT6ZMj3szXfF0estcSfLtPmSWM9COKzQLbsUnlgunZMWZacZvGtE3Jfr6nlDaYmkerAG9qnKFDJDXEeUi+zdldfsFrM8cuFLrJENqCqhIka43IDTFDh6Vd4NlDjDZhlib0dI2w6WH0QKajawXFXsHyOqrKozlSKJoYpTKwZAVRN1S6RFFX1HWGqXhYm4K+7VANNCSpxi3gdWIg8h00N3i+SYhEL37/JiJtasLMwuks/CSiWEZIBy6ryx329IDRtqAlZaKpVKpCnEZYjsRmt6dWSkzRQB6xalrMtqJrC+4WC8IipmgUDvo2i0LC9GrC2whPNvjlrz3hqtwyGDjsopxCbhhKKi9vN0hJg3k4YBPl4DkElOzyiFWY4dsa89uEfhVQmj7rqiBzJQzNpa5kOimjb/hYRkW2q/Cykp48oqvXJEVEoa14dnSOqTj8zxc/4uWrlKNRhKp4lGlJplTIbgdlgpVCWwuiTiKWOlyro+tsKklBLRJaTaVuGqzCIKRDzzNqTUVqTarq5yCJ+YqIQFtWXGUJ/9sPvocnK1juAEs0dKZHvHuHaVtMeiM695izQMUM+jSuzlYOcCpBJiLSN3saRcc2OtLNjrd3VwSSgWzqtNmGugMrtbld7ShWMwgi/MZEnJwTL/dMelMkXUbzKhart0S7Q2a3f4IZqEzaCZ0VcHz2dTwzAWXG7PYFgdeHXce9qY5unLDexbi6Qts4rHKNQFkxT5esqjVKKCMpBQO5h+eYLDZbLi+/z9MnAakx5OMnXyfeZNw7fsrwxCFqata3IaPhgOBgQNA74bvf/11Gk0P2qx3TkzG/9/z7HJkqc33NSDZRno7ZfLFmJPpMTwOKQkJFxeh8Qqvj3rd+CaIc61jDkSP6qoyn2FzfvqEvfFRRolgGdddwsY1xw5Y0SLD896Ydmmzh6wHGgcQybrE9FbVI6HUjhOZisWO12NDkGbNig6zonLsauycBnUjRJYVCt7BlmwcNFE9d6thita1QNwVpv8a82RJbJb3aJMxCVtche8fhtEjRzTHi3R3zwMHJS4Qr0cohquFQSSZSU3MwHPMHX/wTku2SXIaBPcbTOu72W/JO5/TMoYxz+kcBumfzoIBynRCLFjYNpSXxjaf3aeQBda6yWM2419M4GgzAtzlyC2ytROwbeo5PtL2hP3XJ7grmix16p7JeLOmfelSrBXnPYnX7DsUWiMDl3XyFPT5nu6iwuwbPLMiqHcMjl5iKzd2Sv/tf/1f8jf/gP8WvNdLORo8bjFZDVCW1UiO1Dm29Y2+Y6HmL0oZ0sozUqDSVTKok6IaK1skUmYzbq4n3P78p8CshAoZt88UPPuPI16iFRxlHNMi0Ystus0dPN5S3FbX1lstWoT+ZMjga05/GjDsTfTLEs0wqKyNbdqRJTtcYZIpKvisoUchnEdeLF8QpWE1OnaisNhGqWHI4eIyiGihpSH65JehPUZw9hw9OUa9bxKRPXu85m5zgDQZsY4ds8Ql6XiC0KU2RoasBXzuY8nzzhoFQ0OwtQvgoaoqqJNRajtE4CMcDy+Hdy0/5+Nv/Equ0I7AlTiZHlHXJ4ARefbrFyVwMrSTLdzSzGNdwedp/xE20RgCbGj7+xjka7/vCA1sn39e4Q5tO2uKNHrPe39HuC4TUctbzuFtHHMo+20WCNZYxD8dISYnh+ehywdjuk21TuomMVtcs1xEPTyfk8gXl8gCz55B0GkKouMMSJZdQNYGkVHRiQ1U2bNIbFus9okzQvAPuYh2pikgtk77TUlYQSyqNFyLJEeX1DuEZXBVL9ELhapMzVWwsR6HaqUh+gBs3bNSa015GZ2tI+pDL7StMSWI8lLGNQzYtCHnL7O2M1dWXlKaOm2XURcq+zlhqCn4ukWsdp8MDpKrFzlskYXFwbiJqidGRTt2W6IqFrHSsxAYt69APjqnlmqkaUO82SEcBeb6kqG0yURLvMw5OjimihEURk7Q5vndAFFcYeQ/P1mk1Qc+p0E2Xy92OTR6R1gYDKvShwzpf4xkZo4cnPDo7ppA37JUK2ekodjVNV1P2h3RphNaV1A0QdmiuRVlXiARkM8FyIU6gKSsSXaJvhuy3Eq0lfbX7BIos5eyDAaZik1+taN0Bebtk/2qDffL+OkqNF+RCwTh9iL7bkMxXqJJA6qm4mxDFPEWOXJriJYksUCWdfdjhmQaZgOv1BWUhsd58yqr2qbZrZkXJPUVn0bvkdPhNpobG6087tkGNnmyRhYEYOhj7O7ZrnbJ7xch9hiO7tJJKp3VY5Yak9VDaObI3pblsuCWFqODl5SvKOsIocyRpgDSyMKstZSQYDJ7gTPp848kR//h//R6zj7YopeCzyxVeqZPRcX58n997/ic8de6xCrfEygJXErwQG55IOpYxQRIyIhCEryK0sUq2W8IkYL68YSTGhN0CkXe0/gDF0omzOdsoRHVtmv0OtzCJ5R1KXSFtFfS+g7yOiXcbijomEI+J/SHzosCuHfyDBkWSUSUTXcvJbBej6xOVO4w45GI+w9yryI6D8lPc+0ARKEJlFUk4dsFdviQwp0h6SejKjHouniaxnBUMn3kYvkWl5ijKmoQWt6cx36csqhI1lQibENdz2G92NHWLMtqg5DtMIZNke16+m+F3LZtKRvc1onzLw+FDkjRkly0ZCYdAlbmzFUb9IY4Q5FpEYzjE6y3L2R7T3DHfRlR1hq7tyGY6mlvSm3horYku9aiiHaqecDj06R84XM0N9qsZ545PuiqppS0+KkY/QvKPkKsawj11V9H3B0hNSdNKxImKkvt0RUC7WRP80i9ys9khVRr5LkLXFeTYQSQludBpmxpN0WibmjwKQW+R+wr7rYKmlEitCqJFKQV7SyBKHbU2KPjZXr9fCRGQFOU9d29dU5kexmaLMlTQegPKVOZqtyCQGoSesfn+9xC5zNGTAYqek6ZDQiJmboqcZyywOes3XFy/4sC32aZH1FrDdrtl+3LDKivpmgvqrsLr96kUgdQ43CzmuBKILkPZuuzimJNzjZEUkA2HDLcdb29eEvEllVKiJinXqxhhCuQGpqNjTPuE01MLQ6tI132a6prNTPDow6+hnh5Q5BHSImO5XvJgGPDqR1/y+GzA3/xXf5PbN8/ZlRHezsE9GiCMIeFmx4lscrW5Y4SO69/ncNDH9g+oG5jPXlNLJZ4z4PP4DffG99GMPjeLFLVL8YYKqTCxPAOhJSjpHu3gkG8//kXyeE7SHpPtv0RdWuRqxry64H77mB88/yGj0yH93pTPL7c424Lg4DG+YSLKGKVNSYWGLbXQ1STagqYxuCwlgskhRVfg6Q2arqGYNfG2YeV43FdTiixFsXtU1IyUIdqZSpfJCNnAc1O6gcTF7IphZeAeBhRRQdVoHB4atLc1ezuhniUgZBS7Yrtc0yU2XlxgHAVopkS7VFGUin0jsJKKyXSKZuV0skVPcznoD5BVnfZuQ2jVdEmO2x9zvbtlYNnslT1m8Ig/+oM33P/6KdOJR9EpVFKIrhxg2AZtE2P3HbxuyCeff46y7WEdWvS0BOvga2hGhXxREjctthhx+2pBkXTY9wSjGlpdQmxTDL3HwLaRzlR++z/7d4mijD/84hX7tcRf/+f+Ge7+95yf/MlzmnZL25h0coZiKmSiRkF734NRm1RliaoIGlQUucQwJZrGJq9CZLOkqX9+qH8lREAgWF/NsM/H9IXOXPhoconqrjBkC5GbaBh0ImVrtTieQC9sFhvQ5R2pYqHW18TlHtPzWCyHXIc79psVOjdoo1MsVLZmgq83FGnGaithxQWib7DO5ky6CmNwQutqmK5HF+h0ncpNmTKpfWqnxa167LuWbrvg6KOv0332gtUiQvqWzi6TeWYPeHeQcBg8RDktWG/ecjhRCI7uM+obFELh+ZsfU3gW1/uM73znV9nOdiTL76NoLnUm+Aff/S6/9hvf4htnATmCq1nEydmYWs4xDxVWxZJaKsh3JZPJA/bJNbokeDg6ptuEiKHFs/6QzV2MOjrkcZeRJSpy38K1XcbTCaLIUHWNdt1hWsec/GLF7PN3xLnKxfwCf3jO8u017uAMV12zyw3M/ZxoWGGnHjgRXSmIfJlWcTHLGKNewLBAj0MU00JqW2RVwxYy27oh0CLatiNsDLrNikooyJlNoJbs4pJZHFGlOm3bYJd9lk2KP7vBrgSlI7OPBqhSjdNCZbssNi/YpCHZPqRX52zmNS/mBZ6skMg3jBwLO/HwfIHXtKiJhT0UiL3KNk/pGwFhltJlAivQ0DQZfV8hnZpU24RNveDy5Wue3Dsni29pLYlMH5JKFsfBiFQtKeMWfZBzELns6jt6yhRzYtDJEU5tsG6XmK3P+uYG92CKZSzQch3HVcjWCXddyLnvkGo53t6l7UIGfkBPlbizWnwd7uwemVpjy5CJErmEwmpBl6j2Fa4iI5Gyb0xoBEKvKNqOugS5irBkQdE0KEr9cx0F/j+LgBDiCe/ZAn867gP/CRDw3l5s+dP1/7jrun/4552V5QWeatCpGZY/5CBtaNUYxX6MnO+ougKdPZtdxETykGSJl1fX6KMOQx5zdihR1gqOPWT1+QWXxS0D22MfX7FTYFy0FLqGHDjEsxipGWO6MUVcU0oFhw/PKVSPuuwY9Q0qI6MrDYSqoiY7ljc7eqdn6KWgJxW0hkMTyxx/9AR7m1BcXTE+lEjna6beIZqVIlVDDg4nFJ7Hxx//FW4v33A6PWX+S2usqym7xTWquSE2JeZfzJg+fETPczBwyEOZXbiki3Xuf+OAE29MmHck2ZaRbNIYBpLm0k0bis9k/PtDpDKkzE2yVUY+UsicjCydIUsntGqIVhr4qgR+gSsPUTZDzO4SWZ6yjJfspYYMGc+zuZyvaEVKutwiGx5pu0ELJcabUzJ3h1lYWEZGFx/gWgXpFoQX4IQ7Gvseob6muDJQVl+ytAdoik35tiFybbqqwjJk1KlMUq8xC5NNLdHtJQw1ZhkViMrFbgqicR83FWzymIGSsIoS8rDDHsjYzgmaPkbpbUjWKpI/I09q7t58hqhrtqqCY+sEvomuaBimjzBstK4iTOZwNEX1J3w4NLi+WhINcxTd5iBQuBQBUr1B311SOz6rdYbWGhz3Y4woJrb3pIOaJku4+bzh3umI67d3XL9ccz60iIoQc9rh9B4iqgK7J7ja36ELHQ+Dty+umQ77nAyPgRSls4nKGUPLJ5s3sN/wS/d+ncqW+GN3zKApaRtBauiAgqHkxI2OJiXkpoayV5B1CVnLEYVMYwk61UDZlXQ9lS4tyXQFNn/BItB13ZfAN34qCDJwA/wD4N8G/suu6377/+lZqixj6xppoWDvCtb7CLSMcv+Cy9tbJvIIqQedqoGZIEtbnPMphjOlzUKWUc5ut4aLPa+UCFt20KqMiW2RZBXJ3YpMWNjGgHsqrJU7pKVF0cY0eGTzPda4JFGH7G4lptMGSd7iKFMu05RSzQlEQK9vs9EOUdIFhtWn6QokacaTX/1LjP0eq2qNK6vIa53K2fLw5APEQOLHX/6Ac8sji+ecnx+yTUPS+ppwvebg/AT34UNevNkgHcE3/uovcHwoY6ljnBOXSgjmcYStCi4v5oyf/gJ9XeHz4nMGFzJW4DO0PepCcGXWVJbGOnmBVjjkNZjyisYHrTeizMGtbOooR6dFciY0Tcz0zKM3/g6zn1ywrnYclhKnHz8l3e/o6QeYtUIVR1TlAikTZJmE3TdIxIZM6lP1JJqwgtDixdUP8ZY5P0luOW/7KOMYW8nRRhZVWZEpLVZ7QitSjjWNi6rAdQPW6wum3glHXUWnWKR6QD9bsWl7jE0fBZ2TYEe6CqFrkIRD7Kt0hcS4pxDqHo13x+1nf0QbzhDSgAfjAyJLposy3s7fsAyv+Nf+xr+O1fZJ9y0nvRE/eP4DJkc+1z96S6l0dJcp5w++xe/8j3+PqGmYaDra+B6StEUqTCJbpcsWmMspI9+l3N1RKD7HjySivUS9i1mlK5zERrFKispkck/C2tnUaYPXG7PN3qJaCq1v0eoBu2SPiFaYWp8wucSbDPhffvxdfumjx/zir/w6//0/+R8YYTOWajZWR5E2GHJJLaBrayS9RGTvmYeapmAkBZjQGA0ismmlFqSf7yz0F5UO/NPA667rLn9qNfb/aghVImwz5EynKSt2+xVh1+GLnLN7R3SVQG5VKqVG7mzIc7ImR5c3yJ1ApUWTYD+0eVxOGJyabBYJ8bymCwYMDzRkV3B39Y7bbo7hePSbhBezBiXNiFMV4Qw4ejTC9lrasGBbOuwbm76VYw0/QCoUMk3DNBsaZYxltrxb1wTDBxhNhahyHgzuUegGy5tXOLLHrppjRkPuBQcUfohRdtxcgSSpSK3KKja4+fQN3/z6rzAZNdRSh1HVrO4qjHt71EZFqVoUTSErGqa6wfMvP2PkWHx8dEyV1USU5EWO7xsErYoSR+zTQ/L6AlcaodUdL17c4j316Ps+5WpF16gkdUGmm5x4Fou8QOk6jIMRo1qnbWWMfUtlGfT1mr2nI9Co4y3hFg4+mLApClRbppYzrNYl63ZIeohc5HyxvyZdZqQfjznvD7i523CSKiRC4LYq+mMNgUPtNQyExvYqwbPP2VVbaFL24TVm7VCZGvXmBi3ooRgpmdxS6zqyoxPYMnQNq1ZGklM0ryV9V1DHGcm+QnfAuGfj3FT8ZJWxy2rqpiOvc+os4cQ8JdQi7k2npJ1Ep8dosUr4ds5Kv+SLyyU9P8A3DhBSSm/yiHJ9Rc9qyLIGrQvBH9MaJUGgI6IG2ai462JsqaPrBLpwmboqkmaRlBty9uhSSBzXlM0dpwONOoa0nTAJfBRLRSrHJJslXbJgEHyHl6sUSXUIU0GTC2onwy1bEl1GKmVEVVO1Go0qo0saclmh6jp70WDlHZHaoJcFupBJfk78/UWJwL8B/L0/8/3vCyF+E/gB8B/+eQgyAKkTIPa0ucGL1ZK0yBj6Q6J3Ep4T0PYylLKjzUzSJsNwBgyx6Pds2lglVMHAoREhTWaSah1i0HE8GmGcBPiJzNvNHdQLXLN9305p2ThuTu6qJAlM1Y40KsjKjqePDnA6gSGX7DcTalWQr3Lq/Uv633hGruwpspr2ZkXwoE/dmbx69wn2fMT50w8IlyVCMlDEgCJcQm/I7Mcl0/6Gsa8SVinB9IxadDSpRrzLmPYGREXGvgCzlFi/C9GfHWE7EmqqsCeEiYO7V5BMg8a06JqEat6xCzLebiKCo8fUtcKBm/HlXqdYLZg5KiNTQiQVGyXmH/3936Hc54zumzx8+IxUGaP1JQ6GB6i+zGwV4uo6C/ZYqx31QMaKh1hehyT3EO2abCk4OLSIEci5B7ZANyzWUc7+7pY4bTke9OkWOSv2TEYOdBWtppEkK/pxgNEXdImJrqZMzoekUYZs9dlernBViavthvrdGvdUYZMsURMFZ6JSG+8r3WXZ0fo6/bwhrAu2X87RpZSeFeA+eIgVSHi1zXL5ikYDbZGgOgG7JMVRZN7lW8Kkoi8NMZVbNN9ENmpOhuf86NUbTKnBnKgUeYEf6Mh1SU8PcCWTWm9pAosUlaFhQ1zj1S0NKqZl4LoynmsitwG6khJtC3xHY6rd5+16hzwesNovEIXD2BLcvv4M9+EjArWlpUIMbPwbB4uWk9MectdRyBGaYiHVGnQ1XSwwnJpUBEhtQytK3LRlG0AXSuitINNL9GYPAxOx+nk2o38xLEIN+JeB/+inS/8N8Ld5DyX528B/Afw7P2Pf/wkfMS2HOmkYPjnlYDTCaGTarqWaJkRFBq2CZvtYao/BgcVQCrG2Oku9xe0EjqMQNhmHA5/drsWQPcqJixVGbN7G7LOYRRURxxaK5UKR4fZNUBTKtKI8Vuk/OGRggCMfkm43NGXLwldoMKlv5kS1TGmryItb6lIlrtcM7w1YJCVGt6drXAptx5vZlxiWQsIdWbwk6E34/ne/z6PzM5TxiNUiQzQOo77MJy+/j9jDy3qOr444642JiwvqccCgnrB4+5YPnn3IuzKmEwnLz3J8w2Ilv2b/pYm45xDkNlYlkVoG5fwFEKsYagAAIABJREFUk2kfV+lzMDjhD9+8ZFr3WKQz8u1rfvT3f8Cv/9pfI2kart79EXqtIewczZ7StWDKEiN7yo8//0Oub9dobsLld1O++VAi6E/R7T2H1pRWxBStgy6ptO6WuoOklNH0mvvTJ0zO1lQhpFVNulpje0cYjqDKMiz7CCE35GWLXKbIkYGk5Yz6KttWRgn6OJTYPZNoalHEKbFhoJgdSZTR1AVVUZB0MWrlo/o2ZgtXoxtO5ClXQQJliyMbDCyJxHEJ9IbrNGIzD7n7kys++uVvItsSclbiBh35fkT46o95+OiQB+YpfxC9IIl1/pL/kHA/I0tNbrIZVXLHSa9Hz+gzW8yRY5X7v3rEYnbNQlYQTUu6CTk5O6HfcyiTFNWVaOuKeVhg+BZi22LLFcOHR6zXc0r7ELl0+P3f/cf82l/5Dd7cfEmj+QzcQ97Mrnn0z/4LyHmOoXkIuaWpa2S5QRIQl2CKPUUiYagtS71CyjUauUXWKuRMpTNVurih6f7/TQf+eeCHXdfNAf50/mmg/3fA7/ysTX8WPjIcTbvHH3yHRhVoZkBWdYThgqCxOJ4+QlhrtHDARirQydGNgOpIwgT8TsPyHKZhxTxJsN2KzumQ7iJ2mYUmZxSqwMo13PEB79Yber0x6XaBkME+GKFsOxwBte0RKAaXC0jTCresuHcy5CdhwONDi1gqcFUdqSu5fZVithrWyCC8iej0CltV8HWT232OabT0zGPKPOT87IAoTOBVjuuOiBqZpss4c+6xOdL48OSI8nKBdh7wdcXl+mpP6Eico/HlckH88g3Pb2NsRWXt5VxezPnobIp3OSY3IvR6giO32LaC6GrmixzZVRCFIJKWmJXJxWdvuJ2veD1/zfhwyrf9AddXt0yefUSZxER1yV1U0N+VWK7C6cgnSVp026LII97lOmY1JJhGDHOHulVIdwsqySfo+UhCoqnWBCOFJNPQVZnDM4dS1rG1Pn2hYo90ut0dSSRTrkK2bUNwHOC5EotCQ0liRpbJLMuxyxrXs6hVk15Yv38NVxvgmOy0CCMqqcqCYtMiFJ3z3iG7d3usRgdPZfvujlczG73f4CctS2Ey1QwSdYG26IgGOUnY4AUGRb5mud9yuhlwcRIxv97jHJpsyUiLDVrjI2IJrRJc3+7RnslYuU3qJMTrKfvFDaPRMWWegWTQOgpVWSB7LnfLPVm0xH54HymryRY6/sDiZDRE1RXIQ1KlI/EGXEdz6CwMS0YYxwSSxnZ1SyXJmDoUYUqg6+zKmgYXWY0ouxbZVUBr6AoNGZPCjvH3Bq3aoJQFtW6QGS2kPxtO/hchAv8mfyYVEEIc/CmCDPhXgOf/dwcosmDgNUhqxXrfoXUq06MhaqJiaB6rusO7J9ELJdJOp675P5h7k19LrjW77xc7+v705/bZk3wsvlZVT4JLJRRgWZCNqokHmhkwPLAH/k888z/gmQceCfZIgEsuyFI1ev17fCSTTCZv5s3bnP6cONF3e3uQHhhCsQBDsME93AHsUay144tvfWshAocoVxhXZ6haxxwumEwnVMeSQEiOI8Hbwz1N42AKm6czj70FJ0oi4pzBYM5u2eOMLN50bxDGR3iZQp+D1aYcOhPlTLkKz5j+4SP2+RZrt6evdM7DCf2TCpnprLcruqzjZpGSOBZX2gA3fm8waU8E+X2PJWuk45GphOFoQFu/JC09fvpP/wHXf/NbgrolH49wE4tSCKLgQJcvaXyHX/zlLzBLm9ZI+PSzPaPzIbevb7FbhSVf8yf/6T8BR3B3s30vkw5gGI4whIk0BJaUNIbGpz/714TmENprtKznq3XGrt/jb2Z8b/wRaWITmXckvoEl5xh2y/5lgRbbbIoce6sxCALkDm5di3CbUvWCYN7iqYbGUjT+hFWbEIqINooo+gq/adn2K4JpwJSAtXNGfbjDdjru1wn6VqduDKZ2R+cEqLrDakd0sqDNa1RWUFUFTmaRbrZ40xCv69AiF9mV7zMmbQvPgOEg4m/ygvMuIIojouGQw80elcFoZLPujwxGJyTBFlUNMcSR6cWUt79cIsMGK7A4dS9oekXU2ohmR1Z3jEKF4Ro8e/yEqsgIMjgaGnqusasSAssliHXeHI48fvaYcR+TmJJqu6IrU4SYoPqWzaJncN6iaQbb2sbwC9bLiu6QcW5b2MpjqW4J2hHLh9fEV1Pu93OwTPKsxohH5CpDLxWalyKVQCgwu5a68jGdEtlKzMqhMBpa3cGWLUo26PD/Te6Apmke8J8B/93/Y/t/0DTtR7wvB978B8/+ztV1sMrS96EO6LhkqC7E6AuiwEWlQ/pDhWk5eFXJIHBIspre9CjaI9mqYRxrKGUydQ1MX6OyHB5NPMhz3t2+RQtGXHo+F6MhjTQok1sO+wXlquYPT3/EyaMxI3XCMEh4CCRjUfMIn7/6/Fd0cUuddAx1Qb/X+VX1bxnZA45FTYdC8wQjzSScXDLQRrSmoG4LDvcLXN/k/suc0jxyejJlebvkmLeEzZ5ff/o1o+GAV28WSJGjPf0IpQt+/+kS9JT/+X/5V0znp5x5HsPnzzn5/pBAHzI9ueLd7ivsbcWnr97xp8+f8YOnM371+heEfcAq+4znfIBXbPm03BPlGl9++VsG0QjT3/A3r/538jrkv/zv/xvGE5Ouqki0HNZQsiMQObE9I/zJU7787DULUi40m42/wTgYDBvQRhqe56JtoAhaHNsgCg3Ozzzyjc3d6h7XrHG0M5wm4/6zAwfrLUVrYrU2nSgothWhmJDVSyz3nEa0bIceMQWlayA3O8ZXM/xcoLUFw7HFXdJjpClJozCGPbPTKe+St9zpOiLLmUURXVtSyJaZFHQzRWS5FJlORMbybUogF6AfGU2G9Puc+iHB1sf4gzG/XvyavdYxMhWdmPHHP36BXPsc1Yqu07jfvuZqMMUbDxCDju8P5+yPBU4UcnF1yc2rB5KLI9KJafc1E9/DP/PQ9wW2KsjnHvvV+1Kh63YMxwHHRKNQIzq14Y9/+k9YZQde3v2G4PIF2e/eYrQZlRfQZgWYEbpYIUyJXg+o+4zAkPSqQrU9tq9oO+gLEO4RrfaRuoC+/VYa+I8iAaVUAYz/g73/6v/tOW1XcXf7QCx17tojz+MzdoeMLu459jpTy0G0JVKGjLyIRvMRwZqiKTEPDbPhFMvtsHMFg5ZyJ/EiEz9zWNlDtJmG6ReMgiGaIehyh7RseHqlk1cttd4wEA4YObvcJHtwiWeCY7Fis9xTLFwsMmrbIM9LcEyavqVrBYHo8EcDVBByZUFp6dgorq7OuH9zy2Gf8GB26OWG8l3GZHpOIELMMXhtz3ASY8Yx795ssTuHNFmy+Pol65uE7sTCMlLCqydIK+OTy+dsDhpucEYjU47lhs/eXGP/hcuLH37IZB6SrDKEKyjbHBGdkn1zg4tB68d8tn9A+7QHy+HHHz9j0Bwwg0/Q/SksvuTdsURTW3plc99+w2g0wYh0sruStcq54BRNWHi6IG90PBqsyZDOUNSdoNq2eHJGXt3jmxJDWdwu79ENRehOaDuHC1x+ufkFzfKAY8VkFyMc2+Sm2HA+CXD2EqYh/TFl3cFsn2PPBtibnntLxx+biKajOjGJVoJdVjDzJyQvvyQvc3R5IFvvMB2LQvRYuUGuTIZzH01p9KsDdmbQDU3ui4RiJ+nrDT+YfUTZHrnwrqj5VxjKZzg16IsDjX/g3euSH31vyrQ641gmTFuNWEiSJKNBEegubdxgxjqCgNgbcjQXZJ3E2OUM/QjHCDimHf3br+HRGfF5QL0HJ444mZxQlwfMY0eSCUazj0jahjfrTxEKnCKnsaaY5oqu0gl7hz35e1sgu6PWXdxe0BwllqPRaR1h4VBrEtFB1+nfir/vhGJQ9hKvFCReRVgM+O39PXqnOHNGNIcDr2WLaxicnjo87Jdku4b5+YzONHgkhihfMDA8MqFQteBgpLidwhYOkegIB1e0XoZleQhXZyQNqoHB1H3EnD1+G+GNRxxXKf0+wbA7ZGnTXYaYxz2eUPStSdPXtKJBKoNO6oiuYvr0jJP5Y+7Xt2y6kNGgQBKQK439+p7MzPl4GPGqf2Cd16h9gPC2GI1F6M04PqxpNbj68Alvr99xWNxxs7zDsk1G3gmz0zluEOB6J8SBg/JilCH5g/k/4hh+xW++vON3f/tLpOlw9dGA88jm5+sjp76FbTTYdkjhGDz98U8o/+KXzH/wA54+eoyrj9HjK2zVc7e8Je0tfD+nuO9oOeLoFyzLIz/+0fd4+uIp++s1ebkiDufIsWAkPIp2Rdt62Jmklxq9V+HpgvGZQ/JWsluWBCch+csV6UjgNS4rd8nr2xX6N2v62ZFn8gSSIaP5nOxgEniC9G2KMVREpoNRHrFLmzAKOctt7g9r3LMBizefcdCHVF1BuS4xnffzIFo9oFQrRGpQr0pWyZ7+sOLZD/8xRxLckctKV1yaBuvDlsN6hecFFPMC245YLe/pVpLBi5C6PMOfmajO4sr5mpebJf9wdkYbnRLaQ4bS4Ph4yvrXv+KLmw6PmOnZiLqsaDkiOwu5P6DFpyDAmvnMbcX6wSCQJSMxJ2nXbKWPLxw03UL5LVbW401H3H2z4tO/+RmdZmOJlrrZI2oLUxocMg0Rt0ipONYmgVCorqLXTUyhU2tQmQ662WJ0AYa/I/uWaeLvBAkoJPfL14TBCf2w5sPJFdv9O2SRY/uSvvbR+pQkdYlGDqOPL3lcDTgMGzxvgueF9FbBYNHQzyXTek4iM8omZVYESENxNG0mTohn2ZR2y5X/EzarNxxLDW8+pdkXXAxOsWYB27IkHur0DWTdHtfU0e0AczxhnG65PySE8QlXnzxG1+Du8IooGlBlPftlwWkEWtcxGAQYueQhu8HQpozjhPq4JBBzvIGkWCwwns7QtkcWx5zsfsNXYsPk8SWx5jA8mRCEU+q8wwkb7sQQsprAj5GahXb+hMsuYjkMePX7z3nzueJP/uzP+cOzS3q/Joqe8Md/4mMXBR8/8vizD3/KXbIntiwcXWMwdnk4pvSNyWq/ptzuaffv/+s24WdcmCbf3Dpols1wPiVta6a2T7upcE9rpBZhaxYaUJs1oeeiZYpWFlycPeP19V/w2atr9K5CLVwWX/2Ch8Li4rLDO59Ql4LP/91XqEHIs2ctwxMHDjpuGIIbMZQm+0rRfnPDItQQ7hibGpl16JNLqlc3NHZO35u4riDtCmS+J3z0nGnTIvcZt3nHeFvRlXuswMS3fAy9ZtEkfO/xj/k3//pfckPF0+MjynHD44s5bujQIBFWAsaAR86I68sL4s0tN1XO9y8fEdgtu76nSRpOH3+ErHsKqyC7yWl7m0Cm1G3HcDhDaBH3qxXRtKEtegwayrVkf9YRmR04PdqgYvt5S+gpehzWi3f8u7/8Oen2LaHRULQmZqdRBw2DEo66hd5IRA/KNMgdi7CoKegpLO19RoOtoY49Sq/IjW/X73wnSAAEJ2fneKMRSpZ8/usv8E9bpqNnJKUFWgHKJXIdBvYFY1NnYTRMTAeaAmseQdFSzjpkloJpEgsPMzCJo45CjOiLlKyTjF/4OGuN3DnykAi0uqCxC3xziDd3WS9qnn/wB+yre+6/vsOoTGzLJs1TimSPPdKZaKe44Zzd3TWrVYayDYITB+EfyJc11+6YcbNFCR8nbvC6gN1NSqcq9E5QVUvKxRCj2NC8bHCfTXg6uyQVJv7XKwLbYXQWIrOWw26PNxzTtw3sWwzDoOxaImXQoxN6CtcesjRt8vRr0lWK+0MDbWvw+FlPVcakZs7hi5ZdmjG2hwyQWL7D/eaAM7DRO4Ed1xRHh4Vo+Xg+p9M8Os9H7QTjuc2uWDB0Jri2j+OMOTYNlu3QlhLNd3BdC11C2e2Qco7qdiyygtA1+fr3bxnMRziTCXG1pBRDBr1GJSuePnrBV0VPJTLWDyWdtFgKxbRwcC6hKDUOto7d+ISHis6skfaBJ/EnHD7QSG4XbOoDyXFLXa0oNcEsNtFTk6Rc4zc+3UxR73TC+RDLDhBvd6zlhqO45eTRB4iHW7zTEFsrsLQBzsTAHY0xk4rg3GeRFKis4eTZU2Rp4ZgOy12PpytGc4++2LO1MsJmhDm16RxFYBjotYloJfboSCMmuF2CKwRC+OxnilHssM2GBGJPvjeYBg2WY/Ji6vK//e1XZHfXeJ0g7wQYDRguTmNTGwKh2+h6TWPpdHVJfOg5GmC3Jo2Q9HGPlzXgArImKgTJt6DvO0ECnudy8vgDpMwoK8lHH05xwinO3MNfNZw9eYrrxDRZj4obanHGROypJbijmLxMsXSbtmvBj0mbnjAAIzEohqAfNhhtizcIMVRNKyrMWBEY5ygjJd9UtO6R5u2eTnWUVsVqs0Y2BfHARaEoiyOOazGwTzm2Fdbxht5oaPWS2fCMsgT6nrq+5/jXG66ra2bzMeHFM6xGYR8yxKhF5gWZGhOcJNR1jWcXOEedvO5pjjlSCfrBCUq29JaG6HqaqiNJXabxe4OQzu6wfUHrhjgi567q0Xc69skpx+QNo5uPiYc2Xff+xemyE3L7GmX6CN+lGQgWbwpOUKSlTe/usS2Hrv76fRima6J2S7aLnv/kH/8RVQ22MvDsDs0zMF2LvGrxZEOhJGpfox0NLOd9eMg2vaYtSj65OOXdV1+hBS6HN3fUgUkczBkYIyqt5+LU5bo60NzDUVdM4xGZX+MmEi1uOd5O8QyQWYlrwPX6jmJ3pOwkV39gMZuElKHGwzdvqPKUbr+l0FrK0qDuEyLbYBQ5DCaSdw9fMLYvkMY9k+mU032PY2v4gcbI8phqBmkQsxcGZ1sNI87JtZzK/yn54RvMiUfdV3w4m6PbGtt1Qj6K8aWGMgTUNmm9wjY7PO8coTc0tk2eF4x9j2khyVwPhYZzUnHhDXFzl866Z1b6rJuCJ7MRu7alNS7IUmipaXkfJqp3FkrY1OYBo/XQuw5aDSF67E6ncCSB/j730Cmg7xVNU9PVJpg1ji74tgmi7wQJoAtWy89ptBF+MMWauJiNjfJGOKOeJEt5U+/58PKHdG1Dbq3xnBmRNoTeo3VSBiaIMkAYClszqDMPU2wQicNB6HhexMDRIIEeF0+2DK86zo+n5JVNbkOoVSQPgqBcslcddV7zxfqeme5gRwOq7ZGdf0OnDZkNh2j9gEt3SydT3qzXOIcN37x5S1JtMfIewpDf/K//krKWuAKs0gfT5cIvofIZXz4jR6eqekr3G7bfPJD7GsNkyyExsQ2oPIuuPqClBWkaog/X6HJOnfsEcU5R64x2Dc0HOm7+hPXuwOB4Qx1eYG0a2i7m4vKCpv+IQ3LPKt+g1gMMu+PXr/6W8/ljjslXMLyga2qmw5D862uOesd0GCMLk7R+AMPAkgYc9izSjkkoqKRGodXoQcbxaGEi2d6m1Ozopc1yteKr9ZKwdtCvzjCo0CIb0iOhaXK3Kvhgdkp4ZtIaFZvNEr2VyC8lN0HKqLqgP7HRtYZi9kMS4aDMFTJvePOw4quf/4adscYLTzgxFbcPPeOzCYZt0217AnPIq/wLhBbxvUcfsGn2OLVPkq05u7xk5p2x/OaW4Cqi1QUfzp7z28UtamZyPowIhhMO11+y1SxmjeJRcEY0GLHN9pw9uiTxjmzLLWZoERNSbSV1KEge9ohAYY2HjMyGetexT3egXDS9prg7MnsxYrVdMBoPsfyA8P5AQ8Xl6YzbJGdx8wXC61F5hef2VLWFptUYtY0uBH3TgdmAbuIYgkK05MoAYYPU0DVA5qiwx+x0CiWAv1sn8J2wHFdtz4kZ4w9shrJm4nmYXoe6L9ntC/rG4YV9ArZi7gZMqxHpMSGt1myMBSOh45sOnQV0FpnR0BorcsMgtUroB8RDG9N06F3QlKCvbbpFxVH40NWYQpGvShr5wCrV6LsIzS4QssYJHCajMzA76s7E0SDZHCg3FZoMGMRPUF5P1ZdomUTXMrA07lbX2E1OYHdUHKg2B5quIcsMAl1HUwcmfoRvCfK1xBoJYmFQVjp7cmrfRkqJyo+IukELU1ojxE0OMNDoa5OBOSGNplRZQNs3jCOdPndRxwJPn5Elaxb7a8KhhX7qkNsh++M1maVhTMY05pjg4g8xbbCHNqbyUMaQcz8mji5p+gRfCgInoNBdqqojUjZ5bWC2BqESmKXLXBUIKfEHEZE3xdVNVOPj1RbrviXPHuh7k9X9N2AoDppkNvFY3+9ZXN+QPSRYkUVVNOSuIil8Fn7D129f8pu/esX+m1/QrbbsCh2ajpcvP+XTN79l8/YVduuxWae8ySGoe/qswpQ+qBVX4wlNabDLEqbjMxpHUB0qsAOEK3FHQ5pSoXsxYqBh5JLBNMb1A0aMMAcebpayK3K6EuLQQ298PE0xkyOGgxFmZ9C6BpmV4jY2p+MxNg7TUmF6EbrT4Zg+pjJphc14corXFKSbN4jUwtYdjMAkLTPasidNKlwvxrd0xnZLnWnIvsboBVqv0ckay+4QhklXaDRVBy3oSsPXalytwe4zemwMXaIagfh7oP6dIAEJGJNznk/HTKM5W02nbV2aoOTi6RCBRCqL4vWStNyj6g7XDslUzpSedVryerths16z3R5Zrg8ckxyz6gmsjlHbvR+VbTLQdAZWAYaB8G2SYk2vCfp6xaLvqQqb7fYao1yiwhEfP/6Qk+cfEgc1Ly5+RKR5pGVB7ZsEY0ndhHhtzbnl4hkDcv1IfTDpesHh5TtuZUuel9iHntHsEr9+P6ZaVCWIAWnRQyuw3QbPGtNoFm2XEfQdxyInX6UYdUyrdJrEoC4bUl/R7pZ4ukYWpgSyZeD4ePaYkIBSCLL9ka0lUY5OrAK8YMTEP2NSuAz8M+xaohUKLTji1gW6MWOqjTkc1wxOLJIsZ3d8i/R1otGEuitIdgtKbHJ7iVYmiK5834fWahpCbCUYWj36ZEgQm4i4whp4BHqD7rrQp5xGT/AxeXF6gtnoWPYce2qzb1K++eaGTfv+UpiYJrHv4gZj/LMztjaUTc799p5DsiJb3NIWGrtE8Je//LekTcE4sIgHc6adhms31GKIsmPOZxN6a4QlekIzZtvs6A8VeyWpS8l8OIbYIF2uWadr5N4E4bLK3ou7Rp5PfSxRQYVsIJ553CqTxnApshJHDKhTl2/evmYlVyzrByZSp2scpN5A61OaBU2bMJY6k/MhRe/x+c0r9ume1WaD7zsU25yH8oD0S2bhlFqzKBoPzVG4GrRagdJbekNQKkUlQQqoTR1ZB6isJtV7arcnlxLDLuhan9ZQ/D0dwu9GOWDbFvf7Bd19TSMrfvjkI6oTF68RXAzOsU5NdtsF1skFk0GEpylKu+VC+LRWiJ2l7FNBvT7Q9DvWaYvsWihBug3PPnzB8GFAPrVpixZPV8S6RhPFCAwMI6c6OPh2hWEpBHPUw57zKGBvHGBfk+5rtstv6H1QUjFoTsD3qbp3bPYDJqcjZuEZg+mArz/79yQ7D8gQVkDb21hRRVkkzOeXuM4EQ3cJrJBcg7oxqBON1bvP6KRLpm0YJQFEAj0Moa04ah3pdoGl9xxyjYeoZvpuRWQGGKohW5dUrk4lW6ympfda1O4a0/c4mCVXp3OMlcO7WUnx6R2NLTG9Gr3M0ZwR/S5FjjxOXBO7qphMxhzuC9J5SiclTd0TTAaYfYtXOaAUGydmbpgcd4qmTrFdj8ROMW7WWPYpHw3P6MY7Tk4mZLuavJIcyoKT+RhPSvxnl8RhxO2mQ+t6Xi7fEC2XPPgNkfMhzvoeS3N59OgFx2TLTbPEkorBB8+pf3bk7PSc2lhx8/mGn7/7NT/+0VNCt2NflFiDC7pwQ//NgtYY85NnIzQZ0N2/xep0mqPJ5Czk0hlzcFKejC85LJZ8+vZ3/OmTj7FsjfDqnC9++4rBbMLVJCDbHLn33yKHL/jkRchhvafWFHZU4estRjRB7jucMOahLLH9Fcf1Dn04wKVjmWxJ9zqDyQVfv/oZUvpg5LzbbznRXTZ5zZOyoMwMbrOE7O4ItLjSoBA6U6WT9RKl6Rh9hW7rSNHglxpHo8eLfNIyp0NHlza2KSnKFsOx8LXqW8zFviMkkB1Tqn3P+GrOQMVo4YyxKCi8AXl7ZFvpKFMnMkwykRJ7Q8LGIwt0qHNqpRj6JoxP0GXNpltgHXtySwdbIFuNUvX4VUWluaxNHV801FqFb2msawstqnGqDllF+FoN0Qz7tCFaxhyTW4RVEI1P2LU53lAgp4+I1Iq6HdPJHSQxh0HP2dWIXv8xq693zP0Is7A5egv0zKVLNUx0RvMZum+x2uU88SJ6T+du/4DmachtSa2Z1EZPnu4Y2gY7zyfCYJllCM/CNh3cskB2J+wcQWAIHD+gZ8cTx+bBLjDxGE2G7BY77jSN06JHeC2B1tFfjNGylH5fcgBsPaewjsjmSH/QMEWGKVxmL54S2Sa625IkHU4HS+1AWeiM558wkh2JVmOaR3BnmBVExSn1bEe2LMlbg3AQo6SGmN1iJzVe3yE0nfHA59BJynxH7LkcjYTzMKbwAmbakInK6YMh1+8WaMtvmFzpyOsc9+wSC4e6abj//eecP79kcNaBcNGqnp3Z0GqSyajj/rVAb3yObUXVNfR2Ru01pJ3AcFuE0mibHK3QITZYtSX9UcOwenZOyaPOZOF7vHr9JR/MLpifjBhOTnFkhtJ8lN8SVCaLV3uq7Rq3z0j3gO3iBR6DQUwfWlTJDjUacmaDOXHZVEfuVw8MwjllY9IUG7J8TGjZiL6gK12adIFl9fRdQ6U8LL8lzzWwLaRW0OBgVD2d5pD5DWNpkKkSW+mYukZmgWa0CKODHsr22z/6vxMkYNomTy5nlFHIOIyxgMopYZGhTi8pnIIndkAuW9zS4r4rGfpHZDYgURl1bzFVOqZzYLW1GPVwk1VMJyHZsaa8aOjlDU60jGmKAAAgAElEQVQ5YB/v+NiOOZQCr5KUncKyPdp0h+lE0JU4lol/GTEM4U2yxhhNMPc1WrXF8lvO4jHa/Uu+6o949gm6N+awSRl0A3pvz+nZI5xgzPLTrxAiQdtZ4JX48RQZxahcYp8ZDPAwXswRSUbzxYG+ttnIEoMaw+wYOTGytYhaqPsCwZBjdsDubPRmxH664qwcU/YWM6Mk6yfsihSp1eSdRNQO0eQc3YXQktxVDvF0gGxrLKfD8SSV5jOzhuzfvUEGMUMjRDNMWtNmVewZjy2OVYGldFRXEhsxdd8RhQrD0XEajdp1oJSUlqKtt3h9SBXkxLFBgYd7W1NbPqE/Qcka03ZZWT1Cc9CUS7q/YdHnSK+AsqMLhwTDAbVweTrN2OYV928kUSd4d3PD/fYljy4nZG7LfXNL3ej4Wsaj0xOWDyWaEXL/eo2GQTCaYcgGrTGom5bFdk2s6bxOSkZaz8G0kGlNWiyI3Svq7q9Y7Zc8N85wZj5Pzi/xDI/IcxHxBUVyjX32j9iaR4ZNQDlo8dQpXxxf0mUNWtPipYrR6QnNocOwBbYpiIyQz6tbRjudd6tXlEoSdC3JYUuQCup+QTc02CcO7sUMR3SUpkOvNAxT0eUWDTWjvqR0JUZdIQnxdIlq4Gh1qEbSaTFdnaI7IVnR4wkFSqeyG/iWJLLvBAmEQczZD18Q1iNkaFIfU8ajIdqkZ/O7W/wPBizvCkYzk2134GJ4SpMZBHZLr40Z2NA0W7LERmtKBuEcTZi8O5TEVzPSzYKBHdPoGuaNyXWU4ncttV9S5+AUFflB4V1ChUMY+4RDnQgdX3aIQGC3kptVydR32beKKBTsHjTa4hq3MLDCOcfIJagCUtXQZhqnlzF9EeOdOBhxhK9LCsMl1gVt3YJo+Pyv3hK6HZfPv8/dzS0f+Dqr7YGD5tCbLROtAMMmEmdYVoPUbHpHUJQH4o3DW5VxcmGRtRZOm1PoDipria9cHlqbaWBiGQlF3yL1I5vdGnPgsH2pkVYauTqylgnhkxFOEiN7EAE49YDeXtNNIkQ2Ir97S/B8xAEYRz672wcqf8TU60j3As3TcA0L3bRYZx2BPqD29szDH5BF7zgvh7hmQmM8QdNL2sIA3eYhK4lH50Srr1nd9DihIA5zPv3thqJRmLQ4gcux11g+XPPjH/0hJx98n7/98mcc1gs6UaOSMf/sn/1z9Kjj3B/z69/fULYHPvngKV4pEJGLrsBuCnzbh1DQ5WuMbszIdElHLdP4nH/z7iVeXzHwzph6HrnfMo3PmI410rrF7FZo5oSqOtBst6T7FH3+mHFice4+Iuh7to6BPegY+j4brWK9TDCVpNFvOXtyyfHmjtdfvebq/DFtJOiTEuPFjOkkZr9MiM2ek4sxeZ3i5CV5rzA6DeV09J1JJ8CQAZaeU+tHpCFopY2oFYbugurphYZOjmY4lJ2HY5fYnaT7lu7Ad4IEmrYnf7tlV6WUfUt0PsBd68ROz+mTKyx3wnq4prdMbCPiQIEwXWw7wEkVTahjJ0O26ZrIVGRiT9YdEUGIVloceoHwUhav7pmdjdDSmMo9Mq1ibKlRO0dE2FMvempNZ31IMCqbu0oQej77VcO+kYgopvcgSiEvdCZ6SFq3NK3EqxKcvWRpjEmOSyZ6wP5o4LgmtRkiKsW2LbiYntNoe7woQB33lPOCbi9pVg0DFXOz/xVFG6JVLRMB8sSiRyH6hlaCt2/Z1wXOach1ljCLJdk6wJ/aGEZEsW+p+pzhJkLM3pImEWH4AXnX4skxIQmqSkisiM9f/y1N6RLaAEO++PIlV2eXGKZL7FdUVYhoYy5ik+Sixw4s/MagkR2JZTMwCo6Ni+05HKVAHltMV8M3WoIoQjNj9K7C7ifgJJRaiNAbTPsUXwPNLlgvDywrm9v9a6KrCwzZcp/W3JoalnFE7Fvy5TWu5fLs2cd8/OM/JdYhaVLS00+o+oK7d2/5+c9/jn0+5qdRyLNG553u4OsGB7PAV0NGY4fk10cWqyWngzGnl8/J6xInavCbKa2WoWoDz7MYaIp8EDCQLml6oGsEjd4wVya2aWDasL4+MjA9/IccpcHl+YTdoSTOV2xWcFu+w74aM4gGZHnJ8OyU7AiysdFMi3hu47Zwr3fEOSzlnpHR4ekDHvYZuhoSDAyaJKMdKWRmoTqNXkhkp+h8C9GbVHWPr/dkEhy9RvQextinPqbUFnh1RtbpeK0DZH8n/r4TJCCEwn32CMqcOBMEnkVuGdzdrgmmB0a7hrrOGM9DzNZlmymGbsUm/wYhW4Sy2HctYexBmVOlivWhxjMlRdbQ6A9sbiTVWBBXM6K+x1UOt+0Rva0ZuBqRdkKiDoSxoNvs2DcaTdXyzd0D3jwisiJ0LyXfQR0amDS0hcKQJm4Fsep42GYEJzrBHrrQIXJ9Girad18Tv3iCOzxHuBWDJqQIINkm7F73RGMbre9YHRc49hlZ/UAtbFa95FHv03aSTb7FdCsaOaDRFG8XW/Sq5lbXGYqQTwcWL7yId22C45jEzpJ3Dzp/9Pw5MqoYyhO2xYImbjG0MdPuDe6biPOrR0Sy4d3rV7R5TaMSrEOO7064+t5TRg74wqQ+8WkPHTfZliiNiE8EdBJHq+iqhnlg4moe2YmOSho0X2LnFkeh8M0aET5FjRXzpCOSHauhgWnF/OATm8bqubz8c7bZlt/9+1/ym5dfMrIsFlWJFwqi+Jw/+pN/zicv/ojfvf2acGLxow8/4YsvPuPtQbG6O3A8bmluvuK37275x//5P+Unl8847Hvmp0/Q+4Zm1bFoOubnjxkPQv7oT39A8VDx6eIXnLqS8OIZ6//zM8qm4MVPnjA/e8z6KJES7FFAl0rcF0+pUolPz8BwuXg8YJloFGVCHPl09zlfvFljuxnjgYldzDj5UNDcnuIPPUxZ8du7BT9+FPPB08dcv9zykw/HfH2/JNZrFn3AnfoVeX9OdlhiNjXSi1BdSaAUmW4g/B510JAKtNLAkg3CM3B1DcPsAUnS9gjTxzMUpaXhu4pKa75VLPSdaBEKYdBrJl4xoB8E2FMLvYNwOEL1JnWoczEb0MoJStOg0tDKGkOZdGVPvtBxuob++Javf3vNsbhl5nk0poOiYJe1uMGMsbDomwQVKBQ5sScZDidYnoslc9wooDgoyt5gUy7RGo3e9kl2Ob2fYYcTzs6uuDg75WI6JRqFnM0CmrYmdWZcfXxGc7xF6TnCSdkWG3aqRQwiulKhv81Z79fcbY+U9zVmNebJjx5jejGVV9MoC+/MQaQadnqkLGo61WOYPsO+ochMdukamZbIvCXRDtDWZOkDRn3kvt6hJxn2Ghb7nkDqLFXCsG2o7RTHD5gPZkSehyFt9JVgs7rjdn/EHcece0PuVwsqreC2SsnuFmzrLfumw5MaqSU4e36GdaZxEo2wRoJaaTjzIWbXU2g1RgtDV0eUElfXOQkdtPAS19K53El8YXI0IqZjA92UDIYXOFEIWsnyYUddFRi9SV61OKHG9buUF1cDHl2d0VRbRnrJWDe46bcQxpwGDqUoMdoDbAS9Br/867/m+ssdjm3SA73lguXijxRd1jBzY7JlRTtpaI8GUrMYTQShE+I4Ooc9bLcmVVJiRz4D4wz3OKYuK6RekmYdy1evUWlJoCRlk9Os9yi75+mTIYY+YN9LDKtidDzFHA8oC4nYG9ysv8Q0xyyWCwZhRBBNOB3ZyNzFQ6NvDLRUQxMNlQ1en6OlHXWl49caag/InK6taQKJPjWRZksvTHY1NK6J2TuozkDJFr1T9GWNp9xvx9//b0j/e5auCTrVYhkFZ4HgfpFwzO5Z5ffMJgOkiNgVHqppaLSK4chFjx6xb3WqtmXZLdCXBrsvBU7s09cex9bG7XOUUeOlBffvviLsHVpNxzAaCi+gFxN6BbvMoTEyjts1SbbBjnSe2s+pzZpBFHN2NWPujzm3AzpHo902rPI1supINoJdveSzb17xq//jV5TSRatbqtUO/IhAjCg0RZu17MYSvw2xQh8xiFjoFfv7GvScLOuxxJbyNsOJKlxHYSZrrO4epe1JrQ6nPZKrjMPhNX2xIl/lWPdfkvdr2u2O3bsFXVki7CVB31FFBrKHvdWhIRgPYTbyqI4Ne8Pm2Q9fMMgksddAr+E+dfn+40eYmouhYlQjCXYaQhaIVjBTCrPaEbgBid5DIvCMhrx0UKaJYVmYRU5emew1nX6oCDWT2FaErk45CxFWRGQbdKYg1C2sUCHkkES4SOUxefFTrBHs+pI+VfzBxx/jn3xMXrSI0ynB+RzLPmHx8g1N37NcrbBWD4wGU4bTAG0UUxQVLx9+zr4rOB6OSAWVMoh9HVFuqLqEVftAZD9iEmtQGRjH4L0cuyyozY53h99wv1m8V+ANfLRHFo55SduE5PqApRbzs2NINjzldPIM7XzCVWQxePGI4GLK55/+HjHQWbd30JZYtuBLeYvIJCKI6HY9apRzKF7zq9+85Ob1Pcfcwhmc07UGluPjawZ0PZE5RjodmDU2PaarYymPoJZo+xZVhShD4qmWriow+gS9LWmODioAU+lY/7GBpJqm/U/AnwErpdQn//feiPe5A495bx7yL5RSe+293fD/CPwXvE8/+6+VUr/6+86v2pru9+94maW01yYfnI0oTJfHEw9pe4R+j2f5GMM51nFD0jfsihW+ZaL6F7jeV2wODc1Iw9ZaxCDmYVPj1B5hKFFOz+PvBeyShlhZLA4NQpc49gq90TH6lOujS+8LzKCnftuQhwVWlWBNL6lViXnoOVoGnmxI6w2dsBn2Omrm8Cj8Hv8wvuLu4TXXb96x0QR6VTF/8Q9QTs58cIWZ7NgIBQMLNhXrXcoH8wmF0tjevMMTPlupcA2dsnMo2yON1bNIbuEuR9iKtKwxQh3HbHi1T5mIiptKYr5dkmgHRAgvhs/oB+ccshVt+gh92qIOkn6k0eUu17ev8YZjnusueW8Rz1xkkrIa5YQDj3nnUfQBp17LptWpRU+ZrTHjSzohKVsPeb1kMFQ4A5/INeiTLdUgZKz3ZBaUxYFpGtJogmKqoWTHtm6IaoVlVRSaQNo2hQOTOqQYHfnxyQWXrs71suLn2YRHo5jfPzzw3/6LP2c++pDl2wes7Q6ZG3TGnunFE7aLHbqjYT4/YX60YTAgq2zSw0tUZxI0Ia4Dvur4/fVvuf78jvlggI/GzBnitQv8eIYRKlb3Oclxy/DsE55Nf8Lg6TN2twuOdoPsfPzxczbbmnZwyuB8jPlIo8s9WuHgTk+Q6z3rwOXKm+B+kHDlnGAtTLTIYuSd0VU9/f31/8Xcm/zatmVnXr+56nrXpz7nnnOL9+JFvPciwlFkRtom0+kkIVMyNginoIEAIdGBDi3o0aXLHwAChJSpbCBAAiRMJsaWiwhH+EW8+tb3nnuKvffZ9aqLOSeNG4ksOyJJYSzFlJbWWmMNzdUan8ac45vfYG88InH7VErjlENe3b1mOZ3jhBHvNCP6YsIffPknqFJQGiEdGlkvsGKfpmuokwBdVCSJoswVlugoaAm1QnYOHQam4WA4Jbqx6Uqf3CpptfmXXg78N8C/+uds/xnwT7TWj4B/8tN3eKs5+Oin13/IW+HRf+5oq4Zlk6ITj6TnYDsWA+eAw70JStlMghHeMETvVmyalKpbY+Ydy/UtDXeU245NVmDZku2u4c2nb9DzK1YvP2X+9JbIWJDtIC8rLp88Z347o8g3yKqkFhF9MWLvyKdvNYi8Jdp/2wuwKjSuC62ZI8OCQNpsFres6w3L8obb7ZSsLXGSAUYbsnd4SHy+z3tfu8AZj1ikVyhDke9uEK7P2D6kWFcUnUPfCjClg1xFROEZzkhi2xFFeofoamyjAd2nmJYUckmxyWialvouJ9+C1+5oqhC6Cu3FdEaHURhs6oxindHUWxo0tmixhI2PIO9SDMsnkluaYknZ83i12tB5mvXaIb2rmM4WBHHOvBOMggDRM8gXFcUuZ+gJDns+YTAmVXO6LmPaaMxQYZYN11WK2boEMsYNclwvwG4dhkbMfhNh2KCwSKx9hltNYo1QwiRqJO7JhN7wnPjI4ezRhDpw6AUOZtWjNCWNzrnKSzoh6AyFn4w5iCbYbkCyCeiGJWk24yv3Rxz19hgeDRgeeExzxUKuePbyCqF32NQsXEnZakzHp8wqkqTP3oMBjuHxwXf+Psb7F2gnwnjvBDm8x5/cznnyyZxZp3GbIflU87WLQ7zjPmnW8IQFuSp4/KLmv/+v/gd+9Pu/x7paEY1sokMfjcTb69MGJoETs622KOVT6RThu9w7ijhxQ4zBHlN9w+fPvkDYHVVV0jglHS6ibTFxMNMOaZtUjYOsQRoWBAa21khX0fc7KCRK+hiWiRAFigbX/UtmAlrr3xNCnP85828Cf+unz/8t8LvAf/pT+3+ntdbAHwsh+n9Od/AvjLqtKUuFLEomF3u47gGtUfOsNnh0cMT6donXttSGpFURlmsidY4hYrZ1w1i5uCeH/OEf/iE928fe61PtloSnB+SsyXc2Rn5FpkoGvo1rR1TY9HSEaXQ0nkBUb3BsB6uyybBQ7RzhRAxGe7RzB9NaURkZRmBjOXsMV9cEvRMsCdJIed28xLzM6J8maOkRH9tw7VG3Nk29Jo4FFglSghNLtvMMe+RjDBUn0Rg+3zBzNc5wRFXOaaJToiKFrsQsShAGeDamsMgrydCzqIxr5NrH6Ld4VoGVW9TrlmeGZpwH7L98iT65h+ND6kvIPdSyoLy/B2vB3YtP6TlDMFvy6gtUZ1J6E46FjRNoDE/gFB5ue8d8/pyo/z4D7RJOJDI1qAC/aMh0jRcO8YkQZUfQ+TRhhetHdKJDtAXCa9HSxdx32RVb+tqCRYEc9klMGyoHdS44+NMLvvPhio+efknfPqLQku6zx/hmRFu17F0MWb7OKLo1q9vnLG+fE01Kilyz2XTEhsAd7WGXO0bRGDPb8JPdM4I4pDeaEBoR3/jwXUwdI0uHyJ9guWc4Xo9vff3X8M9PmM9bHpdXRHXNkpCdsGjDkvSLL9nspzg+TCYnqK1AtgWi9fjT25af/PAjPn36EXuvct47aNn/7f8Ez6gZhAHL6xn5eoYcOgRKcnSyR+UKqvJPmJdAURL5Jk9erckXGRQCjUR0FgqFVXd0QqFjh15nsW0yDAGe0lS5z8aFQJuUtYUaCYRsaYwOs26xQotd5vHziAJ/merA/j8LbK31rRBi76f2Y+DNn/G7+qnt54JAr5/w9b/9Taw6xaSPtgyGDMmuXvODjz/BPdjHrmzixMLoYlxzgd3vMzEgLBLyyODF4jmH4xO2Ycb642fcVDvuFhUTs6S2Q+xW4QUxm3rJxQcWm/UrxOg9+mbGjJAo9Kh3NYblE49azGZEFWpeXD7BVh4DXZO7Iw5OfFQmYXBEWi6Yb03651/l/q7giXhKIm0GUcj1pYXstjh3DYUfs1rWZNUz3j18hGi37A8neJ7Nnhfx5uZzbuaXdByy56x543vYtUtpTKm1SVcqVN8n6kpau6DWitk6xOgBgcBhSj0NKDBZ64xq8RqGv0z9XYPMAt8EkWcUykf3Q97Mt9hSczi+oM3XaG3ihxHFIifPdmzOj9CrNc75mNoJqCYGA92HYsZOeggFzpFDlBq0SYvn9vCzDZ42KId9tPkG4YQ4RoUpFbVtI5IBcasQOxenp7BND1unNJ1F6PbZChPPuM+D82ta49sY8ZB0ueUP/uB3iOMYUxr86l/7ZX70f36OLGdYBwnLbcbmas0Pv/yIyNd855sX3HQSX6d4lqBVFeG+4F9xv8VH7VMC1WO7vSSxB8Q9n2XtEvQfYO6N+OHlS+KTBxgqIV9PsYMR1zvF9e1j4l4fqTRVIXly9ZThJOLZ4gWrmznffPgtGrHlJ28e8+XdJ+jlK2bKxp5qrH9PkCRH9OOE1eopydDh0eAdmhaGo0NMKnTwdd6MSq6Wl6y3mpdPf0DVLDCEQc8SrJUmCGyqUiItgZ8bZKLGbix00JJ1Bp4tqFtNZ9iIsMLofJSW6KyP7WY0TYhrbKl+Tvz9VZQIf5aEif4LTn+m78BgOKIvLDa2hVAzqt0eUl+zMQ2GR/fIixQ/8nCMEY2/IYwGGNIgkAY3QYqabjDrmtdXf8BdGjNfXGNWawJ3jyCI8RqNEdiU1Qaja7i53vC1k2OaquJWNhz0HQorxA8sMCXZJuZy+YI897g38emE4rLNONobkmqbo32HzjYQL0O8fYtmk7FYNYT4zC7vKO417PU8XimBY0osK8UwbVwRkdVzdAWH+y5a79jUFoeDryC+0iO4+oxsW1GUikLPkbVEWTV+F+J2K3LfRNQmrmmQqRwazaiVlLuEIm7YyyS1jBlGE9rBHUbV0jNNSi9GtCamrQnbHbPbGy6nKeHIZ3JyH52tcF9boDvaOMMoJOPeCbVrotc3xFVIG1c0bR/Xr6idCDsXdLLAawKEV6CikKtsxhCJxwBpCurORqmCZi5wxx1GvI9tS5pSgi0hHFN4K9q1RWhJPK3pJkdMiksi75zX0TVfPB1iVCnrouGLP32BHDVUnc2FcNEHB8jNFHcq+Majd/ile9/h6ewzIvuITK5oXAc7LUjbNQe2x6ZW9PZ9ShkwXyswhxRexatnl7yczgmdMUksEfEAtSvZ3k4Znuxhb0uWq6ecXXyNhW2yevYJXzy+ZXxh8+z1Aqu9oqym2MWSxjVImoL+6QPOjge4RUxeSdqmRC01tatxjQkbvcHfKWqzJPKGHD9wuc2ueTYvMNEIX6FqkwCDLivQyiK2LTLVECIQvsWubfEjl7bKsErQnqZLBUZQIQwHX0joJFZb0AQBsPuZAfuXAYHZP0vzhRCHwPyn9ivg9M/4nQA3fwEV/kzfgeOTC726fs31vKa377Lfz4nEhDp7hXYt7nnnWKOWu9c5nqNZqAy7CqhiRTdtuXtzhzBMlrsEPzSJyxB74pAWO1p7zGqz4NHwIUIrONyymS14all8+MF77GYpnVQEacg8n2N4ORaaXuBTqYyalE2eEEiTF9s1FJK5Z2Hd3ZEkI8IRxKZJ2Nr8eLrGUQKRtrx7NqK1bmi3QGUgFFh+Q9g7Jd1uydWWeNRHWhoCA2wfKz4mKPo0Yo4nTdZOD5Vm1GaJlZvINiDpNI5jEFgpdR1R+hoDxdgf0DpLyqLCsS3MZU5rVtAI7O2cttNcX29xHI9KaiLfZrkq6dVvMCwYBX12A4fZZUqbp9ShRc8MKQ1F6ZrUhYJ2gVn77I8tbuocz3CZGDlyJ5COi9Of4HoenddiFwmGr7G0hXvk0xo2RlOzslv8oqF1e3hNQVJ7WH5H6xi0EsKspgoTwrjk6cxhufuCWtpU9oQfvfg/+OXwX8ZxSnwBQVTixRb/zm/8PYpNQ2ftiK0WYTpsbhrk1RJ9r0eYtuyf92jvatKdxpjsoVNFW2tevH5Ou/AIPR8pBF2XcfXlDUW95LB/xL1xjL4/YLjsMb/LuZ+YOAdjXryas6+Pqbcr9mwJo5ij90759PstQdLwrQ/P0TuXMixoTYe6ibGCjiQZUssdxVWBORoRmBHjcUTt2Mwvr/EkUPtoWVCYLk5dgGtiNiGlKgmFoDYEnS5xTROjA7Chp1FFgxAhoqqx4xbpSJRwMMwGB/FXkgn8z8C/C/wXP73/T3/G/h8LIf4R8NeA7T9vPwBAtjX1qiIKNZuNpijekD7/MeG9GO8Gwndtuq0PoiMrc+KVQcGMx59ccr29o2fCi9s1d6tLev4Q7QlaJUh1H6+pOZ2ckWmD4cRlpWrun1iUucssy+g7Y2SXcrX+Ah047NsRKugQZo+92CVPK3SdszUEvcrFNCvyueLkKGGarpl9vGP0zjmGA2cnj7h1n5Ou5/yorHGiAE+lNCVIB04OzlhWGXtJiAo0JXPI9injlMhpKUYjtKg5eOYytToCADVGdyVdLbDzLaXjYYUmcmGjZElbauzYwnEtrNUejdXgbCUbG8b2iCqreHO95fGbN9jxgEcnJ0SDPq+WnxOmNmvT4uTBBXFjkEQHPEgqnFDiGD7z+RxDVFS5TZiAEbpEvkeVlRz3HJqmpdAGw7DHNuuot5LuwILIwsHGD22ssqW1HUyRQ2fQkwZd38ftNFG/owwshLRRrWRXZNjOiEY6fP+jj7l+/ZqBdUjdL2jmG8LKxY0bXr/JsfQId+RzOoi4Xq5YrV+SGTG//re/yxe3kp5Rw3mCMAOcsU0eHHHvcEjmlBjKpFtUfJJtmE9rrlZP8YcBu5cKu73lq+9/mz3jgMV0yv/+8se47pj9vsNuUzL/dMeqLhFyy+Nnl4TC4l/7N3+dSpck7x/wW999n7KEvCiR8YKyPaDnWqyvrzkZTHDGLrMnC3Rr0Zpzgv49hsaYRX7F0pHcrJbg2riWj9CK3LcwKnCMLVKFFLpGmgLP1TRph1QaS0VURoarXcbuW+HRcmfhOYJAFCxrgWU5vC3W/X8EASHEP+TtJuBYCHEF/Oc/Df5/LIT4D4BL4Ld/6v6/8rY8+Oynf/33/9/ml4bBtM6pM40tdizSLb4vKFLBwcNzZrsGy9ihhUtkWzSkZPOK6WqLaOa8bh2ePf4cf9CnNhWWBaE/wJIrytxmPEqojA2FFRJsLKzYYXI+wkwLGltTq5oSn1HaMGt3yDuwvB5Bz2fo22S2iyFNutWc0lM0KKbTkCKb0VqCarpEyYyD0wdsyyGqKzEBt1FcmR5RryHqjzGFglnFlbfkJDxDODZu4mAjcA8dzEXNzj6CwXNYlpSbBtd3iZyStRB0wsXyWtpZgDnY4W49nLilUz50BaYZMBAK27LphQ5tVKC9ih9+fsl29Zxh/4w/eV7xwXcnvHPygHy3ZX1V8PkX38cnpBdGiFxhOgOW5UuGwZAmHmC1Kba2Mb0InQzwBDTZEnvgYcmEqmwo8y2u8LCNMUZREnoShYEQLQY2rh9Y0d4AACAASURBVLRoBx56s8MpO3zfZWNYiLWLNjYkcUSb+uROxnR+y/PLZ9hVgOoZuLVDFyRk0zd88njKpL+Pa5fYmYPzwVeofv93ccIRTu2Rr1yGYktlniPEIf0gYJDcI9kLsc2EpNnxYnpN6wUcliXG5AhRFbzJ7mguZzjjhMWrO2xRMr+6pjUDysUn3PR8jDbjMJhwN39Nez0leXDM1x/uIVXDcHSAThdMDr5CVi75/k8+4XZn0d+z6fKUVXnFd/rvYpsJ168/Yjw2cb19VLnlarMiQLB8NUPWLo4t6GSD2drEFujIotMOsmoxpIGWgnprYbk+tlmhZQqmSYfJrqppbYfYqGhUR42JWynqUf3zWMP/wtWBf/vnfPr1n+Grgf/oX2Te/2c0NalbYFUZVm9CnWmavMQTLk6WUudb8q4FaaIne9TLkv/rh79PP9RsNiXVqxuyImOy55KuS+4dn+OYOyrpQLclN1fE7YC8u0P7AqMx6PINDy4uKOsaTJvqtqVVO7Z5xVlviBobNCrGzGcEXp/OgFU7p7jcMr7vc3m9wDM7woOYveMJV5+3GFLywTv3md+NKLqCj//o+9Q9m0jvYcUl0uzY2S2eqkiVZiT2kKEm3wguvICd7RI6A4JwhDV7RS9MELwV5HAbC8c3cGtN3W8JfZedNKnROHVNayUoZ0lTBlh+iGwLTg7PuZ7NePz8x/zJH/0+xwcP+Vu/9A1ubhrc3gW24dA/XNLerJFJRtNoLMch0DVmZ/JyccN9B1z7CC9YUGcN5maLcWjTdRFu1SNtl3RVRdU6YLqY4wptCIRdYucC/I6mVFiGxNzkOMIkjTyCAlzLwiky2nhA2lTYhg2y4Pb1l7A2SZ0NPN7RjBr2Ysngb3wNPYypHej8HlYKY9PiIPRYmorTe2cUSmA597n/Yc47p2f4oz2Ucih1TLq7YnPlECYuV+2Kqq0wlcE6yHFfrzn75gNezGbc3c646q5wdIuVFaSyIr+pkYuSq2TGSCvOL4753ve+RzyW9A+O0UpjDO5D3+Xd+9+gF47ZtDXkJqnlo9sOz6oRFjx8GCC6hNZ2WL66BC+nqgSr8ubtYSFaOs/FkpI8DHG1xNy0GIaJNmoiS6MaG4uWrrEx8THIqJyStBO4ZkfR1OjQxMhsHDvH7wLWlD8z/H4hzg4oy2Lz+SvO7h2zXeScJgmrdcJik/Jf/qP/mlP3kO/9vV/hzfI1Lz99SZ0XWJua2fWKq9cviXoXnL9/wdf2fgl3UJHmBU9++ILh2Rlng0eo3Qp7OMCtAgp3Ri9+SGc51MUKOx5gbg26eEFdOQQ3Grln0bYdSbt8y+lP57SVgdMIrKjl9fPXPDz4gNaxKDaSlbfjaM9BU/P5k8/QbYPdD2i0xXFvQByHzDcF2fVr9vfPie+dYhUav1xy/XkBgcfl+gbbGmEmJUfnIcXqhLv0GUHuUWqBaY2wvJQBDXLQp6lSjG4NeLj7Ed1iDaFDby+hPxnxS7/6W4SFYltV+FaApV0Wjz/lM1mTza7pfSNnEoxwTRdteyT2iLCeEcaH9BOJP7qHNXvKzi3pp48xjUc00yuShyNaFeMkNYW1wjVt7h1OMGoXc+jRawRFYCE9CzY2bewS6BKRdFTmHk1XYuiWamBjlwVqEtBPbZaWpiod/vHv/I/85ItnnHBBU2bsnb/LgwceS9XQmX0mZ0ecTwYMB/ex5QK/VKANVutbDMvg21/9Fu5xiGgSVp0iW9b4bsV1YRJafY7ONW82Mx7uvU8ZFfwvP/mHDHsJ8uvf4aMf/g5Hzj5vulv8QjGvUk6ShG+efp2z4df41m9+l25b0uS3FH5AYHdgum91CGYN2o9Z3Eju2oJg/4JkNEHQslm+lXWfzgv8M4npCPbHfdKmpci2mCvF69mGMHeYUYOwMMsCSYC3rMlMQag7Ele8VR7uOnTQkFcBE0NxY1YEJVihJpImbaOQrYddKio8cBtE+XPSAH5BQKCrKlx3wGqesutyyvaQRZMRFSUXwwveffiApjch//Q5le7Y3Ja40Zb6RhEYEYcfnhPUJV/ePKP96Jrraso3H/4qKupobclimTIcF8wuXzLpxxAr+rVJ28XU0y2lWaPLENtR7PoKlUIwsOl2Btf1ilHbR9pbunJKhUbWfVQLyjCoXU1VFcy6CnfbsNssaDBob2vuj0O06mE4irDvkq88ZGLx5sVzotMDdoagtmriasna9AlWS6arlEFvxP3TCesvr6nrEt26VPKOPceAJMB1fGwp8BOQlovQDe7BGYYl+ODDD/n6V99neH/MsBmx+/j36bo1g7hPheauWFLOGt59ss/uSDAcGcRngvL1DH84wVEC26jYbhVZ6vLVfQsx2cPcbXlqdFjLlGYQgNnjQKRI6aJFjB7UCFMyKz1Gro/OO8yDDhqPpmciLXDbNZ1OsCuBUXeoIERXW7RngWkimhzD7KH9hE+++IjRICByPHx1hCmmeF7Lfn+E7yUYoiFfK3Rssv/wPfzXAeOjAUU/wagDijZAVwVV1tFJE2OzoXRrCvm2I/J88YJ5taQ/mWAog06UWDuTPL5ioCXOYJ9Howd88PX3MPwhtVBs0hZplPT3v0LYLshDi7woGdhfoT2e8nRTMwgdgkXKQvk0jqDn9QjdlEnvECe02OYpt7c1p7HH6OCE69/5Xd595yGPDnt8/9nHJEPBLu9wlMZQkgaJbZh0WrHrBIFrYjQeraVxLclSCoy3+SJ2J8mlxnRbIgN2GPhGQd1oAsOl/EXOBKom5wcf/S6HvSPG45h5+oam2iDGMU4UsesK2i+fsdh9RhhdYMqOGzySvQz3YITfFJxGQ7747I/QCZwMBnTuFkP12BVbevEEf77BKmqeVSmNo/neO98mb+dEjknsD9COZpfZJIZNQUl0qQknCbc3DY2/Iqp8igf71K8yeo9Osa2Usk04DAV+Y6IDg8tFzng0QsqajbPCiY9pZEm+cgllw/hkyGx6zcnkAqMt2UmPPiF1AU264aas2V49xrh4QG9gc3FywavqS+pOEAYCa1jRbraEuYk9chg1HukO/KMzwiRmP9zn/YtvYB+OCIwQ4+CEJH8HN51hOz1Eb0PdedSbjGX+im8OxmxTi9rwaWNYdEvWl3PuK4NN0MO1UmodgQlFuSB0Gpw6IMsvueee49sjUquiFHMcMSFyLML4Ld3e6AI2tUUkdzSGjWo6kAJVabTQpJZD39shdcymawiFwRsqsu2Muy9fQF2im4BmbDGLIWFCuDfCd0KiriGxK4K9PTwlCSKbKh5iBiHPvv9jqiMbEU9Q85RMlcjbmmLYUS4UkfSx7Am1uOFR/4JdU9OmHbvVjvDoHMdrOR89RHc149NDzPAevpny5voGXTwm26ywvDvevXgEWuPrOVn2mLYNiLOM4f4ZtWvj+TWG3uGlC5ZtRnMQ03QZl68X8OWS/nt9rvOU0/MDEIIq9SkWLoYliJ0KKhvfkdQmZFWL49hkhkRIiW9oGmXiSY0dKMxUUUeKqnjbjr3BphCK2BPIqsExQEsbfpFBoGsV19MV0yLHe+7hhjFnTkt/eMRieU1arzg4Pubs+LvMp7fkzhpWc+52KfVOEn1tyEc3T2mdFo8hbdPy5MknnA7fR3cVM91xd/cSDBtPeCyGipfdjNODrzAApG2gyi3zvEYOTKLW4jbL8dc1Z8cT8rxF9RuSmxARd1hdR2f74AnqbkUU76FKj8meQitNksVUnofdaGTlooKcWebh3qywHYdS3lE9FXSkKFdyNV9y9emfsq18XLFmvss4iHvc++p7DE4i3MpkUZqYasbxxQFKeux7CjXaw7Us4vOIpOrAGeD0Qya+RRcMsKOKJBlzEp/wynnGbNOi6jmDcMDt6xVff09ysD9EyxIriNilSxjUtNYBg6bBGvTZdBKrTgmiIfuhgycrRtEIL+iTmppBAmE9QDshFSbDtUb5JtrtiGjRtolvBShTohGYdgOOQ7DT0Ll0pkYLj51vsFi/4PMXb1AuWBubwpf82t/4m4iuJNtU3J+cc5dvsYeHGOKQIKpQvQLzrs/OWVO8eAZmj7QoKF++ZLVqsCdbRsMLvNhlcjSmbluC0CHyTsm0xXdvRsSxpOkMujpH65quFciy4HW94N3TmEwGfOtghJVtkffOqDfwo0//KYtdylfuf5u2uKaQPmH/iGc/eULr31A1Pu8eZqRnp2jD4eVU8XvrW7pXX3ASnPD9T/8YZVscTEYof0C7kyiVUW8a7NhE9wzKqmanBa7pIYwSWzpID2TTIcoW7ZsY0qY2O4I6oHIzdrXG8Vx8ZUAq0U5AWWQMh/UvtrKQQOCKDrtLULYijiWvsw3x+ppV2xGaCu/FFccXF+TpCm9r0rZDRN8jSHqEvsnB/Xe4vn3N1d0lA0+w7QR7JyWuaLl7+gynLtgYFralqG73mHw3ptu1bLwCqwPP6ug5h7Tyii4I8FUEdUVRSzrXZoTLPJlhGxEyBaELYm2jjTHTboFKLTQdE9/gcWcxKHLKwKN0U/JFgB5sWOc1VA47O+IsyXmyvaPJLUpZUlVgJDtUJamLEuOsh2XYPBo+Im0E4W4K7QTP9Eh8RW9/jNN30K3Bvh2zWFTsnfj0hE0viChNQVH6iFFMfP8C44s/5rC5x7a4ovXWRP2A+ctX9MqM0eAA2wPPgarUpNzgHAb07ATPHWJaJY2/pV8f4u3HFLkiUjX9QFKlMW7PRMU1DhOUnWHaO7rARRQhrirw647St2g7G0NL6p2DY+RAhMsWEJStSRwGhKGHp09pxzWxGRMEFtXGxRq71FlJEsVYAxfhzlnnsL5M6U1y1DIkLVuqrsEsCg6PDnm+/iFJEbM/DDB9j0K1FMspuTXEMiaMBppbDXvOmDB2+fTpM1RW4g56mE1Ne11y66+xthkrqXl5PaPvBcShz/13vo376hl1BT1xhhFv2WS3TDdzPjg+Z98Pefb4CdvnT7mazvn8xz/B2k3JegXhuEVenGPnknjcY1rX5N0tVlujPBM3b9i64GiNqW2UVdJWYNCgtxaVI7B9cGtFJmq0Y6KbAhML27DpZI1ULo3r4ZY7tO+SFg5/FbTh/9+GoTXKzmhSQSUqhs4Jo5N7PJml9OuWMAEd9bl98ZwszzBNgziGq7biYmTTVA631xuCVNG6oLRL7GvKF08oJ3uYjsFuJ2iygjSK2I9yVrdbTNuj8yS+55D2XCJzRyotzGLFLpUkvZDYCHBGIV3VYhUTrHXKzlRkaUUcuozux+zvDrhLttjiiO38M/rDfQbhAbNswaEIyYOK11ubrlb0By7p9IaXkc/mukDVBdglVa/FEwrLUrimx/ImZbK3Ro7uE7qC4LpD2pJ+AOnaojUsOhkwcQ20pdnv9VFmgHMwIkhsCkMTa5tluqF++opd5qDWb/B9gd1NULWka1pWRsq+PaZoSiIRokyTKE7wKhsrrMmzJS6aeOBg2y1NaRL1+/hOgtpKbKDBwGtdEr2lVRKkj17XuMOaolaoMmUdJ0SypFQWItjSrm3aqENVDpG3IaoG7I3OeW90ypPyilH/AYO+RWI4VL2MM2vCKis5Jia7m+L2HlKuH5O/WXD9WlN3ioPxmHr3kmTvAc+mM0bJmHo157aaMsgfoqWkswP6AgxS0i00esasyEm2EXu+gzeYkCuFtGJE1pAXGXWa4/UDvtofMMszuqrCGFqcnkzwhse8/P6P8T0TVwuiYMz1i1uU0eEGLkZe01oGnphxu7qDouTjq7ety3/z7/8aSTIgf52yosPGQtSS0nBpqxY3MLBVA7i0gUa2Fq4qka1LYyiEMDBtiaM76lBhtzalAK+Q7JwaU9ZIYSCURP0cjgD8goCAFgpVW1RtycW+Q+4Jdh9/TB37VFJRPB5wdNKRy4SR8MiQRMM+96RFVQT4e33CqmU92WdspqxMTdo25I1mfZsSobCQWLGiLjLuiiWvn33O6TcFrZEQhCHlsytu4o5Jb4id9RkehPT6grbYsl5niHlLmHTshgHxsqD//tdxC81ufknZ2uwdneBaLv3kGDsesmk0+skdV82CkRVz4A2Yts+IzQh74LC4nBGWS6ZdC5sav+iQbUftxQjRcnKwj2WFVIZkVJm4UYiKffxlgXMksPIa21vT5Db+IkSMa/ydhOKYWxSe2aMTGYPwIcuTmuPXjymCr9A1ay58D6k74mFAkoxZNztCxyPXDa7fIYuUNyIgXpucPhizrmGRdfTOB5i1wjMdxEgQdwNiM8TyA9ZiThVE6Mygsw2ksFF1h95GzIYNw0Jir30aVxPYDngmZVnjmJrKHWINW4w7g7/zW7+B86Pf5d3hKW40Yplv2XNGmMmIsTflTsLl1RXt0+c4WQsTg+XlGscJqYOGV9MVH/QeYPs7wvEFydERmzJHUWK4ENghurK4K+fYtYOPJts0GFFJta6ohx6nk5A3hcm9M8HycsqbqzmrJ1OOD44JXLCHI378o+9jSINHDyVT6459IyGwBIW1ZXu3Zn8yYm+0h7N/yOko5Q/rV3RWQ/WmZaE1rz77CVcPxsTxX8dINJ8//pIsAHtnUYkWT1u0tY3WCtORmKUBfokWBlrV0GhcbNpCI7RJYBjIWNPtKiwzwTALIqOjtSKk0b0l7tfyZ8bfLwYIYKAbk/u9PutyjbtnYC9HGLXE61xKd83tG8n+CWycCpV21Hc+XSBo2xlBfx/P3Gf94jGHZh+LmiA1WbcZ++OOzXJHXTbkvoWJQeHkLLOWg8slt7mk+aqitrf0ugRThGC3ZMsr1DpCd4IoBOmb+D2PJrcZP/DwIp/CaeirgDTW5EWN0hWdKSinCzp/RzIwcFYBtYLMmmOOLGpl0QUWzshnfWeiVYbvJ3hFg+yXSFuTOCHgcvtmydn9+whP4CQ9VKcR9xyGUlIbQxhqPNshdS36XsCyKfBcj75toRKDzXJNNe1wLU263OHJLUZoIcOSkXuP04enOMMYI60RJmTZhEQtkLUkUSa+LvE8h3EJqoORpbHiE1QMXm3hBgkFmtjf4qYTKBukELjKoGlqjNgjuN/ArYusWxq/xYwUdWPhmjU6iaCR7LWwvlEMHYfwXPPo5iFqYCJzg9ZyuVlcM1g35HWI3W6oy5b940O6uw2PP/mUDYKjRLBY1IzOjllun2EaHpPY4HadIrsVZq9PbWhE1/Dl1YJqMyUvWj782ocMvZBoLBGtQIYtm01OlKVcVzWykTDscdZzaLKS9TpnkynMUtK1ii+nz1g+y5B6i9hYTK9nmG1OO31JYH2X4/Exp+dnPH/RcCskI8vi0fvn5HVLHUkKR7K5M1m/WYDWGPjYQqOGLYNKYrSKrRI0psBoHbq2RhmAcEltjWO0CFugG5s6tRE+6CbD7xSqFdR2hrAMDOvndx/5hQABUIzimLatEHlNdNdR9UraNkbuMmTnscsKBv2Muazwa5d89xGq7DFOYpp0TlltiT2XWZFhOR0XexF0PkKl1IuaBgur1sS+j167OCcNeqAovZRsZxJ4xtsz3E2BT8jE79OaDVunZeiYb3seupqxGaLqGl+4uIam7g1w647WrNi86rBOXGzXwek0rjZxDnzK64aXqxuqUuCcL2Hl4TTVWy655bKSGWqnCQoLe7xPrkvezF+QmAmb23OivXs4p4owSrAvLfSpgbGpsVcmXTWnNI550PWwVIfvrGlkhN/YuLrP6/IV09mUATWdPaAPBJWBM/TpChvfbSnbhqEX0jYv2OSS0nUxrS1rZTC+ylgJjTcISOuWnjfHNfZxhy6NleHXPRpLIWyNNAwMM6OphnTuEFM25DuTzrMZtorU1PiNyabraGvwzIomKykI6AKHQtb4tcvB6YTttkPaFe7tDsNxuE0b1OqW+LjHwO7Rs3uUfsXWaXl/co8MgzowMaY1T5INX+0f82x6wycff0ZydoQzSOnsjvXTnN5eRJ5NsXct2j6jMhr0zsboJURCcXX7JYWM0dsVaW1zpGKC01s++8EtpTZw5ILDaA8rGbAy57xeXdKoAZ2eM7+5Y5z0oKvxyoKD4yFJH/pK8/7AwziY8I2/+3eIthW2aDFXS8qyYlYuEJ0mM7fErkddKraNS4KiQzGUUCc2ZRrh6ZqGBjoDIQIaGmIXmlLiVA2O0mSY1MJEmBq78bF0Q/1zVEV+QUAAVtk1unBxlEmt5rS1iWBNicLrGqpBzfXcJB6OaYwlWd5g6ZIr0TH9eIduoHJdDl0PexRzW3aIdEvZ7LB6BnnVEguflVGDbdF/vuQ7H/wK7r7mbr7m4PAEpyvRysYRFnZ/xNjpM7TmNHKI36zIdYNhWfhWxrq8wRM2WSHJmwpBQ3LWUWcWhSxR2qNzK0QmiJKac7vPfJPh5IJVs2Q02SPsFOY25xv7Fq/1kk21oe4qzLQDp8NOJF88fklRFtyffAPXAD22sbXDmatZllAHE+JexFSvGI5iNsqg55bslMHNzWuKoqCaL9maFd3dimjYIxHHJHtDRGJRZC0vb17xutsS+X08YRAeuAz7B0RuyNnRMReWg2kMMPo+RgdNobCUh+x3pLRE04j+/pZt2UemBV6vw3beKkZtKx9flKgDiVXb2HaNX5h0ucJVS/ATynKHUntQVmwpePV8jvZmXL0syUSfR/sWk9bko3bDhXYwrIQvX/xTljOfd06/xs3lLVESQ75hajnsnt7xvy0e89d/49/gX/q1X8G1DG5u53z+o4/ZXqd89zvvMjn9kPvfnDBbL9lNp8im4/1vf490t6Y1bLrliryqebp8QlqsWPygJFAFrjfCwWRqTEmSIe/sH/LonWOWxRS1m/DhB32m2xXfPvsqv/0P/i6FXbOdKf71f/BvMfJt8tWWW12yvL5lsH9Me7VgenXH3fMCWXiYQYkpK3IdEtgGi0rTcxSZaWEWEkMppNViS5e201TUxEbIThcEVkRbN2wChZQ2gW9QpQIj6RD5z14KwC8ICCgtqIWLF0BjacoaKuUg7JahYVPVglhLujgnzRVNp7BbCQXITUt47NGKhmYtECc208U17d0WbI+7+QJfR3iyodA1llSYOoT9AGmOMZc1R1aNzh28kYva2gwmDqb0UAocmUC7RdUDJkNNu6lpnQFCNaiRwyDsCIwhRt7gOYJXlATbJetyhxVEjA4cGjlBZRu8MOaof8rjp5+wqmoqFZD0LNxQ47IjMSPyMsMIA7osYNZ1vJe83bzM52tkH7w9B6ey2OCg7RB/GGBbCuGbKCS+7tGtd9SWYtXAapuznM8YHiastxBPDojGEXW2pbMiNuULFtNbQkcg4hEiShhnJicP9/ECl8YI8X0TZXqYrokYmf83c2/yq9uS3mk9EbFi9V+7v92dbp/mnnvPzZvOm5nOdNrOsssl5BSmRFElgYTEAMGo/gULBilqgEqMQIyQkBASA2YeMCqLplwCZ6Zv9re/p9/9t79+9U1EMDgJWDizbMCi8p2tkCLW6H1XrLf5PQy7FE1MX67xhjURlpvWZxA3mABMG2CrnmA0xFQdUrfkWUAbtjRVgG8i4jhnl91FphuMVXTXp3Q2YOW21IWhySzxaEqwvKYpxkSDAW5pKKIMbyaZ+m/x4GFGV/vc/93H3JznnK8Vo6Fifr3ESFCLDS/mGyxztlcxo8GM6P4exYXAhls+KTP24gGxHyJSgXY11fyCrCrJ2pJ+W/Nk8iXOh5e8l3qE1qHvJcTJmGgwYff6gutXK/6tf/ffo3hxQ2FWdFeXfL3JmNyfYNghmyHKLwmy2zR7mmYjCdctw+iEgQs4nawJnMF1W+o4RpYheVgzUA1eqzFjg9kqpOwwCnwdU7oalfbITKCEhDonkAGFykj2PLodDISgLzpSbekKHxs5+BXiQr8WQcA5h7UdjR4QdYZSF8jEki5gFyj2xIACQdkYbFHgIfBFSpGXHAwVu+uCZmPw9gdsmzFRf0AQBbxav6b3JZ5S7NYGP/JwtMh4i5/M6OMdnoZJN2EYChZlTzRw1JMQaQ1HRYCdDFEEaDfg/KYkSCE0lkKE+K2lDTRBZ2iHPrlpGAgBj+5hP7tGSR8vVCzXc5QRyMyHe3D3wT0m84ITseayfs2ucOhZgl8ERHHH9ipnMygZmoQq2zFINUVQIh+8hatKwkSxvqnxU4tnWmobwKrHH/q05QUCiWzWeNsFL85eg94RZkNmJ+ANt+wf7WMKTb+54XgSoe8f0hNxcjwgUYeMoxEtbxqZOq2oVUgUNkSA6Tw0NbvhBWo3o21Bhm9aaAdNy7pXaJXTej7ermS2SbAHDq/IsLsR0dii2CBdihznVJWHVgM2xQ19k3PjWpTfcP7yChEEHMkJp4srVHbN9GDAO8d3KJTix198j+Nvf5O+dPzghz/j1fOf4OmH3P/Nuzw4PEarnqxdcvp6xyysOX77hGGY0MkKfz+mf9kig5zac4w6j8kkYrncUpuecBPRm5Lh8T63Hz7iPf2QoqlIW5+Xr57h1iX+LGB/fJdGh6xuetzQ0i1CPsmW7IeCOHNcF467hz2lSdjWZyQ7n1VZIIOAMB3gNT1BFtGNBMLrSfqcVkLoLHUFBkPUGRq/pyZiqg3bvCDSGpNBJD1a1bOVMDIC4xmqjUELRxn6+JnGJgHNLqcvfX4Vmly8mff5V2ue8t0kDRHWYn2FDWLavkN7hiQPWaaK2bIgn/a4JqTpBLeU5qbOCRJF024QdYqeKmJl8P0pxsH65oI09Vk2Dt8qlDA425Ifpbw92ufWlx/zQO9ztih5/PgWgfDpuoKvvv8+jZUEgcIahUPiNVtIJUHhsVIeo7Jki88kFvjKRwmJmwpunq+wlJROoDpNWxZsREXiQnQoKbYdpt1ROYOOO3RnSXyPn/34AlcX5FvDx/NLIlWiakcbanQ4IRyCrifcfXSLdbfj9r0pqR8xG0WM0z2SwR62B9MbROzhScH5csXm7JQXn5+yMiVveR4PvvqQthQcjBzaD0kOU7xyS3D8A1tGIwAAIABJREFUNgfxGOdD6k0YJbCxewzGLbYIiBNFEDm6zifzfcLAQ/UespA0g4qkiOnGAa6owLTYyIPWJ6CgNj5OSIbaw4iewuuojaapLK9f37BbXtEstlwstvSq4eDuQ16e/RR2A957cIeXz16y2OVIV3BnvM/+43d5fXrJ0i/4+H/5C975rS/hGc3ju7f4+fwFcpewfzJkvtqShIfsxQ4/9JjXglFeMnw44YsPPmHb7Hj73d8gDhQ6cfRXjpUp6GtL5Vpuj+9xPJsQDqdc5Avy64z944C26mmbki/OL/HpEK5jebllc5Xz4vk/Z/HinEFh+c//6z9Gj99CRxUDhvRexot2zZ/8p3/C3/9H/wZZvONHH3/G9//sB/zs088xYcSgB902FF0Aoxa/t3TVEE8UON1jXYBUHTZ3BNKDUNK1hs4B0mE7iZc4wt5RBo4gc7QiJFINm87+0Dn3jb/if/8KfP6vmKWnVQZlprT+BnXTICc7OjemamDPZSzHEr+N8cUWTw+4bEv8FOq+Juim1OMav48ow4hah7DJ2Xoe202LVoqsqRiHkiJKOCpSlvWC48NbZCf36W8uib70iFEgycMZtRQkVuOMItERve7xw7sYseC6rgkmKZ0MCcWSKPDpOof2LUXZsh1KksJD9BXeKKGsbzC5JLkbEDY+6UODNUOW8wtsIeiamLLfcfdwSGNDBIZh9oqj0RHbegtFiedXrNctA93xxdOOOtgRtBvq0W1Cz2P/YErYK8R0zM2z1wSjhDIekuzmnBnN0cMZw9WGkey5nUzJpx57Vc6tkyN0mtDZQzxPMdQNtQW/12xiCSLH5HtUoqJZS+I2hElL0BQ4ZqRk7MIBng4oEsuoWUKg6LIIk3dIL6OJQnQncVaQryoYSupGYq6W5FHPD/63H7A3TfA6x46CRMfcPD1FNYLES3k9P6P0xpAssLmidT293hHWHYOpz8nJO+h5zdn2JT/6+BXj0YhwAu08ZJwkTPaHzMKe9bzgKNBcDyqe/uCUdt3SK8g3HSqxdFXN+WrJJErpZM80OqE5gNW6JQxuUE1DXi+ILmPq1GOVtyRqH4KMoHO4uKDbT3E/bhg7xeEtj53t8Xc7bo2ndH5HWwXIreTv/fYT/MAwLVKuXl5x8ewc1QrixsPFFevOsu9Zil6gnaBni6cTGtHjlEdPRy8kiADZ1VggNCFFXCHpUGVAF3VEvU83aCDrsZ3m17pZSAmHch5OrhG5gHGNY0jcanTcsnAOaw2hFnhNQC63RKGmRyEZ03o5rmxonCQIHGFWsGhq4sYik4S8sqTasbM9XtZQsaPxJdWmpPJ2HL97j9qrmB1N8eSYsqtJ4jGlgpAxUdpTz7dke1PuvNXQeBrd1LTcR9PgFRXSRchOM+1LeiJEPyI0CW4vZ7wPKo6QlcL0Le0i53h4zKZfYFTPpgmYTCr69D5yeIkMHrHOK8IsJTxIyHNH0hb0rkZtrknHETdNyyZ7Rdwc8bXfTei3iqH0MIcPkUGDyiOW4jH3pjll64g6mByElL6HanLiuxo31rRG4VUSN5Y4E9A6Rzds2S8mMKzQfkWkI0pVkw4spgdbjLC+pnIGKVv6MmfQ7WM8jVto+mhBHqSk1mfjPNLO4lUd6taEbOM4/fgZ89UZbRi+GV2W+yzNhqSFrMkYOIEsBea+Iahj2uaM9dmG2btf4rq9ov35Df/j04946+WQ692cw1lK0wS8cyuhdQovbimvPuLB13+LrttykbX4QtB0Hl5hmE5CsnDEIPDIsiuO03swHHD9+RdMpg944HvsFpeU/Yhiohl1Ecl0wMou2WYt41Jw/sFfUPY+/sBycitlODjEmGsmh2AnHvtHE9pFTjC9ZDff4Hm3Obo7YH6quft3voaZr9mcvWJ4a488UHQyYJKX7BpBKmNc21P3DpOGhL3BqZpehqiiI9QeJZoq7lFG4nUKk+ZEjcZEDppf/Cp0PbRgpobd5g2d+JfZr0UQAEEtfGIjUTIn60dMTcfOGJxoGClHQYhHw7ppSKxPN7SIsnozYdX4WBOC31BsMrTnM/IkhepJvBjfWyMjcKWgiRytJymt5enljmz3PzPavs3vPPkWwhugtw369pggCjjwPKq2JPVbGIYE1uCHI2xdEPr7xLplzB3kfo7pA/bznoU5Aioq0RJGAbszgY2gr1pGI9hWjvDBPkcTwebzFpY7em7g6C0iLDN1wsnwmNNlyf6w5cUio7soeF6MGNdrVrGjyzzKYksdlnyRf4/fmZ/AOMQmGl+2CKto93v2xJbh3iOGVGwuO/r9AF3U7B2OyRHEnUIGLeO9gMA/JpMdkxasdqAd2iRoW9KHAr8VdEVDGAlWA4Ff3lBbhVWADVnYFbbziUaCSjToesJyVxMKn+VgQ7XOKNZruvMVN0HFIAGvqpG7mk8vf0jRgpSWo3emvPz5KXXd8Y3NgMHJHVJpmT7e4+ZiSbPu+OD0Qz770feRj+4zU0Nme0fsnwTsRQFiOuLitGSnVqyu5uR5jUoiUiGoipfUgeDbX/9dmlqxzi45HDyg7mrKpuEffOcP+ewnH/Lz6x3RaMLdE8X81RlP6y0noymrZc7p5acMtaCPWhJvym6+YxM4apszGUv+0Xf+AWkkKK3g559+gQ4ifu/vfoOAHrvM2W0vaF2EHyjG+0PkosV2PWFRk0mNZ94I2CzTikg52qLE+B59oxn6DVsfIhsjooap8diUEicMIpaoLsC0NX3ss3EZiYVOaJKqo9cBhfl/eRP4FeCR/wz4N3mTb3wG/AfOuc0vZMk/AT77xfbvOef+8V8fAhRRVVBEBr8aEOgdbaeoRo5hH1E3ELqSrugRNsGKGkyIKAVGtBApRFchcaSRY5UZbGQIakmpr0EGBJnC9h3SKbpuRzxUdNUZC+8u96IhbWbxhCMcakYmwteCphXU2uHLgGpoGLYxVSyJ5QzPk4gowmt6EhFT74GxHsdOknuKJIgoy4yjr9xity4JAoPdegyrJd7AklnDl/fuUk0qjqt7FPmCJE4JJhW7ap+3JyUZNxzKgLmDr5h9urpmWq9xTcPlWqFiRezv8cXL54zanvT2Q8xhiidTgmrFpi6IVUgTT9l/aLGiRg/3CUxLH1mScIDfb5kODthMYvZ7S4Ujtil1ZBF4ZNshw9iyM4bJuGa5G6LdipYIfeBj8wQpNwRC4zwf0QX4G00hM9bbHlde0yQNy8uGwHV8fHbG0WHKxbKiRjGeDag9S7leEA97uOyRbcNePKUwguff/wF97fOl9x7x4cULtq9fcOfOAx6c7HH35ADteXztnSfUecbn2y3fnt0mDHsWF8/R0YAHRyPyteP5fM1dBszefsj8skSul+RtQ2a2VLLj3skhm7wjkmOezA54pq4p5xuM6QkTw4KMVT7H7xQ3hcetvT3wHOcyJ3214WpxQ/mbj9k7mlGWLfL2kHfk+8SJTzRJoFmwCx5wPD6haC+I6patVFy+fEYfVKQ7n0I6BtJQWsWgcBgRooKeWkYM2oqdFYTS0cUVcudoPcU+PVliqGqFkgVWhFBnRGFI1nUEgUCUAV3yq/3vb3IT+G+A/xL4b//S2p8Cf+yc64UQ/xT4Y94wBwCeOee++jc49/+03lqaQGDwIW4oep9Iw6iWGFGhbEBdK8JIERqDSX36SqMiS2g7RCgRfU9nNOvdEOV1iLbA2YAiawi8isYX+DKg1h6JdAR4iHLI4MiS7RY0V4ecJVMOdo5cltjUZ9pGjKQFG7NvBMu4Za9o8VVHmAlyA5X28cQMzzMEh4piFRL7FU0To5XC6xpmg0N0CDVrYj2lqR0ygMO7KTYA2Wyp6iOaHnZVy8XicybhkNj6DCf3sY1FxTGxqDHNlFytGd94DMYRg3ifFzfnOKP4ydlnPA6/STzq6FzC2K0QSEJdYHKPRKeMv+Qz2t5iVl5T+keEqaII9tF0SB1jXI9SHWmvsHqFlyY0wmcaTdHNmir20aXACEm0NOBasiRCrFqWYolQCZvNEoIErSNOLYiF4OpsQ3grYjo44PL6BbWQfOu9r/L0+jnHyRBlSxov5PZ4QJvfIb0fYYqerhUYU7JqFrTtimh2THA85cvRE9q54/jOY+ZdRtkqhtbj5fUVo8ERRwd3mA5jpn7CSn7OVJfEs4Sw3CKEwEYpdiiZtjCMDaGCcCO4IGEXznkyPebi9IbN+hxvEOOtrkijPbY4Kl5x/nRBNJE8GUYs3Q6TwNhXDNwAHWT4HbhwxN5EMY0iLtQM5ddkqzU/X9zw1nRM5LcUfocqBBuvY6odG+sT2oI80Uw7w9qAchUikkx6RYdH2ylsuqPKDZ0nSKxH00va0BLisMqDXfsmP8Ob0rVwv5oz9NcGgV8GHnHO/bO/9Pg94N/+f+L0f8WUQ9EzymHT+YRaE8eGWlS4XNMEFmFbemvRLqKtezp8lO3IO0W8M3hakHcWrTbEoaIrLCIsSBmwKzO8gUcWWMZVRtmmUDXIsKMsW77YnaFvTYjdgi5+l7e8mJWoqYoekVSkTY2RHiLzqEPDRsXcGh8yFR5EPuGmJxM7BsP72GhJ0x9CrDgKWnJvSNrAdWCItEfe9KQjj7EHzhO0RLRTwV45ZicNCQ33j++jRcmVMTBvufPOAW3d4dyScRFhx0NOT68pi9ckoylvv3tIIyXxcIjoa0yeEA99gtsP0KMjZF1iBNiqRzRDxMBndveEtXMEy328NID0gNhtqJcpedox7GNsAJYA23TEziGDPYJwTVfvvaEiVR2hibk8XxG5HRdWUhbn5GXJ/GLOKAnxXc+63kFq8BY9Z+fn7A1nyLDmL86e88hKPp1f8/7bT7jYLbjIahgoDtKIp+c3qDgnv8x5/sWGcWHxD2ZEtuMnH73kIJpx/fRDzj4fkssNppB8ld9gnpYMJykvPnlOdnKCcEfgbZEKXp4/I4wHRLIjTccs/BHH0ZDpKKLo4cjb8dnC8MEnP6GcLznYH6KinKVbsfziJcvshjg/JRl/k4HX8O7xff7X779g9eyM7bRl+uQORX0H1RrW0wWsoB7umAwfI9M9nrwXcvWDnLeiQ5b+kt//1nt874efkd4s6FuNVAVb45GUmtx2WNvjRZJNCDpzYA2dkURuSOlXqF6TVSVK+wwqRyYMqROUWhI5hzWKHRrRWX5VUuBvIyfwH/KGSfh/2AMhxI95I3L+Hzvn/sUv2/SXuQNCQNuFSG1QskPGsBEakfVo6QiTAcL1dEhaWxOUCdpf0Ho+qTP0Yc96q9HeG6XbssyRqabzDUHbE7QBzkRotWOHRxQ3OOeThIb2eoMbBpBn+NGYJlmTrzTr8ozYP2R/GNOXE9pRhthpLlvJQVrTKENHSxSV9MuGpvNxmxWe1HhFCTNN6w0YO4GNcg53DTsxZbrXYzuffrBD+CFmFyLEiNYWBKmmujEYr0P1kuPYI7g3ptcetWqxzRGaELWWxL8ZYzb3sTjU4YCxfwj9Bq0apDeAAKSJKDrDYJzgy5guCSmZo0LJtdSkXU+fhDRNReBHWDVkOC2oDOhA4hU+UQNlOKTuFVlmoIygK5lvKmTvkyQbXj77KcfT+/iHAdcvV5SeIpE7kLd5+fwMkyakE0UaxFgHrbdlsVjxePSIrE8p8xuuyhW7bUGUe1Q3K56HGt96zHPNcn7JpN5n1a4ZbZc8sz5/8Nvf4tbRHfLtDpeOyYuSLz75iGgvxKgSr5tx0TjSxTmn25a2q3hy8Db2aMpHH/4Zg8kBxc7j6HjEfH1KeSaphx6dBb90jPYOEXHD2fMVYldTzwuq6zPOX51yGEsePtIM+w5rBdWuJwhqVusRZaEZBxqdCtp8ykW9Yvb5itmXFSwtOvYYRCMu/QxrG/w4YWJKdnGDyBpq5REHAmlKikQyEBKNYFMneLagCRWibmmsJPEsbSdxiUB3LcZq4qRnVwoi58BAqwMCOoSE/pdrivx/CwJCiP+INx0I/90vli6Be865pRDiN4E/EUK855z7K9SDv8wdUEo5Ys1u6ZhqKLsB0t/iyRAveZPEM5VCdxFSFRidUUlN4ALariEQloH2afsWr5K0vo9tHKYJkKamVz6665E2xPodrXHEXU0nApzXkYopbdux2gpoLvF9jVQ+9ekcXz9iMN6gyzFR0jPTE0gMzXpH7GvKssdXITaQVKIgCPeQXkslJLNNS5EYhq1jFUZM+pZMBjgqXDEm8zt80VOsMoyMSFYpw8Si3JDMzBEqxUqLED2Bb6ltQryvsUnPSKT0fkqQdsithlAg/TH+SOH6hlamaGmZyIa68PGPUgZNj+8cPhplW1rpkXqCQsWIuicUsIoTRG+gcXTiANIC6zy2ypJdZGArlG/YbbcUi5aj4ZRFB4Ne0xc159uad+/fonL7bM2CaCSZHg14cOshSvR06w1O56znCVXecfAwYr885Gc/fMG7dw7Y0XClNjySU16uXvNgdpvs5oxc94Qu4UvvHWLlXSaDPXa2Q6RHHO4HFK1jNBqQKkdXjFh2N3zt/l2W1Y6RzGA2YF1X1HrNk7e/TIOmp0UmGTN1RBbOERdr5KBD9ODJjsUKTk9vaOtzNjc73n9wn6ftM06ePGScVNRtw3x+QxPuyK873pvVPLs85VtvDai6AK07PFliR2O2vqB7/ZrNVLM/faNwlAUl3kIipEfXxMQ+DOqW1vdRUUfag2tidrpg1BXUgUBog7aSBkteauLAElUO41I62bz51kcW20IYKJxoiO2AXdvyt34TEEL8+7xJGP5rv1AYxjnX8ItipHPuh0KIZ8DbwAf/0sMcqLwgDAQmUUhuiNuEMHKsG4fyNDiHR08rYoKuITIRTuUUnqD1NIO4Y1sGpNKQKo+VKaHrUUpC6+h1gSJF5S2JFrTWEA198iCiP6uJb+XsSci8iM/OPuOYfaIoY/6ix00HyHjF4TTltckwi4STwxmi1wjfQdjSZo5ERwh/Sb4b0YUdW9HQ7lq8fsQwCskiEGVEazKcLAjOPPrUsReNqOMSJdf4a9iG5/ikWJmgVE/t9yip0L6heS2wsympmNOnPkqPUPd74iZgF4WYjWEWSjKtCbyOXkYYGVNSIn2FXx9hA4HvBLreUYdDZi2UMue8VQTlmi1wkSm017FaXBGkAWar+LOf/pTy5pQw2uPxV44QWLb5lmKzYDkac/O953R1zCbpiO6M+crgPqu773D24mf8i+//lNtJQnjwZuLT629Y6i2vn61RSoGo+eiDL7j7rYfoeUiRNxzsjfjo8jl3krcgrjm4FzO8+5gZMZ98/IqDk5SiXfO97y+4XJ+xP4h5+mzOvSe3+fLkfX56+jnz5UvWL25Ibu3juY79owPyCvbTEDVLsBcdi90ZF/UVXrMh2kqyesOrmw11fkXICG0F7WjGnScHvHV2l5enp3zlt7+Ov7um6kOSWPO663l6Nefz569YXJzzR3//H9Lmmrfu3MZsFPtVw8/yjORyQxPDZ5tLRGn4ybMX5PWWoDM0UhBPQmzToRvBVgoGGkynQQtqZwg7aBpNpKCVPQ2SlgTtl6jeohr1RlhEdrSdpexjfPVGkvxvtUQohPjXeZMI/LvOufIvre8DK+ecEUI85A2Z+Plff6ADrRAW+iYkjiRrr2Jde3hGkpgdbdcjXERoCyrn0ztDoH2EE+jC0XYSP2gBR24kkzSmLipa32fmtXTNAJcYMiFoeokcBWy3G/zI0qYR1VzRE1BWDfFoxFJe4b3sufVezrUtyNYVizv3OHh0zIEX0FPSF+AaRdH1xImiMh1+P8B3Zyg/gVXP3ugYmWS0XkCQO5zKkEGAdAN6lUGqkX2L3w+wpXmTx/BT6ibFo6BVErn1CFqJm3YMbyewsRShJlAh3aLG0zGZhqRs6N2ItYQoEpiso8s1xi9JWp8urNDKYNoUjaAkQHgZTTphtbBUbsXp6ZZQpZigYb7ZsXt5TugPGM72uXl1ia5KqsMKcXMPF3b87PnP6SvFzz/6hJvXV3znj76Nu+kwiyXbxuf15oqPf/Qa+ox2OubJ6B2+ODsnTgIehQ/5pHyKmUeEjNF3NvTzjvt39ikaQVMJjryITdRT5BXH3QG70wuuC8fs/ohUH7G6+D6P33/E8PUBaQTDdExrG9Zqx8hq3t5/j09OP8TvJJ9//ppiveDW7AHXnuLByqMZdNybHRKvCz7/3k95nb/gSBzQ2Yp9DpBjQ74XkBZbdsWWIi443wRkqxZzHeOlVyQ6hZlPv9bcGUI8HDBfX6ClYXse4SUZS+MgOuOjz54TDe4w8hruze7xz55+jnSONlQkPRQteLVHpxV+XdNFLZMooGgKYuMjdY8nIROSiTCsTYQX1XS1IsGntCBFjew1JnCMPEdpHYEVv2p04G9UIvxl4JE/BgLgT4UQ8H+VAn8f+E+EED1v4s4/ds6t/rp3OCHoTI2WKXVUoSoH5ZBY5HhxQ1kZep0gpCEwGiUtoasoTYQFykGDtYpB3tMHIzqTsfI8pHUkZcWWgD4q8RuD6FJk0CB3jkI4rIjxhCGRionWCE8itCDWCfXA8OLpHB1pem2Ymg5vU1AOYqqbNV1c0vkRM6YMhyHZdYu6LmhETGgMMhqRdSuSykdHO0onqY2h6wxC9Pga4iyHJGVY5PRCstMCty5ZLK6ZDSNqsaXppkwijW18inWPTnzM0qPddwTDgs3O4QufZiDxOCWqOub6CFn7jERLoEH6Gm8zJIsMRgYkriD0E/ImhQZWV2uw2RvRlU1FXlV4fYdTDdvdhm5zRbV8TZ6OuNNqFq5G4fHZJ3Me3z3hTlrSHgdUpYHjnmZT8fqzBavFkqebKx4cH3FVnXHnesqd6YxSegShh0Cxk695/2vf5uzVx5y/uCCTkvmqRfYZdx5+icNWsDiaIqVDBj2D0Yiw7Ti7+XM+2u749r2H3NoraYuCm90cEGQbhdAFm6sNxwOPYDZGtw03q5y7tzoeR4adnFAsn5PJc7JdRV913JYpNg64m2v6Y9D9PpNUkUU3OM+C9fG7Ddsyp01rpm3Pgow700MGic803SPctLC9RO4/YlGdMU4Pya93XJyvaaqc9fZT9HjIuZ6juil93SFMQG1btOeobUdrNJEUNEIgXYk2DpU6usoRqppA+GSdYECGrDzKyNIagUWgjUQrQx8IXC7wtKL5l1QHfi1mB5RQLk41AottJH4UUfuC4baAztIRYWRLKw1jkbCTBqtrjBwRFA1OWlwnmMU9XQijUrP2G4rWQ3gWGzlUr6ktpGVFE4S0wkObhkE6xomM6ck9Is9DFh27VY1v1wxnb7E/uUXf7XhedkzCglCmXIuW33/yFfTxkO35ioN0DzFyhGLG9P67aK8iauDPvv9D7hxGzOcF2a7m7skevojgwOfxwT2UyVm8OCVNDpnNDvnJy0/Zm2guig0Hg4BxH1AXa3okN8sb7tx9jNqLCZoENXqjq1DuLM/bKw7CkMODI8qVYXhrgMDn6rML3nryCDH2SQZjZCRZfvqCMoxxKmWzvmGsE0wjWS5fki0rPjj/hPduv4XrFcFAEMmY3o+RS8unu+ec3HkXaeY4NePpy7/g3p3b7B8+wKsLdiZDh7cZaUG563l98TkPHryLCuEvfvgBJD4Dseatk9+iGoY8/fQFM53z5//Th1w0p9j1Ftmn3HvvAYPbh9y8eMG6Lri82qLLjOnBIaKVnLx1xN/5/d9HOp9+l+MlkqqxLG8WRAGkx0eM4pBXVxKuzni9/JQn938DE4d4cUUsB/SeZXMz53/477/HRG7Y5K/5o69+g71jn/T4DtnNDmkUctCRxx6nP3vNvZMDXv7kJf/8k4/5w+/8AaZr0G1P39ZcX1yxK0q+8w//iKRNaSLLZrXiYDrGtjWN63h+0zC8uWbw9l3eP3hAkUY863d895/+F4TXPb5psAOfOtM0QUloAxqvJVYOrIPOQheCaOnTgKAsKUKFUZKw6FEB0FqMSzGyIfEspjNUIiBQIXm9/fWdHXDCklZQKUXnSUxdMpE9ufIJ6DF+i+skntPkqqGWLdq9QWZ1aY20msrVXDaCsI9YegWiTmk9iS8L4htHl2jSJgTdIZzAiJ5R03LjFgRxRNL31K3mYTBl712HrO4gwgFVl6DEgrGy1InHvfAuk2bBbv6K658t2NWS+VvH1D9u+MqX3yM6OqTdLtADn0HYcbmqefDoEYVpSPOc0cEMFaUUuwU2FxQ6IZAL1r0k7WtMWbA3NxigPfD55NOOyIckmpKOZvihJq82XL+4RkUJtiyQzpJtHH59Q6d9uDac7zTTacK18PCuaqpSEW13XGrF9dWW0OSs5yueu+ccHd/H33uAzU95fPguq2pNIBJeP8vwooyHDxO64xn1eUVbXuL0gONHlnf1V9hVGaUu2RMHvH75EWHasG5HDIOSz25O8Q9mjJaGV2dXDETBdP+rLAfPKa+mFOfP2ZTw+vwL6HvKVrA/6ggJEOuKvXTGxQfnNKrheH/IyfiE0+0FXgBh6JO7kGz5HEqf+0/ukRyOWF6c4UeHEHRM9+cEB0fcqvd4sXzOnkvpGs3S1sQ7h96eMYsv+dHnL3l4mMDDkKDbYxyPCe9GvHr2nGg9JBp3TB6/S7U4w4iK8djR7nqGcUrRrGjiAR013kwQtpq+dxRFR23fEJ06p7DZhikrxImHbmp2YU2btcwO9xgIjfU6St/hCvBVxjC05KXPqJHUYYNxPqPAYGxJm/hMbcXWgOw1qu7eMAnVGyKV0D2OHhO+aSMOshbhN/wqIqn67ne/+/+nv/9S+yff/Sff9ZRAOY9OCxQ9u0KibUwHNLGP7B1C1hS9I/ACfK/CDwxxo2ldjVCCyL0ZVImTGOEpfCtwwqfV8g1dty/pVIowGVEb086GJFIi4wG3BlMO0lu0dk5ATNs76u6CXresL5eYVYyuHafVNSoW5L3FcwptPILWcXgwZtNt2WVLVq+2tDrh3uO7hP6MODIc1tCbkpXZsVztiKUzYTCWAAAgAElEQVSPiXOOfY/GChaX51g/xnUbLhdLrm566vKGl5+8YnqUMjoa8dOfPMN3kK0rpsMB9Trn+a7CUTLU8OymwlQFTRcxvjsk6GPqrKL1Qi7mr1h2K85fPafalNSqxRUw2Uupwww399HTmtX2lJubM3a1z6vnP+abv/N1ikJzEIz55MWHiElMM3+N7x+R9VfMny5YXla4fsn56RZRNAyqEjuImQ5jYiMJZwGjdIJvSl7kp9w5uoPrO/xJSNX33E7eRpxM+Np7v8HAHzIaz3jr1j6//Qff5nV5xbBXvP0b77B3tEc8usVeHCMHCf12S+JptB7iNT5BaPDjESLb4WxLGmmGLmaJx0EQsG5LdmefUeQVrz/+kOG0YLmTnL7+lO2i4N/5w2+yi2OsGqFdxw5F6+0wnUB0NWUj2HSGaDBAlpZSOfaP73Ixf81mt+NovEc0GLDRW4bRLW7FAYt6zYaSD58+Q2wb0tanniia64xESfyDKT/+8Dnn6zmJMTgV0IcWYS0m8umqBq/z0bKjFhG+UzSdwpmePNTIpifUhs5EdK1FJxZVa3zn6OoQ1TqGxuGso7Jcfve73/2v/u/+92txE7DCUQJKN7RFiPAcRBpTb2niAFtYWqkYtIbIjxmEBmEstUlpdEugEmzTIrwEMNRtTqQ0JYraOHRkGfYKMU2otx7GH+FZ+0afQGsStyYcnaBVzjC4x/zpNf6hRtsx26ylzDvaYMvt6IjY7jA3ktndkNFsxmw24/n8Gdu6YtTGbD86Jz2+zaEKSa2lcz0vP/8YWyjwDd98/6uYRtKqGpH7bNIQ36uYHD/i6YufoOuATbbiIFDIYI93vzHhMNhnW7bs3wp4vbpgqI5Yz9d4KN4KEy42Pa9twevrV9y+/4h3Y00iLGvVs6g6RvWabqI5+/CG0WjA8cEx6+qMZHpAEAZIYpInMT/57ApXDkhMx/HDYwZpS5M3PLu65v6dCfeOHrDXBpytS374sz+lrucUZz3Hd55wHkBTG6QfcmEd4WaHtzPs9nPOfpxRPXuFKR3JScLN5QY96pkwYqtzssTnvZMDXj+94dYs4YvtDZUr4fsev/d7f4/PRz+ny1vuf+MhwUcvYOOYdh3WemzUlqPJbZTZsS0E946mFJOQ7aZgvt4x9AIOZM2LVUl7tePq5YbZwZZg2nH2ooVhgDGC4UgTNnscTyWFmyPw0GLJemewCvZNSNNsGIwks/UhS72hbbdMvAOEVBR5SXV0QFV3hK3D6hvKICGNAsKXFaOmZ7ExoCy3tgNuv3PIOEy46A4o6w7Ze7S9xQt6qASVEQi/Jokitp0i7lNIszdCOkFH1SukbFHxgKq2EDeAh7IOL2ipRUTftUSeIcPipATsL/W/X4sgIHAYY7C+xAsLmgb8pqONwGSOJKmwhSIZhcgSrgZgrxNiW6F6h/U6rHFkfcsocpSez0bXJMajChx+CaWWUNeM/YrKCaSCsA+x44quiWi3lulEsq2v6LyCIzNid5igv7hE5DUTB4Njzf34PnNdsH//IUn/RkYru6kZH++T5x6PvnyLk4fvIGLHn//oGX1X8c6tfTj0yVYFr5YNw6Fi4o+wNPhhh9wFfPb0E/qyJvIsRviU4YDHwQHq1kN2n79it2u53lzxlfe/RpVvGYqQeTmnHY7AdxSLLZtLy9d/8yHEPeuLknQ4pHWGersjO6+plluOp8cYHaLcARdXK9rTax7dPcGeaUYyJL074cz3WbVwNHtIrQx3BgGfPf2MZj5Hf/ku9aclofbZXQiuFy/wgkMKq7lz+4TN9TXrrKD/cEtnK4LxAfXygheXax493qf1BxTZAhlo1juDHIUcjWbcLC4Zhwnz4jXv/9aXuPjomg/WH/Dlm9+jry17j+8RbDVHR8foScLhgccnLzPKvifwQqRvmIQdF/mOw8agZUjXwi4vWNqSxcVzypsFXv8C33+HqCqoQwttQ2Ust2ON2FO43pJ6MYXJ0F1MS4FnPVohSJN9PL/kJrhBnmuCXPDZyyUn/owsPaPvJXmxJQr26IIJpVize7WmpqHeWE7zOdNBj+ke0i0rikFII68wRY7fVPS+RrYKzwikErQd1GFHDCibUReSxtOEtqfxLZ4UODJC51PVHgEGOQvJclA0RJ2hk47a10TC/HrDR6yTICWqBBdLlKexWEwzRMQ1ggj2epaVI1IWda4xsqBtAjptGQ18ZCWI9nxkliM9TVAmlF7LqNCUGGxrSDvFOoDgf2fuTWI1ybI8r981uzbbNw9vfu7vuXu4e4SHR0R2ZmVWZg3Z1UMhldQlhJAaIbFBiAWIDStYIfUWxA4WINGbRgiphdRAQXahpIbMyqkiM2PwiPDw4fnzN3/z99k83cvCs6USXVmFqhspz+pesyuz1Tl27Zxz/z9tok1w3AaxedOxWPsVUePhmz16D3z8bBuzqii3lwSewWh7n0f3HnJ2uWDYclhXDTNsDuYF+3d2UZlFMAoY7e6R2A3bm5A6WbDVGVBpl1Z3THurICDAa9eUlcWnX/ycdBHR2DXXZ6+IsHh3dMxoa4et7oCo0Tz98Z8SekNGW8fckg3T6yvc4YD2bpviZZfZ5Rk3wmB79w5vb9/mwYNfY3zPZn4Jk4sJVbUiFQLRNnh47z0KAmbnr3GHbY7u7PP01ZqnF0uuby5JIsXXHj3k177+63x5/pxokzB9fc54x2cU7DO+fY+Tl1+Q6w5lEXH33fdY/dmadl8z9Nt8+Mn3GdoBL3/4kvCdPca9AflyRvvuIx73I/b3Al4vbnhy+QIZDBn3upinGc9mTyh0hBH22B1rTj58xd/52jdxtsa8XjznrWjI9HrJs6trbs7XTObn2Ae72Gy4e++YxctTrNCidgXRvObSmWKsXU7OP+T56QSyGdptc6e/xWRa8v5dm3J3iLpKaQKXsReS5hnx2qS2IawzDOmzmD4ncAcsFylst/DshJuzS6q6QSUhxrZHu24IB32263t4Thu7XaOdAEtZ9J02i+YZ1y+WvPv2NulHNwilGT4IEZkiMgqyLKNKU0IEma5Q2sYWJaUHdayxUkjCEqeUWLWJKRu0a4PKaeUmmWlg2gKvVJSGQq00shSIDpSmRS4FZqHBM/iV1hNAaLRqcIIuWZOhtY2pMxxRUGmTBqhUhaUbytLAVg2NIVBugVeaJCuFVjW6BJSDJyGXJi0UKhS4gNAdyjKnqxWpndMYLkpkONjE0mLcPUCmC3JVUJ7Y5LeusBYV/bKP1RcYjcHVYkW2uaGJFcawZtQeYXohW70+k/kSf9Rhapo87h0wyV6TI3mWvuS97lfJl1e4Xo+rcom38Xh4W5IbDvP4BcF2h9xQGK9nbH31G0S2wC0C5ukUGQekyZKT6hltHBq1Iq877Kwt4uyGuNuw4/QQtsluZGDWl6wnHRbLDV0ZoN7axv8s46w8QZcbsjRGdMdIpWkMRat2+OnLJ3zlK1+jLEqcNkzXp8wvX9IPtyncgOlkRdMZ8vr0GVWypNBLRCPw3R5f++A3+ckPvsvSuWRrNCROF/j7Pk6j2EQx0aJk5zii/2APp92imyZ8dr2kP2wokyWGTpivHHpuQV0teb5pGLkuT8bP8OKGep7hFTGmarhepzRuxd7tIxJzg1Q188WGnbsuVqsip82G1+SrhmRzyeXlhJvpCUQNgbogrlJsx6QuAxxy7E7A0pOINqiVJIqv2d7Z4nKdsrPTQrY9zI1itKfRUYHRtJFGztnqFVKWHCa3kHaDLT062qE/tFhvLIyux07b5zq5QRVtAm/Ck2dThB3gbHc4efIxg36fI2OPVTcjqQ0qGSDNFOWWZFGDE5ugLVzDxEtzUktgYKBK0GWB37YwrYo012jDIVQZtnIRTYZwFfXaJGzZqCpFhy3q6FccPoLWdB0TWa2oBZSiwmxMCl/T04LCznHXBrpjk4sSchtfF6RFQ+MpRF4jzQZh2Wi9osw0phpQmhJDFCSNh/DniKRH6tc4skEUNtKsMTs9tluS9eqCXjvAxiROVuiVS+FayLJimUtaakbWlJjSwux36fRd6iaDAC7PUm7tHdDtuTTONpv1gvVyjioSRuM+z58+JWz5pPtwOLoPzTmTC4N2HHMzLbi5vGCFi3RjTi4jDKvENCfcbJZUyuP41tuUzYZqVWBvH+NYAZVb0erusT1ok+c26bqkGUhOz8/IrtYUSc3OrkG90ozuC37yz2aM7BFCFDhkzJIlt2SftFmTrKd4gUV/FOLWAjsXpLohmUcMWm2ay4hPph/RFVBmEVlacRi2Ob8+YTzu8nwzR06fE94+4mB7Bz8wCA9DXN3h1r1tbtYx8y9f0dIbYlVhFxsmF4quYdL1XCwXCtEmT2rwEjpjTRpt2Mw/4uzmlEH/NlUxpdIhnVbN1nBAsH1Ixx7glYLW3pDCz/HyAJ8dknnEWsCzRLNhg516bO8NyJMCV9k06QTaHpvlmtC9Rbu7y2J2gdMYpBqEq8mzCYHlkPXBOG+Td8FSBsOBJJn7XFYZptegRMN+36J0tkizmFa3T7tnsaoEBR4WNe2mg21XPFnGPKr7/OCLK7rHMwY7W+y1b3P/8W2e/PGPqQwDO6uwBeSmQlQOSyNCaRdbKbRVYAiBRGMUDUvDQZsKy84wLZuKCrsMaYoaQxckTYFQEjuNyIMQNtFf6n6/EkHAMEA5gkRAnQm0BhyJ7WZUpSaPakwtsFcm3ZYgChLSxARTUpomg0AQlT6mTqlNi6Yn0SuNISS1knQaxTJrQK8xS8gK9434hsxQuaSxDWaTnLrsEQQdetYYy5OYXo9K3mAbgp2dx2izxs4KbqqSrtNiXaesLkpu0jPSG0FrY3G4E7PcOKTa4b1vf4vL8xf0jzvcHYYUVsAif015vabbSji7mlD5Lk2T829843fY371PYcz45//kf+ezZQ3thAdffY/Rjs/27a/yT/77/waxzukJaG7v8u7b91HtNS9OL2i6FvPTK/p397FlCw5MPv/wBG/ssoxKxvs2f/i9P+Gd/T387RglXNZoypXgeHTE2fVr6pMJT2+ueOvOiC/PFhzv3ePoK19HDWqC4har5y/pDcYMR5IvTq4oVmsip8OO2WHr8QNkoCgaTVHE7OkDgsDk8jzinfuPqDczFiqkSK45fPxbzNcX7Bpduvtj8rMTTq9fcXRni0Nxl9LXVNkS7Wzxu9/6PdZhA8kOk2iBnUmydM5XwweYuy6WYYGVoiKDVC2htFkYK6azExbpDd1LheFDryu4Pd4itwwio8YoXKq+xrEM3nu0xR989oz+1jau3aGpLS42V4jCxRIxE6WZXZ/y4M77zOYVsUzZWoecza7Y80IiYTPueKyUB1aBIyxaHYNmJohDIHFI85ivvn2P1oM7/O7xV9kKDILhLvgu9+8/5mc/+jFOHqCsjMhoCHyLqlZIbaBljVNV2I1Natg0sqKpDUqnxC0NVGFTagdIwY5JcoUnLIrcI9CCzMkxil+SEOBXJAigDZrCJFM1pukzaEryEirXIC0VonkT/XJyqsqgCn2cpqAqNWZWEjUS7TWopkEbAf6iAiujFm0KFpRNCx1q2o1DLSMSpcibhmHaQboZZmcXS42IkhiqDMPOqFwPd5Ey2hswGo6ppc2eMDhZX7Ld3cdH47hDPqtPuLN9l/DAIywH5IVilS1gE+HvjDiw9slVTRJt6HV3eTl/xux8hhtrju/uc/XqBGPnPnY3JBx6pJf6zSmyByFfvfv3mU1fUlzOaIY1b7/3Pm1p8mW5Zvfh25TGjFiAHYQsTq+4e7THyhHU5SnFJ1Blp+QnAe7WNmF1izz+iOm5TUtqOo7Lk/MTnKwmtx1udzt89/QH3Gw2vPvuB+ztDXHCih9++HPEYkL78C1+9vnnHBzt4nb3KeKEtLHoeRVNUNHeH/KVo1t89PJzgu0uaWMwjXM6ozZ/9If/nLbTwmxrPLNFMJQcH7zDqNPmo+cv8GzJVnfM5lTx5XjDV/aOWE8q1CbiOa/oPTqkE5rs+cdUSUJRdVnZKfarjL29IRelQ1NVpOSskynqWhFfJdTphO7ugO2WwMhdgn4fvbA5N1L6xRQj62IEFXXu0zYNysBiXc5pdEJz01BbsEYTdSJaSwtFiiNAah8RNDjYnAnFPWFiao2qMlzrza8jYoUSGrtuIfoezSSh0i5OCaPxiCxr6Lk2r6uCNL0GFSJFRq4Evi3YaEWYl1i8Kf+VTYPlCfxUsaorVC0IG/MNRq/JyZwNRiUopIuvG1KrxjMLjMoidEuS0vhVTwwq8rrGCEMoY1a2xHQMHENRl5JUmCjtUIscM5Nsa8W8bFDaxTArtDZQhYHlGoiqABfKIsD21oS1heFHdNMKUWqEqjF9F02OskI8BC1d41FheQ5NWWNYY8o8QiiDJHIo6zPc2KN6/ICh12cSv0S1jjjqwTtffxe5SHBwsds2m3KJVdusI82zzz6ms9enwET5NnER47f2aO+bfPrJH8P8HZTXQy0a2k2XbHINpWDvzkOQCam5ptUdcClrvM0lYa9LVJp0o5SqSFEdj+IqRzYuntumMlLUfMRksiRJc549WxC0LjA9j5a35u79R8SLa66jKWUW4voexdUaMWxwTIc7b7/H417I6ZNX9EYB1y8sOvcqTGvMajJBphliUXG6uWC52DAILYx16w3wUqzxd4YsPvGoyznmvTb6ckk6qZFGStWyma3W9G0DZ9sktmpCy2Vr74B+t+K4kbxcvmDfHlIZFo++ccRaWfRMC7WpuEk3dK2U7k6PYXuEUyusQYsNCjdKmDYRm3lBOa95fv4Zi+UJLbqMnJBOuM9oO8XVksiLiZKCgejS3Q5Io4o6b8hdE65mKNfGlQa+oyjMGvPaQPmaxjHwa4vU7dHqVKTLBqMp2PZcAssk0wn9gc9iU9A2TERi45gFwvVp9JpstiS0j7BSh6hu8ERDVMSUjk1ykmPYFd1CcK00mSkIK4Xt1GTZm22+dixWcYMvC4QyMW1JbUtQC4raxlAaap9aVShHY6GolIV2FGJtYmrB/596Av/qJkyUtlFZhe+ZlIWJ0iXp0sX2ctxC4yFZ6gYrMFkBhpLoUmJIiQpqnDpDVh6RynEyn8rNEGtJow3KoKZfO1QDSFcujiVpqhZWmjIdlOhlxczOectxkF2bXK+In56yEOCGPRwlGRzuUUSXyO0DesuK+fWMk2JE3++hbJfJ8pK+uaDKPa4nJ0wX1xz1bvNnf3ROINbErZKxeUinH7JZ5XSc28ythoFoca4Kvnz1AresKP2Ehw//FvHklKuN4OD9u/QpSFcpB9t3mKymJOYdjDODpW64XmfMn32M0+siG5sontJ4ir2uR6s+xt4LEJlFd3uXs4vvYssByWLGZ9PPMWTAZrngSB6zuKypa83nH33Gi6sz1M8vSP0d/t0P/gE39QprFvN3/63f55/+4/+R6fSE/aMHTK9WzL7zY/S+Zmtd8N0/+A5xfELYOyadTnnn4QN+9IM/4Wz6iuq6j8ecKmi49+0HOMuGIhDM0wg3jaily07Qo5IVofZZaM1b4za+bLO5WlGxRVotEUlBsMqotwU2Gg8Hud0iyDTrS8VkckO8WlCsE3pBw1fefZu1KBnsbGGmAbP5U2R5jh1kTDYa12gx6gf0TM2FVOxbLiUFpXSJNqds745x9RZPb16x85akmK9xOn0uXn7O4e0hB+0+0heYzRD8hnr9JXXSIxm69EuLarDNcOVxEbzg3oGPtdPClzVCChrd8OLz55xcn2BHkBoCR3sImSCaHkvWKKEwGnAxaGyXUqXYtkFTljSpiS0tGirq0kP5Jb3CoZYZloJVmeFiY3rQVCaUv8IYMqEVWkh6xFS6hUNJujGQuqCoJcIWLGuBWQtyLAJZYmUGpixxCkXiWZjKx6xqgraDUdfYjqRUJVZt4VWKuWFgrlyENJBpgW5qaiOkXFk0A8G+35CKGjtfkcw0jWliLAKkHWEf3qZWkrR2qRav0OSs0w2e8Gi6K2oh6VttZtGSju3g2AN27wzYbNZsO4rg4D6Tky/ZmDl3dm8hdxscvcb2d0nrZ2zPW7iuw+VqRctqU7ma1njEuV7TtdpgN5jrgvOnLxDjkl5rh5vmktlJxjBwKMuaO+0tLoTi3oMuzcriJrmidEuWi4yuKTk5mzHc38bIanpb9zi8d4/59Sum8Q2vL08xBh6yWqHqmLYhSAip1xOi61MWNxoZzblzvMt2/xZ5FXG5vGEUauSuz2Hg8eXnH+EaPoFncFYkDI2KWTfHlTtY4oZ+u0tZtdk92qPttui1a25SwYO7v8XGOuH0i88gdCkLl7/17m2MyqNpNmSlwJQew0FBtBhhNwbmKKQqGxovxrJBJhNYdJmWX7K6OcEs1mQRjG/7LKeXqN4t9ExTaYOR2ee0nLCqe/Qzh3bLwbIDmh7IWpDkKWqrwzDKqYyQ1bVJzYbb+wO2dci6mKJFwXJTEE6WmELjigO80sSu1wSeTxOYRI2J4bfwjSuEPaB1PeJsMsXtG3RH79PEE6RVI3RDWmksoVh6NdtCkVQuSqSElU0pC0TYoBchhpGjDAdFQ4OJaWW4StMEXahSirpG2Q2l2VDlIX7QUEgTY2XRCmo2v9IEIgFC1hRCUoscpQw0UHsODgVoQa09sDPIU1Aa03SodcPSkuhlA+abTK1eQyEDfNdEBQXJvMQZOuxbkuhGY/oaIxFYZU0ZRgzlALIZatTBsxVmGlCGGWGzh38QsH3nbZrVDCFXhN0AjC2un3/KzmiXPNsQncK9r7zL1fyE0NwmzRtWRor1yqRo1gT9kNVNSnv3ELMRDEdHqMWSTDXshCYvFl2qtGC2+JyZ2TBdXHDn/h7tTsC32h3WriZItuAw5uWf/wmdaAvxcE2ZrmBdMNlYjA4eM21M7OIl7rrHdXLOTfwazx7hqZKL5YZMxxy0R8xWV2A29LxDsmnM1tYedZHRWSfUhs2ijvna179NdPOUP/3Jp1TTKU3TsNCKl4sZpl5haokX1QSFTds2qUyDcmZgjGDk7TJfv+D223+P1fwMWS3oD3bo7/Qp85JqMeVO+x30YcXN03OcSUqqU+7duYfneKS1ojXcpvYN1NKkLAqcgUPbPKR1t6FJVwwCBx0V3GSapVliTAV457SyBVfnn7LZKAydkcodctvEaUPu25jGFHRDcOkhh7BsKlo7kuTaxU0ctsyGpXSw0oQEj2DQx2jDZn7FTWIRWHvYjYNVZQxdgRKCIpOERRtvBNW1iez3cZwubqEwyRG+y9Xlc9r9baSI+fz5Cq+b0pWSpA8XP7mkzh1yC6RlsS4aVAGGD03ZIGwwNybarbAqByVyksrFoMBQmlSCykp0XRMagswwMVODBoPGTJCNwPQM0tT/pf73KxEEALShkdIn1SmiLDFaNk4kqA0D3aqRZo4oJcqUZFZOU2hwNG1tgK3IaUAJDN+hyTLqysPLXGpTk2xKFjSYtk+zzqisAFOlCKdLTkGnvYsbdIjzBomg3xuyHYwp6pTzj17SbXWw9hy63REiWTG4/wFbOCzmp5Rth+effkqubAq9JsknDLp73P5Gj49/sOZ8nnAUxEw+E6ztkoO3FhhOydjb4eSTV0TRgitmlJuaB3fe5cH79/niR3/Klzu32XG7XGdPGMk+teXgDYbYg4Dsdcb0taLfVRx/7TGn51e0hEAPbvHy/IqiZWI2LnmuucxeUy4zRNbwR5+85h/83jcxUoOpkXL6+orDR9sMW2N+/NFP2OqMGHXGfPLie7z/9nv8djjgT37yhIcPtnj8/gOe/PxHoAu+8ffe5+TJBdFmwdFeByVdPvj6B9xcL9GVjTDhwyffY2uwzXuPHmKsbzg4usW743ssZMBZ9Zog7fGN+4/4k599D9KcLBRE2TlmOEZJSX0dY/U7dHcGBH6bPJ5QzVK29nYoVcFefx+RV1xcPOF8M6X4dMXHn/+IRelhipS6NNmWXYauR76o0V7EyasJk8mcx+MjtBFiBxCkLt02yL5Nx/eYTWAwlnjenDKxMQ/6WE1KOc9IpxmRJVF08KRJk5bQzanKJdV6wH7gIUpJUWfoqsIMIchDagW3jveRm4xrb01gKcKRR1Vpfv7nPyWdT3CVg7mJia0WjojRpcTSNmmhMQKDRJWEjUI1bwAlCKgS541wjCiQlk2iS7zSQgc1VZ5haQNZG5i1T+VufukBol+RIKCxLA/iCBDgWJC+0RrUuYedp+SOgkbhNBUa0NrBykuQGbZpomowaoOqLrAMD11H1KWBKSSetLFqScwG0xAYlkbJHrZs8EcOltYUpaZlGdRpQccPkCODeuOwt9PCtXrsHR1w9fKM2fUV63qD9/Y3mSuNPUvZ3t7h6fmXrG9K7t29zXqScJlXbJqKTughpcXwdsVmYrB4MWNV3XA6T/GKhMliyuHBbepWgeyANmKupY08f4U+3CF0xwz3uhiJw48+f87Nq3OeJyv+nW//fS5KiNcVQikuVxBWFsusYH/nGF9XPJmes9fa4tPXE/Y9jTkQJPGG8Z277G9q0sf7jNrH5JevKZcmn8dXNFdT3j68w2wds5ms+eDtIw7fP0YVNXYYMHjnIevLOVlRYY+7ZI3HaHfMwdYxjhfx8YefkRkOvnfA8GjE4b07nH3YMD+d8cOrhN5uD8PwSeYr2tsVVp5zMV3z63fuY3nbnF19gqozvNDFbTJatqTt1qSbbezdilwZdGRAvMlodw3WQZfN1VMsXWLRpduZkacBrp8ReD5mYGGUNdmkZrqKUema9a6mJ0EhyKUgW06pi4zUc2n3U2Ido8SItrdCVmsuhUvRFCSbFbPZJXcGu0jDJ69LPK2wtIGZrFgPHERYsmuOeBm9xtsMyVcpch3RuptTqz7bgYuNS1pDrQQicXHIyc2cAotKFfiOgSEsCqfGTRu0rjEKiF2BXSt0KdDYKLMgzR20mxEqTSVMKlIKbeIbJmajWfsGgUpJM63NBDgAACAASURBVAl/UzT5L+EO/BfAfwBMf7HsP9da/8Ev7v1nwL/Pm1Tkf6K1/s5fHwMkqJiNKbF0gykbqgaarMEPG+JIoRpNu6WJhYURCYTI6TY1VQV14+GRMcembZTEdYmsJU3LwEgUeZ1jGy61adHVilKsqcoBRl5TXNdEMufO4F2KMqL0IspaYKcGyuwQpw3+loXeKMzQITdN/MxkXc3oWy021ZJpXlHFNh07oSwU6dCgY5t4c0l1OaX1G79FFG947Bs8e/kF3e0et/rbaLtg694Bg1YLkcNssuC8FNzauU0tNni9ENfoIYou4bAmONpjXlxQ/eAEd2vAvbripiiwvT62EWHWEYNxn/7A40wMKMsntHdv8dhQLK5PyFdztm/9bYrnESuWiNIjtTckls3OPRvVdJimDVIpjFrQ3t7h4aMHqFWJ2W0Rlq/xCkmUasrshoG3Q15NkMUDskDQlltUw485bt7BPbxB9HyG+8e8G+VcLE4wN4poVWAFNavVEq8dMN66xbqOmUZz9lomW7u3idcLhOqwdbiHSBrqVkXZzwgSGxn6rJbX1IaNXuVILZifn4MvoVT0gx1u1muWWmCKhqvKgcsFP3z5M1rKQPRa+AaosgLpYpkZZdZnPTVRhkGe1hSGRdOXuEaXqpI40QxdNWy9ZTPPbHIrxfAq1KRitYY8t1FK0RUGAR5ZpZFSkzg5QqbMEoN7Ygd3X6OcIXqTIrEg0CyThKVp0xcVNTW1D2Vm4VUZuekwMCCRFpYSeHlDJAMss8QqMnLfxPBq/BRKTDJDYOQ2XsemsCJ0I2iXgsa3McqK5l9BXuwf8y9zBwD+a631f/n/ChhvA/8QeAfYBf4vIcRbWutfDkcHMBqqzMYRDUqYKGVgqIbKN6hKjbIsRFWR5R6GNDFUjutXXCYOI7OkpqYQDoFfU5QlvmdRRZJBqOjsbbHTbTOPcsp4wzKDUilCryIxKozYoOsZvJw8Q3gundwmL3MSK8K0I6Tjsb5KqXcUclOy3Q2pgx5B5eO1G6bzkhfrz/Ern9HeEdo08HIfLWp++tlTVBQxfOsRu8GQelRwx94mDB0G9kNyb4aSLqt5SlmvuPPoEXq+4M+fv+Te0TGuGtDRBrP0mk3jI+wbxkuf8Td/kx//8EcID9I4R3RDVAxD16MzDDk7e4bWgq2dPa5fntM+OiawUoZvv8Pliymf/vyHbKTJne1D3HVDFFak8YhB6PL+oM1sYpDGGabK2dxMyVVMEUlG2z4vX/yM7//0Ka245lN7gjQbgvYO3mlBo3sM7UMubj4lbgnuL/b4zh/8L1RZw+0HW6TSJY8j3n7rbcqqIFAGkZ8xXPXxRYYoTPaO+yjdoposKMuCWbbAiYZs9S0WTY0q1gjZJZ495/T1DefTCzKRYqyvqLVJ0A5RNy8YyRbXcUrbVARjmwN1iMhCMBJqbWGFNlbp8/zLz/nss6ckJNxk1yjRphZrxqaHbUhSw8Lp9xGbF6wmCff2bhE4LkFLcTVN2cZhkZZ0vRi5uUO4q0m1y/54n0wu+M6Hn9OqE1zXJBchn/zZj9jpjDDHCjlvc7p4TRtNXtYYIXiJiaE9UqNC1zUzRyOSAtMP2FQNVhVj1x7StaBRiKgmsixEHeLrGCuEsopxc49MlVSWQWoYhKhfsg/4G3IH/gr7feB/+oXg6IkQ4jnwa8AP/uqXAFpQSpuOaCjLkjJ4U/vXTY6jQiopMZWksAtcJclzm9BoSLSHluBUKaqQGJWBcGsOj8e8985bvLV1j8F2yHweYfQF8VnN6/MzlqJgchNhygZj3WAFDm5VU7iK3YNt2q0ecVxTyBIj1fTKDWUjWW5SNsslr3XD7s4WTao43t1lEPjUqcWnTz8jPOwyNsb89jff4vnTGVtjj45nMFUd2n5NObGZehHziwWe3KEzDCh1zpcnL2g2NfeP7rFeRJx+dsrj9x4T9kKWWmJEinOpsJ/P2LRjRoWN1DYy96nlmlzWuG6bdmOxXs3YCW5hbNfMzuZsbgrq/g2BN+bu/UckKuHd3WNevbzhcHeXd29/lTi6pklTRndMwp27yPMJaZ7T2WozP8352ZNnXL28ZiS6eDsOfZFxfTbn9OqGJmgxjU+RqxQd2tyxepw8/Ziljmh5AzZ1SdjvUBo5eZrw/OPnCLfFb/36Qzbuhv7hLTqGJF9GdLpthNvBcxVR5LKQV8hMUBktzBsojDWryCSZFxQnJ+TFGjPzGexpZCOgthBmwSK9ZugdIDodXDVDOwZuv4UXgvBaSBFRqBarWmMrG3WlSZqaULYwhi3aMifZJGzSFGV6WMuMqhOh6l2qWmCVOaXfxfPPSKMWmX/KyrxFUBRvHDEMee/oDq+ff0ncmASpQJkmP//599jevYv3fokQBrpdY8wsklzhyoa4imlVBghNWdm0VUlkJvhINCZFIynzCtMxMBxFqxCUxptGpbwBVfZJrRizcah1gbcG0/zLseT/n4LAX2H/sRDi3+ONkvB/qrVeAnu8gZH8Czv/xbV/yf4idwAAF4TMSCof0ThYWYpC4Rg2uV0BBW7tUJYGmcjpYZC2FMQ2ZmOysm3aaHQ34NZwm9/73d/E7jq0Mp/+VhfDclFWm/d+rcNv/J2vUK1At13sxYqPriZ88vQnLGcZjsypRReKGsd2GZm7vFLPuF547L/VxS0cipcp8TRiPVP4+zDEI95ElAlM5lfE6wLnUcDYP+Jgz6asDc6mBVZYo3b3KKwN1dNrOp0hN9YNdWxw995Dpj85pTI/p927Rdb0kXnCTEzY698izDb8r+sNXimRQ5Pr00t2bh1QGQYXVyd4Zs3OvbvkN5fUVosvX1zwO+8dcxNdE7YCTk9i5Drj4NAkrGxGfpfXl69JViny4IjPn/yIlttiOGgTzzegTzD8MR9/9CFvxVukdUUVT6lCwWF4wP7DkGhSgO8TzxMi95o4W/Gbv/5t8skNTW1x8PAdwviSs8sNrSTFCgzCJiQvSvb6AU+na5zxIe7ijLxMMOgijTVqZNPru8h2B2FdUc5d8iRnbVxjOgO8uCA6+4I8nrHUNWJd43dDTBWwqiMcLSiVZGCYLJaCjkwoGxtLrTGmFtm+wk0mJF6Pe1bDT0uLLKu5jid0O8dkVQaGJK8bmiInL1KGA5d47dBUa5TUdAzIEhvRyVFpG88SCF3gFAqcDaiaovbY6m+R+jMsW1L4MaNBj+UXLVoHI/Kr1Zs+h6ViZVkIo0SnPraZYwqQCJq6BMvGqUuMWpPLEktWGAWYCIpUg6tRlCRC09UGKz3HLDxCK2PTCjHSiNI1If/X2yz03wL/iDff8H8E/Fe8gZCIv2TtXypi+Be5A0IY2s0LpHRRuqJyMooaROOhiXGUQSNsEtumkybEQiB7DtaiYmOWmLgMTcG//fu/y/NJhJ3EGGZAOWuYhopRY0BuMRxrEmLa9iF7nZploDDtFu/sd5hPX1NOrqlLB9XMWLkDlmrOvo4JW5LA1bz+5FNKuUtTmDx+eMwqL9FRw7PsBZMXp/jjPt/6tb+L5ya8uD6n6e5TZWtmr69Qsks8i/DP1/iipmzZFCpnq7XF/fGYy+WKtw6P8Qe3ePHFCcKIOdo+YjPb8HPzC+J5yihrE7PgcPcO3/vu/82TaMpN3OLOW23ev/sBUaWY3Jxya/d9RL7m//zuP2Xv4W3IVribGKVr6vMJP71KsWRJf9/DVibrJxW9fgCOR+21aBk+08WEqL5k52jM6csV3/vzP+Wb73+Dr//6fbKJ4npyzsH9Rzx6+G1++vn3ibIb3FDgywZz+xjba/jii59ycPwN5os/ojMIOHz/kHIpmUVTyusFlit5/fwZbW1RVZLrmyUP3uvgFuB1HdJFji9hOwiR0iBdCvTqnKVhc3J5wfzVC55fveZvP/6AnJyOVRPnGY1lMO4PuDM4JKmh7VYsewXJRJKbNVtLjyJYY18nNHffwgg/o2xdMP/ygtY3x6RpTbK+pj3osu37FK0lSSO4jp/SNUa0OgXN0mXrkUWgPVaFS+RnFHFA/7oi2XWYLWK2RltoYdDtSFTlIKXNeOs23d9powyX/+GPv0NZ5Ti6xK41jVFT2wmObZM3NWZuY+iKUrjkKscobRoMDKFRlibNFY2joXKwugoZV6wKA+kJpFmQbjpY8xJpu1Trf81nB7TWN/9iLIT474D/7RfTc+DgLyzdBy7/uucJNDg+DQ2lAJlrDAlaVtSGxJQSPzMwjYzSglBBschIHQ+JwtI51nifxaLg1tjnciWpDI3nOKwuS6x7oIOGJLKwhEkSTFEbRW6X1EWXJkoJvRbaq5nUl7Q2Dtum5K1bDxFn1xQUOHsOvhqjVxc0uY0WbWQXlpefEc1ygp7NYOTimoLlMqNjSiYnr6kbxWCrx2IT0bZa3Bpv4VkOi+Wa0u9CNSUTQ05fnWHrS+46j7A6Li+vY8bJjFwl3G+/j38U8OWHH7NJNWmVMA7H6KCPOf+Uadzmi5MpBwc+925/gNfqI8YDktMpy8kG0/GYZDU7ocdgcMir658RLVJ+47e/QuZvo9JLDjtbrMoY27b46NnPaQV9ek4XqSzy+gk9TzA62GK4fczSXpOdnPPl+hX3j+/THu3jrjXuVpezuKQziIgzaI+Oae30+NrvfotB2mYc9kmKlE1R0397B7cxsaKYzBXE+YqDHZu0qugFXXRqY1k1paEpSZjPJqAlZdogjRU3F5fEzBmaIHSDXQVEQvPq9CVV6RA4GhzJ9pZF0Zh09Bhzc0NUtTHaCfUioN9zoZriWzZe5aG1Q9cNmJ6d4e67YA3RRsa4t8UqFdzZKpBhiE4MFuWX5C8CbrcfIvvgSSgiRTa28I2AbstCpCssERK3+uy3luiFzSqPuT1wOFsorucJOtdslImPRrgOdVmgaqitLqY5o9A2iJyWtEi8Ej93KQ3wQgtRNwhhUjowyGvSwkAZJZYJkfJxzBTdsbDrkrrgl3IHfrkO8V/ltELs/IXpvwl8+ovxPwP+oRDCEUIc8YY78OO//oEGZQEYNU0DhjSptU3TmOjagVKgGh+dgys1ceNjYGLk4NYQjHvsDfpkQc1sHtMRBdOrBTrOmGRTzl4oHAS5BM8rqZKKSytCVyFlVhLHS66iFZG+RCYrVLNknSScf/4hkyimbjRmalP5Fr7XpWpFvJp8xvrFK86nG8pWQWvnFndGb5NuXlDUOcUqI3dLvIFJrnLC8IB37t7G9SBfz9lkc7LkhOmrc/7sJ9/ncHefosh5dv6ErQdHfO2995FCk+qGT376PU5+9OdMLxe4AazSOfatHXy/RIxa9LWCYMUygsT2uYkvGUjF/uO3UNriZnWDIxf0dlyWVUKqJcHuHi+uM4zXa3Ix4GK54uDWXXb3d1DCImhZqNAhbGuiTUie5kwvIgIM7tgOcgtarR5OUbHb1fi9Q7RV8eFHP2T2dI5jOxwf7CKjGlbQ6YWU2YbpYonXKQhDm9t3d+n1xrS7t9A3OaXwqDcRlVwQpTPKukBlIYXpIW2bsixRpWQc7JBeXqI3Jt2WRz8IcQ8ERdkQtLYZbwXUvk9WNJSZydqRjLo9Rv07jAKboqpwPYewCTBdCLpvaux5J6clQ0Yd2EQ2aRWzNDRNuw2ywHHaTKINpbFku30LV3qszTWZKTFij3mRUVmCKtT0+x1WlibRJT3XR9YORrtEmgK0xJQ15Ro8BL5jk4U1VppjS49clfhiRW5ZtA0DjwKd1fiVRY3CNCV5lNBYOUYV0YoaEgWOX2AHAcQmjtlgagO9KihtMCzrl7rf35Q78G0hxPu82eq/Av5DAK31EyHE/wx8xhs82X/011YGeNMxqAOgVBhNRW0aODKnzC3MtqapFEZQU+UughhJTuFIalljFpp7gxFdTC7XN+zWQ4xhgFkknCw97nQGfPjiQ7qmwc7uNrtvP2IWZUQ3FXtHDUN/QCxDzPApR8cHiKjDOrZYNRGyVeMEEJgGy3iFLwtuvXuf/fwxZ5/8iCzJOL73DsNeRcfcoz3Q/PiLJaWx4v6tR0wnz/DduyzKBqyU5Gcb/K2aXLi0D9osioxs1icoI2bZCjnWDPoDshfXRI6Pdis21yv2wi7tnR1W1Ql/+v0PORp1eHf/m7R34XCyYHIx4+zJOa1+zjyJ+Nb7DyjMActXG1IZYTc1W3sf4JuC06tzfvvxu7Tf3Wb52Q3dW3B7p88f/h9nNHyBURmMzT5242O3S2aLDYXI0dYBx+9tobKa5+sZxU2O6F9QthTB7V3et3d4+cXnfO0dDyeC10+f8vOyYVxZyMDi2BnBfY9eVrPT3qXn9lFdyd5BSJPGbN3+DVq1Yl1F1GWBLWtW6xhFiogb8qCg7/fw2yXK1Si5xpZd9nbbtMchtuUxutuQxhtEtcRsUhiErBuf/bZk8zLn6LhD7LgsZktkN6VoTDZnGbf6YwIadK6ZVw0iFfw/zL3Jri1Jdqb3mZn3ze7PPu09t7/RZUZEJtsSWSBFaECNBUjQG+hF6jEEaKqBNJIA1YCApBKrYyYzIyOjv3H70+1+b+/dzdw0CEooAZlVRJUGsYZuBnefrA+2lvv6fz2FztE4CqQK8dKUarVk+eYVanjCex+/jxGv2OYe++YaUyhGcojTNqiNpvNbhoXPd03NPZmx2kWUrqZUHRf+iDCc8cf/2R/xv1y/QdgKxyjKxKdtSkLtogX41tKZHpQPylB1imAY0O1bukDga4fOdkhh8LWmjBxsrelHkDRQKdBCoXPNWEL+HwsBa+1/+zsu//f/nv3/DPhn/6H7/rsheoujG5pWEAgPLQxd64HrIvuCrnURIXiypGgVqXI56JYwcrEOFDtJH3bEvuZ5u2T2bsyH7/8MU99RBIqR7mAdk2+3OOQMpIsz19QKtpvnSH+EblKK9WtyUVM1Bc5yy2qpePT4Hs3DS7bffwYVNPX3DBpB1mjiCwezu2O7dVmM3tB/3RF0liePP0QRkGZjsmbFn97/J2jl8NLfUpKQ+BZn2xPKmNEZHF/8EYotu22K3YQ045gw21GbDrWq+by/onip8VrNx48/pJI9lo5dE1DR0jYx0jrEYYzv+jx/nZF4htvFczzfcvLeGWM9Y6UXjB7cQ8wjTOfhBwmdE9IuPO62V0i9YhCesC3v+Mn9S2bxJZvbt2ivxXN2jOKUqm0oNmuSYccua7i52/Hh4D7W7LjZvOX0/BkL/Yp8W2JEh3v2Affvp7xttwxXZ5hmSaZTmujA7DCgqi0iMIyNovJDxr1hk8cEomEYuWyocL0xw1ogHdgJn5lwMGaMM+6x/phdYfCHFq1yfHlg7wd40ieREHUtRa1oUkPjFbh+i/RK8o1GpyVxeoXbjvAHI0aew7h3eLFvubzU9C9rCAaU4RbPWOLxkGY9pStgvb+jrQNC2bC7qnCmCa5YE6l7+O4pe/EWHfekXc12pxhJyVgpGhVStmusEExGU4TVSBMSoumpoAdtQ3RT40mD9lyCuqeWHdbpEY0gkpYq7PH2EcLZIqVDrgSJgUNV43k+phe0qkdpjZQONer35t9/VDnw/3cIYZEaUC6V66K0wbguOA1NG+D1isZx0VYwEJI1kihQDLshj0/eoy2XXJk1WytJXJes2BOFNar3yasRIp5D0lP1Lts3LVJB6gzQjaDIXXa3bzgejxnHxyRKUZclm8AjOZ6zGnQcnn8LK0UymVJ3DTfdilHQYVfQTkJGj+/xKDqnx2UajLGJw0457IVhm5UcEsNXu7fY5ZpssWR7faCXLlEkCdMJJtvzxctrni+v0Rww37+msFfU25T4wYBLBC+++55k6vLkNGIcCGY/jTBdxosvb3hwL+Lepx+xLz3O3CFFkHGz3KF1y3rTsasDtv2ew6sNzy6eMk1G9JWimY7YH3LW2S1/8ZMPyFrYVrd88eY1GzUlHMdUqwVH3TlPLj/iu7drVlffIFSE5xzhO4K1rXCGkm9fvOZQbdku3zCyDk8fPuHh8SljVbJYb1h89zm7F18znzxhvau5+cUdOtcQO7RGccgNQdHghT7CMdRpS64OTOwEJx4g2xbbFQxjxd66eDbjXu+QeAk2TbD7DWWekFkL6xZbZYRK0aQQ4ZL6KSET2kWNtBa3ylivl/jRI2xVM4l8tB0i0xI16NDZga3f0AUaz4vIuhbVB3yYJNwdVgSBwhOCtmkYz0esa0Vhp5TSZ+uuGbv3GKkRUTxh2wpavaEATtcl4WCM8hWHZU4le9SoocGQa4kxAqIGlUItHfpe4iceKgZPCkzv4VkHt3M4JAcMLspqIqemEx2DkyOqxqXGEo4amqEkDhRl/SMXFbFIWmFACISX0Smwvcb0Fl92CMdnkEtKY8mGCrmrUGLAMBXEKqM5n1FvM+5uc44mp3gt/JtfveYnx0fETsuvvnuDjAxHTszCXXFYv2Nyfp+TuUcYNRz6mHb3ikW5oc877t97iOOHVP2Gfb4mccbMnp3Q1td0+4rGWH7+pz/jrs65ebsl31TcLrZ4gSR+8JD9boefL3j86fs4ylCve+45Ppt0zPLNL7inErYDDyF8Zn1Ge+Tj95Kz6RjrOnx59+aH5uSDGe7dOdGTe/hHa375f/w9ztElrbnmV//y7+jSMfdnKT/79ClbO6B49JbNL74mjS85/sNLssU77rKMbnHDUklU7NKLjspKHCEI5Y5kMuL11Qv61uU//4tP+LeffUYSj6hfPOe2M6yLPZUv+eDxU6an5zyejsijlG8/+xtke4R0Xa6+fcfJfMh49hc06wViJHg4/wiSnFe/XWJMQRuN0I3LzXLBcD5EhTFf3m7584tTrF9T35Vof4y7rzgeJdT+jLDtyFcvsLsRnTtidCqJcVB1iRsK8Kb4bku1uMGP5nhiC1mDGhwxfJJwHChelw7XmyvCY5dh6HD4Nmc+VazmA+bbitvbd4TuhKOzc5pYst7vsHUNXkS4z3n7ruPxB/fotUtW5KTpCfgLxukJ5fEeeVvRRT7RMGU6FASdwcgYMWhwc8GDsaLIxxh8ZNVSDATHB8Gm2PF2/4bU9ekzTWc9HNViJw46N2gLUhpSayjqHjsCYTTGFBhP02mPtLVoI+mdHllLGjHAUw2O9HDoaG99xrKlSAeEM0l297u9yX8UEACL0wukG+Dokqrp8YRCiBYRgNQdh95gBwPIa1zPJww9gkOHmA1Q25pYDul2r2mFIAxnICteNe9I6hkfvfeIzXZDA/SnAW3R4NDR1TVV1mMjKHeWojBMh0NCYpyo4cw/Z6M1cVVTSYHAB10wOL3Puzd73LAndBWHElCGs9kFy9sV2eEa//F7HB3FhAvJ39z+KyZyznD6gPP5e5i4YDaIkcJDRD5FlVMctqw2OU48IziUhCOH1lRsqhtCf4Zse4qmZ7LPceOER08mtNonTU55d9jTHla8+OI5WXVH97YhW49ohIOKHOZnc67e7anqiioKSSsXYSs++9t/ix/GOKNzdvqaeJPyyekTrj7/G6RzQSNy7k/mbFVLpwq+/+4rXrw0jCYXqCik93vypiNtNGux4fLRR3yZLfGbjGWx5Mw9o0/3TNqWs7Mps/kcSOgKi01CHpqCXSVJ+oDW14i6wljNItsxaCIa7VF3Yzy/wAqJV4X40xhVVYSTcwYnglkcoNILOqdis57ShlOiXhO2KWs/xB41xNc+cu/TeS7+OGQxHjAqtnQPJwxrSygkF6Q4dYF0TwnkFZGxuKdzUm/JarNAiJayqxBBxPHFlNtdxaiHxXBO328JpEVVHf2oYphG1HmJCj2KrqedeoT7NUeTOa2U+DOfstyj9jW66mmlR+QJdCPo9uA4ICuPPrVkZUOYCpwuxFQW4Ruq3mUSOax9CblGK4HqHZy4J29zwtYh9yXK6ymMi6NbtPr9doM/CghYK1FWIZsW47kIC31UIoyPrEEgCb2OrM+ItSSTHkkc0omesAsQocCRHqKIqbTBliXxtmM4OaJcLWjOJ4TBHH/Ys7aGVA242mRoeaCTEbo64JWa45MJ3XLPYrJhWp9QNBVp4PG69/HznkML+6bmycDl7lBRvi3YVXf84eOPKYKEZf0aKVwGp3M8x6V4U9CcT5hsThBuSCg7dmhcM6PoDD2CLnuLHQ3JI/A2Q/LlDTd5xqebhHpQcv/9j3CKjmMRMLg44/xkTqYNMS1l6BCUGdvXa5rSkJsVoz4knYx5tSkpdgtscswkHLIb1jx/9Y6R6nh9+z1vXr3karGjUc8pfvkr/vKffIxXVVzplunjx6TxBCHn/Orq18weTTkVc272n+OMQsJuR6sHjKcwcHq+/MWv8XYT2kHM+dEJkXePbKiI/YaPPjjGcR+xWmV4oWKapOiLAYFuiMQ5Tbgkz2oik9C7LqrKqbyWmetTdDsqfYdvXYyO2CWQbXpW6wVxYHFlzKZzOREZWke0VY1oDozTgKrKiUYJdRZhTYOnNY4QGGdAeNtRTQccy5hCbHGmM2r3lzRKUJUlrTunMB1Ou+PYCbnZHah6RbG1tIOcdDog7zpsoDGyI25DogG4jssknaG7lkQPqKI7HCIuCsuhD2g8DzdzucksgeMhBfTGI45r6qpHoOiNwYstrYK4M+wjSZun9H5NP6oRpSTWkl0FiXURgSLveoJAIYIGZzVBJnuoPHyh8MOKrWtx97/vp+EfCQSUMD8MhAwsJuuQ0kPWEit7kD6hV5M3iqBzaLwfHF4oc3x/hu9UTJTHdV9iXY9g4BHVHVZprq/uGBHQ7O5Y3tR0uwjbd6jhCaeDkPV+zTjysX1M1d+Sb9ZYN8auDO5Zy76z3Oa3qI2hG0BceHSnl8RlCeGA8NzF38D2sGO/3pMMXSYfHKEWFk9Int+8wNnc8OTjZ+wqQVQ6dO07Bh7sF4Z0VtIHhs2vXuKfStzJhD+694f83auvUW6PxmffHSC/5ba0jJyUQRBw4iRsszX73YE7p2X5tkLmW5zT+7z/Z/f5/N98jx+4jJ89o60tMi2CJgAAIABJREFU3/z2OeN0ytmDGZ//n78gazSH1Y4HTx/wxw//S6afPORf/PN/QXw65XJ6ztur/5WXb7/hVAtOpg94GMVMjsb80fmfcJ0L/LrnoTeiSnp2N1/x6NEjWrdHtRmj9CECg8iXtGdHXCQjXHXEcvl/YZ0Rqu3ReUt0cYTvwjj2WGuHff41xV2H8HfU+zm77Ia26DkeuCh6QtswCEJsL3n95h2+gDhwfzgBBkfUzYHMXFNpwduuYBTMGaxzQlcTDQPubAM7QXKscTIPJ2rp2hZHaFa3C/Z5z+XoCKOWzCJJV+4p7D1IFI8uT7ipC4q33zAMK8wm5PyBw7umYKpj9kBWNBwT0I81teOThj7VckwTC3K9J0Th1ALtePgmZ1lYtuxpVUeaOejYQ9sCx1rKwsG1HSgIrKSzezzfoz0IVBCQReAeoAsMvTUIr0WVDp1xaYOcrpLEjkY6HXkr0a1HO7Gw+N0f6n4UjcHeStzAw256hJI4qsF6Fm01qrfkhYf0DH3Sg+tRASQRtVqSZT071WIOFV6zomk00dwnHF6g+oR3txuub/ao4wHvPZ5gVy3fvfw1ExVgcgeMJXZdmqFPWSZIF7pI8+Z5xhqYZgNGM0gYkoct1bribVWgi5bB4IiT5IJW76BpWe9y3n2xpO96XK+naSsORUFbe8iop8oL3ju+xLU9Wd1QZT2pe8TJp+f0y4p8e+C71zecP7hA1yGLw1vqZk+euTy+P2I8jkFGvLpb8s3iiu++fk2xKPDGgtn7zxCqpNwY6mbB4eZrPP+U+OiUZJ4yuzzj/P4zrOfS7XdstnuCzYq37Lj++msO+4pf/uIzXl9dIXqLVgoVNHT2wMrsWe22+I6P3a749svfwrHLo4tH/OyTP+GnH73PxeSUi+lD0pEiiDWnT3/CKAjRMiXrSo5OTwiiEEemXB4FBNohpSIgYDpx8OoEJw0I+ymOU+CJmvkwww0EbdhDHbDvOuh2yMoQq4C0d6DTNKIlaVqGTsy9sym+CUiKCjyPXsW4YYpfZrxpXhA1c3ppaU2A9V2khu3dFdvaopqA3fd7drJlkCbMfA8lOnKTM0hS9KTGJCN8T9IIybDrKZuK4WCCNAG+23BQDtQlpVfQhDmq25PZHtdVCCkQXkfrDBiOGrbbljDV7JWH3beAYNAHuL3F70NKT1N3CrwQd98QSYnqO9Kix40MfeXjG0VfurTeiNatfyiH3RYRWYw3wDoQxTnB79ESgB/JSQB6vLKhCTpk6+IKUI1H7ragalQjoHMorabvBfgNbXeAPMImBV4laZXBTy7ZH5a4iY9ttzQBBMcKi0NQV+TXLhtZMqgEr5ocdyjppUuRK7osxw9cXGfEsVrzjdgwvNkj7t9H4jCewO1XPaovWT1fM/mrS8ZJipIBL14XdEcpkyphdnGBmzhsshWFdvFcwc3L5xg/whUOnz54n/4Q4e8rVos7ICCUc17f/m/48Snr6685vn6MijxmScj6Gmo23P1yz+CnU0Z4PP3zJ1y+vc9d/I71/i0Xaoo3iKh6eLO7YbFdoXOB95OG4/GYNnlI33hEiYdfudxGDU8vpvhHAm/VYAcORwOf+PgSs9GcHA1o94aqipHFG9qvVoz++im32Y74POAn8z/gPH1M36xp5ZjN5g2+FPStC52DnKQcDUbs9mtMtUGHPSfjlLK1+CnceT5hXeGIgH2tSEJFHPpUFFSOYVSFVJ7FwaPT3g8nw6QlsS4mkYi0p40n3LWCo4Gk3GcoqfGEz+ToHkHoU7uSYNfRxTl9kJC6CTdVxLuw5VLX6D6kiyX++Bix6xGb35B+ep9FnvOojzDSYUfN4aribDrCH2XE6gzfd8kPO8ZNQWU9blgxrT3CbUM+drikZNEb+l1H6DsE2mGwLWm8jrO4xQ+PuG4P3DYQmBR3b2lFTuv5WB1RByVRL3+Quis9hn5P2VWYWOI1DkaA7ivQMbIvaKxh5Alcu2InFdJaap3Q1z2+LZF+S9u6/DDT97vjRwEBC9SBhVoBAmsgCyR+LRBK0scusqoQrWQkFSbo2R9qpr1AK5fsrsOPh4STiF3p8M33a5LzA552mLtDTCwpOxelLbHXEh+dk98WuG7Ja0czdyOU6ChEhek0nzx9QHzvGfV+Tb7SaLHjzbs9biKwe49w/oT6Vc2vF79CiC2HJuDpcMTZw2PigUQFCdOTM7rW53QQcH0wXEymqCBis1iQjsdUesDlqc/16h1tmXE2vMS9PMZsO778u89ZhxUfzGb85ONPuPzwL6nbA89/+5rhRUC+KxCpIDw+Zz6SbK+3fDAYYmTL7V3Gg48f8jR9n21T8f3Lb6H1OTmf4TqnPL/6BefxKa9uvuXZ8Oe0oWZFzc1uhVhvGB75nM0e893rL7m7/obUH3PdG+avrjk/e8B21zJ4PGeTfQuDMSK/Y3w5hbdQuAVnD58ycY8ovT1WOljdEfoB4HIejZGhYSz2tKFh8zZDtoZMBEi/J04TorzEGIE/qmEXkImGqY0oZgJ9KHC6nvJqy301JBzFVEmJ3WT85sVzXr294fH9TygPHjv7juQsIVYBXj3kqr9hElqc4g3rgceF65JrhbUtV69fsmgsIhG89+AZd3c3TEnwZUc8lpikJVAO0/MRUkMZjTCdYTA7we5rdOPQypY826KbB8TzE8JdS4Fiq0pubr9nEh5Tz5/i71tU0HD18pq36+9RbopvKoKupRkYyCS1r/CrHhLBtm6RMiJVhtatQTgEvaTqGmwcYOuGInBBh1hbI02Pj6UJNOIQI01D5znI/PdD4EdRDvwwDaUJXRekxWKR1HhA73l4bfODtpuM6GyHVxlc6VMIl6xRLGzLoW+wh4onRw+pXJ/buqTNOm57gVis6eoKR++R6h6Bn1PIDfUhZ1fecl1v6G3CZHDK/OiYXvlME4PbK0afHPHkZz/lzz7+mFgec+gr9OGGur5BdweaPmB44aPLjqu1xbhTFpuaz64qEhPxtqnpC8lgdsJoFLHqSr59/Tnb7VvOz2cEwzM8H6LxnKgb44uK05/Nef/iAXZ0hPBStruC21/f0aeS2ze35G9yYs8wO5acns1pBx1dUDN1J+Qm47xKOZokSL8gbyS77CWeikn9PaNRippY2lCzPtyxO+R0vWRzd82h3EDrkWiHQTRnFMw4v3/Jpz9/hhNN+XJzzavNd3z2r/857TpndXfH3fUdrjNn9PgYr45YG0uh3+B3iqBROEmC1/o4B6jdPa4z4GYjaDYJQvXQdfTJgdWqxN8aRmFMFApm/QnRkctQdGT7LeatJugteaZZbzvaqEFqyebOJRRT3l2VfPP9DZNhyuxBwH1/CEWHNzDEwR3TGpSzJPJc3DLmTbZChIptWbBzSkS4xPQu+8oSO0OGbkjaRXSJQNiS3ncYzkaMJgZnW7OTAopbIidhkETMz0LCZEbXdQzrBvwGXx/oNhXCKl6tviHWJe3AJRenONIS7jw6U9BpSaM8yk7/IBduNVXiolvByOnxrYZdi6hTbB1TKhdhA2igo4fewwxyGt2hLVRSI6yk7AVNb6FpUb/bkBj4kUCgB0QgaIzEF5JKxYQNFC7IXmIUHIQk9GpE6FJoKGgh6TCdi0YhdUndtmxEwZP3JozrmLIxZOsdcuDT2TUmVGh9S7WHxd0dG1/jmg6soPdCqv5As6n46pfv+PKLJeHQ4UF8SdqmLFY9JmgZJy4mdRBuzOWDCe8/OmHYGXoR40c1VZExmSqOvJrtbsfuzZrRiWKX3bBcbMiWJXZfoRyPl1d39K3GkyE337+gyW+pZEwSR5yEY9I85u7uezav39K6ByxjbH5Aelue/+2v+MXf/u/c3i555A1YaZ/c1oTC5+31gdf7O2bHJ/zBR3/EifcxXb9HqYBoNGfshzydPcZxQ0xlaHcFIhBo61Ksl7xlQ3LkMz1OeXL/lJ89+wRvFDHOaqaDCUMx4cpkRJ7i+Owp83BKrTV6otDrEqNG7L0G6/W4qmJrD8ixoNM1Wbvm6ChiIMEJYNXv6bqWvoc7VZOVLlrMwe1ZGZ9CCFJP4HqGum4RxsVTWzqnZB9siEIg3hEHluORonKGeL1P+ME5ZWBpsh7RBCgs5BH7LKGzHoGaYMsl2bslzl1PvTDsioq4zDE95E3JKhFs3xxoax+38ymbkqE74uhyQngQlI1AIKnaLYXU+NEQmQSoWHO7q3Fcn1nsEwx8Ru2MfVvg7C2TMMdoRZ1IFIrWa9GOxpMJnWN/aKw2lsD9QR+jExY9dGgpaUxOHTaUssJGOcIHT+aoyhK4CoWLQtPWDr1S9B5Yq7CD9Pfm34+iHJBYRNMj45rWMYi9wPghNBV+XGKtQTou264npMZ1PeqDZt/naNtiW4HrR+h+j9xaAm/G8PIEP6yxt5Lt3S2MY7qrG44eP+AmW3Ix+ymRylCJx+uXK7Zew/l8TjgdcHxIyfqMqihZFkvcdU8vMqxMGHswH7/P982X2DLErwtQJ1TNFhU+xlExVJJRGFLIb5mfnnL1bkvkLrktDLMhOH5C0AXkoufmu+ccigXvf/LHlPaWyBg6kXA2m/Hr5lsm+5Tn333FtrpBDoaUuSA1HU8ePeBMQFxVvBQNXn4gGaR89MGH6JHgq7/9DUejhMXeJQwgOfkYtd9TLq5JP3lMsq/Y7PZM7h+zuV3y8eUzFq/WhPEl75v7LI4WHD6XbK+2fPn119x/9oz5vSm5zXl3teSnFz9Dy4rucMuhq3h4fo+L5Izvf/0bFncRXlqQ9gOcpmMeCRqzRkZTqsKgTEerC0wRMTEhRbMlHEa0O5etu2EYuLxeu1wmFcvGoZCaxlQ4fcf2UCJ9xdCdULoC29bcFfDhk58zuDzCa7csHUm43pPYhM623KwLtOyZugO6scW73aMoaZoB6ekl+he/Ih0P8eWBWk1J4ohyu8HrFNNRz7a4RrsFfpPw9/U7vFFCe7dhfjRkX7SIYIBfC3btkoGIKVcOM9/nepkxG6aIrc/gPGLgDUmHY74qv6W0W/rWojwHt9LYIMDYFvoe4bkEWqG9EuG4iLKnaSQJIQfnh/cgMIjaYCJJ23RYx0MWDY7jkjcOwqkZODVNG9OJGqt/15T/D/GjgIARCmSPrAW+LwkSRd0qehuy71oCAqx3IKqHJMawEuA4grIQ+I7B6h5dR7T+mNDWtNsNlW3pGUFdcHTk0EUp90ZzsA5noxF5f01VSYp9TR1GXAw8hPE4fHNHeqJoygLZS/ysp/Qy7CDh6emIIlug8zUjkRLHETu94kiN2I7GDHY5233FzmyYjC548uwpdedxfbNiEI94GCqi4ymugev1gbAXfPDe+2j5Ab/96nMejAcs8hx35HAdZjx+/D4/+8l71MsNt9s9f//1v0KzQYsB4XxMID1ui4qPnn3E+58+w2kkn735gldff8P1Zsf4fMw//fgJ7xZr1l98RmM8BpOQ22825Ivv2V5rfvP9rxn6J0weP+Tes2c8vjji3W7L3Ve3fPXia7rmCTf9HdXNLT/76/+K9fMd3z5/y7M/W+DKMcdncxrdsLle0XkhyXHIwBbstUFnG9KjI0TtkZAQYHC1pe5y8n1Jp7cUsiNsptiyxZUFss2o2jF+IdhyAKto0HjyB5gtFjeoAop7Lu7aEEQhnrDcubf4/ZS82jIcT5mm9xGdJRc1Y6+lsROcWYxnNYtBgdApnum5iGPKRDOqPELukzoeX332mihWnMUnuK7gcj7htjdo5TL0I4Iy4JV7w7sXMadTxWF9xdb1mY2O8IWkpsBkitHJlF13YDCF/fWB1Xs1algzvD2izQI6Cupe48oeYSyB7SlNQlc3hNGeTS1JCXBVzSFQaJXjGIkIamQjEdri6xZdxZhEIIVLoCsGsUcuYC801ulxZYNsFb9PW+hHAQEweAqc3tIrRd51OL4glAZal1Y2RI2ELmPvWFzlo0WL6iQeHh09ZemhnZzBNEJqgWM1RVARjhRaR+SLmnd2TXIJvZmSnnjUXUcnC8ZyTl14CLOhn0QU63d4fozwNOXyOdXOkuVLnCePyBaGOEpZ9c8prmLwDixcgb6B7Ezwk8lj5vElzbDENhOGVcfQ7zBORScumBjJerFiMJ+QiJjIqZCtz/x8wM32FauipX+3x9gE14v56pu/p1m3zMbHLG5fUacBs67DSVNerb/CIeTt1Rd021tGR6e8/PwL3t6+5cgb4C06vjFfczy8x/EfPqN/XfHL73/Bfm3wfY/xrCMSlzS65DevXhApyXUWMmxd3lxvSc6H2JHHh+n7fPDxe3iDgMPS4Z/+F/+UOA158cuXmJMbVBuhxzGl1fz5X31MdgeDac+7L28JRYvtc0hSirwCt8ZNQmYqIDczNt9+Q+u/wilTWrtHhw4622KlT/wmg3GIrTV1qljtHbI6xYY1TuvSRx2bxYrx/B7H8/vcvHzFQXjMygH9EA41qB4OqUaohm67pIvnWC+kOmxQJuGNc4OzFviPjrmrr4n897j37IxmU7Ps9wzXFUsv5GE0xcx6rm4NB3PHWXpGM/bYWYh8hedIfFfx21//lkenZzCLiPwjBpXl2otxH4/B8dhd37KrBOv9DYqGUESoDgpTEErFUBco4bPsUsZ9izY5MoaR0GSdR4NPugMlLHgVbefj+AWN9Ug8SZV5WL/FMTG+zWhED75LT/F7s+/HAQErEUZQRQabQ+iGuLWhVBJHQuh77I0h9kIMNVqVqBKshF4YWk8ibMmD6YCu+8GBpffOmE80Yq/IAk3oKMpsxVw+pCsLuj6l7gxV4xPICt8fsLENdtXg+j4D7eN6FjkMyeo1pe+jG0tyrOnLlkl6xn65QbkRJw9S2qrDrRVq5pHtlpTXLo8ewovyDjcZkR/2eOodz3c1m8Ly4VjgXpxx3I94vbpjOphAllOnHXk35Chu6bTm7s2W1nWIvTVVPMTd7phNH7K7e8t6lVOul/z0k/f4+ts74myNLTXZbos3STh+75LXLw980XzNp+oxmyridtvgKQctLX5yTqxyTOfyfHHN2I0ZPv6QD5PHmPBf0k5iLob3OD25IBEJJ4MY9fQJy+UV1W3DaDSgyXrmY4cw9Yh7h81uxVF/Ql3AGE0uOiLRUJYWXTc0u5xAT/H7JU1b0+oDsuvJuiV1oBB9QTAYYvOOG18w7TpqUZO2lrbVqHVG2IR05gDVCDOI2OuGWQcqHJOWL9CjEaKNmTgtO+tgSpegbGlDRRhI+qsDnRsQDnxWdY/TuKT7Oe50wFDWmHDA62QHpcd64TC/H1NqcHCRbsDgOKS5/cEDslcG3TfE3n3yXjI9m7PLK44JSNKAZmSJ12OqOmPuava1RYuORCVIG6L7hn4scSsP6/VUtaQDfN+Q2x6hwVQKacBB4zYSEVhsIMhLjZQKrVKiqKBZDxCRwak0taqx7gjb7OklWC8EfsSzA0JaehecKsWKglZqukZAD9LrcBqFCBWN7THGIGSATTpUCY2CxGtxRch6VWAGkrmeM5mntLsWI2tOVUxdZ+wTwdbk+J5Ln62QlQtVRxEFVLpk5I8xZz7UGUru6afvc9bWTIcnFPItV3nGsXvByGl4488ZPRbcvrjh5rZlHARMH9yjrmuutx2pWTKNjvmGAOVpprnBRgFbMuaJS1/57L96RTXxyHZvsDplacHvAiZRxVJbRnuFDOHB6QnjowHrVUs8M6THMd9//Q6dCKzT0QLVQHMkxrSBw73kPpdP7yO9IbMPJaPlkC/fvOVuv0aZjPuTU7x7Q2Q3B31H3Toc6YZ4cMGD3Kef9zx++BOiYx/qCX9w8oib8oZ4nDCzPZXd09aGsInwh4Zd3TBRQ5LEwewE7WmDPhjE0QX+5oadE4C8IR76uGZEtXvHUkPv+biyoVIeTVMR5IbCRph+gW7HaF1y5xrcOMKuBXHkU46W7K8lQ9NSBxWyc/Fal1V/R09MIOaIUmDcji42CNnSdxoxgFhZbK1wZEJY72janpHRbMKaYHZLpI5YFhqpDH3uoLWmHgj6xiE6HSJVx0glLIoCY2B5vWB4f8bA6anLmsGpjx+POdgdq8biVAsaRxKGHmnrsm81Yewx732syRGtATegPeSY2NK0Chcfp3MIREPXaQonRJkGT0NoExqvpDAwbSR17+AKRWU1fenQeRkqDDC+D42mqWukl4BTQv2fIDT6e3wH/kfgvX/YMgJ21tpP/0GV+Cvgm39Y+9fW2v/uP/QMa6HvXWRfIZwYYWuUlLR9j1v3ZCOHqDSYGobKZ1c4mNAivQbduRxqHy/VpPTYVU0Wh/SrGt8B6w0RjiQcxPQHS36V444CDnScqZRmOiJOQyJheCM6png8u/8pnewoXY/pdE5319Ee9pzImHDs00Ut1fOSYah47/4l04unREXJy+wWlQfMHgiG5YjPXm9w9I5X337Hww8e4x4d82w9x4ZghWB92NLsSnZtz8hxUIuaduhw8/IGZ5CyuntB7gTU4prrd99gWp/x7Ihll7FY3HAppsyfPmRzs0S2MeE05L2/fI+rb77gKrvjTKRcPb+mEQs4WLp3JR/97OecPztGSMWrVzdsr65ZZDUPH9zHPxvyfV1w9HbHxSSk811Op8d8vn7BdrnHixNGA8F48Amvr9+SpgYVDqnbnKZuiGcjAqHo7irc1JAEHmo0wH3bszEui3cVh2aNKjriUtGd5dy+e4nTC+zBJ5/FKLlgr0JEtqDY7SE2hFcVRTD8QWtvVTIbO8TxhKJoiGghFujyHN1/jfUUxh7wDOjuh96CkAei6AmVf0PauVSuhyMUVd9hdj/U1Z4RPF/9lqfqEhGXDIMjqj5H1AdW5R2Hm4IPT8boNMAeOmTUMfMmtJuWciBw2g5Tt+hoTDCO2L/LqIsI5casiwbXddgYwawWtICjB4hUE1Wa3oWi83CFRjsViUipRE0lJUNTYQOF6lxq50DVu6TElFZgZEUvWyDA7zVCS+q2wrYgjQt+i+w0FgcRSEz5u+cH/jGfCP8H4K//v0lr/xtr7afW2k+B/wn4n/+d5e//n7V/DAD+Xwq0PQKLaGukMVjpQJ+yl5DkCtUKjLDUpkXZEqdyUSZAhOB6kt4IfN9BCI9I1PRriec7pMDKaTk0Bj+MOPZT8kbh7yQFPW0yYeh5rJY5gdT4I5dqd8fqN685fH3N9uYV+8OSBxf3GQQuu+tbVjcVF75CObAxClms2OsNxaoE31BsXbKlxRsGdIWla1psLpH7Ht33eElIGBpubpf4vsDzDP3uDlPcMfGhNpq2LnE8H8fULN5s0PuEh8+e8uj4Pg+iCeP7QxwF232BqfeMpynTKOHw9o6uc6haQdZpZsmIYtPRmph4EvGnf/knzB4+YC4CdKMZDy94+OQ9LsJj+q1mYBv273Y0s4gHs1NCDMJIhtLHp0WFYzwV8nB8xvzygmQ8YHicMp4d0dUGx7h4MkD0MVHp0uUKMVYUN1uiumDm1KjQ5y4x6HJPVZU0JiBwJMoccJqQqGkRXY5sdijXslEJu3rPthHkZUEpa9amI6kNxmuwecVYlThhRF9L9rsOx3p0hUG5DYcuJAvW9O2QVXOHHXnk5ojx8By8AhlKXCCVM5o+YRgNyeKSzrSITlN1JbHp6AcBbi8Iwg4lfJTfE04sThRyiEo2hxKTabqlAk9iZU3aGcJgQ2s1R1FHXzRgKs7Oh3QSKi8g6wyallZqPAmV6ZB9QGoMdRLT1g6ltOApdG/QqqT1d2itaY2m11AYl8YVYHtU76JUj+wE1kmxjsTk/wk9gX+f74AQQgD/NfBX/6hk/z0hpCDGUDkunja0nUU6BhkWiNaj0h3hRBLvLdKVlCUom1OrnqSDtnHxRYuwDYFWLLsDR3PNTqfEQuLuPVorUGJBpwZI0VANJzh9Rtj3NI7HotLIO8lJ27OKW3QiydjQv5a0iY/cDRBxzFHtcetkZMWCxaYj0hl3ozGqBWc0QUhFqlpMnBC2FdJK/vCjZ3hK0pERp4ZOWEJ/TuiuybuWB13C53LH4OIB6ekp0auXbPIt8/g+u+2XPL43Y3T8lJ/89CHDNKbIthxnFX3n0TdbYhuh/J6mzni3W/H26hojXOY/P0anDttNAUEHhwYhXAZNwdIL+KNP/gKShqAT3Lz9AnXdUu1bzt4/5kQOkZ3grj4QOhHBaUQVaiZ1hxgljMcxme4Y6pIqdynZYwNNdSgIj/5v5t6kV5dlT+/6RZvdm2+3+t2c7p7bu6gGGwTYZgJ8AAZInnmEGDNmxMdAYorFgC+BJyBctgqquc3p9t5nN2utd71d9pHRMNi2VEL3qIRrcnIUGcqIzEE+j/5d/J+RxyfDsiyRuad5f2R1WTOKGXeYOB3vkYvIw3HErpaMfY+TAT0m7KJHNhnvcdBHSlmw1Qeecon098QyQ4Se7NQTswWpm8mXGXbSXNprvs8fWDAyCOgECJlha41xCq87Nqrk9NCwqicSS1bmCsxMEzpeXi3w5wOnbGLoFKUqMXnG+GjpZOJ8nsgzcM1AW0mWyeJtRe4CoZNkmwX1esXpcCbngsNJIEvoTMWYAudWUgZFrjKU0azbwCl5QqGoBo0SAQpNMolJQB4rZB/I1omhKximE7WSSA8pSAopUNYyjBEVI1oqZuFJhUM6SZALVsLRTB1FvqRzpz+Iv79vTOCfAPcppd//rbnPhRD/BjgD/0NK6V/+XZskJJ2RqNwzNglUhfId1mqSVMR5pt0ZbCkwyZDrHBUb2qkglgEpDdqMnCZNrlb4fsC0Em0qehvQvsdKw7DJ0MOEyjXD04H69oJaj/jZ0zwduVqseDM98Me/+kf0wlLu97z77jv8vUTFV/zJ578iXe6x7RVl9ZyfvvF8mBu21wL/5MmrRJ4vyY1HtjmlKnj54iVfTb/l/vVI0cK+lCy6I+9lQ1V0DKPmdy6j/+b/4a6+Zvfb9+SbL/npZqRwik9e/mN+8sufI9Y537z6G9JfFrTpiMBQ8T0nAAAgAElEQVRjspnK3aCuZ3722S2mvuN//z/+JTKT3Lz8jGOmKLTkJ58/o77K2Wxqfvebvybogm0VuL5cM0fDV2++os4sxSx5Gj0ug+448nh8z2df/inrPKAUFHZBms8Ec0U/dpQZpCxSZppZ3pAfDHEhOR1aKq/pY4/f9eRkPCWPe/8evbAI3/H4+xlpWrQs2KrEWSWMCfhdZMhAvPO0a0N22rPrBctZMVQNSxvYqGt6IVjOES9aPnzXcHu3wa5LRNtRLze4NLJNjlIpYlXjzgKNROuZxVVNOAZkjCwucoZG8vbVd1z/53dc3X2OU0cWJHoJXXLU5ZEuzBybGnUYGeYV6thztgPSedCKMq+ZmpZka7QoWFjPtDA4BReiZvfwQO4MNZ6vF4HXb544RcGqkrhZMaiZPEATJMWsEDT03vJiGTmeBSIMhEXBpHp8GxB6hZsGhNMs5chkFbPOCEohO4+rM6zzzD6Q65pYho+I/APX37di8J8B/+Jv3b8HPkkp/Snw3wP/ixBi+YcWCiH+WyHEvxJC/CuZAlUwIC0lkjI4JjLSLAle4rMSWwkWztPHgIpnQhLkhaDtA9oG5kkS3cjImSxPnKXkKZyQbcu5O6M1HJ4cRShRU4WxkjyvSVlJrQuCyjm1gbSoEfczfv8erSU3189YP7OUUwF5wBSf8XItKa9vufl8y+LakKYMfXnFJ+sNhYP0mNEGSV/AXu1Yppe8eF7zZjgxpkSoFNVOcLG45VlpkcOJV2Hkm6czQxP49FOBZIcrZ4RVfP9wj3jXUd7XnMc3FNWan/78JS+f36CvDXEE4UvefzhxzgXNvaPOCtTrb8mGhs2yIpgSdGK9fMYn1QXZ1ad887tXvP7mFa4HnyeKF1e8XN+y1opJn7goF5i6wCnFeWjpHia8uUT2H5hFRjiPoA26XLLWEaFycDMzJYSeruvx88Srp3vsacdp79jFN7Sjo6pG7LKgS44Ps2Z0A4Mq6ecZeXIU28hWJXw74qJnyhPzaeI0K/JyZJ2WNFri54rywjD0iXQ+fBQhlYF1USJmC8lAe8AvIM0ThyMYqxHrCUzOh7ajmRqELZmU5/ZTQxY0aXC0/Qc2UVHELSI5jo8f6OtIKp+gXtKPETEIDnvPUexAnPDngnVlsMsN1bom6wNNnxNExSkOPGWK8M3AfHogKzR9O6Gdw5LotaQcR0zR46PF5onvhedkEkpOVH3E9xVGlTC3CGVR2tMnwaQy1Diy6HtiWZAPjmgWTB6GPDDOP+wO/HuTgBBCA/818L/+u7mU0pRSevq34z8HvgZ+9ofWp5T+p5TSP0wp/UOpLE0S2M7ilgW9SWgNc0xo61Czo+8iQwlWSooqEQykGUQ1M/uBMXNEYXA2Ek1CnTxyODEYSZYUpyePGkam7ROydGwuVvRzYDocef+uRVcJG85UTnLiSEqe2EqeVy/5r/74P+Nnf/Qlx2Zm3+w4eoF/fSIYePbsAlsqlmZC2QuSWSLyEwvrqDEYDLI88sGN3Gwj1VqzKlaczJk5HUgxwgi31ad4l2jjDCePdDmnD1+TZo0Lifdhh9sINi9/hkmRL65+zSfP/wM+ffEFKZ/55v33PPVfM54E1z99iZcrvnpQxDkg6oz9WXA67Hl3eAPFSOV7vvjyihc//5Q/+0//FNUvKQvJ5cuaUt0CObMyVMMZoxKq7+lvQOZgvCHjHi882hkm3+FPCSF7WmkR/oEP00Bod3RPj2T9mW+P97hyR/m+pO8DKWhMWJAdHMYfURSMJ0fnMkJUtENkdI42zag04aJnLkqyONOPEpTAnANjAdplhKJlkJr2KX5szVWW5FUiWcEYPcG3+EwSs4B3E2FeoZsWn0uWLiPMMyWG7795YHlhsQvL3XqDmQPdPLDwS067hvmpJ8SEnhyVqpiNYsom1ByZZMW81DT+wDwO+E4xODg3B+Y2o28autDztNvx3Xkk+vjxn61KxkyyFgKkZO4MC+Ew3mCdxshIXxXETCFCBzpCZhFpAjUTY4YdI8EEeqGQ/YgLM34+IApPJgKyL38Qy38fd+C/AH6TUvr+bxHDFbBPKQUhxBd81B345u/aSKRIfa3we03dJYa1I50MYu4ZMlg4wRlF10DIIwGD2ciPQcJOI+qPR49nI8hajzWCOQsf9eOne5rlko25x4wZ+dOnzNkRl89sP3uGEJbF0vNH+iXjPGLXS87CYeOG6/IKrXJ+3x6x5qMm3/kgKHzHXCTGWFNry6/+oz+iPUyEuuLl0zvc9AmTnBjHwFAVPBPXFLcT/Zvv+e7bd3Q3itubaw7nHdu65te/LPB/0dE9V4STZP84IIeaq5d3vP7+kS/LW26e/Yz54cS+fyB5zVdv36BkQl4E/viXf0LTzgxvW/7018/Jrzd8WhuWi+e8/PQZU2nI/vK3/M13O9Qw488VZWa4/eWvCc6TS831n/wZb4+v2X94Q5afYHOBHAfMouLw+AjFEuUiUQUmnah9IpaSp92ebLlknie6NsOrPVYW2HagjwcG75lLz/jwxLQ/cV6eWdcKmS/wbWRfR1bhiqZy6PuJuT/xioLCNB9r341hHBWjd9wmQSgjLkYsDlsl5try+HDAOEjpkYuLG6Lb4wSM54hdrVndLDgdj6RBIiycWkcdCqLpWLSW6rMVu72m9wPTwz1L9Slqk2HPIEtP97Dj61VJZgu++eYdX/z8M1Q7E5/BVX1L/vQ9YgRRlKyNZCo25HPP1BSYauavfvMVX3z5BWNXkI6e+uoF0Qz0aBZxou8ixIIpzSSR4fBE8bG9nLKCeTYo4T4eH84rcA4lBE4roozkckYEg0oBGQSxnjFuQTe2CBNxnQH9w53//710B1JK/zMf1Yf/xf/n8X8K/I9CCM9HvZP/LqW0/7vekUTiShlOeqLZClQzkwrNEEqKNNEb8zFjkCLaCYZRUIyRPHxULJqVJvaRjRlJEkznmb3AGYUtLriZJXma0OUdsR6hjFhZMYoJggIhyK8MqiyY7huiT6yXA/7K8JDAnUqurlZ8d/+BxSExlguyzYpNZemqmf1BUJYLKhnZb+uP+fOpYT+/wfQ5LgPpHb3TZNdrxPnISQTiONApS8w2vLjd8lffwln9jmfFHWrxsZhl+fyCVkoOuzdMLhFOPYuLgtx06IVGx8jm+S3bGeqXgfj2gtB29K1kfR3pxIGtuOONyajrW7Q+MZWGol5TWUe+umEKe5KUVPMKajCZp9v3XFys6EbH4jIjekk8DtQbS3At59YwtTkUJ1anDX41s3SG4+xposW7HQcr0f1Ee2yI5wktK2Y30A6XFF2P3EjWncJqmHcRMQcarVFTR9ePzN5SrguMGogho1xo/CwR80STaSo9wKNgnc2ctcMfN8hFTpBrlseIXwu0GOHYU4eCQzqRz4lCGth4Bp9YlZdcLC95891rJv+crVI89DPh7KhMjx5vydYDIkuoJCmKjym+IXZkJ8146rioal73j1wse2KXYO4YlMFkA29OJ0prKW1iI+EcHOXVhruLz/jL8S9wdQQvMErieosnEhMEH8i0IYyWLHfMCJAzZkwEmTErx0I5uga8iCQViVGiZCD1NUIZclMRZSDkBhH/Hv0EfkB3gJTSP/8Dc/8bH1OG/78uJcGNlqKC7NQjTM0hKEolUcPAZHvqWXNWkkhCK8tgPHiFih+FG2cTiLMkiMhRJSwWEQNr3UJWI/o1qTixzL/kOB/wyjO8Fdz+RIJYsli9IJwf+P1vfsv17Zo6e0a+CLyorpm1YBCJ5bAiLY80Tc/11TOCDYQHQbh4z/1QcSkt4alhmI7k6xVLt8SuR7r2zHwYSLZhpSrOgyD5jk9+/gWXRUnn4bgIbOZI+O6GYG64uL2iFpKxfIs8Bro3sF71vOoSoXpgVT2nyNcM+1cU2nAm4ZaKZyO8D2dUhA9HwVIrssKg20ee1TXT5TOqco/SgvP+iXlV8Pz6hleHr7ldlLSXS6b9kfqiZDUa/MIRxZYkDap8YtoFwpxozQCM5MEwLl4xPxak0nOtax6eHsm3Sw7vX3HuBgqveIyanJ7CTAy+Y9YZ6d2R0iSGU8SZlqoQ3JmeD09bJtlSHDToM2K0hGykOzrqusSZktoGJhMpO4HfrOAc6PRMVjTQSR5zR4oCYwcqdUHTtkSREabIfJNRTSvU4QPlL5Zs1CUiGQplmVRAyMB1vWDyGdmzyLDPyNQCZzUmKNzQky0039NzK94g7As8kf1hx3An8fkMaoWLnvHtDNWW948PKH3FTblgjp7tKiFWlnRKZASiaImmJAoHMaOeDX30CBJpNqS5R9mCuXLQzTAGWqOQVpHNnlF6EAqZZ1RhppkDugQTA6mLkHl+6DTxj6JiMClFZgUu1bg6I/mZvJ84yoS1EuMsbeYRyiCdIdqGogO5iLRTQTGBCAGkJCHxKqd0M3q1wgdN4WG9NcjsOR0N4pQ4rUcutePNTvJ8Jfju/l8TX49c/HzNM/0Cuel4dxoRKw9jZLuxXGTPUcUNzs5UncEWhqqsiGKksiDslrvlit4GTnbmk8uSoWkpi4mCI5vyFtP3vBNn8ostdmPw3R1XS8/7h3v+7JeXVL/6khgKfudbysOO4amgrsE/DswxZ7MdmB5rWAasDKTtGrSjpka5lonEy5sb6vKC/+sv/g1fxZZlveB2uyFbrcgv1kS3IimoV9eINPK7D2/5bL3kvhsQ55lULLjWzyk2jh0DJgTyMufoFMNiRpxzlgUMpxOza5kGzSh7wqsnft/t8c0Jd0y8PvyWLBhGp7ArydREbFrTR5gOO4pnt6Q4IzaSYTLobqCf7sjzVyzmiMpOTG8ywrVDPDrOJmN9JfAFkBLhoDkuO6pTRjMF9I3l4dRRR2AqWfkRuZC8e7eDhWJlCkYLed/QqoAtNY+7v+b5L+74R9mfcWJgnQwv6jXZ3YD4rkSowOdffkE7HpF64rvvA/kc+ez5is9FT8mKyg90MqdYwLwJ1P4Fj+HExUJwdXGBHx3rn3/Jv/7z/5O3f/WK/+S/+edsfvo5zAKvRkLMyEqYxghZjpAwuxyhRpKXBOnQ2tC7CRUcflmxPOUMqsPNiU4UbHRLN2vMSTIsMnL2DMYweUVeC5LLmPnD1sCPop+AjIlDiOi5ofSQVTNkgSIlhE/AiIgRMQ5QtWQm4gtDPxgME12YGJXgJCNBKsh6Ois50VNKS5ZpusIQ0shpf8BuS4phZpjPVJknRctd9Tmf/vJn/Hr7koZHTn3OlbYcTz3D4chvvhnIK8HkcjaqoKlgDDU+b8mrJdZdkMkevZ2pasvLeE30mmzOqYxmcXFHTcRnAX294kJ9TpbuqNcjUxEoVclwrnDrjHyZ84mbEOZjkJJJoq9GnBYcDkd2zXccp5bpoSNmG0xfE9NAs4JnasOXv/yCy7sb/st//E/5ycUz5jaj2Nzh+x5tFOaqwC4z7KQwHuzZ0dPwfLQs85rKC1Q50Q8DSwqyqgJ6xNYh2BPHAx9ODdo9sL/fcXz6mtQd6cuO8/kVh/MTb8IjVZVTrkourgy1zTBTjvP3yFRRigrVN7inEeSJzHcYKzBZYJM/I19eMdWWeG04TQZpE9bYj5mMPtA3jyxjoB5zwkKisoR8nLFB4fJAFuE4Cdajxa4seWzwSXJZR5rekTeBbLvl/HSmHwIvbj9heq/wR8ly5YinDL+2mBza9y2pK7EULO8CNo5IIShlwaEStNGRK0OuKnI5MnlIjx2hnVBiwaxHNkJxUVt2386kwpDHEuNnKFYIIZmdwMSJZASpd4yqQyQP1pB0wqtAZiMqKDjNnKzDJYUsZ2w2gJCEpSAsNIXq8VYTThGSZEqJSax+EH8/CksgRrgwmr4IWC4Q54E5K8lyzXwOTEpj58QYPOEcP360iqgUCT4SURACWRIM+QyN5qpS2NxwihNLueb8fsftxZLN8oqKmW+lQR87LsoV03XET0+sqi0fho7DWfPZJzWlURzbI0F0iOnI168TVq2oqiuqvCC4QOZzVDZRGEU3K5LVzNMZWTtm58msQRWaaVAUq0vyR0m2FIh9wypfMURJzcj081vqYmA41pzsDlFvqbTmH+hbDs7w8vKGv/n9K6y15NkVu90jJpvIiyPy7pIqWzM0nuLnmqoqGGeJyiU3N79k0UHQPbuLNXkA5T4hqhZs5Ng7MhTCVxziyMJawrpi4IwrO3QY8aEgGyJ1VjC3Hhd72jDx3nnG2OIOI+fjX4ITqGVNnR/QD/d01wtk+TGg6CLozw7gSubwhJCSUWZINzC4QBkt59kSxIHzlHBdIi8vOKl7trEgzhobT6yyK45zpMpKZilwakkc9xRyxqWISWu685nRBfzFil0csGicNuQxMD/VtKbBpROf9CUXVzXffT9SZRHjHWq9JUwWh2edJ4TaMNY9Ku85+SXzuUDJ94zhE+ZppBoDo12wLXdkQiG7htwF/GINQ4OWTxz2Bz44x7a45fmXHw95bagp6iXD8YDLLfkcECqnOI7ErCS4hpTlpNgi8iWFHzBIehEwPifNZ3SR471mDhW9iNjoUHFiHBSq8mAMi33PUBcoffgBO+BHQgKJxHmaQC5R5Qnta7LKYN3AvnrOuvmOLhnqp4JZJWbvSPNM1AIZNCpJ8tzi5kAMnjJPHMce7xMLuUCmCbta0Ry/Jis/x25zVlaRN5cYNyN8g1UL3u5/x+lt5E9++WvKF1uaJ4lZKuRJY5fX3G0rJtfz+PDA1d0nfJLnTDExjzOnoqcK0O4K1sozlYa1CxwrhZ00REE2zPRZRI0Rc1Mi5hxdtai54ichcWo0RdD85PkLmlvF3BoerEYOC7w489PPPmd1uOX9/DXn04BY55SnDfPg8RYu9IKruWUeJgqvKX+q6Nsl5aVgOtdc7idENVOkD2RqRSsFpYl0biSdHxiUZxyuCTLRe6jkhj49kc4nRlFRBU/rPX5cEeI9jRtQTUPrPMtgmPORSmXMqxuyny4ZH55w4YQyN2zWCRsW7KfEeBo5NXtE35FdR+ZHw6xyTDahJ4nrISwSaTwy9iVnYXimJ6ZJ0swDZozomxcM8xlvfk8zrAl6gyl7ohgxixypFHry2GrFh+ZA6DKy/BGV77iMGjtv2dsTa3PBz39xy9vf/BZVC7JS0qF5990bLrIV++KB7KDY/HKBbx9JY8RXl2Qbz2KwtLlAPex4UjkbCq7nBX2pWGYl3/72A2/evWN6fM+XFz+j/Mkt/6Qu2c0BaxKKiETCUBFSB3kirAUce6CgjB6nclxzZioypgEkGmE7otUELYghIwuOMbdUTUKuEu5JU3QBW3hSIRBKk6vmB0ngR+EOJCSbwnNjHdoWqJWi9B5RVGxVT1hdol3JcBOxViJMhlYSo2qsCcxGMCLAgwyJPnmmmMiQzHKk6aFMCqVX9O2Rx693GBGxZkc0OXOr6LwnbyUvP19yebNk7s8kfSIcBSFp2off8ebdE7vjnrN7Tzo+8O3pgbZ3eNtQj4I0KbZrSVrklG6HM5FtITkbTcwt42XFzfWa7WqBDIGijNTJsFQbTiFHbwWLm4xCWwqTc5Hf8Mmzz7m8zikvDJfP73j+/DmX6oLVuGDyC161O3bHe2okU/bIOaxh4Tkxsu9atsLQR0V+67i+gbqEYC94bB6JU4uVE1WVUcYX2MMWbwfEPLAeFHI4komKdpRMqaGbHWYoYPOG8XgkDIYxu0RJRbdoMPQMakT6HJErNrmhHEGFE87mjMOCykTwI2VtmFVNYTJIHSYeaaMkJIMsBKXPmGKiLhqy7oGDCMxyjbcBs7R49gx1Q1RLVmVAjx3XmWIeZyYhMA5s4RmlRGiD41uO3cjT2aP9Ajftmc8RqSdSnNFaYkpNn0vMBKubLeeFRo5LOnNkfgJpl0yiY+5bZG855iPnfmZwZx7PDXM14kNk2c00fcOh67FjYLIBqQM+Weq5QKojh3nP2WtSUWPsEZ85QpTYRmKjQOuBzng0A0pqSB7PiE0RkZXQa/w5UfUzflKsjwOD9XRtgBycMAz5kkmtkEpzGn64TuBHQQIiJY7ugjEtED7DzRXZUiFbSxoWZAwUuaI4FvRzwHpBNIIYJgZnkblEOY+rFXkmkVIgcgXWEN1MrRUP+3vMpaBAUhSGcQCzuEDHkTwPrANk9SdoteLNcUe/2xFOLV41PLiGMWacuo5zVKQmcfCBIhmCckxnyWAiRV0Q+4FxbIhSEIIgtB3LNKNzR+ZnUogUqiDLFSJVkBWYbc7CBEpzi6k0SeY8K5eE/Jr1csVtUVFWN/jTgctLxfXnv+BOFNxcLvnk5ZY0SDAj4txD+pbzeSLqM8N9YKYhs3t4VLQ+R+gcocTH9ta09GWOmRN2eSCtJxbaYDJoyolUHJEnQ13OGDExnhuGeYf4IOncgJxO9M1rqrJg5e/QsqZsDJN+YIyK1lYEW1OVN9i5Qi0chdqidMu5myjkxNS06FExlgWVmhnajrwUxDowJYkbAlok9FmwWU5EoI0d9ILtXiHPBWtXkvuRJ9dSeg1mTQKOUrOYodo5Xn31wMPbJ8Zdy2EemdcKmy85NCO6eMDPiaaHG18zMmJFyWpjKOIDYwIZJ+oQSHQMscPhMNMSd3jDcRKEOFPaJVSaxkxUXlHrgupywcKukflMcTzQl0c6PyJNRila3NggxIq1FyQmRhGJUpMbiTEwTwalBBmGPMKQIr7ticFQZQGXRaRpaZEo40FBoMGvE+7QkYJETS0y/cgViKQQjKnBDDeo0w65tKTZoktBoSCxQZUnuqBZeUscIl5EovYIGYguY8oiWWuJIlBpSxsiYRK4WPDUt0hjefv6ieeXG+zYIbWjMEvYXKCN4sHMrA4jXFlyK5Fii88m1rXlJZ8i40DfdFwWlnBxwULmKN/SjAtS3WEeJGL7QEqKczHgRw3DmeAC56ApVMQXW/ysMCvNsrvkPjpyl3D9IzI65uMZXy7Ik+asJ25vDcNxifkUMjHxEGcyueKPfxF5PVnc4kwhSnovePv4iOwl53eP3F2V5LeXyExwPL1m6X7KpFvURUU6G1I4oe2E6CdkXhIvC77+q0dE9UB7EHzbBKq5RMsj2aqhu8/Q2Tv0NPDN/kT3tuNmW7O41NzUFe70hFjkWPMpRaXI0hHvVuCesNZjasWt3fBY5aQm58bd4KuB58sl+8MlumgYTw0uRNblFuEMJm/4vFrz+2PAqiNmDjB4xipnJSaiTpz6lrLK6FOOKZbE2dFahzor7KwgaubtTLKOT39yh/AlTf89q/qWs1xQjxNTcJzime9iy6Y20BlE7tD5gjwImmzBdumgzzjGkRS2uKcd5zfvefnlM7Y3N7TfN5R1yXLhcUhUecv3Hx5p2VFIxcXtJXa9Yhg8IQWGJPn+fqAJCa0Vc2rBBFRUpCyQ8AxYrIpMlcHaHjFpfGUwCqTWjAjOp4/ucGkizowsjkvOG0dqFMEYljWMpmXsJZAB7g/i70dBAkIItutE1r0i9AK7SZi0IJORcQIbJTpfUIw7JBax8TRPhihnkDk6OKLIiXlkDhV5HCBPxNFTFQpkoLpcokaDIxDNAj1LuiDRh3eMRUktnlHXGVZrlIcYW/Imp74ruMsFh7Sk3hr8OFMJweFxz34pqWXDGNe80J55dhxfHXFVjvETcrtlmg11PiAnw1U44VcL9v1M0BrRRrxsEXZBLgpYlej3M338niTXBAfSVOgUOT8ElkJjLBgsv/rTGx7fL0FLlAqYzmEuDXKlOcqOZ21ErjVhNrT6xLHfsZpWKKNI/cxaCZpzjl10WDWhspGMLV1ssd2IXLYM+4nh/J7j046kEoMGdwC16En2DlOtSdGjWrD+yFhOLFSONc+wKuF0w4cA2bjn4uI51XCHrhLpuGErFSkTGOnpjGF4fyJUEErDIiwID4Z7cSBFQRYT0Sj6SnIXc3KVk3KHGg2j9xjR0E4dMmhsaZnNxLgpqbqPJvbdF1fkhwyS4Pj6gt1jQXanyLWhlz1q9mS7HTkWZz2uGdgurhB5Is/O+BlWpeHNBGsV8EuDkZpp7CizS9xqxkuD7tZk14k0Wm4LTVzfcaVKvn33Dd8+vOVlrhHTFlEoXPOE0JasG5ExcKZkmQX6MTKZGt32jEoRO0fXSSoBIfesOkETZ1Iu0JkmpiWjvCd6w54z+inDXFjm1tNYhfUlK9Ew2Jmx/8P4+5GQANT6Al+2tFn4WD2VjpycYpNb0lwj9AB5CU4zTIq89DTOIVzEqAKfJkKKJA+jUqgk8XJGTBld6invNfba411Gbnvy6RlTPIHaUpwl4cWKUCQ6ZfF2xPeKxXxiOV2w6868dQNLJEYq2iDI84SdtyipuQiWqRkYTzNOWdR44jRqKs6MJmDakv1ywMuc1dzAeo0SDhN6sg6GhUZliXQS+G1GOm+oM2iFoRzOjNWGi03kaG8p9AEbF2jTcfNyg59GzGngcK3YFjmlrim/PyKuFe1eEnNB3Z9R7Zn7vmNztyWXilYrhiJhHx3NVjHbwPTQ0fiBx2/vKW9XxL5jt/sA9YJLYfmkqgilpncrytuapSxQeUVYRkZVsygqbBEJs8FUJf7qzGUzU2V3DCFga8A17KKiTgIz5iwqw1RKXv/V7zBzhYkasbEge/oz1CtBOKzQekTZNdJFxmqmkpuP40wiQkdVXjIfZ8LhkeLlJeHbAfEPrhi6RLs78+yq4thOjJua5vA11X5DX9ZsthXHKOhdYq41JZ67u2dcbhTDscAWE9YNnKeAHiZGWzMcZjrleGGfYy8LcqGZe8tJ7rniJ6iwJ1U57ZtvEaVAlxNPf/6O659+Sn2jaaZIOxxRe0muEl2RUSfFRIYTZ4QbKZXCeUGUAi0Ds3dkneJQSlLK0H6AZIg8oVRFmeV07oSuBCk6Qh1ZesnUTnRZIvYF0P5B/P0oSEAbRaZHnLMsc8d5msmyHCEyApHUnRAyY61LehVROnFuHStlmCkYxYksZTityCdDSI3e1rwAACAASURBVB49R0RRgm2RbcW9GPlsD/ZZjtcZeTaQpZqbRUksKoZ45G2j4XBmfZUhqIjlFa+/fcK8lOhuJtYGoWsWY4NYP6c7NvjuxO++bVlcLjFCkbymVj35coOeJWQz3/YjP1lpuuaB1+eJWbyniIZ6lTPLktXpTFfOSAcLAftu4LX0LEVFnq4Y9wOHkCjsxIdJc3r4LZ+uC2KsCYBdlFwXEpWWhL5j8cWauRtZrQuc1Ez7ARGXDOLA7qu/RkwKvQk8W2x5O2rG+z2hOhJng8CwudjSDyNx0vzxH/3H2FVNPLdcX7ygWzzAUJPGhtPoKTzI5Wcs1pJh0pB7tDD4aY/NF+R9Q8oqVrZn7zVXMfLii5LuXiBVYsoq5HHmp7/6Cff7M/3TgbIocaHkrg68e/uGk5nIYsWidzSXPWYYWbpb2JRcDokPTpDmjnZ+oNSXxFNHWhu6tuN6VXN4O/IqBF7Uz2CZkwtNjJarS8M5y1iFkeWqpOl6wviE3OaM5RXtU8vj0yu+vHuBsxu+/voveF4a3E2imiKz6vAznMcV1wvBQs1oOdH2OSKbccee1jiK9JK7TwVXf7aEg2Id4P7+WzAnnjKNTQriSK4DfjY4NdNPipBp0irijgGDYMoDeQDvNWQlc5xYKomLE8n1FKbGzz0qGlZuRBSCWHnEmCFL8UPewI+DBEDiY0lVNRxbjZhyBga0mmm7SFUosj7iZEamj4wuI3OCvijIVjP6ZHEi4aUnSo+IGmRiDiNmLMnCwCgG7vMFZZ9YbBxPQ4FeFphiw7Aa6FtHf5TEtmNyG0Joyf2ZxV1N/vtrkvbsvj/y4tclXYD+//4WKQPGCF5+/pzlQnN/mPClwXdPtFqBMixEyahnHqceczxR5CsWUyLLIicdWRx2fGUi5iEQq4KblNPZGTFN9CnnK/k1rl9xmzTBntBe8SJb8urtidtLzWpT4P3AOllUBSctaPcZSUWqOGEzi95Ymt5S7QJH98QcHQtfMYlbym1HXilWMWNaSSpXsa+f+P7kyDNPXW5JxcSEZFqPLLOf4wuHexKszp6nNbzME+egqUpPCp6HaUC3kLIju0nwi5WnLm+RJ/fRPXKeU2ww4pqgEj59T5rgYlVgEkyxJ8sSo4vIjSWfMoq+Y/q3+nukG7LFjBCJrkwsnxInc8RkOSY68n5N279jXV7T9om41Ny/eeKz/3CFlhITJTYkkthQlh3dkyAERyUktszwssYgkHpi7hr27/ZULwUrVdK7kVpblhcLVKaQec4nm47dk8ZeBUot0TcninHJICN3i4R7F/FboKnRSFS2YGotxAtsfCL4kQD4SREzD8GgFgk9TsQuwyiJYeQsJM7nyMLjFeRJISMoHxEXhrgfiWmBsB1hsyFLDYURxD4w2eIH0fcjIQHIVYLesqoyDrlDjZrUCM7tyGwz9PWAmCWt/hjwkytLnrWoBpIWBGEQnSevI9PkSClHhYlMKobcIr2DveOwSQxNxkJ2JLlnt9dclZ9yOt0TiByagevg8ToidMEiLpApMDrPor7BP82ERYS0px9nDramZoC9JqsjcZoY+i2THMj8mff5xGflBb4rwJZsg+QkP9YLiKdHnsIC2yTa5h1+FxHbO6R0DLPA9i3TWrMpdpzOBjd4zpmkTJZMKnrpMeeReZxIWUU7PTLHGX30lItEPzaYJ0OoLefdjh5JvbrjZtMzFRvucsHpWJMWE5vyE8Iq4cKS+tvIxXVB+3qHyBOV2qBXcFN8gakdwyHyOij64onzqWEsvyC5GT9vWYYjTGdcnJgOipfVGi02nOqCvhTkJ8f+mwO61mjfkE4HZioWtWSyoBhp0575HDBacjjmiOmjZPydWjHuWsTzE80bR75YkQXP/bnBL1Zc2Iq2eUAVM6Pc0pcnnpkVf42hvvBM2mNshtmvWVxEum0gbxY8TV8RJk1WSExfkgnPk5ypliXx4gYzWqTQ5KWgnWr0mIitY+xmns2a80KxrDTTWeEKxSrfcrKQR8X9U+TieqBPt7ze3bOQiQ+NwvlATB2ZMejZMdg1+dhghWIxCIaNYJwiWS6YkyAzGRfDRCtmQqxZes+UnYheQaZxnSJfDoTOE4LBxo7YadxsiFeG0PywLPGPggSkFFzaDcdMUo49vTMoNdHNLZlNbOsBPVb4GCnSJYvtCXpDN0AzVwgTyJzEqJzTDLkeCHEm+ZzgRyxLvPL0teRZpZl278k2a4bTRJSPNO8FnyxvqXxBcbljsyqxxSWumjg0oP2O2c3kn15QKsv+acebhzfU45JaWo7vP3BarjFDwpwCy/qW61XFMMLdHHgfdmgnCOP5/2XuTX43y9L8rs8Z7nzvO/7GiMjIoaaequm23GobVoC8wBt7xQ7BygvMAokNfwIrJK+QQCxsCclbEMIsMCsjsJFxu+2u6urKzIrMjIjf+I53PiOLyJYaUeluZCHVkV7de897htV5zvOc4fuhn1fI0hHCB/iq9j2xWZCwxdgBTEazqbjY5ug+YRwnnk6OZWKhueTKPZPphHCRwKwQ3Zlvure4o8eHhMP+G2SaYkXFAk0rHvj6ywMXquCTF5/w6//2v0GpLplMgl40ZD5Qm55YNpzbQBY95Y9+zCfnZ+6uClL9lvdT5NXFDfP4NZPdYkrJRZYyXPwat0Ghbc+mmZGx5M2Xb3g+PxAPge3yCrTFZ56FmqmHBBsE3g9E8Yp6YYl3ktiPuLjh4sJzChW/+Kd/zOKqpJ8sZ9mzWtXMw4GpyOl7R3GvyK9WON0zmhkSS5XMGAy6qHgqAn52LOOCrlI0dcLXX+34ed8xJxUXumCdvWS5q1BXIJ8vWS8dh7Ph+vWax3ji9anCjDkfN5cktxbtCkgqqsRy/8V7nNO80FseLr4mzdeUTUDKnCR0HDtFcrFgGjUXhWFoA6hHqCoefOR//Z//AZ9//RVSzdgpxWYaKS1TSCAGRDkRnGDpIn06gbV0pibUFu8j0veMRkDVIOxMSHM4DbS+JKYDUii8LbF6JjCip0CU8lf7ApEUgWk5Ux0UQ5mzlifMIBE3WxZtxywNrU3I0xGtDIkX2NSShoYywH5yeH1EDBlZophMSR4g0GNEhpA9ifTE88BoMpLLNQ5PqiqS6Kii5n7oWcz39OfI+mpJvYA0WXB//ppZQLEp8V/t+eenHbrK6M4JVTOxlmv6w5nSP7N/p/j1zTVjMtMOhmzXYa9vyA4n+qpFm5I2PNB1Of4XHd//wUtOvUbePzFsBfBBoHJbXnFWlkwInBa8eqHQ+zNmM1N8u/20zgqWWcJTkrAxLecG1rkmKtjYjNF5TBaZppzb5oJXf/kTtjrlBQWdPyPzmjTCciFArYkhUhcDQWas6TgvSyotgO9z6WbCaHD6AjUGgr7ncyH4tE9hbfBGctI1yzixj47PqoZRgrpdUgVFviiZO4WyPaqcSes1RR4IosboHlnVNLpnqbYMVyfUpkEER+I8ihQrMkToeJ49zZiQfLyk9YasNAghSMSC81yTuiOpKsj3hlSBqFO2dcbpZ55q/TEi9ZRKMZ0N4eiZLiaESagHy7t+YOUCnZe8VAV2SgmhRduJV81H7MNIfWgQZs8XQvJyu6HaRpSqaYoF5AnBQVwUTGePf9jRaEfMBC8uL3l33vGy3DLqiqHMUEaRaEk6lRgiou6JdSS1glnmBCc5pxNF3+DpyK56ullTyBw/GvwacuuYKUjtjCoVpZD0oiCKjJiciUbiKoWaPnAgvyv9ShiBEECHa0TVkXcRz4JCTBS6p008qc+owoiYa5qs5Z3PUMqiVEdsCtajoc/XGN8zBUPuPWMTIUjWjg/yzGNCVkh6/SWfpFcYO1PVhrmPCNGzbBZMquLFiwVxNzOGA+NiQWpuGNV7lKy4O39JuxfczBnbzz7Fjnt27JDW0e4V15++5F074O8irz8qqF9fopTkYTiyNteUnyrOTx59WPGu9Pzsm6+xD4YjHZ8tPmHYWbyIbOUKz4rT3JNeram8pqpecBI7oijIiwGOwwexiEvBxcUNG1XB4UD+/Qv64QG+tvzBl39I1UouL7bAklFF3ChZLm4IWYpLNLIvWYuUb3hg7DwiSUhVg2Tk+uoVYTxxfivwoqf3z/gsZ/rasBQwlyn1qAgqIk4dZxu4vE5ZhDXlaSamCZukAuFJmzX7rme6lyySLZlMOAZPWRSkpaJ/NjwwMdsDa+ERQjBJzSRGrlnRDZGLWLNIJP7JMb0sWaiSUQ5YYxByTwwpp+ioVGBvWj45vcJqTaxgXV8SisD5/R1BWqbhjqW4YGonTOoZ9Zkqf8Uy6XnIIp/Kll5nvH/u4e6Rq6sVb7onwm3G5VkgBkGqF+SrLXLqKLJAXa55++jYVtB6RZsnNEKwi4r1ck0oLslTkMYSFTh7SZk+o1NJPAR0k+LmQKk9rtDMToNuUUkgjBV1mPGyAwW5rTHJCF5hsopkONG6lCTVGDEyWoFYeEqnGFOH8ks8T790/P1FREU+Av4ecMMHgPB/HWP8O0KIDR+kxT4B3gD/fozx8K0C8d8B/jowAP9RjPH/+lf1IaWgyFvwFXf1yLVZk+d7nFlR5D0HKam7IwdZspwtK5EyHB5JZcN0dhwLjwkWAWgnMVqR2oCZHScceZJhC8MYFvhjz3s18GLZ0FrLqnlB72dkUiCNYpg0zbrn+eGJ4/s3XHx2xWreMPVHbm9uaJIRJ0razz9nHkeqrSBNLinLNe1hYLsoSU+WP/zZG25Wn7JqUuYA5rijHRf4MaOVAzfZDf/yy/+T4CW5yZn3BhLoDwODXKK6E0W5ZREFQxsY7CPNMiOKQJEXpI0jvSjZiBI9ekKd45YFV6PjfLukz0+8cC0ySVn4jBeLj+B4z/61onItsi1JJkmylAy14YYFuAXPyZFRLGgqS9jt6EpLVkvuHzXLzYa7p3s+fz7w+z/8bZyI9O5AlmqC6Jm7kUQVrLIl0+rAvT8SwsjUbunMPWOUuGJE5pJRDDQ+pxVLTsWOuJwIzw4zwWn0ZHVDd25p4onuaWCMJTq2PMY16ySSh4QoLM0qoTM9cSgpij3zUfNVG5jkma/kz6i71xytJTGPTF2OdimyWpDWC1TqmU+OzaJks7jlJ998xff4DD+O/NPhDlVX/MbyCp/PnNtnsk0CAyzzBeHKo5J7VLLFpxXiNHB6mZItOx6PZ+aY8P2LEt3coMOBmx//Ngtf8LY+8LOfvidpn0HW7HVADhqWnmqwmMLiKsVoe9YmckoSpM8ISqEnS5ErepGQzCOJrfH1GT8OxALSwTDZSFpY/HKJ7yeiyUkyizaH7wSR/UWODTvgP4sx/jrwV4C/LYT4DeA/B/5hjPEHwD/89hvg3+ODrNgPgL8F/Fd/XgchRIZY0jJw40pkLVHcoBKPbGrWUiGSklKNdGbJCotOS87CYkrNOpWkcyRVgSg1QjiEd+Rek/gSnMNZSRwHovYM/Yk0KRkOhsPzwNI5uqc3TNMOsT9zfIIuSnKV0T1m3Lc73r0xfPlHTwx2QueC9a3m8qMFqvmYUDvaYY8fPY+fH/jiFz/h/dePzJzZm567r95ziAPWP7Lv3uGO33Do7vniDz5nPPbU1wuyz15TX/0IWdZkZuYCTZSR9twhVMe0DIQqJ0lSrq7WrPNP0KphJGG1zllrgzx53tmI3u3x5ZKqytjmDauPXpJbz/Jiiw4SUaXswojeVqyvt9Q2IlgTpOU2fg+dWaZvFONo0MbDqPBR8rj7BXe7e5YqJ2pPXVVcFC8p0pR83BJlzaLQkAr6JdRzwxAEbjmQJ5E4e7zv6eaZdlcwuhJrz1RqpAgNXb9nlgeUEbj9gck8EOyGbhgpCjh4jxHvEMVIqAYEHXSSuVkh0pkjJePs0cmAj44vz+/o/U/JpCOranQ/E/WICY400SyShLnteGoNqjshd4+kaeR0f8/bt+84v7lDqZKLxS1bp7hIL6ilYDdn5DvN1EksE2k2MS0K5HTGhjW5qJme7hl9IKkFwwRlrkF06OmCeHxkDhXGziQ9JHJCnRNs4nEuZRwyaqHxdUQ5jVSB2iSEWjGOniQJoAVx4ZhCTlAJiVWk2iB1xjwkiGEmDQ6nHU4Y0lJ99yT85w3QGOPdn87kMcaWD4Shl8DfAP7ut8X+LvA3v33/G8Dfix/S/wGshBC3/8o+8EzjmcsUkIEmVgg501QL8mVKWSU0yRWZysm2KaHIcUsHOkcllpmMLCaQpkQ3I0WBNzmTtrhy/KDa6yN5pZDrkiF4zssBGcDkZ/YhoE8PDE8nfNhh+pE8KKwseXjzBfadpBQjupYUUwnvJxJVMT3MPP3sFySnSCUF0ewwoUdFWMmUOfac3ryjSSsqXzL6AhsFx7c9j1+95ZO/+mP+yu/8Hh/fvGCbrMjCgRflNQdx5J11GGmYQqBerLms12hRUCaSUHliqCGWrCT0MWKSnMtPP6LMFTbdYsYnmrZADJp2f0+yTAlKgHVk08RSa7RPeDgM7KzB7r7grDVJ9cxuONHFMydzpA4NRg+Y0rDQGxZZgpMnslXEmgMh74k2cpI9+VJTpa9oR015XLK6fM1VdUnpLOmTJvGSRFzR9ZZzGMmkwTeCQ29w45Eyz5h7RSvdB1rvpFguNvimQYqZOs/QY4IbEkojIWqc6GlkyjZZoBUEE3h6vEO4I0VfsX/QuO6e1iuqPKewC5oGZLpmSLY8zY8Yd+J8Vrhmi9guGE/Q7lvuTeCxCsjCYXXGSteYdEu+kOxFpDt90EQMdkFSN0iVk88dIYnoCbAZ4ZiiMsvwoKBOqKqWQZQkhcDriNeayafYhUV0CbL2iAnMIBm7GhUslXF02lH0kSgzhHcMBtQppzopIgojAlOQFNZSq0h2GDHGwzyxjDXH+N1O//+nNYFvISS/C/xj4DrGePenhkIIcfVtsZfAN3+m2ttv8+6+q13nI4VyHHrBbZ3hGk+TLphEQ8NXyOmW86pFdwliHtjlHtVFVlMgMY73xjNXAjsYikIQpx6zKJG9IPOOcWpQWoJpyfr5A/nlq/eY+lOySeAbS2hWiBCYTxM6ieBKrH/i+uVLRjtia8GnyxfEqDDHI+/+8D33Q4cuF4yTZzYzIUaiTulCwmLVoGeYyxIRc+ZUMrU9OhYkHyf88JNXVHbBcRp48h3N+RtWq2tsB/VB0Z3O9IuJck7o2sj6wqPqGwZSqoMgVAbhOsyQ4WaD2lvi0nC9vcJrx6q9Ybl8IPOR0I8klysOdwc++fgTtM5ZlRP20BNOLedp5qfv31IVHvdRAZ2la3eYOuFn778kppoSBSvNelLUiyt2g6CZB+6PhqqI+JiS+5xf3P8LbBixVwuauWXDkurqh9jDE9N4pionmM6ssyW7U4soHEu1YLqQxHGPVBmvt1uep5F0qzH9ile6J9BSjA3idsTz4WBZOSeM2mOeWmKlyNUVrz627M/vicmKm+/dQt8xi4TX64QxFrRPX1PsSsJ1IA6ObXaL8eDUl/x40xC7kReXF+S3Cy6vllwjqGbNY7WmnWdcf+Lh669ZrAJPyaeo44Gb4oeM7JBWomRgfbmm+q0Ff/LP/gmby5zm8ha3ERzaCpE/svooo3trwWakRUsmIucxYywga3OKhcH2AddInFTEcyRhZMxKmFMULcIn9JyR1MihhUxgRErwClsqKpGjhQLbcepPqGKJ+46A4C9sBIQQNR/0A//TGOP5Q+j/y4v+krz4S9r7W3wIF8hSTff8QZjjudix1q8RTlGVAWduseuB2RzQs2UUga2X9FzTljtmEclthlADLoH+HMkrjTs78kYSuoyscTBYbFqidECZgftnx299WjGNE+9mw8tcUBclOzOxVRVDorjxN8RkIImCOOb02YQfD9w97ejjI2V1SXlVkOYW39Q4vWE9DJS3G+IckK1jlW1ps5lpF4j7O4IIpBuN8Gta82E2mE3H6/ljHp9mVspyrC/Y0VINE4W4RRfgupmsnfHrE8P1iiwqarmkSCPTJJhETzu2pD5AlkHuKMQ1ubLMhWbpO/xHF+RF4H46szx5jnrF9StInkcSkVDla+rUIW6umZfPTOeefXgmdRXZQmMTT739iCF6tqs1Yu2pH5Z0meeydyQZsEiIU8NrSqbjnikLVMOS6XbGfmH5l//8Ky5e37DaJNy/7wlTylWZcHrc0wVwfiRZNaxaj1WBpD/wNvdsYkmrDsguI2wWZFJgsgxmi3ESi6MfBKtLiSwT5OBZzhmnYoaTIzxbxiwgyxoxTfjdG47NZ8jKYlizvb5himfWZcEf2a9Je8Eimxkq2CWBpT4RUk9eL3hXFVhTkQTBKk9JmzPd1OFKjT+naBtI0GTrhlPoKO4Mq6sXzEoifcbvfPJ7/C9f/gNU5vAWzj4j6JxatfRRIQZN6jzZMBEsiFyjbKQrLaF3ZFojm4FhKKjlmegz8JEqDcxSILXBpRXSdgiXUBaKbvzuof4XukoshEi+NQD/XYzxT7mDD3/q5n/7fPw2/y3w0Z+p/gp4//+yCn+GO5AqjcoCqpH03pMNFicG7OCxyR3eBJq4Jk0bBCmJzUmajhSB7zO6a0PbeqKJVLnDjQkqGlwfQRhMnAhC4ruOwXp8WmBDAs2W/EXJZiqoFxtmDapboAtNtUgZZWD2KWJb41LL8/s7Hr7piaqkaF5wfXHLR5uaMA44vyNLBrKLhnKTo0KBsgVpveC8G3n+6o6H0zNxqbiuPyN2BU+nI914hsHTz5blNuHFZkNlPbfpgqS6RJUVy3TLxcUtX5lnhl3L87sHijhRLFcsLzdcXVyTVpd0bLkfjnQPA+ddRwwDcRgowok0z3lZrXhz1yJ3B6xJ+FQqZmlYZFsW+QXWK04mMmeeNK2Y65mzm/G1Isk3pGZJVDllUWDPRxK/JL9QLKqGRZngvOSqWnJzuWCb5thFTUg1NvScnwzt4x27uWUlG4o2Y3mTc1taqHLioqIoE5pJcmENidK0vebxaEmloUdTh5JJzqSD5ew91kliOjJvHKbLWKQjjV2j84LiZstTnIlKkWgBdUZmz+wed0w02JDis5nns+F6eiYxjqkXLJhw3UjmPc/nA+PhGT+PiKYmmxPSdGYzZ/j+TIwSpxY8dob0YUQecobNjHm/AzdSOEEaNQunWasVSRnogyJZOoTWSF0gkQTlkGWLthUyWkSeIFSkKwRSRlSSMQRLmASigigCdq4R0jHqklA4RgStzLCqQ7Yaf5qIXmKLSBssOv3XuEr87Wr/fwv8NMb4X/6Zv/4H4D8E/otvn//9n8n/T4QQfx/4feD0p2HDd6UowJoRX2cszRJ/fOawX7D5+JmwK0nqPeo543CayASIINDSUqwysBmiH8kXNb4TeOWRYkamgakvCWEiaQVZKrBKEDKFtpG4sty/ecOL9QXZp4ant3e8fL1i8yJlcjPj0wxDhktK7h7fsSwb+qdnFssl9DOZSGmD4Xn8ipC94If1FXNfkCyv6PcHhiqSZYKHL9+jKslv/s5vcln8Nvk64bA3hCX82lWC0xkibLE6IlJLWq7J0Vy9LLBmIu8OZEuHyWt+43slrYQXqiRrUiYPGRYfPGWa8eI1pOL7HB/PzHeWXhy5dBVqXdH7BnXseFlcIq4KihBJ+xzRDZhScv+wo1GS1FW8ffMlS52y3C7h0wZlHVcRzo0iLC+IvaPrH7h7/55qtaC3Dul7shAoLl5QR8UsPVcnMNJzHI58/f4Nu+cD1o6odUqxaliNCU/dM659ZphHlDW00uJGjSst6d1IPQcemgDPPenrkukAhY9skw9K1M/AWgy0qwrfWvbuAVE0XHeC5kbTHTVS5bRTRyxqNho4GEb7imVp+OR2SXs4YESC6h3TJFjGwDpbcYgKqLDnd5j1J/S543DI6W8q0q4njZ7z+cRFppk3DbfXGXqfMK3OzOcJf5WyDA3nrGeQAnsyiLs72lOLtBKfTSAEK6E4TZGzciQxIYyGmH1QITa5wEVLmVZMISKjwUWFMIaYBQI9kxFEbYguoBKB8R4fJHmYCDrHzxMf1vd/efqLeAL/FvAfAP+OEOIPvv399W8H/18TQvwc+GvffgP8T3wAjnwO/DfAf/zndRBDoEoi+ugQ/SNjqAjLgcMc6H1gaNfEHG7LyMUmQycz3qYMzhMWKXnWsCxgEBnnLsNMCeWgyAhomSIWkTaT5HVCrjKc9SwnyVmM+GamKTb4aOmPOQd65v2Zna8JYsLqjMRlmLs9H92uuF5dsL18TS8daeIJqqSsUlJngJF9eCZ6wzJ6wpPho+9vKLMFhfWY3GNipEp7xr7HuAVONvjZkroDVwqe+hNJIzEhsnKawSnenxP6uUXZjJVJ4bmjTnJUDBxOBrAkoaA+eXTnyTyUq56r5ppqu8BeLGgPf8LTaUezlFwER5oX7LMeVxQMwx3b9RJdlMSYMQNnEphKmiFHuIzP9ciTP2GHe3ozEKLiNA9ocaJ/2vNw/pKff/2Gue05dAHjUjIROTxOjOcT/rljGS2fffQZVSEx5ZptrNm8UPh8hXcnhnPKIvngRscHSSwS5CpB9ROybpFzxo8uL4nlTFQJaR3YGkjtBVtmOiYSrVl3KdnHGd5JZB2Q3lI7xemwJ7QHqssVxcJiTzDHmfaYILqAKZ+xVSTaBbOLlDFAqZmiRDjBqI5UnNlMGdmy5sF4Fqon25SkVc7OJygfETaj5IB40pRVilcNQUqaEf7oF/8b9w9HEpkg+kAaUmbpybMPZyOsz/Bek04Se5TIkCGDo58iaaI+UI7TgM4swpdUcyBRnlQqypCh5hRNIFMSk2ZEI6ilxOTfbQT+ItyBf8Qvj/MB/t1fUj4Cf/vPa/f/UUfA086ybB45lCsmdaZ0kaQLaB+YzEDQOYdEUT8aunBmlRakAswcie7MeRzRIcU3jjR6ThK2R8PzKpCPFUs345MUP40khSZkOe6geNpMXK0qmm0G7ojmmikEMjvg1xJOT1A/MpYKcfmSbYKyIQAAIABJREFUGkUhE4Qt6ZaST+UCj+bpncCNX1F//3cZK8P55zt+53d/E7Rgrx9p+5mLnWRTbcn0LdM8cFYeb85oGzHSIJ9yaDL0N0esTzneNpReQmsxhwN8doVXGSSR6TgjtUCgGKxCZDPpYs00TyznwJ0bwTvcTY176iiXa5RcMBiLTzxyGnFhws9gJsltmrNPJMaPpF6wzE+sijXTOpKMKaV75u5Ny1NqkZtnVmrBulrTtgZ2J0Rm2D32/PDFxHxZsEosf/iHLS55YnwIZNWKdHnND370Q+SiIk4Zp8uB4ouM1eh5Hkv89MjBjFybhLDJGe4GOge+bVhvE0S9ItEOad8ztApZZ7TA8iKS2JmVs4iio16n2EEj1wlmTjDmjoPMab96B2XGun4mesU4CHS1ono9YsYneALf5WwahagsTgHrM+64oouaqU1RQZHKe6Z0xTb3RD6i++qeZHlNsw4cJ8khnnnYjdTPI/11zuX2U5K05Vjt6R4WnPcnKA40k8TmltFIyski0ST5yOgFY+KIIsN7S/SOghIzzoy5JM4ppeADdl2XlKnG+5RB7WlMDi5jFAHlPBQZblySxGfm7/AGfiVODMYQiTGwI1KcLSKZUPuJuEgYS01qInE8U009ttxQ5xvEWbCQA51wLMoVdpEzv3+GGAmDI4kZT7kkRsBbhrxBholQV2hjGWZN273n+Y9yzk/PvPrkhp2TZMMDzXbFcH7L+59kxFyxLDROHtm/6dipkeSFpydhc59ypyKXH21YrhuiecV+ekcZbrm8fc0fffUzntoB8fzEKTiyRc7HleVHH1s2xRY9zyydpn+ZcJie+VomLDtHttmSxgl2grE2yEtN7Vd03XuML1gsSn7y9pFNqOEy4ZUN+KZgHwaKPGcoDGqxolSQGMlisaRKV2iXsJscpTdMR1BVgSBn3UR+8WZHsY1oD5dNg5OOY/dEWr5AyWfU9pIfXFxwOH3FdD8wN/oDvKnMsc0zny5ueP3jlzS5ZtlXMMJf+sslqF9DesW7h2f0EtJuz9nMGJewN5p5PqMvBJ+JnO76x2TvHvgXX33BRSYIWUqhHaWrqd0ZPZz56fjMKtug5ZEhFsi5R1Urpuc1NrzFtYpT77jOLimiQVY9U3vJmM8sfufXEeMZXdc01Ypz6Rl+eqBarJBFzi5MfG+lWaiX2GlCdz1hLnmYDNlTR28euIlL+qpiYyBZNmzWmm/Oz6xGaOQV0/KZt//swBdv/xEv61u6nyiamw/QVGdqXv/2b5D+j3+f2GrOlSJ4BZlkFgX5MDAHSY5gsCugJSQO/JKpHMlkJJcNUjlMkRH6mdqMdKagyHsyEs5pQJkTPlkibUYaB7z0yGIBwy+Hgf1KaAxCYBYTutswTgey/kwSFAcbyU6WdPaIKsFWBabck+6OJKkiJJJaRkyiSXxBPntKIq4IzLFCa40cR6ZCkCRnpIbcGazqEamBssR0gud3HafjiPAWpOR+N+NDTShBmMBumMjnW9Q6khAwX2RsjKK4zKB1hHbk/HaH281oahb0DOoJ9jOzPxJU/oEX/3hkNz/xdt9yGHe05zNucyacLIvxih+KwGpVMQaLrBaovEOWDbmXBH1DuXxFXTXsHwx+6BjiGRnueY6ecWzJE8U0W4KVSGO5qjfMqWLO4HmXcBA9JpWIdIN9lZElFYKOrguUypFPklQsOD8+cXqe2DvBMD5hvWbcn7l7OtA9lpyVZ55PNLmiOvdcpiv01Y+o0fhOM5sDd+5INxiGx57WHsgKxVJnnLMrmljTFAmvAMqJxHtkvGTfv6el50LNGDmzSqEKGSfZYzLJzgYyrxB+pioU8VBBJRD2xDLtmbOUpAO9HfDhgM4EWdZQrHtWoqTwkug2hFnSC0fpJcm1xK1OGL0inmd6W3BTptSzY4gOt7MI09Luj2wXn2ATh2oURVUgVcDikCZy8gPtYNBWs9/dI7oF1hve3v8xeviavEjQaJJccjACGx25jyhrKGwgDAGRJiQ2MnkJcqCqU1SfIUQgDgXWOELeM8gOdxAkacpYAEVADp6IYRvALxqS2CMuZmazIEwj9ruvDvyqeAISExVl80TeVsyyxycOGSuKsqCfevSsSJVEDTUsPG6RUu0/IKv1yROjg03BfBQUfcm82hO8IikqQjA4WVLEiV5MIJYE55BZj7pp6DrPT775hr/64ofILGOzWSHmQJye2LUWuYjMZYvuM9LSkl9cUIaCVJTopuer7h1Jl1FvNHl/5id7w0ZVPDzd8WBPLHuN1gUXv3tLHDT3+4EwwPIWxHlJtUi51JbzCLuff06aNBwkWGkR9kvyxlGH13z6b36EHhwxH1mrhM0yh+aCi+sX9P2BRE4U2Yq9G6lCztf9wNJLvChJkwl3L0D3fN32zHpPlbwEEyhCwV007AZYhyPncQAzYsyJX/zcsSpX5EuNngzuGurDkvvzn/CsLMYY1hcL6vM3kF6Q3ySEwXK5WPMn//s/wSC5MhtUmXKeI02a8TDNVE3OZaI5iB+g+hMP7hGpwfmBfH2J9jPH9sjpOCDaAbfYokVPkVXUaUZiUzbrjPM+Q1nLWRTcrC/xC0duBbPLEHbNfDgyp5IXTc5pEshiJC0bnBWUakYV15yeP2caNOWiwuzu4DZH5oqbYsvl7TWf3/1jfvKLO27Fb/Fqe4suKnb3Jz5a5Tw+HwgxR4eU3MKbeeLh8ZlVWvDj3/19VipBbV4jjoHQtzzIGXO0kCSIuYAsQugRqcCLDJ+l1HREXWD9RCxyinRmFjlBa8wgyWOOrQVajgRfogiwWSN6QxdGxGCoU0HbFTRhxG9K+v67WYS/Ep5AwJN2CbE1jNWEDgqCInUOuolCfLhuivDIcEYERbF3jCuDjhK/MHhtaXoohMSWYPoMoVPCyaG0RYyG0UaiSlDOM8uB6EpCP0ICeoz8fHwi2eSEcaKddySu4uIWVv6SLTWlqahl88FlbxJO7hEvAjxK2sdnDo8Dp4eO433g+RjYRYkIF8RmxdX3vkfsNHFOGLKZpPBYUdD7lNANxNTzvB85+5nufKTbtcwzlErijpK+GWm/PtNPPU5qsipFqJQh0yAfKbJr8rhg3SW8vFxRrFJiV7DfD6TtCbuznPKOY1YR7MDwrqS2CZSgS/C1R21qtllJ0mQ4Y+mLJXN35u5wT3tsCTIixxtM4fCxIOla2tMz7TdPnNUHQVfGhCpdUtsFm+1LXrx6yXq54SJdUaVX7HtNnZ5IM89RGBo5oqoGazry2SHGnMkK0tOEmhxMLS0lTWlZZyVCWsYIQyU/bP0qzxgq8mEP7sN9kUZUDJlFVZYqt4hB4oBJlVCsYRCo1LDvV9TqQOwhNzP5mIF9QA4VWgtErkmyQPSvaC5/nW7WgGZRTuTbidEotMsxnaVyitR5GiPZbBUX65KbuuHi4pabpYB0Sb6tMK3HLi04RSwciXV4kZFmK0gssRoxyfLDwbNZIqJB+4AznspotJX41DCPE4ZL4uwxZDh9oqxn4kIjswWnUpLZgFUFcrYU7l9vd+D/9ySFJJQQdIHSHutTYpDkruCYdbg2UJUpWkSyfEOSemK5I3QZc2lweLpOEzQUDFTBUaWa0kAoPOakMVhEKij6D8q9MpTEOKEKCRbaomLXnXj86o9J6p5Ze+b0mXNQhG1HcJYw3fP03LKbOqapxZ0sYk6x/Qh1zrqo6LxkkZzR8YFKSq5zx4uLhiJ6RvtMfZmgT567Y+BylbHQARctDJGXL7f8xsvPuP3oFculoqo8L198Qnn5guLkqVJBfXHDjzYrimSJTBJWSiIfamy/w/jAkEaSGLiUBc1qZlUlHNcZO54Z9iN63nGykvxaY2ZHKgWiKFhmL2mkYCwa/G7Axon+2RCmhIqIUqBkgtf3JHnGi5trFhdrXtYvuKrW6LlEFg3BGQabEcUR3xSkNiGmghALmtQg5hncJbqrMEOFN5GpP+CzkUYsCNmA7545pRXNiwKrUqrqTMw0Yc5JypwoA7mU9H4iKSS1WKMvryivKjrruRvOLMLA2c209QXOeQY7cR1mMtdyGh8RU2CzthxDg88Lgg/YNNLLht14ZO8jJvScfMqrRcpyM/HRSqGTiBINvstJOkPnnuiHASNnVJ2x2TSM0WO8IlsoFlVG4i7p3BOqM4R2opmhKRwhBNJswggBc8SFS3ybYAvDoCWmKSgXK04uJ20Uk3bkXhFiQior8mlALx2baMn3KZNXSCMgl2Q+YEuDtEd8SPHiu7kDvxLhAEKQig47azI2mDSSO8FQjDTpFfJyJukzlAokiaI6p7R2olp5+qCQk2Kdn/mqdUxKkilIpOLoLDep5Fk6ypigugKXSiwnUiEhCGLHB86dMTjj+fKLE3Ptuc7WmKXlZX7Fu/07QpFSrCqE8RSpZDofeTo7cu1wakSKBbFKWScFgyooBkfNkaexYR4PJPuWV6+vyULG5YUhhkB7OOBlQ5UazoWnYUFT19Rlw4vPvk933jN6x8tXK5Z+i5MbZLCMeQWuIW4n5LOgvWjJpgVpO6AbiVcSq2byWhLKLR/nkn69pb/bcerfUSQl8TjgNg3jGDndf4VKDW3nWTQRt675mI8Zck86L3AmIV0r6npNcbT4ZcP98WcUFy/59JMCihQ1Wo7SkMiI1EvmZMd1URKaijCD3gaiLblc5ozdA0ezZykC92fBu4d3pMWKvZhZzAtcOqEYONCRlRPmkHGZZ9wnPbmBg5W8LFJklVH3EWOPFDZjn77DZyV2FziPAf3+S1xZ8voywc+R90WCOB0ppg3zqQR15Gq+Jl1AryJv38Dl93OkjyRdiisVt4sVvc5xDynff73hoCRNyIhmYK9KKpmxXS45Hfbc//QN3/u9v8Rl8T363TtEpkB7huSRYnNN+/4dNipaEUhshR0NMakQyjCLI0ELVJ4irCCznsQ52koiEkl5hEGAKXuykOKEYZaOJNd0wiJa0GeLVEuq6BhlhveSUkEfJ+rriHn7y4ffr4QRiNFzcpKyyIiupSk+iCyWCBgcwRpMFYhCok4zY5XR+wpRTHCcmYOgVYYkSFSIZG3OqTzRiAWHc48kEqvIQY2olUDdeexyglYh0oyoBqLNsLNmzhJePn+Nef1bLDcNR+fxsqAPBzh76qbm2A5opZAE/Al8nXG9WGLmHdpqfFLS2YkQK64iJNmGy5tfw2YeV0c2w8z7PtAsPugNzLsetb7gpr5gZGQY7olDzvG4Q8aOYK7oCnhdLLCpoCgauv6J1VBisw+A1DEPzGXCwkqCTpAyEMeRMk4EuSGQsFjXZOoVg1JYkeBrR36WjFlgu7pkk50otSaXa64uVmS3DVk70XvL3irM8xGTKPCOdXPNmFraAYSa6FtDmXh6uSbNDoRcIOSKNA5EbWjbFGpDJiPcKlYPOSpVdP5npCIgXUBnCdQZ8zJDv5PEruAqu8Sue5wdabIrhJ3Q88j8aChvSqosgh25J0f7hHVT0Xcz7TAgmi3az+wfO1YLqI8eleTMK4moZ0TYcExH5LSgrnpU4TgPBcsqQV6lhOMz24888m3BTknyRcHaJLReIMoF3p55TGdeLV5yHS+pljXHAMsmMD9GrmXJTEpJztpohnJF9X+3dy6htm3pQf7Gaz7XXK/9OOfcc+6zkiIpK6hFCAElTTXplPbSMg3BjoI2bETSSVdBG4IIioEoYjoqpiMoItgyMYp5FlWpx32ee/Zj7fWaz/G0sXcll8o9pGIS97nc/cFizjX2bHyDsea/xxhzjvFnOwoD3kK+1NiQmFmFl4EUZoy2Q80qiIEh5AgPMwJDLTDGEKccEyROFuh2QkiPUIFcCYa8oB4dg/c4rchjy9TU+DZxvNa87IWhV2M4kBTrZYH2ER1KktHkU0dUgbQaECLHmgxiyQQIJTFpxHQKJwqk8zRtiSsVhwRdvsENJc4d8bnBC4HzOdJB7AypLOBY3i5VDoZwA1PIKLNEniK/c9hwdfEuH40D+fGALQQcC1bNGb2RjH7gMI2YpFlUCTMoLr65J9sKopeIi4yqOiFLkpQL3litOV+VSC9YxQXH+jVWJ49YLk9vk6y8ljEpxwt3RVopjoeW480VvjD4jeXb+0u+/vzArF5SuIxpvGHpNuw7xXE7cWCgLC3zTHKJZ5pg2lhy3+B8TsxHhG0ZpozcrJkVBSdVBOXZdUdSnyCPmGZNLOesm5GegnBwCFlQFuecBEFZTdTGYtgjihbvRwZ7hXMjh77F2p6t79iljvZqwoXudvlwP1FUA7J3DFlEDwuG+jZD0zd+5wNGV8KUowuLaF7jUbvASsdpXFGtzvB5iRDndNOG4Dp0iIRKI2xk68CFUxp7wPYOZQcGV+CSZlXVlPOScAgU2Qpc5AaPHQLBb0jySGEcbmwJ01OcOKOat0hX0OgOaxTXe0HaH1HK0R4PbKcRmW43rkmyYTWtOXSRVbOgetsgtUQuThFJ4LAcQyIVHUM7sVIN62VJ9BpSRxAB5S1prZCmZJQBFQrU4PGDROqEPo50hUAKiUsBX2laOkLRMz1xpFoQ3Io2B5Un+qWCYMlFQ5zlcAAhc2JevPT+eyV6AkiYdgJXOKpMM8YWdCIPHj9oCpEoguBw5XDznKptMY1nfx1QIZC6iS72yGPC4OmTxmtJlgR1VPRZQbCWaCxm0LhMkLcJTqAPWxSCehpJJxLvEkt1ymXvObvpmX3hbV67MpQrhyh7mhDRXtLpjqJMONsg6oEyRqblM677a57WR4x5RPMDr7NwhpMnb9BlLW/Wa+ZvnfFa2XDMJpqUIesa7xKlhy1Hxq9/SJorTJGzlhXZl99k9SIwe/1t3ttc8vr8ETyeI3ZLsuJI7UG8WHG52FHMS6Qp2Xc9ctYjXMKHp2TdlmXxmMzuuOkPjPueMOaY1vH4ZMbelMyVpjclY9/j/ZpYXGKuC/ysIlZHtM6p8gXTNDKKOUVfoNwlmcw4fhQJWY073nA1vcvKdozLHHst6OXA62cryptTbIoE5XAR1ATvtR2PFyuefvE1ql3P+xcRvfmY+RsNpq+QYU66eRdvNW6muHi350zOqB6XlNEjhEGbOVbsWZTnxO6GZ6sZdhipnGB3VIQwUJ4vkRmMTUaZgSFxOj9HxpyL7gKXZujSUQvHONU0K8ssn6O2W2glbplRDYb3v/OC+WxG/oU30Eoyd4GPrxNfeOfIcvYm2UGwfdSzyCV2aSjSDDkH9AnBBEyUPNJzssoy2QJpErITDLsRExJZIRG5wQtHVjSoKPF5iek7JgNlXNIPPXVRMkyWeZfdZhJQR9QYYWWILmJMTcqO5GNkyhxUhrqbOL789rt/EokpTpR9YJe25AO4UBAHCHuHzgI9nsVjR5WBzQNTl9FrCCniCgs+IDPQIUGVM8smlAhgByIjHg8knAkUwWDLhInnKJsIRWRwE5MVSG+ICtLUw3XEH3qqtWN2Khk9XBwG+gIujwOb0WO1ZZatWD99hm48z07OkfWKx7JE7QxVnVEWGW8++WFmT19D6jnboOic4fnNgOq2jH5kSpZZL+kQvLi5YXQZmYIUNaevn3C62rFYN7CuKdIeZoFGSJKCPtxQbgXxoMhaQXaiMVPNNApifE5qPcNwYBAwxsC+OyJlD2IAG5mdj7T9jum4RfQ3jPaSdhPpcdwMHRdXE9N+x2AdcqiI/YaTc03Z17yYNmRS8pqILE4XPKkqTP2U8FHH/rildoZjV2H1hFQ79DRR1ZasGanbnje/dELWnLA3c4Z8YhsTC7Egk7cvUw0+4YVGDZ5VSvhk0dozqwumXKBnE8tqRqY1tT5hKipOntW40wInLqhkjokSYUtCLjFtzrwZGHJJDJEqO8UXLd2HI30aqbsW7zS7IUEM+F1HiopiLjiOHl8miqlncTajjwPo57iqQXuP6hvEfiJKD4sGKocyCqEjU3RsyonHbzxG2QatHc5aZJGhpSMLBWkUeCWJRSL0E1M4EtWeosgohwzNnkUp8dJTSI03Ee0cjXQsGo3yC2SIuEKTtZpJ1+gxUVhP0H9gIe/v8UoEARkSiyBwesZ8VEg1Q2QjQ2VQBpwQjFNg34LVDj0KrM/BJranHuVzWBYEPdAOihQsxzEy1REX7O3EI4KZySlDJLlEkSS+vUJ4kGpNvrqNwqLu2O533IhAn/c4YzBCky3OOatKQoAsCYq8gbbAVImqKnClgSAx8xpRLdiXkpA5bLVmiIJqFVjEGtFZ9s83XH3nPQ7blm1WsVANQ1C8u7d8eHlNNRUsFwo7X1OXM7oGYnXOSVExxoG+hThEjh8mrq1gGPdsFIhe0ZGQH18jsh5xrFBKkWSNdfI2W3O7o5Rn+BG+fbXjGoXvctwoef6iZ3ccCAeHvu54fhgJm+dEe2Df7QhtoAuW5DT7LRxnA0tRY6ShWwUsiidPn5HNG0RWM/SC/WBJdiSLgtPqHerTM4rjKQFD/khS1k8QUSF0onAFupYcpKfIWqIBmRzr0eNmR6p1Q/MjS2RzCsIw6xzS1LRdj8SjT2uiPkHcSE6FQTmNO/Y0vqMooRGC/CzQbRIMFT7MQCpKrxizI6VR9FVC+p6SgmqROI478miIKLpuot8bhpSRwkDRnKBHQ2Uc3zYJpyZKHNFHTuYZB3lCUAuUTxwPA/Fig+suyZ+cE1yNFjmZ7DBG4CtPmedkvkf0gVgrBIa8kqQRVNEy6YxhyNCxwGcJX97uEhU6zz5o4tBirUT1B0IhCHJCFBByjZQv31nolRgOJKUY80glEqnwtMuOzC7IJ4evSozriSiSA3dMRFcyZR3EAFcT7a6lVZZoFXoN3ARgRTrcoDIJtoFiwk8JkQvSMGJtYGYUtmiw+w7fKKJ3lDERSkOlFd3o0RQUMuMwHTkee946XSK6I1OSZEnx4Xc8b73ekxtDCIYiQX5iSWNFsQjM7ER1/jqHSyiyEfqJ+VnOPCzolUPZkXEb6bOeOlrOHj9jshPvt7ddfXtasRoV1+P7yB/+MrW/nbEPPmKzhPQtzE5ZmAJRe2ZlQWpP0cqyzy4IU0Moek6KHB0N23zOi82HFMmxNDluY3GngYOzrMuJ7pBwdmTTfczgK8pswfPLDxA28fT8hCoq1JMF1+GI0HNKoenNiFYZSmW0R8F8sSK9pch4Tv1GTh5ziuVjUB7rBVZtGLeSUoIfLIt8wSYIDv2eXBQot+Lm+XewOqfycKOhOCqW5xWyzEk+Z58rmFbU3QRpzTROCHvJ8URgngTie4Lz158x2Z4P3r1mvrR4NXDc5tRqjgwerwTdLpKvMs5lwYf+a8SwotQ5W3WF6Q1ZBYQDmzFRNhmrmUAYg+E1TlXL7EdOOZPPsHLkcLMnf/IOrz99k+uDJxt6XJGwu8iy1DgifT4xK2EXO3A5k1eMKqdQt0+nRDFjVowED5qM4aZjkNdorai6xGAkfRhodMZw7BFeE5ZrtOoQStOIFtfVOBdQcSQ0S1QaCVN46f33agQBCdImfCFZl6dsbzwyE6giRyLgKAh1ThCCnIkhHJC9wkVLTBWp6GGYY/tIclckJCZv0Rb6UiOPPdJrrAFjO6KWKK/o0CSOlKVgGgzzzLBLgcok/BHIDtjMs8kK8qsOzZHjZuRm6NnsHX5KqEzjlm8wEzWqKNm0lplzZHOJFA1xtUY1DXQ7rp3iSS5YzGZoc8Y3n78gn410o6XMZqSqQdiJj48HqjJn7UsyLdiohiwTPLrc0wpBszwQWZDySCnW9P2Gfd1TxILVUBMWHreriNWatYsMIjJGTzYazsqKfmYofIlIC9Lwgn5b0BQ1i1oTm56LbyncNCfpgSwp1FKxGEvmz84JXaDINMJBGhLD9pJhETD7inUD0wL8tkccArNn5ziVmLnApI7YY6CcHLttJJtpnIfRw2E5UNiATwV67hjzgfq0onGC53GOODxnZsJtAs99CXlgNgn6RYHWkWypOPocpR1PZyWHraPVl8xZc1SJMo8MBQg3J5YHki5o43SbZ7CaCAcYReJ09hglWxRQHSqQE8V5hhY5b5wUvJjOmNcNWVJcyhvsuGWRTvAyEjuNeDvD+5GssGSXFV1dI68GZktNP7WEXjF/c83NpseTEKHDqNsnAAKFV44sCLr9Gbq8AA+xKVG2JoQReyKJOw9VTWc75uqcXh1wmSAOEcmIjRU6j6SkQFRUg2cYEqKxvGyn0VciCBChWRcUKWObPKUWLLWgDC17LxnygpkXHGcdqo+IrELRk/eKLk1k1uCFxYYRKQypjExywhYls1GA8kwpI6QJHQpiSCQhEXFEu4RUC2TZc0wgtKGfgBQhTdjWgetR0rLZ9nRtItSOmcjJZpbFoubZl56hPiiI2Z7Re6I0NFmgnFe0Q8fTsOc6WopgEY9fh5MGe8iYLwtsr7CD5XSV8EWNGAvKdofeTIiTjqzOSH5G5jsIj5GPSibrgCOreUIOGTuZ8G2OajWHJyPi2qBqxRk5mJIibXCXI/v1wDzUrBePqNuRthQMeUYVc4yaEWaGVbel+OKS64sjW7/BKcl5m1FmJaUoCU1LrTWlkhzcxJjnLAdNXymeHxLnwTJ5hXdHxpChQsSbM2z3gmKs2S4zoh+gEMh2yTS7IV1WTDJy/sjgZcZMLHFnC7rjnllvmeKcmI9UsQLZ4bM1yWRUKuAWpzC26DHBtGCtM44BbFmzyjVpWtI/huY0R+326A7ysWAsDItgONxMxGUik5pFAy5GnBF4Is6v0MPIlBVUck5aeDJl2YmRdZO4uNR0WYdUFTNmLPyCURlgSemuycYJuQCbF1xdTbzXf4MvX/4AjAVlVeAyx2QFlfEMDkoDoxooVUYSgsktmcs9flGQ9o4wKIQqWLe3r0n7akOyBWr0VDons4Jr5UkqI/cDPmn2o6UQknFqgMOn3n6vRBCQUtCbAjsNmEkRc02YPFeFoSkFQhbYaPFRMuTcbubQa0bdYYYMWWhSTISTgGgFxIjUkgQcs0hylqrpycb+AzHPAAAL50lEQVQAyrAwkm5weAG61rRtS8wKquzIdNREkaHzglolLq4uOX/6GrNmztUBTHaDcGcYM5KfavLH73D43R27Yc9ri4Y3nn6JVdNQlTmbactjMm62PYtiwWgcIRzZXB7p9xnCGILdEgbPux95Zr5ncaL4M196k6eLJdbmbEXP/NSwMK+zGTreOSYoM/pLwVXfks0TJ4s5m+GabmqQYUY57JHS4LIZwm9IQvF8fuTUN5SPTjDPC/pnB+RhYFYYDt2S0xQ42pHlWcFr/pxl1XFzSEwhks3fRs87op9wxSNSDst+BFVQvalZTZqDNBxuOt69+IBpdJhoeeutZ9yMGQd/xUIvyFYBbzW6bPDDgfbomK8eo88t1p7QOwEbyaR34DRplFRNoj2p6Lodpc7Q4cjqaU7hDLoq0KGjsw4nS5I5cOl2jEPExEhUE6Nz1GMPfs3z3iOzEdV41suIbTOak5zOOILU5NUpdmw5OMv5WUmuAlLNsOGCY1/woz/0ZbppILcBeyyIp4FaOJZrRbPW+HnDPCr2ZzXyekHqeva5QcaB5HoufvNjvvGrXyMPkqOak3UXlBRM0YMwyCLQHBwIg8skS21pg0INjp7I46mgU5LNPJEfDgx6hgyKKe6J/pQhWDQG0U0gBEoKikKjU0HmLS9bQ/RKTAyKCPWoSfWIXtQUZkWWO7QUOEqOyqOiJLMREyV6GMmTwWiY1IH2MNEdB9QuYtJEiDnRlxRDpBwkep4xaUHUgiqOWOuQ8xKlMtygiS4gOoltoTSGme8pDgGcIfmB1F3yYrvhODvSeYEWLUMKnE1LoCXmkfmjN5D1E7TMyP2ED1sSGdc+sXux4dp9SJo6bvY5w03kcPER9uZ9/KRgXSCzHc/bkesXEZMrRCqYLxcsqxnVqKGP5Czoo2RKlmEOs/LujbHeo5lT1B1Vv8EsK7y7XamWZjmDCTxSK3JRYjtDW0Y4lOQiUpmG9WLgIlwwuonDR46PpwN+CLeLpNwKPRtga+jCDrF5zjRuee4ih3xPdlS0+YLWBETTEnxgHwIqN+wuLON0pGgtAUvKKmrtUM4iZcO8OeHNuoA0UdiI2mq8vESEnLKamBUarxqSjQxjYFXWxKZCD4lyUSHWgkk1xOr2LdFynZBXBU4nUldytJbDscfvJ4JPrOPI5Dw9kegFpuzZqEDbCcxo6ZwnMzldAmkHRJujVInPBYMYWCjHYt2wXJXkciILkoI1ihKGJYONuHmiVBmKBaFZkE8CcbQ0jx0/8oUf48vPfojWePS4ZVgZ/NRhqgV1fbs3hhtrxkUkWUumEtrMUL1FZWuGTDGpCeXtbZ6MQTLZgVKURA/ZSUJFQ60FQgUmM4MYmBiR4uW3+qsRBEQkGIHpGrwH1/f0LmcWK2bNQDkOpGBJncLLgkEIhJ+oXXObolk7ZmVPO/fUTc1CT4gUmZCkSpBZBd1EpSODk/RVxLcDOEnqLGWtIbN4r2mniSHPCMtIayOtiDzfj7hokdqQ7RMffPsjnjxe4x+tUZ3AZxmNKpiVBcZvkHnCXhm4PsLNBZxYjJ1xbCxn51DEGq8l3aAYZSRLPfP5mi9+oWL+hkVtNB+3B0abKEtJLS1jmCh8oB41KmWU2mCevU4WTjmhplopUqhxs5LjoFCZR4Q9wVao3qPdbYqz43iBuNmyCx9iUsnVtmfftrid4+a4xe5a8uHAlCsO6xXhJNEXGcNKQigYskgXBqLoEX3iW7sJN7acZpFcVsjZM+aMXE89l7sNRYqE0pHvJ4bjjqSW5HlJmzzZmWNsegrxhJCDqGpiuaJY1sRJIDPHLE8oJ8EGYn7E24xgOq7EAWknxJSBrAj+wLBX9CiyFAlF4mo3wBAhBJIb8KKgqhc8zU6QmWIaauQ1YMHbHcYkclHReItwGVMNmJHZsCSKxJg03WZHkC2L+YJ6ptG5QOcnDLJnlkZskCgBGRNyoWnWJ6h6TePf4PU3FV/8sz+IUeDzgL50cFrg2ohH4i3EOkPhyI3mQgosE2PhydSEUAkrI/VQIYzAy4QICREH5rJlvISoem6SI6qCKu4ZRUGuIarhpfffKzEcSBHyNNLNSko6nIiI7JxObDGbmjSUxOUVWudwGCkWc6ah58UQ6eURkwTeV8xMjogKOR+pxES4Egy9Rpee5CStk+gShM3RosNLyXLd0A4dOpcImWH0yOgzbGqpg+bDb77g6VsnmEFgWNC8lbN8fsrp+hTRzGiHgZv3XjB/lOOF43B54OpmT0CxejRjZkoeFSe8OE6493s+7D9mLhvEmJAiYFpHsZxj9ILX1vF2CHCzY17lXPd7ViljyDP0UTBlkYsxUlxl+MLB0EJxpF7MMK3GZBM9A/s48GjytG0Evk15OkdNHne15f1v9xRZYJgLtDmAGtjuR467A+MFqDdgbs7x7Za0b+mlRWQV7ByxmqhoIOXIyw/4uKgxaeJ923L2vqZbzajq91gezmkXkjMtuRosQkoqnSGkpus+QpuSE2OQWcEoBbLylBHKmLjOnpCeX5LyNXW+Y/PeJWHYMmt+gBgbdD6w85q3Q0NMEMUH2FBhygVVChjnMfMFRenw31LESkJdszxbEk4b0mbPlT2y6tYYfSSdWg6j5Xo0VHFLZ0vMrKFDoEykdRGfrlDdcDu09Dnj5Y4wr+iOsL+5Zn4y50Q9YbGI3OyuuTSCzTjxWC8wDJw4g28s5fyHCf3H5Kmn83NctUf2Gl0cEZlAdDV+FhGDvR3/z9Lt48NtRFWWzhWIydBNA0JD5jQxz7Ay0gbPrMqwsgc0NhtJuWF+E8EIVGFg/+mbjYrb3cDuFyHEFbdzl9f37fLH4JTPtj989uvwWfeHP906vJlSOvvewlciCAAIIX4tpfSj9+3x/8pn3R8++3X4rPvD/dThlZgTeOCBB+6PhyDwwAOfc16lIPDP71vgj8ln3R8++3X4rPvDPdThlZkTeOCBB+6HV6kn8MADD9wD9x4EhBB/RQjxdSHEN4UQP3vfPt8vQoh3hRC/eZeW7dfuytZCiP8ihPjdu+Pqvj0/iRDiF4QQl0KI3/pE2ac6i1v+yV27/IYQ4iv3Z/57rp/m//NCiI++J0Xed//29+/8vy6E+Mv3Y/37CCFeF0L8NyHE14QQvy2E+Dt35ffbBimle/sACvgW8A6QAb8OfOk+nf4I7u8Cp99T9g+Bn707/1ngH9y35/f4/QTwFeC3/jBn4KeA/8RtCrofB37lFfX/eeDvfcq1X7r7PeXA23e/M3XP/k+Ar9ydN8A37jzvtQ3uuyfwY8A3U0rfTilZ4JeAr96z0x+HrwK/eHf+i8BfvUeXP0BK6b8D35uL6mXOXwX+VbrlfwDL76aivy9e4v8yvgr8UkppSil9h9sEuT/2pyb3fZBS+jil9L/vzo/A14Cn3HMb3HcQeAp88InvH96VfRZIwH8WQvwvIcTfvCt7lO7SsN8dz+/N7vvnZc6fpbb523fd5V/4xBDslfYXQrwF/HngV7jnNrjvIPBp2Y4/K48r/kJK6SvATwJ/SwjxE/ct9CfMZ6Vt/hnwBeDPAR8D/+iu/JX1F0LMgH8H/N2U0qcv8r+79FPK/sTrcN9B4EPg9U98fwY8vyeXPxIpped3x0vgP3Db1bz4bnft7nh5f4bfNy9z/ky0TUrpIqUUUkoR+Bf8fpf/lfQXQhhuA8C/SSn9+7vie22D+w4C/xP4QSHE20KIDPhp4Jfv2ekPRQhRCyGa754Dfwn4LW7df+busp8B/uP9GP6ReJnzLwN//W6G+seB/Xe7rK8S3zNG/mvctgPc+v+0ECIXQrwN/CDwq/+//T6JEEIA/xL4WkrpH3/iT/fbBvc5W/qJGdBvcDt7+3P37fN9Or/D7czzrwO//V1v4AT4r8Dv3h3X9+36Pd7/ltsus+P2v8zfeJkzt13Rf3rXLr8J/Ogr6v+v7/x+4+6mefKJ63/uzv/rwE++Av5/kdvu/G8A/+fu81P33QYPbww+8MDnnPseDjzwwAP3zEMQeOCBzzkPQeCBBz7nPASBBx74nPMQBB544HPOQxB44IHPOQ9B4IEHPuc8BIEHHvic838B2RqYoYexeKcAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Boundary attack: 100%|██████████| 1/1 [01:55<00:00, 115.96s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 2600. L2 error 2572.478 and class label 866.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Boundary attack: 100%|██████████| 1/1 [01:54<00:00, 114.90s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 2800. L2 error 2430.8635 and class label 866.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Boundary attack: 100%|██████████| 1/1 [01:52<00:00, 112.77s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 3000. L2 error 2310.2468 and class label 866.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Boundary attack: 100%|██████████| 1/1 [01:52<00:00, 112.94s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 3200. L2 error 2190.2302 and class label 866.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Boundary attack: 100%|██████████| 1/1 [01:54<00:00, 114.96s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 3400. L2 error 2091.3186 and class label 866.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Boundary attack: 100%|██████████| 1/1 [01:55<00:00, 115.80s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 3600. L2 error 1989.1289 and class label 866.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Boundary attack: 100%|██████████| 1/1 [01:52<00:00, 112.55s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 3800. L2 error 1911.7917 and class label 866.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "attack = BoundaryAttack(estimator=classifier, targeted=True, max_iter=0, delta=0.001, epsilon=0.001)\n",
+    "iter_step = 200\n",
+    "x_adv = np.array([init_image[..., ::-1]])\n",
+    "\n",
+    "for i in range(20):\n",
+    "    x_adv = attack.generate(x=np.array([target_image[..., ::-1]]), y=to_categorical([866], 1000), x_adv_init=x_adv)\n",
+    "\n",
+    "    #clear_output()    \n",
+    "    print(\"Adversarial image at step %d.\" % (i * iter_step), \"L2 error\", \n",
+    "          np.linalg.norm(np.reshape(x_adv[0] - target_image[..., ::-1], [-1])),\n",
+    "          \"and class label %d.\" % np.argmax(classifier.predict(x_adv)[0]))\n",
+    "    plt.imshow(x_adv[0][..., ::-1].astype(np.uint))\n",
+    "    plt.show(block=False)\n",
+    "    \n",
+    "    if hasattr(attack, 'curr_delta') and hasattr(attack, 'curr_epsilon'):\n",
+    "        attack.max_iter = iter_step \n",
+    "        attack.delta = attack.curr_delta\n",
+    "        attack.epsilon = attack.curr_epsilon\n",
+    "    else:\n",
+    "        break"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Unsquared Images"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Boundary attack supports inputs of unsquared images. The code in the following cell describes an example of creating a Resnet50-based classifier to attack unsquared images."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```python\n",
+    "# Adjust image shape here\n",
+    "image_shape = (224, 150)\n",
+    "\n",
+    "mean_imagenet = np.zeros(tuple(list(image_shape) + [3]))\n",
+    "mean_imagenet[...,0].fill(103.939)\n",
+    "mean_imagenet[...,1].fill(116.779)\n",
+    "mean_imagenet[...,2].fill(123.68)\n",
+    "\n",
+    "model = ResNet50(weights='imagenet', input_shape=tuple(list(image_shape) + [3]), include_top=False)\n",
+    "\n",
+    "def _kr_initialize(_, dtype=None):\n",
+    "    return k.variable(value=np.random.randn(np.prod(list(model.output.shape)[1:]).value, 1000))\n",
+    "\n",
+    "head = model.output\n",
+    "head = Flatten()(head)\n",
+    "head = Dense(1000, kernel_initializer=_kr_initialize, bias_initializer=keras.initializers.Zeros())(head)\n",
+    "new_model = Model(inputs=model.input, outputs=head)\n",
+    "\n",
+    "classifier = KerasClassifier(clip_values=(0, 255), model=new_model, preprocessing=(mean_imagenet, 1))\n",
+    "# Then call classifier.fit() to train the new weights\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/adversarial-robustness-toolbox/notebooks/attack_decision_tree.ipynb b/adversarial-robustness-toolbox/notebooks/attack_decision_tree.ipynb
new file mode 100644
index 0000000..dc70a77
--- /dev/null
+++ b/adversarial-robustness-toolbox/notebooks/attack_decision_tree.ipynb
@@ -0,0 +1,318 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# ART decision tree classifier attack"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This notebook shows how to compute adversarial examples on decision trees (as described in by Papernot et al. in https://arxiv.org/abs/1605.07277). Due to the structure of the decision tree, an adversarial example can be computed without any explicit gradients, only by traversing the learned tree structure.\n",
+    "\n",
+    "Consider the following simple decision tree for four dimensional data, where we go to the left if a condition is true:\n",
+    "\n",
+    "                    F1<3\n",
+    "           \n",
+    "              F2<5        F2>2\n",
+    "                \n",
+    "         F4>3     C1    F3<1     C3* \n",
+    "             \n",
+    "      C1     C2       C3    C1  \n",
+    "      \n",
+    "Given sample [4,4,1,1], the tree outputs C3 (as indicated by the star). To misclassify the sample, we walk one node up and explore the subtree on the left. We find the leaf outputting C1 and change the two features, obtaining [4,1.9,0.9,1]. In this implementation, we change only the features with wrong values, and specify the offset in advance."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Applying the attack"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "from sklearn.datasets import load_digits\n",
+    "from matplotlib import pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "from art.attacks.evasion import DecisionTreeAttack\n",
+    "from art.estimators.classification import SklearnClassifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0 1 2 3 4 5 6 7 8 9 0 1 2 3]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.colorbar.Colorbar at 0x7fa24585ca10>"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "digits = load_digits()\n",
+    "X = digits.data\n",
+    "y = digits.target\n",
+    "\n",
+    "clf = DecisionTreeClassifier()\n",
+    "clf.fit(X,y)\n",
+    "clf_art = SklearnClassifier(clf)\n",
+    "print(clf.predict(X[:14]))\n",
+    "plt.imshow(X[0].reshape(8,8))\n",
+    "plt.colorbar()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We now craft adversarial examples and plot their classification. The difference is really small, and often only one or two features are changed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Decision tree attack: 100%|██████████| 14/14 [00:00<00:00, 1546.08it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[6 4 4 6 6 4 1 2 4 4 6 4 6 4]\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7fa24570fa10>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "attack = DecisionTreeAttack(clf_art)\n",
+    "adv = attack.generate(X[:14])\n",
+    "print(clf.predict(adv))\n",
+    "plt.imshow(adv[0].reshape(8,8))\n",
+    "# plt.imshow((X[0]-adv[0]).reshape(8,8))  ##use this to plot the difference"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The change is possibly larger if we specify which class the sample should be (mis-)classified as. To do this, we just specify a label for each attack point."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Decision tree attack: 100%|██████████| 14/14 [00:00<00:00, 1073.48it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[6 6 7 7 8 8 9 9 1 1 2 2 3 3]\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7fa245684f50>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "adv = attack.generate(X[:14],np.array([6,6,7,7,8,8,9,9,1,1,2,2,3,3]))\n",
+    "print(clf.predict(adv))\n",
+    "plt.imshow(adv[0].reshape(8,8))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, the attack has an offset parameter which specifies how close the new value of the feature is compared to the learned threshold of the tree. The default value is very small (0.001), however the value can be set larger when desired. Setting it to a very large value might however yield adversarial examples outside the range or normal features!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Decision tree attack: 100%|██████████| 14/14 [00:00<00:00, 1586.65it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[6 4 4 4 6 4 1 2 4 4 6 4 4 4]\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.colorbar.Colorbar at 0x7fa24562fd90>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "attack = DecisionTreeAttack(clf_art,offset=20.0)\n",
+    "adv = attack.generate(X[:14])\n",
+    "print(clf.predict(adv))\n",
+    "plt.imshow(adv[0].reshape(8,8))\n",
+    "plt.colorbar()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/adversarial-robustness-toolbox/notebooks/attack_defence_imagenet.ipynb b/adversarial-robustness-toolbox/notebooks/attack_defence_imagenet.ipynb
new file mode 100644
index 0000000..935d1c9
--- /dev/null
+++ b/adversarial-robustness-toolbox/notebooks/attack_defence_imagenet.ipynb
@@ -0,0 +1,1893 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Basic workflow with ART for evasion attacks and defences\n",
+    "\n",
+    "In this notebook we will show\n",
+    "- how to work with a Keras image classifier in ART\n",
+    "- how ART abstracts from the specific ML/DL backend\n",
+    "- how to apply a Projected Gradient Descent (PGD) evasion attack against that classifier\n",
+    "- how to deploy defences against such attacks\n",
+    "- how to create adversarial samples that can bypass those defences"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Install and load prerequisites\n",
+    "\n",
+    "You can preinstall all prerequisites by uncommenting and running the following cell."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import sys\n",
+    "# !{sys.executable} -m pip install adversarial-robustness-toolbox==1.5.1 tensorflow==2.3.1 Keras==2.4.3 matplotlib==3.3.2 ipywidgets==7.6.3\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting git+https://github.com/nottombrown/imagenet_stubs\n",
+      "  Cloning https://github.com/nottombrown/imagenet_stubs to /private/var/folders/_n/8rjttmq57l7b1bym2lt7d7xm0000gn/T/pip-req-build-eu272ttv\n",
+      "  Running command git clone -q https://github.com/nottombrown/imagenet_stubs /private/var/folders/_n/8rjttmq57l7b1bym2lt7d7xm0000gn/T/pip-req-build-eu272ttv\n",
+      "Requirement already satisfied (use --upgrade to upgrade): imagenet-stubs==0.0.7 from git+https://github.com/nottombrown/imagenet_stubs in /Users/mathieu/Documents/git/emptyvenv/lib/python3.7/site-packages\n",
+      "Building wheels for collected packages: imagenet-stubs\n",
+      "  Building wheel for imagenet-stubs (setup.py) ... \u001b[?25ldone\n",
+      "\u001b[?25h  Created wheel for imagenet-stubs: filename=imagenet_stubs-0.0.7-py3-none-any.whl size=794838 sha256=5b9b8d262457de69b9ee4fcadcab3132fa5c4044499848f7ef5e5e41ba671df1\n",
+      "  Stored in directory: /private/var/folders/_n/8rjttmq57l7b1bym2lt7d7xm0000gn/T/pip-ephem-wheel-cache-64ze3dd5/wheels/33/0f/5a/c83688c23a05eb9e88527a8944da56dbe007c86f534b0c1dad\n",
+      "Successfully built imagenet-stubs\n",
+      "\u001b[33mWARNING: You are using pip version 20.1.1; however, version 20.3.3 is available.\n",
+      "You should consider upgrading via the '/Users/mathieu/Documents/git/emptyvenv/bin/python -m pip install --upgrade pip' command.\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Load basic dependencies:\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "\n",
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "import sys\n",
+    "import numpy as np\n",
+    "\n",
+    "# Disable TensorFlow eager execution:\n",
+    "import tensorflow as tf\n",
+    "if tf.executing_eagerly():\n",
+    "    tf.compat.v1.disable_eager_execution()\n",
+    "\n",
+    "# Load Keras dependencies:\n",
+    "from keras.applications.resnet50 import ResNet50, preprocess_input\n",
+    "from keras.preprocessing import image\n",
+    "\n",
+    "# Load ART dependencies:\n",
+    "from art.estimators.classification import KerasClassifier\n",
+    "from art.attacks.evasion import ProjectedGradientDescent\n",
+    "from art.defences.preprocessor import SpatialSmoothing\n",
+    "from art.utils import to_categorical\n",
+    "\n",
+    "# Install ImageNet stubs:\n",
+    "!{sys.executable} -m pip install git+https://github.com/nottombrown/imagenet_stubs\n",
+    "import imagenet_stubs\n",
+    "from imagenet_stubs.imagenet_2012_labels import name_to_label, label_to_name"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load images\n",
+    "\n",
+    "We are going to load a set of 16 example images for illustration purposes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "images_list = list()\n",
+    "for i, image_path in enumerate(imagenet_stubs.get_image_paths()):\n",
+    "    im = image.load_img(image_path, target_size=(224, 224))\n",
+    "    im = image.img_to_array(im)\n",
+    "    images_list.append(im)\n",
+    "    if 'unicycle.jpg' in image_path:\n",
+    "        # get unicycle index\n",
+    "        unicycle_idx = i\n",
+    "images = np.array(images_list)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The images all have a resolution of 224 x 224 pixels, and 3 color channels (RGB)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of images: 16\n",
+      "Dimension of images: 224 x 224 pixels\n",
+      "Number of color channels: 3 (RGB)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Number of images:', images.shape[0])\n",
+    "print('Dimension of images:', images.shape[1], 'x', images.shape[2], 'pixels')\n",
+    "print('Number of color channels:', images.shape[3], '(RGB)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As default choice, we are going to use the unicycle image for illustration purposes. But you could use any other of the 16 images in the following (just change the value of the `idx` variable)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 576x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "idx = unicycle_idx\n",
+    "\n",
+    "plt.figure(figsize=(8,8)); plt.imshow(images[idx] / 255); plt.axis('off'); plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load ResNet50 classifier\n",
+    "\n",
+    "Next we are going to use a state-of-the-art classifier on those images."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This loads the pretrained ResNet50 model:\n",
+    "model = ResNet50(weights='imagenet')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's look at the prediction that this model yields for the selected image:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From /Users/mathieu/Documents/git/emptyvenv/lib/python3.7/site-packages/tensorflow/python/keras/engine/training_v1.py:2070: Model.state_updates (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "This property should not be used in TensorFlow 2.0, as updates are applied automatically.\n",
+      "Prediction: unicycle, monocycle - confidence 0.82\n"
+     ]
+    }
+   ],
+   "source": [
+    "# We need to expand the input dimension and apply the preprocessing required for ResNet50:\n",
+    "x = np.expand_dims(images[idx].copy(), axis=0)\n",
+    "x = preprocess_input(x)\n",
+    "\n",
+    "# Then apply the model, determine the predicted label and confidence:\n",
+    "pred = model.predict(x)\n",
+    "label = np.argmax(pred, axis=1)[0]\n",
+    "confidence = pred[:,label][0]\n",
+    "\n",
+    "print('Prediction:', label_to_name(label), '- confidence {0:.2f}'.format(confidence))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So the model correctly tells us that this image shows a unicycle/monocycle, which is good :-)\n",
+    "\n",
+    "Next we will create an ART KerasClassifier wrapper around the model. <br>\n",
+    "We need to take care of the `preprocess_input` logic that has to be applied:\n",
+    "\n",
+    "- swap the order of the color channels (RGB -> BGR)\n",
+    "- subtract the channel means"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from art.preprocessing.preprocessing import Preprocessor\n",
+    "\n",
+    "class ResNet50Preprocessor(Preprocessor):\n",
+    "\n",
+    "    def __call__(self, x, y=None):\n",
+    "        return preprocess_input(x.copy()), y\n",
+    "\n",
+    "    def estimate_gradient(self, x, gradient):\n",
+    "        return gradient[..., ::-1] "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create the ART preprocessor and classifier wrapper:\n",
+    "preprocessor = ResNet50Preprocessor()\n",
+    "classifier = KerasClassifier(clip_values=(0, 255), model=model, preprocessing=preprocessor)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we will apply the classifier object to obtain the prediction.\n",
+    "\n",
+    "**Note:** we have to swap the color channel order (from RGB to BGR) before feeding the input to the classifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Prediction: unicycle, monocycle - confidence 0.82\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Same as for the original model, we expand the dimension of the inputs.\n",
+    "x_art = np.expand_dims(images[idx], axis=0) \n",
+    "\n",
+    "# Then apply the model through the classifier API, determine the predicted label and confidence:\n",
+    "pred = classifier.predict(x_art)\n",
+    "label = np.argmax(pred, axis=1)[0]\n",
+    "confidence = pred[:,label][0]\n",
+    "\n",
+    "print('Prediction:', label_to_name(label), '- confidence {0:.2f}'.format(confidence))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So through the classifier API we obtain the same predictions as from the raw model, but now we have an abstraction from the actual backend (e.g. Keras).\n",
+    "\n",
+    "The classifier wrapper allows us to call other functions besides predict.\n",
+    "\n",
+    "For example, we can obtain the **loss gradient** of the classifier, which is used in many of the algorithms for adversarial sample generation:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 576x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "loss_gradient = classifier.loss_gradient(x=x_art, y=to_categorical([label], nb_classes=1000))\n",
+    "\n",
+    "# Let's plot the loss gradient. \n",
+    "# First, swap color channels back to RGB order:\n",
+    "loss_gradient_plot = loss_gradient[0] \n",
+    "\n",
+    "# Then normalize loss gradient values to be in [0,1]:\n",
+    "loss_gradient_min = np.min(loss_gradient)\n",
+    "loss_gradient_max = np.max(loss_gradient)\n",
+    "loss_gradient_plot = (loss_gradient_plot - loss_gradient_min)/(loss_gradient_max - loss_gradient_min)\n",
+    "\n",
+    "# Show plot:\n",
+    "plt.figure(figsize=(8,8)); plt.imshow(loss_gradient_plot); plt.axis('off'); plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create adversarial samples\n",
+    "\n",
+    "Next, we are going to create an adversarial sample. <br>\n",
+    "We are going to use **Projected Gradient Descent (PGD)**, which is one of the strongest existing attacks. <br>\n",
+    "We will first perform an **untargeted** adversarial attack."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "472bb80f0d704e9784a544f262cc00f2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(HTML(value='PGD - Random Initializations'), FloatProgress(value=0.0, max=1.0), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(HTML(value='PGD - Iterations'), FloatProgress(value=0.0, max=10.0), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 576x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Prediction: mountain bike, all-terrain bike, off-roader - confidence 1.00\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Create the attacker:\n",
+    "adv = ProjectedGradientDescent(classifier, targeted=False, max_iter=10, eps_step=1, eps=5)\n",
+    "\n",
+    "# Generate the adversarial sample:\n",
+    "x_art_adv = adv.generate(x_art)\n",
+    "\n",
+    "# Plot the adversarial sample (note: we swap color channels back to RGB order):\n",
+    "plt.figure(figsize=(8,8)); plt.imshow(x_art_adv[0] / 255); plt.axis('off'); plt.show()\n",
+    "\n",
+    "# And apply the classifier to it:\n",
+    "pred_adv = classifier.predict(x_art_adv)\n",
+    "label_adv = np.argmax(pred_adv, axis=1)[0]\n",
+    "confidence_adv = pred_adv[:, label_adv][0]\n",
+    "print('Prediction:', label_to_name(label_adv), '- confidence {0:.2f}'.format(confidence_adv))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, we will perform a **targeted attack** where we pick the class that we want the classifier to predict on the adversarial sample. <br>\n",
+    "Below is the list of labels and class names - make your pick!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "label 0 - tench, Tinca tinca\n",
+      "label 1 - goldfish, Carassius auratus\n",
+      "label 2 - great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias\n",
+      "label 3 - tiger shark, Galeocerdo cuvieri\n",
+      "label 4 - hammerhead, hammerhead shark\n",
+      "label 5 - electric ray, crampfish, numbfish, torpedo\n",
+      "label 6 - stingray\n",
+      "label 7 - cock\n",
+      "label 8 - hen\n",
+      "label 9 - ostrich, Struthio camelus\n",
+      "label 10 - brambling, Fringilla montifringilla\n",
+      "label 11 - goldfinch, Carduelis carduelis\n",
+      "label 12 - house finch, linnet, Carpodacus mexicanus\n",
+      "label 13 - junco, snowbird\n",
+      "label 14 - indigo bunting, indigo finch, indigo bird, Passerina cyanea\n",
+      "label 15 - robin, American robin, Turdus migratorius\n",
+      "label 16 - bulbul\n",
+      "label 17 - jay\n",
+      "label 18 - magpie\n",
+      "label 19 - chickadee\n",
+      "label 20 - water ouzel, dipper\n",
+      "label 21 - kite\n",
+      "label 22 - bald eagle, American eagle, Haliaeetus leucocephalus\n",
+      "label 23 - vulture\n",
+      "label 24 - great grey owl, great gray owl, Strix nebulosa\n",
+      "label 25 - European fire salamander, Salamandra salamandra\n",
+      "label 26 - common newt, Triturus vulgaris\n",
+      "label 27 - eft\n",
+      "label 28 - spotted salamander, Ambystoma maculatum\n",
+      "label 29 - axolotl, mud puppy, Ambystoma mexicanum\n",
+      "label 30 - bullfrog, Rana catesbeiana\n",
+      "label 31 - tree frog, tree-frog\n",
+      "label 32 - tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui\n",
+      "label 33 - loggerhead, loggerhead turtle, Caretta caretta\n",
+      "label 34 - leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea\n",
+      "label 35 - mud turtle\n",
+      "label 36 - terrapin\n",
+      "label 37 - box turtle, box tortoise\n",
+      "label 38 - banded gecko\n",
+      "label 39 - common iguana, iguana, Iguana iguana\n",
+      "label 40 - American chameleon, anole, Anolis carolinensis\n",
+      "label 41 - whiptail, whiptail lizard\n",
+      "label 42 - agama\n",
+      "label 43 - frilled lizard, Chlamydosaurus kingi\n",
+      "label 44 - alligator lizard\n",
+      "label 45 - Gila monster, Heloderma suspectum\n",
+      "label 46 - green lizard, Lacerta viridis\n",
+      "label 47 - African chameleon, Chamaeleo chamaeleon\n",
+      "label 48 - Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis\n",
+      "label 49 - African crocodile, Nile crocodile, Crocodylus niloticus\n",
+      "label 50 - American alligator, Alligator mississipiensis\n",
+      "label 51 - triceratops\n",
+      "label 52 - thunder snake, worm snake, Carphophis amoenus\n",
+      "label 53 - ringneck snake, ring-necked snake, ring snake\n",
+      "label 54 - hognose snake, puff adder, sand viper\n",
+      "label 55 - green snake, grass snake\n",
+      "label 56 - king snake, kingsnake\n",
+      "label 57 - garter snake, grass snake\n",
+      "label 58 - water snake\n",
+      "label 59 - vine snake\n",
+      "label 60 - night snake, Hypsiglena torquata\n",
+      "label 61 - boa constrictor, Constrictor constrictor\n",
+      "label 62 - rock python, rock snake, Python sebae\n",
+      "label 63 - Indian cobra, Naja naja\n",
+      "label 64 - green mamba\n",
+      "label 65 - sea snake\n",
+      "label 66 - horned viper, cerastes, sand viper, horned asp, Cerastes cornutus\n",
+      "label 67 - diamondback, diamondback rattlesnake, Crotalus adamanteus\n",
+      "label 68 - sidewinder, horned rattlesnake, Crotalus cerastes\n",
+      "label 69 - trilobite\n",
+      "label 70 - harvestman, daddy longlegs, Phalangium opilio\n",
+      "label 71 - scorpion\n",
+      "label 72 - black and gold garden spider, Argiope aurantia\n",
+      "label 73 - barn spider, Araneus cavaticus\n",
+      "label 74 - garden spider, Aranea diademata\n",
+      "label 75 - black widow, Latrodectus mactans\n",
+      "label 76 - tarantula\n",
+      "label 77 - wolf spider, hunting spider\n",
+      "label 78 - tick\n",
+      "label 79 - centipede\n",
+      "label 80 - black grouse\n",
+      "label 81 - ptarmigan\n",
+      "label 82 - ruffed grouse, partridge, Bonasa umbellus\n",
+      "label 83 - prairie chicken, prairie grouse, prairie fowl\n",
+      "label 84 - peacock\n",
+      "label 85 - quail\n",
+      "label 86 - partridge\n",
+      "label 87 - African grey, African gray, Psittacus erithacus\n",
+      "label 88 - macaw\n",
+      "label 89 - sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita\n",
+      "label 90 - lorikeet\n",
+      "label 91 - coucal\n",
+      "label 92 - bee eater\n",
+      "label 93 - hornbill\n",
+      "label 94 - hummingbird\n",
+      "label 95 - jacamar\n",
+      "label 96 - toucan\n",
+      "label 97 - drake\n",
+      "label 98 - red-breasted merganser, Mergus serrator\n",
+      "label 99 - goose\n",
+      "label 100 - black swan, Cygnus atratus\n",
+      "label 101 - tusker\n",
+      "label 102 - echidna, spiny anteater, anteater\n",
+      "label 103 - platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus\n",
+      "label 104 - wallaby, brush kangaroo\n",
+      "label 105 - koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus\n",
+      "label 106 - wombat\n",
+      "label 107 - jellyfish\n",
+      "label 108 - sea anemone, anemone\n",
+      "label 109 - brain coral\n",
+      "label 110 - flatworm, platyhelminth\n",
+      "label 111 - nematode, nematode worm, roundworm\n",
+      "label 112 - conch\n",
+      "label 113 - snail\n",
+      "label 114 - slug\n",
+      "label 115 - sea slug, nudibranch\n",
+      "label 116 - chiton, coat-of-mail shell, sea cradle, polyplacophore\n",
+      "label 117 - chambered nautilus, pearly nautilus, nautilus\n",
+      "label 118 - Dungeness crab, Cancer magister\n",
+      "label 119 - rock crab, Cancer irroratus\n",
+      "label 120 - fiddler crab\n",
+      "label 121 - king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica\n",
+      "label 122 - American lobster, Northern lobster, Maine lobster, Homarus americanus\n",
+      "label 123 - spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish\n",
+      "label 124 - crayfish, crawfish, crawdad, crawdaddy\n",
+      "label 125 - hermit crab\n",
+      "label 126 - isopod\n",
+      "label 127 - white stork, Ciconia ciconia\n",
+      "label 128 - black stork, Ciconia nigra\n",
+      "label 129 - spoonbill\n",
+      "label 130 - flamingo\n",
+      "label 131 - little blue heron, Egretta caerulea\n",
+      "label 132 - American egret, great white heron, Egretta albus\n",
+      "label 133 - bittern\n",
+      "label 134 - crane\n",
+      "label 135 - limpkin, Aramus pictus\n",
+      "label 136 - European gallinule, Porphyrio porphyrio\n",
+      "label 137 - American coot, marsh hen, mud hen, water hen, Fulica americana\n",
+      "label 138 - bustard\n",
+      "label 139 - ruddy turnstone, Arenaria interpres\n",
+      "label 140 - red-backed sandpiper, dunlin, Erolia alpina\n",
+      "label 141 - redshank, Tringa totanus\n",
+      "label 142 - dowitcher\n",
+      "label 143 - oystercatcher, oyster catcher\n",
+      "label 144 - pelican\n",
+      "label 145 - king penguin, Aptenodytes patagonica\n",
+      "label 146 - albatross, mollymawk\n",
+      "label 147 - grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus\n",
+      "label 148 - killer whale, killer, orca, grampus, sea wolf, Orcinus orca\n",
+      "label 149 - dugong, Dugong dugon\n",
+      "label 150 - sea lion\n",
+      "label 151 - Chihuahua\n",
+      "label 152 - Japanese spaniel\n",
+      "label 153 - Maltese dog, Maltese terrier, Maltese\n",
+      "label 154 - Pekinese, Pekingese, Peke\n",
+      "label 155 - Shih-Tzu\n",
+      "label 156 - Blenheim spaniel\n",
+      "label 157 - papillon\n",
+      "label 158 - toy terrier\n",
+      "label 159 - Rhodesian ridgeback\n",
+      "label 160 - Afghan hound, Afghan\n",
+      "label 161 - basset, basset hound\n",
+      "label 162 - beagle\n",
+      "label 163 - bloodhound, sleuthhound\n",
+      "label 164 - bluetick\n",
+      "label 165 - black-and-tan coonhound\n",
+      "label 166 - Walker hound, Walker foxhound\n",
+      "label 167 - English foxhound\n",
+      "label 168 - redbone\n",
+      "label 169 - borzoi, Russian wolfhound\n",
+      "label 170 - Irish wolfhound\n",
+      "label 171 - Italian greyhound\n",
+      "label 172 - whippet\n",
+      "label 173 - Ibizan hound, Ibizan Podenco\n",
+      "label 174 - Norwegian elkhound, elkhound\n",
+      "label 175 - otterhound, otter hound\n",
+      "label 176 - Saluki, gazelle hound\n",
+      "label 177 - Scottish deerhound, deerhound\n",
+      "label 178 - Weimaraner\n",
+      "label 179 - Staffordshire bullterrier, Staffordshire bull terrier\n",
+      "label 180 - American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier\n",
+      "label 181 - Bedlington terrier\n",
+      "label 182 - Border terrier\n",
+      "label 183 - Kerry blue terrier\n",
+      "label 184 - Irish terrier\n",
+      "label 185 - Norfolk terrier\n",
+      "label 186 - Norwich terrier\n",
+      "label 187 - Yorkshire terrier\n",
+      "label 188 - wire-haired fox terrier\n",
+      "label 189 - Lakeland terrier\n",
+      "label 190 - Sealyham terrier, Sealyham\n",
+      "label 191 - Airedale, Airedale terrier\n",
+      "label 192 - cairn, cairn terrier\n",
+      "label 193 - Australian terrier\n",
+      "label 194 - Dandie Dinmont, Dandie Dinmont terrier\n",
+      "label 195 - Boston bull, Boston terrier\n",
+      "label 196 - miniature schnauzer\n",
+      "label 197 - giant schnauzer\n",
+      "label 198 - standard schnauzer\n",
+      "label 199 - Scotch terrier, Scottish terrier, Scottie\n",
+      "label 200 - Tibetan terrier, chrysanthemum dog\n",
+      "label 201 - silky terrier, Sydney silky\n",
+      "label 202 - soft-coated wheaten terrier\n",
+      "label 203 - West Highland white terrier\n",
+      "label 204 - Lhasa, Lhasa apso\n",
+      "label 205 - flat-coated retriever\n",
+      "label 206 - curly-coated retriever\n",
+      "label 207 - golden retriever\n",
+      "label 208 - Labrador retriever\n",
+      "label 209 - Chesapeake Bay retriever\n",
+      "label 210 - German short-haired pointer\n",
+      "label 211 - vizsla, Hungarian pointer\n",
+      "label 212 - English setter\n",
+      "label 213 - Irish setter, red setter\n",
+      "label 214 - Gordon setter\n",
+      "label 215 - Brittany spaniel\n",
+      "label 216 - clumber, clumber spaniel\n",
+      "label 217 - English springer, English springer spaniel\n",
+      "label 218 - Welsh springer spaniel\n",
+      "label 219 - cocker spaniel, English cocker spaniel, cocker\n",
+      "label 220 - Sussex spaniel\n",
+      "label 221 - Irish water spaniel\n",
+      "label 222 - kuvasz\n",
+      "label 223 - schipperke\n",
+      "label 224 - groenendael\n",
+      "label 225 - malinois\n",
+      "label 226 - briard\n",
+      "label 227 - kelpie\n",
+      "label 228 - komondor\n",
+      "label 229 - Old English sheepdog, bobtail\n",
+      "label 230 - Shetland sheepdog, Shetland sheep dog, Shetland\n",
+      "label 231 - collie\n",
+      "label 232 - Border collie\n",
+      "label 233 - Bouvier des Flandres, Bouviers des Flandres\n",
+      "label 234 - Rottweiler\n",
+      "label 235 - German shepherd, German shepherd dog, German police dog, alsatian\n",
+      "label 236 - Doberman, Doberman pinscher\n",
+      "label 237 - miniature pinscher\n",
+      "label 238 - Greater Swiss Mountain dog\n",
+      "label 239 - Bernese mountain dog\n",
+      "label 240 - Appenzeller\n",
+      "label 241 - EntleBucher\n",
+      "label 242 - boxer\n",
+      "label 243 - bull mastiff\n",
+      "label 244 - Tibetan mastiff\n",
+      "label 245 - French bulldog\n",
+      "label 246 - Great Dane\n",
+      "label 247 - Saint Bernard, St Bernard\n",
+      "label 248 - Eskimo dog, husky\n",
+      "label 249 - malamute, malemute, Alaskan malamute\n",
+      "label 250 - Siberian husky\n",
+      "label 251 - dalmatian, coach dog, carriage dog\n",
+      "label 252 - affenpinscher, monkey pinscher, monkey dog\n",
+      "label 253 - basenji\n",
+      "label 254 - pug, pug-dog\n",
+      "label 255 - Leonberg\n",
+      "label 256 - Newfoundland, Newfoundland dog\n",
+      "label 257 - Great Pyrenees\n",
+      "label 258 - Samoyed, Samoyede\n",
+      "label 259 - Pomeranian\n",
+      "label 260 - chow, chow chow\n",
+      "label 261 - keeshond\n",
+      "label 262 - Brabancon griffon\n",
+      "label 263 - Pembroke, Pembroke Welsh corgi\n",
+      "label 264 - Cardigan, Cardigan Welsh corgi\n",
+      "label 265 - toy poodle\n",
+      "label 266 - miniature poodle\n",
+      "label 267 - standard poodle\n",
+      "label 268 - Mexican hairless\n",
+      "label 269 - timber wolf, grey wolf, gray wolf, Canis lupus\n",
+      "label 270 - white wolf, Arctic wolf, Canis lupus tundrarum\n",
+      "label 271 - red wolf, maned wolf, Canis rufus, Canis niger\n",
+      "label 272 - coyote, prairie wolf, brush wolf, Canis latrans\n",
+      "label 273 - dingo, warrigal, warragal, Canis dingo\n",
+      "label 274 - dhole, Cuon alpinus\n",
+      "label 275 - African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus\n",
+      "label 276 - hyena, hyaena\n",
+      "label 277 - red fox, Vulpes vulpes\n",
+      "label 278 - kit fox, Vulpes macrotis\n",
+      "label 279 - Arctic fox, white fox, Alopex lagopus\n",
+      "label 280 - grey fox, gray fox, Urocyon cinereoargenteus\n",
+      "label 281 - tabby, tabby cat\n",
+      "label 282 - tiger cat\n",
+      "label 283 - Persian cat\n",
+      "label 284 - Siamese cat, Siamese\n",
+      "label 285 - Egyptian cat\n",
+      "label 286 - cougar, puma, catamount, mountain lion, painter, panther, Felis concolor\n",
+      "label 287 - lynx, catamount\n",
+      "label 288 - leopard, Panthera pardus\n",
+      "label 289 - snow leopard, ounce, Panthera uncia\n",
+      "label 290 - jaguar, panther, Panthera onca, Felis onca\n",
+      "label 291 - lion, king of beasts, Panthera leo\n",
+      "label 292 - tiger, Panthera tigris\n",
+      "label 293 - cheetah, chetah, Acinonyx jubatus\n",
+      "label 294 - brown bear, bruin, Ursus arctos\n",
+      "label 295 - American black bear, black bear, Ursus americanus, Euarctos americanus\n",
+      "label 296 - ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus\n",
+      "label 297 - sloth bear, Melursus ursinus, Ursus ursinus\n",
+      "label 298 - mongoose\n",
+      "label 299 - meerkat, mierkat\n",
+      "label 300 - tiger beetle\n",
+      "label 301 - ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle\n",
+      "label 302 - ground beetle, carabid beetle\n",
+      "label 303 - long-horned beetle, longicorn, longicorn beetle\n",
+      "label 304 - leaf beetle, chrysomelid\n",
+      "label 305 - dung beetle\n",
+      "label 306 - rhinoceros beetle\n",
+      "label 307 - weevil\n",
+      "label 308 - fly\n",
+      "label 309 - bee\n",
+      "label 310 - ant, emmet, pismire\n",
+      "label 311 - grasshopper, hopper\n",
+      "label 312 - cricket\n",
+      "label 313 - walking stick, walkingstick, stick insect\n",
+      "label 314 - cockroach, roach\n",
+      "label 315 - mantis, mantid\n",
+      "label 316 - cicada, cicala\n",
+      "label 317 - leafhopper\n",
+      "label 318 - lacewing, lacewing fly\n",
+      "label 319 - dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk\n",
+      "label 320 - damselfly\n",
+      "label 321 - admiral\n",
+      "label 322 - ringlet, ringlet butterfly\n",
+      "label 323 - monarch, monarch butterfly, milkweed butterfly, Danaus plexippus\n",
+      "label 324 - cabbage butterfly\n",
+      "label 325 - sulphur butterfly, sulfur butterfly\n",
+      "label 326 - lycaenid, lycaenid butterfly\n",
+      "label 327 - starfish, sea star\n",
+      "label 328 - sea urchin\n",
+      "label 329 - sea cucumber, holothurian\n",
+      "label 330 - wood rabbit, cottontail, cottontail rabbit\n",
+      "label 331 - hare\n",
+      "label 332 - Angora, Angora rabbit\n",
+      "label 333 - hamster\n",
+      "label 334 - porcupine, hedgehog\n",
+      "label 335 - fox squirrel, eastern fox squirrel, Sciurus niger\n",
+      "label 336 - marmot\n",
+      "label 337 - beaver\n",
+      "label 338 - guinea pig, Cavia cobaya\n",
+      "label 339 - sorrel\n",
+      "label 340 - zebra\n",
+      "label 341 - hog, pig, grunter, squealer, Sus scrofa\n",
+      "label 342 - wild boar, boar, Sus scrofa\n",
+      "label 343 - warthog\n",
+      "label 344 - hippopotamus, hippo, river horse, Hippopotamus amphibius\n",
+      "label 345 - ox\n",
+      "label 346 - water buffalo, water ox, Asiatic buffalo, Bubalus bubalis\n",
+      "label 347 - bison\n",
+      "label 348 - ram, tup\n",
+      "label 349 - bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis\n",
+      "label 350 - ibex, Capra ibex\n",
+      "label 351 - hartebeest\n",
+      "label 352 - impala, Aepyceros melampus\n",
+      "label 353 - gazelle\n",
+      "label 354 - Arabian camel, dromedary, Camelus dromedarius\n",
+      "label 355 - llama\n",
+      "label 356 - weasel\n",
+      "label 357 - mink\n",
+      "label 358 - polecat, fitch, foulmart, foumart, Mustela putorius\n",
+      "label 359 - black-footed ferret, ferret, Mustela nigripes\n",
+      "label 360 - otter\n",
+      "label 361 - skunk, polecat, wood pussy\n",
+      "label 362 - badger\n",
+      "label 363 - armadillo\n",
+      "label 364 - three-toed sloth, ai, Bradypus tridactylus\n",
+      "label 365 - orangutan, orang, orangutang, Pongo pygmaeus\n",
+      "label 366 - gorilla, Gorilla gorilla\n",
+      "label 367 - chimpanzee, chimp, Pan troglodytes\n",
+      "label 368 - gibbon, Hylobates lar\n",
+      "label 369 - siamang, Hylobates syndactylus, Symphalangus syndactylus\n",
+      "label 370 - guenon, guenon monkey\n",
+      "label 371 - patas, hussar monkey, Erythrocebus patas\n",
+      "label 372 - baboon\n",
+      "label 373 - macaque\n",
+      "label 374 - langur\n",
+      "label 375 - colobus, colobus monkey\n",
+      "label 376 - proboscis monkey, Nasalis larvatus\n",
+      "label 377 - marmoset\n",
+      "label 378 - capuchin, ringtail, Cebus capucinus\n",
+      "label 379 - howler monkey, howler\n",
+      "label 380 - titi, titi monkey\n",
+      "label 381 - spider monkey, Ateles geoffroyi\n",
+      "label 382 - squirrel monkey, Saimiri sciureus\n",
+      "label 383 - Madagascar cat, ring-tailed lemur, Lemur catta\n",
+      "label 384 - indri, indris, Indri indri, Indri brevicaudatus\n",
+      "label 385 - Indian elephant, Elephas maximus\n",
+      "label 386 - African elephant, Loxodonta africana\n",
+      "label 387 - lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens\n",
+      "label 388 - giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca\n",
+      "label 389 - barracouta, snoek\n",
+      "label 390 - eel\n",
+      "label 391 - coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch\n",
+      "label 392 - rock beauty, Holocanthus tricolor\n",
+      "label 393 - anemone fish\n",
+      "label 394 - sturgeon\n",
+      "label 395 - gar, garfish, garpike, billfish, Lepisosteus osseus\n",
+      "label 396 - lionfish\n",
+      "label 397 - puffer, pufferfish, blowfish, globefish\n",
+      "label 398 - abacus\n",
+      "label 399 - abaya\n",
+      "label 400 - academic gown, academic robe, judge's robe\n",
+      "label 401 - accordion, piano accordion, squeeze box\n",
+      "label 402 - acoustic guitar\n",
+      "label 403 - aircraft carrier, carrier, flattop, attack aircraft carrier\n",
+      "label 404 - airliner\n",
+      "label 405 - airship, dirigible\n",
+      "label 406 - altar\n",
+      "label 407 - ambulance\n",
+      "label 408 - amphibian, amphibious vehicle\n",
+      "label 409 - analog clock\n",
+      "label 410 - apiary, bee house\n",
+      "label 411 - apron\n",
+      "label 412 - ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin\n",
+      "label 413 - assault rifle, assault gun\n",
+      "label 414 - backpack, back pack, knapsack, packsack, rucksack, haversack\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "label 415 - bakery, bakeshop, bakehouse\n",
+      "label 416 - balance beam, beam\n",
+      "label 417 - balloon\n",
+      "label 418 - ballpoint, ballpoint pen, ballpen, Biro\n",
+      "label 419 - Band Aid\n",
+      "label 420 - banjo\n",
+      "label 421 - bannister, banister, balustrade, balusters, handrail\n",
+      "label 422 - barbell\n",
+      "label 423 - barber chair\n",
+      "label 424 - barbershop\n",
+      "label 425 - barn\n",
+      "label 426 - barometer\n",
+      "label 427 - barrel, cask\n",
+      "label 428 - barrow, garden cart, lawn cart, wheelbarrow\n",
+      "label 429 - baseball\n",
+      "label 430 - basketball\n",
+      "label 431 - bassinet\n",
+      "label 432 - bassoon\n",
+      "label 433 - bathing cap, swimming cap\n",
+      "label 434 - bath towel\n",
+      "label 435 - bathtub, bathing tub, bath, tub\n",
+      "label 436 - beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon\n",
+      "label 437 - beacon, lighthouse, beacon light, pharos\n",
+      "label 438 - beaker\n",
+      "label 439 - bearskin, busby, shako\n",
+      "label 440 - beer bottle\n",
+      "label 441 - beer glass\n",
+      "label 442 - bell cote, bell cot\n",
+      "label 443 - bib\n",
+      "label 444 - bicycle-built-for-two, tandem bicycle, tandem\n",
+      "label 445 - bikini, two-piece\n",
+      "label 446 - binder, ring-binder\n",
+      "label 447 - binoculars, field glasses, opera glasses\n",
+      "label 448 - birdhouse\n",
+      "label 449 - boathouse\n",
+      "label 450 - bobsled, bobsleigh, bob\n",
+      "label 451 - bolo tie, bolo, bola tie, bola\n",
+      "label 452 - bonnet, poke bonnet\n",
+      "label 453 - bookcase\n",
+      "label 454 - bookshop, bookstore, bookstall\n",
+      "label 455 - bottlecap\n",
+      "label 456 - bow\n",
+      "label 457 - bow tie, bow-tie, bowtie\n",
+      "label 458 - brass, memorial tablet, plaque\n",
+      "label 459 - brassiere, bra, bandeau\n",
+      "label 460 - breakwater, groin, groyne, mole, bulwark, seawall, jetty\n",
+      "label 461 - breastplate, aegis, egis\n",
+      "label 462 - broom\n",
+      "label 463 - bucket, pail\n",
+      "label 464 - buckle\n",
+      "label 465 - bulletproof vest\n",
+      "label 466 - bullet train, bullet\n",
+      "label 467 - butcher shop, meat market\n",
+      "label 468 - cab, hack, taxi, taxicab\n",
+      "label 469 - caldron, cauldron\n",
+      "label 470 - candle, taper, wax light\n",
+      "label 471 - cannon\n",
+      "label 472 - canoe\n",
+      "label 473 - can opener, tin opener\n",
+      "label 474 - cardigan\n",
+      "label 475 - car mirror\n",
+      "label 476 - carousel, carrousel, merry-go-round, roundabout, whirligig\n",
+      "label 477 - carpenter's kit, tool kit\n",
+      "label 478 - carton\n",
+      "label 479 - car wheel\n",
+      "label 480 - cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM\n",
+      "label 481 - cassette\n",
+      "label 482 - cassette player\n",
+      "label 483 - castle\n",
+      "label 484 - catamaran\n",
+      "label 485 - CD player\n",
+      "label 486 - cello, violoncello\n",
+      "label 487 - cellular telephone, cellular phone, cellphone, cell, mobile phone\n",
+      "label 488 - chain\n",
+      "label 489 - chainlink fence\n",
+      "label 490 - chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour\n",
+      "label 491 - chain saw, chainsaw\n",
+      "label 492 - chest\n",
+      "label 493 - chiffonier, commode\n",
+      "label 494 - chime, bell, gong\n",
+      "label 495 - china cabinet, china closet\n",
+      "label 496 - Christmas stocking\n",
+      "label 497 - church, church building\n",
+      "label 498 - cinema, movie theater, movie theatre, movie house, picture palace\n",
+      "label 499 - cleaver, meat cleaver, chopper\n",
+      "label 500 - cliff dwelling\n",
+      "label 501 - cloak\n",
+      "label 502 - clog, geta, patten, sabot\n",
+      "label 503 - cocktail shaker\n",
+      "label 504 - coffee mug\n",
+      "label 505 - coffeepot\n",
+      "label 506 - coil, spiral, volute, whorl, helix\n",
+      "label 507 - combination lock\n",
+      "label 508 - computer keyboard, keypad\n",
+      "label 509 - confectionery, confectionary, candy store\n",
+      "label 510 - container ship, containership, container vessel\n",
+      "label 511 - convertible\n",
+      "label 512 - corkscrew, bottle screw\n",
+      "label 513 - cornet, horn, trumpet, trump\n",
+      "label 514 - cowboy boot\n",
+      "label 515 - cowboy hat, ten-gallon hat\n",
+      "label 516 - cradle\n",
+      "label 517 - crane\n",
+      "label 518 - crash helmet\n",
+      "label 519 - crate\n",
+      "label 520 - crib, cot\n",
+      "label 521 - Crock Pot\n",
+      "label 522 - croquet ball\n",
+      "label 523 - crutch\n",
+      "label 524 - cuirass\n",
+      "label 525 - dam, dike, dyke\n",
+      "label 526 - desk\n",
+      "label 527 - desktop computer\n",
+      "label 528 - dial telephone, dial phone\n",
+      "label 529 - diaper, nappy, napkin\n",
+      "label 530 - digital clock\n",
+      "label 531 - digital watch\n",
+      "label 532 - dining table, board\n",
+      "label 533 - dishrag, dishcloth\n",
+      "label 534 - dishwasher, dish washer, dishwashing machine\n",
+      "label 535 - disk brake, disc brake\n",
+      "label 536 - dock, dockage, docking facility\n",
+      "label 537 - dogsled, dog sled, dog sleigh\n",
+      "label 538 - dome\n",
+      "label 539 - doormat, welcome mat\n",
+      "label 540 - drilling platform, offshore rig\n",
+      "label 541 - drum, membranophone, tympan\n",
+      "label 542 - drumstick\n",
+      "label 543 - dumbbell\n",
+      "label 544 - Dutch oven\n",
+      "label 545 - electric fan, blower\n",
+      "label 546 - electric guitar\n",
+      "label 547 - electric locomotive\n",
+      "label 548 - entertainment center\n",
+      "label 549 - envelope\n",
+      "label 550 - espresso maker\n",
+      "label 551 - face powder\n",
+      "label 552 - feather boa, boa\n",
+      "label 553 - file, file cabinet, filing cabinet\n",
+      "label 554 - fireboat\n",
+      "label 555 - fire engine, fire truck\n",
+      "label 556 - fire screen, fireguard\n",
+      "label 557 - flagpole, flagstaff\n",
+      "label 558 - flute, transverse flute\n",
+      "label 559 - folding chair\n",
+      "label 560 - football helmet\n",
+      "label 561 - forklift\n",
+      "label 562 - fountain\n",
+      "label 563 - fountain pen\n",
+      "label 564 - four-poster\n",
+      "label 565 - freight car\n",
+      "label 566 - French horn, horn\n",
+      "label 567 - frying pan, frypan, skillet\n",
+      "label 568 - fur coat\n",
+      "label 569 - garbage truck, dustcart\n",
+      "label 570 - gasmask, respirator, gas helmet\n",
+      "label 571 - gas pump, gasoline pump, petrol pump, island dispenser\n",
+      "label 572 - goblet\n",
+      "label 573 - go-kart\n",
+      "label 574 - golf ball\n",
+      "label 575 - golfcart, golf cart\n",
+      "label 576 - gondola\n",
+      "label 577 - gong, tam-tam\n",
+      "label 578 - gown\n",
+      "label 579 - grand piano, grand\n",
+      "label 580 - greenhouse, nursery, glasshouse\n",
+      "label 581 - grille, radiator grille\n",
+      "label 582 - grocery store, grocery, food market, market\n",
+      "label 583 - guillotine\n",
+      "label 584 - hair slide\n",
+      "label 585 - hair spray\n",
+      "label 586 - half track\n",
+      "label 587 - hammer\n",
+      "label 588 - hamper\n",
+      "label 589 - hand blower, blow dryer, blow drier, hair dryer, hair drier\n",
+      "label 590 - hand-held computer, hand-held microcomputer\n",
+      "label 591 - handkerchief, hankie, hanky, hankey\n",
+      "label 592 - hard disc, hard disk, fixed disk\n",
+      "label 593 - harmonica, mouth organ, harp, mouth harp\n",
+      "label 594 - harp\n",
+      "label 595 - harvester, reaper\n",
+      "label 596 - hatchet\n",
+      "label 597 - holster\n",
+      "label 598 - home theater, home theatre\n",
+      "label 599 - honeycomb\n",
+      "label 600 - hook, claw\n",
+      "label 601 - hoopskirt, crinoline\n",
+      "label 602 - horizontal bar, high bar\n",
+      "label 603 - horse cart, horse-cart\n",
+      "label 604 - hourglass\n",
+      "label 605 - iPod\n",
+      "label 606 - iron, smoothing iron\n",
+      "label 607 - jack-o'-lantern\n",
+      "label 608 - jean, blue jean, denim\n",
+      "label 609 - jeep, landrover\n",
+      "label 610 - jersey, T-shirt, tee shirt\n",
+      "label 611 - jigsaw puzzle\n",
+      "label 612 - jinrikisha, ricksha, rickshaw\n",
+      "label 613 - joystick\n",
+      "label 614 - kimono\n",
+      "label 615 - knee pad\n",
+      "label 616 - knot\n",
+      "label 617 - lab coat, laboratory coat\n",
+      "label 618 - ladle\n",
+      "label 619 - lampshade, lamp shade\n",
+      "label 620 - laptop, laptop computer\n",
+      "label 621 - lawn mower, mower\n",
+      "label 622 - lens cap, lens cover\n",
+      "label 623 - letter opener, paper knife, paperknife\n",
+      "label 624 - library\n",
+      "label 625 - lifeboat\n",
+      "label 626 - lighter, light, igniter, ignitor\n",
+      "label 627 - limousine, limo\n",
+      "label 628 - liner, ocean liner\n",
+      "label 629 - lipstick, lip rouge\n",
+      "label 630 - Loafer\n",
+      "label 631 - lotion\n",
+      "label 632 - loudspeaker, speaker, speaker unit, loudspeaker system, speaker system\n",
+      "label 633 - loupe, jeweler's loupe\n",
+      "label 634 - lumbermill, sawmill\n",
+      "label 635 - magnetic compass\n",
+      "label 636 - mailbag, postbag\n",
+      "label 637 - mailbox, letter box\n",
+      "label 638 - maillot\n",
+      "label 639 - maillot, tank suit\n",
+      "label 640 - manhole cover\n",
+      "label 641 - maraca\n",
+      "label 642 - marimba, xylophone\n",
+      "label 643 - mask\n",
+      "label 644 - matchstick\n",
+      "label 645 - maypole\n",
+      "label 646 - maze, labyrinth\n",
+      "label 647 - measuring cup\n",
+      "label 648 - medicine chest, medicine cabinet\n",
+      "label 649 - megalith, megalithic structure\n",
+      "label 650 - microphone, mike\n",
+      "label 651 - microwave, microwave oven\n",
+      "label 652 - military uniform\n",
+      "label 653 - milk can\n",
+      "label 654 - minibus\n",
+      "label 655 - miniskirt, mini\n",
+      "label 656 - minivan\n",
+      "label 657 - missile\n",
+      "label 658 - mitten\n",
+      "label 659 - mixing bowl\n",
+      "label 660 - mobile home, manufactured home\n",
+      "label 661 - Model T\n",
+      "label 662 - modem\n",
+      "label 663 - monastery\n",
+      "label 664 - monitor\n",
+      "label 665 - moped\n",
+      "label 666 - mortar\n",
+      "label 667 - mortarboard\n",
+      "label 668 - mosque\n",
+      "label 669 - mosquito net\n",
+      "label 670 - motor scooter, scooter\n",
+      "label 671 - mountain bike, all-terrain bike, off-roader\n",
+      "label 672 - mountain tent\n",
+      "label 673 - mouse, computer mouse\n",
+      "label 674 - mousetrap\n",
+      "label 675 - moving van\n",
+      "label 676 - muzzle\n",
+      "label 677 - nail\n",
+      "label 678 - neck brace\n",
+      "label 679 - necklace\n",
+      "label 680 - nipple\n",
+      "label 681 - notebook, notebook computer\n",
+      "label 682 - obelisk\n",
+      "label 683 - oboe, hautboy, hautbois\n",
+      "label 684 - ocarina, sweet potato\n",
+      "label 685 - odometer, hodometer, mileometer, milometer\n",
+      "label 686 - oil filter\n",
+      "label 687 - organ, pipe organ\n",
+      "label 688 - oscilloscope, scope, cathode-ray oscilloscope, CRO\n",
+      "label 689 - overskirt\n",
+      "label 690 - oxcart\n",
+      "label 691 - oxygen mask\n",
+      "label 692 - packet\n",
+      "label 693 - paddle, boat paddle\n",
+      "label 694 - paddlewheel, paddle wheel\n",
+      "label 695 - padlock\n",
+      "label 696 - paintbrush\n",
+      "label 697 - pajama, pyjama, pj's, jammies\n",
+      "label 698 - palace\n",
+      "label 699 - panpipe, pandean pipe, syrinx\n",
+      "label 700 - paper towel\n",
+      "label 701 - parachute, chute\n",
+      "label 702 - parallel bars, bars\n",
+      "label 703 - park bench\n",
+      "label 704 - parking meter\n",
+      "label 705 - passenger car, coach, carriage\n",
+      "label 706 - patio, terrace\n",
+      "label 707 - pay-phone, pay-station\n",
+      "label 708 - pedestal, plinth, footstall\n",
+      "label 709 - pencil box, pencil case\n",
+      "label 710 - pencil sharpener\n",
+      "label 711 - perfume, essence\n",
+      "label 712 - Petri dish\n",
+      "label 713 - photocopier\n",
+      "label 714 - pick, plectrum, plectron\n",
+      "label 715 - pickelhaube\n",
+      "label 716 - picket fence, paling\n",
+      "label 717 - pickup, pickup truck\n",
+      "label 718 - pier\n",
+      "label 719 - piggy bank, penny bank\n",
+      "label 720 - pill bottle\n",
+      "label 721 - pillow\n",
+      "label 722 - ping-pong ball\n",
+      "label 723 - pinwheel\n",
+      "label 724 - pirate, pirate ship\n",
+      "label 725 - pitcher, ewer\n",
+      "label 726 - plane, carpenter's plane, woodworking plane\n",
+      "label 727 - planetarium\n",
+      "label 728 - plastic bag\n",
+      "label 729 - plate rack\n",
+      "label 730 - plow, plough\n",
+      "label 731 - plunger, plumber's helper\n",
+      "label 732 - Polaroid camera, Polaroid Land camera\n",
+      "label 733 - pole\n",
+      "label 734 - police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria\n",
+      "label 735 - poncho\n",
+      "label 736 - pool table, billiard table, snooker table\n",
+      "label 737 - pop bottle, soda bottle\n",
+      "label 738 - pot, flowerpot\n",
+      "label 739 - potter's wheel\n",
+      "label 740 - power drill\n",
+      "label 741 - prayer rug, prayer mat\n",
+      "label 742 - printer\n",
+      "label 743 - prison, prison house\n",
+      "label 744 - projectile, missile\n",
+      "label 745 - projector\n",
+      "label 746 - puck, hockey puck\n",
+      "label 747 - punching bag, punch bag, punching ball, punchball\n",
+      "label 748 - purse\n",
+      "label 749 - quill, quill pen\n",
+      "label 750 - quilt, comforter, comfort, puff\n",
+      "label 751 - racer, race car, racing car\n",
+      "label 752 - racket, racquet\n",
+      "label 753 - radiator\n",
+      "label 754 - radio, wireless\n",
+      "label 755 - radio telescope, radio reflector\n",
+      "label 756 - rain barrel\n",
+      "label 757 - recreational vehicle, RV, R.V.\n",
+      "label 758 - reel\n",
+      "label 759 - reflex camera\n",
+      "label 760 - refrigerator, icebox\n",
+      "label 761 - remote control, remote\n",
+      "label 762 - restaurant, eating house, eating place, eatery\n",
+      "label 763 - revolver, six-gun, six-shooter\n",
+      "label 764 - rifle\n",
+      "label 765 - rocking chair, rocker\n",
+      "label 766 - rotisserie\n",
+      "label 767 - rubber eraser, rubber, pencil eraser\n",
+      "label 768 - rugby ball\n",
+      "label 769 - rule, ruler\n",
+      "label 770 - running shoe\n",
+      "label 771 - safe\n",
+      "label 772 - safety pin\n",
+      "label 773 - saltshaker, salt shaker\n",
+      "label 774 - sandal\n",
+      "label 775 - sarong\n",
+      "label 776 - sax, saxophone\n",
+      "label 777 - scabbard\n",
+      "label 778 - scale, weighing machine\n",
+      "label 779 - school bus\n",
+      "label 780 - schooner\n",
+      "label 781 - scoreboard\n",
+      "label 782 - screen, CRT screen\n",
+      "label 783 - screw\n",
+      "label 784 - screwdriver\n",
+      "label 785 - seat belt, seatbelt\n",
+      "label 786 - sewing machine\n",
+      "label 787 - shield, buckler\n",
+      "label 788 - shoe shop, shoe-shop, shoe store\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "label 789 - shoji\n",
+      "label 790 - shopping basket\n",
+      "label 791 - shopping cart\n",
+      "label 792 - shovel\n",
+      "label 793 - shower cap\n",
+      "label 794 - shower curtain\n",
+      "label 795 - ski\n",
+      "label 796 - ski mask\n",
+      "label 797 - sleeping bag\n",
+      "label 798 - slide rule, slipstick\n",
+      "label 799 - sliding door\n",
+      "label 800 - slot, one-armed bandit\n",
+      "label 801 - snorkel\n",
+      "label 802 - snowmobile\n",
+      "label 803 - snowplow, snowplough\n",
+      "label 804 - soap dispenser\n",
+      "label 805 - soccer ball\n",
+      "label 806 - sock\n",
+      "label 807 - solar dish, solar collector, solar furnace\n",
+      "label 808 - sombrero\n",
+      "label 809 - soup bowl\n",
+      "label 810 - space bar\n",
+      "label 811 - space heater\n",
+      "label 812 - space shuttle\n",
+      "label 813 - spatula\n",
+      "label 814 - speedboat\n",
+      "label 815 - spider web, spider's web\n",
+      "label 816 - spindle\n",
+      "label 817 - sports car, sport car\n",
+      "label 818 - spotlight, spot\n",
+      "label 819 - stage\n",
+      "label 820 - steam locomotive\n",
+      "label 821 - steel arch bridge\n",
+      "label 822 - steel drum\n",
+      "label 823 - stethoscope\n",
+      "label 824 - stole\n",
+      "label 825 - stone wall\n",
+      "label 826 - stopwatch, stop watch\n",
+      "label 827 - stove\n",
+      "label 828 - strainer\n",
+      "label 829 - streetcar, tram, tramcar, trolley, trolley car\n",
+      "label 830 - stretcher\n",
+      "label 831 - studio couch, day bed\n",
+      "label 832 - stupa, tope\n",
+      "label 833 - submarine, pigboat, sub, U-boat\n",
+      "label 834 - suit, suit of clothes\n",
+      "label 835 - sundial\n",
+      "label 836 - sunglass\n",
+      "label 837 - sunglasses, dark glasses, shades\n",
+      "label 838 - sunscreen, sunblock, sun blocker\n",
+      "label 839 - suspension bridge\n",
+      "label 840 - swab, swob, mop\n",
+      "label 841 - sweatshirt\n",
+      "label 842 - swimming trunks, bathing trunks\n",
+      "label 843 - swing\n",
+      "label 844 - switch, electric switch, electrical switch\n",
+      "label 845 - syringe\n",
+      "label 846 - table lamp\n",
+      "label 847 - tank, army tank, armored combat vehicle, armoured combat vehicle\n",
+      "label 848 - tape player\n",
+      "label 849 - teapot\n",
+      "label 850 - teddy, teddy bear\n",
+      "label 851 - television, television system\n",
+      "label 852 - tennis ball\n",
+      "label 853 - thatch, thatched roof\n",
+      "label 854 - theater curtain, theatre curtain\n",
+      "label 855 - thimble\n",
+      "label 856 - thresher, thrasher, threshing machine\n",
+      "label 857 - throne\n",
+      "label 858 - tile roof\n",
+      "label 859 - toaster\n",
+      "label 860 - tobacco shop, tobacconist shop, tobacconist\n",
+      "label 861 - toilet seat\n",
+      "label 862 - torch\n",
+      "label 863 - totem pole\n",
+      "label 864 - tow truck, tow car, wrecker\n",
+      "label 865 - toyshop\n",
+      "label 866 - tractor\n",
+      "label 867 - trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi\n",
+      "label 868 - tray\n",
+      "label 869 - trench coat\n",
+      "label 870 - tricycle, trike, velocipede\n",
+      "label 871 - trimaran\n",
+      "label 872 - tripod\n",
+      "label 873 - triumphal arch\n",
+      "label 874 - trolleybus, trolley coach, trackless trolley\n",
+      "label 875 - trombone\n",
+      "label 876 - tub, vat\n",
+      "label 877 - turnstile\n",
+      "label 878 - typewriter keyboard\n",
+      "label 879 - umbrella\n",
+      "label 880 - unicycle, monocycle\n",
+      "label 881 - upright, upright piano\n",
+      "label 882 - vacuum, vacuum cleaner\n",
+      "label 883 - vase\n",
+      "label 884 - vault\n",
+      "label 885 - velvet\n",
+      "label 886 - vending machine\n",
+      "label 887 - vestment\n",
+      "label 888 - viaduct\n",
+      "label 889 - violin, fiddle\n",
+      "label 890 - volleyball\n",
+      "label 891 - waffle iron\n",
+      "label 892 - wall clock\n",
+      "label 893 - wallet, billfold, notecase, pocketbook\n",
+      "label 894 - wardrobe, closet, press\n",
+      "label 895 - warplane, military plane\n",
+      "label 896 - washbasin, handbasin, washbowl, lavabo, wash-hand basin\n",
+      "label 897 - washer, automatic washer, washing machine\n",
+      "label 898 - water bottle\n",
+      "label 899 - water jug\n",
+      "label 900 - water tower\n",
+      "label 901 - whiskey jug\n",
+      "label 902 - whistle\n",
+      "label 903 - wig\n",
+      "label 904 - window screen\n",
+      "label 905 - window shade\n",
+      "label 906 - Windsor tie\n",
+      "label 907 - wine bottle\n",
+      "label 908 - wing\n",
+      "label 909 - wok\n",
+      "label 910 - wooden spoon\n",
+      "label 911 - wool, woolen, woollen\n",
+      "label 912 - worm fence, snake fence, snake-rail fence, Virginia fence\n",
+      "label 913 - wreck\n",
+      "label 914 - yawl\n",
+      "label 915 - yurt\n",
+      "label 916 - web site, website, internet site, site\n",
+      "label 917 - comic book\n",
+      "label 918 - crossword puzzle, crossword\n",
+      "label 919 - street sign\n",
+      "label 920 - traffic light, traffic signal, stoplight\n",
+      "label 921 - book jacket, dust cover, dust jacket, dust wrapper\n",
+      "label 922 - menu\n",
+      "label 923 - plate\n",
+      "label 924 - guacamole\n",
+      "label 925 - consomme\n",
+      "label 926 - hot pot, hotpot\n",
+      "label 927 - trifle\n",
+      "label 928 - ice cream, icecream\n",
+      "label 929 - ice lolly, lolly, lollipop, popsicle\n",
+      "label 930 - French loaf\n",
+      "label 931 - bagel, beigel\n",
+      "label 932 - pretzel\n",
+      "label 933 - cheeseburger\n",
+      "label 934 - hotdog, hot dog, red hot\n",
+      "label 935 - mashed potato\n",
+      "label 936 - head cabbage\n",
+      "label 937 - broccoli\n",
+      "label 938 - cauliflower\n",
+      "label 939 - zucchini, courgette\n",
+      "label 940 - spaghetti squash\n",
+      "label 941 - acorn squash\n",
+      "label 942 - butternut squash\n",
+      "label 943 - cucumber, cuke\n",
+      "label 944 - artichoke, globe artichoke\n",
+      "label 945 - bell pepper\n",
+      "label 946 - cardoon\n",
+      "label 947 - mushroom\n",
+      "label 948 - Granny Smith\n",
+      "label 949 - strawberry\n",
+      "label 950 - orange\n",
+      "label 951 - lemon\n",
+      "label 952 - fig\n",
+      "label 953 - pineapple, ananas\n",
+      "label 954 - banana\n",
+      "label 955 - jackfruit, jak, jack\n",
+      "label 956 - custard apple\n",
+      "label 957 - pomegranate\n",
+      "label 958 - hay\n",
+      "label 959 - carbonara\n",
+      "label 960 - chocolate sauce, chocolate syrup\n",
+      "label 961 - dough\n",
+      "label 962 - meat loaf, meatloaf\n",
+      "label 963 - pizza, pizza pie\n",
+      "label 964 - potpie\n",
+      "label 965 - burrito\n",
+      "label 966 - red wine\n",
+      "label 967 - espresso\n",
+      "label 968 - cup\n",
+      "label 969 - eggnog\n",
+      "label 970 - alp\n",
+      "label 971 - bubble\n",
+      "label 972 - cliff, drop, drop-off\n",
+      "label 973 - coral reef\n",
+      "label 974 - geyser\n",
+      "label 975 - lakeside, lakeshore\n",
+      "label 976 - promontory, headland, head, foreland\n",
+      "label 977 - sandbar, sand bar\n",
+      "label 978 - seashore, coast, seacoast, sea-coast\n",
+      "label 979 - valley, vale\n",
+      "label 980 - volcano\n",
+      "label 981 - ballplayer, baseball player\n",
+      "label 982 - groom, bridegroom\n",
+      "label 983 - scuba diver\n",
+      "label 984 - rapeseed\n",
+      "label 985 - daisy\n",
+      "label 986 - yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum\n",
+      "label 987 - corn\n",
+      "label 988 - acorn\n",
+      "label 989 - hip, rose hip, rosehip\n",
+      "label 990 - buckeye, horse chestnut, conker\n",
+      "label 991 - coral fungus\n",
+      "label 992 - agaric\n",
+      "label 993 - gyromitra\n",
+      "label 994 - stinkhorn, carrion fungus\n",
+      "label 995 - earthstar\n",
+      "label 996 - hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa\n",
+      "label 997 - bolete\n",
+      "label 998 - ear, spike, capitulum\n",
+      "label 999 - toilet tissue, toilet paper, bathroom tissue\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i in range(1000):\n",
+    "    print('label', i, '-', label_to_name(i))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As default, let's get this image misclassified as black swan (label 100)!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "target_label = 100"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now let's perform the targeted attack:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5957d15a625649c7bbe8daee9cacd2ee",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(HTML(value='PGD - Random Initializations'), FloatProgress(value=0.0, max=1.0), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(HTML(value='PGD - Iterations'), FloatProgress(value=0.0, max=10.0), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAcEAAAHBCAYAAAARuwDoAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAEAAElEQVR4nOz9WZskS5Keib2ii5m5x5LLybPV0iswjUGD4AMuFyRI3pC3/IH8EfMDOLcccB4+M0M2BwC7gd6qupZTpzJPZkaEu9uiqiK8EDWPLMx0zYPCxVz0sVNRuUSGu7maqiyffPKJmBnfX99f31/fX99f31//EK/wP/cNfH99f31/fX99f31//c91fe8Ev7++v76/vr++v/7BXt87we+v76/vr++v769/sNf3TvD76/vr++v76/vrH+z1vRP8/vr++v76/vr++gd7fe8Ev7++v76/vr++v/7BXum3ffPlmzdmarRqNG0s9UJKmbu7O4Zh5Ob2hhACEiLNlNoqIUZCirSi1K3S6kYrG2Ur1FIxa6gpjYBK4NWLl0zjhIj747ktqCkjiSiB8eYAwLIumBnRwFRppVFq4TzPhBDJaWAYMofDRKmFtSxsZaHUlVbBFG5v7jgcbmgFtAECJkZZKtqM6SaQsiCSEQQwTJV52TA1PGYwqlTyMPLq5ed8+eUX/K//t/8rPnx4z5//238NDaJFlmXjfF7Y1pl1mbl984LbNy/44gc/5rMvvuLV/RfcHO/493/13/P23S+5PL2lbjOBl6Q48MUPE9MxMB0h58Rnr99wflz4L/+Lf8XytHGTbtDauJxONK2orShGC4bhbS/jGLm5ySSJRAJ1jmyXiP7Bv0B/+Kf803/5f+L3//R/wf2bFxzuJu7DxjE0/tkPM29uIj9IkFvjF3/1U+bzwnyBVmG9gFkkxRtCSgy3B6o1HtcL33678md/9sTD48yv3j5wWR55uLzj9/7wa/7kT/+Qw+0rDjeveP9w4cPDzLfvHvnwcCbQCNaIbSNo4ab9JUnfY9tbtK08NjBG7sd/ThpeM3z+TwnHe6avf8T54Zf8m//q/8b29A3t/V+i9UKrHwkZ0g28vHnB16+/4OG797z95hu+/OEbvv7xF/z0Z3/Dz37xU1o90NrEi9dwcyv8/h/8gPv7W/6//79/x9t3H9HLDdYyQ0qEIIRkEBXygolhRRBJ5HREm7FeZqbDyBdffcZnn73ij//4D/jZz37Gf/vf/nekODDlAzEkQki02qi1UVWprfn5UEVLQ5uiqpgpa1FUYQyRKAIEVI15nTEzJAgicj1HiIIJYgkRIUT8+wRAsL6/wZAY/NwGiBH/jCEQj5kwJISAmBCICIE4DEhIDNMdQmDbVlSVWhUE0hD8jC6bv76Y3wOBN2/e8Md//I95+fo1X379NX/7t3/Nv/23/4alzMzbhVY22rZRavX16F/UCq35PZuh2jBTtPpfpTQiEhgCSIAwJExgi/1n+v9XCdRNWU8NMSFaIBgEgzEHhhxZW2WrDSEiRP7zf/xP+frLH/CTn/4tHz5+4Pz+PWVduLkZyTkg0W1JA8ygKagZpa5IgDQmYoyM40RTYykVkUBImRCUEBtlXVnnBWFAmAghIjGSJJIkEZMgUahUmjWMglljKQuqjWkYiCEwpYCZcXo6Y2oMcmDII5+9+oxaK++++zWmSkBBBEsRRABQURTleHPD3d0dy7IwzzMvXr3i7v6Oy2Vm3TZi9L32dPnItq1ABgkcXhyIMbAtK9YUSkGbMp8v/oyYMISG36NZIYRMHm6u92Bq0IxlWViWxf9shvS9uu9d04ZpA+2L7r+BaCAQCQQRfzAI56eT/E5OMKWEmbkHEUiWiDFggJpSaiWGQEz+0M3o/94wU8wMVaOp33yMkaZGUFB8IX1z+ntYv+UggqmigLbmzsoUU3+9vbVRJBBD7K9jfWENRIgxEWrEdD/r4vfSGq2/hhsFQYIh5ganFCHG4JuU/n7qn0UQEL8/wWitsK0Ljx8fOD8+sa2FSCDFRBAhBP88pkoIgZwGBNBWUS1Y/9JW2D+UUVCE8TBxvElMRyHnyDhEtiRIcANnKIhdjcx18QQE6S8XMAuomzGa+VZJMZBypJ2fePr2l+jykcthoKTKJSi/WDOXY2RJgazK+7f+2bZF0Ca0Ekgh8/JeyEPgcAg0A0mJ8sL4+qsDx6NgUTldID5VxsMNa83oAkUa5wWWGqgMWFKwCigSIkETW4mUIujm77lWCCFw+zJwuEncfTEgh0yZlDqrP++QaUSQ/UsBqNqYt5W1ViqwbIXTaWZbG6h8sm99Ac38K0oihcQGfU8DCCJ0Izb0NQ+A78UgBuNATglTo9bKsszUWkkpk2NmGIY9Arvuf+sb9Xp+9kMovndDD8BMjebWwu/Hdmfm9y79Z4x9f3enEPbv+/nFPnENZjRVRMDMHSwSCCESYyQQEPyZhxBJw0iIiTyOiERS8rOlqm4X2gYoIQBqmFZMBJPo5+XhIxKEYRo5nc7UUqi1oq1hzT/L8XgkpcR8PlPWFUUwCVirGH6ezMS9l/VzfLUlgqlhgeuZFczPgQgxRHISd+waQBWqouZ2DECCP5/d/CpGM6OqB/FmbhfUfYmfwf0A+lO42jf21+j2UFvzLaOG+Stf39f6XkP8dVNODHFwxxNAzBAz1NyRxBYIYuScSCEQg2CmpBho+DOprbCsM601XxdTmhkEt010e25iqBi1VMpWaLWB+RpK9/Sm0Kz/TFW0+f2DUbeCxtCfR8AkdPsUuG66bnPdEIYecD2vmZpStfp5MN+Tsq8nhl43727YeT4rfe38DDyf1//gX/wPrt/qBF999hptynpZ+mKOGEbFWMvGZZ1JMTGNAyEmwjD4hipKLZWtVNZlY102jtPENI3UtqJakKaouQE38ygYgxw9kl3b4sZAFEGopWFm1FoIITDlgUES4XBD1cZaVmiC1MKQB+6ON2gTzo+LH2qE9VLZlgshNiQY4zAQJZKHhqlyOs/UqoyHG2KMpBDBjG2ZARiGiRCEFCOocX78wHY+8fD2HbVWzvPM/c0tN28mSAEbE3Uzat04jEc+/+wrYggsp4+UAUq8sJ7fsp7eoVaRYGz1PSkFfu+PfsAXX73keEgECUiFIVbS0CAVVFbfwKFcg5Q9gICAkUATdRtQMRqNRWEJ8Id3Ez/64o5v/83/i5/9P/7vlO1Mayu308iQIodhIMfIkAZyGnn1439Enm6IIZNi5NXNxJuXt/yf/+A1L+4HvvhiIkUYZKKo8H/83wkfTsZPvlXeflj5u2/O/OI7+NtvjLkpsz5iZJQJ4h1ynxCrBBpDrEQr/PwvfsrpwwfKJaNVkQL3x8z/9V/e8vu//4J/9n/4mhJG/p///swv8sJf3b6CsrKFWwBCXCBUmhUezmceHs+U2ijA06/e83c/fwtaEZtoFqhWqW2gaaSWSC2Jm3RHm+DdaWFtG0kyhpCnQBoCdy8+I6aEhISqsS0rQYRDzrTWmOcLD/aen9iFdSu8ev2SMR84jrcsy8o8L5SyUeras74eoPXA0c1qQATGKJjA5bzSqj6f6yggEcONZAq70Q9ucCI9AEsYEbPo79M8hTIzaqu0Ygw5ki0S00RMA8fDkfE4kEMkSuR4/4JhHLg5HokpI/kIIRCp1703zxf+4i//nKqFQw60snE+P/kzCZFvzx/42d/+FdPtPXev3tBapZbtitp4Rpr453/yT/jjP/p9/uLP/x2/+uYbtmWjlcr56YFSNmL0zK9VBQXR5hliDyLqChI9GJYAIRpmglokjIk0Tm7Qm7LOK5fTxbPXWokpMQ4jW4PaAiUIG8a5rDwuF9q2QSlsi6JFkOSBhiT/jRt/IYQMQSCCSWDd/PWXeYYQYTDEGmKrB8wh9me/kaIwTInXr17y8vYVRgUal/mJUle2mmjWyIMnCK9eviSnRJs3WmtEjG0rfHh/olXlw4d3iATGMKIClT1jMmptLNsGIWAxMp8L8+NMGjI5DwQGUphAV6xszPNMKRtLnamt0Fgx4Hw+E2Pk1avPSTHRWEAqQzRMHE0UMfIQPcBokRgTU07sqMRcK/PyhNWKaCWnTM6ZpoaaUMtGa7U78z0bAt0TgOb7oIaAIMSQPwlGfgcnGGPsgaJ75RikZxXuiVuPYGttBBNiaBACEt3zi8k1PhUJHl2pYNIPqu3+v2dbaoR+vx4RGq1WjwSM7ogbQZUqoafQHhGpGWqN1hqa1B2fCaZ7xunRvUfAhqDX7NN6dNeaQ1CmioUAv5HV+n365/Kf02aU1jg39SyzNNpQabV4Bou/ljZP3cXM36tCrSu1CDEq4xB7ZKeUdvHss296bR6VBkkEwbNWwbMcMY+iTFDboyW60w99bcSzdECJWHCnmhB0XamnJ8rySKsLy5xoMVDTQIyJYTiSh4nxzcooA0GUGANDaEyD8fT0gRgWbm8qQ3Io2QBRTznNFJFKCJUUAzlHNAmmbiiUgMSMxIFoiYAyxkokcXzxGbBRpgFtG7kGXt6MvPnyDW8+f8nt7cBCYpwiaYiQBggDKp7FiIUOm3iMYM0dRwgRLUopSgSC7VmzP89alPPZA6d1LdRivhc7xBX2jNAgSCRKIuYRbUqRjRgih+nAtm1czmd/lj1zTCmRc2IYhmuU6miJUkqhVn9h6xG8fZLtPecYPePRZyhzTxsEv0dPShyC7KH0FTXav3brsaMo7Nv7alccuUnRs+EYIuMwMgxjj+yFlCIhRoaYPErXRm2R25sDbUikKtQSUN06CmOEDmlqq6zbijY/L61ngkEiEqGUjUs3tq1pv/E9KAi/kWX9hxmB9YxOBELr3xPPQoIIUQJJIgRIIdC2SkA8m9vX7HrczW2DKrVWWmmg9my3+nqbiu97sathsytq0NEqiUjwM2vySebeM8X9T/vrqim1FrayYub2YF8nEQgSyDkjYqQUSSmSDxPaGqUsHtyol6lqqQQJxJS6Y47PGay0jnaBGlSE1TYUASLrujEvG2Vr1ObZmOGogTlM1/eO22FrDlB4sua236TvZdkDda6ZeUqR1qqfgbZdkTFfIuu2rj/t/gyJHcHpNjlI/73INensK3m1in/f9VudILiDWC8nzIzxMJI6rKcoWYVajWWuhNiIdWMYRuJ0RCwSwVP5MZBi/A1YQTCCGCkoSZStrLSmaPMNIx0hLHV1eDMPqCnbsmAY67YQQmRII80a1SpalWpe7Evx4CUEAqbdUOSI5EgUiDSMRtPKuhVqbWgtoEZwd0EKvtDa4VA3JYJWt6xa1aMqWRELiCXKsvD08OGKipR1Zl0uLJcz89MTeYykHFkuCnLi/jZzGD9Dq9CqMv/yA0s58+H9O4SNLJUUI5+9/Jq6tb5/jWANi15vUFU39AbSzD+z+AFEoZnRFGoYId9SamI5b4QamGTkMB5gEKKeEN3INhHJHKeJ8Rh4fRvJh8C6bpg2zo8zugj/7//PL7g7jnz95WfcjgNf3t+wtsp3y4n3p8ZP3hXOS+TjJUH+kt/7wY8YpgP5MDGXwlIKW8tUHa/2LHm1mDdv/iXYSlvPoI3bNPHymPnn//tXvH6RWSVzLkI43hFvFnS4p+ZHSoyIBXIDmmCbO74URyQaEqBSqSHTloW2eW1ITNjODdXG+fQLBGPdKq0qbQ2YCk0KpkougSCgxU/fOEy02rjUE3GIvLx7yXw+83F7Tzpmbl98xjLPbNtHcs6M48jNzS0pZU5PJ86nE0+Pj5wv52uEe3q6sNYelBlEiQQThigYkaLF4bAdvYwBxSjVM7yEI8IxZZoKRZ/LE6Eb0ID/KklJ5uEtKtAMWmOImeN4dEcfIjfjDTkPvH94pKnyJh/IMXM7Tpgp7z6+xazxJ//ojzBV1vNKKYXz5cR8Wfnw7ol4DOTXCY2JljLn8xOX+Yy7DDdmQwj81V/+FX/913/JMq/UrTJKIHYDJyH1AEGvEF8/4R2+NKypBzWtEqKQp0hIEEevikaUFBNDGgi1UaNAikgG04BWelBslG1lWxa2y8p2XhhVSUGIAc/g9BODKaBB3QnEgBCJYSDESIyZlmo/qAIh+Z1YRK2hVjw7C9BqYZ4bZfmGd/JrpB/wmIWYhOn2SB4zx+mWGAMRIwT4/KvPwWDeFpZSKbVSSkXV8aHVVlLKTOORkAJxiMg8U+bNs63W2KqyBSFtjZQ2lmp8eLqwG7UQI2GciCi0eLWP0mHQtlVoHvTsgdW+96KYP0fzQCjmxPF44On0yHcf39JqpZZCjJE4RIxKaRWIgDt6SRGNERNBtUGvoZsaVdxeUzzVIcg1pvmdnKDXz9QjMWyHqj+5Qi96O0Yfo6egfnA7ti6ChD3i7Riz+M4R6HU8r8c1bX0jyZ4dX/fL8ybbs0J3ontdL8jzhzVTtFUQI6YIFoFAiMGhoZhIIaJUjEYIgRShSUXlGdXfa4wd2t7/rx+z5/qDR1TiUBTKuq3XA1H7feyRHQQkgGql1uZEBEnUHqJH8YLutlbmeWUuCzEEhnDmcu4Q6DMK8HzZJxDZ9T57LUgFxQghQTqgGtjWhkhkGA8ELQjKIR1IMTOkiZgydy9uGA5HXtxG8iisIbjBscSYFNpG2ZTT0yO6JIaysbbCx/WJp1lZz0qrmdBGCI9Q35PayKATiBKTB0ClVarvZbyCaUB7NhQS0BDZEN5+vLCUyBwHLi3y/mnk8QKlQVWjNUVUSXtY24MDld1QGilGUpjYPEVEqyHV91StoOakg1p7TbgBKlSrhNAomx+bddtQgPPF4c9lpdXGw8MD6+qReCmVZV0dLYn+WZoqUfU57jeHwmJMWKCfHa+z7Vt+HEeiBJZ2oWnFgQq51o725+1Zkvl5iBHBsw5hr3V6IOpRtPa80/dzcKihR/lQtsYyb6RoxKCs6+bVjuIZxul0pmyF3EkJpRZA0eiZd2vtakOutXwRQogdPxTnFMTQ793PkZNsCk0987Kq1OA7Yze4z1/dVOwmY0c91FfE8SoPqoMCwaHbJk46yTESO5koihvZUt3mSc/kzDyQdI6GZzYeSOzI1TMKY31tlW6MRZ6z+RBA/XO4zQpXG2ImqHpgItHRLVqjdtQq7HbJBLHQyzKBFDMpBw5jJqfE8eaIqZFSdij4EzthPQEJZh0p4Jrtx17DFu37YedCqNcIZd0AJ9Gk5HwHiL7Pwo5Y7HwO32Na7ZqpOQrX0bjuF2LOSAidNOnomZl1PkXoSGQ/wLtDEOnPQK6ZvXUfI0BQd3wq9Hq/YvqfkAmua6HVQm0V6Q9TDYLWvrTOKBsm8wh3GqhVWZftCoEiEGJAa0W3RoiRHDIaPLPS1mimbK1QaiPSPjnMQiJxJXpIIOSxM4OKfz8lgvkmNukHXBt1nQliTDdjZ3tmTBqIcjwcmHLisji+fjgOYJ6Jla12ph1o9agt5dDT8p5lEmhA69DMEDMpBqacqbXx8PR4hQZqrY61B783SUYeDLXKtjZuxkgeIudaqFUZYqa1idPTRm3G6cMHUOP00djmSlsq0hxS8UK+olpp2+LPJCSHcAjkkDiMiVIclk3DAQ6fU2vm48PCEA+8fHnoxIeBH339mvvbwBQP5JB48fIVw3Tg8OKIhExd3FhN4wGxRt2eMC189+7XfFR436BY4dROFItom4gycic3bMuZ9fJzmDMyZ46Hibtx5CJ3rHLktCrLak6gAOYaKAq1uVH5KEIMyunhkRRgDq+p4cB5/CFPj4WnuTCvhWVdiboRpSKqSA2oQGmVFIwhKIfDDcebWy7LyLwM8LjQzitVDYpSugG2TpqR1fxA6YIIlDoSh0hLTqLS777rLDgnhHzzzc+dfDEMrKfG9k1jzCM30y2GMC8LLTa0NJZ54bIU1ISUJ3Z0STmxFWVIbuzefP6KcRj55qc/Z26FkLuhFDdve3A5pEgIQh6ShxLmyICEcIX0W4eqadVZl0EgBIYpEXNEJVJV+PDhzOPTzJBHUkpsm5GHgWpez3//8adIgK8+e+Xvlx1SXM4b2hrzeaHUymWeqVujlIqFzk9NHt8MKXF7OLpRDu5gt7JRto1aij9DM+YeEITozlO1dfjLHWrDDaiKdqMbMTMncTRYtkaMlWHZSDGj0cg3icNt5iJCa40pTdwe7zgvJy7rmUAihYyGQMFoElHJvc7aiNGIYQ+In52hr4/D60GNWBtIZAgJE6+Lp5iYYnKEwRpNA60GJAuSBSsNrQ4dixiSEiFGD5IExmHgMI4M48gwZH784684HidyGljXjePNHdvaSDGwc3/MoJqTAJttRBmdpGUw3imhNnSraN1odds3Im2rWFuoeDkshUgSYTj4vnjmGle3Pc0z6VI6iamTZVDz4MeEkCLjcaLVyocPH1jXGavV4eqcSSmTckZ34iA73OowajQv+LRu90NnxaYaMIUiHvh6YnXNDv7jnWCrlVabv4TQI0quN9SaQuhsyp4tWY/GQwidMbX/e8/2JASkL64BrTp0QPP6TAid6dU9ve1Rbo+chxRRFUrxTKG2RsqR+5s7jwDNHItW60yw0OnZHRsWr/V5disIEa1ueYY8edQbkx80a52i21e64/gaQmd6NSR0JySB0hm7EsI1s1WcpaZWqTpTaiaUiDRFRLEqxCicnxbWdWO5FLaizI+FthmPH5w00eYP1E1pJYCmK814Z7Tt0bt1Y+EdoL4JtDOpUjTSYIzZCSj3x5HDmBjiLSlm3rwM3NzAgEfHYywkA+YPIJFUhCAwdnZsbmuvW1SkQSxgVhm1Ek0RNZRCsxUrSlkrLQeWFAjDSBgGVrulcEAK5OZ1qCACbWLQSK1+8Er0QORSV0QCa1opIjxd3tPWMz/+7IYLR37+DVAhST8M9Jou3npQrBG1kKxSxdAUSePI0SIrK9UqghOmSF5XdYIUrPOlU/MNbcq2lueanDoBxZpR1oIGh1nVlBaMJRW2XIkhEkNiyANTHrjMC9u69ZqMnw/okXIK3L96wc3xwA9/9EPGYeTtL39F6zUhCZ5d7BCgiJByJsRAzs9OUM2zZNnrJx6Z9vd53t50iEot0AJO+iHsKQilFbQoWykYcDhOxBiYlwvz6kbHMIL6Wq/LRtPGtvUWowDNKvNyJg4DOY6UbWNd117vhm3bKKVCd357DdSDA89QkJ6pXN2PIbEjMj3f342ik4GcUYmZ1121Ea2wLjOPT48sy9I/uxP6HF7tzHMStRaW5eLGWDor0fx5h4AjXOzIkfR6oFFVe6bbHZi4XXMmc2SvAsY9u4yJTv6GnumGEDufIvZAxuty87KiZh25Vh4fT2ylkGKmlErtbNedqONs8v6McZartMK6Lj2zryDCMCSaOMoWQurJj5Nndna5iPpniW4jw7X22bPL1Gu2knug1p9R32/a68JNnIFaO6Qt4tlfzBmR4G1s9tzW43s+EAnPSGEvH+wImSEgkRB78PBJiPI7OcF18R4U6wfOAlgQzAKtNZZlJg+ZcZoQnDLbSqMWJWQhxt2B0dlvbiyx6HCFwbp5thUJXveIPXvs2V3th5TWiEE4TANVlVPfJEtZeXn7gh//wY88cKnG6XLiw8MDAcgIUSFY881EoJbGZSsOSTAwzxutKi9fvSYPiXm+UGpha97ngnqaH/AiQAkDjUZtW49UN6oKm0ZyGhkPN9jmLNgWPGoudmEt76lz5FKjhzAKKl6bvDw8sC0r5wdDm/BOzqQcef/+zLoW5tM7RIVjvCXIQAlOBCrVMBXUwnP02Qv+qspa1w4zKGOuvLjbuL1ZOR5mvv78yOuXt9zf3nIY4WYq5KS0ckG1UM8f0LWyvW+ICmMcHZodIykGxjxgeAZNAxY3+rdmVFtZ25P3bG4r4Xxme3hgrY3HUmnqtRAZD5AHppvMcYhMk0OyZm8wO1BLolngw5TZQuQcjjQZKeOBrS28e/g1Lyfh//KnP+DxbeFf/TywbIHNRmatXIoiVQlbobTK2jZKgqXXDDQF7l7ccbjPvHv8wNN8JofRSQTjQIyRacgI8Otf/YptWwCvnZyeVs+gUv930wHdCpeHE0pD10YD1rcdQimVYZo43NwyjiPTYXKiVHPCUYwBSU4gCykyHAb+6E/+MV9+9RV/+o/+MWPK/MV//+es2685jNEN7DAB4mscAre3tx5IpNgNs7CVyukyI6oENUKMpJyupQxtzha15kEvsaIxMU3eY5ays0y3MmPFeHx8IAThj//oXzCOA3/xF3/O6XTmfF56QJbYwUE3AMaQE3eHkXVZeHp8YJxuuNFXXC4nHh8/EkWIIqzrylY2bg4HxnFgpwRp8LagvQboMLehWkEgD7kH2O42U3LnF5rvSe3Q7LZUWjDIxjpf+PDdO5xskyjbytOp4R2RA3EYICWW+cK2rmzrGbGtZzQQQyKnwLbZtX1LMYjdNtVKDpFhmhxuxCHqaZy41tcskAleu8pCbUotO4Tn9xWDZ/cEoZSCtsq76s/g5uaGnDOPpydiCgzJ6+vzZWOrRgteP9PqQYCIoVSWAmstXJa1B9ONcZi4O95Sa6aW4ZpUbGujbPXaKtaxOra2IFHI0eu1Q4dgx+PRP6/0boKygSlxTGhrrMuFCtTF65Buw4wUBtIwkG8m6lwol0KMgRBHRDxpyMmJWktplN5DqlWvMHlIzgbNw+7zn1spficnGGPPfnpko3vjYnyul33KnNQOkzhMwZW5aOa1OFPxoEA9S/DbVP/as0x1h8ueZPcPoaqIRCc5hMg4OeZeasVw6NYpzo2tlF736Ew7OlMsOIasm0cfYWeZiUfUtdVeUHW4UYITJrT3NRKTR+ohemSJY861VUTF61kSiK3S1GidtanAOA68eHHHvC6s84JWj2JC8qB8HCc3pma06tG69+E4lOEBsOAJrEdqe1Tf8HqCR0TSIUV3intx2te3ga4ECilUgmwIK1E885vnC7OuiK5gjRFhiBlLETFhCgNRhBFIJoymfk99j1l3+NWEZs6crAJZFFpGy8halCEppUCpOCGgzsi8YjWgrdDSQI4TITRCjTQTchmwGLEkBFFaeUKaIMuZnDIvpzcwKolCoIB90qvWu8DdjnS2WtVrBt1INNt7SsM169iNXTCvAeU8+KHSrS+pZ4pN6TT8hCTI4+T7tteCpe31DHcO1hqhG41ts9+Ae3phlCCBIY/E5O1H52VlYesN9b3mJdJJGW5ExLx2OE0TL1++YN023n33Afp+ZmdtBzr8uLcQCCE6gcy0EWPumYv3bOU4kIZ4RXNSj/SXdXG0hF7L6/t+P7/O1DOsNYopM0Yt5crY1d4O4p+9f37z/Gh3XPsZ9XplutbWd4itNj//XhL3/8T2PmSIORLMIfE9C9kdCtCdjYA4dKrVafx+9hsQHOnA94vXoa4//mzvsB5kez3U8Ew2COQhEWKiicOEEr0VQpsSrCNh/Rm2ppTa+meOex7ssLxaz+JDXxfPlpoo29oIRWm9pjzPhW0rCIEgEY9SfQ1CTEyHA/QsUk2dRIY980BsP9RCEA96ld4jyV6D1V5Gal4bVyXGxrKsxNw6qtFbI8zLVGbq3AQcWbTmSAFmCAFtRtsarTlL1dTbOPyJNmgFDV6zVd3LAIK1Xr/H1663/BLluR/9d3KCwxBpzXvLDNg2I0ZDgte7WmuEFnoRGcS86K8oScwbNwlOT9aIaOw+z656bYZi0gkjIuw5tyVnOHobg9fWDCEkV19Ix0PvzVtQg4eHJ1or1G2lWm/f7Awy64iVR9vQLpW1bqQw+gaJERHjsq7IagxjJMRO3ZVAaT2LzJOTDa4U50BrhdIWQhCihn6oE7VBteCtD8D9izt+/OOv+elPf8b7t9+xFVfYuL0dGcfEZ69eMY0j2wullsbTwwPrunYsv/ccIbTqlPtBvE84dVi0afwELur8VnWjFaJ/dqxAOZH0ljHcEPTsTFc9EEj8+u2vuFwemMQZej/+8kuO00TMgWCBSQZn/OpGRJmaZ9OY1yFbrt7LowmThMpAs0q1xO2YuDkMlCKUKlzOhWUuzOuJtS60UlBrbPlATJnDfWTIB9rqkM/ESIiZJEqTkdo2WlHy6SPHfOSrmxekYyHZhdDONDsDwXv7UkSByEiygNFgq5hVf89+Ylo1RAULvietqjusXvs7jEdSTCyXj6gpURImXrsU9Yg1hEy4959rbeuwlDeLa3ToO5gxpcTd8Zan9shaLzRVCMEzMlPyODGOAykNIJFvfv2OuhaWdQOEUoOTAKJTE1tVchJub+94/eo1//RP/jPef3jPu3f/DWbq2WEwUvSgrKkSYiLmTBan2TujUnvvY3AUo124HSamw0hpC60J02ECgw8fP3bkJjp7W6v//F52CAlTpa4rFVjPMwFxqFk9yLNuxHrHkhvt4MZws0oefM1yPhBCclthMOTJuQLrciXvWQOl9vKHO/Y8ju5gWqLVggXP9AJui1ySowea6q8RYnU+ljqZptSGVVz0I0aiKFn2ktBzsH0l7VV3LAElCUxHD2Q2xb1niZhVf9YoEhzO02psW2XdGsM4MMTUAx4PpMw8OIrRWx2kQ/61eLAsgLYNVWVeV4fNicRgSK/XEWAYRl6//IzaCuf55Mz4HgSsxWvj1YxokWhCiokcxclKtdKC0mRPTozW4estuOOuCDElUpQeYHnSYGXzc3SYvJSlStsWLqfZEaZh8OCgFApCIRK0EZtirUKrBCk9AOktcnvA1pqTqEqHfZMHMoc8/qc5QesYv/Vi/XP511mgoSsB1KI9wvYNJcEj7ohQzdCuAhFiRNGrsZaOT/fwzyPBwJXxuWeDdlXysE6eEXLKWICcnAE1zxdMex+NCLqzQVPEtFKbq9uEXmsZxuy1tp7JGkYrxTeKOfwg4iwss3qtAwXAgscmIQaM3uPXm/5bU6RUmgpOhvXDcbnMvHv3nst5Rpv2nrFAHjIpuTFKwwA9S6jqjlp7E7BHwnKtje5w064wsmPfPa5mPxUi9KflKhp1K97gqgWlojRvqQyBp8czHz985KuXL0iHzCiZSVIXy4IBJyonXGpKiB3q7lF48MjZE45nKMzMHbZ0OCYIpAxZI8SR3IR1iw5XrpV1qRzG1XvGgke/2QurhFqoQRhNkKbcpo0pRLbtgW17otmG4X1fwQLxGgE5jDLEzDRFjlNknk/M84lWBK0ViU608h5Nva5hHrwfa6qZGAyto0fEMfs+a+Z9ic0N1g7PtOLsuujYGRIScWcUGlhr1K2wLqvXFkO4RtoiDazy8bsHyuZQQCuF0oyQh87A2wljfS8KLPOFpxj55Te/5On0RCvFSwm9ICMhu8O1CmpobR6kAjsTz0wJRs+yjHFI3N0cHd4y5SleqKUxzy5lWLaNVivs/XCfXEEiwzBiTalbQYKQQiZFZzOWEt0h9b1CcPbj3j/ZVKAFwMUkmnaLYV2KrddQa2+S9jMBLbSePbUdzLqqNqGG1ed60m7rhJ0G7mc9izuAUrSXRTxA3yHr53pkuNpF1JDilisAMQjT4A3qro7TQB16rrUSMCfs6Z7e0xWruj1k50f09enEMVNDgjqZCxgGly7b89LYszDrCAjHQ7cVjZxi52s8q/zs6jXFth4Q0P9N8myK6PcQu/WR/ZH1jN8lcDBrlG2ltYqm3d56M7yWSrLIqNafiWf4MXuZqnUkADW08y+uSjX9tc12ZM+6TfNyknV1maubaj1LDa23yP391293guwk6r0Lh+vDFonE4J3/Za2uVpE8pY4CaXeCzWhVSeJZnEm96v6Z6rOx3CMp73V2w7E7wf335rT0bDBNR49CB2XbVk6Pj5+cvEyIAzElhjGzLhdKeU6T0xjJY+J8mtnWAhb6a89oq9SQen3lztl/nS7ctoKmCN3gpCFBU2pvdPd+vYrpRlOhtYAkQxI8fHzkpz/5GdtWaM24OU5M09TbNoQ0jl6DskYKwto2LtuMFq9JphQ7lNx6P264QnZi3tu4tyt5i0ghxeAtF90NamnUZaWUlc1Wqm0oGYsBi5m37z7y7c9+yef/5J7xeOAoIzcyEqkEIJnvhGDeixd65qtrQ4OiefMahgxXZyxd+SSY/z6Y4qoRAUmRA0ewA6fzyrIUnj6+5XyZOR7vCTEwDI5mHpqRW2IrC5WGsjKYkcaVu2Rczr/mfHlH1QtK8QyZwKARCYmQMsM4Mk4TX7+554dfvOC7b3/J+7e/4runhY91IyaBMGCtEy82ZyBOh8zhOGFWKFtEpFKbslgGFWJTggXaCphRt4Y2r42LGCm4YcvD0PeJGz0rlW1ZmM8XJ1nE6LJTwWHhsim/+Om3xPQd6zbTamWrShyP1HVjbxEQgZAjFozHxw9cLic+Pn7w0sA8I1EYhohIAhlcLabv11rb9QyG3vcW1eHB2maqVm6OA29evWScEiLwTXrP+bLwi198y7Zu5NSdEp280Z2DC2gEhnF0Sa2lOGs2jwx5YBwGylYIJCx0h4Ii3T44s9ORlFo3IDhLP9DLI5CiZ4etlitpCaDRG5maB+tJfH2nGKlbY122K1K0twu4+QloJ/ZE8T7kqotnGc3LOCEkYnLHjEDIqWeB4sztzaUhA66AdTwe2Wrhcn5Cm7qsa22U4mpPLhvhvckiLqhAcEjYySkeDLpc4g7jOhy4qpOU0hiJ0SUGEddvjQqiCXAWrNuw2Rn13uHvWWB38M2aWxEFVGghY59A20THgUW9TLSbbgvRHXTXhd6WjsIMucP+njTUfoansXVSUSCFwDhlWjVK2Yl+epWdJKQeZHgtc08KYrcpzTZ2lrREkBa7EyxIs6tG9e/sBLet9/d0buVON96Kp/K1ea+Zlxgc95euBddU2VqjaqNZJedMTpEuhOLOTZRat46razeavp4xeqQQY8TMBXnpTKWmjWVb2bURnbQSnYlnzfF7MZI5VBckk6I3t2+1MuRIipFxyOTokK9rH460mq61o6rOXkm5BwCpQbCu4+vuOUpgiAeXoiruBEJfVsMhFWmB9VR4kLNnqtqwTVnHmeEwkYZMDBeWXNB5ppaN9bI6G1Rb71VzGCi0HhAE7Qw570NMSdiD1R5E+eGO6k3TsUfp6rDGupwRuWMcE2WbOdfNIc/hhsN05DAdEAnUpjycz11MwLNOWiXHwKvjgRQCaXAx6DEeXAlGhLUUnuaZeV05nU9ctsLj6oIIrSkxD4ScvQ5jYEQYR5iOoIHH2pjnmdgcVp+iutguJyIR0+B0b9loi/Kv/+JXfPj4HR9ml0czsq+/GSEpKVmvFcJWRs6XwlYS2B2H44hMlcu8sW2u0mOa2Myhpa1syALLeqFs5VqEV3OUJE8uXkwSh83wyDjmgYALQpjBusweqZtxtjO6NZbL0ve0ec2l9drN6G0Nyzpjq6CtdJarICGSByejxaDsh0aArQs/lFp6zXxDVGgksIZZobXmtTmCKw15aZNkgmigdeq2EjBJPDycieE9X331iukw8uLlDeNh4OnpzGVeKdsGCtPh6D+ncr1PU6WsG63uGpyNahsUsHMXYLBPOm+7E1Pr2Y54huSqSd6eE1Oklkr9pGc255GE9Sy3H4JuLwzr/b/ixjMGhjFdeQ10ByMWwYK35VSHDW+OR5anlXYV0pArDOp9ynZtaZUuFrw7qhgjOWdujyPzamhZvW1Apdfq3ZEHcafimU7D1J2x9ws6DWVnWboii/XM1QhdzDp2JShVFzOnNQThMB37eipNodTA1pTaLp6tFeuKSrArwOwon5c6vM7me30XOekM4/7UPLkQTB2VCyl1JG8PhXvJplRQWObtKtLuhCwP5FPvfW0EYoqknP3nDUcOhkAtC1oLom7LgkR2veirszYDTbCjjtf04HdwguumXY7M09mqHik4WcPV70Nv+vEHWD3KGAZqc2ZcbdU9exoZphGKQXUs3sSorbgDsto/iG+uHJ2sEpJHljsNWdWw2jjPMzlFDuNAkEiQhFr11zGHsrzHPxJlRGLwjHDdiDfOaDpOIyEE1lK9pmDBlfD7a9RO5R3G/UwVnA3ptbCEkCSQ8g2lVIriUSJeYyF00YCizA8by9OGUBFpLOlMjIGb1y8Zb46sFyVIpF4+0srKZd7cmLXqBBf3deT+yBwFUVQ6ZBeFoF5S9XV0wyHRJwiEuBOPhLKuzPMjEj5nOiTWy5nzshGJ3EwvuD3ecXNzi/QWlG8eH7gsK+dL6ey8lcOQ+cc//IrDMPLyMLhcGJkmxiyNdau8fXrk4+MT3/z6HUtVLsXZw1qV2/sjt3cHFlUKcHf3imk6IjcFJPO+VFrZIBsxCr93bBxyYkS9BqVCw0gUHs8n/us//0ue5gtvnx7AAkEmshiH4A4qiSvRqBbWZeDx8ci6ZMxecHtr3B+U9x8eOJ0uqLq+ZmmNVpR5Xai1cLo8UWvxWmDPGAiJ6Ti5IaA5CZTgGVB0EfZEY9tW5supn6zAdl442SMq9BYa7dvW6yspZUiRy3Kidhje6fVeAxm7E7R6YRdTN4xlW3u5oT0fZAmE6qzKtqvQNMWb9bJrXybPGoK4E1SznptEvvvugXnZeP3mhtvhwGeHF9SmPD4tpKcL7797woCbwyuESC1drg8oZeV0+g5r4vqRNFZb2LbKPBeu/AuDXVXkKjStQHG5xpgCJDgebhnGkQ8fvnNoEa+TTYfDldzm/YH9V/WAmMAVzwoxkA6ZK3siKCoVNKA1YF2t6jBN3N/d8eHXHyjb5mUBcWSo0ki7EyyAGLH3Zipg4pyCccy8uDsSpFHXC9tWKdrbwHorUoqxN6a7yo02JwESU3dCesXLXILRUR8Bcs7uMHrGWNWdm2xGCpGbuxeAsc5nCqBFvHdzO/X9tJMVe2Zn7nydXdyzztZotN6SswuTCOxs0RB6IOuUzHEYEYFtmztE3Z3g1lBRzsyeuMjuSHv7W6DX7oU0JvJxcBu4NXKeGFJkm4WyGFqsr0HsDvmZZWIY1F6eYofJ//7rf0I2bVef6HHarqou+/7p0Z06w9Pg+r093R1SxLqqSrVGu0I4AsE6T8Y6ecWu7+HO7LlVIsqufu8PK3jIde1dcSPv0cGuIKOlsJwvXVxXOyMzsq2Kto2UdjX6XrfqKgyt7uUFf/C3dwfGMfPy5QtaU96+faJslXJeMAIhDtf2C5B+CHqE2fu/6AFq7DXPGHzza2tsy0qNLltVLj5ORnHo4ap6gD1vSroiAs+1g6Y9mzA69b1PdlCP4By6b0CB7YKeC/Ppkfl0JLQdkul6zCreyqK7TFVGomLZa4fWQGNkLYUoQjt0EWpxtY25Vta1eT1HEsN0RCwQLXYWF4xDJKaIrjNrWRlLIyRnyoUYrsK3xQqtKtvWSAoxFVIy7m+PmAhLC2hbqaVRN3Vji5M1VLxRuLaCrhvSQJoQyWAj20UpszJEJYtyd3vP3f0rnh4fWJaZ+bJQq9e+VhEnTVik6q5Tq16XWTen54uhpTnJBwiWEHOGpTYj9L4pNe//iiH5fohumKq2Xhv0+rpYI4sSwvMhzsEj8RgqAC1oz/C641JXREk7fbE3yat+mmGItzr1M7WrbXg/WuiwfqMJqAg5ZqY8cD5dwBoNh9Hmy4VWKjkPxDjwwx/8iBgGygIxJm7vb3n4+IE//9cPFAKVQIgOj2Gxy5/tKIyTanZN2V74usKM2uXczuezE8aqcmVO4pNCxJ4VcPb2XukGO7LLsjmqtNVCSpkxj15Tkn7ug/+gNu8VTBIdB7NdIGTX8e3ru2eT4mus+wkVyCmSU7r+OvS2jdiVe3ZWscN9zp73sqShHRYW6X3Vvee7NYdqnSIgSK2oKgsXr6ttjl4knMDXrPq+FM+Gd/u8y6TuqjW28zlwcktMCXpgcq15yh5f76vcS3jdtllfY+3iKp/2Qgbh+jpNW++ZDB3F8OfW1O/DxPcqJTj8LA6Te0aYiNORGrxOu08vCtLVkaq3XSC7tXzOSP++67c7wV1OqLNBa38xxZ6doNHVwf39zO22R28xeKSB31Tpgq5e1Hano3Ty7i46a4oYNNtnRQ3XzYv1URp7q4UJda85pV77ar0YC+4E181JEgjaDNPIulRWGjE1YgwuBNxJM5LEnWWPpmMM3L84cHt35I9+/8dspbJtf8flNPPweAKJSHaH7wV+H2Ei8jwhw9DOfNvtkjdg5xSxWtlMMTzL3M5ntBby8ehwSO1njd0g+Dq3Du/scICL5T4/bu3PviMQnnmH6hnMWqgmXJ4emB8PHIbcJwV0ySJzZ7ErFUjIPqcr420KNdICLNtGEKNaJUqiWaJUOJ8Ly9ZozYkY4+GGIQwc44Q3LwewimmhzauTYUojZHeCkoQcJ1QS23JBtbItjZiUYYpIgpd3AyEEljVQtkzbWq/FOVkJSd4fZS7Cbm2BhMujtUipibIE6gqHoEymfPnDN7x49Zpf/iLARyGlE9u6cZ4XUOWQMkESW9uoql07NFCi30sM4ap9KPuWdp2xHvgMTkTQRkiZmAdSCn2UzOL1315f8fFYhRyUxG4DjRxcpSTE2g1Hzww61V1bcVHinK9FvtaMulWcFZnpmKATa7ph30kmElzdqWqliQc9OSYOeeT8dGa+nHzunzaWJWMayXnieLzl9//gD8hpYj01xmnix7/3A37585/xN//u33gDPk6O85FiPqvPegtQq15u0RZAn5uhvWXFyXGtGeenk49hSoPXH7vdcRskPSPnmjuF3tIQ7bnu11DWVpCUSEPGxzw0ivlaglwDhhzSbzpB8TMXgniAqXvu94kT7GWhIUeG5ALjQ4pe6+sfq5qx1U4ojF6/dr05LwuZeCuIdAIQDaz1zLYnDYJAJyVaczWrunq7jWSv32ovAbRgaDB38mKdV9Ab0cWl7ui1vhBdGH2PJK6i1TiE66pcbne9N/kZrkVAu/JLyLk7Wn8eIXaRkS7x6MxYIFlHQjrFNeKtXpvXmFMI3Qm6HFxIAzG03mjvSGPqM2BLLR407MZ2V1v5LddvdYK1PUcHIpCs9+xEr79prx3shlnEM7I0RIYhMw4TqWeE27axFSeF1KZkiUgMSMqdzuyvoc03chp8dtnea+I+0VslQgiEth9c78Uz82hKfG5JL27vD6hj2leNvuIqL9UfcEqp64e6UsGYR2+mLa6g8NWXn3P/4paXr+5Zlo3bmxusGqfoWW7o6ig9XQaf5IV01pI2vRaSJUofiupkm1od4qjqIrdc201+8wF2wRQvYuPnzpUaukya+Tp55u5qF85QbG5BI1hoaNzYmhIW5Sc/+Rkf3r/n688/5/72FpHQx+R0pfk+n+zl/QsOqky9DlPXhSTKcVDGnunXpsy6UVQhJXIMvBgGptoYS/HnnCeHN4pi/Rl8uDywlUbKA8fbWw63rvzz/mljKcpdHBGM+1EYpSHlAShMweEmFAaJ6NqwYoxphM5nDUBrlRiNISWv226Fpiurnqmr0YoyTZlgiaePH1nmlaePjyyXFazP1Gwerabszm4IQlQFcaPXaqGJUAVXWOqNkxWXbkO115Lk2ktbq8N1Qz5ye7zxgEn0mvV7HUivYXarvocT7nhr6yr6faxM6zAgOLKy9lmfefDaSFOnilyVj7yIda3d+ASE1tuSmpNT+qy+L774nB/+4GsqM6VuvP/JA+fTmWVzss3rNxPDGGg6Y7XweH5iKiNPT5lleSAkJVYIxfsaa+lCysGdl9fJnvWGYwjXCTOe0FrPuLy1Q4CKzzqU3NWqujP3lzBfdzy7BVBxgeewW25Tx2yuTPbe1oRLlA0HYTgMXu/Nvce0349nU31agcA+wYXaIbqu8JKGAzGPFPMsn+6EPBgHmj/zZj4ZolV665Wbkp25zv7x8P0YZVfWwtsWTCnVetBbrko7aoncstuD3s9bi9KqsWu37mbL14qu9MWzXVclTxNDHvoEi9qZsR6Im/CsFW2dYtUZ7j50PTpjtFbW1dsbvGUC76e0rjFqezuXkCzQamGt2lvSEq2z4zWmrirjX7aY80z6pxjHAbUM5tyLti7sGrx/3/U/IaCtfeE7xMizExQTlMQustpDBoeyUiCPmXEaO1SSaI9P2Fr6BG0lZFdCkOQPqdeU97j0+v2+yyj00SKtECyQWiLEXvfq6gqYDzV1p9e8QN+cRos5hdsQWh/f0jFPanIZoykHUhKG49ShBGUYB774/A0vX95y//KWdF45Hg+UtXYBAAhd4qlrT7FHTK7Y2xugOg7hfVK+gd2Ath4VOl05B/Xv77hFv8fo4ZTDVNddG57locxhDZc7Cs9CtqXjLhE0VDQZbfU2guVy4pcBdKvUzz/n5fSS49GJBy587q//4v6ehjA2rwuvlyeiVQ6ykrtVaGbM1WFcyy5QfpciBzMOzYg5E8eBdV1Z5gVVZynGbyOlNFIaOB5vONz4tPrFvqOdFyY5+GsdIpnK8t1HMHeCOUVaE3II6KpQnMxAz/21Nx9HEQZJPXtraFvZNNBKdX1GvUHsyPnhgaqPPjamuNh7jBHrDMTdCfqWVJd9auZGvBMwrCsMOZ3A2xBCh496kuGZQFdpCbe3HKcDVUufDNFZxrI7wM7cq8WzgG6J+9QlgnY4vDlkYB06LbWiMZB35rK16yQCZxs6/xrZA67dENVnB2EuPv3mzWf86Pd+zNPlgfN8YrkUPn44sWxCTANffPkleRCarrS2cjq/pZSR8+nIsj4SkhFibxVRqJtC8mGwrTVK2bhafjp0Fna5LOM6psFc1g0zYvS+2TSEq9Tcp6Wf3QnuqYirttneBeJr4fUWD4BFkOIMyZCStzCNA3nKjmiJXJ3g/gz3X9W6Q2zdCUgfSDwcCHmkqk+54Vry6Rlkb0torfpe1Z51RblyvXfimH8U3+tDzr0OBlvpE3D6Pqi14HNGPIiqrRG9nIqo2xuH8b0tRXXPmvx9Q2+/2ye6A+SUmaYDZVmp5kOJJXipwRz/BHqzuhllWQkSuLu5IabYVaM2wuMTCj149STgWv/ttU9RL95qqxTdiDGRUr6SzWxwNCTlRI7ZZe7a870Ow+DOUgutVdZt4bkD4X/8+q1O8HgzOTO2q7xHnK05HSa2bePDxw+uo9lTVcW18sZhQAzKumHNiNEpsdPNkTQkWtsHToGEAqrP/YK9ZrZTxnPaWVyV1mCwoR/ygokRurTb1ueO1dohBKM7JnDqU7uSBSIuHbU77yCujr6Vwlb8UAYBc44xf/XXP2WaBoYxU6vy/t2JZd68PtbhFZIx3ESmm4m71/ekIZKHRFlW1nlhucwslwu56wbmwQvaWfa6m99LWYuzpZLXhu6OL/ywVR8VtdjiD731qPFa+9GO80vfkE6WiNkz3ZQS0XUBKC3TNPkMshh4+zBzWr7lf/mff87xzWvS7ZEwjUx3NwwpsWzmZA0qQYx0yFhzoeRLaby9PLqmaBbSNDHev3KoKvSJ46KUcqHOjy6kvFVO5yfOlxMfHt5zLjM//ean/OrhLaErpLy8+5L74w1WfITQXQgMKtgMSWBsCSmBp8cTj48Xls2HOO/QebRAypl0e4O12inxgjDy4v4lLz/7jPePDzw8PTIME4d44Fcfv+Pj0+mZkdt79nZy2eV07gbWR96M04SlwJB73SMI2hpLmIkxcjxOpBSZhsT5dOLd23fkITuJg55ZZOVcHpm3E9s295q5uRjyMLJcZq/rdIdXt64m0hX9vX7Ye6bM0E7SgJ4RbvU6qBdzHVa3/dL/rhMHrMtOBXea+7QEAf78z/8d33zza0pzYfH5osRw9OnsIfDhwwPn88zldMJUeXj4QJDAz//ub1iWhQ8ffu0IRkdlzApb6YhSb9AH2GtQngl3xmsIyLVXTSgSaaqUYkirvYYsxCFe63KYIV0b2GRv1eg9rSgxRO7vXhJyukLKmLdhWN2Q4MpU27ZwevpI2RbU+hABOhMyxF6CAavuWWvtRBETYhx59dlrbu6OlFpdxaonAMtW0WrU2QULcnY768nG3gPndjWnyN5PJzj5TdfihCYRkrhWbDVfi/HgUnPaihMY14JSuGw+tUZEicl6PdiebSCfwMi9xrx3ay/LQqvNhR4SxOwlpOPtLcMwMPRWiPlyopbC5XzGR+95a8TpfGFdCzFIn73Yhdd6T2hrnpmKRcyg1sbhcOTm9oayFcpWEPGgcuzD2YeUO6fCBVCWiwtTWBx6AJKJZG5FOsT9OzrBccqdQdTrbOK49t3Nkctl5vHxwaOA4NJimF3loLR5YzYKGpSQI0MeSNk3fis+q83VVzxyZrfpnzjCPLgTLJtHe6FDIlspUI20uQp7bRutY+ZBXb2FDklecfYeBgZxUVrbHUjf3KU2VCtl8WLycDNgovziF98S4jM0KaReItj7Vwwi5Bw53B94/fUbhmlkujmwXc7MpxMPH95jD5VgiWCJOIzk5JOnJcAwCDEYp4eNrbRrE/vh9kgIwWnmpbDNm0eJXYDAJGDimxvZt22H0mSfP+bN3ikqMSgSEkXlmqU/nBY+PF7403+WGF/cE6cRGTP5eGBImaIbWhtJGxrFJaBK41Qr67Ly4f2JGITbu8wUhTzEqzPeJaHKunI5nWnF0AqPDx/58PCep/MTa904f/ct+h6aBgiZ/80/e82r+4mi3ld1FGFAWFbfI7lFVITzeeF8Xjx4qZ80heNCBIfjgXVeOJ27qk/I3B7v+PLzN1Qqp3JmSJkxjCzn2ec49kxk3ycxuNxZKR6wRamEGDjeHAnRJ7aLeGZaa0VrYciZFy/umabM3e3E27fw67e/IuaB493oYvE4AWwupz5ZfbtOlI/pyDQNnQSyB3NuIARh6I3pMrgxc4p9F6boaKdibKXuYMIV4cKkHwnr0G3POHG4bTcZewvS3/3dz/nZz74hREeCpulACCOpt2Q9PV0QOfHw/tf+edYnn8iyOAIiEgkSyXHqJYCtQz69pLHDmHurhIBghLCP1Yne54d0IlzX16xOELHkZB8JULVPftHuVdn7VHdFpUpMmePxxtnp9CxKPcs0LdD3SS0b8+Xs92suGn39CpGQvEwk9HalWthFrSVkbl+84HAzUKpDvp71KFtt6KbU1SXoUoCQHN51m9WJhuZEvX0QQBDxXuWmWPSSRez1Se3Z8mE8AMJ86VnW5u97WRwWlNDtxLUdwXrM1IPnHdEwJ9cFiaxlo5bK4TiQxkwc3Anev7jl5njk5uaGGAIPHxLrMnu9TtVbGpqybS7SvY+72wUhQnA6qMoz/G3mU4XykHnx4iWX05lTfcKkYqIMQ2A6DOQYnck8+h6aL5XWCqGjcTkd+r6JV5j9d3KCtZNUUPURKNEoa+N92Vzt3WCfUUZIJEmYJbbF4ZnWqkMhKXNIkTH5QFlEWOfNWRuX3suzq1ZEhyzXZaGG7QrvjePokVSpJDPGabwedqt4MV1rn0SfvOcHnx6wF/xFeVZHMHU1d+nd+f6Ju0N7zrBMjXWtjpP76SRFVwhJ2cVqW28UsihYCJznlWJKDYa1Akm4uTsy5oAWJ95ohVYdJgLXpcw5go7Uorz98JZSK8PhgPRmZBNIOaE0Wh9TUrvMll/XWA6TZ+g1iCsuqDbEKiYRiT5cOCFYzFhIPD1d+PW7j7y5vSWOExoiTQSNGZPkQ0dxBqaqF/ab4vAxSquV5Xzm3a9+yTiN3L64R0JkzEesRXSLaHYW3FyN1RJTFQaF2BlstRmIj2rJCK9eviCHwHF9zyCNN3/4I1IUhpw5lZX3H9/z8fTYx/j0yQjQ+/OEw5ipdaOIKw0JQtHKUivD8YbP0lccaibU4IhEVxYSC/3gCUOX12tdwSdk30dPHz/63gmjF/CzD3utO2u2VrZVeZLCtq2dMj9wczySUibngcv5kfO5knJiBIp67S90QlnEiOb1FzyuA+tEg541xCCMg7cI5MNAqZUPHz5eD7/txTVXwO+voft2cTWbFHo9SMjiDrCaoDXw4uVLpnFi2WZqHzTbtLI1b7RPRGIMTNMBM3VB9QA3g58P73XzfdhqpXRhcZ9t5zydXdgnJQ/MvFWkddazkz4CwpAmBhEWm71XWRuURlFXo4m5swT7s95LA/TKQq0wjC52vpWNZV5JwWvoKWaGTGcmJ0ppXC4zZSsOqwdHcfbpEfuEGg1ew61BnrPZEBiGG2JIPJ2eOF02zLyVzGedRtKUGdPAcZqQ6BMUfP5hxYpnbk07WSwCIaDigtgxOREri/fkjeMBEeHm7ravucONeRqdk7Bs0B0MRmdcupPdSYDPk352p+vbO5oRe1aa08BhPDBNI//kP/tHfPHF56ylsiwrP/u7n/Dhu3csy+z1veS8hPlydtg15H4+fZ12/c/YvF9S+kBsIVGLcTqdHE3EOBwm0iF3UffE7c0NQx5Yx5FtXbmcF0cDtWLFmNsTwk6qeU5g/uOdoHbaPN3ZSaBZY503SlNcm1l6dhU6JTZQiqu911quaiUjQ9+kiRCcSNE6i2dvlPcVctC9rBsqgdplxXLuGnGdLZVivGLePslBujBvc0R8Z1LSmajRWXdXqSTdRy09L4Gz8MwzxN0Jmo8R6a90ZUlFyS4uLbjJdY0kLAjrVhwvTw5qhAjDNHDIkboadVPmU9cODB6JheDwnUyJGh02WbcNrYaGvZnYa1QoNHHWl+o+c6zXIz9lRsmzrJRrsPYMc6/3SX+24jqt61I4PV28fpIyJuKsxD7ZQ7o0kkpBi2feahDHAVrFto1iK+tDQfWWm9uDT9TOAzlDzWB9wPF42BhLI49n0rgg0vu5apdeEyek3t8cOKRErt8xoPzg89ekGHnAh3CeLmfOy9zFoZ3gsdeWg/RIOghN1A0Wrlm41UocMjdTJj+5IXUY0KF66TVorsGE7z1V66VaY7nMDhtGr7Gk0Weroer1u9Yo+DitUoobrZgYh5Hc1Wu2ssBZehYgmAq1t+bs9bpgz9ndntvsSL/gGonDOJBz4vjyjmVd+fj4cGXw+XiZ7mX2eGln/u0v3Juud1g3BGjFjdJh9GjfIbKNYq1De6VnrW7aUh7BjBI3AoFxvAMzmq7s2WYLBi0iqi7NRe/w6v05OSeGYaQWQau3OWmvHYFPi4gxULYVM6GoE/Sso0pTdGHpnQAkn66bPxrAkZHaSwwWPUgOIeKcsN4v2RQXF+i8B+kQcQ8sPxX3tk5GEcEDegnk5H3I81JYltaDkHjVSE3Z6115SJ1xGnc6HdL7OvdGf28bcRjT0SPv50vBORUSQkcn3AmeTxe0NVIeaeLMYLoAtvS18PcMXWSEqzMMwQNk2RWE8BJS7MHCkAfGceTLL7/gxz/+Ie8/PvL4dGJZZj4+PFDLdi0lmLloOn1UVG/GYe9+NNuH/HZUoEPwqso8z1fCVB4yx9ujDxGOkWkaOUwu1jHkTMp91qKqt/YUD7wkD/ynaYduDQuBeDz41OLDgVIKy/bRnU1be3Nn83EfKXlDfXVh4qZKNIhVKTmzSvJaTgqUpbKtG2KQJFHpM6WaXXtKRIS6VdcpXLbO4HRI9nBzxGeW+WEIaSUhoD47q9jqD5GRFnYHLj7Cp6u2xOhbw2mkXVlAetFcBenyYC4EDlv1sVLJXLtJojlUnLIzV82Nlm6FrRbqeSZGIyXPSmJnXoUsxMloomjxPrjLKVJWRVKimVLbQq0zT08fSDkzZJ/tNQ4TLVTK4uzF2GENuoTZ3mIShD6exptJfehE8sGgaj19Xghh4yBClMBXk/GDG+FlaBx1wx4qzYRhjEiAQz8kW1ldBspctV6GjBCJh0atlfm8oCfhURJpOpBvHQq5uXGNTqswhcJI5cWUMb3lrI3VGiU607dcFlY+8MXnr3lzHPhsU6bauI9KBS7bSltXHtaVk1bi/choxoCrDrEam1Q+vv+WddsYbMNU2IB3795xulwYx8Q4Jh7nFV0KUHh1d+vQVdWrAv00JmLKVJ1clLgbvcDcaygOs9XZySmCQFv5aB+6en7tijuBy2nh1/qWmHyad6vVB5A2l5UiJHJIjHliGkYu4eLQUZf22oEdkdoJH5Eo0bO1w4FXn33O+XTi7TfvKVYdNd+JGH0iq3REgw5VYz49RfcexTBArxcjQgsbqwWmu8whZJ+CADzOJ9SMu5ujizK//gxMeXzwutRhvPEa6eXEtm08PZ2dHRtcfm/kEyfY0ZfP37zks8/e8PjxgcvpxMePH7mshYoHR8c8kXMilkQrRjQlmFAqPstxrl21yp3CkLqE31Z6zVNZ1wvf/PIbFxCPk8/FwyHQMXk2r/i0iet4qxy97SC5E04hIMEVVLQHl42CCqQcGEZhuvH3Ps8uftGqO8EcU8/yGylVQnYx97JLkRnXs2wSu0SiZ/+qDm9XcTKcjK7VG4I3HWoXLPnhj3+EqnJ6eGAxYxf8D5ZdSH2HcqWQEELKbMVYqksA7nrDez0yBmFZXQFKqWx14r/6V/+Kw2FCCLSqPD2dQIIr7xrsLCS1Ln65s5d7f9/O9JWuOifBHaeKhwJSvA3IMDQZjAGiYBFmu1DKyvF2ZLgbuf84QSo09WRonV144LIsv0GY+o93gvvg2egOLg1jZ0JJL6ruPUp9VIZwxb0V+ocPnRbdqMXIwaE6rUYrroMZRfzm4YoNSy8Et+ajWqr1BuPJs7yYsw+mDb1lIngDcAzOVKzSCCRcL3HvlfOpDq3rll4bajsZYa9f9dj5ajBiiD0T2+l9vR2jI6kpB4dtOilHW4PiWWpKeJN5jj7ANcTeIG2E5Fmh1uqDWFUcFsQZrqaNdVtorZLC0Qeyxuy32KGxjvT4JvffAFyljUIveUuPItUiJq5ZKFYRE7IEhhC5y8aLLByCMViDxSWeco69f9DXTVqF1sjSs4Y+WSNKIizKpoqVwnbxCR/kyJhdx3Vng2VRhgCHGFlzpjShtoBGf0ZaCm2ZuQnKqwxfDeb3hbH012/b5o35poTR+7mGANaZtqrKMp9RbSTceTaFy/nM5bJwdxzgMLCus0t/AdM4uNSXunF11MHrqqlrOppFUEWk9AZtdzS75FmQSDVjwan/unmPmYTgg4XbuUOPweejxehD3tU1dwM+hmpnR1+Te9mTfGFnTFof/TSOE4fDkdvjHVp91l2T3leLQ2O/6QCFTyxur3m5cLF2Qoqz0n34abHAcfBsM3eYs8iGmnJ7e2QcR168uPNsTxcE4WY6eHnClr1mcT3gO6fryu3qsMTxOPHq5b0jDZ2gRmd7I6EPp+iZUPDpJvsIJjO78gw0uV3ZS4PS1Z8Ijuxs28o4Hbi9m9hPvYjP4WydKbtnkSF4L58zy9zWSNifibGLOpv4XoidIR+zk3jWrbCV6rqeXU9VUCIuhCDBm8OdV+FQ/BXF4hOW7B5w9efZmQ5o0Oc6sPkU+7v7O7Qp59OJ6zdlZ/j3dtRuFaN4zVVrB5IN7yHsDHVnigvbWqhVGUYfs/WTnzracJiORAmsm48Ya9Ztf3eCzz5oDxj3IQp6zQAJjmapqI9kw5vnu1K/80SSOCwcoVJQq9zmkWGIHG4Hmg6sG9TqyIuaOUv6P6VFYsy++OvpiRoDbXOdwxwD1kIfDGud8eMQ3q6j56LoRguu/p4npydXNWjKmDJpNNbLma1s1+ZWP+O+yTFzFqh5ZogpdXPm01ZWVK3rFjqhBSAOiSSRSbxfEHMiTVXPVtMYaDG7NJZBaY3DOBJjoM1eZ0h9xlrMAzFFjrcTIQg3pauYi0f7CW/AX+ZCU59aLTGQRrxOp66csiyudI4oQ4zkGIkh9ynjwRuXmzNgqxUsKHd39xwPt670YbAtG4Kg0TPWlJNHi312mZrXTSzIJ9B0V5vZW1iiEWKvhaYJaT7Z+Xh3w/1h4n4YuIvwMiovolJTxaqyfVgopt7AusPDqrw8vKZo46lsYIkkrxkC3L3ZN7EHGvpUicFnjQXzg/DFMPHZ5wfeP3zH21//nJMFFoR19Wg5/ujI7XAkeYMZC+KqNG3lcS3865/8NT9/euLd2/ecWkGiKwypiTcVr0pOgcNx9MGdpbHVim7FpzzUylpW9ATVamfXuYtpHYaqpkgTni4XQthY92yiw4aHIXtU3oWD94nvKXX4pff+hmHvT3Wj1TrqEILXQdKY2cx87l5tbK3x7a+/I2CU4gog1G7Dr7CpZwS2+T57/90Djx/P/Oqb71i3lfly6cxcJ/ZMkyMnpbkGJm2/ub43cAgKM+zi9fhOmkZrJcbIOfuE9Bcv7okxss6zoxP3iWMcOaZE2Qqnd48uQP/FBFtjO6/oVhlDIufIlAZKKX2ivFzHOxnG0+NHvrHG6enCclm65GKkVW8xeTqfiWufqyh2LcFkvE4tzZ27s0ONVrrxD/R6be1nO9Bs43T++AxFdqbq/Yt7Xt69YFtXylbIKXM4HLylqTVKUMSUEvw9lK4BmiJRhOn2juPNgcuysK0rH96+ZVkW1nnezRspJe9PDa6eY7a7YsOoDFNgmA6UPnItJof2Yy8L1a4UE5Ii0XqfoXJ5dyKIcH76CAaP7x8ppUKtvZc5IM3QpSca0fdv6bMgQxhBIuq4mguXFI9h6uZtPU+PJ86X5Qqtr2dX3tp06yIn7rxD8vPRzEs3lB4MerbkkGUvI4XeWxk74/4wTdweDuQhMwyJNPkwgHEYSTmTB69DSzPqXPijP/xHCIGf/+Itp9OFd+U7sK2fl/8EduheNC2lYE2u2dneM4M+C14jbjyuRNu9PS7qNUpzo+z1jBh8JtYuiO2bw2nbuwoERm+ncNjPyy0VVR/g6c3gFRd13cW741WSamfAuTK6sY94IkWkzzNTBUnRa5XFmZeS/KGE2Ns0cvZ6TnDSwubWzINx8160pk50iT0y3CM2VSeMNC00K2hKaIpMg0sySc8QzBzOIbqjHfKIJdde1Kb+DLCrVmhMfVZbq/3g9Cw17NiCXJ0gPdMNIt4c3EfZSPPMLsfMYRgZgzAAoxhTMEpQNPhoFKsVH9zrAY8QmPJE0MppLT5VQiZyEg5ToGljq2uns9cOz/ZTJcY4DcRhImGUdWaz6ONwFydvRBRnvTfwjjswmK1xahvvHj/y3eOJ+TKzWYPc95e6E3T1FTcc0uX4mhkhOKGIbjSMRpU+Hy24hdqbn3eWXKlO0dbWG9n3IChkRxl6LXLvQ4+74InHJX3qgWdVXkPv56Eri8ScCD1bbL1Voay+5rtAsidR/cV7xr9PM28o21LYpDJvK7VWR1D29CbQs0p8XqJ01SauhcUrNLr3HEpzCF0ClFXQECglIDFymEbv3SqOwnRpTl+D1tjmlRgCWipaK21zeDnQa9QivYVHYCetdZGAbVt5OsGybJ494YHl7tRK8V5j6aWFfYBv6LJ1e/ufoFc6vtfUu1rJPqg39LJJ3Qji9qJWD2QQGMbRxdKbXjkIpbUr0tU66e6a5/R7kdAF5WP0nthlZp1nd6iluDRj6kooKfcz+mnNymXDYuyyglZRrb0eFxwKzrEHveyiQF0yUVmWM6ZewxNz0fa2T7+wvbbYbU3fHz6CzuHmIIm9n3r/aHvLoPXePneqPqtQECy4IlKLjV2s2tu1Og+jG/NmbjciPcN0jBcL3XZ1m+WBUWYYvB1iPAwQHDKOBHJwSDkmV79qqty9uWccJz58uFCKOjrVs8xdMOF3coLTlNnnocUYuLmdOsS0ElF2H+vkRLdyfrjrJ+l88uJqVFfuyIGUI9FGTJPDdMVHMZnQBW9dgkwMx6/gSsooZfM+uzxhoffpqEOHYoFI6oeNnvMHJgZG67P0anV4LySnkyuMh8lZl6LUmq/SbptWQm1MbcAkkceMmCLL6hFOhwnGnHrEg8NmQ+xGdKTUlVIgNP9KyTd3U2XZim8wEXLy6dPD0aOzeV5ozbg/3AOwziuYkXPs1OVAKYWPHz660kST699jULR0aVnfXCY9IiuNcRoYh0AU78kbJBCbIWWDdWZQOEji9hChKWlpFKqrUuCzAE1gxUgilHEimHAw19TcZo/AjnlAzIhpIBkMPZqRGEjTSBoH/viLL7C28e3HJx4uCzUOIJnPDxP3Y8KWmc0q4bwRWsWGEY0RcoYY2MrCWitLdXJRyKPD8tFY2sb2sLIzEwmR43Tgogvr6gFPuJqxvToVCNapWt3oHQ9OCccGN1rNs6Ztvjh5pouT5w4L+xR2c1KM+RxAVxFJXofOkR3/vLs58MVXX/DNr96xrhsSHL7ftj4E1brD26E2cYZis+AGRgQZBm5fvPQg8nyilI3SB7sq/usyzz2IsmdUf4dXu86txF3CzY3yNHbCQScz5eQQbcJ7N6W5+MC7b9/ykBIfvnuHaqOWmaLGT//qJ248O/TVSu1OL/X+6j6pMjgbtDVl3irF3AmWtXQYLCJth/R9sovPiPOswv28w2ZpchFn1daXrpObgmuGtuqtB0HcGQ3DJ5MKejCsqsznM5fTE/PpwrptlNYIHS510pRP/NgD9v6YMIzz5cyyLizbijZ3lyGFZ8Wt4P+u9EkP4ND4TlJRNZ/OEjLHMWI5X1sXaisurLB5K8jrwwsOh5EQE9qUn1/+jqKFQfw+C9Uz5pxpKsxdElK1ItGeAwe1XkPpeq5XtTCeRQJivMpMSgjUzVty1Lpucg+ktGd60iUfk33SpiCCSkISHKKrOJWyoqYUig9MHxLbuvJQK/OUGeYBF+1s3N5tTNPEkBMphK5WA7+ovyTExNPDE+Xi4+JKK1ct2N/ZCYZOCInBpxnnGLtmpfavHeLvGoQi7AoXdP+/D2+8NoH2yCXgg2gFPsGG3biI0IkqArZH3n2B1a7G3nAqsqLPUTe71++Za3iOCKw1b0sIHtmpeFYaOjssZSeAWFfa1+LT7GsXSh5ieM6s+4aXzkDcy3QhBlLcQWyfYu5TOBQX5N4jYcfM98nz1ywiRiRKh4SVmNyVWXbHHCNOhY9OPojRiS6fakDuA0mdqRZxHO25OVpwQk9UiL1q6AKODbR1RrAwdIOYY3Tou9ehxtjrvyiEwKTZp6U3o7bmPZzgcLIIQxSSGcl2J+ijrFIUXh2PfPXyFVqUrFCZkJA5diEB+pBkirOAW+7TuMx8MrU2/2rXqkmvbTn8ZZuTclII3uTbHdUOPD3D8MKVp993NB32jFHIqbcXmI9wclSis0n7M92ZgjEGH+kjXJ/FTi4I0b92+Gsf6ZX6IFVxNlcP+vZovFta8V3mz9qfqwu1B9Lgaja5rBg+886ZrnrN7p4Pku0Hyvcwnp1L8P1LP7Ep+2y7pj4VwuF7j/570R9TZZ0XSgiUbQHxiS9alfmyIOKsZ9Qz6b3Oev0vdPUaVcCnEbiaTnNlqR5oPqMb1tfOPbn1zKYzCno/r1zPp7J/7L2Htnv/riYdpLcIoL0OGjrkX31cUy1X0WoXDZFPds5+L1drh5kLedRWr6xy6bZ0z173z6EdUYHnnsiOP1z35N6GsbfOqH3CcDdIMTJkz5padSGA1odKC956hLiWs1ztQ0fn9mdo+8nxQNowdpkaM/oMx11bVhwZiwFrhavWKvj56IQdt3fdj3RFHRf9709K5IpO+Df7/uhBvBPGHLXRzmyH5l0CEqGqT69QfwZqF6+5L1sfV+dloqsX/y3Xb5dNm3u639PWVvrAXg1og1pdnVkIVz27pj5NnuDaoNbZoq0W1BqlOrsoh+QRd+9PiV0AtZqTbJppL5h6hBH25uBkxOjyaLvTa9ZHeZhDQ2Iul5a6QRqHgTEPXERYQuhsyn7gg1DaSm0reRjIQ0bW4j1ei6vVz+eFNmQOwxERx+WbKm1z45Y7c9PYp25ohykCUxrQKXC+GJe6obWyVSOniZicKFGr0VY31maT1ytWZ7A+Xt4TBG4Hr+m8e/+BGCNffP45KQl3L+5Zl5WydtHm4HqMW9vIKXvv4R4xB9dWDXFA4kiQRrDG2jZkXnvfX3C4Wbsyf1MiEYKQY3egNCQKxxdHmsBNVUIrTPOZy1JZ5gLa2EplGgaG6UAKQg5GwwFOLSvFVv7g8zf83hdf8PD7F+auhGEW+PjNt5THJ9rNPU0T5eJN6B9L5N184W9+/iu+OT2xaqNFkOxZf+kSTJuCdU3ZgO/L2KRHrs5x2KXsYofQ1+J17ZQSIQYOKSEBxulAjJFtWXyixdqb0juDWRId0SgEInkYkWrMs0PZqsJ4mHjx6iW1bmzbjIUA48h5mfnVt99wmWd3fn2EmEO70CS6zexOb6vV92Dok751cyUkMWKKvHr9imWeuZyeoHY1FsPf79qKtDsU7Zitm/AcMjnmq4zVlCbGcWTett7wXahUwNfL1Ovu3sDfKNXPbIw+ly/FRGvK+bQQxcUkcs7cv3jFsq0wnxnGxDQNPZiplG2lluJTMHK4yoJdg+zde3cVENuZdL00sTWfMxp73+PQx/oASAqMY7ySXcxgXcszAQZxecFaKQ7wEvOAFPVJ8D2QD7FPW+ltVNpclq3tJBBRf0ajBzNDTOxDh63DnXvg1HqbV6hGNFdXUnHC4VY2kiSiJEQ8qM1d8zNmD5C2eYNmpJdDP/8JQkOrB7PH6dDPf6L2ILVUY+n1aXfEfahvMGJ2mypda7SZcX9zw81hIuR4FYnIeeDtt+85n2fWpVCborXDodfIwH+/h5WeZRqtbpgESsoehCUnHmpTfJX3cpqxbYW0hiscvLBhRbh0D71VD1COt12i7ewTZcQ8k5bYrlKCv5MT3CMZAohqZ9p0xXrd617StQf37fkciV2jTu1sTK0u/IsSrxHbHiF1g9JTrf7K/XWes5wYelPvJ/fpm7prCF7F/fYvu9YiYoquiNGsa+aFK3SCGbl7kRgC1iMw6w36glB6UX2Xtvo0ZoMeqO8hlgLS2JuZd4esrXW2ku28Ha79jibU4vSn0NeydFhHu5TXLrzcmvfzpJxp3XBfm6N5NneE/U3o69vXSQKEds2IPKuWnlnvjcx7Bs81Ihf26fBCih5htiiEKoxFKdEh0r12GNSbumMnQOzRskNaynG6ZRgnpmFgqw3Ea7mXb9+ylca2NRbRTn0WHuaF95eZh3nmvKxY2HF/30seLe9r8Fxvabijas0PpgsI9OcWev/Xp2l+z1Ji7H/umZ+qay+q+dr62+6Qyx5Rf7IPgF39KOUEomhLu0/DzFhXl/6L4TmDufbvSUdZesbm8KI+E2Tgmrk8i8A/98ddO0ylB7N4xvWcZT7v4KuxMq/1216z6dnCnjDU5nvAOoSh3ZFi3fjHtHuVviTtWVb3itD494dh4Pbulm1bnQldnUThENwn2dte44bOWuw7t2dEu0D9Ps3AOkIV9pqQuCGz+Pyc9r7P0AN4ESH1Zvjn95Pre5hxdajXFRO7LuOuP3DNXJ/zXcDrsor3rD5n4lzvpanP29vZ4V5nFiw8M2mR8Lwu0rWH6YGIdFi1t0E938Gnd2xXdG7/z3tNfY9IR+dCP8OqbrdyzsTBpeIOhwPDOPiQg3m9wsBX1ucndug3lmtfp74PdT8fYVcMcgF5Fxj3QFyCItU8ODQXABdcIMTMFZGaGRJdGrKt5QqRQu/h/B/exW9cv9UJfjw9EUTI4wAYj+cnx9Wbp+QxDq4eUbvJLX2zJzAaqsWdXBaWumAncWZijNwcbj1TmSaOpj0159q8LiF0SKsbAgUJkZvbW8AljmqtrOtKzpkXrz7DzJuUO/eCUivnZaNuDtGllLi5u2UtldKna7fetGuAzpvLvsVEiolpGqm1cj6fuVwc699FpWOKHKYDmDFf2vW9g/iEZMf2m5NrkjDkgc/uXzEvq9cKLLIVN0gxBrZ6QVshzoVUE69ff0ZKifMyUmrh48MjZsbdq5dg8OHjAyFGpuM9wzjxw9/7IfNl4bt33wFCYvD6kSRUK02d9Rri6GlQUta2Ybbx8s2R+5uR+NkN7e7AU2hEXbnPkYy4QHhVqBVrDVtntCr1fIIUidMB8VGspJh5fXfrgUZRkihpWxmPg0tIGWzKdSQMODf8LiUkJepWKX0iwkXh3393IqfC8fPPCFr5L//sv+ZnH7/jp6cnNlNubm9pBk/L4q8nlYAraTRTSmtXg7xL9UUtTIFeuPf6bLOttyskquJ73OM3ajn5MWq20549c8oeqrRdDT8aRuNyPmFmpOgqQkkcpttqZRonXr16TcCJCpf5wmU+k5PDWsuyspXVIfthuMK11nL3X244VPZA0NU1fvGzX3pf3CH1AERcfcN2o+YtEK0Lc1uTTj/vzrAaG41aV4/oq7EtmxNzphFJkRBHJPrYIVFvUHeptgJ4hpSS+CQPESw1EsJ0F2m1sl5mTuvC06++wfrUjN/7g9/jX/yLf8FPfvK3/M3f/DVKolrt49WCTyKIkfPlQinbley21y7d9ghZIvuwV8PY1uoQYxjIOXB3e6SUwuN2utYp9+wtp8gQIsfDLa9evmCHnC+Xb3l6OhFUScK1DOPqLT0osufQp1ZvSj/e31x7gs2Uddm8VjxG51f0wLZoJUhkzGMXFxFqrZTSJ8xTCWwe1PTnGENCQmBITtazUtmiUTl5KSZMSEqUZWGthbqeeSYlQlXXhhUruNSiz7DMaUCB0vrAWxxFwSKlzMyLEjURc+L27kCImXWbOc8n1s17nff5tUbtMOQe+fTEpnYGf/ASUGs+VSLG6FNLAHqLWAzRx7slH2qADVjLXB4a1i5Yn9RT1f/96Wlx1Ca5jmkxJziNw3jN2H8nJ9i0OklFnbFYt61HYKGrHvQPuQeZ1/ey5689AlPPXvaH4VG19yPFmNgltGVnSnXZKLP9sO+ZYE+da72+iwSPVJxKXjvryL9arbQYqCV0mCsSVAldDWR/DXVr5nFRryOGGAldSkibN/+LCHFIZE1O0sAFc3cnaE5Jc33U1ghJCBbIKTuM25UpVJ+zW0/7rL9GdRIAdmXQaq9/YdaHXRrLpXUdVZ+8Pk4TtbTruu6v/cyO6koxEnrPWyBPIyEk7t/c8fLuQDxkqhgFpYgTlEwEiT3r77GOdoHdWhqYK8JI70OzTnUO5uF8wAkNXg/2UTZhz+61w0nWiQBy5RZjCA3haavEFlhDJgbhu3nm3eXCBjQJTH36tnS2mdoOyXltoe6R8DUCdfHeK5wlPt4I25nN3UB28qIqToaCrkvoLybXfU4/+fYJPPbMdhbx7Brpcw1z7rC0Z6aIZ6u7A/ZxNYEWnWCwDxO9oivXX5/vQs06qUbQ0JUyeuq1lwFDNy67z7syQ6/1ks42xfeu9ayXitelg3iLDc9sbnRfq74xeq01DT7rT60RQ2BKI9uysi4LWr1VRaL04DBzf/+CcZyun1NCnzKAsyRzHljCSu3kONvtwSe5hu0fR66Lw3VaA3bN9PpJe0YMOsNW1a5qKE2f2Za1NfL1HT+9rGfh/U/yyd92lMjRg70+LyDxaiifhys7mmImz4jEXle9Mo7tOqpNe190xLkDrc8HLKUiujvphIn38rVmvXbtwcIzFUyvWWHoSJrpPspLnm2jCWbeguai676hPAjx1qy9pEXvb+wr0wkzfAI69HXc16q/129oe+6/7ZmM79M9U/SZklZ7ve/6HH29RCB0cZOGss+WlP/gyf2H1291gpINQalaMN0hl8g0jGhtlHWmy7g7+2kIXsBtvS+EeH2tXYB7Z7dJfyTeiNmnNwegwzkp7FF2b4GwPiPP9NqmYAh5zF0cOl4PeVOjtD5jra1oFZpEWlZadmcj+/uZw7N0hZtmEJo72zTkZ+hGzaXBEGrvbN3Wzfv8evNuzBlthbWs7OCSFkVKZQtu5FRdii7nAST5RlejbRGaUFuhUTnPJ7a6kdLgrKw09AbU3jzdYcvLsmBMvIwZRFjmFVOfii0WyTF7Xak36ceo3L0YeP31PX/4h1/z5Vev+fqLO+5vR372Zz/n/dsnvuJzDhG25KzdVLwWqJpQFebm2c+6NCRCKt4nlOSGzRoP64VAH3EkRhSoAgUDCQwhoMmDjWah98gpta+ZSaSlREmRd5cFlcJJlCEEzppYLHO4fdHNoRNApCNkW4U0CDeHTN0qrTxDhhF3fikEUhp6ncWcaanejuNfQuu0vxBgiH0mW+pB39rbVtbaaz3uUMde4yh9/8sOOxKw0pi3J8o8czmf3MiETvdOE7c3t9wejszHmbKtvPv4xHleqM2bt13wGAipv6IbFX22MG4kq9+jtYqIMmT/QSfn7PvGunJ/vz37xO58igj3S3srSWBDQ2DtMwwxV9B2UXZAM0PKfP7V1+QhgxhDTry8veXj+48slwsLrvSTeqSfO7Fjl8mKMXF7e8vp9MRSVjfS3VFEcZYsXfRagSG5uHldK4oQ8uj165B7KcB7QpfZJyEEdRiuXmtX7ihWnIcQc2Q9L8zz5rX2Uq5lFuvSe2IdPeprpb3kkLMPcX46PQEQO1egx0Bs2zOsrOr9fwzKmL08JGEgJWMYYNtWStlIwRm5ratc+c/7+CVUSeMAye1GUOcghBx8PFfdaLpRy+YtN+ZEMoJPlrFoHqSHiEgmSCV4EwWIo1nVFEIiD07KC1EYh8R0GBmmSB6FUjphMfXeoOaJgHU4d9f0bZ1M+Rw89aArPLPadrbtrsHTFFL1n0aqq/cMkYGBQOh9tI3NvJ2mbN5O1ShXRP4qHfn3XL/dCcZPIt1rVvcf/Bv4pMQgz0ngNX/rMVvPFntVqmcUu9u364vt6gTSqcjWswXpmnk73fo6gDP2Kc/Qoy2ur7n/p6rPBq7u/TW9F1B6t0+XKXJ0rmcT5nUXV2nvOxk6lTxc2VL78uwwS2vaa0yBnZ2nTa8Nrn72zGnp0NVeQjdQ3ojuvWlCiLmvja9Z+7SeaGC9IF1r73vbaze/UXPwVY8R0gDDlDgcMjd3E/cvjty+OHJzHNEAcy1Uc8Fm1yzcjWWPDsWPSbOA+nA7pHoNlRRcONz6ZGlcdaT1NW49C3x+VoLXlD7l+vW/F3/NYsXl7uiERDyG3QOnXdXf77WT/j55+jvLbTf4Tql/jjytr+0Oo9knsaNXE+T533SIaxhdlHieZ3Yt2509t0NP/j/fH0QXEt4nwNfqjfkKSMw+P24XEw4R8uDDqHuNXPrzlp56fFo773Gc3+knxJGdL7yH3RK4rutzrv3JIbbnn3V0Yq/d0lEAN2oi/qvuWDE4qqLSp50HhmHwEoqYt430vS2dhb0PpPUe5MrlfHGhZIMQIsPg43AMriiMf+798/zG5r7+dh/nJubWzwM3Zw7XUntvYn+6+2MysO4I98kJ+9Bj3Wtc+2rZp28q14xGrFdxha498KybuQcnnvF3J9iebcX1cyH9bPRpES3S1JvHfS8kVCOtlet9NXnesTkmZ3yH5AlHDBCMkCLBItG83Y3OGt+n9/SN8Rs13+vHfP6o/i7dPqecGIbsX2Nm25y5ab2PWHQ/bFez8Wmcdv3ce4+O9vLCp+/Hvnd3BEo8+0sSOtM6EnEbZLjUG/t5/ySzVPvNP/+PXb+9WX70H7bai7N4Y+ymq79hTi571rdh3Vp/02tFnH2InTYoS2E4DqQUr42rjv+7qgwGEfVaRjf22ryJOAwu6Fxr/aTeFl11PqWrIdBr60WvneC1GG90VcpaON7f+Cw4uSAF6mquk9r7G2v14qr2Sd85DgTpDK8YGG5cv/DmcPTm0dmpwnVzem5Z+hDRzG6hWOdCKwZ0iJENpKIpdsHuBGnCih/ceZ69QV6yOwS8h2juA1yb+uHL1qi68u5X37kCh9O7QAW1QKnPe+pwM3D3cuTV5ze8fHNkOCgtnFm3QJDK+9OZt08zZ4X7kChWXTjXXRMtJDQqGkfMEm6mAq0MtACr+mSJlY3euEKTgKWESiK2QA5CiLB7Pjfxrp4PTqoJISA5YXmgmEeDtQXSJ3u5NXeAcUxYgxmjBSB5G88yz+jWqJtrzoaU3OHKfiZ6D13fp1cfIftct9AtGrSuCDOEIzlnXr56hZryi5//nTdApw5z59jJQO7SYxdbiKNP5bZ1u+pO1uas6bmtLNWoy8L58MT93Qum6UiOJzIragXrAhHgvaYiuCB/4BpQNnY9285A7Ceg9ZaEmOS61gSXMHPDLs8BZjeo+3SVwk7SALqItHbihXRj5vPzfMahRAMRpmkkDyOlOcPxFx++Zb7MWMqQGzJFajPKuvGLb75l/LN/zXfv3xNIHKYbjjd3PF0WyvnC+byy2Oq9bghEnyfa8DNXej129+GlbZ3I1YXm64KEwNpcwHzI3usbWl+h4LW8rVRva4mZpuYjrJqy9/GBOMnL1GvruzM3l4Q01V4SAMm+ntLJarXOBHzqjDUns/T2/U54CVdHE4J0gz74gPLorTVjmkgh83h6YF0XSnN909Rc2efNZ18xTiPrPF97V6kQzgO5RY7pDkEQFUrZOJ0foNtLz0q3nt1zRUgI3heo5udaxkAcEsf7G15+9pI3n3+GSGSczmxb4fJYqJvLVTp5q6Nh1ns8e+Su1jwoTwM+uWLjSnDqQXJMkZiyO+2m3iYlPq92iIlhcKdfwwy1kiz1QM4jDCdggknc88/fzQl+9ua1i5A+lWc1hT2LgWsDr2dBO5PKo+39fff6hKohVa99HZ8yrvYMcc8aBU/Fr5j+Hvz1+s0efbO/d8fd9ZMIzHSv8zxHOV64Fmo3XFp7w+ueWdpeP+p9QDu7tKfTe4/iVbcwChDRpFjXE7Vej/P76JhJhzCuwx179uIuILLTyv2vUlcs6bXTHUcPETGhdHaoqboCRIcdtOvlXVe9h18eZTm2XptSWmXbfC7h44cntG6sTytjyjw+FbYaWDdjXQw9hh7B9siz1xtVxIvpIbgw+d5bZI0IDH3YaAqd8Rgdui21IX0EjMG1hyv0OurVMGMs28plmXvWlAjH/z9p/9EkyZbleWK/S5QYcffwII9nJSnexaRnaqQhGMh0A40GNrPAGh8Bnw5bCEQg2ExjuoGZJsVZVmY+Gsw9nJiZql5ysDjnqnl2d70cqfGSV5ER4eFmpnr13nP+50+2hOhIzrFUHYiL0/lArZo07h3EPuJqVQcWx2qC7Z9U7t47zbW0uZkE3cx91eupogpvQmtZ2cC5GDvY/Gx98AQJlnDi6U1oXdcH2mHKaANSzvekdX7OKteUNKg3xl7t/FJ6si7XpU6bNbXnwrXd33YPMecV/bC28bSD7GmFvWrvniYh6KuEEGz2XigiSpRp7+MJF+Bc0bM6+NeiUgoROM0LaVl4vL+3OKJiHTHrsz2dJm5ubjkcDnoYzQshaHpAMDMM07s/ea70o3grfHUvaruWPWPS2J/q50vOSsWXsP7dun887XpQqL8x4dc9wa0T43NHt3am9srrzzBWcps12/0quZ65EQ718mifa31PZ855S1Ron6NKsXXl10DfRjTMteLNcq/a/E8T5NX5arfbE0OkjwPzdEJcZc6JMi+r9lBf1tv+p2HEzgwkcA0588xz4nA4qqOPzRGrtLmjrj2HX5E5V1e+szV/T7p5KwJkvQFnJ6N2LjQWv7i2Lxus6+TJOaGfufENzi4x7uni+C9+fe8h+L//5/+Cw+HEn//5X/P4cOBtWqhFCKidT2HWN5BBCIiLesK3abu3wzHrvCW7Qt9lgotEp0GYNTtNB/ZurYoEx7JoZeKq5gHaciBpmq3O4gTytCAtjNJMX0su6gtZ9b0W28CXupDyQpKFcNTuVERIJVul3+kGWNuGbBcpBkQ8JWctCmuB6omA6xyxG2nardPxSMozIoVS5nUD9IaVOIpCR0GrlC4EYtdDpwzcKfXq5lB0QparHrj9uCXnzP27t9oFe31IikEAZa1GlSrtfQcIOS26sJxw9zBxP+sDfvxw5KdpphR1ginV8erqJ1xsXvDuTSUsMx//cI/feLIsSMmc5qwfPWiu4dXOAjxTVYOBKeMCPLvYoZ1WprpA8YGcKh+OD+xG1Si20j3EQheg1hmqTg2KVL757iv+/tv3HPtrxv0z9r/1IzbjwN3/I/BuSco8FsEdEj56nl1d0vcd+4sdeZ55fP+OjCcNJvmg0b+FcRy4uNiQSiZlFWWXUpE5IaloPp7T2DBBkJSpRXh8fADnWJJ6acYY6fuei+1W3UeGniKV43zSmeGiDOZp0krXoV3AMtvhUoXOK7V7nmYOhwMfbu9shzdTBfHavekyIot97sX8KpssyEcEKLN2Qq7TjUvDVhV1cS6wDnyiyZJ8IHqnjGaDbDebnr7vSUlHCId0JKes6zdgGZjCUorB+jZTlZnl5Ll585Yqwut370lpYT4e8M4pCzIXZNFq3Ytwd3PL8f5IEWUw3999QNNSei6N2eccqpstOqOqKFtTANep9diyJHO3sq8qagdWEuA0Jq1mqtNklFzduuGKOAKdGlcXSNPC4eGRZc5G3rLcQDsAa1lwot6YOPW6FO+YbczozeNPyoxzmK8pHG0uJyUTYmDsexunKOEjpar2j87RjLRryogU5qwyGhd6XOjZDQMhRoaNdoxv7u7WBmMtwBBc7NlsRn7ww5+w3+/55JNPeXx44O//5q+5+XDLz7/8EpGkPrnar9qhqMsk4Ih+oI8jXejpfOTLX3zNz372JY+PR5Z54XiaVcuZsBrErYUVdvDphTGmcuh1/GTM7WCm9WqtJzgpWtTnha6L6lpkEH6uqjdXj1jW4oo64wS6YVTrTDMhoXzv+ferD8HHhwOnSU2zcZjZq+jCeNqJYfWLsegaPm6lqR3pFs8hWvWLOcs06u/6c54M2FpYrKBVJv4Jz8fw4moVgS/YDEqd/hvbacXirXNABF9ah9NeqpWDGDNLYQ1vG4YPYf0c0mYS1g0Fb2xIrySUWjKx63Rw33SW9rMrsnqt6ubkzGldRfdtNqWQqT//U2GdQXqLsXFWVq2dYC2/jIe7dg3PrMdqzKrpuPDolIBRTLxfq2fXJTqfOR4njn2k5C1IeNKJ63+x0yIGZ9V/k04oRmsuFQ7vIkVVgrZktIMqds99e592UcTVVUd1mI7cPT6wbEdqP/KYFmqvMGkcejQ6qpJPad00h6FnN25YRDg5Z7pHZw6Pbp0pidNMQbWsg2EcCSFS7h7IeVoNsr3TeaXO4NpiwTZWzYlE1Fu0SjEHIvOKLVXdRnJZtYnQmMaNPSjrfKbN855q134pB83Zs0NT/p03GN1jGqPF/rZ1yk5WHS9Y54l+pqahOy9pm6+Kzvx0IzOHmPMbedJQ6ujB/ljXWBWm00mLzmlSGVJWcXj1FpemwmPrigvzMtsmd57ZdeaN60wcbu2YaYSdmnBUzDlK4U/xSnBz2DUlqGaR9iOcafn0sHD2mZ3TotTbHtVm+BiaZLgaLQED6+ifNH76nCkF4Hx/rU+tXv9X8BaJJOcAY11PjbOQNbqu7V1VO0h9vvXxjk4tH5ujULFYopyb/tit98ahRt24SK7CnAuPJ41DyvbxdMtw9pwHnIttxz33a3b9G0s9LTpeWubEsuj9rbmyDuZtD7JJlknQ5LxvtbXW1k3r3rz9e8xmTaoGBHhna9mQMYsVczan9xi6Qm1j6hWN0Z/9pM3/L3x97yH4r//1v8E5NXotWRjiQHGFJAURhxOLGLIFjWnFwMxyjRjiRENofegIruJRHVL1jmBT4hYAel5X1WAyvaglJbWFihpcK0FpsEtZlLoriRh7+iESYsC50aI/EtJ8/uxB8yE+QTG0k9J5nx5MTafWeZU1dIMyL33S3MBiA+c4jnQh0AWdNWzHLUPvyfnIssxMU1WmlPnwJZNtqFZWo4xqDgQEb1oslgarGClH1L0heKVEb7YXtuiK2sDNkx4+uZyfSrHe58lD60z7JDjuP5w43c8GgQhiePp9PFDnyNtNwKVH5h9cUsdO3TuiI3ZA1JBVB6T5RE6J48MjUsDnYDrJRAiRoR/VBsmCTv2oh9JUCr3Ny6qvurh9AVcQPIsUvv3wnp+9/gYuhW3O/M03X7K/3DFc7Lj+6CVeCmVJ3Lx5p13p/orNOPDyas+jd9zbKes6Z2QF0fl0F1hESIeTpiR4+Ojlp7x8+RE//ZufspxetybMrj2IKzqTMBhX6kLBIa6jSObx8KCbRmhr1xk0qLO8XMOZIGD2UDzZDEIMCJ0iI8WqcNPBGXd5fSqkzZSNYeTaoDRrjI2LUZd0Nmj8yYEO4Hyl9ZbKegyNG0FL0VhyIdeJLg6E0GkHKQUvDVbWwkjtBnUmiFPBcq3Czdv3Ch+fTtooohrHnPRQ67qeKhqKrGS1RKN4dFGf31Iyi6gLjg/eNlJHb6HcR4BS1s1+6PTgaCOBrlcHqr4qqzoXdVSq9WzH4Z2GHBOUJBK7Tokl1hHqSKUgNVCLpdZ7O8QFms2eHt6qC61OmKakhYxZ4VWzP9zuNohYKIAVfy0TtdRMamzwvqeUpFFzmL+qaX5jNxJjT6kayr0sM4DOvJ2ysUHjnNSke4sQePP+Hndzz8+++lqZ/dPE4XAip6oRTww43+HjYJ9ZdZb6QXXdzykz16oG9bVyOi1M06zETeE8Ny4a1VbXSkPXi+uhwe+rVso9Id81p6TioGYLXi4sqUD0EIK59lS6PuJ8oItKHJzkSE2FZElGZ+Lkf1JM/he+vl8naEd5bRWJLR9nR1wtVs0a/Lme7K03bJtslfWwVBG9XTWj++se1SpDt57yYF55IisG3Gg4LS9OqzYh1YoKzw0uAtpsr7HhWpWJ/V37ny3RuHW0bV7jrVvD2vsQFOh1oa7VWrUqpiU9gDCOvXp8Op2n1EXDTZucShA9uCraQQWlUgdnjn/te2kaLC0vNF2io3hPRh9cScpILNaDN1J+42+rp6oWKy44/CoqNuKHiJaqzik0ukw8PNzTkzgcj5w2kZ03nVXQqrHUSsmFu3d3LNPE3Yc7pDo8PcEJgytaFGwKEgPSBXzvCIPO45phjTOYpM1vVXDuqR5jk1aG4Biip+87NuPIZ598yjCO3N6+Z+ZE7DuGvme/3zF0UedORXP8nJioWrS7Eb/ecH2AumBQN8zTbDKCc5XeGIHio6Uc5HU9IdqNNMq6KB5oS+osS9CZ3JP5xJMvb5VsSzVff5bYs0Gr1GX999bQaRH6tJBrFLz/1IWjPQf2G5EmS9LrgkFfStZyuBBXB5U2G/QhnN9frTaaMN9aWRervj/BIPjzJ24atfN8sp7Z2LAaR4uxBL09Bw5nEqKCzuZUVxhiZCnqGtL2gFbIyso4lLMQW9Qq3ZrvcxeOdrqxMR27Th1YciaXxMr0pbXb6H2xgqZVnWcWKU/eL1Z0ihJxpK0p+SVdqnNP2KGriFzOvy9ntro4Z39nBb3oM4KDcVCyTrJ5dSt8nf3qXcAHRx8jySfSsjCMI69efUTJlbQUqrR7VHHSI5KhZpNQ6Br1LpB8+3x6611j1tqsd0X/1mVr+2i1jq69w3XJCuvObAgbbRaJ2L7fNH+t8NBfXVsr3kOoNH9kVjOJFQb5B7++9xAMXm/q8XB88iB6oh/0BiRj+YRocJfYBazrhWiWS7XqwZmLzdVEL2wWhaqkGInEDkAf1eMzV7vhpVDRQE+RM4NT3WsUktP8u2zp851WNKKdoENWvY8aGRgc6Ryx2TxR1csvKT5dixBjYBw3SlXuzSM0OpzXmCPJgRChpMIyLcQOnj+/IOfCPGfm08R0PJLmooGqmLtMydRS6GOvWiCn5s4NimsuJM6pODajkOluu0UwyCkFXM5k51kWTOPT4E+B0sTjAl6x9RgDMXoz/a526KsPZM5HTnXh629uuR86vvn1j4ih0l13DFF9R6XA8TRzuDvwF//hb3h8eOT9+zdUF3DbC3ocF1XYbTe8eH5FHDuG3cDFs5HrzZZgvpze6QPRXFgaqanEyNJB8Q7xwtV24Ppyx6vrZ7z8+BX/7T/73/Duwwf+h//xX/OBD2x3J3bbkS8++4RSEm9ff8PxeKAuNgsKyg5zmD5OqkYXDR37zY7tuCVNM6/vv2U+TbrpRz2hg9dUb1+1Y3X1pGtP1MFf1ntk671l/NFE0W07UmPgptGjWkHiWyBxxEdMNqEzoEajb4kmOpeyZwrdjKkOb8xM/QECZVHUoh/1umYTQBeDCVGpAl5hL6mNH+vUwSl2qgfzotZYoWM+Kasxl0qpCzEO2qF1KiOXpOuooIfqPJ8MZuzsNbLC+y6YkYVqu2op+BDxQ09ZFnJu3aY3t6Owznz6Xk0e9vs9wzBSysxE5nCcV8KNbnmqXUxzJsbAZj9QRTRpxYg7uum2GKueq6tLPvroBZIcp8OJaToxzSe9Vt4s7rwVC9VBL1rnoIV7yslE8Q1KTToHM4mZy5pSL84jklmSdW9e6f4xqpQmBkepkFKlj17lB8dEWsxr1DuqKKu8Wezlqlreq3Ekdh354dEIfGElh3jn2HQbhk3PsxcXHE9Hvkpf8+zZMz7/+GPm08Ldh3sejyfu7h9UE+sDeTqSTgf6TveMIW7oQs8hT8xkRMz0IxcoJr1AbORlxYPAiscX/bNmdegNHs7JrpMHFwwZKVAzeqCJITFgXaV60yK6JylRKOKdIFOy56u3z5+/9wD8lYdgnhebbVlFa90ZVgGf3eDPnVfDd1v1uTKdrNpdZ3FFqM5gvSfVE0UXJ7n9Oz14W4Ve7cHNBgeK+X6u1XsVy/pKGhQpOkv00Z8zptp7RTee0ATxTgkwXVcpXhlczjuDZKJ5blpRWFUsXb1XWMauWZc9EPS6VcGHQD8O1DqvDEAaewrd5JaUCPNEriqADQ1OswpXO0KNoZFsrEcUnlNLuwSi1XerKp0TxDdWoOLlRZShu9nt2O031h0LadFNoqbCUjMLkVkcX75+Q6mJl9e/xjj0CoqY1VLs4MXzZ+w2I9txUKeYbqDDsQX6oWO/Hwl9II6BrtcNQN0olHlIdQahaEddnePthwfu5sTVs5f86Iee7e6C7eB5/dVXnA4Har9lN2z5jR//BsfTgcPtLcHDZb8hzY6T7/FxYN6o4XgBc8+IjapjAc0OyUKalZGY0gI4uthjgw2dvwSPdELxjrL4VYemVXBduwv99Yko12Es6faE0IYj67MQolax1eZBpehsRYxNJ+cb+ksFbRujAGeHJZ5oZW2u1A89F7s9aZ55uP9gySjl/LPt/612YA77fPp3aUmatWjdRRN/h84kAnbo6RhIqE433L5XO8GUzkxQZ2u4qRS107HZlggxROIQ7BlVR6qGAunH00P0NE0kIxstqVgTqgekXmJDlwwpWpakxU85d+04CC7YngFd1FHGKc8cbY55RooM93IYW9UIHx7lCjhUq1srueh963YbPSSjJVS4ZJCu3lsNAhbWOVgTz9IIgooyLJZkUQzlQjTJx1uij/cOX3Tuu5xm8pxYTpMiGiKq3c0LknvSZgMuc3+fEODVi2s248huu9FCUapGfLnWQ3rcdgC5IMtMkYWSCymdOCa1ZGsOYHhv61DvbGPTr09Dg6eM0SxoZ68aP0zTabCla2sStexDn1VvTPQ1e1GMqV7BGZko+EANVU0N2rjAOf5XzQSPHx7RkzsieKpVdh5lU4bQPdkBDOvT1WEPWF0fXB/U7NljTLLastYWW7C64II0KEcvRNe3AMpe4YBayKUwT7NqSCSrz1zX6VC7CktJ5KXaa6iup+sDwZIomhCWoHqnEJqmK6L0mqAJ0nlRP77etIitPK+VWhKnWT0lYwjW7Wb6rmMzqR2Zjz2xD+x2FxQqp+WoC0wCrgRcFeaSOaXEXBeN1em29M7zmDO5Vgbf4ao6r1e0rvF2w0P0dDHg3AQfHtbNwuk5rF/myoAYu26qvPr8mo9+8HKdBR0PE/OceP/+ltNpYog7cMK//fO/5Pl+x+/+3o94dnGBdwdIFR8z223gd3/7xzjxSO0UesxlXbyCakA1RLFQmCly0ug6F5FiJI2UoVRqP1Jc4C///ku+fP+BH/zwN/nxb2+5f/8t8+nEv////BtCN/IH/+2/4OrlS/7Vv/iX+OBJpweOd3f8/E/+jFP2xLjnNDo2zxemJXE/TbjQEbrN6kKRSyblRDomHqSQ6kSpC70f2I4DZZmQUuiHYIklikiU4pFs3YFUak5W6DmaREbvgW763omVh8EsuBRair4jxo6u7ykpkaeJlJOuV4v4keCse7Ijr2DPmm4Coe3RrQTVE9PE9xpfdP3sGb//u7/L/d09f/M3f8M0TRweDlpV27/3nAOuC5VU1ZzAV0c5Lnpw2kE0bEZCjJp96RwsWlhlEXNyAt91XF4+p5TCzfubc4oFRdcgBsmb+bvY/Gnot4z9jqUspJIoCV0XXoyhrwSPm9s7SsnMc9JnO+rBOQwdzsGStCsrqZJr4fHxoPWDbYpKpA303WAm5DD2A9f758wPr7m//8AyT2oe34pgr+5HXbM9tDy90GvKSOc0RPqUE3jH7mKvFnZZZ1rz6ahjhJJAHDH0RvQopqNuG7s6ufQx8Hj3wOHu4bwZ28hiHEeGvmfbK19hygqBPt7ekXNhScpIJc36Hmqh73vGXme/r28PvHj+nH/6B3+kekcg5YHNxYbTcebiYq8chiJs91t2FzvevP6a9zeveffuloeHRw7TiTklnASd+cdO0zfKcm5IsEmiOf6IVLJR1NZ6rgTTU4bVXAMaZC+EiO6jVtjhnCbuFIPUs5DJ2k37AD6QqgU2P6lHf9XXr/AONchwlRTn9tHs4GpAihhOfhZ96mGhm/L6XWKWY3Ke9Z197TxPXcx/GcdVBwdQer5YpaNVizlrCAbB2pzQDuXGDVG9IPbwt5mYP5urilWLaFuvIlFLXBCt9rJiKkpIqYmU05nxao1ylcqSzby3VnA9MehpE7tunTdK0PlYbcxB65pzVhivzcna/LTNHEpJ2nWJ7Y+hFax2na1C7KLO7pRSjz50dlt2+z0ff/Ixj8dHpvlEvxnwsaM/aBhqJjBXx2NKdHEhiWoDxTakELVa3TDoISgBX4W42MzHVaooIUW8ppJrsrveu1wyQUwS44EqHKcTpyLc3t1x8+Ge7fNM5+D0+Mjh8Z77g+C6xM2HD/hh4Ce7H7HdjIRnl6TLay5nYXp84MP1NcfTI29vXnP7+MCXb95oJxh6E8cXlnlhFojOk6sjuI4UYDDj9LkGfVzNHqvWbMQq9KEVZ3CkdSrGHq22DrxVnm1dOyNDNqh7TSc3aU7r+IRfbvwMS9L1650RfttcXgs6aZZl1pVR9Tlrz9YyJ00Cx56DRrYUvYfeIKym00SkIa1W5KvpvTPWqBQrdJymqdT186s7kM7WlSjig+q/anNssO9t5tntenoX2ey2PLt4xnE6Ms0nEoUsRQtqk3nIOmZhfabw51mb1uCuPeTWpbZ9RH+tmKVgQ7Gcdi6xU4nJvCSK6YEb8uVsnca+o4tRiV7+3H2WkKhO6IyZDno5my4weIONa5ur6oNYROg8dDFSSibnSqoLJaGaVCOPueYe5Dz92NH1AXFadKs1pBGTvEWEASIFL56+D8To1qzHzEQ2SzZtAMxhxkFNmcPDQY3ml8Tj8ZHuruPu7h3393ccTwfmZTLtsVtRtZWUbntwlSeMY5wWOkLDH1ZAUfd7/YyrNtfOBf0ee858oGmu+67HDYGS1EauVHXLWvM47X01DgjrE/QPf30/HFr0kwUzrapVZ4DQI1m1VfppKi4Eo+/XdbE6adWsbpSlYLY/5wyzJiBvlHBVCjx5SBxIFZLh6Fo1ybrReK9J8hhJpw3Li5wX70oRN5gJZ0J375UZhm4YqSortI+9tfQG19iBn+ZZL3xZKLUwl1mrlBLVlHqIVIRpyTgSwSogDSL19P1ITknF+hZempf8JPlCmJbJMhtNV1YS1Sk7zgE5L3adLKh1pSSarixGuhAYu46UFz2w2/q0PeTFi5f85m/9Jn//i7/n9duFbT8AgccpU1xHnoSUhXmaEO9ZqigRJ+i9DqOnq579sFEtUxVCrmyCHtqpVrIUQlUXl2wkkyJmDyZpDdYNXvC+crh75P3jiW9fv+ab93e8+PwHDJsNt+9e8+Hmhg91hH7L5dffUJzn+uqSly+e83y7J5ZK/OI3WI4Hbl9/xcPjPV+//opffPcN8ud/wlKEpWBVaOF0OHIQKMWTi2ZMzi5qWnXwiCTmuSJSzNU/KRXdaNreqfawGlHkDOPYYWNeks2my1uclkMlF1kKUpS05ax6ErtGq2QBKyZtvcYQVugUEZ23eQcxgVeBO1Uos2gnWYWcMo8PR07HaYUVQ9BDvB0AzmbhrM8y+ux5Y3THSGcd4MPhSJoTUoPNN+3gDqpp0+fY66aHEPsASajpjAg1KYw4RShC7IndwLPn13z28afcfbjl4f6OR5k41UVfw+kKFlFz/IBTdqCDbPT7dq31EPJnSj1aWHsrznV6GQz+ta46BoZRzfAPp4mUBQ2JxFiM4IJmQg79qOb+dl9FKqlGfHUMZvWmMolKmnQ9DMNg6x8rgpPNKStddIzDwDxVlpxIyYpr7/F9pBsGQtfROYW4N9uR2PWk04klZXWpqhXnteOKzsgkknEusNtucM4xHR8RKsXPzNOGw/GAjIVxu8cjRGA5Tbz77oZlOjIdDyTJJMnkPFHKQrZ9z7mO4KKtTWx+3U5CvSfOOWI0FyKbwbaCQtPJFKb0EUKM+hyIp1lcNhS8JVlU43fsdnu22z3T6cSyLDweDmoejl8JVd55imu73q/++v5OMFm74Uwr1PAzxFBPjeU4U+2tMm6/x1hBXpSgUkUhRVuKesgZK1S3gDWpwaEXsjnW64NgRSGNWXWuZoML9l703/mmV6k6CUJairN2f66Cpq6bnuwJdKyH/bl6yFmrJjHWobJiIVRr1fFI8YiFd3tn7806n5QWnBP6Pih5orj187voNLQ2OMzHmdW9RkCW9pAbnGOswOqTzRM0p7HpCylKj8nWZnVDxFWFAMUHqnSUOjNP70nlnuQeqebB5TnSMVHoERdw4xVsthxT5ThPbGVBeyS9FzmoRZhkHUbPNodJ5iBRyLrlOIXMRDwFTxLQVDDP4DqiD9wdb3hz+8CSK7jA/eGB7ISH6cgxzRTniK5jv+u52I9sNoOycEdvHWfAXYxsth/hpkvKR5fw0QvuQ2CalJy0zCfm0yP3ztEtlVQcuVgNatebDF4inRMTy1fEq36xYvZNVddvj81cRWUeOXiDbfSx8t4YiDRHDHMKcrresq1jr0+7rmN7Dlol28LuQzQrs1nnYNIQk2pkE+yxNDJELZl5nri5vVEz5ln1XFLqudi0Z0yfXbA6WkkV4hh3V3TDRg0mkgqbvRWhiIUtw+okpSJlzmkmNrdvVb++xWrYkrM1p5/jdJx4f/OBNM1K7hO1FatkpKqhAQ7G3Y4YurNDSTKWqGsEumwoSmNg2tsV25hdIJgRYPAq/1pOE999+y2PD/drR9zMsnGO2AeGsSP2gdA7imsdpl6HFoRs24kesiIQNOh27QSLgDcUq+pa6aL6xNbYMfTjiqZ5r+SUru+J/UB0yh4vpVLLoukQ4s7cA5slur7He8dma2S+qFB8SguCw/uetFS+++YNY99zv9X0DimOu7t7lnwi1wROkbGlZEs2yYh10D5ounubs54PG6uyG6uzavfXQkf+i1KFIkg2roA0NnC7umIE9la+oDrFWnQ8IYUQPL3pCouUdR7pWiHUzpnv+fr+TjChD6yPdlPM1JoKXvBdUF2TWNXaxKV2UOmTUc0Bf1bM+AnYqQ96E9jrI5irHrjROpuibHTwBtUYDd3bMLXRoGOn3VIpgi9CLArdaOVVjL2qhARXddBKqVA9EoM6wdsbqy33KkQcQl4mfbepQbgGyUi3pjoIIEnrBB+MzIWyXpe00A+Bvg9IcdRkaVgCofOqD2wHYNtYbTZUs0Er5vrvRpM2+AkXFCoo1ZivojOrUh0zOk8dNj0+T8icESKOgZwPHB+/YcrvSO4D2fxJvXtgkIXsOqqPML7A7/c8LIm744H9MNOZlZZDSD7jpBLKwTwRFTPIVOsGEl6q6iBFQAIZz4xjaY4YLjB4z7v7ma9e3zAtFR863t3d4I933BzumZcTdNC7nmeXG54/23O527LbjcTR69l/FXBuy9Zdsq2eqxx49uaGzYtPON3f8vDuWw6377l7/S3vCHRHIRXPUjRx3mc0Y7IWoigCICSEggShBiVL1QosBU9lQB/yUlTzSh/Nq1Cr7xAwptx5sxAr3KqzjVaqNRsVLUVMYG0HoNMxNd3QEVwgl0VzHdsTU6Ihf2klB4hUUl44HSvf5u+UuDFnJVcVQZ1fqhHoW/FYgYj6PUUCgf3uFZurK96++ZZ5fsBFiM33FXVjcgLONKq+C7gKabKDyazfnswhwJTE+olttlnh8eGR42FhDJE+BJ33k0ESIjrDra5yffkR290Fsxk9uNNJs0WNKCGL6WarEira3lKrzj571ykbG+i8Z+w6TodH/v6nP+V4OGGOq3YIasHcD5HNbqDbBEK0+bAdlk4EOvDi8V0jumjl0u86haaL6idZTe4dQYQOGHotAl034CQQ4oQLGsDrnGMYN3T9YPmEjtPjRFoWfd5xKnov0HtN2WipOs8/eon3nuPhoEgGuj66MDBPws9++hUxOIbBE73aqZ3mhSkd9WJ5IUthSkk1pyXjY0eIvVoUep2D6kHYYOqmVba1aeuEYjCoQmNPjiXtpIvYCGhtJs0EBME7jeNqXX4qFZ81c7TUrJFwXcfxqGhN15J/jJ1az04R/7hD0EfrlMQ8/yQYtCAN3j9DQU/0H3pC2f+2UkzrOuvc9PMg6BBTQUpvB4r2hMUqImdC86ZpW3MAjSVU6/lCqvSgGBzknxg+nCuQ5hRvr2gdXl2dJPRnVgOubQ5ZtT33Bv+I3ejWvbqWi+dMM+k92YbivqrRtKD07CVlMpVSNVUBp6zT2NmQWRIlC2RdU0WsMnTo9/W9mo0H/Zk5JX0IokeqowXVipijuwsM/YZN37O7/JiLy09wdebv/votj2lizs6kGYHp0TFNQmFWvF7u8Gnm737xDdPhgvHXrtn3PUMouAyHhyO+QF+8Hu6l4f+TEkFW3V2guIK4TAgwBGi5Rq/vMw+HyteL486PvPj0ihfB8/M3r7m7OXC3CImerWh3snF3bP17OveWSMKVEakBpDNIWD+3l8J22/Hpj1+Rp0vmL16ynE7Mj48cHk483k3kojOFaTmxpIX3Hx55PEw8fLhlOh25uXnH8XTkm/s7DstMwFPICmc6oQwq1ylFn4HYtUGIaq6qKKxNUelEbR2X2Jzd9FMqjVBzb4uHAyBUZ7C40EWl0qcSUZmWbfIWnaRFkJBt1BCiGjyUYgiMDiVxTuOeci0abUbUzt7pHDnEysVuZLPZ8umPXrF/9oKyvKWmRI0o47CCE0cX1AxBhhGRyjzrMyhZ9bLiIzg7bG2vaFFIYikkNVeSLFSDxSmR7IKis6FHsl4zH8AFYU5H6knNxzX/UO3chtABkS4oIWU6KmKTy5mMoaQW9dKMtiEvy0TolMCm6opC6D1D7Il9JHSeMKj9XJEZqYncxi3BZovB4UVMWoMy16t2ak+1mCXpe9bmW0woXzTmyFCUKtmuj4562vMdve4P7dDpLZ9003eICEvSZ068bvynaVISoOWgKqeizVX1XKidmlTH2NOPG5ZcrJPW9+e9Z+g7ci2kIsTQ6wzRmPrXz55ZRqvG422HDU7gu29fM08zOU3K3m/9hfzyYaR7aaW5QejfmrGGSUKC0//w9pykwiyNFOlX1WBo9nXWUWLcjt5333fEAf+LDkE9BJTdrQ9tsysMym2l2AxEcYzQ2jRDThsMovCHwgPn11ist1zd8GurmNs5ZLM9O5fWATjefrQKlSXbYpcKLcxRUEz5aXSOEUyczQQ86INipIYG4uo362LUhxu6TrMOV8KeQbm+KvHD2Q0RDzlXpmWhq0LndKNcnEW7iOWhSZNfKFMwRq+J4aXALJC1W6QoVOd9oOs3eghGZWOdjkdcbb6MsJRqHbR6YDr0QdmMkS9++Ot88Wu/zV//9X/gr/70S4ZNp244PhBd5PgAh1lgmCEmgiTq0vOXf99ze3PFZy9eUv2Wa59wZE53D7jq2cYtVRypiHq9Vp1rLsVpK+N7Qp/wfSUGUa/Q4Kje8c1j4u/eztxMjmMY+b3PP+H6csNf/+JnfPvNt5y6PeIH+qqpAFt3w94N9P47IgukC3AbXP1IKdQONDg3s9t37J9/gkMPSf1VN3GV+k1InfBTwc2Zn/78LW/e3vH6y5/xcHvDz37619zevGeZBZ8ORAJJMpPMFITjpqPiKFVjnTqKaTULReBY20Ne2r6jSIbN2j2ChIAEbzooWbvsYO4sFO0O+whx8CSJ5OJoaD8n7FANVKqmKHjHpu8Vmp7nVpib6FmN49UHI+KaTMEVul4YRuHlxxueP3/GT37rE65ffsTbb/+Cx7tE6YIGLZufZt9t8D4qszQXTu/fK/Pa/DqdV1ePELoz+GmznWrFQK3CUhaCL4QYyVXZ2TH2eD+oWw9VtaVROC0Hpnwwdqx2XS54hmFDcB7GXjW2VXWHMs/aCaKsZW9M8BC8upHMmd47ujhqx++LsrRDR7fpVLahIgvV5AGpGts/Dnog2GfxJolQ2zxBXRP1ziBCTSop8PYTa8nMKbFMmsrjnBCjI0TT0YqQFk0RiV6RgJa20keNnNpsNjjveXvzjiUlCArXHk6ndX/T1HrtzlNKa0tQRVnzOM+42TDPE5SCGVsRgmcTOk65UhIE3ysxCJ0/vnjxXHWb2z1dN/DRi48AR/o3/4YPt7c8POR1X13hsnVvPaNeihrpl/Ni9yngYlAYGId4fbZLKqS50HUmJ0ELsuC8SgdNvuS8Wg/2ruN/FTGmCyqKz6Lwi1a8NgzV+6uF7/q/mx5q/UicNTCrmvDs34foA9XMTnmqC9HXanZPtZb1ojmPib3PrEkxwbKIzli8QMtya+9hZQ/5MzvM7ocizk+bV6A5XFiRTfuGUh2Ndqof7UkpY6QDVgZgxWVhHDrGceBwOmgYq/0D7yviCnlJ1KyVv8fRDQHXq4C2VkiLdqWlJLxzjN1A9Z4a1f5MStEHPDaCkWoFcxXiZuT61TX9LpC5p4YF6WB3tefycs9+uKQPPQ9l4pQTYv6MnXdEJ9wfjgQ8X719z7zMbF9s6T0Mg9cAYlBKeVQRuau6Mbhq1QsZiZkaM8VH8JHbx8TdceLN+4m7+4X9ZsuLyy37zU4Dld1KLsYFQVwixI7f+uwlP/nkmnj3mpIeYfMc11/gLy7BRSuSCk4mNOOsgOtwbkulIs4iV7zTStL3uLFC13H92cf0V9dcv7xkOR749PNPuLu55aH+a9w333Fz/0F9LmWgiFqhVUCnLcLQfC2doQkWCN1GJM1fMtg/dKJkoZLFbBetjHKsM0rXGJ81INWbYNvWqWB+nG356YjCezuAqBDyuRAVm807paUXB7UUoleZj4/azYqDVCvDOHBxsSd0esCKEcX6sccTWJZMzQWfFgukzjq7t32hzY4xd5rVIabN5b3Hu0rwQt8PRhwr5KSmFcqEdrjYIU4JZFEwiZCz8YmOSErS6J5xs6EUIfiKeFZma+gGnFmViXOrI1GSRKRXwkbUGXqIkdCFtRNUSFaUEOPdaueo0XIeSyRlmRNULYoRNS1vCTa1VJYpG2TtVa6UEo7K0MeV6Yh1MsWaDikKd4qHTLFO1qlDVs4wz8qdyDo7lWricSvWa1Zmc4yOWtVrNMTIbrPj8uqSL37tczWIQMcq85RYUmJKi3WzkGOlxIJzGoOke7jjw90Dx9NMjPcEH/jw/j0IfLi/4TSfKPXchet9b7teWx9PD6d2mFiBV+xpMGmEaOdC14U1XF1oREdnxa/Q2Xy2nOmq/NLL/Be+vj9P0A+K7duGruXUmcq9buTt8HPnh30tfXHG6LQH3K5Ipckk7AGxC+EsyNZm0lh0KqsHsT38weuhWq0TXHcCfdpxyAp/EAxafXI1msj4lzv081F8/l6jApu9kaDdqkPPV7de5DNJosHQIpbgnSp+0zEMW46nhZQmVOAJEtTiJy1JXzXqg9b3USu/qH6SR1etukzEGohxC8Ejy6AZhlV1QbGL53fvIJVKN45cvXpJvwkkuaeEGelhd7Xj5asXXO9fMHQbfv72W/zDnZk9OE0XcHB/OFGy8PW79+Sy8MW1DvM3vcNnh3KWKjEUmy/pco+0tZBIIetMzUfERW4eT3z55pE3tzP3j4lXX7zio+cX7HpBI3SdQVjYvDQRQuE3P33Br398Tb1/QzlFeD7jZSY++wLX5sY1Q50UKqsJ/GAHZKeuJusm4XF0MOidfra54lkNkD+DnHn8wQ95uPnAn/7tVzweFk6nBTIUGSg10LUuRdQEoTePxOLV2EpNFdHZraKANsN2WjxUx1xNZmAVldhDX+0AbMkoahjudUN8utRdUVOEqq8dYov66vQ6NvN3xMwlMriAi5rmXkvRDtFs0tSxX+de/Tiwv9hr7FgD/51nGAa8CxxPEynrm6lGQBN9OPUBqMU2KNN/WYHnnRammmru8L4ybAY2my2n00RhoSw6z1fSSaTSaPZGZLLoMZm126ipUEW70xKEGE4mT1AT7c24o0plrrPuJyKaXC6ZiunSgtfZa98Rew079t4pQUqKfi47BHV30Dk8TmVZOgsVOtcRRKFIqmh3lSvLlIw1qZaLJSViVBlDylCWs82eVDWalyI6FqnaXStzPZKrJbeIsuZLUQctiYBokQAa/1VrMRtH5RaEENjvL3j16iN+87d+h3meubu9o1bIS+UwHajHR2oWShZiXOiiGQOgJI0qcHf/oMWAbYHOayE2TdNKljofgueW47x8G2J4nuG1M6JZ6OUqSNu/nWo1h6FjWhbNVDRGaDtqOgs8n0s7OH81NeZ7D0F7dAxeEFbSS/GrlujsjWhQZK0mMrKfUO0C2CEUrBIWq4TFRPP2GZQA41bOnF4nccYYsu9zyhICzBasseL09dSLUSnOtXnJ2V9rJ2gPvTsfFi2hWES9/kBWZ5Dog+HN+tr9mipR1/fTRvCNlo1361xGjZQFCMQwMPSK/QsF73o8UW3gpBCdSj5y0eF8cGoYPPS9BpGeErkUptOiG2ocgYWUZoWc7AFVGr+xqKzCzDKBPLC9cnwarnj2YsPmoiP24F1R2QWCNNF7VNNu8Z7k4OubW+a88PmrS56PAz/eb4nVweR1w6sLVRyFzm6dznxLzVRxzCny3d2Rrz/c8PXbA9/enPjoo4/57Z98zmfPN1xtOz7c3/B4eGBaCqU6JC0EKby63PJqs+H+yze8mY7U4UTY9lxuAjEIlO+AHlXtZupyQtJEPd3hhx3sn1MIJOmIfo8PlzpvLtoxiBOcQTBVPBDori/Zjz3//F/+7/idf/Ib/Ol//FNu3r/n53/ztxwOj9wcPpBE2IZ+lTgUqSx5sdlc1q6zqkOI7v2B4DqChyhaqZesQnPaKLoVVaDvKVisUxZKXcg1G3EC64gCw2aLiHCcTyAqvVFrsqrrH9WLusZc9pXYBYIfwAgO86Iztgw8zhN//Zd/y+3bB+7eP0D2lJrBV1I3EoKj6yI+CMVs70qNhsx4K54TwZmFoX1A75zO48y8u1YlzKV0wgeNLbu4GHl8OKm0Q12zqC6DE2LtcDXo6nKOvgu0ZhtXOU1HRNDPFj2bbQ9OIdNaoSZv7zUjTiG/ZqMYgqMfOkKnHWC0sOQ5LzoHtO7FdwPOee3ga4twC3inDUNaNHprmi2cO9s4ychtBb0vJRU18hi3+FLwXV5ZnsXivSo6S26dnRNlTy551oKvCcwbBXNJ1FAZBo1mm5N5NQPOBcZNj3OOD3e3VCqxDyqnWRLTNHN8PHCaTiqhqGoCUUUIXaeHfeu+0O5WEHoT7ve9vmYyI4PzOeLOvypMYodWK/6cIQNxbQjPKJvxP0SbnSq9pXAEOhzPrq8Zh5F3b98ynU6krKS8tVMsZx/bf+jrVxyCZhdstFU72s2U2VF8e6O+YZzWha3I4pqU45+Kc51tkOcXWrtKlT6c38NZCKv/NWPguqZO6IVs9kugxBcXlPW2Gm9XWTu88MRTT0mhgerEsHA73KQJ7R0YHp3QDaQN1VVCYpBd+xxOD2acioxLLUpNryDiCaGn7zQXq0jGuw4vQWnPtagbvNfgVnyzd3LKesoVOc3UCvOUCcGx2ZidGeeZpLeutSJ6AJldV5GZUh/YXHiG/QUX+5Fx7IhOiSTqkC/4YsnXTpOvJXiyc7y7eyCXwtvHieAibj8QrZBpLhMOqE7Fr0EcpSZlJ2bHkgLf3U782c/f8d3tibcfJj79/If86PNPud7ANlbe3yTuHx9ZsibKu5LxvnK9GXg2DBze3nAzP8LuQH8xsvviOX7wSH4P9OB6ZQUvJzg9Ijdvqfs9PlYqgSyB0Fe83+h7zqqJqx58GHBebLbgiRc7wm7LP/3jP+J092MimW+//obl9pZb75RSnwt97KjOMQFIJhdl1pm/D23A7QI29I9Ee/iyCKHURqCze2gFFdjMy3RSVU0aas0mitauKvjAdtxop5MW3UBTXk2pxQ5BRTSCHYSVrusYhoE0zRq2nJXwkaQQJs8vfvYlH94+8HB3tC60KHSfqh40MRBE01FKLYQY1K2lOkNCKuA1zqfqe/HBETuP6gkCWSDXRM4enx3bzY5xGJhOR4QmBYDqVSJRatQZnH2WLhpDVZSMM88nQBER7zxdryL4pA0ZUbyGSxtBJZi5BFL0EPQRH9U/Nka9tqmR8KwbjjHifUcqSkYJEqyh0V0tJS1sllkZizlpmHfLWa3N8svm97Eb8LES+mrPHczzYhFc5+JbCx6ginmqKsFOcBpk7R2yhjHrSkrmq9qKj2EcySnzcHevsomq3sTRxzWV4nSaOB2OtGgl7z2h6xRuNWegNZ5Oqibt9B2bzQaAO3e3kl2cnQG6Pba5QNv3WwtpZ0gTvD/d/9tZoiwzvW6i1zoEx9XlFfv9nuPjo8bCHaZ179Sus55f7x/4+t5DcCk6C3Qh4Aw3FvvRwnngj5jXmw+2gTyBFK2FEufVpbzqHW2p2cF51VPZnC5Y7E7sOvsz/fk+qGVby15zRtqRlh+GztNqaZ2hiv2bYbGInLVRtrhygySty6ztgttP9A1DaIert/duHaQajGvV3V5TZwA2+G5nN5oIf5pOLClpFSp6YnZdNGd8tZKTEFVDlicy1Rin2hmXqgV0rcqScxmW7DRINzfoDMUnYmXc9Fxd7tjsI3N5pBxO1MPCj378a3z+xWd46fAS8SUi2XG93XLYjDy7fsm4Gdi+vIbY8+23iWWGY9lRjyP/8e+3/G2s/En+awZfuOod2y7w8WVPH3t2w6V2q8vC/eMjb27eczvBu6PjMC0cTo5Xzy754pPn/LP/+tf4p3/4I7778itu3r3np1/9nL//xdek+3suauHzZx/xfLfln7+64ONdx5YDThZe7i+IMVD+5O+Y5O/5ef5TiB39bkfwjt47XFqQ6YDrBthuWTJMWfBuxPtRae04auyR0HMf90xhw4tPPmF7ccnu6pKu7xjigW478Qc/6Pj1iwt+/8Xv8fh44k/+6ke8vzvyJ7/4hsd5phwPusY2I6VGplm7juZ84rpeD6FVQ6OwbHSOaOvEeV33x3Qi1UTfd/jY4HjHxdWlshGjSonCohZ0h8OjFjx5toJVLcU2/Vahtzyg6eZicL0jJ6GWWaHAvuf5iwueXe/YbHu6oeNwf+Ld4zvmmpDQse1HTZYII+CYpoNalw09MQp0ppIqyvzMjMpQTAnvoBsC6lqScZK04A2FIUbGbc9mu8GHioQjLz4bePnFwLwo8nGaMzkLNUcQFUaH4PU+o90XToXp4MhZIcBxb8J+H1nmmQ/lyJr4EdT833fRGJ7gqnWxueqkL1psHNq9awGvkH0fIxIEVxTqJWv6ey4a6K30/YDfbfQeLNaVuGgaPt3ngmUZCm6Fv2dJyugUr0W7KOTcOYV3a9D3IGiRm7KSdmKMQOV0eLDOUZGsoR8Yx4GPXr3ieDoyHU+UnHn/7i1NOqZFUyGlxDInYhzousg4DIzjyDSfWHLSqCkxwk2tHI4n/DSxzAbNLota3q0niZITnWvYotOOVtQY3Pe9jQKenInWoDTwsaXKp3UOqt/z+vVr3r97x+PjIymZcbl3JsB5Qk/5xx6C2YagLVjTe/UKLZKVYUd+0vIp86q9qPVcZxhVl/yZJGRzszU81OyBgtFve0tdVhs0tEPCui7v1ey1QbDWtTUIgSaWda05FYMO9MLRZBXWvjmzrlqFzPZ5miVTa9+bKSvrR1aqe816mJ4LnaevrYuglMqyLOZ4f447CVEH8N4pvJDQoe5ipuLiLCUAZZNi9yDnRdv9JASB0U7whjCA0A+Rq2d7hk0k1Zmc1ULrYnvFj774NdKpkBehzI68VHb9wL7r+Ojlc/ZXe3afvEJCx83drYZopivKMvCLtz2uTJzef8sQEp9dB653A5M8Yz/AS3HMGe6OiTc3R3761S03R8frR8+29+x6x8f7LZ99vOO3fvKS3/mtj3i4+5Zv35x4c/OWL7/5hnI8shHh890Fnz675Pcve15tPcVNOClcDq8IIjz8/CtOjxPffXePxMj2+RVdH9mPvcLaxYzHQ2BJldPcDNvFuoCA9HskbvjO7bkLG/Lv/RNefPIJXV8IbksfTvhh4ddeeGQ70n3xBaepsBte8vWbB76+PSE88Hg8aQHXd+TiqVnjyEQmPfxCv1K9JQQkBlytajlHM0XvwAVmObGUggtih6A+U5vthtCbdReOcIrkOfPhw62F/ZpThtPkgs3YUwtk16mIXWyoKNWCf4WuD3R95OLZBa8+ecF+v2Uce/7m9Hc8fLgniUAIDMOGLvYm0taZVpXMMEb1qQxK8EG8kZp6mjTIRa8EG6nkmvFSCGRjfXqGsWO76ymyUFm4eLZjuxs5zRNLyjw+JOapcHxwlORW8od5PpO97lWx0/2kihLFulE9gV3swRd40KJYXWAsfSCqWUULvC5V7fXwmn6jcKumFChnS/eMaB5l4s5ByYKaV1QRQhgIITAMI7UUTqh3qHMdXRfYbDojfmiBg1OijasBRNGgYJ+zbZh6OATVzomgo7dq68yiwxCW+WQQYyAEz9APbMct11fXdCHyOkbmeeJwOLBCkra3adFUdaQkmvG4GUeyZJIUPJquglO3qDmr4XjJyjwtOese2/ZJwzaapd1q/1etqldBLU9bELXgU7TQidB8s0stuJzXwASNcSu0LMy2dzcbzV/FDIVfcQj2/XCG/2C1RhKCjcRGPSuC0wTt1MIU1Z3FiKTa/fkzrFjtpofaIEydn2nnZ+4HvcoR1GZNk5NxSpd1ooxLPbgULw9maquxNjYHCUB2a9pEs4uSdrBaNaGwkmt/0EqR1a0gG7w5xEGHy2YfF0OvDK/mjRgU5mkdM5i4uNk2tSorZ1oO1jTN5HxOodAiwhF8Dy4QzLUk5YTgiXGgeseSvYXpKkSwVAUbghdCVdnz9bMrfvO3f0Q/dvTbjuPpxPF0okrl9Xffst9cMXZbvvr2NTfv7vhwd2BJjovdRzy/fsGpeKZFCOwYO9htnhNcT6gB6BkuX0Kd+HC44/h4z/HNd/Q+sot7rZWd43FaeP9wIm6f8aPPXvHysuejy4GPnwVeXgauek+aZ+4PM+8fZu4eZw6nmc97x1WM/O7Hl3x6fcWzcGJTM1I9fhZuf/oN5MLp/QNVHB9fvSQOI5fPn5HLwvHxAzUnyjIjOKooEhGqx1FwFIrTxBHinTJWP8C3J0dMjxw++YR6+An7Z1fEMuNLwh8PkCbc8QNSHb/9O5/xgx+/5Nnzge/e3fD//v/9T9wej3x1fKQSVEfXLdTBYPK0UMSsvkKA6HEGyzXozRsA1GWdtXVB15CG4Bow4YQlz0qauIOyFHO68cqkNSjVibBMJ6WNG6PZR5Pp5IXQRbq+U5F3B5fXV3z6+ed8+HDL63fvuTseOKWFbtjQD51S7PMMpXmTOqoEJKmOrw8j1QuLA1cF31JMzDjch8imH9ntnjP2nt0mcHN3x+ub9/ocB8f19Qsur7Ys5UCuM6NTk4m+G8kJ3jMxnwpdB8FXrRmcEFHYs7TNzwt41cd5UFZhgO2212gisflbaHmOOvMsxoRX5rqYuYBWt9ppV2rQ+KKh2ypJJdvYB92LtvvdSorz3tOPpl0NI94F+n6jXZFTJ5xl0f3AB0+g0/0sBA38FZOQmaSrGfXnnMhFcw+rKCu4pbUrTueIPrK/uGAYBq6fXbMZN1xc7On7jh//6Cfc3X/gq6+/IudMWbIZOQSdBRpJRuqMc3csy5FpSaRciFHF8gLGvbDzATm7F7lmm8lKpqSYpVYwqZIPazLPE+CTtYPA7qWIyfEcfYgWZmBr3JKJdORkDVE7wFxLuv3+r+9nh0Z1Zu86Y+Cs/W2wDscplBs9aVHHeSfOuC1uvSkNxmwierGZoEKRZ+hRXRCUQucMFtUsMguPFF20iKj1mG0IEpTo4kHZdXbxglOISZskWWnpVZ5sFLBCsQ2QPhsS632pood2F/RyVSlrZ+xtTqnXItjh3y69Hn7ehu9StQvMuSjRzHudH9S6OkK029Zip5yJdooRgWJn3WojAlmgZakNaBAVFuPYjCOvPnpBN0TiJtAfBsJhQBw8Pj6w6XaEUV0lbm5umKZEKY6+2zEOlzzOE2kpeIwNOmwIrqMcdePutheUFHi4u2eeF+bHtwRx9M7SBvqBucKcYXMReXl9wWfXGz5/PvJ8V3m2qfQBUkocp4WH48xpSSwps98FXo2RTy4GPrkc2Z5O9FSQgGThdH+nwtmHROgGLq4uGMaRy80F0+nAac5ISpR51s26KjIRXEQzGjMimu9I0sJner/weFe4u9riyszV1YgrE0EKTgp9XnAlw/FA9J6Xn2/B9exi4JvvRv72Z39FuK18czwag7lHvCPEWWfTNamAO0MJlVq9MnC9zkN8DPrsiChxxj8tlG1+7fQ+t1lzmQVZjJHcijls/iJQzJDdmcWZC222kojOa+cUlak5bkYuLi65ub3h8XBgXhZSLQxdoAsdZc7myORW6r/oEBxwFrUETrKpUNw6N1pji7rI1X7Pdtvx7HJgKZnvbt63PYvtbsPzF9d8eEg8Ho/EqF1RDB1l8BzuMzULIdh4I5g3aNSDpjSSnQ4Klbhisyznoe/1YEnB9MfeGcKje1Vp4m4bbTgr0jUVQmx0kvXQ6yzatTUxop+hG3qaxk8zC0FZrR0hdIzDiEghFdXTllJ0JGT7mbNrF0OwvFSdnZ2lV6o1bkk8gqXYe2WSw7kjGoaRzWbDbr9nHAa6qLq56+trnWfGqHKNqnucOLfK1koVJGemqeh4Jptz1WAaciPstGxK3wiFzq2dm2s3loaKQTMnd5wbDhsGnK+lawjeGR7VOaB6DufmVKOkBSXDVGHNU23X68nr/4Pn3Pf9ZW/5YTEq9FjNZFfE3pSPtoiwBaQf4Pz39jbEEfqo3ZpBk7lkqhQ6r4PpaHDn4XSkiEEoaIJ5KZW0KN4sWecB0mkLrRWbJ1WFZtuhilVOfa8RGxSjhDfoU34ZJtUSqt0UPYiKCfBxQEUH3F69Ph1AzQQHu81ufQraz632QDrvlGFpe8Xq6GLQcEoFl6zadI7OFbwLdGPgHMdS14rLBweu0geFtrJpsZyZTNYKKVeKVxeOcdex2Q/sLkYurne8KEIfK10U5rzw9uEtx3qkxkz1PRn4y5++Zfj2yIxGaPm6I4jnw80b8rJw9/pbrvZb/s//4r/Bu8qb73rq6Z54C9txw6uXnzKOI/uLHclmg9vdjv3VJWOsbKMgy8zpYeLLL7/l9eHEn/7ZX/Knf/F3nG7v2VfP73/2Eb/xYs+PN5VLeWDosoqOj/rg50klIwuBoe+5evWMWgpvvvo5eVmox0n1lHGr64eMF3VIaQVa8JHeiaYA+MKPXl7w/KKj1AV5/4Zf/MmM32549ePfYNxd4E4QasdH+y+ILvPw8z/Fe7i8/Ijus8D/5b/7ff78Z6/50//7d9RF4dYihYSanQ9uIFKJFBYR5pxxnYa21uCo0dGCVrfDjq42+LSoFKE6uiXhq5IVYozsno9IFu4/fFCEwqf1MJCGxOgjaBtExYWKi5U4CnEL271ukoskvnz9LbePB05JCP3Adq9wmPcwLeYrKSqYL7bBzFURmlhULoBtRHqYe8btqBtRVbHz8XFSQkbIuNDx8sVnpDzz9t0dxencuPqjyj9qMdasru++j7gqpEV1aKdZi8NU9NlXSFgfpy5Gxo0+3GXRyLao+ztd09MGj3ey6umqtINRvUAbWcxomtQKUbRY0Q6urF6p3jl8iMQh2pxVd30f1ET/cndh7GBlIOcKXUi4op1ecNFIf7qvRK9M54x6sKpAf0N0nUoBXGQcFa0rrvEaTK6RF6BS80Ka4fbmPVKEnx5O+OAZxoHHx0fyXChJqFnJUt5n2z4NHpVKWippMeTNSF4+1/VQa9FSfYwEB0MXkFqZ5mU9pHHgezturEnyjXXv2s9WbWU2WBWH5kyGnuCdGYhvGMaegxVp0akkrHiVGlVRKzsvVqiFX90Lfu8h2ETXDX/GtwNQF2WzDcKz5gXapzpXWFZW9b0mm4t1NlIWqMXEj96cCOCYJqUS2xVuh4pCmKZX9J5qbFqkIfGi0EF1hhuFVefTZBbYYdeKjVrON3L9FYVBAbuosrbsGlbp1gQIsY6wi529j7JqKhUGOhcD2E3+5Qssypy0gTTurKES0bgkDY60atQm3TqutAGgM4q2fbYqSqKoxTRsnXaP3eAJRHrn8S7jXGZ6WJinzFIWDSJ2moJx82HCPRQkjHgfudpu8A6m0wPz6cCHm2/ouOLl9ZahC7j0QD15vLtnv9vzxeefsNlsuLy6IOfEkib6PjJuOqQsSJ44nGZOxyPH97cwLbx7844PN+8JKXERI59e7vni+oLrrrJhIXqDjFPFVUEWvTfF4Jau70hT4fhwh6SCyxUfo+omvW5SwXmCeCOZBCUZefC+Un3h2XZgM4x8yJXT6chDmshdx/jyM0rcIqdKBD6+vCS4RHr/dwRfuXjxjK6L/MbnLzgcJjY4clVT50yT9igDT6FYLShTOXcjaqDuILqVsEF1ZNH5UnXK+PNF7daC12JwHHokCMcuojr1sy5Kmxh7Pp7MXDRY3uua6D3DpmO3HxFfOcxHCoLvOwYZ1dvW0AkXKvhiqieHq26tHXFnMXQzkMJGJV2nyFHJ9rnnRAiVaVYS2WazJz8U5vnA4TDj45E4JkIn6/zWe7Uw905ZkgtKQmkdaS7adaasD3gIDueNJi/q4tI6O1sy6vNrMz6x7NEqGrW0skqc/FKn18Y3iKPkxlg1OzRLOQ9mn+Z8K1BllV+4Vg2LaqCD98SgfaIaZIshTVpAqCOfFeu/9P6drnsjEKaqgv7idaaXcl7NEHJKZrixcPvulq7reHZ9zTwtmAUqaycgVRsIf2abVwsNbkhZLQWpbiWntLMiBHVxic6r7KK0RB/lNfgYaRtj+37dNW0/RjkWaxh7FTBLtmBWd10XVZrjsX8htsei+wLnA9Wu3Po8/IPn3H+2MT/56i+vJQTP5W6n0gT0ge26bu38RJQIHkOwitEysORJZwQM40DX9yxpIZVMThOlZAL6MF9cqQ/d/eMDpWQ6qyRzWsilcJiOlFyMianvATsc9SY0cLMZaXPeYOwADaIwbLH73g7RtribFnC9kAYRxKEHUCNZNHpFLYm0QBiiMlcrWQfRXTSqeFXNUR9ZJmVcNailmNu9Dx2qOdKIqZojIp7QKcyT06LzTIOUey1tWZJ6QrpckQx5dlaABCVT9JXf+L3P+G/+xW9CqLhQ8HQ64O/U7DedNHrn3XdHHu4W3n8jTI+OYb/Bx8BSJiXxFH0P5bRQ08J0+57PXj7j//Z//e95eX3B84uNVYwHIjD6VjxpRVhLNvurzJffvuXn37zl6zcPfPfukZBnfE183DuuOsfHw5bL2PGTy8hV77gIhUDltKS1GBCBuSpEvkgmRM/ucourlTypTZgTR+gC3TAY1OWg6sbtnNgGaTB2zTgp5GGgxI6HJTHlzN+9veduKkw//H3q7ppYHbvNyL/67/4Z19c74nDASyIcH0kPD7z/87/mw92Bv/jZLT9/857/17//c26XyjfJ47uObrNZrbumJXGaF3wf8X3ExU6lEL4gvpLKrJurSsPpfId3nmXWZ6rvlDARsmrOTseTQuCD2mjpfFpnnlUKWZJ6SNbM5dWOV6+uLA+kMIwdw9jx+Y8+4+PPP+Li8oJxu+F4d898OPIXf/a3vH97w+X2GTH0PB4PzClxe3+nDMaqZWiRhHee7bjHiacsHimFPM+6GWuelToJeY+Ljn4YGMYt/RDpho6+98TOs6RJuxnnEeeYDgs5ZdKs4dCh62wEouthSUrdryXYuGTGB8d221lHY05STn195yXjQ6CLHS39Rv18pe314ArO1VUvuGQtplyJirwEG59Yx12t+O82g95nK1iDV17CMs0K/R+Opn8Ugov0YdCZqY8sy8K8aNCAE0W4ihQtvgFpYxIbn3Sxw/tAb+t8mSdyytzd3VFrpR9HnUt2mmjhRAvG/cUlS1p4eLhnnmYODw9KTAzeDiQImG5YTJZm+2gjb3UG2zbGfugU8g5NixpUwznnxYhU7bTVdaBkItTuENNDYkKKUqgl048j/bhhv9uy3YwcpiPzMjMvszJF2/FVGwxs1UrzczZiZjqc/kFM9Hs7wZQ0O6zWor5/3lhZISisV87QonPq5u2DJ0TNNnMGNVYR+r7XJG0qxQteTEAs+jNj39P1HWPOlJIIFp8QvBCLJ9WO7B01l19+k1bBNG1e++Btw1TbvidA85NDsIWcYtVkA6BrY4M2A1Obv5VSVrTUOa/dWoNLddmq60QX9YaWiu8isesoueKWZA4YKgAt1UhABns4LKi0FiRVcGqtpMPhqlBszOtnbJi8mHYSW6yq5FD/0HmeKC5RZCbSEaTHdTpwqrOnLHA8ZqZTpdgBjFW0UhO1LCzTPbVkmLW6iE7/c+lALz2fXj/HBc9Ut5ATfj6aXKVVc54lJ3KaeXh84Ns37/jy9SNfvT2wOT3QpxM//OFLvri45Nevd7zYbNiT6CkMaFLFqRgDz2u3jt07XyukwvThbp2jtLmuFsvGvFsH8LoMVts8cQQJ+OronKcGgSh0Iowp8TgtPN58YDpUOh/Jux1JHNKNdC+v8GTqd18hxxnJiV2AP/jiORu38B96hfLfL3rPYx/V9ip6TdsoBR8DIap/qAQ1Gs9UNVlHCWZK/IoE51nmZNdBoa+cZs2MlIx34RzEGt3q/OMERRoEavH0m4H9s0tyzSx1oet0bjXuAvtnPS8/ueDy2SWnDz3z4cQvfvEL7u4c+8stw7CBHro0M7sjSwZXMyKOpRS8h80YcBIoS6AsDqm6qXuvxhupFCQ7yqzSn34sdN3Ifrc1uZSQZk9Nzmb8nmXKLEYiE4Eh9Jpp58wnGHs2XUTlW0pwWZIVkKJG3tFHHYtUvbZN0lVNRqVel+2ZVs9Q8f4Mi8IvIU7OsbrXtBazWX3ps+2IQfWgS0rM88zj6YhkIEHf9YShU0tb78glk0oium4l7ogYMQrl6VREf29rWEdWRlYx825t6oRpXvTPjQ099gPOt+BjiF1HztlchvxKAnLGg/BYjBtqEl45o2Yr/CR6+NRiW6VlnXZBmwcvFiUmWiw5py1DqTYWalu5E5WfqQ6M1WjTaePRDz0PpwPTPJOLcinWnEwai9b+gT+Pu37V1/d7h3Y93nmdW3nBhY6AM1aYHhbOe4Yh4J1a95QCKWmHFOxgHMx0NecFyQsuLypStvOsOshJrbTVwaFTSMtpkKW64DvSkqgpG9yY105QZyDa/GrChX1wp51hM+DLeA2HtcvunHohlqpBpaHXhRTW4bpuqBU1xnW+5cRFRCo5z8pW9KKmu0OkH3q22x1LyZxSou977YK7yLgZmKaJeVnWGBLnMgRldwUXFG6qVR0/SqFm9fDDK2kmGMOqGJ07CuaTqlZMtS50cWB/ecF2u6frOvabkWH3HErAlcjdzYnHm4k8Q8mOwzs4PIAUNe/Ny4G8ZKZ0R60LXjTlfdxtiS6wu/6Yq23Pz37xLfN05ItPLwkxMOWMr8KAwRsCgqc4x+uHe37++h1/9bff8Od/8TOmuUCq/MFn1/zOix/w61c9H2879iUxLIumG4jn2AqUrIf7ojIxeh+gevop48QxMGqHHXQj8tEKl2YYbKQjs+PVB9X2u4AaA7AUKJ7RO7ro+Y3PX/Fpgun9I+n+AZyQNgM/+7PnPLz/iB9v/ynjdiS++CHD9gUfZUe5+8Dy858TyjX//T/7I/7u9QP1L17D1SXdDz7jcTlxO92TKkxLYRt7+q5HvLdNUDcvX7UTCGj+isLYUV1JvCaQKIuwR1zFZ920lDBhFPfo6cZIHzq2cdS5tBe66Ll7eKDf9mz2G4XLe8f94Y7lZwe+ffMl/dDzbHfBGHsO04nDPPH3X/1cO4lhJMTAi5c7+jHw6qNnxOhY8lGfzSRIddQlkqbMw/uO5ZR5uFuoWX1yxUzNL57tuH55iQbtTnRWFOTkqMkx5Uq2SDQR0dGDM14ARtTAEWLPatMhntBtaK41DmWPOttW65O98dwenPcJZZbpbNa5QAgai9W7oFpn34NorJIPaj7tg1dhughJFjA486wtBpwoarHfUlMhTQtSE4/HD6rD9lHdbKQQO/MwNfedlBIlV4KPauSe1NA7x4LznmKJLWlR1mgzB+mjNiZdp9mnIoV5zhyOj+eGxhAvkHNKPWY6YjmtzQZN53kFjZtSIo3qVNw621uWZKMcvbKnZdZ10RqY6M8sUiv6nXfWPesoS5Eja0hsXptrYZknTqfDeudcCOuMH4c1V+cmwbWD+h97CLaWW30ftNOBpvc7zweDaZawKkXhO3NiQFmQ1XRJtWSTLJgVm138YmJPsQsjT5hGeB0S16DuBM5ZkKd1YYI06c/KKDp/6Q0VEX3QcE/+XtaZAVb5nFPCjWn3lOn05O8adVlf2xhaNrwNIRBE8F51UI1Y5JxTrV7x689S54Vqw2WnbLcVkKjWTdu1FnttfCPa4qtQ3ZmchChTrh8G7cy9/u/9xVZPiBI53BXqMlNmyAuUBUrS6+KcDtNFEjXP1JoITgf3QxcZ4sjz/XO2nefhcGT/EBWWsBDOJ4CEXmGDIo9z4f2HAx/ujjw8HAk4Bud4te354fWejzaOZ52jmxM+Z8S0ZrlqzFBDu8VmIT602Z4K3mOrlZ0WDN5rYkFpwtVaDQhoG6UaJigR2GKNBFzBDBw8+7En9p797SMnSYgUuizMDx+Ytj11XpB+wMUe12/pn13rWoie3djzgxfPOc6e55t72O8Izy6pR/iQDxAdVZucJ3qotvbNrcgYoV40XNS3929dh3Au1BojuYjO0ApFtYdm/zX0vXY93o4KsTlOVHuxEBzLkkjlxPF01HST50IZt8pMdl59PXNhh6eno+9HtpuOy6sNXefJ1StsnpNVtjqn7b1wOqi8RSFNhe1FOi4uNlxf75nziTkXgldpRfANqTGYqz2j60bp1j8XezZd+36HypTErezYljrOusu040/WX1on2F7T0YzosX3MrbAnVVGf9ny7EHRWKoritH3B19bN6gPsvHqH6j4Z1qgo7YySvnKbRbZ/tr5bWQ/vNqcTB0702dMiuKz7qo5rwkq2Wn9GVc1y8IHOtHv6vNhzgawQcntehCcOWk+6q7bnrExMYS1YQm6m/+Zc1GZUVb87tK3YnIXOrPrzJ7YbQ0ucL7UoadKIj+f3+ORL7M/Xv/oHkVDgV9qmaTRS12uOVNcNv7x2THIQvHYxIQSWnJgWJWVkyfji6bMnpaTWTMuJnBaz+4EQB5wL3N7e6s3AWnzrzJfO3PqLVmWX+z25ZA6n47pUcynIMtlgVQ9o7MHFV5NcxFVu0Fr6lhe+JlFUWZ0JAGuxGjvW4WOvQtJZadRdv1HIwsgMgicLLDmvuW5Ni+idJ3rhcrvlcjPyIfYcppk0Z+ZUGGIgOEcqiVwS3hc8Qhw0Qkiyw/vIdrgkhEiI6rqTZ92YMmrAHfzA9nLD9as9u6sNIQ5cPbvmB7/2iSZU9wNevmS+E24OM4fHBUqkD5HjfNLw0nSAmoku46jUecaHwMevLnl+/YL/6r/6Y2qa+NP/8f8Jkrl/mNlsemIwWrxX+6gYikH1wv2HO/72L3/B4+0dlx4+3kVebTr+8Fnkdy8cfS7EWed/PsCpJrI4cugQCQiaXh9thuxDVIuzYcBVqFlnCZ0dhs20OTpBxDYIX+ywcyuZKDiY/UIKC1vZMEggV6Wv9F5FuX/8k1csRI6mpbrqYEwnwpuvcY9bGEc9oS4vWeaZ98eEy55fv37Fxl+RyjXz80tOP/6Yv/vua27LA8lleslQHClptJAe+oVcRQ8DJ9rhiaO4hRoUEvfO43ubS+egeZhJK+r5lFCfwsKu23D98lLDlYdAqYVUM0M3sB12SiyqFV8dFMfxYeI0ndQE28GHrye62BHDjs8/veDL6Sse5wPTadLDpQxIFt69fkPXOS5fDIybwMefXDB0HdtuhydC2fDh3Ym/+dO3nA4zd7eP5CwsqfLDn3zCb//ej7i9fcf7m7c83D5wuLvXtdw5tl2kimeZDixSCL7Tm3ZWQmnHVyfdD7xYF6KwZ1mqMgv7pgc0JChrF6N7gazdTsqZIBCiyg4IupbEDhWt+tVqz3UDeMdp1tdONVFrZZqPiBQOvhBjYL/fIVR8L4i5ygyDSkXGzZaLiys+3N7w5rvvNOe7Oqok5vlxLXqq7e3TMmswL8phGJwmyUeP5SQOegiZtV3f9wY/6v7gTca167VTLlmL3N1mJKeFZVrUiMHrPppL0QDwANErsYzWc1ctd1sh74oVYkWfubRosbE2T6WlZKCw/dDjaqG6YkQlJVBlQ+b0YAzEGFmWmXSXSCmtB7uOfCytw04Db8WsZP19N8SVmPiPOgRbkeRDMHcNxXab1GD9tqoVZjsYpSrLSqSSkyeJI+Wk/y3JmFr67d7rg92IIg2O97ExlKqd+Frxd7HT6scYq2KYuIrP/1MekLRy+T+vFtrHa0Vh+82Kk1j1Q2v+tCoUPNXXdUF5zwp5NGaoBllqC98cYlp15YPHY76EMZKXgrSD3ymMF736O2ICeHFG9NGtQSEab9BO8KqhbEnY0ePVDJRSK6dp4XScOR5OdEWItnD7oSOEmdVcoAq1LNSy4OqMk8yVpU7PJevG2xhHVEQqS87MKXM4JXCe3aAbTXVG52gQbS6EWthIoescF/ueT7cdH20jz3vH1lUcBVerHmwOm4hBlaDFzZPufOUrOrUPo4gKx9veaB3VWgmaaPc/XR8NKVF/kPYgWVhnFYLRAq96Tw2R0QlFHH1e6JYJPz2CUw2ViwH8Rt91PxBCz7bb8CwkPjt2PF6MvB87xi4QHUTvlMFYsRkVqyeje1rBrs+UQrhtRuhsduLNy2vcjLb2tGOtkgxS0+e2bValFIjQhciSMiWZH2OGNFXyxFlHO8/EkLm4HOm6jhh6+pCUSVmU6JAXzQcsnWOzC/Sdp+86+j7SRWU9xr5j2hY22wFEY8FyqcRcGLeBfnB0nXZIMTTXKKyjs3mUkTYasajd4nVDdmsQ1XpjHVqUnckbhm6JX1GuNjZqXWA7VGVFelrH6WwkwdqpaXt+7nxUfqUa4CrnVPNGK4hdQ3CqpSH0jJue7X7kNHXEwWk2aVW3oWoJHSpL8IaoZdMyPin4rYhvi3uddzvt8JxvfpuKyrUuUey5b0VAu15PeRDrLi9Y99UOwfMM1a2vea5MVNZtVmku/Cd7cns2zYLSNtl1G16f03O3rz6quu9GIyI21E+MmOVwq8pNrQMbV+I/2/p/6ev72aH75xJjx9Xza714ZpR7WiZrtU1zFVDShtfk45SU1PI0UkkXVlWtYa1a5ToY91tiF6lFySzTSRmJvkOhoqidoKs6X3x2dUWuhbv7e/WuW2aDx5TAk8qCKw5X1Dqp2ABcA1XPi0oE7RacDnwdQNQFEHyTIdjQuVPmVUUXQvQ9ToRaFzuxVeQvkm3z1/eSS6IfB4btyNiPjL1GUwnCYV6YU2Y+LpRU6MdeYau95rqlxxM1JU53MyUV5qRp8T5c4H0gRp03+CiktHB4eCBEz7AbiF2k3wwMG89mHw2WTRA6XOj47R//Oj/+/Avefv2WD+8+8NUv3nB780hKnlod1xy5iML/4Q9/wov9li+/mflwSPzHr95xzBW/3zEMkS9ejFxfXvC7v/GbPN8N/PrLjZI/NiMxRoahIx0eON68Y7i/Y7x5R58XhrwoKzQvWnWLo4aIeO1WRCpzUgf+LHrYd31P8IHRZhtOmQEQOqWEn444EXordhy6Joleba2o64YXRIgGG1KFuV/IMTHWgV46Sxdx5IB2C35ACMyLzuJKgLjb8Mk/+31k6Hj7/gNVHP1mR9dvuLh6ge83+Isr5psD93/9mp/d3fBvX3/Fz9684U+//AX308yH6UROgZw9ZdZMPH2WIIdKdVUH/ji6oGSlhG4coRvwIbDpA0Pf8fLVc/q+Y9yOpJy4vbsll8xSZ3JKzMcTS86c5omry2d8/OojptOJ6XBQxnZKXOyfsdtcMKdCLkIpM1ILcVDI9HK/J4bA+3dvSMvMMAZj5g4Ku3ae/cXA7/7R5+CE25sPWthWyMlxOkQ9FCXih0LcZvKijM9lKixTYYgburDh9Xe3uiYX9QBWbEl0CO6EZa6r8QBAHExsbvKkmtTqUcXhon7BzkGMChcX3YskK8SWzcSi5GKEFjXfjp2yvRV+18il6nVuFjcbZUAWPTxL1oL96nKL9zAvkyJJIVoBbAHgluriCKZpE07zxP3pwBi2bOKWD7f33N89UBLUDH2/Icaeu9sHTqeZYGxhZ443McR1HCNVOB0fcN5x+fyZFaOqP+7juM7JmjPN8XDg/bv3VkQ47X4bS1Sc2uOVrDZwPqJRZVWTb2plGDc6v+wHBOHh9oaaM8NgWmdaV5nacaA6167dCyuyQ2dz3GqwiKxzV/Ws1flr33XcP9wzTUdyrkbA07pEosl5sjVUhuLl+fgPtoPfnyxvdkKtAqjktdvBabUs7jwbrF4rxFqKVQMtM9CwZmmRTPr94lTHUhyrFVMz1m4zsGo4shNRmzXboDQqqLE47ea1CsYgyHWYt1aM51rjXGWw4t52RKre6UltIGYDp6+tNGuM5YR1cC3RWOd32v2VJvxMTQ9kFXnbv+3mUu1aB0839MTOE6tQU0QWyCFTJWm0kL3nWptbgw63u96MCwzOVUd4hxwztc6UesK5DlxP/jyx2XZcXPa4MvL2u4pjwtHhCVxsAi9GxxfXAy/3A3fvZqZjIc0zhykxzQp/fvLsI1IRDlOi947DEbraa4yLCBHHUirTaeJCMp9uPUMJjCUgc6QmYVoqS2rerVo91idVJlU3lmCD89Xdo5r2yOv3O7s3bUaCkanaTFecwTDmyOJWUpVYUa907mosQudQwS0OqurRQlGNYi0ZukqpLbXdstPmo32OgnTAfiAW2L+6ZswnWGZICy5lQq10ThnXxZlswzmbvesHETvw5Mnq9Xb/dfO0AYCrdNHRdZ6uczgf6MceWYT8WElzUXZlzixzZe4yp+Nisp1MSlkJFV2mRI1skiI61xNHnjMlOWRjbGbRDjZNmRJUA1wz1AVOrnJ/mxEqt+8m3XwFnOtQc/BAF3rCkIhjJS2F4+GAFCU6SbU5iHh1d6q6yTkzEhBnfsbOgxfTiymhzpkeuFbIhpz42NixYo11XfeHp84mtG3Arf2k3Yk2oDEzf7M7k1Wnq3ZvTV/lvZpmOwe5hifdmT7v4JBg3R0OKVVlUE6IfaSLHWM/Mhwn1biK8iy6rlMGqGmzz+CWvm4phVo4Zzm2nqoWmz/qWKiuaT91Zfsrm77SdOFPrsj6XCGiGsCojjvt+cE9OScsN9Zb1FyI2iS1yKWnaJyA2j6uK/uXd+e1w2+dntfixqGGBM08Yf2o9rGoDQloH/OMWP5DX997CI6bUem/VhUU0YpaDWorS1nWBaO4fF4/jHcGwzh7hxqdrXEgjtXh5HSYEFGFPzjodFPvuwhOSCWt870G54iIOcwIqahco7kMRKJBmXUdhmtMUtVYIhfWC3NGku1Att/7al6fRawDVG+/YGLPVLT81E1Y7a2cixCizSArsghMkGohnU4saeZwuifGjhgjXbdlO26hziwhE/uOED2+G4lD5PnlJdHBdHVgmRLvXt+RlsqcNlpZ2Y7ovGfc9jy73rKkmfvDvc4IOm+zSkcXR3o3EOuGWDc83z/n+Ys9n77cEaWyHD8wH284Hh21BP7on3zOj1/t+K9/smfvK//x333D49tHcukQ15MlknLH47EQY+IxFcrjiXT/lqtne36wGyneIaXj/nHizXe3XMUTz7sDpCOkE66MOAaWLlBCoJKoaGxSlEKzGO+MpbftAz4GUtB8s3xccFUYyQqfRZtXO4Nsa6I6IYdsZBrw1VnnKcSsgGumMtQBVyO5JDUIjga5WmFTQ0AIhHrElYyrj5Ru5k4K3TDw0e/+Ni5l8jffspwe+PrPviReP+dq/EPieEX/B7+tYv//8D/BaUIOR3V7GTfEpAd8DRq8mpJa4FWUzaw+vGKbfiAYbOWKppinWqiL573NCzMJFz1xs2GZC48fJmqqlNkjtcMXz+m+8M3hhhAgRnAE+q5nOh2ZT0d8jEqn347EruN4nFnmzNvvblRrZ8b58zRpwZUKIUa2uz3LlPl3/983VCks81FdPsYNu33Hx59c0cfA0AcIR4SjdqtzYey3bPYXPN6duHt7w3RM2GmBoD6cOobR1479lugDUXTU0EdL5OgCpVTu5WhjmWQ/QwkjtaoCDjpwdrg6h08obOctVs2BCx0xdHRBO14xqL64YuYFQHX03UjwwcKZhWVWNGMpSQutquL90fQYtWZcCIShJ5fKXBLiPF0/0vcb+nHHbqlWFOhBEN2Idz2Hh4nppPO99VxxME3KzO07I8GYUcnj/R0AWSrOBbpuVORkWdbir1KpntWtxXtnBERnnqoLRRLDsGe73TAOIzF2fLi9YZ4n1Tp7T7YidrjY4UTY9L2ie3d3xhQ9Q59UIZ1mJTq1Aj441ZAWO/0omnJDgKKxeHPKVKdZow7LqSxPZHMW+SdOu29K4lfhob+CHdq6nNYj/ScTlTPpSYfsxqzyzhnlmTN7i1aJtaPb2XuWFb91rhEWGtfIrZi189UiQ8r59Z3Ox5yKAa1u82vV4VA9lXZydV0xZ1ybMxTavkRWcFrs11pVu+Rqg1OtgglaiYt9tnU2GRwhVI08EbGZS6VUh6cYzVrhLkVTn1R2di2xKqvrldI/jB1QWHLrYtob1Gvhg5pDR9vAVrNlabWWoxFCjo8nbt7esu0CvfekRedgz3cjYxz54nrDZ89Gdl6IteB8gNgTu56II2YIpRKWCTd70uGW4IRjeiRSON2/oAwVVzuW00I6zZQx4zqbNVVL6pZCkfBLhhXtN4o+qFuPdyps8DhS6+Bq4exUH3CuXwtD7GEUxLpsBwRLwNY1UIHqmhLJrUVZ67Sb5ZjDEaqukyLaVTQWcnk84Lue+Oy5XqPNlmyD+jpN5Pc3uD3Ii+d0PVzu91w8PLAbFLasu5HHqfA4ZeZTIU0ZL56adR15J9ZFrXdwrXKb160+OZ5c9O+TaPEX0QOkZtW06SxRiF7XQfOidY51NLHOWJwViSXjg9HUayVZBFmMejC34NKUdE2npeJKoZRZd4tSFY7u9d1rYQYEkwW5QPCREDq6rmcYR06PCWFeoWusY1ONuCMvWOK8OiWJ0fmbo0mtygr1TtdVMVTKP0WFpJHfjOzSKspf6rnbomwIxRmpaA9rY4e3b25OOQ3SB2ODNh2h7aGl6HMvJr6HNrNUowvxM6EXTX9ZisKsNATDmPqGAlDtfdVsKFqgVq/OU073HrG17miSEtbxVs3Zxk7OulPbraUF+ToLcFaikPeOvu/o+4Fh1JQQuzl6mII6jOmLPNl3z9ezkZM0AcivI9a2m8n6v9sT6GlpQW2fjz7gYkddVFGrG/C5A16bzrapfs/X9x6CBYWHQj6d22KqVknekhuAUGFOMC+ZGITQt3RfoWR9qBv1ubKgGr8BJBoJwK2blzdZgCb2CjWZNZJTf7s7DspU7TUxOniHrxrLohARyvn0ejy5IHhLV28jymC0etcc4+1h9nb1nQVb6T4heKyyy3qxg1d2qEPUBEuCVVWO6CJjHPD0RLdhmWemacLbdIckVJ+ZliOEhdgFhuBx6pxMmhMlFz4cj3SucrXfEzeBq5dbplPieHqgpkquVQ/bLlBwTJOSl15cPluZubUKOVlhIiAkRBx/8j//GX/5H/4923FHH0cebh9JU8//6Y+/4Hc+u+b3Xnied0K5ueF4SHQXP2ALXLuF7pjo376jLzPP7m4YJziln2to6VxJly/h0bG/uOTFRx9z+nBHvn0gP3Ms+2jWbJGSE3lJnPzI4npCFVwFybqJERyuR2c4zhOqJywCfgYxFxsRFvF4iXTS26K3SrHLeBH6We3TAgEpHil6vZZoe1/DUMu5kCnmstk5ZexupowvjrkEsngkVkoW0p/9Je7iApEBf3lJ+OLHdIcDm+MChyPpf/i3yMcvcP/0kau+8sd/+EdcPLtglhPbTy/Z/+CaL9+946v37/nuywPvXt/h44ZQe/rSEyTQ1YoXIboKFLKH6jyhH8BFEgPVB6QbcQE6nzXlO+yofqbKhKPSD8EsuxzFFZLPlJxVTO0U9IvdQIgduRTToT0SvFCKajZTNjOGUnG+Yn7y6qmbNGHCBW+yDMduiPQxsh0i201gdyVUNzFzz+B6xrCnbkbkasd+d8l+f0ktnpyF03xgKRmC/qw4BFxwHN/MnE4JNz3qBt0rGjPbdhnDaEzskUzm8fCg/r69ro9c1Wi55LJ215QA1emsS7Jtux4kk0WAiLjAWqF5j/ed7oa1kOpExdNF3SGzU2eoENVF69nVlXppHk4sy8K0TFoEHicVgXcRFyO+6yA+MsfXdFcXbC72vH99z+P7A51UogyQM0EEyVnRt2TyrGoK6Npr4IDT41wsqaEbBi2kqtDHyNXFhjQvPN7dK6M2OnIo1lA4K3aChhOHXm3Lug7xhe2uZ7/bUtxCP/erPnA6nsg5U4uuk2zzveAdiCcvygKNZuSQnM7tfFEzA3w1WDNQMVmZD8QQFaLPlVgdo4PtdgvAjSUElScnqEPwoRUXpkP6xx6C6rvo8F20w0FAAmOIq7hRuymgc2vQo2uU9HLu8mjuK1UPDLGTe2UytU5R1KS2VarNycEHPZC0simQMy1EFzgf0k0X1uo0OfeeuFZd2n6p32ZzpHMt6Jw+UGf/udZFYRulvV5orhB6YDefw1rP1VbsOkbFknWAjlXcxvbzeIIXCGLdm91Ew9jFqzPNnAuL+WF2TlSOECD2QtdFtruRrotshoGKJptPS+JwmpVCX5SxW8mcloVjmVmOlSFMbPBcbQY+f7bh115sud5Vdr6Sxg21dFxvA1OBLy7h2DsOeaATx6thoe+EwTl6J1xFT+eEISfk+Mjda6Ec75B5Js+eaQ7InKiLOoboeKLiXGnK+vWBOl9b1cppSoozayuUqSdOYWjnrRPAoHCr6ZWNgOCoJkKvpRprza1rRC1YK74lCyC/VDyKdectoSR4D06lIy4Epndv8NOJ6rTD6q8uka6HLLhhMJZlz+WrSz7Or/jJ6YfUrac6z27ccnmZmF5UqkTmKbAsjtPkSBli0XUXghZmNhHT/E2nhZX3bl1XtWhRtyyaHamY+bkybnFla4A1UTvqpi1zAQuYQh2IHGvcWNBZvY9ibMcO54TiHCKeXG0TNYJGjIGud2y2gXETiDGQJFCXQPUd4kakqv3fdMqU/Mjh8cR0SqSkbjvWvmMjLfp+AAnmNtLm96LaPdDfO40U03GhzptLUYBdU93b03/GnOyf6X/OOjPf5m9uLZBa8bsyeW3PESol2x7T9iCn3V2MvXIbmJRYF3tqyRqEW6q6FJVCEWGMKmuQRSUXmm2oo6hmyeJ9VI1ptWxIBC1j5DxrXBEt7d6rdZ9UhXrToqG9LSGeomu+i42kqAhAzoXOniXNLKzM80QMnrTMpDSv+zaoiXgx7kdtsXqu6S1tn1wvj1vJTKvm78k9kXUNit0fh5RMSQvV6MMidX1mW1fenoUzTvb9X997CF4+uwTvKUMPCJIXBu/Z9oNtFEXhgaA065wL093E47sTZVlYlqwPTBQ8PaFGyIMmT/cLBK3GnIB4ndDlrAeX6uwg2/A0BuvsirphHJ54RHbBM/ZeLemqGBQr5pX4tBVWym51Bn+InA8859YUwGCLveYGl+nDEPUJ0cib4ImboFVniJSSmJfTehAGq2D2uw3D9TMODwcODwddTE5jngKVzgl9AOkqRBCvC2J3canJ6eJZlsx374+kpbLbX9L3kcvnPc5VSj6w24188YOPGfqe3WYDPuBC5Ntvbvmbv/qG6bQwTTNIBjkxpaIp6+meoST+yW9+wW99cs3/9kfP+K1fu8KXpHlw+2uuFuEPH9/xw+3CHzx35NJzOgApE487PELXw4UXfhgqU7fhTZ948+E9f/UnX7ONkWebkWOFdwI1L9RlYYgDQxjMhq2aZZKQO+3i+67TmU+tOKkcxVMFTmbh1MVRZ5/9gFRYjor9O28HafBIUM9OyUJZFIatOTFGz6bzJCnkmgjZ01WnZIfA2kWLd1QXmXxVgoqF+nUuEinEPFPu7njzP/8bSoyki0vGTz7j03/5ryBokrmcJuT2juFqz/4nn/DiNz/i9//4D/jzv/pb/t1/+HOe7V4yfP4Zrz7KnHLm5vUtjx8eefvmlsPjcY3S8VHzJcusjD3Xqf3V4BXcLXUiF9HXFKG6B60Hg67fWlmZp60o7YcNfeyYThPT8YT3kRiCHS0V7zaabOBVFtPFpB1gVw0W2+Ocdog5V+7vHkwHhyZ07CL7S/joBz3jZmAcRuopkB6BsCP0V8ynA6dj5bubG25v3pOXSlmEVHQO2P4vFggh8vL6JbGLPBzuSXlhPk4aWG3rolaH84LvM7hM9MoCPUzGX7BDJIbR5ps6ooAmp9DZs+8s9T2GlSUegm7ap7roiKSNR1C3n9SkUGZB5om42tGHDUUypTzifcezy5FpOnF7N+unS5Uy6YGyGa95df0Rb7675/3NPfMsVCKziBYMXUc3RJJTCZYzwiGyoIG+utN5O5RcSAgqUXM4fIUkcLh/sM/MKrIfx4HdbsOyJKZZ5Ww5zfRDoOu8Ec0gLwtdjJzmI6kkhWedY7e7JIbInJVpu9g8LpgzDb1ySXJJOFGXLQ1ZXjTiTJTF72kjEp2Pk1Q/HJyQpiNlPpCdOlHVnLTxqna4Oi2GS9Hmq7M9+x99CIauW61xWieFswoJu8iOVesRfKDrOsaNBsHmsmi3Jw2ntUqqbVTnHo3VOdy14WlTddkrGZW4yRzc+q8V3S91tUzBHP+whuGXHF3Wyg771Qop4cxSaje78UWfmsR60+u4NgBo2DaYlinQd91az9RaDYoV1tgpZxEu3qvOyDQuhrSoFqpXj8laFcLSU9PpNwSP7zv1Ve3UbebxcNDk+qShl/0wUpZsg2PUsFd7TJsNCdf7Hc+i40cfv+AnnzxnlEJ+uKeaP2Pnt5TsGF2h+EIfK8VVTr1BiUknsDF4dq4wugS+MPjK6IXROTYxsOs7unAmTml2ZAfO9D60a20OMM7rrFa9+bRYMYhGte5u3WiaXtRuosK+zjoYcZxHCVbxiiET69Vomqfzuj9XsUXn3c6QANeIXjbDqOh7KwlXCj6cCMusJsRt2C/g44R4SPVIiB3b/cDlfseLyytNbkh11d76CGFw9IMjJbcSv3yI4AKDN7jYmHedZfh50BmcWLpGtf+chUl787n0WNwM9nsl5oQYVgcUFUSb5ZeXVZhMKNaE2ozORLIueB0F9L0lDCS7Jcoq91F1YsucbL4VyEvh/nRkno6kdKKSCAGyU7cbnCJNxbqAunZCarHV92qYQInkXKiL3UMpawem39dTinrwnrs3y/k04wVXtaB36AVyDd0xJKnaM6RrU8ceQZRk5UDZqzb6Epw1Z6IxWi5xeDxQSmY6TUoq7COlCn2nc2y8QwhEiYBnSU73M+cJ0dEP5uxEJfb2HoJQi2NOCzYex5pwuw62O1ZdrM6s0YL31rDk8z4reu00sk47xGp7FmgXmQ1hwcHiE1KUgU5Vjr5mR6riVmeTdeVz1CYfPPfaum7avkdDx5qpvSEx8vTfGW5je7W49j2y/vf0j1r7px//aSP0n399v0Ri2OhbqElPbLvTxalDeoMiNI5Dt/1xM7Dpex7vHc63aqLgor4xH6p2js50g6KD62oU1978SlVbAsVrpaOsJaGLWS23gnoALrVSBGYjjHiUoJNFZX9dgFw11sOHqKzShoOWqDcxZa2UnVF6G/vW63wy2EPhLSWj69R5flkS1WyHnFPG6jgMXGz3pLQwzxMpzUyzxkM573CdwwXo40AIHeJmEplQVETfb3UTjDtHjI6SA9FDv+shKt25BgfdQOgDu3HP6fjIX/713xPwbMLIdrPh+uoZDw8TZZqpc6UmcCEgITDExC46/vDHn/Hbnzzn//g7v8Zvf/yMw7d/wXd/+QuW6qjOc7F/RXAdm3SgJysE6CtzmKlSSaMeTtEFBsn4/EiMwj4k8uD54vqK3Tjw/GLH3p2oPNB3A/2wwTPgpadIUsYfFVzVgbcP5pSiwm8AX0f10qxOQ6m9arcws11DzegxFrHpxQpVhfRV15HOnG19W6mkQmdvcyElNpSaCF4PzEJEvKdrsE5FYZqqG+iYDYGYEv2ciDVTXAfjQMQzXsGjn3j/4RdcbK95cfEJn378nDj9Dv/um6/4xbdfcV+OPNaJXBfcJjPsQbxnrpEiHtyAc5Gd2+DwLCw4Dxf7CxVRW5rEdNTQ2bzAsix8WO5NDxaNAFOR6shUk1hUuhhww0DXd2YB1hGKozp9Tod+IISovruuEAYxspGRKXzA95H9VU9Jmce7D5QakNwjpce7npTgcLgDItH13D0cefP1W0I8EroDsQu8/GjLze2Ruap3rBMPRchVEypqTaTscCGy2Uac75kHR04Ff6/m2qUsBmcOxBC46K8oJtPJOSv7FaEzIXlwPaUIDg3AxQrZZjpdDJIUL/Q1apyP9/REq8hEk+qBYYzgnCa1i5BrYjnC4f6eWiunadL9YxgYho7Li0sKhVQTjg7HBqHn9kNiSZ6u2zJ4D6PjeDgxTQt9F1TOUwK1VD7cLyxzRmY1sLYTRQmEdpA454hmNdgPPbkUlqwOTCYywzvPvCTmaWrzIO1mO6dd3bIOGZAi5BjxQSUqeiihe7SzeyV1hVhzU2U0ApKPCpsLOCl4M0mRpGTK6pXMhWiJivPWjlRahFSLElsszF29UqEBqprwwdk28R97CDYvSGcPS9OGpLLgpOJlsQobJX64Ducsw24IDJteB8ezVc/eU21jM87Sk1ezLs0Guc7832JUU9mGaaekUUp4vUBh5dzKWlU5FNqMIdAFh2S7KaLMvc7kCGVJlAZfUFuj+3SAcv69YdYq+Bdw8kRTo5c+ep3RnOeQOiPKRVl/Phi0EpSCHKOnmBm5sw5T05zVZHg1HHDCsFGNVU0grlp1C8l55lPh8LgQXcD1ltlXHjkdF+bjTE6iA2fvqQRebDdcbzy/8elLfueLj3i+7ehkwSWVdmimGSxuIriMLxo1FIzGNcZI9UJSxTrBRzqBMQ+4Xs3CY8i6yRpcowbseh21U2u33aABu9b6n1V3Sh5be3Q9uPRqr1xOg0tX7R+s96Z6oOif29JYYa0Q0B9OtU65PkEAbD2KzuqFNkfM9j5bpxm0K1gfaAjzwvTmNVxcEl6+ojGUvdONN0iBstD3gavrS8KbwHKakFC1g7SuM3RK918W/X27RuM4qG1UqjbfWtAwWaeFlr51rZS90wgdp1Fn2hZnHB5fbW033Zw/X+PWRVRRRKdUnbs2TXBaNKV9GHplIhPV6qtURRqCGoLPSThNhce7CW8yk1qSxQnNnJYjnZsYuxnxHS4KoS90A/ShI/phJVfM06Idn8zksjCEnUmWPDHAPGm327ueNcZnRVcc49CTvCMv0eaebu0onG9ojy4S7z3Bh3WmhQEIUhsoo8hQre7csYiK2nH6/LTuZEWrLBVBGbuqr0wpG27VjiLtKmtyRN/hh6BD4KIEHB8g+gFPoCyZmitjjrhgXZmUtYnybfZW25q2MZF5jJ4BwidI3ErJtSeuVmN46thoGPTZbihWLbPJyBRpKzkrQzTra7TO5ikYKWjX3cYf9qTpDt5QGjmTcwy/X/dUXawrZvdkz+AJq/RJG/m/4Ot7D8F5nnEIQ9AbGNDD6FQzUjKyTAoTFUcfN4zDgOvBj4XR9YQecs4cHyYwv8e5HMhyMv2HbWUGX4DHxU7ZV+0DuV4P3tlRa2GaJnBK2gkh0sVhhbuUrBPNaFjo+46h7+F0QGpWAXmqbHcju/2Wx8cDZTYrqurwFkrahPHize7He9u4CxQNOm35WXrPlD7chx7v1P+zmQ6XmpiXmeH/T9qf9cqybfl92G/MJiIyc3V779PevqpIkaIoihRF2xQgGbBlCbBfBEN+EWDD38Gfx09+sA0Y8JsAAX6RbMuCGsvswCoWm6q6dU+3m9VkExGzG34YM3IdEeIlULWr9j33nr32WpmREXOM8R//Jk6WvBwNPhqHSAyey2oPk/NmcZVSQXPGhUqMjhAnwHH3sCenxvFpBRppyTRxtItwOiaePqzEEJCbHee68EM603Klrcm8Rn0EP9Cc5xdfPPDXvrrj3/s3/iJ/4/d+Bh9/g54ekbUiKaCLkRIulyNOhKE1IjA6CMFxCCPgyPRDOwY8ibHC7CeWYcd5qZCzFd5sMKmLVjxqJzEIzeqQWIisdHNnO21MX6V92jNmoxUxhxKwgGPpwm7RYkWzmbmu9wG0WcQQZlNmJCQTlMfBhOlVoZZq10p6XRTT5YWuK3TBXqfIiuAQNyA4gtq9FzopwTelHE88/Z2/Q/z6K+4fHhAaTQreK7fB20Sdjhz2Nxx+/pbhn/0TTo8vuDcT+8PE2ipZC3HnaQQ7ACtm5eY9D/c3jHHg+al17enR4P42gorF1qhJJfDC/rDvJC5PK5mSZkQagWhC84244DeIvzd6rdJYqVKgeHwDvB0s87IiouwOdwyDuenUqqzziqonTAcQeL40Ultx8sy0G7i9v2VeFh4fX7gsleclc/CJwa20EGgxEneNA3C3P7Af760JEvj4wycu5wtre6LkzG0MjJOwn4zIsZaVWh273R5VYV4scqrmZq42uz3rmki58qMhxVjZ3u7rviS8yoyqNtBqTW/trHjFmMvi+nVXtliKzSuzVDuEr3W49/reG/TYciZp44Q1Rz12r3MHB6JzjMPIuAsGL9fGtPPU2lEUH5iPs6XqxMw6O5Z0IWuGAqJC161Tsj1rzdu0lOmroetCid5s972QdMCy2ytKZySFKLx5e8fd3R0pGbP4/FLIOfUirqx1sac6d1OUsumpB1tNoK9SOgM37NnsGvBNGuawM3EzODD+Rq9rntcCqF3y480VSjcD3u0MoU+ef54iGPqkt+TSy6x1lz74fiN16Ekba0mUdmJonskFUDvwbx4aIUZSKqQys9vvifHG0iRaY10tF8q7aGy3bg8VvEWfGJuoY87aGAYL0/TRvk5kS9qyTtB370pzWTDbo4k9IQTWlCg5dycDW3zDK6zcUu4uBn0HuvnsbS4NNpa8enP3HsZhsKAxVY19Z7tAizcZh5EQuvVS369s665h2BEZqa2wrq+uMDMraz94FaHUSCmQLl33VFdLohZHWZTBH+xmqla4Q4w0TdRiUGMrCWhoK3wxPPBXPtvzkI+U739Ne/mEzhfqqmgd8C1BawS1JO+A6fTMm1YpztrTwtawBZoWQlFmCj/MM0/nhUsuDGNkGo1qbSGlW6XBEAEzB6Fuk2Ctfe/nUQ8tDqg3hp0okFLvcHvjo+bicjWQ6Pe9GovJBPJikK3rcPmWQN5EzTpPDU6lP0O+xy2ZYL6TDJSrcbFIAfX0c4KOoplpQi3Ep2ecQhr+PjpN1P0BR8G3FYmFOlZkaDDAwxT41Wefc5LM5VLQHud1EVB8hzAF50aceM7zzLLOHI/PlJLIIdlz3yu4tC1U2rxkLYXbnFNan8QpfQ/dGZAbxCS9ww8uWlMovu9eBKjdrUfxfbqwqcyEy7WBetO1fvbmDc4ptIUYYDoIQuF0eaLUhh+FMQTuxoB3SmoLQcxjdGwV5xq7vWc3edaULaXEVSQo+7hHfENdo2hiN/UQ6hfTqZVi5JCcktmqJSU4xyDOilnXITsJNnm1dmWgW4OzCSReWbW1NUTatRFrBdt3dbs0h61YDEHpsUNYVJw4oCNA0zBxnYvcxgswJutWBK0PseDgvJ27alN51UrKBclwvpjZ9TIb0ubdSAy+m1SbIN8gwi4VaNuS7AoYIn3lZM+TITVixqfQtqzTVz1kSYnlMpNzsUSHnGl5c91VxHU8tui1mTLf006b7ROmd306rX2fCNfXpLwiNxtLuzU6omQOoU1c/zsby9m/Pvf9bNne7n9/Dv0X1Lnf9ocjF0ptPD8nmuo1MPZ2P/WLF8xzj0JOM2t64TDecF8eGPYDwz7w2f0t4yD80R/9Ec/ffeSrt3+Rz998wWX+RMoXPj0eWdfMNI44F8jVYJ/dbm/L6W1nqJNh27GbVkd3zd3bcrGcWIZhQUlaCV7wDqb9HhHP5XximS/EcQIXmKYJHQabkhBOpxdz9F+77Vt3TTDvvz4LV6Wuxcwl2GJ87MaptaK+H5jNppAYAvtp6gSj7WMxa6escHN7xzAO/PDhWy7zgq/W0V+ezzQt5sqhEMZb0EBeBkBYY8WLM6JEg8PwzuDA3AgxMO5Hij+TaqbmRMm2G1RVfm/3U/79Xz2QPnzL+ZtHWnVoE3K5odU9scBQM0ETDsV7+5l2eCqLT6g0Mtk6ODyhKZLgU0n8o+WRy7JwXBZubkfubgei9pZ4q1ZSzV7MiYnWtYucq7XpzXua85TpFuLA2OOpOK5INxd22qBlsHPGgkl73qBGoAo+e6KpNhGtSK3kWkmtGrkkGAPVd7KIa5tBtxBoBqv3onvJyQTiAdBiKeaY8bN3QvRKKAn99jvKb77l/Hf/AeUnX7P+W3+d/aq8ecno5MmHgDyccG/P/PRu5G//hb/EP/r1r/nj778nvJkoh4FnCVRWvNhe29ihwoenT5S8sHz8iLZC/MwS6SmKajdfbkLJNtHs99Ge21Go1SHJIyuWVu58n07skPTBGyIhAWg4NyDiWJenbhJuTaH0Q6zUE1WVda12lMUbdnc3/JV/8+d43zievgetBArn44Vv/+S9pYQ/vGMXIm+HgdNL4+nxyN5Hxt2OYbBE8dubgf3k+fT+zLJcwCVcbDy8fcMwBl6WTyztwhd3e5vunyA3mOdCLY20XKilkWcl+EDA9IdaTfw9+mieobkb3TeTYVkGoDlLWWahUn1Fu/uUNPN5bT/CQX204pbS2byTm60yBEOn/CCEMXJ/c4s4Q74ajdIK0ve0/dQ3GQoLqVijZoYCrqfLNJYFWnF8/HBkuSTyWtGqRHfTyVQXai0WXFxtB97rhTXqYsODF0tsqO36Ngz9iJFau44yOobgqLVQauFyPrPOSy/+Sk2rGUNsU3UnSpo05dWGchvPbIvlCH4wiLtka8C2KD642sS5/h21FXPY6dC1v2KS1Zpab82teCt+rptOZ1qfLLcq/2csguu6UooxhhRAve1arpoih3dQWutwghFHUl5oqVBdMDf1ZlDAzc2OWldOpydidOynPS9+IYndhK13DsJmj0bfuxle7zaBvnT8uHcb9K65VguaVAE8ODcwjpHNkTHGSC3b9Nd9Fze7euTqgde8dUJb8bMWw4DSzdtStHeCjl74tO8PjIa84fFGalODUJwzCybvOgnHo04oagGVbCINtb9Tm6Ia+yEVUPXUXOxnYFKP2qcrL77DDY2qjVxtOV+do4qnEoiSiVoINdPWFS3t9TU20FoNmtUC9OwuVWRLve/whHOdwLzp05raIrvvTXKzIyIOwaQtWq84/7ZxsL2eiV+9QFGhqWez5nBhQsJAOLxF4oBPM9RM26J0Ni1qM1Nj58VEv1ccfUum1v6Z9b2h2M/vPnlo7Xsj1eu1EOh/x1oWt2lEmwnOXWdOmYkCRstHqO31QfYKUaFeLqTffMM43uJu3pHaynI62mQhlclP/PTLB57nF8555mUKXJzHtTNa7KF3YnFhTdVo6WWmhYYX4bA/2Jph7qzRFrpzVOsFrkAPhkVgEIf3Az5YM+ZUQAIiwdK6a7XDSoTaTNQtrhKiQ6I1hVXNmnDcW6TX/i7S1JGSZxyjhfkOjod4h3fKbhSOzyfSvLLb3/L2s6/IFZZiyEQtiWlnQn0V6TCtSa5Ka1SUEDZoy86KkjKqhTWlK9nNiZEztKmJvKVRLsZIPZ1WalVSqsQAQ/jRZNTsHLF0li2UldehDU8wmrChMB0ZsjOqUTrs13jdc/14AHF90sq5vHpsCnjxxqFwr/tIdZUmmeA9wYWuO64Eb+46jy8rl3MmXTI1bQHG1tRYIEzDaKLY87Y5A9H3ak36Gr7fq50tb3tEjzRnU1dfTyne3Jmq8fCbWLF0mIZZNlanvr5nff2J/fu6/kcbL6P057/bWir9LO9/X/Xq0GNnm9WETVeu0pnfWGaodvKi8PpetmSPf/6z+B/69VuL4PlyoVRlXSzNXDUiYoecd/YhGYZr7gijD9ScWNazTUk1kDKkwWJE3ry5Jc9nPj6e+OlXX3NzuOPj4wlZ8lUPuDnu5FIRB9HHPo0ZZToMkc2vcNO3SL/TaimkZbWU9sE0T9N02K6s5RiWfN15GMOo58/pJnFwlK6nGkI0fLr0g7tD04WKU7GoEyd99O4SD21mLK62x2kFitph5r1jjCPTEGku2E5PrHA3BHEBsz5QaveQVEYDI9sIFeoyo2q5eipCATPQHsf+jCqVYtq41ijOU53SnLJvhTtXCGmhnM5oT1I3razQWkFLQ9UOmNYZZEYj4Ap/bhZWJmN5hVRcFbRCqlagp91IjA6pCcGgjM2PzKt1wKYxAlFPVu2SEI8LB9x0YHr4Cj+M5A+/Map2GAwqVWOr1qpdu4YRarrmkGYhzbbnanad+uGuakQobb3f7LCRr94mdmeHWcWuizQjwNA6A7mZjVTT1Q6+al9X/NiJdUbMmkTIpzPzH/xjpp//Cv/zv0B5es/Lx0dknZHlzP3nv+QnX3/OXGcylW9yRUuzSJ2kRLVDq3YI6nQ5ksrMOIIfIg8PD3gXedLVdoeMRtknIaJo76J98Pac4hEJwJ5WKjUVQpzwYeJ0fOFyPiHO5B0lH8l5ZposSskFs8eqWLu/vx8ZxsjN7RtUhU8fT4yDuYsMY+D2/i3TGHi43/Hy9IzWwu3NG3769e/yfFr47uMLMXhicAwBQrBmUZvdU2nNBreq2h43OCCTs5LXhdoK87KYsTQGSZeyoircHEZqasxayblxPC5sRv5ttAjIDQbcyCAOwZuGyQhzvZ8yz2Ex+r+2a7NcymqwYF1RsDiuH1l3IXaOeMOOWdfUQ3hdJ8b1RBTpUGRwFCqFRBh2TENkXRI5WbpGcJHfnB758MMLrozQnDFCm1Jr7hKgTjrczPo7O3KbhbR2H2TlVQrSC6HgeqHf3HGs8W41owWzefT9nMRB89cVXOui/v/ez+r/9D1K6crELlZPfLCBpLbXcdT+TgdJhZ6R6K9/Lm4DQm2NUfuKxtFXTf1PTXKk2Lf57VXwtxbB+ZL68tfIBmBemKXrOcTbDx16irOWSgzGYEs5saYTVKFlYbcb2e8OlMFswbJmzvOZ/W7C+8DLaSaX1rU4cv1gDPfdLiFX14Ky+e71vaB3gSa2h9PqoMC6ZBwXNkbfvGaWVCyAsmu47KB0HaMPOBWcyxivwOJYmrOuIhdL0QArApvucIMDTMvYgG7H1gkWus0+Sqcnu+4nLtRWqFop2T5Q2SpKf/8OD3jbXXlh3O1Mh4MtpMXbwV9L10sKCJ7aHeM3klZpwujgjRMmf0Xc+zWtndFGn+7tmrdIVy7oddmu8npTmbWRPeResBiTVpGSCQ6maNFHQ/T4pvhre22FbtvpefSK+RvR0hGmA35/x5zEpt9zhdyo3bjA7SLSGkUrThteO2TCFpKsfSLrn0+zyQnv0YoVWrFsOe89XiNXqen2EHdGg1m92R4Jur0b2L1Ko4lp06rYARMMhkBV2anyeSrsS0Z8Yhjh5mZHqo30ckQfFiQY8eH+buLj0xlZMus8czmdjEwlpoV0NPZTJDRFXYFgSe14YYweDc68M6MYFb4kzpcn6irgVqoWSksMw8hud0BE8cH1vXu2CdCHq0g8jI6mQi0Gs9ZiE3ATgw6j8wwhsJtMvhEYEedZ52LPeK4Mg5DWlbQs+CFQWubjpx+YVyP8eC/s9gEnliof/UBwI3kN1OQYpsl2al2ik3KmtMrusLekFrXP00XHoMLhdkCbY4iTyUBCxtWKqonlcd5gYOfM5N0iY6/kuu0ZcC50rVw/kzpp6PWgNyJH09c0GdgkVa4f+AVtZqPmqkeKmLzBCSrtVaTvbBMtvREPEklLYj0nA+bVXmfrk5dFSJpcYHd3g3ee4+MzOSc6rGHrBrdNhGx4ZZ+WXu/zDdXaiCv9/1Httmd1cyGyh0Nq9zwWi4xzfb0hrqfH92d4u0p95LWf1Tq83Bm30ixLle31NO27af8qY/JiyELwBO9IpVJq7WfSK1pT1WRQtZ+ftaNRTv6cxJjLJW3HJOI8ykhTJVeTdzvf8OKYnKfURqqFYQrs34w8Py6cTi80PFk8tzd77u5uSHWh1Ew5raTLyuHwwO1t5LJ+TyrLaze2XdzeWZm3qwV5qvalMcJmou1doIgVKa1GO15aoqZqOxPnmZfV9o+DZxB/FZc6q4qEMACVnA0ecbLBoZZtt2YjCnnZhMf9w1X6A2GEg9p6Lpw34YWxGe3DKbUHuKo9eLlkc9IvnYXW8VDb7zqcWv+upk5mOoy0WlnmZP1SL9AlJ5oIzTmUiDnQK8bGNDbc6IV3o2O3FUHFINPu8BCk08M7lNifa6TYJOjZpj8MeutxNx7rlDVi7LSyEiPsh8BuCEzRI1UteVr7orsFKNJ1Qhaoi6r5YHohTLeEwwOfZlhSYThWpFSYPC4KcT8i2sg5ITUTNNtuoDNcdLOLstvGHKaC41rJm13bQKejRwssVer1hNgE/O1KTon2DZvBxW6TDUjr5B4jkwjdwR7HvimHVJCawK0MI9zfHTg+vrA+vcBXC4TC/hB48zAxns6QE+v5zOnlhf1+sGgeuws47AZG8ZzrDAI5Z6QJu3GH4kk14JxnHCYu85GPzxckN0qB0hJLnrm9uWM3jmxm7yaDyMaMjJE4RuIQGCaPj8LjB89yCbTcD8Jg3rnRB0uQnyZCHHl7M1Ga8nw6U7XC00qIcDwa+SMOAylnvvvhNyB7kAfzpByiwWG1sttP7MeJ47NyqY1p7xjaSBjMr/Tl5YisiX24sWeuQS2YiDzArQS0OVzbk6j4sNgukAo4xEXEG0pEy2RpG4ZjPZg2gh8IPpKxtYTFeCm0PmnIVgRt2nDb9NebJu9sNZGLSSBy381ZEVRzMxI1D9D+/RymOzV5RuR0OnI6HtlPB3bTvgdn1+4jKwhGWnv3cMswjKynF0q2nEMrhBuZsR/m8iNUsEu7roWfVzmF7fgdiqMYI6WTaZyFwlIo/wABAABJREFUXotSu148+nAtglvCR+0s0+2abtskOgKzmWyD9qRwsfQdu/j29RtJTvW6Apt2A7tx4PlopiCvez7pkGo3nOiEudqvrdvopL/l128tgofdjtbs8EcsXFFbY0aZxpGb3Z0tpb3HN+nXvXJ5Xmm5Mg0Th8Oew36POHh+OZoXXmv4YqGQ87oApv1RKtUVmnSrHfFmXCtmdwRipt5NKbltjR0ijVYLToT9fn/1vtNaybXgugDbK4wxotWWshUrgmMfzZvazxnGCQFCH71dNobZof/vIPYa1pTY7uKtbvcaxqZV8p3xqlVZa8Kr4qGLvT1pNgmFxSsFK8BNTX6hvQMTT5x2gJBOa3de6ZrNnHuhDr3L225KKywO67DHCL98u+dvvrvhp3cjE4XkbJL0g9CCI273a8fqtRMg/PYsVFumlw6has+bUxxVlMVVVsm0OiNDZNodLNoGIaoyNMjqqBqo0whxJEXXPWitIKW1kBtc3n6Gv70jJ1vw+zuPr4Xqi+3TAlASsqy4vEJZOxRc+zxoc6pvXKURrjt+5FzIl8LoA9HHa6KHvR2D+gzWNO+hKqXrwyKIQzUCHu9MPJ77JOFKZxE726uJVEvtcUo9PlH//j80+LQpIQ48vH2Hq5nzd3/Kjomv373l6TGhL4U/ng4c9yutI+RaTF+b1kSlsflbLiGTXeN0XvshY915HMzKb9pFnBi5RglMRHbT3mDqXrBTyiwpXT1vN9eQaX9gmgyWo3iGw4TzDsWMK4IboUbWs6cGR9hNUCvreqIU+9zEK+simBhz2c5jtF3Qmri5Ddw/RFCPysh6rpyfzpyPmWWu3N7cMU0HLpcjKSeWpVE2q1mV67TuXejaUGObr5eFdS6k1XxIUZvupjAQFJZ5ppZEyZZyn6ux2J2LNLV1TC61Mz1tx1t7+onDzPtdCFyDFzAERtj2l4L3E1tqO2KMXSNnFrR2GZ4XJNE7NWXLwCxJ0LyDeICy43heWecL56dMnpXJB4KPtGL3vIi/yoJUrZCi5vQCFhpAn06hGRlN6BwMrqgYshlF8urqtRkjiJjEoaMtZtfrrikjxuuwf7d9ztvOz4kwHcz0WpsxaNc1/Qh5EXBmNoBuqyWltgJVycVYoLv9julw4GbaM8SBy+VCTpmXl0fSuvbpHTaRveso4J+rCJZaya3fACVTSyHnjBcIXgiDJ44eXx2+OOY5cX4+4rxJAx7e3PP23QPv33/g+elozhzq2MeB6AJLWs1hoSZUK0U84lqXP7SuIezmVn2Or2rxLSLms2AHVbak7d2OkgppTldWk9sgkMET48B8MY1NsgwIY97hehH0jNOG1Zv2xKmN3uNuhwgElJQS59MZVcOjraM1wY/2Fkgw0W30gZQSuRSiCEEc4xhwXkglc5kX7m/viGG4UvcHbyJnu8Ed435CGyyni3XZfb+Ra8aJJ4SRjazUEExEXvFUomuEoPzqzYG/+UsrgmPfqYm3fYRTR1STBGzaGpnCRvWy6548rQqyOIvsK9pvOPresbJKopYLwoFpP+AHT1GIvRAW9RSNtOmedveAHkZkN3AQm3Dq08kcMN58hrvdm4+nKkEfCKpQLUVC0gzrgrycu7TC+s/cNUvaHerNGP3V6k6AljPLeTYNZhQ0KC00mofWDbvtRKioFNPKAfSMMtUfNRxSzfFm0yiJ4MJmDmDdQxOox0eWf/AM0RZSw09/ys3Xn3EpK+dvf83+q9/h4d2XHPdn3LDyB9MNn3aJZxJF7WBvtZIuychAQ0C8sIYCZOazoRTOBZNrBNvTT/sJ3+UbzoPze2IIhOjwEnBEUsqUkowlrI6cizWq7o7dbuIyKFrg/v6eOERqSaj2dIDmrkVwH0e0VdbFmtS6mmm1OjH3lHQhRM9uH2l5pawzwT/w+bvP6DZAfHp+4vHTifm8kpbMYXfHNOx5+vjC8ZgpVwKFVR+TrAh+sGY5OFuXLJeF5VJYl0QtiuLxEtj5AdXKusy0aszQ0pvlUfdWBKuRN3Kt19gjc1epKFuD0+3gnOlMbYqxJsv3vRmYs1StxipOtXSLtv6IlS7tcdBKpuaFpgPKaFCz20PZQ9jz8njm6dMLeS7UAnITCX5Ai5nlizh852i0LjlTfZ27vAt2P7euFBQrFNolI9LPsc0JTDox7MdSg01wgbarTds2PaJ6zQA1wkt/hNQGBvHCbr+/2k3mlMmdvKKb5kz6uNrswW3aIdZcyMGK/O39HfubA1++/YLb/S0f3n/gdDoxX84s82oFGQViXye9Mk//TEVwjCNOCtS5P+DdZaLjS7U1CygNHjdalI8LSilWkWupzMvMy8Ux7nd8sT+wzuaDJ630ByniYuDg+m6qX2wzs+2SiL67aQo5FUpOJuQXqNl+rrnAmB6tda2RtfQB1UZtCSlWMqP3+GkkdyeZYgu9a1eysZPokxCCYeadGry0TMm5wxh0W7XXqbupsnnhBR/Y7W7I9YX1Mtverhtsb6G/znlqK+Tcr7EDo6EYOUecN8xHhXE3okNAo5nH5ovh7k3tIRimycisDtoy004zTo3ccojCu9uBfVCkroSWcVq7Xkz6xGdXQdk6XzpLVWzvWPskWE0nurmZWLGxBJFWu0PPxjDrO7lWCy1EWoi4h3v851+xjAMpRFoTZhVOqyO5xJgT7lIp9YJq5VhsT1DbisPMdCUv5DLj69qLkrd9HAr4ziozPV+pxigDTybSfCM5k7mP2ojNYrFKgxiks80iDteZag0J3c80tx9BpiDq++y5aZ1eHTmKCEsAVz2jBrsWa0GeX6jffkeiMmtl3L3gxju+uN+x819y+M33dniHCC7gdx7XCm7JtJqhbB+yXE8b1R4uLUKrQlRh7IzA0kCaTcbzvJLWE7tx4mZ/AIVp2lOrUioEv8O5gHc3tHVkf0iMU2UYG85lnAdVg8wE8wdtTXl8+kStmbKcDIlwpa+j+r5MRrQJafEoGQ2VJS88Px5Z18q6VuazRSVpAZHA5big5ZnTsbDMnUGMYM4WNvOLM+a3E0+IA9oqzp3w3jFFT/PCqIHggzHEG5RsO71S7OgOLiLqet5e33e1fg5IvRZb4ZXkYRsDs3zeDBZEuFJLh6HrBxuULTWCzixvzfaCzhFCZzwPjpw9OZnOpxSxdI28UrPg3QghEkTZjQfGwXaxNCH6AQbpnqAWteakEce2UQysPKnvxcbio0zz2F4ZmWDToOhr2buup+jXoOttnXlhamfkSteAXQUkHY4VFaQpJWe2aKOqRtZS3SBMY2a7ztcw2YpN0A0zXUluZZ4vdrYU5TE+8fjxE/P5wjzPnSNiq4PWWaRlg1j/rEUwhog5wmC7Ib/N/jaFGFQQOhusB4W2wny5sC4LrWSWdYFL4+2bL7m9eeD80lgvjXV5ppbFxnaEfYCmlTXV7uZgxJDQRfFUB6WZ5VA2pwIUilhki6oSmrGaBDq5QxAfydkgxNZdYXynH1fMRq2CHW5C33l1dqe4K01X6TE8Tcklm01Rhwq2D+5HyMh1x+GdZ5r2HM8XUqlXM/JaKhpMoOuc+QAWMtr3kCr2wMQwIuKpzbIMx2kwxlewyaxeDFJoajKJYbBcOQKUkkgl2/UQ2EV4c4iMXqEmQss2VdGvsVxfvXkBNgMXc6UTJMx3sraKNqE1M/w1yF9Nh6em69GuCdy6zdZMwNtETBB+e0t4946LH1glUHLD1cZlbBQClISfF1p9obXMOQ3We7HiRdnFiMuJUlek5Wvv6fvD6tj2c97g89q1bM5RJFADZPNRIwBRe4GnG0oLiETMv7CHMjvtB8SrPZUdg+7aJUs3Gt7+rAqsThhwTJ3E0UqhvZwpQPLC6oV2PCGHE29vb7m/v2M3DV1aE03EHiKqBXEv5o5TtxFCXqd12tU9v3Ubvq2pqQ2kW/RdzitPjy/c3d7gxRGGiXGcjJFdGtN4sLR09rQ8MO0uuNAbGxpSgxXW6o1pK7YTOp1eqDVTy8WuU3yd1xDL4FOFnDAJ06iknDm9nDmeVk6nZL1eE4IzVuB8WclL43KupGx7aBCkm3mrWBFsaqbsPlgTLHLBO2GI22uccNdsumZsx369xPk+KZltodtIH26DdbbYJuk6P3tPglwLIfTwXGzyQS3gWoTesClupu++7F7SWiEIwbuu2YzILLTi0J6isa6NvCZqFbwMnQgH47A3N6zrHtPOUVWTTbkWUGekM3utm7HoxrXobkSGwlr8EYZCWZi16Z1EO0TbTQS2NQHAZlSirVzRL7vxO1zai49NmiZfEWdEv013rirGnagWkSZiQ4WtFk3+IWKEyFyEZVlsRbesiDhePj2xXGZe4+v8VvFR2WJCX8+1/6Ffv7UILpfZGJFbOnMI3T+wklLi8ekj50tknnfs9nvuHx4Yh4lf/e7v8uH9e75//z3zUjiuC8P4lt3Blu0hOiqJuig+mptCxLoCGaph9It1F6Uf4lRz7MhLsm7HWg2zdzK8AjAoS/qNGUKPRXGNnH80ffWLFAbzQjH4q12ZRMu82sLbuw5/2teXHpWy5Su6nmBgWLixFDcPUPugKs6fUXGUtDI4WwCXkjlfLixrojTwfnhFA9ScXaLv+rCSEGnE4WACXm9O/WteqU1Zcw8JdY2soH61lPlB0SVRU+aw3/PmcODNzQ0P+4GpZCirJR90zadgN9528zY1E/KK0PqhV7v0Y7PPl831nMB2FHhgEJi84zBM7DwEV/tCvRf+QVi7nd4xNC4+oDmjtVJqRmlMy0xoK3r8Ac0ra54QF9i9GW3nki5oTtBMzlFbsVLknDHyqlG4aQNNhBKAIcA0UIaK7gtrVlJpjG1l0tTLGUh/P+DMcBp7Ta0vchzSm6XXI4Y+MVjnKtCMzSk4Ru8JalCZOmsgW15op8ru7o6wv0HShfOHbxi++BlyE5nnI6enD6y7G0ociIfQafQeGTzOBWOg9iZ9OtzY9ffGaDaWc4fH1Z7ZWit1taSRIQxoEy5LMWicYhBmaVzyhcWtTCPEqPzuzw+8eRde2dDF0kV++ObMumT7zFomLytQGCeP9zCM5r+51ETOjXPuelK1pnl/uGU/DEy7idocNUORjhSpoEVMNE7Fy8AYHcfjEzmvxKHivOKDsbAfPzxak4Pt32sxa8PdbrKptRl5K/i+o+uFrdJ6c1C5G3e8e/MFvbZxOR2Z54t9pmITDq0hzbTKwQfEW7Ghnxf09aewybzEbNgcJmPSBs30gnHY44Mj+GjpHcExbDKjpRtj+4xzlhQTnafIQisFH5UwWjMtteKiEXRqTtRWKJq7bMGGgk1vrdJXNGG0Ri7arrImWzvUatOXbktCxQq2tK5V9LRibl+yoV/B9M6UTorp7NBXCbY1DkXtzFDaFRky9x47d9vmQdpHV+83jzRrHGrJHN684f7uvu+tK8sQaDWQUr3yTexwlh/BoH8OOHRd197990NPZPuMKaVwOh2JMVDySi2VIU7s3r3l888/Z1kX3j9+4jInzsvMu7VR1KJNIgF3GSAXy+3y0rsKpfmCq828HLXH7ygW3dGF+61W0+9tsMK2INdu9trvYu8cIXrMjcSmgq1rg26y6/Q1EgeueHWpltzuxOGHwTq8HtNiZsTdpFmtYBhK3OnLzpntUSqIX0xU3hrR+77fqSzrCmTCsOu+mfZhi86gBS8DzgmpFBBlcuaHGWKkFkfqJJ1SbSFdWqOKg7UQAkQUcuk/N3B7uOFmN3EzBlzNaCm4pj0Cpj8gfcG3SQw2VqnW0K9x1yD112pJ0K83mO0VbOqMzrELA4NrxjYV0xmpVyQ6cm2sl8QywBKa3UM9mkVoaFrxZYbnF3SdyWWP+sh0Z07+lNWKoJpxsDaDtUS8FewiNA00GVARqncQB3QcaYPJa+pcaEshZ4X6elhYEdzM0yuyieK74F86xGOjCL3ztA4a6c2+GI4jArH92IZLEWlQM7pk4t0t4zix5sRy/IR7+xleGiXPpPlIFptk/WTG8gSDtDYt32ZlOEQzqhhH86/1zSYSg+AMymnVporWzPRcVVjWikjFu0orSivdlFsFdI/qwN39xFc/2VvUknPUfEtOjtOzom2hzmaz10pBxGRSITh2Q6BpRUvqTGW7figE59mNgWkcGKaRtFSGmJHSv8QGEcsD1UoYPcFHSjFCBSg+mJwb1DTNzcJ1HY5dh7LDMBjsV6VPGUrusiK2qb2fIzGOPNy9oaidPXldWJbu9NStEMF0wDhrOJwYJwHsxreaYVWg1tY3UhFxAR8jUgtVC9LZ6D6YJMy70FNuoEUlr9mQto5CeT/gfaDmxSa00HBBDSHSaqxo39CuNGz9PWyvTbZ9tmxxb9boNa/4qlRxRgTSjnBt5tbdNs2aftM6a3VsoUf20PQOvq+LtsFrK4JXLWEfXDavUmtGuuYQezagB/Fi11evr6WhrTIOkdvbA5fThaSJGD2leHJpJq/6EWt0Qy2v/+pf8Ou3i+VJ+NHz2bvPrQPsS9dUbCe2zkaFnltBWAnhyHm+8O37b0lrQhQO8cDeHWCG5+9fcHJEgHldKbXx8HDDbjdS1ZbtN9FuisEFaml8eP/IOq+8fHikrJk0L9cC5Hq3EJx1SdIa6+VixcIPpJRJpfZoHDCBmOA6My4n65qMBAOE7i7jeMXtabRmThnRmWB/OAS0WcSRVutoaq2sKff9k01nliIBOZsZ9jRGhumGOA4sqdoHJ0bKKS1TteK7QH9LQXcEVBxrXvBaCYeB6TDwxde/ABplnTnPM9/+8Mny43oRGrxwc3/L269sCvzs7o63Nx6fgOLQFlhKoZVKWfK1wMvWeQEZR1VznDGdkDUqzqWO91dw1SApjLo9hMBhd2DyEUkJ5xqhM37z4JFhYBxHvKvs25nh8syh1euDW3OBUjnML4w5sXeK342U4QZ1nnJ6QSm4MiM1w3KGpiw41HvKfo/EPeHmc1q8oe4+57isfHg+stTCOSfyupLWhbwoZan8xXHHz8aJhzaz1wxke6C82JhVHK5BdL3+B9t5bLpCabbJxnmzwLputg0G8rmCNNQ5e/iloxI+0s4nWl0Z3z4w3t1Sn75hOX/kr34+4f76X+DXL4VjUmbxlOpYbt5Q+RFrcYwgSsYKQym2H20SbAqNO/vaashBTVZUW2m0ZAeeyw4pjpvbew4Pt4zjjmEY+fInv+Du/g13bxItV86nE7lk5uN7cmqoeqbdjvu7BwThlz+D+XLkH//T/85mrIc9zjtimKiu4tuMQXFCK5XTi3Lmwket3TBfaE7BK6p23ztvRfMyf6TUyv5GuLnbG1wnUJtZ9+3VIpFyajjx3OxvQWGZrap6tUk2epDBZDjLvKDaSKmRlsLbzz/nX/vrf50ffnjP+w8f8fGCyEKI5khUmzWCgjkHlWzHu6XH2G1jR2/szbIxMtNiSIJZu9g0riHggrNmRmxlU5czqh7UU3WhsKDVEj9SOdp54MBFYalH6rqwhXhf1oVcClVqp7L2dVLrzMwpMAyRu7f31KJcTuajnPJsMKf35i/a117OOX6c+WrNfqVWGCdLD7ms2SKbeuCB6WUdDK5Pkx2KbAYf55qsOG7JFpbDRNVi03rPn0RMNRHdtnvVfj4UPj19YJ5P3XVLGEdPjAdyeaG0Lgu5FlTHq1Xjn7EIZmlICBzeHHDOWdGoDckBmYV8OVu2VKskn1mWlXxJrHVljKM5ofvBlrZZuLzMtk/BSAjqHHEYmHY7cnUolf0uEILndndj0MxlsWLUKrVkal7Nuqpn+1kX5fBisoBSU1/VBcO6N7YgW5fQF98iV5d59Z0Y4jtscB3B7e+1ZtZGoXdDGwvUNUFrpWZHzpk129Ra62Yj1N3RW7XOL3r2+x27wwE3J9ZULHetWRfWuhOPCGiyD9TYVEKpxSAHr4TJ8/bLB0IUvK+8HE9cSKw5s6S1Q5LCbQx8MQ7cTzve7ffsR8WV2r1CfRfuW5EutXUpieBc6xO/KQazMxJEX4PblACI1E4a6Xg8myvOYE1JT74WNalA+ZFbhpcGbYX1QsiJ5q2Y5pTQUhnmM7EUdqMw+IAeBqo4jo9P1Lzi6wVpFdf1kdkPVB9I44Tf3yKff0UdH0g3P+H4fOL7/AOny8zTbCSM5eLIq5BXmIZI9J6BzK5lOu0NegMi1fCw0BuMzVFmCwuV3ly9BjZf1cLWKP+oo+9/oTdenpISNZ0ItxPB3XCZnynnxk9uB+TnXyC/fuL9y8pjdawquP2e6pScL4Dix8mm9tK7//YaDuvFoDZUEM1Ic2hxNjXXPtk0yFJxUggPA4fDLbe39+z3B37xi1/w5t1nzOtHUjkzXxaWNfHy6UjNheDfEELg5uaBIUT2046X50/8w3+QKW1l2JnecAo7bIO5HVA2laa5Ukqi5JndNLKbBmtAu22hXpEtJecza154d/OGcRetkKuyJDPAHgZvWtCccS4wTiOtwZJs+nQS8D0NAS/sepM7r4HSiVyH2xt+8rOfs6TK4/MZ5web4HzBh4Z0n8yupaF0dqOZwwtS7f2p913vbJZjKZsgX5wVrFZM89f6bh0CrRbSOuNcxLkB4zln20k3IeWFUjLTZOYiua0mC6O765TVClIHusTZ6zS2qjBExzQN3D/ck1JhWY5QCrVmnAQj55Tad599ZSSm09bug2pJDxUfPUMcueTSm+6teXY2YW+w8PXMrddzELDu0Zaltoe8Ri7ZPs+IeooL9Mlx0zsr83xiWc7s93vGcWCME1G8NUs/duvBEBcbRf4cO8F/9a/8BXIp/PDpCVUljiMheG72e/I04sRE6/OakehxQ+Tgb3gI0xUS6deSNWVSXnA1IM3h92Ze7F3ESWQcDaLw0VwFvn//A+uSeHp5JC/ZLMxGh+5dj87pb6xhhsvZDmSHeScuOfVDvTtgRGOHOd87YQW/29GmketKR+xySFVc1VdmKjaVrTXjqsOnaMXQO3Kr9v5FuLk5kFLmclrM6zSYS7yj0qqQM5zOR5a0oBIRvCUBdCzb6Mp7QGicu4uKdUdDMKPkNV2ouvCbb0+E6Jh2karK3Wd7cinMq7EiB+/Y1YqURBwjuwM4p6SVziwMlNbIzYpTcw4fBnPJ6SkSHb+gUVBx5H6Qxc64CqN5qVbXzEUlGWxhjU6llmRuO6FD205JlxWtR5pPqL/Q2ozqSggmdJ8adgCGSnGNY4yIFxY504AhFjyNkBuuVryCBo+8vUVvbpl++gtyPPAyvuWfffvCf/7/+n/wfJx5/+H56n4xeGtWXS9cD9FSCg4UDlR8yDjXO2rnTO7Qod5Om0Kx5AvTfdmfDdcVhsHztRaas/QGr45Ysc7fRxrW2LTOOstPL+Rc8J99zs3tA78z7HmXA0+PiXJaeVmKeV/iaB4GZ7D8dLixM+ViLkXjaOSTWgq1KPNloaTMfJlJayJdMuLtsza/g07Uqo11zVwuK/sdiAZOLwuqR5qONB04Ph+5LAuXi5JT5nL+AdTzcMgMcWA/DZxPTxyPz0Blf7M3Asq6UlUZh4lSK2tKuKaE2ggu4Icbbm5uuLu7wUvEE/jhu0eePp0ww7uC94VJlBArIbwSzLTvsdRFgzxne5aeXl4MDl4SDsfgeqxXdd1Ps0uEHJY0Mw3EEEwfV6vFfzXjCbRq8gjz+ezsDeeQYvB9zgknEPr5kVaTISRvfAPnrPwXjIQTp10fUpSSC8vZ3o8Pg51B3hHGgbGpBc3mxjRZJNY4jZ1VaUVJxMhP4h1Stzgiiw6T4JgmMxgfgk17nx6fEYTDLuIpXM5mmh4Hg5rr5sXZnKVcdA5FA2rOpFrhfGEJmZYbofd7IsIQBuM8aLUVWtczm8jePis7Z+3sbtUqpQ0edv5LR/hqLczFgo5N9mWBu7ubHfvdyM3NHcMw8fx05HQ5m7DfbVv9js6oXG04/8xF8LPP33CeZ/70hx+oTZFhoyEHBKXsRnBGcpDgkBAY4o7DeI/33QhAKupMhyOp2ZKpbYv7za9fzPfNm+i8tsbpdGKZF2MD5WpL0iDEYNTiVqyrsM6sdxNi36dpDwQWm168c12Mbuyxkk2+ITFcx32bum13twW0hq4tMz6M0akdSimtJ0zbh5iLGYhPMfYpsENhbvvArZtqtZFSopRMiHu89wYxtNqnKwGGvrdcUTZXHK46t1oyTZWj1i5En3DeAox9FfDV4GTvCWk178jQCIN9prWANLtZmhrcqc5svsQHswbT3lHSrtdnC7zconUswgRj6Ts1BvF2La2/s4lDtr9rsEjNlaorzTeaK4gsiKQuNQGvpuxZpFE7AqsOzmo2ccEVvLMECaf2AKkPuMMBvb1FHt6wysipRb69zPydf/rHnM4rL8+zsWdd5DBFbveR6CPRB84IRxHmJp0Co50YsO35Xq37Xn/LthVhs6LyXWis3Si8dj1V7fvu2LavDii120d1Gvi80mpjfPiM6CMP04GRibsxcvAOt/m6ls4EjqY3HWJARVickbWmaW8Q37pCK8x5MXOAVOy+r607Hm1jrO28mjZyKWZLVgzOX+YEMuP9DSqetDjSIpQilArn84VWBVd3xBBZl8gyH7uBQ594tBnE7QQXPdRGqdWSOVSNmRk9wzgx7Q8MfiT4yKePJ3suyAgF57Zon429aJ+C63vY5uSKULQGaTGosOWKR2ieHiodrnspxJAW5y1j0rttoduuZEDjAihaldC13H252z9rY5irmC4TTA5hk48hSGPXMKIYcS6YH6FlNjYyigse31M7EEvFCTEa6UhMyO99IPYg49LJKc71Xf5VY2dvQZyxLIdxNA1zf5qXJRG852acyL5PZO41T1J5bfKsQLnN7tc8ibsR+MY23p4A1CB+6UgYfXK8TmaqpkH+8XN0ld11eFTa9X/XZlpNp0a6URMwEwfPdBjZ3+yZxj1PLydSsXWLXYPOC2mvT+ifSyLhbhfGsfD1L/bMc+LDh4/MTZmPvkcE7YjTxO5mh48j47RjGCJugnGMjFPsAtNG2HlyGrkcL6TlYodLFtbkkbgz9U1R6sUy8E4vJ/JaaLkh6hh2I8U3nl+KiTKttcBhkGfuglEhMKhnrx6pNolqzmStpLQaDr1RMbfFbZc+kJpZrnUosza9kiW0k2BqrhwfH4kxIrd3pFqY0xlfPFXtwXfBtIm5wwUOZZosdT4tiVwyw6SEWHDjwC4MlC2bbGNl7TpRqBtBV/E49fg2daS2UBqsoeGDEsWO152fzA6sOlqqPM6NYee49QOXcmIpR7wbcC5SW0LTws1hzzh41mzmAsVVK1Kj4fS7FHsSeb/lXUNdpYjZjLWihKZMDiIW5JkjZC0073BTsMm6m3rXNqNu7lpMs73TOkDxliYBFF1pVMoK0Hh4ueBbwbcToo3mHdV7jv4Nev8O+cv/Di8N/vA3n3j/8Xv+we//Eee5ciM7Pv/qC+7/6uccbm+5vX/D/c3Ew+2O6GzPOxCIKsQ//C94/vYfMZAJWq2paFD1TKXgvTEuq3qaCmTrLJxF07OkxU4hqVeiiHpvwcAEnIw2IZYTzSvqlNA9N9eitJqpj4a6hK8nbm4nfucn94wO3v/TH0jngtx/Rg0R1osZan88gsAYRpNSlAFFCdrXFOlMq43gKzIJMoxUVZLm7pHpaa6RnHJOL7RjItUzHx4nbu4fGPcHPv/8J+x3NzhWdj7w8O7nII398APrujKfzlzWxmnJ1Jq4ffsFiFJlIJfGsp77htRRWyWVTArKEuFwgPsvPMN9ITxcyOmJuWTO7RNLOrIfJxP3D3uc95RWSJfKvCRqVYKGHm9UaAXySalaWX3nDlQLaG3NPD3jYExlJxVfMrFZJJkfHNoSl/Mz5/mF83JkKQtJCySHtIgGa/rVZ9Rlg+w6iQcgV9P2bmHcm2gq9Yy/jSSyuSO2niDhB4sNQyDgCW2gNMW3SsPTRAlxMvmTMz3fbjrgRMhpvULhVfPVtsxvMtKO2jRvCTXibJ+9lExu1aZUVZaaqE6J+9EgVMFWTiEwxJEYBlJOlFpYl25wIvWV7CSGcjgRghNUPfUaIpw6SmuEwtYTgjbIkqt00QxKrC57YGcCfvVUTTQKfgfjfebLnx948/CO1c2wg/xDpF5KJ9wouKGTawKbrfafqQhWlyBU9jfRHmzMULs2IYSROB5APKoB5wdzqg9AaEhU3Chd5C5EIj5CritVEqGZQ13TldKEmgFRYwnmap1Y6xOVWM6VaoXg2YySNxGmOf73aCR6h6j+2nQYHbdRqpi/YwiI638uGDZfFc3FBFWbYNwogTT6h9m7O9s7ate+6dWqx/WdoDijOWvrBq+b3kWFlpWSK84Z3j8Mgxnu9u+3TRWWlmwieUM4zA5JtDsg9LVV6/Z7rTTEmQuIF08kUGQlqyOLUJzlLLaa2eyy6Pu6QZTJCzkZBNqcWYg1b1fUp41Xa9ODivTg4t59W1NtO1OxTtzeexcb+04QoXdnbbtZG8KEGbN7Wouvs5Z2/WMFp8qwFGJLNMkgShsmqouk4UCd7sjDWx7XzDfHD/zwaeVPv3nE+4Hb3QMPb97y9c9/xs39Aw+ffcb97Y6Hux1RbPmek6cmh/v2D0hh16+VuXs4adcHVt02NW2uPP2Os6yc7oZvux/bY2xvxvVr541hq7lfFxCCSV9qQbShy0o7nxm0IEF5uNuRcuPmu0d2pdLiQA4D62ruGG1eDYo7HMzft5qu0V6odFRC8f2zRDzUSqmWxRa8jTfqTDeX68o8Q84rxVXGfOFws8P7RqsJtDGEHc4J02Reu5fThVoza5kt9me0iAYz21dDitRE5Vcae9+pqmv4ASRW1CeyXMhtprIABSdWvLw3z8+cK6UoKZvpu3MRaY6yVlru6SvazFUJhdoNqIvtck0DqDijz1rzLeC9o7XKui723muiqk1qxnXq104FFYM76Y2nGZBj93SfEullEDqrGPvZHXR6/S1C9368Pk+izqasvnsEyzg0/+YuV3MmdSjYWSQ/Qi2214Zsz9Lra9oa2WtMWjcEaWpwogv+6hgj3iHeGLbjsMOFQK2VVjefzvqKpGGTXBO5ToRNPN1bzdCjvr/cnovrUK2vl0z6isL16+K69dnmciMO8JU4OqZ9IE7mdSuh2AHTKptIv0No18/hX/TrtxbBP/yDPyKGwGdv3nB/e2D4i18zjiNffvaZ6eDWxOWy8vjxxHbKZV1J88ySPO7sr93z7c2O25uRL766Z4iRPM/UnDilC2s58fHDMykVpnHHEEd+/ouf4hC+/fYDtShjPLBTuLnf00ohzRe0NOpamNfC03ntewJh9Y46CD4IIQi1VGq2QthqI3bSQHP29bmaTZQm80Z1wXeSgwdtnF6O9uFjS9shBtQpkiqxCjfjwWQKZSNEyPWAEazYhziAHxkmh4uRqph4fk341hinHW4Ixt4r0uUZDYcRZbwG++9qotr9zQEfBD8ppRSen082tfrKm9sbvvj6p9T1ifwQuNkNyKi41ePmHaOHQRKTRHTncW6gVOuEqRVnqm9ctWTx1S9GCdeAiCOGAcHjkj28MUaC2B7CLzNeX3BEg5cwoXoUIQwdq98sqajE3YAf7JqIH7fHiWWxSWczsRZXqNJo3KJxoH31E9r+Bv/lL/n2ceb/9H/8v1Il8NXXP+X+7i3/m//oP2bYT0xv7ojBs4uREDxxCPjBEwZH7XTsqVjyhfzOL2E/8v5P/zEfXj7ymZvZodyKJ2KMxSbCJo/PrrPd/LZ4iDin7KKxe6vPaHNosoT4wmxjg5ovbUzerOlGT2gVqQ336Qn39IK8fYfsR37+r/8FPo97Lu++5DffP/H//off8f7xyGOzTp40m2cuAfGONme75zQjCG/efG3NolruZ8rNJsYYmKaB3W5iiANxGHg5vnA+n5jijugjT6dnnh5fKGtl2u0p82IGENGgfmdJODgVoihLMhQnpfwjuY01q9M48ubtG9Z15uXlESeNoEo4K/N3heXDyidf+y6ncudvefOLz1mWTMqFTx+/Z1kXbj67I+5GPntzQwie3TDRqvLrf/qedE6ci5H3SjEHk7hlQFbXiXCpk7esefdesRbPMy8z3/3wp5zPj6ALQ8iwU6pfaSZetLtRBpRgDFUrKaBbcsEmJBd87DaKXQXunAetSMudMzDiQyAwGJu8GUGt1JllWS1Qu8dKrfNKyQYPQ+XS5xutRqDb7yb2u4lUWg8Lnq2YBIM58TYgDM72ia1UYgjcv7k31nBtZlhBpbZsDlvYaugwTdzf3rNlEL4875nnmcenR5bF/GBRKKn152ErttJ1kh0BqUafDUN3URDb2fnYfXckXos93V4z9HO0iJARjk+feHk6cnr6hv3NC89PC8tcyCtod+4RgUBAVHpD9Ftr4L8kReK8MA4D8tacvG/HPbv9xLvP76m1cjxewAnn88LVIbz2QFw80mmvJiId8B6mKTLtRpJUcoJLPaOlktaVZUl4iQQXmabREqCHSHFmLyQijDuHVk/0lVYaNXjUOfxiuWMNE3vnLljzoacpSO/UmkGfSqesYwzPpo3W7X8UjDxQpeuFLDcMb1qt5i1hvhXrOoJ4SxJQ2+GZhZpNsCblaNcP16EEZxRxy0O0xTEY7LopiwzFr4iY1ZupabrbAw7vhm5eLrSaqHkGUStgBIZhh/rE4G+YBpOFOOcRDbhaCFLNUDJEinh634xih4fjOhB313gQtfy/a2PVbAdr3XUfA52D7rCydXdbrpftLzpjqzWb7Jt2y7Xtwelwjlqnzjbbi8E41Y1o3KGHN9T9LfN0y7Nm/vg3PxCGHV9+9Uv2+zt+8ctfMRwmhjd7pDV8LVdfRA3Y2KrWeZsNW6MdDrS7dyzxG6o+cVDpWYoW7Fm3Tn27AD82GFZ7qKWzlRWDO80tR/oB2uwaNnpQb59S+s7Fq+KSSYuYZ3Rd2N1MDHcPfPnFO1pzDP/wO1oy0+ykIMUguVJt4qtq8ozQxduDH7etO1JNNO9DIIwDu2ngsDe3mHGcKLmSlkz0oyWqtGdqLizzbKbHy2IMv7VnfU6Dib5rsJVCqSa5KZ0YcYW87N6OwVOrGY97FYI2M91PQllsV+xoOFX2twM3N7doO1OrQWglJ2vGomOYAjF6pslTq+LHbuC/mgTFtL+mJd52V1XpziJKC9Ip+SYlceLNhGKdKSVBqzhnFnpa26sLEooSbeK7umf3D7Dv/zc0p9eBzcfDeA+9KTD+Qtcua0CaTVjaG/VaSucg2PpG+56yUVCt3ZXmlbAVhtGmw76vr8X9eLy6/vO6ksNeT3AO1xqCMVV9wBjv18JhE2wI/spLGGKk5tJN6V9Zz9tnru76htkKId12UthgT8v/c16Ig8EioqHvBM1uEe3a6BCwHB7HZfGkpDyFhXmR7lOrPQru6uPDxjW5Wlj+ll+/tQje37zDB0/upI39NBFCZJ5nYoh89uYtD7eNz958zvF04eOnZ0qu5FTZ7SO7w667kEBriafTzPvHb42W28Q6tWmHuMA0mV9hlIaUlePxSPCRaZrIufJyPKOAj8Z+nKa9fTh3Dn9amIt1oud5phWFKkxh4jDs8ION1efzkWW+2INaC1tqzhACSOCUVltqS4EmpNIjXpodWFosyDZJoRYrYJtWsTbDJsU5XPcXbJjswMWIH4OxKYlAhGI2YfQd5ZoLqVpYruWd+d6xW5Cx7+4jKp6GcjqdGIaBafdA9J5pzLSOkdYGx/ORh9vIl1/9EicJz8pOPSEJrl4MZxwc+EBx1mVlDdTgOmyJbXNFGfoC3fXiGLD3XtUhXZsoarvZqvbzayca2Svq1kteOumoEDaD61bRlFgzNFlI2RyKnJ5xmgni+/q2oc6zPnxF298TP/89nnPjP/nP/g4vS+Uv/5t/i8/efcG//bf/NofDjru7HVUL63rB9ebHdKIVT8AH2xUEpKfKV5KPpHHPJxdZ1CNLYdbEwWe81GvEl5GBOrQrnfDR7ACTHjFlbZb/EaRu1yPgrMB7QWK/cK0gtZldbNdUrT98T80XpjdvcK7xi/s9twSm2xHmwDBGC/rFGI0xTtZk9OlbxAgaH59fusbV45yZLpcK6WwWZaUI46SMUyWvlUC4iuwfbu94uN8z7gZC9OTF0Wpl2Jn+7Yf3H21iOS9og9jZkVq3nZdR9J1Amle+//ZbrJUwiL8qjG7H7fSOy+WF0/zCki6UvPLF7Y63d5/jmdjFmdvDjQm5DwMEOJ6eOF8upLTgnefLr76kZGX6YJFCulbymnl8f7Lzh2hThxeLMvLRDu9elHwzvd8QAlqVZVl7nFQkLydKKj8qHj0/Mq+gjXE3WLpGr/s+9imtQ7Kux6rF0aE6WNOAwbZahLoYElU7oaaqGYPQGn6IhrR0B6lUxZxX+jrBdd/ep+cXalOGGM1UwQ2ICLkUXJfCXMkzAhJeaZNOYAzducVBqJFY6EYkmXW9cD56cjPIf7ks5JRNqO8EOhnQ92a5Zvu+raepmJWZ2aSJE2Pqh8BufyD0mKSSK/Oymn9tKdRqJL7VbWlCEZWIk4lxfAc1kC/OTAVyNS++/npUhGxY87+UFPMvLYLBD71777sfFVptLMuKDrAfzb9uHA+I9Ly+OVFLNU2Od7Y7atoF64VlubCuCx6LGfJxJLhe7dUIFk7UnC18t6TiFcNuAs15ovRJKwTCEBingUZjTqar2VxcfLcJ2n5LZ4BtHeE1uLdPHI3XzsGEq/AKsNu/35hjJedurSavlOWOR2vfCb0yrew/XS96Xg1e3aavWm0f513ri7X+Y730Dq7v49A+QeZuz6Z477m7v+vlprEfzYbNR884Td19o1iuYgw2/W6gvOs7PuRKmb/i9X2a29Kn/bZqqJu5rrzuHeg70+v167o5+kHfO2P7vU1/dgjRzOmi0ajZnDIGCki98pfMD8+j0w11uiVp4GlZ+NPvHsku8rPf+z2++PJL3n72GcPoe0fbYK1sDhWoTaitFHQ1eUitiqYFUuJ8OrGcLzzOC/OSOLSMaCZPrfsEbHsX6TsYe28/Ilr267F9rt1thMaWv/g6FvRBgr4k0c2A2K68zrMxGl9eYH9g5+9J02gRW97gdqfQnDOK9I+aXenPhlLJ3V3JYKnWMy7tR9baSKlcHT9qNWRjuxfHUQhxYBwCPjo8kVY9zndmY63UUlgW0/LiJ2M3qn0/y1qkezgaC9o7etSQPVfS0x1oHs1CTUpeGyUprajJqQiMg0OdUqKjOaVWyLmRnIVk72I0Ufi4o7gCLtuuNBi7W3jdh233fH8iX3dTYrtbbUotlryi+uoKdd1r9QgF07d1+LO/I5t47VjNKbE5LNmKy/Vny0GPHqLrBjeCifZIpc360XXmpuV+257QGJvt+oxBzwVtPd9Q1cTv/Uy78g1s4LLv219LZ+PZ63JYDqranzdaDw2oZmCdM7nWznDfptHt9n113NqCxzensR/vI1/1hx1B8pYOUeumQ2ydPGfTXVPfn7V+bkaHC+NVI765IdGHkO2gMf3lhkz99l+/tQiW1p34NdCy8vR4odVKTgv7/cSXX668e/sZv/rVL3j3NvH5Z+/47pvv+eN/9ieUNfNSzpgWR4neMThHUm8i7Gq6GZNLePbTRBsiA1ZIXh6fTPcTB1SF0qr9nUsGLVzIxBi4PQw4F/jyJ19wPs1IGMipkuaKUyElo9QH76iloBlSquScGaLlzKW8GpQrRkbYLqTrmGDz/TDr+DsJ0zgO0R6Kqp3oUVCMqt5ao+RCq0KrncwjYm4ccUBJiCipmA1c2mR5rhLEQerQ2mS+gq0veCsrYEbRicLHx8pPfvoV/8H//N/tOqfG+XLi0+MPRJcpallx8+XEsTbSOFDVk6riu86uOQti1Q3ipZ+pZuOBmVEL3QCDljOqZsWJ28qlkihkrTQM+pzGgTEog1OiU9u/6IYaNajFAnVdQ9TIE00znsrkIIgzoblClkj1B/xPfpdz2PGf/b1/wjcfnvgH//g3fP71T/gP//b/hLv7W2psrAYaIVIYuvBatOGDI/qBT49PfPz4iafnIy8vJ46nj5wvL7z/048cP57g6QfccuYv3cCXg+PnP9nzZgjmDdvMQKI5yNhE4LuUpDqP4lh1QKVRo1hxdxXfAlEHkEqWbOQDEqIeaR4kmGNRLVAr/uMJPr2g4fdpv/6A/8t/i+HmDj8a+UxLtiK2no0c5nKHpG39MOwGkIaX2HeCG6kKu0+GSC2NeU6klLh4YbOgS8mguNv7iYnI7Tix34+M4QbRxh/98R9xPp9xzTP6kVN7oeTMyyXhxHHY7ez4rUpBSaq4IIRJujRjCx42C8DLKXE5ZS7HyrqalvWP/8kPfPxmZhpHYgywcxZ71czqq+SRnIRyzDgqeTSh+eBvGYIihwtjHvDiqLlRUqPUxtIt14xgJVfYDN1Yyo6SIa2VlAs4c5SpRHwPti6loJrtOnZpyqbZ897z5s07VBs//PB9l0wZCzl6i1ZK3dlEi5JTYZ5XhjgwTVNP4EkE5wlxRwgWbGzoghDHwfbTPfV+K0a3t7eoQu5OQiFYU+DFdoqlVoMe42ab1r1DW6PkzJJzD6I1a0O8cQJ8BRUl18xlvti01qU2WuzQ0s5sb9fmuluvlV541dZZPhqnQNRTS+N4PBJjpNXCkhLPp3NH3tTQhArex65pLSAQvCeOkbu7W8Zp4unRDLQv9UxbE2agDDTLfNTB9y76z1gEayrghbIYfbdW82gsWUlSOR8XxjhzPs5G0y0GLcQQyd2n0KAACCp2cQ2RejVrrjbZmHDZ2c6mmbaoVutKXvF3rFtq9mFrUy6iDNGSLBAheo+6nn7cmqU1uNZd7+uPMOLX7ll7lIh141xnN7elKmzdzNbu6etUZ7/9tUN03rBz+zqP9w7vjTwyDqN5p8bYwyKxm9w7qAYjuqumzhx1tBkk2WRzZbDucDcM+BCY9iP7/chuFwnBQYM1N1QSqSTyujDPF86XmZeinKoytcqoBm+ydUy2dusd6NYj9wmu+z1ateutH1bUrNN3bL5/zkGMlty97YK8E9wWRULrW5QuP0H7nsRqrut3v1znKXt5Ne7IccenOfMJ+O7xxON5ZX/7wO39G25u90y7gVrzdSKveSWfTtScyeuCE0cQz8ePj3x4/4mX45GX45nL+sKSL9Ra7JC4OeCnSLzxuMHRBstL9DX1z2cjRPR5r98PQp96xIyKVZxBb8GhVa5U+m1KQrG/2ycK3Z5WASkVpwVeXlAcenyCCnvXuB0c59V2yvVKfjNkYZsqW6tXJw46q7mJTS/S9zxNMB1ssylN+oNZciKXyroKIpV5johUNBijeJlX1iXRWuzsZJtOpDsubY+Kd6Zp3H5pNT3cfnfAScS7EefNh8hJT3JQa5CXS4Y00w7COILgoQjiHeqFWjxaAlrsOhYxMwgJrz/cOU8cBmP4NkMWYrOYoo3eZObN7gphl1qp3QZRuuhuC+79MZyIcrUXA7lCoXae9KKwud44u9OdWISQIWN6nbzNk1W27tDOoesP2lCl7bDC0Aex73mVFATbXteOQF33kduLZUOltoZ9m+K6JRrbf9erXZoZfVhT5bsLjKqRya6v6fp9+hS7Sby2n9jPjg0dsmul/DhseM2e3BEgYHNtvL5/e0MNuuDsdfzsmu4ffbl7/cnXd8yP7sH/oV/y25aG97+8URGHD0ZSGeLIMETu7+/IJfNyMh/QIJ4wesb9wM1hz8P9DR8/fuTDhw+M08Q4TddR2TQmmayNJsqb+wPjGJE2gAotG9SSLjO1KbkKSMBPdwbxOKi5MB9nckpcjmeCj+x2e2IQxkHIa2OZN8/Q2ovIVnCxia41XBdmaw+pTSmZQLczNYbdCMClhzWaLYqRGkKI3N7dEePAfn+wrqybfYsaIccPAd9TJd68fcvDm7f2gLXKd9//wNPLC1988Y6bm0MP8Kwcnz6Z9kcHO/yCdTTBDXjv2B0C+92OX/3s59zeHvjJT9/RJPOcP1LqyrIeWZaZ4+mJ5Zw5PyXWJTFfFv6dQ+PfPTR+1+35mey4iQOTD6zFkrVzMzgiyIqTynAQ8MK5jFR1PcKtIfWC841hL9ZtMSDSCH7l01n5g28hxoGb2wO3I7w7gG0dV0pq5FxxVZCKaRYlsKpQMKcT1XaFG733aBh4+vlf5SMj/5f/4g/5/rgyhwP3797x7/2v/pfcP9zwcO87Icm0VzF4vv/ue/7+3/17fPrwkd/8+tekJbNeVlpu1Nz3td7z8PXn3H72hv/pX/2L/JWff4X6aOYBqriSePjv/p9MH77hbn5PbCsM5jaS+25oYGc70aJU51iHsdvPwcYGZK1wLHgRonM9zBicqzipVgdrh6URy3nU1h1nHPnNO5Zhx3/ebvmmOP7unz7xkirHYSJDlyf056Wfu7Uq82wJ75SGD544jgzjxH6/J8RIGAYulxculyOaC1qLRT21RqmLwe7BIN9NBD4MFktUm60LPAbJ398dQJXz6WQktsF0lBVzmJmPJ776ydf863/9rzEME+O459PjI9+9f09NiZoS33/7Ax9/+IirEdcCcRzxMRjxJQjTwz1uGLjMK7U2xhhs2ilb3psZzqs3uNBJpOTK+XzGO2eNUjF7PieOKKEjacrd7Rse7t7xzW/+lO+//5ZxCgxDYF7OpJJwPlwhUxGz89tM9G2XZ9IJ3+O71rzinONwuCHGwP4wkVLm5fmFVDLzOnPY7/nszQNpnZkvJ3KplNyutnBmIeaujXhtBaX1KdSkVYpcm4lS+tqotmuTZU2K6Y5LK13raDIGQ+e7zKyHC2snrU1hYPQDt4cb9tOeD58eOZ7OpIvt4dKSbcjYVjW1L7W39Ie2MWWNUCjB98mw17fu/2qShs3AoTcn9HDr5ria1jvBjd68n/usULLFYdVyprWMxxvcrAFUKL0clvPTv7AS/tZJUKtdpa0wuObtAy5Cy1BT63ZiibB6S073Ae62LisQXCC6aBKD1nCYlq2JiVbN7DpgujWhiLmn6LWr7RdM7UGMQ6A6oaZs6c85mzNFE9pgr6+UV01fq82o9WJ5cdpANsx9u/C16w57R6tOu+NCny5DMPaodtNidTbtDNFiUrzBYD1vBVELjPSxJ5Cr9M6wx9nUftDrNilZ9JL2fRl0EW7v1jfWYYyRL7/4gtubA1999SXTNDANkbkkLpcXcl1I6dzzE7M1CeeZdS0sc+HkCy8xs/iB6hUNnRriwXlBUzJxvrirQ8fWxZqbjnVgTvqU1l/v62Rt+6cYzKdRS0E7/fm6m0D7rkyuGiXptmNODXppKLgeZusDGiJ1d0PRgcfTzPNx4e7rz7m9feDtu3ccbkaEF9sttkbWxnIuPH78yPfffMfjp0c+vv9EWjJpTgQfiWHkMA0c9ia6vX/7GZ+9fce7Nw/UMNCct+TrnGC6oQ4HyvqMaJ/Qrg8J20nCNhkaDabDbNL3Eq5dM3Brb/pdb5zVbd+HV2r5tu9p2aaR8zPklbcPb6nTyF14oZTK7M3Rn9x/7mal0vtj54TWzL2GJkip+GBMxOpq98O0H25B2OU6mdZiifPdfZ6US/+se9pB/zkiRi7a9uzXM66zCb04S4GpDW3gnHlVDoOxS1upXcZUjCF5nSzMn7N1B2BXBb8WvHZdYFOas/vomlLRc4xsGhLbgwvmyekdzgXUV2NqO4MGXcX2jCVzPh1JqbNgm+O/n3hgzfE2qVgBev3stgmntHJ9JkTkyhuonfl5ZXZi0PT+cAAay3zqn8PrM2XX4XVQ2f5M/Xa/uL57t2fKeQsSsD2jXvfD2xmWa7ZhQK93md0p20Bce7HBrk0InmEc2O137JeFUivShCyZ3O3hrjvvDQrZxtArktYnwI44cX0/0vkN3cDAX1+NfWZ98t4cuEDx/bzMJfV7xl6zbjtbtyXJy/Wz+e1z4L+kCE5h7At0UBprWail4dyIOJjiBMFw+qqF9Zy4hMTzeMHJwLs3X7CbJqZx4Hw+Mc8L42ShsZlEk8a7+8/YTztqtr3mshTzI11WKoUYo+HwbSWEgbdvHgAh397y/PTM46dHci6slxfmIFzOhr+qxI4N0w8hoWZzTkft4I2HiTgMpGWmVcW7ES9mj2TThDGtpnFHUyVn08REPxKHyM3DDWBG3CAE310KELtJnZByJq2ZJa98evxoDxTGChvwlHnloo01Z/NVnC1SyI9Tn1S2rlP4/M09/9F/+L/mzZsHxuh4eXni//cP/mtO8yOfTj8g0hiCEp1j2N9SzidKnmnFQYtcUuL9ZeEnPrB42IUdddwT7u/w08jx/ffM5xMxOqJvIGYCIBoMOlWjfUlnebV+k2uPu1FNtGKJHloT6bxQGWC37x6jxs2sAGFb6hsdOjQrgkWcufWPA7gAfkCHHe3tF9Q6UDUwhB1/+3/0P+btl18wTMGmUKqJwJfE8/Mzf/hP/xm//qNf89/8l/81qJkZiwqBwOdffMVXP/sZv/vLX/Krn/+M+88+43B/zwNnWkssKZEV5k5Vjzdv8UVZSiKvR4b2CafJpC8CSEYRmjOpDC0j6gli4v/arKFh8teFv7SGpGaaTW+WdeI3Xi6IS3YAbKzWdsFX+Ms/+Sk/Gx/4zfsLTs58qmbnty6rQWthQJzvMT3gQvf5rMVs3NZk1PgxUPNKOZ6IQYlDJC1mUzhET+wB2o2G91asQs2dvt+JZ85KXiq2ZzteLjiBKVojpIwEF9nv99BgSZWX44Xvv3vPzc0Nb94ox6dnnn/4yPl04vxyJKViDkTBEYdojGMSZS2wKhoEHyPnS7brWiziyTubQoyMIUgIV8gQHIcponQGZjPNXgieYQg4HAHP08cj3/7RbzqiU6kFslqBNnN7uyU1A01ZkwnVY2dwaxeP20EM8boKMULU+Xzudo4Gge7HiYe7O77++ms+fhy4zAupXEh9gtwIaf2GsH/0M81p39XHAZwjZ5v8hsEIhmmxtUAcbf1yd3tHypnLh6XLsgyJCNGaBPFq7OYGUxiY4kT0jugcb9+94bN3n7E77DmfLzw/PnM5X8gpkdLSsVTpz+urs5Re4Vj3o0K4DZ+2p6ZVE7jTqDXR3xUxBEy+Yt+iOaGJsN9P3BxGPj4+WRLMiqkBBBCP9Ggq0dp7wX9ZCfyXsUND7Osf6xZLtZsop9XS5Hu4LM4KoWDC9GVer4h2ThkHtLo5ukgfdZ2Z13pPCBFR7YGjRlahT09mgm03RPBG0ba09pE0TRz2O9Z5ZV5mWjHvR5wd1Nv+SrR/v1ItzVmwacQB3jHsdpjNWTYXhK5J20b0IZrdj++MLOcjIUac99a1blh2/4+t2TGWWRfR92ikbdKT7iKyriu1WQHc9gQbw8v48ps2qSBOubk9cDjsKXmxzi5nSumLZycM0T50S272138fnFG/SpdZODGyTQkO2e3gcEO7nDu/dDGfwM5U2wY9u7NtP2O7CLseet1lWL8xRE8tlZI6RbyzT6+tmdt2K/2h0G1+uN5OZBxNLPWcccecG3OtTIcb3AjvPnvL/cN9t60yR/68Ljx/fOTjp0e+++Y7Pn38RF5yP/CCMR3HHe8+/5yf/fLnfPX1F7z77A3j7YFhHNB0NqmKmiQ5bwJmHylxh/MjzaUrImJFsLtu4DpUZo+66yYJqtCukonXXELXzK2ntS0+xvaIr7/6lOxNx4aLqPPbmcHgg0l7Snv9fPo9B9tUtj1rtrvaUBgnQs2V0szR5arz1L4LEpvifAhEuiuLcxCd5UH6YO4i3sYR28+3K2Xe+QjOd/MIa2pLsfeeUubp6ZmcM9oqx5cX1tW6+h//ki1SqVpYqlW8RlmTTVO5f79l6TurHmfVoUqnRvdvmz1cNzQQW1Rv3uhs+zXB0Ji0rmwk3db4UTr6RqDpO622NSivO8Mf79wMyref27GN7nginQlvEWytNS7zzLomm3o7QuW7SHxzKfrxDk1/hA5txcXAMu26vf71/R7YJqNtJfXKXe58hs5Ws7DdZs+K34xO7Lq1Zi41IcbuDOZN7+d/NHE5eX1d/fyEjX8r2/Hxepi0Hx8s268+6XYUb2tjbKXoCcHik4LvqFk/ozYWfT+Grr/cdtb8ll+/tQje7A+GbftMKYVSFmrOXE5PjMPIFO87Vtv1cgTyWnj//gmHWU55Zwyl3W7HOA5mBYSJMhElRnPxCN5e+t3tRM6Fb7/5hpYbfmcpEPtpRFU5Pz0xjTu+/uKnTC7QfpF4/vTEb04nSoX5InjX8L5ezamDs8JaS7Lg1mgdizqQ6Hj37kvGceS7779nni/kZTGNDmZke3vYm+lw3dGA2bk+XUZjV2m+QhIGyDi0dKg4FZa1El13VPFdq9Rbuk+PL+ScCGEw+6zYEa2aEC2Yt6bQWEnthhAVlcw333/L8/MT85xpKtze3BMD7HeWTDGvF8bJNDjNmV1cjEoNih/27IYDbTdymQLu7WfIm8+p04RezsyfviEvJ/ScTc/IawcqIoha0+J7lFLtC2oQYgy8uZ2Yl5Xn9YSTgWGIlnVWesxPF8siZpZeOpHFbMqMQHCsQlLHw91bZHfDrz+88D47fvq7vyJOB37vX/kd9oc9z5cX2ppY88ynjx/4b/6r/y8fPnzkH//hP6XmSug2cgHPw5vP+OLnP+dv/Ft/jb/1t/8mURtBG6dcOacT63zGpwtZmxGvBUQbl7ijTDCO9xbV1o4EFYI35nMIgz38mF60loQXxeYY25m2mliT5ddps1geWiG2AWow4wJjydtR7jpRbLqxCbENVDfw/aczz15t57oDWS4Y+86bNWBSRCqt5CsU5hqMcWCMAze7PcuaeTmtBj13PLap2YZ5UYL3DMFCXFWhNoPlwsGKYc52WMdgp86leWuO1Zrj8eYebY3n0xkHXC6zNcMCx+MLv/8Pf59h8OymYAGtJRCC4+b2lvPpQq0XxFfwiZILOTd8MQH8JZs7lQQzDn8+ne0M7OjNNIxdm9tAhMZMCI79biREzzCOvXHNuL7j7/sAWlNKKgYpOmcRXQpI6I25o1UsaLz2aCBVXKUTjTpLuBWcd+zGW8Q51tb3j2PEibOVTc0s64Xj5czv/8E/IefEutpUFePINFoUnXqDOZflbN7HgqFMwRIKGmAEkR8VDPpuvXO4tGGRVSX3xYQVhtBjz3rbRPTKbhzwzvcz05CoZU18enzuU25fkTgxzsMYTeqhSm/XMEsKrsVw++dGqKHrRE1sq9eUjetmoZpMopVk4QcitqcHpslxdx85noR1aVRn0irnJ3CB1m0/XIeihxisgfuzFsE1ld65Gr3fBfPbDN7hgqNbdVsX7zZnDa7Th3dCq2bKbB6bsKUROG+uKNIEqmnutDUIlm/XujAzZUtNiM7TWuvRMMpjtG6ypPIaNtnsMDVtT9+ztUbDGIioGhGmr3BqymS3slxmtCnTbkcYR0paUe0WTk5gCLZcrabHSbUanCBbenP/kNHumFO6hsr8DcFyyCzowl33e957WuvOL3EwzUy0azvdGHFh3O3w3rqu+7s7fvPNr4kh8s1vvuV0OvHhh4+oS/j9zDg4pA3ksnKZTz3YuHepqj3GpsEoxMFS7tM6Uy4XSjzzfqksWfh8uGEnAVkz0pKJ+lGLFBJ6RJSyjSAWN9Vjq5xn742OnycT+a7XXZJN/+7amTWaeFRcd9WBLcNRxh0MO85+IjXPt+8fecyOhy9+ws3dPcMu4oKxaeflwq//5I/49OkT7z9+4vnlSM6Vu8MtP/vdnxDjwDBOHB4euP/8C95+9s7s01q2wluVIEbx7hzJ6wMrrbFiejyNE75VYrvHtwEnFUfXJum229SrR6p21bhcGaWtN419j6MeqmlvpRlMtl2gpwxLEc5rI6E8pcpC5kN6Zh4qq0y0IVDKmbxWlnPqLE/6wWNd/jCYJVcpFa9C9rknntsu0XWW6rar0ma0feMDdOZj12jZoWVMRlFozSBI7xwQjGHqPMEP4GHa0Zul7qvaEw9KzbhiXr5WTKBlR6mNNRcLm3a2Cyx9l0hzOBVzdaJdDRy8YXG0KmhzrC0jUpHcTXVFKF7QVg29yhnvlBg3vabixYTy3gfzKO0NyPZ7Y0u2DcvD/qqdrdYIbI2L9r2Y61OSrbX09f/09Z+GofK6P3R2b/heRHI1op1KZ56L2HTYD3jbl7W+37Pfrby6yfQH35xwlrWzUe0r3Y80kVdW6jZGdT/mpq2zzVM/U+2ZyKXQUIZptDXAxq7PbZP99rqxPef/3LSnfS69TrSvXyWdlOi9J8ZgNBcxxnWTivMVHwveV5zvaJS8PqtbeLFijkn7w0QIv7XM/fYi+MPTEScWxuicMBz2eC+M0ayPnLMiULRaBx8D0zCwGydCHAhx4un5kZeXZ3JTXGnXD+kQJ4sOyaalOh2tqCEGoyw5WULDyxNOPOuYqUV5eXqhKXzzx+9xzux8alqJwRsO7Cy5ITcHpSHVqOHqFC+OiKNWE9AuT0cWOXI5nonjyF/8a/8Gt2/eIN7IK99++6fmnD6ZpdLzOVFzIWVzD9mNXbQZOksMWHPmfDxRipF2YgwMMdJaI6sCEZHAOA7s9zvuuUNECGOHGWIgRM+XP/ucm9s9P/v5TxnHkVIb59OZ//Q//U84n87MLxdSKrwcZ3wo7O8y+5vIZ1/dktPK+XxEa0DqQCsWk3NaVx7rgt7dc3s/8Hg8c3l+4rsceJ6e+MM28iyBf/vrr/lqDIzZ4+WFeHmP0wyDYe5h6iyvbGQi77wRP8T2gbfRkcaBm3BLbo3H45EYnSEBvhHcJm5tVBeoEhmcTZN1Xc0A4POvibfv+HUOfJwz/+V/+/eYm+N/93/4X/DVz35CdFBLQlj5+OE7/s//t/875/PFDukG3gX+lb/0l/nf/8f/W8bbA9PDHblW1lwQybS2oP2hGrwQm/AcPReNUHrHsmakVJ4UxHumm88Yxnum3YFYL8SzEuqM1hU68iE0BsMlu9VW6B16QaTiXSAEkwZ4F3CnM/l06c41goQB1cDvnx3/LAl/8P7C+3Pmh+OFuUL8Csa7N/zlv/lvIofIfPqO0/OZD+8fqTXTXHcdaY1hGLi7v0fVhOVLiKR5xYeJOO7ZALzaMrVmaqrUrLycj5RSCG7Cu8h4OBCirReca4QxgFPO8wVtlWncExEaBjsP8YYYAu/eRFoz157z+cTz5Xxln6r3yDhQ10rOK8tSmJdq5vm5EgbFh2BWYU0ZdYdXTy0JtBB2PaBZTFp0WqyIXtbVBNeaECfEOBlaQe32X8LtzcQXX9z1QiDE3Y5pN7IbD+wOd90Qvxob1HmWfsZpMUlQdD32yxuUqMUKiB+8wcEbIShulcmKVS12NlV1RrTDIOVp8OQC6hteAsFF5vnC6XRmU9/ZjtczxRHnhaIWxVV7IkPsetDlnGi1dumAdCJe5fRyQjGJifOewQ+A2vXs7U57XaF2aUcDB8uaEBXWlMil4CUg4nnz7i3edw/ephw/HklrJq/m35p789vlxj0oALRId5DyV4hp+9E2ZTb2hxse7h7QaozluVxYy0wYFuIghHEhDCsuNqRgJLw+ZUKjUvEu8pOfvWO33/3Zi6BlWDmm/a4vae3dNFVbzsprjXfeMQyxU6lX62i8dV/jOHYGqEFg4owcUEu1MM79jiVlRBLrallVPkSCSheoC+uazXtOzH+vlNwXoOZX6pwVkQFHwYP6voTte6sf/d/G5jPyqdiDVivz5WIWZ9FuWhci0QlhCN0uaqUNjal1zL3niEHfAXbsvnVo0DtbutNhBN93Pw0orZKL7auceDQlShFcC+QWOJ3PqCjzZWVz4KilsV4W1stKWgu5e5cKjuAi0Q0MfqBJRat0kXCmFHMBqX26RmwpPkZoVbgNoF6Y4sjZjVya5zkrb5wFbooXm5iCIFfuhl67Yls9yDVCRmtGFEJw1Ay11J7oHRApBhpLZ4hee9gO6He2nx8nyrjn4/OR754vzLlQXXzdb2KF/f2Hj3z4+Mi6JFKy6zn6wM3hwH6/w08BF73xC/v7/rGDTJONyemMseiaic5asEV7qbTmkVgZmzCUgl8akhz16EwXjO0GXeif9Xan9QdctYJUg+mdEqThBLzzVBdIPlCDpWOv6lhz409PhX92afzprDxm4bF61gb+khll4fG4EkZhNx1IufBRoPDKKpZuN5fT2rt1zMx782zFUA7vzJGliYO8ds9IYzQbNP3q5E+X0JTcus6x76s7GSX0HXRac6fo2/Tbtj8Pwaz4crXns/nu5Ws7t+DtDFPRbZvJ5rvYnVcxf97OJnTCNEw0FbJqN822BmAtNvXU/v0axjmwwtC2xdq1ECrarcsqTYtBw3StbMd67J6hpyUYAmL3fd909RWd+eluWmH6+7BoKzqydpUxOFO/IZjlmdqJ4Z2z8Nw+ndsOEaoXTJZkP9D1/1779zaxeneEoTsHKbDtqztpRzvy8M/vGelD5JVtud0HnUlr78kYyK0nRLSuHfTeCC3UhpNK7nINm8y6YkK3M9hOZcH0iL2ksOkXaymd7GUNSakGez68ueOnP/+c86WwrpX5tJDWagx/3aZargjlsqx/vp3g4e6GEAJv3r1BgLwkcs5cLhdrcNQggNAL4OGwY10vnC5HpjEDjRgc93d3tGJaHJu2jbHWSuXm9pbP3r1F3I7LJfH9+0+0mpj2N4RabGmeK+enGTDMv9XCuZhfIcUDHvE3DLHhfKM0IVchpZV1adDMhaPpRl/vd6v2E71DBj98/w3x+RPDYCP09LBjjN3hRc0pxAG3w560rnz/w7d2APyoGGwOVt5HBh+AgrSCHwLDGOiSLea0UFrq1nSBmlbbMYwRgue0nph2E46Bw+HA/rBnmRfSuVDmZga5CkMcGEbH7SFycxh5c7jlqJFjm1lT4ng8o+rQFihaKGpp5hKUu33gYfQcDiOXceTT7h0abng8PnE5L0wE4jDisiOo4Mcua8CW0dLdMAodsvHeRPHlgrjAMEyW0FFA8cgwmiyg1b479f3hfCXISPRG6b65pxze8I+/+xP+yXcfudTGOARcKUjOqAtcLom/+/d/n++++45WzHlIU2Y4jHz9xZfcPdyxuMScKnU5GYEiCkFsGm1qeYhOzV1j3E/EMBju3RRJGWqj7W0ivLupxFwYnyfC+Zn120BZIWrFOcXv+6nXafpGzGi0lhBXGIKFAg/icF0feYoT59Egd0Lg/XHm8ZL4r76b+XufVh7jyOI8KexpzRxSwnJm/JNHbm5v+PyzL7m5OfDt939iEWX9+kkYqLVyvpzMJCAMlqXZKmhB1aQiZolmxgtPOZPPs/m1eoh+IISRIUSCD53EVahpBWlXFnWtZqi96QKPxyNGJBNCtMgbFHa7PWlZKUtDa6ClaMnpBQY3ME6eWRNLSwRnAd5NW49LK10qkRDNiBsJ0XHz5i3iAi5ejICzWkL6y9mkUmvaoMZ6LTitGVzvxRNd7CL2Rq6JNc1dy1sIwSRQGmzd0tSYulUSzTVEJos36tIpEW/cCG/3U+/1EAKVSs5LJ310GLI2Y1kHW48cpsma1ayMQzQ3q7SSczISTTFylQu+E1pMPwxwWU+0WglhRJy9Lyvy3va5cWsyuRpJWKNm04z0omkri3aFZp33xGkwk49hQFtjnTM5F9KaDHXAWLExBAYXLIqsVFIplLKRmLZ8DatQDvNrdV6IXiiqpM0EuymXy8xyWREvNiL6gvjKr37vd/i3/91/g9oCpQqnl/fM88UGwG3I6e+pqvL+/UdjwP5Zi2CrjeZaH/cFmu3xhjDZ/iElfBSGaeDd23f8zu/8ku+//5Y//uOzWeHMJ0Ic8WGglErO7eqY4tThRTkdT2hVnl8WlqVwPl9shxRsoalaeqdhH55ZwW+YsjGRzKkfuHaP1oGH4HH7yUIcW7R/bqG5YKJfH9DOQLWokmRyieCRaP6bOtqHXFI21/XROi0fPFrBiZFXNp+/EENn4nlyWkjrxfSEzltHJ+3qvtOCpUxsr0uqdUhlzSSETx8/cjldGHcTJVVLFQ+RnR/Zgnt9MAF6yo2clHWpzHNiXSplM2Wujer09bevuGY7LJdnvAbefP57lNt3PM0nTnOjDiOi4BdL1NgeE23VTHP7jsH0jH2y22QDiu1gRfBDxNmJibau6drYan0I1LZ16g6cZ2lwzpXH08LzeeVwf2fmBN5DqTyfjnz69JHvv/+BT58eoVqaRxwDD/cP/OyXP+Ph3RvWbkNWm+Ka4Jt5T5oXqAmstVnmZWu1d932MLquOYqDhXMGijGUT54sQp4zfk7cBpt0VTssfGUQ9onQmS41OtdF9RltGXzhUuGTRk5z49xWvn9aeDwnvl+Us3rmoqyuUVV6eVVolePxiKpymA54hTFE0JEQ6UxOTylm2WfobKFVR60ZaQ6h4vB4gVaKaQKTQWlOLAbIyfY+7H7dyBXqTJAuAtIE1YR0iECcsx2MGrO21so8V2JwvLm/Zx1XY26qvzbGrTYjjDnHGCOub/pa30PTLP7JOyPrRa+EwZvuUjtcOEVc9IyjrVOqinEL1DgFSLjuvhw2tQbnbeIS302enek4MUcX0Qat2n3vMFE+Djc4nIf9fsL7YD7HTa87RBOm0x1QzHWndgcfQ5C0T4gYGqH9KL7qlBu1OwzZJNj6s/LasGuz/ZmEzoZ1JhKPwawcS+663E1X2KdZkT5VS+uFu+s8uw9xq6Vfpu15F9Z5IS0Gm6KYIL/a5PyKrm179M6NQPu5vf1kuJIxxGbA1heHpbru9bwRb/xWjq9MVDWyCSFEswKUgOG2cp2Icd5Mybdri5BWI3X+mYtgyQUa5MV2fhQzuz1MO/I6czyeETdymG753V/9Hv/+f/A/47/9b/8rvvv2T1jmhfOycjjcs9t7lnXmckl8/sU7DjcTEdO6fPfNe0r5jpSFUvXK9jp89jl4R9PF2NGb69YmWqUXHm/i/VqU1qnkVQr4yjgOxHF/hQHTupJS7jdR43B/xzTtOM+JVBslmVvD7GbECTUly6A7jEakaZkhRjgYFDMMQ39dZn0WR+u4h2FnYz7w8vzE06dKdJ4oDsioVkudr40YqkWSOMPZvZmRkOdMTZlfL39iEIYz9ugQd8Rxx37aG8SyFlKdOV8+ggiXc+V0zDw9XqhV7GEqCrmSx0aeIPtKDtXSDAA3f8KXMz/7Gw88/Px3+f989y2fcqHc3uD8RDwFOtnOfmZPetCSDXLyDRVjGIq7fiFQEe+J+wE/BmPgqdDKBhtJX4S3Xjwq4iLqI8+l8mlOfPPxxHePZ/7Vf+0v8cUXn7MbBjRlvv3jP+ab777nH/3hH3I+naEoUxh4uLvhZz/5KX/9b/0Nht3EZV3skWzF9pdiUpshdNhJtudSO/TeaM2hOPMu9cLBD0RgjBmXMuU5UBGOTzP+MhNvCmPzxJvQj+7VUIHaA4qD7aInNfZ0mpNR56PnUx754zrxT55e+NPjiW8+Xng8Jj7IjhMDp2TuShItLxBRlMqH9x+YTxe+uP2aII3bccfoPXEwjWodjQwjUiipsFwWhEZKZm8mLVqahihpXZiXhWU+U3JimozRa7r5akQWrSzLhZxTzxMUujyWmlcEx1AbcRi4v78BlGU9U0rh9Dzz5uGOn//0p8yXhTF+4nLKvDwtZriRK0FMGziMI24MnNKFS17ANSQ0XLQ///qrzznsIh+eH0klk1vBOcd4YykaQ7CItegjy5IpejLnqeYNBWqZ4Gy35sUmEpMEjIi3vMgCFFVrHKQR8YSOeDkn+F3AD/D2zR1DGDkdj+Rc+P+T9p+9tmRpnh/2WzYitjnu2jRVWaa7qqt72M0ejkYSNUOKJGQAAYIE6BPwG/KdAEmQwBGNhmPaTvmsSnvtcdtFxLJ68azYN0eAKKg6GxddmVV5zN471nqev41BRHnaWBEYxRbhp8TmkdJSryGXi27CnHruIWxUgFlKEeoHjtQa2bqLXJq11YwtAihrOxSw6npUVYy52dVion5n2BTdTiFqiaq02IaQaGpMlBDPUW+2Cs213+2Zp/nMkZ49jItQrn3xyIcBUNpkxLrEMvAut1PjBnOLiowls1CE2lqM9aLupND1Hb7zJBXIKokXu1hK0eSiqUUSKKQcwVKCQN26lRCcTuM/DA4lyZuTQpDpYo5nHq3mhEaTY+bwuOPL3/2e/+d//S8IYeTP/uwfMZ1GptOJfrWmH1bsdydOpwnfBCDjbmSe5OGMMRHyB1OxspZ+WLULLmBtQAdFiYk0ThKHlpMcolWUYbloQfYLKFdaXqVcKFZZjFV0rjtj2JWK7wYpqO07+gpoUYHGNLP03uWcUeMEVHKMVDtzX2XatsqSKRIFp6XDy9qMiOZkU4pzkHDqKn4qUW4t06d8Dd36s0RdK34u2paljKQ35AXDV5GkErs0STqLNpQ8EacTpzzzukbGaSJnLTBkQiqQjKJUxRQKMSuyajBOkQOoMxWXj7hwlNB067jPGl00G+XQxmFakohqyR+5LhugYPGqofxpeeK0cH5OC+cyB0mb0Maf1bqm8TtKyURftCEpw5v7A68LJCzd6pJPP/uMFy+eMo0njvsdn//mt7x9f0uexQaxvdpwebnlZz/5I54+e8rVxRbtHfiW9pNlilYApR0uQKmKTBCYTUleZG0JOaYNajWDKRWbJGGlJnnYo+lxuucwH0lA11DQskjDc+uArCLnT1YTauFUM7up8vAw84vDzC+PijdT4H3IPATDEc+pKkIzWDhA54RBc3NzSd8NbNeXDP2K68trYolYIzaU2DJ84xxIORGnqYUdS7ZoNpEwQ61K4vT8TAiBOQRKiOgimcEhZQQNVrgoSImIUjIUEdqX0LidKjw/OYnoITeoLYkIJkdp6Pjmq29bCIVmWEs0XU0WokMrCXGfplZ/5RIrr8nZUarBa4fR0lsY5oTGYrVYFkpJxDGCqngr1qRxCsQokK3Kog2o5ZxX1IAt4cRyiaQaUFbhVj06WnKS4lulwFg5S2wzv9vOor0kw5RYCTmSS0HZ5f2WthFj9JmjoyociBq2cYulXYYGg8oGlZoCsxS0UjhnKNlCTdgWk2aNbl5M+XvXYhWncaSUzDSGNqjKWaOVbulZlaIKqUH15yCBxVdYVFN2LjelnJOp5MbXOoxdLkHZHoWnqx8ScVLz90W5wD/4tJdNUNTFtnGL4ott3HkVy0nNmWoSvXcM3UDXdfRDxykdmVPh1ev3/P3f/4a3rx8Zj4kcFTUbSk1I9q1YJJSyGGvY3FxIufEffgkKpBFHsRCcjidykpR57yzb7ZoYIrdvHnn7+hX/+n/47/jn/+k/4//4f/o/MB4PjIcDrnPYznLY7RmPJ+4f7jkcj3z5sOe4P3DajZL4QqEohVldYfue7fYS6xxKGVIIROWZT0feP96TWvee0orcaWo1lGLaahzxVrdGBcmntN6inWezlVLgYhXVKMaQSbmy7qwYf7WnojjtH4ghMDeVZ5kaNzTPBCr723v61cDzjz8BAuNR0s+FzHZ0fSTFRJpntJX09BQiOaZWIaRw2qKcZBsqZdrDAk5s2NI6r0Rxpqr4irLKAteUzOPxEWs0zy4uyHFmPu44psybV1Gmqa6XUoBZBBudN5SsOITKlDQZSykzNQU6Bd5FjvGe+XSL06B9z7cB9rXy3PRYIl06iG0kyYc2NtkAWrZe0+qC5tpgIQ1ohdeKMRaOp0RnNb0bIIv9wiEf3mqkHHlShlAdn397x+9OO4Lq2Vxf8rO/+HNePr/m7W9+y+3bt/ybf/WveXjckwJ0Q8fLj1/w2fe+x//uf/u/wXcSjZUVRF1a07hp0V2JmjMpZ5HDFcWUZ0JNmFxaSAECTWXho3ZTgiyCC5AL0VTxgbmQuD2OrDJsg8I4RXENosktZzJbivHMvufoNLtO88vTPX/z9pa/ejPy129HguuJtpOYONWhS0SXTEfF14qNEV81f/LiiuurK24ubnDdCvvkJft5wrmOKcxMUySmwDjuGrwbG0QlA2NWijgH9nVsggcriSi5MDhLZw3xFJiytCikXLBeNmLj5OCVoHVFCpLM6Jz4RkmiA8jhCEAJ4ldM88zjeOL2zRtunlzxox9/T3KGnzhsKdiciSER5sT8/o7D/oHNRcdm7aj0wmkVh66GFBJ5Sijb4bQmxEzKgcfHPaUkvBPIcZojYLB+jTbAFCmqkqnNHC4XY6kJakDlCZyiv9icB9hc5LNjyGgKvfU4Z7CrgvKFKUzkLNx7rbAe1oKYBRGCWG/a8yFLkNVWwjOq9JHGlMSPZxwmWYgSQp2rFNbazstIqUTpaLTBdNJGP6w81kncZMmVFGbmGDkcJRHG0kprrZRyxxgke8cEjFZ0zgmVJFdGC2GQs5QWsp5VFVuCMnhvhMb6jucutbg7qx1KLT7sCm2LJcsScjbAYhqa5T9QILWKnzunc7gGubDyPRdXlwxDTz90cKykMfKr33zB11+94vAwM50icVKQLblIt2MDedFKVPkff+9jupYB/QddgtoIL7A49zvvqNbSeXdOmFC0ChFl0apjd7/j53/3d8zzRJgmVpuBYT3w+PDA4XCg1CRTEwpne4yNlFJa+4PGdk4yOT1YC52Ti+wUpfIlxtxySBsOHbRsbTmLOrSTra9q2kpeqUlhgXk0omrtPbrFJRlNC6vNmE42vNVmI2pQ68gxEB92ZKKIb6vwBTHJREpVrNdr8QXGiGqiH6O1xEU1j5H1imps25Ga+k2wCSqKWJdk9sKiYJWtNKJ0IleB86oSCbG2AjeeJoli0qaVxNYiHF1OYhLvXbOzgM4tIKoaTNGoXCElaauohXn3yMm8pzeW7WrD8d0dIZyYnSJpK0R2C/YVm5NtEEvr/VNKlHNaclBpZZrimUsi4TeeioT9grSG6LqYyhHSH8MUZo6nhHcrzGpN33VY69jtd9w/SOO5yuIfXXcDH338Kc9evKDrPdZpAZdUpWotSkwsRSeKEZ6spNKEh0qg3moWVFYUiLUIx5OFd6nqA3RqqBhVWV9cYFXB1QPaFKTWuMm9FRgZDUBbDiHy7vHE3Vz49pT5/G7i8/uRu1ApVgK3DenMm4iEXOOMo9Oay86w9o4X2w3Xm57LTY/rB/TNNW4KXF5ctpe04LLF2OaRLVFaC3JBG4lTU8ajTNeChwuxCh2QcyY264puqj6Bhxdep1KUcGKodtABuhM6QCtR+lkrgopuWDHPs1SV5dT8eIo8J1Sv0U4R58gUTpQgvXrWGzaXGy5vVqw3PSlKmsy4y8xzxLTBhJzkuVkySaNEpqWWA1ybfTMn4c/OskchIqlklkzQD9m3YmtZlreGeQknWjNzCKSs8EGgu2EYJMgi78i5QZZKU5KUS08xnn14qipMlcBVrVQL25COU60ttTQe77tiadU8s0phrCh5ZYArBC1q8aYtFW9mpzF0cja2LSymhEKxWW8oqjAxNq7wA9dJ4wmFNpCvV4Hee7quZ55n5hBQpNaSI/BmTukcQ4kS/q2Uisrqgwp54QHr8s1kA9ZKYbUM/1IEIgEIKKRiao4cm0JeWeGZt5st1xeXbIY18fSO6bDn38M6Vfvs6RbGQuZ4eCQE94dfgsZKRVDJBaOUlLUqgegElgjCzhmLwqCV4+79Hf/qX/5LUlNYXV1fcXl5wd39e3b7Ry6vLsS3oRzeWawdKSWhvQVjsH0neXdeYW2l9xoSzPPMNM2SLdiizUqBkpq+t4KxHV3vwCB1SrkRw7FCzoypEsdEdyG5ncppMXOnRK6ZrpcXcb26QCvFvF61KXaCWsiqkmuLmkqZeRQf4OXlZWu02MvWMc/41YrtdktKkRBnnPMY1ZSVCCRai6Zk4ShTETVTqbmlrwhsEeIskyTSB1almgDjxBh7OO2EXzBOegiRjSXmhHOGvvcsDladDRaPrQaXFToJTIaRr396uOMQe1buJWWz5le/+5a83zM9h+SMlOrm2g4/4V8llahVYUmoBrUVA+d2AWrkcMl5lo39THtrdINEqZbSiHCjDeM8sz9Uuptrhu2WYRiw1nJ3/8D797ekkCTn0FguVis+++yHPH/+BD90MthkgZSUVhI9ZZRcCFkL16JL82xBVzW+ZlTjF2qepB67Gfh1kRswWw0arKlYp7m4ucE6hcqPWGYiiVIzrh16liUZxzKOI79/9ZavdzO/vJ34etL8frIc6KluQJFwKrbkj0rQA1k7fD+w8o6Pri65Gjq+d33B5WZgc9ljhxX62XOGOXBzfSOfD5VIJTJU2y7B1KbsgMTdOVy3oltdME0zp9NILYVIaBdGwSrVekTbMCbBKNTc4G0lsJhkNioRDVnhu40G57SELm8vGU8Tx+ORkgw4ge/TGEV442GeA8fpERUVJomw6Xrdc/3sks3lium4I04jp8c94xjwVSOOBBFAhCCpQzXIpZ9Ks29YgenLsl0gn9MzR1Cl866231H+c24B8i2qTBW5LEumtoBvVKWsNc5qbq6v8d4zTjMhijLXKE2MlVzgNE9ig2ib2dBKh8WWImeNCGkkmSeGgjIVbeUaEnRIFJ3aii9yHhO5XfjaWhbM1liPsz2qEytLjLFViO0xRnO5vaDoii2WGCOHw/7M6Z0h0ValtWyH3ndcXVxyd3fPKQoaqNvCorWSrx8iMYhXMSXhQE27mM6h498lJdvWqcwH4/8ilpGwETmTwjiza1CpcZZ+7disVjx/8hGX22se3wUe8ygZpHC2qnnfap8ApRK7h7tmXfoDL0FrkKkhCeErKV4tHNco6I3UKNmubTcZnOYUZll1c2W3m5nmA/tdZjxa4lhwNpDGRAlwPAZSSqgImErOEyWCygptxaNWdOWUDkx5pOh6Tq+nSpSSTJ8WTW3Zf+pcQlurocoCiFGRmCu6eHQRmEqhsa3FYN7vmLXCXlzK6t9UV8VYkjZEJRuBbSqukieq8aDc2WQqP5eWUdFKzUunZCfQCqjyUC2molpF7adzxtSK9nKZlJbKIOWajuubZ6Dg8fFOjPihTVkFnDWsV2tg4TtV63GTHyrnTK4J5xVbp/BaNiGUQTkPzavojie6+MDzjz7iwnd87i2zt6TtU5KL6GlE5RGUKMWUEbxDawk5wPimEvSSjVps+1QIZ5uzkRxNJf18tcV9Ke3JxlO0p9iBogdqk0QP657NdkXnxbh9nCLHKWCcxTXVXNdbXr58wvX1ldgSVCVb4WPkLZFHzRoDppPMTFtxS7tHtdSS0VVCtlPtRa1WJI7Kh4QqlWBUs2yqZtQ2ckEmUcFNU8V5sF3LpXWJo/Lclgt+OUb+H+8Kb0+ar0bPMTsO2pOVwynXUmoUhUStmWvvWfmejy+3XAwdH12tWPeOblDgK9mCMgVf96x04ZPrJ3Ro7t/fU1NGG0+qiSkH2i8PWrYvu9L0lwYXB4bYcX8LIc8wZWqSDlHxtQLGnIUwpVW0W1/RWrhK1BLbqDCmg6o4HGZcKGLTSImh78hRE2dR307TjNEFbwumVC6HawKBkJonNmvCIXPKgRQhJ9PqycRjWATAoBSIWaqMtHOoaihZ+PxQQtteP6ggS82kVvqsVCeDYcmoYqFYWRLnJIpEY84Dv9IK5SBEaVmJ0VBnK4HTtbLqeryWNpxaIORKSM3XqDXeCMUhi4JuMXhAXRScod0TtXGKSzqPwupOKoiKIkf5jcSOK2Iv47zwtVmM+Bape+p7R7KKx4MgSykHrDE8udoyjhPjYS9pXaUFXmuNUVJYvKg3t5dbnn30jLlMHObHpvyuIrSVeKdmuZEPwpn7o11CtFzRxTOpsijjqW3oldejNwNZJ7QyZJVJNbUhDIGmU+TqyYbn37vkcnXJym+wfSbWA+jUIjh7xJXSdCFao1py2DkA9g+6BK1AhSnK1JVqacHBcqKrlm4yDJIXoWoGEmOcIWtUNoxhJtdIPFbSaDlUER+YBjWMcyCXJOSzrq31gGbHMDLRmcIpHZnSRF6wpqU+rAW7eie5cfM8iTIPJYpK0wRZmnPUmSsdtjT4S4lM3KjKcfdIKpnBexQVazxGW7K2JG2J7c31xrRIuFmsGWrFB6mhHBy1/THa4jQNWsrUskADNPhbPkimpTxoY9BW4MxcC053WG95/vIlpVTevX3LNE2ywlDRBrS2UlRqTCOuDUpZ5pA4TlGg3JpwRnNp5aGsURJelOtoqzPuONKd7rn5PuRth+2s+K62V0RfUPfv0DGDSihV0DahVUYRZaI08gAoOtlOs4SKJSBlTS5Whqk24RWzXIKGrDuycWQ7UMwKzAllKuv1wMV2Rec7jHEcpsBhio3sliQeP1g+evmUzXYtMJ2GoiVUQec27ZqKBFI52WhLlYSWAhTXpPDivZqxZNUKSymswglTEidJOQNsay4wLcpLymqnduQOSoMqVJ04VcUXZcvfnk78394Udllzlzqc6fGmpzMaryBU1agC0QnedB3Ph4Ef3Wy42QzcXIqPLq8UuHYJ6oKrO5TSfHJzg62KX0ZNjRrtPbXCXOT3l4ZyjeoUdqvpb+S10MUS8szjfkeZoaZMWw5EtKUlvkobde6bNL5iTDm3iEhJi8JoT6Gy349YH3Gd8E7D0BGNokThJ8dxwpZIKTPr9Zbt5ppd2jGPUSb7YgiHTD7Nwt+1K8QaSb7JpYpoJytiC5Loe4dRhRgDtVQJ16gVRQvQ1uI5TGRyVe0zWoQPLJaaDTVCCUmiurRpUZFCJSgFsQZKnAnBSUFxyugKa9+TjUCgqRbmJJ63rJDMUOvla2b5fMlzukCKkZDDd8RlnlraMFkkGMJqR4pRaq6ULCIgW7sxHdpoQpyFZtLyOnXDgEmKYgTuTTngdM/N1QUHq3n3up1DRaG06A4k0KC9+Vqzudzy9OVTHg7vuX2kwQG1DQZNMZYlbEIkDUv4xXIJLmXbzQcs/i9SFd9WqZJPO7hBBDjaklRClYhuCVKqTTzXTwZ++NOn9OoSW9fYPhPqgSqAlDQbKUNMQSwlTflbqxaxzx98CTornFqQvMF5mqUhfdWLYGYeMbXHr1ZcXV3w0UfPORx2PDzccdyNHI4nYsySWJJEmFGaR2boPM5Znly9wHopz1RacZpnci18/utfC9RqKzlHNran6w2HaaSkTCqBM6itpbF5wdPl+ly8e2AV0mlWxS5QQiRp3fxOCtMy/nIQvvHh3S3Oe168/BhnLIMfpEPwWlFLQkdRyeUwcYozh9OxffhEWq+UoaSZcb8/C2Ekc1vRmgdlckuFudU7mc5LMMEnHcPGshm+j9aW03EmpcLbN98SgghujDYM656u73hyc03Okf1h1+S1i2JPY4xl1XtWvUbhuQ6FYU64nFE5ioFYLdBrpWNiXRXx3ZfE05F//JMfM6L59s1veLc/8SfXzxkuLtAP36BKxLcA5VytcN8porTB2LYBVkWoMJVKLFUuBoSHkefMnM2wS56mPns/xSm2GQYuN1u8sugsCuUwz2IxSVIAWnJp9gFNyAlyg2SXh4gKUeAtVCsszfXMx6rSSskaRyjyjmWOFfWfqoXc/F8uJ+rhyO7xNebwnoEANTJPiWPWvLU90zzy/v17bvOJ3+bEV8cTozEUrRmsaZ+L0FhDxfOLFav1BZcXF6yGgSedY20NV76jNxbTa5RTODcId1KtXA7esxnW/JPv/Yz3d4+8ftxze3fLt2++RpVMXyWv1aElpSEnxmnHeHcSSL4qToeASjJM0fd0g6PvLFElsipoZ1k65qqSNCeB9YSbH+MEKEyU+idlBD3a7/ayZVRRKUpvHiIasnLoS8BVYHXRM2w7+r7He08uj5RyZL1e0fmBw94zjZlf/PU33L87SjiD4iwoy1iJNOs9Do8fLsi5ME1T830WtJaQCDnoRdSTC+gmKhTuUBGzdOUtm4+onA1aX0nLvSnoCruHmfGYiClIVFsWiqa2s/PKb2WTadxgqFkQkmhaZnLzYSong2GphFqoeRRaoGU1o2qL4MtYJ6HW3km9VSyFlINkMWtNVULbTPNIzhlnHbkUxnmSy+e10FgNIW78vhSMLxfser1mc7Fl6D3TeBLNhrV815OnUHjfQ7FyjVTVWniEjqkVVAu91ko0B2Xx6zXqQ2goDTU0oZIRVl0t8HtmnANFVX77yy+5272j11c4tebVV7fMx0oJChIoK+dLSVEoJSUIVJ1a/vMffAl6Jw+OTuRUmeaZRWRcciWOM52xOGV5cnnDn/zxn/DmzRtqVITjHfN0JE6JMEXphVPmXK7re+EEL589Zb1e06960Ir3d7ccjkd+9/N/xzzPbDcrnLNs1wMZS3BJam5igSUEVknigGqTqUwBy6EqJleLQuD5SgmJpBQLW2CtHBQ5ZnIqPI73WGd58fwlznu87TEYVr2l5kjYP5JTYRoDc4o8jiNd13F5eSGZkEaRU2SKebmVce1D3RmBAFLJhBSlRzAmVqsVZui4fLHi4knPZy9/TGd7vv3mDbuHA3/z179gPE6AtGqst2u22y3f/8EP2B93vPvVW0oKUBoZnyvr1ZrN+gpvHd44LnaJfhTxjC6BpUJl+b+OmVIrp7tXxHHkH/0v/kPK+or/6stfc3wc+cGPn9KbCPMtKisRM9XKlI0Uo4YJY6oUH1exUMQCc6qkxT4oR49EBLaJuKWuN9/gUsUiF9BqGNiu13hloRRSSMJDNPk9hXPNC0oRUz4LtoCz8b0WEQxlJWkwZLFClKrQRKjpzCEvMVDyBc7IlWxOVWFiou5PjId3mNMtXZWhaJoDY/K8UR23jxN//5sHbuOBL9LI6DRTr8FYOmVFCp4/XIJPNle8eHbFZ59+n2dPnmCIaDKMFVLFmAoGnF2hrCVhqFXyelcXW372l3/J/cOOv/3FL/He8u2rLyFlOi3B07YaShbj8DhHDqdZqrtKRZsOYyRpRHWGbrNiWHVojmQVmsBJS2BmS1qqFXnNc+E0n4SDbRty3ztKLhwPcuhprei7jsvNtr30BVQLpqCQVWS1XrPebNhuBtbrjjEcmePE0ydbNusVp6NjPCl++/PXzHPENtoAksDrSmD9BTHwdiuxXfmhBdoLPKptwRoll+CilSk0ykfO+ZQTKQWaGwHoQFmGfo1zHpiAxOExiDaBQEWGfYXCr1atuWKg5Mxpdy+JNMg3UNmytCsrDGa5BGslxZmUJmy12DbIy2MjCJo2Ihzsh0GM+oed9I8aoT5E8SrdjbVILRI5M80zc5yZw8i/V9Jbi8CYtVVoGcV6M/D82RO0UUyTcMZGG5pp6KzBcLZDdctAJhaLTCY39biy7mzEryVDOLV/V1NQBC00TSmxFfh+4DiXUvQaIonM6fNHvvwy0NlLnFnx+H5HGCsqtWg33S7BnETx21RsOcTl4PnDLkFlaK0MAWhTiII4nVBUuk6krvvTgddv3+J/8Wtub+94/eo1p/2J+TSJ/LoWKWbVtnFtYoo13rO9vOLy6gpsJdeMOXaYEDHOoWNkPI2ihJonjDFcrNfkXBidxELNYfG9NHtllaBsWcXl/0ujc2jHKsxzJZZ03pqq8xhj6K0FpziN4gPbPe6xdmKK7fdI0vzrtEUp+ZCpCt5atFakGLEINHuGBapsF7lAjQKhSABcBaslq1RrKfIMUFlTUXzx1dfkCK++fsvpOMr0hphCe9/z9OYFm+2GzfaCVBJWO3AGb9eUlIjTTC1wOp6Y0OiquQgjQxnxeYUuTcjCgixXOpMxNfFwvGcaA4/ffsG8uuer23uOxyNvyzOKd1xfPUeFI2n/BlMKGwxRK/ZOUuUluEAqpWLJnGKLFXMabRFewSiU1UBuPBgyZRpwVrOEQGutMEaTqoQMGBS2iQuU0SgjHsP9fidirNrMwc0TqNpJt6iJv5tc2GaFsxgltxHvw7+7oNaLl0q2RZ0TukoGaiUzFkkiGhPsQ+bV6cjtceL9bHisnlENzAoSRTZ0aA3Zho+ePeXjp0/56MVTnt1csR3WeNsxxyi1X1oo505LJVRWPRpHwaKVa2HN8lHzQ8eP/+gzjINf/fYXTJMhpZlcEyEGapE6K41m6AbmECkptDxJ0wpRISoYa0FZhTGGoe8xxnD7uGMOEWt6lDKyZRexBum28Sm9vL7yGjvnuLhYi79NieUqxUAqgZAmnHeshg2Xlxsuby6Ypj3v7u+4vtly/eya3d0j79/ecbgfmQ6BlPZ060wuWi6vLAEVP/jhH+Oc4/btLWEOvH1418K3BS1Ise33KrWAaRFuhJSEu1OamAOZdG6BMdairMFpj9Ze4uRQrDfX4uFr/G0qR0Fj9nsJFwgTJUvnHYjJX2rWWotDqWfbikgI9ELvo0ySjGFdyTWds1vbUSJ3Z63MYUarQE7ysy7DdsqixnTGye9UJlJOhDzJQJcF4vZOkDfVhkeFErqmStN7iHOLOyuMo9jYlga50rIhpW5Ms+S7cs75aeXCVaDWzcpRqiZmqauS7ODmHyxZlOPtn9VmnAeJdNNaCZJnOrTpoFhiqtTcPrMtwDo1vhqjzv9cW83meoOx/wCfoFzKFUoSfqU1iucwY6z0AAKcphH98IjC8PjwyPu3t+SYyHNcjhVMk457a3HGYpxFWcuw3rDeXhDVTMoR4zzaLsZMTRhP4m8Kis53XD29piJQWoiJVE7nh640IlTXpcKkTUftYVg2jRBB5dRigcBUjbLQdx3aGklBr5XxcEJZe053CVNCU6QpWunzRGWtkNG5CNGvmglVTlBNzarFF2WR0deKd2J81U7esFKkIHf56827W06HiddfvyNMEUvrItMGZz0X2ys2mzV9P9CNvfBTyjL0PSkE8XjWFiCbocZKVjMdE7ZYTPFnODAr+fg6VbA1oaYDpUSOd685nk7c7g/sp5n7bOiU42J9jbGG/Pg1Omd606NR7Fv/XGm8lthUYE6ZzoN3usGflaUouTZ+YKlA0Q2eFk68ssTRLaHGQr1KVReILB9VOY2nZsuQ96SUxbtYzz9TOwXEpG2k5V0CfoXPWNpGFo3TB01Zm36bCEDXjC4yOpRamVpl15wVp1i4O03cTZFdMhyVJRhPQmT2evlUKo03iidX13z2/e/z4ukNN1eXDbIsnAJMOZ+NybIVWAoecAKhKddeR1FCOm95+fELxunE0IqiFUCCKY0S05crShk67yi5EmpsTI4ctMpAaipmbxTGymbnrOXtbWQaRzpvMLqpAKuUxy72HrHDyBQumaXiJ4Z6PrBzFsX0aT5xla/ofM9qNXBxueI43fF4uOPpyx9wdf2Ct98eePtmz+7tLdPhRC4J31emqVCKGKW1c7x48Qnedzzej4xj4nH3iDRcOCjyvaGgtCgrVV3i4KIMOap5cauknJSYqcahsBglaJdCxBZDt6Ef+lY8nQlRho3jYZQQ7lYYHJOITYxpof9nFaaciZIm03QbSjakhdOvtS0PCEqyELVFgaaKdapKhBp1Ce1HQq1RIqahnFt+UgmNspAeSDtIZZQ2LVB7MdQX2Q5Tjuc/IQRSKmK7QX3439Iu8vbsCt2w2C2Wz4Om7yylytCrWrC6QssKrkrzIgo0vQygMoFK5ZcBrHJY4wgB0agUhWoQboUWQFCkGrLVkRljWW/XOP8PsEjkksk1tzBVyQIBRVZSvNj1PaUUgViOI+/Hd8yTTBA5ZWJt1SDK4m2H9b7Ja43El6XM51/8nuH9e66eXeC8o1sPoGFYD+QSqTlQmy8wpsLudEQpLYowa9hebokxcRpHRL7dbB0NU64lN89KOafcL8leqknorTNYaxgPBxECLQdlCeITywVSZj4eBFrr5PD1qw22FgnI1gbvnXh+FggOkfzmTMtuTBgjFoBYEkkhlVNd40I6x2p1gzGG9+++5vF+T85VYphaNp7re7QzPO4fRU1nK6fjgc721JyIY5BwgUk2XXnYoebKeg0frxyXvcEpRayKkJV8CjSoNKMzXNoeUzRfv/o9D27Dy5cv2FbFF+8f2O0NT3/0HDusiW++gDLKk4WWRvFSKbE0358ihMzuOHOpDRcbK7AeTVFWVfNJCWyaqhTNhpzPGaxyubdDoB0hSkHft0Negx9WaGupGkJqviVE4q8XkqYJZNTS2Yf0Xcqw3shkIxzQsjPqRQhQ5PDSpaLbFFyrIiRNToowF0osTFMlVMOq60jA09UKXQ13NaMN+AYfqpS42G54cXnBpy+f89HHz9FKc5hmxsORMM1MJZBI9KsBZy22ilCjV6LIrqq27dpKtmIVXujpkwv2u0surrb4wYLechxPhNsoG9hUWjqLw7pK11cqBmrGGIPujGwhOdOvelZrC1kRU5VtyMoltzQxaKW5WG0pFHaHB9CGi4uLpp5OOGclQ7MumZcilSolk2Lm9v0jMf6O7duOi1c9/aBYrwdu3+55fDez31UU13SDQusRZWdSyqRgSBHe3+4pBN6/e42xnhCW5004sXlul0JpUHttsF3bxrSWpoYwz4RpPAeDlFIF4icR0kRWERXkkjp0vbweTs7FgpUza7UlpcCc9mhd6bTI9b310nRTIykVwiTRbiEFjO3wTmqSrHbEGAlRMouNsXTO46xvwhkkLm35CKPa4I+cq0rhjG68rGxn6+2Ai4aUTuLPy2JvyTVjqm7iEbFQ0dpFln5F2QrlmqtFYiVRCGyupLw7hczStFFqplIwtuWQlkAqiTFLIbpuF1tpZ6P+joAsK2m4pIIqRbpCrUHpSqqR3g6sujU1zKQYWtayZC5TwVh5b0xnaQFcWGcZ+uEffgnWWoV0rRIEK0BeAV2/U60BaU6kGORFa9txVc1Iry3WGHmjmvRY+vYS7+7ucMcDejCs1it85/F0uM7hZku0LUy3BdBOIbSsvLbW9x70TG2XoNEteJYlZ29Z1StVGXkTa1sYlvZuI4kY00FEKsoalDVygaq2JuUsZbulAh7rFNtVJ/xRkiigzkvijJRK1u/8UaScSTHiEb4yNQ+N76SpYVit6foOa3sUcDhM7HZHNsMa4+x5+7WdRVvNNI9UCt3RE6ZZ0ihyESgmZkpsbe91+VmgM5rLlaJ3bXuoEHPF2JbuUiI6ZwbtqCoTdrdMZuLy5U/xxnP37e+JQTH7LZ21VC2JFoUmiNCNY6n1LFVOqTCFxKaoFj8l8yPo9mCoNhSIiq1UyR2k2VO0XmDtdg+24cVZ4VGUUVjnRXaupNGgypV85gKl8eKsoWoXqWroiWzDsHCS5QzqSLSbatAZoi4+X4KQiiZnDUmEICFVilL0nSPVwkXnmZof02pRKMqKnNl2Hc8ur7i+uGB7sWEcZ6Y5sh8nxuOJqCWxQw3iM4x84EsXT6bS9fzZrTWhlWaz7tlsVqzWPdopVKcoDuzBiZozRlAWpSzaSLlsLmJ81wZJvGnvg3WOru8Ip0jMWf4dXahZPjtLDU7nOyqF3XlA6UXuHmb52Zbg6LYt1iaQKq0tYJ4rx9mwGw0ff/KE7cUVp8eJ6XhsmbkD1kUUBusslEzJlhwrYzwSQuF0ekQpCeqXoOpErYmYRJFpkAYYFltM+QCL11pJSbJ6RcHakIhcm6R/EVsI7TJPM0VZPA3e1/IeOd+hjCKVI0qVc+2Xc47SxEJKy+Uv7ekJhRXozmicNeSqqUlU4s7Lv+utg1bYm0JoKtG2BbWiYt2q2pozDImfAN87lK7Su5oA5DlZ3o8lEKG2cGbVgtGNNlKSrNqTt+gpahaPpxaaKefUKtFgMdyrFlCRc6IURcwtyoyFnivfuWVkc2zXSBvc5Vm32lBVppDFAWA7RqLUJhW5oFme7xYmYG0T2xmBoa21OPsPuARP84nee37ysx+QU+TV66+Z58jxcCDEif0xYbTDuQHXewY/YI3FOcscJqZ5Yhpn5jEQ80w4NNO4logzgyJPEykGfv2LX6G15uJyizGC828vrlh1PSUV0hxQStF1vbwxcgqgjaMWxdCF8+UmptdCiokYQsO+lRzYNWOrw1QtZl8KsR4pWtMZTW8HjPVUpZgeHymAtR21gBUWgCkEVtZzcXVDqZnTKDFRWhsh5V3XpsnCerVhu73gcDxyOB447g+Mx5FhNdD3PX/0R3/Mk6dP+erLr9g97vj7f/2KoiKX60tePH3BX/zln9P3HV999QVhnlqPombtVxIAXI7UMJIOe1KIxGlGKUVvjFxEtUGKxrDZDlxdrbBDZXZIlFISWLgmjQoGVTR9Ffj6x53mpXdcPrnm2G+432wJ88z/5a+/xoWRT+M1V91TfvLJE2yZYPc1NSRSjkyh8HA8coxCUldTKSZRRAlCURWlxT5Ri5L0DCN1TGgtxLZBuAMlYQ3GWi6vLpmmE2/fviWXQuc6nPNcri+xnUNpK8NPEjM0VeBNS7twleSdFrWk70PN+rwtV0oLLaiSxVE5G55raQWhRaB2219ghogKAUxm5aSR5EXviVHxI3fBm5jZnhL99RMuPvsBT66f8tGLT7lYb7ncXvA3v/sV/83/66949sn3uH72gpgtwa348ttveXjccTMqVsOKa+8ZnKF7nln3Gu002lfqFEi7E/s332CcwwDbleNHP/4Bb+7e8/Mvfy3m+WcbttbRdSviKTI+zoRpYh5F1FJSEZk/BW3lwj4cAuMUCZMYtMOoIH9n4EiRUmF/HNFasRmuMLZ1aLZtuRYRnC2wVQZCKVTl6azn6YunfPzpS6knIqBL5uH9np/+6TWffm/L7z5/w7u3j3z5q3t2dzM/+ckfc3NzxYuXkWGV8ZdPCTHy3//Xt7x788jb1wfGU8LqocGG4smtqsHnxVKjYR4FISopEebINM7EOTa+VTrsahJfoDJdEwGJtvu0e0Cf9mcERUt8J9YKNKiLDG15zvK5r8Jhr9YSM5libpy4wfuB9XoghJnTdEeuEWXycseR4kiOk/CWyyVBBYQuGpwTmDRlCoWh70EpdgehifphRd97nLcigE6GkhNpPgmUW1pYR4xy4fY93sufw2Hi7v7A4ZAIyTBPk4QuGNdsFU3bUM9bjzxpSn4m7yWrOcxysXq/lmCGeW4jbXsBiznTI9J1KtSbpsr3MYqSFONpYp4nYpooWcqVqxEkKyNIWzrKgOi8oVrD29fvW7fiH3gJ5iIdXE+fXpFz4HB6jzkVTiepNwpR4eyAdyuss3Srgb7rGIaBaTzhJvnyMUViam8+yIvYAqRL29YeHw5SNBsC3juGwdF1vfjtciEYEZt0nfRklUILYBV41Rq7hCsg1UpRkgmq4MpSvVTa9CfmW7XwRTXLmzasWvqFoyBG/lwyepAXUatmUWxtzt0glVIxh7OwQ6LbPDVL2e96s+Lm5gpj5VM9jxOlFIy1dF3H9c01L54/59XXr5inwNv7W2Ke+MnPfsiTmxt+/Ec/Yr0eSOXE8bDncHxEVVgZDQXiKVDzTAkzJQgPa63B9E4exCKbnrEW71cM3SXKBZKJlJDk85uEmKhZtidb5OK5VoqV1YTVwHq9RQ8XPO6P/Luf/w7mE2wGpn7gxfYT+rxndXoLRVE0hBLYT4FQl3oUQQaaKkSm0bZkK8GlWltGe5Ebp7r4b1XbhCRLUNrCi7hiz2Ih6x1TSuiUiJXzJWjU8kfL4NU2ObFjLN9DnTeVsnCFfGdraJxirpKRqtFYO4AbUC1T0zknrd2dpljLde3o5shDzmwuV7z45CM++eQH/OQnf4bzA94P/Oqbr/ni69f46xdcuoHSZTKKh5B5sztR/JZ1dqiVIRZFUIXOFpwXHrukTJ4j82EndMMw4Jzm+vqS/XzkGEYwMGx7+n7FxcUN4+NITXtJ00HEWiUmZgK5yiSvjEDZda6EWbrsSMLDKCOHXBZpLnMQdeJ66FuN0gc13sIftWW7bfryDFrj2ay3PHv+jCmcmMKJnPbM08T1jeNHP1mxOxaO44lSjszTTNf1XFxe8vGnJy6vMy9/vCKkyG9++ZbjcSLnHSlWjB5QtZKYzoI4ahNNFE1ODU4r0vge51k2tarF71xFpFFKRivXQp6lhzDOE8RCaVSx7kSE0feSCqWKwMQ5tc+SkhAR32A5s6hsjabzlqE35FrJ09Q2qUXpXmXTKqXBnqpFILb2+irRY0opZlqLewuzlpojmphRKohqgZIUMcyc4tjU8plcUtNjWIyxonDXkkk6ngIhFFJWxJCIc2BJKPHONj54SZqxZ1QFZZpitRJzkIup81Bav2CBWoV5X2BqpXSj3apQFRpUQw9rhRjEKynRdhJkUY3AO6Xks1ALBaZ5tk/HUxuA/sBL8PmT51IES8H1HT/9sz/heDgwdF8Q5sQ0RnKOPO7fczh5dvs96/WGi8tL5vHINB4ppeJ7J+xvTXIra+j6Hus6VtfX2K5jt98xh5nHwyPjKTK4C5RWxLmR6UFW4up0I/tlao8UUqmttVw+JIqCWpRzVZIuypLN2dRVUowt/yw3YREEtMkoI1Flzkpbu2lFpV0/kEpBTzP9ymOcwyrHxqrWhDxSa+Z0OlBal9oURm7f3zLPgXmesdbw5NkV280FQz/wu998zm9/9Vvev7/ldDqRoiS5HI4H1F3h7//6r+h7z+3+LTHNxNNESYWH+T26ajrVMU9Bql+0xvcrSZxBEfPMHI5s3EZENM7gkqJ6Q9AVbXpstpLVlysYTdSVECVZP5QDORuedivi9hqrHJcXN/h/PnDaPfDml39DnME+jDxRgT+1HooiGTjlkdvdI37o2FxtGDoJJDBGo71p8JSRktyaKc2gLhOvvJ4xSkN5LlV4Qg3r7YrtaSsFn9TGR2aMEyikM5qsLEa1pJwoqfpFVVpZI7EIDKyqQJ61XXbUjKr5Q3dfg87E1KhAyyCWokCFqyc3GK+gHNE50OkmkAnS3FAqrK3ljzcr1tcXPH35jOubC/rBcRwn3j/s2e1HSnLkKmEM1Wi01a07sxBDZNYz9zEwWsPds45iKxdejpswRXQ1jIcJ6zLTnDju94T5RC2RoXf4wfP0oyfkXJlnaRtwzktknstoL7/e4+GBeDqJh1IXjO/QxtGvLaoqDruj9Fe2NBPduNSShO97DAecczjXN2i8ZW6OAa01nbPtwhXKxGDZ3T/y+S9/jhs6fN+xXq9YDRdoHMfjyGc/eMFHnzxlO7zn9TcHXr/6Nb//+m/5u1/PDOvCj/8UhsFzffFH6E9f8tuLX2FqZJ4Gckrk0hphVBQ41zq0bVIg7TDaE+dKrRM5R+Y5ts+J0BVCQzQrixbeLasEJIyyDZ6W5KAaEkWp5qnLnA4HlALXZ8xaMQyDoEXuSC0JlSK5njhOEaUV11dbCd6PoQkta9veKqFkcq30LTnJCBgrkWW0bFelJThcCUdYgRRSa5sQO1JtfsIUnUD5seKcDOTG2CbEMnwQCihylIjIUgxKS48pWfjSmuoHCLXBoqU23217fbwx8gghKU5VNT9jStLGo71wVDULD+o7Li/W3NxsWZrlH+8O7B6OFIpwhd6hjDlfYNK+UYXi0pq+X2GsBae/O5P9/38JroYV1smLqa3m6voC5yz3m3dMRiT405xIOUjieqpoa/ChZw4z0zxLmay16LbiynRfUUajrWW9WtOtBrRWzPPM/rgTzLzBxouxubQE/7pwMktLvW4fVW0aQqolIULJtmWMhLTK/75JHtrAwMJRFKAqUhITbC1NcNHJh1YeGIX3FlMqLqcW1CrcWmd6opoJ89gmy9YflgVqqfkkYbMps71Y47te0uiN5t3tHfvDkXmeSVk8T0prYozM08T9+1t8ZznVgxjsJ8l4nA4nNIbqOWf3oTS6yYErtQUnJ5SqOG9wWkvTA4pUNU4ZjBZ5cc2VbIQLSzUL3h8C1QZ6pfDGcnIdxitefuTYDz2vP/8Vc03cThFrsvjJdKEoQ6owhRnTW7wzWFPRSgYZbYxM3KjveAc5K4m/u5XVtoXlItOn6zxd3zVuuZGQC+Sr1Vk1Wo1AcmU5qD98ZaERShW1Zxb/oHDFGVValRINzqNtrTS/E0qM+MbghgGbJor3mFzpTBQ/7ZQEDgecVtx4x6pzbPuOwYuPNOXA7nBgCpFaW8/iwnyqxuNmgetSTExJhF7HacZ5xbDWmGLOIdgpijRdoc4Tcy2pZXkaVn3PHCLTFJqqWZ3VxqYFPRilmny/SJJIC8L2pkNrw2gCKTXYqy139cw5V3JJ7dkq6CpbZq2FlDOGytKoXgvi7VKKOM883B/ZlE2DwDq8G8ToPkbWmxUXl46Xn0jX5Ddff8nt3Tvu9jOuK+Dh8mLFR9s/Z7Pu6LzHW8jWN+HOklYiXImIJpZsE93SlSTPVs6aDLo0q4ewXDRfIzo3fjq317plfy7iqSTboWoq6RSXhBfx0UpyjSgXl/qp2p415zu6riNqRVASD7eYzktrMEm1mdDromtXbdgq7XmQujbh3lpiSpF0poWPk5AKKU1GVWxtZchKzk3VaALZQ5fgikYJIBfkkglaShKBlloW23qGR+tZvbN4tgV5KUrQHnm2stgk1HIu11ZZZVmtV9w8uSHnmRRnTvsWeoD8IspIibBuUOzZGazkEtQN0dNOLUTkH3YJXl5ciYm8VlIGVaTrTlnNarvh+tlHjGPg/u5AToUcC1prxnEmhkIs0ghgtQElMUan+SThzvOEdR0XNxesTc9n3/8EpRQ5T+z3B1QSEtbpjmIycxZTeR7v23ov3ITrPcZK391Sy6EaPh1T4TQHllQJo6RJuhZJodHLtHd+EQ21GklnVxLHA4V0PIGCrusFBjCV3sJgpV6mGsuxFuYpnEU4pcWhacSAaqhYBWE6Ms9HDo93GK2ZQpYm7LbGX15e4jqLtxIQrQqQIIdCiJHHux0xJKbDCGg6GzHK4f2aUipzTCJYaHi6dwPer+j9hlXvuBigpMjjbubCdJJNyCTqVa1QFpKeSSoRpz01Z+y7ryBH9PYGY9r2u+r5i//JP+V03PHFV7/hoCPlcoDiSBhi2TPGxECh6w2uyxhPExLQ8Cmo7TKTNJ92EJWCsyLggSgez1rRyrDd3hDmTO86SkznB0DMxLnZBRb0TWE63SqUmiqtfX2yhC2nIvAfaFzN0m9YNLQhAQWhCFxurEMrc46yctdbjE1Mjx0qVbzWhDkS80gqlYihM/DEKxQBdu9h6Klx4v3tW37x+de8f7wHawTyCjM5BHIIpHluwdE7op1YbTakbPi3f/dLeg9/+Rc/5ebyiuvOUo0ja1HH9W7AqIl5PzE+joy3B/IYubce5zou+gseDnse7u+oMVFDOENPMRbAi+K9wngKVJ3RW0vnDdvNmvUwcP/w0C7aCLW051tiwlQpTNNR+iuV2JJ876AqEQ5VhXOS3Wtb2sl0HKmNbLUqQQ588evK/fvK5ZMDw1pz9WTNs5fP0Lzg7WvNz3/xlsfHiX/z3xY6B//8n4oXliTN7l3vsdaQ8tBCxIOIRnSikkkZnFM473CtrVzXCDlL8fKSXkJt2wjkVCVOz8pFmXOkZoUrkqBTEOjflEHOlWSpFCYCpcKrb15TaiGGudkHFN45hqFnGHo26zUhROY5ME2BaQwcUiHUeE5IKqan6EIIIkZLRX5GT0UXdQ41t0aSZXovGoqSBQ3LJVNqlraWRt1Y6+hdR86VGETMeByPpJQxiLe77z1hDqRc0MpBC/0uNcl52+BxJRwMgsfJxRmz2Mi2lysUFe8gpcTJygUf4iR2jarJWj4jN0+2/Omf/wmPD3c83N8xTYp5dux3j8R4whiJZy4pyhllHRXbEB9IOaB05Xpzg/2HCGOUNqCkEVlraUNPqfEw1rFab1B6ZpyziFDmJJ64JFAQSrfV94MKU6KKMjWKFy9GuemtFuGDM3LI5JjEBmEEZ66lJYxHgVYUYtBtea+iVipici+IgEHM0PWscFJNaSjKNFG4ynnc5p62SquGpy9rdEoSsaR0I3C9FpNnWnyQihwFL1eKpoirLDYJKg2qlaSYUuRSV4A2Xi69ThIZVpuV+C+VBBLkxqPGORNjlr64OTJNCVU12Qa8VbiVyOZzzbR0TvmdlYUqlpJUCnNNIivOVaLjWgrEOZkCZBlRFZUjMKP292Ak/QHjmfFUY7h8co32mvJFlZqpVq2USSQ0uSXSGyulvmoZYNv3kI+IbFk1qzMsKfLpFvrQ0lsWWLIfBoZhjfeeGCR8HUQ9XMsH1Sdatg2jNRnhclhS7YGlU27hA9V3fjjVJsclwq2BOGcPo64KXSWyTPmOYgyqGKSeRxJRchXlnjGwcmKUDuMJphFiIEwjh6NQAEXJczCdjkzziSlOpFlES2GaKCbj+p6qKo/7R4wu3D+eMLrjsJ5BGVyIeKXp0NTazNepkuaM1mKd0dXSdZqaq5jHUxYjZ2teKLkNg82vlXMmq0qKSbgnZIsoOZNSbB41qZZCIbnPVbrrdFFgW3CFlqb1lMv5kyl0rRzIqYnY5nkiBk+OjumoWtN4IiW4ebJhu3U8edZDWfHV1wPTqHi8TUTlmMeKM6K27lNlThqQpBP5ZvbsE5ZNu3x4j5tK/Lwp0bYoJD+2totQjnTd+KjGGytFLSLoWHjHklodUZHPdMny+43HsT0fH7Y1dd7GTUtNKZRsiN9R0lf4YNtoyEiRoj5y247kn33gskX4VeVBQJ1RlZRbPpxSTZkt3au+c23Ll98pptA2e0nBMQ1hUw2pkN9bHqAP72djX5cBt8orVkqVLN+2DUrZsMFmLf7Z2P6dulRbiTDINhVqrc1XbBwoc95QUefDqgU+1HMGq2hNZPs2/xBhzKtXrxrMkTFG8WZ9h9Fg9AajJS/SWs3moiNkxxQyeYY4Klz12GIouTClwrDq2W4Uw/FICBOhQYZff/Fbvvnqc35tPArF4XSU7jPbgdIUreVgyWLYd6pBLPEI2aByFPhNORFLKNBLUrl1dKv1AjIJDk098zXLBaqQD8TF02u6vue4k34w33kqME6GlBJhd5AHx1hO6sD+9TvxpukqvshpxnWeYbsWlax1zEFgzfVqy3q1ZrvyuM4xh0DOiR//5Kc8e/6Cm+sndN7z5Vefs98/cn/7ljBPfPP1a3KWpu+cC/MptIdK4IBqKtXWZl+hZSDWpvosmKq4uz/ysD9RfeZ3LvOXL7/Hz55+hIkzaU5cWsPgFTmO1JBYZwU4qo6oNGH/7b8g646H4Zpjhv/rt7eYp8/5X/6X/yV6MzB0PQ44KEtRE3tGDlSiLqhOMWw6Op8xnaEWiIn2OYKqRWlYtCFVQzW2ibvlXYtBskJzTWjX8+n3PuHy8oK3b17xcH/P29evUUqx2x9JBZyzbQho8KzvoHrwK2pJ1BpJVeqwXJINUeserT3UCarENNVacdVgqmKt2oTbSedlqQoTHcZfg4U59uTjzOHxgZICIYxYZ9lcDqwtbLpCyAfy736HngrKbHD3d/QEyCfGecevf/V3/PbznzNPIzFGjvtInDKntG+H8ITvO+52IykX/u//zc/pveP5xd9yebHiL/78H/Hk5gl/8tOfMWXN5uYFqzFRJiOv52Q5nSbuXj2SQmGlHTgLBnkew0QOBYJ08uUCMctr9f50QLfqslrFqF2oOCO8+6J7UapQcubhYcQYzWo9UKxFY4khcdyN5AqpVEwtuJrouoF+2BKOM+Phnou+Z/30iidPLrh6viLMd8yHI3evHpl2B3prePHikv/Vf/YpYTZ8++WRaSzUckdRmn/2n3/MeIr81d+8ZfcQ2N1nVLWse0G1tMl0ToM+ItFtcgRqbfHOsuo/NKe7FueXZhnwnRXBUFP8i99SLfwidFbKW6dxbJeHcFRWeVRRnA6zUDZGFgJrDTnCaUqc5gO3DzuskRCNNFdp7cFI+ouWkIOSKjNBhGQKTEt8CdMSGC5/xVH0FyUqrHMMw0CpmXA6AFLFpq0M31AZwwlrPavLgXEcOR2PHPY7drsdpcjlZ41Ce3seZHKW1oxSOFuioGBw8posdpRUiCVyd/sovklj0UazWW+ouRC96CdCloD1eUr83d/+nN/89tfC66ZEzQMldYQQmgc8NthVgg6UlvPPNei1Fkgh83B7i/6HZIcej6c2EUp8TUxifr3Y9DL9VFBKY72lxIwulRwb56cMxnipLsn5nE7gvEOpSqkCCUzTiZwTY/t6uUqZqbddw6Ob56z9HrWZtpYYLJWkbb0qTWnEb62LBF9J/mkRnDov3ph2K35QDVWqEmjTdqLwK817I99Gpp1UZAKseUlVmMgUZtXM343HdEOHQmN1ldLSmFvbhMH5ntV6wDjhALeXl1xeX/P0+VP6vufh8Q05jeyNIQDTPAv01Hr8cmiH0bI5Ib9rzmX5Vc4htktLeimJEgpvQ0K5yMdj4JNQUS3GbTDgm2+wlor0zskwoVTB7B6gavSYKLFw/80rTCtgVVq8OKpY5iIpHqFK40huU6WE/vJBbLB0vnEeGhsvt0xsbYtdEIDS9m0F/dBTcuHy8pKcM3fv3wOKaQpY51tr9RLN1PyCCz+iRVUs0tCKROwKvKOUo8hc2j4P5fx+2malKFoqkmqWn27phMyxkmKltE67TMHqinMK60A7hZ4z+nhEH45wOKDDjKVCSaQUmeYTqSTmaSbFSEmeWjSxDQBhOoHKhJCIqXB7t8cZTTgp9qc1H39yjzKOcYqkUvH9GueH889YsyLFzHSaUUULhFnPkgpBRqq8XlrJVqEQBXUKQYaHXJoSXjbuqk3b3FV7x2SLjzFRmgJT0TjyVBrsL6E4ULCqnM3fNQtUl2ImphZsby02OVT11KzJoV1WvrLqVtTsqNFxOka+ffWOWuHJsw3zbLn51qMo3L2xlKwlCECDkdrSNjSW/w90YAnTb4lTWtCLfIYM9HJwnHnVM6RRZIMEzlGRxrT0qpY9VJczS6vGV7ZBtoq/NeWIM5pilbS8t01LhnT5+Re1suI8wZ/fv2VrA9mya6kkKwEntWu/ZhEOrmaoujZOVwRMxjqJNpyhLvFnNaHOaVUa1Tjf0pCR2oIbFgRHsXBwaqHr5f0tou4sxmC8lAxIubCB1ven8oLqVA6HA/eP03lBsVp8g7mdBbVx13VJ52p/LUtNbVRumOf/X5Tg//glePv2W5TSONuhlSGcnEzaEVaryrofUC0PNKWJMifClDkeMpv1wGa9JrtCjpnx+MjxcORi03Ox3hDezcyT3OZKgfMCb+Yq3MvHP/g+1nccjkfmGHl43JNjIKcJVWuzkBVUkYQDIXVbvQ5JJn3nWA2dNCPPMzkIbOutxvQi864oUggC46SCDlKFklNF2QYqWoXBUPGoAi4JaZIRaa8pi7CjiSmSJuZCiUHUntmQYiHMEd+tubp5zmKHi7Hwzas3hJjo+44wnzAqEaeRcBpRiKS6NAilItN4RrE0eSsSOeyhQWHyIc2kKpFeBo2tjketmbPFffWWV1+940dXaz7aDHyieq6Vo1MdxnY4Jcozo2Z0Srj5QMkVaz09sB02FNfz+HgUtd2wJQXN2/0RFSM1RqYYmUIgF3B2QDERwyydiYBOBZWL5CY2wQloaQyvBls1Dkmor7V5kqrAUb3v+KMf/RG3l7e8+fY1tSjevr0lxMJ6ey2vkmoHXBG5tbNOBCjFoinoWkhKeMQyK0oonPJIKEeyEljFViWboLfSEFKgIodTmSPz+zvq3R3zuwdqHNkMa3J1HENAecvQWayBoEUq351G7O4B3r3Gl8KWismZEAK55sbXyPcoViTxaZxJYWJ3X7EHR1RekI9SqUlxaDzn7795yzFUXn78QNf1XF0+5/FxRNuOXGF3DKAq1g0YZXBYwjwzhVGi1Ipi8AObzpJaUlSoiVQyh2MgzImYUhu25KBLWqOKFnivwWpygcjlOI+BaDIhSuiA0R6twQEr77hcdYzzgf3+Ff2qZ7sZuH145G6/J7oDxV3x2acfc3P9Gd5IbvH9/nPG+Z63778mp8THH32P62L5zdffkHNidX3J09WWl9//Cx7uZv7lv/gdd7cHfv/bd2ht6LdruRCtE1M1IvCYYyDnCrh2tdXzZ865TgIl6qKYnOWyK3JV6HYBGiStKM7in8xeYyxYWuZv7+UCVAprLV03oIxCW0NKkZmZGEaO+wNWO6x2YsfKUprttBOf6nLYKAQJapePlAGLqKtkYeR0ipRaGE/yM2xXl6ScOM17SdRJI1pVjC444/HWoDYbEQXi0FWTovj05ubpnoOk6Vgn0Ws51ZZ9utAIstDEsfXKKoF1KVAojGFCR8UcNc541n4tQ3AN8myhqFUUqtqI/Y1iSM2cn0tkaZlZKAgZpipoMcRp41AV0WmcR+0/4BLMSQJcW7YUNckEX7NsOCFEMJrqZC2myJTchJzyYpgWVVSLdD1VCW41RkvbQhEM01kRrYSSMdYwrAec788RZkof2qVFg51lxCi5JTno1tWFTCXQJjtrpbA257PpWeslPFqmuBaiLiq2lNqEwRnz10pR9cIZ1Na43SZGWqLI+VWTLVWr9oBog/eG1bBis91weXHJ1fW1+BhLZgySUjOOIzQuzFmBKCQV4bvJM+2FXX6OZWJVbdSCNrHW9hiLJ0sjW1ZWmqjhYR7xYWbtFEoXjKtMObNpKRjeyMzrssJkTVdFIFKMFHheXl2Tt5dnAYXxHeSZw5ywKdEtp0e7sM9w2QLW1HL+e3kXz/ug/Ofl9T3Dou3yb3+njGa9WTOHmb6Xg+R4OuH7noI0hyit2ybXVMVVoLqcEnOOTEnEBilmymgos2KsB2I9Ua0CDV5JU3hnm/eqvcaqKGpMpMOBejygUkSVIhQBotTTRjdYjTYsFUzJqBRhbulGLF7VRZzVXoO2XIiP8oOfjZIxEjAin4WKZNrGyOF0oj+e2B2OrKtiux2wzmGdF4GHjOqN+2zfp8ViybOiz2HNuvlpnZHf/zQ2lrl+OHBBtg/V+CHdfn714a1sqSySEHNWUso3wznPMKwpBOZUWa0cF5cbQinEUqlVNlelLM6KLUA+5rLZKQXGVPqVDFCrjSUlGNaW9dpibYezio+/v8H3hft7IyJPk4TfMlIkLb+HqKhzLSx9l6o9RhI2sSRYimDqAyzVht8in9yslgNZ/r6UgsoLx6paBVW7KLQkXi0mca3lIkpK2i5kkynn11x/iILhfMB95y+tvmPn4fzYnGPPUopgkAaN9iVqrdDOdGVkDLXGtmfG4L2n6zxaQVKQk6AKuoWPaNVsF6qcP7PLk17P/0dLfWrnVHtdqlKoVDHtElse8PM5pxF1f+sblWCbD8lDnL/+d/5Ze81QCpa4t/MX///+1/+4RcIPGG1YDRuBk5K8SJ11lJh49dVrrHd0m7WoG3PbjBRQhFA3xuC7FsMVT4yzfBD6vqNzlv1+T0qJq5srrLU87A8oY1hfbPD9QNYKdTqh37+DmqV/iiIVHEl8YFZbeiuWCLkQl5xIi9Idw6pntVZiKE+Bw3hgnkURSRVLQFVwOMo/X7b0QkJVRWc6gYeyxMKVJF/f2g5FIja+UpUqCexhxlqP1x0XFxuuri744Y9+xGc//CEfffwRT5895ee/+SVfvfqGcTwxTiPDyoOpPNleYtYbvuxeUc2JKUwtWLs9PC3EXJVWfeLkAZBQKGmtLmjxnFU5OLEO5T2287jOs4/vGcPM27e3dO8Sl86xNpaPVtdsXU9vOqw2rE3Ba7jWHc56/OYFrLb8J3/5ffJmyzSsSQr0zQ3jbeLzt/dc2MiPrxWut7jOURQcjicGV1l7j7T1ZRb5QVWGZPR5iqwtWUxZLUWQtUG/Wvi4XCLKVK6eXmK95rMffI/D8ciXX3/NcZ757Ec/xnmHdX3jAIXDGk975nFm3B95dXvL12/fEuZACJE6y/bu1hXbVfr1Ctd5LjZbuq7DKkN2ihSldmajNPpwYv+7X6Ie79mohHFiM8jKMZg13rUgZCqxFHytDCpBnIiHHRWDwWBjbo3kEhdXtUCk1cpUL7Fdhc1qYDX0FC3CgHGaxYtaI1MMvH7zhnGaubr+NU+ePuWnf7rBDI6b58+ZwkRIQYIdYiKWxFyimJ9bP+VqtWYOgTHMhDCSYmD77IpuPfD+8R2nMLPEJYohW+BOaqH3wo1R5PCTgxWKEfwuRxk8XQtEsFqzWm158uwF13gy8PKjT/n4o+9R6opce6p9gHrguNe8J7LuLU5rjvvIHBI/+uEPWK0trhfq4j/+j/8xCsUn33+K1vC4u+X6ueZ//b//AY8PIz/4kzVvXj3yt//2K3S9YLBXLc0GYoyM02MbutoEWJFA9SwDrVbL5VNQSSwSLYOPFGRYnap49LSVi3IcZ0yLQ7Pa0vtVG6Y4X7JUYdG00fTKYq3BDx3hOBFOk8B/qoqFRctFL5axBKViqxVri8oNsRVLW2xDSAqSUZxyxBjLXFoQQG6DapWvn2rFGMd2fck8z5zyiXXfoy4vmaZICBGlpMC3aoNJrREDqCpRKIKqUEmxvVZWEpp8u/yLlpCVHJPAqhRiVoxpakKr5glPmdV2xcXlBTFnQk7EWEhxoi7ah+UKzPI6KCUpZEsVWs1BLvXengePP+gSVC2a7OypMaIOW3jCaYzYDOggMNGiqdL6XLthncY5i/cW6yXvMaYkc5XREnVlNcqJ9aJb9ShtCClQgxzkSomUuOYsnVxNOvGd8UGmhFKoKX1IJWnTgTZGkgeoVK2waSaV1AK2S1MOVrlsSmUYOqw1WCcN6aHGprqUGae0tHRtxe8iH0JZ+YfVmovrZ6gG5Q1Dh/O2mYczIZw4HR85HR8Zjzu0rqx6L6QzpcG6kJIcWDUva2ZTaZmm9sp1QaXk/SjlnIDS3rzz+6aa/8dqQ28d2g+oPhCLppSALtJed6kc3nSUKubfohS2QoiyIfv9iK2OjXWSTtIEBNk6Jm04pkpnJMzWd5Zh1YOC3eGIWjk2vkORv7PfCUVR1IeNcMmmXzjBUloYeqVxuDLVSWB5x5MnT9DW8ObxQXiE+3u89xLimwLhdKSkSJqnc6xcmGfpmLSWNqqissIPCtMp+q6X3886rLaCdmgtTSglo6YTHHeYcETnEa2FI9lP8pBmnTEgKtxlc1rgqyqdZ7kUYs0t67RtWe0zXJZNWQmHbhT0vWfVewkDVorRiQo14pCwdxm+7u/v0FZzPO45jUdCCPInzpItGyOGhFGxTc21bYey1SlDa/Fofi4loRFd1523T+s8AsKLL/CsGmzCjMUDaJRBaYs23Xf2+jOTTaZgnMZ7eUbQWoq3C6hqUVWKdKkjRIV3hhSrcGXN0D2O4lEWFbTi7v2BSpFs3SrQ9eEQxINba+Ovmy2HD92Ri1ZALVt4e09EeiFKQ85qx8XRuqAMnE8jaKgU4hFdtk6UqERR6qw3kH41LbBl+6OUwhpLVPpDBnFDl1TNojj9sHbJ2ffvwSR85z98+O9E+StIxBIUIrxiFdENFarGGEct01l5XdqNLWIe0yLd1JnXBPnBFS3rE85onNLt92n/vFbdvud3LpkmvCzf1WqwIG1GaqishSTndcqZxaco75daftUP70XlrBRfrYZ/WJVSKhZdNXPMOAPrdYcCwmkihMRhL51qe53p+oHVdtu6AhVdbxnWmouLgc3FCm2e4nvN/vHA/nSU9BKrWT3bor0mzpFE4dkPPkEZwxdffUVKhavtNVYbXjx/zng68rvDPSUnOrUYeVtLc6rkEMlzAq+hNxK5lhxVizFfdT2679k6wyqtmOeJnCJqDKSYmQ8TEcXzp0/ZbjdcXV5Sa+GrL75gLAlVZYvJLkjP6FrhlMJWh+scq+2aP/7jn/Gf/if/BfcPB759fcftu7e8ef0tX3/zG169/i1GFbQqHA575jDxj/9n/3M++ewHxHEmJ/EMxhB59/6Od+/es3aOTlliSZLPuF5h0NQk8W1jnOTfGYOIBjrd0k1AZ5HMm6KxGS6N5UXXoZ9/gn7+fXJuqsswo3Ph6uVLnq3XTI8jeY6cWmrLl6+/5TROnH7+Bf12y3/UDVx//JLvf+85yjpscoym44GBwURWG/D6Alu+x+Nuz7/+d7/hp5++4Pn2MxwZixI5N4WstEAtSwVLFa/lYkNIyRKCOT/4KYk4yVjLMKz5J//0n/LNq1f81S9+wW6357g7YZ3B9ZbTYc/t69eoKmkW6/XA5eWWTz/9Hv/0H/8lxnms6+i8wjvVqpWgSEcuUwykUshehoutOmHGmfnXf4N+vOOlvketArep8jDO/M0X36IrPOs7Lteejcl4HL6u0KmSdRDLQcnsppl3Y2R/OpKSVAuFFMhJBi5rVnjbc/3RCm8Vz9aOdWf54csbhs5JqTCKoDynkPj1V6+Zw55f/Lu/4urqCqsjd/cP/PrXvyDFJAUBDRnoO8Vmoxsv5QQiThXbebbrHqV7lKocQuRwOvLk5gnPbp4Qo0BXthnRx+NBjPzTJLanBkE55THaMvgVruvYXF0R5pnd/d354hjnHW/uCjc3luc3lzzsZt68/z3HE5xO8KM/eslHH13z7Vfvmacj15cbVr1n2FScX/Hu3QzqxJs3bwkxYd0V0xT5m7/6e6jw2Wc/Yn+I/Hf/7edQK9cXHd6v2A4fU4sjpSS8Z05NxOJlWGz1XrnmM0Qd0yQ2knbS5mauJyY5/F2P1gbxZWuMlyq4zWYr4jolX+vh8bHdkrJNW29IRToNjbF45+gHy3rwhGbZqUUQuFMYKblgtcc0kQwaUpDtszauUbytou4tRYltadn8SqGkQ2PfBVGo7X+vdCEXjXU9x/E9X3/zLTnVczqScZbqWhtIiqQirTxLkEhFs91ssNZSmnrWNGvMNAZSykyzWNdcg7Z1w85jDKAUxnWgM2BJaI5T4ur6iuubaw67A6fDiYf7Ww6Ho6TGKIW2HlUVOTTrV10CNMB1jj/905+w3qz+8EtwSQ9QbROpC9m3bF15Uehkiq2oolFK6jRKiqQwE6MlRuG4rJU2hJizSK6rxtkVtvNSBlmRBgdtpN055tbqgJg/lbzgS86kXhw9Ssn3XqaBZvjR7Y+iTV9tqZIOLSelpVXeTEolVVGnxiDr/6Jcgza8tDFDWQlo3V5Kg7TvpODVOEu/6sBKN10qgVgiqSRJZA8ZVaTFPMaZkjMxjMzTgfFwJM2JqjtikrSZ5fsuvOMiearQJsLveIfkcyfJKk29phoXoYt4u1QpGCQVRysDSqTM1oG1Gas1poJuX9MZUYWtV2vxes0zvvMo1XxTLVDa2g7rOozvsV7jXMJ0jvVmxeE0Ms5ymZgWY6cWb1N7kJew4LpwndT22ROBRTlzBcvW0sZepbBOAtuN1sQSOez3okKeNPM4EkPEOydD2nrDxcUF282G9WrVuis9XVdxrpKzcNQZqYNKpVJJzYBb0DVjcsSFCRNnsetoie6ba2asIquw2mGUEV5CVUyWX6Ish5XRLTHJNEGsbL+K0to8VFMzGrnQnXT6DZ1hM3jWvScXL5eg6elC4mK95jjNPB5PhDizP+w5HA7EeSbF1LYrqYLKRpGzoDXyOaLx7WIPN2KWa+SjonM9zhjmOTaluGxdne8xKgqC0hYb2WBV237V2avpnGVYNYmikpDuXBO5akpRnMbAbp84HAunU+HlaUNMPSGMzPOJh/vI0Si2IdENhY02aNs++1WoD6ikoEip8nBX2O0Kt28bUhQq67Vi8E7YWLVUSee2ETbY88xktaGs8cqigFz+u7bxKGFHz1meDRkxVqrZrJHwZ2O02JxaxJmo4xW0gSjGSHXyWbGxEm3zPNM+90tqUhZB16KNaLueQLAtQ3TZBNs9IJvcYtdQlaoSH7h41XzU7RJM6dzWU9r3S7minShB+1VPN/Roq5nn0FoiBL06q+7bTybfu3zwd5fS2lgaG974wbqca41bXobdkquc/ymfm0FoqODyuyolBvtFjdpWTznzlKSFyaaf//BL0HdeBBIVdCnEcRKIsxnKdeKs0tG50GEocSbHkeM8Mu/vOR56usdeGrWdFsVizIzjDBQ2L5/RDxftfM/Emqmp4FpreAozNUYwlhwDvfcU2w6LqiAbdDV4K/mGCkU2EgHmrKPznlghxSzeQSqdB9dZVPFEFLoUkqmESRNj5s2bdzzcP6JKbakTUNqEoXWl6wyXlxt+9mc/5uJyy8vvveR4PPHtt6/Aaf72V/+Ou7t7vvnmNXEOhHlu4ChoIoqIdYaus7z6+nfc3r5i/7gnzgk9XFG1kwaKYUC3oFiVMzVVpnyQN7tK6O4cE0K6S6hASOIXyg1S0xpyNuRoiL2hBCMNCymIjF/B1Vqz8ob+dIDDEX0IuAxPnj2j6zv+6PkzqoJ9zhRn8TdrTG85hozTms3mmm2Am+cfce0PbIcH8LDyiv04kRo20nkHQUFO4AxYg7YK49pBjDRLnC8BY0VlV8q5i84616A3ES6N80jIkc16Ta2w3x9ZMkCHruPjFx/z5OlTvv/Dz7i6vODp02tBy1HyuSAzx5GUA6X2lOrEhhIrJYlNoppEJcF4QI9HrkhYUzCmMpfCYxzZlUIdLvHO8fLmipUtGH3ClIJLkVISkSxRZOueVa94EhWr0xFuM9pkDAWPkcDmzoFzQkEYxWbTcb12XF8OrLuOqqzI0/1ASFD0it3xxOfffEU1mjfv33LYHSlzlOi1LI3yIJDiHASGiiWgrRRZhxLJU8RqJIPXdfS+Z7uR4tvDw57QLsJKZT1sKF1uB3mSwbHWFmWlmINEjeWU6TrP1c3HbRtNzCVwTCMxZ/bHzNu3e1692nE8zZzGyNPnkcsnj4Q4k2rkV795zWF34PK6Y7V2/NlffMrl9cDVlcStefuUaap8/7MT+8fIq68suwdFPX1KjIVvHxPbix5vLX1f2ayDQIQqE0tiConOiXFdIb16tfFTylrh2rPAppLxrrDGSYefs9KfqKXWqht8qyISLm+z6gkhcDhIpnDVIpgJSSqaYorkrEnFELNlnCyllWJLsEU52ytCliJfLSFG4n3UinUv26f0mUJFYOJ5lqJuoVEKWsvf1ypwqxjkhc87HQ9M44GSM9b2pDSTa5Dd3ShefvSS6+tr9o97xtPEt29fczjuMUkSj8I8M+dKLjK85ig2jRLEQiZNPq0RnkpB4gVrksq6qpboOgg1k/JM5ZFxniFGapQUrd4PZ8GYhBcotPEt0Fwu1M70WKd5/e419uEfYJY32kgyAouCjIaQQ6nfUYzV0qKJknhkosiLc4V5zOQaZFo30tG3lKqqqqgBygw5iIYjuYJWhvVmQ46ZOAfB5a3FKMOw7lm2ypIK8ylBbqIFJA9RztNFBSVTXqr1fKBopzFWYaKmFE0xmrrwBFUScpSKHMcR7z39eoUfOkmCUQXVwbDpxe1tNco5lDVgFbEkDqcDx/HEHGfBu6mkNhVZXZpkX/idaY6MIaLRON8J/JZC27CryLirOMtVqSIgWTiy2rrllJbA6Nry/Nr0JT4c+bOUh+YCvTV45xmswRvN9Uqz8op+N2FCQgXhIlXKmFLZdg5jDCsq1Vqs68go7u4eCavMMFyCNXSbDU7Ldq2peA2bznNzcUHX9dKth/hHi1q4FAXVCMdbgSIp//IByRQVycSz+kvSgpTwAylxPB7YP+6FHK/13P1HrQz9wMcff8rVzRXX10/YbAa6YRB+IUm8nPw7mppNK/Yt1NLQjCVVJstrXauhYiSiTAmopNA4FL2CC1vYWtg6CfL21WOLPm9bFRFN2N7jiqPrLNb3Ao0pI0pd5dAYsjESY6fktXSm4oxcTsaIWhklg4JRiicXW5yxvL1bicJyzudw5WX7Eo4LUqXxwWCLwpSC1a1Eu4UQV0RlK3mpH1R32ggfV0slTJMYmausgNYL9KZybjwSVFUIYUSbTnJB+471qmd/2hN2UbZm3UkotBYBiTOV437m7bc7ifsqhdMhMk2Zy2IxqiNFxzwZDocT2iiePVP4zvL0+RVdl9jfVebTjFJ7pAxc+gVLzWefLbWekQlVi3CNLZv4nLxRmyKUD2pwrZcarrb9nU9M+RqCMFWBHNsGLst1gzyWMwrZmZbNRhSQlazkLBPfom7pVxWyQJy1LLuc/FXbeawrpBYPmBeBSIPHxH8tPl75vqqd8YvaVS6okj9kNZfz2Z7JMX2oaivC/9l27mhthdrIcvaXFjNXcjon3QCytdXaknRqO6JbPisfPItyP8j7kJPU4ZFy85LKBS53z3fOwjMWtkB3qqGOEgf5B1+CnZHS2JBOoui0cniVCkllciOMSwzMs+F02hOXS9AalLUcx0KsE6utpV8ZSgqiXawWXQx5V5lTYjyJJ4mQ6HrD97//fSqF3/zml+SccJcrvDJ026dYY1hvtoyHkVdfvpWMvePYUhgcuhpscuQEsRSmEplKpO8dfrC43jFYR60RWWddk2FIw3ZIhVgjr+7uWa3X/Nmf/4yuc5wOO1FBeVC6cMwB5on1NDHGmanOpDgR9weOx5E5Sxq8Uoo5J/I8sfaOwVtW60s2my2///obHnY7/uSnP+Xm+pp3r99wOh4pJZBJWOcF1oV2uTWDcpaPca/lodIYYk5McZbJKmQR62iL6TSuk4d5LvDsas3Lq0uerTZc9QOXRjMoePzF75lOD0zHWcQbxxMVxdWTDeu+Y2Wt+Hq6S+5i4b/625/D1SWrp08pneHik4/ogyIcf0efA9sc+XS7ZvjJH2O6nm9HWPuezcqi8oTKEzU7aL5Ega09FIviiFKVoI5o1VNrQVcpR1XAHGeOhyO/+MVveHx4IJxmVCz4xcBdKy9efsQ//8/+cxnAnIZW6JurhE4LpAUqd6jSMcbIMUe0CmgleYS6KMoo6tGiBrKDg3VYa1njsRRulGWtC2s3sfbwaQ8GRw1DU7sF4YWMwvSe4fqCldqw1hd07x8w/hu8MijlUapHKc9UZR+xKmFVoTOJwRqcLpK040XiPyiDdp4n15fsTjO73cjjceTV/YHplERtagpaSRpHzBKeHorBZVFqegquwUalZrJVaAO9GcQfPAdyyFQSxkoySsmFt2++YZpG6TE0htV6DQpSEm+WCCQCj7sTMa1ZDyuePnnGn/7Zn/Ltt18z/nJi5ddshgvGjWa6KAydtMG/+eLA1798wPUO63TLihzYrl/y5OqCNG14CJXPf/c7Kpl/9l98yuXNiv/gL3/IaVfRp8C35o5f+99QEOja+wFVO8iOPHvx34F4V0tAZUdBt89P2zCoZ4GNDCqIiE8vxnqacVu2RqjEPGGMZtNvMMpTs0OVijNezmetzzmeVVWMLSwWnqo11XyIFzNGBsSpCVViUJRMG9TqWQCXs/hKQw4S5JGagMTKRZZKbl4+i0ZhleSLrjpB0KjgzECJihSyhDYksXGlKVBD5u7bd5xu99LWbh2uQI8hG0sB5imTZinZrrQomXYhg6BptSJ+zPOAVShaLj2hqwqQUMqgcJItOiUpsC5aNuCq5P2hwb1kSgmCMjThYCriOZRh7x8gjOl6Ty5CmssY3ohfYyk5Ah+8IDlnpnlqhaSiuNTtDbfotqFVVps1/dCdoUu30SiXMejGm0RSVS0ZXXNxdSV5d9rKNmAcqVYeHw9ixk0yWXgjD6I1DS5DzOS1ZKxq249WOKDMgWmOpHmmpiidgxqGldQmLSRz1YpUMyFGlKZxRJWa5IWvcaJQce9vCWFiGkdSlBDaGFObJGVSMdqiXc9ms+FiPaCNFWFOTJRUOB5OGG0Yp5GQ4jnXT32HD1smMxl2lqmyKe60wqDpvaMaSzUFZyy97c5epFASj+PIpzmzUZon1vHUdaxyweVCcQNmFbkbT5xKYtofcDHirtdsSmXrHN4VYpo5Kc36ckVdd5RwAqXY9AN99RI03FSOxsBqMBzjzN37E9cbh7pweG3wpqco8x0tqBHVn1o4s2aILRmjC7Um3r69I4bIeDwxjhMPd49M08R6tUavN/TDIMpkU7l5csPD4y3OiV1jaZkoOUmnGY2DaLmjlSLpMEKfoGsW0ZUCWgWNrgYz9JjYkWOCEPFKSdHs0AOarx7uoTpq7fFKsWnwonUO5QR6XA1bnqyfc3P9hOv1JSWP1BxaMlBg6zqMNVyv16w7zZP1hovBY5VHF4NKzWfmVFMUGoau56OXH3ExRYarmfd377m9fU1COB+lNJ2y4BR43dojFAopXF3wNaGWmtiAiYwgFdaolqIkkKha0JwsWanTKHFp1luctWzWAzknjqcDWil2+x3GGX7/+y+4vb1l9yjPS06V8TDJQawN2hvyLBt333l87xmGAe8VxnpShuNBLBvjSRSu0wh+X9g/jBwfE+/eHXh8FPW1swKve2cbhClCq1JkGFpSSEopYvxXy/kh730+n3TyV9sLFyiDklrbgypnpWIplZgjNUrq02JfA0kfkhYVaYCvSzYuiloK7S4VOqMJZIwWv7byop5NUbWasSQ4XUxN3CJKy5ylVFo1sr1m4eq0lXHTKAEldaFdwJXpOHL3/o7D/igXelFYZVs8XEtHSohCt+bWP2qXY0igzqrOy3Qb3VGLv7Zp+nVDWJSSTVsV+bdr28wls9XImUl75hbu8MyRCloj56FAumdVuUIaf6wWgdOSpvWHXIKXNxtCmNkd75ooQSCPYb1B6ZF6u5MPj9JMKTLtH7HWYr14M6oSGbtzHlxEqczLjz9mu91Q1hPVJw67iTBHVslQsmZ8OFHqxDRf0Q8rPvvxT4gp8+3XryX0VxlOxyOvvviSkkGXjk5ZrvoLrDY440kqEgigC6pE1r3DdZ0YQmvh+LBjdxoxSw2Ilv65py8uwBiS9uRSub27I5F5eLjDOUtudUypZgqJUg6g4fWbtyglMVAlV7mYq0NXT00CTXZuRb/q+PSTl3z0/Alf/P63vH37mjDO6KR49dU3vDFSlKqUwlqHs7aJGupZriyfJ4VRrolJWo2QAWudeHsQ+G7V92w3G06nkcPhyC6MvDk98JOLC55mzY9Ux/fsmnI8UMZMv73mOGz5zXTkdZ75zbffMpfKG2+52q7ZDo6+87zsDG6z5fs/+wHFWE77t2g38NHlC2x9ZB4licWbjHaFqw7e/v49f/vzr/jk4xd875OPub5ecbUeSM1+4pQRcYO2UCymt9gmTiFnnClQTvzrf/Pfc3v7wGE3UXPFJEM/9Hz8vU+4ub7iT376x2In2Hi+/vYb/tVf/Q9437G5uGDoe7artUBvxlGLtIYkdSIz09sVG+tRs5a+tBSopWBcT2l8kfeO4ek12mTCr48wHelXPUPnudqueLM78H/+d78mYlHDNS/XPf/h0ws2znGz3mD6FcateP7iIz767E/4Yrfn92/uOezeczo+ctw9ME8nPn76jKvtih99+pyr7Ypu3QmdkDUkjY9Sb4TTwguh2azX/Ef/+AfSKuF6fvXrX/H5b37JHCaS6ui9Z7tek3Ul6SaYqJBnKWPGG5SxGCXSj3k/kqajcKMVrq8u8N6T6kwuBYcB4xljIOXIbr7Hec8n3/+Uy8tLfvTjHxHCzKtXX7Pb7/ni6y/4/Zdf8Fd/9dfUYijF4Z2i85qu9/S9lyqk3mFVYuzg2bNrtts1Nzdr+sGzHx/ZH2fS/Z6cEvtdwVjD47vK+Djz289/x8PdgV/8zRvClLBa4XuP947VasXQbVC07ShFpmmWmDdVZRjKnCvYjJHDP9Qgz3yVy88Wg1FLKkshTi28vWvwaKtvO8wnTBSExmiLtX0bxNoFgKh8c4iolv6UsghlLBanLMr4Ntx7bDsbKorxKGEP82kSEV0rNtDNDC/bOHjvoVbKLCEOXW8wStG1QU9FsezEEHgXZu7u7mS71ArvPN51LH43jYfimCaBLGPU1OLlAixVQuVrJmcZLq2RC9LUxefs5FLW0uspgwcQzTmVTqLfDBZPp7tmvzHUGig1EXIWyDc34SIfuh+XG00bw8XVBmsNu3nXNvQ/8BL89IffZzwd2Z0eUErx8vkLtNKkUkkhtkSL2pI05DYXNU5t/qfAonASw6kkvORmiqwpEeepFYOKZ8ta6Zqb55mKxnYr+WAk+RD6fmDA8PTFR5KFOCZshpIgUkk1CvavEqoKp1mLomYx+htjJd0ipUakKmmS1tB14n+LWHIpDEN3NjGrIgZvaCq7hkmr9mCoIpxKKVVI7SLp8pIhKnwmKE6nkdvbB44nsTbURhjnLLFZqrQ0Bm3PD0XJAnto3XrxvtsSqWVaLU05tWwF3nf41nRurUTbzTVSqYzTzP1ux3FzSVCgvSjr9vuR+zDyfh55P554Nx2ZSuG3d++5DCdePrlkowsbC52t+BSoOVGmQHWVYEfqFPBFRAKqJQ2VKuWZvlsRi+bxNDFse7I2UKT6avmcyPgrPMnCvVBV4zOl5TzOrS8vA0ncqU+ub7i6kr5LbTRVVXznuLm+lvJNLxFPKSdyrugoqRgg0u9qmtJZtOWoLBcErf9MG41poC1ZRBMLL2PgPPQVZQjaEJVFdz2zNhznGW9A0ZFjYNo90G3vMbt7LjR8+uSaB5M5WNirwuQtTy43XG3XLXpNnTnTRSWsVZI+uiUmq1aMEjGb8T1mc8V6sybGSIiJaiCmzDTNVKsotqmnm5igoBpXVlG58TWNc85NnRijZHrm7/jHtDZnXcBCjknB9MzD/SMpR2KU7FDgvGVK76doBOZpJqfIPJ3wzuOsTPErP2CVRRUlal0dCWNgbsrqkkt7xi05GLKyWL3CqEQK0rqiWipMLnLgqrZxpJCILUaxliqoi2rq5aVNpv1/0zr3lsQm1+DfVAKlSEtKlTtIDvcWp7ZESeWUm3J/bnaClm6la+st9VK3ZhVTmKhTaY7r2mDTD0lRyyCck5yhi4dPnTNP5U0QRKxihHXEG/n8L+rymEQ9rrOc1TFmSBkVkoi3rGm8pHisVamEHMkqo42Vf54VphgS8vmwzkPVuNhQoOaXNGZJ5tENBpUQ7lwkjIQl3KTpV+Sxs8K/F/m8kz/4aJc0mEWjsiR/gVzGqhRqzmAUmwbR/8GX4D/55/9THu7vuTu8xVnLn//ZPyKGxNdffksKMj3lthlR5YHKVWGqbRaDyOwixk501eFxTNOMMYrCRLWJ4/0j8zgz2AGjHYNfoZTleDgyngJFdaICnGa0MWw3l2yvHB9/8n3G04lvX70inibmh0PDzDNWZZzO2JrRVdSQEU3vL1i5nkNV1JTJbY8vrWzzyWpgWK8IeUnSF7I3USgZfKvkMA0gWWKDjBGYYA6TkMqpyJSYMl23kmgvLW/m27d3vH31lhgnytJTZ7QoxGqRonQlsI+umjmIqblz3TmiqJ165wegVjHXU6EiW+/1zbUQ7Vl8Pn3fMZeICiN3ux2/S/DR9Q1P9FM22x5bO17dv+bL3S2/frjl68cHPj8+cMqZd18G1quB/8D/gKfumosB1l1ldXiUiM5jpNqJOTq66YjPTrbATp+N6tY7Lq5fkMh8e/vI6nLNjZYDTFwC7UpRFW0q1nicUSQsoEi5kmKRlJPQKoByJceCM4YfffYDhnWPMlLlNIaZbvD85I9/whRm9uNRLCk5UkKgBtDGoa3F2IqxolLLMaJjRmfpxpT3QgzzNmvxOM4RNQdsQ0eckoNmVpakPdH3RNtjLq6ZSuD+cE+nK4YN8bDnOP+eTZF+y+c685c/+j5v3/fcPa7YP64ZxxMvnl1yselxtqJ1E+vUiqntcDJZ3nuEJkhVwsD73tGt11y8fMlX33zFYTwxh4D1Xryw44zxFrvyWKfxnRHPppJmjVwgqYIu4Kv4znKRCrVpDoScidNE/X+T9qdNtqzZfR/2W8+QmXuqqjPdc2/fvt1AA2wSpKgwHZQ8RdgOhx3hsL6Mw2/8eaxv4PAQDtohS7ZoiqIoiiAIEhB6QPedzljT3juHZ/KLtXLXaVhsOBrVOLhTDbtyZz5rrf/6D7XRdQM+hMsu0XvdmU3TTM6FcVou7PVlSapv805NzluglY55PjGNI8mSLNZYoR999RUvXr2iwyMLTI8jy7nxeHpkSbNakCG4GvEtskwRYWA3fMay6anpF+RZ/X/F6cTWdQrJlVwYzwvLMrOMM63o6mAFOp1bIXl9vdH1BKAaYa0fBmKMjFMjF0c36P6wkXQyTOqg0qRRXSMZ2xQmnPfEZtOdNaebYU/fRTZDx/39A3WpT8OEuvVfLAqXKV8g3EpTaZaoPEtEIGuh74LtH0tCROi7rTm0jJTcSKPu4aSq9V7Odo+VgusDbhM14qo1nItqCVhmpDb6fq9GItkRqljYb6Pf7On6BpPTfSeKUsWgjNV51tcS+kAthXkebXeoJCxv/AYt3ZrWUszc2+WCsxi61qoheEKtakBeDRaVUkEqaVnwHl5/+Zqu7373Ivj4+JFpeqQf9M1yXruezaZnu9twuD4wTwvTcTQylVr7iA/IinWjmVddN7Dfb2kVzueJls40lwjZ4dxAS5Bbpswj4gKb4YYQO00Kr1jGnjDNZ8QNPL86QITteUfbbuBwzTRpIK+viVBmWJ3GTQSbcmYcR4p1UA3MY7kiLTPPGXwisWrvmrUbDbDuAhOetmpWQYLLTj1Tixi7yjSPIrRWLPNOu8JaktH9E5qn1jCfDXNo10NjTVGOsaM1c71B6Ezs6OVpKtVp1Trz4NjsNrz67BXjceT+9kFvxKgOKL3vOC+J7/MDP//wDhk6rvue3jt+9nDL1/cf+TDPHEtF+oEOiNsdcbdlePac/tk1begpwbNMM7E6tgTGJfP2m685yCPPne4pc1p0RJdK10cO11seT48cH48sKZOrGlQ7WXenq/4Q1qR4Qa/n48Mj8xSYp0XDlbMeqtfPr7h5ticE2zWs+x2xTEvv1Xw49FQpVMlUYzwHS58WX3BU1YrWorIUjDlqXaY0qEuhTIl6fMSdjvTO40NHjB3Fe2VVikBVGcLp4UgnhSRKWnDO4UqBfCbffWTsv6H6js51bEJjN0Rc27EdFHYW0+s1S0O3BY/eg0476IZeK/GOJnAeR6bceFzg3du3xvJT42FMz7bqS3OpYMYAiMc7jAFtEgHUMUZNwhqpaDzX6qGKJbR3sdOdc55paDNYaqCtOl0RhfObZTLGoI4wPlCK6LOF7r1z0ciw0/nM/cOD7rCc4+oQiL1wfDgxp5m+2+K8J1eopfCrX36LD4GUF06PZ9KKsuiTRXQKA+oEWS4/p5RVa7iOC1rQLgk2Ruposs79tp9fPVft+iOopKdq4G1ryuyWqk3Zej9jk7De8AUnHUNUuLbre2LXEUN3mepazrZz1bMil0RaSS+AGGKyiYO+B21ROYU1yjXbubMW6JZopdJSYXWUwnxptcP3ZhiRkbbQKnQRJAq+gmuiBS4veHR/vHIf9B6tev7b2aeXVPefodNyI2ITdgw413A+4ETDJJr6ZnFxuqoFKUV1hSbnqMW0jbKeGXxCCtWrW4oih/v9QZ2rftci+O7tr1iWhc3eImqcGvjuDztKabx8/YLzceQBR6aRFMDEtUhDs9uMkctmt+fF8xfc3n3g8fhILfdIW7jePyeGjofT0SyeRryLvPi91/TDVi+gqJVZKpnj8RYJV2xf/ZgwR851Irqew+aa+4d7vn/zLTKOyOnIPE3MZ2VqSQjMy6xdZ87qsWdm36UlmhSOp4mpQLX0iGbQnIAVVHWJV52UCZyLmLgUanY2ATacqBSi1cwyz6oxKrqwrk0lGOKqEkEuj6pFtLRVvAubzQ4RR67qtxeNRRmCyTmWRTtDuwFC57m6ueL3fv/3efPtG+7e3eKcw/eBTUrkOfFwOnM631E2kW/mIy+eP2OzGfgX777ju7fv+P48cs4Vvztop3p9YLvfcfWDLzk8v6Id9hqH8/GIa4HD9jnz8cjP/9t/y+t946e/H3FUlnnCSca7ymbX8yI+Y/x64vF4Zpwy2SKJ3EoyMIlHlopzcU1YoZTCu3cfCB7G08gyaRDzZhj4wZeveP78me5LKLASA5p5k/iAC0IfoXktBtVXasj0oaPzkeYTjcxUqxo5oHTylUKvBgJCOSfSaWT5+AF//5Fn4um6gTBsyE440XSCqJFlybx5eIfrA/NVr24czlPnBTlpnmY+HSnPX9K9eMmuazQ6rnba9KQ0U0qiesFF9PviaKhhfF2tq5zKJZwPVIS7hweWdMfHh1/y81/8jGK2YDUtNO8JXafXpVZKEnUEEfMxjR6JjlwTmpfbKFRDe6oyu40x6ZxZaYljs9lSciafVDw9TmeLI8MgV6cwdFUhvu86gv2vZME53es0DP6vjdv7e92XWb394rMDu13k44d7pnkyxxuzx6uZf/X1vyHXSuy8mYpnPZebmAl6IIgoMpESKWdSLpTSdNffLiWNTwXbazDsKu4G9QfWvZc1a0by6DrV9z6eT9Sq7i6AGn44RzeYgbalLLRSCE7YDQNx6Oi3A9O00MUzfezoQ8d4PjKNZyvYmXmZWVK++Jl2bUMQx9Wwx4fAXb4jt0zLCh+WWYXvCT13chlVTpSKxTN10CquZZoLVK8mImXUBBy3ZNgIQRyC7khLSuRS1IJRPK7pREanTYQLUf1+V3/SSz6jM9KQahtj15t0ZA34VQZpqcIasxNSIS+ZloTq1GiltTVFwlZDjct7o7IeIZVCbJVnL55zuDr87kXw2c0rTucTv/rlrzmXmTL9ipIrx/szaa6UpFW5rK7gluIbveBLIARHlUZzUMrEebqnthnnG670UAPjWJncwpKgNk/slX6OS5Q6Mh+PNIE+eLzXDt63mfPH97QGG68Hwzg+ktJkBq9qy9Vtd3T760v+VfNq7TRsdmwRZrNLc64iThh2O3yMrIzasrqWL9rRiAV6+bY2HtqFxRBUO1M04iYXTY11loIMzdK4dSostRCikXLWrnrlU1nI7Mo+my1xIAy6gJ5nNYatoj+72w6ayp1OFDLTMvHh9gN/9hd/jquOz754zTRNnMczfdfRtjsezyMPacHdfuR2mTnc39F3kQ/zSBoiMi0EGvuuY+h7fvTqNVdXez7vI1dOeNWEwQf2L16Qzgu/+vqXfDgeeXy45cYHpB100S9RtZd4gu/Zbwa6KMBCyQvzvEBo6lksT9qnT71Fp2liaZVffv2NMhmD4+rZNa9fPWe32fCDz1+z3W7BC8UClytNNX+1sWQlLSAe8VqImlPtkICmZ1d9oGpzFKeMPEfDr5l6vplkouBDpXMR73tC6JQI0PQgpRS61vh8t2XbVUKCF71js4uEGClLg6TJHKQK04zME36eCBU6LywmjdDGJkPS5kk8iDiqD3qgorIY1bgVclU5zbNnz6jNsdln7h/v6IJXhMKm29oK5EqWbIkRTi3DvFNIyotlREZC5/BNkC6Qs6a7uFUX1xrzrCb28zRrWoHtTfHNYE2dOnUyqgRRzVteVCSea8J7z7Pnz02b1piXrPZspTFNK6FBeDzOutM8FZYEJxaVFqAaxnHUSbNVT22Cd1HJKk2nMWmiTEMJhNDou0ArhampO6gtsK0B1XvG2160tmY5pooElZyfHtnVsUp0JdFqo4/xsjO9GFe0BkX3tyUtrDV3Pp15cB/NRchzPk+M55HkFybnyWlRjV5VZCM4jwRotpIpNVFT4ePHD4hznM9n3ZVWBxXdeTZtZNbUENdQWN2K8drx11KV0drUKUeotJpJbWJMlXnVJTvLget7gg8sReOPXAuIMytJH0jY+ZmKoVTeCpeiLP6T4tXEEoVaVbZoUbRDqsYeN+dpsVfCTYg6jTbdDypz3rSZlt7iJFCz4xd/8Wv64W8Ah3722Q+5u/3I+bjw+PDIX56+Ic2J0/2JPm55cf2ZLqxLVfaQd4QgdL1AUyZbpdCkkMuZx5MGHIYoSNoiVXg4nUl1wZu90Ha7MTPdhVI1rdt5x/PPPzNXgYpvE49vviPGnv3+mmnJ3B4fNQ8weLWForE9HNjfvOJ4PnE8H81CCK4Pe7bDwOOtJgv0nZEPouLMmK9eLukSVdPsAq/lb6X/Oi/ELuCKQi2tGCnGeSrB5pxGrpklKaOrlEIVRxSHUJ8KoTFVRUy4Xxvj+QTA9fY5Iuq4Tmv0om44V4crlnnmeHok18ppLoxvZt6+e89PfvT7/Pf//j/g/fv3zN/p5NS7yMfbj7xNE+/eTDgUhvDe8fIHr9nsdrhxoqfyqu+52e74hz/8ipvrA/vBMTjhh9Wxiz2vvnjFm3fv+H/97F9zezpzPy68jFukdTYJdwZzVbq4pes2DNEhLKQ0M44zsvFGZOECnWm3rAvv03nkOI+8u/se74WvvvicZ89u+A/+w3/IYbejjx4aLBZkWqRQgKVCSoVxSqqFC2qt5pyDmql5oaREykn3CBVy8JTg6QR107FnvYoWmhAyIVRi6PB+IIQeVzNzbaRWafNCXxs/PlxxKnBVHLtOOOyh85E8NsgQsqfNhcKIG0f8qISQEiIJ0XWnqMkES9ZwZN9oLtA2HSIRb/5D3iDkUtXp5bPXn+FDT6mex+M9ffSULDpRiU3CVckQ3llqOYIYJOW9EJwavnedHnLdos9B9FEbQdHp/OFXXzOOI9NZw093u61l9KlFXjD7w8kMy/vgkQppWswppHF1tePZc0WDgo+cx4VxWnjz3VtuP97rpCDC3f2EPwp5VKLYkmZwiTklbXKqM/2dNgve9djCS6E3tAh6pwbkm0ETy9d1hPOeNbKqrtlqoo1QqavblCIXeS2CJnK3S0Ja1FB86Hot/qsNWSsKSxRnEgh172kI5yWxTGeWXJiXrC2gWLEQXXs4cVgbT/Sezik5sbRCymqMfnt3p+ewyStii1bArZFYm8xacOIIdh2qMxKUeE13z/MFum21IFJYpkJivuzjOue1mdzv8F1EW0ghFJV+DX2v18YE9Mn8TVUX2BTibGAEZRX160mIVDG9YHs6E5yDoESZEHudbHNS8mVNtKqBxK1Vc83Re6+kxp/+8Z9RVqj3dymC/+Kf/Qnj+cz5mMgLUJXEEMKAk8A8FVKq+jCGQLfp1ZWgJbyLhOBxsTePxIRIIgSPd6LCSmm4oOGpPmg3NS1nlgzezwTv6KLCPss04rxn1w+01jifjogbOY0z4iN9tyF4iK4y5cK5m/H9Bt/36ogBdDESYmATAtE7hinjVhjICTmfabUwbJV+3hb12ksla6gs0Ra43uASRTbUMqkpvTw0QlfYbLbs99eqPTLvvHmcDH83EgaVGFYD2mawuhIh1u4Sc2w4Pdxr51aLdZ1624ynR9u3aGevAZQorFgWpmXCBcfVzRXzaWJmZOg7dsOgbLBSudptGPqeF1fXbPYbig9IrnxxeM7VZsvL/ZbDpmPXB4YucBCHy4nv3n7Dd2/f8ebuPUuF6+cv2F51Co23SrAus1nL4FcZoGgnm/NCaR0FhxgchomV1ZUBlrQwp0rOI8MQ+eqHX/LqxQs2m40iBqLMyFpU7L2UTKGqQ08TDVD2XhmHovqoVDA9ni5hhZXgFKne2eeBd1UZzZKoFEoAFx2y29JyIo2qJVSXjmqk3UrLE9KETgKdeKJ4XC3kZcJVZRbrXkTTogJcCj8F00Ep2UYvmE5Z6tyvQbauBXMgaQbFaWrHzcvnlALffvuO03hEUztWFxxRD0wK1RXEQcWzkHQ3LoVKREIDVylN9zWLFcHgG14CsVOdnQsRHwv46eJS4rQ2ANhuStnK6nSjXUUzB4/mGktaeHh4UOebECifRAU1lI1aRS7MTltCQNaDEtOY+a5HRNTmzBjSDUhluUDGrGbW1QhoBsfLxXRYOQIrl0GNvTAfVS2BDbFg4WKvBpWbgDKLRQlPyn78hMRh3IJmMKhI0weiKbM0OI8fVIaVy7p9VEi+UU1bZ+wBgW2/xTn1SS25kKY7ak4r/RYp2L2x/mo6AKwuL6vUoq2FiHIxmr9EDzVl1HqKWjc25WK2hk3tE64m7RQNUQAtnlhDARCCfr/1/6/OOc4S6c1ATFV+v8HkrHp9SqWu76HtyX1ApShFPXoxty9vk2CM6lMrhMu+8N/18VuL4H/5j/8boFLbYnIAxYS7KFAd85y1+xAtgv2mpxRNH/bBEfuObrOh22xIyx05z8ToCV4YfWZxVd3XXdD7gcrxfE+tCSHSxcjNzbUufMczoet4fnXNsiTuj7cKPdZ7dvsbPvvBM71mQXClsQwJ3w+4ric06Jxnf7Vnu91onEjODHMlughBH5jx8UQpmX0MhBh1im2VVLJRvPVhjr3aRtXUcM1CMQHxEReg6xu7/YGXLz4jZY2xmceRB7NToqk3JatJtXNcSoB8kl+2FsGceby7V4/A/QZBlOXYEqeTUYdbAmcQdKkmK1kY5xEXHDfPn/Eo95S0sOl79sMG5gXIvNxfcbXf8fzmhs1uIO4PBIQvrp6z7ze8utqy6Tv2Q6QPnoM4lpT4+dvv+fWbN3x3+5Zus+MPXvwh+yshtYyrCnes93QUNOfRqYF5a5VSEqV6Cv7ye69N+HogpZSYlkSZz3R+y4+/+orPX79mu+kvy/dV6JxLZU7J2IoJ5wJ91CgVH8LFv1XhxgalKaXaOcRFXPCqD0OFxMESPxbU8aV6Ub3gfgslkW7B5Uouybphg5yKFsE+9vSu0eNxNeuegvUQCjgnBBPwu8s+qj2hCc2ZF6IoyWI1IC8OF4O1Fgmc7qM224Fnr55zPs/c//nPOJ2OagRxmXD0sFPmYqEFoeB1Vy2FJpVKgVAR31iqHtRLypTaiCJ4qez8gAj40BFigaAHVa5NfVkNMDGUmRicOaB4hb3sIHPeMS8L8zwqHBg80WmCemkJpBhUqRZvxl1SOUd6OtnECaHrQYR5PCPSiMFYxdVMp72oTAH1oG0ZYyVapJJYgkjF4LWVtNYIUcUx9tPI5uUpdqNmg0uN3EiTjmYTJK1pYbGi0pqSVURd7C9FMMZIjLpLnlu2JmC9pxrefXImIOy3W7rYm3C+cvfxRGrWlcNlh+nEGuyqjXWQ9XdZ0ZZVdqEPaxSvjZJdj1arzawXgRia61rJS4OscgrnPF3Y6PUsRRsPuz5hXe6j94Nq/8A5veBShVVkZy9eoc222rdps1bdumNW+z5Hgxz1eUHTP7zF/XVxQP9txP1NimAruueLYVCWY1nw4uk2A2kpnI/J/AZVC5PnCXFFk9FbYZlH07Ao06eLG7wRTfrBYDDT4PjotYnxVzSLLWpNlN3kG+IjIp5UChUYNltSrtQ562I126LXC23oaFcHXN9TWsOHjsFH+mFLN2xo00irQr/b0vqiTMWc8T4CyjarAsEJzTscGq1SiiAS8H5LdY6SJkBYSltZzBSEuQqnacHffaTvOzZ9/6Rfa6vXojqymm2f6oZEGPot3nmm6UgpmeAjwUc6c7Loe9XY1FT0ZsyzFpkQEFGHBFA26ThNfPf9t7x49YrXP/iCUirjeSb0PWHoCN4RSuHZ1Z6bwxWdF6QW+q6n84Fge6JSlU23JPVZnHLmNI388ruveXN3Sx4i3W4g7Ab8ADVooVla0k676QTjxNMkIBKpEsjoPzuJ4NbhL5Ca4+448fEuM02FmmB/dcX1zRWb/Z5us7nkitWi8PKcNPBYGYEOLyqID124MOCUbm0JHcYOrKXiW8FXvQedd4QiF6ee2lR/WlojiS7sW9fR+o5k7xm2m/OlEBr0QXWp47LQqk6m2sl6cI3glDjlWrMClaF5bahKwRV1wyiOy+v2GZyrdD4rpFuSNp+iB7lznpIy3//6a27vHvnXf/Kv+cUvfqkaNTvMm4m0dfCpun9pCo95Z8q0os4jTaq+kaLhuTlXcj1ZYZlxiEohUiX4aJ9fL6bMzjlos5b2prq5pdik4sNlUqi1UHNRskxwRAvtHvqePKQLISiGYJOPQow5KYsYp/6p11cHWoM3p0ej5z9NycLK2lZ3GiXD6aSrzjFaSC5Sgab3lhG01QmGJ1Zna44qgjcxOC0hteHDOq3rGRedicVdvJDdqCrZwIl5/RbMPIxcMzSnBJLQac6fJSPUkmzvZcXCCRKEwXfUTou+k5VYt75a0akeIZi3aGxipg8bPa/Mvm3J4K34XdIyDJzwTe/VlbG+1pS5Fo1iMySlnR/wPlDaTtdSn+QNsja3yNOVXFerFiOjTS3WGJj+z9n9KlqQVW+5kpG4aCRxhk6smkObWrsQrcH8HYsgVV9ENOPrWrNW/GEDbWbJowppDSLIy0LsIHTuwtByISDOMwyeGHuEBK3Q944QhZxVDxK7Thf+nVBrZjo+QKvasYMaVIu3w09F8yyZeWlWBBshqEWS6zu8d0wE5tpwIdK7QN9v6PpBHyBX8dsNQqM8nChVRaAriaWuHYwXhIy0jAa8KgyECcIRSKudmgilCakK52mh5DtePLvh2dXeiqBY/FA1jN9gilUO4BzDZoN3gePjHWmZ2W53BiNVxEGMSgRYih6QOS9456zzEXJT3z3vlQ375t0b9s+vOTy/4XycCN0R33eELqqVXG1c7fc8u9qTRNMO+m7H0PUmIDY7qVpIWQGhpWROy8Q3b7/n/elI6QNsOsLQ4/pGDYVcdT9xMRNANXZNPLhIk6C5bOZvusJeuQVSg8fzA3ePM/PkKHh2+2dc3dwwbHfEfmA6nix/sai1X1rs8dRD0zu9Bj4qPNxytWK+qESmKaEra7uLSLEsZi38zvCZipCbdvtJnJICukiLkSRKBIk2sfuSCa3RRafa2THpXrwVWoy03l86YLXibrhWaC1biGxGatbEEKo1BVqoamn4WuldwaOHJdXjpcM7M1DPmbfffs+btx/483/753z/5ltlW67EDoNgDf+yvxacU6hYFKvTXZAzeVFV3WtJ6mRS7Z4ThDwrtOh9ANfUX7Qqk1Ssg/ciRDPbT1W1l16V+IDJFXLFBdXdeqeWa30XyX2vUDUOH6Pq9rLupmijisi9x4fIYb+jtsa331ebXMUmAm3kVQVgOXtltSDUA7UWDTbJRQuRSpzaWkaoboUi26XQNFE4TpwgRX1Z9XlReJ61CIoyuWtT6BfXLjNlsfdBWtE9Wc50vlNNsJ09zuvecZ5V1nApQV7AqxsOVS5rpmJ6YQi2h9P33KP3dKwaS9cPOimtK4SqYl2QVZRuchYvSKkmxbI8QexPVQ21oCYfadFBhCCEqnFiIsIagf4UxOAN0RBDORSqFi9rb2ZVzz0VfVZ4yIqzwarKI3wqgvr99ZyiKdLmV9LT71IEu6A+i3fvP1hChC5+G56cKvO44GOk32wIQX0Ia0lMo8KnrQrn08I0Ncp+YLONHHYb+j4wJRXeHm+PzHNGjkoGUY9D8LWzbhOQSptmxGdOxhgrK5spZSYZeXj4qHuH4KzTg+Z7nB8o86QSgjSST53eELaFE5oW5BCZsqMkKF7him0/EEPHYbthdjPjlGi5chwf7T3S/UZBp1gfoRNPRXdPnRNSWri9vaWWwmG/pZROb56iRI5gWXjruXA6PkKDtCRNyZgm7ZBEK4pz67bCksDFKWbeZs2fC1HDLQRq01DUX//qL5mWicPmis8+f8nDwy3vxbM03d3dLYl2nrh9/Mg8T3ThvXaLzrGJHf/hH/09bg4HdgzECNI73p0qf/zL7zimhfj8JXPp+MU3bzlddVz1GzY+snM9XipBiiWzd8xuw+z3jLXntHiG5AlJyGiDMeOZi4ftM3YvKn/41UvisOMnP/0hV1d7ask83N2Rl+ni/SiiZARMD4qoTpDWSOOkLj5VmXG5ZlzVw6UTITRNaAiuIaFCy7QmJLvKTRkz+CZUJ2QnyNUNDWGUjlBnInpIlJpwTnh+fc1VhWf7xjkvvJ1OZC9c9wqzt6Fj9oEcIr+aMr/8/pb+5UvizZ7Za7GYTjPTMl8IHV30auknorKh2CE+KAHBR4bNgaXCv/nzP+e7797xs7/4OQ+Pd1RDL1zweng4QWo2WEpJLLoQLHinkFwTNU6uc6WRqKeFlnRicSL0TckHKXhjfQbdeTODNMIQrHMXaBagvh4qxiJHdC8WXVCDbHM3wik7Vg9HS7IRr/l1udJvI6GLTNOJUhdKqfgysaSDDUAZceWSzFCK0CLErseFqE17W9M0lLyScjZJhb6PzhZTaz4goge+D+orrEhC1kQZEWVgfjI5FXNvqkEn0VQsYmnoL8hIpbLUbGWWi75NnJLySlbm7aohxjUdKGyyLYu+9jadjJHacXXY676y6ejUaORmzaGRd1wDXCX7hda08DcPwUV9z6uiNx7MhlIpWEHWCbdRikGoeWKqeeXi6A6wFdJ0oiRNhQ8hcNiY1Gq70SbIe1ayrThlxVYjDGmouGYrijM5iiE8634U1AdXjAMhzlGKM02nYvDOJnm33eh787sWweiUdfd4d0cumkNVa2Oe12+q+4wuBGV3OrncUBqcGCiW4LzuZfxVz2azobgzJc0sOXEeR1pSkarvdMe267YgToMoWyOT9AZfFCJ5ojRXljZzlod1P4s0B3h8V/GdakZSKZBmyhQYzPEBO+Ri5wgE3FkLd3WN6tDptDY2fY/Uwjyp0H5aTuA8PkZwjoKZEQd9AT3gmu5Hcsk8PM60WtkMPbVFc3lXCFaMJKCNeWMcz5Y8oHZOaVlAoJINjlBYNPYWLKk8bnKqRK/0bO8geLW5mqbEm7dv+Hj3kT/6W3/Ej37wQ/rNgIgmG9YGjznT5oU3H+85Hh9V49MA0d/9J1/9Pr7b0LyGxvrs+DAVfv79Bxbg917+iKUGvnt3Sy07fvh6y67zSNcRXKJzRUNv8STpWNyWmY65eObi6LIw2wQ94plrQPrI4DyfffFj9lc3/NHf+wm7bc/jx3tO03SBlWvWznY7aK6ceK+BxmTVM50XhUAtGqbWSpSgsT3WZcewEHwxlw51F1Kdoc3r1SNVKDZNud1eC7BEavM0NJapGZpwtd8hOFoR3pxP/Hw+Ep2xT/uOut2wuMAonrenmV8cz7w8PONGAotzZAdLyszjTHQ9XtRFBfFKInJKE2/qEIC4QD9smc8zv/r6a7755g3ffvutWg9WsUK5QkiGP7i6Hl1QVQ7vpScGJWdQuYShtklp+BL0+0S8dvle6elBmjLA0YTw2Clkn5NNkpfj4rLUYpXLqTVeVGKMs52hTVrOmeGz86TjmWVK9NtOszOlaihvmmnVk/LZHv6MiE6ywGXa81HdgaoVwWYTW66VVCupVGM0r2s39WRVEhdcRhK35uRVyM3IOHWtOyBQ2pMJ99rARgK7TnV1Hkeu5ckou7nV/RAxWVUpVfWaVtO6TSAEM03HUTMsNZNagtborJleRfzrlDfOZg7hMXjYmmiXqdagrMUkZygp43UIJXiFoQfv6JxcSCs1qQfG5NRJK1WzpVNWF3mZLiQuJ5E+HBgGz/ObHT4EotmotapwrwuRnDPTtJCWwuQyzjd80PMzF+UYqJlINkMRPWuD7SNzVjmOGmprqHtrGEHmt3/81iK4jAtLWuwN006aJkSvAa4aO9KYpxMxeHwLOAqdV6Zd8AOpCrmC9z0QePvunnfvbiky0yjQMsPgSd4Z1itmV6U3lLN04DYqyyt2G1qDOWvXKa5RXaeECqf+ezVVypQMFnPEFUOuBUmwpBMLCpV473j+/Jqu76gtMy2zHR7qkEEpZvKcKHmkNKGkgI9C53UPpw+b+Qc2CCtNwnB0QfVu83lkZWnZGEdpyQ5ffShbylA1C9D5VTvXcBK4gOMiuKY3Uk7VUAKBWphlVlw+Npa56FR+Vg/Cz1+94jTeU+tCCI7zaWI8n/C1cO479rsNz692xH6DOMfbd+9xTUWtMfbqriKe+/nMwzJD3FJT5u72aLCNygu+/vaeF9eB3ecduEAXGgsCS2HMjaUJGU+VwJwbYSpkv6G4yO0x87gk5rpHfM/14TnXV89pc2WqE2lSOEybgEAYvC7anXo75iUZO1QbiTSrJqvKk9Nna2JTtV7jGB0xNJZlNTQQUhPT4a6uJ2vKvae6hvM9EgfwI+U8IlW7U82YM7LJlFhS5eQ6TnhOqYJX/eDdPPJhWnifhVMRDqWRBapXixjvA8F1qr3CqbOKwGK7pUBBXTU2xLjl+ctX7Erjjx4TXXfFP/0v/4RpWRinCfGO6Dot4sqtVPakQcE0lTyM54k5pcuqQkSDq47zkXmcaF53hOmcdP9cFTIchk5hwKq77vmskqKUNHW8850S4DolgakcIZlNF0ZgsClqUXl+SQpLeq9zyW63Y7MpNnkVgu+gE5KI2Wd94uQq6wErhi65iyZSnGPVwa0kNe8gRn8hbLS6ZumVi3OUPoUFVxrBecvQs9rujda0emTOmlqfjN3aUAj25NUWLoZocOLKxPR6v9ZCkEqQDNL0OUZNBlLV/bXYSiKgayAxu7DQdcT17ETDtkvNFFdpruGHgKMRU7a1ieCaI0SVSuAcdayUSe9jbzmSpWSSpbevyRO12rRur8GjkKtE2/Xaudx3js028vrzF2y3W66vrwjeE40+vG5anSiZr6RiBuJZiUx+JUyAKxWpjTkltcdM6vZz8R21ydB5Z9O2NibZ9IO/cxFMczL6fbXpRW/q6J0uM9eRe5lxLaieyld8UL+4LnZIMe2H07ft+DgyTyOhy7igcGAIapVTm1wiltYiKF6QArVky5NTPzmlA+tAWE00q52aCVRTwvsEIV3MmNfJIWc136VXund0nm3Xs2w3xM60PNmcNqqy6VRAm9TPrhak6q7zEoZ5gat119MMskSU/JJzZjyPTxfe4J9U9Wat9vUkhWR855UN9slu4rLfFXTSaM1cLbBHFPUtrNBE1Dx50VywlBbO50em+UxtmRAcpWS1mqORlsiLLz7n5urA9nBAvOd0PlNSUdNx5y9w1WkZOaeZ5jtaFsaz+rr6EDmPjtu7MzEMJHqCczTvNWA5F1JRIXvB0ZyjVJTgJJ4ikdO8cD8mshckRrbDnv1mT0tqQpBT1oPcqYlx13V203OxlSrGFF13hm0dO9adnj3AynNZc9vUsKlUqE2h2WoNjgYXQ2teGz9XaC4iPoLTFHCpDRf0MHFOVIKRlVU5u8CMYyqVUBpLgeO08PH+yKPrmV20a6LvG14Fyd4FXHUa+tt037wqnopp2JCA8x27w4FBAl98kXh4nDQlA2FJCd88rkZb0Fa8oLsbp2YXijQ1liVBSurbGuNlh9tyIS+LGl+IYzGwuFVjrIq7WNxBpS6KYuRUdZ/tOzOViBdjaoW/9Ayx3lrJUWZCvxJ61gdbBc+NeUnkUnEuEMI6dRmiyyfj2Eox/qQQik0zakSxPlsKpwd/4T7q4Vz059e6QpJc7BBdDATntDC1dkFzxKQRzabN7D7hPFZYpoT31c7N9VezZrgaNGmrMJWVOIsWUgck1UOaZbTTPa9KF6yIO42LakCRGYpGIeEgbntca0SfzExczcdd6+zcdKQlUUrDe2BlzVY1gxDkAg9roDqXfb8zKNfZNaxN5TchqHTn+mbPbrvj6rDTJmAtgjZmi47lFpqrzUB1Qg3+CT4tamo/TjPLkpgmM1VQk0vNXXXO8leNsNYaKc9/Mzj08XSi1kJoOmp3g9iREPTQtoO+VNXSSU668BeIseA9DNHRSSC3yjxPtAoOTx88sQOzc4BgpBPTqTiU/ThN2tGfxkeCD2wOG/WZ83YjV9E8vulMCIGu9ZpC3Crn05HH44nNZmDYbFTZI+5SOFpVOvr9xw+Mjw9kp2SE3WaL84HnV1eUlDnefiDNizLREPpOC1hNSkleYUnnHF0Y2F5v1fMvmY9fgXT/QJlnsII2p2IMMQXdXPCslkoCdjg1WtViH3xUWLSpu412rsayNpjPAWSLYyl64262G1rtqLUnl5nv3vya2jI3z284n87M54maMnNK7Lzj5TDQB4V5v7y5skSAB+6Pjf2zLall/tW/+TPefPzImBZcCHz24iXdMNBdHfBSONUzY2ksxRO8FrxcGjUl5qTwDK4hHQz7DYfNwLluKLVjrjNjyjB0xGEg9o4QGss0I9LYdAPOCAFrLE1rlTRZmKixPpekAmUhXijhysD1qndzKo1oCKuLosqAKr5Vo+Q7GppnJs3RnDmUSARvcGQI0LRQ+epxLaigVzISFnwWSoYpF+6XwjIlltOZsTYiGDtU7/G8ZFpTSCn4jhZNWInu250XvGt4ydS2KFmnqZA7DBsE3UfXJnTeE8TrwS2NUhZz07D7BoUdy0o0EHWVUQJcNFRDD9kYO4YB1NlfGOdRp9GoRS3nZPeiSYqqemc2m85ySeYUtnr+a0htLboCaU04jieWNNseyamrVIQqGpZaijUkTuU21bR03iupKiVd1dSMMsopeq18VC2mSVGCWNpM0XVEnmd9/3B2AOsEWqv5x5qWGQvQRUwyVUSJMTb9CUJ0qknLXoXtPjirxwVn0pAGGk0laHwTFZGs14GgMK1vZIOFi3Mqfep6go/UrLu0IlqslYGq8GmtuktrNJY0qfZvjXdyQgyR5y9fsMwLHz7c4STQhy2b7Yab5zd8++tvef/2HTULaXHGnYBtjEgI6lbjveUYVpoRX4L3KnkxBCw3dWfa7nYc9gdubm7Ybrf0XaeknN4Klgvqe5wKuWRSWshZaKnq3tuLZoHGSOc7ootsxpFlWTidJm2IqqIOyoXwmsjhHDklhfOxzMHftQjO84IqNJzefV4PjVq9MclW/LmwOn5TsZDEijh1VAneUZZkrDztQoPTZWsTXUhrd7qaJoOs3VQyr7+V/WdLZx36dcxqRR0EGuBC1K6hauLAtGScQAyest6Izb6uqvH1dD6RnIMYEO8ZDj0xRvY7JWJ0Xc8Ulb0q6JsuzulBIU3fsKaHrA+efohIVo1XK03hCBFqzjbBCdUO1GLauLVTfeocbZo0jL+atKSZMJS1+xJPQ29+rLBXdNcRg5IIbM1NKZnHxweibNjutvS9/p5lStSiyeVXfUewBfyz7WDL6olp9lSvRea79295e3tLqpkhdhx2e/rdjuHFDXkZme5PLAVKVSskJTFZJiLaMboguABxiOqZOHe0FEhVWIo6rMSuI1jKeZp1Ib4dNGxWF+J68KqvYrKdn6V/5KpwuKVQiMFhzjk1I/DtAlU1hNqUqK6dqR7WlwayKauz2TRe14kyBPDBUBGN3HHi9F5rFQsjsCmuMtWqBgVpoXiVcGgkl0Y4VTNm18PXa0SPBkyqubVTGrvY4dlsZ9kEZcESwPaHXexs92JEkJYRgjoStbXoa5fvQBGUWi+M0JWIQGuqsYxmEoEwp0mzPL1OH60qmxpnP6usqlczoW7FpFC/2ZMrhK+n+TInjucjfR/oOk8/6A4Mqmnm9NnzbvVasXfO2TRZ2iUu6VPBu23m9GxZwRRbU6ypCTRDVkox7apOc3rkGRxnBBlhdZBxRO+fXI7QUOuGGFTabEWg96Q4Lf8aWWUIkcPeaxAJCAE8NG/xdK2pdZgzPkVQn89Vv2gHnt6bln1ZvC4aa1Pdp+v8ZYLzMbC92iPnCT7eKzms69jtdjx/8Yy7j7eUkllXXRn9vaP3RFCxug+0uth9Zy2NnV0XQlFTxueqfYwx2pmCSTv0vok+mqeyNoOs+sR1vGqNZvC2D54YOovoa4TFUnfMbs87JWut1n55NVNojb+mBv72IjiOJ5wXhm2nb2LVQzbX5QKzhugYNlucNLwtloPTQ3yeZmpbKA187OhCZHEZBI4PI61lrg4bYozUVAwKURgi2Mjsq8IMu2GLOMc8a+VfJs3Gq9Loh4HrwzNaKeSHIy0X6pIQHxg2A7lW7o6PSrF2ThfbpTJVZUupHED4+HDPkhP9ZiB2HW8+f81ut+fv/p1/j3me+C/+6T/hdD4zjSf6fsvrzz6n1sb5POGdMIQIeeb+/T1RhN45jVVKBSnQhcGMhAtkFUiHLiDB6U6jYgdRM/sfcwVpDdIKsXoowmzOMc4OxackhrpyGinOkfQrcAjzLDzcwQ+/eMHnL19DdQybHjeeiCXxP/x7f5u/88MfqkF1zrx7PHBeFn55OvK4PDK9/ZbzMvOLt99x9/jIOc1024HD5zcM2z395orjY+Xd6cQ2Nh7vr8gd1NkRnCN6+PKHL/nyq2dc73uudpHYb3Cx4/bhxNu7mTf399ydM//gj37A68+/4tnVnqELyO4ae9pprbLMCw0rfga9KTqhaEUX1T8w+H5FnGjegfdI03Z6ZbMtM4CjZDUtqLLuXbFGTx88nO4Jp+ZwLhJffE4NHafTkZhnos+a9O4Sxc+cwgnZRL70r4i1IbVe3Eu6zcB2t8F/vGX+eIdIpQ9CTdByxblC6eplL1mDHmYuKsy0DcHCX6v+u82G7bDj7/57P+X6Zs+33/4v+fnP/1v+0f/9ewoF32nj5rxCbZvQUU+ZfNaCVqsQO22acklMc8W3DkegHzpi3xkrEDZuT2mN6DprXI1sURPihGHzDGQ1lq9aNAXG+XyBt2ia0dhQbWIXPIfNnlITy5zVtMBpBqiTwDKbTZY5S9WUlYhkxTY3hZ69wd41L9TmqGWh1QA104rXtqEm+CRNotUMRWEzNRfQKWs1ihGNG7XkDrFsU5V/OaCzBAepukpp5tQyxAEfHL5TqH4uyZoC1VFGH+iHgd3hQN8NbPotc56Y0plkFourdEq8rl9i0LIeRItOS9pApVHt41znLFR2g4uO4fmGSmUaR7z3TFE4tcyHh3tuDs/48Q9fMgwDrnmCiwzDoM1kU4SpALWP1KGn9IHsPUtVwuQpTcw5I0kbNec0LUf3sY23+ZGPdyMf7v4/l/dacKqvbEJYVxjVCn4tDJuBw/WB4/nMw/Go96zTdtaJX9s+VumMbeWUnFRN3mb7XJrKe/6aleBvL4KlJB15LbBxBYIbumcRlPobOl26erLZTQFNcX01lK4MXh071lDQZcmUvLDbbIjeIUktvEpWunEz/YeIwzX1M8SJQRXNGFmV4gp9U3F3LlWT2HOh5GIhurp3S6ngq9rq5GWhpkJbFGdufcKL4+7dB8Z5phs0HocK6fnC3//7/z6lFPphy7Qk8nSkC4Wh69XmyGfUU9ZRlso8juDNdSbrkr/VhsND0x2rq8aGRRfD6gWIOq9feliF76BRizlINI3rWUN0nVtdi8S+7hNRqz1w63xZipCSI/iB7fbAfn9gmk6EKHQl8cWL53z16gXnx0fmRbPgHuaZn50eOOVMOj1ynmYexjOnaSLXQnONbjdo4xDVbm59gJel6nsHDD100bHfD1xddQwRhgjVBYo4prLwOJ44LTNTbux3e57fPGfoFUkIGgbHMtv7XtSTMRmLtiiv/hLf4y2YN0S9xZ1rVOdowUHxqqFhZS8qK7g0s5ISE1qvtMaVmi3aleamrhdts6Mti+rZXFNORgBx6pebXKL5wCZ2muBeGtkJyXvcZoPbbWmPR+365Yma3xq28NfXqEKvprox67iDU4amntJaoH303Dy7Yp5nfvijLzme7ui6wFKVBKQVQ/8+BJX11KwhwyVrt63uLJp20myydSHinVNDVml4VPzt8aYl0++biz5fm81Wi2CrpJxYisaG5ZLUHaQFY7wGVsqJGkMLc9KvUXG77kjFOShqECE4lY9VZa6u10wDrZ/OrpUpuArftcM0Ak19WuKr6XZljatYd4YXyQKKDJgpqX6PVlkjlla3LO+8pRHZ3lGUwRuCJ/RqYrFM5TL1g95DIUSGYctms+Ww23NeAkzANFNZ8CufwTm9t72/8DI8QkYDxLFJU1k+jrjtCX1gc9ipEF8U9M9OZVFrysZmu9UsxYqhXMGY1PXCc2jeQwy04KneUZ3CsaVZE4FeGwWynvaWpSVkWri7f9AGvqmVoZpXKNlFq8l62sHV1YGXqXD3cM+Hj7escExtQBMkKEzahUE1gEEL4TJrRN08LlTTuQoKHT+RKf67P367WN7rIl1axqH0c+8F30WlXlenL0KPH7PX01+nGRTSDJ9I40SZzIn87KAAAQAASURBVEQ6F502SmI6JyhCGRdqzpzHE43G1e5G88Js/F9qwkdPv+suu8DVob1zHb0LbIbATb9lnGaO40hzwho944Mjl8ScCtNxJE0zLA1K41ECDmFJFaqnzNBS5vb9PTk13r/7QOw6vnj9BUPXc/v2DWPKPDzcst3s+fIHP2CcRz7cvSdPM+m4UHwi+5mSC2VJPD48cnw40sSy09DlrUKkCidXhGzwjA/6WcWs1tZHUs1gmxIdHDhv/60aXONEp0M7Tcu0qPlvg6vdlpfPfkha4Lvv3jKOC973NHfWzr4Tho2nLR7fHNEpWWRaMqd55iF/ZF4SQzdQt1DqSAiepkIiYgx03qsgF4/3HcsyMz4e+cEPXvDZ5zcMvWPTC9Jmal2YpsychYfHe+6Pj7Tm6ULP/mrP9bNrvAo5SGVWBuM4agPg14IPOvmpU3yTTw557FpcFvi6K9JJWZTo0rCdkD7EDUeRFd6zDnV1D6l6ZJeiUDhDRHJPHQaa2f41r24wIpXcEo/HiV9+944mEec3F8awHzrctueuQes34DQk1ocefCTMZ81sC9rlhE7dbGiOVh01dyqwr41lnvjmL/+CbtiDbHj75j3fffcNt3cfcNERskOsQTyXgt9scF3E2f4REYj6HjaPPTeWZlELzpw9xlmTHJaq+6pTNgeTVYMmKhWqaVLUJUZtwpp6XM7TyVYGKs4PzhFDT99tWHflHFV0Pz0snOrEMKRPUgMa27gliKe0Udcv9sNzni0Ee7ysENA7xN5tzAVI7I8W0N53akRtMhfVCequfRVxl1qoKbOmcIuo68qm39H3A9vNFu+cmrjXSohiFoc7fc+iY54npulWofvacOJYfGVcCo9jYrfbcnMzETvPZhjs4LZEGXnyzZSqf4KldHg8LUZi1N2jhf7ptUiZOk7gIMRebQgXNTx5/fpznl8/Z3c4ME8T796/53g8GtpiE5RoE1LXEiW6LoqbAYmeRQS3ZKZxMmN2rVluDSCoWU0asEK0jm22zNKJTiU36z/npXK+G8lzxuNYCczisVxNM9iIHRINHs2NVCw70RqQ1ZBgvTd/5yJozFltBwx2W3FdnINiS1drkkBv+NUT78LQasqUq63a2FovjMqVGltToeaqIvHWqIMe5BfpQNMDT1meuo9otaL+f8pM887Ti6eESvBJ/QCxblHMbLYUY00mWBSPzi0rC8u0PWuO2DIuTHHi8eGRYbthMwzMwwC1UHJimUf6rqPrAim7y/4yz1kPkiDUXMhLYll0Ea9du+4S10ukh6y7HJDrLKiTn+1lxLGuaVj/Kqs/nzWqTl0/lHbdLoy5tbv1vqcb9pSycDqdWJZ8ibBRLeaKx+v/Vs1nzoWUK6ksyjY0jd26Z1mdJpSoookNYdV8VWUdCrDZ9HRBnX1KFiqNVArzAilncil46ejWXUIXkKouJDWVi0VaNVq7Ler0pl9FXVJN+mDuLG3dTl0eRXv41h7T+n1DNtajkzU/TnjSXtk00VaJXQxq4dN1NDKteqqrJBpTbRxz5XFZuD2dcX4gDJHWFFwlJWSCpR9wQw9B8wDXvbMujbjcu+sEqAeIt/tFL0Iplfvbj/g4Udny8cNHHh7uOZ/P9nX2eg0u0sbUrtuavMDK/oNLise6e7LfXzVbhVI1XijnZDtq/brgNEh3IePMiFyvsN7oOWdE9PmtVmm8uQXJ5Y/D4Viyprc4F3RS107m6WBafyX7VyuDfW2614/1abrwMS9nibI0nfPKahdnX6dB2etrulyHVq3RFHDhYkwRgzJp1f9XIWDn1BknxoB4p7FdYquRUnTnKA2a6lFTVU/d2Ad2MtD3A14cwa9Uk6f35ClpxS6FfW9chypbFy0mreqhkKu+F5aSkQ2V6rueru8JMTKNM+dxVEeb1p6O7U+u39N1N8mJV9jVeasJrT29GdjufB0lWZex6+uWy3e273j5WbVU0pzMq1kuj2EzFNLYSejYpTrnarvgy3B+ueva5Yz4bR+/tQgeho0+FObkXYqGQ/oQ+OSYNvV/JRfUfzH0dlEazvQ40zQzL5lhM9D3kRiuaA1iLTBlZQjlwjIpRJH2leY0aQEnbIYNLjha1V2g8wrUqwN/4jTe48Uxie7MNs6v3gIafCyCUJCWyc1Rmydbl5jWJHjr8oIXXBPyOHHMmX/+T/8p+/2OP/z932PnPYcuUFphfvyONn2kHN9rav35xHxeON2qWHTda3grBj5ieyugq+ALdJ0K7hcNqqxeH4iuV+p+GpXNqgQAFK5q5kpRlHnYGrRcCA6GINAyrSw0v6H5rbK0DlcM119SDi+Z3n9Lvr3lOD8wpjOpnhEp/OmHe2T3kfx4Yhln/uTnv+b28cTdXCkNXGj4Yn6TrSBMuHImnh41Fqo7sN8P/K2f/pQgmfN0Ikhic3AMW08fO2gLaUm6VyiR21l4HGHhhthteb2/put27HqHtJFcVc6STNbig8LjK4nFm+DdXZo0o6xbY6Rnh05nNg8qgcMQAudAhfdNJ6xm5r421VkbpY3CJFCEWDp8i7Ttc4qP1NfPcfOAPDqOOfOL08LXjwv/+a8y5+J42F7x2bMX/OGPfsx+t+fm+hmpNZbWmGhMreGGjjsn+Fw1nilncip0olIQwYN36pgSPK1TEkT0A/Nc+Rf/7E85TZnvvr/n4Xjil1//JcfTPTjBRSV9BO/Y9QO+2zAzwCBsn/ekNJOWhU3fMfRqtuBEDQzKelLZGeRao7ciU6PKMHLOl8OzoU4mrjn6ZESxrsPhGWVlUkIzg4ScE9PyYM2eQZIbZ+41aqqNh35Q6MsFobmKixFfNYC1NciiBl4iEZVtZ2CFxkWdjkTPsFz1vCmlUh2269NzqtZmTT5a/Ct6DUOw+wqk2+FDz/b6is2grlIgtKChwjVpA1arqH1czrRpIU7KQfAmVaElmnEmxuNIzg+c+o7j/cB2u2O7VVRrXhadwsRb+nxgWs62OtJRJ0QjnqCknDkDzbOtB0ptjKeJsiwsH++oOZPnRQ0mXCSlyu3tI4/HM0tVU5RgE3ApBVcSrs0m1xHmcWKaJ6bzwpJ0t/pJ9aFKerph4Gm7I2vh09Juamosq4NGYy4LeWxGctOm3qHvQ0ur7yosx5GF+VKwLxlSlmBxaW7/mgKoV+y3/UcTTLaLYLSohs15q8o2+a3YcW0awikOfdXadem1sN2UdbV+ZamlSR0rPpn2WI1X7coqnKm7yWpt+Bp/op+pRsqrtsU3ZerRPuk3RAuyrVNx9joV5q+Xiepp1II1P+vuw0fSPDG9fq36Q9EHr+ZZffcW1aaltJBmLeYiqv3xTohOOx0XTPuHINFdzMWx61x5OsSdOfAsl9tjvRxyMSRW9pPXh9ca9lZs8i66E/N+Q4x7+s01odtoJh2iBCcrLnOtVKm8P5757u6RfDyTpoW3D0fuTyOtU7intmI3pWO1k2qtUZdES1n3wjHQX+2p6Uwaj4QOhk2nXbEdMqo9giKOKoHqHC40Ygxs+i39oDExl+JkbWlr6KTUGisveX0Pnxw3njr4S79ohfDSWaIPpNAsWmjlo6ETlt2t6/61XYAN1bGq60eD0NPqQu46pCRmCRxr480M347wl8fKIlCCY+MDaRgomw11u7WUD52gow14qeiB6Ypq4Yq9r2L1oa5dtKirke5g1Cbw8fHE42nmw4dbHk9H7h8emefz5RlqYKSUYGkOHhc6OnnS+mnCyyVjQOF6YxZK03u5OUerap/o3dMOdQUdZd1DGynGVTF40abzdunRaVXIKGy8QkMhBDXesGQXnTi08Q62D3p68LWpaXCZBNb74TLdfzI5aZOkrfGFCXt5uuwZc+u5+XQOaOyV4IL9NUZ86PBRvWmdsWapBrs6/SYXtCsri1GanjtPx5ve1ILC7SVDksZMVb1f6Sg5kdOihUM8NehOLueFnJXJiTgTqOuKRSckQZpTGQq6881LI02JVopNpfZza2VZm3C731b4eGVKK5pQTUOdSYtpyMtKPJFLkVsRlKfuSZ5+3/WM/aQ+fsoZLk3ZwGuygFyuEZevlYYRYNZtIhdy1rrIlAtM5C6f8+/6+O0SiaJsH630jSpJD/dW8S4Q4sZeObZMb4SmAk01rlorsuP66orYRaZlJpXM1jqo8WFW0amrtNAYdj2CYxg6ZYYZndijSdHztOAcdJ3CKjnroexjzwqSZINSpmVhXBZ1TqiZYbOhG3q8BLxUXPQQuWSY5ZVmXJo5vKtGbz5mzuPEP/6n/0xZoCgUQHVMy8Ldx/f6RkolhIF+d6VRKkBOhWVOxC6w319x2B3YDluO88S4JGpZlAK+qHFxF1bPPfVSXXIi50IXDD93qi+qWV07uq5DmrGkWqGWyYp/x3b/ghdf/pQ4HOi2Nwwx0E2PHPYH4nbD9njLcTzy8fYt5/HIP/+XP+fP/82vSWlRs2mnZgZ/9AdfMPQ93/7qO9q5EeOBVHuywLls+PY28VIKn3/ZaQZfX3m4PfL9uzf8/lef80c//dt0Hh6PD3aAZvwm0G8in21f8pm7IoQHPmxGausQF5lT5jTOdF2P857Y9abf067TW9GrlhQQ7Z6vKFkoejMTaGIP7/qAZKSqBrXVotB21T1NnlRj1lwE58kSTGivAn0XHRIdwyYQgeEUKTny9tGTTo5ffoS7sfCnH0+8fTjx/t3IXCvnVjmdKqlF5nHieDzqzyuVn/zgc37yxWdcDQP7vlcmZU64PkDw5KYHU5GKw+HMxzRWFTWHoJqs6+fX7G4aL7/4nDdv3/OX3/yaZc6kxSp4VZP67bBXGK8XkB5kwLlrnBfG05H5PCq9HitILlwy2vo+kkvh48d7Frt3EUfXbbUYleVpf9r0WpZWSH5BxHF9/YKSF/WWFNXsZlsXeK/G2SH09LHDe43Eir7D+2jFxlGqOgblRQ/yru9owOPDPaUW+jhoca5B2aWdaBSSWAENgmQ9b0pTnaDqarNaO8aemtQnWY3FzXIuQLcZCF1k2x8Isdf8Q1HtITSmZbbAXJNwFZNvzY2SHD4M0Aq5LhfGsg9On4WhY9htaCVRc+Lu9p737+703qVdisKxizjjYdB4moQtKulqfyDESN9voFbm+3sQ6IPHVyHpYk1JbHQ6zU0mC2ke73pyzeScrKlUjWI/bC9cjsfjmdPp9EQOwpoAt7qIrl2jTWPmYmPsNRPJi5WfpgVkrXHr34giPWuYsXcrPCxr+6+fZrtcqkHyl6HLtIZ9b4XwdyyCF63KeldbQV1Zh2AQyNrhN+3+VrHpp7s/nW5W8ah+L7Guq8Kl/XD+Sc8lXs2DV3Np7VraZepcfzbtafK2H0Brmvs1L/NFehG7/sJ4AsPZ0Q5ZWqNYR9uULfGk58kKncxJU8rjZqNvQi7kJRtdv6nhb2h0Q1RSfbUdAI3owEVP7AP9pmNuhdSqvoUNs5NS/9GwGhjatbqI4S87kyfob4XbnXfWtXndoeaGjz3D9grX7ZC4RUSlIT5GujDQ54VUK5t+SyvK5nxYJtUvecfh+pphq8xP7515mipDz7lA123o+x3D5kA/bAnO4dbJqup07r1jt91BXahpXMc1c5hR8oT4gW6YCH0mZ+3cci2knGzfIqoJVEElYo/d+ruvk+D6FDnRab+y3jei+zM7oGtd9wh2bQvKzrXlevNN92dOp5xVZ+WMjCAeWi2M45nlfOZ+XFimzJzhsQpnHLOC76SiVk/znBmXzMPxxPv377XRqpXXVzvIz5GSkeyRrObMNH85KJ9ac33tTuQTyy1nMJnQiSP0W5acOBx2tJoZT8F26spGFjOk8LZUbai7U+gCi5GOdPqul4DV9ZkM0aarFSGyM8BZWkKpT6gQYOJtNXjnEqm2Lmme2Jc6bdkUZyjDykZXoOkTTOvy7BvByNYE67NGXE8BuXxPRNaj+ROU7On80AnQJmIjoHwCCOn3sfVG8KpfXP0va63qw9rUFCCXqsHWtvtUlyJN2dB/V//K63ki2nRBz41aC0vNzPOCW++BdsG8kOzsWZOLbGXJCkEusTONowYY51Lsd+rUz1icRjTZyJuTNtnVJmM+GajW33u1uiPrOVrsPPz0Q4TL+u833qxP/7bxVz7kk79++h9/Yxa/XCcT3f6V7/3JN25P12n99845LinP/46P314Ei8J6uncBZww8tQMTZQS1SqnpoiFsRd079IASPeh84OF0op4eWPUd50kTw8eULt2TiOBi0QMtqEzCuWgYt0KOc86I0/TlIELvPVIzrSQUy/A4F+l2gXFZWKZF3Vh8pFS0O86JVJKmEotTzz3vkKzU+GJRItX0Ls6tB456Qj6kUQuQz1QKa2pjxdFtNrz64UumOXE6TYwOlrJQQ6O6QpKJBWg+EaJmMVId2+2W1iAEJXY00dglH7y9n4W2dgPS8DYKlbTQnFqWdSGyG66Y54Xjw5EwHNhc/4BUPHMKNDdTZKHb7xj2e3rtTrjZdPiWyOeRVgqfffUVh2fP+If/k/+A7W7gP/1H/yfefPsN3775lnlawPf0vuMnP/ghrz/7nP/N/+p/jXONu/s3zOeJx/cfSPPE1dWB/X7HsBlo2VGlEbpAHALSCy4KiwskKrWr0Dea6OQzzg+4c6MfGt5FgreIGPfJLdu4MPbWghjR52TJynrPFWUSVw1JLhRyqqR5fbA8c26k4tW5v1VcMrlwrJdGTZzjarsleEcajxyPD/yrf/ZPeLy75/bjPYJwtd/Tuh37Z1um2w3xfMvyeKK+HQne8+zqmmVZyAKlJXJe2G4jn79+Ri/QOSiL1/17L9QIvlcT6eACTjxRIq55nEREIrQO7yJXW08Yep69esWzl89ZcuXrX33Df/6f/mOm8cQ5VdLSOPPIzh/Yba4ZzxOnxzO73ZadD8xp4TSdjYUHfXEE0NWCczRzTQkx0tVGmjOgdmggzMvZmmDtzpWj4inOU1NiYlFBeq6X4y8GR+yGSy6iUFhSY06LHewjgrDf7+ijJrBo1JPuOvv+SQJDVleqNTVdE8wtYb5peoigDXqpynpsQS3QOrfab1RaNBi8VjKqhww+0HsN/PW2by6LNnVz19EanE8nSqnsrq60cQoqY9leXzGdz9w+3pmI3c7XCi5VmswMIdA1cF2P224QNABb9SDucgalRVGt0PWanbqoJCzNypat04z3nn6n2kW8+tC67R4nwna31XSas+7mHx8fOJ0emccz8ziSJv2dOvcERYfocBHanCk27evHClmu09FaCddKap9T2tOnP3026wgkn5a7phdm1fviRDteY0W3Vsx84elHrjW0NViMlOmd3iPBTDJ+28dfyw7VTluxcC54tnVpfLLHY52s7Fey/742sa1ZOKhh9rVWPfCsz6v1k2lTPmVI6s8odZ0uGzTNxxZnnqMoq1TtFtR9HMQiOPT1iU1cdS3WtV4WsyrGdE+/5yeMPPhkj2SvtVR981rJ2lW5lcbszUXDqVFsjCTbG4izZW+rGnwr6qizdjGr8asPukMp1gU753QyMf1gvdyAdo1qsZtK97Tr8r4KSIjEYU9Nxjh0aMEOPcQNLvSE0HHdeYZQKZseqZWvfvQVNy9f8eWXPyR2npwKp9OZeZpZloXQqdD3ardjt9lQsppWL7MSLGrRK+u9v/xpzSM+aFMUvFl5fjLCOVG4ygO1kUpiSTOlql5NdyLYTsdcf/7Kh3KxVhTAgnAtEWGNzykNcmnKyLMufN29VdsvIWsmoUKr1aaXZZ7IrXL/8R3Hh3s+3H7k9PjIcZkJITA48J2n33YMZWZz2JFyUaRBzGn/QiDR9zAENSzoWiVQ8WbQTVAizHoQOTOBVjcmk3GbU43+vgVSYp4maI1Xn71iPE10sSMvywrQGVypJ4ZC/5mcEss860Rg7bwWkvbE/nQOaQWsKfbBX8zba1tJR+ud+fQ3K2pTRc8AdQGR9T9YuoU1wJcDZyVG6D0v6JRXvd3rnxg8O6/f6+luqJfJf2UBr0/L07nE0+tYd4bOKTfhoiG0c+6Tz8FOt1YrVUyXKFAskUF9lpuRBz0hmpVX1ym8uJ6hn96zdpFaUUkDaDA4NsmuZKOnAqGfuxYKqfY71gq1kNtMcRZJ570mZ4RAMs1i7NVr16+oho3i1QglT5O6wWvyBD7qwFMu//w0y/+VEfA3boSVmYux7z95WC+f/Fee5U++3YUMiqIYv3F/Wb1of/XrsbXI5Tf5/xlBf+Pjr8kTNMsmr5DKtMwGZalVTzWXdNDUdI3eiAyxZ14Sy7ywLkWD94SL6TY09IYTBy4I01nFjn2nzggKxRbdPdR2CQelrGLpRPMO6YWlFU75jOoTNbxWolNKdlDbrFaaVobmLGG6kMpMrZVkLvddDJZgrYemawFaIyV1Y8A8/qhK0FlKxgWh33X46IlDIEbHdB5xPnDY7e1GE3KaSMtCSpUUikGtTSe8mgl9hw9qCiAi+Gy7v76jeNUa1qrwbqN90k3OBs8oyWCshTlXUmuEYcuzV19yWhxtFjp3JvozYXMN/YFumOlK5Q++GHh93bHfBIah4+//j/7nPH/9A05L4sPtR958/56vf/095/OJWjJpWQgH+PEXrwDHP/q//J+JMfD8s2v6KBw2gbJEatIuNHZBR7QeI6I8PVhOVKCrHoEq1C2lcTwfWXLi6nDQ/bOrOEGFwQi16PShw3FDbEHfiu6s59IMwi6XM209/JdSWbIKtIPzWuQiiHikFvoQid7TR6dKoKpT2y//7M95uL3j3/7Jn3A+Hil1RARi30HnoW+EXeTZ56+I1xvu68iHb98xfnykNvju4y3H00gzr9GAY7sZuHl+oCuFWBvz0mmsjHdkJ9YwCMFrgkfLKo/wTs27c3OUpfD243tSTsx/+udc3Tzj3/8H/5DoIlfX17RWmc4nnKtEG5Xn85E0zZQ88fgwcbyD1CoiURne0UMqlLzgDwfipqOOM9Iaw3YgdIHWjuRcOE+Pel4YzLqY7ss1JYOoJECdbqpzVBdUdjNnAii0KNoEidepU0pWPRwqCqeqxGLYbFRnmDp9371XcbS2sYg0a3aLSSCfDJ51DvSUlkmrMbQ87aVymsnTbKxyg2ddwKO7wVYKuTVKUn2iCxFxwpwSpTXGZUJ8YLvbsek3ph/0dEFZ4tthYJ5nxrlqEyxqBhfFk+bE+w+3dEOk30STFQXlM5RCE2XYqsQlUWeHSNUz2jsW52i1kKeJ1hrzctT7Jgx6fk8zw2bDYf8aYsC7wG635epmyzg+Umsxd7CenFXqpebhOnnmmlnSzDSPql0Gk3qtCpq12MmFi7KuIy5IjX8q5itB1iBGk7vlp5lQtAFt3lIwqie2VbMofDKZPCGiDVadhmqAHUsrmrTxuxZBZ1hq+WTHp0wkPcwudVm4TFwqT9FOrtgv6+wGFNN5qK5FjNAQqdWzLMl2hfpdcym42szSSTtYtelUtwERb+JcrwVtUWhGaPowNTOVLarZWeUP3rvLtLTu2kAfmOp0+rvMoZfxfZ1urWtr8inBSZlyXuNeQuiUqYogToNDQ3CUrOJtTbGul5/xxEqtn8z2XHYzwVtqOHJhq67TZGtP08xKXlhvoNVqyYUOjyc0bzKBTKmOnCvROWIXOGwGnu16+lCJrpGP95xi5N3dI+8+fGQ+j7oLNIcSvWI6fXjnGLY7nGss80wXeg6HA9O5MZ202K1aRUVGNJGDKkqoEvPAbPoYpVxZFmXx1Vp5eJhIC2wG9QT0UbG6mhQeXieq9UZvVSN+Uqnkos3TpZu3a+O90zBX59WJyD5SUeutlhOlZJas/pEhKPG8C56uC2w2G6RVuupwrKn0hT6d6JaGW2a6WrjpB9KwYbvdgHdquTcvOuHY4bCaeutQXPX1eJXpqG+kXJ4JeNoFO6m4y72Vub29ZZxGjscz52nmh7cfqLXwh3/4E96/fUuaRkpbqG2259TM9aRdoP8LE9cmTaGa9GR9fu09LEqdV1s0dQ1BoPOd7RDFmkWd3mttiupZbNKq69IZj8u+e53U1vfJOWckEzPGKMW8LfX183RVLtPyuqdcd6mrebR9a9ZMyXp5/c0Kp3EZmk1YBnmKfX4pQpZCdVUdmkRJGjiBv8JkXcGy1Vt1fQ3OreiSjcifMNzbOg1mIWWxvZ4zG7h2+Zy1gLRSjbms1z46j/hmGY8alItBzAB5SSTvmKdJgwa6ga4L5ja1vl61A1Q9YWKF8ZpxPRr6uT549VFZv9CGVP10u56C/nU90S4D43rGX2BGniDUTz7WM0zEdpganSaX3WX7jc+93DyXH6QHcy0F/jtQo08/fnuobuzJOfN4/0hrVX3pHFbCsV++qW6nVspcyC6zTOpwn1eNjfNgNjYxeu00vd4cfRxM6OxZpgUsw+v0OKL+gp35RWsxC1EfNLVdgi4K43nieD7a46Dp8r44PVCTGmC7vqMLgU3fM3WBlDwp6Va5poyQWYpBFkan1wNSEOdxrWrSc4XFnPnF6yQyp8r+esOPv/ox4zTz8e4eJEGaaO1poV5dT14K5zSrq4S3g6I1y08rSBdxog4TrTWutwdlybZAbZXjPJJK5nQ+Xh6KLkb2u43BEw6M/Rb6SI4OFwa2/Y58FtK5UKaFsY38YFd5/nzLD19c8eObgdvvf8H58Y7/7N/8W45T5pt3Rx7HmYd37+l9oO2uyK0x58YkPb/47i2fv/6C/8V/9B9xfLjlv/on/xnPb/b83T/6O7x58zXvPvyShYVJDTFpbWEpibkkqB6yR/wCMjMujSV57j+MnI6JzX5LiPD+7fc453j1+hn9puNw0PslnTWhI9shHb1COeI15bwaUQBEJ+U+4g2t8LHXf3aOIB5XMq4U7ueJ0zRzPD0otJvVQP5HX75kuxv4g5/8iNLgxz/5A9o4cvPtX1KPd3z89Z8zTyfu335DigPLaWTjO/62P3B9aJx+78T7u3t+9e23F1KZqw3XPCUH2hKU3dYKUTTb0EUheZQ5fbH1qnTO4V21hITC1VXP7THzx3/y3/Dh9pa7xyPb3Z5fv/2Wn/7BT/nf/+/+t/zZn/0Z/4f/+D/m3Yd3/PrbX7GuV8WJmXSrPCcnjZ9yQZGR4K0JA81LdA1c5fT4yDTOpHlWRMTo+YPZh/Uu6u9pz0s2slyuiuYkI8yI87SmrGzoQDyegkf3/c4H2xMKJSdqUsgdacYatVWDWGq7CNN5VFgv2DnQCRJU3E/VXL7F4N8lNZas/74ZYxiaanudoIkalXnKzIu9HufZDIUQnOr3cOwPB2LwXO22iPOWd5dwlvyRaiFTkeiRLOpduxbCqlpMtRcU6pKZWia4SPSRDg++MicNhnYVKM6m0kyxtcJh2OIHz7JNOnXHSBNhKomUM+fHI/M0kZaJ6+sb/uAP/5C+7ylNeQ3ilT3cd4HTYyMtkwaYOLVHm3NCgqMbBiQW+tLIRY0jijVQmDxLmuVJUi+SGKxwIyBhbeyiXvOSLpCpYKsc75E4aNCCB1qvn5/Rn1PQr5XLY/7UWCLmhgRlyr+1AP61RTBnG8WNQbU2pLKi7ZfCu+7bGqsui3XyA1bU9pN/ZHVs0AInJqg1X09R8oJOaurmoob47dIBaL6fOgws80IuWX95YwauWDfrHq4UvTmzyjxiCMwuWyW33WG1N0w+6SZ+A74TVn0Z2L5OtFPMKXE6nUg5I63qurfYfoKnCa2h8JDYsng1GRZjYLQVtbWp+hLMKwr4OG/G20F9F52YQ4s3XaTtK2vNlKJ/qhpgWXpAZJ5G0nxkWxJhKdw9dtwEhYh9gzKNLKeFMo3UZSFKI3pH8bofTa0CjmlamCxnLrfKNM2M08hpHNUvdrPD+Y55KSb72Bi0qfT/5j3iB3ADXScMQ6brFlIUdtsdsduwJDVHWEqFVNjjERdwQeOOXHuyxTPMxXYy+v4E05xFS9723tn0YwbKLdGWGfLC8fGO08OJcTqRUgKUpFRypqRMEU8Rh4sR1xr7wx7ITCHiY6TVjkkc8/mMOGXGdvPCThpzEK77qF661XRa2RLp7eGST7wzYGUvqmXwxQZKjLSDabecvt/q1ZqY5oXGiTdv3vLy2UvLV8w24bQLW7K2FY52VHPZ8cETxbRm1b63rESSNW1BeDKsXXezpt0ymv669pB1SmjNJklzZWk21aDRZWLrBzWfXjWE2uBV+/xq+9T1TFkZ+E9Ap3s6bBX71ulM1gn2Cc1ZG5GLraMuhC8o1Wo+1Mxyr5nnJTYNlaq72aecQr31gvOI86zb0TVRo+SiDYGhFaqBtXPRhklZX49eFsSvhESzvDOUgvVcMMmP/BVkcD1fV1RJGc3t4qBUar0YeAOX892ZW5DqJ+3b2GtckT0NrVXLO5xC0Q0h2vt5mY2qpt1nW5quP6s1Pedi1+FcIPgN83jm/sM7uzZrIbT3U9zFt0D9kj8doD/d+K7PxtOxvb72/38+fnue4OOD3kjBYEwLkdWprtkDoNCjmhjrEt3jcU4z27yN/w6enGcArEsoISIBbq4OtCI8PjyypEU1RjmTk7LDfN9Dg0zSnWBWLc80jZRcyYs6z3dRjZa9d6oxdJCyMs2qg6lkOu/Z73Ysk+qNsqVcuxpRhqm+DRLUP6+YWkU8RkxQ1myMkdIKU164v7vjT//0kd1uy4sXz0xUqnuHUhtUJcysQvGWlGqcl0QpFRG9Mda+RR86DeKsruI7b42I5TRuBtNudXgcvQRqyeQ60crCOI3M0wPL+Z4qmSoVKZ4oV7y5+47v3/yCD2HhKhTk9Ix3L7b89CrwrN+xCyMlFF5eDWyXwMMcdSoQR6pCQdl1j8eR7u6Bb959y+PdLW8/fiCVkX/9ZwN9DHz++e+z2Qzc3s/c3Fxz8/yFafKqelg7cPEaF3ZImNnuMzV3nLYLX371I7a7A8fTiSUl3h/vmPJM3+/o+562taDnJeFE2HS6I9I0c41/6fvA4Wp4Yh6LpmPfP9xxe/+RZZxI08J4vmeej7z/7pbj/cnIPI6bmz1d7Jlm9aV8qCcyAqFjENg+u8F3nuO3B/ou8uLFFQ/jzMdffaBkweU7urbwqp646j2vv/qcx3nm9jRyOp95PM4Ey9N0iEUV6aFUWiGVymJ5gWpW7S4w+no4lFaMJKZs0ZLgOI/8xcMvcNnzX//xf83XX3/Nx/sPjNOI91FJOkVdOAKO6pQ0FPsB5zumuZByoflCc5klTUirygUQT9zuqKHTQprzRf+V00Krnujtmhs8ue5xRDyuWGhu0akuxIDrgzaDJDUyF733pWE2bUbYEOg7bWbEInO0sAqtOFox6zJEXc7DU2iy1UUrIKhXb636+u0wdS5oMoQoMmRUIs30Q80bKJCKo4pje+j19XvANXrbv/mgE2zNymScjyPzOFKymgL4oBNQq5Xghb7T6XFJCdccDk8InmHocbWZZG4GEgkdTFYEyXmvw0PO5FrU1B5Rs3LvCEOHi4G6M1Y/BZw6ENUC47iQciFET82ZaZwVCgUljJVCsgk6OPUGpuqeLXa9NbVmoWjZhqrdwxoFIfTmthNFiXi7Z/T9hsPNK7799df8F//J/5NcNBVGeAoVWLMvtXjrM9FyUcr3ujlaB7H1T+ETWJ/Le/s7F8Gyjq9+rcqmvbGf/JT8zKUrcWhFXqfGixntysK6dGw2VcyJstSLl55+jZlLW/Vfu2WFlI3VZtKKYg+gj4EuRrabjeLuYoXsMpW6y0he0cQAJ47gA00WbQStG3Nrj2Et0cWLc+0wRa3NxIum3TeTVpiOpn7S8VEVrqi2K9WH0DRsl9f29EcHuXbpZHJVc7dQNIIqGzvLWcTICvNMZVaIZFksWaGq28Q84rpA6LZQI1J7hs2e/fVzfD6R68j9WHj3MPH7+wPeKzmmtMrclH3bhcBSssI19p5WdN82jmd+/au/5Pz4qAa8LfGrX3/LYb/l5fMb9WrNmWlu5BoVikoLzSvlfrOHfqiMU2FZNN/NScW7SgyN68OGXDuSZOtgsV2YTsJr5tnQq5mCI+nEIkqsKktSE3aLzqk58+133/D1N782pl0DWUAyc5kprlpnr+ScaTpz+/EDtTXus/48LYJCc4Uwn/n45h2tLAyD7jS9eIJz9E5jJV77gbEU7pNmDNYguOhpfSB40YQBm1yoGqyboz4OKvPQ+8A1Yejkcl+tk3+tdl9Z2kqqmWWaub+/4+e/+Avevn3H8azJIIWCVMEXb9dJm1gRZz6y5TK56R0ol6OkZJWveO+JsZGCt4nLJhRbj6xf4JzYBGf7egth9ZdInBWBkd9Y5Vy6edGbbfWFvUx0AnoH6u8vlzPDWLMX1MZZ4+4u33glR13wGSefTE52TeHJ5kP0+zTbT61H6mrAvT6HeubZBF/UJiQtszZmeSEXJdfprl+hbZF1p79qI9fpsVyKYrAT8ym5Qj9fjalXprSx7Q1lajTdDzYd5hu20mENNoBxmi7nzTwnmjUPuSkbWnzAUbRBE7VLU6JLtTNx3daur9+xpqwoZ0KzZLUoOCQIoY/sdnu++urHbIY9V9evKHPSvWlVlvOFDbpOxEEZ5YqcKAcBH1jD2C/viO19lbVbLT2i8Td2jFldCKJ1XcG6Mb2Z6uVBxKyRxKETg1vFB812dxZCKqbHc8rTojTGhzMlV2IcNDbE+4tTQ5M1IocnzZ5UWi7M03QReDof6PuB/XbHy5tnTMvE6Xy6VGLvve31BDKkkkgGJW66HtKk+0u78M6bia7ddD7qG5SKpaJLAy/4TouguGBkFYUSUlWT6c4HVsPluRTmWjRkt3BJudc4FWXPijh1Q7d2VVAGqlR3gYmmMuEcbDbq1pMWjWpaHkdaLUrqsEe8zBPz4wc218L26kD1O6o78OqL3+P69UtO779jvv/I9w+3HB8f+XuvrpVRepPZ7gaaXwjnhe0wqL1VTUhpBNEjLOXCh/fv+H//J99ScmZ8POFd47tv/5JXL1/y05/+LW3QSuVwuOL5s488no48PD4SOu2YP//yhzx/8ULfe3HMi1HP2xknkS+++IzYdVzdHjSVPi9IXTjsrnHOkaLHO2HT9bRaGZ0mb8zLRJrhfjyxLBPn8ch4PPJ4e8+//ON/yT//5/8Vh6tr9ocDn33xnJuXB0Lo8ftgk1jm2+/fMj6e+LO/+HPuHh54XBq5QXOe6By/FwNdK+TpFi+NfR94tjvw97/8CYMLbLPHx8rvbbfcnk78+uMHHn1l28Ojixzinm3nmVtiStm6XN2L++sNEqPurZbKMs5QGv1OXWt6S5eoCcoCJUErumOnTjyMd3zzzdf8o//H/41xGnn7/q1S1F2glog3otYKPbkG45yYlomuVzcUnKc5LTAeGMeZXBr90NN3gbJMJC/IYobaVXc01QykvRdqVsKUiKdzg07pIeg6I2Vq8zrUiJJHxAT3BAdGwtBD16BVKoWGqwVw1JJ1cvKBGipPwk5BJOJdVLE6VmBMBiHVpumgiSBtnSBKMx10JaI6v+Cj5lBaqkSt1SZjlUL5VUcsldYSdWok5zkVnahS1sZPCUGVUmdEdPeoUiGP82oxV2slz5WxnllSZohRm1AzsleYyyarVi8MTGWN2qrMirXTl2Tnbm8HnEZnvX//Xs+I5liWhSaB3CpzBZzHDT2+FaieIJprmkpRmZuPKp63CLImjua8Gp031CaOigsYKakQXGC76Xj52Uv+x/+z/ymH3TU325dsQs//Nf4fydWcnKxu1NogqW6y2+xp50fqkjRYuOtZi8+lEAetL+NyVtnPtEBVyPavGwZ/e5SStoqa5vwJjm3Dx8XpG8yTU1Sb4VZWEUZVzgqpilOIoBkGTmt24Ak5ZbJUsE4+L/nCQAN1NmjewSWDTC4/Qxf8gQaMy8I8659UVoG5BXmKvh7xtltx1hX+BmurXjpWXVvaPsMJ/dDZz2y46Oi2ahSeczI2bCV00ToivcO9051ULYmUVm2fXUPUZUZflxVenMLEpmVsTSej4AsrC6oZ81V/NpSUSSnrDrLYTSTC+fGet9/+jP35gbSMHPY/4XB4QTlWzuNMbZ7mt7RQqW6A2CHese0Dna9MfUVa4+XNDd3U044PuCXRe6BVZldRyZLCM95FnNPA0fM08/3bj6xZbHcPEx9vz0zzzNkYaj4GjmNl9+ajavJEJ6mK8OHjR7abLc+eXRNi5PGY9L/ljBNhv9kg4p50S2aNpikkml23sn9LVs3hMk1MpxMf7j+wudqyOWzY7DdIH0hOaClR5sTD/QPTOHL7/p7xNHF7f+J0mpmzQvnVO4rz3DIQpJGax7XGKTlO50z88JHYHF2uRN8YusxSCqelULxns9nTep3m3LDj3MLF5L2JHtI732lYaxfxUmiLTh+l27KEwLvThJszN9eFuTYKev+N88RiMppcMg/HR1LWCVCh/kzFUWrWa9aq+cHqDrXrhBBE8zFN81qxXRF2Bpt+Vj0oywU5kZV5bZAt5vwUY4QVfjSY1AVP7AYtZLXQnOo01w1fLaYJ/gRVWhvw9WfRVvmDMqd1b2k/+5PR8rJ3RS465VbzRUJhwBTrpKhf9dR4rntJ31CLQlknZ+UaOIJOvracU8uxckGi1n2ZiBpdx26nsX/B0agWCFwuu7eG7uoqCW/IEIaCJBP7K9j1FGPngwcnNm1yScIR12y/+XTtcqqcTpMVQT1LSln11Mbcb8bIdF4LZNGCqZ6kHqpHQsS5oM1pTTRnkUht0Z9VMiLCsoxs68AXX33G4ebAsN3QD716KTthDRJfUTZsZ6lnulwcbUBRtvbksvY0mTZFytSMoBGGoEOTc3/DIugqzgtdp7rAlBIrUa1W1cusVGbvTdMjsu7MAZ2e0jITej30lmT6nNSgQh97nHgm243lScXW3pkxdC40FO7z3tO77YU+vNo7SfR0XU9ujQ+PD6RpYj6fUJJEIERPCJ1R4BNdUEGqVIUlnBdqdRd2k/MRcUo9x6y6fPDsDhtNSPYeF7QI1tYsNLRZwdJbrYmjiSN2Pbt+A3Ukz4rqt9bISUNW6VQnScqIFJx0NBxzLuSaqSXpvsh3Zu+mgbDzpISRmrVhGMdZ4QNjBDcPH978mo8f33F184rnL77k7/73Ap//4d/meJo53t5SaqDFG9jcIB3ILiF94Vno8dXjExw6D9sf8zgn+OVf8CAjyzIRXSHVrHZjGYoEhB6h4GRSM+d/+0sEPWBbFXV3Ea+7oeAR7yntL5S4IxrkJG2FypWe7oMeVKGaR6qapOKbCqebjyx54f39B4VgJCgVPegOJDd9EINzxucQfv8nP+EPf/qHbLY7tpsduReWCMv7E+1h5E//9Z/x5vt33E0Lcy64ujZtCsGU6MnB844NiGcKe1pt+KUiY+Zfvf05rSTKPNK3ylWr3Oz3fPnqJYebG148f8XWR65ih79+zUcGqsvUUNS6DfhBPNBv9nSh4ktlXB7IUlj2z0nO8bOf/Rm1Nv7oxY+YszI0p5r5cH9HyQlPI5fE7d0dVSpFGkjBoc9fasoGzUslOE90quccOjVdaKjtnjjPkhMlFyuAjmjSqTzPpGmiD2Zt13t78BUREVGjhN1mR0qF83lSu7xoe8O4JS0Ty3wi14hUd1mFpKykrlVPtxaHNcIr9BvEebwRUS4w+wVL1Sbxqdk2710f8QglafK86gTXc1LPk/UBak5JarmqGH5oHcGBhE5ZoBZLJL1l+2mHyzJOrE2CmthHNe/3jthFDtfP6IJn20XuT/d8+/67S0GlaROQUqbOiRYS2Qe6oOz2sRamnOhjr7+7raG6YYN4p41cqRoyS9XnUnSqFhquQVoSx+NoEKg2OtE7WhUbAPyFlCKoB3LKDSTqhaqRhieEHTFGPt694Wz5reLA+cIKGrZWyOnI8xfP+B/84D/g9Zev2R22dKG3NByVvqjk5akJic4zDFtKa5zHCakLOE1WyaWi5iBOEzqaHXzSoAu44NjcDIRwAYD/BkVwPZAuEwiX5TJVtVnrlk87yXXvp13l6uqw7g7X7q5VFdGKQYVVKq0Uo9CuBByt4MHrqLRi6UpbNoYdmsbdikY1VcPT14gRfTOD3YiOXJ8w9zX9AtzF4QUr3D6YF+JFz6d4e6s2xUb93HLpjp2x9lRb5OSTx6q2i2P7Coc08zItpSi5z3YyzgneKzmnplGhVxPy1Mv30o7HBe00NComaLdd28VIRPwqTG7k5czD/Ts+fviWd2//klZO3Bx6puRJ2VEpzKWRq9fVtCS8OLrOkwGfC0GEq90e5wOPS6VKwudKy5XSRJf3dT1GykV3BurecLmPVoQAvWerbYdsAUQroiePMY2rNSrVgp3XsUCasoE9un/oYm/7Jy6U/9Js94s+bDoJCLGPbA87YuyRoM4wQuP+4ZHT9x95eDhynmZSUlKGEqVWpqERAEQTEWgNXxUW0n2SY3ZAcxB7ltY4N3B0dHPl9DhycrfEw4HuusPVistra+vU5cfrFJJzISe1GVvSogxQQ0bePzySUuHV3aMKz5OyZb33tFpY5pmKI5a4nhe2s9ddfF1zPanU5in14kOj04WxtkXE2JQ2xQnkZb7ohlWTqs4tMaq5Ri31MgXBKhdUU4TaMA2a12ZzXcKtR449bfp8ZHsd6y5M+JS6eJGcoSzyWk16cPmGlrXSbBqznSLrHo61uVnnxvXr7L0UZwbq9TJXrnd0WydbURSruqrBt824FA0WSYgr6KNpdoitMY4TyTuNNkqJLkaN7Yqqi26mn62lEkMk+qDwo4ithPQpqzZvNsSYmBpjVUyOAmIFwia8SzKD2PdY/15Mk2fa8NX+zGBl/cVXfbWda9rlXO6PVvX3122RcSJsF6soF+z2e7bb3ZOHdKm/ofd8OjWfmpJVd4ioI1dzGH1+ReqM7+EbLgivfvCSzXYgbnTQ6YM2Vb/t468pghrnK8ZKK82gvKp3ttSKd4JvXGQJxiEhOE/XRVbRaULtqtYlPk2FsCvcWbM6oojrlOKOTgPRRVorTMtiP1sdZOaSVSrgvDL35qOSHEpFLERSXESc6olC8KSsb25JyvZ0LiKivowtPC14Q6cuvMuyxnVkqJWS9HJ5iTQaKScrgH51sNLOo9lDY4dNaZmcFN4tWcN1V/lJqF7dQ8xSLMSBhqONj+TWCDGAc2Q7cMu0ICL0m95g3IiLDdkqrEDR9yQGRwaW1kjLI8fjPbtf/xvi5sDzZ6/58vVzbo/CcYT5PHGeMkvxZAIiC95XNkME7/BHhWVevXjBbl64nRaac3S5AJVlcgpdFYXkckuXGxiDeNWazmtsFXI5bdfDlhahQmumxl8NCeqCpsHrwYA8NVZOYBDBh8jN9TNKrZyXbIXVoOSSjYBQ7Po6NvstV8+vlaBUIRoR5sObd3zzF3/J+7sjp2lBwQq9oUUwbasjeoWNXWtIEYZsGY9oPNRifrkhbik4jtUxS+N0LrjxEf/xgc9++AU/uLpS0f9sIbRe2HYb+qAP+TIvLLMWwnE8sqTErryiVsev3r7nPM4cXn2A1jhPiVIafexotXI3PRBLoN9sdPcVtVn15nZQDJFpaOOQEVwF15qu41ZG20puMdMJgOl8VKZjVZNt71WKsukHEFimGcMHtcm13XoMHSklTuNIjJHeC476m3WLVdKkz4i/iMzNfNv2YObodbEerHVWX0vsP9JQG0UuRbCux6uIWorZeVFWnPfyEta/92qgbqYBIBdoGLDJFVgWBGF2JpS382U28+quFTMt95RauL9/UAjTNUJ0bIcNa2BAXhrLrCSQmjN97GyHWq2QgnONJsX2fzoJl6ww4HmebY2k66fQil6OujaQ+hqDwYRKolnDCpQ3obKOJ7kKTf2enTWD6ypHn19RF69c1PLQdqZOdGWk+1b9/Z/dPOP6+hpvKRgl50uBXD/kcmI0MLTHi1B8sGKbtUvINumvZ2/viIPnb/+9n/Di5Q2FhEjjsB3MLP7f/fHbvUPt7lz1Ka4Ze3HFYdEKraJQp0tIu0l0P6W/oHf+Amc0Wzo7/AVKBaUnrzd4a03fXK/ieii4Trs6Zw/COjyId6bzWTsK7XhqRfcaQQ9c3WPaVIb65IWoUwDibJepD5qGtpoGx6q6c0LoIiFotAgCuVkLuTpSVL1WreTLa3Jdo+vchZ3YqCoxcWLzM8b8QqepxuW6BGfmt2ITksF9WuS0Myw1a2MhIE4JAk50EvQCHU2da2hMp1u++/pnHLY9u+1LptJIDZx0SAmcl5EP94ntpqmg1XsCwv5qS8iNoVT6caJ3bxglqdOJq4RozNaiDirBdetpohZ1pdrDJ5cHT/9vLTAGf4rm5LUmNqk8TYIEQxNW/0B7eEozk3cXjLFqewVpNsF5vED0kd1m4HC1Zbs54Igrt462FCiVx+PM+8cjU06sxH7XVHjsvCNGhaQbipPVReHyomtsimvaNLV1ErVGwA7x6sBHIXaOuN/S7bZsdhv2tlturWk6RlVyj/6+CsH2mwEXI2lemFNhPE2M48LdcdH71ne40KvIeC0YYl242QWKKOJRVyp9g1qFVbTkYsD/BtqjU7D3Wj5LzipvaYrC+OBwlrZumzqcOLa7rb7XeHIunMpiPzMrO9XrdFVr0mfbpr1mz8E6HKquVhEUdX0y2vyqD3FPA8sayiuX1stQqZXVaa+zouucC0jmFMVYGaNPY+E6+3Eh1FChuoasp2Yr+l7ZRLW+8FUPuDLbfUnKLvUB6/Quhb4RCNqFEX3Aez0vWoj6vUohldU0ulrzt5YNVZA2KmWx3XjVZ914U3jrZexo1oLTVnkbF9jSrftYkcv1XTm0KyvUScCLs4i8xul8ZpwmTaq4sHbtOjaVuLWmu2hxnhg7EMfj8YwnE/Kggcysk/hTOVQnosWQMEU3Wq1IaUjVJcs6LQJsdz3DvmPYRfptIG46RBrT8URbfnPa/Ksff00R1JvKDFJwxVmhMqhrbRY/LYJVYc0qhWq6wuCsirdqAlftgnSprXBn8Nq9J6vwTW0b6DcR55tOALWQxqQX1kZ18U7H72qXUnQkb0WgE5xaTF4KYMmFUhcqxSjs6uGohrJyucHWYiTW7Xjn6PuOEDu6fqM/blnswF1rnk6ZeU6KVeeM21R659XxwlI3mmj3pbt9BTZqexKbAmaf1fBeXWtyqkjFHO0dQ6fd4XkclZUlCvmuOkkt3BBcI2Qh+srp8S0f70786KvPuLr6W0xV081Vj+R5nB75djlz/ZnteEMgBnjeb0g4UtIbeOMjZ1nwoinkXe/JHkpR4XAIG3tERffI04y0FWzT19ZWtbNXQoFzqBmwFcLcqtneFVorVFcQxwXSJuuDmWtCEDrfgXNqHixijD3wqCtM7yJX+xtev37F1f450rqnPd9UaWPj7v7Mt3cPDN4RLuQAiF1PCJGuVzLOMmvKfZkVlUgZ7ZoDNunqR7a0A4J6gNbocZtIt48Mzw4MN1ccbg48u9qRloW8LKoLy5XUMsVr84AI28OOUuF4nDgeR073J05j4t3dRLA4qtBlJETEJ53CUMie6nDV/H2dVwJay6qtq96KWiEU6JozmAlaLjQnRO8gOE7LRE5JSRxUYvQ66aE7cJoiM/urnUJUWZjnxDgVslQW04L5oEWw1BnQXMS1CJZcL1o+lTVlGgUkmoeqM7a52LOD3UvNJoP1SLUVg3iceG0wxQxHqsLkiuMKUC8OI9AuQvx1jGxmG6lHjDaXjmbMyLW5F3VyWZsIbXdxtRBypXmP92LNlZoPzIsS6kIQpPNI9KtvOs71OBc5Pt4zzUeUxKTsyYvJdWsXZGWeFKJ2Uaf46LVZiKJ1xEi22lQ0Ia/2n2KaPJ7MtC9OQoaUrEsLL/r+tqZC+PvHRy1iuaoM4lLD9L3MyfZ16ApBnx/Px9t7gnQcPKQl8ylLeWV9llpoecL5qK+lFFrOSAZnRVCN7TPSGvvrHYfnG7ZXPcNV5PmrHa1Vfvanb5nG6beVub+mCBoNtKRJoUKDN9Q9fh1bKpKzxhTlZk4WDZzD2zS1spZaVRaTEmhMUGquKj5oB5tNO6RsqcJp0ptkv9Og09o1Nd2NXmGcRSHJru9t9xeU7ZQLfYz0sYdWScuignixG7ZCSxnd96/8Nqdw86cyBcEmksK0zISaaa5QauM0Llr8jNVVix2qLV+IHWlJTOPIkmZyWTRJoDalRUvAR53aJKgvZ2kZoeJ7By3qHdyAxTrSnLSQZnt4xTph0ZtiWfeeteBcIwRtNjbDQC8dWwncvv0Zf/zPRk6pZyod3u8Irmf6YqDuBs4xGllCH+a7OXFaFn71y2/4eHfHn/z850y50N28ZBMGNn0gxo6r/QsqmVSOHI9H3r57T82VZVrwLtIF7eq8OKXGWwvksMnNNXx0uKakJ+fVaxUc/Vbt8qY5mcdjgebwQSH3YPsbkU/6SYPeN5str14858Wr53zx+nOu9gciwnEcOZ7OPHz/ntOHe87jmeGwJWBsyaqGDji9L6ac0IZEp2/WRrA3BGRNCknpk9VVoc2Fw/4Zf/D7X1FKIqWRZRp58/03vPnma5XMmKvIehzgM0i5IHulGcVI9tTmubr5gu2zQO4OJGAsgZQ9OXtaDSqVkEoyAggCvov4EMk1k1Ih1UKqyaYqoSmnnZXZ5kKH8ysbu+L9/5e0P+uRZcvy/LDf2pOZu0fEGe58c6jKmnogukiRLVIiHyToc0p6FPQVCAiCHgQQoNCk2EIPNWbneIdzzxCDu5vZnvSw1rY4SbCzwKwonMq8N+NEuLvtYa3/+g+J3oQQFaouLpuvpu7zWgpFYNsywXuSnwjRM81JC7/V6Ty/N/OetFrW2fFs7iXqZjN0dAPGM//QUmmu46MSyK7LVTWx8KzlEzGUwqQRrVO6eo7Wrg5AysYfnd448D6akQ2oTwyZotslBL00WlPDeu8dPiQ9y8YpIqPrNMa8V/mPnhUg3uMRUtT8zbwWelXrxKFtDAG8V7hvSkfVWrbG1lY6ZYcSSy/0jnaV7XlOl7Oah8Sk+YLXi5J1xGlAQYiTmk2UrLpR7/YOXAfrSj5RwFr3Z6NQuqEzTfkAivjZ5Vz0M4tO/17tmY7Dh4k5HTgdbjkd7+h9gupZFyWCKXu40FtVaD1COEyE0w0hJmKczPN24/6HK5fHTYmM3VAIcSxLhaeN33zzlg/nCx/uZ3pr/P3ffMNyWf/wS5CusFPJi0Fts+qBysDcu42etUqpfVMGlHe4EIgGXzpn30MjOhUSeyN15FUDGnsISLePvHXKuirl/VpI0fPi9hbn+l6RxSlQcmW9boSYmE4HYlDBfMnqX5rSxBQmtvVqXpzDuR3oQstmKWaeh2LD9lyezbydYF1JZ1mv+OLoXMil8/Cgc5hSus3EFIKNUZlg3juydK5dha+5FFRIqiSCEKL6sQZBnMF7KMTmJ61ga2OnBHf0ctdC3oF0nFPYr4mjd8dmbhwlV4JvTK1zmBPHecLFhIuRt7/9a37zt/8jMr9G0h3T/JIpnbh+8ZfUwx1P8UAPgWSD/Lf5Ax+eLvx//t1f892b7/nXf/XX+JT4T19/zjwfmGLi7u6Ov/jzP6e0jQ/nd3zzzTe8+f4HaqmsSyZFh5dolby15x08HU3DaRZLBaOS9/U5PPZ0Oil03p+opbIZ9BWNxDS+hrF4szrNFTimxFdffs5nn33G119/rS4iImyXK+/fvOXXf/v3fP+r33I9eg53J3VAaZVQ9RJszhlDTju/umXrOiLOOabJquDeqMUOM4PVFKpbuTt+wT/7J3/K/f0Hvvv2G5brhfvHt3z/zRt++PbtLhXSQ8qpVIJqr8XAWfHcvPwjjseX/MVf/mdMNy/4zt2xbhvnGqklkIujt8Acj9S2spVHavO65lB3lt7MV7cV1lbwUddqdwfFzfaDMuG8Nz1ux/sM/dm/d3EXaqvIlg3CVYnCtm4QI4dpJornMEcaDc4aKqtRZOyQrTPYttXnuDEDnRXetTHFyPejNSbrPq9XTTWooMG9aAfpXVRZFEoOUbmUrpGWC223lt2HiziazuedOs0oEK8yJifPxD4tVlDqvvf4g7eEBDtUQ7Bnr7Zq43UMcpZ4Tb33XrkQeb2SV5uj+4CLgRQ7KeooKUzRiD9K1Go0cm4qqK9aiFDHklNnqVorLXiON0d675yfHgAhxAMxJebjgVwquWrKvRrQjDm8FnjOBSXqWUFQe9kZrL0BtepM2/R93p5pijqS2Sw70cfInI7cHV9we3xJbYm8VJ7WK6V0YpqQXuhlI07CfBSmu5np5S1pmknTDG6hy8KaNx7ORe2mmgrnBa+cBhq/+PUb0iFwN0daqfz9v/kN6/UfcQkGY3eW/pFjAGYu258tnOrQpnWIXu3EVGyrotiWqzF1AjGokfHQhjSnMGKx6lnwNkQOemnZUrxcrpq9Nh9U9nBWKFLJk4r9xzRxe3NH3lau/mLeeybgrBgA/uw20G024J3XNHdje1rdh9jwWB0a1FnBOehNnRn2jqD0nW3qnWea4vOosDdz2deLL+dqOLgz2rbOl2JQCG8kqW9tpbRGKbZAK9C6EhvQ392dWQk5wQVLs0azC4WA9I1cLvhsGXBtw1et2KV3XMu4eqUusOQL3735NVGuPLxLHGPkj3/6J4QQ+etf/JYf3r3jcStk8bgU1CvTRe5evOa//M//Ja9evuSPfvoTfvnrn/Pv/p//mu+++577+/fkXE2/3MztZsy5lGEZgiMmT+0enfkqO1aqU3s2u/57UVJYEBUKjzRv3/UCEm+dwmYIBdotlp55unzg229+Tc5XOoWb2wO3dyfevf+e3377Kx4uH8hdD3JfLQnd5mV0TEOl6xs6KWnItB9mBwaJFXOg2VpROM1maSKB3ISH80Ltwu3LF5z6jRoTP2Xev3lA0HmncxFxnio6P3YU7ZAriHhubl8w377kWlBY9qiuNGWrlLWYHdqzi4oMdnHT99O7p1UVsduuVWg+aq7hzelAyzq/3raNYUtGh5yN1FWrISQ6Wy9Z51VOOtjMsbXBA+i4oJ63OIMWa1EWog/6vbXoa2pil0a0U7bpDD6EnW1cSqH3ToiRmBKXspmpoc3a9NrTc6l3s1xTGFgvXYePFQnOGOfoxcSYqFQ9N4LD1Y60Z9q+rrv9V1l32Hb/zTaYiuj8tdkMvLROSpGXL1/Re2Nb9NK7PJ2VN9E9Ix1GeqO3TO2ODLvEQWxNl9oorYPNR4ebSAje3oOe0t5H0pR4+eoVY/xQW6OJI6XIcZ7YijM9rXZ2taj/7MhWrdLIpVg6hhBCxIshA6LF9Twlnv2NVTM6mYHFTTwpOWeOvHx5x5Yz67rQWmdbK+u6Is7x6aefsi4T2xVCVC28eEctG+4wM82Jw21kOt2QV0fwM33zUD3idc74cH3Ldl44t40lCEvyytG4VMjPRfL/6kvQD6EsCpLrg9cOTmF4b6ylQfFWvNqHgPNRF3nWljmGSIjBSAZeZ0W1qjWaQK3qqOD8URmn3tObAzakN66XhTRF7u5uEfF490Rz7Zm8IkKMiZvTHWu8Il4Fo63qZdcq5vgA3dxotErHZAZuH752BtFE7BLURbqtWSv+pmL2UnRYTrExnInjY1SNo26gosGqIRJ8JGez9elmiCsCXQ9LvGOa1P2+XosGy2566boW1EHe2otSCwx3iwAueLvg1cvQS6fkSt4qWy6IZELFZA0F18H3jGsLW1lpeN68+y20C29xzGHi06/+mEOM/N2vvuXb779XKr4ojd9ZkXN794r//X/13/DJ60/46qvPab3w3Te/5c2bNzzc36uWyyc63cgYegjqX9cqOUSvMxYrgrR8dPuAXWFgo1qYDrA7PeydFWjenre0arBVpfdM6Qvna+O77zK5LEDl089fE1Ln/f0bvvnuV+TzRqGYzjLsco9mRLCSn+Ue4iAeNLMyqmhDX2N7NjjQS1CsI3F4l8hdeDovpOS4ubvTosQ53n73AW9pJTRLTRFv4vYGXhNMaB0vgePtHYe7FyxVCT39JtCdEiPKpqiK9E4YczE3iGK2D5ruq151nqRoghCiZ54Tp+OB6/nKmgul6nzOoZ6QJasF3mg7UlD0pBg0F0yL1pvFfVlR6cclaNT+XqsiF2JJ4VUL6VohJm8FsMJ8IUZiCDoeaY2sjbGm26fI+WqX145M2aY27Kk0LZR9GGbXAR+zMkaayqvG1zjcu7P11+pONBmzsv2y7OM/NU5Kf4pTJpUZZdiJSG3qavX6k09otfJ0/8ClnXnM97sofTBYe224an6lDtRbyu1GJKXqRYiYlMwGmD5GRdbqqp95DMR54uXLl4Ay3XMtLCUTp8hhTvgiLDkoaz3rnDKvi/qBBvVFrr2rzlo8vg/3HpUtTEnTRvqAPNqKFi4q1bm9uSWESDxEXtzdUUpmXVdaE/KmjH/E8fqTT7hePOe4KmM2dKoTCzvopClw93rixSeR7erwMtPXkUIz0xEuP3/P5bxRn3Tu3LxdfFdrUP7QS9AKGSarosSZ8sbbnKxX4yahFO8QmA8Tx+OsOjWrYqWb+3iIxvzr1KJMvrIVpWs3HaK6tiHiSd7TvVCrskYrajxduupRjre34ITX4TNq0/BUnzrVLfhYmcUreavBlj19hWG3rrn0BR/UCcaF/pzDJXW0jWCHU27dLIECQxcJDW9sLT0cHSEqc7NXJWuYB7ZWhF1nX945kg/28zo9d6iNXLSSdF0PjHVZNVOx6yEm1R5m0C1Zij0cCUj1uNxAMvRs2H7Hu0ZKB3VmsJSJVpsSBWLAuYSIQjOuw3ffv+X+/ROnNHFzOPIvg+c4T3z3ww988803vHpxS/AHfvTVT+m98+H9D3wzT/zN3/17vv3+jl/++pa//bu/J4UTU1qZ5ieG+3+torMUpyQqzAs1Z3Qe1fUS9H7YeTU8nc0Mt3tpe6XbemMrWanqzczMe7IuU2cxravBwG2cEYRcHY+PC9LfsiyZ82XlzXf3nB8zvTrwakTuTa+or8kYycaU08xGpagLIAYNObx29aXgW2eOs87E48ThOPP69Sui9/ziF79knjzHQyBFDVt9fLwnt2rkJqG2RS8cO2zVsitxuL1hmg4cXn7FdHzJRgImktwQfOHm1aeUyZPlibZe2B4fgELwBo+1SmmZrV/Z2sZaOt2pFZiXmeQPlNx5Ol+4nBe2JZshdtXC1TnWvFBbVdhQLPAXp2G5FY1b6jqfDQiuVJtvTWySqYtaw7muTHP9nD14nQlR6n75jRSJ9bqyybazI8tIb+CjZ1CLFa7GGZSOJnCqlrPXztobwUOadA4dvX6+w2KSLnRsJtqgZ+3wWq+0j85QK3tsHGUzKTN38IOU4p69lrXQ7sQQeP3iFdu2sZyv2sUN5rk0fT9GBnIOnR1fVsRpyPAwktbYI4tlEsfNzS0ijmlOOC9aAAr4OHM83fDT/+SfkmLip9dNRfRFzwfXFN7/zBJTlmXherlyfnigNkVtctFcTlqxfQbOaa5m8I4pTkqUaZros2VH6yPrskHLGphwyTy8+YF/9f/671WbS1CIv1TOT/cc5qaFVrvRJyKNKTn8HJgmh+sr5dK5+sYXLz/h67uvOc13pDDz5vt3PD488c2v4CFvdm05BptTzN3oD74Ex4OOIzbFstuqkfxcNc2OaNeYUiRNkWlOes1YzI141U05H8wZoVnlZ2zKXFC0Xz9wkY5PtkiawgrW81B6Q5xjPh2JKXLz8oZlufLu3Tt86jTZcEEv7t71UPRXZ5KhQcpXoC0GT/CCD3rQ7XR8FA4TO/xq7QaNmawDk230lRHsqtW0LuBW624coAezfmBi9Obgg2omezdtY6dm9BKUjHjIi7Lwkgu6sc1yihBRKyGsQtSDSMxGTRdrV9alc9qFOTUjp6NQn4/mihMBjzeG6rt3D7QKt8cjd3eZ4gSZIu8/fOCHt2+5Pd1wmCc+++QLct747fff8EMK/PJX/4HD4Yj3iTdvvsO7mRiPxHSilEzdNoXjSqMHh5eOE3ViKUUZuloJC0QzyTboUYkXuslGpd9o5Kb5iy1vRs/WQ7WbRGaYSZ/mI7lq1Xm9bJTlnnUrLFvh/bsnlot26c6rlEKakioMYtCNpMW2OieJmASmIW3EU3lltpqFWAoJ8Z4YZ+5ON3z15WseHy58+9vvmCfPzSkyJ88hBS7nJ+2QjQDSdTijFlUILsyIn0h3r5hPt0y3n5KmO/La6RKJciSGynb3ghoa23Igk9nuM51qsy0lo7SeyW0ht0y2A9fj8DIR/EytcLlcuVw31qVQtwu9ZtIh4YKzWXnD23533lAUbxdJ1ZmaFzUxkNpw4oghEfG0VQ9TE8tgGCMSgsF6yi1oDWUTd9hMXztYg7ioRhEY7Nma/t1umNV4bFTEJAydTjat4tR1TBGkm3TORNe2ZhrKcB82bLVrQSxDA4iJ9scwc/SJHTO7UHciEW+nlho1ROe5u7nletVOa3RQ+qPMx1SsKPdC2bKmd3inCNZ4X86+J6lr1TwfCT6SjgkXBG+m3MQjN7cv+PJP/ojj4URCMx7LVtjWjccP9wp/lkVnypczl6czj4cjS165rFe23PC5UZYLZV2xBpkpBaYQOKRE9J6WVQIjTShNYW+3M5AbNXcuW+Gv/od/qygeOnOdZ8B1pqSSOepBi7WamabA4RSQ6JC2URdYgc+/+IrPX33Bp598xvF4w1//u7/mu286yUGvBYdlwBrYryLg3//1ey/B1owpFdRax5k/Z0zDKUa7Fh8CIQamWU1au0EE0C0Y06sT/rJq9l82hl8zJxUatSpDs/iuWjOr8L2HIIKEWava0pVEosM58nUl+MAXX35Jx+Z/uVDr6Ijg9u6Gw/FgCxhENCngs9evuDkeSFHBo7/9m7/l8eHBDlwQP9NwuEVfa8nZUiz0EHSiM4Jqc8eyZnxQf0KF8IcTjOqpxIb5LSgE5boMpzZGtbKuGXpFascbzKdcEZ1nfJwd1qWb52JTx3oAc6ZQKNu+2XhM3kd8iiZgVbihkVVi0jt5U53W7e0nvH51x7ff/JYP799zXS+Ulvnu+28Jfjxzz93xFmnw3/13/2+dgTTHZTnz5u13XC4L18uTLerK6XDi7tXL3WjcmfNFrZWlVErRjs8Z/GCMbkrX5IIgQ2eoerEpJYWiYqB1NfPuvdJxtv4yNze3/PHPfkarjTVvXJ4uPNzfM02JwzyTYlBUrCnsU5wyTIPTtW2T/vHxkVvfiVWaoK4Hdcvq/rP1lYYgPnI4zHz51Y/48qvP+Rd/+c/527/7Bf/h5z+wlco1N47HmdPJM736mj96+WNjYiZSSAQXWJZCKY3cFTWoTqjOcd5goyJuxvuE+I0UHf/sn/+MfH7FL9v3nN+vfHiTaS3rSACheUfuhetypdRCDIbeRE9vmeXyRO4qpl+vhbwpc7T1SrG5nhiJpG4FJ3A8HEyPqoLlbF2caJgjuWakV0rvbPmKOO3mVIitZuFDftTE6T6yYknvBnM8oe/rf5oSIURar6xl47ourBZ1pdtE916hEkSZhyKikG1Xn99SNmpd0AIwqhyrq0NPLcWMLzw+OqKDXBQa1h3abD7J7tdpv5k6NJNG+MkW7OxDopTKN99+p1KoGHEpEpJC774ZIUy0m6105uORk7/hcJyZpkRBzavf33/QBAjrXrecFR6NDd8dzYrfiW7WeStrdiyrRdIJdArpAPTASW4ppXB7O9M//YT245/o5V8LTTxdPDZQJnkxyNtg4XWj5cwvf/FzHh8e2MpKbUWlIF1Y1iuDdeMkUIoiadgZWn64IMptAVSSoUdho7pK8ZVQEjEltf7rFR8mjnd35CbcP135+X/4Nb/4+S94Og8bOGOuWPM3pHy/7+sfuASt8pBhLaa2SDj9oENIhBCYpkiMkTQn1nVlWa7a3hfVDjoXyVXjRJZloWzbmCzbqxWDB1UjhmmGQKtKcbJDMlrZwTC/HlKI2xcvyLVwXS2ypG2jJGQ+zCrUtK+YdA7106+/5tXdHTE6Wmt8//0PSn4Z3+gnWne2KJQp2FtRanDXS1D7M7GhveL3RHOqEC07pRumr8WoOfOLbXJn3aTOneqqHY7rfeiBtVr0o3oWOxTsIcszOWl8dfvZMj7erlJ1Z4y/uincoVltZoBsr791OBpB4v37d4hz5LxRW+Xh4QNGB+BwmPns5Sf0Vvnrv/krs/hqSkZACRR5W+3S64Tgubu7IRedkWrSfKEs2QJhxwxKmZXOtGDihlVZVKIHegGpzZ2nh0AplW277sxCnTtWQoh8+smn9N7JpfDOv+V8fiIYi1iTO9B11Op+uXUcXYahuY07wGzCxopFy+JmwmcTg3cRnESmKfHJJ6/58ssv+dnP/oT3H67kKvTaWGujeSB5Ti9e8vLuJSEdCXHmkE5MYeL8tLKtlfPW2Grj/nyvHW3pFKkc5qAaUimEFPj668/Il8gPvzywLdHo7EUZjc6BBI1syhsjx84F7Tp6q4Y8gDTIqz7LLMb+FnDNqSmCU0ajwzp7MfG2uI8IEtrVKm1fD9BSNkbU0Ij/klrx4vfPHdtHrZnfrI0M9FBz+3NXeZSaxW85awCyDAsvfUCtq5bReWOWlvGzi/3JBu8l6xscoJyAJjpnji4QomjCTC37rNGolDqmsE6wAbV5PZeMM1GbauCiCK1WPtzfayZjVO9cFzxSOq4+X/Td4P5DShwOB+7uTpyOB9aW2Vrl8XKmXa92kKj5tQrvsTmkoPQ5vRJqy+TiyWvDgXnDNkICL54UEq01DjXhXSKFg50zHRcTEqImvDhPlIanqZ1grVwf7lkvF377299SGpSWqTXjJCgRx2QpegkWek1mlVkoeeP8+EFn7EnJUCHMeyfccqdtkAiKyGWdYYuPpPlIXit52/j+zVt+89tvWRZtAp5VuuPg7PAP3IK//xI0K5zW9eSuTRe8Rx065mkiBEdK6gl4vW70WvB9OBJo51ZQRllp5gyPmLOJe2YzpQA2X1AUyi4Sk1aUkq1yVdcOH5WcMR0OiHQeH+7Bqflrq411zcQ0kdK0w57Z7MrOl0UTt7eNN6eTxbU03t0/cFk3tcMScFHX+9PlogG4BoPRin3gVhuOVqGb1irOis03DbqstWnnMU9Qs84x7IZylqdSLcfNlajQmB2/g6Qzjl5ntjKtWoq3x+a1yq4cXWwfxbOoY78PqlWiKhav1kqqzRmhpylOeB843b1kvrnjV7/6tWoc1wXv0M4WMci38d2bb6F3rcRro5jXaqdTajN2oM5Q85a5f7gnJmX1qScmOpMiMPlAatCKQitqdl6VBo0DVxFp4JRpG6yw8MND1asWtWVlD3aBdw8f+Ff/+n/i5YsXfP3Vl8ynmS+//oJSNt6++57z5ZHWsomn0VmXzTgEQaoecNlWoahKWs24RYj28NeSraNtOO9JznO4ueFP//yPEXH8q3/1/+Wbb97x6vWXHI4n7l68JB4S8ZDoEmgSwEdqD9xfG71dqNXRmmcDikDzidYLH+7v6TwwHRZ8iITlwO3tzJ/96KDWbV2FzKUVhfLUgmZfs108O0DQqvl8ou/NZnE646mKAI05qHQt/jADfYFsz8cFZU9XUbeQ0eU3S56ptZNLZ8ua6hLivDO66UpOGQbKfLTyP/6K3uOCIja9NijVdAv69vQC0QtQa0YtOosxUdU6q2nnZESV1iDn1S5Yx8gB3SUhRSUrMUyEMBHsZ1+uVyW77U5Tdt6i5Kh1ywab28Vm2rq6FbrXgrOVjhBVq1cbIs3GElrc51wRn2lPTzzlZf88jseTaqK9daF29nTJStBpHamFJ3mk+sbbN9+qjCkd1LbNRTX+8FpUBpQ5LDhy2Xh/XXGohKiLziPTfCCliTlohNhqeYf3T2fOj4/cX554Ws5sRWUcy6pylNorQ18p4rQ3aLCtqxYV24r3UKsnTUKYUBP344QEj4RAmhLpkIhJiZU+JHCey3rh6enCY86cu0lndDBlhf9YP/9oONRsiPro1OzXDAsmG5B6p4GcpVQbjndjH2oVVXvdDaTHa3POcgYNvRUfFS82H+tuqShtML2auoTrehfN9wrKNi0ls1wveihMB9PuNUJkt1lyRnSoTYNg1/VKMJ2RjwHoappcngf/3kTx23Z9jvPoH3limpv+Xq0a3Im3wa8ZOhdjgYYY9UpvCnu0rrMV8QJG4nHefM9tVtDG4fU7/88uu/F8ZRwow0FivCadkzlx6nvZxdwvhpG3mnl3s7CLIRDTxOF4ZD4eefv9Wx7vHyyz7fmIElG24dPTo76Wrq9H3d31q7fnY0wQrRyXK+LN3aWMqt1mS3YgVjqtjuzGpm0z6klqY019v93Tnb6vjnWMHfbIHydcrleu199Se+OzLz7FBc/p9sj9/crT5ZFtW4zOrw4i3jp7/Zj7Pvwu2OXe9Pd0Ez87M4HI1lWXpuknIp40Tbz65BWPD0/8/O9/yfnSuLl5ye2LV3zy6ZeqD508y1a5bg1Q84Alr5owLhPgxqQcXKC7zrqdteDoHvER3zMiZf/sxx41a2yFdS2eZu9kGB2vPn+/swztWVo3jfNq5CC2S00/pktLNFuuNzUUd+OzG2ZbPF8AO/tTu0T/sbaz21odfwS9GP9nl+Aw5NcuRbvA3SLUuiLZF4jp/cFkB6NL1CBo9SjWLr5WJWLIMH+WQdJSC0RXu6XHO6IRVNZNzUEGIuDtv1iuiWon0QtanL1eQyf2/VPVKqJ3R+kW5C1t/xhqa+RaKFtF2qYzWNEUimmedqcqlZF2I4p1uoUmb37FrY7z4z2tZPxLXUOlq3zKBeUamEeFkvhaY1lXvDg1tLAnqTmUQnATThyldXKtXLeNy1VnilvZqL3Za7Fn3izOrGdEPCWsqre+XHGtkVqleUUXfNQP1AVHmqLGMhlL1Ydgf1Q+1MWxlsJ1Xdla1aBrhYieq5Hnpf4Pfv3+ZHlbRFzP+BCYTicdaE6JaUpM86xVSym7qLR2/YBarUaRNgZYVfbTlCacm9B0Uj2olXmn2VW744eMMXQY4wA6sOUV3ws+Ct2Si6/Lwm9+/Ru8D1olEQg90EqhbAsl6wzCB8WXAUoprMsCTTVHmngdNNzXXOOr5eQFp++tCZZ+oQcyzgxcD/H5Gbgxk1ICEOIJQSNlnAQd1vdKK5laCiF6y80y9mPQRaGm5WYwjB08vdObuZF4FeRWbU40cbkD3TLKjEbtgqehyc0YwKfMr7JXjt4w18N84HR7y7/8z/8lP/vZz/j2N7/h3Q8/8H//v/2G+3fvtKcTQQlCnbUUg5QChnDhnJCC14RqExfXWlnzSq6a3n7drlqdHmeW+0cu58vu2zpF1Td1rweBOgqxa/d2mNcMGEpVHqVzY07jdgs7uh4Kb394w/+0njkeZ+5uTzw83HN//4G86jOo4lWR18y7UoYTZrVD93mT9S7WhDSGgHsz+yvnvWmz7pgPE48PT7z54R0//+VvePHyK/78n/0LWg+U5llKY9sal6VwuRZCEnz0rBVKVwjNi3ZZAtzO6o16e3urbOglU1vnsjWWpXK+bvjeOJ5esF7OuDDTy1g/omxVP5icZiag5/N+WJRe93SWNqrpIeMR2QsbcR5xwrYVYxsrU7da1mPudZeXVENlai2EkNRmbtv25Jb9bq5W07Xh3qTXeHCKGNFVAO5MeD4y/dJ8xMXZBPwqtYKOl6r6wnWlW85hr42ybZStqOxIIAW1hSyWjepEDFHSS3HL6pTpu6eJzqxr678Du3mvsUZF2AtYEY1tGtBsKUUhOyc0B+uysiwLdO1UhwSiirKbx4UqranXW1BbwLU0Nf02kfx4FcX4xB6D7LeVpVV+/cufE2LkcJw1SCBOTGnm1YuXRB+YwkTwkSlNgOf2dFQ2d+vqoWzjl1Iym9NE+7Vt5LZxzWeu5Yl4jBzbLbXqrPZUEzTUCrBmHs8f9PLbsjY3VQN4NyA5p5mZMRHSRKVzf33Cp0iYooYfF5Tc54RrWbhsZ87bhXO+crw78tmXn+I9LJcrl/NFPUvNwOF3qL1/yCU4KuKSMx2YutYGww0lBNWYtPKsq1NtztAJFXXAUGMsXTDO6UHV1CpIdphvRF4YpGGv3VlemPN6IW9ZDYF3DRkq4n16OuOcI60rh3QkzXfajdVCRZ1hJh8IqmDXTWtt/e5QjzIDvVMSQGsV6To/GQbRGkBqo2GnzC2JWtkOC7O2V9T6PtQdx+3vVbqmLowOUv37tOJyTmymZ12yeAarlaYYP7AL+fdKzb5kVG/jcMFmjs2cYEVfXxsHfmPvpJz3pBB59fIln336GVIKwSnbrde6u7n0vWswgaTY7zBI2DlRIpCIzSwHRb/SV52hhRg5eIWgci67VVWKnpFDNxaBGpPrihxDeawSHsQJMRanmCGyjI6FzrJcuC5P3N3d4FxXJtz1ojCcQbaIs1SFNsYz+DFkt4p5HAgjkaBiYmnDpHROrjKhEAI5Z7Z1Y9kyL33g1SefcVkqD08bJRfW3FlWuK6dJJ0gegHWjiWzoDNlEXwcn89kc7ZHttJgU3nCmiuJToizwY0BEU1AEUMEhqWYNkx9R1qkqwSodmWRKgRvq7Uby64/d1nDVq0O2FDavp67zYDpTddcH/Ci7uUhjhfYo4d6G7O/Z+xgnEAKv/t9jtjtEhzf53zASydOk/5Ms3QcbMtWq9nymeSljY4WO1dGTNs4i8Z4wdn3K3IitYGRzRqjeG+2d6yAHxtet4GOOtBzqzdlwXdBfVS3TaFamkV5DdmHVfxuhPrqRlbgxz5zhlGHFePSNWEFG+P0rl6bvfH4cI/zjmWJhorNTLNaiqUQOcQDU5pox04MiRSG9rtpd92dsrMFildv4NoKtRdyW9nqogHjc1LTgt4JbaY3WC4Olx1+9XSqmtvbGaj0x6YGA94Z9O5polIe4zlRmlkBeqFXYc0rl/XCsi2seSUkz+E0MV9munT1p90arepexZ717/v6vZfg6TTTrOosNXO5PJJSIkWP81CqDqT9pPRb1xqlQVt1HjGMdhtFNVZJD5qW8z53iz6ZTZQuenXVE9yka0ELehVYdxGmaSalyM3NLQgs1wutVW6OdwZtNeb5wGeffcb56ZHHh3umYyQdAq0sLPmKo3KYIyl5YvCkqN5/b67vuV4X7k4H9T4MEYmJ169f03vncjkjdKZopA2TOlyWurMFS6lsa2ZkdI22vJTCZVG3BI1rmvAkcu1sl5UQNF8NVKC8bXpwDGbuPGvSwOXaf0cfp04Yo/NhZ6R6p7M2VwSpulEsw4K2CbSIjHxHO6ju337g6f6R/+v/+f/C4XBkeXoibyv3D/daSRr8S9PNWfoQsldb3MrUXDZ9/qPAmGLUiEBUW3pd1Kopm/3Yze3E5XxhXTdKWRGRfZ7grROIIeFFhdTyfAZqkGjvtOpxNJx177U3K6bELgRh3TJv3rwxcTdG+tADbRQ2g0Enwv7Z6pzhmaquzitmWOAch9ONwnUpMk+JlILSyKeJP/+nf8Zf/u/+C87nztsfVpp0mofagwbJTokoGipczXxeemMrF7bWd+jwuj7qOr97RQyJF6c7EM9LdyKkyHruFCDOr5jmK8HfqqFzOSsigMPVoLKYpiBraZ2tNJLzxAC1Oco+gxVcq0irSuIxgpyI2oXpSW+618MMHray0YDLdbPwWyXv3L76hP74SPnhvVqieXWbylmtwrr59CqyqfBk8B7vdEQi4iCr5szZXr0uqglbLhdjbutzbDSdpbQrKTiOk0oq5umI94H504nHx3uW355xLhLjjNsKnbx7Dg/muyIfCgH13pGgsGwMCe8aW9UEhafrRXXCXnWGknXdp+OEF4VRW208fPjwDFW3rsNV05x674gxmFm5V5/XpDFyWDHWEUoryph23da3QrQhKOytSTUdQedJrS30LmzXjVo7l/UHQPjtr35FComb+cYSa8IOOSvoJEzzzDQdmG+OpOPM7YsbDqdZz/8gPG7f8e7yhj5VDjN88aNPmabE+uBZnjb+7b/5hrUsvP4y4NxE5YbtWvjhNx9UH50tkD14XHLITWeaInfHI8vyxOX8jm3zPBEgJiRGzvmBv/7F0fyDO9f1Sg0ZfyxMHg7Hl1Dh6eGRvG5c3l9p5XlM87/6EozBU6uwinaCtRYb2Fu0Bxgc5veKaZA2xlxhVJgaz+EsSPEjmrltrj2ZYv+ZivON0Mtm7MfgdWPFEKhdw0bpXbvSIrS2qXVZmri4J3LOpKamzaUWYy/1fZbpjaKtFH+LN7HZgPc6UD8dj7oIjbk4T97IOYmtNGpfdUFapFJeiy5O563qHKbg7XlmYcPPwbz0vwPz2OXSVPnWZfgPquSki1Z5I3JJu5S6j3xkdNY47ZqVaWTDap1tYfE62BwNaWzrSl86f/PXf6Pfu67QO4ejWt2NGa5WfAbZtsbIhBQdSxqsqF6QTtR+zpl+aZBytm1DmjBNiRQDV6zCbMUOCv2ZyXeFp2LaM/cEy6G0z3IQgMT0VlhXjsjzTMs7sLBZbO0JCu/s1585S4yuVAkhKEEA2ZmGFZsvGTswmsdmSFGJP0Eh8BgjL16+4Kf/5E/57tsH3r//RrtpD72q4Xk384DRRWn2WzeTZyOtiLBdVdowTyfV9vlJMwvTbGtfD9MYD4R4UPs1UXmF0Zm0yzCSm91h2ql9NJLrRn7SClRlTh3tFIMhAfr4+/76tMu0eZpdkr0L0ZutocU7jY5LzaQ/mj3y0SE1xgxuxKxZRW8vVrDzwliRrapVXO9qpN93GFdh7t51duhEu/Rpno26z45+OK/GEs00hSONXteUVyuzMWy1n4VT4lTrqI2c2bM50QtExgdsAv4BA3f77Bid9li/Xl+LRI+L3iDCqPBwr8/MZ0Hfk3y8Vo0wZ9t6vM5OH9J+WtXncr1eFA4vkEIiH7Z9/avP83gIwvF44nA6sfWFuR1xsdDcAVCyYa5XtnbZ1/vxReJwmAkmcCpslL4yHU7EFJFw4Joy7783d6WMPU/tyCUKYfKkY2QtUOqia6t71GA7Uu4z4XKv4yUjXnUHLjRCh4kJOqzrVZ3MPgIw/mNf/4BjjJIWpkmx7WADytYbqtU66CXWIbfMtagFEq1AVzGxMxssqZ0RmCegbCuLGNJoIxun+6Ybc9MDLAXN7stu5MbpjGRdFnDm9ysdaQVH12qswbKsrNtKbiut68wuxIBPDpYGUonBkYKno4Pu4+1MnAK3xxMxBJ3XeUc8RB1qe91869ZIKXB3uuMkwt1dZ8uF83mh1wu1Pu4zfu88Lqi2KfjIlgu5VtXsxQBFWV05r9QiRJeUnTVmgUVh2bzpoXCYAq05smg1Hv3QRg7WX8eJJ7iIJxAItisUtqul00tFqrp5ELwRF3Q+NPY6QEpRHYFa3eEJ0Ihb1eZtVnUqPNpytk09jJB1brg0DQc9TDOdRiVT143rWjjN6k5zfnrid5er7uRadd7kfcUHR28Goa6rXhx2kJ1uDkBnSpHd53NAmKan1EQgpZWPmanCehg0bHPFMRO0y3Ac8j4ogajYxpomDTYOKdgBEjhMMy/uXvL65Ss+ff2a0+mIqyun2fHjr1/y7jHT369sDyvLeaEWR6vK8vUh0HvQYsxmPMHrGvfiKA3u375H58zK5D3ePjHPEy+nz4hz5NNPPye0jZQCNYsxIR3RW8EJ1udEm0N1tXzLavMX9yMQuncG7wO9EYPNrlzUYmfN1NxZfLEuQo21t02fl0uBdb3y3ZsfWNeNZrlzIpjTVN2f9Pga1x5VZ4x1wLPmhxqcJ7mo7G501NCd0OqyF+V2AyjMaoYc6/lCXlfKtnG5XKAHelMfVRFPTHq5eRtDqFRQxwRbHvN9hTgdShxJPqi5urFKvShC5KwAxoqQZdX9EszfVotORYp8gDip7ENn4frAe9eEnAHzYxea73qeejPm6IZ8lKKRU2LNxLpUvHPcfXqDeGGtleYKIWUdQ3nd7Nf1olFjZp4RXDIrEZ3l13XjfJ85n+95Ok+EKRBjw7vOWq/Md9EIQ8L3b9/hXeDWf6ajl2QjnQjxFPnxj7/g8cPC9798y1o2Cookhqhd8Bwn1ZvWjVwya8lMcVYeh3coibrjPGzlidoy03QixMTpZqaXTl6EsikJb72uCqMLv/fr90cpKfvDgmetajKW0ph/aAXdKKjQVeG1ZifqQH95rq6QvY1XdtRmFaFCJGNO0KzqI3xULY1DqWvoqHSzbhL9PSIq34DhZ/iR00S3eZFX2rcK0bUCqTaziDHqBRK12/TB20awnaq/yKpYJYQ4y54D4SIK0WiRYJvBO7wLprUxHc+YHYwZ6OhsxgxN36SCO1bqDPzfO309Vcb8YtwXYia7VtFZ4rQMpl3no+czngUfLRAZd6U9Hz1gRBSCoavPjmAwYrfOnz7U/Pv8QiEu/am9a6pF8x+bFZucoZf9cBvzqjEbGm+rdf0c+v7nefbUWqM2CPYx6vxIa1+/l4Bi+XB9d/NwTougEQ9W2whD1p89qBnDFss+6R0OHih3DMFgMAtjFk/0gcM0MU8zkzlq9Kazn2kKTGtFvc67WYF1Wn9GARDdX+PBiNjM2OnBW4oZzxdwvhBTUveTUqB5UjjsJvXOPc/i3f4+xn4yAHiMTgxOf/7N7Avhmf3c9zNBF5QZCej4yAzrB5ymT7E2Nb/XQ/p5/2Nn05C9/s65w/Nl0ewSHOfR6FgM3NDzqNuFOiq4jxwlur23Wup+IOac9YKU5w6Y0VEZsjJ25vPn8cyCHm/fGTxsY2RbA2aOMdxkOntqhnNWLJqWUQtW+SgEwD5vO+N+R+g9UA+7onW+p/urS7f9pJ332OuIELzmMG4NxDVbw1oo99bJuSmjfZxxH+3b8eGUbO5MUvDF40PBuUaYA2kKu2RoK41SKsll9UgW+6wcuCAcjkn1ilZcjEUg3s5Jr+EGrZX9jMXOUdWhDEegDr3SmmbLdpru++AoziRlYw+NB/l7vn6/gbYkus309voxRE53t9wcT5wOM61qmy+t07aVtmVKLna+W/XpBLxumD0ZutohiCgdthoTz7zyctOd5UyYq3dDh5ZpTVg2pdNOc0R6Jx0OSoiogrTO+fxIq5UpzjgRSt+IEgjOEw4zkIhBh8Xn80opjcPxCKJ08dZR9mPVi7lbuj21U1elxV+3Red+wbHmlWu+sFbNGEwpcjweEVGj6QEDK9Rjot3SEefxIdGbVr2ati20bu7uKHzVq3YIa9aMOWcnSS3FGJjo3MfSGfyQHOhHRs2o5mx0eg5cK9pBm4SiVl1Q+wE5LjSbiZQ2IkmCVcVGwjE6ejB4ohWbx3UrfiisG+TSjGgTVKzfKh8eH7iuK7VX5sPEuumzENFZ2GDBPT4sBJ85HA96YCRBmqNuhVYyjw9Punlbx4vYrDdyPFnA74D+gOYKhK4zK/GkqESEVtTUfQizB/wpVtC1ZjR6p5sxJDVldj6aHZ7n5njky08+59WLVyQfcLWSHx7I10pdMi0X6BteMinAatKYbmahznlkSsimnRNNDSdenm7px0bFaXdxXfVw31bW3nj4cE/LE+cjbHnjMB2oeeV68TQRlVQArihUH7ynGnoirdjB73A21+19FAp6YIoYK7LVnWwQJi0CpjmpEXqKe3HrzEax6UGiEUXEvaLVYiVqcdWtFa9959+oxvQZflCrsUALQnFqu9iAaUrE3rhcFlprVsCIzjSdoztHc+bFWivXc2ZdN7aitn2t5Z2sMywQvWgn2Mzc23tFAYZMrKlUVaUTzl6laAqMc5q2gIgWJh1jOVgh4tS3t7bKVjINIVswLrWhAcJ6LjxnOfI8L/YqSxMbXLemo6Rl1TFDTAo9T8cDIXgQZeRu20JtEMNM8oG7+cRyXXj/7oPO2km0XHm6PFlqhENiInihtkIpG+RKF09pDXGdLz97yc3tkTlpkb+uM+u18u//9V/xeP/A5emivsxTgOi41o3cs6IUsbKJNiVhmojzgfl4Q2Fj5YK4gA8z4iK9ayHZpOIMak4pEvCULZOXTJRZz74QSS7w+Vc/Iq+Z7//+W7LNj/+gS9C5IaS0q9Ru5pHsPESlGMkleK2AnFUWMuAD73UEZZeiM8lEa313Qvm4IhxVpIh1LvIsietN5w29g3pd6wHlnN8vSrEFLIIRTrQi0LR0by4kXV8HynTVBAuNvdlnm01x9c2c87uMKkM3y7ptymxqjlyHostmYN4TfNB5kihtX0XY+kF2LVEB3Rh9YJHA7+q6xCq7bloe/Rle/PhJ+wxTf9Yz03V8TmraofOO5xpZnn+nE+ukx/8yfrI+jHEc6cGkPZ/te3t5H79ufqfSF3u42hFW1a05vxedmrH4kcu/ONTWbszsFCEYWXW9q8+gc26vDOnd4qmsK3E6E2zeoYQL2avcNrrmcdEzCB/OWjxlAQpDtiNmYq5m3+P9y8efVB+sN/uElRKpYaOlI7njS8XXgu+V6Lpm1jnZ3ThExmfq9JJ1AXEqsQHTeaLQXesQstNZmP3WUiu5VHXd6RBSssR5t3eYuqaejRpGGoDYexjzpbEORwE95qt0u6Q+ms2Nznss18GsHH+c19moOFF/93359X2dYTPqLm1fU/tesFcizn6XYJ091qgqE3kws/dud7wXe68N3QelPM/+dMxonfW+96wL1ZJd/6Y8Iy76bWNvfPw7u30O+o0y2mGse9s78b6/xtFhdxmksWcUTQxz+fh7B5PXBo/7s9djedjaPRdwznmaBd1oHBhaoIjq70KoBPG6JxvU0shr1svdqEHBOUo3t6tufI8xvHKq+ZzmmeADwkEJezZMT/NESDAfJ/WfNbMJZ3/G5+2Cx0dF4HqvSNPABReC7Q3rjj/6nEWczVqVzAT1I5chZ4iKnr3s5iP/y1+/9xKcpqgmq1eFJMQ5Wq2UXLmcL3y7ZVIM6oYyOY7HF0yPju5NFEonRt2Ma7ZIIXOKWepCrZXkFbrZFhXf5txAOjGpmNN1raC8WZst1wsijimpRrFsVS8w0ezCsmXSwRNni6cRT0iOkBzH2xPH40z0WrWtWbWMcZpotXM5F0qpGktki6CUwpt3P9BaJR08wXkOh5lSKt9+/71CDlEvvDnNyORoRyG4CEQjjmSk64edlX2gFOba9UH7SMYbZNfxWKZYgSZayVeyXrFVJRLFPU9vvA/Mx+N+mPcGW8Ny4+z8D8oAFfPnFDy1N4oZELve9X1HT2nFaN22yczTsBkpUGhWEZvpsU4A1G5qQDW2+Wtzqgui0sjU6li7zn3BkWsl10os2pU570nJ6+WA0Ek0OutyoaLUeo8Q46QHd1ViwrJt+wnbBEqGNW+sZbWLRY3GPR4fEzHohm2t0YodRs4OvKbIh0fJQNHPKHHqQjdruQbUrVqHvCCimrOyXfn7mvn000+5Ox14dTzw1YsbltwItZG8xx0TNRcel0IKyiilZk1DEKWTp5PHH7QbKzRazfv8hw4hCYGAiwfEB9aoMH9GICZuP/sEmeB8fYt0SC7oa98KvWWaWwA1lFAPd/UAdRIZ1nN6YDf9984/O8B0FUEHp/ra83lV2YAH7wM3N7e6770nHg98OX1F3lYuTw8a11OrKsyDwvWuaUHWjOhWcjHmclNkSJxGaIlKEZCBg6gwvbeqcVPGRddLQOOuxiHarJBsdhmM+a4iZvqshxtaMwPtMPSFPKMI+t89dMdlsSRb694w/eQUvFkUKjs0eG9mEmbL1lRXXV0nuECcEh1H7hqirI49hW6oiXdBvTyzEnCoJvewwt6JcHu60Z3plB3aNo2derJZaa5KvvJdd+KSC3TP3fEV27Jxvb+ybSvL9cLp5obji5nZTdzEIykGNmYuZWFdMz2CNHi6ZAgbty8/YToeVYs6wb/8b/635Lzy/sNvwWVefx3VqpAbvTzTBn4DKj7AfIrMp8ThJpn9dQffqVRG0DktQvNIT9CCGX5AXhrb1smXCzU31qsSytqKzYNt9v+HXoLea1r5sxOD/rDWKsUySJx5vcUQmKdArpnDMu8dovceHxylOopre7W5p0q7jyu2545nx/f77/7uASl+XPWNjgX4qAq1DeD1AlX2lXVoMRC8I7fV8rKiUrwXJQk0MfeLpowsNd9tTMSPqt/Kto146qBNUBhdqVZCfbQ79vrGvGtUmp3+LKVQAMbetv7zcKYZ/6c/Z3Qf1mnbAeW8zmdVN/hMTGk2E2z2eYrNGXZWlnzMUhPrChW+2hlxo7Mb60Bkf20gzwL28V4/6iiHqHlQtjvmmGPfPj6TMRMdxuLqdapVnAfyqNBb1wR6e/3Ba0fsxekMwj6c1kGazijEdT1s0dfu2qjxnz/Ljp5j473qZ6aaVPxHaMjoBbtCg3uHK9Cco+TM5XJmuR6VvBMD0Qg3szgW55i8J/lOcKp7U6tjPWIbiph4cQrDNjNU715fYB31//4BgyjsXZv2Dl0cISnxSj9P2DNARX9LZySt25sWczsSZcwq9e15733sxrKvC3vKQ7umz88IXTa/VTRIbftCDEhT0bNeguizNrKT9MYAAEUqikzbpTc6ltEq2vseEp9nVOB3cIzRNzy/x3GsjFdvTbLY4N/Amd9FOPZ1zP6Xx8yyNzMDt18mTch0vNNkdhxaRHbT5tose0hzGPt/d+QyFmjTF9FoVnR+1PfZ9+lsXGewu9ZZ0ILCNuT4mSN+SkdLytKWZqV+F3qp9NKe73PMOGAgTPspof80YN8YJ9J0YJoP+mxrp06BUhPd39HZOJxMH14UynUfIRBi3bz3gg9CE6+FqjFOh/es+i87lB+BdnvdUK7ayVulrJW8btTSaBm0ifc7a/8/9vX7JRKTRsLINEFvxKjQ4lIWgjoA48OBuxd3HObEzc2B4+nAPKk3pBobrypjMF3LHuRpnpHOi+bZedtcWR92yUVffFfoS4JtDkuLN8N2s7pqlJ5xIZCmG3x0dAfhkDgc1cVA4VFP74IPE3FKtKsOcI/zpJ1b1eT38/VKq5XlerH4mIoX4WY6qVShK215W9UFPwbtnp7yGXNPVPYkw5VTzDrO/DBBB7rSEAzLrpWeO6ulRnhJSNAoGu02IyJQXdwvUE2xP+Kcx4dIaxqk25qmao9EbGVDQqDhpe7uLCkERJJBHfZs6DbTwKziKjUveth4664NXQgS7DSpWjxkneEoMWjUMIIL0JszG7JOJ+8byo4YSs9Iz7hNLcnCQTvDGJXZ2+KszjNbJpeGixPee+bDidYaPupzVhlPp2zWnTYjEuwXRcf7hvqECs09p5Y7F3AOk8lUlm2xS1G9clWW0tUxqHW2Tdln6rjjkOZppXE5X7gcL2yXK8TIsXWSBNw00wpcEA5BSLEhTmfGxuPXaG0v6rjTO+QFaqGhv79u6lN6WS6autEy4jquTrjWyV0v4xiVIOPQAOUojuY6zcjCrQ97PV2hesF4mgsU3yk0DWHuVix0MWlUoxZ1LdoscHaQuQ5H9acMDhyNWhdl8m4bAsTDhO/KQtWNoVW+NKhdtZ0+RmY667KybVkPMUyXa0VaB12LoshU793MIxrOCHY1V5NiaKHtglo71qLhzjlno9mrkXUInl67moJXoVdlfebezXvY2WhDFGFpGnfWeIZXMf/TVoxQtukeZdZ9UWsb5YcWB8aXcG5EiQ0kXUsQDO5rDF21rjVxookbttdaB98Ha1UNNtpkZ7hGNOBbtSJYkNqo51Vb2wJtW5GaiQguzBz8xOQirXcueWPrK7lnuoeQImHyZtr+Ez794jM+//wTDvOksUm18O3337Pmzmc3r+lkcn+PNEjdMeFJ4thGswJIrypbS14bHBeoxbMtQstCzZqAg0R1NSqN4PScaFkh3PWyKSpYmkppbLzhTNv7B1+C6lzQ8CFoV2eEm96qVqvWBfqggscYte313qvGrXVjPbF3jKMO7RbLMujsCrvZ77UDwIIinv8gBo/oxlWwZhRn3VhI5uLivWmAAsqctIprr26cVafa3XUw3ZDbq6laq5FFADHdoPPUbF2VwTLjIB9WS6Nb3XUFo3LsH3l2WlVqf3P8F90Ie+U1qrvnTnAPFLV5UrQhvCZnWBXZR4dlP1bkufLaP7mPGgmsgvzovSCiszl5rgKfcfnRlRp0YV3rYPbus4yPZyIfv89Rn9sF89xT8tF/e65ARTBWm/wvzJ2Mtt/8rvMTmqaDj9cwnknXtbs/g+F8bN+qyQSYm07bC7bams2QFTjD24EUAq53vB1owVvqib16B7jeCL0CGssTnYbO2nhk/97nyasJsw1xkKpsPukKU+aul3zOqo8LhnY435/f696pWLcyOj57nZqNyb6unOjsdV9rAz4eWPrHRwLo5/7Rc3AGLTrbc6Nl6jZv00tK9r/n0M9VnZHs26s+K7F57tDvydA5jktwLM/BWLf5cuvD/1Tfe2P8YHl+nV2Lb1f/Z2dE8BYTp/u1i3IYlGTXPtox7GfB6GCeF7LAR88PxJjNz/PGtv/9sfEMuWnPjPNuPIsdBhwPwH5ff/4IGHNanh/9/qP9x10mKiPTitj2etPiw+YSjJn2mPUONrvazeme6P750h4wMwg5511uMzyenVMEruPpLSENQgk4PEM8IF1/6yjKGbNGQc+rnRH57ByUs46sxGsOan++AMAQD6x5+gdvP/v6B71DcZ7bm1u9+IpWdmUr+IPn5pQ4zAol+uCZ08TFX9k65KaBjV06PgWSc0horHmhlLzDdVttFsaocoBxJu2D3GDQWEHfXJx0gaEXTrUF1ETJBjEKPgXCNKkOz0WSV4ZXjFoZNFTQfXN7w83tDQ/3F5Z1s0pc45waTTV9udC6Xjg+zjgc6+VKq0LSUC6bMQROh1vWNauFm/3O2g3+s4M350zO6y5mdlWLC+91wbSqh1ftekBE0UNeq0ODj5y3BI/IfDhSSuXh6ckkA3boWQfkXaC7psbkTZCKMehG2Smap+Ycw/Bbqs4dxtZoPVjnoGYB0m3Z1GfDBBHPlOwSNOr/6EbVRmtAvuMCNLjFYB11hlFocJBW6HosOYSUJoV5mpKt4qwCdW/VspjQPLhE8x3ni3rH5k1JKk11kqU2vNfuXeexXj0DOmxrVoJNK/rzTFtXYkNcI8VkBZMWTVO/MXhKN2+ShqczA8cYmb1nFpjzQvVq0nwV/XcRIXTtDlyplLpR6kppnSqdlA74EPEh4lqwIOrK0+WR5Xrl6f6JXiuHnAlxIqVbZTWaHGBZFtZloWwrThxVtFjyYdLZbst4p6YTXRzdeVrz9CZEIsEFNYfodT9KFLZzTEcluvg4mUTkYP+sh1l3Ti8RKwJDjHYyG/Fov6C6ztHw0JQj4C0iK4SkyI3JcwZxCFTq4KKu7+Bnem9qrdWbzlV7pxQlbWCXbwwJcU11kN7ZZ2Fewc4TvKdUzRpsJVqXVHBVcypLVbSpAyHo2tOuwD/fgQC906uy3lswrpWxdso4WMU+S6ed6rY25TPUim7QgpOkc8pu3Xr09OAtbaZacO/I3ez7xe9MrhBG0Wi2i84KTk0EauDsJkIzODFXm9pVP5ymmXmaOM6Rsmws1TpXr/NWcYHz0wVx7/jw7i3OwTyZqYa59BQBcZ6b209wVZASCZLZrp28KMvYiyMl9V6tJdN8RVCdeafTRKUZIcxEF3k8Xzg/XphSw/tIr6p59a7qDDUOH1Z931vWO+sPvgSzxdVjh/iYtezP0qrtVjVmSKGqsmPfO6kCbCCucOC4AHsfGpoxL1PGHMi+SAbWrZD4cx8zLn9+57BtyjZ0QBD8wI6tuhm9SG0VyphHqsyglGLGwhrbpFlgdilbJ1HNkqjkooykLvul3bvCh9U8+zraqSjt+bnX2ZmI++JVyPFZqW6RSjbjcPZHmnq23t3ckFLkxe2dDpvnmVoKd6ejQSN94JBGBIBSMrlkSt6o2RZbNcdikb2q3ucGH78+Ebw3txc71NzoeHmu9BHVG+3dnAjOvAHdmNH1AYeO7pQdEvEm7RjwnF6C9h5MEE93pq3Tz9r1rhdS10pxzEuwn++8J0raZ7BU9aHRNIHMuM4GbMx43v5ZQydOZ+C1Cs1pp6ffNzoTsdmKarS8QBJIwRO910F/rngaU4Cpq8HBFBxT9OSiZurL9YF1e6KHgASN6OmtUbYK1vm1prPHlCKHeYJeeH1wxCjMs2NO4Jtaz5XlSi1Z54J48PG5g6YhxRxWMC/YpuAFaLckOGqLNOkW2+Oss9EiRJwGK4vzuKg5o+KH7k0P6L2zwv699+qJa2zo1s1lBUfzgRAsEFoEhiNI01n5eLC9a+SaG5eiE+ia/6jWZh56J6amY4Jodndeu4TQNQDYWRfpfTQmt9dbSrqaBDSh+WevU2WlK4Q8CrYWE85XquyLTtdSNepZTJr+EPRCCn6cm10lHOEZeRKUda9jI09KiSkd9mag6diQPvSQStvfW8DBrB7YzNgHu8xkACPirEubwCkBxzljUMu+dbFBqf17Xeu1NWU7DweH/dueC1tBuK6rNT8qbC9bgwzloXB5XDWasT5zHazkVdaxF43NMl6F+rLaXVGV8e+9t1l82ff+lCLJR9XuiuUsNpUUye+/A3//Jfj4eK+ttr1I561Z8noBdnGU3liXlVIqy7qxLFcN0O2F5vqO9T+uZ87nq20QTSGnsb+RKFrBpEkXpY9HugilbnpoKU6ImH1YwXwv7cOrHeqW2baCXx1xc9ycjgR/xEnE+WgwkWdbV8Xyi7K1lstKyZXzeaFkzQDsXd06HI7t2iFXlsuZ3uFyvuplV6vS6V2ibI0Py4NBaZUkET/NysbbMoLq14KL1qkrrbeUVS2yTLnbygqtMSXdQOpe0um5cpwO/Kf/9M94dfeSP/76j4jRQ2wGcyjcJd6gA9HXef/hkafHB+7vP/DhaeH905UPHz7w+Pio89XgNEEa/V2+m8uNOBv4d4Lf7EB2NjtTA/Bs1lWl2owvY0Qkb7CMVY5BkFxhhUGZGODSNKvXJk09Gmu2ZAJjcbTaTQc6IyJs+UKple3Dk841U7IhvbZzm0klEJ1pz8fjfpBtW8atC71WztcnhXScV8mFeELyxFmrWOfEtGONWlZqXpHe1bvU6aFfrAiMXbta7xzJO+6S5+4wcTtFDjjcw0I4eW5uPMV7HrzjVUl8VgPb+oYfzm94892vefv2O+5evuJwuiHPr3H+yOXhSl4yl8uF3jt/9Kc/5vjqhvrqwCSZfzqdmUJgu5loztHLPfnylsu7byh54ebulXZaaBeL97j1qtFdA4ItIKUSfSd6x5xmYoxsk6fWRlaPICPAKOrhnCPOOpcN0wSiIboCpKSXXDaNZ62VKI4pJSOreZ2Pb9sOL4YQSNNBiUAtI8m6bqfnT2vZnFb0EmxNn82AQH2z2Zf3eCccol4um71mkY4H5jTpa/dRz4yCGVpYJ5gT3kVjZOrZlbdFC0cr3F1TWHeap7341nGMiedH1udgoFpBPPukV9QYBYnKrpprSgbpekE6B7cvX3P74hVt04L76XLmuq4IRZGVMnIzlTgUYjSt74isM7KOFbpjBNRdxAXPzSHRc6H4ldqE0p9AnCpyghYNFdWxdufwKXBdzizbylwrqVV8Ek43minrneMwnWit86tf/g1rXvn001eE4Lk+FPI58/avPvDwwwNlEXr1OFGiZEBwVegZ9d09TWw1I1ePJyBtpmyVdTvjY+B0d8dyubBtV7XgFHj98o4pROJxBhHe39+z5oxb2j+uE7y9vaXWtm9A1WDx7AqDdoi5bgyvx1zK7i4/mE5D2/axY0of4LjopYZ1fGKRSuPrOXdsdFPj7w0Mou/OD6MR8AOvl8Gg6gYn6uatVfMNSym0ov+pfzbt5mwI6cy8d3iL7u2n2L3TNDtRRLvfYhc6KHVbxbLaWYmzjEVv4bcGG3qvlXHLSn93ula1q7DCcFR1U4z85Osf8fLuBbc3R3Ve8Pq5SzPHHO/2jZbEERFupsDtMXFzydxcNqZpIsZI7o3SG7n2PR7m4xHQ4IA6UZ9LcfZ+7fB3xiQcX40Rv8I+nBjs0Gbav2cvR5s9eDXFHj4t4qoebPvcd++htWvwOgMqrezVYUftrnQZmUuKjMrUoCAnz8knwKCYDx3h0GsPVX3vw59UX8HzOhy+uAr77R2j/c9OFLpNKRFjxOOgV30v1ay/xOGkGqhfEAq9bfS6UfNCWT0hamqHY6ABOg9yomzBGDqTd3z24sAhBa6HA2ttvH3/jrw8UbeVXjM+evM+VdmPwncB8QFaoRbNzLNWHaHRS9aEDKwaNyaUM1LKcI5Cd5/NTPdHrh0Dz647g4HcbV49HFmcdW2968tzXr2FaX38bcYPdniDFcfZoMklo/sZsy5vWmUJA5MYq9j+ycnenfXaKc50k06Q7hGPke/0QnOV3cR86KKlil2+okWE1aHDYF3cCHweMgybT7mxH8YEeESyuZ0Pvqeh7K9cdW8iym9wzaNaOv0glU0Mgy06urEdL3NiH6Pm8DXr8KrBrB8zBbQBHI43zmBlNbUo3c4yJ3Y2PeNx0asMJHpHFR03+D60iso4FmQ/Z2uppjcc+Av7rFNHOUB3eFHjfd27AkGo1e3MVS+OEPVnhxEAbfBZSko2q9XrDPz3fP3eS/BPf/anLMvC3/3879XxvatYsrZMLZrAXerGJVd8DkTJqvvaL0GFSlspBO84HieWtahgVUGwfcDqB3kmRJwTM8VVokvvqHPL+JREwAVrwxtSnJ0zeqF475kmv9Pne606o5gnYkrkiz6IbdE8uWXTOeWWF2NXKhwUp4QEIU2z7RMlcLvgoTbyplZQa9lUB5QtjNcLYAnNPhJj2t1TnHeEFmh5o7XC4TQTU+Td+ydKKeqF6hwxGvRWsJmIcHs68V//V/81tzcnvvnNr2it6u/qHbFw1MknrdrJ3MwTn718YR2o8NSEx+b4u5//B371m9/ww9v3PDw88vj0xLKsz2QKNPNsHOxIQFzHh7HRVKbgo5aLpZooX+xiaQPKUPZuEEf3jhy0Ae5FO6roAynOpJioTmd2rXuD0p7JQJ3nf05JWaJlOTNCUl1vRCtUQkh78SUCvSoz0QtIdAQ3q35wFHNV3XvonZodNIfP8gzBYxmY3lLNu8Ln9uYMWrMOgIYPjpu7W25ubzkcj6QOLJmGY1srzQdiDPheoK64lokUAgVHIS8XWi1M86eEyTPHSGC4+3ekF3q5ElPlNAn/9KevuTvMPMRPeft45pd//x2Pb7+nnJ/orhOnWed9LtK7aja7b7hpolxNHN31IBAbTWzbplDY8QZSUv2nU1OJYSyPCI2qmlJb95OhLbmpY1Q0aNM7le+UbVMozgsuBOI00Wul5qJmCcFBhd48uai/6yho3LiIis7YnSWDuGiZfVXn1c7r4VyNpOKMwdyrXS5Rky+8DzTMpFzUYF+8OS7p6JNhEhvTpK8/Z2XkOkwMr24tUhXp2QlCdtmpAYTgjWmZS7G639ZWdbqMnF1eTvQZtEYvnbIWtmwRRC4Sk6eTwVVc032fx0XSdEzgfVLyyhjVBCskQlDaSVNiymUruNrwTSFndflRsU4IAR9Vd53XC0u9srQVF4U5Rg6zI03gRDWnUxwWgVpI3t4mUhGmY7SQaUePHY1TKmybIm5KkeoUJwyDy1Y7ZWlQPFGODFK5nyp9aqyXQl4ryXtCiEzTTHBhD3OvPSNdOM2JQwqAXuJ/8CX46otPuV4v3N2/peSMF0fvhZKvxBh1kTqB4mhUatuo3dISAETNdiV4ahcaBWeaGodqZUa3pQeX7ILVgVFrQ6C+j4xCcJxq6MXX8MhkGj7xeoGYc72yjDrqQtIQX0mTw3u1Tau1w9mxrpsJ9QsjiFHZxR0fnnl8Hft5vjPoq8OTc3zHSAJozfwprRMWzLlCa39q65zEmduCCuTFzAScQZI4NcJ99fKWz16/xPVCL5vOEns1Agx4unUpSlvfe2mB7jrN6xD6IPDZ3REpr7mbHecXE/dPM9dl4e27ey5XhbbV11N/xLCTGu9BdWI2K3AOF5LChq2Z+8Xgt9ps0Rh+QZzGOXWsAxhdltGYzT6qWaW314n9o3wzN6JjotHH9bAT73eiDYZCdDv8RkzXAGG990SSFnTScK3jmhmDt0bdOz89SLDnWbvG7HRzLhlTZjHXIxEhhsjruxe8urnlFBKnJtyGwEOtfPv+nuVww/lu4tIaS29srZMblBaoTWU6zamZcYyRHrJSyO2zOLiN2XteHiOvDoEXEU6+spRH/PrA+vCG9ek9I3C11MFsVYZflUacHPPpjkvQzDxKpRdLpzcURKTTi+pgu5lHiNcWbhhGd9MEqq+tEqzEPmusc5WOskBl7OmRzhLxflLhvVONaLf9jHN0pw5Mz8kT5hwyoJKBQOxN1mjF7XEN8oIMPVkxpxJRGr5k/R0u02T4jej62pEl0e5uH/l5diYwyD5Dxfxcx2sZMq5uBJxhMjBiv7qtfzG0YnTVH0EYVGCrhWLjJNAzTPeIIWeA2HNutvfEmQTK69m3e/EOlmvPuo+6Nii1ZEqzSCvMGQp9ZjiHBM33lOrxQUcbwQeCC2xL5vp44RgnJd1N+lNSDOAaDrV49FHNStIUiVNQNFErSi2W1dme7oQqUPrQNRu6FQZDtatPc/R0mWFqBK+uMj1X05jrswkuquwi9p2g9R/7+r2X4I//+Z9yPV+4tIVaCsdppuTM/Yd3lK2wnFdC1cXeaiHnBdVkeVxw+BjMTcXTrxfauuCk4qiAVk/BK0a/5mFTZnPHPbOv4YBg5VcLuhGbH5ABSIy4o3ZbrgkSBZccIVql7jsuVFzMuOS4OSSCOyDuRCfy3XdvOD9dQDw5bzhRUkw11xS6h+ZopkGSUHC94JLOFNqqu8S2p26OVndfz97U80+8Z3hMXJseUC+dZwoJR4a24LjVGWaYDNpcOcTIf/JP/ozPP/mEen3kmq+UsujmqKqk8k5tp6r564XuqA6Kb+Sus0/fHMfu+dPPb/nzL2/Ytk/Z8sKHp0fOy8L/+P/7K7757i3v3p9Z1kJuVp0RVYCNVsbWnKqzu/NEl6it0IrC5rVVXHdqN9YUnvAGXWWDgXrrFDH42C7Bjs4hKqjbh4ja4hmr0+HU5ByYUKLSsiy66JPOW1re1MOmN/UWhWc43Dac94mYPLkUcimEpiSqa7lQ6sZog0cMkzJ70SIJ8JPakdGTHobBK+rUhePhxJ9+/VNenW75LJ143R1fOs+7dz/w3//il7jPviDOt3xfCg+t8Fg7l+xY8sRWTiAzIgkXjxxOB1xdKS4TojoJfRLOnILjTz77gk9Oia+nSiJzfrrHv/+Bh9/8W57OVxUMe+0+I47T3ChSaT1zc/eCTz//jPv3D7ybEtenC8v5wrpmltzwoud5X650WZDDAYkRgrr4qL+qcgRwaHpIhyo6L/Yyunn7Y4e7GzZbU8L7mRBvyHJh7Xl/ZrgIBDpX9dOt1QhBVpj4YMWtxeREk18YmWu43fTNCiwVqaoG0QnRC0ilOvWr7V4DYlureJcIXtmz6t3gkKAwLq3ho41JRneDV++C7KA1qmTrWjUHsfVngwKwMUJrlFXTIbwoYcj7QG9YUoWiKFvrlKHPtaJWz6RKoeJC0sKgqmY15wJdQ3s9njQfEOnkbVEyaB/PoyBVoCpBbrmeqbngnDoh5a6uQ0WcXl6TJxTVjKbJ4aNo1xc85w8X8iXjW+B4qpzuJnwQjofE1JxyBVwhHU5Ij9y9PLJeFnpwtGIlshfcFCF5qprCUDuU1qFWdStKjrooAiMhEIO6PjkfqE2dlPJVNYJVJQ14f0C6Z55v+QfuwN9/Cd7ff2C9XjXzrhSuXaGLurMgzatwMf+9qmyvLg2tA5wyLw3KCSkRWwfn6XlTYok0ei86bOfZQb/bqVjNrHbwFbXQNxryGBm4wWx0u/uDEFXU21Zqc/gqhDXggqcwbJcWWtc4GC+elIJBE2rsm0vTENSoYFwKSSuu64TgmSdPcZW15mdN1tjMCFrPGZlE1GDaidBEOKSZFiIhJvCB+eYOYiJ59eG7PR6ZU+SrTz/h5e0N/8V/8i+4PR4ptZCLduUOdYexd84owvVGFu1+0SKid+tcet8v6ug8LiZuDhoi/Mc/+hEvbl7w5t0D5+vKdz984LpmzptWZtUgFqxLH4avHTVa9i5Yp9wMmtb5XP0oX3IwBTv6eann5WB5sc/Xgs2dsrmytOEyYU1jCN40b2oiMOD6Xsrz4eueu05GB1obQdQPVN+/w9WG1E7ocXeg7yL7RSxWuna04y/rCt5p3FYI1KAHl5POmjPfvn3L5XwlX1e+x/GmO35zPvPUGycqL6Tw9QFe+MSZE5fYeXeo3H8S6CGAD3z2ZeLmVminmV4DoWcCjdeHwiE5fvz6Bbdz5Cat1LJx//CG+8cHveRjIJ0czXv6rMShMa8O3cFWyY8X2rLhWid5j8yTsiFLAGMrdxvUDP1fSio7GtmXW1vUsmy0SiPIwWkn6IPuZ7+7seghvtUN1ztbr2ohNhyg9sGiRib5lohh5BAKdGfyHF3xaLMCDsuh7KYVdbuxg/hO8J7Xp5fMU+TF7YF127h/eKR2ODUrXbu3uXCn5K7RUk6MIIVeJEQ9mQwOjnR870okaWIAkhCddk+YAbYWw2hBh6Fato3GzKF/hOLo+aWkn2qFQLVV3Xuxz1s7wAG99vax44yalEAnZzW9D0Hh0tobrjlDivRsqIO97RzRB7xHWdhZpT96mmtz453+p3RPXjUs4c2bH0j3kdvLDXEKhBs9h/M6gr8LNVdDzOSZ7Nv1M9m2FVk7ftGiQ4KQN2Wwqxe1pyIMNnNDLJVIC6TequnDPa7p/ZH8hMOTZr8fif+xr997Cf7mV7+k5Mz5fKbWzFNZdWhdtBrstVBLY1kVJvJuYOJeXReK7NKKw81JGX4hEkujXM+0vNkMrpKmCXGB0hR+s7mvJi2IXiLiUBur1mhr1irL2IeYr6B4pdpDshnfoxq0BptjNNEquXrWfKVWOKZAcJHjrB5+kOjNsSzmrek3vIOXd0c90N82SmhE1MGk1kfzVC3qWtIbAaumtY8lGHOwE+jd4ecDTtyeIXb3yRfcCsSqf/fTuxMvbm75P/0f/w/86Muv+Jf/4i8pOfP/+G//W5bLlePhpE7y3sgqwxe5aVEwlNjqX/M8/gY00cKE1jE4wslTj41PX35G68I3b99x/3jmv/8f/g3fv/3A9cOZki39rYvObtALtfcKfbOLKeGk45y6WNSmYaKlZnovYE4xzpmDTtMN0DFatKjgtjuVAXjvTa6iNI0uHec+qp67Iga1VK6XqxJZrOPzwRvsbC4vDIKUComddHVU8UEzLUth8hOhRYM7jWQk0Jeino9dD5jzeaELHESIUUN0m3e4KDwtV/7t3/2cOURu5pnuhOJBfIJ45Ogyn4WFl6fEJ/OJ+FkiLrdcljtNyabqnCQdaF6I4Q7nHBONIJ1bV0je8eLlV/r5bO9593jPb7/9hm/ff8DFyOQCRx/p3iOHWTP0Hh/wOJJE+mXjfH1HrgUplTkEjsly5LoGD5ctq5tIE0JIhBg4ne5I82TpKPDD2+/J26qXQje3FTrVKzkiTFq1n+aJnAsPlzO9FtrWLbOuEkIixgMj1Ryn3dNEwqPaPpHOtmVKabgeEfxuoO29gnjnoqkrnqDIVLBqyTdOtzN/9ic/4sWLEz/+yWf88MMD//7f/wrnIjGcFASUxvVy5nx+ZFuq5vGZgXrNQiuDICdIBFwnGlTL4E91nR26prBqPB6orXN91CipWgviIBh7tplzjkrKtEtXrrLfiTK5rORtpZhbT4yqTlB+vHIOjP5lzjWN0jrlvChpsWXEwTQFehdqVY/Y6FTCpGmquldT9BxPiRCFXjeVtK0VSQ6fVHMcvMcRcd1xOT+xlY3vvvuO3huvXr9mPh34+k++Jk6J9ayImuueujVFzfbhn+q7S8lczmdqN41gEO2+S6VnzXDV/etoooze1oFWaEDNyhpO8wnnI0JAxHH0R7wLyufY3br/gEuwbEUFnE6Uvm50dT1MTXiJ5g2OPDWdQWGdiC1qmxHoeKeZa4dWhqVUagN8wXmh9eeqETquNz1Ykx5Ievmqc7h6ZBpLyISgtTdcrzo7611F9VUroG2rxJBNTArNBzxKlvAusJUrdbiFtMayKOuz9grmijPgnkpj3Qo5133+5fD7ZxFSYDrohsVYqW50uOItGsbTRZPavWXTYSn1n33+BV988gl/8kd/xOeffkJeryyXC0E0rcPqIe2QR6vcxxRNL4x9NmhV18h0dM4uAq+sVeke37Vvra1znDR+5auvPiNOiffnX6notAHO4Z12X8E1ZV7J8zU7Ltsdqh5Vq216c88z6HjogyrNWF0KE5nXoNMAUbpd3FRaVNG5uu1/dLEbQqCMXdn/Bz+0jzw3yc+OF25/xSaVU3G+taPe6fNkctA6OXtqa0yoG4he8IVlXQnBIRLwHRantmI+RFrwbCFwiIGXtxOHJPi6Enoj0EisTH0jTY27KXDNja1Wqle9a3T6vCev++zgo1bjbaN3xyadhc79duVcNtxxJjRI4mnOaWhysYli19w9P1jPPjCJ8Cy01rXpitMCy+CDNCXSlDgej6R5Umuu1tSowJiEOnfTfqXZs6lFDZCvGGNUdKTRdw2sKGuxWTdSDbJ2+s8dVEvnQLDcTusiBu3de12LsW1mpq3bIc1ayDgH05w43R453hw18SBeqK1YGOuBXFa2bdF5V4pqi1afV4xYYRei5alqM8qWTTtobM9eFXNsCH6UwDZDdCJgukj1xDSGJs8abD2stdvrO3z50a3Rm46aPiIpGdRhjHj0smwN6XU35Edkh1V707mqmExjNMLq3ifPUO84zxGqoWJuMz5HVNp6yTbyKfqs8lZxvuilv2YuVx2PLOtGXRuP7848PZzNEAT72bDljGTB56BxWnmY/FvDs2VKgVIV4lbCq+ws8r4X/Z1WC4JQZNth8Z3Z/R/5+v1i+VWrcBmQg5mrRjfuNG/QlVY+imUpPMagZRtbq6PefWMYW0ulls5WqnZXLquxsSiMMSi5vjadKR10gdQ1K6EhF01gqQCePsmwwtPeS8QE+4FSG6000lIIkjmawNhJpPtAcDc4iVzKI3nQeGvjcl5pQ383R2KKYPBR6YXrslKLmbsiBp90Yoykw8R0mg3r7wSzbnODweoiLgSd06B/J8bAUla6CD/9oz/iZz/+CX/5z/8ZL25O/Opv/5aH+3tNko8eoaDpb2YbNfZs01vPZKL4/nzI6aWhDjVhJI0blNW7sLWCuMbNcSaliT/72U94/clL/v6X3/D0pB0u3eP8hHdCkk7vKnQeVkg6dHd7R6cWcs18GXWhDjs/vfQ7SnxR8wEnahQQLGRTMPlCKUjX9aN/R5MeROx7us5xclXkoNGIXT0zhxUXY204ryxkY1F0rfGeL0GU1Rq9Vb4j2Lkog9WHRG2VS76yZX1+ITiQCXzjQqR0QVKjOM9VAmme+OT1gZuD4MuFUCOhF1K7MLWF42lmmhPvzytPi743pDL5TvA6Rwve45K6r5RVzd+vsfFE4+31zH1eCLcnJob3qFARrZZtDdTeVKLhAkkgSFcXo1KM5ASlODXIF6B3puPE8Xji9vaWNCW2y5nSqqW6e4NH0QOrNda8GYu2mt/rZj87KDzih7MJOwxZ62B9qilEbRVHNyNwb2QPLf1aa8oGFYjhBu8cpa+UKmxbASdMR4XLHZ7jaebF6xfc3ByYj0d8fCDnlePxyIuXJ86XzlbOuCDEKWoV3yvVDnknje4bIUGMHkODyUX3GSYoKSOs02uRNIsWZMF51dw2dSGK0Ssjs6u2MpdsBBGvcz3LElUTjWLQqI42xEW8DwTTsqquVjXIzqEZha2hTx581Eej6RWKzAwpBqLh3N0LPhmb1ezkMNs+76C0lS0r+7aVhkxQvcKhuWQtjkXIq6JC5/dPuOB4XC7KzKydulau35w531+pWS9jnFAaLMumY/jJ2yitKPMzTUpcbJlaFUqNQYtjGVDAriMCXFftaUM5CVIgL/z+K/AfuASv66JzlqbD1+GggvdWAenAblTPPih8pea02rY7HFgQZPCeIh6xg7/0pi4SFrSrxB6dp4Xd807/fc2mS8SiVYIemN1rBQPKntKE4o7zjSAekYlk2pjDHHTuFwTnLSrFLKKcJCTckGvgcnkkb5lLNzy+aUV2//4B6XB9urBeMz1njeuwzrHVRnRK3XUy5o466xidSmsaobTj2l5nZ8XcaLzAlDxffPaar7/8lOD0EHu4v+fh/t5MrY2N1y13THj2YbR2Rzt269p7V99EMAjESAZDxjIYa3buBb0FuJknSi628QXJjVahrqtunDi6fP2L3XpdtOE0c2Pz6YTngR/WM47X27vR88fF7EZ/tiMHtY7ZiWpUdb4iiFMHohQ8RdBhfNeZju+iM7YuH7ni6OYDdE7W+q5hFKeFyqCr63ylG8Khhwx0/BRpzZPF0soddIRSO2uvPJQrU2pInOg4yiTkXFguj9RwwvtZP68OEjzhEHFTQtKEbBVXFUIXcfhpVsr6YQbn+eHxgdwqp/lEd477pgSbNiWN5xIzhahjHwWc37RYNAjaBUd0Gs2lbO6izFdzOnE4go8qeelaUNRaWJYrtRW9x7w3s2aF18QJLg7DZ2MlCjv7UbwQojm7YIiSdAZVsNZGyVUv+iZ2QGPRQZhGUq3OgjjI1vnXQmtQ60rvlTSr6cHtqwkRhTLFF7598xvulhvmpOfE1z/6Sm3wekak4Hyjb5m8LSr5EaGJMouV+OSUCCRFYX/nSMkjRQvBkZqj+0hlFN30oT4orPzsrqLnaIgaMVZz2+dtmvenZzv2OSOCFKG1ombewRsa1sil2jngrXXUz9a5gYa45+7cEDlrSu212qwRY1M3cxD6iGntMOTIZiytahFaikKcYudJuRYokK8VHzvbosSz9bpQl8L2uLFcszky6W/FRhTdbB6Hc1irTV2SStUIKVHWfBvSGD/0kHqGlKzPcfxvuS3sDPZ/7CUodCJ9b7sRR3dBVaSijg/VDg7nPW3bWJdMShg0aEbVZk2kjjGe2julV4IFJz5TX0xrF1SXpX6DnZIttshp9+mjRcsEEK9QmbJ6q0Um2YXYAjFOxJBICVLs+LjhQmUKnugjgYRIYnYvaMyIbFydMlhr3VgybEV4/1YP6+VhUTurnI06r1Bfq40QVPLgnbOLyC4p1D2+VTVpLh0kaJahd95y9Qo3TjikyI+++pyf/ugronPUdePDu/fc39+Tc6YNY3K7yIaZ9AAhhy/ouHPEFrjHGGk8L8LhtjMOLYeYvgvujgd665rvFwTZmrHbmhIwXFK2usGJjMseY1TWZjZnRs/ybodmPwJM9ySBvZt+BimhqTOPsmq9zv1EA2VtuSAIUwx4gbxqSkAtKnXoI7EA/Yx2xmdXjWEpVX0oB6Tv3H4hjgJBN5KzeBwNNG69k0VlIblpV5KrWfDlzFwqkg7G4HNsa1aYaHJ494KBZEnyhCnhpgRxQjZwLdrB4gjzTIgRf7qhifDNt99yWRd+8qMjLgrvL5kPrdKmZCLzoLKQnnUuFSN+CWq/VRu0rLMmHyi9U0YCAioTkSa47okOql3yrXVyyVzOT8QtcntzIgSv8GQVM/H2xElNvsdIoVEN/ov4ACFpUVGq0htcG+4bkEujrhUXhB5EpVDerLqqogrNCXPU/QVKuFInl0opC+Iah9sT0zHwyScHcML1Uikl86vf/oJXL+64OwWcm/jpH/+EvK4sT2ecZEJstPPGul5pLdFRy7jmOjF6YhAGdznEmRAiqQxLL1v3ztZL2ZT81YteHVHHDd4QCekK6UsIlF6oolpFhagtVcJwXx8sS9Ep6pWiJ0TPslXKVjTdwYpnEC0mO3vRN5gJ7XccZOyy+5+xJpWQ+buXYO/q3hQG/c72XquNsjVK6US7qMtWaL6R7wqteNZrYd02Ht7fU5dK/aA2gGIB0b0WG3UYFNPHmWCXYMuUVf/EmEgpaTEsAiOSqXdNT6krnc5hPuCcsCzX/az4R8GhYk4Q3tuBbuWJWtU4jsejYeRJD1unH1DMGW8HnnfaASI6lxOnKeLz4UAIScX0rVnVVPenND4OUySZJkk7mWY0fPEQRA/o+RjN7aGTkjBPkXXrLCsM4+diqcjHcCTIRCsrW1mYX16ZJk/jSquZ8/lKr4W7u4mUAv3hiohjng44hKkHWoO7O319Fdi2zPnpQoxOZ4FOoYVW1Mg5BI0fYTCkTMfofMNJYUrq4vLTVy/49MUdn7+45SZ5vvvtb1guVx7uH1gui8IeJsJV5pWzitsujT5uGV3AYvMZmjm00xEXcc2rOL0183NthKAaSEvb4eA6TIH//C/+hJ98/im//PX3PF0Xvn//YK47Qb0gvbHmzHOm9modotufZh16JQY0qxVf8IGYEjvTwdIFtFMWW8QaqEzVzSHWMXbMtQehOY+EwOF4pNWqgm+EZVvx3pG6um245inSELSYUAvV0UWrj6WDZ+TD6dy5I9Rmna8lSqUU6b0Tm1an0xT10GseHwI1BghCDBtNOtdcKR18SITgScHxdKl8uFxxsSEh46cj7njkl2++5/3jIw/rmbVm5tMBvOM3b75lzRsv33+LC4FLhGspXA8RYmB2E6E1+lVDhp04Tnc3fPGTr2i10LLZf7VmEJp15KDkqobZCZpdHbrXW4N12TSvLWe8c/qMQlBheutqcSfC4eaA9455nvDBk2I0ckMx+G+jbBvb9cLp8ILbm09Z1sqyjGT5zloWtpIJ0eGCxqXtnUMt1LrSeuNwPOB84MBEnBw/+eMfEedAnzTm7NUnyujelgOn44HDTeByWfjhzRsdz5TOdVl4Oj8hrnP3MqpeuTcu54XluhCPt8zTzLJogkGx9PMYB3lLi+DaGrXp7Kr2xlKrFU+T+hV7Lb62vOJc0Gg6F5gPRy00WqE07VRD8IQQrJA18p+L2jXvcjJHb3phmgoQJ1ELU4OEqmmY+3DvdsqbWLoK1pd1tYgpIzNZc1FL3s+RrWa2mgml4Ddns1FlWHv8OKShqJ7v/LAgXljzSs5F1QNro21qrGCwhJ7xTlPkgw9m9FGpouTFbHFYKUVScqTYmQ4TcTriY8R5x3K9UnJWiZl05lmdY/SO0NDif6gX/P1RSsPg1ggvblT6teG953Cc8SES02QO/VpZx7wNhAxng2C6GVfbJXiQIy3B9fJEzprZp62wQTJdH361ofMuTO8qQchtU/o1mmZ9PAaaNKqrpEk4HT2NxmWrdCkgOt8svXHsLwnygrx9S2uPxPkD8x1QF2qpzA+eXj23txMpdq7XBUGY01EhVItf8l49E3HONtZbnIcYsepQoHR6y+a16PTSwOnMVAS8TqKO84njYeaPf/Q1X3/yCZ/f3XGKnr/67W94+PDAw/0DtVSmg0Ix2GB8uMkPxsBwfdFLpqGBrEZb6VWr0Kavo9ustFYNDfZiziraquGckCbPf/YXf8zjZSHS+eHdPfcf3rPUSqsTglb6SgfYrON9hj81s9vpCctgWD5f1s6r76FyA6wl5CPgYVyCVSveXhVDVSchRRO6CFUUUTjGA61WvBO2nLksK94LrRe8j/Za7XdZsoU5bhEl6ec5LgV7DXX8p212nWcKyY2Bi5JtDocJcSrk1deEUb7HJdgpTQkzihjAh7Xw5t2V5gvNbXz+9R23t3f87dMv+Ltvv+Nvvv0lH84PHG8nXBCerk86L/MTLgSOn9zhQyTNyoab41G7vnjVz6o0Yoocb07UUhXmv154OD9ojl60rrMLbBW2RtvUscTbqCOXTS+vrGSD5aIewKcXJ3wIlrkJFEV2XtzcMk8Tr16/JAbPYY4K322bjUtWLo9PPC6FVzcv+Pqrr9k2WJdOaYXSMvdP91yuF3zSQ21rWWdkZJVVtJVOYzocScnj4sTxmPjzv/gRPgk/PHyrkP6rl0pqWk4a/H0MPF0e+P7NL3ESCOHAcl25nC8cTom7V7NeZr2R28JSnoiHI9PRs1Q74yzkOFjWpSJGDV+B1tRTtzYKBRHHFM183Au9NrYt433HuwnnPIfDkWVbWa8XLDYCEZXfOEPvnQ+Ih61uavfYlETYu1NoExsnmLPObhlZq3Z9RirByIOlLNRNL8HeBKqjT4qwiV2Cxklj2zJb3mjF6fnXEgSdy3nVp+h+KlryXh4WuoO16bppdgnWrDaVeibZ+WAEreAjk0s0qVRxLFUldN45YvKK4qXO6TRzON7iYkS817MOG+FI43DQIOncwFeYnDeCzx94CW7LqjOaSXe6hGgzAxXNlqqHlu8NZwaxmE3amAU5r5X1kje2EWQpQ3gOIU24EG0IrCQJ4Vn/Euxgz6vCoS4oOh3RuWCcA/NN4u6T0z5UPR6O3N3e0t49cN7OiGtqUusj3kfWdaUsD8AF8SvTfMuLF4nb46f0Lrx/84anslGWQlnNAUbQoN8eOJgRsA8T4vWSFy+clxO1bipyNXwdgRACMc3EdNxzugYDVVzHeeHz1y94/eol/+Kf/gU/+vQzpHWePjxyfXqyzdFsVuBgsButZMAuvo65SxhLZLAsDXRXCyubHfRqJgReqOhG0rmtKASpPw2hc5oCTmb++Mdfcndz4t2HDzxcVt5lhVm6VXZ7wVVs/jk2R28mhrZCZocnVOqQUlQ4rI561v5X6Qql90Av2sl2adqZ2ab2ztO7Phsv0JIybmNKdMDnVXMsvaN7qK6bpZRjGBa2rvBpSsHiWfousBUd25qRib22ri4CupaBri4oIaq5dnca+ErwhClo6nbdWMuF+5z59uGJfDPh4oFffvjA3/3qV+SmZJbPzvfcvnrJX33zK3797gc+5AsXV9iKWmERdf03NK7ow+MTPkRevZjo3WmHVBslb7TcKFczN7d5aEzK/ixiOlEfnwtWNGFhrQtly0hS26sqSmRQuZ+uI3GyW5Mdbk7K2HV9ZyiueeV8fiIGT81BBc3rlVoK27JRtw2h8/T4xK/rb42RKeB1xrpcr6xLpm1DgO5wXjjMB2JwfPLpp/jgCL4TgvDqs1ccTpHjFMF15mlmKxvff/OdHpRF7cAu84VaGj/+yZfaRTXP5bqSponjzcTN7cT94yOP5ydaV8mT+gdHYpjoyXOYT6SQFJYVwTm9mHK9qDNNCCBlDwjHUBJsZDMMyJtkK4Y1i/UkJwbTvDcl6A15mKGYbKVqt4jbCYm9d9W6dnYNY98LYtUHyrCnFC1ISlv3bl9sNijSYRCyOzrSMKMNbKSijFadEm6r2k0Oy8JebYRw2ejSqa7bVhlOMFps993n2OPobOsV7ztP16C63aT5ljixwOOA+EKjsuWVfnnicDqR3MSLuxO9H9jKE62rCUHvjeQ9TZyNfv4RnWAerC7ndZhpgvaAU51HbVSn1kbROaYQ6DFRk3oK1lbNEqiT88Z1WZiT4ukjbsiHhBf0IO0dasYGPXSBgJh7vREhgs4Eo1NCjJ896TRxenlEBaSV0/HI3e0Nl/VMuF8Rc8GPUYfK28NGXSohLYSUSSlwczrw+edfIBL4N+GeWlfKWqlb3w/DUrQyCXHC+4CLCe8d0xRowHQ8sC2dbT0/MyXBwoYnQjiob2RwlLzSajYmpPD6xS1ffvYJf/azn/KTzz5n/fDA5XxhvVzIy9W6ctk7o0G8EXi+BLteEpiPHqJsVMNK9dmJ+fQZHd05cF0l9d7pnKdZtz2G7HPyeO/4+otPOcwzP//FLYjw7v5qUPYYutvCMQimjgBbI+B093yxDONj57Xi1QPneb4y3pwLCrk8ixyaid51wB+CJsNveaM7IVqmnQ8B37QjFCun2yDlONmZoYqwNfUPRWd+teriU9cQm7c4RSZG0y1dGaTOPmON4wlqo+KD3pwhElMipiOShbVcOefC2/MZHx2HduCbx0f+9pvfshbtIN7WldunF/zih2/57vGBRVayNKgZacLxqMHONNWFnS9XvM/c3r7SF1YGvJWpubJd1QfUeYXjQ4iI10MpijOJwbNkwTlhuW4UOtGMph2eVlE5DU1lJzLk2o3pOJsJup4FOa9QOstypXhHL4HWCtuq7iT5mm28AtfrlafHss+bNLXcs66FPOQivXE4TsQYiGFmnhNffPaaNEXWywPONb764o7DMTCHQJXKNCVKzbz/4Z3O6iUSvGNNV46nE198+amy05dOTBPOJ043idvbiet2pT7oBahcA9PW+UQPnmk6MMXJMg+F3hckZ9x1VdsyryFdaiBjFnTD51jc3q313UJS+RIxBhyqc16uZ5ZlxYkZRdi+UbN7NA/TOwT1RpWu7GnTk6gemsoAXdRu0BEINCrGM/xIOmSkx3HbgjFKq509xiHooPF0npx17CAx4h3UphFHZVNfKIJqNqtdgpW6y2eQZ7P/vC04D9c1EkmkONEVesT5oO4wroNUNW5pwjRNSEwcjwdCdFyWSinCtq7UVgle5TtSP3pDf8gl6F3EOTUlluGX5xxTnDQ6qVzVKaFWGvZQu1bs0esAd1RwYjOw3fHcdB3TrDDW5emiuiIjISgs1o140dU+zQlxViZa91qZh6CamS7C4Thzc5uASumPHG4LX/440c327PoIy0VnBW6Cm7sj8+FALhMfPnjWVcXzH+4Ll0vnvBZKAe9mDYuMnS6F8/UJxCPe8hZFIY7rcqXXRpoOlk6h4ZfeZqK5rKrT6kEp+Cny1SeveHl7w3/5v/lLfvKjr/nJ119xdzjxmzfveLpcOS8ry5a1exJHbR3pqpN0oizTkYunmqThtl924bruIK8dkF2arVet3KwAUQ3hKAPtWZvHYMcMvI8aZ/TnP/sjXry759vHv2PJlZZ14CBUzewz9wu9WD5me+rB2TDBu5O9S3NNVEYyHrt1s4r8Ctkq29pN/C9jhiiM9AyAddN59BQDPnimg0arPCeTPL+nMT4dh1yrlbyutskdTtkZFgflzXVFiwltIvXCTcn2yUhoiJ7unHlHQs4q/O1r4bvHM80J/+HtD0y/gO+++443bdHnG4R6feJdrzxcL5SakQDRO2RKexK6iJlVtIYUu7C3SnGQV8vFLEoo8UFh+daamhZ3NaCYfGBKkWmOOyqhEVKedJgVbbAnltcL67btkHboBsXPKk5urdk6U23ZPB85nY786Z/+FOmN9fGBy3Lh3YeNOE28uItcl5XHhydcnJnckei92WF1fIDT7YHeJu6fzlyXlVJX7YJm9T+9PC7UVZ2mvOucHxe2Tch5xiXH6cUtMSa2awY8h+MLdb26nDmdjrx6ece2Fc5uI0QNp3ZiM3Qr4l7evubV3We0OnNZlGg0TV7j3Vrj89s7pmliPh418TxvrIvD907JjrouZpyt2uT5cDS5y6xrLa8oe3N4Cpsva2Ofq2s9qIe4erAOL1jLHLXw3Fw2Wqu0TddzsE715nhL7xoITEeF/B1iSFBh6Up+dLidZOK84PyzsQtW+DmTVeWyUuqmF1KtLK2Z9KtBF5XI0JCW7f6pxgJTOUOzzznnDB6mBr00RQhao7RidpfaAjeei7RcCnVrHA4zadKEiFY9MXS8Ex4fL6zrakiOY0rm3vOHX4LBhJlmCNuUGRditBqw70P2LsoIw6j5znsLs9QbXcMR3f739GDqxEnb3ctZuw+3H2wwiBKasaVyCp80HR4bqQXnzBRXiCnw4uWJdT1zuTwxHRvhEGk10VtiW65seSW5SkiO+TRxOgVKSZzPjqenjVwa50tj3WAzL7pol6Cut6aXEg6cvudcMrVWcskE57VIoKgmZ3Q6ovE/reri8lMgxMCnr1/x1Wef8Bc/+2P+6Cc/4ZPbVwQ8pVaWdWXdMmsumowtFmbbmzpvwHOX5567km4iWIcNyA3+E1OEqxjXkjw6MDq5jrK0eL4EezfBvcBhSojzfP3F5zgfSe7n5K6uQSOGB7OOM/GFXiJ2CepM2YbiBtW6IU0wiyoPZkysl7WTIe4127I+qurx1lWA7ZyzbMMCeEhqPhBTsh82aOzsc+Vx2YsVd72qY79YzqBG2Fh8jJdnWzD7O7qGnVm46ezBie6X5hzV7LFUp6rz8n5dyL2R88K6Xsjbha1uyigWz7IuSClct5VWq/rgOqe6xt1Q4dnPlGoEstJUwL3lHf4T7HU36KVb1lyh175fOiEF0/N1ZSg6j58SyTmTABVq2cjbVR+MdFpVBq+PzgJoLXnFoP6YIqebG370k5/QS+bdt4r6fTgHppC4O97SH564vy67Y8wcPXMKOF/xvhHkDie39B4Qrjw+Vc31y53qO+tlpZfK6W7WjvJyRQnkntQCd5+dCCHw8vYF3iduXn7Bsiy0+h2H6cDN6cDi1YUmJs98CORtIa/LPhO+Od1wmG95937h6ZyZp0T0nrIqspRS4Hg8EltjK4WH95NKOkrB9c5io4g20trTZJ/NSSn9Z/XrbE33YQMbIRjL9KOvzmA5WmzVuAi9ruENy261kUK0QN8YEq13tuLMgKSCV8JIdXU4H+77TZqGAwy50OA2jBBpcZoBW6vmRPbWyPZaY9BCvRjHQLQu0nOzwfA87E7Xbq8VXxWRUbu6YuziQpw0bQVRY/sRj1drZcuVWjOtbzTrPr01RSWvrMuVET81pfqPZIfaDOR6yYQYuLk76YUYPDQN0Oy9k6s9JtdYcuayLMSkhJlt29hqobWsQm/RaIt8rdrVADEFhEpwGN1f7aigs+VMbxCnSf0L54QENXfVsUsnTEIuV0rzNBGOrxKvfnxD50Snc/++cP++0Kb/P2l/1iNJluV5Yr9zNxHRxcx8iSUrqyqrN06DTQ4xGJIgAT7xgZ+bb3wgMZyZBkh2NzgzVV1VmbG5uy2qKnJXPpwj6lEDMAvI9ERkbB7uZqqi957zX2+U2Pj+Nw+8f/dEEDXCXjZ4vUFwMwzH8el7wpKJx1dq6dRNL/F4XPR6yFo71ESgC8NBK4N1qxxj4nh+oLbKVjZGbrTcCFNimmZNIfGebz++493Dmf/jf/Vf8W/++q/47TffcXYL66cX6rbxyw8/8OXzFwvAtW45pw+xMHCu4UUhaE3AGTuxAwjDREOOHVvUh3xvSPBO8CY0chikWQu193vRcRBvHilVjLquAcS//fiB5CP/4rtv+fL6xs9fXvS/Mz53Cso1helk8K2n5Mq2ZqIV0u7MX4yRKQSkW5J/66Y+1gvmfH4kxsj3f/Ed4oT/8P/6j9xut69QJraBWKp+F02jr02h+Hk+fL347HtrxlXuH+zdoyombECMRzXP6n5px6jWiFoKiHA8HC0/NOJD4LDMymHMyVoABrUX1vILvRU6hctt5fXtRRHTIFwHXMeg58IYheAqXlR5mNJC7SpUqa0ypJnazQ5LYJZIChNP53d6IfsLtVSutyutD1oX9eiFqDU1tSHOMU8zo3feXl7UShQckoIObUmQFnBFkOr45vCNFcyqeG3UAqPfTU3XtxcQR/CaLPP49A1C4z/9h/83o3fyegM/ePruA8flwPvHd/w2TvyXaeHLp5/48Yd/1AGjVlLS7z2/3ci3lffvHvnm4wcur+/Ja+by+on1y5WXX35hSONwOhKig6mAG/jUSbPnww9n5kPg6V1iWiYeHxJPD5GPHydcGMQ0SEvi4d1CKYO8DUpx5AKHp9/wl3/zHscRGQvH45XXl0xtG61XXvMLJRf+8NPvmZ4/EXxCEA7LxJQCyxQYvfPNx0da71yLclXRUq+cdFocOFHo07lIrpVbLrTitLw6RaCRgjfxmw6725YpVYMERoM1r6pQjolpmr5yDrbHlaFweEyqki1ZFdmnRbfXeZ6ppZK3DZ+i0lsIruuiEmIEp+EFGNztRC/fEQNN1L/IGHetX1zC1wXJ/ue8cH7/yGiD7baZOFzpKYlROfuhqWRtqOUIr57E4LsOXiEypOJSZ+0Xxrrh5gouUrasnxEnuJjI2aFBDPGfu+b++L/VgGI0oVz29mJ9IxUeNbK0K+7t+6BUw4mdw4dOLpW1bARLgqhWAZRLoZbONic9yIbyMWI+mRD1S8ulGsejB7KPQXmi5BQqCx0XBq1pP19nkGbH48fEnlqz1Suv1xtERw9weDfz/vszxeS1+VLJN+xCiUzLmTAVvbBz5frWGHj8NMEAb5tEpxl85xnNqQ3AOdI8I63QPZS+0W6FKJ4YkwYDe8/D6YEP7z/wN3/5O/6Lv/kXqmICXq/PXC9vXF9fuV7e7sW8X8s2TThkKfTRxBlOdh513LfoPUhBJxrl23ZowfOVPLcdy8Jox/2SCMnKU/dm367Q5OPxSK2djw9npHdeX54Ry9YEIXiNn5oPi3IgTrg5rWjSMAMVBvXeFS62iLTRbRF1Cp2oLWVmXhb++ne/w3vP3/5//471uppA5f5/+jwO80sK1vvmiSHdL0DQnFr2yVBs6Nqzafdt+D6Fj7t5d7cKOSwZRNQikUIEFwnBM5klIMyRNgaSq9bslKttyF258dvKNEdmn8hj3BtFWtc82iCOxxgJPmp3YYNW9dIT4yb3vsToI8lH5rTgvKO1yibC2PTib2iFVgxqzO4Fuyw9uWzkvBHnoL18XpCkqTGjO4KvuDZIhxlwqkocjVY33f42DTm+XLXhuwdtCo9Ri19//PEHHbKcoi7vz48cj0fO7x85Pzzx/pvv+fv/7Lhsf6Csnbx2/KRB9vmi8vrHhw+cjg/MbmJLhfXlC7db4fnyQu2FbS246GnR0ChZiZNjGxvnx4nz40fEDebkiCkQloXWMjlfCDEwTwvb1lhvjVIbqUaOD9oOUreZViZtm5eN6/pKLhtvMmi98HZ54bYGDnEh+MC8LOrjMw5vngKtd56vN6ATknpma6lmTQk4F9R8n1GubjhGMzQhaPBGDH5fou49qxiEXc03PE+zcopae6Exh0PzWQVH9BERoYpmos7LTKwNJ97ye1WE1pwOzmMovLijMzihNU2yUe6YOwVBNZGfCaa8s9PFkEGNe1OL2V53pF5EC0kJzsIB5M5DStBg+x1Q6gQNFPAd5/VyHy1TiTQZqpotjSFquejsl+CuJ/gTL0FjXBmiOrBtuxFCIM0J5yAkZ/mJmVahrFd9U4bKiL3BbM5OkS6a8iIIIXaDk+x3MqGE90E30B02cwrnDYN2fFRDaRDdXooVXTYZxMuN10+vDOn4pWr8UxXy6pjSwrt3DuePDHF8fnlTz1NtvK03au74sOi2OirOdT5++0BrnR9/f6UUvRyMFNJkka4HkgtCJHJ6PDNNMyOgHEwr5J7ZWEkyEZy2MEfveToe+fbxkYRQ1myB4YORO74Je1JssKDo3TcZRC8978U4G4NU9g1R9CZR8Qn3lBPdmezw74a9KCaBs3/jdwLfIL893ODrZdnskWjM0fOv/+a3vPtyZFtfeb1t/Pi26XvrNRLrnhgzbKKLmpnqxaLInOAkUIoKexzQTe3mvULxtTZKzqy31TxZmjur5b0NinUQNjEYXjReTcyOYZNoQ8PQazVhDeZ1cmKhxV3DDHRe0I1fvv7hZJ9EIXZr5hBtDoiC1fRoIsqaL7SuSf5lZLrLmsU4HFGCNYSDrJl63bjdVuW2giMKdoiqICLXqpO/l3+SQ9mqwUjBUcfg+fULLniVvw81udfeKVs1IY3xS0E3CkW9rUVcNLK510buK9UKbWfpBIG8qaKy9WaHoB7y+vqiQhAHHRXF/PLDj0gQmuvEEDifTizLgW8+fkdrlZ9//pmfPn3if/jPf0vZvoBbWU4TDw9HXHe4IZQl01rn7foHLm+/8MtPK9dLIUZYng6EBz2bTk/vcSGSGdRReb19Amlc33RgfPm8MqrjMH1Sz6erpBQ5ng+0OijrxrZlrutGmgfzIej3XwfT5HEpcT01RnM8nCdgcE4z6/VGbfos1q3SW+b6uiEi6o/0CiuPWhhtwwXH8XDS+WsYRWGwZ2mdQx08nAbrrXG9Vl5eNU6yjq61Q8r43MMbnC0Jrqq/Woc/d7dINbtMqsXhBdFLcJoSDLiuqmdw0ZP8jJ+CCqr6fnDYkCiqvo0S6F1TqrzVWXmfDUqPeuHZf+Psxk6WpKNpLoJIxAXh/JCMK9WKrOj1EvRec0S3teBjxDurR+td5Zhu1vCAUIkThKSbY94qjw/v8D7y/OWNdSts202XrFzw7s8o1b3/EJ1ie6tWEKkvlLeG9dpUEFGLBk7vh2azlPVdDSh7NBrqD3M2ZQ8bcfXnOA2bxZJGZD/EucNeehBYZNXQy7BZzM52y6RbJ2+dnCFnaHXGS2Ca4dQ1e3HLhZKtYb5mfaBpqq5Eyed5mRRqmLJ9wHUK0ylusEvsnUcFEQIhBuXkUPioj0azEFzHIDiI3nNIifOyaFHxLnvuevG5nZEeGMf39a3YMwK98wZ12gW3ZwsOzSuU4X618Azj/XSYECUdYIdDf3XYI3wtAZU9lUZ/nV1FxtByyw9PZ6BzPs4qWLmqjcUbNKnTqljHl06OXjQBwwdtGFcj/D+NbRsGhe7y79Y6OWe885rM86sNcFeU9l0tek/KMLWsqePGHRI1Tm+HQuVOdbHb/Z09lPv/dpbUmcAp+GCvlYG699dPf37txWDXjUbVZ8osGRoD6PT9KJ1eGjU35RRxOFErjNK1xvN0tSApjHz/UDIM9u7Auq245nExfL38x7hvDvc4Oufv6Dj2texcsh6cGoxca6dF/bKLpeB0k8oH/1UdOoYFP3d9xlpX8Yl4x5j2IAdV6y7Lgev1wuV6IdfKWgspbEyxkZKwTAmKQBXi5IlFKJeVmm+8vl14eyt8+PhIWiLRLQqxPZ1xIbG2Tm6VW7/SWqHmQtkG260SfbG+0E4dG4fjgeVw0meiVbZcyTkTkqI0GtGnm5AXbVeYpsAU1YM2tsoaErf1RqmVa6kq6ChZn6nDpM+2PYtjqLUqmjVC7PUOTjst2TIxCCM6nBR6z6yr/V7DLiYbZu/Pm3Nfuew9FUlsrrXnfk+xkq46AhH9/npTA7wTLKNXh6BaO20r7GIzRY/sc+udeXlNUew8o+20hXHUpup3omIfh/J1e4Qfdr6n6O73hArkvKrUPUgtd2uVhqsbEW+2DOf8nQt1Xr/HVjspzkzTwrp1OhnnClDvfaR/7Mcfh0OD9uq5AcEnlsOZIXDbCtF73n/4hrxlrq8X8rZxzUWPDBdofTCy4rStV54OjxxPR6ZF0zL+9m//ji9fXuhVt8B5WqxwUyXb1+sztXVqzfZB6zgXeDyf0bLICylNPDx+T6krb7dnJjcjI1FK4+2tsG2dbWu0utJrZrTE5ILaLJyj3TLZPiw1C2XquNEttFAoq26rp8czU258+aLBwdWgv9I6MQXeP5w1pWRVrrDUldKyGrkdFgAs0DqHQ+TheOD7D+/5y++/5bQk3d7Gfmgrd9RFL2TxegirYGQQvT44bny9APWCbXcIYoi2Q4BOhdrYYcS7zlYoCWMHpC4H2rNnG8eAPWLzXl/S7fvWBnXH08OR4IV/+y9/x49fXnlpv6fUpokjIuotM2otxIhPyoGIhJ2AY3dwjK4cgnT9XnuvDHZoY/D3f//39DHYarX1tuLEsYSZAVRnvYPDOsx8xIl+4MHsIc4jyS4k7CIafX819PsECyDWy9WNQWsD34VkNpLpqCb5JpbbKIMxKq83fTFb1Mb7IoOK0MakvsbdRN27ZSJq/qIHklfl4ehom3gfuKYmfB8Ap77A26ZBmikdNBlDOqV3LusFHwOn+KBw8Cg0lGIYdLZSVFhmm6T3QvKBNBnN4B11zZT7cNi5lYF3XweHvS+viYVfVD1kdFYVLbEWYasbowmtOfrQUPutFp5fv/B2eeOnz78QQ2SaZ6YpcTjA+4d3fHj4hhAmfJj49MPvefv8icfzd0zxxH/73/wn/vCPv7CcJkLySFSB3Lv3J5DI8w+fuJWV4dQvRwm07Hl7drTS8f5GSEKaHSE4tisqZssFHMSw0HLh9ctGb47WHJd8odYrkzvyzdOiyV4D2sOJKc0IaiNL50Brjcvb2x1GHL3z+vxKrUUjFsfgtl7UToMGmIc4sd5uPD+/kNLMYTkQgnA4RMQ9shwmLpcbt9tmcYeAeFrToIvRO9MUGSnQ2tfg/32p0KQG5deqJcKkoChDCF9HKe7jXgdnxcghMGqmtUzwk+XDJnTU7tArcYoISZOn7PsGNAd5DI27G1pC3Psgt35Xn4qg2at9ILWC2bQY2m7TauP2drl/VAkVcY3m9OsvV7WvtaDK8NNxheHwwzGFxNPDmZwb5S0zav/TL0FxTr9Iu8F9iKbA2/DeE9OkilF/uys/dco0Fegej+OEEAPTPLEss8Fi2kG2tw+I1QtpLqBNMs0M19h0PLgnpDQRgg8cpoXNCVu+ESQgw9MrlK1T8qBk6KXrQWaTx86WDYsL6g1GE01PUeu4XoK5g1OItkf0QtpvDcUoLfEh0aqmxHdrlHdeJb7Dy50HC96zTInzYeHheODheFS8f1h9Uxv0XrUteVjQsGHlygPKLra9B2UrfLmrRMbXzXn/57Zl3cWQ9rjfMb+xbzN6uTkEb3mI//Mfe0jw7i+cU2T0mQ/vHqkI5+MLt005L1VRGpHh7DB3Gn6+s5D7170n2tvvYl+6fdH2Ur9dLveWcYR70LW3ioexDxHGTThDFkb/ypHqz3dm1rfsxM49KHnYz9OvfVe7yv3X1m9FnwdkGHIwwA+GG5RW779PHwo56n8VUGRDfVJ96EXYqomAEINmnaIn+5Y3QHA4N+5/3+wDnfYknaGK3J4rYX+O+m5TMWhraJSXN0HSV6hX7pyt2PZMs1p1C3pv458+C/aOKWJgFOoOIzjrfttfr966bfnqGd2ycpClVA2bcKL0yqS+r3meSfNCSDNlPSBj5cPHR46HJ97/53/kur0Rp2iagICPjuN5YkjAf9KEkERgdKvPco6SNcB6y42BIySv1E1WC9O2VWIK+KiG/to0GKB3FXPl3JgPB6boKLaVpRiR4ZUPtmguFZ1sZhf5qqVoVSmE0RUW1EvQ23AS2HJlvWUYqmQcQ6H1aVL9RTVVsRuGJnV7z5spt722yBRD4arBLs4SpZxlCve+UyP2dt0Ngr/akvYEfmcmfMfXuKT7/6saeNA12N28FA7LigYQKxkfu0fYaeBC1eSkHW0J3mSjo+kzNnaltgaB9NbubRFjf65bp9k5ph5k/VzU0mlBX2uPvn5ePP3SvtJA/39+/DNwqBYyhqRqzHqvwsxsrfJ8NThxFIZUpmg8j/d3iGs+HJmWCZ8CdRR+/vRCzZlRK+fpQGuQW6HUV5x3HJYJlTwrrKGH3kCGcmc//+EPpJQ4ns6U2+Af/qcfmKbE4+MHPUiKY7wlcj6AVCaq9X1BbfbCOoUa8mtlvWYmF3CL4/byhVtzzMsJcY7bj2+6Wcx6mLvemYIjTkcAeqlMU+Ld+ai9cnEQ5sh8Xsh543q78vzpmc8/f+avvv3Av/7md/zVb37L9x+/5V/+5rd89+49UjbK5Y3rbaPkyuXLJ7bblduaaVbD4kWIzhHEsXj/FWrEUhmw9EAxSGF/lgE/BLEeP6246nRRIynua2BtQ5vjacqlKBavh7r3ltwebUN0XuMz3WCOnkP6Dd988x3H7/4Ff/jpJ/67//7fg3jipB69IfD6+sbnL1+orVNqvwt9Hs9nTocDOIU6estmtXEKpZaqkmpnk/u2UltjSkkPADGBSFK/1ES8X2IKKSpvc/8wC/phwhI2jFkVN3BhgMeyI7E4qErNalquXsAHWo344Dk+LcQp8PDukd47L58/geukyatQSAbBeeY4c5Mrz2/P1NEoaGhyDdDxRFERVy36HeiYtuCJRKn40bnkG7Vbmr5zlJ41AHu9Mnqlj6JWjaqezZGzvQ6eNqxaqSpXFB2q5HMKkzb0Nosuqm82NGrrFoI/+PLyzLZlfDSuy/jc4BcErZwSkf38JAUVXW1dVeWjFeq2cXm5Ic7z/fe/QaiIZJ4e3vHd999yeXvhf/j7v2M6JqZD5Hw+8u03v8UF2NwX/up/e+Tj//I39OyQ4Xl8PJNSIsVEH8JvfjNR8qD1SG2Vl9efuG03/vDzJ8TDw9MZ8Z3mN17zyusfPuuQXTqn84EntxB8JPoT+Ii4gE+F5Cq9Fl7LK2Ub9AZTUBHKOJ9prRF9YIzBPM8GHStadJCo3symdMX6qudnHRlwOLdStsrtohaPz19uyvcLTMvCdFh4ejpzPp94e7uwrhnpm36eQ9TyALvZxFn0oUHW0hTF8ssDY6hXFTSoXGF2sw44VW+L83RL9Yp+4KIQ5yPRndhuN15vV3Lp1KZwuA5tARfDTiDQzK7jutIY3VlAtl2w06zxcblXo4RmRm2U5iyEJJLizDKfNHQ8BVortJrxY+DqSt4U2QpRIfbzw0xcIhRP9YN8vdHb4GE+I7NnapFW/ww4VIwAH87Z5GL/k2HTbGFvRVavFojfuR7Fa0PwTCnRXKeNSs4reV2RLkQXrEesM6jQdzEGlo/XuCv2nE7uZfs6TSn/sMEQTkc10PfRGc1B8cQohOSVl5NmtSzGhe153U0v+WAHS6uD3g7IELL1KWo6g5j53BGjFYoJTDGQohp2e/NMh8Tp6UjOnjBByStvr57TaeHDuwe++fCO77/5yOPxyBIj63olryvbupJz0ZSY9WYlpF95OS9aqqo1SOzghYpJ7u/Xr/7oti2xp0LssIfBpXfm233l/Ma4/zy5W2V3ftHZxma+PoQQBn44gltwk+O7KZBLVXuLyfwxsY0mQ6i8Oxsh7gQ4qnoREfM9Grc20C31DhOLLbu6Ie7pLboj/YrjMz9gH3v0m77f/b4h6haqeaoWqWQXtbh9gdbs2l3cxh4NZ37YgaXteKeRePNE7w1nVUHizdzr0baN6Nm8JmY0urUTwPAg4nRDE88Yev058Qgap+XsO8RS/3ffb7OEm5qzevl6YURHua0g6qdCnEL/9nDodtYsON3MyN4izccgSFAoz7YINSCrKZuh4Qy/5o9dUA9ojBGH3K0tKZqYqummIijEnLdMSIE5qSJcYwMdzk3UNni7Xmmh0GLkHI+k80wfG53K4X1gepzpq0ea5+nhSIpJebM+8PVIK0LrR0otxPnK6wU+vwX1nE3afNPQIIGcC60OWu6EWNm2xgjKqQrqqd2RAvVXFmqBXoUoKlQKwZsXT8VrKZmXt2tiivfDkAgVJpXSaGOQx/7MVmoe+nWMZh2NqJI+Ribb9IIXbj6AK//kWb8TgaJ8tQw1rOtnRFX1MUYNk67jfgropbnbLhRaEu80P9SJUhn2a8ZkMZO9UZpyxSIRxN+5dvsV76iSc+5enq10pbU9yL7NYTy1V6QsaFC2M+O/Z+Bj0MWpgAyLjByNUdH6pq5h3b04iN7yUbXIeTSLiLTw7f7HvfJ//BKMmkqKw9/l4SAEP2s6VPKMkWl90xc3eHvTFCMHLdVct5XcK3VUpA9CmBhOOa9eMrV20jLjxeGawjB5U+hrSkrMJh+NR3jQLcBFRoPRHS/Pr/z00x+Yl8TD+UgfkdYnvv/+HR9/+w3Pl1+43l7Z1ZLl5mgNpnggPU6cHoQQdAPKa8PbzRKnSHKRb3/zDnHC85dn3ZEsPMAj+mZFNTP7sDA/LDy8P9JqomwRLx165ttvnvjuwzv+8psP/NW33yC1cbk+8/MPf+Dy8kKxmLn8ttFKw4m+/sGZAVuSHpZDNDGmGxQ3dFDYBV1xT2dx94+ITm2w433kJhprKLu4Qh++2op6/UxkE5xCpMP4Q7GiVW9b5PDu/jBPBB7jiefThdPhyJYzt8uFWjM5r1xuG7VWtJG8EXxUv9LxzOPje17fXrjlFWkKh1Y3tDnER8Q7nba9Z5kmavXq2lAkhe6MvxwDkWoLX7+T8sPaMnzQdodg0MsOOVcTH+nhgk7R3Rrqh2Y9KvSjkvZq5vOcO7jKet0I0fHxu++oPXNZP4MIYQrGTzZt+Paq6vUp6OnQBkhkEOkmM98DBFQI4xlOJeUpRHwXGOq53a46gLa3K8IgHTyMztuXVzqQRyNNM4/vH0Ack4jy95frvbBjToHok0q2xqCVSs0dlyIueWpfKa0Q0x4WHwBP7RrKnKaoPKPFX9VuyVFOOUM/dNA9HE70Ufn06Uf2vrc0OQ5Hzz/+4w/88MMLPjRCbIhPpCkwvGMD0uxJHkYa+N55/PiRKAvr20YuG+qbFY4PJ+iB7TpTu0OOE+fmePxWM4V9qmoX6Z1SAriZy8uNz19eeX298NOPPzOnyDIlVTp2zdesvfDN0wfOhzNi3Ydfnj/Te+N8esL7yOV2NWhVUYyYAq411u3GqJVWB9tW+fL6RaE5U3wrZyR6+Qw9yzKFlcxwzmL79Lm8rTdKycgYFqZhhQC25UymSAULdQ9Wquv1smLSf7yXFs9zVJ6uVuMQgRGQMSsvXitYvFv0gTkmRDo16MYbYkTE0zoWp/d1YD8cDhbpplalt7crpVW2qjDV6J3hdVJPc+Dx4UDZGpe37Q7Ve3HMIdKcaBtLX5G+GbLhWC/1zpu3W+Xj44GUhEtu1FJ4Gy94n5jDGRf/jMSYfWq2ZCu80wMqmLk2JRXO+BiUv+mm/nNeN7KqitKaNcW/0xXec0JzKgHfG5TFNsmd21IoR+7bkHfKGTrZ//A40UMj98HlstL7IPqoG4DxJ050sqOrlcF5R9n/nSV9pCSEOIhToA8hSoTh8UX9OTEF24K6iSobDke0Ql8fFA7ozhGC4L1uYQTPlCKHZeG4aJXLIU0sMbKVQs4b63rjervRLDtzFxvoULSnwMh9ArecHl1Z9h3w1xsg/Gpa3P+dTp7YdiXYf77zV/Zf7bPifnl22xe/fkj2v7Df1u0RZB5PYHKBFMLdP1SsYDnbBniHKRkE55hT5DjPnA9H1tuFW+/3reX+2xhP58QCAMJuGfnKbfHrn/prjmPfIu8TsL5IYr+eAgPD/rv7C2wb5v6LKrasBL3++24VF6U2pKqR2OP0IOqOtQakd0u6cV8VzX5XwvavSTkEIKqHr/a7MOmuzhPdfJ2FJfcmevmb8q9VQ0us36kU3YAbnRbUZyVO20aaKa+/BgfoH5oVi23QgxQ10KDbz3MmYRcJgLuLjYINvbuvbAwV7agWYH8v9Oe23tm21cAHoRMIU6LnQR+V5eBYgjOeURQ27gU3GkJTYZwMYoLkYbtWBhURrV9zXtd2H1SsFESQ4ZGYaL2S24aM9k8iBhl6Pm11sN06JUUN6h8NeqP2TBuFh/mBQxzKIxZYbxutFebpBMNzveqAV4teyD6Yh1k8XppxjCpOGXBPZRoDre/aP5e2WWnLQ6fmqr5pEeshHTuiz53vb1Yx1QfY0Mo+zO2DHbpIqOJSNzXN3O3G+e4CFssaHl0Bs76XV3PPFdb3PRJjold9dtw9U8KCvYNGFjIG0trd3qGDui4Pzp43QfN/W8U2Sfu0DkVJnHjlJoctZKJ3TB6aktRKp24NwRNcIvpJz26J5is07+Qf+fHPWiScgxg7MTZOi4oSljZIy8TDx2/JpfB6edUiz9ahVUYpfPnlE2/PL/hLxPfIfJg5TEmjh4LnMt3IrtDfFCMP80KaVAwzxiDME6MPQlBf3XI46gVaVRU2iQbqyoPCDW+fN8rF8+nmSFNjPtzI25XL7YJ3gcfje+Iy41Pil/aiyeV4jTmbZtLsOJ4T0zz48KRB2n/7n/+WXFcuXRPKX+sv2hVWL8xx4vT4xHIQzg+RTif3jnON7fmzilxa5+Ai6f13/OU3f8HffP9XvFtOpNy4vly4vb5yfXnl8nZRqLZ3WtUBIIjGzvmhpHgIyqNl680afrcE6IJ3z4loliG6X4IOU3QpzKd/IYTmLIcysBffFj9oAmN4GI4q3vgpvUC7hjPRpWi8VlDIrhFJw/GhFT6FQnOFdWx8WdVfVouQh1AiGuc1Oh/OC795955/87vf8he/+S3/Tb5ye3lmTJ7hHc7iBrtJU7ux+Wk5EqaKqBvc4KZxv3ib+eE6juag9GE5nmrKDVbS7HpjkKFl8Ek3sqIQ6H5R4hTWan6jOuhuYYiw5kofsH3uTEvl+EGhucPjiUHHTY7WrNi3d3LthNlxOFTeLm+8vDwzLzPL4aAfXEnsJqveLXLKd0Q28nq1S+KAc4ltnah10MZG6YW38gp00phwXShNexSHV7Tj8vZGTIl5OTDFSHx4IG83bm+v5Gvm+e1CR1tdQtSN95wSC44yHK0r/B+8cDqcCT5yub3Rx+D94wPOOV5enhXqLhd9r7LZW6JXtfCb8v4v1zdFdVJkk0a+Gn8pgy0ktjjBGhnXRAmfubQvTJdG9IPjMTAlT+t/oI7B8Btu6pqBKZ61XhnDI+8So0F5U3GGhEbfVt4+f9K+Q/PQURuhZg6xcb2tvLxduUrEE02Mb0M6MPdGfe18+fzGbd1wcsFLo20zzq/87d/9AzlnPj6+Z55n3n2zELzncTlxGyvP9R9xdTBx/urd29Ebr2jSaPo5pXZaiXQvFGnEFHUjqx5yZVcSuibQvQ38g3LbNCZwWu5h6SKq1GSoLauPxi1b3VlTq1gIuxBRq47amkG0JyiXTH7ZiMGT/KI+xQ7H6UhKiZf6oqlYhpg4icQgzGkixcjtcqWWRq8bjM7BezpCchN0qNeOi6qi7hULe2iUXPFhItZoGq2O9AkZgSl6UtTP+uaUvtrWzhI+8nj8yNPpX95n6N47rxejlv7US9BHK3V03Tx+qgxMTkhRiEkhm6mqAXP0wSiZ7gcxeUJ0hC74run+MSq3phi2U2Os/9Wk7PSvxxCqUVKTtdKnlPQw7jvHMwy3Vp9LDJHRtV9rN42Lvbm6/ejBznBmGaj78W4qUXO23NeA/itjcqN1FQF1+/3x46uqbnSGNNqoSLdrY4DHkULEuch5OrCEhGudcruxXS9s14txOso3afqMTs67beFXWkp9WMXUkc7bQW073N5uPcZ9gwbua82wS3DsXOCvfuwblT48ssP73PXJNk066+Lb61+cCwrZSVQgoGm8W5gSsTbSUmhtEMrA9YJrmmEfRufjhye+e//Ex/dn3j8e+PB05PVy4tIreZihve9f+zCYFvNZeUvG6LQy7tvFMIh332oZ92/qzmPqy2j7qG3D9+K0r68Gv/rZ9pr2exrPGObByhUXRLeAVjXwWlT15kSDzWkd6RXngvWmGbTqI85HQzgc9/5JzFZi1hFn3KEPioRkqQgd7wLdd/YAZW+JIM4EU/o2u3+y9e1B44JueWpBMgWuOKR1RJo+76UpJ4Z9bftrIhqSLTKYpoR3nlsIGqJvWZLOEkNAlI9vxu87vfDaaLiuhn8x9fSuGq61s64FSTDcgAA9DObJM4K9+ILJ8S0hyH6fPsBJVVWuwyLuqopVatetqahSNudGXgvVwja+vs+7+vkrk+68t2g5IQTRTc541z6EUjI5a51SrZVW1B9btkrNTQer/Vxg2H9vfLSdSbuS07mOd5rycn8mxk6DmNdTRPs07Wzf9QMmFvi6Je5nmW1/uxIZ+Oot3DES+YoeCRhSMr6ql0U9k87vqJw3/6DaetR/bKdV/3WNGuosGGKq2IEYHNzte29NzyZtIhF8H3if8D7pUNVtgza+XIsTEq1jrH7QYJSi9UzqNddTIEQVNP6xH3/0Ejx8857eG/l6owhsdRCi43CaCNEx2isO4eD1QxhSopUbNQ9iPHF67EQfVQDTA204Xi8br9vKnAKHOPFwOpNiJbgJJ57Hdxp8u65qzTgeFiM5K61W3t4uZDrPty/60AQhRMc3H850dJPxHmIUljmSnHBdNc+0XzYGjsv1CyVfSfHEGInnn68KpRBgCP/46X9Sv43xfvVLp1boryrIORwemMZEYKKXwecvXyi9cC0Xng4n3r3/hkkis0yc4omH9MBvl/ecCrz9/CM/vb7w/PyZ21Wn8CDCsHU/Bb3YnLfDaX8AWkWcME2aqCPBHudmIhJ76LpWpTNEGVxVOatpWoZHRlTYCjXyM4amzziIXU2ziB742ovmcTKpgCMmBkKpKrpwSdsGvA/cSuaH2xcuKfLd7/6ad7XxfTNIvHRaL7S28nQ88v584i/ePfDd45nH48JxTjx8+Hf8b97+Jf/3//bf8/e//4ELE7V7tqI+09pvevAGLb5N8cgYg5vLdwgNtMWoW5wUMoh+h4e115LWqOpTR5gRd0ALEJsmuQTPXW5U9LWSJoyqELeqIlXavW0rg8zLs2fLr5Tts6qFnx6IXi1BW9bJNjghpUCKT7x7erSDwuxH4ritK7lkdutKdAEXAsdwApRHZwivLzdKaaQUiXGmvHuHAOeHR0SwBP1OoyLe4+2weH29mE0pURu0rNVH0Ag+EeNELRv5euPWBv2aSYuqL8taKKMh+Urw2mUYYuR0esc8JZxELpc3fvzpJ1przIcJ5y3abaC5wcHxzelMrpW3203FazmwnA+cn07aDRgd+e3G5flnjtfEcgqc58Sc9LPk24RPj0xxwk9qv9hy1bzJm/65DkeXAbFQ2sYPP3227syZXAtvr1dul8KXX66suXC5bRxORx4e3xOjI0XPMgWWFKgWjP/b35x59/hAbydG7fz8wxduVw3QaC3z8CDkzdG5sObMLz9rM/pPP/1MaxpN1kxlNbqjtUkpHTcpBdLUbhDjhISMn7Y7hKhDu14kMUaWOBFDMCM+XC43TV5xu1WiaF7rZdMUlmVSe5IPeNd3zz01d0ZvtJ4JQVs0euwsS8INwY19kBOz2yinLqhNC+B0OnOUAVbsu66Z3gafv7zhnAqkcMLx+J4xOjVvujGmgw7tXYMetjw0TOF4sM+pZ16OLMcH3l6fea3POgzLoFvK07wspHm3LTn+8IdXPn36CrfPhyPTPPG7v/5rzVT9Uy9BHx3StBFCF1B351M0O87inRyINEaveA9hiUACmXTydV7JfwJrr+RRoA4NNPeq4owpEqPX6dI64kQc0zzr1Nc2nQCD3KcmpU3U/+RTuHtQfHBWsRQQw957VxvBAGtb1sm1O/X1DR0j1cgKOunZdqCGb429Epz+2Zonx9gh3H6XE/rhCOJJEjmEmaf5zDQc9Xaj3G7k25WaM60WxEd2NRro6yp7hAn2xMIu4jL/m3FG9mDsm95drvUrX5t+mTvurwe52geUtxGGCmNMSSO/Gh/3LEC3xzl42z67KsiaccatFraSuZSNPDphSsgEScx7mbWJoFY4nxcezwcezkcezkcOk2eKwsNJw6efHo68vh3JXwa12mbKbvDfHXR7KsVAW6O7QmTo67Lb2pTi+9VWN/aVWGzL9TaRN3WOOvUR2gtL3/tk9l/BYFLvg/6e5LsgZ3QNdlB0yXggij1n1WBbjfzzzilMvPe9dT1k9lzG3bM3zHMmli242z7E6WY40KJYEeXnEMHXyt64sSMr9EH9FYc4usbS0TXpZH999hQTxq6yQwUMXZGNmvXzMkzJum0avVWrRnztg8jYBVdduethKpD9WYpB+ZphG5Lj62ZRqm4srXZKbrQA3asisBVhXYGmoqTeOrdVW90t05symqnXVYVcm26ArjlK7axbYd0Kt7VakTNf1YqiXt4YAiEFhsnHfRiE2DVyLnpi8uSiA6PaumZ93VQqZ95fff68EyYLrxbZecEOeNwwL56hKs55xvC4od7hcefgnYY/4PEhamqK1XQ5V6ySTC9B5XiVT7s/67sSyjapgWkudgXs3aO3K5NtaDT+zQ0LyRjGrxiSsHN0IBoK4r1pO8JdT6IfRB0s1QI+kK6/37Cv0VkUW4h2FuIQCbbpKmWze3h3IMt5+8uhw0BpiszsZxahggtme/2nyNf//McfvQRDqPrNLTqFRtH2AYa+uaeDU8y/Z7Zcub5m3r07892377heHW9vQ2XiTjid3rEsJ9I//J5PnzvXH7MqMaNjDoF37x+YlsRymi0SqAKO0/kREWHLF6RmTr3gnGeez4xWKetVuwoP0aboToqJOc3McaIrmXNPatB12yFVKLesB7R5Ghua1fn9t98RQuD3P/2scV2iPpbZJzSiSyGeVs30K1qjwzQx+YjrgzAcUw+8Tw/89bvvWX/+hZ9/+jtr/i54GlN095QSGy8MesYuoq4JGCLESf1he2SV2MWmQnm1n9z1ymYj2PmC1qBWUXm/CIPMQL8vvfIXfVCdEoxdb0M9gD3IpHBVQQ+66pRjLKit4/nLFy458/vLlbU1/CGRgmdeJlqtlC2z3YT1rRCTPldThGUKRK+ZpMfZEyfHv/1Xv+Pju3f83/4f/yOXlxecS4gEmqs6jRjsrgiQI3lP7ZamD1rAOwbD6b8PXS8ZWtWDeWgGaAjT/gpbdqio1SWkr5CSKGweQlI4E32vj4eZPiC0gYtwiBPRQ3AVh1qHRs3UXMllcLnqtK+HrCMlT3adLo6ybpR1M0/mXsDs2cpKLkW3Hu9pPdOHIjHOqQhnMJiyBibfA+eDCi3CUJg1OQ24wOkFeF0zwUfOpxPremPcKmJ+yGVKpOOBkiu1NnIZtIEqUz1crm+UWujWItBGVVFDq7rxOIdzSQtU7dJ1AcLU6R22XAkh8v7pRFkbt0slx8Z6zRzCQpxmhjSFzHrVz2ecNB91ha13/v7LZ1obvL1eKblwu14ZA85P73BBubUhg540cEKcfn23txvX68rPvzyT18F1BRHPFBN0z+1txTOzJONnbXsruZDXC9vqiS6idWYKSZ9Oj3gfOPgDtVTWTSmWh9MTvXVLABosaWYMWFtnXTc+f34ml8b1pirpNC132HpPWeqt00tVWDAsLFMgOFHYtVX8sAFNotlM9P0P0dO9Ni9oelWxJcXEccw6+FLt0nNAoDV97TSHX4cW5yLB2aUrntIKtTdad4yyi3L07ELUt5ec5+HxHeI8z18u2v9XCt45TocP9D5Yb1e7QANBhEkcPgbSPFtVUuWWG9ftWQe9eaGNPeEYELVV/bpYvDuFfyerkMp50Lv60mNa//RLcI9xatZ35VHloyY9ODUFOyhFV3bvOsEP3eyi/nsfdPqdF880O5Zj4FgC4xV87YSksut5icTJ48OvOZw9T1SNmeI7JH1TQghamzFmvT72zdANpjRzmI8k8wft/iytlhmEGJnGdM8yrU2hqSaq7mq940wp14cZzm0a6zvGLmIVOntKghBQY7sDgkl8I4JrXSPk1o0+ih54wzYxG252DlBEVV47IeaMrHOW0rJ7N39l0LlfDF9112Ib8Lj/I7FUFN0wdVjQVgnbju0SRkQvWONDdJvURJQ9MFnzvTu3lsk1c60bay9UOt0NfNSOPR8tE1CAVmkxkGIk2UGvlg/doJ04IsLpsFDLIAWdQEfvjF95mnrXr721ev8eYd8Yx/0PEX0Z921QOQu5vzpOHPvuIrbhifv6XiI7v4Dyr0bS7ZyMR6umQtBsyRDNV+eFYckx0Stfd5gdWy5q/DeOozfj3ppaY5RL/JreMrD0F4NH9fdUDhxAvKoJfXBfkQC4c+pi6/Ce57v3v+VckOSQSdTfFifu24bzRB81fJyOdN1GW7Xnwfg4QZ+32/WmKTddS5Rb00297grVWvHD4bNDvLtvGt4FqsDomhNZSyFvHn9ztF4tTH5/T3deWjett8sb21pY10KtjdtFYxUJN92GGuAGblYet1XNEb1eV1V11k43fs7Z1jFs8+21a45vETbplDVTcqZuWUPujasD3cRjTMSQcEeFP0PQ4toYPc0Jh2WBAVNU2HMUNaiXWtm2Qh8rGiP41fvXxSku4TwEbKvS8y54y3cdqBBxT4OyLVaco4upTZ0iC1qaMuzCMG2EDO0KlU7bFZjd2R/2GRGUohFv4SLB3gnhHkc9zIeFbnbeBw1KGSgnuW+fXtNvnI84Nwhp1g0Q/XwosmiRim7PBN032v0z7MyvbBe0XYI7uqNeVw26oGvYRa2D620jlD8jQPvDhyO9wub3Q7bj3SDNg8Mhcn7SCchHmG7gpXI6wBQzYxmICyzzxDQnYgz40Pj4MXE4nnl1kfzqcXtZrFsZNIavauSNKmTpaMp/8hP4gEzDHuCJ4BzL09P9zfFOD67j8YGHxw+IL4gvPF8vvN4uXC5XbreNh8cHhCOlZlqvlM+rFuOOQnGNz68veB+0Nf1OaDuW+UwfndwaKTieHp8Q18jt1ZSakckHksBpSnw8PLIMyL/8Qv7yQn690Kh0qqY9eBVRaACx3mYSdGpn6MHoQvyVoAGUv9KHQuEM0aLevd3dqeEV0UlxFBOr+D0VpluCzrCkELEAaUGvIY+QweK9BoMumrpTJdIZZNGw4h+vX9hq5q1fKUCZ9ENzEL04vIeQEtPhwNU5fG88nk68fzhxnJTrFf1EE5yaW797esfj8sjD9B9JdLZWGE1wk/rPStaNt9RNBxEf7xeOHXvqsXQGB/Vhl5y7P9dBtKOtD5WIa2Gy30FTfXlFSNNkh4GYmTeYYEI9WIc5ESfPw2khJiFOUVV2242UJp6ePiAyMdyJT58+8Xcvf0ftldqEbc1st0zrjTYac0pq7HUwnI5tdVTED3wSLRhFlKcqWiIKQjpOGtnVdSiIaWIX+uS88fn5k81FjlIqt9vK+dw5HBcOy8K74yPbtpG3jTlNTNOEK51Quiaq9MolX2i9Mh1nfU0snu3l07MKgkx6nywjN/usHFAteO9Z86yCitkxTsLDWa1VvWk/HpfK9faG+wTpGIlLZPaeyUXERZwkQjjgQ+L3v/97Pn96ZlkOOOd5fS3U1vh8+QWAVjI+OM5PBxiD9XKhbIWXlxcbBgVHYImzDblNW81bIwch+8F2sXi7ujFa4TUshKw1cINBnJ9YDgvH45kUZ+JZAwHqpujQ8+sLIsLHdx92TJTaOnLNLIcD794/cb3d+Onnz6xb5e1SCS6QwoTsKvukqEzwkeACMUW1riB4aVyvb5SiOa+jD8KSNHHFe2AwDIKfihZoI9UuknhfFrQBqH49Q2onl32EdvgpMCQgIeFCwteIGvO+wuj7DxEhTTPiPc8X9UtKU2tFPJ4JzpNCUIprOamQrjazjuhQpxB+YEqTnlXWSD/GuJ/B2vViEYAi1GwWHxMC9aLIklrjhB9//GyX6J94CcbhES+cnvaHNqNt8J20OIJlcyXnISZYBsuUmKK9iM6zTJ5pVrzbOcfkPM152pyIXWtlcELrXncfpTAoVQ/4EEyl6eV++Yl4gjtonJhXaND1bvmU7g4DjJbBbeS8Usqqwoqkh7OIwzeozROuWia7c211aBBtaVbomxZA02oQmE96+G3lpkZR5+xrUS5Q16WunEs1Q2rJ6qcy0Yp4sUnP3Tkc9G6ziBe584AKf/46AU9BAJ3IlYfcVZ2/+pNugfvqKmhR5hjmU/JAsNfU65/vST/6uwyvW+DWNtqASqAjVA9br9zKqlU/XcUmzb6O4D3eKxrgxREQDnPCtyOnZWYKybJjlZMYDSQoJxdRnuSvfvMtTjxvm3I7v5QrWytkp5yWCmkty1X2ZgkQ7xVattzQMfqvckH3LXGY51Xj5GwdZFc/3rkt+zW939MsLMcWVTnPswZQp6RFtCmqAT/Xjoij9g6j0vpK2bK2sDQYznxfxgNi13fvupd3u5gnl2wb/RUvaMPJznPsG1Pr6tHbiWw3tAGcgbWt6FA1TYkYIsE8gzLUs0kKqmoF5mki+UgZ3Vosgj7Pxi9jr+8860UyLIhyWNUS9wxV5Q5b0a93ONhuhdfnCyW3O/yn1KHxqhnwQ0tgo7CtBek3nASij9zWQi4N7ysinXVVeJA9XHg0XHOEa1CYrpmHeSjXqSN1QHwwhEW3iE5jTtO9bFhLswNdKn7M0CZkqDJ7jgfSdCCFQHRWBTQEFzsVT/JeN6xgUGUMX0Old3RndE6nBecytQwNAkmJaTiWnrQdwezAOqDKzpAoUi8eccqlDcd9k7pz2M4uQzd0o3O7KjwiaGjDGBq/Zo8HLgRCSpY0JdqXGaP6qb3HVxUjFUMvetsD9THkZEKcJzgN2Nw1DDFMurWZAlhM17Cjdwpbi32Je5rVnn2rz8V+4e5agLva2+thtXOkYzRjMxTJqq3/eZfgITvmOfE3f/MNznfW8plBQ4JWNW4j65vjIod5xi1PLIfB8fj1dp6nxJQirUZaC1RuuOZZzhPjOOmbBORi5HHQ7W/nEZLT9vfg1Wbp2yPBJeb5HfRGzxdGq/RiaQNjsK4X3raVNlbauLHVK1u9cT49cHo64HxH3KCNmdYht4ZLompIVGFXa+OyrSCOw4d3MODl+kyaHH/xuw+0VvnDz//AFCc+PmnT+mmeSAN67jQ2irxyu3b6peEHpOAowYEfWgocnEmfUZwdFQHgTIaOBhUIg3sfkV2aItod14vCQc5/TUVw6OEmfagAyQ0NMqid2AbFearTCdO5oL1d3qtdzne2oXAYbtCk8yl/YRuV4kS3zB7JpfPp8qpQcldus/RMDIHJeVJyHI7p7umf5iPLu0em4ZiGJ2KChE3oRUiT4KMgdRCH4//yf/rfUXH8+OkTz28X/q//z//IT19eeKlNc1qb+QFFxSWt6GU1p0lhvRjpdWjerQzK0Aupj4L3nejKHYJeGRT4VbmvQZF2C86HRTexoUEGMUZiDHz8cCDNgcMpEqKQEvTR8FnzOl8vGznfuL39wu1yI183DQixy+zX8G2r6gGrBt0fz0eSdb8ZP2Af6qw8T1eIVrpDuoU1N+1PA4jODjnvGE2rguZp4cO796QQWfzEaJVar6SYOB0OdwHV49MT07xwWS9sZSNcIJfIZhVRYkKW73/zG8vvPFBr54effk/eNrbLBUHzJ/vo5LXoIVdgfXvj048XpnnieDroZ647Rq+MUVhHhiw4d8L7hU/XL2oz4WcYjtulUCvUokW+n5+fqbVoXJ2DlPQ9zOsghcTT+QmcZoDqJbiXsybSFDkeF7sEK6dl4XxYLJTDk28bZSsc45nYZoIdvh9PHzicFgT1+wUTsbWuYyXTQS+HpF7a4+lAbY0vz9qrOary4WkKXN5uJHnR1+N4wLtEcDO3beO2btzWG3lb9Twtal1pteF8IrmEcxYaHWacOMqm/tlpieBhNH3IQ/QqZjPVpxb1ChJs+BtqctdLENzYxTFC7oUyisKRQ+wS7FzfbpRcrVFeSOmk54l1eQ4UhUnBlohmBeFj4LC4OTrB1131p3oQF5CeGb3Z+aEahD6w4AalhxChosXVaUqIc+T8FfrX4Ab+zEtwWkjJ4x04N4hBP4wSh4o7zRQrXn1b3kVm30likV4OxZub4rQyhEAgusiookn6u69J9AXoxsU50ekAp2ytiAbnqspzqCVgV3jWQiub9lLtWXlmKvdOPYT4bjmDetE650yBOkhpoTXHVhWecl57+7wpa8UVxDkOj4k0eabFU8r4Op2bAk26blLeOEPNGjVxUDdVqwcfLNlhfOWknAzuOr0x7uqy/c9mCGTP/FT/49e0k32b/NqmoKbrbpDnjp3L2CdCoTvAdyRWhWGTIEETPBqN4TqNjoSBs0l9iIAJhDBT0d6v6Ie7i4h2fmlvNPAiJHHEbt7RX/E+svsRbSBgDKLTct6H44S4wXfv3+HEs91eVFhk4pxh/kDZvWbdhuGus2PwXvMiGwzjFRT+tFdSIGB2EtsG7x1p9roGi1sTg1anSaHLkBI+KMfTwNb2gfjIKI3bbaXkznqr5FLuHPO+pgvKfZRWkBiU9pKdB7b90KpxpKsoyHsPEWvv1u+n90GxxpZxRwz8fZUVUUVmCEFV0049jIOvG/Jur+lNi3E1nUYP2HmyYIqiiMlo+jr0pgbrMjKtaTavG/q17clAog/I/YBT1Sh2aTRaRbc/U2JKNTSiNFqu5p3V79s5D6Oq+AULSjDorreGiS51KOqd2hvrbdPD1N5jun4dahlQ1KAbXK5LbKcPk5s1heFqHWytWTqO06zP2oheX8sYom7jot9bcrN+vkwUGK2H8HRY1M5ROr4GEAdtcFtuTFNkmSNCQPCwdlpV9Ch6Dc7AKX9XSlM15BjEqA/UlGZEnKa6jME0R/Ymj8Hd5cnY1ZP2ufPeK3/ctSdzniZ2vtQZWjCpne/OK5aiEPScbtRcabZ9+bAAnnUoHNrFPt9exVuj9j2NwgbYr4Ogc55oF5kLnj48bUToqgtQRMI2Q8b9+Qp+144oH+1FX/NuqTxaKfdnXIIf3r3H+cHuo4reKWw1D0IVqJHhPWPSLM/kJkKsRLIagX3jVhtbrjZt6AMiIVBujZq1jaLRSUtCgiNvG613PHrIDm8+ueEYzVNKoIsjcmP0Sttu1LqRb69M00wIiTEqrWdSCkzTgRESI1RKrpRc8PGAcxPDotyWwyMxdP7xx3/gtm08PERiCBSTE4tXj9V3f3NmSpHTY2K7OVJa8AgtZ1UuxY4LjpQcKShUG5MqFKU7pImaN70eAONrOKC++Tb1YB9ZpZXtwgwK3+hlN2itot1lzsQv1uI8NNC89kZrWO2I2LTtoXuaVf+02Gih0ZaKiyCLIBFtQ6dTpNLdsFxVR75stL4T3eDCiheNs9ofuuB38UtiigutFkpbiSJM3pHEaU9CQy8VEwDgvMI2vekG2zKC4+k0czxM/Lt/9Tf8/PmVX37+B8rWGd7TRdicEvhOgsr4q35vAY3UiylqtFgfDDyNRoieEP0OepIQAoM+PANR36YVkDrvNQ4tBJwJEB4ejqQUmZYZXOPt+glcZ2pqhF+WE21bef78QsmN9TZorVHt9Lc7DuX4GtdtBZdwUctKnddhsFjfXa9WKSNCjBPT5Igu2MCkQq7mGjnD7druooMuesC7EJnTQkpB018AR1OEYCjX3Fsjbxs1F1qv+O3tfiCfH0/EKfByUSiyrDd6a2yXjZUV595UM7Bp2n+MGl04hnbZRRc0qDtG7ti0181AcsONwvAdvHYrhiHUNZOdIxondjgeSXHi+lLItdwFIVEizjnWUvQfOFU599HY6JSbKhOnFJGh3Jd3npQmoldOWmyAKblxK6ulQsEU1e5wzRu0yjIfSMmz3pQWSsczwSfmlDSxqKtC9jA/IM5Rugq41u2NEQLHabFtXfOSz1tlDo7aVqaUOMwzpThygVY3bm/PTGliSpH5eCBME+umEYS3Ve0fWI7xNKlAsK0rMjopeUst0vzmsRvqg7sPWOKEFJKGhTRVqh6XA+ID4iPBO2LQtK55Ppi4RdjWjVoq+aZ/vm7qVezNU9vg83jRmMRdTOadXsZXrUy6h5hQlE7ygxQ954eTdlsat+y9DVqtUWqhtkopN2rNuo22jkvWNmPirOhRDrbbueh3tOVPvASdH5ZhyVeuaejq7MUzh0lX7K7RZnPUwtyYAoNMHwXXBaloioQEgh+QhJg8dQy62zTCqg/G0A+7x+nhKHLPzpucZzQPVc3lKVphatXEgdbbPTJrnxQ0FUPjj3TaVRixD7TYtKOchE122lwcEBfwDpYlAoN5GvjYiXHgXNMw200nb+8Cy7SQQkKsxqe0Tu1o1JcXwuSRMjT94D6RaZmrhlvviivbuAwBF7grWPVf2xqxj/DjTmfdObJhUnuNwlI17K5+HOI03Dh0Ruo0r76vEHTrdQcgoo3oo1GlqPQ42QW7ZioOIVPRxCDfBO90y0pTsu1GvXGtZYsBq6raaqbwQvMmZd+EnVODrl37IiYXFx0S3BDeHWdG6ywxcHGOPUPC40xTJPeDcXRoRX1dLuh70KP6qGQYD+0135Zhv4c+8fqMOPPexaQeWZNiu6iJL4ezXoIuDIYM7dVE47Bar/S+sq3ZBgOhkHluAAEAAElEQVRPNH9UHYZ4DD0YnHekXqgjoqrKzKimFHQgYtCUOG0gH+NO+N+lr8YzBXEWMD4rDGpKWG8w+T7Z7+Z8TRDpZplUzlOqZ7iqQdhl3yo9MWtnY68Vmg49BoJYKom+jmI5p3dfoF30d2K7G6Jj9T/dtv7WLbcSrzmUbVC3ruk40SN+QHPgPcknWhjUquKV5CcalVtVq4RLamUJMTL6oBRtp+m/8ih68bTSkK76xG5/EASJnqRBySpWioHLy8a2rQga6/X20qgl4HtgSp26Fju0h/oM7WJurTJQznWMQa362k3JMyXPYYmEBLiu2ZwhUluktUQvWj+lW3jUgoGkv24pDSfafRhiUhGaHdTio74Xo9Fd5/3je9u4iwpHXLAov4y3UoLh9hypQd4y3ndc0Au2V6dhCq7iLTA9+WQ1X5HeOiFNdtEGeleBYKlNvbtDUYpWO9I0fauWfEd8Btr6oBddI4jSGnjTBpvILySHyEQfM2M0Ssm0VpVbtv9+dLOEdG3q6B1tV9kzlv+kSzAMNcR7+dVJK7gW8RKJ85FWBrk1lpg4LwdCVI3Mmm9c84VQHb6IqkC9JyVPiJ2liVrTiqf2wnW9aRCxBFvTlVQOUWOVlnmiV+UVggsc50SrwrU5ahVy14zNvaoGsctoa4xaGaERzZBbOza56Oc3b5WyFcRFQjRuyMP5AM53Zj+UR0yV3ivPz6+0oiR7CoGn05OmzfRIb4W139jCoCAsKZLmAJfCKMWmc4Xudo+ahikpdCmyX3B28O5DyFBYU9MbUAOzrf/6KOkG2FtROEWimohbNxO4tT54z5iEMQmZjSwb8wzxJLjHAdMgbzdqK2S3aSfePNMGrNcbpQijJpDAfAg6tDjBuURID+R14+XTJwqdsomxLUUjtRS3BBdwYw8yVkWqH848Sjap9szAjM7i+M2795xS4jxPXKKn12peSUdDyDY4jCHWUVmRCfyCmf31ou5D4Ra8p1kVWHDOBi+NV44WZ5ZmNfDrB2ng50ScZp6+fU9KgVt+po/BIZ2prfD89pnWCjWvarkZyrvMSc3mZVvt6taS6bhE5Yc9bPnCtq16uDflrL0MvCSG99yuqyqxR0DCYOxCABueolM7Ujx7FXWtm31+ovrdatXDRRwSAmIlsqNpYIP3EemNUQulVSuiVlhJPNSS9GvrHf+ruqiBDhzQwHWExijqyXROv3+8fo3SzFcbdn4KRh/U3kk9EogacdUb5dooeWNMnh4dfVGuaIlH/JK4Xq603pjiRHWNz/lFN4NlJrrIcYnU2livr/YMamLP8XDUDtRroUeHEKGjyMTB4VNkniammDjMR6Y0c33+Oy5vr+St4H2krJCio34UpmlBNg0xnxdHSgE3KjE4FcP5wPz0Ti+edcV74XBQeHk5JEo58/23Hyi1knMFDiAnZu9JAvM8kVLUSizveI3qyT5Mir7MywnnPXVTNWiKCj3e3l4YAz48fc9gsJU3Woc6HFve+PT8BR8C83RgjEYPjl4715c3QvCkGMk2NEtTX7XnRPSJEBISPOFgnGHV8H8vCRCuH2/UXlk3DRZ/fruSS2VKMzlnXl+e0WoyVaTWrYIUfFqZuiZCCUN/b6/Co+NxZlkmYkqEECjbjVYyl+tKLpVelK9/fXtTn6E3C811pf9ZsWmLM47L0kfcpLltv2pm8AhzUFVZvt3QxmWvxZA+0MPQaqSmqfsxRlLwlFkbzEvZNAbKzxD0TffBM8+L2gLMGtDp1NwpIeMdpEkYKRLjGZHBl88eutOL0qtVYWg5l0IxviDSQBphTrgorOtKLY14GviDMD97SlZvVSuD1AMhQDgG3V2KxyOE5UD1HS6FSGAU3diSDwpv5c5E4zVUYvScJo+Thg/uvtnt196ve8FktyuA8gW6v6nozTi9O5BmskZhn6gxbk0hvXuUmP36wyZyCRpC7Y4TjyeHW2B1z1R/g+kKU6E6Nc4GJ3TpCoEPOD1GahHWa4UCLk+22Wlbe/UbPRXCwXi0pHzDaJ3QPbFpLqBrRfm/DkjTCVUDCO/1Rbh9K1ZljW8bB9/4L//VX/FX377jy+3GrVR+fH5lLY1frEHaB2ceJRiiSraxzxA2aDg0YcM5RQL23kXvgj5vyvTrJTyEOGudU6dT6sbPv/xECI7arzp4mLJzq9kCfx1I0E2SgR+6t9bWdOvyWhgaJk1TmmJkzZFcvpp60zxrvqilbYzDmW7t8A6hFuPKbYYPIRmaUhA6bsb4TOXIQwjKhZmSWT17BkcPFeakEIinoz6jTuhVXx8XVIiQ0qRZuj3rtr/dLI5PsYtlmlS84HQa72PHZZRv9XF/HrH2ARWA+Z4YRchlh66E89OZw3wgBa3cqjfhVgp0jbievXJKDkdznafDRy1blpkwPBHPFB2nD4+ICWGcKTmHhxFUNBSiWYm6qhlbhu4UPRh1MKTz9PCeeTriXcI5R/IQgvD08J6UJsZUlA/1EIKDkVQVflNrQuFmW+isXKRMOvhWjxfHcfEWFt8RN+PcgckLHx7PxL2Rwam69ro1SunkrOrRGJXDu15WS/lRX+gcLVEoLRoe0LXgOvhAG52/qt/pkBQU7ZKhgpJSKnv4fu1Q+yD5RAhRFxhR+wWjWVHvV/9waTd6H7xd3qitUYd1V9rP76Mw0C7GjqZJgWgKjhWxl1y5DkUa9qAV8Y7r5ZXgtdbMeadq+THUf2te0966Xsh9MIyjjk5pgT/5Enw4KYRU62CgcISmUMwqU6+VIBp1VrbM7e0FNyaWtBAF5TiGwrLba+F2KyxPR47pgHOwTYPXLzfIjeV4ICTP0/sD8xR5fHwk+MAYim1/efvC6je2lAkepqMjOs/kjjjgD3//I1RPzY40RS3unAY9Db0IHZReaGMjnQLTIqyfb9R+5fB4xIdAI7FeGs+fik63ZUZiIC0nraK5dXz0nD4cyVthfPlMHJFxE1xyHKbEa668vHakViIb8Zh4ioFJOj60Ox/krbGziEMFXMpRRZNF7+2pO8xJb3Y86Abg3Q4ImhJ0F0EQvkLCgn54bGUQL8jkODy8Z3n6ho9/9YGHb8783U//gU+XP9CPP9LTG7VpiobIRGfQuOHovJtmWhW+fCqMDULXn9ebo9BY3Ru4QXz0JKfxU711pEIsntSFUDtSm0mmPQNTRf4a4h2CH3tEmkJssV+YnOP//F//O0rv/OHTK8+vb/x3/5//xKe3K+vtmToECVqwWR0MN1ir7ogO3by7qEjHhYAfgTE01Ny1TgoK51eGRfCp+Oh4esCnwMvrhW3d+M9vL4BBRgxtBXAKB1vEDiFGDscHHB3fN5yPtnF5QvA22XtkqQiVLWe2XIhTwAfHtlVqsaxHEZblEXBs1zdqLaxbNiuF/prLojBo7/p9+ylq7JUPdumY7L99NU478SAYFJU5HRaOy8R0OBBTUilAH3x+fSHnzNPjAylGLuuVXDLrbYWqHSXeOY6HE4iwZhXKXNfVviZP8I5lifp7lWI6mEEgkvyBvGb972x4+/j0nm+O32K6HW6vV25143RaSDEQ0mTcp1ph/NOJWhslZ9zQ+rzDtPCXH/9axSQdfd2uF5zzpJhs0HE6HPXGul65XS9MOhHpcNMq33/7G5bTSfl6s3MI8HA+EYOn1pXRGiVbAMZQHvrt9VWDMV5fmJeFb3/zLSEkvJuQMWi5kaaJw+msG7IP+BAJPuJ/+72mc1lQSRXVL5Qu9CGsm76X6vEbfP7yyrZl3kwtW9pZzwunOaMhKmX1eJiIKbKcDiogah0v2tdZeyO3QqtVX6u1qCVly1Ywrg0ctalKdcu68TlLsbqumVIqnz9/praGiyqAGBLN8rExRiMER6ObLU0I02zwcaeWxvXSrSig2iDvKPmFWi76+oqwzDMpxvtdVYqiOnGeVUCl2iYLrPgz+gQfDgctRbxpYJY2JyhejnRoelCU0RDvWI4L8yEwHYIlxQMy4X2i9A0kK3E5Ks53gu+cH2YNA54TPjqSF8OmX6lO4as+BjEMRlBDvjPpbh+D26Ym+9P5gPeRlPSh3rbVSlS9doGaXUgGTIswHx2nFolpYj4oT7QeFXL0PNIbuOGIXtsvvENh1d64vKoxtueisFQaJvF1xHni4emRyXuGOLpztBAYveK6GbiN91OKSLmKvm9+Q9svtN5o51z0glCkdOcL9cFTj+XAN+W2uts5JUFq16R1kbvPrdWNz5++8MtLpwctqDyk98wfD/xSG9cMfbtQW+GSrzQa06LccB+aJxkGDOeIU1ShTVXbRg9O8X0HbgxKV+zfdx2EwtCWjiDcualx31T1f3t2ofPGO1nyxNj3uFpwwLtjYg4n/u2/+C0/fn7m95+fWYvmQXa79Haj5QCbVocdsqoyc4IOa0OUHwqekOL9q3HBkAiRX+V7Nq0rGoPkdUOKUbesGKwZIh2NW9av3KE9kzhN/Ygx2ABjcu6q21uKAmiZ9DCTsPPJlMxYI4Oq5AR9LaMVn663dV93desyX24MQTeyinKsfqCVXcVU6coXe+dJcWKaDkQfCOKIi8LCXXTinmcVgMwt4Bms80S1s0DsGRsI0U8IzZrVu6brOEd0CUezZ9KpEd5rfdFolbopWtRao90GFMcO+PqhKAcVu/wtaQUtRJ4jNB8UpXKDEDpjeL68XPVysVzK+XDWOqeo8iDQeDSNQRxsVFoLbHlQtsx1ZAgzrQtBNORfk5v0sq/dEUSfk5A0+CJOC4zO6UkVt7lpni4uWk6vHbsjMXrUaEeLoxy90n0nBQtzF7XCqJdXtGwXx5R04HHswrhOzhPRQa1Vwz+6bnZ6GarYruYb9II3JKmjNWNQEPHMMdK9p6fIFDvH5WvvZCn6/K03jQS83CKlFpwpMKdlUvrFOy3QNsr6lgdSK9KUU2Zo0XXrYz8279mkwz6nmhjTLI7ZVK739mxN+dKbTv99qxoKMLIhfmXcz9p/7scfvQQfT0dK66zljTaG5Q07CE5VVQ1Ncu+FOUZOy4H56JmOytOVMvAxMkago2o75dEzbqoE33l6f6SVSIyaZFD6lU5hW98YNvk45zkfT0gHJ1UJdFFF22290mk8vjsplyiOdS28XTLTMhHDjKQBQQ9vB8xH4XB24BKlGNwljtuL8k/pfIYR2N4yMjpTQrMKSqW2zuXzqgbgXBjRm2EehvekGHh/WKBWesl60QQPTT8g0tW/N1Qno5eB9+zSDHH6779yaAaVWsTQLpoJ6GWHUwVpM+hzBH1t9ozXXJpygl4Vc7VkPn3+mefrM8NFag38zb/9De+/PbH9/pX1WmmXTsnw8papfeP9N5GYhEFR4hvla+OcGD3giv5+fhJac9QwFLq+bvjuSMMbRCWas+q+VuD8U+3WuIc+O6/2FcSz56COoXmKgvDhtDBOMx8eD/z9jz/z7//Df+K52AYvDvykQc8GhQwz1PdhIgiGhSbs07bmF/opsNOwLu3wKBZK0PQSrOrFiikhop5E5zwxToSYWI5nhYd6NRuI1c7YdhxDYLRCqxs9N1recEn579K09qc3DYvwUTeDraxaUN26xpPZIJhMAPL2+sIApmlCgm0VduH21g3aU6tT7VaVpCn4OlQGR0ozy3LEWZjA6bCQpgkfA1sp7H4F6YHoIC8zdbd2oMkfYwjJLsHgVDjkRHnX5BKVRg3gRiSMBfU/OqiZug62tZJzoVw7ZKdWKzre/G0jKwyalmTeWPsMTBaSYf/Ix8ro8POXN4KPnM5H5mnifH60IIf9+Au0qttp2QZeKr0NbtugbhutFJBEzYNpghgmK80d4FdCEM7LieC86Qk80/Kg8PF8oNbK5XrVUluJDAkMU92KROiOmtUo7lxHnCZGyTTpxmi8qkp4FD3pCC4BIkQXcSLM0VNrY54ipVb9fkrl9fVVQ7U7MAplu9KLMGrWXzskWhAawhw1b3lXrO95y/pBceSt6uX3trJtmdfXqHYaXY/JXmhjMC+aJFaycty86oAmJqySjg5iTSMRdwHnuNMY0NqgtK6pMrUhYhVdQ9/v2oaJ0PRC3VPQFWEVRRq6Dnx/HAz9Zy7BUjdqBx8jNOVXNMO4E7zneDozbIU9pJnz8aQeONdxvhG6R1xCRM3VGiJsNzlvtLZyWws1Jz7MH0hpYnaNPhxv12d6K+ZY0XR17+H9+zNCJEYoaHB0nDzfnN4ZnyTUJjw1obqN4lZarbRawHXlxZogXai5sq0r66ZrdnQH4hJJPuKI9KRp7odlw7nB6TGR8+Cnn1R84w+TBsz2QfCO02mm9sZWM7UWcr7ytjo+RfUjHUKwbRKcBJw4qouAs0zDgXTFsnf/pLNVvrcGooeiiAlm4N7b5f0+IWk6R7M/nPmLBsL1tnG5XbhskVICP//we3JemRdHK43AO54OkbIlXH1lYSNzIzSHlMG21V8lWQwgK9avMyZGrphvctDrwI9wVzSOLprt2ES7xLwYQf61HUKkWXqIXlTdJkebEeyDKRoTJ7rYnOeF/9V/8a/5/Hrhb3/4mVtpvNzUJN/dV8WodE2Ika7e0t70A+OaRyysQGQ37DrzU8Hl7aZ5j63Y++J06CEwjBNlQC1ZtwpeaV3hImcevcNy5PH0yM4AVzp07U8bfeiE7BzSPTI0K9KbUKe2Qc+D0SD5oNuApcG0bMHbU7KrXb1q2h+o3KTzgzjJPeha2JdkU4VqjBFjqBDGR71AB9C64kreObx6ZWirStsfHh5UkSz6HL6+3mht0Ie2CEwh2fas6s+OXmrOBaQH9jg6LypYCWFSPr5WlmWh3g83413tSXMihKoBFLsO1Ttvhm1RuMepPxRRXur1bWVbleuP0THPwewocT8raeKQMFvpwVAUSSprFfqlkLYr3heWORKiozbLbm2r5WZqH2Brq6VX6eXThmZzrlvHu04M+j1J14g3DccX80I2K1R2OB/M64vFhKHqbtmFc6KaC6cJK97B41mN+pt1G8ZoVok2YFSkRoNusyb0lKyBGrlTfGEN9e5iCaIuAM38jKZNUMXmboPSTcs8ql6Bx8Mi1NQo3il0SiSEjee3N6RU2BTpiEmHf28NGN0+761pIEat5Y6IiDNkB0Ugow1vuhl1atUvLkq4e8CHdIqpRv/kSzCXjY7Dh1knaSNCAWJwnM9nWq1st5XDYeb8dL5L4xlqsvY+4XxizlVvdPteLttGKy/cbht5S3z38QOHOeLSoHXH5VYYQ2XJgvI8wUfevz8zhqcWNcS2UZinA9999/5ejoufkLDw5e1HPj1fqKVQ6oaPXVNJuvrUylZYryu5Xuij8W6emQ4aX+QlEpogdGLMiIO4TKy3wctzA4TFReMJ1Nx9PM3kkmnXlTIya7nxuoKTwXE+MqZFLwhpmkrhFN4YInQrMtVcSk3IA+6NSrVpTUhIUbcux112PRj29wpwtO4oTaFTZ1tmH3C9bfz86YXWA53Iz39ofP70mdPpBD1w+ot3LKeP3N4a5IlZ3nB4fB9QG/lyobVueCZ0NhOReIUgRjOo4+tFCHvBpW4Jw7r5RnBq1m8DaZaTKmIJGF1/HtCL5URpbCLRaWpFq1W3NRc4zwv/63/7b/j5ywtbb3x5vXLdXtQjKfeBEAbKmLZGL9yroHQfMFgJ5QtdsCiw0bm8XrltGyNof4uzC3LgDexUcUlpmVErW+2UWrhenvEhMh2OxHnmeDxacLYmbnRp90twmDWAoZ0Wzk8QPLV0nXqLCsxSCogMDSvvg1L0SUmztmKUrbIX5oobiMGDzjukQK9ZBVgiphoOaiY1RXIfgkjUOjO0EWNvFIkxIU64uRviOo+nowVoC6VUrlerKBPd2iYfjVO2gcaeZe+AYZJ+OwQPh4nTOejkPgbz4aCXYGkaP+iapsKoI5dUJmhCQ2kAP+3B+jot6dXdEL9RSydfV7wTtttGmgLH46S8cLTLUAINwcV5X4QgRsRX1jq4lYKXjhfPQ5+ZUqBshrA0rzFuXuMha1kJ3jNPGlbdh9fYuHXgXacFLdEeJeOCI1Tldb33+DDwvinXGxq5NWprGkXonFnGHI6goiD2CDFt9jgfjohzrFX/u5QUCahVVbeUmbytPH/+pOKVXuySbCAb4m5qGRGIorqL4GdCUKtGiFEHoqQKUnpHAW+lywbAEml9sBnPJ75pZ+HzjLhMqxtjDKUR0ACAvbOwW41WtXB5/aFhGk1MHzE0QQwJaBGAmul76ySzpwyvQ1OuWUP3/9RLcE4fqX2wZSU0h2H/MTq8C4wWoYkeYi3guoZeD1Ttg/eIn3A+EWeIowMbg8rT8oEH98j704HeIg8PJ01WEK3+2Q5ncpkYomq68/kRxFHrfg93js1xejoCkU6jDcgZxF2Q8oV6vTBujbEO+iYs5xNLmJhLIG2OtAbKFpn8B5xznON7UpgNjuscToIPnWkKDAZrfSV0x7ffPOJa5MQBBvQKaZkISQ2/Bw9pmliOj5St8Pn2hcfoeeeOxDQTwkwv2kTdRb1aSRwjahr9MF5HRUmaX9rMiIsMvTTNeO0sCHor2fglI6+7sG2Ft+tmgjxh3SpOJsss7eBVmvz73/8jb5cb/+U3/zXvT9/hnLBtr8Q5ccsX5kVh2vfzRm2N623VxBh/RG0Hol7A8qbw3+QYftB8I3XP3COhVchFtw2nh1dr4Hu/574q7Ghs2bCBy7hQO+uUvzBOpht3Kl54dz4wp0D0/wu22riUxmUr/Hy5cbmuKhzIhXXbjCroTHEiRgtn9hMu6a87hiYRNVN9xjkhUTlehRRNoTsKvVTe8mqXhLVJLJ5QGmXVIWrUldvlCz/8+A+YWUsHs5KhbspTx8Q0awFu60Jrgd49tIa0QYgHiOCkKCzbq5qiZzXY+1mFItWrN20I+BAVmhR9v4dPLHG5C0g0f1J77fb82NttVXQnB1wYiBtspVvd2E25WeNl06TdmnVT6M25SIjGywl4r8XH2QqhFZLv1FHU4tPEMoWDwbsakF6Lhornst1FTMqhayv9GIOXXBFpll0phKq+yymp4KX1XRE94ydYonFHvVERctO4M/EaITZNiWmJClEatdByodfCVlZqzThTJFZ0IBkiuA7tpjzUUBEEn4eqfH3QZ3k0ixCz5pgdDRM6IXnmOWqfXoiWMDU4P5w5n06oQnkwmxDkcDqSJq8bNgPcfonbZ2JokbFD1d3HOd2zdpWDmWjlyDzPtD7Ifei22Sp7YlOpaqlpe/9kG2zrSisN779uZ+u2Umslm2ezjEYbg2z9jGIK4ZobvQ6CC4wALJ5cip5/A7W0DdF+RdTjGn3Ea+SMokhe7Rqtqt0Kr9Yh6VprlSbNtxbLolZBWSek8edZJKb0XuXs2y8wtElAHBa95GG/BHtFekC6ww1V44hPOD+DT+ATodxIY6VaRuDD6YmYEv79ERnha4SODn6s84kSChWFAB8f3jGA63pF27CrTkVxZr15Pv/SdSKtgnBD+EzbOmyDcR2MG6R54cgjUyvE3Ig5EHNgnh8IfuboH1VS7ja9BM+DmAbLwdN6JX9Z8Sny/v1CZObBPcAQrZrxArFB6CxJGActFv75p8/88vLCWz9xETiHiYlArlB7Y7fqBlNxbT0r3GgmrNrqHfIcfI2jGvdL0FvUlcJ7vWmQdR/aHffp0xsmsELE49yE9wXCXpBa+PnHH/n0yyv/7v/wv+d0+IbTYaL1G2UMLrc3krcPrtdutk+//KBhA2FS7mtAL5myeq2QSgmJA2IlVJiqUK8XSlkZXmheNHbv1xClsZ3K8Q8Vh9BRJlHXOREUbxGViw8M9nOOx3nm8bjw3btH5bmmyOfXC//jH37il8/P/J3vvF1Xvrw2cmlspbGEwHE54KYTPi2UtmkvI1WhoqFbVZwigekrx+5UZVVXVdJt+abxWf6AF88yq/T95qyqqmXW2xs/t46XiGeiFT1gk++koJ19h1kTRXobbJunNqdRUX3gk/KwtJv2aHYVSizTZMHHms/YnG2CdJz3Kkax2EMXNSyi1kqOm56JXX23QQKlbuQtM1qnWHA3AlvtOgyvK603ljkSg2duGnuQt6p5lhIQLwrles98mO8Dmjj9523fLscw4ZxaUvawhKs6rzQmq2RSmhDvoYtN/IM2Bltpe06AXihBBWaHoYKr1pVbnmcVFkXvNb1lvSEIpelN4Ts443XTfCAtRyynibIW6lZw6wu5CFSNYKtoSfGufM9bZa8l62OwrZs2lKDVStI1TaaXG3sAujhwwTElzzJP1iyflOJ08HRZeXxc79V1x+OReV5Ih5koibHHRQgWdmBiQYwHE62+i5MpKH9lExh9cDieaMahqZ1FeyWldbZtV4Zu3LaNtW6aXCQa9dGairf22Ms1F2rrd7pAEXphCvp7N2sk8aLJQW6OOmivjtp3pEBTjgYav6jGOG+vh2iYOR6IaD1FMBjY40a/54OLfZ+KGg9CHIz7B/dPuAS/+fA7as9Mp0TuK2/ljd61JXxyidN8VoPz8qg5ej0o/xSMC3QRCRPOT5TlSPcbFE06uFwa7fVGcrrhhOBwDs2CE3h694ExhFpVHRX9wpCOOzoGmd5fET9wqTO65zjPjLTweHpkSm8c5jM//XDjD9x4OnpojoenM6fzAUmfwV9x796zHR+BAyITDw/vCTFSeAZXOB10E4Q3NDX/A6TIdHiPlABvtrmoy1sfQINsQLMNp9lzfnfGx8hWG0mq6ry8J6SFXq+MVnGjIEBM2litIc6DXrXfrQ3dlvTjaVOggoy6nQR/FxFIA2oneGFeJlVXDcBFcBHnEuKaDSiB2oKlenSGKNeGeB5O75jiwuvzG6V2TZWXiW/f/xZlBD2tw5oHI1VG1OfgNEf70GfKZWX98mq/ZreJXu5qTfWuiSkVd34R4+P8/tLeQ0f2j7IyW+a7BIWSpdnfN/pWmYPwl999w9PjA+8+vOPtduPl7cJlbbzddGofODDBQsIbRKkevGM0delQOHnLGlUXvFop3p3/Eui8vr0odNMz0zzx8XsNWP/47p1OxmijeM4NNwKuB5wsyq1IJ7rO6XhgmSerzvJ8ed643SoxqCQ+zQnxwnrROiUfdXNejgdwjlw0Gss59WW2XOiusQ7rP7RNLAWFl50FsI/R78o6Veg5amnmQ9TRpDTlgRVSdvSmldfXy1WHo6pw7tjTd6Lgo9cEIRHmeSYkTdoZdFrXIUOzfy0bvqO0wlSYt8q2VXJpmsMqDgl6RPaubRB+F1Hsl6BT4dlwGgjgx06kdHNSqj8y+aj+SCVOGbXRa2W0yu3tjcvrDQzm3rsfr9dXLfVuWa1KAcR3C+IWasmWaRwUCXMRUMXwHkbNUHuVStwcY3Rqz6SUOD0cTNErdwFY64PbWohBO1ydu9FaJUSvfF5TX+3xkBVqnBacc9SmAepT1Aq2PS1lV2E7Udi/mhBF1caV2rKKl2IguYBLmnSVtgnvb9zcZvx9R1yEEenlpp9h6XoGWvSONFV7lp7196qV1qoJ0TwB5fPqqVFaxzd9nfemjW5B28oFyj/53Gv8pnVTOuVNhV0dqqXoIsJojeE0wq//OZfg48O3tL7hl43cb8TiKWXj8vbK5BNLOqjHBK+5ea2ZMs/pgStBse0YiWOiykLlipBZ3za2tTLFindaS+O9w00B7wKn0wNCoOZgU5ZnuIaPzaqWUEN16ISob7r3J1L8huNh4fEcGPWVy7MnuoUgC6eHhcNxorDRyET3QGtCzjO9R87LO+IU2XplSGaZNDFmaxmHY4qCk8QpnOkbXK83e6PMk2fVzM7pdDUsp/JwWnDNaxakNIo4khmXaXplih3rPihX2Iu2lDevQ8AYzeTsqgbQKXDYUzcsVKCbOMi8Wx7SFFWs0AbdRYZPOOk40SR6zezUjEnFMlWEIjiOy4noEq+fMq1UxCVCcBweTgwRtqaKwMDQ7z0dmILwcAhq2t023tozq7zdVV8KScHeCyZO8E4LdO9a0bsidreDjPslqAG6GDNkXrexC4dUQLCnocWY+PDuzLl1Ht4/cV1vvFzeeLl2nt86ty2zbZnaoDVh+KDctzJupEO0xBh9P/2aaX0whcScJv7yL/8C54TPz5/IeeX69sw0Jz6+/8Cgs52P1DG49cHttvHy/IY0h2uBGANxiiQ6kc5hnjWma17wIVHKM73fiEGl/NNhsudCw9wn53FOmI8nPWja29daG6BXfRaH23Qzcp44HMGgcefUnKyHjkFGIkY56ES/R9HV/rXqS0QsA3mwrtrpGIn3DV4DgTS0IKaAF2+KyYmHDw8wdMvvvenhWzolW3dh6/hQibEisjEoBkErPYGAG14fe/W6fA0kt4FoFx04BC970If5Tc0sLjSDlLXcePSmWaNrZls1PlELP/X0XW83tu2mebajwzSZmlaf6dxW+54iiNdEFfHGx3NHOiDixTGRqK3QM4Q4M5siV+0jKiDsQ3SwGRCHsGUNKEjXq/5eNngwutYdiQaMb9sNYeAPOmR1qxLrWGA2ltZj8HLvdgn2TYfjkAhBowq9D8SgPvHaoJWN3jTsmuGQtprEfRhXOyyMe5hSU+FqFf2pKFJzKBT1mvqi1q7S7tCok2aUxH7pfV1i75w9zgoKBB+8Umi9Muxrc6K2utH7/T34ky9BXFC8lQPBJablDAeBdw5HJHJAN57K6IVeV3LJbPnGkAIucEwwpUDPN7aiE1UpN9I0k9IB6aqSPCwqPV7zjdIGaVESmVhMPquvSCCqGsy/Y4xKWzemMZEeZm0v8Jk4acDy+d2Zv/6bhSkemcLhHojcJTH4npw1Df22qnfp/QeNOJP0gDZHqAdx60+a9l8yDs/RCz12pqLChOuW7ZC34lX/qB/qCrNUDqMSMiqWEGgymJ1ehCFpZxy9mT9GN8g2NOszJH0gPMpLFiOLg4smGhLoog0ABiN2oDroSTesYRCz1lQWnWaNP1RcRsNy/Wi42hlpQmRinieCLzweNaln9l6nrpfVOAldD1zOtNEpNHp3iEuMmqnXC+vlwna9UnOhtD3HVNWxesao70k77wbDVKDY3+9AaTUFppgqVKJaRPY2hKpibr4m7WiCEGvGy+AggzR7junE94+e0T05VxV09Mo6ql52A7aSTYikW1Sc1IC7lWZFpJ0QPN9/XPDB8e790Li0MuEDHM7FBpS9pHjm8nbjl5+EWqCuQohR7RQ+sTj9a40K1O8rvBfy+UQbuvtPy4Tzwvr+QO/KU2vBb6CUxo8//Wyy+EwuRbNDncNNM5a1ACKsuyIzmGo0Dd0ac2W/6jScXPnoPtq9Wih4LcD2UdWxcl/P9WuOszaIz7PGgk2ThkJ/8+Ed05Q4HQ8KJ9asrQjBa2xW7bSiKnNtSGg8v165XrVOqJTGumWq8VNtmF3MuCTllXQzGsW4Y+OqtaGy45r5boeGBQQ3ay0YnigTw58IU8e5ZpB0U0h2NEJKuOAYpegG5lScNIVItLB8ze3VTr8Q9XUCO7dMxLeWrDB+aPgkHKYDp/PM+fFwb+oR9oZ1b5mgWCyhoSEuwQh31W3O+plPqRGDwzvlxvKmyJK/q3zQLVwGOKeKf+cYs2aghqroho+RVrNmKYsnes9xnvDAtnXK3g05BqGpe/fgA31E3KaoQb9uauMZXi/BkZHRqEWDz71X1CA4VYJ3b6ETveswI+lehzRk154ahxoVFVDkTc+JTlf/pXea/yoq9BrI/Wv9My5B/ZB50cDUOUVVuy1HgyqVe6ptozen8v9RKGvVgOjROEjFx85Amx1a15R63dwWRglI99rI4B3XTQOXMQ8cvtnaM+xoiyBavNr31nGX9APoPMGrKhXxLIdIEM+STszxQGsG6bgZZJDzzdqvb9TeOJ4C0+JJy4y4SC16qAcruSxSVALsheZgHIStQLYPIOZpDHFhNBWAxF5JtWjyuWzaTCENQ230MBJPbwYRjaqXIaIyZb+DOUoy51KskspAcJw5E2wDGrYpmXhD/ZT6UxXyqhY5ZIbsHVaQjnTlBBgTOJ1oHY0lXYgjEAXojdvlppuDU5EGRTfiYko48cLIhbpubGumblml70OnYmdf2L0Wyu48fv2sdvuH9m12e6hlZESGbgR3nsPZDvn17yHo61IqzkOMkIJHXCSFyOQTpTRqbbySuVBoVVsGrquWBY+q28ZyOmq4cUVhqm1FvOPxpAEPJw8DbehGKvircmMpEEjMHHmdHbEW8jZY08CHiI8TR3/gGBZwEfHeNopOOpnvzYaAaVbVWz2qCvS4LCokKdrOvl1u5JzZQmDzmW0tdOdwcVbkhG78rdaLOa9wWRBhdK0Hum9MZmVoTS8B561OzauIxTl7sCy/VHkpwZm1Iljyjg+eaU68e/fIFAOHaWK0Ri0eFwNunrSYtQ6FYHO18PlGCIF5Wnl9dazrRi/NIEQNYhDjhu23t1EIazzHLCyqntRlpe/wCQPBicqN6xAagS4J5weejkjWZvOmv2eIKphpEvUSRIe5FHWjtwQQaMGEg3H/wJmoTXnzzTZoVesKPiXSrIIohTzBO02U+fp17gH6hjXi2XvyxhC7lLQCShEeDaBvpQDjvgmJfby6swj/Ieb59rg+VG1qMZV7OpWGgqv3WYaGD4DTTNkxNFsa5ZSHOKpzSFVvMq7hhugG2ByuoYrYgVroGHuhBc02eu/1dXUSVW2OWMO8nl3CwFsl174s7NF8d/TIpgVVlwv7YvrHfvzRS3B/xrOoj2O0oG9o0bJZ8YFSM7fbhTEKYxTm9MDjb35LH4E+PMt0ZI4Lj8uE5wPjnBm2MYwuRBIy/N0cPR8+gHRirAopraqOlOB0gjO1m4Q9jaMhTHh3sm9npo+V1i/IHGCKpDCTwgR0zchjo49Ka2dGh2PWZJXlEHBBaPlKHwXHE0IjDG8PhZLOdVtxIjw9KtR4OjZqqWy3TbkEIi4FXIga7LoVRt4YW7amaBh5VeVZL+TeiNFCI7ozqME+RPYeV0tOIc126vj7kUUT5hHoIsyigpupN9YOku1haZARiqAQCTrVtSbQGrVnPv34M8fTE0+/+ZZ0PLBHFs0PD1qJdFPDduBA7YVLXcm18NYy2Uy0IiqcGiXTLxfq25Xy+YLv2nTQW7eDSuGtMLSeRVk++zQYBiJOhwAnQnN75589st6IgiF3EQCil5SCP6aSpCmHIEFPgQC3tfBmfMUYsHzzDY9PT8xpIobA20UjqErVXMKctTpobyNvXfmyNC+aPzmd6b1wu2LK0ule6BrdzCQPvDs2Pj4WWh1qynZmURiaLbqbxm/5Ri6FZVkIIXLd9EJuVVtQxt3TN+xACbjoWU6PeO1SAj9xaIE2oA5vAcRJr4rev1YsOYdzQvWZmmbl9EZnHjrh3zZNO1H+aVDM5xmdw3XuXZdp1lCBgdolrltH8uDteuXLS+Vy0/iq2V4/nFp3vF3Ezi6OFAJb6eTaGOPAPB/AP7K0TpyvbFvmy8sbueggPYZOVTry6MYTYjSIXZ8LN5x9XPT3DEEH2pyLpZdU8vMLl8sb0XtScMSkQc2n00xIntPhyJQmYx4GKVSCH5xOJ0IMdDSya13VJ7orPZzocHF905CJj+3Jyqc1eaj3Qs2Fn378hdoqueQd7AOsNFl0xHPR4YPw/nzkMEdyVjX4ZM9sLVnrveYJ551uhd6zHJPSDRaVN5rVtoVIZZBr1bPruhGnyMyMSGCZH5A6kDpwMmlUXQwqqrI+wfP5qBeuXc9vl42cG0v0NGuEYAxKWait8e07jXLD2eWExp1dLjeu28rn11fj6D3bmrldV7U+9KGl3qOp+r1UdSvsEwaDkKJ5Xnfkq3810v8zP/7oJSiyp3Y446G8xjfVhviBF6+EfylgRZR+mTmfPuol2APBTdoEHwuteXwE578Gn05uwuOoLTMYxIMZTsdFRThOK0DUDOsJYVYRRVTMP3kHkhCOjO7pPdHajTo8zkVtYA6J4CNOVE5bu9D+f6T9+dMkWXYdiJ37FneP+JbcKqurCr1wAAIiweGMFjNSGpP+bEkmG5uRyDGKQ5AGgA1wQRMNdKOruraszPyWiHB/y7364dznX/ZoUJAaCUt0d1ZWZoT7W+499yxWHFYIiDmiK4f5IoDWSAp6nDhbcz9ODpobuj4ihIB5OsJMkBJQS0Xoj+hqaCqIKSEvMyw1/pw2YN4w2tEiDRWciUDo8C9BGBmjvADFKyceXvxlpz3BnD+lLiSM4tyqGD1lR6EGlKFB88tCDKgAJ3J+OZooNCjOjyc83j3g+uULTHMnboGA5C4kxa2QAjJECUs1NRRTlM6ZCoxBwlYK9HRGP69ol4JZAlIMDs2NiR4Y1eTV6z7jhFu/uZYtBOFgX/AEM4VR2HsFKF7pGn1UbZCTnKwh6m2o0ch6q5xfiwTcLte4ff4xbq4PWOYJh/s7rNu607gf7t+i1s0PWB6qBkCVZIxlmqA9oYcZvTdaaeGAQ7pFDgfM8RnmLDgc6POo1ejKIgbrG1TLPn8LqyIW4Pb2CvO8IJ4CtlKwrjwAAjLMBFslVR2uN815hkFQakNGwDQDrQPWDDEmHJYDAJoxNADFPC3FEzSiBDJybbwb905ttpsTd3PygVHbako3IrhQXTHeLyv33g1bMag+uGOMSzIyu9oo7BxjDDgcDMdDxFYUpXaGFqeMHBjAXHsEYkFam+9fskXHe2cSurBIdh0tQGRkOCtJSIh5hrXO76Jws4+KzRrmKaG7S1CSGWnKWA4Tbm5vcHU8Ur8pgmMuyElxOFwjpuiXYMfj5cz8Pop0Sf5oznpWQcCE2g2nrTp8ruhacT5xjHTeLnuIsAjPOwHnvGmKSDlgDgrojG1bXcLQ9sDk2jI0KFJKQJiAGCCZI6AUWDyhijcwAcEDrxlJRAZvn5R/Xsz7HCVFjqwAQYxE4kJvmKeMmAKa6/TMBFOq6G6vloVnV6+07rs6HBwwMxfkC0qpmGPEdI4U7nvKzEUovduLZn8u1jxc1zrRLO8SAsg4HbDSgL7/f/nxvZfgWisXWpg5C0jsQiSB1URO/gIAoANWscwHjBiVGMh0A0jVPSYG2Ro6luUAs4ApLohCwg2hBx6GoRPnl4UHHdObA5l6qqjlAokJeToAkiA4UGbeBLAJwDU44I60fNMNXTeoVoQ9UYKwaUyUdsQ4AwjATJ/NnBpEgG48eEpVWO/o8QSYwio32JIS5mA45FvU2rAWYv8qgnlZMB8jzuc7XM4Nx8OCOSWUwwG1FFzev0e9XFjxwJDUO04HeMRIXOHAGOg+NGu+wAzMcRO3z+DBKNRsRUMOjXr+COrxlJWzSiJDNLDKt6q4f/8O8+ErLMcFbd1wvL1FyoxOCSFjPip6r+gNSBZxMz/DlTbcXF2hV8X6nAfKej5hfbjH/cOjQyAN2gW6Bb+cOoYuLFgANDHDDHxlEoHoLK+A4BmW7NqKL/rogn3gg/8EGA8EgwpnKVNMJC2J8tBrBvH0gmk5YFoOeP7yBV68eoF5ysgxYqsbuhjSNAEG3FxfOWPPs/KMUNn93QNF75WzjJvjK2zrim/ffIX1vuL03QUxzpinZwgxI00zpmnCPB/4d+WMCtLTTUnQGCnwHUDRjpSpuU3Rra8612LY2HVsxZvnGBA0Yp5nxJjQLKArkBovuyUnzvHcIaUNjV5M6LWgZ4bV1t4RhXjGuV+gWhGnCQkB2SPKgkPV2i8AOkqpMACVDxrBnQ0UYD++FhIZLLiTiie3O9QaYkKOghS9yFbQ99cvTQRB2TpaU1y2itJJuDCQuBZEkNLMIlVGYocfgMaZOU3VO3q/7GkJqh29V/rZxoDKygb1dMHDWvFwmTDNbqtWVhwO17TIM+oi7x5PMADLYUJMwd9tQE50UlnXDdo6spOJmpJ5S6/NCJMrtFqwXgDVCS+uDv5OImpX1D5M8tmQBCFJ5XTe3GknQjWgdsG2VvTGNRpCQMhcXzHSMHuZZ55ZhWz8jAkG0GElATjSyccanLTT2O0ruyrrnbPCTvPzYLSnVDN2ZaDdY5KAJRDiDImWfm2jfeDiEpAulLqUxot0+cFrHM9nxImJEnlacFkvjMtqHFk81oZzbXh4OONyWbGug8HMZgDdURjnEdA7N/Iix/dfht97CTZ1i6UhZnUyRhAaNIeUXOvDTtCMrvWGgWjJjkUzginsc7kYEyAJKU4IA27V7sauCpMJgCIltvFTJk5OenaFaWHeFDIECZAPWUA8QDkzCDDjgH+nAksn28y7KRods8sQJKTIlmlK3SvcDFWhO4J0ROVn7a1AJCCFDASQzhwK1AK6GJook9anjFIvkBgRJ7cbghCSuGzUAfbKmaA8sSTHfEYgpCEDZF/59TdSh5zU9xtQYgiC4S85HoyYeXc/yAze5LOFwrZecD494PL4iBwilnnhzDBlILK7RQA0VAQ1zDLDLGHJCdoEhxRQtg3RDLZuXpUpyMQLBAuk+0xA3Elm/B+jfeCEi+DG1dL3kp6Xmw7hMf/dcQGaz2BG8eDnMUIMDov6U/O5ECQg5QnT4YB5mTEvE6aU2FGnjJjbLs/IkTE0pfIdQRWtNVziBYxDFr+8ZmgzoAlho3NFCA1rFqQ8YV469GDUouqAsz1WRhn/4hgnCz23OAtCCZGp0H/SBFkN0hW1KUKgrZnGJx/Txbt0BHhKQGSRPXEPRYckJRBG70J7PfSG5KB+TBXSOyLYCYjLlwj6cKxAWJbzRgrqidDAK/0B5QMAVHnhBMKB3apbl3VsIMI93EEkuLQj0Wu2NbCL9lgq83cvPlsf66XbE73e9g1iMAskZzgjUl3j1lUhkV1sByDqOsZdsB9xfViQEyPauN8qAjrO25lCczlSPjST5DbNE0krzckpEwuP0kl+YWxMAGJEjYC4NR2MHIGYMtZasbU6OJdsr5VjjK5KaYBD6KZCRxgbS1uAqmjZcF4rZhUyMgFobWSle/c+fkb3YdWuXlobggVEDSTdNXadvTfOBk1Q3JWGTlE0HjA/ggY9jWuA+y+62YQFwJTfLETmdqopjiu9d6f5gJQEKcI9SBtQOkLphEK1obfISKdBGjDzj6Ew/z4igmDqh8Hf/uN7L0FFdyHpFav2mBGYKcxQzcTFn/QAsw6gQTWgVGb+pQBIaAgSUeojtvKI4TE5zZRPBMerp+ngVbZjvXxl0N7df/GaGpc0dCfcyNo9bw0GAbPZRqvdtfEFWkHtFYpOvoTwEm2FhJ55CYhRYOY+jIlO8eq8JFZdguhGwOn6mqwmbTv8EwIr2To1hHnyth9ksGqjO3+IjFCBYT4ecJCAHI9oa8Hjwx3KtsLsHh2FDvoOLXIu7nOg4BegJxRo0IEgAiFALALB6D8Y6bQwhl8hRkRJqNb53WzohrhIL3fvcdcNkwQ8Xl9ju5yxXF3j+Y9+iOjhnt0CtFYEFWQ5+MUqEIuIy4K6rbgKCXEt+Hrj4k2uBYK6bR44N0wpMiMsOKw20AyQ/AADei1ky0Kpg3RGYjenn5MmCO1eHLjvJ92H/CB2DRrZjHQyQkhYnj/Dy49fYzoeXJw/ATHjcDggp4Dql16OGQGCKdE/MfBOxvXxhuLgUgm7ASjLEck46zhvBaVVnC8XlF6wPV6wlRllOyNPkxtw8x1s6xmlXFxaFFHv7qHuEhQkYFpmpJgYOCoB00xTcomCuVMOw+6I66R1Q6kdD6czP7ONAsN44Pucm6SRBQLxqt6cOKVYbi84lYrL+eI+lKS2j1gmbZmFG0hQOF2ecgQF7IhijIxlAvyCjEiRxIeARvejwP01bNxiTLishAglCUIUzJmH3XQktLauG12UXAencKel3/jfhu52gzEnqBrWwuIXbu0W08RLcnQLnn+p2rG2DWsw9LLh228zDocrTHnCHAQpCPIcEZPgsnXkHLFuJPSkfM9GoXOMc1yOmGLEs8z1M0yhW9m42uUFhfboiGlGSgseLic8Xk4QD50eSepMU1BoLTDtyGGGSEIvPFfWjWOlVgUiF9zdF6SccLi6oW4403s1iuyRSl4500moNtdOdhzShCVNbmHWfU5OApgZ8HA5YasVtTN0ep4mBBGUdYMp5SkAixcDUNvK9RnglXumMcrD2Y24h1vTykorErlRdCw5YcoTpgjcHmc8zgesa8HWK5oqLo/Mhm1eHDGlhE3D/m5/m0sQgFcW7LTGwxqUW0LtAgsRA35V5cyFRIdO3l5Qh3v8IBPbNz8X3sDA4Q9YAWTAD2p2NKxGDUa6cMz+0Lg51PzlDnZQCPyDldE6UNkPUPMvpi6GfcKO9xqT/6r/+ogKCYA7X3gaRmfF3vzvDiHS00AyNzc4AqzWmRMmXlV51laKCXMPiKlia0qxbSuAAFqNXZR/GPGWj3on807Qxr51VJDzGdIijaV1DBS4KmcTEiIghC/2StqhxlYKyvmMy8M9oISstXVcffwakiJC4r8QIoX5sWcM/ZWA/osBTMqwbrDmEgqDG27bU98nFG/z7htX35gUPuH5Y7it+yX4Ycc/umL+P/MumBZd4uvj6dWam0SHmJHnBfPVFZarG8769j8vUEtmgyZP38woYQ/8HXy7lJmTlmJxKykWSf32GW3EtpWzG2vcF80J1z6HQSNLMYSA2gy1KnVUkczr3hjUGtz5gmEXtnfwAeImEzRFYLkdYM4erC4nGd3rYGUpAhQR3di9UXc1vFO5Z8QULQSkiXBhLWW/BLsL660lXjSqdAypPPC6jcKVXpoj09AMXkwnP4gBC2RRRz8PUqKFWO0N3RhEHYMbfUOQg39OISQdvIOqyvmQ+NJXFd+/HCMkOtChmflzwL4OiTxhh7opd1B08EI4G4k0tSpSypgDEYPDdUbKEc0UOTNpISVa5wURTMJx0Txz/aSciOvkCVobqhtgxBzAv60hpgUpL9DQodIQMEGQUFKglVmjR2ZVN9aOEQKaZNuwQDNDK4BBUeWMmBKqkizTF2fUGjXEGgZBKqLWhm0t6K1BS4PmBs2K5sbrWyGHYehLT+eVl6CShMZoJKHrkH6QdOMDPW3cYipOzolu/6aUxTQ1xG4IQr2kKtdn72TUxkCLO1hGnTmDtCaQ3rAJg3ZHIT0667/j/gPwd1yCaeax0AMTi7fzBsAH6jFgmjxrLZByGzRhaw1raUgByEH3wzqnCVeHA7+8C+pNBHVjRzVnHgbddShBKHrM8wwAKNvoNLvvdaZcm7igt9PSjZ4kHUE8LDJN6BbQMaOUM2rbkJcrxImtsnZD1BlS6eAiIeB8fkTvlXT8ACxzRQiBhrES0UGKbqt8uSEAzZi3RVs5pjd3VY+0iTtEYN6VqFfP6fktEgLmF58CXaHbd+jbCd/84q9RHk+wWiDq1GBToBYAthtvS/IiIA0AYgjqFVYKL4XegF4hzcX5ooSUnXCijqUXPSNUxd3bjvP9hLs332GaDyi94fr5c3z02Q+Q5gmHfKR7Tw/Q2nA5rThvZ3zz/nPcv/8OX/7iZ2iXC9rDCUk7ZiQE6UAkwzJ5OjTnSpyHaYo+H6IfpTa3xXIWKQZTLpBRyMvcIRcIusdZhRw5H4uRB7SzKtWo0Su94Xd+53fxo3/0j5EPV0jL0RnGneB4a4gg8/liPADmOUGioW903u/KzT1NlAJM0y38toGJ4Pmnn7E76uosZEYvDSKSNkEpDdtWcbkUXM4FWAJCOriWVfDixUuSLqh72UOTLfJwX88r1AzTckQKEcvhmkXD3gFR9P3iORGWzoVHGLKTNIMQIGHoJStqa2i9Ibmh9JUthBTlJS/PzpndpVQ0J4D1rrh7f4d127BtDCOWFHeI2xRoFzK9xyy4p80haiYJTHNyz1TsqMX11QHPnl+DTF/F8DbNU9ph3CABUySpqjQyNFtxSzbQUu+0cdzQW3GtHs3LSyWZo3em1pgZz7it72Oc8ZkiteFYzytMN3zbhjdpgwTgcJUpMAeNnZ/dMPj35nDEPE14+XzFYZ7x0bMb5DRhzkeICsxYCC+HBb1XhLZ5sK7g+nBAThFWmcZSw4KeuL4MgvU0odVK3oUJSl15aRi77uXAkpP6YKCcHrEJ8HhmsWfmTcw33/HsBuUM2jpK2bBtK7Jl6rKtuV50RggJa+HsuAlzUOeFUWEXNw14//49elMs8xFMKuEYojVqOqvDkxJlbzh6b9jahpwTlmVmakutJIVtG2JYEYJ5LBTJMTEKDmHGbDPkRlAOje5GZuwg1RwJ/Ht1ggMmYIXRm9/4kTq1qNnnaK5dGYCvP2S1geEbcgoIgfM/kbBvbiYOAEbjD6YlD+hGsLMB1QkP5s4W1L94Z+dwHqv9MUszv3iEQkpuIUYVIbnDAinhorQ7GleIumtDgCGY0S5IsM/S+DeM+ZJj3Ea/w2CcfY6BnQRx7Q4XnwRwA3jliugC20SLJZsq+pyQDswigw1fKR7m7g6IHXz3S2Dv0CX4P2dXGGLkAWQ+x3VTWm8tB+rMztao7ax1Y9zKpqhbxen+gcP69QUATsG0dWzvH9C2ioe7RzxeLvj8u29xun+HN9+8QdSOefibIjzNNL3y9KbNv5dC/CAaL9I+rOL2BtHfkNO9f2OdOrowZkmG/TzlT3ADdhPk6YCbZy8heQLSBLTtSZc4sGXvjMb6HczVJ6he9g+4r0XSmGGzJxEAXvU0v8gY+VKLYVsLgmzoDSixI2pCNCBG2mTlaULK2d+d0eLLjGJno91cMMb9hBiRxKE2Z8ypz9JjoMlCx3gQtC1szfZLMKeCrQChGKSqU+oFYu7yn9h5tiqc6ZsgKjP+auN8KoQOQQBEsUdnjXfpc1wydQn87U868s8aMrgQKGxnkoXnx0EQMWJ3grNhSRiaPe8x58Q5KpXYyBJ83pec/RjQleHfrZGR3XtHhZ9TStShY/Agwv53j3OFzkvA1gS1A60XAIomnZZg1lmswjDlDLHgdoMrtCsO04QpGUwzIefegSBIlZ1Pa47uuDY6SuTnMSeNBRZnEgS95P33AEDojpL58oyZ52Y3Xgbdm4feg5/r3vl6Dt8YGUCNhidbQdOOoGSydq2YUkeME86loPYOTRN9ejN1peRddKyFzxahepdJhnqrZPUWfToLWa9T8F9bJaMZ7Hq31rCVhq10xMAQ9u7I0IBkB7YUotuJCAvG1jsRKf3+Gw74u9ihl44gwDxx6CqRnndVu0NtTwy+gbNP84x5uX6yRnJDYOsZzdJu0WSBaRIxzQgxMfepdZTt4tRrznByJvzTOunQSaLrjIZYnIc/h6AePSvCeVj3obwJYBEiE6OUEIAOjFiWkDlE7WDZNC0J2QKkO0wmEWSlkh3bHVqLKftBqLDA4bsKnQ9MjYcamFjeWqOvn9s68dIX1K1AO/0oQ+BcDUvEsx9+huPpGR6//AptXVHXM22AcqSuTAkHtDEr9NwgXiwko0RTTHlATnSziSGiakbX5iJVgQgLj+CXUlNehhENkIL3799jbR09TUhTRjPg7v17/Jt/8S/wcHePN1+/x2lb8eX9d7hdZvzux8/x7Ljg5uU1ktHLNIiQYBHCPpsS6Rwcxwkj4dz8MAgMdWPHB35GjO3rNH7uZPW5kg/44SnsvfsG4xGaJAMpwTKT35Em9BB31wl8yMQb8VZK8fzukgFPixeWHbU3SO88eODEDQD1/IgYIpa8+Gd22YGIi8NpbzeljHmqaIcEk10WTFJF3dC0MZ0kJczpCgEB67ZB1XBzfA4JgubdcPbDv25MbJin2XFuahCR8k5GakpJCw/HYWPWcPdwwsPpQmF6YwZd74qyMrx0Kx4qvRXGO8mEbsC6KbZqKJv73SpHCCHw2498tzYIZesFVPgGlNKwloQUyAZtvaDEiqoVeY1IiWzXHIEYDOd3j1BPaqeXLhBCwM3NLSUJhfOg6chnH/LCfLrKBJS5bKi9Y66VbjWV8CH1zwWlbn6qGLqyE6ekpgEuDZElUKa6wi94et7Oy4QolK807Qgp4dIVxR4wpYS7xw1TTDjmmV4DXoEaOlMkcsY8JyyHhBQ5Gy2XFXUrmPKEmBKO1wtZxalAe6e0CkApvFFEGkQMU6AjU194+VFHmYBwQ5RC3IREDbUWXM4XxjqFiEUyghyhG6AbcHf/Hqd1RakKkUwo1kbdGCGlIyovQjNFSBN5CYm60FKpyVUnQD6R8QJKKXh4ePR5tiIGmoY37SjaWHioYpoAzdjT7Zubco89qz7qyDEgA1gbB3Kh97+fWJ6WTyP3y/bq+0MRou0VH/uuIJ4w4d1SUPDhYNz48OE54ZEYeXgNnJnGuj7/sVFti8/B+GfKaPv2/+/zMAx9CN1PdkIEvDPzz2he+Y1IEFV2KPB5wehAZXQjRhaW9tFpjW5hzK+879w7G8N4Ik/kTB42vAM/cDsZ/zk6m8jZ6HS8QoRgO3Bw3Vsh77jxZbMq+Y1/c38agzmp4pQhgc9JsXvujRmQYXTbfEfjYBxm3NYb7u7ucSoVRcgILk1x9+4tfvGLX+L+7h5vv73Huax4c7pDfXaDn7y4JvtLwuiZvVB5Mrzmc+MMAR6jRDbZ3tfy0cjT5b5zPPeO/4NH54XQeAw6ZqBCs11KdSIEFJHTcJrwIjy4V1w3OPxJmZOYoBY41oCzesDukL/mEgc8sRe3rkwatx3O2D/mSDHvTT02S737E6QY9i6J67Lv75kd7ogdMqYrhOG5A2TXYIqRGDTN09ithJKjhzFLQNQOUb/kGiFSWMJWOkpj+oFK2BmJVc1lAhRJN4dDK3h5tM5Oht/T3PbP/2743G0gNubEDj87eidt3xxyHPpnGaiPMUs0+ntslXmMcEQBBsQYPKWcEKiJv//hzwugd3dGkegm/QEtdIjRszIa932ItCA06/TZhEG3bf9+7Dq4qSW5+NyZ1iQx8XyEUuKg6AiBmmhRIIeIluiXnELw59HorzwbmpPWclKkaKgbBfXiRSHPSV/2gegO/ztn8TxSng6GEfs1UCIJvIj6OEK8kKyJXrQpMNcwM7gDXQx5y0glwxytkSAIxl0A14MqBkpmwzoU1RGs3vt+/hKRER8NcIRUWx+96c7abcpopjHn7J3flXZqtC4cTFYeYpzJijPvxA/eAW1/34/vN9C+/ogPzN3GBQp0oG8X+nt43E03QpyBAjSG1kpEFuabQTofllUSG0KAVB48EWSMte4GseKkltAhwQgTOtNKTbG1QiKAka7cOmXjARFqwGqGXhr6pTmAQj+9GAiLqLK1N6GolVlfhHmD07HnaXENjpGV2ejiUpTEhXRFiLG6k0YwzhFSGKQbv45CwGDr1VqxbRvmeUFOmWSiQAE5VJ7gvZQhwXB4mYDbihgnbOcz3n75K9TLGdv2jl2oz8RiGDCj7oSAEANymCBV0etg6/FCTKBuaULGIGBDEnoQlApUo1M9HUAa2lrxxS/+GGspWCtRgIftjPWy4ttffU63iY2+p0UMt4cZ8zSzcg0B6Iqq7LbjFDjbsmHJZZhzQg5AyMMA2YsQG5Cnz/wc5lV1GJ2mLQg5IiBgpkU9VMOuXQohIafZDQYSrEfUBry9P8M+/wZTnpHzjDQzd+w4M7NtED/mw3OkqaG3DbXrjj6Yu9wTqVS0ctmhf+2Kbd0QY0BdTghxQspXJKb0jofTI96+f+eEqo7r6ytcX18BcUKeIxo8LmjIMRqDdysqQjCkPDupgF31YZpoKDDu2kB4zFRJEEpxx4TNEYyAtOu/uihEqDUNEnFcFpoE1Ibzw4qyFpRWULXhcjmjloqtM1rptFVCq84InZfEMNWVs/vTZUOKCVeHaxoyqM8G04CPQUiyNFSYk+gECMBhWbBMM9pkSKlA7IicEkqp6K0iZUoEBpy2FaYnXB4fAQBNG3JKOM5HmCrWraK3jlIV8zzh+bNrrNuGh4cHZ9BTAhRjwvn8iMvlxL2aM969f4/T+QxJXKOlNc5YQwYNN3gJTj726H5onx8rQtS9Oz3jgiBAEhqMT/OENGzJGIKCYwk4XsIO99EKzbBUYIoGlRXTQhjXcnKBDhDnDJkio5FMsRptyxYnfQVrhL4nFku1jZgiw5JmXC/ZL6COlCLylLgHVTBdz7g5P8NlLSiVRtXajbPh3umUY/TvNBPUwizC0/oIg2GZF9eH0k5OFWi14/Hhwosqh32tq1+aXdghq7BD3Jpga7xboILeHZHzQtIF1ghYEECJmokh5oD49+kEvQHzYpQHryVD6pQ2kH5MvVMAIVNWAtxYgxXEKr57B0UYT4ISgzZWomYNMCYwCAJZcN65jCp/6MIG9Vkd+w0ISBK9usRebdrg43uVYRgRROOW8u7Cu7Ww/z1OMhlVvPAShG9cU2W7P9q8Ef44KLnmlRl8BgjxWagzKN1rTxH27uipKgPEDCFkIAXk5RqGhMPtS8Q0ozycYVbRjQGfwRvCQZyglwx2D8Hx3MRZk7SPSogJu1EwxdOKy+mCVjskzRAnALWu+Orbb3HZNly2hto7HssZtRScLmdo78TxHZKEkBgUWBL/BoV+uLkEZ+DBfObmNmAfdnMyOsC9L9xfI5/Rh/9cZJgaEaHYZ4ucEdauWMsF52K4Xw1rnHFCxjIvmKcF81XEdAjoN4arg/jGo+QmBF+L0p3a6X/Hvm4EEjICXFogNHwIQaivDWGffSg41NfeR7/nN3lAdAMKMaWcQV1MLpHQsJH1CZdIDLux6GkSY5+JX4L+EPfiajxvGzOirk/PagchIqaU972ulQXmtGY0bZhnXrjSba/WQ1AkY/Wfs7BCr8zcC54u0H2uxJgfdhJD/2bmzjRjCKzcC+oGyrWRD5Bicqi8ORzmXrsgkhEySXulkDgGj9GJkmgqsDlxpnWEQCkAkwv0CRHR4Drlcfh9+NO7ivFzoF9jDWCcGyxEYUTAgilEOiII2wsMxQwxBVQYZVXaIZGz/x6Y+DEQmRh4CW6Zdm0FHXnNaM7wHrtDnUvRdPMZ55jtkolrJj5iah+MCfjsB+BCbXH0DtsL50g2PPMOnTzAngYGQ+2ANj5jG3CnkUxZGueUcehLQfSlVr6/Wjfv6BMwdLJ+ECrUL0JHCM2gRsMFsTHHtd9Y12J+pnlHScVA2Nf33/bjey/BbX0AJCDKgigB0/URJsA1rlHrhvPpHUJImOaFr0NZ7VU1DspFEaIQNtAVpoxoMbCiDZLR+5m3eqNG73i4onBTyApVdAzndxHBPBkdBE5ndK1o7cx4kkzWWMzRZ09uzRSpFwvCATPU3BU/o0gHsmKKhE1SchmGz9dCIDHAEok/6D4TrTRIXo4LDGTJCQzB+dnSg7NCsx9AAOaEGBfkNCOlCY3n3N51BH//2Q8zDYBZQr76FPkKePHJT7CdH/Gf1n+Ny8MDyv0dghkOUfxiad7pkZwj1jh1mTK9Wc2gUdBzxDIBoQGybrBScX7/gPvTBX/+F3+Fb968Y0iu0Z1GTVBb8QXnp3nwJIcIWDDo5IvJBHlKuLo6YI5Av1z8cH5Kqk7OLB66sKoNqg1JqEFNifotHQeLi3qtkuU17LHyRPcJiwYEkDmntCQbBQyZyxlvvvsOf/HzX+Lbu0d88eYeebnBfHyG4+EKx8MRz14+x/XNLX7/938XP/jkB5iWjJgTXjy7wbJMWK4O1I0au7NeeaBaM4gEXN1mN5DgAdr8EIQAAQ1BVtTacLkAk0Zc67KLoiVMEJkwLYKQArpzoVZsaKFjmo9MVnfy14D2o2eyAdERAO4fNJqzT5GQU90KD5BO1WsXGm6fzhcmPeQMFRdHIyLFGVMisedquULvHWmJOK9n6KsXhCsd/ry7u+Nl4muYc8OKL0NFKQHrllBbxenxG5TWcN5WZ5WTaWPibj7pqaAJUD+QvbO7kM15uZzoIOMXzbrStN28GF3ORzIcC12uNjOknPHs2cZO8HL2uiACp0d89+5bwqUx7UzEKURMIaJhWAKukLC5f2tFNsqghllm9cR4zt8iDHRIWVeeWTGxCFjXDTEI5hyh1ql/DEA6h70wZaUUwPjmCaU11+RVQBsW1+kGL4IAjjlGgZtcQqJWfKwEzNOEn/zwR0g5A0LXqSm8RYoRh+UAhh+UUYdhnmdcHY4oZcPlfKEuNWb0sgGtYUkRh5Rc4hKwtYKuHc2TToZ0aysVXTu63lJvuW2oteHh8YytNzzcr3SC2QoUgnp5uoaox6bpQbfijUwgG7Wby6ocRsYYSQikUYh/Wh/dUIOraXShv/Ul2Dux6Ji8TXGX8hATTNt+0ISBu9qTrdQTeZHYPTPZOrTzMOTQNGLn+fmfMf7M7rd/d/HrqMJhT79vzLYACuiDUQsVPMtr/DMbUJUNFqlfcjI6viEviBjEFa6w4Jeh/2/wcuCowytssPoAFMG6wx0Om2FHogALTvaQfZGav0B8IOg0JfFiiI5VqXuUPCFOHfP1M5gJdCtAp5Hs05UxfDMx/nDHyAlDWggIiOhrQ+kNj5cVp/MF7+7ucfd4xv3pjMd1RVUS07s5Q1J9iD7eETjbjONZOWLAd26IXvFCdYz0MGYFCkpFhkOlOCNTtMOcQIPg7jH7vNSftPkF99S6c+PJmFN7xyMAtVOKcl5xOq14OJ1xOq+4bCvWCjxeOi7LBaflgK00nE4FtzfPECTheHPEvEyYY4Kp4Sh5j27iXCW4PhYOOSbvtLm2YuAF1DvZhmbsAAF4ssKBBVuKUItkVTvLOgY+o5QyYNH1cWEsRX+n/HtFxkiC659VNP0mh+yltb6veQXQxJx1TESjOaLSTRED/YCf/Ffh6x+E+hMlDznRZ7TMGTkKSW3eGU8p4Hx94KU/JZSaIQHYSkHT5mxpnx+NteEH39AKBu+6BqqkSvcWAZx9GXbGafMcydya+5n69/EZHuN7xnzVn5mxWI/jjHCka2gezWd8I6UgICBFyhlCcLa1ED1RlztBnKgxgAh9QgzM//yhXSQrU36jk4GMGXcHrGGrNKrWXtk8ACh9IAP+7GA7KjDUswZnexqZvKfLitzIko1iUCmYUkJOE5+vz7VtjChcfmAm7tFJv9jhPiV+notrIoMKgkV29sJwZpsiVOko0zvlXRo5+ukx0JFL3chDx9kx3KCoIxw9qjmCNz4bb2wHeuzpP2PAB6gXAIR9Vvsbx8j/yo/vvQTr5YQQE1JeuLF6Q4gJUzxAcqTBb4yYc3LtUeUl5T8hNLGOKaFuG/pGR4NaK3KOiBE4LEcyobyqMYloKrisKwWTUIh4sjYAcXHvYV5gmACbUcqGh4d7F2A3LMeMq6sZWgGtnA90rYQ0jViyCvjdgpDpZgAkISByCC9DtE93E4CMQ4EiGisrAw+U9eSwYGtY5gm311foaihaMHwLxwE6xPKtc9aa0oIoiQNx42UQlN6GXQ1FKmeLiTlbn/z4H2I7PeC7nFHXE7a7NxCjl6cZvKN2+AARIm4sGwQhZ1jO+PaLb/H1u3f45a++wDfffodv397h/rTicVUUFfShH9QnqQfEaEq8SxEASR/Ax2pkFXZFqHQw4WE28NpAB4gYyFb16CcIX4uWCpEKjdldRmaO8kAmpfkskIUK3x//Xc4Ay+qb1av1KSU8nDb8+ps3+PbdPd6+e8SldaQp43TacHr7iJwycpqQ53fI0xGPdyd8/volfvDZD3B7e4PTR/e4ujrgk995hePVgvlA6DGE6IRj32h5pEuMQi2g9YbTeYPpBmkXDIF2ng44HJ8RhlPFVjmngrKrkwDEDCySYBPtsMzoY0piAN05YuazrSuNlC/rhWt1yui14vHdex64wjSBaZl4KVpHShOmfEDXjsta0Cp/ZveJ3dYzatmQl2vENDHItzcKkwFMkxu8Xk1ojan2EGYeqhme31wxp7NU1Ka4rA33pxM+/+Zr1NpQ1rafMTzwOCNLmZd+CBHm5slwK72QBSFHCvAl+CWiKOdHt0Gr/g6ecgOZXbg6vCtOAv7QHMO/T6K1ofZhUMD1XStjnuZ0YIB4Tg6vX9A652HalbZeEFj1RemWYDKKR4eyx/LI4jaUiOjGBBrGGJEEVFWxFeacmtEmrBmQaqd7kURelmJMNoGgrex4aR9HcmKpwNfffIc8ZcxXV0himHXDMjGuSQKNToAO6R1mFapnsEydyGPoFwSbkOIM7/cwCvwkGRZIN1MzrLpBrWNeOBs8v+dsGVDEAFwdj5hyB3rwdVEpV1nJOm6e2lJ7Q8wReYpozYlY6GjQPUzaWwYfcBmSx3iFtOzvFjtuMN73b3EJjmt0sOxC4H/vjZTpodkZzMgBW0ZP6IaAXY4aH6wQpiQbbkL0kE7D6Cap5TIQD++7y4VguMGPWRC83WXVRhIEmanEwWHcELX2XTvFImJ0f3ia+eGpglP1gxfOrBJvcyD0XjS67hvIQCO2zucwTWRWjepQwWrQuj1VwOP1jffincte8aoPeiW4KbZDbE4ASssBZoq8MNBYywJpFbZt3G/y1DHvbZhrM6sKSun47u4BX3z9Db59+x7v7x/xeN6wbhVNydza5zXe5UV384lxeKySvTtmOQrHT40+gvJBiWYAGboB+7NWYwcoCkoOhvTOzKt0HnCEM1336IiA7ckm8A7Tu8zOP2ToEGOMaF3x7v0DHk9n1EZat0gEpEKtcZZpCrWI1gx39++ZKZcD4bZecXV9wNXtzPl0XEjUcgZv98OuqRLIG1W9mcs0Awy0aGMNxM8VUyY8bYrkiWlBIgJoecdL3guX5POO8PSdR83PNeob2YNdxTvmfV4UeKAPz81BSBmp6vSC1L1iDgG7g8iYczKlPewWdcm/vy0zekuAQ3CjmzsuE1pnR5WTIcYZJsDt+RqlVKyBv7/bmBWT4RmiZ1uGQOLJgB/BGWX0w30YKpgapjxBTTHPM4IEaOIlFzsvlpT4WaE8W0L3C9Jk10MG91FVR5FGwaKBxdeUEzWLni039kdyW8Ku5EPojr74u/F5FEm+oxsFG4Qw1opAzNt/cdZ6J2rFOZoAgbyLrhwtjOJSYLu0pzVC9QGJsz8J6NZx2c6omtACUWemV3Qg3BOSz8OZq2KaMnbdnYzCM/C5+XkWxqaD7ejYOE8/aO155jtJI4QIEZ7pEoCQEiI6YlRqThPXQAgMOyDpTSAxMOM08qwPGM3EB3fUmCUP3MI1yPsu0TFv/tt/fP8lmDLEWWgxJuQ8o7eO8/0jJBrSgbRzWjD54ovUR6nj6mYdbWUgY0gHLHEBDJimK4SQ0dzJIfjsp+iYLXig5eRMMgUQhBesX1ajVQ4xYJlow5VzRIgTrAVs64rH0+NeXWZ3KglOBxbvbLTBoSOaEVc3Dj/Mi8+CXKOYWXVFS6y+twtUBSlQP3e4OqDXinI5OQWI0TmEBIAUBcHd4MVJMebQVR/egA4hTseFxcLWoQpsRRGC4Hj9HGFasDzeI20H5CmgXx6xfvMFOyUXKo/iwiAQSQhhwmm94M3lgp/+xc/xx3/6U9TCdPluJOlUpRB+rGV1reb1MiO56JVr0NB6w+P6yErWDEENU28QLbwMvaBhu8dZ3lChqCnQ/RI0bxJJ7kQ3t5VrAmh8gj1dxGwhQMMwW8YONfZGiUP2xPZpWbCWd/j5Lz9H6YYVcJPoDIQNCBVq1XWAFbAJX8qKd3cLvn3zNeZ5wfMXN7i6PiJMgh988hof9ZdYlhlXz2YgCEo9sUtzWNss7pedCHC4mqEtom9umxXd5cYbbA2CNDlRxtcJ3PEFfi7miebsrTFlJTjsw05AMC3sIOM8w7qhrQ0SmWwAoUm4OmzIEF26vtS1oGlF7QUxCnIOmKfkndYB0zxDmgLaoIFJA8OkOmdmQN4er6Gq+O679zyEO71Oj1dXTAkX0rRuw4Kr45G6N/ciLb3hUsnYrI2p9iJg2rsADZE6L4mEYENGlIgpTYjpicCTcoYZcHNDLkGAQ7XlDAiQsht5DBMOt+MaTRrEyIbMCSPCpHnaPZ2nOq5vjpgPmehMbzDvL5aZZvtNGrQbSqMeLiZe5Dky2LkqC/FSisOJyRMaeGkGiU4MYWHWusvLEhBkAkSgWhlZpAwcnx2Sb7XAuqIWT+GRRjQrBTTtePv4SLj0tCBCcAD3Ib75FvO84PbZc8Bo4H+8OuDZM1rRBQDztGDKB2eN8s8VYSGxQ4wikOR7tLEw6YXnMzov+Gkhc7/4gw+TIoaGrAZERRcBYkK3BGsU6Ud/JxIBZEOwRGLMYJD7xSd+vmCYqISMIZOA6Y6k/NaXYLDIiCFJiC4wp+zIY1kMMASYJVDArCQyRO+3ZAiIDWKejeWsyQEnQsjg44zAO7wQETOHzKMTHBqu/esbZwkhBKQI2OQ+fInxKyYBMSXqpQIZdszJ8gtQeIA+dZjYu9HRyKgO5iL9B5mqwYlW7yNH0Zx95VqqVrFViuJFMitdfivXTvEldvUsr6E/c8jAvNUfkSUYB6x3n10IV2rKgC2Yb59BpwxpZXd7167oRfH27h6//uYNL2MLuCsFd1vF12/ucFk7rZ/M/2wRSA5ICJDkcoOdKUfjgw89Vln9Ckk3oOb9uCQcpglZAhKEKYxjtuMieIxKU2luoII9KgmBqlQuPt6Mu7zTZ06kvhp6cNeRQSwK0WcV3HBbVaxVcW6KooYi/F6SM0KKSEl8jqhQKTAoSgNsazBR5C2j9hUPpwV/9Vd/hfd37/Ht8+c4HBa8/PgjzMuMq5uj+8j6DHh0AqMLM+/+4+yzTvlgRuo6p6H78rXMft/nSMLvisA0eLPB9YR3cwZzJ569IhaK1HNy3VRkATmmuU+bh59xdHUCQunWKedpXZ+yL+vmEil+z+IRYilPMAPWUvYU9hANIXsnM/4+bUgC3BwOqDljTglVO65a+4DlRwTEfN5VN3Ysralbl1FalVNmsTuqJptgAkyJKAw1yhGT5/em7OG0IA2/1fZh4wB4xx6ja0klojb+vWNmtxwz8pzcNi5jKx2tZ2wL4bu2cR64Vq7JmOMuhtduKBvt6NZVvSilPZa5Dm7MNSE092h9nIvshEPkfK9/yIb0M3OstuDOQHB4udTqF5UbiOvm5xQLNBNeSj0EL0AMPRBOH4V6VcHUQdTGz3BKzrzTTUOmMhCxYXfp6yO6SXkICAZM7qmqnb60MEWIynPROmpkR5gSmawsfMmOHWt/6JzV+gfacvOGyBsWR9fGnHPsy7/tx/cny2tGkowlzIAE1EIOWZ6OftPyaaqSeBGDcrEmClJDBFrlz26G3oEUJ+adKdOhgzse8DB2uiwEy3GGmaK403rwFOohg+gg9JpSBpIynig4dODo3Hw8YD5MfoEEZpYFIbutK4rb+4SQSAMOzkx0v7lSOPBfN+pdxoEXJZN2XStC9Mih1qGPD9hqweP5hJwWzFPeh7twR4NxoNde0BoPGzPg+nhEzhktECZbHx5hvbufaeS8QASbGFoQbNOCOM94fvUKaBX91WvUWnE5nbGeN5zePuAv/svn+L//9/8Sp7Xg8bKhaUS3gFYreguESYMwfDMEzDNDMhnHI2hK7WbfLqid3RZRDgIjIWQwBzvikANe3054dfMMNyEjByA5YqKiuLSG01a8mInIgT/3E9iPdx9fcoOlvKdmBM+J02zQaKixcsZTGpiqsIAkroSmwOPjhreXgrdNUVVQQ8Bhyrg6HJH7hqnG3TVfA3VOpz5BLgn3F7Zqopy/fPHrz7EsC25ubrAcjvjd3/s9fPTRR/g//1/+O9zeHhxSMhg2livD0QaBfL984//8qYTWRsbcyOUOIfmhwueRIi85BX0bU54cgeIF1koh2cbX00h2YEwVcFhmutf4k41OJoFDaFBDjsGfV0ezjvV8QS8F27aiVgbFmncBJHQQltsKI5TydAURwbqNz8IIG3WmInqEaYe2E3JM+Pj21qU0g7wFIk0pu4SCwnU1wXo5oWwrLmtDqZ37yISGEQpI5He/WoaXLIuIZZkQY0AKzxCCYM5Ejw4zbQjP5zOCAHNgSy7BSRjGAh5C8+7WmAyRUoShwaQj+/inlivOHv0Qrp2ox6WwaCZiZGiFeXjrpeOyrvjmDYkiplx767ahtorL6bLv8aoNVRtSzkiZOtdpYhivdGbzaVdYSm7SH2ASkNwcoaPy/DidvSgh4pXScK9m90IzjBPC3Xv6384Z1+UKmxNZUhDEdNl1jCkFBCXXI0fC3tPVwtzPzsI2IjPlJDRYEBznmevSYZuU2BQtKXFmXOkYJLJhdQY5ghOTAglCo/xn7UYH2RgiSimw3nYyEssLdaMDuNzMUMuHxftvcQkGb/dKXQHQLw8isJQcYx809uSdlPnFVZC8q2AH0f3CmpiDFVl9QJ+0Lk/0N0eehYcIZBgn8+9Q5cyJmlpeh2MGAh1epeKtM3HmMSinvykc7uhMdu6unZEAyRFw6FQ+qFAlRK/OCVeRbO5MNe8oAVqNCYApZWee8Tmadwm9w6vSUTWxA7SduTpE7XQPgQHBmXrRhzbNGZLzvCCKkZwSBPHqGv18wXp/wtuHR/zib36JX33zNe7OZ6ytY+2dw3llRwHEXXCNKEAYvpUCWPfZRN9nR6ajy4HDrmAUlnAGO88Rh8MR87L4IQRMGFl1xjglPEkXgsOqGAXNGLs743JUucohsaMHhAkh7NLHUFzA9xzciaeWgjdv7vH+/QNK7egSHWWgY0bKGcvhgNYoCi69oWpHV6aCfKiNBQh7N+2QIKit4dtvv0bvFV9//SXKdsHLly8Qk/u/ujaOgQ1ER4YmcFxgo5sLgQe7DjeND/bqgFStV193T7Ma+H4ADDH6ntsIxSW3pVN/VEloZxW8G6cnLuEivu+AoYOVbA5Zq3cNvFi3svGCchgwpuTsZa6p4UgEobPtuhH2ixKdQMVjrDY3dPeMR9jY8jQgsFb3mTTlSkdAKlL2zqwbGjosKMXczsId7G/bZ1Uf1FZwxKqzQ8juNfrhbGkwzfmDmYcShY5+YxYbAjMYBUAUJBEy3CGedA9IYLHgwzykKdMYPnDOXPoz9KaohedPygmXsLJrc59fUfFZucDpzoAxDEAioIEsasHYDuLsWs8H9GehIe3idzEiSPyKfKfMLeUZ240M33Xb8BgfkSOhyKSEx7kh4j6+GPPUhJnPil2MY8yKmHjmpUy7yTG76GbowRm3XdDdm7YfmTSi4NlcKi0may++F7yr8e8mgyQaPIXC3/lwoTEjnsJ/V566/r/lx/fbpkV2MafzHQz02Q8xIcUrmAbUwiiXOdEdJIaAUh5xvjximhOWJTM7SztSOmAKE0bEEFvw5u+Zt/swy4YFiCSHKgjXpDRxgXcyyxjcqRRkgy1x145uDYYIC4mpyS6yVZ8dqQrWlUGNrfXdpgcChKuEKVEvCACnC30aU+bf3VrxB875ypSit91OLkBHFMHVvIA2Pg50Gv0aOZdJ+4wyCDO8+r6RDWIc1k9pggRPLTfFHAQaBXedcojbmxuHiisiDIflgKKCh/Ur/PWvv8T/89/8a3z19Rt88/joGywBPYERVdz4KU2IOQGB4v+cBDHaztKip6QRJrOh1QTHUUEwTTNiSJhixtUy4fmzW1xfXyNFuvtfRUGXjiIMU8617vTqoEBQgzoJYegGxVO7TMa+4uUQPWIIIcACfTsHNCQSGX/kWqvLecVf/uUv8NXdCZe1AGkigUSYLjEfDpgng/UK1Ya1Fmyt4vG8YtuYaiEmkNhgEnAqJ0hdYejI6xlbPePNd9d49dEzfPTRR/iHv/cHuDoe8fzVs10gLjAEpetFB5PXEeBjA5aqKQV2Bt03sbkWUMTTMCL0vKL17gew7CQlSYSh8kTjiLuHO8CAw+EaEEElCwgpOpN5poxiXYsDrnEnskTriNbZXdiEOU/ope7ktvv7e2zb5qxhICZGSJ23jR1gejpGelfcPZyQYsDtzRVHFDmjtYbT6R4xcCyxWwu2CM0JtVTUUqFCn9vD4RrT8YB55qz8ct7QSsM5rOgGHK9YbK2XjchOZeadavZD2dlWXaDaUPqGIMCU9srUu4sPDD2sw5R7OAXmhwoUIbmWby9ISfCgeUBCNUrAUr7Q7qt0vwQnGASHq4TeO65uDqil4fxIota2Ke4eHrH2tvMuVBu6RtTWUJsTzWzkSEa00KCBFzpNNVjAZPff1R5gQRESL8DHsvLLIvAyiZ3uPdJ27XVH97SKhm09Y55nHA4LpsS1AM2QOXlRTEQshoDFjk6W9KrRoeYUF5LHPIQ4ethud5MFyXGX9MyTYFkySi2YDwsN5qvi/vSI9w93DiUBQIVZdZMUyrNSiuw6HQI1XwfmB4bI8Jj+e1yCauMiyr5wDAq6sIsI0kQ9V9cC4lb0PpTAIXNrxKDN97/vdO+E2EGY/7q5XmUcEMMOjIx4cUNkdU2eu7U4m1Bcu0W21LhImXoBfUoxb+qXnh9+wSN3gvrowlzob7T3MaOdmkj26pvxPOZdAhMa3BDcqxCmeAthxN72Aa9VRR/fz4a+h8QFCclZcGGfj3YhfBFS2qt/mCF0btgp0A5JQsLldMIXX/wa33zzLf7sP/5nfP7Fr/DNd/d4OG8wJAABaqz+dwebEJByQJx8iOwdaW+cbdICbMyr3Hopcp5LGC3j+vYGKSTkEHCVM27ShGNMiFEhYcQn+5wEgsXpzUGGDog9oCkd+0UDggafnynDj2VAqp7i1xWhgxeMgXOKEGExo6ngtHW8vTR897jiYWtwnQVnDuWC84MhR8MUGf8iKUNiRuoNpgERBb0qLyYfu3RnmNWy0TJNWY3++tdf4nJZMU9XuL29RcoR85JxdZwQDID0faY8LNVEhqaJy3ToAMfwc58ZKmdsTFafBjnW59ni7ELzSzXgMB8ACPI888ARN9Tyv0jA+d+8TDve0B3iNgD73B0J0yJAziQpmWE+LJAUPMOOMhUHVjjbdtaoeZEJ9cgk072T7872VVOUWjA632gCCzYQWn4WVWhr6ELav5lRDJ5Bo+buiTYaAJgXjTMPPcRdUqMGXLZGqC4UxBCQpyf4dLiRCGgbN8IAnvx1d7IuCzXPslT/8lRc6d55kvgmrNCda+BVMhAEc8qM6TqwWJgnngfbunmRTl1w7xHoG7WR3uGI/yUxJJijP5AxE1NUXflWfUbnwBF8JAy15h2UOIOcQ1MJ8QMEDU4qjwixQxsLD+szes8sVAHAXcJ650+ezYagnWeLssvsrtttTceRDIOxi5aAHv33oCMFQ07Uo04hQrAgenRSax2XErCVgFoKeqmQxCKOjHT17fM0M++E3bCHkn/Pj++9BJu3qykvfEAOzbSqyDng6khBcq0X1r6dl0JIExSC1sThTPPDYAwoDQHJiS5++HYeFANyiIGVyuQU8eJeiq1VP8wHUtxdy8iKV8bfY7RYqmqYU2YAaqEPYvNMsXk+MPvPYbBuFb0V17gJunHuFCKTtxGwQwwmgpA8s065gMvWkZMgB0GvFdu24nA8Yj4sMO3ojRAZTGi1FSLmwKTs6DCh+MC/oLCKdJcIplQYYuGlf5wnXpgh4t39A/7oX/8H/OLzX+Ff//G/xePjA969e0vZQJgcjn2CFGNmR5GXgJR9o0LQNmod1b+P6QBu5yd6fwyY84xpnvHR64+QYkQywzFEvEoTnqWMlGl/UzsPIgsBSQTXMRKiFqCIoqBBLVNHqax0OYcO6MbZhvnspssCQUAutNdLypIixIxgCT0uuKjiy9OGX98X/OrdGdswZQBgWrGdN2ynO9xcXWO+ucY0Z+QpYgHXXpKMS1yxrlwjOoTunrhQVmaoFSvYTht+9p9/huvbW6yr4aNXL3E8LHj27BY3VzdOclEKVXWF9o7SGpKzk0mSoYGCxLh7qoZggHDe3LpiPs6IaUFd+WcMHdiAyqyTrHVz9YyweHYPSJchjDFgUKX26jjtF9DlUnCq1Q9P2clFeZnpfdqohbMITLVidSNpzkBJOtGuaMPeyklZYnzvdbjY+KgixIjeGra6ESYzmoznmDCY7KNDbF5wqI9EhnXXWotr86oXyyTDLBPXB83A+VlaZ9cFM8ZTpQnL8RmJMFmePpujOFNMSInG1hy3yPhQAJx8EyI2cCxjwdBF/d8H5uhpLf79/aP4eedEoggc0sJCxRTHaUJCwLqtOJ1PUA1sHpqh2ubOk7Y3tikmNhlejLOoN5SyQmBYpqMX2INbRli1uk46grI0KOFk8eKle3HXrEM1wDBhtQ3BOrbtiGVecJwzluyzSOPnrBVMD+kdOZPB3ltnUcHOArU0iLH4C1EwTxEaBcjUaKM1IDY6WZkgTgE3hyu8vL1CKQXbtuHuUvCwVty/e4+yNQRV/hnDZN61zaM23NzAYXhP/9aXYJj4wCQm7z6GqwL/4oeHky8jOk2Y2+ao62QUT4p9E07SgsOe7vDmScYONgPI0bxi5J9ByAL7JhlganDc3VyTUj1Mk7MrP1DAhaO9YW30LmVFybidGHwSFSKMgWC+eIIPgPM+thCBd47B9TTAcPkfdmtBB4WXn7/Uin464VJonH08HPxljRlWQC9kdbILEJcKjNR784TtgMnNZ6MYtu2Cv/ybv8Hj6YTPv/4Kb968wZ//9D/gu/fv8PBwh7JtDpUM6zdnVvncLJjwOZr53z20NAExZohQ7C4O215d3bo0xecJrvc5nc6srkNCmiakY0LMEU9SVmPHb4xxon0RISbzuYxKJjQ3jNXD6Hpl/2kALBBG7D4J9BGlC5CFqIKQuMUDuEOFFPppyjhe0fBBrGNKGTmS3NTXRpKSOxnFlHB1lXhue6dwOl24sX0x8oAteH93h3UrmPIv8O7tG2zrI16/fo0Q/nc4LDOOy4E2gq36O6cLfy00KY9uHGyOWqjLGNh9klVMI+1O9xBVmNAG0EYnPBAUEZ9D2gd6yNEtmpMNCCv7kAvzlL3Q4Y8YlVpQfwWcUwuW5YDJhfRdFdNEGnqZ6b3Jc5oU/947DsfNCS78de0N2jrqVtAzTZzZcyUgREgKLob2jTY0ycFAN8onFu2UeVmPDpO+lAYxIieqbZ+tau+UFMAQVNB6xWV9pONPISM0pwRatemu3VNnZw44WCsvd1igHpOHASRkEnIi/J7nv58T302r3WFAdmPut48U4PrIDjvO6HqDWg+4ub4iHNgMz+oRpb3wQpdnikHw8HgmEcnJe2Hi2dgs8jKzym6MEQeYskARMSTHwyGGna5zKYIhCbWhUWbkacI8eeaqAHOekTM76G5uAgDg/vEe5zX6swFml6lNjjRN8Wn0wnOV59Cu3XY9MH1EC7b1Qrg8ZISUscwTgZwAWJ4wHdltJjxphdVT7c3n/uLnRdyLhIa/FzEmzrNDB7wEE2MLoaa4XFa8e/fe3dAzUkiw6Cw5G/qXJ1pr9w9Ccox4B9hxOheU0qg9CgKLPsyszv4bswO/+HIOLpv01t44V2m17O3/vs2Ff1bZKmqhAfA0TcgjDcB4MUpiUrWPT9A8lXhyRt5ANWLiBlyWBb11PJ4e/DNlIAIIeWfpdVVspaCcTyit4rNPPsOrl6/QTNFGR2wU0PZaBmpP/NyjYboq0FhdpZRctGwolxN++qd/gs+/+AL/47/6VzidTni4f+CfLXv5yYo2BX402J5RprtxtqJXt9sC9otqsM/TlJGnjE8//QRTnrCuK1preHg8obaGu4d7CAKWfMR0JcjzRHd/8DA2rwYjmFOWJ4Fpox2ffycLiYkaZFe4iwXAtntXA7HgEMCiP7f+tIINDhMFBdyGrPQOpIRpybi5vcHrj18hiiFCUbeGshZcLmes6xnrtqLUguVAfdxh8aQPiMsv7rBtxSUEHaVv6K3huzdvEGPC6fERMUb8p//0Z/jxj3+E1x+/wssXL/Dpx69hDv+FFOnm0jrqRq1XzISptClKWVFrASxBELnWIhnZIsBWq19KyWUA3jX18e4c9tHxv93+bHRp4GEycv7Yhc5IcfZRgCEnRYw0xu69I7oRxJwniDB+Ss325IrqReiA/Hov6NaxNs7Se+dF1EpBKQWnhwd21piRhKb3zQylK6rDeyHSXADjzRNDYzqCz5AmRDLV1fxSJ0qlZi7+h18+NO9mQU54dm2EYkU40746HJH8nuFcZLANuQQtsGhh4U8v3ZACJAlCXKg/juCh7Nua55KhbBXDrFzNUN1cw3RcgiSPzMeZnV7jM962ipCoxWyds84Ocga++OJr3KmiissBEi/e1SJ6E2yNzcAUeOksmZ6aHRFdDevqRiDDfMIMIXoOX54xTwtyDJgCu2+iP2knoTSjdCao4t3dCUycz4gScFwm5ER0MKWIdHDTBYfwY3CXn61iUNp6ZwOzbRsup0eklGHzjMOccLiaOLJJAYcwQSUhakOSYaTeQdtBc9s2Xv/iqIYZcDmdCI1+z4/vnwl24ukWCY9dKk1tJQh6q3sm1uykBBkbc1RiRizb4HZFClZT5lUWmAhN5igvwehknG3dHJZjJMxxOjiM4V2Xk2gsCAe2Xo30/eIFaNsWCfmJe55i+Pcpmja6TgS26vC5GQ8UQxV2egNeGJu91MINGNwzUgI73XEBWoeEgGXmodrNEHPCWjfXLIZdGM8D68mXbzjLxMiOecoOQ+YZl/OKP/vTf49vvv4Gf/LTf483373Fw/09aq0YSePDUJaQctidNn4jbgfm46LgOW2+GDxKalkWJNdYhhCwXc5Yzyes2+ru/oocIl68fo2UJxwP13i+THj24hmuMkBaMy91ziA9N9C7EwmcD8nown3Bju4LfoHCu0V+H84hTJ1MxX/ocP2EV69fo3SgxyMetgJEBjy/fPUSz55d4+PXL6C1opUNKzZo75htdm1pwtRo+rutK9AVJUbETMg5OyRd14IuHT10h2Q5kG9lgwbOct69fYf/+B//EztCEczzhOORpAXe7QGYuG565fqDKXJKSJGEMBmsN38O5pcaJbM+yxr2X97tMa9O9hk5S2j/dXFjYh4R3G9x+HW6Nk39DDfsri1RgkeN+VhLCyUPvsGJ8hi7TuM7ikZGeVdDgQJuUjFNk+fnce+FQAOBph21d5SSMBUmrYgkDLuRMSuU0hB6h1F9yotNn1ICzC8w8W4uIKJrh3pnlELk2KI2H2mwQFsx5lCCAK571wGhaEWzThgvCAIy7cbgHeMAnfg0uA8FOBwOTrQRdzKquymEGliEqqIr9+UcAnrzhPogmELyiXl3YtPEQr52PLu+Rk55d+1SZTH7GC9otSEXroFoCgQgZIfeY3LtJxmXg7k+kLoQSfSbc9qZsuLrIwV2eOZnYAz0fA7gmCwOSYw48zOIn3HO6O2VXffx4AnwDtn3hhgi5mlCnjKur49ce9GZ63Ad6xLQFGimmA8ZV+2IWjaOu2rlZYhhDMIGJ0SOqnJOe6bkb3UJ9tYgYDpA7x0PDxfCohO/RBB2G1NMXrfZDkUMAskofVq3fdGO+wQC5JyRMym3PCfd7qyd0BoNolOMyIdrevdNk1cyTz8UBlTCQKbjwB1Gr8mraneOMKAZ7ckGOzRNBu9fIMYXb9FQPWZoJxg5vFN9sE83ddkPJvUuuZsixIjDcqQcJLEjW+uGKc3IMdLdw59vjBEITxVM8AF4EMM8T5RbpBlrfcQf/bs/xueff46f/vRPcTlfcD5vJNjMGWEQTBx6iIHVdkzJv3/njMphkDBo2eNgdDjj2fNbzPOCwzwDZvjyyy+wrheshRtoygvm4xV+55MfYDkccH3zDNc54vkh46AVUk4IAmQZ0veOwdykNsuZWwKYxw2NuBRzuox4JUP3B7reGIC+eaU/dq8wUeL1xx8TEppv8PbxEYgB0zLj1auXePHiBp98/BLr+YLTwyPh80pG3pQnTPOE2iru7t6jbBtaqQgSMF8dCafmDMkT1i5oaKix8jv5zd3Kys9SI95+9x1++tM/w2effYbnz1/gxfNbXF2/ZpECYyGX4u45OXSROVGrGhIvwVIKWmtoPp+lndeTbdRAHADAxPYUFu1c63ke4dFuZ5a80DJeiCkKLAhSBNBAMpAxG3DKvJDhF8LwuQ19QE+uywpPhyi7/+QzdA/LdZP9mOkFukzT04zNN3HvRAbWLWHdEslRpNECMKxqqEoJEs0WWMgOKQcNaQFEsgRT98NFksuzfI8FpkUUh5dbbV48N+QYOMuLAXmZ+Out47StuNSC68OMZcoIkhHDAsCNopWhswrqpMvWIAG4uYpumSfoYchSAHaSRoZnF6Bxj045ocaGTYz4fo7YWkHphpgztXgNQDE8u7nB1fUVcya7YnMP0wBhAn3yUODOfY4pQ0JEnheaqVcbkw8iS2BDYmLIKWHOcX9FPN95CaZIvsIwbhcIcsiUlI1dXi/sTr2Y7+7WUusFMUUcb3jJ5RB4ca2d3tNL5MjieHCk0VBKxbYVXpo5QFqDtUbFAY6oK91yLhdDrbIXIvBOEJHPOucMi38PsfyU4n7hmADHZcLuKB4D0jHu0ThdB3PTvRs/oPyadsRKgToFqIEEjN5ZHjmFPoSAw4HxMkO03pXdKA8H94/7jQsQ+8E63AnI8hqD/pFy/0Q/U+VGFmU21SCDtIvHNjlcEjLp6NvGDji6kH/ASxRxw9mgLgAe2GkgrVqto5eKqBFBeflVKRgpESnywiqdhtmt+VzInSIAmih/9+3X+PLLL/GXf/mX+Pqrr3A+c06VEy+HpiNySvbuZvgi0hbJCP8adWLD1EBCdNZXwNX1AdNEd6C+FdyvFxdBG6Z5ws2zZ0gp4fbZM1xf3eB3f/cPcDgccXt7i0kVx+2MVM6IVpBMMRlnItoFrnqnb6LrS+k9OliePgoc8D0xOpe8OEVdhCpy+GEhnHnGlHG4OuJSO87nE87nM9pWkZ9FfPzqFV6/fomf/PhTPN4/4N3bt5jnB6SY90w5QpEBtR5Y0LhkpTcaJMeFM81lnqCJrLrWOtbzIzsyZ+lpbSjrhvd3d5AQ8NM/+zN8/PFHMFTcXF/h1cvnoDBbKENpjQVejFi3ArsoHTtidG0m24fg+w0iaM5EDq4jVddPpcSZUKmV7DzH9odrh0mEGiVFIoQRSZgSiBC+4vOoiNHlKmB6hWqHCZCnGckM5nowjhN8F8qYW7meNgimJWMMLgm7jXc7xO2yy4MnpfvQqOQHCnQAMMOwBUKDpjSj7p3z1Nq47pOzpfd9iOiSCXaI0ccpdqAoe5VxjrDIqEZ9WnYSx7A1jG7ooAKs24XdpCNEMXM2qs7IpIm5s0w/oOaPeSZTediVBVFEIVMygnaB6AV03wKCGFIA4WDtmFJAXDKSBo6IMs+f2WFBtIYyZ5RtQu8dpW48n2Zqluf5QDJgjhTwl8L9qH4ERyJBKVLKwNxLQ1EwLqlH5GniWCYNU3aK5bvriJtlCIy/LyT06t25s5/LVhz14vNKrhmP7uozztUn31zxYk+RoiBFjqdSSthSRKsVKU4Ojfqe9UZFnZzUPGXmt78E/UVaN+pCZuqDWmPLm6a0z/1GREjwxZ1zprmtRvReebBKR0rEeFtTbqahaABx9DxNyDlizrxwa6/uR9f3y22ckXslDF4aMbKqGvsyuHG0GwPxpFUKgVXFNTZA67SLulzoyB89WPL69ooXVGFg55R44Tpyhw43sx0Ht7AiGVZCSAKtHbUWqGUkeHct3KR7HEkUtMoNXf3SPxwX0pdN0GvH1998iy+++DV+8de/wNvv3qA0Z8/mCd0MW69e8Q4cPj0J0r1rGN1DCl7FxQikSBcGCK6vrrAcZlwez2il4HR+RNeGNGVM84SXL1/icDjg9Q8+ws3NM/zkv/pdHA5HPL+9AbYN7dtvgHOArHdI1ukY0+j+RgIT4NNdd52BV9SG8WYZ4eOzE+Hsz8yZeiF4BJc4I4bFT0wJ8/GAct5wPl+wni9opSGFiFfPX+AHH73GDz/7Id4f3+1dNlSwbhu2UihmDoayHACEfd5Q3JVlmRZIFEyTByd6V9Hq6hpKIgZaO6oU3D88uM6r4f7hUzx/doX++hWeP7/h71Nak9VWAFBju410ldY4V3UzmJAnzp2c5dY2FmyDqGDVdli5w1BbRQiK1NVHAT7gBWOQeu8QlyT4ovXDXlFhrg19kk0AQsKRcG8KBLWs0A7oTn905W/YhVCgxyhhXW3Uto2Ld3S00S884/ANUeJ+mdKnUzG5IUUMFHRrEyfGktGNywozxeIQs7ru2MCA3+7WeNJZIEowpMpnMWRM6tBmag2ltF3zLMJZNgIvwa3Q4UXBpmBSRQwRzS+6deVnab3TMMIJcznRArKUghAT5uhcCzfDHkbl2Nm0PLFS4EUHHazzhNgDCx8vinr12VgvqDWhTCPVnUz9NGfERFTKTHGYBKVU4J5zSigR+uR+p4wBI2lRh7WjNUTljDLGiGFPeVhm5Dxh2zZqoDuLnpzo2tNLAVFZIlS1VHbb7v4VY0TKCcvVgt7JiHaE+ekSbArrSgedUdilhBQiaq2IIZHt7wVPbR4H1Qs9mwXQv88lqDbqPHYRpdIJIo0NiOFMQSgzpZHcUBEAp/0TM2bGPfU+IUTM0wzLGbqz/7ghTg9n7oMwNFPiF5Xtvw4A3Vk/zERj9JAp6clmgHVBmowGxNzRe/dpQvhvzgHInvZgJPCYKkLIPqckbLcsRz8u4JvcYTifFUa/BTsAUYM2ppAbbI+ngc92cs6IOcCZNIDPJ+Z5xjQDGROsK37+85/h/fv3+Kuf/zUeHh/x9XdvcXd3j3cPD9iaE1vEIKlx/pjmvWt/ytEaDi00vx7p7leHKxyXgwuyBXVzz1GHCefDgsPVAR/94DWmecLv/v7v4ermGs9vnyGnjGmeEWPGPD8jVBMzkA3x6gigIZwOSNqQtVG4iw4NLiEXzhbGcJ4RUs3Zdq71NEK1JoIwOovBZlVngZG+ihYiNhM8ngvefHeH//Cf/zN+/eVXWA4Tnj2/waeffoyPPnqJ66srxADkHPGDT36A2hou64Z12/D2zTe4v3uHd9/d4fRwxlboW7muFxZIELRWsK2c4UnKCAG4ubmB9o5tPTFWx4dql4c7tPUMrRu2yyNOD+/xj//RH+CTj1/R6SWMAoiXivaGKSfMUybiER3WBnYrtdpIsmDiQdqZoNmhpxgTLGVMkcjHsMTbs/sAl5uQ4s79pJDwpA/joc11o41SlLHqBSSYmX8GiRFheFmieafDfRBcg6etQntH3Xj4zZMHZbumthe+9xxpjm1pGFoYQuZIgmuCtocpdlj2i9M7vMO8+KHd/fpgETpmYJkaKySw4OrWUatiXQdr0J103IkE9tShRkmABnbGIhgs1WoeQhz93PIDpJQCAbBMMwICWaVQtOrm5U4jXdfHAdwhmhchKXI9KdBVUIqiVHeKETdVEHN0TvxMBmowBANu5gU9T9CF7P2UnEh0RblL8o5NekKOCcflyMIY6iYMg2yIgUTzAjbzdSMuXwtPHaF1WNugvQDaMS9kojNmCmgYrH8WrBbYNVeHWqfMAuL+REH/b+hpA8/XAY9q76hO4llSRAoTtHlH6IiNmWJrlL/VkqnFTHm/x37LS5Cb2sDYk7YPMoNrRewDSIU4OGdtzV0XhrYsMhLH6HAQIAiuNRpUcPjQ+PJ4IbtoonA85wwzbtoggCXZH8xTHIxDscZUdVJvHZIYbZrxUq+970a7XNxu0zpmf2aI7tw+yBnDrUa1OlzHCjnGMUm0neijLrw1cPGQWZXQtaFrQ54S8pR5GZigbhXaFGlmXMsSDui14atff4W/+eUv8C/+5f+E796+xWldUXvH5UK9JDoTL7rnH07Z8xZ35ukTNh+c+MPJCrAcD7i+vkb0NIDT44biszbtiuNxwTRPePXyI9zc3uD/8M/+GV68fIHrAzH96i77j/cb1AkVpoowL5BWEKYZsQckFZg0FgYQVswi9Jb0rpDWeRRcs77RXdAveLq8DY1EA7V9VmghooeIBsFla3j/8Ihf/OpvcHd3j3lOuL4+4uWr53j27AaHZUZKgml2OUeecFk3XNYVvz5OePN1QkDAHPnrpTTkFFBrxePlQsHu5QLrhuWaBdJyPFLUrgVSBTWRVbmdV9QY0VvFw/0dvv76SxyWCWX950gZiFPcoTZxl5JpJjXdQvDLP+zvTwCsG4kA04FkJWuE9pMAEuihKwCWzE1fPpDd7Ls4AOZC994NIZqzeNk5pRjIcHZGIi8aQtAS3N2oKeYludtIxCCwMMjWuC8cYbEhjSgN08TZqplBm6K6M0jI7ORjYKHDv4OG9XRTZ7E4maErbQ0Jr8Hni4T4qT1U5OBFQZ49uoe/N0e/xHy2SJYkD/amRGDKZUO5bC6BcHceS+4c4xcCvNMwT3sg9RKmnPFzxktco/UnolwMEfmQodpR2+pdptPRVTHljHmadnG4dl6EQbDP7boB2Tux0fkYgC7AIbPAoPGAommBiWG5WnysAvQKtJUo0bIcSGQR5nUOTaQ4nBslfoB4+XSiV8A6UsoumeqwzhgmGAv+EAK2woIN4K0q4xL0ITJBdM7tWlesWyP8G3nZ9daQPeYpiqF5V2roNFDPkRdk47y7tREkbMitomnHFsiCn1zL/VtfgsPFIjlxQiP9E0lk8IpOO3qrIBbuSn1lknQp206zR1eIH2ARyZ0wXKZgHPwnAY2z9SkFfrgMdGuca3gnaG4LJSHyzzX6C9bC0NA5HzgrK08sqiG3GJAL53KEdQy2J03T3d/ZrSJIi/dY/u4GBbq5WeuYA8QUEYMAk3d6Yu5AEaHGQT0JKn4AulegBurTJAi0Vlwez/iTP/kpfvazn+Htm/fYVurYdDACQVF0CIzGIcvPq3EdFGjqgEa3KiChKc4ZeUowI3yjyoBfBMHrH7zGs9tbfPLpD3B9fYWXr17heDzixfMXmKcFtRoMnYw9FUicIcEQHApvkXIHXraCneguyvBUf+6DTLTTEZVbjbIZkjc0dAexmd+o4JpoPguK5uG6MaJtHZdTwen+gndv3+J8uUBSwOG44JOPX2OaE3rfWKG2htI6DKvzMwS3h2vElx9DV6XWM0bETJi01QpAUWKAVFrtta2gV8JdEgKW5QopNahwTtF7c2jO7dO74s137/Cnf/4X+PSzT/G7//B36TDkRLEoCQiMiumeD8fiO9ChBowLyyHDOvz3mG/8AFEekOLQmoHSIl58dPGIMghQLF6bAaHZXrCRYuIeo54eMRAU7TRO0F6g2rBeeNHMc+YBK4kC+TKMLzgjjDkjxOwes7S6067otbsWj/R2a4VzT8i+x0aRSzmH29DBqAsM7gwDcXakSysEmP1cgRfknLjRgB1gN7iuFaf+yBlYToiImJFQQsCWWFwhBNTaUdtTzJkN302oF7+EcWkEQlhRQLIPEarmEglDE86fRbBrIBXm5wncIq3sZ1VpFbV1hEQvzhiF3qHq6SN9jJ+IiOzM9EaTicPCkAPOYfmZYwy4vqaYPkfK3fq43L2AYeHFBiTFgBQCSukk1cTEsytlt6MEoB1pOWCkioiApglQxDxzjt0/4EuINwZCeYSYYMkTL/sEFk3C73CuBTFkLIcrdK00L+nUAAev6Lt2NIelaQVJVyFkoDuj9++4A/+uS/BJj8cKJ2IPojXzGYOitMpFKk9zutZogjqJMf9N2baL0lIrcjUArmsTDO0OD+hSuIDMhqbG5w4Dk5SBHXvFDDq50Iw27RVgb26TZt3tgkZ17GQWo/E1YJ6e4AN1Z9YNJpQM2MOfh0IdknUoNXCuJrsUITxdggigfVzCCOJlpiFhYhXFiJhqdcN6vuCXv/wVfv7zXwDu26nou0CYi9uxeYywSZ/7qe6fV4TMroFz5Jw5zA7RYZpKWrcv6mfPn+P1Rx/hRz/6EW5vb/Hq1SvMy4z5cIBIwFr5ThTstjGkI2awoLwAhR1fgCcAChjJJABcyyYSngbyHbsIne3DmBEZTBTRDzsfmUN9/hZMYRKgMPSqKGvFum44nU7U5U0Z05xxe3sDiKK1C+jL2NnJdpocxJixTAvileHh6p4kmU4iiFhHiwG10nygTez8S6mAa7/ibn0XMbl5QhizMdNdjP7w+Ihf/urXWK6uSN+mnQaohWbnYUJKfVfzaB+mSPD3sVMdLE5zW8HuljCjgh+8SgWgYs7iHlIY/nPFk5DdKWMYVC9Cfl5sjZm7sqhRa1CtHu4qmGaaL8D3mvheVu/AQkwIEciBXUPTQoep1uDuATy4e0cSN0AHRx46ilCfN4rH+8RI9ulYF82jfqJ7DC85U1wd2JmSlGX8bLwP0KrzESCMfpJIuROMizFEICZIKEAQ9OaX0z6x8oGt8bI3ezr3IINI5OeDPs21a6vOBp34HgJncioAmdvjPGJuYeuKHLnnZaBWPj7ZP4YXlRp4kXV3SmGqDmBSvSjiGZXmjGEPZwDDnffvNi5AgfhMc4rMc7Q+CCYkPIYYfW0KciaDXdxGMolAxVG8EDzZYawn2ZODVHk+JtcSpkh/ajM4O7ZhiRNn0c2ApqhdmTfoHOLulm8BGZDghueCFllEmya/4P/2H98vlve2+/7xAa03bGVFjAnX1zekYWfCIaU0H95npJQYCdQqWqucb0TmSQnPOFStCBYhxta59YYpdQ6kE2nW24XGwc11TnmaIKqo63k/YEZAZUoR+XiFrhSkBviF5eOREDgd7c7IlMTvRrII6c3jHQ1yjpmhDs87E1gn1m5++QOcGZj4XHScPhwSwpyJZw45YFzeykFvVXWCAOHU83nDtm34d//63+GLX32BN998DdNK2BT0DVdfyEHC7toRIivdzZ37Q8w+NxLElJDyxDlenvkuAg+z2iumecLxeMT17S0OxyP+2f/+f4sf/+hHuLm9Rc7ZB8qCYKw4gnG5BDi7NrKaK17ZK7n+SMsCKSvKujFJwTokpL1aF3hB1QUxVj9IDG76weJBRk5fcOOYDEARW/QcQsUI2m214utvvsKbN9/SGL0roZXRbQUWWSlNOMSMUBSo7mUpQFpm5BTwwx//EB9/8jG++PJL3N8/4O7uDuu64rSuQDfMhwPS1GGhoKnidDlBIISiRXA4HhFTwmXdHCalKF4t4Ls3b/Fn//7PUGvF1fUz/OD1K3z2yccUQ2unDjDFPWRWQd9Dpoqm3W80DuIXCFeWjcQOOHV9pEZ0H2OwfBI0dKTAUFp1YEOVz30wc1tvKK05+UFcYqB+AQV3FUnY1hPt0yKNy2Pipd8r/CAVwEiRhwHSveMBWZEh8cC2YdDeGfljg1UeIpqxu6FPKhATO58onJdGnw1KLxBTZ40Ln7uBhApjisaIDhIndHQorteFB6+PbFImKzdPk7fUghyBUgEtAq3GbMIoXvzqPr/qzYlcXLq4rMzuYxIMi7QQKNkRwZ50wAQE4AISD0nC4RlSletma4pmBck4ulAbnRu8CSkwl2RFGQbhhiA+y/aLKXReOm2rO3weRDhDdmTPvDM1NS++FdX4zI7OOQDGyNDj7dz+ku+X8HCOESoMEocAKVFSd9lWrhNHYERAZrXyv7dOyBoAUogwm4EOuop1sqlL4/hrjE9UvcFx20X21ywSWHhsux77t7oEg+O3a9nQGr0wc86o9YgMQZiys0PNXzovwHmZESsX9B6z4uLU7sPPoSPsSr9KElEjolL303tnp+JWUpPnZSl3GlzowktOGLKpRv6NdoP5BTbINUF8mKz2BAv5Z4dr6+CwXnQH8u4Uf3ghMRiw2r3qdgkJhH+mP3sOkjtY2Xkw7e5fZ7pHFDHehxfr6fEej48n/Jef/Rf84q/+GufTA38veFDr3nEG/+khspEwa3fCQvYDLCZegjEnOkHM1OCYO+N3UyyHBYf5gBcvn+P65gY//OHv4B/8+MdO/RbOwhzeJnT4xM2VfTNgh/YIFwSElIAeSRRyGCv4OtgbeSE718wrOvPNt0cMisPIrgHaOxkn2JgfbAC0dzw+3rv3osOJnoK+61UhHn3lc2sdTirG5xQF2Sn9p8vZi6DCZZYSJJAlGzWiKoDacDmRNRycrZY8YoixMMbDkcsK5/MFX331FZ69eIEvv/wah3nGZ598/PQ0HYIysLoV9QsAXIv2gaUf/LmOzp+IxtCZAiPDETAnjRBGQwRScMNrJ8t0Iwwq4J/VqFWCuKZNPcGCdxG7/947WueszxIAL45U+bkGeU376Fj42WPghSQOAYxueNeIGi9beuViJ2QYQIMFR4yGhGkgUuIGATSbpiWfOv01DMkF+HxjjsgtYZ6z/3lA9FxFCYrQ4wfWfR0iZGaWQLZrSOJp5XDtM/bzTCT4rzdnIWcM8p4J2ZB0k6o+BeCRTTgvoAdlw5DibhLQVDnGCUySGF26qfhlT3ZyHLNQ17MRIxHoCCkY6F3nEN+CAW6YsFem4KWkRg2iCfd1TpGidZ5ybkLAAkPGuQivX/2Zy476GMcZYtjUryjj83AuJLrnbFqnNCQGnskRkUQhay5X6uiV6RoWgzvQdF9D6iC172qHqLs2/z1/+4/v7wQTq4iRyH59fQVVxbad0Vsk60+ZE5eCIOWArg2Pj5XtczfEibBfV9JYxV0xdvfwmBBF6OEXAjegAfNCEXOeF3+5gZVJ4yUYvHWPiYykKAHWOlAIzSKQ7TYlF832hjwFKMQJMeai+b6/vhR4UCZn5FFzwk4Gw0vUFBUbSNoYIk3CKwzhBeziOh/o/tl5y4adhTlNhF7/h//xf8DP/uIv8OWvv8L9wwO++fprnM9nnC4XqPkCgVCw7XNHCBdeN0XsDTEC083BKc4MwTxeLTt5IYZIE+TEfLDf/4N/iJ/8Vz/Bi+fPcH19RReeFPHZx59hOh4dpwSyGqQreuUBh8jbsI+Bv3+XMXRsDTAk2HSDIAnZFKgrsDFWKG0r4ZboKelemDQY3E4IkAgLY+7j6IHDKOrvHV4Y+A5C04bHt2+wnh7x4vYW18+u8Qf/+A/wk5/8GL1vJC/AYT2j/d9VDp6jSJ/FYBzI99bwyaef4sWrV/jH+Q+hqvif/t//Cl9//TXq6QJtFH23rri6ovaquM50LRtghhfPnpM1Wi6olfFF6Ipt2/BXf/VzvH//Dp999in+5Ec/xn/9T/8J/sk/+UMML9wcE2QeJ5JBrfnFyLUroT1dKCEhLyzs1L9jNxZDy0QSxrZt6E1RKmctW1mRMh1cWlOU5jIfAhhIOaPUhvNlQ/Rk71VXIiROfMjzhOBRW2QxNr+WiXjEFt35w40OQDZnK2UvnMRdjPz+Bw01GiOyAIiQLbyX9O4BqsFz5yq7Cgh4cXhB2+rq7FO/FDw1o2uAdAAbQwAOVwePI+I5kabEi0WDRycFrKtgK8BhfkULw7HkfD5/WQta7bicN7dG49oUi74eLiTMgJfP6e69w6PdJRoDhvbXDWCaJ0yHhZeQE/0EZN3HFNj5N0o+YE8+nt3CvmcA7EjTOOHHaGfxlJERL7d2vjsBw4CnOQPO9u7yBAGrVkcO2OykGKBtg6mH/PKeZpe50Oc4BJqs3N+/gQhN3s0E5+rm7grUWvD4cN4LVT6RvmvNdzcJf0wP9484X84uz3AnopjweLqQ9OjjMXjxUbfq+vW//cf35wk6CYaC0YhlmVBrw+VyYSU6IjQge4fSW0OtbR88iNJRgOwx3S8EalQc0vT51qjaTAzJDSyXw8LKUElv7n5YRozK16N3ECCi+xyDkJrnFMKgRsw6hACBi329mpa9ErKxzjFkBnspD0J05m38B30QOKtSGgV7hRcFThoJPpPghh5w/lh4X/zqC/z5n/8HfP43X+D+/gFwr0fo/t6Jswf6Ncac/AJqdCwRhQgdF8QZbdOc6fbiFXDwhZGnjMM84dNPP8Ef/P4f4MXLZ7i5vnLOHDDnxaUJPi+ICREd29oYsaTujs9634k6fr/DK1MEaJxokJAmmHWgMq6l9+rwiewQiokNlRnXlJ//oxvc1+KHP3dcxvw/FKWu0F6xLDOe3d7iRz/8DK9ePYd5XBP3BTVmYbjWxEgfSDTIcB4x4Hh1hcPxiOe3zyES8Pz5Mzw+PmCtDU2EES9mkBgpjVipna3aEBBwmBZC5zDACqo0Bor2hvu7O5weH7BeLjifV3z62af+fnjZSwhIZnt1DUcj9osc+hsdYYjsmqSz6+1GCVAM/D7iUHbvCpXB4DR3LXKShXhnntgtjYt9CuziW6NR9l5wuqUfI7eejvHRrZIsYju0vRsQdCZ/QIZBtsuShDMndUJcV3OfXnHSG98xbLCwKSvhORA/2Ktu1q0G60OGNPaQ+GzI4TaXGgQFYg7IOcA0AspuL6QA7QlmlK7A57Vjhsn8UEGNjVmGol7YCwTZ2Zve7Qm7xVLZNLAQBnobO+npfGxqUAm7xldN+A4DC8bi5KzQFcF29BToHaIBKfD9tqYkIaq/By8kRzfaqrPBPziNostrhiuMDSQMhNXVmffwlJ3BmxiXoAQf8SQ3suAxhlopks8enBsqMCJ9e1es67bDu2aKjsaLNhF+plEG0Yl1K7hc1v2ZsYkii3urbUfxUuBIioX63+MSVPAiPN5cYwzRUzQs88xsspnsH90vowBogNaxKfhyrPjCUdtjNqADDvMOwHz4PVzsI/VgtXhKtWd57ToofFBF8j0huF/k8JRjt0SGXy0b4jQh5kzRrYvmmQnIQ7/UCrOCWChY37PwvGuRyKpLFGit4u50T8w7J7+A2Kd46hiakoIebEy3yB6VGLBtpOF/+eVX+NXffE5oTYc/nznEOjSxnF1wduZw3yC+JAYbH1JCmiccrq/3+zwlzjimacJhXvD69St8/PFr/MM/+D388EefonU6R4yUgFY7Wi17IGUMEa00/Ls/+re4u3vEs+evcDwc8cMf/xApJt8E3iVC9pQBlQRFxNqBhEA2Kjq1aaYI3Rd2dI0UmEAO32jQQHKTJEj0SrTTpswGNDsYNYmyneM0o5ni+fNnePHiBV68eo3rmxv+HWF0OVwvtVAUfUhHHA4zWqO2KU0UFs9CujmZvMBPfucnuJqP+PnP/gKPDw/01FWathuAZ8cr1Nbw3bYCbpsHYbbfiPwqteKybfSGjAHn0wM+//yXeH/3jtT6Ucz5diCqxTm2qUIS12CtdT+IRcSNxMWlRLqHWJ+9wMx5hgTGQrXaqNkzQUtkjS7xiQoPE/ROXe+czUXchjTFp+cRBDnzQtlUuL7jtBPCAF8PJtDiKQpGkkI0SogUfTcIH8KdlCPivKA3ekH23jxhxZmLwrOIWXRCIwNjTFrwOTcASBJEI8NTJCL05Neex7GPYkcbZS6HaZ9nhZQQkByKNcx5QgQNCHpvaIXzKIm8OHLKCBJRckOT7ixpFiwSEiU0XpioGlKknq1FJoLUpBg5pPDDuqwrfZkjIcgQWcRqVYQeUNYNvTYXnAcmbxjQKqF5FlO8WCQEXKfoz9kcQvaw78nNBobhuIIXzLYhJlqVjYSTPNPbU3yEoFbQOpEZk8icya6YkiCooBda1SHQDP765jlMDY+nRwDkNHB8FiET0G6usV42PNw/omnF1goj2lJE006t7ijmjDZ1j5eCcClY14KUIrp34SNNqGBc3TIosn/rj+9nh4JlfsrJLzCSGJK/oBiDV3UO7spAjT+wFjPbozae5maDyfgEBSgLFyDYky8kiD+TATUcRfz3Ax/M2UZTwI0VdDhXODXAZydhDLOV1WaIFPT3D6oS9ZkCIMNPGwJP8x44t3+vWgs3fyTbCmnMa0ZTYU9F2HiiXlmdzxc8Pp7x8PiI0+kMbfDhtP/w6jb4JTgu+y7jtfDXKJRmJExOlKOYGtTJSlPOOB4WXF9d4+XLl/j4Bx/j2fNnOBwPtBerbksmAWYUmo73OYxnv/n2W7z59i22teH65gYff/waMo0vY/u7DRJgMiyQCCcHCAuaMR/+jefh8JGMw1A8CkVYQYWxnn6zE+QfMqo7+pKOZ7AsC5bDActyRJ7mHaoNrpcMkbnLIoYYBDlFJ0fRgFyFqdVP1bDh5uYGvXXM88IkjY1H+UhLSV5dR5GddQlxCD9F5Jw9mJeU/SC0TatnGnY310Uhxv9F7/vU+Yp3WTsD2Gc5qg6BjVcxOjN1Sz9PAY8xwHpAHTtjzNd85jZmp+b7Koawe6PuaJT4+pbgTF8mKwSXFgV5+rPH7FyNrG1GlvnnB7uWkTFqRoo7U2RYGA/jAQb1+uGgghBIgume8dg9gWQQeEYnMPbPh3sPHz4nZ1bGFPYDdlilDWOLoeXs0lzN48/N+CiCE0rYXfmpZ4SuWaCyUJShb7UADWQQ8zt11zeTVMbcVfUOEbQPG/U+DVt5MXX9oAV03XR3zQDYIZXaIBJQW0c0J4cRMPOj2mfC3mmLowWtNySNoJuyfXAo7fCLv0OuBxZ5juP4HG4/dx2CzXniBX9eATjiMNZZDK47pASFHJG6r4vSKkqrT8Wvj8169+pbDLFHxMgKV9WbLZ+FDwLO9/34/kvQuScxRLTacLpQ2Z8yobmmg63o6d0SELJhMupJ+HBYheYp0fPNjYHHobdvLHdncT0lhn9j67ZrVrp1XC6Pfglk0ng99yqHJ+85BDByKRoUjCWZ5ICqHdu6+dXESk5CJBNKFb3RWHsw7QzitGJ+NnWyTYgBWYDr6yuEGOgpaWSmin8lAWHgPR3CAkQFd/cPeHg84Y/+53+Ln/3Fz/DXP/8FzAJaq3SXj6xYc6AgOYa004e9+fWXS7g4CDBPC16+/hjdyMAyA9CBY0p4eXuLT3/nU/zkH/wEH3/yKT7+9FMclhnNixBTw3peUdZKSnsImBI9AUUEa2z49ts3+MUv/wb//t//GQ7LAeeHB7x8+RF+8g9+D3nKWKYJpg09NFSLaDmj9wQNEWaM14quVxtQqBoH4QbXD45N51kHnEVUug6BAvNgHLAHv+T5HgNh9TBjihEfv36FV69e4dmzF0xRV8ZcyWRkgWyKQ1pwPR9pUJA5W4sJ2FYeQGNKrH7Jvf7Bx7h9dotvvvkGeTrgrbzBuq4IG2dB5ws9I6+OV2hNcX4skEB3mhQCovs2jguhNyYxdDM83t/juzdv8OL5c9ze3EARoX54iwHTtMAZH9h1YRhetYT9uhlq23jABNrKjew21Yoggqv5iJ4ZJL2XWqZOGnC9G9wJRQOiBUSa2+C8Xqh9TEzT6J5c0BtvlMWLNeWEg4J3VdRaODJxc29EdkkKQYhkwZoa0Dti7oi5uolFRJomZAlodUPvFed1Q6sU6wsCaiFE23tHEKE+UMRN6T8khnTOfX3Or+KHtbLrbW3USbRl641OTIwBc41lyu4jzL/f9niqBghwXBiSGzJt9dZKIgfOTgIxdtt9dqKWa/yGUfTlciHhI7m3qM9C93nlB64nKc5I84SYpn1uJjCcHqmTJiNasV4aLAj0/vTEfg2GmFaoMr+yux4xp4TjsqC2iu1yIYJUSHCc55lnpO+Hrp1IhQimPCOE5M9SMRLnp0xzkXVld3o8LBBE5JDIKSmc04cGBKV5fJ8nHG+vgBNwWc+E7oM7fNWOaorqHIiwF82GulY/B3lGZ+967x8vUDUcl2UfHfx2l6Aft7ylZJSJrLacabcHoY7KVQbMweq6ezU5qtGRLbXrW/AEdQR5urW9R/A/1ucGCsfjQXakjIEvmUxPbZTLImD7QWZ+sAxoZVRRHwyY+DeKfPDdvNv8jUrCOwvvkGOge4gaod5Bxw5eZQc/9MulYLsUvPnuLd6+e49f/epX+OUvf4nHx9NewXO+YnvnMlh2/Gj+6z43DeFJ/JycBQrtkOrpEVPG9fU1Xr54gVcvX+Kjj17h+fPnlLf4cxtZcIx2AaaQMBhu5gwyNZIdTBXv37/FY8r48ssv0Zvis9/5MaEaVZgScu6NuiQTcR1fYKeFQCaaDKxgwKjY3wGXyqhuCY3KmOf5n+mFOg8uA/VeEiCmiCJYHP5NkcURq1PbnyM7oCdLvNEqjOc93jf9J7l2h2fm7e1ztE54PeWMu8cz1MqeVxYj2WyUxzgs5u8quP0Vv+eYfylOjyd89+YN5syDaKzNYQs2Ft9gGfJxjQR1ZyYq/FkBMrR2cEBfPbYoZM6VIrzTGj4g9qRPg6vyvMiOXrFrpwsUfCwwhNlseIN3VqPrsydD+s6kBvE1wXxIn1Obz4U6O5uoQFRFzr4G4geEsnEGeEfF7zUkMB0SmGZjvk4FjipB0EUh5oQ7L5pM1TV1htZ136etmRtCcA9HMQQxzgjdetCCuEHb0OOShQ1j/iYgewoKUQYnBoHkFeMgzt8ny7jmNpMk9Pl56jB48qJAfB4vPvPDOI53tMTlRKYY3qmmFOF3J05JCIg6SG1uL6aMfdtnkr35sRiQ1FwC5gXz6PBHt5efCgiSd/yiTlyzrbP4MaUOmuYo1FHCETH17hHijG5nImM3vmY3+oTqObvNWXPqey+IoyMMI2UIdbcnwuX3/Pj+KCXly/ZeH9fPbvi4A3ZdIBy56u3JIy+OMMYQEAJZjaUWtFqRJdCNPjG1fisbVDvmmBBTwnBFEaHXwxTdwgxc8KrciNkdViLUN23fYZxx+Lbe3Oi4OZw1sWtTdTiELiWIpHUf0gK269P+99veXZlXGUBtvByXeSZ5KRL2iylhKxWn84bo+VwpZMQQ8Md//Kf4N//zH+Hzzz/H119/g9PpjG3bULfmh5tfvDlCkmA5zIgCnM/M8EtKCPp4fdhhv8EGDSFgPZ0xTRkfv3yJ5y9e4kc/+hF++MPfwT/6R3+wbyQTwbpekEJC8ty2NAfUSoZX8Krz3ft3KKUiJqDWij/8w9/Hq5fP8Hh6j7u7e/zLf/X/wsevP8EPf+cnmOcFb9/fo6xnPL79BvPxgOeffEzmXSCLsraGI4A5Zx6S6nL6cfmFQCGk9V27Zo7RjJzKcYhFIYN3yRNUDWup9NDUDSIJc5qQIWgrUQvJCRBD2BTBfPqrBVoDtPg94MP3GClzqEXROyFzMxAyVsMf/tP/BgagbifcvX+P/+v/7f+B9+/fYytM+m5bJatvEDw6zZinlJCD4PZ4xTVZ2cUYFD/96Z/iy6++xP/xn/8z/Df/9L/Gi5cvcHV9zUin2jEUf2OzL4crt+Kj7GEE2IpD2O3CbuqwTFDrqP3i+4Iz8qZuSxcECRFJEi6t4rLWfc9eNu7Vw9UBaUq4f1xx3lb0Qs1VjIzgev78FWJMeDwXqClaY07jdllZ0I0Zdis8/AasFghB6g7xASEYQlRMORHCTZmmDhyyIeUJhynQQ7UrzALUgLJyb/YluJic2X27K5F70qI0jBxSQo7MuKOmj5ddq7wExUkYIdKpZZoTcg5ohTPYIhxLBEuIBkjm+ThNbgVpHSkE+rqKMOHDgNpY3DUl3V9rw5SAgAbTDNVlPwfgcKV4Idi9wDjpitobetlgZpinA2KISIcFyYDqZKcehZecUadYT/dw3IWpEtNChGyemWJR3U1J1f2VAasBbRNkzUjdqFVEQNk2aKdXc4oRpXG/rO4tqxPdsk6XR77cuvGcigmtdZweHtHV0N1wQxPNsNftArOK+ZCBYOQ1GQsWCxFBEnpRaDMEuHewFnRrPM8A3N2vtFEDSVx6Kr/Zw/z/ewnuBbQNkgZnY8E7t6cqzT5oGAVjpMfL8mmex0HrqIv4e3ujO0JXRRgamHEIYlQF2Kt5BtzKU+fovz6cMUaszfj8e/XiQ13AdtjVVCnnczLCYL1JGHFMcCozK+URLDieRxifzxwG9S5HeydEbN6pmuLdu/f45S//Bl9+9SXefPvm6Rl38RrDn5vrxUbH/ISw8znkFJ0a/OT8IC76nacJz2+f49WLF3j9+iN89NFHePXqIxINPNWg9I4ISiaG9g6jk/YeoneaoBeP1jkej3j2/BY3N9fYtg1fv/sW9/Md7u/vscwVp8cTynrG+fHkPpT8sOZdwnBXwQffZyyu4bnKim/MjMbiM3Y8o9YUf+bwg0rGJ7Z9g9NwW2lm7LFLqjR0gHZI74jJSJIZKSPeVQ9npH0N4wlcEBEcj+72ogtiDDgcFpzPEx1mvHAbv3fMomXM8EBoy8xQUP27KR4fHqFqePPmDd6+e4vD8YDD8eDdecew9Out7+tcRN1z1OE22FP3XhUWFD1FmFHPBwBN3AXJ6GgTU8RI49beodr4XDX4fuQaUDDei04vfRe4B3cnkRAo7u/dIX2uNQMPLtO+MzaZtCbeeQh0eG8qIKKA0Mi9pYgYn5jk/BGRoqAWJ810n+03w4i96iLOBKQ7FQQkVgk7znEJDqvHGMkIFWEIdKv6dAlKpNbWiVVmkYbYaqiho0ORnLk59KrY1zj8rufFnOKwmWTHHsQ7GZdk5RihYXAkvKsYIxkf9RnIUQhOrNNe9jUiwssIoGsVhCbrJkoNpa9jG4tEeEHKIPwBu6vKjoQ4XC+qkK6A6PCmwrCCIznrN+8J7jVWNmWjZCj6xZunZQ/lJlJgLICFDc6Y8RHxFzfkoGQlCj11uxBBgZuc7BwPIbOjNmfP+j2Bpv8LJO//+8f3X4LDachb4toKBdmJnoaXdUUItNfhW4dbYLEzq+ouMBIY+VENp/WE0gqe3TzHnGe8/e4tLpcLnteKeZ6xzNHpt/7iKn0YU2ba9LwM5t7wiOCQdKQ6xzpIELymQpg4j8xcMOd1xTwfkKaM8/kBtWy7tdnhcIWYBFYq503u/ELhMinxwpOIUFxVEnmC7sWBtebVK5dkqw1rW/H5F1/gz//jfwQkAnmB1QIb9lHuXB/AmClJdGJRUcRMGn8SXny3t1du70Ya9npZcTwe8Olnn+L1Rx/hH/1v/jETH14zOaG3jmYdTbhxoR2YwHTzxvDQEDIM2TO5SNKwSfHrX3+Ny3pGyoIXL27w3/63f4jv3rzFv738KWCGP/mzP8XV8QYvXrxCsI48CQ7HjNtn12jljPXhgfqicgat6RQBiizUMdUGIDrbbtzyT9cOAIF6Wkh0qKODmk33IKA2VShgVuHhu14e8PW3X+PZqxe4ef0M5/OKt2+/w3p6xPn+DjfXN7i9fo6XL1/h5YtXiFNGnBJqWQlNDVaez4olTg59ARD1tPgrvH79GiIB/auvUSEIM6FPWvV11F4BhwcNATkfYbgga2HkSzPORwD84pd/DRoEJ9xcXQN+WT0VU5zlrOcLAGrVIECaGMtzuRTPOLyQMr4Ng+SG2irOFzothRCwTBOuDgcUw2jPEA1o2xlr70iRKQGnxwfqckPEFCLS4QoGI0OxKe7ev92LEZihF17u0WOfIIJmiu1yoalx65AoSFNE74JWBkQpvNgapSrB2cIilC/EKMhhRpCI0/mRdozT8sGsK2Jbi39fp8a7obZJ3y+WQaAaMq9lyrg6Hjh7l4htq/xzwAN3OUyY5ozT2Uk2fpoWMPg5OVmJWYUBeiD0XLTuUojghR/11WU3B3kikpgnHVAC1mpn4HNMT0YYzqAXAIfDhHme8f5dQevVL3eFZDYm0QvyeVrQzRAjC7TDYSEj1QuWtRZMYIAyO08WGccjS8qR1N6qw+5KlW6EoLq1X+iE0HPOyCKOmnS8e/sGtWy4bBcfVzTknPHRx58ihoC8ZLSuKJeVN6gCAmZaWuvUVfoZEHPmTLYZpBuKrqj1DHM7sO6NUxusO2WhXBvnjjEN4t1vewk628h2htD49VFZPN3+7CgGewy7FdOO5z6V1DzcuqKFpziT3hpaDNDslc5eXbByGUy8fdP5oMg+PDN/4xNi70jHgQrwcnUGN3YIctdgeWW9y0rcjnifSZhXsuJ0fReJjg7HxkcR7zASztsF93f3eHw4YV0rUmbStvocZXiwfDiiDP5ZYKMreZq/Rqc88xHws0wzE9RfffQRPnr9EaOQloWX2eioMLpifsIQhivj6Nr5/Z6YvNgrvuCw5O3NLawZXr16QfNbNAAVObssxBJz76wzzWK4NnTCRxYGi0yf3hueOuv9VX0wC+Nn4fzERD1nb5BpgOFHOtZHEAC9oa0XaL3a3U4AcT9LQlOtN++AursUjcX0pN0aWXSjOxxeoL3ziV5dXWNdN3yXviN1fnjABjozJiQoOnrn8H485+AzHi+5Yb3j/HjGu7fv8PjwiPVyobeszxFJtOL37UMcPeaQmdq73SS+0WqvtbR7aNLaq+8zz951p5LvyS7eRWnv6OgI5h1hqxRBS6SedFTfxviyD8EgcwJMCH5kyzDfJhlEO8+J3tkFDvYg/POP7oSzL0CEmrlm7u5kAaXSZlECi80xzxreoBoEMH5fMyUb0wD4GEU78yJba6iBCQTBT7pWiZawmg/8ZyOJRuCXRUBV6j4tyTDDYtpB67vrjylHRKTxcB0NE3x1k3Syksd58cSE/vAYGzwG8e4iIAFOFNQYXU/oRbowWUZgSImFoviegz+noeVW7WwU7OkvG/O9HZsx7qvuaBVAY5QBdatRsiD+XoejFvkALAgVDBMejYr52EtcP6pmdL3B09kZIJRCdN2RvyBj6ul7cWiU/axq+7k9tKrmELz+xt31v/bj77gEnwyiaW5N9hPjggKmeWLHV6o7RPDfCjKIKxEe0gWJZKBdhWscF6CUFaVsOFwfcLg+sHIuBXpgxSfBD2z1Td7ANj8M+QOrFbp1BUwzU43jviO9lTe29W5tDIOgKkW10+GIw+EKVuuuiWGFTggqR84dJXJh83sKpoXeomWl+/1TAHCHIiAhYkoHzMdb/OV/+SX+3b/5I/zNL79CwALrYGdWCf3kREiEc8mnxQeXakSH7GJkgjokwRDQlfKM4/UVfvDZp/jn/93/Ca9evsQPf/wj1FJxenygdyG6mxaAN1EHkgiWJCjN0NCRA4uCXgJ6E5+hKuZ8gHbB49179N7w8YuP8er2Ba6vFqaiC01yX7ycATP8f0j7s2ZZsuw8EPvW2oN7RJzhDplZA6oAEgRBwsgWyBcZ9SyZTA/SH5bMZAJNEq1NJqmtARIQgWJVobJyvOMZItx9D0sP39oet7obCbPCIS+yMu85JyLcfe+91re+Yb1wPlk+fEdkowlKSXjcZuSpos8VvW5oZZg2+w5igGqHRINVQkajM+ThqlgLY6ym7u4gGdRzCZmWMHb0B8nQegHef494mnBQgR4PEPkMQT9DjIrt+YLl+QyzDefze+R2QiozoK4DbQ1iQLksKLXikAkvVTdreCqc6/3RP/sj3N/f4/u3b9HlGVs1Nx6gW8o8H1C2C57KGeLuKyEA0zTDLKBVdXlDx7vv3+DyfMbPfvIHOB4OeP36FY6nozsLedipCTZjl+GhCCjrBQYeGIIKVEYBlQZIDwiaoIiYJxqnxxhRSsHHhyeHC4EUEpl7taM3oGwbWl8hoPatbQW9FzyuZ1T3OR2+mwKQoWeGGOCHLACjSwttwjLh1tqZAlD8edOxV/B+/46Rus/2VzM6pNjCdBExWAiANWhrWC8riwtnfw4uvVXCYxqVUF/hBava/NBMMIvYNu4xaBW1V9TWKHkSTz9YyXru0jHFAwOQF6Jc8zzRuqsUCBo6aESfInkH5yc6o6RM3m3pzXXD2AuR4DImeBOhLhLn1q5kNDc+5wTZ6fByczgA84xaF5jV/XAIifBnnrhn1eqRUSEBGFyJjsuS96KHCS4ezWQNXZj7Z0bLxM0q4etOzvbt4YA5J1TwoP/49ITWOpLnZB7u7nEEr2tvDY9PH/nPD8+IOeHu1T3gfIa6FTw9P0NDQHRrzKQJdVuxXC7sMmOm45R1zDEhZGBZmLKRJgrqn8+0eIxKZUJVzhJrvTpG/V6H4HBzICOLnnKtNVyWhSSNw3FnrHUzMsFcVsFqYXiReMXpqmWBZ3FB3FRZYCvxanOKL6vk0YFe51X8kutfG7VdYn4AyvW7dt++vZq6HqK9m2+i1ws0sts6W7HfeWkZv89GxziYSqSE87zuO9Ty+HxB+fZ7/PLXv8Zvf/sVHh4er52Ydzq0NHMNlx9Eo7JmrJMTgDgs9NSDiJSoi0kp4fb2JX70oy9wf3+P4+lEbVJr3om6wbYYCwkxaiL3Vp7X0kan5Z+ZJKdKM+rtGuHEOWXE7c0NZxGdEEYMspOoWutYNrJNawcu64bzuqEkYSp8bwCql3D+Jj5pCwfbre/XadzxoT21/b7zHo1O2jCAHPSGvm3olcWNGOcy1BIG9FSYuK7YuyduQOxAxmszsbvts719Xu0zvuN8QL2pePHiBYJGlI2MW9WLP62shlNO+/My/luM3e35OPQptQCXC969e4dvv/kGN6cjbm5OXDu+/nR0yaMw3R8ln3nbmHuP5XJloYpDb3vH2Dy6qhm6Glow9Npgte+J7ArOaEql5m8pC2rvyJnJ4U6FpWmAGcxI10f1Kry5Bk+u6Rj0y6R3ZPexRQjuokIDWX4oTwNoYwcZLO+dpXjtDsTX/5jjgx+dT4NnzeknWwqfFxKJ6FxlPuvs3p3azi0g65suJgEVFtiNW+tuAKLohe41ohs0eMxRN5Rt4/MqnE3TNIBveOxz6v/bhrOOeRya6N4t7WHTMNdJ0/FndIaMLQr41F5MlFt72DW/zGXsoghNYEYSYnWSXxD3cW7eMY89Fp6L2IgGjevLkY+4vpDXSMKYd0YfSQW/LnFnco/5LYsju3aVgBPRZG9umnH2rP2q7+bz7t02duYIiX8QqJCpG7yRQPudbeR/8esHD8HJY41q5SD6six4fnrCr3/1G5xOR/yhO4cEZVXRWvN5SuAsyAXCrBKTZ+jxITuECR0ZhBkdAuwNfSuwUncT5wEX6KDO70gZtSK1bQAEVQTpExKOAqitY102EjQkIKSInBJKrWRMSUNFg7QCRcdhOiHEgHVdXYdF81lxCyzxLrOUsrvflE6fRRVB0oCPHz/iy69/i7/6z3+D/8f//b/H+ekZT49POJ8XbO3izb4ia0IKAWkShAhPE7Cdrn0zn5BSxOn2CA2CdaWO7+b2DofDjC++eIVXL1/i3/yb/w63t7f47PPPEELAcr7wPqgipYzj4cbnUw3WFjIfu6GURnqyRHZ+jUWKBMGyrrg8P+Pd999jWS6YJnZfW1khIjidbokMJG4i26Vg21Y8PT2ibA3LMz9LbR2X9YKH8wPu0j3y/BrFBFaqzwjbXtqIcdHFyMigZWt8TwCGT6sAaEYfn9Aq0N0gWBXREU16OHqw5vkZ5bzAFEhgd1AKdWTz8QCacQOXdYVdNuYthgjKMwzrRoJIcjcSHo5k/SIocrjB4TDh3//5n+P9hwf81V/9LR4fH1CWC5+NQquxF69fYV0XPDw9QBCRPVEjpMzoprLism24bBv+8i//El/+/a/x8tUL/OznP0PZVtS2uTECdVWDJX41Ze4o9YJaKjo8dilmWO/YNteNmqCjouPsFX3Ftm7YLisgCaIRbavopbo3Zts3mnXhvLFFSg1ubm7ZrbSF5LJS9w3XzHDZVm7s1VnkaeJmakxg2OoCoZeQM1WvkOrYMrpvjOL5gQ2k5teywHpDjpTcZC8Sq5EdPqcTi79Ec+Xt/ASFYI7XFBXOtqh9K+5SYQ6httKAxDfTgsBU2HWUFVuuTMwwzjK3M7fhsl4Iq8cLmIhOz+W1rIAAqUQMSZiqwmJk8+DEvE0Gkkao9ny5IE4JKWSEFBARYKYA4l7YpRTdNYqRSWFKMAUuKxGTFCa+nqdKWKt7IWYmOB4mFrxm7EZDwlIKnpfVXWyMRMAQcLmsWNcNMWYEjSTZrRVBEgQKClsFpWw8gFtl0e6cim4KSEQ+kD17OT+jmWGtNM5mAlDHUj0BBAGrBGwaUGHYmovlu6B5ukaFJ9c3+qtO8wmz6g7dBjdZ2LWov+8hOCrHddvQasWyrKilYp4n5Jx3PH//fv7QPqPprhNh9WM7zg+4BmSvysZMSvcKf/9L42nfXd+3u+h7NaxwjUtrXgk1BDBxu7mF1A4cW7yWBWYePzIurFd/sL3pvM4TWYm1MSzso/IeXWEn1GqG8+WCr7/+Bt9/9x3ev3+HbS0oK0M59xLejO5F+slABT4zcpeKlNjpqadCqHbEEDDPE06nI169eIWXL17i/v4F5sMBI3qI2kEP7RVCQdZBkpNrluAemn1YcvnPXNldG5Ztw+65J0Oz6FQkIXGBMSi2R15VI+FlKUyWrmZYXabSnOaM8cdYfcM3aBv31C/8dULi3eJ4XkaJ788FV013icB1drYjAPikW/zk/4iEvbsbX+z4hhCalW0QwgJkF6s/F6y4FUC0gJubG/QOvHz5AgLDuzffcz7hRBozh5rGbMdZeapjHmb7OljWBY9P6oYSLkA3o0uIjfdO6j5n5ayOg0YgCnri3Me3PpoO+EwZxpw+G/cUzkDEtctto/v59NopjSegIzuQz033udu4gkNLJv4+x9bQO6Ohmg1Nqe8P1sb47XdAgd6vc3vpfPaqd5CtUkrThJ2P76OoRvYnXK4wZunOZsLQzfnlcFTjk33Lf2awsnXnHgx0RkZz7fdgXLtP0SWiDgNJ6m6e0b0opRvO2DtsX/uEJK9FtobhxSz7dRl77bCui26nRgo+ySlQMrLNGPPGQ9AZ2u5q1OEZh10dN2GXmEKCOCpXK9PtoQIEhfWEoMLMRVFUMULe3kGzdxuh2AbrAijHAnu3JuIjMfqaNuse6MtnoXW3G7xuvP4s+FnhUTrd/+d1T/LnfJ/bqfMqPEWmNfQf5sX88CFYnCr/7u17lG3DcrkgTxP+2R//EUKIiMqTvWMnmWHo61qvaPVCnaBGMgBTx4gCqS6LSC5LCOK6D+cGqW9dZYegGEh5mJlr1at595BRWsFyWXgTpCMCyAIa9ra2X1hSpAM3TQDzlJFzwsNjYYp0J/bfBLDAWR2ZX3SEacUXvQt1q9srETbgDfru2+/x//yP/wlv3r3F+XJ2hu3oaMecEtBk0Ew4rhdz+zNBihNiiJiPN8gTNW4dBlH6gL5+/RKvXr7Ev/rjf43buzu8fPkZhsG4QJBihmnfK9ZtKYQrSgeqG3ub0GmkFNS6IYaZbMDzGdu24ePjRzw9P6GLIKQMTcPwOABGf8AhQ6it4LxxdraKYjHgYV24wIJgbRWXrWHtgqIZPVYgz0BZASMEZtYRjPD72HzFgkf8OJzuEFVwgfEOh7QNkOBeo0J7tg6XzghMoxdH1MdpMJ+NUrNlYAdBhoF5aocz7GYWeq0aWqmQUfiJH+JWEKLi89ef4fbmDiKKb77+Fl/99its2BBDQ20bzudnbNvGe6Cg+BoOg9uCbWuIie4fzwuv5bJuO1mkNLqv9G5QL4yyyz+eL9TJHQ8nAMA6sVNAbTBraChQTQhT5ux7rRgknZiSC+WdqRgCFzM1AWDsDTDPE4lUbcBgIJLi6yHlxHuysqiZw8zrNJG5va4Lu7jO36umqA1YW8EguY11NXY/2w8nrt3aqEVUZ2IHt+sa80d2xREIHRrdmEMUPR+5eXq6eesdMZLhPvSAptUhRYUpUxL4u7iZ5yDQRBmCKq0BIYqUvAsV8THRBdY76fw8YQjzkbK4p/LUTm9NDYC1jrpVOhdJgAbgcJgQYkIMEa2QVCSRh13MClUgKzvhqiTKzMcJIQTcODGne9qOKK9DNLLru63oYGQW5UweXqAsmO+7YVk2nM8rg7Kl4/Y4AULYt9eOrTJ4/Hy+oGwFIhtU2x49ZzCiNKajxgWEEWu9V2wriUVVyO7cnCFtfl9TiFz3rWEcsaP7HqoBxAAVPr/DecdEMSdK+WLgXEN22tPveQgOf8E8ZeiYqaTMZGS/uIPqC3zK3qTORjWSzegXOejQjhmCuWO5eIUl1MGgsoo/F1K6Q8y+aFlTtLJyEbbOxRoSVICcEinGvXFWMMp+vRoEGzpqY6hk8C6JNkt837X1HS4AxInQ7lazz4S8UdTBiBzVJl9kXVe8e/8ez8/n3ZmiN66IMPB9GVl3o3CwvbL8HWeRDtfRGfKUcDwd8MWPvsDLFy9xvDkgz5mVVGcV1MVQPca6Y1TXClj7nU1FJUA1IUZuKDEkClFbx7KuWFYG/A4G4hC2t1FQeDIG/0aRE8kbfE3BeioorWNrzTce3hdCT6McB2FzG3ZqfgHHIbh3XF5Wo/s18uPPC7+rxcmYq3yCEhjJG12AauN3Xh2Odj9CL5KaOXPPKenRrbjKRp1bHunZft9U2V2JEZa7v73F0+MTDX0rbf723c43xp2B69X+YAWasyLVv2fcLw2RhnLinaoOe0F+Bhpnj8Jgv8v+HgUxsAilvtEgEj9Zq90F6X69giIMayaD67D4INneN1xfh+9d945SgvAA82QKfIqseBHoPS+G7c+wPjOfVY4uf2/bXY/cceUXCLCvRYcDaKKBSmo8ml9Tf6SM0Lx5MQU3RcDYuywgiNFeL8GTzqmlNRFnM2LvvAbasHeNbtWVokeEeQcXQ4TBINpd16y+/mSfYXexvdsh98KLb3/+w7BLDHztMAIGvADX4J26ayDZ4Rs7RBj1hr7HwEi0s2Z7/NVgEEvk94XA3MR5TuhGnaq4IUdXwGJDtuDdpqGkgK1wb6i9e+FEEg6c3BiM0rIYFb0DeYpXz9LaUMYS9+d47Ic69kE/WPvYD/dOkD84umXYmCuOkGXbn7gf+vph27TOU/Xlq5fYSSVmHg08HnrhcNjo8zny3kLMSEqD2hAZehu84ua5wZlP74xMHyyubdtQt4o3b75FN8Or1587NBhgDViWqyWTpoT5dIMUEw6HE7bacF4LogBJPUpJFNW1NqVsqNsTDodb5DyhlA3r0lhZhYhlW9GtIwX6dnZxONcrmm6uBwx8+BnHogiW9kSGx+dn/P1vfuMbjqLWDaWsCHAfyczsPjNxs2pP7lbZU7VDCJSQiEfYBMXL10d8/vlr/Lt//+9wd3uHoBR7l1qoclSBgb6HA0xkGnj0DXEIIhQhZEz5QFaaNQTNAAJ++813+PDxER8fHvF8fvIZhuBwuIWYYNkWWDfkwDDlKEAIEfMNU0ZK61hOBelwpDbv/SOgF2xtw1IWnLcLYtsQUSEBfG86zsXmuL/tcy3R0bEZROk3OyCs2j55wAUwq45P8qOa64WOQbHBsDpUohp2AhS6V4veEZxX5v+VWtB6w5z4Oc/PZ/TWcR8CNCV0EF7MkfrB3hpCDvjpjz9HLRvmOaO1gmIdJoqgM7QZzM6MXuoros5IyjTxoMEXuUN5Li3pIoh5RhJFDu43uvqs3VGj22mG9Y7n52cXkfsMxPh8HPLE2U7KEChq8cOk8x6MlPfeDZYVSNyADaC3aweW5UJxuadBjPMphuiHKWfGeeZz+/T4BEaZecEQPLnAbJ/tmW/c27bi4t6krdWB/7rbFDt7qCDOFK97pLKf426VBs/4a8Dl3BBCRE00rKA4nqgFDy5FzhnTfKDwvTYfJSTEpAzOhUOs3VA75QZiCbW7JMA36xDcQNq6u8s4AcqJQXPKfl/pCRokeSMRvQjgGKErOQa1G2IQJJ+DQoA0R/fxJawrbi7RG/eqmBjCG9zEnLN2I4wvZPL/ThGJyIy/j2dAKNSPMWKaM0IMSDliniPmKaGUDaVstIeMAeiUP6VpRogRz48nbFvFejlzTugjiI6E3oFlc8KUFyRMg2iYD+RAXJaKtbg8ptF1JsCN3zVgChNnvUZf1NZtvy6UfnjDYGDCRTcK9dERPwn1lX/SIbhjs16/7b6Obf/3bq6nGhXcvimNzqm7powPPsv88TO2/0gb+jRhUO7xeCJM5n6jIQQO+terIFdVyNJChYk6q8r85gND88IDunmBqQ6PNHdc8O8UIVPODOaz3lHzDfPa4W06KngNAdta8P79W3z4+IDffPlb/OIXv3S/VPjMyT9yII5PJMXniAa3qiKMyVlgRAxMvmbIZcY0T/jn//xnePX6NY7HI3LOtP8iDsuiAupdIXyD9MXfNs5fxkbnA2MaOVd014K1Znh+OuNy4WBcRfcYl9FwhZBYNfpszMZuoXLtDGNCzIBdKs7Lhq12iCaElBGnGbJ2tLJd4WE/UNj9+Xxy1Nr+Eb2p4L/6MxTCWHCGXc9p3NwHPt9bQd02J7GEAQ+wPjS3YAvB9U19jEAgKcIQCdVDkPLE7m1/n588BxifnVcpJuYPahCs7zZ/EN0UOiZn5DVIrVApEAApRdRe3Rqro7SG9+8/4JtvvsWrV59hnmdqzsZs0Ujc4bVq15d3j1YZjiTmOqkGdJDcZsrOVUJ0CDKge74mSyRee7pz8NfGyKzK2CvE+j43FLnuDWZwA2fqDK0b1Ntvcz9H6if53OyohyZIOPjr++Hmv5v+kdxzKAY3mL+HFK7ogQFXo3X/7907xLBbGoZ9DfOZHl6ndS/2xgxq6CaDjs8IwDpSVCQIoXZHMgazkixkl4R48jwkosNQGtdL0IgQlPZqYoQ/B6JlLEaCklAiEr3r12vRBhYe9Ovl4StuH/mpyxERgAjAGK6L67Nq1hGS4HRzAMAmJ6juzUrYKyCDBWFGYQx7/qJg6PwCcuJcXfqEFiMmDKUAi/x58jmk/yn16m1aW0MKRIzmaeL+xNgZSBCU3HCaZ2x1w1YLWueohI2T7SbuzKEEYJt3+7ju1TDPsPzhbvCHD8ERwLhtfJCcltvh8zj4RuuuAmqAjWGqcSZR2XWTDvyJkelOSPC04u4snhCZNnH84nOYGbbOGUZKCbUWLOuCEBT3p1sA6mngDa1UQjBKLUxzogoAbKViKwVBFEEicXjb0Lq7u3hUUi8d1htaik6/5iPVHAaaXB+lkXBJihOeHt7ib/727/C3//Xv8H/5P/9fcV4u6GMHwgCe6GwyHbjhdtcvmgEpzZjShJyZ0TiqrpvbE6Z5wuc/+hz3L+7xH/7Df8D9/R1uTke3i+PiLJ7duEOyndcAgU77y+XsCyvQ788PvVILlucF27rhw8MTLpcFb96+w/lyRu9M2AgOczYvJlLmwoHrKHugbVIP7GQoSI7IOKF9XPHmwyPQG0I6IR9ucby/x/rRsF4Wd+g3RFHmbbicZmxm3YWvvJTi+D4gIXKzDoPI4UzoZnCRoXeUiratuDw+I51mnA4nVBiKdbTGzS7GiBQjtspkEw2GLMET1K85k+qHF4RWY0mJMIzYHlVugmtdkaeAf/bPforvv5/x8eMTqjHSJoeOfjjgsqzYlgt639CrAWKY5wnn1VBKxVY5A/zlr36NGCP+/N/9e0xfTNguK9GPyOsyomK6D1HHQUNPRTZfzTrWUnh91uLrA8CAVGF+oHbAC1XFIJQQeDQB8jxzplU2SCfi4dgUTEZ3ZygrWaWlM/NQ2igWmdaiKV0Fz4EMTiDD5IScInKMZIGboNjG4tYZMqUu6L1iLfxn1jHqJaatwvQMiRFmilb47GgQWBAkTV40UDvInLqCrSwIISJLwkD/Q/oEXgRQKXfHnGjMPtxwurldnsPnwU3XxxhBhVFKz2VxKJYxdIc5YyzW3omg8KjHfrCziw0uV2kOFRoO8xE5ZTK7AbS+7dIbwCFaFUyZo5J6oaFByhmGjq2tSEnxo5uXGG5VLMwqi96xdzcSDA2Bfq6JkLqoXjMec0QOgiklWAdibFCxfZxkTrYrlfPtdYybvPNb1w2UbkUv4AW1V5RGhqt14HI543w+Q0IGQsbz04rlUuBCGxIPS8X77pvhKLDY/aC0Df+kmWD3BOXmQ9Suo7WmZrCWtg/uVQQQsi/FWVGqtEPinCoCXkHQBd8ZUrsGTYFBlcaYB8EDiQBrFb1SzEqmFFvhAU2qOyJUhwM4y/A5lbMlrTc+cF6Vjc8yujLS8INDcs2lFbJvzLs+ySGwh+cHvH3zFr/6b7/C1199jcvlTG1QH4nM2Oeq+1Pu1YIYZR8h0fop5UzbIK9bpsOM080JP/7JT/DixQvc3dzgME1kOznU2c03AQmwyEO1F1pTBfjiccbWSBSHsirOMaKnDHRBqx9xOS/8/k+wAx9Jk2kF7D6tDI29wgz77KUTClqXBdtGl4goijlHHsLdYfSowFhIdtXg8QD/xD1m/G7fZEdruM8PwUU8svZGR68ilBJYQykLtIbdVDmIu7jUigouFqax+++G7fOGoToLTq0PIbnFFmcWrbUdcjGHcWIIuL25xfm8eFadk0E0uMbTME/9OgNRdmWhBqhXyugdj4+PePP2HZ6ennB7d7dD8YNJuBeR/r6DkzbEK3mFQRoDSWW/fp9eWXCTUgYXBxh9bLvtjh8SFdp9s+8d1ivQ6l48ilxvA2xs4oLo5hFkYrqJvpIYYcbnSILsSAP/8Dr2zp8LQRE0oVX+N5UAUyZ6ABFzDNS2uWSgNb4RDQFA2E0mBucAzphVh88GY7t12o7B2IWlFFnchDiAA5azRncaXxCsyzwDNOhI2/BDMVzDtQVAQqbOzf1LRYenLZ+1kdbhHNb9eRGNSNoRLFz5Bdbp0YpxCLb9mRGQcEfim3eArlEshdpcZwtgeI7KSKP55F7sz4C/Bp2WIoYcohZKZkhopJWhwTz2bXigwpEXgbqErpeCbrq7K1l3qHRHCxRaffzhCBF8/4bSKER6QA6ULvXeUeNEeVnrHGf5/Rrh0q0yyeOHvn7wEKx1cyixcPG7d6YCe77gMDwOISHngc/ym2IM1NcsF2CaOEfwg3A8BL0RiknThKBKYkK/LvDoc8i2bqgbnc53l3dxC7FAKnCpFW0pMAQOcc2FmsrA1eWyYV1XD1cWQLp3HW5BxTgIbC5+D5FzxZTYfbTK8oRkiQ3fffM9fvXffo3/z//r/4v379/j8vzEhxLdyQbi0M6orH0wLyNqiU43eco4zDNiTOwkBTje3eDlq1f4l3/6r/H65Uu8enGPoIKnpycu2DDx0KsNEsA0995oHK1AaLrDSmOxjCH+lBKO04RgESk0tO0bPD0+o1mFKHcz65y/KOjYrhCaNhsPF+xbquwCZhqWFJwfL1jOZ9TC5I7bwwEpRHomikFygBUAGwBUFkK+wDGgJnjoKvefHWLeA5fFXG/vC22U0TFAOxA7O8NleWIWYe/sIkNCLQtabdhaR9cKREJt/EzDqkxdcyowsKKf88EFwJxLL2d6Hw6mpgow5YTPX79C2TbkiQvbOqAaPTklIIaIbduwrRs0BkiKiLUg1ojWmEjx9u1biCr+5F/+KU63dzjOnCdtnkCxE4f84EkpeE2Q9uKy1YLeN9TG62fmMKERkuzgIajJO6ZGFyPO2ThrEijKpZIZXVaYGzcbDGF0X+NhUIFawBSPAOA6YYGY6yIn6o5pvE3KfE4ROUXUVvinGnoFDoeEKUUs1lB6h2iFmGHOESEKbvKMKIJtXdFaxfP5AjNzX13O+ADZpUE6ihpnUi9PI7PO52dGItQ8T16oC0Jwy0AwcHkUs6Zcx7UTitNA4kwtnFun6PIEf57mlNDcK1NFITFxLNFYBMVhgwgiROgdEp3Q5Nd3mJi3Rkb34M8P6Q3cIWcCUaAu3CejBnRj0jzEECcejtQSB2gMNDCRgJFPyD1dyWoFWMDFDKsGq4blUrBsG26OR6SYULABxnSREBgYDRgzYl032ToNElrr2IRjmeBSqd6NBMaQoFtFN46JUojIecLhcCTvpCuOqboGl0bZuyREDZflgq2SHLMsF9QmaDW5ReHveQiOWV4MQ7juJZ8fBFN227ReodJhrbDiCle2JGd8AUOnNEIrhwfhZaHI93JZOFx3CC6GwRxl1RxigA6/R237ReXmrBAEBAVymvhaEr178UpDdWe55hwRoqJWtuajOh7uNakP3RoPyeBziSbEdrsB5/OKX/3q1/jNb77Eu/cf8Px8xhVv6v4bvQPQuOu6ONfuO9wRnBhRXSqRUkDOET//g5/iiy++wP39HebDgZtXM7c/8jRrFoC7lylsgLA8OBSywyMsBDhnvGwbnp7f4vy8YLkUfHx8wLZtsOCsTfVih+xrmN+v4SA0NHmskMdzAddZgo4ZrftzAqRMmcJWaX4NTewidGS9+ZK2wci1/QAPzkCWPmj0nU4jXlSMD21DeydMElHSLNHWQqi8dzROxkCnorgXJ63zuYo+s6EBdiH05QiDAai9QtEcivbiZlSyAGoXIEScbm9w83yL29tbnOWC7fKEoacTVcScUXsHiufsGa97iJ4o1YFSN6zrxeOJCkKYkFJE7xHdGkZOZfAKfYTsdtc5inGNDC/YIK6v8sed1TZNmx1z9DnX+LuGVsEDr3G0YS6noN+uUYjszyLgv39/MAA4QsMZEotVAf99yFO8woF2RbKEEIAuhhQYQUYpA2UC2gNiHJCfr9WU9xzH3vsemiteBI8uIMIgaghRAFOY5ytKUHdt8r6t930WaXwAqXO0vuvkNESiTLGh++VrsLFYPLnDoISXoIlF0lYKtHdoqjtZJwRBTMGJcdhhT+vVf6Z7krz6fNUcXXL4ehCHso9wgu/d1d2DIr+TBViHqnvGeuHa4Jl/EC9QzM16RkweyUNYVur/urhcK2FdC5bFwwb2zlHA2TnROGn+TAktFkMEtHEkVLeC4ftqQv1wigDmiY+jMVw85AR0oiorFhRHGbNLVAyGy3pACIKtkPNwmOj9Ss/WwXL/X/76YTi0s8qcMj3d0By6AjfE4zRR89I6rHZYWYEUoTF5lcJFEBMp+NbVIYPKhAYBnp7OTFb2N/ryxQtMOePovnzwYyWlgNoCemlocPG1jM4KzEZTVpvBQ0xZbVU0D/cNcfbukBvBsO8ZVbVGYt6h+QKqKwAjtAKAjyUjYR6envGf/8vf4Mvf/Bbffvv9blVGiMipu+AgO6VMfZqLSsVIVQ6SEAP1QFstMOu4v7vB3e0J/+pf/gl++tM/wO39C4QQ0WtFBxAjUwNEh/WSQ63+UA9CDzo33KCCkCJCSpjnGfM84zd//y2++fotPn54xPl82avpOAeo5+pZEITMDqlXXHdPiM/LuH8ZDOLo3oB7rXagdUivCAFIU4KJ4LxVJAiyJB6A+kmKRvCDtBrwSeBr8KR0qZ+SsAQSx+sNR/7gm70fqoHFWr0saAeaPVQFNqopmFfnl65uBVstOE0JGhPWlXBPnlgYebOJ2knoCYh8z4OB59VzMQFCwt2LF7isG169egWx9/jw7sP1EAyetdkasK7edZMlHCIoQWzAtq1YlieU7YJeV8Rwj5wn6ko7mcYiCjgiUNzcgA5KFOUP8hcAGombAIObJmQOmtXdhQdiu/8vzNBLR1kNzarPvwCY+7nCsBXmBk7zzBHH72LYHoMT6bULkphCiMhhQmsbynb2sNiOANd98dIiJZJTkOioJFUcqfCiz2c/KYe9UK+t4XwpXrR0P8p9BglDUMGUBp6piIG+lM2LAT5O1MZBgC4BIgG9FbLYM1xaRE6AdEFTw7pyH6HswrCVs88qC4JGzOkA64bLwvmjhgmtVyyb5442slJlMFMBWOd8dT2v2JaCfJh5GHixOw7BECM0KI7HjBAEZeUY4nx2NEtJpJnyxBNJmhNLXM7QNgxyG/1BFSkk5JB8udNdqmzVj4uAkAImDXj/9iOWy4bD8YiUEkYI9jDDqM0lLU7cydO8Px61diw98JmV+omrV8CUIupWUdZC+VbInHUbjelFDJqGPIWvUeoJU04s5jvNSGptWNcVqzOD/6GvHzwEx4VulY4HhH0Uw5FdgiIaYHVCF0b2dDOspTACIyVIb4BVVoQhoqE6DMDD6+Y0Y87RKzYeuHsCvbo7iVexmiJevHrp7EzArBM+MVYa18reEBW7e3431+AMnY9Q3mFKKXZvbRfU7hleAOY5Q1XcG9RgnVqyy/kRb757h1/98td48+YdnxUR30hoUC3epXLYTNaaRo+JEbovBGW7n/KEORwQguJnP/sRXr68w93dDaY5XVmwbkZcvbUPTqPfi25w3hFdNxmUMO6cEzS6+TYE60ooodWyQ1DNGLMklaSSEB0OtL6zMp2C6nA4K70GzlJrN8Aaum2o24ZtW1A9TTyljLvbe7S+4ts3b3E7JdzNCdmrV1nhxgekNgsEUQIKBumAHSIdMnDtOm3on0auIA+9VitF/kHQWsGHD+9hUXF4dQvkI3TKiClSuDtSJKrPkJxQlA8zMts2n034pugHQffDvu9rxIsxJTwcETDNE168uEctDTm/A1B2T0gV3YORmzW0XmkWoQlBG5p2bFvB09MFv/3qG4SYMJ2OuE8Bvft8bpDMmj/fjTMSvyrwgtwjyAANitaAUs1ZqA3WKx0/ZNxe2dc75/hh957sFkDHJeHPGUX0vExu+Bx5vQbqAWd1T5mH4IBkSy00Ut82aJwQMyN61HxeHAYCNGQzPpeSxK6sGSwEWFC3VwTnu6Fj1ul6yLeO5cJMO3bvHaVu7pCSocoNdbA9DYYy5uLC+XprHbVXdDRsy4oqBbk1aIh0mmrAshBel7H+s+6chtYbluVM+n/iKKhVxmBNKdPwRZm2sfXq872GEEmQkxgRp4CYJ8SUdvMrrnlyClQFpVaU0n0WZt6ZgVFdRhhzjNZYDCSMYGP+PfYZHyw42UzQKu8X8xcJNWvjOwghUNgfqBhYt8JipfFzdLBggXnhUvlatfg99H1VYL53xZ2oNQwluMZZ0JgIQnJXu37lEpgZDnNGSgGtzYDRdaf3jqjxn9YJcq1f2U5hmrxVZwUsUaBGKKFogfWNFlqlYopMiJaqQCckQK1JGzpZiAB3N0d/JXaPrTmzzKuHmHytG6Ap4dXnr2Gd/p30p2QYYzUuFArhXYTaOqnl3lhoil69O7VMmBtWakWvdNcQGwRLxe0tvUTPLnwH6ODw4d0DvvvmDf7u7/4bLufL3pkRMgTZgz5TgipMzSny6tBo3GHQPM3I04zTPGOeJvyLf/5H+OzzV7h/cUeXeoeSeyfEWN2Kzr2D9s2L0LMgRfdjFCBPGcfTkdCfRixLweW8omycK7RWUOrmnQCvlQkh5RAGxx57mzdCOINv/VSusapEb0DdULeVfph12w/BF/cv8fTwDb769nt88eIWc7pHikBIEX0zdxEiiSe43VEx/nbpXCTJQ0JrHaQSh6wlYMTIGJyNHAI0k0384f1bIClOz/fICJjnO+SckeeZc7ltQ9CIKJyRSKCrfwgBdePBwtmgYS0kGfXA0mMQssMQhfusOUrEYZ7x+vUrlK0h5wPM+JzxbOB9wizYygIrjdhzF6gUqFZmzzXDb778Cltt+MnPf4bDzZHXuXeYpR1KZeTVFdWAcMYjqtDUdllAUz9YWkdpxcXpHCuM+SIMe6iy2LB2UwQYSHzjXBJmOOQZEEJQRP6i6/DYvQ9v0ynxvXYjJHjZFhqcbwVJI2JgwRiMs31JAa2ye7fqiNScyepchrSJms+QZ6gqNqMO8XDwRAbztHTvDKgd5edCTJhTduKL63Rt+Gri+vPNiWVCn9vWCtAJn4cYAM0wE5zPzyhb2ZuF+9f3LjUYNoRnGNQ7OUFtlRaIU/b5q3HWVwqdtnrFNGXMOkFCRo4JORMxGnZgY4MOic/m+fmBRvBj/OWz4WXlAb08LxANiMcj5inRO9QEVkFotHQW7F40tc4ItFqAZd2wlgumlBBj2tHzeZoQc0JxucK6kdW51XUnO6rLp4a5Q2ucpSuA5IWaqCJqxKQR3RzlC8I17/etiaD5IRjZ6QCdmaS9A8d58jMj+BiFl+HV/csriez3OQSDjuw1HoJbKRhO7SIgDGrcnM0If0gn3bptBSsuZDL1hl4d/zUGpA7txojGhfqExzucUcm0NuIdfbP3DjFKhNKLCWYMoRX//TGycyjNISHXyOUY9tcTY5cZNQKpowflcNmYOCCq2MoGbMDHjw+wbjgcTjifL/jFL36BL7/8kp9gaOQ6oNbptPQpY7UTVrIQAIl00VEOfaMGzDlhPmS8ePECN6cTfvTjn+D1568QQ0avhmI0rVYT6iS3jdDC4QCIuBsNV0SrDct6RkoJx8MBUYe1HRf1tm54fHrGw+MzHh/PWNZ1z6ejYYBSJ9cqHTgisKfXOu7Pb/ZDwIfQtRQa9NbNB9a83mIkHW1lxfmy4OHxGS9ujtCY2F0NoXqgDZM1QnIskAKCKOFewCt5CmSvXRh3bRGSsLoBpRFOtaqQGBCTAJ2RO+nQMM0BBjrj9MLUBHVLujRlpJwQRHc2KUR39qD04SEqvlGQbBRlggiQnAi1tYo4ZfzhH/0cMWZ8+817fPzwEefnC0zNnWQMMSi2jZ2XGFmGMQaYEeISNSzLGU9Pj0AHoiasffNr68J1N0Zkaj11b2YdZSUs6iIq3i8bzv2s+mPAPnuHOqvW0YtcE6wlWFdcLmfUUvaudxy09A8OMPej1KEfdQu00htiTJimjLJteHj4iFJoCZdUkIfOshu6eXiyJqh01LKi1Q1l6+5EUmAh8FAzg8zZk+HJVG+lObFKBooHgeF0c+KatjEj9GdOqGWrte9wd6u0uSNCL4jC2bOERvje973eQes9j3m7v7uBddqNmY+LWif6CB+zQGTXuEJp2XdZLxSoT4T71CICSOopW8GH80ccj3eY5oTNiSA73cbh4IzEUSQGy9Kz+YRknSnPsGSY89FRDCOhaKn7emHeIzjvGcG/vUKNxQG1eYpta6ilOwdD0esFotdiUFzqsJUVBprHq6p7p7omt3fUsnEFuTFIDvy5WlaQrNh9xxGUVlG3BkuGHr3I1Ije+P4TUVKiDIOgx56cz3z/x1SC/+ghGF2cS0PkrVBYGoNX34PN538kKKSS/NE2inNp4Gu7DlCVdPOxNLnw9Gqz5AL80fK3Tqw+wltrH2CHEDiLEtKj6wbsmp3gZBNx5/TeMAIf1Qk+YkILITFIMpjPDa13pMxDcDCQHj0GKYSE5+dn/PKXv8LX33zDVj5wNidg7IeMz+RzCzRjllkWHoJ+8EYNSCFgygmHOePFizvc37/A5z/6MV5/9gpGNjpQVwDU7Y1humqA3hBWJbEBXgg0XC7PEDsiHm8QJDibkQt3Wwsen57x9HTG4+OzC7QboVpVpCRIAairu45o2M89OlWQgYjgTMNOnVotFdYKevFDcBewsgjZyoZlWfD0dEEpHRoSWOJX34AVaMEhVt/cHYqswvuqHdgB+p3q7YXPmOd2w1YZVmelIWhHGBq+y4pTb8g5YCkdy1JZRDWjXVjiARhT5ObVbTdH2CHGzlnnoI735sbdynDT4JX/pVWknPDzn/8MQMAvf/k1NzD7zg/R7uuIrMRa2BkEd5BBjOzwhSy3/RCUhLPPRYMbxZNIoZ4moZzf9YZtW3wF8aBozZ2mR4kMks9CIGwg4LoeLPDi3VbvgvXyBHO7wdEoMUUkQzX69/UrcmQNrTeUjTN1U8HWCj58eMf5Umk4ThOmG7JIOT/raFa5XhDRCp2WylbdzrChh4DuxuIsMl06ALseghiQPdGc4+mIAEEGi+vWxTvdwbZ0T1llEbkuK5nlAqa8hABFh7pTC89sATpnqiKCF7e3CCHg6emM0hrWRlef4KQ1jUNU788RgK00LNsFWSZknXgPJSEKEFWxnN/h8eMjQpiRJ0MtfTftgNBajYJ+Qvjm/71h6CaJYOQ4QZTuRr03XC60o1xXylxKvyIrvXW0rdCLua6YYsQU4z6HL04wC5KgYliNej3ZGxOmAhWfFafMEVQBr++U4/UQ3NG4QL6IGWrdoEKDg2FObq1jK9s+qmC95mQ5yB6avRbatpk7PIyD74eBUH79I2J5r/4LfANmV9JiphNToq6pF4oeTak7CXnCYPgF9Y3TOrZekUyhXdHFK7CUgUC9T3O9D2G06Lg+D6/VB9farn6LLDvFF7lTmYXU3oIGmNIerVZnsnW0tiGEDLFADWOtPvjvEIoL6TxoHi7aOmJKqKXi66++wZs3b/DL//ZLvHv/HtXTNbjYuWmJCsxTuOGzBs4FWb2lRHgIxuolpoDDIePzLz7D688+AyA4nxfkmFzTxPlOM4OJ4nA4kn0bCOXGcbdNMIcZMSXMOeN0OnBDV0o7WjVcns94ePcO23qGBqMjBuAVLx+e7tUrKWLdh+s8DGlQ7nNUd2RpzfmcAgZxKiOrYkyY8ozj8YS7+3tczu+hOQIBbu0EZ16ycwAotN/NBNytg0xU3UNkXbHp/98wutxhn0UnFH6ndc40aqHTUKmc7ZRtw+W8QCxATZGmiBQiv2crSELP0OFq5BcYIVFO0OGaVIeBlsuZtQEIiddSIEbnkBQSXr14jfPDM6t062jC2eBg+7XWacIAoggBgS4ZreP5mSb0796+x+3NLQ9/71q4UXNT7e6MQUGz4DDQTaHsqK40pu+gI81WCvIUEENw6ItwIGPnApJy5mVmON3cIOfJmc1GuAt2ZWh65Nr4+24RtdGgoreOy9MZrXXc3t5j2woul5VhwYmOKltZyWxUsmJLLTD3rJwOCTMEliO6Ch4fHlFrQasrqhrWhZ8rxQRxGz6YoW80ad6WC6IGnGZGLAWHZqXyADOff/TO5POtbJiPM6bD7MgSkKeEkITwbOu4uKHEzc0Nckw7h8GMTFpUUnzNO1PrLJZC4EG0LgtlIj7/a2PEAYxeG8fTCSElTNMBKoYmLBSWC51Rbm5P0Jywtg3SvJCAYspHNzzgOii9caRgDQogx4Tm4vhWG82rvekoW2FSUKsoraLnDOTsTH7uI70aer9gMLUHAUkAJFlYuILd6Lp1H2lF14yTubksK1Rpds08Re4Hrqjxnqqjg7ZyDj3QeL3TXq80ul3BAhEI61AzDFnVKN7DOCd+4OsfPwTHzXGqKYXuCouKmAj3cdA7SASOK3c+EKPzK3V1iyIyqPbYosxqqfW25xYCw+V9UIL7bnQdmuzVwBB7wuDaQnfRADmDIQamPBvJG6Mr1M6YkN7aLubv5kkGwE44qbWiN7b1tTa8efMG3379Lb767dd4en7yB6mirK7NiwqxAJdJ8vMJoE5uUddOjkzD2jpiipjmCS9f3eOzz1/z4Vk3mvjGwRVzqF+Yai+qtDTCcPEBZ56acDzSeWOesuPwToWuHetlwdPHB5RthWhHAIux4CALfncEyI3Ov0eEYSns1h1aagW9cWPd7Y1FPB2bzLtpOuB4usF0mBESN2hzSQA7O7dz84d1h8MNTjbxgmdYb+0PdPeDcMhZeH0kiM9/vPJvZKJthRrTbg2lrliXM9Qz7UIisaNuPidLicbbvV+9JoQsWxWBdLBgEm6ebS1elSa/1tUXN2n197f3eDvPgM85rYP2c/s8qCNEz88TJ0oYqemXZUUIFzw8PODjhwccTweEGHYLtOwruHlkmARuCnGwbZWSD2nK7+kV1itKbYiZxIu+9d1yKkj37M9IZrAZpsMBKWbfpQy6KYtGJ83E/Rm8Ak+08eL+sFxW96A9IQQ62MREQTrndBuipzq07oG+ykDonDJJXZHz72fpJMf0itYE54WdzXz3AjEEVFB/xxFMxbosiDEhBxJLYvBRiEtfBnyLxvtWasExHHE8HNBKgbWGnBJiDigGVKvY1hWlNNycbjxP1Q0czAAjKxrOBubexKInaIC1tkO6yr9Ea9T0qRcVEMF0OOBwPLr0Y+xtDeu2YlsrDjdHJFU6olj3tBtFTjPJhMafq67oH36nMQagAsXRgfIJc3JZV5zP5z1UWTrNSkbw+Cimt7Ki+nyUeyo/e/Ji2oLD4koYdOpEW5rbnS3riqgBUSKiOtELDObuAmgflnZujTZ2QYOHGXNu2lpDcNh7bNqjYR8ktQEf/9DXDx6CcwjU3aQICwHJoafo4uBaCm96IJU4aPKHmMGlGoMLcG23N9MIQBUhOWRZC2qrCIGOHLWwKkhuSWW+vmKcuNHsPSA/dPfNMofqz+CgR7NF9sxdCuoBkEhQOFg12vwQMRQkRIgJ1m0lnAEyjN58+y3evXuP//Sf/nu8e/ceHx8/YttWtLpyo8vmcGLY2aDqcA3ARZdyxpwOCGECJCGliDwJ/vzP/xx/+qd/gpcvX2KeD9g2Qpuzzzwacd4x5IAGwjfRaxwNfNhLIyV5KxtKTISvt4bLUnB+XvD0+Ix3H97icbmQhMK2GjQyb2jGjWnERwkiszJhuLh8w3OhCIG1jvNloXfqwyPOz0/47tsvMR/u8fmP/yUOhwNevrjH/e0NTocZL+9e4Cdf/AR3t7cIIcPMo1cQ6Ak5MsTHCewn8pgeD7eaXYP6Cc7x6SOuPiMQkhsZMa2sbnpvKGuBNUAl4pCPOKQZmodPakRH2PVg1lzIT/wPKtTLVunoagghIwRDmr0gC7wu28ICbikFIUf85Gc/xvsPb8hMNk41dWe5EuojWnmF3MaIYUTt/PrXf491XfHn/+6/w83tESbdiVD+jGkGhJT51huWixtGh+5rtCAGQUREmiKONwfESB9ItQ1qxV1a5FpGd/DAN24o1W0F4RGdtRRuVs7yG/Nc1YBaG8rqpBRbeaDNXMOHww0LOfWOd1sga4OgsCvPCZXnNWrt0MDipaMjyIzjPOOQblgMCA+88rzCQsB8PAIKhKxANtye7jh3mkji6U7SmKaMCYaTscNVMxzLATel4HRzwvF04OerFRpdkjSRnPdSI6wZ5mmGqOKyXABwH4kIaD36+IT7weS6t7qt6L0hTVfjcQNgtUJj9AOV3Tl8rjqKIhibidubG/SjIakAtdBqzgsuALtUOWiEmrmB/rVfy4HC/wJBDhFhPqG1TqhTDT273hSEZQFBWenfWYtbVHayqouPvGrv1EpW54g4WVjczOR4nGEALssGA7WaKoIcLkgp4ObwgBASYpw9t1Cxbgsu64WpRTkjxYYYL3Tjci6KGB3CmjVozIyWcpZ0bQBUCAfLP+EQTErbsRrDboUz4j04d2gOWVH4zYBb3yx1+Mw5VFP45rvPWSI4NN1ciHrwTX94agaHi1RZB6gyxThC/QAkGw7VoNIQA8kx1dsImigYttoRU0BMfLDIbm2OnV91LJzpBIgJ2soOMaYMoOPh40d8//13+K9/+1/x+PBIPN01iOaHaAig2/wQp8uIWHFRc4w0yRba/8QoiFHxhz//Of7sz/4Mw+T6+bmjVSBlDrwJq5ClJgBEO4b8QgRXa83OzeDaTSvO54KHjwseHx7w/v0HXNZnLGXzwGjKVEy7Q5DwvFv95Coz8LVUzpkEtOAS5SxlW1dcLis+vn+PDx/f41e//CVevf4JfvTTf42cBTgecZhn5BhxPBxwf/cCx3mCKpGC5jNaIMCku+Zz9HpjIx69Hvbnz8SAJtgtzjDGGq4B9ZqBRuh+CIp3xIW0e5WIHDMrfmno4o4XCohrkkhL3F8Zu+WfENZUjbtLDGcWdOjv7SrSDTHg5at7nG6OjpYMn0iHcc2Ltl0KIJ+UvRSJt9rpICMGyL/FNE/0JIW4ZZfsB3VMEVZdtgJD8k6HaSU86DWwAwQc1QlADwxtDiFQEtBdUNmv1bW1q/heQL/f1jvKxsMQHhkUA5GTVrhZWhuU/wh1F5ARstt6x1bVsz8rToGHdOt9t0yTSp/Pbg3TzASDqBlBAiI2GiuvFRIM6RAgQWGRM8HDPLN7DwHNOrZaOI9NJF6FAUOLINaG3BqmecI0T2hB0RodVzoY1G1mmAJnXybUy5a67s9DEIUW/vcQo0tEJs7GlwVA37tedSeVrRTqR31OHILCGtDR9mdbhIfXPDFarru8JakzmWvdkSeIm2nAEDzIuDdj2Lg6DwJAFEVMERUVvRiiRuRAhGEUYiP7dVsXSqtckmNmWDYejJwfG5aVBg1w+ZtIQAiK4oSsDx+fICI4HGe4nBcxKh7PC1KccDg0dogh4ryc8fT8gOPxhONBMMWCPMI37GpVN4IXJmXxhU4bvWqckcTouunf9xBcC4klZDaNjUbQXSwtOrLg1IfOFSKGKUUG8m4OYUZh7H0z1Eqct9QKVUHMGepzCc40PJ4GPoeshbEbzx8RVHF7vHVyixMilLBXrZRLLKUiWcCkEaIJ80wrNFFD1ISognVdUNbVIQr6DRqoV7POKKUYInLO2ErFf/27v8ZXX32NdTnDjIy3cV0gHRIaK5jIjiImmvWyoqYINEY+ECEpYlTcv7jDzc0N0jSj1o6Hxw9YtxVoM0Qi5iMXkObscHDxh9whk+EBqLQXiyHg4fmCv//Nb6hDSxnbWrGcSVZZ14KGOmbRQADv6z5k58ZmnfIEMTLHWjf6oVqjk7wvxm0r+Obb7/Dhw0f83d/+HS7nZ3z88BbTdI8QI6aU8eruHkEMX/7mS5TtCb03HI4nfPHFF1g+fo/18R3QKoOGfR6rDpNSyG1oDhFl34xrv3pnGuBSDsKfgPlcwCtiM2il/pFSP4FqRM7ijhP5ehh1Q0wJIQaUykVN7Z5iKdyAR+JIbSQjJSeSuIkreqMbRwwBCO4xigiVGVNM/t7oRtJGIQZjnI17N5ItyTqyl44+U7w9H2Ycj0dsa8H56YIwcc5iOppng1nD5Uwrw5wSemtYt4Wepp4bWEpB7IZkAWtZsayclZsYNNLHdnvefI3QbUeNM3KYOTvSbdNi9KJV/BD8JLdSFfk4EQrsvk+0jlpXXJYFJhEmmYiMuHF8PnCmvwoEFH+HXRMsgDWUQm7C2zfv0Vrz1BXF7SnR/NkLiFYbuna6WWmgcT4ISY6DpaPTY1a5RtVHBLCGWlYe6DGhlu6IgMOS5DMhRhZlXWlWHVzPlSTtDkUQZ0t2cwavd2WjYxPFJDzYCmEgKDjzpYUdX7U5N4IkNi9SvYjgi7OgjG4UslbmgT4+PnHG7oQnpEwtsJGpWdxQYXACrDWfNAwjEsqPQhCYREiPzpVoaFbpGypG9EXp0rXW1eUm3P/PC0lViOwMuQHRs7euFc+PF2gISOmB+25MRIrQoWuByIomHZv2Kys2Tnymy0ZHHVHE2MgIR0Q1En/WbcE/CQ6tdRgqf1KdCq4GuyoY/GJzZmVwY9zaGmotZJJ6nhiENG1zbBgCaEz0LXRoM3p8SHWT3too7n4+PyNowJxnJ4w4BCmezODVRnU6butAisoDSzizUrdI6sbDmBZM7i4jgmrFyQHO5kp0Ovnu++/w3fffUfvmr2vmJATRkZkKmiwrWX+8UO6E4KHCXuVpUBxOR9y9uPN4HbpJXC7P1Bi6a8dgPTJ70PE/Cfs93cm/ThQqpeL9+w8+H4xohfAf7w8PPo3+XsO4yx69Mw5Bgwdemkfj9F0vBfMD2IBeGx4eH/H+/Qd8+/U32NYFtVKDKMrMtrvDCZflCR8+vIFIgYoh54zb23u0y0csneQB8UN37wBHFSq209f38fY4SMYU0fVHY6IujgSMv0fv+zyEPSM3QnU4nr9yMIdp6EBixuhSr+SV7lKh7qQK0bgfDnz+x+0g3JPUCAlJJItvkEeGbaC7uUQ3Lt6tCQ1gupFTvoW+ljEltFJRto1WXML7Jb42WSyN3LzkzGkK62MI3FgaafZRDHWruFxWCpODM7ZFUbthLQ3buqKWggTvUj5hC8OwIyiilB6Rw0Vi05ihinVopwFFLwZrjQxeabvtIWO76PgE2M661RElJIAIjRuYO9jw4f1HbNuGm7tbTFPC6RB2noEZoW9AUTuT3sfbHjiD7ffd4W6HgQfBpVVDzAkaAtQdpEau6CAnafD37s9f9EMwdI5RtlYwUtwBuETCvOBz+FuYStIcOeiqZMH6uhvuVkPeEcCCfphFjJkc9yAnuIHFYqkF22V18XlA7wHVyXbjOWOgsb+fgT6Mobx0n13b/llNARTZ1yDddXxleaHVXLI2DMV7r9AQcLw7YeQijv2+lobzeYWIo2khUsMZabdZSkUIFYaKinYlGQrtAmslghBaAQSIYSLi4tKqWr2R+4GvH/YO7dcQWfiiNVI2/abSdaPVlWJXcZlBNZRWUHplkrtvVmHOu9u9+QbYuqGuBbNOiEITbM66WBW3AvQqyGlm1a+KrVY8PHyEgNIBgWIkN5/mzBu5bagGMj2DQIKiuKZIIh1LeiGxJanudlvstnhSRESgAt9+94TvvntEXb0KtkdgPBjujcgL5p2yx4XBBFkJg04hIAow54j5MONnP/0CP/3ZT3B/d4CCnaRqgGmHSUVHRYcgeNWYk7O9wM1xq66PS8TzHz+csZyfcMqRQaWXB0JsRoZujMwCNO0e3qt7WjWNB+hGLxA4Yx3mWrQpT/smYt3wvF7w8PiEN19/hw8fPmDbVnRRpJsvEI+3ULlgmie8+tER798vePeRs57p+Brz3Wc4vPgMzx8/AHgPwRlAwUi+5h93/EdHGpDMHhVUKU7vnHFoAwyK2rKXBD7TsI6uQJOGlANu7u4Q84RSisOk6kn3DVGYLrGVjtI3QvjjMAKt6awL2b+7jtHtxJowgscM1en7JEEpkAJUKqJe0PsKoDiqQsstGH0o4zQRWqwNYoQpW/fDoBegBTw9vcM0AU/nB+RDwnQ8QkRRKjVX/H00O6+14Xl5hIaAw+EWI7lAWoX1Fa11PKzPqJ68UEtH8/kxZ5+KIBM0NKgJaq2o3k1EP7B673h4fEBtjdl1QvhOVTDPBx7OK23OYuZ7a6lB1o0zd9vQbFjesaiuopimDE37AnJDeqa6QJgZ3yXgxcsvYGZIRxbRG4C2CXC+QAXotZBoI4oYOqZOiweMrtQEtTdc9lzHFWkKyFPcc0ZLB9rGzxRFYOrMY9dFR+TdcBrATvqD8pDMyrm6Cskcy9N531tDTIjT7F0/Z3wpc7ZXStulNFETzTUopXMZi2D1MZKGyMOnGawYHurF34OzUulRhSgR0oHtvPjhwzXCNQCXr3UcT5FzXutewBNzap2duPVG4lEFVCcEmZBdNxxVURvHJbVFrOvCw1x4jtRtQyuCuq6DQ4RSG86XbS9IUp5wOARoaAixIFdD3gyRnGz3H0246R050m+5N+D5iSHBjBITyEzf33+cFvOPeYd6Ba1jA9yhpr6zvwDi/tR9UIjcPeai+0OH3nfYM3hUiTVSbJet+MFCiME8noaxRsDwlowhOtxBqGwtKzuSNkgpCRpdbNwaLXk6B/nqDFL6jWLfiKh/dCE06BjPoFgXgzY+kOfnFedzIRnDgN4HGzQ4tdu7o47RnwBu+qNKMSixbsVhmnA6HfHixR0+e/0S05QA140F18FIMOz2Qd4F6fBm9OqqucVbCIZeG9b1gtYKclTOUho7ggEZh+DzP6Ggn1wgcagYGPECvA/eOfpASJydVH0mcFlXLOvq7Fl29KIRcToipAmqDSF0pEmggbBHjjPSfEKaj0jzASFOEKo//QA0/6dgUK4Bwt4mcJtfFgV6VZl+Yuumfu1t7wO7kVouQZHnmWJd4wx0OHVQPH1NIGf3NapVP1bH4by/z7EeHJLuIPxVvVvngBldydCh/rTiSl0TDGsiJqGE3edTHHkx80/SO9ArynbBsp7p8NPG1YB3dobo3qmccBE9iQZcU8nZZYkwfXyrlRCjm2+31ndW4DzPhIolAOKgbeezQ6Mi7ghlo+xEJnGAoMNAja2YQOGxZU70gNk+g2OuXPPPOvLiBT0FQJJ3dPa7//T7aiKY5sw9KPMZZkA051QqgPQG1Q7bKnoEglbfT5q/N/XPXF03DIhm5BxccsXOs2v3eCVB88Koe5eOrL42XC/t3cfopNjdG0yptxxpNZTHmI9h2BKpm3iXWtBdjzfirwZSBaHJ9giM9YeSr0m1BbaNKEaMHmsH73DhhYjbqlXXcHfXGcPIvwiqaH5ADVeUEHym3HauNLtJIZEuDqazk7tac69lR2nGuhwxYA0uZejs5EqtaI3XvFkAYkPoQOiGZoraBAp2wbkHxN6hWpklOubOTqiUzs+bZCbEOs6RH/j64ZngSj2HBNLy05Rg3fbNb60VCkNy5X8ItKpah2+kClIidls6A1DL1lB9I1bwcLMAsnwqyTPUS3F4PM+Ta7J8AzFFDBNev/4CgxgAPnJk3hkQp4h0mlBNUOBOGU4pNxUs5xVlW6E+eI4TD5kBlhwOE2rr+H//D/8ZX/72G5wX5nftm58QWp2nmTosgMw/5YammpFjYFtvfABfv3yFLz7/HH/6Z/8a/+yP/xgvXt7idDOjm2DZCg7HIw7HI0LMEOWge1s2SE5QEcZadXNfPkM30qBLgR9+gEVgSgGwjNaOhH+HS44JmhV0NGxtxVIqZxtyTdIurtV0J0e3Kav48u9/i6fHJ3z5my9Ra8Pd/R3u7+/xf/w//R/w+PSEv/iPf4HzpeKyZLy8fYG700tsl4a/+h/+mmzVteKLH93hj//kT/HjV/c4HU84nI6Ybk4wu7CDhq9p9UrDhtcdF1LwDWjoBZng3FB7AaR77I+gjYKm837TwD1iOkxImezOpDR2HnlzAwy6nM+kv1cSQ0aK9/i+5LBZiJyRBy+uamVayHSYuIG6q496l/1wecayrpgPNyi1cQ5jRCU0CqYwoZaCUjtypmNRyhHdElotWC4Ny+WMdZlxPN3g7u4lzDgb29YLAOCQZ9eiKSQk3Mktaq149+Y9mZnThFYL1uUZIQZMU0S3gNYVoQXEGlE7iQ1qhratDP1t9BxttaOcn/YuiDpB+tMWd5K6uTnRaWkZMBjJNdWJQr3SdCPPB0hr6FtzBqW7ZhtgIaL67LK2jXPoILBagd4QckbQgGXhs4ql+0HJIiM1zkrTMIl44r1I8dHh8VFUEl05HGf0SsuypS54Ol+T2kNKkBAxdbr7XC4rtq3g/du32NYVP/rxTzEdZkYHmWHywOduJAzGzK6keYGQ8oSgJMoYgFYoaB/+riJw8XugbaBLGFptA61FPBxoSq5E3bb1AjO44Bz7/iDefTmbh5IGY08tyjUAKLolDOnYgHpZr3SkmJGnDOsr0Djz0RqhR0XLjYbbRv1kt46YOhA6bLCItfHwitT/XtbVi1CuN4YGs2AzG4YeK2rZqJeNQyJnyDEjRQY5iJivQT4X1ju1zhDMhwNCiLD3pKiqfkpu+z0Owe7sONZ3o4K5OkP0AZcqPxREvHXmthKUhI0UIo1dGysm6wan4WHQVxnkyMp3RNMISHxQA11KOtzoNWCeglc43Du7EVaAzwRTUvfa9JvbOe/oRtFmrQ1B6fXJm3/Vp3EQ3/HmzVt89/33/jt8ke4flXo480MWcJhL6QoTNCGl5DRzw/FwwMuXL/DjH/0Yf/jznyNNihAVz+cL1q0iJ8b4xORDchca9xZIpGjeHfrnGdV49/kd1w4Zp60HRHeXJyvWdqYfYf++B6N2GHJMgJLZ23vb9YW8zx2PTw94//49vvrqt+w8AnBzc8KPf/pj3Dw+4fb2BpAV1hOmNCOnCZdyxsPHJzoGgWGo9/f3OBwPZCHGSO/QoMMGFYNmD3hHhmvXNbrE6/NsPithNybi/3RCDTcB6hJVdb8eozOOYwbnED4fHR76tRTOSmJ3lCE7+csJIn7/R9dGiNQjgnpHL+N3cxGXQsvAGBMJYLX5hjTIXZw1jXUzflZU0XthtJMXnjFExJS8Y+9upQW01KkEcb1udC3hum57RFir9EpNSIg5eovr191fV8l8oQ5wdBL++LRCj93hUTpE0KWMBJXrbNq8sx+bGK9LozwqRkL9AdBAc3fjbQdEyRz1wxNBEEy9I+67ZWMzX9tuEmBWOc4oLKDpoM8wWcHI+gNGHJzC0G1CnjM34lqhvaNI454TyR5V430myYIZkM9PT1iWBbf3F/ceplbP3Lx+eB/DeQLd94x99unuT7VUlxMMVfEnsyvzPXZEWPkh2IeBg+9VrTUPDOC13xE119cN8Tm6x/ByUe/+vyrB15C5qxa71AGLq0bEUNnk9ICuBhuyDCMhcjVHNQItMZ0GQTs3R7ja0DpaRx/zTBlko+vG2ltHrx0ahAblbibQkqEl7kdifc90HIhflhHwKwiRiRYGQPYg+H/46wcPwTzRCLd1Dm5Lv+wXX0QwHSbqompBLyvq09kPRWJ4pCVz0ccYkSbdC/zxtV4uqK258bOQtKCyiyA5UzZEGYkF9FQkO6pj676RSACEF6YZx85bKAABAABJREFUB/vrxvBHiUxSsBaAxiDPGA/UnLhwuwuhkV4bLt+/wdPzGb/4xd/it199g7Vt6ALO4QyYUoIgEP4QPzA0QCMNZnM6eAWbMM0RU4742R//Ef7Nv/03eP3F57SRqjQTeDqv2NYN7TgRyl3ZRVfvFpZlgYjgMHEGZCBkmjMddQQdVQ2lBMQp43R3glwClrKh1A2XpyfOQCBXSEnIjGxoaENo2+GQc9grs1Oemedn5v6NwDQf8O/+V/8e93d3+OrL3+Dt27f46rdfwXrC3d0f4uZwgykdEG4i8BPFZb3g6fkR0+GIm9tbxCQorXHmmjMWVWyA2zyZy+49GcRoRSZ7pe/YMMThT28NBYA4uaYx89EE3HCmhDhlxJyRckRK4nFR7kbkVWwzMipjiIiRiRVPT08opWCeIwOi17o7GokIqvKQnD0iplbOOuCeiZoysihOveF0c4ubu3v0x0c8nM98Zizwc2HE25h3Ys3tvUhSoUwoQJXSg1IKUjrspJveG87PfE5iGhZUtAWLMaC2infvntE7xeAxBuR1QakN61Z9rt+REwXlOSbEEDDlESZN6KmgoAkwHSeoCrblwoiq1gDpeHx6RO8dH5+eIRpwc3PrnTI3P+sF05Rxc5gRe4ZOhGMFylmTtf3QDR402zyvVNAJe5YClEr7tG67KLo1H9qE7EYYLKy3jYxozWRsi5C0staCYoatUZTdWkOKASkGzuBqBzaaagyLMnjgd8wTZtHdno7QckcL/lrC9x+JHezC9a0yWkyMTO/qiSBLqRhTlLYV1LVQwpEmVPfr7O42dN7aFZ1wyB8wNK1QUcyZWszBql+2xW3psFuXwQyldY4ejNaPmgNbsdagMSJJQqsd54+cYx6y+3WCv8dEffQRsHrRuPiz0Lw4O53yfsDV0qCNo4Ee2KWNRAmphNwJN7sXtBlqIzyb0sQxTDljmrgWhzmH4wBYe4Ogo1wWiG7cm03gfou//yFon/wPGmU3v6kekKnUUo3Ois4PhEVHxcGvQcXXa1c2KkQMDJ1V2pgF7dWBVz3X5G/bmYR7FYH9ZfbOlbo5dk5h9zrlzhqCmzM3cYsufz3vQM/nMx4fHvHx40c8PD64NsY+kY0xUmp0h/sLD1KBB+mqsmqf5ozTzQl3L+6Rp+wVl2/2PoehhyFlJGa2x85Uv+Y50lprXMvRiRDaocwjmNH/shTXm5nr+/wQ9B+4pl70fVZg5hC0uubQyOS0aDieTjgtK+Z5RooJ8zQjxoTn5yecz1wkTKyfnKgkCCFimmemFZjtkgRVZ4QGGiZAdbc8+7Reu97tYVc9qi//+/9Zcdf9GbLdABl+XQhRh2u34rWLmIt7O++HikIjre0GueXa8dn+vHnzuXdyA83ofSACXgarOETJ4iimhBHSO+7b9Rkflbjt6wEQV1/YMM3nM91dPwfZf7xuhCThnci2FeoOhUzJdb1gBFlDACmCzR1SWuEoYgi1xTfxIU+AwV09/LqqwpQb1XB4AYBS2Zms68oMTJeUXBnhfTfcEKVxuvvtwY2Ar0tZsM+qiTh517rzB66dE4EYf0rMiTZ7IWVuLH29Z93XF7Olym7V5w+Qo0IKMxoDVJGrs5KNGaftkO/ozppbQZo/pz4hHaNk1+86YxfmZhFCnI31AGrvqL0h2IDqR44mX6sZCT909LnqhSm96Hs3L65JNV/L8OiAsZKaH4xw9xwojUIgwAjhboXJMiGQXKjCgPEuPARjILkuGddL9T00xsjXGrezc76aYqS/7o6gES5PkXNISrQcFXTpB2VNzjbtDd2GDaffLvUL1/k01F5BRpy/9kApf+DrBw/Bjx+fEILidLzhg+taLBHixrXQe+7p6ZELs3Wc4gnT8cionrohJlofjYfV/OymgS2r75wzBj2XD6ehKuXatRYAZFoZQLac+OURQYo8GMz56aYRjNJsnDmKIEcy8HaSjZNzxiNRG3VwhzvOZv76b/4GX331Nf7+t1/izdv3uDyuJD00VqNmGd0Xt0AQjQQPkwIo6eQ5J5xOJxwPB9zenHB/+wJ3Nzdo3fDw+Ihpmvx9RBeBCqySpg8B51JG70kY8PT0EaqKu7tbqCra5rpBJanm9nTiDCEzTeDDgyJLhOhMwXkzDIPG5u4b1gIMQnZnbbh/cc+A1JQRYsTrFy+RYsLP//Bf4OnpCX/xF/83vH//Hv/jX/2PSCnhD3/+R9AU8b/+3/wH1KK4PEXc3B5Ry+YaIxKY3n58j3UrOEwzkjZErZhORxzuX2B9eIP1iZsXU0Z8qepAcVm8sE4Rh3+xH5z7iBDc8S0OWz4wiy0fEdNEDZdE1KbQbrBaQLqIuLDddr/Sh4cz5T0xIOeMh4cntNYYbRV0v0fqB/hWK4ahM42Qh4i6kMUaEkJkSoX6fCQ4lL+DNd2AsfCFGwYMeL6cUWvFh49PCCGjeZTU+fzMw1JY6D0+PkFFcCcntNbw8eMjIII8Tbicz3j//g1SzjgdbxjrldkdH0zoxVkK5ulEv9dpxpQT1m1FKQXP50c8PD4hT2RiPj8/8xB2+7Lz+czDZppg5qJsCKKRJBOniG3bcL4UoAuibGgSUOUqmUInUS6qMmVGKL4fgbbVGcv7/VZfJ4W7YZgoa6pbBVRQIw+HMGUeCqpeDKw8BGwUUixIAhSlNCzrhsNBMc8JrRCJCv5cZGXxnKcjMLNostrRnMyVEehiUlYayx+qv0+K4kur6DDESlOFdVvpFDO5oB+KGAw6wV2sKmIANAVsW0WFhyOrcL5aDfl4ZE5rLfQ1XS5QDbg5HQEwM3EYi48PXmrB+fmZjUkIiJZIiOkdpRlE2z5LizmitYKtFIQYcAoRay2cH1sDavNw4gSEiNo7ylZRZMP5vKIbfV01KObTCeZ1bDN6SYegriM3Zl12SrO0VggqRDlbDEmBQAb4Vqv7EyuO0wkpBHfwqng+r6h1w7ptMAPm6YYo4e97CDJL7Xd6s9HzjJr42sX9TtXtqde4zjd+R6txbZz2rgSOe5tvcq13n9fZfo4LPoFaR6XvYnnvA8gqNRp7j3kLZxXjObC9M9pnOuCCKLVgXRa8e/cOb968weVyxrZu7gBi++8ZHcXe/XnVY8LBdoicyaWUME0T5vlAGYXRPad284cG+3tU1zKpO+UEoVwhRaZJt+rMUf88bQwwzfaCYMy++LoZrRH6G2zZQdzpwlIkds4TqkS0VjFPB3Z58wEhJRyPN8gp4eYUcTqe8Ad/8AeY5hnbxsTsw/Hk70+xLLTJYowUjROW9YJ1W12r0wl1+7whxIg0TdRB+v24Pl2ffn169wdrUnY/Q3zyc7v/6ICoRanlc5h3F3Q6FGP7MwifHcK7Z1brKXkUjGu2eh/dOAB/bnVvlfjfMDo3oxMINx4ueq6Nawc/7t/oKj79I+LSJEcnam3YSvV8zMbn0sz9ez3n0JGD5ow7FUGLyYXLXH+qVNe32vaObJ/TG1GWwXRttaHWtv/+cZ2aO8fQDGAQh8xZoYTVwzAS+GQfgAR0CFmoQvu5T2eGcKa2iUCCuyMMwMs/H2Tc577vUQSL2SEYhWrofp1HwPXomLvtE1DvRmy3sBusydZGRqP5LJ71YzOhwD3gyiY2Y2eEK5ueLHR2yHTysb2DF+nX7tH/9EYIUEfnaubFXof1K8lfxj4rHuA9PolvYOYzRBIBOQOl9Ml2j+HuyNrvaueuz103Gktw7jhQDtv3XkY+cW/ure5oiCpNNBRGKz+LuzkANapAT34O9CHFIqfEuuyNjRrnxFX4HMJnf0PcbP751IgIDMRAVeinLNfPhE9Qnx/6+sFD8DRPTgxwSnR1Wr4fRN0Mmieke4qBYxhianPvzIzWgKWVXSg+DsX5wO5pWWjHAxdxluI+lY3RMlOOGOnEKorTDbPboGQZjYUBd62x5tB2DdBEf7xuDdv5DBOFiWKtFZDg7uqGEGjK+zd//V/w3bff4T/+xV/gu2+/x/npAm3EmskGTAgqyJmmuRbU3TgIkaScMZ9ucPvqJeZpxul4wou7O3z28gWCBLx79wEa4+4TKA7TiCpSnhBD8P+uyIkP8XyYISIo9c4hD5IBho1dctjp6fkJMQbM84S7eI/55sC0hMsF27piOS9I84Q8zTjMJ0z5iDhE0v7QL5cLWquYjieEFDF269Yqjjc3+N/+7/73nKdcaMiaQsbT4xP+8i//Cr0/4Hl9h/mUMB8nfPfmI/7qv/wltUaRpJ8UhQusGKb5hPha8PHrGxRLyL1BjffBrHNAD0B6GGoCnifqkJY/3UEIv7ZP8dQdKkmI8xFhPkHnE40ZUv6k86DrRZoT7aT8TOpdEGNHjCR35ClDgjiVu+F4Yjc1xegHyTjLKDNYLgXLuuD9h/dcix34+OED5pxwyAlHD/wttYI2TzS1Biq2bUOrfRcFE0KlefnWKp6en/Hx4yM+fHhA7x13L26RYsTpdAuzjqfzMw+w1mAa0HqDhoS7F6+ZMzkd8fT0iO+++Zb+vTmgXgrKUnBze4/T6RbrckYtG6JHJeUU8frVPcQ3yU02NBh1YhrwxRf3TjbioXTZ3FA8ZrTesWwbTAJOd68ANJRePHKIsWAxRNRWUMq2HwyHQ8Q0BQYYK3l0wSj5ABSlbKBNIANc08zZog5oshGCt5jQxSFZGFnBDgNyUyMfgDCpo1S1AuuGutEwWpQHchRAxTC5x24pJJfp5IzuMuQGFcEU09Z8nlt3IkxvCpWF+skDNZ2Xp4+4ukLw81prQGs+WtF9M2fnFHfYuzafw400d4do3374HrVVLJcVMUR89vo1f5cX7NPx5LNLwt9dnWRTCnoXtK5ISnJpihFzSpznmWHSiNQDtkpIk1t3h7aGKIbb2wnAhM8+u4cZsC2bk7QYVnDZFhgMs0ZcLgvOzxefrSekacZ8OuLytOD54RnNCpoVVIvopljXjroVrOsGWzvK9owQlF06BFNSpJhxOM5gUxT2ccXvdQjq3sU51OjuCuqVzzjsxPP7UuKGPPDm0a2xUKG0YryhET9y7RTdKlkGieOTasV2+Np3Nz4w+6zG3F2BCPJeI42sOul9n+PsO50NTRiZlWbAhw8P+P7NW3z8+Iinp2fYCOpVVotjTkTxsXgV7I4K6tBCDMg50s4pBhwOM+7u7jAdpv3ng3dz8AMQYIVO53z+XbCwf4Zh3zQw9G7XrnRnlnn1OA7VnDmU32dHnRZ1MWXkPONwOLq7z8Db6VEpwuSNECK6O3B0D489Ho6EAG94b1oxnxl4CkOrvukSejqfz0hTxsE/u7lmVOAaqpgg6h5uMma9fBbGTZQuXvcPUg+ujkW4Fj+fPubXHpGzOXYqeq0m3a1kjC0ocb2aBIQQ3IaNLEcNimjR55vAiJYBBiPPfH3YdTPt46G9VuvRvSTHmrSd6Wd7t2OuryU0e10foyAZ625nCPon5Xz+k7WC8b18hmJKv+OQw3xBY5cyvE5rZUrBVliMQhECDSlCCOhucQe/3vRadU9QcZYsqCsDrrPSYTHIUFXKnUaXBcBZ223XDvbe0U3RnX1oo3Oxwag1DDH9tVNkZwC7dpfdBE3afr3ZseDaCe0bhe1riNeW3fYe7dY6YWcZo14WYRxZGGPfuIodi/DOsF8rs6HF5D7IdccO6fq6MK71QaTprlWE2RWGH107sL/nsVead/E0kK4k+rm+szUykq+dOXYYZKzx0Z1S0zi4JQNVkf15Uy/8uKZl/4wD4Rj7W/BYM2vduQCAdkG16Gu5e54lduQjRkFOATUxpUia8oSGoENRq6KPMOVPdJnqKOC4C6ZxP3v+5+jS7379cJQSAOsNl+cnf9wUKUQcw8wLXSsXt4eKjgOzFPpyqgbkPCGlhMeHj7icn/bN/nAYLvYJEhLq6hqiTDd8uCN53VwwHhTWgXPdCEMlpg9IAC7LBe/ffUDOE04nzi+H5wht0WToOHjBNYI4/TBCpmD4N7/5Dr/4xW/w9LQ6K5ULOWe/gQgY9mcaBNEH0+bK0pQi5hxxM0WkFJCT4vMvXuNf/dt/hcOcOWcYg2qHdWKk40NweygmcQLo/LzPywUQIE8TJAiSJpgG6DS4nlwMwYfNT5cVQWh+LhIxzYo8HXA6AdtWsW0crEv0zmZreHy8YF0L0kTvzHUjJj8daeLdO2nU27JBRTFlpoU8Pz7h8vQEdKC3jmW9oNTtCivWFflmxuef3WFKiufHB0wpYEo87Lskj1xKSKaI1tE2Q4fnP3aDwIfszqizSAnJgPV6A9QE0RcgoVIAag7NKtAFbeO16gF7NUXJNJO2VUHilBhiAhAUlwsdheY8Q7LgoT2hDiiydmRN6E4QgdBsAaAbyOEYMKXJNxAeHG+/f4eHjx/QrXqMje22WH3fuP0xte6aSNLtQySRaJ4nHI8HdI+KOhyOhD3dHmqeDuwy1+1a6Q/Irnc8X87oZri9vYU1dqA5JyBnlNbw+PiInGbM0z2ZdWp7AcHk9bqjMocjNVnbxq5sFMtj3QwYdg//tQarFeVSUayj9AYLARIpeMbIDBUWstVYZPVKSY+hIziRTTVQkpQE0jnXRx+Wf3zrBsNlfaaFXUiIMWCaJtTesV7KTvTwnY7FrHRYr2gVnG1tDa0xbT1Grv9tQL1wiNgtvpjl6CblSsYrdXl0YTlvK8QMy0oJy3TIUA2Yc77uB8pg5XW5YO0kC/UK5BiQIq917Zdd1xyiuTMK2dKtV4gZTqcjkZNDBWBodWMYgld+BM8CGiZYa2hl8wamsQj1Q6c5C7YWlz4I9u6qmrisjeOiEATBzwkDESQYtaQxMZex9c45Y++oraDVRqMTJWs/0O4HMRoOR0XZBLLyjIFGJkXk7IVS97NHMCf+s5SNUi9hbmRv11HA73UIyqhQB7bt2M6oXGTv9vj9A28f87Lx2oOaPCryAQOaDYx7iOPBPDgAXUel67WV+3FeZ7z/k0pYrpXnKIENI47E9tfZu9Ph8mKsXJooLhdGDjXH6DEW7/j9+ARf9q6Fn50EgE9noiknHA8HHI/8k6L6kNznLvsMyytzZ4qNzo83Tlj1CWEmkkb8TY/50459XxclvBLd/7t/Fo08YwGf6/j7GO99+PqNCnbMQbu7+6iJz235cJ3PZ1wuF+9AWU223tA8Ty0ExTQl3NyckHP0anOMfmz/uVEFAwrS/l3HRYsC3uFPrv/VlQfXTmt/DvhNKkPn5AxUfx7JPfGO2atmiLvwuJuGSRg9Hq+bd//BK3cYrkWgE41EAI0sprZtpT7UfLY3tIouvh8fef8YY13sran5fxsfkfdD4L8rBKSU9hkUNz8yD0chwGeNaekdhK1689gjowUaszhtX5PX3yE76Ye/22BgZ0R7rUEq8/vo8Pw4BNW9LTtG1zI65XEogKMIn/kxEYFtONMVfF04Y/NqlO9wIeBIDHaXpcEV6GPNftK1qP/8YPoq2M2Ndc2FJPuKHH8fhEVTbwZahnHT753vY2x9lH+Ac5G9LCXypW6uMJyprj2cozRyvT77+1PsRhe7O814Mbk+7ePr0652uMuIx92NIr0XBt0O7sH1V1xZz+L78Nh3xzWFiPvCfvoz2KVqfSd0qaMVV54FH2nf032/Cc5CF4mYcmbShxsvhEgPWQsBliLJa6puuB4h0qD16ko27u8VJgEo/fCpqbOJf+jrhzvBsVobmZpR6cQx/D3DTgVX8jFbdVx2RqkdpZJi26Qi5wkxJOpd/IKZXSHXOE+8UL5BVn/9oIQeKe4FSmGnZMP02Ui9f/nyFUZUDDy+pZYNpWxIOSNl4vZRSZaAKtR9CWPIQBR8ePcB3377LdaNDjG92v5gQQQaucn0LtysNY7bDQ0Z8XBAyDMQEm7vXuDnP/8Zvvjic5yOR7RaSGMPjJ1CDDBVbOtCEbLrJ6cpI0SK7enH6rqey7r3fSLsj/gvY1NgSoUO26ehPfKNqAFI04zDMaJuDc+PK/KcEFPGi5cHCATn83mfy/Ru6Bsf/svjGb0z7gohoG8TlkvBr//+S1zOF9Sto1dDtYJSFyznR1gruLs54vPXr/BHP/85Xr64A/qGVhRrJ/28G5lktTTvXBVJJ0AyrLL6Vjj0FdMOWxvA7stGUcLVJjL+KYAGxJgZvZMzWhjZbc1NCJx5aNvQHqCsjIa5vbtDnmYkAUJiZp9qwGme0GrE4u/t6emRzywa57k3d9iWDd/+9ivOqZykdDjOnLXmjKCRKIMIQvR5hQfZxpCBoJDAgOYgAix+yHiSNg+ogHme0bv5bKxjbe6/6IdOSBmtNjw8nXkYicGaoRd3EglA0oQUMoasAKm5RZYC0ly+olhL2aFsmOfoCbMuqxnaWjFyPD2cDzC/R77fdgClFUAN6QRIE6DQ4Hgt7hIFYeZh4AaO5mb2KXjMmaGB+4dGrsmDZBiMgcYAiiNIqNSNRqUsRWO+HoABCJlrWDR+AklyphcDI3iSRvQe8Swb1tUgWn2zvcK91B3ydxlobDGii56WAlVgmrnNBj/cYk4QwdVW0lFKUUGXiA4g5IA5HhhfVBssAE0MIaY9gHkcpNb4ewRAihML8URkqLaVrkNl4z7l+2mIlHkRaQAk6V6s8TOOgOO2F/c0umeR2zt/Lqqg+AE8OB/qh6buLl8sGteNPIeYBCIRMUw4HDKOh8lhXZfJmKCWjJIy10FUlKqoTXFeNqxbxbqu7tRDNK+sDZsb/nczlEbi2E4n/n0PwZESIaM6FPEojB172rsqNl88fccQmpAI5w+7C/84tLuhoXtignokjucRwhjUC9vZkK6KRYiENU2uuWzEnzH+DaNmIXklOOEk7ObdwxYJDoOu64rzZcG6LCgLiQn7TAf066N2hnf008JDldBGjBEqipQzbm5ucX9/h1cvX+J0PHxS7Y3298pQu3bJn1x4r/xH7hdE2IVB9ns6OtXBig3B3VFEqU+SK/xr/pN7t6wsIvxl9jQD8653NCQDRoiBi3tU0yO38HK5YFkWwCHlOc+ACd6/f8C2FdzevcD9/Uu8ePEKx8PJZwmyswj3mR6uh9so2pR4jb9vkGADek/26wffq3cZC2h020IHnZwTggK1bIAGOk24FokbWQSk7ggHu0W7diEA1nUBTFisdNvRiHHP1EXH67Iw4UFpMLD7k7ovaLdP9Kv+R4OjFIFwepPuMKKb1e8dB7umdduwrAvUYeLgCS19ddH3EEbDO9vqafZqu1HFmKeOLo9rfAwQxDvl5jPhAfdfGaRk7XkPYZQtG5yR6O454978TsPr10xUEQDqy0ToMgK4lsxDZb3QleDm+MbOOvgMa7jgKLjZx0iinSqhOa4BwTRyEwdSwlUL8WglQK+dnGvpgmviJJBxmFOEG2Pusy51NrHgE31tY1Zl1Oucfswz9ybFHLJ0pGLsn9JdMiPdE2O4CFUViL4XyyCyKPaRc+O97YW2aJSaDY0d15E50sPDlvem1upow+j8w+70Io7IMFrOxze9UyqiRJfUEaphoUSEQPe7bvYJXrOjM9w/R6ffO/fw7LZxu2ao03c0+H7P/ZWHIPdjRYChBgUswtBRxAN+ncUthftatX8iHBpnVknaeAOiujN3Im2/145h/IpOeK21iq1WaEgIKaG3jkstKG64nKeMmOIuOyBEREspDQFbb2gAohvkqgTfqHnxjkd63RU3Y7VOHJ9P2JVtCShymqAyI6SMkDIPH8EIGsdmEV0U33/5Nd6+eYePbz/i/HjZ2/uYaZw7HcYDT/krjWII16UYcHt3QowRUSLub+/wL/74n+MPfvpT/Om/+BfoVmDtsj+Y5sWfeEpGDJG5dYkPhnqUt/k/B60+B7KdyoAeC8XQrZNok3Pw9t8QjFAOFxKNccV37m7G7DEljNO25rO8QoKDKg99GfCW4PbmSIq43+NaNizLGe/fv0fZKk7TjENO+NGrH2PdVvzlf/lbvHr9Cn/yJ3+GP/j5z/Anf/JnCFahvXglm5DEEMStxEB/xYaOxgwhRGFFPqrVsaGExH/vnbMOPt4d2jdAFF0yTdBDR5oC7u9PQBAsj29hmmFhJpkjULwe4wzrK8w23iJlUHLt1Rcq8Ob7b7CtK6bDCSFEpDg5c5kkF+v0CP3+u+8hAtzd3cAAbK2xeylkPq5lxVo2bFuhPVstSDNJVNMhIQTg6XxGWVeocDYj2hGi+T1sePf+LWJKuDvdI+eMm7s7tNbw/v0HbFvBZaOhdvLNcEgIuphfS8KNQQPv47YgqiKJwhC9w2NYbguAqSH7M7j10UGvgAhidvhLOjoalnL2DXXeA2u56ZO0lpS7rISAFAGZqQtVY/dkjZqz4Mxptm6+Hpz0FTzmzLxo6Y2HX54TINQJtt6wbeoa5xP3oOflCvNlRUoztXzFD8BGFq3GBGsbeq/0pwwBKR5R27T//HyaEVKENTLZS60cDzwxuy6dTuQZeJHk/B9aoNWOtjFaiF2bAnEQTwLlKa3ubPqQAqJmP+CAaaJhhW9RLFZ7x/L0xCSSsqErIJEwibg5f56O+75eC63fRIQ+pHnC4XADmvg2aBCkGNF7RemcAxuA+ZgRckCyaxEFGOdykD39QjeiE933H0GESEdSHqgKymSelxVBFfNEt6w85V3T3GpBLZmHvwq6RRgi1nXDVipRm3p1keqNyNK6bait4fnpGaVUPDw8exTc73kI7jlT/kB2mA/sqaVZl9Xxdt3frPkwV5yUwkPY9rghspyczaa2Y7qs3vqOncMp8rsOcce+xsbuFZY0X9jeoQ6jZYS9YXXcAO4yyJGaz6EUdIh5eHigVMOur4nRDI46zitWas4FKdMfdOQIZp8D3t/f4Xgkzg2j7RyH1/TNG59nePntLDoAIz9tANlMnL8mTAxm7mA3Dq/IwajcvS115Cz2PSFAAyvw7gttn7cpCToG6opa5QGTYiYRoBP+lsiZ2FY2lFp2kpOBh/rxdEBHx/qB0NzhdMLhcMCUM9AEKM5CLIV5hiowFUiKLlcwpkQo2JmN+yfeNX7SScN9KXeWoBc4puresw6Jpgyoe5QCYDwU4X1rLnS2xs7Hy+veGpmAXvHvc2QfD/RWCRe1gOGXCYBWefs9FEwSdvJFer7Qps0G6/N/Yb3t1bten0O7To8//cOZZMPlfEHr1PO1zlkJ/Bkdv3Mkp7DDcygP7DJiygjjeRzvyQbyQqu8MacOgev60/vC1yJbd7BP2W2ad/K+VoWSIlEh5OuvJyJQo9TIlLrYGNNe3hiECJPvVCEmXwNeVI4Txhd6jMq5twUMY3ioIkdnJML2PQswGrN3dmCqQnJT0J3kIkJEiSQ7/r6YAjR68kXn2unakScuvpQ8lcSRRfG8VIUzmV0GtetMx/NtY4/jfWqdmr0R36V+LT4dco3kk25MZ2jWuK8Ju7bexbWNunMwYBFTnl0nKy7DoEvOdQ5/3ZuiFx5XqNShS/9qtaAZsJWC4WU8HlR21uNCJOc/sGNvrSPEgHnKfugHkA3WoJ5Q4XQ2NDfrTskdjQToLexrszkTlmx3jlE4thszy3/464fF8itP2pAdb2/FbwSwLCvevf+wwzqUAtziGiIaOW9QbjAp0cljsJslcyhs3fWBtXMj8gR1U1ozwdgyJ+XCaX4jQ0gOGcFp26Rph6BAC7DanbVU0Qv9EZlk4ZTn3nGcZ0QVvH3zFr/58itsawE9QT3DzBl6g13MZHXW0ylG3N7d+vDYEFPA7f0Jn33+En/0Rz/DlDMZa2KAh5GqGIW89AXezb93GFBoyk0ySuGB0QljpsDFHCT6xkNorwmradv8OVOhKD4qWtnobVgalrVinjNCyChlw7IsyImV95QnxBDx+PyIdV1wOT+ht4a723vEGLGcnwED7l7eAQAenz7ivJwxzzM0FKYopITXn78GFPjNl0zlfvXqNe7u7nGYJvSNHpRrWXFZL84YC2hBEU5H9OWZqedKt5E27NWUJJUBsFVn3DU0tvRj1xUvwgK7+4oACwnTfARiRA+eE1caxIOJ21rolqMbTMoukhbrsCY43twgxIRpZhceA+HZWhcu/s7OOQXmLd7e3BBerw1RAk6HIxnDhxnrUmmc3Slcbr0D2nzvJvux9Opz0eREFnNJibvxCzWqKSc6cywrPnz4SH/NVuk92gi1VZ+hj7GEKruMXorbKTLNIMUb9Lqht81rTEFsEeiC1SoqOkL0eRE8ccDLSW7WcjXIcNML5vEBiD7PF/fz9PQNiSw2rF2JTyHStWSaZ+RpouNUK+iVdHhJPEyS+1UOGzU1kraen+mgM2X6uOY4ngy60EzHoz83G6wremcYdoyG3tRn8gZYQ8yKENOe7h4DX/swE8Vq4moUVgjIzlj2xwR516b5fMohudaHdOAqVRkzNHVzdhVCkt0aWumwClgFcia7tUfmjI4RwubxZluvJKYZMyCjuvxJxHl0XvBKQE4Jx/lI04W6UuMcA1oTjkUNuywqpoR5ysg57U4xaAXSfR0BuCxntFZw2ejGc3N8gRDIaRCl+w7tzSYnoPHMOM00y5jmGUNCZ41QdsjRU4k2tLaiVEGtQJwCMCu6ZcBkj+aif2nHVmaurZcnFuwXfxZ/70PQqzHpnYehs8kGHXpUU3vjZKwKA9T/3+Cr+Z1130LDVWsyBpndocwxTzRwjxuVqLoWrXhYpvqDJF6FXgHocaj03Yx5n0X5k6M+l6q9wqrh4eEjPrx/h1JWwOOVuDe58wRG+y/7w9vN8fEQOEhPGTlNyHnClDO1Q3r9Gb49c8q5b9o2quxrtw2fO8agvnBYXXYX9sPp9sNAVSGMuSnFmWjqEDJfkbo/ZWGSqGtMIcLyhBTiLsb1s9zZWN7RuMHKdKCtU8qZnaVrnjDmGsFcPzdYYoOVB1hvKOtCBCEGaGeCROsdbeXGvXOvHWLpGF3dcH8RqF2Zx+adv8HfsPmcTwK6hJ3abRohKTtZwTukgJ3WrQJIUnQJ3rk0yKB6B0WpjR20MkkiDqs9d+OgcBtkgwLuoK/IOZLokig5ERVmXea0Jy/wGZT9OYB3JLuvrj/H43+MeNBaGspWHT2B20N12E5f58/XUngvHM5ovhLp/m8e4DskPrZfe9h1VqxCW7leK8XxuJpdfAJWOJuZwa0wYOhVdnedcV+t79Fio9NgpxxoDqCuHYbnDkpENc40978zkGaotr8G9YwM+qV+TXE83HBOWyrEFMG1acFRE+t+kApoPlEDRtC3Bs5oowYgAmvdPJ/wE+9Z+GuP+bDJrn1OHusF8x3IXINXmNCin3iPjk7ZXQZ3KEfg+y23PZf7cB+o1btg8DkeM3nqi6/fBww5A4BRRjrqVtuQh8l+QKoKIq7CfEpC+JxeSU5eqLtUTAD0FlHEkFyrmVwP2/0QjCl5B9r3z9yNBZJ1mnSQ8RwZ76bDiWhwHTKA4aXafDTSP0HOaJ/JjpPsXV53A6Zr6PE/9PWDh2D1RdFqoUPGNIH+e1T8T1NmFVoLQtS95Y/un8n9nO4DkD6WJODQiqpg2ZgiQYKCfJIbiCum47BN6x3Pz8SyU2ZKfXaYtdvIw4Jv1HX/8Bo8P87cyDWRSHN+XnA+X/D1V1/hy1//CsvlEWbFN1hSxnsTiNGZHcJDvbcO9QMyxIg8HzAdDjgebnA6nHA6ztQnqmexNTj0AWgcg2xDbzzsgugOE2++cHLgQtpWdifFzXd1H2RfD9DSKj6ePyKEgMNhQrSEDH6GECbMc8SUIra6YisrDtOM43wkhX9sgGa7l+Y0TbSASwEhKe6Pdx5/FLBtG0rv2Np1oB4CMf5lWdF9s2DnwFy654ePyHPGfJoRAzAFxfPTI87Pz9hKHZxwh3ALvU0DB/pGFg9idTeOGH14zw3DAjftVoAuAQ0RXZhS3kIGphN63VCWJ/fMjBT5lw05ZeQ5UQsl0f1sGw+qoHh6ekYtFVNKmOK0G8O3tnoxxVSHDx/eoLsV3jxPeH1/62kU847QhxRxuD1iOk6IOaChQfsn9nsDlnMh+YB5ORpWEN4PuJwLnh4vSErE5LIuMAFmNyQIQVBrw3JhzmCMEdYNZSvIOeN0OHj47IIYAnpK/nNwciTRCjFBSKy0n56fsW0F83z02T1hzgYnukUPkJYJIooYOcOqZfW16MkCbUOEIlnyTbwjBTrSiJsxBwFgHdnTHBZZUWr3DTm4tKdDotdOGiHBIUnrQDSkFPDF5z9C7x3vH97BmiD0mRskSDohk9yLhtphW0dvlbCaGLrYnqbxdHnGsq0MwR0jEcCt+AyNNRCmzKJnmj1XsF8tvlqrwCjgPfDWmmugh7k/nMLWr8YKClDn6Yc3bd0KDDQ7GHBwzpmSICe8Md6q43iccSUOcrxSbMNlPXNW6PBi793dsFxOhEGEA9basLUKBUk/MSaOfyK1sUH5erFyDHKcJqgENjAaME0HP3z59zQQZzF6fj7j7XdvcLo54fMvPocgQizSS7Q0dy2aUWPfbfxaa+jN486c6T9NGSoB0eUotfIQ5KX+JxyC3Sv6lPJeaajQn5KOFnzwplYZIZQzBIoG0s+rd5DCaRirAPd3M8Pe5QxIAAZnpFInNsgc5lRwA90r4A8h+nWGOJximnnx7BWOslyEAdjqhmXZcHt7QI4J3799i++/e4Nvv/0W796983BQlsACgTkrq8LDeoeyWSKgERoSUqIp8fEwI+eZN807hNZZP4vGfd4ytGchOG7eOyGM4F2P0ySbfxBm/l1Fn6XRoCB45RwznUAOh8kf9jFQV0JTSq9DKCvS1ioq2yzGxqju+rjBfBOPpeJnUWwekaIFWC4LvvvuOzw/XfB8WZyYxAosBcNhirh/cYub2yOmKSFGioZbKyiFsTq1k34f84w4HxHnI7aVsy0IYJ9YHXVPTOeso8Nc/9ZF2dHxqEBThWlCTydIzJjmA2S+x2UTWFc0eJfih/XluSAnBnXONxnpQCq9ecgoxjkE7JaAMXIN9HglSagq7m7vfCNKbgBBuLX0bf8drbc9ALr14cXpJfeY82Kv73F9WnSveINQOxZi2D09t1p8jDSNXQ4ibsDt69XEoBk7k3SXFwWBRLIjuVb5eO8klM51EGOCiGKaJj4fLiVrK+cwJuOzcJ7Z/FeFRGPqYODnEJokDNJFq2XvAMXny0GpE2u9eQYg54TTPCOEiHVZ0Xy+Peby4p0SjE47MZAxKEI5B7oAbSTbcCbKfcfJxBFA4qFlnV0vVy6/cz4ekOa8M9Wr+3GKui7zQKZuyt7lB28Vu+zMSjNFSmzruhCab6if7H184Mbz3I2H8+BbavA8TJcQDNapfjKvk9HV25WpSeRM9j0tqCAIGxUiEp+sNZiTeMwPRh6G4nvf6Ov3jtgL+zxNiKkjtcyxUWAxZHDEy7sy6h1ZpIhfJxVDefkC0zwhJ95LccelnPp+n3oy74IDWqs4OElvh8WVY6Tp4OzXUv25viIW/9DXDxNjBEAImI8HwiaVm5Ckgb3Q11FdzGn6idHvSr+4FAkTjjiiNFP13/pYsHK9wCI+oAUUhE6Gk8alFtdH3QKgG4kZX0/BWRkT6B2mS4IgAVHCPrc8X854eHrE8SYjpCN++atf46//+v+Hv/27X+Cbr75Gbw2yL+gAnbhprFYJ40gkJKSsTkLMmA4nvHr1GnPOOByOyNMBGjM3/bpBddqh2/HFtGVCkMtlYdaWV5VmhMQ2J1GMjbFWJk1fLtT7pETB9E3MSCHh/u5mH6bHGDDNbi3ktqwQnzttG0ox1Aqcbo6Y5oCt0tdQNSDlgDH/DhOhxOfzI+Ezd4n5xd/+AufzAo3HfXPOSXF3G4GbjB//wRf47ItXON3MmFKANQ+qxRnVFM0UkhjAO5+fUEvF8+MHPNUKpIhJg8+ABc0IZ3RZ9g3MRFBcWkHjgYAaJiBOkMNnSPMJN69/hHA44OOFDOMkGa01hFbw8PCI92/eM94oJPzk5z/C8e4VQkgQjVjPK4r7X4oR9oyRbkAaFNAZMKeZG3A63DBK6sBZ3roW+jZuz0AXSFfUUh1OZafW7SqkF6UIu+3mcPt5hgauNfV4rjzxz8NK9tuyLlBVnIxQFWFeweHA9zjOtpCxN5yuBqfTSabnZS0FEnlIVRcihx4QoMjTTOMDT0ZvIBNvWc6UP8EBvEBZR+srQlQcb0Y6gjstpbAf+qVsWJdOKD6MP4IcA2JIeHzecF4WnA4HzC47SjnjAR9Rtg0hZWgImA/Uzak6qagPH9ANIoq70y3MZHeA8gkOfJuAguJ/zto7DDS4r91QthWtbLi7PSCkYaAArCsdcuLEgzjIDOJePBw3T3ePQlu1WBth8mlCax3LsvHeYRgV+PzWZQcGQ1kLylppHCHAfGCBNSQEgx0a3HFqzILaVgEzD142rOuFSESMiBqQdQKUuYMQ7PK2K4JmCOiIaqiNKF4MAdkdqSAkCqlLJSQIjtPkBynfx+BQjMNvq2V3owlBMXk+5zQnnE4HTykaRip+6PtkaHjJwomXtZLolyORwIurDqhRxZ6SUrfhgAP8I2fgDx+Ck3eA/nH8IohfdHi1JZwdESTfFy/dOlgp1VqIITu7aufTid/QZjBjkGvZ2PH1xJM9ehdnylmX+g9qpGNG84qotEFmccKNcEOoVtEGVVyAlDPWreDx8Qlv333AmzfvaSVm2DvGfcjvFXN3DVqrFZCIrIIpRdydbjBPs8s3Ak6nG+Q8XysoFxLZ2Lz3ne0T/Z+SbbbPh9xpJHnC/Lat3gWySzqdDt6x+TzOyLIqdfMZCd3tmUxve0ozo2QAAWeY5DAYtlJo2rxVHI4HpJQItcD2FPscE5oI3r95j4eHRyZ6G2etrXU8PT1iygFzPkBFcX9zwpwjlvUCtYasQEoRKUaoKaopr5HDPtM843T7gsSPviG6DZOZExBECJeZcWYgAtXkBVQEJMLCCQgz7HCPkA+QMEPCBIkZIVCKIW0FquFwPAKvWSSpBKQ8AaBjUC2Nz7d+Arlnhu2aEGlQn7tERyVghJRaM59v+8wcvKejuqWB8vC4xN7pi+sM+3C38GecSCnnadX4RyFOIJkQYqBedp8ZGR3Gx9pyJGTXpAGAF3mCTiheBRJZmMrIjTOiOdG7FHrz2m4Sn0f3WBjwW52CTvN3riNejwZTOuXwOlAHXCsZyN06clTMh7wzjUPgZj1PfP6Tm5Rzo7vOuXKmqUTMQ+TtDFpHzTlmdp/fkZn3SXUhjnJx9kjv43VbHfYk4WmaMpATPOF7d6lJSOgW95GOUjR5hWplMIrduMPlPPB5bx/C9sQOcjg1qV1n4tLJwt/NRPy67NNiCTCjmbaowtpIEmHro7/T/vBneO3rPpcbM+Px7PH3dnTxSDsEl4aNGRxHEyM1Y6TTwyUTQxJyTVpRwr7+89WRqmuUnULVkHLkbLVj72oJr1MK04W6VAhRLQGIiISAybIz23mPUiJcP2bfvV+Lyn/o6wcPwWOaefNtHICAgJCFoaN1MsrEHySSIQKGF2erxhlSWR0qTE5DHpwwoDbQbaQWjLwviKAfDqA+zsF/zyRU8wsVSdJALai1Y918PjEG6xBsZUOrKw9Bb82necayrnh6esbXX3+P3375HdYLq8ZRpWoallU7OMEZxwYgCPJBcZoSPru/h2pEKx0iEfcvXuBwOILyFaEzDTivhD/sIyyymcGcyBKHShVCnYwBh9OB5J2yckbmxtQv7u48nJiLqSwLWq04Xy6Igd6S3RqWTmYbD8HAxPHObjYmPoBrLzgvC9plRd8qbm/vMM9HtF74O9ZnmHUcjwfUGvCLdx/w4cMH1MrPE1VRtxXfff89DnPA3eElDqcbfP76FaY54fn8AOkHzEGQZ25szRS1CbbasfaOlDNwPEJff4Fyc4v28IZM0aVwJhtc3jyG3kEAUWcTRhzzzG57egkLB/T5pesBT9A4QdOMmBR5Ftj2jL423N5PePHiJTelZkiHGd0CHj++x/PTE+5fvsR0ODBbEYI587ldW2GFb9z4khdqY5a3FV631j1dwLcJdfLOtm77tbNBgunU8LUR4ePQoaOLQ/HDFHTfYJMqTscZzRilZDCE5NW3D0w5yvD8OQlQZAxZjqEhaEWKPASTZmhSQCl8ryDln1FJTIVojQzgPM+Yjj7rNCGysK77qAOOonTr2MqZ8GZMXiULejPvpArMGjPhTrNLfXSH2k5yxJSbFxad5KMCwDf++TAh5kizATXm75nBPL0KgdQ8Ej0UCo/zqUOr5unv7gOqonheznh8vuBwmHE8HpDThBQj1rqgtqusJzvzdjDlAZcdFdq/SXQiyDg02koT/9hhre/MyzknPgtGMlqQgKGrDNYR0KHC8USK0dc975HC4e7ImVjrJEg1p3cGu3aV+P+T9ie9tmXJmSD22Wp2c85tXuPPPTwaBoNJVjKrBCUgpJTQXCMVNBL0KzXSoAABBQ1K0ERVEiAmSqpkMpOMZISHN6+9zTln773WMtPgs7XPc0LhFMgbcER4+PN779nNMrPPvsa4C1SzfZff6UldG9gqd4wSvMwGXwMEfFYkAZHobMuA1gu4XYcfE0PyVZQ4ApYCm72a+B4NY/Yi7EYkAxnFbWVGI3eh/I+KsrMJvXOEEwC5M44p71OnCBASoXUMY78x/0xiDON+vSqHPf4nhuDebVcPwaszTENpDTEQQhIhrTggcMdnV+1gnwZF2BUZopd6d6YJ4gHBBvNw3erC2xAIldYeK+SWR8ThfccBBZyo0xeRZoqHpyecnp/x/PiEy/mMquraMv9ZfnODcKqkPwFgxo4xThlxJNNPQgKqYZpHvPriFW7ubxGyABa5w/LPygex7Z1R9XEgRh/9XTvE3QZgWgALyL4cT+7Uo7XsTiRMYDbAhEYGEtyZ3/apk2sQQyu8kj0iSQAki5BIsp2OGWkgyaW5hCTFDIVhWyuWZcHDxwc8fHqEFp/UtEJbQdkuiEg4nTfEoWKYEqYp4XCIOEwRhxsGYNYSYBr5DFTjD8YICQ1hpPWYRIG2e7RzhVZFDtGtTx0GFu6nYsycblKChQDNGZDMiVEEFgxBKoJd/KWPsNBIDxUmV/TAVill31l3baspp/HdZUiA5O9SEBo5NO1OKu7P2NSn8B6p05s9TyVodNrpgm7mqXnzpcTBe6EKfrAEL4rzeMBxvtk1agk0K5Yw4XPj7Wn28GlvTsnoFkCT79jJgGTXrQAYKwahxqxZAyRAEimLgoDDMPCaJ4GhQtCcVBEQJO9MZng3DnAnlxIb3pxcSyuBgc0xAaIQqZimiQbe5odpP1Bch3a5nLFtG4YxI6aAcZzddN6P0c5udP1jTGmfcXhWZe7RHBZ2+iydRPwg3gX8w4jDfKD2F9EbeloYAsFRCHQci7wEoa4Uxt0zWbIu6AdJcF0j3SfmaWBEXQrucgM2wRIiTK8WeavyGqboKR0dGQmBKE1TN28HmaV+2JsjAzwCzJ9JklLE94jjMDh8zPOhT4SdnawVFPGnK2rVz0gikxw0ehajQa4aXfmxy5fFRBOAUnjNu9tMIARbWoMY/ZbJOnXc3gRi0VdQ7O+aUmscJEGMNULClUMpPpn2ZfznSR5/7Osni2DxriEZ4ag8DXuHKg6FdRZbF+9uZcO6rThMM6YhO6wXULaCVtpOOvA7xF8wiHfVwX8hQfDEeHVRtLbmB025/oIm3pUYAI8dUcISRfVKaIiZXZYWmBZcHk949/Y9nj4+4vTEZABzCQdfZl5xomK82bweLDZ5GpGnETFnLpZBFtYXX32B+TghZCZAWE3oTjE/ErmaubizoTlDLQ/mBy47U2skBQwp8aCHPzDrii584Q5cICbuAWloFXvn47pk7gZbY7hsivs9y4jIoFGtmSGPCSEBsjagVaQ8wCB4fn7E+fmC9+8+4PHhEa2wk9Va0OqKsp0glvH8fIPx0DBOCfMh4eYYcTNH3NxGtBZRtt4cJMJ2DRAUiCjiKMA4Qu5uoWIoz4pQfJIBSyAADCALLkn2iUepRY1nwASxRn8VDFEqoj17QfFGKCXAMmAJCkXVDVo2pg4YCQimoD7skDHk7AXKkGPnqxMGLWXZqejcvzKypiyusQqJhsvgwVYKC2DZyr5eoFsJ/31rJP9EB5vg91ZEcJiOuDneIQ8JMQHJdxBDyFADLitHoDSE/fUQEYQcOalugBnh874aIGNbeYJ6qkrRhhSG3QcSIrg5sDAU3aAoEPB9GgaShaj/AlrrYxjf4hHD9Xfx/85u/h0jEJN7pCYWEG2OcLj+18xwPp/w/PyM27sbjNOA+Xjgcxo61Nu/uTd2XgSh5oWbLiTWaFAfe8H2HaH1jiEy8eZ4ADodX3wKS2mgQYi/cwGAfKZR1ar+epMhn3rKAkhsSTmj5wnGGDBPk0/IHA4iIixEmAQ2ZnXFslzQukY4XSUVJhwMVI2WeJXN4dCXvn649iAC8wl1WRaoNSgqjarzjQ8m0Ula/TyhoxW3L3TjoqUaD346E5IR2tnk3uOjk58Qrrtf3pTApIq28Qc03VGDpgyfHuLAkG3vCRmVxDO+23XSXs53hN5khsDP2tFFzhmf7xv+EZEg/pEi2AfmnmtVlhXdJq25a4yIIVIH7510X6ATfpMOy3hXwBwx9WBe2R+c1vzP+qTS2ZAMWeUH7r8yCQXwKZJ3T8wFvEGRJBIIccxfW8PWikNDAd9+/x3+5m/+Bu/ev8WynFFr8eLMrrAzmSywwwnuyznOzOObpxEpBlzOJ9ze3uJXf/pLfPHFF5iGTGizuYtDz+XShrrRQNu80PYIJRNATWhMACdjQPaIkOBMw+4JmcBOq/sLSqRLffTfuwu+tRJWKaVcO1dViBJWpOjZroeONxoxRFiP5fHIkm//8Ad8+vgR56cHLKdnPDw+Xf+5KV69eIFpmvDqi9d48fIlDvMLxBBwOSu0bGjbM6I0x+kXqIF2TEZrMAvUGqnGHdtPcUNwbRsADO4QIq0CKjCjTyWCAk1gZYJIgsnEDjMoI6dkYMdYIpIaQgUECrEVEQ1DElQBmgLjNGOcfI/kB2QzkIXcFB8+fMC2FZiwuXp5f0+nfwAIHZoSjKChNKfvBkPFZV3x+PiA8+XMl1jge6rrLqeaM237hzZDzD0Jg1DTOI6YDgcaQQAwj4xJwVmtuLKtiTjwOQnOyk5xgCJDLSOngCFd1wBboy4yhvwZ2ULIQI4R6vl5qg3VumeL+M8ExmHmzxKfZ72QqfnkmHqBZjHKA9cqMCaODJ6MYQY31RC8efMGr1692nc9nSWaum5un374lPdpC+D3MK37QdpXEhF8t2HqGZiKUHswbdulF9fmntIJBEokqjPSg+97cxqQk/HdMQOdHg2obvSR2TRNY7eCdJaxYnd0QqhADE4EAVLKmA8HBIlops5MDdwTKjW3bFS6HINNFfWQPPsNnPoiBCPgkGh16RY9eK8RXn2K90oHlx61ay0wh9Kb8p0Vo6nFHpYeIsQUWjhlR38fINxlj9PACT7yPa+bAhIwTUekwAa951r2JqE2TzFRFjSJAZISrHgaina3IfTFlT9f7hgm5Tp8/JGvny6C1p0JyBqq5l1Blv3mhQBaD/nFjJ4UHSVB9bok3TPC3NlAUnSqNWng1NL15XrAVlYf46u/ag4XutFr8AJo/rP56UnXZuitz5RG4fBWVgwDmZTf//A9/sPf/Ae8//AOl+W8f94Ih9i0w43CASIwLPd4pARiGpkCfzmfcH93iz/5k1/i7u4WowuhOyXJ/CFrqljWDet52Y0GDocbeuX1hmXbfPrjDRvcyijNmayoUhEQMOQZgB/OAiBykZydDVZrRatKhlutWLfayYAU/GpwAoahmVLO4uWwbIX0aYe8u+vF999+i3dv3+L89IjlfMGnjx/QVDGlhGEc8cUXX2A+HvHqi1e4u3+BeXoBtYLlckJZNiyniikbDkNCs4pmFdUWNGwI6R4Sj1DLaH2zLkCKBoS6a9e81Dj0ZHRfgQFJOdm1G0okxhlAhYYzNEQEyRALsEKq/qgGsw2GjV1uFJyV13OcZuQ8oKwbtDY+VnwBUFvFD2/f4XQ+wWAYhwH3xxvS/X0fgRQRjW5G1f0N2X03XNYFj4+PWC4X7o1C8PJh6EGoBPSclMV+H1Eogekm7MM0YZ5nlHrxAkCoLvlOsDq5IEXKDC7bxR/u4CSfxMZBFGOOmDLDk5sohqporQusCe2Z+d4GQFDhuqE1EhYs7MVHJGAYfLfnQwD8AC9tQ4oBhzHtcDFdSjKaVjTd3P827dec8V3A3d0dhmHw5u56Nkl08tbOV+Cz0z1fOc1xAgy+F+wKvyCGHAW1uQexNphyouoQcJeBqMN4tVaEDEigt6opE25iEAwj4c2ciKhsul2lWw4HMyoo7ihDDxe2prCiQDCHNGnFFmPGYQ5egCmvkRiAwsLaTRiSX/+lbCzmnYfgRZGB5yQcsQGv+7QHE9Qdoep2fjseDXOkjc2rn4kgGVEDG9EQBFG5MhiyAhagLm3Zbdb8rB7cD1oC72/dGjMy54PvZQXVqIkOfuaqN0k9FJgrkwhUJ+cZn8sKEnO6OUmKvOYIcA3mH//66SglR/LUW7OCALHKC+d/oF8og5MXrGOyAGAujZB+skHbhmoFyROpa6sAaF4robOJGo2noZhG70j9c+yTpXeZXXN1XX6yUMbk0oimvkpmAnrOA2ppOJ/ObiHVSwAAh68IMfqOQrv2Rt1dpWewMfbocJhwf3+L42FG3pNdObJbo2XTMAzIIihDxrauTO12okS/NDkExDjsN5vXQ6CV3e55WREkYIwzBMDqbDmr3I8yCZzXRECXhuI7rz7pDWHycFFeR0aiqMMYdNyPIbAAqKFUMkefT894Oj1jcfNaEZJC7l68wOFwwFe/+ArTMOB2GDEocPrwgKoFy3bG7eGAu5f3kBaxnVakKWGaZhQTFKPguukZTSNUPWZKgsOmjOMhDOQvq/t7WuAht09NogjSEKRwSizM+hP1rID+5xPJW4KIVgxbIXFoTBFjDzfO9BqFMoctAkiS8PrVS9zeHDl5pYQ8Je5U3bUF3k1Hd7ppoJHEui44nxdcljO2ul1fD66sSSwDpUY0yxZnx9remFtQIDRObzlDEplv68Z3ozt/jK6X4s5SIDagqaEIWdwaG3qOXxNBDXQNSSGiVP6+tXTdaHYBcrgy/kJA9p2WNL4rUboEwg0hQHSn+0juuksEtFJxOZ0xjiMGhwNNRsJy1VnVAgyZ0WspEFlJIQDRg6BNCXEK9p3Y4FFJ8N0Z7RsDhjwQW1Gf7hHcv7YiImCMGRWNlnXd5EEE0q0THbKrrSL5igQOoXbnqc6sZO6qIhjJbUPoXIIAqKEW8hli6L7GhtIK1lo8kJaVlwU2IgxMpqmoTK7wItiMRhtottMlJSQkwS6Wr5W7s+bRUtKRNOtIk1+HSrmL1uoyleRepETtQjQfZvpu0Cj7AaBJfcXqsORavIFj0dO1+OTLJklSgoihupRE1YDaUC4X38Njb+jJb2gU508D0S2/5xy2AlfZuOK4Yv3aXq9jsH7D/qlFsLGQsRoD1a8BPAqk2+Y0elj7yys7NGrC7s0aXIPllFdrhI9i8heyQeJncgylZAIwDEP2/VMvvO63By/ODgv1Mbh3fV1v6I+nv79kqrXWsC4rtFZID/ID3K4H10LuB7FZ8Y447szMXgSnacLNzQHTOCA5DNKIgcJqRRwGjHmApggbB5yjYLkAm2ep9Y4tHUaIREf1jSa7QTxVW7EufMD0yKK5eWRIx/NjLDtkklLGkCMF+GDQrbbGwysm11UBsRJ2hmSIRExTQooBy3lFbWSMbduG83LBeblgKwW1tv1nHO/ucXt7xKs3rzGEgLny3l0enlFawWW7YJIB4+sRtSrKUpCHzDw6hziYVr6iWYKaU8ZdzM+apw7ZBPS9r3xeBHsHG/yZQoVapbVZTJDPpgc3JiUlXqiP24oip0x3EHc6khjRoqCpQWtFjBkxCO49saF3uSl3yKy40Lr5umCk7s/owH85b1iWFcu6sOkL3tT1xs2LYrRdDvXZM90fR4UF+njmHCFgcVu36h0/D7Yx570vaA0QTfTMFXAHGF3obSTjN9/55BRxAXfVy7Jg2wpymhHjANVuwOAxNplOKubTcg7suiX7rGVkvzKM9vO/AtmhlwVRAjD73s5t/szTaiRgJ9NYqTA3Wg4xonhyQ/dpbtb1lnE3I2BvRBr+lEefrrCjM82hVp4TPKu2Dm+GTvxXHwAEPcCVkWPXyUZCJ+h5EoUT32jxx3QF83PTlKQOHvJh10BXVJpHwEMAvMmLKUFicslUh/foItvMiWkAjQqCewr7mojTK8lPWvm7eK/NE1J4lnV5GW3jKqDBPzPc9N/4ntp1D+gljkMPnIAj1HRLqSyC3Ui+Vv7u4AQtlvhvWt2fezVF0ZXs6q1hnmdMk7v7NEUeiF40JcBWGifjsJMIfbvuQvwIj4FSR3V3y4M//vWTRfDjpw8IMWA+Huh9mPnyD3HkL1859qYc3W3ATyg/yJuBFlVNMU8BOREy6Fe6lQ05RKQcQMae7FOdeQUvjYJWFHdX8Cid4g9kyOwWd0G6AazelcvbHJHDBBsHDHlgtty2YTmdYC7eVN8JBlOKZ117BamwQE1iSAkNhDbefPUah8MBb16/wevXLzHP1GzVRiPfj+/fI48jpvmIbkJLhm3EOMwIIWPywWHz+JFpnjj++wTcJ92yFbfy4p6m9aG6a2MAhyI6aYndaUiCOczII6G5WgrUgLWumCQhIfn7Rlo0Am2KammMhvHulvvgALHgXXjDfDxgHEfc3RyQU8bHD4+YYsIwzpAIhNzo+lAN1sAJLwaMs2IYE1KMqBjoJGJnNK1Ylw1lM9wcXyAPka4cahgCP3BV+g2SBmYoP2p7AoYEnww5gddLw9o2PMUTYqLWr1mApYi6rKjrBXUztNUwScNghUhATJ7IULGshOSHPPr+jnDg0/kJaorz5URNkwBb2fDu3Q+otaDUygPY4dVXb77A2/fvsJYNtdXPtit+GPvagYHltusheXOZOXh3c4PX9y9wPBwwTRPWuiGo4nggXT/74U05TpcSGWJOvo9JPtmQvLasCxmmfX0RE6Zp3Gl2IQRM4wE5jYTqYK439QPduPM0gEQwCxhByn/x4mieZDBI9nRwQ4gB0zRjHGcMw+wHOXiYdYqrsoH2FScscF/Zdb/09yRiIF6XOqRsygM/SQOaobqsQht3n5TaECY0YwGqre76udgaJCdPTHENS6f7R0ckOmTo00ZzDrsq908x9PSM4MgAd/jnhcQh8YBnBXd20zTvDPyed8kGbHOSkFwLnDTULoY0Z9xCsGwMvu6MU4lcQUVn43PV7NwBZYRUq4ZajdOhENanVILEQJLuG2pzRy4jRN87ijzSJi4PASGySQjCWgoIonQtL40eXPq5A3vdmnJdCtZ1xdPTM25vb7kvdjeuVmlW0k3DBYIcWUx7qosY7dsA7HBugOw/4x/7+skieDo/MxnbdTwh9W5zQG0NpTBiJ+yMq76U1r35q62hFsU0NGrUQgA6Pl0p2JUgaPjMNgwcsQ3sSAwGad3sFbszDB38o8Oc2f89QHVDrU7iEcouKErlAaqebwh17z7Qoq2bVIl0wT2hwuA4tIJElfv7WxxvbvDi5T1u727cuomQ5LIs+PD+PQ43t4hpQMrZWV6E2nLKbrzsgtONYuMu/iUNmfRnbW2nrTPIM4IcN/ksdJgU6nEYfYo1QLi4HtKA+XDgfmrZcF4XrOsFqgF0p7GO7ACBcA2LFhM4AMDcR1KMD2wzw3EaMY0jpoHM3+fHE1rOeBkGJAiLIByyUj4WKQakkQGZ0XVbAZk7JqUObFsqjvMtqeeOj6SQfCfhb07gy6neMEl/bqLv18DDsm0NxYBLWpGyR7TEiGgJl7Lhcl6BGmA1IBTa1kU/QKs2tFKxbev1cIxspqCCZeFEty0bYow4zCO2dcP79x+wrgtOp2dM04Q3X32F25Rxe3eLYRr3LhZ+3Y3nPZmpoF9lwH6+8fANzBI8zBNujkeM44ics08PgnFgpxtBAXspqx8O2A/9EBJyGrxRdeKUMvsuukg9xIg8ZCDSpNvUME0jhjzRhxd63fX5BKHuAanJfWTRrQt9evA/HxF3oo5Ewqk0JM9ufUcCSi/gvP1Xs2UIUK05QYIyj+Bew/veyXeq5lAko7jM3UsEWgmTifRLQ2syFsG26+Bg6mkx3ZrMzQicHcmqG3ZCngH73pq5iZ0w0x1QSFxD7RIEFp3ODg4xYBjGXhUIEQoh9loKOl2yG3erEJkjqIndDKHUjeSd5HvZbqemXgQjd3tRErQqw8Mbp/Uu2O86w24PRwcZP8u1Ix6UyAhIjEpqsFARzcg54BsIiCAlTvoQwJJgdgTNexvuFtWYg3nhzjyEgPvbW4QxIEchrUwZaAyB800CLLDhcSsV/rfAJRqdCtjn339GEfz6l19zodntjvytra1BrXG3FJyJZMQ9W2VQK4JwigqCaWRe3Hq5uJ7JdvcMjJM7jvN4z8PgkAg7vp5kkdJVmNyx3xDinjBNCysjM9N4cIXoWWAOhyznZ1yWC85PzygrH7Lmh72EsHe6tD8i6xDCCWccR9zeHHF/f4eff/0L3N/f4xe/+BrzPGNdN/QjeD4c8etf/xkfJHc1AIBlW9G2yv1gzgC6Zqvttm7SGFRKSEHQKfvaOGWXreAPf/gDTBXzdEAeMm7ub2FBUNyFQgCUWrFsqxuMZ/Q9QM4Jd+G4w20hRBahSrp1f6kZ49R82l9wuTzhfHrE5XKBAXjxgpTvv/4P/35nlY1xxKfDa7z64iX+xX/15xitweaAFQv+9g9/i599+XP8+ss/w+nyhI/v3qG2M6oueDoZLovhfFaUIrg9RMxjgskJJhvUEmCCoJvvJCYoArSxMArIVH7/vKK0DeftCaWuuKxPmMYRr+wFXt2/wOvbr5GCIasizyPupwmGAbABqitUN6jSdoqPRMA0zLDB3C+TEC3EcH//AoAyVBcCWMM4CP78z35N+BbUG6ZhxDBMGOcJEuhIs64rWqkQtwoDPAvOyWcUsEdqV9UwDwPmacKb16/x5Zs3iJFTBYUUV/OFkHgtUjyg1oLT8wORh3FmY+eZkABI7BpGpAzkgcnuALW6KUUMLzPavSEJTYm7plErD0RJPHiPLnoWZ0+bw38p+cFEBJuUe249kEJGvpkgBlxOF7q+5Lg3v9Hf6WW7YFs3xJCo14uE3kKivk3d3UkL3/W+Oy7bxt8pTwgBqK637dqxBkXZCs7PJ7RWUeqGcRxxOMyULLWGrdBovhcxU4Yji3hKfdeeJb5X6ueNOeGlwR21A4XbSbnTCsId4WVb9imlE2r6oV1rQ6kN58sFj4+POMyc/JubHqQ4Ai57gV19nY+HW3ei4SlAH1wgaERrhnUraFBssrIIlmuKxO6sIgIJmYShtXAgjMC6rCibT4FiyIF7PtsqtqbAZUMQ4HBwf11PmQA4oak0Qv2XxVnBhIJLaxABDrdHjIcJx/sbjMOAYRoA0PINXtRijgiJDGVBcCPx62CU3WUGIAISEpHFqvW6dvinFMHb+1uHKep1MWnYRcK9M+ymrXC2U6vq6d7iNkh9CWv7uKpuytxvYmd57Wav4sUL/vx2uNMbtt4BdTJNa4zBQVN02y9492P+723rhsvzM4ofRL34GGRvGiTgs2kUAMwnOrJCD/OMu7t7vHjxAl988Roh0MwYQvgh5wE30xGlVWwePNtZm+u27ckXJj7g+8ur2qOp+Dsj9txAMs5qY2jq4+MjIUqEfVowkb1ZCAD3UOuCHBOgfiAHsgxziL5n434tSmSIrNoOe3UrE1PCgrWsKGWlf6jbYLVW8fbtW7RWkYaEHCesJwHGAX8+ZM55seJyXvH08AkvX73BON/i8XzC0+kEtWeonnF5zricE5YFKJQnOpGqwaT4YyUg/8uLN8C9i7OKrAlO5w1LOePj5R02XXBpDzjaEWkKuLEZeYhIpkhKaJnN1gyECZfLI9aVEF6rulu20Si+PxQkgIkIcxQDMI8O2a8LLAVM8yuKwjOh5tKYNNEbuOI71e7YT7JJjxK7shy52GfjlkPCmDKOhwNubm64F/Zn1oQi/d780D6MDMzaKmIgNZ1mG1e4KKVMsXRQSNT9fkd3fYJPl+JZn62R/FHBjEMLvAmjR+Tsvwv8fXEZTnBZhyrvnCk9c/M4om6OxoBm377edXIKWZStNBI/IpAih6Lg+ZI8km2f1tS4X6qFh2QLV84AJwVOVArhnvtygjaadKeUkHNCcVlRdeg1hrTHHnH8Ba/0Z2Qfnkl8Z6zxM/YkdnS7wh42DJ6VWyv9KWbTk8M+8TaXz5RacLlcaB4+jNyxBu6zUyS0DIMjRIIBXFFZ43PcO11mPDKXsPsBtUYTezFD3D8CMVPu0yoZvJkoQW0N67btE3FIgYhM4/VVj+2KcUCzSAIRhFIUlzGpCeJWmAaSKOupyqk7jyQOzuFAw3UIrNFFrL97zEn1tYExk7Ap0zVC8L3jPgHCTRTA+7Jv1/9/f/20WH5Z/LGWvdDAgKobf0hy+NIfCA4FXOLWRl1cyBniFGESvfxliBkxZpRSsBXalkkQbELIZMoRQHIPO0fQzVD2yY2PURLe0FI2Tgy+GBWfFqr2cR+4LCs+PjwzY8xdF/bLs7t72J7hFYeBYlvefQzzESnPKOuGVipyoqGwfS5eb4rTdqZVWRph1rAtC0QE83FEFIG2ijQOiDnCQkWqDVspuKwrlsbu8DAfSGvW5tCSeJDvPVQNh5sj6fm1IUCQp2knihgER3WYNPPl0MYYklorIcnkCXWBXoDoLiIApgOh1fP5hIiCIAlDHvGrX/0JUky4ub3Fsixu0kuqM8KGs53xfDrjw8dnSDAoNmhpSCGhlA2fnt7DrOH+9h4iMyRUXE4fcb484/3HC07nguOrA2wUulWEjNYG7syEYbqoDdYalsuJe7u6YK0r3p7eYW1nPNUf0GRBkwcke43WMtZlwuOnR9wdDri7e4FtPeP54SMQFyDMEGvIIbHJU8WyXFBq4a43R9Cn1L1B1WBWECJwmGdPHafwvinh6ZACSXTBEC0gtQAphrIUNjApIqSEEBJqo5mymusGO9zlvp55GjAcpp0ST6uohNFZgBZH1yRu3O8Ip5Kbmxe+qhCHjEluoUSCguaY0j5UaJ8WVfeGl/6z18y5YcowCGrjodfTJZp7zKIf9o78tMqDc8rBf0eSlrZyIcqRAkLqNot8/qzyWQ0WkcK4azYB6o6bFwsTJ6G4Xk2UsGuaZpgaPXfVnEgDPtsSvGgAt7c3DimSk7AjnQ2Iwt1wikxaaA6XttJV5IT41kb2ozaGJTcXcvdE8xQJcy7rsjNSvV3hwR3pKiWfNcLiRukv7l/gMNOYm56rBnVmdgjRHY6UbGMwZUddY8hbwc9EpqdC2+Y5oNR5tlrQQVn+DK5o6D4VMQ6e0qIGQaRDU7RdnN7hSQBYPSRXLhWpGIaWEIJhcWt1CD+r2gGpRWZb8jKgFsNzU6QYMWZ3cwqesqGNZiUS9nCD5veyNxF7z+6QetUGE2DwfXQt9bpr/qcUwdZ3fOKOBV7ArDaq9EO6Fg6Y3wCnDleQPRai07sJDZiy803OZCxtoQ4mRgSjdidYQB5oFI1uiCs+tvtdDtaXE1cdibiLBnVxnj7QdxOB9OrLsnq3JfuCFvgMOd7bapAp5ktsQUBK4544zVE8eTxN23HzCmaHiWtzqruIJIeaoG6gK/BCyd9gKyuTtI3XZRwJJZj2BpSL83GkTdYwjmTDeQMSfQkPMUQFcjLvcAeaAfjUW2rl/svT1dktAqK8HgIn8khAdFlGCGSD3t3dc5fjxS/nDNUGlOrPS0WpBcuy8TwMDaLgst4U20ZN5jiMiHFAjAqRR3a9yxmny4rzesa8TQT7Aht5FhR2dFJop7auF9S24VQfsNQLntfvsekZC97CwgpJj7CQYHZBrQuWZcPNdEDOE9bljHVd2CVHOnKkQHPnLo6upaCNGcHIBqYUyHdXvRGDN2O96MnVPNnEmZ4qCBVAI+lAP1uW7Xupbkge+oajJ5lQgM+kABfxS2c/98aLJtplc4q+G2QPAxl2Yjx4zfdEyc0oTAJSEKQYoOLnSgVglbs5pZar1banS9C1I6BrzXoE0lUT1+mDnGr9CESMRCpIPqQBhtjVhOLKlvDzBcqVh1zlUeaQZne7EXHUQrrS2HdngX6T61rJbvZDUD0cOUbFMJAEFENCClxNdEtDylZkZ1oH4V6KLEo/7BPfR+4svTFySYs6tEjAyyeXVtEdteANuRn/u8FQjRN9a4qUBSlkSkiGa8Zl9UKT+jXzJ7CqRwZ5HFpH/rpai3fDrfLMPKzAro17Z1iHfg+cNRs7xKx74WURNB/Orid/Mw43W/VtZVWHwvvPoG51GAZfFfBZiiLOEFfmQHYUEGwomuruVANT0HyEvz99qq0ffz6sEzEzAMndzvrn/amvn5ZIhMzOZuM+ZoiZIabxs7WjcZnf1FAaq3ZKboYbh+vUwWcUcSBzLYK1/BAH7KkH1lBr1zgR3xafjPI08sFr1Se7jFYN53aBhIh5nnmgO4QK63lgLqmQgNPzCW/fvsX5fPGLw9/iCiuSFQUX7MN9OKHcVbx88RKHecLT6Yz5eMAwZBaC1n8GX9LRBbssKAPGiRNvWYrrJhWXSmhgGEd29tOEmAecTyRdnMPJodNh7zZbMxyOd4gx4NX9LUSYmwWhLigGT7WOBss03Z2mjHVl4eV5olg37jzqVpma4N12crbX2uheDyEk+OLlPUIU3N3cUUcERt78+Z/9BYXDUNRScHp8xN1NRlIX6CLjcBjw4mbG8eaILBVIMyTNe+iy4VuUtuD2VcTh1YyP6+/w4dvf4ZjoIoGVzUUbTzA06EXIWKsfobIB6YTNTnjS3yPkgvuXFdMc8erVjLAawsMFDSuem2JWYNGE503x6fmMlBpyLLB5AsYRT+cT1mVFThHTMDI/LyUsl9WnHT4fdC0RLNoQVXAYBx77paCp4bIxlmpIA0JIvGYiqLW4+JmUcpMAq0oBtO+6g3oRBQ+bIYyY8hH3ty/x8sVLmkmA8iIYPGWcmW79EA4ulYAqiCiR4SyRUJTs/+E5FVPCkCKqArXyvYEopnEExi7lUUjlTij6NCZ92ee+neJwft/Ph5jRGbshAmOmGL41uGUXOXxbWV0hDzQtKK4Hg2AvLk0rFMDT8xOndJ+Ok0+6WfjfTQh+5mFCTAaJG7+H22kZGEv28PjswbCdtq8U9mtDaK41y5mWhWqAOjHM77lI2O3RSlt4YPPQRBoZwh2cQDO6zq1Ej/fx4cIqGcynsu0s3dYUtemV+RgichxQyxm1rpxdzYujT55m5tq9fldJbhNhcxwQMU6MswvYYBaQk3vOts4xRwfceN1Dh3sD8pQRh8zmx31jBTRmUDOPtWJzpyA7WNDc0ILDkiXBthS0pH6eBnrLhmsgQwWDgGvotoL0To2je7eCqwmBoGwVrRXPJAzI40zJnbeRy2Wj1MbN7//pRVCuI/GVoXSNzBDr+pMrHMrpl10exaXBRaWu5wF8AHdM2gtUbdgTcc2ABpdfaA8V7WXFi6+zt5qqJ7NHv4nekf2Dj2IA1m3D8/MJZXMrNvy4c+0dqfRi6IzEAN6oeZoxDAMLWunOFLwmIbDbYZSJT7zNY3kEaC5AJ8OyARUwVaScEftUKJ11dj14SAwIu43ckNlYjOPo10LcfYIWZK6mQw85pZaw7z77rpN/v5XCnYj/jGgRBg/09O4UIpgPM5o22h71ezUAL+5fwhxz39YLUFeMQ3KjAD6wAYRXBGAUFftqNHCa37aKsi2Ih4g8CB71Ceu2IdkKlYy28kWvcoJJg26ua8InQDbEcIbhDNh7hKwYJ8F8mHBzc4SKoDyQzNGMO8e1Kkqjo440iuvVmANYfY9LggjZuKEbODTP0vTGIERBReGkLsC+v/aumO+B698MO9y1oymuB8Nn/1u8E4fuiBtizMhpxDTNmKd53xt3pmPwIFZJXmDMPXOTQ+kaXDITiWyE4Gxffy/8HY5wqNH2Nww90aG2K9LR32++Nn5q9vceV2SFBxZZuXvck3DP2dcm1BO6/aLxPNknbe+au0avVU4m60KziRgTkgIauafqrjrNJxS+d4bQ3IrPfGfoTWhthab0nRVu5mG63K4HABqv0LD2d7cpJHiT6AcTYcbOnqUMob97fTsWRaGOvjS31aOYnVyBFK/JFN5SeJYlGb4MJ6iI0gDj39fWfTQ/n3T8Ldfr70PTBdl1jQY2XPxXeeCaNzI/uosuq6ADVz9LbC+1BkfaQmTR9L2wOsva+hABgzTj7wuidBrJso3BIIk73sZOEOLBDXDkAOJrVzNgRwQ8od7oL90yGf2deUuOCJAHh3l/4usni2AUUtuHmVZd3eliCK45csDD4AMUdpkR0CMwhLu93qUEv8C0RXJqsoEdWcro4kbrezmnK8M7vfnmxh9owglDv9H+8mit+6GXIvH12hqsFXz7w1v8x7/7LT49ndFAdxi4HILQFg+M6AdHCpRe3N4ecH9/i9evXiPEgHcfvsf5vOCHHz4gDwMT5oeEm8PIR0MJr5g1iEYESchxQDwmtFbQtCJ66gNMUZYLJLEQzTczRjVsrlFblhUCpsCHGHF3pGatFEIC4ziz8G4sypv1qVzx8eMHfHz3ATFGxJxwOM44Hg6odUOrBfHAQ3rII1JI2KyQQm8Gs4aH52ds64Zf/elvULYN7374Huu6YdsYcPzV1z8DAFyWC1qpuDveoTTFD+9+56LgiL8/rXj6eMIXX36Nr3/1GzydL3h4PoO+oIrvvvkdPrx/izc/P+L25YSf/QvD8bXheP/XSPmCttL5osaNh5r26Z56uwRFUcPHyx2WYnh4alg/Dfjt9wMmO+Ieb5DTS9zKLerzht9ffot5Cnjz1Ve0NPODympDjhk2BUzzzPzDIAimmKcRNo3OeotImQzm1YCtVnz7w0cy7aJwAhxnSKBjUdsqlkeaQLfKvTVT6skcbqa4bBvF6E7CUFWaHA8J96/v8PrNK3z9y1/g61/83J1AHA6PEfHIw7i1q+yC718DLHkun++RzEOn/e3tX6VsKNvGHbmpo4JCo29l00AJB6Nushsk9+8grouzxoMwe9Zn2Yq/y/xn6pmffQ9luJJOaq1opXjh5a4MDSjrgm3b8O7TR1zWFa/ffIG7FzdAJPno44dPUG24P94jJSchhUBdryrOy4XrAlAvu5zOXAeMwYukfzZz2YFEqBAdsqaotmG9FNSt7XFveaSX6rqQ5bmsLGoduau+8qnbRi5Aj55qlGR0KD52L1ULqFvDslQYzvxd/CySxlu5lAWlFYyHoxt+EArsUHp0M4BlW2HgBAQA6ukRqtQkDhN3yMXdXESSQ5qcZENkIRE4azgGlI2sf3E5TWerq/s154HkxgrC3tURgjCkvZgBwOJn1OmyIgRBHpI7DDEkfJ4Yf8Tmj+dvrcBarvv4FLgayCEi54B1La5IKAgSkAa//5k34/npGQbDr37xi39aEaQA0Z3X9wvlBrmfYcJsNKR/1j5vwPvB/qrgCqLCJ74ry0p89yQ9m8p936JTXbXfmEjqL1p3inBnGOum1d61QqBGBxD1jutyWfD49IytVmez2f6b9a5p/8s7oCCCnAl7drZgN6retgoDL7gEoGkGvSD52TueDmMXxnlIIaL7y96aF0tj58t9G2HOHm0CX/KysLCzKRsnkzGEvbs2UNPX9xLbtuHp8Ql5HDDOIyYX9WsLXH04nDPkjJgS2qaOufO/t1pQWsXheIROEz5+fM/YISVLdJwnAPDcv4QcIj0ylxN9JhHxfHrGDz98gMUDxtsTPj484MOnj0jBEIPh08ePeH54wv2rBKsJQwLmSTEfH5CmJ+jmk2xoMDH0vq43U6ECpQYsyutmNaKtI7bTAUlmIE2QOiBpgOqGrS6YxhnDOKJ6QxIaW01xskKItN0jAGPIiQ7/iK41C31PHGHW6NpiiuSJCMnMUwc4bZ8vF2zb9lmDLbv5cs+PCz21ob8PgUVxnEbMhxnz8YDpcMDldPadU0T3V2QD5yiKf89uLN1JLTxQe4Nj+7MPwN+b6+7E+j7LG6r9Te7vfN97wvDZt9mhNImysz15Pvjn8knyeij4lNV0n3SAjBhd/wk62NRasKwrLpcLjbjzgMIHb3c2qk0Ju+4MZ3PoUf334P+uhUQysYDQpzxnS4cQd5p9n6bMXLZQ6hUWdl10HwZ7Y9LPv+JM7m1ZHQXrbPdAY4hCz81knMyDXJGj1ujwJIFeoVLY0a91Q2kVkmgToQwc2U0lKJ/0rEY+QFyTbExuYCAcxxFG0PFsSsnpMS4h6SdiR7h2pEwdnsAV8ehNFwu2B60b/Of7gAFgN6Z0jWzPRjV/pAL8+sfupytILcCSwSRCpTIgXBUa6R9KMlPY971FfToPwkLudnK1Xv2Y/9jXTxfB1B8HZtqF1qW8/EVjygBoYlpapSWUAbAulie84jwedMEnf9EMWERzbY0FLtKDj9Xqo7hUvhDZ9YA7FTvw5hXlBWytAa3t8gFClNyBXM4XnJ6f8fj0iKfnR2xtAxI7rN7O0v+OWpQe3yPCaSuFBDHBx48+VUlEziPydPQIKEJfpV6nSnghpl4wkD7t3Uxtigx3anEXEihhmTHNkJAQD9GnZI9TGbO/GAmtksEYRBBCRakVj4/P7PRGMlJVN6QkuL09IuWEPGTkzAdV1VCLIUjj4nkEok8nqMC6Kgk9dYNqxTjfAhDc379Gis94enh00hPvVUgCCQkpDJAh44uZkKtpxdYqhtsBWyv4/ocPOJ0/4vn0AV988QIvX93h+WGEWcTd8Ut8+eoNzh//M56eH3A4f0I6PmLyrrvvwijkVizrilobTqeC8zng998c0dqMoF/hbn6N/+LNvwKWjPohI0qDHR5wuJ1w8/IFLCmWZYGEBJEJMZOJuekC0+KMtP6UC4bM7nLVxkm5GBCY7p5zdko9PSNbbfjwwweEyD3186cTvv3tD3j77iPCODFGhtgOYM2hY4Nk+CTIg2AYBozTgPvbO7x68QLzYcYwjSDr2e+PF7UQAk0nQAibjH0vYnC40ItYEOp3oezam5PZEDoQZ8jS35/AIuLCa2tdPtJo/mwdenSIFtz3lEKJQDcWb42gYI4uzE8RPXpq3S54fnoiHC9cm8T+Kwn3S5etYJonNnPTASmPsEry2ctXr2HGiKYg3MMB1Atyi8OQ6XVdiT51P89qqAB0Y7JCs83Jaxk9Ey94zS5bcWY1J+XY+B6JuxeZJ1GsnXSC3lgIzALWlX6wLDgJkkgIqaunewwBvdWgeYBy0qsVPdOwmVDcXrkvLYX3oDZPit/olHQpCwyCmNzlKGWeYZGEqG0jOe68XJBCxNyPIPhZqEYIMPLMQnFWudLAwwD4gefNUPTnzbApJ0Q35dmVAK1VLp/3BrsS2QLhfUsBqAY8b9fq0pv+sWEYC6J4KIOp295VaDBoE5Yxj81caqUeVcUVCbH3bv+0Itg1feI7D+ofP8OY+z9H787FdwPifx+u3cQOk8r1ezvWHGDuAO4T4P7n2D1J/9/o+5XrFNmsG2ijXz50Xz/xP19rw7oWbBv/UmfRYf/t/UfJj/++/9VdKZZlZRZa6lZT0ZexzkSyffL/8TcQ/IOfJTubCf7SqHbXDWLmKSayarlAoWBbwEPTfSpVgNroNF9bRbAe4MlrEkLAODEYNiYerupeq10I3DcAhKh6x8qCHRyyoc7NnT4GEnoA4cMtQjah8YVIOeP2cIvaCrZ1QRwijY+14nQ+oZSCEIBxzLg5Htx/k4y4Ic2obYBZxnoOKCbQxukq911WIclhWbmbPJ0Nl0vCttwAdsQYX2OS17hLL1FM8XhZsIUzTh8/IecXyOkOLVSH9rmv2Y2T9/21P0MO1bF+dJjbXMcIELPntTReXGgzXM4LUkr7/vh8OmPb6O0K/dzHsffVP349BF64YsA4DJimmd6RganqQdQPP/ju9vodbC+w1ze/d+37N7fPfyKnHfOCJf15FXb4AbJblKlVZ9D2afP6A/iv6L4m5L+//wrYaRvuFqTefaoZSq2eDpD291r6mOBfKWWkADcY6PMCZRr8Ruq7KB7UTXvqhOx7IqAXrq4hVA98qc467/FUzMVjaCx8WmSkD3dRrguGXH9WjzDzi00Iks9N8+IYghcbobZSG3+n1vr96lOo7GiMBDYouxZTAfN4of77qBmCEjVShTvlOAPeTUvEiz+jia7aVDXXnKI/F9f/VvXG3Z8fuvr0ya9/Fha7zuhUu07EHaFSh259sefPkOfR+nOhRhi5AwZkI4OLfAFS5OdIjtg1ocuXOjmnmyHQi9mxCxHkHPY69ce+frII5jz6t3O82Li8bT3GxruQ4rTmnNOPLXsgtP1ShblwtPkyNwphgBgiEMwzLvtRDEhwEWhiiS/lukhWVaz+s9XouD7GwaeuvEsiVIGyFjw+nvD27QMeHs44nzdsW0Xrwn2XNgkRMZIa4G4XMUD7ywng08MD5nnCr371M9zf32CeCSNqMz84I7rqiheeS3l13ZBE99OzBK3VO2ZnV67srFNoGIaG0QXW/SyjY0fD+fLIz+y/93q+kDrt42fdNn8AgHmecXt3g7Jt2BYyHJ+eTpjGEcc7GiGIX8PLtiAKUzJI6S64f3GPGANuDrcwAw7HAxCAL3/+C2zrioeP77lHKhun13TAq9cv8Rd/+a/wdHrCN99+g+fThhULnp4XrOe3+NlXX+E3v/oNfv6rr/Czr9/gu2/e4fe//4YziAZ8df9nGMY/we8eBE/v3uKdUFZQV2qxQs+oHHhf8uEGw3CHv/yL/wKT3OBVeoOhGo6Xgg/v3+HxP/w1vm8N/04b/st/869x/8svMY4zDjcD6mKoqyH69eps0GGgkbg6/XwpKw9r1/MhjYAKytOKZtzbmiqiKS7nC95++xaH4wEv7+4h1bCezyjrtsOcIVALywZLkGOfoureZcPdcF68eIkvv3yDISdAG92GkhAiUnMfxSsNf1vJiktD9iaQ0G1xgZUEL+T9rAucdLa1OVqQPPxaERNlPYFhbSgO5XeNlhr3Tc2B/ghCbHmc2bhBnVnZnIgTYI0MUN2JGfu3o25MAdsMKVMiEfOE0QLSxEYuRqC1dV95MCTYcHo8QZsi5p6iwaO4r1GmaaJMYWM24FYKyrZiWVzTmxP0vHD6GjI9hyOF6Wg8g7at+IrAY84SG9PLhY11h6ZLqdzLV78eGhEj+DwJ0EQYiLvRD1kK09RTCmhKRyACW4Y4kF0ZEwOui/H7b80LrvIgCAMh6BzccH/bgMootmCyF7PVLfFips/r6syu3ggy3d7o+VwpF6J3aUK1gtYqkxlUUMTjv1qDmOx+vuprpD7QWOO9oKfqteycVkXOAccU0QAU4/let4I8TxjHkT7Gy4IUViSJuL+9xWHO+x5We59apL82gClqWyBiOB6PJEn9U4ug7e2BgRjxZx1l/0f7F610uttL72n45w3X7/Q5JffzCn2d5tjx8O+bJzir7w9avS7v+av4T7KrRkuNUEIPtd3WFefzGeu6Yts2ZxV113i2xtePZDtGzo6nf3/z/Z1hmiYM4+hF248T49QKL0Z94Q8F0yn8xezO530C7E0GI534OZjs0HZCkAFQD/ZsjnEHD5DjYrwbfnsH5DdPBL4DJMuxTwn9PvQRQX1y7DcpCNzeiI4ZIZCN1Y2ob25vsA0Z27bQmDgKICxKwzDgMB9QW8U4TEg5AyJobcO6bliXE5bLBc/PJ3x6mNBUkceBXXdtSBdFqg3D44BhOxCyA2EZUyCYsyIHwmoDjhjSjFtNmELArRpiaUjnBXY64fz8iId1xdvzBffffoVvvvkOX3z9Cl/dzdDQ/FC/7sN2WRI8BswnZ8A7Y1yn/d1OrHnGm9PcmVuZYI3youfnJ6zLxSN++p4O14Ln3W2/bwAw5MRm5XjAzfGGu29lInfXCgaH63/0uJlCevyXf1+7viz+7/bev+/4WZhDZ0Tj8/dS9uek/xDxf7sDJ33NIU5W2ydAvU66/N28wYS7fvi3DdGz8gKLrapxnjPz6Zdyn5DE9XAFDNe+TuytMimFvw8/D+BFMPQA2cY8U/fEZERSI/TdhA22R2cFMyBRRhWMO8Q+ZPc9Kfy+d4u74BPx51MWDAi+l9x3iICzYt0TU3VnwdO6TXf2Z5QKIPiz5zpJE9qGab/GrrkEUQozaqIZJdaN9vmO1+aaTtdZq0+M4lOnGGUT2iglaQyY5ADj07Z6k19BL+FAXQqau23kSIMRNmGfnevozGY/T5UTbGd+djF/VRLgVD2EvSrNMgQoDkv38PAdrPFzOvQ1mUvHcqFz0k99/WQRXNwxJnbqfvKRPHYYiNAbzFMDVGHKqU21omnZoTh2hLZDLf296gyzUjYuXyOnz2U7s2MJGSEKhinBWsP5mbuvw+2th0UG1K1gPZ+otxtGnC4nfHz6hNEthz59fIsfvvsdPr7/Ac+PHwmJQjGm6LuUfkgYYN1GzTANEeOQdhG1iGAcBnz11Vc43B6xrGcXRJNkEvNIlqwLRUVpEmAhQPKEnAbfNwBx5n6muUdnLeoP3oa6nNCUjvPRQzK3bXXIynyBn6HWsG0XQIxO7g6hkQjEXdO2sPNLOTl1O2K9cEcaIw3GgydUbJcnaCu4ub3BMR/pygFDqRu0KaZDwjgf8ebNa5gqnr5+A5hhCBGlKR4vKw7TjCSCKQ14efsSnw4POA4jGhY8Lw/47d+8x1//j/8OMh0QpgN+8fXX+PpP/gQvo+D48BHbv/8dtudHfJ1GpHAHHe9gKcMOIxAiRDJgiu3pGaYFUc8I9Yz49N8jiiCnCZIS2jTi6cMDfvv+7/HD4wl/98NH/PW77/Df/dVf4b/+3/1v8L//P/zXgBTEcUPdgLbZPvnFMMIQ8PD0iG3bcDweuMPw4jMM3E+fL+xGk9HY9/X9S4QXwC++/ArLuuLjpw/4/rvv8Vf/r7/C0/MjpCliBnIWuN8RTCIaIgaJyIHsYWsNX37xCl//4mf4F3/2G/zpn/6aU8K2II20KqPbmewN5v4lLOBl29zkwM0rIjMUAxIPaGGYcrOGYYi4nSNarWjaYJG/m2qD1YLqBcla22USIWCP9xmGkc1nISmN57qhbYXnR2BkV/E4sBAiqjWUVhHEcHO8YQ8psu/ON1SeK4NgGAbPwDS8e/sBy7Lg9niDIQ+Y5luYJMKhtSIQg6S3qMANFA3rWlFqw+nkxvo5w2BIaYCaYts27r5aQzBDKN20PiFFEtlMAmIeAQ5CuJQrBGpCtAboRBvb7SXNWaJSSOqrjUIMkpR0J5qJu0OIKrZasJWKnAIz9QaiFOpEmMuiKI1pEGaGcSZD0xxpu2wXSACOTjg029gU7FF3AAl5dT+IqxJm7z1Qa4wviol8gaaVZ3yglKqWzRsPF4FbQxTBzcsZKSV0c4m1bSAfOPvelnBvbczWTLVBm6FsLq5HBDaD2cZrpAFNDDUUhOcTc1g/H6b6oy/AEGm4v7hKYKtnNor/1CK4/5TPIDmDkCXqjihsEK/VHh3K71OjT3j999h3Ds5ivE4l4PTV/62Or7cKtYBQADiDqOcYAq65MrfYUde5OWPIEjuD2gqW5YxSV9hnTu+fnxwCZzXhSuIJXmQ7LCkidMEfB0K/UZxppvu6Rfr3k3Bl5klnA/bOv0+X/d/w1ln6zknRXTj6hVX3QOzsVPOujB3m59N3T8PwqVavJCWR/ln333LfM7Ir7wccmYkk9SlqlxI4FX6cRp8oC6wHfQVFViAPw55MwMxJOkUMw4BxGLEsBefLCXUraM8X/Pyrn+F4uEUOhlAr9PQMPH7EPNxgjAO0DrCkaCbuI8knJtcCWMFQN0jZkE5PEAhiLtBhQE0RphWLKc614HlbUB4fsUrA09MTDx9UWKhkfvZ71hEBA7iz5hRNf1veruD3MLoBdookpkRP8c45oVVmVl7OZzw/PWJdL/sz0nfCXSzQnY7U70oQwTyNuLk5MslhyIA7wZga0yZE6OEpsr9je6qGT7adwdifP9iVzMIJ6rNn8x+88z8CgUz9N/VO+7M/fAUgjKkHABNXlJIIPtay22Dt7GXrkx51dUxHMC+Gjq+o7c/Rrkf0z9Yn8ForuhF1TIk+qQ6LGbCzZVvzSdE6I92f9ditt2i4708CfGUImKIaIMIkmD5Zd9lB30EZrrviH6FU1ouOOeKC/d/pCMyPADF89tqLT53WaEygYdeYsjhY511ySuO4zSbYxfvNfYHVdYCmzr73AmzqllxKVKm0ekXUVPje9bQQJepEJimlEqZ2XSn55949mfdzypE7ifv96CkhnPY4AHCPyUsW1BDalanez8ZS6xWRxGc8Bj9Jm3O6ObECcF/nn/r66Z1gyPylIl+ybeNDtyxn5CHj9sUdtDZs60LGp9tPDTmhVUHqYntcXVucMHpNhG/0uwtIDLh03V5OfJFq3ZgAfqkYUsKrL17CDFjXDcV/H9507rcGsCufhgnHwwG3tzdQa/j46T3KttBhQhLoVqaoSuNgEWekBiMRI5IBmvKIkANzDIeEMGQMU8Z0mHBzc8vh0XMV4QenISLmgCGFPR+OFHg/rBRYStmJJYCgOrRmPVkeARD1/Ye56wEpOGiG1k4gC4yOCAzwbKjbSjaYxD3Ute86gwxktoYBaUp0Y4iC59MF63bBfBj4mV2zGIYEg+J8potNCJwYh2FACMA0CdbLBd///bdoEiGHe8SBWXHLtmFtKyRF3L18jSHf4Di9hnz7DZ6XjZl2ywXH+RZff/kbHD99j/D8Cen8DuHpG9wNhkMQFLlHkwGXGNFCgM0DJAiOEpBheGkVgymObUMLEWcLWMOIT9MtZFI8TzOe8wUnq6jbBfooWJcTtC2oYUWTDSFNSBiRMuHfUjY0Vdzc3lMiE6kpu6zrfmixUA3s+D29vIIvbQ4Bayn44bt3ePv9D/j44QeYGPeYIcAaG8kKsnPHcYLCcFHDGAQ5RLx8+QI/+9kbTFMGgmFITHnfisKsIA8TCQJO7+8HcvACW0FYMDqruqMvrRLSNxhtGkPiBLiu++HbjAdqsg5d8sBJuTd2FOavywJtDVvlvvnh8cO+LgA8w08N0ir1Y+gFCE4cGndol8ktdW+gmxsa5EDmrtNdMM8zUsrQJlhKw9PyAIjgcLghm7UU7m9BTVqAQGuFrgaxQM2nUeNGZuZVw6yNhbUU/kV4GlguC2qtGMYJISXoxnc0unmCmu/1qx/GjQ1GSgPMBNZcluBJHjGSCKJKyz36ctJpq5aGWgrCEDGAaTutkH1qdaPQwbhDhAAywAswf6fuItML3boRzWMPLYB5IQpuQtG6xMI9RVtzhC2CFgoBW2nAtnEdpIqQQc4GmJEacyZ50Fc167JiCxufHWWIMgDkgeckUWBBiozdungKR3U4uytqBOKwcEMMishEd1RNlI4ZkD1aKXr3uim1qerNFH1u/xlFsH91F5OO/5OwxO7Lgl4nm6beVcPp/HsrSe2rNWpfAr8H2Y2UNcS+mPVZMMa+p2BsT/EL1M1sl+WC7i4h0qOVONnEGDGNE9Ovrbl5NBOp99bVuzP+stgxbP6tM9A+6yAEQq/BmJ0hylgV/rsd6xbvFjk9kF1nnuINWKtQBCgCWisoxRmD4brDIdtcIBquqxj4vqZPcYI9hbuvavrg2Bl7oX+mEIn3u69lX+QICEsxjZsdG6n2n3W7PvoEiQwo9iJIpqqh1uvPlRAY0ZMiSt2w1Q1bIeycc8J0P+D17T0u6xPevgvYlJCTjxfYwgCJEzTfA+MZc9pQRJk4D8Gm7HkTGgU73nZWASARyHfQMOI8vcAy3eB8eIX1olAZQImP7HTtVhtqMSB6VqS5ce8+B7ivUW+OHC4y37dYM1jgjlSMcT2qQCsrUozI8wTdFKenEy7nhbmbEbsGTVwX1rv0vqEzM5iz+YZxwDzTCqqfDJ+rbvednz++fTfdJ02yjK8THjt5TmMOvPCT+iu6Izn9ebf+YnT3lXD1cdy/I//I536OHHz4ifxRQ9fd0tHD9il7/+0FO5OweUROPwThz3et3A437fs2OKOQhh1NtaN6/s50OdfnaItdf38nDME/Z9/Zd1eVGMVRFN0/Z9+t7Qh0X0j5BZV+gvs9gF//ts9ruP4z2H4JduaqcxR6DBHgfrIx9XGbO1/j/99hC4XLV/w7fb7W7VFxnGo/Y4PrFbLtBEP+u95o2/VX3V1zfPILwfYEiiCAVIMJo7YAMkjp4tPPsH4fvZFXDjrqF6D589czQvf9oHFvvL9/YpDqO8n9RisVZkEcajV/Xfxu9U7mJ77+kSLoN9d96XLKSCEiSkNyiKv2hWltqFuBIsASgMg8veBv1+X5gnXZME8ThpxIeY3AeVuwrdvuwjCkwQkGLIpDvoGp4vnpGa02fHp4wLZteHx6IAacAuZpxovbl67hMxznCbfHI7Zyxnp5xrYRCm2msMA9XDVliKoBEjOLBZJfNVLVdsPuoghJcEgHHMcbHI+3GKcZ1bhIDsaXJ+aE6AGmACm8MQhi4MS0XC6QxFyYZT1jXTeExPN3GCZaQUUyzKySWlzqdoXmOgRnAoD6nFULGmgmYDA0dC2YeVDqxPy6UhHcpaYWss94H8j41VYxhIgpj+7mo57gIRjigTBwnrxjFbRW8Pz8hG01IM+YpglfvHmNsq149/AWD4/UZda64XiI+PXPf45/+Zs/xX/3fy149+4/Ah8FrVCjdaobnvMt7OaIxy8CtvEv8BU+YcaCVBcEq0jbggzDm3HAGAUlNhQBngPQ8hGXl38OHe5Rb3+FLQ14Hmd8Z38LxTcIeMJkAWgNbbtgPRecnsCsvmFGlQSVuB/mgLPbNKAa0ErPtiQdf9OGGIFpuoGp4flcsC4bnj6eCGP+8oDtseD7//wt3n/7HpdLQxoMaTBn0XJSXM8LaotorUuJaGKgATjc3eHF6y+QhxmwiNrEj3IertoK0Kj/Yk/HlI3gJ1fK1+QWBaGmWiuWdXOtXtgNDQCuNuDwFeXLPEwUjExixqHHj9WNxcHhWGrXAm5vbh0+r+iuIyIBccj0lupwpsA9fsPuQtWMg8SyMsx4GLlDh1EWcNk2lLpS76cN4s1Bp9U/Xs6UFoXewLoeUAxqAUCGgqJq+O9l2qCl7QQjcV/KGGnkUEohUpQDJCek4LBopKlHMzbW7DcDAkgIqdyiOpnJ0NT1b5J2nbN4kehpHQZDzJ4UMiTuZrUhpoGTsG1QK9C2wZoizzOGlFCUMH0phqYkuJgKi4Ax+YJop+2NPKwH2ipaLV4s+PwhDahehHrPXJqhVYNVAxoQVBGiIpk/j+cCmGGtKxSKIUeEKMjj4KsVeqmaRT9TyXeoxnVTzlcHMua4VhRvEtT3krAAM0Go1DdH4ZMbQSPtnPmsZ1cHmNB5XzoS8k8tgh3G7I7qItwIB5koePUCx6Uuf6BBfGpjV2P+8NMmiPlrXCAr+p4jxHgNyXWIodWKJtQamncWFgMSIiwnzDNNmEln50MDYZJy91XsxtzrujENvFbvOL2jN+xTGtCHOn8YgoDtBl+y4C/5zc0t8jASkhFvYV3oTneKBjHf30GhgV0KzQQ8lcN/ZvDrdu1ODWXzEMhdwcpDrzUPzPSXfltWilWdpt09PPuug901WXPNGVXUN/YP6lDBWp3cwClP4J3ofl06c5ZwlCmF9rR+4wM8zTOdblqFKUNpY8y4ublHCLR4Qkx4XhaEYcCrL77EeHyJl18UHI4Tm5RN0Yrh3Ao2NHxQxWiGQSKJNmPCEATH44yaBDnxEG4xoOYjLjdfoOYbbHlGCQkXCzipoDR3uMH1ujw8PeI//+53+NkXr5Bfv0STBkXD1f3/x515iCyIfRcYxMjy7VtkUbS24fHhPdZLxu2c8OHDe3z6+Amn09mfrf4f7MWGbOP+bvFnJw+ZPR6PuL+7oweoSZ83/CN8do/tynQ2f+7Rd8T9YFO6kXRYPURatzkded8V93e5w2kdmoTDbeokruvSqoMoxG8CIun02tNnzM+BPl9/NpdaQDAmd1Rte6Zl33V34+wd2jAiNExUqEh+3eBMzVYbVNSdrAQmV5IKr3X9EfJj6BAynVBC8FWFT6v9EjPZ43rOdf9TRWcmOsO8H7R95wig71IRrn+ut9iKbiOHz+6lo0Ei11lajVB6EAgIQSIw3gqCbisN5EiGqbPMQ+Iqo9+/6xnHAyAAHgHFMHNyJNiEiV0ROZhSxw3bGZgwoySnP5fO2C+NsLCIIWgApFwlQa4P7ffEPyibvq1cz8DGldA+pfo96g3KXh9EryYsCDSFiK5ZlACy64GQbN8Z/rGvny6CwhsokRDYNIx8wPTgDgkrdgunFJFDRmkVl8uK5PonuGC+7wDqtmGrBeY4ekoZKQ5IQ+KUkckcejyd0JoipwUhBEwTne5HYarC61d01B+HEefzGe/fv+OBASDDkFJwofIFDw+PeP/+I87PC7TA+2XZD5DWWLDSSOILEv0OIQqBh6DGhF/98lf44s0XuDneIWX6PtItgukBy1ohiF4E6fbCRGxi+6U1WOXLmHLCMI+AOOnABFYVz0+PqKW4H1/cGXjrmQ70AYpSG949PiLlAV9+8bUXQTK30BhzhRBRS0VdNtRKXdQ0A2FOfImy4Xx6QllXHG5ucHf3ggJ9E3a+QdCKe7sKSQm1nlmASwUCMM8DgAFHT+Vezo+oRSGacTsf8PL+azydn/H24wecT4/4q7/5W5Q44b/81/8G880tpsMR3337ER8+fIfL+YKyFiznBbUUfNhOMC24PRwx5AkvDi8xDSPa63vkISFNoI5zSNA4Yxl+gU0TntaA1rhDfntZcLo8YSlnmFROQDHib//z3+K/+W//z/hf/6/+Lf6X9/8GsAWwzSlF2MlQ0zwR9natqnSShnTLrAqzhhA31PaE33/z/wZM8OHdH/DD92/xt3/3d3h6ft7dUKIjDVqBulWUdUXMnsbtNlPjNOJwGPD1z77Gn/7616hKDZW53MCMBSa5ecKVRZyB0BmKoPuKRN/fFVRnZg5DZKhunlDKhs3DUq+FUNjomu66LmbVNVSrnFjM65sfrTnCJ7ZISYIQlSCBRwk7i0+tCloeulHBuq1YLz5ZwqBFYcVQQwPACUMABIlIQbCVimW9YM5kTveCXb0oqZLt2BzS78XfjAk3OXGCtIA9FSJJAJGVq92ciACBshdhO+upEQGr7+g6wWGIrultbiZQe3yVlx6H6lI/jJ00tG0LReFDggTzAtLJR2xqTCtglXu3NCBydsDWKs3SAUACDocDEYDHLuWibVgcpJdTwsqbGzvE4BFmLCIBCcUaNm0sLiYolWuoDAYd2OCM26VCt4bq12rbfGfpzb+qn1nr5pXEGfeeOJ8jcyiDB4ebGEJk7Ju2Ai3rDt2TxAikNCB5KouECLXiP49n+VjoVHN1CxO32Iv/vCKoZhBn75hVbOhUWOqRerKBme6dm8BcZCrIKaCz0fpGpAsXS/fh81+aL6LtO6Y8ZCRlyGRwv0Z2Fem62xJBN5sdx8lhVBaOIEyn5qK5YSueng6HQHFtPj7vzg2CkCPlBP74kB14nVRhlIKoVhbB1naGEwdJ776VxJSmZWdcNl9E+zDWUXweguBLZ8H3G6bwMw8AiUUxJVhomKcJEqL7LQak3KdXHkDdsZ3+gCR8DA5rtVYJwYgHm/4D3VrPUTO/ULRP030SykPeH7SmDcu6omlDKQVQ8UV/cng3Y8wZF9CIVyDIw0BYHcA4RBwPGSkq6hQwjIJSMs7nhtoiit/jh7LhYkBYVmRlCK4kQCLvUW2CaoKqwTtI+gaelxNKpYCc1lURZdvw6dMnnE4nbNuGnCnapfuE7Pe57xhIZb/ujLvvZ9cpDWPCzc2Mn//iZ7icFjx8+ISPnz7g9Py0y4yYAcjuXFuBaoVqQ+jM4sD8yGGIZISOI3Ie0YoSNHFmrtgVphLA3UTgmFF3VIFPO91Ll7Iavr5EVkor7MgF16lxf9Kc6WjWh0X+pc483adNQvc7cdOLaRQ6v3Q/kJ5L1ws2lMhKAHe06sWjp5sA1x+6Rym5HyeHm4g9OLvvttD2vWbfi103QQaoUm/qEgpJAXuCAvT62fY/bnszxN1quD4T4junwKkpOgu8D639WjIkmcHf+6Ttk6GYNwVdpuTM5CBEpyBEpSzS8CBGPpOMRgJCZtBuq31/StZmYMQ6gtAuTLJ/oObp7tnPiMAz2Vw0L4iEx1tvcFjgxQQt2j5lc2eNH7E8ITRR6Ozy/jx1EhSfM4GaN6JwHadfJzLvu8TOaA/nOkHq/rjuoT6T2tbWGtN4nFMdPa2HuZDYDVPEn/mf+vpHQnX5A1pZAQiWsPmIKdjF4VDAbYdMG1LMJEjkRBcGp8i2wsXpkDMkDKhPK2qtCKmL7Aln1spH5fb2BiLUrsCAXSAcsz+kzN2qdUVAwMv71wB4U3pYpjXBumxYloJlqRSKwgUJdqW69+wuKxUaFHEePtstCNKQkAaSJwxwyK+6835FWRYf3wG4ywbho4Ln0xMenx7w6tVLvHz5AtvKhIdOVuFRoYDnYjHRIWNTT3Qo1LskSQgpYPKkimEaUVvD6bIg54z5eMOXT4GtFJzOZ07fOWEYMtMEHEC4XM44nU6YxgHTYaKlmmAv9l0XGmJAs4Z1WWht5Sbir1+8gATBVjZsS8UPH9+7C1DEkEbcHe5AlmxAjgmHPOKECF0pccmJms/tdMLNFHGY7nZk4Fwatqp4+3HCeVnw+PEJZdvw/ukTAiIetWAYMsa7A2LOGFpATBmpNahF1MrmBw1YlxXvP72DlAUpMtUiDwnrcsHb777Dpw8f8Pz0hJevJ0w3A1GCCoRETWIS3qfauq0WXYmYqC4MnBXB/e0B97czvvryFX7/99/g//R//G/wh2/+gHdvf+ArmnzfnUeUSiZuK6uLvisk6M5+O8wD7u6OmOcDhvGAohcoyNjrByWfc59YUiYcqCsgwJAzSCXvKAeTK2IePnuvG9ZydpE8J9zocBM6Eq9MZeck5c4zPbomE2VlRy/MNDR64QoCBviOp5Gqv2ycJFsp6OWbkyFJVLWuNML2n7VTiHySa7Vh2+gVKxqRw7Snzl98zVCVh3Q3Z5DAIi5RycZ1SPSyrXQFGplPihBgKvBjh641LhYXycg58MOCUpggZJFXAzTys+RMo/5CuThiIlkne4pCUDbgzSVNEB7as7v9dNg1xEBtsRLqpytfgoUrAzMm7g1z5rVfL7wuiyfLh2yICJjy4L8rm/ttLfy4oxcEAxADJvHrIAGlKvIW9qZHhdN8iQ1Fr1IDSYKiFK5bU8TYEPyaGMDdJgR1664u3gShucyKDUU0uzbeJrCgaBUo1WuLGSCJkhs4w90agirWsvl6iTvZFA+AJNS2kSmr6vPa4Z/nGNPZPd2HrhcNvtlGzV3f60lgZ+TdD1PMfRdj5mzIiiykenOJnRCcHhzcnaW7z5MF1QutL63FsWqB/7ffMBEfuLthqqdeaGNmnic1QIAQ6dknvpEXXDuG3sn2NPV9VPSOtL+QtVZE9D2NeyuqIZhSb1OuSe49RLPWhq32aZTXKYbM3WejJZhAaM3lYlnqvNg5pUi4uHc842CIqlBw8o0xonO/ckqYp3lfOpNs41M8OA0NOVO7l7N/73i9d2ZM4tHuBUi5y+is2LoSw9/qiloapoHByMH4zNTaACEJSdEQMyfV1KfOIM7uIq7GHVcD0JCTAUFxczMhjwlTGtGqYl0DWjFcHh5xMcO8rYjDgLkYQiqI6QizjKIDVCvT5J8e3axaAalIacBxnnGcDrgZD2hrw6f3n3C8/QIx3sBao60YAFF2vx2O2SGWPpGBUV2AP6sQF4UbPnz4iIfHJ88V5PIfgazgqrSuagaSsYIgCAlUnQwGf66IQiRE84xHNaa8OyTb3wvvEvneeMgu90pgAfPJdSd6wRwt8enGu/7Pv7pOFuBzIP49Va7M4dWb45Q8fcExt9q1Z45w5MCdjYboOzP+jB782r1o4QWwswmvqQqcpOhlm3waJg2MrRZNiwjVXqE/+B5LIUAKCCHzXshVx4rgRtnmZ0AUJJBmlFJAynzTRfpG1nW0/owAJKsFIR8BECR/x/cw1+DyKNhn4+lnLFAjlMeJ0s8d12MqyEQOfsb2yTR7Y0WP3+rQIRA9hHvocDkaLLCi0uJO9vsLCCxfMTGicp7GoiTmtWQINSBq3Dmu5uHpIXT9pe8zd56F14f9PwBEfLUT/FH1zD/XdwK2B043hwO5I1WIdsWBJwLBriQ2v6RdXkHeR3fRkt0G86e+frIIDpnyhG3d+CEGhz/MnzJjx0Nh+FWPBDBORGtDCnAsf8WyrjCZMSAjhcSOYSAkELruxbvcUt1tAhN6jpXItUvsovEaZBd6Ry+sDjrQgX5ZnGHJRanECNE+uvvPTHK13RFxqzD/rDBoc6ZmKShlw1pWJHf7QCDEIK0BVrzL3vaXpdWKVhqWtSAvBRGGJCSOxDxCV+7CipfUWguL90Z4Z55HhMSCRXgxEdobOAGMU/sRNCWiyCljno+UmASXpzTshXDIA2yenambmQAewi5wbfX6EJoqEiiluDkcEYLg8nxGaw2XVgAx3N2Qwds2kmLWbSWUGgGDIk8Rw5QwjnlvitScPyEV1vc/QZET45LydANFwiAzAhLOK3B6OuN/+L/833B6esLx5oA8jji+OiPEGaIFhgSThKoVy3bG09vvED230lAwjpxib+9e4f7uDeql4ve//T1evblDTAdYW2FavHv1JT8M2ROwJTsawad+j70qZYPAMILIw9///e/x6eMDWuVOJuYIxIhiAWsznFempIfMQzlA97guunv4fsu4C4nBcDlv3F8lN/z2g8WEHXNwn9qydaLT4MxIF6cHNnG1VUcIRp8sg5s/d2N17Idiioka4UqdH8kZAQ2E/89PZ0ANL1/cfRZ5ZmiFmXYx+S40RdQW0MxlB37IdYh9GgeUoGwivWGo24paCkptrnGl9Gn0qUAbHaYC+LwEXyckslVgbmQSYoAFQRH4amAkdO/nC00D4NGnnvgSDCkaxoleqtHPpc0P6d5AdBPwmAhVKgQSFMNeLAkLRgTfNXbSG6VQJN3w0E8BSI6+iCNOaoYaGRIQ0OF5tmDTMGIcRkThbk9bRRIgjaPvw/juFX+Ph8SzfF3ZUMQQOG2GPvYZhiQwXyMxM5A6vlIYQVVhqMZ1l7aGdRG0SqmXqiEYd8Ja6y7QVxhUsK+sCJN2PWYhN2IcqCfcyDFprctgzJ9JTwcRBjyrNl77/pIYsJbC1VPZYNpo5xYCplFx9fH7JxTB1tX+YKXfNh4QdStIKeFwmHDNkvIJp4/MAIJxN9Pptikz7bn10Tp45W6CPCZnPLmGDZ3izF1BKbIXQaDj0QwJDcIolWqV1j7eqZ7OJ1wu7hlauAPZd9PuRgF/qIKAU2lI3kmyUwk+zZgzAkMgswpK+QK8EyWFng9R9yNVGIZpwh3fCDw/P2FMCUOMCHHbO6eu16MEIhPSjdzlDDl5B8U3VV2uEmPyKeW6CNmdNKxBzaFr6fsc7JPrtq2e5hBc91MJh3QmpXLSiQ4j6GcFWkScBWYMsBRGxGhVTn1REHKiOLqfRMJDSlrFtq4o6wqtLIJ5DoijAJH7sR2CdvHux7ffYLkUfForaqn4xVe3qC9GfHp8AFpFqBlZKm6nG6xrxQ/ff4CKQRLweq54/T/7c2i7QMszgAgxwe3hiFevvkROM5ZV8fi44OPHJ8zDgGGY0JtHUucN4ouSGLqwOwIGbFvboXpTxelywem04LwUrEV50sl1qinbhq00bJWIRHfWibHrAie8/uI1Xr56gWHIqLV4B3+NNtr3d/6Ml1bQ3UvQ4SUQ8jTwXetF1Xy10AOoXXG1Ixa04+vNkF4lbw4Bq/bdHQ/QcaRzkILvYeoTcmbEmnVs1RGKfQQUPofs4sUZnsH3u33P5FqzGACfhDo7WoxENURDdEKLdYO0Ph2I/0wQxYqjkzEC7988Tuh5d/396czwfn1yzjyz4EbzAJECz27sk2yHJgGBRcKm1keijlqZuQCd14FIGKcrNTYDu9Z5Z2H6/s0b8auDTuCZWDeYVog15AifzhTiu28gOmJle8qFoKEz8qFGxydWXL9anMjJgCUCUWJDTQHFXIij/HzTFJ3V23xwCWgKrO7PXJyRa727Ek6jpRm1x3L1PDXrDF7/XYzwcQwCA0X9qEBtLHY7j8SLjlbuaDsRyis8tsLB5Ke+frIIdvKKekTFeiH54fT8hOPxgNsXN5AQ2T2Yt/Z9qeskGa0NRXlj8piBZtQ4ud/cuvKChTjv6eqEXunt1+Av+S787/Rh3wtqRY4RESNqrdQR1YZWGh4fPuH5+Qnn8xnLSk/GDguZAapXyi2TjkfuHAUsJJU+eooGCx3W40EBVWyXMwwC8SyzHV4RRsWYNszHI27v7/Hhw3t8+PAex8MBNs8AAlripJFSRk5kg4ovLKvThPt1lKjeNbLrpyYJiGKffR5mhZVWsJYVvu7ezx4KjYHmxAxripp66KThsl04KUhCkIh5nMEsM76468J0960VSBTM85HuEgCqVIaIxojxOKBqw6X0sgskMUjZsD4+4uHjpz0U9P71DY5x9peT1lTBAotgqfif/vqv8fbde3x3OSONA/63//bfYkoR//f/x/dY1g1xa5jjET+/ucen7QF/89v/J0KOuH/zAl//7DX+5//V/wKtFazLGd9+9xa//e03eHF3j5//7NfYmuJ0Vnx4/4zx8B6//MXPcHtz7H0Wo3sEKOUMs4bkDhqGiGbAerkAABMKquHh0wmfPp3xdN5w2apPIeFK7GgLlq1grcAgYU+XjylgPky4vbvDL371c/z8519jmies64oQZkc+SCTpAvgenLwuG1R58HbCWIeHzN8PfIYICVgEWWCvhs8GTiIikX623c1IHIIEUKt11RCfwCPZ2KUUaFMEhyzHaYCBoc7WSZSAF0G3fwOo2a1ewPkwc2r34kIph8OV4pDptsGUcKAEASa3cRNCYOu60OTBxOU8G5/JA1PLzYCcRkzjLSh/ajv0uB/+TtYJPnWbUQKggRo56blvzhKObhidQpdh8OxivmofDoRni1//KIwHM9DfVBxi63FeTh9BTETJzuuGdau0fRCS8poWoBYEVYzJZV9e5MZhcCMODku1skEec93vqakxycJ7hd40de5A/6tUBgr3SRBdquzpIj3IuK4BtRkezmdHAqs/YxTIn9cNpSmwOlzsulvrz2QjcUqsNyKejSkBddvQSsVWHBFxi0JJJEzWwsqwP8sxARawrNs++Pyxr58Wy/sLTIo0q2sIgsNxxjhzocqKzRct9OnURw9zG57WFHkkYcQC91+qfc/BrmtdVpRQkHzZTr9O2yGHTqftey3zDhzAjpWr0gjXGsX7XWdifql7wvyeuh29Wfc70T3qkgtUOWXyYRS/4fy8Psl213zfi8YY9pTsHQ03dmjzNOHF/R3GYUBOZEymFBm27E1C2NlM/OzB93EAizLglO0Q0JM0OjKtjRIJdRi6FidTRDcHqK4zhDhEldjpusZSVTEOM2gdza47pehwUfRlObtGkpvIdgyuBUUw7q9iRPTU6NTF1a3vKzKGccI8H9En8JwTtCrvQ6OYN0jAu3fvcXo644e3P+DTwwPCYcY4ZEJ6fl2GHPHy1R3mcUZMDWlQHG66P+ECCYrxMOF8ang+nWEmuLu7Q04RZbtA0ohxnLCWFZ8ePuDLr15CUh9/XGBuSqux1vicQxATbcGY0ShIOaBWxcPjI56ensh4hHpTQxirKXce2tyDVdipp5QwHQbc3h7x8sUdvnzzBj/72c9wOBwRpL+e1OD6Bgk9mFndzYQG2Q7ee7Fzadq+ngg+kfdnC8F2PZjsfqL+Z4IjMd78aI8tAyesnk7/uZ7UVLHx9XGGKt9xeLcfAcTqZP0gbv3HTj9K9xNVoHZpIKeyblJjCu7difnvHALVz51pAsZh4u/kzPUcO6uc36+/pyFdbRt1R6t4r2ISpJT3PXlT2REx1bZzGjrqFGOHl/j5iNwAkww+sTuWol02QRRgiIlNqXbru54pShswQsCU9YwDM0X752JMkcuZzFBrn4h5FmU3NfdjCyFwQtJ2ZenTAjA4D0P2VA2y8eOeGmESIDFBVBHU0IzmETs5a2+wiIoNKdJxJ7nG0olJqzanbQ5+P9mAlUYEachDBwixVcowQkcGwL1sCF3G4rt7CGCK6lZ25uenSoXB+Qm9A/wjX/94EVTFui0wA1Ic2bXeHN0VJezEjy4D7eJGc/pq8diLYcoYUtwnllroQEB8uGE585Dve5E0pp2D87lz//7EK90W6F/HB4KY90qSiXEK3UNG5UqUIZzD0MbUDQW8uJkE5ETtlm2Ni1xQ2MuQCXP3e9mvLRfnPIxoL1T3y97tsm4OB8wTma0CwZAzUoxYV3ZXEkjbjr0ou59VlJ4iwe+Ye6DtZxE/fJkdK3cSTtkqBbSJ+WTrtlInFdNeAHPOiDGhFR48h2ki3t/HNH5zBNArshZ2VVMeuTftUzMARGrVYuSEAwUyIurW0By+yMOIcVJoo3dkDAG1LWitQBrhkiRsgr797nv88P07/OG7b3A+n/Hq/jeYZk77AYIozMx78+YVCTtNkUfFzX0mgmELQmyYjgeczmc8PDwDCHjx8gWGIWJdz5jyiPlwwLquePf+Gb8uv8DnRLK10GJNNy+CZYXAME3UOGXvQtMQgKXhw4eP+PTwQBs1UyBkJ0VwwlnXAhVlIyMBZgFpGHA4znjx4g5vvniFn3/9M/zql7/EzfEOIWRKhqAIKfv+rgBG/ZaaC4HFi6BdiTHoyIQXvthh+tiBehLPWAjpGsMD0x1RRDw1Qq/s2EiUppntBRLAXhTXRu/PtPX9Pd/NmF2z1kXlDgu2RuedKGCqhGvgqCjgkSou39GNkx7jz65aTvNUhR6EPI0TAKDW1acKn4yDeaOZuMNLHlmmTFwpRVlwMpv1lCK6XVxr3ayChT0NdEIpBa7d9QZEeRp096sp04WnuTXktlYWwWRIge5Y3dBiqxWbT03c/7lffOSzNkWmf9Sy/ijeaHSi1LqWfaIWoczLkU1ORsmn1A5/9q+MvQmqRVFKIwEvRWylYCuFKIMFhEpLu1IKC+FOYrpC3gHAPKTdccjM0ApQm+LSCp1kcoQ1QAvP0qLV93fZZSIGWXXXb0MVAnWkyFxe4QXfJRPbyqSbOMTdvEAgnuP5zyiC1sh2HPLw2YPGiaC2gstyQWecRYiLSTuLjlq9GCPZYsKE9w6QdU3h6OzF/v93WHHXAjqEoo6raKv7jmIXIDvck2LGzc2NT6GK+D6gbAvJJr7HYsYWu/CO63dGaR+rWqVglJIiQS36owyr7jEoqXe8vDG5d+uD+XUBEATNd0rBGZxBBCGF3UWn+7HCGna/ys3dOWLy4s+ptznGX6s7IjiDL6QEayTo0FCWn0l9T5RyxpgypkwDaomuuQz+wgnPuOhCe7OATmKpygMXLuJtfY9RWeyLsopKFHcJ8gmhCspmOJ0qnk4bHp5XXM4XnE9nDDkhp4ScgTQM8H8Flwsn+PNSsdSG490L5PmIUhTny7qnrX/51ZeIKeLL119CVfH2D9/isiwYpwGhRqy1IaRh/wsxYxon3Bxv8XQ2vH37PV4YraoCCoJT+M10j4jZnKmbhDCVGZuBFgAT25mG61bx8PSM//h3/wm//8PvADDDjKzPgJwH1KYoDiPThcYTSgIjeiRFpDFhnAfMhxFDjjtb0PxghLEA9Z0dW/zo7yRPYbXm5C4Sp1IQb0xZEJw+tZ8L1mEQ9N2N76C8sYPLAOQzs4AY+9Th52j2RPhKpuzW0xrUi5X/Dtj1vhExKBtQ/x4xCDkCKoAjSGJG5nKKaOKxPR4iK57LWd2tJFpCQKAt2r7PDzgeji7zucCKQrWy+QsZ8GzKlOjclCLzIPPAkF1hB0F7NTjiFMSPfkWvJp21Pg7jdSp17bSAekBz+YgEQLLsBZrEuoQxRqQ8eCVlqLdEhhLESIha1At18F2aN+wCXCUYEIeS+0Tk0LP4ACJ9B8jr7hiAw+HqjNU+PRINUjC8pUmHPhNak10SQSccoRFEAKQ2uPcMTAQhs3GZ61Uyp8FQzd1tSueQcu/O2KaEoWX/Jf2sE9m9jgH3OG4dnXNxvZI01grRyVI+cwr6I1///xXBNKBHgUCwL0K7jhAwftgYEVNCjNkfqIhoDcEqrLKQ7GSBvQhSdLTLHh06gNekDrfsWpNtQ4gBwxghFiDi2Hal+8xNTjCjJCLlgFIWZvR5EWTRSIBFwIqLc+lCwJ9HPeNu6urFu5SG6oyp3Wg3R97QQgJD9t2giexGwAg00GWHQ7FrcrgEQnq4+hKfEyX/We8eWftYBA0eyGlcEIsIMvgy9EOv096Tm+6qsvDnIWIaRhzGsSsT0Y3Rgzcc0SFZ2+Fm4tsqbgHsL0gFfVHFA33XbSFtO0ZIJGndVIEqKKvhdG54OhU8nlacTheczmdMQ8I0ZNxlwpyd1LMsnJjOa8VaFcf7l5hV8cPTM3BZEZJgOrAI5pzx5Rdf4Xw+4+9O/wmXZcEwDpCoKKgIcSBlPGYgJMyHG7x+/QZPlx/w9t33iEPC4WaEWINU7slMFWVdUbbNfSkNaaCMR7zhamIwUUokjIkmj08n/Me/+0/4/tvvIGg8LIT2VilnyLahtIoUmBsYnfQhIcBCRMheBKcB82FCluj3Je4ANYy6WdXeHYuH7AqYwmAwrfx+sZO+ImHdVnezbPg/6f+rrwv6f1F8ro5K+AFnLsBGQIwKcQYe4EQYMzQBrFUUd/zQom4Dl7g68Ca5x4yleC3GNMEXQCslTEpSXUoZeciooaE1g1mfPgghtoXOKYLM57ZxFVNrwzAETMeZyMD57JyBgkmBaYDDnwEhgYztEJEjyTBpSPvhWUvx94TEPnE3KEdm9880Jh7apRWeic4Ly7TOcma3UOjeGuq6EtaUiCR8j1vdqO3sjWrkvaQ7kSEm3tcU048IdTQ26ecxkIKf1X3B7X+mRSbHe8gGQsBe0kMwmlb0XMXANJq+cgnuABSQ0UJis2L2mVmAUFfbtY60uodkNn1zjWgqqM4AhZJ9urM8jIEJKUdkS3tho+6PzXyQ4Dt7XgetSm5DUzRTJG++aMBgjvz9VJX7R4pgSgyUbe4uUby7yju92lu5AETjDosZXwuquxvQMNapwiHtDhTihBT+gs5Mk+AHLSkxZIrRdabWtkOnqg2XE7H5hAm1NSzLsj+QKbGbqNqwuuQgwHp7BK2ELMX3CGyGO5VWYCFAJWBZLoiB9HBEQZ4G5CHvO2NphDrF5RTLevFdGckzUUi3ptdf8CLYobCrRyLgsI25PEEcFAmy9wz9wOk7Adl7euwvYwgu7M8R0zTs0yq9E9XvBx1zCCk6JdsyWqQRb226T+J84Gx3ihg8tqnvYC0ET/T23iwnjkmr4XLZ8OHTRzw+PuOHd+/x/PiMrTXEacTtGOF0IzyXisuz0jg9JoRhxJQFf/Knf44vlxVmBbUW6O//HqoNp7IhLAlxOCKOI7Y4oiZFvnmF8cbw6z+7wfPzGb/73R+QZcTjdw+QRfHzu9fQ1vDx229QlgvmI5DGApUzDtMB83yDFAJaq055H5G1u3EE3/Vwx1F9/2pRsVxW/Lt/9z/im99/g9/97ls8Pz4wrisFjENAjAAjgpSHawxIHfGIgvkw4uWLO/yL3/wGf/kv/wJffPElggwo5lCfNV9FZAhkf/4HF+xft/7Ez1KceTjY/moSORlp6s7iwXfO3EeyewOTKGKIxp1g9zMFOpua70+SBLOI6E1x380Pfjgnp+PXrXpD5gYX7u4UUoQG7olba2hVXUeaESwgh7R7iRLWYwFGUARJexHlvomNXqffizvP1JrcYICi/LvbI7pkNMWEYRj29wa+A+u8dImyN2Vq5iYW/XqDqwoBUtX9/QVk1/D2/Wz34u0XscvAzEfu0PkPoevnuA7hLpV3K2eyh2PI0Bh9pya7y0yn8A5Dz/j0Tsd/LfXRtJ+q1REEyQ7h+qqI0DLP4+wrLm0NTa/JHbSsHqDKP1u0Ub7hzfS6UEIyRN4DarFJHGTRq67vo7SojfSNPR4IYZsQQYwx4ny+4NEKVAWtBYRpRJSMZV19umPz1gIZ7RkJ0shTiCG4vtl2AtlP1rmf/Ic5Qptg9RtSmyJKwBgH1+TFvSAEVUitaLVia4UC1sBJLQRW99Cpw04EEWdZwjgGdzq9wche3C+8W7c5yQJgtxJDAhJpsKfLM/veEDGOGSlN3AG2CjX3oAv0K2Sn1iBwSzbvQj5/KRSCzdO5lW0g0pARexGUXnyEjvZGV4uUKYFADDu9tzm9vkPENALmJC3A7lgjAIsgXxmuT/pORLG/mITbu1wfjmkQrk4Oo1GGEf0BbC5EbSi+UwsAklP0LbmnpeuDcvSlvPDQp2cB7dJY4PlDTeAmAs74s7S/IJfLho+fHvHp0wPevn2LsjXmw00Z4zxj3VaUsuG0NehScTxkTCFhGifkmPH1dMupyy5Yy4L350dclgsupSDEDfc3LxGGA0rIqMmQ5zscphF/9ptf49OHj/j06YIkGc9vnzCkgDfHF/j48A7fv/0BLQbMh4CYCyxcMI63uDkeCfO1xgnN95pmdOinJRXZkOqTAQbDslzw7/+nf4/f/+4bfPfdW9RtwRiYtDGNwZGTCgjz+KI4HB4EEoF5GvHi7ga/+uUv8Jd/+ZeIMkCQUdTdkrR6hyy+E2SH2220WCQdohfxxtWYAcdXc5disKG1/rjsBw+RBMJl0OYTGdBZKR0m7VFKERQ3s0EC1GOJsn/PUYiQbHFjzfBDUqFXKN6fe7NGJxGhp2REgEZzSQ/35c3UNYqOtvhBCRAOUzNME/VxUF6fUtWvGRuS480BIgEp5P3w7xOIhAQJ2fkKndLGFJWerze4scFOmvSLy5QINuy9eYxwooH/OVd87KsLddlGL4JXkTybJkD2gztGj3WSCLMERHGmsH+vBp6fXX4B9c/B00HcjzSSYgJibgC5xiTliNBrttuTdR5Fq4U6wMZ3N0Y22kTRhKkifcxSINkFtTbkyOvn4w8Qsrt7RW8qiaiYN0Y3cONzIeIAAyIa6tbPSSCPI1KeEU4R67KhOakmCO0qU00AlJaNQdBlF0kjNPwziiClAYZhyogafXQnPbeq4bQV5JQwTWRBiS+PswT38CQ0pgCqVpTN+xQBk8S9axKEfenuvDZI4BQq3o3DzI1fM7vqwEkrimIcIkK6Q8/D4wvLvLeu4eFIXfzGwBl6Bkhj12xAMNuZUkDfTdLbL0py0rL4AxwxTIPrJitqNebGhcC67cXB/AbsScqsp67la0ihu5DwM1af9KITIfoeht21QdsGBO7FusasU/AFQDRev1oL70cMYFimeko17630082ox9Pd5aPvnQRxGPiy6AVaGy5Yd32TATs5I48DYPTejFEwjglDGRDHhGEecHNzwLoWSNgwTAOGcUCOGW0o+LA94LysmOYjJCTCvgLU5YRWK4ouWMuKy3nBsqw7SzmPmabrKpCiKOuGFYbT6QnLcoaIYlsveLt+hyEKphwoAQnAzas7vHnzCsN8wDgfMOYJpjz4TTn57gcdXI7gLEK+NPQAffz4jHdvP+K3v/09fvj+Bx52Lo+BRMQ0ojZ1GVBFEnHiUHBCiOHLN1/gL//lv8Krl28AS6Bnp1Le1hMKwOfXTDG4NrMzErsfbu/M+iTAXDVxeNrfZafk0Q1pJDzmB5FVaiJDoFcrw635TpKWsM+NfIABxtWABYV/9amUUKU/TtydW99H+vsVImImHKqVHsLmexAR5bRlQHT6PUK43h+lgbsEwTRzDSBuNSVR6FxkrnGV3io6uhH7zsu8qVZWEgWuqkH+2RS6n6fzpXe3E2ccuv9lHK6WdDDsBcxrP+D7uehN/s5r9AmazUDnIHREjLvx5JAtHAZV6+9w/1S8412Y3+OK9h2b34SdTYrPJsD9HNxBbwjY8JoJJLGBiuYJ905GCX1Mbf0c4zk3zSNZwsV/h34dUvL1UB9seN+tudNLpSZVvfkyM+QUcDxMDm0CkkZaBCoZv8V3oHVTaBWvMYYkbEDIbN2PuJ/8+kcMtEloiQPpsXkXVHJ/cj5fMA0ZY2Kh60UkgQ9QShnNf4GtVifIsJOJ0hmVabeP6m2WdfzdBAgkXVh/qnwhnIJ31DCmpccBMZJyXlvBVlfPvqPDisFJAwbAqdASyDbqwt3+EPhbxQcV4pRoWkN1CCqEgCGOrkk0ABWtKjTbDmUQzuWL0g8KoLmMxH1DnbUn/kirO2Sk4JOcL51pzaTYnB3FqYzGuqp8YQTs6DpjF9JI2/YiydDK4HBr/6zO0FWnRgPOOgw7TAOfEFcrnEYSH9RlK9yFzAOggrY5HO2QbHSvzmmiRqs1OqMMeQRihLWMj3jacXsJ0ck3ZMHVZcFmK9ayYl02bFvdGXghOcuPHHO0UlCgWJYztrICQvLW0/kJKQiWJGjBoBGYbw948/UbhDggxBHSMrpxQb9P8pm+oCdvwyiRyIEF//R0wcPHJ/zw/Xu8f/+JhcuhU4O75LdKgo3pPpHF5BZgwfDi/g6/+uWvcHt7xwLqh0A/Y9EPN9+Vj0PaD2Vvr4DPppD+7/adM5xA0uPLiI4Fxp75CWON+zeJPKgZjnAlvnDaYxETXKE6PzL3a6bW/95p/Ps768Sa3swanxMkTozdTsu6laEEt2RkcaMnNHdBtTSXPTUIAoaRXqGfEyAIl/oZ5nDg3th2Ibp/MAWwJ7Gjnw0sOJRvREeornZ51k9w52jE7OQ1v3fV0Ry5/pgfFUEB72uIbiOnyrw+0+vED+/Me/H1vYh1ssL16vs5yp+h/n5IqHswsj8hPLf6ueTPAgMBrk2xePyVIQAhw5TIT4Lvo6G+W/bvoS5hMGAYOOHtLPlq3gRFaFTUMqDb9pm/TkEMtfVrBKg3MykFzNMA0wDTAA0RFiIdrKLsTNVgCU0UA8zzYV3KBj/Xdyjjj3/9tGNMfxkTC1ceOY2UUpFSxOFmpk2ZQ2QRAtNK2xzrtYSC7eS6JLj2JkSFBGc0tYaYk+/nuHS+XFZnR8oOUwYRjDGj+4CJ480wgdXNEwSqTz7cpcRO0AnBvUj9F+NY6b+jd9FeEMw5mCkN7slpgOnuvjHlESLA0+nZoSogZsEhHjn9NgPc2aXvEYK/0Ga0eYOwyEH6/oavTEziQcSugYqdJs57cHO88+LlWVtgQU2B+5VLWdhS+tTXi7ALrFyzpqgGEkKC7HZQN8cZQ86MiDLG/cBsl61sbQWELjh+2gBCynOQiDi7ZdMgGOYR93e3sFrxIIacI26O3CVHbYBEWEoYpwPmY8VwOCIdJhJuTBETYBkoj8/YlgtSEGhMOD1fEFTw6vaCVA3Lpjg/P+Ph3fcYxozX9xNaa7h/MSPhBrMM3K21gjRNGI4zhhd3yNMRggRB5r65bjRzLhvEMhhtQ+lAihkxgIkO3uiVdcNf//V/wDfffItPHx5wOS2EFkNEDCMZjYgoWrCUbX9+mbtJdGWeB9ze3eHFi5fMx4zcC6lRxxkkoO/G9xdZARP1BgV7F999TO3z1198T2V8Tjhl6671EwTk6Glxznbt1mgWlJZs/jv34gp1lycjUY5TSKd59lQZlzul5ASYxOdRXA8rLEbiRgvijSmEoF2AoWnwyZW/a7cpCMGZqH45rPnka8R4grCJ1p4q4WRI/vrikpNrowGJn1eqz04/P8DFpyy/fr3RMBMSzAXoo0HvPoJPvowhI0N3r1QwdhQ+0e4erVF7y+UklYCxM4hj94vla2yGvSFv/jFyJ6e4cXoHR5s3AeYIEdcu4qRCn1Z3BAQOj3uxbN7MkRbHqdzh3v7zVRsSKZr7NM//36CJ74saVzXzTI/hrbixdlCkFDDCTeDNiYgAKMDrnZygGAl5IWSYZqS1oNWGCxpKFJjrGmvlZ4kBEBVMjtb91Nc/YpvGLqN7x8YUIU1RCm/SMLp7SocahKkD/UU0n6h6FyQBMFeoc3x3+yZTxngEYSdiSiNpU+SQ95YqCDAkspJKu7485sJ886V4F57CH6YeQeJWp/1ZACzwZ/qBsU9u0P0AiW6jxiLIjjmHBIVi3Vb+TtlfdmeHWbMdPgF6reBep6oz4pwo07tX80LY9wb9/+tPqgmoCwpk4m1l9QmOH4jQi6IUWrnF3f2iF9KwH8QVZHD1R6w5ASCmhDwMEGcgloXao+gTadGVsg6nj3dHnZhI+BmTSx0CSSDTOCJn7o9i5D2QZpCmsBhhISHlAXkcEYeMkDOkbi63II9Ja0ErGyHYKKiloawVWipUCmHoyxnr5QTogLKuJJwcR4xxwN14i1oLtnXFdHPE8f4eNg7QNAAWnXLfdpeUpg1JI7orEaTvcsR1raA4uVR8++33+PYP3+FyumBbC0IGD/lE6Qm9F4HaGEAcE1MFJHDfPk4j5nliYkQenOwREJQIRpTou522d7fanIzhrGGejbIXw+6p2O9tEJKhwj9EJdR8GmXiiUGv0BhH0F0EkB1SbZXuUf3QVD/0d12W8D3pfVeffIMf/urvQPK9FgN8DW1Q9A1dMGcBCKAmDhG6SbYZJDl71SOECEFjn9JkPzr7dNcbX/HC5O8W/PoF/v+mvdD5gdeDAVy0ziLUmamdGMZrrK36O2h+TnVUodc+h1EDHDI2nwDNBwK/f3Gvj3xevFEIfnaK/6tOm0PHWw2y64vFJ/geImDO9hZHhOiMf9VWE2VWNwohbNp7ZnqHCM9D2G6+EH0xpNFJdOo7ODi8bZ0k6RZmrfGS5s749AYqcPIdhA5Mos4w7vfS9uOPiIX57tNoENDEUNXNMSAIjnZ0wiGN+m0/Y//Y1z+yE+TDvpzOEIEHUjo83R+GDhFw+AAsQsKAZa14Oj+iZ7PlHJFTpMekAS39GJZb60Yc2lOph4Pj7P2lSv2eb2jWcCkVEgSDw4WEPM1JCOK6qwHjYUYeZoQ4+UPkxrD9NELfEsoOzXZHiJjY/ajRjPvTpwekLDid75BywGEa0ckqzdwott9AgWd78f1jvldEjHxAUk7u30eGV0/ChnZoyXPiaCW/f59qfOGsn9AASi04nT7xsF8WCMj8HPKIeZ53EaBIgkhG9u+VnFzRHdlTohNM0HJ9UYSfP4rg5nDDa6s0RxiG3E9aFrlk/iYGtKJ4+PCMD++e8O77B4zziMNx5kK7gvCeVORxwssUESNQtrNLagxhOiCMM95Mt1BVvPkVfQXngXvfaRgZLroUpJsD/tW//tfIQ8LLL15QlhLUmaoJwRRZK2wIOA9CpyBT6NbQNgrowgDkccAwHpzBK0jCp6OUxTWiTtsPCWVb8Nf/n7/CH775FlYvSIG+tSEkzPMNamt4/+kB2pobsrOrH3LEOGTc3tzg1esXePXyJV6+fIkQA2qpjt8JSSex72F8ckEnQPDd6wcinwsiI8kZnOIFStWDTvtDhAgBp+1ePFNObI48G1N8Iuo+E51lab7CSIcZEOZMdoIJYLCBB1TT/gKwARJlMvgwZ2ZZrgVqBQ3dQzOzcTXltZIANEPgSMQ5pNKLNnlxcCTRu/wrAzAHHhR5YiOj9vlUJujm1AYWuBRC/0b83MF3wOY7UulMbEF1so749Bj7btTfwxwGCIR+rnCNY+A9p/yH2ZJRON1Rq0s6BBLJOZ3B2n1HHT10trnsvIwucCdHgUVfYJAcSbRzQqGYN/dO5gvivq5+rqI33gnXIJFeiTOAQaCN6SgpCEwCMzBh7nNssFZ5TiMyzmoY0d14usOWeVFUNcxDZ21SZ0wiU8O6VqbtlALuNvPuDLQVhrFXd26aQgBswLSJR+TF/SznqoZRfefLZR+I/tjXT+sEwSV5KXWfIoL8mJx/JVh0zQ+7nqYbvR33A2AEQEzX1KiNC64NEqBq4w1vZCuNc4YEoG3+8oeOx5NQUrTxwdArjt4nQUj/mZEar5TIABPsMo79TNhhEBbFa3q44+xObFElE/BymbCVDRIyi7p+rpdkh9kfTD6F/ds7UxYKuGtIjBEQprlbYGrBjm8ou87eXfrounf6nxfB1hqW5YLWKlqp7E4lIKcBKSXu2SIgXog7xErXFj9N4MvvHS6+QmriLS1z9IBWK0zEf3948e/Xyg/Q2nA5r7icVlzOGyRETAemWRRntJlwHxQzk6K1FT+8qQ8LIWKcSc55Ad8YJwBQ1FK5P90qQsz44sVXnK5uJh5kwdA2YDOgU0MsNdRUEalH4fWqFWmIiMn/innf0QafoIpVwLoxL5l3ddvw7u33eP/uBzYM8NBYCGIKKK3iwthuZI8K61FYKSWM44ib4xHz4YBpGrmzVd336uaTn/kz1Td0fUds2rWcHA8Y53R9dvshaft38cfR4cLO3OvTTgzharbtExJJKLhOnT55xpwcoicCg25Q3CF3TTyM+rPlspuYInpwblNF0cpIoJSvraiAKImRzegZsWiNqQ1XI3F/TZr5NMjGrPvvysBTn7Aqdgi16wxNwh5fJTBAuRoIgXaJzVznKX3PJDuLu6Nb4k28OVKQ4wDASUzoml9O9bW6H5y4LAmuuxQguIRKUrcnvN63vprgnw87RM1PL5/db92fDzOHM22nGdKppsPjsP3ndDlG+OzdtY4mREAisxb5XrqUxioA3QkyDX7+OyvWaVkwt2qLRgi71R5uQAi+Xyc1QypM4Qmy4Zok7pNwjAhiiGLYRGnA7c1giObyr5HNSWARvCz0PBXRf56B9g4l3lCjFCEwUzQ33zWruI7m2HdXIWQM4IlF7F+Y4wcgZGe3BUKR3R/R/AWsga9sdTEl6cWC7GB4bTRzbaXCYkQbO7FFHGogDBlCRIiJdlO+P+k+oJDKYqo+EfVezickF1SgciXJtYEo1tMFyzRgPV/Y+cwTQoiYDwefCK/p1r0GppCQ8+AwU2dv8Z+11h8EQdvoVtJTrMX1eCmyq18LxcLseowFx39mqQVqTHI/3PZ7QGZsyMlZpYoUDCmYX2uh+F+c6WaGZWNQZW2EWqfhgOCyAe9v0MzwvJwhEBymI6cNE2hQmC6oVXE5V/zw/Tv8/u//gHXbcLy5BUTx9PBEeM/lBiERHYg5YCsNtW00nEaApAwJGWvjYT85JKZOKCqosGCwmxkGwRLYKdfG/ZBYYhyM0lx6yhFNKio2mBa0egaNmg1fvPkCr16/xvE4o9aVxtZBdpp5HgZkAKYBpVT8/d/9Pb795juUjS1P043yAzVYqXh+JtmnVTaBOU88+IIgDwOOxwO+/PJL/Nmf/QavX75kE5cov7BGMoK4ZEj8QOjPgTpbOvohjJidqMaDaS/UjiNJCLsJO1xXFmOkrMcnO+3dOiibkehwLhz+BG0Qm5sHiHIy4vP72dShHQ5ks4ToA01wMwQ/N1IeGY7aohdx98yUBFGuLVIaEHJnHyoqDNoiDJ3NzaIcY4BFQfC9VPOdV2LXB7cx9UOaxW+HQnml0Fmz4qudLpvqbNBrms61adjJSsqJh9e8cM/pu/4UGS5clJPhMM+f/WQWEAm8LyZsOrq3qe27NMKVu8zF5+7gZ5Lsi0lxaPSzlqfPKv/gbylT48/KKex/b96FSEpAwj7hx8Tfk2CVp4uIT9DC+2+Ivnf06w02x9b/PgSEnjrSFLAIhmBXbCuRFhEaC9CQ3PfjDr2HlDCIYUiB0qFADkmpdBIqRR1qZa8xdCcyD0j+qa+fLoKdzRTS7unHh4HuK+awGGugOEtbvNtJyH0iEsEeVuovJuE5+IMHiHzWyXh3AA/Z7Ps0wp0UcdJNRfqzdGVF7f8J+zQYfNfo/f3++a4d9hWWlf3/v/7FB4kGyNWttKxLN8Qd2Y1T3N4smkHMdoH89YHk/1CQ5dacXND8e8KlG9FHyCBy/V2NE6k3y9fr4bKBEATjQJmBxO4aE0GWKBw+s30vwf3p9ZNWd4snnKMYR94PPs22Xy8mOndyhe8ezdBEsW0VT09nPD2d8PT4jK6vrIXsToBFMDmkIxJ3Xc+uewQnFgnUCRl8QBan9AOoxpc2+B62AVzqGxBUfC9DmJkiZBYVRWTRaoWHaBIcDjNevHiBITvpynpT5SzB5E5AGtCq4tOnB3z4+NHlLIK+h+ZzqNjKups7iHkOnDeJIQUMQ8bhcMD9i5eYptkHLTaFTf+/7b1ZjyVJdh74HTNz97vElhm5VFZ1dVcv3ERCGkmYBZIgQYN5GQIzf3SgZwF6mRnMBoyGECVSosQm2V1VXWtusd7F3czOPHznmHsUu7ME8E1Mr47OzIgb97qbHTvrd74T2hkS022iBqyCzPD6dtQo60tfV30tLHLzc8G0u/3da1+w9HudI39SFwZTdtVgEXMKja1LM1zf04HO4+iQBojVjFQAZ7qBoSK/k6ESi17nOp8TOSi7Jaohl6uhEl1UWvuC3YszTqk/pwVOsJqVLKVsEWcv6kZiN7S0IXbUef8y6x2eS+ewsYYwW78YQ2s/UQFrwvZLBDu5PtT23NHGtVVz/mG6r+21O9n2XNruT2YDuDB8fJG6BMB3y5+n1c0ebITXSnlT0j6/tkjYdSINecCDDzadYpCrdp+h5bDVlLYDjaicWDp2fRmAENmalBn5Kby7QOnEma6sUYzk3SJ4hU3jAOcoyry3v+56N4F2Q1KVttqKCqSAOlUcDhO5HAeiMEPfcfhsrpyX1tEb11KZmwdJrSsy+shUXAG9pClb6JySeVnWH9fbBlSCPt6+veFhCoIuJpysVsi54LA7MN3UJXKKKrDpOjw+3WC7ieh6MlOUGiApIKEDrJEeSj+wU87fSkKGmaoZsQKxFEjhBApAcHJ2htV6QOMcbY3tJgieXLfCd0s0KFsVcrWhvrABkuaVuRgRCMCDPk4WIWdy4Q19NCYZIwzP1szb9+hiRN8NFOzoYm+DTSMLyBMKUuiQYoepjChlgveYpcQaRqwUCwk9KgjOIEUVx1r1gXMkUzdYesRSShhxf3uDP/uP/wlX1zd4dXWNftXj5GyLHIASDTWm3nVWUQoQpoBe1uSoZbDCpt1iBOICjNaeIMVSU3XNg23pLNUJrCnbcNliYJKVYoRPJ1cAnDRyHA94+uQpLp88weWTFzjZPsGqE3TRos1CJCKzByyY5N0B91e3+OM/+hN89vkXuLq9xT6PKFIhEeg7Aj3Gwx5QwaZbIcQCxBuE1CH1G6w2HU7Oz/Do8jGePX+O7ekZJBILxzoVjX2wGXqwqdvFXNxozmMx0EH1pngrmnuUE8FIL4R5MgG8F1LUDNuiHg6CCdicPAE6Nli+G9CqTNlqtnluxpUr5iT5pJPWHmGgFdYTiR9nHQ5QTahqiL6aaRR9j4JCpTMZZAo/mbKsExmQshs7OzNBEkLw5WJErhZh8wVmZcxwtybvZhJoQIMpeWaXLOsiTGG7I1IMlZ06BgmxEr+QfZirBIii9e4mGJI3JsveqKkJd9fR2pqIG+igoM4JEYYOtbYu8Z0j5YU7XwRVRUZqgJGQEx3a0rYKZOVr+8j8V4FnzsxJizZBvtkNa9eANqiRWU/L6tA5gjolJVDqZEGCmqOufNZiaFHT9QRWVazWvXn4MEerNLmsRVBDwVR65NIhoKLTOUrPEtgelvcoklEDU6zRUssR4W/GGNMWwv7ixn6ma3KPEvA8ebMBws2TSki2GtqKBVGmWYJWQrZNAKsCscJQou7hmUdsNGN5KpYe8B4bdUSORUjSepJSjFgPPfoucvBrVc4UhKWJKqyIbF7fIoLzQnOAWhTsDdNWvI/ReohkTtHDl8VdH0ubtDX06MuUhXugtrCzIZw9Kq1OHq6L9/d98d8Lrb5HFgr3Wn3DYPdhlFmeJqvCrIb1N3GUU0So7NlydKtaSFJNiJ2fNBjCT5SHD5U8lbe3t9jtyB4Rrb7iXwqm6pyFxiPcNp7GGOJhZrXFGCJWXwi2v3GWOTC9wps1/5OFFCCY0a25ofxijOi7HuvNFienZ1it1jY+q9p8Rp+yYIe/kBru9uoWb1+/xbffvMLLl68520wrUYKB6OlqtT1BQBciJFQIJoSY0A8R682A07NTnJyeYntygn5YQULkeiMAgcauee22mfTV6FQJAJVikSflwnvbZm9c5qPpMtUimBm97T2y0Pm8++T3eX2XsmQoQ4uI5mjT9MJS3xi1VdMH1ofqMhwNtCGOmLQzAVG0fk2r0ZPRhhGly4Xfh382U5UUqO9mwCyIg9dDLRZayJgfqfk7av8XjNAi2OdUpWz4QIEQoq3hfE75BvO7ia0Xj58bkkXmSgBHpcPjtgc/9+f0Z6GD60TeTqpANCdan6T74wJ3LhkV+gi0og/XzxMED+rI5kyL1Nbz6HqllX9k/kzvD/BFb0QGzUXz37Eo19ZPPJJX15l8ugpt7RsBTmUnLRgVKaztVjs/YMZFAQwlmgP8m693o0NtBE6MVsdL9GQ0Z6zWPU5Pz6G1oOQjfcnCzehXRCABithHTn92MmLzGo/7A3ZTwbBeMYdeGO0dywGQgNV6A4jgeBihlWSztRasV2SFH+uIu90Ob99eYb3eknPRhLJqQC5AP6zw5PFjPH70CBcXF7h5e4+7wx5aWHcQYeNyNL9KlYX2XEaCMKy2SO7SitubOwyrAddvb1DLFhcXZwQVRNb3plogYjVA2+xaAS0FuRYO2TRJ8XRI1/WQEJHHbI3DyZjSKSoxGg9pJAP7/rDnfMCRjeuroUdKPYb1FqR4pmc05snuzdLPQTgap2R442zXr9Bhhf3+iFIy+qEnQa9NHR+rM/Rng+ZzLIk36XdxMvSv3W1JNhUkYbNaI1wmZK047jLGOuJYDhiGFdarDbo+obdhrYoCDSQSr4ZjbFPcy2QenStrZ/6lbKbOYFpWp5pTc8n83Mn6Rkf0qxVOths8ufwRnj99jpCI0O37lQ3rcOQgafWYJgS++foW12/v8K/+5f+GL3/1Nf7yl3+F+/09jodbIFRsTwfE1GG12mKaCsaR6qmLHSCKGhLOL87wk5/+BD/80U/wd37/7+H58xf46MVH6LpkGQRaUrX+1Vp8OokpIRMJj86sFQ2d9nZWF9B4q/EFgP1gD5xVoBZrpRFLj+psSGNMdEy1sj5rHp4AWPUG/PA1djBF037UZO74tVl/VEvMimjlkG2xFK94WxCjArHe3VoqjrmiH+hcJGFNtQLQpBCjVXQUtS6yVgIg9Iygq2UQosSm/AGm402QLJIpBh5Rq0PZ6LhgaTho0y8NyF28hENDveosNW/1yWp9ybUSO1G9u97WM7kWV65DSonZmpLt545zmNO1Em16gm+mZWEkOFAk2lN59OeG2KxVs45EfacGCSWf8jiOvKWqzdlPXYdOYhvGTUQ7gFgQoiLGvjkJ9DIog5ya40pQPQ6ArtRvgWWU4gAZk7tKjmqJLD/krOgy2X2qBR5ex93v9xzL10fUCPJaB5LSqwL9MP3N+gSXlrt5IOA/QghIXcdwtU6Wz7e6m+sr9d/1iEgtuuDU6aNNIkcn7XVeo/LIDOYRFkOqxcjUVNCMopzuMAzawC9+ZBVqCq7nV8dZd+Krb55cS/fYF+tuTLdGi5h8JaacMY4TxuNEOHtz6Ngj5LntB5e6122FbvNuTfZbRFUCrC/GvD73hjCvqYgV6W1mIAFSjjyMRgYg5kl7oCjNE+demjcPU0ziEd+MEmQkBbhPJ1IRInPz9Dr5vNVGZFH8nX4pwIFJXSfQPOE4GWDIit2NsFkWB9DurfV1uTfueyXeu1ddBIFAVJtAFjWmB6EIRInwQ4rohx7bzQbnZ+d4/PiJwa2rkRZwpWuTeP6hVXF7c4tXr97is0+/wBe/+go3t3csyFeOTErW1Nw8c1tPyhfXZRgGPHp0jidPHuP58+d4dHGB9XoDj8seRFMANe0yS/Hds9miKv6Einb55ObL62Kd1c/lIgNhkeF8xqVF/s1qmjff6MPgSPDqN4M5GrHH1+Vvz8+m6rhUbS+aiamlhV/Vo4Lq+++RHlOAwc+HGYQH6U319KByfp6LhT3kHFktoi44kQB3UTHXRpluL3OkZF8eKbrERHdwK9/HQY4eubl75uvtZTcXXQlLI7nMIv21zW970F4zH6W2l/5szTVsEbVatqJVb9taQj3CXn6ccQwrZdzDS+/e8raWwF+211p/8eJ9/WFcNrWitVtUP/PsJ4OTKaiSPEQgNtggolVglVNKVDn/sUDRWY9uNQBUr/rXnue71zuNYJ86LlQAPbjdjhsQLbLLE4z8yxgEYE3Hcw0xgOnJavwSYvDZr77+Cq9fv8SzZy+w3Z7ggydPsR5WzaUrVQEpGIYVSwWVU8VLmdChou8iZBvx/EnfiqCeJ28rLoKSAvouYdP32PUcyyJaUTWjtAU1OLUZxWSD48PQIXY9JAXUINiPR+yOR+x2I/puxLg9IvaCNNj09lpQVFDUGr6Lsm5C5lkOjLW+F28pSc4GnwQ1ikGV1RQRCMTRiqpjS1mG0KE3Fpdh6FGr4rjnBAsOOwZi7JgKNDRozRzj09tYlzbupAIBjMoCAFQl408pmPIBRSs2a8KPD61Pjq0hb9/eER27PUWQiBQSxizItUNWIOsElYSuEwz9GmEVUXJGniaUWnE4CjarAcPQcXp7nahAochwKiqi0MQMVTFHNiVTUD5KazoyuuhYawSItq2SsdlscXF+gSePL/HRBy/QdZxjqBJRpwgb2WkHjr12UTpECZhqxn/4s5/jL//ic3z2xZd4e3XFcxoDVDJCEKwS6yu3V9fIxTljBUclc0XfJZw/usBPf+u38PHHP8aLD18ghoQpsx4OVaQkZkhdq1YgFATCy6xmRqQghEp+kfNmlGS1Y1U3bJ7J8LqbMpBWRWdHv+mmRbxQlaw1RGACzqriWaWuYz1mMqovAVuo5r45HiCNBGFpneNBCRyIrK0GxhFcKpUTyFGtruYp6cwPDonaJJAPs9p0nZTI6uNkUBI8C2OTDIJxkqqdKVh6rfBOaYzM2bWpOFHYy6soQM2Q2CGkHsn9iRD480J17uwpwV4Qizm5rXme/MVB5kktQegAF4va+XOLxkXmPdSHRrNqQcl5JhB32JLV5xw70KbWRJu6EWcnwqPDoAoUq7WbrKR+gOGBl13ggFpmQSKHC6rC2+ajuxFCT74z/eYDqrPtfQyWSTRWr8kAlggsOUUVSGBtl6UrBQqgpSDaAPDsrURCXR/lBKUU7CDI02RtLGhOdYrdu0wcdcm7fugFVQ+rH+S7oXOYad4kT4zl8hd9Su1cu9ensz/guWdPsTmzAdE+tj2B9b1iYbCA0OcQI/phxU+RmQHF0VuIESFwinrf99YE7YV6kLi23aW0OpIqWmtG81kNAViqzRY0eqK5wxRNKWnVRoprDhm9S5k9nLm5zj7dDoEzf7igNlRenYv07nV6FOlcjQ/YEdp7+/Ivom1wCKV3RzClEqwuy0nbTrpdbZ/FIj5v/K9acTwybVhxsKbnhN3+iGlk02sx1Jn3+qSY+BwiVsZdVh5mWZEmR7MTHL4TNXgKqi48TP7cUipaEKOg7wds1mucnZ7hZHuCzWoDKwY/iAjmxB330Vn9VQXX17d4/foNDscj56wZ11eMASlyb0tV5GkyQ+EGhvWO2PdYrdc4Pz/H9mSLvu+hapMCzI1vAIO2Etr+biewGUzf4PmOv+vp6oO/ufwyl0iZbUGXi20jjph//WGkMJ8VP0MW/wHL+HlhmH/THclS7h0dDttXCQgKthu57HvYoE0lo9X/4KHZ8jlacIpghNKshbmoOEpWzVjNgRQ/wxCNOkfpYhGcs+4ECKnlrH+wRXsyR2e+FN5SIRIgDoLzLxN4P5vt27ZVvGlZLKtnmyzt3OTHokqPnGEqRtSCGNvHFkEzaxHMYAFCqrXIsw2oTYyaM1ItArX99h7EFsHZOpHuT/3WmwTbC5o++uviMtclHZTuhjvYfvtMTB++7lM3YvKBC9zT6DXSZsh/8/VudKgtdy58ZG8kDlaHCYVIOhhl2QSDo0vHFOHiKAf3ooR9YM+ff4Czs1OcnZ1jNawwDD2iCCZrFfAu/2li781m06OWir0pCkYCwoe2fiffcMQA7cmL2McBl5eXuHzyBNfXO6BeMXXaBdTjBORsMN7AumQAIB09HqsVTNMRqoJeBtSccXdzZGQTOWw1mFIOve1cpfJzzlXEOf1DsnAfJSKYMpkNojVSd4me2+EwsQ3EmP3ZpiAEGwlRbz6ixutPChtSaZEC0YNGh7YaHBGPccw4TiM6YeWsWlRaRtYC87hn64VFXXcTh+YOw8Dm9jgglwk31zscjhP2h6+bQn97dYNvv/mGPXFDj9AlxKFjNDIeoQiI/ZrRZs6oFcgV3EPHtphiVQH7D5WdgQLBqtsQiGDPN04VAYphnSxlVyzaPOJ0e4Ef/egTnJ2c4unjSwhIuyYhA5KBmhBBhiOy6bBDNIaZtaRMii8/+wq//MtPMY4jJADjcYcQKi4vN5xKnjk0uuYjVANiYo0kpYDVesD5kyd49uJjfPLJT3B29hgp9Oz5VO9NY2PvmI+N57YWykeJTPkSSAQ2p4v12Qq4Tp74UBjyUhowpNrgY80Fs1qgWgqu/GMCYiR6unA9RZjmDXTLWZIIjsg05J21MBTH0QQby2P1mEbFBbEotFDfRmaTOmsFYH0+QqKgj4xq9/cFk44IxTJJtSCGytaqIKTjE0Eufi4ow5M75g1Vyhoj+RmY5WBTvAHrCtewQOmgFWXGL5gcitfQKnbHEaUWrPvBxshR2ZdcrV3EC7doAQH/R1yBR0rUMQo08B0WaXBGkLV628pCiQv1aLDePMD0AQR58h5SOg6xMJI9kuMSXd8zZWnjkIJlfpLYbL/mfxEIlgv1bjCnWVXJdIOAWidULaxDCuezcr/NWFsalBEqzKGwYcMWfAoEMfZQZXZPvB5dFTWTPKDrgo0GIz6l5glJAE1ohjtY5ij1HduxrGYarMsg4PtM4PcaQW3HBsDc+B6k8YnS8gssu9AGKeI7vixDc4HadIjVmjRmXcd0JtR7EGdEldqmsN8OkCjoUpzvxTwGiEWC4tGcNm8uhIihX2G7PUGXuoVrjLmJeA795p44h1PXimmaoAhIiXP5drsDNvsDSq1EuPm5Ezs0Mi/9Ev3WPssRUF7/fBAB8rco0BXGQ2CRidVGQmieEL0nWE+SK1aZ0WLux1V73+W9KtNr6n1xsNy8TbbwGjojNoMrV2AaM/aHEfe7EcfDiPv9od3v4ThCkvX/zAvN9y4FsGnZ5PZjisrRXgs30VerebDiMYNHNHb/nJfG9wqWM0wxoN9usN1ucXKyxWa9Rpd6Ole5tGhTrBXBZUVs7bWSqeP++h4317e4vb3DbrdDrYx8meBRCBJEOfC5lELWDmFKO0b2bG42a5yfX+D07Byb7ZbDT9uxnNNezV9WzPU6+3s19C7rR2r74VGk++ktJEYLb1okpfN6tTBjGWmgTc1oEVlTAea3u7w5nY0sZBqL+9AZAOHRatvaxRbbXNpZQkXaZwQzUsH6xRTzOgRLnXHt1JCMi0dfRCyy+DbR0CQep/mx7wufQgRMvQvlCeLpvTA/t0eC/kx2/9W0vD+rf7K2F0rLqsF0hCzuop1/mc+g2ppD/f0Wz7bQ6r42/qQOqott+2Sxd7aOCkPtL5IDiuWnLPZOLGOmbTHdOWgAHZnlhM8TbK0e/hvLl3kU3CLC+R59IV1Wg1sF9YyZ1a0REKz7oBEUOGFLjPNHfM/1TiMYfWOTT0C2e7c8ezFPL0YQmpvnZSx+uG0Th9WAYTUQZaiKi/MLlJOMw2FEngoO5WCTzm3KcmQtRAI9JzHWhNXJBlUVxzzR2+6p3HKZkAuBK2IAD1HW5U62F/jBD36KLz/7FlE/I9tMZqdaDMIexQo4obaWTGaJienP23KNmAbWBvYBn3/6OabjHj/56TMU7dBbM3YXHE3VY5oyjoexgU2cbcDZWlzw+nWHYRiaspospZYze/KSkVfHYCm2lg7FwukkOXPOGdPhAEkRfWStq6igZPIW0okButijHwYcxgOO04QUO9YMCr3D1XaDEINRNs1e6f3tAeNxxOtXN9jtDvjqm7dMDReFSkEN7C178oMPMU1H7PZ3JuMFUyk45ozVOmHVJda/KsnAc1GkFBFCBx8iWyvZMcQ8ydXQIwhwPOwA5bwxkgNQoYRqLQ1acHZ2hk8++RG2my0uzh9DEG34KG8nSmLfJGbF5s6HqmLaZ4zHCf/v//PH+OyzX+Gzzz/D9e1bKEYoCkI3kmvz2COrYr+7h6Ii9oIQK0I/Yr2OePLsDJdPn+K3fud38LOf/hiPHz+FINln0aueakbOE/o+oU+MmKvXa1WNFEBI3QUmnwGFFCoDr7t4WliyFRmEYKRiEUjq+ub41VJtSDV/LwSuH8zRJMhBUOqEojbhJQYbzMoPU5twr5Z5sO4FQBUlextSssjQDKCl3IsRr3sLEVKcU4GW5h66FRISJmOMGZWQza4ycirFlT7J3D2SEWuvYrug4mg9jV0ITE1bXel4HJuydXYp0QJJ1rYlFV3syRNbCcLrakInEdG0a7T0Fkc76eyYWoqRNMhkjmlmUcHsQ3MgWLcmGlKQp4xpzOi7Dl3qGjGHxDmNzHdl+WeaWHYAWANcbbZ0IqSYfuj4amUJZJwmaLFSICMP1iKjOcI2yGBO3YHRWeHkGcAGI4sNVobXpwE15HayoQfZMlnRHJ9apZGO05kkzSAJM9iLWSvfnw4f4MjdWjNqzZzoYnSTbohjrSy1KCCG5PXU9fE4/s24Q734PkcddrmnKu5vzR6cjxlpzoH6LdklTG/EECAgPZXWh5EjvTDzeHwjPD8fmIt22jV3YaKhniSUmYHdDE/Xd9hu1uj7HiFEVMc4A+ZRWO+RPRf57ezeK1BQQKo1Dobc7/c4Ho+z9+bRVbD1MMPPmp17bjo70PaQIg4cYhTMhlkqkxklOntQ0tbFng2LfTGryvqhsXks9m2OqIydBtb4jLn+4pyr9MLZFlIrp5TnnPH2+oaR3/0B+8MR97sDmVEgQKjQNCHYAGINLM77wW0pKPlutGDRgM6I1cbSAHqRQWDjW7QhhkluDIgWnlnQkK6HDU5PTnF6ctrSt01D2yKyWdwjpSbUbX1293vcXd/j229e4Zuvv8XxcGDaBqQm65Otj3HQei2ZTlxA13PCStdHrNc9zs7OsFlv4LRc/vxztsA/mc++cKr9AC1PR/s+925xaPTXvGoZKTXXHC0qafegM0IVqgbqmF/v90FDyk+fEZ7fibogD9673aE8dMwFaPW4h2edxjB2yebsuUioh1qYQ1nMUVu7V8BricEjufYjixKb0RVYimERtXmUAzqCACDBeI7NcfIG8Hbb0tboYW+lR2p+z3N2xn83NGS1mLw7wM/0y4P3m9eb8Yi/m0+bEDt31i/n/aOqD+5DfKHbPVY8QBLbz7VldPh73PdWt0CL5mT+t9pz+uf50HBPpjPzx/YWwYyQn23ILJPuYXkdVRxh3v7NrFf0VfUsnp9t+ev78d3rnUawW3HYKNRmdFVPE7LfLcQOgmC0TtJQPZwUwYepBrbIU8EYinluAREZAQV9jEhYng7ecNfx4Vyhs5ekotiC9D3ZaMbjiJQSVqsVcnTGBSFqrGZAM062W1xePsL2dItuPaCWae5NgR94h98JNPPevek+TwUKIibzNOHt1S3OLs6wWm8wrBIqjpbDTqgZKMcCCQGrzYYwNi3WR1voYUUb0RQCtGTUMmE6jiiFI6IUgpOTE4QYcBz3jfBbQsCqp3etldRlCgpUKcV4VQ1xWI3txMdv+FkQYDeOOI4TemsDGfMBtRZsT87R9Qmelj0eDxinCVdXO9zv9vhP/+nnGMcJFyePMY0Z375+iVoq+mGFkICw5hDfmg+IEtCnvq1tFxP6nv7rOE5GzqzoUocUo+X9ORFcECGVKMDQ0zAnSQiq6JGgUtD3POg10xkZ0oDTk1P87Kc/w3q9wdnZORRARoFIZTRSmc7N7lxUevFRFDEUS8lGfP6LX+HzX36BP/njf49fffEl9rs7xFhZX5GKi80FogTcXu+hpaILlLkuJXRDwsnFhpmPdcDp2QYfffgCZ2fnGI+VNZ1EPLW3VLgSZYuGpY4MueIHG3BmGE+fU+HkkcquTZewdKEDTrxOoRhNDqh4ko2kUTUCCyyYTBwv69B4N8yTOiQTMzAF9hrBWKxemZKbGu6BpS0DQHKGwHokrL4lKPTsQdQxqiJ2iVMLjoKQM1HOWJB3+8g2y+/3A/mNnYh8sh7noedA51pra7wGOJKNDCnMeLC3T8wRpO4Z84ipHNleFRJRsQDGkWT1tEfBCLEBtRmGrSne7m/ME6PRtECuczMRIYiamtIXSRxi3rxHq3fCHQ8aQJ/sEWLibrUm+dDeC7BHMieBIB8zRGZIgq9NtVqplSq0WLrUppggAKJ10XMXGiuMt5q5Tc0GaqyF7UJd1/P+8wEQnhNFIO+6sqTUDGANRKgbRWKwvlmNNt0zRARQJ7BvNBDhbsjkqQCqAbVmqChi6hbG/9df3zNFglerKxlKsVROaI8tIm4+oeVj5/6zGnz0JhlHBNUauC1tkRJfYy5t2xxr0K9WtwsGiMmFbO+x6IwwrNVG3RDgwr4hSoAoR3tsT06xWm/Q9StMSYGwQJ4JrP5tXpcIGtGuRWnilE5mcGqps5dpxrL1z8C8PC6ISxB/EmIrKEOEkN+Ske3+1YR/mjKkCKbJ5i1KQHRvzpyL5imqscoA6DprwzCP1FuPNLoHDYYPJRMGH4j2ElN+nMLhkRoP8eF4wG6/x+5wQB4zpnVBUQNBdBEnZ1sgKLIcWsuMqQBzfltVuUlW8/xlEQ24sKrXeg02DswHzl4bWpqBI2vOTk9xdnaKjUX8TKlUMgS52zxbDxNsgecTFYrdbsR0mPDy29f45qtvcHNzg/1+h1wJ5U6mOLt+MO99DxhwyXvK3BlarQdcXJzh/PwMZ6cWCUpoOAtKiTWNW4Qx11ClAaG8bhQMCPOwF1XQ5sZhrklZ5yS8H7AuH7/tg7iOnVsh/J21xQr2TbH3nZUJlT5fUx0hvXBkdfn53pNmijK0aM0ioqZAdfFl+x8EiHFmseFTt2jBb8bvZT6Gi+hrsd2MJuegRywzZVRKNKx2nh3v4IaH/Yn2ga4fHnyG3YO9FwmvXQ94ZNOWHrCzMjs6+E50Oe+bd/UBDn6x57TX+xBx7/2lbNhOKCPe2TGZMyCu77x1SyxTMscl3qPJtjhxPljBYg8W9U57FBHPLHgE+nAf/H19LT0692kXlBdYIyLIwatOuN5Wpu2RYxwoz/5z55z+G0SCU/VYoz5Q/uM4IvacjE2FTBmq7hl17B2JMLqrIJgOVDBY0+nrElGkqe/Y1TXaAFfzcGIabMMmRK2IlfQ3u/EIAdgnaP+VrBjHwp68lFArMFYgVLa0rDcXWK1P8OjJBzi9eIxxeoPDVKDm2QbLf1fhqJIaOVew2MHMOcM7gBUcqFoKWQ1iiEBmzaba2BZS9yhgh18koloNiqNeAvt9SsH9YcR4PDakXuh7iARc396SLzJQSW5WAw+iFT84NQCI0bhFC5tTzzYnjV4uMugERDAEklHnqoi1INQJuVRMWtGvBqQuAsistfggRFAIr69vcH1zi/v7PUpRnOQRqpz5uF6v8dOf/RhTnvDNt99gKtlmQ9rgXhNQpuUcOCJwBhFEQQ2KVA3+jAoVtankhJTDesqKKkIyQ5gzBIqaFev1gJ/97MfYbLY4Oz2FiiBrQS0TNB840ieldnBcszgvJSrr219/9QqvvnqDf/tv/j1+8fO/xNfffoH73R3GukNFxcnmBF3X4+TkAtCK+9tr1KzoDJFGrz1htdng0eVj/Pbv/hQfvfgEP/z4E3Rdh9Qn88Sta1Ype51Ff7VOmFPs9MhpqBgxhyCY8kiDZ1xtsWMmIJejRXjsk4MSiMSIiAwaPgx11kYUazfgXvulnWW/HtQmS6jQLgdlHxuAqouxPFBDM9OAK5wPuCLnyWo+3Rz/1oqsnL8YU7BSRJ5NV+XnxZQQE6DjyEkDCsDqmVCQdkWEgDA1oBe0pQO5pgEetgoIoqvKrBGNINiPZmxFUK5vChFd9N62aj2Y1WqInRkK9QlJ7kkb+hHoe/bsTlNpiHcJHELNj+EZmUzPCDhr0CkhxYwJIEAI1KVlApR1wKpA6LjW9/s9Sq7YrE/s/rwUwQxHbAg7nukW9Vv6NHl9EwtQixkTT0F6tszVg1gUJrau3uPrKX2xQnUtY3Ou2muhdPwE8I7EdjYDUdMJwr2qheUQCa1WKObJmJvRnESxMW10AHjGF/b3117vBsa4ddWFNRdtbQHu2ASPPFotC2geAGNqHvaU2Hdk3KFtU8xbgdKLqoIGBfdJye61pobr9gUH2PdkNTYBaosgBFJJRwREnJ2f4emzJ7i/45QDJ7d2JCgnVxgqVMlS49x8PkIqSESIGaVkHA5HhKCNeLp5anTD2Ytn915gqRhvZm41FzVqNAtMRFoKg4FKbCg1QNrIkTYNAM4cQuPI1IbVi1SJAjMvpSgPHoe7Jm+z5TijGNlHGeap6qVW5FIwThnjSLq1EAM2JycAgNv9DogB94cdRzCB6QkOHbUxUUpDFUOYMwRB2mFQ4SmKVgvx+kHQYmvlT8n6anTGof0ReZpwe3/LlGw3tCkQvo2UTQNOFI/PvfNtXuRSeY9ffvkN/urPf4lvX77E7f0djtMBuY7oLQXXdQNCTDgcJhIQKGiI+oiUAvr1gNOLU/zoRx/j8uklPv7Bx3j8+ClSbwhoyFwLbJGPwJlWGOnO3rq74wovRfDzWsRsMR/lZVETsyjee6ycwadFxDp/ll8PAmT7/wCTU60z+4ks7q2tY52PsJLse45uSLvIZ3fWJL9zMwaW1ZhrP2iR03f5Qd2BCWpnwG9jMQmBZy7Mf9e5XcsjQXc2RNDqaD4yySe3BEt1qo9ICwKffCDCNKrFWg/Wbda6tcmvy5pHVbytaqAkbfcSYKniJh9mNITOh4HX0eJWbxmLJH/0mpiPlWMmQIAytRYKPpvMa1zR1sYBJQ9X035qitinZsw1Rd8wGwygs6wsL4/J9LtAFX342Wi6DWY4be3CXF7zLgBVtJQ+1GrDUdjTDIW8GxPDtXvXD/tgwxAn419TLl7fm7KsihAFQwoEkGTAWXU41qJy1lZV9N0aaVhhKkeUnDFZET/ZlKFq6cCi3n5JD2aehsDPXlkPknPVutB2xsqiYumlKAxNS0BKESkG/ODjj7A7/h6ur27x8tvXkI755VzVJp6TjzBa2moSTqmvUIhmHA/3nBS+XmEc7/Hm1VvUR2d49vxx23DKns22ylZrgU9OoPIejyNiYkS4Xa+wWq1MshUaaCy7Lliz9xreHlEBjBOjhdT3iwOliEKv+3A4QCWwn0oFdXLADWuIAtLd9X1imiEI/BQk4zEdc0EurIHtxyPud0fc70coAvqhxwcffQgAuBn3OI5HfPbVl1TOYWCEJxE+sqbmgnLMGPoOfUqIkXymkAwgQ0CexZg4lXvoyNRyuLtGno4wdxh9v0YKCX3skLPgzcsb3N7e4i8/+wU++vgj/OEfrrEa1qh5T8otdeq2DaeMHDJSimyTsctmcWPKinFf8a//6I/xf/yv/yfyYYeSR+wONyjliKePLjGsVphkhVwEr15fIU8TkvWrDec9VqsOl4/O8OKjD/HP//t/hsvLJ/jkRz+BhB5VBnOiq51rsUhmLviTCH5WHA3SbojLaaRTmPpgqV5r0zCPPBh6uOkOU1B934M1eGYiKgpgBNB0FKITpXjrGj1o+gsAbIhyrYh9D58zqKoNjCKgsaz2HPnoDDKs4cR+QK3F+kTp+ZNcgbycpUzgTSSkwNmJzCyxB09ZWoJERkRqumA5IYd1KKVG8yhf1TAvfv7m7poU02zsYkCUiDKOqNNk09s5ZDmlZGC1jCQJPhkGAuhk7Qy2DjZ/u71vKRkOdmE07o6eq11r6ZFiDp7vAXEK9HlYnkiWMs+VGSsn+CNfK7BZr6AqmI6FU3vgIFWLyosPtCVYZkgBpSrGYj2S5WFrhgOD3FBCC3xAoPp+GzlHshpxKdqcUAAIqUU5Zju4F2roYEl8P58sU9XnSlpU52ZTBFESRCKmfARsDquYvVDVRincxWSyyz1ZGv7fdP3n1QQD7ar3nhimlpMUgm8wrNnZINOWm65Wc6JTO3unZXlzjU1cWm2rsSogLDwLIEQ2mpdiAuz1smCDKa0ArEa7o0VsgoxiGNY4P7vAsBqQUkRusfWMwvKDAlTjtSMqLACoeUIJARWcuXd3t8OwYt/XEiXaUt3ikaA2KsiYInqQAihERmBkkDHvSygIXcfm9wbDdu+rpS1cvCwvJNxwJxcQdPARNtVrhkbf0CVyXXpNoUXj4vfPvxz2B9zf3eO4H5FHo3YKZH6psOkK3l4iCgWRmmT/EkLgg1jKxVh+BHO9QOixCQzpCYWWjAIgSCUbi6FryVWacX13j8P+gC+++BK73T3G4xHVJ4O4vPgh8PpyiOg6RktqG9Rg2ApcX93g6vUtbm9uME57lHJkKtX6KoL1No6HEeNUkUf+LK0Suj6g6zp+DV4LfIyT03O2fCDY5BJ3OLCopSxSk0DbB/FAUUC4fqWTIgKgJiCYEQVa/cYVr6NV6VBL+76fKxdQhddsHKTDPURYInN5BmtVS0VymnirJ7VmO/6fWO1bhU3xTr7tMH+P/1pE0CIa2zvbupZ04qfA+ybFnGsRGE2Y3bO2RGYbIeQ1vYd1KwcVOcLQ39+yJuLjxIwmrhQSe7VRcpTp2u7d0ZL2/svIGh4jLssBdEP8AUUwR7Qe9S+eGzJHVhwMO6eBFRYSOo7K1KREft8BVF5r9TvjmCI0gzdvn8xyN4fiNEwy70/7bO4so+wFWYnvp7+FNIePmTEsZdPlzvabiFsXrLb7Zidq60GXEBtjlmab2mIP6Ih0I84xGX0YkX73evcUCZN2CeTmjNbkUQOZT1LoZ+HVOdz1VAwEKKEiVx6KqqTpCYizBbcDHL1h3h49ODee8+6gIEhA1xOxGitX2b1YWLqIgadCa0GZgJKFBzABJ9tTfPD8I5yenaIbInSqRK4Z+oJTDIBJK6ICSckgkgINSh73ACoKjjhOe7x69RapS7bfC0MVLZVU2MQt8yOgHwaE1Wpx2CzVkE2gAoVpvVrRu8o06MXTUcFrES4gPpYJlqI9IMaETgGJCZroFHBGngDF05+d5dcV7oIqgAY+EeD66hpv39zg/nqHw4E9gCkmpNShlIzpeEQeR4j0tr8ZEoEQpQ1CrRGQrqJL5uk6Os2i0Bii1YmYypkOe5RcMKSCPnFaQhC2I0xTxhe/+hxXb6/x7/7k32HKEx4/OSfFHgCOMu/Ny2SUAwSkJEipQy6KqczeZq2kiPvyV1/jFz//DK9efYuc98j5gJpHa46viKlD7BJ2r95gvx8x7QtCFKxPzrBaRaxWHb82nJ347MVH2Ky20JosmhlJsdZzNqMWd5rmtGFzSIJZFvPA6adXQzoDMVF5eA8aTL6SacGa3ROfQQNVOetORFo9j6nEGS1IL7ZAojZDzRSqYMocliw2q5OvF3I7Ag3pIq4lo8sklX/JGVBjogFaSrVoNVCDteZEcZKmJoNM/ZoRMnutSuIGKuTSoiaAy9dyR8KoyVYKDhTzZny3OqUy8xFjQNcxalejwdOx2GBuRnIigfXpOg+kpg40YvilAnV94AqyZls3mQ2LWIJ+QboxwwwNZQ8AudKRtnqc87E62CYFsmWxhCmG42hP3phfHDU5+cxQmSM/mO4RFaAIDWYpEHJUNkPDlk1pjooI9XoNbuC72WhCuX1VkQt5kHOhM5RCQJRgSGUzXKUyuLUsFwMoDiAXkAs2pY6j42KA4giCkvkM0fYgH33Y+LsNIPB9jDHGOtGmjxtFmUIgWlHUwto6W90m5HZTgiWNEunBvD4YawWHfj7MQYsdDBbr280AAPLkXkLkAdZF7h9oIJ5aOXMwdBExcJLAahhwcrJtxe5F8LMQvWaJ+Yne0CuKsRRoCUCZUMYjrq9vsdlscX9fEIKhZqOi6ysPvYFSPAXjG9X656CGepxrCyVT6YkYgMbIBTwvztoSjReL0XZIKmsvq/W6gRDc46u6yPUrjeXkVFXGgCBBG99rngqmY8Z0mDAdJ5Q8cayWpU6n45HAIDCNNHuQCu9wAtAi/QZ2ssjAlUMQYX1YFdXGz6hw3WKyyNH3MxNA9O3LL3F9dY2QgLPtFr/9Oz/DJz/+EVtiGG5RZskL3JwvhdF72QGUANxe3eHt2xv86tOv8PmnX+Lm5pakC/mIUiarfUccjweOwhpH1Fxo6FMwIu4OsR8Q+4TUrxBTbwpNeC9+dvjE89GaQxs0gQPTibxbKtWgkXMX1UEEnjqikZFqsZXJRZ7xhu3sem2tAfddxF2JW0RlYt7uTy17EKOg62yenjIFBSiCJJNLO70mP57OCggWxc335CtBuzEjGb3mPUfLhOM35ia12qdFGTMTjmOR5xTarBDsie3swQ1r2wf/ebBWjfmzvTyBFkn5KKVgSPUAT/1xZxWo7tTw831sU3tfu7c57cefklfYtkN4iiAGjLGz25Ic0R6Cb9T2zQMGafdsL7Eoibo4WCuNo9rtZh2erbZ+FrkJxEZJSWuNaMGuzHtVnDSeu48WDrnyNmRqCsGMem3vbUs0y2EIbZ2oH5WtU5ZNgrhOIxDKUa+eAcnsu5gp5P7GRtA2r7iirq06DkjgRG2xUNy8g6bko7dKRCRE5Dqh1MzhpdE3A+ShqwVFiilNFnijRZkCU6aGaDoezaO1OXmt1xWyoEGrCLUixEQOObp2ONlskRLYY1i5iD6mw8TYdBIXH4PtqQGMDnlEEQXyiOmwxzffvkFMa1xfkaVmqiOGATg9JbFyin5fLoTeQO/CVOYhrIH9fOM0mYEhm3qxSC+awUpGG0fWDZmFAEAMPYbeo3PyUR6Ox5YKU6vljFlRpKDrBnRpLvRTaQumY8Zxd8RhN+KwGzGNB5ScEboeiorD3T2qKutMYp1tJsgRNizVD68Z6k4Cef+UQhzUcAalopbMupNWztfrA1IfkIICk0ILU5D7uzv84rP/iJvbW5yfX+Lps0f4R//kv8EHH7xA17tnG4GipEcTZi2qFkx1MoeJEYAI8Oqr1/j5X3yKP/vTn+Ovfv4pXr96jXE6YJr2KHnEdrNFlzrc7+7Z77XPqEUh/YA0RKyGHsOqR1ptkVYd+tWWvKgFqEW4GuqDmQO0WkHBFPcMHddG5u6ZN+mIaIzW6J8jX0fAXUU2pRQKlZjGDgrFZPP0koVSPLv8LKIYHTRlhkSYOjM2YJMlMwDWW5c6MhIFUzYV7EVNwYjXTVt42SOE2FSP81IoWFKCGfEQIlJn/XGLkoQDfPJUWi2SjCxUrMHVklLZuQ5txtTUU7MDZlmkaduHaUuIMeq0KEct/ewpXFgftEeQRO4KKlAM8Q2PnrXpKhGBDIt+PwXYl8mUKA0cDVMQTm9X/yARcFB1IItNLUxyiGEkPDgQ7ifXw0BX9G5MwcyODFl5yLkMbbvc0sV8mTkYlktkhq5DLhU5W3tEmF/nz5at7zd10cCGlv41cxE7vjYpMSYa5ukhAIwhyUGBATVwfaWWxm4Te5YkCIopKO5bWTapTOxcKDYD1bPOOovFb7y+pybo9TUjazXlITY7DQbKqNaj4m0D7hVKNYJYsdlzqsjTSAixwZM5BilxE6EG/Z3HrHg0SuHFPAyzcLijIuEwTtjvdqzZTSNWK3KFahWU7F4jG9X7YbAZgx1GLRaxRUYwxZUFFc6UKyoEKTk6SyE14+7+ClWBzbevEDTi53/+C5ydn+LZi6cQFOQ88iAkkvhUEYgwhE8ODAHfM082TDfQI+37FYBKVKOQ4BpAa+OY+UbdZee6xDgjw6od5FpK83krmAYNHQv+rEcmS3nx/cpULe1Ae5jLhFJGnJ4MqLWHdhGpC9jdXZkRzCzcB65x1YIoFEoBzN7wfp0cgVFvYYrEooUYIwv/qg1Bur+/Rc1H5OMBNWeUDBQp+If/9d+HAnj8+AOcn1/ghz/6EU7PzmY2GjFQgdUyQgh8JvWhLxEvv36Nr795hb/4+S/wi198hl99+hnevPoGu90dcilYb0+QEkfaCIDpeECpBaljajUOCf2QsN702J5u8MHHH+L87BQ/+8knuLx8gq7bIgSCSGB1NEeAAoDzWAZLYToSGtpKvnM9w0sS5u1Hg75zWobFMormqfvHzIpem8ISQYsqYcbRJ31oU4uA121avCQO6TcjYwhbVevJhdV51PSFgTUYAfl5spjIjI60NKWQss9+5ujm2p6HwB1CSNBSbB6FtFSeRTWu+Lx52lE1wZwCj4e5vgbcw6wn6d/T2HuWxd+eUfqMTmz3EGdDJ+qAGHP2TG/xM7TZQ9+b6mjmxXY156jtvcwGVgGIOzYzGMfRw9IcK+6pyMzSxOULs002xwiysBNN33LdWuahFmJNqnKwb2A6XB3ZOqd/0MgVbE+qMayrcUP7hIdSPHU/P6fLtqU6GAEmQTQEsrtqTu3ne6GoQCAQKiwsH7O277aC32METWiqT2WuBnAgsW1FakJbajE6LsxCprB8cWnAh9FTaaFDCBGbk7UNyjVG9soeMzeC/h+FiCE1+QcngmA0YH+/xzdff4NxPOKwu8ezp89wsj5HrTyguRZUZAzriFW/xmq1wjAMZP0vipCs0Dp6QwSnMWguiBUYOhqpJArUCTfXr3DYHyH6GLvbI2rp8MMffYyPPvoBghD9WRMADagBKCEgBYJhUjL6JZTmzdZaGf9KRL9mPj0bUjUZEs33sRQDGCydWQgRdVUxZkeDcgMWHX/kp+wHxNi14v/yKvmAOk3w1E/OR+R8xMXFGjEETErldHv9kp6fKDQqEKkYkCdEESSD5MeYbN8rEV0TnRFFsQME9P0afTdAFvPtqiqub26xv7/B7v4GJU/oVwmb7Qb//H/4p3h0cYknzz5G3w9WO/UIDywKObEBYEwZQIQigMwzX3z2Nf6v//v/w2e//BRffPY5rq/f4u7+BhkjihRcPrvE6dkJjvd3yOMRx8MOJWcMPVsk+lWHfuhxcrbC+aNT/Oy3f4znzz7Af/sP/xH6boVa41xjAY0IjVKFDzCOPpPTmszUnPdgNdBS1BS0I/bMm07JkIQ8/M4x65o1PDjvphwghmjkXExa10pEYIuy0OSq1RTF7gmzsatQBFvralDw7EQXECKSDXwRLGxrKcNFG4zZR/bNLaK0WpVEEQJLUzIDksXNoClOb6VpbRbOlWpnQlxrWK+u09vAQOOV2YI65hbhBSvVOJl4WxczGsX4aZ1u0NtP4Aj2yjcnTswAN+BMSd8bT3n6fVStyIZ3CC19jgb0ieY0lFrgHKVo+wUSFahzqfpEiib+1h+rUCVSNQqj16zzsIImY814NC2ONu3dnGvKJ9OYuVbDPYg5AiZxbZ6sOUumy3Kl/egTmYGOB5ObADjFJFPr3uYkSAKEHuYEFMpDiO1M1aJNj0Kq4Q7E5o/O+vxd17vToWIpRs9tG9OJemouRpRiwBPzyBSk4IHBHRrkOxiRNHrE0CpaRgBLbAYERnukRrRKqRERJOv3AgDvRYIAopljNioh49EmC5P5ZHavai0YR2sW7zpsT0+QJzLNWBcTui5CpKJMsGwQPaJi6L7Uk54oHyeITBhHEoDf3e1wfX2H12+usDkRnD/ukIIiRvNMDfIMMRQaAPbUWCtF5T1AxFhvtPXLtcMsLqoUqGqN6DF2ALShbR2lOk0ZMdBpYETCyF3EUbR2Fl1/qh9KOjl9UmzWPcZjByATcFTrXPcNQLJMQRWFSEDsOqJDjdczWb0XFTM1FahI5r4o80it+few22McR9xc3+J42KFPHVbDgIsnJzg5PcH54wtsT08aujMXeqHiBAXCg5o6x53Sgy5ZsT8ccNyP+PrLb/DFrz7HmzevsT/sMOUDqk7YnKzQrTrS35WC+90Bh/0emhWCiK5bI6UOq6HDejvgxYunuHx6iR9/8iNcXDyGxgQfWAQ/fAvPnNETPWzA0j/B2kmacrXISazh2ZSS6yfPjog9lzfUP4zirEdVBKHzaNORicE8amlnlhHzHG0omlpslIgQfmYwxc7n4Rnrgj2nGT9/enE04KIxm2eqWNqPtG1atJ0DJ+SIFm04mXgNdobUQx8H+PgJtzpkQMsAACyueN177mH0mjQ41URmg+/G2Y23YhlVLx18hQY7ywvgLWQeLuyX16dKcaSlW0WgMY207/MZfZjubI4cBzD7xEw7LvbC1sOjPACIC0dKRVG0mHGe65l+3y3O93tpDoU7aJFnze6Addg6r1mL/Ph5nRlGB/c4JqEWl3F9sFcAZsSxegSuKIu9FlWIGuo++CoAjhL22qpYGvj7DCDwfUN1bZVoBC1PI/QAY4jWoEk6tIAARPZxkCsvtYfzBw3GUI8KTJWMFFMuQKlY9cxZZx/m6hx1dpg5nmf2IFtEFMQmoU/c9L4zI1jQPBFVVM1M902cUrw9O8Xu/oDjcSKGUICSaATVIqlihflaChvwU4AWxVQmKGYjeHt7j+vrW7x6fYXLsMLlBydIoSJKYZ9T8bwFPWnKK71aDuqtbbP8ufv+N40CYb2iZPKTRpsj6F6ip05yLpAUuW5q5epAlJcfcGDWn9V6JYuS4zAIsNn2GKceu/09EXHZ0xYRTvRLr9IRio52Lc0Iem4+V0VBgVh0yi4bQ9MVEofnCtxe32F/f4+b6xtM0wHPnzzGdrPG8xdPcXp+govLR1itT4Bsk9QL5T8Em70IQCLQSQCqWCqnombF/d0er16+xRdffoVfff457u/vsdvvMOYjKiZsthc4vTgFlN7s3d0e9/c7bBOZQ/pug77vsVoHnJys8dFHz/HsxXP85Mc/wnpzCg0RGS0mbYrI01Fu/P2bYhGaIqBWjmJaKt4QLM6x1LeaN+ROi3iq0ZQy5YVppzIV9r/1K/6uowXdeZTZaHgkVLxXzLMOFrYkkUVf3pxc4sxQkrZA2avHVzjjSWZKsEpDvyosywPWPKXyM3MuGDMniAhA9icJmOpkDp4YwAJuEQAI2tB6U8zR54zaf7HBx2FIXQqM+HN3C0YRY/yg4zTvE9q9uyKf1xyC1pbhl6difa+dHJ8OrhF+mF8KcA3cADUHAmiyUAAYU+pfS6/W6q83R8Pus3h0brVxB+7ORnBWLFboak6RD7FVa21x9L6TLsDujdkON9bawCml8DOD4Q1qDb5hC0fE13tep+bUuZwJ2chK0Qb8Iu5jdmQMe9icI3+qJu+1/eg3Xu9Oh1pY6x6D2mJGOAqpIqCS6FYEiEBFQsgEeVSv8lfmf2GFYHF3xWmhVKGVEctqWNFoGTWSetLbDpDnl2MiFB4xYMAal0+eQWJASJFDL1OHqXJIL0cLbVra9vGjS3z04jnur95idzcioAMkYFRAitgIKYZG4k3gMSCXgFoFMW7Y3J2OqLLD8XiDq6sOn/7VZwCe4sc/fowA5tAZjbFW5ukiURtTU0knNQwcphtjaNtYMyPbNhfL8lTB+iF741aFkYMWI611IRoMKJJzYdRm71PqZDBiA8KAFG7V2CuQAFVG1pthhbzZ4P7ultGvIXF9yjtkAmA0bBJaVKog72k2egsxeHMMlTXD5soq9nd3OB4z3ry9wt3dPd68foXjYY8XH13i8dNz/PAnH+H8/AwfffwJ1tsTDMNjiCTkOiGgIIbRjIXNmbRWC1RrCcnA8T7j7Zt7fPvNa3z2+Rf4+utvcHv3FsdxxFRHFB1RMRnqM+H6+hqH/R7TcYRUReoFXS9Im4I0ZPSnHdYXHT76+CM8f/YBNv0WSQZkG0+TnR7LtlMqAR3BkNXLDAXXk1ERQgfHKOdcuXdmaBrSMNBFDm3vYS0CFrlZ/5u/zmtebJVQG7vj4mSy1iJFNAMCWM+uMnpgebKg5fNEEIXIh0bebLtfy2h7QaUNA1MU+y77NYNlCBgBV9TWuhAsciuqkOijyKRFc3MYx7SyAC2KFmPm8bqls4qUyYF3antij+EmQBdZG11mbNQQmZh79CyyI72ZYO7xRdNVsHuCrQkjxJmnVg3ox/Mx61xGaMUM7hzpeSik8NYMN5sWiJiirnM8B4DG02nh+D52TwF2ZpOlKksz9kWVAEA39iINqSw1gN2TxiJk+6GL9/bh2HlJ8g8OXLCba89DbRIt2LDUq9XLYwyWsq9WTzRjHBh41ayowZ2COYyfjZ7AE77vut6dDi2leUQKT6GRTYXMBgWi9Gw0AjUJQo2IoaPHgQrVgNqMiofac52gWXAz3MNAztDxcEQVTo3wi/1ybIzuIhlPNAJ9WKHryH/Zrzryx2UFMhVSl9jUnDO/f37+CM+fPcHnv+wgkhFjR0WlAqleP2KKTgLYsxgisk37jmFFhR9HVEQcj3e4vQn44vMvcH4+IKAzNg1FkGxRob2/z4XLFblkpDiQMd96kdzVLeMINWUK8dQompJLKUFhwJtSW6pSDZnWdwmlVExTQRJD4wrz5zMFHqVRtbTCt4hQYdSC1dCjTGtGatOEJJ2Lmv0+c/HRcvXRuSRRUYWEBvQxKxArYuLkc58oXhU47He4ub7DF599hlevX+PNm5cYxwM+/Pi/w8XlKT784XNcXl7iww9+G8OwxbHQkallBKQiyNQmXrecoTtYpaKOwLjLuH57j2+/eYtPP/0KL1+9wv3u2piCKgomqGTWdlLEbn/A9dUNMGZGrr2gWwnSuiAOAf1Jh/V5wosPPsCzpx9g1W8ASSgTU7o+2LhFezXAgUwtuhc/VYAr7dhQw4pxPDZ+WO9/mkmCBZKSpbUfesBLIygC7qvVT8QUeHv9IsLBIhoJCAS5tBStE1u4tubzGGId1J8EfCkU2bIyMMcrWMQ2qyNzhCvM0HptSKz/izP/SiVZfpC5sbplTay9IpkzmDMdMm1oaXMQYNmcbI3n0LYHYlFVk2lP15ku8jVR3zhVq2V7PY0DfHicFqkVd05ai4DtsAOhsEhlezrToxbQ6RA781X9jDbLgUb8D+N1tdQlHRVt8sRH4MMEe74KLyMxiospMXIrpTkYFYoCJz23hxJANEAqS03FSl5essK82s3H82h0bsNK7VWUfd8jtnfU6gCfMlMJwiNp/iNYDYVIUEaIYnX0liVod0N9/X128Hsny0sQ9KnnQ1S0ELbUgnE8sqCbegtzGT2lLiEiUQ+VaqTPjGAQrOnRFickb2CN8AbiqmADdPWIz+tiClQa1cNIbxPCWYLD0IMIRyIz6fkETqJXwTRRNrte8PEPPsLpNuH67RW61OPqzS32+xHxLiIK61gVChFGY9M0seWiYxqw69eQELA/7jDlDC3AmPcY84jL52u8ebvDyUnC2dmAqsHuRUA/j4JRc0GZCtLKDqMHZr6PXvy1dTFX60F6B1DrewNS6rSOEPgAACQvSURBVPyM2CEjkCfFDj5M16VTrI+tOmlw4SHzlpOMDEXAo8ePcXJyguN4wG53j/3unoOLDxy9VHTPA5k9ZXJoB6ECJCRnAQWr7QonpxusB4JZQkitxlA14w/+7u9xbbuIlCI++fEnODs/x+WTRxhWK4Q0oCIghIkKeLOhODg0HEyxRXQ0RKPg5ddX+I9/+hlubm7w6tVLfPPt1/j080/x+u2XqDpiWCV0fQeRczpexyO+/vIr7HYHQAP61YAuCi6fnmF7MuDkZIX1eoUf/9bPcPnkCc7Pn2AYTjBNPLQpDVBY2jBg7osU0jpPWhE0tJrRDOuXFrG4pIix7bjzE1NorEpNAFTNGTIwQfU6ls7GUedaUjN8Tcj4DX9H1mXpvAa7JyYagikuOnYeOZqbS+cOysHagHH1oqXma2U5QVKcP9wj2Qo4fVyMVirUamlgIkMR2MpE2Tanujn+pmiDK1PYnWozgPDod/klQCMLEP8lSwcq04wqJBroQPR4tvSuG2CWMzA7X15j9eDNrQFfhOLRVvAIRVtUxECBrSdtNiIMTNPkQxj5SXDwJFVLdQTsXE7xtv1ga87AjqnnIOAQa2Os8nQtb3fuNPVnKdUGKBuwyynoWgBmD+HTSKrrGZc71fn1bV/QWrdqtTqfVBL+m3NRMpHzECB1zBAEY9hKSRDVImfTg+1B4c4AZff7ru+lTQsC6+eRxrQO4QSE4/FAtFwgjVZRTn+PiT0uVQVax5aHdqHydsgggpScNJlpjIZGKp5yoMC1dEMhDdU45SZDMnB+oNaCkid3iQDpkWJAruyoCD3BUk+ePsXF2Qaf/vJTHI8jxvFzjLkwFYNiwB+FCNMSJVP4O/M6qOwU43hACRlSgXE6YH884u3Vc9zcHJC6Dc5DZ4LFVDAafyEVg5rT4B7pAzJE8xobvVNLQ6EFO25g+PJoh9tormq1/qcZ1SfuIdr58x5FDwx9RhfbVQJOT8+gWrHb32F3P+D1mwnHkQhUzZn0Ys4qIQKVo2ckQFeHh7VWxSb22G4GnGxPcHJyiq7r0XUdbm6vcXd/ixcvPsKji8d4+vQJTk622G7OkCzlrSKo6FEh4CgXRdevWGvMFkno2CKGWoFpCnj1aoc//rd/gbvbt7i6+hpv3n6Lr775HFPZQzGh7yO2pwkxDIihx1ffvsb1zR1UqXm7LmHoI84uTnB6vsLJeoXtZoNPPvkpLp88w8n2HF23apRiw0qaIjadyHMkswKCKmJDiTYtsjjAMyKRitrimmCsSu7hm2scohkobw52MIBnhVURVJsu4IxOtRqi1VlM/tpUe48Jo7cf6OJ+GV26EmsmWRzU5ahJE7Nqst5S8KZZBBAy7jMmsOdjX6G1lVjNifgLR0VLWxeeA9YLl3UlmHKEKf45ysUi8nPjz/NTzTJyDJmhQJsa98iYaeHgLRcGUvM98Txg21H/i7PL+NL6WrpnYs7/kkC9PYvXQduLF78LAwjqTK8YFq0Eghm1Cdh+FTpSMTmpemjOiinOWYa9LauyZxNBrYb9HV3kdVwziGLZhWBbzVQt5lQ2KGutlaIyUxhMzon6rOwTNZmNkWxVCtI9RmOZyWoIZ5Nph640bwj+52++3k2bthAT+j3+MILjccLLV28wDCucn0fElGgsrfgsttldTOgY6gGu0EWN802RS231RehsB1LfNUEHSOINAVZDB60RXfQog8Zrss+U1LHvKk+kgLKPVmFROI9EyGlc4ekHP0DRgG+/fYOrt9c89IkjmxqdmAujM1jwblBKwX63R98PONmcmBIZcf32Df7sT/8cn/z4BS7OfgI21vaYcsEhH5FiQgwRXeiRhsixKlKQjdLM9620VBhH6Mwgdo4TKXlijccQZ6lLxsrCIagoBQgKTazPhmY5bXOdrxXW+qAK8hObMjIFF0LEk6fPkB8/wuXTC+Q84fr2DsfjHlevv0IpmcAkLag6kk6vG9gCk1aIKaHrBpydn+Ly8jH6LqHvE2LoEUKH3/3dAR9//AmG1Rpd12O93qJLPQoSNANdl8wztvYbtSGWxpTRRUA1oExkNigQ3N+O+PJX1/jii5d4+eolbm7f4PXbr7HbX2OsB3S9YDOcYr3qseo73N0dsNvfYDoytRpSYrvKYF99j1U/4PGTpzg/v8BPfvrbePzoEsPq1GDtNkIIhuxtB2hR0wFMGQJZrd0o8rmWCtyh8MXOB1tqXFGhGUpXJsFQsk6r55PGtcaGplQYhF7myIPGgSARjRwpFQJf39Cg9mV51GYUNPpk8WR7QsPk6VwC05iy0qAIMUEDUcQh2AxRJSiskeQbGwuDTf9Mu89iQ7UrM0qsB7iBNnEOxAJHe84Ib7niM/tooWBE4rrEuLp9KoAsWpBCsmS+pVMhofX40jGI7IFumr22CFcBgpAA1ilVbQYnz5y6hhWx1gm2fjlrEwcdA2Ib68jKORFg2AhzYGq1YdrOoOU93A5mM5mJAqOqS5Ymp0FZrVaYpgnH48gIPERGWUEt0zLLg2siw1Y1Z6stQ1mk3O053TGmY099FoV6p9ps2GKyF4KN9VIaXXFjbLpX/XlAh4R4DbbWEW/CL8rmErr766//rD7B+d/+F0GeKu53O1QFtpvMImsfwTl5taVzUuCCwwAdXIcZqlzcS7P4PlhNKw0Jpl+4ICWTbi0GaLQmaKMbI4LI0HDGNgCHVAsMCQbkSRvdl4Qep+ePkXNFPwxUqtbvohAUEbLJ25FphXOda2jjOCGG2HhPS83Y3d/jyy++xtnpBuNByfUYO0wWvYoyUo6SOOkhKBDm3iMP7nxuoHvXTcDsUPoQ3up1E50bkPnCCsdRuQDTh1HM74T2xga2gzvfLTUkASenp6aUtiglY31yh/1+B+QD8kQmoFInjPkOIXYYhi1iGtANJ+j7Aav1Bmenp3h0cW6TBRQiHYAO683WZMY8OekBBNZMVZFsCp5X9qWlN4rpG6GMlEhyBAUOu4zXr27w5u0Nbu9ucHN3g6u7K0zTPXIdMSSmNfsuoosR0zTh7u4OVdkvSq7IiNTxq+sS+r7H6ek5Lh5d4unT57i4eIw6RR68OrX0Gg+mRWS2obMLw8io1Aqk0CIhLH5OPtPSeghbLclfs9hDmd3v9jM2nlstSdWIAygPD6JOUEGwpiVQV4j+ngvY+vK3GGX6/cgMcgOBx5Qdpiy9zk9gSYFINicrkQWlaFOsYu8JmdfCimTN2VPrM/P/HjgDbR2L/Zuf7fLvEYaDd4pjETS0iJWLW4E4IzwVhPRDlU3iEkBOvqVhwCKVzbokPLCSxc34UjWcRUAjn4bxnDY9Yz6r4Sm8vKi2TH6/wSLF6lM0QKfX0+FzDyfTpR7di8xZBXdMci7I2SZaIMIJ/ABP0dqHW6g5i59iqVk8/Tz3rC+NInERrtMCrBexWi1W5rdwBKiYU+ZtFY1GEkAQYiMggDrvqUvtA333m6/vqQlyE7KN73FqL0Cx3q7xgx98zAW0aebZocCqRvejIIWjzLUt82pDig1CC7RMAlOESrQj1y619B6NZzZ304RHEqpmTNMBUiOi9khR0K96TDmw8baLLLp3AbFL0MwD9ezJM5xvT/Ds2VNcX73BuOdEhFyAUCoUkUvYBnQyVXs8cvhslASoYLe7t0b4Hrv7e3zx6ZfoU4ftySlevHiEH/7gEjVGlNgjBL4nZ6Jag24UZLHCcGaBOtqgVq0Fuc6CVtTJBGYvMwjrtbVWjDrSu7K0EupkjahzPcZ5GcXSGGrcVrVWFNuLYEwNgTkK+9UBIh1ONhHr4QQnqy20FjjRdA0cjJwzEFOPYTiFCJvmRQTjEegSDQ91p6W+zVMhHVSA1tDqEsdSidBMvFf3bLtuVs6lCG6ugPu7A7786iXevr3GL/7iU3zz7Zd4+fZX2O1vsTveopYDSj0ipBVWmzV2d7e4en2H2/sDDocj+mFDPtBVQuwSttsB61WPDz/+AZ49v8Qf/MEf4NGjx1iv1qQ3Uzb9jhPTwAUTexQTEXlETRoyToUK17IXqIDEOKPf4ApSZ725AMqg7RXPX7WIxZ1KInalKYFkWe9SDR2ovvtoaD9nByHPQSafTogkoTCnqcJn1QGwrI23UXiZWWKrQPFDxBzZjo5AzfZ6o/4ikbTlmZr1tz9kAcRRp2x0hGwwkJogODahRU7mJphBynY+nJPU62xunGHcrhIjpBYEFOtVLEghIEm0HlauL6kNbUMsgne2F2nGIbQ9BADx4bkNlOP76dGJ9bqq7RE82lHL3gDeO0j2GisHmBGnUaiWMX5Y+7JcFrz8obCWF1WIVJRKVHWXSHgyGV1jasOn5whqCSCCoslTNXYm9u7pYq2sgpw8+u4s4KHMZpj8L4AzoTnH1BelopGU15wZbIRggDqrIULtd+ZpMW4cvfNg0UPzG6/vNYJQ9kyRDxSW6xekvsNZPKOXpgaFVW1FUF/s6qF0BebxJWH2SNyns3st7hUoDVIIi8XVuUlcDcnogllqNh/FvMUYgUxgTkwCIBojAedooQZsN1us+x5nZ2c4PTvB1asbjIfJai9iRlBJFO7GQy21pGgHa5rI2pFSwjQecXN9jdev3uLLr15hs04oH1wASoi0ezWemgoWbQXRxr+Jqs1B0MreKUeUFa/HVG3IOXdFVVnEhqUePJWkdsDcefZoVnQ+dmLf97SGmNIKc/4FANNafReADthuNow4K8Eq0rHfa7cbESONYDvoecI4HjkUNySupdSGxBPjmZ3Z9ANUFLkywoom007rthBP5Elxd5txdbXH5796jas3b/Hll1/jzdVL3O+vcRx3KPUIVRprproipinj7vYexykbQxGQUkDqmdofVh1WmwEXjx7hydOnePHhC1ycP0KKndXeuHreq5VDRQTQqfW1NSfUlaSYEWFaz9ccSyNo1wN+S48lzRPmXx2x+WBrYfqznSdSXaGlsTzT2CJI9X2vBuGff9mjS88KSK3LWABuhA3FYulftBRUtc31j/OU5UxQPT+fP4pHH17L83YE711rgKFm2WWWdcxIzbp477aWitZCRM4CPutcg3PDxgVtBvrBn9pCoOotAr727YC5AXaF/N09rbanni2Y16hFf+J60X8nLN7S4jPTiwqQKkznn7sJlCajdDoKeJ6KZhukHZBzNh0Gy7Atntf1tzz8HuA8tv782u7dafL8Jz5iy9Oi3s4RGkmAtDKpdw84aMwdkBbpq+GArG4rtlFiOq/d6IJYYVmR+HWX6DtecXd1ryKC2LEdgZPqBVoFtRbkybyJzidHW89ZpVHQyl6aEAKmaUKe5hkpqYuIQYzCax5Bkqdi6c8RIkC/GiAyc8S1uhloAGMXSRS9O1h+uWLoE9brDtOkGCeOF4mRzxFSRJnYY5ICYaRfffkZ3rx9hX/xv/wLfPrLzzAdKeCH42SpsivUWpFST6/FhGc1cBzRlIEQE7q+R0oD+n6D7fYc5xdP8OzyGV48/xC///d+ht//uz+xmXoK6ARoYXN/jGbcFLVG1lhaDcOiClccdlF+GEnWOiOpamETfQod2vR6FyApUCktWtBCUInzuEpDvRlDTppbPdBEnSeJKarJTy09/8Ap7WMubT20KMqx4JhH7McDNts1TjbrOV2TM2opiF2CRKZBa52HgA79wPSkad5pHAEFuqFDyYI3LzO+/uo1/tW//N9xc32Nq6uXuN/f4eWrbzHlexynV5zhuEo8NLVinAqOU0E5HlCnQ1vWJ88e4eR8i9V6i9T3OH90gpOzE/zh//iH+PjjH2J7coKUOgxpiyARpUQ2ehuxvFSBSAFwAJwMGQFaEzN7RQAtUM2MOIcErzeXzHFZxZxIQnMUwQbZ0qBUopGhjOxFGiVXpc5DcrCLOaRMc4uhtQ2hCK5ltbXXANRofawta2O1CG+OE0VUqy0ni4iMfUbNCRNluWG3v8P1zTX+w7//D1it1vjkxz/GZrvBo8ePFkqdACzyQFIn0Jh57c9BGv5qA36YjHpFTy1LwXRgbaOkXGn6PEVOuqGxZ1rUol2PcsT6FbUSNBKDs8vBJ7E45V0NNmfSvYEifJ07HnGeHO/OCRG77oB6GjiYoUdziv086fzgmJHDbpnpxHqEDNOLvCdz4IV7P01mBO0zY3SzOKcaq7WeRYlIIbWAw51yxw5U+8yZXU2bnoeiIZer1zEtmxU8PDGL74Tj3s9O8Lw9PyXCsAaTBT6Kvu/Q9Ql54tgrb4nwWrEbTUnSYgwnDVEFfvrTT35jSPhuxhiYzYqOXrSJ1F50tn6oVrPQ4C4W0zUMvBolE2G2hhwKFOwYZovt+9kWthXaBTEkcCbVaK/xgrLdaYhAYa2uVmkKiu/pKZAAQYSopxqo+F98+AIXj86wPd0idIJOE3vcDPpORe3jjQAIe7pSEk6dLg4hzuQklIJ8m7HbHbG7PeL6bcYHP3jKyC3QIJNBTB+sNcSb4UPLLDXiLA7ZavPcvBgfjAsyL9IRvEXzb9samXe9wIm6x50Cm/rdMM2+seN4F+g6hq+Q6nUv6/dRAWo03e9Fd6vbVha/i0WlEp2lg9kC9+hb2sWAGKpqXKQedblSVOQimCbFzU3Bq5f3+PP/9Je4u32L4/EtDuMeb2/fQsKE1B3RhR6r1YoRURHsD3e4vd0hggTjSQJSCFivOpyc9FivB3T9ChePTnF2cYZnzz7As2cf0AApWvpFQGUbU7K0GNfatzU0b1Sb3C833PfcS56+H1UdZNKSO3BgRPHRWl18EC2qzHWSphhtrZydBQ6DXwZhtVrtW1qDeLB79jRXAZVfgBneWbNbr/icpSh1wnE84O7+Fl9+9QW22xM8efYEIQpKPX9IkaVzhKGAZWeXEbTCieM9yppBRHyZu2i1lsb0JIChURXWO2HZJVL3ORiHe8W6mnNO2sCoFvQ1WjKR+Rx5/TCGxXr6+fL758FTv/N2gJZgKW1ysNCAiz03N6C9QNt7ONDFsRRq0dXio+3+fezW/J7+OS1zp8wshUB9U9v9zgGH/6s2OkpxZd0Ag0HntOn8eTMEie+0mATir2uoaL5uOf5LlzcPX2cHSAVjF6stgnR2IpcydWTuO653GsFsAhX9PUYv2jqSk2kh0uQw0hAASSJqACoKQq1AqUgikL43owak6LRbbMw2IhhM2dIftuclVzb+OrdhndN+pXC6gATBetWhlIgcE0otuLk/oOs79KuucQMCgjIRnh4SUZRaOe296ztER+L1AVIEYRSEykGbJRRTZZWIvUhUGUJAv+pYGxonpBRxsl1hmgT74wEh3AH6Gm9eX+PVt/c4v+hxetaDAJCuCaZYw3dWrke0Immu7JecJqYvu+TfN1RoSAYsYe1vtVqZtHHTgh06BRjBl0g0lg2iBAIQO9Z7a25pNgUgUeFggoXIEtAAWJoZgLWAlELj0CW0vseYFKHnLEaUEahDU2BVAUSxWWEVgsJnqRVTpRc4YbL0CXkmp5IwjQVff/MGdzcH/NWff4Ovv/oKr958hd3uGsfpNUoZoXWHYTXg0ZMnbH0JCfvDEXfXO0y5sk5p4j/0Aesu4HS7wdlmg7OLM6y3W/y9f/D38fzFC5xfPEapEeORBAZxRZAWp5iIya5FRlx1thYJwFpnQoHiWKiAY+i4pkaGQNQcZ8Z4E3DseoQYkHOlV28kwc2IWPTV0H9WqhAbFQV3PFWBmo1mTziOpiqdsBQhyThHWvpJrM6bOUw6dqiFNbwY+O5SqelU6IrFmKw2OkKr4OT0Al2/wj/5J/8YEMEwrND3fVNI0kxXQeUUZlApw1LD7hgBHUgsD6uJ1bosCyjYly+Wkg7onEHE5u6FwCgN1ZVqBYyYoprHwhq7gZK0IhRDu5qz40hWB2+JwWDFwXQ2yFaLOajK80dnT1tkZNlXRplmeJifFvcmGK2JzBygUsE+U8p/I0HINo7JdZsbewP0FMuYBcMMEBLgjFuAgtkVEu/XhlxlupRn0tlpaEi1TX2wOMD2opohNgQvMLNcqY0zq5adqPw5ghMxmEzavFl3VKw1kBG1qLVZVUzjRCBezvDE/DKTqUrnGEEaq5nLy7uu7+0TbL6mR1P+A2UkNzspakbCPXrnGzSP0v5Nx86gsII2Yb7aIqj7+0ZHYcELIMaIoph7QkAUZZCIEAWAIQRrxVQqkuo8pgVzlBm9om+1HKY4bWJx1yFPbphksUmzIhJz7SyhYHOuLCyHGrE4vZCSR4wjOSivrm4xrM6wPekxIznnSAiuDOERBFojq6NFoymgapPCqzUPO1I2WB9P45LE7IUKiMzSRXNscEPo1R5fb5hit/12D9OlVQGrx9oLqlH4WoO1uBcjak6MP6dFRksHL4h9KNNv2r7oeXLaGg/OOFbs9xlv3tzj5mqHVy9f482bN9gf7nEcdzjmnQEvJs5XXPXwkVp5qjgcOIm8RXMSEFNA1wX0PecxbrcbnJyd4tnTZ/jg+Qfo+5UpOUOweUrLF8aRmM1v9ohsPg/VPXr4Qi62WebXo3m6ao6C14ONFmwRHdqx4+96FCLzO82vUWipbA+Ii594/cUzA8K8Q2lDDS1lZwp24d+jFRdbkCbtWWLqsAoBT589RSkFo01nR7vHxftgPsstmlnILux5tRlqbefYwTP8Bp89NEtj6+qhySLCenAtoya/C3s/J8hujqHaJAXTC+3z5wPG3/PP8qi1bdQyVp/P+EIw8GB3BQ/OcMt6mVz5craPMluq/rRizrU0P6PNq2RUbedfAkEusMhYllk2XxMznsoMkDyQBw9KtYlJwzcsRcYyDZ4W9Tj5wb74froDGRRiRnY567H6fSzXGKazqqee57d71/VOIxgHZ1ivlmKw6CIQhRYV1vYgcJpXAJjL5x1sRAPEGopbaOw36F+0YW0orOfXaaeUOe+qjf7IFRlHMoUGfYYWRAFWqceQIoZEQ1mKMhJSM8zOZgK2aQbp8eGHP8DhcMRf/dVn2B92CIF9WkPfI+eAw3hnHuHEQ197AGIheQHCCKCD5oIEwXYlEDlgzBV//ud/itvbt/iv/sHfxe/+nd/B+XnCZrtMDQWLJnjgJg5C5MgoVY6bgvEpKlOOYk6FWiRSKjDmYikHoeMg3otEVv7UB6iOLW0EsI6H7HRqaKnUufncUVx2b5UIXR+pFZQGcLReza4HxMiytVZUZEgKWJ1sIF2H0aDxFANp6H2eY3OWqinuzGbvmATHqeBP//SXePv6Dl9+/hK73Q6vXn2Nq7evcHf3EtPxHiXvECLQrQJRwqjY7Ue8eb3H4TBhv5/QrSK6dYcUE1JIWK8D1oPg/NE5Lp8+wk9/7/fw9PkH+OgHn+DRxWNIXAOISKk3AInVsaM7DXQQvVfP5wiOE5F7yfrGxDgSsxZUBBRjIokiCIlpOZ0q695BUFDh6EZn2IrG2h9MQ3ozdM4KBycBmB1WhOZY5JxRpwkhRiTjcVRHHivT+zFG1OAjyIBRZ7nIpTZQA4SDnlVIRqEqQG/MUWo9bl2PlBR9Z8hOO6dEdgaoRNQqlnFilsVTYZy9ycbo7Leos2KLFhGH2VqaD+cq3pSK1/SQocI699wc7mOOqM84tiyxRaUUkky4YQ+AhDQbNDUybqjR9i04Tl3Hldlg8zMp4a7jOW7TKMgsdVCURPLBamnNtVIrQlULNgwg5CPXWsbYgWb2jWjE/VqdFo3ZhChGrtEMEiw6LW3AsgckTstoPi20VJaLLOoOxhAUWhbJIr3C3oCQojnudm/mEPpg9VlSrccZBaoBoh6ton2fwU5ALlbfdQi1swIZn23xXlV92Ob36653GsHgqQXfVvP4JEiDyZK/0N0Q/00F0W/SNkeCQOp3w9f5Vzy68qGqbhzpGWK2lu7hBItsJLQNd9h1MAUdLJU3Y+9k4ZEsIxO+4cXFBZ48fYpvvnnFgmo9UvhiRDB0lXuqnpZlWE8lQ2+NU9BFolGcFQAj7m6v8bUGfPPhh3j8+Cly7nEcE7bbNfo+LZ2q2eNyNhfMiKlqCIgZQWXrZyaEnqpvli/XAmnmEbntiR/aFoGrOc+LvZ/7mryOiPZvMSeCh3lWKnwvpraKK80Um/PhFSzKgEUu4mbQe/8U0yGjRh76/SHj9cu3ePXqBq9evsLhsMfNzVvc724ATJBQkaJYiwezFGWakMcJ03G0InmFhITUdehTjy51WK0DVmvB2fk5Hj++xOPHl3h0eYn1eouUSH1Hfyk04+N7AFg7g+hCzpo48juW9nJIgjPHNBvn7xVYow62Xp4pm/f4r/99qXOXkcJSKtqrTaYQwoPfWwYldMCDgRfq4j3b8YGfG06LQCNoQgjN8SECnGCkFBxUwvfwtjlIc13hh9yXxnWNX842snwy9tjZE5rOaC3wptXVnRQTbEd0z+wlM0DEMz8a6rx38DSmWGnBzibRavMCznfaFpRrpw9eMdfhHFkxp349KnUQjvrrsWxzsfhpccb5LEtFDYsCdV5XZ2ex0+ZMQE0Jy7xeVUkd5w7FUlG3X/Mt8s/CnPfwx14+/2+6eD6qnQ97PnUptndcRHvizt/C93mwyr44pmOaPL3jeic69H63n9WdpzdFEFKyer55jCDbSBQ+UC0WjootSTMW2mLEUji5IDroJnSW9+fmF+v4F3+KTCOU2mRtWyaDNLsirprB2kxEkAkxTGaoA0R7CDpAM6D09FQUyRBFN/dvsdvd4d/80R/h22++xR//0Z/g5uoGV6/vMR0nHPfX9MjDAAjrTC6MnGDRo+aM6XjEZj3g7GSN9WqN9WaL3U5xvwc223Os1qe4uDzF6dkW/9P//M/wB3/ws8ZocZwymRPscEYkO/zLXDQ/k96WhR82c6uaQIZA9G2KhrpANqPHvrpSDH4saEirPE6opWBY9danaGOXSvMVoeBMRgH5B6HgTMiScTzu0aWI7XbVFMw0ZRwOE4KQIIBTqRP3TM11lYKQAIlANLDNy69vcX8/4ub6GsfjhNvbiPv7I/743/5rvL16g6++/hKHwx63N68RQ8X5Kbkn+04wHidcXe1Q84QyHl3SkSGYAJw/eoyLx5dYr7ZYDWs8vuxwfp7wT//xP8Xv/c7vIqTe0K1rksHDlFPhMezTHIXb7luv1UilaXD+AGmUUxIiQuqRS8E0ZdfSGPoOfd8RUV0zMixbIU5ObA6+pZrEMgEpeqWW/xWLrvt+BYViyhxMGw04VbOnl7WxcHhDv1q01FpizMg3ur0i9pkZwfr4XGGqCsbSUV0lQwPa+IyKTCh7maCVshpDQBfmonFRHwhLB0URUCWgjzPHKhfZPPpqn233INY9rtFlk61SfWKdaTwWOJyRICYm19lmyR5U7oQghYgUgwFoZr4sMaehVHd4zYg55ZidyfoQwI0QfBqEoaetHustVQq+R7Le4epOADCnvX2D7G6aXhQ8iORCm3RhesHWp9rZrcoGfzbNk4KMdb4yG+gqrXUkRMuYQa1uSgIU8555v9NERz244TOZtIb2OjmNkQdDtB9R5ppgrcxQ+M9gesHTzz6+yYOW1oJnJ8+t4JhHAmQaoMx0U2Hrxx/8/m//xnDwe9ChD8PxlmM3oZxfBcwOsjFhcDke/Nl6X5aSYgJilrB5lb/WiRCPTuf6gBPGzq058yEGzOteeIutPaD5efN7n56eYFh1uLg4w2G/Q29N9h7RegW0qH92nj1IY2ypqhjHEX0K0EpS7xjY5H88ZByPCgk77I973N2fYL8/PnjEB36joCnDB9HsvHRtnea0kL0DXXPznB6+OxUVxWruYPM3ZGrF+wPnuWrf2QgA4sJqSHpXYvOdOOzdf83CG38Und9v9vL4vtXqePv9hMP+iOvrgPv7A+7u7rG7v8P97g7Hww67/R36TtB1J+i6gGGg4Q5yQC7A8TAhRkHqGQVEIV9t13WkQxtWWK06rDYJ5xcXeHz5BJO1acD204KJB3tkjrOJlsfIrqz8qWxl1df9u997uBBtmZwVRee9o4wpPWb/9mLLxT4vCD15/xh30oLYfi/OsP9MF1GAZwPm4E/auQ3zry7Op86NyW2b+cJgikh0XoOFaLc/PbpYPuvyz3npHas8f/e70QaHFgMNKOJrj3lNnCTAH9Wfu9X42jN7bINljDPvxyIb8zDiWNybPFyb2XF3afmOYLXn8PXSVkJ9IC06r4ff4/xuFiC4UV1EdV5KUrvhhyp23oS5/QJt/TxbgfYZvn764B1m2W4vsGWVB+/pv+tD2Rk0zYCXufdzjpZbEcfW/tfaie8+0jtfgHdHgu+v99f76/31/np//Zd8he9/yfvr/fX+en+9v95f/2Ve743g++v99f56f72//tZe743g++v99f56f72//tZe743g++v99f56f72//tZe743g++v99f56f72//tZe743g++v99f56f72//tZe/z+d5fLkneA9RgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 576x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Prediction: black swan, Cygnus atratus - confidence 1.00\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Set the configuration to a targeted attack:\n",
+    "adv.set_params(targeted=True)\n",
+    "\n",
+    "# Generate the adversarial sample:\n",
+    "x_art_adv = adv.generate(x_art, y=to_categorical([target_label]))\n",
+    "\n",
+    "# Plot the adversarial sample (note: we swap color channels back to RGB order):\n",
+    "plt.figure(figsize=(8,8)); plt.imshow(x_art_adv[0] / 255); plt.axis('off'); plt.show()\n",
+    "\n",
+    "# And apply the classifier to it:\n",
+    "pred_adv = classifier.predict(x_art_adv)\n",
+    "label_adv = np.argmax(pred_adv, axis=1)[0]\n",
+    "confidence_adv = pred_adv[:, label_adv][0]\n",
+    "print('Prediction:', label_to_name(label_adv), '- confidence {0:.2f}'.format(confidence_adv))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can measure the quantity of perturbation that was added to the image using different $\\ell_p$ norms. <br>\n",
+    "**Note:** the PGD attack controls the $\\ell_\\infty$ norm via the `epsilon` parameter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Perturbation l_0 norm: 73%\n",
+      "Perturbation l_1 norm: 2%\n",
+      "Perturbation l_2 norm: 2%\n",
+      "Noise l_inf norm: 2%\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 576x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "l_0 = int(99*len(np.where(np.abs(x_art[0] - x_art_adv[0])>0.5)[0]) / (224*224*3)) + 1   \n",
+    "l_1 = int(99*np.sum(np.abs(x_art[0] - x_art_adv[0])) / np.sum(np.abs(x_art[0]))) + 1\n",
+    "l_2 = int(99*np.linalg.norm(x_art[0] - x_art_adv[0]) / np.linalg.norm(x_art[0])) + 1 \n",
+    "l_inf = int(99*np.max(np.abs(x_art[0] - x_art_adv[0])) / 255) + 1\n",
+    "\n",
+    "print('Perturbation l_0 norm: %d%%' % l_0)\n",
+    "print('Perturbation l_1 norm: %d%%' % l_1)\n",
+    "print('Perturbation l_2 norm: %d%%' % l_2)\n",
+    "print('Noise l_inf norm: %d%%' % l_inf)\n",
+    "\n",
+    "# Let's also plot the absolute amount of adversarial pixel perturbations:\n",
+    "pert = np.abs(x_art[0] - x_art_adv[0])[..., ::-1]\n",
+    "pert_min = np.min(pert)\n",
+    "pert_max = np.max(pert)\n",
+    "plt.figure(figsize=(8,8)); plt.imshow((pert - pert_min) / (pert_max - pert_min)); plt.axis('off'); plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Apply defences\n",
+    "\n",
+    "Next we are going to apply a simple input preprocessing defence: Spatial Smoothing. <br>\n",
+    "Ideally, we want this defence to result in correct predictions when applied both to the original and the adversarial images."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Prediction of original sample: unicycle, monocycle - confidence 0.99\n",
+      "Prediction of adversarial sample: unicycle, monocycle - confidence 0.82\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAcEAAAHBCAYAAAARuwDoAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAEAAElEQVR4nOz9WZMtybLfh/08InKtVVV7d+/uM/W55wJXBGECB1ESjRL1TfSgjykTX6RXmsxgxgcKoplAkARwcYcz9OlhD1W1VmZEuB7cPTKy9u7ua4YHPqCzrbpqryEzBg/3v8+iqvx8/Xz9fP18/Xz9fP2HeKX/tQfw8/Xz9fP18/Xz9fP1v9b1sxD8+fr5+vn6+fr5+g/2+lkI/nz9fP18/Xz9fP0He/0sBH++fr5+vn6+fr7+g71+FoI/Xz9fP18/Xz9f/8FePwvBn6+fr5+vn6+fr/9gr/Jjb7755S/3/AkBSkJEEIQkQiqJXBaW8xkROXxXO/SutLrRtpVt3ai1oYBO93z1+jXL6YTgb0wZG4fkjbh966gqdKX1zlb74TOSQcS+uW4rW13RCnSQnEm5cFoulLyANhto2u8vQMqGDera6B1UQdHxIUHIKXM5nUjLwnL/wKkUXl3OKErTxu26cX3cxtCWVydOr0/8+i/+Eb/49Vc2P1W++eaPPH54z+39N7T1CvKalE78+neFh1eFv/jdryhL4ptv/8z77574H//bv+P5w5Wn+khvHa5K10bnhto092cuictSEMlAQnOCJcOv/kv48j/nr7584Nev77j73/3XnH73H/H5r++4PCx8nuCexn/R/sjrduX6BLXC7dmWq2T8ng9IzpxenZCUgMRaK+/XZ/7+72/8i3/xnrcdvga+fDjz1ecXrk14rvD1Fb65QtogVYX1LdQncn0i9Rvn9j+R+7d8ePqe3jY+y1DSCcp/Sr77gtf/h/8MyReuv4cP7/7I//I//zesT3+iffc/ofqM8haSQgER2+JUO6l2ttbZmtK3FW0bUhKpZB5ew+VObK1UePvUWDeFzRY2kxEEKYIkYFFEIMuCkb+di7uSyUvhfH92Wuz03mm90TZoN2i90XujN9Cu1F6pbUNbo/dOb9Cb0rsaATYdB6IrXK+Vrn5gBDuXIvY3gv9JkjTOpv3OvEyKkiykDCkJOSdSElJK5Psz6bSQySQSaTmRUuF0fkXKy+EevXe27UbXDtrR3tG68vJy7sH57sL9q1fc6o3r9kRbG21rQEPVzl3vStNK7w293owI8fVA7Ve3wxtHOIHtzRkQoY2zLfgm0QQa0J6V9kH37/laIgmR5ISTeFjuOJfzOLO32yO9rki9IdrJyxlJabAvH539lYDsz44f4rOd3pWu4IQAZIRCShnJmbJkSilkhIT4nTuVK03r2MulQEpKQtGmrI9+Ox/zYGQHDuHvJ+iZsT4pZbJk7l/dc7m/0FRpqk4XQr0pbetc2xNVV2w1lXw6k3KOh8K2oU3Znrrz0JkQhJQWX+eC+rO1G63frldu1yvafZ/tXd/3bj9GJBzu7PvtJ9UfJTy+fzwKqOn6USGYJPmjdSek8f99YNo7JD+EiE04Zq02YTuk7BPyg6tqE4+DqqofHdLj49Q+oy4M2RdJPjHNnRDsAOr0n73Z9zVUUBGk6/hOXDIEYTxI7cD3TquVJtBrdkHUoStCH6uVRMiSQJXe7DAHS1oSrDItrJ09kp9DAXKGkhWk22D8PKm/jwoHYpC4ofg8/N8daB3qxrZVbluF77+jLmdyPbPdZUiJVeBP5QPPVLYr9CZsq63huSRyKpyWRk7iiCce3BAqKVXKUjl14ayZJdvhF4GcYElwzmJnPwH5hNARlNQToneg92R5MkEiBUkXlvt7lvt77s5nSCfWUyMtBckLkhbABD4kG9d8+mbC0okunAZ7E2pVUthHnM7oAl1siadtShhd57yjqCRiTJTjOXF+agwn2XdVhSRKH/cVp4pPX/qpeXzqkk/8LUFxL+jE7xfy9EAr/uWcElkyOZfBmHNKpEGggmonZ6X1xnZbUTFh86nxJrHJaHdh2fzBMYhpzcTlSBdBkxz2UuTTvEKxY6IpNlkO65CwtVCxMx1CEBFU5jNkv7oqtTdbuWncMnicC5n04osfzf2H9/blDDQ2Q4GuqAT97QxU0k5n2fkF2pAEkmwtm4Oo3pt9y4koQFvMqff5uSBJaLXRWqepCWqjT1xw6xBQ6vpB8nMU9x3yYub7++g/uRyq3UAPLtzEwcl438GcyxWNv2PsBIXH/+WjZ7+8flQI3l3ubCHbRkdp/noe8+v0trH2Ts6F5XQacq93RbcQUImUM4VAwIrkBJLQ3mm1QbHDVLsLNyeo9ELQA2jvrlV+BC+YV1a70Gs6MjxAe3Vi34WgKrRmv0suCEJzxh7nQoLTqyHftXZoK6xXzrmQLxckZZCM9s5CBcmIJO7Kwv3pAamV64fvKalTkvKQb1zuYHuCGopjguUCpztDtSnBZw8gmyJ5RdNKzp0uSl+ALkhLfii7EUkqQKK27Mg2IxVSAz48Ad/wfas8P67Iv/l/wvZMLhspdZbywFIu/Iu/+qdcHl5xlzM5CadT5pQTX7268Opy4S9/K5zlBBUjSBTRlcR7LpdnfvHrt9z3Ox7aawdUJxbbdn4hmVdL4ttneL9COt0jwFkrpW+ktxm5fctDTsATqfyKfHnNb/+T/5T7N5/zy9/+JVUz/wvv2c5Xzn/3C7RtbPLKae4KWmHbBgGpuph1wKAdtBmTk9Z5+pC4XoW7h0ZZBN02WDu9Lmg37S/52RURFjFBcLm7M0TrtKHNUPMpm9ZWFXIWzkthK0ZQ2jKlFepaqdtGEqGRCTQxjvREpiaUjWkNOc1RMJD5+AoJPD47nRHGUaZh482pkKWY9gc83F+4nM7k5Y6cC+f7C2XJPNzfk3OBsqAKdX3kdr3xh7/5mq0qa0rGFOt+TpMY05FW2a5P1K1RNwcbWidWZsMufqZrznQRxIWPOp94if26/09vdo44m0AwC5aDFBInCk0am9QBngfvSiY3Y69vdeVaN5ILPL1ekbaxSHdYXSEJWnDBmialQV44nX5CEEpDpQHFznO1Tekl03Mi+x1yMW1xKYkswjknkii3eqP3Dg8btSq3x6tZzG4VARYWJAmp5F3I185WK713auuUklmWhd6UbevgPG1ZMjknbrVRe6evDW2NrZvIQjupQEkORFsBVcQ11H4w2oVWfFyP2jZu10ejm6xDww+h2popzeKSVVNi13B17GP3c2TWmx+/flwTzAlVRbqQlKHXHIhGbfIqSm9OwiKOEg4kfdAId81Q0d5QdYNECEG/QpMbggzGfT9V7eYo+YWU8hjLriqaSUGYJaxhQsXGPiTf4RHxjw4qfnDtng3Ytg1JnZRsTr1VUlFyXsx8LAmho72CdLQrSWyzgx6WopRFETHYobqhPaHNETNHTVCSTlpu0JX4SsjxPTIqydB172jdaPWGPn2A6wdqWhFptLKxLXd8eHxHFaWXCyVnOgUtiW1LrBlut0fQlXLqvp9KY4P2TOJGyZUTK+d2Q/VktJTMZLi4+eXSYUtC6oJ0YQFyF7jcgTyg8gW0O0r5guV8T7mcKKfCqWToCcn+kxZEii2ipknrMTrQgRZlek8OWlBvdphrBYKeWzBcV3hFUC0TMBLEhaHRa1gpoPU+0Wl8DkoxjVCTa6C9k7qSkx4sFwHyDmQ+abb74RbXjGJEu6CWyQynKtMZGKdpP6E6aUJiZkshkySbicy1v5xN+y0lU3KGpbjlJ9Nb4XRekAxaG9o7vfVZf/FpKK2a6Tc09rACHfW7STN8OWfbWReEn/iWgjQDPzu/9X0goQnygvGvWGRfR5VYAwNJXRWaa1IhgF1TgW78q/NCm5TpmS8YfiiRMdZJmNtXjEd17XT1/VWFlEiCuTaSshTbl5JtrAXTpHpvpN7prdGa/VtUaNIQjPjEEVBXM7u3prTaEUmIdCQ3ZN3MxZRtDtpDkTFTrsb2YUqHNqMzBaTLRK4SBrRBX0Ox9zVtvaOtomrmVbOC7Vql+Gspie9p2vddMZ4cZ2Ys6U9YFvkJIUjOiHZSs0mVUKV8UDq4LrTW6X0bBNa1uzSOnTUmkZLYAocgrSvVfRWI0NdujMDPa3f/nPpBGmDiEzNTVVprPv1ESoXzpVDXRt3aGLqKMSdJfojiEJJRhK4N+eTKxdPT9E8b+9Y7b2unpMR5SWy1crtt3N/d8fAgEHq0NtCbEYcAuQ/rnQh88aZxd9kQ2di2RNu+Q1riw9sbH95uxjRoiDQDmYIx7H3TGJukHM5d54xwz4kzJ2Bpj5TtidZvn/AUdOBPwHvgL4C7af7P1Hrlz18/cj4ltnYiFzHm7ruUNuWEQlvR2yMrjZV7ZCnkpSBnpZSOvBJeI/AMrAymf7t7ReuZ2t+AKq9K4lyEfBLMSWcHxZ+GcgHOLuQCPBkdqDMxCTigCYMjjjPdatrUBZ8qOSvrtdOqYnDchicpkbuBKzpHlK++blqpXfmw2dHNIoicgHuWIm4ats9fyxVJNySt5G1Dex0WiSPGc3bZzPScPuk2CGZvM5UkZkcnztd8T33x2/7eTbKJzJmiJxJnDJ78dBxdLpkvf/2Gtq08f0hs68Zze+al+6Y1pdaKUW5zZu/rN1HjbnJ8MdQgbBeA89wG4+vA1TX4S3ebYQYKpDP5jJlw68q2Xcejk1NK+FiHVX3ryNY/MRj/YjN2qknHkhsFfMxmO6YgH24TxoQQrChVO70lAyAipPOZciqknJAk3F8Wct79vkUXMye+N1perzfqVmnVRlJp5JQ5gdNHonZlcx9sbZ2uja0ltrZyWyvlcqGclK02F4QVtJvlgEyPs1gT2jI1rCWEbDBxZKxcUDIpCSWbwtG6WUNu12dzMVEnmCr7wiDkDJpd28bOAapoNbBau3MFF8ztJ0XgTwjB3nZGY/JuJzwd6n78DsQ7+9ymg/qpM0eg8E7vjjQOHlQ5anuBFF/cI4ZiATuTizx4TTZCFrMMDi30pZP4oDZ/NPD5Qy8RegMSXYSOUps6+urUtrFuN05tpXGjNaibOfLTuJciYj9tq6xi65hX4Xq/kFLiu2+eeHx3o7ZAPnbqjVBm9B6DlIGQAplKypDPpLyYySMniiTS0ujA3QVK6ZxKoZTEF+fOuTTOuVLSxonECaXQyQpU91U83+hJaNl2v6lSr5jsaAnRjPAB4TssUqXYZqRMYiFrdtuTgQXFTMUJC1AQjJdLgq0Ca+f94xObJrb6TO83O9BJkOQOcwJm+u9Bi64pJUFyRpbFNb0+UGl3y0EPUtQJXWqnVvtszgJdzAIg5mnSrmitI5AlJ5tDw54ByWh0oH7fMQ/GSClTcqHVyqw/HSnxE57DcB4HA5/8dftBCEoJBKYcjnE8xLUyozFxOm6QC4hSN7Mjr3mjlW6+IME1u2a0nHyNh89m13COEsu5i5rGOLsoQJGmrn2p+5viyISo+5TeKPurPp/e1B9tjDzlZnjUmeesvOlYxaMmMswB8YkJKYfWMaZEnE/dNfL9pCJ0JHyVCkgaWoy8YHAqNsYuQu/NhIpkN+0ygM/YfUmIFEQqvnS+DAMiGihUH9FOHlOQYGi/HakNpJLEwk0ilqILR603LA0vwPfLoEkJ/tmNv+tQJ+2+MZ1ZuvDiDvNua2hMg6/7uPTT3355/agQvF2vpn7KWGP3N9nNZdrUGNSw08cQZ1qHIeQ0ZJTvUNN2ODBD+rfp6Kuh9VlOxtgSFrG55NO0PDFmSB7JZ98JJhKRTcbAks9n97fgB842JuYNagyqxQeMiZGF2qA2j3Cj8XR95vl2Re4tYE1vr6j1AbkPxGnEmJKSls733z/bnGUjF0fzCP/jv/gj18eVtlUL1GiTLVzGFA4AYl5vEczReP6M5f4Vdw93nJczS17Q5QMiK3/5lfLZK7hkYUmJz0+JJSvCM2LGUBJC6SuJzolKaQo3e26rsKny2BvXGIueod+T5JGSfk+j0FgQ19yEV8BlsJpY++JzGEesmSbx9rGjzyt//vBHunQe+5Xb+gglI6cMp4qIMTjT8oLLxJrIiJ3Jy4XEhXp9pt1sEkqYQ4PAdt9290NVn27mKhDItXNd60QvigS9AEvK3C9nuN/YTk/kdELKxfaky372yUgWTotQ8kKtT9S2RxcPbjmdxX2b1RFevJZcYE0CcFqF0DKDYe4fsPPXO7RwIShcbytb3ziTyXmhbisJ4fpYSUU4vzqZoSdMm1QPxNBxv4+12v3vBGy1sq3rjvubqY4RIBFgOP7f+hHAjqUYnwlBaG6HflUjomulpAUp0Funrc0jOPf7pPFbXHvy+/eG9D2oTZL54dJ4noEvW1/bryRiAUVi5h7F2IZop7ANgaFu9hwCIYCs73drijR41pVUNs7nC6Vk2jm77cekV4gRyRfI4sB8ZqNq2hONjGmEIJASIjbeXfAbIFxvK6yNLB6j6mQlixqWPVDZdKXs+5smAlALvkAmGrO9ntnYuMVLeverx3kMG6sHpOXkrggPuhzC6EeuHzeHwkHgHGTUuI4wciCiiSFrII4Afaq7gzEe0AVCegcM0I8fkcAjpXYUlMRCdyUl80VMmwgRNbUfEdEQ32nSNONb84PnmdlAxlBUXHWP7ypJd+wemkQsQHP7fGejyY11p3+M2Fzr1kC89vP01kyn222jrm24BINhjTOje4API0hjzBIR8zmVkliKmSKWLJyzsJxOLEW4v3Qui3JfhCXBaUkUEZJUzKhjdvyilaTK0qobkH24HlmRtFNU7YB5OLN0RVtHWiatmcoCUuhcgBNsYj6XOGj9DJrp6uHkYkEtvRVUEk3OFrSZKmyN3Du5dxbUInVTHLBA5+Km449wgtFNyc54dadD8xxZ9Kqq+yvUmTNsm9KTktLOoAUlu2nPfEbKcxU2bTTplNJZTmI02GFdK63uaT+KoJpMW1+E5ZQtNN5Dwq8fnmi9Ho9FkK2Im8kSpRQUMeanHvwztA4HHBKaStxMxn0AT+uoJM2oJprPX5MxwrY1UhO6NCQpvVZUO61VWuvU20rdGtetjnOWxEx7PQRF7E9Tem1jGLvk3H8mPj7/Gktw4HcTH1Gnw9DOujZat/D93vWjdVSy87odRAzXanxQMI13qFG7NqgI3WlIsJQD8cCkHvxPXdT4noSx3ihI4KMff4yDit46TRLbZm6WXPqwpFlqWqe1Pn1/UibGWuqYtyRB1HzAMkKi4vMOjlwLHkKw2YAy5udXMbofMR/MGQGTb29GK+pBlAM4iAv02JPQognmNlMCjOPqqx9rGcJTJ579A9dPCkGwA4QKObQ3/ZSA/YlHxe6FZtLjRvNsXtzuxS0FKOYZpZc9TymJB5dmgWWXreN9XngzNswUEtlBSkhqn1/j09cRVTtGJYSgY7zBdOZDaj6QhvJElxvbU9zF7tkG3tzv36ryzV+/o9fO+nyj106qZV+c3g1VdUVbG2NCcX9n3N6E9aUoD2e4nOC0wMMC94vw5Zd3PNzfcyo3Smq8XuBUsNQDSSxyI7Fy0kcycFaLMh0CcCyIktXez2rCcKNzo/K8ruj1aibSqmxPlfrcLGIYTE0uAqd7yCcKXyJceKbQJNHSYtpOuqdz4sbn9LTAaUGujeW2Ic5EV1G2xfKuVCagILhf4sV+lmzRtNe+244wr1gSQRY7ya29R7V6YARcn7opXdnBGA44MH/4po1bEz5sq+XgFeF0uXB52MbThTCLO51083Euyx2nE3z2cM95sWPaW+Pv//pveN6OtOJcDEmJZSnknLmc7yyfsFbLNewvcnTnb8u4hf3tOZ9bN2GW9ULWxLY+A5DPGRGh3/ycfcD8MesTtTYenzZH6QFAdiI55cL96WzBE1NoZ9sa7WaaYA8Bk0JbZXxuiMSJr7+c1yH8PtsHezdHawK6Nm712aKqSZ44aL5DyRB+k6Hj+d5K5C3RHXyr481g73YemjNjswooaYk8Woy+NteSJTSkGe1HNmBcthdjDVxhqNXm9PgBcm6cH7qZ5xF6U56fbqzXFZFMyhaE9HL3B78QM+0Lmd4tHCppIALXLtUCuA4pbG4xWbLnjXc39WcHCWUCXuDuDY9MVTfxdmWrng9LGuB+l2JHH/FOB5Ng9DQQZ0GgHtAqHIT5D13/AE1wQg2TthY2/12OuZQWt71/8sTZCzPjtAOijhjkIPnj63GIwrYuKVl+mqvAIhaCO1t2ZgF6vNv+XlcxYaV6oEXPnhmJysM8oMnHCGNjHCHJC6eCbcj+WuuNbdvGR3b0uaO9REbtCLFtFrLcN2i1W8CGO31Ds55jBnqfljzQ5QFEKEIjyY2cNpZcubssPJyV05LJJXE6ZUpWcqoIakIAWDBCOcEuBFELdR+aVvyYIGxYmNGte7RabaYJd/Uk14Y0m2tHh2U6aUJKI6cPiGwWyk5iSxe6FFpOqLhJWDJJLqR2I5UbKa9kNgqVkwcLtWTmoYbRroofTFcLk0DXhGghpQbSB71Fornlw8GynOgt+Q6FhgEBsXY687PS2XOpbMq0rVFv2/Dj5BS5TkaHTd1A77kxterY69404mIObDOuJFhidc42ZnWf5GymmyxT4x4CyKz32K58ZGMPIem6RO/dLVlWDGC9rZZX5oDs6I80JNJV6NLpajlswTtaa7vJM3zkaoF0+yw/ZuKxCC9ZzTizcdYkO8NkjzvQw13szHSQPDOw3dgZZyuHzua8Z6zafgj2e7o5aPeLzYxxj53YJdLE4F9qgpNE0W5hh72Z4Kxrp/ta9e68SyDnYubBvMdsSHI3ixjvGrN1vhLPEpWPVjxC8Oala55Du24bSRKJQkqJ1DuhCkaQ0WHj5sIKkRQ/+dIUPMI05h2yxWWNS704G2FFU2WcL9K89p++/kGaYOTLzZE2KcFysqHN9mbEzUOzyXMm0sG148/9RA4Umg7sZL9ESKVYzloWC/RYFqDT+55j9NGBmNdgps9ucxsO9/i8/7/7wSwBK7ov10B/ZuZpvkAWsSmQ8gQeTFDfPPpJtXPiHPIbyyMUkmRfY0Nbz9cb61bZVqU3YLP17O5QT4MY7FebwdJL8BN0rSvCB5b0ikt54LNXC198diKVTMoLd/ed8wny9Yr0RtGFLPCgRigXNSF4ApKacByZJLrTQccZOspja7S1UrbKqXZq71TtZO2sGCpvNLga1S9nyGVFTh1yIZNQMjc+p6WFfjIfblq/BzKl36OtUs4foD4i8kSWGyIGVG9ZqAm0RWRoN6RaduN36gvaMiIbKdWRppCS51guFol3lx7Q3njeNgvnHoijuBEnCiQY/eu6A0Qzdyb6trGpmj8pJzRnUskWCNMizg5yOZFz4vFaEVGvINO4NVyg77QaDD9L4ny6kFKy6kiqnBzd1+p70o3POI80gRNkM0CgMy+ZLCJiyrIBTbPotLrRW2fbNlrrXK/rke518lN2tQoiSdl6orbOFuZVzESp4EEqffAFWSwF5uWZ1hd/v/x3Qfb4BcF4RVf6Wg2EteA+k2BRLFhFPX1KX8BnU4zMLy64MNmnGHyAuYpV/th3eRhxgHC3ku3vJObIyMnwCg5A6GYCTd2EwFxwpPWGirKcL0iuVHdLoC4EPYhtm6xeu2voU8mmE3d1q1kIwupBYGvfEBEu8kDKwuJBSAZCdDfDDCtgH6lkURhpXiHUFBVFLIjHGZ3EARVG1HFrnsAf6xp5vcsL/v+J60eFYHOE8tLHF/Oote+yTgxd7CkHNovYxo5Yjpq4EXFXKglRqOohrx32sh0vFiZmb3We7HXdHfnDDJN2LhFZYyZpiWwMW9ieLPpK98/EmEiOPFK81ga6Nf6XTLv41O7FS8Zp2G6Vx3c3+r2jzWRmnlxOnouahw1b+26iVc9V2wXZhBwPPpP9MMd7h/VS9/GsFW0VYUPEUy10RVtjvV5hqyxbJ6tyScKSzBdobF6n/LgDRgV2vWHHsf4dD6n2LEw7iNN3iuoQ/r0NW7VFj+YFSZmcnkAXlqYImU09TGfDvrN9D9t7mq50rTRHuOLIXZMZRrJkK3u2CEWVDFxr54ag2dF1bW6eCaV/thSY6YeUDHiIB5cIZpFQATWTVkru6/MCBrF3vfdxTlrrEFp/7zsIrM0qKTVFpJn5qzXLGUvBpGYiA1UrXZZSouhiWNPLDIbPxYD5hFziWCgjoFo1chdzYDgkY6bWlAzkdKXlAAI2nlSsSEQbAZRREQqrwuTMubbq53U/LsFb7fc+vua5seqWn1ECLshafV2Z6X0mUBlxA5oUKdn8ywcf+k64I6rdMrJRTbtyNxP3WPNdOz6ehpiCUttmtC+eOtbDLGAgW9seIakD4Oj4HYkrx5jg8MXrtL/+eVVPObFNTZJZyjKEICLjjj0WYbAUHa/PQABinrH+HrL0gpYs/9PATXLfapiJteFR3P45j6DXvvN/xt1NcliUZ0aDJ6D755y2DnyP6c+uRv/9432Zr3+QEBw3nR/S7FCOY5iFXCZHlL8RkUN7Lb5doI5NmwSPdM95SZ8auAf4iiBlGTTXu7JuvphdIQtpsedZTMzRNh2zliZYup2/OeZnA9wD7hwZj5OSB5Oe0YEG3Tb/3PiKcv2wsj6u1DeN1j1MWxLnu0RZElkUyYK2hHbPQeuGfDwhx4fYx+ZHLNinDqD2Zgx1shH3rVGvG73esJDOFVjQvqGqFgWojQuwiPDmLNyROZOczR3YzEfXXtXKglh2gb0y1KsXV3g4V+1ULJ8TEVivgHC6eyDlQjl1S6XYbjQKyj1dE9QbWq/I9Y/o+h21Xam9EV43caNuTpmSMktK5MUQ4lmVkyrfr1aYoGfoKbGpsud14gld3UARUFIy1Clg1UG8HFx2IqgC0snF96FzYBKqfbidzXy4m6TyspBKobfVViaZn3lbq1smBMSsH3sIhd279871+dHKuJ3unI3JHkl3pJDpTMch32nWxtgNaGQln+Byvlj6hgvsutn7rHa+y2JovN/64SjtGnEI5CjnkncmG4Feg6H7HZqbt4p4XeDQfe3agyJ2D71vPYHARbxkoQBLoqdOozoP0/34yH4vG4fBuqjveuCFEzvYr5exjbaOa7shNCRd0GZlFk2130bVot33JR6pOZujZfq/Hp4RQwjXTQiy2jwgDav2dFnOY01NU+wOFKvdpO9rHtToOpivZ5iAY79sPSImb552XW/mSlhOHt1efc88z/Bk52erdYR57rM6bBy7Wcse0l8uwcdLMuaoKtRtz0n/oeunfYL+v0nRnJ4ooIYaUk+kHkKuE3oAYgIyiUfZdfNVtGCKXmHDcTsSf2keWlc8KgCS9ikpXtwM4f6JiDw184DVZdS+12ec1hMUUhZ2kBEabI9HcvhDPPF4knHmTt6L5x6QU6yV07OKUm8btw9QSiblREqb0VMz4b/eNupW2dZGq7sdP0RI0jT2Ywhf2KPZDsQQnxEfthU02OqV2/WR2s509gAjTTb/czpxzgU0m/ksmab9/ukZ7Y27BIskXpUzWWRU5JI4s+4n2lqj1sa2KmtTnjeLHmuaqGqaRNcMmmiqbK3TXG8PE7XWRlEPPvFSziKNizP4SiL3R9rjn6jX99Qo6dSnwxtmMTCTjIerh1q7LI07mpnpumkpUaVDI5Oe2Vqw764MOsByXRXozX1eRg8HH7kGuFSigPJAwijdCzGPykrBGIPTiAmcMJIlP2vxfdOmoW4riEcjInP97SFkLBLVo6t3IicsPz3ZwUgqFC27gW4cS/uj5EyXjHQrn5YWj1LUG711ao4CykZr6s9Lktx/5fPVjBLas40mIgez75VFuSoieYw3ohGDRwkcQPTErQwYp+TBUIrm3WfTmrk2klfGaU1pfaVXs87YYeBgLTzw3yC5nAa3lAzSN/cdZjP/6YZpff3FXZxvqrIXxhsvGxlIH7bs2ILe7fAFmA/8IAgpC6hYtHTUalU9+NCY1tsqa4UQ3B+yC8C4v4BYpP28xvu6uP9QZfAwC3IStq3vJmBj6DttvgjsMJ+8VWlShMSG0iKl0D/mZmm19KQ54n+a2g9e/yCfoKGLmeUOluz+H9uY1hh5M+6lMGJ0oW4+5ARdDHVLM8TZ2+TsTxGz6Tj+Bdvx4JBtawOV9N6GQ8pkmKJbd1DlkC1uo468up0DK1UVizUg0XEBjAJ2IeijDQEokq3I7liwSeI6fat2SLA+behaOV0K5WQdHnpXVmyR1/VGa5X1uu1EwhT1pI1D0rOPLwTQ/uhgrN00ZwSrzr+x3p54fs5s9ULnPM3TTNqX0x33+YwCWxMDEL3z9bv3bK3yeoFLLix3hSVnihgj3YWg+XjWbWNdTQg+VeX9FiYdN/cm0wbsOY0bnU0SlTBXKayVU+7clYpIIgtkCkUqHeEK3Npb2vu/od5urJt3IKhOf+K+rIJFn6ZkLwxzAJzOHTk1rnVDaqZWd/b3zTS5KQjqsOwewRbgrHvaAx6YEGbB5IwtEC/NAxImf0zUOuy1Ir2Tsu3FbCIyppD9ZAVdT2Y9XGhgBRbH+UCsBkcwrGB4BNPJLlSmNxLU3kjSSZpYtJA0hGAwShvWsmS0J+p2QTVbDWGt1PY9TSraNtSjDANQWES30MZ4IgIyHSSLOk1Lc59l7kRRzyikIMgkmI5wfcxnOpKShCIliJXQPGQ1s3ROibwUet/MX1UFrWn6PgftYtg44rUyvZkhdY/66skAUr8dWMxwEwwoE8sbRkf8bCuMsCx7XtdmPM7jFoL3htDKxcbd1GIc2rqiyt6hZDIR6GH/p0WTuKebNSXkcKjD8+TnK8oHOiDAKnFZFSZnqXF45u/su+p5lolOGoBd6APcTYtIBJNKWMsIc+8nhjZdPyoEBy36HzL+fDlhFx/qVRDQXfiN7hJOcB4Fa8EdiZZMwmvkyYnQJVEiX2ygEWf7IVQw1BIo0PJcdt9NvNdCggOjRuEA1TLQsKEU10JCEEalFbEghrsHQ7rPH257CSjxOaqwJ2ZPQjc4lO6CtiNm9gOQmyXAewRi9WCD7vX51NFMCMEejMi1z4934vh3MB7BChJsHW7PFur+4dWFS4JLuVBSoV2Km5FDwzFivCG+L/t9G8pNmr0mGRHLF+oqVMWquQTz9REHI7dDeDOTXz6BLPtRyoWcMup+yshbv6giXZHsvgox4GS1Rhvr2rit3UPHLTZBxI5Ob1ajVTcTGrV2ECV5C6XAoSUlWAr1ZHtTq6JdzHw1VZQ5nCllMCqr2N9Huy/11j9DoE+1ZsMJMBQqP11ROUiC2ONhfQ6MkOn/O9wZ1TfA82gDKpnWvPM435NA9pHf1dmrmPTpPAwub2e0NavzOBxqyYo5350LJZ94df8l27bx9dcV9GZ7gCIjwd1m7gG0xFT3KxDd/mJER2fE6HlEcE4q0dBadBce0xOVYOaRdhHfMctAKlDUci3jPO5Muk/3ms6ZMiKkm/vnkvO8nBNSrE2VxR2s073iXOg0uonf0ocoCMg7dK4Z0KsXptY4KZHWYZJavDFBdnC6Vlzj3X2J++V0lfY1ivVC9lCdCNkQ3fdx7rAyovjdyZyi20e4BeKhKTbef6L0EH0HRD7fmFkqGdLZNNsp66O5L1MrqCZ6rz9Q+vLj6yeF4L7u4Zp9oYUM4WROb1EzWklycJIibs433qNhUtjoU6GTCMnUPdDACvdOqvEUqWZCQd2EYrdNOZnD2Yt4z2tNVCYYFS184cQSAcQDGaIFjhUbcWK1XSWXxOvPrbL67flmROu9YlI2c4mom0HEiz+KQ6ZJcscRaJ4rpK1ZL7KS0SQjXLg16xc33Ek+7KiMGmjvcCAdpoVzPbwkGWPA0o3BXp+faLeN95fCSRty/py83NGL0EIIjlINwtWZaNRB6YQQrHRRMx85qu/AqsKmwkqySMbxs1+9V+r2BCS0LLttoSxQFqRaFF8zO69b2C3h3wRFJWIxszZut87t1i2BnbBAW1oIXSyxW8082KTRUOS8mLnI165kM7fWk+IlULxqfR8FDOYzMa++peJ1BmKJHM4BfnbTzy7x8WAUPxeSvGuIawaOOnQ8K74nfBzBFz6TGM/0+YPwg2D69nz/vKckSOSHedCXHBhlA23UxkhvMDPkiZQz9w8Ll8uF3/36Vzw/bXz//TO9ZRI3AxETgFCUOvnZTIgP+9Y0cNlxJOzBR+JMc6hks14S3Gp/2uAX45MuCCVab1keJynqODGC14wEdQxt2GcURuqECyMTECYEliV7dSK7Y+/b4AWHQJc5TmLMQUGbmY1dEOxks4Mgw1VO8QrqIWySzYwY/mqRbIWmNmz/2oGi9vXw+w+NcrJ2RHGA5NQmvggj3ZEQgHmY8/H12M8Aw5w7FlH9Zt1TzQLMqPmMA69YZUQL01NRurtvULNJdu1uwVFUPtVl6NPXjwrBUVIpTaSl+jJ7aF5H6xKwZHK2GogiVn8gkmOHvT9VY9iK9wlz80T0L/EuAzYEPeavm45t1DliU9QZTnM6c1QxXDr2Xqhw9ppgxTciH2w+fDoOQVnsgIjzhrJYfpCFm8tgXlkIRWA/nOEv82XMiwnU7EQqea6bGQwRy/XSaFECVdt0+iZk380BPJxeAW7Zp6I93O5CHci28t37R66toW8S+qCUnsksA/2d3G8pKdN759Xnn1tnjH4DgasITZXcquUH9cyoIJ8SZSn0dMe5FPq20W+bm2HgpmdQZWuVur1lXVfW1kinC8JCXu69LctCEUHSByQpeTmTUmFZbDFOJ7huye7bFFojwmEkOaMMQdWFrYNSTQtRYeuZEWDi/L+3jbrZT2/WCUGxYAPAcu0Oq2yMJjIIP3nuhm/EvldKMibpDDai8oYWOOgwrslfrYKOhKXuDLj6EeifeL6w1ymEYeEYQiYYsoPFsCs1A4jfv33H7XbjzS8unE6J9+8bW431EM6XMDHvJlXVRmsfaPWJ1h5RrTAXpvcI24hEHQ1SD4Lx4wjxyIelmf8nR2K2vvCS746xeRVszh49LilZFwy/LI0klsjKnXEWtK6u1cfQI7zcKUdfNI3t5nfOy52BrGRWsu6pOzkEaduB8ViTF66YwL/DAj3O7/RbJ9DTzW1QXMvv1c3M2i3qmWZCc6zLTMdH5rHXPHVWK9axQuNrw7zed7rXSW7QBiZUlC5HYDIUFbUuRBHAaCDX6pWKBRh4LVwXzSmRSqfkRKvmAjDlKw1/dNYTqp113SYf4aevnxCCTtRp156CiI2pumlxWjnLQRFytrDcuEz2+IEbtXVcAHb1Q8RuNQuwOgVbHE53s7Bpnc9JmIOCIk1l8TOhRAmvME31BkOyyS7obO6mkaYkZFNO/HUTigL01VFLSmPYlq8yjcnPWFIzSeSSyCfrAZZEIHvHd+wgxwCiQ7MFHOpuGnUwMKKkgikMIQqHQ6IylA/LqxXwe33/4Ym3zzfuT2eWJfFZv/c6hCEEC0tJQKEnqJ9/TuuV7ekdeJ5fw8qnJcmeWm+jkGQ1MDktVpn79ozmTtsUy5g403pi3b5ju75nrcra4NQ7RYR8ugCZRe7MlccVkUbOZ0opnE6rNalV5fJsftXeDIGM+oaAOelcg1XPuZSKSKf1QtMTVhtEBw9otVE3q9PaWthcorB2rLULksgf8IIJlh95vHZy2M9JzoXL5eQRehZt2SNYYpiImL4ZgQYQPuEB2dT84iOyEuXA20K7izmGy8KZrooMITJQE7jESbx/94HHp2fOd5+BLnx4t3Fbu4M1K7JgA7vsZKed1p5o/ZHenghuf9BmuoyzOJKmdf/ER9f0cUJzzcqoseJzDw3gmPg938fWIeVELuFdtUCN3jzkSNRqfqZMS9vw2Y6BSHJ/qhApNGPXulj/1NNlnOPGxiaruQ0S7hfGXTG+IoNoQ7j5vk2bPQT59Lk5SZyuiHRH5IleI8q5etnGZub/FPefI1qPa2/QyARTEvEI6zLcXKE8WDDTXmQixtQ9QnUnW3V62x83IlKHZcGflzsiG6P7gWu4ITdyMWeZZQSEZdEX0393tUjcHwhMH9ePC8HFK/hrmg6cIcCk6hXjzdRpSDqMdNM9vNdayvuGdRgh7KhXUZn2QmBUNRiVBoof2NqCBgejs5yTcPTiP7oLw8FU4s34d4obGJquru1lN6Z0055ktea/T49XQ13u80kZVIXedgNFOmXK3ZnlvHC+O41nbs8r29PqAQ+QpZAlIUu2AAgfU40yVwTDtfEtBWeUoem2oCP//dJMHRsQ2kX82NXdZADKN99/YL017u5esegFvU/IZTGtzBtkJpSlWUJ2Pt/Te/Ugns772+p0lyhl4f5yZ8I2uyaNsPQz555opdFq49aBqjx15d1WearWzuWNJgonzulCkYXLkikJTvolRTpf5gslCb2Yj/Px6Zn1aaXXagc+IpPFz8JL94uDKzKcUuGhnKhto/aNbe20pjw/PnJ9fsJUvl3gDbV8+HMtj6nLyu649WclIZ+KmXS3ypQUax8pIIuQI8ipCjQzz5v/yIVv5Ne16L1nD5BkJqeA35lEFx1R18eC0z547woxU0u0ExuYAd2nPGkdqvDhfeV2VdbVOo4bo0l8eLqSbxvXa2UpH7i9r2zbRq1XoFPGOuxpJ1aCyzXnSY2yNBs/f9PS7wtrr6lVoaZtnT7yef1EhwxBmKz6Y892mIdpGIESZecpgqAeZxAsaFzJgU+yBB8TzLs2DyA58fD5a7KXvFu3K89v31sA3ebb6sw5Atck8uJ2ZDuAw/CzveCxMuitOVgO36CNrTvw3zzo51gr+QWviHsNldPGEk/M2fylpVhFovPlTC6Fkmze37/9wLbVyXwfG9EHMBMXvrOJfw9ymR1u06YNV1ZlORtvHREGaaPVxrZiANdB5EhBO5/599IE02IoySqD7BK7e3sVa/xqgm435co+d1VytvYwNmoj7qTqKLtPO3n8LSnMk7tmZLbe/pEQjHS6CJvVeIFATbYwe8uOmKEyn3htHkkp6pF5DelCq9Yh+yqrPbPZRqYsFt7tvpvkQo3Lwt2rO169eTAbN53H7z7wNEQlZBbr3u2Oc5wZWOzAx0793EFo9NRH4nEISY27zvOCgz3f45RCptrH3TT87vGJ67Xy2y83Pjt1K2pwKkg5kZKHtWuneNmrvFxobWPbbrTeuV1XvxdcTmcuF+8K4Y04E1DKwkkLrVVqXklrRXPlqvC+da7NeoG96qZ5LnLmlBbuTlASZH3NAnyWoKTOtVjaxXbd2G6bgaMQEsPcF0JQnf+LuXm9k9MimUteeNZqlWy2xrp1bs9X1ucncj6PSj6HK7TDtJgQZDdBBgmnZD0He2OkHM6MVLKvjw931Ddljgh15qGeKD/nOZTkiDr8VuaYaZ8I4NDhVHah7gwoxpOle9rCNMqDILTr+amxJqVqlEUDtHO9CiKVG5Yf9vT2GVB6XxE6ZSm02kwIhrbq5fRm7UOm+RysKeNvObymCr26aW9SBIKBmsIug59q8oIdY44h3WQcijnqc+AmefF6BDx4bpD27MFFfV/VlLi8umc5e0TjM/DOeVVjEvzm9QvLk6WIpY+xLPG+DHPjmIfaGC0SPOwQEeRnPUjXWvdcxxem4qPaErecgVFEfwu5ZEoxd9f9/R2n84lTsXs+Pj5Tt/pi6GHBMDAR7txwD9hwZmLT0RwhmJUYswKUUoTzxfs2KkAjbRYQZ48J4W68p5zKATR86vpRIRiJzCml3ancleztRI6hXfYgbUpdQ+hAp7FkJVGsA7if+pwLWiBtG3OCRCxbqxuamrchcQLIgkhxBICbAVp8w1XvSRpypKU4IDvCHM2M9nl097mIIif7bKNaVOaWJ5MFY0xJrKxWKlbiiaUMM0hc58uJjHgd0G7BOF1GDb+IxjovZ7p2anTd9gRtOS2WRI9popFrNnKXGCQ0/ha8sG2yZHFxn2eY1+38W+BMRrmchfv7xJKLaaopjZ6sqtZeydI5kkfA3KB6hRcMZGxVeff+e8rlxHJ6RegZ3RlRV9MQevfGuEVgKZhFoPPh9o5VK+dXC+fTwmvgLgnLGZYEr8U0y7umvL9ufP/9d3z/+J7++gRXgQ9mcirZOxJ0f34wOkNtHrBga1XrxnV95un9I7fnG229Oi34GANxhUAcJvUNxfIewfjiMK+/uAZDcTRvW9GHOWd2hY3yVzLIeJQ0GwCzqQGcaNyarJReQswUqXWi/gQaiekBhfCHWseDAAkxdEG8+0F0aMGrfVhTYcVcX4gzyI9m/PFlQlmHD+lwOsXnI34quxyDe4K/jOjabuCmqX13NSHX8g76hiFIDJRY4n0zIZP3VIyID3A3JUOqejDPrEkaGWTz5YcwKl5VZtVJA//EiqiVU+xqCepDwCZLaC9JaN387Ef/2SS7JP5le5CzdZovyaqz9G4+/VJOqCrr+lIo4Wvsr0Yg7oS7D5GqyOCkJfhsMA853vNyLgiNp6dnam+0trqZVAJ3HbZ8lDkL9KKMOIyRBhNBRncnlsuJ892ZsmRSsfd7FisCok+wVbabgcXEaI388T68uH5UCOZQK0MIioB0UrJQ+LE9w7AtrrU6gYrZpxPdzDfhnBcl5Ux23+AAJrvX3H0c9iwhjSrsMnVRRtueIB+mzt3byrxTw+c80OFugggKG+aJ5qjF6891r9HXWkRa+Xok/zsVYxYnH2feBU4kipalUFKyLver26mnpNdAd7kksnoyqwBejzRJQjV5NXgZBQLi+8NsMhFlMJTkvs1Ye7Pxx3NlWAhPBc4noaRMJlsjbu+SreAd0hNS/Smt2s+IQ+3Uplyvj5SkCPcj2jBkj6rVClQ1p7elqWXIxhiu2zNbb7R7K+J9J4lXAucTlCLcoeQu9Kuy1s7j0yOPt2f0XGyDP6wISkptJPK2yDx1shCSRxbaNGqrrNvKen3i9vToKacK3YMIogFhSAgNE15zBqKx2Iyoy+kKF6Lg/dpCs1DvdedW3AOzGkBNiST9g6kv/GJZhuAVgaQJq0BaOearvuBaYQVxH42ikL3c25RtliQPk2IUrx9BKK5JpZgjGkQ1TWbmlPucDg6keC+FFT2E9D6+fQ4RmBG+vx4WcKQrnTRoPzCEDUdAvHWS79MQLK6VGK/zPSQ7P2geRDONNYmB3diPLM7bXjqfJqGoVoJtNwXOt0ueD+cwtgHSrR3TpCl/6jJwEiZKAc+ly1JMA9wCNDC0d8cbfgMPvnuRT6B7FM70/SmCV/b5hSa3LInezeGpuqG9uqDLnxSC8Vqw5Ni0cLWSdhNwORXO92fjoyVbTdAsdE30VCj1mS7Ktlm5OEGmRP6XMOB4/agQrH74LOIOF1hxCl2Se2K09ehL3u4+0/SK1g0tiZozZwpLMnaSEW/6meB5Twg1B2fc3rh63SpJhO6VHHLZh6wKNezc3a3KCat+7jA1Kkng4zdBae+ZIYLh2G7OrOMgj1JvMxJEKOk0gYKQNp7uqkpqje2qaK92qL1LejaJ6iHGx7yzHUH6aLP58qrXFzRzl30+GKL12Iux9R1NBQ0RrGw/jBK2JjUiSliJtBPCgnAC7kW5l06hOpJynwBK6p3z00reKjeKPyj8IcnvZ5/j/eZBUoJ14Mgs6cT9OaHrE5VnMgXrMJ8OCRWn/oG7rpzPFy7nzK/TmTOQWVE6Tz6nt+vKh9tKeV6hVtoSBRZ839zUjK+xovS1Iz3x/vYN24d3XDfPWaybA+MJhQ88NcHlSbiMvVP1VC1FUkMRmrCb4P2OXa0mTq9CX/P4bnDGsGGI55t073yu88ZqfNJLSohwKov/VmrPPN7wc2UFx0m75sNh9DE/UE/A6Qqi2bsGxRc6+WR+obaaxD6dPVIvm9XgcmfdA5Zc0NZZ31twRG1C85KKvVbq2tAZ/MIo6pzEaSH5+Xa3SQw9TMBD43Zko83X19OAWGxdsmT/bh+yV6uZsPOc6+gcAbUYBgF6MqEluZi1Ik5CmgKTHMwgatXsnDZ6hg/PN7L7QW/XK+vVekeOOQMlCecllAxIWchJocmoXzvvVlBeyiY4U5FRAOnTlwUthTxVHbMgXsxMRr3pGTJGaVer1RKvRemaeXxScs7UbbXi7teV2iq3daX3NvaNKM2meyF0HQhkpwHg4PsEWM4n7u/OnB7OLJeFvJh16nReKCUjZxv8ck7c1so37TvW62oWSeohl/CHrh8VggY27RCGKrWryhHi6u/7NDQlAtpaE05DJOWklEDiYugluYCJrP4gRZnWp7ufLgRAznloFbGYoYgqfi76UUCNkJEgAEeUxr93TaePA8YuCMeo/Lt+sGTYE+1Nz8YwXuk+nPXWzGmcoKTizX3dZq27YJqllymI6sxU0Rpo66gqDO0igIkjvFlYj9GrMWORIwoVNd6YMatkBLkvWEPcRD9ocsnHXLYGWyORPbQ5DSSZUBYxDUluHSkWXYsLY8sxOXFOGydZLThIYi9i7ErpK0tPLCWxnE68Bi4qWNXHxpNaXupzrdxqJW2V1DqadwPIwXw0go0cabfGbdtoN/Ziw30q8n24lGOUxswczNiqMII1Yh/CjBTWAMV4VddG74le4/U9esfiQ9US171m5UtmOOY0XTnt1hLrYhAZpUe2tp8zXqB/3dG6pGkN9s9YT0QsulfVetBl8xPlnLncnck5UYrQtw5P2YCl2qEvKE2V5rbKQOohBK2n3X42UaVWE2AdDgUAhgTyew3jj5pPqReLGjWXoE507+Znz3hoUfMPGRqZKyGMQhrJ3RzTOsaZs3+6z2sWRGKmyOR7t66VulmZvB2kmqk5+gAGX7OiE+qxBvMeyZD/6WCmjp12oMv8vREChM1op4l5j5012fuy32XcWT3aU5RUbc6yGgi6Pj1Ta7WGCt1M8dHMQMfGxP4cT9fsvrG5+Z66bMgls5xPpgEuyWMohFIySykUx+FdEyk3SnlPzZuVTtTu3rEfl4L/IJ9g3tfFhpxARNF1JZye8Z/V7Nwdt9IbqSjnPhUk7ntPrgHxvDWNuG17V2mM4GqviAp9m23++4EYMkkdVU7JxF3t8IkXxHV3gs/JTWMK2duedJ9nLu7rw6KhlnPxHnNzMTcfY2zeDpwBqF2pNDax+oHihKZuApNYCT9jw2rlTrScXVsI4s3JQ8lXNwWnAVSO0XT7CNUPlKLmYxR76iKFkgqfnU+8Wk58VhKv2NOwL6rkrrRbtcK/zYoO195JkvgsnWmycD05HNMVQSi7jk3qkCpWTs/zs0jwxTlz0TPvtxPPW+GDnrg1WLqwZGvXVIBf0/lSKw+yUkhoUx7Xjf/f3/wdv3965Jv1kfftRuSpH69Ia/DI2BBGLhP71tm85YyKt6fB/Wq7Luj7nZDi0YD+sNatHk4ZnxuP3bfAHzbYjZ/v3jqrF9qLM6UpjWIJFjLeh3v70JLGBZUi7vO0dlOx5kEtAvRQEcLtPsvEmVAdiNIx03Tqhoy8Y/As9vNiGn9JEX6xGWDidFh7K5xsEke0k/yMFzdF9h2hOfLf+73so+uHwCMdDHyEeZlQKBMgDsw8AMh+zYC49862eYePJEiKLjFhQ7HF95Ia05ZabdgQepEdOgwEInRtfHj3/eCF1llh2JpISSiLVdoZYGkaZU42p73Luhx/peabbE77kQbq2m3TDe3Kst2Q5tYHISqBMPjrS6yj8X4iIjWD/TQHa+26ITehSEckUbV7sQghLD4G5YLny1jzTxxRA32YKTrimGKeplneSGdYkOGIbtX89Q1zs5kFAC53dlYeP9hS1EMt0U9fP+4THMuxH/BOCBwLShlrN2BkEKx9IYknQh5U3yAonYSgf2vWjlSnLAbXCFvbXQ6yo4rdxm33zxMs096J/FCdPjuOtkgU7GAQo7gwTcnKNqc0kv/Hg+bL5VgMG1+OaGvSxMIEc0Sn9V0jDdKYLDM7GvOSbCEIJTm69UonsRYSmrqoV8BxwgtS1jCJRPCBpztI5pQzlyVzlr1xbgKKQkFhMyEo22a5dr5AZ8k0Mj0bY1C65UMOynH66YIHQ455XUqmnAqvlsKrsrDJRuvWseIkYrmCItyp8kCnaPM2Wwpb57v37/jm6ZHnurJ6jlyQUyymRdsOFWH8mPUCS4Rvnr7wIoVhp9LAYruZRnsUSDYmMYdA7f6raU/jbMi+/kEXQy/wqM0QerbOztVVhptgV+ViLLGms02s76sf0aD6YkwhoF2IxK1tON0PfYeh5e+FLgxkhltVEU+CHx0yugtzvD6q+05jmRPJeiJDIMH9LMZzxpD2Kjjxb/v8DoAh6J/DHtqUp0MlxzfNwGWJ2NKhi5KHVh7fDSF45EsqfaxXWMUYfMm+t93W6V5Hu5yIpRuIyC7iNfbVaqta6dtwaEwfOdBBPCLmGaUWLWYgabMi6xLTP67v4dFjfo7Uxp7MZ0vdgoEnT3TriRDAPRCdf2I85sXx+mgMEpzqeHU1l1AUVFARB4ndq4PNlkMlF7E87jhW+lMewZ8UgrIPUhk+/zhu+9nxDRhWnTBPiGmGqkMljX5S9kWXGK0Nc1U03RDtbhpwFhNh72oVQQwJHxc2TCl7ZQF73omMsiN4syzuQQrHldehlZp8TJzOJ0/18JXtQQT7NVhQTkNAAWy101f/tFqyqpF1FC32wxGJ+xzHkySjWBk1QVhOVjKoSadLo5dKFJaHYHAvt30+wJg/tiykcDIHh20bst14lTufn5TFy2blpmjdq2Ks1p+UmjNdYWnWDWKrFyCKmcW6CIXMCeHsh0oE+rKgeWH5TeEf/+IL/kX9G/726Vt+dTrz6nLmF5czD+cFNthQSnFV5rnSnjd0q/Tbyu3tI2tro9hIMJTR084cXMfOXF0t1y+KyA5JNaFtBaQgJEvMTUryKi1bD7NWACb7wjBLHRjLRKXBWxLWXSCHcGEHe/FTjTR6MBbdvbPizwyt6WU4xr7X+gNvvjw5/o0DOAT1noYsC2RhfX5mu91IycBgTxaEouKBY9fqQMHASlub5wdG5Ipp1DlbgQLrct/RXp0veNi8C1PLa+svRtwHQ5aPxs3Ot4f42HnWx0vguc4oSZIX8+6INAtEaxG41A9f/MTKORP/9GVYxmIMBu592S91MhuqTkI+AgPngTtfQswcPQCLAt0bfd+Mp+YlkbJwvkITZQuCCHwVwnQsXsRBHGy77CvKiFwNfjAiS2MOfgBkgMVP0xtTdSO7hwm142bZmVufzMR+vjtxOhULwEkJK3wh3m5OjcukREqPpFRZyr+nJphCNw3GOhjtdGNHF2OeU4TnOIQjoMZyYPZ2GZP/ZaDU6d8q1ggzxjAW+OVC7dvEyKWRYTsHM2EqjiJcSCcXkvZVGc+VGLvf85C4P01tPoozbpo/29oMJHaPqkg6aM874tp9jR8LaIsk0wQiXp/Vq4yMJTouyDiyM4Oz1I5iznWPLhTwAKHOIso57dGIs2nQ+bHVePVtyoilzmH5UsG4zPopFC/jdIroL4TRPHZJ3HHmi8uFt6eF14uZZi/Zev/R7Bj0oKvNmGqtjepd63vv9AGKHIANvrWn4Wr8yzWsTx4O1yhsvYwhxFCF7tr0bnsdvOOw9Ps+z7fFaXJgnRkMvryRM87jcTAz9vTk/eOuOR5f3Wn646/o4fcuUMZfO4jwj7bWrAh2sojxCAVQDLBuPZ7vOxEB215eTXJyNrKfT0VHw9ydT4Qg3Mf90Vap7st2mN4+yZdLOoT7NHWzkOjQGgwzuQG2KVF3eASWucCb1+jA4uUFMRwuma2t81T8j7333fSEaXPifzpiHg5twmIk2r2OrQUyWQT4sZzenDT/0YOCTg9Dj/tYWJ/6/kZk6OBln5ILM2T5QXnk9BKBAW7XjpiTWhvaEqU0S4OJW0ZcSZ/Go1Pdq8THOsGL66dbKcXDxkGoPD1fabWZK00nLeTwv7BZW7jxtl15ehbu7+5J6TzmmaWwpIXarepGxnLxdIQbB2nvDmURyCnv69qNCRs9ONNKmH8x5YGcxWuwSe8cnKXzbgukuaahKs/PV1ISLpeTm13mLygTbvto7ZJYyyYLjc5mAtU9/CL8CfF6LlaFvuAliZIxipxDuzXz6OXV3WDkdd14ah9sXtFnkQPpHa6chfOZUS3f2hPt4njBzKJxLtKC+axW+8AyfVaTwskKTW9YTMa1gh2XYoE2Ek58c2rLyKeD+1S4A/7rf/Yf8V/8R3/JN28febqu5Cg3tVkWxrP7F9Z145vbjX/z/gN/eHxk83zKLmZa1JR2QMrUSmUCqQo+YeG4cUZhMvmmPuZpaqlDY+8gInNj1UfC8kHgCbJ4RJ+Yr7eUvSde7da7Nzlt9Jz9GJs21cPE/JHANYb23G6Wd+pAL7T7gdZ1L71+WJCQ+LPPIy4vwLy/7gXmFEvcH4nZ3fmd7nzPVzJKepkFZosVskpE43x6gNX4fucoKSSobQwt9nXvoLR/VgTPKZapCP/udiCweQ8XhmuMQ0L5M3pFt2qJ/m2KXI+0IWf0A2D5gZM8m/bC38nIk95j6kxgReqGzqZ736qCz2FYCvxpakFrhRO739Am1nujBpCIftYOaCQ6nMh4cV/mpOx+oZ2/xl7cPZw5nxfqprQK19tm9WO96MbeWcRH0wfs/IEr9mT/TlRGSjmRs/Ud1aoeK2M9SstzpkiarDtiYKXD07ZaU+1aTavXH+DN0/Xj0aEjJJxxdnqz0l7aAhVzdKcMzqt7rombHXrbvIzPjq7EmUKSZK2YZqg8XSEKd9/Mjlq7gPii2Fme3xdnRsFAdgv7MAcwPkaYncbj1VrHdBFqqR4+7YE/E9OQ+NJBy7C/zSjgzvxpbnFw1BEpGhGY00ERH/Eh75BDMr529fJP+0ZpRCnGxKbnGkiQnSGXzJKzNQMVLyYgO6qPW8ctInCkx2GNM+pnMYvRhLE3D7AKQSCmHQbTtYr7iS9fPdAw87HiPce6mcdXUasVi/L97cb3t5X3W+Wpzr7AYA4vmP9MRBN/IeEWpBcIKPwhh6K0/uXphrs+Fjecfo/3/Dj43FMKOkyj88ZMw8k1qx70mlI4rgiuGFv6cmi19XGWQq7taP6lZvpCCB7m71ONdY2XYiLjrOtoBhBzP0Q7D+Ldhe4cJRvCIYJSRmCd9jHwXXuWTzOzjzZ3EoRBry4U50VTYkJMk7PPpBBYMOrR9uBXPq/jOCazoPoNd5ayP1HCmDi96dosh6G4YPKrd6VP+Yc7UNgtZBIOVjW6lUkEW2xPZ2+e+xIAsf8WIEWZtBjnznNLySynYvcXJVWzDMR6HrT3saLHHXq5etOsxtpFgYIRcY/7HwXYxBN/mtO6388b92xtG2lzuyHhh8Uw/IQQfH/7ME0mkbF0hx49QyZf71CQXjwvWsjoSAyu1LqxbdsosGqNHxdPOvb7aXaG6UnAbrpMng81C6BgpFmS96LzzYgebAFs3O+QIuy69wOzDDCcspha7WG2622ld+Xx6ZGUEvd3d1ZD73QyuhE82tO2S9c91F5HwquMgxB7p+C+LN0jDp+FnoTlzsyVMcvmEVDX6zOIcHe5c802mx/y7oKsG7o9GyPF+5qBOf5TZtRQyAVy4ZwLl1T41a/f8IvXD1zefAZ3d3wokKm8xvuDRjPazQ9jNFOMhPMQaneQN+GygRHIRtZE0kxWE4hSBFnEo0TFSse5hgHwu1+84cvXD/zd377l8fHGH6gsovxqW6ht4//1r/8Nf3x65Ot145mIyp1QmAebNKYDAoezl0kUoKVutTad049S2QGSJ/++gnfjnu/1UpjszGoIwKDRuJ0RWHDo0cU8+xSuXdl6N1pVLERwYqyjQXJEZfvr/WrfWZZIbzDfbJaozjIz2eNYdwHv9Wg1Q01s2TrZLlhBaANGCZW5FJUCleicbmkUi5ncl7ObEzejGS+p1XElU4S8WAHuyH5db8LalZYsWTwPf5O4xWk3zQ/MMS96vNbtO2aGefmG/dnVyhQmB/vnU+Fc9gjX2m/U22YavwJpL9TxkrnP8iXG2b2xccrH+IWQlV2w2qTTPAaYc6F168p1BJwEHQnns7WvqljBiZTidBdEEkux6NBtM0Fa19WVmmn/w04omI9a1NseeaESWRAp3F0y53PmdDpR0pl8rpxOjaoGztfnCOSyYadd9TwazSZS+zQQ210ZLZQlsUAcyQb09Qr1NtFu7cNkrwI1W7R3NID+aLM+cf2oEGy6N53yTDHLCRxjt6M+wIy82OjD7ioRrWdaYbcK/Z6rIKNO6LQxQ/Njr84S1VpeTCy0mpSSJ/AbEZmpkenkYVrAiIBi10zHoh2xQ1dT9bdmmmBdKpliybYyRW5p6EfV12UquRRRoQMhc1ws9WAAdZOYTp2+p/u3Zger9e45Ta45esLQAKOOXKMvoMxC2Bnvciqcl4XL/YW7hzuWuxPpslCTsOEh7KojIIoUwjtQu455KPKxdTFwnDrvjwoQLgDxPTPTqa343VJsHxFaN8G0obzbGuvW+Prpyp+fn9m6Z9d5ZN8BZ35K+E0vhdk8/GxOJS8GPwkenTD8J/yIB4Yox3+PT4RWs+tOh3FFCkRo6BFgEvv2gvXvf44xqqerHSccCsLxqR/P4TCcwaV1gEWcllS6B60NewrRadJ6DFpUr6RMOZ0spaYpWquvtQNQ2CPBB9jNI1fPytz5OjhwFKc9ebF4Rv+e0wf+QRkfCTAyf20P7LD7RQRqSsdyh133RP0fXK8X/x4RqbqPb7Y4Mb8904vzPI2D1EMbnWjROXFv5gdPfRqdA4uwNPTQvH1+AYDMejGNJxQPCT4f47WflAxLhOs2cihTdveGK0BmidGx3p9aIH3xmr78/85MDXyKmlEmrCR9j39Q2IWgX9YhQyf5Ax9L4uP1Ez7BWUJ3KlMnRvFvf+Qf+zTBdLp1Nt+2qdQZu1Byhj7iuTzCMzthR4PIiKoKBSQ0nZz29jlh7w9/RPck5ixW01Oy2e+02qJGl+WmrsGF726ebJwpVdZaKaqcitUP7O5rtHqN1ukBzOEehzgV6yWIM95Ry3asT0OpHjoEKoVQReb1VKw03fOtIjRST6BmSpMGUZO4CSxFkBJpHVaFIydYTsLpPvPmd5/xxS9f80X+jNf5wutfv+H+4YKeFm7JfHwjEjgBZ6Aq/dk6XTyrC0psDNqF3hJbVKmQziKd7DVC5S55ki9xV6aFBYQziYw9+ynyknriX7595sPtxtdb47mHeSRzWe6sTuH6/DGLD/AzkWworYiV50oorQvVD519PoS3jc9MUgPLMJiDzofN1ngOZh9mOAdvZo4I4GVtt3BN1HxYiVMulEWIWqpVvXXmcInZgRgh+9Gqyx+lGkLPqSvZuRqFcxw8tMMhFea9GHSpHZpQ14qkTioW0BVthUI8JAemtTcrcFzOnO/v+NVvvqKuK999/WfaVtlE6K2y1Qpthb7R9YzIXQQsW13OxZoqMyWLR3BSb41RslzdKIGDANx3FAJUFerm2phpsgPAEWAO4xFb43xKpLxMy5IObbFkWufjdYzTHZDJ85IHjw6eF6SfDLwvuYxvta4OJrzH4mhVzVjzjnJtG1kbF6zvoXrVmWUxTXbtGIPj+eVgXVi6DyO6+8Q4NcCBoGwIjdYubDWRcjWwmhOkzHIukIR6M791bS/hwpBC81J9/Dtk/IzvnMbHS07/PR/P4Lw+tpe+zPnFDX/k+nEhGOdiBC/O2hLj8NuBeTHRGd44M1J3/mqaF8q3fyp3Eug3CMNenLS/HeTtx3d3gAwGMIJb4rD2ZOXUxrj9v3DeO5KReZ4awtYd1J9c1/2wDv+e39fyovz1qDE5+0587IasE3iFfkNvx7nOq9a7V3MJ341Hdg5GqRxNdwPtycAS+ZRYLsXzEAWr8mpVPprXFIwho0MmDEt498Pi1Zf3cU5VPOwtR56Bb8bSxnf2iFxjZB2VPjoiNJQPtfO+Njad+zWKWwaUuYTWcEN9Yq+CzGTQihWQ3jWvfb0nEtjlHGn4TQjQNL4VD/b8vyEE9wcOV5RGnRgThNn35SNu8KnB++hk3tyJTg6jHwPX+c/DfaYPfvLZGszRzBT7UwYI2C0eCtZepyyUxVwckTAedpIRcNqVljutWa5elLWL8SUxs/bIAZQdWEj46NjP6j4b3XnV7MxW9vM+/rZvGK3rR2zsqPnHodoJbKcXnb90/O7L3xH3E/swtm+3AIB9Jmm4VOLr+9/d12Af4e4mssIL7iohkUrehTJqwVvTns15tsF/FCZ3TsxqOsejtud4+sexCLFsLwThUQJMCy72x8Hcrs4/JrqTOSDlU7fSfU1/6vrxPMGzmRj65qPuyQ5B1mOwVkwqtMIphDjKM2sV+qpofqEuE8IuygaNyo8ueJggGOOhzf/sWKHvOZYssF5tG+vNEHU43cvWvTePUPtq/fkqDo19mweatA0oYn7CJIuVd1qsVmIMPy9iWlDdN9sEfmM0Xd2aQ3rPD7ROsd6p2coyqarVW1VlrcmiBSeXxh71r9C2HQc1rCRTPUbQaVdq7eTs3SDGineEOo7PdV2R2snffsbtEZZfK3J3xKDiq9hFuWYL9dHwu3T3W9GMNs6EHZOelC1bw9nsoGC2ZUf5qRCEmja6bigrsI55b11Zw/yvvqMaiD5BXsxMF/G2odVNvD7IKEepKrFZ9aqWapIFkjX9/ZQQTWL+ltFdqW3x9BcfnbUtHTQxaEwtW7QhVA96OC8LS1G2rdFqZ2vd24MF44UQsAfGPBjZDijsZQcE2HpbUFZ8P9bEOKDIfgZlumebPmZ7tX8dPBoQ0BHQZr7Ah/sHLncXRKz819oaW6tc15Wm3XqJeoupRCXxSO9n+/Ew2YyVJ+xiFWMM2FhnC0VJzVa5yYvkbx/gCLDR5AFHHnA1CEInYIppNHXjdruNO7U664F4D85uVpfkkVXMtIxblIJ87PupvdgdJ0QrBywHLjx4X07Wz1Cs8Ed3oR6xAyJWc1dLRkuieRGNMA3XnKzBfTmTdeHSTiS1R2115cPj+/2ePrqu7GXdBFQMFFuBbg6t7Y4DFou1SIL0WVEIfVsGODiCr5fgMT4iYSA5GIwMS+2togCLwktYaHWgbmC0rfn3FYIpJ/d0Jk8wd0VfgnHFEQ8JLbvE1sBJLuDUgz+6Wj+8GN6L8zxfs5/OUEYg5+kz0y10Wsf9+zp+t9aw3l22GdEIk2Ge3ZmNTneyyFO153uOlOREytkbBqepYkLkzDHtq92vd5BknEQs92Hf4DgmXrcxDqimqKNoH4wU7UDnEH6DZtVRXiDX8Hd2THPsXm2ht8bt+crju0dYL9zaCUk3tlZ49WytXfoSfj5DXEPhS+63COb7YgPS8PNiltJkrZS6hv+FHcmNfTLaiTY719Z59ECcpngtQLUyXiUbWAiqEevUrl6LzhKTp/H6eiXYIzQDVElCkq1N6n6/pPshPBzOQJ+OMZPs/xxkaackohPHsY/baOTzxX7aB1prJP/dwmLiezwsEzqN5xPXgJezJWOHlz4GGW6E+PgPCcHdFSNjTkz3Hc+a59Y721ZJeeP56Yn1dvO6kp7KosGs7Pm9d4sEpoImixdwv0/wg/AXHzwvSaAnrEuNhO49VmL/5Yz+wDZ2HjWz4+5+/3jRrEgz054Xe9r4T+4DkzbyqQARJ45IjfCFP7Ch6Ro+z0F7nvvsZzDAajQ9COGWvCk2qRgAFDuP5Xa1OIcePR7D4S+7LhPOEE+TGd7fWL4DfwteeVyeob06iNlTCeYFkePe7WQ8MfjpGTHD+YDJi3vq/M8fF4Q/KgTPpYR/0iKMaAMp7HTcgWpMkTQO2mEggneS7Eg9uyCwD8i+kv7bPQ09/A4Mm34c1U9dOv+hRwygfp+tbtRaSc3QzW56sU/txNsHsYGYKUEYbWskW8Hg093Fe/Ul0E5fYNs22nVHkJGTYxq99XzLqaM1g2SrTTa0NGEvgohrWlHBFV+PZI05a4QsdrR16lYP2rkrOYA1Wu1ikXAtwekGuVR+/2+v/OHf/YGivybLa377a+Wz13d0/R1fPjzwq18Ll7PsZjt3iKRTNqc9aTdzu/1WRDmHADyZUNec6QluNBZNiFrd+jD72FBDKJgZ9PdPlX/9tvqcladtsxZLD3f0lujfr96U1SLjLueTqcSrr4n7jTZs3r25XykDWeju0DcQ26xbfTWg0iVQtzGoKA7Yu/uXtOwBPi6EVT1NgcnS9YJCzZ9rYCzmjZvCb62xrQFS9kCNXfB83Lh3vvf+Zx/ms/ntGUiBjDRJO1/HdI24wvWiRKIo7OZA27NZSe21sqL86es/k1NiyabBrJtZN1qLji8EcVOrpcMIG8KjgRzPXTUlwMoWxqSbNDOH52IVd7YK2ue2mtOa7AJ6KHXh0phZlJh/udbK49OH4Rtute4f3CHX0SodaCIAOtOCOC/zntsW/hWRJf5a78p6bQ6md8FymEZc4vPGe5dKomvyhgH24a1FzS3/uFhnj1QuBuAXIdWVzyRxva3cHj+Ms7vvL85ArOePsqAsNJKxlKmIAApdG+3QIBlkosEphZOPXoyoGuLZymhR5i+JgrifNILrhiE4Dtuc6HxYuB8XgPBTmmBEgEXlsgk1mpPZUhyOEWnGEXY60yH9bVJ7RREYZ+F4DZSh466p64F45olq767lOe4dJZf274dWaYEvad9s5SOUNj3aPu/QOZkaQU6ZnCLIwxKfUSGxoCosi9LE8lXmZYl/qCNApJO8OJ1FV+pAdvv6OOJxpJMiGZy9hVQ/ELFvDxE5ql7XcSeqWjvbKmxiTDvzTJLE+8cEuvHh1RsukmjtjFVfMBZuCyyuPTC0Ka1egqxb2LX4D8LwKZipRonghJmH6GFt7P2tVm51HcvXPR8plUIS6085NM+cyMtiY02K1kq7riCdLLprbAPYyjDfRF5d0KIx/j3yVUbIuI491DC1xajDx+TEHMGqfRCSWwJcK5/pbCa93uM+eqDBgZwnepbpZXthFoQf3/vlEQuBlwghEPs69L7xzf4DCccWwLWbIwWgK3XbrIKP5/9FEfOPOpv73/GftfnqDvB3gHHIx/U6qrE+4hphGDrnuc6z2BdiHOyxQkIaUaGH9K/QxidNdN9Nf30sjA6f4vBtzoKTXVuNVt4xnt1P+fG+7/7LsQKWFsSRF85LOnJYLSQbklutcqIs3kT5dKYgnKoVA6heyIBgN+K9JFP2WAiZ7mwPrLW5hh8CUQfwtqsf12sm2sPGOG1LWBsmGBm0F/JRpziF+Uzo8Wfmtz91/bhPME8HTUBaEGCyCMhombGHPxGpFEexHOq6RZBJLDiMlItxvMekdkISEXpNpKhsP4IMFLo319mU7EWuW7WKAbvPQkeFGAWk971xJ1P4O3x8aOJ1MdNCSplLOZGKWNhwEiusDeRy4nTqnC8PXJ8f+dC/d9S7b0QwQgGSdHLPozoKwIoMzUPx2qvSHflZx2ih0fpmwh/bAy98iJlap5mIpYxIA81m0rjeOlsVB91C4juEt7De866ceC2Zfn3D9quv4HSxpuSm7PoB9NsvWIhyXU0A6uZvpOjJ5CtbCVyvodHIzgoEIFl+T9dK08a6feB6/T4WH/I9khKn8z3SF57Pz0it1pmpJO5f3Q1mWa/PPNevCd9/Uxt/VFOQHO1YCqUU6mZ90ATPSeo6SmmhRi/h6wFFe522VBnWCx2dMU1Dbjs1Df7wKdqa/j7gyfn1Lsdn+rIMv+fBZy6MQO4XoDH4p7F+OXwgygOWtJClEF7BdfNarOjhG8mtKdXPVgJElX5bzes8mPXEyDB6HjI7uJa4u0O7PSwqykxajc05IrEtRjUlq6UrERg2VnSaF3vwRmgvQz0D83ktJ3cpeL1Ur/s9CFT2Kk87r9rBkGJptN1dD3EW5zHjeXgnjuFU4Hwh+GgQbVgFZsJxAeXV6/bzM3ybfR+rCOQzKWeWy4mSE/enzNYWuiSkVsrdK65Pj7z77jujMS/HKAKnpXApZbR62qnIpvf0dOX6fDM/brMxqzkWXbD7aYjSLmkKbkRemExiMkHMHYkgQX+eFTm3keTu/VkxgFrdijPkbZTb/PcVgkO4dxd0Y0Qyw6L5G75Ys3pnzpXR2kh1mDrtnzPS2YXgDw1+1+7U7elBcO5vIiqOq6OsPRj6heUZmFHT8ZGhrSBR7T25EExeRV921MGOVuPA5ZxYlgVtnV7bwcwRMzB66eD5h6HmD4A5wcK9zJs7y0tBmngwTB/z3AGQmmbpFeQtZHxfYu07cYa5s3Wrx/n+6T2nAo+3L3muJ86etB91VuXF/YIQDgCMXdPoGIL/OAepj3XcNQJ7/1zg/iRsnqC+nCCVzOe/eMPWG+vTM9u2gmCHe7mYH7A3tpxpOVsybzctNMfAXetJXszZctGyd7jokGwt00TfqtlHOu3jRIfaw+80zU8P2zf9vYOIl762F7f+keM7EetBQkw3mLBKjGV2oRx2KdaFnY5314AG//YvhNlUxouDbufxy7wAIYHkxevzQPbPRYqD0ajumqAzwJ1fvJzJEcK+nOvLy4CoUFKmpGKgUxLbtlFbdfCMCwWZ1mpevOkKYn85tST7zAYKCQAYeym7tUHZrTzdg1XCbzuWwu/jwnOOSXRMcbSEeVCPpdWZoMy5sJyz0Xoz32zdGNabpQi5pBGcp2JRub11S+WofVgvZt6th7850OM4A7MwnLfPN81S1PLHaxw2/DRtgniQ0qF02XjYT14/nizvG7BtdS+Gu/MvczCNf8SgXlCBS4roy2dNntpY1Mi9nyTsJwl3HGbfTHMrdPpmWmVCLJKs92msbrKdBN180I83Z0J1k8BJQjmdvJVSHmYjhT0AZr7UNKKShYe7e7Zt5cYNbdEjjukZSq9W8/FcLqScwx0xhmUmZ0HbfmRSSpzv7qzO3mO3Ls66D0amsaioo968R1upTp+0F8O53nrnD1//gXfvv+Hv/8lvKHcXfrOcOGeJ1M0xf53yEg/PHd6cXRD6pA/AJPxMgmm8iuVIdYUvHgpffX7i+0fz93zxmXD3cOKr//x/z4bwvF55fHwEgUUSvygF6Y1WP3CrhVTO3Kh0tXy+NAhAsPDOTNIEXch6NiGZb8DmQQgvRHr0+Zt8c+a7UHN3M0+yH8xo9vKRSVuBgF0XG8DgxbnV6eeT1yHKZXo5Fn3WImJKH2X0ByPxr6iiNGOWaoyYjtf5tECwIdfivOo+yh1whjSf6O2wrP6dkKliXWMk8k4VdxF09vCY47qMK5rR9U8dymkI+0jMPJgL5+XEfT4Pt8+72zuetz2/Lk8CMATii3B0UGvOIgp92wcnYvm6ox7neGsXjIdxBl9wDTA6WeDgO6mnBblRlYbVzR2ElA48Kibfto3eoPXdpHkqC6/O9/AK+OWveX5e+fDhiaad1hs5reS0EbmJYOS0Xje2tVLXbss9WP7R3P/JTZi1jdDGZfpBPCc2O2kYZxoUpBaBuqfEGaFb/E9HI5NhWumfun5UCNbbappgM0ZMN3+URvmo8agf2M7xmRBEZlbqwWAcVQQCsnnpC+L2O4eTuccdXQhGoA72/e65cvac8AvqFAQRjiy/vy/8DE6OUUgy7dHkKVGLsFS1pHVL5xCf6x6wkXKmlELzmPDZbD7zg67N+2PF0DxV1MeZJA9T1jDviJn0mpsJgp/ga4GjQUOF1uetN0sPSEm43J043Z1Y1+YFAxJN4aodauXf/fFrat148x//ltNy3nmtJ9hGKTspiwv/TFL3eQyBqSOa2AY4M+AwI4aZuPN2Ux63ztqPH1UHTTlnUjnxu69+x7qtnJZMao307gOtbVyrma1vz8/ItlHFChO0+dlRXSfWyH3NqoJoYvS/9D3q0YlEZUQihm9iaDXB739IZE2+x0FrvpfDZ64DJwwKHADxhVAdGsHsm/zoPfdjd7XC6j6GwYcmHmTPEEJbU52A9cykJiA0D+bw0rA9+npMTGnQZXw+fJMSFhYDB+N86H4f46ETWNBdC5keeQQkH+EKdWuOFe8ePq8Xa3n4lvtJzZUsw2ozAwH7jPsFU/B35wkp+nvK2Nu5qM1hdDqdBwfy8zWaAwTZjOAbZ4yjg41A6vTlBiSrEqVC0oVSCpf7O0554f50AXWLlCRqa2xb5bZN+Z3NrYHYGd22yrZVP7OfmMTY1yk3dRDIPJ+daY2uGCp7vqzuNKIuDg/t72YQgp+ltPO83V/w49ePCsH1/SMgaMooCWSxjamT/foHrnF4CSHV6CSazgRsCEV79wrrYZLhsHgKhLm/joRwJ5YIZhixLib89hR5P7vBED4+wePfL9aUKNG2fzSo3vxCW3sGOjeCY5u2eF4KKRVSWVhc+Na0sW27Azk0qBhfbduMnWks4OY8kB1NZjuMRUCzIKeTVRWR3Wc0zOpjPgra3GaeSCdhWTJvfvma11++5rvvH3l8XulXZa0WYPm8Nf75//Av+cXDhX/6uy94/fpu4uEdaeZnNFR6Z0UBOpgKXl1b72hqtNEFOyDFjvRjwXszH+6/ew9/fFY+bEf62vACRcDlfOG/+i/+S5aS+eLNA7cPj/z1v/gfeF6f+b4/cX36gPaN220jv785LBneuhH9t5k3edBJItNkp8+muzA0JpANnUfk8vT+p68Z5hyF+uSSZgwpmB87uA4Z+/IyIDQDih2gCbbEKSXKstBbo10/pZ36P12iSzDraV9EDXBpxnypoQVNV5LoG/eJ+QeDdUGlQBrRjGIBTpOrIbt+3B3ECpgvyE90CIBIGBUwn2DwiskQZds4Edq0QLkUDxYplvP3I5flljIC4Za8TN0pQK1nw8Rv3Df8Uj7oiz816G7/rHZTZiMyef+OTTAn9pxfVXO3qHeSD4ECCMYrtqWRe0IlkykUCpfLmb/87VeUUjidFnptrNfKyWshPz1faa1jvnzoq9KuysZKo/F8XT0gxobRw0QZLg+xFKU0SzP2AKh5Pog4rnK+4ILyYLWLiLaBwqbz46sZtZp6Pr7+D7l+whx6OA1E8tQo3js+ubPvCfTtxCi476+NsHZg19ZQkncNTgMiyzifJs91fGenoBdCc3p+RFoNcw2BECZN8CWRxsLrLhQFvJWN0qpjZm2oNieUiIgESWambc40LHLOBKSkRC7Zo8/2UHFjDDE3N4Eg7lyPeoiWmCyiVt1FsD6AbrYy0opk6EBW+yYMH5MoXRqnhwdef/HA6X4hLXC6s6Ld1RF3uFlvipUoCxThCxe0nYoRaenJK+PYanc1p7wgdBn1e+zemK8n6oMG1VzXldu68fS+cn3uPG0r196slyCw3Ro5V7bbyulS+eyzL7i7XPiLX7xBX3/Oq1vndnvm7dN3PD2+5+vlzPfvP/B3+ieqqoGnjhUQaI1a667Z+bwjuU8xM3LyVIu95ySH07e3EptOggz861reLJpe/Bz2fgdIOr7t74kcDn0IOft7tnvuaQ5Ra9deS6Rc3Me1S4kXWMmCgaJJ4EChMNeZ1MNbO5OWabzxvSGAdKcAo4ndWmB5t5mcsgFHn1H3lmB21F8wUN05jllxPbdN8Uhg7zzuiEdjDj74ABCh3cXStmZnOgLqxrq49lVOmVISSynehFedh5WRy6moCSp/XGxNnCtiD8T2ZWdgzvOkmbY7NsZuYl1HXIMdbhvx/7+Qrr43quLWpY42oWtjY+X69MTb777h7v6e8/lLVG3utVZa3Wh1pbUbVv3D3GFrrVS2PebigP12Zq8yh7uFLzNOeVST8S+PYgP7Z47qCNM9OPiGg0QPnp0JCDLcGXK83SeuHzeHelkWwTYmbPUxoZj+PtwJdb0Qwup+ktZWes8fqflNZaBOt+PgXALFqorv91Vi80c49xRdcCiLdBDV0zNnphJls/zfwzziP61Z9/bm0XKtrYfxmwBUs0trY1UldaVM7CzlhOSFVqv1x4LRKFann96MD/VDZQQhfFX2PCH1qIW4ut8k9KQ+TCLm5av2uQ5IR7Py8OWFX/2jX1ifwAx3CZYK126Fs7fVPv9M4izZXL+RJmO3sd9nyE24aPYCCJbDVdkb3A4Gg5tTeggbO9CG1uHd45V37z/w7psnPjxtvG0rz1Ox9uvjRq83bk+PXO4uvPniDW9ef8Y/++1XXFIh/dU/oz4/8+6Pf8eHD2/5u6/+ln/3h9/Tt8raOmtTs2Bsnef1mUd9oqlSe6aLaX0iiSqNKo2mnSTW9cBUUIU6HUL1wkl9KuM23g5htAOuYA3H+jJDZ0Dd5zjfak+px/Y0/h5n+yjG7OiY9WIUBQADYKcz2ip9XcczEuw9TIOjRNvBSfDvWmcI7iNfib/zPsKgRH+W/xVgA0FdAESUbpaISPV7qnr7LAaN2DrtD0zBJti5AeKgdZT92QGmMVT3IXlupKWI2HvbVrneVqttOmGXlK3bxeVu4XRaKN4lY1WjE6lTcBTKeR6zMtxH1YVzr07/5WP2a/1CdffpeqjmgnVbkbmEFGpFPHRyzM+XqkVvO3Duoty2R+rtmfX5LV9++QvefPEFrXdu68Z6u7Jen1hvz2zbI7p1qMrzduNWV5o6JO3CkQJit/e92Kl3v472AuMXtmFxJvQAMuezEMW7X5Zn2jX/AYvsRyKA5RCC+snrJ6NDgQjw9FRJ2QWM+w72aitHwTBxhTEpy13r03IccW9XM8lEaxlmYHpYGD0ucxDcEII6xjVwSEqItGnRbeH2SjehfQaqcWEY9/WmVRHEEWpqEpghsmI9wFptvoGx/fadsTSRv+ZrHZp/AvYuyTtTGUNOLt5790Rw3fdaA/0HshTCwVNOiXJKpEXp+YqKVZPoq6JXF8DgyeJKS5kq8ETlUTfuvUmuO1eNg3awOhJ+EKI0l7MlVR3anE7o2FC0e0q78n3r/LnCd7Xzvm5cn55Yt43Vm4ImyZz7RutPlPyaX3954c1nd5we3PSsQr4X7k6/RG4PfPXLO/jVG97lzPV65fr0gfX5yu3DB959yCzJehfWqlx146bVaogmSGrmuK0qrVtZPlBysv1QFXoCDRO+77OO5HkZmxXI9+gfj1yvIImAC8fDKr7WO18wn9n+Adm1VEKZmO6LCRM1FDlcGBN/Pz4rvuiAMJU0ikpo7OGgxh2zDx/1mGF8JA3BrEEbQfxxHDzCW1NzLSKBhv914ioDoeKCKebvL4eArUaNksQZ5L6m0bc0xfzEgl5QSzXqXigwJchLGp85XTLLKZOKp/74GMQtGnh9i96Dw320ELaszYvH57wfdlW0t8FPrP5vaIri5DRHJ8Ra+AZ7Y4HcfP4OgEv2QJpi/1a/t6qiyczKT883vv3Tn6m18/S0cn1+4vlqUdfaGq2HqbWPbhozArK9tfOg0XRUI5phYvoTQR7F4iDSMefx8cG/dgqdKH36v5qA1/l1P0/im/TiqS+vHxeCccseD7MAEDQNfrvXNnwhbeeT9tKJPLScCan5K52MaCL1zuwAPdw90KiAesCC0ZC4b2U3LYUkNZNsG/fZj5g48X3MhA5+Dt2DeNqIiJgOWDJ051ZOy3fqjvtT8rvFWk2AJwjKgUtUOWprRLvNe4C3dRcLAOrdBFhn75TeZScgz8txscz5vHB3VygXpZZHRM5AoT939L1Fu1ZM49WkkApbUt7rxjtduXjsnsbhHKb60YmPHf/vPrgwG83nwhpOKWvvbL3zx9r5+w2+3iof1o3nt++oz1fed6U6Q71vd7T+nlI+4x/99p43nz9QlgWrvFPIcubhq3vuUd7QefP1W+5++Tue333H+z//nsfvvuHtH3/Pn79dWIDtpqzXzlt9MgaWofbG0qwizmNvdFWKNhJQPUAmaKstBXonucZihhLheKyO5yNgmQGvuFe8faRBY8Ly4gzvmoDEP+PzQVsM3DPAklZnri/hpHC4/8BZYmk4qWSgonRWL3/7khumF0fHpuJ0qGGRAJXG/DANM6HTjyQl6YL0dDDy7ozR1lgSyOk47ux81GsbkqpYgIqvbcypiOWX+a0omL9x0+4R1p2UoaRESUJOwvm+cLoUq1YyuadSaDzFtfyKBSFN4x3KSIesgmo2UOD73d08qr25RudfjVZjyAvlZ+ZROixmWX3TSyFl4XRKkwAx36HxLhDPJfzw/pHfb39LV2FrsG43brcntLYhBMOVsMdnDjt8xL542yawCBrzzx2EHzONT3sZdKTeUm82Ybrz93AbXCmQYf9DJAB3UGTYGuRlQPYPXj/RSslNDJMkCgNXHDeRo8lxH/MRHe7CJyY6CT89ftPIMrE/bZqLf9UUHI/IcskfiPrIWKYvfeIan3WThUxwRRxFxIznih7znYHh0BbFc878s0ksGMgH1Od5jy8H6tkRcinJtLwN9ggsJ4Ig+GkM2eyrgzglCtrmHQGfT4XzyYt/C6hHtY5IEbfFNg8jSWIH4F//zbdcHzdOv/sVD6eFuwBYNaPViluLxvwaDfMlWELBsR9bNwxlvlOU75+Ud8/wfDs5dVgj57Urt96p3ZLUkwBduePMPSfK9g2pNljuzcSV9vqMxuCEu1dnvvrHv6SuD9z+8nPWp2eu7594fLry4fFK2xp9azxvN9a68u33H/jwdOXx+7fcnp/5mz/+ifePj7z78JZ1u9FEqVLRZCBHuiHgfnIKcSHYNR0sEbPcibgby2n0g/+C08v0W4wQkWLcPyL/hhlo1gqGr0h9rXe6e0n9EmQ3kaGdZ+Fyf8dyOZOKlRdct2dar1YAXXbCVQ9j1xauitkBMQvZCHrb6XiIceHwDVUr7h4exF2DtJ/oMDYsHT4DlUi2d0tN3tOlIpYnpUQuFtQSLWhtGLZGqWAAryekQVmEUoR8ylYyMWHWr6RWCnH4G+02lqVhwFUVmrgPNJLGkWk3fN9HjsFMBLLjyGTMbo9ynxGL+FqqF3VXb8klENHk4MIvuq173WLbrlFrNCcoWdhKMR95MyVERK2AdwStjCiefRzJA3hG95had9P+fATm6c3erV2CjQ+J+2V9dhF383HRsFiHT3STsPmnT743Xz8pBGPmOyN7sQlRxsifc3D1HSTXdNQkzEOfEp72WtNdpY61l8FBCEvkdFimFf4p8f/y7R7CcCfqkEdjXPpibtPNYipN8abh6qG8TgNTwfF5jsfxOML3sWUgN1grHIvv7UJ5vCJQkuyvTJpaysKSDdWeT4XLqZDzjrO7Vde0RegrNDdRirAkpfbOv/q3X/Ptt0/8xS9fISfzeSQV2BLaZiFocZjKOpjhlFXnawE9WdH3TeGbR/jjd/DMCTgB36LArSvPtTMaOmhDunIvdzzIibx9Tdo2uPzOUYAn3k8r9fD6wsPr3+B5G6AL2pax50k3QxnXBrfKv/3rr/n667f88W/+Le++/ZYl/yv++PU39KvyuH2gp+aBNN0jOZ0pnvK0t1Y5ZKD72L5JFibCfHMUgHOGrMCgIQGkZKTk3c1YcVMiuzSb1znobIqmHjcex+h4ZqwgROLycM/9Zw/jK/2p0rZO7p2khvIUQWseZkD1mwYUmi+JIvBECSrdZzv5Ge18NadLK4SmsVluqqbgDD+0gex/O5POli7Qa0e8c4SouhAspHIafsADG3ABm4sg1SJVlzthOSdySW52NkHcrS0Fffhs/K1sE8k93AqfCiL03YnKOAcBGJJgSK8Bese7qp6KJbvskDB74sdfENkTzRWljeTF3RrWgdotkGdJQs8WTVy7osnOm4glrWSc9l50khisSxU0Qzfrjk4N2T9i87EE7OSgvgehXpqBKVxXnrbkXz8EgO6LMG4UOE0GP/1xefATQjB51f9Jqs/3OwiyFzP9KLPfBiNMznj2ewcymM2E8+2jiWbMV6b57WbGo+TaqzzEC7u/TF7OYxLk9prOvz4NAvydoYB7fllr0JNHfzmjS9n8AQMN6nFdTBNM01iBDOfs52XzoCQ3cSZHSOSOdDWT3FhcW5wkQkZ49frEl7+4o5TMsmTya8gnt+WrcBOL+6psrLoXbYNG05Xvn58hCX/750du187l/syZzKmbMBzMNHKVvEB2iECFPUpUFW3K2yflu2flw9Wo9lKEU1ba6zvucuNPX3/LU4sAISXlExn4Fd/y6y6UxwUWhVdfA3ekfu/c8eQ7Yode8MPpR8cs064a9ISkgmWjCF989SXnz17x5pevuT498eY3X/H9N9/xz//5/5s//ukP/N23wtPt5si6mTagav5BlOqaUJZOT90iUoPBTUl3Y5vU6nIeG9wGPcRnd3/QTu/+kYDFqmj/mC4dhUwaqVOw38heTeM8paKk0jmflfvLzoifb2qSPXl0X/Jk7ZPVzF3dpNd8PfbqZYIc5jAfHxkMe5wjtTQqs+YwzKUqOzyYK4sh1hbLojz9EwlUEzmd6K2zrnvt25SSlwAzQd1lj60GyCWRc6YtZvVJi5rcTjqYlmKxEeIKwM6EHZiop9Z0S4PS0Ey70jf3sTXzoSfsOa3XSYCN3RsgRlvsm5JSNtP90ISDj+7AyUjLU2K8jnLvbVgnSok5B49TP2U7L4/vKwZici6uAU/751fJ5mfF4xO2bbPNm9LohmfqJRwYxLy/FabgPRrmKHhtm52Pq3vZxVS1Nur8yqCbHxeBPyEExeN5ItpyzOZwY93/8ZHEl0lK7+glyHBKyTp+V4x9DZ1H5y2zP0a+0CcE1/4kl3tDyAhRszIOzaFU0XT/wzQ+MbX53e4EPebSMAST3BwDFHdm2L1shCEIbXli4324Ob5n67g+pwHqx/xEkNxAuldZj5vZFTzy1asTX/3u9S5oT8DCKMlUJVvOnD6zTnNVr7jy9nqlIvzdnx+pN+UrXawIAMFXhWGyUkzoCEREVRxoiw61v999UP7++z52eVlMMKd2x12BnjJPHaIw8jmZb+RXfMevu5KfHmwe/c+IvjINBfNxmhn2Ns3EwmCH1WS3SQIJloIsyhd3d7zRgva/pDfln/yjv+Lxu+959+e/5aQrbx9XWsu0vlrN1rZ52okdxO51DEuyMPua+h4Nu2OTIY9GiasdRkzXflJmQ2K8ZTSjIQMJoUbcL5D4IN4DOpyo11NukgnBfFLOJ+X+HMLTup7AVHfUqiRQzH7IVs3m2PGiGuNRE1c/nH3ZpzcEoS1Id5Pr3usu3BQxXuOtLstMU0mJEoA3Y3t6yjSvu9udycfxCgeK0mn7gSLnxOm80NQNndKJmrYDufv33SY7CUIdSl3z+qM9tHVPNq+bpRfUakXCSxFrJbWZHzKXERI61rBrAIA+llKc+c3BJzroxB0cIYB6HQ0Ghi9bpgV1vtU0ipj4q2HaDuB9iLLc5YHVM3brUjUT/DXJcM281IGPT53oRPc//cbj5+VbY9+xIKgU/AehTzmbL8sR/tD1k5pgEKfEhPZ1YGfJ8dvpIhjjGPpeMYHjp0H73k6J0IaskeTLCg6Kun/tU1doSOnw75eSUuYTNG5txHH4pMy/9nFPw+E4OhvhPC/1UGIrDyfWcPI4muk5R0b3Ev9IcrJxQbgpoA1aBT2SV0CIWOmmcGtmMi2jOfDCw/nCKS+8kpXblxv83cbpfeOKMY0lncipIHmhUkwTeoZXWXlzVv7JvXJGycNo+fEVTKvSqXTePjfePjXeXW0+eTmTysL5DHlR1meb8SKNk1h6R06J372+5zev71lkQ+sj/e339F5Jl39HPz/Aq0dkOSMPn9t61HUMSVNCU0FGVe974BWW9rPDkoPMECHdCWdd+K/+L/8lf/W/+Us++//+K/78zXf89b/5n3l8fOTbp7fU3obOqySLCjYgOhLxgwELxsCS5AH8Uu30plTECh4MOOxQJ3m1D69SMoqES98ZEToqBsEuvHD6Nx59FHzCLgCHmcsjDSNg5+m2sm6VtXZG2DJmVoxUjMASgmllXaHp7s+ffxLxrF02puQRmkmQnChZKAluV2Hb1NOpBIoiWUcrnZwjFeQQLzuulJMbfrJF+naM19RmeayaduFXEnmxQvZpaFh2wK2xrTn7Y28E03y0pz0XGDzBX10jxFMjQgDBHDQmYvtacmY5nUclmNY6tfVBH6jtp2B+SAme6SbC0cEh9ra4Zu+5I+IRdznLuKe5QzxcTtsQFt17jrXW2Fr3egQxZ/v/lNTjtAbbVq3maO8Wsd7MHhzBOFPout9pDtByykgSvqTh892BT5xTBx/j1DoNE+vgPFdtYH188sevnxaCokTwS6DYPfQ3rslUo7PcCQGYpiWcBYsnfxOHwg6hfFII4ojuWBtwlnF25mdPol07QsbRs76QGzoEyf6tF6bUAxbd/zwIwgNkjcWIhbHIsI/uEdP+ASFIjDk7EAmnuy0IqR8KV445BqsTMSG4NQ+dRhAWstxxf/qMh8sdd+crW7vx9P136LOJwA2lyEJOC5IKnczXb+H5Cp+/Vp668ruLkkU5aR1Pj6U8EKibiFYq3z9X/u77uqPacqacC8sJSnbHvypZGkuqIMKShV893PGbhwsLFeoV/fCe3ityPsHlnkYjXe7Id1bnVtepKAPQk3PflIEvQc6h0Ax6GWEdvp7pLJzSwn/yn/1vuf7VP2Ltwu9//0fWb7/ju554XJ+RtlE7g0l1tZ7xiDKMwsHwxUP009QjUje0e2K4JObkIdy3I1E3Mu31WNvY7BdYe0hxfztSBabc1wOFxa8hACMHAK7ryuP1ZvObTPUp5QE24zxb9ogxoOZheQcQFz8RkilmvkqSyAkvv5c4FTgt5i5rKmgTRJPRf1EHETKqtxwjJ/eHiYOGpWAVTRqod4mwLJ490MNaDGWyB8xgctBSpAJ8yu68HTVOxCK4J52V6Fij7jS2LjtzAf19c0Qgl2y1icViK9Z1pbUNwvrWY1v9/It6+yjjLc2bFRu5CJbT7YLc5QqId4U//h1jGyNzP3bv3bT7nQxf0M40E+1smws7rcxt7DR8xQPspxeAXaabJ4vOmZy1x+cdtC/2pYnUur4XSAh2rno4HT90/UQrpbzfTMM/46MTjyZ8MeKj3JoPg/3/SLRicw9/nlhtSGuiG8h3x3kKZD3ZAYowZZnOvaqnGqiPuR+YmnqIuLUn2lH1QQAOWtmlawhWlWnTFLMnqhyF3ydFWfwoL7c2BMfIXQzjPp/WfC0FJELV1RiUKktnR1H+5/mucPfZHa9eXzgXS5jmdOZ0d+J8PnG5T5xOpjTJJnz+i884LQX57nuua6XcfY6UOyS9GtpjVeEP38Dbt533366caNzpI/eL8uvPhSyZxCkWiHc35c+PSq3PrNt7bn3huZ948+rM6/sTl7uF5Zzp2mlNef/3f8+3f/gD7f07Fm18+fo1n53P/J+/XPjtQ+ZeG3pdef+HbyjLQvnzB1JeyOe/Q0tB7850orlobG/4OMQLCF9QLn7gEpQzpAWVe0hnlt/+BemzzzhnyHT06U+U6yP/5CvhNw+v+Mv0T3n88Mhf//lXfPfhmf/Pv/6aD7eVd/WR1hPUhEqlJB0hHqhjkxfAjmLot2Am+to7TdVrxQrplCxZ26P+wl+yXMy03p7DZ2TtnazU2K7dmnC2KNaXBSqGxSmlqAsGInTJNApedpxdLFpopgQIFptTdq3LAn4scGN2cQQ9pvBnJ/BkJiQpJGE5wXKGpRTKUsj3zuQ8Qft2bd581+M6R0EIu392C3y4HyKnXLCgpXy25r03TWY677rz3xeWoYBDE74+nttYtwC3be/xae/ZZquo14VIg2unbCUPSzHTa0rJXR/2XykGU+tWqduU0jVYiPtf3f0x+qbGXHpogg6qu637spwHv4t511q5Pj17lyDvDdutjnCtlhebUmIU5Y6xhIvDu1wEB550iR+44h5p/y0y8iZHl5pPrfn07HCxwGT1mLTmT+Wt/9j14011Uz46qGOxBz8/xGkffs3GkNBIZqK1r7lhRvbPZUd6Mg59fsE4Qvr5wfIbmrVDveae5UT1EYI8L4ntlEylvWAisqMk3B8r4uaVGd8G8vtYsPlRPN4OPmJEu23f5xUCfJgbjlrr6Gjlwr4ppA6l++GbApHKKfPw+cUqXeRkha5PF8q5cL4UTufEchKS1fbm/tU9Sy48fniCTUnne2R5Bf0zhIIkQ+ffv4d3KF+zkancceWzu862wJIWFtnjt759Uv7mu069rdTrI+X8QLlc+KKcuH944O5OOZ3gaRVah+fvvuX93/89XStFlC/OZ375cMc/fVX46l4odKv68nbDxO0HEomFxaJOMx6fWsd+q3aLenXr8TBKSUJJ6OkVWu4gvUbSHed2pXz1Gz5/eMUpZ7i9Q9b3/OZzaK8u/OPlK9bnlV//8cIfvvvA3/7pisgTz7crVKFVa5WcpSLJE6EHsJYYlZ0RN3dG6I422+DkWmAq1vtQogGwE30+u3/5Jk7vwbr3vKk4LYgHJoV14kh+wxQlbhZVD4mPM7zD0GxmRDWNJCwh1iHG6DepjnJyEfATQlC87m0WGGzMn5mLcD6ZZpSXhdPFBJeBxMb772C9dtPgphGFoA82EUaJvf2RM9cFtqSsq2kjeVqHyKedtocjAw2ONjG4UFgEpAdYd8Y4Ujr8/STuFvb8xIQHqRWO9V8tCjMl8xXKZlGxQ0cIyulWQWtontNIR4CMf0m7Ab2ST147GcwXam3Tnp6frVRcrQMs7tWOsgt0EzxH75RM1ZKG6vUPFIKxjg5CR4BHgIt5Gz7mraPLyMQjB98cvPMfJgDhp8yh3kfuFNXE5+BUn/wwffVGq9tg4CMYLgRgSscJSkww6m4efR8py0FA/pRaK/Mtk9nrJVuUpjaLjtq34LDMxzkpe9TdyycMM4PZuFXwBqxzROVBmvr8AYloKzssYctP/rx00HrlhfY6a5CulUcUlNvda7NDb6HgSi5QlsL96zs+f/MZv/jlF5TzwnI5cf9w4u7uxKKW/P7+7ZX1Q6VWr+2Z30BWirxBuNDch7Wpp67IlYxwEkhsKN9T1867P8P5fObV55ClkDiPcadyx3L3S778rPDLNwtvPit8/krISUlYlZindeNd77wH3kjml1n4P35x4TevL3x+lygFzs+NdMOi9qSxcQMRrstp7I9FoO524z1Sr5v2Pm9REritqBRa/p6eTrz9n1baH95Q//Ff8vD6gXR7RHq1clYiyKvC6Qy/S1/yizev+Txf+Ptv3vF//+//FW+fr3xfnxBnQI0EstDTFHigts4N9Qhe9gLSZWGYZQULbJ0tRZeM5EReCqqQHjZ6he2dmQ7HKRFnF24ycSViIHdV98cVsYCkE7x+fc/D/YXz6WSpCMtC6opKM+HcjJnftBqTZjfrSkg6LBJalcnXb7yu5LFFLPnMuTxY3t9iPquchbxkUoF8gryArw73r4XTpXF7EqpH+4trX7uBJqSuGMBOyunk5y98jw+FWjvrrZkSggcFCSARMnNI3ByumsGDJgZs71tZyRQRpMWERlmcWbvWmYrvhdqOZA+Ua8EnFFQS2s2islEnPjBNLVmgnQI1SqaNoQXaMkvC5XKh5GLFsX1/at14fnpk2yz1xRoJN1d43IApSu2VVoUk5sO0JtxQW7TAO/JJ8Uhjk1EWLYyqGbkG0PcFDDQQPNVf/4h7hszwic+WQfuGWQo6s6/yHy4A4aeEoOf3hBP6RYYGhtLska1aNwgTgOHYVUairJsvhjl0NiEKlpg7hOWuOcYz4iu2MLp/MebtCxsEKwl6BOSoWl7bNO4dVx3uxEvf3GE5X4zLPmjISZ2xzZstLgAjEnVgdWU4chFI6i2qAvIdHr6r9ztP6WaaDvTTPUfxhV8z58T5cuJyf8f9w2vOl8zpkrl7uHC5OyErlrDXoN7MDGKKyJ3tGxcSJ3/NO7RjJaZwk5fKhnBDe+f6aIypf76R2PcxJ0iyQFl4dQ+/eA2v7hL3Z7GWfF3ptbJuK6t2tiS8IfEqJ/7RXeE3rxYuRciilE1JAjWZNtJY0SRW1lMsJYQOUj1Zqyp0f06YJn2RJJqFYj0pe36m5sKHr+H27i2fvV5IvCGLMcmTer7ZKVFK4Y08wH3j1/nELx8u/Lf/8m9pVflwuxL1GiH5E6O349BP6UFrycz/OZVB351mBztILUDzkpCSyK5t66nSxIJqxv2QcUbCl5eJiEsXUJhvKWUhLUI+C5f7hVef3e+yJGdSKfQuZnrwiL/erZqITGknO9PyxXJry1hr2eNuELzbygU5Kemy+59SEVLBG8vHaVROZ2PC26pmuegwCsazw0SNl7FYhlzmBHNhWWxBt02meCA7p0pkKOrOn3TIdj+OEXkoU3qmRaEOq5SbInPkR3YDuvm0d+kQ6+81zlY0A4iSenF2ZsEb7GU09Xbq6srBd8gYE5RsrZNK3nl3A1qt5k9szl+iEEiApAjCcRoU2amrdwv0Gz0cbeON33lQiKY02kkdNZ+dJj7OfI8V2P8VH2NWpGTfdXN5hU9SD9//h14/KgSLRxoldi0uJgzitSdtQCkn0pKmHlgRWGLMsORslUrsm+z5U75O457i6MnrdDpBtoodLDHEIs5Mehor4kP71BKYCv0PwQem1dqY+pSgKexR0rEWyW2ysviY/YBonxYrmWl3/FuDEfqP2+Jbt67WOHmLdGeG+6hne3f2O0RLpoHMYv2lU06JL3515v7OtrnTadq5PTfa+szTunLdNt5fn2i18t13jedn5fGKRahdn5FUjeB9cXJKLHcXiiRSWjiVhV+9XliSjel0znx+PrOUwvl05s3nyj/+ynInt6pcpHMnjbIKtM5Tu3KtN25/+D3y/Xf81bnz2999wW8/O/HZufAfvz7z+QJ3olYaqjYrfD3qz5pGzvVqG5OT80Zft24rWsgTSt73WcW6flg2Rif1Surv0Nszf/qXwneXB1599RtOdxc+v1gu45Is5+z8+oI0Ze3wG+75v/2ffsNff/2W/+a/f+LttfG1WjkvK821PzfoyTQ8YUmJvMgombcjZz0WuxAxpF47m2wWjn/raHOXXrF+cTOUCkbaPZ0iCk4LQloS5Zx5eHPHw5s77i4L+QxJzwgLp9boVCqJns3ppx1O18woTSC2/uFaUBhl9cJ9EtMZFsOYXgj2tP8jFxdcJGiCJo+8rJm+JbdnHxYF3zrTjJqfD61+30ZOQl4WeuvUq2m1Q4eNcysvjWgG4BG85qpJiCCtSIEW9ZJhYQ5OyuLzzyUb8NU+rF1Da/BniGJVB72YtJmR7fwauLDKURK1UgXMjRRBPNkEd5C7jzWCRJ6en1wIyuijmFKilDOw0XqzZrttFyKDUOPY+0GpLVmVpBi/p/8EyN+Flb+ni9PASzue+zNncHF494VsBOuiUfJHr4+yc6JGn+3j7/7U9RM+wV0zmnv9hbCy4rqOFyyQlGFxGmjGiGMpmTIqlRyF4Cy3evg2aix+H3bnQGKRlA4mcIKYI2Q7qHmYlbAxx1uBGJneOcS2xCaxfzhMvLz4XlRnCNNP1xe9xCL0fEzQRzG0O1vB2ZZtzGonnmH7Zv9u0NmoKBEy1R/ZmoWTX+7zADMRyF9rGxrL03pj3SqtN56vyuMTbJvNY9sqKYXP09F9yqSz+ReyZJaSePVwprg2cFqEy5I4LZnLpVAKnE4mANcVdN3otw7V8qbWunJtV/TDO/K77/higXS68Je/uOOzS+YLLKEh2yZYuHmPFlzTXrRmhy9PnHb8eqHfx3SCHtQOZHc0mXUltcrzt99xLVfk/hVVE/cpkbyUluREOS3WXeSy8Kqf+M//4hV3Uvnn54XeNr69Gi0H9EsusFUZ/r3k9SlTEqQIo7pN0LTOg3bB0mWgX6tXafezzITZWuMUMxins+rQ0E+JfMqc7xbuX50pSzJTJJnEQjklchV6T+NcSVdyTmbG93u3hGueuzbDRLcHIThfMqCy574lUuqES3lPusdMvdVMzBHgNrQLlyvdfeQA2szvV7duhc5TNq2nmnCIIiexSuqEMJ80dEo18rBs0QnQ6B5wBbuFSNwfaMF9O6/aNyMC9mLg+37FPBQ7v95U3mIg5mLphGXNeGzsj+WHivOCzua2Y+3NvU8JVXHzezfTdQ/K+DgidF6LAMPxngQBzEAn9lQgdRP6+5qOEzcMzsb/puWZ1132UysSud0T7/fv7/mNHuw07asv1o9ePyoE+9ZBLGJpv5kt2MuqK5Izkpfx5OyII5ys0feraTV/Vs8u0Pzzfr+yFEQYte5oldYa79uzdUvYl88XbBZ8kUYge3HeI7xDGSEoHyGQfb1is33R/ZRFIrCZgkDT7ugPX2EWcd9HIqUI37Uoz9YtJzJ1naKFbU5GGlGcALYtEJ1XPnG0vBOJE1xxZjHlRr/UOvKpc3rYRvHfp3Xltm3cPjTWa+P9287zEzw/wvoUwTfKpmbf1FYNjNTVeprxC17d3fPP/uLMFw8L/+R3r3g4Cb+8s8i3RWS0uYlzXlJjyY2vr8/86b3V5lyvVx5uT1zWK29kY3m98KVk7iXxmsRpgzvFdPjs+xDNDl/sWQpXn0w/7IFT/msn4Xm//Y3ua3dBeUb5U3/icbvy9V9vyHKm//bXPNzd8Zv7VyznTHptkZsP6TX6cOKiG//k7hX/13Xjf/n9d/w//rsPvG+d/z9pf/4kS5Lk+WEfNTP3iMjMd9Sr6mump2d7BjuQxWIXgCx4ABQhIODv/Ish/IUECchCSALEEsBgZrbn6JnuruqqekdmRLibmfIHVTX3yPf69VDgVfnyiAg/7FD9qupXVb/DvAoTieZKw7gaak1dc1AGTYE48XczVXzVKoLs4LMgw6VrDSyVVLt5H2bPIxNji+axlmE+TpwejpQpMx0yx9PM8TBRjplyyMynTJ6g5gnKAVlWqqfiqAosMQXmDrNcelcAQBm7S936quN5isCcIKUGaUVTASZKtg4nKkazxwlu1YXcerVSaL050WiodDtaDwPR5EPHC1WskKrSVouht5ER8LFktJaT29pKXh5NhlqPf+NZ7XuPtlDFF573/BOv35nFlGVdLubi7g2tSrs2q+O5Ay69mwKPDhVRE/SmBgGbDDPi4O551BjCQbDSZmllH57Og3dgVWxsLHvVrewd2175hGbakQxDQVkx8GjUcMPuFCGXuJbuZN3tIT7u2+a0t426tp5XuzU+NglXu7WxCu/bFhdm2zL/yOPzSrD1jd4bIyPbAkg7LSgpkdzvLAg5ZfND7+5OsXjCtpG3QYvYWbQAEWxTs9rr0camf6TVdi0+CHQWSnBTL88V4fOfR2h2PNIe1do5NsBhs6aYIoyxETEyTrSgySnRuyKRNyMCasm6G9DaUIzSxk+9u2UyWtCn7TpsC23koQZgjddvGFeKlD4uV1vjuq6si9KuneUK1wvUFXrdrM8mK9DQvkBvtHohSaau9+ih8OIIL+8Trx5mXh6EHz2w7RU3TZsRMw34tMa1Xnl7eeT8+J7L4yP5euFYF073E3eHzJep8CDCAavBOCY3beMUs7KHLMknT3xsI04V4DmW4d4YCVixxRncdYjVnfmelYsKHx7f0WTi/PKBrEJPd6gYQJSSKJ4fIA/3vOydP/nRC+py5c1hhtr40M2t3dXqX2qOSi9qCMpTDETcRoglq9zGv9XF8a6IehARSMlp/5sVoqJ+rS1GiMB0LBwfZleChbkUcs6UKVvj2KNQjjCdEtM1s0rdSDeqaHGmqVdf0hrrzIl0O2nWVkulijBJFvBa4JDMTZhStr6C2Vipo+uIK4TWlVZdCUZIQG42azSaH/sy1kZzw4gWtIl9jHADaRo/7/Yjyo7kuZN1e0glQeAzJWDeCJN7yTsspsSuv6EXlVf1xsG3XReik1K4pm6iO7pbC77pwyIcMcKUzE2+y6fWrtZEOrx5O4V5ozBClBHX1W3/3Vx/u58oKrCNDc9+iXvdn2qboYhP6+6ehrqxQTVjasyrgyvttN62yxDKeXcPz+/3dxyfVYJr66QOKdtETe5qEqdTjfI95mMB3HcrZgXibpck3mFcIKtVvWw7zBXWS8zHc4yWcuL+4Y7aKpfz2avDdx8wjdU7wm3beffQIEZ453T09yZ32Uah48hBDOvVMPt2bG5WZ80mqwtaJl+Q2ROcSzb2mcLs43U+X7hel0Ee0ia7e7INb5UnVosXhuUT3ajHBmNrFZKx1jIKdKHMM6f7O+bj0eaxdp6emjESNbFelXaF5YOyPMLyPayP0BdjHGjzyg/1A7CyMVQhS+Yr4Ksi/JMfzLx+MfHVC6txGqVSh6LZvCt8//aRX/z6G/7h73/N3//yV1zPV5bLwvHFzOu7wg+y8MOSuRMb7RA+FgpW+rr1XAMrswYgawAvB1+xETYZtVsDewW6HaZHZBQ6P9GZBX6KcFbhzwU+aIf3H2CpXBESdzzwAyTNpmhTo7yxFjzzLyf+8M1r/s//23+fX1xW/u/vzzxdz7x7esvaOkv3smq9u3tvW/RWEGIL+G8A2e7S5fyNK288oUDPJowP7okoqZDmxHRXhidgPhbu7idyKeSpWMws7xOqLWfm/iFzKEfKO+W6VNa10juUZDlndyer8DIdC6UIdy+83VDsraqsl8b7315YLo3Hd6ulMFUI3+h0SNy/tFDJlBJrsz6wLeENawW60pbOuu6Sznde7xgTBOhpFHMfu18x0OJvNstBfFvt48Q7oS8eBqhYHt9Oxm0XLyDWngmwGqM7rWVATLE91Ki1odqd2W3uG6XSWIyypTjQ2bHEB1Tb36XDZe3e3Mwr6LhC7L2ztnWzkvj0kUSYS6FJ5xqu3cifHKB6dxuBowIYaEhul59Rgy67LFu3Um2KK2Y1At9me/rJdTd2so21ydNNs9XeaL16zHM3KrLtkv9/j9/bVHcg0xAzste2G8zeeW8H0rqdgIC33CitOMtQU6pO8tBdnE7IJRteGJtMdov2Gaj5CLHsHujGR41ZjfG7sKG67XHGU4z36e5Z4rpD6G4x1AABgjE1c86sa2atyevLOjPDn3fEHwKFsZvo588UFKmYi51WFKeaC0JdlZzViC5kAxvVvnqFtkJbleZlj+zGqndAWIF13JNIISMcRDnQmZN9TdIpHh++2TBiFkhK0Fvlej5zfXri+uGRulSolUkLhyTciXCf3EUYw7sfX984sT/EPQNJhL1HYUsI2E38QLV72Ltfm3bDm2veaB93CFngSGclUdYrSaDXK71O0JunTViFfZln0jwxTZmH44GffVlYzwtfpEI+C1e9ILXS1woNb2KsW1sk3fbcx+j8EwWgYg0AETzs4qGFqKiSxa28MtoBTZN1RsjFvDfhNDBQpuYiW+2KoRyjEa2oIlh5sTlbnunxlClz4uXriZR9FrrSV2WdE7p0rqXaOqtWnCEerkyWKF/EYqMdi4P1XTzIClJ8NH3bEftgN2b76d9N8X6obhbYkApqCtA155iXIQaGhoiv8FxZ55X95TZZvj+J3UAAEgF33arn+IUM2M4fenm75R0MD8vYr2qKMKp06e5ct0MTMiq8VUnEKuvIthc2+e2gZp8bqruB9NfkRiZ73H5fRNsfIsiOLiLYrDx2A/1sIOMMGon9GyDeGztbat4//vg9ZdP2d5KQNIFYFXQbKKPKSzNrLZdMc/p03FcRK24bN7YsF1pdCObVcEP664/XxSbAk+WnLGNcrFbgaIM5aL17ZXR77KSp+z0+uYfcVWmIXmNmdk/v8U8vQaTVzJA92a2rtT0qSc1ulA6aXBA52k7C6XRgnguP5wvLdTEyhDqqUoayv8pKbx/DseQbJaWjzQHriGVp8hmdO5QrHz5M/O3/8shXP078+GcnDrOVpHr3KFzfgbowqr2ytE5bV7RVVM9AJeGsC5+nuRw45IxwZlkqf/1Xf837V/d8UX6CHmf04c5c2p8Y4y9y49+dL9wVpcjMl19NvH5Q/uRw4Cdl4pTF7W1HjbplOKjiNRxtDCXcf2yJ3EHUGGvF5z9yvm7mfZjQ5s7Z7TE/pxVsOAITwj8rV1auSL0gOjG3E6kmeP/X6HqE4w8hZRIwT4XXXz3w4rTwWhbuLoVlTvzNY+G/S4Wn6yM8vWVZFb12asShoi3SDfBhZ4FsAMAe2J8hBSlrMVdhsETvLSYzT7NZMrtKbXQM24itF6vwpfBB0RXO3i1g1ZXWO2tt1lD60tDWyRawgsk7297Z3pwP4kWgLdetoiQplB9O9Nr54qtKXWD5sE1FKpYPGCs8LeYhPvdd/hzOFUjZarX2W3gNkGKd5gBNnpjuDXpzjN2m13a40mXDvsKUxBvEyW3EZBBOxZTFAdm2RYc6Es+LRICZNHXkhRXXXhdIKsx3xZ2jK8ulcn5ahpJpHVMgHnqJuiTe8pOojFm7GQY9CtbiityvnV2vBa9yP2rxcxarRd8Eb2C9vWO/cwaJaT+AsgH44ampxuy3lDkb32hV56M9DmWogmd/N49IihhwyOThGYtz7t2k25zehOpul8pHx+e7SHggdZ+3F6hkWGp+gVHMNYYtUJfHTuI+22pEF0lmRo+ag/Hw4bXwCgUhkMSZpSKJJHpbwuz5CO6R4CfQ/3NFaPO+h3vbxO4V8LAoPC3jRijB7tndahnMsc1lkD3XxYT5GDzELeMIwZaSaaJeAQf7vAjGB8tmfaAk3x2a3JLJMuIttVWenhbO5yuXy5XEzCHPVuGim1s6RXULGqi5baAiWplxFqoz/5LHnLpWaoP3j0/MRWzzSiLfdTRtJcx9IpHWmLTxkBpfFDgfEj84wZt7+DJnXqZgr6oDcB3xGdWdu9AtHNkhckkmtPYOpBsk+xE9219XXAFuCD3yy2yRJKOiCNwnKxhYHdmmVqFVejWylrYrEkQvbeRDIrWJfEq8kpUfV+Xc4OW8ov3KY8aTkLe9vRfKm6UiN4v11jsRfzMGIsXLgan1lsth7U3JGak69kJvQq9qbKIt8Iouii4OqERHt/WB6L2ykLql1GunJbFCBE29rJkaQYQArZDmghal5EwrFm8dzxqdGlyYpQZS934l+zdJQlO3mG/iZltLdNOIZYJbkurkkqG2Yva3uGJYL7F3x1rbAWbxZ3FBsN1biKDEzfkRddbmNnlJ3GXZFNXBv/WsW6WoMlUvFq5ixJndcmS/L5x+rhjosXTjWzk77m93X7LzmIxx8AeN932sL+STv0pcYPu2XbTvP/A7LI8xsGx65RPvHXO1/9gOp1j49FOf2y7xuy4fx2eV4GGekWRU8LQf4ACqrgytjGiD1evPafcAbwhYZXTR1o6oKQURQQ5pK967PTa6WtB4XWzx3B1PbhlmqwJTBZUG7gNvrZnxZZ1OCUH3cWxUPjEwtgF6rKPUd5MsSNbd4pfR5mhLgreYUnGquHVWF7SvtvBq4jgfyPM8Ns40F/OERiA8JsSrMtwdQXvj/PbiZJUDSDa1JImcDD5rzqh0EstYoGUWZIKarjzW71h+9Zavf/sr/uAHP+EPfvgHtDoxnzKSruS8ciiNNS9ceAdcgUJB+BMVHhDOrXHVyt+8+47HLDx9+YIuhfRBeeqN4y/f88Wrxp+ejGyxls3smJYz84e3TOe3FBb+5RfKfzJPtnmTVb5IsZZ0Y/mpVEO7agzETHbiQXQpt2Ow5HbAJxRbKMAx37K9bl8brdbYaH7eboCjiBLxmYgwqXby+/dIr5z7gbWvpPM/GFK/XtHWaQW4v4PyhtfXlf/w/gMvv3/H09r4W848fWh0Ohe//2gJYxeKRevurB3z7kZQ+XbJGZJkpruTxaUz1vR2doEtQq2N6we36lFaEmrZYvZxJGc7M1kn9b6qFRmIzdKSu+47VeDD25VcGpBZJ+X6TpkOiTc/wvr2DfBWSCkzlRlK5+6u2sKPKiUKa1tZ+mrK+SN7RRkk2JRuWv6MHZzG9IO65aOmVLc3PftQk8gzAhe2N5hZxMykUXzd/xyVHKOkmCfkj3i0OXXxBEA/lXJMWA/GuzuTGapUTSxaKPcHjl8obam068r1qbGct9rMbdkS6+3xTWj0lh3wGcnISEoBBc3LkJKQDh+Pg0aBb7p3PNmhyWfHTcCrpN1vWJGHFHnYalVvVBFduCUufix55WauPz7UPYtWTMXcvIkJpNIxtr0Nhw1OdEsZIvXzpwd+X56gxEaJk/liCcQeQbsxOabkdLDB2LkYTBEmosCvD2zFA5+3MDjIGGFAt97208BIvZCoItADqG03JLAxyDaceCMYPx727eNDOCpDGg0o9AwFyfZ5jdygPpYqrWV6S4MgZJ/dygyNeFSxhNvEhGqmHSotd2r1uo3jza7ok+ciyVanNLn7xog2Fb1aHObD8cz749Oo5lLXlb6uCCtJVrKsQOXhOHPMhTcd7hRKFXJtSFvQDutSSWJ1GK858/h4Zi7C0/meqRUOp7QZzbXSlwulrRQ6LzK8PiZWVVbdZP4mikKQ3SIYkbCc9qtgbw7sluhmShleHq893xG6/Tv+sbUYMQ/RHmqarGYVy3pFFqEvK61Yh/HcO9Sru24xUFdmck7c18rd5cLdlLibEvclU5tyWZu5eRPG7ByI3Ob2U7Uvbi0OH49kMfOcvSJKgprt2br3s9OoDIJaXp/2j86dvfGzCIgmix3vm5TcsM4sdogq69UTrleLM18fuxX8TmYN5RL6QkbSs3aB5vH/Bo1k3SLS1i5pgFm8OovIKF13g8bhJjy6mTqyFfvevzsEsugWC9X9a/sV4jMS19wjkfhyJJbGnHwse5PI1i3Dc4ekNxJC0p1bHyWp0qvQqwzBrrm5xfcM2UdcbChw2Z7Pn6CTRl/CGLl9XHSfd/epI7yBKYBitNGKrZdNDpteEPeUdcsCCHfl7xa4Q2XfvKzbt1D8wwgxNLSNxRAgevNx2Z3nc8fnY4LjLLZhGm0Xp/G2MZqs0Hbvxv5xhIcjMVOAfdzM6BCvhpLS5RbZDpQVfuIkNFUeHx8RsdqK9uaow7EVtpVkrX1sYvsgnNy41HRbHqHkno9T1+5JseLuEo9hpvnmXgdgTKCi1DgpanlO12jeqehSWcsFSkZKMoUmyVudWCWWlIQ0m8Uzl0wS5XTKtLXy3a+79exyMo0myx2x8RIKE5K83mKUA+tKX6LsEfzm69/w/bffcsp3HNIBCwpWWr2Sc+VOGjnB/+mfvuYP3txzVmWpytf/cOX9h5X5fWW9rnz39SPHWXihZ9r1ifddWd89sDZ49eLIH/343sgtDer1Hf3xO76sF36EUlJC0kzplaSNq1pN0tEFxTtSa3V3ezahkcutBQjVBUmsl6CmhnDfAaFYzB9tRF9o4SaMv4SrrFcf20zXZE1DtaHLt7BO8JsCDyd482ZI4bFbjpC+Ah4L5AeoZzjA6xcz/6684lfvzqT+xIfU+ZAasqq7xSy+2QIquusgsHfgbfGXDDRlpnIwRXg/o3Ryu9KWxvXDQnOiS8iKJsrqSsBcd24Z+zrJV0a608eSaVOEOXV6g/e/PSM5c7g/kZ/g8v5q5dgOE8dj581X5go1ozAz3U/eXBZk9WWoCZjI05XptJDOyYq49+59IMxuTz7Pxuq83bkm5H+HKfOJI9xqe3r+/pSjN12oDysRMyrkJGuqMdJbphQxwnKz7pw7OSrmSMItHLuY+U18bc8TTDOHrNQJ+mpf1/REnRb62mnr7kZ7QxGqp2AEton6x0K2qjbVQ1rJwyBNiX6kEq2YiC3jsU9/bGO7Z+YpMznBT1KiLtbNgohbe91Rc9F2eFpH7qaJgxjPHXjZ6Rc70u5ONuU8Goonc/drg96SD1DbnXMn6P+Rx+9VgqOY9M6gGqjLIfaIhXku0OZTEFdIgTw+vrHR9iImT7aafFFCDfWyT4IVrMZOOJBPIDVfsOYasDypQHqyzTBhEQwrTz7CIfs7dKXt7rNYyTsBu4ULAhr5y35NYzRZrtMWyvSYgbukAv0Go684aw+dLAo4WZf3Md9iFkRyJRge5ZzVLZHnhA9rnaK1IzmjCbIsCBWwAsAPdzN3kvnJ6wM/fTXzdu1cVqWeOqnCi2mGKlQ6Sa1zReqdvl5py8T6dGFNSvsgNLUiu329IMuVqqt1YrfWHpvbPOqmsndF+bgAW8Ud/328aee+QsY8ogZIPrGUP15+Ua/xhlK2LRbx9RvKx4v4GNhrCo9eTODVK3rUiYzLJEWLogeBU+LwMPHm1T05N2q78KJ2Xi4r1Eavwvnc6JddqzIVrzwkhDdG/HdTgD7hKQ+SUNQFVSzfszUvHN5jzSc8qHfzuGO/+c9JfEc/N2c2XD7GU7ESe6JCXjvdY1TW7b2xpkxdIM2CTC6EHflJyp6WoiMWZozlKHgOG3s7hHqshNs+mgxm4+a40xulHfF5v3tl+3lM2iY8tzOF5goEvQ2KjjPsuQP78ZLtPLLdVcjErjpGcxC0xHtHFqveY6w3JXVTZuIl06BvstMF7NhD+GSGLASkN1873St7Wf7yqBn6HDOOJwjA5W7VnLY2d9nAmbmid2MjWApdxKt9H9lrO6sjvnd9BnDjJNy+Vxnn+WiLD/2yzeGnnudTx+drhx6CDbVVNEiikDtJhbprmWMmYtzP7s4/Bmx2yG5g9sAmufAJd3okSSdAutui/jvutlJv5RLrAWtOOlKfYyTGHPUh3D6FGzadb3/t3rZbtIF49XyfaJeQJLEk4AHRJ4EpGWpbzSptHasY05o1CU2JNBVDzeMcVmjgNCUrECD3tNR5fFhJa0efqiv1LTcxZ+Fw8PsVsKikTcbmRtjGeekrFeU0Lcy5ARMpHfj3fv4VP/vijn/10zt+ciqcf7NypfHXr+HbqXB+9SXfzZXfcCalzkOGA0LuV/IqTB9+i1wKlw8TFXhSYS4X7qb3FBaOeuZe4YXCtSlLw0pyabIcq4GE8IonkKKHne+drmEBbnMjYv3ix7ryh3X5cTNVe7kofrFB7R6n0JF8b4LD1lnBlbU0q6Lz97+C+xPX169Jh5njGGTxd65wTPAHiZ988ZKvvvyn/OWvfsv/+xcT06u3vPgq8y0r3+rKr/925dtfV1qrFlfs4h0fYtH65g82zTRbzug0Q0qsgjVYPltT1KfragXRG+Z1SBM3iPsTcbW4/e6svg2wpW0/yG6efCBrsPUer9ag9pTIRcy6FeXp7Yw+TNy9vrdCEmTbgxRso1vKiF465+87799W1madUYwlvHF4kxSj8/c2vCIgWCBWGMHDMRcrSqe3ytjZKqBpNwTBNO6IbN1mSspk8bSPcd7t+eP6yXv49dTpyFbXAfV9GnJSoHeUhdYbS9/6BWYRimTjH0w7RRHYhWJjICtCt7VFR6vth12WzU7eKRaXbFC3/prScVfl7ho7bXETZiAet1sS6o0u8Ed6dqoR7snFFHYzKnLsiqFx2S1DUS+9xe5Msim+YAT1jko0aIBo+LU1o75186f9g/2O4/NNdYPgsFN2ytaRYVxAsXZFSawhY+Mjxs7ntPJ+AG8FlI6SOgOsqy/C/uwEO+bTXgztNJ9/M7hrQvWmHszvuLvdXe8fIhbnUHzbeweTVLDam878GApprLFwQxlLc8/mijZbZKFrMr97hzRlVI19lrI1Ik3+3iA5dKwvmdXV2+7dmmAqgTCadBrKi2Pmfp740d3Mj08z9ykzITRX6A9JaEX4wX1mzkpR2/RvJpiycpeUmcp9u3DQDCwoCU0Z7YuH3S30bixL95orRP1FVLfNwxa3HSg7kJ6vfvU1MQZsv7duFtpudndr8na+9VMf8ZNur8RPKaawVrgutLdv4Xik3t2hpSCnE3qYQSyZ2sKDiXI/8eLliS9fvyJdOut85VonllZ5fLhyvaysy5W2VtaVmxJfAy+GEszZujx41/nRRJVIAmcMhtyMa8QdDCDqcJPINsa70bj5zd9m61uJDhXWP9Dy+7RbWcDUheQ5omUylmoo8GD+a09GaqtG8bcqINZceXQn3z07yDhHztnqvQ7ijo5nHLJgWEOye8/NqroRD4pbL7tnfSbeb9fWbh8DQ1btbpftVXGinlrjZG9Iu/cMDQCNjP0fTFwED3ckygHamoyZLPCcPXlD7tn9UXY3pM/lZ/q0DLQxUQdHjVYF1CqJ9dZH/eKtxGSMy9ho2yHyiTy+bXyfO2TsjjycJfsHM1DqxPDd+T4lw28rBH3q+KwSPN7dowgt38gC+2CCw2QLRp30orVzfnziw9vFWH5te8DffYu7d+wQydassW+/iyB4+R8ZkQwDPPtJvdm1+tH6TcNt0vl9QeHbI6SA/1hcgLhc6YClcET8KiGlI6mhtVvxXt8l8bzJrY4s3RsJ+7kDdBXx0lqW1jDd4wgoMRXl/sGkQFu2hzD6dWbtVp1kyP610tZGoSGsLMWU27//w5mff3niP3515Kd3R+4X69mmzR7tqwwvDiA/FS41cbkWVBupNSyS0ylp4bB+R0dYF6ySynSPyBXWJ8DQ6pLgcQgK8dhwQ/fNUiUA1g5V64b1bBJ306IQrdeaCxVJoSVdIewEzkdI99mhBGnQAJeIZ1l5nLr4pmq9oecn6v/4P1Pv71n+6Z+RvviCw0//GJFsHgAqcIFZYMr8sHzB4fiKX7z/Nf1tJn2A8kHJP33k4UdPfPjuLefHJ84fOst1OPTQlukqVE8oR2aERCnJyw0ae45UMLaZWQqSbBgSyhaUsudoNFZdSWKU/ShUcZMAu6EGyzVMIMXcmilqZGpGOyyPzR73auSO6R5Op8TrHx0oc4Hk7aGauWv7AusFrk/e2kfPrLqyYOxZc/BtgTTxa5Ngng5oV65PV1oUz0Wg7+Jxaj37UMvhxa2yLWniFjAbSMhbXmVSlIrlHH5CkPq60mxAVt29HvXmzWFk2baxultXzudqa1nMi1M87j2Ciw5wmTEXebNelkng+CIz3WXO33bWs+UI3sjmyKuIAHdMnu+X8FyCjjCN7YlbULD/JQoIrOuVul6NeLd73zzPJMmbXdJ972j44gOeBXzcLOAxC2oD9wxXbGkdvYP0wReJ/kVb3bFAfZsS+bzq247P5wlG66OBVrYzby1Q2HJtvJHr4XhgXSpLW9ke8/kt7YTQc8QUF9Xd5wYUDrQRoyS+yW9PLXE+d7uOgqwxQLFoJZl/fHcf223J7vuGQmM8ErexmvDWRy0/XA6PHmt5G8jRUBlGHq6dd5e7FoOelEjYjcaVU8qU4kQB7WiuWxpKLIQu9CYbgOoCmsaPr48zL08Tf3h35KfHmRepM/XFqskAfbUCu0KjiPIwwSHBURLqJl3SzrFbLLNkqCjXBkmtWWeWTlFFPAHbqoH4YI/du23AAYoFNubCiDpvLwaV0kHQ3kMXQEPDuoxgF1t8T9zyTv6h4blQ3Xl8/HM+FxIw3xdXUne39maNed++Q909KSmROubqjb57wOEELzp8wQM/WN+wLhfepTPTlDhIZj1NdJ1p1VIaWri6ejIPTMqOzq3vUhlFBCIuaOst52KwQ41iE42g9wZHR8hqSjSJWRYp7ZPydZsUsUUa43bT+9Mysy1R3S1LDdZ2DKOnL9l/nbp21nOnritrXWjtSusrPZrEosMK2ntWQu6ILWXanBBXqsrHTUQSCa3W+mrjRz4TlGLAWLfHHBVyxkrY0QHG0Oxe6753b61AG2/tlaim1Fok+4M1/zaQlmBzuKnt85yEVjoyWc6qFZK360yHRCJzXbKnsTTfLpucHPshNPNg3odRtcvBi3uS/aNt8k/U5b3IxvvwAeqtoWmLLXaEUdnFZdIoLhMxvZv7HEiXmzqi450OZtnAxP7RhrN83Phet9w80CeP39NU127mU534PuJg+aY4nE7M08zju0f69f0Oc9+exdKPt4GMzbuTgP5aciHofnhnIHUvXR6gZMQMbm+JyFXfB36DPRnhaKm+GGT/2S2YPQLbJajCLnjkEx7nZ92PRYwCrTnRdxs0eyyviIwNIFmtvUkk0u+S6S3gaG2Cc0rcn2ayW53oCvLeEpaXWCoGxNoFtkkwwVl9Cf78q6/4Zz96yf/uZeLnJ0hckOvjcFkuT9lo2pj+/upgwOF6mIxy/wSld171BllY7zO1KuenzqKdx2Zl14TGVIBiVowhY3FrbwdRN0PQkuB9pcR3+0ve3u9KzDaY0cd22RHb+/x10TCw3b6Q2HRucWnb1kGCzWcUC2ay+fWbzF2GEu1PF9pf/qX1NfwP/iWpFOZDppFYNZGxuOV0hLsvhOk08Tq9ode/4x8e/44icBTQlyfK/URKZy5PK1fvpBADdBDLF421qb4+xMvv9cWsv+PxRGuN9Xw1d7MjZSONmVVhOZrF6/1aB3Ft+33gVkbUbkxAshJbCXHGbgIt9CSkY96Eqk9T72btWc6w923XxtOHyrvvFtALyiO9N7pW4vL7RHMU87o40MoJcgFEyNNMa8rlw4p2KIVhLQxll4XsRSc0xooNSwUastHpo/tNlDYbRsa0W1+7o1e7SZG9PFP0alYP6k2bd/o3cHHvaqUNi5hc8qnKOZOnCb0TmBL6oaOXTjbfM/MXM70p338Ly7VCDcYsw4NxS7o02r0ZaTqUIOgAJ2PT3C4BHyirCJaLEB70SGNb1n0yJvRuKTDhqr5RzjsVtxGX9n7kcNinmyIq8SjP5e3I+XUBf0tw/P0KEH6fEtRg3W2af7DPOlSnseO155IkW2ApUaaJw92JularE+nni3uL9IT4wzbWNlmj/mhYAUNJubILi2o3MLL/QeK8HnPrupEj0vZm+71v7oNnijAsgLDuZFgi42L2r+LxhF28AZuUyKkRZ34FqWUw/IjaiDGZVqkjrF5rJ7PV4VM6a7OEFSEZTTryj735MBgbVduux5LvvC/uMq9PmZ/fF/7kkHidhEndbYiOSvbS+pbDpIHJzK7pgBZTBNn1buyrSFsY7FdX6mlfGQazFmJL6B5lf3RI4OBt/n0kxMlGgSJhHwO7FVouw5GbiwQC1lsF6gtddu8ZXdCi0rq/NMLCvZOeHpF/+1fw8iX9qx864WfdzuFrZEZ5SMIc+kWiXFsiSyGViTRBWhuph1swOciKZ3zmpBsWsgl10USeClGsGY04lA4AIBqeDQOxY4yHkPPLhmLxvaVRBGMnMaOeq4EYPL1FUMz66asRR1YV1lppPdjJtmhElWlOpAfxIvKwrkK1fHyLiSWLM+bJ3ChdDEBOh2wtiCJF6QaI6oiZat+NmMpYjzF2Y6/7c3qoy1bWYGb73n0GuHS/4PZWln9+1wjGrfWQLYyayON3/2DKQlGhHzw3L5pwu3bOJZFb9nCCWhNiVUvRikcbglvHmhnMUnby1a8Zq/uZLWxCobEt+AEfn7/N8xmiAc4G19jY+Nu4bYMYe1AZne2j1JoDqI8MleSFEboMmBxX+rQs+fj4vDs0gEXqrrU8Poc1bV2u/rfemfLEoRx9khOHuzvmuxOPbz/Q+gf/EOMh3Es5jhRujjxtfsIxNjFJG6XYz7L7noa7YreKEKy5pmeafuLQIcBuH95jerJtiigeECt5eGS7CbckZfO5j+u7ge/CL+dMymlssNhs2mxxUzpdOmuDJslIt81p444luuAsWbEYTOtW6EXZLYNqVXeWuinBUiAVfv7VzL/8gyP/6d3EPzv4dnMlqh2ceEb2vokeCmI43Jop4haUTC/XUdQ71lRMaGUXhMVyi1Iprsrs3ju7eIabdBtyZNvApAHGY4qTu1ikhhL0vFOJc9q4RjRHXHnveHpjpLZiiDuB0XdeBY0V5wzDDp4sBn5OC9QrfPtb+L/8l/AHP6X95/8FWoB03uGQDKlwrFav8Y5uMWEnM9k9dtLhQJZCfqywmtDrNx6S33W48E6QJJHKwffoai11ViGo3ObmCgCLeRt28a+mXpUjbwsg3JA2DFH4YjJlUczKSlj+Yp4yqSSUK15LgKUnnvpEXa70fkakD3AqCe5fJqaSR7GN9x+U87WPNZySKbpysvZLae3mTkyF3jrrsqIqVt3G92cSy8PdXIXcEMbYKfiEePEOuUVQagBTwMrCDQ8Dz/bdXk7dfHz48xLGAp9LGU6v6L5hoUFBxOaoFGtinCcvLL5UeoWLgqowHZy8qNU4GLqau3WkxsQNblWwNuPj9n5vFSDbOov9ETFQXwtbI/DxhB4KdPZmGIj7hHkYm/g5SN1GzFm/yvYQQQjbnwco2Zjl2trNx37PJrk5PqsEmy6oqscnbPLHsAXSccTcWmdl9YC5jIHLM1YodlVvtWEP27p4LNGfK6rTRIA4mUDKEZfc+8p2wzUwk0kYW8BjhO11GU26bj8ZP/dqtPQeyZ77uJpY3HC4aoGtBMleCcekxfe4gmOqiKEkcyFFIejweFbtXhzXYi70jiSlqTHJ2pJolXEuQ1tiQKWrp6fItoydrNTVXtdmyCoh/EDgz+bEF1ncqmt2vmbl7mgEdXN7JvbYwhamuIeguF7oam7Ut8DalaV1Dll5yNYqx0ujEK7xkRskOhSXEjFDjwVNE0RvSQXR7rDitpfY+CVSbHb7xTbbRoUYP415DgsiPiY3pzRzI/agehK1wjjn9hFRNdTy4QP84q/g/oi8tpJ/CkZcyQLV8rRelMQf3B345tK9jdIKUslOxjErwSy2pJb3pbvwwDYnyWcm9lTaZIaI5YZ2tbhd94LYvk6G5eMPMn5y7HtrObtnLTEKW1g+q3h9XyN1HGbh9LIwzYLMtifMUoMk1tE8z8nAbwFRKwReivUhDVA2zwqSzBOiGxEiZ7MIazZwm7xgdEuenxr7LvZB1ByLZ7zVbz6/236PLuYRj95Hqra8S8Z70K3D+SjrFiIk0JSYHLPUi81ndLvmNs6BjN99hhVqSmiGpo2lWWPZ0TM1UIpbL7Z2d2oq5Ku/9nE+bezzuPHN5Igi1+NdO+NE+0626k759u28giltv7Dtk1Cu4zZCke6UIc/uUbY88r1lm7PVlI5PbO2ofv/x+X6CPNG7sn5YEUmU02G3WRxB+HNUbdTerJlumZDUyKlTTkI6HmiXSrtGropQa6YFG0jD6kqbEsSaKR7mCdm5gHK2xak7Oqhqp7VqOyEqXQCSvK+fdiMvsFOGu819vS4WP1msioP03cCLIPJsmNQC2updJSSsgp2C3nSkLahUkucD2nNOxXqxBQ5br41aGcnw12bxqbVbr7Feo1LOEOvbJor1BJjC6IYMu+uy1tG1mduyKX8iyv/xkL1aTYe+GMy1vHnr99Yx61iVAf1HAO52qc1YNYhVlYvC3ztdnqXxZREepsIUbt8IsvhYhRUw6BCuX2zzJ9LhDvKEdutuL2sddM/n1HBjLsYM+PZQ5xXFre8UXACG2NRy8+rOra2u3EKpNH2+NbeJj+/ffgv/9f8D+eFXyD//Z5ursRyRKbuFXvnhIfHPX73kz1NmUWvKnLvyeMb6Fkqhiw46eGsLipVdBmUa+WsZFQl7mHDjA7buShnv62ujXjEyRe8bKfEZ608qW/nLHajIhcHSFEnMRRykWtWidITjMfHyx7MXhDfmOE/mgs2pwwySC3lS0tSt20MTUp5IOdMvtizv75RjUyqVpo1LU5rCVMyCXGn0Dnk2gkiqDgaCBNIZhSoAokl47Jubw1FpTpk0TD31BrX+eWXkQw856+kczUvTNV+XARZyYcjNLMIhT7fx71AEe7AR+8PXYQ7SUSmowrVeeFoWWgserexuKBahDqZmsFHt3DIK9Y9Y205WbcFQY25bDrPc7g9VItdF1zZMaw2d8OxRVARrlG4b3FzHJgduzBKNe392jhugth8i+7cUy+INOdKQj+7jdx2fVYLX8xWNBSRCv5qVN5V8c0P7a3WszqfVCO1jpaUpkVJxgS1MU6arsq7d24a4sgirS4q7BZyMkvYISUbQNKrKJLXKGbqPBez+2+c6GvrdFmbJxdhxXZBm7kjbKK5s4pw3yTUxe3EfznvTbkBM4rO7Twxr0pRBR8z69FhNbFjrOt0Huox44UZR07HoNjE+HPV+/q1EdIDV1DszUNTio3jpukEIcd0mDuWizqvsYxu6PWtsohpKr2HrZdgp3ag8gyk4xLKdV7bGp3buYW5BmdGU3YWbQdTyykSQ1InSfJp2DXd2xAR7Ln22Tu36YxbV5sHpUWwp2Qykv/1tu/uOuss2zhRKPY3ZQEFrRT48Ir/8eyua/Pol6NXmrgGqHLLwcj7wxdx4rMp3i7uE29UURw8LuKLa6KxmuXNFUapOtp88ITsXXys9gtzbkCZRcsZ7DuZhffe+f3tYQCClD6C0d44gso2TWA5kLoV5vjOSzQzzQSiHYm69Xk3gnZq3vEp0FdtvSZHckWaluay/YabNhvhTt5qtXBWaMiV3hbvyz5pAlJ6cqesSrV3NQlFnOGv3YJYwSBTxNYSph0BG3puPR9I96NkhHQ2hH/G/sGw8vaP7Curm2cpioJwcQh23jMWQWt7myhSFlUQjyj6qcn5qXM6N6mSYcFGGUZWcbtaGpr610jYSin3ZdO5tvOdyJKHPnn97Nd7fbhRO6LHxKANt9O0Myk1o/fnobtfawdKQ+arjvb4cd/ccJ7mVvZ87PqsELx+eTOE3gESq5q6Y7o+3N7X/V5WqldyMtIDHhfLBLLvS01beEXh87KyrxcBUPY4hCcq0CU8PgDqvwwcn7cIyZmWZqZzcutxBq6HE4qphydhin8rsQvhCTY3WrOlt5KoNRXfbtt4UtwxvmVkq4UNKURljV/hZglma6JpMqDcrjNsr9Ka03h25rjeBddtYvmtUoa5DB5uSngiije0Hb6A76G0wdeXUG2WwyFa0Wzm2Fkay1xSUbqXRkoY63TBATwY+fF9yVYZgNyUY7igdy3hzUTpoGZvJhUkz65osaEow3ZkiLGW4qRCl5eL31hAamiPQ7It+R0wYQycb/V13UziUlm60m0+iUHwtaPJ5b/5kNsLP3UoS168d+f4t6ekJfviV1RmViqxnLEJZOOWJLw4nfryKtWPq0JqQW6Mv1XBKB/oV0RV1Jdgw2m/vdyQKU7bY3jQ7mKp9E0wBRBNWYHsCnb38VYblqlyv+7VmaCcXczuHoCfmcGdwiMB0V5gPM69evba6ktmU8XzCgNZ6gdxgXpnYxGjE/VrrUMxln3O0Gsv0Sfz9NuY0heRWXrG9XxByFzOs1VjXHbW+g2unXxdjPwYaJG1CP/TWbpg21vKYSdJYHfbvyE3zcTIL0AW8Kl5F2AsW2NqMGq/JleAYyqEEXRE+g2rdxUnkYr9/e+HDu5W+OJPXQxdROStTUBqduoOp26HaTNG3zLYlZfvay7tgAWgfgC9SGMKrEl4wvRmh/fBu+yr8FLblBauf/BlN9QkDImxe/fgt8JlXPnd83h26uKXgZcMsv2Wi9k5OySj6mGCPmJAHqbBWkS5gdnNhGzN2kZKyiYPa+yhSb1GjQA3BA7Rj4JqwkAbciAF3ym9TyEqQNgzn97FwY5i2SJGS8kSWAtqs7mi/HdAu0eV5ExiqOoLEYQKEJZB2ey3qzCoyhHo8a49qG76QAbN+VE3jCMTO0Yg57vN6pINELtJWi3NEC9xNeAS+wHL9INa9Wj6b6I7CLabEnRhzY2AFrtBtbsA+r0mRBJNsNtIkQh74NFxRsTXC+vI4aAKmI5Jn+vQaLQfSbIWX+2LIvugT9IrKbKzYPoKYY/zjayBEtXHZaQX/V0eO6UCcstvIEps15k/GPdsIO7Nxv/5iWiTWVyf1ij4+wj/8mn53or54wP0GpkQK3B86r0h8XSdy7SQevT+gW4Ducqu90Wiog76MOolif/johutasHY/OZGKWTq4skEsvjaV7hahCzOJ+RFEMkgfnccPR6shWcqRnDOn+WQhEE/BMU+GKdaEMhFCLLsQUwuBuE8uj1xXhjAfgwige3HuE7Qj2YGlTUi2XomaYEqJKhae7Q2W5muid7IIsy8H9bp6NhThsvd5VgaAGCtG4n42C/DGOT72aqThyHDF97UhqtTMSINSFTMUNNaUemOCPZ4TB5m6Fd3YybCEx9HDgxt72ONnm653pjq3Mc54tr1uRzbA6M0gBhRI/vbBJwvwfYswt7OPrSXjPgYg9uN3fWwzgmy9JHElGHFYdmCajcrw0Ub8zPFZJViX55vLTl7bEcXJHWLKMLgUdnTHj83skVjB6gtsZ9Zmpx5eF0PVjdh6tzAtSgTGHbWdItrdGniuk1ZDf6nsEGz4DuJjNyMvpDybx2QI1zoQHmAWimJMJHwD4T5w2bo1iPjCRN0ItQ3V1WOeafN7BJnko5ZCOVpkO+QNedCxXe3I1pScU6MRcJfhtqVcCSa4B74UOKWbfWLxJomZA4LqvrMCfYjGUNqo7uwnsXuQZEq2+06axNB6cjdTnEgdlJigUCRZ/hrzCco9Or9Byx2c7GNVQVsli/eN5ID2SqrNz1u3e1RXDGN+97HU7UhqzOHt+XaIavfWPj7t4+J0orFCh9Do4xwhk0SBVuHDB/RvKu0HX7LeHbf3FWXO8HDq9Ek4LTNl9apDVoEcbaYImyq1VquQkkw4FHM48+lDbVyCxOKKcIvuiCuhDkWo1YHuTm4I4hrG71eE431hngvH+RU5TeQ8W/w9fJS90RWu505OnekQIxIVoVdbkOQtHhn+g0qk1d0cAXXGGmo6ZIIpct9zWZAmTD7llwq1qVWMce05JWGKeLzqVn5vaJBt9DYjP6S+rX6NUMWePxAjJnsl6OugK32t0C12m7PxBCSoxWIPqdLo0khdrL7nUPpq1Ou1oXWTkjGywysWG9vbNW1sye1UNoymPkYLbO/xugma7Zvqbmv4OKUN82NQZ4Mp+7EYCnC3L5Sbgb19/+6w93n7LSdcbk9toSdThBByeCP5684z9Hkt+Pk8wR2yjaO3xnK9Mk/dij+naOwpbtFZaSArYb0rRjtZKw5pBSvECqi9D1HKQY1w0D14mqJETn42N46+2rMBD926151qbqENDHRuHihtKIfdKyknq9HYFTQPhWIpOMbYRNUaCfuii/wWO5F4nouhuE4yRdPDPqtEVlvrG+utEQs5FmfEI51QopCaWhuRbOM3mFhx8z4G4itU6GRRCvCHryb+1auZn7yerGfhTmmXpIgTMceQhvvT9Yl69Rn1rH/paSho0U4SpSTlOKvFZlNmms06MLfwrsxenum5kO/u0MOB1WOJlAdIM2s+0DPIbM/UVaEJSU+kXpD0iFQhcwUXGMENu1nyO3DhHIFNsQcmivWxPY5t6v2JRNAeBBAbaHGhrtHRwM+VdiADCc+AFTtOT2em375Fk1gvwYcX5PsD96kgJfFjJ6f8SoVvwkV9sw03176qqf6unVQXwHJ3YxmIK4dwSWg3JZqcnRzFExBH2QWmnOJxycmILaWYzurNygwcZ+9anw5YSbkJ0QR1c0WqVwxK0lmewqwxt5oCJXeOpZInyLOBFkWpS2c5K+ql2ILqr2sm60T1tI2hr7IPhoPNYl5m63iQrHIOWGxVfXVk2RRgWL4itt5TFOTSncDeFgGRS5hEBl9sn3soWKXDQVoLYR8gUzu6rmi3PZ3Vy+D5s7ZmLPHNErTJr0unrZ11MdZw9piiNf0VVq106c4WNevaKrdgciy0w1iYYxmxBXx3i3iPhPz3EDPxuDtI4L/H5tmPmozTpZ3ACdn3CRXD7sIm74M9nqzBelQqU1XW60KvHpPc7fWIGsFOwP+O4/coQX9U3X7WLqzXK7G3cnKKc8JM1lXoy37I7JjmxOE00S6ZXndKUCz+VDRbh/pFhhJEsG4LN7FHF6J9YweO6g4Oe0L5bwPzTDP60bIamdJOMtwXFA+G92KKNO41gfZO71aizOpeClGKDN1ilhY+aojm4c8PJWjJrrs0GmUjk0hsHqOWpRwLMG7ULM4oy6e3PuSbNR4O10KnAD99NfOv/viBHz8UUjb8lhV3owglKXngrIgGWc2X3qFdxXZADasuDfeKsXs7JQvHpAYi8sSUNmWedpuv55k23cGrr9CXr6nNrL3ISrxytVzIskFO4w6cSH1GdCVl6C1ZzCbIAfuV69MehRnco4TZID5+zTdMuv1s3OlA1xoOJBkfiPEdxP1uCjBgywBm4Eiikp/O5N9+Ty8JLQmmA3qvkApTKvwkK3NS/o2mQYrZ46u9EqQP+4nUr6BwvTYDNcVo4znPAxz2bvH3ktOuLJgLKS9mEHIqiTDPhVIycxEr4VVnVDN5xksIZnf3FRP51fbasjYvttyGu9DiSo3uAu00d+TQmU9KnoK9COvaOT9tglgX3DK06jaqzT1OrpB25paIE4M6o3hHzsWAoFqMNKwGA0RBPLPBTSUWQoj2DVkOl7OGVWOx/j5EyxhNpt28WyGfXWTMKdutwypC0RT+EARrfl2v+/m2hXV96izXjqpllBZx/Z99Lzah0+jJDI+ULcXKXNi29sYN7nTV5hjduxXjAfYLWDZ9IpuqDBU49E1ooGeHIF4Fy3KGu3qPy5u1PU5KIHvT4Y0k1mc1TZlpCqCirHX15s4mC0OGbnr9Y1bp8+OzSvCUC6o66PrgSmCtrAnO1/fM84G7wz0pJ3JOdG3mh1cPfI5nO5K4o2VcWUU2JyCJCXNPGbINwGAMqaHVutKr5Titl2rxrASSrcrGxvWz5++yuSrHfQxXYUxwvJsxmeHz3onC7b0+kdGhQbU7fd57a22+UF8pJoVbM9dqDTeo34M4j1p7clUSa8jzX7zCzCgknKwAcV0PWLxoIbD/KAc1xtzlG2al38+JLx8SL2fhuIn08ajhKAsgtxEC7HWJIGd2Zdhkt34VaAGnXTCF21cs0TuLLTkptBevafcvWE8v0XRiRV1B2T20Ppm7g5XRbic2nAgcDvQE7Wx/3e405tJjyd3IUqKZJkpNEQcUqkELJhVyd5ao4tJF2Lt3rHyYsbek2bbRqMPqMSCCRRlL6mYi/IV1tT6EhxmOByuz9viBUhopH3iTG9MRjoETU0J7sS4LvqZEfVh2skoDI2FrfJrzVttTtnkNwGXekTbi1KrJ2xNZP0tziRdgIhVrjZSm7EIlxYLYxtuBadfm7ttwYFrstOMEEsOprAJPGepV6dIHmDu/71zP6vIhPB2xT7EiEp0xEOLVqbOFFbm+F+9HaO+fSqKnxKrYPvVCD53qscut5NfetXmLp0xRbiIhFEcankecXGUAIrnOcFkkBgJab7uz7zRJ6FdXIkOx7gBQdAUJUXbIEyUnRCMWZ917qudfd2eRS1ZGmxlbHNs9dKyowI2hsz3jcx24rWU/QxgboRxH3vde3vp7kqDZa9r5vaXsLszw6oUSHZ/dMa21edu0Yk2kUyYloV4WtDXq0jaFvhPXtxvx08fvUYJGgrnuFgqq9FppSbku6rkwD5v7IVlOC12ofZewywHhZPGr3G9cUYgRbZIjsxjDoS7UVJOqKcFWO+vVlGCeIKvRqlV2iMb0xUh3ADbU6Av4ZmzisnK7GnRItC2oTErQu8e9YoOIuwBhUJobVjYrdXNhep+2vjt/yiakekS1dwtP2Nw6dr+2kFsVRCYXKF6SyyHa8+lOhBIUTpPwxX3iHiHaD8ZzRRpE3J+6oui7cw7yisaQ7UGCP3BcVPyHsOJNZ0KaIJ9ody9or75gTSdWOVinEjVOqxCJx6EE7dzWhNRR4GT98dpHT7xFMQfYUQHJNOnUWA/KVrfVpz0FtyaYer6eNiDgGqhHcp4r2jEoupEDYowkVo/fUa3G1kCgTHBdkKdHylFgEl5l14+hBCXRk/XP602h+Xxs+NEIFnm7U0lCmYqDllup1X1yV9Tbt9mYKjJSdGSkTxSE2ZTg7G5SxUIWPZTfpgR7r/RuvfsiRmRWcjUrfBeNqEm4VLeS+hbHv55hOSuRZhDKJKRhXUMJ+prz/SKTeamuj2pFJbwo/lQyrUHL9ny5WyUcXAluid5g7O4AkkEg2b3sq8vf4NBSSM78K7HyUmYfAwOzZjYlCGGWqlh4I2KjQz7YEBgQ8J/3omkqhbmUcapaTQlkwcqzicfpcqQhhUES382NICrumr1V+xvAUl9Ct/ss5mZb7tueew4hNqBWdrLEiir03gd4H6M85BLbmTUCRpgSLIUpZ84lU6tQ3eDZX19cYOmzR3t+/F53qIi5CVQxs9OP3jvXSwUW5ulMLolpTqzNkuZDmYUV0lyZhvdOJFuQ3gslp+ISaPYq66uhwNbcjG+G2pbLld4MVQW67SXih4k0BnpTiIGmwip8FjWyF8QUrQEkn9BRAXaMxmad5cw0z9YPrUUk3+OVHtDWwYl0gSq75SEFyCOAtIGvNu57M+P3vjqzHg/3J7RP6JzoVVmv1oopH2Xcb9FCUeXVIfPmrvDDlzOvtXAcQtGRyJ5hgAzXrJGdlNptIXnq4uaCTVETdrPqM0bACRB2DKQcZy8m7DUxSBhm+vkc4T7K9Qn6SpIFoUOP1qrNYPHTW7Qu9PUCvY6i3NlHr4uBki4Fd/y6ek0umLuTsBRhASqTWF9Hn8pxU1GPcsyRRH6l/32/nETojmBTKMOE55rFHwTqQjt3Rrt6IDLTJSsvUuOrlKnqG/wwI6rMTYwk0xcU9fxZsS7kAqVMlmaUYmvLmOdeO8vqFrVaGkKWTJmFMmcHXIlpPlHKzOnujvkw8/rLI8fTJnAvHzqtKrTVc30t1kVfgEbyFSRi/OQae15jzLBi76uxwlPvls6wmpIbaz4UrV/XFIOT09IuWKrmRuxNh1ImQiQlkRMcRWypT7DWjoVQn1kJo/h9fFn1iBCi5p3aoVSCYpQR8rZ2fL96lq6nUCVrVzXWncMow2euKCyJPRd3FXrM0voG6ojXi0BNC+JmsAIqfq/J3c/JgWwA8rinyDPG4tsimd46dd3aG8WyGejQZdf+qS3Ny18vfrEadYr9GmmoI99O1gYvmenM4JCH9o+f8fBKuD3E5IKFoBZagysTteUBLGwM9oDVtcCOrPa7jt8fExRIWbzw7nYyVfPfp1S5Xq+UnlDJNPWKCS4FE0LBJmLtZleM5PXdwE6zJc5qqTbxTn7ZOkgr9E5bVyv5FIFuh03NFZPua5ERG+/3PmU8FbrPefHrmjd2O2fk9eRSkNboUdTThYvFQQyJJfuzl3MbQ2qvpDIWW8SQnpN3jIF5kzJKSon5mNGerYTS0qlrNQ7RvM1TVpgQ7l9MfPl65vUpce+h/8Hm8jnZV5wINBqsVUvgjzQOdUChTitVdhqMjFqHdb/hyZ9lVJxwwSQCkTciIeXaTgnWK7SFRHXy1G6aWoWnD1AXtK1GwsDmKFLfe0p0Mr3PKIlGQin2pf2mZU9xFVlEd7FBdYUXLguG8REW7w1SjX92G9H237YXZKdQta3IdUVLQnNCpmRdCgBJyp10XkrmW4Gzt0dQf03V2MvqzFBJwvA0SR5CKhC27ZPuHQtsf6m3TUoJjilTJvUGvYlpmpmmO+bDkcNx4uHFPXcPM+DApa2s145654K2i2zY1Rrm2zBwGit6FF1AvQgHJO1WMnDttCXCF3hpOn+3anhHY0exV2BW2pFRbWl72QlmImb1qgwGJkPZ7naWJAtPRJXZ4Y2I1xls7/E5Fawb4+5Ue5djuPTwdlUYzBw6Zmw7+0GSkrMaS9b3Xgt3YQ5tDE2agyaLCQbLQINRI+OtfjuhBDdgLZJcIYG0iJbujoi57p5tGBhRR00VzS4Hdm7N/RSFEdJ7Yyv+vl3D3iPjVxcODkrCBPZ90yuqVlS90jxkEjLL3zfimSbnPq8C/1GWoHAoBZKg90EdtKow1+VKp7PWC6oFmC2NIE+GjWRLPNWahoAQPAgtQpmgZOH+xR2lZK7NlNySz9aB4p3nuylE9MXHfkxw5CEaylptIed8u9BcApsCtq9tn/nfHDHX2j3+YgNcHAGKW23W1T1TJFGTWHNVr+4+trk6GWCXEJowQGG0aPtbE9nzsRANJ6Sfx9dAOiiSzI+ektGrAXrP9JrIL723Q1hnwCkLLwt8eVf4wf2Ju0lsw2hFqKBm4V2ruKvK7iIC+WbwCU2jgbGvtr2ijgUqrjHjUOWm07ETaXozlNuuVzS9R+qVlApT62jvSFugN1J7RPpqhRVUh7sog6cOLIiu5L4JW1KmHY6oCBWhr0KrwnuEb4AL8Ij1ul0aTN1Kvv2MiR8hKCtHanAmP94LaoI81lNU87kxJgZ6DqDBMPSGKRnsxg5cFvv1MMPxZFajwE++OHAh8fTtE3ruLB6DrliLmWk6MeJigtXnFKBHbp8Q+XqojA4jfWUQgnruaO6sixUemGZhmhJTKpzmAz94/ZKHlydOR1vr0OlJeSTWgFl7VsXFqtnADQa1311GBv6HXQ6uYA2jcyYfrEi8YUp/d/dt6TVjy2T7IFB/GB8h/wYE2S3FYbwJaIY5ZV7Jieuy8Mg5IC9393e8+eIHxG58PH/gcn0y0KF7lvnu3HADUYdu9F81bs4FlrhNGItiMGBdUFlZOWjRNWGgC9tzpRg7Mu0EoaIsfaX1CBk4MUZ1GC/z5JWb8gQeVrI1mJBUiepV+8hXxPZvlNbHwzu4EFryThm6IhtDouNDfecWVpcxOyTpiEk2ZS7bdglRXYqNU60m00ZYfr8Y+Hi+PnV8XgkKo0waOcE8E9bKuq5c69UUT/eK9FVJRZCSncu1i7upjK7HwdRLeE+9STgcZ6ZpIrViLtC6INK8KGoIky1vLemmWMKSsCBrc0XnTLcY4FBkboHeSrn9L4a8ojeXIEG8BLfJBtkg2aZLkujCQCX21ujabAmx1hQz3B3BHxSaRtGu+GzQ8MP95ghJQLKFkSRbqSpbHMl09RG0N/qut9dhgvuD8HDM3B8m5iwe/A8ChxEi1garF+B+BgXNqmLr7SX7xTXceO4GDiGveBWVzYKKCGPvamWsakXXC7TV0Gh3N6f5tcj9TNJmgUTdgKfNdwddEW3kUR0fqxY0mxLsWC7pIsqjwjfAe4TvEa4Klw7HDieEF2ReinCUxsQ++5EB2uJxf1fe5PMjnJ/OmRhW4zirW4dWMFbRqInqQuf1XebHXXj1/sL7s127usZV25iO/jxNxzuT7G9mYyPLYBJvrOIYs24xdho5Z5iMXj/lwv3dkVcP96S8BaS6diIpHjEglQfAzETnFY3n0/24xXP7OLS+ufiyxcdVQUIpuA6wmLsp3ZwYpdFUvcDRbtvtR/lZeB8wMFVEKGJM06fRiF6YDzP3dw/jHGtduC4LkUqyDe8m0Mfae35x9qI47sHHg9u8Ot0EDC66sN6LLvmGO96s95xluGaDfVu1UTVisZDE/t5Dhk+JlDKSZwNHrMPSVJKFvLyU33a7MpSOCKO0XoCG8R6fDE3JS9L5F7K9L97jwGa3q7jZRDGgvtljGT33mOZkRR4kDPfge+y8DTdT/5njs0rw9Z+9tsoPH1aj9Kbw/So5F+7v7q1UU05ILqSpUKaZ6XBgSjNzMvICdJamtH3/kgZoZz7MHE7FC2Nvwo60F/ZKrbcr/eZR1T3taihLm44QH8lidKpYqxe35vT5wlWs1FTvpJ1gDcWHXwcg7WqnFjKnw0StzVq++MliHYgYVTl1d//GRVMhpdnLy6lXd1dT7qrDDRAs1o5Vm+hqOYeWhpcoB3NTHV1YaJtdmTTuS+J+zszzDLNVSqq759UqaBPW3kfqQKjj2HZDfI0kcd+8EpZexGZM4FpNXVe23Sub5LSrHHRFpJGXA9pnWppoKbv1qeYG7dXuQnW40KxZgFLWC6LNlKUnjWtK1OMJne7pp5/SJHEGvi3wb5Oxm5/WhYqwIlS9Qj2btKVzofJOYJJsgka6F53rmycGnD6yHbEi9eYvu1jHeE1cmvtuTlusuONJvx/eQbsgr1+S70780QxvTsJvijHhfknjUc0rYSSj7NgiBEZx0GpWpqWDdGqzWGC9LrTq7n51TprX+2vNlEzrSu+bml974dImWwEJtC1GfNHoARhrIKFq8ca1Nta6S18iLEH10Iatn1AovRmaL/dQ7gUelf6k1LOxoLt0unaKExwlGvxG3Cn2f7f3h4Bu26Ymqe3TzUupY43trdZQEmPrj3rDI6izrX+U0dViTPfeDNTNupN4/61S1O4uPAf7Qbzr8XiFQAHbsyYxyzlFzm3sQb+GbCBYRDhNXq7yaDmdKVnKVk1Cr1ZWzqrNmAchB6PeFd7Os2h7LfTbJw6rsSGoupwYNNedn3qAoDhpjJm7egbS8LisbMlk+70nHk2aitGilwmrpFRXv8F9IdbPH59Vgsc3RwtYV5DmAe7eoYsH3yebExJSslmAJVNKYcoTUz4QmUxtsSrwQ7y6UMhFdlbNgI3jK9iRgUY2n/ItWg/kET5gbfZTBIhj7AdA3SGYOLq3HEL3Fk9gP/uw7O7NJkMoObnl+DHLKj43YotDOYqj53CzyIgfiXH4PX4UpY+GQ9IvbAuhlG1zj/VVGyzCXDLzXMhTtlqLKfhVCsM6wAXfcN7tMKo8u+ozU2P463fT0e1eQ9jR3WLxZxetpN7MBeRxRZFCCx7ECO7sH9XVsXZSW60jiHoWoyQ0Zfp8oJc72vSKRTJPwPfA33V8Y1x8Uwu0TK5gfd8rjcSiK6s0Vvc8uM1/w4u6efohUPno0P3L+02P2rpz0zDQsaDocoW+IvdHkh54VYS7SfhqSrwvmd9Wc4lGjA2PFUcl/oixJxcugxStAY6iKbNsVla83m1tREWnEHS1wdK8j6aCNndnD2+C7tz+Sm2d1ro327YLDICOs0C5VYLazRVfMsgR8qqwdMvJdcaxxolGEqMvUN0ckWEx2nOJt14CelRGkQHyLO5simOfuzr6Q43fHZgPoky/mXbb1gEAfVHEHvdfn8sL33rb+8Tc6uHl0lCWtuC3rzicDRxNgpFPcFhDNon120s5I8WaBCQHtKkkVOtNlRmjU8jgWwwiUAyHn38sabYfbI6dZZ7kdhM8f7/6s928R4e8CyVo5+y7edjmJO7Z9EO2ovoJjCwU7/tUUOPj4/MpEpPJMH1jsmm+Qm9CW6xthbdLMJSUE3nybP59F1bvJj6XTNHEupjf+e4lzEchTeLuRxNSXR1trhWtUJiR1OnHM1ISSY+0tcPj6i64bu4vMZedl4c1EBUCI8XC69BWe3VHVPGlZ8WsW7AQ/bVYZKrUZgtsvS5EMWwAeh+1T5/PvMRNxFu9oL6WK6SKSrE8NjJo3ty11i3WquwLliyqaZyre5I3C/YsZUNRUqzB7zRnDneFyAHuztaTpqRqcXZj0SeqRpbi82ew57AjCCFurhfrEpCkeTUSW9u7iCFBEYoNZCg8HqAh/YrIyuQSwyLADBZxnE20WTHvbhKiy8HmPc9omWmHH7DmI+9JvAf+DvjgZBjSBMcDp/sHHl695u5YeDgWJ8QoD+++5fD4jvVXf8n7t78miaHKA9Xjwb7Jg0o+QMdtnmTQcgaw8rhV0x3IDYsQcQvZtFVeITVBn1bIV7h/RX914ufzS45Pna//4tdc3p29MLaSchvl8aLM3YagN52R02TViySZK60YJb3vSvFlZ4dOhyOH6Z5W4fHpgv7mG969+8Dx4QVlmk3Ya+fp6S2tXunXld4sP631znU9WyEJNWV4vVZjZh6yJeov1cdShwDb6gVavH7OUA7wuMJlV1Gi+V5s2UrzZTcEIzRWCQdItzzitro3wqo5NbIVAEjuQqzWO7OkTQT2vvJ0eT9WfG1Xnq/mYfaFIhs/6HjfPnVok/Ob8L7FTYZmow/kqBMaS0jSphzYQPn2cWsmnucEqbOu9nxm9SXK4eA1N40jEUslY3JCjwdqrZZT3a17jYgMpwXC6OwhqSCSuDxdqOtyex9Emph/bpcb9Nxd/ewBTAGL7FJ6QpQJ5n9J4y9aCu1Y6Nn8Mqsmi5d7jpNOgJpHzQJLv7uoYByfVYJZLNmyHI1vkdSVYNBsE6gmtGej5xdHKNs9D3CVsTzA5ihjOniVeSI30FwsTVe0NSuA3WMyQ7BDOhhjKF/7CAJbh/KNTrJfljdrFZtoUmfU/oENzXZDq3v/swGXEPI+m+E6SBvS6H2/tIWbmnUq2+LtvsbdRcJ+4iUWBESkVyQsxaBvx2ZxodxxCZ22SwvWWrFk8lQYba3Ub0DH9Bl5SRhLJZJNP3XcWMdim240xnVrHXmuHBhIdoyrl3ESp7kL26KNhqTDgjZcj6h9jWmRbF/pQMsHlnRiSQeeEB5JvE+JpWRKm8ZnDvcvuHv9hlcPR16/OmGNdpX5eCS/PdHefQ2P37P2TlZlCjjkc6KyucYH8t/9tv3ugijWhspGAIxn0i0304ps2/5gbXBdkRcJPUy8el1Y7pSHX37L8enKUq3aiIXbvSLIqJQUEmi7GzHTwYWldZrYT44AuRTyVMi5kPNEV6HWxuV8obUGaWY+mFBT7az1SqsX+mK5i0uP5s+rkR7U9lGt5g7vKlYe0Kn+7hHehi70YI816dsirJ6OhTWcAKBpK0rtUYRNDfVwjbZBOomk9Szi3VvU6fYMdiSYHFjbMqbJ2Iyd4BvcWl3P537/m370l+frw77FfKXxzojVh8wwi2y3rghL3a/kz54SaBaGIeTpLpJNGYqfJ85k45uswg6KVLd0XS7uZXjK1uIq54mUMst1sWoH6LiPTWvbPyJxv3vrLvbP0PDDotv6BW7K3p7fJ9iZcZqyFZDA5rKpxf41aOZet3XDHIlt3D99fFYJntdlNwjCYY48otnr8gmXs/L9d5EX1LygSielBrlRZiizoeHeIc0rvVfyFA+8CZWuyuWDocvcJxAhHyCROerJFsjBlOPpC7MIL48rS22cL+uYi+bGxuQPOIp7u39dl74fJ198aukXfY/8bJGtraKeDyjANFt7H+vfa1bcx37y/YJPu694Zsew0eVCtsmHzd0h0vxahdFcWIV+naAI1yCaNWOeTpO3osnmKp3zxEkbL3rjwOrPbUurJKGIWBU0bgDozVMEybMQOZjVBbo/TxKERknN+7vNBKFHM+A9RPesS22r5WGWA5rT1hXAY6IJAya5XtwKHOofSPR8pKcDLX/Je2b+x/NLlpKpd4nD6QV/8sM/RqaEHtK4ds4TZZrJh0Q5iLeKAuQVPNxBUtoXP+LdL/8nHt/9htwhUckssE+VGDg1FEMdW9engmiGGn9QdJRvE0CyE6ViCUTT5+/fwYdHaEfkReZHX7zmxd3M//6nX/CrhwP/1d898s2l0rXRUSIMXa62hnokUNOHV1lkIk+vKB7CsFJnYqGLOQ8UXlImk02ZKvTaWFEu73/L+iQ2ZqL0y4XemldvUa7r1cqkXRfL5V0Wi1W1RmvCk1ecUq8vK8nY1y1ZN4pEon5o6EVZurEaW0tIFo53R5tvFjqNmvoYY4Du9eVTMiAc8Mt4VupL1YBWd6JeyF4lkYfgdRiol2ECpdTI2fg7XdlqtD/3su3kevwuMHKgt0VvezqlZPl84d7116Khda+gVbw84e2ObDViuBYTD2U+iHweekglg2S0W2pTKs+BqRNmnCyocqCtjRoPGRheYJ5mpnIgeXWrfjhSJHFdLtTq6/YGHOwUjwjRj/U2nONxzQR5MtkosgHW3mUDjz6gItDXwvqUaJ56Ev18g78Qg7053z6vAOH3dZFo3XKQvNZgTolSMse7Mn5GGuUxkJx1AkhRJUUaZEvkNS+QkQ5S71sbER/xiEO01ugV77Nlit0cTXnkBAowkayobIUuQlrbiAuo2IYIlIGD5VFI1kJK24DbHdBbH5XJx2uqhmi9EoaIWAdsJyQEt/MG3cU87ydhoL5AOKGKZMuT21laESMMxTfSO3yitSdf4OKyWd1FW8hJKMVilTlnSoeCkroF3oR9jJVRIKXv2FWyc+TEtxvHguxeCDbrWPBpG4fdHAgM6yfGOWlU3pEBhU2mmOWXekN6MH7tWpoymid6mjnLgQ8c+G0r1FSYppn5eMfDwyvrYXnHcI+PXe2FF5PHaXXKVubvcILjA2uaqCosCLMmppijgWv2AM5jvQPj7m2BiPvh1s4usd7jWsY1231q7UbVvSzotHAQSFPmR/cHpCrzdIXF2um4oTOs+hHDEbevB7M4eQ5goeTZ9rIk8pQph4nNwmfHjPV4X++0ao2Ak1em6bV6wQqz8FqvViUm/l4jXgjQ8Z7cCGrttmJ/BWNVLTWCat1njBhm+yrlRJKJ5mQ1gyLOqtzNhwzijFnUkmQk56vPSzKfzmBp2/yEwPZYYG875aRjTiMksrm6d+th2xA85yvItkwAdxf6ZEUfypHLLGws3o6He2V7TtwC7D4GGoWj99fb5N7zxudRMnK7dZcZJEtB7ErPaVjmoXhyypScXQZZH8qUA+ZtcUx7Tpu7W4Ub4GQnQVwJpmTFRxhFCmS734hl7ZSgakJbohHVt2J+nwF4V4zbyv7dx2eV4HLJW9m5UU/RUGROiXm2YGsuwvK08vgu6jt6Wd++0pqw1H0Nys6eZBgdCoCtEkm8uNPst3BLSEzMk1JeJQ5LQabEunTOZwt2pyRoEeq0wx/NWrWYq2RIJqtvoVYOTlWj6bdtGVVa9YLZzXazYhN3nGP4FIvfJX/+54oQm+Ts3bPdtN8qCSpNG6LdSEZ7V0QsnOZ0Z7Bd45tYmYf4ncrM6xf3HOaJ+9OBQ26cpoWpXaFe4JqgJVe/9mxVFOWZ37w7EBhBAf978oKVz9hwpg8zWo4bghyyYnuPyRVbG4Pb1zzWGXdghj6pL6AN0WZLWTJKofGGnmba/Ym3kvmvrpkPCk8kXj285J/9y3/OcT5wmI8kaaR0HZt+bP60iTfo6OMT+vZM/eu/pP36b+H8DdJWfsOF72n8rHeKz8UAVOCCWq0cHLjgTdDzNp43PbJkW8JdfB+oAxNXg93+Jsv3yLIy9VckueOPH17zWiqnw3v0ojxdvFeeWxstgaRG8XEeQttjYYfJ4s2S8g5kBb98b+WyjYsmI005g6ou9srjhzO1NrNGtXNZzqb8ngyWj0jDXuJ6zCZ4bykUUBX0DK10elar0NAgF/O0CBaLyl6RJXMFGnroqCj1amM2HZU+K3LM9F44XIW6dD78djGLULtxBMJ5kTblFx7J3huXy5VpMvdwa8qyKr0vdK5DsUv3ZtnBVHXAugnwPtZZCG/77iX/8g70hKjzYgbdY5qh2EI1xLrr/VbCjDXtwL/ng6V0eYqYBskupnZvxYbbVYyIxJTQVIxE1fq497B8I6zS28q6LltKWGigFk/ffEziHtXP5U3SHWREcYZEGQP1nEF7A1BukEdIEH/3ZkX5usvj1n7f8VklGO6UiOVrYiTaqrtgUxLm2RKTs1vvfVy+Wg+93ZOp94aJ6glB2Y6P2CZx/OVC44aMoNukAzaI2Tre9xZd3Lev/SCYtbox2kIJ3lS679GQx6bzpmOFK+je+2DMDdwScaP9dMXiT4nRFNJ7MN4ARBzhBYq8cYHEGNyoHD+3WOHwJEiGaSpM88Q0TUzTTM6VlBuQx+7XnRQfeGN3nd91iAsy2W7h5rXhjnCd2T1HZbNOnP0lyhYpF4vzOYPPoL5bVl6GRHbVL1ChyUzTA49aeCeZb0lcJHM4HJjv77h/eMU0WYEq0ahQtEPKanmgTZXezIJpT2f605n+9ISen0jVCkuvA2t+Akt6bOsW8TraDi2pMdQB85+dRweXeftbyL+6IEtClgtpvXJSoYpVqIr2PMEnBONQWipCH/M6GM3sYtI3giRuYxMmW3Vb9aa+23rUbhZ8q41Wq1mA2mluAY5u9mkTgNuj2XmGYBRx95qtR+3NSG4N7xph78+xTka5r+JA1Eva+cOKN7hLYuSppJPlkZaGBDM2rKNdLE7w0EM2i6s3U8apd6+y40XBfWQ0BldMdm0gdwd8Nf6J8WaLeW1ocPMAdTy+737XHgxMVx47EtNYc2q/bJ6o7T7Er7e7/M1kjE/E0htAxRR7jyUQDp2ocrTz4tx4zPw5YhnpGAS262w3eAsMni2TYVUqmyeD22NYnbtr3349f9bPq8LPu0N7swl4TOQMx9lYOP1JmQ5t2xyBWg6TAf11OBDQptRFoRrzsulKx1liCHOaLA4hE2CtRVQt1qDaNkXsE2aEL7EOAhXWM6O6flarVQn2u6xq9yLOIq2NThubPornSu/WwUK7te8Zh6vDOOdgT+sGQsZak/EvbJOcJ6sxOrtyis+HcW3MOkZFFEkVoYEW6Fb+S8StEByWpQRFmObC6/uXzKfE6QvI2eOUUjnzRPfrzE1hBV1X2vpEojsIsZsMXCm9m6EeejK+PHy5z5mL5xAJwdZRrSRRpgzqEcApicUesfJkOsSJKwVdHZBYM0NxC3DshFGkqKFiTLB3Vfm/frPwPk1c7l7x8Ool//xf/jvc3d1bzioNxUyE7lat+Dmbdt6/+8Dbt+95+/Yd7959MNTbOi+eKkd5xRsSdxy4o3HE2/AQcx1ITYfA2Ta8jWPP+yKPHVZzvVpe4IZh2Q+nr+EAZ/37R3h/putfoPcndDlBzRzbhfsC7ZRYulXA6YGABbrnCdZma9RShRpKtco8JWOpm2ICf/ESMsT7tvSFGSWX0Wtsm3W90tvC0+NKa14pSdncnOMIZ1nb8Ly/JecDp+OroTzW9sSyPLGeO+vF5l5ovLxvnOYGxwJTZmGmqqJPDlBbdaHgUxMXkE6ZGy9/YM95vVpaDruaoeLSOJdCmSfrl9mUuq602sza6esu3SLmzNZvJxJpbqFFD/CxU1ySrEhGKKfWulVtiXnvFkMdeX9D496GFpJYD1Zb1uJl3hj7JZE2F6mIp19s46LNwyxZhtKwfMd4LiwENXCCEbhUV8sn7tYRZ1g73YylvQUX4MvCkzIsybw9xjjMA7eajXmDKk0IWf1Qt7oQKxWZ0ghP6FKtQEpbyb19otn68wDux8fnLcHVSBm1djQnqlpaQBerQlGe+tDmtW0b4IYBpBgKc7pzWF0mA2PBxjzH+QEvhjvam7i1MTxLzQLENRJznfG1T1sbDCrMdTLymhxlDrT1bGLid0U28z18EbDl6aSov2eIVvJmgg9PoreYirY2cd2BQwO9OuKSELKuwmQ45+PGzI1wOMzMhwOn48x0FE5H+1w0eFUqtSsLyrKutOvCZV15bCt3SQYhYG8hiG4zN4oN7AdmL2SIN8XfQnDpiP8YgpcICbIpEMYiDmvNlKgv+hsgYlfoGEK9Ak/A26o8TXB/d+Lu4Y77+xPHw2yf7wZ2+rqyXq60dWW9Xnyndh4fzzy+P/P23QfevftguUYCd/NMSoVUM7lfSctC7onerlQ6efSs2KGBZxt7PyID9Scvjed7REP+xCc+AS7wdB3Oj6iuBup64Y7OQxLOGZqYtTb6VbJhh7H+HRy3EHO9W2lDV1BE7p6je/WiDGDxe1V75lq3ZO51ray1Umujte2CKSwXbO/uq4ngoDe+p1Qo5TD24loTvVoGU1117Mu1dIq2IUErSkUhCmB0cz9JHzvPfncfYJkyLSWvbazGOh8zZveYssVMzRCza3ciub9t47j75P5aNxbVmME9EzLcz/7JON8+FWK3f7br3f5umslXWHjCZLsb9mP+/FAluuwMw19d3uiQxOMwUO5EvFD0cnu+uPA+VLo/0z6WudMIBAM9rGp1j0jffUBIOyAR6CNtFxOGAtfeEffwCdt5t1jk54/PxwS/fhw/p5yox4PFFJIRRN79JsxVpRyFw0txje2ED8Qsw4bFQBByL2RNowVMEmMd5ckRgzcrXZsv2O5loaLSC2K5SUu1PlJ1sXjENYR/LCVMaTk66Li7Uy1PblsJ9gQiYmkcIrQkI5cGrDyPdTG+kkQ4TjM5Z6bDgVwKh8NxKPHtsHOnlJBi7s/eKgMzuuKfkw1ZcuXQNcrAeqM0r7mpXpkjqXCaD/zpP/kZ82GinBKSOilfaXrl2t4DVoXkvCx8e3ni1fuV9v2VvyqNJTf+dJ75o6lQJEXpXxNi/lvVHioYYnEOAo69WZzlJ+LxxXje8fx230mw2pIooysDvo7VZFtWzLVG8+fcDaV3gViAC5m/RvhOrf7x6XTgX/yHf8rd/R1lyqhW+vIeszY7v/36t/zl//znfP311/z1v/3FuMdXL7/ki1c/pLm76+F+5nSa+OrPfs4f/vANWZXcG+UXf4G+/ZYP3/wt1+sHXvCewoq6zzK6Btw6tzetqAnrsJqKEVkukfVkd9Iqox5suOhkz8sSRd6/Jz0JWmZETvxZhocD/OtmLtDcKop1FMfPuVk6O8HTsXY+uqCloGk2Cyr7HuuNWldLiWhXtK88enBvrU9Wb7YUJKXN6+SKtzs4mjGWbFJzX6acMVhm3exLySQKmZmp3HG4v6MuK3W5sl6Vpw+NeunUS/cxFr4/P/EuXcl3hTQnaxnl1hsi3g6IUThnW35iJg0n82IdrV9iK4uNs+ShCLIYUF2aV81p1dmvC7Wbd+Nmew/E4/PmqFFdcUWaT2tOFksZI3QYY7U2bxLQdViJIkqRRo0GsbjHJBgybummUTLtNn8wLMY0KnL433usBHwu3KsRYnVnfA3vj+/3KU1MaSakfGrKnKDKysrqVuH2vM+PAQT9hx61iUOOOC8j9vywdwQShSRlrOBIgJOuUGWjmuuKqvevDCC2W/fP0OUnj88qQW1b+SPtypoaKanlniCe5O0XSkK5bgJgS35kGAAj90WHc88nKJGTkXC6VEKR76DDMK2Si1lLaMb89tVykhigLGJfbur7l3qAfENB270acLAKLsYuZSg2EUdtJVIjsrlQpsl/3hEh9ugXtgTQKEcUb9sebDD7hoIYr/kiQQFDq6fTiYf7e169eCDPmZZXgh9lVSdCgHRqrVwuK+W6Mq0r71R5R+fSrbCuOq08NpBE0d4N++HIY3zZndiEjhifbISH55aSq3CidBRyixBDgAxrcbevFYLyR2OiUrikmYWZg8zMdw+c7o7Mx9kVq9LbSm2Vx8uF7779jq9/9TXffPMNv/3m2939HDkdFw7zgdPdifv7mbu7idPDHfP9nW+2jr54Zfluj9+DKrVefIzr7hk/gbxH7Md/Tzhb2cBZR53b5EzJfnuaIVd9fYOguSKycp8yL1W4S8JFhUcPI0UloG2JbScc/Dn1Em3dvrwm9lCCvXvXltacqOH7vq60vjrr0hz5m1Vzi/BHdR/B39sZleq7MSKbx9iDOt9a85iiAd+9VVu1k5rSF0hdrONGwmRJSibkfbC2PehjPqSwx+PB0nHcmxIXsS709gTNFWBvzV3pfczhftFamM7yQUf1kxjrYVmFwoq5D0tsL9z26ybO8VyIP3shxGy4m3aLTZ6//9le0/0aia0d8i+59HTXY07ZMgNCWicbr1yMHYp41wrdX+x37YftzjY5uJdvfAQqwgsWcU/ZjfN4nn0cUP9xlt/z4/e3UsIIW7ROPV9IKTPPNkCpTP4gnXqGxwtOZxKmY2Y+5jFxpr4iGtJ2tqKxTU/5gZwLTd5aJ4hgp/ggBqsykw0FFOVy7pwvnXVduF6efArUHkumMSfqcaFezXpM7p7c0KP9m5Lh9OImYPGSbalYfpweT2NMcikc7+42NyfbvYYitLWx20jh698xYqMxJ3PbdvOYSkUJBTtzmO/40z/9E16/fuCPf/Yjqlb+4dvfGCLTvlHHVUEby1Pj3W8671vj182su4PCPxEzU3TOaCqQDiAF0gWzQCfDobHpPL7k4hgRY2yGJTjKvfVgye0nryGjMS6DrWaVMXz01c6RVaJeNiFJ++FASwfO/JCndOR6/4BOR/7oxU+Z7+4o5UiUvNbWqNcL3373Pf/mz/+cr3/5G/7iv/tz1lpZ6mrUeIR616gV/skf/Yg/+dlPOb0oHO4ypzKCviBC/cFX9JcPIEJ7/44PXwtl+cCJ1bsT2njsubUiJqjH/ItacMTlSMNajRWUybbCpgR3loYEWBiVwz9AuvLF9AJNEz8r1hz53Cae6DS57pp4hKodd+Grr3tRB0/QSW4JqCk/aSu06iXW+Ecdrm9cSE2G4gtercT6berFnnNpmLVez5TZ4uNtbVwfr6yXino4GBgEtibONj0b41Cdrn64s9y3yLPdE+LspjYJg9+TJCFPXuoxwrZqiioLnD9cuT4++r7dhLPulgXgRRPwYuJYvqvDw7HBATxMEo2ziY7qaRhB2zld1O9jc2OAd0f3ikkpOAR46kJYFU1G2OfjjzvDPX5LQnfLMnuFAgMLiSyJqWT7SjNTnoYSXfpCT43FW8jJGikSmxGEj76FiT5WjKOsYtzX9gKh+AKY2An3SFHGOumt32QYBFoJzKLPB/ATxz9KCe7v73fqWd1peAmku+nlLfpk28bxGSUVUpm8wWca9e1Me22DMzpI7ONnosYqizZGIzbQNjSeGBZg9/favtAx4cHejJEVb1RqoNErw2z7ze48O0FlmDC3C0AD8XmsMhSD3Kxv2fnejeHWd61wNgvN8v7mQ+LFyyN39wdzbbWV3lYP3i90dwtot/6CzclIvSlNLQWtCtRugtjypQotTWiU5qfR9IJqIwVV0YPrgRxjsYuMf/z/RJLOIbmBMVyGGv/b2Bjg3DYNjETy7Q8CKdHKgZqPLMwsaYbDHXk+cffwwHQ62GLv5uqu1ysffvstv/3tt3z966/5/tvvuVyug8pdponj8cjrN6/54Y/f8MWblzy8vOdwSkyHRFLPUxuWUkJKgelAnk9M8x2ind7eWTpLNMFlGwNkI8DcsvYYIY3YK0GSsfjrdg5rv4V7+7yMnHeEuAJXk1cOn1wVR3wkYsi7PRkxw01lqdUK7SYAo3+nxD5xQlEa85y9qlDBYm0bGSMsn7B0zLjyPe40fTCCRu8d7c0T6lfOT08sl4vFrKt3pR972aYhuVcmhKNme6GtoG0bsw6b90lkJE5a/HHL690fgTECxGxWyrCdx7G3XgQZijDev4O94+Qpb3WPB6tM/D792rcLJBNFGT5ShM9+3e+9UPQ3sjnScZ4/xDNLKmJ+yeWY5RqbHJbIhXYFq5gLN+fENE3MBwuLrbrSax/3tJf53Az7ti7H97DubuYmrMA+FOnYYkMxbtZjeA7iPONUquMePnf8o5Sg8UISUAzhlcyoH7l/Ph8wyVBSZmY29LkbeRmbwyy7++MLpvnA5MUAZ8kghbPX/7GQoGIUO4Fpd8ta6XVF2wpazee+gmrD/Cd2T91THyLppjn6LuVIyolpnpCckXYhaaeUmeTuExEfJRlqDLzeZwth9NEgW9mmWqu5d3yBiG+MnLfVX+ZsBa49AqmOEqMZKKmQc+LFi8Sr14Uf/9FLpjLxzW+/Zl2vrOe3NL2y8o5gLa7XhacPT6yXatE0Y0aAmlG3lM65V8gFmU+cObIyo+lgLu72DdIvHNqVnHaWXKzHbHUoA0wE7hKBYxJ+cFBWhatWSuq3+y4U3z7NZ7cpJGKgpaC5cLn7iut0z3dMnPOB9PIHHI/3fPGj15QpI7rCWtG28P033/Df/jf/mm9/+x1/9Yu/pa3GtosmP6/fvOHHf/wz/v3/4M/4j/7Vv0cS66xOVazP6pWmK+tyodfVlSGU+Z5yKsyvruTrB5YPj+SWKVwR+pj/ISjVPB2dzSLsAs3LF2nfal2KQlE8OJrGOSLheEVZ1XowVoG/L/AbYPFBC7DRV1cgLgnEKlBbtZOmtLVCEB3ylt7RMSZ2jxpjNItZtcaUi3Wezw9W9MJzDPGoZsXAEkR+rRcdwKqVnNLkHSfM5fm4XAawrW/fc3laXVEr67JSl2WUNOstSJ9mv1vXeCUVs3ouo6vM5rg3q2M2C2wq4IC65ITc+doNyxKrP1r2QIxMZrLYtJOgNAZ5zCQGIACi28x4TcfaliRWrCGlraxrbEJnN5YUyraj3caMvmIR77h6ipsb92xz6GZ8uJr3OahwG1uOO9zvNbGCCYW8yW33ao3SlwBdacl6cixrpa2NXAr393fMpyO1d77/9Xdcz9db0BeAaGe9ffL4SAHGYUJaHHhJTrYGJaqsVszVbmk1I56YXZETzN3+uy4wjs8rwRSNaUOj+uaKVfc8F2SsRui9U/dEkEC+sSD8prMkJs+fu7EOREbxanUh3rvcdHGvSyVie0lsY7axrLZrqFsKFiT2mxUMlQbaTQJi1TPSNJNT2ii9xU4WZC4NBbUTJNvFfAp79yR7E3pj2NQtJPAK8n20tDFE7+NSLG5hpI/C/f3M8TDz7vt3iAjff/OWta5cl0fIK2leicDcujTWxSp3REsieqNh7MqukLEYoPn1vQt7KahkEkdHuVaFR8Qy5gbIHohst+x1G9ccxcy7bh29d+tjCBdRjxNt54iSXSoZKFwkc06JM8I1CceHO/LpgfkwkbPQ2sq6rnz3zTd8883XfPPNd7x79551rSQ8hvrwwJdv3vD6qy/5wR/8mDdfvWE+HobLPRLU6ebcNGtmaCNrcTAX+nyyJILznbHRiHJpmyWgjmKthFf0o4iHc62XGM0fFKeGIzfM5id3H/5qVT50ZaGxiPD3D423OfFYC5cetVaN2t9bp7bFzpWswHDv4s/m1ppXbAnPSlxxxFboW8qBT7RZC+5MDstfQ9gIqpHNl8Z7hxC8sRA3qdx7Y11XwusRiehR1MnT5Ua5PLMQsYbKY+Q26G+WgFg7nR4sUBODNQtdG6UIh0O2alXd12vInWGo7X7fybNta++tqWfyzw9rc3bb/ebZSbb3644rIC7HipWuUzWZduMhYVP7e8LLOO++OEPM8N7y2l9e9+eze3l+sx3Gegl+hZ3alGUmUQ7FvQ12jt76uIW9VbbXwc/N1JAJsn9VlegkYpc0AJRkeyBT1sGiBYIF7PI5PQ9XfeL4rBKU4z1gTLm44SSObERRaY4LZaPT+/WWdWFZLxymI3M5EEUQ1EkC4VGZUuKQTdOHcgDx0ljQlpUoFaRd6RdHir3Smim2ROKQJmpuaKs0T7/ZFrdroq5mCrkfvemKaEU5QprI8x0pWSmpnBJzzi70PRGm2gZbu1k3TuR6di2713WpXM8XhyaFhHrlexmznRDqutCaWJAiivkmYTpNHI8zf/rzP+DudAdk1nXl//Pf/39ZLivrB0tUXmkc7uDNj2Ws33XpnB9tMRKJPGvlnIXvcqIhnFIhpQrpCfILkAP1eKDlwnT5glRX2hXgjOTF6nfilm8OpOhL1wVTp4D00d/V3hKazxf0hnFs84aSjDEktveMcuA7Em+xpriUzJ/89EecHl4iSeit8fThwof3H/h//uv/lm9++y1//pd/7Y2V4f7+yI+//CF/9s/+Xf4P/8V/xnTIzIfshc+TSVjtxjgElGKUe85xI5Ag302UKdH0Dev1nvzhHfT35m3QhSDKiDAUiQQQC8UY2yPZJVIVI3pIZsWS+5NucOqXZ+U3q/J/+3Xjbx+7NQPOnfxHZzh14AWdQtUnQ8ML1KXy/sN3trzyPMY+S+YwzRabSSYaEzuAGuK9N6OXumVMyuaJcEUVLtuI/2Tv2bbhBXfjOmB2rG7pLc/QeO+dtS9maaZp8NVqM0+N188n905SGfmeqDG402xgIvtmUuz919V6n9aLC+0xop37h5kf/filzX9KUBKqycBQZRQk1QBGww3o93wDdB0uCTfdlxAhz9lqK49A5TjB7gjGut7EX3NJ5DINebLWlbXVsRwZAPyZZvVwkEbH+J0XYqcyGJ1O4ny3oTb7g3tjrMqiKdWwLJVw1o7Nyt2rO/qDsXp77VzOF3rrpL4BH1PUty3Snh/7JwpdnqSR6W7ZFVKBUuKZIRW19bp2Dz3ZmfqEzcE0mYH1mePz7NDIi3M/cZZQCrs5cEiRxGqEmrbONilYUvbeLFeNHuo2mbWuXBdDFV2VpXaLWXksa7A6Hd2tqxfj7c3jJHEfUZLHnDVZwg3qs7y98dlDMsoVUTuahcke2msIhltLSZMpYq2BOj47tuP8YSls4E/HYvqo1YdwQ8kOJHM+X7heV5Zrpa7NGHXa6TTLrTpblwAVtdqN3WKC61KR2pHWae4ek6SUA+4Wi+4Mnaua6w0pTMnaFAmWoyVdhxKUuL8AnT2mIO57U/T77Ir9xhlj44Ja44FdaDQx9993Fb5VeIeSPUE9Kv/X2vjmm2/55utv+Pq33/H27XtabeSUuD8d4PXOhAABAABJREFU+eLNG/7w5z/jyx//kMPxYNZ12fywWwzZN7VbaWkqIDPRJVsaIA1KszUyPxiZZCl0XXdrW9lQ6SZ6Nq6gAUBzs4sJYk2oChUjo7zXzrkrf/nY+fsL/O1Z+fVirN6aYHq7ki7C4bBAMhBUa6OvDa2+N0Lg6Da2rVWbvxTNnWWM/aBzuOs0wJx2K5/VU8T27Dm6585pcnDslv6wT3bxOCOeZFLqlDL5eX0sIsXCi0BHhZARx1TFKkopI/4oQDJAmTIjbtmxStrZE/+rK4W9ElzWxuPjlWku3B1nE6L7ilW7PXtrsTw7wqsj2/dhMQsjj3hnBg1lp2rsWLvn7UIWl0sGFvX2K3IK1K+javMX6gVCwsQqGwtyPFvk5smz98R6kc7msEkJaR6XU6Gpy5PWR2qHzZ3df7hQU7YVUEqmiZinDh3g4aNxjluRm7u2P0vEchn1q3POHA5YRxMje3D+YAB9c/UGsLbxNs9h4nPHZ5VgWyzwmQ/WQuNuugM6VZ9ul0Yy9tU8Q86Fkg/sJ37fI9UWtT1EEuHxcuZ8OYO7oR6Xxto6y1Kp1Yrv0oFr9R5lqy2Cm0FNfj4oZUMea11Z1iuRoyjPF4ovqr52qhr8lJyYjzPZu7DHopEklGNGu9LO7m4J+Lo75afWorDFDsNTHuywfQ8t/H2jrGHMQ2v86tffcL0sJuw2wxxo9BWWbzvkTpss4Vm1si6VD+8XsnZyVxbH/ykrhxcQlXtTX4Er72vhEeHLNEGaSf2eLomZt1ZJZljqPvgtNrVt/hLJR7J5mZpATbK/4WeHBZFHo3CvVlEzXAR+cRZ+2e3zp9pHHhXA5Xzhf/g3/yO/+fVv+Itf/B3rsqAKx/nIT776EX/07/wT/jf/+X/K3cFYvUYw6a5MdTSt9tRoJz4p+XQgM5Ea5rZMixW1fqxQlP7wFX060L7/O1JfUFYP5MfE7J9197Mbn0aESUguVi6rwaXD0jv/07Lyy7Xz33zd+cU7+HrtPHVYxapqlL+Fkhd++IOJPBXOWGWnfrmi3eO/w4Vkbr9Gg17JebK+jUwwRUH2THSzW5uiK8PlJWtDKmgu9JSwAYHWLihtxMrNuW5xfmAUNUpOtZek1sEi2/3oCr1Vals8hrjFdDbzyyrBxGiOl4tCEeaDsbdtQSYaR1SFRKViPSd2/E5bL5eVf/jVW168OHL6yQQqJK9Q1XeIdn+9j4WzKd/h/BKMT5CsVRMiyGhCaWczYayjsHgbDE0Z58w5U1IabuDWqpWhq30owQCV5k7AU1A2BRjFPW4A2E08U9kH48296QrENVEQzYTu5eTSqKJlRZfCKsfBwrYfrd5rAg602qhrdaC+WdDPAf+nD7E2cCUN7uF8mJinmZevEncv/Lm78uH7d5zf7y1MB0zdgOC69v+V7lC3QoJaHnfddwG37gg+khS3y8lASCnHvd2aBtqgVXUGvrdI6fa+5L2uxLtUV+PVbyjRBz9Q0dYHa6gtypQhzyPQ0Hsf1q1J7WgGXEg5m2BIyd7bulkcYvcmCXpLRCcAcbMo7mfsmkDFyar0R1fqlIz9CbeISN0SFbAxyY6E106Txtvv3pFzYTlfaauleGywR3dzYveyditsXNvKem30ui3Y5haWcpukO+uVhnAnJ5TMRRJXhDcykaRt6SRRZGAYCzEfrvB0ix3bCnHf/KhQktwxpuMeQsyJx45MsiSWLlwU6rKiPUGZ6K3z+O6dsTg7fP/99/z211/z/bff0bu1Sro/znzx5gt+9k9/zg9/8iMOs8UOu4OcuGIXJXW8A7sHB0W2DaMRz9FhMVmcOtGnmdYPLDXRV+/QIbrR1HezMxB6rBkzSoawO3c4K3x37bxdGn/xpPz9VfnNk/JuVa5dqRoOVp/6JBzmO6bDzAqsbeW8fKCrpS6FhWElqdo2J8q4iZSztXNKGMO4LiOFaCBq79tG32rogtLUHZ0az+Xv9Qa1JihvvTbmEnbFkAUhk7p17ZZAQJLMhVjEKoL7PrZ9bSM5Tda3tGTrlhIxx+iYHls7Ty6cmhWC2+kFvwV51ltRjHUyeUHx2saf7UsHgdc7Co1lYWIiLDm2uGkop3AJarj5Nnkx5LP/nkSGx6Anc+mpxo7ZH9v5A2J+DDVv/7IBAvdWsPtgvKe7815BNCFdh4Wrg4SzPcOwJHV/jfh5H2u+fV2GfvC7fFZrNd4pyT2MxcHFrvvQ7bPGc+0zJSMJ7/PH55vq5kISYSINt13v0Nbd5QXbTMMvvvcRGpFmUHAxgEczgRohCNVojGgMOpN5heyD18d/mxKEMIFNqYoGm9IGNwE5z8x5HkOyLAuLd0QWhFQOpDQxH2ZjaBZLAtZuvQVFkieUV7vONXKK/ALF3lvrxysgl4ykQ2iL7ft+4JCtTGYzpdBbYITGer3yd5d/QFKiyH46A3AokA3RNYtRnlul1pXL9eLUv+22aoY1b6g7Sycn4cR7ijzS0htOMvGXZJ4Q7tKJWZO5s7q5RKMTulmATmYgmdD1LhvJGWfmUna/1SBUWIVkUUVDtEsb71FJKInHK7yrSn062yJ58ZLWGt/96u95+nbmUuG7777nb/6Xv+LD0xOKcDoc+cmXb/iDf/JH/Mf/2X/C8TBzdDZx09UEW22je0fCmb1D+YVk+/Sh2P3V452lLixCuXraaoLppDcz9FwJhkxSVVaM+PIPDX7V4S8fO3/3vvG333a++dD5nsYZHXE1Tdu9JUm8uH/D6e6eAlzrhXr+FtXKnK3t0NosuNYWbxKbhFYr4ongCSFNGTlU2rKwXhf6ukbJGftMSWgWtFcUofUryh51+zqagJTNfS4JsVx7rjXu3tnF4SYtmZwygjcaXR0EiVCKgQqpFl8eQyeQpHM8wlxgygcShdWvkDEBHuDucBJjgV8MZK6u/Gzbeu9EeTY3U4bTbGu8rmPJGtvT7yEZrpayLZfk8iffrJ9NSQFBFRj8hiEPHBREvkxyJRrNiK2CVnIGr45Tj5Zlt6vso+NjxRhAcCvMIbuXVNWLhgOeopBT2jUQdyWtjHSWgYr9sOIkcRXdm/m7u9qVBb75/kyNF8gHIc2AlVMehvFt1zsdn7Up8NSP/7VKMNwRxmCELl7suLFZI2KLz6UJNnBWJkkkDb/8PGfKlFmvSquQmjfPpHkOkePxbqtLsrUI0smSs5FlTJtqt+7zaqjzho2muI8aRxmGggVhLjMlTaS5QMmkVJCUKVMxf3YyViPdw/hOU4uNX9Xdw9NWUw/Uma3hfw8EY/FT8aCZWU1mZd4oQwkLyc4VgMhILWpoUAVJZeyv8Jerdnpb0A6XZqXH1rbQehtFxcEqY4lNJ0vHe7ZVpBcSwuTIMBUd7h4ReL9aHdf7npnIjmAihqOeeD0w5ViAoaKR2yU98gM1WSxy8LCVoEuqWMGC39TE10viqjPITBahiNWo7ZeVv/nbf+Dt9+9YqzE0j6Xw8v6en/38j/nhT37Ii+PRXCopbYj02aHskLlNuJnK3TtYNI8ZLg2pylOayEUQvZIQDjXT1syZzJTNW2i6ygujjd23Cdu1Nda18dtz5zePjb+5wN9c4O+fKt9cKt8uyntVbxjkQkrccpLEm7sjd4cTr0+J+SDWqw93H5GZpJDUWuO0BBtz0yfHXaW9mWKqaoSatlZ0rUMJgt1AS3WIWE23QfCI/fVulHRdK5ITMpXNIlYjS4QSLLlwmD3xOkPvjT43ehNy21quSYMSygJfHpqMXCZyI2QVT6loSm3rJpy7rU4DrnaeCNVJDIpL9NHbT8TA8DRB6kjqROpR8FFSgTTp+HxWB/rD07UDQd2VRdQ1VmGvskSNIGVVq9LYRNGuTlOCnj01oCOyuZ5HZw1/GLO66xiPkNGxQwdZSzZMug0Ew9rvQ5bZ3wcvA2M8R5w1HM7atsIaqowC5Hqbxb7thXAbp+z5lu6692cRQsal0bd2nmameRqdiuqi1LoPtQnRXcV4A3aO7KDic8fnlWCzh2wNEKWrxVyMDGfC3UWJV8mwRCjRKzkVcpqtqrfC6W7m7n7m6RGuV4Xram2Bcfedo49ERrKQphNIomfQXNH0tAmq3ulLdSa3Bd67Cx9TeobIYsEnn/HD4c7u6+U96TR/FC+Np2nN3UKPV7Q1S0LvSl9Xq514f0fKmVK2Lu5EEHuH1qBYvKVXOpagTmtorbv3BKKD2D0C9NosGViaeYamTvSzE4SUC/RGXR6hK2s1gVL7dcfG2xBXBloXLiIW/2QhdXMJHoAZocy2vMvBhPk3V3jbhB/oxEFnOlez4KoLnrhndCDg2PzPPEI7tJcGApbI+RFAuleTOdAp/GJJ/OKSWDiBHJjE+kysHZbrlf/h//VveP/+PZWFnBKHeeYHb17zL/6jf8GrVy/44niii7BivRpb+9iCMXXgyDKUeldoK9LctA4w0WHJM8IErZJFeLEUyrXQmZizMM8rOatV35AdBh+MA2VpnXfnlb98p/z338Offwv/y3fwSOfsBoHKjmzta7qQmVLmD1+/5PX9iR8/ZNJsBaezCPM8Ww3GZAqgq1B7QlO4MrenFm2WX1s77arUulCrxz1349Q9G7GhVkt3mo1Y40f29d8TBvT6ipaEHPI275Gj6Md8LLw4HMbvlYVFG91dywFMZgTL97W5Yc03Xg2rg235fNphuVha0rKezWLqyWOErghDv+De+Weua1vDvi6njMjBVocXPzbQZtOaZ8wywQWtWx2GOoDh3XLLykMSkSET/Ae7H/FKS9buTD1EISIUEbpkmoCk6p6LbDnWVgInbmKsNwPG3ZoU90jT2SzCcd0Rs9yOrpbfvH9jzD8BdfefSfbXVnXXdk9ZzhcvPfdcCbpAEE+1y4VIIxo5l0Ew8io70eHjeDxwOhxscKlcz3B5grpu42imtM+Rk5OCqf654/cky6v/L3Tt1LZnUu3NfftZ1dsCYbGGvOEhTNxYEm5bO/Xc6EvnulZab6xqjUmKdHJSDnPxArywLvCIuR7Xq9f2q803jFi8Da/ioNmFqy3IJIaQkgRzy7sZu+/9RlC7JVimYukHtaNV0KsxMVuzGot6PltM5TC51emLf1eSaFPEiVKiDJmiKUOeiB6FXSOmsTk3dv84k87cpk3UXLNg7Ue61Vt034QnPG/VeCJOkWMHpBTwfWz84OqCofkCyBo6rUDqNC20lt3tw82sxvSLMyvDJT2YbJ+kajOYpv50YIa45/5awrVaky0UQfKBnjKPH95zvZhbThJkMqe7O/745z/nRz/+AV988Zq7u6O5+rChSWqJwepMzMBvIXCJjXhrt368HfYEH0lw/wJE0bN5KZpaEnTqEKXSIvn/DJbq8bTyd7+u/MWT8D8/Cr++Ck8oCzf6FrA1OpWJUjJfvnzg/jDz0y9f8PJ05OXLEzLNrA0O18S3Hx5I10x77FbsulfEY4R7K3isCawuZ+1XWq+0HmzA7buFXlw5RBeVneVg0jXdupwUo6w7WU7gxoEq2ml9ceLDZPGPptTUqMUYsklxy2wyj09X61zjwVQNE0sNFHVVqlec0eF5CTwaTOzdVt8v4PTs97D2Mozi9erPn/pQnnvjopSCIBbS8QupBtdhG6/ka9t7b6NNx8h9xALFFFVJCSnufeli1l9YUruSM1E6sMhMT52G1RSWPcBGzV3x/PCHMYmRP94Lm+7aTkWEQ5S6NmMVu5Mr4oUyRn87kYEQ82pY6kICKV6lqHGTk4qQpDAl8+CVXNxV62uxt6FrtHks0JVoCiU452eVeT4+/lEVY0a3dQ+yD/ZvmOMayKcb200hJ2XKsfwcHiG0urBeK9cnZb0o1YV5TaBJkNxJGY4HK1ANsCT4VjzYfl29uO02meoNY61zs6c2qIKj6OGmFJAinsMTSJvthyl7GYmZ6Cjfr4IuiyGebhCv1dVaJHE0pVqKCbv9+kqK5OL5hokh3qwMvjcidQu496ggurnAYrW5wLZMD6WymJtheS6wqz+nzbgpmWysPNgg8OiGDdGyyYZGKL4gJEp9yoRmofaJtRkF/VPr6cbqS9u1RqK0bhbO9qHdtzDe/UYNGCSMuejIsJzQnHj/9nsu50eUZhR5Mi9evODf+xf/ki+/+oIf/uBLS4PwpWmNZrMrvEbvmV6Npr63ebYinp9QfmNO/WFjLB9e2/fr90ClaYGuJG8/xKgZKTxp4u9U+MUH4d/8cuXfLpk/XwtLGxGzzTLFvReSmOeZ4zzzRz/6gjcPJ/7k9QMPxwO8vkdLYQI+XAtfv3tJkonz4wIqaL96/CuP/MxolRVqa+2Na69eTIKohb7FfgWrjhL7Z8+GHhPeB19AYkmtIBkjtonH62KZaqP2C0mO5PlAqpO7Exud1U8ZKQZCx7uLuFW6daWxGNm6mgVYsb5/FnOG0QIp4J6O+IgdevNtwzY41olNo8lrU3aLK0RsIUZBTAkmSfTm3Qz8o9teFhcxsimSDpECYQUiuif4OznPP1Wy1TKu1bLlhvIbzBz8vTYHU7IqXYtEkfddCpYoyO/O1TPZnp0d7PFYR+e3HkXnZizGdK2rlU2LakhB4vmYLKOIJLKULf3BS4zJcJ0O+A4IRQqzHJjSTE4TShBJOrRKQr1wjph8EoxM44owH/NHHr/nx+fZoePWb/8oIxs6kLHAnFwJuUhunQvVhE2H+s3Cu992lnMzf+7q6CxZqsRUBLJQ5pk0Fda10aqyXJ5YrleW5Uxti8UD9ba2nnmTt8r+2hO9ZjTbZCYSXbrbzgoXQXsne7m0Hru3NqSb8k5JOB6P9FK41k5bC9qr5xM2c03GHosFvRu3LRboKCt8/q4YAgVl1IWTDh3iAJu4BOBKP1ixfmExYRNUd19mcQckCkkLs3SO0nmRMi9S5iDJLZSNoaWA1A9knXiRDmSvJZq1kXRG9URjpiMkzw8TKqMwsEs7jd/9iYKZt2fo2XpNdJGtl2LEP9wylLsJmQ7ACeQIOdG78nS+cjkvVuvSd+Y0Fb764Re8fv3SYybPMOhYyC6OkrlbkpuCwxCIKdVi4ylO41SGBpeu5LsTRZSSkoMXy586X6BkJR101Mx+Wiq//bDyV1f4rx+Fb75f+OUK33Wl0ulWk9ARsO2lJMIX88xDKfzo9QseTgf+8OUdD/cHjqeZNBdKtv2SZWKm8NM3P+C7+ZHv331nIQItJBWKew6ss7rrcI9b3VhvkZ0s23iJmMLE80+tUldY+WYtRSUkX6UOHhd6F5pImGMDNwRBrrXOcq2WoK9Gwrd8QntvPiRSEQPVvfDYLO+1qSm/Vs3qqm4JbrOYiNSf/eQnjzsZkYyRDC+weQY0SB0RA7M/anSZL07eW13Dzc7e9CtX9WGUsIj88zdULLm5r3CNqpkxFoOOHo3CyMfT/vxzummZneQRj+fPh8lcoy6bmjN0R7+bIOvtP28T7hwH2TuOQHSAJe0mcRMYJ2MnkrbZ9ufRrWyEXcl4IDpgt5EurY/s7nnU3nutV7g6+EjCujZq7VzXhVWvxvreD61funvXnnTtI2Pgdx2fVYLDUxC6DhfmkQxahKC0y5RBJsLtubbO2jpOUmN9VNq5mdAQgckmLMqTyWx99/JhQvJk9Oq28uG7b43VuVxodeuCvT/M1eh/F6X3jK52a13CKkqmBLXBGesyL95ME3eLdWsVNR2UJInj3QltDVk6NS+sdUFqNSbnWEx47NSVerhZVa3rdYqNN+DlABJJfHEPYeEdCVyn6a7ZVx+EhSjFZZsklWA2lh1aM7eG9An6zEEqD1J5mQqvcuEgZiFJLO6Y7/qB3JSX0wPHNPkiVrJOHmM82LXy6uqt+UmKp0LgCjVUuitCDaEQzy7OHxDGsKSCZmsSJgpyPyH9CNwBR3vyuvLhsnA+X0c3BDAl+IMffcHDw72TUmJinkNRG9jorJ2aRU/VP6MoPZkV2gW85MVAtYGVJrGNU5K4y7/TeufpaiUrp8k8IUXhcan8zbeP/A/vlP/yN8q5wVO1a4l0uqOdsBTCpnpzmPnyeOBP3jzw5uHEq9f3VirubiaVwuRlue7SzF1KrF/ecZzf8Rd//29Ze0J7cUTtwisq9oRTwlM6LZxn5IahGEPJuX2hSdEsI63AHS9kTxUwueDWkkJtzf3ayWNmeDzLPq+YEmxtxUhvKyoTkmZStyp10yFRju5KV+Xy4Uwwklu3ohpt16R4TO9gZul4EKGTpLk1mGG/HneWYhTj771bbCzWkKclhd7oi1tj4b5xWWCSz/dA7Gm3TnXc0U5Sjz1ie8EKhpt1aGRMi1NHt/RbnqNLnHjmnUxMSTik4oDES85VIxJm3TxjNzowsGzasSqzu4Vdvgb5qPnlTMr4BARWvDFOEnsWaly1aQXNTmoMt+5G9rNCK3jxlIV6XSxGmDLL1XL/lnpl1YupVw+iS1xBDHaIQlr6s3H7+Pi8EnQ6fVRIUZ+UVNUKQU+ZnIQyiyHTeFBV2qq0pVNXoVZLq9DW0WRxHjMmBA4T5MT8cG+VOtwHf37/SF1X3j49Ws6brnTpyCRISwYB2Vc/j8XgA6FWWFX8Mh2o2er717qStHPIQmkTzWNRYNbFVRs5F5A8WJCKkLMl2PbJEe/SQKrHwzKU2b0o2yKvqlxatyT4vU/BO4dHB4zBGSsZyYnppSAFLh8qrVo8cuTkYAHynBLHw4HkgqZrp3qFBFFTYioRe4MinRON8mwTxiJNXIxE009kLTRXxJc3d2SU4/srqV0Q3pKoqC+ffe3T/Xm7ClUNyNlaVaak2xgNBcjOZaGIKPd3wmtJ/P+I+7NnSZYkvRP7qZm5e0ScJZdbVbeW7mo0uiEg2JyR4Qj4QvKJ/zefKCMUITgEiYUApjFo9FLL3TLz5Fki3N3MlA+qZu6ReSurRfBArzo385w8EeFupqbrp5/CDWAz/tZl4fvfKbq0qL/JW2WeL4zjQBgnp3jy+kqDU6PbJIPuqm+fuZ3ihiT99EjjfiuIt6UsT98Rnt7hqCTQwiUrT2vhXOG7Bb67KP/hPfx2FV5qYFWTtUbxm2yUM6/vj9zfjNzd3XI8Hng1DJxS4qvTgdOQiMfR+KKGCWKihgMSBsb7G8YY+TokpqeJv3j4p3z4+MBvfvcPlOoDgI3x1B0zaPSBWqrnYk0DGhNQQKQQJDAMgZACMRRo/9bkpetydVYOU1KIR5oYsjalyHQwGQ1gFGNqBlMkE4fAOB0JaSAMI4MUI12PGFjSdCKHowGycl5tor064YSnwSRZATL62a/M1upULAKy/t4Krcexhm49Wzmu5mrDurVuEYZoN97SHZYBiEyhmiNQTdnXVTfGneZRGEqjK6HNNdu5ars6gcmpz+ds7DF8cma6wdreZYu2xB1Rr9eV7BR1pYNm7G1cafZ6oJtoCYxjYjoMtKSOZeIWOu+ta41Nb1gf8YYb3e7DiOB95FTLNGAgyFKLi17y12xBAs158/R3WTNrOLOulZwr6wXyIjaDcrd2+yMtYnXZ61Tu59cfNYKtEN3mnYsafD9KtOMbhGlo0Ujbf6WslfmlsC6BdfFD0YqenmkSBQ0DDAPD6RVpHCnzmbyuXJ7PzPOFx/mFUnOHV0jyicrFNyIEf8gd8LjnOzDBcyVbxHPHOSO1WNhf1p0RtAVbcjUUXJx8R9T4TkMyxyBlKJUyr7ZDskIaCDF1r6bJdakW/qfGJdiuUp3nb2cAAUmJMETGO2sSXS7FomkX4iZ+MRpCbByPxBAYsBaJOV9QjaCjrYP4LANVkigHCmlndfZBUmAxOErNBBmpPm/s8vpIDMKwLMg6orxQWzSqXNVCO5ze37gbQQ+nRJy5XzAATbDIsB9wv04HuB8CcATuAJgvF+svXc0zb8KtWlmWmWUdGYdt7tn+RjpVXUtv1v3JcK+6Fy938tMv6ShbvPVnfXkHz+8gt8nWlVIqz0vl3az8+wf4/VL59y9wEeEcYo+Ym0mxuQWRn9yd+MVP7vj5z3/Kmzf3HnUKRzVUbMu+SBoNWCUTGgfG2xNpSpxOB6aXI7/+4U85jCe++eZbaq3dG4fcn62l93p9TaG1/IiITzpR4imSxhatqIEP6u6c+7o2GrDeNiSWKZFaQRLjYfLKidgHuiFSKjEdmI4Tw2FkOI6McWVImSUXY7DJoAWmg03KeH60SeIdGa7WikUYjKACUC3kuoBUSq8NWuJRxOtJXptvD2O9ttVaRbQa0lY8Oo/BACoSXQoOIJExVFLQDkqr2SKlro/ciNDASUrXpT0++rTx26d5fBovanuvbgS1IW2u5b2FdXjUVQulFnL1dfBeyhgszm/pY4efIQjjNHB7d2yqj1qLG0HZDDCbdRFpk2L1+g5czg2X0QygLYJiGYMYxbO6sns4d5L8fs0Iriwoa/F23wXyWXbtHNt27h2MlgT/0vVlIzgGU9brsjGt+LZYv2CgijJfxLwssnuXmTxX1rlSqru+Xu8JzauKAWLg7vaO6XRguDkhKTG7l7DmmWW9UOdGBRURDaRom5YC5ul1j37b/J44cOEQ7xkyxFmw74NQQzKmFcvR9mbUeV2sR2USr1kGj9Z6ids/RnYHqaKa0RrRuBNfVcTrolX6nXmDi6NWEUMRxMD06kA6RF69FgiVh++eWMtqhxKLAFMauL+/J6bEMCZTUWV1Je+G1eeLaFE/0JVhUI5jNGq5aiNlShdTGLBoJtcHBj3zMd+zhpGPN4oOwnxKjHngzXxPqgvUB6DucvmuTNiYdJpTRPuzece9tt/Wygy2NQf7ZCGBDc8OMXq0IjBOkyt5GMcRUaGW6hR8n3ym34t9SpOT/dEwA9gO1GZAt1tTlKU6QLAUpFQbgaRKqSu1VNZFmYvy/aXyMCvPL5WXUrgorLKXG2+wB766PfD27oZf/eJrfvrTt9ycRiSmft+e8XPiCgcREAjYGKjhOJIOI/Fwg8QTf/HPBm5f3/K77/6Bp+dHPjysJh81uAHUTmXYKwhNWn2uZpyMrSmmaKOAwooRGng0k/Wq/7m2tXJ0X4we7kmrdW9z4dq2NF7g+bJAhNsknBhJKZImQYbEoMrybGWV8WCTTXJeGA+Z8gMsi0IyNqc0mCzFYHJhjslqLDiur1s2tGIZE81W1CxrJa+FXDPF6QlUq9WzRMjYvQ7R5p4ijYb6gCKUcqHWFW3tFO6Yt9R2jE5mUVuN0Y1CT3/skRvSxU62HyGDlW46b3OjTHMV1OvtzQDG7R1U8V4+M0Dm7PgxDNvvtZ9F2fh5myPQ30fd8PQ40J2z2Fh76Jyw2ztHP3m7HlR3PG2+ZHZ7uoX+bZRY++BalBX1dib7jFZmubL/10tJHPpS/cHry4wxQxtLkWnY+PaZBiqo1CzGxtAWaqnoUl2wGoDF0wHSdKCtvsTI8XjgdHMLhwMahLzaNIi1Zta8oE69hDg/X4xGn4Ohwiwq2C3C/nLlZwtaEA3WtIpJjuZCUbHUSE89QJUVQmBMiUD0lLt6Xah5xpvgdGmt2ScSxE0YXfAaH+MGJ2/el4NKktGjD6eR8RQ53QAUKplcrWchSCCGyDAMnG5vXdlg5LbVINGWSnEPyr2olsaIAaYpEOMW3YsjqlBn6qByqM/EeuGxHKkh8bQa/6dOgUMauMsnAhHqo0UIzePw/7YG9K3u0moXujOAwQl3XVo/cddaFsnhXgCdDV4EYhoMVARGzAxoURY1JC9uoGxfdFP2fb/sxG80aQ1a8YkQdWNqGXhRiFqtId1P4FozpSjzCi+r8v6sPC6Vy1xYqCxsxqxJUBRhQLi/mfj6q1t+8pM3vP3JTwGvWzvcvwhGuu0FGnGeztYwHcdEmgaG8UAaIr/45S0hwevX90Dh8eN7d3OCR6sejWjzSbaFt/6siCQIkxCiTUOosfRWR8Qcq71P3JbIi1q0pHFb2quoAelgjZwNGqtz5XQaLdKOBohpeqtcMhkYxoEQKqc1kKLw+FDJGcMvOWNVCEKQ0ZwpEngdC9WugppjWqtxSpY1UIqlTUstFHV8rnpfrgoF62aLKgSNLofmNqoGSrVJMBYobEZQXHwjRg5RFpcxbRHgZiq0nYGrn9PfRGIgxNCR+U0Pd2d/86/s/Vxnte3VXQ29B1xCp4Lrmu0q9buXWDeAXhpq5ObsIl4fytIb8u3WWuWw9nvdBMfySbVm+mBxJyboKWA/Y60Fo71rR8zvBXCvQ5pBHza98YeuL7dI+MPJaOcSYxxzpPjeBP+hD2ml+a2GrC36qJWSlR8+PvJxWbitr4gp8vT0wDLPhPFAUkGWFYpNBgghMqbBvI6UKKUSw0IplXVdt9u4WmgXzna/Yr1iEoScF7RmGxYqduiRhtg08tpamkJXcr54b56nf1I0xpmWBo3JI8f2tLK7H9vY1rrqrIxWywgRiSNRovEGz5WPHzOq2YkKAolEigP39685HAZev7YBxxRY18pHf/xkDhb9LKsV618KTAq/AG60eYcNzeasEpoRr0eYYF18twcqE89pYJGVIa8csvKVviWwEPhIk1BbZU+fOSCjooSgzgFIN4Q9KuxCBVTjBjSIM5t7CttoGpyMue3zp0K+Py/XO/FHrmvXshNJN/Ep1w6NuPJQTYhWolamAK99lM5jhUsR3q7CReAxWGwQgNfHE1/d3PCLX/ycn//qa47HW4qMHq4UZr2QNRuJgYIGI7+ODIgkGAXG5iwIN0djPzpVWM6JNAXSceBwf8O6ZrhcKLlQl8WRkobsTVH62CJxRgKJppQbNKRUS/9aXZ8+lLpxZLb5btaDGzgMEyEGxsFIkJHmXFgvWMmGGl9q6YY4q1HJlQVvZatANUeYjCmflZQmmBI/+7nfR4hUDTw+DzZCTZ8oJbPMT1Y7dE0ZSy+FGtNIGw9XHTlZMzVn6tLqu+rOWrQZjOL1tQDjKRpV3Cf+khAINdjUliDewoOlC0NgOARrYp+9P1i9nWsPA3dd0ZMGwcAyaDHi7RivsQUuh5s76XsCNgzgNBJyYXF+yt460NoIdirKWji2dqJ2rbmwLEZKoFWN37lCyTbpp3iqtWd+uv2MOxNhDoQ9Xjffm2+8f+ZdlBrDxvoi0LLF2wHXflqvVsCyGP+4k/9HjSCWgelIy+bMf/rWn9NSmSe4z4BFdwVswSw/+Hy5ILWQDgeGITHPZ5ZlQdJgBMfB+9NisEnIMSIxEsaBUKrnuDM5509uqP3HgQ69nlZpFWVjRzCXInjaU4J2cvxcqoHcioNOWh9QAYIQkkenKVlKoNHLe5pUd/fSPEDb/s0zJgSrAwanQSrqM7lWY6rBBCBKIsXE8XTicEgcj9EEODdXw+7Zy7i9litYcbgCg8JrzBi2KK33PwNbH1jxtg2bcm2e3MgcR7KuPNZIrpF7bkiSgEc+c8N83Suejg3QSS6awQtbTHwVoSHdI70u3ssWOcsWaVyH45uXfC2N/jPZfcxn4tLkZNsrhQ4Bt7S61yBUt8qN011FUZIIx2SOx2FUjlm48ykbFwwclSRwdzjw1f09r1+/4f7tT1AipdFmIcxKJ5AoiEfpoD6fUqOgqR9RxiE4gz+cDpE4BOIYSccDGldyrSg2VsuUZO1Q+FwdZ5bcEPp6Nhx2rQ5yaswgdbdd7IoEEm0eZxpIMXKYnA+2rS127kuxUUdFbW58dUhFUaNUlOyL7ukypPgPKzEMyDAwvGo7l8glcFks0i35kaozZb1QiqXM+t4XoahYiUCtMd8Yg6zvuBYbSdUI9yUFg+b7+S0UqkAaDRCjXbfQVwLE18ei0/YzS9kGiqi1WLjjscneZ3/pom375FX4Xhzc1r9H6dd3YcQfowlJiKbzUnPEwvaaPhrNz53t+eZGlmJglFZTrpao8HF33rLWzkszEP2w+Tu3npQtfmV/1/4k/SUS6KOyGserNu9210y9lS90e0c39CH+8YG68I9olg8SuD0egYoeZ6hQstfZ9AIa0TLQj4WAjDZWI6gxnTTWk/Yord7RpFMrfHx8MohsKUi10DoF4e7mBFU7sawZqkAiUkNAJ/VoKu0K/tZQWas18NsBrJ3NgWp9YBJMEisGmIi6YKTZvpA0pnm70T4UxPujhmTClcbUn71dNvjXD5DXYpqzomC9XikRvdG2qNHHXc4rYSmQrdbx9u0dIQhpMiM9yIKEwvlin5HXlXVZyctMzRmDH+5OGHavIZhyfH07Mo6JSkQoBGo/yFv/mBKovBKDpSwELiGx3r2ilpXnb79hxgZdHoCfyGuiFCy3Zet/0cJDcbo4qZyiMArWWrMr8LfLFK6gP/Jv23MEXr2+Iyblw4d3nQpNBIvKUzJPuSo1rzvrBVtPlb3gS0ejpwv979nP2DZ8SAkS0XSPDAWGB0IoDKoMVRlL4WZQpjHyywq/rDaDfkG4/fkvePPnf0lOiRwjejxSj0eWpbKu1eMfWC+VMme+z5BV+NO3E3cTHAU7HINa2F8UXZV5LoTYFEHglz/7KcfDRFkLlzIjt1AzHJY7dCnoZSHXQi4ZcjYatbDTypUdH7BvSju3mtkj8bSYoj6Mk40EitYM3TNwur1pqZWsPvE+b29dDpV1Lj43LnA4JMbJvCYl8/wBlvNqrUKra0KB4QhDrRxenpFQyI9u2bpEbfIVMH7V4F9NCtY1c34+sywrtWY6OrgqQQMSJzNEYspsOT+Rl8Baoqf0SwtnuoOtVdHFoknj8BeUZHMVJ4uAjWCqgs+klKYr+lyxDWSzRU/hyoyY2mkOoWwOXtiOUQyJN2/fgBbjGi6F3MpI/mbd33Q+1/m8kLOSV+XykqkF5kuxElc1Rp4gxtjSlqsHff6/RqMQPHtTfSe0R5nN4d0M374uGcGZX4IBsqpFqJVClbbmHpb6p7ddbYjeMYxXqd0fu77cLN9C5+TDVclGUjsbTU5dG2Fcext/oCB92rL3wlqavm7e4L46hirLPJtQh63dO4oQh5FtebdFDm0kTDK2eevzqVYIB7hijGhfbgVbGNT/MGGrVESMlkeCEEruXlf3N3YIqRTN2wgpsQmqXcVCB//kjWB2W9u4oUkFJ66tsC5IMQMYhsDd3cQwRtLR1/NcoFoKtNbKMi+UxVI5mkuXh00g3ZuMMAyB4xiJMVHFMKJ2X5YC3WTJ0j8Hsaj4JMGQsdOJXFcucSSHwkevWb0JB4TsQACrH2UqL035qa1HZ8zYf/XVFapsqFpkt1xuyCQIh+NELgfCR9nGrIntF9HWtFJdYfmeX1m8a1n6UWLtnZ9m1HabCNlxdkabeIR4RuKzIT2TkmplFBhUCSlwizV5uDvOm6+/4ut//hd8KMoPpfCxKo8VSl5ZMBabApS1Ui+Vh9XQtV/lyjiqU+epNw/jbWhtVl2XLu5uTqw5M00TWpWZ2UaBrQM6Z6oMSF7QvDhhQfFoQ/u6yO792J1V+2Dd/S6oWpYmerrOEIG+3htyxpvRZSN4z8AqBrbJ1RRmUFIMTGNAkkIQ1nO2VOzS4BXG5zkcTAmP00IpRqZ+lYLpl2yyQosQ7Ge1KOti2SStjctSPUtS0TDYergTXNZCzYbhlKYDxKdL7PKLDT4i+88PgRh9SkO3dbW7GrUBZ3wPrnyIvsFt5XdGpDvhSkvV9Z7lEBjGCa2FvBRKblkwOgi6G6Fg9b68FgN6zYbtMKxRZZ43RFWKlRAMzNYivytycFeUoX1AM+BS+u93ueo6QbaJHYIRhYfYFBXWvmL1CfHaoex2eueyIVgGLfy3GMFf/uJntpiD0Njn8zLz8uEd61K5PENvQA1x1+/RjLSSyRR11Gg19gCDgwyIwqTOPNAL1/ZAlnIPXQCLtnSn7hZNkGoItjgoqgWJGN+nCEFnYxjuV/N0q3sV9m1olkK1bxYqlMjVNbiQSRTGKfHq9SsLuUMg58zl8tKVamgF3uAjUIIrZP/MmhdzaP1Z1rxQtXBzE0lBECaESBxH0hgZfc7hzIWGeiu5MD+drY9zbgrde5agK6NxjBxPicN4YOBgBX8aE38gdNTapvxNeV0Qidzd3zPdfcXDNLHKhP7z/x3rfOb7b3/LY7kwAgdduS0BKRkWyLXdC+ZA+SELUQgpsE3ebhmElifQHnOb7tSdIRdu72+QoHz//dDRJiEmpnHiMIxICJRgSsXqt4aObJFBU06t/9NmObaH9nVwQWw+QcfXNCOuWD/n27eEMXD84YKsCxoWtAh1qcQKhwoj1uWY3rzm8Cd/wvCTP2U4/Ix7VQZV8vMz7y7PLLmw+H4owvdneHoszI7E/NvyyHfjmfpngfvbidccEA0sqzlwhz1vrf9dpbIG41k9GL0JGgeyBOYixBliUUZXNgszq03Vte2PVttqCrf9r6EbG4aj8V6eLzMxRo6HybM6zc01w1J2zmDws91HAu1Q1nbGCiEZPes0wWkYyLOQ5xdqOXPRdxRWYn1FzZFSMi9PysuDkCWQ0oGGWMcDBqM4tI+rO+x8NwQKQbfJ6QQbtKxlcdBbdCNhmR/N1gupRb3jxgn1d8QEVRXx4eSyi6al6x37StEAbzln1nVBnQR7yyjZLpTViBysqR0k1W1/zMKwAe6w9DHBAtQQQE/EVAhDoubCMi+0gGQYJg6HEzkbMUhLXdUqxvDlg8R7OxDF+sYx+EuH+oXGXuUgPUleu052tt3LNJW+C06cTzSJkJJlyoZhYhwm5uWZZTkjNUKxTwuiVnZU0/1onwzqDpynq6+c4M+vLxrB4/FoYfXgCkuVNUby+RG0siZTGNrw7nEX3bVoVcp2E92zcRdETR0nmsJRH/OzOQZ1l0fukd0uzyuI9/DZwgZ8fFOxnOXeS2hLri0abTbxUwytfZzVE0R3P7LPDkGtb24cCY4E20cU7T43LgjbaHu9v7kj1JoSWXO2UF8tJ2EowIT4yJQ+AbtanURzpayFvC6QhWaxrz/VVjH4OJIUEtboa/9eaQhH3xttPTfNMBgR8+jotBQDGiPDm68olzOXh3fUXDkzIBWO4pyZklFvauqBXYvWgpPbijk+W48QzrJhn9+iMXux9ghynCYjO2g1VEAkkEK09hnEYO/RCaK2wiEtotyGnG6pnU5+1T6f3evavnaPW+1ZjgdCOTKMA0K1MT40L9z7If09pmni9PYt3N5BGBl836NeqNm87DW38WDK81L4OBczaAofS2EeMo/LQsiBmzqStJJLJZbaQUi+lbSnqFJQUVLjbYmBGmzuU2v/iWKkya3bq0cI7hht1uLqEHVZaePS7P6VUi2N2ZrI8Qiq98f5ngubhDTZbsY22DYyDoHDBKEGyhipk1JrIegLpS4wHynrYGhqFWIUT/8nVAuhzSSUnQyKsNeNWxThJ6adA1Xb9Foc+giqshnuVmlpPKvFDE/1iRO9+uK9g4GNmagHbi7XjXxEqZRqFIEi1orWJ7mD1++q6YOAk7VvMtuizo2Cz8KqFi1qMEx1ipUsINkZmiQQkxli1cqaN0KKVirfAlH7i92jXunI7XyZ5t2Q4+KZIJsX2gY0bzuwxcvGF2zZpxhHUppYs2UKe0ejyOZY7J54T+LwI0meH72+aAQfPj4RY+T29oAC83Kh1kIc7pliIYyLUaJly/GXKnZodk6p+GBewKx29qgQmzu4g4/Ycsy26aXa7MKyGuNBaYUZX6Qaqgmzp9Bqd9FtEC6jiVxkK3w3N7TNImtRBmr9TYYOtUjFGLZ3fINAVkWqknJg0ZUfvv+eNESm42TTMcrqEQZdctTrc832xTQR42BN/zGwnA06PcYRRC2lMopnnJTLxwslQT7Y4X//7iNlLZSLG4pq0x3ilqTzur4BBSqF8TnDZab+JFBOR2aEZwIHEsZx45OOHWgUXeIrz0i+kP7j/wQ3bzj8D/9n4s0rarB0aFsbOFFZrYWgWk9QCRUOtgcDlWGKxNFrKNF3sddbsYMhQqk+SFbdW2dFZCGEgRgCd/dvSNHqmsXnqOzZisAi4DFZzThIRGtG82Igpx3q7UtXP0uAzXIMvc6SgknE9PqOmAryMAArjU+qJxUDxCgcxsAwGaFBXc6Ux+94eD7zzdOZ92Wm5pl333zgmx8+8lJW5lqYLws5FxqOKIZIjIH/8J+Vm5sD5S+VV7cTgnDUI2O5IYk5OHNtAKxgjlQtTszgDzQX9LISijJI7D2oWtUCp1a/rnbGMsGZ+03mjLezWUrsnIiDJaIQQiWmwDCNbGrKV0bZgA1qUca6wjlcUFa+en3kZjxykwKnKXAYKmNSOEAZAms8Qhbk4s3ueiKEA3d3VqN/9RbSWFB9Yl2EF1wpqxJDYhyOjOnAvnnMTs9ApJBY+yDjHla486o+8Vpic8qlt01JECimj1ZdaJ68CKyzGeY0RkeNXjvx3cEv5kQOcaCoca+WXCFrAyabgcXaioO6vjULa8ZU/FxJQFTJkntrlSr7HAsxRg6ng8mXBGPEMtwV0TlAXDBoDfyIR5VaaQ2KtTv95kSZo9HkQ6hSvbZn8jDIgAZlHctmYbsDZ6PPRK9Nk6Ws2wxTP6HSyMGN1Uex4MSAhJ9MN/nC9UUjuCyZFCslj54n9sbTMFh0EWzwqArkIjaPc2fTtKUIAlbDAFMUDfmlZqCkFh+ho17XMsYGM4bFufx0e28BqR4RNhaO2LwJf2wxNEiILUWrDn1uN6fd8bf2bou0rJ++oaS2yAi8ZujCWGrlooWUDWBjqLfsz9TeX3u7QfdXxO4pDFZLqFk86jMvL6Xio6HsdWWpBn4Q62O6XGYfRWVCF5zrTxsdGXjNpfU1Grlzzcq8FJ5zIYVoIA7MFQkqXYBRX9sWuZaCPHxHWBbiyxM1TYTj6B6meXVVEhUla0QcglCMGJaAISZtaLF74dJ1YHeH1U24SkEdOGFgSXOYYjQjMAzGiTgMA3mwUxpj6lvqJdY+KkujedfqNeSq7fN0UxboznHbIuNWZ+kk4Q1soAaICuNAGAcDkOyIlHfBrQGoBiEGY+IhL5T5hfX8wsvzE5eystSVl48feXr/wGNZuLgcoT70Q1ptJMDDC5el8uHjC2hlGgJF4HUuVG/NWWtTSOZkqGIjt9zx06K0voi9kZJGhty8fj9zlU3RKdoRgXYG1aOkFj2J1dYIPs5KeoTZ0H07RjLHhQbymlnmTM0jNhlg4yoNojbMOChaEjAQwoDUbBpbBoZBqMXo1UoRLqPB8+PSqPKqD2n11iK0ZwksSvLRPTTD1ORg02cttOiTInSbC2itWLYeVTdDY5kq0BgIyfSMxA3B2S0leN3R/ycBA1LIVudXqN7OFBrxexPILqt+D1UQtdakKtWcQW2FErtrm7cXLRsXgkdf2wSG3pHkp7OdBXXC9z3bjcd9mzTptl5gvK3t+YIYyUoI1xk5/D0s+9sGkZv+KuWTXsF9NlDaSjeQzQ4hS4tM//D1RSP48nIB4PlldnujxBg4nSabCTUeSArjUVnmCs/WUtCqd1bjdryOZAjG26kZlmJIwiU3Oh57uqADIpHheINIZKZ4bWil8TP2vK9EJPoE2KahtqXZKS6fJyfNy61cX62DzwTL2U1ZS7Y5ghhirM3uy65QxI35E9nQiYfJI4YNTDPEyJA+KS5iLCfTYeSrr18xjAemZDnuDx++Z1kurP6483lBNZPfXyilcJ5Xq0HMIFKJA0goVIdjB6x2yOqciRiYYg7K//PdI98+X/iXX534794cWEJklMgtwqROPx8NSk91FKQqzAW5PCH/t/8r3L+B/+P/BY0DNd2QiZxDZV3hZR59ekYjBJsIQ+Z4hGHPLVtxEobtZ5VAIRqZtVgNYs7mgQ5J+eVf/pybm5PJ4/Mz333ze16enwF49fpVT6fFmGgpLRvBBASbFmLjdVaKKrka1DsWwBFvxYiZNgeGiKjgNLJ0vz2CVmE2qeMAaBEuz4IWV6YRxghDMg7OmJ8ZfvgbZHhDnRZr76Hw7Q8P/PV3H3n/+J7Hl0eWUsilER9vjl110oD0eCbGyIdvf8fxMPJP/snPefv2NYw/43g4AZF1rcT0ihRWWAp5Lryca09dWRuApbNyKUZErTBoInJgZmXVYll2lDLbqJxSZ1SLD1nF0uuuDBvoqahyueTermFDgaND6vtmE5I1MicKgUy+2IScl+nAy7EyLJmwCgdvLzmOgkhEpxO1Toj8Gcu8sK7RkKbcMSThV7985OU5Anecnws5v1BLQetiIL9o5wW5bHqChjL2NY61G4CmVP04u/x6pN/IiR0SpnW2x/P0aHcQvD2hFo9OtFGTW0pfZOv9a4pfDYeIloDRILr0OW1d1ev2jPbz4iPmJNbeRxxigLGVlfZpyLYERlwSvMVF0oRopMQXFrJjCwIpmA2oOHhHBs+1eWjvLT491ewBREF86MCMiJCSycUgRsyfe3bGMw1VURZyzVzmBZELNS+Nx9zW0ktruzTD1VWztWYt/plfur5oBNfFPO0sBRFDFzLY4Qkt9PbQOMTGAk7HlrTDEWgMA+ojM9wv1D1bu4foKCE4bZS0CIHt6ffJaaEbT5us2HLQ+w12r8V7osw5bknYLRrsnqEXn3qtAvfs1DxnrYZiM++igma0zMgwEMVSnIYWtSs6ZLrXX5LNSRyHiWEcOZ6OTKcTh8FAxPP8BPg4Gq02d7AW5osNHy65dmJZ89wqQS093P0fL6Q3v68J5Pu1EEvlz24i7+bAbRw5euuCjRnc+DhNkuijXbQWePeDkUU/PyHTiSAJiQNZIhqqse9gKZKkZpCNGSb0vqStzrd9TJ8o0b5UvMvD1riqMk0TRzeCoEYbFyMlZ68TVmIuxMFJvd0z9m4Xa3pWc3HUi/lam+e8e+TmwfqPm0jsgemyE8XWM2VkzWLOQ7C6cUriEYwQKITlhVAnAheb4gHM88LHpxfO88yajfihVvVGZFsXEErwBvZixn09wzgO3NzdUDTy4eGJdVWG4UipFRGrJ6vLrBkgJYTalXg7k7CxIRkZsjGcNGSsTQlv6f7spQcnQ5M2Qm3TQsVbFEoutCnptZqRM/nwo5j8T/HUfvZ3aVG1Co1mI0glRoUY0RqYxhtER8KY0SAoAyXCq/vIMMLTOTKMhcvzQlkhr15bd0UurdShdq9bPdydZg+kP7F/PSK7+mFz6tpQ3PYLzReOu8DIec4MoZrsjPS0HljU5PX51pPqb7SLfbo63A6RazEHi4kLaG1jmUpDSbcAYIfj9Cxa6wYgBpIOzhi00ajF3gZiad+msen5jy3Fc3V7rloNbGQytbVOfP4MqEfv1JaE9TaDrR3DZGgfrTctLq77LA6stfDJp3x2fTkS/PABkcAYj6bIpoCuhSeUYYgcGVFRsiq5ABZ4WZqtJkpIJDUoRjE1AIOasViEXIOhN3U/cQrQyro+GYEz6dp3cVqw6NKoeoGQnIxbNs483ziRkVoytXrtK1Qa/x3Z+TWdnDgO0enLAHHyMw1WA6HDdmgpOqohOgvW17c+r6RhZDze0CHYfj/T4YbT8Za7N2+4eXVvqNIoTIfJ+RJN4L766c9Ylwu//YffkNeLj5DK3sPUotFNuMhNGEyxFVxYdsuZFWoRPqpNE/+/v3vhf3288M9vDvzpYeRXp8CrUThgLRGpqs0PfQpoFs5JWIBlgfw4w7/5/xJu77n7+a/QNLBmKJwZ5dlSZDlTxaJT7R6RXdLzOuysTNha0XwEj/mbpT/mvnVimCb+4i/+kpenJ37zm39AEL799ntubm74+S9/ZmnDZtS7V6bXRvhK3uyPtSqzO1miMARzDtQ7xAWTM+YC8woPCo8VfcjoxdY9BGE4JIYEx2ODeQtRlLGuhGVGlpeNFvz5I4/P71mK9+1lO0ul2jBZslmKkppRN6OSgTpn/va//o7vv/0AwJs3r/nn/+yfMXjLjqpQ6uARZUP6uYL1xYzuZLYKr2L1x0GEfJkhZ0q+UIo5YS0timd8JAhDcyy9zzAglArns6cgo6dndZcRqQGWZD3FY5MFuH098tWvRg7jxBQTYzgxyECQd8CZhhJ+/eoGVRh+Zpmm1Q1Hqa9RVf5qhY8fMv/2XyU+Piz8/ncvqNpxDNEi0b79okaMUXdn60p1huvv6/X5qt6G0/RU8ZfH6kZlkN3rA4GREBJDOvS0YxWjSHTmNUquLKsz6kRxB3z3oY01ZXvba5GulVKFS5mNaKQ2B/c6EhRgGAMpGfnHdGiyA1UHak5oHQkIS1hZ10yJUDX0tpzqLmIPUNx4t4Tw/tYsNY+jVe1UNmSp/wbdilbBw2H3S7dZj81i7Guc7fFkt31bDfEPX180gs2TKw7tV/fcyloMnJKd6UEsMukDZAMW8gcDbVgLhPXyhWgQ6+Dz9Pp8rxbQRTHuvejeB9FaLdbQ3PT+v/ZC0dILtTajyjdYpHupLYVtiMBitGhOmqnNYPUcCJ5NFYNWSyDofhJ5UxjbAjfE6xWDhNc0YgpW00qRYRyZDs7QHjCmmhCMFi4KMo3de8bvtdbmqf7IIW0O2Cfa/fpb6WnqFeGxGP/lD0PlFCrHETQGjsCAMKoQK8SsaBYuwSbuZHWWiJczGkdiiNQUqDohsRWEKy0KtWh/896uHD8X6iaxSusRlE4IvJ8Mbo9hDxtC4ObmhKAM40itldnh+cuyklJiTLEtjDtLtXvXjUxA9gdQ23BTvfJkm39pKR4fw1QUzRUuZ+R8MdBJcbo5B0ob3ZVs4uShgGglqE1FiXbIKMWi/d4Xpjv5FvNqgyNSoyNl7bEq67pwmYWHhw+EAOfLmTwYQGbNxUYE6bV8dMYmdSeBLSJufXSR5v2bbIL0Wa/7/etjk/hEBtWyPxXd8VP2o2VRpxia0yZVACKkMTCMTpZOQstAziMpJKuLulyFYHX0ISY/585v7MOlb7DjfHMXWXMkjYa4jGwoxXZHSunZHu13SaNL/uzqLudVILJB/HrrSNg/rUfN29P371tc3aNktezCHsLw2Q3stuLTSEf3v+N1QMnVM3SbJW06rBbxyMt1nh/UxrgSYzAdVoIB7sSMrHj2TbqH6qel1Vr397f3A5SdPlMsY9ju90eeV/qL+llu2refE3/tPj5oz/9HsqF/xAgCospaV5TAUCb/JJsO/7SoTYMYkvW0LJbiIFm9YAiHzVGJGJOHmvUeU2AkoGpAh3VZqaqMdwdiSkzja0SMLGrNmXc/fKCsC3hUtmI9PZGKFkNtakhQRyQqKeJ9QQkRJ1lUM1Tn8zPLPJOjUKPScLWK5fO7mIiNfZowFNvcGvF986vzE4Rqjm1JRoHW2hogMk4T96/M46ssPbr59BqTMCThwoFizS82+Hetnn7tt+T2s+VrbMvbmCVzKvYS1x8GxxkaAfkE/1UDv7nAL0m8viROEhkkcESIClKsheJ+kS3pIUJOI2U6wO3BJsjqEV4EHs7A3KSPuhrz+xwrIcE07u9KfQ2NSaJnUqM5U33MD9jfO+w4ITFw8+aOOCVu37/icj5zfnxkPRu3683tDT//+c/cM1RqWSnL3A1rUxLiDCLVpIjP3Gm/crF6rScInNTgQvj9f4LHD1DOQCE61bK58gLn1DMjEmEdwLCv5/7etVrtY2uX2XnqEuBgzcJ30QKKFq1dVluzw0lIqfDh3Xfk5czfv33D4Ebw6eWFl0tmzTaNu0kBJRtJZy1+32bESvD0Na5cYiBr4aYeqLXy7uM7Lsu8k6ZW23JF3pzg0gL84L/Tp89aU3kIjKNwOJiRun3tSkMix2MAXSllRCt8eBDiY+T12yPHY2Cps58hC4VmViQo06F2xpwu8aEy3sK4KHHMhGKzAINEEqNziSqqmbU4cfnuUt/vzRkxc9+dX//12mS3KY7gDrA7uAEDkqUwXpM5X1kxN4W9J4+93r9Owfr3V8alBasi/r6N/gykKlUXo+xLDT3aDEhlVeP/FEZOp7i7PQO+SYqkyd47pci8ZHIuiJjzEPyeaolWIojd7LsBaqnM7UHMgZt3hnGvrwTPwxr2RKzsUbM7OgJtPmb3WP3b7JNlUtoc0D92fZkxxmHsm0W3FEuDRxeKI8SwAm4VpDTiY6s/uHtJ715zT7YXK1tBGNuUNETLRcdEEKsNqve5UQNFmpu1eerii9qkxVBuNh3eJrj7zDH/9xQHaio9IrRUvmxKUi06FPE197/shU6xaFbBkJlRiJNRRqWxTZ+IDIO1BdjaFUqeWeZnQ4hFQRhBo3lYRJZ1ZZ5n8pq9oF/7s+3D/M+8P9X+043b3H8r2H/EeScN/RqMkrjCQzZhL2It+kWCDd5VS7+NahMPGCIMIzVFNHkxPdqUvRBjj4CuXH/MgVC2pvQ9eot+q9ojPRCndFV//bbXZvdtXE8aB043J1SVj+8/sOSMfoyUUjgeDp4FtWRNdO5F/GBaqtzvKVokmGqihsFrMWpMFSI0JLOqOVvy8oy8PCLLC5IvbISazUN1hblHnIraPL5G9dTleKvLdUMvG8ou+BqnaOnZyZXYoVj/13AaDZKuhVxWXs4vpGxG8DJfqKqUYsxCgukWqRmpBkyjDRnu4eqmwKK0HLX1dU3jZN82jVwbovJase9iH/+fR0Bec0oOFhrHQBr8bBNoNFM5F1QKQeyMasiscyUG7al/qD16DdhMQ3tWi3zLWnl+LswXZV2a4BgysYHnZXfeP83i7P7SQ8LaY6dPfqU52O38YecrxtjJNGRHp2ZMNMUAd6F1EVuxpdaGLt0yP912VMX7fLqtk6aL9nsgbLyk7vSFolZeKrLVwX0tW8mg5EJes2ew8N5T8bq+Gim1CGuphGrUig19bJ+F74c9U+iGrSHPW65yM4SbFdfrJcX1hEhHrNLrkLr9UluEnUHQ/b20LN8Xri8awSFZJDe1gqkFz4YmA/MoBVhAGalyJHl/iY4zTB4ahgT4TLI9hyMwDIkQA9NrU87MiqEL/cbXlVgrd6eJNQmP6zO1CLpuQrddbVNNEG3a+0R3k3zTJ7Gm63l9oeSVy2IIOfNuhBQzEg1OLUDx8MtSqgpBrcA+mtYXSQxT5OZ+QoYJGW9s6oMmb9bdvp4efsvLx9+QTiPxkEjcEcMR4s+Y9MBv/+EfePr4wNOHB9Z5tii8CUvfe+mytCnQtgbBl2G3NjFASkgcCeNg8+pssBqq8GGB5yweExXW1JwNW7kzI2tMyJu3hOOBdH9ATiNTGiCN9nvjSLoZIRXQFdHioJQAjIhmYJcGbDvmP9CoECuUgNaBtVhLB0AVn3QgG0QgSOQwHfnzP/8zvv/hB/7DX/818zzDt98SJfDX//GvkWjAlDev7vmTr78mhESIA9OUmKaBOFRiqtAmhdcbtBqPZ86VIl47LmeohZCVOM8c/vO/Izy+pyxP9Jl1tYIUNCiFTCBSUIJWIFtTchlsws+wqcvt5K62bmK2IEoEidxMI0MaSKOQkvBPfnri9jBwdzgSJDJTueTC33zzkSrKDx++67Pgshvz5Xzhu9/+nhiUaRRSVIYBi9DEJqeYZ1DpOV2EMaTeWgIYBZva2KFaK5fzTMmFpc0bbTJYQYKQzJSSiA5OSwyTcLyNHEbh7hiIYyIw0Nz2Zak8PSrCGSRzO1UOaeTjd888hYXjKyWNEFgRUdKUqIghQUvlw0eYL5V33194fqz8/X9RlgXWORFjYpgmi1iLn52Ka8xCM3BbG4F/19GgcnXUYgPdqQcL7tyHMRFi5HCceluGqpIXG3cmC/68ZysLDbE/f62ZWlYLNGg1LzXS7+wHRqVvm7o/FTxgaSj35gw0/VBq9Vpjbti/rkdiNOfg8nKh5g+MKTANkbJkiInkKVEdkzkDQZB5hdUQ2d3XTtodJMVFWkGrw8qCzxWsba23xbTfa6O6bbpJjOLpWR+xRTRHR3ctbVJ3xrTJrlJ8b1I8fA7A+eT6o9yhvfei/9Q93eo3LS4/UtBgLRJVTMjySkfHNXaOUtWGS+IevX9O9KGyuuYO993XGGIIaLL0YlkzS1l3EQ/0O2zpriqbfmmb3v5MVq+M1WaOxWBRmrHL2yFHLSo19Fu7x9ANYRgCh7vJa5cwjJGb2wOkAR0GpATCGmg0Q5tXZzRypIpKROIAAZb5GaSwzGfWZbZerObOiVjKQRx9uzdwTY42d9EiHm3+OD0yTsAUbEo2jWFd4SAwiWIES3TfvTpJgMYBTQOMI3UYvHqh25QHzNhJGpGSCUNA1Dzu4G6ZWl8Bjf2lB6p+q60W1RSycUz2Ld2UUBdoe7YUrVZUSmFdVoo3mC8yE1MgTYmbw5Fa7bAPg9VmoyPfzL9xI+BPnwOevsbq1tVWQ1okuC7IMiOdFNcOdRHrrWwJkCDipOQ7/3aXDjNvF++X2s5aQMxRCdEJqQPHybg0b48jt4eBV6cDIQRmVca1cHtcjDvdiSFKMZDN5VKYLzN5XY1hBDPCCH1Ujd2MKQ9p3pUvh/T5gGaYVSGEQi3VCBO89llbpoWtXcI8eEdkhkgcB9IgDMmBGMH2vJYW2ynnF68hywpSiTUgU0a4ILISpkhV8V5ChWSRzFqqkT2f4fKifPxQePpYefxYWVdbi3FQpoHWpryzc3vHXLuOa+m2LYvXlElTN1tmwY9p15mhRZ2tDthkXj1K88n1ilGuibTz3WqTytZCtLsHtcjdWzOvWiv2UVB7tA5karVfz6O2SCtgeAFDHVsLm2hAnI3IhhLY61sfoeEbqmFF6hYdS945QuoBQz/o2n++WZMNkdvnj+I5o2p9gQF1TnLdfPsua9o1waZPPDjyum8cGs/0H77+yBSJnfWT9qh2lVK5PG05dOv7ETRF6hDhDHKB6aSMwUhYc66c58I6l+09x4GBgDARSCBnekXad1SAMQTGceTw5iuWeeZ9/cG8kAbF0hbp2XT3ooGQCzFng1XHuMu4mLIbZCKtCZEzOQuXl4WSKxefXzjdDUj0RQpCOoxUrSx15nAz8au/+DlxDITJaqCTjBSpLKwsT5XLeyVrYdWZkEGKnT6hwsuZchHinRKnMx+/fwECy/PZWHU8lxbE0Fq77JlLjezWyNm11MbTNAGL2LQN1BTzzTjwswhxCIQx+qwu4c+q8jNVslp9blKDbi8kCIFwcyKOA3o6wJDIayEsmaElBACJA+HwlhQHTuFiN1onQlFvHM1WOA0ZJaPBagfamug1QU20XqNmWsApQjXwOQQO8moDnOtayHNmOV96vWQcB05ytD6yYeRwe+LNq9umrX5E3pshqBC0R6wiAxIiAxeiNj5EJQZL1RVZqVKZNRMqHHIkinAIiaDWc6nN5YvXzxAixAlYglunQEWI04EwJFKyIbJ/8tXIq1PiJzcnjsPIq/sjKUWUxJwVCQeelsI3LxfmZeX88MjLeea77x8NeJMzFZulFzJEFaakHIcKPufe2jY83A4gyVJhvU2hmFEXClUqMS6oVhslVitkBxsFGy2WBo8IQ2CYBm7vX1lad1BfBmNEKasyZ5gzyLsVNHvxUPj660fu7/F9EZZ6ZJoSVm6BW82ECC9zZV3g8Z3w/AS//Vvl8WPm7//uuff1nk7YFAePUj9vovaMTVPMrUWhfX2aWQtmFBqPZwzdugCeSdrJmU0qNTRjD0KLA/XaSwNbm3HASKtaIkktolP13rsqDNEIM9ZMl2sFT6uqj9BSWr2zJeLEnbQUovHdXqAOBWQmZ7gsYrMCizYoBcNhIqbEMNlMVzRS1KbGaK1wsUCi0djW1jHe7HC98jy2Y+yOwQbZMYemFMtYjIP1DhqerRU/zfEUp5PscYED9IIYucbhPhDTf0MkeLWn4Da6f9OFxB6pehNq8x7tKlmoq/Q6S4jGUdfqjTElm8snFl1o9A/1FZHU0jEG4o5EQnVe0D5Iq7FSmJS2gazG3lPpOekuxGZRNk/cQu/W4Fs8F968i5h8JhdGAEsNjIeROCQzKAPubRhTy7pWci6sWihaTSi0Ec368lVbz7x6ShljSrFaYN3u1yPAth4dIHN1fvd1281ZaT7tVXVdzRsMFW6GxHFI3FRhqkCeKcXqvKJ1R28W2M/3U98rqU4V5uhBiUdCXbzn0IybKEgw1vwF8WjTSICN8m4Hp2yRq++ORYKZSm5+YJdCrUrJmY8PTzw9PntqpHnhhmwbp4GbmxOTp6WC85Y2RbFF0ds6teJ9x/rtFGWTsDaQtNdUfF9bVqO9X/X1DjvgFSIwGGAmAWNKHOPAEmGpig01DX2+5U0MHIbA68PEq8PIzTgxDokhDj5oeiQG5c1JGFLmcVUo8ELEmHdK71/bJQj681XdVpxmGMzGExx53bgy27OF9sQ7XSbQa8E2WUFcoQfSMNjYMM+aSJdSU3S5ZMPqZDaF6iO3nh8tZz8MptTOg/Uftkj2cHLQlqOrapmouVDKsynwTpco3YlsEYdt/YYabnXfdr52OI7rq0V8Xc2py1S3npZZqsWdiF0ktLekOwvbS4r1uvOBFlkH0/Syq8GhLmMtXWvhN2CMVo3ppz9TE3ixtVLoLDeglsrP7nCLbi3Z2pCtNoIqhgGJgWIM35Taoktfy55q1s52s6VzurSYBOh+GTZhUqSjuFsk+mMza7W/Tq8cFKcp7hmJL11fNILTdr/XHlATkGKLaAwCoLoQS9rttzX1ksU+KcB0GJmmkYGBKBE5+b81d2cQ8/pnN3CniEPyaG9srCwD3nVJUGHsPUg+/mkIrsSbG2VMJDWwYTC0gmaGKAzRFFMpysvqVQEXqvHm1gAgqW0QDKNceXmVyszM5ZJ5eHcxI7iuve6m6ihBN8Jtzc7PM7zMbEpBNnlpHuHuQNb9fuummK3XCNoYlqaATeC1C+tcIOTKSOFP7u741d0tUz2QNMLD7ynl0frgisBR+z0IljaVAGsUq4suTygjDAb7lOkr64e74IYUW+hYyVX5UOFGAkHESMuDgFg9zoocQqieolFhVYALoT5hqEqbLyFqbTovz2f+83/+Lzx8/IhWJcbIMCZSCBwPI3ev7/j61z/ndDwwTvLjHqHiNGBKaZR9tSnBsgk65sQkgclrjcHfwKp+UCV1j7SiXGpmUGXaK74hITcnRgzG/+Z45uvDhYfwwlOsIKN/mXP2q2ngzZT4i7u33N8eiYfRwF5xIEjgMBpL0etj4GnJFP3I+3jh44fVG/YDhdr7eJks+pwcPZ13yE3P0xFUPX2+WAYktYZ4u0KTyerkzq2/zudspome1h2GgZvbO4PZh+rZYDO2pVbmdeUyz9QVympMPn0ghsC380JMhbevjpwOI8s5E6L9QkpwPA4cT2IqRCOS37o1/UCQypiyMTWSGOKnasyjslrRtZUt6GWTq0u2rxjNKGwcoNX/29axgERKjmiIvnYtTWwKpPY6JGz9Yds5bylVG0xg9XqNFV2CawqzLqUUw8k6xRqD0UQaheMOdLXbuxYH1AorleilklItaImJflYEjKJOBoYwERlI6WAtn8uFtazMAZsUpDbKrc7lmplr52xuC7m1A7Xevx4KOrCS4JRxq24vbUZ196c9XZvY4HsUjOP3s338kevLwJhTMsW7TRW9uoyguY2jwQq3IkhpQJRAyOrzyuxQtFRL8JpAw3GYNyNdAiW5txVC91SapxDTwOHm5ETS2Vtfdta61amuVqt5Q20lPeGmG6NADGZwx2iz7cKQrOXBkZzETTBAWZdiWJ9a+z3Ol0LJK7Xs8t39VaHfi3pvY/PUeiQi1uuou7pfE4D+fu5Yyv5PbH2TR7OqNhUgEnxApa3GqpW7OHFzOHA32deoA7FGaowsRHL1tPAFal4Zp8QY7cNaq4SoUpbVIrYwuNEIFn0IjsjcPN2q1ui/Ahe/n9BaSVqkgfGHmrcbLH3qB/3779+xrpmv3r5GFL77/Xc8Pz3z4f0D8zxzOBy5OZ04jaPNopsGDjcHXt3fkWL0Q1+Y13W3F76enqWoPgF672mjdauJ+73KMCAp9elYiBCDMGG9lEuBGVNWB5TR02YkQcaIHEYGAkcJ3N/OfPWyUp+t4O88bwxRSEF4czrw5jRyGBMphk5hFfCaowQbXRNHpjDy1SshhIFvD4+Udd4U+l6xS7AIfD+/bDvU9kgVaitLVJvTF12ZW6RVepRpwBF1hOKm7Nv+FucaLaWtpUfRYiWSZfVIrG6oxnYvQRNJI6IDqhHNluoragC9jw+wzIFhHNE6oBqoFS7nmcv5gmpmmx3Y2Kna26sz9GyUidsl3bm8CmB8zVsKdFvg63eoWsklI1qNi182R8Jqv9qS73ZuWui5Dxphl/ExfdoZoj5dq3Z/dfceVzdu3zRjJLu/N2PUMwZNWHrZQC2bU23e6qZS7RwFNTsh1cdW0Vq22odssX97b23Pvv3SJx9t5Pc4sUkPJneRofVR+8o30lsH0hDVhk7/I64vj1L6yYlalMvHllKxpzfobCVjDBL2T9U8eIcqI07LoOZyJBXCIBYOBZAThBHqolYuyi5tHjEmZ9uQ4LO7aLRXEA4jb39xYD0vvHz3aHWFZWPJ3+qgbbeiP6wte8GmE1NXqAttyFhKkSTCcBxsQKsThRPjlhv2PStZefq4gFSqLLR6ZC3KmitVI6o7L1uucZG1zmgtzItTobVnG+UKjHDlNXmQjG6GsEuzGKR99DTacEWJYUtRgvJcC786Hfnl27d8fXfH16cjI5jB+jjwSOKyPnG5zLx7/MgSAi8pMmnlq3rHiE2bD6VSPz6jU0blYB+zGGXWJcBUYTI+MaDaM16Uy2idhKchcgwDgtUfO+qtfZXQx0PlsvLv/u1/5Hg68n/6P/yPBIH/x7/6Vzx+fKLORqn281/+nDev7/kXf/lPSYORLRTNzGVhWWdeLmfmsjI/r0QGAuPOLbZ1DmSEsgGuWhax6YIQjL7/5gT1gnwrkNUmPGDe9CUHHi+RRZUzhTdRuB+iccueBLkbCW9uOclIChO/Dgcu0w3TN98Q62aQ7sfEcYj8+deveX1/YhxDB5KICqMrFnPOEhzvOErif/Mq8u7hkYeHJ5Jmfvuta5XOogNKIjNYSrY2Xab9yKh71Kb0QOYXpAYOBxsddvYJF3ldHYBjKX1xsnprr4gkay5jnhdKWVmW87XH7/eCjoTRZZ+9eyIcw5FTGhgawGStIJU5z1QKf/83gSENvP7qLTFNQKTkwrtv33M+n6meXOzN6BSTNQnmlM3ZWzJ2N9WMOc3Q2Rq1GZghhE6GYNcWnXVTWAsv85kggWmsBgxiovXoBTFXRoGibQbojhzTz+3eCIoYnZtoNWYl/6UrVd/o0bS/avuGXap1F9NUVbI6vqIUpAi5NMrDSA2ZECqigRr9TTpJfyCWCakDa1VqzVZuoc2daEodPP/V9dU2Jg8aUKhh44JEghjFf9Y2jBnIFWpvKKGNQuvIZrF7Y8rWUxRG/tj1RSN4uBmpRYmh9vBWq5Fl1xqIaQb3pPD6jjqyD7G6krM00tzpmIQ4GgTWvCwXm9AWx8bWpDD4zDhDpK2hFakdPxQgpEg4TrAUxMcYgbGTbwpOMZbD1kCkaMn+ZT8L2CR3kiHyQoyerjOFUL1e13tx1NMRbTO8DUIxrj7N7pV0NOOmbdvzGiv6VsDuSNBiS7EjtEBEtn93w9eOafs+eM0uSiDFgcMwdQWvPoVDgJxXlvOZy+MT5yHxnKIBaFSYq/IM/H5deVwuvFsqWYTbyzOHoIyXCydR0jQ6lKI6IGKxtZ1ncyqyQbqttmbJwqqVbJYYWSNlEqpER6Fu9Vztj7ilG8FSlfNl5je/+Q2gnJ8v5MWQxClGfvL2Fa/u7xhGqz0RQGurGwu7t3LF6Kmo0NQd2BDQuiUK2vq672J0W9fvJw7IatXyVSvfXRYWVRYqNUVehwMHVe4AWVd4ekKmG+QwckiB14eBr25vyHmLVO5GqwUepsFRyXsY/v6UNmFwhqY4cDie+Prrr8lA+Jtdvdg97I6m27OctNL5jo+x1XOk2MSIeV6QIA6W0E0G8agqZzQGaon0KRYo2flv24BY47O0Z7FIckVqQXyyfJRASMFZY3xlK2hWSsgohZwLRQualRwhPHxEZGBZhOfnF3JpdeRmlD0PU9qZ1t4XV/cDiaW9IvTIz4Owq+Co9T02F3XDSmyAm+rRbilOfyZ563nrW+fgFNeVVYsbkv0t2T3sU5v7KG67TM823u6rYND3qNUPGzCnOXz7DpeW9UrJAhBR79vVAtXzELo7A75eMQ5mHGOxUpK2Omns8mcBnBm1SONkNi1tik88wxG9T7V9THV70gRzz6KlHgGrR+jNsFoVM3yawvzk+qIRvHtz2r6pwArrUnn6kAnhwsujcQtmXd18G/dEY5qJ3sPRw9UI0ykyHbsP4gpJIfgMLowJf4wHokczFWUJ5nlodg0VQMaB9Doab2OxpuVaVmwidO0CbXi7bGFKqdTL7MTUJikpOWvGNGKQNrYvlKwZUUOU9SIylaqO4lT3SqhoNqaUrknFRxD4YVJXwHkxFo+Mp0N9eGrj0QyDWpNtq8k5uKYJf6NIpZqRjNEo5hKJwzBxf3PXD2927tG8rKzzzPP7D3x4euEHKoMoEwcmTTwW5Tvg355f+PbxiedcKCLcPkRuljOHuyOv1xvi3S2jOEy9Zrg8QamElzOiT5v2ND8XWClkZjKUEYrJQPVBr6EdcqAhN2IXTnMa1qK8vJz5f/3r/ze1FNazjRtKIXGYRv7yn/wJh+PBZkH2MO6TdIjfklUW1/595zxpNcCGyOseBogoPm6NnQ6j/a35vC9l5X/5+J7Flc2baYKY+MkgHAE5n4nffAtvvoLpyN0g/PJ2Ispb7m/vsMqSchyNmyC2VgLrMzDAyd4ICtBgzyFBOnC6P/Iv/revSacj/9O//p/dSWr3GfoDdCXZ0gz791WlrGJyWa2wfZkNeTsNkxtToz60kr2y1IVQLHoYVBhHI0Je5nPfi6rK6lmjGIQ1r9ZnWC5oPVtPZBw43E7E44DlHbQzRS3MFM1cVmOYcn4iPn48U4ry7t0L62r1+K7URRhTsCHHi9XOK5VlySznmbJmri53XKXBNHt1x8k++o67gdTNFLZaf5dcURYWoqfkgzaHxvYhhmhzID0gWMvC4qTPwh+6tvrltXBvr2h126tTUNXq/aq9FEOlp6K7PERggGk6cJBALQJEc0BChWCsOy4mXroRxmGihsF6+WrZQRq0gyC1KnmxAeKhmv60LKAnSxsApwHxdoGHPbkFHo1KskfOnkoWdbIU36Mk0dP4f/j6shG8u+t/L2vl/PgCtIkQdE9Jko9L2jGF9GBdQKU667ilxdYZ92xgnc0Tq9EQXzEUhEB2eqQUmhehhKQG2yaBRhLKcFTKlFkkoMuCzP5Z0ngL/B6D3ZUSCMn731YrHsswISkxDBNhSL3FMNcMqmyt+3rlodl0DHvPFqFuxWh/QEfhVCyqMo7IQqnVKZs23CO02WcWJYkDG5oxRmxED2weXLva7O7NsYDOthCM0NmQVpVnrXy7Lpyeng12ng4cQuS/fHzPd89PPOWVFWWVarVRtSnpw2ki3U7IFGBsj1jRxdLKS/0AvHDQlu1X73WKlq6I0E5Yldgb8rej68hPzCmMqlSd3cEY7QlbJiAJh2HkV7/4JW/evCINyfd4S0yZayIgkRgHZ/rX67XrVmD3bd/g4LLjqN5SDHRxviCXhRASkgYHP1maXSQyTUekFKiLj5CySIbFUIPIil5ekIcPRIkMEjkOkRuwZulabaaki1CtymfnuEUpuq2auhRoA2CAIS+L07IRkCKb/LhxlZ2E98BSQCNUx4JSbUp9V1Z9v7R367TIpKrNAc3r2oEP+9UNsqsbBmuXKI7OzFmpuSDJnJzH8sySFtLg2zFYOnRt/Wu0s2ojqOaL1Rk7KLTrJQcj7C6tBoTrjFG0yNG+Q9Vfu+ef7GkYT226AfTeRcHm9109tzu5JVhZqU1XkRSpFGpDV/fzsCn+5nK3NHLdE33vraDI7hzR7UKbxmv8zxUtVrfrdbUG3KstR7UhNlddDKuYJh9cHdzwuC53JHO/F20pW0P7192em0yZQxmjEDRSQ/vcTbhbQrz9vsl0JOBOTS2+O409xq+wvT4gUCNagk0uCcqXri8awa/e/qT/fb7MzE8X0OKC4w8XAjFsaZV2K4CDMtVQTWI3Ps+ZnFuiS8mXxdKSR0xXign/Ul6IGonhAN5wS4RxCogMwA0xCEOCy5J5uDtTn18oD+ZdCC2ttV+q4P8dIRRLXdZsY4EOBw5H672qwYzKy/lMLcW2aC9v7DxBrKnc4MjQJcgS1d1TsVkTNnDUxkcZCKN5Za2Q3Nl4avT0VvPi7dMsON6tr2fQKo1PXckYbZYZwUhKxpEp7rE9rAs/LBf0h3e8fHzib24mdIj87bff8PD8zLra2y4WZjKIcoiB4+tbDq9ukRu7KRNKpawzVZ950W9QMnetdozF4TAYG8yAbSITNcY+uWCvmsyUG3dpqrBi/ZPKK9pcNbBex9tXN/yP//K/5+Z07EgwVZufaEkzm15CsKGrWm3w8dVwux4ufGoIBZI9Z8AYTmXJNkrj4zN8fLE+sHSA7FRXdSbGgdvTPWtZIBcO0SnBMnC2OnQNwOMDXGbi7SvG29fcjBEdo7Gb5D7Su2fV2+ilboD2oDIU6xyLV56/1sq6LKzGGwYaLGrLFY0Qh4SMW3NRk7IGBGpzFGWxf62SfahrEz//X3Sn2PNrpVTQzOIz+6pea4c2/Vv870RTxgXr+6wZSl1Y58I5zFaO93Nwc3tgGD6ZOq7K87pYH/JLdd7NnWsVxRrAu3HZycrOCPbUSnco28+b1xS7Qx32dXs3XskjwiyrqR51nIKnYLM6wUS2iQ2RAaLhnkOIRNkh6/tduEHNnmXyVOLn0xF+JG5UYDEDWhwEaFN7aOHwZgTBwHo+v7Vigc+8ZI5H5XgUKBGV6ACnADLhnex+37YmQ0yoBlZfy6sEgwhhdHyHOmvVrpwH1yrbVFwbGK4OLqp9GELfNgfEGC2eQDGcRxal/rcYwcfHj9SqzOfCcln58OHMOhfmNRvySazvJ2rjdsebRxs3X3BW+EAaLIoRF77ufbRwelWkGNSWqD6ZvKLRGrbFBbVmQaQQ0kJr8o0xME2jNYY6hZVoMS+o1CsDLQCxEIZKGg5Q1YbhJqtFWmrJpCK01FHdeYa7uE18U5v/ETAuo4ozCPfGTquD2ITkQimZNuC0S32XlM0rvMbibCCfHm8LfXyNeORZSyVrYa4zQxyZolNkiEE3FCjzTJlnflgzF4lM9UQ6DCyApAHvgOCegZgiv/rZ17y+u+NmOjLFgdGJp4PCuq788OEDWi8Imdep8NVNRcWMUMN9RklMcaJoIqtQsjLP1RCTseMKNyJtf+qsyupku1Uj4xQYx4mff/0193e3jGO6OoeKRditmI4frhDMQARvCbA+twbckX6IFTGMjis6aZFA6035RKGLR+niHxSAG5QVoTAwSbRhpA39VypG7VIQnQl5JWomYYTyxZ0DrQY594IJbcZiq7WFFtmIp3KD9yc0p7qBejy9amA2O28tK4KYck4hOZVeS+ta9NTn46XgG3kwI4oZUw1x5527kfB9U22kzO2zdlHOXiu6gYskxjgZr7fL9LoWYnBMmh+pl5eFGLNHMqZvVGCupUeT16e9xQb23y317fLWgrUWCXY0opoDXw3JiVpzvjEA2essUbDPZmwIUaH5VQ256DLkWSqKELKPptJqmyKZmgs1m4O2TROptBmhDVVq4ruBcWq2Zy8NYKjuQK3e/uMliuB5T3FSiha9t0xW1d3s+VyhZrJeuKyF4BW2Vp+OY0Z8uocJf3Dfo50F2Zw2f09xGe5YkF3mof3SPt3fAoCKEw+IIaGlZIL3YvoD+S24g+VI9bWsXGGefuT6ohF89/4d61L58P3MOhcuL3PvCyylWKrNjXF1QxhitNYCf49xioZsS64hfINq9qjHuqbNYwGqrmjI1qeWEnUc/KFcESw22SHEMxZamBE8HifmECghWMPtWlzh5Cu/DrDZWgpTsOLrUjMFJ0zejq2ztfQEPzbbavNtmjfbUF6CtXZLEEdvbTFjLtbkm31sjoFOtQvM9n7B4cGyoa9x+D4QXdHYQVCKt1G2IbEVJdeVl3zmlAxd1yQpibWqz7VSzmd+lwu5Km/qymk9mkKdJlK1w34cEodx4i/+5M94fX/H/eHIEBOTvw8qrMvK777/PblYH996U/mnp+LoYXPyRYQUBk5ELnUgF1hXb605Gjnvzk/fohtVllpZqjCfX4DI6fYVt/e3/NVf/Qtubm4YtrPsKUkzgitK71/D08J+2lqytNVZ2XbclFQ3gnYgRdXSuTQA1E7RARa72kFIotyrsqowy8gokTEYcpRS3AJVJK5IKcS8EDUzykBBWGMih0hdjYmjm3anyjJgnhX8+3QGr8cTfRbmTuBtpqLXj9STxN6CUqlWoxrUQShmWI3EYJM9Bp8mYJ2u6LJaRFLcCDoIoo1Fa8CxkqvXtTd9sL+0LTlCSgMpDNSUqUvmMlfW1WgYYxtTACxLtihgdWU+mPHXXqzd5xXsngRrEwoEoobtvGkzXfSAz/wml8BdIznYfYRgHpdUQQZBduCNnX989Y06v6ao7V3xf65AKEJ1Q14QJwxo5Q2TrbYP4s55MxJmrCzCy4uVleYXWlsrW1Zqu8VG6BDaG3VrbeetHQlzbkzel8uKNRY5zMSNzXCzEIeBGEYzhOO4WS02I7jfDftTOuAQaIx92+ukHcut/i1xQOOADA4kzKsPGFi7IRRThLuQUlnzhX2W58euLxrBh++yMdCfMzXXTUgc4ZXLziAEnzYf8AnEDjFu3vQe2ab2yRKEuobelL6xm5vEKJVaZzMMwYQ5iLWkivfhFS4gyQZvhkQaJ4oqZXQQzVqcMi30kFlULb20GuqN9UKt2XPZ5vkpMI3W8rFeGo2Z7Vbz+iVGOlrJFYFo8RpSM4S452ipuOrkw1rNgXaKyF4jufKKwCLZFkWAOR8tcHFhla7c295nlrIwVoMHx5iIaSCLp8WGiA6uwIrV/Spwd5wYnc0nEHh7PHGaDrw6DdyMwk20AaoHQLVwmR/RyzP1+ZGqFTlMKJ7eafLY78uK45b+LR2NVh2hWEVoWEBrAzHv93yBcxZyHhiGxJ/+6Z/y5s0rpmkiiCOPsZqLgS6ymaku963WYosaxBSMdNq5bd3Ep2skbycwWast82gtfIPAzQktK3UWWrNWYbOnja1iDDCKoeBC628p1msVXF5a7VOaN9uV51bfVdxZ9DcPGjqjkQCI+lDUQEwHlEANEMdhJ0vt8PqrvD5agSKFwuq7HgyRQ+sslx2Hpb1NcQ88V5s4v+/jakF1mzhjcxsLDT+5uQ/bJXg6MEbKECgSGdRi6Y22pFmp9iZuwEyIrMFerJtJ8eCUjZ5tnx69it12Rqszx+wcnWYkJLhPVRUjzAYRP5tsek03abL+TVV3kbWvH14vLA7vD508NPjQ46sbvVq1zSiYIVobliurZTrrbo0+zVqwVUY3lIPvsf/sKmrbL5BZRdoT1irk+UItmRJX42Km+DSZ1HV4u1/95H7MOaMHcddP2p5zv5q7ZxBBYySEgJamUwwN3OKYHs/Wse/RH7q+aAQ/fNfQjxuXJS4otSirwUC9GOrUY0EJDhAQp6rC+SF7g6n6j1QpNZicN6e2P7Zi9cfZEVUT0r3CYkFZULJeSGFkiCNDiBwPiRVYAM3GBCHDiAyjE5OY/xxUKc8zZV6RUJxaL9DgmIqRM9dSyZfi9FKxqWn3Oqz/aFti51isDenEjsGlkos30e/y+RKcYqobQdkcBdS9sWZG1FKpNEdf+nuoe9SIKak5zxyq9e/FmBjHg0fsBRkiNdkBRbARK8DtaeL+NCESiSHyp/dvuZkOvL4Zjbw5WuvNAaNlmudHOD9Snx7QEJApohrIymY4uEaoWeG9Ulz4O0M/YilwcTAiBnx4OcPzaqKapsSf/frX/OSrNx05bJ6wHf5SK2veIf3EtSLSUzgq0pG4ur+xpmFCsL6/IKium+LwQIdB4PYEZaW8k17vatvl/hFJ2peStBqtXkuprtnWOAqxOWS9wNvWyVFwaoq2cUiKiAEp0vabIjAkCIMZwUqgJojD5M/mi90MSm33G/1boahHlBJs3iQtGpTr5wObMF8zqwNuOjvR7lKFKkpQtXaZth/sv7a/RokMYUCGALEwuhOVV3V04va+e+dFqslUrRixvUQ/J00vdWHohlB3D3Olm5VOXtEyLy0Dh4pNL2iAHhHKWjbHvn+J6zEM4i/AvvfUb0CrsSgFEUd6h+7cquruTbdlaq0V3bCgtG6svBjjjgFh+NGrabDN2Nl/t8qdxftOwb/fJf+7O5zNn1pmWBcYTNdPosRqJOmNALUPtf0Ro/wpFmD7HNcTP/IAoS1AsDF7hGj2SD3D4kM/NTtrTfPKvnD9EQJtv8F2dtR6AstSemQoiA2w7Rvj9lure1WW4kvRJqw3I2J91EbS6iBMV8rWbFmzUVBdUEKsTMeVoIUyG+IvV1xwIiks1LSS0kSaTj2i2iumtuiOr7H0W+sp0yMlJxo2/rrmbMjO4m0QJigVUdmZRN9wsegwgMN0vb+vVISRIcLlsrDM2aDvUawPP2wGEExwarU5JMV7x6obrDBIf5Z2BQ22laL+Xla/WMvCx/MHbuUV43T0g+nOSDSS51ArUwhMXI842j0+VQNVQ299OefMZZ75r7/7lofzC7/LSkrwFYb/eCaQVXdRIE6QK96Ovr33UiEUnA0lcMmwFCFX0yRphGmIvH39C+7uXjEOo7+f1bqaYeldDT/2ADR+yOqExS1XaIqxkyB7XU2kUddZc42QfXJBQCUSxhGdRlu/Zl/87aYAPx0LSyk8rZnkCexWBUGq9WEFPN+qQGbFRu3m7Y6tpO0RipUmuwWg+fWxTyoAKNTyzPN54T//3bf87d/9V+sP1a136sq3bj2RErA0qf9G2WI2aOTsRj6w81JsEocEVi10XsfmXEiP3czhEVuDPdVguw3ChihtToXRkkWLkr1MAEacYPM+P9OpoD6LEHOIdn4NGs1RLOyAPW68jYfWGG1KbtGO9gcInsEIbuBKMCMZWuamHUbX4HuuymtTtv+J9ExJk7mWWr3a6l00heBzMMV7qIUgAzUqcrqQ18L55XIFmml2eIsCxSdBmN5SCZbuL9t4ouCYDXNizWmSVpPcYqztywSEeT4T4sIoFtmHaIahZQHsOXYr4jptm9jNFrCrIF6nFHY/3zkZBs5RCErAGuvtI9UGrGu3Rl+8vmgE9x4SOyVS1+LN0DSy935j7df7lGa1zYoxkFIguKqq1aHfNiK+19ajZANVFPNZSqmEJIxDpgaBbFHEpQI+rqeEgKYLerwjTdbbGPz+N0JcpxHTZgQhDhEdAmU9YC7Ema3FYbuqFqoWUwJemA5tqoHsd9GELobdsFFHIsYwUCKUXMlLISUhjaHv6+axqK+Pedlacv8Z0ngLP90oR38iHVoPSi4Lj5ePDOPkn7MdPImhE4aP0Yxg4McvA2XYV1Elr4Wny8w/fPs9D/PCt0U5ReEtVtd4cRqosaneFm0AK0Yt1p57Uffmg5A08FKE8ypkR/OlEaaQ+NnXP+PV/RvSkHyPvJeqRQL8YSPYZ5zVuo2oaihHESOrjht/ad93ZynKmLEriI2/GgbiMNjIMFzuXWmMUXk7Fs65sKylVdlcnxpKuueKcSWjhSzCxdfIgAyWVm7ZraEp5X6ZhMUQunI2x/OF5+dH/vp/+Wu++fbbjpQMvj79LbrRMviSdgq1YLlEz/woOFqzGWjj+BHMCKooOe+NZkvnSRdpa6yXfbnIMim7OMOM/pZVaQC76GkvvAZdqrE91e0jts8Fai7uRJgRLAWqIZPQsEXu2ytcTxRsakLdSZI3X9cWgbuTafPshEYY0N7NnmvrUWvHWtxZvr7fvhCWRfGQsr2+Onl0jzTbewWjyUtpsNS9YL77UVhjZplXdwntapnJhnYQzOkOg7iBCpadMqST/YZbOWmpXFUj8K62P45t9/Y3c2C0WIbAWj98dmNw7IA0AJc/pxtbmkw38dnBL8SjONkjzXsN07EYsn+xlaqGqFdy9o+5vhwJtgLrCiUXXs6PJiir2eeCbaCSiAHiQGe/6Q9XQGfpBzo5k38uNtG9qDUPNHrSNuNq8xZMQGfvLWwef1FM4Ud6MTcvM5enD3ZPIRqyqwqSAS02jNf7c0LcvK5pimgS5mWlVsxQa9t8YRwKQqY43FijHwhhV29qu2geTFe+O2UCLg/Bj4jDJ3XLf1oYrx6taKfk3f5sfQV7SL9WCguEQAgD1iKSycXAMs/nZ1L4npJXyrqwriuJ0KOMNa9cyJyXI8SESCGEwEtdCSQmH4d0zt7TBXzMle/KwmOZyXPlXDK/iZGFka++OpGTMKXEKsIq4oqrMGvgXDdzqyVRS2JVa4N5qYEZoYSExsgoiSFNvLk3RhhFWcvalUvs8asrkOCMHc0BckaexrbfUXZAI4YMEWLcak9t3och5ZSqpvaNAEKQV6/RICxxJDAz0urUAjV0ur0YtCOlK8K6k4Pm+T4sK++ezzwdjuQxbfD0VjfK9ucSLbM7oTb/MgYfQ2UIns7DCiyXmb/7u9/y/sP7HvU2QE2f09NqWX4z6lRcWhzOELZ7LNlSd94zz4L0/2lLd+5ARuo1ct0hEGlGr4EX+tXAZVu01/ZGfLhzLdV6Mj0OCbVALj3VZgHFany2Xn5oZUTb7kCIIyGGq6igVGXNxScucPVvJkDYswU1aH8IRiWnOwPfn4/+DgFhp6+70jb9L+BRnK2HtAz1pvXEFXyDh7dUg6+OYga7ERWoQEwBJHJzezSuVtrIJZO6hv0NJGtEj80sGsG64rqtVgjq/NV2plJyrl83iudSWNWAb0Iri297X5cXJCSqnKxGSCINibevb3rGp3sE2mqMrV2lzTHEmYVKz1L09e5tOi4zO6NnQ8w9YNt28ovXlyNBb7TUValr5fJ8Noqh6gS8KaKaTMEkL7+0rWqHyPP5JTiTjKfzajEhNB7P3KZDGStLO5rt7quhCc0eaw+NY7S2i9A2Ime0PBrcOw0ESQjRW/KKHXARVEY0WEuEYANxRQO52Lyv2D5cbWLBkCrUytzAHFjufKuZNByUK1hpN9+3rf/Z2krsI9yD2WnGWor3EW6vld0XDYMDmyEUA1CIJqqktnl+r8J5PpsiLoWas/FdSkTVV79UZlWLXJK5GTEIc10Z1DgBQ4Wl2OHIwEtR3teVl7JSVgP7fP88E0d4yidCEBY/cFkMOl0pXBAuNfbnihpBE2sRpAgzwkKw54iRIR6Q4cDt6Ybb0wkw6H1bS5GmUHZyV+mGsBb1VJu6d7sDkAmeGvJaszNn2AoEB9ioectq/YIqEE63VtNx5owBp7kqIGrkBMZ4UbphUmBXrWwCwPOaeX+eWdNIGelG0HrBqo8DUrIYNmZw3ahBfOzYZvwbw0leV7755nueXx7ZkzFoDyuu5arVwLQGj5J3Or1a318utbe2WhTmgDcFSmshsRepL/Q+TpIWYYRNgV1fFla0n4bgrefuRFr6LxCpSA3uz2w4Bd90NO9QBU73pQjBW61q/31zpLNTuV0lf660preViBv3jmPWLfOw+30RpYqVJEw9eAQYrBTRkbxuBHX3GSC0SXJtq4xkRHZ3s3M+dmnPmIxd6ngYjZqOStXK7OWUVgIynWipfXEjbFsTKY4cth+ppaSD6eRBQqN1Jq9Lz+RVbc0l2/7XdUZCRsfRmSOtxv76/sCQYm+mDyH05yhFWbJRzK1r9lmGgJvatkebNLcrsEfoqlOqGVnCbo++cH3RCD59+GACU2ZKzf1AtTc2wXea1OZKNXy5/YahpgVjazdNSIrKWhcbv9F7jLZNNq/ID4u3XuQV63NqCNLoyE1Hf4YxErRaUrCu1oNDRCRBHZAwuPEU1lopMTIEa9bVaN7EmIQUDGOuwFKdGFiSpW/SRtJts9paft6eNxLoRaJa7EsqV1vhsNCK151q3uobqh20IS1akeZo2uaHIl571O6Ruu11jsLFGkZDolqszjybN59CZDD4rhmIRcm58JJXQq0cSyZWJbldflwyJSzcjgsB4TJMRqILrFlhrdSl8nyBYYDXByHnwG8e4HISbkYYCEwEP4bJXQynXMKOZ8sqALxfE081MaeJEAd+/dWfcjjecXt7Qxqih8LbFa6iCl8LrFVC1VBzTWb3KtnSoNqnyzfkgmCptILXxlw5AVQ1NVCjQpwo8QDhDPPFIyH3/IIwDoG3t5G1KpdSWbXyMRdCVZIKeS1kIg/rwjJX8nSk3LQ1EaomagmgHj/qCBoproItISCoJDRM1OGV9/pBTR8twmD3ft5f2j0pvVYPilLWCloYxkgapAMgZMXy2F77qdXPqEc4sWclGqrSZLhUZZsmYXVoCfhA1m1dW+hmHr+BgWievw84luBk3m5IQiNyxu/xxyyS77WVCVoE4r/jz6INu1A+L4Nc6yWPDNVqurKTpv47O6fdHskc4pQUqQbKClwHC9AMm7/Un7u03RP/9w6K2RLsVzLtQMQ0BAPh1RVBGOMBHCNgaVHHhnoLl0ogZEEWb7jxCGor19ngbQO6WUR4TAOpRl5KZalKcuBkM4QhuH6SC0Oa+MlPvuLm5sjXP3tDSonU06Mt2HGHz/smrd/zk1qe2u+ta6bkzLoaaUtzJhrCPkTnxvX7qRt10B+8vmgE55cXf4OZjaeN7k1Zq0H1nWuNp76ldQt3AW+pcAhJVTLZC+3XT7ptwG4RvO7TctANg67gY54gebEcVScUN39eqUg0o+VrSa6G1ovJUF46GR1FFHqdTIHVEXWN609CuYbzaitmuzfe9qTDsg1npfvz0mqrsj1XzyS5/bxCkoW2Fu4zep1AXXH31IxtCqWuEDwC9nfXanRUh3FiCJO9ZxKL8L2Bn1rItZJ1IzGaS0HWzFpWhhBZymjFalVyrmiu1FyZPS8SCZQivH+BIVqNS9UOEQ7e0Q5Naju+pc0qwnONfMwJQiLEgbdvv+L29jXjNPkUhU8SV7L5059KUq1e3mrKpHvXuJernknYv94PT2tp6ETBXj+RZngG+5KBzr5Bi8ys9eWU4JILF83GE1vMFMVgymMulZfaekhrrzEagjZ6+4jHhmpUgYrsyT5ohBEaD4YiAjROn60HLofm5bux7g6dfWYoBgqzeYXdNiHZvizt6ZHT7oSGASQ0GVX6Oa7qQ4w98nFS+p4rdJltxgVsCkGpGfPw25K2yKr2MKllC21r9ErXdBxAJ951dLC/QEtbY7/flirvkcb2Xm2R9uCnlsJrac4rudtFCaajDSHbI2GRawaqvudsRgFzxOyGZKsfS/Dz0yTWEbLa7kUNaa5CFQMIDU6WIT4Ltft7tWlqA3+FLH1qiC2HralxWu9T1jB4f/WsBfHAY6MxUy8BKsJKDJH7uwO3tyfu726MvWqfvfHzVWtBS7k+3W2vtfGUKvNlZllWLhc7U+0Kg9U6Uxqdzcd+v5SrIsSPXn8EHdqEOoJUj048DfIjnpDduEKsPX3RvK9GzrFqIUcL1RXzigTjpgTHDCi09EabZdho19Sx96LtkBiUPfPii2v9b0UVwyIKMhRkaBGEUBwuX1ebVDFoJUbLfRsDRdiVY6Ftf8OC2rsEp+gJpCDXpY5gRjmhrKLMAsuycHl8Yb0s1MWnyQtev5S+T03x9EqXtsPhp9CNvzEGNVO3vVawvi8tViMkBWx0qgITyMg4OaQ+WJP0RGIQeHt/w83xwClGUghMaSCFwMJCoRLmmTUXfvvtd3x8PvPt+wtrqai6kkvSUVK1wGotnFYr9ukFNSZkSoxaGCiEMNl9lcV4ORecKMGGNqXhwDBOxBTdm7UnbutjLSMKWjo58zbdugslTfVHaYTU0jPRqhZ5Wx3OzOD24u7V0Hg0KwPIgKZkCzkOUB2hKW411ABAc4CHAJoCVUYGsbmDxe/ZMO7RNGbfQVu3oFBLtPcexKD6TYPJZthVjJFHPBWewsAYbbjwpVd8XI46AnGLwppBKB4V5RzdaLgsJ1uDxaeVb9JGv9/2tx9VNwFUKpVs7U5qyL3NBtliGRuN7aPE4nyp5mS2zJ9aHu7au9dWGxYWj6HaPdqdq49Rw/exspRiPJzF+VzJu1r8FoUhstXhQ8sIGDAmSLLaZduzvhAeEORmtJ3zzRlVrDXC06OfrOH2FuaoBI8erczRpnN8+svaz5cGT9mGgFIp0jiZPRhRi5pDttXJNMoFZblU5nmroQdVomjX+4gS6QORrtaZ/U98CddckWXl4eEd6Er9+Vf8AQz6/1+vLxvBJmjuyVueGJpS6Fvojl1T7Ja6iBvrCWoLU3Ai111Kyz296G8UWljUwA0ld6+rGUIP+Wj1EAWKrIhs0Ok26Ld7MFc3aF8V8zCRQPXeqBCE6tMwWgqk3ejnpt89//Y+uv17wBB9xcO77KOA6mrUSH3BkvBjmqN5naL7z4LG2L4x1W//bd6qEUVn475U2YktQCIlmCYoq/XSHGNiisLpMHAcB26GgTGYxxYkkMlkregKl2Xmtz98w9PLzPNlRVVIY5u7GDw/aUogZyUKlCQ0ZneCMQrZxPVKjRGVwZgfnN5QC4Q0EMJoE9TjYM5SUBpiI7h8Fi3mNWvuDn3PKXXxNeVv0YPVZLep4O7sOC1Zb3pv1rGtr2zuj3qrhI/gRlOkNbML1jvWhi9kgYuog1LEd8CctNLmwm2Ftk0APG3Y6a2817alCmS38ypeM3N29RgiQ0yG3vRsiAlUvFLU2znztKBCkUCpSiimgEUbvyessj8BzbD6fXwqw9KDHLYY0TvQ1NexpT2rGzdHKVaqtTVoWxPdPk19YfsdtI+zqGbf6iG7MyuibSb2VuttvV/qhrBW27h27mMz681j/eRZBeiZoP2z2+/2aKcV+MTuqlaFxmDUZNSJPOw9/bWehjTdaLSHV/qn6VbcyZRNS4Xog3pdZtqjKNZzGtX0X9At2FzX1WutlpKsYo5hEO1p+LYMPTclXIOi2l9FjLCrVC7nF8YhknMmSug9zf3x1fZf649Egh2hZX+Uam16tVZH8vrlxC1SivcMmlyXcl1u+7Hry0bQa2KdaSU4b6WzbajS54zGhCMfm8dloV/Jq01MKM1t/eSGmpflT7kxw2s3ZtrWAiyXvbc0V2vUhFt3Bw/IM9R181eyebyNh/HZEYNrXrzYPiAxcvfVLTGZIiGKPb82uK4LPziaNXTvfF+sCKUQ1gyXhXqxtWjgA1BLNXVMr9jBE7lKkbb32iwj3aFqZcFPHURVvO6RjTEnRsYJbl/BEAZiGDidBg5jtjSwCLfTiVfTgT+5v+E4JL59unDOheez0SY9XZ45zzPfPX7ksqzUoEyHG375p3/GMIxMU2IchMMxILHyeM6sKugQGCQwSeBwjByPiYmBAWFBWFn59p3ycYbHOrNK4de//CvuX33Nq/sbxnF77CY+hdY/aSjBq95O344YI8PQRmM1hN6OnUW3qJq6k5iGMOtMPc382SKvQEiR4SdfU6eJ87cvpLxwSyFohDJSqayaybFCWqizUl88PU7ge4l8I5Fb4C7IZ6wWjQUsRAPAxGSRYBystWVASKqIVGKq3N4L0VtufvbzG/67//2/4He//w3/n3//rSGxqe6IbNypjXKrnZmqDX+Zjf6vJKSEbdZcCE4/V5w9xeLCnLd1FQxBbPM5t5aTqqBFCbJzABtrT7V/q8WixUoxkFGwHuTQdUrYokDflz5U1fdOWwtM27+wgLaesWaVK22sGqsp1Jw9CmyCJHbT4vqvfW+p1y2aEWBMh129ke35BruvEgvKTicppsQxHRSIjNPEdHPavX4nd4LRknnmqbU6oSClRa4LvUooAmP0KF4hCmFIqCiDT38PGpEciJfQ90F4z9PDYyfonrWwhsppOhDGiU5sk71nxoV0LjvfQbtfgMwQ1sLv1w98//6Jb75/tD5HmsMontevDDFxmA6fmau5rFzy0vcgYI55hSsmo8ZHXR01jPdZf1r7/rHrj6RD2xtEWh4ejA+yIa8spUZrBdmiwrbhLT2loXvLV5dyrQD2Bg3tjbd743klI01vsX3u9lZbbaLWXmqmLgXNpXtGLNb3+HI5W5OtDMaycggM03Dt0TX7hden1JE/Vw/UvGsHuuQCpfgGeb7dvSH7we7BPMptXlI7utfsJrodNDFhkP06tt8zzWNI3mjN+cNg7BzCgM3MtMMbBKZh4DgM3B8P3IyJ9+fVQB25MtfC0/nCy3zhvKx90kEYhNtX9ySvR6VUSbGA2AzAUGCo1icaQmQYIodjYCQykKh1pdbCIspTxdJZItzc3vD69RumURxIsE+f7eSjOz072XAhDBI6Fd7ea6YN5cTkasO3fS6fzSXby3TB1k2PN2heySkhZKuuVKBGVKymnEUpwRSeQdcto/IclXcOzDmluM2u608hvb5sJYh2xiz70QLDlhpN0YyhqjBOibdv3/D88nFr6GY7n1uA28b9XEd1FZ/57XX/q/sK9DaotmpOjWmVKmnN7dKVQl9nF+YqDdYTu5A3h7f1C1o4jVeswFzNup0rdmes7X87S82RDq4Er+Zbtr+6w+1/XtGNtePeH30Xc3oT95YBCsSQjCJtv1IOmLMnMudam8PRfqnuCCVCJI7Tpmt2BZnmdFtrlZeZ2v3l0qDx282LGKdqFKvXxgCHwd6vSF/7uAbGmrpR6tM5fCmKG8OK9LqVikJx59G/N8L6nR7zNRI1fuh1XYEL7x+etuSik4lY+qEyDRM3x5tNr/l1Xi88r2caBdWYJlIckBTsufwq2fZwXRZD15cV+UcYQPgjRjA0d7SlYD6B4nVodupYlW4gNuh/E8yGrJS9nTIuTO+HE3pHBRC6bZEgNsOuX+3k2GJGVQaEiNWKKg2zemWODchRKnlxTtTLhbKuTlbcDhSIVHJYef/7dwzTyKuvXtmzl63QFMgM+YLEAQ73WDx+RlczsHMpXLKRz17OC+eXlWLMuBuSsEWx0s+lu1SWWRSkwx+bt9ud3kbe4b/St4rNwd5Wy0Ap+TIwfxgZD4k0itXfFggjMAhvfvqan795xZQSIvC6VNI88823P/B4WVgumbK2GordZ5DKNAXGIRCDZXfHoIgWZrKBG58Dp1cDr98eOUzCYQpdzGVxb9AjlAMgJG6GxGmMxNEES7N9ZlbrozQGn830NVXfGnJDUxqbLqar7SrO63rtd2pri0BN00fdIpV2xhXCquZoHkZqnQinE7oIOp8RqSCFixR+yzOPFL4jU8cj+uYVBwncuGF+I0J6+5rzm9fEw0iIiVDtPFRp99EMmE9AcJYkPLpHoCwz3//t31Mk8VgO/PDugd/97gfev380uxVsXcwuZVoDc1ZxSjI/Jw3oIXvw0s4qJKAKstjwsCCtlujGUDwh4ZPJNyqV7pWRc2VejW0lBidk1tT7hbX4KCR/2ZwNGTqO1Xrbuh9jgKoWjZb6I4xhuv/LzgiyyXA/T5/grfYvv3ZxtUvaOB4YhpFxOPyIbrQWLlBSDeSaeVmMjKOu2d5FoiFfE4zNOISGNvc6tE9DaUOMRWI3WggwtOc69Gc0mXcg0Oh7QURLZfGgahj4w9envqDfjs1ZtVa3qsplXpmzzavsLEyIVbt2wdAucvBFdFDSLt215IXyXFvY2++j1MKW5hHWnMm1IsXqnqb3hLxUZyFbwEfT6VVw9IevL0eCO++5ZXA3r9qNnls+2e0LyFafUnZelrK1T9hb98nV7Udh94mye2PFvSTdfQpetPU+mN1D/9iDGxS60SMV1mW1npfVajL7Zl0JleU8W9pivSVEtXYO3Q6SVAdl1IKyQp0t1Trb1PqclXXJrEvxKIDdWdS+qtsq706x5zn3+DPdkT2bsVYD1VS2+sL1EvkVkDgCA7UMaI2gTj5b6B6xxEAYojVig6eAbTDrmlcn/r7WFIIV3WNUhuhNtX7XxRVkXs2THSerR/Z6XL/PFvk4xVgwUEdK0rwBS314BNhH3VzdR6vN7GmlsMjFz1UHeu7WqXu0bWtaYUalhwJ9zbsc+WuT9aOSBiiZIovr0sJFlQ8l81gLD87uHOOAOlF0FYM5cDhS7m63UK1FYTSFK9drxMYuon4+a61cnp9ZNfGwFD4+PPP8/MJlnvsm9XYanP2jAYQacK1FcF3mdgd6t9Q9bt4RObS9UPU13sne5oa6k1kruRRHDCsGWfG5hFq7I7r5ubZJVT/N+O/w47qzYbodUfnsPnZP0SKyH1EUn0qWfPLz5vynNJDSSGyR/O6FZgT9Vc5namvkkWd/x5aK9ohVHV+we5628/bf7UO0PaNAR9M27dl0Z3DaMkfvV2e31+j30aNptrQ3m+xt9c8tU+KzSMi1UErZ8f/CHqzQztZny6w4yny736qVWla751byarK3lwdPf4uCtBYcrPvAOGp3NfB/5PVFI5hLQxa50anOBRfDFvUppkg9XaONTqyHN+2riWl7utq/FLVZgN3dNkZ8Yd88b+/XgamtRggEFWJ1xgzZjGHbLLJ5mLlmYzpYVuol25QJSy7T4NK27gUtwnpZKLnw/tt3pri1IMF0XhVlUYUww3qhUXmVDOvsno4a/D1TKRIs5KpGOpQkEKMz6XymZqGFidX7eRr3ckfJ4TIOV5FfTx/3ugwcDm94/ebPOE5Hjodj9+JzsHEmZXkh54V/95/+I/9lHPjvf/1nvDrd8B9/+3seXs48zWYA24e0yWwAohWdHxGOjKcb2lBLaVGvQ4Tw5vcaGk20PVD1Nt54EHNqORHCgXCYjK27Ga5mCLuj0hKYJmuNvUc8iu1pQGnLsZ8NoL7me2XULkv/qDSyMzZGjSaJEVQi9eYNIiPE71nkmXcosy68y5nvzpV/8/3KY658n5U7feZrVlIKvB8TdRyp48Tpw8hNSOhppB4HZJ2RnN353nr9oj9FJSIaySpEP1ItO57zysd3H3j/wwPfvvuOp6cHO48aXMHaE8YQScMEg1KPRtBQqndwNiMoV3gHu/JiU0e8Z7j4JJfq1iSYXSOvSgiViCu1KKZh16XzEBsSt4EWanNhNsH+xDi19KIF8PYaqrTB6Xj3xieQA4HW3sTuLdU4eWsbrLz/R71+OdAqbY6WFYbTgTSOHG9uGNJEGGxaR13zZuC2D7PX1oKejW2gtTMZTNP+Xlbl8rKShtHmnNLAT4151lI/pTtLcmX0YmpBgmVUvIUbXmwtQzCijPI8N5WHqHCpgq5QZnh+ORslWjNgLvFb9LutR8aj715L//LlGeTNmHVvqRmuz7ei/W7D5dvP/M9in+scDh6Jbjr8ky384vVlxpj9f/xP/fRdO2CDK8uu+9ftdbzsXnfld9SebhK298RrIFevbd7O/mE/GZUi7kWKn1ItjipS5z8tHv21/kF3/PcNs1ordYX5MhOCEKUaxZbvVAG0CLquJrIKpQhrp/vUTUhEzFv02WghiheJm+G1Z97qj00A+2J+prLVvbt2Frrn2H4hGidmSkfG6Z40jYRxRGqHYFq9ENCqPL2cmS8zjy8XUhh4nGcel6WzlvT1lS2CECxNLFrMAIovJtKjjXGIPq9uiwugMe5YSi9EsZmTjIQ4Wo53r0gc+Uv/bIOpt/sIgNHGha5D+59NXrR99l5bal/QfX3JvFhzjuqV/90QxaBxoMYB9UR81shFAz+o8EMRfrhUnrPykAEKd8yEaiBz61MdjOBgzWiJaAloqUg1pplG7nAl2O2J1NVkb5415GQpmTUvLOvCWnK7Y/bXli42GsOcBXLe0Rj4cVN2nnyLRbxWpRvQ4+oe1WSpArFWb/9xqGf3/n19u46wNFij6drfbbeJ16kmlwO9ei/t+9v2SdqLN0CUbXSPfprBarW5q7JD+7f2d09VxpRIw0BMyaLAaOkGydXP9/4Bqht3MT20A95YzdURspopuRl7b3zS1n23qze2cPgTII6Ipfh7WrafQw8IQu1Tda45Ty06zIsHPfvCuz+86dOmaIxOsOzqsdtOXfsQVz/o9qHd0ad/2+k7doLHJnt7O7PxAbfP2PLZOxHxW/6yKfxHTZGoLccbitFCVa55CFFamciwXdnmg0nqyv3qqZsDsHv8vUl0/WnefRvG2xwBxQrKFbzxy4S6VEev7T5rVcjFUp8lk51tQL3JsiXvEPPQOlOFn6hSbHTK6k39IoZWo25kwDlXni8rEBCdkCESpmQP0xq1sV6rIUbCTULiplRHD/FqVgMaDWaNSxsF4mNoaqs673eY5j64/+PoPpOJwGF4xf3rn3K8+5rh+AZiZCUSQyVqQcfiwjYChcn/9jffzfz9u0weE+PpxOXhBSkKqyJFGGQ0xF4YGOORUYQUImE8IrXCMmPe68iruwN/+fWJwziSSH6ovd4WBHQyINIspCyAcYiWkpnXhdEJgIOnZkWSHwDr/g2yybgJ/AaP3qSLba12hq567SB7OiqvrXTtRAxict4yDE10W65j9X151hdWPXOu8KyR3zDxg678do1GQAC8D/CcsLpfPPDVdOKnp1vCaSKdAkKGuTjQoVBTtfFijAjBWI1i4wd1xwWhMCEkEokQI9P9PeOcu/MYYrD6YpGeOTACrMSQIochMrNwKUKf3+dXCB7zx0SrKZacuJzPFC09xZzrNV1BqdioMgER3dqevK16Q1BwpezaAnedJZCC1X72ZwaBJNZfm4NBE63UIT3S9E4d0iCkQZFUkOCfG4oTcauXX1rRy9ChWwrQ/4iCRCENR0IaOB5ujLzBEZgq17//BwOjDqIr7vD6UuQKyWuAeWWtuRvoslbvld6cys8mcfipkhCYjjfG1zmMmxJFkbKiJVMudjZawEEVWutlLQq1WJrRSywiGCEKmcbeNc8z58tCznl3zvwN5fq+9v/0xy/1//s9yyevF9hIDdp7fmr8rg3LhlP5w9c/bpTS/r131r+PPekelv3Hs8lo2Dyqzcjt6hzuuW2ed/NQNhBtOxibB7nz6CqegjX29wCIhA7xLblQVhP4UnyMjpOzdhewf077IPqH9TpGy5qInfqySge65lLI6wokhEJsYAyw1hIXnEY7FIZAGBx23uoD6nPXdspeVany6aH8zO/u3llfc189m9wxMk6viMMNIY4eiQZEiynINHraMgClRwIFYysZhpEolSVcDLjgvTlGFKCEkIhObCzq06rRzogjEkkxcjokUorey4bXMoMFgTEiao5BqxVKcGL1slKjTQVvLSkhePTjD/+ZEYSulczYNY9Le7TQeA91R67dStOl62fZ6tOyyWST16qV8/mF8vLCU17JpfCCcCZwlsiFyEUtXkWsOX6uNksjSmVcC6d15eQsHYL2cTWmSzZP/OrLnZ2WeWgeonqLTkyJYUgcDxMljzw79UsRh4qJPUlLt4aeLuwf8EkUZNGFSCAm53wNZjA2fsxP1p+9ty5XwIZtJdl9lnS3ff9+9uNN6bfIuHFx726yIzyvIkGkBYJsdE5sx7xZIsFqZ0pHWPd7x25IPMsQQ3QKtp00eC3vOsW+Bxk1jz2wJRft4Vsqe7/6tdfqbHCBjXey39g+e7/YzksrAQkzMfqcDwlItHsS78PsK+NZDitFbp/Z76trEvtBZ9fBwIxG0Vg3xdwdgE++79ennsKPGK29HZG2Uv0kXP1Ksx17A7g/7f3T5MechuvrH28E2wcrxsfWUgzC1aa46NlthB97N2c49zsvzgfaaILsAQz+amNi7DPX4nPCxA5fwOQraaBIJUtljInDAOd54cnnal15/j5VYb/Wih3qQHAez3160lM+/RAHSlGeir9aLA6zYZu593IN4hHhEKk5U9fFPkMgjdEY3/1aLoWcK5IMaJAav2IArZUlr03t7u54ux92vIxNw4cgDCkw3bxmfPOXDMOIpANDFIYQiKwEVqbhnqS3rOszpSwcPD32+qufMB0mJDxT8sz8dGFeMw/zM2s2bscgkeNwYowHcp0YipCWR2qpNi8xCsOUiHFEhgNhsKG4IQxIVCN/joKMgahCWOMGDRbhvDzBC8Rwiww2pSSI7NK3f1xuq5qT3fRUr2WsSl436PyiSm64hKaUmsHh6twBMFOZ5xf+07/5n7k8PcKyujqMVAnUUSjDzIvY6CmAMq/MLxeUF4o88E0Q/pME/uVf/jk/PdwSJ7FhpL4uKj7waxTrUHLSf/W6YK6JhhS0dJtxAh1S5av7E//DX/0l3337A//64YW1XEBfTP4wdhWLBdk9maW49m270Zl1xNtbxmgE25d5MefMDYEnY/pXu0rGCQ42A7ehlLj6ZeOlDJ8rLTUlvekSs2ohupJcF1ClrDOlVLbRUE7RJ072v0sntYbw6yqSpec3ThxzNsR5eIcUmFKw1sdcKLqazoiVtS6uz9qsBpPVMAwebdkZTZKMCN3lzkuMJDFqwb2ZyLmS19a7WDf+h5KosqGrm1Jei6FIL09PiAjTabKIMEZiTByONmc1jSNBAmOM5Fy4nC+WdTnP5LxCGECzp/9sIFXR68b0Ugp5/YwO/keuK/N1dcn+P5/V2LbX7qkO+wsVWi9vX4L9P7UvEZjGnVf149c/ygj229Kr77rlvf4I6b9bW7Np/6dP8v0d5abbE7CpeaOvcpPkPUR7Eu1QDaDiQ+VYVYhq5Kp5LX0UztX9d2dbu7fa/Le9B3zloLSb6p6Q/10cMGFOl/FQRhtTEqL3sWhAa+jeaPdM29vtlQPaBSMEoeIwf8UyttuNbG/ga9GVtk8TiDEQ0kAcjoRhRNLYyYsbxVsUn0oQC1qFUxCmEHh1ODBNI3M5MwO1FHLxiLrh10Pzjrt2NgVULRLEYd4itg7S2GSCOqLYwSfi0VLA6ke+Acu6IvPM8TDZpIfgnq14ZOzowu6z6s5YeYRd1Bgm+kgltLeRmSzIFh22GpFC5xJVjyR8W9psNZYL9XLm/PzI+eWpNzDTULUhEFJiOExIzi6LPj7M6ymrC0HOrV0gkGKLGoTalHcIttbNOOwKnhaBq3PN+thez4gcDyPHw8RxOiBUSjl/En9ZCrj2KMBleV8Tb8plF5FVg1Fv77I7NE0+N/G289tLIkKPcDaLKT0J82Mee58Gokqom9ePc2Q2h7szwOyebzNvn9ncXStMe4V8cuavFUCvX9IyHZVaLbpuFJCtXi0+JcKyHKBJCNknisj2OU2Din/A1uTvjFC9aLndktpi7O7P/3RjWXJGBGqOFq27Z1dy7pGsBHESEDU9JVZPbudK2m7uol3dbtr2KsqG+t9du4LMHzB//Rf9udtv7rWb7uScrjAbUVgHPej2XrY2nyN59wH9H7q+aAQbo1fbi0+yGoYM1DYipgllJbjRCsVy+eKHOMgectseMvTFbrUCRZhzRiSQqtUi8r55SOjTKTIOcCmFuWSeysXTW8WdP2l315+rarbUnjdYdqosv8Jnr9hWVD3VapakGSEzfOMxMUwT0+nYFsjGqoSAloLWYsKdm4nXK1q0ffQhRCMiGEcjNl42xd4FUqAl7w1fYguTYmQYE+NhYjwciNMt4fAKyBRWgipohVGRpLxJB45h4Rcxci+BmxgIqvyvD89cnh/5+OGBx5ezRYFNwAWYBhgTV1Kmiq7a00qShHT0+Y1xJ6K+3QuBC8HTOfZPFeX9wxPycUYVToeJt69uvZm3xSqpv6DJZ0tdF7WxT6VUyyDslssEwAWoX31MKGCpUaoSRxACB7USJijkhfy7vyd+fA/zI5Szv3kAGZGQiGHidHPLr//pr3l6OvPd795jE6yePSVfrPUjRtIQOJ5sr4ZxxMc1sGD9WCSLZqI7FG3qhf3YGImkik1VKZWnpyeWxaKj42Hi13/yCx4+vuc3v3/sRqJopZQZqckYU/JMyWvf26aXGrORdo3VNt6+KeWaqafpm6u2cbUZf+3cVjVmmC717fhfvWRTu1q856taC08zyEOYsLRutPFK1++AsqJEm0PYHCb/paImF9sA3U8eon9r0dC6GgVYkAYcCYRGgh98LidCDAUJkTTeEdPA4XCyMzlBSImnpwdkbajpTWarCnkxBLs5jLL5w0FQjUjWFsJiY9K2ewGI3hzuQ8ZMx0TFEOuF87IS08DN/SszgqPVeoOcEOD8/NKohilmPy2Kd9q1jkkAhkNAU2J5KTZ5ZNtN/+8nhuJHrj/0L9dBEgSJBIkbsltaClZ/9PVXPoMCjaf5C9cXjWATntqjpd2dfg4TpanhZqz3RvuzG3Hhl2h1nVoFrWFD/ah607DsHIXdXbgjqcZ4jK7qHRedxwKqOFDmmpaqHfU2wmPvJe5N5rXTsfdU2j7sHk7YmrNl95t+aNUdAEvLaX8ArZ/wlLhRNS/fRz2peZUattpD9fSEORemU+3GmmctSIjE8UDwsUTiSEL1QZVmyCuHGLlNkdsYOQXh4OOeQilILl0G+iq09gF3CkopTpu0RYWCGGN8V1z2s4boszRVRDX2upmXmswQFgWpLEslxsq8+kQQX+P6ifbdj5az6GbTGZ+IXd+vqs3P9de2utKa0VJJIRH2jW+iaCmm9ES4CWLk6f6WK825qRxQ7u2JeYpCDcIQA0UrPuLNlj8E0pBITlreIO4ZBy+4legIOTEl02nKfG7k7Ew+Hz58YM2ZNRfWUkgpMQwDQxq88bp4S41HTs5icnXJdgY+uwQ6GTT7124u95U/LmwGSN3A7T+vlSvcQO6jyGYkLPixKRcd6aBtaVqUbHXqopszA1vv455arzmyvVTihrnV568f1tapVnG+SrGhvrAhO/uv+me2+mE0/mQU8uoUcrLplrZE9oweWYkBHczAb8ws1dfq0689HsNSyu60uJOprogrhgPI60oMQooHCJbdWBdrK+qsRBU/p+1ON12m4ulxAiH5fbN7mHafgP5omvN6df/gPwibjRBzym0vdv2hn4V92320v9pkjC/fxx9NhyqwOuXYJtyuSsJmlBtJtbFOmJLcBTbsh0silnomQhyD1fxejPqG6ogj/8zsnExNmLsJK5YCKXml5kqZMyEKaYzbWgRjldgj6vZXLVaPa7WhbWbXNuqjl1qbQt0dkuL17n0jtknesnnMvQnWtHTOhkakN3TazMFmPFQGaEwIQKw+ZWKy9EVWY1G4vFxsAyUxxMBptEGjeXVPXAvx/0fanzzZkizpndhPzcz9nBjukPOb6hUe0CDQ3WhywRZABBsuuOBfTaFQuCCFm+4Gmw2iC1VADah6L19m3ikizjnuZqZcqKq5n7g37yuR9pTIiBvhxwcbdPz00/mG+eWvXEBktM3W5pBHKidTpK3z1az8IsP93I3tZX2itYXcVkpTypQohwzn2FCzdQK4NDoXnp6eEGb6q9nHXihT4f7+hvk40ZNxk+axuhXyEcoNAyyalakoa1W3uk35P57gUqFTmWY43mRvlbIXpGIOFIwc8sc1YzsvJY2ZMjBmOAROp7c+vEeXM8f7mcnXUwVI1vn9w90reir8s7u7AZBeVPlj9T6GnGjryqsfPvCurkiB98eM3B14OsP7tY4c2DwfOdy/4CDC7JtKgUbdWd8yvmUaCfWGvR1orFX5259+5M37R/79X/wHVODVV68R3yOlzLx4+SVru3BpT9Ykt1bEs6SjhCR+Gmw7o/XqbqxB5gkQ+nKxWr8Ia7rXJclzVm647reNNVDtQ9BJ83BsVmfAcdjOILf2ufI2Oznv5z8hxdg35zJREXqzGkZyN2U0CxRhVR09B5dqeevWurV4Wzt1aYj33wwBPAa9GwhkrcnLZSo5d7QXr1v2kz3Jl0omTxOH42zsLwqt1+H1GkZWx0c2lR+lJJ0s2X1cl8Fi7y9abcF6GVbQyomTSuSULM3UZVNG4qmApdPe/IS8fMH9L74efVCFxts39tK9ZXqPfJ9uYiph85OSdaSpCbl1AKKD/IbV2a19Ueu6m8JPkBJG6cyoQd427EgvJZNd5jCY7Gyb1fH8ilcLNdJdf+r4rBJsrV0BS+zp3NKP3Ix/jZomP1d2z3hV4+MeE6HUZLOISUrz3EYonhEyTNdDqNiG6jUQn5s1OT7nBefqZIeGSjWbehDejtyE7ozZ7X13dodbbYx3vJpVf5YeFczDOtHxbRuvIRE2l9kGdWwCW/AxZDKs8ySKsiH1skRtnGwsPfGwHrINL0w9v0hPqAq1d9ZWWdWUK2rtsmo1vtDwUs1yTM7ysRkGa69IhdP5QkrK6XSADlU7k/cnvCydD4+V4yRGfecUfMY+0z30KMM6tzFyq0Ixe1iss3t4eNeWXboeU3/XaIYVrabU59LWYxgh3deQhQOX84W6rrTLA31dmC97QScwmUffU0JTGoAGujEElWYCqTWBtTMjHBHu/TOPc0ZbZpksN1NSomShi6mjYNTc2DmeiY2xHpyG2//cW+XN23e8ef/A4+PJwkjTRCKRa6K2dYQRwzq3erwYDzM6bM3JlcciQzjthckOvHLlCboylO3JNySkDq9ufGy337Y5FHY5gk3WxWV8Z28RnHiHDUFsJ9r6idrD61znzgPse/kWu+1TLLLb57qjrIa5PYZM3PMz79HYVEyZ9xYAN1t7gwZh3GiXS4tHjSiDblRt/vFtyP3+gWYdoER3SKzjyPY2inlHy1JH373aNp6zkXveydt9vaUZO9ZvMli68tVzKXTjKW27HG6sMUOlJ2rtnD5cRpH7kFnjlcSckci3gxlXjjNRbWP9Xk3Wn9Z5Hx2fVYKn08WtohRvQfhH41vIE1EsfeeCIcypXcFmAkMxZrki9gVFssV9V/CwQx0bQ8EgvvvdhdJrYz2v43GMOSAsC40Z8qMbdDiZ5bhZnFu3+NjQTbyEwHqYEGp537ljb9kIGC/f4wXRhN44IOhZgS67cIvu8hF7ma7VrLDqcECrEtv74L4Ib4/j7g5cRwFvPGUE9lTgAbgBmQx1mARdEzThqVbOy5kfJjPybpbKLI13D53zAmuz+TyWzNoya4LV56Or8rCcSFU4r2ceng6oQimJMittqUxPxUoHTo0vvjjy7bdA7va1JqidTkZJ1AusF7Alac8nJI7Tgflw3EpSu6MmY+Sztb0SFyzGJdg3AyPZuhhYNlUi10M9w2lhPb3lcnnk3fsPPJ3OY26X9QVzmplmDEDwAiRbneKYst7h/Ii0TllAeoJWDLAyz7wQIdfGu6mTy4EPk3BIyiyJg2Rui3DujU6ixp4Qwr7fmWDq89/JJO+PZxqiLhf+17/8S968f+ThwxOq8P7NI6Vkbu8OQEdlpWujrTZubgq6sOlI7iNcHi8nYw9v998fY0piNqS7mMhXZwzAyl6X7o3n7EK8g8fidl/bVEMgOjvLcnF73BV7Tk727eCibgo952Sgqs52ra6oVlpXlrqjAhztD2x97GNHQwa1xgqGAsfWV/Ii+ikbfVrvjVoXHh/9k21lvVxQbSANSXWz2a7yRZkYhmu17IpR4VlH5aF3JHAKpRBkwuIOR6hHzz+xNuXHH94M5XK5LAxmnYR517rrlJFjz9n45DKRC0wePy3FWqllLILWd4QBgnAQm4NpKuQpc3h15MObR/7i3/0n2hoSy52HMd8TyMFR9WknI4uJj36hd6vUHUaSbJfZrKc/ffyjGGOu1mH85XpvEki7wS0qIZZDS7pCDGtQ1UiMA2HUDVgT8D1tOrw6wayADUjiMf3WfFv4cPvGMZ5JV4R9t6lCePljpGSbJPIBbFeyZ3cev7FME5vDJjJQoSImJPNsZRExMM9rgIa9M6ziayaU/Zyph0i726VpJxCeM4l0tZB19dCOJhPGp4f3PP70DxxuvuZ4dzs+05rQajJ50+H9Ygxlv7qxzXycLGBznGZaVw6lsLTG7DVHSxNfb+aZrbUhl4W37z9Yc+JJqGvjeJypq6N1u3WoTwWH+68gE4fjLWU6sK5hZIS08lCN+AbubOiwq2Nv58qzv/gFulFktbpSl4W1LSz1Ql0W6mVlWR5ZlzOXi8PEXX8+nD8gCJfzI1Ub/XiL5mw1keuFV9//RL6cYDnRW+epgkjimKyze8c6Wy+60qUzIRyScDdlDmXmphxJ88xp5+kk9/JiZW970N5zVVtH0V61KaxdWWujOilE1MvVniBVEPNMDOFbh6K9HkO30vdj6GsucucRqo0edyn2m4aC9llzj2J4d3vHLjw21d1+iK8YCP/MuPYmhFLcgPCWN69yBFh2ePrgkt0fG9LRPijoxis7DITr45oOYK/N/Z3GeYxUTlfvau4NfO2EhKTi76Su9MOw7G5QQwsw4UcBaTFZ+gwMNBTdGNCoJR0WzXBIeutclsXnHOpavYFAeI/ZntHljEo2pDou8AiZbOQNo3bSnYsgKojzcjJswHwzMx1nXn75mmm64Ze/qTy9/8CP339/NZ5Xc3c1G5vSuQLf7NGie9n4TKb+3PGPLJGIRK3f63oNbA/TTcDtEV8iOxNMBa0N6Vh9tghURqxeVYz9oSl1aVsxvoCWPgSisaEvlrDGahYTzixRsiH1u7NftA7JqI2e52nnkplEuFwadeflJSfgjMGPcZVsCz5Hcn5nKk6HiZsvbiz35lvpylIOozqipV5KMIq990put6PXeKbumyLvWgP5+bU3TpeFVjvraRl/uPS/YVkTX/7qX3H83bfEh5YlcXrIRDX43yr8eIbfzYVfHOCrF4L2gqpQysyrh3ckFeqxk2vnfGbAuXvvPC2Vp9OZn97+5OOW+er1HRlH9DlAwKjCCmWexrh+96tf8+VXX1teAQFnU4WJoEQDTxV34PB83f3po9WVy+mBp4cHHt685c3TO358eBODPYYyKwaEWUCb8uP77zmdnvgP//F/5d3793xo1p0kzwcmEb7rncm9z4xyg/JiKvzu1f3oGn6i8QZDLM+YsXQ4FG7vX3H38ivy3Qt+UngB3AlYdVu/ygjuTanavH7PV9ildR4dBNR657ycPVRoxufDaY0F5msXbzFVsJCqKzARRF1hxLCnUIIdrTjhiRguNwktCTXJ4MvM8ZFIaGN7O+nGwJPEcqGd7g1PN0UWJVVBlK9YC7SQAwDToSDPUiNgioO22dR2Te9U8azNEX6v5/RhEHLLN+u2Ja+OnYzfZkci1dDRdQWp1GxRKl2xHKwYJVrON2jq9Lzag6vBoBScBNXkXZNGYaJQtvpmjbvvhLDPryJbTWUT9znylawAWHqj9QdbQWLdJdra/dbFFGFOoxOF5kTDwTDgAh6SFHJK3prtUyNl95VJyFPi8PLI7Ys7fv3nfwYt85tf/Av+7j//Nf/P/+v/zQFNm5UbXLHjqmMJWOJAdPDKfHS/sPLk+Yv/zPFZJdjNtfsI8hzW6fZglqsKWuXxFUo6YulibSTDORRXgvsYd6tbbmjkEMSEkm1IBt1VxPLFLyGqG8LPXWKR64HQvRfrg5ZCsal/Tj49pQZRF6Y5uzm8mTtlzgMYMJKdPg0pqbn0qmhvjGYUu0fZNh8EgnNns5pXitj3necAOOxeYSBNFUi09cL58SeW0xvW81uk3EK5MQDRVKDO1pTT51pyJ09qi0IbNwcTQF/cW3Phx7WiVA65ksTqJTuWI9G+5T/ojcenxPc/vNlYalxo55LJpYz3ejwr3//w3oWX7DZ5RsjcvfgDU5nc8hSOxzJyoCN7rjgbRgxqR7WOQa51YVnOtGWlns6sqbGmRs6ZUpyiPeZClYc37zg/nHn7/h2n84m37x95fLpwUehixNVNhPdiBGQp2Xp/7J0HrfTHk3lUCiudJyolKcdsCiWJg0eycbeuHS5ipuZBhEKyonthoHItb2o/iijTwa7/5uGJtx+eRrF0dNzWETMLYgsXKD2EYzXw0W6lGuTf1/7OwBvO3HOy5JiCSHkEtV3alqemUGqbYW5d47d9uYHQ7Gb7lFe80+bkBJpUrvv9RXRlf4w99YndPPJsgWHQzXOVnTeZwgV45h+qyaQeJNhqasyiPwF0U98OpswHktWNi2B18QvgQpGxwX2ugsA9Xkf9+RRHv4438fHpENFMG9OQGZvH1L23sYo6Yt1Zh/wekQc3jz5hbavsPEO7myzsKqNWdfGOEuRx54EnKIeJ+1f3vHr9Bb/69jcIhfUlLE+nLWqw8/hNdu81T8j0sWrwEcRF/YhmIxhxiTybs585Pg+M8XEbj7JbJ96X1OPO3rFONgW4Nf3EQ/SNLtC6hfiiMee+TEcV6rLBl4dMw5SHsutd5pZi/B3pNiEeerASJ4Ocb2GC3XvsNkzJmSwGBNJw29wYjMMamRph7s3d0dBYswfes4VNRXRLK+wmqaD0isXtm3r7KH+Ocb6P3HisFevxsI2NKHT6FsryfaPB69l15Ach0ZYnPrz9W27uX3N5/yvK3beUMjFNQr49wElAj5bgF5jmC4ebRqGANr6scDcnNH/Lh/OFh6WS0om1dy61c+nRUqUStVz0Bq3y9u2ZN2/fxujZfy4hrZgYZ4P4KxgwFixE7qAGdENDXrW2BPOI4SqBX1U51WphSLWAomodU3k3H3h1e8fXv/qG7377C25v7yiukBVlpdO08pd//Zf88W9/z48fTpyWHT9iMUOIbkrqp2TEpuKNy1tVWFb+v49PrphhQrgl88UMv74VZAJmsTEoM5VsXpbAJcELydwkoGRUEguLITA9sHFZ3FI+CF07f/UPP/LuwyPn2qjdQE2tVzeiOqSGdYN3Cio1SjrB8j7ZRYAAqVyl/4dAqqEEWx9hfGAIhqwucCZxonl2h41P72xKQkImyBW34+b5boJm0HO5bal7Ljv3GC214ty/P3PsQ6Imd+xzFli8BmZ4NNE9PkGkGJBu96wAbTV5UbLx8DR8XFvsfwvnVu+6EXWO1rUyBjhhTDMeAPdwrxHTJ3riin4yjujttw+XKqZwFaVJHulRxAtvdDsv5NyW7pHxTCKBqI+3FffUXalEuI8N3Qqdtw9PrLWNNQTdZGvJHO9u+MVvfsl33/yS/8O//D8yeRPu1NpG+h3zPuThvNkw2qCbN+1wvXF3wR6JjDMcCce7ZGBWrufsU8fn6wR3X0MfKIMpYOQCLM5hkFYJFNDmEIWNHvWGptX3fwzPj8HcsbMltwRvaODdsV/22pW+NsLz3MOXw2IYPbTiZcAtnjxi2Zuu3HIFksx7MtZ4Z45PEWqQMelDoV+hXbZBGF2s3aoOoKLtad9lGkXxmxUeHq3EtWLjhoASzIqPWr1IcqMsT29584e/5ObLyo3MJI4kDkhJQCEfOmXq9u/cLeyhyvFYyFl4Uc1O/OrlC6Zp4tIaKpVczdjI4rlLtxA1WbXt1mjTA8RqC8ki5L6YPePfY2o9txxCyvrW2d+HkYwZO/JsbXVVlm7EVxsN39Ze+ZKsaFhFOBwO5OLdoNXW44cf3/Dw/gNvfnrH+6czl3WltT5oAQ05qWOOu0+AdGPWr90BOdG2yEO8SYSHJvy0CMeUuYvE6HCbtq+a4LLLzXU8jO+yv2qjK7w7V2pr/PTuwsPjxYzDnXHUHRjkYNz9ah/dUtSV/ha/cWUZHoAPnFPYhr5BPdVgS07c29ut0dCg+7U57FB1oEzHgDThxYRMiPvYHomoT4xH88iFpQXifT1s6lRf4UltIhx3MiRO971jLyXbbtpGyRWhKW7dfqFKDoPU8Qa1VqtH8/0pIZXCww0kPVsua6ijMW47wFe6NgYjL6qiYfqP71327xUlOj5H2H6WkLWyjciQohoPI0Ne2z5Nu3PiPfyZ/I5Wi6tUp1JsbesHac9sNbWn84XbtXJ7d8fNzS3eoc/nk4+O54hk+6fv6q6j/GnfOSjG9OZuZpoLX3x9R5kThgT7eeMI/hFK0Hyp3YB1nFA6Fkac6zyi6dq6ix+6eh+5vYfnQJjArwQEfjczQ5la08o+3sehK9Yv0D9i3KDNnjmBJq44+aJLxEBUaegwU2Y5W1iis4VaQ4KkJNzczqSSzQNMyZBY44hn1u0Vh+Zli+l3YwwZm25403u7C5oKbSi8sM4wRv6dsBtHNikZ7wJB2g1P7/6Bpw8/8MWvn/hCZo7HbzkcjN80z5n5uDLPajVVOXkgMlHuD5ZfaspNyTx9+zXvThcelgukC3PtQGXNQmqCpkQXoYmHc6/gtOG5G7RZNWHNRtkUS2jCWBCq7tFfLaWx7kxwbspvPxM2sb6JfHOvvfNYG5on7u5eIMXo3CyU3Pn+r/+ev/9Pf8Mf3z3yeF52j74nLd5MQ2MOMsHYMYs/WLhDzDQRKsLSEpczfFUKt+kGTc5pKE4M6ubzkuwzWSKn7JZZNoF3QVm78v7Dwvmy8nffP3A+PZmyCB2iSm3GsVsIYX4V2NgUpXbMxU0kdPd6sn9Vv67rjV6hVUJMqrvpoRQjBxd98yz86e+izQV0I+VmCljyULBxjC4qvkkjzx5lByXtW6MbAKm10cNs/F5CRrWdUOqbQS7jPPu+E7s7MRxcOm4oaIyfPeN6YeSvZbdnSWY856lceaJJ0pbKkZj6wp5rp0e/Q8WiR64E9lDA7Um3n1env8tVdowvV1L86ogxGArQFZ26sbFdX0CNyk88x9tc2Z0uF0Pzuw7YZLgZJu8/PHL74o7Xr1/z4sVLLhe17h/AuvTrl/jo6eInWzet65ZOen6I8PL1LXcvj/z5737J8WZiWd6j+glNuzs+rwT9TgGwBEY4RIdkt81p+YuOdGvfsrmC4aRsyuHKKkm2cMxzE7eadgrQXfacPL6emnkdIQTbbrjifx5yG93FQzkF/6gvwFjMEiwr8XF8w6iydeSG3oyCJ2m2sdHO6EROnKOm5MIY7gwPdVCnjeB2hBfEvLjhiQpJLcbW1323bXHLT0d97jZQuj23L+ZR+GsTRz3/xOXNX1JeNUoSyLdIPoyl9qEmflzhy9I4JkVJ9CTo7IUaT9apfFUT7OE9z1Oj505dq7FoiFnEfU/oyfaM+uzRozTNzEubj77zeK8swvhJQ2SFkcLVOdu5aTDq3N3c8Orla+5ub0mjMBsu54XL08LD+cJDW40Qff+MjviQiPNE2E329WfPBaefEnoSExoXgfcJaq/Uy5njcua4LkiZIE8GJEnB0AMRrLN+b8qH05lL7SwcWRZl7YlGofXZe1cWQuVZrZpepa/349djC3uHiU6mS/auHRALMrlB0QZnpa0rC7MGMYYDt0S23pHZDIV1jVSGk+frLsflhtFVbbzi0aZAzIl7wjuaQdUhnzZJPh5tM5797y0MaNVBBq1s9wx7FcLPuRL/9q9uFmgXsyqyt5KxFEWs7Z3hqqCRM9uvYY2crd+/u8Ic+1YGDWJMW+vVO5zEZ3V8HnEPiXg3BfGSIY08XwzQXhleZ8w2UbFb/7r7w/4s9W4S2rb0TnK94DIKdccIQzNPZaaUCRFrqLx2pWqjJMu1Nw0MReA07OYiQhNvJ9b6YMHZH2nK5EOiHAvToVg3nlDq1y/w0fF5dOgnlaCPwE4ZNrV8WG1meUQB5b5IPrsy+Qi2O67rid0eYIAtFW2enVvWfvtuhsZOse6mKWVSmr1+x+4RITYNmii/RgKzbnYeY7S6HYzyvil7Ndc69cm9Fw85sLlxvXXqZR3h+Cjkb71Z3mLz5f1hMyJ5M9aSvXlmslv01RGzjPcXGe3H7BKCNy/dGQ+y28z+bPXhD5zbGyZpzHOGwy8gH4ggy0+LFXDPdwbZtRAn9OMN5ArvH+jApcPSLVdSckKOlrvBuWRzh5XGurWoZr8QBcx4iHxo2v0lFGHLruT2AKG9wtlhJ3084rc6lqctsCzmvL+6v+fX3/2CVy9fDoYNUE6PZ97/9MDb05l3dfXQ1vasqVieIeis0GhMGsbSllUinmGspQ32EI2+f8rw2FZuT4+8Pt1amQWCponcce+tG+gCG9OnC1yWzk8PT5zWxpnCusLSM1ULrR48tBRKcLUce2/WmILd4XPSnYtWaCSJAHJ2A24LkWaJd6iE8xsejKoglLF/rF7OEJw5ZyNeWHv4BSMUeqXx2NbJ6M7TdwowtJDAKKP4yNog7Cz7Z2Lry6y6NYzt1nh4KMArY4shbzYNutOuqtC8yjQp0oSsFvSTgdjdVo50y+3ZTtoL0D0QYpNDQ/GJGKI65XE1K4mr4wW3wKjJl8U7TcS20GRedlJTiNtq3NX/Pfv+0fFcsO5/rcpaV2vcHKxbXu4RYWYgSnXJSZgPR+fH9Y4XvVO1MSXM2G554CpSjp6tNv6SvEtO1pE+2lkt5GOh3BTm24n59tMI4p87/mQ41I6tvYhZRK4AhwJSmii5m9An9+GGj4EWo9tRLzIapQJhiQneHUKIVitD8CUlT7Y8ixbaCPckSlF/LltwkgolF+YyO7t7J3KEkcsMReTylnnUKfjb6rbEYiAU20gC6HIBEediNHJdxLxdVHeF/TIUrvY+6rf2V5dYyPt6oGGSMc4Mi3YcTj0laaTvGY2Oh3HhAgfxMIWyXBqP776nNiVN35PKC/IXBw53hXflJfSZbw6duWy8GS3BosLf/fjATx8eWS8VmjIXK9lWDqYIDo11Wfjw+EBOyjxNtNZpa9seXvbMGDsLON4ref2lxhoJK6Zt8uMT6/SZQwZ9IyGwDNsu59Kb8cu5l/7+p5/4/e+/5/T4YPs4p6vrR373Y6s5Qjk6NLC6weQrhi6ZphlK4nh35O7lHS9evDYCZu08PLzn3C4sF+NJLWLhaEmR24qw8C1dCzUXesrUW7Ww8+EOlYl1fWc04B5G12EgWvioLZZTysnR182JtFURMVtdr95u+xo5nnFCqPW8aZ3dkZKV8uRSQDrHo8KyclouhEGzifJt2ka4dQgE3exL/18K2D7mWdpU74wQ8c9GzlWv5/L6/fYLhp3RviExkWsAnqpLm55s/NVaSY1QYXxQ1MtkdBfW3fJuY52oozNFDUWek/XV1NjXDP7R8bzi8sxlmn2PCffPpe4RAPXyJDZn0Md6G4FuIUP19/V9IQNWEpEyhV1o0Xhzk4VgNeS9t7lzyVBy4tXdPS9vX5DkgFKMF9dLSbIody+OpBNwvow643xITEfMGMjFdcrEh5/OnKo6K1xYDzJmclkaT6eVP775QAI+/PQTfV353PGn6wQlbNidqx8KjJ2Q7qaYcvKcVzJ/Jh4whZm2yTSbfE90+mzbV2dX09cgW3doNKGtIL1zaeqx9LBsxBjj08xUCvM0UVtlDTRms+x+b1s4ZiyrfT0KYPWHu03lx+pEk90XYVsqtSmXVYc3UrIwT9nyYym8YeveHAt2f/c8RMJQdT9ngO0f0Bjnx6ZyNJmbv9c+V0CFzUtde6O++QceH74npRtSOvKif8t9f8lP+Tec2it++yJxq2kzYgSWDv/5+/f89P4B6ookYT7MVjCbDuQEc4LH0xOPZwubpqQsy0pbNlJjk2+Z/fAm2bZbGOaiFiIJgdt7H4COj8nbd0bClTTFw1QGAJKdEuzLBamCrsrbH/7If/m7/8yyGMJWcrJ+h2O84Zp8Kg63fka+d3sG83iMKqtqQUri5vWR+5cvef3yG5bzA8vpAx/ev+X044kf/vCWNz8+EAw6Mc8e++B4+w3TfMdXv/gl8+0dzA0tE3p8AenC+vSBiiM5PW8fiqarMTHlrNbL0XPvTZt5KWlCNds+TD6amjaHK1KHw3kJURdhvv2KE2/ZYzWhKSsi2YTW4960vF7lG5hCdgpQxhrfg25yTmw9SBnfEVcbIg5ai4LFZzOnEAXnY/HsE/h7G2An5UaEoYHQqCIDoJM8/6ZjMW51whFKk1hTIeNULX1SY1Q7qVhNXcAdU9z9ysob7oE3h+7WvBEFJpcHFs0IlP7QFdnGOKqZ7ELdCvt3kT8b/rJj/9nuHSM1JSiS3PDaaAayG7VZlTkVvnjxitf3L00J6sSqFg7ty4VE5/7lDZINpFWKcJgz5ZgpN5APmTwba1OeoF06lwf35H2OgoYTVc6XRlN4vLyl1saP/+kP1Mv/BiU4yuBkG/rN8N1Ci4paXVPvNA+xJOQq12PRL7GLindCELbdtc3tzimzX3aFZbHNkCdLzN4V88CW1dBsxRtIppLJqTClyWqxJFPVWEKiDmr06hpHf+ZJbIvs6gef/XW19+7VErURI+oIPZllmlxYR0huAG1Uh+xUoIpsfKeDvsmXmlt4o1OG7oMZblDEwpTdnnWJvMc2JLBEuTRTlt08FeXMm7dvuFxOHB4Tx5tH/sndryj5SEnGyvPXf/0PvHv/yLvTE0tbLRSoBv8/HA788rtfkr3M5N37dzydnng6PfH23Yl1bSxrFMIKOStJOynpUM4jDyJ4fhiC81R7d6tVhoNg75fGu+t+TJQdmi9+bZqh90rvCw8fHmht4enpiaenJ374yVoddb9PICS3S+6Ely0XQAf3oqdDyJ77iaapiuc2cmaeJo6HAwllOX3gsjxxXk+8e//Au/cPPD2cnSTa4fP7JDVCrReQZGAMAJ0IjkboJArJFf3GNGkIUBAvITDauzAG4780N/KESRmMp7WiXv/nVrsq6+DC3OZsv4uezwY+ntm7oQ8141Gkj2BTmxvIfkHruBZuxCpMG5VWZ2cXhbzxfzYvS0pcrwmRTZiH5xZK1GTq9h778PbuQzamuLGm6imgBNmb64psa5sN8Rt721iyYo78XbvQ1maeXOrG1iLJUfNugKdtmw/vzp8rWm3N80wuiakUgnkFsbCi7aPNMGhdTIc+O7paeYqVI22eKTjtZTZWmdssznZjAxOlciUpJRv37vnpif/f//A/8cWXX/FP/9l/y/nxgb/5i3/PH3//B25vXpNksn2XhVwSZZ6YDjPT8cB0OJBmIRXhi28S8/GWvkKvyvvzozUeXw2l+tQeHBBka6VfPFz7mePzSnBnQW2p5F1Rpm4ta7Vb4WhXq1Ay2s1n1GIR6hTxXIIL8sgddRmex3bYJlgWsW7lt5mSYAaWqsagkRPzVCx3M6XBwC6SyFIMZeVPf1W6cHWHvunij08Zv1RVajXrS4N3sCsqYqwKPVB3QuphmSaCESI2XaDomzYaptjzrpTCb7aFUSPc7uMzFCpmnoq/h4PNx4yF8TF8TInZzKAV7Z13797y/n2ifBAON0/88OvvuLmdOaSVdVn5n//qH/jp7XtO65MzOzC6kx+mmd/88hcUR8oeDjM//PF76lrNKqud2ixcEtZ8NAEIUS0e8wpkmmAK20Je8bIjy8rVrmfH/QojJSHjHz5OvaG90trC48PC4wP8+PYHfnz3E+tFiYhJ5FLHFIR3sZes/j2MRHV9nBWk4/3loqzDDLS5FEPk0rmcPnCpF07rmffvH/jx+/ejDnYTtntFKKz14gTxYUEVU4R0hEYmoyPk60XEGqtbreuKeOcT7cgoJQGRBsX2omoaQt0MCuhegxelADlvQzEUwt5wHC6TewTOrTlOUD5SgvtrhI0TMyyujFVMsNEFKUpQVEZkZbNVZRiErZv8Sf0TglB8nH1NxT9DQX7q/D1RhbpBoNKwbjV9qxOWj5VgKCL1p5YuA0E7viK95u0yVQwkZnLAAHc9jN8Bnd6eNnk+bZ4nI4PI0y7MK1b68EwGav10yDhiVjL2326c2Dp6GJVFpF5MCYpYZCgnY7g6P534i3/3P/PNd7/kz375T3l8+4a/+vf/jvPTys3xtdUT5pNFDJJQppm5zBwOBw43ByiClsTrrw/cvYZ+hr4q9adKfaq099Yl4+nxhGK1vfrcGP6Z40+EQ8MCtoLzgm8kAe2CrkMlkktmniemw8R8c7BygzREb0jjsbF7t7qSunpLk2Fubewidli4wXqQWohFkoVFDo4qzjlzmK1wmWzhL9GEzp7/S0eSJJb1wlpXxDvyZsG/NhDP4CaNBbJfLP4/ad5B3QdZ6ERbEOkyeu2OtakwINTigAIPh+AIsxY/SoQOHRVqZJFeFMsQLtYuxawhR5IQlFdhxbrKYb9JNunuhgzdlaqil3dIP/MP3/81dXlnxL9r5d3pxGld+P6Hn+ja+fL1K5Ik2tp4fHzir/7m70hJkF55+/Yt//CH73k6najr4gJ0K9S+PsL46SOUFOo6aK2M13VDyH6cgopwqQtC3eUfzeR1QZzoTTmdV9a6sqwLDw9n1rNaerA72YGaALOIlbggstEMqzvCPeKaJvtcVs/L2OmJJAdyLpSS6No5PT65UEzU3ug9MR/uePnVxOXpgeV0oocn58AU8QVz8/oXTMd78s0XMN3ZwteMlHsSB6bbRwMPPLyx9bCsKJUWeUtJXjO4+TUSu60JLNZy+ayr4Si6d+IID1qj7lPBgTChevYQdFXdqg5jLFCkVXpdt/ncK00YOAMcKS4wIpT7Lg/TXEywhha9alUW1+wYjcMm3CVZWUKritGpuXJN9u+ReRfb0X9ScsYABlBFDSCYVD31E0aMB+NL4e7+5Vjny+nE42WxtWZkv5uhahf0MKopujZUksYLIVnN8Bc45hlJGH9xzhwPLyi5cCwzh+ORr37xS1LOhiDuGEFD79S+sq4Lp9OJ5XLhcjqx1MpSV2pzdDEMBL0Ah6l4Vxnz8A1zlEz5ENTvO6nTV8tBLzPvf3zHv/t//b9ZLmfOD421Vrpe6KyE8TakgNgyZ2IAhV5/VSjHQnqc4JxR7RzSgR8ff+CsF9SJO4ix/KQ1c338aSUoPuASTR4FzdCrkOpmwZRcmOd5uLF7BRAjsvewuhq9U607ctndB6w/4SbAzcJJpDRbI97i9WxzeILTEObhLsXmsfBdpmujtgru3k9iVsv+iA242T3xVHFdNaqxYe3uoN5dLYzXhaBhUtlCCCMxrmAaTELf0XpYXGGfR19F30i7tip2li+YasLbHjoK5H329uzre+tPQ8CoG58GO5blgbU98eNPv6euj1wulbo2Hi9nzuvKm3fv0a68fvECzUpbG6d+5r/8/nuEjtQLHx4+8ONPb1ir1W2N8o4o4biaZx9ZDQ9uBD09b9VHy6XuHp2hM/cCSnyuIzS/S3YE7NSNnNaV82XldDlxOj1xOa+sZwY1l4WSwtINSiZhtwwRdNCkxSskXzeRiBt0TalYmLjYZj2fzwRCTzzfOc033M03VgO1LDBygu7VJesAfrj/ivnuNenwAi0HI0XQhKRbKJXp+MKedj5Av4BU0GAgjTdRRPfdEfxdG8ZvKY3GalSGDeIH6y/XPL/k63iMz7USNIPtuRLs5onXNsLbz1XMYKLx6RMf2AD4RMhz9tZhptvVHtRJCsJ0tYwYqHdlCANGVYautaiUxU76szxzINVjeV09q+y+RQ2jr8sA1O39qlAcJWdu7+4HO8pJ4ZE3ZvCPddqvrx8DlexdoyZ6EBlgABLJws3Rwp/5YHnKebqn5In7MnP34gW/+6//d+RSjHO5AWdorbLUM5dl4fHpkaeHRz68ect5ufC0nFjWzrJ2Y3XpfZjThykzT9nCv2qk7maEtm3r7ReEVgvzLp3H9QP/8d3/Mv7WaXRdUK2EEoxozDDqi+MGUuHFqyP3X8xMH26R08yHDyd0hXfpj6guhkcZDxGOF589Pq8E/YnEUUDJGKStA/YEHM0Lm+fJunfk7dwQbsEt2ZqB8sLCX90D1GdhiuSx0w30sW2IoZ1iIyZhEhMy+TijrdNrHRZkCMz5UCglM9284L7fEst6Svak6/lMq5V1rRv/JRAtlKozrC9uRV0zOQwtcz12YR9gqLw4x4Y08iRYqGNvN6nsCm5DEesW+tguZX/fWxYa522EBVsvMLBmwQk0WwFux/IMftmWCtKF9+9OtEVovdNq4w/ff8/5dLYOC6r8+NMPTKUwTcVanby3mkZdK5fV8q+tN8/fBIe5kzen7LVCaeAferNGq0IbHT1qC+/DBED2BP2wqSQowAzo1F0JGm2Uy46ElXAgG98oEDZGb506FLWJr44whRH/qSjRbnql9w3c1dWQ4j43x9sDX3/7NSkXcp4drGKAgEZlysJcEmW65abccnP7LXVVVpJ5gz2hXVhapamSb16heea8JlJXjrmTspJKGBeZlCdevL5leVx4fGeGRKP6XrLOBRLyXp69i4KaS4xWB89o+B62QpoLp1LW0TooIQR/qwBZ8o4sw/d2KD43iIYf6mhCs/02pCOhmHYlUFuO3GpzIze81NVJtvfakyAiImy9GpEX9l/svsfPWz73I6/k6tl3m9A/keh+34r0bB6rn6vP7ySCTAVt7dO0KX51KYrMkOaJSRNrcxnl152OxeoyJ4+8xdxmKIfC17/6jpu7O5LnUCdDsyBF0J44NOG2zbx4cWR5/YLTF6+prbP26msbpnmydkl4rq+tSGv84e//noeHD0CleYPywVkzcp7Ql0ZiYcKJ3kTQ3qj15MtAfA97K7ZSyJN9pWyKMWfjJnk5v+TL+TXpZUFvBP6ycn78QFu2ThhX3ss/4vh8K6UdhVJQ+6SUKMUYxlNOTNPEze2N2V7qQqzF1rlGVdGV1pzkt3bv9HD9vNHRmhBE8T1WBdtEJxFKyaSpkKZisfketGl+qvrEz8L07HWzs9uc3sN6uZhAbSH7bDRV1fMojFCCjBUdXsnefLPPBpJ2A3LExt38jG1zy/a52IfjleMi19bqdifG762sIArpt41q38QVz8ZIrz4Xig7G+a7K+bRE4oFaK+/ev+d8OqFeP/fw8GB1P8dpzI126KuTOOMbtXd6t/yviuleSWLQeTah2NzLQzttrfTWWHsdBeNGOmxvovFv/znIopXmHt3mOWSUMkhHd36opy+0dZqvQVVf612tjGdzl3fCj51noCMkHkxHzY0ekjDNE19+/drg3Uws587j+xUV4wKVlMhTZjrekI6vON7f0jmyIlRNlsPr8HRZWHdCcm32jHM1sMdg7iMjuXC8m5E+8Rjj62FBoY8cGmMc403EQVkVbS5Mmn2yKcOhbj72pEoiMaWtJCBATnmURcX+iR+3jPX2FFHaFBGbsD+3/TuUzrAKdSg7xY2l0Zl8vyuu83ot0gjPlZ6HwPf5U7uNbHIHHUZX7KX44/au231xwojnNpQybmTr0RmqjEBj90jDiDD7Is2Wg1dNBgCJ5SBCmTK57BTgNrikkrj/8iXHm1uXq4Y3FUCmjGjyvp121LWz3LVd5t3k1OHujvnmxpSgKvr0RD+feXz/nsv55FR2lt5CO8Os9tetayWRN+5RoLeVZXmPSGaajiPFBQYkywF0FEMBJccb3eQbXpTXUKAfGmhjvZysrV7fje/z5fCZ47NKcF0sRDQXs9osHGmMMKUUjseD1QN5rZp2IaVGo7lQ2OXWxhp2Noy0CbONoBOHHHv+bCfIc7b7D5XzzJo1rrtmneafvbwxm+RRK6PBR+ifL/M8uElba8NqVLfwLU/XKGqk1lvvJ7Pout2eKVs4MmSh7d3rpPhQgvHaGuw3MEgiRcxTu7Ifdy8r4/ZXUc4I60Q7FatIETdYNuSgooPZvo+aNqzOUeDm9sD97Y2tgdU6RqgRBaIo62o1as0VR3ZzO4gOqneUqL657f0ySEFEg65zUyZqSNveA3yxo5VzBVV1x8JPGAeM94z1NXwXVYsMaEOycDcfaKyczg9Ub1zctdH6uq0/l/jShYazF+E5cBhrtrp1infu6LWNdS3eL05SIcuB5GQI6aCULwqrvOaS7qxrSRI0z3SZ2boqeKI7dUjKQQ5Mody7snih96mtZODFfEtOUG6w0OHJmHpWFupoxGWW1QgxfXS4gjItbrmgQCoD2rd8EKrUtZFSZ8GUnu0xYc6T3UmiabYbOb27gRDvuM0tV/P88ZMNZSCBMHW144i7Um4MjLdaazXbZkKZJtKUUYz8IDGZsaQrVjBiueN4RlV59gBWBpLCAEsCWq86Wlw9pOBdZHyP6Y6oXDK9d06P5zH+67psY3D13snCtk7QkPJW5iEJpulAKUrtq7137+iq1qczFHVTunyg68J/+eu/5vbunl/+5s+sqTiFJMJUHOXrL2AOhc1lx9lbnEi7TO5txvmHA5IyL7/5FsrMH/74DyxPj2z40N266srprO7wLHbPCqId0UbKSm1nypSZDzNNlbV2NHdmrDKgNWFtCW2JUxUefVn37s2yE8icEM3oYntyHJEN+Mzx+VZKtaM5rLUd2klMKc3zRLQGUQVJ2eLWVkhj9sBuceuwxHQs1uu+6XjY6tqGij56AUnf211+BvjC6/16Gnz5EKwsIhD9z8Y9SwKZKMU6DmwtbBhwe6GT6GOjjTGKZSTeviP+4Xe3j/uCHkowDeNACMXs/1APwoon/wfbw3ODJsJg1xZgXCdYKOJ+squZiiap3fNuMcZxlJKZDpNb788scDyH5v7XjqhmnNbDw3Rmle2JTWMN5vq4pKqFQ5o3v9W+2Qtqb9pciOd4B0fwSSioIUt3/2m3QnF37xuNvjYLmWp4ccH6s71AQ9iPesPWhLhXqZHTc8Wk3kTVbRqn68uI11mZoQjTBDm9RPM3Y2bs9QWPzwJBhw/QLdSPjFTC6gZc7Y3uHJSSIE1AEsolkQXzxndrx9x/h7nLeLVtzYSn1I0erXZvo7o7N97PwpZCSx119zKJoLmMyyo4/N/W4ugKw1hG4wG2Nkg75TgsXdzDjJB2fNaUTcoJ6RZ9GMdOXniDox2Bwb7N1M5bdAOScccwWvNoCLD5SM+O0Rpjy+/F+htxpa7UJXo7YtgEjTPjtUPOmmenxVNDAZUVa0tktcYWedMOTdTERFITvaJovYB23v30hrqsfPnNV0zTbGCtJCNUmzXu4Z5iSURpl8Q69n6gY8EWg64e7u65rZ309o9bB7QwUnxMe1eWxQA2TVdbDIut8jlD6p4uSpiZokrtBjDSUIJdDGfRhbUpFy95GN2GkiCTIC2hVdgNKC5APnv8CWCMbfDL5WSFr6WMwQJXTslJiEOgKtA8RNSCjLpvaEhwT8BCASmUazYS30gc7+8hgrvzsmmNPNlCSV7Pcj6xXiqX0zoUbM62aFo3gWCJea/SpxNdpwN92LuFPofg9ijDAatFbTtOZfyZLDw8WeI277aVh/fcZ0HFM7zu6QWfYJQCxAbsYNBpeW5VxYR6t2rfpIMbT54pYBWkmyAegRkXdr2qE5Y7AA1AlLauVjLx/j2XZeHDm7cs5zPLcnZwyG6BOZtJ5FvGE7rA0yEQdCjD3hvny4XeTLinlHdKci+QLIrgAQMbweQu9qd5gI0eTw34EaGtOC7nhR9+eIdIIHNtHazVUMmh6s27NO82IsrxZLK/ZKzzEfVxAVgmpmnixasX3N3dk1LicHPL/euvWGrh6TQxyUTiYKUxwZ/YrKC+RT0RbOTWznjyaS9JEVlJkijlgGQot6+oyxnr2bRxVg6L+BNW8Rb1CEMybm3zmEb48WpYXaVsFwyC/MvaXekns9bdswxd2/uWBTdjqaHBnrHNqL9jGtGMXY+ej/eGTKHNdy/q3q12o/3bf+qZU/rpQ65+zmUiaUG7RYRCJWqMhhNqC5Pjn/yv2ultpS6PKIlG9n9XIgcaOAqyOLG7g+E+Rf8llmP3mpbtCfxnVZC1U1vjQeFST7S/uJDyRM4HDvOBV69eMqWJQ57JeaaUm1FU393kF28NRa3UXCyvmBPaTvS+cFrf8VQf6MfF32oirRldG611ni4P1NapjjvoETlpZljWrhRN3M6ONJuBSZBDQhyx2L0cLaWOpsa7tz9xOZ94WBYudeVUThy+mbmTe/LpwKM8mBwbxNx/QgPyj+osbwXTIpasDub4vVeYkngy1BdoJLl75Ab7mKvNr7TZTOERprz1lYp4u2xfYZip3zvowSyK0W3g10Zd26agyJDMM+2905vlPGLppkCajUUcVrnuH8PqojTec2NRsedOyFT8OXcb1Bqo7Y7ExmAR3p8gAY12b1BEXO7JYGvfpLEjOvx38bdAGoZTaGGLBJqQboIjKBvxvObA/+ys8+DJOZ8vtK68dSXYdBuL7RBG8NG93Xj3DdK+XdvylZ1G46Jm9U1R681meIQXF0xCY1yHd/DxcZ0z3kYmjtoa/WR5GsstrkCl94Rq9re2MLFga/n6nnvByhZu2eduRZCcydPE4XjgcJitTmuaubl7gSwT53ZE1PT42s27i84AFaVqHnERW4tKeGiffHUFwbzmlCdTVvORXI5YhCXxbBFeDeKI0AxDxQrluz77+05ZjPT0mPfxKF4KY3l/UJIblF0DhBW2YYxnKAhrgRSrZkuDwAjhilytwSv9JQDZiZUDZS3DUNGgn2P3IV9Xuv/Fz4xxSCwj+DejG32e4bQBG3m/qzy/3b/XhU6iMlvUY9A3yqBIlWSgFeM93hv+V5fcUklDBW//EzGPsPfOIhcLnb5Zvav9gcPxhpSUKU/U6YaSD0zzVjKmjlCOioDeGzlX66cqmdbO9HZhaWeWfkZLRQ5KXqywX5pAq9bf8qPURsgH398uK9RldpRExLuqM+L0Zkjx8+WJ2hbenE48rQstVcpNZj7O9J44z2czT+q2d/6UIvysEizHaVswScwCcjofc8byLk9jOYDWG+e2GHF0bcNKfm6F2gDv+enY7hWLARl6Y6NNMu7AsmP1WC8LD+8fzPNsnSSJnDJZZqZp3tUg/QmrIAuiiax7p0ORMsOsHI7zuMSIDojQU6bVznIxNpUWJAKxef19ro69ZGH7cVjInxoXMXMpSgFEBqgYFx3DuGCYGjJKMYLcpnWPvD0flqpA480PbxAR1stCb5DcQt+HrELZWSgzGGHCTbPzBmITM4hYQYuSMaIBIxUXKxHxEOW6XrwprF9xOoBkA4BsjtLuCCFv+er54Bbn2oazFoLkk+kwGKU/u6G237vjkUmMWHp43DAKBoMm7ObmyDRPHObM7e2Bb777isPhJbfH15SS0ATL2jldGmvPnKqw9sbamylENxKEXcTCNVKO9/fxL81EVRQyT/mWlKzofToupPwCaU+ongD1EptrwRkz5aQqYz3sDSOUHcz8egBbY7T2UlGqWHgu55Wu2ZShWjSp1dXh61uYXGgbvy97ROZmLBtnbJBLZKfI2lpHf+q4VkwNeqJXGX9MmpnK0Unf61gDXXHe7m0XgQli66juzyYWSuzq/Qvd0OwYYb8xAe8MJ/e0l2ah2aZuiI9idyvzKhPGeDUXL3dKg8f8mnc4mKiIreYTYf8wY9oVcl9RTSwXARZEH3l6gB9/+AeyFA5yY5GEXMb6mKaJaTK8RyqZ4/0t880N969uOd4eeHr3wPJ05v35j1x4JB0rh0OCm2Qc398LZYE8F4sEXGCtyofHZsw0zTdxUmvnVjIlTRQORFlEZaX20xjD0zqRKOSayFmoLeg2G6nA7f3EPGXgjuUy88gH2rrCoh/bgc+OzyrBtCMSNst4C8/E5h9F5iKD27FZAoPR2HHHQRlabizysTFjRuPvcuUBDuMwlGQIBDWo++oKyJ4lck67/J0GPFyG1L8WiqFgu6Gm0NGTXrIt1vJMihrPtwUNVjFPtDnHJbJDMspeAF0/g+53vm5fIar23yNHgVxboFdo1d09QbbGk269dzO+t4Wxs87i3pd22bkBIWifAXU0PAFlH9KM70oURccn7frSPcTTPF/qKMjukPzWq9el4Zvdrr9XgPtpiOvGkZ18PYmMsTUvTfyKEAkMawi7UdvFCI44hTjozgsFNRThGHhbnClnck5MU2aaMsWJI+7ubpnnG+ZyQEVZ3QI+V6wuT5ORWDuC1p7La/tccNqEGVmEeWE61kegPQWrocopQTog+WBhcMkEylFI/j3U6DabPeYwPKfd2nIZPvaeauzPeBT3esQFtXhoNZlnSVfU6dZGzWjcP9bPVQY2Zv65kRgaWtgiLrop7r0Gv1rTuo1lbDS1nofGcuUgtSQDAHR9+Pt1DTpVH0ND3G5mq4zHfPYy410t+tKvz5HNuMs5kUoycOAI/6obGf2KMcrWwI6AIsItu7m1te4dPBzBSVtovXNaVpIWZs4bwbZbO4d59qbTE2kqrKwcWcjHBmXl9PTI5enM0k9UOSPFZeWc0SbUg+VCU7YyjBVIufO0mOxrgdeKkjpvU5aSd9NJ4iQb6xhC6Q1aIRNMXOEdAclKK0SFw8Ect/Nke6qv27T/3PF52rT8cduN7RdyFaO3deybdjV4u607UxMfVy0++7dRqROQTeO53N09yiYc1WXWl19J4FAObOEUU4TGIef5PxzJiLHLh0DY9I8YumiCwJdm58XqRJJ7IVZ2wlvGOAP/slqvwbo2lvPqAr0ZKjWV3Z1CEdq6bRq987xbd+iY3tkYYo0cIJQ/QA+uLpeIshPkiUyW4AyM9zPvr9UQrrupGMp35w6EYLk69Nm/PNRhu83zOjpq+/agGHw+rSlq83qyWAMyoO49XFS/17JezNMqRxK+SUJa71Rsq6Yop5QoIuTZ1kxyfsOUZJw90qYeIWjBV+oKZ596JoSDWUO+7CNMZZGQPFlYfZos3/L65Wu+/vIrfv2rXxltVU6stXN/Ud49Gdr0okbzpO55SOT+gkDBj9aswDynQlZB1kZqwOxj1Nqo04tDxEqHtGdqTSEn2EKkI+hsMziMIoVxBs9WrMvZ/Z70ZTLSItnrumRb72urvHv4QKttQ/J2GQL7o7Uo26zarkxjSbauQ2DFal9WA/J0B4Lg71NPIFOhzQYyctuIzf7bRmUsUXbvp/v39sEdSEP7bBYDsUnaSliEjTszNd1+O/bYtRSUBHJgUD5KToNfeUtMB09tmAaKSLExSuwMzt39/Pvo6eD5jx5RoBrzbSU0TYL2TVjWldY6ZV6Z+oR+qKznR54ef7DG284cpGVFklKS9ZW8La8RCm+++yPrUjm/Ba2GXM4qfPO6sJyUH39f7RnEwIRlyky3iflL9/QTrM4u1K2cntwsgtRV0CzOPbwLC03mhd++OHA4FrQvLOeF83qi/2/hDk0fm0WbZR3exhYjGIMvMb7ED8+uM87dNlL8b7uCW31+DZHt7xLWnX9OlF1uDwbyKx7Ev6foG5i2uwihAIEszvBin8necy4UkvbNOrU8TAiVTM5KLgYQSkmgC5qSQ7s3YIqOOKrnIJFh0Y5jeGXKQC6q17GFEhbYeg5tIzVsVRl+7HY5z9NeWc9x7701HVa0j+GwuMd8bkpu75NueT22vOB+fj1nE7lPCYEiViz7nDjBrhmUeptijDUx7rPzZCK3YkAlEyp7UFXYyAGICk+3D726Y/y4ykOZUhXM25Qw372rQSBCczYygHmeuTkehiGZxMJtp9XAdWno/6BZC+VnAlbdaHtuTJhj4IAX9XyJF4vLDhmdc6I50npPxzDWyVUO9uNjy9r/Y49dbnooGJuf1owdKi4msnmjw+K4vtSoUbZ9LjEdxI4ZHxne3h5YYzkxDTT5WNab97sFPuTqvkNkbDdgT6MWnnB8YENg7+Yo1o1uV9iTWlzlrEMebewW41mux0XGnG1rsdsaVDW5FcbceERXgjvxsm31TbaOPKbfMwhDUvfGzGsF7aw0WDu5JK/bs/fM2fia5zKTpDAfrYuOTLYmcsmAkj33u/OtLM2RzMhP8w4tMuRkyB+LQnp6Fd2x69gHfC+WhKgaGUlEKn8uD+LHn2CM+SgBg0hiPhw4TDOHZB3ckczaVxrN6gdvjqxLZV1WVJINSOTGzKHdWVlu0bedkDKNZCGCWBsDUWaLoa5OnNytVibt36QL9LxboqaIpKTRrDHtVmlrxs6RirnQbnTZZo04BmwhIyysXb0GkO45FbEuF0cOziDSSWSS5BEKWp0uLjbWJtaD/DiAL7Fsmy3u2mki5LIzJXeL3YRQQtLkSjfg6p3WoVpdqS2gCEHFUMe9niHAd3QHWCh1Z73rz4hP/fifpoB3MgpAjA1xPD6JRHFwzrXNrhhsWrv12xvGjYJWY5rQpqjAQiOLMKWP124ImeQrUFMBjVWyiYkhi9zTCyOjh80w9tQn7nH15t3fyj6wYlB/spCy4LLBvtz9Up2Bid4uoBWZTQCI55ry7AYbK10bpxOs6+RoW7vPeSkcD7dknB+rd2heJzieLsb4E+0Dngv18YMtuLTn5tsdUdOvu8/knLm/N3nw+Hi2dZSjyawBlXp4obrthuvHUVvOBY8Bb6prKo3Uu7VMc4F5RXLtKsciy7b3WuusQeH26Vceo2Sv7ft1r0nG+PkEurxg+81Hl7sORPvTuZEc5m1HvYbOdkQc0fFjbIvhxCYfR2s6q+0CXZE02wnudW9GL4ioIdoHUM6jViRyzWhRmLZZCCamXgPJa1Gy6WDo+xcvXjLPB4rMiAoveMVcDlzOj4Dy5de/QHvnxx//QGXhcFjNE8QQxOV4IB8m0mFjZrcSlwl6R9rmIY4Bdm+0V3u31grRILmrcjovXE4Lzfs8fu74fFPdgLhf/c5+HwpC1DyfQFrmZNZwz52WTAFGvz4RGWG7EZIKj4ZNyYyatf2NR/JsL4i371fKPrR/ABnc2krJckM5JUdB+eWcOX8XEXLL/OoBrgdHGTV22mXkPMBDb0PYet1jKND2zEMa5nHcQrY/Dn23xfj3jxQKUFwB4v0ELUbiXgTiTCquuIZlFdvU7rch3eLfMT57NRhz9Kkt/jPjtPu17v4eofL41cZzGiawaYZA+o2myGmvhK7vFXmg+HRX9ZZWft3d3MrwVkL8CKPxaZzmfWlGztXDYUNmx3eNcfGwVGtW/O/AsJQKGetoWESYklPIie2bnnbz7fOW1KjTcva8nt9QnOpP3XVtvUNrrK2SNLpHGGON9om0iEcf5GqgwpOEUPjPp3C3B/ddFa48IUaI9Gqr7E6QZKHZ3tXf5drbt3o0iUHcrc3dtZQtf+TzE5Ns5Ase9RlJMz9hvHPklhl7ofsaHmJld82PWdqFwA6PQfHJD2NyGwxsDaQgXdw+P8j5R5J1FwUY8mjbX303VmGQ7pf89qoynnDEnHf119ej6a6I2NiJ832O/L23WbO16IsyZICnOrpHT6w8rDDNB+b5SJIJOszzAcSoA1WUm7sbem/MjzP1oqR8JiziFF5gcaT+UIIexcHtnhR64XrxDcam1qzEp6p1rqlWm6hcr9dPHX+in+Bk07xj5rdB7Kyt8uHpxGGeeXEzkafCoWROT9CXijhAwGpOdhNd2SDmfsUrSL0rtsDf6P4PJgVsqvuG/rOltL2KFBkuNmSPO4OUjOTEzXFimjIBwGut05tyPuuGaFOra7Mw3Qp0A8jE0/TG5WTMOGtwI3bfeI7ES2nfjMo3ene37ErlxE8RvpCrNxuvD2jfaKkGrFgK5JuxSCLNYnyVnpBvMZibdg1hkuLZIkzRxZ+hbenBqyNW6n7S+NSJz3X6uL9x8GyeSaLamEkx4yF72Yl32SCQhcWNqNjMpSNRjaL24h1l6dWS7d3DyCmRSqak6FJSPOUiV0pV3SBAdJfyjvGwdZE0zjQB2FcXaq3S28pTEp5S5vTTW+Tmhrv7e0q3elMpgt4m2pp4/2TdxKUqPiD04SEfIE9ot1CUlfZYrZSqRtMSmnsIp9opye6Rc+b+m6+4PGSW0xuaKlvWqtuNWLkOy+3X2d4Q2WkJP707qjFKWBAZ4efkrEl4aDSJMM1H0lRJyUgP1n0SMDa4WVis68q6rvHPMc+lzN7XzvfJQEvKuL8mcbKMbXGrRH/FzTAarZc+UtyfNuBkNwZD3/nnFeixn1OEQRs5ZaYSETAZQ2l3rowecx60CbpgKyexpJ12HQ5fmNR9vPW2d+IpRYQ0HeyfgUYPw1xsLFO8T9LxDFWrMTVpp8sKfSL3bN0bEpCcGsNfuHcLv96+eMGLl7fc3n7DVEL+KPPtQteV199lVCplKtSqJPk176cHfvybh7Gk8gzHm8zxMHPMN5vh0mFNi2mbYfXsxqHbuq/daxIfL9RL5/R+oS2demnQLdLwM9M6jj8BjCko1ntss2kgYM7Ne92lEMYIZSpM80RSp01zlHdMshXgxtxc55xizuweLjL3dCS7wdhi+vG33XnJwp6Sd9/L9pVLtuJ/CYOkIaLkbLGG5v0NLR/lYQj1EM5OWDSv9WmR5VXXIv6ytiR8se6sqmtP6rl147Vdu6IgGVatC2O3IsEomQJOvZmT22361SCx+4eOYQs06zZ+Pv6h1Hde+DYFcn1F4eo8fNNtG/9Tx8jQjXm3Vw3vfW/Pwn7bC2kABxJKGpbspmpRs6bF8x/SDYSUhky7thJ1G9ad/B9mKOJxNh2L1N8hFqMYIrrXlb7al0wT0w7kdBQ4JPFea76Zk4OJkmzWd8oxMqAyyMTjOjtzxt+TUcOPipNbZBeWMl5VRhh7M73GQlBHwO724/Uhu29y9WV5vpGt3OkhN0ByJk8F6DsPZ6cA/Z5NO0mvu4YIfo09OlfjsYPdREzojcWys/IiTKnPXmrMoWznh9e706X79StsNrx5RzBy4x6F6N75JWsz71zSbstH+VSMlI5h1atrXtdNpu2vhMc7JMgujziIMXaG3T6/ayjheFa7iChbhArMu1ZIGrlv3Y2FP5GYfsjTTM4TKU1eRaRINjU9yQ1KJViibm7gcpg2rx0gMWoic9mASilQ5ET00N8g1r/v5VatvMpqxDt1se/7RXhtLHx8fFYJ3tzfWOJ9tUlZvd1McyRWSYWEMpVdTVU5cLidWNYz63I2tojaWZt1Y2ilot5yBsU9Q7Z/+/N3giuyjHiweVf2Snumio08NiFki1dPhtorh8w0z0zzzGFOTMXyMZISuRgE4bxkalVuvYvB43lhrZ1LW41LVBdAOHAY2czWGst6GXmybXkkaMmXa/NnSgMV2XslapPGKrgKVRlRbqBgNyGVnk2m5Rqn6Tg2QQjC+ApZE5SkoRXDNg7rMMavD8CCJcZZk3lW+061I0e2zdm2zPo4Z9MRXjTtP3/y0GffndYppeQE0UJA/COfl6Z5y8GokrP1EavNLOjuxbJarRFp6tYVo3e1kHjOXimx84cC9RZhrVaR7sl2EWPyUKC2zcD46F3UujGsCywr07TyRVsRMe5UcuJJhZucmHNCtEKvtJJpmsjWVdTmXxTUUIDrIkZu3Re6MjpWDFm/ms2yCkT7rVBRSRzkpeq5wRE4owAqGcUYm3Are1+2sGUWZMyP/d5mJjukv6XiaQDrqJDcJhTE2pfdHCjsQtSqRBurmPw5Zcp8pK5112JNHIEIIxmWTKEnzNPvHmVJ3iLdjN1nCPSdLJUM4mmQhFiu1tdg7826O1wtyj7GJ9r1fJQVd0NsIZ71TJZETjMbrnNDnyNWRpKa79GsFi4eoQ2XAGIeNB72lTDE2M0JIR99ihzR3Ed9ss2/DM/bhW2v1lvwvBlG+ThzmI3dqDDTpHke1x4q2oOlckPKt0jO1u5oijURpCBfYK1ALpRcyV8+sa6CpC3fDyApk+fEfL9prHq2wJTVd+YR9gRTfnVJtFU4Xyp1hfVRaWv3NMFuvn+GYWp/fB4d6nHtAEUOT7UrOXsu0Hv7iQTgRMki5J5pOZnXnXWHXNsGe///zbSLH7cVqztLwP50bQ1uhy0xZ7z1ur/kSdY8nnefV9gX8w8AiCPudPBfxtPscGkuBHX32FtOYnuzyMwRFqNeo/2u8xjbr/abN+69GXxmJZmi2LHQPB8U2f1zzOGGrtuMZbeYdjkUwC2xNHj8rrDsu+sOcM3PobBkzNyzh2LMQyjVDUAlQ8jGxg+QiiXAh2RGMORvB8+Nqnttuoss+G270qXTpCGSh5W5ByvE9206N0sYwbxu3feNc67TYGgfY6Qk7UxWRg2SmESthOOKENrGJLrghZU8kG2qtpZJSDVLPjgx45RDZotsDAUtXhpiYeHYO7Jbx253+08+8lY4Nxb3PqW40fThzyRjn41OALIvR/F7CSM3ngOoMaIiPu6qpiyloz1/JOy3nNDYCANmm1L3siM3lkoZDbjj4U1JBEWHbPtBoquL3Ss1G117vtg4EMHEoVjUPPce+IRtQG3vxxq1f1yvrzCq4vl2r/QzpuLu0O2b2Bil3d7dL8GrKBK2QLbU2u5hN7Fshk36dCODcaVxEW+qkBwVL7t31UCmZvDyi8G56zaG7Ii6o9MFsNUPionzIHcf27jrSGOFsS8jLL4bok/nc66OP0mbpgpr2xMh283LBIebxHxTKDdHxsZYV7isXigjpIJFCfvGEhItlACih9iwqmxckVwcmJDHylBhl9uIp2EzyaVZsTsTE2adljK55SJeU7MN0KrRhMfE+9oqdV25XJ5GNwNUCS6F/Wjui8vjknaG0xb5y0Sz1e55w36tJwlKJX+BTVr7Yhix/Gcb+eBM7uJkth0b49YbWxJetnZYvmHQnchzgFNY9mENqyM/pDg7TTeOw9q85UsQM++FWTzd1QY00d7EeFoTQvC0borYxbCYkC8e9sqOmBO5Jli34VQrHk5WY2Qht8k8A02QlJw2NODVPKnaPPfGxIFJ5u1hr1aV+UKilnRPXUmlgAh5MsOjxMf8s1m6dx2wEHXC6OFuaYgUSIUTcBTB2roJVVxraRBo742F7aHylL04/sZaTZ3PtnYSTBl+eWdnv32EFeXcbezzcUbqSltCUJufpppsnhtbGIdYHwXFvdRdw9zNGPOc++R9IUsxtp6Ri9/N1+4tsl9EELpYTth8KwdjtE4XpSfraKAKrVprLRMiMsqNhqE42xodXlSxvTIfby0EW3aIQ6wjQutKHgpuN+tq1+hVt9x938Wcxh4sxLZVVdZ6MXak4Xb4Gk7zx2VmIpCUNGXK7Wzgj7KZU3F2+MZX+sploz+sfeudKCeIbPcwb2TbOUNyhXte/A7r8xMg5cJ8OBo0/zOHyY9MmWGew5BUWvL64eoGjGaPJPqQnhJ68XksmUOemHLxtmP+DFIQMinbQKvmITy1K7UuLKtFfYROPlrYdJbJAm1TzM2uye7PHH9aCcbXcIfCk9nyeSZ4bdCt0n9TmmFIKSa4DGizaeeByAwlaqN71U1CkJFTZH8+28APzaL+myvrdRO2Y1OqvVNX56VrSm3WDb17K6Dogj08YCfERhlKLXICo40J6nmpsLL2lkh4NXtRN2zmjxRgeEGb0jKwzWGe+eU331hfPm8op1hSvdY2eBpbbdS10qp91XWh1i00a3Jvs/LGBHheyEB7akKzG4gDVbSbt9U1nn+bj/hxLwYlydaGJXI4bB5cWIJp98XYztebOcZHg71I9/d0S9+VQ9pZ4mGgRPdvi541mofPbDD61rljV7+nanNqFnecu4MmJBuHRCKLwcanJExiX4Ut/zNhCvCQ4FBgtb5dFuZsq52pGxNTHL1buFd8XqZiAKhcEnNJHCagQ8EjGcuZXq1rgYgJEzWyRrdh3FhLz+Zr750OaLlezbA4fY3VYBrZcyjG8Gyunn5c38+LZRahaI09kLw8QPYfpadkc+3+mj3SzotBIU2ex27WFmieTCHnQBzunmgYbpuhMaI1XelZzfPIznw15Nm1MUb3dSSdtOOgNY/EsAeDuSnWkoAkRYp1FyG8I/ZjlIaMSJLde3VwiuwfYD/E/mm5/r5Zpbt9npTBlv08ehGfdc97KJAQY+PU3f0juhXDGXpitzl7Fy4PneXBerYO28pDnII8y+kmJBXQ5vI7BPFm0UabK5VE9pXRZ/We4a6H0E+nLXbHP4pAu7piSeM9t5huq5Wnx0fL30im9UZtK7VXmzZ1Tyt1NFk3hwAqbjI/wl6OqEyODowFuocsh0ezE78m1FzydyhZ9+v16qP78eitWW7vfKHVxvlyorVq3qoazBZwq9SQa5568Pu5smnb1WW34HF0aPB7inhLGBje52ap+ZP5u3qhAwGPsPSVMB9mvvnyS/4v//bfcntz3DRk2m9pO06PJ96/e8/Dhw+8e/eWtw9n3jycePf4wMPpcWfg7KnDZMi+MOi7W7ypmaKO7u9LC49+P6qb8DRBp0BDPEepkoL60cd2oqQyFHLW3fjt3mgr+7ON0JsLqGcFsSl9nARISchJWNrKZfGeZqgZBcuCRRuCOoqhBM0rFZiyKZxQvGNDbk+YRJgQjjnxai68nCdelMJdyhwVkpc03CG8zJnXB+XrOytE/lAX6vqBtX6g9SOtDx9zWx0ni0rcvRTyBPNk7z2XiUPO3N4IWuFOFdYL5/e/p9aLLYwipFLQbuhnaZW0gKaOSuTnt7U7YTmyXoqjtJ/tnJRMeM7Fw/I+B0GzeG0PedrbFmpOQsmbkrXlI9GaEUtQ7er33GCJOj3F/t4dwRy5y1k3VKSI1bEN4zc+H8sKX++yQ1xqR8T67YUBHMe6nGm1jmHQHhEQO2fSw3AUIiwuYnnMyGfjPT0Z68vGP+pvwjCIdlHifl063JKnI+v6SG3nzcFwBTWWfvL3SYEdkG1xjrEW0GLvMSnIaq5ZVtsDI7cjsGVZtvykA9dV1PN6dtSmULtRl0mov015QqKehTf/8cK7P15o54qupgW1hYUqyLorkdCZNN+hdQEujLRxNw1acrbacB/SGzlQJKE3Ng8P64m1NS7nvMNtfPr4rBKcbw5Gu3TeyhhGN/ds9m1XY+lPvZOyWaFteEl65cHF4AwOQTctNKzPnVewt4Zspt3zHJM6/rpZhUOIhSBVn8GI++zVRIQqN4TX9fTJxiwzrKrNCx71KaqjO7l/ytdjGoLFciI63pGUkW4ILDPKLWe0n6osAQKxJ84C81z41Xff8fWXX3J3e+RwOIyhyDuL3jaRMItQBG4OhZtj4fblhfunC8c3B6Z3E5d1pdbG6pyng6bt6gh0me6sTAs5pmTCI8BMcVxtQmxDI31ncG4KxMAvacyZyYQwcvahnTQ+rxiMPLxf8Tl6dvlr61hwouDsQm5vWHWGUbVfV3E4xLYns4AkYmESbDBuyCWrb5rnmXmaBoVbCoyrQhPhjPd7lw5a6e3Mejlzvpx97xRH3OUxv97m17w6EeaUKFl4dZuZc2LOBtYSnkCfzKv0iuSI0qgDajuQp2brtm17QMKQjDqzYO3e75tYjLJ9jR9j/DbLbtuqsNX5pTwMl31K0L52SMrwvkJxgdHL6Ua3ofs5ZnvcUc8fAjaMKdmZqiF3FKc18MpewWor/Q65GPVehON6C/llzxT+aZjl8Z45Ryjf13fyZFUgcGP8sDFPrrTt80ZEYjyiYj39KNAq0cbp+U5lDNnAiLKPoo3xGPOUUM/9bWvft1+STdbGGtr5dmC2UE7R8WK3VIYUtbM71lSgN0ZfzGjabG21ruVyfE9ipORCNqxJsmbeIsZYI8UjTGpI6yTR8kCZmCAnWgtAzc8fn1WCX3z3BZfzhXfv3xGsKSlnjnf3TFNBJVG70i8XA8hgeQZLpfXdMOwPFyDjj9ZjT1L2gvvibu5uigUL3YRC9XU1NuewppSUu/fainu13fed6bA7A3gWtbSNVyb3aCR7+KOaIvYCw1ZN4S9t9ddSLyBVhEwWC5LJfjEl6xyeaVsd4raVx+0n2XmCYuwiX7y45f/8b/4Nr1684Hg7DeFiDCmbIkhilvz9YeLLL+68blBZtVG18Zf/+Q/87d//yA8/vuH9+w98eHjgfL6McQgPN5byyDN7qDG5dT6J5fiWxTlDdwM6ImlilrBkIDVrs9LTkJW5BHTej8rHiV+w/pEh2bTT2tnkiJfoZPpeEu+gHts6ytnW2FpXVl39WvHCY6F9tDas7UY017XNKc5AJOK5wQSURD5M3L98wf3drSFAU0asTB7InIDfAx/cMGvtkXV5w4e37/jw7mEo/rv7rzjMd9YWMClzriTpZjQk4XUu3M2Jf/rtRCnCw4NyqheE34O+sbY9QWCf3PByxVamCprR84Xa3OupVg4kO2g+5QaYGAjmGNQiWzgt7LoYumfybN8iyIRltrZPyUNZLtLFveVOppGgdrRt3sY4uvHvjK5k7r4PGtrntEdDAAZwj6s5Nv0jQ1G6qrSNpxZCLvNMYULX5uTgZgQ3D62nQHKHIxxK0HPIuKINnlj1tbo9yubRGQNXIs2TtWhzdZbKbKCh5Qltyx63dKUNI2MTkSHbuzJCtdeH5dJJ9UpOa96m3U5zg0VidO3KU04cJ2EqXt6QcFm8UdiZ3KmsLAQocK2d6nPbosSO68jYcEajY0hqZBqXVJw5yeZuWh1tXNKGWgaO862VJiGD1OTnjj9JoD1NE/cv7w0oUhcTJM5lFrlAY+bxFkQ+2cMW2CkXY5RRyGrhj77lHyRqgMQZLtjiw/upG2tqZ3AKQE6+15OHZ3AgintsvSPSrdu2e5meYRmMGiM3lQNd6N5RN0spJ4OrW1spK2pWGAly/F6t9RG+tF6wbhEmbLP5Ls4kikb5qyPPWgVVD5ua0JhK4bfffc2Xr19zf3fD8TBTYvOzC+37mIURESHG6EebVJhIfP3yDl0bLw/C46sD7x6OnM5n3r574HReWFavvYG9ZQD7jeRWq7pgizCN7OZtm7gxywSnVQgAibkfp6pv8DBsNsU0+ARJ5FR2f3+2cHULDsS/I6Juc2JMLCGwI8ohz5lCBoG5X9OLxLV1ouQkDJyrNeqf6wiLKh9qt/BR6Zzde8hFuTnA7W3m9n7i6Wnmcj7YppfMNM1OhQaSzGvMCY7ZQq6/eZV5cUx8cWPzm9dGWlfa5R11eSCJWvF4zINsIt7MkkTOhVImVJt5GLLL5YPn7tV1YEjp/TzINt77GMpuTuy0vWGhI7eeiTXsf08RD5GR7r0KZYVFtnuT0UxghG13WjiefciYnaKOz2t2Ywqeg7AGPVnU7pZkKPdo3xH55ZFj9atmL6kZJJnxhzC4fEGFF7h/tt3L7qtBVasRJ0S+er+3ZMfF+pHbIbvfRU6CUUeltaKtfWoLjR9Chpt3i/M0Jy5PC4/5yXLEuGGSAhOx3T2hZIFpNqCSxl5i23sdZe2VqEtsas9kWAKfYPGI1xTRO9uzNidpbFWzKywVdZi4jvp84vh8ODQXpmPmF7/5FbUuPJ7eWp/ABmhnrWu4CLatJMgQdyPg4UhRg92SISMsAE1IZKwuyuPzLsDTs2eJfydXgPvpTtkAAuZJZrfUbZDWCikp2YtxVIXmOYqqQtdsPRKzkppN0DQHRZGFZvRsvHRSXLGiQKVRLYwYAD8YSfSsG8elpZyinMEWfcnG8em5cV8QsDydaLUZk6gIMs+8uL/n//Rv/jVfvXrN8e6FCfExxN0WAiHcIuR0JQ4AZRIBMr/75gv+yVcvae1rWl94+/CBx/OZ/+H/8x/4/R9+4scPcFZ2xddxBd9QDnTRlGxeJxCn7xqT9XxXsfMoZW9kRtDX75FMY6kjkiUEXAhgbDPO0wETqW1nEjMeOERIFASPKkvBqP1SHjKpdrzz+WoK4eqJ03jo4CdFnY1mmoYPH9cGzBBu1nPkQ1Mu5xWZDHX3zqXXzcG+LunAenhhyioVUr4jpTnMJvMy/YeUO6+mzOtD5l//+cQXt4lF7NkvUvljfeJ/+vD3LKcTU7Y5WsaDhWJSXyeZaRKKZFQuaO8GmvIwc40FqQ6/T8mpyyzHOpSRMBRnU0Mq5rRbAMJeMqE0qho5swQhgEQuyY7YJb1D1U5TdW8tBsPzbCIQBuumj7cLmRa1mXRHIcn+hIRSoO8CS7tp3ISNeuXVBhjb7QZ0rSOHODxAdlotjKxsIBu5Esp2lT2QRcbYe90qnd7OtL5gD+v7XQM5IIMFJ30UKI295emggKf3Rq+Vdjqjq/HtjnIUZQshCu7xYmQjE+Q0kTTz7ocHnt6dkSTc3t+QLdiBTnqle4sAOXG8PXC4mQgPG0xBdWDtjbZe7N0yWxZLo3wl08mGQlV2OeRAAZp9EJC61G4gZeabFx/bBc+OzyrBJjF5guiG1qHZRtHRKsQLcDcI4NUUhz3AEM4JacbSYta0mVxhx4XlMey6sZF0XE293kXcMh3tXET2a8+BLJ3WzKIO886ubVmAnM1q0Gkm5ebkui5gwVztLrz/mysAAQAASURBVC40FWm7+yWLq+PItGFru+Utzuc5uEtdIiRXinkg6gCU6Qi5NwrClAu/+vWvef3yFV+8/oK721sfv36l4MS9y+1317NuwxZlDT5eKZGl0BXub24oOfPbX/2S+9t7Xr1beTpX/svvf+R0ulCJko8Y/83yHOOfxMoTdqAR9s/S2dhQYmZVRl51/4kx37q/gvvLaecFIEQdZ6sbWCveb+RoBKLW1NaTW+oSt4k7pKFPFQZy9TpWHutqWFi0aXJFa4J7qY3H84U/vn1LyRNTmZkOMwfpnHOBaeIAHIEyw1e3hd98OfOQvS2S5zwQjEZOrOwgJ+XLUribEt/dHLidhbWvnFvlzfu3PD68pxdF5kTuZtBlop7V1v1YgR5x0ZzQqdD1QMpCz2w5+7Dwfc7mafYeoyac+35+hyH2bN735nnIBvegGpGd06tPAsZc48awaMek6w6L7PHQAJcoZjyNENFoJwKSnDwjJeZpT2VoJAEBdjOUeHfqMrzPou+f4HFEx7skN4c1GwF/ICST19cGYry3sCZ99NP283P4/njD3tFqURFbgla3HDWwhrje3mSQ2wNop6qVSSXxfoLizEk6OeLVadLAmIrmNLyulG0fbFvwmTxXoAu1drRX3r35wPl85ng7mzNyI4Ojma7Ux8p6rpz7hYuuVwHrjpUsaYNpNWdk3wZ28zC21SVE+F2JokAFj/y7Q9XdufoTChD+hBJcQ2AlnPl+opMM2aM4zNXOsWSqUx9EopVNMUUIQLzVRapqrAwSgAx8f9jPo1d5jPoeeKbdmiwmjKUgWfbsWhH40Kh1mahNLVS6+SPmhWKsJJobZXbQxFOl1U7XxSTebFZobhg/aPOw62g6LM4i4YtY8GJob5CZs5N2B2M8nhcx+jYJGLcoeb5BsJD87fHIv/03/5pvvvySr29uySiXh/dckxBbUXEaukc3PNDOAt9rlGEt5QnIlPvMC+28vH/NWpXfv3nHu4dHnv4fH1ieHqm6VTLujyRGppCyK+IUm9o3ZOiOK09Nt3+LuIHSPIwLaT/RO4ciunWbRW9rzK6Xaa1xaXV3WTEyB5HhZUNY2J7Ed+SnjVlHPGc8nl7YkKatj5y42UFmhur5gqbMmgtdhKJYw9LLStUHzn//X0jlQJnvuL2b+ZJb5OaWVF5yJ8I3wKuj8PI4sd4dab8QGslMnAlHR80ghUO+oQi8SM5/erDmym1pvDtX/sff/z0/PD7SjwY8mdvsOTZ7plpXt6fHLoKU6EnoOSPzgV7PSDvTmoE/4lQRyKLcHI9M08zaLvTeqD1qsPq1B7ZTavaTbhfaeWyOD9yhzneKMNm6tkbWHevFkRz2DiLmbTUMKNJo/vs0rm8PblSJh9vCPE+8uL19pm7D6VDWc2U5LbSq1LUPNpsU5AwxdB6CtbXSoRujSWvRMHcDxGiHfo4nirXdhy6F2A57aa3QqzmCU0dF6X21mkBLlO2IFux06YyxAaX1ytW+x4z+DNA7vS/WXARbZ3JjnL0ZIc0/Q7Oi2Dg4n2+tjZXK0989IUn58puXHG5mjvczZc4cbie0wdP3Zy6XlXfrIw/t5GVKdtTeOdcLh0XJ52zvV0y3WeQtmjEnBi3lsCNkhN67t9TMebKIpFp1wfOI4qeOzyvB6mCJ3p2l29z+PQHtEHKxvl2ojEFLeVh1Inhxo44NoWO/+IQNBnT7XQzWFtf1uqqwVLzeQqSbAnW+udEXULvn6UyWZcdPDFrdhDWoVfOwkhjSSDOo059tFe72rFI64r2TBBluu5DNnU8mQCcv+E85jzAlu+/xnCRjARExdveSM7/97hd88fIlf/bdt7x+cW/UY82AOftk/xj3Z4dffdAkDa+ILSw0yH1dkU7ZQqsvbg8kgX/y57/k/v6Wv/y773k6L7vZ2PJpMSUInleRAaqxv+nIFwabyXjeWES9MVyfYPCQa5mq3S30nH1u/Q8jbMLIyyKeC3OGoOioMdhoxvszLOqkeTxzWMASEPdAKvvl+yBv95rXWlEV1pTIKE91ZRKhr51JK7dpoS5wPmXuS+F1qryWxCtJ3GZHeqaMLmZkdpXRmVWyoffmZACom1LISVgDvZdneln4ADyCdawoQjpmUofUO71Wo3GjW/5+iIfNexPMii5YwWF1uhwBpmmi5DI6nhcmespIW11IRb0fQ9dtq3Abx+1GuPfWEQ1WzJ0YAKulC72z+6jgHouHgXTLIqIxHwIy2dznOTNPhVf3dxwPE69e3aDdOCdrbVwWU+SdbtRgKSHZFV8HJI2w+QDLeoS89ZCD9gTXZTxODpFAJgOUGRAOepO9PbCFIcHlgI2PyUfdgB2bdb8NyH6vhUOyC6UOg3R8pG55uF0YeYMF7WsjZBPyHuLvgIhR+nXnkO40JMHT45laG00bpWSWS4UOT5czl8vCh3cPPD08XdnEXZVlXc3Jaskijl0shdQxY7PFGBm9wn4Y4tnHJfdyKdYfnz8+qwSXeh48jNob2hYLX7EZ84H4iYVi9HrbW6oTVwYzielI3cAQHynA66eO+7TGs3DaJpRSVrpYeNXCItkUkg+VlXGYAuwK2ZPbowVLT3RNFt4QvBFqoql7ALqOEReBPHm9FW4huhIkWQ9EyZBLYQoSY98dUSS+J7TTJGaJ+a6YknBzmPlX//Jf8Otvv+Vf/vlvOM4z3795x+Wsm4TZ6cHnxyY0bKyGAPHPZDOQd6AO9z4y5NR5fXfL3fHI//6/+Wf89P6B3//0jqfT+SMlGGQ+sRkFCx2qRD4xSmI2Bvq9wWtTH+AjXx9O65bTlvwGPGoAqtGiJjxevbpmrMfeOyUlVAqBlJPxtm4hRmF3Shunol9sgIuSkJNZld5rlH5ZUO1UR8H1upB7eMKdDzJREM5L46bDLMIijaf3yqs5822+8FpmvpREOUCehZwn/3w2Jej3z9nZR3AmlGlCstClmRU/Hail8hPCW8y7L1LIBbQpaWmEbxRzkYYgibVka6R4Ph1xLLvaeM2HA9M0GSl3TqRsLDvW4CJKorZJ6GzgCPH6tbHPRYmegDFvys57c3mf6IgD8DbAiiUwYmxiLeqI7iir916cbhN5StzcZW7mA9++fM3N3cQXX9/QKlye4PFp5e27s4caKy13JFUzHgWrix1cCsJkqPtQNaxronXxVli6Eax4Q+vA6kxY2H9tzRRgT9t4hVGw+6cI1rEmeYebuqFP43TkWvVt/3M0fDTsGyMDFoAOk4HthuPzcfV0/cHwadbY1x0R41S2/oKbcCwlc7nM5JwoeUKBcztzOV/46Y9vOL9fXK/ajFr3npWeFFmjBKg4ofb2kM7fNKJF2ztfbf9rQXf1h58/PqsEtemYcFugCZy1guSf7mLtbgbDxvU1QpAEZ+cQy5FsD07DEXCzOqwBoBpILA+DPLNwkLByPH8Rwt2/xGtYsgvX3vpAXko2YVd8g0YH7LYaPLst1qYjwr5RgJvITAnmIlQV72Dt6q0kyjEb7VeOmsdtYEINpiyjuerITwmUw8R8nHl9M/P6OJEjJ7UssC42Nrtxto1xbdGHAhTV3fdY4h46/Uh7bsTaczKxcn+c6e2Gb1/fI63y4/tHa16aHOK9M1rM0r8uk3ieS/vcEe8fiyg8h9Z0q3dLMlrokPtV7jiVgtHGbTVK3a1iQ49t9ZqR84ncyv4xN/YOcfaaTfB66Se5TF5TZ3tjj4Y0C9tA8613FhUeUkelc2t+ngXhU4dkJPEJMcVQZLMFh1WJm8Rhx5un+JbKY6v8pzdv+OnpkctshASpJXKHWRMpGzFFlhnhBX2t1PMFkWzQeH9gM/462swK1yQe6vb3jhCjAxqukJhuROzNexvpzUqLJrzm3UWhtTDoavxq+om1suX7t1pL3dmCKYB3jkVJCFKE491MmRM3t5mbw8z9q5l59vZcsRelI2lFsBZVeepMs0AV+iqWbimCJ7QHuiZ2zjQlshpLU9dOr/EmzUdhH4zTAb5RR6yKpwO0xeJpnsrcNJMkAy9tOb/rQYoIyz4NYVMjVwM6jEnHYdDFo2s2j8PIcONiX+A29ksMvk9bYFIiO9POzXiinU86iUVvzqczy3mhfejoSYdxdfUe493ciHX6uoHX9XrGbavFHIah5L+SthujjSP3c8fnGWPc5RtD2SMUocNokCD01Ct1uQmztGPhlzH+tnFEfFJiGDojG6hc9XmL1a9jcnavJ6YxhyBNoQxtcnPCWSoMJCNYHZ1ky2cdjdyfZVFqtV5UXTp1qdTgvxPIWRESk5g1fpwM5VpXA2GkBNOhcLydCSclJlxjjrB8IVksZOP1UjGh03HmeHfki5sDXx+9FEIVLme4XK6FjTDCqGMN6LZYQxiFEjTE6Ud2yhjP6JM3J6Uk5dXtgSLCr796wUTjw+PJujSIx5SHIt6KnmW/Ifcbc3tqhgDd/9ZtIhMO5vGF0TKiQV3o1dFjrhjMCRdKKWbEVIOBW+2ZtTbSlNzTDEi7mwNiVxg14lcCcutkMUBd45HNkpdmVFY9zHd/s+Y7sjbrf9ikuSKpiCtBkQ65sZFQ6zXj/dDM1xb9osKqiR914cf1zP/9+7/l/fkEczWFtSRKF4RCbmrNoqfCdDiyPJ3RS4NUhhIUQKXSxda2dmWSZCUKKRtoIhyDvpF2X03f2Hg+Ou6NxzgmN2hITrS8t1Ldooud//wQrNnq8F6vZTtCg6SUqGubjNv17uWBMpkSvL058OqLw5aGiEeVTioW0hU6ee4UD/nVKjYfA4atQxkrnlLwSMhFlNYN4WgKoY8p3LdESz5U6lRMliJJHmKvY8zi9bac9lZ+ZoVZ4ZqZUdK7PsMJjMGJW28qTdVTCCFPjahD8ChWMplcexsGYbS1Sur7O2O1ktVTHO6XrK0iSWgF50Nd6bXz9PZEPVfau06/PJMHsj1k5P0VS2EpVmqWEZNrWYYSti23U4BRKjPyI+2Za/Dzx2eVYB1gAB/FQD1p0HjZBs7FrenQ4D4hKjtwwbNZCRaG6t5WPOxVI1PgyrWVbdMMrz1DKlBmoUyJabYcpDoCMIXHksYtxjKaZSVLN4LtBPOslAzrYhXbx9KpqEdEQjgqpQglTeQvXlNb53Bv9Gt1uQyuQhspz2+ZvwAYw0JOYtXw2a1jlJKNc/LXX37J169f8cUXr7l5cc/lfKLVyrJcqHW1xRhCx/dpOAxjte8NBe04ZbJtuigKVh00SHtFFUOf1PFAU+K3v/yW+5sbfnr3gXePZ94uvg1dl226zjIzPYRg6MIgSY91oWlMhxfXbByiPkkS7DxeFxUPZ9fvSHdU2G5PpSRG4qDGBSviTWfDEFEHWnkecuRz/WnG3WUsFLZ8t4xfBRh4SpM9QzJhdSiZnAuSJ8gFSkFzoidrcXRi4ytegUeUC52M0C6Ndm7uK8Kb84mndeH98sClLQ78UHqe6CK8o3HWxvvzA0vrQDGU8k0xqVVBshFFi9oz996Qu410YCj8NJHThJ4tlBa2dJeN61LFWT+6mlByi8vy2O4xBprUDdDk4KNcxJS+WIsibd5Uli1TrZroWsZa7MNwdK7ZZOCymO/Rum0qSFLybOHPF19nypyYjhOI0KqirfJ0ekcphcN8tHVyTMwI05odGWosOjMGohMxY3l4LAKliKdK7Mhu1a9uHOSiFh4eaFAzumo3yrWkG8paXHpbS7dkOX8lrNgRZbnKd6HOkBJsVzvjcyfoRWS0Pxte4gbgwNNrUJW+ONJXO2lKpCkPxazhBHnuj5LQLIaHaDqU3/juMqU/NTR1Vm9Nt54X+qXSlmr5adiwG3iawQ1TVVPCoXPMmVBK6iSpvqYw41HSTq6HnLABST5y6ZOQvuvjs0rweaW95DRoaiJclJ2+SVXQjnX67TsGAt9s2xTZX8pUSCXR+2pKc7+4R+Hc7iPDvXcFEG5NtGmbbZFOU/Zw1OYFDp0qEMWcipJkYUrZFFpOlGz1MZdLA60cS2dl813waStZyFPh9vaepspcG8vlwtMH2BJZkdhX9sncVLCOFh4ODU1RknAswq+//JJffvM1r794zfHujnff/8DlfGZZjN9U/IqjRMOfaei/K085uB1sMQwF2GV7qX1qdsyX/WlOkEriz375DS/vb/nPf/u30Csf6rrVET0z7GxP6FCMI2SiG5hq/zEhkSVbiG6j/WArd9iMxa2FEBZCGr93y1oSaUqDyKD5l4GpfI2qmLXoQkHcSArI/UZ4LGMqr9sB7RwJokGoefeHVAzIkgsylKCMPn8nom5PWf0NkxrjxWVpLA91cMr+1ft3/HB+5G8+/IF3lweeWKle3hNrOafM3f09KRXgaPvhiCnBZYVmfK/JB70xIW3i+ZFzIadC14o2K7/xIigzF534PrwDVfNSstOnBd9vcS7RVEwBTt5dYsruselCW4R62rhBFTGFOGhKbG2sRK2ifVmeaAsvlmxYgPlGSVlJ08R8yPziNwemg523Lp13b1a0V06n98zzzGE2KrI5C1VhXrL1o1PIk1g95tneOQjRxz1Hj0IfN6O8Ia8GDspZUUm0vroBZ4ZcdUxBvmKlscNYdBKbnnLyjJ03fWWkhjcWJRlu0G7K2o0HiRxwKMG+bb4ONMN69HXL55Y8O2ONDAWI4I0EKl2Khcm1w97z3F2aDu3JVs+F1Xp4nhf60mireYYIXt7h0iuUoMWJ3Wi1HRYXFtS7tHiZVLaa8ABZ4as15MZGIPOp+ML18flw6FA6m9ITt6RFhCIWqpBsfdk0QWmBO3MP0j22MbHxe8yTLFNxhnsbycgBeoSTrf+uj0y4wzmRilCOQinCYRYONxP3L48jF6TNXPba7MtGNtEbRgo+mVU33yjzoQMrvVceHsO0sY8Usm3WZO97JQn9SClR5mIoxuoDP+r3HHwxSiK8t6GI9T3LmV9//RVfvLjjv/3dn/OLL79iRlifTpwfH1kuF0uQb5pjJ4j9fxLgpE7UEllI1MeVsLtictzi8s4CwBUiL04TgbtDIXHDf/3Pf8dP7x6of/HXPJ4X3i/NPcIw2fznwUfZdxvxamGhJhaZs1ufKQ3FLoi/q5pnNVo3hWrfFH94n7ITkCTLC6WeXP/JiEqFJb4t8Q2NJ8HpmqImzbbVoAQbI65mxWMKsiQ3BqfZlWC2LgHFIN8JoyQ7tYXfv3vPv+srL+5ueX1/x8PDOx4e39POnbZ0C68r/H554n278O7yyLmtNDHBMnZsN87a8+lMToXpUFyR2zgM8Npu4HPK3B7uiPZh6rR9ORdSKsgxo0mR2pDezehSoYfh4W1pZLIwKRpsmzYOeSpGn+jjcXOYnEvV6ou1FmqysqPesNA2Fo6Y5yPzdBzPel47l+qlD6JeErBNwTzPlJK4OwplEg63R+Zj4v5QyLPVDx6mzpRWN3jVujZMNnY0ZZoyL+4PBvZrnceT8nBaON66V9vNM2m10Ls7AcOoF3I+IAiHQ6K0xnqBRmP1NanNqeh8xW4sAG585UBiCuJ1kKYRGqrNeTW3zWhL1/dIsmumBpBG9A3fs8nBaymb3GhuUag65sJ/3vMe61WBno7zu9coaq3QZUPWY6mWINiPD3ex4v2mq3dqsXGohsFlLxC0m3Ksi1DPk/enjUhCePzqv4soAxwOE9M0WaslsZKQrt3LVJTNnH5G4PKJ4/PAmL4rhharMjFh4LHaSIrLdY2e3dZt9gSanFg7Bj9WMuYRGpqvXlsXNiuD5adt0tuUQLakdTkYh900JY7HiduX3lOGbiGmi3K6KHVc2wVswG4F5mPncCtApbdqoIXdwOUY0lQGL+BzLRiWcKvNLSWJR/VmvtYvS7IJx4w5gtNk1HTfffMFv/jqS/7Zr3/Jd6++4P3Do3mA5xPL5WL8nxobJt5kN5IuyCNOaQi3PhTflsbdsqrWCTuMDstpyl5o+mdvpkxJB37321/z+t0Df/8PfyD3xodLuxa0ypZr6JtF+8m1NW4ipLJ5Ydviteh+yukjHlm7ZB8e4JVVgCssMQxZDEmTXegtnmA8toW+tDtdV08EHgnPgcjzG+y80CKJnLK370mWxHdFaOFuCyddaueHh5XHhwe+/eI1VTN/+MOP/OH3f+8Polwwb/EtlZNxEpkQjzmONjjN5njRhZQ7eWqjSDvmN4LwMQc5ZW4ON6yqrCi9GRVXSoWcjZORosilIqvVXVqHdXGD1ABjxoCUSJpGuRSC9TzMphhLzszzRCmZ6XiA3tDVcuC1V6TGU5rEmG9m7u+OMQzIY0POjbVXGo39jhMRSjGgy+2hMM+Z+xc3TIfMzWxCVItFA24OhmivkXGJFEBTppKYymGsxq4LpzMcjonpINTmJEiXDNUVvhjuUxBSmhAxFpPeGrpWQ7778uregd72pxlOhM3pcnVbS5aX6626Z7UR04fCuV5/7gFKvt5jvs/DsCML0kf8xFfF5mHuo30bRZ2bmV7+oF5U31s3xLEMH84Mmg7bZYTqd+nd8q3RVKHprlmx3dFqFlul10xdOnkyF86cPE+PGX9EpOaRBPNUOB5nUjE6zKUqrQtrdcDPaM15ZcF+8vh8OLTutbDuWuCYaG1qneOv4q4hbX3Me+vuxbRdn0HF45njY0mcw3PQVvmi8SfMY2Zd4RwKeUoc7wrzlLi/LRzvCncvirGxkKltobYzvIP+oZuQ61vYIM+Fcij0VGgIyzlRl07TsyH35ouFFZohbcQFcisW4qpSRwJ3K5ZjaKVEGuhYyRNSCqVkz0FaLuW7b77k9csX/Pf/6r/jt999x5d3L837ccFfu7E/hGdkAkkcXSSbFzckhBNKe8hBxOXmtgeGV7j3EiJyILtxDoWkYuGHVzczhTv++e/+nB/efuDNX/wd58vKGkl9NdXl9bzRUzNmGNCx53POzk1byDm4e2QotgjhhUdT13XLM44H3i0V3f0jTkjPQ8YyluZOm24eMs7KkYwBRCR56upjzx8YHuDhMJNyNk8wZSiOtMrJPfJiSFEatXcurXN58xM/Pr3n8eEDT3UdxsMqQhU4ibLIzsn29ZC6q+O26zriDkR3/7q3Tq3V0d0bYtfjAqELkJToqRiJQxJDWGYhS0Y9pNlqpmmi01lJ9N4c0ezLPYmFI2PSEfcGsTwiDV0vGKocekqk2ftgDvEjVw1VzdOzfOLTRaCWUYZUChupwpDZSr0sgNAv2WvRt1xyGOY5Txxu76x3aKnjL5bzarx89YIy5cE3/P7DwtPjSpkTvdj9xQWrGT+2tkqa6N3C+bVWS8nWygW7TlJzErK3VtvqHN1gUSxUjyCSad2jaSKMhOVucK46aCS9MsplO23sBkkKU3Hj1EnApZGo9KOFUpMkpkPxdJHtkjF6O/BK/L+FoeuOyl5HB+9rw32BJFCsibDWRtXV6k0PE2UqwETvQltccyWsXtDlbPKiYSM9ilZbEda3vVkypOT14OPuY6XzueNPl0iIoSg1SiDYANDdJzTKF8bn8An0xdR6Hxma7dii4yF4Y4FpWCKotcuAK1ijJCHPiTJnpkPmcMjcvCgcbwqHm0SWiUkOrCqsNJa1sZyhNaG1fX7OwpOaCk0TyzKxXnzy0wp5dc/IzZBsgrUH0pQ+8osaL2KxCKQ7P4MrEMnZvcFsijCZ9/DF61d89/WX/Fe//S2/+9WvaU9n2roSHktTG78x4aiZQjH4HhPfvEHf2sPIFC/N2klUD1WKeM3kzmgZ6wYhqYURI2hze5hIIvz6228o5cDhP/2BujbW2tnX2VmqM541fo45j3xSZpqLgQKcySftjKJtmXhZTWujOHmsMx2n7Daheo1a2lC3bngN+yt06c5mkbhgAHKu4hVEgmF7LNhy4rN5+TlPxv4zTbbxfT2Yg+xWd2uc18rDeoIPi1vB5kHQlZYTXWBJsu0Wz8PFHtEuFlLDqpaiV6MNQ/ewUNsRTGzF7PGbhBh4LMLyItfvmDwvJJZD7qq0Sa0kKowGNw5S9obLPsNxLU3eCtbzjEm8/GIqiExI9ADEdWLsbyxvnlJiqeKUtCYIXU9vXKI+ba0akURfvRN80gGcw7YkJWVuDnf03ljSCYeTehPtyq1gwCoHctXlieV0HrZ6jRZKfmzg0YQ6xUkujXq5kARqq6aAXOkVp2fcD3NXRv2pIjRnlepqxmxfr1EJGw782mD9NPxwbPeRxqJluljGHBXyXC33maymeRTpD8XqyS2JKzLGx0onlL0PtB22DwO9LYI7HEK9NFJOTLezsbuIUZy1aixgpvyE1tTLkmSUDSVtJG2gK8Z/ZLUxKes4R8PAd/ayZ7mYj47PKkHxeKxjwGF2S33nNVufqM2+TBohKEOZNbHGnZEPHNcOk+i5Za95CCMhEIUmiUWcBaJkpttp1O1Z0r5bcTuG8pqOF4RG0sR9P5CnidYTvVuX465CmiZaziyLUCssVeiaKMcbZJo5NGVdO61abmCAEnYWUQdqhFhSIulkzAcNpDKEYLxeFmMH+frL17x6ec+//m/+O373q1/z3cuvHNpu7vz7Nz/x4cMDdV25usC4sy3M7Hk8Zz81AeU5wb2AD9NiQ6wwcgORAN9fGg91R1mR2TTKsSS+/eo1KWV+/cUL3pTE3/90oQ2vCi9ZcHJmhGBUjWS/qjKXmWmamdwjHOHG2LW+SIr3rVzSimjbIq9j3di79N6v5iYMgJQT+/DsdYPNvXDZrjtIuROAo/euOCctR3WcJ+siX4pR4Hk4MB/N41pV6VrpetlyxWogA60r1JWuK6qVc1MWhaLmlanuLXAhvP/ugKARHm1u6X5kHWxr1GZZtn/HazuL00d1M07ZJpKRlkh1RboyzwlVq8fcsJtQq9OLLWc0Z5Cj197aO+ScKFPm9u5AMMWo5+GahyrnDIcMfRHjpnCEUFdlKkqtxviUxLqs6DrRW+I8w9Lg3EAunXf1iTIr96/NQL59cbB0xATTIXFztPG6ubkdO6Nr8m5ZidansbjKNHN/17hcYF3hvJyobSPO4CKbw4F5Izklyqt7eu/crjcErRwRBfGhDqxK743aKq0JaxPWtbMs3UGIhqI3btIBH8VWZd9vE0SsBlKdxH5vNm+LW4BMTjooG0uZh5eVg2U81t142AxqKTDVvuE8Whrz+bGMwhTIcJkEspIkcbifLIcstl/KvEt5qBmDvXUzmpqV6ATTkUH8Ehc6ysIBpZCJVnutQV1B19gH/RMPdn18VgkOrEEMdgrgwjaJtqCdssx1mpGAK9FsaCNauz4G2AXfjMPCEXDiVxmzKY48y6TJrW9hgCbaCBlitEdzJ7tHcGiOOmqWV1ubeBunQtdEdYXVmhUi52lGcqccFjS1WAO7ENSzljNhkSU3AnKy1gRD2TDCfFa2kXhxd8vXX7zmz3/5K/6r3/w5cwqPzZTT+emJp8eH55GQ3c+u5GTrx/W8aH7v6UAowrCS7Ev2NUc+ztt8eE2Uz3cSKEm5v71hWSuv726oa7Uu5MNLlZFHzsVS6D1lgpS5d8sRlFKYsqFyc9ozQITZGmvQvOmUPPm/5fG3tene2xVMHNuDyTeipo/RzuN5fWzsV8FOgRXE+7rbeGIjfCRMJVNy8nrCbOwuxfqrNXUuSdxq7WYsGljISwXW6rukculw6sqBzGR38HFww6tvTylCkOoSTDvPD5ve8Sb2fS8Mxa8WdIY7J1wihu7aVkSgu6GlAs2VtN+orxgIpFk2yEjRbaSSR0LKXDjeHQdZvOqK6oW1KUuziqEpmU7vLjDpcGhCFuUMSIPI92vN9CSsq79HB5VOu5wpk5LmiaMWbu5nzFMwNGspOLgtj2XW3JjfmGdcFq8zReDxUTlfgNRZ183GqOvmbYvElAi5WKf5UovLveDx3Naf5VuhtUqt3XJZq9AUqEqyOKBH4EwZXKUD1MFvfuQcSMvrSMaYz9ieEav0/mqGiO/02jeDaPugOVIdgkhe4hc2HPQUzxXC93od7j3XCN2mgzEwGWmE762dEa5dnXVKDXDpoGhbU+beNzqrVopmz9X6COsugvx5B3Acn0eHPj/EEv5z8RzH4rkM39RtXcapHZybTj8aGDP2uzszXp0VcOsc82WD3mQbYEnJmMpzYkoW7krZau8StlHbKrTZZMNYLwm0AFJILVPEbbPqye9uG0w974C3ZLq7u6e2xuPjav31knu5AT5xa0UKzjxjF9bmXISySewpFeY0c5hm5mnm9d0rvn39NffTkQO2oARFmhriq+uzPlg2KtmzZ4ccuZEY3rA2t7ybi1DGQ+wU926WxqxEOHJMl8jwGhQMVg3MwP1h5p//k9/w5dt3nM+PPF0uvD0tpoZHixyb09iSJscLZKFMhTLlzTgYzyX+Hu7xFCtYv3txT2uNhw+P1GjZtFd6V0I8/pRGnap5sw3xqETMoSmDPoZDQ6D6da6Wr5gwOEwTJdk7BDqanJBjdyEvGBX24mkAMwNNiTe0VmprrL2zqlLV0cp+TvV8JEQXC3EjxYSQYOvUq3SRAWGKmRSD00RoCEEDJZd2GyMNWXhlpMbP2+owU3ZYFi70AmummId+uTRSSqytM88Tt7cvPUPsQPVYrPGlnlfLVpN6SBhB500azZUfpHM5271rg1ozvfuFBFbtUOHUz3RtSDcey9NSubntIJnDMXP38kipSl2VHE1gx+GuDdfJrfloInyaDtQ1c75MrLWyPD5Rl5XLcbX64IsXybvxV6YY42J10yMhLy4nUgwcqgdUX7CsjdO5cXNQ6o1yejpxPp38HKWntIUfMSGvPWoZuXpuEI+87dyzYZj6fov0CObRxZtf9QIUt+1z1GpGdYCdnaO5cE3oPiXm85udkJ5d6DrqQ0UdeOeMXqZFDMmqtSPdDJNCYcI7q0iieUPudbV01HRcSdIMcyGJ+XhLznCRxeg+6+e9QJulzx6y++6bOyVz0Zu7T8MgNJLtmG5FXZ/IFaAGrhFr220cxJCwc3ZtU+K7iHV7z7sOAEnSKM5Fxby9LiMkrHH9hHH2SXZjR51/XsduD7b4SDQbBDdzOjlRrO48DvfuwmhOIlYvuWtvN5gdsOecUmbOhUMp3B5veHFzx5yL05i5svfc0JXVN0Yh3tlwF2G0bUpwGy1hJ8DN6rgWdjYJYz52s7ybKRvzfXpPME//kDNfvX4BdL64O5JFebhUt+KfS7tdUEISIuY15SBiHG+n29190wawaEqThU3lafd0m/cn/pmxpHaKMP4dxAT9maUMsVA2EEkMzzWW2YVc8U4OTm6N59ZwvsdYUIaT674OFdSjJrXRtLN2ZVFl6VariDByQerrcJud8FDcE/X1bb8TYvnEOaNMaG8AjTCX/S7tJ3ushxiHfdXVtRezP7Y1ZaAEK6ZfaBkHnKTAtPh6ivF0eeKG3JRgivw52ZRghWXxFIbaA/ce+ObkBpb9a22Vps1LBmBxpqHLqVpurYeHYIZiyupjbOOno3h/M8pKAZkF0UzzqEWtndPSWLspoTWBeseJeJ+cbCXn5uTbKVa2ya3i5UB2eH2kNGpdrWtbgV4rdV1GvV4TlwujrMmMdYmxHLMhY/6uJmnsKTtfnv1ZehqlM3GYrNkp0eA4VmdMyruIWN8V9afxETv6J3L96MhjJvB0iq9jN0a1Q+pRYmWECqqWyurN7LGoOshq4eBSZkSEdfEC/3+ER/h5Jeh/DTLhLnbFRbEtEqUEkYB2Y3MDuDA6FliiHIf/borHNmo3Lk1kpKcCddn7gnkG1+09QswmgTll7stkaL0G/QRP3ZwOmYS+JE/cOSLVG+H2daXWhl4UbWFZFyavSUHVeiaGshjREjHWHLf6J6e+Sm4la+uszRBixszgY1Pgm29e8suvvuB3v/mOX337LbfHg32mrmhrPJ6euJzPzqKzifsYxzkHnyVsoxxCd2fFsSlA8VN2xRFskh7fvBK2yEYEbL7H+FTf8ubMJfHLL17w4jjT+7/k+zfvePyLv+KyNhYHEOw4EH7+uJbLO6o8e4PoxaYYF2fXEFSmVqO/4B5UY5RntmZ1bxzEA4VxsA2AjaRHLiKUiJcnhEFSUmBYt2cUMXIYGe111MoapA9uXarEC9impVN7Y/Xedf7QnscN1LR/D9mwsw9UnbCjQ6aDVk4fTnZ6sWvWpXtozAZVaYgkMoUBqPC/WcTVy5HazqdcV/NctfoCkjG+hIGLeogtwvNGYScinJYLRTvHeTIijaqGii42HwLkLOTJFEPK1m4sS6JM5hTePt3Szke+/37l4aFSDp4mKTc26L5Yb9cbam98uJxRaRxuFo6H7TVrg/NSefvug5MzC7nMlPkGkehNuq0kAaQFXMs87clbopUXd7TjEXl8YF1XWppQb1cVZWGixi3aOtSLr6eGRaSOY0Vu6itCeGKBlJvbI9M0cT49sZzPFkXw0Kj4O2va1xF61j858XlLI4W4sfps2+5qXyZBpgA8uZJtugkdR/buNpg5LNnmuYTD4tEVVds3tUcK5Jk74zXNgR9hrHYdDQjMOXciBiwyoK3T3PhJjmSvXaAnJiZEJm7mCbq41994XJdnOICPjz8JjLF39tjw+M/LZ68sSRmTv+VJN89lNIccgzWM7+0fLmzsR7+XRpjPFui4rUSa1JRXCQ9PoTvYLs0ONW4J2q4ko2NWVetos6LUIMkWsUlLCQK2PryOENgj3mhhK6NKSyRxuHVu9Na8rMM+nrMxadzfHfn6i5e8fnHHi9tbSvZQQq1oM3q0y3rxRc9436hRyk7RtQ19SPUr6b73j4aAGrlAYAMlhXkYAs7+PPT9lce+eUU5JW4PMyKJr7/8kgrc3hwhLdSTdaKMahSNifbvXoK+U4DbPbY7bEbRIKd5plQHI44TXY+3HhbsXnv03efi/5sY2ujjtu0+jAbdPrdZ8HExcc5Fhvc58MKyGYQ2DraeO1tUId5XYo1LzHZ8d0ubHUMIRjUlYjCBrmqdkpKDTtTDZePxN3BW8rid+NBITKswohDjidUU7PMoQ5A8j1C2t6sCG/eUDdwT+V/8Xbsr2LxfUmIh2VQEKUZGkSUx3whlFpgyegPvn2CNnKBAmmZIJQYcXaG2ziqgUjkeO/PM4H7tGBn7sqyWRqnGEDPLkZx0AMz28yvBYhLvlixalMpElsx0sWbKGfF8s5WBhTeVcsg7Dx1Xm2/jp1BfYbbTuueMxQGAOWeSZOpyoSZD6I5nUzdAgj5tLFgZWn+/TGOuJCY9Jlg3w5edgYtqRC1NQXY3OiVy0j7ve25dxN4TRb1LhsY6H7s2gq5u8HXZZGqsct9LkdMZ7d5cPwyuVG9d29wXU8+NZ3cSWs5Iw9l4Ph3FiOPzwBjXujkb2wkUs4tyQTzbMR42GXN4mKnZtV8gYIOaqbaV1ptb9brpmcroiADKqmdTtWGFZCNCztWUTZ4ySZTitV0fLmfmlrnVyWpMKOjaaLSNaSXmoArSE0kPZJ1RPTMQgdqpiy96zNoW1LhFA8Yd8fHJGCOyGF3b7c1Mk87CyrpklosL0A538oK79IJ/+me/5V/89s/57v4lr44zk1aWyxOX04m6rPz45gfOJyOqDpkoGAgji3W8EBidL0YkfqegroKRQxHrUBpDN0TObjtpW5Bqxfb7w7xdDCgEiCYOaebLr19TDoX//vzEH9898L/8zfdO6muE5MtaTdH3Rm3K2pSbdEOZb35m5W0K8/r+ey/V8olT8s4AadtQDCNBd0LiSpP5kIRQ6a4RUix8e4Tx5xWaIOmG5L0XLfaSvR4QZIJ0dC9ydYE6F9LaKbWySmOVhZ5cSCpMakQMVgEQxMzWbDmJU0aHUu1s+SDUWDxQlr6OeZWcmO6Ke8AbG0m8eO+NpXWylzIKm4CR5kXGOztgyo2cMGTm7vfB8RodUPI8b/lJceWchDnPVv9bF1Y677H6v7vbDMEl6sws0yTMd5lpypSSeHF3z+3xxpDldO5/daYuK9Ul/CHfkK2wEVRYP1jUYGlP1FZ5OF9QVUrBOo6UBZGN69+AE43ULhxy47Y0bxOVaWrk630tRi3WjVVbNA/FIcCL21ta75zPF1pr5JRprXNZq4Wj+2oyp4E2pdXK2uCpVjeTGn0V2kVMHjZ/hrKFS1MWDjdH9LJAtRpQ68nqkYuIVIWnJxmyDsJtvLi9lIzCiDBt+Jdd4FSF1MX4bvN12ZWJ5Uh8TmaAjPx3WM6BoM5uX++bLjshSnhJiNvBiSzz2J8h77b6544kHc0IdOn0ValnoSdI2tBj59grpYhx53orq5wyr27vt33+M8fnPUHZkH4RQpHdV/zbKJQs/DSMfPFwoRsKyRObKdmW7bDnOx0h0rB7rUty34RRODreuimGPpjv17WRVKhiBL85ZzO5RvnG9nmrbbFYv1ngUdRspwVdUWeztEwR7qyxlEilEK2Y5rlwcztRpZFQSmmUPA1v616OvJQ7Xt3f8+r+BbeHA4eUoDeaVpZlYV0WLpczl+Vsyj8sLoydJzzA5IImFNs++Tdi/rv1Gz+E1R/W3KZiZGc6blCKTykhJZSNfSaLcDzMtHbk69cvWZoyeUeHKDoOD7SrM0c0q2PrrTvLiWzPrtsaiKfYK8Q01htXazEuI7KdO97gWTjk/0/an3bJkiRpetgjqmrmS0Tcm5mVVdXVPdMAhiAPAIIgz5D/nD+A52A5BIkBMZjGzACD4Szd1V1LrneJcHczVRV+EBE187iZtxqHlhk3Fne3RRdZX3ll3P94p4x9bjbO/v2x+yJ8IZtDPQw92cI4ClrV/u6F5Nmj8V063aHcw1v1cVeJ9EEanm0acyREJxB1i7i7V0nbmJZSF/KKgXTSffgrhkF7H17gwBJHmHM8tjj5vf2tiV1/U4KRv09Etw2bg+JvqZ6zD4+z01pFq91X9bycZKV1404dYcQMyTtB5HmCyZrczseGNhkpnlOahxLUnqizdRhpvbG2QnqfvK6006VSZcHg/JZ3Nd3R0bYiuTO1aiCsDNY9tqM1oXVLS9hSiHIpkzGSEjkbKXTPBegkj1V3H98Ir3dnblmbe8Y0+grrJU7eSJrJbuAFlW5yukYL2/pc+UYZWyMs3lAiMZ/q+2q0uwqZHlO9nUd8HW5GpH1+Z/+Mi0VCZp+VR8w7jYWaHJiVBI9s7XdZ3HLaOGHdg5V433gWk6mj8xPqqHmhLUbiopO1A4tzJL+fMtqL/PzxWSU4jZxgdxc94tHmtpbJUWmq9GoWrjrUcgxaeF8euZCenPD7Poe1Z7ew78U/vFrcOc8UnaAWVBNVxdCFx0xbKtfrhausfJCVx6cHvvzFCUhIsganFj+2kIg0sdyH2mBN5QhZaWvykFvDANORU4qtF6i0ycI2GaaSeDjO5EMmPxqS6YEEOqP9AVpCeuLX56/5s9PX/MNffsWb04HZJ355fmG93fjhw3uutyu3l4sTZas7Gc5K4ajP7HmANNB6nR2ruSvK6BjBSMwjWHhgjLEp+f5qeYev0WH0Rg5SZx194SIhn+3+NDE/PsI//EuW6VtOv/2eZVm43W40lCuNta2WP2kWFuPFPN+HhwdOh4N7o66CVWG3MeywdXc4JXqHZqlivILizgvE1+SdGtftPWFC9biee4ApVmDEgv28xgeZyGrdAKhlLAcpifJ4tHqnnFAaVZ79QmJ56Uelvaz0F0aYSYr3XItkejdwRilWbrF3ygRIpUM38ImhTKzxrQYSSxtN4fqhIjmTjzI8myEgfTQMwAIleZ7J+XAjtRA8VRZSbkx0iu48Bic7L8UL411a7/e8ekG22aEGdMvLSu/WDmoqmeNUyJqpUuAZqEp7mGhnJc0fWOcr55KYxZG3AsWX+8Ht6+pO/PyFeXfLTaAl3hxmBx+t1CpcnwPjasTiLErrK2u9cKmN79bV8pEpuCaVrG9Ieub8IMwHH3O2+rxa+10bI2s6HGNse6j2lda8Dnnt1Na4LbexuLRvYLqukJr10ctTIpcdDiLWTYQwHYiio0Fv8Lg6Z2i4emUzCFGlTGnTl8kcla6umLvXdLvM1t2l7w7T16OsJuiH70KwIp4eUibFcvm9M3KuavLF0klyr80FUjai8+Tyzoj+Vqs9x2sBO+g7w4D0CWQSJjJTKkzzwVifdMfC8DPH58OhI/br8fC0WREqGM8bABM9NfOysjHR0zcDfNP9Yde6Nem1cTbgbvmHoNLkf/F2LJLN8nMEJyqDeVy7WJdrX7zr0mhrd2YSR5IiVvc1wALbxIknKz2/b7ks3c5nYfbNYo4YY0p47ZHVh6Uijn+JsICQeia3zJvjmS8ezpwcXq/NyGuX2431emW5XY0jtDWHPqtbNK5696UPm5l2t/DG2MZPYyz9Q+6FbFMhe9E15icWfwAKt1lzUefPH10filpn+sPpxHw6Mh8PZvGui3/eRrLpbuyxZH2RbAXxQbbddVPMoRh3npwk20Tqnee3vPX9U+jOs9///XXuUMac7jb9fkjG/W/F9qkrPeH1cO71ZUO9CiYMwdCHqFnXoZC6d2QQN7EkZlgEuntXTlKwN8FFg7zZ7kb2SimeTnF2FiH1ThcZRMuS8BySs0iqzUdKCtrvCMhHv1AfgPBIx/WGB5i2vTEG0O8P87RwLyhhEZjWoFWLYNRk9W80C5uv0smHTmrdetM15aAFHb3uPOc4ykPYPDTBBGb1EK0kL2eS4a1sxNFKq53WYF0NQFFvpgSTtCHjCpVMZZqtqHsvw1TViaHNqDMvO5DHu/HoMpCprRk6tVcjuBsgiV10YY+MvA9g3O/VeO6x18O0C1knDI8qFKC9Z1e0nyykrV2RHuVVLid0d9bw8O6vvt2N7r4Lw7PT0KT+cEnNuky63V8KdLUwBI7I3vsNm7ShwaSD0vAxa4m+JrR56UwXtCcSxdf0/59KEPDeW5Mt/GyIrcMsyJStG60kkGIgk1rp9QVdPrJc4PbC8AQjPCPZrI9MITVFlmZ1dWIKULxeUKsPmANPHk4Pxjfp7AI5mzDsNzNri3hjTlUul5Vvfv+e89OBh7cH5nlmnibWulCXe3SFsFGMTQVIcFFbtLjyKKWE6zQsaskwT4lSQLPVJk2+SbN3QU5SeJCZp3Tkzw4P/HqaOZPQRXn54Qfq80c+Pj9zXazeqO9WfvHYVB6KTnZ3DMmlZB9/+mSL3P8YijC7ltdNDMd7NqyZu0KuMIobJz3YJPJkJ07mczYm694ulfnpka/+w9/w/O49/N2K0rhdQVKml9mUU1J+9dUv+eVXvxghtdY+0PqV5yssFVsP7GMF8TgzJCXPgRo2YRP92PYQjrE3Y9OHgPAziW69v80miPHwk3lYX8Rge62JNejF6Juef7hRDiuHJ0AKJT2Qy8Tj6SuaKktttLbQ1mf6IfH27cRtWXi53sZVQ5RHKFLCEvaoQ0Q19TqDdrpYXVQQlRiHYkQrtmfs3YQ+anVxZZ5ovdHXauFAjJN2cUtCU6KQKJKYDit5qiSJqINfLDy+Yus7B2TeFUtfmyvZ1QHY2flrjYM12DplgVtvLNo5auMkyk0nWheWKTFnoU8GvzsIFJQpB+hmtrxsKub1rBBdIUB3UQprHTZpRmnDYFE1I/njy8qywuW6oX8FI2OeSqKUhPBM4sJlecOtn4xIB6jqOW6t0DttNS/0+uxKcLKd2a4rfWnUl05dO+tilHbB2aAaSmbz+GJtahf6Ov5M6pkCNGl0aa6YNqNVImYUgICQEz4eg8h7v5cCkBS13tXWTROoWShdzJEMFK7r9oZzekbNqtgmk1SwOtriyHnzUuuy0HrzEtXY1RlJB4ax7vI1F5hmhjLc6nQyMDOJ+fOtVDQpuTyR84HleUaXjLTCNGce5qNRweXplVX76fFZJZgnK9ZNvujJlqgtgeSaHfUi1kJIM/Q80aRERxArMeiYwEyCVLVan+QhN0fvaOQZIikcaKVUyCkxT5Nb065Ed0AIQbz9jeWhRM3iMqhvWDEyBjoWjpupgz8xVqGnOO0dvlADPRg5mRDAIwclm4UFtkCyJOZUOOeZY8rMKVk38mVlvV1ZLxeW28K67qiYxC3x7ddhgdqG0dBN4/VtFfm3AfdjfNLvfnfGzXrcTvRqsezcLEFG2IyUR+4AkkHkw8PJifl0YF0OzKcjTZXTUsm9k6ubu1354u0jX33x1ksZYFmUtWaWdjEvIO59DIX62CSEPpTRfqDiqYYFOl56/Vxy94qyjaCN7XayDS3o29f5VE1udEOk92p/95FOObvlC0qiN/G6yEIuli++e67hHe/n1I4UeaFsdVJJknWB97mxvnGCBc5dCQwiThlexp217i5Hj5rUhIP/bH2lbnZArLNYKbHAw9MYxpMr22i+qmr4deMPNvqvfV6fbh4P3UKl2vtgmWldqa1TK/7VWZsgQRcXKNzR+nc4oCP3FpGA8NaMu9g72XRlrZ3bUlkWWJcNoRvtzTb0qsVeuxoYZeDiPCc7UK/dogKtqrdc8nl0zstAmUrUIWNzGZD5yK+FsSGuVDbglue4vH+e3YMbZNrdgXJihbEethUeS5px//t1Jm7oDHiVRS5Q9p3t2c2/uqyICJsSHpzri5R2HU3UUKTgMjpwDJl7wlifX9cxY3cIQ57ZOFlIOiVBpXsnj5nejUavVUsn6FTwGrmxV37u+KwSPH/1xpVCbDPIJXE6WRF5KrOjQnHLJbPkA1dR5hnePhmJbPUCGAXWj5l27VzF/m7AFg9uSOL08ECZisGbBcTrmqbIM45/ZGz9rglRK+CflpUyTRyOE4dyYGImdYMzJjWi5qBxa7XRe/cu97uF44ow5FN3KHKNWrvefMVajNsDZeyZy4vCmcQXeuCX+shZrR5w/fCOuv7I9dnqf6wL9aac7gvgx58tHC3qXbrZahaHVbkT/DtFGICLIQd3v7wWuvoJT54LvWIXTHJCJaOT/b23ULxWG7Quz6guHB8OSPmCMh9odeFXtytxe7b2lT9//JpfPXzF26eZh3Pmhx9vPH9c+O/+xb/kb7/5xijqdGvuGndaxDesI8q8kg9SMNqHUpHdINp2/Lkj5PN4fEd8xsYcJTFxJ6PapqNZ0bXSE9SpurVrBA7TZJ9tPSNNKAlkMiJlK9ExYEhrSvA9dX+iqDuM7/PRjMpbnmiteJWP8Tna+nF6OQdVbDBaCwtWb/OT3DpvTZ3qzHL4ILRSIBdkzahatxRJwiRy11zWitpkVA/UauOubRlgHRGhCRjC2N/o1G/hrSVMQbemJM9d9bWyvjSuqdB7RhNceuO8Tswl83BqTKWT5QWh07RY8fRifUJvPY9c79oaP3z4SK0rta/U2rm+dJ6fF7779sXKSJra/UliPk7Mh2lT/MWbI3NvcDmPvkUGcAtit5ambuFbZsP+lmLEIiJQW4HF6QQxQvpcsq1iGVnqnZxzLEB22TDZmK1Ls3Uz2Wf6zr10v+5uj49TYpJ4tHvryTrFryCaHP3cyZjnZv0wXYEnK0sp40QuB3yPRpC6SyIC/SAwzUjvzHc0366gXXkiYsxL3mB7+BqpoVI3j1DM8Mt+hjIdvV4xo5pYbgd6yzyeZ5u79ASvrvv6+BOeoF9Ku8FshcH1KK7t0472RrKQSUxaKGLQltSVvOd7XIEuZDpaDSFmXqIhraboQTZFIbTnWWSrmUJxmLDbByLkyDdlZZ4Kh4OFQCM0GeC+YRkBUaHaW9pCjqEgdrLXjJGdVoy/7RYViuVCk/FJTmRmCrMU5mRZQu2KLivcVtq6jBqqqOKS/XlfeYNJ8LHWIZTNczEFPfJmO7dGh0WpW85hpyy3yElY+LuHAgbsOmcG/FES6gwYxhvYqb1y65WrVqp0ypToWujnA1oTfdo6d+RsHQK+eDzz5cORN48T51OBnpnzxJdvH3i+vfDhw8Kyti33JRJu+RBS419lAGuGFyybcgtk5etZi4/E6G+5sO0iWxg13eXG7WWPIETOQzrWQb3a+2XLoW85EMulqSok94KGRzPMEZBdpMGNHkXIvp+t6WsMhI5Pi5TdOPjZfL9ErdDwQkMAbSNiPwSs3x+tD+/L70U9f+SWYmvOhNMdEt/NUhftJI06rVB7u9WukSfsZA9XSgfpliNMVairAfIaUJtQjZiY5qjYqopqQm+J3hOLF26rNpbaWGul1WYe5dK4Xiq3a3XeTzMsxG5thAwF7ubNMt7VOr8MF9iEnnSjyuso02S5vOL5FVHQLMyHmdwypQirG95G1p3da8ooFg0L3s+xnN3bCi815JN1sjFFZtPZts+CM9jc6VK2H3UsG3F2LfNOY3ZMpm0BEfcyk+3/sW9cPmwcPjIGc6Qy4hxJBpmK7spMDBvi0b+Ubf2mMgza0RE17R5ARhIBIw53ueRRlybCutoeSvlVof9PHJ9VguXgubDaHQovowWQdXZnUJZJEie2htO5W9gi4QMSndbhw6lxuSjyPdSLeWgiwvxwJpXJLQEhZf/uwipPAR4AWqVfX3wp2sgWsYCyzhOPjye++OKRCN0svXGrzQuMYwmrsWGsqyXHJTESbOKIOv89d5uDPhZcImumkAd9m9bO8mHhfDjw9DhzTiee8gNP05nTdKSsHZ5X2uWZer0MuO/A1TlKZ4tk7ZSgYEi8ZKVp4PJMBE0KnTtWhEGppQB9JNmtqaVPBDocmiTRv3AXNhBbHZLNkrM4hd1tGGVrgqU13l3e8byu/L5dqUk5PRSmgzU6pk1Qp7HojzlzzIl/8OUDv3l7dALqxGlKtLczKn/Jf/QXX/H//h/+DX/49v3m7HpILZCqsXDTeBoXPN66JnMPwpGdgWObLQ9ZFptkmpSc1c++Wf/idXvHY3bKPu99mPLoZJKAk1zIJKROkAs5n0A2wqD90I7b0Q5tS/6ELziJR4t23qkk+3tpAmQLZw5ib9+rLpJGKyVXgOvgW435F+smv0MgBhLZuqAIrDZg6yysyTbEJpyNFkxFuS3PjvyLc5tAPLS05dtSIk3uEToalgq1W3Pflib6BIds4djbrbLUyiQnpiYsOVuHisVIMb7/rrMszq1GopSD7cNsIdnmXKKtL9S1cnm/cHle+f7bZ6v9W9U8yG5yLJz/PaA+CZQEB64UbpTjGSmz3b8m2sWazMbkPp0sLHpdqnVl6IVDwQi1HZRzuy388MOPVCcOV00DBNjx7hq9Q1ohNVKakTRZiRTgvJAwNTMOWnYD36nMPEcbheu7ZPO2/kTR7o2KW+QEh2XkI2DE1FmctUXyppDCAB+mpZ888BxqoxhxsezXZDal3eumdgK8aHs3WyRimtBWYbWW0s0N4T4sfPNOE0J0n2hquUqqrf1vv4OUK8fjsiFlf+b4rBI8TNkF6I6tJAvTnAzgMoc7Gxa5maySLByQRuI8DSeqtUxOnctVqMMGFqajNSYNW8zQWP6gIt59nCFB0mQFutaoMuLMdv15OpDzxBCPfv8j7xiFyUkM7DGSr4zPbLT6AV7QncB15KZbZ8Oj8ni/leoLBxWmrqRufWB09e4Bg5VjsxjthLt8nyvCsJaMwWhXleNrPQDEm6u9pZ7Z/xvrfOcxKoxw6Zb7ku36LrOsTYlajZsPVVNlaZVbXXmuK5dWqWp5Xpv7xDRjCN4sFkJCOOXCueRBQp19TkqypPibhxOq8PWXT9TW+f7jhaU2v8fteXQ8w1YKMXITsRZglFDelf7FuIfhure6raaEO/Uq2zqXFEEfs2BzMs7Ykg3ZmYI6JgwQVcuN79B+olufTevA0nf3tpEabMxE6t0zfPaTOmUZm1UXBlq4/H704LztQQ22CRzx0NNuuv27pyccKz8iCHDXNUGaC9kB0/fAmD9rle7MMWFDtZEbUj+JWfuYt7aslCz0YjlAAda1cUtCnhqiyb1O+PhxZbnVmDgPVrg1Iop2Y+DuNOrauV0MmLKVVW4bLYswOWVbGDimP4TRmBr7OakLa28KG7RIAgbw686X2iGpK6hcCC5XSYnzcmatHW7V0KsteY5R0ORrIVkoOEWHEvd+1GVFeOLS7XkDx7AJfJetEgi/uEs7SeTUDXfheAPMk431l8VRtpJtvbhcue8eu1lFwZyFZpeW4gZpbDnHRUuU1pkSTFF/nZIbqMbxrN4jMOmoHRjXG3swHsnvqWvMhQGj0qob3uNnjs8qwV88Ho2E9hYbqzPNiTe/KLaop+29yWnD7GkTU3aEVbKawhAEX546be38ODcuL52OD5jfaJBOS5pdnRgSMYW55b2F5HRiSolTKYPiLha1CYjwQCslKSl1lnDpg9n+UNEs9JsJmahlHJ1P9tYTMLvCqHRy7swHHUlcSwhPZClMHU7AW4VDW5ClwtLpiwm9EMAWTXJNM4auuyRyRet1cHl0kPbx9lvsauMqA/QqvigIWe9Ek9xrD5TuYYsB0w8FWHQoQFurC0qiInQSC7D2yg+XH7msK3+4vFhui1DKVjYyH+YxeBOJWTJvpok308xjeGBqOavsIZI/++Itv3jzhADfvfvIf/NP/zV//OHD2IB3YAisc7n99Crr5wX1aaf0NwPCBmbD+YRFudOIcePDyDOTLIvnTcSI5Oc58XQqTHNiGq09/Bx9pa+NevWadvfKaEpbVpZlHWCSmKvEdgqBwaPoAL7t8YaX6JsfQKN9Ew7e6Kw12jg1ci7Ms0HHR6pDoCCGPByXCAFmlGktztfvkdUuMUlyQFAaL1bLuFgHgCrNXs9izUOqFS8fq10/Hz3302DVhbqsiEzkeSJVSE34wIXna6Y9Kcdp4v13K7dr5ePHF+vi7nM2SC2SGbmnPLlBU6m18fHj6mwpxRR3UaQLWROHKfN4yuQ8kWXeQPU9O92ih7xuE7Jm8sNELpnz0xtKnkyaKPBSabXzflruG0B3MxpvtXLuytPTGy7XKz/8+APrCrerAfmstrDTaKhYG6ZDKUw5j6hG1eyhTosLp7RXfkJx4LYuPmdSx/qPJR2/q6qRjmOGmUFDbaGKQk6ZkoIYd4sYLN0M3nG4ATLPp+HVKULT0RTMvkd3GVeA4XhEKmq/tHpO1FzI3QDto1zN/xvt+SLV468PBLID8PolEmA/f/yJYnnz4I6RhyidMgnnUwqPmUDMWX4kkcQsiFKEkqPDA15XBVI7qXcOB7FN29PmZu9c32Gl6/bg9nCOpFJrw5JzjlvZzHoxQWcJ2uz0t9bFIZBbCnQ5IDmz9tXIwZN6rtg0i4SX1uzcMgkqVu+SHDQQeikjTJIpDhtOupHCjkIhjS1r0i68OXugmL3N4wGCmeiVeFZfxDiAba/+xsueK9O7Bbcdu7OF45L2XzqUYEvVup2LcTesHRatXNvCrVVa1zvrMBBiJedR2zWJMJOYS2bO2QVN5CbEN57NqSThi/OZpMKf/eIXpHzgh/fvWOrqYeRY8nsk3N5i2dFpJ1M8Gl51WJ0SVijDyxtZ5ld60IgRvG6JNIqM52NmPiTyZFRXEX63R/LvqvTWTMBVy5v1Vt1j0BEmi2EQtrW/RQUEK2o372QgObfp3jx7nJmkRyF3XCPmxiI6OcWeDe77zaPZ+4TAyCWCcN9qR0aGArAuLaP5K0Nwd7H2YL1a2UMox7bGAysqFi2oi7BcvFOH1SPQtXG9rLRFuVwXlmtnXRu1WR9TA9e48M7uzZWYTR/75lEJIqelFtqeyiDGTm5xiELqYUhbBKlLkFBnMoWSClMulFI2JTgLKXUOqpsS9HBwV0VK6BnLqd5uJ9bSKcnRsd0+V9Uoxlrv5qE6Can6IumixmTTGbXNnpEbtdw9b7V+m+koQ+G4v2RcsuLGq5qcily3kZlnjxalYWjQG7kb65OqDiVoSM2yGd86gcpwUCQFnCUWLdvv8V22O05EpIFP7n8gRBzzMOg3d06AjD3x+ePz6NA5MWXhF+fCNAvlSYfgrsAN6H3Xjbkrczb6tpQhjYVo6YUKSFOWDo+PjePJrMbeGG2NmoZw27sumeBsLJJIXUg1j1CXN19hrwATQs+ZPm3a5ZAypO3Mh/VIq8qH8p5lvdGr1TmtVLpaD0DRFGkH8luHKDel7HIpCZhT4jEfmF1xZrG+e3koKhcpwkiL7Aj8eBWvGwspb78yZjd+UKwPov7ENO9n34EOFpl+7e3Y/fSMk1nK9nIClc41v7BK46M80xBTgr3z4+3K2rbxRBhFzVNOnMtsPJWTLbQZOGviSHL/3hS4uAKE8BjhL998SX34gpy+4NsPF/7rf/rf8d27H6k1Hqe5bg/rMkYpe/wgwpeJLo2VdZTWxIiWjBEX+IZqONZEtjEQEQ6H2WpUkzMFFaFMibdfH5jmRDmzQffjcz4mvXdu6426dOq10byTuO4UuU2XmRHDKx0JWx/X7FRmmgzC3gE1VpYNXWwh1tY76+LdXwPGbw9DStYGavZQ7gb0EKaxLHYWGZ2r9z+s2Vsa+RgVD5G1bswwvU5I78DWVxRsYEeItHZqW5hqNkHuVValm/dxfd9ZXhrnxwPH04RoQ7Lw/ccX86DXRm/Kuhp1VqsdbZ3b8kLXjkzewSIVEiHgYtPJkCOCMJWZ4+FknSw8typqOdHSTeF1SVRPEWcpJJk5pCOHPHMqM6XkbTflYoC9Q3GvebM8uyq1V0vpVbgtE/NUDD2/bOO1rsKyJG7XK7fb1dfG7qt4ecYFpDY0QkK+AHOUzniHn9SLiWYfhuz1exHetFZlySjgfKymMjHPG+VjVWv9ZVELK2Hpaj0P61qHoZblzF6lbDCD13WQLphSH6bWcHM8DGKuka1tyzfLvaEesTAXPstqUTbZDxb7AOrPH59Vgg/HIyUJx2MiFyjZHjgaHHQVekpGnqUmBOYMh2KTMDwaZQA1Jl+PLWFhoBzsEp7xcIaHIFVLXqAbMeAyLPE0LIq9ZbGX+10DAr4VnEsIORhIu5wKWQz9YpvcJydDcPRJErJzNaXG1jKPbWYGyMWt0y6mBE3fiUPDdZvDoRXvZ0pfeSMyzguWeH71sK+nWXfW084LjDi8fcSeRRMGJshYZ9Niik9R57rsrKlRXe3UbgDfKurNhPGO335KJzeYkpCzjq4XE+YJTljrlaSbmPU0jq8hGRZtUeXL80SSzp9//TWHaeb3333Hsq70HdPLNgcxUq4AiTyOAZlMCY63OXgrsiG7YEuES7Mzo5RMyXmgoQ/zxDRn5ql4f0tbJykFWNxCW7Wqterq6kXS/ScMlk0V3qN43bfudg+G7nSDRy0MLwgSdIPeDmjz2bbxMQYV864Gg1La8py7FfbKTBfQTLbYGLgQDARu5FoCtRj2VdLNmr8zuWItRnlICEHBWOAELOPUaGujuVfngRkbxyajtEG70gNt2b1usFmjue5phz6ATyFBbKeWZN5cwiJYZjjbKBg7lXuGsuth161IvFfjKcVRssT69QE4ZltcuiuCV5TaLUfY3IjuCOu6cvMoWQ/shQhZlKkkqtc5DtkeRo0mcu277Sxj/4Vhol1p67pDA/t6l+S9MIVg/smljLVfcnH6OJe5eR4kJpJkeK2ngynB2r1WkRnVxLrumhaE3GFbu9u4ybZjxe4r5USZimMQ+nh/5LYNNraPotjA5Gy1syZH9jyj+/X808dnleCv3n5BSsrxpIg0SFc7YYLuAsalJxkDg5TJ6qPUk9JdzXpMGPJKJBShS8/Skdyh2eAmNb65FQtDlFxMBaptronZFFnc+agL8+JY/5kErVZ6rQbiKWZlbzBeSJPlKqbLCerEIoCuFrpByUfzajiZQjw82WT1anF0mwR7Mnt62wzB212x9FqB0BAe3tQ7Np/Njd8s+k0gMYR5KExR5/zU/UzL+HnknnYvD4PBi70RrLdZAZ0bWjo8gM6WR+h0bmKs/Ys2miprbdQO12pGTDkDq5CrCe8sFjacijUgnSYdoIKZxBOFmcysFi62peScleFOevxX6KQMv/ki88unI53/jG/evfBf/pP/lh/r6hRKgycFZbMnEfHmrJbHoxuWLCUj9TXe5Q0Cvo1ykCObUCyz5X7mg3eQxzzJN49n5nni4TSRvK2HpA7l5nm4xrp2Pn6olrdyBdjaVuC9N1si8tFRQ2X2hEo12jOdMOi3WzXuXZTZfGldbK4DOhDPlBwpkcCMkpLJOTNNyVGgI8GwG4Htbux3c4HmbB3CWzCl+OuRl0nJ6ODovi573p1lZ7AFaqGbgVpVDWHOpmS0V6Q3qghrVSadkLmMtdyb0KoYSXlX1nU1BaiWyNdqF+skSEKbo//mZiCVNHGazqMCyLamyy9M5pTkHSrcQKoqpCpog+Vq0Yv+WNlvsEQhZ+HJFSvTnhM39L+ydqVW5encuNxuvP+YWat5gBSFudPPBzrwfFm43tZXpO5QDp26S3EEnCEX8wTb0mi18eHDe1r3elIDF5ginHxPYOjsqRQve3MDAHHwVOL88MDxfGI+HinTBgRZLjfqWnm5Lay10ZrVLv7w48WAP2N9m6xs1dbP1udrZ7amhJTCfJh5eDiOno9xrHWltsq6Xqh1YW1e6+rd7QmnyiMkde0DxPWnjs+XSEzJhIY4t6AUjJXC4sazWn2XaBno0ZwNVhwYt+p0RiHjsscJS09UtRygOKuEqBBch9mFWnFhJn3Qw258ozvPSL1MoGO5vKTZvRDZoQxw0OemCAHj/ZxskffuKFjfr0lwphrjuRRxi9UZIBKJmUKJhr2ayK0YAs53WIQdNk3Epoi2wD0gQyGOfBnxe2jMcPGNj7G7+O/CyI9AWF2v1arV0kgkUSdFJ+AIWhQO6kqwmYAT27qizRpgJpAm5CpIsvxgTsK0mqsf3m6aGMo+QtVRHhxhYUG89EfGsBDf1SY5ApU5wZfnTNKZv/yzX/P4cObvvvuOpdbRZLfrljke4j0GUBhdsSWZJxMRgTS8J1eYGpvfyayz1UiJ+LglYfVnWVvkLaoVOjejJFur0WR1J7u2OqsdsEn1/uYIIe05HYNQjteAHSjKla5bz5qtTKFotsL2UtDUaeEW9u752ULORosWQn+sqi53a2W37IZXbHNUTFl7PeAW6LF5is4RXTwSEvcfj5HACx5Hvp8uo89gI1DQbB5zK1ZiIzgDj71eZaXTmNJEI5lAJEq0BXHKtkC6hkFU0kRJZfOcMkNJChYCjU4RwxscotqjUl7rSiv0JVN8bRWxUHy0msLJrGMxRsQjZaOBm4pRfE1TMiW4imMHGtdb5XZrCMJhci5avKSkK7kYO406sK6ktBdpFjZt0X3BpIRizoeKUaOZ0W5GTMkms6wXpPUyzCmTc+F4OnM8HZmmeSP1VpC5U7w7Rc2JWg29ezvOrgSb3zPeWFfNmx7lNPd74G7phQPg8v1u/RPyT8d/+1IQVZO3KrLxFX/m+HyJxMGVRUyiHBDppLxYU0mOpGahJlKCaVOCUYl6k0pnHbduky7cnHzWuLY70o2YWIrdfLQTKZPYxmmTe4IwjAmG4jdyWjpNVuMlFStElZzpWWkppO8GgIgjz5jneBW0JubkPbtcWMwHEyCHZBapoeUEWqJQeGA2pT0lkhZKPZBKCRIPyyN6WC7qMQyMoi5/1N7kwkE0gjdjSdg3vQ93CQZC6pgXFol103+ys8X3Vr9AScgMeuxWu/MIzEo/VXrp9FQJ5hKwno1dlXSFXOGgRla7TIVcLXyjcW9JoEDyUqfwhA0H6EXkOIkum4c7giPDu+3DuU3Anz8lvj4faP/Zf8I37z/y7p/8E/rLixs/vk73Rbjsyknc8AklZh3c2+AtjfFN4QHmCcmJPFs97Ch6ncyIulJZO8gtk7Ki8ozSbcP1HcN9xY0Db7lV1Dliw2+Lm4t62GaAqxxIul0uJe7TQ3cjl1eyGw6JLJ188LKDyXJltVYkJco8GS/oUOabaFfTQLHEtmt6nlNSsNHYsSwXqzNzwRb9OEoy9LBhd3z1eOI+IPAhy1KsZwck7i8sChz85zaTOFnIGe+jmeGyXmg0UsmWd+3TJx3ErVRpQKQoqXCaHrY96fnIIOU2z7eMkHSWzMwEMjFYZkUoc2I6JFhmep1JB+NRPeTk4nIDYtmFY8cmighzGDmeh1RgrbCs+KKp/PDdCz9+f4HT4e6ZPi6NpW5KpFcLQU6T1eKuqwGEjKKssTyefb0Zs1Xtndotz9t7o9fV6mCzRW9KyUguSJ44lJk5T5wfHziejiPhEMcE9FI5JuitcauV2gytY3lDi4xcKlQHF7baWNfq050I9Ieth31k6/NHGnVtts72DcxtoZjSv6yvSzo+PT6rBOfiutY3XMRt824VSRJrC5OEkgo5eUG3dgKuYpgZdRLdRO+WVxFJtKs19GnFJnY+4QhOj9MXQ58uiyCaKMyWaPf8hGLJ8aVX0ERX5zkdW9wQgCaJxUK5fj8ymUDMozSnkLopc+iUyUKoJRXjAvVz7os2bVyyW8smXVQSiyQ+OKvOOSdST2QNF9Cp1lwBDj6VXVJnMDfs5i9yejJ+t3cSVjUyPtfvz7KdP/JBScjThBwzcurIobNOL5CdZ1HUQ0yWo0GVMtt40AWpYi1SspAPZUDyXR6b3Zm87YxmkipFPUeqrqTiWXSnsO4WrI7HAwOxfP1QmOXIf/4f/Dk/fnjm3//uO261cQ0P2c89ktCiXusoG5OaU7+I1/XFGEXoNpeNycOYMsR6GPp8tbaiWrlcq+nHvPhySwTprIgg3sjUGFYaSXycgp1dQNXaIUVoMQIKErUR/vTdSx+6M3z0eNhYCGqqNElGk1rMJCmlFKLO1vJfMp41Voa4YomoxTwVz4E6BZsjBKujWkULvQkrfSierXYW9xJ3+c+dVe963Nehz7inTAK8lbMvop5ItZB1JnktXMzBoRzpwbijivTVSwc6IzguiZJmawKQMzkVDuVka9/tmkGYI9v4bV5IdhasbIZMmo0gQSdoxYr3VdDm0YgUvqLHarrPXzMPP+UgGykEA4tFGApzgdO8TeaxVL56W8e2VQAVLrWxejcO7UJvRnFnbZ0618vFcRDG5tTakc319pnyiNlgmMneGzW58RXsUJqsjKSYE7BBEcIAU3oqZLEuMKk6EpeV2hrLItRuJR9JOmvuoFYHao7MJke7QurWF/ZyXcZaCnOq1pVaV1pfLGTuZv5eHm4TaX+x52uvZMqnx58olveQ5s6rsNj5vK1sT/ZNKXEUi4nntIXDZOd7qyrXJKzA+Zg5Hhw9R6d5rOCLL2Gawuo1mdI6fPjY0J4pnE3Ci6PfMOtHe0e60NsWthj/JUihBMVCCqgiB4XJvBVJQl6N2cFIvDvHCSfqNpaKSe3SdTdGJljKLrqV0JS55MQtWb3kVwFH77bR7L5N7GW3abbasv2k6t03n+I7xTbKOMRCblkSbWchxfuGXSpihbIpMR0fyI9HeKzosVGtAtDVntJ1MSHt156dI1YRehVSE6NOypN5Qn0dSXPFxpamaDVLe1ZTnMm/YsNvfur9g27hHVvpU4a/eJP59cOZL4//B7758SMv3z/zvl9BPZLk7XuM5h6CCVrH+Xx35a0AOBRwkB+Ukm0NRPdsT7i02JDtAk1ZVjunUfwlpslYSxAgJVLerHhROB5eKXgU7Su9LWZ4mLlB2yMZPEKw6rore4hA0/au+DfWoul5GTmicb34sTO88MiNpWye75uHR86HI6+P59sLa6tMYmTFLzRa72M/5KhnE9vX0jZFKDH+odf9e8fWUJBQD/7WLtAzWWemZhEoxNaUhX0P47G0W9ujphZ+SCgzYMnfM1MuPBwPZMlWy5uEaUfSvNK4abijuzly4NSUJmY5ME1P5DKTNcECerDQb6+2zFoWdERABXrxULOx0swzpFQo+ewh02znm84UCyQR1GSHw8ThUMa4RXJgVUOHXm/iitBaOf3w40dut4WP795ZB3vd5XbFENoJYRaTz8cJW/9lNsacw1i4hrptcL2uXK4ry21hua20Dq0L6rXdaTLvWw42EbPT582HK7U23n8orNXqCFap3CbjT9VqOe5uOZyxJntXltvK4t1I2HYE67rQ2mrGZFIOU2Ha91sce2CvEtU86z8RD/18TnC2Ra0OfCBt9npczjMeFMnOlL8pMRFhcnh2bULrULQjSVkbVFVOR3t/c4LCKZuu2m67IqKcDglV98YadMlEYk2KUHVFqsJibDa5JCQ3M2hKx6ouBTJkrYhW65JRBJ3Nij2qIb9atw02Z6+f8jBbEkuIT0SJeaZYoyErks+T5w2dXUG7WWsEPx8juhxnaBJMNDuXh50QHAMxYG7DmxlBRMEVj3upmylrFrX4OX1HaTXY0tyOFN4Q7Y0mOsJM4z2qC71Z/E+TCQgZqG/fXKVgTStxMA6GYKyucvvAuZhy6Z6heaUL9hCKu5BLWHnxOBHuFHhzmODpzH/6H/8Dfvz4wl9/+wOXZeX9y80Exm4/qO8yC5taJ3PxIivx3JthBrx3ZcnkkpFiSrDSNnQn3WK9foOC5ZEBCwX5GCd1eeaSfzD+eDRF6d6doFm+OOSvbrvLcrw2yyUVA5+04J78NHA0FA1mbEXhOGN8JQZ8vN/e6x7gPDPPE/M0e0NTGd6bKhx0pjQrG+jdOnnU3mh1dWVnz5HFiuVT1Z03uCECR64MUK/l9fgDs07MdWLuZ6Z+JMnBDC336kdUuMgIEavAnJ3B0rvbS9RSeg3gp96C/00sTTGR7uZYValqIcsyCT1HgFe2c+zP5/lk7UJPLpvU8yE9oU1Iq9c1S+TP7f1aQY0Z3vZL3lhUtmi9jLUhwFQc6dwTWpQv3hxZ14kpQa2V1VuzLbcFAy9Z6mSW7RqBxB5SfSc3RKCkzLFY2dIxzwZeQqwxdldqs9KJWFA1bQAnEeF0TOQ1cV3D2PXncNBWKPc4xhLyeejdeHiVSuvVCeMtF7p6mUZ8enAHNxl6QeEnGJk+PT5PoD3nXf5NeLWSfDnbAiriFFKTUGaIQidrDZW4rolehYmVQrU10uHxbA/dmC2IsbtGdGZIojwcwnz0MECxDvOQSavQuJEXtbq8KRnc3y0zW58aySkSlSyLN8sU9AhrcbaFLiyrxdkPIbDXiD/Zl9WwCDOzJ94tf3DI510GwlwTq3bxBsEmgy2nA95DMdq17gZVidWw/bzLegcoAoQkXigaIVaHy2wKMLHFHf3OmhpxcDuR+ZJBjA1kjlQMjWZtbioi1nF6EHe6dpKpDPKCsOZkn1uK2sQMSb3jWZQ2vDK67wvGx3bZnWxzIJIIb48z53niH/8f/xHfvf9A++c3fny+8HK7GUfq3n7wa6k4erg3trv2MGiyWrGSDKKdp0KaTAmuq4d5uhe8yeauWxjY8nCtdp+XbH3RYraCRAJcuWRDR7ZqTahVBl2LPaOgamGu6IOXcyGrNcHtqiy6z3XsxksYa2MQA4zXPYUQA7lTuillTocDD+cHJwYwo1Z2daOClSTofDAFXjK1VtrthvZglgEKhkj00Og21d2Us5bd5Fju04JmlWN/5NAeOPQzBz044cP29hR2RvJ787TJYbo3riLSlgujdm4cMVBeW5cUihbvKrO699aprdBS4SAwpbtd+upIZiw6WK45d2aAVkx/W22eOvAOT0moCq16TqtANLp9fctx4+LGbMmuyNQiBaf5jCocD7Ojk1fWtfLhwwdnyrFrHPx2hx2ykyuvj5KM9YsSff8MJLQuN1qtLOvNCAt8fivQVKjNlfzJImHvnzfZGUbs3mAb0QkxOWBdTprnz1dUVxyJYQBjgaV2i3bw6hz7daViLu3Pzps/5+denMSaoPbcGAW3w2qwhZjITBSmyQm2c/KO825hOymqlk6und6StUhRE4xNC6rCMZUh/Lp2LotbMMOa3iDLqOwYpIz5gX6iT0KfE1VWbrKg4gz9u5oUHLGYMAaZlK2YuqCkMiFk+nQADCAjGPdnbLBW4eUHCCaEnDOH44MxXByi4DTKrztrEj4kE2zHYUgo0X8uFnYwsQ8mfh2Pt60QGGCOYPDfGfa7D+jmReFVJL1zXSvX1aUIiY/yDdP7hS//8pecv3pkmt8wlaPF8PWZxg8oN7+zDqzm3cuJniY0H4gcWMCTJXWkVGfZSBzWxOMiHFW8rxq7r/ubl1CAXps5cng7T9HeZ3/NCd4eMtPTif/if/cXfPPuI7fWeFka7259xyLhXqYboTlBVh2bcCrW+SNFLjAHgMNCqwkLd6oLrd3E2P36HO0NIO2N23IjOkeUXMhlHu/ozRrcIs2fN8pJbN80Cn38tjWEFTfAsnQ2ojjPWdqPlp/PxRlxPGQk3k+vrmMCxLVhTpOVImmx/JaMgR97UncTF4r6UCZKytxa8t6ELie61UxO2ZHbGhg+g0AYYbSNZfLiKhd/RoSdLWWgtRm3rytTNMgMFNZOcmGLbHWeKclYrXGNptb2KCUnbuhieTBMMYWISFNiytlqFGvANRKrG3d1reR+Y2pm8NdmCq3PRjG2KB4WD/kIwwTp6p0rTFFKVnIFSY0sN3q3+8xrJk+ZtTamqTF68zlKSYcc8FC+bJEMVXh4OBqFYTclMk3ZqPScraB0A8Rc665jrx+JTJEZdf2x9kZtQYO97cT4zQi2I/3ir6qdx6oFJqaSePtw5DBXalcWqfTV8ghjnnfRpYH2HEckSSx6Eg0LhhH6yVPEHzek/J86Pg+M4UCXTitXo5/ce5UiSDFanSIz80E4nbA4s0OQAcgdrZ1UF+uyvSSr88GW2MqBTuacbSugxnqx1IWmSil4S6XJwQIectg9cUmZxInAg13qC2290Zy1IRBqoYOMfyYzpeKkr1aYOZXZuxGfLKmeF0Qak1jHaYDlCv29hXZJUErmdH4waPHkGxQskdmNff99Viw9HQX+5llukyTmvbnA2FrehAtklqkgA4Y+rFnkLr9mCzE+zzhPa8rLdeHH55tbwBl9/haR9xwezzycnpjyW1IxNpbEiRtXzz0JMtxAYZIHNE1UHFYLAzZPquSp0bJpm6Mmvli8BMCBQXsAKLpZh6MtUTyablvjzjf0ucxJeJMLT3PmF2/+Ib/7/h2/++FHfni+8VxtzUZ5HVg4ckqM0FCcquTE8TAZIiMQfp6Ux+HZiUxPddzPdijSlUFo7X9T7SzLYq+LcpwPsFOC2jptWUm5Wjsx3QSLkFHxIk42wWAB72ZKVeruagZQCq2eknCco5loGutlAFu6W8ciiGZKnpinA4IBXpJ7CnHt+0PHkjxMs3mqdTJvowUXqsU+5pxHaCtUYBxdrVwnUQwJHqCUkklTtivXBpPQ3NPeEw0k6e5N5Q04IhBkmtHqsruntK4G6ppdwFtkwuRXTFyiWCpGzfMPg3Xthha/1RXpyqFZ5Gu9uRJUI4aQZkr4rBYKn1weZLU9WB0MotW99KKkVMm5kpuwVEvj5CUxHSZKK17aUqwlkNeqJolQtxe0y+b1l0Masrf1zjxN9N6p1UN665VlXbl9+FQJZskc5DgYi17ajZp2luSrpZ88nGCRqL77eyaT0KTI1Hj72Dkulcu1k1RYff+2bMbTaJe2A1ntQ88uHVG87Cztd9ouYvTJ8ffTgp9Vgsf5DU0rfXHWAhcOJWOJ3ZQ5ppk305mcjYk90E5BuqpTI+XGkl+s5ifdkFo5NKV0uwEV5TSZdYWHl96kE603q9cTkHm2sJK/R6MUoEGtQr46EbdmZDmQ9A1dLzR94abC0o3abMoJyROSuyHEOiCJlg1xlpM10kxJtubB2ZTQ2uyGv/gzkJaZ6gMpT0zz5EajgocOW1Xa0oji/FUTV4wxZUJQ55IK2jDLGVr/LFMANoHBlSl0V17hybmWwODY6uwuPewyVUMdauO6NC7XxnVtm550T0fp/PCHb6jXK//gP/8PeHx8yyn/BV1WyvsDt/WCJqcv0op2ZRHb+K0XF8vuwXIDbWRd0Sb0lpilIbLadZtuChAG5ymxkCNxeq9jCIFt3++B2sHtKMDbhwf+b//JP+K2Nj7cKi9r4/uXhefLlR/ff2BdV5ZlGbmQXDKpTEzz7CUt2QWrX2KwgRjCLGXLgaqa52Ls+6HEN+Tlz2YgHPxFglQK08Ebgua60ce5QlhaojUxsEUSA5n0jpYZukGqtjC3oN6ZoeN5x1Fe4f6pWJG4pThcsHWwiIZ5Fb2rw9eNJ7J6eLeqhex7rQT/LRjPKx5VMcvehL1Is/HwugYL++ld/kjdKLJPb+wkliMM1K6Mte4rfSyNWxfzqLp5BuIY+VTdi5fts/jrImLUa9pZtFKKGwLjCwtpeigYL3PobbW9LxVSR6ZMS4meMqkn2sUAGyoW//8h3yDyfgI7DM4wZI2+bmKa4HBicH7mbPnow2Finov3bxWr05snTvORqTgZXGpEE+Xs9VgykEW2NAzMZj0OVTM6n8jtQD4eTIY6gUFKtrcEk6kD7bRZ0p5fkztjZIvoOEdut9IcDYBcx2vJjQGna+Z4OlNrg9uNJsqqfl5XiPZsUZLjaS81ZTu6fERccmcX38mOkKfDQfj54/Po0PmR1lZqvSJiKLCU4DAzch/HPPNwOJryE2NbN7qhCU0zsJKwztu9YQsldebVEpzZb/Ewb/x9XRUtR2PYWIyqTQ6TlSvkSLA3QzGtxreHZLcuM1kOzG2iaadxRVbLERxS4ZSLkVhOCr0aobHTmU3lYIW02azh82myjTJZor8vnTRZ6UhuM+f2hgiZWG1Ut9ICgXXp1nvBPZ3WJ67qBf8q3gfQC46HIvx0svZ/M17GHdBlTLw61dkA2o/Ptt64LZ0PLwbD3xZIWPmd9999x/X9e/7iP/0HnM4nDocvEIG6Jm7ZkJdq6VkajZf0kabOF4nRqBlU7Ypop1B9UQrIgsizqUrtg/5LYCivTafp5v3p/WMyQCWvxscFZQKezmf+8390Hu/58bLw1z88892P7/jrv+t8fLny44ctkD5N2QyYqTh8zt3E6OQR4+/chUnKztFWJFkoU7SOgmt9fYNsslhFvWmIGvjGCHaR3Ci9ey2ljE1fFSAbnVb3ccrFCWk9qZfiApOXIP20ChYszJvz5o3fK0P7vXq4Z8+2sTips+X0gurMlNwoNE8YVb3auEgSynE2rtXJOiK0vkNgDmG1V9hm0PYWRp4rP7Vc9x5A1XQXmhYrnxHEvDERpkBk4eo121qzutLO6s9nZQHGnWnhVMvfWkGi9yZ0pphIigtKLlZQnwSWZr0LOkbnd4OdsTeaCt0dRRIzM4cTPLyJdSKei82cjhPHwzTKCZ+eHjmdT1bAnxLaKqkbKMgaF3iKJHK+vizmElNsSZfuyP6Tz3GPEF9Xqq6srPQbzrzjJwmHowcBxKuH8ehVW61biXr4RZvX8DYLgZsiLtTjibyu1NVC8y1HMb04xJth2Ahlq2N1dL3pnq3MKAJjezFit9UZ1FSfOT6rBL98+xVNG8daqFp5bhfMKYXExMSZ2S2aEVyKuHVyl93DmKteAHF2GbNYU1OmMiOSmSdPGPuTTMWd4uPBN392q9KKFDoXtEAriekglGOCPqH9iJ6U/gS358Ll44kywUOG00PicEyQr5BWWvWC/VJAE9OxWL/EYsnp8qBI9u4S2r1zdKIdj9ZS5aMXKksQFNutdjGCZYDsqLAuiRuGNjVrxjoiSygMNaRhUcfJOaIp7RG+r3eShxRV3cDoDAusdmXpnUvt3LTvwKU2N5vZ1AhQzfF44unxLTlb2O7LNwvLcuV6uXl4yBT1oRkwYmkGZW59NfStHIdi1bTS9YUuE01P0G7wXMNoNEow7tjj9uv3Ez7xeKEPA/9+UCQs/uElJ85z4i++LLw9n/jyi7d8vF55//LCZelcbkrr4lHrhKbEXXd6F4Sa4Oicn61vwi0l4XD0psquGEQyKenWzsZH17oCQF8ZoclUnM9TElkmjiUz7whpP7wot0U5kDziYVZ76zdHu/mNiBOJe2PW5DyOEbpWxEEknkNSU8IDcBLlLKNNUhT0M563Rj7SwY5Z7OyLo383HeaI8GLK73CYyDkzl4k8JebTPObN6vu6tRCqDK9kXTvr2lmWPqi3RCyHPqwj3eY9AuXNkblJmhtXlp9POY1xuF9SyZGZQjokpjlQ3YWE0kTp3aj5Usn0DFr7GC/1mEtUQ39q+vzEmgZTUk3pCciN1oXbYqj6kn9eWC8VuCkiN263aoTXKfFwOFFK8X6RidSc2WnUkN6Xigd9HHjZUFYrmdJuHdQcjaxZvb6ycF1uLOvqhoCyUVWa4V39y6jMhmM4nMTOyPC4wQJSEqfz0VCsi5dWNYWevarBwurh0QcEImqxfce/EhQ6FKhFlRKpTD/pXOyPzyrBx4c3dBoTwtoXWN1CrFA4cOQNORaBS4ctRWosMlbgmpGePSFsSyZ1y9XJNCG5ULJGY2J7OGdZgNmF07b4VFYaC6rGldfVSJlpGdpW3/SsE/3lZIioDMcjzA9xXljFwUMcQQr5BKlYfWRK0I6K5oYVBTVmQHtmnc6wJHhxTyGsDYlNaTMtSUiL1dN1ErVbjWRRawacVZHu+UZJQPeckGV/VH2OY1DDo9j9ai6ouAL0deDhq7V3rt24XwwN5iEnN1ZCjAToaZ4OnA9nEJPij6c3rNMBrR+oVIK3KtWjAW200rWhXOyG8r6y54XWr1Qyrc1oNm0e/K5BdpuEwC3FMibyhFsJ/VhUO/kXruL2jgB8B/ztUArzNPP0+MDbX3zJy3Ll/fXC++fOj8+d6814GZvuu5fsjqPcxbLqVppKTsLT05G0Cz+BEfmejhvopqpyUeV6Wfn47rp7nxVwz46uPh8mjrPhSVW93GJ3N927hSxV6BrerGL9VyzfpKreZSI8Jh8LsWJvWy6bOtDuy4c2vFsz6ruV0ewYiCAAQjKqbqramE+uHBWb13SYySlZ942UOZSJ+Tjz9NXDyFf13mi9UldrdtubKcTbrVJypffVQRmmzEOw/Zw8M2ddIXVbN5oGKxFiIWrRV1QMbjwK1v9SQpqph55twCzvqLG/ItZitbRRNbclJn/6iCUeBg0omju9J+rqK/1V2dtoZKxWlrAsirCwZpcfKTGniQCTiZjnajWiyv3mgUEqvwsxd9nVgKtdC+c2Tp4zbb1ZvZ+TtDPCISacIgnT1CJu46UwXHVXs4jl4yUn9DBBTWTt3qC5o80CHYaKDoWQhsHM8KvzLp0yXD97+y6tIql81kCBP1UnWJ7oNGZNFKlkfbAbOBgkOPUZ6Q1pC0bj0kZ4M00HZDrZWAg8lomTfsXz9Rtu6wdDbXawzrxpd83seT8fMjH0aLRONRGbPXTkyoIZlbMntrdHejxWfvGLFdoB6QfywZScsQ5XA980Ox8iTA/dumW4JdWmF6xG5a0tSA/B1LUYgupraw1zuS27zTmBnnxOlJ4sSJnWBKsQLYPwcVEHkNgJxNtrJBIW9tWsaNoa1m6KYQMHJ4RDSiYOfSGvXZkEzpi+ZsIJikNAwgB+xDIIhet0GvP8SM5H2qxUqbTV0X+TkRPMYue8NW+UtkdOddCroNeOPi/opRoowMeFHrlNv7RJ0HFnewUoMDoWbLVM9k8sk0HGQyAobWQgkQXOCeZ54lyEL2b4+tFh2L2x48Whoyyr0z85i0rOB0S2xqZg8PzzlxaiN8OkAzcDQuRuSNQEPXVablw/Jt5/Y13WW8MZSCZO6cApHSwX5G1NFOXxbF0oxnA6XUpw6wqZ3pXn60Ktlevlmdoal2Wl9c5t9XZN0QszlA8+ziHYAV1WtK0EKa8MT9IXxTBQys5Ms4a07GZpUMxFXRKvv4/ZHH9PWZhmHOqfOB4sOvP0dKW1levlxrquRtLsVIuD8Snuf5zXhaMLWdkBKCJlodF7yAU2qnAzIyBn8+Jbc1ybz/VdN2MYjEvzZBiFyFKFlzUH36ifv6+bV2TADvXndg+wyCAJUO1oU5YbtG4coyULyoXSV+BIy5MBfJLy4eMzOWVOx0LJidInzzkaPV6aPC/7iSpQN4DG7BFmMj6PBYuIUc4cjgcuL1eW22ocAL1T10TrnV6vFK2oZmpTlutKa2qNwFXpvt7O04maGi9c6d3C1tlN/taUZe3U1DbnpHq9L5EF1OH535szQdAiFrpFRgpj387q547PKsGUD5bjcbe35LPHQ8oWgnNE2N7sSFi5QZ6OEbmlYKGypT6ztEuUnGB9rX35CIaESlHEZIs6vCJXr4x8yBiDI6QvnAmC8Zqcqll29QFpD5sR4Y0/l3qhNkNJiSjzQydP1mkAOl1WlMRaJxeA5nGtqqYEMyxrZe11WG2ihaSbN1rV+seJRXGJmwjbSIP0LqR4D/EtQ4n+3BGb38h7zWqqIohYSCSJMGPWXvVx6QOd6ecdbOSY4nKkI5Io+UCSiblcyWos7l07q2+golbsL+qsN7uQGh10BV06el3RtXlBv4fCiYUsI4fxal2Pb/F1nwXfvuv+je7RpdjekshYlGEqiRMzp0l47KG43HMV/wJerjdnurATT9OD5bvHYU10D18Z2YLdR3CtOjhfPIKfO0wrl0PiWI3lw049ATMP+cxDOXlBeABA4HzoG9GwgHrT3jIZhZ/oRGvw48cry7LyLMJSV0gLa2tUXRyYYPnNcFQSQJQt+ZD1qliBpxteQ24YMGZMS4Ddxk1tHkXIBXt/8q+9wttD1oMIwIB0uQRK1L1VVdAJ7Tc+JLhehRqcq3o31TthuI8CeH5shMZDWUbI2z5tpUNqDM9NmWYrObAwuYyolLKBZ/AcYkqJqSTHMaS7e5gkD6NDdwq16WbipQzZI04bwxZEtMQK0YHJxs0Q8g2RQldrL4Aql2Zh0JwnVJ2kwwvoAoEaqYJPjt3fBm+Tox9FMrk4g09KlGzAF+2dltxLIyNNmVlJnsOWptTFew+6x5jFxvVQJrIklr6iqaPNcpldlFU8BSRmCKB4s4OYO3+OnYc5fnAQUhL/TDgUn3PNd8dnlWAdF5mRbKgvJJHz7FZVN8RRt1De1tgGyvTIND+O7tZdV7o23uhfcJx+OdIuzQdrkmSYhBm3dhdUO3WxZ4rmmMk7FycX9JKN2DvLI1ZcvA1OVAylUgyBlldE6hDUp35yZWSKliKuFBag0vWthRd8XAOXMsW4FOiz8nho1Fq5Xa+MRL/nRvXQ0HOFtUPtpGZE4Xq70tcVbQm6p9RUDJTRt4mMdZp9oUZeevTdkxD5ds0iiopQHH3S3LArfu+IjmJ5U1ZKr2bbv/vmR759/J4vfvkr5nNBk+VJptMTabI8LK1SGih1rJC4x6YWSmt95bZc+fj8jL7c0A9Xg7uPdkCK+s6PhT6SfYHIFBefeYN8A5vCdK9vpyH9DfZzJ3RmH5tb/PNTwgWvITF1mmGamJ7ekI8nWrO1Wqt1917XZcd879cSIc15KImulaUpSqWz+MXt/IVCPmVOfzZRZGaS01az6HnB1bvPx7atO+5NxcBfvcfegFpnUoc3Ams17sZpXdH0wlobGaO0qrNa6DlvW906CdgYNQUtK/3gxqx2btU7ZHhtmVgbAAe/2T3IpgNphG/qIDHFS3Ka83tbQ2H97ma5yWzlG0IaWKThMH5yWBlOKolCo2mFFuC4EI4+sSKkEuVZW+2geli0I0blH4Zfx4j7k5KzMi2dua/GIZwyRQpZEofHR6Zj4XSYmHIajY+P2RGgHg0IHyUPzuRuDXSfxTvcGPXYbQWRhqSVHGInvvypDH2tXBcb69uyklJlypWSE7epGDlJtvwr/UCZMsdTp+TMYTqQNZFuICk5VaIgg5IycvKbQW5CfcuhRrYzoyTpHKcDCXHGos46Wx7vqAXtjePlRq2NZZqcUcZRovWIqjrlW2dVi6ypwlIrzy8XLreVH58vHiZfud0q18vqLD7KTTuLWp/O3iLitcmBYEscYsBregN5/7njs0pwbHuxoTI5m0nWBAyVNtzT4Ur4MZUj83T0AdPBOzdPb4y9TGyom1r7mckZ5nXqpggwpnptfTyEbZqjo6jM0slFSDJT5MHKYEb51PbkYW1FGGM7wmotKHkwu3ReQBvSbfK8lnnoppR9e0Xsd4J1XUnteUvCuqAxiFSFuUFtqKOnGpUuDV3MPk+RSY5eQ1GjOu7U/h3ZIF/EYeGrJLe6El10WEahV7JgCN1uXq+gRCEzWulNuDxfeH73wuOblWk+wGRCPpUDSCGViiIktTxUlAWElaxigro366N3XVdkqchtxWNYZvx0JYitd6zW27T5n0wwbHOm92agF3Xrvf7bH7rlvwwIYsAQa8qMN0/NcDgihyOnt18yPz5hmfnGbV1prfLy8mFEDO5Pb0LCHi3Tdba6tDF51mcycaCUmfR44pDPnPIjOXWyNAQDFy21Du9TVaN1aDwGt4vVmEX+TjUjCQ5SyBnmwxFJmUOtJLEO5q072YR3CdjvjO5fVbHyilSwYvdOlU5NiowSHkdFBvWZeMNsl6dbhH9DbJrRaOK1dEXXTmpGY5gmZ9BJyZjFss2t7qIeo2EqydZeNlJwWcVzX7pTPNsmCdLvMCHtbW6QyrZQFB+AZr32qoTn0qyXhHg8RhLzYeb0cOTN09HqSf044CnjicHwYgyEye3LSquGBO3dehzWpsgCRk7fnC9ne4648wj5hjhpzViOeqq0BL0W792p3vJIKM3qLJsWknv6c0vm7YamZfs+PCtgNAXQzfRWL2EJQ7LkhGoxhe7MST0rE8lyqGujIUzF7n1dHSXTCt3N5tjBKja/y7Ja6Vq5sTYjk9eeuEhCGk6fZkbYxgtsivHOIwyFGLIkjGQ+RZS/Pj6rBGNh4WGGnKwUIsAA9iAtUtf2miRP2s4IBUlK8gWBVLL16fTWKEKRs5/bhr557VpX64t2ODgyjGLWixjqK0ddjf8tyWyJe8GFRKSvLWFssPd9KHXzYSwcmwjibXjCarlMqffVFkTT5NbdjVidgiWUD6Vzmt+aR7jehi9qSeNG0ZVJV9ptpa8VnWdjFXn3nnq5ot6UFMmmLAitu0fFbErPhI/ShF1g38awaOKcJm5JaNIsNKo2BAnvDI+FdZuZ7SSFdz9+TzkcyEl4fHrk6Ve/pBwP5s+kTDmdSFpZq6K9Mh2UrA2phdY667Xz/PyRb7/7nnq50ReFakTK0hqpVTxOM9COTBiAKgIyIpELH9axHX084244iJW/+UyvLIc7fLq/HuN1mOFw5M2vf83DV19RjkfyNNHqQu+NeTVP8Hx+sHnXgOKaEPh4vXi3eEAnDn2itcq6XK0Wra1Iy7CciABtP2CJ2p/caPu1ef9808HWYG+zUW9lMzhui5G9f/HmYAg+NYMMruYJYoJMPIKyudgy0rjqNYitrfTWKLMhWuu60lrjmsyI3UKP4a07GcAAxrhcGNJIqF1YVqgJ2gSpudBJnZRWUk+kagjWlDZDI7giq0IviZQmDmqKpNVmvST7NlYRMdiIJOylyMPJdttYucYGPErdvN4lWe5V+oqsnXlWptI56n1O8O97WPG/cH70bjyaaU2Zb9YMQFuitUpdl//N5zYDwyDHVZqVm2Vjh0k58TzdKKXw1dtHplI4sAeI2LhpVwfphIGJK0Fj00EZgLvheX2yPj93xDrou+8MnSJYT8XH85lpniiz8bdWGuutcrusY989r51LVT5+/MjlcuV6rdS1jZsbnYP8EsMwy+lP3u6fUIKboLdQyDRCUyGs7PF2ie5kLUsMjRZoRECC7stCeEEQm8RKKAKEp0GMKlaTZJEFR3YgY0GHpxPNMMXgTKOvnjlWTnjsVqtZgm6ph8ch24bdlKBRuAViXd2gSRQP92ETqvdydiqw5PWueUezRBvFUYBJhYYYnYQq9XI1+qww+dQAI9rVNFe791zt285iHuHEMVEeXspkaQTGKPtbuoihRWW/+I1i7Ha9cHn+wMuH9yRVTl+8JXvHaRDyZHQbSYrpZpkM0JCVXNWs6nRlWTt9tQR/UA2J9h0YZrcqfU+FbA7vDwnremfxxT2/UoTbk8Q87kdsk4iymxdUjBThcOTw+MjD27ekYq3B6gq9eei/d5hmQF3hqVuijaVXWm8WWnZF2FNDaqL2arV1mqEbMEQhCEV3j7+5U3oXAog79f6B2e65aTGP1j371TupHw7ZyxKMVKJW20eGuvdYowAOmJAi9OZ5/ezlFy6UkxtHK9DEow99i/lwJwy7tz5jUzrxTO6RNUehRpAjIISCkNSb8Y58gx2tNwem2MJIyWoQS7GNm4LM3NfMPmz+SfgrHIVYL46kjaN7FEbwAL9TAJItXL86j2Wrjeq1zAI0L+kKKtl9GDbMaxGhzIJxi+JgERnE2auA9jrOqX6zCaye2P9LGpEfDYcNVJxE2oLwvVkZacoJaqdMhdvDgS5CSgZpys6E032yZFcPqj54wysNg69HA1/dSkTcU23d1gC+NwZXrM/dRqweAvd+ckSsq72KcpaZplZuUVJmco7drh2WjizdeUtX2moI5vBh7x7Ct0/skzuB8BPHn1SCgm2Y4RDuj2QKMHME97x6txCnRqzfN0SSDPkwFPMorKewdWKG1BtCt957O0aMECJDHo6AhyWDUWc7wDxSBFOMYi2BLIxkXkjUEqRR4JFNm3gPtyT3zyuTDBVJTzROe9PINquDaaZcSPO8uf3eKkeYQZVcVvRYPVveKelEe1O5vH/HervSb+8Niw9u1aS7MgnBQopddoD+sBG6hY5M6jQixp+cAaJno4fb1mEgAW0jXX78FlluSFc+PD2hAueHR978+mvyPDMnK/WQNNMlmzLvjUwxcmmuHPOBt+dfcL19z4d33yLrYsADH3Px8HLJ1oQ1hYSKPbLhKGyIBzjENGVzBZmi1vFulfvPCcvbhGfw+hBjDXr761/xxT/8B5xPjxyPJ49smFDu3WqU+oDps1kcPveHebbQZdgwvbPWlY/yjrWucN2Qhq+P7t3LtTcvUg8gCb42JxNaxQzH7q+PBsYuAEMY1V6YjhPTYTbh1DprrXx4vvrN6RgiJbq/u/LdCazRp7MrH283ruvK8XYzYAoRMvS7bEabtqzRVWD1fRc70/mAvQdibQqpsaYNrFNyYpoK6NZNQ4MleTd3QUJ+PJmRm7LVBo5+hjvFrLKBA/8+TsvYXornym3/rL3RloXfrwvf/jFzPp+Zp4k5ZbIzSpUpcTgWckk8nk/eSceVn8uS4rnU5GjYfDy4Zyoe/mukbEjMKDi4rsKtYhV4WulVd167Ik1ccQdjkYVO1tVyuF1BUmddf3AGnDM5J04Hz+uKUqaJ8/k0xsHWTMgHwZrv2EZUhdttMUYhF30fLi8sa3DRQl2tm0giSCV0N8KOGBE2pwSh9sq1Xsc8bkcEiZ1Z3s9xPMxkUU7lSKudW12orXFdVicKTzvXS/8+0/95JShhov+ENo1apGCOUV844EzfGi0sNuSUuDVqCsaUYNBE3V0zXr8bGNnWtMc2IrUbr4937RBgFiIUJ+11Zncsf2YKcePfG8gydg5LGLVxhSSWP1HLmWxWT3f5a0p4KMGmxuYSOlMyZG+13DvCRJk669rokhG9GatIr+Oc+6nc9vXu37hhhY21t1ki3MsvbBoD+ckANWzTqtTlxiKJy8tHVISXjx8RhfOXbx1y7ptZPFQiBjvPYQE6G0xWMcW7mgYQDcs/5i1AIdtc7ufrzlrcZC4h4O6P3eIculDCArs71B86uaEynx84v3nLnCdyLkSj0eytknJKuzU48HPjpkoy1HBLoRc7OSXWeTbgRFdqM2s/7rOU7H33dCgda5QbyshyMEaHlUbtLMmVpEdMuyf9SzEKO7xYvhQTQB3rs2l5bPdu/LB84E4JxlPFozp6uichTcVqZmuluhKMdEOvyfKUrVFRZzRyuUAAj2L4ZHzOpbevA6FrN+8o8uLKnfEywuSYR5wztNpI0jblNZRgH97n9vfd4lD1UPDwu3amB5s3pI6GVLg6I0yt1qT4kAolJdY2k6fEsRamyTzxUjJ5ssbjXTI50K8SpNKJQ8lD3hjqWMmOb+jiTWgXSIvQWSyXuFpO2FhZ+jaRURwcXmjHnRGTHRdpiFjNds7JuGF9Tc1zJ5cynr7WzrK2MR6lCaVtNGnX63qnBF8uN5ZdKDeaRWd9NaL+a9+FAxSwRs2dWw2woqs8DQjB69yoOhl9Jk2M8jMEUm0eSNhkv3wqLH7y+LwSLPEAycc8KtUiC2gKyEXLtnAVahccg2wQei1GNCs4lNVdxWwuq7plEzVGY7PEvZB219qHvOKhTbkZ2TG+KCwU2voMWrBgR0U90U8vrjRc+e02nirWn3B/GWtpQY5EvguirEoo0I657+LCXfM0eB0RiNYGQVmVfA0/PbxFa6Vdv6evFz7+3e8sV8jNgSQgkcPD85z2U9iCpvAlQTZjJCUrneipoakhFUNCOAI1gD1R5VT7ym0VPnz7Ddf3H0jA6eGRVeB4PvPwmy9IU7H76d0kTe3wEZZr4w/fP/P8w/d892/+Ff12g3ojuCaTK+nRdNkd8M3gt/Byb8ay/1pBh3n0elnr9rL9EwvMrzeizGGASOH85dc8/ebXPH31S0qysh9L2jtxMyDSKamTEaq2kJ2+FOyCksyAMr1j3tB0mJhPR38wJ3zXTeBqM2F2vd24XK/c1LsLjHygGXZbPZ6HQ707gkg8i737MEcebjBej2+9K09vj7s/ds+xeESym5G2tu41eFYHqs206PlsIIh1eaC3znU1a/vigYzbslBbBT5SW0OYfXkHttC7Ecez031afto+jxSLNTcuXutWnDh624YicHBlb8s+cZwP1rV9MkVaK8M77b1R6+LE/FYDuqxiDYFbA6epi//C8eiBHvJd9sIVScL7Zgq9v7e1VpJhFE7HQimZ8+lAyZnH6cRhKnz59pHjPPHV09Mn63c/Z7uRIOG8ykxANvqzAi1ZvS3Fxnld6yAo91j0T4xuA15oPfOyHMY4v6w33l+rNeFdDIbffb8KICvIqixLN9TnsrDWSkvFIkHSUFFj2ArKNmBpF0eFeh7J92I0TghMS/edFMrTJHanah0GonWUr9a3sln/zR3+Mu6UuUyUZIw1Ed7WIS8/f3xeCaYxHG41huXUN6LWTdGzWeQ7AeSbzTZHGihc1WRhEg+ZWjhpd/GRzebV2XfbYRh3oRq3v0vcB7GkxJV3FLIaUEZ1K/7ddPhOcuruXEnGfbmtMcZgu8dQpjquP5ImAkY95npXGWjtKRVUG7lU2jJRju88T1Ot9592N/p2xsHd+Pg8ifcT9FrLyKVI6gMVOTg4NZ5ZXD8bYKAuC3S4Pj+jwPHdO2pdKV+eKK64e6u8fHxPWxrtXeVyu/Hu3Xuu795z/fgRaZXUfZmP4QghxxZgEBgUT/HGGFPZzfXPHTsFKD4920vb/BiLiCmYNB2YHp7I09Gbzu68ACCgfuIIHWOg2G8mX1cDEugjKGYM5WIh/lTm4cHYNZS6NOrNcky3bEAykU0Ix11E812R6AKx5TfU16AKwwzVXRQDGIXks+42qCrqnK/NlV1vylIbpTVqFePrTaYci5vsk2R6MzRwbdY3sDalZvO6cnY4ukRfgZiUtFuq8XQm3OLvZu0nRw2bsTSIQoiavK12IoLGuWxzkJJwOhysH6TnXNuKwfGbPdO6dG96DClZTWeKfLua0jcDNqi6/Dn2osBBUK3ijV1BU9BIwlrNE6y1G8n1QViniflgEYPbyYyMQRce5ARYSUTH2H66RA4utoCFVRXAUbtxT9HYAPf8htXn6YNtOzjIL7zteKFV6rqy3m77HWB7c1F06Sy3xrI0ltWMnpommmTDKmZ7guQk6Ypy88bPGkrQ9UekDaxSams2Hk1vTQZZX0m7F2OqqdUVfUQXOzvcR+yXZL02PXcd7EAbH87PH5/PCSaHsXmt37oEM4qSc2KesZoj722Vhkc2YBhjtMcGxoEhfadAhuxTtHl3hRyx7rgZvfuuwzMUC80GR55au44UytD1nXnNzpavXgEZwmSX2zPlYJZJXDGRhzL3PxDF7ap4u6HXOmn33OrJf4QWCz/yVR6OZfKrzTPaK1km1ssz7/72t9TrC/3yYmNDH8bIvepwzVKwc3ZB2krqDrQQA33k1Fk1sbZET05o7QLUvAPb5bV3+Ob3lO8LH//4DdPxzG9aYzqfETLv3/3If/tf/Zd8ePeB7//wjpKFL97OHObM42liQjntktbBdP8avRc7fdgLr75vf0h3SvET9eiK0MDc90qzk+kUkkwkZjpvWPma9gqlMpahX6vnRJdPmfO3H3V3L3/a4twfKQvTwcBmpSTWpbNWpScrnZnKZMTOKZSgl6tkQ0mr/31yRbz6TU05j10Y846IoeT85jtGlN2bjmbBXRvvPrzw8eVCXyp9jVIMG8GelaLNCJtX8xaidjUoEbND4wdPtn9Td6kU63ZeWxujtdSMrI3ijDnF87m9KCV3DppH3R3sZ97G/nA4WqeXVwsiT5aayP1Ab82GoJkx2LVzPFR669TV8oq9w1oX1rqg1vtoy486RHJrXvXpYR6NUmvj5bqQU6OvME0LC5W5TLz7eGPOmXOZx1JOYnnelC1sOs+Z+ZjH2E9SmFIe0Y2UMknSYNCaEuSurH210KB3YhjnD/s7YQa43DBTYv57r9jX79PufTBX0BWW7PnItno9ZBgPjojCQEF1OEW20xRY1pUPL89+Yh/3HvnntpVjjDuxr1bNiKu9+etuBGYvw4qys78HsPdPAGMyw0Xbe3ZE2sARd8PYl03bjS9G/jCEd/wz9M0ujMourxDHyM+BK9L4/AaN384bWYFPheG4BmG5vn5hf6Fx9e3LTVTdXUxfv3V/3TidhOUng7iY8BhHLjKGTtBeKKdHJCXK6WzWcxggrd4pkRj3T55U9uUGDuxRQaPANxnZroaFicXfcW8QgXVZqLLy8rKQ5wv593+gHE90Tbz78Qf++t//De/fvef7b94zT5nl9sDjw4nD9KW3ddt5IfsbFENyBnLsE30n90siPPVXZsYnz3xnJumrP7oPoQi1dtbrSp0TtZYBnAzvI3TzxnzS/fNbXhPkZ1fMTx1GGLHlAcHYOFIWiiZ6F6+9SiCWu00jD+sAMEnWekkEvB6uDC8plGAaxT62xpLVkIYSxDykpJ1WrfmtqtVnHQ6VpTaaGuF7c0+xJ6xmUGTUw+3nSSQPj94ymvu9EYhBg/RH3dcnYxZGZjFstqGYoSeDioxZDG135+HiiHKl9/t9HRgB86gdla5m2DYVyFaULgK9Z7rkAZCyCFZ3gz25x2YXjMBQj9Upm03XPQ20itXT5puNpXRDPNbirabEAF5FxBT5lDh267gxRIgrL+vTmswT3Fl+o+XQa6tY7vfTcEbY5dV1e31LZenYByQFB+ykLKQu7mD4OooZ1m19D6CSK4pIa6nuw5Qyxqm6pzeMTEeRh1c+4H93OkSNa9bnR3fX1O1R/dnkT27QzyvBoUUNEDHPyWjDVnet/XoeeIPh6Mcd7M6lhvIJqqoxDx0PUwX1fijB2GE/cS6f8YBQ6+6MIxqAnTfqBu8+rdua2d/rmPwRRsU1lnWaN+/PFuaWh2P0kdOdqyhi9GsqMsJXsOXGAokavIrjMZMiSZmeviSfH/kCWF+e+eG3f4NeLo4Oi3HEFaHXQ2qIaiuqFunkSZAWoTLbNFNNzKqILKRWrdmnYt3Ou9IzSErcnED5b37/Ldfbyu2v/iW1dz4sF67XK9/84bfUtXKrnZILf/vjA7/+6gvOhyNPh8LDqaBpq/JIY7ZsCBrCIM73hZQ8RRtxhTaU1065KXgn4TsDwJ47Nsuni99Asp31m9/z4Yfvuf7Dv2T5i39IOUI5CIecKVE/p/i8T4isWx4ZBel/cmONyfH7qr2z1mb0cWvkpS2sl3O2MpM5sXYxWqwwXMoEyRpXWzPVUIruCWbLmR2jy4qPx1jX+rqhrSKu3K3vX7f8jibePDzycDyxrCt1rbx8vLJeV176bQNj7I6UrbN4mk+k1pluL7TuBf3+7F07tVfLRfbmyNUYFYVkZBJttfcfpoJOmbYURAp1VopzqorAlIsV2e+eMXluIYlw6NOnhBh/Ki/klkzOmcQ0YlitrkirFhEArKZEje5YITlQaKDJY+QdnHJtK2mtzhW78pJuJCxPn0tmOkxGoj5NSEnImjleM6ePZeyDhMmMQ5mZSmE+WkcOyXks8Y5a8W+kS1yc3sk3GylM5G+9JDr+3KfggV6tdCgXWq20tZJmYV4zaRWWWpiahR2v2r3Flp1s0mkYD5avDMZ5U1R1qRF5ptbOy0djZup9KxHrxPrpDGKI3WYz8hR1KrqRxSUaIGp/te8/tZ0/Of50sXxMr1sLHbOmItxvioLNehkLbm+a7A97QAnPaqjucGl1u16cafywG4xX2t8sjx1jhW918zhefRa9U4R35vxuQVt3A1d+n4zm5hewfXT72/7bzhJTlIFEHV6g7D/iirbYkj1aSHo6P4Fk2u1qCY/qcPSd6RP+0jA0Qiiac2GCs+PtqOITxixSe+dyvVkH6pxBrDdjbcoff/iR623h5WbM/h9vF9a68PzyYjlEhdo7Kpm3yzLQoIQ16IZI9znt6qUR4opL+5it4cHGHPg60dcLO552P+YxeHr/c+udtS2szYA/3Ru33qYz13Tg8DAxnzJP5xOn+TDqEA2gEYCr/UKR+6Xwev78tz7WnW4eUH8VynaEbXJrQJOM8bBLeoufkR8M8Jd7NylthMk7F8iMQB33MXJyEdrtn+ZKkgNQ1JELJSd6tj6fvSemYh7fcW7k1K3xb4fjwbg9K8Vyiq2OQvzwEMd/r+WDYvcSNaBq4WcLcTVqlCz4Wk47Y1MQZF3d2nfkc9qlGnYzEvRbRk1nntrGmIR5W2LGX+iRlBLGx+kxBHXDFiWSrp/Em5RNcKt5pjk1BEN+JqAiJEfUrjlTu0JNSMmsJFZZ7/LmAMdpZc6FuU7kksHrIm1NddotyAN8rCZbE/Ncdh68uoHp9882FxL/SeSpxVtwJVK3esKi2dNfhk9ozii0qanYb15b2EypWcZDx3h31MBKrXqedYtZdu2DMGVbM7sV0w1UaGFXdtfeT0HoLHmlk376+BMlEv59X++XlOQJ8fgaIQJthC6MUMle9diX8XLiqKftKn1cVICSUhhV+yG276rDwgUL5XRWLBfmXSnE+TQRd5m3gY7zdN1Sk4hbXoIJHffSRDcBOBC4Y3TuF9IYftlvjXurdFPzofzy7lxg/hFIlG48fEk5vuGr/MRyeea7BPX6Qnv/3qWM1fWIhkrr5jupNfiFNNJpvXWaJgwLaQS9a4cPl4WX68q//F//Ld98/45rx9uiiFvubWxqm2q7oMrmHdRe6csLrT/wcMgcpw3sId28wS4W6tIeKGHbDNJhdiL1bcHI3XgF0CNe06gPHSCGnafo4xjH88uFP377wrfvrvzdNy8swBV4fPw3PDw88varL3l8esN//B//I/7sz37FdJwpU+bNw4F5TuQyObbppww8QbIBYEbEUY0A+Xq5Qq+gy6Z4+ivzUApQyJOSJ5i9ZrY3g/GHF5y89CW6lSdXfrnco5p/9nCgRcXQeK1VonPG68OqdwtTSTCD5JneJ7ROGGLP+ku+f06stfPlY3h4E8tS+d0flWVt3BajRES6hVY7pKSI90D89NpbcbiyIlTWVahVkGLhwN6bc5TaEtHVnz28CVdkeZrvx8WVf+9BOL2hzcf3BJoSWTHCgDQhpewQrytdOpXVkPLRO+/VGAeopaspP63LLjSZfC6V/vJiisZR52mHc8C2tpMGdI6T9TkV2eXV3TCwW48wqSm0w3Rknmd+9auvKaUgmMd6zKsZd6UOebfl5AWLfsQ68ChV6vTSOeXMCYYB8Yj1W2zu+Xe1dbCs1snkIjYWtVqXCMSNnGaRhVbbWItgr7WuVAsPOtYDMyYw+bHZUJu8vZtDr6tNw3P6tFTq9fF5T9DZS2A/SDqQjduXWZzKlqfbesTtVdhmDe5ESMxmfJLNev1UAd6pfV/U6iWm4lB0iYSsbEL7U09iXPaVYtu8Mxne2v5e/dTjzvwzIyzgT+SfC4eIu+nCLYR7BTm+DzdVEClmjc1ncofp4Q2SMu26oLUi1airXudIREB086pCOZsraCN9vS18vLzww/sLHy8L758vfLxcuXVLYm+s97BfcLYkNCpLxth0jfBQTH3M3T5nYtGEyPOO/IJ6jL/bvCW5D3TeGxOvhozXStCu27oJ448vN3788Mz7j1c+XC4sqtwUAzG8XLmtjZeXhbdv3pJT4fx05nCcKUnRXjicJit4Tvv72c3jjuPNxlnd4/bSmNYQUStNwQRqIps3bPTEYyFKrDufzoCRDxYdwXK88fPIu3ot6mAAEVDv9O3rv2PzGl7pxrkSj7VNeCjaXIrvL0VL9lz6RE6Nw9TJqVNcwbSeKEl483BkrY3LbTUGkJZY1kbr1+Ehwjb35in4+N2FMnf71r0clb4ZrjvEOl4mIC6PJPdXxsG+TrF77jA8hlhKElclZJ4MQ8zAgCJKD25QzDv8xAALb7/bWoico12hexTNwn7GZxgzv6U4rHwFmnrnhg5lZaCJXz2aSazQjWKGw9KU8/OFqVjpRk6C5Gyco2LsVTha3AwtUyAbicLOU0ZGOzNbiBFrM1lv8hdE+zAwSklWsuCo2pINFNnbdpqx7HY5xXFd2QzvUa0S62VvSur2DHtR/tOOyKfHZ5Vg0xvGCGPWQVgNAb+3Amkzwn5aUG2W82Cb8L9ZPtGsH4ngNxCtk1aPq8efowZ8r1B6F5alD2FrXqrRXXngZNxFKNWhzGXjL43QSfDPWdf3+J2xGdBtk9jE+KpLHjLtmwq9s/Z/6vCi/V23nLvRs9i2gBfWynwg5xNv/yKxXp7p+m9p12f6u2+sdiZYvsfhSQUP5fmIgTa6NBrwd998y29/93t+/+0H3n+8cq2NtRvfZFSD7o8he/K9geL6jPB/s8t7E94aMEKfd88kB3BMbF3VasJNmcgtk6fJ6933BsxuhAY7OGOTvD5eLpXff/uRb3/4wL//u2+4tM5za7RqhcHP79/TmjB/8wPT4czH9898/Yuv+PWf/5o3b5+4/OZLHp9O/OqXv+B4PDAfN0Wor+/n1UilUjicH+it0Zbid2gCTeZG1okZpamhcscK8N0enbQHI5tLWXcahtDbVlo15plrc85QK/Zel8VMUy+VGTVaw8jWzVDaCY4kME0Hci4UbnRpRqNGglpIXXmSycNZxvG41k4/K1++eTBPYLGavMu18v75mb/94x9YV7heLWKUxxAKqRgwJMyATzaQ1y4GdODOPgrF/TOz8b/9CGM6moLbX4tHwGS1LhmWhnOt7orLhPZOmenWjWcUsoxcltpn8+5Rff5rNz6NaEm0Nmu+XVImi3DPqKKje06giSVVcrpwvbwwz4U3bx+ZcuGYD8xT5unJuk9ED0vAexuCqDVB782pDwfByP0Y550m7pqo0hASc8ZQ1Xhtn1ZS6jwI1FX5WA0Rm2exXOZq+b11gCI9u78zMCIisr2+yR3T5TsDcsxhHJ+VxJ9XgmHJGHrBB8L/pK/E5GZJRWzZ0VNjUSQ/4Wi7a+8f0HcZqElwhBWuLCWuGSvSPi9gdGzuVojXFEko1hFyvFcxo07OLxcR8X3sOSzBbRh15B7Gf86jN8zAPvb0ZlTfbea9BxERbdkZNDbVg30FoEf7YAHJyHQidaU8PBnP5e0ZrQvcruMSun+43cwstXG5LXz743v++P1Hvvn+HT9+fOHleuO6Vuso4O8eQt4FbXjZP2VcjQUp99cb0YBYRxrnVrpsf4qFrODhqn3YeD9qEWvwn2V71rtl7mtlWSvvPrzw4eXGrTbrA4nSuvNAVlibokumqvLu3Y8ISp4S1+sFZOXx5cxxPqBNkXQklxEzH5ZxzLOqDOc+cuTW8WQL+6skpKdhQGgPJX8vwmP9JJf9cZHknt8YaX9jV3WFFMJDN95FLAQdFvdWP+kCJYBbuhkzdm/NwFK66w+3W+9bVwl79p693i85ixBWTygqtHbg8eGBZVnJYgq5DasfQ8LmAGzIIG2OmTWu4UTK6S7/OXKnu7CXdT6JeuBYhO7ZSOz/MHxdvuxkUIQzN9vLx0sAsoU5vZNFbw0LxmwQjTGX8hPhOtjlRf3Y5WeDsHq7BzPxjCOUgSAe0S6XF31EALrLS0W1c/N6yXK9UFKhpcZSEspCzplpCj7oxDQJB03bvHrXlxjH++yn7JZszEUYDza+WcxaTm5JWNeQzDQVlEZZO3Rrk6VNd3iz+53vUpH72vFXucDdre1JW9itoZ87Ph8ODdL/4idWM8NsYDoqq1k3EjhRMLPGrPiSLaZN2xKd9r5sYT75KWXl9962xSKyu4fAs2Oe33woGNhmJbpJbMMUY5DuXIXh9hN/9g0U78nbnUTYzq5tz9BweO/qlsuIeboWlI2KKsZf5B7aDA2RZt7k/nCrMiibNPrsYVB3mZ9I+cDxl412e+Y2gb4807/7o5GPx2YYm3873r1c+d337/kf/vm/5l/8r3/N6vWAFrLAenANsM5wtLbF/wmb/nZ+FUOs7enKPoFuK1aDlWWE54ai9CVQezUh46jI7VI7VZigi3N77vPKYlftPozvn2/8m9/+nmvtvPQN0t1a47bcrFOCKvVWkeWFv/vdlW+/PfLHb//A8Xjkiy+feHg80/+x8utf/ZJf/uYrDscDcixIFo7emKH2zXe4984sv5uTcdSKd6LUvAE1LKcbykg9jKAj/JNaYpd6RahYWbVfp9nnWosEVY1X2Cuse6nug+65dTMoArSiqFqIdrldqXV1JpnNu7ENoB4C2kBWOVtWq3VH15PQ3DmnxGnKlPnAsqxcXi4srXLxLhWt1u3W/CK14+TM9kr2usnDfCCnoAgQ5mJNhvO8kfvbMxiPZd+xPg3yc2ULyYEhNadYR0J1VsM4UhHrGejnyNkYTNb6QmuVS1u8rKi6nPZ+glh9Yhm3JewfNDze2FYd8zDrXVhjCwQq5lUKzeNl3j+1R1cMRq1zkoY04yaVRfi4XskCJyyXjAjzPPH0+GjlF3nifCq8eTwQcbQ5F687VUSCxSi4R/fLy1Zj9orp7nntCZubJTXfFwbkOicoq2ESlmK1p3UVmhttbrZt+mvYFvcq8u7Iyn3429Tk4B7+zPGn0aGKo8WsgB3deQlRLht5kRgkL02I1yQJqSdI0VMcZOcBMs63s1DdpXKdsuUM7lQ+RPDReEkDSn0/GCmB5rzF/gcSTcei2Z+3d0Wkj/ftAdAQFiybYNjP1BD4+sn8xePe+y67EoqftHJ09yHx/xPldDDmi9MbOpl6vdJro91Ws+DrwvvLlW8+PFuDytvKdx+e+e79M9/8+JHLUkeNTVxfvNOrg3+3NOdOmN5bsVvoIUvmWCaOuVBc5I+Sri2af5+3Ermb/TuUZ8hnH5cxrCkGczdSAQAa2tQUXlVYvEGshf/cZ8iJMmejdapm1ijKUq9WWvLcua4Tq1758HLk3/y7f8uP737ku3dfcjwf+eKXpgy//uqNw/edCWmMU2Ty4jH3xd7qT2qAA+lm1VuubQy4GZeqxt6yG5fBfhQKK3ajSyXrmei7TOxZYyEqFlXcr79Q3AKuCLG9rru4S2+mCP1j3b2V5iQaURwddZjDM0g2HzlnJoTzrExJKBga+dSsYN1AOrHE3IPsNmdBkp28Tm7K02jlBlYykUQ8X7cZQyN/NdnDDXr9fb7Oj5RlFxYU7326e73IAEapKmvNTrlmBOvXqdKaoxoVVLM/h4N1ukU4btdleHfD2/M1oa/m85VAuLdn9wZkrOvQSj6GVjcJa9URSTN+8shbY11QBCvALxNND+SMd+gR1tyYcnbeU89F4vyog8D+zsYdt5ps8aHAVDKtew/JYaSLOTAiRqwOlFpGDnRb47aj++78P3XYMHcviZPdUOhnPwd/LyWotGU1VFeKeJ9BzK3zguwSFCb4yp0AMEod4yJOdEnDIh9aYj96fqpUBFQ2yyNt62AvEnQ8xh4FtGkbEeerjEspo4NDNPyNxrBxRE3UqsHLsP/wFp1mlzMcesrf13fG99BxElZaLHgcniqjQUEo6QCSjKUde6IYeu90OKPrxFET9eHKy/HEermxfv+etrywtpV/+90P/Ff//H/lu+8+8rvfvzeruzkFUQfpQlIQ75TN5EWxPtY9vPFQlq1G6pPNCLLvcy58fTrz5eHIUTKzJGtsIUazpRLCUl0SSySZ2YeEB1ii7y7BNo5M9tkQF81bD+VBXpXpKCuNpSvXCqvu71msPuuY0ecbS7uCWA3gpd3QBh9WN76+m0iS+du/+y3H+cjTmyceHh74v/zj/xNf//JrDv/Ff8b54WzoUYEmzdqEbavGlJZMuzVr1YpGnOB5a2fqsrUUtbZeSyjD0PbDEkgSuaCoN0TQ7g1UUSY6PQnMxQREdQNkJ92DRNuaXEd+shnKu3cyCimzOvfmhnq073U1YbtoG9s4iXCcJgK5KxkkJw69U5z/tD0ex3iE4tuIk72cBLvO5dpYarRVYttUcbhEz9HWSAzQczydySlR0kQSYU4m2POQC3vRvc3OncCMfW0VQ3imddxDd4Vaq+33FQP01dWZmHQxztVL43K98cdvv3P+S6VWuN7GysZhS7vfg1w3bmRv+DHKqcfSGGjFIBBv92mVnYEVcHK5CvLxg7F/TROP5weu61vrWJ+M7H3KmeJfycfwfDpZbeP4715HB216y4WUM5JllDT0ruRrZsoVycJtMXLykm0fr63SF7W1P5wNm6O21xXx2DEm2rfwsURZ3Kfv/6nj8yUSA71o4ZH7nBm7udlsS5uGnVVL1Ntsgj15vNk+H5O7t2hdKOs2yON67Jdt2DW7B2fT/ve8jdtPgUAaSKQB19gpw9eWxE4J6t11twcfNkdoLd2pTLfuwyIfCbFXMC8ZlDJh/3t41BGEticE65+opNOBnJRpPdBrpyF8/3zj3//NH/l3v/+Ob75/z4ePV27rikaXZpsVUnKlnh3wkeLc+xWk24Iad2lef8oW7phT4TRPnE9njocDk1gIKOfkSkFoLngCkHF3wqHlGR6/cSj2EboZb+ygKfJayqjj3L1vua18++M7fnz/kUtv1G5CNmrsgtl/PjRUZlqvZoE2Cw1H13JjUlFut4tRb2Wh9cbvfv8HbrcbX3zxBW+envj6l18zzYXDMe/Gj7t1q2MvRdnDFr7ZezbbmCTfL5+Gc8xzS8SCV/c6VDDFp/G6WEmKmhHSmt5HL3wjJZ+bpLYuo2/ijip3W+Nj2vTVt21WNxLmbd5iTNHw0lxJ7m7FjMJGAHUSwmE2L22tBrLA8/K7xQjiTbYJrlGnIvP9HEZuyANxi1Q2CPu499A547sQDW8IflbcS4590YqJs9QNoFICO6AzvXeSdHIRlvZEq411qdQKpShrrVyX27gBq0e055S0ycPtWTdZu18Qsv9jWIzKIKcej+khHkOBbjndtTVDi+cXpmI1oVMvtFI4uOtv3MPJ2icp5FKM7i3uDcb47oHv0nfIUumUkhDJVDUD7XhshihNibUWciks68JtVcf72fpJ7mnbM25CaXt2553VTWa9Tr/+1PEnPEFbisqE6it00L1xMiag+2vmvW1tO0TysG9sjtou2W4nCAEP1n9wrx62Wb8XoRL/SujUSCU7g4pslkoscqtNacOSM7XdSVI25Rb3SUC6Q73HXSiRLLbzvrYQtrvb36y+ep/lCgVLdsfm2wWWnKlkdQ9hq26y0gl5EuSQOfULvTYWEf7dt+/4v/+3/5L3H97x/fffjmT7dm/uwXsKL2WvPRpJPLNIVb2421Fv8RBCJklhmo5MOfPlYeJ4PPDFl1/w9HjmmBIlC2kSwpVrNCuqxzkEPxmsEIwGftCk9NTZ8it+NGOR77GwRhTCWVCAl+cL/99//df88fnGj+s6QikJa/h8nDLTlDhPwlkz13XhVlf6izXJHYd2VJTL7YVlsVDp9Xbhf/mf/xceHh54ean84he/4P/6j//PPD2dORxOhnbm0+PnjNLXRd3baFikpcuWd9vUqGG2ETy8F4ZctzWh8Z50x7tZqyH2groty9aIzISMRQZqNf86DUH8Ezf+M4eqsrY6Quqy+/v9GJjXfvdgeOqitwEmmk4moj4+37hpvTvDWDJioAtrszMRRN7ykzPxM4fsxte9yuwRkeSeIN72KEyWLdxvX5bnshZTqFoDZOB8Ulo/8fDmzLpUXj6+sK6d263z4eXC8sMN5wYYSjBnSFlHCDImobsz8jo8OKTccNczVne39fvbHlBIfZtXQ3Cu9Nq53VYOh8LpODGXmcM006dEbxnJFpLWDmuqHB9OFJkGs43cDaJ/qRmdA9+RMnkWWjfhM5VKzonalMOi1Kqsq/L+5Znlvdedd5NJos5F5LndnRBlmwmHa/aIFP3p408owbJZ2GGBuAdldWLJJ80UR9jnkdikdzQFfsvE0J3JovbPxv5ikxOKMFCbwOiTFV7KnQ0njNZE95tNPBxwrwh/SiQJbGFRd8aSWxajO3JgFneAndfneH2IOrLLm+d9Utgs8skHX68jMF98nzHobNbW9bby+7/9ge++/Z5/9a/+Nf/ut7/j/ccPXG+3MWJjl4sJWLOa2Ze43V9cGV5ZPLZ476OUJlLKBigohfl85DjPPObCOWdyUQur3llMTiWnneRRAN2NuYXMzdIcN9O78VayedOKC9RdvhAVVBJNhUuHH1fl21vnwxohElOuuRSO88TkbBphLubUmRLMTmKujdHYdfMOlOZ9z15ePtJb5Q9//B3LcuVvfvtLvvjiDWX6NfOhcD5O9wMZ4ULfN3uQz35d3mNXwpTdT8rrFbKb1py9Zdm9edh3sdQ8CfMud9Rap1dn5hBbZQBlNsPIQuHKoa9ITsYE4t5FV0WS7TdRFzyvmADu/VtT1LtN6Le5y2uq73HZvDV1GTFNltMPLzVIlQMvsK1hBe2DrUd6G6HVlDJzOey7p9l9BopTbK2FETHy+MBWjynb++I1v+yUldyhpn4XjlNsDx2KgYXkBG3uHGZj31qXhbWuLOtqef1sRBfqtXibobTbpEnv8pq7pKrvK8tLBzdw00/Xkr/dnesARzW0G9dpk06VxurPmxzrkXRFc4erUFqFqVhO1u/R9hZoGgOMRfV0oPyt2bCAl2konSkb4CspPB0nEtbCqzflVheWmlhWo+AzmbSD4+8X1e7HdP/rTx6fV4LiSjDlrT5Koba+M8JD7W1Xtyhbg9TpPblXp/dM726NDnqcUOq6h+IK6rcYffSyz3assRiDrQ5xuw9Vw+cZUm2bfOn9buzGRkqW/FUPPkuO0gndKUIwLO+nQ3svqnbqSjvRJ/FOCf6koapEuCXUri2byKRageqy+8SHD1f+6p//Lf/+b3/L//O//+94fvnAj+++33XodgSub/wkFsLIOcIhP2cxm2TZqLrMEpyng5WjZEOYHd488lAmflFm3pZCKQ4SjUFWIwkGQaXRRc0jhKH4cs6UgL/vmtkaGxCukGTLs74KiXWEWxe+WeB3F+W3l8bt1r3DuEGc53ni8XFDD4clMSUHkBygTJV6U/rq5HNh8ADrbaVSafXGS8ksdeHbxzdM85Gvf/ELzqeTeYRTGUTPeueGxxrarI6hKiP/sVsDr3tq2pt/InQqkMtMAHMG+GL3O0BJwnTckNjrtXFrq+/B7i5PJhcnxwzJWmBdV9beaaq0daX3bp6lWv7Rcsae71pezQ9hPFe25N54hb29PiKlvmTFh+94KOicWZdOa85HilLcrt46PFjX0x4Q4XW1lmS1kctMPxcz0BxtLlgLypzdNvHwvYATHmzLOBSl0cu5Ucv2+lEs93VzTEButnObmAwqU0YLnMrRU+Od83EmA9frjeeXi5NKd65t5doWwqeNY3juSXakAewKP01qJqzXX87J5ixywRqjtD3PdnjqpWdaM57dpJUbSlMoFXIyJHLOsLbVkM+PD0zT1pGleJpkDEyy6ER2h6c3e6o5m2xZdUKkktSM3ibCeT7y1ePMWhvL2nh3ufLhcnUZsOJQhTE0A5Q2bEAZ4K4/dfyJproOfHAzS/xi3UkBIzeW5F7fNtza7cG1Z4wZHR2dXTQKTAPJMybHfJet7qWNe/iZyNFOSd0jgcIyVGS39zZLOREeX1h27qklZR/UTo7Q+/TSuskKTIGrhJXqo7UzCTerNe2ExCalQvUpjBqb7Tl8/Jtyu93469/+lg/PH/ibv/sbfvjuR/71v/i3fP/jD7x8fM+y3Hz9uRcewBefykQgwPzcQfEyLEsXhqOtuYx5TmLKz9r6TExl5rHMnA8z09OZfCzjfDH+Q3gQ/dIKSb2vmjdXzil6H2zotT1SzqzWTVFoeNDDsTJrU3fvF2yBT4eJ09snDlPiOFmYyCjMEqqCrob4Tb1YJ/dintEe3IQaaIOONTXVzuX5glblb/763/H+3Q/krPziqy9B/hHHw4HT6TAErS8Qvzt7ljG/WH4qGpBFnd+2VvfrY/uO+p4aUxeSevvYEHRj7bHlXSeBY2JwxwTydmjSZuHww4lcZiYHH4zmux6dGUTGnmJY1uZ1i/4svVFr43a7smcgsX6eZtw0tXKVGOxYr/hcRyo7+9qji4dw7VxRNBEUGdKtJdsIFivQGrfbi42Hc6JOpbjHYmOSvS/eKJJXCKhtyK7R43FX2iUIJUAzc7ZxK26I23IzVHmHIsGOIujpQPvyiXU58/T46KjTztIqq3e0R704S+H5+YXbbUHcG4890fZRfMDSKwrJChsmByUNSMBuqSRMFmRJlJwoKVM8vOwTMAyE7XBWL4Xb2qjdwsdJ7L1dkhXdyz4itK3JuH4SN0LVyrMkiYEi3eCTUkizUu0GLZ8viV6rI5Zjp4SM2Mrdoo7zU3fl/vi8J7hjExjPEKEItQFNkc8aCVuNN/rvkUczzr7kBme8ZTCjjDFyxovaNu8QYSqTbdx9uIydV7ALhYK/JTQcO3Hi+yxOkVw4R78/YANa+e8phPq4quyec3cjdCeA3T//eAhPxZky6aEgh0Dw8XMaFc33qCvXJ2hTru+f+Vf/9F/wN3/7W/4f/81/zcvlwvPzi+U6e3XBGErONvZOpuyyVi5QthIp7/Vl81nmMiIAEuGLGCNJSJk5zDNvypHz8cD89SMly85Z0ZGyGyUTKdPTDnqT0uiNd0disA3/3RBvQ/bKpI2fd4c7EzyeZ375my9dEhkApq+VTqKrGKNH76Rk4axcgCKjFCDuda2LZd5W6DTq7ZlLvvDycuFwOvLdd3/kN7/5DW+/eMObN0/8Mn9huap0T7k2Imtp510MIbMrKNG7FXR3DJm3M4D35wzY/EZoIWONB+1amo1CK/KJG6myWXZabaxSLrswK2M97AE9IXCadq715mTVeDH5wrIsiOcj44g7691IpK1f5h54Zs8TdmRXoRRBNVnziQBqakR6fO2GAvdIE658Wq+0+mEgnOd5Rk4nNEekxLAIYzT3W7fj4yYEofmmBE07zj72OW+tgUxBdWdEYeTYjcigUabM4Xwwr6YaEf3amrWwcrLvru6Bd+XvfvcH3vVOCpR3jP2yE1kaC0GRSR3WmOhdWZdtP23hXEfOJsuZT7kwDwbvACndyz8wA6YpXG4rkhrHyVG6qmYsdze8yi4Cttl+JpuAQ0msqtbGKobVLfSSsinUkimz9ZbMZGq60drq46go6a7GGbWxvvXO66jE6+NPdJbfTrhVABmKbLPRY73slcLPXFRjAWwf7q8ylxqr/h7J4cJCtnvywY6c3978HUPhNxdenuVk3KrZz8bwxuxc25hFlnO8tNsbmzUe7xx/DnaVvXLuSq8OXEgeClYY1Bi7RQIykH4hG5PCcrvy1//ut/zhD3/kf/yr/5Fvvvuel5cLy7qMzstjrMbCtvqcjfcvUInbkg4EZooUMCYky7RTgpjiS5LIZaaUwvn8hvN84M3btzweJn6dJ84JkrQBexAXUJGHUbGSFCuZYDz33s/eDf+9AmT/+2aFkzJ6PJNIPCq8qZWvvvrS+pQtKw9PZ55OR1ySsi4rSxJaS7QmZGlWgJ239QY4UbMMQZO60GtjXVb2DCp1vSGi/PjjO0QS/+yv/id++fXXCP97TqcDb948sPFJ2Lyomfeuboba2vgTxxztd1oI1hgY/WT9iVvCYVcOU8q1bRgbKaVhebcgk5BYH9blpWMApeGBd4etx7V2Rou64BUVZplRjYa9VoJhHkZEQDYFYcwxjXW1Uoj9PoSoF1TW1XJDYRiITJsnNBaKbNsuJbQ7V6Uo0OgdR8ja9Vtdud6EqRSEyUL8amaIetTCngrj4SUhVZiPycKnWyKSbT9tYzPmyuchi6JJaChZhazZy6XCzTVvKouiWe3+1bhK19qpKG+fHpnnibaaEqzdSKif8426GiWgRkRE1NE9Dhbs7i9twzvCu6mY9zcX8wQjTy+yAYDsb4wuQi5Yxrrp3sYoj7x3s/XVI8+bhhzfjGnLNc7TRJlnL5FpbKkP6y2pOlm0XhOHeeJyEZZFWNfqnUFiCdie7d1kzTxNbIjonz4+qwSHpddCwTniciew7PH3wcKf07q+8Hts982y394RWubVOSQ8KBkbmSx+P91DSJuQiLW5oS5lm/idItyyiPfX07tn2m9KefWuHZ9dmPcqjqBqd88RFGvRIFR2SjLcm23LbTIpomalK9fLjX/1P/8v/PVvf8s/+af/A88vL1YI3vsIDdi5zcKyXFt4ubv9yv6XLdRZJiFn2by3KW9NTP3eRDLH05nDfODrL7/ieDjw5uktb7Pwm6xkr7nL6p0SRVwJ2saJhH+ne5huBxZ6NbzDlvBxuKdIC86MhOZCf3wgpcwjcNXOL77+BbVZx/TTofB0Po7rvFyuBqVeKqiQcyUns8Zk591EjhhMmJWeabmh3XrEGWG4oyl7RX8Qrpcra6v8+Z//OV999Qu+/OKJx8fjbthlPEcYl+ORd2F9vKHuNjCvAvJ7ZcJOMW3sD27F7zLssgk92Ye7OtDiWtEfz75vlUxq+TVttN0624zgzdA85mTeS2n0Hspv5nw6M/aTf2u90vrK7VZZlspwvf2aNzVOSaFSxfEAqlgoFaCNPRRDYt+zC31vTZWtiXDbFZu1VmmXis4HsiTrSt67hds8pxb2A1mg2XrIJY/o0LB970KFm/Lbz19OadTLiVpkpe3xCWIEBJP3SzRScjMERBtJO1+8feKxP6Creem3tlJbJct71tviefBdCHBHqNS6YQv24k6wfLiUwpRl5Ok2I3oTFUm8FCWARdYVeJyrNXUqQ9BkwBrpAskAPtnzhAY+FOiRk82kksnHA10bta3uCZvDZGQJE6UZEK+ujVIal4tyucC6p/cJDxWTNenVvPzU8flwaN9ZmRqqYRu9FO1IUhQn2gYMUl67pc0LEbb8bfw+Tif7hL+AeHI+XOrsNVjuJgdisPv1Ymn3jvMLjgC+x7NlWO/BIbgfHh3//Mwhr8NSmyUzvNKdJ4YLu00AhWDaYzzlbhB2Ppfdk5pMuF6v/Mu/+ud8+803/L/++3/Cd99/x8vtwupNSpXYrJu1Rdo8wXsFuAmuEPLZDYzDbL3KckleE2SCbSrRvkXIufD2iy84nR/4B3/+H3KaD7ydDxz6ynR9R2oVqZmkjazVN1MeZNniIbdQZ5+YTq8N6zvFBxGYUYe1asrWiiaKi2F8Px5mnh5PnI8zbx7P4wwfPnyk5I8st4V1ruQizFPmtiystVG988Je6QiQPKwztQO5NZb1Zgz/vj/qcuOqjR++VeiNf/ZXf8Wf/fpXlJI5ng48PZ7NuIqH8X0yQHQpDIKwsIPvcz8gut1Q/KD3f98MwD1wI54m3a87vxR5s9kkXvcu7UnNJNSczcjrbbvebmaGge9pku5pBs1u5O3oFVXjfQlSYVZvKbSPnihI70yqlJS90HzyrgoRYfEQ6uBgxRWhUYktYiCe3jItdZJUamumcHfPH3EFy4TsIXZePxlUOwK320pt3YBlKRHBaUMv7AyZUUMR5oKOqE7sScuJOWhNBWngIGT35g28liahZevp11XRbMwqh1asrKc2lsPMcrPaxBqmym45qSq1mYd+vW7QOvMEi5Faj5SNyw8xWrmcDQGasnmNIWMlbRAhwwzYIGpY8FjJiKQ+8pMBqEzZg9giRB8y85jNe+3Sx5xkf49g3TBaOxu5e75RvSVTd6NBu449XHv5xKd6ffyJVkp7K3NbnIyH2OUCo+Zul5dLyVGIu9DA6/sZTsDeI4AxKFTf1HGt2OA+ZOrK5t6rTIiU7b1BB4WM+9tTDd3dk+r9X2T3NdyTzQa3HIuFcLUpgYLtuj3cCJtIeJjmRcrQRnfmwO6bWUOXlxv/9J/9T/ztb3/L/+ef/Y+8XF9Ya92EVigYvCuHC9ORAxK8MN6W1HiknEk5k7Hi4sN8ZCqFfCikksa5j4fZaJIESil8/csveXx8y1/+h/8Rx+nAA5Buz8j3izU5vSaSrmT15sn+NXzn8YiOeH1trb1SgENw+txu0jZZgjmUoEZNod378TDzq6++4Hg88Pj4MNZNTgVtcC03bstCSso08hYrCoOLca8Gc7Yw9TwfaK1Ra3UicAtpt7rQ6sK6XFluN0iZ55cXfv3rP+OLL554eDgNhRWKKfuKiJC17am0GXU+ba/9CpKyFeDD6421AXJC4cluofh4erG4EV7b2tyYWfxzGmaTWj5exemsOvsk7JjXWPeKGT+pgwQYyVMSyED2iWZbs6Lkog6Y043NqRtILudsvfVqKDwn9W6Wf9tTREY4sDUD9vT+/yPtz7pkSZL0QOwTVTUzd4/tbrlULl3V3WgADTTQAA/5Nnzkzx2Sh4cECGBAPAGDM+f0oIfTC7q6qrLWzLx5826xubuZqso8yKJqHpGZNQOruhkRvpjpIiq7fMJSd1oLYiQsC6F01oPFzo0hnzrP+vAIULEs0h0jJrjyTpAM1QZZRxoqJCQoyKzij/aZFta42egBALhYZjJgNcNRk2wGaKa8CkFrFFxLkTKLcRThr3RsV2zbjnleAHbcLhAFpNhEQSBJBzEM0RQjkmY8h0CIKSHGKIkwnafIY6a2VKrIVPUg8tAr/4QQk5xiAqDWL0hik6ygCX5WRC4iUECJhFo3iHFADAl5yVr3LZ1MSq2I2sIJNXyvbQP8XsXy6z8pwLMGQ3c4TzNxHtTDQSYa4AajnhU7qtTUIxj4D04go9g9KaDQrLqmwroWWKtlmJ2kyRKdzIpgBTenLUZZUbYfm4p+wIWhxRslLlIRgtVZyWE1qytEza70BdV7VBu7BHKXY8bd/R3+w//wH/DVV1/j7/7273B9c4P9/qCp6d2eoEPHMMsvdMJOLWhb3iFpXU9IoBCb1jeKhjdtRgxjwtnuDJvNFn/8D/8Yu90ZiMTqmsYN0jDi8vwJIhFSLcCYAKrA4R709jWoAHHJq3Uy5UNZL8SaN6XE3u0Xu4vJqnrZXKTkd+FaUffvcHtc8MU373C3P+B4d4Mn51t8/PGPsNlucH6+9bs+efoEH3/yEeb9EfP+iDdvX+Pm+j1u7u6xP8w4HGfknLHMR5RaFBbL0sxZLMJA2J2dodaCZTnIAVwUZo8Zx+MBr7/5GqgFm2nCT378KZ492QnKTlQFjRm1F1bdb9w2t1sRJX5ar5ScI1Navkup6pJYugxBBDM1JV5vKbGdgxXWad6IrrEme87aKrS99p1TIcwaE6zaX69Z2yRKmpXrcNVQjMUVGZEDKiewciwLf7BmgdaSdcTky8SVsZ1GZ8SlVMxzRqkVFxdiKVRUxBAQYwJnESaeIEtQBdsPp8ynKqpLr+jqxIkEMYYAoIpbsRp4frHKZys5WlvqJmTRUX2DaGwfCWgJglnwuXCx3UhT4zSjcsWCUb6ZWnY2WHIwyqZisxEXvXEJ2yP73f42ZRlB4SuhQOnBwlON3Ejda0GJ1tsG+g0ls6SVZ2gmkccVSda4qnxQwddazIhCH5nBiiQ0BELNGhtlRtZY6bKMmmmbHyR1nV4/UCdI6BU824zQ4eSI+W9aJLuA633iJuFNAFnAFd3C++f0rJJpjStBu7rj42Yu6VhqVQ2ri9t1Y3E2sQpK1vWYFL5IXunGaL+w/dpMMvlUWO2puYdDiALWq1ZWYyn6dzVw7op5XnBzfYv/9J/+I371xa9wf3eQ1Om6rCxAsyYjWYalMUITEVDlJThxD0PEOA4AJRBFz1qN2nBznAZM04gnT57g4vICf/pP/gmunj4FMAIIyIsK66xQbGUBD0n9Fgm4ew9aCqiEVj6CdsRpNe+iRCpuH3ebdgx2RUksSSPMRk9BiurnW9zf3ONXv/4NFs0Xj4Hw7PlzbLYTdl1M0OrilrsD8v2MaYgYonSQn8YD9vsj5jljT4xlkSQYFMU+JQjYcpTEoVoLCBklS3smwZpnLPOM63dvpbtDiJiGgPxn/whpBBCiMMDQIoJy1gkVtC7wpe4Ht8+u3uMTL4oJAv8urT7PKvBWlU06bi593A2uoLUs2X4QjTOcnPb2OOdvygArK6xV+1xQb41Yk1ZTq7xEhYhC9XdzjMocJQu0luPq2YCcvalMLtBzrtLLUK/KBaXrjDIfZ8yH2hRSAFLaZZqXzlXS4r0jej9tVziJgGpCXvlKqKpwWh1pg3Tz5B7LaO2XtxO0UeflRkAACBXbUZKRhqACHxAJMnLbdwbKLHu4221NHKNXW1wp6eipd34DYlW2mm9qe9wvB3OrY4w+hW6paOUhs/E1w8LkJqlbXPbChPaQGrAGp4qsWaIlRQErj0VrLueT+OzD64cBtHVCBrVkWrhPivWAqK85hIBI8aF789GrUyNU0+TCrnSaX1+eYV57FSixOnH3UzTPmvQFq64xuvnPLMFc3wDqMFJ7ZmECpM3B4m2V69qKDafWZad9+fEV1YbsDZZD3+4hjz0cj7i7u8O/+df/Fr/+9W/w9Zdf43iYJW3aQGLZaEeEXyDq3JcafEaQTC4y/z4wjAPSNGBIA4boWFBIg1ioV5dX2G43+Oyzz3B5eYlnz59jt9vh4uwpEm1gkRNKkgmWGXDggAggtvZHTAE1JVCVZAN0ir6DqXsGsKXG2+K7bet7a0JAgZNQOTaNn4G6MOZjxvvbaw+Ul7Lg2dMzmXdqpL6UiqVUxGkLigM+iJ/g/MVTfPPya7x/9x7v3orFDVSktAB0wJKzBOo7jiD8iRDHLShmTCzAxcdsFmHFYX+Pb19+hV+dbfCXf/Vf8dFHH+HHP/kDV3UDtSy8NmsRhmwrbpN3usHK/Xd6sf8HpkrDajD7s2IlINaRYlVzgBZjq5pRWfVMVq5g0gJzVX5dRLKlEjSLX9iM2Y4VhGYxA8LkDE+Ulbnb+ZDKBdbXmzBwRbUCjCBgtdwdV1VAC9uUKkouIMxdbkDsgBAlg3UYRxFyVSDEllwUnkJzD6w4rhKYtKrZ+KIKvZCijqPFuUAAsfYGDZKpSlET0kJ0dlSpy1eQx7Z2YwQvm1Z9zGOZVeec0gCpn8waKtLMUIg1GgdAWpP0MeJ1FJRMQJlg515hl9AKAW4hRrIhlS7kJXtTCauyNobsSdDD2+5tHECNKpZM4yZ7eo8RrPBS18GMAEYYBu9kX0oF+GF5z+n1e7pD9fG1m2CnQHBtAwXQxaPat0/vuBY47UNWJC/MzQLfQOniH0QMwVc7GW33ICFyZRp8qstgLaDJGK+bIU27WTFiEzBm8er3qI/vyafNwu8UnZUma9oede8wMebjjLubW/zlf/lL/OxnP8Ph7uhZjmZp+8KRZl4SuTVnYxaNFN7jLYIwDAPGzUaLYRtu4zAmpBRxeXWBi4sLfPbZ53j69Cmev3iOcZoQsIG1A2LhWCAwSlBXUZA0LktWsfWtMQp8k2gOshc6VskMMyLqAzptPxoT6bfP4kFNcwRUCcsF+8PBLcHKBWdnG8QU5VlGYyDkGhCGgDAknE8DzvgS83JE5YLjURFRlgKxfCWGMofmGmzURAhJ8Cp5WCSGnbWIl4FlnnGT3+Hbb7/Fr37zJdIw4vPPP4OljIegtVXd/fwf248uU7mLkzS4wf67QB+Xdw2/j4v7OkohtzmpPNZnmgXsV3FJWvG7oTNZhuQqJGFsygWh+yN05ZsbvL+cGqiVjDAMZUrVgVXynI7RXxP6tKQLeZpSEDPABVmtN/G4mYLc7hkCEAYAJQO1eOG6jd+plOFtp1hrg+URsi8lmFDvYqYUpGsIAcylIdGEiGIKq+5p7SWDSfNeT++WjrB+PSrcHaOqAGyCEIC0hGJCqKGbl4UZjCcJbbqyZzqOTASkZ1qwRCOS8sRaO2HFzVqsJwC00k2GRH9eL5M/iDV71MJDNhBXt6opbTZ9xVXWGGvRbO9So7dk+q7r+0skYLhzsj5FSyVCrNJmJ7e+Y6jsBCcNQ9Ew+ABAQZoDLNvM5IUcJA9OK+6c9ZhqBblkfL/xCWP5DoKpRYid4NEPyLEziJQT61M+w4BpZEagqxA24JaJuWt8w3ptb7Xf6+sx4lVmtN/vsb/f4//+3/+/8POf/QK/+uVvcNzPEtzXLAKxooStxJRkzcz14u5mYaqJSLK6hgFDTBiGURDftQYIkFYpMQU8e/Yc5xfn+Bd//uf4+OOPcHF+gXEcEYakh0HS0XOWkXM3B7FdtFksAjgk8LRBXY4Ix1lyAaxbBXNjcoE8huB7YBqv/xI6PVU+Y7ajJFCQHgxJ5Q9MOBsmLHoAJg32r+x0hlr+nu6hSQMBH774AFfnFxjjhHfv3iOmt9jvD5JRONsaFCzaQsi7LcCylLegVMAhopSCeZYMvForvv32W/yXv/gLvHv7Bsuy4LPPP8VPfvJjz8Kt3RJwp3BWFYZG7h5fQ0tsgCkEvm4dikunGYo7fvAvWRhDLC8Vguwj8NhYrVkSMDRZxTTrUk3hi114o7+EJk2cctVElYIHAqhVelkig93M4iOnNze650Y/qyFo6AXmYlfs2N1GaIZFacpLlnhXCEgDMIEAbSIch4TNNuMwi0WIfEStuZ031tNgyRexsSZWHg1dYykUnaXONlipAFCIEUJ1z1nR5A6/lEfGoCUGqviZ4SEF8wzOGhMlQKDfjH5sTC0uD5B26ZEXNDFV8wNUoPR6qMUoQA1PAWgoVPZuMu+M8mW2aKo8O5e+jlkUE/lLAQo6AW/l01VgKdRV34ytXFgt7/Y1Roshl1xbXeSJwnV6/aAQ7I6a4jDCffq1FJ+Cr78fKjuSqokyu8C0/7IuqmhEbbNAQjOq/8FXDrQCvm1vUOeefMRCNFFsvjjnGt3iKEG3lHVd3kfXr9OmVasxJh1UKzKXpd+eW2Zc/1Dh+Yz9/R7v37/HX/3V3+Cv/te/Eb8bq5api2vNig01v5VdwF21lsGVgsT4UhIBOE0SCJclEGVjnAaMQ8Ll5SWunjzF5599jj/4/DOf2WydNrQUq9BaXrUrwJgXhwAeErjmtvKqWBhN9EL00fVlU2RWqkI7IE0f9NR1pTJMaZDCdwLGNGi8xZ7Xs0j4oGz052fnONtucXtzi1IL9seDCJP5gMAV4yAMsHJxP1ur+WOENAA1YNDarwUt1rO/v8d+f8Awjnjy5CnOzs/xBz+22Ftwxim30iPtZ6jTD3r97WTJ+p0xZmFWGzMLABSl9eea2IXXrtrfmqJoQAyOzsGAA+kDSNE8D2vaXi02d5bCyqNh8qQLbrgr7lSycrtf/wj9+7GSMLL/khR6D0HTLFn7/2VNWotJeQuJ0lcljhdTFAWHMhiLejCsdhQnPMwGpu7eqvNiwDw/TLx2byqSlglWy/Y8nQpxUtYla8Pq8qtm9WkCnssTWxP9bxMUJxfDE4HNNW/LQAEO/G+C0Gv7fQ7K5S0uTfD1MQc2wzx8TaEzCbDaSFXCzJoUj6eIQdZkZOmFKrRUmS3hVubGaEKwmiL53ygExe2lGHAoQM0PvhZCwDAMWqOhE6VHFpxZmDoMcV7+E3XB3SAzay1Yeok9S7ezOxPiTor+oForJFbNTujyOVLti3TBlAGoz3w9TIl3EQAiIVxDpDfG1H1ax1UlrblIp2YrkwhkYzQiWl/MjHk+4ng84P/x3/8/8Td/81/xqy++UAHCLvyAoGUmdlNo7I98zaI2vRxSwDgm7LYbqdtRMNsQA8aUMKSETz77ET78+EN8/NFHePr0KXa7HcZpwvNnLxA0dsaQrt21MvJxES9Ax2gJ2vGjAtk8aJHAKQHjBYCEmitQZtRcETzK1VbOBNdK2er2rClHBJReqzVFg2H1mxnAuJ3wJ3/4Y4RhwPbqHJ9+/kkjmADXj4ZoheIa44wABUZdFtSc8cknn+HFiw/x9Mkz3N7e4Ve/+gVubm5QmJCXjBDVVaZIPXMRxpZqQA0AkmQCbJmQSxaLUEnl22+/xV/+l79ELgXjZotPPv4IP/r4w0az6gxspQqN8Yvg6d2LcO+llXQYmTVByi4ES8moxRQ9Owu27KahMJiLMlb52wrBpUURK5g9wbpjWAp8az6Itu6q0HBlcO6UF1Pe7OOwGHnjyquz5lqAcWB96VEeZ1oX0AQAdZJBsjvTEEBhQgrSLd1wbFEruBZMOWIpGbuN1OLlMqLWIrHhCs3ULsg9VioAQDPDo7xgcWQuFQUFB5ZkjczK0IutL6ReL2lTYv8vMKO4QAF0LTtLyPMS9JA4gH3nSEO3Xm4DqLAX3zFLEnYg7/7zUHGEhRHbC50M67e+CTvlwfxwqyzkdTwuuLu9h6EhkZVfiOvQ753n2V3aPgTIqbGYtRhomiXawW9+1/WDQlC0CiOgfra6cUFqm+QMtae5O6ZNVw5Y389BDzZBi+2pTaxplWvh0UcFiODZVTYuayrqo9Hxm1vVn7cam48QNhBzk7XpKlM5/Zbzjuo1Tu7lI3FfSoNUWlGAuEsYd7d3uLm9xU9/+jP8//+X/9VdbG3qJpCVqg0soDMLbH4xigs0DQOGaZTsWIq6RgHjNGI3TXjx4jk+++xTfP75Z/jggw+k7idIyQSooTCyZmRlFICLYiDKIQFD3ZkdZSuCBMdRIPTjgMrFD5IrvzhVkpqm2l+neLTm/SJbQGqKSQUQY8KTy0sM2w0uPnyKqydXq3W0n1abpems0jEsKKIGA+fn5zDYr2mY8M3rlzjmBct+BrH0Q6QKoAZRerho+rayW62PhSoUC2Wf22G/x6t5wTffvMLLl69weXEJk9DydVurE0pTYbI+Z2K/OQ1i9Z92+O3YVgYji6bel0ysHtNZa3pmW3igKSf9SehJoBskWpYagMrOb2ElA6vNlhut6IC6iZlQJMaqh1AnU9lvhPYlGXT3ENWGCFqTKQkxY5J6UwpBu2EIsVEAhkRgjihFkrlylmShmrPwo6p1jW2qchkvKmiKCDOyxRtrQS2CZGOjTyNjIBM/tDoztvcMIUKq5oOBJuM0HuPyqWe2tlnGb41AavMHAHDIvr6RuqgRTbqtqfOhgo8uJ0SUOnbZ2ZEoGKxZnBn7/dFj7lL+S9LJJ7b7L8siMXo9z/ZOViB2u2utGWydTh50Lllf3+8OjUEPg4BKpnFoQhESsYkK7xQg56qqz/80sZarLG4fkkO3GLQCXl5fBHg6rJwVFoHTrwIAimoxdajH0g6FsD5dnTDvXquGsqBp09XcFN+R/WlCs5YWoH2MHlbf1DN8c3uLu7t7/Kt/9a/xN3/11/jNb37r7qKVigPVogy/aB0iRVALMA1ikW/OJPElUstL3m63uLg4x+eff4o/+PxTfPjRR3j2/DnOzncCkm1HSUyiVlpCBaUyFk44zAW/+uVvUUvFRx//SLLQLDFUT1zBAEZBJhWmAaBEiAiomUFLtaPVTc6OsWqAeuy9oauts0JOEVhjehoTJEt/kFjoLkVszrZ4+uwDnF+cww68eGoU1SJEEEXJODQ8S2bQMLgVxwR8MHyEJy+eoaDi/dt3+Hn+e9ze3AhfK4QDA6FKrKcUxlwEJmqICRykv1zOcnZyKZhVO6284Itf/Bxv375GpP8b/ugnf6AuagJqAJjdrW6EVVmmbBZz8aaiZPqlEQts1eSsBVhiS6N8sfa6TcDKU6HZg2x+8I5hMbBS1JghWZfmlVGXnsUZ3cUFERQERTwMDNLawFXYAM39D7AnVNkYssIDehyyFk98IBu7HxCB1DPh15QpAhARo/ROJJKuKWYhV81ElSMXUMqCUgsoJp13QQ0Vu7Ot1h4eUUrF8VikY4ROm4q5jBfUqjmxpeIBYHJ3cSkox6PWFWtZlUH3mZXAflxgpZ1YrEa7dHsvuQMUCDQ0RdRqNZ0g3MUtdB1UGXRnCbOEczQhZgXrzMYN7TVV0kGr9nUBjHICVFkBcBFc2LyIeV0VhKIgI3MWLwQpGLsBHxhPD8AQIyKZH6EpAZWtvdcavvKx6wdLJBzdPwjMTX9DyyIyoWjGIrsa0u5j0p/8g+jPsG/aD43Drtr93hN3CGadkFBHWH/mZETdX+TM0GId7uPuhLOdTZNTpi2f3q2pcGrN6AJUdX9cX9/izdu3+MXPv8Df/u3fNcZyOkxTvjwBpr1ubt4YJcklaZPbGAw1RgTaZjPh4uIcz58/w48++RGePHmKi8sLOSBkEbFO0CsRy9QYy1JwOGa8f3+NWiqePHkBnhTZQU+kHC7NM7T4AUFhs8grZ8m/0ZNSZwm5/tv2aE0V/XvGbSqsj4C4thI20xYpjatvWWKW0Ym5faTlH6nbj6xACXGbME4jnlw9QWDCbrfDsizSXBeMUCXlPQQRNCJ/LGYkXIQjI1mD2qLzrhW3N9c4HO5x/f49cs6SpBRbwJs8RtDiii1LDp2y1LSvB6Tj2rjE5Nses9Jvz7wajcr9u/PLPe/trER07zM3XYY7RVW1PolP2j5Ax1NhXn3qxsDgzoisTigmgCsDCGpZKEeWR5PXjlhuAYfGdldzV1qzWrQ13ekd1P1b/ScAVAWAlzMne19AoSIXAhX22DkzK7ZxAEgs4QCAalAvXwVTAFHt1p61VhPiVVHPUrc4Dyx8oJWvwDLhfaGl1MAhLm0duX3MrEInL9UtQ2XtAaiJOBYsxJoWV0NxXtCxQKUZWn9Qn2nJLBa7ruJ6rhlztdrPilw0MdPASa09HMv+tCfB6VVcRHzy0IfXDwpBBmv6fUAaR90kTYjRrEzHqwTLHSNAhVByM7/kEDx0g6zjPBAtv1stQz45HVNVSC4q4XsFqLNclkWUYGqX/WRCBkBQFHTTtuUZQKytt19/mWuO+uis/rLWbuGhjNube9ze3+P/+6//Df7iL/4C3756hWa5dgxex+YJrTZc3W+iiBgCthupgdtst4IBqB3fp2kS7MzdDp98+gl+8kc/wccffYyPPvoRQkqi0JC6RWz3NFhb1Ca+vbvD4XDAL/7+S9xc3+Grr34LK/zf7c7w4qOPlXEvKHlBPtyizhllAZBV20ZEhDVpNbdgY6KNPEP7S/eKSgUlEf72uaRLWSoknhuOINXirZVKCgnbaYtxmCAVcgooThLfIqrq+mrryRGQ/oKdwsQAIeDZhx/h/OopSmVcv3+Lv/+7v8Xd7Z2AG4NQlgVMhDFpu5pFmVgpzpRDIIwpIhfFJq0ELhXX797hy9/+Gk+ePsPl1SXcqdPREmAZ19wUTbPs9MC3uJIqbk6kUGbTuRClnceKpv0+rp08ZByuXXevEQgIZm1RE1S1YllmrbnLzpgsQc0MVgoAInlNqx9JHUPVsgwp2Jeegwwgs6XAyxmdoiW0dZlzVBGWRQJk3ihYLIMKofeaCTERhiFq6ycAsHlaqYvQss1AoeERQgFRxbSZMFbGGNW1B6Hf+/sDKhOiwbhNWvdWxBKvZcG8FOwPUpJT1Gr53pR+jcO7HFx9lFAWaeybIbWcdKygQEjL0AQYpAwj4EHjFLlhCEAOGIuh++gJMn1zRZs2CEIxDNWytnS56Hp23obKjFygTXOPWPKMglmaVmNG4dK1krKEmIrCkl0giQiMZQlGVat1aL0Qv3sp7fpeIejBcj1VIVjWlFmH6qLsoCdcAakWd1FixkppfUSDaItpL7rhc6J9mOkOsteahus/THvl9rpltImrvlu2ThAKb2hoNW2wJ8vM7TuucVPHQH0N9WeVVjDvr2/w7Zs3+O3vfotf/fJXMC3Z+Y4/s42t4Rqa0moxQLH8kia8kPauG8cRu90WZ7sdnl5e4tmzZ3j+4jkuLi+x2e6wouPT5TdtFIySM5YlY39/j7u7W9zd3YKZcX39HrlUnF89kYQCkgNdloyaswIcmPgJqBxgwLm+OVAhttIMe21ONGDrb8w9fdk/dso61eEVRKBhcK52T6WMLzWR1T/r87txEWGcJok3Pn2CGIBpsxHM0bkiVIaBIATtkCHDXxM46cE0p4LF3e7v7vDtq1cYxhG73c57P/p+kGrI5iq37+sYNYHT41C2LisljLmjZyiTpfUY2c4Vr17oi5TLI/FqX1JqbtJaRJsvWXv61d6tapJPr2i8ovW97Omk2txUCFYbj2dHikRIkJY75CDdWlxDKlRCNBHdEUJr5xVjFEixDJikYeX6J13dGu/QNTD4MESWxCgARFWA6KutNVpZQgCYg3puSUGtNfGFWRQnd4eq18L4XTFq7vahEy7i0SKnC0aVovNcV8pkDfXx6FNndYdAiEX3JWhSFvP6LNmnVXlcnR0zJlwoGh+XPa3ab1KaNEsyVpOyQq/No95i1dVuotnKD7Q5dBHVx22j1fW9QtDIxefkapr+ErUI2f7pASIwQhR/tt2hat8rL2Q95X9VzX7bGf2bAtRkLj4E19ZZNlMSNIIvjC2iHdhivmS7tZvINo/2K1HAcEoeDfa/LQRJbCNCOiMbqyiVkGvEyexwd3+Pm5t7/Lt////Df/wf/zPev34NQGM1Hd+x7M+gyRoNfQfKaCNSjNjttogpYjONGpMNGIcRZ5dnePbsGT7/8Y/x/NkzfPLpJ9idbXF2eY4xDjL0k7WHrqnEJEmbFLFgZmbGMAIxFXzz6hvs90e8f7fHdrvD/c0dhmFEShNiCpi2AeAMKnvpIhEkq65UYGIBXWu0xd3/+tWVfSlGfYU0j4F05WVvBnkZs1qvng1H1WOOzEAuevCDaL2JW4q6uEfbXlNQxqflpnaGUhQ3648++RhPn1zh5ctvMIxvcJi/QmaWjNqiGLHQch0SoONKQIpRIjXa307ovaLmBX/913+N3331Jf67/+7/in/5L/8lzrYTpnGApaRbiYMTqFmuRZU6bZtTug21Y6R5SjDkJQtrCHMvTgQ1EzgbQ6qC7h8CyrKg1oxjzsi1oglTIcZxHPU8Vz2jAqOXl0UzLCVZRHriVZSyqEYTlFE3YRJ03YZkkFhR8Hf7SRGaYDVLUc/NzSzn3uLTgCqPQXEv54bppWF+4VNUgSOB6B7sCfCyyPpV6bJuwAAdY64+NBkcRVHahloRUwTOt6IYFAG7Zm2uWwrE60BSzhNTlPdKE9CVG/9iNf+ZgZkqcmaBJARw6tIiTqBaEfJBy6ska7Lm2hL20L7KKwhLzdCtRY0dRgnAkIChc9SY3R9V0LCapixVXSrUGMDiMsEuZjHiSmWpt60VKAsIFUOKcq9BkmLGGpGLABbwrFVjGlNw5UwVz6IGTjkqT1Wv3kM0r4fX91uCneW1oqA+PmdPMKZhCoomFxjrAtdWU2WL4sxY/c72sN6Utcm6htJrPS74XQit3oRpEGjQOY+5TRkrrWG9bNx0x07r8On38VBwA9/3Qm4Z1/XNDb7+6ht8+dVX+PLLL0Xl9PVlZ3rGwTxW0q01kSK6p4RhECR3SYyRIvhxGnF1eYUnT57gxfNnePpM/g1jwjCOaDiBD9eAoNpZZVARjblk+UckoOlR3ValLMjLEcfDPUrOSAMjDRFxGEEoCB6cD36A19WbvdRfHRGnAUO78w/49pimJ0zMrB5ihmeLVgmuU0kYuGmeBINI1+Qct076fTfYqPYe6dqPGg64uLzCshS8ev1GEPtncfdV3ysbndGI/kMf1xYmd3d3iyVnvH3zBu/fv0OkKyTvY0itThRrt7Cjt5j11S+VTY0gbl79nFi8zWshXyCHg6ulCAg+S4ZrKYLOnxcRgs6YzRNk5T+qAGfFki2LMD8TmoUhOJ2W2l4D6iqbz3R3oBZCLWJ5BO055AkaAGouTRjbPFjg6qC8jxolaQufIP5zfZ6VL4WTfnO1ADUzxDwNSJqhyNEK3MVSKcpPJGxSobnAjaKJ1BAQD4EJIzAEE5mqg3OHQEiQc9LnOYhC2iAEjNeJZdjp+s1dJvePqjCXBjQhytRacNu1ttyMJNQTxLJuIQTxePheid1gKWk+vmqJkTp2LrAnkq4Ls4al9HNiybe4r7vJSRLjAlPDxnX50rwTRpMWLpD4Yss9CkYQ33N9vxA0zcRuooOLw9AxpU4YOl1qUkR3r4XE3GrCSrSHUqTGZtAsR+lu3o26B0l0fyDQsO/0zzbE7qsGSH2yyw0m/eEN+u/DkOnhLljJbnSfx8Mv6Rgsy8mSbf7jf/of8f/51/8WN+/foy4zoEDCvZIPACGRJmmeuPhIIJGuri6QhoQ0RGdEwzDg4uICH330Ef7FP/9zXD25wocff4A0jhg3kx5KuYqhz5xYuwyIxloYx1my4W6vb3E4HBFDwNlui3/6p38odY1zUaFQEEj7knEA6uLzYEi8jAAUVCRUF0AijHq3RtsAc3c1U4yVxz4muEm7v0vTVaOteb7Dy1cvcf7kCpvLnTPbpQLHwkhxwRgzah2RsjD8vtDbXHKWmGj+YwrAOIz4J//0T3Fzc4PDPOPdu3coy0sB2gYAKggeVW60HkhS8SsIg7vXCMu8IOeCn/3sp8jliH/xz/45fvLjP0CIA0KMKHVxl58wMVblSpMmNEvUmGQx/6GaSM4frdZP98RqoOGWDYteVgk878Eo4qar8JqrbCgles/jcS/3M1p1ODtlURrXWQ4VjILKMwoDuYgA4BA8xuV02Lg9QJKh2VdLUVVMST0Tw2hg1J0azIxcjpB4XpunfcDA4gO1jm0AkGdGnqGdXgLSEJGSAdM3Zuq0q5mLCaPmTMQHGe60+s1ER6P3QIREQaDSusSiSAAjGjwmtE0shhiQwDhy1UCAUbcoOWGQXQ9RareheybuakbN4otIHbpNt/oroVty0R6O6kkwRcXrlmUOUQgMs7qr6yLlCaUe4VBsRBjSIJbgLJ0dsilK2vIqKz5ybyoFBCQE0ABQBJalSHNkSMskLoImUzTuWDMr3zfqHx7M8vT6/RJjuOf31LTNByrEQ0nS8beTjwmDa77eKi6CLqMTJ99dEVQ3L9aHELipwd1XV8F8JSpFV/P79hqRZQmeGArdPe0NiauQPcMErmvpjJubG7x//x5fffU1Xn3zLapqyyv4IfsPmQXY+7NllFFjf2lInklIFJDSgO12i+cvnuP582d4+vQpzi/OBCU+Rsmi6iXTyeXxT5+faWkVJWdBymfpMn6222AcIigc5KDGxvyER5Quy48lMcSSQ1ZuET4hDP/SyZ/szKsduW6srn8pw+cCMIFLxnLYoyzb7p5irQjcmcBVEQoKqx7fxal61CP71w932kwoteDi8gI5F7wZ30kicqnSNicEiZ2QCGhjxJaCb222vC9ekXrR19++xu3tLY7HI6aNdZY3gYJ2TrqzV7n4+CQepzRYTnR+xw7VfpdOc+SKSK0ELoTKGcwt3b+qq7eWKkql3rJqCYs3zV6dOxa828JqAUq6Va1mlKlCrLE+33N3RQtTr2Gta5ICK0OHLslN5J8xxcqZoiUEuRCUfalV18HGDkZegJwZgQXnU4AYmhD0eZONv+oaFkm4crc96/9NgDTFpIedM5Ej95efjeQbc+jDF/JooSMOvFIQoHvBzOAkJUXSkEDuVoMVErTvtaU9PV3weVQW75AJzWqB+vYRWT919xpd51I7IShZsEJnHayfKlW1P2+rkTlTbmai7jEr2IDXZ5u5u57BD16/VxcJP8hQ7bjkrvOCjjEQQmy2IbO6ZQs7Ya8GRB1zhsT8KlUMBGh/EFvxTiDggfWlLAJgcW/EvkD35LKX8jp5SWNglj0IVH2+a5K9BCXRBAloDbMgSQG5ZGeWuRYsnPE//cX/jH/1r/4N3rx+jzyvF6LpFQRKrRkuAbCc2KAW4MXlBYYhYZzE/RkoYdLC9w8//BD/p//L/xlnZzs8uXqCYUwYN9JPbDVVI/xKDbfV3rDTZ8yiMHI9YMl7lDyDS8Fm2mFIWkNZGUQauypAJEZUIcgV4OUI7G/FJcILGAXUp7aZaf3AQbPeLHMEaE6YviyWvIoBHfQsdFMIOOyBt6+AKQD8KVCt7Y5w9VqBJQOFCogOmKYtRkweb7N0bbMAT70BXCtSivijP/wjvHt6jevrI25ub7Esr8W9NSaEInRbakZmSfXmKHHSKUXkvGCZF5eub169xv7uHp/96BNcXlzgg48+wvn54FZp1i0qBo/VrY8o/IrrmWddVvb9dRcUV1TORnL+UzJ4g+4LSRF3bV1XjGkds3gIbHuMetJ3tLSwWORxmVXo23rCC8mrfubUIrTNOGoiqyvj+q6F8Y91EaHQt+vRPSNmTUyRF53k1IIjVTIDCw+o+j3OAcgkcy0tyapAlJw0CApTzRmoQBkEeq3C3N2yliULk88KGRPYOqDIXHoEFVKrtELdskBDUWqED8NLHQeJfJ8muRn258AtvUh+Gi2IUD4cs5YGmdu1tafwrdBwhrhmizTfBqEkQiFAjVMXZrMiKIUowNf5yJ1MqljijBYSMFc5YwFa8hMCuhxwARdgUb4KoJXcrAp27srR1Q0cgmBTF5nzd1dktusHhKCLGOcArpG6cdxJZuVHvcxon29XH/LrrGq/P7k6tNr99n19SHsOu9UmstlV46ZlMqv2dnq7NrLVb/0pt1XwQ2bst/rHK3cFqABub+/w6s1rfP31S7z+9g3290d4bRebUtM4kddjkv0t0F4xBcQU1AKUDNAYIzabHXa7HV68eIHnL17gydUTSdUeB8QUEShKSYpTYZsQAZ4c4nirtkYwV27Rbs3F0/Et0WkYBp2rAuYyawKG1HDlUsE5o84ZAVWQqGxeIM8KfeBJoG6Yp8rHyX/bRtoPs95YuEieQSU7hRqsl2vqgFvAtVRBnWdhMlYKYFzGEgrasERr3243yDnj6skliAj3t+/BrA1hWcEmELyeiRyOz4r2g2c8LnkB7Qlv37zFy5ff4PziEmdnZ63N0ckZ6elwtTbdPq6Ft1mRzcqlbh2qYVLq/jkMW3fmcynqTm/PJwBci9zrZDvB4p41q4xLGx4TQeCwmlDoPYUWyyoBUsvpcxUvipVbkaIxJQ7d+WnLE9ydY3ysDc4xjPV+bmG7J4glfygKBmuFKhrSekPKUpiBIlmTuRo/UwGVWRODBCySqZWYEJs+TN1e2VibpWt/Sc6EWKdQfE8zNPqrd+v3bzUuwF4q1oRgB1qnblbLmvUidDdZyNfNNtPwZA3sw7eR2r4RRGEjjcP2iluBfc/2gGAA9fKvPQM+1tWs3WlmmbRWktHG/93XDzTVrW7VyeJ1GVYKOdXLhWq9ADuB1YpZRYtw3mZoEVV1PpMxvcloAg0E7+jQM0f/eQKDpoyj1oqydIxC6sKBk0XxGBDMrqjrj5wKQ32alhS5C6p36/z0v/4U//bf/Q/4+uUr3N/eoyyi7VrMy1BaQqRVvZoBJg1xQowRu/MJaYgYN6OCZgeM0waf//hzPH/2HP/sn/8znJ+f4/nzFxLLCOIqSYhuI0FTkLXpH5gzRIUSfbqqS4HVZbPfH3HYH3DYHzEvixqJBIoBIUaca81VZalvqseMwhXHUrDkjPvDHvlQcLxeMCRgMwLDEPB0HPS5GR3Q4nqNey9I935zT+qLVQ/ZICRs+bjFBHq+B9VZMvt0TaMqF2rO+UOWReJyVvJzepGaGSJMTSEIuLzYYbMZ8E//6Z/gzZu3uNu/xf1dQF6K6KAktY5MQWlEyY8BSgNCSJiXGXlZcFxm5Lzgr/7qr/CbX/8Ku+0GT59cevJfrY6l0q1JWywv+wlRsjI7YdW+07RPhhYoc0FZqnTnLuKqLEtBzaVTsvV7loFbOsUYFcSLfNIy8uyh6vgQmEtG4Sq1lc5GRJlodSN+dH3MNQYD0dEXZnmmDiVEofftsBMPyUCdls3qCulWrXvb5SWpJaIDYFKLS91rBQUULAMR0i6rYwo1ZxAz5pLVuqnrBzjeb/dMeBK/W6RwmDLtyNNUOHicQffAevg5GpZ+nGPSb/cJUHIy5MyKIN4V6fHY9HLGUhizIs9UwF24uUrrsdNDKf0DpU+j9fBz9YsAGqIoKequKKY0aCPtuQqNZT2OFt2uEEVsnpfmPjcvgisDURUCed2ygpkkd3VYJIFqOTkzj13fKwQtXPDYZXrU+jX9+ciXuriyaBj6tyC8aD2NEkA1FUf95KJGyOnyNlfcnsX6s0II1ZDO+2E8xgj693qADBO8vqEnd1kzIiMMEYy3t7d4/e1r/PZ3X+Lbb1/j7vauMSVuOgzp4VjFNAgCWaaoJyklaZkUxR0TtTTi/PwcL168wNOnT3F2do7NZqtQYL0pJcdAaMS4iD/d/1n8sTJpGnvFPM84zkeJBVmMqfuWcREi6ZCOxJpZJzBrZanScqhWAbAohCWhIVqB4MgPsomNSHptmIBTAux3QGRZ1ZqqbndFtfeN6uPZ5N/vFDX9j6NanF7fcQiC7tP52Q45Zzx/9hwp3uDu+iBxX7foo9K7ZAkSdbBUphiylBIcDgeEQMgmKcgsHHE1N023Gwi1M+ceBiKPJRkAto4a7iTXdbL4tWUHs7VM8rWx/Sd9likwGosx6XUKxVVtXoSWwdfrP/rf2vbd5ua8pFapftL/EWe4EFTBz0TIYUbgiBhSS2RR4YsTqmcmGBQiET3gV31HDDIhojHlh/Rj4+5ivOjWQTSntlG0+ub6c/ZNCxWxb6iNXIA5HhGCZLCKBhNHdELLwgkKS/lD7t/SxYyFEYMk4xTbz8riLq7ke23egZxrm5Oxnn5eZPNV5ai2dTCj5cF+o3kHWPevM+Zbnejq/u151BgpUNGB5X339cMxQWppsL+Pf7Ub4oP7IJj+03hUTAkxMniWBS/qN5eHESgkWeAiGYlpaD3+WPtr9ApfrkJzAnDc7cxqKEZ18l5Rnmnydi3kAPB3zOlk3gUVP//iF/hX/+9/jVffvsbvfve1Zu9VCAx1cYs4BEgcw1KaIYcvpQkxJOzOd1J2EIMHwsdxwOeff47nz5/hz//5n2O3O8P5+ZU00rTBunYhPyIkxlm4U7fJQA6k0qfojpScMc8Z17c3uLu7RV0WLUq2nV+TS4BiLo4RJWfpNrEwym3GUjL2vEiq9iHhcmDU0TT4CGRIjZYySxs+WZWfaTtk2H+ENXVxo5PKAlZNXU6VKkLrfbdnkCchrI4Ic+sq0m85K7Mkci+HZUWOFPD8+ROcn+8QQsLLr77Bq6/faDZdQeCkMFsLiAQ7tS9ct/vZUO8OB8w5Y8mLWPVDkj5v8wxwQSCZZ85NOZGEGIh00b0PPUJrtdOrb9riMNwof9h2Gn5vZ11FEHfgjL62+3cCFWB1izdRx5U1Rtbdl4FcWcE/WM9KE+imCwkPFeEXkrhBg6JUWb5ErYuUCfEWgSJiGJSqq+tVAUDq6+LU/SINh4s9SPpudmmjNtOgNPewyJyxJphH3gakcP8RNUu+KU/poe4Y9MA7YWVKJvMsszmNEygGpJhUWXlkLFxR64JlKbivR30xIKWAQXGhAcacK46WOMGAmBcVNbNgfeaCXCruM2EpjBQrODLKjJVCQVHbu8VOgejcwVbHsFLqmAEu8NIaXXsrSXFbyYCFyawdc1P4tERZsPrx77l+wB1KXcZXjwBD/r+1Rkq+kI/fzybaPuOMyQwD5Q/V3+duYQm15jUZeS1N92qneLk1oQJC0oUJdEK05oZw5csZ7w+IP53yvGTc3t3h3fv3eP32DW5v76Qhrj2HXeFq1p+dJo0RSbZnQoyC/2mvxRRxdr7D+fkZPvr4Izx98hTb7RmmcaNBe1oNcmXVqkJXKjvDMJ+5QFUSoAW985xxnGcsS0YuRZkbdQSnB9OCv4AKDbV40gAaKmiIAIvLg5jBoWpQW7MjGUK8IShvruvB+9pKjRBTcAbvJMRta40OJObH7bvdJlEIfvic0TDDs2Pbdj96OUkZgZCMlQiIJOnfl+dnuD0TEIOYpSCcAon2Xsnpr3ITAsag7QF9/IN7GqTuswBOYQIDIH3vEISpUzsRFeydAYzuuatLNaUgkChlstQKig+tMTSh6SzazlzjD7aIniXdCdDTur6HC9wJ9Qdr3wkZ7s4n2f4TrGqP2ZLT0oPuZf2Jjx6D1xIbjj68oPW39rfY74xagwrBXjD5gYKAAFjsFUYszWLRNY52EPW7VhZgseZASjsQZCgKTexGVZqDZpEHbWAQtRVU9OL59sN/ZSkqQgSmcVC2KOD8KXUNxEkMDo8p6x1YoaOHHFCqgFLkkbXxt9RVVmbFGFU3Jrea3GoWIGdQBTJrNUAvOIOc+RglDyLnKnkJttedwCADvyebYaPp1fQfCXH01++ZHSoHJQXrDq8ARG6ytkeKhv2Y/7j97f+1+XffjzqxqkCwrK4PKDJG5lkZD2B5lHK3XnMTkC5SZH7pvWXJHxFWN9OvlAWF1+vWZbCeKBn2d63i5Lq9u8cvf/kr/OrXv8Fvv/pK/ORg9Ac4QpFgEh5ggsYwIMUBm82krlAtgk9SBP+jTz/B8+fP8ef/4l/i/Owcm2EnsEZBlRGTS0GQOPrWbaVIS5EY5ZBUTXyggRGT1Grmecbt7R3u9gccDjOWufh+Rm3Twl1R8Kk/LkRC2u5QQ0BcDgh3FXzHYJLYWOaIBdHRJnwRuDyewqokRBxbIXxnQVmJiSGwoEeLpwgki6qQt/timEa/Fqas6CY+t9VQumQq+yWQKoXKqACEMeLDD57geLzHNI3Su48LmARfkpiAo+xTLQ3xxMxg/5NEYGStywvs6Rude1II1EEUKsOwm6DlF4FazzUQgVOUZ+hNqEicq4aKwAGJlckB4DCAa6skrfkArln76HHbHPtnWRpFhXxtila/lL3L7P/oZXvnYOd+McAFXI9gKuBg3VG6T5AINGlYo0pm9EQBVzpD0lg9TNGSmHqtCZVZ27I9YtFFAldGUMCByqwYGEmtVwGcGIb23VKrW/UEaNeI5DLSlOKVux+AucmHafBcgP59o5omGtQKDQk8JkzDgMrAwiZMm9I4jgmohlVUIf7oCoQEoigweLViuRTl+rhXT5DGgC2B6DAbXQu9L9n6VB4FP/cAUC6Yj9llgZynhDEljADu74/IuXbzFgKSDkdKZ36q/49d318s3/v4gZVEFUJs8YmeJNpGrK+WPHnqNCVI765mwgTVGjy70biEJUQYUZBZdnIf01pWo3HNCzAXDptGz/14Ok2TurfxQKmDaRxcC+bjEe/evsNPf/pzfPXVS0FaKRYHlPsTsXUqUggmfU2ZaRoSUjQUGNXaA2F3foazsx0++eQTPHv6FJtpUrzObp96p7l2z6iqLUtVgNZ5VV1ZEnePHLiIrCC+h+MRh+MRpWasMV4MQLw3Rdr62gEbQgCHiBgHUMgwlAyLz8oak2f2eexKFR97hNkr5AsPwCxCSURXKiIvnO5MDrhlWSt4KfK9RKYjtKva3kAFWxuDELgyu27aPnVqx851byKMw4CnTy+RUsDrNwuoWh2YWoWVVjcLFJBClA7aajlVBq6vb/Hq1bd49uI5tptN+0r3XVJLQuKMUdx1VFTOqfVhng9T9khpICQwAyEyLOXGIMhCWCexkcEf1gJD0lmfbd2P/pA8eFstYtacOMg5JpaYkzzMlJjmNjU/j+xxFdBq26Cm9HtRvZCU3dBiVqagyzgs1mSKqcWRBI83rNyh4okCmAM4YaX8+HaYFRsI4IpaohsJgClqZsGRWnOqnNXqvU5ljNGzsOXzcdVEwPmP+3Rt7XretKKQpsYZX9M/AgOJIdmmOgay8ESwmHLwBwvAQEQNJN4BZsTCoGlESalDFJL92kzQ7GfJ8FyKQd4RcqkY0oClVEyb0gQad7WdgAjDYXDYzZwFwMFih3mRLPZFkYR6WcW2OD+geH2/Jfj7am3c+Ebbkd5Z8sit+ORFTSMPNn8VsKZW1lUtEqOSgstGEZgJhIqAgqbB2WYTBeVYEpX3w636s0FT9fzd3ZYdYTVh6zdHKRl317f49S9/jX//7/4D7vf3KFlTezUnPDBAkRElhObxOoAFjiwETJsBY5o6JBgghogPPnyBZy+e48///J/j8vICY4panCyjrZaWXQWWLujBZ3BLQlBwWklhBtIwYkiDW5H7fca79/d4f32H/eEepcxoAGPCKppg6lQXV4ila/QWQIgJh3GHmCqYNWezQJMfWuyrJeqqIARUELZj7AqNCSQTlPayKP9tVBpPkD/EuuPDLLS16VGOjAkbIahSYu9WSVwJMQAhSIZxZ9XYtGMvUfXt3XbCT37yKV69eot372/ADERK4MCIKUnhdHdFEtfbsc6oyKhVYmm/+/IrbDYT/jQmpBepSV4XFJ1UY1l3M/XEaE4AS1xSt0DT3mUtWVFOEIBABYbJWCoQtE5WEvuBwCOYKkrdNyXDBJlvBgHR3KBiKVLQU2WCF0kUEZk4oGgsolBoJ3NJYPbC51yznM8q8+FyADh70k1f9G3KjDyvyJModnvWREKpBQhSIwkdVUwRwxCQQkDsQb5NEAwBiNSsbhNymvUYHomsVmYc3PIXHhIhgjaMQRXVUw9UR1ZMK9CsYOg8LgS5S8Lqud6aB/qGxW69bM3N5cos/fdIQBN8gykCHDClASlGLFXQXgIqaqgYh8FmC4AxSAtNF2pLFiSYoxoGgibDOM4Ggg4XgrkW5LK4zbM/ZuznjJqP4LLgfg8cZ7jidjwuWJaMt3e3mJelyZUu9P1D1/cKQasVMj1ELKMua9IsDYUwc01akdvtc1VeUgV9zTjcZXKCTSfPI7fG1sTxUNuUQ2DVi+vwsxyOoM/r0gZZEyGaTxYrTgk0K5eAXv+rpeJwOOLNm3f427/+KX75xS9x2B+wzAtWGWKAhtWowaF15yslcYNGRXcJKSLFhN3ZFpvtFp999hmePX+G3XaHMY1IhgBjMZ3QLCHRWgleD8bQ2I/rRBB5WZALcNhn5Fxwc3uH+/1BYN7UWmw73YuNfn36FW5qBYP6DkLyOdU2JZovr3G2fWWVpnWVMSlvss/N91FehjGzh0xDvhBQgbqgLAeEHADeOkNgn50NkluMTW9KpgVBsRobPE3X29GEpmqmkCamF+cXuL8/ICbByBRFRbTtFCNqUsuvVp16lwWtt7y9vcW3r9/gfn/AxVKQUstY9IEacopTpa4l13ZKea3cscZZvSZI/HQat9QhsJz52ra1V31gGojtjX2sd+kCkKxdWzMVSL5sEhwDqHMrU2iKVRD6JSIBCCASIRgrgGT17u08Gv9QN2iwZs3U+IE7r7uYnAlPj9cZ/+i9XhY7D7S6n3F6lR/wPpKdHJQorcVkQ/ueu5vQMlHxuBAUBZT1jJOfEfOm2HjbvfvnG2futs6f9vCybFmnNU8wMPVFx8wEskSe7p7W+YLsq8yISSz+gbIk6ZUKIgYPvQUon02VsISgsfGKCREUBnBJ4JoRA2MaRTZVrhjTgpwzMhcc56iKf8OxzaUBvXzX9f1C0LMC1RJgYVoOcVP7mGCLyvnyUnO1NAvXwIEhQughJPtKe3GAXvSToZMfyvQ8I64x70ZUuolqJQhh1JPALK1pqJcB3HudCWWpeP/2Br/8xW/w7/7tv8ebt+9wf3+Pop2v/Z56gMhigZ0AJALGcYNp3CIk0caGYcQ4jvjgRx/iyZMr/LM/+zM8e/oUF2fnmikqY6mlo03lQPJDoZqsfYm2+SH/KIumlRe8e3eLu9sD3r6/xn5/QKmLKiVhtZcrlaLTNhttecdciTPkE1QeY3qrzF7RoCU2o3EHnGjEa36iy0mrz0iwXgVbdWc3CBkoM8rhFnEIQL0QAaADYpALTBui/RIsnkSq2HXxknbIT+hJC3PTMOCD58+wzDPGSbslLALAzCmph6FizhXzkoEqDIECxFUKYQbfvn6Dyow//pN/hKunCzaUkHrtyTjHKhZCvt+RpAA+t3d0/TohpS4jY/JUASoqH1l+74WceQOc5mDvdUk+nWZCLjDEooZmMoaua0vQxC7J9uwPh/wonERhCNphICUQVQxoFjkzsCy5y00gAFFRrRq9uvhSAenUQJpxSaR2YSctCJp8ooKLegqEel8sqYf83LeyPi0OB6Gx214tkQ1ovS6708YAOHhSSFWlpRfqgcwPjCaou0UUOpbc9e+Qew8uIngilTCbqJn2XZxdZweUDt+1w1m12fkxEV5eK2MmsV6jag+uitaKWgOWTMi1YikZQwrYbYK+V3G2rShZu9GXisIzas2IATgcj5iz8JXD8U6S++ayAjF57Po9YoIkeIhgzULspAQBzpFhTFk1TaPO1Q1VfXNWpSeHGlmsN6rdh2Jwf/WpVirxD1l8zcZtlpgRp44tBMXUe1BM3DRngvjDZc/1+6RuCR3P/d09fvnFr/Gb3/wW796+x/3dPfgRlSNQQIrB8IB1GKQZoFGzusgD4JtpEgvwU7EALy7OMU2TZogFmM2xftIaQaFfy6CHOAQ79AG1ZpRSscwL9vcH5GURlylLKrTS8+pseWD99FJT2Q5prZJGTZA4p8JSgEiywRKZG7RCwRtPJN3D3ZVXO23ElFScfFcHa1YcV0Y5LqibDGZB+ye0MofT57X72D2M8a+ZFj/4rtqBJIkMu4sznN2d4+LiAoH2WA43IIpAEAstcELgglBZkzWAoG42EyY5zzge9yj5iFpmWafKEHdCU0xIzwivVP0AkDRTjSQ1uFVjh1JK1ObBaAyPSGKERWOl7iBh2wOtPzpZcznvkhXo4ThSiIJA4qolnFiGss6ByDOciZpgApltDQSOgmPKAk+GYMkthpzCkL6VUm4hwlj3sEsRNbYegpYaRPJSg6AKgThRJSHJxtHg0ACguvisFo+uxjtUua7teezKCqNlQavHpirfch7TL6spHPJsrlCvyZpUJW9Cd5OBLnUIgBbuE8MCr2QWfFsNWaNq9GSVdWaekYJoQJJZWBtas2YyV6Epq8AiaLUSndCYjY2kfZXRHJOAWDADiC2ZMdaAWAIsi7nUgsrivi3EiIFQtAl25YR5LhiGCfMiAB6biZBzwbvrO5RTHN2T6weEoEyjhqpxqPVGyTpXQPVNBiTVmOLJh2CSqV+W1WecnfQyVnmcaEfqs3aiksuxzdWdQSTxQnHTt+fIAWP5HFuokU8EV6feKmfpdBpPXEAtuL65xV//zX/Fl7/7Ct9++xpLXpWf+ndiCBhidIlirpdx2GAcRsn8isA4DBiSNFY9PzvHP/jjf4APP/oQT64uMMQE08I8tb4tgK+bjd3oLYC8e0F0vz2QF0IpBcfDEbc3d1iqaFPW28GUS0/DXpnE68u21MowSmHURVyb42YAlwwUcZvMRVzBKUCYZFhpNCtlxK51nrEJMGM6D5c8mBZLErzP+wPSdtcV1ZPqrw/nY0ZMAzG3JT5NuOqSx3uFkAQ39OLpFe6PRzx79gyBrnHzfq86OQu2KQgRCwJXre+TvYoE7RMILMsBh0PAMu9RliN4DOLqixpURj/GrlSJoghADurWjKgkSl8xzw3acVwhE9pigxUSDAqWYKtjYuLhuhNIY4JQi1YYbEgRcdNoD00HBgGIJKpJ6AShbLzCjEHiklSkcHcEAAD9EUlEQVQESsuSy1JsQhAAhrigVsZ+Ec2f+mdxex5BYrmjupcpNDpnAAuk52TolPXq/genQkDd3BqGVbdzg0Vr8+0mzNlpiCuhlKAWFIGjKZntrFn4yZMIyDjlOj/CM89g26MChmXNQIwwODCaKuMMQ5AyedfUzs47x1BvmSSlZBOMTKKYwD7TRhQ7HNf1FggPDkHVmxrUgPEkCS3bQtfgWL6cmZBrRkRAIQYGy5fWWDgnHJeCebmTxL6yxZIz5nnBcV7wfdfvWSJhbp+2OTboEAISRylm7IrXBeapmhnhZ6hPbSb090H32onGTdzcYBFCgNw+ZxtBhC7MDVdLzfVlaO1cReMoNaNH12+i2Ny1MiJ3bxKhlIrD/RHv3l7jV7/+NV5/+9bNbXmuWqZ6sGKkzpVCrf9fHBApYoiCcrEZJ2w2G/zoRx/g6uoSF2c7bMZRR6Im6CnT79bKxiq8WMoZJAYlrZaGaZBSiFywvxfhd3+/x7zMKJy1qFjnEZom1y72+9tfpuFVBmph5GVGXmYsy0Eh9khZ2AEZEQcFxo3Eov2HhFBthS3N2QqyyeNAoo+cal/tV/+LG3tgAHlZcHv9DjwETPsL8LDDMCVY5BgU1WLQvTNGQwmC9KL38rAANyWE0eImSsgSjhNiH8YBT55coeSKYXgLLIxcjAalqFmY7iJdxSkgUkQJsg/LsuDufo8vv/oaIQ744z/8AwzDuStS3eE42SbNhlKGVxXZv8XcZfAWQnBD3AQFNc919elpvilB2h9R64JC7bQ1p4sqBEHdn9RtkgkL+yChO7PUYnTVzpENziV2G2efCAIMoMAYEJvlwYziIOXilYlBEsJiiHouO1HKAEpFDYSFWawnZoH9ypAEEjvHROBq0PsWitA177UzZtFsdOMYMPArRMXplNAiO9aBIfw0LqjxfeebGkclQjumvWK4jqXL6+Q8cl0/WdZhJh0LWDw60N6Alg3a3L5ohesxrHshzn0CG3m5koW/6tLKmVyHtLl08iAk5S2VEJkBJCBVEFUlccv0JWymiCER8rDVkJCUhkWKCmD+3dfvJwSZTC1pE9MrECEksSyyZ0EVlFpRuCqWZWeis2kz3QqgaaF909H2X3IrLsYW/+kDqgzWrDZqCWL6DNusYAcnmBnd+cldELan9uaVvM0oOeP2do/Xr9/j57/4Je7v753oTagTB2kKGiF1SGpWEZHUAw4DUkxIMWIIUhqx3Wyw2+3w6acf4sWLZ7i8OBchSHBtsjlOqKWj94qJvif/lySMNEQM44BpHDFr77r7+wNev77G7f09Dvng8/WcBKyz7pra0l92+EUjLqWKAJyPmI8HRXu3ON8RmTe4Z4FZG5Gb5o/qTIhNaWIlCFN0Opcr+yrYWrdBNkexvL8sR9y8fw0aCWd3V8COEKYzEYEkbiCymKn/IxBiex2GBdn0WZOJbJl6vliki8gYhgnPnj3FMhcMaZAkmGUGKLQUeSLUUpQ/Ro3XZVQUzMuCJWf85ndf4jhn/OjjD3B5cd5tOa3mLr/UdjBUYEs7m0bfpmRYeXlQi6GHD4t6FpdeCLK1y2pSWMA6mhjrFacQBPzd9qmJYEFWUjHg9GYwkDY9P1EmwE8UQC87sdrVKEUdY3T+ql3dcwcATxiSAFHEbtOdpnSdKjMEKlNf0OawMWr5QgjgYJzCaJebhb0CGTfaEYHLEIQqcbnKHBkAqiVQnRgEXbJEE7dCt1Yb2F9crc+CM2uZQpWysqiutsZdvS+D3oCal6AweJHOI6XmBrd7+kwkL7FgZuQuIYAIGDH4WLlW8JKV0VjikrLIxsT8X2ZgASFUOZNIWZQThWM0pLDdJoI5ACU1nYmBZ1eXjw15df2AECSfmLkCQ+g1EFLp3hEtjBnpguriayrCA81w9ayHe6pX535CI3L7Q7QrPcTVNF0ACrojbgoCtObGfEB2TwePVqK3MoMQ5bO1AFwZ87Lg7m6Pn//sC/zmN79ByXbAeuYCzwINQUiXIFpkiNIRfogRYxQrcLfdYZomPH32DBcXF7h6+gIXV0+QkgDcFibvJGDZr7Jcpkk35cL4k1iAA1KKGEcppgUijvMBN7dH3NzucXd/j2WZQWiCw4DEV9vAXTaa74Gsn7QLq6h5QS3Se7DW0qe3IBfC8QBMOzuajEIqxCOBs4y/10a5PRrNImwD6wUdUaOB9gGWkpEqZSJ5KTjuj6ChYNAiY8v4dJ21O3jmZmxaqtBBh3kh1OWxIB2xOUwCMGwGPH/+HDkznn/wAa6v32O/v++EUdN+rdWqwuWuhMl+f4+b2xscjgvmpWKgrAzHkhRas+r1YRchnmKS84sqMfWON1qGcYihS4VqAocrI0XGfJQGq6ryADAkoIAVDUJPnnpNWlyIXbiwdhgRAVTR/JGtIwlDQhrehUBfrwCSKbK25+6yb1svmyO8ahxHgAErjpekJxX6PlGhZfL2SpCkIJPObPvtzY3AHBCCCEfWuBprFwUBljZ1tSOuTpYzIGdgdbagvMysYqPP/uTJa1XbHJHu63ryj1zyQG/SK3tiAu+0q4Qo3bUU9SRIvoDpWLohyrIJlPOKZvsWUKbotexr1vAIJBueKpL1kOrWXxQDGWPj1idShuDlTL7aHfNq7uTvFCwAfk8hCHDDxmVD7D79qGg6bae72bBBdpkKopvfS/5+oI9sZm3sqiNeOFVZhmpF7Ri5bKaN75Sg/HFFGobaq57/EmR5pHVVxd3tEe/eXeOLL77A1y9faqCY23ygByk2a9TmHUNEHEYRgEkE4BAjttstdmdnePr0KS6vrnD15BkuLi8RFcaI2dKrJQMx2lq7Rk6doJI/QxTrbxgGgUeCMOx5rri5PeL2/oDbuzsX4FY0YJBOZgE1wfNQAAIaV60VNWfpPl4WgTiCCBiQYLnuZ2AubecYaO1xiDz43faklTHI+zpdff6jZ70NSwmiaEaZAP0e90ekXXHt376zcvvqhhHWOZcBfa/4pj2jqgLmMR/bAGCYBjx99hRLZjx78RalFrz65ps2yI70A7qhd/NgZhz2e9ze3uB4zJgzI4YCilU1LcI6/q575eeJEBPBmo5WBUFezRdSpxoirdZPeBxjSOpSrXDlsA2/ddew+3L3r92sZ7QVnEuTCymC1SKU8h5huBVRT7LdQpmeenrYmOaq7lb/61p5QBwnEEi6kxsVO3C7lRI15YBWG+CD9/1gLkA1d6QydxMmVeOCXi/YeA5162BPcizR7uz6YyHWnsfqfEgiwIsKyWBPaZbIw8uUSdYz6wKQtcRN8ZOLdBSRxS3SC7N2gTl3iJh7XMfepUN4aRG1v/uInNRJkgIAVKSg2VQ2d9HXZa1WWZ3s/+snasmSvmu9SGFZoQeRnZPre4WgIxmo1K8sW1oLe+xAYJJaMajOVDQE06B06MTsfn+QSH9Zr4d6bK+w9nMgS1FmORTF1AC2hdJsOwLWAWQBgiY/x03DkyQNApMKQm0VkxeBnsoLY55nvHn9Ct+8eoW//9nf4927d2IJsrUpAiiyWhmNuClAhKIB30axCGMaEaI0v92eTfjoRx/ixQcSD9xuNo4a4fO2fxavC17RBOsCYLVoBNLu8IyagfuDCL63b97h/ds3OO7vZZ5U0dA1GB4c6x9qO8OmWakFyIyaC2opWJYjllxwd6iYZ25MhgKGtMHZ7gl20wZTSkhUTqxNSVgxgpeR1BNqWA1ICZ4evkPQfeweQFJTNC8L0vGI8XCQGOmQTr5P7i5vHnnuf8AVD3WVtRhNr/zJFQHwMGC73eH50+fY397D6jebHDTVQrwVNqtWl0vY748I4Q77+wOOhyNSHMEUkZKVGskZDWZdk9Ve6Uq6Zt/fmyBZem393UPD6BRGQYjZbEaklFCOAbWLr7i/pTYACoD1TKDhatdWDwaGBHv0zGcr9ueKgEWZubkWaytmMouQEqpqmJJcXHTPW/mQ69ckhelEhBQt8gjXjWuVMI5KYJxeZLBqrCjjwcItQsGB1EUH8gSjyoRiIBFGHX1WLIsrtMEAMsBRk9HMBW+uXie1jrRkP6mqpazlF6a8+vxDcOFnUwYzOIhCnLOBKIiSU5aCUguWknXjpA6v+gFgAdCuQM7HRgfdskkeBrVnq8KZhh72jbzUK+aAGgJ4YKQQMKYkiWEVihAj7curD0GJqvbE1Y3hwU/hcWum9vD6XiFodRxQYWczYY3sCv0a2oqZslbELNqSQeCwas0C7ktixioaF3XuSVWs1spRJxH7olAGUEOF2fi9Veae9O4GXsTuG6c6F5Gkd5uPvbKj3kvD04J5PuLd+zd4/foVfvfb3+D2/l7iORrsNf4Ic4EaEQTynoHSOSIoNFpCiCOGacS0GfDs+VN8+NEHON/t1BX6kMmvYlcWpAe0zEA22/5nQrAQY3+/4O3bO1y/v8HN+2vMy1Ey78iYY+9q5W5lbJnYN4KhKdKVUcuCUjLycsSSGfuZJaNLY8hEETFN2IaIaUgYQlciYU8xhqsWYWPX68n3dPCgWKOnFzGVV1ph5Yo5L0jLgvk4IxBhSC2FzZhOoEYDTicmnJ2zAp3p4XtjXN7lJwhpIGymDa4uLvF6s9XP1O5bJvJVCLK1CBJBRUSYjwuIDjgej5jnBdM2gTiKW7cvuFMGy+22Xd1kG2lwTqxzD8LsOzR3XT+CYEoXjOOAFKXfXC0tBsbKkJc+yVfnyMwC51q5E5wWQ1XgYxV2CwOx1pU7XrA6SfOVlecwwEsEB1GApTFvFbd6sNgS+RYFFYJBQejbeYGm/EOVR1NA+rgma9wttUyWYELPZqPoTcofa9BwVTEhyD4mv68n63X11kR6r8Y33RX6uDboKDHVt24dZoopwKPnxvL0XiVXLTnQUVaJ/ZVakKuEeFgbB/vXWPhhyaJQ5rJ4VqzJ9GC02+pOQIEwGRgE5HxFFqUgAagKVI6YkILBzYlilcs6454BoJKjCTU86O5ftzv+re9YQ7t+sJ8gzC+vZ6edfcuC4pWqQic3IDR3DFClVxXUR88EVmbeMu0sxrC+qtZ6WM2O6zdVfdVdYa+VvngiDBlJ0YoaTCj6S67kmoleUJaCVy+/xbu3b/E//ee/wJs3b3E4HEQAGqETmpAjqJeIQCE6VuCYRozDFkOSmGBKkrTy+R98js8++wRXV88RaUITEaptrpQqknpJCicF3LLy9kqpBWVZsK8zat7j+voab96+wf3+FodlL5BRZKJC4K2sfpN7iaILmpXYSOucTAgupeJ4nPH627dSsxN2ijsYEEPEMAwgLCAUjENHlD2duImAdvDxmPbWC6D2ndUn6JFvVZY+Zp3GaGzefguPUhycNvowiN2hV9Lc6u/dOsxACdjutvj4s+d48/7lI/dmFwZtbOuRcK3IS8HLb75BjAF/OP4YQxwak4HRt+oqJar2W8XzwKbmRCRl0pUYlQoc7v/BP312gRhBRc+Zze+7mAq3M2GzNPjE9ro8o1ZCLkCqhBRbK6dqZ4gt3ggv3gckdb4WBiWp6h8XAmVVOgOBJj1zq5pDgSB03qJ1cyEGDGkQi5N76DF5WIhRcW21UH91kQpAGSGxJEFWc4kqUyGSmsRGP9Tdgbu/uSEmPbLGD+mzKT8ANAERsPZoHo91Hg30lpR56ihLB5kFiySA2V5rghUD7umbtfn0sszICoVWmbHM0kdUPGjaGkuHSEQISRJngoFFkCkHkr0+jcIrttPOKUVYWluEFCQebK+Y+9fdzSuYu27F1iT96PXDTXVtMtz7tV2MdAysvUeq3fAKbFc1RPtulQJX83EHD4p27ii9ROg3dUaVMn/PzGI/espDOa41fdizO23VBmRMpCcsQFxp1++v8eqb1/j5z3+J65sbLEtuLiayeGfHDLUkwtDdo8JlDWnEEGXjY5RC+ecvnuPTzz7FlM4RMJyw16BZU/1LmlHVDZMBbU5MGneumJcZywwcD8DNzQ1ub95jzkcsZda91fFDNb7QGJeTnq530SQT6rARaxViPy4L3t/cgpEwbSYR0EGwEYcYEahqN4ymVj4QVOYtgJ3/7xKA3e9kikxbd3+Zus/1rhO0Gql2NsgVDJ8yAGiNla8KtyURWtF0ELK4DKmBqOsDATEfR0J6RtidbXDyACW95q48vcxwrKXi+vo9NpsRn332iRwll1lNKSSGxv2aohDUy2EdQAKAguJWBtdmcXiCi66RPBstWaJnKN1wuf+N+cGrTa6wnzFmoBTyOm77SIgQFBZzq+it7Hx6TCsKk07ZPktigUziXgomBHVe7u5F6EIAHlBo4+0yLj2DF61F0uOXxO4CBxBVMIe2DDZ/QtflPvheNP6KNd/rBOGjPJyw+oK1Z4sI7XXljfKjE4RsQpCkXWdlVEvmaSmucJ5dWbK/S8ZSJGu55OKg7/fzIi2Pinz2mGsrE7HQTQgYxlHnVWCZvSkSthNhGCZsN437ifHQBNsUgdHC4AQHXRfXa5CM5hXvbD8eAzHprx8skehyqNohcFqwFPiOQ6A92A44d26g/l41rBMQ5OWHqpBVNaEj2Qpqwu90jsqpXMEmExy6cjpkC++Y17etmv4dAmqZ8Xc//Su8fPkSt3e3mJdZYxzscw9KgCFREwJBUuElfiCp1TEGxCSlEVdPr3B2cYGzs51kcU4jhjg5JNcwigC1prsNG7GtkPMjdT3lZcb+fo/7/QHv3t2gZvH1H5cZh/mo7akA9124BsC6Xp6ADUCBxf3wd0FprliWBS+/foeb61v88otfohZGiCMur57g85/8sWBdDoD0pUuoFLHniAjSlAcJvvdShpRJnirCQoO9Bv3YZW46EVAGjeVB/CAuomFKmnTU4mlNqz4pitdEL2venDvkCbPAZOHW7lFUqaNLIKmf4gh1RD4i6mz+LifQRC0AVHAt2N/vcXd77x6RXl5Rdw/D2KpdqQTQ80tCQERk6QmXH0HTMMdNLhUVBchFsjrNooaQzFKquq0EL7cPYMn85ZwWG6gdSPXcsCpSRZWQQAEJhCEARbN4jYHbTKx9GxZBoZm1ye5IhFCBfCzSi27UYx+lur4qBucjVQWqJLMq7msh2N4HFAUUHe6ciFUteUEZwMwYnTWJVVi0Seyaphv5WO3oCo/2wSDlGdwJF+sLIN+Q31bRWWbkRbOkc4ZrT2xyTvFrK2OARFVK1Lhmac+q6iYVvM6WSLOUhh5jdOMgJJUbbZEkpOVcFENZyqNSFOG632cE2iPGOwUX0U4eKarBQFhiwKj5FBQikrKwmBgUGUwCaVCDZs9a8hq67fqO6weEYGMJp7XK3BECmN1y7YGzTQDWEw1K7nfi+GE0Ma+fasKN0fcLJHvPN/OxsSsjN/YTTO8y4WWf4I6LNA2CWYR0rQWvXn2Fb775BsfliFKyC3jAXKHkqBPye3QLUDRPE4pmASbszs7w5OoK02ZC0qaWHgskYdhRsKF8aVb92fpJs1gLy5Kx3wuzfPv2vaW1IrP05RaMSp2vq1KW2ShxFm9NBGU4GiRv1or8nXPGzfUN3r+/xts3bzusUmnWG6JZRgRiEXuz4iB6TwoD5+32eU2v3L263uRmt3X0snpX2YOCScu+EFK0tjGEZgmI0HQB2o3GHKLOjC3uZz7wbuyAadxqaWqA24C2mnrH7TsMtTbspdri79Qyd/OyYJ5n1bBPFALmbgRtPCu2S23ViAmBCVmVp9NvGgC6JEaoObiqN5SrVulLWbV5tHtDVKEUJVNKYnqXr8d0LPFF6xRDCAgVqB4TM6Wmm5EyYWThFTkIwlEC5I2shfwD631FAZayA19yNFqzdbb1ecgxST9Nej4aOcrng8YfNVDpZzVD8FtdcTlZZxPI5lYl5VF8+klmhYujPtkdFjwwxy8z3PsGXatSJL5XsyLW92ZHd66j3hORwEXHYrCZYFeIZZlV0DBp4pVz0ga6DtZOOqaECRA6hYCgdYMhACVnHA9HFTBHpBiwGaIgDaWEIQWMQwBiRI1RugZFWYYE+ZrFIuX5UrhvXhz2Ff3u63uF4GPG/4MNevAiuyZqLYrsAPbRAeIgrhtzIVXxUbtqow8X+DPyzFQjYutIwQiaFVc72jSLjCwwqPNpm2WHYHVPf7gy9BBQK/Dq1R1evbrFkrO7RMTFEtXCMPndepKZuT+mAVMaMaSERMDV1SUur57gz/7sT/EHf/AZLi/OMW0mBaFdYEkiFIaTrgHwBWGCaDwMSd7JGfNBMgiv37zFYZ4hAFBqqUI7qFiRNHmuc7fT7OtQq6V8t0PCYO0yUVFKwf1+j5vrN7i/u0MKEQEJiBOGcYc4EEKsAERhqFVcRcCIOADTSMB8BM8zGLMK+bUI82gtN+EBtDo1G3ulLjnEvn2iTadhwPn5JTabjda52kdV+Hn8pLsNgCUvipSh8ZHacSCwIIkoA2MGOIsfvi9iJjAStKyo18NUuAp0l2CJcuGmyXtTaVJNfI9cNcMUjJqrMr21ILQrIEFSbiTDecm6fiTCrdSqDLJ9VUUeOMtYctX5SxBY0H2cwYpgLj2ji80u6Zl50J2zsMZSiyJKFaFMTTgx3cRm0u8i+z1EGBedV74Xdz5P0mB6RBS+cxCFNKaCGCOmYYTV10G9OFwZnKsLlJgShkF9bp17zbE+OkH+cITrVxiMqm2gXOkO9hmhmRCiK1T2P6ulbBNXqR8JoOhVaKHb98osiGy6xuIZmt1DhMrgnNci3pQJMlBAgTGkqi3fAlAqoZaAGBJKAGKUHqHjCJQSwFj0LEmLsMoEkHqPCmCRXsnuhcbmq7fFW1hCN0uetZQJSEPEYRoQYkRMGUNKGIeERBWRCoaREWPGNhGGQKCiVrgpgqJ9Ik4jSEERHuGiq+v7hSDzKT9pe2Na7FpVczPbCuy7IM3J91siR3N3aLDWXz/JgNVfXNgJ9xRNiUyQdZcxqE4DtO+ZvHU9z+7lwlpM9SUX3N8v2N8va4JWDdDwBy1G59ldpM1rQ8IYB6kTDITtdoOrJ5f48MMP8OknP2prKSuOx7RRVx2aaoOqrWZqkXGWZUZWtJacM9ALC+r0P2LT7bpF7awGPURVUvJc+xMkiEWaBVfGshTM8wF5kWxLChGIE2IcdF1Eh3TNm6TZbhwC0ijaMVMRd6FXuzbby/fEBqUL4TVRKiDWNiL5HraXGCFGjNOElFITlaaY9fET1eDtqirwyQnb9Dn/sD8DgGZNM6DQXfYF4hY3lo93+qlaAMFgBnWtFY7F/641o9bss63MAsAdLLHrhEGrVWJ7WRpyFyqzZAdqpp0xzkqCHosqyRK1VBRztZi3x86qdiqxdmteL0fd89uuwJRP4QswcWuHxs9RU1JxSvI+RSv+ZzCWLJ9ekpzbWK2YXFxvCSJ4E3WZnSYEi9Ys6jWwJpcoSEbP+wQSj8H9GqtSYXyqrzYS2XVSEkPdavQu0PUt9Uhy/4cqf9FXkjraN/cjYEqNlEC4C1Zj4szi0O3JpXeTE5PnOIhnW86HhHciqFbB/dSkpRjkGTVINmxRLGAp5G+czGtsVWnkovFmFYiLKmQ5ixOTo5yHVDWRkgVjNoAwMpBSBSqhxGbUeLxWjZGBBKJyCP+tQrALP5yqZWIpyOYEy/qsAGuhh1uA3AkIkPu9qb+PLhQxI7hkl29YYX62m3RjaHBVQZlNJyB10R0hvz9cmjxg2IjSircRGbhimWf87d99ga++eoX9YfakKZmWlHfERE7Ypt3JsRswxIQpDYhDAg0DNmc77HY7/NEf/QR//Md/hA8+eIEQEriLFzTdmTDPM6QrRlrxW9trni2hQmNrkGDyNESEwAAVZKrSTsTqHw2TkCtIO0Z4XzjYM+y+ADpBmfOC3/721yil4sMXH7V0PYqgeIZIhO0mYDetk3bimDAMI549f44PP/4RxgAMgVDevUR5/xpcD+DMeAiNZQK8Mw3AbUz+d4Yc2dReki2xzUUcAqbtpNmqLRrovEbnaFqkZcNJVmTbF2ofBUPwT8HaoNUea6DF6D5cBfJuGkYsvGCprY4ukpSPHCXJGbmIcOlbfIXAWA4zluMsjLU7BxbnDbV/TbBHS62SxafA5qzrYnWlIttIrUKNnRLa3gZZ+6plcrkIAEE+LiIg69xohaRBrWCuthKfToaCIS2DxiCQFjlKs1ZmMXS8QgnG2sixT5sgFKtUYlPslvEhCzD+wipglqrbr3G7cNf5CiQhJ0XCEBt493EmhL3i+0bNZoytxq0sc+fabKQ2aKProKUAcZTkHDMUVmTtOtpjhkH/j2XR7cvLAsoFiAFMwcfRZGWfGWihqP6G8kvtxuRKhX5R8GtlzRgVlCx8krTTB7BkAnFFQcU0ThhqRQxF2qIFEfwxRBlfkCa5y7IIFN1qrlUaJldGyUXdqBV5WZBL1u4iaAZFkFIX7zJBEO+RnRGWz47jRvYviUx4zJl2en1/F4nVYUZTG9gWkjuth0Vr9FVXgWRyCtbFWAO8FVLoye0zsg7KjKBaiR2H7zJJA0SoKec9Fa5tDnKoPA5lOpH70PVTygRyKXjz5h2+ffMGS9EgqxGc3o9cy1BBq/VWRBGBpDlujAkhJUybDc7Pz/HkyRWeP3+G7Wajfmxq9+yYfSnCOBHqeu4+rz7OYIXyhJQCGAGxkAh5YnU1rt2fDgF2Eq8Vl6AlqejBqQU5z7i7uxWr88kL0SpDy3KNgTCNQRhCsIw9aGZowma7xdXVVXvO/hp1SNLMNRgxdz9tL5idX1gctP/vevPWpCHcTzTZlJL0hSNy1zWcnG2uYoeKhrouNDch2NMzGB2G6+O8zWOEBKQYUUJeWRNEcChCELwbh8YBfF6lWOyt53WNdnt87MpVrTR2IVhrl40KKNC3fEFCF8WFgw+sszYqc9PccxZ3bOdRICLNf2DtdNHG0xiy3M06JwSK4OpRWX+a0Dfp2HTs1AtB7hJ+ZVaFFRHH46h6Kn37+l4gmnyWAjCqALOiZQAxVYSq3S06jaPMBbWrXbM0Mi6ShRu0zCIFjUGbm31FEWtl/jRz0Zap0aS9qHCOlIDQqYKmp6pFH7r7nF6PvdaPS/htgILsiQ4UFEs2RoRSEENQL1QQ2DMNGRFVMEWUSuAEFCKkKrUvpWRPYOzHYPy57SMc5ekkoq2hpz4BrZM1bP0wCNPGSlvacv/QzL/fEjRwX7+bjaCtvLhW+gzQ1dDdd23uQEeZYK2lOWEaRd2oacVROrdXe6k9Jch/+uQcgN0pJU7Mziw2Bh3VTRW1pYiaXPd3R9y8v8EXX/wcX371EvN8gLVJ8dGwUghZ3l+ApPwO2Exb6SGYIqZpi+1ui3/4J/8Q//hP/yFefPABnjx9KnVL/dVxNzvszFB3EyEZ8272SDu0gUDbERQlkWd/2ONYZiRVOgxBXnirAjtxxxDRtEE1aP2quUqH8/sDDrNo/u/v3iOGiA8//hB5YTy7AoCAEEZcXJ7hfHvl8UxrgzWNE84vznz0+/stDncjOEbpLhRVeyV2wbFamxNCXh+RCstAQ5+FGQmYEsI0YBhHhJQQgzVwdae7JIiYVcGM4/Egnal1DDEmF1LOrJml/Y0lNJC5k1TA6hTk14o0jDi/vALf3OD+cHzA/NxVSo8fWAcxVqVlWQSMytD9fZ3Uwiss2vnxqIlcLHVdS82SuNULKpBkIdvx1drdVIUpLaUgLwuW4wLOhuRRGwPTPViypL4Pg6q8rjS3pBpRx8QWD4ge2zZBa0LT9rvUoin8mqJkvyvjtKzpusjalUHGYNZ+NGJo5A1RaLTwu2T3TiWt36XKQiNzcaVdkvGqCiPjrcq4S9UC99LoJATQEOEgDE63Rs6C1AJCdwZtbDCPrX6JsZjlnmediKSXGT6xHRHnkf0/u9djSqLTqL5ALHxNXaMRLK8RI6UoUG9EqLHVQo6jdBG6uz9iKeaZqKCUUGvAOEo3+eOs7lMWIZpSRC4Fh+MsrbtQ5AxVXq1XBQRRqLAqtetpMCTLODMj7w9uEP2+1w93kfCFZV8weZ37jzwia414mrYrDW3tbdM61+qBa1cqwfuoTxNi61+489+bLtlkpRAPGXVQ+xQRvMbIrNrKFcfDAff3UmR+c3OtzMdG1q+u/kU6VxIGG6OUQ4SUkIaEcZxwcXGBFy+e4+zsDClJfzXJjGsM7NSLxv4+g9X15mnbvTZJcCSaNCSkkrTGRvt1deFBky+k6wYfCZx5WDIRRWhpR0KMUtAqrsIKIGC73aAO4gYS/McR47TRuOCaCmOIGDuosiVFxCEhB1t/FcrUkUPH29uhaNaCzR32CtlOukQBYgTFqMAFXedrarTRL46ljdfauAZz7eJJHedwRtP2v5GGCULWGC0jDQOiCp+HpN+fIkL/DkDq0jSrrnkHquPX6v3UrVxYXJylmLtdYLFyyQiRPcxgNE2GMMGNptuyqEVW2rqoDGqKrCpsRISoKCySDNWkX3O/wS1N23tdaJgrr73U4n/mcTKcYNs7BsTSCKqc6J2FSrvl7X6x8RfdS+UIYAoIpMkyFRppabys7YjewChS/MgIJFBsgRmIQVGI2rcY3OqYbV06QWhCujkgZD1q7eYNbvSmwupkZGsyWq1nvxhKw84/ZS1aOUfHv5VXCopY47NuoDAjJkV8qZKxyxQQNMlO1kXmYkaTraFka2t9ZeWO/DtVt/MIneiPq11dtU36Lu/hyfV79hO0mz72GglCSgW4trpBswA8GKyQKn2hAxP7IbaJuZcGQoiDWsCnSJp9Mr1oC7KRgdlKpdplvgJD2+2/2P2ej0fMxyO++OIXePnNK/z2t7/F69dvMR+O3p2YIEFa+V3/R8JgB22PFClimibszs9wtt3h4vwcm80ksyoFeZ41OcDgArpD8MgaE0QYOZwWpOmnuO7UFQMCUsS4FZ94DBH3+z2ur288+SOggiAMnWApzIBFyDabrXS4HyeElPD08gpDGvCP/mTB8Tjj7//uZ9jf75HnDHHxBakFItGmxyFinOKjc0hE2EazmQHsdsDVFfL1K597Y44moNeH2/8iasyXNEP4AYUEUEigcYswTAhpQIjSwsqsRTtoUXlBlpXFNE0Yx1EaDbPBfhVxAfkYTdiuY6CnpHW/P+DVq2/w7uYWaRJr9MEHOwXIrHaPc4sGidubGcA9rt/f4ezsHtN2Eis/q3LqAlv29TjP0jj5uKjwUeclMyiLVVe4IteCiIio7XAoBozDgJSStN5aMrgURGZkdZbUIhmWljQTTjRzm1ZW5crEZq8A27xPDRTLZK76P2ms2zLNPcO8U5Bsx0VKyl/F+HtAc4k+/kVYiUStGTkXKdWwUidqOKC2x6O6OqtCD8bS8t6FfI9iCaryNEgAa7U6FAIoDk7DtiD2v7IITBmqYhPTmueFKPe2koSm9Lf/MhilLACzFNE/mHhnE/SjIzhSHAHeZ3NMCQMnLHFZCZtche7OzgNqrTgcBVlmfwBqUS6XGMMgHU2WRcI9h2UBhogzGlCZkbPgEuesIZiyCJBCtdlAvQLkCAsK8KdnBfDkwh8qDuyuH4gJmj1uS3uiZShDJtUohLaNMTSHk2ktKygfwskO2CEGyJI1uGM1vTkAYFXobEo596NdD9XIYv16x2gZOB4O2N/f4fXrN/j229fY7/eY5xmncTOfj8/V2rSYFRiR0oBxHDFN0iw3pWFlHThunzHQzmper4i+FxoMlOsVulY2G7FCAzAkTHVCLgXDMEDQ+bVwnCUDq5LlorS44DRtMKQBcdogpgG73TlG7Ui/zAueP3+B/WaP4/6AkguO8xGgiphEEI+TuLyXJcs8emuQqwpr2bkhJQzThBATmCQO4YzsxELwu5DJLe6KmsMjn9cdDyoIQwKRxRT6NH79PLeUc3ExKbYlgqdz9xq07UKzonyS+l9yDV7cQDOyxlN6C9ncvpbd12KQHRFo6nEuFUuugtSRxSILaMDjZj1ULl6/V9Ry65UME4hgRilZ6zvl3RojiEWJCyjyLM0Gdld1F+eSNSHfm8eyHduzrZDblkmfqTtvbxEZkqp4HHpsYvk9NIWppwm7LMFal9Gsz5NBrwQHzBpj1rNiP2k1X+MjhQWFp1omnm+XZNCWqp1sFA9VCv/XGrcIl9oJQXTSunosFOZ5MYDw/i6PmUR6f0ZTiGSZVgVq/jw+eclczI4J2q1zgHjoApOGtmQ5raQwUUANhEFbJeWcUEgUGbDFnpXfECGUDAs6ers4la2SyBW6HBEZaPGgPLnyQu5V1IGyzfihLHjs+l4hWOalCZgQQFYQd3JZsWKIVreHh4//DhMnKupBrh0aAktmHvUfNGGnAMk9g/X5n2QYGrNKViW7GgN5LFGYRsEXv/olvnn5Ev/xP/1nvPrmW9zd3IJqMaQysLZQGYaoyC7B2w8JBl7CZrvB+ZMrbDZbnJ2f48nlJV48fYLLyyuMwwYhDgAkC0uMUwICkMIAogDrDXG6ThR1YqUpCx1fln0IhDEN4JgwjAPGzYjd2YRSpZ7Qb6f1k9O0QRoGzLMAYRsTSykpzNGgxa3AEBn/+B//EzkgpeLu7h5/89d/h/1+j02RjLDdZof9PuOrX3+DOBE2l42Z3y8vVmg327MzjFPA/de/A8KoGrxWaUvWFIyL9fE/O9JVtS7Jxn1c7QlxQNrsEMcdEDfgkFDQes+5Rw5NmBHUuirtmVJqxYDGKobQJ1N0VqlTo7gt82yuUAGj30wjhiEJcHypACTOV2vGPM847OeHjM2WgTSdiVUbNrgrUcNBJPHgw7xXJh60rlG9MilJDKpveForaFnAWJBBwDKBaESmAxBMGAhTfpht2A0xTuIGHVQtUMtG3M0iTEizmFkFXEVBQRYsUM9yqShEWi8oQtcyYEo166RZl7ZvYCArJGOo3VYEAqfWuNcvH79aX1rrSARwIOQsiXtibUVYBqp5DGZlvnGQ7vRB6yNdiWWB8RojwCRJdquLpT40VK1bMVOrE1M1Lyg5K2+18fZKZUEP1mDpQBKvta4bzXV6MgBYQp4vSO3oCWaMaM7hyaOHGJCYsNSCwuyZvQpLgUCjWIZb+ZkXgVSb54JcC8J4QFlEUcil4KC1hTFUQJHVSkkoOUhsuwrQX2EgVkH/sSuap1DjllW7UDSZ+Iib4uT6YcQYY7aVDY2325S2OEE1hfqYALS79edbP+fxmVXtVSe0Hohz6pgXnd7OGeYqPLP6r6uc/V9gZtzc3OLN2/e4vr7B7e2daMm97Ows2vW/DhUmSexrGBJSipimEWdnO0zjqJ2pNTu2DQFeNqLWXnhs/QzZhYSx4LvmpydRO7SBMQlwTOctDFFSjqdpg2EYEeMszFr3Onr8zIr+5b6bzdbNV0LA2e4MAHBcDjr/COaMw/GAgQJSTS12VYsyE93vECRzNgikGMisL9auIlCr2X/tlbzOJmvMo9dagbam0H/mpajqPuYVBQAGz0PdPcziN8gq07AdPcaIw+MoLYZjTYYBaOaudDU3q8BjWhZz6w+I0j35n8q5dY+8k0uD6BBLsLaEhV5WkTJEb+eE1ki6WbmqtQMtLkFtjP3SgmROjP4MtLPCbnV1iR4mdMDNRWsWj35WHBYECtzWqfuvb7GxCqOHnlDaorUY02P8or/UOjGas7UU5UItGGKtGlEFPbBY43qGNbKKokWDlSuoPngSGFLj2epNCehg4mT9bK1ktCu/CPWfaUvjq6vWliuPzOvEQNDaW6h7U7nt8+llXr520swKszuaAGdRPnTRWRWXGtQdXoFBAAVRBwYFICMpXZosAGJhlBhEZ9NMaWItlevW1LBQurCrWOmrsT8uj+z6PRJjlCKIJeZHDXXfUp17VBZjvafa4oNFteWMEYiQ3nRcwUVelxiYzwJeka5psr0lSNBKCYgZffrgtbzuTomZU+o2+PVvXuIXP/81bm/22klb3g6AFI1Gc/fIz6gpwoEscSRhMyWcn0m/unEIeHJ1jk8+/RhXVxfYTooCE4zlfN91KuX6HmVN0Ide0yHBmTTqjGEEhaErXxHCHDQ2ZrEPgqbvZ9ZekdEPNHXPs8EkGnC2Ocef/IM/xvXNDf7+Z1KPBhBKnXF3fI1N2mCHp/6tUjLmwwGDJkgVLtqYgDAgIPOAigjwAq2k1YaZskc10sma2cga3RHIy4mbciECtmhyTCs4FgHYpQ+taQPw5CJiSds+aq++ZZGiYYpSSM8aFCtQt+ZcUXLG4XCPeZZsvpQGnO/OcT29gwmC/npAtcZcXNg4ngvAQFnK6nAL0wa4o5NeaEvj46I9MpVJqoBuakQBMAOWJOP3l/uZYlUjCbyWdaF4jMkwo+bSKTHsdiBDmx0XuBfGs0P0dimqB0fD+EE9Ro3hG5Sd/Iu+gLQag7SMD4BahPTIR0iFm70s5x1gaEbtIoDRkYoC/Ut8vkJ5QBq1DCeignCs4jKE1vraGZXkLRaQHIWuI1fW1lmPDlLRUQgBwoNCdzJNZ3GgheprDoZ3u6nI7UwHweD0e1hG9uqENaqAWuaWCeGfIMsoFjoLuv4hGj1LokwgRk0MDMBQE4ZlEKSYjYQJpnnQsp6Wd2Gzv7+fcbc/IKkrYCEgr1he9Y4SrsCu7vHD1/d3kTj52zUgBkzte+Cn/o57iTfpu4dFgUA9onY4cYeamtmrH6ejVJdGrxn1n2tfNc2+e4cZ+7s9bm/uPKPuse/28tgSVeR+QYtsBSx7GAfstlvsdlvsdhsMo2ZM6mljv2s3B/VB9LVn/fvU/Un+8U5McQA0zmCactCMuT7WZlaeaWpNx+fVYwno6sZ8kd0KEtQR0THNLSXrVhAIGNKgnTSkZY09ycpjOuA6uQNrzK6fP/eP1yhutxD2eh/pky3WbF1tocOlqtUiJ9QwBy3jriHVEKyo5vT58o7NQS2F9hERbUWSvczyBdhxZC071fjNusSgPYy7P1uW9Ood90I8prkzoJB3LSYo42Vnks7zbAYMtJrZNmnu/0bLvDTrh1eD7TI2zaqw75MoHetZnMzNz7c+vFOsG+PxFWjfYSiSyYOl1HPL61uffKbxl/WjZd0YFku3yKTlnYrVZaUT1M6vrlSpVXFB2V9beZWCWpG2F9159umTny6n6ZZbYfNoOdNtrXTtetNcXcauPaykhlreoe05+/N9cOjurOdLIdGMVtnoo8UMWdcgQuUFB+lewVKbOSJJ9mvsgAh0cmVkZEMqqQwqYkUboIXwKKM9+VntNZ/394vD/x3ZocLcZIpdT4tTSfmIGGy1Ie19YwgBAIjENRatizAjcOlAok3y4OSZHTF05VKuReuumLsthoC4SkyQf0tdwBV48/oNXr58ieM8NzpxAWgwaUAaOivYBUtE2m4Rxw0QB1xcPsHnn3+Gjz58gecvngoUk/v+IYXC3fdlPFV98BJD8QgaoSkHldZLHEw/k3Wj2gpKieQQS/xqvTtyuAukvktgi2wByfbZQAhWCbWMXI643+/x61//Fvv9AXkunqBBFUghYDtNeP7kOabNhN3ZBs+fPUNQkOpmfzTR5QfdLP0aVMPUeIyyH+ukbfkPAzpy8DUhIATENGDabBApYL47IA4j4qj7EAmlCkpRQfZRAZbK3d3vgVYuaz0fBDIOg1gZlSUb7ng8+CGOIWAzjjimgywpG0C1CojaHs2xm4tq8MxaV0cVQTsdEBGSxqOWOfvAzAPDtSAvM2plzFlTSip3Yzf6UCixWoUWGhBpIxYCoHtgyC3rJAs7RywQZCzMdCU79fPR7kRq2Wlpp8GlAV3rsB5CximkHX+5j2Qog4AStJGvCRSN3ybAEtO7O8H1HDvjbFmghDYG24q5azXaX07CtpFmhbca32JC7uQKMQDjAAJ7tjlUSBBR1y9UeABZjPyE2unkpx/WKGetBolp1mX2T3CtKDy7IDaJGyIQEkkOQYGfv6qrH6glt9lDxZOtOMbKNG2PjIfaZ8iSaQgoUVKNU6wYhrY5rHsntFyxHSdc7ppr//6QcZwLjscjcs4glvIjU+6yIgwtWcMvuQPH/Y7r+4VgM5UeyjtuB0A0Rv2749FN6bHNe2Qw1G22xZ/Qax/2XnBhJAeHYNq0aB6KLkPGTpvVajG35rVtmUMMYJkzDocj5nlGXpYOUkpu0Apg4Y1zvS4wBAxDQkwJgaRn1vn5BS4vL/D0ySXOzrZaZOoPdxbS1uWRNYfaJr4+bb3sc30MAbpm5EX4Ij6a0dxsHfm66aZNg9Kvub7e4UP7e6ZxVetZuCzqdmvjjnFAigNSSNiOE64uLrCdpnW8CN3z+6nZYffMu3727LG8xoz7eAngPiWShIVxHNStqdmGXDWmQD4Xg+JqM+/2pbu1WVd+IKC81Bo+MzeYL7WCGCf7bAKjkzNW5xojtTidHxfWEclPyf5sFrhpxFbDl3MWF6P2dKv2PIuN9ZedFRUmvWgT2WCT7xWk/vVuP42GuMVG23PaD1MmASAlgNnQLAOk7dZaCD4YMlS5660TEsAIz7o2ZsxA5K6mrqNjCrTyKDQq5K4rhLw6DDJGgfBijZkT0gAtpg8wr1hbwQeqWTcB+ec8S8cKkBmTq1tQv1f970EUZ9Z79OxSBKp9BkCMTu8+FKLVmFqOAwtcmjJz/9iJECDdNXePMq/WwU+mWulRO9QUFss9aSKlNNDS+5sQ1LOaApBjR5MIiKEgEpBzADjBrXQVfqUyCArKvZSeEh+9fsAdyv4pibdE9GRZq+IYUnjkQU1wuaIn32obQQo2Dav2MPEFGGK6QZOR3cu4sjUBNKrnLGgDzj1Ywwnc5b+35wJqcTDj/dtrvP72De7vJIZTlupESDr/EIE0VMUZNIJJGIYBF5c7pJSQKOHq4hJ//Ed/iI8/foE/+smnGtuMqkk3Afidh8StjnUou60prT7r1qJrkUnmz4KHmsmwNQOsNY4Aq1nhcWf1sN9WNTvR6hOUMSmckTTazJiPM+Zl6VrtAogJ0+YJxuEcCRFXZ+f4w08/68CrVfhpTKoVAXfrDddnwVFT1YNmuun3Q3ev1doQ3NJL04DzqzPEYUQgqfVriSOmJlTUvCiQsjwjjYP2HGxrLZaOYEdSHEQBIaHZWetQi/aZPG0C4L/rmpfSd1dRGyIFTCGgLFVis4A2Vy/wsm+uyOWIOe8RjsJ8lyrZc8e7owjBkjV7VO8fIWnmZjprdjWgsX1N6AokGn91Gmhrq/x5ZR31blDWTgUryW5XT+4BDpphSCqsN7OUMLE0gh9Rv1knPNb37pTx1bNFMGSzlFcMqhMobWc8M9lc+KVUcK1IKTW3OQHjZGAUci3Z1m19XqmbO9n6BUhJwEnWIoMVzxeoBiVnAqUXPBqKGVTpXjArKIKJCL1U45c6x4iSoigJWeHjVsLQxih8IkbBA2UESNxX18U8WQotZHHYXBV5OZh7vnElEEBJ6Y3t/SxYn9HuNyGSYArbVWvxxETbPgZwPDLmhbHMWbJuqymHIjQPi8QZ727vsSwLbuYs7tTvuX44JugzoRVBi9ZlwdgmXORjpnnRim59gaijSVU4qjN/6h8M9H8T/L7me5e3WKhDs7IkWdeEOLXbmQZmj9Z6qvu7O9xc32CZF8FEVFeVgMfSyvrz2EEgDGMSt5RCpI0aB7y6usTZbieF2Z0V0DJTLbW5W1Pn5fZZ+7PTwG0NALjryg9fu5eE1DTTsXO7Stytaf+w300PVpdCv16ro0WQ4PSyiAVoFKpXZdas0wnTZsI4TRhG6d4gsTmx49gAkCujUECNCZLbXNxBKn1qqe0poRur6ZjUXOZWw+bmvjH4qIgUJrzNpS/fsZVhstQqnYtLAyigw0lPRXT7pAwSnTZOyoAKIHiTj+k8Hiuy5V1/qDOunHxrLshLwYwFRITM1ti21QfKMKrzdhmajrnq2skg276qWUQaFwxkq9ExSxuXfrU79iKwS/e5nl+Ek9eUT4Tu+URBgNRCUEW3Dc8UBrN+7CeA1t7qVP6qi46GtjVtYbvZMGDuS5mIJbtRyw/SZTTLKqUo5WB6z8iaFOJnV34KOJACFNjb3dyacJO9YTbKtvna4qx/X33frLduetR/gGT9gsYwXbFYdUdAZwUSLFtVLEzjpMbP7cECU8nM7hEWjNfGYO2zSYUbQ+nQ5Edlv1cghX/UOdUiNGhrLAoaY5B+AoiBUQr5ATHvC8WG2LPkQQDta6eVPnJ9vzuUHvmnV0HBkhchNhV6QsDR++xJGZURW3/w2uDNj1yUkQQrCid0rk1qA7KiPd04Dibooj8nMEBcuoOkn7cUM4bETXLGMi94+fIb/Pa3X+L+5h51NpcX/PuDFzm3gvWYAs4utwLWGiOGccTF1RlefPAUP/7xZ5jGEWpCw2ROYIBjbITS7Y2PUwkmNPbsUmktnIxA19vVftODzEL8BdUxM+0zQRFypUVPEWavkEdEJJ3hSZNJdBIVjNu7W9zd7+X5dtBYsPtCinj69AKXTy5x+fwZthfngsYfohCEcqSSC5alYEkRy24DHA7AUpBIGxEZuI92D/KyEt1AtwTbmROmFSVwS5DnhTCqdQyNEcxSihKirzFi0mQkQdRhQLuoS6xzztKRwRhHg8g7kWw6kEBAigmbcYslzyglqyB+5MOne8cmrEUYVX1NdpQx7zP2dzOOQ3HuzCxwUe4UYdlxsMTd/P4MoHYZnQrHwVAPcSdsbXRiQD4cq/N8AuIAcCUvrWzGZmg31CxdMQE0btSZSoItHKWZdHI/wIOViiGBQAia3Fhz7gADGm93X6eCPfid1PPhMVnWEojagLEJel67GD4A0DhobF9etrZFY9B7RTlfgzFzCONetOnw6Rq6cFu/1ZQqMvHTC7k1MzbF3KwwyX+g/iPNewagEqkSqpM3pQJa4mUwhl0ZlpyKHqxDzrH9DEXibrlw50mA3pMwTVoqpSAUBvaQF1tzF/FiUMSgrbyyjgXIVe4fJ4BHQIyg4v0w7d+cNwJAf3UGZsZ8rPgBGfgDANqmJfTDtN1wzavpsaz9xGxAQb04JvyIu6/2/IP7G7oa4SeNO0c5A+57BtZCTvdS1NLaXGfyQ+ItROSo+tYv7vbmBu/evkVelhOV8YTJnVxmacSUkIYB47DBMEwaI5TkIfOjM+CWpWd3qZ94ReD9fLg/vPJ7qyGk1Uf7g8Td2oWo9UpmUTRuC8EMNLGi1lB/KllWvHLRdROLI5eKbO2GABjALqDMLcI7SRDgqfhSPySfMzfs6rlkw+oOceDVgfTUjK6uVLRUaY5bKQA0IKQtOG0EBaVPKVeLUKSLMGZj6Fb+4jFSF2rK+JJspLnzVmvPmgpPcOSgECWEMAwDkkKRhXgiDAkS39aEJxNidlvLHBTvSvBuDijk1hgDjakZTVS7x6miZHvbi7pOArL9aYKXfI+95hPcPsrtfUfDIaXOlRAkD0uQy2Fz+wEpauKauiLtqrooPTZvH1ekED2DFkaLgCvTpFB3p3ik7OsKKZYvWqsG7lqytVgTg1tym86JjTaNThREIcUmBJnlnLP9br8QaV2w8Em3ErvLSbDa+tsLGgumLjvXXacyuNP4nRGL0XgINmjbcLPCbAvJhXDj/lY33BaPoB7BygJOb+cXYpiQub8dcQSgRKj8EF1IloBAUeRI7WSJzb8WsyAbcpB74RlgYqQV+lJY7f1j1+/VWT6aK8UH/dhNJchZLSOzdh+zXI1u0qSMzOFyYBlBTQBIwSnaBoIQumyNTn+Q75iGyQGNQ5RGTYo6UdRtlEtGLgu+efk1fvurX+J4uIezDWoj7Stn2hPN3ZYwTltMmx1220vstjtMk9QNymDEz+75GjY5gjJyXY1Tq6LzZoEh0WRl/LSauy5Xx9NY11pS80mKtj3m1S47z4EsgC0WUeVFPqtxpEyCFbosBTkXHJaM2eo6UVsgQkEsUpIkjwDJPCx5AUUjCBOCWtm0yjZSEckEtqSIWE7ILcgYnQ1rZ3hKqAgoSKC4BW0/QJ2usKQBoLjCTuQqWW9gtXADvC1MCBCg6g7lI3mXEVlzY9JVe0GKG7D1oUuT4HrGAFBN2MUz7A8zpu2ENCT3RoBVAHaM2pI7XHlVAUuQ2HJVqy9lMu+dMure5GvyrL/N2v1mQcIu7mQMxxmc3qdnrIBo4Ccyk2ShsHZdx/bQgN4x4i9b6ciQtIODeytkTQpLd3bp5BGa4LBJhuYBknUQyL5pSAgUETEBDAUtkLijLZCPu1SgiDVYOovQQNuyxl1t5XwVgxWkizAf4yhCcOiEIIDAEZWlLpKZwUWoF8GWi1cCyLJWWxySFb0GQrMM5NpD40DWRRWNtr5GDP1PPetpzcvX9CFfNdBsKUEgB9Ew4jVlpmh/zGAYqYp+ZRYoxYB+gnYWeRqwulxYVXCN4Do4mH2qYr3nLLFCRoLKuNU1sIQucpazlAb8twlBWzcjwFaYXRFCUDBic4dKaxSTIJbs0lYYaJvSv2iMTzWu0AbsrWmAzmfd8si53cxv2bR4dgL12h5Ftc+lYCkF33zzCu/fvcPLb77B27fvJOU2oFG65kobKEdULQwhgkMCpQFpHLHb7LCddhjHESkNHv9qKq+uSSdYTdjSKeUpA7G2MvAV0vkp5yEisSyhFlWv+fv6iFLC3DQ7eaO/d5tvY4a6CM4vmvCspeLu9h739wccD4sSpMZUtAdioiAwRv6QloEquomesBgQxxHDZgcuizCwnGFpqaJFtzn2xMQUVn+DEogSQtwhjDuks0uEzRmYBnEBBavP40aGZviap44anYWoDB1trayfmZ8DjnLPDo1HwgDdXhM0g9D+7pyLfibsDDh7UfrthdnaKeZYq99zUfcfFz62z6cfJIIXfNn7Vb+rwtaUrxCS6GXZaufk+05j6k4PMMuYLDcLwuCMgYfOarYEHbMQVPByBHNoAPKBVJTJs+Kg1okq0yOkCe6oAjVg1POh62WoJP0aMYsQ5EGyb0tFrUUhDaXpsBXn2+q3vGZWnYEQSRKm+lhgBWNkseCjQd3FBk2melxHCrIHRg9R1zVQq+1dbZ2tyZqodI1ZCJK7+mk+vYOfqBU99IqKqQ59ngeolYyFcQK4tn3T8Il93uWA6UN2dF1TWAtk0jUVlqEepyr/Sm24qu7674euZ9aypnNfOvcd1+8hBANSmh4wa0Lo+qzpAdBDU81F4EF5X+mOAMnn75NkRlW3i1npdhwGs6qCZj+irCZnghQmENyOhtY3KTOthDkXHJcjfvqzn+HXv/o1fv7zX+Dlly+1FYyLJ1AiUJBYB1ftTRYINQxAHBGGCePmDE8vn2KzmbDd7jCOkwI266nvhT8Akyyk5n1QpmtJRo4uURiP7Z1gHLIIWp25/K+DSdL1LRxh2I3OVKAWXm1JGe6B1o9F6tA1AEgfadmQUirefPsOd3d73N7uwZWRQvtsQMAQBBtVfLBVNF8Kq8xEhAgMjLQ9w+b8qWj8VERoa50ik4SwvGs2y2hccvlMA4ARiBPC9jni9hzD0w8RtxMqJtTAKLGKd0J7uIHUWg2dIqKnM0bJpKO2xMLbx+juY3G1pV5GwurpCrLshRm/D3mXMwZuj4D1yet0lKY/rnYEKGoLh5PuGW1R2mdptVqdIKTTF08+bDJR6R4qCIdRwOCPe8mWLXrDYJZfEgsi6ZLG0UQ4oXLBUmZIVDdK15XYBGGypJSOovo1sziddAkhbHajeh3k0baf7R7C4gRGlVrZz4MtYc+OzfMRyzJjGAekIT74MCPrp9cXl+A0yiy9Ct21zUDVspWiiDklA1ypZe4Cwmi4nMyeECiBVruoSom5c7qDzCzKKkOnr/zU3Lw94zTV2aSokWvs5is0v3oBFAgpaR7IOKlQNKu8V9aAJZugEqSlIZlAVR7JjeCa0WOsUw9+1X9qmORcPSvWIf2IpKm3eyzkuP+ADPx+IWh94U7rbJjDuuyBVBMJquVDfrLOYVVW9IgfGJ2Oa3GkPiWG0AXozV1hwpOb1iQWR3UB2NaPxe+vrHzOM+Zlxvv3N/j227fabqZJXdIMw4ab2bQaIrF0xphwvjvHdrNBiAHDMOLs7BzTZqMMRtzDvWCSUIDqXUaUDxhkJ/0DGiNVKeAfr7bRvYDAqr6MIEDFsPo4dzV3eh+z++3V+S6/E7Rex9qaFNxc3+Du9h5393c4HA7I+aB71iYRKSGmAcMYME0VaTDbRjpjM/fZqhFxGDFst6B8Lq2M+Bo8z81qY7WqbcCkmZZBMmdSjCBKQDwD4gbYXCKMO4GyikbeBIZYdmRdVi2eCXOParw1NIvGk6As10CtEbKee67kdLtn8UZVxpgZqGL9sKbcm1XMXc8vR3dRum0sSRRKi4elFL1Q3pgfAwhFrXfSwEIwxhBgLYRJRW2rtYWsiSLaSJcINMajQwhJnhcVOF7KRyDuqsLimkOLGUIt4UjiXo7RzpKsnYNG6HzGQVCWWq864yGq2HZjgQq/lKJ6oyT2arfsrQy3GCFCkbkpPA8EoQoIYgYNwvdCCoqzC81qs48OqzG5FyYoPbHEvBI3FZWr7EkAQeLHusxRBKEtXS2SGWn3dz5oPOj0MrbMaxW4dewwy6jRA/ntm6plfMQzU93z1tbLVqwJKuvPaSUvRpPtO8xAjfC1FxqmplhZ9ilDMcE1l6CT7c7gunYvKUXEAHDq501IKaBPWCsnTRUeu75XCA5pWrtONPAt9Xjr838KyNqRroaylOSaaqsfUsepDtz80DKlJtGr3pQ4w4LuEgOQdPssUDPon2wZmaVIDCrXgsIVx/mAw+GIV6/e4He/+wb3+6MZsfKfQQ6auwBsLCIHMRBhlwY8Pb/COA5SHrGZcPXkCXYKKg2YIJQZ9MHbtrnyQjw1aXGyuAAsUBB9QdhXyL+9+kMFJLUAgzBdQ7XQDxNrNmhwIysmYfYlS9p9LVKE+vrbN7i5ucX1+/cCLFCOeqhGV5I4RAzTiGkTcXZWMCWpc5P8sur2i5lHw7T1LeO0ReUCEIOXKtZw758m/xqQBonHjhtQmIDpqQrBJwhhREhbT9QQ8SvJAERaW1RFKei9EBLnNWzR4ErPEM2lJ70IjW57YIgVU3HlQZQvDUQLlmY5cYUpckmtUvDuIOJYM7VIIqiGacC0GZ3b57Ko4OwVLul1BxLBLoIw6cOyglNbIgRLJ5cgLuNqQWiunjkYh4QQI6bdgNiZCIEE7mpZunZjJt+IVAiSZHvq+dfQoNP3NCZMU9LM5YBa2LV7j5Fqel9QJCAiWYeYpIdnCKQWz0rbVjVLCSYGg6TtPB+di1lD25VZ1nZIsr8EUGLN8oMyOnOHy7+ahYYkFVf8GIFZBa/6aciwTiV7nVmUq0ba8rBMBXOXYmm1k+aK/86L13+Y90vlsuJykpYh9Dal7jWbN6migMAhetmCrVVbWSgKVfCkF0lWss03odkPTTT+GKTvKE6XsUiLKnOPr2ZjFgOz06Qov/2aUBtDv05+pr77+gHEGDcO/G9PyfBntcibDFgGZIWhRmaM3vro7u+Gm+llpG779Y57kNgJWJlJtUwvG2zjLlwVgByNuQCMw/6A2+tb3N3cYX93j1wKWtl5e7L5uC1WFDUOOkwj0pj0XMvGTZsRz148w9nFOU6b967Hp0XWOvHKBC7deqrW7NYsdeX1hOYNdH96p9HqejayU81+VQXd3KbS36sVj6cuHtOlEOhBr3j/9ho3NzdYllmTbUS45SoMapwihsDSVzBG5JkRuaKEIpYECMwBFRJPkgQUcWXSuFEX2gcI20tNoxa3Hxpl6L6zHrwAogQKAzBcSK5+HEVYqcsmeqwpKuyT0o4e1GZ1qbelQmN4gjgTKKj1aKOA06grdUCXrdr2RWuMFU9UMmuLBfbdGlQG5Nlsay2JlAGmaYNhs9WmzWKlAOKtYWYUs1RI911HKu5S3U31u5nw80RHjcWZkda8IjKXYZRm0UMQ0HWzboZRmXkyxBIyqmuWBAgta1QZZwzuukwpYEjJaY5UKbP1yXmdkCJKZ0JKQSxM66Xj2avs86XuJzicGO5NuZUOEfJMzxCGWUJyb+neIkol+T7LXoUAL3tiMMhinkFNIAjcmCu7/tmOy6lhYbE/rlImI9Yz3ADpr16Rbq/o5xxRa/0N5uI8ndWw6DNgZe8gXougamsfF6iSlMZgBI0TRQ0bWTsq9wx5uZcaJa4LrT2L+iGEZIJV8X27nWI9qF43rSDmcNJSej1FGYo4XYQH1/c31TUzn6gljFAnKGxAFnPTCdr8zfQH09qr55yDO81MY2Euimz6cA2qufEkq7QBz1bj/npfsT7Y792/V3G4vcf1m/e4e3+Du5tbLEtWTA4zxc0N0A5CCKJ9pBQxbkekafRxhkDYbDd48dELbKauPhDUNqAT0o3hwdFForoTBBnFUp/FpSkaPXQ8vVbSLtJHNJDDLnRv6+RSUp6fuaJw9f5bQoS2u8GFIFURgm/fvMP19TWWZRbtXDttzLlgSAFpGjBExrRNiCkgH0UI5pDFcKlApYiizC0fs8REMIJGcUOlzZkc/iKuDPufqRUttiEdHJAhSkechOnEEZaKSyosJDfUegCSYnT3jENWUAxmRS8h8gbJTcXrPu6nz16j7p9eauWxWnl5kc7lJTeMHWN2YItb6UN0zw01KW02GLdnSDEhacsu1ZoAAEsCHmq8FnMBnBDME0CCx+phjU7OM6TdlDGraRy0h6bEAq1jQRhJp6mZesWUggZV1TMyANptZUCMQExo5SY24mDrYJagLqX+HDcThjG1cRuwj2mB+kdzAetv5mnq4tK+XBXIK1NK3d6W0OO6zgKgeNkFtA8eRRVUVVFZTLNS3iHIKxUGQhS8ZIfWQ2couEMWr4AFtCzkY8NzAfHwv/azeaGoowspd9IpN8WZ7fEdX/b86wDr4i5kqXCKkPwAsLi8jdUxWN2iDRUnWJIwqRA7OSauiAUCcUuw7OwG/6B/1WQN4HtFJ/eVN73I5zuv3wtA2wpL7RkCRQYP1XgxomuRbcQ24N59ygyPjVBoNUc2IQCuRbS4QPcp3yv2laIVA5BR2oBJodprrkABvvrqK3zxiy/w5u1r5GUGa1E0kwjrIG2jBVCWJNU9hoBxGjEM0jF+GCJSYpydTfj0s0/x/MVzbMYBKQa0LgjrxTfXTt/zzddYGU6oodvMjvSDbmb3PftVstDWxCzanS2PxKJcy4JaF9rOxUbhAeUqup4A0WZ8+eWXeP/+Gvv7e+R5RlmOnfuQQCEiDSPOz6+wmTagg+7hJAKg5IqFpFaxhiyxE+X41iKIEQCaAMpAEGg3oafqiBvuS4YeTlZ5R0HgOU6gqAhyfltyosJy6WsVrZmqHyxIgD86tJXSkj6V+7W3V00DYeuGLnttXTYKZxzmI66v32O/vwf37b56AQWgIR6ZZeRnHSABaYhDRBpHL9VgBqJKCRup4aHX7lEAt1pGnXNwzdxS3auAJ1doRxRqKe7UhDNMyRXiBYiQxsGVY4a17+rmBhEAEs8DYmSx0MNKkjkSCQAM0TBQGSDCEKM/t+d3FrMyu7fVtDWLsJcTjjrUK4i9y4+E4RO6NayAAUGb+HIDTRPd5FyoMNSHVY2FW9Jbc9lZxjI8mS8EwjBIR/ZKXfy4NqVgnUyp4w4yH8vJsJlXtTJqR3NG65aIhW69AbXiVVknAmKsCJFh6FMUJNmugiBwauLZCEHeK6Q5AGx74YMFs4BqeFmI0wUhqGvb9rcHA6kmTLwyZP39plJ0MgSd0P2e6/stQV2XwnIzMdeVDShaiDPaNk84G2Yb0IlA4JasEhIcH7QNnJyfWZkfnRCpuImF8eDBPE362dk1QUlABb7++mv89O9/infv3mCZjwKArBocgaQRptfbECJJcH+cRqRxwDgOGMeEGBm73YTPP/sUFxfnmIYk2qOzSnKNxsYnIMfFdVZVGN2AE6uFEBLDLW0i9GdYvsRNi2Og56tekaevWQsdgMSC1sLVQNLJfiWqmcSPDjkkSyn4+ssv8ebNWxzu75HnBWWZlSGPegAj4jDh7OIpxhCBPZwbV3NpcQQyA6nIP0UukczTAkk2iD5nGaJ0inasQ10wcY2okhMh2l5oFltPC6bEClMTgSZ5icpclNEZcAFAWrSdFHGH/HXbJRNy9rtvisdWVADVisoFBQXH5YD319fY3x+wwtlcUa1y2n7fT7TbEEUIDsMoZRxotyLIgWZIjLzWiqWWdn+SdT0FgTdNesnSXLkuABUgDVEbQdsS8Kre1RF39F7jOKo7T1j/3D3bmLkVk0t6PWvcUDMfKbRKClvnoflxmJpn4OFFjRxIaQInQhDk9GN6S3tS21Nh5qZ4+VGT7Td6CO5otSVornoVKgXN42Mf9KQk0x+qYc3Kd6XTDUBMyCxux8oSdvBuI23KjW6iCmg9/AaT5onyJ8msxq9NFe0VMXM5+gKoygC18MR4UW8BAnKuCogdEZhQdG/FQ2+jde1VAefXMA4pwZNaXKCFZs32PN0Y2yPvrn8zr89/kxDsLDwjHlaGbJLeBeDDb8uRVsHpK6CX1Ai37C1/jv5uAXpD6KeuZVD7RRihKM8V61s1/UNCBeSLMc8L7u/2WOYZtbY05pZa2TaOINpr0C7oQcsfQghIY8BmO+Dy8hzb7bYttm0+Nd3LzH1r+usK5IN1o7b2BFieVz75hmlLVZWDpNo6IcBgs2qRoD2oYjGfZwUCVWUOp+wkuPADoCnIBff7A+7u9zgcj8h5QZkXSXiIA2II0jNxM2HQO7B2Osi1WflsDy82BxUoaqU6cABHV3WJGJGyM75VpiAsFk3wVtK6/2wrU4FllgatcdD4TGWYu9zUas856LKCTVEDTAsH2Nu22FacMAuu4vY8ZrcAc87Y7w+4vz/gcLjHkucTbab90JKuFptqfMf/TZsR2+0Gw0binmxtn1ThNJzGxECtAUPWGCyM8WjcJqwzNwUYXjpupAjUwq6ZmwLhiIUmLNS/J+VMob1PwsRTh9Qj61gkuUdduRY/ig7L14RK90NaMLG9YvZVT7nkbjODnXdXvtKIWWHSTMMUVZbmzpqZ67vCgOEooRMkgnJUVU+R8QYCrCOFMWGyuj0WD4/wA4uROjttG1/7ufQDl+8GCqozsmg3q6nbHYNb8+isAtI4FkWh9RaeMY+FZoVWyw6F7wU070Jcq4SAqpaVTLZWsf6Yo/Pn4IJUkrMIATElnw5MoW2zB0MAKpZ5RgxReo+aAmphKUBBG+RGgSX33gW3/7evUXyMvz68vt8d2rs5beeCNS0ko6iHlykWYEQyE93UkWZhcRd7M+pvWrYyHkPDNy8j/KNNENbGNOCaajcwK5Al8Y/nZcHxcEDOi+LTQRe9CwB33ydFShBYJHFrUAhIQ8S0GXB+vsMwjs4MTYA1jY1EcIKVKL+jtqtbwCYEZS0KqhMBda4pw6CVtOMAcluAUFCRuy4NohkWdfW6eF4/mS2iwyja7flwmLE/HDAvM0rOKDlLOjvETbzbTNiMg8Q1ATCqZuMKoVaYAmVuvqoZKFWFjQMxYZV9BxHYFqEgFbA9E7D+Ay4EW/qsIEwsAh6MQeZPxshcCMpSRlPPNdGCdQ8E4QMqCDPaw/WLJzGOWhllKerildZGh/2M4/6Iw1FoDn4XPyhY2eN2JLr7Cm2TZt5OSJOsUZmbFkqAunFNkWSPwS6wVlKKnEIBMQLDIIqTWXCBo/bmAyQ21vZC9F9VZgDFYPVotVs59uHUoexYhp9l//WuK3PR0inX6iy2Jjj6Rep+t6W0ReuXU8++/d5bgEarXosKWHaQK25i1asSxsafbOvks2Q3BzsCU2vHFB8ovWRC3Vp4PSIHbX3EBQtfw8b4qQlLVQTYXKjGJHUuAU1ZWs1VBaAperVTJuUeRdygzAjUAXyQ8RNVBlhDDMzqspRzLvQhWNI912toWSYEGVxnUBqBMIBVITUw79V54JYf8ICDUfTP/6AJqNfv3VmelMEYryBa93ZbX7ba4u4KZlt4bK/JbXSag32zf7Bl5vWa3eojBAGuNXWenDRXMxEeIiu4HI843N2hzMsqoBmiZX5VvY36wKNihFLEmAZcPbnCxeU5Pvn4Ezx79hwhWsKKWixVa6+6+jlx80h6cAyEXFhqWE4uyWYE3FFRyGMBqKK5ggiUuoQNAopqmZEs8QUSR0yMGAZxQeka0MkiN6Zge6Dxh1rAJaNyVubZLChjOIWBw7xoRhc74dWlYn5/lCa2G5ucLkSSNk2zWou5atYnRVVEulYzpwSmmIFt0JrJZ4wk5+bB5ADSWAxBgBAEcEh9y0GmZDiFolDJ84R/mf+J/NlrIVgeKIEUgDRpsyoaUSsBuJd2R8uMpWRNLDmZGGlSiCnyNocgZ+DifIenTy6w3YwYx+R4qGE02nA56bsYAoEHOeRJHMAw4c2oiBGKcykPC1Fh2XLQsqImOABplhx8IzWpRWOGjhBjC8LQOJQxRIApreM16NFhdBE692GvihJJ656ifNEUdOLTDVgva7uUZt23Wju6b4JNfjcQdY3f27etBnqlYMtYmIBCCodmD9FbPjzmCgGpyjEpw7Y4t1x23ky2yurWQKJYqvBcGxEAxQgOQcqbAJAKvKpNqsWaJdQqWMo1m2BXimQdOGvMEGgeHcti1MelURLghrEgxIhc1DJGgmU1EwXpCBECxtHm2oT+CetBQcXMGbEGxJBwKshcPj8QPD0d/e+7vlcInqYZkMZpujHr77wijOZeFDJ2RwOf3PWBpF6vDHUf70Rkmy7p8dJgB/nKrMfoYewg9yh5wXI8gqXFdxsLa0mCOtGZg+x5IE8jH2LC2dkOlxcXePr0GS7OL1vhsgkIlrqtPpHTxhIVKFgjBji9YgreZ4sNX7MyOGvn7KJg1jE6MTLkuARVTlzIBRF6XIFUhUAjwb2BtVvvXi9zcV7FcjBg4RW31VkxNDtU45ysWjHniny/oFTlwhHCKQKASKhZiqwFl7GCgrhOgBHgBMOssVoxmSdhXWpjLlEbfwVJEBElAKxu0sFqFdWVr5qT3kKRaYy8TACip6W1c01+fVwIgoA4iPSiMGCes+wXSzeKXLKtLtqqq+Jm2m6vK5LQ33Y74Xy3xTgOSEm1CSJHuIN2Am9sml2IMuDriRDAyGDKkvTVQ4O4ANO9VhCBZiQ3tyKABqQRg8bCW6cBy/D1O1MLfFiuQHttrdq2LEVbIbO02PPdemFHwHop/Q+93wnHZOf26/V3tgXxFgjAfl3t8apgnfXe3NZNvA1YXQ9CRsyakGSNwsPKMpIPd+hV3NYoEKEGe4Ylp+lXiB3tqFZGqGxiFKiWJaowZC4I23ZzGx6gqpokWKlSrO26bB1GVKQapfqEpaRJ+rdUmZtC14GlLCam1MplTi6CnkNm5JqBkAQnOqy1ml7U9Qav3MESuNo+tvX87uv7LUHFuGtBE7v6U8protUV7QuQvT/Y6rvBToOuumW6scvKNmF6+GwjjCjap8VxVmoXYeWHRy7ALDGtPGcF1W0Cu8Ve2BeTAmGYNpimEbvzHS4uzvHRRx/g6bOn+OSTH2G72SJRhFsXSkylSHwoaJq9EGnLlRpSQnScVFIwaonDkGZ6EVgQ6Csjxwz639r7s2ZJkuxMEPuO2uLud4s9cqnMAlAAGkCjl+lmD4XsFo4In/hzR/jAJwofhiJD4bC72Y21gEJ1VVZl5RqZsdzNFzPVMw9n0aPmHhFJgk9EWuaN69fcTNejZ19KxrT3mtoOAJLFhmDZbAxBSAFXdjd0z+JHDKtqDXBbUABKEcLJSBpi0KdODl8v67OfDtju9vju21c4O9sgEWG1XuHi4kz3qCCXHXh+hYvhAgMNAHdASZqsuGC/m7DfzbA4n24FUM9YkdVAU9WlESK2uSmehhz47XaHaT7g/vAaKTFWa2CdNrhKj5AAbNc9hjFhTBJcUUDgQnXeVF3JmbMilsjRt4gaMKbKHZnrHyTlfiglnJ2v8fT5U3z5zVfY7fZSfiyotJwckuJSMZTBHFbWw4D1asSzJ4/x/PkzjKs1SMMkADURmepO4T9K+mbPqyqcBE4joNmgBBkzoMwTsRYhL+IKX1ick5ihCZIrnIgZmpD6vi4GQ/NWEtLgm1QJlvEQpJy+MQBKQD39X8BfbguCSEFMEkoUkZvTPk03mP2NAiqkteds2ZUYOIkNCBPQkmIhtMA5rcCQkTL3tiQMia0De21UqFq0GDPj81LfiqDZD8vquKKlvvanOL+khYhisdCWdchgtk9iE+ZeVJ55UuI+qwq4tBKxjLKmkMtgWMajbOE8OmE+zDhMGWmS87te9eg6Qq84rFc1eymMlMUFLyUChnbsgGgZVqpsZxapmimDUtGsPfLjApXClORIoGAG+f/8ejcRNCTdkF/Ue75jaAHXOAdLhGxuHA0hVbf4hhBWIhi5w8gc2zPGOTgBtCsiAJgDiQ4sFyBnFA3U5lIPguAf9jRm5lpLROj6Af0wYhxHrNZrXF5e4OrqEldXVxiHAV6nLxwyl2DU9olgOwFUKuuV1CnyYViQcQpqLYCTogZDknEbyBwXZK7ObycApYj3ujzoDIqrjCksV8uqNkTRVF2JkqZ+MkKfMR1mvHktBYmvHz/EGRjj+UbaLwVUZnSFsWLJLyjZhjpwEfftaco47Gvm/r4T1/8+eBk23IVBT5H1ymDMueB+P2E/bfHq/jX6nnFGhJwyNt05pjHhMGVQYoxDApOqK73ZCj8c4FA5LdQnlnAGU0K0oG3etwmiury8wDCOmCZJOO6canGrk1nZahC+Eq2hS1j1Pc7PznBxcYF+GMRG3QkkFWUEmFw/iHgujQl0F8+URB3Q14B1UHENQjIOC9CyNUAmjee1SAafpsKGJYy3dbO160idSLh5TyCUYE4b4KymVVrsBbQqGtd5GDNH1a3LmzcGugEVMXGUJSMdxVS3E8PPcCOd2vlwGiZ/mPbATiRnYf5nO6tQG6l7jNfFFa1HcebP1iUSZFXyLy4L6UkNrLoFs9TC2IIuEjzlG1fcXNxtdNm+4AkhgupPEDMgOcUkCTkjALNopzoCSq9JFyA4g00SLIxZNWp0TAMDPleVLUnumtQJY9QpqfLharomhvTnAnlQp//Q692OMSl8HQ6HcDIsyY3bLzw1kdxazNaxrj2sb6sa0sRh2cQORFLxo2aCqFdRDgUw28SJlV1c290er97cYn84qKon+eGQHoxNhRNAUu6ZUo9xc45xfSbFPzUlVNK8i/6qlr2Bql6dPmvgd9ZDmdTwy2rTMtJiHGjLdQNWBDZ1owKpGOA7Kz+jhWNzOL4W7lozYfj2AazSFOBSqH1laifbsvV6wPn5iL5/4NnbCzP2hz0IexCAPM+4ffUSxA/x4PEzcSDSo+DIt5fsKx1BMkztgN3djJvbvQQGF0a6I1A/oTzcaDFOKetEBvRZYp72Oyl7s53vMZU93uxfYcr32M3foksjgIcouEXCPUp5jPO8AnOvR1NjqEjUphZYwScQQvxV9wJAArqhBxFj0K1ycm3aB9JE4rrL026SQHnFPwVA0VR+XERiSZr+zMbZr0aMZ2t0mhi4T4Shq2rJvpPzUzT3qxOhVovnastu1OTuFpxM6qgEcw6pkgFxUSmXJWuYahAsC41503rSd10s7npNZpGNO3MmjQs8NyZjDnhFPIQ9cYDhBi0hZgwgaXvuUGJmW2fAjRmNyB5AnlVrYLtUNEUjv0PqCucF1qYS33ieitnddG/dDkLWFUzPzQzN42kqepHqzIZmalioHc59VG2dOAyJSEuSMfJs2hFSwlyxADNQZsPLov80rMdBgo8LIDhJ1qqQ/u3CSotLjBm4380S5zhnpETYacB83xG6Too+d10SL3KNKXTnHqPTCjeJJJSLS9J5ZVCnXu1qA5ChlKrtY4mXTYQTafTefr2nsnwKq7+4DNAW30VR3OYn8EvHRNAmAkiuQ+M8QLpR1a349MX14PsdOuI8dWCYphnb3V7i9AxgnI8Doj7ZJCA7IARC14/oe4uFMk84lcRMQiPVTfs0zPurcl1icyB1s67qS0fEjKakFFAJMnkqJvXy00KeFBARA4689GVfdgO0xsMt7mOLOQEChiFhGHswerf5lyKu5fMsRnECYz7skacJbdWK2m9U2XNmlIkx7zP2+1kcWgqD5h7UF5xd9JIBTdJq14kVUTPnaY95nrGf3uBQttjO3yKXe8z8AswbTBiQMGPHBQesIbbN5FxydXGxQ21ctW8/GsiL65KgdmJCSiGpecB5vmcF7hYnuVirloBZy+2UDNf6Gjxpp13Xqy2lMl2W1Qi6z6zv+L4eU3OZs2WagUhvnmtRshdIuJC94Hi+iIONw5NqG8jqOJL3YGsgiK1Uj139Ttacgve0OaIFLl4lD9mLgPkT1dRZ4buInsx+xsofiw1dMyJpfbnZTSBFkmCQVGShVH0XyfoCNWvJbPgtZmGBmw8sX6ctFUHUnma/gzKkOYRjmKaKtHoGF9ZkCnUfmh+022vFrKxQuEufhtc0LKIUaAYwtpXS3ayMXtT++k2ybaHmnuHyOLQpMygzkLiawEgIWCmM1M8o6NDlDghFGAjkGkA0a1gZB6QCLhrYFQaazSTBgvdFXQ6pe7eg12+7fkDGmIogji9DJQbtCUTFXeEtdsVVAkRiTwKCIV+LvSrgd0oUOiUs5j4dyRx0sSS/Y+cEydemMhfOpTAD17e3ePHiBbbbvfDmVIDItdkvjQWU/HTqhdd1uLg4x/nZBkSSaHg/yZwkIWxFXq1vqlUqXyZyVYRFRb2ohDhOhwk5F9CsNr6uh9vuGBj02X6QAp6xzpq16eCqSMyunCW8wRkVBcCan1SB3jRrICTqcLY5R54Z23RwwzizVEs/35zjYiWVM4ahw2q9AU17cEnIXYf9vsdcRow0YRrvwOMKvFrh9Zs7fPXN99jut5KAmQ4AZSRcqy1wjRr6XQDsUC09GcAbAAcAdwDtgf61sLt5AnAPwh7gR2D+KXJh7HMB72aUzFgNPTZjr0gzOG4ZeCm80YkkvQBAop7QYvAMU7Zxs/AE19NAmKG5zB4obBIP5iLJtdWm444f+u/QrbAezvHg8hEePXyMcVjVPdeVseBsjoeAxEM5VrInzaNKZD9xWspICVckYykWVhERJoG6Cu8G47J00fmFFD2IqtGUl0ywcnOyDjYAY/6sflxQqwrvoz61Mysx1Bf00HWW31LHmjVxclHGY5prJh87LlaWjEtGU8+ICSgJlmHI1Y2mMTUwVGNwVjucVWKQ7WeAshJDYW5jYvC6cuLAYovpBNn216RZ2Lw4MFJQ5k4ZKX2KWTRCBmclF5R8UEe3ukbeqAMBO/hWFpY0yqJUzZ8vE8O1eba3RDjkKiQQFWQ+IFHCocxSoLoIQzb0NWH2XBj3hSUxCREyJbEHkj6DDqnvkFks+mx+C3rWOnXOcuwnljikPjJqp6/3EMFAAJe00Ci9Lyj5j0tIRgSTPa8coi6gj43Z26m5V6sNwFnMKM8w9CC2NkFGS4JcxmFgvz/g9vYO0zTLIW2wgP3oIDTVgBBiIcbj0KPve5jxO2tBAo93DISwcqcCGJULgyMqEeftMMvTktQ6A2zlScqRR54AkLmbV3trPSrtL1YuQLzdQgYR20Alfr4drn6WPRzGEavViHnK4mBg36YO3DPGPvkc+n4ASta4QCMRjMN+xu5+j4HFzft+u8PNzRaFD8g8Ad0BoMnHVXBAgXn8FhDuAEi7SBmg1+B0AOgejAmgGxlDkoMn6sQzR0BFpYIDi5dj9nhEO9GV+BiUef7I5tJ9Dmn9RNvF9XuEtWRRfxUOTgu2PYrLmyQACtfWf596DP0K69Ua69VaMtmkFh6Y4WkDjZu2hNJW9kjaUwcDxDMTdTnhDMQxNhBDi8eo+S6ugoSJlBDGwMpk1tNMSmHjehQjgCHNiaVOy+DGk1IyuxAir8LQcxmk7hyIjwxfCVuQQGoLOm7S9GBqLjBPSgpzNwLtbXGAhaDN4SL1AyNht3mlMM/UaeV1bSOX4kQQUIe7YgH8gVFpWTnYybdsUeyEujLpDb7w+YR1oLAuJN7ubZFr1mIA2orCRWEySJGWiqhsZRyMqc/oVLWZtHBsKQziguIJK8RL30JCQMKYKGmvRNDOGwHEVBlR5Wk6anPTnrreXU/QZyGHznO6BYODOHa08YKG+C01E/S3EG3NVNKxOogmgMnhxQPCTXWmnpqRsOqDDfJX0lp5S3eFh8T7zBkvXnyPX/76M7y+uVNOqSrFzJvPE7lSQk+EIXW4PD/DxcU5xn4EIeH6egfq1spZErKm8GpD4J1vBkDqjMFgnlFM+mUIF5gqQ7BaDWDuwZY5xeyeKvF2aahqLLSIQjz6ilYKYhSeUYrYoeZJXPyEK6/ORgAqZ68HgHVsJRF46HD1+CGG9QjmV5hncWIxlRNASOlCoIIng0YwTTLPOQGHe/zudcIvf9nh7KLD+WWHl9/f4/WrHTbnHVabBMYejK1nIbqjLfZpwnr9Cn13QI+MxAzOGZwZcz9jngsOdwVTKZDExoRx3WOkDhd0hiFdYJ0eYKQz9fW2EAzhGD2M0z2ZBcJT1ykcdEowbKGoenEGWJ8gAFiyWncDDskT43CfsdtG+1f9XZuqxJMg5az6vsfVkys8efYYj549wcPHTzCOYpO28VjZq1pIVO1SWWHK7yu4nEB99W89E8qYpTQIs9qkeePqgU1holRhMU7TgjZtXubw5YjTkKl6UzeLA7WcMeOw34kWYzWIIdQ61lc6hWvPZ8qCm2aYxVMdPoowWrOOpdOyUpWvMHOKlqMCaeUPsUVLjsywUTA/BsY8m4q7GA+pc64Mk807+lJQUPv6PhUj4giMEZk/iF8ly7nOpRJWZySs76jtCgy3ra9h1RJ+mj0kBXoCwI69YIaAqAHxECZlzAhGj6ScVJ4ZOd+LL0Uv9tAuJfR9Uh8ADbPoOolL7XpQ6tH1BSnN6NVTPWAvmfMkGL3vQq1NQCIC3nO92zvUqAzseKhBciF5Rc8rZucXnQC61OhnhhyhkBqNQ/xknZv+UGwDNf4u6rAjM23U0cDCklZvdztc39ziME2eQai1+ygXApPoBLkMfY+hq4G+uUhF46gKKKwxeG9BblCpxgITzCwghuDKXlomllKECFZgTmqD6lzbBuMGUT+b1JGVA7SUUFkz41ioRBzhYsODUUvuDeOIXIpXFIdyvDlLBpyul1p17lKvXF/Os+DFOeHujvHqNeN86rDPHW5uJ+z2E8bVAC49OM0Q1KQHBvfI2CN3r0H9XiuiMECSgJtnOZfmsSjwKU5JPffoeYOe1+gwgErnzgmJQqVwlehi+SnAKk1oMVqDWSN+ZAxfBVhjSsvM9TndlHnK2G0nTIdcN+306nv/pPuduoTVeoX12Rqr9RqjSoJm/yUiz3FqNhjzNo5OARU5muRj3HyUhpZk0eCBQFRgql0/awG3yloYQJo0WNtbWvWdebV+rehwKY3mypjbzIySsyB8LJz1UPcgyjEmlRU9Q6wMQAkaGc+B4KmoZA4y66RpidmrmEci6BpvwMuRzbNKaX645Rkz8RjxrxEjS65IM7eUog5E9WybWYZTIDoMz/bSEkGGvdppgeOUnD2oUpx/riMpINXghIvDe1z3JXICMqbgnBg0E+L5y+rxKZqulICuCJHuKKFwJ9UmIPDSFwZrLlLqhJHpOtadiRBl/di5kqIALisFLffbrndLgn1F4GBoXcnAAkHLhpB6ILEGc3I03FNwiY2D13wzVvQ1qIBAqBkxksUBBtVVWADHXyEWiQshIwGcwcjY7fbY73a4vrnGze01DnmCsz4M1YoRzA5ohNBG3KcBXeox5xkDelxenOHifF3tMkXamAMBNInA+WCyWUsWGqYs+vwSqr4nADSCqNPUUgDr716lNT9fRd7P80Gnn5TgTZU45yJpznJBLowuWWxX8tRvIK3ETIpCWJwHPFSFCH2/wmokXD18hDxnTPMBJRfstofGvhFNxPOh4P5ui8ME7HbAfr/H9v4epTzAdHiAaZ5RMGM/ZeAeGC726IYZKwJGMEbskLBDiAw6hk8CLlbAXAjY9+gw4gyPgPwIZf9HKFhhIkJeyekdLgnn5z26IYl9jDQvpYQuqtNJQiI2kAhkK0AdV26bwR4TOh8ksXjqk6gAJ8bt9T2++t23+P67l8qh6mHQWmwGx/EQEwjDIMVzH1xe4fGDR7i8uMTZ+RmoybpCGp4Tj4Y4KRUrAgvLEVsqpmNj4Op/Mq8F+bJ+uurtLLAXkKQJFs7e2Qgk2YNxqy0htO8kccW8O6gEBfTJylfJNR8mHGZlplKvPrNGqqoGqOSY3BxSCcMZQ2PIpFCziWOaMwJEyn71DHQmrbKEPGRIHchSqoOBiDkeClOy2NmnWSpeQEsguWzqzDw5/MQl7pIm2VMVMJc6F2MsO53xPJN7dcteyIOz5kAmCD41aw9r9hbzYIW6QZAxCb6J5H0C5H1nJbClVEix/g08PFeAjcm+15tsmSmVihJJIef1QCgpgXtCmTLyrJ6eSeyB1DGGvmDoM8Z+wND3UqGCZnQOCda39MilaBYjoTuSU+TdVPA93qHyj5EDkC1PK+eEJ+S3S1PUbP6i4bqC8RQHG6C/F76PWhnnZKJdIVwWqzhNM/b7CYeD/JRSamotm1qwW0YCWIkjqUQp2dK7rqu7DFbbga0PTGAK8+IwD0nj5dZL9n/quhk69GWx+Vt/QjwtrZP4GQk3XedfnH1rKgb4NphtqzpJ1DW0PIFA1/XggbFar0UdumfMlJG6XJNLQ3g0S5Yu7t5iwzgcMqbpgLkckKYDiCZk1nRsWdygqQCpELpU0HFBmhndVIA9iUrVxI/cySmcCMjCTXJOGOYeHY/YYIUyj9hPA6gkFD5gnnfYl1usxoTUXYi62zhKY1QUTq2iSQSPym3qptpamWQlkQXCuRPEFlQYPDMO+wnb+y2m/dSeAxWjHEE28KsVPlLCahyxWa/RD70yRiaGWDMU9q420hDvANNBVlpu+ILHpKNbBqYtqTQ7WAUgsuY9C0P8HRBtIFKGkOVU1LGZqtHPpp+NSghtYOwaECWCNl7tA4XdISl6lxIMQcvlifg0Z2zO5tVrT5jHt2wc55rNqRj1YMDLGunYU2qOuvDfZMEIRgSNQbH5m2qzIIGEIDHqKplnqldQ17GR7UP1VgUDFrVCQYvV7LnjLbPDVntsJYK6dgu4gNdCDRDCQaWrD1pN1Jx1rQurmlfxlMEOZ3+RLPIwBfXykrhpsCCpEEYKl0TtLJfXD/AO5SrJdACQzC9b+1VlaBFJkS3I/MQhkht233xba5YAaab4fITZsgkbz5LQrH6j8q0bQwCmPONw2OLN9S3evLrFmzf3uL8/YJbVh+14P6Tqeh5aMW86kcAY27u9BHqzgGDXZamlVWrQq3FIhrPrVgUiD5LQBiadgGEOSTp0lGCbWdJtcUHOkx44M8TPQaURWDAFFlKCPY6qKrXDUrQ4aNJ6YUeMioSJJk64uDoDF8bZxQrzNOHVCyClCSBB/NPB9mYU7msFjIcRaSSUV9d4+eYlDrngAGDa3+B+dw87ghfDQ5xvNtjsCONhRndxB6IZ9F0HkCYl5wzsc8uCDr2xuuhywsX9CmPp8QhnKCjYlt9gmgj7O+B6T/j2lvDTf/PP8Pgn/w6EAT0kZjF1CZwLKHOVABfQVGAhNYp8VE0mqKsSzFp5QLL57/ZbbHd32GsdxvotJNNREnUyuANxBpVgxyMCUYeHDx/h+fPnWI1rZTLa2nuVKgX2hVHVQM54ab5PH/ekc5PvbVmNIMcWpbirJdkzV6dTV4cWjCqaXUIXF2CegZk7zP0gknFhteEFBD0mUcN7i3S6RRLCtt/v3VZduZWCPJsXpTKQWeIzZ85CxOYs0kNXmaA+iV1KsisyZgTJCYBUmWeQlmI7ZOnDiZxWTCEqkDy+IpUJraoenWRtJRdffVpZcwybN2/RY+CRvepsUiadX+CCKBF6r++tWotaxbyBW6ke3zI2cylSE5OFeSgw7YdmxoFlXpI5mxMKaUaYyoMrUxAYdFky0kQakLOQwpcavzkfCF0i9GlGlzqcrdcYx0GLIEcYWNAbDWNajaeD8+P1biLI2mxa3LLeHONXjjYGZtPyneOhOiKzd4EIA+LVJQHojKqyg3MYMSSgsqD6tnKAh8MB9/f32O/3mKYJzEa01EbnNhUBFAcOE6u1A1MJDMOAfuirFOFQT74uxgRXVYiNT+fvenfl3lh1/qyOCJHTYsAKK5rqhbP5t8UFC9hL9652SQKyut4LvNmoNSkUtgSkxiEnhqQDA4Zx0Dg5sbe6VEskRd4H6SeXGcPtPUz1VlAkIfdk65+AXNAzoc8d+gykSZBQtzXnOhlJN0HXRebSj70E8TJEUrzvMXKPMXXIYOSyR94zdjeM7Tbj+nrCxXdP8Orb73H54ArDw6u6N6j7FFZEYFLXRtTDqeGeG7gmcdNmhYe5MA77A3bbLW5vbrDbbXUvwqloOhWYsDvjMGC9XuFcnbJ68wZ2BpMqbIR9j3Y+0yQYyiNdO+PQGhq5oGqGYmqJIIPzpSwZJA0/PezPR/tTlXaVGJk5gqhZewuUB6AqZJtrPPtVMrM9Mk2NwHL1sGzjMkVbUvSzF7kukjSfShhLgmRrUmReAEmB5vNQpzWrR6r4wYCENW4PlsDd7JE2FnMEIoBUIqxik1y5BAkRlbCYP6gV47V5V/WmTiOzOxcus74Y7mpYKq5rlgtjttAukwphDCDD5bRcfB8pAUltu1yqk0yVbuvKZV1UIcz1XDgzaYJFIXCSzEVDN8Mq1x+zVvB75LG8pxn8eL3bMYaDE8Gpi8Oiaf+mCj3NKYbDFduAOpyxcAeGMMFAPkjVhD6UX5FDVHPlpZQwdIMuQUziC8wz4+b6Nb79+nO8efUtdrevUHIRu0Wqx1aPLGR7xFY2jB3GVQ/qk7rKAsMw4IMPPsD5xZmUDiKEJMQxpFRbJbFreshH6gDqMM1Z2bpOAczseBPAh+ryrpiiaALHivNSq9JdXtHZg6EIR2KfalXVxaboNQ6mdoNy17Ny1yNSGvDomXB1JWcptbSdwKkDDWsh1tMec55xcXGBeQd8idcgbMHYYnd/i+3rW3TDBl2/Ac4usVpnrAEMSBgOIguvXmd0hxlpxaAewBlcemcQ1qqCxAzQzOjuGKljjGcAZ8bqvuBun/HLmwNuDvf4/v41Pv+Pt/iHf/gt/u3/8d/hf/t/+vcwbNW415PzdQZFIj2DAcxgRxsAucQuORHXm43ahg7Y77d49c33+PLzr/Bf/+K/4u7+thIcJ0xxrwxugJ4YHz57gg8+eoaf/eyn+P3f/xTDsBaoUluHBG0RoFKqIdY8z5UpsioPIBhRMCaVedC6kkIMcmMzNKBxbO5jq8wuKvHT50j5cysZbda7bCpIs8dZTCQAoiJZb0inZAsROY0Am0LMCvJ+70SEAexLkpJcbmetkivDPKUz5lk0J9lF+1wRc4bEIWqbB6/iKf9adrhcXxH8YTisWLUck3xso3VMnvc312UmeLwhCMFHwFrXftwmaesg/7iCxAcUIIsYiQ8wLZMPzbc2AzxDo5CQS8FUVLMUzFRu83OipOdBCRFpppzSM6gQOlY6YIy9zjVa98XmWpCLVNRBYPCSWVNJ4L0WgibkktHvO8+HfPJShpSIQNv3WQR/kCSIoNpDAAr2TfR1dy61eb35VHkPNJyd9dNmf9EMMirmW1A3K1GoHJ720WCWOo5pmrDb3WOeDp7ayDkl0GIxVaIhhHRs+r3mvhtWI4ZxUE/N6qlXW6j2PMvuYcyBAYXdk9AJdxV1hGLcF7MlE4g+ZZUddU/dIA36UBioEmeQBHwPrI+6RWYbq3p31oQFrAmECsZhBHNBmWfkVCQbPSVRURaSOEcCCmf0w4DVasBMB6QMlcwnpNKjywnTnJXDlFAbKpCfPYEOEjafZoi9oVOOFoReM+BjBigTulm8bKno2Z4KDtOM22nCzf6AN9sd5vkNpm3BH17fyrypsmttrCmUkatrZ5xuhBPDXVaiuEu9wKMm/85zweGwx93dLfaHne8cgMbVPege/L/1eoWLi3OsVyuM44BGp3OCw6zbv/jSzmTgPM1NhTW5BXhpM9QBKoK3P0k/UNSXWruVO1N4NjjjxtW/iYkM46uOHHw0hYgjSpFKBnPOgQiSq9cqF8renxEWlwa9OW6Xsh6JmgQbAZMkkRSZEZyDlrYx9s8+5wYTss+Rm8bhTGdkaozTsBKYzpppk4URSrJxXV+dX5ntFa2a0qAarS+pbeeSxSkO0H3WPfQ11b9V2+CJ1zU0yMxCwixVHFJ8rVUjoHtTlHZ4+zF+FAZzZmcUU5lljPEyXmhpTrR/H/s5nL7eYxPUxbRq4MqpUVLk27EAXolYt/46lo8WI2KxKYm+mdsHGm9bxn6aQABGzZU456BPZwanQ0AwHYgGb2u7vcer199jt995D04Am3/F+NolQt+L63/X9XA9QN8jjQPG9YBxvUK/2jSB7LVFqRk4aK02IaSVKwKM2FQAIGOrs6r49NAW1xm1W+nr1aw9L/G0/6YE4UyrQ6KredAVSTlk2USCNE1gWfNUw3GZB5F4DpMgIpp8dJkIh1UHHAowAZvzAc9+conxNaN8t8dNucP1lkHYArTDm6cf4BpCSFYgpPsOtO8wzSsUJKS9zmOf5fAMWeyOcdkzAJ6dcE0ouMEer/iAb/MdXm1v8fV31yC6RsIL/Ozlv0CeWSShVAmQXWYZFM6+gEvvayZPWhIIWyVZsx4rMVeOa+x7gdc5T7jdvtYkBYFb1L+KMyTKqWsPDx9e4cMPn2G9XqM5DF4egRFPVi7BKYqDoK8fEoRIiDQWUY2GgsA0MIaqWsZVRqmephSacExuZ79AMC9rGR4hUHVu9YpbSAQppKoaDO/dkbZIgLv9HtM8CxEsDLefq30tWbYVJSglz1KeR21mJoxKNwTL81uzJgGYJK7YLtMWldwySg0JVQl3ebE/WVfVn1LbcsmaQ3Th7gBAmf0oBQpsZrZA/toPM2Py5wXeykGoIHmWIXNIEmZlZtEkeD5Xrmp0k36EJlOloI5XokUZoHIQxt6q1mhIQ021KH0kVc/WbEYKrAW1HJyueVcIaRZXfiLxwh6SIAAiYNAcxZ0mgUAS+JwVHvI7vMvt+gGOMXHW+lFtbw4CEfOamoPrRlLzsiwEc904l/48XgfhXHFzqzZVJSBW9i0SmBgnVThLFXnOdcyBa2R/nFQKVKJolEqvLvXoOkkX1vV9zSHazK496G7P0CNgqovimR7seATqhPbY1BRS0n59ou3LCOkRJ32KMhoRZm6kP1VwNLZDy/avUaKapYTBXYdMhM5U03qAEnVIJHFxXddhtVlhvV/j7GyN87NzXJzvXaruxwEMcVEnMLZF7OI9EzqN8UpgiRciYOxMog0g0CXkzQbo18D6MfbzhJup4H4umMHIJSPnyVdgnieUOYO6hEJds2+s86/5T9WwHoC5ej+jkYSK5jXd7ba4u73F7m6Lw24f0nLV5edizgEIjZBzR+O4wmazkbjACKhGiV090xIr6Lg8gxEiRC5RbJiTwpV7SRKCZKWropIJgSRxQWhXEKVlTqnEmB2R0tFw7WQbt2+wblJWwy+rZ2cJv0thT91lnoC+Kn7OglbF2tfvKfxuzogyOI2TC+LDdS8WPD2E1IeH3Ab7losCSqVgw/cjrzu4CKS251JSO12p9+01z4ZTG0PED/a9h1w0BNUbcVwt0RfsTEaEKzIkSgBS0Thb2V3JtRqXQZ6TpCvJJUNJniL4yPVvrikzpo/ByrS71y00xMmkwEBHyg9Iov3+EAlbbf9lh0sde2PkJo4DgusQUr3D7Ol/YlcAQl7B6olFAHrzDE8AIWHoOlGLaAaTptzKotGSZ0zzvubXi5uuh11KgSQdQLKO3GafKOFsOMP56gLr80usNmv0TWc24MVNJfKZCzIknolzRq5M74mL1SNRdHvV/uBNohLDCvTEranPWyu8eBA+t0RSBUOYq6SZ9VN9HIBVqBA3bUan+V/7TrzXQBYKIfE7XUrYJ2CHCcN4hsurB+j7Aav1gM36Eg8vnoBWK9BqwEXqgQRsC2PHjFvtdQXJgTvMExIXDGOPgYBnAMY4uQSUYYPtwz9GHq+Qzz7F9eEav1n9A77rXoJfvgEjA2XnM8rTDoftFv1qhZ66Gmy+2AzzyDNh0dY1edUQQRB5FueKw36Hu9tb/PazX+Hm9TW+/OwLvP7upToOBAYvAfOBsT9wqD1n3tIEpA7nV1d4+OQphtUG0Cgx0j0Ip8UhwM5GgjhsoROiYD5/BaYKLOFg6jmlyAjCv3fCGNeGqwTX6igYGdnVj3axbpL0UQmhMZp2GIvWqyuWFgxtIjDOGkg+M5CBojF5xbOhAMQkNS9tpVh8AiyGpVWFtjQtXknDhko2TYzutf9rI19KGfVEEkgLHlfGoeGDfI/gDCEF22CZCzJn8QZmjXk2RxsQkhXy0HlOWiUi6TpInLS6mJwkwtW8cuqbYgukzC0zJG6SAc8CZFm7VLwuNAs7o04pw9hp3K3ifoNx8y4CgRKjM/QShpNgplL10dBrP0s0QpckHrUjyVg7qId/34cE87rGJzc5XO8kgsb9kJ+S+sWSU5MrcrT1jrwTgF7bjfYBS3XjZwOKoDvp2xMlOUYiP5ye4cOQmSIvUpvaNE/Y7XaY53w86DgvH7RhPl8FdKnD5cUlLs4v0HVdyG3KDRcFMAoTMrN4N6rHj2VSKXaYyTXkldEICMbiAM12YQTMEI7bVe1vZ4FP7IsvqqxlDW5V7krXskqB8g5zu+nUtGdu2y0isBRUfSf5TedhwLjaYJ4z+mGPzRlQHibwMAD9gAEkTG6WtZm7hJISppSQOKFLou5YkxTEPU8Dpo6Qhk6clYYeeTjHdv0Ac3+BqV/hns8xbZ5gujlgmiWcJK7Km+sb/OZ3X+LJ06d4/HgU7zkrpEd1HnVSgbvXtSHHZCoZF1FdzdOE+7sb3N5c483rN7i7u3MJyTfM0hU1p0ja7LoO/dBjc3aOi8sr9H0Pk+jCAHz3K6Oi3xm+qdsdmKiAxiNiMKJEkMKlrITSmbqG3Wr3G+QVF6QMVBHYAVy1ZnZcH3/o2+IspdDrrDk/qxeiXcUJpHlzKnKm2g6IJE0WmTOZxO6Jpqj4WtiRatieE/SAkvkmy54Vy/OrHAPhBCEl1ByWHI52eK6xVQWpqsKU9q24j0FAsfg6Navo/grRU0lIxyl9qfrCi81SZZ4VBrNqpCipeYUhAoHtK0c8UyVok6ql/gBp4g6GZnZVumU4gkCBCPrfJJNMhcBmDIwOYqzesoZr6lIBQHVKVCKYtbRdX2K6TkLqg3T9luvdRNC5IFOPaWu1bHN8Wn+LZ6XloFb4U3ZUN8MPvzv6ih0vVNQ2ItSZ3riE49+oKQfHJSlpJWVmzeOYgXLA/d0tvv/+Fe7vd9UOFi5v1xCUFh4VQiVrMI4DPv30Uzx6/BBj1zX2jNgcF2gSbFF3gYNrNKoXaNGoI0palTuoO4mgnlPVUuq8AdXVs4VxFMUBjii+oxKu6yQUGQaWmLBgJPypt12BWwlXSsA4JK0nuELfJ5TUg0vCfjfhweMNrh4x5kzIOUmd4wKU6QCeM+73PXalR8YgyXT7HgmMBwSsKCH3lxiGAenyTBxxLi5R+g1248eYqMc9BkzpCof+z7C/6XC3+yvsp3YWv/z1b/F/+b/+3/G//+//Hf77f/MEqS+oyQyA1loFNHECnnhbV4EURnkG5wnT/h7XL1/gxbff49effYbddAfGHBsLTLhhSks+QhjWPdabFZ49/wA/+eT30IcSPyZRuKOLbRLEnssKX1XUCNoGrs+e3Eoo0lUO3xNKOMgUR35l+V6yGogitRgBE/glkJawiiG9xgBbNqPpMGE+7AVO45z12XmaUErGNM3IpWD24HAhuLOelaK9WMUEPy2eM7c6ulUCGJjxwOzE5AkAQFZRg6u60IVktncjtQtLTM22RHQIw6+RF08qHdrIEqdqskENnbCV6tRLeKYumJeANLATV2ZIAnxlOudckKYZoh/IkmJOGZCiz+YZzlTYyLPGJlpsZ+ZJoMJsfzrGfdxDIkDLvvW9pF+kTkxKw3yMS8QBataFS+iTJL4PSE12UzP8dHPLxFvc9zjUiitvu36QTdA2QjifeKLigVqwPvHl8EjN7F7fEgQcbXDKDTVQBLfFwFndZddOzpAS6+GR5NGHaZn14e0zpWTpqCIJ0tJKlmm3iShmzxriNg2qXBMFBOKvONJlJ06WOkpidDSfYjhRDMERdUz2yQ6rcIsyx5oo20pOCRetSEJ17ww48atcm28Mqo8ku1TL6oxjdhpfIZUmoZwopQ5dxxi7Ill2UuVCDYFQAZKqtUpXMJ734G7AdjtLuqUsh30HQqGEm25AT7XUVUodmAgH7JGZcUCPacrY395jf3OH3e4e0zTBVHJJ9UeH7QEHzSIEkvI1FjnSiAoRMwVbYIu0WCuJCAO03e6wu99hu9/hME/iUGG8mzN/VRKwPSKSPLWr1Yhu6LWsjhj8PUjcbH2GTFVTI9JO4HoDIxMhpnqG2ic6OkoFIt1aAdpTHthVNpZ1ImMUSAkNq1qMK29p58HEsZpxx0ICZDzVM9pGpVJdsO9VTZK2796atWSSf6s4i33k8eyi+cwq9UdGw+1vnGAOOla81teNrAddXUaoCXq8F36X4MUCYl1wptbcYWpOw3GdEnjzvBVPS4kJtL0igjqnoKqBjbFJUFzWQYqLizp1DjkpMgO5Y6N14E5tsGBkYlWPGpOhYzV8uFxaZoh9T6R6JEJSRiubNsnGZgkN2GyGRQsewM+k5WdVh3H0IdkJiNCzFkLItmZvv95rE4ykrm5Lta0ZuZJfCzdugz+7RfD4Mih672BVlbuggsQRxFCy0kJtxozSKNr1oyLzkgv2uwN2uwm7nVQdODlJ77D4gvYpcvu1/poe09Adi8HYjfbimWVqFz96CiweLpG4ne4kmWemMqsapyKG6qgjufDgHK1cKRH6oXLPzMWzywOS+WIYekxzxoEZ0TiRSFSXfeo8O463S8b5a64M9Zgrnv9SAVUP3NCpRKtqNEoD+j7hbCQc+gEDJZf9zcPTrD88zmCekdYjptxjeg3MuwnYy+G8nSV2cdutxKtMAX4EQDwD+RWY1pixxv5uize/+QJvvv4S16+/A88zQD26DhgGoGMCtgWHuwm3dzucYQ1Qh6E3EFTA7Y1wKeFT71nPmOeQqLXPeuFg37y6xevXN3hzf6OHtVMVscHZHPCGSOlmm91sVri8PMM49FrJQrw33cMyweEQgDpwwAm3q7P1ai0/hIROy1xlaMU8LK+kBCtjQi5ZzQq1Zl57NJ1bcgaNZCEkpR2H/L65EjFP/JwlGB1F/GuNxpnq01+dlQiawcrsVUqkPWO0EmL4mxGDMY7UWD4ZnQfXr9xEF6mTMoFcLHxrGXJrTA478wIdbnYGS9svcI2ZxcI5ZbSXwhgIUlorIWnBVWA+iK3wwMlVmtJCnTeDcbBSSsbMgIACrCbTEPWYMmM/lboHJMzpxBmTgDHAwG5moBTkGW0B4LDK/tkaIwEqBpDLLDGEDLBqEZy4ZUaZzetUcFBKhFwSsmfYZsk9XDL6XvDf0FllHXlk6EckTpJs5R9DBM1ob5ScQ2aYulX678Ijg/yZVvpiiy0JD5kNqhns8do24Ox3Tkww54w5Z+ynGYdpwpwl2TQISF3ybPBLVOZEjtDM0x5h/0+5Lv0tqcgiB8NH68eKWN0puAh3laAZHWw6kPybxbLX6EB6YwKs3lao51Lr3tkhIJinnNtArIMqsvrYSikoJGqrmgOy5ky0jBqsKfJSWewT29jR7Ad1QnC6wcpTQTJyuJbduHJTRzKGTmDpYjNg6An3PGOeCvJhUu/7DClzNSL1PUruBOlqEuKMV5jud8iHPXpiPLw4w+Gwx267w7jqcHY2YBwHEDN291u8/v41hv4Rzs9GWdsEl3GSwYIOtyYvIftf5y3aA4nnA16+fI0319eeqssqVQj+ln2ddQuM7+g6oNeD3HolpwY2o9ec7ZNDJjkz3WxP3KgKwfWvJh+nc7x2PqieTVcFn7BFei/mKUi+eDVhs46BFzgBiyuOoSHwEIk7JSR0YjcyW58+G+RTwCTkuCp83F8T4+tTNoexuheW6N76s7JfFlAUNT2R7AJ2DkvbOQmsW+B3l8IAAjVmwIWALtVAcSIgDQhFdnkxwZDjxxgS5YpYBl7rVjOpRMqBEWHknpFyQpc7ZTgEDLpMmIYJBUAuHZiL+sdwHYLihJroiQJcVDxjyQEY2mexYHlFFQVisy/FJdGaO9k0B1SZESIwT6Km71qm8NT1biKYw1YqcmXTk1MI+rbtWhCNo+NoCw+4NpD8n0UD8TRT/Vic7p2gknpNc8bddoe77Rbb3Q7TfABzVvVAgiu+YYeG4M40KcChXY55FG1wRQKSt282VF5VNMF+wuE+VI9fDEDASJ61gqRCNqm1lBmAqCrHXiW1pPWyeq11Z/Fl6nxRuABF7QlF00TZWDVDvam57MrKohIldGztAYf91KjEJP4LGJYqhqWNC3BJj1ICU4e+TxUkzIk4yctEGaZeHntgBKF/uMacC77Id5juMw63W+RDxrwHiBPO9huk1EOCK3oA52BskfkNcs6YDnusesKnz5/g+vYG3+YDLs7XePT4EudnaxCA69fX+N1nX+DifMCzZ5dIvelDNVWbwwcCA9JAjaOpLiWcna8BAj7//AtcX99gPsyuWi8glCyVRuYAugT17xkI4yh11Uz6S7ZIlJwAJx2L6c5I1dsxwIbC5/pJoLF6O4r9zAL7u2W+Wh+d2eiVDAVCY21Hb1FBpkkKomr4QrIMI2YqWBBAxcFNm+bXofaBKq8mUd311AEMFD54BhOFPBmd8QggEJlUpsyHSWhyABucDGj8GgHD0KEPRYnzrGenM+nJmIYWy0oMHzcytjDNYe76inlPxnJYS2TKDEBrXPZ9hy4lmA8sd9JX556stqKkY5GdH1mk13mCM+zguk7gDiOopmUrqg5lxjQVMSuBMTNjmDuUnLEHkPsJ0yS5lQ8sHrtk62Ll5op6NLvPhzHbiitLlT6zagcSKfNoT5UCItYwN/VyBjBnYY5m1GhbAmFKM4iAcaw+Dm+73msT9IBtBphJ6z8JYFCyMILKLlN8D0B0pK58UnuMlvSsIRp6QxINSz/xuxgGYsd7zhnT4SDle7aSM/QwTSdUp2SMrhJAtflR5B50d0ykKoz5IKmpuo48sWychhHAZpz+Q+0QGCGHooUnEHqqcXcecE/JMyUYpW54BYZkZNdE2VLNPGOepb9ZyyopOguXtFVKgdc9Qc1jAtgeL1RnSaTHFJGCEmJ/3NQRPt6ChOzxXpRmAFlsGaH5jmS7+f4OfLuVekxzBg6CdstQhA1OHbQopXjlZqDMB2D3GsRbdOsVLjvGeiSsVyPOzjc4O7/CxcUVhmFEAbA7zLi92+P88gyr1QAy8sezx8ZFWJHlCaRGHUl22wN293vsDjMOc5GSYli42dh+W5MkOHUcR2zO1njy9DEePX6EzeYc4gTu5n5/hwHESvLOLfv3rbgjtiNT0cW+qz3SG1tQ0AT1aA9j4Npq1TSwnfAwQZMAtI9EACfysBowhEmAeFRD1elVwkpgKhL6YXZkqMMjWZ89mAsSJcU5HObJYLcFC0LtFZckK9fklR7qMbczlrrkKjZA+KNSqhNbVUO3p0kYSWNi67NWIqqJ4SSrZhK0bAS/Z5clVbAxJQUEw5W92ik9zk7VpcXOpDIfvSYb4WBuqQSdQvrK6iAzdRlznzAxa5gXgbnDegWUskKeRMKcWOa818QEPItWrDotqd7H0HCYn6NYlnzRDCGIZpsVHGgOMyVoFENsqMG1Qh0RME8d/tFVJBjqZuwAIkHMhhBb1U09fJbp3Kt2LccRsXftzD8UP9XycCoWa9Q8JjDDNlLZuMM0e/2629sb3G/vsZ8mzdqhnKAz9aQAD6ATpHpSh6wdl8w47A4oWfKKGhJwAohKAF1iJEGrumKVsEAHHDi/LvWKnNS5QN83lVSnLvPLiyiBS0Eqak/JWaRAZkylaOLw2m/kfJ0IcpaUZ4ASL1QuihnwwHJ5hboOiaXWWSMBl9mBVBCddZFAmMWGV6S+G6Ui6mCCJY6XwwuI7er6DfKra/A8CWe5l3ZyN4H7ERiNaO1RCmGaAD7cAne/A8Ye6eIcT/oNno6PdAc6rM4fY3X+BOv1GRjA7f2E719vMZ6d42JYI1EPQsJctuAy+YQjU+BgoUh0nhl3r+9x92aL+/2Mndo1Ei/eMCA1ZJcIXZ+wOVvj8uoSH//kY3z8k49xdfUQiYZmf0jhqzp2sJe5iUTw6GJGZnEtSC5Omhrv+IVWloBqffScG8UDHLHWe/JD7sZgFFsQUp+SeDUaHUkamaK9ZU7iRFYYHRIKitSb44LENal7C7qKwlYAc8FhPijTznAHrJSQNOE9AK2qMjZEnZV2l8xIXVIphBvJeU6kyZ59CU6un63irMnubQ1J/R9gpo3lXMJvq2Dhfas3pyCRSBzF2WTkamcFAcOqzpcBVb8HuMmz6eflVhHIKhHt6jXNHWZNPDGLWAph/Ve2MJKnmYVw3ewO4r2r5oCJRQMx7SXX8P5Q65VGEhBtlpW50sTm6mSV50n8HRLDYsaNENpRcSGTCCWfhvF4vT9O0AdnhmDCpIZNQkHHvaq6hEpWRFsPKzekug6SwyZxKXCxjmzD6lEsyE6wWG7ApCQjvh6PpYBrnprgOAe2l9HYW6iiG5+4m8+WobvyjAX7t7RcuEDjPtzzi4IriwOjuNenkJLEVM3J9EGhcoIkPq6HqmHbVUeei1aTV9VE5oJ4aJgtF3g4SEE9SmCkrmvAxuenOC0lArvtCuJsofstXFoysgrxnCwgzsA8YZpmTFOWvzmWkqnbzcx4/eoN7u+3uL3bYueevYzUi+3sycNz9P0IUBIp7LDDlDOmwwSe90BijF3CZtXjYuxwedZjmgr2UwF1CdRLNpppyri736F7c4unzx6i5nkNIBuYLJdsAQfgXIDDlPHmzTVubm8VcVZpqBQCIwtS56I0UP7r+h6rsxGXl+d49OgBnj9/ig8//ADrzRoBIsMGGJcse+b19t57BUevow/tGa21CIKK0TJ7JCDG/4HgMLpUc8bPDEX+EPU/VwjRNS81tV8YIEElwgSFq4rIyblg7YQk1WEK58aTaKg0kdwGV4PqSXeLNEF2lwips4r0yeeVSGuKmkSwQK7uUarMQs9dmAXXkAiyBI0VPQvSr6vWd0lCb2zshdFz54Hr7doCoMFxHBGhGy0SXxpPlpTbmHPxSrPsey7kSJxmhSuxNZOcmVKQSk2H5/RBk7pbjt/10CF3CdwnVdVKntdUgKzpznJhHKYSzhgrUwj0vcIeAx7TxhEX20DN4luJSzHm29aWzfby9us9cYI2hrogmSU7CJEExw4d0JWhoR4Cl5ZmC62+f4lddfM9c4ZxiVyfF6I3K0CoWjRXoLaHPGAzEBlz45eAXEmhZWdH2lcDc2WQ69iybGrR/9qDbXW+jidn99zGmLiqVVx0FETSm60PbTOdct9sAf7JbHUKrZFQcUsEjRBmNl8+AQqTWpb+gKXk5mANiwztDfknaHkpOfAEUtWSMBeUCFDvuY7FvZm7gsQzMB0wHTLuDxkDzRgpa2b6pGut08kF3//uBV69eiNcZal1/lY9sFolfPjkEuMwAgfgMB3war7Blg+43d05MR0HwuP1gPPNiAeXa9xtJxxud8IFdFAiWHBzc49DYXz66Yduk7XdJFtzGJIimJQsayoc8H6a8fLlK7x5cw1Leg5IH/MMFCqYMesBtaoihGEcsTnf4MGjKzx7+hgff/QRPv3kE5yfn8FjihfEkBnCadt5ey8NJJDWew9oM8xJEQdDpG8Wq4s522jqE1i8GEHVbkkPa6rOFGhar/2zw41qDsxBw5weGGobNqTeakxK14V5cq2am8JDROg7zRms48lc8YGpRbvUyXoEGJcSqSqx9gR3dFZplwtbfA3mklErSdYZJ5f0jMVRZxaSdT4EyVAqh9aTaPjJvu+6hNSTxwsaop7VcQSIEAHTc+o8CRhaG28P7TuHjbKh274Vgdc8M/Is9RYzS5UPYkaaE3IumDKQS4UmUueargCpMDZjDzOHlcLoDxNyV5BYbIxdT5imjHmqq2gjSgkYR40dLkoMFT8Vy6QQnA/heNl0cBVbcdSQvOP6gblDl5dyhqkHE6GgoCYOMej2R6vN0KgicTUVBoTTHh4+9avdeONgjGtYHPKSZ0yHnYj+HFuRy0xrBMIiI7B2lgAkqXgkVn/hKrskKpNIALkesmpT5Mr5eV/BQYThRmIlU3F00qwiCOGiAfNKlQwYgbDq2nTJ7BGSvaNwzbaQOs3AEqVSVEnYe+4UQWjmECmWbI9TLZ5paNWXLVUirTBwOGRc3+5wu51xn4FJ96GAMIHQa7yVxGWS9/Hg0RN0wwb7b79B3u0kaD4lPLg8w2Y1YhxG9KnD3Gd01OHi8gLdsMd+kqBfoMc4iDqRuEcqG0laUAqQZyDvwW54HwAMYJ5RyuzSoEylAyOUsQJbVa1mv/b7HX756/+GL774QtR3BvenLpdKkoddpL5Dv+qx2ozYnK3Q9X5YnMG03jg2ZL8CMThW/zC6ACMuwZ0al9sJkm48Ko1Mek406C+xSAni98CAq9KrncYBS2E/odSkzwWSTJmVYSZBrpURFsZK5k7NGY8JOEy1RjpXOYcCi8k86XRPU+pDBYJ2HTq1uSct38OMGnfH7MxiR5GRjKES6lClJhU7I4lEtWq286otq/+aKtAknZQk/aBpv2zju45rabbIqObW2c0STHvb6FRDXBFqI2xACH0qDKs7mrigU2a6Y0am4t7MuVjxY64JSHqIhDibDkFiRTH0ml9YtDZSwFrAxeI/S5HahaSJwRMVcMeYKUnhKWZ1ENU07mr/lDVjPZ/R76TO/X3XDyeCFDdNT53qD8xxhhxwLRZHM8c0+kauZ/utLGyYBGyC0t7Rk/poiYdcL0ljtRdboO64q1BQJUDj2ypGtzknMJJUd8513KlPoL5riBlMknSOt66brxrJQemoWxDthQRq4wOBknEXalFky4jBYf0qB9jZ4adU14SglTE6jMPYHD6fqqqVySZii1skLVGdJkkQd3y5AGxxP2ZoYDn40yHj+nqPm/sZW3eCJVSZosKV1+ejhAcPH2N9dokXb15jN+3FvjcMePD4Ec5WI8Z+kMPIBV3X43y8QD8OOEwHGQs2Ff9yh5TXoHJQdncC8h41+esKgMQpFp7V6YA0HizBavYx6j4bU2U7sNvv8d8++xVefPNCbLMkFbEZ8Bi/CBBEkhJQEHKHblAiuBYHGTbpCxxA8i3nJRC1ytDElWXbJESYq5JbUMshENFkNrp63xxNnAgyg10iNKJJWAwB1UVIkJhF2SQuEqqdEsAJU5ndDGEu1HIGku6B5StWhmuv8YZhdQlAr9qJLhfjTgHqQFSLE7cEWhjIoRtkmMm2mJVgsOQsZagEV/eGXOMmBK9PvdtejRDKWdY1Vgkn6pbsk3g/FiTJwhtBBkzsCLvTGFIZIiOnjMZkExlbXa/CjFyVNseSeynV/V41fYBInpmLerDK8uQiFRoKk+RBLlAiKCEV7qFBwhSUImPNJQPzLCpnFtw6TZIBSJJKsMJJhqmHCggoGVSqA5yvm3JDzOXodFQHRbzzeicRTD3VZKwqila0K+6qVIpITMpZkBbqNF0OxdPFgCuhCZXDqejPBbLseTOlL5krY56lZE4nxgSYTBNLGg1jj/PzDfpxEG8mzq6ikoKk2quNJfCRYEjZlczoEoMNDomQhgGp741kit2OBJjl8Kknmg3ELbSoqlE9pe5GHDfICCOjPvi+fAY+b5kbE0vgaAEGTlgV9XQkIKXOfQ29u7Cf9inZxqlNInNV4whS57pc0aNMRgSgw2HOuLu/xc3dHe7ubnGYDo4oWAFbmkzaY/KQADk+ajnrO6RxxOOrS6yGQYsrd+iHDRJ1klGegK4npG6H9OrebbXCpDCmeY+b7Wsc5i06FKSOQQOECHYJXUcYO+lZ1F6yQbYDrthXhJj9O8b9douf//yX+OqLL/H1F9/g5vpG9oLq5gpMcBOEkEh4yM3ZCg8fXuFnv/8H+JM/+WM8fvwMzL22XsKa2t4ovGuQ/FLYdFX8kagnmN6Jube4fL+2U9RZwqafuA9MJLyILKk01mlGlWxliJohkHp7innBY2GN4OlRGVPCoPsgKrVgj2dWJjBIU72sVUzGD5ASCEbRtayZntQdKHh92puJOnQekB2+U0lDMh6FXVTmvHS1b4LZHMNemOlAbYXyK3poU7PD4l2dmj1MzljL4GrGIvm374zwnGaVhHgzel1oVxHrOYwEfzDv8pLlh3UXzPNcQxoym2OWvHrYSUjV3GUnQIZfS2Hc7xilEMZBik+XlUiZcxFpctaMVvM8Y54ly1ffZayGjJJJk4iweICrSjRncbDLOVX1KUSFalN63/VODNsNhFIIlFWnb+mytHXxAhTbVeNmDfaNt02GqUzqPvr35kZhJ1CCOqt46+iZC4qYDQSZa0wStAaeIA5C3/eSiHjsVS4psPyNRFDX/hMTVlqYpwJOjHFsMQb1vfyQjcFiJWuqpYqE6tSNQFnyb24eQAu1kZiQ1eY2bqAdNEPTuy3xHWfZWD284qm77DT2VcfAetAIUO6ZwXMconmI1b1qjb7CRs/zHtf3W9zd32O722KaJ+cwCQkZ6hShHptmQxFyEQCkEwnw4fk5NqtRxkYJXb9GlwaYqWa9TmDukGgUt3oYNEjppPsygUuWJFEJoJFAPYE0nVuffKt0vZO3UdXO5GtmTt/391v8/Od/h6+/+ArfffM9Dvs9jMkDqiqauLREMAliW69HPLi6wCc/+Qn+9J/9KcTAYkTQxhKRrMLeQvprrwUM2kwUAZcWrHHi4cCIWR8BJScdVxGiZ5Ih0Klda8by8vPCQsgLi2cwZ6AhB1QdUBjFEa1RS4kvDusxaGHr1mnTJ8T+lzIBgq3U63nJDFpmqLq2NYVbqXmM7SX1lyjdCcNTxAOmDbNxtJ3CCRsAICRbD+svTJ6OmVPzPunYK55sUUW0lFVia2dEfQ/i8wogeZ7kx8YUn2G12xlzXBhbkNgSu5apBhhzZnUqSmD0vp8FEulrj0/TAYfDHtud4AWweGfPWiXjMAG5E4mysDJTmg8VZJKfOgT+EAqIH6AOJSL0Q69liyCZH4wTMb20cjgNVo2rHghevWmfVK4KmRkAJYxFPRfZXObrYtmThS1da/ZFMC6B1abl2timyCEhHGu4rS1cdqhID8fRpW67HvhvnqBun6lYyo5A2wuFX8YiUvMt6T9Lj0Q5p2q7Uk7LBx3W6fTVEi7bujgcArndpesGZUyKIlJjWvR9T1dUPXcNgRsS77uE1WoFDKK+mnLCVCaVbBTgoYm3E4FXQOo7PH/yBPv9GRIXzPsd+nEF81RWL2nxqzgAiRNWFyuAGaskWSw4Z/RIGM3HumP0lxfor87R9WukboXV2FckH4i6Se9gk051VkzgUrDd7vHm5S1++9kXePHtC8yZq2csMVKn5cJCailTl/cdoSPC82fP8Kd/8md48uQZQD1I0wIuodEcg8zW2HoJx4vCfeP4qxTBancBW+5Yee6IJyN4vJ5dpQGW0HeSNeMi96qjVH2RdAzE0PycCYkLOBUw1cTUtg/J/DxY6kmyDooCsFI83FGFD8DskhWwVfWebH8qQ+MWmmNuop4/3wGC6xI76Sct9orLoh1XiwYcUqlRCJPSL9iYAa74yxOURHwb2J2IvwgLL1LTtNmZZY9htTY4tIYkEh/1PbqUVJtWfL1YGUXStImF5TCuNqMQuqnDMqFIXwoKS4Fji1ktJYupwDJuMdD3IwgJXcdYjQVznjDnCZqNEtMsBHUqmkj9kFDmjH0q6Io6MCpjLJLi+z1j3uMdKtPoNG4m5yLusr54BgAiKQqwx5VijXqObEv42/Tl1ThXW9aSGEaJnADGx5SfISeEUHiwTCnscNsKpoTK6TczPrEK6gTU+o42hH2RYap2AcAy0cARzgLlLD/6P8rfBZsqhXGL/VHG65w9VzVHjAk6wYU0fZ5kmDxBMLmTgTiN2JpUisnxXqmryOFopZTEUcXUJBPAmbQqPEFA0eqPETAUpI7w8OoK037E9vol8jyjG8T7z1Q4ydZ/BogJw3pAB+C87zVDxixEEAOoB2gAuoszDOdrpDQidaPkrW0YosqARHuZrRUxgAzs7w+4vdni22+/x8vvX0tuS6h07nFMpSWCSbn6JDbDhw8e4NNPPsXl5ZUSQGMK2l2ynY+ex/7dCeQtTQSNBxmciv2umuZFbRilK+dfdf/tOBNzcKwIdm8YqMghNVVkHWH9t55f2T3OEi6xTG6vK4nk+UPVjsFGcup5rFXa2OhBIBKRsyOX9AI6ifzf0eXOMyGT0PGBqWvBTJpY6RhXWBHu5n4Is3InXBYCmAieWxS6vq4laVRZcgidrhpBDFOPOIAhGVkaadIpoa5z0v3pxG4r6lB51mJTCbMwPmq6GsdevlN8YWPhWUJ68kolx5zFPlkIcy7usFoM3wwd+p6xXjH2U4fD1ImqswDdXCSjzJyQ8ywep5RQMKtkasxU5xm33ne9kwjmYAeyIEwALvl0xkeyHjaOC233FMQMcDRbhOBYFiKhXH3FPNVF2wCgt4Hk+li9rAFZRINsSXbcaQD6ACJP31yfRQAsA5cu2BACpLCmW0uebFoRiFFB5yjbdTxNhkj/p/oQ6kOWK9Cpd6NmpQC0gsA6iHpgnndOaKqkuOy5lYL7Tv7mLsECeQkAa1CrPWkZL9zNWysddzqPlHrnbA9ZAs5dBiYy/AsUQupGdKkHujpWgYtSUTIl9DQAqSCPGzAXPNg8xNCP6LveA/nt3ZQIZ8q9no0rlJxxANB1Pfp+BRp7pPWAtOo1eDr6+IWdbrQbUM0NK7ctThi7wx5/93e/wJdffoXbN29w2G6D3VRCRKB11rLq8M0WRR1hXA/YbEZcXl3i4cNHWI1rlwAqXJimJTh/BmS7BCf/pe+LI2fk0NjPLhMpITxG/07CLEZDEbXf51LrYZp+FaxInJAya+iLwWBo3CQUw8GaGDp19R6DJSEHF6mOAIsnS5p4uYYmMbe5Sq0WolWtKCqdkno8N1WabF+Xi+hrTVUKizjD8/SeIIaApKWJBMa+ic3bue6SMkvKwDruIjGzeIYo3RvXYFHsUYmz7g2bUrU6DFWVfmRCwppzCYgrNfskjCZ7gu/MRb15O0DzhhrOYWZMVIsrMwOZsuQb7lZSfHo6iBeq7Y067cystsGsji6FMXYdOiLM+n1Su2S/J+S5wxYdpi5LJqKSMM+ifaEkscWEGE5x+npPAu362aQqO1NGPuxf4IRawDenBRj2XQs0Esqd+v6yE0FvtSw4oKY79SALd8wNXYhWh2afvYEFAYQQT0sJVwddjfVe9gmGtE5QPQA1DT0c6FsCtPxt45Z3q9qR6hccumsIIQRp5KmqylKnUqxw1XXWbT69pAUpWbPRVGKQgyShrt4qbbqLv6qixDNVMUwxgtmubdxgSkm8C5MnU/KFqwZ+8UTtaUA/iJpzPZ5h6Ed1YIiMgRDBsRPnhnEYkWkG54zU9+hWA2g1Ip1txKm5h9i0Ftt3UirQoRGbBMqY5glfff0Nvv7qa+y3W+TpoAiFqqbZEqqDQUxeqsmqfqzWIzbrNTabM/T90CI0lVoWVgIfpIXVNMc7gElsxylj2EtCKzEtr2pDa89ctJEK6HGFU/3e1f8LAg4IQ0GWatBKA4WqHK5GU/tHZFoJ5KpDMqxvKdj0mWTwa8RE10qkc3L+RroI0ifb0pC3T4SQrDpsQuVE6vKFD2kBUNQOERWBGo5JqqZ2N1ztX8+JO/VUdbhwHEoOjWET6ufloGx7Kx6OY6h/M3Nj1jd8HjPKNIkJbFdYNq6qf80MpekkqcZocmIQJ41jzigqkFjmnKwcSyZ2JrIwQJ2Giug56pSWEIsEOBeAScLBJLF/kSTcDAm/KfjHEUFfJ1XDdW5zMwdYqv/ZYWPhUh2NMVytUgHBqajXzTKOzPLGmborEqemBh/M2mQASTBXC5t0P/RYnYmXaEqEorku6+GntgfTPZhWKuDvnDPevH6DlBhPnq8wjAOGXpcvlPAgBeqGvhlAq1qViJRTDsgkeCswBW/XWN/PZs1myYGmSpoxH3YonKV0SH1c1l2lNRjBQqeqN9sXUqRb91y40iUCNGJnqdvCvCEAKO8XMGdxKc+SO1BcoSsH5fYls9UtUTr1oA4Yrx4CBRj5EQBG1/VgAmYLTC2O/gD0WJ+vxc+UenTMWJWsyISAIYHGDuLBXGqNOxInFbMnJ7YxyZoki3+a5DASTzjsb/H3f/PX+Oqrr7Hfb9W13S4LAAkXAZQIXd9hNQ64vDjH4ycP8fDhA1xeXmAcVjVuUFVvFcktD3IlCtTeqk8cfaGw5gSRFAcb0NX1d026vZVIfQHkXXFEkPNWJXiJ4xIPunq+K4MmDbPqryWvbcNl617oOTDVYFJCaEnmF/OsjkNG/PpmhXhlL8RwAxlclwDqI5ZRZtxwk92LXHscAJVmOO73Fzy2jXYwUFOwKWGu584IrWpaQL4fbgqxPjyRhZ26uH8AJ0+7Lja2sJfxE8W/OKwPuAoimdVxqXqeAkCvf9s0qyqb1a4Xquqg1oG08libca3ZrDLmXHDYz+KBWop7hYJ7WNYwhuaDLtmLMBctnr6Ze608sXaYYoYUac4Z9/udl+R62/UDi+o2PGoldljCZAUUU3IlANUIr88sWFsjgoxQfToCXuD4rWSKuVxUr0fz0AxIu0tCAPvk3pke0+uPBSAjI4RhmEYYmLHdbbHZrTDnjK6o/iYAqVkZox2iCnOkhE2dbDT1mDZeFzSs5ZEDCipEG5NhDis5T9UYTqY2MeJLNWdkp5bcQAQrKlwQouXaw+ioqEG5aYGcQDNrktvMnoIpZ8lEEU2rHOYWdrv2SwndagwMAYOQlStUibfYm2JLHLpeVGZF9kFmqUSpg6rNo8+lBiaHfJHVO8+IADy/A1CTMHz34gW+f/ECs1cBgaoJS7OivoxJ4jj7vsNqtcLF+Tk2mw1Wq5VqK1RdSgsObLk2YdUdyy7WEiduuT3fcv9aslaV5kytZTjY91wnQLZy9k7owDVETmQQvoA0WKi+XxiRwfJ+3OhaGUlG0jCK0hAVMLxwbRwnYEmpAXTm/UiBOddnlDmsmhX29RSarT7AjkMjdTyB/Zz2d1VStfmpBsVeE4iVzq18lodtELn63bu13inAh28/q3lJMaCBBAcrUbtKdQ3r6OsdsjWQv91fdSlRqXmDHCfL350WNBehTUKDuDCyqUl7xQnImOcMFFJnxqJS33LMjDkDfQYmEjsiK9M69p2rvSvTwtjvesw5g5JInu+63k0ErYKnDcVyCeVSCWE4g7KAClVKqFyZwb5u8qp+sTRt1HhPJSnuum5oSxx2Ix0zJOxEyfpKHVI/SJYUSMA4aR9OPI9pDzpUYTDpmSy5YH+3xeFsjb7vMQwDei1ndIR8vMo2RKWVOg9uNUA5mrd3XxkJI06NDTzgFGaJm5RSJcKdFQZ6Shg6K7lkTj1d5T6XYi4iY1EBuI4oaZ+aXi0Ip3Ywoes6TQV3dwe8/P4GX3z5DQ6HA/b7WVIw5SylrJIkDEimByMoueqQEiv9G+AMQ1gd1p0pIGGIkrId6ppLfSDqhkdC2i8qlciR2poePb7Ao0ePcHV5LsWFSZkq3UZjF2aaMeUJX33xNb76+mvM0w5AhlTeUJasQIg/KlJMxoAlwjAOOD8/w/Pnz/Gzn/0BHj95qvkuLYesxEFG2BaQUp2sSVoNQNS9rAjdkJp8z1QV8S4NOuzaQikTsMB1y0vsQZXZE3VaPXfN+Gy4xtWzAgrEO7C5bFysBCEBvcYd52xVThoOVhie5SrouauqYFQb6nvmFg8zmadv8DcQuHLIX7ylpyvAbFCQwQrJGiGvY61n3tSg6NqwhRhzGLfPx6LOgWbD1zDqwEgEGGlGX47uyEMMTtzUTTTkxY4P9BwlqvPS2E8ZhHyfWI9grzGJuRJn6hPSZiXlz+ZZTTNBKlU7YV8yOBdMw+yp8JgZZRIb4H4uXjaKQVhtegy5A6eFxuHE9Z5I7GRj8YPDtiFhh5zIpePFZAfPljP2z0siBPghqi0dc1Tx+LRec+TSomTlaItQNky2SxHxsjyVwfzMANTTMM+SViulEKMXpKIFZRYiSDWNUl0THHNWPv26jv5G4O6MK+PwU1dKVLrJ6g7qpM1JhRZSXJx3JIC2b3KITyAQ6zMiQ4hL8m57wHa7x/3dFnPOmGdhWzJneGJx9aCMOy0IgBWMqrTaLhPVsfr2kaqJ4FinJYIcxlinkUjSyG02K1xdnWM1Dp76qnGXIogGoWTM0wGvX73B65dvpEqAZ8hRIoj6q/IayTn9rk9YrYQQPnz4CJvNRuDTnIhUJR9PUASq6BRRiQo3EthSFepnFkC0DTfnCqcvWn5nyCvChXeC+uWSQLhdxLiogjbUA5UzFEoi1V0UhFmxetT/xL4qGVGYCnZ70+zDib4+GQ/tYs2IwlcB/JdM6nKtTGKu2iDzv2VxJPHtDPUGw7oZDMuvBW6i6qPe0OHqWCHjjITXfsf1tba52bSjvo7S5hVunuOwNhWfQmySHEBCB2HEkoq4dyRldkitK6WUIOlKx0WfZSJNxA/MTG5vz0wo6mGKUuu3EhJKYoxl+EcSQV8Qcw4Wzj136oua56rmIwpEMLlHkRzU4JFJag+rSxlWsR1sg3M1z9y0y8ilYMozUiIMYw9OnRedFHd3Qbqb9YjHl2c426yQhh5lzkDO1QwFOVxmCkxUQgCCtSL2I6Ag5xnzYcZhe8CQOqzHtXqOGdGoLFNCQtKySDAEDdnoXGKh28DFhpPXGWRAuDhmi6VRb6eYK5ASqBvRQ5Lydknc8FuJIahpF7YV8yRzCTCcb7a5qdoVYDHgk6QAdiqhCPn29ha/+If/hu12B4vVzNDkq0XGTx2rOoPcKceJjSUnXLg2B9yhszZuUda2C4WenSNdst0a9tGR5I988uQRHj96gKurhzg/v8DQDS1PbCpOSAmdF9+9xsvvX+N/+r/9P/HVV9/g+vrWy3PZGtvQFI8rMk9IQ0I/Ei6u1vjgw0f46Ccf4Cc//SkePnyMrrOyPjLvNpeI7p0itmp3kuelILItHtrLEWp1hApO3pUtVSBs6ADXZ+LJJCJw5yukDKLaxlRKZcuoEykJlJNQ4iPV4YWpLZyPqHBSdOI2sJGQmFFmgEtB1BYqgmpmFWfZ0hIZ+9LhJ860npSkdFPW1vpkagdLXOdrhDC2y7U1uKYsDpF8q3x8qsRtpuSrHprgZZu6F03ooT9X4Qn6nmhgqh8G4pqS1OIzPC5ZnlgdxGpRALchJ2MTOn2uFhsHkZQVI4AH9RUpHZgzMs3KkI6C34plC1M7YmZMmTAXRodOykaxnO+5n1FyAc0HiQnMFY+Vwugw4D008H2SYFzIwANRAtSV3TaAmh2CHirl9AJQNARwMbiljcjvW7LUIim7SmZw1v6LDcGAR1IzERH6rsNmNWIYenS9eCZpRSbXmgaha9nrEQdcsqj0ilWYDtJdq5Sxkdj6tK1ay6bSbKA6uN/WAHn2d72MS4BYD6UAebD5qbjceB2xHA2mq78JUHUXN/bYmD4rIpx5zri5vZX6hQhENKxOrUoCH78jAc8tt2CC6jIsZkS+D76nsHW19bR+ZF+GfkDfDzg/O8fV1RXW6w36fgxSoKDJyKmUXHB9fYuX37/GV19+i2++fYFpluDfZh3i0NRr1jxph7HH5myNqweXuLy8xMXFJcbVCinYZyMKRrgbCaGtmf9uBYGjy+Zt8O7SIwf7J9fNP4INX3FuVal6z+EVQVPULAcFscEQpRA4Qdp0BHsVBKuK0OIc+W1zNYIVxkvLBlFVmksoq3NrsJ3vITd73a5NSwSbDsNM4hs4OqOylXVtyYYXpxDxwrILXWM+8dXx47T41wQWOM46gkNDR29pPzYt0jvVveIKe7KWshPF1PBkSctJbMeKHwqReJISa372KqDYGS+UUTipf6LgRmagJNEY/uOIYHPVljpbkZBC7Ii7qUrkpo1Y5ojAKhVxrUzQmTLSXI/knbkwWJ0dUkoYaPAq913q0PeDd55YuOnzzTkGeozHjy7x8OEa12+2uJ3mOswQfNWo/JQz7sDolKstpeDm+hbjahQPVttZGeSJ9SL1gOQmWHrprmvSS9cp5wurcxbGA4A1wFQy5ERZQfOV2hychlSVQuhMN08ddCQIqxJwmgEqMAuXAZWVn/KK3vp8UtWFcZPmGJQLMBeSIpvqtVmIJcciafdUZe626Et78eK3rW1Ug1GEF2Ql0iYpVxvSOPbYrFZ48uQ5nj/7EMO4wjCMqjoODlHQOCitHpF3jN3dDv/L/+P/hc9+8zt8/vUXuL27qwZ3akbmHwrJGvYEXF6c4cNPxA74r/71v8QHH3yEDz/8CMMwyjqYJGv8jY5FjlGFB5NCLI6VKDhcnDjsRgAjyZJKGiHh8NF7dqiXXJHrg9pnNW2Yq+GW7ZkYopiQYNligaKJlB2p+VRsIQQfdIaYB/EaJI3BrAhOIYHNG1WRqzNrkQS1DjLxr+V5ISOCAHqrZUpteEqFwThhtrKm/g0bMV+qOZuRxd92liMjR467lsTIUlsSoMHsVGsS1lHo6Kiutary2dtd2oVJmAatSu/qq9AviEFaZKAogHAfIYWdJJjWqZQZzISioUrZF1VTrGmRYC6MkYMkjjrG3fYe8zxhUH+Vmkhbvt/kcgyPi+s9RLBZ4pb6K+vyVo7ADjAqnxE/GUH0n64Ck3BpFBqSzPGMCkCEzu1ylmUkbqwAgFSQHsce49Ch61pu+piTAlyZzaaSqGOd5hnTNIskynUeLfAHArZo/4i7pvazq7wCuBqQVqQgfRtygz7fHiyTKNp+o8TMnNphGgdoe+RE3iDvWK0U/yyFMR2kaK5zcWwrGMIg1KghJbjgCOX0QnGzuk2HgQjaP7Ell1YhNsauS1iNA87OznBxfo7Ly0sQdQBpRQwikKWpsnVVpuP+7h5vXt/hm2++wzffvMB2t8M8TzAvuqRngeOyEbw9EGFcjXjw8AqPHj/CkyfPcHn5AOO4UieAADPSsQNHRczhPLTbANe4tIt3/Czge/l2zFBhyshIvX+iPV17B5/lbrodSgngkUQo7SaqXoiNGpDRxibrIZE8pFBQJe9nafGsZz2cUl5Cbxx7PRTWn/er35pvkhHg00SQfOCNNErA24mgLygiFpC9CDKiNUfwJ2QhyI9vJaJGxNXOTFX1GdNQNsjAgTiOJyzUIuRA/Cx0bwNcNWdXO2UdpwXns8aKOjPjNmJDiOzqdasqVxklYJ56sAbvo9RTb01R+kcGy7dfUrNOAqphMZpJ6BOK7Oo7OoE4KCtG1fUAJTGWwvgVBA6XQNSh71bemaiYxAHE5R4CqCMlqgnghHHocTaOuO92GADMVONA2TuBuvOTVo4Q7kjrnqDkgu1hj/v9Hvf3B4zjClSyODU4w1MbXmhBfRqJ0sI2UDc9Hg5fziI6cq0tA/LAOD30pEHjwbXYOSEjFATxcDNV49tcUw2ZKMGysi4yIX1fBgWgiHMSJ+QM3N7e4bNffY7bO63iwNCSJ0kdHCyuSAlNSkDXacXwxVY3K6B7j7CeBDemmwcvQm6MyGgBwHq9xqOHD/D44SN8+Ow5+q6vwfZUD6uFlcSdm+cD/qf/+T/hs19/gb/6+T/g1ctXOOwPQJGQi5SA1Up62u+1LasrRxJ/SH2Hx08f4V//6z/DJ5/8Pj799PdUc5HATBLvTdwiR3d2iPM53rek57IoEm/PO8M470IJLBWxHZME6Fu0HRA+DE7D3bBf8lsTMnhspTFBzTboMSajQtoagbgDk5TqyVxaRwbVQljKsaSwX7pe7JuqzdBD5Z0503iimoAlsq8zDgEtHHCaa7d05mav5spcMdeu3Z0pCebkhYqjYWOWeHDxkMVW28qR4YojXiSFWXvynpNMqzxe99KdXZQYOZFSAu6w4cNQvKCxEUZwzC/C0npZrtdSGA0IlOIyBpBAPABUgJTRsRI5My+RCDEm/s2FJQaaZOesesVms8E4jthv75HzrAdBNQQk+PHdJPA9RPDoyCkAGoeiGKROsvlwfHQZBk8Wj1W/jWqHI/mSUDNBhCrsKWltO/KhaT/GvWnQdNdjHEetKG1wFFkhI7vs9yvuqdJq0WwH0yTxLbzgfIB3c3n1K+NywjFUmxZgh4P8oPhKBWiPwfcuwQUuztZA1nxJWuqmNcO3g9dSIx1xHWvRLAxVnVFwf7/Dzf0W293+6HUZXjhM/vci64nvn+1NWC6bS2CwapNVYqdANFLqMAwDNpsNLs7PcX52hrPNWZUgTqgFrCKI2RVyZnz3/St89fW3uLu9x/4waUjK6aPVkBKKtsANHj2SwPjVag0P/XAYMxhsaESLmCIcNMjJ/4k3jAZWe5beWMYwtuOvM2BUmDzS+lDtNp69epob8A5B+hw3U5J510mp84RkULEx+pbS8fouY2kNt8gYuBUf4rwWjQlh4sWZjHOrAOdkZDkmW6OoUTlxhWMaF62ZFwCF5bDJ1r+fk9DGia7YCamejiPcVBkhIl0XBmoy5DA4VntwyBlrq0xhzO084nOsS0aVEHp7Yc19PAqzqTIaHP0/lFfpNB3e3Et5e68gY0fgrQx/vX6wTZDgTATARohkYY7SqzXY1bG2T4AImLMEhKZOJJiBEqJjuH9SNmvTdcpddi0hOHIAcf4EoBHU9bi4eIwnT5/i+s0WwLVFmcFiUCwogjtoFvJBCGEW4+pUZnCZMHJGmWfcXu8xDivkKYMgFakhZzcsRPWKpXB4lrBa0wK2iIm1irXF5glQdL6GXS+epyJckSehPbVvhkMShBMzEbgUEuO1vauYa1ZhIfWSdq3oCwlCALf3BdMh482bO0zThLv7O+ymGa+2E+apYFKubUlNBeZlREQJVhuv4gIOC6BvaExL1gPbaSqxJnqQANJSWWBXwOLq4gIf/eRDXG7O8fjqoQTEI+AoCqNr8R8AYNoTdneEL37zFT77b7/B/f1WvUHFzihBwMC8z4FmCxJORKDUYb0e8PjpFT766Dl+//d/houLh41qzdYoF2ieS0NI+oCmD3N/PxX4C7QaBFkbSjRqUhLpI+mahJIQEcXVnTlGFmn5N7XwZGSbLWs6nX6/JHjlijg2f4gAsSsTOiroCiPz7HGEEicv860lSOXMWbH7Zs4IxIg0mfOcceJ4tBdZcbTQBYVBmtmYqFkHx4apXTHSnMqnjEYMIKdydLeOvWlpwezFN4ozok4UG65ScVsl3fKe83GnWaHTV2BYSPyYT268/p0Cn8dcCykjaUmsYuMrLXNhej2tUJFL0XSDqhRmaFkpIKcOUkLtHKUwSqlpIyu3++6d/0GSoAEUxUWwK3CCta+3dUp+yJOq8MTIzc06UuREFtxPE+sSB7kYtMCuAMBqtRYXeK2qTrr5EroUPORKYDlUGuQCzHkCKKHXgo/39zucna3qe84RxhWjZqneBVY+tbh+Lf3QtWN4zJnr+luO3B0B6HgXrPwQWIz2poluNG5sa5zcA7bawiXzy2E3Y7+fsb0/4HA44PZuh0PWYpg5i0prSQCN+hg3iMoBOmrkdv2WCeCknarmIa6pm7ylBPQpYT2ucH4uEuBmtcY4DM6MxMn6wWsOoYZ73Nzh9ctr3N7eY7vdSQFPmFpa4Qfm0KG2G0XMqRMJcH22xoMHD3B5eYnN5hxDH8Mh2k3i481Ek9ttySQErirudfRrtlerMqAi0lan41Dov1qmgCthAWocWBgz6w0KTTUxa7bFUdRt+hGiRYnBQjmdqfbphucJi/b9brhsgCkuFC9wVT2rtHi1GZsNkZs/m98NX3LiidpctNrXcZD+G73qWzwM74AQdjPWSKVArPUnhkzYWT6JOqnGTzY2duPMfI0C63oC3bvGz+mDsgtxkDBcb8asMEGDJUbAcwRiapRESSVFiQcX/MSu0jbfjX8EEbQg9BrBn+q5VbuR6PTV4cwQgc0lbqTG8XmaogRJoIwJrMlUC8giDJtBSO5L8ZzpyDiLMLEAJZLJQnNDdrIgjx4/xief/CG+/PwlEr5C9X6SNqwUiKVZ4G5GQUHJIzIK7u6upc5VP+B2m/D5bz5Hnrf4s3/1kdY/rVXxiBLQGwtUTzpFSKaK4m0WlkvTCE5xL4vwju4FQeLi4vQ7ku5yoJ92Xkqp8XMiNEgnWeOtLFC71nyQOJ85F68jJtlgxPHluxc32O8mvLm9x2GacXO/Ry4ZmffIyNjzQccYCv5SAjwGsGIXSzDWed8t4bMl8Lw3SQPuFbS66tTpb11enuEPfvoJzs7O8ODqocSxRmJTP8LisRo1IAN5LvjLv/gbfParz/HV11/jbnuPXDRDjEqCqdg0hJMV1XyHNHZYbUY8eP4Az589w5/9yZ/i937/93Fx9hhd1x/NMV62T/Ucs66TwSpEwlBCWcNo6sQiXEVgoCNsRX5eT/s3G+GzG5FNCJJeILuNRjESBEJTKsgJV9M2ufjQZaArCbnMEkTt0zGux3yRFyOuNgWAQpiSnknxKGXPUWwtNgTG08eZDRBtX0sCaHTIx1iHsFzR+onQWa5jf6t4n+5kEhfwqFP27DQWB2rp4jrbK5LjXrQwdqyaYWTXiQyJJyaZkZNbPNvA1WI+7yM0ovYUAGBlHlMy0txWp7G2mZPW9U5gjSUtejZISteALKWs7W9PbroyVPe+692SoJUQIeNRjOyFgSJKdwsACGsk6cNSfdaQfSp1n2PfCpXVS2gB8EvdFRkXVu0EphYdxwHnZxuM44DUEcpEke0Os1JwVAC0A1PyLAlbiwTMb7db7HaHI4BwPs65Jj/qdcgR/3ElhDXAs3JqUM6fAOU/7CBYe7YnelaPjOE6Lt0MZx7Y+gterqo6Ig8QhrhbF5IEtZlxv93jsJ9wd7/HYT/jcJgxzRlzzgKkwl5UGHGGidr9conQzni0DbbP2qoKk6EEi/1Jd6QkAF3f43y9xuXFOc7OJCdn13XV7hSknAg3fun9u5t7bO/u8eKb7/DimxfY73aqmjPGr/qgVRuknIOUgGEkjKuEcbRsNFc4OztDUkawdnbsGUtAtQ9GpOCLRc2fzGbDBKp00M4n3qTQhnGr7VF1iNQ/Ty/WYkWPF7Fi62YCEeVXfK6HwgmYwKJ4YabFuQkBDaHpIwTdgJuuGbMjfNKWGjXhkqpFuD21HItHY9cVQheDgZ3VgKEaCryYm78e8UjYTG+g4ulodoKcaMfNlnTjeFQnhnpM9d7yoEPj4iVyvFSnJHvr3JDDItcmI9g7ThMs4AIlybxNEqznpa4DuJUx33a9WxLsF+5NJq74htXvJT+icBIlS5BjTLOTUo9+6I7ezASU0oGnrGRb44K6APyL6wTeqhN3lMkwVdL5ZoMnTx7h/GKNYZWQJwJP1lJkXWUjjBhNhZE8ZmwGOCPPE169vsHlg/MjzsXkmeioxjBD/fFl6EAkD6l/lS0ju5khFDZS6qpN4vSy+MwBqmZbAGa4NRmmcEHJ1fOTTY7tBvllfZcepTDu9gfsdhlffP0S99sdrl/uUGa1BnFByXsB6CRHrsMAR6Sn8UCdoCP11hlffhN6iroBRsoSAyjeiCZcyoeLzQb//I/+AKv1Bpvzy5qQ+NRCnbjBEOn4V3/3a3z5m6/wF//5b/C7L77A7e1rgPcweakDO0G2tkzYGEbC5cMe47rH5abDw6sz/OTjj/Do8SN0vYWlsGGkt+6mmzWWYz41JV78PvW9IRKqq1nfeVej7/mKfaf9KoonGnX3kng0n+KY9HfS8BUiJHp3AuSG4IWOjsi7EoJE1Zs8l+PQLykRRCeY1nJyWU6BE0WCfmJ9KyGsFzdnggAKIVoi11R4W3Qe84/CGAoyswMwDBLrPM11AlFgOfZMP37ubTOWWOcYTracqwlTqnVw5wn36Uf1obA/BReKB6tgVQZCPUiqTQCYkdWruANYax8ao380/vZ6NxGkAFI6MOlfCUyiCh9QIkTBIEr2D4707f6WAQyR6RNlG1O7Mc07AUGQNa6bT2ZoRef6+XG1xvnFJdabMwzjGoeeJcA3q+t/e1Z08eClQDiFQE9m5Cyp2wyBxGrVp4lTRQak7dvf0H58fRvuP8a8MKjo2kMXiGsbYXVwNKEI7bZSihGMkaw/yghonyUX3G+3uN/usdtP2B8mTPNBAC6Ja3v2nJC23dR062tgB5N0HtR8W8fMLbH3AGG4NRE1USHQ9x0uLi9xdXGO9WqFYRhc6yCP8HIw/iF6j223B+y3e3z91Qv87vMvcH19jd1uB1bDvEgnDCp2eDWNHOSs9IOE7Ixjj4vzM3zwwYd4/vQZHlxd4WyzUUbNRnQCUhYUj5ebG/AcHd20z+848ovzFFedjft725jo9G3oGY+snnHkpASfI/y9hRiyfU91nH620wm703L56MSKGogFalO3XoC85ZPYH3IN2JJCLmCpnmlrdnHWbUuomamOjRzIfX4G66cQiTVGvmP+IMfPHGDHtVFY/G7Hebz1HPCP9Yuj/bNnT639YnMrbl9S8Dg/fa8hzIlRvWria3bAVcL3nKNANc7GeZ++3msTBPSoMwNWM40hSFgrE9hwmCFV2TuvO1DbavBWzejh6oVOJmAFa2vMVp2HbFSxedeFUG6LyDLxm8gkeUQvr57g7PwSj55+iMuHj3GYX2E3F8A8CptEhKyeU1IUEgmqCpU1YEukPWWdF6Hr+uYg+HgX69luuTxQ5uLENqw24Dn95C3J22kqt9Qe3sAUmBdlcmU4hYGwByt3XZJcewx0HSF1aSEkMKbDjP3+gC+/+Rq3d1vc3R0wTTO2062McHWOwoy9cnBLK017TJdoWwnnQjIxwie+gi0MEAi93s26flSAzTjiT3/2B9isVzjbrDV/7Psugkkgut349suX+O6rl/jP//G/4hd/9wvc3N3gMB2AXNAjIa0SOBHmXdIwiQkERk9A3yWsLwas1iMuz87w0Ycf49//h/8Dnjx+hp9+8vtScaQ5mKevam5YIE0b9ckmlhgerjJmoEkzdvQuVdaRcYLwNpfZcOTHWJ+aBkvb0DJJTbU9U6+8te2ANQOxF2KV2tzbi/fcbWYxdvIx6yIsmIBEVig6JnXipvv4G6R19WIsIQxhL6YSrlO8fKvlooj/F4Tr6E3UJ+uc7K7Ng1TCpSRrbt7m9nS7SkZMQrdRz/tOGhJfsmD4+F1sU+BnuYVGaJs8WMpcGXpn8JHhOg4rJXW6UVSeFV8TyglbeHu9N3eo9xbgk1VqsyBJJ3kLqtWA06IkjvzPfqg5AZY9oAJI2BxApBSuPOcCpCtntQAUixe8uLzAs+dPcXe3xc3NHZDkcEU9ee1OFZnMkrC3ZOQ8KaGdUcqMaZ8xD4yxNz18JeoGGjHOrw7JPLkYLdwtPSbr+smrxuk1/n/Nise+m+/9Zsg205nxWZJYx/MAhiQqnzP2hxn7w4ycxWXZvPZF7WvERILzTwrvNhUS4Pb0a+5yroogjo9aerjkM5ddLeCSMe33ABhpI0mvV0OPcehVbZwa4GAf52IwEEeKkiUf7Ytvv8Nvf/VbvHr1Cve7LeZ5QsmSRM6QU/QAThauMQBp7DCuR5ydn+Hx08d48vQxHj96gsvLK/RDr9XBF4tj4M3trSNC5Ox6+BzmYbZwv2sEsJmyPmuIkNrvT6HdipwW8Kt/OOpjeNsO7YzqlRC9vhbTd2/RgBMiDNYuCUutTSXacdDHCK+eGyxoR0C6gDL6gQUJDEfEKHxCl+nMAFBxGkidmBaw18Th1XHQ4u7yqrRlYUum+ISMzdiVZLDAVZvVzjy8p5Os4MdhMpEAnOq8YqiaYDzqwKxtPnpVmjTvahsv6pm14YUE7M0cqPoN6PKAuQOIper9uxYV740TTNq/BiBaPTzLR5eApP81ayERBqHg7XIgFYBq3E88kSfAIT5XqOXGiJQTWLzgACBS64cff4R/Pv0Z3ry5xYsXr7TIKrWSWOBqCzMSSYA8ZcJ+d4c8zEhpjWne4eb1Dh1GXJxpNXAt+olsDiKQcAGOAQNyKMyb1McPAYSUxDNT3DCaE9vMLzfcpyFprpjhbapkwD10e+rlnboJtn1gBg6TqD/v7ve4u99rvwWZhHgY8CRVPdcJ6WwXHLP0TRgpAR0DIdwhKZKsh+YAQkb1XJXGi1bzuP3uNYiA7vkDUCnYrHqsxgHoh2a2djSNmJYF8gOAeQYO+4Kf/+XP8Z//5/8F37x6gdvdHTBPixRRlXgSGH3qkTpCd5EwrHtcPrzEoycP8cd//s/w0Uef4qef/gGGYUQ/aGo0R14LB5Zmh3QNlkZBx5sUfuq4HF+E4HOhLy2CO2oq/CanUxUbMlpN1ElKqe9FhYqZE2rzS2YM7h+xJDbxMmtTJRstIXTFiRZLdmnhbRQlHCe7YaYALuqVbd9EZBvG6EvkI6w3qqSu6rnmpTCwE6JjYyFYLPRpWEEr8buzX82pyrmOMCi74vTbm42L6zGpPH0tYXGRScyYhqU6nE63GvViTduRGFvjuo4JwpxUa2OS7hK/a+AA3isJksbRyQjawRlDYxuou68OEhFOa/B6JAQBPpb6cp+cejY6Q0DaD6vKyyRHqwretmKLZjGJm424zK/WK3SdJtoqApAVfhhFUwZ1ClDmAJLnAyTmZsY8T7i93WIYRlzuGH0PDCt9Xx3aa4mi5uwvkBBVYPBlCuopx1CmT68uzXWbKsdEunbCyFUPQ/H6ljx6TFb+CtUeG9QeggwZu+1OsqTsD5gOEwBLzKtSd/A/dnUV2e62aiUJ75BEyB7bZ7YNgpZnqpGBVRIsuh4FXID9bo9pf8CrN69BRBjPRxzOzwHEArzky+kgqvBpHnkRabx5c4PXL2/w8vUrXN/fYD/tUMoBQK1/CALyLLCai45tkBJJ4zhiNfZYrQZcnF/g448/xZPHH6DrtZYlasyjnQUOW/5WD0wGGscqJSxManeP3LnBFh+10uBbDv9WHFKRieArDsiG3QWA9XzEPT51RToK1Bg/+9b+druPwU2gDS4NGwzzYuzthN6L6HxN/DhFDGTklZtx2je2c0BFtI7PQ6PxuYrSwh4txxjx2uJL8gQJb6FZp+ZXc5I1b71dxV3PfMTNrc8CR8re8DJ1Xi2bcjxa+64Sx3ZI7vYDw7cBndjkvE1zxFn2Y3Aa89P+EBPEeyrL67Ioy+Ya/cLu3EBUq4PHMTUEMKxYPJzvviryP3pDq5JTIq9pJd3U4Eg2NQSpnZETHlw+RPkg4+L8DH0nSNAlqoDTpVIDORHMzKqC2wJcUMoe+2mL7757hZwTzs6eYb3pcDVAVZUF4nvJDQPUgCEtP4RTQqZgjM74RgC7BiabTyQ+VJaR/9QPwC5Vkau1OwcagKHFE/Dm1TVevb7G3c09dvvJ96Pr1QY6LSQ9y3iiDLDYdWX0HYmnp5lsXftK9uLshC8R0Ntn90iW2J/tyze4u93i8y++QEqEdT/iYjwHcw9gqOdsueDKEMUbhUXQ+/KLr/Hrf/gtPv/yC3x/+wqHwx1KmWKGPjCAw2HWfKiSsSKddxjGDhfnG6zXAy7O1nj65DH+/J//b7DZnKHrO1e71ZV4O/GQjrj5evlk0cQSNdfokZGsee/0KatxsjK8lpgaXWw7btDqEVK0j3GObd8VsR77AcNxSNSAcVGiX8sx1IeNILfNtxNe4uVAAOnE6I6Rqz1fcYSAq7xr1VbC8BticLRE7RFf9lIrhHB8lt/yvGLTBR8kry05g/YwiA130eLbQPKt9g07TubfYY3EkdDRPI66VJykGYp9rMwVgiKukxCjBCA7c+St6VjL+87Y4nqvJGjERYcgtyioEMAAl6ZLPwJEVR1jE7bDl4S4WobwRg2DwFFFiuoA1G6MP+O6mMDhgFxlshpHXJyfS/aQlEBUFLgVmVjB2siOsrnDCwIquYDzjHm/x+tXN+j7DeY8Y87Afq/V0inyMjb9KsmZVFenS/Bg0oAETF6OoCCvn7C9UdgnRpUAoZ/dICDzkwLEFKrPV1gtmVHmgnmakA8z+sQYuzD2pG2QqF0yx9HCpSchdHK/o7qO4OgBbMutfp9cyb4faGbwLPlaM0/glHG+2WC93uBnf/IzPP/gGfqhr5Q1spHmNGWxiAqCBYzXL6/x+vUNPvvlZ/jVL36F169eYp4PFelGoHNIUyLdSV3CYRwwrkdszjd48vxDPHz8DEO/Qpd6mNKb4nAM0+t+NMf1xLk9Os4BNO1vB/0G2RxzyhEV2hoXBwmuiPEk0ooEJxILOj1Af6edgaNIg0GDqWAPV6SDKhO0rYE58DMn2o53hLr5TyV/9amoJiTdGz89Ea+7tsvOJbvppKG1QWo5TZR5sTLtyL1vbUfKA8W9WTTXIMhT+98MDlgQwHeSiygVNn2hXRt/nGGOUosm/PCZyrkqG06wHgqDTRdG8AmI50r26UiWft/M/HpvKaXmKFluvWbXGYy88NxxUaBuniIzJywG9EkIZdUIKZh6YtUFyXPEduJi1NyYIZ2hBaCfrTYYB9IExqISZM1T4wDvBITBkpMVicQ2lXNCSgU8HbC/2+Kbr16CaMQhH0ATgPsBfc8YRyGEcdOMebAK6K56t/EukqC2x6I9ZtR+GV5TwgRWXimCQT0goj7uJP+en636ZJln5GnCtJsw7w9YJ8YwSiowAJh9jQiZJMk0wJ6tQhZNxifWWLH5Jcqq5qkB+RJfCpiKlQq7Z5uPjAum/Yx5njFhD+5nPHrwEI8fP8F//x/+Ha4eXGJYrZr1k4nYB13b0mwzvv7iW/zD33+Gv/hPf4Ff/t0vcHv/Pea5xgM2F9fTMHaEoSesVyNW6xU2FxtcPnyAn/zBH+OD5x9iGNZIWkmgQbkGB7ownN9/SCN6a/CPEa8OfrSawS4aWJpj7DkGNDuJEWZq3j1dhobbM3jUNilSqt6gSyJBKcnmJ3LNBjXtFT+HcUbtOkSkf+IBW3QFNisp1Dx+cmFMBxMYDKoMHEAuBVW3rTD3QGrraFgkvUBxo/PHKSJXZ0jwRV7wNie3ZxnRH0bhCnl/N+z5CaIWx3QSWhc3K3NOFeXbQFmz9ZitOB1h99rmsl39pyotCBJHWnx/js/BD7veSQSNMzxKyqt9OhNoSNEmbHoxroP3X9Hgz0YgagnUlrobZ1Y7ajYuFyDrUSuAKQ9Z6/N4W0bc9ImuSxiGHpxncJLwgASAc25Ul1kLjyYWm5R4hTL20w3SLuHVy++QuoTPfvU5Hj68wiefPFcvV2uhqHRnXKjl/QxEd0GmiEIacT+49ZAYUmpMGnHPIP0wcmgnHkyW6htd8nAUk7YkizM3rYEYXc+qAheOtCtC0AuAjtmzxFHikB1NGAGC+izUGchnq6FU6rjMDMIMzPs9OM+S7wkMKlI/8vHTC6Qu4XL9FFdXD7Bab0CpbwEtrmsRVM/Q9FHMeP3mFq9e3+DX/+0z/PLvf4HvXnyL7W6LOWdnIJyYh/1JSqyHcYVxHDCuN1ifr/HBB0/x+MkT/PTTT/HwwROk1IuZwK/A1YTPZLV4lgd3wfVF+1urAajZcxwu9J9GUlvA2BKrMSD5extIo+NxHbW5QPRcf8uYDMktnYt0XrRsgdV2zaoGbddmieBOIlD/UmE+WR29ljCxNsgNPmJEMHrbZfIJAQvPXIMXOm7jJCGx+dER4WKceJ4WN46kNNWicLtT8YM/W2wmoe8Fd22S7smJHH0MozAHC664ZekVSrG7Rfesfh8GTwu675qmqA0RDV4Yrzrd1UThb7/eTQTtICyYkFZnDX9AYNZcpOuhTZV8x8YlbORUAgv/zJ731tpvTKulAHl22w6ReT4yQJUTZ2+NkFni+sZhQJ4KSi5IfUKXCOWgB1F7mHJBYkY3JFEBc0EpM3aHA3KZgW+/xv1uD3QjPvnkQ3z0wSP0XVLOJNT+S7VafMzQGcl+RWGRuhG6xdp4Dskl8+kfhTilJLFsyVQNlmuRGJSSEEEXUcWOiQIgx/Mo+v5+kH0oWTLWp64Tmqm7PQLC5XbQbO9AItbciLZ3puefAcwQ39Je68ElR9yWtnF3u8V82MFi8TarKwyrAc8+fojzyzP80R/+S6xXFxj6MwmLMOwbk3DLggEQ6biwrN/337/CL3/5Of7mr36Ov/3Lv8bt7S22u3tkzGBoHcATRLBTG/R6PMOwGjGeneP86hyf/vRjPP/gOf74j/4Y69U5UhoaGtGSC90lgnRSQtHPoxNPPg9DGmYCz3a+zKbibSwwSwAQPvo3PGqMA7UerKfpwYIA6riXfhlkyLCBT+Bkq8azFAnDCaDzdhxmBCRei3PRqlmNkWz5pPeLD7ISgc2ArcFCrnxrC6e/WeKBtzwf4WIJI/FpXhCtBR8jTEcNlSBUO+QxhL59NpGViGFZOgRdYGsh/kD3ZNEOtVsZY96t8UoGjp2XAJGyY+Jsb+s9DA3wPnVoMYpuYw26e6PmOjAZhJX/0U3VBY4ZzK2lzEAJqeiXpmo71JIkxTbZ5micQBtnZzhPbEtw4Jf7CYwEog7jeo3zywvkWWx8PYsTRulEl23FIMWb0sScSoBzFmJymGfs9gfcvLnH9eU9Xr7a4vJqwOO16qgMM5BksCFNq2aj8n12LpVcEvQyLlQJOTE3a7TMqCOOQeWIi05qQ0yqeuo6SwtVkUNCUncepdskdY67HpgbbooBNo9N2YuIYisZZ1iplbpLyhgIO6K/9WkWpel+d4f5sMc871B4RgKj6xOePH+A84szPP7wIdZnGwzDBqnrZZ1SDTHngIkJ5DlqmYHddo/buy1++6vP8df/9S/x5e8+x+3da+wPB+Q8aSgLIW0Shp5w2O+FSUriYdz1PVLXYdgknJ0P+PSTp3j89BH+8A//GA8ePkbfn4FodEJyfFhtXHY+oJIKwURgO3IG3w3yc1hpw2tO9dEi1vCZAqI6dZ24fcpZpDl1LO7gHmsnXE/Q2jSTb5qwR9iKV9t7Tj8rdB25rQT4d4Idwn2Ecai4yjy1vWM+tS/vu0x6qqvg/JIT2fe3c/RIvKENNs80424erIRowTE0IfFGl0JXdT1o8bwtWLtZDZOD4HkebHp1fdsJGnZgfZ4CNYzqTHs45vttvKPR6hXMbtyeFap78nZOzq/3EMGwiLZQiqHNrlX7zYDq8VvgX4gygOvT4yNDl1xijIGd1SRs7EJ9r5UMdcioslY9S4ZyxUK12mxwfnWJ7f0Oh8MBPYs9Kyc5z1abT5a3wErHF4KI6rOM4zDP6PYH3Ly5w5vze7x8tQOlgsdP1osRMQhDyzeGBN7mGGIB/0RaHcHSz3HlzCzDRXPZEhfWSvQVGE21l5KogTtN4tyoXRFOCNgljr4TIoiprruEG2gOTR1HF/fTPNxQj6hIxRmecQgCLwIz5qkmgfv7+zscdrd1ajr2px8+wMNHD/H46ccYxhVSP0qwegpOQoabGsmDUZjABdjdHfDymzf47Je/wV/9l7/A7d0r3N69QSmQAuSKfbuuR79K2O8OyDMjDdJH3/fohh7jJuHsosdPf/oczz/4AH/0R3+CzdklqDuDpFaL3qDm6FOB3TQpMtYEK/zKsHjSQB0aAlYZlxgY3Fyhq8o1N6yTP3AaVddxcnOrYqiWuAtVbWLc1MGshLmwjj9OJxJjU4Ea4DgBC4Q8kvU4VGch7Z+o/qeWGLDp2xcEIzZaITj03qCzSqCW9kBms+C9jxAGwhXOX/VNqog/Eoe2iXYfjwmr/Dg9C3DRNKdjYF9vGQ8DHr7m8opxlBVSg+fwKcIHBA4ONsnYdkMLwtvE1kPYv8Wj/j23k/Q9fA8BBH5giIQDJplTx0Ibr8gj6YEuIQOLrVmiY8lFmq6egsYQLwaBeHCFyWBNIxZWRI2sMT6VEBxzEkBFso4/eniFn3z0AQ73W8yHCXscBFgoIVFRRw6dNkMDCiFOPEQokEwzeb7Hfg+8ufkO/XeEz371EMyP8dM/+MQmDAJpArTFHLDg9HQlUjJJu0K+xxRTPSilhL3xJghWj01seEJgxE5ntkidGVdwKSo5GuDIOnQ4P9uAywwue0xTwS4H1R0zGmMxQbmE0LYGD4oHMNe9qUxgHUPeIeeClAq6QcAyJcLHHz3BxeU5nn74Cc7OLzGsLpC6DomEkL8V1bCmw8oiAV5f3+O3v/kCf//zX+K3n/0G2/tbTNMEgVtC6ghznpA543ZX0E2kIjCBOokHXJ+tMK4GPHy0xsNHF/j000/x9Nkz9MMKlugtkH7fPzuogc1oz4/uG0CwOGOrymGNJfXkjet2hBvN9mLIw2jn0dq8jQDGtkXal2YiUY7EFQEhAiGYUPY73PZnI/dfqVhj0zRm2BhYQQsxmq5OzM+669gWCNfmk21t2pnX908QERjiDw832qdwpu0R8yA1/uDESgdhedHT8ccTf1Zm4Lj7t7/o43nfK7bOSpgX57Sp1Yc4h1OkKUK8XZXLcfrP8eTUe022LUccb5lAA5/6j9Kl9wnm7yGCpXYMckeKBY9Up5Y6MGvGwMJN7ao4zQhU5l1lVeIDk7GYYQRq9jp3lQPU9mjJm5ktDEAS9djDB5f48PkzvPj6BW6vbzFrBYdEEqlmykyfpXE6SmgLOqmhmHeYuOCGJf7ti99e4eoBgdMnTnBqTBK52N8Cj/VTVc0Sn0ZhnSJHryBKp2GBUgJKqTXB9Jf/TcmRlSNRLsJUOCKSDDxn6zVQZuz3HQgz9nt3MZEV4hApxSpGE2kCAohq0kSEVIlgZIaKDkuI4ARK7ESw6zt88JNnePzkER49/RDj6lz7orYmncKEhUHYpLkw5sK43x7w/fdv8PlvvsRf/9XP8eK7ryT7T5nVTiKEfy5A5hnTXo7tBkBPQgSp7zBuRmzWIy4frPHw8Rk+/ugjPHr8VGpNBkcYPXr+uaDatj343jfGPgc4ZQYoN/TFiODJKwBTDI0xAnYSBzihjG43gKNtRn0zAKxJtbV91Lcr3QXrjCz70DspcfguEaFTFUizt7DcMWYnqK8KeiIcdxI0SqUuZmTEmrcI8DJWfqsmNggjD2sVSbW+yToeXj5f/26m/jZieOK92u+7XmkZgbqP5h/QPuUQy/VpW29u9lQ1Y7B1sr6XI+OwvUv4QuMYWHFQGIsSwBKzNVk4zXJeoY0FHXZNw1FM5OJ6T4iEtaZSSkyL3Uy+9i5cnMT/MdWEu28bhq9lUc5Dd6nrFPgKt5NDRdKAwX6VAMl22aUeRXIAQCPACR9+/Hs4uzjH9c0dEhG++uIb3M53mgCuU9tYRSZzKaJ6c25fPQ1zBjCjwxaHQ4/Xr7/B61cXuH7FWK+Bsw0BJYGp88PijFQMofC/hd8thaxIBBa8b11rCqAVTxQDrkdFfTS53TWgLccQ7EDDDM0jmnD14Aqr9Qq5TNjttijlDaZpxmFXFkTc7Hzm6dTVPkg4nXE1YL1JWI0rrNZVXTxNGXlm5DKDueDs7BzjOCJ1a3TdgOcfPMBms8Jq3CAZoaHKSJn2y6QWLpD6XAfg7nbCN9/e4usvv8bf/+3f43df/hZfffc5tve3PmZRrxRhBJBtuNJ2l5A6wvp8xHrT4+rhGhcX5/jZH/8xnj59isurJ1ivrtDRCKBXWORafUUX1NgGQ7jHlQsCIiUWbcoMl85J4S3Z8vr+ax8uxtCi3WMEEe1BMMnmiHFs30F4Z4lvyXe7vUe0SLdNIWIl/tafzvIQUyVyMjxDyScuJ2LUTH1JtIC6XEeOkO+5ThKb5TJTJYdyS2AxblF8b8kANzt34rtG5Xc0wFODOkV4WxzddbZySR0LRRskGaGqtGtjI0BMLRHOvEkKTy96D88ztZtkYyquUZI5mhq0VUxXL8qoVXfyELuO997OU/j1A4ggvMelS4cd9HjcHNVSCTrm0xBXnRnQcByC2Gpwc+jMXvSKykbvjKuTR6rLqRHZBKBwD6YOj59+gAePLvHbz36D+9tbvPruFba390roikOvdV3UXpGYfCMZokaklMF8wDzf4/b2FW5v7nB7LSrMszXASChs8m7YHU8dFoggNLmAuQcHB4tIEG3eyrouUBNV2wgqMjFJs1lLFuRvWNq134nQIeHs/BzjaoXd9h5d6rHb3SOlgnwgWRPvUw6Oe1SqXYxtHB1hGHtsznpcnJ/j4uLS39ztZkyHrGtBePbsKS4uztEPV0jdiHGESkBdJe4BKOK+MxM4QwTUCdjezXjx4haf//Zb/N3Pf4nv33yNl9cvgDkUyCVJFF64eOIGg8PUJVCfsNr0WJ8NOLsccXG1wUeffIqnT5/jfPMAq/EMwFA3kmw94vkjWMYeyXBU4RhM2q8RMkV7bltXhMDssNf4wigBdBUWtQkqOP7D2l84sS552kiXRzXwWby8ob/aEm/VKcHacnU+YGF2cAqmf1KCSoALW2Qg8I05hayJxX1fMcQNUI9DI0781rkaO/KDrsBYBMFv+XUdER9/eYJsHM/jbQ9wbCTuue1UnUdDKkkyHpExrAUqdZVgFqkaqxrTHG2h7YCWalEON52VO0XHo01YO6zFBSoRdPuuMpoGtkc04lRIxHv0oT+ACBJSP6idKrrCsCLgUim5Ps+WPBRA9XxctGqqVQuqNrVMzMCj7fnkdUINI9YQP7sKfJu49qFMNpAGENb44MNPwJzwzdff4/b2Dt0wIRcWeY9LdUPXTCQ5Z6m2biFuwwQqjDJ3mHmPLV7hxYuv8PO//gV++ntP8fDqY7D17QNGPdzNsgSA5XqwYI+6ZChEec7Fv5Pf79d9N+vKmgGHuXIKOg5jQqjv0aWEp8+fI+cZT549wDTNuLm9x2G/w5vvX6AUqajhM9AYidT16PoVumHAsFrhbLPB5cU5VqsBq9UIUxHPmd0bFwys1xv0/QDqBk2R5KxRs1RLZpwLSTHnPeH6zQ6f/fp7vPj2BX75i1/im2+/whcvfof97haY6kkcVwlpIOy2GYe9xIZIeIk4Dw3rAePY48HDS1xebfDJp5/i4aPH+IPf+0M8fPAYw7CyHdGRGLfaK0KtBM2lwiIRnBYrbljJueIsqmlTayY/J0t4kZkfceeoTNPx1TZwFFNdua3jJWdx2/J8PlQfKOonYLkXkm+KILYMITwES7fYEqDTGMKneHoaTkjf8mbznoxK4NoO1tvJj/OX7Z3T4witUFg4XyO3k/5/d/lIabGrFRmEPzi8k5pH3zUChoR+FWXU/H58qUGybX+xpXf3FQiedxAJ69tH3EL4iefqgi8eef/av58IEjy9ETfQUQ/4sZKkzkoAz9SSoVkz9Kfl5NHocCkcNvKNWBz4Uhe33lWvTjswVJFSQgdOI64ePMY0F2zOz9CNPahPoJlAJYEK4DnxtLuSi9jsOGmh3YzCpFzUBKaC69s3+OKLr3B5OSDnjyRMQLFC4M21Wd20IPE1q7jgHI3bZIbryy3zDh8BQ9wHatdHYcfzicZ3DJERgNQBqcPFOOgZPEfOM85ub7Hd3qNMt5jnSWIm4553hNSPGFYXGMcV1psznK0vcHl+hb5PGAZFSiJ3C0gVm5txDdH2s0DePk6dVxEHGM7APDFubw/47e9e4tuvv8FvfvUrvLp+idc3r4A8efA8mNH1wLgmzBNj2mdYHcvUSXmpYewxrAacnW9weXGBx0+f4vGTp3j65BmuLh9Ajs8SoTLAXfi7kji3UYDlTHD7JiCqoZyz7xtRLXN1pF4DYGXA7JwI3g0u4uFaEi9mFgcq/S5ZCr0AjDH4mnRMFdXaoIQ4VtW0DJIKalpEaMiMmQEoZj1d7LODabTN6RdVdXJMABmIuChql3xeTqGTL+QSf5FT8rjWukqR6z6xvk4Il/rX8EzTV/NufOCIMrRtONhZwoQjirWYF6HxR9CpCA4PUmDhdlAMYeS5nW+0M6J5nP3ziVH7HkVcdIT7bKkdnvWWwXmgP80YAtw0g/hHSYJkqgZzkKGj2VHl/RrpZfmgHZAqNOpJsM1wQiaHc6nicJH8LUOteyS2KbcTkB0/Wz2tEo6Ep0+f4+z8Ak+fPcWb199jOmSklLDdHoAskkULSKwEKEtQbynoOsk23XU9xrTG9ZtX+Id/+FvsdtfY7yd8+umH+IM//ImoehqCXIkOKMy3OXT1+bddtl4K4/qKAUhypNU63YrOoJgtTw+DpXpbSh2dlaniEZQGXJx32KzOsRnOUEoGq4NMm81mQMIaKQFdx+jHHsOotQtJ4z9SQlvpxOArePX6/N++CPMM3LwEbm/3+OJ33+Hbb17gb/7yb/D6zQt89d1vsT9sAezFqNYLM5PzAfk+Y7fLyHMBuICoB6UO42rAMPZ48uQSF+drfPzxR3j85BH+5b/8t3j89BnOL66QugFAHwg3S3YbYiBpsnGzcbveph74yjxqyRcPb3kLhPvBrzdk7ysBMQRhy+WvUCUI3r83WkkNczo6d/Y0Lf5iBHqkVwZglnhGTdu3BOn6MdquI6DbmAKSW56PIwYpMBwK8BS/b5BtGMiyNE/4GLM1cUt53nHZgv+AR8MV98Zasf0TQZ/DN0DV+/HRe/X72o4RPgIhM+PgKavFgazkEh4+ntLJfNu8/NtepuNnF5+MqB1/c/xZMZm8o+fDynY19P9Eg7zIa33qeicRrAfCTNypgYPWZZlbxizCWSRqy9nbgOOkDJANZN3X+lTbRtrCuALAsJ0GdT31wrdEODs7xzCOuLq6xMXlBa7f3GKeZ6RDEgNx2PnGRsMiEUpldkbR7CvMBfvdFvv9Cwz9gPXqCpvNBp/+9CNQB6ReA+GdiSZEB5kTO+BE0ua6hFLznLP1I79nmKBKHI2rusZ9mEGfAHfurINURJcs5iqBmbEaCNyPGIczXfm5qvzsKj1QRkh2mAPQQ4U7hSNLGmp96z9uAgpLsLwMJ9jhzjPj/rbgzas9vvjyJV588x2+/fpL3Ny9ws39G5QywfLbICkR5Bl5miGhCDZlmWM/dBjGHufna1xenuHBgwd49OgxPvjwIzx+8gyd1rcXaUIWzCRq4SfM0E8+3re686p6n0sBm4q75Sb9/ES7S0U67QKdOoM1du3YUy+uasPwRaJpzE1sliiCic9TlsEQlZFaX5jK2y2xqnbW4jQOg40kzNY1rG8zs4goAgb35pbI0mT1ykVQfD7SYzoJkv7gD7YnLkb61u/jlLxpQ5hhXEeNcXiSArGQ+c3LNVgcX7uaLvUDt3+2dxcDOdZ/1T09BYvNDoaDvrSyc8zwaA8vJuDJVP7xNkEc2w6CmpCo+mA2BE5xsIfMxsqcFD9Euw9gWWekGnzt2E2FLQ1wwllKvW2Lk8gIs4ZtmDOKIq7EjLHv8R/+/X/AP/+zP8X/+D/+n/Gbz36L1VjQUfIim0UJRs4aSO7ODULECmcgA/sDIXUFQ0948+YVfvXr30hw9eoMH3/0GB999BBWvqjrCdSRD6ruJ7VzdOZAk5Tp5EJOmuaSUVGLnSg8yQyrKxgBONYotDY7j1mk2DhSsgB3FsrJQ8vKqtG0gCBZyANWccwc1Z3sxM9/9Jtc8WY4kASeGXkC3rwG3rzZ4m//6jN89+Jb/M1f/yfc39/g1evvMOc9gD2IClICpjljfxAbpmTV0XmiAyFhtVphWA14/PQc5+crPH/2AFdXl/gX/+Jf4oMPP8LFxYPgIRxSLEvCWDfPuHxDwkidOuyV96kYqBK2eJu9C9ur+lzFJlRPYXMZWo4qJR8DoomDAdS4yzhmIvK6ngSgd8cS6S2E+CNDU5+VGbvDFkQJwzBKb8FrWXqzUehcrZ0j9R5wlF8R7O+Ymhlx/ZqnA8GIREOfsbqfxv/5cw3zTSiqMpRoo3fYMd9xNbDwTtysIoYDv7K3zjy0/hnLs+tdLG74UrkDCUc++61zirSN27v+22Mxo8r6qJHTxPaoP6NpugRZKYDtkYXecAjtgjLsbV/v7+wHeocupDygyTnnX9m+GBZz7s4OeWWjqjv9squ4sPJ8BRqOv/xzC98uG4lbuQN39foEDAkRkAgfffQxHj16gPPzc6QuoeuSeINSgnlPmtqRGUjJSI0pHotkJckZPTK4y9gfdihvrvHy+9f45ptXuLzc4IPysAaux1qIqIQ7btkyMF109rrU6lKely/Z/ILkHYHWsvNXtZsQ5YgUYhtHHnkMRQAhFMA31UJREjjJ2jNbSrTl0TEv4niwjyfCMWA35H3KE+OwZ9y8yXj1/RZffvktXnz7Jb743WeY5h2mstPVmXW9ZPY5F5gdrc5LkgMMQ4/Vqsf52QoXF2tcXp7j6uoCT58+w7NnH2AcRpAjY9t/O5Y2D67aO0fsldyZLNPeqJvf7D9Cqir9Qg6/QF2rXIkmBIPPpbWrzSDjyZ91T+PmO+ftDdQvEwJf1JRCk9+FCzJnTPMBKSWtP2kMQ4B5nwNVJiAQpOOel1cggHEAC/HUPDMbXlD7btG49uf7FkdQsQw70/CuEbYr3+5MeCrgSe81PHyKgXoX8TRYaFmAEy/4ki2ADrRYvvZ7DuvXDIaDnS/kaDuZ0aj5cLx2Xrgh0IL4NHGsbasmBI2DTnHq/P8LIqjJ+auKbHEo9ABX9XxgJ4gkeFkPbOTuHZQUsRtlbxZCHXG8sKmHkpSwgUBNDC28eVQZVqSQQhsLpA4SBxAaAPUKTGNClxgdD+A5Y3u7Q9EKEyACpR6JEvpOJJ7slKig9AVMBaVskXjGF58XvHl1A+L/Dk+ePMLV1YiLYQiA1qDFsH/sbTaXIa5jy314JmDHdsYnz09RNWRawGVQgjcv+z6q2zlbRh2ccvApYJ8DgakHdZZQHFDZAXbiiS2nqDVkIKrEZwYOh4Lf/e4ab17f4+/++jd49f13+Lu//a+4uX2J76+/AJcZrgqmDuOYcLYhTHq78pOSz3U1rrAeRjx+co6LBys8ffoQl5fn+O/+7b/Bhx99hKcffIxxcyHlkYikAgbExZzI4t8SEo0QDx8rQHyMCm1Xj9i/SNBgpACV/y2lRWa+QNTgfX/+BFP1wy9DQhW8zJ/E8AGhIh7rRNINqqdhtqBqxiFndClhcM+ZCu2BLqGOOACiP08nHzly1Tc1/vLZUo76MlbB59f2ElS7ZmCsuAxAVIjB8SEdz+idV1SzRYK4VL8dnfNjTAEc92wrd5SIKzxtfMTSxts+x3U9lp0ESezkIqfl/WMG4W1Nnh6JxGhXGAPElKIaR3MUdO3K6bbsen9R3eUQIjwaQo4pYKKEGBalYXgWbTYb2DwUs5yHZ060Iuo0QpuRozp9GEuyPDTSndinhnHAuBqRZ7FxUcegooZjq4EFYOkRW9wIL8/lUtDRhJILtnc32O8IL1++xuvXtxjHC5xtenQLFily03WmS4iioy2hoxnVBamEpK7aCd+mMILQNYXfcRSGXE1QoMW7IOf+yFF5lfZUhgxtGUAZbDFCRgGAC7gkd6qa9hm77YxXr7Z49fIGX3/9FV6//A6vXn6L++01DtM9zM5GJGUtxJF2cRJJ0/8RYRh6jKsBm7MR5xcrXF5e4MGDKzx79hzPnn+I1foMKQ2iotfxngz7MZt5XMPl2iLubNw9DrBfX/adM4PvO66TOOYdCOB4eOFwEzVcdPOJKrGxsUZoNWRpqnTzQjaG19r3qZ2g2FWj1hJN76AZekV2RyjrPZM/lpXj57o3lbmuv2VLzEmonUPAiO3f8Tyd2rAGUS7bXBLGOJkj2WzRe4tHTjXTlMdbfr8gKEcSltt/l11xAN0TBLSFLNe4HV3czp9DRrIjjK74+YcQQOAHqENt+RrOCopMTx5y8u/lLwH+RO0hOIZVheBebS4UwLuZSHv4UqqJnEi5e0cxxpw1s6mU2b8joB86/Pm/+BM8fLTBX/6Xv8HrV9fopXosVuOInDPyrKEApaiH9Qxx9Q8ps8oMLnfgtAKwQSlbMM/427/6L/jmi9/h3/3v/i3+/F/9GR49XuH84nj5jcCetoss1ut9WFEnSNDwPTZBiJANkTfILHwM2mqL+2QzvNqiGUXtwzswZuH0+DmzpNRTN0LbI3bqXJE/l4LDNKEw0NEa04Hx13/1Dd68vscXv/0SN9ev8Otf/0fc397h9u4a07xbrEhGKgXzjvBmDy3mKRMg6rAeBqzHHo+enuHywRpPnz7Ag6tz/Ot/+2/w8U8+wQcf/FQSY6cBpVAjLUTEd8yTV3uNBIIIYxCLHJcirjoWL0hE6DoW23bjQW0f231KPpolug37YwUal5cxr06Cub4T+iLRIYMWRdmM7NlsPUBGJW/qgcQdhq5ThqQce52a6HFM/wLxa71VI5JvmAdAwjFkYdp2dCtiKHNM79lAqN5o6O7y+1M3k2GTxX0lHNEW6mNriMqCfL3V+yZcviXU7p3t5dG6HsPJ0tmq6LhIvwsUT6ZpucePtjIwrd5XnZ+FPy1RTbUpS7ti3uOmjeaj/5wgpmzwqPqrY47grdcPswlqW2S6Zj8rx1LIQiZQ6azCfGwx5lc07poV+G1iy9Yr6ucFsL4NahXpLY+uNyRAk1LCk8ePMM87nJ3/Gvf3W+z2M3KWzCGSFd/YOAPwGqNj7ZLZmxRxMDKYGTfXrzHtZrz49nt8//01VusrrNYdui5Kd9pWdI6Is6ewJAFGG3VE86F68IlNVKpleDorX+uQXACL+K2Yj5MXhz16P4bBNtI4R6/asH6EkGA5eufJgFnrys1zQc6MqUzYbTNevrzFq5e3eP3qNW5vXuPu9rWkdIPoOi0xiK8CF6kSEc4NUQKlDn3fYRwGrDcjNhcrXFye4/LqCo8eP8GTp8+w3pyj71eeFK5xKjHYWUpzce1sne2zH1Rh2awGZmPCcd6ilTYbFBaYm9jb0XknWwxu7jkuOuXc4cxnZGarP6aNJvbF2pb/oRIzaR0r9y4N+2I/EbYckqxPqvAgX7RMxxLTH2OAisS9nzDMBmcfXae/jHffKnVyu18cfSqao8HN74qU4oTiebNfHFBbQArO/LDSAWvfb8ljnp3qGMUKno8DVbu0whEhwCu3kBBfY/DCG731HyB/qo6vpgqtFG9BMhAH3eDDpv8F/vzH5w6ttiHr2D+kUI7HBthMFZ7pJBWhziIsRLKu5C/VtFj+DQHuFqcBm6SIo0u9H1AxxseFllYk0JdlBtWwecQlUBJJ8J/96Z/ik59+gu++e42Lyy/xi1/8CrlkKfPE7MkfCkOSM88ZRAVElRNK6MHTiD7VODyAsZvvccgH/Mf/93/GL371Gf6H/+Hf48///E/w8FGPzSY5ABwTQMFaaXnibOzLg6ErX4pIGJLFowWovhMJuigTw9kCh+WJ1JPEUpJI5bFYhB1UZpGsbHntK0pA38VdkPA5vyKBKgBbyQ5j2VMHMHDYT5gPGXc3Bbtdxm9/9z1ubnb4/LMvcXd7h29f/A7399d4c7cDlxmXV0CeCTfXfUUuOaPkPQqgkq9IxN3QoRtXWF+cYXO+waOnV3j29Ax//Kd/ig8//hgf/+T3cXX1VGyA4RJPVUYf1XM6dMkVbna7JXlSwOEau5XVuC9EL3DnicCJaukaI6NGxcngoe5F7OlY13ISw4eLj/46Out+ZvRkamJ04fAJDCtqHOzzxHIumIHcV2ZM1dAOpw0iozCCdyOu5iI0EqCNkxmag/X0K/5ZGQ9j8mLvlUhXHwNfowVRdoHZGzBoqD4RAoNJ1XluTA/YkI0LOkbo0alI20v8NmLcIjnWrEwphcVgOkkgRCKMu3DMSLW3Wpgzj12OCbBhyc8Xo1OEygWiJTo1BTi59G5Iq8jkOS/Upy3ueV/ybOB9cYILnmt5tOUD1cksQccQXuAcCG8hzKd0EA1L347EDpQNzCoIvHfK3D7lzFYCzs7OkBLhbLPBer1CSsn7sWoQ9VBIW/K3qXsY5nnodhHY/YzMwO3tLaaZcHe3wzSxBORzGFpYvuMlChBE1v6JV96G9wKO8UDgE4AsAqJA+rGnVkWInmp3cT7aD4srwMKiSmfzsthhC/LMmCfG3d0Bt7d77HcHTIc9pumAaZ4wZ6nX2HUAmNRj1RBsVEDWfZdYfdISST36occ4DNhsNji/uMC4WqPvBrRMVW2lEicExCiSduRz3zb9pq1m/u3+Wls1KXzbynFRZXt32b+NtR2DtX9qC05JLvWhI/7fPx57qLbjado/Cafh2UB02suIj37vjTKaF/g0AXzr5MjwexicobLlWN5itzrV7vGe89Ez7QsLyrZ4/JQ28C0N1d8nX2ghYfnNKXwfj6/cb9flXeOqzIN8OPaBOP7TGYMFlJLivx+2Du++6Ie4kP54/Xj9eP14/Xj9eP3/43Vssf3x+vH68frx+vH68foncv1IBH+8frx+vH68frz+yV4/EsEfrx+vH68frx+vf7LXj0Twx+vH68frx+vH65/s9SMR/PH68frx+vH68fone/1IBH+8frx+vH68frz+yV7/K8In+jo1p0aCAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 576x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Initalize the SpatialSmoothing defence. \n",
+    "ss = SpatialSmoothing(window_size=3)\n",
+    "\n",
+    "# Apply the defence to the original input and to the adversarial sample, respectively:\n",
+    "x_art_def, _ = ss(x_art)\n",
+    "x_art_adv_def, _ = ss(x_art_adv)\n",
+    "\n",
+    "# Compute the classifier predictions on the preprocessed inputs:\n",
+    "pred_def = classifier.predict(x_art_def)\n",
+    "label_def = np.argmax(pred_def, axis=1)[0]\n",
+    "confidence_def = pred_def[:, label_def][0]\n",
+    "\n",
+    "pred_adv_def = classifier.predict(x_art_adv_def)\n",
+    "label_adv_def = np.argmax(pred_adv_def, axis=1)[0]\n",
+    "confidence_adv_def = pred_adv_def[:, label_adv_def][0]\n",
+    "\n",
+    "# Print the predictions:\n",
+    "print('Prediction of original sample:', label_to_name(label_def), '- confidence {0:.2f}'.format(confidence_def))\n",
+    "print('Prediction of adversarial sample:', label_to_name(label_adv_def), \n",
+    "      '- confidence {0:.2f}'.format(confidence_adv_def))\n",
+    "\n",
+    "# Show the preprocessed adversarial sample:\n",
+    "plt.figure(figsize=(8,8)); plt.imshow(x_art_adv_def[0] / 255); plt.axis('off'); plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Perform adaptive whitebox attack to defeat defences"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next we are going to mount an adaptive whitebox attack in which the attacker aims at defeating the defence that we just put into place.\n",
+    "\n",
+    "First, we create a classifier which incorporates the defence:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Prediction: unicycle, monocycle - confidence 0.82\n"
+     ]
+    }
+   ],
+   "source": [
+    "classifier_def = KerasClassifier(preprocessing=preprocessor, preprocessing_defences=[ss], clip_values=(0, 255), \n",
+    "                                 model=model)\n",
+    "\n",
+    "# Now we apply this classifier to the adversarial sample from before:\n",
+    "pred_def = classifier_def.predict(x_art_adv)\n",
+    "label_def = np.argmax(pred_def, axis=1)[0]\n",
+    "confidence_def = pred_def[:, label_def][0]\n",
+    "\n",
+    "print('Prediction:', label_to_name(label_def), '- confidence {0:.2f}'.format(confidence_def))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We observe that this classifier reproduces the prediction that we had obtained before by manually applying the input preprocessing defence.\n",
+    "\n",
+    "Now we create an adversarial sample against the *defended* classifier. <br>\n",
+    "As we are going to see, this adversarial sample is able to bypass the input preprocessing defence."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a38d47b099c5453dafa1e3e9305d282a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(HTML(value='PGD - Random Initializations'), FloatProgress(value=0.0, max=1.0), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(HTML(value='PGD - Iterations'), FloatProgress(value=0.0, max=40.0), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 576x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Prediction: black swan, Cygnus atratus - confidence 1.00\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Create the attacker.\n",
+    "# Note: here we use a larger number of iterations to achieve the same level of confidence in the misclassification\n",
+    "adv_def = ProjectedGradientDescent(classifier_def, targeted=True, max_iter=40, eps_step=1, eps=5)\n",
+    "\n",
+    "# Generate the adversarial sample:\n",
+    "x_art_adv_def = adv_def.generate(x_art, y=to_categorical([target_label]))\n",
+    "\n",
+    "# Plot the adversarial sample (note: we swap color channels back to RGB order):\n",
+    "plt.figure(figsize=(8,8)); plt.imshow(x_art_adv_def[0] / 255); plt.axis('off'); plt.show()\n",
+    "\n",
+    "# And apply the classifier to it:\n",
+    "pred_adv = classifier_def.predict(x_art_adv_def)\n",
+    "label_adv = np.argmax(pred_adv, axis=1)[0]\n",
+    "confidence_adv = pred_adv[:, label_adv][0]\n",
+    "print('Prediction:', label_to_name(label_adv), '- confidence {0:.2f}'.format(confidence_adv))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's also look at the $\\ell_p$ norms of that adversarial perturbation:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Perturbation l_0 norm: 99%\n",
+      "Perturbation l_1 norm: 3%\n",
+      "Perturbation l_2 norm: 3%\n",
+      "Noise l_inf norm: 2%\n"
+     ]
+    }
+   ],
+   "source": [
+    "l_0 = int(99*len(np.where(np.abs(x_art[0] - x_art_adv_def[0])>0.5)[0]) / (224*224*3)) + 1   \n",
+    "l_1 = int(99*np.sum(np.abs(x_art[0] - x_art_adv_def[0])) / np.sum(np.abs(x_art[0]))) + 1\n",
+    "l_2 = int(99*np.linalg.norm(x_art[0] - x_art_adv_def[0]) / np.linalg.norm(x_art[0])) + 1 \n",
+    "l_inf = int(99*np.max(np.abs(x_art[0] - x_art_adv_def[0])) / 255) + 1\n",
+    "\n",
+    "print('Perturbation l_0 norm: %d%%' % l_0)\n",
+    "print('Perturbation l_1 norm: %d%%' % l_1)\n",
+    "print('Perturbation l_2 norm: %d%%' % l_2)\n",
+    "print('Noise l_inf norm: %d%%' % l_inf)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Comparing with the previous adversarial sample, the $\\ell_0$ and $\\ell_1$ norms have slightly increased, while $\\ell_2$ and $\\ell_\\infty$ norms have stayed the same (the latter not being surprising as the PGD attack controls the $\\ell_\\infty$ norm budget)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Conclusions\n",
+    "\n",
+    "We have walked through an end-to-end example of using a Keras image classifier in ART, creating adversarial samples, deploying input preprocessing defences and, finally, bypassing those defences in an adaptive white-box attack."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "emptyenv",
+   "language": "python",
+   "name": "emptyenv"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/adversarial-robustness-toolbox/notebooks/attack_hopskipjump.ipynb b/adversarial-robustness-toolbox/notebooks/attack_hopskipjump.ipynb
new file mode 100644
index 0000000..1052a6e
--- /dev/null
+++ b/adversarial-robustness-toolbox/notebooks/attack_hopskipjump.ipynb
@@ -0,0 +1,2456 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# ART HopSkipJump Attack"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting git+https://github.com/nottombrown/imagenet_stubs\n",
+      "  Cloning https://github.com/nottombrown/imagenet_stubs to /private/var/folders/_5/f0k1rpfj41x01f5_19w2vrkc0000gn/T/pip-b1brxr92-build\n",
+      "  Requirement already satisfied (use --upgrade to upgrade): imagenet-stubs==0.0.7 from git+https://github.com/nottombrown/imagenet_stubs in /Users/minhtn/ibm/installation/miniconda3/lib/python3.6/site-packages\n",
+      "\u001b[33mYou are using pip version 9.0.1, however version 20.2.3 is available.\n",
+      "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using TensorFlow backend.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from __future__ import absolute_import, division, print_function, unicode_literals\n",
+    "\n",
+    "import sys\n",
+    "!{sys.executable} -m pip install git+https://github.com/nottombrown/imagenet_stubs\n",
+    "sys.path.append(\"..\")\n",
+    "\n",
+    "%matplotlib inline\n",
+    "\n",
+    "import imagenet_stubs\n",
+    "import numpy as np\n",
+    "import keras\n",
+    "from keras.preprocessing import image\n",
+    "from keras.applications.resnet50 import ResNet50, preprocess_input\n",
+    "from keras.layers import Dense, Flatten\n",
+    "from keras.models import Model\n",
+    "import keras.backend as k\n",
+    "from matplotlib import pyplot as plt\n",
+    "from IPython.display import clear_output\n",
+    "\n",
+    "from art.estimators.classification import KerasClassifier\n",
+    "from art.attacks.evasion import HopSkipJump\n",
+    "from art.utils import to_categorical"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Model Definition"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From /Users/minhtn/ibm/installation/miniconda3/lib/python3.6/site-packages/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "If using Keras pass *_constraint arguments to layers.\n",
+      "WARNING:tensorflow:From /Users/minhtn/ibm/installation/miniconda3/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:4070: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
+      "\n",
+      "WARNING:tensorflow:From /Users/minhtn/ibm/installation/miniconda3/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:422: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "mean_imagenet = np.zeros([224, 224, 3])\n",
+    "mean_imagenet[...,0].fill(103.939)\n",
+    "mean_imagenet[...,1].fill(116.779)\n",
+    "mean_imagenet[...,2].fill(123.68)\n",
+    "model = ResNet50(weights='imagenet')\n",
+    "classifier = KerasClassifier(clip_values=(0, 255), model=model, preprocessing=(mean_imagenet, 1))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Get Target and Init Images"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Target image is:  105\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQEAAAD8CAYAAAB3lxGOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAgAElEQVR4nOy9SaxlSXrf94uIM99zxzfnVJnVNXZ3VXWzm01QpCnRAynbMARvBGthG7ABeSPABrww4ZVhbbTwsDQsw95ZsAzIgghatikTlEyJhMxB3Ry6u+bMrMw3v3enM58TEV7EfS9fZmfWlJVdSdf7A/e9e8+9J06cIb74hv/3hbDWcolLXOKrC/lld+ASl7jEl4tLIXCJS3zFcSkELnGJrzguhcAlLvEVx6UQuMQlvuK4FAKXuMRXHM9MCAgh/rIQ4m0hxHtCiF97Vse5xCUu8XQQz4InIIRQwDvAvwbcA34f+GvW2h9+4Qe7xCUu8VR4VprA94D3rLUfWGsb4H8B/sozOtYlLnGJp4D3jNq9Cnx04fM94Oee9GMhxCVt8RKXePY4ttZuPLrxWQmBT4QQ4q8Df/3C5y+rK5e4xFcC1to7j9v+rITAfeD6hc/XVtsuduhvA38bLjWBS1ziy8Sz8gn8PvCyEOKWECIA/h3g15/RsS5xiUs8BZ6JJmCt7YQQfwP4vwAF/E/W2j97Fse6xCUu8XR4JiHCz9wJIeylT+ASl3i2sNb+obX2u49uv2QMXuISX3FcCoFLXOIrjkshcIlLfMVxKQQucYmvOC6FwCUu8RXHpRC4xCW+4rgUApe4xFccl0LgEpf4iuNSCFziEl9xXAqBS1ziK45LIXCJS3zFcSkELnGJrzguhcAlLvEVx5dWWegSn4wvK8Pz0YzOs378tDM9P+78L7NOvzhcCoHnGF/mg3527McNxIv9etJAvRykn+8afBmC/3ObA0KI60KI3xZC/FAI8WdCiP94tf2/EELcF0J8f/X6N7647n61IIR47EtKiZTyid8/6fef5vVx/Th7b609fz3pmF/EuT7pHJ93PE0/v4zzexpNoAP+U2vtHwkh+sAfCiH+0eq7/9Za+189ffc+HT7uwn3STPZpfv95j/2s8Fn7+Fl+/+j5fJbzu3icJ+33Rcx0j2v7eSiOA1/M8/BZ23jSuX+a+wFPIQSstXvA3ur9UgjxI1yp8Z8qPsuAfpob9GXNQJ/2Rn6OlsECrNq80PRFH8D58ZWP1R1I6X4qDFiLFazacW3YVZsSibHgBwG+71M1NVIIdNdhjUEIsAakUiAsVncI7Hk3rBAYC1ZePGcDFoSQCAvWGs4O7/r68HlgL3TsKfDQdfiE3z0OX+Q9/Lhn+tP281F8IT4BIcRN4NvAPwd+AfgbQoh/D/gDnLYw/SKO88gxv5DffBH7PEt8Gf05MwvO1H0Aq1skIKxFConnRwRBQFmWJL0+FktRFCg/IAhD4v4QLwi5eu0anu+xf3DA2mTC3u4ui/mMMAhZ39giiiJuf/Aus+MDFDAaDsmzJW3bYawGA1IKJzQ0SAEGCRhAICVobd04F4JzqYDlwaV7es3vi7oPD13TxwiHp5m4Pu++Ty0EhBAp8PeA/8RauxBC/HfA38Tdir8J/NfAf/CY/R5ad+CLxKdxXH0Z+Dx9eRZC4LGz5sfAWksQBHRti8XSmQ7dQNhLSfyAIIyYrG9Qtx1t29IfDLly60Usku2dbT788A7XX3yVOIoYbWxjjaXTmuFoRBBENMY4H0PXMlpfxwrF7PTU2dbWgLaAwRMCjKXDaRJCCAQS6BBWrLSSle+CT3eCz4s593Eq/aOz/cf9/lF8Gu3gqYSAEMLHCYD/2Vr7v606d3Dh+/8B+I3H7fs06w486cY9bvvnVZGeBT5Lv+EJavnH/P6zdeaBE3D1WAFgjHls37zIp8OCMSAkk40tuk5jBSSjCds3buEFPlZIwjDm+o2bdFoThhFddxspLW1n2FjbYmNzk05rhJJ02vC1V16nq2qKfInwA/woBt/HNC2e5zvzwRqMaZFIPC9wWoI1dLrDUx7m3DQ4UwXkA3Pg+VLuPjOe5t5/mmf/cwsB4XrzPwI/stb+Nxe276z8BQD/NvCnn/cYjznm5/7u0Qv5ZQiGz9r/Z9/nn2xPKYWUkq7rEEJgjEEIQV007OzscHBwhO4Mr7/6DbS2rG9t8cKtlyjKkulsybUbL6C7jmE/oSwrTo5PefOVV1kbr2OMRkqB8jxq29EaTV23DNMxV6+/yO07H2CkYO3qCwy3r5L2UsaTEffufsRHH90hnx6B77O1tsnRwT5NXaKUotMdSiosFoF5zHl+efi4EOrnuZ9n+1zc91Ft4bPiaTSBXwD+XeBPhBDfX237z4G/JoT4Fu4Juw38R09xjCfis57003i9n4QnRR4+D7nm00QxvkjVVTxGAIDzBxhjMMYQBMH59rjX5+h4hkGwvrUNwme4NkAID2thsrZJECYM+gPybIm0hunJEfPpjJ3tHcajAdPTU7BQlyV1U6MBLCipiKIeL774Kk3XID2JHwbcvHmT+XxO0RmytuHr3/4WcRhx50c/ou1aemmK7jo0DZ3uED8R4rScawVfMh73XHyRgv1pBMHTRAf+KY9XtP7h523z8+JJPoDnyRR43nAx3n/mUwOwWsMqRm+MIU0HJL2EdGOb27fv8uIrr/LC9ZsI36fVMBj3mWU5VaeJoogwjlC+Rzk/Yj6bEccxSS8hy5ZMZycA9IcjoiShqGuK5RLddAgEvV6PtWSDje1NhqMhXhhSd7cpa823vvvz1HXFD3/4J5wcHnLl2nU8JTk5OqSuy/NzOtNcAOxK0NnVtp+W/f9xNv/jBPtnDfF91rD3J+HPzeIjHzcrPvT+kf0sz5dz8HmBwGKMWRFywGhAgLEW5fkYY+n1ByRJyub2Ft/41s+iNRRFQT8dIoRgfX2dPM956403mS8WVGXJYJjStR1FVXL37j2WyyW+HxLHEU3TsrOzTRhF1G1H1bRUdY0SAiVXuokUtNqQJAlbmzukvRRjDVJI3n7vxwS+j67n/L2/+3c4vn8PK8G2NVJwLtTgLMJhsNa5Bs6EwLMLuT6Mz8Kk/LxC4HP4Bx67+Mife9rwRdXqTMN9KFT8BLX3qw4pFQKJtQZjVkqzlCih6Cz4foD0fNa3d/i5n/95hqMt6qrl9u3bzE6njMdrCC0QWpDN5jRlyWI+5eD+R5RlQW8wJol66M6S5TnzxZLtzS3GozUWiwUnx8d4QchkvIaUgmU2dwPcGnxf0Y/71HmJqTX9tI/yBW++/g2iKOLw6CPC3oC1K9dYzmfUViOMJQgD2q4GwGrj+AQXBv5PczJ4mvD0Jw3yL1p4/bkXAgCCxwuA8+8vzYKfQKf1BdovgER5AUEcY6zAD2MWiyXf+OZbrG/uMBiss1gskJ7Pcr7g6vUb7B3so5Ti/Q8/pGkbktDn6PiQMHQcgus3bjJfLHjv/fexQhAmMXlRIKVk0B/SGUtd1TRNxd7+Lp01rG+sc2X7CmHok/b6jAcjAt9HKcUsn7O/v8sPf/w2y6ygXBbUdcONazdZn4yZz6e8//77q3NyGo5enecZBfl5fA6+bG7Kn3tzwMWKxWMHv7X2IVLc83CuzwusFQgpVyq0YTheQxuLkJIXvvYyb771M+wfHvJLf+lfRvkh+3c+4vXXX6coKubzOf/wf/8/8D2P4XDAaDAkL5Yc7e8xngy5detFUAHv3/4QbeHKtWt88803sdawe+8+773zLuPJmH5/yMnJKW3b8MKtm6xvrFPXNVEUsTHZYJD2KcuSg6N9NJbTxQl5WdKPJ+TLKU2VM0hCfvM3fp293fssFjN02yCEwPd9enGClBJLS1VV1HWN1hrPc3Pfo+HQ5wWf9zn9JFPn/5fmwLkgeOQzXA74T4KQzommlMJagR/4bIwnJOmQWy+9wmA0Iu4P8cOYg4MD2jzn9//Z7xFFEVXV0OQFB6dT5I0b+FZQFhltWWHqhMXpFH804M1vv4XyfJK0T5hE1HVNPOjx4msvMRqMyLKMq72rxFFE0zTcu3cPhODKzg7TxZSqrtjf26U/GpKmPWSs2PAliTfGFy/w7o/+jH/6T36Ld999jyQOSdOUJIpo2xbP88jzjDrPkOphX4FZkZPkSgg+b3hcJOGT+vk05/HcCAF7Pps/ejL2Mdsdd/wiU/Scc47zA1hW5DFrVo6hi22Ic2aZg7lgR7jWPg0F0/HYHq/ByAuEtTMqlES7vp1bLxJhBdIKlJWcBe400v1GdA925gIV/vzoq2w7BVp3D66VFUipHK8HtTolgzGduzISlNH4QqG1RgURwu/x+hvf4+qNF4iiHsO0D0A9y8mPp1Rly2I5Y3F7hrWaJJbUIcxP9zg6uMtkfY047WGEpKgb4rYl8H2quqarfZqsoCwKAhQvvPw6QRBgjFPVW63ZPzgg8H0CP4BO05maw+MpvpT0VEAiAnygH/VR8Yj9gz1OFhXXvvYNlgbmsxOwLXu330eaDltXKKMJAkUYDlkul45QdFGDPGNOPnRNf/KeP0+C4lnUdnhuhMDjiCur0119ujDrC3FBzX8gIs64Yuff2YspKRfbcm/tQ9vPbYbH9uVxENbiPdTKhQF74aVXYWpjFcIKhLUoy7lA6DxDK7oHDRs39p1YcA+qxfHmXRftQw8zBhQCoVmFxC3GrMg+tnM0ewRSrJJ/jLuGWkrWNrbYvnKd0foW165fAyGJ/IAkSWiahj/8F3/AW2+8yb17d/n+Dz4A3TEYDLhz5w7D4RAvDBhvruN5HnXb0hsOGK+vM16fsDYeMVnfdAO91dy6dYs4jlksFsznc6QQhGGIqSpG6QApBEkSE8UR0sJyuUQISZYXfPjjH9Hv9zmZTbH6A/r9PlvjHreur/P1V2+QZQumpyf8njKcHhyyOO2waLpOUy7meJ5EGveEnCU52Ye4049nSn4SnlXBlaflwXwWPDdCQJzdkMeci0U+pCcILsZ8LxAvLuzxQBDYB3udhZF+8ugXXvoz9Vs+Jv5gHpY1KHOmeNjH/kZqixWCi2LPKQBODGDPRJlYfTTOs3/+QLtdBQGr4B8Wvdp45iRzmgFW4Hk+KlC0VuPHPUaTNb773e+R9gZUTUenNbPZDCUk1hiKoqAoC7IsY5j2KMsSoSRHJ8dorXn19deZL5eMJmPWNta5duM6vV6PNE0xXYvWlp0rO4zHa5RlCdYSrpKPsiwjSRI2N9eRUqLbltnpFGvdWVR1jef53Lp1i939PbzAxwg4PD1xAtYKWgs/fOddfM/jxq1X6Q9G3PvoDid7+zRdh6cEbdu4ay0d81mps+fmoqZlLjwHZ9ueDy3gzET4vLThj9vv+RECPGn+lT+pynP2+aLQ+EkzAs7MgAsx4guDzY3Mh7lzztH4pNYe7fMDTcNgz1X3h9rjfHJ+2Ew5+96CRCIuTERurApYJcqc93VlR0gUWPA9nzPflhWCznpgO6RSCGMR6JUW4c7bj0KSwYi2s5T5jPHaOtdv3uLr33yT9fUt8iynzHOapmU5X1AUBXc/+oi1yRpaa/r9Ptpq2rrGGENRlkghyLKMVndEUcRgNKTVGk8q0qTnjuuH5IulEz7KY21tDSEE0+mU4+NjMBbTdrRa0zQNTdPQdR1+GFIUOUEY4Qc+URRyeHjI0dEhb775FkaANoZhmvLKK9+grEqs7jg4mXLlxsugYqIwRC+OmU5PwRq6ruFJgt5xCx6YCM+LADjDs6K/PzdC4PGDDJzVvVLjwM1sF0wEYcWDgX6hpXMG3AWt4ey/OYsbC8vDafWP2oif3GfNo4k4F7+3GGHR576BR4WOS3e9KBjc0F+9F06LEWeptNax35RSKKVoG42xljCO8PwAaQRt1YHVCDpnqggQysOLfLSQbO5s8uJLrxJFCdeu3+D6tetI4XHvzj3atsVXPqNBjzsf3ubOnTsoJXjvg3eZzWbESUJZZPQGfQ5PDkFCfzhgc3MT5XuEUcTGxgZxFLG9sUXTNCjPQwaQDgbYrmO5WJAXBVEUYaxlPB4SeD51VWGtxvMkURSgtaIzhjAMOD09ZT6bkfb7bG1tcn1zg34vIQxDJpM1/DDk+sY2Ukp+9/f+Gb/wvV+kLHPSpM9Hd+9iUNR1h9ENCJcK7e7zxbResdIEHpgIn2YW/WmH9z4L4/Bxv38cnhshwNnJ2Ec3u6ff4gbF2TA6n6+FeHh2B86NaPHAyXfRySNWqSbuAHaVne7a+Cy31IpVqG3VpzNNQ6yO40au4MyLaQUIY5FnTk1rnSCSAiOMm70F+CtNvg1Dd12EQKgH/gS/3yftDzDaUJQFw8GQmzdvEciA+/fu0jQVhwd7dG0FgFQ+QdInTnpcvf41rt14EaU8treukER9mrrGasv0ZErTNGitkVLi+z6TtTFBGJDvFbz29dc5OtynqiratgUrSOOEwWCA53lcuXKFKAgZ9AdEUeTMAWPQxjCbudTgMIro9RK0Ns4RGIY0K1XdmXSWPM8Jw4DhcECv10MpBYDvBQRBgKLF80FIy+HRHm1n0FrT6I40jVgsFsRJyMbWOuvrY975wR+xv7eL9ENs167qEwg8z0Mp/9xJKIQ9z5swxrV5Fkl43rSCi3hSvsqn7fNzIwSEkFj7ODXNPvT2bEaUUjwI8YiHeQL2gT7H2RA9dxpa4x4C7AVXkEBxYeA+rhdPUsOEwZ7N7tYJFITAGntuWpjO0XOtsOcCzFuFqpSSGCye9GmNS5311OohTUYkm1uEUchkOKLX69Pr9Xj11dew1jBZ32Rvb48sy7h+9RrjpM/de3fJ8pyT6TH7R4fUdU3gh4zHE65dvc5bb36L4WBEmS3p6prlbEGeFUgEEsH+7h5d16J8j/X1NbavbCOE4KPdXY6Pj5msrzE9OaVtWwZpys7ODpPRCN/38aQCbRj1B9R1fR6qa7sWrTW+7yMEpGmK1obT01Pu3LmDUILhaEjSS4iIOJ2esFjOCUKPXjIg3HDOxbquUVJilKSsa5S2LLMl80VGf9DH8zw2t7comoqDg31a3fLee+8xPZ0TRjFBGOB7ikCp1azvyERd19F1LV3Xng/4x0WHnndB8Enbnkhlfh5OTAhhhfSwxqwGoUYqhdHuRp2xvQLfB8SK8+6EQBzHlGVJ07YIBFKeOXbOpLobjC4+rJFK0HUd8iy2uNILQD7kC3jSTb+oUTjnlUZIidHaHf9MIK1Uf7HSSJxD0mAFyCBgsrVJ2XQMh2OyrGB+fIoIIl598w1uvfYqO1eu8K2rL/DmG2+cH+t4/5DBYACA53u0TYuQLic/CiPqskZ5Hq01LMuSqN+n6zpAEHg+pu2YHp+gmxbPD9nfP2A+n7KYz9Ftx/vvvctsPmU4GWGspddLOJqdsDaZYFC8/trrfPDuOyiluH/3Q3pxzM0bLyCliyhcu36NnStXmaxN0ALiOEZKSRAETgOTEr1KH7bgcgKkxABZllFVlSPwWI01Bt/3KYsK3WkEUJXue6lCptOZ80EMh2itieN4dW8M93d3yfMlH3zwLtoYDvfus7+/S1Pl6KZhuZjR1BVau+cDLuRQmM/mGP7zBGPM800WsisPlxACayTWCIT0EMpz3m0s1gtoqgpPKvwwpqlrylojVUQgAjfDS4mSkqqqnG0sHQeg0xrlrXLPPXXOFjuzJM697zw848NP2obn5oXADXptUEiUkOf2ZofFKIkWAiudKSNlQm8wYrS2xte/+Q1miyUvvvgSH7z/IT9+510m62v86//WX+GlV19la2uL68JSFDm665wwqQuO70+pypJ+v4+Ukn6/z3I+J5eKNB2SLSun4jY1TV3RS1OU9PCMprWaSFpsqMjKkvlsRp7nxHHMn733x8wXMzY3NxmtjTmdnaA8ySBNuXb1KmVjOT4+RgVOpb5+/QWaqiAMQzzPYzIaEUexiyaUJdL36Pf7+L5/fv26tsUKCMMAhERI4QR4XRP4PmEQ0HYNs9PT831015JlGbPTKScnJyRJSuTHCOVRdC1xGJL0U/wVOei999/h9u3baNNyfHRInuc0bb2a5UEbN/O7+2+R8oE/4HmYEL8MPDdCAIwb9EIgo5hBf4AfhhgEa2sbKM+jl6bMZ3N0U6OUYjqdkqYpvu+htVnNejAej5nP5ywWC9qmcqmmUjsn2UM3+iy8twrFrWyNJzlTzlNvxQNno7HKCR8rEMJ57YWShFGEikKUJ0k21kjGI/rJmJdvvcxrL7/M1Z0r7O3usrW1xc7aNi/sXGNjY4PrwwnBfEmWlXwYeoSeD0DbNDSdYf/gkDAM6cwCrTWtsbRtSxBIlvmSuqyRUlFVFU3T4W1a4qRH27VUdUtZOeqs8iIWywX3799jOByQ5Tm9tMdgNGA2OyHLlgyHQ8I4Yj6bMVrfIV9mDHops+kpp7NTkijEDwOubO+4Wd33YKWh+UFAtYoiOCegwZOuApBSCuUp6qbFGEOy0hi01sRRSBJF5HlOnmWUZclisWA2n7sB3TSAIEoSdnZ2OJoekXQl4Yp1+KP33mb/YBdrLfP5jK5tGfZ63Lp1i5OTI3bv3UXr7pwufdGde2ZqftXw3JgDnucRRTF+EFJr2LpylW/9zHdoNHz9m29Qty2D4YjA81HWsLu3RxiGJElC2uuxu7tLXddMJhOMtbzz9jscHh5gu5L59IS7d24DFq1bPCkpyxJjnNBwirtcORPNT2gAF98/MC0MfhDSqYAgThBBgPAUVggm6xu8+da3ef2l15gMx+ysb7I+nhApKJZz2qala1uqsiAMAozpkAiKPGM+m5ItM+qqoo5G/MVf+ovMpidEUYi0kGcZg0Gfg709wiBwQkwJfM/jaMW6Wy6XRJErCNLrpaxvbNFoQ1HWaGOR0gPpcfvuHf7kj3/AYjFnd/ceUsBwOMDzJNPplGvXrhGGIb1ej2S4RdNUNGXF9tYmd++8z87ODjeuXeH09JSr166R9p3PIo5jgsjdG4AgCEh6Tl2XQqK1oWkbyqqmLCu6pqUpK8qqBCxN1yKks9fPhECWZSyXS5eaLALCOKZpO1CSwWiN1nT0hwPWJhOUkui248dv/xBrLU224Hf+yW9x586HVNkSaV1JMmusEwgrWpc5jyD/ZO2BZ0UK+mnimZkDQojbwBIXfO2std8VQkyAvwvcxFUX+qv2YyoOCylJhyOiOEYbKIua0/mCH73zLm++9R3qumWyvk5RlCRhTLFYMB5N2NzaxPd9jDHc8EO6tiWMQoIgZD5bEkUJgzSgLnOi2BW2yBYLyjJDdRpbn9l/Z47Ds6A75xSEB3JArB4V998PQkZrGyTjTW69+gqTrQ1UFBKlPba3drh55TpXJlvEwqOeLrDzAkTL8vA+y7JAW0PdNM6ejmJ8z6esa7RQxJMJJs+5efNVTNsQej5He3skYYRUktnpCcvlklxK2rbD8ySj0QglJVIK0jQhikKyfEleLpFThRCKLC8Z9IekSUJWNys73SXbxElEv58yGg5QKzNDCgHWYLUmDHyqPKOpK5SS3LjxAmvra6sEoeuk/RSllKtH4HskSUyvl5xHGZSnHINx5efxlEcYGDylMG1HZpyAzvLsPBTseR5pmpKmfZbLBZ7nURYlddEQBgkCSZoMWBuv4Xk+QRgwHk5YLmZ02iKNZDo95f5H79M09Xkgyay0p4eoJ2ehZvvgXl/EhcAhF/89aODBv4d5IhcDwqtn7WPm3bNwtbjYt2eML8oc+GVr7fGFz78G/Ja19m8JIX5t9fk/e2InfJ9gskHcH3H9hZvs75/ie6667aA/Zjmdo7RBCsGybsiynLIsyeezFZHFUJYVQirSfh/Z95iM1wGPtY0xSkKQjDg5PmTv/j32d+8zHhv2ProDVqN1C1YDGo1CWYswzt53HB2JFmA8nyCOSdOUzfUdbtx6kfWNHW7euM5rr7xCGgR41tJWNeV0QZkVmDAiL3LqpkFZwfFsziLLGI7GeNEAKyXBYIjn+3RFAUHL2prLuR8Ohuzev4swhsXpISbpIb0AFQSM1jcwgG5bBAY/ComtY8bVbYvwQJtVaM0PEQh8VbvqvaZBWI2wls31DYpeD993s18vTTk5OqYqcoS1bK1NyE9PyIqKjY0NKqHpdM3a2ga9NHHUXqUoy5o4DleCwznbHNlJ0a2iAy5UYjFaO1OmrmmamrqqWC7mZFlG3bV4XoBnPTrboc9+W9UMeiliZ4esaqibBlqL8leOZKmoq4I8U5RVwWw+pWozjk72uHvvNovlHF8JqhWzw674E8JKzqorWasdG1O4SoXnA3IV0XG0DUcKe0BZe3SUXiSz8ZPvJagVQ9ysPgsrzwqor/hgFrViupqfghB4anNgpQl896IQEEK8Dfwla+2eEGIH+MfW2lef2Iby7M//yr/J1WsvkPT6vPjiK4RBxN07t+nqGt/z0E1D2ks5Pj4mDAPyPEcKwWAwwFpLlmUYC1Ec0+uljCdrKM/jdHbK5sYGndYsl3M21sa8+/aPmS9m/PZv/SZN7VTQqsxpuxYpNFa7DDOEwAtjZBAgPI/eZI3rV2/wxptv8Rd+7i8wGY6JLGANcRiymE7ZvXePo8NDqrqmaRrCMCJOYpIkJYhCvMBDG0teFAzGEzptCOOIOIrpD/rUZUXXtijPw9QNebZkOZ+hpCBJEk5OpkRRzEuvvYbv+XS64/jokOOjI2I/om0bpHKFQsMowA8C4iQ5p+EKKQnDiPmiYH/vEKUUVV1ycLjH4dER2rhFRSI/4GDvPnEUUuYZIurxq7/6q462awxV2bC2PibPcwB2tjZJ05Q4jrDWMBj2CcPYFQLtOsqyxFNqletgKMuS+WzK6ckpZZFTlxVN16I8f+U4FFR1s1LZNX7g8hnKssQaifS88+lSeQFSeW4y6SUs8yX7hwd8/4//CGs0oRewd/c28+kRh/fvU2ZzF6b1fKSU1HWN0d0ZEfvh3DIrVnGjMy1QnJM33QDSD4uBM03iAQ3l4Wcdxw05o5J3nttHmAf7SOu+N6sJ6IvCs4wOWOA3haO3/ffWlRLfsg8qDu8DW4/uJC6sOyCVx2KW8bPfvULaHyEsLBcLPCHRFqqiZLlYMhyMCKMYrGUwGFAWJScnJ4xGI9bX15nPF3RNy1IvScUXjLUAACAASURBVJIeYRhS5gXd2IWC8qxgZ3Ob8doGo7V1rr3wPsfHB2xsbDCbT9nd/YiuzrDC0FmBlZL+cExvOGB9c4uf/d7Pc+3qda5u77A+nBD7Pu1s6mLiUnJ6cszR/h55XhAlPaI4oaobWm0JVnHqOEnodEfVdPTTAUEYsswyhHHZhBhLXVauyg8Gawxp2kdrzXhtnc5YirykqSuqsjhXwaXymOcZJydHrK+vI4Cu6Ogrhek6PD8gWmXudU1DL44ZDvsEvofnTej3E65ff4H5YsE7b//4nLocx7FL8lHO17B9ZQfP85hN77O7u0eSxO544sHagZ7ynbp9gU0pEZhOU5UFTe1y+4s8p8wyt62pKYqSvCiJ4hiEoG4axx9BMJlMHNkoCGg76zIQMaRpCkKCUPTSBBkEdDoiDEOuX7vO7u599g6OKaoG3UEUJdjO+WTatl1VVXbRmzON4CFWOY+mFl146nngaH7gKliRzh6igV4cyGalATwwK6RxA1+Lc2b4SgB8wsj7gvBFCIFftNbeF0JsAv9ICPHji19aa614zLoC9sK6A+lgZLvOMp0u2d66zunJqbMjhURJhRGa0XhCWbf0+0PKIsMYi++HVFXJfDbHGEvXdhgLYeixmM8x1jJfZAyGuctOi2JOpnPKqmU0GvIv/cVfpigzfN+Fpt5//12mJ4fUTY014AUBt772MmEUcfXqNX72O98hlB4ekqDtqJcZy+kJVVVR1RWLZYYBorSH8gKUH5BEEVHcI+ilBEpR1w1CKJIoQVhQKCSSbL4gm89WHvKIuqzwlaVtapCKwWBIVtZoC8PxmIP9fcIwREoFQuGHEdZTBGWGH4X0kgSxIuh4ShF4Hl3jtJO6aQnjmF4crMJlHcP+gE4blFRsb19B65a6KtHWsebSQY+iLM/JNVEUnXMQrLUURYkQgqap6MUJy6wlisJVyNfiSUldN8ynp1SFM+eM7mjq2jkGqxrTaYzumM2mVFVNHMe0ugMcD8NiSdM+RvnEcQhYdLbE93wXAWlq4iBAIUijhJ3NHXa2dtjdO+AHf/DPOd7bpalrt06BfsAHWDGIHYdjNXjP070fSVQ70wcejGGJFeZc4ZcXHQ3nPJQH/gArJNKa84QyYThnkCojzge/ltZFnD7XkPxseGohYK29v/p/KIT4+8D3gAOxWn9gZQ4cflwbaTrg6tWbKOlz794udz68zdbWFkkYkC0zwihiNJ5w96N71HVNEoUEvgfW0kuHLsS0oug2TUNVlnSrcNJ8mROGIRsbGyjp0TUd3irx5jvf+R5JL2b/YB/f83jzjW8xO5nih86z3jQNb731Jm3ToruWwBhsVVHkCzorqOuaZVlR1BUnpydUTcP61jZrG+sYBHlV4Qchg8GIoigpixyMQSmfwXDIIsvZPzqi3+/T6Zb5fE7XNly5ss3x0SGjfsgir0mHQ5Z5yfHpKf20xyiKsLpFScFHd+8y3thmsr5BkoSsjUbny4UpKQh9Zzot64V70qTEj4Jz/kGe5Xi+T384pp/23ZJiQnL//j16aZ84cnH+4XhyHpYty4q012c4dAVHZ7MZuu2YTSXj0ZDcz84LlriiJZZssSRbLmmbGqs1RbYEax0BzEKWLZ1j1BqUpwjDkLppODg6xGCx1qUd9/sD+mubZGXO5sYaBkvTNsz354Dkxs1bWCvo9/q0rcZYzebGGmtrY/L5hPlxy/To0PEGLkzxZ7OuFmI1c6+YI+djesUruTCkAYxSYB/M9FqY1T6PZJeKMw1B4FmBtgKzkiRagrAC3wg8C61w7fy08LQrEPUAad2CpD3gV4D/Evh14N8H/tbq/z/4uHbCMOSll17BWovWTsVbLBaUUqCUYpllLIsSLwjoVmSUpnFe567IqeuK8WiEH/hkeYbWmsFwyPb2NkfHx3SNc4iFgZuZmrYmDAIO9vbo9XoEnofVljTpkaoe6aBP09Qsl0vqRUlZFvhKMC8yQl+yXM7Rdc1kbZ3YH1BMO7wwQlrhEnilIkl6NNrSNC1VWaI7TV4UxFFEFAZYLGEUom1HGPq0TU1/kHJ0eEBeFMwWc9oKpJ8QJz2azpLECZ02HB4ekURO5XY+kB6BH6KsIFQBZZFjdIcnJVVeUtU1SJCeQnqe0wzCgLxx9frTNKVtGpTnI6RPP+0Txwkbm1ssFzPWNyZYIWnblpOTE/KsJLga0u/3McbRf6MgxBhHwsrznCiKKcsKawxt2zKfTinyfLUgqUa3LVZ3lKzMiNVaA0J3COWhlCJSChX6SN+jaVx9BK01SZyA7SiLEuUp+r2UKHbHqzunDXbGcDqfsb+/R5bPyYqMqiodiQzrHKQ4M0AIl2KspMJKRauNo33b1aBfOQQv+gLOVXXrlkKzWFileJ9VOQYnT1xOjIs/eme+GVZtnjNcBQaJdAPKaRgPFbt5dnhaTWAL+Pur2KkH/B1r7f8phPh94H8VQvyHwB3gr35cI1pr0l7fFZtYLFDKQ2vNdDYDQPk+QRQzGE8IooiuLh2/fbVCbqc188WCIHCOsMXJifPGK484iiiKgrLIqaoCgWCxWGDaGokLp21sbNDvpwRCIAKPfD5DeYrI9yjmc4xxKvFyOUObDqVgWeTUQBAOsCiCKMGPezR1w+H+EesbEAUBvpD4SLRu8BEkQYi0hq6uCIOQQCjKRUaV5ZRlRr+X8sHb79JpTbo+Ym1tndAP6XTFoD9Am5Yqz2lalzK8tr6OBcqypBYGrGGR5y6Pv2kfMOGEW10oTpLzyrxBGMDS0Xe7pkEoj14aUjc1m5ubtG3N0dEBe/sHSCkZjSbM5wvapqMoK+RshtGGbJnT+DVd2xCH4WoFI02SJOf2tFIefhAQhSFYg9EGoztHt1YSpEBplxWpsTRdR14WBFHkVjYO3agy1hB6Pv3+mKatWWYLtLb00hQ/itHG0GqDthrlucFZVIUjHlUF2XIBbYUEdrY3eOONN2jqCmyHL6FFEveGVFXJ/fu73L+/RxDFdNYwmy9WUQ4J5ixiYFdaKDwYsRKzykiU9oxz4jyB0kIt3b4uCuCIZhboVhwVsRJS9kJg8lniqYSAtfYD4K3HbD8B/pVP207TttR1TZ7nWGvxgtBdxPO4d4+NrW22r1wljEKqPOfw8ICyLAg8jySK3H4rplye5ywWCw4PD+j3+3ieIs8z8jxnMhoT+h4nR4eEvk+2mGK7FvQGm5sbHE730VoT+IG74Z2mF0e0TctivqQoc4SCWndMlwWjvnsAA9/lvGcmQ7cd5SIjjEKSKMYTgpPZnCAMUNaynM7wfZ/aK89nzuV8wXKx4K233uR3Pvh/2NnZxkof6Xns7+/Tdh1xnKBWxQnmiyVCjgnjlKIoiaVECxd3t0KAVGTlguFggKfcunxN09I1LaEf0DRuxlTKw2iDUh6ekigpiMKAwXDI6ekJURwznZ4SRRFCSKRQJL2I7Z0dqqpiWSxYLjPqqkQpwY3r1zgr21UU5Xm1XwP00r5LMrKGrmuRAifIpQQlaFdZe6ycnWVV0a3o3cYYqqYGK9HWUDY1cRzRV5JFtkTWFVJ5HB0e4vkBnieJfY9b168S+JYPf/gDfGFZG/UJiBj1B7z1zdf4y7/yr3Kwt0uZLZBowjhl6+oNirLk7Xff490P75KOJ2gh+O1//DscHZ2sooQChQI01nbn2oA9T3U/q4OxGu4rHoJRTiCf5atIo879Ee61ymXgzKx4zoXAFwUpBJ6nuHJ1hyIvadsG5QnaJiVNE0ajEeO1NSaTsYtlRyG+JymLFe/c8yiKnCzLKIoCz3O89bMkoK7rWCwW7O/uEb0WMhoNKbIMsOdZx/fv3yMIfMq2wmhD2Ti1URnHX9dty2I+Q3k+TduSDgcopQgDH9064o3VkiSK6DpNXVVkyyVFFDHoDxBAlmcYNE3XUDYl1kpXhMNoWmsI05S607TWsn31OkK5PIT79+8Dllu3bhFFEfv7+yjPXwkCjzjpoaSka1qU9Ij8EBm5ZKa0l+L5HlJK2qbBWEOcxC62X5SkfWeX9/op2jhef7+f4nmey1h85TX2D/bwfY/FPCMIAnr9AVHkHK0Swd7eLkJINjbW8X0f3/fBujCl0XZV4TdwdOcVh98LAnzPI/A9hJT4YejyDAQIpUAI9LkWs/L11BV101DXhrzM8a1Hf9jHj0KUcvUSddPRNjVta1HG0NYlPQlfu3aFtcHLrA1TZFcy7KekSYxtK6p8QVvlDHoRo0HCYn7CcrGk5yvefOVlhusb+FFMnZe8+/77HB6dMB6M8JTHyfSYw+NTnAlgQSrOFkP1VpOT1h22a1eZ5Ra0WNWI8OiN1rC+hxaW7OQEqTunNfwUV057LoSA7/tMhn1UENBLEnTnVLlqbR0pIU5i0n4PYTVBEIGUXL1y1aWXliVlnhOFAUkUswwCPKmoa7cIBVawXCxcfT0hWC6Xq7AalHXDaDQkLwuUlHx0f5denNB2HZ1pMVYTKh+vJ6makqouGEYjwjhhPBrSNi6On+c5vV4P0UvRxi3m0bRudR0DRL0eyXBIPT1mtpjjeQFCCOq6witD0sEQL4hZW1tjf2+fX/ylX2YymVCUFVXV0O+n6KamKXPKIkMIQVm3AGRFge8HnBwd0jWu/l/V1PTSHn4UY5TCjxMXC1eKMIjcbCU8hHJe9aQXEwYBVd2c59qfleO6dvUqV69epawKfvD9PybLC7y6Zj6bn4cPv/a1lzC6xfc8iqLE8xo8FeIq+iqCMKLTHVYI/DAkCIJVtqezlZUQSKHQ+iwxy6KUREgPi0EqRRSGrE0mtF1HVdQ02pk6fhgylE4AzOcLl88g+5wcH5Fnc0zbsiEbrn/rNRbTEwZpROAnbG9toY1menrKcrlk2Hf06nQ05p333+do/4A4jPF90NkCZVpevLJJGgiK61e4ce0a65MJh3nGj995j+WyIB0M+O3f+T36gyFKeWxt76A77UKsrQuJ3js4ot8fIVEo6XH1hRdZv3GVZNTn//6Nf8BybxeMhbPycD8FPBdCoG0ajo+OEFKRpCm9Xg+ATrUkcUgYhaSrbZ4UKC/A93yczWXd+nkYeklCP+1zGp5yeHjIdDpFSp/Ad/z3KIwdQ61paNqGTrckXUqrO9Y31llmGTbriNMexbKgakp6aYyxHcVywSKbY9D00pS+6RNEProLKMscYw1t11AUJUVV03Wa2WKJHwbEgz5pFGGB6fEJUnqsb2xgtSX0QzYm68zmS9ZGa9z54C4vv/wyQkqqVtOWFYvFnEGScP+juwRRxGRji7jfI4oS9CoXvqormlYzjkLi0Kc/HNIZ49KnPQ9Tlhht6VpDFAREYUxdN5R1gx/4YC2d7vCNR5Yt6Q+G53H/zY0Nmrbl3kf3aTrN8fExW5vbBEFAs1onQAo36HXj0n290D8v3BGGISrwVinFZ+nhgqLIyfMcZaFrO+edA5SnXIm0M20Ap02sb2zgSUkvCBjHAzrToa3FC8NVspKmqWt6vR5GNxwf3qcpcoLymNHVLfYO79PNfUaTEWUvQvoBRVlSNx1B1KM/XkMpj67uaKuWXpgwGg4xAk5PDrBNyVoacW0yZH3Q4+bVLV5MXkQaTdof00uHvP32B1y5eoPRaEwc9RgM+o4huXrmrlyv2J3PKJYFAR79dIObO19jtL3O3s8d8i9+93eojg5An0Ujnj2eDyHQtbzz9ttEccRwOOTK9Z3zmTvpDVC+h7Wdm2GbijiKaboK3/PwQp+IGIulLgrCwGfYTzk5PKDMlviBU1tPj08IgoAo9Knrktl8ytbmBp6SjMcj6rLFUwEiCqnRGOli32HgkXcdujPUTYffNJQnJ8RpQhL3CMKYnas9suyMb5CxWGYIFNPTGWVVMu4PkUZTZjleGGFwlX47Y7EGdKupm4a8KumPh+RlxWQ8xtqG/rBPMk05ns4osoL1oEfbGK7d2GKxXJKkKUWZMZqsURUlk9HYJdAYg6Cj7TTSOidlZSqXNCUc09BYaDvNMiuIoxApHUuvF0V4QBAF9NMevjDIKED5nlvUo58iPYVZxb2V8tzMvUqfjqJoxWGQLtXY94l7ifNXWEtZFnSrRUI86bgg1tjVysKCumwAS7BS8w3GaYZ1iR/4SIFjEwqLVC4hKfQ8gjTlyAj2D08IbIHN9ihnc+4vDhn0Q6qugcBDyoDlPCNrahoBXddSFyWNwUUuACMsXVURJiGZsdw7nOPpmjiUGClZ5BmVqNClIj84ZK2X4nUF333lFkGUMNleZz5dsB4KOmPpjVI8IRCDku//xu9hq8b5QuyUsJmzc/NVXnrz2/zpj75Pe3jPVTkSroL0mWvgPInpwt9zPIW0eC6EgMDVzg8CjzgJSXsxQRCQDgbn8eYwDAiCiDAICAOXw26wWGNpfB9PeighqFbJOUZY+qMh/f6Auqopyo6q1syXjqSilERYw2J+Suj7hGHsilbEAWEYkcjU1ek3zvHnohEeWZYznoxdkYuqQocew94Av+1YZhmdscS9lLpuiXs9qqaibkrqqiSJI7wgQAYhndYMBn1C36fIMnxfYYVhNBkSBTFVXVKXNYEfcOulV9i7v8vu/fs0nSFOepyenFCWJcGGjx84FTuJYuqqdAlOvu/MlTzHGkMQ+ASB7zIvezFZXiGEpJemNG3trqcxroCHELRNTa8XEwQufGn8gJe/9hKTyRoWQX/grq1SivnpKdYajNaEgbfiB7hVhoV0dQOA83sZBKEjdq1mu7ZzpKG26/A8RVkWAMRG40euIImnfExR4rcd0liEH+B5iqpt8AJQVuMLCKMeYmBY7h9x+8d/ShTHKF1x995HqCBkuLFObzBk0EupT4/5aPcjZO3qNWR5xmyRMV9kSE/hBx7K9zi4f8A7791hZxiRbK/R1A1+GLJ7csid9/ZYGw4ol3Nm0yNee/E6vXRIPBryux/d4aQtuXJ1G7qSfn/AoDRkp8d41kW2dusMs6jwgpTX3/w6P/Ptn+f/3T+imZ+4Slur0OTjLIMnbP7MeC6EQNu2WCytrgHDZDKhsx1SCYqiwi0rFdDv9wmDgEApEJKmbWi6BqzBDzz8wKftfNJBn8FoRNO2xL3E2cAL6eiui1PSOGZjc42ua/F9n+n0hLTXJ88LjHJe2cBXDIYDisUCcOprEHg0raEqK4IgIIwStPVo2pbRZEyrOzQwjBKyLKeqS9RC/n/UvUmPZVuapvWsvfbafXMaa93N3P02ceNGZGVDVZKCrEoS0UhZDEDMAAkkkBATZoxgwKRmCMSQAT+AIRJCTBnAoJIkMyKDiMi4cXt3c+tPu/t2MVjbzvUIIjKrQlmly5b8mptd2XE7x85a+1vf977Pa4i6DEht4QURvlI0ZcX8KMK2BOMwUDcVi+UMVyk816HveoSUNHWLo1wWyyOqquLq6i3vf2AzDBrX9ajqljzLmc1S2rqiloY/EIYBVVWaBTSV5Hme0TQNSRKjbIUIJZYIWK8fsW1zl8/z/PA7cTqbsix5eHggnC+YL2akaWqYAbaD45i7vefY1FVN37XISdvh+g62bR+as11nXmvD7TNKRt/30RPkQ9oSjflZkyRh1Bjfw+RElNKmblu6rscSmsBRhk1g22gBVV2zKXJwjDLz/vaWuizp2o5Z5OAFAePkB6nqmrOjE6I4pqpqPCz80PgSNus11lTNFGVBluWsVls2mw3fufgIWyn0qFnMl9ze3nB/d8PyO981uQttxfHxgjBO2exy4tCnLkoCP2C7esQ7crmYJQjl0LcGRaekQ69syr4iweI7Lz/m02c/4nq7gSfTFRwcn+bvv7gB/AK9+jdYf9+KTaDtWmxlEYYhylHc398yCgiCgCSeYdv2hI8ymoJBjwhLYktJPeoD+lo5Cm/0QBhmnbAsXM+MGz3fBQFpmpJEIUJAXVdcXDxnu92SFxl1XVHcVgzLDgvN2ckpJdA0ZmJxfXON67poPRKEPpeXL9jvtxRlyWw2w3VdXOUQeh5Sw+0wMLYdq7s7pB6I/JC6qqnq2jxXW7Lf7wh8n3HojMlu6CmKjEU6JwjCictn8fziks1qzfn5M9BGSBInKfvMLOy6bmjbnmFomCUJdd2Q5yWLxYxxHNltt2R5RhLHdG2Lsm3oe7MAheEjlGVJVVUsFguSJEEIcDyXZJYiLInnuQR+RN001G2LhUYKgxHr+356A44ox8GybIZ+OtdaBocmhDgYisZhRFgWnh8gpjN/0zSHqcSIiUE3bXILhMXx0Yn5F8aGqm2RlqkQ2xEkPpbWbPKafZnz1RdfEXohzRR/7rgezdBTFCWb/BHfcVllO6qqpKo7qqrmaD4HJk2DtNF6YJvlXL+9ZbfbIaUkTWekiQHeNHWH77iMmEama0tOTo7Iy5r727d4rmK7rqgqI5Pe7TMcx+b9D97js7/6HCEtHNfHClwaenY3t7x88YLf//t/zP/y5adQ7BHCyJsPEuaDColvvv70+W9YFnwrNgHbllxePufZs2dIKYmiiCRNTVd1NGWmtDRVWeAqB+koHFsy6BE/cJHK8AibypS4fuAzCo1QNuPQg4D50YKmbljM52S7LVVRs5jN2W933N/ds5jPCf2AXZFjW4Lz82fUZck49hRFTVVVPN4/UDVmwQ9DT77PcKTHMI7kuy0CQVFUxk3nedS7LdV2Q1lm3L/9irbp+eh73+fZxQXaVcSRT2nB6zdfMVqCfuyZzRYo16cfRuazJff394RhyPXNHSdn5xRffkE3Dtzc3OA9eSykZezH2Z58n9F2HXGSMLPlgdQ7ak0cRQRBYFJ56pKqMmwAx1HUdT359xP6fiQIzKhvGDt83ycII6qyQLwz3hvHniSJKMuSs9MTqrqiLEt816MbepTr0baNefMKTd3UdG0LmikU1KMaK9q6+gXw5+NqhXIcHNfFcT0zNhTm9en6HscSSGFhK3lgDtpqeq4q4Drb0rUVm9UjyvHo2p7HzYbNdmusytrixcVLum4g2+Vsbu85mi8Rns0w0am6vmOxnHO3rri7fkAPsN/nlEVF33W8ePU+x4tneJYiiCOqfcbxfEndNuT5nirfk8wXtN0C9IjrOWyyLfN5zH/w7/wJX//+NXVVI1XADz77nDe3X3H+6YwPXcmLi+e8+K3f5urHP0Q0mVEv8mRW0gemBfALt/7ftC3wrdgEpJS8evUK3/eJopBZmhoxTdMwjKAtwdj2hImh0/R9Z3iBownXUEoxiJHOsrCVOds6tsJzXMra0Gf6pjfiIkuibMVus0EIY2lt25a7tmM+n5NEIV988fnUmDP46ygIieKAJE3oNx3b7Zb333+fuqm53z6w2WyxlUMUxYRBxHq1IvA9qmJPke8oqpy2bejalqs3X6IZ8DyX12++JghDA+RwHOIk5f7ukYsXL6nbBj+MDwDNtm15/t4L2skBB1AUOWEYcn7+giiO0cJkEDpKmZSgJ7OLZVFVNUW2Z7FcEEURGiPBbduGMAxMaa41jmOMQbZtuvlNO9B1LXocGdqWCoHsGpRjKitrOvsrZZqNaZoa6pJwaJqGtusOHgJHudjSLLSmrqmnlGDggPaumwZLSqq6pulaYmtS1kmJ7ZqekY3EZSTPdwhb4gYJ0hrxlAJRk6YJaCNXdvyAWTIjnc3YFzltWeB5EQ+Pj2yyPeOgsZWibVu2m41p/tqGi9D0PfcPK7KswHEUWZ7x+s0b0iTg2fNLFosjHAt2ZcGb12+wXkiS42NgQrHHMWESs31coxyHduwJXYfEcwhfXRAmMbusIWtrfvbZV6xX9zh9y+nxKS8/+pjb11+iVwXDE/x0OgcI8U0GBzwhcfjGs8Q3TcR/kutbsQkopcj2OywBcRSS7TOa2vQCpEFAYEmbbDTjIsuWuF2L7RhgJRNwxLIEY6/Rg7Hg6mGgrmuUskmOT/CUi0RQ5HuKIiffb8jLkouLCx4eHsmyjDAOubi4oGtbgiBgv9/Tti2B7zJO2Os0TXEc48DLi4y6awiUzSg0VWukycPYmVQiW1A1FU3bILTm5u6arMgY9Mjp2TNOzs7xXA/dD4Dg6OiI2/sHXr16RZZlBJFPU9csl3PKqiJJE/I859mzc7quo21bo99freiGwbAERpPkk6bJ1BzMWD0+ku0zg/ZqW6IooW1bqqrEdZ0J3jFiWfYEBxVkWY49wUZMgOhoxo6DCQkRQuI4pgcxDkxHJSPOMszHYWL7S3zfx7ZtLMHBl9EPg6FGu64Zo2mN3dRTxdAgBkFRFqYnoGzyqiBNUzzpYVkCZRtiUdf3WGp63n2HZWmyMqPpGrxRM46a9XZDUZVowAsC8rKk7Uzk2unpOfPF4oBIl5ag7wdGLdhnBV3bEiehGcM2CkuEFIXp+biuw+buhmE0YTCWVNi2SVlyAp9ODxwfHzEMIw/bFUoIUtdlHDq6fI/dj/zuh6/Qg+DrT65Y3d5yliYGxjKMpi/ABLiZYNwHa5L+hnb07kYA/FMh0r8Vm8AwDKzuV/zwL36AI20Cz0c5phxtqhrf81gs5kRxRJImBGmKkBaWtLBtRVVXJjdv0AbI2TY83BlZ8VNohy1Nc2y3emS7eqSbMFmh73N/f8/y6Jjtdo9lWQeW//3tHdIWhJ6PlEu+9/3vc3R0BMDbtzfmfMvI0dmpaRIORig09h2b9SOCEWGZnkdW5AihicKYEUPufXv3wMssI45T3n/1PlVR8+LVeyRzYzHZrB9YLpfGURkE/MUPfkCcRCYSLE340Y9+hO96hyNUEEVUVUUUhsxmMyzLwvIshrFnvlhwfHJiFuhoaE6u6+J5LtvtDs/zcBz3kA+wWj3S9z1x4ptm4Djiee4B+VV3LY7rIfSIcr1D53+3y1iv19R1jZSSOI6Np2NSEirbJgojkiTh9PTU2LCrmizLadvOLMxRG5rT0LPd73Acgz9TSlEUGa0YscVAFLi0dYMVBzRNy9A2JFHI1WcrimyPdBV+EjEMPbcP9yilmAUh/dCRzmfEXWjWHgAAIABJREFUWtB1A8oSNJ3pMYSewzJNSWdzLM/n//zTn2BbmtOjOUeLCO8JlyYlYeCx328JgxQxl2R1x+urG/JsxzxJ2GzWKNfh8f4BjTFI7TZrkqMzqlWHUpLAarEsycXxnB/+4Ev+8Z//kLPNGpXODtmRQmuzWQpzrDscCiZZMpPR6V3f4bswXPjrK4NvxSbgOg4//elPCQIf2/VNE0YY9t368QGlFPe3AUopvMAnPVpy9uwZcZoSRRFhGCEdG41mHDvqqmDsDUJr6A2FeLvZcHf9lnK/o20b9NizywtGAacn52bT8Vx2u2+UcIujJXm2Q3kudV0zn59yfHzMzfU1Nzc3hnbjR3iuT5ouzL+z2mBbCiUdGDssDXo0hhkEuJ6PUi53dw98+PH36Kf0nIvnF4y9JggCbr56g62M2CbPc/q+x3EcXr16xcPDg7nDeS4vX740ZbIlcRwHwWRasm2CIJhw261ZhNJmu93iOC5KOdR1g1KOaf45Ll3TEXgBaM3Ym9FUVRacniyoy4pOdiyPlkbIM83/hbQOATAmsWfk/v6e9XpN27aH9CDDGbR/4Y2ptaaua8qypOt6Y7WuKjbbHbYj8cOQ3W5nJhDS9FqOjnyUUljCZfNwS9sIoin9SAuLoam5v7vjiy8+w3EVTQX9ODDUFdo2uPEwCJjHM5TjMCJI0hTfUfieQvmKYRzwJ31E3kPf9oSBhxAmB8GxJa7n07Q1fd/SdC1D1/Ls2TlVZXoeTdPi+maDkNPvQmMZ3cPY8/Zxxa5qUQ3M/AEvjhHWSJjGHF0+J14uydsOaTtmktA0DMNTKvXkTXgn9/LJh/ju9ctg1G99IGnbtrx48QLHcch2O4IgoMxzNpsNJ0dLA7AYOqS0CHyPtqx4vLujqSvKIubkdCp/Bk1T15RFgZ6caJbtYFsWDw/3FGXG69dfIcaRoszIC5Or5/k+86Mjzs/P+eznf2V+jiybLLoeVVVRaXMuPTs7w52cbZZlMfYDXTuQZRmOcmnbjqFpjX9+t6GqS7PA/JAoTVC2wnF8XC9itljy4Xc/4sc//jH7/Q5X+abZ6Nj048jFxQU/+tGPeP78Oev1mn2WYds22UNOFEQsl0dIy0KPpnchpT40+J7yBNquNSWuaxNMG84uy/BcF2VLhqGnaRqENk7E2I6p65rNdkVZlnjOB4YZsN/TNg2Ra/IOhCUmXBmHAJjdNuP6+tpsWrbD2A80lXFL2pY0xzPLmqYF8lC+e67H+bNzdvs9ru/hhQFiQseZ8fEwbRg1Sg2MQuD6Ltl+Q9U0OL1FWzf4jkVVlbx9/RpHSVoEUkDdtjx/dsl2t6FtGyM4ksYMtVwukRqENRiR1dCx322Q0mFXM6UeafreTC5msxTPVURRyG6TI6yR46MF88WCu4cVm9WKKIjYbTPTG0Eg/QAvCLEsQZWtGLVmtjyirwuGMTO+k75FDxV+6BPPF3zx5gotBIPWCGljTf2dpwbquw2AAw3pN+wMfis2AWnbWJZNXbdIKSnz3NyNlUNdV2y2KzzPQ+uBu9sbXOVxdHpG3zQUeUHbtCRJQlma+Gzbtrm/v6fre8IwRWgo8j1Xr1+zWd/RNpUJ7khihBR4vsN6tzU4bDTClhR1NQVeLgjDkKoqub25NkqxqiLLCuqmQUvF8sRj+eyYwA/xwhlDU3Nz9Zq6qVFCcHZ0xMnZKZawyXcZ69WWi8tLrq6uOX32nD/5k3+Lh7c3rKoH9tstbpIwWy7J9xlRYHDqs9mMOElYLBZ4E5Xo5uYGrTVJHPH11284Pz/BcZwppMPM173puGB6I4o0nfHy5Su6KZykLIuJGViz2Wzo+563V1f4nst8nnJ1dUXguSSLI9I0xbLloZlnSXN3z/cZ3dBTVTXLxRHZZOuezWYA1FWFtCy6TqAciet52FKxWCwMk2DQ9OPIIDDNsn1GP4yE0YyuNx4QPxjo2paqrNhWBbELSMFmt0Vpl75t8BYpyrIo9xnuMDD2HXVdc3J0grTNBMXS2qQrDwMPqxUSi1maIEZNXRS4rgTdIy3J1fUtjuuwTE/wPIVAo5TNcnFE13VEkUtsB3zx6Zd0XUeYpuTbHScnJ2DB/lEz1D3K91ivtpRZxvzYJwUseirdEfoJSTTnj//F5/zh3/t9btc1f/nVPTM/4u//y3/IT/70f2d9dw9CoMfBOETf6Qs8OQ21mPiFv2J9/U29gW/FJoCGm7tbTo+OUa45543jyKB7wshHOaYB1XUtQ9ejbBNZlu32iKKgLEv2e9PoapqGIAjYbDdsdzsscc88TY2BZuxI04T9dqoEck2Uzri7u+P4XDGfLVjMlxwdLykKszieAk1c10ia8zxnGEfee+89vvzqK7Z1g+269FozO1pSFyWLJCYMXIRlTCBxmrA4OcZzA7745HOaumO/z/nw+x9T1zWffPIJaRBSVRV/+qd/yh/80R+xOFoCguvrW86fPUMgOT09p542p91ux9nZGbvdjiAIOT09xbLEISeg7w0sRE4a/DAMTS8hSfCDgHy3o5aG4DObzVg9PrJZb7i7u8NxHLbbDfN5YjbQosAJIpqmwRokSinKoiSKDORVSptu6BlHoza0lTr4DgxXVExOQpumMaRg13UMCFUpRvQ7eoeaYZxsz31vICDCAD9sKXEcj9jxWN99zfr2yvQEdi1FtuM1I4Hr0rU1yySk3nvMEoNYx7YMck0bTcIwQVIcL0TZCk1P3Q4oZXgH69WKr1+/xvM9XGfEsp7i8DA6Bilo6xI/9Dk7OWadZ7ipoS15nkc/murFkZJqv8f2fALPB8ATgu1mRTd2OOEZYhAoYB4HCDw+ebshsl1E6xvK1aSPEdOp/93JwC+qCX8zscBvvAkIIb6LyRZ4ut4H/mtgBvynwMP09f9Ka/2//XWP1TQNSlqmzAoDxNAhLTM1GNuGRlo0dcNQNQTKoa8rrj7/OV4QEqczAsdDlxWuNASaNzdfMowjVjeSNTtsW6BcxXyxYHV/hx+FNG1L32p0B8vjIzPb7jqWx0f0nTmDe8ohz3bstmvOzs4YB5Pw63oKFaa8/93fYrfLaNsOq9fQ9SaOy/dJj884aTr6rufDDz4kCAKUtOkb0MJivV6hLLD6nteff8H5+QVBFKOVz25fsN9lWJbFh9/9kOfPn1OWJVmZoZQiCiOs6fWpm5Iw9LHtJX1X0w0WWjp0Q0unW2zho4SF7YATOPixwglcoviM3XpP3/c0bc313S3N2GP7ZoPZViXrbIftO3Rdg7VRnJ6eIEZB1zVTzuA3b0RpKWzbIUoTkDb0hkL01D94ahQ+5Uc2Y2saubKnG0fatqdpOqq6O2DGHcchjh0zotSatqtNsEroEDlL3Bcx3VjQ7kH3gry6Z/fpTwiHmqaRBGnM3HdxPIXtBoSOh0JTtR1+NOP4aEkSeeyz0pyrhxEXm95WlHqgKe+gHdmWBgQYex6OZfoTyveompbHhy3PLy953H7Kw5s3LJbH3N3dcnJyQpCkCEtD13H/eIfnecgh4Is3bzk9WeD5Hrkz0uuOelszNC1lNXISK16cX/KztzaDN0OOX2IzMgphKlUh0NgT1WhEi55h4hiIX9IYH2Bof81R4TfeBLTWnwC/ByCEkMBb4H8G/mPgv9da/7f/pI8lpWWY9l2D25vSfOxa2qZht9kaFdm0iCxLIGybxfHpNA4bubu7ZxhHiqpiu9uhtT7cES0h2G53jGOP5zjGcFOM01zbzKb3WUYLJHHKZrMhDiMDMo1dqgdjqe36kWQ2w5miu33fp21bHKfme9//Pr7vT/BTg/1K09Q4HzXsdzv6tiXwAxbLY0Y90rQNdV2RzuZcXr7g4WEDls0f/MEfkM5neJ6HH0Q4js1qtSKKIt7e3PDBBx/gOy5f3H6GZZm7WxhHCGmR5yNDV1NucpS06JuOFosgTYmC0JyvO4XVmDN9HAdUdY3nO/wLf/fv8ebNFZv1BltYfPjeK5q6YhaFDIOJBauKAuUZG7DyXIahn+6ipq8jhODLL7+kLGuyzfqgHwgCE4YipTV99Akjb7Isdwfth23bLOYzkycpbdq2pe97tPanxKc5SZLQ9A2ONdKPPb126ewB21ekncOf/vn/QV3uENLn/PwEaxTs85z162ssRv6Nf+1fJexHqtYcad68eYPGYr1aE/mKtio4ffEeb6+uuX57zauLS9IgOkBdAMoywxUwny8o9iVCa169eoUWkO0LsiwjiiJjmZaYI2Vs7Nyz2Yy79o5u0KTpjF5Ksk2O6FosLdjnLdmu5Gb/KecffoeXr95n9clfMloYl+WUofjUEhAGwDBpQuCd/xyuQzP216y/v63jwL8OfK61/vo3iWmStk2WZbiew36/n/BfPZ7ncHp+xiSUwhImbLTvWpq+x0YhLInWZnSilMNyeTSp4gzr3vN8kiTG910e7+8oJthnEIXc3twzCgG2IprPOT0/JZrK3qIoGPqRKElJkuRQ3gZBQNcNBwFPOk8nVd2CIAgpy8poCZKEsu0OrAG0RirF0BgFne8HtN3AZ599xt/5O79DnCZmAxl6NpuNiVMbR7puOJzpHWnz9Rdf4nkeL1++pK4NulsIwWw2N5OV7SN67KAfkBraMmfdVoSXL/Bdh6Hp6dqcjoEBjXI9mrZFTxoFx3EQY48tBdg2vusx9D2hH5Hne+rHmmeXF3R1g5ok2SAOr0ee51y9uUIJ2/Qx4pi2bQ/KxDAMiOIQz3NRyrz9irqh6TqyrKBpGkMinlSFfd+ZzXyaQpg+R4BnQ16VDE3PQI/n+7y9+RrPc7hePeL5pyzmc+rtjuu7Dfu8RAnoup5Ra5I0MUEjsxl5UaCUpK1rHvI9jZZ89fqarus4OloyWxxhKwdaA6zp2g7L6VCWOmgolOvRdT2PzdpMK7TGD1x830cpyWa3RuuBoR+mPMWRpO2pdWdyMsIQPXbYjUGpe47C0sYTgTD8QcMZeDr7j4D8Zr0fhEP/3+ufV0/g3wP+p3c+/8+FEP8R8H8D/8VfF0H2dBVlTlFAU5W0XYctJVXbEUXJpB405WM/jqiJaiOlUQcaSrSg7UzApW3bCEvghwFxMiOKQ9abqdvtejA6+L6grBo0lrmju2ZGXpQ5z86fMwyDCSB5NHbZfBphXV5eolxNtttTliWnqdk0Pv3s5xwfnRCGIU3TsN3uqMrSoK1HI9BRjmvksJ7L4viYoWuxbEXVNhwdHZvzctPgOA739/ecX77Ad/wDeDVN53Rtc4j0chxnGqttKcqK5ckzBGbkWtQZdZGjdY+ThLRFTlZ2/NX/8xO++PTneGlo4tOOjgnimOOzZ0ilyLKcMIwoij1919FPUBMn8JHKCH/GcSSYjFm6N8EgT2Ki/WaL0BrP9w548iiKDmPEuq6nlB+mFGnBLInx/ZDFoiXLMvKiZL83sBbbtlmv14cEZsTTTFzgOB5CWlRFzf39A1EY4kjJxx9/xHyemMlAXWBPgbVxmrDebFCOR9EYr4MxClUEQUDsz4gCl3XemtchShiGHluALSVeFOMqEwXvKmV0FHFs9CKjiTeP4gDPd4iTEMdxGIaOzWbPYpYibZtdVuBHIVmW0TYtwna4v70jfHGJtsYpcUgjhoEkiklPzrC8gKEqTAFgvUNB/yYn6a+9/qYb899GFqED/NvAfzl96X8A/hHmp/tHwH8H/Ce/4vsO4SN+YBpYl5eXRnQySVqHwYzetLBIl2YenMQxAlNGdn2PENb0R2BJQV1XU7NKHh7j4eGR1frBvHFcj7qumc0SBoTRukch8/kc23VYxim77YZ+GCeug81muzdv4gHWqy1CaFaPjyyWC3bbDY4ySrnN2th79VSVdG2HJSxu7m6QQvDs+QVNZyqB2XzOp59+SlGU7POS2WzOYrFgs9lwfHyM6zrcvr3i9Hd/l81mQ11XrB5MiZ3nOV9/9ZUZb9kSP/BwlGR1fc3x6Ry5mBPEKfv1lsD1ybYVn/3Vn/HTn/ycDz78Dn/3H/wxb7/+hKEqoe9MZ7xqEMpmcbTk/uaG6zdX6KHn/uoNH330EeeXzxnGjuViRj8M013J4LTapmUcwfddPvroI/b7PU1dMww9bdsQx+ZOXtc1rudSlhV936Mm7JmtFI7rEXgOEBnS1GJGUVTs99kUTWaz2+0AcG2bdhjQ00YQJTGr7YbA9bHEgFKgpCAJQrZSg7QQ0ub+7p7r5Zzf/t3fYxSSrmkQuqOdglCWUUAaJ+yqLVXZskxjhq7l9u6aqh4ptmuOFjHL5YJ+uKNtBr778Xe4uroyFULXkRUV5+fnpnk49oRhgmWZ8atSirprcJTLxYsZeV3hKMnD3ZrHhzWX7z/n5mGD1j5KgysV588usJXDUFdY0gLdgzYMQo18d0XxmzqI/jYqgX8I/IXW+g7g6SOAEOJ/BP7XX/VN74aPHJ+e6e9+72Mc25ynm6Zh7Ax6+cWLl1MKsKYftTGV2CYKy7Isc1ec7k56kvWaKUFNN9T0Q0/XGRZAFEVU0uQV1lVl5r5Lg9P2o3DyLkSsV2ZU5voBi+UCgCSOqavamJr6nr5uCFyP2PfM/F4pPN8xiOkB0ym3QNqWif7Oc95eX5POUpMC1PfM53OOj084Ojohz3OOjo549uwZd3e3gNHlf/HFF9zf33P99i1RaCYgt7e3nD8zoU5haEprEMzTGK2hKCqatqNqatAD5XbH5599zmb9yP0qIjmOOFrOKfYZbV0TxBPstKywLQvbtjg5P+Px9gYpXbqhJ8v3eIFPWZZYUrLf70EIpOMShAlMvP0g9MmyjDiOmM/nSCmn0WRM13VUVUW2zxn1YPoevo/rDocyXSllJMS9xg88wyNsaoZxZDabGUlzP5DVOUXeMgwNrudxcnLCw/WVeT/0mof7O+LAI4xjgr5GyoIoiek7M3HoRhj7HleZfkSZFxRhQBh5bHY7hLBwXYe8zLCkomkN1Xqz2eA4No4bMPaaqjHq1KfmrWNLwtBn7Du6wbAtm6bi5OTEOCXrhuP4mOXZCVVbk61zkxItBsqqwvE8HJXiyIi6LHi4ekNTVTAlYVta8wQ3/gZ7rr/55Ffc9P95HAf+fd45CjyFjkyf/rvAj/+mBxBC4PsuUsiDOs6dWHT25I+3bcfc/bueXoOtXIQAPwynfslUjkWmibPdbOj7gb4fcBwXxzNcv/1ua4wyXoAscoIo5OHhAdcxFBtPOQel3mJxxIuLSy6ePWe327ETO6RlEaYJerrLbVZroyNvW46OTzg+PUNKwTiOuK6D1tB0DULC0PW4rjHW9H3Pxx9/j9vbO6O+Q05nYPP8d7sdtqP44Q9/iGMbhd/P/urnJEnCer0+ZAD84R/+Iba02W13ZOsdXhgRpwmOo9B9zzCOPLx9y/2bL9BjS7OzGYqEfTOwWu2x/ZCj0+dI2wZhOAJRGGExsrYsbKnY5RlFXfLi1QuzqB0HGnOU8oVEaKZ0ZY+maTg+OaKtK/qhxbIcmqanbd2JC2D4j03TUlctXTvgeoooCnAmMKfrGp9G27WMuqdvW/opv9D1PNQIQ29m9pvVjnZb4LkBR/M5P2taXNfGDhWO47LfPQDSHNOqgjiOqOuKQUgsIXBdj7KscByHNE1IZ3N482ioxxhIqOP6CMvm/GjB0BrgSd92CEsaH0YcHXBp77//weHI1HUdeZkReL6pavd7ojQxrIhJCFQUBgIThBECSVnm4DjUQNw0+GFINJ+Trx+mkatE6HE6CHwThyqeIAO/dGmt/9luAlPgyL8J/GfvfPm/EUL8Hmaz+uqX/t+vvMZhZD+ZW2zLnHeV7TD0ownq0JphHJC2jbRN9FbXtYYnMC0o3/ewbIG0JF7gGzRWahRnNzc3MAV6nj57bsq/vODLLz+nqhsuLl9wdn5OFEWIpkcKwTw1DcE/+7/+MQBN0+G5iq5pub+/x/M882bqW1NBhBHL5ZIwCNAYVNYwzc6LYsdut2cxX7DP1gb3PY68fv0a5bpcvX1L34+8dC/pu45PP/2Ussz55JNPmC2WLBdHXF6+5IOPvourPD748GN+/unPaDYZn33+JccnJ6TzGW9vb+nzmrKoSSKXZrdhm+d8/cXP+fRnPyDwRnQZc/fZn7HdC/6lP/qHzNI5jutRNh1t1ZDtd/hKECcRH33vYz755GdsH1dcnp9QFKZxFycpXhghbWM8yvM9Srn4gcvp8RF3d3fkxR4hBFEUGZ7B1zvTENPQtR1N0+J5HkM3ohyLqioIggjP97CkRGOCQmfpjNlsZiTQTUye51TFnk5aCG1xerzkfjuy322wu4rIdxm6nLE3EFLbjQjEiE4l665is9kgbInl+JwcH09hJJBOZ/ab61seVo8Gw6ZH3nvxijCKWO9rrHFku7ozTtdZiqcclscLitxAXVyluLp6TbqY47guddsQBMYWr4cRrS2kEjS7isfHRwZLEMQh3s6F6bW6fP8jPrtes8sH2rbm5vNPKNb3iKFHa2vSDn+DIjbtQcFTwtGvWKOHv/8zmQ5orQtg+Utf+w//aR+n7TrevLkyYZxNw3K5pKnbyY11fHCgBUFgRkfdSNf1tG1HEHgEQTARauUBk6WUg4gs2qZjsTymahriNCUIfCwNd9zx7OIFRWFIOp4bHMqtqqpIZzPyPGO32zGOJhRiaG3yPJ+ScMwCj+KI+WKBUs5EyIU4jimKwnzvMNB3HVVZ8CbPOD4+IQojPNdDCCYen8d+lx8aZ59//inbrbEnO8rl6OiEph95+cElhmcvOHt2wePjIz/95OcAfPTRd4iTkGJbG7/C4DJgc7/JKAbBvhM87jP2dc8iSTg/fUmUxgRhiG3bVNmO/W5L29a0Vcdua8jF6QQs3Wd7Li4vzPRAWAdaUBRFdE+YsKbFcWw8zyMvjBlrv98bapGtJhpxyRdffkFVNpycHCOlJEkChKURwuQU+GGAmBDpdVURRqFBlVEbg5PWOMqmqzrK/Y5ZmnJz9Yax2CDQbLdbAi+kaTq0ML+T5XJJ39T0Q2vckhoeHh4RQ4Pn+8wWi+njHPXlW8axx3UUWpsKJMtyFrOY45NThr41PgLHPJ+2bZET5n6z3aAmMEo/auq2xZ7s6Mvlksdyz+3jA14c4C9Sym1JuliQzGKUNA7YEYHluTw83LFbP8C0IQkhp9AT/W538GAkOqgI3/FnvPv5r7u+FYrBcRzNn9505O/vHhjHkflszna7YbvZ4Pm+wY71AwhzvjchowaQaasJnjGVcMNkL7aVYnG0NEGlntlMhr7Hdh2OlsfEUUoQuERRNDnZeqJkRlk1RHGMq+RB8db2NcLSJjBDWrgy4Pz5swM8Y9CaUZvnIi1J37bsd1s8ZZOEAavVis16RV1XWJYkDFPKuqFtOl69ep+rqzc83N+zWq0AuHhxzvHpGWGa4HoRygtQyqEfer77W79Nen3N559/xieffo5yHJ4/P8J1PIq8QLlL0qMT8qpDKI+HzY772ytOTo/5zvsfcH68ZHFyCtJms9/RTuDPpqrpmxzfddhtt1y8eMHFi5es7q7MmNJ1cV1jQiqr2igqhUXb1kipiMKQvuvY7Tdst1uUbcJh3cmhuFo9cnd3TVXWDEOLq2ws64jj6ARHGRuuGaGZI0bbNBNj0sV1HPquI0gjfvLVlXH/1SV1YaZJm9124iRqIyEvS24f1ijgvaMzct9HDzbWZGTa7bZU2ZYkNfSlYRzZZTm73Q4hwPUcwiDEVi7rfUm233G6XJLEMXEcYlvgpHOuvn7D7c0NStqkSUqPmYQIKany3GwKfoBtO4RRRDqxMz3PIceE1Lieh+ga0ALH91nva37ykx9z//YNEo2QwiDZf3nxTAXAYUD4LmTkFzYKE5H3q65vzSZwc3PDYjYnDENm6Zx8v2foO6TwzMLqenQ/EIcRyXxp+gW2xPc9E6fFyDidiyzLoh9Mo2UcJxmFECaJNwwZ2o4wirm5fktVFMxnKX0/cnp6xth07LM988WCqszZbjcmMHSaVxcCirzkxfNnHB8fU1Ql+ywz8A8pJpgH2JZFHPjotmH9+MBQV4Z0XJfYtoXr+Wy3W6JIM4yaq6s3rFcrVqtHTk5OSNKIxfEFrudT1B3ShaIZEG2D5/nYXsjs6JQPpeLh9oaff/oVt2/f8K/88T8gTo5xA5f3lgtOFin71YoPT48p84qmqYyCMlQ4cUTeNHR1wz4rWK8f2W9WuEJT2Bbz5YLHx0ccL2CWzkBwGAWiBbPZbCL+fDONMWd6xWw246c/+QnZPpsatRU3NzcMQ89yPifwfbo25+b2Nbf315w+nnN6doYf+MRpYmhGvk9ZlNzf3SEnxNw4juix5/jolPvbK/TQUJUNSRySS6iKglcvX5ljWFmz2RfIpuL5ZUMUhlhC0w2apm2I45irrz5nvdnStS16PiNJjSApL3NzJEDg+yEnJ8fkuzV1U/Ps/Bm+azOODeMwcHp6CsPI0PVkWcYohZlcKYf5fM5stmB1d0+ajNSWyWFsalP12MqmrDv6oaPZZyRORNm0/PlPfsqP//IHjNkGqc3o0BIShinB6JvMVOCd3qDWf+Od/5evb8UmIITg8vJyCrUcefP66wNiTFgWSkp8z8f1vIM//YlXZy5NPwzfgBQmG3KiFP0AwzAyYDLjfN9DuEa9disE/ThQtQ1RFAKarm95cXnJ4+ODqUBc1wiUphQjLwyYL+Y4jsObqzfsq5IoDPExDsC2qQ3swrJwbIltS4LAJ9/taMqSuu+mjIKBphmp6obj41OWy6VxP2pTuqZzU4Y/rtbEszltN5gcPsfFkiblxvcDyqIgnc3MWJSO7W7P2fkJ/dATeC6y98nvO8rtirqs8TyHJA6xopCyaelEgWWb+HIpjBvv/PkZlgWOPY05ZUde1yRpgut6eFOj68lXYeLMzBTgCcLSNi1D3yMErFYPRnMf+GbuPnk/HMdMDUZtMY4D+92Wrm+p6socRZKRumkZ+wE9jGRNy6g11thf0sHDAAAgAElEQVSRHL8gcF9y+/ozHvYFq7s7ynxn+kmOS1PXNE2NoxwCx6aeOAt66BiblrppcKXN6ckJTWsQalLaIIzMOQ4DLG2azV1vfAzz2QxfuWZzKvdYVk8682l0Q9vVSNsmjCKkZ4AzThAB0Pe9yWUczR09SRKs0MP3PbJ9ieOY199NYgNE3Zbc3bylrQvsJ6zgJAcWk29Y6CnNSD8Bhw4CYQMlfQcqIn5Nv+Dp+lZsAkFg7v5d12FpzasP3ieY0oKV7ZBOeny0wAsC7GkTEJZASIthNOQha6LYDMNgXhKtDdt91EhGbAliMBBR14blLEEPDZbQMHZkuwq7H2nqmu3dHX1VYQuLpqpp6wblOgc1WFVVNL0JBQ18n6bIqbZbNvf37LMdvdD4sSn9PCz6PEcPNQwDWhmnmgmmH0BM5WtRoAGpHJp+BDGdDy2bsR8Z2xHlSVxp40iLAUUcJ9RNi5+MCN1y/5gRRnPmqU/VdUg/RM6XjPsMwhqtJH3gUbcDQ1PjDxZ6LAFNvd/iKTOhqeqKvt/yW7/zO4YW1PTGXINgFCP9MCBH+0AkYlLz9W1DttuZs3zos9+useioJqHWfJEiLYUlbXw/pO0Hmqqkb1ssMSBET98prLFlqEss26Vte9LZnDc3b9nt94gyY/GyZZa49Ayssh19lVFtbxFCUHemZ+M74Hs2c89jvV/RkYAQhLZiqWyULcmnYNYoCvD8gFbYdE2Hqitk4IElKDvjbYkdj2W6QGpoxgEhIBkEjnIRWOzznQHdSAvHdrG0oBuNVkUoG21ZOLagDkzWoxg0jpLYtkJ3I7brUNuKRkv6usSmQaMZLHuaCeopq1Cb6PnB7A3GG/DNBmCub/oCpofz69fft2ITEAIeVys818XzDEGm73qSOAEgyzO6vufi4tJIhMeRIDBMfNu2jV9+yiF4urqun+5U5sVwXRd/qgDMLNwmjAKUcz5tpQaL1RdGGWjZNpaU3N3dHeSvm93WJCQ7LgSCJIoRwqJtG+5ub1jfXPP49RvaqiKYRySi5+r6a+r1DmfQWL6P9H0SIfAC01B0XWP5XW/MUaBpGqqyoshzhOMhlTk2WNKhbmqCITzc7aze8PssIZilM8ahoa5LNtstyrZQtkXg+yyPlvRdS7bfUxYZeV4yInhYrfGDwOj+MU3RNI3ZbNYM40iSJIf+ipzm1E3T0GszNjPNUX3YFLuuoyhyqqrGsgT7bMfj4x3ShtksmpqnAhjpuoYs0yRyhuOaTb0oS+q2oes6xn7Ac30s2+DVL15cTj9LR1Pvya++4Ke7B+pyT3hygSNj9p/3RkDlOOzLEd9z6LqC1uo4u3xO1bYM/UCel3z43ntYAh43KxzPYz6bk0Qxb1d7hCWZLY44Pj0y499hxHddXHdOOp+RlyVJOkNY5i7vuApHKsa9qRyyLDcxZCczhmGgahqK0vAdpC0p65oE2G62xHGKkB75foe0JY7vUeR7k57MO5iwX7eK3+EI/KpR4NP3f+uhIiZi6uggFPH9gKapKcoSS1hEUUySGqWa4zhYWlBVJU1jHUwqehzNWG40bDsm1aEGXMfFdZXh4k3Ot6cpwtibLv+oR+q6Ymw68kmu+nQOjaJoChJpplGPpq4qQxYKIzxXkTkee0syCs1gaXa7DWWdIS0LS/c0fY/qFWLQ9N1gSk/NFLdlURb5ZP216LsOITRKWIYA5BrdghaCXo80U6inmrBdjmMmE0ijlBsnmTIC8jwnjmPS+Zy+7ynLgqbtDM9PSZSySJIFfdfAFFvmeQajHYQ+UlpG9y9NZkBVVaR+Oin+1DQT7w+vqed5WJbFkJvwUmkLEwnvGYpRXdfEcUrg+QRRDMJiu9siMCEv0pbfvGkFJq5+tzcpRMomK3Lk0HN9/wXZ9pGm2vPSiXHHyjATkpBu6JFSojFjYdsWZrQZp2w3W+raNOBGPeC63mT0sdBCMI7aNJGnoBgvDOnykjzLqYLYNEa1pmsrlOvg2R4aM/7b7/ecnZ1hO6ZisidOxpP/w6hONadnZwzjyM3NLRdSEcQutrJpmhrV1IhxJPA9BmXTt7+8Wn59af+b+HbgW7IJSCkPi7mqarqhx/V9BILl8hhpS7As4ymwHRzHxnWdw0iwruuphK0pqsqUpwjkJEmNE7CsEUYbx5XTkcGk8bZ1acwvjXH1WaMgyzPQmvliTuCZbvg+M4upLE2cuO/7KMelbVqc0DNhmVXB+uGekZGxh75qGITpRYx6JFAOvudjWZKmaQkSY322lcmzH4aBoe/p2hpbKbqxoB8FjutPm2KB63smvEPaeK5zGF9qrZGWRGibpm3Ji4JlnyIQU7kbGX9+09J3I1VtGl9PopinMrauK8IwZJ9nhux0do7n+7RVychoDFRtC0IyTlFjfd8fejTKtrGERTcMpv+QxCglsCVY0sL3XaIoIAgjlONhSQPrWG/W3N4ZiIdSDlEYMjKC0MwXKaPuacqa1eqR0PUpihIQWFLx0x//mLOZTxxHeJ5HWzZEcWSyJgNB4jn4SYrvh2S7gqpsadoe15mmDp4LGuqmZrvd0jQNbhQf+hxxFLHvOtAjbdvgei6tMJOgsipIkoTNdsN+v8f3A4IgRNouVdUcJl9aa/I8J03mRLOUzz79jPu7e5azBeMo0ULTVRWdk6P7Djk1+Q7X0xRACPS7dmHxNy/+/1+MCAGur2+wleHkXbx4MZFxJMlsjuu6EyE3IopC3MnIopRi6AejutKmSTJ0xm/wNL6ylc3p+QnzeTJZhI0hyLKMSnEcg4naK1C2RGBNbwoTcFpYgrquqcqS3WZLP464GC+/bUnqOqeUECzmvAg8XKW4ff2auszpx45hHKYpBwjLJwoTovkML4qxlYNyjAW561pWjw8mjajKCQIf5IAfJ/SduXOvNyuEJbBtZRqCcYTnuOhxpH5y6kkjvnpSrCllY0mLOE0oq4q6biiLkv3URGvbdlIqtjjSVEp5UeAoh2EcvqlAqpowNu5MoV1c38MSAs9x2e+zA1V4HCYasbJZLud0bUHXxRT/L3VvEiNZcqf5/czs7c9394jIyLV2Fskim93NJlvqZQYzamimMUBDgNADnSRBR+muuek6VwE6C9JcJOgmHXQTIAiSMI1ZurkVyVqyKrMyIzLCw/e3v2dmOtgLz2Sxik12j4BqA7Iy0zMqKsv92fb/f9/vy7Y9IEQTJwGTqZuUQnpEUYBGUzUV+4PDbTVN7aTJGE5PT5HSst2t8T3J+f1zHr+4YBiHpIlPfbPm6Scf8d137vWnEs10PmezWbsiYNfyYHGK0dC1Go3raCR+f5Lqf768uuajjz/GdB1+OGMymbDPC/KyYjwcAIYsz5ilZyyGQ7L84BytTeXEa76iKEp8P6Jra3w/dIlOArzAqQiLvCBpaj755BNCobCdZrm8cnFrbUMY5GS7NbvNiqau+qwBPqcLcATul7DRX9YG/CbjK7EI1HWNUh7T2YzpdMpgMHRXHSFpe3x1FDn/uTUWY/SxVdW2XX8i8DEGuk7TVDV51qO/tcZoTdO01F6FsR2Ejn/v3GwOVR74HkZ7GAN+6KP6o6RpO4rCtfWm0ym73isOkrp2IhHddlR5gacUd+4/II4T9qsVbV2hTecQ2rpFioAwTkjTAePZjLxp3U4Uhjx5/ClSSuqqwGpNXVX4cR++YjTSKqqyZLdzsFDFkNJTR22F7znWgqec9FdI0ROGXGXb9fMF0vMYjieI/srSNK3r+ecZVdsQxRFNXRFGEePxhCRJaRvnuNM9wl3rltfmi55aRI97l32tIkDojrpyFtnxaERRghRO1enCYSKUErRdixDmGCwaxzEISRInjgKEJcsz1psN6cC5M70gZLO/wfcMm80Vs+nEob8GjllQZ1tXHKwq9//meRjcgqg7Zzy73ZmlknRtRytbmlazz3KnafAU9Su05N3+gNE+YTQgjEO8wEP0daXA99hsNrx48cLxD9oDaTpkNHQ8gcFwyD7bkyTpEfH27LPP2G633JnOKbKcymhQEEiBtQYpQHftsQP46p3efuGi8HIuSflSTXg7/k7gxaI45u2vfY3BcEiapHTa0DRtr8yzCM/1RbS2VFWNFC6YBNykN9rSdU7ZVVfNMYCjaRps/0B4vkRJaFqB7e+zDljhii5aO11/UVQMUucm86QTe8RxhNYd1XZPmiaEYdiryA7EofPFa+kAGypKGMwEuoP6cEDrDhUkGCWIgogwjAmSGM8PiKR33I1dQIfsqTGWtq3xoj7GWznbsBSCpqocoMT3qQQQaZIoOfL/u7aiqWusSfr4tuiYASilQ7RHSYLWjTNWqRopXV1CSEXgB470HLqrjvJ8Ou3i3NNh0t/5oW0bhAjR1gE66SGYt3mCjgqUYvWcIJPoHiKCkHheiFQ+2kJdObNXVVXkZdGrQBOa1oW1tJ1zKOZlwWZ3IEkHdJ3GVyGZlly+WCGQnE3njCcLclOz3R24ur5mOh0TIgiDAGssdd3Q1C1SKLb7HcNRT0cSAmOhql0wahqkhL0icDwYo6SH7Ry12eJMbGVZYTuNikMmk6nzgAQ1UrqE5tFoSFk31L0s2bhdjUOW8bPHPyMMfZRUFEWBP3SJzVJAHIWMhynCaCSgX5nALxWBHOEht+2/lwvBLx/9/060CIPAtUw8P6DVBqV84sTRfMui7GnDmgB6QYpzEXrKw/ckHcbJXZsG3Z8SbhN1VOBRVu4EUFcFcRxRF87WGUeOR2e0xhqNUAprDV5fDCtL18OVUvQADOMIPdodKa3RHHZb/MjZWUMLTd1SlQ3SCwmGbnIMfB8v8FEC/DBEeAqhJG3dcjjsaduO4XBAW9e0TUVRdO5U0DSkvSxcSYHVLVIJTNdRlTm6rejqCF8pojgGLHXRYLRGWCiKkvF4eMR9Hwtg1rX4ttvd0Z47SGLqusGTTksR9BMnjhxb8eqiIUmcR15KXIZiMuj76N3xpHZ7QovjmHSQEvqKOPHRXY0QIITCNWyhrBrqJkNI4ZKV+kJuVVes1hlFkfVpSxJtJZ0RvPuN7zAanfL+j39E6AlsV1FWB+Q4JSssaTpmu3Mditdef+ii7NMxvh+y2+QIBEma4AVuYRwMBvhBSNUaXixdXsLgdEaaRCjl4Xk+k/GEqnBBLMqT6N6ghjYEcUzgB9w5u8N4NHbpym1L29WEYUhRupqTNobpbMHzp8/Jthse3H9ErEKarmGSLvACH7QmjEPCMHCLgHXx5PZztYEjZvwWJCJ+cff/ohrAS0rxL4+vxCLQNA0ffvQx1kIcJaSjEVprB3pI0p5b7ztHFC6ocjgcHT1UxtjjEQ9c5l2eZ1hcEAjC0rVQV0XfPw77RUT0R0aJwKLbrifYGNarG6xxGnOBpWtb0iQljlOqtuFQuO5AW1fk2YGiyClHJUmSOvKrr3pOPCAE2jgGvh9LhuMxBkNRb/E8v29nuiO8xXDL57cCgtAH3BVIdy26P3Y3VUkDmLYFLNPZDOX51FWFFO4KYY1xHQipUOpWQ9H1OYQly+sly5ulKxo2FV1b07UhYBmMhujOTco0TRmPx7Stiw7vtCXs8wduJ/7tsNa6Qq6FwA+Q1pDYFKNvTyMKqQL3HsmKoqzY7ne0umM8nSCQXF9fcbO8pmtdHWiz2SK9kD/8w3/AO+98gyL3eO1BxzJcst3vuLx8wuOPL7h+UvMHv/sWo9EML3CORNtvFkpK6rrCGMPDh/c5u7OgrYtjMXObZXTGgJAEvkcQBkiluF7eIKwk8ByT4DZ+rW0aPCkR1hV9F4sFq9WK/W7HfncAAfcfvO7CXIUgSFxoaxgFRIFHHEVILWk7t3GJVqCALNuT5Xs8IbBKYuyrz/YvT/AvnvC/3BL8/ELx6vhKLAJSSMdbazVRHBMGDvpZVw2RH1DXDUHgEwY+WIPuDHVV0ymXRVhVDgfWtu1xEnfaHfV1BxZDVWrAOJuq5wpnWZZjug7HenWnDE85oqzWnZN3lhXgbKvauO8VRRFl4+6W0lP42qKbjjov3E6vFMr38JTEakNel/heSDpK8OOIMI5oug5tNFVVOvSYVM7T4PsIKVz+nHWpPtZYutaRlWzncNhtWSCloC49ijKnrkvSdMBhuyWOPPI8dp2LwN3Db6vUty0/33eBn9PplCRJKPsilycdmMUYzWg8IopDd1+PYjrTUVSlSy8Cuq7trxLiaBOWPfXIaosnPWwvCsJGTlCkFEjV8/R9jAXpecTpgLpuuLq65sXVC3ZblxHQaRft9cYbb/Hu179FXjQ0TcNiPmK3W2OwZNmBYrvjxeEKz+x5+603mM5PwFqGaYiQkq5p0V3HaDjk5HTB/Qd3WV5dkm1b4jghKDqKoqapG9I04ezOHaIkIa+ts3n7ksFohPID93fvaxd11dK0NcNBSlkUbDYbl+w8n6FNQ5K6NqiKAkxpyMuC08WC0/mc8lAj/AFFkeEpD08oaq3Z7jc0VYky5hcv/L/m+DspG/aDgDdef5OyrhBSEUdpLzzJ8aTrP3t9EYoeqdi2HXXt8uq1NsdcvkOPrnatM1cpLyt3rFdKHJn5brW0RwXg7b1Zdy1F7uizjlNYIKUijEK3CCjpCj6DlKryUEbTiZo4ntLh+uBe4CM8j7qpsUajTYePQ55pq9kd9kdsdBhGLqSD8sgSsDiARde4uofA+dcb7ai7nlRILJ3p7/mBT1kWpOmQtirBpNRVRVm6q47qXX+e5zHqE3sCPyCMQncCEQ722lSaunbS4jzP+vTlFm21a9m2NaEKaJqGMHQnBq01ynMngVtKkJMw697f4SgrQri2LUJgEBhrUcoJr6aLBTerNZ9++oTtbstuv6ftWhCCixdXfOPr3+K3vvPbSBVwyHYMhhLZtMSpZdD6lNWBqsnouoa/+sEPKfKM737v+4yHMXGYUgtXFPU9F5culUuunkzGVJnTZwwbaNqOpm1QniJJUpCSyXRGGMQU+QHl+y6LsWnZ7XaM0wFl1VJUBUq4ZytNEwdg9V3hOQh8mp5JecgyPnr8Me++fpdQKfzhkGiUcnlz6bQh0tL0dR/TtggERt4e+b94Yv/irt+nEyF6p+Gv5yT8SiwCQkqEUkjlxCZlWbjef9uSjEf4od+n2bo04sAPUFI5hVld03adi7LKcrJDdhSyiJ4TX1UFVQWzuYsUvz1uB/09ve2aXmzUUJUu4svFanUkg4HjGXqKKGmo6tZxD4BAKmoE690OPy/wfB/le9i2xXjuIXABHS6ssiwbAiuRKuijuQV+4FNrTdO6aDS0wHYttmkc4qzO8T0f3Wr3wxgaY1zycO3EVJ6SiK6gKQo8JYkjz/EWgbKuGVhD3DsoXQEP1nbLeDIjz0u6VrtU5dEYJSye56M7Q3bIGRwK4jDBBXIIwijsoS8RSknaThMEnkO8KwDdP4aGMPQxRvZdGPcwdtYRiJQQ7j2NQrSF9XpH21oGgynpcMzNdYkUMEgnnN95RBQMXct4NCRJQn768V+6jaGrEU3DIIgxdByaGz7+9AmLsynvvv02Re3hJz6+lKSBR7a5oe5yDnWJFw+wxgOrHFRFgbAa27Vsdns6YxlPTxnECb61GC+mthKFz8WuxRtb4uHUWd3pGKYp4WhA+/Qznj57xuLOeX/iURgL6/2efLPEe+11doeCwWRE0eTcLG/wpcd8ukDKgLbt3UBfMm//OgUgvJJL+GucCn6tRUAI8d8D/wS4tta+1782w+UOvIaDh/y5tXYj3N/uvwX+FCiA/8xa+29/1fc3WrO6uSHPC6SnjnHccRwxHAyg36mTJHEFDuvqCFIpJyutXSW5azt83z2gjt6j8SMnLJJKHIsjRVnQtDV1JVFCYNGYrumPt5KydrqDsq6YjCZHtVcYRWgDRbGjaSqk9IiTmPmJiwPbbXcUO3ctSdKU4WiEEhLpB2jrJnAYhsheCRhFcZ/+UyKkpTMNnhf0VxSN6TrWV88IwqinJDkZtLa2j8zqMG2HFJZiHxNGYxaLE3SbUNelaw/qFmv7a46netCnZTgcce/eA6IoosgzdFsRBB5xGKD6rAApJbrtKLOcOHHXMatdrzyKHFzU9xRV7QqtUgLG1RGUdK43a2XfNXD32kB4IH8xk7DrDOPRiLvn9/DDhI8+fUwYJ+SHA3/8B3/It7752w6ogbP3CuuTJGPKbE9XNSSBRxrGSBszjBu2myWb9RKlvo7jL8Bnl5esLi4JA0WgJL6U+NJjNBoiPUlRlXSNJk6GTBcPmN95k8aAlQGNGiKHY3IEwvNJBmP09MBSTLgXzUinId1hC6VkfjrDygGbi2f96VWSJAOQHoesJI1CkmhI0dUYYXl+8YwnT58yHY6Zz8/otOVmtXFv4K8o5n1xxmCvHejtRH2f/VhM/LLx654E/gfgvwP+xSuv/TPg/7DW/nMhxD/rf/9f45iDb/c/vo8Dj37/V33zuq65vLhwR3JjiMOI4WjIZDLBmj4Isq/mto1j0RVF4X7fti6xSGukEnR1S1u1Lp6safAjH4tmlDj09fV1ThpFRHHIeDhA+J7rdcvbieVW2dvvH0Wha0v2/ddDfmC/39N27r7uBQHzxYIkjgmCgM16464jt7HoxkmEPd9nmCY9pKLA4IAiWmvCMHDdA09xyDJ3jbEGYQ3b7YrAD0C4K5AU7tTkIByCrm6w2lCXJXHcMRrEVLlPkQW0bYXsN5Sua2ma9hg75vf47zwPwWqyfYUxLoh0OHJquSAI8Hzna5jLCWHoUomLQpJlGaPeF4+9DSR1V4CX1Wzb463ELS27N3059+dLpaNhMZ2C8BG+z3y+oMhzyrzi3vk9TuYLNts9dVU6Q46wLBYnHDxBftiThCFhIEF3vP3mm3z2BJJ4wDAdU5QltdV8+OFH2LLhwd1z0BZhDEpaotB3qsbVgTidcnb6kLtf+x2mD76OtpJNVnK1cWTpMEmZTsc0DDl/+E3KqqbRln2jaHXC1WHL+snPGCjNxI9Iwxg/jAiCGBWE6NYwGo2P1ziMUx16UqKkwgrBi+trLi+euxBb+MLTwK/yA/xNpMO/1iJgrf2/hBCvfe7lPwP+fv/r/xH4P3GLwJ8B/8K6J+FfCiEmn+MO/tJoalfsMVoznkwYpKkzrGhNmqZURUnRdhjtdkKLeRkRZul94pKr6yvKogAhaNrGObC0pmmdwMWBMyxSWKI4dG+YdIjuqnAFQHCaem2c5XU4HmHM1tmRTedaiL5P25X4QYwfOMTWdrul7RHUSZJQVRXGGqwxNLrGYLDW0LZOYGS0diIgKZjNpzRVwSaK6BpHrQGDbWp0U9PoDqkkRhu0NVgh8DyFBExnsNqA6WiEYLe5BtMSeJLicEBY8wvtId1TgJSUFEXulG6eoixLtG7o2oTxdNxHmjnW43a7ZbVaMZmM8H3FeDRke9g5RabnHiHZ30aluL0MvLSzGuNanL3u1Tk7efnASgRxFDEaSapO884777pKuuek4avVDW1nKdqa6WLBYVvQNQWH/Z718powUGBbyuLAMH3AyeIEo1vOTk95/vyCq90SiSWJE4ZJzJ3zO3iR4zoGQUIaj1icwe/+/h+TjmeEJw+5zDuquqWqOnZ5S1Npmu2K7bYmDCOmswWeUeyLHa2UbFZ7fvDjj/jgR3/BxG95fT7g7bcfukVSQlkVVGXhWtcCFrMFRnQI5QA1Qgi0gBfXV+z3e/ceWfPKgsovvGevzM1fc+L//1MTOHtlYr8Azvpf3wM+e+XrnvWvfekiEMcRv/2db9P2IE7Vw0WX19c8qx1LUClFFEVOnuo7mEgchQgLdVGy3qyxWpNEEYfsQHbYsz/s+zz323trhzWa0Pe4bhrauiJJY3wlnaVXazwvwPc9ksQlDF1eXiJEbwZRgtPTU+pRjbWCoqywUrkY6yyju74m8p2X/dZXL/tOgTGG1c0N4/kcXwpiP+xjyp1aL8tzpJSMx2OKw4FAhAjbUVcdbdOgpAJhsFZjrKXtpGv/CYEwoDtB2ZR0VcF+fUMSBZiuwlcCTwl017oHBkFVuQU3jMKeBFSjlCTPndbi/r27ZHXdh8BagsAJZ9xC4U5fgzjBdtqZbcIQjKWra9Rtl6DvQsDnHtSj1r23xQqB9CxCGqx0dZKvv/N1kjBhuVzyydOnfCYu8P2Ad7/+LhdPPqEoOpJQsV4tefHiGR//7AcEyvDW6/dp6hxfWpTv5NST8ZDZ/HWizkNZwSCNOTm5QzAYUxaGYDZhJyNKJbj7xjeQKiSrJDQNTdNyvVxirHNR7g4bXtw8ZzQec7V+zm6/42x+QjjI+Pjxx/zwZ3/JiycfMKBi98zjz//8P2I8T4gHA26efUoy8JlNXiNKhpyenaJtxzLbEIQXlHXNNi/49LOnVKWTC3/RtP2yReFVhsAvcgUtgs8RSD43/p0UBq21VtxWfn7N8WruwHQ2J02dLj3PC8JQ9ek6FW3bUVWGyWSMFa7XnoZJL57RFHnO4bCnqUv2+y3b3ZbVes1qs0Z3HWEYozwJ1tI1HZ2u2W63zOfzHpJ5IPQDkjRCeBIrOOYdlGXJaOScX1IIxr2PYTKZ0DYt3uFAq12l+9ZXvy93DnbaT6CuZwwKnAW67cGok8n02F5LBymL+Zy2rthvnaa/rGqkNWjdYrUG2WHFKzu6aQGBEgKJxBrjKPRKYY1PVeVgDVK6rzHWxZc7DUXOzfIaozum4zHa9wjCEKV8tG5p25bJZNIHiFa9/wDAHKWvXdcdCc9ODNP26kuDQKCUf7xC3RZiX/nsb5+bfiEweB5EVqBGQ5pG9/i1Af/q6i8o2hLdtgySCIC6EYyiKXEYMogjBC2PHtznG+++yX5/zWiUOqcgriUayoD5fE7btoRhBEHCobIYOWDfNlxerVjnJWE8JPZ8hAXdGbZbhxnzPZ/DYcd0MQHGXC+vePLpY0c6Khqk13L1/FPK/SWeqDB1TTwbMhhNiMIUq+Gw34HRjMdTRBBQNw1YQxInnHr1bB8AACAASURBVJ2cUGQ1zy5fcHOzBt29vNfzixP/qCH5AorwqwuAxEFIseJYGviy8bdZBK5uj/lCiHPgun/9OfDgla+737/2+b/wMXfg/sPX7Gp1w2azIQxDTk9j4jim0xq/032WnSv2WWs4HPb4voeHZLtxDEKlnK+/KAq6ru3v8q6/v9vviKI5aZrSth6bzdZNxNFbR/eeQNLUDZ1uCJR3fFPLskC3xqX95AWb1ZooiSnLGj8IieKhqyHIjKoosNoc48xvbm5cfHl/NDadRgpnUqrKiiCKaJqGNEmYzRy1FmO5ePYMox1QsutarHapuJ4vEVJirO4fAuM07MojimOXcS8EvidpqgrdthitqeuGoqq5WW3Q2pLlLm+gbRpEjwNL4gQ5F+z3W9rWWYOduMjJt+u64np5TRwnLBZzskNxfOhuU3GcGcd3UfO9XuB2fNGRVUrZl7IMooeHKqWYjMZI6VOWNfvsQF0WKCH5wV/+W977xjcJo6Hzj3iK2WTEn/zDv4egwfPA85ym/2az5pAfejRcw+LknJvrJdKPIR5T15AdDB8/uaLqWlrVS7K1QTc1m61D09+5e97zJ108fRwHNHXFZ0hCL6De7RkONHfHAfHrZ3yYXZOOYr7/e7/nCFjaYpRBWUUofMIopmhqdtstk4lLTJ7NpjTNipubJWXd9AW9L99Tf9Xk//wQ8AvMgS8af5tF4H8D/lPgn/c//6+vvP5fCSH+Z1xBcPer6gEAbefAlEHo0XYNjx9/SJ5XpGlCOhi7Cq6StKZzgYxty+bmwGdPn1JXFU1dsVwuORwOeEEfiy1d66xtaqbjEcM0JYqjnv7iVlKtXay2tZbLyxcOSTYagrRMp9Pe2lyidUtRNHiexCLYbtbEccx2s6JZrrl7fofQ9zg/P+NmuWS73SCEexgTKXvWoWU+nUOvsPM8jzIv+l216a8CI9I45tOPH2O6FgXEOkUI03c/WpQv8bwAgaZtWqwBP/SIwggZBJRNR9c5mo1SiiLLWC6XrHc74ihhMpkySiLyLeyKAqxmmJ4ynU6xdsz52Sm+77BoRZG5WkrTEIUevvIIPI+mqolCH328avlHdHfb99lvYS+3p4HbB9dZl+G2ko21GOljpcJ0oJRHFIT8/Pljnnz61BUShTv5NGWJ7ylW2xuS8Jw0TZjNFmTbjOWLKzo94ru/8zustzv2ZYeXDmn9kGgwR0UTHp5/A+n55HbE5WbNi8s1Ty5fUDY5BktbdShhGY1SppMJVVny0U9+gOf5Dr5Su02mzA+0hz2X10tmnuUf/MnvI9UEI+5S/tbX0Z121uzQYqnxpaRcbXgwOcWPY6rtxj3H1jCcJZRJwoW9YbPdU+SFCx7t6wGfP0V9fnz+lPALrx09Axbs31IxKIT4n3BFwIUQ4hnw3+Am//8ihPgvgCfAn/df/r/j2oMf4VqE//lf+x+w0FQ5u7LpC1YlIMlsx/n5OUWZHeO9pHDy3xfPL7h8fkFdl2y3G66X1z0taECSpAyHQ2e4qWtGowFSuUjtqCcMRf0uLOztjmexXcXWuF32lnDkSUWOIPB9qqJ0u36WoZuGvCiwKuCw3+IpRRJHhEGIaXPXmZGy758r10MOQ/aHA+vVmrM7d0jHL4nJURCgpKBrWpIkpipzjHa5ilp3/dXBHJ2SQriMBbebQte2BL6PEBIpFUmcEAYBm/WKDz/6mLKqODk9ZbNaMp3OOFnM8KRkt9tx+eICT+KSn3vM9nq96rmL2sGs+9pAmibHxOAkdSm9ty7F2/g39/9ukcrlDWJ/kXdnbpVwtsfAGRdtrgJB01iW1zf8/OcfcLNeE3o+ldH4UrFvaj55+hmzk1Oqpib2Fefnd3l/9QlB0vMJ/BgVdExO7uEPZsRxwnx+F398ivQisiznZr1FGx/lBwzGA/Ll3lGgdzlxGCJNi64qsmzv0pj7YrPuWuIwZL/bsr255mQ+5bVH54S+ZTafkdcVv/Xud9jud/zwZz/mUBeMRgF1V3FzeclbD14jTlPWq6V7DuOI6pC7054UTkTWR+v9po7gL6oVCGtdXuSX1Bdux6/bHfhPvuSP/uEXfK0F/stf5/vejq5zbb78sGc6nTtQZdP0Ypb82K7zAif6uHzylMefPOYWG321vKbTHXGa0LQNoXZ466IPBK3KAt8bUHfuPu523SHj8RhPOqYeBxdPlZVbTk9P8TyP0PfJDnuGwyG668iLA0VRMpmMWW9usMB0MSLs+fPnZ+fMxhNWqxsOWc7l1RVlWTEYjpwpRNB3GHR/7VB4SiLBZSeYjq5pmYxH3CxdFJl/i+HG5e7VdYnt7buqNzWZTqOlpuw00gt6iGdAFIZ8+vgx/+Zf/QWr9YqTkxPu37/P22+/zcnZPYajFIRhu91QNR2eknS6ZTRKwWpeXD7nwYP7hIGLI2+aW+ehSyu6xYMfDge63kSklMJTHvKlERbncpPHnU0qd2N1GgKLwsMIQdsZtNE8fX5BlpUUWclhc3BdByl5592vMxxNEEriByGe75OkIXE6AjRn9+7RaI8oXfD6W/e49+gdJtM52vo0XsR6fWC73oPv0djSnRaloK1KRoOEcZyQZzm7POPixQVd06DbhqzPVmyqkjSKkBjOJ2O++9u/xd17Mxb3TpCeZDASEAx5+NYDbDqkMIJYenSdpq5r0jBAKMXJfN67RgU3N0vyIiPLDux3GQiB7UExv/IM/wXj1YX2eIrAPSuvovc+P74SikEpJB/87Kc8fPiQ7XpF5IeYtuXis6f8y//n/2V+suB3f/e7PPn0E0fs3e7JdjuultdkRcZkNmU6nbBYLBgMhhhjeP7Zc4SF87MFyhd4PkjlU6E5OTkhSSOSXhkI6ljsCqXT2gshekyZ0zF0jePfK2F5cfGce/fu0XaarilpG59BErLb3rDd7lBK9QtGRhQnjMYjOmN4/uwzhsMRj/o4r7ZtKFYZxmg8KQn7CX/37im73Yqbqws60+FJ3wmokKTJAG26PteuQAmPOApc3UD5RFHCG2+8xb/3B39AoCRWdwRSsL6+ZHX1nO3NJdVhw8M33mF2cse5AjHEaQTCEMcRgzRmkEYIYaiqnCAMSMKE3fZAFEVusksXJ+Z53jHSPEpifN/HD3yUcuLV2+O/6DsB1thfuu96QmGEopYd/+bHf8W//uEPaIoO21run5wzGacYCf5wzPzkjDt3TliMF5jWoNAo33L94jOSJOabv/3v48dDgjChbFpuMicaa8yB0PdYLCbsiz2nixmf7nKuLpYs5qcUVcZ6tSSIA7abFUW+oyxy4jjl0Ztv8OjRG3z3e98jDkO26w3DOEBg8NOEQ9tyaBqaDrabkGknGZx+i+FiiJSCzYsLOqtYrdakixHSGmbzhUtfXq3Z5weWqxV10/ZKwVuY6JerBr9s/JJM2PZt2a/6IlDVJXEcc3NzQ9u6Cup6vcYaOD075fU33sBi2e92VFlOudkcLZuD4YDz8zsEYUBeFDx79pztdst4OObO2R2iKGKzc0Se9XrVBz0ER0DGbYCpJ6VTyPVUHikVpuvI87wX9XQc+hzDruv6O7ek61wMV4Ez6eR54cCcXsCdO3eomxbPU/jK2ZulkqxvViSDFD/wqGqXqhN4ik1ZUTcl8/GER48eURy2HHZ7tJLOVRmnWGuJowSpBLXnaDVpEiEtjKYzvvNbv8PXvvYui8UZdVNTlQUIy2w6Icv2lPmB508/JYgHiP7vFPTeg2EPwmjaBms0dVNycuo6Im2rjwwFp95Mj+j3uBdKKd/D68VXom8FCl49qtpXWoQvdyxrBAhJXbeUVUPbaZ5+9owQj5NhxCgdsi8OSKWYn91hOHJH/1ZXKKl48423SZKI+XyKHyZ4fkxetJTaslnnhMKjkB37oiXyBHWeOSNSUTKZLRCRZVtu2axvEI3GtC2jKOLhnXPe/NrXODm/h5EK7QVUXsDpW28iMRR5RtcpwkFMaDpWN2uU8dH7jko0yHFA4HtoEXF2974LOM0yysLVguIkZnOz4s79c7ZaILynrh5gNNYKpHiZKvSbji86FXzZ+EosAnVZ8eFPfsB0ccJoNGK36dhtN0ynC3ylKLI9Rb5nu7omDgOsbbFCMxql+GFAGkXEYcTT6yesry5RvocMBZWqsEVD4Ena/MBhdc0KixEdX/v6N8ibrNdsT7HWsl6vSYOQtqqIfB/hKZACazXS84h6O+hrZ6fuQ5KSMHTMACyOz5f41HWD1g1RmJCEPrqrsK1gkETs85x0MMBTPl2j8WWI1TX7zZbDbsPVxTOa8zPG4wFvvPEa77//Y7rOYdGVcbtm2FlOhgtq6Qg3o5kL83j08BHvfPPbjOcLwuEYvzOMTu7if/oM6UWEoQZhyMqCzeoFd+/ewfcSLAItJPs8Y7dbczofEYUhngAMdK0lz3b4nkB3FXlx6JOGR7S668VIFl8qQk85oYvyeifkK3kQvaLd3VH7HUpAJ12XQ9cd+XLP7uKGYrNBjgbYwYgmVCTJCaPRmIHvM4xiPM8yGIzQxhIEU9LTBwgh+PGP3mc+7/r2Zk1VHihx7cuiqGA8BjzCUHB+95RGV7RtS6QGBPECL4G7d+4wnkwZj8csTk/xfJ+b5ZqLz64dfzAMuXt+hyiMaMMDxWGHsIqgbgnP5ngSDB1dsaOwLaarsL7HXhgunlxTf3jDe2+/RycrposJBh8rQnTb4oc+bWeg6ePf1S/Ply+jCn/+z7/otS8aX4lFoOtaPvjgA4aXlwjPJx0MmUxmrj+bO3vmw4cPee3hAy6fP6euK3a7LVlZkA4GxFHARZ5z2O3xpMT3FJubK7qmJI4iuqpkfb2krHJE6BMNEnb7PW+9/hZp5BKJdNchJYShQ3DfbNaEYegy8IRAYlHzOYfDwVmWvQCsQFiHP7fa9AASJ6ltGmez7VqnGmy7jqwoHA25UazzDIFbSK6vr/nwZz92Cr6mZL9ecvf8Dvcevc4f/9Ef0dUNVZ5B3TAZjR2KbeQswGEYMpvNHEUoSRnMpwzGE6LhmLrTnJ3dIR2OaLXuabqaoqi5vnzOo4cPmc1nqECQDkcUeebaWdLN/sV8ihXO1uwq8SFGgxf4DEcpTdf2WYrhUTlore3jMh2chd7H7mAwrgbwKgLLWoun3Km1zHMuLy7YbTcEkcPDv/fee4wnY1brNXfv3ndo8yZ1cV+3QbTSo2lylsslnq84HA4uwLRxXZeT0zlhGDIejwGOqdd13fDgwQOs4GW9Q4qeHVmz2W37ek7EYnFC27rFxGrD+z/5CdvtlrcfvsmmOCB8j8FgyEc/+hHSaKzoOL/7kJPTO1RtwPv7mE8//YBm/SFvBQP+6v330X7Hyd17NMJD7HKqfdYXVV0kxa3ZC3hlIf31x+3Xf/49//z4SiwC9Io8148umYynbDarnu7rzCoXFxcsFguKokAoSTJIUWHAYOQKfIHnUxwydpsNCIvu03riMGSz2bA/7HowR8Ph4Oyjt7x8hXuT5/M5WXY46uZ93ycM3cQOfY9VVfToK42QiiiKsbp1R7yydGYhKfu2onG58wjaViOkKwB2dU0FhGHMer1GSkmebZ1GAI0SgrZpqOsGK+D89AzbdFR5RpPnTlKtFKPJyB3JlUeaRFggShOSJHEAFqDVBi8Imc6mDAdD6kpRl5pWO6HK9dVz/DBkcXqHdCjAtLRNRZ5rhsMh1voIYfF9Sdc6vftwmKI71wUIwujlgyYF9CYlbkVCt/98pdX1qnrtFgIjelhMFDoF470H98jzgiiJGY5GeL7PYnGCUrLPjnTXorquubx6wWQyo2mafuLXhEHM3bt3+fGPf0ySJI5ylKYURUFRFEz6MJsoCsnzktFkTJ7nPH/+3PEVgwAhJXlR0PXWc62ty4jUhul0zOuvP+L5M0XTwTAZY6yhrDqer9e88fA+5ycLPvjgI/7y/Z9zdXXFR+//a3ara6Tccufd90jHI1QkiAJFVdQ0WruMS2Owlj5dyHKbQPzXLQBf1ip8dSH4svHVWAT6frPLra8pq4LZbMZyee1aY9ZiuoYXF8+o6wIpQHkCaZ0PvqxritLp4JUUTmTk+1SHHN25h8NoTd3UEHgIIV3qr/RQQhH5AXEQUrVOLNQZjn3vOAl7oq9GKIkCpBEu094PmE0nVFV5rBsUecZ0OjmyDpTyUaqlLJy3P4kjyrKka2qKbEeZO8oPaNc7xvXSi6KgyCvSdIiMNJM0Qde1kz9rjRcolBTESeQyGaMEPwoZDgeoKMGoAM8IsqIkywrquqVtHZwlChVxFKCEpSr3GD1Fa7fgGd0wm8/werFPWeY0jWKcJkeC0O0JSRvXXhWe6qvdqv84resO9HUtKwRSyP7WJF/ecoUEKzCmwdqO6WzMvft3+fTpBbPFgpPTE+LE0aCHwyGHfclsNmC/P2At3NysWW83fPTRY6y1zOdz8l5+/fHjDxkMBhRFwXK5PE6Mozuyrwll2Q5jWhCSyWSClOLYCSnK8ljZV0oxn0/ZrNaOLRlFPHr0EKMSPvnZzxkOh7RNQ5DEXC6vWa+u8IMILxJYv6XcvqBaXaC9jv/7L/4CP1T8B3/yR0wXY9aX13TG9P4L67iC4kgF+BtMp1/+t77yi4AQOLBGWzCeTpDW8OTxx6jAB6R7MGczjLZEgY+xhnQ0oGob/MBnNBrQ1AXKV0xmk2PfuqkrGlOBddLdruswwHK55P2f/JRvffM95rM5UkqevXjq1GrDCV3VMBqNnFdB+ezyjK5uiCJHvE18wYNHj2iammy/wZOKk5MTp+vXLql9t9ux3e6chsDzGY4SmtaFgYZ+yvL6mjrbkN+21zxBXTtNgVA+s8UpYZIivYAo9vCsIRS4yLT+Du6OiLoHozj+gTYaX4HwPXwruHfvHhdPT1mfn1IXB4RtGKYxnicZDoec3zklDAS6LtCmI/IVm/UVt6CV+w8eUtYdWZFxenqKla5VGQSOTBQEESoM+s+xn/XCfahKqaNgxdhetmZvJa/9g9n3sKXnY5F8//e/hwpjOiNYLBaUpTt9DQYjPBnSVC2X1884HA4A+Mrj6vqq/5oBn332Gd/4xjfwfJ/5YgFAW9dH0GroB+i2Y7W8oe2zFG+l0E1VghDuihnHzOczrq+vub5+wWq1YpimTKcToijg/fd/SFVVvPHON1mX14ikRSrLSB+4eXHJ5O4Zd2Yn+OEZD6dj7O45P/2pob58Rtc1/PSnP+GN1+/yreG3qLqG51eXLmdRSFC379dv3Bz4whPB51///PhKLAJuGMbjCbrtsKHbyY2xIA3L5ZIsyzg7O3NtKal6a61DSg8GKVU5YLN2uu84cjTg1nRI5cAaVV27dOPehpxlOdvtlq5pOVnMjiASPwoJDOyzAwaLrCrCyMeLXEvx9uF31xcwaXrk9lnjzDVt404BUfSSHVCWOZ6nCIMQ0IzGA3bbG5RyWYNl6Xrnvh/h+yFFVXNxccGbD18jnI4J4wRPd2A0vhJY3bn3AYm2feSWdlZg170oybOKIjugu5Y8y2irgkBZutAjTYc8eHCf07Mzh/DqlXlGt9RVgbEGdIcUkEYeTWuRSjCezgiCCHm8MimkUug+xMQd7CXGCpSQL5/iXubsbgCv9sBtj4Rz/ezJZMLrr71OVpYIT1HUBdvdjsMhp2s0tjXsdjvm8zld1/Hzn/6MPM/xz32WyyUnJydsNhukdASo7WZDWRSkaepOItrw7NkzttstcRzz5ttvMR6PsL182uJsz3VdHylVURQxnU7pmpqrqxfEcUxVV1R1yeWzp+y2G7Rp6HTH8pNnyK4mX90wCwfEsxlv3z/lh0HHJLKY8YgHD+6Txj6R72F0R1VW7HZ7543pW3pI6RKI/4bdgc+PvxOLQBzH1HVJXbd9hJQzv3StxtqW2hrKMqeuKqyxrLY3xOmAKInZ73b9hxWTHQ4UlWt3+aFHWZfOSWid7z3oBS23EVq3NmDfV7RtS1EUJPHgaO/M85wgCEjiCIRlMBwgpGQwGFFVJUpa6tpJl5u2dSCJvnV2K91VnmK92lAUhYOXSoHpufIOa+Z2pDRNidMheVlxfX1NFA94cX3NYDSkE5IgjlFGE0jncgMcaUhrtBAI9VIU4lDmFcvlFdl+hy8l2lrCwGc4SImTFItwtQcscRhQ1yVZtqdtatfW9H0O242TOUfpMYk4jh3d2PMDpLxdAF4Oi3VBmfaVV15tE2IdcATRZxne8i/cgTgZpNRdR9U0GGMI/JAiy1kv10wmE8bjMZPJhM1mQ1VVvP322y52DZzCryy5d+8eT5484fHjx5yfnbFYuFzBw25PEARUVUUY+ui24XDYO8TY0PEALy5fsFwuXY5Abw8fjUY8+fQxh8OBLMuYz6bcvXvO+pBz9fyC7Sak0YbVzRUjCVM/JOwKXj9/k/Es5d7AEt4f4d17yPf/4Pdp24ZBHLjcyf2B3WbziqCnf0P6a8FvMr7UQyC+/GrxlVkE3L2Y4z3UPTMC1TMAm7Zit1mRJAmH4kDTtOimIz8cEFJQNTXGWsaTCbPZwnEEmppDltHUtbPiAm3josHW6xsm098jCWNullecnDwgDH0QPmEcMe69A3VdY7qOTjcoHBC06zpubq5RSmJ055hygY/vKZq6xmjjsFnaBXgGgcd4MnQLkXB6B095jCYjlOcoPtZClhXONtxLa63WfPLkCTbwuHf/Pq/du4foNJ1pGSRuUka+j/U9jPJI4wiUoNWOk3hz/YLisKXM95TFHqvrHu1lGI2HhHFIVWZcv3hBVeWkaYLvecznMyazGaPRiPv377tTjx+TDkYo32U6qh5sYrH9KcLt7vZWoyIUrdF8rgpw/IyPpwLAWEFnBHVbUVYdFxeXHHrPQ9u2nCwWJEnEsyJjmCbEg5gf/ehHFEXBW2+9xXJ5TZoOaKoGg+X58+f8/Oc/5x/96Z/y/e99D2sMFxfP+eijD7HG8N433+Pb336PNE3Z7nfcrFfHDkvdNlR1Qds2lGXJ9fU1z549Y7fbOZR6GBCGAY8/2TEajzlZnPK1N99gv7lBNyXf/uYbrC6f8Lvf+Rr/4T/+ffww4ZBn/NP/+J8yHiTkecPVes12t2U8iLm8uuby2SX7m62rAQgQBjCi//2X04V+k/F34CQgEELh7rn0VFxXmPKUqybf+tgPh0PfbjLUdYlnAqIkoa0bOuN2qizLejeh+/VL80oHynH2lHK0WCWk2/kRJOmQum6P1WfPd1yDxlqSZIjso708z3MkoD6/T/SnlrapyQ578sy4dCLlwJ5KOVmxEILpdMp6tWJ5fU3g+8ihcyFGUdijxzUaC0ZTVzlRH8HWNJ0LtvQUKlCOD6AkMgzRSiF9DzyXadg1NWVVctiv2W9X6K5iOIg47EvCMGA8HoHV1EWG1h273QqMJokD0nRIEAWcnJywWCzwQ5f/QI9yuy0AuiJgX//v7cRuuLu/OT50veDl6GS7zdLrNzth0VZQ1g15UZHnJXVdURQOwNE0jQtVjVPapqKsMrw0YDQacHq6IIliTk8XXF5e0XWd+0w8j7ZtWa/X7HZb2v773bYI19sN1roA1DAKieO4F0S5Tk92OLDb7ajr5khjdieHkMFgQJqmRFHEfr9nc7ni7/2TP6NrC4rNmuLyE4KvPeLsdOS0HYDCR0RjGj9wcmUlGQyHeIFP3V9RddsnaBu3+0v712PB/l2Nr8Qi8OoqddvWeOmbNkfISNsX944tEO1089n+gDYGLwrRXYeKXOHK9fQ71O11ALBWY61yKrmqZDwYMZ1OCcOYwyF/qX/3/b4lNkSkAyT0d3h3otDW4Hs+Uhqk8Fz3QEjXuw/C3s/uimNlWR0DOQLf42SxIAwCssOO1c0NZVmTpAlhGFEUBfvdgabTeEJx2G8J44hy4tKEwaCET15kjlKjW9quxcoI0To1XN12lGXNdrdmubyibWvGw5TQhyQJSdOE4WTUe+UVD++d0XUdi8WC2WJOnA5d1qCn3P3f8xB+cHQ/Onegk/4IYVGII9jSvc8CYeVL3OUrCwLQs/Sh7SzaGKyR7PaFK6TmBXXVcLNccsgyxqMRVy+uuDKawSDl3r171NawWq349re/TVPV/OQnP+HDDz9kPp/z6PU3uH//PkmSkOdOOzAeD3jrjTePn93tdcD0z0Qcx4BLQG7bFiVcfH00HPLw0SOiOKbqY8murq6o6xohhGNIth6HXYX0JJtc8/GzK+aJRFjLg/MG6aX4nkfduFyB0nR0jWY4GFG1FcLzqduuv1L1GyE9dOV28fxNxm9OF/tqLAKvIpOt7ZWTPbDiFiUuhDq6zwwWYXH9ZQNt1yGVoilKVk1DU1YIJanL6hWJqlP2SemScRHw0eOPeHT3IfvtngcPHhDGEVoX3L0XIUTv0HvFAjscDvu4c90vUhIhDHEUEQY+dV2x2awRCKbTBCFkX1PwSZKFqzs0rg1qSRkPU87v3CGOYz742c/Z7fYURYmHWwxbY8g2K6y2rK/XfPzBh5yendA0FXfvzBgMEibjMbPpwoWGAnVZID0fJAyHCZPpANMckEYxHM6YzacMhgN8z/L/cfcmsbZl6Z3Xb+2+P/3tXxPxosmws2yns+QCC6iySiVZQqiExIAhjZBqxBDJwAAxAoEEEmMkJqBiYAETpKI8AMrYrirb2TkrIyPiRbz2vtucfvfdWgzWvve9iHgRkRlpJ2GW9PTuPXefe/c5Z69vr/V9/+/3HyUhSRRhGpCMxyTJGD+K9NI/HoEwUYbODdi2jWBYAdxgsJW+VHupPR3gFbWaMl52CQJKCaTQrdhNJ2l7RZoVXC/XZFnBZrNjeb2ibVtGozF11YJUhF5Akabs0xTZa0HX8b17zOcz3n//J/z0/fd55+13eeedd7hz5w5Pn5+jlOLdd99ls0t59933CCMX07RoBxR9VVWcn59jWRanp6e3pcSbVWbbtgMv0aVrOqzIxnHg+npJkkzolZZQX1xc0ImW5lIUBAAAIABJREFU60cXbFZ71qs1P/xnf0z64mMCav67//a/YGGCEwRMRgFdXZE3kv/rH/2f/N3f+R02dcoP3v+Av/jgI7oOMAeFoFJIzcHhiyU+nx9fd9XwjQgCrxFBcpNMupmIN19LKfW+SehK2Y01N0phWhau52mwZtOg+k4bdsibshqazWeYrNdrFvOD21WH47q4wwqi7yWWre94nmtjCgPR9/R9PUxqD8u26AbXX9MQwxYFHNtGGgOk07JvexM818VxLKKBS79drymKEsPQMJSDgwPG4zG79Za60F4HVVXRdIKu76irhrKu2e93CCHp2oLxZIRl2tw508taw7IpW90oY1sazhoGHuZiBn2D79ks5jOSJMYUPdNxPFiHv7zjA7ewUNMUiEHEJXv58pMSAoGGoxiWCQywUaWQg7jGEIMoSKDNU6ROFjadYpfmlGXN5fWKDz/6GKXAc30NdxGC9WYzeDB4bDea79h1LQhJ3Va0bU1ZFAS+z+HhIfv9nouLC87Pz7FdncnfbNYEYcJ8PgOh3ZVd22a1WvHixQu6viOOYi3yMgyyPGe1Xt+u5NyhUartWqpaa0a0dkQMAq8Cy7SxPIm0BVMiRFdh1ymJZ7GYhNSqYZPumNgOuC7S1tWi7/zGb2KbFrKTXC2XXK2WNxJBrZtQ8kZoebt1+tTM+GwZ8NMLrZ97fCOCwKvn/vIOOywd5c0b8TLDfKOqAr2n1Fx7AyU7mqqgawy6IbFmKb38FDfBpFXUO606LLZ71us1URJTy55RGOH4Hr0QhL6HY1o4tolpGHS1QmAxns5AKCQKP4wwhw4tKXssp9dLv0pDS13PZjQKCXwHx3VA3QQ0iEdjDMuiaxqKoiCMAqbehDDyMUyl8Wa1TiwWWUneFFhSUq23RFHC7mJLvi0I7AT312Mcz0cZBn4ocCwLU8BiNsZQDWVkY6qOOPIJAw/LEHi2RRL7eJ6tvRFsC8N2UENdXzsVDTkZQ6A67TmglM5XyK7SAbo1UIYOCAJAaolwawFKy4brVtL2AtOw2O1znj29JisK0iynKrXsWjtQt/RdT9e1tF1HGE6wTJO0yMjKmsXhgqLpePzRQ77/ve9rhWdRMpvNSJIE3/eHHAY8e/aU73znO+TpFmko2qKiN3VS9/D0ROsgTAtLCDzbpjQNsA1M18N2A2TfaQ9LepToiaIY1zVJ0xWWYfDJh+/rAOLYHCQTQsfCDizevDvClDGTOGJ/ucboJB4SFcQk8QS7szj91n1Usadf7TGdkKrudbxUEoWNEBIhu1u9xe34K0oQfCOCALy+6eHm8dflDF6VQ2qirb4btTdAzeFivtkG6K2GQsnuNgn54uKCrKi4c+8ubzx4i8D3qXvtAuR5njbIMA2EAst1cLA1Ksw0cBwLwzDxXA/X1iAO2XfaOqup9crFFIzGWnHWyx6GDkWltBvQ9fKKbL+n7TVm3XUcwihhsTgizzKUEmyWK66vr9jvU9paI8M1g3BP2xosL5+R79cYxIRJguVpt6a6KggDl9H9eyBbZNfgOrbmAEvNDjA9D8NxcVwXz/e1h4NSOgdgDFRgoUCCaSmgwxyoTHXXYBg2St0cZ2GaFl2vaLoe1Rk0XU0voaxqikIn/na7lLppNPVYKBzHYrleDuTjmsV8TppprLvnOIxHY8bjhMXBjPPz57pnZLvlJz/5Ce+99x6WbXHnzh0tEFOSO2d3WW3WGjRzdaU9Jz0bmo6mqkmmEx48eEBV13R1g287dF3LwcEB08MF6+WadHtN37VMpxPqouL64hzPD6ialquLC13xsSxs26bYXZF1NbWASeLx937n7+BaJo5l8/z8Odt8RziKsRVUTUuRpbS9Q2ho9ykFaIDj56sAt7nUzzx2M16Ft/4i4yuDwBcYj/xXwL8BNMBD4N9VSm0HLPlPgJ8OT/8TpdQ/+HlO6IsAil927K0G/dWUyi2e6eWxQmj2KoMuuypLHNcbehQKqqoAoZNHUkqEqY1EtfeBNfjtCUzTGpx+DW306TgIqcC2cRyPG4vufqDtBAOTv++Gi6DtUEJweHTCbH5AXZZUZYU1oMizNCWMYsoix7EMbMfk6PAAJQVFkVMUJUVZgICDozkPP/oJvu9zcnamsWiOg+paZNtguA6e72EID1C4jqYpK/SW56aZxvN9DNMa6MwCcWNXjjEkrTsUgrZ7NeuvdT910+O4Dm0rkQqKqqFuWoqyoh6y/vs0p+latpud5jpKyeHhIYvDOcIcKje3KPlm2KZYPHz4EX3fc3Z2wur6ks1mw8nRMW8+uM/BwQGO43D//n02mw3b7XbIeYQ8fvQJSRIThiG7NGO7XxEFIdNkxH67pahK6rzUhOogYGxNMV2bwPcw+oiuq1F9S9+2WIaiqXKqokT2DV1TYqPpVL1qWF8+pa1K7HsnHCYHdE1FEsU8eOstgthnPJkila6uxHHMOl3juJqvuFqtXxVU3FzZr7/p39DCvqQt+OuMn2Ul8D/weeORfwz8nlKqE0L8l8DvoT0HAB4qpX7jFzmpL6tpvm5lAOo2N6CUelmrVgrEALLkpaxVCKHdcQ0D13U4P3/OaDzBNE3iKKGtayprcEK2bMyBNXCb4OoVneixLQPDsDCEhbAEQqghvqiBnqM5/zcBqesaDMu+RXElibb27mrdwSaAakg4mYZBFIc0dYMfaIyXZZi6261pqKoKYQgm0ymb/RZDSMp0x2wco6TAdy0c0xsowob2AxC6ySgKQ+RQfbltlnI0418TarnFfgkhtPYfE50W0LbqXaugB2GY1J2kbGryqqLrJKv1BqSDYZmk+4wsz6jqBsfRZbiqqRmNRgN/QWv9J5MJTRNi2xZTOSOOQtpaNzr1XctqtaJpGibjhNFozOnZGV3fsRgtePbsCWVZUw4Jv8lkwvHxMaZp6tLfeoOSOoFbNzWG7G8t1SxhaH2EbdL2LYahMAyFY5uURU6232uIS9dRVzVttqPcb2kQdIHP6TzkPE/J8y22cUQS+Sip7cU7pbBdD2EYmJaDGIRh19fXREdzDMMiHErEr1zRXz45XqnE/mXtDr4yCKjXGI8opf6PV779E+Df+kVP5HXbgNecyxdsB1423ujfZQzlH3XLx+OmFXNYY0nZYeNohWFREEUxVVlwenJKEgZURYbrujieh++6NG0DKBzLxvM8wnCkqTymoZ170I0npnljnjpMModbw1ShFKbtYTrgus6QVDRxXN1ubBkmXd8zOzgczEm0kxJSu9RURaFbTZUWO9VVTRRHvB2+pffDpq2Xv4OVm+1oe22dyBM0basDgR9o09Rb5JdxWxI1UEipPwtdvhuqI8rWCUIFqoe81OpOyzJ4/uIKy3Gpm5blcs0+z2jqjjAMbzPujuOQ5yWr9QbP98iyTJdiLYvtdje0RTu62UtKPNejqXQpbr1asd2ukLLDdxLW2y3bXcpiPmeX7smr8pZMnIxHVFVFMhrx+PFjQNfc/cDHciw2mzVVXREMduuO52JZBoaQWKpDWYJS9GzXS9LtGkP12IaiyXO2V5c8f/aYrqp49603CY2es+mC5w9TtqsLttcjkr/5bV3JwiDf71kulyihODq9h2lb3L17xjbbMh6NEKbB2+9+iz/+Z9+jrTe8vNX/LBPm55ldXz7+MnIC/x7ak/BmvCGE+B6wB/5TpdQ/ed2TXvUd+Gyv9M8SEF4+/ulcwutyC59tpBDaEkcrEesKe7hDNXXNdrMhiWOthDNNzs7OaAxB22mQJ5aeDFqvoDsKb3q1DXVDgBhyEQM0Qwx6AaFszFAvxfXqwmSAwGkTkYFmJAwLlMB2HBZBgvYSNJFdq7PKSjI/ONRyYwFB4ON63uD+o3l8N/80rVdn6rte+yHqlZD2KuiVBGHoFY7S59zfBliBMLQLtJI2aVbcNgJdXW2p6xY/jDh/ccVicYjjeqT7AstxULagqirSNNWS3Ciiqkpsy6JtGu15MB7fnud+v8c0xK1gxzZN6qpGoOv3pimQfat5imbGr3/nOxzM5xoIEoTs93s+/PBDgiDQYqGBJbnb7dnvU1zHwvU9YhmxT7fYjq27P12HfbqlXVcYQiIMk6oscE2FEXmsry5YbZZk6R5Zl+yuz3Etk9gT2HTUZUHb1UjV0XUtu82W2eIA09JIuOurCxzH5uC4pyhyHBRRFGir+KFkLW5QTL8MZdBrxi8UBIQQ/wnQAf/j8NAL4K5SaiWE+C7wvwohflUptf/sc9UrvgOmaaqv2gJ89v+X/z59zE1C8OVj6vUBQapbCKNjWzRVRZHn9LUGS8RRTFmVxFF0C85wHYeu62jbltpqEIbAgiFBps9FKoHqhyAwnJNpmjp3bmoUetd2g2Ze6lKaAMOwUIpbBLopDKSAXpiYpo0ETMfCsmyk6rEd91ZZidDlUdN0BrpvjzBeBqfhlWNbDtZNlQ80SMVkqJwYSAV9p2h7QAi6TmqNhmHQtj1PnjyjKEss06Rpetq2I6xadruUKEpYb7ZkWcbJyQmE+v13hlxKFPg4lonqe8q6pmlq6qrBMC3CIGR5fY3s9epB9Yq60twHFIxGI/q+x/dt3nrwFji+toLrJabtUNc12+0G27aoqkqDUgYNwOWl7p1wZ2OybIdlGpydnGCYhg66skXJlirfIZsCQyjapqatS8o8Y399xXp5TbbfcffshNh3CTyXwHUxDcF+v6OXPUVVIpXke3/+5/zad77D4ekd3dkYhriOjexaLq8v8JEYAtJ0T1FVpPuUbmALfn2Y2FePv5LeASHEv4NOGP7dmxmslKqBevj6z4QQD4F3gD/9eX735/f8n/76dcnDz5zdkDt5jdRieJphiNsVVZFlrFdLDANCx+PqvKOMY6q2xXMdoijGtEzm8wP8wCeOExzHoe3aYW+mhrq6hohAr+/KQ34AqZe9AgPVy1vYhuwlSqhbnwSU7k1omhrLsrEt+3YrgwJh6nIcSlc3bEt/sLat8xvCtIbOXe0CJF55+TeNOoZh6qSfkiD0isM0TbKipm5biqKilwKJoG4aLi8uCaIQywr43g9/zG63JfAD3n7rHRSC5+cv2O22dF3LxcUFx8cnNE1BPB7j2Daqb3n48CGPm/a2lCeVxDZNuqZhmRcgJaKT7LdbDVx1HN045Dis12viZIyUHUdHB/hhiOEGpOmeURJzdXWFaQjWqyVJktC2NYaAyXzGs+fP6bqGbLfGMnvqJmMUx0RhTBgmWJZF19WUVUa+vWJ/dY7oawzVURY5VVno9vLAwsFnNhmxGY9YLVcIyxqMSlq8IMCwLK6WS1bnNcl4xMHJKVL2HMxnunRdlyyvL2m3S4QfIosMaVg8evQIWde316a4kVa/cjP7RceX8QXhawYBIcTvAv8R8LeVUsUrjy+AtVKqF0K8iXYm/vhn+Z2v2w68NKr4/DGfPvamDMjL7D9f8uKH/oSqKuklCEMQ9+3g4Kto6pq9lFR1zdrz2K03+kNRiuOTE/qupapK2rbGsbXlmHFb5tEdgbZpwc1qZTBXlYOk9iaJqT9nhex6MEyU1BbeUioNo7QcvUoQ2l5b9QbStDANg172GnaiFH2n9CepJDct/YYBbdsP2wwDgQUGSARCarHPTWuPkgZpVpENLdZ1K+k7Sd00rDdbjM2eZDTl2bPng/2YbrU1DIP333+fOI4py4LNds17730LpSTb7ZaqKLi4eMGTJ09whoSoaZnUdUMYRjiGQVasqPKcMPSx7QVFVWBbFl2nbdPCMASgbXVgzPIcWdaEgU+e6+an3/k7f5vZdHIbKLu2oSgyTAOiMGA2HmFaJpvVkrrICe8HyL6nU5KuqzHp6auMdHWJagtCz8I2DFxft41jmOSuMzRNmaR5QVY2CASWkEglmUzG2KbJ4eEBlmGQ7ndYtkOW7Ql9j+1WW+TlL14ggoix72L5tu4VUDp39dqVwC8oBPqqAAA/W4nwdcYjvwe4wD8e/shNKfBfA/5zIYQWucM/UEqtv+pvvA6B9Ord/nV8tU/nAAQ375K4FVgMb+it4mr4XcN3vexxbBvbNpEoLFPfTfuuZb/b6n502yFPU0zDpO071lGIUpLNZs3i8IAkGeHaDggTywIhJIbQ8Iqqq2jrRiPNep34My2LG66gZdu352KYpmYRDK2zSg0OQjfOy0LQNi2O66CAJBkNzVU6P+A6LrajmfYGWrWIUIP3novtuNi2C8Kgl0o3I/W9blYaVgibzY71estms6WsaupaqyGbpmG322HZL+haDfA0hMHl5RVNU9MOWxvTsjg4PERYJoYQdHVH23ZYpsVsOhuQaRXr9QrD1O+563lEcUhdZJR5pp2oNtrmzbL1nr3v+4EUZbHcrKjrCj8IGMX3aJuGURJxcXGOlFpt2Dc9XduBkjiWQVMV0LWc3jthOo01n3GUgBLkWcZqfcHq4hnt7pJZ6OCYNr7vYlkmXddj2g6267Pe7gZOYUIQajiraVnIpqBvte2dYxncu3OGYUCVZ9Rdh+g7jL4j3aVcXl7ilIWmUk3HjDxP+028cn3q5eMQyW9L3nx+UfDLTAyq1xuP/PdfcOzvA7//dU7k1Un+2RXAp467ncw3k/6VpJ/+wW1gULeJueEHwxBITEMrAW1PLz09x6GuK7quZ7fd0LUts+kM+p6qqsmLgo+qSl+grsM733qXZDTBwLxFbkdRTBRGCCBLU1bX18PFr/sFoiG/EAQBSZJgGAZpmuIO+8vtwByUSk82V/U6Ww5sNluOjo7olSJLRhiGNTADNEzTdlz8IMA0TQLfp+87XVKbTPH8l/yAomrYpxmthKpp6DqJZdlsNztWqzVPHj+9vTBd1x3eMY0Gi8KYxWJB3/eAwbNn55ycnBDFsYayuA7CGMxS2grDtHjjjQeUZc4nH+sFYde1xEFA27c0ue7VL/KMTz78gLzIUUJxeHTEbLGg7Rour65Yrlf6dQXaj/Lo4IB33npAHHiEvsvV5QtsSzs/u56HbVvYpqCTPaaAvqnwHIvx5ADHcbEMmzzNWS+XfPjBT1hdPGFqS95+9w0838GPQmzb1ozBTuJ4AVleDjLsEM91h6tNofqOwHfJsj1ZljOfjmn6nrouuVquOJhMyPOMTdmQpSlRUxJNZkynE4IwYjZfgGXBwJe4tWRQL9uyXysYep2S6GuOb5xi8NWvP+et9rrnDWHhsxP95XefWU/d7LnQIM6urDFtm14q2k7LfseTKUJBksQDaKTUMBPRYyBpypzzhx/xUVnRS4jihMAPuHf/DRYHhyglaeuG3WbFfp/qfIBhsNutQSY4pkG+0zr7fbrHGRBp280S13YoyxLf8RGhy+pqpTX5Ste5DcPUScyiwra1bqAsS+2WnEwwHZfMcWgHMxJhVgSdSdcbtL0kLys2uz1dD2mWU9YVo/EY2UPbDVWItsMwBNl+i+05TOcTHMdnt9riez6t6jiazunbFtOzmcyn2IbFx8+ewG6H73oUWcbFiwv8By5FUbBer/F9jzhZEIYBu31Kuk/Zb/Z88skjXlxckOcZlqUt28IgRPQ92+WKzfKaMAyYxTFtp3MIrm3T9R1ZnjGZjhmPxxR5RlUWzGcT/CDg4vKK+XxK5Jg0fYPZK/q6xZA2bZVR7i958eQR5XbL6f0D4sTD9gKCMMFxLJq2RdFjWw5hEDFs5gBJWdaEoYPExPRMfQ21HY0UGG5A1XaoTsuBO6VQfY1rSDzfJQhCkmREh+Do+ADb8+mq+pVreejTUK+f5Z9bHfxVKwZ/WeOLdAFfdOyN0y+g7/68FFJ9eh/0mcAgdNJLKYUYMGVKCAzLxnN9hOy0PsC0kBKaRpeshBC0jcIUAtsyWV9dURYlEoFsGvow5plhsN/tcB2HKAyJAg/XtvRd2zC0IlFJijynq1tAYQmBUD2r6wvoO+qu4/piie/5FIHLx48ec7A4YL6Y8/TJc6ZTTda1LIssy9ltN9qkouu5zErcOMENItwwxsBin9cUlaS/1sYpRd2wy1KUFDRNixRgmBm+6+P7mlZ8fXmJELBcXvOtb79H2zYE8YhtscdeX6EEHM6nhLHP1VJn9eMwZnV1SVvVBL6PYztYphjoSyZxrMt4u51ubOqGBF5RFDiuy+zwgANxCEjGkymz6YzxeMx2l1LVFcfHx5yenjAej/B9V8uzZT+0fWvr9NlsTFHYWLag7xui0Md1XLpRRFbsaZqUXbqnyVq6fAd1imoLri4ueOd0SjSKMUwP1/VwXG3XXtU5Tdvq9uO6xjIFs+lUcwkdnySK2adbmrZnPpmTVR10Ctv3iOKYqm5omoqri3OqvMAPPYqy5vLymng2IRnFxHHEeru7XcnerDK+bHZ/epP7i41vdBD47HhdbuDV7172DLw8/rPjZqNhDMk13RsvcF2HKAqgrkn3ewzXw7YdPdF2GxzHwxc+fV9j2SbT8ZjDk+ng3qu3EevVmt0+5fTomNlkiu+7tE3Der0lLzSt5vT0DMMwUUN9WBgCyxRgOqSl1tVvdjuqpsUwE6JkRDRK6JVit8vI8pIojAYfwBrZa81Cnq7IyprjOyZHyQzHC6jqnjzfIYRBXepuxlYpbRuOgWVrA1fLtgn9kKrUiLSm65hMJ9hZim05bDdbptMDwijEdRyuLq/44V/8iDzNuLq6Yr5YMJ5NqYucxnaoqoJJnODYFtvNms1mw2a7pmlq7NwkTkLtA2gZmJZgPB0zP5zRdy0oSdd25FXJxF7w7d/4daKx9lo4u3+X3XaHaiu9lpM9rmORRBGya6n6lvlU8yKrpkGpHtMEZRq0ZcNmtWR99RxXGDT5HlSPYwmkrDBNheN4CNPVORqlsepd19N2FZY9CLFsm8ODBVWtqIqCxeQINhvKosSeLcjLCiVg7HnYrodsG+qqZb9LKcsSB8konGEPgBZDGrecg89f0b+c8Y0JAjfji4LBVwmKXnc8vIypnx1SSsTQAiykiYaXmCgD+q5FeD5xHOnyV6uNRKL4kCAI6KRifnRM4HuopmG/L7AdrRaczxbM5wuUUlxeXJGmqZaNmiamMOk7iRP5OLara/qyh75lu1uR7TKUIajqmiCKGY3HHJ2cst+nrHd7sjTl8PCYrpdI1VHkFVEcs91sWG9SdnnO3be+RS8FRdnQ9ZAVOkGZ5zlVXRMlI6ajEYYQVEXF8npJHMXQ60TiZDKh7Tp83+ett98eujNhfXFFVzc4wqTapeRFytXFJVcvzpEoatkxHY/Zplv2ecblM23oYZomV9fX5HnKweGCIPTZp/shiLUoJFEcolDkmcaih2FEWZf89Kfvc3rnLslohOd5xKORljnTM0pCVssVVVMTnR7SNeagPdDdjZZhUFUlVVXQ1zUXz56RbS8p9muS2QjTlIOU2cRxTMLAwXFclOEgDJNOajJ1r3SHKEMzWRRp34emK3QVab/Hd7T8uGlbqqrCcjSNqZWK/XZHW9V0Tc9utyM0DUZCYAycy3KfD2i9/4+UQnxDgsBtF+BXHPPqeNXP7kYS/LrA8EWNGBoAat3+Xc0R0NZPs9lcTwy0824URYwnU+7cvUvXaxyksByaDixlkoxnSKWYTmecnJwyGo1I9zuWyzVd2zKeTBhPxggMfM/Hc30M0+T506ek+x19U/Pi6VNk3zNdLDhYLJjO5mDYfPTwEa7r4nshbSDJypowDIjiMV1vsM9KOmkwnh0Sz+DNt9/DCyOyvGSf5tpDsG5xPB/XD7Adh6aqMW2LMI4o8oKry0v2aUpVNRyfnfL2t94jTffsd1suLq6I4xjb87jz4E1Wl5fUXUuP4uT0hPXVJUJJDNnz9OHH9H3Pk+fPGMURcTyirmvG4xFxHBInEZv1muvrC8JQJ99Ur6hrjRBDKaIoxJCKszt3mUxmtH1HP5lQVRXPHz9mv9tRZBuePB1T1xWnZ6dslssB2Nqz3+3o+hYh4IMPP2C9WtLmGUbbE7sC6pRFckTnQF7bjNIUw5SDZdrQBt2DED1lVQ+QEV3xMSyDOtWisq5pcW2HtqmZT2YUh0fEo4QeQTRwKaQlWH+8Z3e9JI4TrtdLWtkTj0YaglM3FEVJ0zavlAiNT+9tfwnjGxEEfpEhtUwP+NlqoloGq1VyL3sNNPtPSkmPQdtKmkZbiMdxTBAEjMZj7UqTZroBSJiEQYDj+3hxQt8rktEY3w+xbY+6WdO2HWmWYzkunh8QhRF1XYMQTMYjijTl/OlTHMfh8uqapm148+13sB2H0PfZbLZsNzscx6bwGyzLZL/P8PwQ14+olhuKpiOKE703dhxsx0UIE6UEvqeFTV2ny2fxIIeWQ/nVtExcz+XRJ48Yj8dMpnqboQzBarPRWyVT23p5YcByu2Gz21L1ug/i7MHbNGXFw48/QkkJA1h1MCSjrAqapuXw6JjpdIpSktVqyfPnT5lMxpjCoG1aulYbvNqmSbHfU8Qx09GYJAw1Uk320HdsthtNZRKw2WyQsmO/2zEZjwEdsIu6oioyiiLn6eOPefrkMV2WMgk8jNmYwDWwTYEX+Jiuix/sMAxBXekqjukGtF2HaapB0mxi29okxTB0T0hRFrSNxPcCLMsk9F0m4zHhKKGtb6zZPDZVRo9eEW23O30dBAHL5RKigLFt6tXg6+p/v0D2/4va8r9o/LUNAl905/+qn9/ICm72fL7vYzo2ZVkihMCxXNqmpBjoxzcluzTL6IVB23XEyYjQ9/BcBwwTKWE2XxBF2hK7KCvWqzX7NMM0DbK8oG4umEzHHMzmmjWf5/RNS55mbNqGVkmaXpIOYNQqz7lYbWnbnrOzu7r1tWm1KYkX0HaSKBnjhzG2Y1MUBWDw+PETJLoXIE6SwRvAYrVaEkXhUDJVNK3uitPM+y2nJyfMFnMwTZq2vX0/4ijh4sUF/+IvfnxL4CmLksV0xvXVFdP5nD/6kz/i8ePHHB0fM5vPiEYJURzhuh6G0M7LV9dX9H1Pmqak2z1tVRMOCcSubQGo24ZWCKIoYLfdUNVfX8a1AAAgAElEQVQV19dLwsEyrik1iWlxfMDBXOPaXNdlsVjcIs96JKkp6JqaqsjJd1uMKsdNbKpyj2dGVGWL79ooIYhHCX6Q0DS97s3wBpPUoYfDtCxUI+l7iQ1EUcRqvSbP8qED0yIIfOazKS0Kx7bxQh/Hc6DiFsLa9foaMg7nfPTwY8rFhMnRgsVizmI+53yzu5WZA7dVrF90m/BV8wS+QUHgi9WAXxzZbiW1X/Y7YJBg3h4FN4ARpej7HtlqV6LxeAwm2K5DFATYtkOeZziey3w+x7AsmrrGMk0CWzPr1umGrpeUVcN4MmWcNNRVxWQ25+jomO12Q922mnwThUgp2a3WNLbL5vqavu0p64a/8Z3f5I037mMJk//7D/6AzXJFZ3l8+2/8GgeHJxyfnvA//cP/md2+wA8jTu8IHjx4QK+khmcYNUVZMkHDKsqi4tmTp9iufg2r1ZJPPvmI8WjCfHGAPWDYylJ782V5xnK94ur6mtF0wmq1ZjwZ8+A3/yZh4LNvSi6fPMMLA86CO2TXa66urhhPpxgK7h6dECYJXVMjmw7/8IAwismzgqPDI4osZ7vdEHoB9+7eRUlFEkcEQcg+z3h+/gzHsjg6PsILA7K6oO47Hrz7Nr6v1YFFpunIdd0yPzggSUJ819N7dyRloZ2EADara7L1NaKtGfkOo9jiYD7HsXyKtkcKExybZDRhMj3AMFx830UJiSFMsqKkaWoEQ1NTUXDgL6ibVgcE22Wz3UHv43ouia1XIU3VDipDcet6FCUxu92O+/fv8eaDt7nzwCZxbRbzAxrD48GDNzn/4KOXN/5fcCfws64AbsY3Jgi8bvwsFYPXHf9q8BBCIF4JFmpYakklh8qAQRRFOtnTNFR1SZIklGVFUZRMp1OSiTbBtC1D02uThCQOKcuKXZozm02ZTrUzT1mW2swiCIiCgOlkSlYUGnDq+WyX11xeXtFX2mcwrhrGiznzxQFHx6dsNxtMy2E6W3D21rdo+57NbsfpvXvcu/+AKApJi4LZ/ACJoSnLlotpVsTjkLbtyYoN+33Ger0mCAOdeFOK9WZDVdYoBaPJhP12S57lWnXoubz/05/y7PwZ3/3udwl9DxPFJ48+JkszRocLPn70MYvjI0bzCZfV4Pprgec43Lt7hzfeepNHjx5hCk0tKsuCtmn5wfe+j22ZeukchRwezAnDENlLtts9pmUTRBFNXbNJ95yN9Z65rGvSPGM8nWC7DrP5jKbSVuJSSqqywrFslCEGUEnPxcUF+W7Dbr2iyVJGgcfReEQSe8znc5oG0qLQOPlO3npE2I6Nadt04uW1dGNn1ytJ23b0XX87sYWpZdl13Wgak2kilHHrYt1LCVJiAHGSsN1uCdwAz/OYjRdYqsOybMRnin1/2ZmAvxTZ8Dd+fGY1oB9Snw8EDOXBATryamLRsnXbp5QSywDH0pZgddWgDEGaZuR5wcnJMZ7nkWUpgecym03x4xFSausz07KougrVd6xXS/Jsh+dpCSzCoSgrhGHhBwEPnz0jCgJMx6bre3w/JC8qqqbj7P59VN/jxQmelKR5wcXVFYujQ03CTTPsocmmyIuXwW/YE69XW1arlQ5Y4xGn1glRHHF8fEzbdNqlp6rwfF+LoADTMjg9O+bk9Jh0n2LbNrvNVtNzZEe6WlGmOWrWsHzxgqIoNKwEgWEJHF+7H3/48EM6BU7nUhal/lfmSNtmX5cs5nOsJMYPQ2zbJYjH1G3F4ckhm80G0zQIo5izszM9sYQxBOQcz7WZjieEoU/oexiGRtLXVUvZ6Ex9VRQ8f/qE64sXuIbBZBxxcnTKaGph2y7V0MHYdS2+49LXFYYm1tL1Pa2SdL3eKqKg7yQacq25lc6AgGu6lMD2CAMPw7HpW81MKIuKPtFS7NAPCIOQYp9SDlWLTvVIAbLXdvVl27Jeb15/bf8l9RB91c30r0UQ+FLWgD7g9mc3k/52YgwHfcr+Try0CCvLEmNvI4TOD3i2oKkLrq9XNG2HH4WMxlMOFoe0nWI2W7Dfbtlt9vSdJIxDuq5juyoxDG0ycvHiBQJBFIWUVUWWZ3RdTxhPiJPkFvulbAvLdqnKmhfn11xerpDAvbfeZrW6Jis73nr7bfq+p2gaDg6PydIUw7QpC12i2qw3PH/2jPF4RNvou5UQBgcHh3i+TzhYjslC8fjJMxzbYTIes79eUrcNWVkwnk6YLWYYQvD8+TMuX1xQliVN1/L3H/ybLNc57Sbld//1v8f/9vv/Cy/Ozzm9f0ZVlPz5P/+nYAkuN0t2f/RP9LZqNsNsA964e48//MM/5PLFOQbaQXo8HpFMRtieQxBGyKJieb6GrifyY6TsMJRB30nmswVBEJKme0wlaOuKuqyHQC0IwxDXsxGWiRSKpqm5vjxndX1BsV1zOh/z3jvv0CqbxdGYIq9I8zWyz2krF6SBZ5sksYtSHUXb4HgBtjKolaJtGmzbxvFC6rrDdXy6vsFxPPLiiskkIIpH9MLAcm1cS7K+2lCPOixPkQQR5vEpa3fNsxfnTCdzwiDSYjTHou07njx+zvX19V/tBPqK8dciCPy849WVwGe1gwqtz75BmUuppZo3go2maWgHz0ChtH4+jmOUECgpWW12lHlGtt1iWiZB6GMPnoZ1XWBbJuPxiKooqKsC0zCZjCfs9jt2ux1JMmI0GTOdjojCmO12h2E5KAS7XYrt2ji+z2Q+Z/f0kjCKkEpSZDnPnz8fmlhGXF5caItyYVCVJfHZGa1STGdzuqZls92hlCLLc5quwTAEJycnmIbBfDbn9OiY5XrJw0cfs9tvOb94gS20fyFK8xf3ecbFixdUXUu6WnH29pvMDhcsr6+5uLok8HyE7EmCMY8++hBTgWPZGMLAMnXnnOc42ojVdgijgMPDA5JRQg+MZlNO7o2IwoinH39ClqWYQnD//n2m05nmM/SK0I8wMKiKHAMIAm/onmzoOoNu8HLMsowXL15QphllUeBPI6o8xwqndK3CtBws06aoGwyhcG0LN/BwPQeaRpuBth2msjU/wnVp6p6mqvA9nyiKWG/XICBNU5qmxzdNjpXQiUkFcRxj2hZd1+Ha2pMyiiKiMKTIc60VmBxArxFzRVnoitFf4XhVM/O68dcuCHw2GQhD04UAJQf3FiFADfZlptZhG4CJBnagQBmaMGRbJlQ1fq/wekFr2JR1jRPEHE5mzA5OqbsWR4LnaCLvbrdnvDhkl5f4fsnB4YJ+29EKQVG01LWirDqapiAIfQylSOIxUWxydHisMdrpjsALqd2GKl3xfPWJFsyUBQ8ePGA2mzH99gIDSKIxvhPxp//8+0zGY46PjimK4rad9+z+PSqpLyoMwS7dk2Z7DFN7Guy2a2zbJg4j0jTjxYtLgtCnqCpCP9DZ+brC8jzqMudXfvVd0jTl+z/4Ael+hxpYgOcPH9FlBZYSyEqC7PC9CNcNuLq6Jo5j4iikTEtGDyLWyysMAUdHR0RRpIOW5XDvzgO2uz00IFpJ33SMp2OOTg5BKsbjMUHg07YdRZERxzHjcYIah/R9yzj2aLFJ04JKSupsg2tIzHzHi49/qmk/VUHd1DRdhedCb0NZtfRKIGubstXdjq4QqM7CkGBjDXBlfY35nodt67xFJxvqrqfpQBgOruNiCH0NOYaB5zh0ssFJfCzH1Fh22WGbsNtumM1G4FtcXa64c/oGeIpC1Lz45BK57zGw6IxOt0PfdMW+tn3wK+YG/z9LDN6Mr3pRr7Qe0SuJULr/XgiBOQQNoRvnwTC0BbRQOJ6LtEw6y0DEIb1lIUrJ0eEJYRJjWg4XF1rYMp3NmM9nFFmKf/8+liFItztkD8+enQ+0HAk4zOcL7ONDLi/PWS6vQZhU5YaiaZnMF4zjhPFowvPzF9puK03J85w3HrzFr/3md/nBD37AdDolHo25Xq1J4jG2bZMkCa7nsVqt2O/3hFHEt977FdbrzUAH6ri8vByMUBRZlrHfb0n3O/bpnqIseevBO9y//wZ93/H0yROkkszmMz5++BAhYDqd8vjxI9555x1++7f/Zb7/gx9x//59fv03v8uf/+mf0Xctf+tf/Zd48ugReVlwfHyMsgy+87e+y3K5xFAGz58+54c/+hFHR0e89e47ZFnGvXv3OD4+put7dumO0WTE5fWKDz5+iCUEplAURY/rao8GpRS2bXH//n1sx6LrGsoyJ4pm+K6BG47YZznPnjxlt1pxffmUH33vz+maCqUkjmMzmU7xwoi+VxRFxePHTyl2e944PsbzfGzLxnUdPM/B7C2SJCKvGtreQAmFabuEkYvjBlxeLimKEqU0Lt52bNpGG8LUQ8XItmwSz0FYJvVgPWdZDq3suX/vDRpD0u5KFBDHEZvNir/4ix+z3+/R3kw3V7OmwXydJOGnRHQ/4/hrEQReHeIzQorb9mGGUuAA0bhpMlIoTGEOGdvheUIMTSe6MuD7PgqNBuv7Htu2mUxmNG3LZJbgui5JFPPw4UM2qyVCCN57++1BNCTww4DLqxUKk8ViSpblqEFbgBBEYUQQGlhFwWazYbVcsl1vaKqKLMt0ZcHz8TwPKSV1VfPo0SOOz+6SJAnz2RzbtvnJT/4fbMvg8vKS3/3d36UqG13rrxvarsX3tOpxPB7T1s1g1+5R5DmL+ZyiLLEtA891iZM5dVMi+44805n+NN1zeXnJnTt3WK7XZFnOO++8wxtvvIGyXbzA58Gb77G8uqRXHckkoafn5PiUO/fuEo/G/PiHP8F0PRLf4uDggLt37/L+++/fOvsmozGO51LVDSAwDNhtt5wcHXH33h32+53mEwymp1GszT+7riEIPKq6QEpDJyaFNon54PoFsmsJfYcw9KnLCqM3EJaJ67ikRU2ZZ6S7DHqJMbhLoRRd39J3rTaWFTceFgLfDTAxhv6BQm/38ow0K4ki7S+oBqs8Q2ieIraLsB3tdrze4IYhfS9J05TTe3eJI4/GyrTNnhKAOXAhX1XLKrTR3lcbkH1OYvQlAeDLgsLX9R34z4D/ALjJaPzHSqn/ffjZ7wH/PtAD/6FS6h991d/4WcenLJc+Fwg0rFMp3aIr0JbZGAat7DAwNO3HAKUkqoVetGS7PV3d8ODOG9R1S1EVGLbu1b9BZI/H44EwHLFbr1FSs/cc16UqKlRR0bYdrqttxzTa8KYBpcOwLKbjOUnX8+LyAkMIDo4OMQ0D2UvG4zFd17FarRCmycnpqQ5GYch8cXjLODw+PmS31Ry/2WyGUoqr5RrLsekGWtFsNmM8GpPtU2zbZLGYYwjF5eUl1gBOtW2T66sL8jS9fW/jJMbzXPb7PbZt4zoewUHE/fv3dYXA0makmjjck2Y7RrbJfl9xzCmWZbJYLDCsD3nw9lsIdB/CydkpTd9yeXEx2JA1VGlFVdccHZ9wdnbGOYq6qWiamvlcNxKVVcFsNhsgK5qgbNkepiXIdjuUoffRlqE4f/4U19R9GJPxiOvBc9AyTZqu5epqw4dPP0ZgMBuNUVKLgezA03JjqasCsu9pmpai7kH2eK476A5SmrZjNJ1RlBoU47j6vSqKgg5FX9XYhkUw9A3Yto0c9vx9r4jHI4RnEzsBrmUOyk3B/pXP4IuxQl82KV4zR37O8XV9BwD+G6XUf/2ZE/gV4N8GfhU4Af5ACPGOUqr/uc/sC8dNS5Bx+9VN7f8GD4YA1WvI5eFowuFcizzSvW4l7ZTeDiAlXV1RtB1PnjxBSUHg2Oz3GWGYY9oOUgmur6/puo66LBmPx1gDwNMyLTbbHVVdEMUxyWhKr+njbDZrPvroI3b7Pfd7wWxxxHQ0xnY1IHQ+n+sJNRhkSil571d/lbIs+eSTT7hzdo8g0LCO9XqtUWKyZZwE/Cu//Vt878/+qWYJ1B1+nFA3DaM4IkkS9rstXaf9D8siZzqdEEXaXnu32/DJJ59wfn7OfD7j4OCAqqpwbJcwjHnjwduDSlLRVhVVo2v1ddVyMJvy4x98nx9873soevr+IxzH4WB2wIUXoZRgPp3w9PEjbEebyT5//hzHdVgs5rqu79icnd3ReDTfRypIkgRkj2lprwXPscnznKapuLwscGyT+WxCXVdI1WF7LsurS54//oQnjx+i6pTlboeUHUkUsDEktu+yT1MUgvFkzElzpCtAroNhWdiOS1VWfPDwfT788EPuHh/fEqXTrAHH0bmjXuG5HmHoUxUl8/mUJIoJXY/5eITvBVRliW1q09Ybh+P5fI5qW/74j/4IV5gYwkQqwQ9//C84mo4xY5/L3U4b2BqDE/enLvNhdfBLaCH4Wr4DXzL+PvAPB+DoJ0KIj4DfAv74a5/hZ8eQ4n8VJWIoPfEHuyAsQzA/mPGtd9/l22+9y2I6oxkMPza7Hcv1iu1+x/VmjRAmWVkQ+SGWaVI1DWenZ3ieTyd7lDBvjTL7vifPMq6vrgh8j9FohJKK4+MTgjBgs03ZbPYczA+4e+cOURJw/uKcg4MTxuOJhlMqra+v24b9fo8Qgvl8DsCTZ89YrTa88+Atrq6uePHTD/mt3/otAs/VlONabx+aqqLve7wwAKHRZMiOtmt0eXS4g5zdOaPIM66vr8iylLqu8X2doR8nCdPJhKppCIKAe/fuabFUpc037967S1mUXC2vOTg4IE/P+elPfszTx08Iw5DZZEI6eDY8e/IcpQx2ux1l3RLGEUEQ8MEHH9D3uv8iz3KCQYW5Xq+5uLzQVuL37jFKIhzbJopCuq7F8F3CUHfrFVlKkadsTQ3bUErRVxVdU9E2BZfPn9IXGUL1TEYRAl01cAyt6PM8Dz/6f6l7kxhbsjTP63fMjs139Nnfe/GmyMzIzCqKrM4uQI1otRBCQqxgAWKD2NES7NiyotXLZssCsaUFUksIIdZIjVBTdFZWieqsiikzhjf4fCeb7QwsPvMbL+aoqKom6ywi3O+7fu1edzvf+Yb/kBMnmjCQDCcaPRaU85jBUVYtdTNQ7mosoiMRxRF5ntM2NVVVCsfBg+88VSDy533XkcY5211JHGqyKGYRBPSmR4cBUZTzzjvvcPPqAqUUgdbUTcMv//gjjh4/olEB3lqUfzOl/ew2/85b4htO/79usNB/qZT6TxEl4f/Ke78CHiJmJPfrxfjYV725ve/At60vSYi9+fh9xHSA98RaxB7/9Z//bZ4/fcpyMWc6nTIMokP/g3d+uK+9kyRhdbfm1asL/vkvfim6AFooxgB5npMXUy4uL9lsNpwcHTGbFHJKtQ19PwiSMNKUZclmu+LVy1d0dc1bjx+yXC5AiST53foOHSUcHB7S9z2Xl5d75ttms+HJkyes15KKB5Hm4PiIZnDc3dxy9tOf0rW1gH+mU0g0H374Mc+fPydOIq6vX6GjmMm0YLVayeipLMmyBKVEWfn1q1cMfc/R4TFpJOOv3W7HertheXDAq1ev9k5AVVVxeXlFqEPef/99+r7Hdo2UDwqePnnGs8dPuFutePXyJavVmizLMc7y+7//+2x2ojD//Plzbm5uWK/XTCaT/efFe9LxJA6CgPlsxma9pq0bfBJjs5SiyInjmCwJWa1kZOn9IOIu1vHxb95ne3tN6A3OW9EFDCX7MMbgI7FX6wcDnbABzSDKUc67cVIRMZnOcYTUTUe5rYjSAmcDcDJkct7hnCFJNPSOoe2YFTkqi4m1lHvxSCwSxmGPx2OsJQjh/Pycer0VXUWlePjWY8rVNUeHR3x4eQvOj1mCHScD94JC3yIbNALgvmu0+KZY8H2DwH8H/APknf4D4B8hJiTfeb3pO6CU+sbi54skCNEOVHICjv92enLEz373XyGLE+qy5HixJA5Cduu18P5HmLBSYv4Qa6kFo7Nj5ssFlze3vPfee0REo8VXwPZmx6TtwYlE9ieffAJOkGHPnjymbVuqquWDDz5gtb7j4aNH/Jt/59+grhrW6zsGKyIdTVsRVh2Dcbx6/Vq8/9IUkFT48ePHrFZr3v7hD5hMJrz/7gc453h4/oCqqnjv3T+jqiqyLKPtWh48OOMXv/h/+NWvavrB8vyHP+T58+e03UBdbcmyTEA/F685Pz9jvV6hlGKxWKCU4sMPf03TNBTTgjhJ6IeByXROGGicV8zmS8qy5PbuhocPH3J9fc3//X/9H/ztn/8B/+6//+9hBssnn7zi6Q9/yu/87A/44z/5IxFeSROSIuUgi8HAp59+yrNnz/nNb37NyckJjx49whiZYNRNxeHhIau7W3So0Tpgvb7j8PCANI1I0ghvrciHL6Z0Tc122zL0HZ2xXL/8mI/e+zOuXr3kD37+M9pRVLWqKvIk5eTkhJOTM5wX89RkLPO6OMYpRdsZijjh6PCYLMvZlRWbzY6jZELb1my3ijzVzIqCdjrFec/NzQUesHZGU9UcHi1J0zmlHdBRtM+kprMpr1+95sHZCZOJ6ElGUUSYZzx5/oyTWc6qN/zT//N/h0DhXA+KvcbgfbNbec8XioTPz/y/YmN/8eR/04fj69b3CgLe+8s3LvLfA//b+O1L4K03nvpofOyvbEn8CxB5bfmA89mEn/zoRyI77RxROLrvqIC2aXHGEicpSoV0Q8/t7S3ee5IsBR9QNQ0q1iSzGbeXFzRlxXQ65eH5A7alCIEeHBzQddJlvlfxtcay24rq7mI+Zzlf0LUtXd8SJxFVWdGZgcXyFBVEDMby4ME50+mU1UqgosYYdrtqVO/tefvtt5lMp7x69YqmrPCB4u0fPCNJEt5778/Zru9o2p5iIkKld6sr1usNr1+/4uz0nMNHj3DOURQ5Qz+w2Wwoy1J4/fMFi8Uh7WBZbdc8P3ubBw8e0HUDxXhSZ1nGn/zJn4xfp2RZxqeffkoQKA6Ojzh/+IBdWfEv/vx99PqObFqwPDgizlLmiyl3mw2L+ZzBitPvw4cPOT4+JhvLp+12S5alezNU+R0MBEBR5ASjG5NWAVaJS0/ftDSNTDLaRkZsFy9f0jUVRRajgCxOaPueV69eibR5GJKlBTqNsVixlx8QmjWKIAxI4oRtXe49HwZjpQEZhthRuswrxWQ6kc3qg72H5apt2W46Tk4LJosJaRTT7SomWU4caU6OROEo0zFJEqFCoVavVzuOpwVXF7dsd6V4LgR7woL8T315f38nmvw3lgVf/3Pf13fg3Hv/evz2PwD+dPz6fwX+R6XUf4s0Bn8I/OH3ucYX1+ezAT9yBjx5lnJydEA46tn1bU8YBOyqmkBHDMaw2pUso2is02IIA5wx2EF0/pu6kVn9dof3jr5v2Ww27HY7dBQTRRFd15IkCWqs6T/4za+xveH6+ob5YsKjR+I4c3V1O6akPcZZVBhirWE2XY4ngmazWrPbbPFetPUvXl3y7NkzPn3xgvff+5Df+73foygKXn78EXebNX/4h/+Ms7NzLi4uSJKE9XbH8fGZKOkYhw4jht5SliVpmtP3PQeHhwxdz+3tzfg5IgKt2Y69gfl8jnWOuhYJ77quefsHPyBOYvSoqhvFIUVR0LYtZnBsNjuc9WRZQVxkpEVOZwaWhwekTYLF88s/+hN+8MMfcH50wunpqfyssUynM6q6pu97ZvMZi8V8PxXp6ob13R3OGJwzdF2Lv9+Ezogh6NCNfo0BeZHz6tOPSJSlSGLm8yl1bzAo5osD+rYhCEI6MxDYmDCKODo8IU0nVJVQxMNQtB+jKCJJY7qqxNqBIBQfCe8NvRmIA4WOI8Je4Mmb7Q4dhBydHHN7fUPTNETTlMEP1HW9p6hPp1Pubm5hENdj7zwW4Q1EkdCYvQpGmXiL8x6FcFb2++we+PYd1tePBb99wvB9fQf+nlLqZ+MVPgL+cwDv/b9QSv3PwK8QF47/4q92MjC+pze0hSdFxnw2pe9b1mtDGGgmsxk2VNyVJfMs48WnL/no4485PT3lx+/8mGpXs91sePBAGnZFMeNX7/6as/MHHKqB7aakKXeEccIiyymKQmilyvOjH7/DZrXm17/+NWGi+clPf0pRZMxmAgG+vLhgNp2jgoDdbi3vxTi55nZLnqYopZhNp3Rty9B19J1hs9kQRxHHx8estxvhNmgoy+0e2//i9Wv+9M/eZZLn/PznP2cymXBxccGnn77k5YtLttsNTd3w9Mkz1s6z2WwwxnB4eMjR0THn5w+4uromDCOePXvKMAyEWnN2dsYv/uiXbH75x0SRSIaneUKkNTc3VxjTk6iYZ4+egFPc3N3QNBU33jKdZpyfn1LkBe+9+z4/evvHOOP44MPfYN//gDjWHB8fc3RyTJrlRHHE0dERk0lBURTSTbeWx4/OZWzmzJ6917U1Q9eCM3gc00lBMC+IgpAkCsijgNPDJZO8YHFU0PWO169fQ5xiPYRhhEeR5lPK1ZqHDx5jnGNXbolDcQju+o75cs5NW9O0NXVT0ZmAUIn5SqBDVBBQTGasdzUvX10wnU5569FPCcOI66s119d3FEnMJEtBCboyiULCULHbydTi+voan0Y0zYCfpSyWh/zkJ7/Lyw9/I+Pd8I0Zv7onunxZbWvPo/kOmIC/siDwF/EdGJ//D4F/+K1X/g7rq2CQb/4OdBCgPHRtR6RDrLH0Q0XZtjx5UoiIhoGYEGct1abEGctiNhclHBVws7rDqwgXhPTesdtsaLuBqq6xdcNbj59QFAUfffQR3nu6rsP0g3ALQs12W6KUp64rdrsdi8WCxXzJrtyRpTlmcKRZwcnpObvdRjrXcSwuxeNc/+z0AZ0ZRI+Q0R2pafDe4r3l8uKCpuvROuKdd94RJqH1bLclgxEElFKBuPrEMavVimEYuL66ZjabcnB4uB81aq05OhLwkcfLidn3tF3L5uqKoiho6or5fMrR0RFltUOpgMXigDwX6O9us0WHinKz5erqigcn52zWW66vr1keHLDebCgbcRJ6ePqIxXzGarNhNp1gzcDNzQ193+0btG4QsE4SR1grAiPGeJSOkPH/gBrkPki0BuUJFERaUBTjBjgAACAASURBVH7DMBApNWIlQuJYEY66jvdj2DSTa0kSKRuV0S8izzOSONo/31ppNvZ9j440Yahp+06EWqYz2rbh7m7NMMg0xgwDPhGfyiAIiNN0HGDLe27altlsigoCsiynbVq0zoWT4h1h8FXbHcaB91cv9XnC3PjQV2QOwrT8pvU3DjGI8ngG0ijieLGgSFOabYntDXGRE0Qxg4N+MDhj6QGDo7MG1fdcXV1xenpKmqQoBU1Vs9mW5ElMkSZYk7NaXxAlKYfzOYP1vHx9STdYjg8PKbc7wa/PprT9gJ4UTBcLnLEMnUGngsjrhgHdG7pOXIW2mxVdVdE0NR7FwdEhyaizV5mO68sbhn6gKAo2263Uy0pzeHDE6u6W29evePaDZ0ynE25uVxSzmKurG16+esGT52/T1A3driQkYlvXbLc7bN1QKlgcH2KaHtMazp+8xXQsBcIgwA4Dd6s7HpyfgXMMfc/F61c8efwWcZJxcX1HPluQZROu12s6L43Rg+mclVlhuxbvBz598Ru6vsLanOWi4ODwBwxdR+gs29tbbi8uePrsGcuDJXVT44xhMZ2Sx5owcCLt5hRhHBFYh/IKazvwIZFW2GFAeUsYaDqEtDXJY6IkRqcJdd3gLJjeYoeKIgnJJhm9C9B4XJYSRjHDriJUAbUzmHLDwSxjHkW0k5lIs+kAlOgMDP1AQ0OeTxh6hwoiFgdHfPjBe0Sxpu9rPJ4kzbEobOCIQk3fC2qzr2t8MeVytSOsa2Yqxw0D8WTJ0Fvutreg3NgQDHCBjEFRDkf41Z3/rykP7unyX9FN+NYt9VsdBL5SPtwLJbXIMqH+joCYqirZlCX5dMpssRDMvjMsJwfc3a5pu4b5fMbN3S13qxXnZ6fMg6XgwKOItmlYrVb0ZuDRk8dEkXTN71Y3ZGnBWw8f0nUtVbUjzVIevPWI6+sbqqajaWrqsmQ6LQiDgKZpSNOYw5NnhKGmHyxeedI0Zb1aMThLXCYY50kzwbAr5SkmGWkS8+LlJ2RZxsnpEU3b8s5Pfsrq9oZf/PNfsFgs2O1KfvmLX1JMZ5ydP+THP/4xwzBwd3HB1cUFByeHzCcFl59+irGG25sbMI7ZdIZ1BmMHcF4ER1VAVe4oyx0/+MEzXr54SRRrNts1F69fYroO03Y8e/aYxXLOw4cPRYO/rfemoVfXV5ycHPHw4QNu7+6YTCY8eusRfdvy4sULhq4bzT8H+r6nKAqsMWw2ayZFSpokVFWNGsU+ijwn1jE2DkQcpG3FKXo6JY41ymiyLGOxmJPnOUNvCOOYrm5BKdIk4/TkRIJcb9jUFcm0IPSazW5HPhFm6NA1NE0z+gMMhGHI0PWiUBRIBrHb7fAEOK8Yup7D5YKLXMxRdABh2GHDmDiOmObitxilufQGooRJmnNw0O4zF++lD7Ber6RnEAajH+H32xufc57+uv6BuvfJ/Or1Wx0Evm5FYUiaJHhncc6ix0ZLVzfUVUUcRcSjQKYfhBTjvAccWZYzdB1N0xKnDW0rfnrDaLSZ5YWkdEnCbD6n6Trp4I4pf6ADFssFq/Ua4yxRKKPKvMhIkoSb62uZtx8dkhUZQRhx9/I1ZVWznM2ZLuY471kcHhBFMUoFdF3HbrOlaWriKKapSiZ5NtKAB/FIsEKMsr1lPp1xfHSKDYRTf3d3w93qjo/f/wDT9dR9gzUGAkWWS7/i5vKam7tbDs9PmEwmNHXNBx98wHq9pihylAqYTiccHB2gP5Tbou97ilwCk2AG3uXjj3/DydkpKtRkhWj/XVxeMp1Oef78OWVd0g8irHm4XLCYzwW/kcQsFkvSLN2nsXEUUte1wLzVvUBIg3eGNElwTsoh+KxMVkFAXe4o8pwszQmDiOl8jlOKfhDknY4jQKZDodZ4Z+SkVEos30a57+kkJ8Lg/Irb2zuc8+goJo4MyonwqQKqssShqOsG6wxHh9L5j+OY1IW0xhNrLdcbX7vrpdxzVnwaAwVRlBBGGsLx99t1eONwX9y7/o3z+zv0BT+nn/HG128i7L9p/Y0LAkpJJhDHkYBJshRrLEHfibeeFf++QCniKKJyNXleEI+Y7qbtyOKYvu9ZrdbEcULTdBhjSNOUZuiFHppYERwJAtquo+ta+la49kmaUrWtIP6sJT87o2tabC+vkaTCd1+t1qRZRl5kqEBJxtH2EChxL0qh61pRsLEDcawxfUddVfSzGWEScXZ2RqQj0izj6OiE0+NjfCAkqaZr6buO169fsSt3qDAQ5Z4k5ra+w4dKvPlCDXiarqFpam6ur3nxySfc3d4xmJ663HFyeop1liBUHB0dkmWZjCBHrcHJdMJmuyGMNG3bkeciJGqt5erqiqbpWCyWLBYLoihG64jpdMpkIt6M6/WKvMiZzmbCFIwj4jAYHZ5b3Ei9tdZi+hbbC2ln6LoRViteAKEPWd3dEI9TmyAIieOYuu1o6pLBWawP6IZ+ryattRiHBGFAlmc4DFGkiXUIVlimxhiMFbkwYK8/mSQJXg2UVcNuu6XvBtI0xlpDGEjKHmoZd0Za9CeC0WOi6WuUCoiTDDf2D0IVMniH9QIoIgxRzn0eBPfGf79qC7/Z+BOE6Gfrnkdz/xnun/dNgeBvRBD4XAdU6H6AJwggDJWMyEbIbBSJIec9iq8JOkm5O40ONWY02Hj04Jzb1ZokzUizlK6XEQ+RsMPqqpK0t6lo2440jsmShKIQW+uiKMjzjK6suL29pWsFKzAfjT2sdaxWK7rLS84fPuLw+AhvoOstXsF2t2OmlHj+7XboQKF1xNnjx8xmBcOowNubgc1mR1U3TOcLirnQij999VKCUOAZTEfdtJyenfLo8IT3PvgApRRvPX1KXVZUVcPRyQlKBzRtw/XFJavbW7I05W/9rZ/x5Nkz3n3vXbTWnJ6d8dFHn/Dq9WuiKGI+n3F8eMR8PuP09JTLy0vapuPw4Ig4jrm5ueHZ0+eASGrNZjPUmJLPZlOKyYSua+n6Dh0JWCuOI6aTgigMCAMYBsPd3S13t2u6riHE0oTSoMszsRILQiXinc5we3VJnMTESSapN4pQKdqmlZR+sFjj2W52qCQlSmL6ocfYnjAKCQkIRl3CrpXfryD1glG1WRqEgzEEKmAxE6/J9XqN8g5vLXmW0Pc9zjrpNQwdkdJYa+m7jsl0RtcbtFZ0/UDghJ2o05geS9m2mN6MWhjqO5cDX5wGOD8C6PdTha94rgIIkYHdl9dvbRD4JoEEoRN7cbF1bg8X1UGA9UinOUmEU7+ruLm95fT0lOlsRrXb8vr1a87PTvevF2iNGtM4naYMVjr1eZaTxjFJFBPH8QgyUrR9z9nDh8ymc9pAc3d7y2AtVVMTpyl5XnCvbW4GS1lWqDDEGWkIyqkzCGvu8Ij5dMav/vT/JUBRlyVn52dsd1s2mw3WWJq6JS9yrPUEcUTVtjjvKSuBBhdZztHhIW0/sN5suLu7I0xiHj55zHKxBOvRscYFo7fiWrAC8ei8e3F5SdXUrLc79GhzbsdTtO8GLi+vyKYZOorxXrHZ7vjd312SZQXL5SE3NwKYStKYNBVs/tHR0cja9qSJ6DGGWpOmCYECawx6BO5EUYQORYjU2xBnDQpFmqSkaYKORPXJOYe1nr6thd0XKNRoGy8HQMTJySlttWOwliIMCcIQHUWUdUlZliwXhwxG7pUwFKLUriylHLSOzWZLEmdMJ2KV5kbB0TAMSeIYHYm24/2e7fqePM33tGJjDEZ5qlFc1o6TniJJpExNU0zQQSBBy3uPV19GBn4LaHi/6b9k5Psmlli9kTV8w177rQ0C37SsdXT9QBgo6qZBaxGkTNJE6MB1LSgwIAhFYMQ5S9M2Mk6KtCjbAF3XYRFdeGMtsVIcHBzIH7QfSMYpgneeru0w1qPHE8IMg4wMjRUAzMEBh4fHeOdpqpY4TjHG0XU9UdPv2WgeCEK1R/KFSlGV0ssoqy2X15copZguDuh1T5yIGWZcZGTTnIPTI5aHS6azgu16Q6xFNagf1qw34j0YRTGegCxNMf1AHEf4MKBve5Ik5WA83VUQjmIZijhN2ZUV09kcayyr1Ya27bi7W/H0nefUjdij/eT4lDTN9+IlzjnCUOOcAGPm87nQkjcbwFFkKYv5DBUoYSg6izE9zngCdW/yAVkSo3wPLhmp4GIQ45zYdsn1YOha4igiDBNUKOVJW1WsVmvmyyPiMGToaqwxRHkocHEv7kJN26LDkFApwlCNp3+DGQZJ272Xe0cFcqr3Ur4V+YT5fAoElGVF27UESjGYnr7vwBpqN3Bv6tKNzVDroKprIqUIA43WMb2tWW02NFWF8n5viLOnxI37+quQg2+uN/U032DWf+6Hvogr+Kr1WxsEvk4gwcN46jbSD3CW2EvnPQhlRrwtd7SdlAFpmqOjUDrTQ4+OIyaFkICsc2x2W/LJDGvdGOE9p6enLBYLttsdWIcZ+e1pmlPVNVmacn15yW67BSN1XT6ZMl0syfIJoDBWkSQZs9kCpxSL5ZLJZCLXHUVOT05OuL2+oaxF+382ndK0Ne++9y7WWbrO8K/+/kPeevKUsi65vL3BYdntNugg5PDokCSO2dyumM/mBCiqtuXp8+csDw+53Wy43l5TpCknZyejE84axpsOFaAC8dC7vbuj66VUOjs7wxjLp5++4OjwiCzP2G1LDg5i2rYlDDW73VYwBl3L8cmRQJS3a2azGbPpFLwT2T1rR7JWPCoXW7rWMvTSi/DWEIYBKoA00STxRObuzo2EHMMwPieKw5EctZNey2RC0w8Y63j3vff4F796n7/zd/+e4B6alqkRuLAKA5y1hKEWurCORHXaOaq6Y7PZkUVQFBPyQoxZlQrQUUyohbkYhIrDgyVdP9B1LdY6ssmE+SLAIffXML7vNElgMDgrtvE319eYqubpgwfgwSjFZrul3InojPWfbeHPbeZvWJ+T2buHHX/DRnffUG/81gaBb1oeGJwjQaJgZwZ8C3HkiSKhgIpjTwej1VUfiXJsVkwklS5LqacCRRAnRLEmcSlpnkl62Isd9Xy+IECEJT/69W+w1lFVNUEkjclAg1OMZpQ9cdJjjcUYOSV3u5Ikz/ZCpmVZstsJH2EymQhE1YqP/X16++itt3B4bC9aA+vVLavNGhUobi4vSJIUnJxseZbTFS0oSNOEwRmZXScJk+mE/t73PlBEkWY6KfZsN4EN14RhSLnb0fc9eZqAtSyXC7qu4eHDcw4PD7EK7u7u6LqOy8tLePacIs/xeGaziWgbjF4EXduRJBGR1uN96cfGn2DzrQ6wgyJQMDjR1RtMzxAEpFlKFEfy2DBgxiDinEEFkpq3TU2RSKpuvTTH1tuN8BLSnECJPqB1Mu8HUF7teQlSt/cEVrFar6mbhjgQAFfX9aNjsrgPNXUNWsqKIJBpw24XY62jH4woFAWaPI6IQ0ZWoKLrOhQy2YiThNWd+FJGKNCBbHz3BmvwLzAi3GcA8o0wCr+iH/A5qfFveP2/eUFg/PDWegZjJG3EY63Ui87fc8ELHF7GTp1w5J1zxKGma1uZLOQ5hMEYEBxZLrDWsq4AxTRLSfMJdVWxLRtUINTTJE8BRz6doqyjGjos4mcQ6FD4CVZu2K7rCOOIu7s7vBebMecceZ6z3UjdX263++ASxRHT+QIdaa6vbrm9vWW33VCWFUkSUdcVOhxHon3P+YOHBFrTmoFIayaLGcYZbq6viPOcOI4IA7FAH3px/Y2zhPliRtd0eO9om54iywSBiSjczadT6rIUUo8CnSTyWXQw4vpBRzKlCUIo8pSz02PauiFNEqwd0Hr02vMOcCLl5YygLZUnDBXGO4wxOGvolQfliJOEQInqngoCsGID1rZGDE1Mhy4mog4cBNjBCE+h6cd7Xcoh5xxd3xENEXEUEYViNjMYizMtOg4xg8EM95qMFcZ5JkUxukp1dP2AMQNlWe5Na6MkFgSnEdEZFQbyO1bhHvEJo3pRFBFHMclkMsquhwxmLDNGF6yvvs+/+uH75997aMhTv+zY/aXX/WugEv9LWV/HinIoumHA1aIWHGsN3mFth3WOXOVkeY6OI6yxwuqLJZWNIhGIXCwWQv+0htV2h9IRWZETRgFpnGKsI9ARZdNQ1Q1BFJMWBarTTCYZNzfXYryBl4wgDAlCGScmkUhxJUkqrLOx62ysIctSceTVMXc3t2KsaQyz2Yy+78mKAuulXFChGKV2Tc369paha8YNFPD4+dtCh7aGME5J85yTxYxgmlKttrx++YoginHecXR0iMex224pd1tm8RKPF6Uea7DWMZtM6OOYk5MTzk5PRsGQku1mxQfvv8sPf/o7HB0dsVrfcn11yb/2Bz/H48bpxpaHDx6wXM557733SRIt2IIwEAdm5XHGYqQ5gzWGvmsFy4DHmAHvrQSqYeAzuKzCeVAqwHvL0Il3Y6AUYaRlJh9qdtsd5+fnpEmOdaMAjJH+hHOOpmmkuZskeBVSNw0BLTouyPe4kHjMACKs8wx9hxlEpwDv2G63zOdzmqbBOMft3R1hFKEIUU5Yibum4uj4SODGOqTvLHhFpEN6K8jASMf0jRFb9lEd+7O6nS8IZvD1J/iYAeyzgTf2y5tN9Tcbg1/3Ur81QeBbDUbeWAovrCxjCVD48M1uKfsT2HmH6QWrr6OQyaSQDnYSY8wgGnBtCyogThM8UG4qZstYnHK1w4aSqkeRZrFcUO624CBLspFOPKAjCQJD01LvaiIdMZ3OmM5nDMNA03UoArIsYzqbkaYpVVVjvePg8JA4jrgeNQBN36NCNVp6L3ny9AnWDIRRyAd//ueUZcl8NiOKNUWaYbzn7PycB+cPSEPF7W9KNrsNzdBydrigyAvMYNjdranLEg8Ya6jKks16Tde1xHHC8dEhs9mEo+MjwLPbbrm5viZPc3abHa9evODs5ISh7bi9uRkbaTFZJpTquq6EkZdEqIAR6CN0b2m0jUJwSo1afh1DL0As0duzeOO5F+dQgdprNQotXO6Cvh9AeYz1xCh0KDP6ti7RcYLyhiiOyGYFioDBOaJAREtVoLDOYqxwFVCaMFQo5QjCiDzLyfOcqqqp6obZZCaPpwneCbZgGEKwYIylKmvm0znbcotLEmZZAmHAMPQ4a4l0hHWOOAopzUBjDDkGTYQz4NQ9c1CCoXL3eZhkT+rN8d+X9sAb++KeP/CFhoL8xr9tzvBbFAS+uL70wT+HnRgjqGNfm8WRhkCNJ3JI33UMfQ+I/HTX1YShYr25o8iK0cM+wgVjJ31E7qlQU5WNWEpVNUPXU1cVVV1xfnZKHGu8k1S/ri3WS2MSkOCQTfZpYxhppkVGeXFBXTfMJ3O8he1mh1eew+MjsjRD4Sm3W5q6ZH1xt8cIpJMp77//Hl3fyvX6ht4NWCXOR2XVAIq6qmjqigbP6vqGu+tb8T5MYvEULEKs9XRtQ296tqsN9a6kqiqZ4XcdURTQDx36QhOogIvXF5jB4JzjcHnE0cGBjGCHgWdPn2KGgc2INgwC8M4QBCHn5zJ6VU5Gb0II0gQBmMEQBgodKpJYY3rouh7jBqzpxGNwbJre17z3p1kQBHjnaNsRwRmEWOsY+lpgyNZiXEPfNuS5SIL3nfQTAhSDNdJt9x6PAIr6wdE2NaFWBDocG7YBUZSgGBiMZ7upiMKIxXyGjqBpawIfspwvCXUMSKkVxxHT2Zzru1uyPCdwnvnhEXXXMpkUxOeaMMso2x1d2YtArULUhj1CikLhCceb3I6nffAlUZ379dnBed8Y/CwKKKU+BxT+K9cT+OtY31kl9b6TogScIk40lgHGmk/tteHvm3FBEOKcIPgmWQEeinyCsT1Kh/hQ46yjbht0HGO7jigK0TrC9wPVdiOCIuUOlCKMAnwIZV2RjNbfzkMYaYrZZESgidFlEIhIhXeWtm2w1lBWFQdHBxwcnDItJpTVjuViQd81hGHIZrMh1AH1dcOLjz/COMPBckldVZjBEEcxfdtx+VoUfFUYcntzRZykXF1cUq7XnJ6coKwXJuFiTlFMxSuxaqirEhRMJgJ4As/d3Z0IjsYxx8en6Dji/OEDzs7PWC6WnJydMJ1NqatzFvO5fJ6mxg09WZ7i8xxrDEWWopSIdOx2W6wZwAs0tu87AuUIAwXOYk1PVe+wTnoVZhhg/Nt577Hj7F0p9QbDTzKzAAkOxgg8N89lZFmXUi4IoAy8dXuTlgAIg5AwCKQrby1d1wp1OpHA7R2kWUrbDRhjGbqe+cwSak0UBWgd470RLIiS++tejEWpgKaWqVMWxWR5BjpgUkzokIyl64U/MYyfFTyBCsb+ldzfn4P7vjEG/FLzb3zO9zUduV+/NUHgL7TeyIW8F9SUHUdK95ZUxZh2930vJ/0YMIxz+K4nDHusV5J+a40LB4IoQkca7wbCMGCS5+ggINYBVV0xjIIYjLp4QaDoR5286WTCZDaXJlJdM5vNWSwXVGU5BhQhcZTljn7UwWuahlCJQKe717APAmnadS040cRfHiyYz6Zs1xktLdPJhE8+/piuN3R9z9mDc1brDfPFAVpriqLg+PCI48NDVpsNQz/weveaq8srNne3FGnKdDajmE5wTkag1hryPOfo6Ijz89PxplYsF0uePntK3w8cHR6C97x8+ZKh60njhEgHhEqRjXZj5Vbo1B7I8wwzDJihGzer0IK9szhr2W7XNFWJVyI57sa6ue8FuivMQofWAVEcE4VapgSecdogrr/3eAUFaB0ThiHWSGZyf4/oUHozWmsCBNHpx05+MEK6o0jT9wNhHAIBTVuBcyMqT4EKRo+KDt931HXNbDIZsxSxtDs6PmK322GtpR3txXSkqb2TUacOiGNNGAafKQl9CeX/neGDX5vkfwk49A3r+/oO/E/AO+NTFsDae/+zUZX4z4B3x3/7Z977v/+t74IvkyC+tO4znf3vR41RUyKoc5627xmM3JxJEgGeMNSj0UiIR+C6eZbhqoo4ywl0TJplGBQGz3I2pcgngtdPUlGcXcy5ubmmbWvyIqeualw/sCimUExZHh7ggMHI6ZGmKVqL1Vi1K1nf3aEUzGdLvJKNHiUxdV3RtY3AhpWAh6y1owGHY7deYUfp78umAR9weHhE31levnzN0dERBwei+hsoOD444PHDB7x8+QKdxrhAEG1d23G3WlNvdyzmC9566xGz+Yy267i6vmK73TGbzYVo5Ry7qmS+nJPEMcfHx1gnsN6TkyMODhZkWcJmdScyZlmKMQN4SxqneDNgh45Aa6ajzHld1XRdgzUDQ98JX985yt2W3XaL9ZY9Zs4zEqasIEIRodQ4iphkBU1VEgYBcZzgRqBSmubYYaDvenQoprBxHBOP8t/OOJJJsicqRZHGti1t04hug3PUtcC/s6InSgp0GBPH0vNpuo7ttuToeEGcpJR1hwoEYWqswwdiRd60LdlyRp4XrG5uaZoGlDSLlVJyIAUyOr3PAvYn+T1D6v7e/uLt/h3Wd8UYfHF9L98B7/1/fP+1UuofAZs3nv+h9/5n3+O9fG06s/9l7E9/tW80SQfZo7yXm2KwBErRNA0KSNIcYy0hCPNwzAZwFq1gUuSk+YTNbifAkpGb0LYCCAkigduePXrA6xcvqZuGoR9IdEQYaFQo9Wq1K6naRjKQKJJRkZETb2hbkjiWMVWaSZoIfPzxxwJ/9VA3NZvNhslkIuVAGIqo5jBQ7kqSJOXJ07c5WBzImErHBIGMI8uy5PTkhCyJZTw6LajrWsg0zrLdbnGDIQpCnj1/zjs/eYfBDLx6+QqlAtI04+BgSTJ6Ilxd3bBcLjk4PKCYFPvxZte1FEXBfD5lMo5J8cjGHuSk1yMwx+FHspfgAcxoBy6/V4MxA13f0bTNmL05tJYNYoyl6zrWm7XAv62j9A477aWR6RxBqLCDlYAxCMw4UAGd6en7fm/84nEY4/anrhtly+SzShAR0VoPY1ARmHAk2n9K0bYdVdNw4BcQiMiI1qCCgDDQWCN4BuXuR36KIJAeQxAq6YWMpanpB4JAMZtNhQZszB4x+GZvb1/tfwVobl8aeP+5IPH1ecE3r7+U74CSd/YfAf/297r6N1/3/hry//3j+6tz/6u6T/UVoEYe+D0L7F6qyhoDkR957Ubq/VEPDgVVVY26AtLJ7weD9yVlU+/RfcVkQrXb0doGax3bzZ0QUrSm74TApPBYKzTTPM1oq4qD5ZI4ivDejQrJjiSVQOCdvKdVWTKbzVgsFlxdX7DdbkmynG15zcHhMUdHx7zzzo+Zz+ZsdzvWo2dB24lSrdYRbdPy8vI1m+2GyXTCwcES5b1YkoUta9MTx5FkM3XD2dkZh4eHALR1w83NNU3TcHx6wnQ6F8rxyKQ8OFgShnKKaq05ODrAWzsCb1q6tpHU2TlJ1UfXIGMM1po9jt05mQzcB4T789AMYqvu43hvyCIKPHq8jkiB9V1PaKW/EgYiYa6Q7Gs6nTEM8vf2e8y/RofReC+NGadzAmQKAtI0wVqDUsIABOldyKxfss1+6OnNIA3oe16F9wLuUsGYEVh0qNBa03UdRVGMikcBHo/1Ut6ESrAYRVEQhAHOfvVZr/aZ7tfvD/9GYPi+/QD4y/cE/i3g0nv//huPPVNK/RLYAv+19/6fftuLCMXyvjny9QnQZ4Fh/4P7f7PeE3gnmIHxde6loppaLMKdFa8B66Q+01k2nuSKuqxEiz9LePDWWwy9nOKvXr4kCjXaKZ49e8qumHCwPMQ4T1WWmL4jShKy3OCDgDTLCe5FT5IEfXxEojXlbkfX1rR9K7JUwZYAaOoabx031zcURc56s+Lw8FA8EpOYR2895vnzHzCZzbi4uKGsXlPVNXGSYK3db+qz8zO8hw9/8T5JEnN6fESepgRHh4ShJsuE5OKc5dOPP0FHEcvlkuVyuddA8N7LTD2OMUPPxavXHJ8csTg9IYn1WDcHjKW2AKPivYXDhgAAIABJREFUkCyLqatxxm8d1hp66+h7jzMDzlmGsYbu+1bYlZ3QtwGCQLrk95v//n3oMGToOpyT7K7abtFak49SYfcIwqEXym+e52y321G2S/7+ApbSMnlwdgzGnr7vx7JNS+alFI8ePmAyndEOlqZp9911FUDTNGy35Z4OPhiLCgOMcagoIBsNSu8tyJJQE8QxodaisrwWqrcOFLY31FUlcGSlsN+Q9H9tdnyPCeDzeIDvs/6yQeA/Af7xG9+/Bh5772+VUj8H/hel1O9477df/EH1hvnIvvvrHHZ0mfnaD/Vm6fTZi+FHQwkfhsIOcx5j7AjjDCjyCGcHtM6JkoTBWnQUjtLXEf0gf0CvA7xS6ChCxzHzyUz0771ChyHLxZLeGII4pmsaVtfXOO/Ii0KivffoUKNQcn0gL3JQcHN7Jx6GcUzfGpIkRjnHrt2x2axp24Y0izk9E6+AIAiZzuc0fU9qLU3XcHt7xyQvePzkyWcEpFCTpim3dys5uZ0j9FBuNyRZxnw5J81y4iiiKStsNxDnKbtyR1ntqKsa7x3z6RQ7iHRbVZbkecakKIi1xvYdcaz3ZZhQt0GHiYCCRh+Fzogpi3UO5aX7XpY7mrqkriq6TvAB9zeujEO9MAfHFPe+1pfegDweBgHVTsRE9CRFh6GUgUrtXZ3uXzPP8/G9iOafCsANcl9570fPyPsAMtC2HXXTCAw7zwgGT9f1gn2IY1SQoFTArizJ0pRhzDDqtiWNcuqmJgomqPGQiqKIVEcQa4YRPh6GocCGI4Ei393d4owdR5b+cwfat63PKMLfVgB8t9f83kFAKaWB/xD4+f1jo/1YN379C6XUh8CPEJeiz603zUd0qP19zeS9dHu/5pqfi5l79tTYVHFOhBriKMI5+UMKSsxhTT/WfwLdjGKZ8YoktpheOiPqu2VVolWEDgKePnnCbDZjGMSGWimFC0Loe9I85+jklN12i45jUGo87XoircmzDGNkjDUYAblM8ky4DfUtjGgx7z3z+ZzdbkcYqj3Q5vp6RTGdMgwDq9WKIAyYzafEOuLx48dSN6/XvHj9gtevXlNVNfFI4/VOGoynRU4Uaqq6wsUJXdugnCPSOcvlAq01+iTk6uKCnfecnByRZxkoj8ITxRoVjCpDviBEobwbQT7yeJYm+0wCRDGnN0aCQFuz266pyh3OGrqxJxBFMSqS0kwgtGIO453DWCtkHO+RvoP0fJTUfQzGkGtN3/YyDo4iuqEnVAF935MkCToSb0D3RokSKtEQUKNLdd/3Y2NZmIPGCnNSx/I38uPzBzMQhEI6k+xFkWXx/nXuP0McSF+iHVpCD3mR0Q09VVWhCKjqGhdprq+uuLm5FrFbvLgTfU4M9L7f9dXrzUPym0B233X9ZTKBfwf4c+/9izfexDFw5723SqnniO/Ar7/thVQgsk/3dF5gT1H9unUfAO6/vv/4Et17lI5waiSthAHKW9I4Yeg6OQ0muVBDQ2lePX/6DGsMkzTH9AM61UymU2yoaJ3BIj8zDANajbWcF5mwp2+/LWNK62iqWqilyPsfhoE0z5ks5sxWay4vL+nahtl0QrmryIpCPACMQetgFCXd4L3n/Pyc169f8+TZM46Pz1hvd2w2UldeXb8mCmTy8eMfvUNVVqje8vTRY5YHC84enrFcLjk/f0Bd1Xz80cd8/OkLjDEsD5YUfsrD82PyLCdQAUcHS66uLri+vCJNNMWkwDlLFkc0bUWkoO9aIh2ADxDDZ8Uw9ESjnkPbivpSFGnqeieCLE1DXe3Yrm/xVk73NI4IQiUCK2N9Dh7T91RlxWAtbdfu7clDBZ3p0KGoCHnFOBVI6XvDzd1aaLv9wGQykXsKOUzubecNYvYtyYYImxAo8iwXVWo82+2WsmqZTBcj63NK1/dycq82YMG5HScnJ1jjmOZTsizDhR7btQQ6JY1j8Ia+FbBa3/dcX1/z1qNHVHWLVcJRuW9EqkAaqXsm4P8P63v5Dnjv/wfEffgff+Hpfxf4b5RSA8Jx+Pve+7tvvQbsu6eozxp7bwaBz/UKvgY9pQRtOWoNBERamlhJHBGFoUhKRaOAiI7EOCLNiHTCdDKRBt1uS6vbfdbR9B3t0DOZTLi8vkJ5pAmVy4lMGEIQUKSZQEVDjRtHTqIsDIEO0GEkzL6ioKxqhsFQVw0KRRLHHC4PqKod6/WaNJ8zySbkaYGOxe68LEvapqZvG6bTOYEHa2UW/uDBA6wx+Hbgk1efMIwjuLwo0KHM8XEyg9eRRicROg4JtegKBkoIMHZYoke+vw5DZgcLPKIu7EfXp/vMxVkj9o9jN91aS5Yk9G0rYJi+o20auq6hrcp98BX4LoTOMww9MsYNR/7AiOUwFms91krpRABmGEhHybP7kzcIPcb5vdim9EjiUXzEfk6E0zmHGQaCUKFI9wFmsVgwmUz2zUCFp6tbSl0KNyAIEChvSJxEYy0v8GU3NnXTNGVd7ggyASxZa8V30di9IQkIyzNSnuOjyb4hK/oDGuu+uz3Hdznxv01S7M31fX0H8N7/Z1/x2D8B/sl3vPZ+KaX2dt9KKawyWGfRwf2YZzz17ycF7ossC1kyKgRnPWbEnUfOoJWM7JIkFrmrwYCDvh1YLo9JJ1PSfMLdasWLTz/m+PiELomwpuH4ZITBqoC2EZ9CNzgB0wQBpu3oohrGDKbvJUWNoxBchFZeutp1jRk60jQe8QQDx2fHHB4eSoAY3Xz6wVLkc05OTghTTVxV7DZbLi8vRXzTGNq6JD4+JhpJUWmWSUc+NRwMB6xXa0zfs7lbMc0mOOfJi5xskmKdIctTpkWGtwbnBopiwtAF5EVMMTkWC/GR/w9yalqvcM6ANXjrGLoO44Ru3VadyHVZSOOY3a5FB4qm3DH0Da7vwRgCL6NZ4yxOa/puwMFoVCppdxhpkkDRG2m+BdbivaTZphsIEo8PQ+KkkE1rDEVRwD0AaGy06SAQyfNUSD6DB+sMQRCh0OBDdOgoJgmRTmibAaU1QSjAJD0SuMIoJB5CIhUQxRprIrwZUBqqrqH3luPFDNf3VOUWa3qs96hQ44yn2dQUac71zQ1plnKa56ywTOdT6f4rhR2JRG6PIFbjuO+bt7G6RxN+xb/9lYKF/mUspWS0cj9iup9X32PonXNY777Eu/6sPxrs56zykNSNUaTlDxpHZCPwp+t6BmPoe0OUFmy3W5puwNyuGIzh8PiI2XxOEIRUTcuh9zSjbdf5g0diajECRe5n0cIYTPbeegI/dcKec5a6qijLHfP5DOccq9WGwRimiwWRlrFU09Y8fvqYH//0HdqmYbcVB+BdPzCfTveNryiKaNqWfujIJwVpkTG4gTTJaOoBrwIevvWIsiz5+OOPGIaek5MT5osZRZEL4ccaHF7GVCi2mw1pEhOoAmOkESfqQPG+W58UBWW5ZRh6wkARJyFD1VBVFd5KFrbbldzd3TEMPW1V88nHvwHviSONDpRAfWF0PpK+jff/H3Vv8iPbtud3fVa7u4jIyOZ0t3n1LIzAFi5hTxgwZIKZeMYMYcQQBkgeYPEXeITkERISA5CQgAESDJggWwwYwAAL5EGVS0VVvfvefbc5efJkZkTsdjUMfit25jl1u3LZ5taW8ua5cfJERO5Y67d+zbeBVE7PJQQqJ2w+X0mwnOYJow0qBBFn2WzJWXAgkpEARhf+g+NwPAo6U5t1TCyNzyyBGyWoQJtQMWKd4q/97l/jT/74C4Z+wNrAfnfNyxc3jFEETbqrFlImkXl3944cAp/9zue4thY7M6XYbDYYDc5bbF1xPPZ0dcXFbkuMkf2LK37/936Pb/7JH/BX/ua/zZtPPsHWNWGeIceChlcfrO2PJce+c+/wY6Hix6+fRRAAVpLIGTl4LgXO33PMEgh4Bor44A6UBg/nskBS1/NmzQrxBsyJyteMcyDMEzlH6trTbPdiV60033zzDZuCB39bzEtPpxOfffoLYSQau6oDVbXD+wplNLqYWaaYiCmQUkQFUSdWWpSFx2Fks21RyuDqZpXMMsZInVt5tpsWpTKHh0fG/iQQ30Yg0O/efkvMgmtQRiy+hLsQ0NZwc3PDi5c3zOPIi+tLUbrxjhAUY99Te8duuyXmjCn16LIsWPuE0ddaoTXFvtsW5p/o8s/zSD9OGJ0IcebwcE+IC84axv7Ew/tvy+85UHtD5asVhDPPM1ZZyEruUQiSzltZhlabEjwFIbh4cfXhfKrlTMgJG9UK8pGPPq9rRvQLWLEC1jqsl9eqqoqcMsdhoHMdSotVm7VWbOQeb9HGrjJoVgk9PJfJgrLSUI4xrbiHnIWVaa0W8dFlxriKOSxUZQQagkioV5XjT776kr+qFE3doJXgDMQh6MOt/JMCwEckoR+6fmj0/vMIAuX9rWyxkuao0h84/0iM55slp//TfSuPF7SW0ggnfIkM44yzTyf2brfDGI2JSoQ6OxF7eHx/z+XNNadHUdjZbDZ4X3HsT3La5UzlG/b7PS9fvkQpkTC3TuO8K9DlRFQKNGgl2oZaK5lIWE1deVk4iqJb71CqQinFq9cvpKY1AjbZbDrxvS8LfLvd8vt/8AfS6FKK29tbQop4b3nz6SdiK5Zgf3ND11R4b+gayTJUVqVxN6CLXLsIZsqoTyu5d6aAXTSeuEwsUwmkdYVVz4JE1ByPj8yj1PzT0NMPR+ZxwllIAY7TiHeWqhJWovgAKOZlQZURqpzkos4ruXEufy85Q101DHkocN/yXoxsGlsCh0RQmc0Pw4BSRpqKqCJJrmQSVLJMpbTQyFOkbWtCmDgeJ1BSctR1hfdiIqKtwThDygthiQV34AmTEIBUthgrPRSllNDRSzBqmop5mokxcv/+jjefvWa32/E7v/MLjFFUdVVAZtM6Gn1ay3K6fXDg/VNd/5xHhP8sr3Of3xRhDutdIYnIhz2XLivzmSwSUSRpVq0lAGs9kLMssGEc0Uo87re7ThSE27Z0qC1JWbTKDKcjzXbPl1/8mtPpxO/+7u9Sd+2KMGvbFu89m21LTAvv7t4Wl9sKVFqzgpQDcVlKeeDlRCoglUyEJLoEdRawi3F+DXzX11eM44hKmRcvbhinnvGhp61rjv2JlBJ/9V/9V7h7uOc3v/6ScZ5wVjMOQp+tSubijCHlSOUd9UY6/+fm3bI0KyxWayV4ihhJWRSNY1ho2xalFH0/orRs+tPdI9ZUbLpG6LzHnv7hwDCe6I8HlqkX1WeVccZRbxydr3j/IMaqrqmoqpZ2s+NwOhWj1IjShrp2TJMoAgnN2IikWM4M/VBKwoRFPvuzZbhSmVjKkGmeBeJdOAfnsaF1mmUJnI4nYRPOPcZYjLNFHFXz+s1rDo8j7969p2oaqromhCgy8nEBrclLZH+xJwYlitJKCxbBauZZmn9xFpSgNDItrqr5o1//IV99+VseDw/84pef84vPPueXNy+4XRYJZMbIqFUVtGuGXPgwZ/zwD2FmPqQK//8HFvpnc5UgqJETVNtCAY6ywZ53ewUU9GNPtSKvWZZIPw4ihW00fX8k54am6bC+WjEJ0zShVeb6+pqrK9mQwzAQl4VhHDidJMY451eXmSVMdF1bUvlYOs4aOENWC7quiGVoZ6mpMdawLAFjS/3r/ao7qBHZrn2zJ9YtrrK0h0NJbxVN09D3Pbdv36FSJkwjQ38Us885k+sg6skpYHWmbVuMskBFCHHV+huGnhCFfu2UJmfFkiMxCLa98nY15LRaMY39Wrtqo0SrIWXaumGIkWUeyUkovkZbcBpjHDFklHZYL1h9bRxKW7SW+2OtYV4WcoSzgpD0CZ599vOMcpramIJ6PE+QkIwgnMVMK06nE7YoE7Mk2lZBkvGf84a6rqi8YwlLIRpJ36iqa8b5VJ43ibHKIriTZZ6lF1LXkGGYBkQwRSzEhn4kzMIYDCFhXGaZ51XcZujHVTnZFUr0MovGo7YOUhA/g49W8pmHAD91kz/7GVWyifzj//bnEQRAGi9K1FTOqV5CRi3nBo9dSuTUggikRMzvrHZK13QJiZhgnCYuXt1I0yhlYlxo3bZYVsli2m63GGf56usv1xPFeFcaRlZMR9NEtpYUwup3sCyLWGxnkdHKJFJUpPPYEwG9LEvAaEHXKSX2YlpZvJEvAOdF0NJ7T9KKS3uNrTyPj4/c39/z4vWr1bZqt9uJ8Uo/kufANIz0OlA3FTFOHNOEdwZTyeK1Vjz1YlLYWZGipLwpR2n2eXkPIc6krNAG0fNzmnmKHA/3WAVxngnTyDSPjP1AUznZICCjyyWSlcHaCuc1dduhjcNYjbVzOdETbdeScizmqIVgEwIoIUbVdeR4PJJzYlkSu221gnOAFVBktGYszcV1DJmz9AdKQP7Nb37N7mLDbrfD7evyekqC4SJKTqmM9qx1ohPhHYfTibmUb04prNOMp5lxHGndhoeHe+I0EZd5Xaun01AOMs2bN5/KZs6aZZ4xikKiOguoCrnto8X7DA34/VnA8yR4pWDmp/HgBziaHwgEP4sgIO8vFQZfWJtQvrIYlbEarJa/m5TIMM1zKIgrARv9qavcgZjka1oC797ds20rrNbUMfDy9ae4qgYjKDeUYtfU60l/RvJ98ur1imtv21Y6wSozDT2UlPn8npUCUz5AqRXPv6NwFGRWLWmwJCHiIJSynBohOjYb8fgztaerPFUjX3Vd0zQ1m+4vsetawTk4GVvefvNbUobxmHn1+iX7yz0hzUzTIGWNNjRNQwgz0zKSlYwHc1JUlePu3VvGQdLvZZpQStLvuq7FWTlPjMPIu9tbhpOUKRcXF+wvN5wOR6y1T+rJGaagiMqSk6KuWza7i9Jc84whQE60bS1CI9pxPJyYhhFrPMpo0IqURdx0GYc1NT7LdZ2xCUoplHHrfTBGSgCVM5WRz9B5w2eff0pKgdPxwM3VS6rKYzQcjydOx4lpnLDOobSUIzFGXO1x1nCx3VI3raTqOTMvM+/ffkvb1lztL3m4u6XpOjZdh1KKzXbDN9+8lSAVE69fv+Zit+N0OjGqyGIr3t3fMwXRU4wpYp4vYfV8638Pd4Bnzb4fARr9hcgElFJYpaFMB8648TP/Wxev+fP3iELr9IQa/AA+fH7S8s2Irn5T+7WLbK1Ykg1jz5JF973dXtM2zeqgc2Yf7nabtU4+j6Ug8fh4T9O2+FrqbHJmGcXlFjLWmiJAqrDWlIabxVrRE1BkQvHLMwVwIoCpxDj2ZXErfFWjkoiZ+lqEL3Rl+eUvf8nhcJAGFSK+kTIsy8SyzExjLxtiKrJdKTFP7doHEMIWhLjgk3Txc4zEZS4MPUN/OnH77W2RJxcwTX84knOmu75mu92uJ3OMRX3Hy9iMGapqEhvynNag2jTNqgIUUyZnRVXV3L17z+F4wLui77Bk+n5cacEgKsRNXZdu+hNxJoRQoNaSPltnIabVf/Lq6krISLMYvSzLQl03axvpPIp21q2liHOO2tfEOnI6HjFKMRTsvzNG+pEK2t2G6XSgqtzTOjWmuCbvqX3Fb37zBb/61a/YX13gvCekyKH0KcSfQT1NQICfMvR7rr/x8ePf9ecfun4WQUAXnfczm+qpeaVx2nwgMWVzJuSwThGeW0J916WUIsTEEiLh2VgpRFH2qZTF+ortdoP1nqTgOPRlbGYZl8BwL5p8zpl1Vt+0bdHVl1N/Kv8mBgELLRNMo1hR1XUtwhjer4o95+xHmpzi0Seil5YQZgHfIGWLVSJNtdlsSEEmAipnrq73K5rQlRLCGMU0jcKFEKVvlmWk73se7+9pNy11U6FilpNqjkWQY+Dh/p7T8ShW5uVzeHgQ5t7+sqPrOi53FyxLZHexp6k7od8qAxiapgat0c6zrWSxoyhgJmnyaqWpfFNO9khwgq2w7lYgtMhmOGNDtNZgrfQjlCqeCRO73RZjnhCGII3lcRwE91+ymBgjY3+ia2tmp+iHE99++y3LvHB1vaOuauAoKs5IL8G2ktX4qpIokfIzstMZpi7j3Kr27LYdxiiOx0e6yy0hzFS159u+p3KSwX311Vc0XU1lLHPKHA6nMuiSz9nwzGL8JwSBD+ByH232PwtaEH4mQcBoMRGd5plAYi4zXmMM2joUQjX21gGKUOCl527wuUb8ritlQVadTj2Vs8JuKyMla6246PqKw+lEPh5J2nBxcYHSGm0MX3zxBVVVCQV5EaejKURqpXk8npjHd5xORyHdANYouqamqitAFs9cTuVpOnH3/ltpkGlD23QicR2X9aRUSnE4CumyrlqUz2AS4zivpJfffv0VdVXRNBUhBNpNW0oYQ4yBruoEO7+E1QQlxcjp+Mjbr79knieMceyLzkFcZlKYVy2+Xbct0tqBv/6v/esSDEPk+vq6pN0TKSwsMbIkRbN5IXgCY0BLfU3oyTlirUCSrYYU5hWnMAwDKRuM8SjE9ckaw93dHSFJd7+l4V1/FGKXymLl3Qr9V8Z/stSHSYg9p1PPZiM+kFpLv6ZtGh7v3zPPEy9e3mCMoa5OVFVDt9lgjWa72XH79p6+77EhsGl2GC0ThLfffoO3jv1+z9v7O1KMbDcdKQSWcSxNTmgbwQFYq+mXEaMdj8cHSJG6qdhdfMKLm2us8YT373l7+1ZQpqps/j91qn/3Nn5OHFLqqW74Mcrx9z/jzyQInBGD4QwRzZmYBMRiskwMnmMHzlp8ctqpIk/9/bFPqXOwiGtq3Pc9dbeRYOAsahbzicd+EOLHIuYZXWH9LYsYULx58wZrLV988cUqrPny5Uu89yX1HhnHEcjUTV18Ek3Bu0+CcCgQaZlrT2KBFiMoRde168btT+K5V1UVqQS+nKHyntPpACqx22yL3JbIqcUURYpda1TKZbPoki4H5tLZhowvbk05Vjgj3HtvHfM0c//+PeM4st/v5Z5PgYv9FXVVMc8L7+/vCb1kP9vdhoi81rE/scwLRGmY6hJUq6rmdDrJJCBJuo3SxW5tIKRI3TRcXFwwzeFp1GcdIcnU4ozQPN+/53WxXjMhA0ox9D3XN5fSYNOZx8cDL1+9ABTeebqmkc9smTn1PSFIqm/L9CcXAlhYFpZppttscIU+bo1lV2C/3lsqt2UaBkJcCp6kQmvhA2w2HeM0cjZC0a1byydAOB3mGU7g/E39NJSABIIn7OyKn1txVD+eE/wsggAq47wm4TBBnIQUEDJiD60FPKK1FuFPo8lJQVZF303zvdipnFfe+cOpx1cO40Zq71Bx4Xj3LTevP2UsuO/pOHBUBmMVXlc01krnfYlctDvCMLPkken0QIgRTTHXTGFdgDFFlnkhFqfbrm2kM66MGHdGKUtiiEBA58z7d28ljZu3UCiqyxKJGiatiYWTQJHGqozCpIUwHjmOPXVVszhDXASrYJQ44qIVS4wMfc8yz1hf8+bimk3X0LYNGUWMmf1+T1XV5CwWbVtVEd+/Z4qiSrzpFNWmxVhLzolsNX2YiSHQnNN2ZOR4HB8Jw4mIaCm4ugbj8G3HdDgwhQVjzhZlslDruqGuWpqm493dregVhExOEJaMxVBXnpwW0BXDMJTMENI84KqaZruln0aapmF2lmmUkm6OmkRgCBNJZbzKtJXGGcWpj0zLDEbhnKezNZZMSDO+crRdzTILtVsrhfWeaRw5nEZ2Q6DdyhqzBawFGpVl5DoPC4dDL5OoruP27h6/DLz97R1LP6NSwColfSljUSlhxW2FqJ42NXx4osfzgaifAkBCfvgD+PxPvH4WQUBrkWV2fpEbXohDAnCZVnlplHxQ6FKHK0VGYaRXt7IP4alxcs4cpM8QMdrS9wNOi5bA/f09S8pc3LymWjwhGF69flEw9lJq9CeRFn/58jUhBB4eHvj1b74QF6PKc3t7W1R3xO6q6zbU3pOCaOkN08R4PzItMnd31pFTpvaVuCYDTdMJas1UdJ044yil6U9H5mki2arUuHKKu8oLIKqf+Oqrr0mLnC73x8fyPPLRvvv2ljBOvHrxkqvra158/hlXL66FvVhVqNJb0dYSitTV1dWNSJQ7S1oCcwhUVUMoHospJWpf8YvPPpdezlntVym++eZrbm/fkqaR/fXVuiLr2mO05vB4IMeEr50g5uaZvhdX46ZuCN7z26++ZAkT0yT0ZKM0MUjDUhPpNl7Gs+G5OpEmxLlAm2VcV3ctvvJY67i/v2f6w4VNu6FTnuubq5UBWFUVzol0fNPWZCUjZesMu91FURyWAJeB29ORNI1cXezh8YHL3RZrPdYb8TOMUm6JpLnI17+7f89uf8G7+wf+4T/8B/z2y69Er0BllLYr5D2vpJ8/G1ZQnf/dD5QHP+tyACha86AxpARBG4K1rLioLIgqcdQVCKhNmaAkQucfwFGf+weCGpvoWl+krK3cboXMi0MUi6xx4mK7ob244Ne/+Q0osN7x5Zdfcnv7tjQIRYTCWsvj4yPee7bbnZxC8yyaBjljjCZGAZ0kxEOxPx0Zx5HLiz1ai4adMYacE943dJttmY5A23VsN9tCG9ZobTmcDnhflYaq4+LiklCkrepCZ5XNJaO87YsXfP6LX1A5z7brsFmTtcJYi3VOpLlLySRTCYN1jov9FUbDQ7FxP8tyDcPANIzsdru1OXuekJASr1++hDCz2e+o60bGsDmTszQ1d7vdWv4JfNqDQ072lKicRxtRkBbJd8cYZnKKBcWoVqju+bOdZ5m0oM9oQMmY2kamKzfXNyhvCHOgL0KmPopXQijqRrX3cj+sQykxbDn3FbwV/8G5zPfrpsXVFc5KuVYV8liCQn6TbNMYIRcdp57tbovJDcucxEnpLCqTomisfVAK/DDo9yykc/6zetop62f1F2o6AFkMOjIoFcl4YnSkM5JqXWjyyyUSRsvXeXJw/vq4BnqeGSijOfUD201NWEJ5XFhsOmes0bx6eUPOkbu7Ow6HQwEuSfr+9vYt8zzhfceLm5egZIwlmHIBMD08HLDbc4S6AAAgAElEQVTGst1uaDcNVhvGoiuwrSrBoNOTkuKrr79l6EVy65NPX0tKrQzXN6+oKovzlqb2hWiSpdZGM4wzKUIMEWMcn3/+O6QkHPscE/fv7/n6q68ECltXbPeXKGsxVYV1HmcdrhFfPqMd1jnGaRbsRc44J6O4tutY5plpDsyTvGYK8Hgv9+XsGJSBtATGaWC73coYMyxoa8ShyBjmEIsoqFC6FaqoQFkqbxkHmWCMwyCfW8orUUdrjVHgrKWyhrO92VmHIMZIVrmAykTaLPQ94zjgnOgGdF1HtorDfCSnzDQt1PO8BoDzFspk5hgw00TMQXwRQoDNhnkRUdemaagqySDqukVpW7gDNfPyJIrTNCLPZq3l9eVrrq9vSMOJZQ4QxUWJ87gzs2YCWSlyFtHajyPBx6jCcyCRJ3n6gZWP8BMCwU8RFfkckRt/VV7iv8w5/32l1BXw3wO/BP4E+Hdzzu+VhJ+/D/w7QA/87ZzzP/qh18gF3eW9JWdH20mnPOVE04im29k9NoSAKSyynDLBJIIKZJXRKq+L4RwJz38+N9WGceZw6Hl1c7Xq/eUYMSpJUyyK+MX7O7GsfvX6EzYbAXrs9lvmuaJyjtvbW4GBOsdud4Gi+OJVNe8e3vP27S3bzYauayVKx0g+juKAmxJ11fGH3/4RISzUlUMbj/eevp/ISTONgbrzaO1FTGSe2O/3hBDYbnckwJcRpdGgVCaHzJITze6C6CyLNVTGcX15zUUB60Rjmaxm7Eec8+Lfpw3b7ZawJJYgqXRCEZa5mHhocoIc4fbtO7755hv+xt/4G/IZhIWUI7OS+900lYh5zjDOM8Opl7IiAzGhc8aUMk5k4OLq3TePIkY6jiNzXFYx0rf3jzidUWlHiDM62iJRZmmahhhgiUGsxcaJ9+/fS8D5fzMvXrwoDdjAPEhTuOtEEejcx9htt9x++y1ffvErLq5viNavpqK/eCUckeFUjFC1ZrPZoqxoHHrn1lFkjBk0PD48Ms+ic7DpOqZl4eXr12x2O+5S5Itf/xqi7CZpI2SRbMslCPxAKfBDKED1PELkLCCi542F77l+SiYQgL+Tc/5HSqkt8H8ppf5X4G8D/yDn/PeUUn8X+LvAfwr8TURW7F8G/g3gvyjfv/eSQJYl1dMGX3mJzcXS2swLs9KoLEo0ORuMUgRV7BtLOvRdv6s69xOeHuDUD4JIK5yEFAOPDw8SFDZ7UBpnLF234fHxkdNJQEA5JfaXl9Tes9vtmKaJHDPTKCw4Zz13d/c8PD7StS1N3XA6DcxnRp1vGIeB41Gsz+/f39N1DS9evOLVy1egoC+kGe89OSvx8yvaCOdZe922hCWyhJkUFryvMFqzpJnhcBLarHY0rqKpGuq6xRqHryqZFmRDVFD5iq7tMNYQQyqjrk68D5eRlELJ0MRd5/HxkcPhgLMC362dxxXB0pxFwLNyvozWS4pLMRhF5u1hCaXeFpfgJRjmOZd5f2Ap7kzzPBUegEWljNIC3dUx4qonqvmZ7hxzEp5ICSIKePv2LUYbGt+gjSEuY8F5+ALdVpxKaTYv8yqe2g8jb9/e4q3l9dUlXddReYcNmYfTiXGamKeJY99zWbaltVb0AJTC+5r3776m9XK/T8MgDEMt2ejQl2xH6t9yasta/rikfX6Yff/+efq7D8BCuZQWP9It/CnKQl8hKsLknA9Kqd8DPgX+FiI7BvBfA/8bEgT+FvDfZHln/4dSaq+UelOe53teIxHSjNXi7uu9h2LTpFFYO2ONps8J8SAUPzlTQDey1J4jrdUKM30CYan18XGWmXJXeZlpA9ZF+tNANA7va8CQU+b+4cB2u8UaS7ttaeuGaZzIKfDu9g4yXN/cEI2YhMSU8NZT+ZoYM8MgJ0jl6tKcFFPNnDN/+V/6y1xdXdFtWpz3Qm9tuiJdBREttFPnqRuRrz5z2Ku6Io+yaKy1OGtxxhNDYp5mZj+x7y7Eq1Br6q4Ts01jRTWnqqnbmpQi43FkGEZ82ZjnSYJSGaqKeRa8/xkvAazmKMty5u4L7FuQlT0qBaqmwRgrmv2TAG1ijAz9QNWANqYEaYCz+7BgOJaCxXfGEcYZS1x7OGcpunMgkAykYYnikXAoLlP766vVr+B6uyPbzDIHIUU5x5QWhmFkKEHjYrej7VqOsS+CJ2plJ9ZNRZwiZrTEkAgxcDye6IdTSfkdyxzQlWQG3jv6w5F5u8M6MVRxSkkp5jzzOH+wL/PKgfmOht45EPBhOXA+PL9/X5Un/ZER+p+pJ6CU+iXw14H/E3j1bGN/jZQLIAHi18/+2W/KYz8QBDLzNMoC8AnnLdqaVfvOWTn5l1lmzEkrrNFEIw63RiviOYSWG/P0O5/zIVWEGqT+e/vuPS+u9uvpc31182RZblzhA0Rurl8wTRNN23F1uafvew4Pj7y/uyPGSLfZyBRhCShtUFrMSMSBJoudVZmJD8PIMPQ0TcOLFy/Y7XaM48jheMI6S920GCud6MfDgVQkro3RTGMt0whr0U5jlMY4QRouITBNMypB3XV0FxdsLi/Yv7ghLAFXQFYxJTYXeza7HZBWIcyhlylJ27Z0OWO0YQGWJXI6ikRkU3coBfv9npTDit47HB5Fkquw47788ktyTnRtS0Cx20lzcBoXTqeTAHKsODufTidUQYt674usl6D1lFEY78gxM59GDFGUj7NsBVOYgjFG5pBwqsL7ik8//ZTb21sq63hxfS09g1BKNTzjsDDNC0uQRm3d1NIfqCuudhtp9vULu92e/W5P225Ec9Fb8iRKw/f3j+gcqYo0u375SkqaDGPfc3V5weeff8bv/z//mNPpyP76GmUsc4iYnPj0s0/449//AymDFYLsXPsAkLMSenEuh9vTnPAZaSivkUOVvtj3Xin/QKj4MwQBpdQG0Q/8T3LOjx8gkXLOSqkfep3ver7Vd6Byllg68zEEgdJmIdwYDckIz9xqTdKKqGXjG20wSotwh3BOzqXW0+t89P9SbymGcaEtgiJ9f+QsGDHOC11rMdaS0KuyjtGax8dHHh8eOB0OKKW43O/pirqt9Y66aXC+ktNvnpmXmYvSQb9/eOThXgLH/uKSjOJ06sXdZllAGZYYS6feME4LMSc2bYd3DmMch8MBX9W4qsL7mqYRW/BlEtrzOA3MOaGtKad/SwwRhS6Y/Bqs5nE4YpF0ftNtqasGX8npZbUpMN/A/f09d+9EFafbbkixKPeoTNvWNJXDV1LXp4LybJoGYwxN25TfL5STPXBXfBHevPmEDJyGvpQRvvhDqGLdZVann9N4AuTvFEp4D0U6TOuzv8RMRhiSjZfeitGmZAxF9TmEUjIsDGkQanFTyXttGppawEN1VbMs9wz9iFFHjseW6bKjbmWEKNoSFXN/xGqNLdJmwp3whLjQn3q8tTR1TQwL4yTejCklcgz8zi8+54//4A8E51ICwDrj/55e3g9Bgb+vHHh+GaX4Pgb+TwoCSimHBID/Nuf8P5aHvzmn+UqpN8C35fEvgc+f/fPPymMfv/HVd2DT1jkti4wFjRWuOllYgkkMGhQJo4S0cc4OpEbWaKPQ6dmU4OMbwxOYorSbi1X4xP5iJ/V0oQN3xbIqhEhcEsrIKb8sC4/378k5UnlP5WqaupbnzanUg7r4HCh0FnrsEhPzNEknvK7ZbDfsLy5xvmIJC1UjZii6BJGmbnGuwvmaTEaT2W4vqLxjHMXgMmfWutYYReWdaNeDcN8nQSF67wsa02C8BaNlI8eMrTyuqksZpdHGMo4L3mVi7rHGyPtwHl/VtG0jCE4D1so8PCeR9I5xYR6FiHNzc1MCqFhvZRR9P/D29pZ37+7WksJYyfYEDCafkz57T8iiW1WFlVKigKR1EeYULYGcRdhVazGbzTkTQ6LynvpctmiNMiIyGkS0QCTLc15lxM5XmJdVldkYRwhiMjqMA10sn4fWVFXFPEjpl1MSCzaladqOGASNqtuGrmvZbLZFGLVimGfG44EQZX0rSt1OhjIpUCh0hvSjZ+qzDIEfHwn+ucqB0u3/r4Dfyzn/58/+6n8G/n3g75Xv/9Ozx/9jpdR/hzQEH36oH1DeIcssPvOLNkx9TwpexnM5yymTAqqcCNZovBPgxhwjdlkI8WztndcREiBd1zP4onSlM9IEu31/x267oWka7u/vpQFkPcd+IBdF2nE58Xh4FIGQnGi7msp55nHhFAKLlp5A3TSyCVMZmZHJMUKZB+8vr1ZGoszbF5quo24aYfaRJRhYx/Ziz+W1ZR4mHh/ey2JfApdX1wXSCnUjykfzEqSZ6msud4ZlnjkeDsxxxipNU3j+w+ORSWsu9nuurq7RzhNzZp5mliQEKxkpad69e48rctxXVy8KGEi8BZ1rsUbx/v0dDw8PbLuOaRqYx4lpmnjz5o34AaRMTHA8Hrl//56vv/mGcRy5vr7m4uIC6xzVNHLqe6ZJDFvC8kQPfq4SFILoF2itcHXNEp8+33mecb5FW8vpNGDmGWcFsOWcIS7iDbkEMaI1RhyNV08DI3bky7IwpoCbZxQKbwVkJOIxgWWZCUExzyJr3rXCzwjzhLMGbT3OW5LKzBnGUSjcVVWJWY3SDOPAb3/9Kx4e70Udb4kYFElJVysXybxzUp2V6DN8b1NvHQ3++Ob/8+oJ/JvAvwf8Y6XU/10e+8+Qzf8/KKX+Q+BXiDEpwP+CjAf/EBkR/gc/9gI5wxwXVNCYMBMWaUqpHKGkgClGIKF1Rumih5fAWYUzmmg0i1YErVCR9WRAlRvL2nmRAKo18xLp55mmqUUfoD9gt1fF2ktqrRBEtyDkyKuX1yIrlRXLchDKaCULaRgmstJYX5Oy6Bu2bcPuYi8a/OOIPklt5pxnmiPOG6YymzdaVHlIMPZj0SZQoA3HfgAS291OOuwhFs1E6VvkJDWvzhBURBuHtpm221BXFWM/EEPE1SKpXabLQh0uJVjXthitnppNCow1VLVnGHpSECedlJNkQCGiM8zDKCXS8cTxcOD66rroFyYe3t9zOvX0xx5nPS9fbnnz6af4WkqGkBLeCQ0YhO2YUkRbkUTTFCRginhv8M7hfUM4yaTGln6CLeVPDAuaTF156soDmpQghcA8JN4/vsdoy/WrC2JKpVmppAwJgSlm6iXgrMHZjDFJDG6zIitHSmLB7gyYthH+A6KGZK1Qv6c+siyRZRyIw4B3Fl03ohcxDvzmT77k4d1bAVxoWY0qZZHRzzLiXjNWIJUDf6XNn6/vjQs/vuk/vn7KdOB///6X5N/6jp/PwH/0k98Bkg4ty0Jxa8d7R06eZEWQ8jzKyzmAilgNyokwZEqGZbGkHJmCRqWEynqNkkkJ6EI/76RKFODxNPDu/kHMSayhPx7wVvQE5lnm30sQzrd1on8oKaAiKbGsbpRnHEY5IaqGaVqKo7F4E8yLeNrdvX8HKXJxccnF7grvGw7HUzHF1KS4ENOCtxVWeaz1JAJLWFAojFUyaiqEoP50FDmyM6U6a7I2GF+h5wVCJCbQxtFuHb4V+m5CanEXnCAZF3ENVhlCKrZiTU3lHUpnfGVRuiYvMM8jj4cTyzxRV5UwP/tB5LOU5ur6hqppycA0TqQg3hGVq3n58jW7iwt2+z0hZWIWANIyTYAu0F1N0zUM/UCcl5Iqiz5AbTTWSePVFW2+WIg4nXdC/TYCLxaOvgjTnntAyxxYhhnXWoyToLEsAbKiK7qT0zKR0Wy7tgiaymEzjAuHw8g0DWilab1hKad81244PD6grGbXXBOPgbe33/Dtl7/mqrJcXV5x1XUF8QlNs2fpT4AAoUJWGHLxJKSAhVjX6Ue8oicW4fm/6rvNSJ8T7n7s+lkgBqWWCyhgRuiy8GRSmc8FIPLhaqswWfTeQ8r4RVxwjQnoEEnP6iWtClbg+QsqQCmOp563t+9QOfPpzSURzfF0ZHtxw/F4LylkFCCTjPtGxnECRCNwLmKf1rkV+78sM9ZYUoLHhwce7u8YTieOh0cSkRgTzlZYV6GyXtV/T6eBHCCGzKYTNmDOEY3CeREu7U8novdUVc1hnhidkGWsFVISGrQ2K+xXG0NS4L2nMg2ZXLwXRd1IG4vTmqzg8PBQ2JoJ75zYii8zuXIyfXGWi8tLQk70p150/bWIiGI0l5eXXN1cU5cF75fIJ59+Rk7iMDwtC3UjWdJc9BKmZaYfB0wRSb2+vsb6nl8dviClXGzFLdZ6QEq+VXBFKfGQWBaheUdRcZqLtt/qO2ll5KiN59q+RJGxXjAT0zQVMVr5uWmMaKOpSy9FBFtkPZ5OB0JYaHxdnIclC6mbloeHt9i+RxUb8tt3d/zxH/0RvLqh++YbTNfRvn6JMZb9q5fMIT4txKyKn4asWaWfnfr5aROvG1qtqQEfn80/hif4vutnEQSgTDvO8OAYRKMP2YDneb+Gggx8ggbZoEVCXOu1g/zBSOBZTbWOV86dWK15PBxRCm72W1B6haKKz97EEqTXUBnBhgthJeF9JZBYBdZI70IZkRk3RiYW8xw4Hh7pDyJjHnLg4fGBtt3i65a6boR+imjd+0ISKjrK4plg5ffzTgxUUkoMfS/CFaX5dSYvuQKyAkVV12x2YtG9hEAq3XTnJIhYKzDtsxS7UufUWhSMUxI9xHGU2XsmFlEN6VKnQqox2rDZbri43GOdYxgHKCf42YzFOodfZih1fimBqeqKafJrI/DcB4gpoY3GFEWmlBKJxPF4IqcssGR91utnvQdnIZWV0VgMSQUkJVyCeZYmrdIKV1WM4yxj4Xw+VVlLMbFGE/DTsgS6VrQacxCK87lRqZRmHCeWSRCJh8MBpWRq8Pb2lu7qis+1RmtQdcU0z5AokxZQghjiw9H2R1D485L/gYP9nyYAwM8lCOSyqJIqjaAFrTIat7rWCFZcVo9WqsxGWb32tD5Hy6eI+DRWkTJDFpsuaZc8R0ZzGia++OobPnn9hq7yaC3z43Faihy2xuhashWtca6i67ZYbTg8PnD37hZjHU3XgTnPbBXTMHB4fGDqe6zR1BsBlRyPR0Cx3V5ATjhrqOtWePz3DxyMpJ11ZVbi0Kbb8Nmnnz6Rdcoma9uW7XYrtOHyu5/9+JZFLNzOozcAVfwDTn3h5efiAJQSS9/jvby/ZZ5lqhLnJy3+KAPYHBK399+itNChvbWoLCImAn3TRODLr34rZi3WyabzXhZxkRbfbDqM1kzzTH98EAbeNMpUw5riSiT8BO2EbamKhJcx4jJUVdXKJNxut0WGXHbKWXcQpWi6DfM4FHKRLfdLAs04FjejJFbqja3kM6kq2rbhdDry9Ve/5fpqz+XlZdFdFJryqR+YQ4BZE+fAqR+4vbvDGZGAs01N0zbS3I6JQ1gYxkmyU0FyyZxAseIEZJmWP6vzz324Zb4vzf/n0hP4F3GdBRzIor0nOOoERJQSVdjzaFB0BdRKgTUf1D1/GnOlQEZ4558oEV8Ib6I3uKTAt3f31N2Gq1c3hDljrKJp6tUaSmtd6nEBtkh63rNMM4f+RAI2y1wEMjOVdYx9T386QYp09Zau7dDGShNqHAUdOA0sc413jtPpxDAIDTXnTONt8c/ThTOwEdSgFiOTpmupm4Zuu1lTYKCkytMqbiLAJem6W2OY54VhHum6DU1dC4KwjB6NseQE/alHKbh//w6jNRe77Uq71TlzPBwgZfr+xNXlFfMwoq1ld3FBVdcMc+D27TtAso9zmitqwrkEdaHyZuAQIzGKEtLZmUdl6S3EEPFNRVs3qxGoMZbVhCQnYkzr5qyqag2Gy7II4lAbUlGxXjdK+R7DU4CcxgnrZ4yWILDZbAiL9Bn6vme/39O08jo5S3k19CNNVaMR3cacM13T8stf/hK/7XDdFpVFHKdfFmJMGGXQJQMI5JJRyBLW6pkvIXy3kO5PuP6CsQifX+dc/nzimWKDLRBaCQhn2nA54XP+KBV66qbIsz2vD1gbLhlFzAmK2OjXb2/Z7bZcXd0INBiB5J4FJJc5EONMigljnJhOFM64MpppHAUjoDUWtcJ5K1uzKchCW50bmom2rqlcxTxNtE3DxfaCTZtQypSUEq4ur8spLjbWZ+LLuQQw1pRZuaTAtjgxO2uLtHoQA47TqeDmBQdhtKAatbZiiNK06LrBe8e7t7fEJD2ZaRSPv3EYix6faBXcXL9gGE7kovevlV7fg1ay8Xe7C3SB9BpnBNA0j1KKIKSfVTdinovOn2QmqZzuOUaRX7Oi5GusWUuHHGKxmROcg1KaeT6t3AtxKa4xp7FgTygMQjEKMdYV6fGiz48wG+dpwlknHIgkuJDdbicio0Xpydph7RuM44BWV1ht8EWoti6qTc1uC06o1NLDSogb05rjPi17pUpgEtboDxGJPr7+LI3Aj6+fTRB4zvgTcRBd5v0C+BA7r7NX69PPpZxIHzEF80fOpecbk8hrdNWlIZbyExf7NM78kz/+E/6KrwVF5xQXVcvx2KPy2XhEZtSPD+8ZJ6kDyZm2aUQJt/QlMhAWMQyJGfphomo3NL6TFDxLZ35iIkXHPM5sNgKxrZqaF+Elp9MjWptVpgytsSULEQ1/xxIDJjsISSYghgJ2EdSe6DQIkm8YRDotxQTFM3FZFnExmmbmcSyLX3Nz84KUkjyHNtRNRVPGYlprHh/v2ewv+PyX1Yrlz2R8W6O9wy6R/X4nG1uLVZvSmpQT8yIa/yrLKXw4HAot2uJ98Y4sY0OtDa7AikkRRSbEBEbT+hZAFIS8YS4GILIxZ4Z+wtcV3loxDylKyzlnwhyEVOUcdVWjshJ8v3UrSlRrzabtuNhsMIZVUt1VImwzzhPaCIqwPxy4e/stb37nL7HZbpnu77HO4eqKuYxww3JiGmZQogtBCpL2l+wUlQvQ7MOG4Pn6rpr/u5qBzx/7Kc3Cn0kQeP6LRsA+mwiop5EJkmorMqlsJPlK5fsH1X858TOsj5fHWOFD62PnNG5aIl/fvuPzTz8Tqq1zIucdI1XVlOaVjKZiCIS40DQVta/W95xVIZ4YYURa49ju9jSbHVXdFMWhUPwOPcMwoLXD1w2+rliKM3CIkXkQ/0DwbLeif6eKUlLKiRwFRy9dPkGwhRjJFFamNdJhN+KcPE0zMYg9uEhiid2XnLKyAa+ur2nbBuedWG2HSCSs70spha0qMekocuziqBMJqNU/0TppmJ7vFVl0CDOCiw9LpO8HTseeGHMptWqquiEsC0oruu0Gp4shjdKksDDHsAqHnPUmYzFWbduWcZg4HR9RnWF4PNA4y+X+gjDJ2FGfmZGFcFLXNb7y9Ce1IiRtQZG6ylN7Tz8c2e/3goYsCyhEQbNedBUbL36Nvqlou5bHt7f4yhNRqDKhqJuaxjpBBtrCGcjSBM6ZYioj611/RxD43t1zHhv+BPjwd10/iyDw8dtde75rdlBgnuppi+ezrXU6C47kFWfwXEPgfOVzwfW8f/AMl3EONEtMfPXNW5p2w5s3b5iD1NIpP7HX+v5YpM7T2oE+nY44X4EW63KUXi22Ly4uePnypajrWivvMSW67QbQYAzKnOHAFXf3X0u6PM6Mg7j8aKX49NNPVyjwEpZn+HnB06MVc1iKhiHos9dgUU52RUVI4LYCLZ4X0S6s6xrb2eJUvOC8p64rzoap4zzx/v5eRpBKTuiEUJ+lZyFiHkkrfIjkRUa+50amKV1+Sm2cUuLweOA3v/4NS4g0taj5VHWLdUOZEDi2uwt6lbHOiE06shbWtVI0HeZlLpMBi7VO5MraFvqRZZ5wxjIsYmJyVhIWYVvQRsohX8mYT2uDK9iDGCLJJrSS4H+mRytV/DGUIqfM5W7PbrMl5ETXdRxKCTuFQLWpMVrk07Z1gzJa4NJK+hJK59LUPf9WmufzrOeN7h/cRz+SFXzf9bMIAufrw7T+nNqXpmGRBys/SUyJaQlMi4BzlhCkjs0fB4HnEwJklIeSxqMS30CSWnndQYsRx93DI2235fNPPqX2zWpbLuYiinkacN6zJIE1OyfmJmGeaXcXdNsduoiMXF5fs9lu0Vg22w273Va65daWskGaRf04yNcwSOptHZubDTkn2rbh8f6B7s1L6qYhZyHlxBgYTz3OexHvLF6Ic1iIS5RUGqSpaQ3TvIjHYhRFHWsdXdes5ieiFiSz/KWfqWrxELRKs+s2MiZUop0w9gNBZYiy0SOZ+/t7chZ47jwK8nGzERGPENNKS44x8fVvv+Hx/sDrzz9nu2k5PLznMDxSd1ve1GJ++u79e+ZxIKvM7d0dTdux3W/XJqgwEhUez7xENt1GtBZSYi6sv6ouHghaRppKa1zliDHJ9KcYoBprRfk5JXxVM44j07zQtQ3WOd69v8M5z9XVNc57Tv3AcHzk5uIVdeXRSCa2u9zxWNdSwjTiXTDPM4nMVbchlyxRqQyxZAIga7VMBdTZa+4nZARr8fuspP64JFDqz0kg+hd5PT/Bc0nz9fr4WW8wEZNAiWNMxTYqlaxAFGqfJUY8w1uRU15TD73eqOfAC9EO7IeB4/FIDIm6qtBKMQ+T6AcW4YuUM9qKoUhdS9Ns6yvatsM6S0wi5rnZbrm6usY6USVq25Z+ODGM4zqjlyZUYpwmjqcTm+2GTS3CJHVdY71Zm5TSQ9I4awmLwHnbnKlagyvjrzNF2lpLyomQojTFZnl+jcBufecw1oksdpQSYxgEkGOdYQ4LOUdqVUvNWtCbbdUwHXuG4UTlvBiyVJ6cMv3Qc/fwjuHUFypxjUzqzJrCxzBhreXly5d88uYTUInjQRR5mq5Fq8xUxntaG4xldRM+y4ufG7YUUk9W0jid5oVD33MaxYXYFkBXTGXEayVbsFazxEg/jiKvFgJVySpDKX2maWQOLdZZju+ONI30Ic5ozb7vpQejBatydlTebreSFRpDLqVFtppPXr5m03YcxyNKW4xR5JK5qQIEOnOHziv349P8gw3+Qc7A83TiJ2cQP5VY0nIAAA+9SURBVI8g8CwVSlnGQ1IjimlGOnc9zw2/LLbUoWD6E5FIKQnSueMqXIN1gz+/sVk67ee7nQAVpYb2KhICRBQDECoHdYU3kvaHnPDeMU8apQy+qqnbFq0t2liqtiMrwxxL/8J6fNVStRsMoqL81Vdfczg+UlWey/0Vbbth6I+icjOJLp5zDtc04AzJKHwj8GI0JJWI81JGgJoYWUUxw1KYcCkLzbogo0JIxEWC5dmu/PF4Yo6R/e6CHCOnw1FO6rDIidnUPByO4uxjTkKGKXDdtmnQVoM16Mph6xpnLdfdC9S7d0zDiYf5UcA4S6aqqzXbmKaJeQ7s9jvBejhbMA1yD63WpLyIpHs2uAw+LdxcbzGuxlfSjNTK4nxDf5ow1tK1W7SzOC/ZTd+fWMJIa2ucgeTEyn4OgU5ZnPM0OTP0J3KM6KzRSH9F64SvigFuyOSomfoFoybisuCdY9N2nIxBaceYhcPi5gE9j9T7LdnVGFORgHkZmMeAUgP1xZ7jMhPjglGGWDa/zhkVdWlYZZ5J4nzH9YSGy2Se/+h5L0kAeLbJvuf6WQQBmRkXr8GzQEKZjZ7RaQqZ7edzpC7MN9EdLAHgT40Ky/N/9P/n58zn6ItMFCQpSBjtUEYYecJtr5jHiWEY2DQ186yZxsJ373ucr2i7BudraSZaJ5BPbajqGm01S5gJSdxym6ambvw6Unt8fJSgZzRd16FNFrEKNBvbMS0zuodPP32DryQbCBnmeWGel5IhSL/COUdcAkollnmCDLVqcK5aMQan/sQ0DlhjGIeRo7FoWBWPh3Hk8f4BpRTX19dy2unM1dVV+ZwUh9MR4ywX/mLNOIyVcuTy8pLaGOq2pWla6kYATWcDjrOdl6+qVUE4ldEdWRSap34mlhOSLCjSzWZLtemofPOsAVyMa4oUuq08Z9DUq1ev6E8nxsdHybRyIieNdYJSXIKUU13XcaoPjP0o/YBCgIphwdRtydIEm9B1YkKSl0RdN3z2+We03YZ+mgj397x58Zf4/LPPeau+ImtNWJbiIKxJsVDirYXS0E1RylKJA3IaPuWtz/78HdnA81H4n+f6eQQBWLu80nx7Nj8tlwCKnkaDMscWm7GYnjbx817AavH0Ha+X+dPoqpxFyTjMMyon7op6kBCcpAyYx5FD0dpT2tDuLgQg0zSkiDgb1aLrV9c1la9kgxjDNE8YFFdXl9RNxf39PSlmUgrFqkpq7re3X4sjcCXWZjGKdt7ZPUlpIUSJu5FeMQBeeZpaRnpn5R+UaCMoHVBFJMU5R4oBX4xXT6fTWtLknItFmMhin++n9579fr/eL6ntFw6Hw5py+kqmKUtYiIijs7VyuscYCVH0AaZJ5NWMEf/JVDAJZ2eis9AH2jCehnVtVFVVECQZg3T5rbXE8BT8t9ttkTvLtK3Yxc/HE0qZApqSmn+eheMxzzLiFQBYJSWnksazlB4CA6/rugCFmrX2nueZylWcJdGqumYJCVcVGrxSYvnmHPM0iawZnjALsjKlhC0b/TmI7ZyhfpwJfJjWPzW4v6/59xcMLPRkL/b8jT9nTH2MA8g5kcqoMK/6gesgcH3e77rWn8j5g6gL58AQyQssRuSsj8cDYZq4v79j6gfm4tnnvOP6+poXNy9YYmKaZ6ZlKYu4pus2K75/WUQvYbPd0m03VN4zjmPprgvJR7D2aV3Eh8OB3cVWpLQWkQLbbDbEJHDmc23c98LH19YyDhPWyL1smkbUdEp/wGgJAPv9nmFwkDPjMK6b3FUVpqjnbjZb7u7uRMOwbBBf6mBTlH9ktDngvQCXcsrERdiBIYTVzTmmRF9k1zPI6FKdlX/SuvHqqhIPwrbC2A3HU894HMW4JAW0VoxLxFqxJz8bvIYlCcIuxgJTtpzt5cP/1965xEpSlXH899WpV1dX3zsXGGAyIIIhmHGjE0JYEJYqbEZ3rGRh4kYTXbjAsGGriS5MjIlGEjRGNmpkY+IjJq5EQHlKeKiIQ4A7A9w70896nOPiO1Xdc5lhZkCo27n1T+7t6lOV7n/1qfrqO98zisgyFXASOGaLksCErcbZ3EBxok1fHMav0U1rdwA0oCobEPmxwBhqpzUja+d7J2YZZVUt08Dx+R9RxOk33uStd95i6/CVKjRNBE7jGgIJsCtmu1UL1cXum9ak+B5egLXwDohoTXndXgZK1DiileOa4F8n2oqpttb38VOfr3M1gamXWoFt3IJ7vg//0/l/jcuxFSJiCJOQwSDh1Kltrr36akajjO1aC28kcUxVW/KR9th7Z/cM1lk2Ng9x9Lrr2dw8hHMa8hqYAFuWmEGKFU1K2dnZARy2spSF9jjc3d31BSpjjh07xsbGhlrpy4WupbMBi8VCy1tFCZPxjKKYkSQVWZ4zmUyYz+ekSUJp8Ya9ECNBW7I9GsQkcez7G8YUxUIvZh9SDOozj0YjhqMR+caG+sqjiDASiroiDiJi72dP0tRnCtpWW9k+pQ1akyhgmHtvAoG660QTlowxLOZzH/mYqS0ljIiP+oYkdkFVFURFRJxGxElMdfYMYRgjhQqXbDAky3KM0QpFja1nNpu1xWObZCS9HnS5UZY1WTwkThJsbX3aek1dqi3BWo0CHIUpo9GIOIwpam0V97EbbiBJI2pbc+r0DmESEwaQ5SPSYU6+uUkZaX/hKE6YTqc4I0ToQ+A/r77KY39/on2YiVMB45zzsSDS+qwtTfr7uRrr+VyAekx7QV829oUQgGXm11IIOHBB4707B05s6y1Qf71rA3WCQA0ltmpUq3d/10qkwOqntnsajaQxvk2mY4rZlHK+oJyrVTtMUhLfZtxaSxRHreHLWktZaAsyamEynTKZTsk3NjDeRz6ZTDTfPtQgnvGZs7x9+i1GoxHBtUcYxAPSbMB8oepwGEgbNVlVtfdG0IYBqxahF3AUNm2ttC27PhXVEVXX2vegOc/QC9/ZXEudF0XB5uZmq5Ivy2+Jjy2oNViprFqNpUnlda4xTlbak3A8BlSVdla7QbdNRY1hmOdeoGp4crOEaRqmBgFt+/faam7AvJ7680l8A5RKPQgibUOUhtd0OmV89ixS11hbawMVlhZzYwzTWaVLEadr9NBoabSqqimKkjTNwGqOgIk07qOua0KfyShB0CaNlWVFMEhIYh86nA6YV/qbjTZG3HLLLQxfzymrR0F8dWVf9845Xbo1RUabALeLaQQXechfEvaNENBAoGApAGgkH1RoSGzjQqEGAtse1wTuGKNZhSLCwqmn4L3rrLJiSm3/6ffYWsNpyzmT2ZSBr0A8KQrGkwmf/NR1JIMM62CYpqSDlDRV1X88HjOZTHHWYUJfxsq3Lh8MtK7feKw16oy/ILe2ts7pujubzUgGadvYojGCNU/SMNSqvG36bV1rhyZ/jNpNLCY0GpoqOj6fzHzWYI0JVSAtFgvms5kvUxa3eQlNMFJd1+2yS0Ny5xg0Salx2UVxzMgYXy5szmKxYDweMxyO2p6OBtNG64VhSOKrCxsbYquaOoq02KpT74eIEIYBdV3ivOodhgmCd705TTU3RguONrH8zefPZrOVYzT8urKQJim2dlS1Fm2xVYWt1NXcBITVde2zVp0PPqqWvwWOPM+ZL0qoff6Cj/moq6oNczahYZRuIGHAIIoZZBlhmury1VqsWAxNVitN+gAOaZOoLHZFGqy4uFbe0r7VgSYrYXmJr4OLELyvfMUzIKtrmXPPWgBb69q5qq33Gmpn4yzXslJCwayqtdjlHpHaphivqlkrX2F9mKsEISdPnqQoCo5ee4RBPiAJtW3X5tYWeOv69vY2Vx0+zGy+0MAaE4KDLBuS5zn5KNcIud1dyoWq98Vcb6AoMIw2tIdhnms24Hg81RtaYJCl/mnsqJvAKZbt13QtH5MGgS+IWoBT12o5GRPGqmnYSuMqzp45w3h3TJTEZHkO1jKfz9jZ2aGqKjZGI188tGY8VpehMYZ5oYE/SayXzHw6ZT6feoNfQRQmbT/AQ4cOERn8UsP5bj4hgQ2oau2Y3DSa0TnWyk1RZchkwGyugi5J4/Yzh9mQJE6ZTWdtlKCIoSy1qpGJlppk401aTGcY0aK0m5uHGOZD7Yo0sxivOURRTGEtZbVgPl9gAqeenkg9LtpGvPR9Ki1B6JhMJ1inbdWnkzFbV2pz0ywb8k4x5exkxmQyUS3NaG/HZDgkyVJKg/bUIFi2H2+0Ldc8CJu6mHsu//faBnCBFyJNQtSl2Rbk/RYi+H9CRE4BE+B011w+AK5ivfnD+p/DuvOHD/ccbnDOHd47uC+EAICIPO6cu7VrHu8X684f1v8c1p0/dHMO53ek9+jR48CgFwI9ehxw7Cch8KOuCXxArDt/WP9zWHf+0ME57BubQI8ePbrBftIEevTo0QE6FwIi8nkReUFEXhaR+7rmc6kQkVdE5BkReVJEHvdjV4jI70XkJf+61TXPVYjIgyKyLSLProydl7Movu/n5WkROd4d85br+fg/ICKv+Xl4UkTuXtn3Lc//BRH5XDeslxCR60XkTyLyDxF5TkS+7se7nYNzK/l8tH+AAf4J3ATEwFPAsS45XQb3V4Cr9ox9B7jPb98HfLtrnnv43QkcB569GGe0n+Rv0XiT24FH9yn/B4BvnufYY/56SoAb/XVmOuZ/BDjut0fAi55np3PQtSZwG/Cyc+5fzrkCeBg40TGnD4ITwEN++yHgCx1yeRecc38G3t4zfCHOJ4CfOsVfgEOiLeg7wwX4XwgngIedcwvn3L/RBrm3fWjkLgHOudedc3/z22eB54GjdDwHXQuBo8B/V96f9GPrAAf8TkSeEJGv+LFr3LIN+xvANd1QuyxciPM6zc3XvLr84MoSbF/zF5GPA58BHqXjOehaCKwz7nDOHQfuAr4qIneu7nSqz62V62UdOQM/BD4BfBp4Hfhut3QuDhHJgV8C33DOnVnd18UcdC0EXgOuX3l/nR/b93DOveZft4Ffo6rmm4265l+3u2N4ybgQ57WYG+fcm8652mn+8Y9Zqvz7kr+IRKgA+Llz7ld+uNM56FoIPAbcLCI3ikgM3AM80jGni0JEhiIyaraBzwLPotzv9YfdC/ymG4aXhQtxfgT4krdQ3w7srqis+wZ71shfROcBlP89IpKIyI3AzcBfP2p+qxDN6f0J8Lxz7nsru7qdgy6tpSsW0BdR6+39XfO5RM43oZbnp4DnGt7AlcAfgZeAPwBXdM11D+9foCpzia4vv3whzqhF+gd+Xp4Bbt2n/H/m+T3tb5ojK8ff7/m/ANy1D/jfgar6TwNP+r+7u56DPmKwR48Djq6XAz169OgYvRDo0eOAoxcCPXoccPRCoEePA45eCPToccDRC4EePQ44eiHQo8cBRy8EevQ44PgfXtGxqawXdxAAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Init image is:  866\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "target_image_name = 'koala.jpg'\n",
+    "init_image_name = 'tractor.jpg'\n",
+    "for image_path in imagenet_stubs.get_image_paths():\n",
+    "    if image_path.endswith(target_image_name):\n",
+    "        target_image = image.load_img(image_path, target_size=(224, 224))\n",
+    "        target_image = image.img_to_array(target_image)\n",
+    "    if image_path.endswith(init_image_name):\n",
+    "        init_image = image.load_img(image_path, target_size=(224, 224))\n",
+    "        init_image = image.img_to_array(init_image)\n",
+    "\n",
+    "print(\"Target image is: \", np.argmax(classifier.predict(np.array([target_image]))[0]))\n",
+    "plt.imshow(target_image.astype(np.uint))\n",
+    "plt.show()\n",
+    "print(\"Init image is: \", np.argmax(classifier.predict(np.array([init_image]))[0]))\n",
+    "plt.imshow(init_image.astype(np.uint))\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# HopSkipJump Untargeted Attack"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Without Masking"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "HopSkipJump: 100%|██████████| 1/1 [00:00<00:00,  1.22it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 0. L2 error 15147.211 and class label 112.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "HopSkipJump: 100%|██████████| 1/1 [00:30<00:00, 30.74s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 10. L2 error 8259.469 and class label 359.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "HopSkipJump: 100%|██████████| 1/1 [00:48<00:00, 48.43s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 20. L2 error 6047.2925 and class label 359.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "HopSkipJump: 100%|██████████| 1/1 [00:58<00:00, 58.10s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 30. L2 error 4446.662 and class label 359.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "HopSkipJump: 100%|██████████| 1/1 [01:09<00:00, 69.60s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 40. L2 error 3667.595 and class label 359.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "HopSkipJump: 100%|██████████| 1/1 [01:18<00:00, 78.53s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 50. L2 error 3021.8582 and class label 356.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "HopSkipJump: 100%|██████████| 1/1 [01:20<00:00, 80.93s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 60. L2 error 2527.9243 and class label 356.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "HopSkipJump: 100%|██████████| 1/1 [01:33<00:00, 93.12s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 70. L2 error 2139.856 and class label 356.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "HopSkipJump: 100%|██████████| 1/1 [01:35<00:00, 95.85s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 80. L2 error 1847.0266 and class label 356.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "HopSkipJump: 100%|██████████| 1/1 [01:39<00:00, 99.96s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 90. L2 error 1587.3302 and class label 356.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "HopSkipJump: 100%|██████████| 1/1 [01:43<00:00, 103.27s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 100. L2 error 1390.9678 and class label 356.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "HopSkipJump: 100%|██████████| 1/1 [01:48<00:00, 108.16s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 110. L2 error 1228.7748 and class label 356.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "HopSkipJump: 100%|██████████| 1/1 [01:52<00:00, 112.21s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 120. L2 error 1098.0759 and class label 356.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "HopSkipJump: 100%|██████████| 1/1 [01:53<00:00, 113.12s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 130. L2 error 1000.0699 and class label 356.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "HopSkipJump: 100%|██████████| 1/1 [01:57<00:00, 117.46s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 140. L2 error 912.61176 and class label 356.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "HopSkipJump: 100%|██████████| 1/1 [02:08<00:00, 128.22s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 150. L2 error 842.8174 and class label 356.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "HopSkipJump: 100%|██████████| 1/1 [02:18<00:00, 138.04s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 160. L2 error 778.2984 and class label 356.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "HopSkipJump: 100%|██████████| 1/1 [02:23<00:00, 143.82s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 170. L2 error 725.36945 and class label 356.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "HopSkipJump: 100%|██████████| 1/1 [02:28<00:00, 148.45s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 180. L2 error 675.5015 and class label 356.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "HopSkipJump: 100%|██████████| 1/1 [02:31<00:00, 151.85s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 190. L2 error 636.62787 and class label 356.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "attack = HopSkipJump(classifier=classifier, targeted=False, max_iter=0, max_eval=1000, init_eval=10)\n",
+    "iter_step = 10\n",
+    "x_adv = None\n",
+    "for i in range(20):\n",
+    "    x_adv = attack.generate(x=np.array([target_image]), x_adv_init=x_adv, resume=True)\n",
+    "\n",
+    "    #clear_output()\n",
+    "    print(\"Adversarial image at step %d.\" % (i * iter_step), \"L2 error\", \n",
+    "          np.linalg.norm(np.reshape(x_adv[0] - target_image, [-1])),\n",
+    "          \"and class label %d.\" % np.argmax(classifier.predict(x_adv)[0]))\n",
+    "    plt.imshow(x_adv[0].astype(np.uint))\n",
+    "    plt.show(block=False)\n",
+    "    \n",
+    "    attack.max_iter = iter_step"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## With Masking"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "HopSkipJump: 100%|██████████| 1/1 [00:01<00:00,  1.06s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 0. L2 error 12889.198 and class label 354.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "HopSkipJump: 100%|██████████| 1/1 [00:38<00:00, 38.48s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 10. L2 error 7398.0615 and class label 359.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "HopSkipJump: 100%|██████████| 1/1 [00:54<00:00, 54.56s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 20. L2 error 4836.4653 and class label 356.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "HopSkipJump: 100%|██████████| 1/1 [01:05<00:00, 65.75s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 30. L2 error 3370.0251 and class label 356.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "HopSkipJump: 100%|██████████| 1/1 [01:19<00:00, 79.80s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 40. L2 error 2583.0674 and class label 356.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "HopSkipJump: 100%|██████████| 1/1 [01:34<00:00, 94.31s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 50. L2 error 2119.813 and class label 356.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "HopSkipJump: 100%|██████████| 1/1 [01:40<00:00, 100.32s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 60. L2 error 1762.1847 and class label 356.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "HopSkipJump: 100%|██████████| 1/1 [01:42<00:00, 102.18s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 70. L2 error 1546.3962 and class label 356.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "HopSkipJump: 100%|██████████| 1/1 [01:50<00:00, 110.12s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 80. L2 error 1386.7443 and class label 356.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "HopSkipJump: 100%|██████████| 1/1 [02:04<00:00, 124.02s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 90. L2 error 1237.5015 and class label 356.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "HopSkipJump: 100%|██████████| 1/1 [02:13<00:00, 133.32s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 100. L2 error 1133.0155 and class label 356.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "HopSkipJump: 100%|██████████| 1/1 [01:59<00:00, 119.38s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 110. L2 error 1043.9689 and class label 356.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "HopSkipJump: 100%|██████████| 1/1 [02:06<00:00, 126.47s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 120. L2 error 964.1652 and class label 356.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "HopSkipJump: 100%|██████████| 1/1 [02:12<00:00, 132.32s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 130. L2 error 896.62427 and class label 356.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQEAAAD8CAYAAAB3lxGOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAgAElEQVR4nOy9SdBtWXaY9a192tv+7eubbFSVqaxSZZWtkoQksGwChwEPPCPsAQwYmIkjMMEABSMiPGEAwZDAjBhAYCIMEQ4D4UYYAhOyLUtIslVSVWZW5svM17+/ve3p9mKw9+nuvf/LrCbxc+itF/+7956zz27XXv1eR1SV1/AaXsMfXzD/ojvwGl7Da/gXC6+JwGt4DX/M4TUReA2v4Y85vCYCr+E1/DGH10TgNbyGP+bwmgi8htfwxxy+MiIgIv+miHxfRD4UkV//qtp5Da/hNfxkIF9FnICIBMAPgD8LfA78FvCXVPV7P/XGXsNreA0/EXxVksAvAh+q6g9VNQf+R+AvfEVtvYbX8Bp+Agi/onrvAJ91fn8O/NJVhUVEMQbUwisQwChsd2PXtZ9OWwbF/rgP/widEv/5o4+i28yuJg30R/Cj9OvH79aPDj/yIv64q96ZEQmA6icY3xdN0I/Uxxeqem3z4ldFBL4QROQvA3+5/a2g0o4ZcN0rf8wWQsCCsR0M/RITpnV/NjsMMgUudhRGtsoCBP521SndK6mQomQSoxT+4uaWGvvfy13d3O5n3b4IYg0SWmwgkNcEtv+AdjrVvdPUX88HBsQCiiC9odvmq5AwJDMLsDvm+orp6l0L/HB3LdMAghVUI2C+XVcM5F+2nfq7wMtpsPjqAoR6/J3uDdlcGjDqx4+r3ARgLQNg1Wtc26+GFlF2ommn4wqEBqxFdQosCCmpdg64fjxB7frBrltflTrwELjX+X3XX2tAVf+6qn5XVb8LgFWCjUqU8scmoOr/NQuseIy/qnxnj2yRRmkLXfng9rUKqNRvJF9FSGc5BdZChwBADyMVYE7K8op5kN3XFbCKBhVilEEWEGpyRQ2OWe0kJqEfitAQgP6D2w1nLPwQdPfcvASEECpHYHai88ovzaLbftzczl/aty4EYAZ+nrbvhq5Ep18gVDBWCDaq3iQA7KjT2h1Ftf+16t8S2VgTFRKFsH6sTEANwswTAHjpdtbsyltfFRH4LeDrIvKWiMTAXwT+1hc9VG38Fuj1MKgvBu0EGvoL1q5QRR8DY0KEqPm9gaGdn1qxsdIOoQeXm2Wl5oskdNExhI2WasQu2IF3WyJCn72t6Axy2C2xa4cZTIcKVTmsKCjJdozaOEzrTlXQ1i+B/9IMzG2HsFc0QEZX7TkhIGQcCAzCzuZ0rC8AAozrBwYlaWrdSTsEtlE539H2JjvZvFqBXXXuGLrkuQQqhnRXFCCdg0nAGj+LGrUdVfNSupewe456zKfTbdU+z0pQKnBsUUKQNbXIVPmJjV4q0lwt9H8lREBVS+CvAH8H+EPgf1LVP3j5U9tTFMH2jlHtUQvLBvGoJSzPgRP8ICWnlA7P3VitGj91o54urKLtawiICDYMyAf1xRIoHEfZ4ErSaDxpr8WoN86OmJiDECP1IJcAMVFnjH2wW1MWsonOCW52K8D2p6JuxwCZb6PZeVEzurZo5TjzLhClkpJ5JbBq1TpBMVgnKWGh/osXEJRsU/5e81uwvVRVf843hrYJRiyhKf3Oq68uweS9etYodtmRLinc/QHAlqpNF8myq0SToFP0qg7Wg6j/gmLXwlPgsKqpMOxI17vpoqv6VThKLCIqkXTUf/X/u4l7qWR3FXT0wp0qlioGwRonWLxs/ru9GSB0eUgAJALLrk63AQFKiGxJAa1JsK/vCSEqpddxt8xuO8fYg+aRwP81gjKRH4mbatsZ2RXzXOPHTn3hq4XN4Y1CWJT+ghGna3ULd7poGGMbw8FGvR4hnFASuD+b9wt1kGaksAaCWClKUCPtWkdQc5btleqOYB8479x7ybp2hqIbn+0Qa4JV6w2+RGNG64p2fp1Vf7tRvzd68mrADvufGD/oLSpWm2r6zCHADygEgtBVUNsGGvC0UlrbVdXU305HiOOXm1Bs7IUKWL6UijsxLsMvRUSDYJVXJ7afKFsjF9YNvyvN+f6qKLqDI4iFECWgpEsAwOFr2XAox1peqkJ3RKSWUV4l9Madh+h9N/5u+2StBuyAgH4VfpHnJSgjolq/qqH52ohiOwiAbzkEJq59Aaf7bRIAcGpX6p6a45Y3LwRVQatOz4u26u0t7SZPY1A931AXdhAA045pc4ZfbubolCz9z2HX6vly+/+rQwS6YPwA6nnqbbB2KiromdSccAuUILakraB9JmG9zfea+lu+XNLqn4IT+QOEopnvjanbxY2NvxFKW7zwNjauWFh/sYuWFWDKjQsdaPaEtr/LzWJfaCwLrygghF9aFsuBkLDDhcSTUot4qaaGWg1w5WqdGqRnHBRoFtn9zigw/cEJIAn0ZLQdIzEQhiCXIF4Ngs7U1YITOBUnolXmN+haBV8wn53i+dVFhc4WvUow2PmwdHXLPoS0VsgUvsjD9moSgc5kRDt8Bs3I6xmMaTZdy7i6SlSEeI6XsUEXuyTXf4+bymna0TGUnWa3VmyTMVrasIeyX1yv3JB9+t81GlmFrmYf0+HhfkCmV23L5dvKdnBw4wWmhoW0ddVfShwda3FOOjVvDqT0KOfmXVn76856s3voDfkGlACDhFcg+OZkNpC5ms0O04EnxmqFct29sUOs6xAXnYmzi2xUJzGIkb6M/lKVPyDyZTZWpSHY3dJ1l01dLhGIdzQQ99s10Jp66uvNeP9/Ngz+NKF4mbauoCVoTsNYFMdUkC5nK4hQBv5Ksbu2BtawoUIAMyDZoNxw5eJvQn+D9u/Iy+pROj731jae+0oNIKXboAOpRW6DYhqVpo+j0m/KQhx44Ut7l9v22eYlLTnaoRYYf91kO663/dgktPXPigotvXytstOmtYN2AykkbV8DAYjaBdvaB7qrJ1v3u3cDIMggsCn9p4KddVQAWnUkyLreq0blrltCLKH7WSiSq0PprosyZ3vNauSO2hpV4GXSwCtGBDosDWitMz+i8dICWpvh3JTlKMtdNXUo9DY2+I7Uoqmnqj0iUgsm3Zl0+kOPX/ZH0W0kIaQjCm/x8k4HNh+1LV4rToLNGqepUlITtK5kFBB5AgGuaFbWksYO2GK6zvKS+e+NHaa559Q4EYG4u+vEYWNTuO5Vd5Z2gX6h0bz5DPImtgGgigAKyH2pK/bBztYNiBjft8D13RuQY8Cy6j8pldu+5iqMlY3r1Y6GFTfhhtrDROlUwcBA6B8JBKQxnJi+2FZDvlHtS+AVIwLesNPIc7pFMLdEsx3XehCE7LJsy8Yv7SxRo1IY20jQV06U+P7GHUrQFb19Q9prsGv5Wu2UTKT7RWo5QvoLqpBr/5JoBeT03V11pxzkAmU9oG6MwJXSSGebmop2gaq+IK8W1dB/N7DeGJmyQ4sK0Ktm92phoUPSTEuDq8RJbM1Am05vM95Ovbv2iFgQrQOkalXJPbSiiyltE10a0+t6s4Ybje/cnP1Jqp8qLZRe8i1tTRoVgtjhntA+1yEKX7g/eFWIgNQfM6fWeSlShP7m4guI2i4SXBX0YmM99Ilnu3mNtnzTKXIO7MsEEgusd2B4t8O9Z0v6fl67hexdHmqaTbexnCJbEkhnS3SeMe1XiZx1LKovCy/FFAFE2/6EoGS7p0IUTL3xX8a/u5W3ivhmzzeVKLN1BUo/51fF5Gndl01D4hdgfn+5jbdVbyFX/6sBolZ6CzbKts1++W2n0jIQg98TDQ0xaLJGNzhBHXMiXSzSq0nBq0EEOlZtpA27Noo3+O7efV9E4V5WsurcUq1Qb62rt7EBgixwRucv6vummCmdP9O23hOdN/dpB2MCcBZwD2HfT9F50Dg3aoeQVeLkGmkwTttRBSBmDUXe+CyF2iDwsgF2VKBq806rStU0plebvx52xtiS2b6bUNhcaQvSygkNufhSGmLX1N9CY65ozPvS9KS7bO3AXD9CvsRmsXizjZNgyx4ZqZe5Jnz9nvZBevd2CW3N3Uy2eE+NstKLpnzViQBeh4RWJQrcoFtLr+mNo1msgJZtXsnR6os7tPKdscpQYZyfvbbAXsE9Ni81DMMLH+LVyZq7N+U3YsUb1wM1TalLBo1K6yIeui1WRI08brfHrtL3IpUgG6qoUnaw62XiAM0gNu1r2vxX/zYbN2kInfRu9M3xO88NbeP4l7QQBUhrX2/Qp+5Zj8uzQaB3NFTbnndDTb46gw1aAoN/VhRKNT7OoX2yTwT6a7Dl6qXGA0UokEpbfPNPNxESUjMIofFL74BXhgj0+IAXrXoTtOETbYTvutCmpNz71UevTcG9OazhWVbsY/8LcDNaV7Zjtja3TY9WWFxcgDcNlHSEBtlkZ7LxvS65KTL0UUK6ZXrI4Lisal+VsYD25qtLOf33XtekQTk0hMzh92ZUQWOmsm1/GtKrUNWmil6LW7x3mwztwt2eSMzW/nM3cm8c9pdMX2hTPzLxgcsFLS28ksjUyOgJoekZsNPGeAhsxmh1hlJ5ImqafvSL/uiG8D5Rb8Eo2JoI/MvgItRgo5Ne/mzcnFLtZHZNVI92ua3BiniO1CL4y63QHgy4oy0tuhjtEKcN2PR8b55lqJe0JkO7e1BzxRocZXM4V3Q29vYIcqAxfDaEzBnb5Cq9vNfljrrge6nsUiEbtkJeG0o7ZRphrFTqxbsKOVvoiuxyZbnGfHEFdOhVrwLxN1WlXU5RNNBe8ZdvuQ6B8uhkjLPWWwk7E7E7OF+bEdDM+0r69W7R8Kv9yb4a8ZEvnec761m3Wja8xL4U918ZItBawFv40vSwg7WNECa1/rddy6bW0JuEHDJy+hEXV/fFXCUe+IU2nicTOFtHU4/KTl3c1Wa/DLnqgN/stUdU6q3cwVHPvUPYDl7a1f+t4ViQvJmscgPn62M/FdKshTW9CnZAN2qwX8BAE+Tg7uzeZAI9Y9lGp3c/I23rfdhFElopUnyIsOSQKd5XF+KDx1/SpuyY5Go3Adp63oCaHk596X3SozNXR8e8OkSgWL/8vl8xx03FqVy93ju0d2JdhVgFtR0k2p6sBq1StiDqtFbVm3WnRLWtavxowhzsxp5qi4+nm1d8xI5q0PIiBYqqkQI2+6K0+m9LMPu8dlO7asbUIxxhr/KdMkdX5XgpEegYUbqPCyAhOeLJoql7vBs2dIPazBGKtpdVWvuLv2aol/ZqymHYiGJXgKJ9uNY5dsKG0z7YWJVuGHkjNrZYW58wcX+usV58Zz3Qhhl6svnSZCn9sf3LCUrrsdmxdsIVIu0mhGC1PcPe/es+VYuWXXCIsYm84pRuNYBi8ScFO6bdoO5751otmnazEEWqaP0XKrb7G/fpOqte9/f3BAgMov1nCnVpWupn279aHvV1bs5U53C72Ih4yMaud/PX9bgAG6x2e+77y9afdQvYlaJVbQYrfF8tX/7kq5tVRa88htxtfVev6tDsnS3mggtZ9RUYdp86q6utv/Sk1pd0qCZqHSLmsGmHEWyj921ojPcaXdUO/wLTi70UumS3PhrZ0fcU3crY8zLYZTeCDkXPtoKE+14/6ztSlW0lulsXbePCujUG1JaBlNom3u/8LiRT6cf+56YmfP78aiEgre5fJzjxD6MSsmUXkG0xWPx5RteeQzPxbavqJkXAVoKzNu8Sc32dXTtFw+ivRtydEBgoOzm3RP3YO6vj62+XYFOG6axzp0shUHiLbdXZlC0Yuut4JdnpIorajrV6x3BDnNQ5x6sRnYf9GLrHmhom1qIPuxyF3Qq0f6Utm9AxsPXhx5YEROSeiPwDEfmeiPyBiPyH/vp/JiIPReR3/d+//WXrbKSZ7kg2MDaA5gzALlF31/ftq0H7WbWNCLTHlzsLGG92RDtr0qu3xrQNIqAKEmDEOMNhTarFVV4v9qbFvRJxm1EEse13Y8AYg0TeBRh0yomzQ4QKYVPeYEKDiY37Lv6z+bOIceWifXEZhTy4pCwdk6RUFBmNdOA8K7bXvlyx2ft6bQ3Oi9E+G/hP49o1CdK7r4y5ykLQW6KeEL2JED3vULdbjdfG/bXxDN3+dlvZ+Fl5AiqbNyxYuzvgwru5HZ30vTEd9+4mMhvLVuCS6fYs7DApH7T0ElHgJ5EESuA/VtXfEZEJ8Nsi8vf8vf9KVf+LH626EIkCpNiIRrN0Mkg2Xrdm4wh1WHA9ux5dfS4Ftdv8QaS7WTcQVrVVLT1Udct1cQHxupfIBgHoin1tpdRoV8gG1+nYxa4SbJrrtSSkZedZ3cbFEGxikYW/XXPzrvp9Bdh1Z8MYqGy3v1spUDba1caZYbq7oJNxV2lVi84gNn77+1YhKl28bOd+LoIegJzV9bOhItRcdkPXqvtJJ0axI+i0VdSs189tb8PtmryOHLLFxTplusFEndU2tj2a3Bflpd/3zYkXp4OoOm9WLc9GWJfARhUIsNiXnib+sSUBVX2sqr/jv89wacTu/Dh1ubFVUBVsC+axm7yRb5c6/ain0FIblzaOothNxKDDpfz1gf9sjK9+0Tf0x8oL2iJCUh8MkpqYtAsquATi7nfH/SWuQ4puu7tsy4na3nbCpI3bNNqc8DG9Z/2k9CSToIJBtiGp2E1i4Tdj++E2R+YIp6oSGig1RRWMUawKts7EY6RRb10qAOPppBO2BYOqIYxSBsMBEgpBlCDinLgizuptTIgJAidVqMVohVGL+PmyarGC/7NYrDsvsQTVEahF1bYbR4BaMqnXoUt3dijiDV1sAhg7hEhaWUB86V1Sfo0fu6A2qbTR6/1OqLa2Qm2tlB6cf6klVIpaZxdx8R6epNn2jEyFbSUlY9vEx1fAT8UmICJvAn8C+MfArwJ/RUT+PeCf4qSFs6uf9lsoVJLS7kgkWTZUzBhILKyoxa0uF96utHYV9mwM/qYAWofide45j49H8Mbp1c5iAW5v74g07DPZNlS3u0Ov0ui2Ol9Xa6WbIWpzIB0IaZKGqSErOybnmsRYxfjNa7eqSXBJOxUxBoZQrRRRl0evqlzkgahixG3uOI5ZrVaMxhOshiyXFwRJTJykDCZTwjjhzv27hEnIk4ePOTo65vHDR1xenJPECcfXbpCmKZ/88APOXzwlAPb3Jizmc4qywlaVO0XnRWOtwEiIFfU+ujXGCFWlLQuNcYEM1HuiJuEbe9//6DEK3frSlN1MwdatyzaHVetcY7sQxNdsfUg30rRtANv4LbWOlva2GbuNL9rBW/VfBjBcK0tCLKWfji/Y/R5+YiIgImPgbwJ/VVUvReS/Bv6a795fA/5L4N/f8VzvvQNUUCC0ExnRC9Ysnd85E9k2Fm6ISyLiwqUUtOuKblt3j/h1cksWwyBvz94C0jmKTKepfgRmZ/f3Imi0GZcDg6r1BsG6ol4tncXuEAGRfv9HfnpyGpuCS3zR5fud47d1rLIneA7vXOxCHeAmAWDboKQ4giLzkYamQlWJ44SyKFCGlHZOlUMyGjOMYkhSbh3fICtuUBQFk+ket996G8Vw89ZNPv7kh9x7+z0Gg5T945uoVcqqYm9/nzhOyWvOVhYcHx8jcsLp6TmRiJuzyk1WKAK28hl3DUYqxAd2iYrbG0W9We3Wxq0XSsU0akCAYMTHPXhiXaNYe0LSEiMUgUsttgnSaFC1zO1VR3V4uIERhOqE91rLrXCSV81suiitqr3NXH9v0unXt3IvIcWVj8wMfL/dMfqXwU9EBEQkwhGA/15V/2ff6aed+/8t8Ld3Pauqfx34676cH5OhncgdSkw97lLYGpdsUL6aEYqjuD1hIHHi8ioBsrqlwmXVVXa/UILmdSbNRmoJljjKoK0+tz3tXmSNpJ9Ih7bfMbVlYw+46Ljr6qPOwKoj4dA2jzp9VBttwRfohud3p6dztFVq24KAtUpR0OQXEOOIUJhGbvPZHCTi8OgGpa1QgfGbh9w8fINwL0LXkCQj7t2/T1ktiJJDlsUnGFGKwnLt6AbH169TVhUmMJSV5WfeeY9ynbFczLBxTDAYQDSjCCxhqZjSqQsWxWhMGBqsVqCWsioJgxCrPsBKhSBWqjxAdAjMN8wzNeXz82D8WDcORpXNOgOxuASjTdThxspK90loWLSY3tVatix9Bd0Q5loT2aYxtdvWE7TaWrgpBKtjokGhPuq+wqZCsVacS+LqOJwfO9uwOMz974BTVf2rneu3VPWx//4fAb+kqn/xC+rSnaJLvXO7CEmrcYe0YRj185tU18EQdI1LOG6cSGwVa0JUKqTamIOOIbLXz873EPFMp6NQb6zP1vORtKpBt1ACkoe0N93ArZfZRYQRMFcvKhpxqcuCjoVgQxrqQSOl1OUc29vKdaw0XoKyLF0qdev0axPAzVv3ePr0Oaa0/OKv/WmqSjm+cYP7X/saq/mKs8sZd+++QVWW7E2GrLIlJ7NzwizieO8YG1QYI4RhSF6sWGrFKrOUqxUPP37AJ5/+EBMKaRJTViXjyZiD/SGff/KEzz7/PsvzGSYacOPoiOdPT8mzC4JAsFVFMAiwWY0oQ2808AgkG3OxOejehZrA6lYwoxWacyDbz9YMoUYehxD1/qolC0UhcN6eLZtVr9b61+6IyoZBeCbX7J/ufm+Q0X25KtvwTyIJ/Crw7wL/TER+11/7T4G/JCLf8c1/AvwHX666erKklQE2/Th+s4nX0YrmYKGLL1Vdu6nf2ggrnIHHU2ZvLRetqB8QQ8v+ihoRxrRZKvqVuqzOnUVscEgw6hQOW0caavehrSH7HCBlO8bQ6cIuuSlUMSxzLwHUFqqY9l0EOAZnTRsl5k5OKBUBSOI3RC3etOf3a3AWAecytLbCWkscO+doSEU8OuT5i3MswuGNm1BF7B1NEQnREszRdW4kQ6aTMYv5gkhLHp2ccLE859b0FgeHU85OTiGC9XxFts4IQid0rSQgHY14+2vvkg8uMGVCZIa8+eabXFycsyxC5przjT95k2Ey4ZM//H2KaMleNCYrSypyqnXpOG8KrFe9cbqLmZu7+hVmNdv1ElR9wAjU52UPoKjPkHhiuYu6N1Jj7Z1q2FKvWCVCbRkUK4QCZSroalMJrA3KdRrjTj1dmtOIpf6WhoiUTQa6mIS8futQ71V82/BjEwFV/YdsjtTB//bj1gk7lIDmpQC+qV2h+uoUZDF96lpTyWamEtlIxaNep+wHebTg3qqR4nOG1D8ycDltyma5uqC49Y58lEoBzprejz9pv2gIFExVuER6k1B5yt4Q9Clw6c7DK0AUucQp1q11bVx2uqZnXbreGlujJqkSSOxMLFKg3sjmiIFlPJ4yHA0ZX7vJJ598ytvvvMfde28RhoaigunBhIv5grisGKZDksGQIIpYXbzg4vScQRozHBmWqzmL2Ql5BZPxPulwyHKZcZnNnNSBMBqNOBofc+36dfb29gnThKxUVvkDvvMnf4Usm/O97/0hJ8+ec/vaXUoxVM+fkWUrUGdAtOv23EWtQqld+1iLAcxXrcDlZqIfWWrwelnlZ7CuRTa3pPu1I8HxLulaY3W2G8+hSnXr2uKol/+0aqm7Gt921VN1FXywiUMmgc4z7iNPs0YiSO3LlIGfQB34aUJPHdipvsSIWDAhxBlmDbphLAkAIsfMxW8ibwZCdYIj11+U7aZOD9Nf6gkwa6iws0q6JahJcpcM+Oe18lJ3bcjYJDBeVA3Vx7LXdgshIOidHGhjy7xLtOYApq3m5ZCQEADLTnoSJbQWjKESwfrNb1UJwghrldFkynA45vrNG3zzO79AVcFyuWQy3kNEOD4+ZrFY8O133+difcl6tWK6P6a0Fct5zoMHHzA/vSQajRkMhuR5zs2bN0nTlKwsWWcF6ywjMEJgPMKbgKIqGQ4Tbly/zXg0warFiOH7H/4RSRRRZhf8zb/xP/Di4efOPamZI4qdsGdjfFC34mO1QiS26LrF99bDtHH0uTd3Lfu9UuPqHNxqntKar/u1rO02XTyoeQDqljbEJc3t3K+lky0iQC0Q1InG6vsOW7y5y8EIWPCVqANfDbRnh+nJy4g7lL7yupng94QbvBXZYsltDXM2l3Y37A6bmNWV4azJrZtP2Wo09Dqoz3MaeE6zvU9rr0eri7YHVGprniMoZd1WXLrY4SlOZD1tq2nciJs0CYBsw/XqxP6KysXiW8ftjDEEElAqRFGMCSOOb97il375lwn3rxGuK5598gnnp2ccHBwhlSCVMF9ekK9WXF6c8fThZ85tON1nNNjD7gXMF0suLp9y8/pN9g+OOL+85PTFC8I44fDgCGOE2fyCOIqo1BJFKZPBlGyxxmaWyXhCEAnvv/dN0jTl2fPPSEZTjm7fZXZxTrZKEZkTJzFFmQEVWuEShXoPA1qia6H2BbU2pe3NVUOQgC395t8M0usS3x3y8IEI54IzWIbSerO6ELvcqPULg7TwrtBG368LbskfDZRblbqgoaxb8qrXxHl4ZYiAidl4EcwXbFoFKnea0yBOvIr9Qm0Sg1odq8POr6y6NTzWxzS2S3Qq3+UO3sg22Z442wXOAtK73XCVHQQp9xV2k2nWBqm6H1+UT30AWEu5rhqDkkO2mCAMiQcJVoVIBlzOZ3zz577N8fVbTKfHXF5e8mkYMbu45M69+zz64RPCacBHH39MXuQMk4Bnp8+IopSDOObe/Te5uLzkw48+QkVJkojl+ZIoNBxO9qiskq0z8nzN4yePKNVyfO2Y2zdvkyQR49GEg+k+cRQRBAnni1OePHnEP/+j7zObL1nPlkiWc+/OTY5vfp3s+XO+99GnIAZJBZODqSoyVUxokAHYuVMXnINmlzbbQtVQztas1xzdtJZmsXdUc941Lvd0XP+CcgEp6gPr3quz8RrGjk3Pw0bQCa12ukmPWtUlcg3pbt8DvCLqgBFxERRTXM8vunddvIBDVG8JDEAqQQlAhLD25nrSWScBUR8C7L1cLgqOzppc4QVoxa1dvkJnlQvIPD0Rn1NgY4IjZ1y0eavvfSnYIXM2l2rxsWMeUXBWvax/IEXou5Eh8GUr583MBTGmOZm3Nz2iQrGB4e2f+eynZt0AACAASURBVDrvf/s7PHn2gj/1p/91wijh8YPPeO+991jmay5OLvg7/+v/jglD9vam7E/3WCxnvHjymP3DPd5+622iIOYPP/mYSuH23bv83Pvvo2p59PlDPvzBBxwcXmMy2eP85Bl5kXPvrTscX7tBlhWk6Yhrh4dMxxNW5Yqnj55QoZxennC6WnE8OGQxOyNfL5gOE/7u3/5bPHz0kPnlOVWRIwhRHBENhqTGoCjr9YIsU6pqSZg6i6Q9/0I9CheH74NNpOYjdQyC7FQFelT95XSmXpqWxnQkgSv0kwa6PE2hZ6g2RlpXdqQunPtVVgcaqnXZudjgdOF96wFGbc+nK1LTcvd/EAEaQW69OU4xpNhBAZkL0O4R5bxtv62F9kx30CUA4p1qSkDWcb27DnntpCUFnbdNyAam9CwPntI39Mg7JspOmcYEJH17cdNCVrfen1Og9TjWT9aZnI2AtZggcJx/EHHt4JDheI+3vvYO04NDBpMDBsmAh0+fUiwW/Nb/+5ukNmC9VlaLJWenZ5j794lyYVXMyVdrbDbk4vSMeH/Kd//Et6nCiHg8wQ5TTJYxmI54+2e/xv50n/l8zs3RHQZpSp7nfP7gERjh9p2Is8sz1tmaJ48fMdnfYzweYQYR1yIYhgdE8gYffPgH/MO//xt88MGHDAYJ4+MxQ1KK1ZwwGXF5Nmdm55jAv7NxVGLUYGPQ0wpjTHv8vqpnu09ItYsxEyiWOKPcEK+6Dgl02WcBMgBd9Rdv49BgA11p0i9sYyyMvQ1hXev8Pavyxvsv2hL+Z4sJXyAdvhJEANwgW2GcbjI+pLBgylY8Cox76w40J62E2B9eKfxmiVAqSl2iK5/ltVK/s6Rlp7ikDS3F9qfVDFQaeHG9PSjUO08mUA7EmeXnnTv+q6l8/3y+AuNiwwikXk6DWMGooGqIGpJiXBkpey8c7b0gOFZsJo0Pv6rWLjrNAioYE3iPVOD3v4uxd0YoS2ArAgmoqoowTllEI37hW7/KG/fvkqYD9sYTABbnCxYvzlitCuazcy7OzlGtGA4MWTLi4vQxz59+yuHxEYPpCCsByyxHiwLCiGKROXF+vmS5XBIT8MbX3yOOA6wtURWKquLJs+eEcUQSxlBWlDbj2YszImMYBTFDca9jn8iEYLLPk6ePOTlZc/dnvsnCVpydn4MWPP7kI4wp0YtzAlsRxwFJcshsde48ByKwaE89qm2ogF84tyvVT7TbWOrk7gWNF6l+65WwdIb6GkKgXLX4VNtodsa+qTt1uCEuNCued7e89TignlFIj3uZ7nM/IrwS6oB4dUBqfuitmUP3gQCRuEDQCCHziyP+ME99kNCpCK045SzDnRj6jaG6nxvynGrLPPGeAWqrrtfnmgpqm6xDHGl8vFlDs1VwueiwiIr7w6ksijszbgNFxTIWmHspRGp6hTQHfNziuwQiLlizbK3Gigt68lzf4aq4k2T+YSNNsjMQJTCGg2s3uH77Hvu3bvBzP/s+SMXe+DoHB4fkec5v/9Y/5d1vvc/zh5/yj//gH8F5yXQ65aMPH3F77wZVOmeyNyYMY7Ki5Pr1Oxwd73NwfMjt27c5PL7uEpoUFYdHRwwGAy4vZ1xcnGEEknDAKluzWC4JRBgMB6SD1CXJnM3IJOBiseDR40dMognRJEKrkslkwuNnFwzHwqrMyeaXnJ2e8Jv/8P/g9OkzLi9P0CwjAHINiEIX/4Cfv5rYa3czAUg3H58PPt7cI9rjIR6HNxCrtUE66Jnr62LeALiDCHQlPggRUVRLd5BsBxGo8ftlzqJXWh0A/ET717f6I7CLBr+N9w8oFRGC7bhMxEsFgVuYxIJVl2hWALV+U3uRQXWDFnR5u5tJ05HtZz2zgCMAXVOC0zhLH+NRQl5ia4uidROstvDcIqB+Y6Q1NKzdVC6hx3KoGBfX5PX/2t3o00rXJ/f8KbsEQ6ZKCqzUS0PNOUYftebP5osE2BBQIbQxQRxQaEkwGLF/eMR3v/2LjEdT1nlJWVWcn58TiEGtxS6XrIsl88dz9sYjViuLBEs+OXlAVc159733uJidsX94wNG1fe7ev8dof8R4NMbagsoot27d4uDgiNVqhaolihOy1Yr5/IThcMj168cYY1gWBaenZxgfJrzOMsIw4p233uTTJ08IJaIUeHZ6glKS6RAUvveDD4jCkPtvvctkus/nnz3g5PET8nJNGAQUhVsxY5xNL0gEBiCVhaXn92qJvNupPr28vT3d5qtVOb1q23XUVoQtAuAuS3u/Du4QkBBnF85dexElKk7D7LkY65ePdrSEzZ60noYNBtaBV4cI0CXIHQuYMUiz6/zONOL863VknxeBHRPWXh1N1J2IMxjWR4ljvC7dBoEoILFTP9Qax/i37AZC5iMJXUy/ey2yrZZO3fCcvC7vhEwhUEf5ldzH90fguYypj4p6q78VfByEbdQdx7EcApgQKAOq8A7GPvbv4xUqCUFLTABiXcYh9e0yFKI4YRztk5fKcnHOwdEx9958i2/83PscH99gMV+wyhfkyyWz8yXL5YpPP/vnHB0eUq4rJtMJpa1YZJdYqyxXpxgR5vM5RVWRpkdM9/coyoowDxjvjyASoihhcTkjHEYEw5Djo0NEDGdnZ7x48cKFcBclRVVR5TlVnvOiLNlLEpbLBeMkJY8j4jTk2bNnPH/+jPff/zZW4Mxajsdj3nnnm6zWK7QqeXpyxo37X4dgQJokVC9ecLI4xailLH0S2ZK+lyWCwAyockF9pJ1Tp+qVtEReOdTA4ZsmwLrN6lTjMZ0NF02gmNE/j7IpFXj+Vx8RkMZtrI2VwqXL7JwhAWeb6EWtxrSBBh3iJDBm3XcqdeCVUQeaKWwIXcew1hzEcJvYGHHviqvP9rtCzXMa4cZv8aKzIKE7Z1+qe2G2S47j5DqLe9tZjoBW22nLOmJXS6wEVReqUcsSYnw3KvXxPN4gVSOIJjh9s00E0Epy9VHeAmtdKmvxUWChiEuhroJaSxAEBEFAUVUEpWIGA6IoxVphtbaIXiJV6d76g0WCkDAdUEnIvbff4e2vvUuapty9d597d++hEvPowaeURUEURCRpyu//3u/y4PMHBAhHR0ecn5+TDBLO53P20pSPPz5jNv+Evbf3eG/6DZJxSDQc8su/8qsM0pSb126QF2uCsGIwOCIdjtx7F5ZLFstz0hSsJqjExGFEtl6T5zllWbJer6mqitJaVusVp6fnGGMYTsdEJiRWZTKdkiQJh4dHREnC2cUluTH8zm/+P1RVxWq14Ic//JDPHnxK/uQ5z2aPsVWOMerSllX+mLgEhChG3Tl8aH3vLlmH7SSO6SODl8w7pwhbtGl/R0ykcMFmDfL4BxNp42IC0AR02ZFN6y+2X6NDCn+lcXnv1HV7qsKrrw54i+hAhRUhWueurzd3aJDSn5O26uK9Q+uODJc+0UW9uWrLvE/VFKhiS+/hVyUXxWn7c+rXXHVjADdkQAcKabNmXn6TAjTwkoRPiuntFdRcRPHv4HJqgVjFqKPpop5IGMHKEvE2hkjdW4GKJAGZUEiGBOptCko0mTAeT7FVRX5yyej2IW++9TPExDz8/FPyfM2zp48pCxdvbMKIeDhhMBxx595b3L1/hyCIuHnjNsN0Qp7lUClnJ2fkeU5VlS7VmIk4PBoSJymLx0t+9hvv8cPnT4hWa4riKagwXg6Z/syUMAy5ffseaZwwnUxJ05TxeIy1lgrL5eoUySuSNGQ02qeqLHEUEiZJI6o7Hb1ksZiTJAl7e1NGoxFBEDAHxmFJGo8JKAkjEKM8e/6YorRUVUVelYzHAy4uL9BhwrU3jtk/PuSHv/dPePK9R5goQcuCQAUJhDBMCYKoMRIaDbHWkrKmqpTCVlgrrTTVbDYvkXWYhfsYY5hviORFG23aoyUKa2nDlNfOGVUHg3r6vbFJaOiHO2riuH0snWh4CRAvijZG5S/g868MEUAMqhVLQkKKxpgaolSqUFgChFLc5JvAvw++cFMmnRVR9ccp/RntOi2IBZe9RgRh1h4BDcRpGY26sNk3V2cmECdQlGCqwmW/EsiIsZSghbc9CGodlxHAZq07ypElQ2gUa0uCwGArJYwiirQgrCJCv8DhcJ/h9RuO4+3vMxpNGA1HvHv/Z9HYcnjjGo8fP2E+n3P3zl0OhxM+/fwB8+WMk7Mznjx7RrbMGEQJh8fH3Lxzh2+//x32pvus5jPKrGR2Pmcxnzf5a548fkhZlARRxPHxEce3bhKZnM+eJbx48YI3jo44O3lKURRMx2Nu3brFdH+fQRQRes/D/mRKlmUURUEYhhRlQVVWRFGESMJ4PKKqLKenpzx/8AATWA739xiNJqSknJ6dc6kXxGXIKJmSXLsOqj7EGGwgrLKCoFJm8xkXl3Mm0z3C0HD95i2W+Yr10ycUq4IPP/yQs9M5aTpglMRoYIglRN2rkpzEUZaUZUFVrlFV1k0mJ+0ImLUovu0FamGOiQ02/6IwpNrolIBdN9JAbaLevWdbcUPpxitsvICmphxmh8HxCqn/1VEHTIhaZ5gpqJz/unJ00RiDSEgcDYEca61PkCkMBkNWqzV5UZLgEmYWfiqtWKx3JQbGUNkKEwhlWboceALOkNaYDt08D0FWrbrlnRWur3UQho9TDzFMzIgX1RlCbYGPcIp/7lULaYJy1AwI4oCDG/us8oi9vSHz+Zz5i1M0TnnnO9/irW/f4dbB1/nOnTd5/1vfQueKDpXnT56xtzcFhTAKKfIVYmLKypIOUlZFRqQhhbXMVivSyYSydPFkcTjCFiVnL06o8oIwSnjy5AkXF+fMLi6oiooPP/wB5+UZe4MEW8UMR2NenJ9wdHRIoQE/97Pv8cMPfkAQBDz89GOS8YCv3XkDYwxpFHP37m1u3bnH4dEhlVSkwZjAGOJx7LwUxqC2IssKZzpR6w79APP5nHK9prJKpZXDhShktcwcAQHm6zW2sphRwtnjc9JBynS6R1VVDIYD3LsXLQ8fPWK+mPHxDz/AquXpo4c8efKIfD2jymfMLl2UosVS5c6I43AM70HYDRG176ezUbu7dvPcSxdxNrZ3zyxQGwZ7/vwmG8yOnmwqHy1cRUQSYGXtq6sOiAH1Z+dLEbAGteIPfYRIZZy4HUK+VkIzJEpG5NklRSYkwcBVYgShYBAY1tkaSv+SMKNoVWLCgLIqCcKgPauPTzzRhSVO//eJHBe1hdWIW+ilNskdSqk4ry6JMATiMvmqKiUlGgRU4k+pWcWEQ0b7+xwfHfHuz32Ts8sZb7/9NX740cf80Q8+4ObxDf6tP/8X+Nq773Ljxg3uScVyuaDSElmA5EtePD1lfbkmnU5IxDCZTFicX7AOA8b7e8zXawIbInlOnq0ZjccEJiS0FYVWpEaxScBiteLi4gmLRUY6HPAHv/8hF5czrh9eZ//4gLOLU2xkmIzH3L17h9VaefHiBUHsROp7994gXy9JkoQwCjmc7jMIR6i1LFdLgihiMAmQyJ3RDYC8cKnKkiQGMYgRVqsFeZYRRwHDeEJWlpydnnquVVCVJbP5gouzM05OThgOxqTRAIlClrOCQZwwnIyJgpCz53M+ffwDPv3kE4qq4MWLZywWCygsZelCZytiynKBrVw2I2NAVVyMf5PEpSv0R6hUoErRKKkN5vYNc2svlfrn+zH7bd0WRytqGzVuqLQKvMWLg+6tR2xu7E2jlYc0ILAV5Q66scM50cArQQScK98iJnRJLeIB08mUKEmwCEdH1wjCkNF4zMXFBVWeEZiAs7OI8XhMFIVUlaUsnb/k6OCA84sLLi4vKfI1WbbCGoNIRaSGxjcsirKgsc9L6QJ7Ou7Hpo/gXI8LbXRId8lJJKIuXTYKGhriNCVNEkxoSK4dMTzYZzLc5+tvfZ33vv4Od+7c5dHDh9y4fp1bRzd549Zdrl27xr29Q+KLGfP5io+TNUl4AGsowpyysDx5/IwkTSlnl1zkFUWl2Kxg3xjOzmdkqzXGwHpdkucl4XVlMBiT5yVZkbNaZ1RVRRCmXC4LHn7+iL3DKfPVc0bjlOnRlPPzU8r5mqObe9g05eL0jP2D2yyWc6ajMednp5yenzJME6I05vbdW9jcYoYhGBcHH8YxyywntBFpGmO1IjQhqkoQhAShIcsLrIXhYOQONFUVcQqDNGWxWLCYz1mt5sxml5yfX7CYr5z9AiEdDrl16xbPz14wLFckaUomOX/44fd58vQRWiqz+Tl5kbM3GvH2W2+xuHjBRxcPqJ6VWOvdOB2rvgvsajec+yzdMeAyRqqC/nbssvp6N4eMqBq2UluqtPnzri6h9T6VdQ864kDnJGwfalViB6wryiE+lZy2xb9A2H9l1IEwDEnTgfMfV3Dj9h2+8yd/nlzhm9/4FuuiYDrdJ4kiQrV8/vgxSZIwHA4ZD0c8evScLM84PJxgRfnB93/AsydP0fWKi8UJn378CaBUVUEYGFYXS2zoDuVbIPR5uWJxceEug1k7N9Y6A6IRQQJ31j6IEqogJhoMMUmMBAEqwuGt67z/nT/Be/ff5XDvgFvH1zk+OCQNCpazOTavyIqC9WrJII4pbY4hYLmYc3F+xnw2J1uvydJ9fu3Xfo3z0xPSwxSzUBbzOdPphKePHxPHsdNSA2EUhnz+9ClxFDGbzUhSd288GnN4/QZ5ZcmWmUsYGoRgQj759AH/7Pd/j8vlBY8++xwDHOztYYzh7PyUu2/cI4ljRoOE4f4d8nxNvlpz88Z1Pn3wETdv3eKNu7c5Pz3l1u27jPfGTEZjksGANE1IhwM3p/GQwShGjLgoxdKSFxmrdcZqtabMC/LVmtV6BSh5VmBC67wDqzWXy0vmF3NmlyWz2XMiiUgGA/KigiBhuj+hsCXx3pTrh4ckgaEsSj79/vdYVhn5csX//X/9fR48+Jz1/BxTloQSooFSVt687g3EqLgX2zSeqhTVnPq8vpNJN2H3IRSDSxR3iXfn70h09KVOgr8Eenu8+ZKirJ20ldpGf7FflTogIp/gPK4VUKrqd0XkEPgbwJu47EL/zssyDhsTMN7bJx0MqCyslhmnlwv+8IMP+PZ3fx6bFRweHbNcrBimAxaXlxzsH3D9xg2iKMJmlvt3EkoKkjQhThIuLmakwZDpJCbLFqTJkPl8xvzyklU2IxjmqLhsCwHO5ahiyWvjnXUOB+vwDM3Eex0FUQgHCdO9a4wOrvPGu+9w/cY1giQhGY24eeseb92+z+3DQwYSkp1dohdLkILZs8fMVksqtWR57vTpFKJwwirLqSRgcHiIXSx48813sTYnCSNefPSYQZJgAsP5yQmz2QxjDFlREoeGau+AsDKEiTAeD0nThNlixmI1w1wEiAlYXKyYjqaMBynzvHSnCI0QScQgTZlMx+zvTTHGMNkfI3EIkaKVkMQR68WcPFsigeH+/TfYPz6iUrh59x7jeEwQWEpVhlFIOhwwGo0QYwijiCgMkMCJ3ZRKGIQksSUMAmxRMLeWqiqYL+Y+P6cQRREmCBmPJ8wGl4TBc+bLgGyZk8RDhJDxIOXo4IgwjIiTmIO9Qy4vzykqZW0Nl88yHj79iDwvEKmc3i9KQQH+DWIYiNQFZhVqXTyG1IbAdW+DqU9g26cEngBIp6iBysJZ9w3XI2BhXDyJf6CbS9g7s5zrcUPt33Q9siG1NDcNYNfeCO2ziVxxUK6Gn5Y68GdU9UXn968Dv6Gq/7mI/Lr//Z9c9XAQhcSH1xhM9rn3xps8eXJBFE0p7TmT5ICTs3OqssRIwEWRs5zPWa2WLC4uODo8oqocxxATMJ1MCCchh9NjqEKOrh0QGIiH+5y8eMbjh5/z5NFDDvYsjz97AGFFVRUuiEArKgICVaggHAjVGrQw/q3CEfF4wHg85vrxbe6/9RbH127x5v17vPvOO0yCGFMquV2Rn52yml+iScJ8tSDLc0IrnJ5fcDafs7e/TziaomKIx3uEUUS5XEJccHR0xOXlJXvTPR49+BSxlvnpM6rhCBPEmDBm//gaFhgUFUJJFI0YKIjkzIsCCcH60MXAuPRrUZAhkoEuEA0QVa4fX2M5GhFFDvFH4zHPn78gzxbYSrl1eMji4oT5cs2169dYyJJVlXH76Brj8ZDZ7AIJA1a2YlAFzuCqXtQ2jpKuywKt2sQatqqoypIiy8jznGw9Z3F56Y4HlwVhmPjXq5eUeYUtK4p1xnQ05ujWLcp1TpnnUBQEBqDCmIBsvWQxD1ivl8wuTiiKJc9nz/j000+4nF0QBkJ92AsBCUKMGlQUS4CWFtHc23sC6mgPl7la/MEy56PzfiiUEGNsk5nOh2h52xH0zq4v3TOBChjrVBIDosaHAhhsIKAuwWqTNbpOfdbd8YFD2coVd1NrcCGRPmVvAy8hAPBTUAe8JPDdLhEQke8Df1pVH4vILeD/VNV3r6rDBKH+K//Gn+fO/TcYjia8/fb7xAl89vQTyvOMKAyo8jnDwRFPzl4wSWLm8wWBCNPpFFVlPp9jFSaDAclozPTwiCgMOT0/5dq1Y6qqYja75NrRHh98//tcXF7wD37j75JnTgRdrxZUZQFSoaViAhf5EyYDTBwjQchocMS9d+7zrW+9z6/80q9yuHdAqgq2JI2HXF6c8eTzBzx79ox1puR5TppEDIYDBsMxwzQhjUNWVpktl0wPDigrJRmmDNIBk8mE9WqNLQpMaCizktV8xuzinECE4TDh5MUlUTrg3W/8LFEYUWYBL84/48WL5wyClKLIXRbfsiRJY6I4ZjAcoj4MVxCSNOVituLhk2dEJiDLVjx99pinz587j4wqaRzz9PHnDNKU1WKOpCP+3J/7s5RxQmwt61XO0fE+i8U5mJhb124zGqcMkxQVy3RvQpIMCIKAsixZrVaEgZNIQFnNnepzenLKarlAV2tHLMKIJBmBWNbZGrVKVVVEsVP9VqsVKysMQ2cIRi1BmGKCkDAKiEdjZosZT599xge//z1mtiAJYx59+glnZ885efiQ1fzcuWDDyElTWYatSh8C3nkHIDhbjxEfbOPOGqhI44MXjYmjjKJzatT56OvnN/YLoCYgiAo0g9LnlxUrLhU6AaHmxNYFxFaYhlUHVTetoFckfIpql8a828h2u9VX6B1Q4O+KM6X/Nz6V+I064zDwBLix+VD3vQMmCLk8mfMLv3ib8WQf0TXzixyzNojCerFmNlsxfTNknKZgYDqdsl6uOHlxwt7BPkfHh1xezFjla+ZVRTocESQJq8WS6qDEWlgsl9y6ccTB0TX2D65x942POHnxlONr1zi/uOD5409ZrWdoZUGEIjRM9g4Y7U05vn6DX/jFX+bunXvcuXmbw70DhiainJ2QLefkdsnJ/AXPnzxjsViSDkekgwnrLGdtlenAEZNgOCQtK5Z5xWQ8Jk4GzNaL9jShVdarNcZYAlzs/ng8oSwrDo4HFBo6sXy5Zi1LTO7OAJg05OJ8zsnJc5e7X6EsSqb7AVqWRFFEECfYPKNcrRmNBoz3JgzCkCQ8ZDIZcu/eG1xcXvKD7/+Rj8RWBoMBSZJgg4jZ7Jybt+4TRiHnZw959OgJw+GA48N9pzMbF7/psgnngKF+K3OAYMuK9WpGvl6TZRnL5YLVbM56tSQv3O/FcsVkMqJUYb3OEXGG3MPDQ0QCkjjAZC4LsgYV43Ts9DYJGY1jTBxTVilxMuHg7h1Wjx7y+OkLVmuXwidNUygTqgLWRYEtS2cQFP8KcG3zzdbp29uXi9RgvSguiBYUeX3KtN79/qyHpwSBuPOjtTPaaunPCTipyVgXo1JJhYqlwkW0i48lslU3NqDe7P7NxLmzVTS2xVol2EihfvXrUH46ROBfVdWHInId+Hsi8kfdm6qqIpv0kN57B8bTfS1FOTubcfPmGy71VBRhxBCYAGsq9g8OWWc50/Ee6+UCVSWKEtbrFefnF1hbURQFleaMkpjs4oK5KrPLBfO9jMEwJR0OODmbs1qX7I/3+Nf+1J9huZ4TRTFFbvno4z/i7OQpWZ6RWihGwlv3v0GSpty5c5df+PmfJzQhAwwmK1nP5ywWZ6xXa9ar51wu51RAOh4RhDFBFDNMU8LBiHA4Jg4CslWOmJBhMkK0IjGGZWmYLy+Zn59TVWsG6ZhsVRAFSpFlEARM9/aYrxYUGnBwcMDJoycEkwRJDYVaIlJ0YolXMUmaMEiGSF4RRxFRYEjDgCzPyPKcLC9IgIM0piwLbA6T8ZShtQQm4ObN21RVQbZeUWmFrSzj6YjlqqIqKsqqJE1TyrJ08RIVrJYrjEAVZwwHA7L5gkE6xSKsqpK9MCJb51ycn7JeLFitVlhbkq8yZxjMM2yZYauC+eyMy3lGHA8oKpdZwVYrVEaMRzm2PGCwn7iESsWMKEwYDEcUeUUaO4IzTIfcun6TOzdu8fnjp/zeP/1HvHj8iDwriYIBRnPWVdFsknr/umhP8bkL3UZvvEU+FFSwjmCjXjJoXilDTEBJ5RM+eSJAnV1MqSRwpNH5rhFb+ezU7nyJVVARrNHmxGkN/dSb9ZuOOqlQ64Yq16rUR6KDzsudd8BPTARU9aH/fCYi/wvwi8DT+v0DXh149rI6xuMpd+68SWAiPn/4hAcf/YD/j7k36ZVsO8/0ntXuPiJOxDkn+5t5G/JSFEVKJZXcVJWrDNkFeeCpYXhgwzA888wzDzzwyAMD/hc24EnBDQwPDBuwoCrQjUhRUpUuL2+T7WnjRLf7vddaHuzIvJc0SZdKVcBdk8wIHAQSJ2N9e63ve9/nvXfvAWlkKQ8lkYlZLJe8ePmarm9J4whrNIRAls9JkwSBIHhFN2iapqOqWvp+ZHdoMVHM2dkKJTS+c2it8RJ+9/f+Nmmmuby6wAjN93/wfbbrNSaaEFJ9v+cHP/h9hr7FjR7jPUPb0lV7RBB0dceh76i7hvXdDW3fcnrvMauzUzySqm0x1jKbLajrhl1dgfcoZcjnc/ZVzWb9a8DgPAAAIABJREFUGVG+pHMD5W7HOGx4+PBDbm+uWRQJ+0NDfjLnUDXc3m3J8wwTn6DdiJeel29ec5LeZ3l6SpoGThcpmmwChkpJZBKqqqTtphCOICQmttR1xTCOVOUeGyKS1YoizyiygiAkF69fkRUFSWQILjBfLVFKsdlvODQti6xgPp8jXGBb3uGGke1GcrqYU5kD3jccVIOQ0zTgptpRHmqGoSN4R304QABrLARPWR5oug4XJm+EthHdMHJ1fYXHE4IgihVFvqSYa0pfc3420ZDC0LK73ACSx8/ehyAmQOkwxaidn61YrZZUhwW765HN7RU+DCj/NSO5ePs0Fe80DMDXdLxHXYkK4MK7jeeVAuQ7KHYQAR8kqIlzSAiTlSgIeqkQQaC9wEnw8hgTclT3mSDQb68B4uuqw7e5im9fvT3tvzUvHevTLxwHAm7KtPC8zYP9peuvm0CUATKEcDj+/e8D/wXwPwD/AfBfHv/873/d50RRxEcffRsfAs71pHnOfr+nkQIlFYdDyaHt0cYyBkfdNPS9xBjDWFd0XctiscBGhrouEa4nny/I759wc3vL2DcIPNZEE49g6Iis5eriiizTWKsJLpCnGblKyWY5w9hx2B3o9g1Nc8BITXM4EMWS7WGHazuWJ6ckaUy9HtBRhAzj9P8gFVma0TtP3/e0TYMbHVVdk8QxcWTwBGwc0Xgz/bu7nrzIub3ZUdU12/0O3x4IJiZJM/oxkCYpznmur29IE4P2I7GfuvDWGKSXWBKabsC7ES0VbTVMwik5ErRCak0kFDaytH3P6Hpm8xVhnJp1SmqKvGCXpJyd3eOw33L6YEkIkmHoWa8btvuG9ElEURT4wXO3uSO2Ed4rnPc0VU0cG5qmJXjP0PXsdnfUVYMbR4J3+GHAO09Dg5QgtMREFuFGhNIoq4jRKKuRRtH3I0IMOKdIi2TaXHWD0orC5sRJQtO2DOOIDzB6z/Vuy+Xla5pqR1mXtF1NexxDBj8J1IQISAVKwWRHVwxusqO/oxEHIEyioaCOVvW3X94wYdpUCLjgj2rV8Uh59whhCWHgrUVQv+3NHJ2v05VDQBB4P4HqJtbE8fLwrhJ8tfV/blIQeNfofPtz09ngqDd4K3H8NeuvexK4B/yDo3BGA/9NCOF/EUL8X8B/J4T4j4DnwL/z6z7EOUeeFWz3e8rdHqkszncTLQZQxmCFYLacY8eIsWsY3YBSEhECo+vZ77fYKMZYy359oO3XCGVJ4pi6rmmaimZXEyvBptrghh7NNWmecnb/jCKNsCJgreGw26KtIjaaerej9w7vOw6bHU50KKM4NBWdBJsWBAk2LjDJjK7rubq84fQM4shilMQgca7HIEhshAye0LZEUYQTGc2+ZtxUNENJkS34/JNPGZ1jcbpgsTolMhGja5kVM5wfaatJJi2jiNXpKYFA0zR0CPCWfXWLHwdcP0xS5SONBqVI0xTtAza2JJGlvjV4JxjHFqEgy2fUfcfq/JzZ0HFzccXF5SuEiDmZn7Db7RmbkbppkdstwXnKQ0VvOoahnxSEUjKOjjRN38Z2oJTFaEcc2alJ6TzBjXjnEGpq8inn8GPAyUDvRqr2gI1TpLUkUcdATgg9kU4pipS+7Tg0e1wdyFY5Jk5w3jM4jwsOpcETqNuaqi3pqo7ycEAMHQbB+f0Vv/Vb36frO6A/9tgkSTanbRtev37Dq9cXWJXglGFbb3Bjh0QeeQfHO30QOMHU9T+KeXyYGqzybWqW8OCnYJpOvt3S06RAhOk5P34tTVm8pUC9+0mOv8mf1xX8XKtCAOEXAkzrX/qTP7f+WkUghPA58INf8v4a+IN/2s/ph4GuG2mqiuADURzRCw9SoaQiz1PO7t3j/sNHRElE21ZcX1zRNC021aTphNLS0tC2LVVVsd/v8ddXzIqCSCvqw55y03D/rCAyisuba2ZGs99vCOMA53POzx9xs7mgcw5rLM45GB02iRn6gX19oG62oAytG9kcahaFI5ulWKMw1oAvccNIsy+Jkpg0idFCsN7usJFFhcBhs8dGhq5ppoZlVXK42XFo9/zgB9/njz7/P3jw4DGDNEitubx8wzB6kiRFKQBBudsjxAnRPKKqW1KZ4aRDR5NgCamomj2L2WyadITA0A+EfkAYS99PT0ylNH5sUFajlURFgqiwnCRzNndrYplwt78iSaY4cSkE6Szn/oP7tG1HWe85HEq6tkEpwdMnj0EIlIN63eDsRDX2AbI8R2tJCDCMA1pMng4hJagJM+YHjzDquHlbRuVxY8A4xaGf/BcueJrekyQxhZXsywOya5Fac3N9jTYWrSNyo/nWk0dcmMDnP/1TpIDVYoYlYlnM+N73vsO/9ff/Ta7eXNIcNlPqdZJx79Fj6qbhLz/9GZ9+8QWL/JTBSv63P/4jbi7647hQH90mA0GMiDDJiPy7C0aMECNvY3ZlEFO8upoK8tsOv3STylSI47jvCLiUx+bjz7f6f8lG/vqx4Ffv819Itv759Y2QDUsh0Vry8NED6qphGHqUEQxtT57nLBYzTlZLlssThJxGZQZBU9f0ncNqSV1XlGU5Yaq0piiKd/bkcRzY7vdcvLkkzj9isVgiynaqr0fS0OvX12ibcRgmk0rTT/HEI1AcBtwwsN+t0ZHGtwP5fIZRishGuH7qgQcnSOOYcXR0bUt5KKnjmNmsQABlVTLgGMeedmwITk4QDj8yGE+kc7rRMQTD/UePGdVICPD69RsA3n//feIo5nJzidWG3f6AkJCkc5QUjH2LSnLiNEJ6gRSCIstRRiOlZOh7fPAkaYKQiqZuyE87rEnIihlOQGQtJ0WOEZosy/j4N7/DZTWnCJrLXYm1lizPJw5hnCIRPL94gxGS1dnp1NA1BuUkIyPeTSM+bSxRFDGOAyEMGKsw2mKNQUiJjSKMMXQOrFGT0St4wjGSre9H2u7Y2Ow8VdNhdESRF5g4QimF8w7XjQx9xzCMKD8wdA2ZhI/uP2Tx7YzzeY4cGxZFTp4mhKGhrTYMXcksT1jMEva7NYf9gcwovv8b32K+vIdJUtqq4dPiM25u1ixmp2glWW+uud7ewTh5D972CAgSHaeE4HBuRIxH9qWYdrzwAik0+WKF1wqvHMN6i3TjhMR7O/MP02njF8VCgl/o+Ave/Zzxgt4w9SQ0iHFy4/4qucA3oggYo1nOM5S1ZGmKGz1KC9qTU6QMJGlKXuSEMIIx2GB5+PARhJGu6WiqhjiypFmC3Vv0VtF3x4tQEGz3JUooBILDoUGpChWg6QYWi3tUTYOSglev35AnKSaMlMPAGBzCGHwiafuGtmuZpyckuZ4279BTHfZUdUmWF6gkm2K3/XS6absOTyDOUtL5jG6zZrMvsVqhhGDoWuomIp/N0TZhtVpx9eqSv/2v/V2WyxmHpqQte4q0oG87+qairEuEEJTddOcr6x5jBtY314xty+xkSTt2ZEk2HY+VIk7SaRauFJGNCUYghEYog0kLbJYR2Zhm7JFaIGrBcAyBePz+Ix7pR7iy5vbHP2JXeXQ/sNvuyY/jw48+/AjjBoLW1HWD0D1OaYKSSKmwUczoRoIQmCjG2hgjA2An9qsAKRRumFr0QQWEkpPXwAukUcRRymppGMapGPhuYFBgvGG+sDjXs9vtSVWGXnjubm+oyh1+GFjJjn/9t7/DfrNmlsdYk/Lg3j1GP3J3d8e+PLAoMpZn5+SLE3722RfcXF6RRAnGRAzlAeVGPnh4Tm4F5ZOHPHv8mNPlkuuq5C8//RmHsibPZ/zvf/SPKGZzlNLcu/8AN7ppxDp01FXFq6sbisUC6RRKap48/YDl44eky5z/9X/6HzlcvDk+scdjH+E4Cvw6K/O4flmj791kMnz1hoRfGqn+dn0jisDY99ze3CCkIs1zsiwHFxjVQJpERHFElib0aiCWGYmagBdgkEETnMTjSbKUIltwZ265vr5is9kgpcGaiDzLsDqhH6enST9UjC6Qjh29Gzg9O6UsS1iPzM4y6l1N3TdkecLBjdSHPftyj8eT5Rmpj7BRhhssTSPwztOPHU3dU7Yt4+jY7g+YwpIsC3JTENDsbtcoGTh7uMKHQGQizpanbNc9q/mS55+94FtPnyKkpB0cQ9uwP+wokpTXL1+g45jTxT3ik5gkLnDjyOgG2qGlHxwnUURiDcV8zjh4fN8jdcA3Hj8GRjyxtsRxQtdo+qbHWM9BTIRgZzXl4UBRzI/2WsG5XdE/cJy9fEk33nJ7u+be+QOUXeC6DXkcI0WED4Gub4lGjw4WkSi01kRRhLIam1iCFtAEtBip25a6qqem2uC+xlBUjM5MY3TvcEynidOzM7SUZMaS5DM6X+NGg04T2q7Be0dfD+R5RqDn9vo1fV1hm1tWj+5xcf2acRexWK1osxhjIpqmoe8HTJyRn5yhlWbsRoZ2II1S5vMZQQju1leEvmGVxzxazjmbZTx7dI8P0g+QXpPnKVkx55NPPufho/dYLE5IdMZsWUwKyb6nH3oePm15s9lSH2osmiw/49mjj1jcP+XiX77lR//wj2ivbybNMR4RPPp4fZiCSr5aXz/dv42wg+Nk8m1P0b0dav5qUeA3oggM48BPP/mEOImZz+c8fPJg8vVJQZpN1BqCR4yBgY4yjsh8i9EaHRliAoqRrq5RWnFS5GyvL2nKPcYa4jhnfbvGGk0cG/quYbsrOT1foJzn5N6KphlQyiJmJ5S+YZSBqm6IrKYaR9zo6foR0/c065Ykj8kThY1SHjzKKcsKbTTd2LA/VAgUm7sdzVXNSTpHnjqassRGimliIxnc1KV2g6MLDVXXUCznVE3L8uSEEHqK5QnpPme92VKXNac2Y3Cex+cP2B9K0jynbkoWJyuaqmG2OCEychox+ZFBamSIiK2ndS3eD0ivccOID5NVtykdNo0IQtCWDhvFKAu5smRZNj2140n3EA8GX+TIo6w3aInyGnVUWMpUkvmYyEZIK49WY0OSpWijCSLQ+IZx8AQh0HLSgoTgGf0UTNq1PQwtNrVoMxV4pSVt12CsQQaOPotwxK2PxFqT5jlvfMWr6zUpNaG8oNnueL2/Zl5EtGMP1iClYr+rqPo7GhEYxpG+bnDeMfpjOpEIjG1LnEaUPvDqeod2HUkkCUayr0pa0eI6RXV1wSp9Dz3W/N6338fGKcv7p+zWJaeRoPKW5cKihUCUDT/+yT8i1D1CGggbon7Hg2cf89Fv/Q5//k9+zHD1CiE8XgiEf0uO5ojDe1sIfr4chLfGg1+yvgqh/+XrG1EEYGLnWxuTpBF5lmCtJZ/NJreegjizFCrGWI22kkRneCYCTG80w/GpVZc1o3OMIjBbzMmLgq4dqJuath/ZlVu8G1FKIwPsyzVRZsiihLJp8YuOKDKkMsf5CfGc5TntOKCkpiwrFssTRhRN26IjzTzLMUPNoWwZPSRZRteNJFlKu2nompKuTaaxnlXIJGIc3NS0NIaqLDF2wo4vlnNiMz3ZuqbDmoH3P/o2F6/f8Ob1K/pxR5I/meS2TYM9M5gowhpLbBPKsmHUYCIzXVeahiAC1ipsZIgiS5YllFWHEI4oTxibDismX4t3I1oIyrpjmSRYa6jHHungWx9+xHK2QGhNNptTFDOUVlTrO4bg8c4RJxoZFEoLsixCyMk96IHeB6SQ2Mgwjv07aMwwDvR9YBg7tIamrsFBIlJMYglNQCuDrxvMMCJ9QJoCpRX7oSW1oEJPJBRxnHEy85SXN3z5l39OnCQo1/L81UuUjZivVmRFwSzLae5uuHrzCtmNiCAoq5rtvmK3L5FaYa1GGcPV6yt++rOf8WCek94/nU5Ps4g362ue//SC1TylKXdstzd854MnZPmcZDHnH778IevLinuPHsHYUxQzZo2nvL1FB4lA8Kbb4fct2ub8xve/y9/47X+F//Pihn63JoSJY/D/IQgD4ivsKW8J0788DuntDvsXpBP457XGYZjY9K4BcpbLJWOY6EJ1XSMEmChhuSiIrMEqQAj6IdCPPQSPtBoVG9RoyFRBsVvQDZ4kmxFMhzgcEEpR728pkoyz8xXjOGCs4WazxmUFh6rGqwYRTrEzxZwZ9X6KRTJSYK2mHzxd25JYSxKluEHTDyOL5RmDu8MB83hBWVa0XY3aS25v74CAGhRJnhNbQ1c3nKxytBR452ibhuXJZJWOIzvFdStF1zZYU7BcndI0DV+8+pIPPtQ4F4iimK7tUWVJO5/TtQ1SGgYBmU9pmpoQPFoLoshSliVd1zObpWgtSbOEVKRsh1uMiUiNoSyr6QvTD4za0NQ11zc3FI9OOVkuJrKRlEhtsTZCSkli9GQJHhxKTmKfKLForREiIoQJNZZYjfOO3k+osSRJCPWkQFRqCiLRSjGbzSZ0m7UkNkUajZJTX2YYHJkISAsuBCKt8QJc27OpKrA5ViquLy9p25qhH1jmKSZNsXIaRTZty/3Tc2bFjL5psUiSbCJUbdZ3R2enoaoPHO4q1jdbNvWeb733EG1Sgh9YLlZcXl5wc3XB6uOP0VrT9Q1nZ0uyYs5mV1JkMb6qKZKI7fpAfBrxeDFDGMvYT9Zloyyj0dRDw2yUfPz0O3z58Ce83G6+Ml3BO8fnuy6/d+8Gh+Fdl/AXgWJfrW98EeiGHm0kWZZgrOH6+hIvIE0zZtkcbTSJSKYxivO4MCUEaaVofaBuGqSYrKdxEmOFYLmYI6QnihN87YhTSwiwmJ+Q5TkIaFvL48crbrZ79tUB1zZcXla4lUTuA/dX96iBrpsmFm8u3hBFEWHvSdOEJ0/eY7/bUl3UnCwWRFFEZAxZHKMCXDqH7wfW62uU8OQ2o+1bor6djtlasd/vSJMEfzMgnk5PYldvmc1PydIMbQxCSB4/fspmfceTB0+Zcus9RTFnf3egPXQkUUffjzjXsZjNpvFdWbNcLhh8oN5uacoDs3jGkDmMBobond7C+0BdNzRNw3K5ZDabIQREKmKxmCMGSZxHpElO1XUMfY9kSlOKk4RhHN+h1Iy1SKlxI4BDyIA2GiEERiq8GfHOI6QknqeTnDY4uq5Da42aZWgPbV9PEeMOEJKz0wcACN9R9j1KKtLI0vuRQErqYVP17Ks9X778ksxmdP1A0zXEUU7vppCTzfUtSRSx3q+p2i3dIHBNS3ayZNI0aIxWhBDY1gfevLmkvD2gpWI+z5nPCkwU0bUDSRxN0XRKEmnF+fkpZd1yffmaODJcH5pJo9I07PYHbKT54Nvv87M//wyhJbZIkGlEx8jh5pKn773Hb/2tv8uLLz6Faj8Rp99aEoQ46op5p2D8SrQEXz8B/FU4Bd+IIqC14sl7j3j48CFKQZ7Pmc3nU1fVebx3KB1o64rITGoyqzNc8CRphDITj7A5VnWdJJyIgDcK7xzs9pwsl1Rtx/LkhP1uS7NuWZ7P2W/33F1dTp70JKWpSrQUPLj/kLau8X6kqlqapuH2pqJxz1lkC5wbKfcHrIpx3tPttljgrqpJ8hmJiWnvtjTbDXVz4Pr5l/TdyLd/47s8fPyYEEGRx9QKXrz4Eu8F489GFoslPorRzrNYrLi+viPLFG8uLjh/8IDq5nMG77i4uCBe32GUQWnJs9WKu8Oecn+gHwZms+moro5AViECxSwnjVPG4GnKjqY5oJTE2klfEWlLkc8YR0+ammlkJ3oSkZBmOVVd0QQorASt8W5kNsupD2vuZ+c0s4bqribNYno3oiOD6/ujwSfQ9u1EFnbT/zlEND7Qt827pCjnHNXNGhssURFhdcwEexKMfmQYR6y0KDGijUJ4j1QF2kxshhPreXO4Y6gbNutbjI0Z+pHN5pZqu2E3Duggee/JU9wA3bblxdUtJ8WKZ7GZbOXCM4wDy9WSq7uGq4sb3AD7XUm9GBmHO9579j5ny4fE0pAWOU194OxkRtt3bMs9TblndrKkd0sIHhtbXhy2vKcL/r1/+w95/jsXtM2Iigw/+ulnvL76ktefLiBSPHz8iCff+y1e/dmPEd2B4MMxkCaACJOb8e1+FzCNJMPPSYv/KqCSb0QRUErx7KNnJCYhz1MW8wVGG9q+owoQa0HTO07TAikc4xhQemT0U7iGMQYnPMpIhNSE0WO0JbYxdVvBIBn7kb6sSQtFpA2HcYM8eLqmoVM9V5trTrITZnnG559/dmzMBaqqmuTERcpsbhk3hu12ywcffEDbtVxvb9hvtmTGovOcNCto7takcUzT7KmqHVXd0vcNQz/y6uVzAo44fp8XL5+TZjm5zFEnlmI2Z311y/y9p0R9R5IVJMk0Euz6nvfff4/e94ztBEEsqwibtHxr9h5JUVCIKYPQGkPdNByNr0gpaKs9VXlgubxPnucEBM6N9H1LlhUkNqGUMLcRfhzRWmOtxfcjvRumJKJ+SvxtBomx8YR1VIJstpqOtXXD/HxOwKOFpes6+qF/l5NgTUSkNGEYaYcS1ze4HhjAh8mi23YtUila19IfNIUcCLjJTxBpkihCK41E0nU7pFREkUYJQSwUQvaczGfgPcPQkyQp6WxBsZizrw70dUUUT8yEzWFH7xyRNnR9z3ZzNzV/tUUKRe0CF7cbykOFtYZ9eeDFy8+ZzxIePnnMcnnKKOFQVbz64iXyvafMUoFqBXmSMisKilnB3e3d9Lv0I1lkmcWW7NljslnB/tDSti1/9rMvuVxf8+HY8/jsHk+/9R0un39BWFc4f3yuhyOG/+1FQIh3rzm+FkfxwF8FEfCNKALGGA53O6SH4lsZh/2Brm0nFrwUlJGj7yMO3hF8QCpFlPVoOwErcRNGXAVBGAPOeYL3BOdp2xaTaWbLc+IkQgVBVe2pmpK7ckNf1zx+8pibm1sO6wNZkfH48YcMfU+WpvT7PXd9T5pEeN/TtR3z+Rxrp3t7WR2ohw6MJhNQd/Wk3nbDZCZXgqYr6foaEeDi6hWHaoeLHPd8w7l8QJzE0/2PgZPTFTfXNyyfPeNwOGDyGN+2rFYZddMwy+eUSB4+VAyDn+K2E896vWZwI1IHRhx93zOfzxj6nqqqWN9u2e9rxjGi6XuKfEbf9zRNQxSlU/CG93g7KR9DEBwOJdpMPEVrLan3jMExOk+ip2gzaw0AfgxEPoMYzKg5uBo3TmYtpRRxkmC0RKrA4Ev8rqFrEmwEMknwh5FSe6w1uK5l6Duk31PX8YQlM5qyqZjP58QqRkqBwSCEYhwHpJGMIhD8ADJwqA90Q0/vA5kPrLcbtk1PAJIspmw39EPH4ARn9x5wsly+Q6QrKY9gUk+1L+n7nlma0DYNfWSQIqXaVPSuIYss15cXOK8Yeo8sDKbXrFYrbJowBMfZ2SmD87TbNUYI5lGEdyVDuUONnt/86Bm9Ezz/5BXXFxecz7NJN+H88XvhkMfwVPFOVcjXJgVfxfl+VQ884a0XGoum/WaLhZxzrC/X/PjVj7B/pknjqTdgjKXrGpIo5uTknG0RMZvPSOdznJUY5cgiaDqJcpLgAkPf0/UdN1eXHJoaoSaR0ISzititb9ne3jJ0LVpJTJJyfXXN6vSM7XaPlJIXF88py5ybyytiLTBxglIrfuO73+P09AaA168v0FoxKs/q/hl5nKOcp/Lg/cBmvUb6SfrZdx2HukaIMLnbCJR3f8HF6wuePP2YIp/zwbMPaPaO9z58wOxkwn9sfnrH6sMF83v3SNOEP/nRjylmU3BHMS/4yU9+QhLFaK3Ix5x0ltOEjpyIRbFAKomMJc6PnCxXnJ3fn+y/XmCNwUcRcRyx3e6I4xhrI1znMLFhvb5lHEcKmzC/NwfvieMIrTV5ntMOPS6KEcFjRIyPPEpqdoeGq/ULmvWInClmaYGNJmWgMQajNbnKKJIF41LSdC3tvqOMDmg/sD9IOt8RhGRwA82+xVpDksQYa6iqA73waOFYpRHl0CGLlK7rcX1HkmZ8cbemOuxRkSGZ5Xg3cnVzjTWGKMsYR8d8fk4xEwyDwkhBN0w9hiy2rOZz5osTpDzhj//4E7QMnD9YcXqSE8ujcS1SJMTs91uybE7kFYd64MXrC8r9jpP5jM3mDhNZbq9v8EwGqZvNHbPT+zR3AyZRpLJHSsXjsxN+/KMv+OGf/Cn3thv06QJ3zI4URyqyPAbMTtOBrzmHAkfJMuDeBtXK40Fhkja78Ku7BN+IIhDbiH/8l/+YNE3Qs4TdzQ51bPTd3d4QG8t1eokxmjjNmZ/e497DFcV8TlFM4iJlpxm09wNdUxHGHhMc/egZx5HtZsP69jWH9Y5+6Ah+ZF9WeAH3zh+wtIYqjtjtdsRJQh9ZTk5XVP0OYyPatuXk5B5nZ+dcvHnD64sLnLXMbU4aZeTzJeMwEtYHtLQgKxgHTICAxDtABKJkUqE9/7Llo48fUg6exDnuPXqMGgNpmnPx5fOpIXga6JqS9XrE2gc8e/aMm5tbuqEjjiOePn2KEgqtFdZaRIDYRZhYk6YpVVXRDz2JtiSp5ma/xdoIYyxt22GMRQiwNmLoBtI4nUZSYwvBs6sr7p0vaeuGIRpYLVYorZBymv+LoyIw6MkQ5ILn+voNd3d39F1PpjKUVggr3zUGhRAQNME72q6mqWsGP1LfQTO2bJsbtFUkWcZut5smEEpwqBvOU4sxEZmIuLi5pO8E+WzSkQQhcV3L51dXfP7jnx29GTB6h2sbvBY4L8nShJPFCcZOJOvZfE5iDUlssInBe0cSG4pFRhkGRlqyNEYwYIzBakUUJ9C3VGNPN/S4Yc3pw2c0TXvkNfREyVQglNJHI9WIijNiP3Jxu0bWPbsWFokjLgqE9GTzgntPHjFfrbjsB5SzCG0Z+w531FLB0Sf4tTGAgK8FcB0ZCW9PBCEC0X3FRPgl6xtRBLq+47333iOylnK3mzBSZcV+c8Xy9JQ4iRn9gBKQJo6+3rK+GunahrIqeHAPRC4ILtC1LXVV4Qn044jUFi0lVzfX7LYHXjz/EhE8lTtQrkuyWUGcJJycPuLBg4LgSU9tAAAgAElEQVS/+Ok/IbKWw+EwdfBDPCGtQkApxf3790nimJm1DFIyDI5D5UAdJshJ1xD6nnZ34LDd0A4tg+tJs4S8mGG0wdqEKM5ZLFd8+PG3+Ys//ynNfk9kYsr9Dms1o/c8fvKIP/vJT3j46NEkbz0c0FpxuCkJac5qdYqSAu937CuPckcqkZJ01cA8KeiHHmE0SmkyP8V17e8OxEk0Ne7Gga5vEdE0BTG6oG0dm+0dTV0T2w8RQrA97Lk66XiiiklJKAUVgUJO9KGmadhtD7x584ZxGLAhEJIY37VIaxhkIDhHewz50FYxekc/DsRxyoP3c3aVJGoUsU4RdkLHDcPUE2jblroeMAYqIbHziMN6Q9N12FEy7jqiQhI1NZ9uXmCNp0egBLRVz6NvPWG739KHfhIcKYkUitXpCiXAK0eQkuAG9rstykbsWujqDiUD47FPsljMiSNDnmfsdiVCes5On3KyXHJ1uWZzsSHPcnbbEu89qRCoJCVOc4SEw2GNCgG7OiVqK5yvCSEnjNcI15BnCbOTBV+8vCTYGKcDwpkjFN8Rwlss+Vfjw3csgV8mERD/Pz5iviFFQGmNlJqm7RFK0ZQlcRJhTUHbtmy2a+I4JgTH1WVHZBJO791n7DpsWeG6jtlsRl03lGWJ1lN67TCOpNl8UpiVe169eMHmbkff7ZASilmBVoI4sdztriaSEQGtFWXbvAu8zOwZjbrh8ss37KsDbbNnfahouo6gDMuzmPzkjDTJSLJTXAcXrz6jHVpcJ7h/uuL8/jlSaMrdgbv1lsdPnvD81RvuPXzEH/7hv8HNywvW3TXldouZzVisVtzuD2Rpxps3bzhZLDidzUiXS2KbIgRcXFwQQqAocl4/f8GDB+dExrK72hDPUjppiaMYpRRdW2GNIJmvePr0GUPf0XctdV3hD45D1TKUUxbh61evSeKYk5M5r169Io0jZstT7sdzpFI0zmGBrNeIQlLuOwbX0jQVq+Wcw77GIlgspitJI3pMbxiGDmMVURyjlWG5XE5MAhdwXuDHQJYV7NoDo/NkecIwxjAKZOqQfU9TN2xu1hSrllpK3M5hwpax77ifTFJnDgeMkyTjQNu2nD84R2nJarlEhcAiKxic42a9RmFZzBcIN1K7iihSEEZU0Lx6eYmNYlbziDg2CALGaFbLU4ZhIE8jilnK559+wTAMZPM55X7H+ZNzkLC/7WnbEZPE3N1sqMsDJ6uEApDH2Losychzy7/0N3+bf/V3f4/Lu5Y//fKaRZLyt/7m7/IXP9xzd3U9TQW8J4hJ2HUsA++chhMg+euQ/ARov1IaWn5VMvk3owgQ4GJzyfnsjBBF6KHDj54hRGSFwMw01muGoccNI0ZPkWWH3Z6xqtjUNcvDgb7r6bqOeZrSbTdc73Z4cc3JfE7XNWg/sJhH7LaTEKQsA/l8wf7qivyB42TxkNXJipOzFb6qID4m12Q1kY+YzzVlOeKC5tm33+eLT79k23QYGzGGwOJ0RVvVLGczsnRAyBpQFPOC5fmSOEp58clnDO3Afr/jo+/+Jm3b8sknnzBPMpqm4Yc//CG//3f+DsvTFRGCL99c8vThQ0Axf/yA9tAQJdO15f79++x2O7Is496DU5Q8kMYPSfOMcRzJ83waETpBFmU44ShmKUkqKHfQKkUURSwWC9a3t+zvNlxf3WBtwXZ7w8nJDBGgqu6waU7nOuSo0MZQVTV5NiMEjzKOwU0sPiEs2oxTVuQxjGU4BGQYsQuN646k4MjSxXZCxptAu93R+DV16fEY+n7NOHa0rWVEESmF8AprY4qPY+4+e87d5XP6ViB3Od1hxws8aRQx9AdWM0W9j1kVC3KfgJZkaYoIgT443CgZ2gGbWYyammjBOSITQfDcre94/uIFcWKJrEbK8V1U2TzLGJSga2tElnD//IyrsiE6LZgv5sRZzDg42tZiVUez36PjhDRJCAqUEFSbNYMfsNl9lJto5HmRIoj55PWGuc7RfTdRrsQkjRbHO30L747378xCP0cWCXy144/v/ZoDwT9zERBCfMyULfB2fQD858AC+I+Bm+P7/1kI4X/+dZ/Vdd1UuSNDlqXs3ICSAWPAd4GubOlch2s6UmMZ24ZXn31GnBYU84y5jQlVQ6Q0fT/y+eUXKOcRg+PQ7Si0IIoisuWS9vqKJM/o+p6xCoQE0rNTkmjBMAyszk5xw8jCWmJlKQ879ts7nt27jx8ypOqIpMHYOR98/Jvsdwe6fkCOAT+MzBcLkiRhfnaf825gHEY++vAj0jjFaM3YwSAkd3drjAQ5jrz47HMePHhMmhcEk7DbV+zLAwTJdz/+iEePHlHVNYfqQKIMcZYjNRhlaLuaLE3Q6pxxaOkHSSQsQvSMoScSCdZMyPRkZomLBGVzskKy3+0Zh5GubXlzdUntR9SxwGzLis1+h04swzAgN3fcu3eO0IJx6EiSBPk2bTmA6g1aW2bzGTOl2Y8D6/UaKSSRjmhNi9pO/YQkSVC+xzrPoIZJzNSOtC6i7Vuc63Auxto5RSEYuoEgAv3QcNgdiMeE3K6I3isYfEW/gfUoKJtrdp/+BZlzdJ0inRecpBabGHSckhUxZh9ouoGkWHB2b8Usj9kfapyaAj7joHHKUDJQ15eI3rOtPVIGZrHFSsGuajDJCU23o7q44tH7D7ndfsr+85dkqzNu36xZnZ+QzgxCahgGrm+viOIY5VJevnzNvfMlcRJTWs8YBtpti+t66sZzVhgeP7jPT18HXLzA+C/QeMbpQnBMb9agBWL0BDHihEAQIULPV0jSt4nb/FrWwD9zEQghfAL8NoAQQgGvgX8A/IfAfx1C+K/+aT9LKcnZ2Rnt0BGNMZvdlmToues6dpstWluyNMeoBikzhNYsz06RSjF6z9XVNc57dk3DbrNDMoFJsixBCni13YEfSawlzSPqapKtjnKit+wPB3pgVszZbDYUNsergCkimptpsLJ3ntlyge37KQAzSej7HmNbvvPxd0nMAi8MsZGEYeB0OSefZTDCfrdj7HtSmbJcnOGdp+s7mmpgPo958uQ9tjcbkJrf//3fZ36yIDZzEmOwmWa9XpPnOa9fXvDhhx+S2IjPf/ozZJBkRUqmc4SUU4DH0LKpygku2g0EJKv5nDzNGIYB3wRscCipmKUpddsSR5bf+Ru/y8uXL9ncbdBC8tH7z+jahkWe4dwUC9ZUFSa203gujnDjiIk1IYW+ahBC8NkXX9DULfvN3dRTMYY0ncJQlJLThEElpHmMEIJhGCYh0DiN55YniwnXdaLpNz1jN0Ja4rzG2AWz2Yxu7LByMhyNIZomPblhPlh++P/8EW29Q6iYBw9WSC/YlSWbF29QeP7gD/4emfc0zXSBfvnyOcFL7tYb8kwQyi333/s2ty/fsH79hsePnxCnOcM4EicWCNT1gUjAyckJVf0KEQLPnj0jCLjbVxwu7kjyCGstUsFyuSArJjv3YrHgqr9icIH5fMGoLYftDtH1yFGw3/SUZc3l/lMefPQtnj77gD/55E+/4gv4I/RQHMPIhMAxYdwFw9etgyCms8PbyLx/0bLhPwA+CyE8F7+mC/mrltaaw/5AJCx7v2W337MZRuLYcu/B/WMKsED6GOUlo+zpRjepA+UUtOmDJjGW6OyUNMsZh4GmqYnjiNmsIEkirq6vqOoefCDNMy4vrvFCoLUhPznh3oN75ElO13dUVYUbPflszmw2Ox4FJ2TWMDiEEPR9T34ypxYDy4UkTWPq+kDdVcxnp4RhoKoqbJYhQ0AZgys7vBUkacTgW3722ad873vfJ57PMSZhuBrZsWG5XOKVYhgmoUzbthgV8fzzL4jjmKfPntIe0d0yFZybE3ScsNveEtxAGB0mBFy9565vyJ68RxLZd9CNmhpXBUxh6cJIQHB6eoa1EcI5tBSgNckY4+xIdprTlHtub1sePnnM0HYYE00BGmVAYOivNWVZ8vLlK7zQzBcL0qLA9f07ZWIWVmTdOfFZizHT169qO7r1wKHb462ibBrGK4/WmnEcyLIpq3AKEJXEUUo8QEmN60ZcponRvL54TRxb3lzfEif3WJ6c0W5rtlfX7A81xsPQjHgZmM1nhLBFLE4pqxYT7ejbnpuyogsv+OzFG5ph4PS0YLG8jzYx9DVaG4Y+IO2AkQYxThoKE8UMw4ju7tCLER8CSRqRJAnGKDa77QQYGScNB3hmw0jrB7Ik5+Q0I/gBOTpc6YmVQQ7QtC1TGpLkrZ1QHEHuoVMIfcwjUOLdleCdy/C4FaOg6H8lcPyfXxH4d4H/9muv/xMhxL8P/N/Af/rrIshgqlBVU3INyE3NOAxTA6ofiPOC1ELnITSB0XuMTojTZKIHO49R4IWgHwa892g1RZkn2YpitiArMjabNV1dM48kvU9JEknd9AQhqPue+1E0SVabkoenj3ArNzWZbqdQiPKoq3/y5AkmDhyqPXVdc28+h67j05/9lLPTh2TRnK4WbP2Bpq7xboBypPcjXR5hrCU2ESen9/Fdj9Q9Td9xevqI7rKkyzrCaLm+vubBk/dI7JR4tN/vWcxn9H3H+P9S9yY/mmXpfd5zhnvu/E0RkXNmV3V1Vw8k1aQgmARNSoYBLWQYFrwx4IUBwxv/CZbX2hgGvDBgwAsDhq0/wdDWhuGFYUOgRJndzR6qqqsqszIzpm+88z2DF+fL6mKz2aIhCihfICozvsyMzIqIe+457/t7n0erLwM8fd+x3W15eRi5ePYEQbT+tmPD0EbGoFkMTO2WUyf48z/7EZ/8/CWLRc6L736D5eUVVV1z+egJJk04Nufx5PaInQNW3VIXTzBpgTIlqB3eB4qyiBIO60iMwacKv3jJcdcip99msfsU/8QRzrWJEALee4Z0R7hIEFMeRa6FoDY16YdrNtPA6bBDdYbd6UTbtlEgs71GSkNdL87f24GQC4zLEErSt4Kbm8+pyhSjFN/9vQ9ZlwuUCAzDDp1onPfUywXb45bEGE6jxQF5pmn7maIqqNMVVZGybSaaU0NVLXBOooVEK0VW1aSJxDpPmiQxR7Goo2DFe6QcMHXOMjeUi5IhNeR2Zrc7slk9QumSw+kL8rLk1LRMw4TQhpvtNeWL5wTpz0/4gMexWNUsHzxCZgW2b2M0WIZfdvuUP5/FAuqcG/zyaf+VZ/EkfgNRhL8ZF6EB/gPgvzy/9N8D//j87/nHwH8D/Ge/5s99KR/J88jkf+/58zg6jMA5d7YGnQhCsCwL9FqzqBcx/mBnZm8RXp6DEQKpBMPQE4pA8Ar6CKO4u73jfnuLRxDSBW5wrFY5Dkk3t+RpyTpfo5XhYr3iF+2OhfOR64Bmtz+e4Ziwvd8jROD+7o7NxYbD/g6TpIQQ2G6vafMTIgQSb5inGSkkb7avUELwpHrGOMeebbFe8+nPf07fdhybjtXqjs3mKbu3b7i6KkhTw9svXvHoBz9gu9sx3PXc91vKsqBpWj779FOeXsS+fV7kmNKyff2aq4dr5GZNUbccdweKNOG0H/noz/8lP/7Rz/jgW4/423/023zx2UumvgM7o31g7EdCobm4vOD29Rtev3xFcJabVyMPP1R85/lT3DxzsVlhXX42dASQkeJkvCDPUz788H1Ox5ZheIFzW6ZpZFGXaJnSDYE0EzRdz2AsudfIoUUnGSbdkGQShopEl2wuLmjbhuPxxGhztNYcDlugJNV7cBkTisSkLBcj93tNmuZI4Uh8fDIuipK98qAkQmtu7m94/WbN7/zgdwlCYceR4GamfqQ7tVwUBZt6QdPvabuJi2WNmyfeXr+mnz3tdsvlVU212iDurrGt4zvf+TavXr0iSTTTNHNqex4/fsyiLKlSSxYWSAl935AkjmGO8elnFyu6oWeRwN31lrvbLc+/+ZQ3tztCyM+f2oTHT56hE4MbeqSS6GDPsSAflXkA3iFkQrQgfBVB9q8CD8brb2In8A+Afx5CuAZ49+P5Rv8fgH/66/7QV+UjVw8fhW9977sYfcFo75kHh5t7ZAi8ePEcKRu8X2NDdmbTeXLvkVJGbJhUeOUJPjCeRiY7MYaBgQHrLPMcWQBVVdErRVXA7dSjtWKVbwhCkl+m5GVOVZVk91s6a0nzgs3FBoDFqmZsBkwas/XT0KPTQF2tOe5P6CwhXxqEPU96yoCVkZ24XC5omoYvXr9muVrivSOxlqv1GvHwAVebBzRNw+VlyZMnP+D67Usg5vI//uQV7uYVf/bFF1RlTcbEzz77CY8X75MBZV2SXWUQYLWs8QH6to/Tc34A4ei2Bz7+6GN22zv29xWnq57LizXtMdqAptpj25bTbY8+/5sfPH7E3ds3KJVSOEvTHMmKnK6TSLXneJzJvGcuC4o8i2mVALIoEXvFejxRf/t9lFIkScJyWWPnma7vaY8NPsz4LCXPK2wqmecGHwKJSfD9gJshL7IIkB0tzs+sVhckSUKwBaehYW4M1vWYbMODByM3r1/GcJINHG+uORQZRb2gcAOq6ajlGj9bhmFg9uCtJdUCO800TUtTFpyqjLvDAS0keWpouhNSJYwTWOvY7XYYpTGmwM+Bfhy5vb7m6dOn9F2H0YqyzOntTDtaRL/DuokHqwfY4BiHHVf1FRePHtBPA6dtgxAafETpmyzDJEuMqnBdy+2rl0x9j5AyDpkFkLkGC+KdS0AqLBkiDAgRl4gYGD5nC0NEuX1FVPYXrr+JReA/5itHgXfSkfO7/yHww3/VBxBCRJKu7Gm1wHhJmlYYY9AqAWp0YpiZmdsJHzQ6SREC8kUZ6yU6LgpVFYs4+90Oax3WOowpMVn8/cfDnuACxha0bUNRldze3pKWZZyDT0xk+DUNm80lLy6f8+zJU46HA50/4KSkXC4IzjJNI7f3t3RtyzBNXFxccXn5EKUU3luK1ECA/TwiFLjZkqaGaRxprOfD736f12/fYoqUpVDnM3CPGQy3nx/Q30r40z/93zA6ZRx6fvLnf8Ji8ZTttme2t9z/qOEP//CP0d0TDqefcto2ZGVBvVhiVCB0Fjd4br/4gpuXnxD8xOmgGdoFw+i4vz+i85LLh09RWsee8jxTlRUSz1ZKtEo4NCfaoeXFe99AqTsSY2AcGZCo0VJmBilH8jRDnHounximfol1E1IaRmuxIiURMmYWxpFhtPS9pZogTROqusBkGSFI0tTjRcJ0cHhnsVOHdZ5u8GSZJfFEAnEysLs/Mu3fkKUPydZr5nEiTTW61BiTcjzcQlCUpmB0LVVdYYeWSQSkSEmzjG7syZRhVS1Yrtbw8o45+Ph7nKVMc4TUPL7cwHkGxE4zSqpYF6orJIK+bXnvgw9iWzZ4snli33UUWY4NjrfHI9VygZ1tFIwER9vuQEBeVwgU3dBASBh8oB5H8rKkXK9ptrcI4eOud5j5C2YkQIT+/NCPR4N4xTpKCA7xG3qEfxPykb8P/Odfefm/FkL8LnEZ+vRXfu3XXs5aDsdrvE3QSuEQSAxO+i859c65GJEtUoRQzPMUeQLdiJ0t+TJDGoFKFFmRYzJDvYyJs5dv3qBkQlkUPHzylGkc6ZqWX/ziY47DyLPnL3h0+TguIKNFCcF6GQuC/+z/+b+BKO3M0oR5nLi5uSHLMtqho7UDl/maqnzM5dWKelESCDGuai3eO9r2yOFwYLPecNxucd5ig+fzzz8jSVO++PwLbOb5Rvqc7bTn1c3n9Pcn/pd/+lNWjy+42FzywfNv8MGHf4c0yfjgW9/l5z//CdvdiY8+/oirBxcs12veXr9FN4LrtmdRpYzHHftTw2ef/Iyf/+RfUGSe0NVcf/TP2B8Ef/DH/4DVco1OM/oxbotPxwN5IqgXFR9+7zv89Kc/ZX93z/PHa9q2je3cxZJHZYXShuAcTXMkSVLyMuXhi0uuj29p2i1CJFRVhfeeX+wPEQAzwzzNjP1Elmb4C09iJX3VUSxLsiJD6ri1HRlZrVesNivadqC5H5lCQ982zApEkDy8uuBm77k/fE4+91R5ipsbvPXoNEWnFYX2gOY0O+53O6xWGJNzdVXTD/HmWV4sUanm9eu33G63eO/Q3vPB+9+grGq2xwHpPbf316yqknK1JEsMF1cb2qZhtVmSpgmvX31OvV5jspTB7yiyByxWS4LzlEExJTDve453t1g1UCwuKE4juYCqqnj+zQ/56PWWQ+MYp4EvPv4p7fYG4XwskEvBO5LQuzf15YLwqxTCd50B9Rvvv39d70ALXPzKa//J/9ePM1nHq89vYR8Ys5GLiwvGYUInmovNFVme47RmUxeoVDM1nnm2TNNMUWQUuoA2xFy7iUWgJDGISjIOMw8uruinkXoZlWUywDXXPHn2grZtAMjSAu89iQ/0fcdydUnTnDgc9vjgEQjcFKvfXsQxXOE9D+sl680qCj9VjNNWdR3dB/2B4BxununblpenE1ePH1AtFqQqQxAFmWmacTw0eBcwg+KTj3/GfrtHFwaTpFxePuBm9vzg28/Bx/rHwyfPuNve8eOf/gwMfPitDymWJc12iCZtl+KC5n5/Yp4Ex05wdzxxGizrxYLH629QqZoiLTFaszsdOO73TNPA1M8c9pbFQrNcLbHec3fqefG8RhuJEpIwBeYwx6JfLJ4wjxOJ1mQup5HRJXk8HtA6IdEJCZrOdnzyi0/ou5EHDx6iEs1iWcTE6ygJwZGVBUEapmlm6HvKqqQs4+er6xwJAZOUzH1DdzywWi158+olfbtDENjv91RZSTfOBCGRATYXFfPY49yEcB4C3Nze4EVPdpGzWmzIkpzlZo3+xRd4N5OahBBil+nUtGyWNVcPHuLsFOcIjKbrOqZp+hJzv9vvSKSmpGRGMswT+tRSVSWXFxtumyNvf3LL03+roKgv6fYd1eqSerUBNTD6aC6QmeHu9prT9ha8jYxNkUQz0jtRwbs+gCBSmoU8v/8uvxH+wvt/1fW1SAwG7/HB40uHDJLd9VvGIFhdrtnvd7DbxVHUsMbiYdZURUWR5mR1ipQCncTV7t3MgJt9tL8YwdXlBqs0JjFopeOIa7rj8uIxddVRFClVVXM6nZhOlmqR0vUdVX1BahReCVzvmOyAkAFzFmZkpuDR0yeYNGOaepyb8bOn94FRK6wdOO72pIlikRTc7+/Z3d4ztA1SJpTlim4YmcaJ9977gFevPuf25pb7+3sAnj1+zNXDR5TLDWWbkVwVJJ3Best3fut3WL5+zccff8RPf/QxCTlPn65JTUbbtCTpBcsHD2j8jE8znh+PHN6+5PHDK1588wMeX12wefoQUs3uuIuaLykY2wE7N+SZ4bD3PHv/Bc9efIP7t6/ohoZlGmEvSaHp+glrLUEoGAeUTkjqkrWbOcx79m8OJAkcD8foZ/CW+/st19dv6fsO5xJSGRkRV08eRJVaohmniZYJI+MEpjonG1NjsG6mSCp+9OmrOP03dAztjFaxDed8LOg2XcfQdVzfbjEBnj58RJkfCE7Hh6kQnA4H+tOJxXKDNx6nPMdTQ384oAWkmaEslugkZXvsOR0PPLy4YLEpqbMaLcEs1rz6/CVv39yQKFgullgR8LnHDTA2PYnWlKEg0SnlomL5QWRnZpmhoSHNErJMIJw8x9hLtseeH/3oh9x88RJFHEn37uwfiLGAeIlzdPicC/jqRiDajL5859+olfhf+/I+5uA3qzVlWZIv15wOR6Z+pqwznPP4eSRMnrqqWDy4iPUCrcjzDJNGqaU/67aUkswuFlpyf0YyCkGS55Rl7FuXVcWb16/pW816tcBaydXDR7CaOc4d642k7+7Z73bUdf1lv7oV0DYdLz54wtXmilPfcX86schzRJLQzwNFIylWIB+WMExsT3c41ZOVCePQobUkzXR8YlUB5wdevXrJ9v4t9/dbHjx4wGJZsbl6TJpVtMPMYpPR3jiEHMk2OVqVLC8f8i2VcPv2DT/7+ce8/ULxd//eH1Evr0irlPcvNjxYLTneH/ng4il9c2CcerQWJGWCqSuacWQeRo77lu3+juP+nlQEWi1ZXWy4e3uHyUpWyxUIKI3Bek8ILavVVQz6qAzlJ5x3pFkgJAmrdMmPX/+Q02nPNDjGsef1mzc4p7hcX1Hkmnl6yZvbLW/vCx5un/Lw0QPyuqCul5RZTpYrunbm5voapTV5nkclubdcXT7i5u1Lghvpu9iBaBQc25b3nn8P70803cD21KKSnsfdSFWWSBEYXWCYRuq65tWnH7Pd3TOPT2CzYrFcky8WmK7B+ziVk+dZxIYddgzjwJN0SZ5qvI+L/sOHD8F53Nhyahq8EnF4LTGs1hvWqzX319e4hWeQHje6aHx2Dp0oumHGzjPT8cRCV3TjyJ/86Mf88F/+C/xphwoeJ4gxbBdtTv6sI4Bf3v8BYhvxS6c5pAmMv74e+OX1tVgEhAg8f/6cVKUE4Xn1+WeUacXFoyVIiUgleRaLOHVVY5JIsFXq3VknYEeHcCJOXaVxXl4nCdZFLuG7LVSeZ4g0QSnBW5Fi7ZF+GqkqjSCCS188f8zdzS37XUeWpmfIhOV4PJKVBevNGqMML1+95Nh3VGUJ5BHtNUYUunojMVqjdCBPDKfRM3Y9g40WoNE6pjEwDCOXVw+5uLhgaFtC2HFxccFyucR6wf5+S7F4yjQ7PJ5Up8hBQRlZCEnbslytCCGgmNkfjjxaPsTmjjxJUTanuXlL9+YNYzeQbQyLukRWJd04MYsGqTXaKISUjMPI4yePkAqEjltyqaJLYbFcINKMOs3xLkJVEAI/nVBpSqITjts902SZxgk3WURQ3N+/RUjI6pzJOnQyEbzCmJLl8gofJN47jodjlIu08e+q6xXjOOHtRHCe0zBFnp+fWVy9oEi/wdvPPuL22HJ/fc3QHEiFQBvHNERWgdEJBToeK8oS3IwfJ6ZxRCrNwwcPGaeZosjxSoOQVGnKoiyQIRabR+ux1rFercgTQ5Ff0nVHpLQsNzljNzDNA8qUlMKgM3BgHvEAACAASURBVENrLVVRMRPzBIvC4H2EhS7yx8hiJs8yTuqIMXEnm9Y1aVFi9wPXb14zDV10Dvh3EaCYEYj3+NklEN6NDf8yJRBCQBgBQ2CYiMShdyCSX3N9LRaBoihZLde080wSPN/84JukZUGiE6Q2XKzW5HkGCLI8R0uNSCMsRCiJ8wGZaaTSmKBwkZMTz0RSgg8oPFYBbkQSMBouVimhTZHCgZ9pDj3aRprw/uU1tu9JhGTsB6YhDnNorQk+0A89k4tE4CzLGduG/rBnd3vD8XCPFZ68XrOsc7JJYXcnghjAOUKikMEipEYJDyLQdR2+bWOhJzGM3oOI47Cln/A2jTULoUiFJlEST0JVL2iniXzhEUzcbE8U1QVLp+nWM0leEtYXzM8bGHpCorFFxjBa3DCQu4ngLZCyOx4wicIOlt62WLfnt/7W3yKEwDRakiQFBN57rBhQIcMJhxvP8k0lsX7idDjGm67IOR62SCz92JFtUi7LDdoWSBdHkCfrGPsOO81I4RFY7JQg5BRvApUyTZblas3Lz7+g6Y+E7sT6/YlVlWKd4/50wPYnxP4tQgj6WcSQkhIMGazNgu3xnp4FiRAUOuFCa5JE0aQZJo2t4TIvGUVCN87osUfZHKRkOw8YJalNxsWyRoXA6ON2e3HmMQgkx+aAVBKhJJc6ZRKS0U4UThCSPOrWtGBYtyxWa8QcMGmK1glh8ujEMGjFGAR2GNHCxdiQilqzqMwL59CQRrk5Jgfh3TjhuQ6goHu3+Q8IIb/kC/y662uxCAgBd/f3ZGmKzxQyiRHMRb0EBKdmZLYzz54/J+DxdqQQK0yRoHVsrSmd4Fw8DhgJdrZM1n4ZojQqZVWkaCXRMoZHyqrAvP+YEHoQaWwpth193yOrFDkpbq+vqVerWPQ57KMhWaaISrBa1jgraYeR3c0btm9ec/dp7OsWVxULLXj1+jOGuwPGBWSZo/KchRARjrJZkacZUkq2u3s+u79jHEf6rqdtGoTJUEnOvtsjc8PQDBSmRDgRYRt2ZtSKUQtWyxXejQz9wP3+DqsnMhOpyA8vH8DsORy39E1Pc4q+xdvdlrwqmKYJgUcFzXK5YHfaYn3PcnGJc5EM7KXEe884jthgMWl+7n7EqnXX9czzTNft6XuLlIJje+Du7hqlYZWWqGAIvQMaZgen00ytNiRp7Pi0XU/fDlg/44MjS3Ok0jjnefbiBU5ajJvZDg2nt5/w57e39N2R8sEzjKr5xUeWi4sLMIa28+RZzrxrmOaZR996yjBNMDmavuNbL95HJHC322GyguVqzaJe8PruhJCK1YNLrq6e0LY9s5vQaUaarlmur2i6jsXyCUI22NFidILJEvzRIyXsTg1Gah4+25AOgn48cWxbqixHGUU3DCyA/XFPXS8RKnIkVKoweUbbHGlOWwLzlw+xd87ML2uC71p+X8kD/ZIrmILo48IhopX6NxUHvxaLQFRMXZIlJfeup0wKejfQdgekSKkqxWK5Off8DVIY+n3H6BRlWZAkmuA8zlkccesmQ0wdeiA1KYn7ZfUeiLx67wnWYr3Eh5Fh6PHjTNO2aF2h9A6Tp1RVhSlL+mmMSu0kMHQ9uTUkRcVlkjCajKNU+BBwIXDY7ujaBiUFUlhGb0lsgnAh8vmVRgaLVAIhJF17oqoqEiWx84wQgUTISABKY24hiCjpHOUcAauVQfYJG6nQKiEoQaJShAc9ARpOc0Otapbrms4OnA4tUz/gVUCniiSRVIsNbh4R3uO9JctSPIaiLFBItJFUKsE5R9/3LPMl1tpzRyTuDIQApCbLKqSccE2DyRKU9gzDiMgixcgOHaZeUGYleVVjRcLucEAxU1Zns9FXlN7H45HDYYfHIxPFqe3QTvD6o0843d8x9ke+YWpS2zKMA8my5OgsQgkCjqIs0CowjiNVtWTf7xnGkaBErGGkCcZIlBIgAz5EW3OaZCgVeYRz42lODX1RkyUpNg/MU0uSpmR1hhg8zkuOxyOPHm2wOicLAu0lIhHoWWOJHRWvAg8fPcJ5z5s3b3kmE/IqRSjNOA4kY/w6FHmGPWnm8Ze1PgUEecaMvksEfYUY9ssbvY+/5lL+SojAV66vxSKglDpPnF2y6a+Ze0e+jOCMi80aJROQkmme0dpgck1apSilY1ZgiNNm/TDQnRVXeIGSEp0k1AuQmWe2CqEi+NK5gEkShqFDKMHUjQxDh/SS0+keSFhv1hQZmHTB7alhUdecuj5WdvMcnaRM04QuM5abDXPXsr29wSuPt2D7EScEUgR8cBSJIc9ypFSM40SxSJgmh04sPtiYhbCW0zSgk4TZt1gvKNKccRxou5Y0z2LiUGsyY9Dex8TZebhGhJlxsjRty0W/JCDQl4a6qpisxY4jDY5uaPE+PskdcSufKMk0xLPzsTmx2+24XD2mWOeM/QmPiNgyN6F8rNhLqbF2RAhFbhQTGiliYTZLExaLnCTRaAVSSfK8pKpqirIiMRlSJSTkbHc9b6/fYozBpJqyqPBpPCqtVxt8CDTdnt39gTItaI8d4JEq4cc//CGPSkldR1vy1A2oeoF3M0UhWKSCfLEkyQtUc6S/G5mmmdRET6LJMqyEdhrY7/aM40ha1QgV2Ql1VXCco+Rm6kbSMmP0cRai27ZsygW37Y7j8RjpxoUCndJ3YxSTe08uoZ1PLPIN1WrJRz//KLItlxvmSTEKj7Yts2kINlK0RIijwO+UAirEGRkXRNwdxKFGfq1yRAI+LgD/v2gRArx+/Qad3COAZy9eUOQFMkgWVUlaLhinibqqqKoyugeShCTJcNNA2/YEExATuKPl1JzoxjEioRLNw8cPWK8XVGWF9jL2jiXkeYr3BXOYmQZBohUCRZoGENGQ3ErBMAyEueOw22N9JLsmSQw2DW1DqyDdrHmRZ2Q64c2rzxm6ButnnA84O8YvqsypygXVekVWxxRkYnK8D8yz4/7u9mwjaiiKHJQjrxfIcaYPge3uHoVAmoSuvWF5ekhmDCF0tFaigsSoWAidvGeeZ5Ik1g/q5YK+75iGnqHtmU9HBHESUmvNZCe8kiRS0LYteWKYveNoG8QgsH1LWa+iGUmmZCaOAmcm5Xg8RSox0f/gnTtnPC6Yx+fMc0/b7BmGGGPOCsNyvaTIS4SMfkh3RogdmxOcLFM9cTqeCHgePXgQcWaHhkQqHj97zCdvX1PngrJYM97d8cmrl/zBh88QPpoeLy4u2O22+HFicIpnlw/ovWf2IxMC6wNFYkjOb6iEL97e8NEnH+OtJUk3XK5WHNqOth9Y1jVIz9E2bEzNZVrRzCesnWi7gdlOsdLf9aRJhp3jlGXTnEDEEJsbA13bUkwLfvGLX5AKSXCW7e6aJNdMc0duFjSHew67e+Zx/AosUDBzJogJGUlD9pfKcnHODYQQtaTe/nWmBuL1tVgExnFEKc16s2G9XlMtaoIDMUvmXuFEzFQLIQg+4L3DCIO3I7O1kUmvE7yHubOMaqB1Ef3tnMM7F4MneiAEizIGrxUqeGQQSC8wica7+DGS/DFKjBEQOc8M3Q06k6zXGw6njqqqEcgYEjGRox/ajqAUly+ek5UFx/t75nHAesfsW+bZwpiSmoKyrFhuNrTTjElTsjTls08+jZXooSM4xzgMJHksBrazRSSKoe858gZVLFGM9MkCb2eknEl0iZxBq5lxDgQpKcuS0c4M1p77+QGhNfVyhSBwOO6Y5hmZGMa2Y5hHsjxlGgdkllGWK5a6wk0pEKcsh2EguZlZfnAZ5wJEpOAqHRfXxBiEs4zDHP8/Fwu6PuK08jRO/aVFBkow2xkvYvtLKkFeFqAURZZTltFM1LQN97sdRVFGnqBI2R3uSLTntDuyWCdkWUJRFWSZwTZ7xFk1Nk0zidZ4pjiZaiVzK5l9IIS4M7GzQwlPmjrGtuV4OpBKxThE/0FdVxyOJ7xLSLOKNE9jYMpIklZjjGY3bnn79i3TNOHnE4uypqxj7L2qK45NQ12WcbufaF69fMl+v+fR+oK2OTFYQOfkIkM4hxQBZ2e+rPZ/BSn+TkpKIhDnzkB80EtARhcE7yhP59bheUfxV11fi0Ugy3Pe/853WNU1ZVFig2caZoQRuBCQiIhZdLGlJoVmHAMIhbUOPwWsD/TDwChmZJZgBsNkJ4KMQgvdS5QENwvknKKMQVr7ZdHFuZjr77qBqqxZLpdoDKE5MeSB2RUM4UiZVGRpEm/I04ksTaP8RAakVsgsodoInIXx1DC7GZPkBATJnJGWOabO0dqQKQ1ipp/ikShRkkHEM988j+gsHvhUJtHGIAX09kTZKKZiw9C2kGUU2QoTAugBOw/YMZD4Eq01SZ5FjoFzUdOuU/LC4/zE7GbUOBL7gbFFaBKDEgKRpozHCflA40LCOI2UdU6WZUgJ8zwhRIoPLhqJXSCo6MfTOgaz6rokuAtMI3FlhggCtETrFKkSXBD0w8QwDgzDSNt3MQWa5dEJME3MduLg4dR03Dcn6rLC3jkSlWKd5M3ba4STPF5fsFxd0voRe2y5vrlhs74kDZa0KOJw2TAyjzOZVOyPB+pFjhQKkHgPfW+ZupGyLkmTBK1kNDnJhCAmjIkRdpMaxmkkWIfOI57t5uU1o0lJpAYlWSxq+nGKhmXOD3QhODUdP/nkJ6RpgpKKvhtJ6gqtcgIzOs1Y1jXCu3isP2dfYlPgnACEyA15R28mxAwB7twoFDEo/A5EKsSXytJfd30tFoHEGOrlEpMYZudRKiEvDFJK+i7uApyLLQ/nzsRV70lUglASqzzTPDNNM87H4mGUXQaU0fTDgPeWcego84xJ9ajEkGcpoxvwzhG8Q6ioyNZqRIk1Q9+QGwNyQ5LOQEqdSSbncAi8d5wOe5KsoFoYTPDY0TH0E+gUs1RIPFol5CZBedB5ColCKMk8zrSnPcMcz7NuHBmnjm60yBCTc6WIHDklC4LzSFUi7MQwtriuwY4ViVJkeWzfjZ3GuwERoltwuawpE4UQ4TyQJSF4rHPs9wcOhwMAVZozMqF7QZ5lGJMRvCPfZCyl4frWUBQlJjUoQXQoFhWDlJG6c96pRdNOIM9rqrkhTRR5keDseH4yKWLDNi4A4zSTKbB2wroINxnGgftdQ9c32HnGnZ9y1nl+6/u/y2LxgB//8M8YtUDYgbE7ITclTRcoyyW7Q0/fDyzfXzONB/L1koSUw65FCBGLhSYujFWdk5icPnje7rcMw0D1YENZZKRKkaoIIh1s1NMpLXHWsb3bgfMssxyTZDx68JTarZjGKF+d7UiaxuSpEALnPevNJZ9//gXNacfzx++Rq5xp7lmVD9EmCmh0lZKmJi4CIeBFnAL80jci3kFFf3nWF+JdhVAgHAglQP3Fw4A/24t+3fW1WATmceLTjz4mBMizgnKxwLlI8amL8vyESgjBE5jpOk9SL+L4qpB4BnyYCd6Bi867tm0I1uNlABmwM4xDh3OW0qQYD6mKY6RKSwQBN0eYZMCzvf+C4GPuXKCwc09VlhSFpJsmTl1PYgzzONA2ETBSLQryoo5fpEQh/HmkMwicBzc7dKmol0s8nm7co3SFomdw87nyG21KQkiCmDH52fATPK73OAmNEBSHnokZP8cs+eZig0gSxuEIwtN7R/AxIy+JI9dKKSZnOQ0dXddze3PL7d0tVVVhzYBVI1anEALVYoVTsf1XFiXL5TKSc7TCETBn/8C7G18D3kU7jtIKgojzG8FThALvEpyLrUOpcoIGYRTd0HM69lhnWa5XiKC4vb1mf3vNMMdk52G7R5qUP/q3/10+/PD7dK3mveeCbfqa2+OB6+EzPvn4NTefjfzRH3yLxWJ5tigNhDCjvUYJyTgPeO958ewZDx9dMo9dlI8mmn1zwvnYClWJjilUY3hzv0VMkjSTiEqT6DiNOk8WLUEEgQ+ezbMrwtvAsTtw3J5AwLPn71OtlighMUWGtZY0N2Qm6tTEJJhdh3MjYpoRQnFqjjTtES0EQUl8iMffIMOXZOH4379iLiAI3kHJIUMy8FVl2a+7vhaLgBCS5XLFPDvyvCI1KdM8cxomssSgxwlhElITZ9ad9fTDyGTjaWnoOtq2ZZ6nM4bKn58qDjc7QvDM/py4y1Ks9yh/Q7NfMuNJiTeac+5MJRpwdsC1itEP4AOJUjg940jIsozeRoqR1IrEBdw0MrUKkxYIJaMvT0iS4BnHHqcz8nWJzjPSPIvzDT62AJEBJRXOWnSSnheNCYLCKoVxATvvkTZhEBIjFU3fEaSg7ydu+pZx7CnqitN2T5pq8iJnGieMyRBC431s5SklYycgMZg0Zb1ekxUFY3vCjhM6FQxJj/cTi3wR/QS5JB9yrJ/ohh6dBAxp1H9JddbFSQQKaRQiiHjzSU1QmiyvwLl43k0FIUlxMiBUgp+JIa9ywThOXL+94c3bNzGiO43MziFVwjff/xbf/d7v0XYd8zTx4qJmf4gh+lNzot/veHu6BXnke+99k/XFFYRAXS4RQjL3M7OzLOqSqweXPHv+hNvrNzT7fez0dBPdYWQcJqqy5uHTJ2RlwWkMSBQ6kVSLBSoxuBC1YEWeM44z0zxS1yV937Hb7ejbkc3FBueniLwXoHKD7zxt1/FgfcmDiwv6w4AwV0xdy3Qe2x6dY3/cMQ09yvsvkWHhHBAQARThL80BhOgmj7gxIbF/afuv4a+YHvhaLALGGL75/gf044CQmjwrGOeZ27ZFS9CJRifyvO0xBOIU4TBO2NniXKyET3bmdDox9uO5dy3ws6MfOjKTIJTAWQcphKAIRL5hCIGkSnAHh7MzXTMggsG6kf7QI9OMNEtxHoKUmNRQqZJBapR3WNGT5yus8AxjH+1BSjO6MXLlvEUxR21isBy2RwYNXoQYkLKWMfRYazG5IQDWOOzkicSpgB1nnJuwY6CQCnseVxZSMJsZ1T+kamqmsWe5LAnjQNfHo06uoqdPa83ibOzZJYaXWRqTesR5izA4pnHAZoa2bVg9LHFuxgVHVuSMkyAtBNOpJU0j8NI5j9IllpkkkTH9FgLK+/N8hyQEi8AifCTkehmpuTpNMJcpF5eX3N5v+cWnn7E/7jmcWqZzJPn1q2u+/9u/ww9+9/eQSnEae6pM4qeZsgxUc0I/nBimFmsn/vRPfsx0aPg7f/j7LPOcPK0ZhUVogRE5QbnYqixyVqslQ9NQlSX1GJhmi5snjJYURQFSsllvSE3OsT0hkwSLx08z+8OeRbmmH2a6ocMJgQuO0hQ442J2JXhKk9DNE0IITk3Dx598zPfff0KaaJLLNXlhuLm7xjqHCIJJCKZhwM9zpAnK2AIU4R0+LN7uZ9zoV4JAsXgoENjI5gIxIsKZPPxvgjb8N3kJJRFKIaUmyzP6vov9+9lhlgsSc+YMDBaVKZLEoKRi7GJAZLaOebZ0XUtzarBTDLKIMyd+GDqGATYXUSkuhECUSxIbJ8pmOxGsx6YTwz1kWR37595SUJJVFUpDVkwM44zzHu3BSMWIYLs9ktCjax1n7KcZr3Xc2itNkIpxcEg1kgSJNIZSano8ymhGZ89qNAcWgp0J84Q9D6UonYB3TKPDecHRz+gkoRu7yFhQltavmEKHniV5JpnmFo+jH09MYUGa5QitgVgZ3+oD5WbDXdOTWodODOliSRABrROcDTR9T9W05FkBWJSMIRpTSdK0ROUZc+cwRuK8QEqgdYgSBB6dZgjvCGGKY7BIbPBnT6tA54qMFA8cdgfcPFFVC8p6QX9okNZR1UseP/4GmYnmo0VVU6QFP/r4T2JE147IaSJPCwSO07Tj408/Y/Nszfff+zbdqEnKhCRRVEbS7k6MtuU09ui8IniNCIpEa5AggsPbkd32gPWB5foBVV5gQsDrnDFIFAmvDzN66cjrdYSJYqnLkvRRxfzTl3z+6hWXjx5jg0Mqhe9hezzS727R773P4dRSbZY0U8fb2zsSqblYXyKUYbQCgo7sACHgDIJ91y2YQyD8yvb+q1py9y5KDF8KSX5Tu/CvtQgIIf5H4N8HbkIIv31+bUP0DrxHhIf8RyGEnYjL0n8L/HtAB/ynIYR//ps+vneO+7s72rZDakWe5wgRWOYZdVVBCBFdnZUEEc+50zTF1J2OQot5ioGhRBnSKmMcR7x1mEyTpgapBCFMQEbXd0xi5KgkhRCEyeHthBUzUjr60dKEQOg6VvkGlcUUW5plSA/3hwN+6JEykJc5F0+vUJPgcNrStTE+W5Ql9WIRt9+6xHmL84EqTRFaMo0jeZXjJs/Q9wgZcH5ChwRvLcI5fLDsr78gTVN87bBbCyhccKBijznMHjkHmtOEqlKeLK9wUxYFndZinWcOcTeQaskwxEp1XdY8f/qc1GRMY4MfBkyiyVODkpI8TQGJmx1905IXCakxhNHTtB1ZVmC0i9mK8QRJglSAcoQgkNJgxHSuamcEAi7MiPNx5suzbAhxQrOuefTkOSpd8NGnn5H2BW135O/+vT/md37r9whWQiCSn4KmKhY0zRY7TBijqdMc7XPqfGK/vWX38hb1wfeA+CB4efOa+1dvSI3CKEkiJYnULBY1Uku6ocfOjjyvWV8+5+LR+0x+IsiSSdXk9ZIeATqhqJa49YnbqeRpuqHcpNjTHnrJxeWGMFbsvniFVoogLGWxJviM06mnJKXIajobd6uv3nzBZ59/zrpecXF5hWXm/n4bC3vhK3Hhd7d1AIdGiljrEu78efQBVNwppIgoKAnEB4t+ZyD411gEgP8J+O+Af/KV1/4R8L+GEP4rIcQ/Or//XxCZg98+v/0+ETz6+7/pg4/jyJtXr0nSBOc9eZpRLxZRY+U9MhEYk+Os+xIZ3XUtzsUdQD+MsQWGwM4zdojR33ka0alB48iLmnEauTncU0pD1qZUqwqvNUKD9AHlJE5FnLjrOkLwZHXKMI9I6QHFoT1xOB5x1iGUQKcZF+srClljCsPuds/IGEM/w0DiHUo5dJJSVSUyeMa+wwFFkWFxpKlBmwSlJaemQYhYCBTBctrfMSYGOsHcWKSIuyZ0QH5LYP9sIsweZVvMccGU5gxtQtdksUYiImzKzjPTOMVBJe9IVEKZF9RZyiAczTjgvUOFwOJM0E0SgxaSYXePkCvSdInWmq7TNKeGhVzgpUcGFZkQzpEU5tzKCsA5QzEEfOWRUmG8QKj4bRflpDHpuFmvCcIg9GsuLi7o2pZ+Hnj69ClXF5fsdkfGsWf2DicCl5dXpDownDoqk5IbCc7y7Q8+4KWGZVlRl0u6vmfE8fOffURoJ54/eQwuILxHyXBONdYk9yfyYs2DJy949P2/zfr593EhsGtGrm8ahqnDlCWr9ZKJmscvfou+HZg8HCaFdwVvTnu2/+dPqJRjlWSUaU6WVmhTIk2K847FRRp3B2lM/YXg0VKiZMxZXL+94+0XXxCExUvAC6SPHaJ3ElJBLNiKdzPEnD0EAUIusO9IYt7H2YO/gB/9y9dfaxEIIfwfQoj3fuXlfwj8O+ef/8/A/05cBP4h8E9C/E74v4QQq1/hDv6laxqnGLQIjuVqhSpLlJU470iKAjt2dG2PdzYSVILH+4C1kbS4KEuk0NwMN/R9j5BxpxAAZx3jPDA4zzT38YYvqyjB4N0cgWEYh/iJUgqdgPQOkUzUmwV+t8e5+KS2wcdIr7VkpiBJDM7+v8y9R49kWZqm9xxxpWlzcxHhITIjVenqqmo1bDZ7Gj1DMRiC5GYGXBEDbgiQ/4E7YgAu+SNIcEcuCHDHIUiidXeprNSZkRHh2uS1q4/g4lhEZslusIdAHiDgHhbm7mHm95x7zve97/NatvUKYy2jyYh8mNN0Dc47vHN0tsbh8D6n7V1oM1kTFhIhmB/N6JqKdZrSNoFaAw7RWWzb0lmD7I9wdon1Di+C9dR/TvAyeAfGYBFs1zfgemItqYpAPvb+UGCSAnugAKnO0Ox2dHWHbhV1UQMdrs8ZjWeISJKkMUkcsbzZUC2XTKcnRJFgMh6xKbYY4+h0uPtMD5eSFAISgW0tTkiUlrjUoQ5EaCEFyAMe67Ab0AjyNKMfKxpjePvtr2GMJUljlFAslxf0RlNtWmaLBcWmwnYVq92e1e0NWazA99RVwWjwkOPFMd72nJ6c8OLqguvNLdJ58ixnlGec3TtDpwkCRRznDLIxixP4wT/69xhM56SLR1yWlsb0NLVh2/R0raW7XrKetqgs5cF8gRGaXbWll5LNcsff/uQjPv3xnzGIel5/OOSttx5hHCjpqZtl8JsMx3hgMVvghT0chcN7Y43g6rNbdrttqH/5MNkdoHwAiHrxxYQW/osFgBi8Adl/YSdwMkBGfpNQKLz//9/H6Zcm9hVwevj8HHj2pec9Pzz2axeBLEv53vffou8jkiRIOJWD7fUNz9oOlCJTijRJg2MwDjCRLE0QHtqqZrVe411HlmmK/Z59sWO32x3y3INRxDiDd4bk0UNu+o6+a8hlSjRQICxYi44kUZSQ5xld57m8vEQcAkqkEpycnNCOW7wXVHWDl4rRcMB+v8fcdCRRQts0bPc7+q5DKYWMNNI1rO96RkenaBmTRQlJmhFFMVW5Z1+WSCmZTCc0ZUEsEoQ3tEbQd3uUvAMRCo3Oe3ojoQqTTjpInaDsasxFxW51R57GONMQKdBKYE0fikgImqbD+YYkjxmZASbZobZQ7BvqruP++TntviZNEzxTZLwhjjV1vcVaHcI4sxxnWiIfEScJuALT5igpUAiUDHJloSU+9uH/CThhD7cwjROh4q20R3iHFxoQfP3tb5AnA25vn/Pp55/xTGiiKOFrX/86F08/paoMeaLYLW+5/vA5H33+Q2LlePP1c7q2JFIeFSV455gOR8ynr5MajfKC4SDj5PiMaDihrhzxbMpWptRacP/JN5AqoawkZdPRmZ6b9S2ucSgp2bZrrp69YDSdsF69YLPbcnJ0RDIMBb8fvfc33Dz9gJyG7ZXmX/zz/4zBpipuNwAAIABJREFU0ZhsOOTu+Q35IGI+eYM0H3KyeIBzNTfXa+Lkgrpt2TQVn119Tls3aPvyzu1D7LrnlY345XBECGHDfd54kALn/IE1EAqK4TAhf+NU/7dSGPTee/GbqAW/Ynw5d2A2P2IwGGKMoiwrkiTYLcu2wfQG0zjy6QQvHcZVDJIFSoK3lqqsKIotXVtS7DdsNmuK5Yqb9ZrWmMCiP1SsTddjbMtms+Ho6AhrLHu/JxEx+SANFywc8g521HXNeBx6tdILJtM5STJhOnX0XY8udvQ2mIOsbeito663RFKRpjl4GSr6fY9E4Tvo6gZjeqbT/FX4xkAOWLx1RN817Nbb4IlvWqR3WNfgrQG5D/UQAUzBLoOSXAmBRFI5d0BPKbyLaJoSfECsSSFw3lOWoZBaliV3t9c4a5hNpthEEqdL5LbG+p6+75hOp2itadstUaTDeR9D0xh0FOoWzgkSqYmEwDqN94bIe7wLslopXzLvDqgrJxBGIST4SCK8D7AL4dCxIhUKpTRFtwP5iMFgwF9c/zlV35L2G17kGR7oO8EsnZEnCYOjFP15z+OHJ3zja2+y290wHg1C65XQEk1kzNHREX3fkyQpPh5QNAonc3am4/LzJauyIsnGZDpC+BAAulmtD+KbiKLcMjuZAhNubq95971PyKYZtuqQuuf6xWfUu0ukaKBtyeIRw/GUNEnxFopdAc4ymdxDxI6yq9Dekk9yTo/PqMqK57dX3K1XeGtCRsahde19yBV8qRF+tQPA8Krs5zVeBBLSzxkMfbimlfiS3fAXxj9kEbh+uc0XQtwDbg6PvwAeful5Dw6P/dz4cu7Ag9de88vljvV6S5LEnJxkZNkMY214o5KcNA3FPu8dRXFDFAXM82q9plyvAsq6bynrmr0x6DRBNZ4o06zWW7J0znAwoOs16/UGYwzT8Zs4r7BWBS9A2wVM9uHM6r1nV1dI44ijmLosWd+uSYc5dV0RpQnpeIToBJUsaas93lrG82MG+ZC7u1ua7qXUOwiBlHIIFE3tiFtBZzsGg5y5nlOOAmz0Yv0ctwsiIGPaoBSUoGOJuD/D3TR4vw9mESnxWpPOM3zRgRBEWtI1DbbvcdbS9h1l07K6XWOtZ78v6JogyxU+IL/zfIC8L9ltN8HnACivsL5BCEHbNtzc3pBlOYvFEfuiOrSnJMIaRKyJIomMIqTWQT9Qexh+Ab8UMui7Qj6eDcfVQ9dbKIsQEbGLOB6fksmGtrbs9gVtXRGJIdd/8zd88xvfIEpHGGeJtWJ+pPmTP/kjoEVr0FqiteZufUtRbhiNphjTsTi+z93NEhnFkI1pW8G+MHz09IraGKwKrbnGOmzXUmzWeO85u3+fwXDIvgg6gCyL6dqGZzNJEsW02x2joeX+JCZ7/ZQP9zcMpxm/97u/QxzHCOtwyqG8IhERSaqouo79Zs1sOmE4zJnPJ3Rdz93FLU3bhYq+8AfYC/zied4fjgARDjsURLWnd/ZwbgBcf5jaB8eBDzkYv278QxaB/xX4L4B/ffj4v3zp8f9GCPE/EQqC299UD4CAoN7uNsSJpjcdnzz7kHLTMsgzBtMJ48Eo2FO9CUtcbVmvLnj22We0TU3b9tzc3lIWBTqOgnBFSqySNHXLbDxiPghCne1+e+gUeOzKo2Zhpb28XBNFEaPxCCk9s9kZUbSkqms609O2HUpLailo1rdkWcZmuaS7WXH/7Iwkirh37x53t7fsNmukkOhIkysZvodzLGYnICRxnDDTETUV0kQY0yGdZDIaM8gyPn/xCS7uUUBmpwGx3m0wfY+6LtAohI7oux7vCLZiUmQe03QmwFG8RSSKar/n9vaW1XZLlOZMpzPGg5RyBXVZ0RSW0aMTZrMZngn3Tk6IIoXWiqrbY21P13WkiSZSmlhruqYlTSLs4ahFnmNbg6Cn6zsirYgjjU41Uh9iMA7bWO9etq9CCzVcoDFeOoQBoTR9nPPei6c8/exzhBTh9buCrq6ItGK5uSNP7oXFc/6Affk5t89uMHbMb33/++w2W3ZVjx5M6aMp6VChkimP7n0dqSNKP+ZyveLqcsXTyyvqrsIpT9/2SOuZjIcspjl13fLRT3+I0RHjaY7ftzwvK6qyoL/bcVndMlSeP/6nv49SU5y4T/3dd7DGg5LoROBpiaTELtc8nJ4QZSnNZgnOIbxnOh9R5wUX/pb1ZkdZhjh74UKtRwjxJbGf+LkPPZCVns5DWAEsWH1QEnBYEA5FWvcP7A4IIf5HQhFwIYR4Dvy3hMn/Pwsh/kvgKfAvDk//3wjtwY8ILcJ/9Xf+AAddU7Jdd6hcUtU78Al72XMvv0dVF9TVFi80EkNbN1w9u+DyxSVt27DZrLm5vUFrzWA4JM8HHI9GVEJQtS3j8RCrBK6HVKdkScIwTemSDlF3r/gC3jRsncV7T5qmRFHEWCrKg3W4qWqkEFT7PbbbU1YvEOohm92GXilkFgxCbV1hW3AHTbc88BKSJGFXFKyXK8TZjHwyI8sz0jQhjWOUDDLmXGU0MsLZGq3XWNuFO6t1uMahTxy2EyhzkO56iNseE0UgJFIq8jQnlTHr1Y4PP/qUptmyODlht7xlPptzfB5iv26XW17cXhArGOQ5URyhvGO9qhkMJWDDydL7ECg6yFFK03Ud+SAHILeWjWnxPkiTtQChPAEDIQ7F3MMiIIJyMWjiw6bXO1AyQsWCrvPc3tzx0fsfcLdakeiIxhVEUrLrWj79/Bnz4xOariWLFPfuPeDdnz4lygOfIIoyVGw4O3lANJyTZQOOju6hJyc4ndLtS9arFdZ5VBoznI8or4pAgd6WZEmCcgG9tt/vKMuSqtpzKUIxN/MJu3rDZnnD8dGMtx/fI42CbLtsG37wtd/i7m7LDz/5GUW3Zzya0hrPxeUNbz58SDbIWC0/IU0WZFlKWWwxB6PbtijxNgTfCi+RB0PQrx+WhhSvDfQe8Yo+EBYO4TzeieAejX79d/n7dgf+81/zT3/yK57rgf/67/N9Xw5je7qmoewKZqMjushhljtGen74JVRB0htHRFJy+fQZn3z6GUI4ttsN17c3GOvJBild3zGyCa21FIeva+qKSA9pzYbK9CRSMklHTGYTtFQBmHllaFTDfldzch5aYUkUsS92jEYjrDGsqoKqqplOJqxu1vhowHyRkseasqo4O73HvemU9XLDrqi4vHoeDCmjcTCFCLDO4LylszBRwakmgXGegzOYFz3T0Zi7m4+AwAPQWtGxJ05GtElNt7VIFCrScCApVdKSGIvQMd5ZkkiQJQmfffIBf/UXf8pqdcfZ8THniwdE77xF/Oic4WCAc47tZh3O+kpibE80HuD9nqvLHQ8fvkFCj0wCe6Dve5QKaUVaa/qu4664ozeKNE1xSoHSyFfylHAnkkIivccIiVQg6fE+sO8UEicEvXFYZ/n8xQX7fU21rynWBQKPrCVvv/V1RrMA+4jiBB1F5IOE43wM3nJ6fk5vNelgwb03zzl//DbT2RHWxXR9xna3ZbMqEFFM51uEl2gn6Oua8TBnkuWU+5Jtuefi6gLTddi+Y7/b0TbNAbiSIq3j3nTCb3/vu9w/n7M4P0ZqyXAsMPGIR99Y4GdjKgtZJDBtR9tWDJIcoTSzo0ckKlCZ7u5uKas9+33BbrsDDN6GRfILic/L8/wv3s0l3n3RDvgSeuBVEVgcrN7OfMVdhFJIPnjvPR49esR2ueQ4TrhUhovLz/nT//P/4eh0wQ9++7d5+tmntFVFs9mx3664vr5hv98zPZkxm01ZLBYMhwucM7x49hl4uHe6YBxFRJGgyjR+bzgeHZPPUvI0VJBximgaYWpDMgyCFiFEaEECTduy6wL/XgrP08sXPDk/xxmL7mr6LmWUjyk2d2w2W5RSVFVBXe9Js5zxZIxxjhfPnzEajXn82iOUUvRFR1XucVhupSTWEXqsuX9+wna35O76AuMCEzHLjhAoBvkQmybsVjuUr5BCB3SXdVgVMUxznjx5i3/0B39IrCTe9sQSljdX3F1fsDy9pOxveVw9Ynb8iDgeIgmiJ4Qjy1KGg4zhIEWIlqZZEccxeZSz3RSk6YA0TYNis23RWnN/8RZ1bJjmGTqKiOMIpYLxyjkFvkNYjxMGL3sQyaFyLcjwJEJRCUUrDX/10x/zlz/6EV1V4nvLg+N7TCcDnIBoPOHo+JSzB8csBgtc71BYdOQZXj0jzzO++b1/hygbESc5dddzt+8RxlDaLUmsWSwmbKod88WcaltyfXnL4uiEqt2zWt4SJzGbzZKq2lKXJVk84PHrT3j8+hN++3d/lyxJ2KzWjNoBYmyIRglFv6PoFZ3J2aw9M5MzPPkWo8UIKQXrqwuMVyyXtwwWObGH2cmM7a5it1yxKwtul0va7mUBidBB6cQr78CvHr/8+JcDRzQcPAQC8RtIo1+JRaBua7Is4+5uSd8b+smMzeoS7xJO7p3w+htP8Hh22x3NvqZeL4Nl0/YMZ0Me3jsjTWLKquL58x+x2ayZjSacnp6Rpik35ZaRylle3ZGkCel5jHVBzNO2IfxDq1DNttbhWodMFM4YyrIMKUHWcLvdBF28CSgwoSSlMaj9iordIXLMBUyYFpydndF2PdopoiRmlOdoJVndLcmHA6JY09QN1oYi16puaLuao8mUx48fUxUbiu0OqyKslZxlCdvGM09gcDwOtmGXMMgd0sN4Nue3vvt93nnnaywWp7RdS1NXIDxHs8nB7Vhw+/lz8mwKqiDLLHEcUdclo9EQH8e0fQdO0HY9xycDEpPRu566rimKDVEkg604DrmNKofpPMO2mizSr2AWQngEfdjSRhCka1+oBaWAznuMEzghadvwM3rT8PmzCxI8x6NQE9pVBVIpjk7PGA2GRD6llw1KKp48eYssTzk6mhEnOVmUsa16KuvZrApSqymlYRf1pBL6sqSsAjtgOj9DpD2bZsNydYfoLPQ94zTl0ek93njnHY7vneOkwuqYRsecvPkG0nuqqqAxitFsTtdabu9WKBcMRs2mQ04i4ijCipTT++ckScJ+vw+7RhkxzDPWd0vOHtxjYwVCfx7EPcbiCV2dXxL9OkBEoaXNr6/4C+9D/OhLK/JX3UXYNQ0f/vRvmS3OGI+HbNaG7aZgNkuIYkm1uaUqd2yWV2RJBL5HCst0MiDNYsZpikxSbm6ecn19GVpaiaBRDa7qiHB0+x3F8oYl4Y70zte/RtntiWLN0XCGd57VasUgjultQ2oihFYgBd5blNakeYYxhtdPT1FeYKUgSbLwIryj6XoGY0tb1ljrSZOcXEehzdeF6O5tWTIYZmilMZ0jlgmdbditHV1/xd1Hz+heO2WSznny5DXeffcnGNMihad0jlQoRO95MFvQSo2QktF0glCSr7/9Nd7+5reZHB2TjCZExjE+vk/02XOETokSC6JjbQoW2zse3L9PpMMd2QrJrtyz3a44ORqTJglaAA5MZCnLLZEWWNNQVsUhaXjM2q4YuzF9oZmnikS/nOQWKyx4GcwvrYckXLRhqxraXa0AJzsSGYqL5W3B9mJNtS6Q4wF+mNIliiQ+ZjabMIyiIBHWnmEyxjpPnMwYnDxEiIif/PRHLHKDGugQfFoX1ASNRLVsYDIBNEmiuHf/Hp2t6HtJmg5JJwt0D/fPzphMZ0wmExYnJ+go4u52xcWzG9rbluQo4f75GWmSYpKC5Wob0PNtT3J6FGzV0mCqHZXvcaYhiSJ2wnH59Abz4R2Dt76FkQ2z4ylORfg+w/aeKInoe8eh2vfLMYIiwdMHZdCXFodX0WMahPFkQC0E5ED1xQ7hV42vxCJgfM8HH3zI6PIarzWj4YjpdE6sI+qyYp3nPHr0iNcePebyxRVN27DabtjXFYPhBB3l1OXnFNs1mSRUkO+u6bqCLM0xTcvqZknd7BGJJh3mbHd73nzjTYbxANv1oUIvIUkGdF3H3XpFkiQMBoNXZBZ1dBSiyvoeqSMkEuEteZ6EbXc8RkrJPgmYa2kVxji8Ca7HfVVhncN0PauyDPwXKbm9ueGj9/6aquoxXc1idcv9J2ecn7zOH/27f0jfdTTlnlU75Mk0RYmWZDxjkeckScJ8PkcpRT4YMjyaM5xMSUYTEmM5PT1jMBrTW3soygnKbcNn/mNO7p/x8GiKigWD0TgcTZRDyTD7F0czvBCsvWU6yJnPE5xtSOOE0XhAZ3rm6YI0iTl0VQPKSkjQMVp8Uca2uT3UAPwr/QBIUk/wGzhJvSy5/PwF282GeJCg1Ixvfeu7TMZDlusV9+8/oK9r+m7AZJxhDGQ6Q2pN15bc3ga9/q4rKNclXRes5ccnRyRJwmQyQQHTOKZLEtq24+HDM7zgi3qHFGipaJqa9XbLcDQhSVIWiyP63uDfAG8dP/rpTyk2G95+9Abr6hIRTRgOx3z04x8jncULw737jzg9OaPqY/56l/LpZz+hWn3Ok3jI6N13sZHh+N45ndCIVUW32waqk5RIgjYg96ESUIlA2SK4An5pvNwvzI1ij6UMvwxEyS+85788vhKLAFajddhGdlVNNJmx3i8ZjUds3Z65i7m4uOBscYSsVgglyYYDVBIzHI+YTAZoLdgXJcV6CWKH9Y4kTsgSwXq5ZlescU5jZU1R7A9Bmp6qrlEEpsHR0RH7pkBZRRzHRFFEkoSWYxJplk1Fnud0vUXIEDribc9+H7IKlAhHirqusTI4COkEfW8Pv1iJaRsaYpIkZrVaIaWk2m8o9x0QfOp919FuOvwpnJ2e4jtDY/YcrypGgxilUsbTcdiSK80gT/FAOsjJ8xDa4p2jtS06T5idzhgNR7SNoq0t3nZIL7m+vkQnGYuTM/RI4FxP3zWUpWU0GuF9hBCeoyiw+KSUjEbHWONJlEImIUockiBcVxJxgI0MgYLQqXaRBx+2t6GCLV6FwwhMkDYLRzqJGU4GnD9cUJY96TAsNjqOWBwPUEqQDCfBSOYj2rbicnPNdDqn6wrKKvhF4jjj/v0n/OQnf4nKcx7lGYP5gGpTUVUV7XTKIssYpAnFvmYymVDXJS9evKBrG5I4RkhJWdUY6wKkw3pul0uwktlsyFuvP+bFB4quh5PonFI76sbwYrXiyaMHzI5P+OyDD/ibd9/n+vqaj979S5bLG2K54eE732KQjVFjQRqHZO3OhixI7zzSGzgY5cqXwYPeQSwCEsCCjgPCzr88ERyMRmvM4RhxeOxlV+arvhMIVklL27YhfKOpmC/m3N5cI6VGZB5nO57fvmDfdkgBWguMDz74um1p6hLbtygJxjoiHdEUJcp31E0ZRDNdCX6AEIrNZoOWGiUUaRSTxY6ml5i2w7iexvfQQZYnpHES8GNSoiqQkaCqauIoZj6b0jQ1wzRFWMOq3DObhfisvt+jsoRYKaqqwdqe3CfUZY3pWqr9lrqsiOoaicW54BJ3zlFVFduyYTAYIVPLtM2xqSGOxaFTolBSkOUpSiviNCdNEkZ5hhqAUykxiv3uGft1Rdta+j6c/2WiDrp8T9PscG6GthE6AaxkfjRHH7b1dV3S9YpJnhPHgXGdJAnJYIB1gY0odMhRUMoCQT9QAGBBRKFuIIIL0B9od6HeJcFrnOvw3jGbTzl/dJ/Pnl0wX8QcnxyT5Tl91TOanlAUDfN5xm5f4AXc3a1YbdZ89NEneO/DIl6WjKTk409+zHA4pKkq7M0t1UufSJIgpcQcDF5VuQXfg5DBsCYFUoZOSFXXtMKyr1oioTg6GrJebimKHbP0mNe+9hirMz59731GoxH9viOOMi5vb1gtr4niFJ0KfNRRb64wywuMNvxff/5nRKnin/zTP2S2mLC6vME4hwxo3KC/EBz+/tJFCHRfTBnTucO7GF5XDHRkDKgpwxv9c+1F7///UQz+WxtCgDOWtq+YzKZI77j66GNsHKGRONsxn09wtiaNh1jvmIyHZH1HFEeMp0O6rkJFHYt5CkZTm5a6LVg7hXMmuAuNxVGxvL3h3Z/+jG9/81sczY+QUvL86hqlFNPZFLOHcZqRTlO0idiWe0zbkWYDWtmSC8XDx4/pupb9bolWMDs+xnvLyIak9u12y2ZjKdZ7tI4YjPMAHJl4kjbndn1Lu19TflJgRoZYC9o27B6EipguThjmA6SOSVON1o4kDZ0UfCDRCifwV5boTR1CK505kHtTrPZEccz5+TkXn5+wuregdQXCWUY6RWsYjcbcOz0liQS2rYicIY8U68+v8ZklyhIenD8K1upqz8nJCV4OiGN3yAdIQqsuMUCKEA1e1CAG4AS6itBTQS89LvZQv9QMmICK8/qwGyBIu63k937nd1FRhnGCxWIRVHp5zmg4RquKrrFc3jxlVxQIIFKa65tr8jxnOBzy7NkzvvGN76OjmqPFAijYtZqJDTuZNIoRvWF5e0dvgiKvaRqiKKJrahCSwXBIlmUczefc3N7w/OqK9XLJaDTg3vEUqSP+7N0fYZqGN97+JqvqBpH3yMwzrgruLi6Z3j/lbH5MlJzyaKbwm2/xs7+2tNsLjIn42c9+ypPX7/Pt0bdpTM+L60ucPxitlA+Sar48kX+xsHfgCArgVeCoCYuv9EQWzJe+xv+6SGK+IotAeKmOyWSK7Q0+iXFRyGJX0nF7e8t+v+f09JS236FTFVj4fUBKD/MBzXDIOhkHW3UqEbVAVBoZWeq2pGnbEH/uPfuqYr8v2Ww2mA6OZ6NXiTqRTnAx7PcFzjskkiSN0GloKUZxTBInaKcRCbjBCOsNta3wrQ2Qya6j70OSj5wHhFhdl+hYkcQpxJYxQ9pdRX1WIT3UfRAWRVFKFCXMm5bLiwveePQayWxKchSjbX8ArAaLrr5RcF9iK092PKC3/cEj4aAxFG3g+FvXU1Z7+qoiUh4zEgwGIx4+POfk9ARjK6SMEULibE9LhXNAb4gFqFTT9h6pBJNZgJBK9fLIBFLNsK4HMjyeFsikwE8l/csrufaHiQ/KS0Ad2lf+gIQDqQXT2ZTXX3udfV0jtKJqK3bbgn1RYjqL6x3b7ZajoyOMMbz/s/coy5LoLOL2L245fvuY9foSKSVxHFOuJbtqF8JIvcfbjufPL9hsdmRZxhtvvclkMg68iCTCI2h8iEQv9gVt25KnKcxm9F3LZ8+uyLMM2za0bc3l88/ZbtdY32Eaw+3zZ0jTUS7vmCdDZnPF8YMn/Cj5N0zn4Bjx8ME5g9iRRhnOGpq6Zrvb4rWjN4CQeBkIzoJf3MbHvDoTfGlidxAKhrGHNqgJg53nUDD8qgeSQkSWndG2a9q2D1tMHEhB1Vu8sLR5HramTYPaeZbujmw0JBll7LZb2rYlTTP2RUFlOkajIXli2Lc1buc5GDGJ0/TglY8OSsGGTeGJIhGy9JYVs/mQRkmE2FOW4WLKsxSEJx4NSaRkOBzTHMAiXduEP32PVopBHOOEYGMtSimUVqyWa6qqInUOI8uAFKOijxSRBQwMBvcZDTzbuuZv+xvSmyFXNzfk42NSIYkTjUIRSwmig2lAdXXaYIVBKhlAnxFYL2mKjvXtNfvNlgiJ8Z40jhgNB2R5jkfQFR1OKbJc0bY1+/0ucBh0SN9Zb9YBE5YOsNZijCHL1CGBSSBlelgAvihOJXickDifIEQLkce/qmf5kHgkVIiKcx532NUKETbA+XBAawxN1+GcI44iqq5kdb0inkyZTHKm0wnrdQg0eeutt+i6Dl6Dsiyp65rz83OePn3KJ+9/wtmDU0aLBaYsKbYb4ljSNA1JEmH7jqLYMRwOyUcjEJLy8orV8pa27TDGMBqNGI9HPP3sU/ZFQbnfczSf8ej+PW6LkusXSzbrJZ11LG/fZ6weMCsMiam4d+/bTOZjzqee5MEYff6I3/uD36fvC4bZKORO7gq2qy3OqnAhHPby0ocUYvcl4VWkOqw7qIF/1eheyoxfHhfCEOKXl5OX4yuxCHgMdX0VTjiHc2jtIeVg4XUZ3eaOjWmI85x9VdB1PaYz7LcFUgqaqsXhmSymzEcLrLXsm5btfk/XtSihQEDftSA8q9Ud09nvkCcZd+trjo/vkyQzEIYkS5nMZvjIsGglzhiM7VA40oPC8O7uBqUkzhqsNQGBpoOAprIOlSakNgR4xnHMZDjGS0cfeXa3Dq1ixnqMGsiAC/ee/X5P3WmUELiPHf7M8unTpxBrzh884PGjc0RrMa5nOIiwaY6KHD7SOKXDQiUFfddT1RWbm0vqYkNT7qiqHd5ahNdEwjGdjEmyGGf2XLy4pGyqkOuoNYujBZP5lPF4woMHJ2g9QkQRg+EYFUVoLVFaIaTCE+K2EQKDID7sUL2A1u0QTtEbQYRGHw614iAZ9kYgHDglMLGgrRv6wnB1ccn24Hnoe8XxYkyuU56Xe0ZpTjzO+fGPf0J1VfHmm29ye3vDYDCkO0SXv3hxw/vvv89/+M/+Gb/3B78LzrG+eM57H72Hd5JvffNbfOc732IwGLDZbblbLZnP55xqTdv3VG1F13bUVc3NzQ3Pnz9nu93inCNLQlH3k4+3jKcTjo9PePuNc4r1HV1X8523f8Dd8im//Y+/yX/wH/0+Lsnpyj3/8j/9l0yGOfuy42a1YrvdMB5lXK5vuHx+ye5ufXjfAqUaF95T94omGD70P98ZDCMBugzlmy+BxX6+G/CVLwwKLw7hCeEc5LaOfKoOBSUBqsXbQMdxRYGzFuUdXVujXUyU5/S2w7hwp9qXe6qywhhDud8Hsm0CrjHgNNZ7lJLkWYYSijweItDkgxBqkp8O8DtPFo0x9HTek+dDpNvR94S7ZBx2EloJBMGy23Yty2JHVTjsAX82Ho9RSlNlFXINk9mMdbrk9uaGeB4h+xEDIdimCX0fzD8WD2NL21SkyQDRdaje0DcWpRUqVoEPoHpkIrEqRkQaoyXOO0zTUBcVxW5Fs1kSm4bxMKXYrUmSjNFkjPOGtiqprGG1fQZOk2cxg8GIUao5OV5wtDgmSpLg9lPyhgPzAAAgAElEQVQKqYM3QCmFkopAtsswsiM9KNQaPJkXuBriVB8uYYf2XaDjIMIOgNDTlh6sE9SbEOdVljV121FVDVmW03Ub9nsYZQP6rqHu9kz9nPF4yMnJgjzNODlZcHl5jTEGpSRae2zbszpMtr5tqOqKySTUa1abNb33TIdD4jQhyzKSJKHvG/ZVQVkVbLdb2rZjNpuRZzlN25AkIZw2HwyIkpRqu2N9veT3/vl/guor6vWG6vJjUI95eDJGCk9agiVCpBO6KKb3NcJLBqMROo5o63BEtQewKodkbSnFKz9wsBS7w1ZL8mq3QA400EYwbXA7/wv6oTGw44v00l89vhKLgP/SSufxIVfQhXQVr1wIJCW4DU1rENFBceYdDsv+qsB6h04TbGlQKiOOY4piQ9cblPOkxtFEYLzFo4JKrmmYDMfMzmeMfUZRBHWgqhQ6jQ4tsRFMB6jOsN1alHT4vsKJIREJkh6pNTiLQnKU50xFEvzskUApRd3UJN4xyjJkpDlaLEjimH25ZfnZHXu/JRtkJGlKWYYzcGctWsQUxZJlFjN7o2Ta9QgcVkQ01Z44y/BW0ZuGVKbIXrAr93QH5Npmu+L57TV13zIeDUg0jHLNaJCQTycIETQVj86fYMyaxeKE+dEJ2XBEnIXg1DhOgzQ5GRDHCq1jtNYhMRuQsiInerX5TL0L5+pEknqHBOJD+8pjscKBT/AOeuOxzuGdZLur2Gw2FGVF2zTc3V5T7PdMxmOur665dpbhcMD5+TmtcFwtb/jBd75HZ1t++sOf8tGHHzI/OuLx6495+PABjyY5XVlycXvLZDLkzSdPUNqDiIh1TN806EPFPMuC4Gu3K+l7i3ISLRXpaMyjx4/Isxl1s0MrwWfX1+zaliMtGE3GKKvptg1KS9Zlz3vP7zgbeRI8D+91yElO1GvaThyg4AbjLKPhmKZrEDKiPRCz8S9vhAJpPTiPFXyhuhQQoodeTpeXPY8W1vyysIjd4eNvZgt9JRaBLySNItCRYwLTX8qAEu8CIdi5ELdkjOHAWQTn6YxBJoqurVm2hJ6ramjrGiEECdAKies9MpJkWQICPvrkIx7ff8Rus+Phw4chD8BW3D9PUXUECSH8oROInQpGImtxZK8CQoSIydOUzGuqbcNarhAIZoMhQkjKpiCOYvTRgGmk6LvQBhUMmIxG3Ds5I8syPnjvfYrtjrKquQK88vSdY9/e4S+GrJZ/xbvDDzk/Pea6a3jnbM5gmDOdTJjPFngExkDb70O8t4TRKGc6G2L6AuHgOB0zXxyTT4ak2jMeDxgNRygP4/l3GU+mZGqISDMG8wlIhZeKSEREWYwgtARfXlPCg1MejIUDwcZ7SwoBZuFfxmKB90EajFTUztB3jl1Rc3e3Yl9UrDc7bm/vMH3NZDKnbXpwnkGaUxUFu32BMwYp4d7jx9xbHPPeex/z/rs/4u133uHbb7/N8cOHPHtxgfeOe0/eYbO947vvvIMaZiil6dsW51oa33BxcYHSmgfn5wyHowCn2RaBW+l6rDXESYzpDHLoGMYxF7d3HE9mzF2IaL+4usaIjtvPrlgtd2yWK3745/83xeUn5HnL//Df/WuOFcR5zmySY9qGdeP4N//7/8Ef/fEfUzQFP/zwA37ywUcYQ4gdPgBGjdQB2Hq46/8q0e+XDUNw6AC8emLA80ua3yAuDuMrsQgcLA8gPJEWh5ftXymdPA7vJVKG9pIWDsvLdBaH0KEHrVAkmURgcEF7g9eexvlXqgqtFUrCarVkcXzySlcdJwlJkhLnGdY6BkcpxhqiJCIWEpFbrO0oyz1pnKIjjelDLUCKmI4GP/bE+wgnwk5DRxGtqQ9+/IQ41gwHOdZ1bJZrqqpBJpqmazm5f8J0NmW73NBUFVIomrah6wXGrGiblrpqD/w5x9O+ZDobo1XEw/MhaZaQ6Ih6tyOJUiItgvU3T1EPj4COzEQcL44CSFRYJtMR0/ERMRDlETLSEEM8iPF4lBQIrRFS4KwEsQZmIGoEGuEkrVBkwoH3AXFuLJYQgOEJBQLvQlZkUOZ5NkXJrmq5ub3js/c+xXtIBxmdDWCR1XobMhiSlM1mg7UO0/cgHG3f0Pct9eG4cJqcsdvsuL654pOLC6IkZTbLWa935IMJw0UKIqPc74mjiOVyyeXlJZ01DMajIPKSKgSfrlbkeU4SJyRpSpqm9KanaTvAUTUNLhZIJyn3FVpp9MDiWsGIIQPT8LOuYJhqTic5re9YFztmUQxJgosSpJB873vfJ5Oabem4ubvjZnl3uKOFyeA8CNkDEd4fioUHl7Dw6tDzD+j2AGs9TKSfWykcUB8WgAOE8NeoDb8Si4BAIFSMsC2dSLCiR4tARXGAHwgoDuonDTYCqoDa8sLj7SGcQRq6psJ0EnOg3+qesIOQhxCM3tNuG6Tq2bc7VusVw+GI1lkm2ZB4kGIRWOGCKCZSKCkxrUc4zXR+BqLDAdkgRckegcdZiY4TIpHQ7hrAkqSaySAnj2PiPD7cHT2+g9FkjtQFpmu56mrGac58OmMwyJDKU9wW1MLRblqqck/pLdo1NKsNo+GY7dWGcrMkj6Yk3xkRpRlWSrIBxFqhhOT4aIr0HXUZobxhNMwY5imR9CRxzGyUkaYeqTQm1iRRzMvutFICqSBSMphQrCGZLPCNB5fgTIMoJWYkcUId1ILBHmCtxWjC2V8o2t7R28Ad3O5Knj+7Yl+VFPuGxtgQGCok/pCebFRHbwyD4QwtNUW1Z1+3HJ8eUXWWpx99zN/+zftMjgY0VcIRkvl4hM5yoiRGSsHz5+/xve/9PmVhcWJHXzc4FXaRJ+f3cAriTKN6TxpBrSREEpWkREmOs4a6DgRmLyzD4Yg4URTFEm0ln374XlhA4oiT8YxZpulyzetvTqjqEY9GCbvrFdIIUiw+HzEezYis4vw7r+E3O+xqh0oGNK19VdH3IkIIhxAu3NrFF4tDmOwvd10EO/ehjKD5OS0RX3zRy5Xhl//15fhKLALgSWYN+hb2oiX+khsd6/G7L160tx7ffkkO6XyAb7ogROnp8X2oKQCH1KLDT/Ee48NRIgZuP7mivmt4+OARr7/5Jvkwo7WGWMekqUCJCKUCtCOJY3wX4SKPVClxHBBaaTIhOeQkOmvoupb+uA1uQyWYTIMS0hoLzgUCsfdoHXO7umO/XXNkPVmSksQxg+GE4+Mzyv0e7wXr9Zbbqwt2u4K+NVTVnlQINk1B30vurp9T7lcIMWIwHqPTENTSNhWDPGHy2mNwBmc6BvGBP+AMeiBRUYpUCXGSMJ5nSKexxiN1hJChjSeExxtQkYfGoA5UptZ02CxCF4K9lIhMk1iNsZ7O9Hgj6UyDdYq6aSmrUOzbbgvarg8BswLiKGa9XFJVe/q+5XhxTLHbsLvdkOqY6XTGbDrm+PiIi4sP2G479puCn/3sr/n6179OXLzDwx8sMKbHeMfDR49YLldsNhtubm5wxqHjCJyha1rG8ylP3nhC3QZa1GgcYU3HyckJ89NjVssVxfoWa3rm8xlt3XB7fUWaZNSm4+bqiq5tibUmiiKq7Q1709IOYKZT/sm//49JG0WsY15cPGdTbhlPvoX10HSOstjQm5xBJALvMAKEwuMQLzuEAPZlBFmQWQfDFYedQVgIhAi7ZiFeZg8Gs+bLsuHfzRkO4+9cBH5N8Mh/D/zHhOXlY+Bfee83Byz5z4D3D1/+p977/+rv8x+pbw+feHE464R3RPCr2hsv9dDukM/gQRwgCubl38Nk8z4D5wK+Sgi8CmKV2nt0XRMlKaPpmKoKqUcIBQqcixHKYR3gemZRSn0qiJ1AHkIshZBBLhvHCOtBRcRxQu89Dh+iz70nd46+tHh6qqbA9g6E5/S1Nznab2jLPVXdEOsYZw37omCQj0IxSksi5Tk7PcEbSdXsqaqacVUDnpN7Cz7+5GdkccaDB+dMZnNUHONNjzMdMo1JkgFSDFF0h2gwjc8sWmhimZAkCanOkEof6MwKIZPAAUTiNPQ+aNKtEUHXTugG+BRsbYldTONCpbtqCtrOUtUNbRuq/puiojUdxXqL82Cd5/R0wfHpEUJ5nOtxeKwzmNoxnB6h44iPP/4IYR33HtxnebtnvV5z/+whT954jZOTE2YPYl577THr9ZrNZsP8/ozhYMDTzz5lPB6QRyM27Z7d3ZJhPmA+nrL7f6l7kx/LsjzP63POufO9b35mbubmY3hEZEZldhVZWUzNrBZSSSx6w4IlAiGxYolUwAKxQ0LiL0Bi01KzKNErJOgSC6q66Ka6KjNroDIzwj3CZxve/O587zmHxXnP3GPIgWpaCo5L5rI3XHvD+f3Ob/j+vt/NjqKu6IqKrdbEScLE8/FCjyQNkTalb1qs7tBdhyc1bZ7TtBVCt/RthU9I5Edo27K6fEnXVPgP7zIfntK3NcNsypPDwTKYTDFWIT1Bmk3Y5isCLTG9ZXGzeret+3d4gC/veA8PgxH6Pa4hcfsz4AgWcs7g6CZ+3fXrRAL/I18XHvmHwO9Za3shxH8L/B5OcwDgqbX2X/h/8RqAd2+Eg7CCQ0s4rPntsseT/3jDO/orow9GfsRdS+cMLI1zAByiAi0xkgNBiCQMA968uWQ0nqCkYjAY0jUNtZREgY/1nY58IQNEX+MJn14beqHxPYmUnqueS4HoLSJw7HlSSfdlaENve9pUOKMsffq+x1rLMMuw4yGmbam6Dh+Ppsq5vHyLkpLMStpKkSaOxsvrDK2wtG1LU2tE2zO5O2W92+AJTbffIsYjhOmJQ48gjAjiEKkVSoAUEUGSkKUpVnYI4aFU4JB/QYBS6haEYpFY4x8YjgU1Ct8AJkb4nePR04BUNMJQLRsKavresFxtgBIpFftdTl6U1E1PGCi6rqNuG0ajEcYaitxRrU8mE4K2JfRhaiaE2QDbtGANTS9YLFe0bctkPGQ0yri4d49e93gjw6tXr6iqirKuefP0DZPJhPPzC1CWcBBS5WusMcRRRNM2SKPxhUBGIZ6QxEmC5wmMbpC2Q1oHUKrKnHy3dRoRuqepG0y1pd5saDH0IuXiIuXNbk9RbPDlGZMsRnshYSjorYcfRG7mxAtQvg9dz83NDcnZHBl4RNEAJ9fuznIXuepb7L84mHZ3OwxwiBDsO0bhhi/PCXyzA7jlH/7a+pVO4JuER6y1/9t7v/5j4N//Vdf5VSu+DWkchFgh6CWABK2Pf5fbfwcCRWHfFf3cAI77IPvedailPKQOBwfhdMMtxvT4JiDf76jK0g2b7AsuHlwwTDPqbc4wCbFJRBQeVYgqKjkkinzSdEgYOAViY9zrk55EaQ8vcF4fIVABYCpsK9BWoH2NCiAM3ZRiLyWx35EAQnlYPWJ2eorRGqM6inUBxuJJp74shCOMWK0WNHVDNsj4KH1CHAd4Knbz9Z7vfvcD8BRGODZl0zUYZYniGF85mLDFIox0BK1KutaUdEVX175z0mep9bFSYHqwfUBe5fRNh/Dg9dsFXhDStB03ixX7Iqc1PWmQst+7insYBJRFz3q1Jogj8jxH+Q5vsVntSNKY6SCgKAqMgWEYsaudkOdq+YbtRmFMSxwMWG02bLZ7TuZztvsdRa2pqz2rfO90G+qa4WjCZ8+fogiIrEAlMV7gsV6vqJuaNEkZjkcEUUjgSRQG6Ak8j0ooNqs1+801UrRILE1RsLm+4vWr5/R1zXc+/IA0sdybn/L68z2b5SWbmxHD3/k+3VDhISl2OxarBVZZzi4eInyPh4/vsa42ZKMRSko++ehj/uRPf0TXVgflqf7L9T0E799wWyIQ9pgUHB/1K9Y/3wGi/winSXhcj4UQP8I1Kf8ra+0fftOT3tcdEEKwtccihqsQexb6DjgQQrxbDk58lHDSB5oqeWyTHCSwhHjn+cR7fG3WdggjQQmsNC6/C6DralrdsFmvGQ4mWDpWueLevXt0UlD1HanvQ8ShTVmhhAfGqeoIQErfOZnOuHaYkI5mmxBPHYQ40wyLE7Ow0iMSgGkwvsvla90jpQdW4quAk9MhFksgBX3fESgPZXvmp3fQ2qnNJklMGIVOn84opHfg/JeSHuEANCi01mjVH/rQEmsM2hroJNYTGOk+FyEktTYoK5BSY/GxJmBTlojeAhHX1xuapiNOM67fXjM5uUMQRuz2JX4YYBtBXdfs93uGWUaWZrRNReF5tK2jQp+Mx0jrIY1it96hAjcPUpQ5q5WiqRsEsNvt8YyTa9d9T69yfusHP+B0foeuq4mSlHy342effkoSJ3i+R3fgktxud+x2e8LAI4wjBjZjd7nB733quiIKAnb7Dd06RwqDkCF1VRKqBpn5rK6XLNYL6v0OmortzRtCTzGIBD6atirp+gZjO/q+Y7veMDs5Bc8njmOW15cEgc/puaYsCwIsg0GC1xuMtMgEhHeYB7yVbzsaCe53e9i/8gAFsO/NDP2i9Y0dg29e/0xOQAjxX+Jez9873PQWeGCtXQohfgj8AyHE96y1u68+933dAaXUwc4FFoXEUrnD+7ZVCO4kN4CwTvTSWtdTdWy4x9fEwV2qw9CEqxUcw6HbQqFx5RYpBKHv0dY9ZVGgG0csMcgGVHXJIMscvVgYIoKAvu/pup7GixCqQeE5MJM9FCh9sFIjtDNkAF8pJAKrHBV63/YYbR0tuJCIwAPpY+xBKl1rlPAxwmKFM2phQAUeyvMxfUM2it3UmVKgLEq61qe0ESgnWS6lJDh2kKxAeh6eDd2GU9D3vSs7hRJrpcvVe0unNbkQhL2la127qusMT1+8oqv2eCqkbZ0OZJp3lNs9UTZktd5Q5RvGswcQuz52EPhIPJIkJvCdI6oOsnNN3aJij3QUs3hxhd71DNMUYy1N07Lf52BhNB6it5p4MOI7f+tDuiAm8H06DSoKaJqG1Wbl6Nqammk2RXkeFxcXXF1dke+2hLMxeb7F8yT37t9FaomQCmM6rO6oiwLTlkhh6dqGrqmoipzdzTX54oZ8t+XBvbsMwpAkCUnCECUF+90WbTRlXWGs4Ud/9mf85g9+wJ2L+6RJQp+mhIGP6Tuubi6JMUhh2G93lH3NfpnTF53bz7ctgIPlHs+uY3xv3jf+XxYBHHOJ92YHvmEU6bj+xk5ACPEf4gqGf+fAMIy1tsGlKFhr/1QI8RT4GPinv+517dGgrXZ56TEPwm1Sg0VZi7D8gjflwnA3gOSeaj1Au5P4+CR5OL0BqnzPfrlGyp40iLh+01MNBtRdQxSFZJMByijm81PiJGYwGBIEPp02R5mXQzdAcWzoSiHce7DudPY8z30RB2luz1EPY4RFSg9fHlh3UHS6wZMevh8jXeWTDomnJFYqZ8HS4iuBFeBHCmkkQjliFqxxDu6oRYnrqmBBSAf5ldZghOceq3zKsqXtOsqyxBqFRrBvW64ur0iyFM9L+Ms//yu222uSeMyHH34MCF6/fstiu2Hfd1xeXnL3fE6blwzGYwI/wJqOzz77gqatOJ3NGA2HGGvwlaTvWhZlidUGYQy7zQZfOsUg07tx5dVqxWAwx2Q1Z7NTsjSljxL2ux2joeV6uUGZntXNNcPxBN02SAGT+YxXr1/T9y3FdoWnNE2bMxoMyNJDJ8Xz6PuGqt1TbK7ZXb9B6AZpe6qyoK5KN16eeATEzCcjNuMRi9US4XlOOr7tiJIEGfhcLxYs3zQMxyNO716gjWYyn6FFT1tXLG6u6DYLRBxiihojPb747CWmad0XZd91s6zLDfi6mb877Y4UAV+nDzyCjo4WIbhVM/2G9TdyAkKI3wX+c+DfstaW791+AqystVoI8QFOmfjZr3VRa3FFPtxJLoxr+x3extG7SYsLs48QI3E85Q8dA4dOcfdp5arbUjsj4N3ljBHUdeU6MVIw1jlSDBBY2qZhZwx107CMbtgUa2hc1HF+9y6676jrmq6TBH5PFEVI+X5N1lX0D60LrHCDRsZKpxdnncysFGC0xRiNkR5CG6R0MNo8z4kS91lIYRHCw2qJUR5KOsnwyIZYadHNoVHcu/6yQCIltJ1jKnKsRr4TJMVgjUVoiUFihKP22uc1RdmQ53u6TtP1hqZtWa03sN4xHk159fY1faMxpqRpGpSU/PSLnzIIB1RVyXqz4pNPvou1hs1+Qb1vubx8y4vnLwg8D/oez1PUTUuaJfjSczDhoiCNY3x1QlmX+J5HYyxVWZCmCRDQ9RYv9lkVBaZuSOOYosi5fvWWf+ff/teYjSYgwQudcynLHCUhSxOm4xHKU6wXC/e3HicYremtoe8bFIa+ztkvr6ArSUKnoxjGAb4XIjxFEbnCKZ5iX7TkVYcAPOHavpPJGN9T3JnM8KRkv9vi+QFtvkMOHOhptd1QvX2LiAeMoxAvdePyWEe18r5yIMfd9H5Yf9zmX80Y+GrZ710d4ZAE/6ITE/j1WoTfJDzye7jZpX94gPweW4H/JvDfCCG6w2v6T621q2+88HvLHrHlx66AAGvUl+iTrbVHJCS2dp7SpUz2UBw54gKAozdFgk0Qbc2x7CgOP7Q2BL6H7ysMFqMUUobopmO33RwoxQKn7FsrdNuzylI62zFYDzi5c8pwOCL0AxAKz8NN/wmLlZK6r+ma2glt9A7ZqPwQgUVKgef7HPAhSE+hrUU3NVIaul5TtppELOh7J8ha1R2JCrAejIYjQOAJD6TGDyP8IADpClyBH4GEru8JfR8/CPH9GIREm97x6TWW3hhaXFS1ybesbvasl1uquqBpGnw/oO1bVuuC0H9L3/d4ynVDVq+v0aahq3uM76NCwcmdO45eTEh6U9F1PZ7ymU/n6KambipWqyVSKazUBHFINkhpy4KqyOn6ju16RZTE4Luc3eieXhf4vsdivaKpa+IkZvTgIV3bMhr6XF6+QRiHFWmMpq96sB2BB21dQt9y8fAR0+kAJTwmoyEIKPZblqsbllevaDdXzNOAQDkhGM+X9EYjCfCjGG+/JQhDRqMhSZpiEShfYdoSTUfkO3n0R/cvEKGgLnKnhal7At2z1Hsur66Iq9KxUk2njNKQdJgc7NvpBrnv42jE6hDGH4aHCByA6MAQIsQxzLcHJ/DO0mXvslFPWNdo/8XNgV+rO/BNwiP/wy947O8Dv/+rrvm1JV1R5IhWM1/xdrdLgY0t1E5tRfCeMzj2VsUBZ2DASscs7KAY71qNwiqU6oikhChAG0MUBDSNo/3abtf0XcdsOgOtabuGYl/yWV3jJwFhFPPx9z5mnI7wCfGilMCTDAdDBoMMA+T7vWOvOcwKNE1Dlg2JoogkjhiOHCnpvtkTCpdfblYrpO9Gl/E8UqtdtVzCYrnh4uQMLSEfDl347yv6tgHpxDiiJMFTiiSOMUbTdB2T0ZgoTgkSjUHQ1i27fU5noG5a+t5JrW+KLcvLFS+evXTKQsIShtGhGOVCyywccHIyPzRrJK9erbhz94wsmdHbEhEGNNIn8xRtWSOUx6PHj2mqiufPnqIxtH1HmiR0WlPlFVk6oCz2fP7pzynKAissd87OmJ2c0/cd19fXLFZLlPJIkohG15xNT/n4wycMkog4Drm+ekvkeXTGEEYRvu8k03rTooRGtw1R4DGenBIEIZ70KfKc1WLJp5/9nOXN50wxfPydx0RxSJyk+L5H0Zb0ncEPEvKmOsCwU6LwYFEHlGMSh+TrHfm+YDad0wpX07heXHM6mdAVOeuqoNrvUW1FNp8xnY5IkozT+Ql4HqZ3snPCSgxHaPDhYDt2BN7r/Unrpo3dhGH3Dj9geR9qx7tY4f/jdOCfx3L9fQtW3Qox3tIoW/uuSNK89xwONQS++hbtoS4AUONJ6IxLIOwhFLBIWm3QVYP0fbSxdH2DH3iMJ1OEheFwQNdpR07Z1FilkdZQVjVvPvucZ/kejEeYjUmigIePP+Dkzh2sNnRNz3a9ZrfbuHqAlGw3C5hkBN6YfGsxWrOvdwTSZzjI2KwXhH5AVVXEQYyfxiy3S0ztOiFxGCEDQVvVlHmJH/s0TcumqhiFIePhBBXE5EHpkJGBjxARSa8ItKTXhrqqWe929L1gn+fsmprpeAwauk7jKae3YKSg3W0IooDxfEYYpGyXN8RRQmd77kxPaKxAhZbJMEOkY7549QK93WKikHK/5/LtgvjJE4qy4Ga1Io4jToYD0jQh3+3Jd3t2ix2ff/4Fby8vKYocz1MM0pQ0GSK0ZrNYsl7cEKUp00FG1zjkZRj4t2Pjk+mY8XhMucqpTcl8NiZOYi6vCubzKVkQ0+oWpd2IsTQ+XZVT7Rdcvvqccr3i4v4Zg3GKH4YkyZAgCGg3Gms7Aj8gSzIMTm8RDFXVkMrA1ahUiPJ9x4plBdKLqLueutdogWOo0i2hNERxSJKkDIdjND6n56cO8l0372kMHzkEzO0+d7u2PSbG7ncLFuNQnYdHHNdRevBdw/EX5wPfDidgQFsfrCPuODo8+34uY0Eal+67lN+8mz4Ux2LoMSVw4ZT7kJTzksdKohBYNNaCloqjUob0IqIwRpjKDRIpD2McFXWe5wgh6BqD6iR+FLJ6e01VFlgEk7ZBZymvXir2+ZbQD0jjlCzxCf2JI0uVkqIqscZS7wp6v8NKi2ccueTy5hKle5qu5+ZqQRzFlEnEsxfPOZ3MmZ+c8vL6NdPhmLbt8fDI9wWbzRotBH6jafLaUY0nA8IsRRqPXe1ITvSNE04pm5ZtvsciaKuOTsBe5aRhTBwnJFnCzesrjAer5Q2ffP8Tuq4hG4xZlzn+6hor4M7plHSguF7cYNqepBuwvr5iWDfYJCbwAjwlaeoaJX0Gg5RdvmO3lYSBcMNXAvKqJPBCZiennJ7dAQzjyZTZdMx49IjNZse+qbk4P+fenbsUzYg4Dmmbht5ofKXwDtLps5MxZefjGYnWDfEwJlZjmlFLXW5o2z3b/Y427+iLLaLZY5uS6zc3fHw2IxtmSC8gDCKC0CfwA+rKFUyDIKBunFTbbIivCh8AACAASURBVDp1vIRBzDAbsdvvaDvNfDJnV/dOPyCOGA4G1E2LbmsuL6+pi5JRGlHmDVdX1wxmc7LRgGyQsdlsDzm/Gw76WpcAHCb4oDli4fD4/svmLb5+5r/vOL5pfTucAGCtgz5Y9AEH6JbwcLyaSGwfgq3c44/Pe+8a+jAReBsOHSiuzbFQ+K5gipTCbSLPQ0pBGPpkWQKNYL/LkWGE7wfk+ZLNdosKIgYiRosKz7ZMxxPu3L3A9xRVWdN3htVyxW6/4+75OdPxhDgJ6dqOxTI/YONrBhdngIeVEqE6V09QElRAUe2pmpbNdkvbdiQKsmRENhqhrWW7y8m3FVma4UmPtm+wWkDfs9guKZuG8/uSs6FT4qmbniLfIqSkyRtXC7AW6SnEQTV5lGWIwCeJU+rKUaS1pmcymJAXe3wvYLdbM5+ekGZDwiDg+vKaP/+Lv6DY5VxfXzM/OWFcTWnKgtYPqOuSyWBI4Hss1yt26w3rzYq2a/B9GAwTpBfgBRLlCcbzMePzGVY7tF7f9RR9xSQ44fs/+C3SyQhPedx7/IDtfovtHWQaowkDxTBLMX1HLTrm0yl93VObBnSLChxGo6ta1ssFq+vXhELSFjuwFl8qjK5QnpOfFyp0NQurUUrS95qur/F8H2MEnh9y5/Q+dVNTlyUnkzNYb6nKCn92wq6ssQLGUUQcRpiupaw7tts9dVWRYEjTGb7noRRIIx1SExfiu50r3Yl3aA/eGrU51s0Oe9h+FR34fnEcByo48Dv8/yIdOC79PsuqxQmtWDfIYt4TX/wmWaXbt3oomLhLaNeu/0pRRBuLkG7iTRiFte7LsFKi6RAiZjDI2G5X6K5DGEN2dockSeiNZn52lySOsG3LdlfiB26OYDY9ZT4/xfpwtbhxgprCQymJFQrTQ5CFBH6IkgeyON2z2S7ZvdxhR4q6aUiyAel4zPjuBbvdntV2R77dc+fOOb02GNtTFjXZYHBIO/as6oIHH34XbQRl1dBryPOarmkpCke2mo1HTMcjJIK6rFksFqTZACUtXd8xmUzo+p44jvnwo48cqlDD6vIa3bQEQlFv9xTVnsXlFZdv32DIaUzPdDxms9+wK3KuXkHTtkilWNzcUBQ5p3dOSJKI3b7A89wJazFkwxSNpcw72s6QpilVWfKzn/+Ui3sPmIxGBFHEYDoiDBQy04yGKavFkrrdkV2c0bctTduwq2tiKRFI2qpkVTXopuby1SvyzWvK3Y7hLEMpQ922BBEEgXAIyyBESN9hOYx2Yt9WYoT7jpSSZFmKUj1t37gu0m5HHDj4cdN1mLrGBgFSSLrSsiu2dHVD32o22y2JkqRCIQ88l9WupKpqbg1YWjea8U1H91fwwPYYKH/p9I8wx5zZAlrgyffGEr5hfSucgLUOxuvinQanoXS4U7sf1rrAPgRKcXAC5hj6C6x2UcCBpey2pXIsEn5pCTc74HneLdQ4GEg32WYss8mcQTIAwPMCsixjPB5x/8EDOu3SFeEFtD14VjEczzDWMp3OOL17QTIcUeVbFlcr+qZjPJkwnowRSOIoJgpjpFK8fvmS/W6LbXNev3yLqTRT/5ST2QnTkzm99Hn+9AvCMCSOUrrEkFcNaZqQDcb0hWSXV3RGMpzdIRHwwUefEGUZeV67AmCnqZqOIIwJwwRfBbRFQxB6DAYZZVGyuHzJs6cVdd1zfnHBR9/9hP1+x2674fLymsFggB8Nuf/kA5ZXVzR06M5yNr3L4voKYQXSaF4+fYbWmhevXzEcjEgHGX3TMB6PGAxSBsOM9WrNzc0VaZri+z5WuyJaXuRgLEk2QJie+/N7TM5mdFajJxPKuub18+fk2y15vWYyGNPkNRePz1gvFg6CrTXFdovVHQj47NOfsVkuqYsc2WkGIdDknAxO6BUUvc9oF+MrgzQaYV3XqDM9QhiqukHrnq7vkWGE9CTNvqYsCvq2I/QDOrtjPj6nvHNGNspoDcgowg8DdChYvdmxvVkwGAy5WS3ojGYwGiCVoG1ayrKk7RoEDgJvrUQdjvvuF8TwArAB2F6Bed9jCG7hRMeTVIhfOUz0rXAC4Aoc0B1Ocqen+v69X1qHI19gXahv5AEjJL78FMOX8igJWOkKhFLKW7EPIQSTbITpDVpLut5Qez3COiWeJEkYjccIKSn3OV1vQMQMEkEQh0SDmN54ROMxfpwS+RHbZkXb9eR5gRf4RHFElgxpmgaEYDIeUe73vHn5kjDwuLpe0HYtH3zvO6ggIItjVusNm/WWIEgo4xbPU+x2OVGcEsYZdbSm3NVkgzFWKeIgwA9Cl/bYljhywKa+1wjEQVXITTjqQ1oQRiFffP7MjexOhnjSoxaCxWrtJiWVYt/tibqMxWbJeruh7jqEsHzno49oqXn67FOsCUA7bIW2AnrHkdg1HXfOzplOp1hrWC4XvH79kslkjBLSUcZ1TuDVV4pit6MeDJilKcMupel6J/yie9abNU3TYAWs12uM7tltMyYTd/J5nkfT1NRlTlkWvHj+Oa9evKDLd0ySCDkbk4QS3xOEWYwwIXGyBSmo6pa2aVBh4ijGlLkVIvE9z+E6pEIIRVmVdK0hjhI8oUijiMl4TDoa0DU94ygiCiPW9Q6NqyttNlu8ICBKEhaLBWQJY993XILv7297QMvclgSO7e539CAHUzmAwo62cryGvtVyQIjDEN4vX98aJwDA0MAEuDr0QTkOFLulOQQIhvfmCQ6Gf+yoyONjD/3Ww+ejDg85jigcEX7xOCIloCoqpBD4QUhXVdT7CryDs7A9+zxHC0nX92TDEYPYIw0DEqmojOBsNiPMUqIgIa8aN5e+zVFKkhcVTXvNZNJyOphjZE9dFOi2o9h3rLs1nTG02rDPc6y1tEXB5XJD12k+enTCIi9p247p/JQwSui0IcpGxGGAF2SUZYlE8vz5CwwOUj0Yjg7aAB7L5YJskLpZBmtpu566bqirmu12x8Xde8xO5iAV9E4duKkFw2zI5fUl//erv0BJSV4UVEXFyXTGy+U1D+Zz/vgf/zHPn7/g7Pyc2XzGZDRgNMgIowiER9HU9DfX9FqzKfdsix1N3TDIYgI/QNkOgaCpO6SAJEtY52vq5x03N0vSLKOzFl1VKCk5OT/l/nyO8RVhGHJycuLmSWrQU8NeCfq2oS4L8u0GWedEwwBT7TBeRl13xIGPFB7j0ZwoHlI3Gt13+JGj7O57g5AexvOwrUFrg68gG6QsVx5F3qOUR+D5JEnMfHaKoSf2AwZpTBYFrGtXkxJC0Gu3h+RswGdPn1GdzJicnXByMudkPudyvT1sTnsb4vO17r9byoFND7D4owP4ZiDAr1Ikhm+VE7CQ4yC+9QDs7jaUOcosqwN3uuD9fMiFBUJYrHzXTbitDRiwVqAP0UOMpeYwcmwtujYUsuPm5obxfOy6BmHAJE7wg4CiyAmikPl87oQvuwYvUSS+pG8aXu9Lam2o6o7RZMp4aKjrisl0ztmdczabNU3dMRwNiYcppjds1yvaImR9c4PuHLvu3/qt3+bxk0d4UvF//MEfsF4s6b2I73/4m4zm53znN+/y9/7+/8R2VxKnGRf3BR89eUJne66vF1jZUFUVU5xSclXWvHrxEt8LKPY5y9WCzz//jHE2YXZ6ipdECKCqSsbjMXmRs1gtub65YTSdsFyuGE/GPPid3yFJY/bNnqsXl0Rpwr3kPvnNirfX1zTTKdLCg7O7pMMhfdsg2p7o7JQ0G5DnJRd3zijzgs1myyDJePToAVSW4SAjSVLyMufVm1f4yuPs/IwwTSibhk7Ak+9+RBzF5EVOla/RWtA0HaPTU2bDFBlGLnfHUPUdvtNAZ311Q766QXQNo3jAcKCYz+fgxZStxlgFyicbZUxmpygVksQeVrZIGZLnFW3b4CGou468LDmNT2ia1jkEX7IuNmAywihk6PtUTUVbd/hhSCeFk7lXkmE8YF1uefTwER9850PuEzIMfU7mp7Qy4smTD3jz889QKMBDiJavswq+Q//34vZIg9tI4HgKWkQg3rGM/Brr2+MEHGoY1pDaioJ3SEJ4B3+UWHzkLVzgGExZFwO724SLAHxl0fLQKzxcoUEc0LzGsebiZKeUUrRVS11XDMIhlagpq4rpdMpwMiJJUnxPsr+5IYymDNOIqqpZ7QuGsynT6QwVRJRVRb7fkSUZWZIxnUzJ8xIpJXGk2Cx2XF1fo+uaNB0xqHeMT6Ykd0+5c3HBdrVGeSmnM8nZJ9+lKzXXmwXnDx7y8NETsixlX5bM5qdoJNpIPC9EqZrBOKPrNHm5ZrfLWR0487JBirWW1XpNXTUOdWgn5JsNRV5gTE8YBfz0Zz/j1ZtX/PCHPySNIxQ9L589o7jMGX0w59kXzzg5P2M0n3BVO9Xf0IMkCHj44D6Pn3zAF8+/wBMSGUVUVUmvO37y1z9G9A5yO4kTziZT0jjFWEO+2eEpnzjNaJuG9X7HvfEIlKKsGvZ5zngywQ8DJvMpum7ovI7eGPZVTeb5WGkwVlDTcnN5SbFds90uafM9oyTibDxiOHCOvGkN+3KLN5oDGq01URziRz7SD52BWbf3+r7H9D3aGkzXY3r3eKQEZegbTSMql1opCUaShAOEJ5wKlDFOwWk6ZFVsSKKEKEqYjU/wbI/n+YhbEJvLXw9cQV8yjVvo7+16P8vv4bafdnhM+O4u8TVn8vX17XECx2WgpAMCCNtbcJC4vVt8iS3NWguRdLl/rUELrJUIaVyFwUqEdgVDI8BaHyvcnPoQ6IWD8MZxjDEGT0KaSHphaOoWKwX7fU5RVNy9+D5Mc9bXJUmgmM2mRIMRnQGikMTzqPoaq3tWy2uKfEMURXi+DzagrTRS+oyThJ++ekWSpKggQ2vDNEopi5q27Xn4GxfoVhPFQ0xgKIqWy+sNJ2d36LoOvV/jBw7pWBblrbPsPEXfNKyWa5Y3S26WN4zGIy78u2SDjPPzc7q2p9gXB17/mKausViUp7i4d87di3P21R4fn+16RxSl6Khnv1yh9wV21rJ4+5ayLF0LDQGeq41MZhM+ffYpvYWgD6nKirKq2G4Khp5P11S08zneMCPOUvwgJBmMGTc18/M77DdrhJKk2YDze/fwlEIKQ1lV1GXJKPQZjifEWUwaRQfiWfc9VW3LfrejLktev3zBzdVbQimZjjPun52SjZ28W924Vmnfd8RBgO5LpGdAG3pt6Kyl15JA+TQWdG8wJqLsGhptCIKWwTylvyoJ/Ig0iZCBj+40QRhQlTVaZ4gwI42vSZOUcrenamqqgaQ3HaazGOmk6qqyY7VaH3czXzL298oFCgcDvq0D3tq2htC+oxYC3An67hq/THgEvkVOwBAgD+btAy0tNG560B6REdYNx1h5ZBQ6ZEG1huYYMLmczh7mB4TtHXX2bYWkxRPQac1OKagqxM5HCEUcx0S+oGpLrm+WtF1PnKWMxnNOT07puiWzdI6abnm73qF7QzpIafue/bKil5KyLLh8e41Ak2UZ1b4jL3N6NOlgwnCYYroGQh/rK4wf0lUNl69vuL7cYel4+OgjFtsb8l3PhxdP0KGlbAtO72Ts99Apn6os3cm5WvP61StGJyO61p1WAsnp/A5RGpNmKcMwpawtP3r2iiAOmIzH7G4WNF1LXpWMpxPOTuYoIXj5+hXXb19RVS1t2/N3nzxmURd06z1/59/7d/kHv/8/U7x5w+DRPeqy4s/+5H8HL+NqvWD7j/6Qm8UN49MZqkt4/OAhf/RHf8Tq7Rs2SJSyDMYZw8kIGwfIWYZX1iyerkBr4niANgppPegNs9kJSZK6ToWVNE2NJxtkBYESpGlKGPkIT2GEU5e6uXrDcnFJuVlxMR/zyccf01mfk/MxZdGwL3KM3tHVEfSSgQw4iyI6o9l0LWGU4FufxkqqtsDzI4ZRSN0r4iim7yGwJUWxZjpJyJIRWjR4QUroBeyv1zSjHi/cMxydoPqEVbji1ds3zMK5I6PJIkLhg+p58fY1Nzc3XzeIrywNt1ODnjzMHbkdDs3BKxzKAr4N6Whu/UAG5L/k2t8aJ3A83yMCalrenwx0y7wHBDC3TvIYTBl7YGY4VkeFgBREK3GqmEfHajFWEEmJttaRk3Dg08dVcrvrklptUTYlDEMGs/Sge9CzXJZUxZ79ZsPAVyQzn1A2SB3QNTXKCxiNR3RNSdM1qFAxSSZsd1u22y3D4YjhZMBkasnSAevNlsZzgNHtdoWvfPwoZiLmbHdL0myAiQVlvuf16xU2HHM6GnF1eekkyoWkriruR/fojGU0m9O3HevNFltayqKg7VqUEtx9cBclJfPZnIuzcxarBU+/eMZ2t+HFm7d4StBVNRhJUdUURc7l27fUfcd+ueTeRw+YnZ+wXNxQXF+RRDHCRAyTjC8+/xTVQ+D5WCHxbMQm2rhhHE85FGWWcOfOGcPRkB7LaTolPRuRqYyXzz4n3+xRqufRow8P3QQ3ZTmOMxSSqlwjK0Uyi1w9p2/pe0mvHftRnue8ffuWap9TlSXxNKMuCrx06q7j+Vwqh5qUwhKGHiKJMKGPbFuUtdiux9oWL1SE4cCF/HVNEsRkacZq48Z+93sH6Iqk5tw+BOUYsKLBAOV79H1PKAKEEGRZRpamqLxgu92RTu5ibUHbWsrKTWT+Ost1txy78PuW876JAHSH8Pl4T3GA0P+ieODb4QSE010wgoMDgNtCh+CgteZutb6B7lAiOSquaBBCY41xxROh3KeVC7RypJ5OB9fDGksgoDMuB/N9BXVDrC2Z7qmkoQo10+Cc7GLO7PSCpuoIPYiDmNDz2G53TE7usM0r4o3i9F7Ipm5ohKIuO9rGUpY9bVOSpI4TYDi4QzaAszvneEqx329JopQmbKn2Ja+Xz7FYiqrkyXeeMJvNmH7/BOkJBtmQYZDwT//kx4zHe5Kzs8M8Q4OUkvuPHtLrnt46XcB8v2Of75BK0rUN680K3/cZpBn7fc7bt1ckaUxZ16RxQt912G6PpzyaNuc3vv9d9vs9P/7JTxyOwRiKouDN05f0uxJlBKI2YHriKCMME65f3jAYDBhkA9p9xeiDjNXiGing7OwOWRajtcDzPB7ef8BmW2Ja6yrvbc94Oubs/A5Yy3g8Iklius6xK3uDAf54yGCcYnTHeBDR4bPfl/TGUORrQmlQxZa3z35GU5W0dUnTNrR9TRRCr2DddWgrMI1P1SlHKCMEpveQBvzec4IqnkEJSKOQPjBsNjt63eIZjdYQS0sUBAhhEdYjkB5REFCalm4Y4wUKjHYUdgq2mzXz2Qgde1xfLbh/r8aElpVtePvTK8xWI/HoZe/GXo6nnQ1xpuv0AiwchgLeTxuOI+w+1r7TenzPtL4iTfr19e1wArgX+o0sqRbEe45Sds4j9of73nEluA9FW9dNkJ45gFgsQgQIqwmtoZWSRkCvLEEcYjxF7UnEIKXxPESlODtJGQ5H4HlcXl6SpinTkxnT6Ywy3/Poo0d4WrDfbDENvHraUNUtpjX4BJzNTxD+Ha6u3rBY7EAYav2SsmqZzCeMB0PGowmfvXlLfnPDcr+nL1oeP3nIb/72b/OTn/yE6XTKYDTmZrliOBjj+z7D8ZAoClkul+x2O9Is47uf/Aar1RopJZ7tuby6wmiNtYo8h91ux363pNgvKauKx08+5tGjx2h6Xj59gRGG2XzGF08/g5FgEk95/vwLPv74Y/723/5X+fFP/oJHjx7xW7/9Q/7sn/4puu/4l/+Nf4UXX3xBUZWcn59jleQHv/FDFvUCOsnby9f8+V/+BaenZ9x78DG9zrn/8AFn53exWrPdl4wmI65ulnz27Ble5KGEq2+EUUAQBlhr8X2PR48eQTDC9BvaKifLZsShJExH7PKCVy9esl0uubl6yY/+5P+ib2usdQNGk+mUKM3Q2lKUNc+fv6Tc7nh8fk4UxfieTxgGRFGA0h7DaUZRt3RagrB4viQNU4Ig4epqwU1eOREVP8UPAtq2pmoamsbgKU3q+QRRgPAUTeXGyD0voDOah3cf04aGblthLQwHGdv1kr/89K/Y7XfI9zCux/hW0DjLiN1/pnHDRV826KPFHIlKBSQgKnHbMHy/9PhN69vhBA7vW98KKP6Shx7ELMXBxR3bhLcTggcSDXPkGsSiRIcQkvLoEoU4DJ1It6li6WqzVqCFdgY3mdC2HZPRkDANGcYDnj59SrFc0AnBJx995FiDEcRhytV2hUVxcjJlk++xumO/y0F0ZOmAhBhPlqzXa5bLBZvlmrauyfOc6XSKjDyiKMIYi6wbnn/xBWf3HjAcDpnP5vi+z1//4T/C9yRXV1f87u/+LlXVsq8b6qal6zviKEBrzXg8pm2uaZoroshSXlmm3px4XuF7knEYkgznNN9x8/pFXVB3HftnOy71Fffvn7G4eU5eCh59/DHfefwY64dEScyTDz5hcX2FtjHDiUeEZnT3gvsPHzDYjPmrP/9rVBgxjH3Ozu7x4YcX/Oxnn/Lm1Wv22x3JaIyKQjdYc8hjt9dL7p6f8eCD++x2W4yxt8Kn2SAlijz6fkiXhNTNDmNiV5gUgjSO+PnNW0zfMRokNFVMU9WOPsxThEHIvmyoipz9NgdtkL6H53lgLb2u0X2JJ8IDYs9ijCAOEyTSwZ77HZXWyCJnn1dkWejQpqWDB0vR0dca4YcoP2CSpjxdrQnTFK0N+/2ei4cPGGQRrZcjJXRW0OKiEXcEfokShCNiQGCR1bvW+FfN5rgkrkNmcQ4AQBwovG0Iov1nmB34BboD/zXwnwDHisZ/Ya39Xw73/R7wH+Nc1H9mrf1ff9XfeLe+7AAi6VObQ8NTgoiFq3zClxBVFgnCIA/kI0Y7NKGUFiEVvekRSHzlIaRjdLVKoD1BvlvQNyFP7j+maTrKukTGHhykybqwYxyPiaKINM3YrlZgDG3bEoQhdVlj25qu6wlD3yHarAChXG5Y5UjlMT2dM2wFb9+8QQrL6dkdlJQYbRiPx/R9z/PlElTF5OI+Q93hpynzkzu3HIfnH99h+3RDnufMZjOstbxerFCBT2/cVOVsNmM8GpPv9vg24mR8hnxkubp6ixKCi9N7aKN4c3lJsd+7z9LAYDAgikN22xLfTwnjM5KB4NGjR5RRxCh1egVCCIzV7PNXjP0pN7uaIRd4nuJkfoLUn/Lko48RNEwmYy7uP6AzhqvLS6wA07f0+5pV03B2fpcH9+7xBgtNjWkb5vMZuu+o6pLZyewg1mLwMoVXRSivJ9+WWL8BA560vHn9klAJMJrJeMSNaOhzg6cUou+4vl7z6ctnCCSz0RhrHL+iH0X4IkIbSSd6jNW0bcem0Q7eG4b4vs+YPXnXM5jOKKsWY9zeMOxcGxTXoRhIjyQI6KTE932nwVCVaG0ZjEeIKGSQpoQmwFbgWcHu8B1IwLdO1TnE4CFvi3kGIASh+TrD6HtYOTgQ2xzvOmbWv1h8CPib6w4A/PfW2v/uS69HiN8A/gPge8Bd4A+EEB9ba38VfPkb160DAHfqlxzq/0cuFcfHciRflkdAhTBY7bjj74wmnM9P3Je7c6OkvVX0tYXe0NuKsu148eIF1goS32d3nZOGBSoNMFvBTX1D3/c0VcV4PMaTEoOlU5L1ZkNdlWTxkNF0itYcYK0rPnv6KdvtjkePBfPTM7Jxhu8pjDHM5/NDsdFQ1y50/Be/9z12VcWrzz/n/r2HJMmAvu9ZrVZEQYjddoznCf/62b/Ej/70n9C2LW3TEwyGNG3LaJAxHA7Zbdf0fU2WSmy/YDqdkEUJdV2zq9Z88fpz3rx5w3w+4/T0lLquCfyQNB3w+PFHtyjJrtbUbYMuct5edZzOpvzVT37MX/7oR/RE6PwzglHAdHbKZZRhrWB+fsLr589QgROTff36FUEYcHIyp65qgsDn3r37aKOJ4hhjYTgcgtFYT7qTP/ApioK2rrkqSwJfMZ9NaJoaY3v8KGRxfcXr55/z4vlfY5s9i+0WY3qGWcL6xhDHIbv9ng4YT1LuFmeIQBGHAdJTeEFI3dT8/NlP+fTnn3Lv7rkjPPF96rx1LVgLsrNEYUSaxtRlxfxsyjgekIYR2/GIOEqoq8YVdA+tZhgwH3RY2fF//vE/IRQghcJYy5//+K84m45Rg5irTc561SGkg3E7El1oJDTmHYGgGHpOiav5ihkF4gCJP8YEHubQNbMAKVC6X34ZaPBvpDvwS9bfBf7+gXD0cyH+H+be5EfSNL/v+zzbu8YeudTWXb0Ne7iIojQiLRiwIRiwLj75Yt98tQH75j9B0FG++uC7Afto+OKbD4ItURqRssghe5meXmrJNdZ3f5/FhyeyurpnhkOQgtBPAVWZUZGZkRHx+72/5buIz4E/Av7fv+bXP/zU078Pj/zBXCkhiB5JvAKEJEJttY1Pnj8JDmgE5xdrPv74Y37vo485X60ZToYf2/2eu809u8Oe2+0GIRRV2zDJS7SK5qDPnj8jy3KCc9iTQq4QAucsdVdx++qGIs9YzOcoAY8/eIdCz9jubtluKy7OLnj3nXeYzApevXrFxeUT5otlFKcMnr7vGcaB/eGAECIi2YCvX7zg/n7Lb334ETc3r3n9yWf80R/9EUWWRpXjPrYPQ9/hjCOTBQhF4h3paBltxD/EZ0/x6Pkzmrpif3tDVR3p+57cFVxeXrCYzVhNlnRuoCwK3nv+nH4Y6PoOwsC7zz+ibUa+unvN0/cuqL94xSd/8ed889XXZFnJ+mxJV6Xs64YXX39FCJL9fk/bj5TTCUVe8On/9ynOOKazKXVbU6QFxiRsNrdcXb9mvb7k+fPnzGcTEmOYTEqsHZE6pSxKjFbY6yP77shOJYi2J2QC13XYoWMcGq5fXuOaChEcy/kUMfO4LwYSadju9mRZgp6UJJOoCp0mkSJupEL5gLWequno2pH62OPQ9N4zTQzTvKDrGqq6ihyHAKEOHL3Au8g1yJI1h74m0zD10gAAIABJREFU8Yq5OSLlOYPdoguJEQUff/wRr19dgRCMWtO0LX/yp19y9u4zWiEJfhfh7ae3fQpkPsbugyBeOJyAQN8PiyFgFLg3r7n7TvRQPxDs/upo+9vMBP4HIcR/Q1QS/h9DCFvgKdGM5OG8ON32S+dt34FfOkkc7s1GwR4Q4qEGinXNQ1UkeoEIHn9CC4ohkGjF5aML/qOf/Cd88N4zlouC6XTKOI5IKfno4x+deu9oOLrd7Hj16op//dM/oWk6rPaIE6kjLQzLcsXV9TX7/Z6LszWzAep9zdC19MNIkZcYL6mqDfvDnlcvX9E3De+8+5Tl8gxE1NXf3G/Rk4ggHJqW65sbTLLCuQP7/Z7nz5+z23UYE92BV+eXtKNgc3fPo9/5Hfqu4fb2lvl0CkHz859/xQcffECSGm5uX6INnMvnvN5uSUOgqSryPJKJEim4e/WKZhg4W1+QmSzutJsjdbVntlrx8tWrN05AdW25vr5BTRQvvvwMNQy4vqU+NiDgvQ/f4/1332PTbXn1+Uu224o8v8d6wd/7e3+X/fEAAT74+APutnfsdjsm5QQjTUTcBUGWFHRth5SS+WzGfreja1pCmuDyjLIsSPIEmymGTaBzV4SgEJ1kdJ6vPvuMw/4WFSpMcIQ8jSvO3STOakxCmqYMo4e+jyapoyPROio/NRY5MUwmc7xLaVtoDgdUVkYH5qgeFtsRb0lTDYNhlJ6ZlojckWiJtXsSu0QcBd1yzjgOBALWOaSCdx4/ptkdUEqSiYan77xLdbhlvT7j59f34MOpSoiuQwMnfNzJVu87hmRvDQYeNgD210z7DGAtOB1pFfJvUwn8mvM/A//k9LD+CfDPiCYkf+3ztu+AEOItEQFgiEX//s19NUJEjXwNdIhThnMwRnDQ5cWa3/+9v0OepHRVxWKRoaTnuNtR5DnRBDTqEkwmBYk2OO8wj86ZLxdc393z6aefYtB0XYeQgsPdwKQP4D1GKb7++psIBZUl73/8Ll3X0dUDLz7/mte7Ky6fPOMf/NE/xPUtu92WwfX040DXWSZ9S3t7w6tvIuklyzKYHJjJkg+fvsvNtubDHz1nMvkdPvvkc7z3PH38hLqu+fSTv2BT1+R5Ttd3PHnyiJ/+9F/xs581jKPjxz/6Me9+8Ft0/YirD5BnvHz5FXdXNzx9fM7tbotVgsVqESm2P/85XddSTkqSNKUeRybTOUpqfPDM5kuOVcXm5R1PL55ye3vLv/x//m/+wU/+kH/8X/zn2NHz9deveO/Z7/C7H/8hf/pv/w37ZktiUtIyY5UnYOHVl9/w/vkH/IIvuLi44NnJOuz69oamrlmv12w399HiW0t2uw3r9YwsW5NmhmAd0sByOaVvFIduyzi09NZxe/0VX37xF9x8/ZI//Mkf0I0jaZpi6zuKNOPi4oKLi0f44OntSJoYDoeKLklwKlZ1pUg5OzsnnyQc6g3b/ZGzNBKMDocjRaYpy5LpdBr7+8MV/RFc8pi2hvXZiiybU7kRfWbo24Gu65jOp7x++Zonjy7IJgWz2QxjElQx5/kHcy5mBdvB8c//+f8FEryPzFJBrGgfvEYIby/34hBVCEHCqUoQvzzxFyfmoD0xaLX4qxAC8fyNkkAI4fqtH/q/AP/n6dOXwDtv3fXZ6bbfcH7Fb/OdMxKXIzAQmVmcBmEIz3w25bd/67c4Xy/jLEBJUhnQQlC3Hd5aknSOEFF88/7+nhACaZ5BkOzbFpFNSWcz7q+vaKua2XTK+skZB3Mk3aSsViv6uqcdK8YwkKZPcNZxdbihHo4s5nPOlwuwHf3QkaSapqro7chseYmQhtQ6nj55zHQ6ZbPdMjgYR8vmWHN9/ZJBDHz47DmT6ZRXr17RVg1Bwocfvc+PZyl/9u/+ksNuQ9sNlJMoVLrZ3nC93ZB89YpH7zzl/NkThG94VS4ZB8XdvqeqKvq2ZzFdsFis6UfH7rDjg0cf8uTJE/owUmYTjDLkueBf/tu/YGJS8jwjz3O++eYbpBSszs94/PQpx6rmz//yM3SzIZ+WLJdnpHnGbDFls9+zmM8Z3cj68QVPnz7l/P1z8jxjPp9HEY4sQxtNMklAgG1HJFCWBVIaBiEphUQIjx8DQ9PRtXGT0dmWMCquXr6kP9aUeYKAmPzrgV+8eoW1NjJEsxKdaRwi2suPEH3/JFIZ0iTlUFURgqcCo3VordDK4RqLCnF6P51OEFLgg2QlFXLquNp0iH3P+WXJZDEhMwn9sWaSFySl5uJshbWWXCekqUGoQNs17LZHzqclN1f3HI578AEh5UNgxX++V8Fr4uT/YeD3NkI4B6Jouv+27/dvL9sDC2D/77sSEEI8DiG8Pn36XwJ/dvr4/wD+VyHE/0QcDP4I+OPf/B0f6pyU7yiJPhwJoQRRveEQxmGICBRZxsVqBUpQjyOu61E+cKx7pB4ZbWB7rFgag1YGM8nBCry0OBsnz2PTctzfUB+OhOAZhg633/PiWKFNHPj0fUdapggVe/rPf/Fz3NBxe7snn0+YP3uHoii4ublnHC3WDnEroRTBWabTJbPZNNpZ73bUuwOOQF3XXL+65oP33+fzF9/wWfMVv//7v09Zlrz86ks2+x3/6o//BZePHnN1dUWapuwOR87PHzEMPb31aG0YcVTVgSzLGIaB1XrO2A3cb+44Ho+YJLYZh9NsYD6fR+5BGzkETdXw4UfvkaQ5k+QLJkWJSRRlWdJ1UT5tvz/iXCDPS5IyJzMFvR1ZliuyMsUR+JN/8+/46Ecf8PjsgsvLy/i11jGdLqibjmGomM0LFoszFoslrbUMTUu12USBU28RfUfjHEpH0VnnLc72cUvgJMU059U3X5IKR5kmzOdTmsHiSZgvVgxdi5SK3o5Il6CMjG1QNqGuozSZ0iKavmSGNEno6wrnRqRSZJkhYBkYybwgSQxqMIhSsb8/ohvF07N3uL9+Sdu2mGnGGE7GLSGggiKZTqnv7mF0FEVB8AGHw4uoKBSEIIi4gpQ4fAgnMpF/854XJ+7Lr5yqR5d52gAaR5Dxvpw4lTFOY7zs+FuChX6N78A/EkL8wSl6vwT+W4AQwp8LIf534GfEtv2//2tvBkLgRAD45eOB4wMb8CSkIOLEczLJmS+muKHjeHeDkprZbEZQgpvjPYsi58U3L/nyq6+4vLzkx9mPqduGw37PkydPWEyWlOWMn33yBY8eP2EtRpp9xU11ROkJi3XE3w9Zj2gCH//4Y3bbHV988QUqnfLbv/OUssiZlVO2hz2vrq5YTC+QsuB4fMlkNsNZT31sOBwOFFkWoaSzCX3XY/uWvvfs9nsmJuH8/JzdYY9zDqWhqo4sVgvW52tevH7Nn/3FJ0yKgp/85CdMJhOurq745puXvHxxzeEQte7ee/4+uyGwv95jheVyveLi7IzV42fc3t6ilOH9999jECMazaOLS376x3/K/lBhjEFrTVakGK25u7vB2oFUJLz/7Dm19zSbLW1bcxcc02nO448uKbOETz/5mt/68CO8rfj851/gPvtLEpNxvjzn7OKcLE8wyTlnZ2dMJiVlUXKW53Ez8OwxgWhN7oTFjpa+bhj7DrzFLxqm7RQ5KzFSkRpJYSSX6yVZWbJYlwyj5+XrrwkmwwVQykQcRzHnuN3w9Mm7kX3YHEhSgwuW3vcslnPuuoa2a2jamuMg0SKarwitkEZSzmbsjg0vX1wxnU5559kzpLLcXe+j5kGaMskzEIL6UJMahZ4Jjndxa3F3e4tPDe1hJMwMi+Wa3/3d3+Pm579g8D6KBECcR0XyS4wJwZsK4aQWQxAF2AFKh+gF7o240NvemyAoQdRvxdevPv9efQdO9/+nwD/9Td/3l448rTveSgLhpKQo3gAqTv6DgJASbVRcqXQ9JlM47xiqlqrref68ZJqW6BEECu8c9b7CW8tiNif4OPm5O25AGIIMDKHjuK/ohpG6aXC0vPPeE8pyypeffIEOAfqeehgjRFlpDocGIUJ88xyPrBcLFvOSujoyzya4MZDlJeeXjzkc93gblWvHsWEUisfrc1aXTyI+/3iMME/v6dqWcLIxu766pu0GtDZ8/PHHkUnoxigfZi0QwVBlOSFJErbbLWMYuT3eMptNma3PaKwn1BtkoVmaM5QxmDHEK2a3p7M1+y9rynlJ2zXM51POzs6oqiMiSBaLFUUxIXdwvT+glaDaH/jm5oYnF4/Z71pub69Zrlbs9oGqvcFozdN3HrGYz9ju75lNU5xT3N3dMgw1WfYYH1L8OKKVJE0Mgxuhj6IwShuUBG9HRAWik6QLDTIgBBitmS0mhGFEGMHoPUIoEhHLf6kUQgiMt2R5Fil4AYLz1NsK8oJE5BRFQpqAUhIhAipY8I5uGFBGo7ymHyL+Yzqd0XUtm82OZhxBCuzYEdKUbrBIGUhUhlSBsAfhBG3XkUymJzp5HIhqVVDqKWPwpPKt+vc7qCDB28OylNNQ3DRIrwit+I5oiIDv7AKFawk6Mi35K67F8tf+z3/ok/Pto3kYjCS/4n4iEMRIpgWX8xmLcsLxUHF/c6Q5DngpGX1gGEd8YhlSRy88vbO0w8DNzS1dZ8nSLOrI1Q3b1y/J04Iyi1JibWgxacbFxSXBBV6+fE0/Oor5kvtDTV3XTCcFSinS2YzpYkk5mZOlBYvpnCxN0WmGNwmDi0F93G8ZqiPHzYHbqxusU5RFiS8KKtvz+uaG7e5A1/fcXd8w9AMIzXq1JDOG+69fUWSSi7MJaQLlbE7V7Xn56muevf8R7334AcJrFIZD03A4HAlNS3U80ltL23QcX+2YmwlPHl+itY6PEdjcdjx59JT5ekLwI1evX9LWLUmac/VqQzFfcPbuO9zud9zc3ZIkCavpkjwx6L4jhJFvXvyCfqhxrmW5yPjxjz7k3aePUd5xuLvj5Rdf0TSWYlbQtB33myPNsYN+QAWPFyMOG0tvbdAqIQRJyBQmSZBIROFRCqwoSJRiUiQYk6CzlGZs8dZiR0cvKnBdbP2SBAvoPEOYBN+PKCEZvGNf7ZmYwNxkzCdrTJIhdASKeS9xg6OrW4IN2MEjpGGxOqOu2yi9dlLITrMZDoHQHqM0Q3BkxYzUGPJySu2huW5Qg0W6jiTLGXFs+nu88nHTFSReCryMOBcvTh/jY+uQCnoNVorYzvrv+A1FfU0hePuP0Q/BBAjza0PvhwEbRkDz7TDD5IGhATpxSmwaIU6ZLICSkiLPyYs8gmiajr5uqasjxXTKbLHg7u4F+HOWkws2dzu6vmU2n3G3uWez3fL40SVzuYzwz8LQXbdst1sGO/Ls4l2MMQxjFPTIfck7T5/S9x11fSTLU568c8nt7SFy3VtJ3VZMlxOkk3RtQ5YlrC8+QCnNMDqCCWR5xu6uZgw9KkuwfiTPJydxiRAlvVPFi5dfU2Y5Z4/OaLuej3/7t9ne3/HTf/1TFosFx2PFn/z0TyhnMx49esqPf/y7jGPH5vUVNy+vWD1ZM5+UXH3zDdZa7m/vwHlm0xmDt/Fqa8MbP8FmOHKsjnz00fu8fPESk2j2hx1Xr19iw4jte95//10Wl3Oenj2NGvxdg/eWxCTc3N5wcXHG06dP2Gw2lGXJsydnDFbw4sVLxrZDC4PtRobjwLQscdZy2O+YlRlJnlLXDcI3aGMoi4JEJ7hEst/c048dWgqm0zVJ4hBWk+c5q9mcoigYB3tSgeoAQSZzHl+syKSkGhz7ZiCdJuigqY9H0kka18Z9S9u2TKZTun5EKc3YD7RNh5Fxg3A8tgQkPgjGfuB8ueC6KCjLEq1SEnXEKUMmDeUsGqKYvCAEUDplmhUsVx1KgNGKEARCCXbVjs3dPSe9N6Q/cWOVRLzREo8Dcwkwfnd2LoQABcJL3ijzvl1FAz0PWoO/kpXz5vxAkkCUSQqnBzqeEkLETj8sBr/9JYxUZCYlKIf3Dm0ifbVvepq6JjGGRM3iCzhuWK9W+CAIWPK8YOx72rYjyVq6rmccbfTU84K8LKOldwqz+Rmt7/F9dHlpmhqpJYvlku2uwXqHUtG/oMhyUplyt7lDSliv12RljlCGzasrqqpmOZszzXOcyVitVxiTIISk6nuO+wNt+5pgUtq6YqILqrpmcANSKpzziBCvTvP5jPPzS1waB3ebzTXb7YYvP/sc2w80vo2qOFJQ5qd5xf0tu80968cXTKYT2q7h888/Z7/bMS0LgpBMpxNWZyvUzzXqpIY7LVLydI3Wis/+3Sd8lf6Ci0eXCKXJywlhDFxd3zCdTvjggw+omorBDuyqlvXyjMV8BbORJE1YLJZkeZyJuBDQRlE1DZlziEQQnKfvWry3ZGlK8I4QHI6o6gwDQhqa6khZGFZFEY1NZnMyKenaiP3XicGTIoREaQh+fKNKLdMUoSSZ0UwnBQaLD1vu73d4D9okJMYhvCOxCimgriq8FDRNi/M967M11lqSJJCFnG4M6EzHn6cUJjP0w4g4CsTMIVON8mBMijLRhh4b9SCwnje+u8R1tDrdcHJ7+5WLszeLQwHQnoRDBA/ehSfljRO+1vBrjQj5wSQBeBgKxllIgeDIiRaEonvDnRIClJEkmSEJCXme4ZxDDoFu0FF2uq4JQmCMQfiGWVGiEhDS0HY9eZLQDQN+uyNJUtq6x1pLluW0rkM4T5LOadsWKSW7vifrO4augxBIM0PddRwOB4RzlI8e0bcdbrBkaUKaZ0gC1XaHnOYUZRHLM2Po9MAoBHXTkmfQtx0IcG4kSTxuaKMGwKxHec3F5SOMTsnynLOzCy5X54S5QHSStu8Y+p7Xr19xOB4QSnJ+doZOE+6bDUIJ0iLDpBpHoO1b2rbh7uaWF19/zeZ+A+PAbXVkeXmJ8w6pBLPVGmOmSKe4u99wvryknE443O1R2tB1PUUxIUkzvHPc/HxL23YsFlFa3egErQ3TyYTJpEQAu92WrCyYzGbkaVybJULixpFu6PBtpN465xhshxsavHOMfR8t1i1YP6KCZLu5JjETKpOylIpEJFQu0A4Vo3e4IOnGgdkJequ1wXuPVJK8yPFYjElItDrRTonbHDfS91HLIiYfR5qlhGGgCh11dWDsc9Iswbm4hkQQBUeTBKMhTVOEUqigaBcQgiMrEnwdUadKKmwAG0qsc/G+3kcNzNM5aWCfPvs2BTww5/0DUcgLpJDfkSKN4qMBRVxpxpixvzKRPJwfUBIAFWnYECoeBgMRtfWtUlr0EQgQAlKBMlEeLEJmITGaIs/JjKauKjrRR9qo0WgVp87L5ZL148fUhx1plpOZjH4YaZoKEk3Ax4oiSejamqHrsUlCnqaUZUFw0SCjKHL6qub+fkPf9SznM+bzOUIKOue53W7pr695/PQZ64szwhjoa4fRnup4RAqB9IKqO6KlQOspz949ZzqbMvYjpJ7Gjhz3e+qmZTpfUK4WGGl4sfma46EiSBhsT11XPH7ylGdnF3z62ecIIXjnvfeojzV13XJ2cYHQkq5rub26Znt/T55m/O7f+wOev/8+n3z2l2ituXz0iC+//JrXV1+TqPeYz+Ys1lPm8ynv/sO/z9fXW9q2Yr06QyUJ27tb3n/vHThh22arGcEKijRnVkwplxP6vqMfeqTR9ARKo5mWJcZIlISxj/yI+/sdQ9+iUkcfBK63FJMco1NUqkFHV+X7mxckackinWCyjCAESgx0bYdSinG0dDbQ7GtsmmDShGGMQa5M3P1LFS3i+3ZgvzvE8ZuQNE0DTiEEjMIinWSxLCjGnM31LralPpqQDsOAt+H0M3tM0Djn6OueRTajHyw6GPq6Q/rITjRJgsVx6DrsEPUDvBAnl7z4Pv+eYuCb44gEIaFOcRAENkjCA0ZARCUuhfqWacsplL6j2/3d8wNKAhpjLf2pfXnQTpdenNxXvz1SCFAhutgSBUPH0UdiTwCtZGR5hUBzrLnb3HN5ecl0NqM+Hnj9+pqL9SUiJnJkEfX1jTHoLGO0luPxSJEXZElCahKSLOH+9p4QBOMwcP60ZDad00nN5n6HdVC3DWmWkRUlD2NeOzqqY42QKX4IrC/XjL7BbkeGsedsdcZclvzsz36GRNBUFY8eP6L2B7ZXW8oy0HYHinyBcwGZGOquxYWeQ12T5wllXrBarxnHkd1+z3azQaYJT56/y2K+BBdiEElww0izi1iBxBjqtuH65jV127A7VCRJ+ka/kCGyJa+ub8iLHK3TKFh6OPB3fm9JnpecLefc3W0Yhp40SyIkOTOszs5OrO1Alqas1yuU1mSJQQWL6zu0SAlakCYmltFagVP4wSKkIEsysjwlSIXwHu8sDsnQeYwJWGlJpWJUFhFipXVxcUnXHBHO4ZSIJjPGUB0rKo4RR2AtRhiUiu+X47FhtBbvPPv9gdTnTGdlBPGEEMVQlEYnCUmuCENMeJ7oslTMcoL3yCAjZFkEahnFZV2Im54yTUm0IclyGtkjpaBruzdGvAqJP6kFPCSCBwlR+Pb9/4ZROAqCE9/Z/IWgQYQ3VhsPtPmHD3995P0QjgBFQo998+jfpkSI78mNuODp7YBykqZu0TpDCkWWpXTtSFM3ccASQCqJEOC9o+16xnHEmIzqeMRL6HWPJerCj86TIFitzqNX4TCSpllcz8o4SLM2MFjHpZDYcaTve5wdmM9nzFYr1utzgg+0dUeSZFjrGZqBzjRkeUY5zQnkyCSlOlRxOCd66qoiMQlVfeD69ppcCMxixaAHknxC1/ckZU4+LVhdnrFcr5jOXrHd7Um1YVFO2Ox27HZ7hJAYnYCX5HmGHUYSYwhacuwGTJpFl6XpDClUTKZBYLKMuqqZzeY469h20ULrdrPl/ecf0NqeYjLhd85/D5XOcG4ghMiKVErjvaPMStbzBSozHPZ7wFPmGYv5DCFO5evYYvsWHxxSB6QUKOlJ0gTCiPQAIbIZR482AhEUzo6EoBj7jsQYlEoRStONPd2+ZrvdMV+ekRjF2DY4azFFdJqK7kKBvuvRSqGEQClBUJJmaLFjdGnyISCUgFTE1mQYEEqQ5RPOFlMIkuNQ0/UdXghGe2QYUnCOxo8xoJWib3q00TgH+6bBCIFCo7WhdT2b/Z62rhEBhNJY7yOSkZg4HvQxvq8jBAoaf5oVhAidD+FURcT2pxUPsKOoy3kSG/i14ffDSAIBtGhOxYrmJBvy1t7z2yQQgGFw1IcuzgO8IwkROaiVIckEh6qisz1JlpOnBdpIrB0Zxg6dGCZlRtVV9H7kUB0oJzOcAzvGJ/bycs1iMeFQHcF7bD/SVg1ZUVAfW/Ks4Pb6muPhgLCOoBTZZMp0sSQvJoDAOkGa5MxmCxCC+XJJOZlwrCuC82gUF+dL7m93VE3sp2fTKW3X8Mmnn+BwzDvL3/37T3nn+XOqpuH6/haP43jco6VidbZGJgn1/ZbFbI5CUHcd733wAcvFmrvdnrubW8os4/zRxckJZ0eQIqrWKglKMoyB+802tkTLJY8ePcJ2jm9uf8GT9RMmRc7RVaQmpT22KCXYHhtKNdL3HecXF4xDz/6wYzqZUU4X+NCjBEg34nxUC3qY3/TCMQw9zfYe3IhSEfeRpIY0KWG0eB/lvcfRMp58AI1UhNHRVEeyPKeYTOiGkdF5Pvn0U/78Z5/xH/+n/4i+6+h2HdO5Q0mJUBKPRwGDHUm0iYHjPXXds98eyQ2U5YSiLOnaFiUk2iQE7aOakJKs1kuGfqTrO0bnmU4m+MUF3ipssIzjSAieLE1BWrwbaMaW17e3UDe89+QJBLDCcdweqI4V0VvjWz3NgOONDLkgzgq+M9OLPnvBRwk0L0REC76JlXjk6W9/0ub0fxuw0H+QI74FS8SRhj19HDco+O+qIgQEY4BMCLwI9HYkdJCagDbREabpWtq+RyAZB8tghsj3Lif4EDjUNcEPIA0qSTGJoUgMWZFzrCrGkx31fLlAWjgeD3z51Zc45zjURxYmuvuoNOC8YBhHum4gSYeTAGbHGALtsaIoDFJF0EdVVVSHI3mW8WiyIMtSgntwoBUIq3j27B28Drgmag3sbu/Z1keEDNxdX5GmWcS9OMcsL/BldMJNspRRRNHSLEtjPz72BAJBCozRTIsC5x3jOODxsQdWKlZGw8iQZeBc9PqzG86ePmW9XuBTuHtxT98dub4WfPi+RxQFEJjNJgyDom1rxranyyqS1GC0wojY3bpw2uQohdMSO4rTPt6DIzr/ahm3B4lGB804jljn8D7q/ksJox3p2oYyjepR3sf9+O6wP/ESCqRw5K7A+RF/UuUUAaS0KJHi3EjfRejwbrejaVu0TBjHkb4bUFKRaI3SmqZpSLUiUTK2HLnmmCRY5xlGe1Io0iR5QqEDjAEpBH3fI3AIB/M0ZbvZYMcRjSEoiXPhpJ0bTtqADwraAiXByvCtouh3OECRpah4aJNPW4HvCQY4IZDhNAP49YsB4IeSBE4oKYFAhO8ZDXy/ihGCoAVOBAZnkSKgQqAbPM4HMhGnwUVRxqlp8PR9FGr03pMoTdu1mCxlkhUxAKoKH0byRYFJDFVTA4JplpFlE5pjxaFqQBmK6RwzyZDeU8ymgKNph7jQFCC1wpgU6yyts9i+J0kCm809IdxzrGr8abB4OEbL6sPhgHOeumoovEHMF+hEcztuub+/53i3pxpq0tTQNDVKmViKDgOPnzxFak1nR4zRTJYzRu+5ub9BFwU6MWgVB6zjMDD2A0mRMl3MGNoBEQJN21LmOV7qB50m5udTGnvBRKVxxy1Set2j8jkhVCgF2kiSxCCVpyxyHj06px1b9JjGIZzWSKkhWGTQ2HHECRv7ZwGJkvQnK3LvLUOIQJkkTQlCxhJXSnAeZ+MQr+kqRjugyxIbAl5KnLWRp9AOBAYgwSw83g/0w4gZ4wXAqAQpJaMdcaPFmDhEtGNEZlZVjXWBeVkyOA9dzzgMBCuoqoo8SRh6h0kS2mHA2jRSWLREEO3e3el9Bif1ImNITEI6mSCJswXt1AO+AAAgAElEQVRnHUMYYjUS3toDCKI+5gNYjvBLZfyDh0A4JYC4TXsQ2TmFiIxIQsephv4NRMIfRhKAb7PdQ/XvQSGQLhAXHLFkEiJOCYeuJ4wWrRVZoiF4rB9xAYoM8lMAOOsiqy9J6Lo+koGGnvlsQWYMvXNsD7cIPSEvC5SRZEmGHTxSG6q2Zd80SJ2RpyWgmU1z7q5v45RZBnSSoJRCKknX96QmQeuCSXokSIEUCU3TY50lz7PoyKsTNpv7iDcYBxazc4bhyCQtqROHMgqhJMvZEjs0vHh9z9h30ZxTSN794EOcNjg3oJKCrCi4WMyQk5x6e+Tq5StkkjAGz3q9IuA4Ho5U7ZFZofGEmBydJYyO2WTCkAxcXFzw6PKC4/FI01Qc1JbPv/maH733IaunT9jvrnj55TV/9Ic/IRBNO6vjgUdPnrBczvn0089IU41MkwgFTjO8cHjrsEKAB2ctQ9/i7IgjMPgR4eNV0J6guA/Tbx9AKAk6wniruwopiHBepTBKcKyPPH78mCwtcN6ihMbagaKY4f1I2wpSnZColKAUTdsi6TBpSVFEXEiqI0JRG8MQAmPbM4yOPE9xwbM7HGA+p61arPRsNxuUSRBIRC9JM8dt27M4P2cYonns0EftBK0Vo4tK2EbD2FqGwRL8afr1/VLdE6W3v10D/FK4uBAIJzxBxAM8yAdJgvTRfUgIpIjP4a/fDfyAkkAgrv0Usc95uwR68xSchB6EC/gQS8yo+BOfTCUEwT28yXp88NhhIEkKtFFMJpM4wU6TOCMIgaaL3zTJUgJQ7WtmiwTbW1zqcaMDIZGJZrFaoA8HGCFP88hf9yOJTtBKMbYdzaGJ8t7TGcv5LLrMnNqSPM+ZzmZkWUZdR+25s9UKk6y4ud5GPL7sEEHFNdNixvP3nkOwYBSf/+UnVFXHcjbDJJpykuFGz/PHj1k/fkKu4P4XW/bHLe3Y8Wi9ICtL7DBy3OxoqoqAwDpHW1Ucdlv6viPRKecXayazCWerMwiB42HH3TdXFOuC41Dx6sU3XLz3jLEbuL27Yhw70jSPQWIddVPjjSFNDUFavFf44HFCIqU6kb8CQki8izOBcehx3p+uaAPBSZwXWBwm0dgxysTJB0sJAkMLYLEukJyurEmS0PUVOkkRQWESxWS2RBCVfk1QqESeQEoe6yJXQYgIihIiJvwiLyiKgrpuqJuW2WSGVIYsSxDeYoxmzBU4i7WWutozn645VHs8KSZP0UrGVss5jDYM3pMZRV1bWmspcGgM3ga8CCdIj432el7gxQkNEBwifGumc5oEvPEmjivNGBwPpjvmdBEdwkP+kAynr5B/xX7gB5ME3lzsPW+yWNRgjSgoKcAFQXQjdLEtCBFlNoyWREdiiVQCqRR930cWGJ4kyej7Aa1qdvsNZV4i3ciYaHzIMGZKEIHQ9wilqeuWJElo64YxDDTbmqqpePpoQZJE7wJrLXXtGIJjksQiFC/I8ylJEhV0VGKYTkqq1y9pmo75ZElwcNgfCSKwPj9jkmVx2HfY0TYNu6uGcYw5O5tM+eyLT7FDT+9HmqGh9SMzEZ2Pqr4FJ2hcTWlr2taz2d6zub1HSkmWJkyzFF2U+NNkfCAKZjS7A3Vdxx1+MmKOitz2XF1FFearq8jL9zjWywlnz5YkcsSNPT9+733sOHLYHU8aABC85Yjk0dMVAY3ycd3p3AhBowIEaQlSnchCGjuA6wecG3F2ZPQWbz1KSUg0jLE6EAKkVATvaVykFIsTinIcesq8xHmH1S3D0KLVBJMYhj5EkBCB0Q8EncTS2Ti8kwyjp2sblBaoJGpVKiUxJkUQaeiHfc1aaebzGakRdF2DDIr5fIXSMWBFKkgSxflszu3mnrzIkV1g/viMY98xnZSYJwaV51Tdka4aokCtiPODeBEPaCRdUESM5MM27OTA9StFAgOEh0lCbA2QPgqSKvGd+sH/+hzww0kC4tQH+AeOgAZpv51pBCBeEk6bg0AEYAgB1jESWWVSCnwI0WxUKUTMF7jg2O52lHkBAcp8gu1HopuxwbtA13WEJMH1PcYotDaEvqc57Bn7jrZKQBSoJBBSSb2vMCpBl5IQLJnRzGYlToAdPVmSIpVCK03wjq5rcS7aks9XK1arJdNyQlXvWS6WDH2DUor9/oDSkua25cVXX+K9ZbFa0tQ142jRJmHseq5fXBEkiEzx+voGozNubu6ojxserx8hXeD+5o7pYk5ZTlFa09UNzbFGB5hNSmyhQBo2m1uKrCQ3CWfnl+gk5fEH7/Ds3cfMFmvO3ztjmixo6orFfA4eurbBjxV5MSMUBdZb0rwkEQkEz/F4wNkRvCd0MGYRFaqkBD/gupb2eMQSMRPWRYC8VhrbnCbgMkEIh1I6vo50CHQUlRUCayMkuEgKXOJo6gqZS5yPa7PeeZQdkBIkFqUVChnZfy7Q9WOkTqdRrj24KCzaZSOjdfh+gNkUrQ3aSHSR4BtJUSQnTL54IzAqhKRtapx35EKRFzlowVhOUMSWtB/G6HI1jjxICD2g/uJMJLyJiFPXjyTCrL+fCIQ/NQKndsK+dbUXnlg3/FUAAd6E2g/ofBf5cJp8ym/3pPINcRoeSiMfYZ/O+6gj5yxIKMsZWZoxdANJkhCCx3sT1z39gJIDLhPYdkSoEZ8ppDIkRhN8XFtNJgUaQXKpqZuacXDM5hpUz3wyRbqoeiOkYjqZMJstGexA0zTMp3MWywV1/cDRj/jtqjpix5HSWdrTKmq/P8YrVqGRrWK+mMV+XY2448hitmA+nXLIcyQd0+mEr776it5a+m7g0ePHbHf3LJcXmCApi5Sz9Yqz9ZzdvmYYRvbH19xc37Pf7Cgzw2w+o5xOsH7EWocbB4qiZH12xqPHl2R5HiXMF0ve/ehDBttxtlhBsHz18iVjP5AlKUZ7CmHJkwSlFc2hIV8kBMTJRWhkcD1+GCMuw40o75Bu5FDtT9uJEKHDLl7ThpOW5Og9+A6twSQJRsXONtKt3ekqTwze065cqzifCS6gcomK8ze0NtGgRWqkE7ENUS7S+KVAGoORmqEbUZkCJG13QHmwIURGI5IsMfhREPr4Os8mEySSEATeC87Oz+I8RXi6viU4gTcaF3xcdU4lSa1R2UPZG3gA+nrpoxXXSTjwIRy+rzT0ZnYmvm2T4AQqeEgeD1oEf40s8Df1HfjfgI9Pd1kAuxDCH5xUif8C+OT0f/8ihPDf/cZHASe9hLdEEdzDsjBCIePv6d/kgHB6Bt6UQT7EDGsHsjQlNXEXrFQEi0itIDgOxyNFnuHDhkQvkVqTZTlWxSvDcragKHKS0+1FXtAtOu7ubunalsJkNOOI7yyLyRQmU5brFR5PZyOYKM0KnDFsqz39sWK32SAEzIslIZfIVGKyhE2zpbUNzb5CC0G1q3DCnQw4PNeHLZkLjEPH9VULQbJenzH0jpcvX3N2fsZqteRYHZGZ4Wy54tmTx7x8+TUyS/FS0g0DfddH/vvhyGI+4513njKbz+j6npvbGw6HitmsZLW8iGYZdcV8OSdNosiJcx2b63sulmesVgvSPGWz3aP8SDnJCH0PwZEnGdLGlkFpzXpScGg76q5h8C3jYaQbeqSLBJ3D8cDxcMCFB/9IImtOSpxzHJs6ouekiGrEeUlbDygpSJKMLniCd2RZgRt79NCjM03TtCRJQaJFhKJbTzpN8S5WDcZoXBcly7q2Bu/pmprN5p68HNB5iVYJaZLhrKXuesZjRZ7OkbrA+SNCRoSpDZHq67ynHUZyPaEoSu7u7mnbDlCMfY8QgmHoMb3CGH1a/3mCDCdtixMB2J2sdk9FwVtCxHFd/p05YfxAkeBwp1QQb4umvjF2JN9tDb5//ka+AyGE//rb4BX/DN5oggL8PITwB3+N7/u9I05ySJyYkeJEjHhrCHJ6k0Rzj1OpJAQhKFTQSCyDdUghaNuoRphmeWT7OchKTbAe6z14hbYwMSXZJGV/dCjpEcEjfKDr+gjT1Tk+gUfPnvD6q5c0Q8c4BlKtUVIhjECmUG/2dJ0nm81ITYIhRCCRg7EbSBNNkkSz0XySg4Drr24RqYIgaNqG/f2eyWzCfr+P5JSupxlHjseKNM14/t6HrBYrnHMYnSCFwpiEqmq4fHRJliaYJGEyndE3DaMPOO84HA740WKU5P0PPuLj3/4YaztevnwVmW9ZztlqRp7meB+4ubljuVyyWq8oJyWbzU20AO8rynLOfD5lUuRIHE6A73ucGhE+Qra9c6fXBpQ67b2HPm4Duo7RWZwd6YeetmtPhqJRJs1ojbWOvu+p9jUmVQTnqIPHTQf6fiTxEqlAjp4gFW6M5jIOie0Hhq5BKYPtIHiPFR68RokIQlIqir16H3A+mtbgI3zX408ahQYhFQhP1/fs2oacOQKD0hqtwcloaOPGWE0J371h85nTzEIqSRgtSiroBFZapFfMyilSSRjtSSbeg5QEBWL4NvjfCIYI8caHMDyAi11cS7sTFoNRIM13BMZivP6GyPtb+Q6I+Aj/K+A/+03f5699pkADwQWcfOtJOGXAECRRk7yPieB0+xg8IURegfc+ssCcw1obJ/3SQgix37SWTJuoB5crEJq63mKMpms7hBMM3hJCRdV2KC24OL+gnE6od0c610bQ0H6PShRKa4b+BN2UAetapklKnuX0dcNqOYuwXSkJLmIX8izH4MBGS7TtXcUsK1gsVtzMrzjcHZjkBbfVLav1OWdn53z88Y+ZT+ccqiO7wwEpIscdEUVVu7bj5fVrDvs903LKarVGhMDYD/SqY7fvSIqUfDahPXoeP3rE+XqNB9qm5X53S9vsOF8/ZTqdM5lNTnp9htWqQCkdiVhac3a2jL4MztEnHaNtYfAR2OMdPniUVnQuagQiPOIUhP3QY+0YZzDEt7UdY2IISQzOruto2wqtS3CO0dloMNr3KDfQdS1KGjrnEDiKLKOYzvj/mXtzGMu2Nc/r961pT+ecmDPzzu9VFUVR3SrR1UhYYGHQOC1hIOGV2SYSBm201RZuW0hIGOC0MFrCB1pIOGBUoxZGFTW+4d6XNzMjYzjDHtaIsXbEzfveu+8VVdXobimVGZERJ06cs9da3/f//sMUIkpZSonEmNCmMkkhVi2/EkrOWGNAKdq2IaWISFUAFqjty5LXMR344DnFwBIizrnKAygFrSvbzxpLTAlDDVxdloVhGAghYEQRWadeOqNLpbFvhoFWKTzlGQIkf9MCPOUFlPKheuAb1k8phvK0+KmVBPqbr3iaIkCdmD0NHX7Z9TfFBP4D4E0p5U8/+NwPReT/omaK/ZNSyv/+6x5ERNCi0BT8VH5p1JIudTZa5wW+YgHPZuoVpadInRI8kSikGkNMcwXccrY0zUTKnocUOe86xNUR1ng80jQNh67h408/I4RETomfffUVog0mCT/84Q84DBsuwxUxV/Zf8gu2aeiagYKi3/RoEZqmp2sbXtxcsTGK/eHAPB+YRRFKx+GwR9MxjXtKity+u2XYdTyc7rk6u2LTbeiM49PPPuc3fuO32Ox2fP14y/Hr15zGEdc0tSJwlt3Qc/nRR9gCf/6Hf0rTOF7cXNP3DmWv0NrQdT0lZ3LxfPmXf4IxLRcXF7QXF9z+7B1aFCUWpjHQfNIQ/YF3byeuzm949eJznEsoZ7G2qjehEqM6p+k6YToJwYRK7EkRnzLiCyUGck4EH5jHiegX7u/vVx1GVdFVX/wa/T7PM6XANE11jLcszLmiu6f9HmMMfdcgGbKqU5roE23T0Pc9+/0et92t7YWpGgNl6tgupxqdnis3oR16rDG18hLh008+ZrPdMcfENM3PwT5RgU0T++N7nO6ZJ0+IqdKRY6CzFuUswXvUGkHWaINyDm0MF2dnHB4OiFNoI0xL5LScCBlqYOAvmus+rdm4uvH/PN/nyWSn1A/YinCS75AIKKCnJpr8kutvugn858A//+Dj18DnpZT3IvL3gf9JRP5OKeUXUkY/DB8RqWqvEutcWCir4eKTpHC1GNEFGoHp53iQApRMyvVU0cqQcy3ryBklisFachoxZoNttoQ0Y6yu1tfa4oMnWcXWKIqq8mTjHJebHSEl2iIYrbm4vMDHiHKOMz9x//oduWT6YSDlp+dZDSZirG/UbhgQEU7v7/Ap4RrLaY64xiI5cphP+McHlmWi6RwvX77i4fEeJZrtx2dM3tOmzHSYuH17x7Yf+PyLLzgdj/i5jjU3bcv93X31QMgZAxwP95ih4+ziDNv1OGvxxyNpCbh+4HA8cjwdGJeRQuZssyX5QOss42Gh31bffWsLKSzYxqwI9S2hbJE9mMsGrR1tW9+KJU54Hwg5oUtgXgKn44FpPFafhKXyA0QEiVJRcpPJeUZEQ2GlA2dEKcYUQQSrFKfDgaHvMZu2LsBS28FQaslefUOEvm8BxRIjSqkqRw95BQ+rH8TiPXFe8EvgNC60Ta3u+l6jgmKZPeN0wjrHTjWYpDjsF7pWE2JEa8M4z7TG83jn2F5vK9sPsNbSGgeufu0TNjUGz5lY4mnk7t17YoxIqTwJzEoGWPeD52SxDzCBD9YO61yRJ5H9yC8r+596ih0sx1/436frr70JiIgB/lPg7z99bo0fW9Z//6GI/Dnw29SUom9dH4aPGG2K1np1kKnjvHqlb34zAclCWQxIi5JjjZFiJRch5FQIUXBt3QSWxa8UVCHFQJZEcVu0Llh3Dki1xF4yRWWUr1TX4+mIEYtRis+++ILdbkfMvpJ+lJCtpniPaVuuX77ksN9jnAOE8Tjigydbg3Md0QuLBMYYQAqbvqPve8bxDZG23pix0Oxqma+UkHOixMy7h3cMZ1tCE7g/3qFQnJ1tOTOWLz7/HLMsfPnwwJevv+TNz15zOo04a7m+viLnmf39PS+6DWI1y3gE07CMM0LGGuHi4qx6Db5IvP36lkMpvDi/pm87UJW8ZVuDUCXFQ+nQKKRs4ZApDXjvadsGpdTaDxf8MuJjQUpmmk/c7R/wxwM5RZhnuhRZjEMas44HwzOzjmfhUKCEAnq12Cj1D6VU0o3R+NnXls5aluCrd6D3NI3DWFNDQXOuk4AS0U0kJ42IrlOIpTr4ixREK2JK3N+fMK6ShopUz4E5BjoZOJ1GYsxEhE3n6uOoHV6fakCtEhBFDDOhQD/sWILndDoBCj9OjCEwvn3L7bt3FRAko3RVYpIroKcpVKL7tyk+a1ohYuBJX/80Hqz+Ad8uAwyrHHlzopy+Gxn4m1QC/xHwx6WUL58+ISI3wF0pJYnIb1BzB/7i1z2QKKnsuxSJaS1zSkFy/kBAvGIDOSJ6ImtF9pEVSH1+sVLUBA9iUqWrrqIVKZa2aYjHhdkpmks4O3sBW9Be+I3PfkiKkU3b8+gDV61h2G5JTphzJJlC63p8CBilKLnQZ8FeXfKD3/zNymCMmWkc8X6BVE+z0AbaTc9wccb2/oE3b96wzBO77RnHw0S37Tnb7Ugloq0i58z+8S0Fy0cffcrrr17z6Q9/yMcXr3hQM4+Pt0SBN+9e06gqtPmd3/63OR1PFJ94+ennvLo849Unr7i6uOCTm0/Y70d+fPtjvn73FVFFLi4vGMqWTz56he0GrMD19Qvefv2Gd1+/pW0Nw2Yg50RnLYfpxLlAWJbaSxdFaqHRhXD0WF3DR+d5Zplnim2Ipwf8PlLSA/50YP/wvgbDKE3jDFYLS65TAWvqrR/9wulwIKSZ2Qe0tpVnUWBcFpzWOOdQUvn5rWrxznO425OMkHxms6kqTqGODruuowAxFFRUiKlTKOcA5ej7nu1mQErktL9jOs1023POLy7YbLYs3jONI3f3j5AMOZ+4eXFDipltv6XrOrK2pGVEmYbWOSiRMC+ElRPw7t1rPvv0tzmNe6JU4VVZjXFEVSAVVW/kyNoN63XE96Hl0FOTEDOrweCvXFdxjfAsY6J6Fc+/9Ov+WrkDpZT/jpo+/M9/7sv/Q+CfikigYhP/qJRy92t/BlQ0HEDcCuyViuo++Yt+wKEuK10gf5DV8DRiKqtoRCuHNRXEapzFak1jDI01iAZbNHmayHTYpmF7ucNkxf1hD2bm2AhnwDQvzLNns93wZv8WCrRty7bfIsZWOySlGdp2pYoachbGcaz9fgYllZPeNA2b4Zzj6UAIvnohAM3GcXVxyWl/4GF8oG133GwT0p5hnCMuC8fDkYfpRJgnmu0OVSCkhDKWjz/7mLREyhz40c9+QkqBnAvtsAEn6LZUJpkFo6rLsHEWbTQX2y1qK0zvR5K/wIghl4DRhZ27ouRE51yVu66Cl1IKKkVigqKrACi9S3RnDj/PFO8JYWZMR/wysZyOhGVZDTQ0JRfEFUKoJ7HWmhhDtVinMKWWlDIpeZw4glhSmLFdV8eHMeK9R6lCzBCVQnCkdHwG7lJKKCUoIom1PQwBpQVpWrQ2aA3n5+dsNhu8nwlZMBSWceZojmhrEaUIKBSapjGMp6UC0T5QciWlta1lPHpU15FSrWSMtUxLXANJqj9i2zZYW7gyG66urp7vW20MKdcbXVFHgRHWMeIHBKBqP8SH3gG/eD1pbKgbAFCyRuS788n/urkDlFL+4Jd87l8A/+LXPebPXyKCUU9+aJVvnnJCF03MkSfa0HOLk59iGoUPX6S8low5lboLSsHmWEt7Da51bDYb5lDNSx7mwMcXN7QbRdtfcHd/z5c//TE3Ny/oW0tSEzdfvFyfl2Je/fVyWBliShHnmcUeIVU003tPKVXWLK3FqMCyBOZpJoaFtlWoqHmIhZtXN1xdXWG0pRlqmo//aWLYnDO8uKZpHe505PB44M2bN7RNwz5G7HjC3bzAOsc872maDm8Suo1ch0v29/dk7xnv7tl2G8jCdtPTbVtSjnR9y3ZoKSlRcqBPG6LM9L1jGK6Yp5mhceimBSk4bUmhpjaRImXOxLxU3YRzzOOCGoQcFJ1zTIfqDjwd9wQ/kb1HYkSVTFGJQEKKwftILoI1jpQWQgxoMWybzD5mQkyomFCqVH3/EtBNpOgBd3ZNnk7IEumHAVDV5ESERMEoVefzrcNSpecpR0RZEnW8a1xmiA3ONKQp4ExH0RbrqhZEtKCtZggaK+s40FlKDIiBExP+kLg52zH7I3LsSNFX20KtybkwPY4Mbc+721varuWl6rnXie3ZtlaxWuqmiCZLnRRIfur1y1rpVrBbPsgO2LrCFCt1qvChFW9e12LFAk8i8J0bRr2+F4xBEaouO+fntOCcMyHWcOacM6lUHzVbBM8Tc0zxZDjyLXp1KYQxYjtTab3W0nYtXdfzPnhMqCqui/aSab/nsHTw/qeEx8jVzTW7s7PquFNmrkphWmO7Pvr4M7Q6Q6sT1trnE8yY2s44V4MSUkqrXLaQc2I8nTgeDwS7I+vM/fGRXYxsz8+xpiEC4zzy+Q8+53d+93eZp1M1MRU4+MjZdlv16apnY+eKoIcFuxloh5fEGOilYyKQRfHis8+Yjkf+/Mc/YgxVGbg53zEMPZ1pSDmSzcIwbFBo9mON8FZxIJY6t35xZqE9Y5rrCd4MA8fjnhA82ghONOE0VVPXVE+ew+HA3f1rglf408j7H/8lx1LoralqSgTJsEwBPdjKl0vyjYFIjDStQ1LCNY6wcgm00kiMZBHazZZSPFO4o0ylelJqxbLUROfD8YgxGq10ldPOidGPUApD11EQsl8IWRAvGAN/9/d+lx/95VfsxwPGWM53Z7y4uWZO1dBkuOwhFzKF94/vKSny6RefYXW70pmFzeYSrTLWGUzbcLwb2e4a/G5LSlvObxR//Ed/xJv/50/4d/7Bf8xHH3+MaVtiqENCXSDJB3JgeCb5POlon4mCPRwDT3tE7RoAWr41aDg9/+vbFcXPX9+LTYCVB65UTYApxTxrsnNad7ZUSCkzqyohEgPF5m8mBavdkqxtgcsZ1RZsb9FK05VKNXU+07iW2Uei3+OLZds6zPYc1Sdsp3jz5j0ba+n6jndreOnpdOLTTz7HuQmjV4GQ1vW0dg6tFdposta0KZOXSMqJogpNaxA1sDjLPM9sZIdIwba17LVJVRViDrSNZru5RCRzeNwzjye2ux1D1+L9zNfv3qIKPJxORG24ubnBOkOWiAqa6+trXry4xs9zDWjNmcFZYhTm8Uh7rtmdbUmlRyshp0CYA6ZViBV01qgEi+rQKVOzOBw6BYw+4b1mnA9oZYh4Hh4fKOmAMwPzeOLx7pFlWThNE9lpdq6BUsgavCTMVOm6KRRSgZgKKtSb36wioSeGoHSOycfaPwtQCrFkJCW6w0qYmYCh1A3B1e+lgI+1rQmdxawEoNg07HLhbpoY7ICoxOP+EWMadrstD/tblDaIEpTKGAGlHWWdLIipQShpNYER62rGYPIYo6r5aPRo0+Al0MQ6FIvxlrb5DLW1vP7XX/G7InRthxJBRFEkP5uf1JkptaL5gECQEWTlLTCuy1q+3RTIz00ahwSnvpKPvkXJ/7nr+7EJFACLSHgmejwlqjwlrXigSK7jg1AgaojqSWNIMkvdAFJ1Yx1LIe89U1mwG3g/R3bWst1t64JNwvF0pBs2aGC+f+Di8orT8cDRH3i1eYFzDct44uF0wpdC4zrOz8958eIFItWgYnCKi9bx3kNaMsoVsjbQNqi8IGWVghpH21QDzSKsvvWOJAojwktuah+rFWkJbDYDTmmkFMq+sH255Y//5E/YdR1FDG9vb4k54Zzho08+xtqGkkfOr68ZugbnNEPXABFdMjEoTvMEaKyx9M1AzoUQfVVnrOQXYwyKhhR89S8oBdsWEMWYdugy06mWu+MDfp4Iy4SfPLfTHX5esAZyhLLMOGuqt4AoQCHjiC8BpV3VvYVCjk9y18qU8SEguna22nSoMpF9QqQuRKsVUjJGr7fuUEAr7NYyTRMimvkwo92C7VsG1bE4IGuGnAlSreZyTvR9S4wLx2ONBk+i0K3BOU2MoAI/ZyAAACAASURBVIxFW00ugRiqbH1wjrR4YgicKGy1IUuVKee2ocuFOXu6vsEviZRmHvIdH8VXXLU70hefr4fHi5VktqAr+Z0VHYOSKoNx5bpgZCXLqecg3g+zw56qhvxzB/5JC/i/Bcbg/x9XfQHCszGHcRYptQ0wxmC9x3rP7CHFtHKsKlvqaXO0a2B7FCBXo4ZpDig51njp3cDtYU/bDyhVjUiUGLIUxtORfnvOV1/+lNPpxO/93t+hHbaIQBSh73vOnWOz7Uk+cHv3jourG3atI1F4tyS06NU3v2Ak05w5wBL3lSVXSJArb70tbTXntO65AnJXl+h5ZsqF4WbDvETmxwf6tuV4fqLkzL/3Oxd8+fCSL7/8itkvGKd4mCaWeXpWslkt5JJonKXte1SqN1gk0oWuzt9zRJJUt92UyCUxjTMpBvq+R0QYxxlRlb9xuksEHbkZDKeQ2e9HxscD03xiOhzwfqTkjJGC1ZZ20zG4hvvHGqxqu4am6ek3Ow6nE4/vHokhoVvBtcK4VOagVpWLEVMijTDGiZAqxmyobVbKCac1IoW0tiGLP5KSedYc1LSnmuoUwpHj8QQp432qtmGdWc1RFa8++pjDfub97T2btqNvB3wsvH//niWFyvIMiYvzc1IUHqYRJ4pd39MZhfeJcRxJfmEYzojOYZTBNoa/+Omf8fqrn7E/PPL5xWd8/uln/OD6hvdhwZR7jNaYnIlS+QVSCkXit3xE6gi5IgQi+VuL/0OFbRagVGQgZ1ZC1xNyvgX+DfAE/lavp41tgLQoNkav6GheKaMZrRNKGUrJpF/ilxSekcMPFRZCCIlxnmjblkYrpnEPpaPrBpRrkNQhwLIsKClcXTVcXn7GPD8wTRMpBKZ54nSqmIO1DjNpGmsp0TMMNZMwS6oU4qggFKKu5phKARIRKSiraWnRptpaaeMQpbhyjtvDAd80NFTbrvOuJ7UGuzH0d4dqZV2u+KQbGaeR23fvUREMijwdWS42mLFQsiWFQswRMxT6oceJQmiYYyKXiZwK07SgU5XtilQVXCmlhn8qoXGmOuRYg1GZNI8cS1NPHa0Jvsrzuk0H+0TwMyWAslUrgG3Q2taFai1mpQMrbZHWoExEaYV2GkkCoeYBpFKfV7CZNFX6cQoesVtaDaV4cm7WCVLFkiRack40TcPpdMJozTIntMn0fQWSH/d7rNPktqFxlhBrlLhzBusMTdcyhxPkiJSIa/r6OqaE95ngA7ptoMC0TPiSWUQhuTCNM9EvECMxZrQtBO85xshyXJjGuTon51yVkCKM3lf+grGo/EQa+vaiqEY73xjuZlOngt85GZTEQE30qzzDvFYTh1+5/L4fmwCQc0EOGWMLxZjVYSyhrKUthTknVAh1LqgyZWXnrVOTX7xWlDDEyiScl4Wzl9eVkBIKyQb6fotxaz9qDNvtFm0NP3r955h1hu3OLNN+wjCQ40TI9fmlmDC6mlOGEOi6rva+KlFUJvtqblKvuiOHENBK0bYtogJKFHY2LI0BbXCAdRrVKJw4shIuzBVGOfb7PQ8PD9y8erkaRAi73UcYvXA6zZyPkeM4M0qk7SypTByngLMaLS0kMK0il56kEtoHSoqI0eSS0EooTjECJnp0EZSGEBeMFfySOB4eMALJe+Iys/iZeZzoGotuW8AjxZBDQonm3DScnKJtBpS2aJMxppCOieQz/aueTCLFXH9+tMRQecTGONpN4ng8UkomhEd22wFRNe8PQIVALAXVatLsnyXFWtc2Sq208pgiX379U3bDht1uhz1vq6AHYZpGYhCWpUqZrW0wpqFxDiu2sjmZiTFgpcVYxXzyzPNMbze8fXwgLQspeGxnV/xoIhlNh+Lzzz+h/KzQFkXwHi2svhLVZ8HlVIXTwjcHN7KyAesmz5O+4AM6fX66xdcWoNKFSwUDVa3+nqXIBaSTf2O04b+Vq67XXPueHMkpoHWPawQtBaPAqPp/ixRCFryvlGAAUZb6KgY+5FkWIGdLypolRN6/H9n21VBiIPLio084awqTBpaFRYRd19ItGrWtb+hZe8bL33hFjpn5q5n+i57NZoOWzDLtYS2Z63OuyTV6fQOnnEAgJDBRqmWWeJRWtI0lWci2kPNCLrHy6ZNlozcUU7BtTc9puvqnbVu6rmUz/JDd0DONE/Z4xiwzt29+Ri4wHwsvX73g/OKcmD3LMiEtiNV0qiMcPDHNIJGcAyULTWO5e1+l0iEEwuOCtJByom2rK1Muj8xvCu+n90zzQt9azpozzi82nA5HTGvYtJe0bUspsEThvRjaLLRtz2Z3toJrPXOsfvh93+L9glKW4+HE8nbCdBppFGghl55l8YR5WsU0lZAVRBNjxKeEEUGwNeJtntFaEUJElkKzM5QSyVbz6UefkHPkdDxwffmCpnFoBcfjidNxYUkeM1TlYEyBlCy2V9hZOBu2tENfzT1LwQfP/bu39H3L5fkFj3e3dMPAZhgYRLDDhjfv3iEhMKfMq1evaHY7TqcTsySCaXj/sGeKoZ7YubaTT5FoSFnnArrKiqltQMUFnobjdbT4tICeB2OslYIEhJpEXITv4gkB35tNQDCiQBSyyIryzuT0NDWoC02piqInKnpbueBACStaWp3YKKtTqWREJ3yZ6WSAkilF6IzFasM0j4RSXVv67VWNL2tbrKunTYyR3fWG3tQ+edo9BT9m9vsHur7Htd2zqizMNeUWnTBG4XSH0pUOm1tDSWC0oHRByMRDqL2pMbRtWwlTOjPPYwVGNbimI+WCcw7XWqw1qMbwgx/8gEM8EPYBwZByFbyEsBCCZ5lHdKMIZSGsP2fqe5RR1aknqzp9TgGypnENJT2SQmZmQr/XjHLinb8lhIWcPTnn6lNYCsPVeeXLZyHGmiGojcG6FqMNrYdjszDOVZdgraWkQmcqbTqlRDJVJde0LQ/v7znKAZscbeqIoTCu7EutNfgMrqG7bGnHb3wnqrvQirusMl5jDUVlomsYjeWj3+zY/4UmxAUl1R7etV11ZXIQQoFgsapaeaVU277WdaSd4nQc0SJMoTo6W60pBbQU+t2WcDpUHYhSLFqjrabrOtL5OReu4e2XP+FPf/xjXlyeYV31Pbw9nqrVfKlSZinVQDY/zfzy0+BvBQGeWoDVZEBWe71vcwCqH0Fd1Suzkm/cjL/r+l5sAlopTG9IMYOhgiWr+MMoS1KFThKjroQKRUTFylX/MKe96Go6InENYBSLkkwMiRAS0S6I9EAtEadpohGDcQ3bzQbTOLLAcRohBEoyHE6RqTxyOhxQVrBSOeld32ONrTcihWV6IIQTKTpKKQRg0QsoRde2aAXZOTrTI8Ws3glV8ixUgLKaXq7Vgqp6yZQCRqoibbP5iBwfsA5C0Vy5c3xe2M9HbGlxvUPrWtqWkmtcg6ey98YR9fBAv+lpu4acCikE8AkfAn6ZeHyInI5H3t/eAhWYfXzcYwycvzhjcAMXuzNSSOzOdnT2gqZ5Enppus6Cdki/QULhLCd4hLbrIUGrNIvLNE5ANlWKrC1cgLq7hcMarinfcEOUUmAMXiecF9KYCPPCTm1pEkRdnoVaWmvmeaIfekqXuGmr2cbx7sSwaVmC8DidePf2LdEHLq92tK4FjpS8INTRr+0Nm00NXGX1GkBWsRMrO3XJtK7Q7AY2R4XWjtNxz+ZiS4yexjnejiONdai25f3r15wN57Q6Y3LBH04rfKXIKGTlAkgB9XS4rdeHpzyrtLiGjdX+4BvNQAPGr99QyCsguB6J3zkl+F5sAkppet2ypLdEzhhjpPiCbjXKlMr4EoXVFooQIxRbntHgpx7xmVD1jKCGGmYaC6fTSGMNKWfEK2wvmNZwvjvHtA2HuxOlOZKd5mw4Q7TCNprXP/kJTdPQNA0qKEyrWWKiFcX+OOLnwOl0T991ABjtGVRL486q7YHMhHkka8WyjNzdv6sAmdL03YAxlpRCPSlLQYJwWOp8uTUbpCtrdeARiXhfePj6ga5paLsGEyPdtkcrg5WWpEaGZkCQZ7OLulkmTsc9777+Cr88os2W87MrnNOkmMhxZlkqgLUbzpimEzFH/t7f/XcxxjDHxNXVFdYW5rkCiCElQha64QZjDaINqIY87wlxoZSEaWroqNIZnyOt6zjb3jD6iYxGx2r++fLlS4xK3L19IOaCUZa+3fF+PJGjR0shyEKJbZ0YnVfQ0QHTcqrGr+9HNtcD5IIaFcEm+q5j/+YerxduXlxjtOaxOdE0HcNmg9GK7WbL7bsHxnHExMhFt8OpGqv27u0bXGM535xz+3BHTontZiDrSJhA9B517OhftjSNxRjFGGaUseyPj5ATbdewO/uYm+vqWHS4u+fdz97VkkMUQlprdkMhrOf/N0v2uQgAxEnV1SXPU9w6ZbXiX4kCglBiprVHFvlugvHT9b3YBEQEYywxXVNKRpVCMqkGWBTwolYXmMoRVyqhpKaxZpGabU9BwgePycolVCBFkWIixkA4BfJVZpw87ehRL2uJ7btCOpx4824iXHg8AZM1w1DL1xACj8cjn370EcYYfvKTn5BiYug2vHhxjXOOsCyEMDOXGaTQSosxVbWWUrVBL1T7LGCday+klKuIRIRh6J8X7ni8xU22luql0qJLgca1TKc9RTK7zRatFK4xQCD5RIoJJQZJCmsVag3eLAn8fqJ4B6bgGl3Vcilh9YBzFmda/DJxe39P9DPn55d1sjFGzs4vaZsG7wP3Dw/EcSQ8Bi5ebVhKWYNbjngfIFV1YHXmPaNvWubTiUMIhJyxyiJacVSJME9En2lly9lOscRULceIaFOJByl6tFLPFeLTeyxrj6wahdu5NS5cmMaRq+uL2i8rxX5/4MXLG0Bw1jF0Hc45YliYpxMxh6o1EUfGVjquSsQcCHvP0G2wzrEcJ4w27M62oMF1huazhmWKxBQqu9I1eGVIeWGzecm8CDl7Jh8YektMkazXEytn0IIhYwkcn8C8Ffx9JsY/HfbrPW4RErpGkX9Y7z/92+gKA/wKktDT9b3YBNCljm+w6JjrWAmICQ551YSL0Lja/7mkKGaVD0eppKHyi86qT0akuVR/gsfTjGka9DzTOYukwHT3luHVJ8xpzxw9y3HiKDWw0rUNvRiKjywxcdkPxMkTysxyeiSmhCIS80DOq4WUSI358oEUI3PKnPcdtlXV71AMOeVqeBISIhFVCve3bxGlKHoLs0ZECAdF2hxYUBTvq6nKGuhhtKBzIM5HjvNI27QEW6220jqFSCmB0YQUmfYjYfGYbctHL67YDC1D15NFSKlwfn5O01Rp8/545EKaav6RMptNw2bY0Gx6tDE4MhjFGD3JReaSUVGBA6cNft5znE5MwOXQY9uOpA10HaRMjAGtDRQwqbIG27anbTq6Zcv7u9saNR8LpSRiLJiiaFOkJA+qZVomnFiKQPYjrunotlvGZabrOrwxLHN1EvapkIlMcSFLwUmhbxVaC/sxM/oFJGFtz+A6jM7EPOOw9G1LUKkeTkowfWSZ4HA3sxsi/TZAqU7EJQcKCimKJgl+KhwOB1LKDMMld3d3HMPIu5/d4UeP5IgRRSiaRWl8BpMFJJJE18das4nluajPpKLI0iEqgMTne713MMdVL5jWzuHXAQJ8XzYBFJvhCusOBF8DPmOowFzwC0+8QKSebKi6Q3qpXvGpzMR6VLJ6lVbGgC5IqUaLOSdiTFhlCOOEVcK8zDw8PBBy4ez6Fc3GEaPm5aubGqqxthqnw4nmouXF5hUxRh4fH/nplz/h/Pwc4xy3b2+xzj67Cg/9ht45coroGJiXhTcPMzF4WtthjaKkhAsNm40iaeisAdeifMuw6+m7AXmpGE9H8rLgaWg3LSntkVX5mEtmHhe+ev01KURKTjyc9lAEpS2guH37jjB7Prq+Ynt1zcdffMqL60t09jRtS9EVw1DGEJMHDO3lNcOwJVuDChEfM01Tvf9iTMSUGVzD8OlnGGPqaG4p0Ahv3nzNu9t3pGVmuLx8nmO11qGz4lBqIKtr7RpT5vFzJe50bUd0jp+9/ooQFhYfOCmPU5rkI7NWqHlmME0VCB0Woo1APe2992jdMnuPFUs79LjGYYzl3cMDy58Ftv2GXjmuzCXWGIa2pWl6rJ2w1tD1DUUK87JgrGa3O6ualgJOGYrecXv6mmw8l8s57C0Xuy3aOKzTLCEQ04LW1cJ8XCKt1YSHe/L5GYeHB/7lv/xf+NlXr6tfgdQMhVJKDSeXigV9YyCuVri/fHBjJ6oy4BsBnQhMQbCyehEY+YX1//3GBABjIqVYFJqsIU6aZAzjs1ZYIcVAChRdhUY6l5pFKA70Ug1EPc825FWS1VHwiC6QMov3mN6SVjZiZWLVeXGJqcpU54Wz7Yb+/IzXP/2SKDAEzVdffcXt7Tusrblz0SeMN+z3exrn2Gy39UYOnkOoOX96DZ+UVIc9KXnG08K8X7i4PmcaWW9eTYl7nD1neLlDjUJO0A8Dut+SSq2IlDpnOR0Z3IZRxSp4Obsg+lCZkG3/HKk9jiND69jeXPHZ51/Q2IaLbY8RRdEdYtsqhFKaIrVlEhG2WhOt5fr8Aq2Ex8eKUZRSHZ2naWKZZna7LSVoMpmsC3MokDOvXrwEvzAMO9xZR2tb8IWiIs5pdrsdxhjiusk658AWnLVQcgXTMmRdaKeMM9fM/ktKjoiqJ3guCemF4is25H2lAlMyezJnq5lo37VILnx0dY04TfCRMSb8tGC7nhDjCvjVjcpYizIOEUtKEEKg7xqcaZkWz5KqR0Xb7bBtizW2VmbOUpkHUHJmDhOta3Ba2Gw2HJaRm90WUzpCyrVlohK0Sk7VZLSsAJ76RiP7oakO68dCtWNbGyEEgxDIAoGA4Gob4ATCry8GvhebABTMOnYRSRQcudNIzutYr1SWYIlgqkRTq4xWGaUiIqlqBhI8BTWVLKuoqGqplFMQFKcpst20xBARu05jUq5BJlrx8sU1pSTu7u44jAcwhpZqK317+w4/Lzg7cHP9ApGCl5kcBeUUOcPj4wGjDdvthn7TY5Rinkb6Cey2IZIQ9uQMr9+8ZRxP+OPCxy9foQaFW26J1xc0bYN1mr5tkFmQvrD4qmyfZs8xeUosaG35/LMvSDng/UxJNzzcP/D69Wvev7+jaVu25xeI0WhnMcphlcV2DUVptFpn7IvHnyLFgHWJrm3pW0VImmWp+v2cqi5g/3DAmNW3T0UKihI0ZpnZbndYO6DiAWU0m6FHaY2PiTRVy7CmqdqBmALOKhrXM08z42lknqb6/ulCKrlOeMyIFlMXnNErbiSI0dXiPKWaIFUKKQXclDnIyDxPtFajjWYYBooRHv2RJdfQkcYHcggUSs08yJWlF5IwL4FUPNPpSIkRNtXUVTJ0bUNjWxrnaNseURojgmtbfEhoIJRC1zdshgFjDK8uXvHy6pqvpxNhipBqBbAGEiK6MhuLWeXFvk6dfn4FP8kD6mxAkWkosoYrCLU1odTcR1+nGn9j7YCIfEa1G3+5Pof/tpTyz0TkEvgfgR8APwL+s1LK/epA/M+A/4TKUfqDUsq/+pVbwMrucs5QiqUfDLlMlNKsdM7I7AOSFmLUyLobl1xIOtekW6lVQREhr3P4p7DHUgplrqDaGE7sD8Kr60u485QLR0kBrXP1GkxVOnt/VyOrX370MZthx8N4Yne2xUtD01tub28rDdTaWjIiiDa4puX9wz1v37xnt9syDO260STKfq4JuDnTNlv+7O2PWGKgbxyqr2rEh3nh06xY5kQ7GKxy7GUkPi6483NMjGy3OzLgjFm9GACpNmUhZ5rdGbPVRCM0uuH64oqz3VkdC2JYtGKaaiKOsQ0ozWa7pW0zIdYE34QQD5pkI6qoNQodbt+9582bN/z+7/9+Hc/GQC4JL7CIp+sczkLxdWOZTiPamgpMxoJKGS2ZgsHZSiWu2X0ZPy+M48g8z/gc8H4h5si7N49YBeQdMfrq8JMiYg1N15EihBSJReHnhfvDPWlKiBRurm+YxhnJAS8BSuZiUx2BngDG8+2WN2/f8hdf/YSbq2swjsV7lBE+f/mStm2YT0esFqISNttzZKqHkTOWtm0rIJsq//3ucY/3kaGvBKIlBF6+esWr3Y53OfGTn/70SfpSjyxVkFC5JiXVA2wt8j9YiE9rpf69lFK1BExUSe0TnXgdNsYCVn6dihj4q1UCEfgvSyn/SkS2wB+KyP8M/AHwv5ZS/msR+cfAPwb+K+AfUG3F/i3g3wf+m/Xv77zqL1bBO60srnF4GnRZ0wh9BbrCUnulUlLFCUr9BYxU+EQQKHl9ceqlEMoH3msihXGcQAmTWSuAFNkfH1lGT7M5r37+WjEMGw6nPafjxHGauMu5OvSKY7fbsSwLJReWxYPImjT8wP79A23b03QtnAphmbHW0LiOeZo5Ho+A8HD/wDB0vLh5xcvzl2BgbEdCCLhmQyma++ORU4xsraHNuYal9H3lPkRPjgHtqsdfSJ7peCSnyJlyHE1H33R0bY/RFts3WGtRReMNWNcw9ANarx75QN8NLH5mCTMQsVlTdCHmwrLfczgcsKZBRGitw/Yth+OBUkaaRtPYtva1eUWmpLZDsLoABdZ+W6pnQKjBsKVUbkgIoRK1ysLxNGE6hewr+83HiM2J0hZizvTAMcWKS5QMoYp55mVElOLd+7dorensFhUjSaUaGtJb2rZDhMrimydK8Ej0tbeeZt68v0XrzCeX52yHVwRnMakQj4daNTULx3nkIlXqumlMpeWUGoxyf/s1fdNgdU22tq1jrwTRimmc6qFd6qYhWqgp3PX6xmMIau7imlz8NDng2+u65BrfhmJtBXx1HyvU6UFl0n3n+vurOAu9proIU0o5iMgfAZ8A/5BqOwbw3wP/G3UT+IfA/1Aqfev/EJFzEflofZzv+BmZmD1GOdTq7KK1QpeKjxrjMVoxlsoCzEXQVDnwIjzZL8LT76rrDljSB4VQkXVDgNlHTqeRoXEsfgIyJib2x4mNtuzcQCBQsmW/P9F3A4029Nuepu1YxoVC5P3tHSBcvXxByoHj4UjKGde19G0PvnCcjyjJGOOIKRMjzLOnlMxv/eZvcXl5ybDpsTtX5a2mxc+BXI4kWrQWButou2pfXZli0LQNZa6/uzEaZSxWN6SQ8UfP4gLnfa1QilI0mwG9hmUYrWl1S9u25JxWj/8ZJw2uc0zTSPCeqAp90+C9h1Wg0zQNUIk5wS+VhBUjhYzkgEjHNI1IiTR9h9aGEANxCVgyY/JM40TTgdKqSq3Xt0mpiqxbqykjODPQyEJqtmgyzYrhdLp23zlnWhJRqE5PqWYkHA4PdN2O3fUZKUUWjlxdDpSoCb5gisFYy5ID07QwzTMI7HY7+qHnmEaCKijVoJNgtEKZhrikmvYTjsQcOR5PjOHEWa6ahhAiSmplYBrLeDji3Q5lDSFmrAjWWJx15NlXxStUARWgVEEV+cAhqC73pxiyD/aB54+/BfcZIHtsqpPE543jaRP5juv/EyawhpD8PeD/BF5+sLC/prYLUDeIn37wbV+un/sVm0DBLzMiCuMWtOtpjUKJQSvBmjqyC76KLrJYrPHkTmEWQSuheo+U+srE8s14pFQONlQforTGPr27v+fm8pwUI8cUuHLXbNsaWb7XnkZXdP/ixcf4xz3dZuDy4oxxGTnc7bk/3JFSYhg2xMljCpim+g0qY5HGkCRjtMXZgiowjzVQo+tabm5u2O12zPPM4XjCWEPbV4VhVrA/nMjHY2UbasUyt3UaYQzKKrRQPfCk4GPC+4BF0W4HhvNzhnjJ7uaGFGLdJERIKbM5O2ez20HJhODx3nMaZ/YPD/T9hl5ntNIE4Jgj48MJHaHfDEiqnnysfg8pJQ6HPUqDbQySer766itKyWy2PaUIw25H03bMc+B0GhnHudJ6c+F0OiKoSrF2q60XhqFvQAtnLlB8g3MRLTWuXEoECtrVqUSK4OMRWy7ANXzyycDt7RucsZzvXmKiJ0bB6BYnlROw+OpalCm0nWMJC03fcD5sKhYzBs7ac85358iwwedE0xjKEkELD497VEk01jIdj5QXN0RfXRHmMnG52fHRZ5/yF//6/+bgj1yeXZG1wceELpmPP/2Yv/zjP4FSQ0lKgiyakCv2RBGK1ApDUyh5pQ6JXjUF1ZY/r4tcKVVPv5VD0FGried6OJdfiQv8lTcBEdlQ/QP/i1LK/kN741JKkecMsb/y4z3nDjTWkOJCCg5vA015msvqSrfVVWdulCIrhVF14WutVrmuQpSq8eWse+MaWVCrIQNkAgWyoErlgffDpvrzxyNiBSeO2Ud0Xw1KMwqTErpp0SvhZD/dcRpPiFSN+WbYEBMYZxm6nuKaahYRIkvwnG13IPCwP/B4XxlnZ2cXFITTqdqT+xBANCFWJiFimJeZVCaafqCzFq0th8cDrmuxjcM5S9f1KNVV8495Yo4Tfo4oEyhKsENLE9P/2967xOiWZfldv7UfZ59zvkc87s2beSszqepGllFjIWhZliUsT5AAe9Iw8wRayBITW4IBgwZPPAQkPEBCSCBbMsiihWQj9wAkHkJCDGhsQ7u77VY/cJWrK52v+4iI7/vOa78YrBNxI6szq6pd6o6bqljSvRFxIm7c/X3n7LXX47/+fwRD0zSEtkWc4WY84qqG89vNDhc6QugJjcMZQ9t1lJLor17zanxF2wU2/VYLqEZwUun7lq7zNJ2npExJiWoqXddhsSpGehpYYsI7T5wSrz6/Yowzz7/xjFrgNI5UgdDq/TFGyDYjvqNZ6xCn6QhS1qEsoeQZa86pJmOqp9RKSo3WJqzQNVpbcWLprQUcuVQF6NRKjJGxjNzcvKbpNhhrFT3oO7qmIYSWGK+YhokDR443PfP5htApjXnf9zRNYBkOOOMIjWWeR3IUrG1IZIabgdA6urZVFuZ5pnFKK15z4pv/9a8msQAAIABJREFUzId8+7d+C6porUUEJFPXFPd+5G5upwhF7qDCcjtVeJcfFCq3HJ1ws6IK7puV2wjj99qP5ARExKMO4G/UWv/WevnT2zBfRJ4Dn63XPwI+vPfPP1ivfcHu6w5s+1BLmkkSyE7BOQlVB6ZkZQyi6OknFeOg6SwyFqIxGGsxpd4NlGgrtSptMyAlrwUY0FKso8bINM2cn+3ZGUMskc51bKxXvvaUybEgQZWO6xC5uXpNTZnQeYJv6NsOMSBOkYuLGDarlJUhY3GkmFlKJOVM07bsdlvOzi7wTSCmSOh6fAiYxtN2mr977/BNoJBYMOx2Z4TGM51GEEOtBe+7dehIZ/+NtZQj2vKaJk1LGk8Ro5x7zqlgRy6YXHGhwbctgtDima2mBY235DrQ2ErjW5xvaLqWTd+xRFVrds6qLoDNdKEju8iyQBkjT589xUZHCVXVfEkMQ+LzT17w+vMXdNtAcAHrHcZ5JRM1GUgqGoumdiLKvrQss5LPpoy1hppbncOfoDaw5AVjrJYfik4ZhtDQElARG4NYBXBpt0EZhEo1K42YuTsxU4y0Vbv0Yj0xF8Z5YhxG+m1LRduRIQSWUVO/hUyaIlkMXdiQKQzLgHEdm03PdrujWksTHONSmI6HFY+xKgvdhvhmZQmugqnKZ1h5Mz0sGDCzQoRXR3E7GqRBcOU2TmAlLPUIdyDaHycdWKv9fxX4jVrrX7n3rV8Cfh74j9ePf/ve9b8oIr+IFgSvf1A9QBeoQAcxM2F2XA0jPoHzLabUFQiTEJS80xlLMA7T6LiujRGb69pH1yKTIGB1PNnVwoKhil29pk4mvnj9iv3Zlq7ruHp1xebZhuAajsOo0UO1zMOJ4cWBYhtMzfTbluA9y5Q4piPRwJOnT/X0pBJLWgO2VW22aj/4fHPJ/sMN/XZDLcKyLHSbDW3XYa1SSroQMM6ze9JzwYZxXDhdvaZWWGLi4p0niicAQtcjgkJzjaFpWi72ljgtnI4HxUOIAnAAxsOR+WQ4Oz/n8vIJxjfkWlnmhbloV4C1Jffy5WuCLezP3+HJ2Ttkq/135yzedrjW8PrFK65fXLPbbJjnkSXNlNPM5YfP6W3Lsop9Ho+vuXo98MnVp8x54t3tU87Pz7DeEOaJYZ6ZZgUGpTneYRWMkZUfQFTwY54QK7RtS8wFWfW2lriA7wnOMZ1GZFnwVqMy5y05opDtlFiK0rc1TcBadZzOunUOIrJMSduAQOMUZOS9185DWshZlJ2oadj0G4RCOs00Z5bSNPjOUXJlQTkDRFTfsBgLYhinI//kd/8x1zdXiIEaMxa544eoVVMBQUfQRVYNmFtQkABoTaze39i3ocNaALwlKzOArWvT/McpDAL/MvBvAb8mIr+yXvuP0M3/34vInwf+MSpMCvA/ou3B30FbhP/OD/sPSoV5inhnsGkmxVFP9CpURAdcckaRYZVyr6LqrdBYQ7YGa7SFI9okoM5y9+KN0X6MyoYWMMISM4NEOtfqgMpwwOwumecZa1HO+aPyFiQy775zQd/3UCHGK4xt6ILFWsc4zlQx+BV6Oy+Zvu/Yn50T08x0PWGOGvp5H5hLwYtlXpLizc2KbSiROHbEMiJiQCzHQYuXu/0eSiUtWUU016p+KeCtw1aoVrUITKn0mx1taJmGIzkt+LZXlVzQByXr70nzQt+1uLsiVKWI6hI0vmEaB1IaOJ1mSsp0bacneIVlnLi5vuZ0PHE4HNhcPqHZBkr2XL2+5niaGY4D3jVsn+148v4H+LbD2qociVUJR8Ew55lcM9YY1WtA813lUrQ01quq02mkSsEZgxNDcRZrDEuKNFRs29BuG8CsqUpmGTOvb15jreXps72e4rM6aK2XJOZYaWPGO605WVNWMk+hiqOUBDXjLdi+w3jlToySCU7FTk9Tpkadh8jjSGMMZtMr18M08r3vfMr1y88VcLFGplIqsoqR3M0CrM9tXf+YmjCJL6QBcvfZaqassGUVJpnXDS78YNLxH6U78H/y1Z3Gf+VLfr4Cf+GH/d4v/BsqLJFpVOhvM3lyaShu1NAwFiLKBYBknKk4LxgxlGJZoiNXUemrUnTzrE3YIspDL4AhaamgAMZycxp5+fqa7szhnWU8HrBOUXTLov3vmAqpZpyD2gi5VGpxIJXTPNP5hilPLMdME1rGeWaJBUpRDHtUTbtXh5dwVTg7O+dsf0HTbzkcB9oQcM4w5oiJkSYJodsg2VFqUk0/BHGF03ig8R2mGqbjUdMIY/SlVgFjsU1AlggpkysY7+h3e5q+BbEU4DQO+NSwZM1RJekDmQosaaHrWprGI6bSdIKRllojy5K4mU7E1wtut6PvMvMwMQ4LRgxPnjyl73oqStdWUsYaS+Nanj15j+3lGf35OalUUoVlLsRBJ9/a4GkaQ7/pGIeRvEScxrZaNLUG5y1iLBur8rs5KaPzvvHEGGmtoaQFUP3JnG/F7RNxKcRxwW96rHfq4GOCati0G2wVxhTZImw3Hb5q5m0tjFPkcFinLMXSN5UoDhc8fb/lcHONOMP+nSfkMfHyxad89tEnXIbK5f6My/2OnBPGCF13ThxOCmpbx8VNWfUy4A2fAOhcxG0AcHfov3EOWoZ7c9DdEpTfMm7dcmT+MHs7EIO1kklIhAVhCYs6hpwQBJMrZaWKcgKNE1J1GKvAiBATMVvlIUyKbKMCXjAFpWq+a66KvnlSOJ5OfP69F9il8PzigohhPh25OHvK8XhFHCvJKJCppsI0XrOUhUiDiYVpifio6kJtu6HKLQRY9Qeurq94ff2K+ebI8XCgmFWdxgWcbxHr1vBUOJ20mJcnz7ZWbNa0xiB0jcMFxzAO5FgJIXB1cyD4DWcbHc5RKauKMQ6xVuGvVjd941tC2FCtSn6nFDE2Y6ySc9YODtfXWDGUWtYZCEuKCwSLbYRcOs4uWtJsGcw1rigqzXcNWMPFxQVnT5/QbvbkPNPEifff/xAKnAYdXvLbQK5CSgtihDlOjMsJ8YGu63h6/gRvB757+C61VKw4rHU41wCRlJVwJYhGe3lWdeCwKjSzvv+yJCVUFbCNwacGY4Un7hlCVeRkCMRZ24NGVGF5mTLFqoCKKaJDTKKp2+mkPI9doyCj4C3eBbqu5/r6c9wwIKuy9ouXr/j2P/o2vLtnsznD7vf07z3DWcf5u5eqoI0+i/Wu/6evSdt5upVZpdSpWhjUyWGzUhwYbjN/rRxUEI+sSkZ3x/89p/JV9nY4AZRZqamKPS85qSgFYIqQ1txeoyfBiAKEKOCiwXqDixkr5Q4vANx5zyK3I5lWPbBbfYIx3ByOiIHL3Q5EmYtSAmOEmGYiBWMywW4wuRBzYsKyb/ZcdhmXBJedbkBrlTfQGqqBNC2cDoeVkXchSeL65pq+3xHanrbtdPwUjxgFmbRNS5lU/tv7Rl+bMTTeE/yGEjPjMDKnE5sWnCyI73GuwQd3p0sf2pbtbqcEJykpilIM3msF3DmFaacl3ZGNOuewzjKNg064lcxsI2Jb6sqRuKqRUaq2xKyxbHc9ZxfneN9oz51ERRGggsV6xxyV7CKXSl7bt6ENzLFZN4LiGVLOkAvGGnBWuzSlUCgcjydqqSTvuDSaP+tTkfHek2qhlKyzBMDWd5Sy0ISK95YQGpZlJueMGMGHwDQtSkGumHWMKAZCnJDWVE0EYkxsdi3kSk0W55RJKImAGKZpJs7K4HQ4HBBJzHnm85sXbK4u+dAo6ay0QcFlhTuNBBEdcjOicGPWe2jErHv5Xoog8AXK4buEgTdYALn7FfdShvv/5ov2djiBCsUWJCvkN6WIERUZMZhVUMRgjQHR4QlnDJLBGYNFMFIQqXfeslZ1HLUCuWJF6cnrGhRUUZGJSuU0zHz340/5xnvP2YQGYypt1zLNmfl0pKLsRzlZjIGd9+w2PU4qh1c3vHr5Aus83WYDTqvOFJiHkcP1DfM04Kyh3W5wxnM83ADCbncGteDZ07YblmXharjmwIhxhjZYckkcxfCNzZYP3n+fQqbkgl0pu3y3Zbc7180lMIrc6fHFmKloey2mTE0gVenFTrVg1ttfSqaUwmkYaJqG0/FIWhZMyaTPJ0K7pWn8KhZbqanw4uoFYgRJGeMcUqC+jrAFjKZgH338MU0TcN4jVvC20Xu00sX12x3GKKvxcLwmx8g8Z2wTsF6px2OKlJwxXjeEeIu1GvVl0xB8Q0qZGWH3zo58KqhgNnjnmKYZYkN33rFMow6eGadFW1HBmGlS2jgpmTRPesI7i4SGvu84nY787u++4Nm7e56cX668i5HgHKdhJKaELIa8JE7DyItXN3jr+NY3v4XrWrZ9py3UXDikmXFYpdl7qIMW/spdSL/WrVZIMyJrjv+F7bJmAMI6JbdeX/EAviDpdvvLPUfw5fZWOIFKhaTCEL5W5Yh3hUayMlehhKNaRfcYE1d0Wb3rH9faUF2CdkYm7ioht2+tZoeZWj1VlIq5rIKSsSQ+e3Wi3Ry4fPcpaSlYZ5GzQK+sjZiN0HjBmEDTGEKwDKeBmBcONyeKgW1cwAilVoIzzMPAcJqoJbNpd2z6DcZvyHNhcpm+yyzzSGw3NOI5nU5KBS6eaiudd1rZNoarlNjttmurL9H7nXYX+h2byx15TCylUIAgwjzPTMMEuWCDXcNlwRnLssyM08xmu2PbteDs2npUNqdyA8fTgAi8fv0KZ6442++Utj0ETK0cDwcowjQcOL+4ZBknTHbsmz2h9YxL5cXnLwGNPgSoSyWatKZjYK2htS2YyiEvlBzJadS2azFIhXmaySbT+UDfdiwpUWrA2kCtgvNWT9MxE/Ytc4iEpLDmJlhiciRmnNlSGtX0q7eKtlVbx0vKJAwihmUcaVyHC5ZgW8WBLAuGwngcqGcXdL06gVorcZoYh4kutBi03VhrwXc93/rwWzS7DWHTaSQbDUNMCtE2FhNl1U58czgJWgwt9yjGrJF75z28+ckvifM9d2BDMW9CAqHnn1qV+A/NMjp4IpqzGyqlOLBVczZjcBYcmXJbsb31m7VSa4KSNT26ZWEtIGSqkbv0qBYFaeubqm0E5dzPfPL5C/b7HReXT7meZh1Vdh5jDLZa4ljJDHoSW89pOJHKQlpmFmfww0QVwTiDw4PziEtsXMt2u9V6QCgr9DfRmRZcYI4TfWk4252x7QsilsPxgACXF090A6+ngw6+tDTS3ukhzuNCWs/1vXNK+OkCOY8saaCmxOl0QkTw3kMF67RmINZhvaPtekzb0gXLy5S1AJoT8zRTvWcaJ9rQAoKxjqdP3mE8DNSS1DEYg2mU9dmIw3thv1dNx67dYX1hHEamaVLad1RMJFVt/+ZV5VfgDnwEaAdjjtjLDV6EYpRNxxiPTRHfeOUi6ByCYZlOOnvRNOQ80jU9gx2VsdgoQ++8LGzWAyAYS6Biqmr9plyY40w2XtdSMyE0XJzv6RpH4xtCCDg3IkZWSfYRI89xxtK0Ld4bNt7T9z3dfof3ga5Wjk5IRZt3phok3hb1qmY2a0dMUwTtY92xCt/b87fRwn3TKUrBFHlTC3jzXXTG/svtrXECIkIMFVsqsRa6WqAsONHcyxivYT6rSnHRqa1Sy0q7lag5USPUO83yNRxa32ht9SioyIshiSWppyBRSfPCP/zOd/hjTUsIHRahu+w5Xg1IAR90qElEuLl+zTTPxHmmUNmGjj70VGsQ0bijxIirWugZxpnQb+lWqrBaC7lG6gxxLiwhrpN8gdD1vJOeMZ1uqMbSdprDYwyu6QiNxdoF1zTElLAHT+2UZHXdXzpzsPHs3QVSFcl3GkcVDUllHckuzDGSx5FlXjhME9shIWJ4+vQdnXbsO5yxtF2g6zsdQDKGm5srttszPtx8uBZCNbVq+hbTBFzMnJ+f6cY24BsVWilEljhSckCqgniuT0diqljrVl1HS81ZeSaMxRuHl2aV7xbyrASuoe9pgPkQcRuV9KLCFCPTtDAOiaadaDaOmLTDk/KJVHWU3HnFATShxVQFiDnfaPcgCcYatv2Gs+0Wa2G/3RJaRWxihGmeMU5RhMPhFa8+3/H8mz/FdrcjXV3p728DqRiKdaT5xDwuIAZjApQZY9bZiRU5VEUwa53gdl+86fHrxncY5XBYv6+1AA9EPfhE7pzE7fcty4+HGPwDt7WgkRcwVjmT9XTPZBqKFdqqjb5U7xc7CkUqxagjKBU0hlhPTmE95dVzOgzJCLVAsXlNI1jZXLQwGWPmk49e8uFPf4DxjbIdx6InQu0wrSHntOLWEykrkWTf91RnlfSElbbaelwAYz27/Tnddk9oDSUpAGa739I0DfNpxBZHEzqaNmiOKcKSM3MdMYsBGnY7ZRMWayhYSi16SlxYfNaUqpSiIBsqxlS8U0CMs46m7VjmRQlIS8Z5Ty1Vp9pEaF2LsZHdkw37vqdpvEptp0wmkVPSSC0LLvQ4awh9h4gKa+RSSIjKadWK8w5qJueRnFScxfugJ1wV0pQZDiPH40DO2tlompbQdqSo3ZJ+t1X8vDFa4E2JWpO+dhQfYLxSqcUY6ftLhnHmcLxBNobxZqSbPU93Z6RDwvoGY5XJpyR11k3b0oSG8aT8g03T4IylxEQI+vXw4sh+e45t3Z3mX8oTUgNnm8C2sZhoaLpAHzYs8QVNaChZqN6oiM62o3NKnFJcWQmDNCY19RY0tBYKxVDIqxO43Sf6wRFIRG7BwyLKUZjqFzf/G/vBZKNvhRO4W3ABrPY4GYH96sVypVAQ0WEAZc8VcnUKD85lhVlqpbiuRCS1mvVNUQ+byFRxIOosqHUlIskQWiILNRU+fvU53cWW58+fsyxZtRA82E6VMofhSC2FWz79UgqnwzUutAoGMso8hDG40LE/O+PZs2eUarE6/K9qwbudbghrIVjqAn4feHX1CRWI08I0T0x1xIjw/vvva/9ehJig1kVbfDlTxGKNsKS4chgC1hBzxhjBGEtjLbZrqSGsVfLCEpU/oW1bNhunZJ45sm0aTBsQ0fx6Wmaurq4wxiIVOiNEgWGYMGIUOmsLxQqNZMq4UmiXTCkG6+qqzyBYseRSOBwPfO+j7xFzpms9IpbQ9jg/kkvBWM92f4ahErxhXmZqgdi+CYVzKXg8S11Ug89EGudJNtP1PQwzcZqxF5bZLIAWlUsuikSslWQ1HRrDAOjgmu88o09EMq4UTGfIJFXAKoIslVKMcliUysW7Tzhvt6RS2OzhtGgKO9dEaFqscXT9jl3bIdZQk1L+SK1Yo+nAG+BfvT3n7/ZHzZocYBRMXtZk4dZKrUgQfFQqgbsegwi1RiobvqBbfs/eCidw+2LLLQHIcKLudmvYrPDbYoyKNehPkkthjplpiUxzIi6JXBKl3ncCGj5WQDvN6lRueQe0AlXR0Qg95ZKxhDbw+fUN/WbHh994n9Z35KpqN+M4AcI8j6uQRKWkhPfN2t7M+LMLtvs9Tgx+03Jx+Zxtv8EC292W3X6nLSrnqChmPefCkAeGz0aGcaTrO8R5nm621Fro+46bqyOb509ou45adXQ158RyHPC2AeeQRp3bssS7CULoaLzHOMs8TqqxmCO4ijSe3nbrtKLOBLBUTuPEkBfaLhBKxRXDbrPV71shp3Pi8IIkqxPOKqx5dXWlHQljmKcZZxu2246uK6RcWBaVCyu58MnHn3JzfeC9Dz9gt+05XF9xGG9oNzuetx25FF69fs0yjYhUXrz4mK7fs+t6SlE8nHOe2glNaVhi0UJeVHrzOGcKwqZVwQ+ctjTFGHzQ0e5xniFnSq2Y1pHXqLLxHdNYmcfC5sywbT0vr17hfMOTiyf4TcvpZmY83vD07F2dVZCWWgv7y2fc7D+laTx0ql2wjAvFVp5ttvoc32po5LqOCOk+qEanXW1ZuQbXVPauyCf2Xs3/ze5RhysUqbjLSr6SNS3OiDgkZPL45bvvLXECb6xWqF1Y+9G6aR1wqpX2lkW0FnLJlJzIsaww2plSIqXIekrfmlZUlEWON0UWbnnaKlpNvC2raq96GkdeHo98IxXaLqhyzTizLMsd8UWpFeMauq6jaVsqwr4JdL2GyqVAcA3bvuXyUvvozVowGsYT4zThnbsDgEy1kqeJ0+nEdrdl317Qt6pd6BrtTRuztsow+OhILJxOJ/q+EhqLF32QU0nEpGy7pRZSydSamJeR4+mEoWjbsvc4o4U/ra8UDuOAkyts3GHyjlgLrbQgmToXssm0wTMfK6fxRPAe7xu60ECpDOPAq+NLxtPAfn9G1zrSDBmrQ0LGkNOMc45nz57xjefvK3jrcGBZEt3GYiSsWguiRUBXlaGoJIwYmqbXKVOrvy/YQBVDd94zzwk/DJymQWcEnFF9hykhG4PfaXrknCHlxDBNTPPMMidCUyilKv1cHThNns0mUL3j+PKoQipSCF3AHA3Xw4AAFoMzE4OqirPb78Aow3Q1hrgqHD979h7bfsNxOuKNo3XCkrSNm0RTglsaAH0685rjv+nzv6kDqDO4gwtVLayXm1tc3K28ubtV7P1SezucgECWVY9xL0hymiNKJufAIkrIUOrq86rKUqeskN5iMqWqHmAtGZ0jTnyBmKHeUo8IrlaCMUxyW1kAyUkBO9KS0kRGWzfXAdo20FhPSYVUdTpvmQ0iFh9alQA3Kr4R+g2IJWah1IJYLTyFfoNFNfQ+/vgTDscbutBwdn5J32+ZhiPH05FlHvHeasGqs+CNhtjdjHO9Qk2lkhfDlA9kMdQMc64q0Bmj8tclwUrA1BVWnApzrPpzYqilcHOYWJbK+dmOWjKn45FhUbYibzxdJxwOr1TZ5/xEiI6ShTgntl2nApEuYcIGF1o8jifvvoO8fMk8nrhebpimiRghtC1dG7BiWaaZbBP78z0i4LwjxqRVG+twJlBqIueIqQZfLU0ZePpkh/UtrVsHgMThm45ymsE5tv0lplp88Ow2HcNwYkgTvWvxDsrGgDiWY8vm3KnjqpVxUEkwUwymWqgVYwpdE/AFSJWcDfMQsTKTY6bxnm2/YddZxHimKvhU8QuYZSac76iuw9qgbci4jnnLSHt2zjEupBxZsKQsyJoGm6xYmHpXLVwrvbfhwt1evs3+K/G2u0AHTG+6Y7GoLFl4gyX4MnsrnICIEKzFVsPFYFiCgBH1ZCyUoqTjuWqLrxZ1ADEr/XXKlVy19KcpwPomrK/9DnixJgaRqMzE8qYeoamHQD1iTYNYS1oWtnar8+PTzDiPbLuWZTHMc6Mh/DDQNIF+0+GaFvENjfPKmGMMoe0xzhLTwlwstla6Tmi7S8RofeLm5oacK84a/MZibOB0PGIwbN2GOS6YwfH++zuaoFDfVCNLtKRFT3tvDYSCL0Eh2LZoW6w6WunwPuCdwxvL1XBinkactUxkjoNOrVtrMakwjRMvxiskCk+ePGXIA/Ki4i/OOLcNYoTD6YjxjrPmnVVm3VG9w60Q4tZa2r6n63ratldexJqZl4WpRBLQhEAtit4rpTLPM9SEtR3zsChLswjUmZJHtlvVPgibnpphotIiOn6eMqUM2GEDjYLB3n33XU6nE/PNzQq1LdQScT5S6YhJyDmw9RtOHJjMhPEW3zY6xp0jxW7IopgSax2bzRbf9NQYaduGD977gH6z5TDPpLbyfPMOH37wIZ/La6o1pBjX7N5QckVQhihyXYVsVcNB0X363FdjoXhs1RHrgqwjx/ULe+YNUlA3cuJNvD8DTUCFW9CW8FfZ2+EE4I4Y5CQW/yVAiErRU77qKZZrXRlEqva0q9BVLbQMdfWiRnvnGU+lAw5YFnWod3/ddwSVQiYtC1JHXr0ylLxQaYlR04Blmjjc3GgfXyz9/oz92Rmh6yhZUYKmhT50hLYlNEHn+a0hLTOCcH75lLZrubq6ouRKKSonvj/bEuPC5y8+JedMGzqgrs7mhhifKSbeCGYd7RUxdxiAKg3d1uFQ8ZK8qiKnlBGTUBYf1VIsLtFYTxoVQ9CtYqy1VqZlpCyVuCxaW5krTdvwwfkTvTMrnj7nyOFwpI66WZvaIN4SUyJTOTs7Ux3F6MglkWpiqdpWNTVhLWRUXarkRC0F75UjoTtTENN0mtYRcYU7V4Qygm2UZsw5R06VW6HY3Yc70lWiVmVKolaW45EoSsmeS6GIUrI7W1iWAXGVZtcQckcdgR6qF2Kp7AwEDCUos1PXd5oW1cKyJIIPiBjCmSfMrdKIhQbnFJy1LDPWe5ZZuyWWmRQT3tWV11Gr//Vem6rUjKXctfTuQ37ebAuPL5ksgSoTud5vABYaDScQcRpL5Lc9HeBWfdh8obWhpwAKJDIw1pWbvWp4n9BpNFVnFUapTPdBFCIrKUMAjkDmXpwAVQEZuPW9zVBtxZLJ0RBtJC+J4/FAmmeurl4xx5FlmHVGoPFcPnnC+dN37jQNUox6oveKNmuCtsSWqMNQ292OzW5H8I3SbI8jxqxtqrGBRgk3a1X1mv3ZltFautiwyMLObtmUyMv5pOjF0DDlATMvpOKY7IwzqtrcdUIuDeNK4Nka5dY7Pz9nHD3UyuQmalXVYx8C1itH3na/49WLVxhY5w3CHSjIWkvTNIzjiDEjzYXHeU8sypq7zAspJfpe1ZyzKwzjcOd8xQhWGqzVk66utNttaHHe0/cB64SjGZhuJtq2Ub5JZ5hiwXWFZl1D13WkWDSXznkVG3FYqylV8oFN37Nvz5nNyLjElbZOn7XbynvjOzabWYNryQiWrmnwK0gN4xQzsW8wVolsDkvFSCLXio+e/kx1DOxeJerJKKDLe1588hEvX7/g4p0nGslWj6zagUZEuwF3qMHbuP+r8f6QSA4ow92zfscv0Ahyhw26d/0r7K1wAiKK83aswz5GPV5ec51WLBOJ23JfFchVSSuyLZSmUibt5Rub32ADyhoyyc0X/z+UjXmpmmjtYuVmZXYhVrBbXKh0XeDzV5/x3reesfMbPsstLAOh0coaikJ6AAAKUElEQVTydndGG1qO1zeUWtifnfP+Bx9ydnaOXyGvyRpqjLiup/hKypHrqyuNOnIhLpHleOR6uKa1LU1s+Od+5mc43+8pU2bpIhe1JfQ9881Mf9Ez+sB8HFmWIyEE2mbDaXmFLdDmTIw94gTnPVZm3GjJc8JcKCNSaD3d2DBPM8YqpPjW+bZti18d1Xa7J0YVS3VBWHKiOfc0k/bZQ9vyzuYD3JliE4bTwGefv2Q4Hdl7g++3jIMOHXX7rUYlMWGxHJaJ2Fp622CyIzSexisTUyoTKS34xdO0yj6dDjc4s0VQ59J3F/R9sxb+rFbIa1VhlFk3V86ZXNJdgXmeF+KS6NsNTQiUrPWd7DImG3wKlKIjxju3Zbc7o7GOMV2TbM8H3/wmbespPvP5P7liv23wBvrtjm3Ysj07IzYWMymT8zAPVKuFuxhnvvvd3+bv/L9/jzsC0FuF4YoOn2Ex4lnWFuCqQcIdK/EtevALz/KbdPbueFu+L/9PDdivAWLQdIYy6+mvLbwJ6BXWXjO+KnSADHUsVK+DLOVYqPMaDlYFyFQqJakjuI2g6i3poF5aaZf0TTsB51SuRKuTplkQacg2Mk0TpxcvWaQjTq+I04xzHhd6Qtcp6URREsx2bbOVUjguWp0nC6dh4DQMbPd7rGwUV3A6UVJWFaQQKDcHXk0v2e12uPee0zUd7XnHNI+rRLuy7YgIKWXatgX0lAahafZUhJg83tUVghtI1WK7eeXthxyLtlxLjxhVfgIYr0diG1nCwtnZmVJ13+PmpwhLnMmfZaQ3vIiwTwm7qTgXyCcVAIkxEmNisI6r4QpwtL5X6W8bsW5DyonWWnq3JYSGlBSeTBVSzm8q34a1eyLkUmjblikP6ykvd3MfOv23fn2vc3McBtLhoC3JklnmWVGjVtMX64UpDrAYTNWc31an3YF0YFkK7f4JpezIhxn/3BCCIWdNq4wxeGMQa8heiClhehUm8a3Smk9JpxZ3+z1/9I/+C/Qff0RMv0yVqtFIAqxyF2SyUrKtnGNfaAWWLznN8/15gvvWQFzAwSzQuAi0vPWzA7JUEHOv4BHU82WIUsi3UEpvwFxD0rkCHJRRgTvWovTQIsxSiesJsOIRv+R/1TysAFerJ22A3ABLZno1srwzcRpbOjtjreW4LFydZv7YP/8+oVPGmH7T0e5b2pWO+3g8cjoNOhPvRB+uvgMsXdcjsXIsylFnqz6QFxcXWKvdg84YpnEkdEoLfosABBVktWsuvN1usWJIUyabrKSV68+UolgK67R6XURPuHke1xMyYzdGuwbjzJRH7GhpcoO3Hh9UKl3BQ4lyUhHYOEQmM3FGYpoXSlF4rd+17LLltB2Yr2fmOXE8Tmw2u1XT0WLpMHZt+zpHaBqc1bSgpEz2npQXarXkJa4b25CJ1LmQYsG5gLC23qoi7aw1WjxenVCtBe8c8xiZa11JaS1t15OKpQ0tJVdSzsyTpaSinZ+sJCgikZz9HS+Ec56mTRRnSFlYXGWz2zLNyn4t3mOdkqmcUmLnHL1tsM6ya/eIM3S+oet7bNtSqyCl0EolSket8x2tuGbA6tRkVd6+PczeeIZVnHRly7r1BLdPumFhHaTUeoBcIrz6yr339jgBMWQxWhTMgjTQ1bqe2PdxkxmhgzojJFIua9ew4htHv11f0s1CWbQG0BhYyvehqOBNruTkrv2yVJBjJteEGMf3vvdtluV93n/vOd22o3GexXvOLi7AWqZx4tXHn/FeeodPzayTidZBrfT9hu32ku1uq3Pm16+IsyoIL/OMK8ous91f0HUd2+2WlBIvjwN9UVaZrm/X01g7IKUW6AtmNEgQGhr8pqHNlhQNMR2BQo2R5aTybs4HFsmYnBlubjheH/Gh5XK7IZbMMI1cXV2RUmK/21G6jm3NDMfjnU7itEzYbDnfFpYZxmFgmgZyzaS84J1jWTKuFs7fO8Nb1lRD9Qudc5iiaMRGjMqIJUWzYSrOW3yy9LJlnA5Yawlto/JnsbA53xBCyziMd8pPIpYYJ8zOYAeFVksSSl8QDPPwEisJ7wJnZ2dsthumZWEclbTWOcH7ylIKMSWmacaaSr/RbooxlpQiOS9aXE2e4irL9YlcDbnoe3RxeYFzhb7fMS8Dr04vuD6d8E4HqsRawmZD6FuiBawOyQ+S8bJol+juedTZk9tnvt5//NfPDVnLhqPRvPau+WVWJ3J7ZICW2b/aAQDIDxIl+MMyEfkcjcpfPPRafgx7ytd7/fD1fw1f9/XDH+xr+Gat9Z3vv/hWOAEAEfm7tdY//tDr+Ke1r/v64ev/Gr7u64eHeQ0/qAfxaI/2aD8B9ugEHu3RfsLtbXIC/9VDL+DHtK/7+uHr/xq+7uuHB3gNb01N4NEe7dEext6mSODRHu3RHsAe3AmIyL8uIr8pIr8jIr/w0Ov5UU1EviMivyYivyIif3e9diki/4uI/Pb68eKh13nfROSvichnIvLr96596ZpF7T9f78uvisjPPtzK79b6Zev/yyLy0XoffkVE/uy97/2H6/p/U0T+tYdZ9RsTkQ9F5H8XkX8oIv9ARP699frD3oN6y+bzAH9QQNP/B/w0Sv7z94Gfecg1/T7W/h3g6fdd+0+BX1g//wXgP3nodX7f+v408LPAr/+wNaN6kv8Tijn5k8Avv6Xr/8vAf/AlP/sz6/MUgJ9anzP7wOt/Dvzs+vkO+K11nQ96Dx46EvgTwO/UWv9RrXUBfhH4uQde049jPwf89fXzvw78Gw+4lt9jtdb/A34PfOyr1vxzwH9T1f4v4HyVoH8w+4r1f5X9HPCLtda51vptVCD3T/yBLe5HsFrrx7XW/2f9/AD8BvA+D3wPHtoJvA/87r2vv7de+zpYBf5nEfl7IvLvrtferW9k2D8B3n2Ypf2+7KvW/HW6N39xDZf/2r0U7K1ev4h8C/iXgF/mge/BQzuBr7P9qVrrzwJ/BvgLIvKn73+zajz3tWq9fB3XDPyXwD8L/IvAx8B/9rDL+eEmIlvgbwL/fq31C3PuD3EPHtoJfAR8eO/rD9Zrb73VWj9aP34G/A9oqPnpbbi2fvzs4Vb4I9tXrflrcW9qrZ/WWnNVEsn/mjch/1u5flFq678J/I1a699aLz/oPXhoJ/B3gD8iIj8lIg3w54BfeuA1/VATkY2I7G4/B/5V4NfRtf/8+mM/D/zth1nh78u+as2/BPzba4X6TwLX90LWt8a+L0f+N9H7ALr+PyciQUR+CvgjwP/9h72++yY6J/9Xgd+otf6Ve9962HvwkNXSexXQ30Krt3/podfzI675p9HK898H/sHtuoEnwP8G/DbwvwKXD73W71v3f4eGzBHNL//8V60ZrUj/F+t9+TXgj7+l6/9v1/X96rppnt/7+b+0rv83gT/zFqz/T6Gh/q8Cv7L++bMPfQ8eEYOP9mg/4fbQ6cCjPdqjPbA9OoFHe7SfcHt0Ao/2aD/h9ugEHu3RfsLt0Qk82qP9hNujE3i0R/sJt0cn8GiP9hNuj07g0R7tJ9z+f2caiJMo80t5AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "HopSkipJump: 100%|██████████| 1/1 [02:13<00:00, 133.93s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 140. L2 error 843.6781 and class label 356.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQEAAAD8CAYAAAB3lxGOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAgAElEQVR4nOy9S6wtWZqY9f0rnvt5nvd9b97MrK7Myq6urGqqutvtNrTNQwYswQzZAxgwMBNLGDHAYoTkCQMQQ4QZMQBhJINkmZctYxAIA3bbbttdXVmZlZV5M+/7vPbZz3is9TNYEbEj9t7n3KyqTvWV+v7SuXdHxIoV6/Gv/73+JarKG3gDb+CPLpg/7Aa8gTfwBv5w4Q0ReANv4I84vCECb+AN/BGHN0TgDbyBP+Lwhgi8gTfwRxzeEIE38Ab+iMPXRgRE5F8WkY9E5BMR+Utf13fewBt4A78YyNcRJyAiAfBj4F8CvgT+HvDnVPWHf+AfewNv4A38QvB1SQK/Dnyiqp+qag78t8C//jV96w28gTfwC0D4NdV7D/iidf0l8BtXFRYRRQzg4DUIYBS2m7Hr3h/MtwyK+3lf/hkaJdX/P3sv2p/5SmPzqnZtVvjzNeurQ/29n3kSf95Zr3AZQALAfo39+1kGmxNVvbFZ4usiAq8EEfnzwJ9vrkOFUtZIAfjmla3rTcy59gu+fGcMvsKkat2+HTUOgVm7qFYt2ShcXQZVXeU19SYoOTFKUd3Z7POwenm+q5k724n4P3GCCcEGAnlNYLsv6MZrW/XX7SYEKQFFdox/fSchJWP16inafF5fh4Bl9zT1QRagfdbD0aonAj+KV6GJbtwT/Hq11zfUY02EnxdtL3FIgGzjFaPg6g85MAE4RwqsOh9vdbJT6S5odUqByIBzGDfCMSfEUl456ALEqGaf73r6dakDj4EHrev71b0GVPWvqOoPVPUHAJRKsFVNuXFdYfdXACXw06Xdu52qNp40ayRgGwk3Pq3XDV1VmaXGr6Z2wo1PZ9AiALDd5xkR86/ISKrvKF6oMgqipJkh1GjnG4JnVtvExKyxQ2gIwKsga6F5q9tfCQIMUnokN+xAzjlECixozUfSPG5GcReatO+JgSD1bbPbz4ONb2td+1gh2OjSJgGA7cXs/I1l5+bGwGy8I2zMiUKsa8ZCEYATHFMCbIU1162N/MonXxcR+HvAN0XkHRGJgT8L/PVXvbSTILcogwE/MmH3Xod4NONQ0h3ZAIPnFsA2crYQVq/gDMl0XVbUIQiKYIAe7WZ1W1VPqIhH1C2Cf91Ciavpqyvvr+u8avKk7owDW8CKkrJaIt11aXxNOziv4LyGVhPFdU+appj6397VzTcE9AKBXpsI+XoiIGqWu2DpoVXtjt2McRuVsx2ov2Nk2h1XB7ZFrAhAY08cjB8OR8KmoJxegglBmw/G3Tq4mu4lXLdEq7fMxp1WRbGfEb9GJOwQZVuNZ9xIprvgaqH/ayECqloCfwH4X4HfB/47Vf2969/abn4A25RBtcMs3WaRWnz1TJCYupMWR4tbbMxWzQi0fbFRbxazExShCEPKZjH4VoV1H1oVSqPxRJ2PxFcRgtxPr9R9Xvh3dwkr9XcaXK/+AjZRoEZwy04WVEP9Tdt9WN929b9dFtepy4llaYFlNfKqiDpMJf8UzXJXSOYQFHTQsk07UnagiaJbI9Ht06uQ3ASWIMw9x25ezcCUne+tUFymrYGvSFIPkPG139glMABdseM6dUBaf6Zc62ktyGnLRX4BNPMeXU0evhYX4c8KIqISyCt0oi5sqnc7C+ALiXSp6ubzV6pjrRcipCO8B0AssNypW/oWhhWFXtNurZ7K9kJW/wZSEgP5z2OcamysARAgLf4Z4YmWX8h1r68byZ/FDvMVoJ6IWta9pvrNR2kIqxI/6PWANuLbpj1pBEzZBdoyFyGB19nt1eJygmceUazkBWhUiXRKB3kqE+AVMAYuW9evntedJcSvf1WtJBJBMBicb0bTiPrNoGmgqv5Oo3634PWJGNyxChudqCPDr4elI6m2IQSC2E8wmwSgEsxa3H796TUWRXSFvauaaYFlW7fcKu0oWeNMSzSp+d8OKEmo+UxFzdsz9apZcxCgBJQdAkDVjrL5ci3vfwXoDH19sfluPVHbDWxMLSKo1MrMztlbL/IaKna2LEHpE3XGWlryedq6v5sAEAKDluFH7W4C0AcS38sVfq6yXFAVNPe2P4QOQlxJAGLoEgDY1r/aCv/OEs3Nbb7t1s2wVbvSxjp8VasaeH2IQBsq3Gg6W7HeTcPulWuvBHE56xFZv5mQ7eK/FdQc2n+ygxoihLVOpoAGG+9tKdXrz4asRzoHUf9wp5mzutkWH0uFoD2XbvOF7SY0gv5W0/SK3+YKcuD7vRs2x7EAhKBpoEEqAVUxlYLU7oSt+mv8Qlb/DlYaTi3Q6B/+OqdAuvZTASSmbXvfCQbCGGS2loU6ELCmSwuu5gRsMNtXwdVCBkJF43YgczPqskt3FYRdfprq+ap65Rp7TQ2vJxFoDUZUN1GohOpWt2vqGdGYk2sz03o5KxA2A5bx6k57ftblUtpfezBrG8O11u+KYits2SgbifgV7Vgbh7QakrWCHLRKVQKPH52mTWtC061x48uhX4PgNkjCumgJhJ2hb6NfXbJuUd1WASK0IWcOKifWdr/bMpEjQJo+bcNVXoqcWrerR6nV3OYz5aJ9c6Mla3eOhxlI3i1lAAkVMV9NgqptQl3fzPrbCi3LftB5r7mKgV3OncrV1Lb9EOKHs27efMd7G/B6EoEWFPXq2TXvNfWsTe6VXcf5mWI9EgUh2lhor3ULUxPujVJzb0BWwGzhz27UXj/bXWJtGbgGpM0S19aItmdLy7UE6HHFi9oh3l6x2dbOpYU4rPqk67+GZlXjvsk3awOUNCVb42WqF02LBQoVJbmqn+t2WlzHQ7NG0jXV7Rj7m98JkqxHyX+uRRV+7qgY6fwyBQQ2Zps8dG+tF3jXCVwzpm3QCm/BElAS+pVdChTiH5nWJzbooa3vQWN3bgSsa+A1IwItltbAdez2isXn8CujRRILtJGQrvnylWAAqZhasyCk9dceSQHMltyyYc2oId4tbps2L69ncrtcjUqKN9LnIiABUtkiMmp5qG5NQIhZ9zeArAD3lW2PXtZaVS/XQtj6GdWwi7fk1aB4vWajC+ZVRJBXGc3XRBYp0LV5nCKu3q6EhF0awDUV48WRikNXq8kCsQhOMjpYI84rVOHVGNu9X+wuobUKa32DS5ASggBCt6alpjGcmO04D8H3+bql04LXhgh4R49XYKQt+myszm2EeQUKmbaVaceoVLfsjmdqKnFcryESXuqFyFtpmzpdlwRopw3tNmU70aFrz6mnaXtGcyq7WFWdqANy2m4z13lXKSq1G+hykzZR64BZI4pxrFmrJactNcjaVqIKi2Ibwza64J2FXw0N21aUNUkL1s3VEC5aLzSGlWCbUb8CbbxaVQdQ1P/7l3YykxCcaMd+0TW7fjX1YRNH6zqshdL5JhUOXN0mE227/9aTda2MulX8DxWaVvqY3LYU6TvYXkyboN2f7QICuBrLu0PRvVovXqk4rjTf9nAtE7FAply/B6DduE0/b5dDGqQjLEpLyO+CbEmkpuYOrTJSXwuewgZhSxqVq7Gkue/WxUNQsq1h9nVbENudI3cVQ6or78YqXLdwatLZ5nplZ2au8sbbRlupBSoJt1u1W2rzrTIibPvmW9+uwxvCdT3d+dKGMJifYdlpJdLX9UlrOSgGTTLItdVgJbS10Sls2QyuJgWvBxFQj6o1MtVcSuBag+9uqWBDKbvueXPLNc/8K36agizwRmdpld3R9i0jwxVqQrBejtsj30ysd+8VrQJSIbfbculJE4lYz7Yfu7Uc4GMa680sIJJBkbekUeMNAlf1raqpoctu43Hdorr9O1TdZuE2tFY6S6Jdrus29cSxLlGTc3O1b7UFOwOP/Qiq1+vbfehESNdfr7UwbBPrcfW3WNunqlt2o4T/k7ZDr/PtNUjnWZtctta6l7py6cyJsHZodN3DrzsRgK7EXrmQu+YT0+lHs2SNrgvVYYJscp+vqEQ0GGCxGAxl10W0A/F2rWVTf7xSJ2tm23F/bxKO1kTmnZrXDjfFbaCiI2wWhO6YTU8kzLo44jb7bsHWnKS7IKta190WoC1EVKVcq/1+CLsNESCoBkCaWl3rb/2trXmRbZvALsOuslbdPKxD8dpEaq1Y+ZbWzGcnyWh1POc6ulP7o1olNsxbriplt6S8TVWzy6h2xHRWqrMiFIjttkoQFtX7IiAGhGDNCK5o/WsBIkIziJYGYToR+BtM3sE65mRnT9qo3IUrhySAhACIKNGui2UH5diULTrXdZxyJe13PVC7dBca1FyLmpsd67Zc6kZvPTKe62h30Ti6NoRui9fiiHZWfrVoNISVX9BtualuZUMAW31pvluNRb3QpXnrFZrzronqiMRcoW/4sKimzmoYa2nCNbe81FSy5rRdE0kLL2tkDL1UJ52J3ohp3mHoqaM016HkvuZtz8FXCJ3dIdTWLanBqPeU6ZaC2YXXhgiobDhCK0nGC8IKUnpO1um4QCloBurWy0EwiNSGumsQbBdIPdclbVStm7FZ1tINPdray9C6v5PTNRW3p8+TP1+2aN3dtqQXAGatSnluXcdFXIFMnUraaG+3ynSKGj8SeS10tB42wpitl9QV0kQHwvabwG70b8da7YKGsOxcGF5+WvtUFQ21076vCrVr2IgfCpVatNnm5+22bboDs82vtzonG492wdbXOgO8vijFLxupgriugteGCOyawU19Ddi1b6JR3uoapLolXZZ25TB0BqGEpdQ8e/3GTiRWaTvbWo2kmU1TC8d+52dLnK+waWdbtrX/66GSGlpbAWpi1K6jCUB5lU4t25ee1OVr7NvAHMta3K3nw72yAyVrYruDYKdevetIOzsUvd2L/2rwjER3EuttaA1WJUZI4V3O3pZSB+svrq6i07haZXXXEMc2VNLShuT2VbtclzE7fVDrL7weUF65zwpqxKLGc+lwonrrrI9J85HU4hS0qwBvcpmm8wlb4Gl3ZRaqC65DESvocvCflbN0le3Nt+1We9NNlKkYjDq/QIK6zqJEWpkMNj/p0Upby26nt38bXPtn2Kl8596pLYljV4WtXYRbxQUkpsBvo+2S+a8KPuy5WXBO1lbO6mZNgq6EyvTUCYtyAOV6i6bALmnA19tegLIdNLVpbG2pVP7X2owqlVzUkUAbSthc+F+vpjCtNr7mcJVa2A5t3cW9dAtXqllvk+AInIbtp+tw36urB+r9WeuApIbLqwEVFMVWxGlt2m15xd1aUqnrd63/I1W/W0wVjRXXvq6kHKkarO0/AQKz3m1W/ZXq26OiVT/V91WDpuP1rtIO323dx4bEKd3Q1Kr0xo7jVxC5TWPcll8AXSpqKzOYFt0+bk/HDvD6vKI7kk58NUJS89yd3ysFtBU+Lro7vHcTOkbU9e8OrKmW70ODk5XMtTOlVKu+GhUJm7eugj+09GLXgmG9GpqcUW1QbC1VtXTXZi74KmJWa4NNsY1UdqtgAM6uFTK7geOOFundrK3e/e9TVeyy+O4ClbXl2jnvDfLdrRJVWUBMpftLd11q+1kLZKPdeJ3RkyxpumKqb6vqFkdXJ/h4gNpGsT3aIhsTg1y57jZvq+L3MxiBso6+WQu2X5UE1GBpTY007nzyKiRbddciX89ZZ8y6Ld0IIKmCIir2ILJhB4rwUucMfAxw/bIfpGa820gNPv6qEmzXmLNLxuuaBmoSCHgv1xXC9s8tCYjIAxH5OyLyQxH5PRH5d6v7/5GIPBaRf1T9/atftU5l3eEGNnDYsLbDbg7DJn3f0MRoQmclrJAqqObBNu82ST9kTVTizYZUVLlj6Zf6K2s22LRHFSQkENNVIEQglsYfvTkZVsQbOEUQu/5tjMMYg0ReVxTTKieCESFSIRRTlTeY0GBig5Hqz7T//IYYYwzRWJBgvSgSqIyslYgpjjyvJBjVKk5BO9+XK7hU48mpSYfUcpFpvWsqAhR6/4apjJytukdcZYbbhJb6sCFy2/ZFu31tf2q9hNS0yl2zZOoyRjcYdTXrzrUYWntzxFpSacaubSLZ4ik7WIhptyxsCbset68TBX4RSaAE/n1V/QciMgJ+R0T+VvXsP1PV/+Rnqy5GQkFs1t0v7egkc9Tqw+soqrDan15QC9biH1Rj3xK3qzUq0qamXYRtRM0tfVc7YobU3+rIc9eRIB9JV4h0n7aMedut8VDSRT3VovvupjodgIsdLKrF5nQtrbyCibqsVUY8Cq27Vltktnvov+s5o+IliQZq969WbaknuBF3O7XULfGLJpSNXLPq5ZYDkIv27c3VvLuz2vrr6t208G5j9bWUa9kph7TSiTipWTZb+ND4iNsyK5Xa1ioprXfa3G6TKNSSggasQ5whxvlw7rZUcQ0R+LklAVV9qqr/oPo9xacRu/fz1gcl2HJHBqDIz2eVW68mAn6xG3y0n6uuWy403USMNker7veq/zvUn06gRz29NTdK6n3cIuxieGsTVIutVAtI0XZOkeaT2xpqa6Ow0Y6u3il5hU4RWEjyyv7crCnZzsNWKZq1vukAl/uFqqpEIVhNUQVjFKeCkwCfjccjaB3WqgjqavnHx0aqGsIopdfvI6EQhAlSbYUTMaAGY0JMENCkHav+BIuqt4M4ofpzOHGeHyxAdQDqUHU+GKkeniZorB0NUD2W3eq0Nu927jRVrgO9tiMHg0oxEpFq02d3RnUtOLWYRasWreMrWOsqDXS9Jqrqx1nV4wbgddOaGfqkMesqdqVf68IfiE1ARN4GfhX4f4HfAv6CiPxbwN/HSwvnr6wkdCTlLrWlbAJujPhssxn1RNbd29FJV4lW9Xhr96EikEubwQHVdk1b6wQGrcWpCpqkn5ubCWrmtsniW6pl1ZuvoNG2PA5OvH7cVvZ2wtp44hTytrrZqrPm0Nu7BiOkTqltDPSUcgWihWdi1oeciCpG/OKO45jlcslgMMIRMl9MCNOYOEnpjcaEccK9t+4TpiHPvnzK0dExzx4/4XJyQRwnHN+4RZqmfPbpx1ycPCcA9vdGzGczCmtxpYUSjPEEVy0YCbzrMVdghTGCtVrt1xKvu+V+tUnjitvhUmu4aFfC2znGQuWd2Hrdj2Utab0iwRh4AuvbZRrCLoCTltTmKumAttS68fGOXqmQWNJcWRLifJjb1WrZBvzCREBEhsBfA/6iql6KyH8O/OWqmX8Z+E+Bf3vHe51zB7A1CtcrrNYBqgkpffRTIVWRtrFwg0mKiA8wV9kIJa1hHest1P7tEPrlRtLMjqlt/Zmt5KfVpG2pBLSkUoOqw16xiLt0qsWFautSDQP8sNTu9ZBqULqxiM1VHU1ZSPMML0M1tgiplcbqO2EIZS4eSY3nyHGcUBYFSp/SzbA5JIMh/SjGJCm3jm+RFbcoioLReI+777yLYrh55zaff/YpD979gF4vZf/4NuqU0lr29veJ45TcOc/ZyoLj42NETjk7nxCJwanFVaGxoQg4j+IiBhGL+A33iHrbiu+nD5TSSi5bS14eGRTT3A8QjIGybQeUTYWiOnUgkN2ZqLd0u4rrqyBmc74VrzVpg+kOL3lJK0+iNvVoZzHXvxuVtX5UOE/7Ygv5+v1QhPIVeUR/ISIgIhGeAPzXqvrfV41+3nr+XwJ/Y9e7qvpXgL9SlVO0DpGpR3lHTiatlmW5Uw7vUr6KmxvxyFyrxACk0F/BMgbNa6ZukXZKph0ZdOttBEUjO9bUSNCrnLLNbW8tJpatTEN1u9d5BfeASYtTtDTRJR1kaSKCarn8qijjjRgHJ5v7BDwlc04py7WOKkbAQZhGlE6BDCTi8PgWpfVUNH14yO3jh4R7EbpUkmTIg7feorRzouSQ3y8+xYhSFI4bR7c4unmT0lqCwFBaxzfe+4BylbGYT3FJTNDrwXRGEVjCUpEyQFRxWIxGhGGAUx+DXNqSMAhxWvFqFSRRNAsQ7SHMN8wznvLV3Xemkopai7u5rHlA7J0UV6Wi31YhdON+93ktW9rWXaN0sL/zVjvgrWNr6ZazQFBoHXWPTRSbwW7RtVXNz5ttWHxr/ivgTFX/Yuv+HVV9Wv3+94DfUNU/+4q6dKfosmHfaad0qjXuRuptGdxqKhlQR+n1QVdoxT6NEVDFiUGNW6fzrqF7VMy6na0mhYhnOkZb28136O0tFi91kMimzaoHsgor7lXzKIerDq0QEfrAXCtRsVqYHRP5jk93btVrvnZ3st763IycUyTwXoKyLBERnHOoCGGg3L7zFs+fvyQsHT/47T+JtcrxrVu89UvfYDlbcX455f79t7ClZW/UZ5kvOJ1eEGYRx+NjXGAxRgjDkKxYkqllkTmK5ZLHn33OZ48+xQRCmsaUZclwNORgv8eXnz/niy8/YnE+xUQpt46Oefn8nDy7IAgEZy1BL/DpwAEkBV2BVrPVIQLb43QlbJStpf7dUnaNGbVa5tl0R+SnshkFgjjh5117QOO6leobzfrxKaqrdtc2Ianf2Zlt+BeRBH4L+DeBfyIi/6i69x8Cf05Evof//GfAv/PVqvMD0vFsb4bT16K98eaZRnWn2u8r2dpdL+21tsSHn3pRseFyqo3EIG11rk7HwwC/g2i9guom+WzX2s3zVRmHjPrJtpthwTvUCABWoFquDUeR141EfHxYGfm0/YKs7URxtz4Vj/OmDkRRJUQpxPjCzlQfqhoi3XWQoOQSYgw4Z3HOEcdxNbqOcHDAy5MLHML41m0gYu9ojEiIlkJ4dJNbSZ/xaMx8NidU5fzklMn8gjt7dzg4GnN+egYCq/mSbJkhAfQERALS/oB3v/E+eXqBsSmR6fP2228zmZyxKENmmvPLv3qbNBnw6Pf/KUX0JeNoSF6WWHLcqvTsPQVWtRppm941Ic99fCLiNoPp6gx+bJ2Bci2wwxWLv0aRBjNqPXWjsHh7CngCEAN5LGi+SQjqEMQdTv02UdqQ7FQjRIpaTCUiodAKkUO9NiHGz00EVPX/Yjc9/Z9+3jphl8FbmwUG7PRniFYbJNo7EYH1gQPVvUg2LHNa6ZTuCnvOAnTtckHxSJavmxKxPV11EFJkQfFpvjBmU/GHiheLhgilxyfVRq8FKCuppCHo9XmIdXujsJJVabYJR1UTS8Q7T8ymauUQMdXQKBExpThESrRWo4zBOcdwOKY/6DO8cZvPPnvEu+99i9sP7pNGKYWF8cGIyWxOUloG6YCk1yOIIhaTl0zOLuglMf2BYbGYMZueUlgYjfZJ+30Wy4zL1dRLHQiDwYCj4RE3bt5ib3+fMEnISscy+4Lv/eofJ8uW/PCH/5TTFy+5e+M+KobLly/IsqU3HBvBrWoTnjSSjrqVtyHYFGar1iTVeNL6XdFMCgdl99QHZQfC10eidRbkNofXSH2m6YqS5NAQ+qZ8Q6Ary1FjZXYdNUCl+adqvtCIs1VVRT9vtjP0Nk1dG/D6HD5Sd3LXAY/E1SYRQaIck6+tqdX7npqFfmv8pn6nOsSPyKu2jNRKYFem7pgIqtOT/RQIVSbI7aq0eqS1IWMTfXwNWh3EKmhjWzZENemg6lZrX57uqmY3NJwjJSJAmDdnGQhgnEOM8e63yrbgVAnCCOeUwWhMvz/k5u1bfPt7v4a1sFgsGA33EBGOj4+Zz+d891sfMlleslouGe8PKa1lMS/4/LOPmZ1OiEZDer0+RZ5z9/Ztol5KVpSssoJVnhGIEJgqQMmEFLak30+4dfMuw8EIpw4jho8++RFJFFFmE/7aX/1vOHn8pY/jsRnek7p2pRrj56l2zUkQIbFFV7oeGtnMfHA9XKlJ7JiDph2VkleX6ai9FX/SOplsy+DdGP7q+WoTgdZnt/0R/k4HZytp5etQB74eyGB7uKtMkc6rerYOnTaA88hjRTr53WrwOvaMq1dKG3YINtKlop5n17UpWwSgbaxw3vqsUiFCByoqXhs5Q8E2mwG7ddYZa72aEPhJ7QEnrW7VB820w0Ob7qw2Iq9DjDEoJU5d5d/3CyeQgFIhimJMGHF8+w6/8Zu/SbB/i2hV8PSzz7g4O+fg4MhHMVphNp+QL3MuJ2c8f/yFdxuO9xn093DOMJsvmFy+4PbNW4wOjphcXnJ6ckIYJxweHGGMMJ1NiKOIUh1hlDLqjcnmK1zmGA1HBJHw4QffJk1TXrz8gmQw5ujufaaTC7JFD3FT4iSmKDPAZyuWKmJS1YEW6KqW3YrGaXIdWzAJaFmZFnZkhGvGeAdqHYhwYfB72ALZ3tKJb0pQtDatVzntG8bcqGzdF9tX2+23KFrhbFXyFWnHXxsiEAQ+meIroR6BiuMH4qmtVSCqJmpjXTZawfVG0qZyZdvuVoN5lTRRxy5VFVnYwJ42tOh43a4N+te5LIyve05rYqtOte0Z10EKOEuZlY1ByTOZmCAMiXsJToXI9LicTfn2r3yX45t3GI+Puby8pAgjppNL7j14i8efPCPaD/jJT39KXuT0k4CXpy8I45SDOObBW28zubzkk5/8BBVIkpj5ZEEQGA5Ge1inZKuMPF/x9NkTSnUc3zjm7u27JEnEcDDiYLxPHEUEQcLF/Ixnz57wwx99xHS2YDVdQJbz4P5tjo+/yeL8lI9+8lMvMSaCKcBYS6aKCQ3SAzcrGyNdoyjK7ily9Vja9nnpCetJ3saFWpa8qC/YLOZFXTWewPjPtwzGm7kFOlAbAtaNrT1KmzGKa9WlRvorJFZeE3XA+OBzr+8KGydI1RbeyudbiU1SClrFnAe166XecFP1qQ4Bro3pqtqlA21LagvWC+8KXyExAXnF7IVg1wBXu8nc+kyMa6BFDK4jArXqKOtJFqjw6lUUrmpB4rxElQtiTCVCO/aGR1ij2MDwjW98kw+/+yHPXpzxz/3Jf544Snj8+Rd88MEHLFYrJucT/pf/8X8mCEP29sbsj/eYL6acPHvK/uEe777zLmEQ86PPfopVuHv/Pr/y4YeoOp58+ZhPfvwxB4c3GI32mJy+IC9y7r9zj+Mbt8iygjQdcOPwiPFwyDJf8vzZMyzK2eUpF1KUp6gAACAASURBVMslh71D5tNz8tWccT/hb/6Nv87TJ4+5vLzAFjlihCiMSHp9YuM3HK1Wc7LMYe2SMA0hATd1zRhf7SwIPTuvvAwR3sff2Px3zZd6E9B1S6vzWj39VUM6a/Iadc+3pSYL2ikXIFU+BwOR37PwWqsDzSTMWjebdeEaltwkmGw2ltXhIBXNCwGNwTpU66xwKa5fwMrVady7WgbrhBj17eYUH7N9orw3JOcNcTdVg+oF2QgB9dFp7UmUHd8zgLNr4iRrSWQrsYjs/Flxf9lGmHWDaYhAVlm6jXf/+ZBdIRpE3Dg4pD/c451vvsf44Jje6Igk6fH0+XOK+Zy/9zt/l5SY1apkNV9wfnaOeestolJYZjPy5QqX9ZmcnRPvj/n+r34XF0YkwxHaT5Esozce8O63fon98T6z2Yx0cI9empLnOV9+9gQC4e69iPPLM1bZkmdPnzDa32M4HGB6ETci6IcHRPKQj3/8e/yf/9vf5uOPP6HfSxgeDelLSjFbEPZ7TCYzsnKGCfz2aQYlRg0uBj2zXiWq+I/P0NqewXooy/VYj6CoTUsNA0mQVvZlX7aH6gbzaO+Mbc+fsOFhahkL48qGsKqsOBvHkRXtCd8gFK796+p8IsBrQgTAd3ItjNPNdGV1HVwt+L3y1ak768jgGFcK2pzjG6FisbpAF1WyLqc02UhayQaCji9ZKukCCjVIFePXjkOowQiUfTx1mraUh8oRUEcW1u8Y9fpaIPX6NIgVjAqihqgiaRbjVRgpO0FI7QN9vbVZvHErAFuuKsMToIIxgfdrS1BhhMNJFUduHIGzRBJgrSWMU5bRgF//zh/jrbfeIk377A1HACwv5sxPzpkvC2bTC6bnF6ha+j1DlgyZnD3l5fNHHB4f0RsNcBKwyHK0KJAwolhkxFGEmS1YLBbEBDz85gfEcYBzFlUorOXZyUvCJCIJYigtpct4cXJOZAyDIKYvMREwkhHBaJ9nz59yer7iwTe+zcI5zi/OQQuefvYTDCX64ozAWeI4JEluMF2eeM+BCMxdsyNRm/RHbePKes4r861Xo+ZUY9mWALrp1z01bxGAmpnt2Cel4vMEbJ4C2dRXrNWVpgKtcFilhRBrk1Dz7tWizRa8NkSg5tqw8r7chb+qDRyh+viBSCG30iTLrH3nTnLQ9h54H+Jl6gUvsN5lQmceO8J6lbDDD7lttQFkw3WhTglntbHBIvVeWZtXXgn/aWt8cI7TAFGpjlCskmUIlKGjkJK+wKISA0RrSUDWCSUqtUJVMUUE4gN6cBCIqYilH0vnqmCfWvHEH2AthFXUi5dZj2/c4tbdB+zfu8XDt+6CZKTRHv1+nzzP+Z1/+Pd5/zsfoo8f8U9+8ilMSsbjMZ9/ccqd0TEuUQ5u7hOG3uI/2Dvg4Hifg+NDjg73OXzvJqpKUVjeeecder0el5czJpNzjEASprhyxX5vTNAXev0eaS/1mXymUzKJmMyn/PRHv88oHHE6Psc9/ZTxaMStgwPeeXDMe++/xWp2yfnZKX83cJw9f8GlK9HMUpYly8sXhKHxQWJotXYqbtuEZdd6ufWLv44orJ+0g8eUtRW/RZiBtXwuvHorgaMKK66lkBbBl9ZmOAkQUbSdwGSrKl+69oL/LPDaEAEfSLHyY1H5N+vFp5U5TlEyIqTlN9XKayCEnjAkFpx6cVwMaFGJ1rW4fN2eqmoi6gksYdkxC3gC0M5z4k01ZeUVsFDadW498QOsrqzOAwiqty0upPJngrGKirDqgVlpK3N6rRDoeneagKpDcEQYctVmz4xIDIGpsvHUaY6lspIHuABQIdSEIA4otCDsDdg/POIH3/l1hoMxq7yktJaLiwuf/8A53GLBqlwwezZjbzhgucwRWfD56SOsXfD+Bx8wmZ6zf3jA0Y197j98wOBgwLA3xNkCi3Lnzh0ODo5YLpdYtQRxTLFcMpud0u/3uXnzGGMM86Lg7Owco54Qr7IpYRjxrXce8tmz54QSUQi8ODtFKcm1jyr88McfE4Uhb73zPqPxPl9+8TmnT5+RlyvCIKAoqkTuBpyDIBYfqenqLdfeNhJFAlQh0mp35AWQjZ87lPb68joCILV0WTMorSPQfZpwgz+2DSGsJBOvLrYaVNsr62awTQCaiEWpOOsOeH2IAB062LprkJAqbrtSoIz4KKhC1mqcql9jdehobboRnwrUiniDYa2kx0DmJ6GeTwUkFiR3qDXe4LZxSAUIOT1gwdq7H+PcsrM1r40HBiFQT8SUHL8PJgRRvysP4ye9sok4odoW6lo2hbUKY0IDpUHDOwTuBVYhVCE3PnDIBIK4ykNdSRykQhQnjJN98lKZzS84ODrmwdvv8Mu/8iHHx7eYz+Ysizn5fM70YsliseTRF7/P0eER5dIyGo8onWWRLXBOWSwnGBFmsxmFLUnTPcb7exSFJVwEDMcDiIUoSphdTgl7EUEv5ObRISKG8/NzTk5OwCmuKP3OwTzH5jlnZckoSVgs5gyTlFUckSQxL1684OXLF3z44XdxAhfOcTQc8t5732a5WqK25PnpOXfe+iYEPdIkwZ6fcHZ5hqijLKsj62093tXshxCYHrYQVLPWHK7xqVHrazqQ4m1NVMyICr3MOu48HEA5pxtsshkLU0kWdcSouEp1rZRkW6F3Zw9JjSjta6l8kkBHDBHos7zSU/haeAdERD1HlZYu01p2Uu8JVzA+9l+sp6Tr5BVr3U5DGuuaX/deaQqdJ66hKrlUVLJSMxKBDPHUv6RLHjcMe/WFaohiG2WipuBSaiO5aCsHmmiCYFnnmV1nH9LK2SMUOOtPo5XKqGCqTfCqgjpHEAQEQUCRWwJVTK9HFKU4JyxXiugEsbbKZ2mRICRMe1gJefDuN3n3l94nTVPuP3jIg/sPsBLz9PNH2KIgCiL6aco//N1/xOePPydQ4ejoiIuLC5I04XI+Y5Sm/PSnL5nOvmTv7T0+2PtlglFIr9/nN//4b9FLU27fuEVeZASho9c7IOkPfG7/xYL54oI0BacxKglxGJGtVuR5TlmWrFYrrLWUzrFcLTk7u8AYw2hviJGQWJXReEySJAwPjxgkCeeTS3Jj+Ad/9//G2pLlcs5PP/2ER48ekb98yYvzpzibI0aRIKiyx9om0MxvAfELqKwYg2pQSZ2tNdLChbYqsBlmti4WMMR6etOs2Ura7MmaOYfeps2ihWad/R4bEPrIZinAi3gbMkAbZ6vvvtbeAYDamd9TYUlIvdmnmQATIi73YrGttl2GDg0UKep0YfWm/bai5mO21frs6z4zjeL9kXMQT+FrS6vA7lGx/pDdVW1u19DPgAYVL6mSYjpPr6lz8ynNmYZqc8RBoLWIXxEJIzhZINVux8j4MxaKOAHpYUWRwFb2BCUajRgOxzhryV/OGdzf5+133yXWmMdfPiLPV7x4/pSy8MZCE0bE/RG9/oB7D97l/lv3CIKY27fu0U9H5NkCsY7z03PyPMdabzmPJOLoeEyUpMyfLvjWL3/Ap6fPiOYriuIUVBiu+oy/OSYMQ+7evUcaJ4xHY9I0ZTgc4pzD4pguz5DckqQhg8E+1jriKCRIkkZUFxGUgvl8SpKm7O2PGRQDgiBgAfTDnCQe+kO7I69PX7x8ysvSYa0ltyXDYcrkcoLpJxzfP+bw+JCPf/f/49nZE0yUoGXhDbEBhGFCEESNkTBwAeosqWRYC4WzOFfbZGrDzHq5t3Nc+vXaJ2DR2R0oNQGob7R/1yHHVYBXNe1rTeOKpDEG0LI2KCqh2HrLQKNdoK2dra/g868RETCoWhZEBI0Lzvv1rSrY3ItDgjcABj6RtNQJJMx6RlTrmHytRKh1Ak3UeUmC2Vp3rwIJ1urCZtu8JTcTCCOwDkzhbQ2RQEaEo/B1V0qdOouIP0DErdbuqLrCsHLRBYHBOSUMI4qoIDQRYSaIEcJ0n/7NWyRJwuH+PoPBiEF/wPtvfwsNHIe3b/L06VNmsxkP7t3noD/i0ZefM5tfcnp+wbOXL1gtMtIo5ej4mDv37vLdD7/H3nif5WxKmVmmFzPmsxmmMhs+e/KUsswJoojj4yOO7t4mlpwvniacnJzw8PCIc/eMoigYD4fcuXOH0f4+/SjyR6wbx/5oTJZlFHlBGIUUZYEtLVEUIZIwHA6w1nF2dsbJ558jARzs7zEYDEhJOTufcOkmxEXIIBmT3LgJqqyyjMCAC4RlVhBYZTqbMrmcMRrvEYaGm7fvsciXnD1/Rrkq+PiTTzg/m5GmPUZJjA0CYhOgVbIOa73xsCwLynLljZiqaFkbT6sZq7JH09qCvTZO17AgSMGuulLBToRCgAR01agGbev+1e+uA9ks69OTRDYSzZrKhtSCq6T+10YdEBOizhGLkGMxQYCzflh8ZpmAOErwlm9XJcgUer0ey+WKvChJKnWhrCikw60TBBuDdRYTCGVZViI20OhZNV0F+iDL9caOtjongTTpnZwqMYaR6XFiJwiCkQAhADVIc+6WVLq5Q6VPEAcc3NpjmUfs7fWZzWbMT85wccp73/sO73z4kDuH7/C9e2/z4Xe+g04VHSgvn71gb28Mil9ceYEYvyc/7aVkRUagIYVzTJdL0tGIsvQ76uKwjytKzk9OsXlBGCU8e/aMyeSC6WSCLUo++eRjLvJz9voJzsX0B0NOLk45Ojqk0IBf+dYHfPrxjwmCgMePfko67PGNew8xxpBGMffv3+bOvYccHh1iKUmDEUFgiIexT5duDDhLlnlZzFUE2QGz2Ry7WlI6xapFnSOKQpaLzBMQYJatcKXD9BLOn1+Q9lLG4z2stfSGPbCCiOPxkydM5lMeffoxTh3Pnzzm2bMn5KsZNp8zvVxQZCvUOQrr9UZjTGWrudqaF+DV+SWNtuld1PUS2syMvXMfzI6idUqxTuBafZjBLif/1aRivXu4WyYFFs69vuqAt2f44OpCBJzxYnUQIlFYGbkUDSPyVUYY9ImSAXl2SZkJvaAPYqrDF0t6gWGVraC0iChGFKzFhAGlLQnCYL1XvzHytaAS02oPxLK2sBqp/MXqM+IKlGK5sFMiDEGVydfvMLZoEGCrSEdUMVGfwd4+h0dHfOtXvs3F5ZR33/0lPv3JT/nRjz/m9vFt/pU/86/xS++/z61bt3ggjsVijqVE5iD5gpMX56wmKwbjIaEYRqMR84sJqzBgtL/HNFsR2BDJc/JsxWA4JDAhobMUakmNoknAbLlkcvGM+SIj7ff4vX/8EZPLS24e3mT/xgHnkzM0MoyGQ+7fv8dypZycnBDEXqR+8OAh+WpBkiSEUcjheJ+ejFBnWSwXBFFEfxxC5M80MEBeFIj48GE/X8JyOSfPMuLIEMZjsrLg/OysIsAltiyYzeZcnJ1zenpKvz8kjXpIELIoC3pxQn80JJKQ84sZj578mM8++wxrC05OXjCfL9ACyrIAFSyB5/zOG4yMwedOVFd5qFp4CUDgJTh1bJ4zZNqGAai28bayWGa7l6tS7YapCzq2I1dDHwhTn558hWbQhSQkyMtGG27DNYd7vx5EQKu9t2ICb+xLe4xHY6IkwSEcHd0gCEMGwyGTyQRbZgQEnJ9HDIdDoijEWkdZenPcwcEBk8nEx7rnK7JsiTUWkZJIjdf8KwONypIqex6IRbTencRWlJc6YOZDmGsC4dRLJKJeWkFBQ0OYpKRpggkN6Y0j+gf7jPr7fPOdb/LBN9/j3r37PHn8mFs3b3Ln6DYP79znxo0bPNg7JJ5Mmc2W/DQpScIRLJQiLigKx7OnL0jSlPLyEltYCqu4RcHe0HB+MSVbrjAGVquSIi8Jbyq93og8c2TliuUqw1pLEKZcLgoef/GEvcMxs+UZg2GP8dGYi4tTinnB8Z09XJoyOT1n/+gu8/mM8WDIxfkZZxdn9NOEKI25e/8OLneYYQjG4IwliQfMsozIRaRp6t1vJkRVCYKQIDRkeYFz0O8NMMZgrSVOU3ppynw+Zz6bslxecjm95GIyYT5fkuc+Djvt97lz5w4vz0/ol0uSNCUj54effMTz50/QUpnOLsiLgr3BiHffeYfZ5IRPJ59jX5Y4Vx/Zvhaaa67e2voBODRWKCLEbWaIrPd116UViOhRNsSiNtmt7fhVqGGlDWCpmL3Q2dK0cbJcy3919ULKSsqEyuu18do18NqoA2EYkqY9ojghs3Dr7j2+9898n9zBt7/9HVZFwbjeTKKOJ0+fkiQJ/X6fYX/AkyfPyfKMw8M9HMqPP/oxL549R7Mlk8tTHj36DFCsLQgDw3KywIXrvHpGva4XiWu4d3NyjcUniAQCEe+Ldw4TJVgTE/X6mDhGogAV4fD2TT783q/ywcP3Odw74M7xTY4PDkmDgsV0hsstWVGwWi7oxTGlyzEELOYzJhfnzKYzstWKLN3nt3/7t7k4e0F6MMAslPlsxng84vnTpyRx7PXDQBiEIV88f04cRUynU9LUJwTpD4Yc3bxFYZ0Xra1ighBMyGePPuef/OPf5XIx4ckXX2KAw70xYoTziwvuvfWAJI0ZxjH9w/vk+Yp8ueL2rZs8+vwn3L5zh4f373J2dsa9u/cZ7g0ZDYYkvR5pmpD2e4ASxz16gxSMEEqALR15kbFcZSyXK8q8IF+uWK68oJ3nBRKUWAfL5YrJ4pL5ZMb0smQ6fUkkEUmvR15YCGLG+2MKV5LujTk6PCQNDGVR8uijH7JwJfl8xv/5f/wtPv/8S1azCcaVhBriAqW0lS9A1G9HRqoMQrW6mODUMxBvoxK2FYbdm1AETybqSONdiY5enZr0KqikU1oHpzRiR4LWekhUeRAA93WpAyLyGX7LjwVKVf2BiBwCfxV4G59d6N+4LuOwGMNwb5+01/MTv8g4u5jz+z/+mO/+4PtoVnB4dMxitqSf9FhMLznYP+DmrVtEUYTLHQ/vJRQUJGlCHCdMJlPSsM94FJNlc9J+n9lsyuzykmV2SdAPUFwTd+91KedDDyrDvgTgCpAI1Em1d8xzjrCfsjc+ZrB/k4fvvcfxrRuEvYRkMOD2nfu8c/chdw8P6UlAdn6JThYgBdMXT5kuF1h1ZHnu9elUiMIhyyzHSkDv8BA3n/P22+/jbE4Sppx8+oRekmICw8XpKdPplLkxFEVJGBrKvQNCawgTYTjsk/YSprMps+UUcxEgJmB+uWQ8GDPspczyahehESKJ6KUpo/GQ/b1x5Y4bY+IQSRUtDUkcsZrPvCchMLz11kMOjo+qDUIPGCYjgsBRqtKPQtJ+j8FwgIghjCKiMEBCbwylVMIgJIkdYRDgioKZc1hbMJvPKgtNQBQZgiBkOBwx7V0SBi+ZLgKyRU4S9xEMw16Po4MjwjAiTmIO9g65vLygsMrKGS5fzHn8/CfkeYmIj7twqhT4PbwCYCCsmEDRpHeqbUVZZW33K8waXTP9BioCUMcPVHWqg2m7YApkAZTrPX/txLO1ANpkx2rVub1otDJWbt6H1lnengC8gtL8QakDf0pVT1rXfwn426r6H4vIX6qu/4MrGxFFxIc36I32efDwbZ49mxCFA0o7ZZQecHJ+4Y15BEyKnMV8xnK5YD6ZcHR4hLWO5XKFmIDxaEQ4CjncOwYXcnTjgMBA3N/n9OQFTx9/ybMnjznYczz94nMILdYWPtpPLZaAoEobFqZ+j79a408hMxHxoMdwOOLm8V3eeudtjm/c4eFbD3j/vfcYmxhTOnJdkZ+fsZxdoknEbDkjy0sCZzi7mHAxm/nMOb0xagzxaI8wiigXC4gLjo/8nvu98R5PHj1CnGN29gLbH2DCGAlj9o9v+M2shUMoiKIBPQciObOiQEJw9UGlQQIIUZAhkoEuEDWIKjePb7AYDHyknAiD4ZCXL0/IV3PUKrePDpmfnzJbrrhx8wZzWbCyGXeObjAc9plOL5BgzNJaetafHuTdo67a8RWQlQVqfRZcFJy12LKkyDLyPCNbzZhfXjKdLVmVBWGYeNerK7G5xVpLscoYD4Yc3bmDWxUUeQaFIzAAFmMCstWC+SxgtVownZyyKha8nD7n0ZefcTmdEAZCk+ZT8DYn9V4bh0FLh2jhVT3xuw9FvBtX6q2bVaLByg+FElRnMnhcbnabxLQU8ephBqgjUO/etNVeFlFTea4MzkhVphW+vosQhIag2kGubYJRhUR2vBOvEDV+YXWgkgR+0CYCIvIR8CdV9amI3AH+d1V9/6o6TBDqH/sX/gz3Hj6kPxjx7rvfIU2Ez598RjnNiIIAW8zpp4c8uzhhmMbMp3MCEcbjMaqO2WyOUxj3eiSDIaPDI6Iw5OzijJs3jimtZTq95MbRHh9/9BGTywl/52//TfLMi6Cr5RxnCxSL2ir3vghh0vOifhAy6B/x4Jtv8Z3vfMgf/43f4nDvgFQVnKUX95hMznn25ee8ePGCVebI85w0ien1e/T6Q9I0IYlDMqfMFnPGB4eUVkn6Kb2kx2g8YrlcQVEgYUCZFSxnU6aTC0IReoOU09MJQdLjgw++RRTGlHnEyflnnJy8pBekFEWOCXyi0CSNieKYXr9fuU29szRJUyazJU+eviA0AVm25Pnzpzw7eekTcDgljWKeP/2SXpqynM+QdMCf/tP/ImWcEjvHaplzdLzPfH4GpsedG3foD1MGaYriGO+NSJIeQeANccvlkjAIkNAfNLKcetXn7PSM5WKOW65YlQWEEUnSB3Gssgx1irWWKI7p9wcsl0tyF5CEeNeuOoIwxQQhYRQQD4ZM51OevfiSH//jHzJ3OUkY8/TRZ5ydv+T08ZcsZxcIAWEYYYwhyzKcLfHLXrsH2WoVkFapCopPElO7e0UNUWwp80o88Kb5po7NPAUCqDGEUYnLqjSGik886g9gJNCC0PmNjYrxR/NJxaeoSUqlW1T7idunJl9lB/ja1IHqc39TfFTPf1GlEr9VZxwGngG3Nl9qnztggpDL0xm/9ht3GY72Ec24nOQEhcEqrBYrptMF47dvMkh7ECjD8Zh8seT05JS9g32Ojo+4nFyyyFfMrCXuDzBJwnK+wB5YnFPmywV3wmMOjm6wf3CD+w9/wunJc45v3OBiMuHk2SMWyymqPsNuGRpGewcM9sYc37zFr/36b3L/3gPu3b7D4d4hfRNRTk/JFjNy1+N0dsLLZ94infYHpL0xqyxn5ZRxr0cYx8T9PqZwLHPHaDgkTnpMl3NE/W5CdcqqMu4FgDrHcDjCFpaDowG5C8jmU/LlihULgjKg0BKThkwuZpyevvS5+xXKomS8H6BlQRTFBHGCy3PK5YpBv8dwb0QahiThIaNRnwdvPWRyecmPP/qR91mL0uv1SJIEF0RMp+fcvvs2YRhycf6YJ0+e0e/3OD4ag/hwbsFnE86LAjC4ipsFgCstq+mUfLUiyzIWiznL6YzVckFe+Ov5YsVo2KdUYZXlPhciwuHhPiIhSRz69HJO0aBkmA7xZ0sGDIYxJo4pbUqSDDm6f5fsyWOePj9hsfK55dO0B+UCWxhWRYErS28QNFUItzZbOpqFvH1Qi6vURx+8VWZ1YpZ6A3h9WnTlXxCDrQLRFMWppazdA6qYKkbFikXFxy2W4mmcwWsPynqrgPej+XOPKCz1lxuorJHtZl+Xz+IPggj8CVV9LCI3gb8lIj9qP1RVFdmkh3TOHRiO97U0yvn5lNt33ubs5UvCKPJx/ybAGcv+wSGrLGdvuMdyMcOpEkUJq9WSycUE5yxFUWC1YJDEZJMJM1UuL+dM91b0+ilpr8fp+ZzlqmR/tMc/+9t/isVyRhTFFIXjJ5/+iLPT5+R5RuTADYR33vplkjTl3r37/Nr3v09oQvoYJCvJZjNm83NWixWr5UsulzMskA4HBGFMEMX005SoNyDsD4mDgGyVI8T0kz5GLbExBNYwm14yu7jA2hW9dEi2LIgC9WKvCRjv7zFbzsnUsH9wwOnTZwSDBEkMpSoRKTpS4mVMkib0kj6SW+IoIgoMaRSwynMvfucFCbBfpfZ2eekTijpHYAJu376LtYX3qjiLs47heMBiqdjSB9ekaUpZJThVVZaLpY+5ijP6vR7Z7IJeOsQhLK1lLwzJVjmTizNW8znL5RLnSvJF5g2DRYYrc5wtmE/OuVxkREmPwvqTGJ1dofQZDkuc26M37kHhsMWUKEzo9QcUuSWJ/bbxftrnzs07PLh1h0dPn/O7f///4eTpE/KsJAr6GC1YOW8xqwMBm+McVNZnkEpHsKYyHXqCXakDKmWz4EIMDtfK8CstD4FixVSk0a9ycbbKTi2EFQFSAWf8j/aX804ramV/vZ+gcTn6zLbUpEGrTVNXwS9MBFT1cfX/CxH5H4BfB57X5w9U6sCL6+oYDsfcu/c2gYn48vFTPv/kY27duk0/iZlNZyRhyv7RIY8ePSbLV/TThDgKQZXBcI9+r1clYgjI8pzlMmM+X5HnJZPpijhJuXHjiEBCXFYQhhFO4Pvf/xP0ByHPnj8lCkI+/PA7nJ+cEVeW9Ty/5Lvf/XWKfIUtHZFzFKsV2fwSVMiWOdMsY5EtOD17ySpfcXzrPkc3jrEYFquMKI4Yj/dZLJZMFnNwjiCwDPb2uJwuOD39CenwkNwWTC8nlPk5d+9+g5P/n7r3+LVtyfO8PhGxIpbf7pjr37vv5UtT1VmmDUWLpmmkglYxYIoQAxBCzJgxY8CAEQMk/guQmLQwQgwQSFQbFaYrK7uqqzJfPnvNsftss/xaYRisfW9mVpoudYFIQrr3bCPts3X2Xr/4xe/r7m5ZlSnHuqNYLamajvuHPUWRY5I13lUE6Xn19g3r/DGb83OyDM5XOZoUFwJKCmKd0LQtfdWcBlYCnRi6tmG0lqY+YkJMcnZGWeSUeUkQkqs3r8kXJanWBBdYnm1QSrHb72i6nkVeslwuEQ72hz1usux3kvPVkkZXeN9TqQYpZzTgrjlQVy3TNBCco60rCGC0huCp65puGHDBY5VCmZhhstzc3uDxhCCI44iyXFMudYIW5AAAIABJREFUFLVtubw4m1lzU8/hegdInr38CBHEbFA6eVSwXF6ccXa2oTmsOdxbdvcHXJh4l/AlmC8gB9jTPOB9hN17Hu87MxZmleo7AFEJCPLH5kAi4IOEyM5eBeHHfn9ezFBy5Ge7fH+KI3Mn/wkdFMrPl7eTP+1KKf7c7dOYcr4G3z1/OorMZeFk3h/z542If2b9ZROIckCGEKrT7b8L/GfAfwf8e8B/fvr53/6y14njmE8++RY+BJwbyYqc4/FIJwVKKqpjTTWORCbGBkfbdYyjRGuNbRuGoWe5WqGNpm1qhBspliuKx2vu7u+xY4fAk+iYKAiGaSA2hpurG/I8wpiI4AJFVlA8yskXBZMbqPYVw7Gj645oqemOFXEq2VcH3DCwWV+QpgntdiSKY2SwJyhIkWcFk4NxHOi7DmcdTduSJglJrOdOJo6xaEys6ceRcllwd32kaVv2xwOur0AnpFnOaANZmuGc5/b2jizVRN6ShJQ8z2fo1EuMSOj6Ce8skVT0zUg/jDMxPVLIKMIoRRwbunHEupHF8gzsPKyLZERZlBzSjAv5iOq45/zxhgBM08h223E4diQvYsqyxI+eh/0DiYnxfiZhdU1LksR03YmVN4wcDlvapsdZS/AON00EF+hoZzf2SKFjg3AWoSKUViQiQpkIqdVpuu9wTpAtMghu7j4ixUIXJCfmqLUOHwLWe24Pe66v39I1B+q2ph86+hMMiZ+3XSECUs0elxJFEIrRecS7M8A7M4cQzSYgkfux2QfMu7WQqBBwwZ/GjpbQw8x90YRwinQSgujdbCYANhDkqdMI4L1AIubZhJcztfmX8YfnX/HeJ+d0VfIT7cBJWfvLX+Iv2wk8Av7eiTgTAf9VCOF/EkL8H8B/I4T4D4CvgH/rl72Ic44iL9kfj9SHI1IZnB/Y7efsaaU1RgkW6yUmNdihw7oJpeZqZ91AddxjTII2hmpb0Y9bhDKkSULbtnRtw3BsiaRgV+9w00jELVmRcfH4gjIzGGHQRlMf9kRGkeiI9nBg8A7vR6qHAy4aUZGkahsGASYtCYBJSnS6YBxGbq/vOL+AJNZoKdBInBvRCDITzxqFvsckMU4UdFXLuGvoxpoyX/L5Dz7FOsfifMXm7JxYx1jXsygXOG/pm5kmLeOYs/NzAoGuaxmQ4DXH5oC3E26c5vnGScwklCLLMpQPmMSQxob2QeOdwNp+FtUUC5px4OzyksU0cHd9w/X1G4SMWC3PORyOTJ2l7Xrkfo93nrpqGPXANI0zg1BKrHVkWcY7XqaSMTryJLGZKdTO453FO4dQEqRAuTl30InA6CxNV2GSjMgY4njEEkEYiaOYsswZh4GqPbJvA/lZgU5TnPdMzs2eBdG8J7Z9S9vXjF1HXR0R04AGLh+f8xu/8ZsMYz8jLMCEJM2X9H3Hmzdvef3mCqNSnIrYN3ucd0TMyIo7MQalEKfoL3GaA0h8OFHe39USPPg5mGaQ4f1+rYJ4D/XN2Q8ntUt4l0b1DoeYb/5UqNC726d5w3we8+8fA97H0/8M8+0n1l+qCIQQPgd+6+c8vgV+9y/6OuM0MQwTXdMQfCCOY8bgQSqkjCiKlItHj3j89BlxGtN3DbfXN3R9j8kXZKkiBEEkNX3f0zQNx+MRf3vDoiwxkaKpa5pdw+VFiTGK67tbFlpzPO4IdoLLJZeXGXe7K0bnMNrg3GwSYtKEaZw49hVttwNl6JxFVS2r0pEXGUaruYD4GjdZumNNnMZkcUqEYLs/YGKDCoFqt0fHmqHvcB7apqbaHqjaI7/1W7/J73/+v/HkyTOc1Mgo4vr6isk60jQ7QWKC5nBEiDXxMqVue3KZ46QlMjNhCalouiOrxQKpTtYn4zTbfmnDOFp8AKUivOuQOiJSEmUkcWnYpEt2D1sSmbKrbknSAiEkUkC2KHj85BF9P1K1R6qqZug7lBJ8+OI5CIFy0G47nHHvBV15URBFM63a2gklZk2HkBKUOCURe0SkcAS6vseK2RJduIh69DOMG+bBapoklFpyrCvk0CN1xN3tDZE2RFFEoSM+efGMKxP44od/hBSBs1WJCQmbxYLvfvc7/Bt/91/n5u0VXb2fnY7SnEfPntN2HT/89Ef88IsvWBbnTEbyv/z93+fueksIEhnkadhmCVhm36ZwkiPPdMBZCj73BjLMxp9ehdPGPx8FpJtZpkKc0IBTkrI8DR9/etT/c9qCf1an8G79jDbhp5/6/3xJIYgixdNnT2ibjmkaUZFgGkfyomS9Klmfbdhs1ggpyNIYLQRd2zEOFhNJ2raZhThtSxRFlGX53rBxshPHY8XV2yuS4pusVmtkPaePvuv23ry5RZuceurxztON8/MWKKsJN00cD/dESYTtJ/LlglgpYhPjnCfI2YgkSxKsdQx9T11VtHHCYrFAAHVTY3FMdqSbOoKXlKslk7eMyhMXBb11TCHi8bPneDW/vzdv3gDw0UcfkSQJ19fXmEhzOFYIKUiyBUoK7NSjVEGSxqgTtFXmBUrPOQPTOOKDJ81ShFJ0bUexGTA6JS8XuBOvf8PMxc/znG//+ne4bpcUPuL6UGOMIS8WJEnKMsmRCL6+eosRks3FOZHW8z8nsdh5Z3eOSBviOMbaiRAmIqMwkUHrCCEVJo7RWjNYMEYhlMAFTzjpa8fR0Q8DwzgyDJ6mG9GRoSxKdBKjlMIFhxOWaZy7EuUt09CREfjG46esvvVNLpcF0nasyoI8SwlTR9/smfqaRZGxWmYcj1uqQ0WmFb/5nW+x2Fyis4y+6fj0s8/Y3j2wWJwRKcl2d8vt7uFkbhtw6pRoECRREhOCwzmLtNPJLCbMA0EnECIiX50RIkVQnmm7Qzo7w7SSEwQ6dxs/PvOfVvjJvf3HLCMBKA/WiHfmGWAFKvhf0Af8ihQBrTWbZY4yhjzLcNahIkl/do6QnizLKcpi9ljTBhMMT58+g+AYup6u6UhiQ5anmKOh2ivG4XQQCoLjsUaKWR9QVS1KxcgA3TCyXF3QdB1KCl69eUuRZuhgqcYJh0NqjU8l/djRDz3LfENSRCxXS6ZhpGmONG1NXpaopCB4j/Vzd9MPczZi4nOy5YJht2V3rDGRQgrBOPREXUyxWBKZlLOzM67fXvMv/yt/h81mSd1V9M1ImZUzsaZrqNoaIQT1ME+263ZA64mru1ts37NYb+ingTzL0UmKU4okzWYsXClikxD0/AUUSqOzApMXxCahn0akAmEFU5i3jecfPeOZeYY9tmy/9485NoFoHDnsK4oTfPjJNz7BuAkXRbRth4pGJmUICqRUmDjBOksQAh0nGJMSyQDM6kIpQAqFm+aJePBhPurJaN4vtSKJI842msla+nHADRNWSLRXLFcG50YOhyNZlqFXge39HU19wE8T52rk+W9/h+Nuy6JIMDrjyaNHWG95eHjgWNesypzNxSXFes2PPvucu6sb0jhF65iprlDe8vHTSwojaF60fPj8OeebDbdNzZ99+iOqY0uxWPC//v4/mmXNKuLy8ROcdTPEOg00TcObmzvK1QrpFEpGPP/wY86ePyXbFPzP/8N/T3X1lncxySJACO8s5n72Ev5Fg77wk83Dif36CxOV+RUpAnYcub+7Q0hFVhTkeT7bg6uJLE2Jk2Q2vowmUpGRakkk9QmqkQQnCXiyPKPMVzzoLbe31+x2O6TUGB2T5zlxlDLaeTcZpxbrPKkdGZ3l/OKcuq7hwbI8z2kPLd3YkRcplbO01ZFjXeEJ5EVO7lNMkuKcoesE3npG29O1A3XfY61nf6zQhSFdlxRJSSDicH+PlHDx+AzvA7GOudicc7gbOVtu+Orzr/nmNz9EyBnHnoaBY3WgzDJev/qaKEm4WD8iXSckSYmzFusmetszTo51HJMaTblcYiePn0ZkBL7zeBuweJLIkCQpQycZh5HIeCoxwGQxNqKuK8pyeZLXCi71hvHJmotXrxjtPff39zy6fIw0K8Swo0gSpIhxITCOPcp5IjQikURRRBzHKBNhUkNQAvqAEpa2H+iaZh6qTSfbOAEhKKzTM4zuHY65mzi/uCCSklwbknzB6FvcpInylH7o8N4xdhNFkeEZub99w9g2mO6ezbNHXN2+wR5iVudn9HmC1vFMPhonTJJTrs9RKsL2lqmfSOOM5WJJkPCwvSGMHWdFwrPNkotFzstnj/g4+xjpFUWRk5dLfvCDz3n67ANWqzWpzlmsy5khOY6M08izD3ve7va0VYshoiguePnsE1aPz7n6m/f84T/8+/S3t+BOA8zgZ3fqd2Sgn2j/f14RCJzc0x0nnzoIQr7jHv7c9StRBCY78cMf/IAkTVgtlzx5/mS2uFCCLF+goggRHNIGbBioZULue3QUEcWGBFBYhrZFRopVmbO/DfR1TWQkSVLycL/FaEWSGMahZX84cna5RvnA+vKMrptQyiDKJZUfcDLQth2xiWisxVnPMFr0ONJte5IipUwFOs548qygrhsiHTHYlmPVIIjYPRzoblrW2RJ54ejqCh1HM91XSKwNBAducgyhoxk6yvWSpuvZrNeEYCnXS7JDwXa3p6lbLkzOZD0vLp9wqGqyoqDtalbrMzrdsVytMVrihEcEy+QiZNAkxtG7Hu8t0mvcZPFhlur2tcOkMUhB304kcYIyUChDmudoCSQz7yEeNXlZICNFwBKURPkIpSSRAJVKcp8QxzHSyJPUWJPmGZGebeC70GOnuXeNpEDJaJ4T+AnhBcM4gu0wWUykZ9xdRZJ+6NBGIz0Mw4gVARmBc5bkpDJ94xte3W7JaAn1Fd3+wJvjLcsyprcjGI0UiuOhoR4f6ETA2omh7bDeM3l7un4Cru9J8pjaB17fHojcQBpLgpIcm5pe9LhB0dxccZZ/SGRb/sa3PsIkGZvH5xy2R85jQecNq5UhEgJRd3zv+/+I0I4IqSHsiMcDT15+m09+46/yx3/6R0w3rxEizJCim6cH86kgvN/Zf3oY4H8On+En7/5yVvCvRBGA2TvfmJg0iymKFGMMxWJxct+BODOUUYI2isho0ijFEwjeM2rFdNq12rqd3WJEoFwtKMqCoZ9ou5Z+tBzqA8FZpFKoAMdqS5xp8jil7nr8aiSONZks8N4SfCAvCno7oWREXTesNusTD6BHxxHLvEBPDVU9MHlBkheMgyXNM/pdx9A3DH1GlhoiEyGTGGsdi0VJbDRdXRPFgSA8q82SxMw729ANGJ3w0Sff4urNW96+ec1oK9LiBdvtA13XYS402sQYY0h0SlN3TBHoWDONI03bEvAYE2FiTRzH5HlK3fQI4YmLDDsMaCVwfmb1KSGou4FNkmKMprEjysE3v/EJm3KD1JJssaQsF6hI0W0fGILHO4dJIyKvUFqQ5wlC6jnoBBh8QAmFiTXWjifTmHkTGMfAZEciBV07p0GnMkMbQxhn5yXfdujJIn1A6RIZKY5TT2ZABUssBEmSs1546us7vvyzPyZJU5Tr+er1K5SJWV6ekZcli7yge7jn5u0r1GARAeqmYX9sOBxrZKQwJkLpiJs3N/zwRz/iybIge3zOOIzoOObt9pavPr3ibJnTVQf2uzu+8/EL8mJJulryD199xfa64/GzZ2BHynLBovPU23siPx9P3w4H/LEnMgW/9pu/zl/77b/J/351y3jYzsdfws8wFmfMwCCYfgpB+Bmnw/8/FQE7TQQfmFxHoGSz2WCDQypJ27YI4dEmpVyXxCbCqJnQMU6e0c4YrDQRKtUop8lVSXlYMUyeNC8J0YCoKqRStMd7yjTn4vIcaye00Wx3W1xeUjUtXnWIcE6cKxYsaI9HYN6xjIkYJ8/Q96TGkMYZboqYJstqc8HkdlgCy2RFUzf0Q4s6Su7vt4BHjYq0LEi0Zmg71mfFbDPmHH3XsdmsiLUmiQ12sgilGPoeo0s2Z+d0XceXr7/i429EOBeI44ShH1FVTb9aMvYdQmoGAbnP6LqWEDxRpIjjhLo+MgwDi0VKFEmyPCUTGfvtPToyZFrT1CdP2n7CKsnQNtze3VM+PmO9Wc0EISkQkcGYGCklmY5oux47WZQMKKWI03lCL4QhhJFxmkhNNLfsfrYaS9OU0LjZ6EXZGeGJFIvlAh+YdQ9JhkxASUM/9kyTIxUBZWbbuTiK8AKmfmDX1GAKjFTcXl/Tdy3TOLEpDDqbI8lA0PU9j88vWZQlU9cjkKR5Rtf17B4ekEqSJIamqagearZ3e3bNkW9+8JRIJwTv2KzOuL6+4u7mirNvfZsoihjGjouLDXm5ZHeoKfMU37QUacx+W5GcxzxfLRCRwY6zt6VWBqsj2qlj4STf/vA7fPH0+7ze72bR1Ttl4knx+X4aGH4chhaEfM9Q/IuCBT+5fiWKwDCNREaS5xnaaG5vr/ECsixnkS+JdESqZlagcwEXQMhZhtr7QNt1SDHnzyVJgkGwWS0R0hEnGb7xJJmBAKvlmrwoQEDfK54/X7PdNxybCtd3XF83uDOJPAQenz+iBYZhRizeXr0hjhPC0ZNlKS9efMDxsKe5almtVsRxTKI1eZIQBbh2Dj9ObLe3KOEpTE4/9cRjT57n6EhxPB7I0hR/PyE+BO8stqkolmvyLH9Pn/7g+Ufstg88f/IBhPmMV5ZLjncVfTOQJgPjaHFuYLVY0PcDdd2y2ayYvKfd7+jqI4t4wZQ5tA5gY6y1KCkJfqb+dl3HZrOZEQ0BsYlZrZYIL0mSmCwt6IeBfhyRzGlKcZoyWjuT6qRHG4OU0Sll2SMkMwogBEIqvLZ4N8eiJ8sMIWZLsWEYiKIItciJPPRjC0rOh1whuTh/PH9h/EAzjiipyGLD6EciUoQL7NqBY1Px5Rdfksc5wzjRDSNJnDO6gaZp2dX3pHHM9rij6Y8MU8B1Pfl6A0GgVIRWiuA9+7bm7dU19bYikorlcsFyUaLjmKGfSOMYzzzIjCPF5eU5ddtze/2GJNbcHju6bqZJH44VJo74+Jsf8aM/+QwRSUyeIrOYAcvx9poXH3zAX/lbf4dXX3wKzZHZ6OY07Jt10PPfQLyzOj/9/+cGBr+YFfCz61eiCESR4sUHz3j69ClKCYpiwWK5nKeqzuO9Q0WBvq1JtcYZiYlyXPCkWYzSsx9h0/VoJDpNWYuA13JuOQ9H1psNXT+wXq85Ho50Dx2biwXHfcP25orN+pwozeiamkgKnjx5St+2eG9pmp6u67i/6+js16yKFdZZjseKRCU47xkOewyBh6YhLVakUUK/3dPtd7Rdxe1XXzIOlm/92q/z9PlzQgxlkdBK+PrVl3gvsD+yrFYbfJxinGe1OuP29oE8V7y+esvlkyc0158zecfV1RXJ9gGtNJGWPDk74746Uh8rxmmiWCxYRbM1+fz98ZTLgizOsHi6eqDr6tkD0Mz8ChMZymKBtZ4s0zNkJ0bSNCXLC5q2oQ9QmoCOIvxoWZwXtIc9j4tLukVHs2vJ8oTe2bntH6eZayvCvJOPI3iIoghI6HzH2HczLMZMHGvuthhviBcxJkoICoQQWG+ZrMXICCUkkVanPMWSSFuQinXseFvtmIaO3fYebRKm0fKwu6fa76nthAmKD55/iJs8w77j65s71uUZLxM9y8pxTNayOd9w89Bx8/YON8HxUNOuHHZ64IOXH3GxeUoiNVlZ0HUVF+sF/ThwqI9zwV1vGO0GgsckhtfVnudRyb/zb/4eX/21K/rOoozmDz/9jDc3X/Lm0xXEimfPn/Hiu7/B63/yPcRQzYY2gdn+TMyO1u+VjgJAQfD8pLvAX7QAwK9IEVBK8fKTl6Q6pSgyVssVWmn6aaC2kBlBOzrOsxIhHNYGVGSxpyw7rTVO+NmEQkYE69FRTGIy2r6GYZbWDseWZKmII0U1tcjKM3QdvRq52d+yztYsipzPP//sNJgLNE1DkeUUZc6iMNijZr/f8/HHHzMOPff7O467PZk26KIgywu6hy1ZnNANR5r2QNMMjGPHNE68fvUVAUeSvOTrV1+R5QWFLFBrQ7lYsr25Z/nBh8TjQJqXpOkMCQ7jwEcffcDoRuzQAYK6idFpz7dWHxCXJaWYMwiN1vRd9074ipSCvtnT1A2bzWOKoiAgcM4yjh15XpLGKY2AhYnx1hJFEcYY/GgZ3TQnEY0jEkE7SbQRCA1SCfLVam5r24blxZKApxCGYRjm7AGl5mKjDYmKCJOlHVts3+EGD26mzAagH3qkUvS+Z6wUpZzl3UopojgiTWMiERGIsOMB4RVxZlBiphnXcmC9XEAITNNIkmaUixWL1YpjUzO2DUlScHd/z646MDo75x6MI/vdwzz8jQxSKFrrubp7oKoajNEc64qvX33OcpHw9PlzNptzkFBVDa+/foV88SGLTCA7QZFmLMqS5aLk/v5h/lt6Sx4bFokhf/mcfFFyrHq6oeePf/QlN9tbPrEj5cUjPvzmd7j+6gvCtsG9Cxk5pdy+lwy9z8d8J4KYP5NTAvFf+Pr7lSgCWmuqhwMSKIuc6lgx9P37cJHKTPQ2JfYz+0wqRTyNRGY2rMR5pBCoIAg24J0neEdwjr7v0WXEIr58T6JpmiNNV/NQ7xjblucvnnN3e08lK/Iy5/nzb85EpSyjPx4Zx5EsjfFiZOgHlsslxswKvLqpaKcBdEQuoB26mRLqppnjKQXdUDEMs5np1c1rquaI045HoedSPiFJk/n8h2N5fsb97R2bly+pqgpTJLi+5+wsp+06FuWSWkiePn3CNHmGIeBiz3a7ZXIWqTzWO8ZxZLlczMPBpmZ7f+Rw7BjtjuU4UhYLxnGk63riOMeJgWA9/sR8DEFQVTWRnv0UjTFk3mOB0XnSaI42M2bOYPc2ELsSUkFkFbVrcdbN7b1SpGmKjiRCBabQ4A4DY59g4oDMUnxjaZUH02OHflZP+grZTkgpUDqi7hqWyyWJSpBSoIOGSGHthNRyZheGCWSgao8M00jiA8EHtvsd+27OiEzyhHrYM44DoxNcPnrCerNhmqb5/Up5MiYNNFXDNI6URUrf94yxRoqUZtcw+pmf8vZ4hXOKafTIUqOHiLOzM0yWYoPj4uKcyXn6/RYtBMs4xruaqT6grOe7n7xkcoKv/uw1d2/fcrnMUWLubr37cQBN8LNk+SfzBN6Rit/rDd+nYQfC+3bBIH6JgOBXogg459heb/ne6z/E/FFElqRoc2pHp55UJ6zXlxzLmMVyQbZcErTEKE8SC7oBlJsz+KZxZBgH7m6uqboWodQs01QRsYk5bO/Z7+6Zhp5ISXSacHtzw9n5Jfv9ESklr66+oK4Lbq9vSCKBSVKUOuPXfv2vcH5+CcCbN1dEWmGN56x8RJ5kSOfpPPgwO+ZKN1M/x3GgalsEgaIo8Xia3T/lj95e8fzltymLJR+//Jiunvjgoxcs17PUdPfplrOP15w/ekSWpfzjP/we5WKDc55yWfL973+fNE6IIkVhi7kttQNFlLAqV0glkYnEect6c8bFpZ7NWL3AaI2PY5IkZr8/zLMUE+NGh0402+091lrKNGV5vgTvSZJ4puMWBf00MsUJIng0CT72qFXEoWq53t7QPfSoUlGmJTo2aD1/njpSFCqjTFbYtZjnC1VPlVREbuJYCXo/EITEu4n9sccYTZomaKNpmopReCLhOMti6mlAlhnDMOLGgTTN+eJhS1MdUbEmXRR4Z7m5uyXW0ZyEZB3LxQVlKZisREvBMM0zhjwxnC2XLFdrpFzzD/7BDzAy8OjRGefrgkSehGuJIiXheNyT50tSr6jqia/fXFEfD6yXC3a7B3RsuL+9wyMxseF+98Di/DHdw4ROFZkckVLx/GLN9/7wC/7gD7/Po8MetVlhJXAKs5FyLgQ+vMMD3lUAcSoG75gAs8uIEO88DgRza/ALnEb4FSkCiYn5p3/2T8mylKhMOdwfUEKgtebh/o5YG26za7SOSLKc5fklj55esFguKcqC/ESNDQS8nxi6hmBHdHCM1mOtZb/bcbd9Q31/YBwGgrcc6wYv4NHlEzZG0yQxh8OBNE0ZY8Pm/IxmOKDjmL7vWa8fcXFxydXbt7y5usIZw1IXFHFKsdxgJ0t7fyRSGVI0SDeh/Ux+8U6A8MTpzEL76queb3z7Ke3kSZ3j4tlzIhvIsoKrL7+aB4JngbarsVuLMU94+fIld3f3DNNAksR8+OGHKKGIIoUxBgEkIkbriCzLaJqGcRpJlCFJI7bVHm1itDb0/YDWBiFmaHYaJrIkgxDwtoPgObYNjy439G3HZCbO1meoSCHljP8nSiIlhGjuvlzw3N5e8fDwwDiM5DJHRYrCSvy7waCQzNZhnn6Y48onZ2kfPJ3t2Lf3REaR5jmHw2FGIJSgajsuM4PWMamIubm7ZhwExWJOPwpC4oaer25u+Px7n2FizdCB9Q7Xd0yRwHlFnsWsyzXazE7Wi+WS1GjSRBOnGu8daRJRrjLqMGHF7EUh6NF6hYki4iQljD2NHRmmETdtOXv6kq7r6caRYRyJ07lAKDV/FgFLlOTE3nJ1v4V25NDDKnUkZYmQnnxZcvniGcuzM66mCYVBGIMd5kQkebqoBfyU+9G7meG8Th4J7+eH0awg/XmhOqf1K1EEhnHggw8+wBhDfTiQZRld3XLYXbE5vyRJE6yfUCKQpZaxPbK98fR9x7EpefoIRCEILjD0PW3T4AmM1iIjQyQlN3e3VLuKr7/6EiE8zVRRb2vyRUmSpqzPX/DkScmf/fBPMcZQVdU8wSeh6zq6MENfjx8/niO2jMFJyTg6dsHhVYU2MZ3toRppDxXVYUc/9ExuICsTinyBjjTGpMRJwWpzxiff/hZ//Mc/ZDgeQSfUxwPGRFjvef7iOd///vd59uzZTG+tKuJIcXdXI7OCs7NzlBR4f+TYeJQ7uRIpydhMLNKS0Y4zfCoisjDHdVW7ijiJUbHB23FW0cUzCqKjkr537PYPtG1LYr6BEIJ9deRuPfBMlTOTUAoaAqWUpwCYjsO+4u1xgXtTAAAgAElEQVTbt9hpwnhBSBJC1xOMYZJiPp6JOeQjitV8bLETSZLz5MOCQxMR9xGJyRDRbB03TfNMoO972nZCa2iExCxjqu2ObhgwVmIPA3EpibqWz3ZfYbRnRKAE9PXIs2+9YH/cM7pxJhwpiRSKs/Oz2cFJOoKau4/j4YAyCYcehnZAyYC1M1dhtVqSxJqyyDkcaoT0XJx/yHqz4eZ2S/32SJEXHPYV3nsyIVBpRpIVSAlNtYUQMGfnxH2D8w0hxAQ7IlxHkaeU6xWfv7oiRDHulJ0o3+kOQ8QsE/4xfPhTuoI/f63Lf3ZQ+a9EEVBRhJQRfT/OWH5dk6QGowv6vme335686x031wOxTjl/9JhpGIjrBj8MLBYL2rajrmuiKOL29pbJWop8CQG6+sjrr79m97BnHA5IKSgXJUIJktTwcLg66QsCOlLUfTfHf23OyM05nbjn+uu3M1OsO7KrGrphICjN5iKhWF+QpTlptsENgatXX9DbHhcJHudnXD6+RIqI+lDxsH3g+YsPePX6LY+ePuP3fu9f4+6rK7bTLdV+j1ksWJ2dcXusTnbqb1mtVjxaLEg2G3SczfOFqytCCJRlwZuvvubJk0sSY6hud5gyI5aGxCRzeGnfkmhJvFxRfviSaRwYh562bfCVo2l67usd4zjx5vUb0iRmvV7y+vVrsiRmsTnncbqcPx/niIF8iBALSX3smVxP1x0525xRHSoMktV6PpJYMRBGzTQNaKOIk4RIaTabzexJ4ALOz7h/XpYcugrrPHkRM9kYvERmDjmOdG3HbrujXA80EvxhQoc9dhzm9yclvqpI3Xyu7/uey6eXqEhyttmgCKwXJaNz3G23KAyr5QrhLc3QEMcKgkWFiNevrjFxzNnykiTR83dDR5xtzpmmiSJLKBcZn3/6BdM0kS+X1PsDl88vQcLx3tH3Fp0mPGx3tMeK9XnKEpBYCBN5WlAUOf/iv/Db/Et//W9w/dDzR1/eskpz/tbv/A3+5A8qHm5uQQiCtwQxEom5DPzYlfiUrC1mp6OZM5wB3YmGHWbj0+7nX3+/EkWAAFfbay5XF4Q4JhoHvPOMISEvFboUmDAHV7rJoqM5sqw+HDk0Dfu2ZV1VjMPIMAyssoxxv+PmcMCLW9bLJcPQI/3Eaplw2Hc0bUVdB4rliuPNDcUTyXp1wWZ9xvriDNE0YPw8IMo7Yh+zXCrqesJ5xctvfMQXn3/Jvh/QaYwNYeakNy2bxYI88wjVAYpyWbK53JDEGV//4DOmfuJ4bPjk179D3/f84Ac/YJnOJpp/8Ad/wO/87b/N5vyMFMGfvr3m+dOnCBTlsyf0dUd8OrY8fvyYw+FAnuU8urxAyZ44LknzHGstRVHMEKEX+CTH4ygWGWkmqA+BXiniOGa1WrG9v2f/sOPm5hZjCvb7O9brBSJA0+wwWcHgB6RVaK1pmpYin01eVeyZ2tndWAhFpGNk9OMwlqEOxGLClBFuGLHjSIgNIplnBV4Hhv2Rwe9pOosPEeP4gLU9fW+wKGKlEFZhTEL5ScLD51/xcP2asXfIw4qpOvA1niyOmcYjySKiPiasyg2FjyGS5FmGCIHBOlyQTN2EKQxazShTcI5YxxA8D9t7vvr6K0waY4xAyh9HlZV5jlOCoW/Recrjywtu6pr4vGS5WpLkCXZy9H2EUQPd8UiUpGRpChKMEBx3WyY/YfLHKAc5UJQZgoQfvNmxiAqisUfHBsSsnXiHAozwvr1/f9R/f/5/Bw6+iz85tQa/xFjkn7sICCG+zZwt8G59DPynwAr4D4G70+P/SQjhf/xlrzUMAzpI0liT5xkHP6EUaCXwg2OoJIMfcN1Apg2273j92WckWUG5LFiYhNB0xCpiHC2fXX+Bch4mRzUcKCJBHBvKzYbt7Q1pkTOMI7YKhBSyi3PSOGeaptkabLKUxpBoQ10dOO4f+PDRY/xUINVAnGh0uuTjb/8VDoeKaZiIbMBPluVqRZqmLC8eczlM2MnyyTc+IYszdBRhB5iE5OFhi5YgreXrzz7n6ZPnpEVJ0CmHYzNr5IPk29/+hGfPntG0LVVXkSlNkhfIiBlGHVryPCWKLrBTzzRJYmEQYmQKI7FIiaPZsTYtDUmRopKCvJQc93vsNJN03t5c03tLlM4FZt827KoDUWpmB+PdA48eXSIigZ0G0jRFJqdcRgdq0ESRYbEsKZWishPb7XaeH5iYXvao/TxPSNMU5UeM80xqYvKBtrf0U0Q/zIQn52JMvKA0gqkfCALG0FMdKhKbU5gz4g9KJt8w7mBrBXV3y+HTPyF3nmFQZMuSdRZhUk2UZORZgq4DXTuRrlZcPD5jUSQcqxYXzaYeqY+wSlPj6NsbxOg5tB4pA4skxkhB1XTodEk3TNxfXfPso2fc7z/l4fNXLM8u2L7dsblcki00QkYwTdze35AkCcplfPnqDY8uNyRpQm08Nkz0+x43jLSd56LUPH/ymB++CbhkhfZfEOGxQcwJ1UIQiCASCOsJwuKEQDBrbN6ZncLMSjxZHf7C9c9dBEIIPwB+G0AIoYA3wN8D/n3gvwwh/Bd/0ddSSnJxcUE/DcQ2Y7ffk08jr4aBw25PFGnyrESrASlTRBSxuThHKoX1npubW5z3HLrZdFSGQJzE5HmBFJKv9weUt6TGkBUZbePnCa+c3VuOVcUILMolu92OMirwOqDLmO5ultRWzrPYrDCn6O40TRnHEWN6fu1bv06qV3ihSbQkTBPnmyXFYlZDHg8H7DiSyYzN6gIfPMM40NYTy2XMixcfsL/bEWTE7/zO77Bcr0j0klRrTB6x3W4pioI3X1/xjW98g9TEfP7pj5DMLMtcFwjkHOAx9eyaGq0k8TDhkVwsl5TZXOT8AEY4lFQs8oK270mSmL/61/46r169ZfewJRKSTz56ydB3rIoc5+ZYsK5p0ImZ4bkkxvUWnWhCDGMzQ7qfffEFXdtz3D3MMxWtybKMJIlRSp5+puRFghCCaZp9/q2d4bnNeoWQErWMGA8jdrCoFEav0WbNYrFgsANGxlhvsSGeob1Cs5wMf/B//T59e0ColCdPLpFeUNU126/fovD87u/+q+Te03WzlPnVq1cEJ3m431GUklfVgccffJPbV2+5e/OW589fkGQFk7UkqQYCbVsRC1iv1zRVgwiBly9fEgTsjg3Hm3uSQmOMQSrYbDbk5SznXq1W3Iw3TC6wXK6xUUy13yGGEWkFx4eRumm5Pn7Kk0++yYcvP+Yf/+CPTv4C7xxDZ2Rg9pcRBNSJE/ITdmSnzsAH3kfm/b/iMfgT63eBz0IIX4lfMoX8RUtFEdWxIsZw9A8cjkd2kyVJDI+ePJ4nnUEgfYJyEqtGBuswKJT02DDigyLVhvj8nKwosNNE17YkScLloiRNY65vb2jaDrwnK3Kur27xQhBFmmK95tGTRxRZwTDM2m9nPcViyWKxOLWCs2XWNDmEEIzjSLle0smJzVqSZQlt29AOLcvFhjBNNE2DyXNECCitcfVAiARpprGh50ef/Yjvfvc3SZZLtM6Yri2HsGOz2eCVYppmokzf90TK8NXnX5AkCR9++CH9ybpbpILzxZqoSzns7wluIkyOKEBoax7GjvzFB6SxwQ2z6UZLi2sDuogYgiMgOT/fYEyEcHYO6ogiUhKctuTnBWN95P6+5+mL50z9gFYx3njoBCJEjDcldV3z6tVrEBGr1YqyLBnH8T0zMQ9nFMMlyUWP1vPXr+kHhu1ENRxwJqLuOtyNJ4oirJ3I8zmrMIQ5DyKJM5IAtW1xg8WlloSUN1dvSBLD27t7kvQRm/WGft/w9uaWY9WiPUy9xYvAYrkghD1idU7d9Oj0wNiP3NU1Q/iaT79+SztNnJ8vWW0eEekYxnaOkB8t0kxoqRHMHAodJ0yT5Tg8EC0mQgikWTzzI7SaA16Dw1l3ylP0LKaJ3o/kacH6PCf4CWkdrvEkkUZa6PoehMCL9/bHiJORexgVwoAYmCXa748E7zCE+a4OGvtz043n9f9UEfi3gf/6J+7/R0KIfxf4P4H/+JdFkL1bTVdzD4Rdi5smpIpox4m4KMmMYJgCYQxY6dFRSpKlKBERnCdW4IRgnCa890RSInREen5GuVhRlDkPuy1j27KMJaPPSFNJ240EIWjHkcdxPFNW25qnF89wZ24OILmfQyHqE6/+xYsX6DhQ1UfatuXRckEYRj790adcnD8iTxYMnWfvj3Rti3cT1JbRW8YiRhtDrGM2509x04iMRrpx4Pz8GcN1zVAMBGe4vb3lyYsPSE1KURQcj0fWy+VMcInUewJP17Xsdne83lnOPng6G1vFhmaoGJuZjmsWMDZbqlbyp//kT/j801esFgkvvvOSxfkFZVly9vgpSao51id5cnPETg4btpTZY4zJUGlGrvZ4H8jybA7hsHMwiEgUfvmK465Bjt+l2H+Je+rezyZCmCPl+3hHONOIKUUqgUgEhSmJv7VhM3ZUhx1Ra9hXFU3TzAEyD3dIqSnLcvbZIxC0wJAglKRrJLe3X1HkMUYpvvOb32JdLlAi0Pd7Ih3hvKdcLng47NAmphpmyXCaRDTdNHtRJCuKLOahHmmrmkWxwDmIhCBSiqQoifUcBR9rPfMoynIOWPEeKSdMmbFMDeUip48N2Ind7shm9RgVFRyq16TF/03dm/RYkqXpec+ZbL6TX3ePiIwhp6qsoecWgW4R3U1poQUFQQQ3BLQQIGijnyBqzY0gQAsBArQQIEj8DdRSgqCFIIJgd1Osqq4hM6syY/TxjjYcszNocW5EZldXdVNgL1IGRIRfi/DrHtevfXbO+73f89YcDh3jMCJ0xvX9FfWzp0QZTnf4lE0wX8yYXz5EFBW+bxNhSMavun0yRbqh3q4FkiT4yxkDTvz6AgB/O1mEGfAfA//V6dT/APwTUt36J8B/C/znv+Lz3oWPlGVDrhQfPH2aRoeReJ+y6Q6HA1EIFssKrTXzWUJ1TW5i8i7FaZ0Q0VIJhqEnFhGCIlqH946bm1vu7m8ICGI+xw+R5bJI48BjS1nUrJoVWmasVwu+PG6ofSBNumo22316E3u4v9siROTu9paz9Rm77R2ZyYkxcnt/w75s0TFiXMY0TkgheX3/AiUE7zVPsJMFIahXK37+s5/RtR37Y8dyecvZ2WM2b15zcVGT5xlvXr7g0e/8DnebDcNtz11/T11XHI8tX/7iFzxZp759Wa3IZpH7V6+4fLBCnK2oZnP2mx1VbjhsLZ/+xf/Dj374Uz7+1kN+/49+k5dfPMf2HbgJFSK2t8RKsz5fc/PqNa+evyB6x/ULy6NPBN9++hhvJ9ZnS5wvToMsEaRitCMqQFnmfPLJhxz2LcPwPt7fMY6W+axGy4xuCORF6vn3maPyGtn3aJOT5StMIWFqMKbibH12YkUesK5Ea81udw3MyPUefMGIwmQ5zdxyt9XkeYkUHiMSCnxe1WwVoCRCa65vr3n5esVv/87vEoXCW0sIE2Nv6Y4t67piPZvT9lvabmS9mOGnkTdXr+jHQLu55/xixmx5hri9Ymo93/nOt3nx4gUm04x24tD2PHr0iLquKbWjkHOkhL4/YoxnmBSZznlysaQbOhoTubm65/bmnqcfPeb1zYYYy9NLa3jvvScYk2GHHqkkOrqTLSgQEtoUXEBqjT/1Bk4/mbc9g7/xGv7bWAn8feBPY4xXAG//PF3o/yPwz37VJ309fOTiwcP48fe+S6bPsO6eaYi4qUVGybNnT5GyJYQlLuZkmSLT6a4iZQonEVIRZCCGiD1YRjdi48BAwHnHNCUWQNM09ErRzOCq78m0YlmeEYWkXOWUdbrr3t9t6JwjL9ObEWA+m2H7gSxP3vpx6FBFYD1bsd8c0LmhnmcIn/TZIAOTFBgtWSzmHI9HXr56xWK5IASPdo7z1Qp5ccn5+SXH45Hz85r33vtNrt68ApIv/9PPXzBdv+CHL1/S1DNyMfKzX/yUR7MPKT+Buqkp1sVpQnKGj2DbPk3P+R6ip7vf8dmnn7G5v+XurmF10XO+XtHuUxrQOAu4tuVw06OlRGvJ5aOH3L55jVI5hXccj3uKqqTrJFLt2O8dZYyMRUlVz95hcGVVIXaCpWuZf/QhSqVuwmIxY5om+r6n2x8JccIVOWXZ4HLBNL0NlDGEccBPUFYFMYK2Hh9GlssUQBtdw2E4Mh0FzlvyYsXlpeX61XOUVggX2V9fsa0KyllD5QaUbE9ZBI5hGJgCBOfIM4HzE8djy7Gu2DcFN7sdSkiKPOPYHZDKYEdwzrO53ZBJTZZXhCnS20RkfvzkMX3XkWlFXZdMfmJrHWbYY23P5dklDo8dNlzMLlg/vKQfBw73R6TQ6efU92RFQWYWZKrBdy03L54z9j1CyjRkFgWy0O96hBFASyYyRBxBxHc5SJyyn2KMCAreYch/6fjbKAL/CV/bCrwNHTk9/IfAD/6mJxBCnEi6A60RZCGS5w1ZlqGVAWZokzExMbUTLmq0yRECynmd9BKVikLTJBFnu9ngnMc5j8lKVkX69/vdlhgjWVHRtkeqpubm5oa8qVFSkZssMfyOR87Oznl28ZQn7z1hv9vSiR1eSurFnOgd42i5ub2ha1uGceR8fc755QOU1oTRU+dpfHk7WYQCPznyPGO0lqMLfPLd7/PqzRuyKudMqNQ9cCOZzXjz5Q79LcOf//n/TqYL7NDx47/4M+bzR9zft0zuirsf7vm7f/eP0cMjdoefcLg/UtQVs/mcTGXE3uOt5eblS66ff04MI91OM7ZzRuu5u9ujy5rzB49RWp8cphOzukESuJcSrQy744F2aHn2wfsodYvJMrCWAYlUjjoKpJwo8wJxHDh/lDP2CudHpMywk8ORY4RkVAprLYN19L2jGSHPDc2sIisKYpTkeSQIzXjwhOBwY4fzkW7wFIXDBPAuYkxgc7dn3L6myC+oVismO5LnGl0L8iznsLsBFHVdY/uW2azBDy2jiEiRk2cFXddT6IzlbM5iuSI+v8XFkOhC3pHlJUJqHp2fIcbUenPjhJKKcRxp5g1SCIa25f2PP0YpRRCBchrZtgeqosThud7vaRZz3OSYBPjoadstCCibBoGi64+AYXCRmbWUdU29WnG8vzmlKguEdQky/jX9TUT7zjWk3t39UwGIMSB+nUmAv53wkf8A+C++dvq/EUL8LqlI/eKX/u5XHn6a2B2vCKNBK4VDIGKGkl9x6r33KAG6qhFCMU1j4gl0Fjc5ynmBzEQKrahKsiJjtkiOs5evXyOloKoqHrz3mNGm5d/Pf/4Zu8Hy5OkzHq4f0TRNUqOFYLVIguC/+Ff/HMiw9kiRGyY7cn19TVEUdENH61rW5TlN/ZD15Ypm3pxQWQ7vHCF42nbPbrfjbHXG/v6eECZcjHz55ReYPOflly9xJvB+/pTtuOXLqy/ob478s//1J6werlmfnfPh0/f5+JPfJzcFH3/ru/z0Zz/Gbg58+tmnXFyuWazOeH31Bn0UXLU98ybH7jdsD0e+/PynfPbjP6MoArGbcfXpv2C7E/zhH/99losVOi/obVoW3+13lEYwmzd88r3v8JOf/ITt7R1PHy1o2xZrLcV8wUXdoHRG9J7jcZ/svHXOg6fnXO3fcGzvEcLQNA0hBH7++Y7Rj2BhGidsO1JkBeEyYIKk37dUy4aiLJDKEInYkNT05WpJ20509z1DONC3ByYlEFHy4GLN9TZwt3tBOfU0ZY6fjgQX0HmOLhoqEwDNbvLcbTZMWpFnJZcXM/ohGWoWZwtUpnn16g23t3cpDyEEPvzwfepmxv1+QIbAzd0Vi6ZmtlxQmIzVxRn98chytSDPDK9ffElztiLLcwa3p6rWzOcLog+UEUajsduew+0NUVmq+RnlcSAX0DQNTz/6hE9f3bM7euzY8+qzH9PeXyN8SPklUpBUg4Qtj4h00QtButP/ZUUgCfXyr2UL/dvmDrTA+pfO/af/X59n9IEXv7iBfcRmlvV6jR1GtNas1xcUZUnUmmVToTLN2AWmyTGOE1VVUJkK+ogyCpklEciYDNFI7DBxvr6gHy2zRYoskxGuuOK9J89o2yMARV4lUTGkII/F8pLj8cButyXEgEDgR83heMSLNIZLCFzOzlidLVPg5wlx1swburZl1+/Ae8KUMhWeHw9cXF7SLGbkOk9e/6Igzwv2uyPBC/Sg+Pyzn7HdbdFlRmZyzs8vuZsCv/3tpxDS13j43hNut7f86Cc/BQmffPcT6kXN8X5ARog+x0fN3faAHQXbTjDuDxwGx2o+59HyfRo5o8prMq3ZHHbsd1tGOzD2E7utYz7PWCwXuBC4O1iePp1jMoESkjhEpnyiaRr8W0yYHTFaU/iSozRIIdnvd2htMNpggqZzHZ9//jl9b7m8fIAqNPNFlebl+z61X+sKpGEcJ4a+T2DX2iDwiK7EEMlMxdS3dPsdy+WC1y+e07cbBJHtdktV1Ax2IiIRPrJaz5hsj/cj0qd19PXNNVEMFOuS5fyMwpTMz1bon78khIk8M8Q40fcdh7blfD7j4vIB3o0YY8gyzdB1qftxwtxvthuM1NS6ZgqSwY7oY0tT15yvz7k5Hnjz0xuKv1NRzta4bcdscZ50BjUwhUBAIIuc25tr9ve3EFziBQhDDDFd0qeLG04fRkEU6vRYvL0W/9LjX3d8IxyDMQRCDITSI6Pk7uqaKURW6xXb7QY2m8SKiyscASZNUzZUeUkxz5FSoE16Ad7ODHgXkFIQMsn6/IwgzWl7oVLcVp5xvn7ErOmoypymmXE4HBg7RzOv6PojzWxNbhRBCfzgGd2AlJFcSlCSTFU8fPweWV4wjhbvPGEM9CFitcK5gf1mS2YU86zmbnvH5u6OoW+R0lDXC7rBMtqRDz74mBcvvuDm+pq7uzsAnjx9xMWDh9SLBU1fYy4rTJvhguM7v/FbLF694rPPPuUnP/kcoyseP16SZzntscXkaxaXlxzDRCgKnu537N684NGDC5599DGPLtacPX4AuWazvz/FfAlsO+CmI2WRsdsGnnz0jCfPnnH35iX9cCDLE+zFNJquH3HOEaVi7AdUZiiampWf2E0btq93GA373Z48y/DBc3d/z9XVa/qux/uMXM6RwnPx3iWZMWijGceRlpFMwmgTjyDPc/Isw4WJyjT88Bcv0vTf0DG0E1opNrstPiRBt+vScNKbm3u0h8cPH1GXB6LXKWpcCPa7Hf1hz3yxIpiAV4HD4Ui/26EF5EVGXS3QJud+37Pb73iwXjNfNsyqBi0hm6948eVz3ry+wkjBYr7AiUgwgeihP/YYraljhdKGet6w+DCxM4si48iRvDAUuUAEiVaSrKy533f88Ic/4Prl85Q/rBJV6y1D5OuzAuGUQSAQf0kHTGlG7x7w66jj34giEELywZ8tV0lVXaw47vZ4O6GyguA8YeqJdsGsbphfrtMFrRVlWZDlGcRAiOnVUFIy+SS0lEGgEXghMGVJXVd466jrGa9fv6JvFavlAucUFw8ewnJiPxxZnWn67o7tdkMzm73rV7cC2mPHs2fvcXF2wbHv2BwSg19kit4NVEdJOQdxUSOGkfvDLV52FJVmHDq0luSFYrvd0jQRHywvXjzn/u6Ku7tbLi8vmS8azi4ekBcz2gHmS2ivPEJaimWJNjXL8wd8Sxlu3rzmpz/7lDcvFX/y9/6I2eKSvMn5cH3G5XLB/m7DR2cP6Y5HprFHa4GpDdms4Wgt02DZb1vut7fst3fkItJqyWp9xu3rW7KiZLlYgoA6y3AhEGPPcrlORp8sR01TiugqInlnWOZLfvTyhxwOO0brsLbn1evXeC84X11QVZppfM7rmzve3FU8uH/Mg4eXlE3FbL6gLkqKUtG1E9dXVyitKcuSEAIxOC7OH3D95gXRW/oudSCOCg5tywdPv0sIR47dwObQonTPk87S1DVSRKyPDKNlNpvx4hefcX9/z/ToPcR6yXyxopzPybojIaQs4bLMEzbsNBD2XnlGmRt8GAmT58GDB+ADvus5HI8EJdLwmslYrc5YLlfcXV2xmAcGGfDWp/Qp79FG0fUTbpoYDwcWuqGzA//yhz/iB//qzwiHTQoOESmbgRNeL6RAYuCrDUCE1EYUX7EIcwP2b5gh+kYUASEiT58+JVc5UQRefPkFddEwXy5Appn4sijIy4LZfJbGZqVAKXV6hogbPCIIvInITJzuKgbnwfuAJJ4EyBqRp3CTN28MzgX60dI0GkFkdCPPnj7m9uaG7aajyHOETGSi/X5PUVeszlZkKuP5i+fs+46mroGS4yGp7UZr1GtJpjVSRwqTsbcJiT64CR8D1nlGGxkGy/nFA9brNUPbEuMd6/X6tAxXbO7uqecV4+QJBHKdI0cFRpKXFaZtWSyXxBhRTGx3ex4uHuAKT5nlKFdyvH5Df3XN2A0Uy4z5rEY2NZ0dmcQRqTU6S5ZeO1gevfcQqUDqU5tTGY7DwHwxR+QFs7wk+MQnRAjCOKHyHKMM+82OcZwYbZrzEEju7m6SiDsrGZ1HG0cMiiyrWCwuCFESgme/2zM5h+0GZos5s9kSa0eCc0QfONgxhZWEifnFM6r8fd588Sk3+5a7qyuG445MCHQWGAd5QqYZKqEZ+p6mrol+ItiR0Vqk0jy4fIAdJ6o6bTmFkDR5zqKqUSdzknUR5zyr5ZLSZFTliq7bI6VjsSqxvWWcBlRZUmuNLjJ656iqhkB6nWZVSQgJFjovHiIrR1kUHOSBLAedKfLZjKyqCdueq9evGIcWLSC+4wcmj0AyBSfLEDGNDYt3jcETlDQTxCEyjCTikPj1qsA3oghUVc1yscJOEyJ6Pvz4I4q6wmiD1Bnr5YqyLABBUZZooxFZGowRSuJDRJYaqTQZEh998gTEeLJaRhSBqABvkURyDetlSWxzpPAQJo67Hu0STXj78grX9xghcf3AOFhMngi60Uf6oWf0iQhcFiW2PdLvtmxuruztv+IAACAASURBVNnv7pIPfbZiMa8oRoXbtEQxgPdEo5DRIaRCioAQMc3Vtyk+XJkM6wOIwIRAOEtwGYGAEYpcaIySBAzNbE43jpTzgGDk+v5A1Zyx8BXdasKUNazOmR63MHREY3BVnjz6g6X0jhgckLHd78iNwg2O3nU4v+U3fvu3iTEyWocxOSAIIeBEh4olXviECIsxTQz65O8f+p66LNnv7pE4ettSLAvWzRnal8hAsl47j+073Dil1wKHGw2IMQ3oqJxxdCyWK54/f8mx2xO7A6sPRpazlFJ9d9jh+wNs3yCEoJ8EwTlqAX0RWGUL7vd3WOYoIai0Ya01xiiOeUGWlzRNTVXW9MLQ2QllO6QrQCp2k0UrySwrWC9mqBixIS23Z0KQmRyBZH/cIZXEKInRiVfQ+YHc50STIaQk04LhrGW+XCHGmFa0eU0cAzrLGLTCRokbBrTwRBHwKsWaEd9GoseU3Oym5BwEvl4AQEJ/GiRKauI7vsCvOr4RRUAIuL27o8hzdCFQpsRNlvlsAcDh2DG5iSdPnxIJBGepWJLVBq0lWhuU1vi31mlJasM4984+mamcqsrRSqKlJFOauqkwHz6C2INIFdu1yRkoyxzZK26urpid7K+b3RalDZnIEY1gMZsRvKTrLfc3r7l//YrbL54zdj3VRcNcC168+oLhbkfmE0ZLlSVzISiqhsXZMrXVpOR+c8eLu1ustfRdT3s8IrICZUq2wxY5Zgz7geqsRniRYBtuwmqFNYLlYknwlqEfuNveEfREkUFVlVyePyBOkd3+lv5oOR4sIThuNveUdcU4jqQ0J81iMWdzuMeFgcV8jffulLUoCSFgrcXF1DZL3Y9IjCmoZXQTfbuh7z1SCvbdjtvbK5SG5axByQysB1omD4fDxEydYfIcIRRt1zN0A5OfCMFT5GUywfjAk2fP8MKR+Ym74cjh1ef86P6GodtTXz6hVDM+/cyxXq8RxnDoAlmZM+0N4zTx8FuPGcYRJs/x0PHxsw9RBm43W7KiZrlcMZ/NeXl7QEjF8sE5FxePaVvL4AfKPCfPVyxWFxy7jvniEUK2uNGRa0OmDGEfkBJu90eM0jx4vCC3mt4e2LUts6JEZYpuGJgD28OW2WyBUIkjoTJFVha0xz3Hw4YoXLqJ+XS7fzs6nHS+tzF7vF0kvEOQp7lhC5zizd9uEX7N8Y0oAili6pzcVGz8QJ1VWGdpuz1SZDRNw3yxxDmfhjJERr/rsC4ZM4xRRBfwweMJOOcRUeB9yojNs5wsmFO+nYAAUmtCCHjniEEQomUYBoIdObYtWjcovSEr09fP6pp+tGlPWkSGvqdwGVnTsM4Mw65gLxUhRjyR3f2Grj2ipEDisN5hnEH4iJs8SmlknJCqQghJ1x5omgajJG6aECJihEwEoDz5FqIQuBiwckqA1SZD9oaVUChtQAmMyhAB5AgoOIxHZnrGYlXTuY7DoWUcOoKM6ExhjKSZn+EniwiBEBxFkRPIqOoKJWRKElIG7z1937MoFzjnMMaknniYTmK1pihmSDnij0eyzCRK9DAgikQxGoeO2WxBVVRUzQwvDJvdDsVE3TQorU4zAknk2u/37HYbAgFpFIe2Q3vBq88+57C5xfZ73s9mjL5NPMl5zSGkFVoEqrpCq4i1lma2YHu3pbeWoAQET54bsiwZs6KMBFJac24KlIoUtWE6jhwPR/pqRm5ypjIyjR0mzynmBaIP+CDZ7/c8fLgmyDJtS6IBI9CTZkISx0hQkQcPH+JD4PXrNzyRhrLJEUpj7YCx6edQlQXuoHFfwwYrAHnCjCa54qvjlFadfrcnTSD/a/0Bb49vRBFQSp0mzlbQ3zL1nnxeI2RgvVqhpAYpGacJrTOyUpM3OUrp5BUYHM4OKZY89DgXwAuUlGhjmM1BFhHhFEKp1CEIMVF5hw6hJFPXMQwDMkQOh3tAszpbUhUfkuVzbg5H5rMZ+64/KbsFRhcpjaYuWJ6dMfUt9zfXBBkIDlw/4gVIkboflckoixIpFdaOVPMV45j2yCH6NDPuHN04oI1hCi0uCMq8xNqBtmvJy4IQfGrFZRk6hBQuGiNCSkT02DEVsvWwICDQ5xmzk4nKW8sheLqhJQRHCIFAfLeMHYeeuq7ZHw9sNhvO14+o5iVjfyCQvBadG5ExKfbypJcIIamMYowaKZIwW+SG+TwVaa1AKklZVjRNQ1k3ZFnyBGhK7jd9SlvOM7Jc01QNIQ8gIqvVGSFGDt2W7d2OOq9ojx0QkMrwox/8gIeNZDZrKIqCsbPoWYP3E1UlmOeScr7AVBXquGO4trhxIs9STmJW5DgBrR3Y3m+x1pI3M8TJ7ThrKvbTBDEwdZaiLhhDcq12dy2rZs7tYcN+v6csK6pKgsrpO8uIQIRALaGdDszLM5rlgk9/9inXV9esF2dMk2ISETm1TNmR6KYUy3hKIz7NDaXWrxAJIBpjunr914aFvn5IIJwKwP8fWoQAr169Rps7BPDk2TOqskIimTc1eZ3IuE2TKCx5plHGYHSOHy1t2xOzCCP4veNwPNLZAecc2mgePLpktUqdhRAS506o5HUPoUpOxCFZfAWC3ERQKSG5lUlgilPHbrPFhYhEYLJkbBr2R44KirMVz8qCQiXDyNAdcWFKLSs3kkKTSpp6TrNaUsySC9JkSTCaJsfd7c0pjehIVZWgPOVsjhhHbIzcb+6QEVSe0bU3LPaXJzNVT+sEKkoylYTQMQSmacKYpB/MFnP6vsMOPX3bMR32CNIkpD45FYNK0M22bSlNxhQ8e3tEDALXt9SzFc45gsyZmQKEoMhy9vtD0mn4ipCbPB5rpqlnmnra45Zh6EE4iipjsVpQlzVCaooiw+MZ+oH94QAHxzQb2e8PRAIPLy8RUtDtWoxSPHryiM/fvGJWSupqhb295fPnz/nDT54gQqI2n63XbDb3BDsyeMGT80v6GJjCyIjAhVSUEwA1Q2jDy6trPv38M8LkMPkZ58sl+7bj2A8sZjMgsJ+OnGUN50XDcTzg3EjXDSlCzaiUX2kK3GQxJqc97kFItD4F5bQt1Tjn5z//ObmQRO+431xhCgNTT5m1HHd37DZ3CYTzDhaYWnyJJyhTRfBfbfRTO1AQY0AC3r3bJfyN0wPfiCJgrUUpzersjNVqRbOYER0IJ5l6hZfJUy1Eiq0O3pOZxMebnEtMem0IAabOY5Wl9W0y+HhP8Gl8c9A9IWp0liG0QsaAjAIZBMZovFeEAKZ+iGKgqqpk9Olu0blktVqxO3Q0zQxBsoyqLHH0fduhlOLi2VOKumJ/d8dkB1xwjKHDTR5sRp5V1HXD4uyM1k5keU6R53zx+S9O6nxH9B47DJgyJ4RA7xxCJ4X7EF+j6iWKiV7NCH5CygmjK+QEWjnslATRuq6xbmJwLvXzRURqzWyxQgC7/YZxmpAmw7YtwzRSlDmjHZBFQV0vmesaN+ZAjvfJd29uJuYfn6fXXSQKrlKSKGSaKPQOO6Tl/WI+p+sTTqvM09RfXhUIJZjcRBABL9JKpGxK0JKqrBIFiMixPXK/2VCWNdYOaJmz2d9idGC/2bNYnVMUhrypUjE5bhGkqLFxnFJICg43TUgnmY6SKSQct1Qqbc2EJ8899tiy3+/IT/FvSimaWcN2fyB4Q1405GWeOimZxERNlmk2m3vevHnDOI6E6cC8nlHPku29mTXsjy1VUzOMaXz6xfPnbLdbHq7WdMcjg4+gBKXIEN4jRSS46Wutv6+Q4m9DSdECGU6631sh7AQZkJD0sZORMMb4VyYLv358I4pAUZZ89J3vsJjNqOsaFwNjPyEygY/pzitPEWTDYJFCY20EoXDeE8aI85F+GLCMyFyTDRmjG4lSMNgB3UuUhDAJxJSjsgzhHNEntdX75OvvuoGmnrOYzdEyJxwP9GXE+Yoh7qmzmiJP4R77w4Eiz1P4iTyxEgtDcybwDuzhwOgnMl2mC2QqyOuSbFaidUYhNYiJbkxbIqMkg0h7vmmy6CJtCFWm0FmGFND7I8VRM9ZnDH0LsaAqFmRp0gY3DTgb0KFGa40pi8Qx8B4p0+qprALej0x+QlmLkAqESuq1yVBCIPIcexhRlwYfM+w4Us/KZOsVME0jQuT46FMBCJEoPJGYgktMakVGvyY7SnxdIKIALdE6RyqDj4J+GLFvt3J9yzg6qrJinCzTODK5kX0AfejYtAfqusHd+NQ18JLXb64QXvJwtWaxPKcNFrdpubq+5my1Jo+evFJpuGwYmcaJQiq2+z2zeYU80Y+DF/SDY+wt9awmNwapFGezOUoaIiNZnhGJyRI8DgSX0pyXyyU3L66wWU4mNSjJfD6jtyN2SGO8IUEAORw7fvz5j8lzg5KKrhswswatCmJ06LxgMZshgk8Xc3wXLvBO+EuTg6TUYvG2dZjYgpFIQCb94C1SQLzbVPzK4xtRBEyWMVssyEzG5AJKGcoqQ0pJ3/VkeZHy63USEWPUEAJGmdTDl4FxmhjHZFjJ8iyFXfYRlWn6IUVy2yGFQw7KoExOWWRMYUjPGXwSk2JAK4tSZwz9gTLLEHKByVMbrSkUk3d4Um/7sNtiiopmXpLFyGQdth9B52RziSJRaovMoALoMgejkg5hJ9rDln5K+9lgLXbs6KxDvr3wROLIKVkTfUSoGuFG+qHF+yPONhilKMockNhOEfyEOCn2i8WM2kiEOLWjlIQYcN6z3e7Y7XYANHmJDSO6E8mAlWXEECnPChbKcHVtqKqaLM9QkDIUqwYpZaLunFZqKWknUpYzpulIZhRlZfDOnu5MitSwfVsARqIE50acD0TS9uvu/phw69OEQyKixAXP977/u8znl/zoB/8aqQW4AdsfyM5qjl2krhdsdj39cWDx4ZLRHiiXC4zI2W3uEQiqpkJnGu8dzazCZAV99Fzt7hnsQHN5Rl0VZEqR6QQiHaYWrQRKS7xzbG434ANZWZKZgoeXT2ncgdFapmlicpY8z+n6LmVohsDZ2TlfPH/J8bDh6aMPKFXJ6CzL+hKdFUTfYZqcPM9SEYgpnvyd/feUSRhPmQJvFX8h3iYPCqQXCCVAfbUJiECIv84v+A0pApMd+fmnnxEiVEVFPZ/jfaL4zKpEldHGEGMgMtF1gWw2T/95IQjCE+JEDB5Cyrxr2yPRB4IIp5Yh2KHDu4oqz8mCoFBpGkzqRBn2kzsRbAL3dy+JQSAjCDRuSiGiZaUYxpFD12OyjMkOtMc9XdfTzEvK6gS+MAqh0nItkmK//eTRtWK2WBAIdHaL0g069lg/EWSKkBCnCh/FRJYb0uy4x/eRICO9EFS7npaJMKWosbP1GSIz2GEAERiCJ4bkkZdCoWTKJRy94zB0dH3PzfUNN7c3aXAqG3DC4kwORJr5DC8TgLOuaxaLBdPk0FrhiWSn/IG3F74CooNoIkoriAJtkpOzijXBG7xPr69UBVGDyBRd33PYb5m8Y7FaItDcXL9hc3OFnUbqpmF/t0VmOX/0R/8+n3zyffpW88FTwW3+ipv9jpvhCz7/7BXXX1j+6A++xXy+OKUojenuGlN2oZ0GQgg8e/KEBw/PmWyXwkeNYXvcJyekkBijyfIMmRmu7u5gkuS5QjQKc5peTVOEAhEFIQZWTy8Irzz7dsf+/gACnjz9kNlygRSKrEot6LzIKDJNmeeIUTD5Du9HxOQQKA7HPcd2jxYiIdBPMJa3SLG0Lfja9OAvi35BohQni3AJ9Ccd4RveIpRIFotlip0ua/KsYJomjsNIYTJGO0JmyLP05vIu0A0W65JRYugG2rZNQMzTRey8SzinKVXSKZzahXmOD4EQrjlu54wEclKUlvcerSSCHu8svlNYP0CIGKXw2hGFoCgKepcoRlIrjI/4cWRsJVleIZRES41MjBis7fE6p1zV6DInL4s03xAUw9CCiCh5mmkwefo/BgtRpom3AG7aIibDICRGKo59R5SCvh+57lus7anmDYf7LXmu6eqS0Y5kWY5AE0Iy+SglUUpiTEaW56xWK8qqYmgPuGlExwRmCcEzr+cUZY7JJGVR4sJIN/RoE8nIU/yXTJQjKSVIhTyZuKKY0FITlaYoawgFwgdkJonGnHj6hjCldm3WzLF25OrqmtdXrzlsN9jRMnmPVIaPPvoW3/3e79F2PdM48nQ9Z7e7QhI5Hg902y1vDtcg93zvg49YrS8gRmb1EiESWXhyjvms4eLynCdP3+Pm6jXH7Y68LNHdSLe1jMNIXc958Pg9irriaCNSKLSRNPM5ymT4mOYsqrLCDhPjZJkt6hPlaUPfDpytz/BhpKoXCCFQhSH0gbbruFydc7le0+8GhMmwfcsoJVoarPds9xvGoUeF8LWtQDoEKWPor7KCTmcTf5TAL28Afr0q8I0oAibP+OjDj+ntgJCasqgYponbtkXLlGCbMvEkYIikKcL+ZE31Pinho0skItunfr4QgjAF+qGjyExyF56y3WJMfWSjNTFGTGPwO493E103IGKO85Z+2yPzjLzI8QGiSHFSja4Z9hoVPE5YyjKl/Q7DgM40Qmqst0Q83jtUVCk2MXp2t3sGA0FEihOkxMYe51yagwBc7nFjOK1EwA0Tkx8JNlJIRSDigkdIwZRNqP4Bs+MMa3sWi5owDHT9QPCBQkn8aa8+PyX2bEzG8yLH2QkRE+w1Dp7RDrgio22PLB9WeD/io6eYldge8koyHlryPL3NvA8oXeOZMCYZt2KIqBhO8x2SGD0wIaMGJXBSpO8nN2QXOeuLc25v7/n8F1+w3W3Z7TtG50EIXr284vu/8Vv8zu/+HlIpDranKSRinKjrSDMZ+uHAMLY4N/Ln//IvGHdH/s4f/gGLuqTMa6xwCCUwsiQqn0TIqmS5XDAcjzR1zcxGxskxTSNGS6qqBiFZrc7Is5JDe0AakzgDo2O72zGvz+iHiW7oCELgo6fO02yKMZoYA1VmaKcJLRLw9LPPP+P7H75HbjRmvaKsM65ur5K3RQpGIRiHgTBNiSYof4kZSLIIn5SArxmBEkpECHCnwQEhBkTUxK8Flfyq4xtRBISSKWVFaoqyoO87+qFDTJ5sMcfkBq0kvveoUp3GdhW2axmsZXKOaXJ0bctxf8RNycgiTpz4YegYBjhbL/H+lNNWzzFOIwRMbiS6gDMjwxGKIk15+eCoYk3RNCgNRTUy2NT20wEyqbAI7u93GNGiG51m7CdF0DpJNEoTpcIOHqkSWl2ajEZpejwq01jvGCeblu8+Et1EdGPyO9iWqA14j7MeHwQuTGhj6G2X7lLK0YYFU+hQTlIWknFqCXh6e2CKc/IiUZohEqPk3uxo1mfcH/rU0jMZ+XxBFEnD8C5w6HqaY0dZ1CAcSkpyU5A1hjwvUXnONHiyTOKDQEqg84gKhA+JmR80MU4IoQGJiwEdE7cvlopC5ARgd7fDTyNNM6eezek3B2TwNM2CR4/ep8hmSKmYNzOqvORHn/1pMn85ixhHyrxE4DiMWz77xResH6/43offprMaUxmMUcwyydXmgHUtB9ujy4YYNDImoAsSRPAEl/wCU4gsVpc0ZUUWI0GX2ChRGF7tJvTCU85WCSaKY1bX5A8app8+58sXLzh/+IgpJlBsGOB+v6ff3KA/+JDdoaVZLdiPHVc3txipWa/OETJjmOCUxw5fQcN52/CbTi2Bv3pdp7/3X/MOpkCSv75N+G9UBIQQ/xPwHwHXMcbfPJ07I+UOfECCh/yjGONGpLL03wH/ISkB4T+LMf7pX/f8wXvubm9p2w6pU4KtELAoC2ZNAyfsVJWbBGOMnAi2OtlKbVKS3SlWOs8LrLVJvS00eZ4lR1gcgYKu7xix7HJJ7QVx8gQ34sSElI7eevoYcW3HsjpDFcnFlhcFMsDdbkcceqSEsi5ZPzlHjYLdYUPX9kzTRFXXzOZzlJJIUyX7bYw0eY7QEmstZVPix5BixGXEhxGNJjiH8J4QHZurV2R5Rig9buMAhY8eoVKPOYwBOUYOyxFV5zxeXuDHIgV0OofzgSl6SiXJtUoMxhiYVXOevPeU3BRYeyQMA5nRlHniIhR5Ehr95OmPLWVlyLOMaAPH9khR5GTSY7Qi2gMYg1SA8qn9Jg1GRIjy3arLxyntj09aQnrfRpxz1PMZDx89ReVzPv3FF+RdRbvb8yd/74/5rd/4PaJLy9ncZIhoqKs5bX+HG0ZMppnnJTqWzMuRzf0Nm1c3qG99j7d3x+fXr7h78Zo8U2RKYqTESM18PkNqSTf0uNFTVjNW5085e/ghY5iIsmJUM/LZgg6B0IaqWeBXB26GisfnZ9RnOe6whV6yvjgjjg2bly/QSoFwlNUZImQcDj01OVUxo3OWEAMvX7/kiy+/ZDVbsr64wEnH/d0mCXsRvgoTOV3WgcQbkAIUiHD6dyGASrMF7zKII8QYQL1NIPi3KALA/wz898A//dq5fwz8bzHG/1oI8Y9Pj/9LEnPw26dff0ACj/7BX/fk1lpev3iFyQ0+BMq8YDafs1wuIXi0gSwr8c6/Q0Z3XYv3Pm0LBkvwHolIavIw0R5bxtGiC4MmUFYz7Gi53t1Sq9Sbr1cNQWlEBjJElJd4mUQ513X4GChmOcNkkTINZuzaA7v9Hu/ShajznPXqkko1ZJuMze0WK9L3MwwDJniUytAmo2lqZAwMfUcAiqpA4snzDJ0ZlE5LxtTbDYjo2G9vyYyBTDB1LkWlKQU6Ij8UuB+NxCkgNy15O2csSobW0B2LpJGIBJtyU5rs67ouOQ6Vpikr+iJHC8/RDul8TKJgYgOm5Xy/uUPIJXmeUom7TnE8HJnLOV4GZFTgAl55sjJ7p2aDITpHtJFQhVPM2+n75yRWnSb1zlZnRJEhzCvW6zVd29L7gcePH3OxPmez2WOHnil4vIicn1+Q7yJ909MUOaWR4B2PP/6Y5xrmdcOsXtD1PRbPz376KbEdefreI/AREQJKxpOrcYa5O1DWSy4eP+Xh93+f1dPv42Nkc7RcXR8Zxo6srlmuFozMePTsN+iPPWOA/ajwvuL1Ycv9//VjGuVZmoI6L8nyBpNVqCzRrOdnGS56ZEbqG8ZEx1ZSEpFcvbnhzasXROHSViCIr6LGTpqg4OQP+PqmX0Skh1gJ/Pj2ZABx8g+8KyZ/9fg3KgIxxv9TCPHBL53+B8C/d/r4fwH+D1IR+AfAP43pnfB/CyGWv8Qd/CvHaEdGPxIGz2K5RNV1uiAnTzar8Laja3uCdyeCSiCENN5JhHldI4Xmerih7weEjNgpZdH7KWDdwOAD4zQgdETWTervnxZJucoY4sBbeq42pOKTeWZnc8Jmi/fggsWJkCy9zlFkBcZkeO/Y9vdM3jObz6jqKvWRYyCGwOh7Ap4YK+wESiuCdwTnkUJwtl4xDh2bomAcE7VGEBCjx48Do5+Q04LgW3wMRJEgKvEFBB9StZ8cHsFucw1hItOS7nBAnBhzCECKdxQgZT39bs84jGir6A89MBKminq+RhhJXmTkecbdbkt/d8dyeYExgsV8xvawwznPpBPIZSn16UsI0ALvPF4ItJIptvzd3V+koRhSqyu9CQVVIZjmksE5Pvnkuzjnye8zlFDc3b1hcpJua1mdn3PYdjB2bHdHtjfXVEZBnOi7A7P6KRfnF0Q/8eDykpevX3G1vUGGSFVWzKqSh48eooscQRpnrss55w/g3/l3/4RqeUZx/ozXrWeYJobesRsmRusZr+/Y3FtUWfD47BwnDftuxyQl27s9f/6DT/n8X/9zGjPx4fsN3/72M3wAIyP9sGHoe8rZggicn10QpT9thdNr473gzc//X+bepEeyLD3Te85wZ5vd3D0iPIacs4bkUKXqIltUi80e1BKhAdp0QyuhoY0A6Q/0Sku1oKX+gHYStJMWAgRtWkBDoNhkkzXmPMbgs413vmfQ4phHRpFV1QTYBPICme4wt4hwmNn57jnf977Pe81ut0OgwDvutEHS+Zfv4WFVhl0AvOwWegPK8BIe4g4VQ7580i+//jo9gdNXFvYFcHr4/gx4+srznh0e+5VFIMtSvvdb32EYAjMfFWbqm+sr9s97hFKkSpEmKVprVBxgIlmaIDx0dcNqvca7njwLCLByv2O33REyhoKOfXDBNps8fsRV1zP0LbmMiIoYhAVr0VF0iPbO6PsAOxGHgBKlBCfHJyEbwAvqpsVLxWhUUJUl5qonjRK6tmVb7hj6HqUUMtJI17K+MYyPTtBSkUUJSZoRRTF1VVJWFVJKJtMpzX5PJBKEN9hWMvR7lHQgery3OO8ZjIQ6LDrpIUNQVg2mq9mtbsjTGGdaIgVaCawZDtRZQdv2ON+SFDFjV2DiPWoL+32I1n7wYKAre9I0wfsUGQeUVtPssFaHMM4sx5se7SPiJAFXY7oEJQPzTskgVxZa4WMffk/AHqzToHAHS5zSB3e80IDg2+98hzwpuL5+xudffcFToYmihG99+9u8+PJz2tqQJYrN7TXnHz/jk69+RKwcb71+Rt9VRNKjogTvHLPxmMX8dVKjUV4wKjKWx/dIRlOayhHPZmxlSqMED974LlIlVLWkanv6YeBqe42rQxHbtmsuds8Zz6asV8/Z7LacHC1JRqHh9+MP/ozLLz+ioGV7ofnHf/ifMz2akI1G3Dy7Ii8iFtM3SfOCk+UZzrVcZWvi5AVN17Fpar64+CpQmpw/3LvDDjSs9wM84HA5YoQwCDzeAEpg3dcdgJBjbAmTA8Wvuv6tNAa99178OmrBL7lezR2YL44oigxjBFVVkyTBbln3LWYw2NaRzaZ46TC+pkiWKAneWuqqPtBrKrbVms1mTXmz4nq9pjOGJMkQOsH5MNs1tmOz2XB0dIQ1ltIPJMKSFylCyzDTt5b9fkfTNEwmAic80gqmswVJMmU2CdMIvd8zHO7E1rYY61g3W2KpyNIcfMBZu2FAonA99E2HMYbZLEPKHJEJClmwfOeI4Wctu9U2eOLbDukd1tZ4a0CamybnvgAAIABJREFUYAsVwBzszQAIlBBIJJVxYXuoFN5FYfToA2JNiqC8rKqKtgvj1JvrS5w1zKczbCyJ0xvktsH6IHSZzWZorem6HVGkw3kfE1J2o7DNH5wgkZpICKyTeG9Ivcc4iYplGBvCS92DcCAGEApcJIJBRngQDh0pUhRKacp+B/IxRVHwry7/mHro0MOG53kWdne9YJzOyZOE4ihFfzXw5NEx3/nWW+x2V0wmBW3bAWEkmsj44GMYSJIUERfsW4mTOTvbc/7VDauqJsmmZDpC+OC/2Kw3CAdEEftyy/xkBky5ur7k5x9+RjbLsHWP1AOXz7+g2Z2jRIvvOrJ4zGgyI01SvIX9bg/OMp2eImJP2TdE3pJPck6P71OXFc8uL7hZr0KvB4E7LE/v7SvQEBE4GsLjRSjsQSeU4GUYHN61Ex3AoR+j/oagIpd323whxH3g6vD4c+DRK897eHjsF65Xcwcevvaav73dsF5vSJKEk5OMLJtgrEV7S57kpElK13d479jvr4iiEQrJar2mWq9C/PbBi18ag0oTstYTZ5rVek2eLhgXBd2gWa83YSFO3sJ5ibUKgaTveozpkfruxffsmxphHHEU01QV6+sN6SinaapgJR2PEUZQy5iuLvHWki2OKYoRN9fXtL04vAnhXKZUqNBt44g7QW97iiJnoRZUoxJnPC/Wz3C7IAIypsPb4FPXsUTcn+KuO7yvQt9AyjCLH2f4useJYITq2xY7DME3YXqqtuP2ao21nrLa07ehmSp8QH7nWYE8k+zWG4YhdKOVUFgfMga7ruXq+oosy1kujyj3ddhZCImwAyKOQnGIIiKtg36g8TD6uhBIGY6oVoAS9rBPVUEgpSxCaGIXcTw5JZUdXWPZlXu6pkaLjNs/+9d89zvfRadjnLPEWrE4ivh7f//3EXRozYEvoblZX7KvtozHU4zpWR6fcnO1QUYxZBO6TlDuLZ98eUlrBowKr1lnHa7v2G4Cmv7eg/sUoxFlGXQAWRrTdy1PZ5Ikium2O8Yjy4NpTPb6KR+XV4xmGb/zu38rELCswymH8opERCSppu5bqs2a+WxKMcpZLIJB7ubFNU3X4/H4Q47DYbX84toR4WgQ4xjGgqiGwQ6hx6AF2OGwtIOpCA9O/s0Ugf8D+C+Bf374+r+/8vh/K4T4XwkNwe2v6wdAQFBvdxviRDOYns+efky16SjyjGI6ZVKMgz3Vm/DBaS3r9Quefv4FXdvQdQPX19eU+z06joIqTUqMkrRNx3wy5qgo0FnKptweJgUeu/aomcJ7w/n5OthGJ2Ok9Mznp0TRirppGMxA1fcoLemkoF1fk2Upm9Ut/fWKB/fukUQR9+/f5+b6OgRMComONLmS1E2DcY7l/ASEJI4T5jqioUaaKBQeJ5nmE4os46vzz3DxgAIyO0GImKFfB5XaVYVGIXTE0A94B1GiSWWKzGOa3oS8BWcRiaIuS65vrllttkRpHuAZeUp1A3VZ05aW8aMT5vM5nin3lyfB+qsDA9+6gb7vSRNNpDSx1vRtR5pEWB+OWiIfYzuDZKAfeiKtiCONTjVSH87/AYCHd+5wRlVhkOXByTD1EQaE0gxxzgfPv+TLL75CSIEQGuNq+qYh0orbzQ1Zcj8Uz8UZZfUV10+vMHbCD77/fVbrLbtyQBdThmhKOtKoZMrj+ylSR1R+wvl6xcX5ii/PL2j6Cic9QzugvGcyGbGcjWialk9+9iOsjhjPclzZUVc1dbVnuNlxXl8zVp4/+Ie/i1IznHhA81tvYo0CJdGJx4uOSEq62zWPZqdEeUG7WYFzCO+ZL0a0ec4L71lvdtRVHbZKPuwwxUEifOcaEOELHug9pKWnv5sieAtG4++2/oqvk6LcX3M6IIT4XwhNwKUQ4hnw3x0W//8mhPivgC+Bf3x4+v9JGA9+QhgR/tN/4z/goW8rttselcqXAQxlN3C/uE/d7Gn8Bi8iJJaubrn46gXnz8/pupbNZs3V9RVaa4rRiDwvWI7HNEJQdx2TyQijBHYQpDolSxImaUof94gmmHes9XjTsnUW733gBUQRE6moEERRRFs3CCGoyhLbl9T1OagHbHcbBqVQWUqSp3R1gxmCsCh43gMvIUkSdvs9q9sV3JtTTOdkeUaaJqRxjJIC0w/kIqOVCmcbtA5R3UGZ53C9I1o6bCNQ5iDdBeJ+wEQRQkikVORZTipj1qsdH3/yOW27ZXlywu72msV8wfGjBVEmubrd0l2/IFFQ5DlRHIF3rFYlo1EwrAsCPSiOQ3S8UoEInBc5AIm17EyH9+qls1Aoj4wIoyx/p38XeBGUi04o8Db0AhwoGaFiQd97rq9u+fjDj7hZrUh0ROv2RFKy6zs+/+opi+MT2r4jixT37z/k5z/9kigfEcUpMspQieHe6UOi0YIsKzg6uo+enmB1Sl9WrFYrrHOoKGY0H1Fd7ljf3NJuK7IkQTqDbVvKckdVVdR1dWjcGTKfsGs2bG6vOD6a8/aT+6RRkG1XXcv3vvXbrHZbfvTB++yHmkkS05meq/Mr3nr0iCxPWd1+RZrMyLKUar/DHIxu232FN+KAEFcIzC9KBfnaHhyWjaMVCR4DziNeRpIfvAUu+A+994hfs9L/qtOB/+JX/Ojv/5LneuC/+av8vXeXMQN921C1JfPiiF4bzKZhrBaHN6EOkt44IpKS8y+f8dnnXyCEZbvdcHl9hbGerEjph56xTeitZX/4c21TE+kRnVnTmIFYSqbpmOl8ipYqADMvDK1qKeuGk9MpWmuSKKLc7xiPx1hjWNV76rphNp2yulrjo5TFMiWLNa6uuXd6n5PZjM3Nll1ZcX7xjLZtGY0nwRQiwLogZx4sRCqIoCQwy3NwBvNsYDaecHP1MRCUZ1oreiriZEwXN3Rbj0SiIg0HklItLYmxCB3jnSWJJFmS8MVnH/Gn/+qP2KxuODk+5sHRQ/S7bxM/OWNUFDjn2G7WtK1BK4mxA5NJAb7h4nzHo0evkwiLTAJ7YBgGlAppRVqHYnC7v8UYSZqmGKVIlUbydRcbPFJIpPcYIQLElKAn8F4RI3FCMBzoUF89f05ZNtRlw369R+CRreSdt77NeDEKsI84QUcReZGwLCaA5fTsjN5q0mLJ/bfPOHvyDrP5EdbF9EPKdrdjs9pDFNO7DiEl2gmGqmEyyplmOVVZsa1KXly8wPQ9dugpd7sQ19Y25HmKdI77syk/+N5v8eBswfLsGKklo4nAxWMev7XEFxNqA5kWmD5Ix4sk/O6LozNipVFKcHNzTVWXlOWe3XYHIng+/MvRHhz6/PzlEBGBt18LiF9BD7xsAouD1dvZv5njwL+1SwrJRx98yOPHj9nc3rKIEy6F4cXFV/zRv/x/OTpe8u/84Ad8+cXngdi72VFub7m8uKIsS2anc+bzGcvlktHoGOcMz59+Dh7uny4ZRzFRJGkSja8Nx6Nj8nlKnqbgLAJFNIswjSGRA3cBp8aEYUvbdez6wL+XwvPs/DlPzs5wxqL6hqFPGecT9psbNpstSmnqekfTlKRZzng6wTjH82dPGY8nPHntcehhlD11VeKwXEtJLCP0VPPgwQnb3S03ly8wzqNlRJYtECiKdITNUrbrLdrVSKFRaTh7WhUxSnPeeONt/vbv/fvESuLtQCzh+uqSq8tzbk7Pqc0NT5qHzI8fE8djJIKsyEA4sixlVGSMihQhOtp2QxzH5FHOdrMnTcekafA6dF2H1pqHy7eoYsM8z9BRFLBiCgQS5yQ4g7AeJy1emoN6MMheMzypUNRC0UnDn/7sR/zJj39MX9f4wfHw+D6zaYGTEI2nHB2fcu/xMctsiRscCouOPOOLp+R5xne/9+8SZWPiJKfpB27LHmUMO7sjiTXL5ZRNvWN2vKDeVVxeXLM8PqHuSla318RxzGZzS11vaaqKTBc8ef0Nnrz+Bj/44Q/JkoTNas24LxCTnmiUsR/27HtFb1I2a5ibnNHJe4yXY6QUrC9eYLzk9vaSYpkSec9iOWdb1uxuV+yqPde3t3R9f2AFADgwd/O9XzXe+yWawVcCRzRg7tSFv4Y0+o0oAk3XkGUZNze3DINhmM7Zri7wLubk3gmvv/EGHs9uu6MtK5r1Klg23cBoMeLR/XukSUxV1zx79iM2mzWz8ZR7p/fI0pTres1YF9ze3JKkCelZCMJo24auC+EfWoU+grVhyy0ThTOGqqpCSpA1XB9yDL0x9AezUW0GVHlDTXyIHDNY26O04N69e3T9QOQUcRRjsgKtJKubW/JRQRRr2jZYmWOtWDUtXd9wNJvx5MkT6v2G/XaHVRJrNfczzWbwzBJPsZzQ1RrnUorcIj1M5gt++7e+z7vvfovl8pSu72ibGoTnaD6hKnc09Z7rr56SZ1NQJVnmiOKIuqkC2DVO6IYeHHT9wPFJQWJyBtfTNA37/YooCoToOA65jTKH+SLDdZoo0i9hFkIcRKySA/NChbOpP8AvBPTeY5zACUnXGZqmYzANXz19RgIcj0NPaFfvkUpxdHqPcTYiEimDbFFS8cYbb5PlKUdHc+IkJ4sytvXAznrKVUlqNJU27IaBFBiqiqo29HXLbHGCSC2bdsPt6gbRWxgGJmnK49P7vPnuuxzfP8NJhdUxrY45eetNpPfU9Z7OKEaLBX1rub5ZoVwcdp+bHjmNiKMIK1JOHzwgSVLKsqSqa+7piCLPWN/ccu/hfTZWIPRXB7CoxR8YGn9J9OsAEYMwgRwi+eU1wvsgNj5Ykb/xLsK+bfn4Zz9ivjxlMpmwXV+z3eyYz5dEUlFvVtTVjs3tBVkSgR8QwjKbFSRZzDhNUUnK1dWXXF+eoyKNTASNanF1T+Sh3+/Z315xi8eJgXe//V2qviRKNEejOd55VqsVRZwwmJbURAitAtjRW5TWpHmGMYY3Tk+RXuIkJEnKAfVK2w8UY09Xd1jrSZOcXEY414IRZHnMtqooRmnIXOwdsUzobcdubRnaC24+f0r/2inTdMEbb7zGz3/+U4zpkKKldIZUKOTgeThf0kmNkJLxbIrSknff/hbvfPc3mB4dk4ynRMYxOX5A9MUzhE6JEgvCsLYVy90tD90ZkQ4CHicku2rLdnvNydEkkJ8PQjMTGapqS6QF1rRU9f6QNDxhbTdM3IRhr1mkikQHVyHWYqPw0ggv8IOH+G7y7cOdSQg6AU52JDIYb6rrPdsXG+p1iZwU+FFKnyji5JjFfMooihgnGVp7RsnkwIqcU5w8QoiIn77/Y5apQRWaoetomz0NA7731DctTKeAJkkU9x88oLdB5p0mI9LJEm3gwb17TGdzptMpy5MTdBRxc73ixdMrum1HMkl48OAeaZIyJHtub7cIrUi7geT0CA04aTD1jtoPONOSRDE74Tj/8gr38Q3F2+9hZMv8ZIaLIvwQmBlREjH0DvrDOPgvjvdFiidYpF/6B/zXVmEXgRw8BdAIAQVQfb1D+GXXN6IIGD/w0UcfMT6/wGrNdDRmNlsQ64i6qlnnOY8fP+a1x485f35O27WstxvKpqYYjYl0SlPt2W/XJBIirVjdXDL0ZbDAth2rqxuatkQkEekoZ7vb8dZbb1FEBbYfsGZASkiSgOC+Wa9IkoSiKMLdDo86OgpRZcOA1FGQ5HpHnid42xPHE6SUlHEZnuMVRjicCa7Hsq6xzmF6w6qqESi8lNxcXfHJB39KXQ+YvmW5uubBm/c4O36df+/3/w6u6Wmrkk2X89p8hKIhn8yZ5zlJkrBYLFBKkRcjRkcLRtMZyXhKYiynp/coxhMGaw9NOU+1qfnCfcrJ/Xs8OpqiYkExnoSjiXIoGVb/8miOF4Jbb1kUOYtFgrPBnjyeFPRmYJEtSJMYpQ8L/JA/gI7R4qBxtWC1xXt5IAnfdaolqSf4DYSiWZecP33OdrMhyRKUGvPee7/JdDrmdr3iwYOH9E3D0BdMJznGQKYzZKzpq4rr62doodj1e6p1Rd8Ha/nxyRFJkjCdTlHAOI6xSULX9Tx6dA8v+LrfIQVaBov3ertjNJ6RJCnL5RHDYIKCzzp+8rOfsd9sePvxm6zra0RUMBpN+OQnP0E6ixeG+w8ec3Jyj2aI+de7lM+/+An71VPeiUcUP38fGw0c3zujFxoxqul2JUqEyLEgDAwhppKwoK338Co9+K4PyNcswfkQUTFQhTcDUfIXXvO/fH0jigBWo3XYRtq6IZrOWa9vGU/G7ChZuJgXL15wulxCvUcoSTYqUEnMaDJmOh2jtaLcV+zXtyA2WO9J4oQsEazXa3b7Dc5pLA37fcloNMIbT2MaFKGSHh0dUdZ7lFLEcYBQJkkYOSaR5ratyfOQBiSkIk0yvB0oy5qmaQKeW0qapsGK4CBkEAyDRUiJRAYqMpAkGavVCikldbmhKgcgyIiHvqdb9/gTOFue4ntDa0qO1zXjXKPUlMlsErbkSlPkKR5Ii5w8D6Et3jh626GzhPnJnPFoTNcqusZibI/wgsvLF+gkZXlyj2gscG5g6FuqyjIej/E+QgjPcSQxg0VKyXh8hDWeWClUkh7ScZIAvjwoBKWUFEDJQbUmPcIfBEOv7l99QHwHaTOk44TRpODs0RFVZUizgvFkhI4jlssMpQTJaEqeFniv6bqa880ls9mCvq+oyoqh74jjjAcPXucnP/1TVJ7zOM8o5gX1NuQTdrMZyyxjlCbs9w2T6ZS2rXj+/Dl915LEISikrlusXaOB3nqub2/Bwnw+5q3Xn/D8E0U/wHFySi0dTWt4vlrxxuOHjI+P+OKjT/mzn3/I5eUln/z8T1jdXhHJDa+9+x5FOkZNBWkckrV7a4Ka2oF0Di+CZLi+Cx707mtQgINIBVDN11KCcHDY0gcgzd1jhx3AN34ngDd4LF3XhfCNtmZxvOD66hIpNSLxONvz/PIZZdcgBWgtMAcffNN1tE2FHTqUBGMhEjHttsKLnrqpcDZEPeNzhAg5gFpqlFCkUUwWe9pBYIYe43pab6CHLE9I4+SAH5OoPjDq67ohjmIW8xlt2zBJU6Q1XFcl83mIzxqGCpUlaKVo6jYgsF1CUzWYvqMutzRVjW4aJPZAkAlf67pmW7UUxRiZWmZtjk174lgdJiUKJQVZnqK0Ikpz0jRhPM5QqcCplL5VlPunlJuarrcMgyWOI9JEkaYxSnjaeoezc5SNyJKAal8cLdCHbX3TVPSNYjrJieMY8CRJQloUWBfGq0KHHAWlgkTVek/Iej5IVoU4sPyCm+3lx1FI8BrnwvZ2vphy9vABX3z1gsUy5vjkmCzPGeqB8ewe+33LYpGzK/d4ATc3K1abNZ988hne+1DEq4qxlHz62U8Zj0YhNfjq+qUf/yUm/WDwqustMICQzGazoLCUYRJSNw2dtDRVRyQUR0cj1rdb9vs90zTltXceY3XO5x98yHg8Ztj3xFHG+fUVq9tLojhFpwIf9TSbC4bbF/Ta8C//+P8jShX/4B/+HebLKavzK4xzBNa1fckVFGGA+lIE/NJRDAw24MRehusAPSkZLc3hePrqsv/GFwEhwBlLN9RM5zOkd1x//Cl9HCGRONuzWExxtiGNR4HhPxnRDT1RHDGZjui7GhUNLBcZ3oRg0Lbf02803obAR2MGHBW76yve/9n7vPfd9zhaHCGl5NnFOUopZvMZQwmTNCedpmgbsa1KTNeTpgWd6MgFPHzyhKHvKHcrtJRMj4/x3pLbkNS+3W7ZbCz7dYnWEaNJTjc06Lkg6XOuV9d05Zrq0z1mYoi1oOscUsUIFXG0PGGUF0gdk2YanTiS6OAf8ZY7A4m/tERvarR3wa7sQKkErz1RHHN2dsaLr05Y3VvSuT3COcY6IdIwHo+5f/+UJBbYriZyhjxSrL+6xGeGKE55+OgxTW8o65KTkxO8HBPHhjgOZKIoTtCJA1KEGELUmsjBCXQboSeCQXpc7KG50wzYgIrzgb8mAKkj/CD5nR/+EJVkGCdYLpeUTU2W54xHE7Sq6VvL+dUz1vs9CoiV5vLqkjwPeQZPnz7lO9/5PjpqOFougT1Vp9E27GTSKEYMhtvrGwYTVlXbtkRRRN82ICTFaESWZxwdLbi6uuLZxQWb21tG44KzkxmoiD/5+Y8Z2pY33/kuq+oKkQ/I3DNp9ty8OGf24JR7i2Oi5JTHc43fvMf7f+7p1s8wRvL++z/jzdcfUIzfozUtzy/Psd4dcPihZ/IXSQJ3X186A+TXx62wv7LhGCCDo/DVjqF33/AicDBEM53OsIPBJ4ohig6Zyy6oAcuS09NTumGHThS0wTYcac2oKGjHI9arMc6DSiWykYhaISNP05W0XfBvO+9Z1zVpWbHZbDA9HE/HLxN1Ip1gYujKPc47JJIkjdBpSp4mRHFMEidEQiMTcMUIaw1NW+OdDZDJvmcYQpKPXIQUoaap0FqRRAlElokb0W0rmvs10kMzhL1gFKVEUcKs7Th/8YI3H79GMp+RzGK0HQ6A1Rg/GPS1gvsS23myRc5gDd57TO/wjaHsW6pyjzUDVVkytDWR8pgxFMWYR48ecnJ6grE1UkYIkePsQEeNcwKcQUsYpZpu8EglmM7j0Pt4eWQCqUZY54BA422BXAr8RDLcqVsaf1j4oVEo0Nzl6gUkHMhYMFvMeP211ymbJhwtuprtdke5rzC9xQ092+2W46MjjDF8+P4HVFVFdD/i+s+vOX79mPX6PBzh4ph6LdnVO4qiCNtj2/Hs2QWbzZYsy3jz7beYTieBF5FEeASdh6Y17PcVXdeFUfJ8ztB3fPbVVUgH6lq6ruH82Vdsd2ssPaY2XL94ijQ91e0Ni2TEYqE4PnudHyf/gtnM4dyYR4/OKBJHEmWHnIme7W4L0uEcICT+ED8mXrmfh+9iwCKE/QV3cPDADuFYZg5DhFcGAv6bHkgKEVl2j65b03UDSh305DKcpz2WLinC1rRtUTvPrbshG49Ixhm77Zau60jTkAxcDz3j0Ygs0VRdg9sGEDN44jR05u8itKxt2VQQRYJhGKhXNcv5iFZJhKioqgDSzLMUhCcej0ikZDSa0LYNUnr6rg3/DQNaKfI4xgrBzgaqjNKK1e2auq5JnMPKBmcGOhpMpNDWQwdF8YBZ4Vk1DX/WX5GWIy6ursgnS1IhiWOJEgmxlCB6mAX8VK8MVgRsVsgWAKsFbdlxc31JudsSSYnxnjSOGI8KsjzHI+j3PU4pslzTdQ1luWPoO7RWxFHEdrMOmLC0wFqLMYYsU4dgWI+U2aEAfH2XSgmUXOcjhBhAe3x3914H8IUUCumD681BoPqIsCHORwWdMbR9j3PBt1EPFavLFfF0xnRaMJtNWa83tG3L22+/Td/3cAZVVdE0DWdnZ3zx5Zd89uFn3H94ymy5pKsq9tsNcRyIxkkSYYee/X7HaDSiGI9BSKrzC25X13RdHxKFx2MmkzFffvE55X5LVe44Wsx59OA+N/uKy+crNutbeuu4vfyYSXTCvIbE1Ny7/xtMFwVnC0/ycII+e8zv/N7vMgwVo6wIuZO7PdvVFhcSc7nby0sfUojv1npwDPdYDmrgX3Y58UsDh8QvlJNfvL4RRcBjaJoLBLw8h1YecoKFV/iEfn/Nxo+I85yy3tP3A6Y3lNs9UgrausPhmR7NWEyWWGspu45tuQux0UKBgKHvEMKzWt0wm/8t8iTjZnXJ8fFDkmQKwpBkKdP5HCKD6yTOGIztUTjSg8Lw5uYKpSTOGqw1RHFEpBVt19FYh0oTUhsCPONYM52MgwtSe3Y3Bq0iJkkgD6VpAh7KsqTsNUoI3OcOf2r5/MsvIdacPXzIk4dniMFi3MCoyLBphooGfKRxSodCJQ/FrKlYX13Q7De01Y663uGtRXhNLByz6YQki7G25MXzc+q2pihyIq1ZHi2YLuZMJkc8fLhE6xwRJRSjCSoKIbAqUggZ9P9CBjnwoATpYbLlhaB1JdIpBiOI0GgC7UKIkJTjLQgHTgqMEnRtS1cazl+csytLrq+vGQbF8XJCrlKeVSXjUU6c5/zkJz+lvqh56623uL6+oihG9G2Pw/P8+XM+/PBD/sM//EN+5/d+iHeOq+fP+OTTD/BO8t3vvsdv/uZ7FEXBZrflZnXLYr7g9FTTmYGma+jbnqZuwnHg2TO22y3OObIkMBY++3TLZDbl+PiEd958wH59Q983/Oa3vsfN7Zf84O9+l3/0H/0uLsnpq5J/8p/+E6ajnLLquVqt2G43TMYZ5+srzp+ds7tZH143GZyLLrym/tXbvT8ARv/iIk+AvkD6Gv/KUv8FifE3vifgxSE8IZwP3c4xmobGlBIC1MBgAx3H7feBIuQdfdegXUyU5wy2x7hwpyrLkrquMcZQlTVWhx6Uaw1YjfEepSR5lqCEIk9GCCR5EUJN8tMCv/Ok0QTDQO99YOy7LcMAWqtAAlIKrUL7RkpB13eY/Y5677AH/NlkMgkKwqw+bKfnrLNbrq+uiCcR0ozJhGCbJgxDMP9YPIwtXVuRJiPoA5J66C1KK1SssabHJy0yirAqRkQaoyXOO0zb0Owr9rsVzeaWyLRMRin73ZokyRhNJzg/0NUVtTWsty/ASfIspijGFGnMyfGCo+Up0aGRhlJIHbwBSqlQVBF4Eow0pEIQeWiEJ/cC10KS6Ds3PNL3gZmPQHoR9mWKA1ZN0Ox6qrqlqhrarqeuA7y17zeUJYyzgqFvaeqSabZgMhlxcrIkTzNOTpacn19ijEGp4CI03cDqNiy2oWupq5rpNPRrbjdreu9ZjEbEaUKWZSRpwmA6ympPWe3Ybrd0Xc98PifP88POIWE0GpEXBTpJabY71pe3/PA//s/QQ02zXlGff45UTzg7mSCFJ60Fjog+ndJHMYNvEF5SjMfoOKJrwxHVDoeE0QM8RMogqArTlzuzlQiCKwxhf5ADLXQaJjW+9Hf+YcL/C4J9J0BIf9X1jSgCL6uXP2wWtT94pgVeOZSMAlC5GzCdQcQHJbVzOG8pd/sA/8z5gyg4AAAgAElEQVQSbGNQUQjP2O+39INBOU9iHG0aGAQeRRxHNG3PdJQyvzdnLDP2+6AOVLVCp9FhJDaGeYHqLNutRUmHH1qczIl0jBQGKTXeWRSSozxnJpLgZ48ESmmariX2jlGWEUWa5XJJEseU5Zbbr26o3I6syEhGKdW2Zrfd0xuLFor9/poki1i+XtH1AwKHExF1XZJmGV4SxE0yRfaCnS/pD8i1zXbF8+tLmqFjMi5INEzyiEmRksxmSBE0FY/PnmDMhuXyhMXRCVkxJspGCKFCaKjXiCwnjiVax2itkfLw0ZItOdEh+gJyH/rZdSzJD7l48hCr7QiRY9ofIuOMxzqHd5LtrmazWbOvGrq25eb6gn1ZMp1MuLy45NJZRqOCs7MzOhyXt+d8/zd/QO86fvbnP+OTjz9mcXTEk9df49Gjh5xNc9q65OL6hul0xFtvvBG0DCIm1nE4VoYVRpZlAOx2JcNgUF6ipSIdT3j85DFFNqNu90gleHp5Sdl1LLRgPJ2grMZsW5yWrCvLB88uuJcrIjyP7vfIKehBI3uBAqwwGGcZjya0Q4tXEd1gDkequxuhQFp/SHWCl6ygu+9fLpf6sCnoYcsrwqJQekMBgF9XAOAbUgReShoDLxmREJj+UgaUeG/D2ch5xGE7LjxhvuxgMAYZK/q24bZz9GZAqI6uaV5qqDsh8a1HRpIsS0DAJ599wpMHj9ltdjx69IgkS7G25sFZimw1IhEh/KETiDoUhBB3niHFIdhUSPI0IfURza5lLUPKzbwoEEJStSWx1eijJfNIMfRhDAoF03HO/dNTsiznow8+pNzuKIuGC0IjZzCOsrnFvxixuv1TRqOPeXB6zHXf8q17C7JRzmw6ZTFfBhjKAK2pkFriFYzHObP5CDPsEQ6O0ymLoyXZdESsPdNJwWQ0RgmYzKZMpjMyNUJEGcXxFKTCS0UkI6I0RiBQSr38TAkfsOk4y93dxntLBgTAqH3Jv/dehK2ulDTeMHSe3b7m5mZFua9Zb3ZcX99ghobpdBHiu5ynSHPq/Z5dvccNBv0FnD55wr3lPT744FM+/PmPeefdd3nvnXc4efSIp89f4L3j/hvvstle8xvvvks0ylAqKAid62h9y4sXL9Bac3Z2xmg0pqlr9ps9xloGN2BtwL+bwSBGjjyOubi+YTmdY12IaH9xcYERA9dfXLC+3bG+XfGjP/4j9uefkWcd/9N//885VhDnOfNpjula1o3j//m//gW//wd/wL7d85MPP+KnH32CMYDyB8Cox0qN9wLB16CQv3i9ahiCw9BIQHiDAp5f0706Wfyl1zeiCHztlfZoBcPhNHOndPIEtZmUAu8HlLA4NN6HYiFUmEEroUhTjcBievA27CoG6+/2VWitkBJWq1uWxycvddVxkpAkKXGSY60jnscI54mSiFhIRG6xtg9egjhCRzpMJ+IYKSIGOvzYEZcKJ0LnXEcRnWnoe0GaJMSxZlTkWDewuV1T1w0yC32Ek3snzOYztrcb2jqgxNuupR8Exqzp2p6m7tjttwgcnw8Vs/kErSIenYWgzEhH1LsdSZQSaRGsv3mKengEvidzEcfLIyaTMUpYprMxs8mM2EuiPEbGGmKIi9DlV1IgtEZIgbOSIP8ZgWgRaISTtEKRC/dS+NMYiwUUMjAERRhPmcNdre89m31JWfdcXN/wxQefB6FQkdFaixQRq/X2kMGQstlssNZhugGEoxlahqGjqVuyrOA0uc9us+Pi6oLPX7wgSlLm84z1ektezJgscxApVVkSRxG3t7ecn58zWMNoPA4iL6koq5Lb1Yo8z0nihCRNSbOUwQy0XQ946rbFRQLpJFVZo1WEHjlcK1gwojAt7/d7xqnmZJbT+Z71fsc8iiFJcFGCFJLvfe/7ZFKzrRxXNzdc3d6Erv5hMTgPQg54IvAaMHebhDAWPlBGhQ8OwZfH/V+oFA5oDwVAEKYK7S9df9+IIiAQCKXBDvQiwghHdNBFO8BnAqqD+kkJnJbQHjrSIuwGQgkxdF3NMMiQa2cterjbQUi8H2DwtNsWpQbqesdqvWI0GtM5yzQdEY9TLEEwEkcxSaRQUmI6j3Ca2eIeiA4HZEWMksFv76xCxwmRSOj2DQhPkiqm44I8TYjT+HB39PgextM5UmtM33Pta0Z5zmIxpygypPLsb/c03tFtOuq2pBoM2rW0qw3jYsL2YkO1uSKPZiS/MSZOM7yUZIUg1golBMdHM6TvaaoI5Q3jUUaRp2jpSeOY+TgjTSVSaUysSaKYu1iLkFTkQ3ahEHhrSIop3nhwgV8oK4kbS5xUICVCQ+JCkpNRIBFYoegGx2ADd3C7q3j29JKyrtiXLW1vSYocLzTCWIwZMDKkTRejOVpq9nVJ2XQcny6pe8uXn3zKn//Z+4yPxvR1yhGCxWRMlOVESYyUgmfPPuB73/vbVPsBJ3qGpsWqsIs8PbuPUxCnGmU8aQSNkhBJVJoSxTnOGpq6xdkwnRqNxsSJYl/eoq3k848/CAUkjjiZzCkyTZRrXn99StOPeTSO2V+ukEaRYvH5mMl4TmQVZ7/xGn67w652qKSg7exBHOTwRAjpeIkTE4f7+B1a/O5xABnogcaHGtHz665f/dNvRBEAj5736GtohCE5aKfAhzCO8m7fE7qcvn5FDunC/Nk5B14wMODd1yTWkFp0+Fc8IULKezQxl19eUK1bHj16zOtvvUU+zuisIY5i0lShRBQCLjxBLddHuMgjVUocB4RWmqQkh5xEZw193zH0Hd57lBJMZ0EJaY0F5wKB2IeAj+vVLeV2zcwGzHoSxxSjCcfH96j2JR7Ban3LzcUVu92eoRuo64pYCHbtnmGQ3Fw+o6pWCDmmmEzQqUYrRdfWFHnC9LUn4AzO9ORx4A9YZ9CZRMUpUifEScJkliG9xlqP1BFCKqRQYc/fg0o8GIM6UJk60yOyCLkXlFIiMk1iNcZ6ejPgjaQ3LdYpmrajqnvqumW73dP1faAeC4iTiPXqhrquGIaO4+WSfbljd7km1TGz2Zz5fMLx8REvXnzKdtdSbra8//6f8+1vf5t49w6P/tFxKB7e8ejJY25vVmw2G66urnDWoeMIrKFvOyaLGW+8+QZtFxDsRRRhTc/JyQmL02NWtyv2m2vsMLBYzOmaluvLc9I0pxl6ri4u6LuOWIcMw3p7RWk6ugzmcco/+A/+LumgiHXEixfPWVdbJtP3sB7a3lPvNwwmp9CCJE3xEaHZJ0I+AAOHdW7xIuzv77gAd5/hw5IJ2DeCgOyOKnA3ZHy5YP4K17+xCPyK4JH/EfhPCOXlU+Cfeu83Byz5+8CHhz/+R977//qv8ov019DfyR393a/Wh1Lw6njj1e7nHZLZh+ooEAhzACqIUBi8yICvUU1eg3SK1ntM0xAlKePphLquadsD2kmCcxqhHNYBzjGOYoYTiL1AqjvLrAxy2TgcHVARcZxgvA9yT2PAe3LnGGqL9wN1uw/aBwGnT97kqNzS1XvqpiPWIbSz2pcU2Zim2yG0JFaKe6cneAN1WwewSd0AnpP7Sz79/H2yKOPhw4dM5nNkHOPNgDM9Mo1JkgwpchTmEA2m8alFS00skyADjjOk0jjv8WiETECYoPWPYfAmAEsNoTAcPmA+BdtYYhfTutDprtuSrrfUTUvXha7/dl/RmYHdehu64c5zcnrM8nQZHMbO4HHBYFUbRvMFOo749NNPUNZy+vABt9db1us1D+495I03X+Pk5ITZw4jXXnvCer1ms9mwOJszygu+/OJzJpMReTJi25Rsb24Z5QWLyYz9ZkfdNnRVw9pasjxnpiNUEpGPEqQvMF2PtwN2GNDS0e9L+r5BuJ6+b4hISOMU63tWl08Z2oboyQOWkxNM3zIZTcONZZQxni9wXiE1jMZz1tWK2Eq88ayuV+Fz7SEoqw5H4a9XIKBRB5Sb/SUigLt5AYQC8Opo8Ovrrzci/J/5y8Ej/zfwz7z3RgjxPwD/jJA5APCp9/63/wp/7y9cQft8+HW9RdDh5d3Q8O4Hd3f+uwckd/iru+xB78PZ80C1wtEH4RGHXYEReBkaXBJJksS8eHHJdDZHScV4PAkWVCVJ4givI5SUdDJCuBZFFM6owhJpiZQaKcK5WXofAiEJTjpJoNZab+gLcGZA1hHGBGXfpBjBfIrte5phQCPom4aLi3OUlIy8ZNoodnnAeOnB0wtH34fce2Ec83sL1rsNSlj6/RY5myCdIUs0cZISpwnSyYD8FpqkyCmKAi9CNJhScVD+xXGIy7LhZfVIvIsQIrgPWxSRIwSLxgZrfJjzS0knPM2qo/ItxjhuV2vwVcgO3AWcetsZkjjgytu+Yzqd4r2jOqDW5/M5aV8QR4qFW5CMRviuB+9oBs/N7Yq+75nPJkynY84ePsRYQzSFZ8+e0zQ1ddvy4uMXzOdz7t8/A+VJioRutw4x62lG13dIZ1FCkKQJWkjS/5+5N/m1LcvvvD5rrd3vffpzu9dHvGgy7Sy7nC65wPQCJEsMasKAEUIgJAaICRJSAQPEGIm/gCGiGFjABAmwGFDGVUXZzky7suzIaF9/u9PuvllrMVjn3ndfZESmy3ZJsaR4cc7e+5599j57/dav+f6+3yRBeRKrW6QZkNYQ+D51VVDkOwfHHpwnYesd3XbLgGVQCfcfpLwucspyiy9PmWUxxgsJQ1eK9qMIISXKC1C+j+kHri6uSE+XCM8jCkc4uXanPciN53rr/FpgYPiWSSxwBuCdPoF/xrn3S43ANwmPWGv/zztv/yHw7/4znvfnRiLeJgRxaxHdgY0WfdOmeoM3swcs9IFm+5D0MwfkmpCSTru8tJSH0OFgILDCcbibHl8HFHlOXVVkWUJTldy/f59xmtGUBeMoxIYRUXRQIVIVgRgTRT5pOiEMnAKxMQ7lZaVCGQ/PV0jPsWqIAISpsZ1FW4H2NSpw+gq+72GkIvIHYixKeRitWRwfY7TGyJ5yW4IBT0JTVY6owzqwU9u0ZKOMD9OnxHGAp2J6Y4g8jziO8IIAoRRauGyx7VusgiiO8VWAENKBfax0BK1KIlsLgTys1gZrBCBJrY+VAjOA7QOKunRipp7l1ZsVXhDSdj2X12uKsqDTA2mYkuc5WmvCIKAuB9brDWHsyDWUr/B8n+1qT5LGTEcBZVlijGUcRuybFiFgvb5gt5MY0xMHI9bbLdtdztFyyS7fUzZQ11s2Rc5kOqFpGsaTOV88+wyFj28FJBFeoNhs1jRtQ5KkTKYTgigk9CS+E38n8Hxqodiu1+TbS6ToUVi6smR7ecGrl8/om4aPP3ifNDY8WBzz6rOc7eqc7dWE8d/6AXrsFphyv+f6+horLKf3HyN8j0dPHrCptmSTCVJKPv7gY/7RH/+IvqtBGKztv+bEi7dNl3ArUGvEDej65qhf5vp/s38Afz05gf8Qp0l4M94TQvwI2AP/tbX273/jV7qjOyCEYGtvXEyLEMolS3uA4U44cLiQQ6+1tRptFU77zh3hJvq7TCp3X1urkUY6fTbPHOS7ffq+petatpsN49EUy8BaKR7ce0CvBPXQk/oBRAf226FBCc/BNOUNYCg8MOcYjHD82kKCJMRTTlZMpRkWJ2aBVITChTV4DnzUa4c7wAp8L+DoeIwFQgn90OMrD2l7lscnaD2AgCRxYBdHAyCR3oHzX0o0wukhIjFao5U+1KGdhLs2BrTEGoGREkIH3Gq1RlrhjCgB1vhsywqhLRByebmlbXviNOPyzSWzoxOXzc8rojDAGkHTNOR5TpZlZFlG29REnkfXOSr02XSKtE4QdL/do3yB7nvKsmCtFG3TAoL9fo9nBNr06GGgVwW//hu/wfHyhL5viJKU/X7Pp59+ShIleIFjrfalZLfbs9/nhIFHGEeMbMb+fIs/+DRNTRQE7PMt/aZCCoOQPk1dEaoWmfmsL1ecb1Y0+R7b1myvXhN6ilEk8NG0ZUU/tBjrGtR2my2Lo2PwfOI4Zn15ThD4HJ9pl8/BMholKG2cnkUqEMpNYWHvTmvehvT28Ea+Lf69PdLeOfDO+MaKwTePv5IREEL8Vzhv5H88bHoDPLLWroQQvwn8r0KIX7XW7n/uO97RHVBK2RtK6puJXt9e200S8AA1dTHBLUmlCxjehgg3SldYeUis3BgEcec1bvIikMIS+B5d01OVJbp1xBKjbETdNIyyjKiLCMMQEQQMBwXk1vMQqsdDoqR/+D4aEwjHo6c9HK8W+EohEVjl6uRDP2CMRVunFSd8HyE9RxvveVitUcLDCIEVCuE5KKkKPDzPxww12TjBYA51e3tA8kmkDUA6mWspJf7tgyRQno93sKFCwTAMSAUilFgrMRb0YOn1QC0E3mDpO4OQHX2v+ez5S4Z6j6cius7pQKZ5T7nLibIx682WptiwmD+G2HluQeAjhUcSxwSewmhN3baHkKZDxh7pKOb6hRNDGacp1ljatiPPC7AwmU3QO00c+3z0Nz6gDWJC33cVhzCgbVu227Wja+sa5qM56oABuLi4oNzvCBdTimKH50sePLqHHCRCKozpsbqjKXNMVyGFpe9a+ramLgv2V5fk11cU+x2PHtxjFIUkcUgSOrWlYr9DG03V1Bhr+NEf/zG/9hu/wcn9hyRJjE5TwsDHDD0XV+fEGKQ05Ls9Vd+QrwuGqnfPz+3UvsHN3Px2h7fGxfz6zs5v9gC+yT/459A7IIT4D3AJw3/T3kxha1s4CKJa+0dCiM+Bj4A//GWfZ+/8y62Lf1MDdUAUYx2WWlpXI729qHfug5vwQhyAFwZcX4Y9hA7uKCnf3qC6yNmv1kipSYOIy9cD9WhE0w9EkU82GaFQLJfHxEnMaDR23sPQgXTdcK4a8DZFI4VwGV5rwShHmnIwZC7VI+BmNZASTzoXXKLodYcnFb4fOa/GuhSppyRWKhAZCIvvuR/WD537KdSB2ssaZ+AOdH4IXGnPgpAKJR0jkhWeS6Iqn6pq6XpXfTDGwyBouo6L8wuSLMXzEn76Jz9ltzsnied88MFHgODVqzdc77cUQ8/5+TmnZwu6smI0nRL4IVb3fPb5V7RdzfFiwWQ8xliDrxTD0HG9qkAbhDbst1t8KQm8ADMYgiBgvV4zGi0wWcvp/JgsTYmjhHy/ZzI2XK63KDOwvrpkPJ0x9C1SwGy54OWrVwxDR7Fbo5Sm7QomoxFZeqikeM6jq7uccnvJ/vI1QrdIq6mrkqYuXXt54hEQs5xN2E4nXK9WCM9j0Abd9URJggx8Lq+vWb1uGU8nHN+7jzGG6XLBwEDX1FxfXdBvrxFRhKlqjPT46rNnmLY9/Ehvq1lWiF/QJfTOpPnm1f5OCHEjQPJt4y9lBIQQvwP8F8C/Zq2t7mw/AtbWWi2EeB+nTPzFL/u8gwV5+04AwjimWvfJbrt1UlZIeWvXxK2G+40ZuUEXgNASgYeR+javcPNxxkDTVOiDOz/RFVJMEFi6tmVvDE3bsopDtvnGZW6t5ezePfTQ0zQ1fS8JfN+1DEt1e16Q+MqFJdZarHSNRsY6dk2jD5P0gII0RmOkh9AGqVwcnpclcezug0MnKqyWGOWhpEQPmsiGWGXRvUtIMriqyI0B6HrHVORYjTwnSHrIpwgjMY7KxJ2vaCirlqLI6XtDPxjarmO92cJmz3Qy5+WbVwxtjzEVbduipOTPn/05o3BEXVdstmu+//3vYbFsixXNvuX8/A3Pnz0n8DwYBjxP0bYdSZbgK5+8qGjLkjSJ8b0jqqbC9zxaY6mrkjSNgYBeW7zYZ1OWmKYljWPKsuDy5Rv+jX/9X2IxmYEE5UuGvqOqCpSELI2ZT8coT7G5vnbnei/BaM1gDcPQobAMTcF+dYHoK5LIUduHcYDnhUhPUUYBUilQirxsKWoXu3vClX1nsym+pziZLfCkJN/v8PyAttgjMwd6Wu+21G/eIOIx08jHSw7t8tZRrdylFXUpb+5MdOscSyveFme+Fjm8M89vnYkbz/fb599fpET4TcIjfxfXu/R/Hdzrm1Lgvwr8t0KI/nDa/8Rau/5l57gxAG9Df4cnv0ufbK0FqSEC2zhLKQFzm0u4wQVwm2G1SLAxomvga/BLbQyB7+H7yt0fpZAyQHc9+932QCkWUO1zRKPQ7cA6S+ntwGiTcXRyzHg8IvQjEBLPAyVcLsBKSTM09G2LNsYpBxuD8gL3A0uB5/u330UqxWAMpmmRvqHvNEWvybhiGFpQHnXTk6gA68FkPAEEHh5WaYIwwg8CkNIRbfgBCOgHTRh4+EHovAoh0UbTdT19Zx1E9nCPt8WO9VXOZrWlbtwk9/2Aru/YbCsC/w1DP+ApVw1ZvbpEm9ZJxfseKhQsT05u6cUG09D3rltyOV+gu5amrVivV64K4WmCKCIbZXRVSV0W9EPPbrMmSmLwXcxutMegK3zf43qzoW1q4iRm8ugxfdcxHQecn78Go7FG0A6aoR3A9gQejihk6Ln/+D7z+QglPGaTMVgoiz2r9SWr85d0uwuWaUCoQqLQSbIPRiMICKIYL98deArHJGmKBZTvYboKTU/kh0SB5PHDB8gAmrKgHQYCPRDqgbXOubi4IKpdiTecOyWoZJwcnl570A90VGwc4L/i1jQA+AhrbhPhN+HyzZH6jhlQ2qWnPOFYh60SfBt++C9SHfgm4ZH/4VuO/V3gd3/ZZ/7ckDgL4EABbmJ/k/uiwCYWmpvbA7eMqze36gZGaQ4h+SG7eyN+BSBQKNkTSoWIAoxxCLq2rRn6jt12w9D3LOYL0Jquain3FZ81DX7i4MUf/epHTLMpHgF+lBB4kvFowmiUYYAiL1hdXR16BRratiXLJkRRSBLHjCeOlDTvckIRooRgu14jfde6bD2PymqXLZdwtdry4OgULaEcj13npee51mjpxDjCJMFXijQ+kFX0A7PphDhOCRKNRtA1Hfu8oDfQtB3D4KTWt+WO1fma55+/IMkSh3gMo0M+SoK1ZNGIo6NjtHZ1xFcv15ycnZLFCwZRIcOATvqknkdX1Qjl8eS992nrimdffI7G9XVkSULfa+qhIktGVGXBl59+QlmVWGE5OT1lcXTKMPRcXl5yvXZak0kS0w41p4tjPnr6lFESkcUhFxdvCD2PwRjCKML3nSLyYAaU0OiuJQo8prNjgiDEkz5lXrC+XvHp55+wuviKuTB89PF7RHFEnCT4vkfZVgyDwfcTirY+wLBTovAGw+vo55M4pNjsKYqS5XxGd/AmL6+vOJ7NGMqCTV1T5Tmyq8mWCxbzKVGacbw8As+DoT8kByUWhTWHlixxgGXYQ837ZspYDkrFTiXqdqG33BF+OWw4AI7+2nMCf93DWou1AlBY4yqfzsm4wUpz0CE8HM/bAOCmg+3uEIebZ2nxJfSH9lUj3E2yuDKiqTuU76ONdRnlwGM6myMsjMcj+n5w5JRtg1Wuhbmqa15/9iWflwUYRZRNSKKQx++9z9HJCVYb+m5gt1mz3+8cX5+U7LbXMM0IPEGxs1it2Td7AukzHmVsN9eEfkBd10RBTJBGrLYrTOuSoXEUIX1BWzdUZYkfB7RNx6aumYYh0/EMFcQUQclg9SHhGNIPikBLem1o6obNfs8wCPKiYN82zKdT0ND3Gk85vQUjBd1+SxAFTJZzoiBht7oijiJ623M6P6azAhVaZuMRMp3y1cvnmN0OHUVUec75mzXx05CyKrlar4njiKPxiCRNKPY5xT5nf73nyy+/5M35OWVZ4HmKUZqSJiOE1myvV2yur4jSlPkoo28c8jIMfAY9kJcFs/mU6XRKtSpobMVyMSVOUs4vLp0gTRDR6Q6lLbrtkcanbwqa/IqLF19Sbjbcf3jCaBrjRzFJPCYIPLrtgD0ArLIkc6SodIChrltSGRySs6F7hvrBQc69hLofaAbt9EOsxdctoTREcUiSpGTjKQMhJ2fHBFGMbloXqgHihliEt8+6mwn9u8/4zVp5G/K/nQX6sOYNt4vfXw0s9M9/GDDW6Qn4wtxe6tuKwcEGGOfiGHPDTvO1LKp4GxK8LQvK25vr9jnNdmtxmHfhyBukFxGFMcK0hGFIoDyMcVTURVEghKBvDaoX+FHA+vySuiqxCGZdi05HvHzxjDzfOfhvnJIlAaE/c2SpUlLVlct85yWD56igPOPIJVdX50g90OqBq/Nr4iimTiK+eP6M49mS5dExLy5eMR9P6boBT3oUecl2s0ELQd1p2qJxVONJRphlSOuxb1oqbdBXTjilajt2RY5F0NU9vYBcFaRhTBInJKOEq9cXGAXr1SXf/8GvMPQd/mjKtirw15cO7Xi8IB0pLq+vMP1A2o9ZX14wblp0EhN4Ad6BKERJn9EoZZ/v2e8kYSAY+gFfQFFVBF7I4uiY49MTwDCdzVnMZ0yn77Hd7cnbhgdnZzw4uUfZTohjn65rGaxjbvIO0umL4ylV7+Mh0bolThPiIKSdjGmqHV2fs9vv6Yqeodxhmxzb1ly+vuKjsznZeIT0QsIgIgg9Aj+gqXu6vicIApp2wFOKxXzmeAmDmHE2YZ/ndL1mOVuS1wajGvw4Yjwa0bYdumu4PL+gKSvGaURVtFxcXDJaHJFNRqSjjO12d3hAD3TC31Ql8LhlG7bu0ca1Dt4Z4ucXxFuZ8m8Z3w0jgKvfg2Gwd5IigHC2AWsl9AHWvO2EultVvU0uireujzi8vuFcvJs8kVLQG03geUgpCEOfLEugFeT7AhlG+H5AUezZ7baoICITMVo0eLZjPp1zcu8+vueYhIdBs16t2e9z7p2dMZ/OiOOIvu+4XpVU1Y6+r7l//wQrnN6AUANCKNekowKKOqfpHIde1/UkCrJkQjaZoK11XHu7mizN8JRH17dYLbDDwPV+Rdm0nD0UnI6dEk/TDpTFDiEkbdnSdh2DtUhP3aomT7IMGfikcUpbO4q0zgzMZjOKMsf3Ava7Dcv5EaNsTBgEXJ5f8id/+qeU+4LLy0uWR0dMF7Zg550AACAASURBVAvaqqTzA5qmYjYaE/geq82a/WbLZrum61v8QDAap0jPxwskyhNMF1PmZwuM7hHWMPQDZV8zC474wd/8ddLpBE95PHjvEbt8hx0ODDrG5TzGWYoZehrRs5zPGdqBRrdgO5QKXGdq07G5vmZ9+YpQSLpyD9biHYRflbIEfohQIVKp22rPMGj6ocHzfYwReH7IyfFDmralqSqOZqew2VNXNf7iiLxqsAKmUUQcRpi+o2p61rucuq6JMWTpAt/zUMoijSNpuWETcBkA4dqKD3DA29Df8M6Crixf8xnuzIVbHpEbvuJvBwx8Z4wAuC+vrX1nsjpi3YMir+lvTdovklW6u99pvfNzd2owBiElWmuEUVjr6LOtlGjbI0TMaJSx260Z+h6sJjs9IUkSBqNZnt4jiSNs17HbV/iBIxBZLI5YHh1hPbi4vnSCmtJzny0UZoAgCwn8CCWtK1Poge1uRf66wGaCum2JsxHJdMqH9+6z3+esd3uKXcnJyRmDNhg7UJUN2WjEdrNhs8vZtiWPwo/RRlDVLYOGomjoW9cC3bQt2XTCfDpBImiqhuvra9LMybF3Q89sNqMfBuI45oMPP3SoQg3r80sHrBKKZpdT1jnr8wtev3mNoaI1A/PplG2+ZV8WXLyEtuuQSnF9dUVZFhyfHJEkMfu8cOy+XY/FkE1SDJaq7Ok6Q5qm1HXFJ5/8OfcfPGI2mRBEEaP5hDBQyGTCdJyyvl7RdCXZ/ROGrqPrWvKmIToApbqmYt006Lbl/MVLiu1rqv2e8SJFKUPTdQQRBIEgigO8IERI32E5jIuztRUY4X4jpSRZlqJUTzd0roq03xMHDn7c9j26biAMHBdGa13jV9MwdJrdbkeqJJlQyAPPZb2vqGvXqmxvQl5j+cYYV7+7zRzCAfvO6h9g6O5kwIVjdv8F0+U7YQSstRgzcNNGZSPetj4PAK7eLjG3XdHi1vUXzqXXDggk7so2yUNI8XVTKcBTTvDkBmocZJ5LeBnLYrZklI4A8LyALMuYTic8fPSIXrtwRXgB3QCeVYynC4y1zOcLTu7dJx1PKIsdV5drdNsznc2YzqYIJHEUE4UxUilevXhBvt9huz2vXlxgGsNcHXG0PGK+XKKlz2eff0UYhsRRSp8YirolTROy0ZShkeyLmsFIJosTMgHvf/ArRElGUTfs88ol4NqeIIoJowRfBXRlix96ZKOMqqy4Pn/JF19UNPXA2f37fPi975PnOfvdhvPzS0ajEX404eHT91ldXNDSo3vLyfIel5cXrq/daF58/gVaO1XhyWhCOk7py5bpdMJo5IRENus1V1fnpGlK4PsY7YBBRVmAsSTZCGkGHhw/YHa8oLcaPZtRNA2vnj2j2O0omg2z0ZS2arj/+IzN9TW+7zteyd0Oq3usgM8//YTtakVTFsheMwqBtuRovGTwoOx9JvuYQBk8oxHW9Xr0w4AQhrpp0XqgHzQyjJCepM1dPmboekI/oNcly+kx1ckp2SSlB2TkqjVDKFi/2LG7umI0GnO1vqY3mtFkhFSKru2oqoqubxA4mnFj5cEJEPT23Wf2nRGC7W8SiHcPHN49Xn57QvBmfCeMAOCgs+LQLiQORApv97578MG7EdY4PIWR7v07HMs4axpwS8mmcKSWFpBSHgzAgBA+s9EEMxi0lfSdofYHpHVKPEmSMJlOXVyfFwfqr4g08QnikGgU02tFNJ0RxCmhH7Ft1vTdQFGUeIFHFEdk0dhBYYVgNp1Q5TmvX7wgDBQXl9d0fcf7v/IRXhCQxTHrzZbtZkcQRFRxh+cp9vuCKE4J44zG21DVDdloilQKFQT4YYBQYO1AHEWMRmOGQSOEOKgKueqLthblKcIo5Ksvv7ht2fXwaIXger12nZJKkfc5SZ9xvV2x2W1p+h4hLB989CGtafj8i0+xxgftsBUWsHqgqSv6tufk+Iz5fI61htXqmlevXjCbTVFC0nc9Qz+gtcZXinK/pxmNmGcZ4z6j7XswGqkHNtsNbdtiBWw2G4we2O8yZrM54NCWbdvQVAVVVfL82Ze8fP6cvtgzSyLkYkoSSnwlCNMYYULiZIeVgrpxK7sKE/pBozxzECKRTq/RCqR0YKyqrug7QxwleNIjjSJm0ynpZETfDUyjiCiMWDcFGkvXdWy3O7wgJEoSrq+vIUuY+spxCX6NTNTcZPoOZUK32bx99I0LkTEG8U7wDA5Obw9ixuJO0vDbx3fGCACQWTgCXr8FRvpAd7gKI27giNw2Bd2OG/fpkAzVN/XWQ3/2TXLkBlZwE/PF0zEJAXVZo4SL+fq+pqlqhDgYCzuQFwVaSPphYDSekMYeSRgQS0VjBCfLBXGWEocpZd26vvR9gVKSomxouwtms47j6RLTDzRlie56yrxn02/oe0OnDXlRuH79suR8taXvNU8fn7AqK7quZ748JowS+sEQxxPiKMILEuqqQiB59uwFBoG1gtF4ctAG8FitrslG6UHjztL2A03T0tQNu92e+/cesDhaOkqxoaeua9pOMM7GnF+c89OXf+p0FsuSuqw5mi94eX3Jg+WSP/iHf8CzZ885PTtjsVwwGY+YjDLCKALhUbYNw9UlWmv2Zc6+3NM1LVkaEwQBit71xjc9UkCSJWzyNc2zjqurFWmW0VuLrmuUlBydHfNouUT7ijAMOTo6cm2jNei5IVeCoWtpqpJit0U2BdE4xNZ7jJfRtD2x7+DMk8mSKBlTtxo99PiRc5+HwSCkh/Y8bGdAG3wF2ShjtV6TFwNKeQS+T5LELBcLwBL6AVkak0QBmwYEB5l7PZAXOXKW8dnnX1AfzZmdHnF0tORoueT1ZvcWL8PNs+0MxNcnsRRgtP2alsC7TAK3e77WR/NN47tjBISFCmwBtAnYEoQrirh8Pkh7k/A7xEKHN/ZQTLXyLb7gUF10VvOmc1BAjKU5lB2ttejGUMmeq6srZoups7dhwChO8IOAsiwIopDlcon0XDLOSxSJrxjalld5RaMNVdMznc6Zji1N2zBbLDk9PWO73dA2PePJmHicYrRht17TFSGbqyvoHbvur/7whzx9+gRPKv6f3/s9NtcrBi/iBx/+GrOjMz7+9Xv8T3/vf2a3r4jTjPsPBR+//5QWzeXlFUa21HXNHEEYxtRVw8vnL/C9gKooWK+u+fLLz5hmMxZHx3hphADqumI6nVKUBdfrFZdXV0zmM1arNdPZlEc//FskSUzebbl4fkWUJjxIHlJcrTm/vKSbz5EWHp3eIx2PGboW2w1Ep8dk2YiiqLh/ckp1EHvJkpTHjx9BYxmPMpIkpagKXr5+ha8Up2enhGlC1bb0wNPvfUgcxRRlQV1s0Rratmd8fMxinCLDiIN4G/XQ4QsfgM31FcX6CtG3TOKM8UiyWC6xXkzVaoxWoHyyScZsfoxSIUnsY0WPlAFFUdN1LT6Cuu/ZVxXH8RFt26O1IfQlm3ILZkQYhYx9RyjbNj1BGDJIgdYarSSjaMS23vHk8RPe/+ADHqqQcehztDymkxFPn77P6599hqMi9RCi+8bV+22Yf4NOvcn+ve0oABCh+GU0Q++M744RsLjruIbEdlQcsAOH3TdpPonFQ37tGg95gQOSShyshMKJYIg7JBgt4kBEYhxrLk52SinlePyamlE8phYNVV0zn88ZzyYkSYrvSYqrK4JoxjiNqeuGTV4yXcxZzBeoIKJsasp8T5ZkZEnGfDanKCqklMSRz/Z6xcXlJbppSNMJdZMzOpozvnfMyf377NYbVJBwvJCcfvw9+mbgcnvN2aPHPH7ylCxLyauKxfKYXki0NnheiFINo6kD4RTVhv2+YL1ek6QJWZZirGW92dDULdaHiZhRbLeURYmhJ4oCPvnkE168fslv/uZvksYRip4XX35BuSqYPFrwxVdfcHR2ymQ546Jxqr+RB2EQ8PjRQ957+j5fPfsKJRxNV1VX9LrnJ3/+Y2SvEJ5iEieczBekcYqxhmK7x1M+cZrStS2bfM+D6QSUompa8qJgOpvhhwGz5RzdtPSqZzCGvG7IPB8rDcYKGnquzs8pdxt2mxVdkTNJIk6nE8YjZ8jbzpJXe7zJAtAY3RPFIX7oI/2AQSg4ENMMw+AwE9ag+wEzaPRBzkwoQ99qWlGhrXWQYi1JwynCE+jGgDEEArzFmM2LLUmUEKUJi+kRnh2cpsY7dXyDYODtdL/77N8d+s6eAwHE3ZBCcYukv1Er/EUhwXfHCNwMAzU94EPYQ3vHDAh3m94xANYiQiAQ0BpHGmIlwlr0DZTYuIShCwe8A6GGIAOMcBDeOI4xxhxktyS9MLRNh5WCPC8oy5p7978Pi4LtRUkSeCwWc+LRhNaAjUISz6MeGqweWK8uKYstURTheT6ogL7uUdJnniT82cuXxEmKDFIGbZhGKVXZMHQDjz5+gBk0UTTGRIay7Di/3HN0ekLf9+h8jx8EaGOoyurWWGrPo2or1qs9q6sVV6srJlMn8pmNMs7Ozui7gXJfUpUNcRzTNo3DXHiKew/OOLt/Rl7m+NJnt8mJogztDeSrDX1eYhcd12/eUFUVSklHheEJgjhktpjx6RefMlgIhpC6qqnqmv2mZOz5DG1Nt1zijUfEoxTPD0lHU2Z9w/LeCfvNBqkkaTbi7MEDPKWQwrjQpKoYhz6j6Ywki0mj6EA8636nuuvI93uaquLVi+dcnb8hlJL5NOPR6YJ0muL7IU3b0nUDw9ATJwG6rZGec/cHbeitZdCSUPl0FprBYExMM7T0xhCEHekypbuoSPyINImQgY/uNUEYUNclWscQpqSxJUtSqn1O3TZUqUMxmsFiREvfD9RNz3q9uXmY+baGAIVDwN7mAe9ahtC4lf/mT5s7r8UNtP7bx3fGCBgi5KEk4NKCPba96SQ8XLF1sEqU6ya8vRetxlVFXGgghOstABCO/sbhDADonQS61hRKQV0j9j5CKOI4JvIFVVdxebWi6wfiLGUyPeL46Ii+3zJPl8jFjovNHj0Y0lGKGAaqVY2WkqoqOX9ziUCTZRl10VNUBYPVpKMZk3GG6XtsGGB9hfBDhrrl/NUVl+cbLIZHTz5ktbmiKAY+ePA+OoCqKzg+ychz6JVPXVVu5VxvePXyJZPFhL5zq5VAcrw4IUpj0ixlEqRUteVHn78kiANmsyn7q2vavqOoK6bzGadHS5QQvHj1kss3L6nrjq4f+DtP3+O6Kek3Of/Wv/Nv87/97v9C8fo14ycPaKqaP/7H/zd4GReba3Z/8Pe5urpiulyg+oT3Hj3m93//91m9ec0GUEqQTjPGswlEAd48w1YNuy/XMGjieIQxEmk9GAyLxRFJkpLne/ZW0rQNSraoGgIlSNOUMPIRnsIIpy51dfGa1fqcarvm/nLK9z/6iMF6LM9mVGVLXu4xek/fRDBIRtLnNIoYjGHfdwRRgm89OuueA88PGUchba8Iw5hhkAS2oCyvmcyWZMkELTq8ICH0AsqrDf1kwAtLxuNj1FnKOlzz8s1rlvGSNB0RphGh8DDewPNXr7i6uvql80PztmVAqrevgUOijNu0gG99x7V52Hy32PZN4ztjBMTha0ZENK4IeOe/g4V0DB23zRY3jpAAtFUHi2BuacSJQfQChoNB4JB0sYLw0LqLcauoawN2CMH+sqJXO4RNCcOQ0SJBCIExA6tVRV3m5Nstqa9I5j6BqEGH9G2L8gJm0wltW9H2LSpQzOIZu/2O3W7HeDxhNMuYzqdk6Yjtdof0eiyW3W6H7/sEUcxstmRXrEjTMSaGqih49WpNH844nky4OD93EuVC0tQ1D5MH9MYyWSwZup7NdoetLVVZ0vUdyhPce3wPJSXLxZL7p2dcr6/5/Ksv2O23PD9/gycEfd2AkRR1Q1UWnL95QzP05KsVDz58zOLsiNX1FeeXFyRRjDAR4yTjq68+RfUQeD5GSDwbsY22REFwEGINSLOEk5MzxpMxA5bjbE56NiHzMl588SXFNkcpwZMnHx6qCS4BNo0zFJK6WiNrRbKIXD5n6BgGyaAHQFIUBW/evKHeFdRVRTzPaMoSL527z/F8zpWgajskljD0EEmECX1E1zkdi37AWokKPcIwY2gH17QUxmRpxnrbAc477DpDKjvO7GNQErQmGI2cGtTQE4rAeZxZRpqmmKJkt9uTzs6wdmBoLVVdHXQo/iJzxP3zjgG4Ow5RQv+1ZrlW/OKQ4LthBKRDPxnBwQDArc8jcP2KzaHc4RtsL27/zgoHKBLCdeoJLK5GJqESaOVKjxJQuP55z8JgLAKD7ytoWmJtyXRNLQ21p5nFZyT3liyO79PWriMtCWJCz2O32zM7OmFX1MRbxfGDkG3b0QlFU/U0raWqBrq2IkljpLWMR0uykeD05AxPKep8RxqltGFHnVe8Wj3DYinriqcfPmWxWDD/wRHSE0yyMZMg5Q//8Y+ZTnPGp6eHfoYWKSUPnjym1QPGOl3AIt+TF3ukkvRdy2a7xvd9RmlGnhe8eXNBksZUTUMaJwx9j21zvCii7Up+5QffI89zfvyTnzgcgzGUZcXrz58z7CuUEYjGgBmIo4wwTLh8ccVoNGKUTenzmsn7GevrS6SA09MTsixBa1fGe/zwMdtdgek0tjPobmA6n3J6dgLWMp1OSJL40LdR4I1GBNMxo2mK0T3TUUSPT55XaGMoig2hNKhyzZsvPqGtHWls27V0Q0MUwqBg0/cO/NP41INCCkEoBGbwkAZ87bl8vDIoAWkUMgQ+2+2eQXd4RqM1RFITBT5CWIQNCaRHFARUpmMYx3iBAqMxZsBXsNtuWC4myNjj8uKahw+eYEPLipY3n19gco3EY5DDbRwPAmHDwzy4s47/HPLtppzuY+1brcebcVOA/EX1ge+GETDOALxb5DgMC6K9Ew707/Zav0VCuRujLQhrEerQV28sQgQIqwmsoZdOLntQliAOMZ6i9SRilDq2oLrm9DRlOh5jPJ/zcwdsmR8vmM8WVEXOk6dP8Kwg3+4wPbz8oqVuOkxnUAScLI+Q/gkXF6+5vt6C0DT9lqrpmC0XTEdjssmML16/YXd1xTrP6cue954+4td++EN+8pOfMJ/PGU2mXK3WjEdTfN9nPB4TRSGr1Yr9fk+aZXzv+7/Cer056CoMnF9doHuNtR5FAft9Tr5fk+fXVHXN06cf8eTJe2gGXnz5HGMNi+WCZ59/hh0JZtGcZ8++4qOPPuK3f/tf5Mc/+VOePHnCr//wh/zxH/4Reuj52//Kv8Dzr76irCvOzs6wSvIbP/hNrqtr9KC4fPOSP/knf8rx8SkPHn3EoAsePn7E6dk9rNbs8pLJbMrF1YrPvvgxXuKjpKUqK8I4IAgDrLX4vseTJ08gGNMPO3RdkGUL4lASphP2RcnL5y/YrVZcXbzgR3/4/zF0DdYa4sBnNp8TpRlaW8qq4dmzF1S7Pe+dnRFFMb7nE0YBURygBo/xLKNsOnotEcLi+ZI0TAmChIuLa66K2omoBCP8IKTrGuq2pW0tntKknk8QBQhP0daNyzF5Ab3RPLn3Hl1o6Hc11nJoGFvxT/7pT9nv8rddsQ7FcnimWzc5osMc6TgEvHfHDZ7msPoLASmI6la54Je2EH03jACAAM0YR034zfsBOHQDioNBdF6A26UP6EE3IW4pSVGiRwhJfWMShTg0nUj3UMXS5WatQCuN7/tkszld1zObjAnTkHE04vPPPydfXaOF4PsffuhYgxHEYcrFbo1FcXQ0Z1eUWNuTFwUITZYmJCR4ecVmu2G1uma72tA1DUVRMJ/PEZFPFEUYY5FNy7OvvuL0wSPG4zHLxRLf9/mzP/t/8T3JxcUFv/M7v0Ndd5RNS9N29ENPHAXoXjOdTunbS9r2giiyVJeGpbekWtb4nmQchqTjJe2HNUYPlE1J3ffkX+051xc8fHjK9eULitry3kcf8dF772H9kCiJefr+97m+vEDbkPHMI0Yzvnefh48fMdpO+emf/BkqjBjHPqen9/ngg/t88smnvH75iny3J5lMUVFI03aOJ1rC7vyae2enPPr4Ifv9DmNu6NIU2SglinyGYUyfhDRtgTEhVVXhCUEaR/zs6g1m6JlkCW0V09YNUkuEpwiDkLxqqcuCfFeANkjfoUWxlmFo0H2FJ0IXDliLMYI4TFyNXylWw55aa2RZkBc1WRY5tGnj4MFS9AzNgPBDfD9glKZ8ud4QxinaGvI85/7jR4yyiM4rkIeu1g51aIK7kXEDcB6qvcMkqJq3E/ru+Dkg7E0erDpMiMOqaiKQ7bf7An9Z3YH/BviPgZuMxn9prf3fD/v+LvAfHa7sP7PW/h+/7Bxvx7sGwKUKb7IhIGIB5WHnHUSVQeIYeA4QZO3CAilBSMFgegQKX3lOndj2WCXRnqDYXzO0IU8fvkfb9lRNhYw8EIIgCumDnmk8JYoi0jRjt16DcbTfQRjSVA22cwQaYeg7RJsFrEQpn2EokMpjvlwynsObN6+RAo5PT1BSYrRhOp0yDAOvVytQNbP7DxjrAT9NWS5OiJKEMPQ5++iE3VdbiqJgsVhgrebV9ZYg8F0vhBAsFgumkynFPse3IUeTU+Rjy8XFOZ6QnJ08QEvFm8tzyjx399LAaDQiikP2uwLfTwiTY5Kx4MmTJzRRxDh1egVCCIzV5MVrpv6cq33DiPt4nuJoeYTUn/L0ww8RdMxmU+4/fERvDBfn544CfugY8oZ123J6do/HDx7wGgttg+lalssFeuipm4rF0eIg1mLwUoXXRyivo9jVWNk6+Le0vH71glAJMJrZdMKVbbGVObRF91xebvj0xRcIJIvJFGscv6IfRfgiQhtJz4CxjnBl22oH7w1DfN9nQk7eD4zmC6q6wxgcFmC/p64qBgbaRjOSHkEQoKXE932MsFRVhdaW0XSCSAJGaUJoPGwt8axgf/gNFDhVZxzIVfFWTlTjNgoHcH13eLzjPntCvC0g3uiY/pKUw19WdwDgv7fW/nd3NwghfgX494BfBe4BvyeE+Mjar/c7/sVGc7f2acBWN+6QPVhKeUAOHOjFb/oHhcVqxx1/Mplxtjyi7Xp2+5xdvmewHkNjoTcM1FRdz/Pnz7FCkHg++8uCNCpRUYBpBFftFcMw0NY10+kUT7r25FZJNtstTV2SxRMm8zlauxBls17z2ec/Y7ff8+Q9weL4lPksczTjxrBcLg/JRkPTONfxb/7qr1LUNc+//JKHDx6TJCMGM7BeXxMFIXbXM50l/Mu//Vv86I/+kUtitgP+aEzbdUxGGePxmP1uwzA0ZKnCDivm8xlZ5OS1i2rD85df8vr1a5bLBcfHxzRNQ+CHpOmI99778BYl2Teapmvpy4I3lz3Hizk//cmP+fGPfgSE6OozglHAbHHMeZRhrWB5tuD5sy8JAicm++rVK4Iw4Oho6c4T+Dx48BBtNFEcYyyMR2OwGutJt/IHPmW5o2tqLqqKwFcsFzPatsHYAT8Kub684NWzL3n+7J9i25zr3Q5jBsZZwubK4Mch+zxHI5jOJtwrTxG+Io58pCfwgpCmbfjZl3/Op599yoPTM9rBIQmrosMPAnoLsrdEQUSaxjRVzfLenEU0IgsjttMJcZTQ1B2+8vF9nzhOgJjldIm1Pf/gD/6AUDg1J6PhT378TzmdT1GjmItdwWbbIaRlsPa2P6gTHGqBODd35Lk4t/raNPL5Wo+wQh80NixAzG258BeBBv9SugO/YPwd4O8dCEe/FEJ8BvwW8A/+gn9/c9bD/2+qAz4u5gkwosXDrQA6MJjBoLQDHFrpqJc8BMvjBR9//DF/44OPOZov6A6CH5vdjuv1iu1+x9VmjRCKoq7I4hRPSZqu58GTB0RRjDEa7SvXaYhA95qyKri6uCSJI6aTCULA2fuPSbwxm+01m82e4+Uxjx49JJskvHr1mpOTe0ynM0dOaQ1t29L1Hbv9HiEEy+USgOcvX7Jabfjo6QdcXr7hzSef8lu/9VskUehYjlsXPnRtg5aayE9AKAKjSexAP3QHvgQAxenjB1Rlwe7qkqLIaduWWCecnBwzHY+ZJjM605EmCY8fP6brOpq2Advy6PGH1FXLi+sLzt4/pvzsNZ/82U958ew5WZCyOJ5RFhFFWfHy+Uuslex2O+q2Jx1lJHHCz/7kZ2hfMxqPKOuSJHRy7uv1FecXb1gsTnj8+DGTSUbg+2RZyjD0yCAkTSeOjPQiZ9fkbJWPaFpsaNBNzdA19F3FxatLdFUgrGY2GSFGBv1FRyB9NtsdUaQIsoQg81DSJwwifD/GlyGeaRh6Q1E0NHVPlddoPHpj8QOfUZjQtBVlVTB0Pb0Fm1u2g0Br12sQBXP2Q0lQK8Z+jpRHdMMeL5T4MuHjjz/mzetzEILW86jqmh/9+CuWjx5QC4kddg7efnjsQ5wnUHGzwEvI75II8DY07sFXLgx2u8w7s4f6psHuF8+2v0pO4D8VQvz7OCbh/9xauwHu48RIbsbLw7afG3d1B77pWwlgNMAei6MshBtZMn1T9WuFY82VDgYsOkvgKU5Oj/nbv/nbvP/kIbNpxmg0ou9bpFR88PGHh9jbEIYhm/WW16/P+cM/+hFV1TB4jngULFESkqRTzi8u2O13HC8XjDsoi5Ku+f+pe48fy7L8zu9z3LXPhk1TVVmZ1d3V3eTQTItygKDBLGajlTYS9BdIgP4F7QazHG210F6CtBIELbUQIDfiqIfDEdnNct1l0oR/5vp7j9HivIjMYhs2yIFQOkAgMyJuvBfvxT2/8zNf0zGME0VeYrygrm/Z7Te8fvWaoW15/4OnrNenIDTSS+5uN+jZnKP1EVPbcXF1hUmOcS6ODp89e8Z222GMQRrN0ek53SS4u7nl0Y9/zNC3XF9fs5zPIWi++OIrXrx4QZIaLq9fo43gbFZyudlgQqCta/I8RQgwUnD9+jXdOHJyfEZmMtIsoRkrqv2O5dERr1+/fnACahrL5eUVKld8/flnXPg4rgAAIABJREFUiHHEDR1NFVPwD7//nOcfPOOu2/D6i1dsNjvy/BbrBX/8x3/IrtpDgBc/esHN7U2EC5czjIxMP4IiS2b0XY+UkuViwW67pW87Qprg8oyyLKI1eKoYN4E+XBK8QvRRIemrX3zG/u4aFSp0cJCnaCWYdvPYqzEJaZoyTgKGIZqkTlE/InjH1PfIwjArl/gppesC7b5GZWV0YPaRx+YHj/eWJNWEUTJJx0JLxMyS7CXW1iTDCtEKhuWSaRoJRP1GqeDZ48e02z1KSQpR8fT9D6jvrjk+PuHzy1vwIWYJwUGId3jM3sMhr/3188D7+t/+hm6fBpwFF/VlD5yR33zt32b9V8A/Jgadfwz8U6IJye+83vUdEEJ8W0TAxsi2f7jWIMT0wA+awgEMqd0hEESlmz/8/b9HnqR0dc1yVaAkVNtt5P0HDjVtYDYrou+fd5hHp7FTfXPLp59+ikHR9z1CSrY3NYshylkZpfj662/AOxKV8vzjD+j7nr4Zefn5N7zZvuHsyXv85N/6t/F9x3a7YXTxxO96Szn09NdXvP4mkl6yLINyx0KVvHj6AdebPR99/xmz2e/x2Sef473n6eMnNE3Dp5/8nNumIc9z+qHnyZNH/PSn/5yf/axlnBw/+P6P+ODF9+mHianZo/OMV6++5ubimqePT7jebnBKsDpagYAvvviCru8o5yVJktJNE7P5EnXwPlgs1+zqmu2rG54+ecr19TX/1//xv/Bv/ORP+Ef/wT/ETpavv77gw/d/zO/98E/4l3/+L9g1GxKTkpYZR3kCFl59+Q3PT17wS37B2dkZ7x2swy6vL2ibhuPjYzZ3twdat2S7veP4eEGWHZNmhuAc0sB6NWfoFPuwYRo7Buu4fv0VX37xc66+ecWf/OSP6KeJNE2ZmmuKNOPs7Iyzs0f44BnsRJoY9vuaPklwUuCcpRQpJ6en5IuEfR01GU7SGX0/st9XFJlmXpb08zk+BJrNBYMDl+R0teD4+IgsW1K7Cb02DMNI3/fMl3PevHrDk0dnyFnBYrHAmARVLHn24oizRcFmdPzv/9v/HNWnY9s/5r3hICoi4F52Py7BPVM24cCL+zUbWwgBBtwBdKvFuxvr16+/VRAIIVy+86T/NfA/HT59Bbz/zqXvHb72Ozzob/tmBGhEeoGI2uzBI2xkCC0Xc370gx9wcrzGO49Rkkx6tICm6/HWkaRLhHAM08Tt7S0hBNI8gyDZdx0imZMuFtxeXtDVDYv5nPefPGGvKtJtytHREUM90LmayY+kaYqzjov9Fc1YsVouOVuvEFPPMPYkqaatawY7sVqfgzSk1vHkyWPm8zmbzQZ3aFBtqobLywtGN/LRs+fM5nNev35NVzcEKfjoe8/5cZHyr37+V+y3d3T9SDmLQqV3mytuNrfk35Q8evqUk/fOEX7gVbnEjYqb3UBd1wzdwGq+YrU6Zpgcm/2WF+cf8eTJEwY/UWYzjDLkRcr/+ef/ioVJyPOMPM/55ptvkFJwdHrC46dPqeqGv/yrL9DVHfm8ZL0+Ic8zZqs5d7sdq+WSyU2cPD7j6dOnnPan5HnGcrmMIhxZhjaapIyqyLafkEBZFkhpGISkFDIKyUyBsevpuzjJ6H1HmBQXr14x1A1lHhWc8ySlb0a+fP0aa21kiGYFOlM4iPbyEwffP4FUcXKwr2uQEkxgsg6tFVpZXD+hiPJzs/ksbtYgkVIh55bLu55mN3ByXjJbzchMwlA1zPKCJNOcnRxhrSXXCWlqECrQ9S3bTcXpvOTq4pZ9FZvM4v6YvmcRvpvBB9AHBKx/Zzfcb5cYEKKU7kPdP32bij8Dmn/dmYAQ4nEI4c3h0/8Q+IvD//9H4L8RQvyXxMbg94E//d0eNRArol9Df4rK4Yj2gQccZVVEoMgzzk6OkErQWcvUDSjvqZoJqScm69lUNWtj0Mpg0hJEwEuLs7HzPLYdVXVFs6/i52PPuNvxsqrQJsEYwzD0pLMU0cea/vNffoEbO66v9xTLGev33qcoCq6ubpkmi7Uj1tvoBegsi/n6cCJoxv2WdrfHhkDTtFy8vuT58+d88fIbPvvsl/zBH/wBZVny6qsvudtt+ed/+s84f/SYi4sL0jRlu684PX3EOA4M1qO1YfKOut6TZRnjOHJyvGLqR27vbqiqCpPEMmN/6A0sl8vIPeh68jyjbRo++t6HJGnBIkmYFSUmUZRlSd/H2nm3q3AuRPXiMifLCgY7sS6P6MoUR+DP/sWf873vf4/HJ2ecn5/Hn7WO+XxF0/aMY81iWbBanbBaremsZWw76ru7SNbxFjn0tMGhZBSddd4S7BCnBJOkmOe8/uZLUuEo04Tlck47WgKa5eqIse+QUjFYi3QpyshYBmUzmqYDQGmFlBqTxdn+0NQ4NyGVIssSApYxTGReYBKDGg0iV+zuKnSreHLylNvLN7Rdh55H8dW2jTwOJRXpck59dQuToygKgg84HF5ES/MgBUFIpFLR/SGEA5norZx4JMJJ3K/bwIem4OijiuY71lvcBwBxyAJq/o5god/gO/APhBB/dHjGL4H/FCCE8JdCiP8e+NnhN/nPf+fJQAjwrizSu8sDjTzMPDzi0DQQIppLLBdzprFnd32JkprFYgFKcFXdsSpyXn7zii+/+orz83N++PEPabqW/W7HkydPWJVrynLBzz75nEePn3AsJppdzW1doUzGKi8oZyVjMSDawMc//JjtZssvfvELVLrgRz9+j7LIWRRztvsdFxcXLOZnSFlQV6+YLRY462mqlv1+T5FlCCEo5zOGfsAOHcMQpadmJuH09JTtfodzDqWhrvesjtYcnx7z8s0b/uLnnzArC37y93/CbDbj4uKCb755xauXl+z3O7q248Nnz9mOgd3lDissZ8cnnJ6ccvL4MdfX1yhlePH8Q0Y5oYLm0dk5P/3Tf8lu/5cYY9BakxUpRmtubq6wdiQVCc/fe8bgHfu7LV3XcBMc80XO44/OKPOUTz95yQ8++gHeNnz+xS9wn31CYlJO16ecnJ2S5QkmOeHk5JTZrKQsS07yHJwjvPeYQMAdNr21lmFqmYYevMXOG+bjArkoMVKRGklhJOfHa7K8ZHVcMk6el29eEkyGC6CUiTiOfEW1ueXpkw8i+7DdkyQGFyyDHVivl9z0LV3f0nYN1SjRIpqvCK2QWlLOF2yrllevLpjP57z/3ntIZbm53HN9fUeZJszyDESg2TekRiEXguo6Ti2urq8hNXT7ibBYs1qv+b3f/32uvvglo/fRLYv7Rt5hQwd/2Nf3/Ph4AAZRwjTCzCJ68Q6MOLx9DEBQgGje2V+/fv1r9R04XP9PgH/yNz3urywpv22uLiCoEIUWHwAVb8lEQkbAjwgw9AMmVbjgGPuWuu959qxknpToEQTRA6/Z1XhrWS2WUZZJSG7qOxAGKQ1j6Kl2Nf040bQtjo73n31AWeZ8+fOvo0f8MNCOU4QoK81+3yBEiDdPVbFerVgtS5q6YpGVuCmQ5SVn54+pqh3ORuXaaWpwQnN+fMrJ+SN666iqKsI8vafvOsLBxuzy4pKuH9Ha8PHHH0cmoTv4B1gLDAghKcsZSZKw2WyYwsR1dc1iMWd5fERvJ27qO0SuOTInSGPQU4gnZr+jtw27XzaUq5Kur1ku55ycnFDXFSJIVqsjimKGcYJqt0crQb3b883lFU9OH7Pbdlxfv2F9dMR2F6i7K4zWPH3/Eavlgs3ulsU8xTnJzc0l4zgnyzTep/hpQh+s4Cc3MQ7gfEBpg5LR0l10C8QkSecaZJSjN1qzWM0IdkJYweQ9QiiMECgZT1khBCZMZEUWx24BgvM02xrykkQVFEVCkkSCkxABFSx4Rz+OKKORXjOMQxRqmS/o+467uy3t5PBSxmZgmtB1FikFic6QKiB2ILyg7XtW8zlBJuSZoe96tCwo9ZwpRObqb/AFOXBdAghIiBgAp5tIpGvFW54M99yCdyDDoSPcU+zdr28wwncJMZjzFh1xnxUZfvXdEYHARK4NJ8slZZZR72vG0ZGXOdIkjB6GaaIoLaMQ2N4zOIsYR66urjk/f0KWZggBXdPS7muSLKHM5jjr2QwXmDTjeBmJJ69eXTNMjtPjY+72Fc5NrBYz+tGiZ3PmqwXeOqbBoTNFlqYM04QfLeMw4r1nv9swtA1d2xNEYH18TF6WYAy1nbh+c8NkJ8qyZLvfkyYJCM3x0RGbux2337zm+Y+eM5/PuLndUC6WXN1c8Or1S569+AFD29FVNQrDvm3Z7SumtqMWsDo9xnYjtre89+x9ZqexFEBK3DRxd93z5NFTCK+ZxpGLN2949sEHJGnOxes7iuWKvJxxvdsyBEeSJBzNV2zsHXroCWHim5e/ZBgjjXa9yjg6+ohpHFHesb+5ZWMn9Pefsz5d025bvN2zmq8oVBadm6THiZh6O+fRQWDdAEnc1G6aEKlHKRjISZRiViQYk6CzlNZ2eAd2crjQgJOYWU7wEgvoPEOYBFc1KCFpvcO2W45mOQuT0c+OMUmO0BqExTuBGx09HUUxYxw9SMPq6IQvPvsUYzRy7JBAlpU4BEnqMUozeseqWDG2LaGcc7mpSG5bZqcC7wJJumIaHXf9LUH7iHoNEi8P2tjC41EPWUCQEsxBYcsKcDE4hAfjHcHbSeDbIKAk2PvPlT50C391fXeCQPu2mWHywNgCvTgENoNQ9kFyWUlJkefkZU5mUkLb0jcNdV1RzOcsVitub18j/Mh6dsrdzZZ+6FgtF9zc3XK32fD40TlLuY7w0cLQ33RsNxsGO/He2QdRDXdyXG9uyEPJ+0+fMgw9TVOR5SlP3j/j+rqi7QbGrqVqa+arEhkUfdeSZQnHZ89RSjNOjmACWZ6xvWmYwoBOa6y35HmJ0rFxVGYzstTw8tXXpFnOo0cndP3Ixz86Z3N7w0//75+yWq2oqpo/++mfUc4XPHr8lB/+8MdM08DdmwuuXl9w9PiY5azk6ptvsNZye30DzrOYLxi9ZXQTuBD9BIWkHSuquuJ733vOq5evMIlmt99y8eYVNljsMPD8+Qeszpc8PXkaNfj7Fu9jQLi6vuLs7ISnT5+wubujKEvee3zK6ODly5dM3YBDY/uJsRopyxJnLbvdlkWZkeQpTd0ifIs2hrIoSHSCSyS7u1uGqUdLwXx+TJI4hNXkecbRYkVRFEyjRScJTRPZp5nOeXy2IpOSenTs2pF0nqCDpqkq0lkax8ZDR9d1zOdzhmFCKc00jHRtjzlMEKqqi8C0ILDDyOl6xWVeUJYlWmUYtccpQ6oNs5k5IBGLqF6tU+ZZwdFRjxRgtCIEgdCS7c0ddze3IGQkuPmDtqBUCOkOrQF5EMeJW+Pds1wIgdAgvIRw6KW9MwiIynr3mp1RwPc3re9OEIi2kABMbQQ9x2GJjA2Td94Bo+JpG6TDe4c2hiAkQ9vQNg2JMSRqQVW1hOmO46Ojg6OxJc8LpmGg63qSrKPvo7jD1Fqch7wskVKSpIHF8oTODbjRx5S/bZBaslqv2Ww7rHcoJRi8o8hzUp1yc3OLVHB0dExe5ghluHt9Qd03rMsl8zzHmYyj4yOMSRBCUg0D+92evnsDJqVramYqp24aRhfxDc55RJC40bFcLjg9PcclkVN/d3fF3eaOrz77HDuMtK6Ls3IpKLLYr9jcXbO5u+H48Rmz+Yyua/n888/ZbbfMyoIgJPP5jKOTI9QXGqUE4zCyKBLy9BilFZ/9P5/wVfpLzh6dI5QmL2cEG7i4vGI+n/HixQvqtma0I9um43h9zGq5hkUU3Fit1mR57Im4EDBaUTctmXMIE2fiQ9/hvY1/X+8IweEAjQRGhDS0dUVZGBZFEY1NFku0VAx9xP7rxODJEUKiNAQ/PdTaKk2RSpIZzXxWYLD4sOH2dnfwFUhIjEN4R+IUUkJT11gpaNoO5weOT4+x1pKkgSzk9FPAJDo+n1KYzDCME6IWiLlHpgrlBcakKK1BavBRDyJM/mAnRuwBBIc8dALv48C3Evl3emYi3JcA48G38+C5ITxRgtRzb3UaQ8mvLzq+Q0FgeGj8CwoEFfe0CcWAvSdYClBakqSGJMQxlnMOOY70o8a66N8nhMAYg/Atq6JEJiCkoesH8iRhGEc2m22ckzcD1lqyLKWzE8J5knRJ13VIKdkOA9PQM/Y9hBCBNn3Pfr9HOMejR48Yuh43WrI0Ic0zJIFqs0WVOcUsj7+PNvR6xAlB23bkGfRdDwK8m0gScGOUtB4XC5Q3nJ0/wuiMLM85OT7j/PiUMBeIUdINPeMw8ObNa/ZVhVCS05MTdJpw296BEqRlhsk0jkA39HRdy83VNS+//pq72zuYRq7riqPzc5x3SCVYHR1jzAwZFFe3d5ytY+Cor3coren7gaKYkaQZ3jmuvtjQdT2rVZRWNzpBa8N8NmM2KxHAdrshLwtmi0V8j1JDQixH+qnH95F665xjsD1ubPHOMQ1DVOSdwIYJFSSbu0sSM6MzUeg1EQmtg26ombzDBUE/jSwO1t5aG7z3SCXJizxCj01CotWBdiqwdsC6kWEYAUkIHocjzVLCMNLT09R77JCTZgnOWZSOKbvSkTNgNKRpClphgqJbQgietND4Jp7eSiimADaUWOcQSiG9f9DAhHva0ENkeNghD2zA+7rfC6SQ35IiDfIeLwo+RI2Me03O37S+Q0HgoJjiIKqNxndFAPbwoiUc3qH4NkkFykR5sMmOSAGJ0RR5TmI0TV3TiyHSRo1GK4udorvO6ePHVLstaZaTqSySh9oOjCYIHzOKJKHvGsZ+wCcJeZpSlgW4kbKcRYPMuuH29o6hH1gtFyyXS6QUDM5zvdkwXF7y+GlU8g0WhtYRdKCqKpQQKCuohgotBVqXPP3glPlixjROkHhaO1HttjRtz3y1ojxaYVLDy5dfUe0bgoTRDjR1w+OnT3jv5IxPP/scIQTvf/ghTdXQNB0nZ2cILen7juuLSza3t+Rpxu/9/T/i2fPnfPLpX6G15vzRI7788mveXHxDohXLxZLVcXxdz/6dc766vKHrOo6PTlBJwubmmucfvg+ESJFdL8AJ8jRnUc4pV7NoyDoOCKMZCZQmgnBMIlEC7GC5ubvj7nbLOHSo1DEGgRstRZljdIpSGrTEecvt1UuSNGedzjBZTvACJaJyslIq6hDYQLtrsanGpNFd2boJZVTMLYWIQKJuZLfdx0xaSNq2BacQAiYs0kpW6xnFVHAntijhwTuKPGUcR7wNh+ccMCF6V3TNwFG6YBgtWhiGWsSN7qOFncNR9VFKLtLh73epeADEvessdJ/le4isQ3W/DwQ2yAeMgBQejyQOHd/pEQr47mcCAkCjrMUe6I/h4EQk3wFJ3P8rEXEU0zYE4dFKRzNNKXEetJQkaYoPgbZquLm75fz8nPliSVPtePPmkvPjc4Q+8K1nGqGjI4zOMiZnqaqKIi/IkoTURI777c0tIQjsOHLydMFivqSXmrvbLdZB07UkWUZRlNyPOOzkqKsGIVO8DRyfHzO5DrsZ6aeBk+MTFrLkZ3/xMySCtq559PgRtd+zvdxQloGu31DkxzgXkImhGTtcGNg3DXmeMMsLTo+P6aeJ3W7H5u4OmSY8efYBq+Ua4QI61XgJbpxotxXjMJAoQ9u0XF6+oelatvuaJEkf9AsZPdM4cnl5RV7maG0IQXK33/P3fn9NnpecrFfc3NwyjkPk5puMNDccnZwcMrdAlqYcHx+htCZLDDJY7DigRULQgiQx0VNQK3AKP1qEEmRJRlakBKGix4R1OCUYe4/REisdqVRMqUVMMfM7OzunbyuUczgVDl6FhrqqqalYrY4YrcMoiVISH6CqGiZroxL0bk/qc+bzWBYSAs6GiGpMEnSREAb/sCmHcaRY5ATvkV5irUWIQCOjuKwjTnrKNCXRUTWqlwNCCvquj5R3Eclv/vBxHwgsb10H7jdKAEhBWEGw99OyGCZc0CDCA7Ew3GMN+FYV8SvrOxMEFAkWe2BNSO4JkXEw+G011QgFHVFO0tYdWqdIIUmzlL6daJsWVDRolkoiBHjv6PqRaZowJmPXVlGzZRgIRF34yXkSITg6OkVKEV1mDlOEEGIjzdrAaB1nQmKniWEYcHZksVywPDri+PiU4ANd05MkGdZ6xm6kNy1ZHtloQcQpRl3Vh+bcSFPXJCahbvZcXl+SCkG6OmLUI0mxpO8HkjInnxccFSesV0fMF6/ZbHckOqoGTdstm230HjQ6AS/J8ww7TiTGEJSk6keSNIsuS/M5UqgDyEVgsoy6blgsljjr2HQ7pn7g9m7Dhx++oJsGitmc3z89RqdznJsIIZq4KKXx3lHmJcfLNSrTVLsd4CnzjNVygRCH9NV1uLGl8xapA1IKtPRkacIQRoRPETLEa0ePMgIhIugqBME09IfAkSKUprcD/bZhs9myPDohSRVT0+KsxRTqABcH5wJDP0StAiFQShCUpJ067DSRpHkE7SgBWZQMD+MIUpAXM06Wc0BSDw390GOFYLI145iBs7RmIogYeIZ2QBuN87Bv2zi29BqtDY3rud3t6JrmIICjcd4fBEBiv+Rw0D+UAG/BNhLacOgZ+gNzLhyYBnGP9EIc1AjkwdTnsMl+w/puBAEPSraHZEXFF3P/QsR9XHzLkBpGS7PvyPMc5x1JCBRZhlaGJBfs65rejSRZRp4WaKOwdmKcWnRimJUZVdPg/URV7SlmC5wLTFMUITk/P2a1mkWud/DYYaJrW7KioKk68qzg+vKSar8H60Ap8tmc+WpNXswAgXWCNMlZLFYgBMv1mnIW5b1C8GihODs94vZ6Q912rFZrFvM5Xd/yyaef4LxjOVj+8I+f8v6zZ9Rty+XtNR5HVe3QUnF0coxMEprbDavFEoWg6Xs+fPGC9fqYm82Om6tryizj9NEZ4zSy3W7xUuA8BCUJWjBMntu7DcMYvQgfPXqEHRzfXP2Sx8dPD74DNalO6eoepWBTdRRqYhp6Ts9OmcaR3X7LfL6gmK8IoUcJkG7C+QSTJBR5hvOOoXeM40C7uT14/EXcR5Ia0mSGmyzBu9gTmCyjs2glMUSj0LauyPKcYjajGyds5/nk00/5y599xr/77/8Dhran3/XMlw4lJUIdAhUw2olSm7hxvKdpBnZ3FbkhlnhlSd91KCHRJsFrHzelEhwdrxnHKbI4nWc+m8HqFG8VNkxM00QInixNQVq8G2mHjovra0LT8uGTJxDACk99t6euImTZPbBl4X47Q/ySE/C2cxj3R7Tl80hElNe7hx2/gxGI5qYxE4lb7O8AFvr/ZIl3wcJvbZU8cYKC/3YtE5BMIerDBQKDnQg9pCagjaEoCtq+oxsGBNHqajRj5HuXM3wIB++7EaRBJikm0ZRpQlbkVHXNNE0kacJyvUJaqKo9X379Jc45dk3F2kR3H5EGgheM00TfjyTpiLMT1g5MIdDta4rSIA+IsLqtqfcVeZbxaLYmy1KCO0RrJxBW8d777+NFwA1Ra2B7e8umqhAycHN5QZpmEffiHIu8wJfRCTfJUiZhCQTyPGXGjNEOQCBIgTnU4s5ZpmmMJqBtR1CKpqpw48iYpeAc68WKYdpw+vQpx8dLvIab17cMQ83lJXz03ENRIAksFiXjqOkO/ZMurchSE3H4Is51XThMcpTCaYmdJEJInI/jsMmOaCXJ8gydaGTQTNOEdY7gPdNko0CMHQ/pdRSK9V4ghGC730VeQlog8eSuwPkJfwDJhABSWqRIcc4y9B6lBdvtNkJ/ZcI0TQz9iBKKxGik1vRtS6oViVKIIDG5pkoSnPOMkz0oFGnSPCHXAaaAFIJhGBA4hId5mrK5i8a2Go1QEudCZCqGQAgT90YBAlBSMIkDNP6wP97u4emhfAiHDBUhfsVlyAqBDvZBhu+3re9GEHjQFBVEkzV+c/YiBEELnAhMzh4QXnHDOA+pAKNjIPAECJ5hiEKN3nsSpWn7DpOlzLICoRR1XeODI18WmMRQtw0gmGcZWTajrWr2TYNQhnK+RM0ypPfkizng6LoxDjQFSK0wJirgds7ixoEkhbu7W0LYUNUV3kXn3X01sttVVPs9zkVocY5hvlqhleb6esPt7S3V3Y66b0hTQ9s2KGXwQjCNI4+fPI03q50wRjNbLZi84/L6ClPE16NktNaaxpFpHEjylPlqwdhFhd2u6yjynCDVQz26PJ7TjqfMZIISYHTGoAeUXhB8jVKgjSRJDFJBWaQ8enRKP3ToKcW7CaU1SgLBotBYN0UegI8qUEZJfPDR5MNZRh9AeJI0JYg42hJSgvO4ydN3E11fMdkJXebYEAhC4pyNPIVuJDACBrNSeD8xjCNmioIfRh3cgu0ULcdcbCJOU8Tu13WDdYFFWTI6T+gHpnEEK6jrmixJmHqHSRK6cUTbBB+i74Iijgj94T6Dg3qRMSQmIZ3NkIBWisk6RsaYjbzrwi1iUBD4A9GJX9nAIYRDMxGCiGVDZB7Kh2ukEgQfmDjIkP62hgDflSDAofjxseF3r42mhED4EH0FkTFVOtQ3ccZqo5x1osF7nJ9wAYoM8qJAJwZnXWT1JQl932OMoR8HVosVmTIM3rPZ3yB0SV4WKCPJkgw7eqQ21G3Hru2QKidLJgia+bzg5vIKKeJvZZIEpRRSSfphIDUJWufM0pQgJVIY2nbAOkueZ5RleRDXuGWzbbDTyGpxwjjWlHlJpx1KKYRSrBdr/Njy8tUN09BHuLKQfPDiI5w2ODeikoKsKDhbLZCzjGZTcfHqNSpJGPEcH60JWKp9Rd1ULDIdM6ihRzhLGF288ZOEs7MzHp2fUVUVbVuzlxs+f/k133/+fdZPnrLfvuH11SX/5p/8hEA07ayrPedPnrBer/n0089IU41KkwgFTjM80Q/BCgEenI1KzM7G4Dm6CeGjaeo0TTHQAwQReWJSEoxjGgeauxopPMpEu3ejJNVdxePHj8nSAucdSijSAJmPAAAgAElEQVSs7SmKVewFdQO5TkhUSlCKtuuQ9JikpChKlJSkOiEEGSdNITB1A2Zy5HmCD57tfs9quaSrO6z03N3doYxBoBCjQKaem25gfXrEOEbz2HGIMlNaKyYXFX+MhrGzjKM9UAPEtwMBcQvgxLd5A39t+RAzUAAVPF6Kh3IgyHDQ3oxTkN+OEvjOBIGD3Vhwcb6J+JVMIHDgU0gQ9uCsqx1CCrx72wUNLhxusiGeMuNIkuRoo5jNytjBTpPYI7CBdor9hyRLCUC9a1isEuxgcanHqcjQUolmdbyi3u1hCuRpTpqkWD/FIKAVU9/TVi1GG+bzBevlPLrMDD0CTZ7nzBcLsiyjaVqc85weHaETwdXlBq0ELR0i6Ej9Xc159uEzZLD4RPL5zz+JwqSLBSbVlGWOmxzPHj/m+PETMgW3v9yyq7Z0U8+jkxVZWWKHkepuS1vXBGLDq6tr9tsN10NPolJOz46ZLWacHJ1ACFT7LTcv31AcF1RDzeuXX3H67AlTP3J98wY3dZi0IM8jpbptG4IxkTZ7AHH54HFCImV6gHvHVNm72BOYxuHQEIspcbDEEiE4VKLwY6zb5b2lBJ6xA7BYF0gQaBVn9P1Yo5MUESQ6kcwWawQ5k7eYoFCJOICUHNZFroIQ+sAX8CgdpcGKoqBpWpq2YzFbIFVCnqUIbzFGMxUKnMNaS1NXLOdH7Pd7fEhJ8vQwLhzxzmG0YfSe3CiaxtFZS0FAYw4uRPeHm42HiRd4oQ43fLRKj2Y690fgvS3fwUfgvlUmovuGPmQI1nFwplYMD/pE3/XGILz1VozuklF/D3gXLxCEOCCg4sYMHoL1jFgSrUEHpIpc8WkYGMcR8CRJyjD0aCXY7u4o8xLpJiaj8SHDmDlBOMIwIJSmaTqSJKFrWiY/0m4b6rbm6eMVSaIJPvrUNa3DBkfhQ1SDcYK8mEeGmnMoY5iXJfVFQ9v2LGcrgoP9riKIwPHpCbMsx2Op9lu6tmV70TBNDiRkxZzPfvEp0zgw+ol27Bj8hBOeclZSDx04QesaZmND5zx3mxvurm+RUpIlKfM0Q+Ul/tAZH8PIfren2VU0TRNn+OmEaRS5G7i41MhrycXFm5imB8fxesbJ02NSOeGmgY8/fI6dLNvt1UEDAIK3VELy+MkJoBA+GqZYN6GDJuqAWrxUB7KQxo7ghhHnIiFqcnFMp6TEG4WwsfklREBKTfCedoqUYnFAUU5jT5mXUTlId4xTh1YlJskYBx9BQngm7wk6RINa7fBeMk6evmtRWqKSmH4qJTEmRTAx2cB+13CsNKvlnMwI+r5FBsVqeRTBQoBIBUmiOF0suL7bkBc5cggsH51QDT3zWYl+mqDynLqvGOoxCtSKGBQJcZSqkfRB4R/UhwPRPYtfXx4fSoMofhdPf3TMtqJL19sswv+WkuA7EwTiNtd4cUhaNCgbEc/34ssRLH2gGobw4FeAddG9UGukFPgQotmoUm8DSvBstlvKvIQAZTHDDhNCeoI0eBfo+46QJLhhwBgV5+LDSLvfMQ09XWVAlKgkEFJJs6tJdIIpJS5MpEazmJc4EfEBWZLGHoEyBN/Q9x3OWbqmYbU64ujJOfPZjLrZsV6tGYcWpTS7XYXSkrbpePnVlzg/sD46oW0apsmiTMLQD1y+vCBIEJni4s0VOsm4urql2W94dHKOcJ6bq2sWqyVlOY9ov6ajrRo0sJiV2EKBNNzd3lJkBYVJOD49RycJj59/wHsfPGaxOuL02RnzdE3b1KyWS7yHvmvxU0VeLAlFgfWWLC/QwkDwVNUebydc8IQRJhNRoUoK8FGyrKsqLBOjHbF2ghBn8lMbwHuUTBDCodRhzEePwByCg8DaCAkukgI3Odq2RmYKf3CWGpxH2TEeDlagpEIhI0rQBYZhitTpNIkjQe/JspR+mLDW4YYRFnO0Nigj0XmC7yxFYQ6YfHEQGM0RQtG1Fc47cqHJixy0YCpnSGJJOoxTFIidpodGwD3qbwzhkDHFFbPjgAiH2dhfa/4Jf4+diz/h3sEExO/5h0r7/yeIwfDt+sdHt8GYCHGIhPdujA8/Ea3EAjjv42ngLEgoZwuyJGPsR5IkOXSSk2geMYwoNeK0wHYjQlt8qpDKkBhN8BNKRRkyjSBB0bQN02hZLDWokeVsjnSBse1BKhazGcvFmtFOtG3DYr5ktV7RtDXaxFk1eOq6wk4T1lu6vkNJyW5XxRMr08hOsVwtYjNTTgfGYhwf7vOcnp75bMbXX33F6CxDN/Lo8WPutres12eYAGWRcXJ8xOnxgs2uZRwndtUbri5v2d1tKTLDYrmgnM+w/nCzTxNFUXB0csKjx+dkeYQ6H6/WfPDRC0Y3cLJaQ3ifL1+9YhpGsiTFaEcuLPnBbqzZNxytVgQERZFjJ8vkB9ww4QMMbkJ7h3AT+2pH17UEGejHHn8QdB0PsyLnPd73aA0mSTAqVraC2HyLp3z0lTwUG2gZ+zPeeVSuULH/hlYaKWX8txexDPEuau9JkMZghGZsLTrXCCRdv0f4qAQcvMIjyFKNn0Q8HNqWxWyGlJLgBd5LTk5PqKqKTjj6oSU4RTAaFzyTs6iZJGk0KpUgw4MNXkDhhX/42sMN/s69/hAC7rPmBxbh4UL59soQHogFf9Nw4G/tO/DfAR8fLlkB2xDCHx1UiX8OfHL43j8LIfxnf9NzHB6Te/0RIUQUYCRGs3BgUz2EPg4v7tA4CQGED7H7bUeyLCUzBnxAKY02BqkkBMe+qijyDN9sSGYrpNZkeY5V8WRYL1aURY7RmizLKfKCftVzc3NN3/cUSUY7Tvh+YjWfw2zO+niNx9PbCCZKswJvEjbNjqGq2d7dIQQsyzWhkMhEYrKETbuhm1raXY0WgnpX46Q7GHB4bvcblAtM48DlxQUEyfHxCePgeP3qDSenJxwdranqCplpTtZr3nvyiFevXka5dKkYxpGhH7i73dLuK1bLBe+//5TFckE/DFxdX7Hf1ywWOUerU5zzVE3Ncr0kTaLIifMDd5e3nK1PODpakeQpt5stC28p5xmh7yE4siRD2FgyKK1ZzwrqbqCpGkbbMfUT/TggnAPvqKp9nIyEe7IYEIhIO+ciIhQiR98YZnlJ14woCUmS0gd/OLkL3NQjxwGdatq2I0kKEi1QCrz1pPM4sYicEo3ro2RZ3zUE7+nahrvNLfk4YsYSpRKSJMNZSzMO9HVFmq0QusSFCiEjwtS6KHTrvKcbB3Idm403N7d0XQ9BMw0DQgjGccCMCpPouNGDj00870EedDP9wWMovDMZfBf+Gw7N83eWJMFj3wkUAX2A23nuBUt/8/pb+Q6EEP7jdzbvPwV271z/RQjhj36Hx/1rK9Z+4CFX0L2FCz80Qe4D5MMbEQ6pvkSGCC0drUMOglYPgCDNJNY5FIEsj3Wl9R68QnuYmZJslrKrHEp6RPDgY/3sXEDqDJ/Ao/ee8ObLV7RjzzQGUm1QQiESgcwFzc2ernfkiyWpSTB4hHUEF5j6gTQxJInBZJECjYDLr64hURAEbdeyu9sxW87Y7XbxNOsHxmmirmrSNOPZhx9xtDrCOYfRCVIojEmo65bzR6dkaYpJUmbzOV3bMvmA8y42rqzFaMnzF9/j4x99jLM9r169jujCLOfkaEGWRMTc1dUN6/Wao+MjylnJ3d1NtAAfKspyxXI5Z1bkKBxOgB8G3EEIViuJP5zMUoBS8cO2A85OuL7Hu2gNPow9Xd8dDEWjTJrRGmsdwzCw29VR5dc5muBx85FhmEi8QiqQkydIiZssAoVFYEfL2PfR+KUn/r1F/HsrYfE+Tl7SNMH7mG3cT6GCCHg8k7VIZRBSgfD0/cCua5mxBBKU1GhNNBmROuJCnEOE/oHNZ6SK948K+MmipIJeYKVDIlmUc6SQ4OzhAIz6DsiAmHiwHHyA/b7DAnoAFLk4lo4ldOwKKnUQHuFdT6Pfvv5OvgMi/ob/EfAP/6bH+Z3XUkIdu/xBvvMmHPA0PkiiHcsAMkRppQDTAWopRTT0COFQHliLHaeIvDrUm9ZaEm2iHlxuQGiaZoMxmr7rEVIwDRYfauquR2nB2ekZ5WJGs63oXYdzjv1uh0oUymjGPkCIHnaTa5knCXmeM7QtR+sFiUni7NvFsifPMgwODiadm7pmUSSslkdczS7Yb/YUecF1fc3R8SknJ6d8/PEPWS6W7KuK7X6PFJHjjggHd+KeV5dv2O93zGbzaG8WAtMwMqie7a4jKQz5YkZfeR49ehSdjICu7bjdXNNVW05PnzKfL5kVM8auQ0rN0dEapXQkYmnN6cma4BzWOaakZ5g6mPwhTY+TAalVxEp4CyJE4VDvGMYBayf6fjikwgE7WZydCEmK9yEqOXcNWpdI52LpMw6Mw4ByI33foaRhdA6Bo8wyZvNF1HeUKrr+WofSGVobIsouyskH7zHagJRkWQQPCREZgIFIyPL3DScRnao7OzFMkX3og4cQUFLghTxkBA6NQGvNMAyUZRkh6od6P3AwOg2xTz8rSzIlGW14Cwl2byvie7+AeyWttyteEIIgHDKCSCCK1//qxhcPU7XftP6uPYF/D7gMIXz2zteeCyH+jKgY/l+EEP7Xv+lBIsVSYgj0Tfg1akJEJ+EQMwUhxkPddP8GeabgIYg4JeBQIogoDNF1bTxZvSJNLc7H+myd54gEpFS0dU2aplR5ypP332caI2z19atXCKXRTvD8+YdU5Yyj6RjrA3Vd48YBk6bkpiQISTEr0EKQZjl5mnJ2ckyhJFVVMfQVvZBMvqeqJIqMrq0IznJzfUO5yNhWG47Xx8xmM3Kd8N77H/DixfeYLRZcbG+o37yhaVuSNI0ZQWKYlwVHj59gAnzx089I04SzkxOKIkWaY5TS5HlB8B4fBl7+8hO0zlivj1it19xcXKOEJNhA10XpbjvWXN/2HK9POD9/j9R4TGIwJrI3IQKj8kRR5Jq2Ocz4ncc5y+g8YgxgB7x3TONI3zbYcWCz2Rx4GPaQ/t8Hb0vf94QAXddhlEQMA4N3BCFo9nu01hR5SvDgZZzSjKMjS1PKoqDa75nPFwfZLUViTBQLmcYogqINwQdGP5EVBUbrmHkJwXtPnzCbL+gnR9f3D6W5l9C6jn19h1E5XT9irUMoibUjhUkQiWEaR+TBgixVGpkkKK1ZLpdU2wqRSJQUdFP0f5w8sdl9L/gheZiQSR5ApPdb4Fsn+n2PMBz2TykE3X2y8OumCSlvlbv+2vq7BoH/BPhv3/n8DfBBCOFWCPET4H8QQvxeCOFXXEbfNR8RQqCUwk0+RjWixfYBM3wYg4SoOZgK6MK3X6TgwP+OKColotWXtRa8RwpJaQze9Wg9w6RzJtfHhp3RJMYwTiNOS+ZaEkSkJ+sk4WS24P9l7s1hJNu29Lxvz2eIiIycquqO7z02hybRIMiWIU+WDFEOARly1aYMGQJoiLYsWgJoCRBAQwIESQYFSKYEETIlgwQIGd16anX3fe/eW7cq54g40x5l7JNZdd9IdjeFe4BCVUVVZkZGxt5n7bX+//uXlGiLQCvF+cU5PkaktZz5iYe3N6SU6fqelFeNttKAIsa6x5/1PVII3t7d41PCOsM41+xCkRPHeWB+emReHE1jef3mDY9PD0ih2H5yxuQ9TUpMx4m7m3s2Xc+XP/pRRYnPC0ppNk3Dw/1DZSDkjAZOhwds33J2foZpu/p9nk6kxWO6DYfTidNwZJxHSsmcbbekEGisYTwudNtKG7a6kMKCdXp9990R8gYxgd45hFI1RwFY4lT19TmhimdaItPpyDSeKidhqfoAIQQiC0LwoAo5zQihoLA2cWt5PKUIQmCkZDge6bsOvWlWB2CVzKaSmZfKWRRC0HUNICttWNZjZq0Oa/vQOcMSPHFe8EtgGBcaV6u7rtPIoFgWzzgNGGvppUMlyfEw0zRyRZprxnmmEYHHMbA93yJK7WMZY2i0AasJ68RDKcUxeC6MIQ4j97d31XFY1jpBi6roWfXz+UNfD/jFDeDlrPCyDGZ+Vdm//qvcQjj92kX8594EhBAa+A+Af+v5sTV+bFn//M+FEP8v8NepKUXfuz4OH9FKF6UqH14XSC+A4rURmKEoEFlQFg2yQcoTMlYWUTUZCXIoBASmqZvAsvhVgqpIMZBlQpgtSmWM3QKiIrGXTDEZ6avU9TSc0MKgpeTzH/2I7W5H9L56EaQgWwXeY5ueq9eSw+GAsbae7YcRHzzGaKxtiUGwxMAYA4jCpmurt2G8JVOPJKUU2rNa5ispyDlRYubm8T392ZbgAg/DA1JItmdbzrXhR19+iVoWvnl85Ou3X/Pu27cMw4g1hqurS3KeOTw+8rrdoI1iHk+gLcs4I8h0WnBxfobWGv0K3n/3nmMpvNpf0TVt7VKXgnE1sMV7T182KCSibGDMFF1L5aZxtUO+ch78MuFjRpTEPI/cHR4JpyM5RZhn2hRZjEU4TcwBH/yLsk7kREqpmnF8AV3Pe6LUX5TCHCOdVvi5EnWsqYtaCYn3Hucs2lS0d86FmFOVLrtETgohVZ1C+ErwF6LUu3pKPDwMaFt/RmUdLc8x0IqeYZiJ63tu21qEMAjd4JehBtSuN64SZkKBrm9ZwsIwDIAkjSOjMQzv33N7c1MbgmSkUnXTy/X9Lyn4D63SD2vu+VghQa7HhJfxIB+OEM/Xc3if2Iwfgnx/xfUXqQT+XeCPSilfvzxJIa6B+1JKEkL8FWruwJ/8tk8kpKglaIrEVDeAUgoi52dnMc/fncgRqSbSmjhb56QfvvcUFcGD1Iksnk0rC6IYGucIw0KxCrcvnJ29gQ2oIPgrn/2EFCKbpuPgAxeNpt9uiVYw50hyhV51TCGgRdVvtamgzy/48e/8TlUwxsw0jni/QCrkkgkp0PQd/f6M3cMj7969Y5kndtsNp+NE23ec9TtSiCgtVyjpPQXBJ598wdtv3vL5T37Cp/s3PIoRnu4JAt7dvMVJjS6a3/3rf4PhNFB84s3nX/Lm4ow3n73h8vycz64+43AY+eruK767+ZooEucX5/Rly+effIJrO7SQXF1d8f6777j57j1No+k3PTknWmN4mgfOBYRlwmgBRRIstEoQBo9RNXx0nmeWeaYYSxwf8ccFwoEwHDk83lFSrrhwqzFKsKRKUzbaAJLoPePxiE8zsw8oZVBF1YTeZcEqVU1boo4IG9XgZeDp/omiC8nDZlNdnII6OmzblgLEUJBRIUytGq2VIC1d17Hd9IiSOB0emIaZdrtnf37OZrNl8Z5pHLl/eIKkyfnI9atrUsxsu562bcnakpYBqR2NtVAiYV4IvqYc39y844vP/xrD+EQUHrnmEFAKQtZGao3UftHK1U0Y+Dhm6EVW/MwdE7987//4Cg31ljwmakzJ/Cv/358rd6CU8k+o6cP/3S/8938H+M9FDQ/MwH9cSrn/rV8DUGo9aK6lfCmldvGfy6GPNAQ5J2SE+FHQSj0DQske7wNaWoyuTSxnDUYpnNZYoxGqYIQmTROFFuMc28sNOmoejgfQM8dGcAZM88I8ezbbDd/ev4cCTdOw7bZVCG4USEnfOHIqGK3JWTCOI9N4oCSQSLQ2OOfY9GechhMheMah0mrdxnJ5ccFwPPI4PtK0Z1xuA6o5Q1tLXBZOxxOP00CYJ9z2DFkgpITUhk+//JQ0R8oc+OrbnxFTJOdC02/ACVRTQGSEFmip0c6hrUZpyX67Rewk+laS/DlaaHIJaAU7W+PPe2uJ1NSn58pFp0hMUGSuTb27RLs1+HmmeE/wM2M44ZeJZTgRlmUFaGhKkkhX6lGAWirHGCglkSnMqSOmgZQ8VliS0OQQMG1bx4cx4r1HqkLMhSwlAkVKVemZV82IlAJJJLEeD0NABoFwTYWNKDg/27PdbFj8TMyrK3WcOelT9QZISUQiUTinGYcFCngfyLmK0ppGczx5ZNuSUq1ktKk9gmdWAyw0jcPowqXdcHl5+fK+VVpXN+W6FqTgxWfxvetlFxDr8vpVMsBVeC+grJHkJWs+5Hn+8vXnzR2glPIHv+Kxfwr809/2OX/xEkKgpVz/XPXmKdcEmhjjB830C1ut1ORmIb63U+a1ZMypVEKRKJhcEU9SgnGWzWbDHCq85DQHXp9f02wETXfB/cMjX//8K66vX9E1hqQnrj9/vT4vyawN87yQw6oQk5I4LyxmhLWC8d5TSrU1i8agZWBZAvM0E8NC0yiIikMsXL+55vLyEq0Mrq9pPv7nib4/4+zVFaax2GHg+HTg3bt3NM7xFCNmPGGvrzHWMs8nnG2JKiGayGW44PHhkeQ9p/sHtu0GimC76Wi3DSlH2q5h2zeUlMg5sI0bopB0naXvL5mnma2zCFfDNKwypCzIJUKKlDETWYi5ZijM44JsBTlIWmuZjpUOPJ0OBD+RvYcY17l4qtiuReN9JBeB0ZaUFkIMKDRbm3mKiRATMiakLCghEUtAOk9RHXZzQV4mRIx0fQ9UtFcRgkRBS1nn843FIAgFUo4IaUjoqhy0mS44jHakKeC0IyuDsRatFEIJlFF0QWGExGhNsqbmHGgYyoQ/Jq7PdgQ/MJxcNUUVuR4vCtPTSN903Nze0rQNr/uOB5XYnm0rGESLavbBkEWFqooiEOn53A8rb5iP0SK9hSUW4qoB+EDnf66koQcGKb73cb/q+kEoBoWoYzJWFZhzjpxz5exRS79U6u5misCvnqga6VwFBIWPVJWlEOaIaTWmVOhl2zZ0Xcdd8OhQXVymOWM5HBiWlnz3NeEpcnl9xe7sDCkUQ5y5LIVpje365NMvUPIMJQeMMS93MK0lzjmstUAtQ8l5bUglxhWHHtyOLDMPxyf2MbLd7zHaMQPjPPLlj7/kd//W32CeJg6HI0LA0QfOttvqT5cdGzMzzzOEBbPpafprYgw0qmVKgSwkb774nOl04k+++jPm4Hn16hWb/Y6+72i1I+VI1pWTKJEcTk80ziKXnijq3PryzCKaHdNc7+Cu7zmdDoTgUVZghSIME8MwUFKtwo6nI/dP7wgT+GHk3Vd/ylwKndEUWbnROUOYAqoz1QmXag8kpUSIEddaYkrYZAkxsPgFJRUi1gZhv9lSSmLikTIXcg0aZFlGjDEcTye0VjXCrBTSkhiXEUqhb1sKguwXQi4Ir9Cm8Ht/+2/yZ3/6LU/jCa0N+92eV9dXzCkSUqS/6F4UrHePd5RU8xuMalBKU4RgszlHyUod1o3idDfSnznOdltS2rG/FvzRH/4h7/7vn/I3/96/xyeffop2DTF4ikionEgfzfHqO/vZNlWxI8/6f9HCGPiwR7BuAM/l/3oN8GFcIH5V1VCvH8QmwLNTStYQylL0iyc7rxlLJRVSysyyWojQ1C7KHKmC9HVeusay2ZyRrmA6g5IKC5VC5DPONsw+Ev3AXBq2jUVs95g+YRrJu3d3bIyh7Vpu1vDSYRj4/LMvsXZCq9UgtIpOrHMoJdFakZXCpkz2kZgTRRZcoxGyZ7GGeZ7ZUMNRTdOSUqYNq888LzTOsN10CFE4Ph2Yx4HtbkffNng/8/7mPaLAwzAQlOb6+hpjq+dCFsX11RWvX10xzzPXl+eUnOmsIUXBPJ5o9ord2bY69WQ19QQf0K1ENBKVFTKDlw0qZbQWCFFjbrUa8V4yzgeUNMTieXh6hHTE6J55GHi6fWCZF4ZpoljFzjoohawKXgb0SJ0GxEhKmZgLMtQ7nZaqugZXhSCtZfaxnp9FrfRiyciUyE8rjW8GukJZClnXj6WAj/VY4xuDXpkFxTlcLhymid70CJl4OjyhdcNut+XxcItUGiEFUma0EEhlKaEeg4RWlb+4QmCEaSk5UtKC1rLCR4NHGYeXARePAMR4Q+O+QO4Mb//lN/wtIWibdrX5VlBofpnriap/QXwUGpTXCmHdFqYPo8HvZRF8tAFQoEswdiDWDePXXT+MTaAA1PhxKS2l1LuReBb+sE5OnrXVqUCs2U0CjUBUxVpZSykJYy7kY2BiwWwEj3Niawy73RalJKoITsOJtt+ggOXhkc3FJcPpyFM48GbzquLIx4GnYcCXgrMt+/2eV69eIUQFVLROsu8MjxPEOSNtIWkDTiHzUp+PAKUtjat0nCIgRoG1liQkWghey+t6jlWatCxsNj1WqgqMOBS2r7f80U9/yrZtKcLy9vaW65ywVvPJZ59iTEPJA/urK9rWoa2ibx2QUCUTgmCcJ4qs4M3O9uRcVkpzWcdYtSKTOFLwlV9QCqZxaCEZ8hYdZxrZ8nB6xM8TYZkIk2ec7vHzglGQFZRlxhqNcxopanbEPI5MJSBU/ZmVJMhR4kXtklMKPgSEqm/MrFskE9knxNonMkoiSoXLAtAVUBLTG6ZpQgjFPMwovWBax0a2LJbKW8z1qBljIOdE1zXEuHA6LSBqQKhpDNZqYgSpNcoocgnEUHkHrbXEpRqARga2K+BFCIFoHG0pzDnQdg6/1ArnMd/zSXzDpduRfvQlSklcc43Suoqf+Njx92HYX7cFUSXFzx3wZx3xBwHhihKry+JjifGoBTz7lH7DLvCD2ATqtxtewBzamuqcyhmtNcZ7jPfM/tkskhCrpyCLWirpBCBWZWA1UExzfYNLIdjuet4fD7TdFimh0RolKp11GE702z3ffP1zhmHg7/zt36PpN7ULLQR913FuLZttR/KB2/sb9pfXnDWGAtyfalWQSyTlgs4Zd2ZAGOJTlaoWKmDCWE1Tmsq8M/alArKXF+h5ZsyF/vqKefHMTzXA9LSv+vbf/91zvj2+5uuvvmEOC9rVWHU/T7jVyWYU5JJw1tB0HTJKishEIl1oa4WVIiRRZ+0pkUtiGmdSrCYiIQTjWJWTSimG+0RRkfNeM5jM4XFkfDoyzQPT0xEfR0rOWFFQytBsHL11PDzVYFXTOpzraDc79DDwdPtEDAntBK6BYZkoVHehVoqYEnGoAp2Qao9ZU3/2aSxQjKcAACAASURBVJX9ClFI6zGkxp+pF89BUYWmMQgBIZx4Og2olPE+4bRGtXqFo0refPIpx8PM3c0Dna1Jyz5m7u7uWFKgSEkJifPtnqQEh2nECsmu6+i0xPvEOI4kv9D3G6K1aKkxTvMnP/9j3n7zLYfjE1+ef8GXn3/Bj6+uuQ0eWR7r+z1nkqj6AlEKRVQPwHPfr6zwEAGrrP7jdfMBxle1AxpKqmcD8/H/3AL/BnQCf6lXWaeALcQg2WhFBFSqXd4lZ1RK1VNeSj1z/8IVy/N56mOnoSCExDhPNE1DpyTT+AilpW17hHWU1CKBZVmQonB56bi4+IJ5rsKbFALTPDEM9ZhhjEVN1TxC7Oj7rm4AooIwVJIwF6Kq3nQpAREQospom6Z5YeMrbRFSsreWx+ORxTkcFdu1bzekxmJaTfd0rOVsueSzdmQ8jdze3CEDyCKJ04njbkOzFIo1JCIxR3RfVgWjRuDwIZHLRM6ZaVqQqbrrjJBVUltyDf+UAmd1JeQYjZYLy7xwKq6eVZWu2K1SaM9aeEoEP5MDGFPDQDEOpQwpF4Q16FUOLJVBOI1UEaEk0ipEFBA1UghSmVEBgs2kIdV+kPcIs6NRCyUHcn6WhtfKRcRKOnbOMQwDWimWJaF0putA58zT4YCxity4Gnwaa5S4tRpjNa5rmOMAuTaXrXOkUIgp4X1F2SljocC0TISSCUJWsvQ4E/0CuRBjRplqZhtiZJkWpnFGyjr+NWtI6uxrRiXaIPOzaOjDeqiq15daAIQga1Hl8x+dAb4/QEj0wGjgmVVU9VTH37j8fhibAJBzQQwZYwpoXctBEsYYulKYU0KFULsjUq5572Wlr8AvaSVXRFWIlT04Lwtnr68q294Xkgl03RZt68dordlutyij+eq7P0atM2y7NUzLhBYtOdaGUtGaEiNaGUKolNm2bevZVySKymRf4Sb1qmkRIQSUlDRNg5ABKSRq0kSnKUpjAWMVspFYLFkKzvUl2lgOhwOPj49cv3m9NogEu901WiXGYeazJXKaZkYZaVpLyjMnItYolGwoGbSV5FIRXNJHSopIrcgloaSgWMUM6OJRSSAVhLigjcAvidPxES0geU9cZhY/M48TrTOopgEismhSSAihONOOUUoa2yOVQemM1oU0JNKS6V53tSOeMkomitfEmEELtLQ0OXE6VTpzCI/stj1CVu8HgAiBWArSKdLsaxWwqvNEqShzgJgSX3/7c3bbDbvdDrNvqqEHwTSNVdC1LJUGZJ7HyhaD4ZgGPDMxeoxwaCOZB18dpWbD+6dH0rKQgsc4s/aPJopWNEi+/OyzqmItsga0CpjnmZSqmrXNqU7vn3vcef2LeCZs1MZ3gu/J6T8eEJbnt1kpqyZIvPAH68MF0Yl/Y7Lhv5SrNi5zPffkWCGVqsE6gRIFLUGv1OFFFEIWeF9fRAAhDfUlCTy/NM+ygpQVKQuWELm7G9l2EqSgF5FXn3zG1sGiQC4LsxDs2obWK2Rff6Bn3Rmv92/IKTN/O9N90bHZbFCisExHWEvm+pxrco1af4BTTjUY0tephnGSmAJSKRpnSArQhZAXcolVT58MG7Wh6IJuLMZZXFt/NU1D2zZs+p+w6zumccIMhlnM3L77llxgPhVev3nF/nxPzJ5lmRANCKVodUsYPDHOCBHJOVCywDnD/d0N8zTVTe1hQXS1kdo0DcviyeXAfJO5G6tFtmssZ+6M/fmG4XhCW82m29M0DaXAEgV3QtNkQdN0bHZna3OtY45Vc9B1Dd4vSGk4HQeWxxntNMJIUIJcGuKycJynl9K4NuXq6NinhBYCQQWJzvOMUrKSiX3BbXXFlhnF5599Rs4VCXZ18QrXWJSC02lgOC0saUF31TkY11m/6QVmEZz1W5q+e6H7+uB5uHlP1zVc7M95ur+l7Xv6vqcVAtNveHdzAyEwp2rU2ux2DMPALBJBO+4en5jiGhmaEwr1QfUmKmyUNUnouQcgXowBtT540c7kD0rBD7dCj8B+WGDTr19/P5BNQKCFBCERiyDbjJSenJ6nBqqen6SsyGpq97aUZzZhWLulz3y2mueCyKAKPnlaVtZ8KbTaYJRmmkdCqWEP3faSrm1pmgZja7psjJHdxYbO1nPytJ3WFz5zODzSdh22Wc/ZpRDmmnKLSmgtsKpDqiqHTUbX8AklkapmzcQh1LOp1i/HhGpdHWujSVEDMXLBWottTI3Edpof//jHHP2RcAoINClXw0sICyF4lnlEWUnIC+FYv87UdWsYS0IJWWUlKUBWOOso6UgKiVlOqEfFmAfe+1tiWMi5lq/j8UQphf7ynO3FFoEgxpohqLTG2AatNL2Hk1uYphGRMsYYSiy0qkpy05oQVLTANQ2Pdw+cyhGTLE1qiaHi0Cfv6+uSMtE6mosGxmfw3DNdaO27rOWzNhpkJlnHpC1vftxx+EoR4oIUFQ9vm6okFBpCKBAMRlaUV16fb2Nb0k4ynEaUEEyhEp2NUpQCSkC32xCGI9ZVV2JQCmVUzcTY77mwjvdf/4z/56uveHVxhrGWkBM3p6Gi5kvtWYmSqBGi1FX84p3/BfOAKFDEaj/+gOd/uZJYV7VDi18PF/34+kFsAlJKtKv0FTSo1fwjpURLR5QeJwSjUmRRkLlmxJXVaPJS9pjqNRA+rxoiixSZmCMhJWKeEaIDqEjwacIJjbaO7WaDdpYs4DSN5BAQUXOaItP4xHg8oY1ErtbRdnWgCVGRDcv0RAgjKda+RQAW5RFr+a9koViLbHtEqapFRKaUhKA2KCv0UhOLR8pCRpJSQIvqSNtsXpHjEWMzsRgu7R6fFw7+hMkNtrc1JXmZKSWTAxAghJlxHJGPj3Sbjqat6sYUAviq0/fLxNNjTUK6u70FamP26emA1oX96z296TnfnRFDYnd2RuvOcE5RMVuKtm1AaUS3JYfEWU4gwLUdJGiUYnGl0qFFS8mJKC30IO9u4biGa+a6EFPJNQpMa7xKdIsgT4kwL+zEFhsh6fJi1FJKMc8TXd9Rush1a0gkTg8D/abBB8FxGrh5/57gA+eXlT4FJ0peENTRr+40m80G6yyrf50XEjJree0zjS24Tc9mq1HKMp0O7M63xOhx2vJ+HHHGIpuGu7dvOet3NCqjciEch7qwiyBjEYSXJS/Lh1IePqQQZVg3B6hhY+ux6EUDYEHHdd8oRDbA8IIX+3XzgR/EJqCkorMNi78n0jPFSEmlhkfqSpVNQmKUoYYwQjHlpRv8fEZ8jiyguomh+MqFD4VhGHG6xj0JLzEdaKfY7/aY1nG8HSjtiaxqCKeQEtMrvv3Zz3DO4dZGUdMYlphohORwGvHzI8PwRNe29UsrQa8anDsDBULMhHkkK8myTNw/3NYGmVR0bY/WhpRCvVOWgiiC41BNl43bImwBlZlnjxBVrvr43SOtczRbh0qRdtuhpMZIRxITvevr2DRUdFjdLBPD6cDNd9/glyNK9+x3l1irSAlyHFmWmsK86/dM04mYI3/39/4OWmvmmLi8vMQYwTwncgyElAhZ0G6ua4NOKZCWMp8IcaGUhLYSJUGqjM+RxjbsthdMfiIXhUoWQeH1m9dombi/eSSKglaGrtlyNw7k6FGiENqFEpo6MTo3pFRQwLQMFfz6MLK56CuValAEk+malsO7R7xauH51hVKKJzfgXFs3fiXZbrbc3jwyjiM6Rs7bHVZqci7cvH+PdYb9Zs/N4z05JbabniwjYS4IdUCOju66wTmD1pIxzEhrOJyeICea1rE7+4Trqy1aWU4PD9y8vakqU7GmDhYBKAo1MER+tGRXJUz95ertvaQPcesVNapWQcB6VIgZZwa8+EvAi/3/cQkp0NoQ055SMrIUkkxEAiqAF9U2mpJEFItc6Syl5DqjdXXOKp5jjFbLgaHKCSiSFBIxJsIQyJeZcco0U0C9MfUN3BficeD9OBEWj88BjaLva/kaQuDhdOLLTz5Ba83PfvYzUkz0bcerV1dYawnLQggzc64R5o1oqjNuzdHzS4VoyFUiXefaCynlaiIRgr7vXhbueLqp0VyNo6SVNV/A2ZZheKKIzG6zRUlZbb5Ekk+kmJBCIVKFYEolybnO5f1posRKZraNqm65lDCqwVqD1R1+Gbl7uCcsNW5cCJCnyNn+gsY5vA88PD4Sx5HwFNi/2eBLeQlu8T5ACvgQkCjOzs5oXcs8nBhCPZoYWXX5R5kIy0T0mUZuOdtJlphW5FhN+Ik5kqJHSflSIcJaNa/nYukkdmPXI5VgGkcur87rEVNaDod7Xr2+BgTWWPq2rT+zMDNMlYpstEYLS8ZVOW5JxBgIi6dvNxhrWU4TWml2q+zXthr3WcsyBWIKVV1pHUlqUl7YbF4zL5mcPYv3yK4eNZFrGZ8zQgk0GU1k+J5v6EM46UsS8foetwgiqoJFPiaGPBcFWuE/en1+0/WD2ARQpY5vMKiY6w5fwHs4ivpDF0pgnSDFiE2SYuouWGIVnORVVvzxFYGS15QXKXgaRqwzqHmmtQaRAuP9e7o3nzH7A3P0LKeJk6iBlbZ1tEJTfGSJiauuJ06eUCaW4UBMCUkkpi05rggpIUg5EXwgxciUMuddi20lSis0mpwqCaZ20SOyFB5u39eQDbWFuY6RwkGQtkeWo6R4TypVGONcg1UClQNxPnGaRxrXEIwihUgKAfUc8aUVIUWmp5GweHTf8MnVJZu+oW87shCkVNjv9zjXUErhcHLsha3wj1SBq5te4TYdSmuszKAlY/QkG1nIyCDBglWaOB84TAMzhfO+xzQtWVlE21PSER9DjRqnOoVjKjTNhsZ1tMuWu/tbQvRrvkQixoIuiqZkSvIgG6YwYYupSkK/4Jyl3W4Zl5m2bfFas8yVJOyTJxKZ4kIWBSsKnZMoJTiMhcV7kAljOnrb1gj7PGONoesagl+z/6RAd5llyhyfZnZ9ott6WENLSq5sbFEkOgn8VDgeT6SU6PsLbu8fsGHi5ttH5tEjcqweBySLVPhc0FmASCShqGFja8mfDXUXSKSiyKJByOW5W0gGOlsFtIa6dspzv/y3XD+MTaBINv0OY+f6gitFDBEZI8EvFS+dBQiJNLW7D4CowQ2VKsT38UxAEQUh6qy+QkYSSmqWccKs0dCPj4+EXDi7eoNTlhgVr99c11CNlCpo8jjgdg2vNm+IMfL09MTPv/4Z+/0ebS23N7crdcegtanBltbWjSEG5mXhu8eZFDyNaTFaUlJCZ8e2kyQJrZVgOqRv6XctXdsjXkvG4UReFrxyNG1DSk8IWrRzUDLzuPDN2+/IoTbHHocDFIFU1Z579/6GMHveXF+yu7zisy8+5/r6ApUDrmkpqh5DpK4xYaBoL67o+21tZoaIj9XPEWNaf2V66+g//wKtV+OOL2AF7959x83tDWmZ6S8u1nK10BiNAo7lSEkZ2xiU1kjpCXMGBW3TE63h27ffEMLC4gMnGXBSkXxkFiDnmV47NJJwWoim8gmRknn2KN0w+wmDoek7rLMobbh9fCT8cWDTbeiF5fL6AqM1fdPgXIcxE8Zo2s5RRGFeFrRR7HZn1dNSwEpNURtuh+/I2nOx7OGgOd9tUNpirGIJgZAWlHKklBmXQGMU/vGBsj/j+PjIP/tn/wvffvO28gqeI8dKQa7uv5pVFNf1+3yX/7jFF6nOgNVAt54KpiCwogax4T4AR557DT/onoAEtBaUYpAosoE4KJLWjM9yiCJrA62EOofPmZxLbYYKEHKpQMkgVizL825gSSVWJFbKLN6jO0PKGW31qsSq5WNeF72fF862G7qzM777+msQoLPim2++4fb2BmMUQkiiT+ioORwOWGvZbre0TUv0nqOvOX9qDZ+UMZELpOQZh4X5sHB+tWcZqvNQKUMJR6y5pn+9Q441Wanre9RmS0q1IpJyzzKc6GyPlwmlDfuzc6IPSKkqeTcl/Gpj7RrL9vqSL778Cc5Y9psOjaSoBmFcPS5IBUKSYkJIyUYpojFc789RUvD0VHsUpVS4yDRNLNPMbrelREUumSwzUwBy5s2r1xAW+n6H27U43cBcKDJhrWS329Uyf+3nWGvBFKw2kHNtppWqHWmngtV7Zv8tJUeErHfwnBOiExRfe0PeL5U0lDIHmTnTlVHRtQ0yFz69vEJYRfSRMaYqce46QozEUMX1jbGVTK0tQug1qTrQtQ6rHdPiWVJlVDTtDtO0GG1wzqJs87KYcs6EMNLYFqsEm82G4zJytduiS0tIuR6Z1jFfyalqX1aeQJHPY0A+1r29XIKKY6vtQ0W1ZtX8YU9ErJOwmvjCL3+CX7h+EJsAFPQ6dhEiUYIltwqR8+phr9FKlEgpNVhCyYySGSkjQiREFohUTUSw5rRJKMxrQ0XCIhnGxHbT1EpjpWXlFJGxgJa8fnVFKYn7+3uOpyNFazSZIgS3tzf4ecHanuurV5W4U2ZyFEgryRmeno5oVYVH3aZDS8E8jXQzmK0jloTgiZzh7bv3jOOAHxY+ff0G2UmMf0u82uAah7GKrnGIWSDOCosPSCTT7BlSoMQq0/3yix+RcsT7mZKueXx45O3bt9zd3eOahu3+vIZmWo3WFqMMpnUUqSo51xjmJeDHSFEKYzJt09A1mpAEy1L9+zlBjnB4PKJ1TQ3OMlb5dgC9zGy3O4zpkPGE1IpN3yGVwsdEGhNCSJyzdbSYAtZInO2YpzrBmKc60C6ykHLVBEg1oYSqC04rSqnmIakVMobVQixXHUHAzpkjI/M80RiFsope9hQtOPgTPhfmJeB8IIdQreopr++fgk+glkgqgWk4UWKEjcAvHgq0rcOZpkqzmw4hq13Ztg0+1Ci9WAptq9n0PVppPnnzhleXV7ybBsIUIdUUpTWQECFF9UcoKEpQwgoKF7+4UuplgYAii5Yi5g8ag6JRlBpEksqL6OgvtAkIIb6g4sZfr8/hvyql/GMhxAXwPwA/Bv4M+A9LKQ8rgfgfA/8+VaP0B6WUf/Ebt4BV3WWtphRD12tymSnFYtoGHyLjFFDMxKhQaZ0L50JSmSTiGum0IpjXOfyzoKKUQplqU22IJw5HeHN1AY+esreUlFA2Y5XBp2qdfbivkdWv33xKv9lxPw7szrb44nCt4fb2tja4jKklIwKpNNY13D3e8/79Hdvtlk3f1ClTSpSnmRjWlBu35Y/f/xlLDHTOIluLtZYnv1CKZJkTTW8x0nIQI/FpodnvkTGy3e7IgF1HlFZCFoUSCyFn3O6M2UiiLjjVcnV+ydnurI4FhWZRkmmqiTjaOITUbLcNscmEWNFoCUE8CpKpSI0ahQ63N5WO9Pu///t1PBsDuSS8KHgRaVuLNYLiDfPimYYRZXTF7KeMzBklMgWNNVVKXLP7Mn5e6kYwz/gU8EsNcb15/4SRQN4Ro6+EnxQRRuPalhQhpEgsEj8vPBweSHNCiML11TXTuCBKwBMoJXO+2dKugBKAs+2Wd+/f86fffsXVckXWtlaMWvDl69c1kWg4oZRAScFms0fMilxq9dI0TW3IpoKQ8PB0wPtI3/ds+p5lCbx+84Y3uy23OfGzn/+8jvfXOz+yIGI9y5bMi1fgl27/5cOoby6V3lyPBWsc2mouSlRD2DOtiFWM9+feBKgHkH9QSvkXQogt8M+FEP8r8AfA/1ZK+UdCiH8I/EPgPwP+HhUr9teAfxv4L9fff+1VViWUkKKOupzD49ClusuUr3LbsNSzUikFRR0VakCL2j4pyHXu/oFIVHPcP7yYUlQRClIwqkyfCzlFDocn5tnTbPaV568kfb/heDgwDBOnaeIxZ/bn5zTCstvtWJaFktfGkhAYZbm/e+Tw8IBrOlzTMAwFv8wYo2lsyzwtnE4n4IHHh0f6vuXV9Se83r8GBWMcCT5gXU8pgofTiSFGtkZXe7TWNF1HDIkQfdX6W4eRkpA803AipchWOk66p3MtbdOhlKHtHEYbZFEEVQM8+qZHmZrrJwV0bc/iZ5YwAxGTFUUWci4cDgeOx0eMdgghaIzFdE2tmMqIcxpn3Ir/SqzMeFKqWo6YIYa8nrfBOksICl/Kh058CMRYU4tO44RuJOIYEWhCjJScKE2tEhxwSnWCkEqGkBjHmXmZEFJwc/cepRSt2SGTIIlaiZiuLlwhYBhOzPNMDh5iHcMu08y7u1u0ynx2sWfbvyFYg06Fx9MT8wLeLpymkfNQpevaVWYCRWBtw8Pdd3TWYZRhyBOmMRxkRijJNE4fJhsfYfWfT/8fGEM1j0DIVR34aw71JcfnN/t6FFhHhaX2xb4P2/jl61+FLPSWShGmlHIUQvwh8Bnw96nYMYD/GvjfqZvA3wf+m1JnE/+HEGIvhPhk/Ty/5mtkYvZoaWsUmLXVn19qf1Rrj1aSseRVYAMKiYqCRTzrqKveqojysuhL/uhVK2LdEGD2kWEY6Z1l8ROQ0TFxGCaKMmxtRyFQsuEwDHRtj1OabtvRNC3LtFBK5O72HoTg8s0rUg4VQZ4ztmlr5mEonOYTUpRKrUmZytqsoZp/9Xf+KhcXF/SbDrOz1d4aG/wSyGUgkVBK0BtL01Z8dVWKUceGc/3etVYIbTDKkULGjx5vI/vujIKgSEnT9zVsU2m0UjSqoWkaMol5mGtloBpsY2pwifdEWeisq0eB1aDjnAOqMCf4hRBWr77IiBwQoqkqwRJxXVtx3zEQl4ChMKbINE64FqQSL/NvqMpQUSRGK/IEVvdY6UlugyLXygdoV09GzpmGTBSlkp7ShJSG4/GRtt2y+2RPSpGlHLm86ClJEXxBF4M2hiUHpmlmmmeEgLPdjq7vOKWRKAtKOmQSaCVR2hGXhKIhhZGYI6fTwJhGznNPkYawRCR1g9HWMB5PeLdDG02IBSMMRhussdXrsH7zotRZvlAFlcVLRsaLK0Y8j0N/oUVQ4HuyIgWUgF6NhC8bh/xIYvwrrn+tnsAaQvJ3gf8TeP3Rwv6OelyAukH8/KMP+3p97DdsAvVuKYRE24C2PY2WSKFRUmB0HdkFH2qnVli0CaROoJdaolUAw1oGlVJNFS8vZv2LBJIolJK5eXjg+mJPTJEwBi73V+ycJUbPSRmssuQUOX/1huXpRLvpuTg/Y1wGjvcHHg736+hnQxw8KoFyCqRCao0wpmKulMGa+jymcWacTrRtw/X1NbvdjnmeOZ4GtNE0XYvSiizgcBzIpxNtU/Hay9zUaYTWSCNRQlQGnigsMbL4gEPSbHv6/Z4+XnB2fVktu1rV1lHObM72bHY7KJngPT54hrGSibtuRyccSigCcEqJ8eEenaHd9IgE+/3+hfeQUo0SkwpMqxFB880331BKZrPrKJOg3+1wTcs8B4ZhZJwmtKnJzsMwIpBoJWs6k7Xoouk7B1pwZgMlOOYhoETCWI0otear1UsixYKPA4Y9WMdnn/Xc3r7FasN++woVPSkKtGywcmGeBhbvCbE20pq2YQkB1zr2m7UXMwbOmj373R652eBzonUalghK8Ph0QJaEM4bpdCS9uiZ5TwbmMnHhdnzyxef8yb/8vzguJy6uL8lK42NlO3z6+af86R/9FEoVO5UiKFQtSRG1hK8hW9ViXKPJAKFWT0FBUOrIuKy6E/FBI9xSjcMvyz6XX1dEAP8am4AQYkPlB/6npZSD+Ki8KKUU8Ytm59/++V5yB5zRpLiQgiWYgCkRiqqeAQlZVZ+5VpIsa4CDktUPr9RKJJISKV6OWi9YttrsqZ7EsL66EsEUA90KFBnCCaEFVlhmHzBddZJlJDoVpGtQsqLFj9M9p2FEiMT5fk/fb8gJjDX0bQvWobTBh8TsPfvdFgQ8Ho48PTyQU+LsrAZ2Diue3IcAQhFiXjv1mnmZSWWi7fo1QMNwfDpi2wbjLNY62rZDypawzDBPzHHCzwmpPUiJ7VtsrJp0ayyubRBacphO6CxorGXTbVG2xbkeZxVaSpq2JedI9/jAwxxxraPvNuRUZ+VSFLquoe0M1pmaOBQjxdQ7skLRtS1hGPEhYrQhzJH72wemsPDJp68piUofEuCa+vORUpBUQtgGu/YhhvkEIq2mLEFOvqLDRKlpvqUQY53R5xX6Ya1FS0WnFFlU5V8ssdKfQ2DKM4fDI7ZtkUrhXFtZAtbiXEMIj4zjjOTE8aljOetxrUUZTdd1WOvw4xEtFc4qlmUiBVDKEkmMx3o0apuG4OuI2OqKFS8p8qMvv+BPf/rTKhnOqzBo7WuJXMfaz6d/+ZzIKcT6Pq6JSytJsL7HS/kQ2SfgyIfx4POlhPi1pMF/pU1ACGGoG8B/W0r5H9eH3z2X+UKIT4D36+PfAF989OGfr4997/o4d2DTuZLjQsRVm65PJEQVX+RUmfNkVAElCsqAbRWMmkUqhFLIVd8t1m5oSeVFZSVyrLPXVYWFVBQfmeeF/dmOrZSEFGh1S69MVWXHRAoZYWvSsR8Dh8cHREiYzuCMpWvaGpKiqnIxCEnvHDVLOaGKIoaEzzWrzjaW3XbL7uwcYx0hBlzbYZxDWkPT1vO7MQZjHYVIRLLdnuGsYR6mqo0oGWPcajoSOGsqu/4EIVbcdcoZa02trnSNShNK1oWcCtpZTNMgELRC41VmniesUaQyYhRY06CNxTYNm65lCWGlD6maC6Ar0julgPeQpsDV9RUqaHKzpvmSGMfIzftb7m/uaHtHo1xNYNam5v7JDMQX67VAvhiD/LJU+GzMKC0pydVEYQ/Fgi8eKVVtP+SKfXfO0kiHoI5ohaoCrlRy7VGIFUor6w3k+e4VfaDJdUqvlCGkzLzUvMQuNRTqONI5h5/q0c+LhJ88RUpa25NEZlxGpGrp+47NZktRCusMk0/MpyMxrXkH8MEKrySUmjEoS+HZEfO8cCthqJKOxfMgYL0Rp7qgeNFRrv3Aj2Dcf7HjwNrt/yfAH5ZS/ouP/ul/Bv4j4B+tv/9PHz3+nwgh/ntqQ/DpN/UD6jOsQgchF9SieBonTBRo7RClkEKsAsH57gAAIABJREFUM2IyUhS0UDihkbbadXUIdRYvJVLWcl8UUbuuElSpoSRFSJ6V1EIKbh/u2e02tG3L4+Mj/asepy2ncaq7blEs48Bwd6QogyyJbtvgjMbPiVM8EeT/1967xNiarnd9v+e9fZd1qdvu3b1Pd+ccgxDokEFiIYQEYhIpBCZOZkwShJCYECkMGBiYME0kMogURUoUJBJFQZGSKAyIFIgiIQY4QGJsE8vYmGPT7T6n962q1uW7vLcMnm/VrtPuPqeNZdfe6nqkrVq11qpd76rve5/3ufyf/1+4enKlpycQS0bnDBe12aj94PP1JduLnn6lBb95jnSrFW3X6Y1KxTUNxnk2lysupGcaZnavX1MrzDFx8d4V1loq0PY9Aswx4owWoy62ljjNHHY7rW6LoW07BBh2e6aD4ez8nMvLK4wP5FqZp5mpFKKqd4J4Xr56TTBwdn7F5dl7WlCrBecs3rU4b3n96hU3L27YrFZM08CcJvJh4urjZ/TSMosW7/b7a65fH/j+qx8wpZEP1k84Pz/HeEMzDRwmJSyZ5qgoy5yXyVFZ+AGElAppGhEriozM5e6sm+OMsTrMNR4GZJ7x1rPqO5y35Ag5F+Y0MZeMsUIISnhirMVZR46RmCPzmIjTjCAEpwS13nvtPKSZXIR5zoQQWPUrhEraT4SthaAEMKUoWGccB0RU37AsOIxh3PEb/+rXuLm9RgzUmLGI8kNUWXQ177UGZdGAOX2zVPnLUkN4g4xbqipVi+AnYJBlUXO6954vs68TCfxR4D8Efl5EfnZ57q+gm/9/EpE/B/waKkwK8HfQ9uCvoC3CP/vjfkGpME0R7ww2zaQ4IGIWj6c3Q8nqxcVUiqnIAoQIVgjWkK2mCckop18tKO+6nKjM66LnsBRcjDDHzFEinWuxUpmPO+zmkmmasBbEGOIhQckkDO+/d0Hf95pPx1uMDXSNxVrHMEzKURcUejvFTN93bM/OifPEuB8xe/Q9PjCVjBfLNGu3ozGaG1IiceyIRf8GiGV/1OLlZrtV9poxk7YJZy0lF2JRhiBbAaNaBKZU+tWGtmkZj3tyivi2w1izdI0qNWdySaRppu863FI4RVQfwDpVPZ6GI3MaOBxUUbgLvao/V5iHkdubGw77A7vdjtXlFWHdUJLj5vUt+8PIcX/Eu8D66YarDz/CLRFMKplwoo/HMOdpIUBV0hgDGGtIpRCCJRhPaDrScaBScMbgsoXGYo1hTpGA8jC0qwAYSsqUlJmHyOvb11hrePJ0q6f4pA7aGIWjT7XSpYx3Du8mrNXKfCmas2vHI+Mt2L7DeAsYsmQap2KnhylTYyaOe/IwEIzBrHpKrYzjkU++9zk3L58r4GIR1ZGiaQC1Uk/Ku/c2cwXsQhuWT3HLqVh4fyMZpdwTleti4g1Z+Y+yr9Md+Ad8NdLg3/mS91fgL3yN3/3mZ6gwR8aFRDQETymB4jQ8kliILJtEMs5UnBfNCYtljo5cYU6qWnSXUxXlCCwoF53UrOQtBTCW28PAy+tb+jOHd5bjfod3Ld57FZeomZhU7NT5SA1CLpVaPEjlME50LjDmkXmfCU3DMGnFvNaiGPY4cxwPvLp9Ca8zZ2cXnG3PCf2a3f5I2zQ4ZxhyxKRIiELTrZDsKXUm1oLJYFzhMOwIvsNUw7DfE5qT/BeUqmmODQ0yR0iZXJQss99sCX0HovPqh+GIT4GYC6UkpfM22sKb00zXKBBGTCV0LMxEiXmO3I4H4jFiNxv6LjMdB4bjhBHD1dUTVl1PRenaclLtiOBanl59wPryjP78nFQ0VYhTJh2VIrdtPMEb+lXHcBjIc8QtfW/nHa01OK+p38paKJWcErlktkEZnlprKGmmUjBi71qTwkycC3GY8av+TlQkRq09rdoVdqkTrRDWqw6PKBW4hWGM7HYj0zRixNGHShSPbRx9v2Z3e4M4w/a9K/KQePniB3z+6adcNobL7TmX2zeIz647Jx4PCmoTvW6GepfXl1O7RE7oQd0jZkn6RU6jxKDkI/ImElhcRkHblggKGjoVzL/C3g7EYK3kmpCofCjzPC8orqQ5Uq6UZV7ACQQnpOowVkhSaWIiZou1BZMU2aZJkWAqOnew/FEEjSaQwv5w4Pknz3Fz5oOLCxKG4bDn7OwJ+/01cawkUSBTiplxuGEqMzMBGyvDPOOjqgt17YoqGp5aG6BUXt9c8/rmJdPtgf1uRzFKpeVdg/MdYgVjDSEYDodItUIePetSsVkdiUHog8M0luPhQA6Vpmm4vT0QfMfZqiNbt0hZVYzRkV6Fv2qnIbiWpllRrUp+pxQxVjXwrDHQwe3NDWapNwTvcc6S4kzbGKwXcmk5u2hIs+doXtOWTDKC7xqwhouLS86eXNGuNuQyE2Liww8/glI5HAfmaSasWyWKTjPGCFOcGOYj4lXG/cnFFd4d+fXbX6cWFZa1VlGOEElZCVeCiNKcTTOxJB3zzspdMM8zMidKURy+DQafAsYKV+6p1gmCVyzKNDGMA0ZUYXkeM1hDGwKmCDFrajDPM4fDnpQSXegwxuK9IVgtzt7cPMcdj4gYLJUXL1/xL3/11+D9M1ar59jthv6DpzjrOH//grm+yfTr3ZEuC3Lw1ODSG1YWeHA+1btEI91ST1xkllM5vIqnpkXSTOppbOPH2tvhBDjB/SumFEpOSkcNmCIkq9RjGj0JRhQgBOCSwTqDcxkrmiv+0GevGg2oc12KQEaDKjGG290eMXCx2YCYRVAEjBFimvQkNonGrjFZJc1HLGdhxWXXa+2iOC3qWKukI9ZQTSVNkf3+lvFmxzxHkiRubm/o+w2hVcxBypFKQJa8vvUtZawYB943WG/wxmC9p2vWlJIZjgNzOtK3BScRfIt3Db5xnDh3mrZlvdkowUlKFFsXQhStgDunMO00J4p1SwFRuyLjcICScKUy2YjYlopW1pG6RB5Rb0FjWW9WnF2c471nGDV1qSgCVLCYRTBUjAKH8qK7HdqGMYa7jVCNioLWoqAanNUuTSkUCvv9gVoqyTtaoxRlaspFmWpBxYwNEVj7jlJmQlPxXjUi5nnSuoMRfNMwjrNSkFctLBtZNCCckJZUTVmLE6t1q+22pI7fGKMnrRjGcSJOkVILu90OkcyUJ57fvGB1fcnHRgfZpG0UXFa440AUDAbBCsx3J7YsugSyqC/z5t8drKje+8ebg07efK+uYvEuJwn0L9jb4QTqAvXNQrFVTypRkRGD0e6A6CgutWBEZcukgBPtmYuo4o/yLCg3/UnUmKyQ4lOoVRGqqMhEpXI4Tvz6Zz/gWx88Y9UEjKm0Xcs4wXTYUalYY8jJYgycecd2tcIJ7F7d8urlC6zzdKsVWHOHAJuGgd3NLdNwxFlDu17hrGe/vwFgszmHWvBY2nbFPM9cH27Y1QHjDW1jySVxEMOz1XrhycuUVDBkvA/4bs1msyUXZZYaRRY9vqppCVlZfJPmlFJV4OVQC4aTFLhuPGXfbTjsDzq9WTL5+UjTrgnBL2KxlZoKL65fanU9ZcVFFKivEmwqGEOm8ulnnxFCg/NeN51XMVEWavFuvUaMVc2+rIM80xRxoVFKtpKJ+0jJGeNVeEO8w1qLtZVsPI3V6cYJYXO1IQ/1rkPkvWMcJsiB7rxjHpVp+SRdz3JPjeNCG1cycRpxrqFzFmkCfd9xOOz55NMXPH1vy+X55cK7GHGt43AciCkhsyHPicNx4MWrPd46vvPt7+C6llXfaQs1F3ZpZDgs0uyNoU56P5alliVoeidm0SQWQap509niPgBQuE85Wim6111ZZMy+nr0VTqBSIRWS6IanaB4eJCtzFUo4qpu90aEho3HTqX9M9VQXoZ1ggrqQL+ifwiwaLplS/YI61IKUc45YEp+/2tOudly+/4Q0F6yz2HWglx6omLUQnMGYhhBO4pRHYp7Z3R4oBtZxBqOeu7GG+XjkeBypJbNqN6z6FcZ15Kky2ULfZeZpILYrgngOhwPDMGHxVFfpvCPnRDSG65TYbNaKGrSFPmzoVj1tv2Z1uSUPiXmhWgtimKaJ8ThAKdhgl3BZcGKZ48wwjqxWG9Z9qy3TJRWwraHcLkAegdevX+HMNWfbDW3b0jQNplb2ux0Uw3i85fziknkYMdmxDVua1jPMlRfPX1LR6EOAOlWiSUuPq6pjdC2SYZdnao7kNN1z5DCNkxbefEPfdswpUarH2kCt4ILTXTFk2k3LlCNNUlhzCJYY7YJGXVM6jdjuuupLHh6TtqTFCPM04H2HD5bGtKz7NWmeEQrH/ZGLswu6Xp0AtTKPI8NxpGtanbEAas00Xc93Pv4OYbOiWTUayUbDcdaU0FiLyUbHiZee34lByIh2DE7b2y5tzHpvx9y/u988rkomkO89s6TAqlH2r6lK/LtmGRp/Ct0rlkopFqymOEoyCnaZ6Ct3sdEyIFRV/4/EIsWC9lTJKiCxvPs0bVWXfEyqtlxKLXz/+Qu22w2Xl0/YjxNmaYsZo/JccShkjpRcsNZzOB6IZSbNE7Mz+ONIFcE4FVDBeYxLdK5lvV4rf12jrcxSE71twTVMcaAvgYvNGete58t3+x0CXF5cLToLKmOtgy8QbHOnhzgNMwmDAzbOKuGna8jZE9NCvXU4IKJMQwDWac1ArMN6R9v1mLalaywvUyYvMxXjONF4zziMtE0L6KDUk6v3GG4HaonqGIzBBGWxMOLwXthuVdOxa9dYWxnGgXEclfZ96frkoiKnZY7UoqdfyQo+oqIdjDniLlc4EYoxVAFjHC5FfOOx1tB3DsEwjwdiVKHUnCfa0HO0I3E6gtVJw2meWJWiStHGEpbCnGBIqTCliSKLAE7NNE3g4mxL752CrpoG5wawQkyRcRww8gHOWELb4r3Qe5U977YbrA80tWKckGrlRDwiSYt6J21dRFNirQXoPSrInQzbV5bngSqFWgVzqn/dvXD6z38bqsS/aybCHCq2qt6cqwVKxIjHGqO4crFU8sKpnshFtQqVdkt59GuEWt4US8DctUgLoiGTCF4MSRypZqrR1sshzvzi977HHwwtoekwXuiuevbXR6SAbxps1Z+/vXnNOE3EaaJQ2TQdfdNT7aKnCOQUcVUvynGYaPo1XWiXELyQasRMEKfC3GTWmxXbtqPpWt5LTxkOt2Asbac5PEadSxMarEs4H4gpYQ+e2mh04xbAjbWwWnmcu4CqSL7DMDLPE5ILWEWwTTGSBy3c7ceR1TEhYnjy5D1KKXR9hzOWtmvo+m7hHzDc3l6zXp3x8fpbWKvOoVIJfYsJDS5mzs/PNL83gg8ecYZCZI4DJTdIVRDP9WFHTBVr3TI3Yqk5L6egxRtHMDpbAYY8KSKy6Xs8MO0ibhWYZx1DHmNkHGeGY8V2hm7lSDERqmXMR1JtSDHhvOIAQtNiqiwQ64CzDklatF2vVpxt1lgL2/WaplXEJkYYpwljFUV43L3m1fPPefbtn2C92RCvrxVn0DbEYsA6UjowDbOmIdJAmdR5VrlrDVbR708p5RseQTjtbj3sq87Jymm4SIunJ1LUE1D49LpZuAu/zN4OJ7AUQ3IEYzPg7k73SiAZCMvHOvGF6OmdKUaFKEutOq4KnDgFEO7mMCoVi9PZgQLFZi268Cazsrkyx8z3P3nJx7/3I4wPOOPJqegADR2mMeSc7liHUs7LBump3kJS1ticwVtPbcBYz2Z7Trfe0rSOklSpZr1ZE0JgOAyYagldS2gazTHFEnNmqgNmNkBgswlYuyD/qlKq1VLhzOKzplSlVMWgU1VUc7mpnXWETjUE8iKW6r1fFHQGRITGdRg7s7pacd73hOBVajtlMomSkkZqSXBNj7OGpu8QUWGNXAoJUTmtWpX6u2ZyHsmphVrxvlkiMCENmeNuYL8/LmAvRwgtTdsp0YcR+s0aJxoFFRFqitQFAlxQmHP0ysocY6TvLzgOE7v9LbKyHG8SZXRcbs+YjhFj/EIZXilpSZ/altAEhoPyD4bQ4IyhxKSCsyFwfLlnuznHNjoWjUCKE1IqZ6uGdXC4bAhdQ9+vmJ6/IDSBUgScwXhP2zd0zkMtFFdUTaiWBdTDnaiMOglNYdUJnPaJfnEEKukurZGFbPR+BHAf1n/vR7/U3gonIG+OamVDqWj6slm8WGGhYVoKUyhpSK2WmpabX297wGhUUCv1jru9Lrr1kSpuSQcWmGYGJFFDUER2Knz2+jnd6zXPnj1jjhmZhWLBthYMHI8KGa0on0AphcPuBtu2WhU3yjwkxmCajrOzM54+farsuk5L47UUVpsVFUtnLRIMdRL8uuHV9fepCHGcGaeRsQ4YET788ENCUP3CmCq1zjo8kjOI1Zw2xYXDELCGmLPi/Y0lGIdtLTVoSFxyYY7Kn9C2LauVx5o1KUfWIWDaBhHNr8d54vr6WiMyC50RoqAYezGkMZJtoVghSKYMevLYkilFsK4u+gyCFUsuhd1hxyeffkLMma71iFiatsf5gVzK4jzPOFIJ3jDFiZIhdW9u6VQKvnhmMy+dgURwnmQrXd/BcWIeJ+yFY17EaZxR+HRKhVQryep49nA4AkZHydvA4BKRjCsF0xtyTVAMFHWEpegcRS2Vi/cv2bZrUi2s1o79rECnqSSa0GKNo+vO2bQdYg01xeWQqopsXVp/aied4pMGoVCTvgdTiaR7r546AJXqBZdQNu57e6vWSP2ibvk9eyucwMnVaVhfqIcddXt21y+tpVCsUbEGfSe5FKaYGOfIOCbinMglaaW73qsTLBCK0+hFXfDZCpE1S5HKoxTFQjKWpm14eXNLv9rw8bc+pHUduarazTCMgBCnYRGSUJlt7wOysAa783NWmy1BLH7VcnH5Put+jUVYb9ZsthsNkZ2jAilG5aNLe46fDxyHga7vsM7zZLWm1kLfd9xeH1g9a2i7jlo9MSYdlV0QedY5xKnjm1MkR521h4bgA8ZbpuOoGos5glWdwN52y7SizgSQKofdwJBnmk7lvF0xbFZrfd0KOZ0Rjy9JUiEXHZOWyvX1NZWCNYZpnHC2Yb1u6bpCyoV5nu8QoN//7Afc3uz44OMP2ax7djc37IZb2tWGZ21HLYWXr18zjwNI5cWLz+i6DZt+RSkT4HDWU3sh1MAcM+vVmhQLc8nMU6YirNtAKhmsphdiDL5RifFxmqhZcQWmcWTRqDKElnHMTENmdWbogufF9Su8D1xdXOH7wOFmYNjf8uTsfVoajHhqKWwvrrjdfqq0aV2jqe40U6Ty/mqt9/FJQyPXO10AEKrRaVdboLBMB6KMxPqWZez4CwUCAaRqkdFdVfJrWdLihIhD2kr+ChWit8QJvLFaK7XrlBqsalrggKFUwqkvXAu5ZOUEnAtpyuQ8L7Ja3BudPJUD32REd0UW7vO0Zd4ALLVXPQwDz/d7vpUKbdeocs0wMc/zHfFFqRXjFOgS2paCsA0NXb9SSG8RGtew7nsuLy9wPtB4rznkcGAYx4UjQOsWQy2UceBwOLDerFm3Z3StW+bT7V1v+oR58EmFSg6HA31fabzmz1l0xj9GZdsttZJKppbENA/sDwcMRavzvceJFv60vlK4HY54brF1jeQNsWZaaUEidYJsMm3jmfeV/XCg8Q7vG7omUEvlOBx5tX/JcDiy3Z7Rt5Y0QDZWobbOkA8TzjmePn3Kt559BCLsd0fmOdGtHEYa5lHJWoyxWAfWOUpRpqMQ+uV5qwzMvqGKodv2TFPCH48cxqOyGjvt6+chISuDX7sFE2GYcmIcB8ZpZp4TTaMpVYoJqQPHcc9q1YJ3HF7uabseJNG0DWZnuD4e+TYLv4WZSPlIqZXNdgNGcSfVGGKKVGd4+vQD1v2K/ahtxNYIUyw4EfIy3HSiAVCsa1mKhG8AwHd1ANQZLNUA/TkRyu2po6jcGyL2R9UF3xIncK/yWbaCJLfkiDM5N8xSsCIKjdUEn5yFlAuJTLGZPBvlwCtlSQsWFtqTI6gZilnaT5XWGOZ7dM2StYAVRDkFMsI5cGgKbdsQrKfEQqo6nTdPBhGLa1od5jGadzf9CsQSs3YcxCrevelXWFRD77PPvs9uf0vfBLbnl/T9mpvjntvDnjwNeK+qx77z4I2G2F3EuR4WKrE8G8a0o4ohZxhzxcSouXQuSDJYUYgxFXIqTLEyZQUN1VK43U3MY+X8fEMtmcN+z3GeiSnSGktXYLd7qco+qwMNgZIr85TYdB3VGXAZ06xwTYvHcfX+e8jLl0zDgZv5VqnCYiW0ga7tlKtgnCg2sT3fIoKSbsSsqBDrcKah1ETMM6YafHWEcuTJ1Qbr22UAyGkXInSkwwTOse6vMBh849muOo7HA8c00rsW76CsDBjLfPCsLhzeB1a1Mh0PC/WZwVTVBDRSaEODLUCqlGyYjhErEzlWgves+xWb1mKMZ6yCz+DnjJknmvMNVVqsbZQANA7MY0JkoD07Zx8VpDRjyUkojUJ9TTZ6AJqq/T0WZNVvkhGqy9FVmamLTsES8p+6Y6P20GhO/9eX21vhBEQUPGKq4exoyI0stOI6ilqKWZBTQCnkUkmpEktWR5CrphLYO6oyYBFuX8BSnAAXnkJkKAvB44mFqC6zCfWINQGxljTPtPZM58fHiWEeWHctczRMk4aTw/FIExr6VYcNLfhAcB6pghhD03YYZ4hpZiwWVytdJ7TdJRhHFbi9vaVkxRbIymBsw2G/x2BYuxVTnDFHx4cfbgiNwxhDqpE5WuZZT/tgDYSKr4FMQnIkjgeojlY6BRZZizeW6+OBaRxw1jKS2A86tW6txeRCGkY+O9wgWbi6uuKYjsiLSnOxZWsbxAi7wx7xjrPwZJFZdxjvMNZwcXFBay1t39N1PW27YrNZU2phmieGEolAaBpqycuQTmGaJqgJazum46ylDhGoMyXPrNdrmnVPs+2pM4xUWoTqHCllSjlg4woEijjef/99DocD0+2tQm3ReX7ntSAYE+TsWYUVB9kxmhHjLb4LFFF0qFiFXpei3YvVaq1DYjHRtg0ff/AR/WrN9TSRmsqz1VM+/uhjnssrajCkGJfis6HkiqBU62RVT865gNd7UxCwQrVWpdoXbIsWUrmr+J/2jD3Fu3UBD9cFByCa/YeeZfiNJS38cns7nAAsY8CGQSzhS5qihUIqGVNV5z2jRT2pVYd6qqjDq5Vx6bViFtFNWioBZI9ZNN9qAk7UzneOoFLICg6pR169MpQ8UWmIUdOAeRzZ3d5qH18s/faM7dkZTddRMozHI7bVdmHTtjRBB4SsFdI8URDOL5/Qdi3X19eUXIkl0XWO7dklMUaev/gBOWfapuPUTz8ed8T4VDHxJmGqXdSNzB0GoEqgW/c4o+IledECTCkjRslFnHNazPSJIJ40KIagW8RYa62M00BJSotda6VOldAFvnV+xUkEVHP7yG63p45atAo14LwlpkSmcnZ2hjMOUxX9l2pirjNxmjBVqdMyhrJgEmopeN9iraM772BvGQ/jcm84FUdBKEewTmnGnHPkVDkJxW7e35CuE7Ve0/drqJW435NFKdlzLhSUkt3ZwjwfEVsJ60CTWp08naE6JW3dGAjV4IIyO3V9h0KnK/McF05FQ7fxNKklpoJvAs5pjWmeJ6z3zJN2SyyZFBPeV20h4+7UjkWEmlUN2lHeEIXcfb3XKcBhc6YYDzJTFtZilkMwACpcanWT5zcO5Iv2VjgBOKkPG8y91oaeAiiQyOjQxOnErlWZguLpBEeYpDL9cJ9kqQQ4dKq53CUIC6+Tpg6nIGFxIpCp0RKtFtf2+1vSNHN9/YopDczL1JwPnsurK66evEc+aRrESLCWpm/ZrNb4pqFgtMtQhfV2w2qzoXGBsRk5DgPGgF91hNsAbVXOvlrZ7XZszzaMttBGz1xnNnZNXwqvpgPGBG1txSPMM0kco51wVlWbu86Qq+eQElOM9MZhvefs/JxxUG76MY3UqqrHvmmwXjny1tsNr168wgA+hIVfsFXglLXa2hwGjBkI5x7rPbFUJBbmaSanRNf3izMqHIejOt+KEsqKx9oMCwMwAm3T4ryn7xusW7OXI+PtSNsGbFHQ1hgLzhXCsoau60ixUESBRaEJOKfQYu89aQHtrNtzJjMwzBFjHNaYu4GcmivBdaxW8xJcq7ZfF4LObThwRunf/HnAWINJll2sGEnkWvHZ0296Yk7Yrb0b3vHeYxvPi+9/ysvXL7h470rnH4pH2YIXhCBJUZucdAj10Vdbpli4q/jfJxQ1IPf1Bn7EBOFpdzy4iYB37m5ASIyA00DILSfcyHTHtlIFclXSimwLJUAZVbLK2Lw4CVR7gAqy/+Hfh04jxgoQWWfDzlj9Q84V3AbXFLqu4fnLz/ng33jKZrPi89zAdKQJgZQL680ZbdNyc3NLqYXt2TkffvQxZ2fn+AXymqyBGHFdRwmQcmR3fc11rcoFMEfifs9vjDe00hJy4A9897ucb7eUeWYOhQtaQg/TfqL3PaNvmPYD87ynaRq6ZsXhcI2N0JZMTL3q23mHZSYMim0w58qI1LaecQzMw4Sx9o7IA1Bp9o06qvV6S4wqluoaYc6JsPWEWfvsTdvy3vYj3KaQp8xxPPL585ccD3s2XrsJ46AVqe5svfTWExbLbh5JraEzPaY4muAJ3pFiJpWJlGb87AmtJzSBtHuBc1fInOj7nr47V2dhnSIfUQc+DIOmFbAgEjNUZeuZppk4J/qwIjRK/BqCp7hMygafA6XoiPHGrdhstgTnGdMtyfZ89O1v07WeHDLPf+MV21XAG+jXGzbNmvXZGTFYzAg+NOr4rOBRyfhf+/V/wT/6f/+JnvoV7hSGq2JJDAYRt7QAC6YWtOVdl4NKfpg813E3I1CrxrzACXDwxrIH8w4gBk1rYObNLECawHVUW5E64ysMoJiIqVCtDrKUfdHv66L1bhRHUJI6gjskkBIKwPLUG1EnwxHhgsprRGHKYUIkkCUyjiOHV69G99cxAAAIiUlEQVSZ5UgcXxPHCec8rmlpug4RVcPxwS10X4rE28+JmCJk4XA8cjgeWW+3WFkpruBwoKSsuVrTUG53vBpVq8B98IwudLTnl4yLUrAzSn6htFuZtm0B8D4AQghrBCFGj3d1geA2pGKxbcSbNwXCWgrkHlX91r/EcD0Qu8jczJydnSlV9z1ufoowx4n8IiO94VWsrFPCdhVjG3LKVFSxJ8bEYB03h2tYhqNqmRCbsK4n5URrLb1b0zSBlBPTMGHqiU1oIdM03Mm/51Jo25YxH5dTXlObSNIOgsidIMqpc3N9PFJ2O3LOxJKZJ03HqtfQ2wZhSANl1tPSWoepSoKa0oF5rrTtFaWsyTcT/pkhNIacDc4GjYqMAWtIXpQqrVdhEt942rZjTDq1uNlu+f2//99k9dmnzOlntJUnBkmAEyqZTFGMi3KFo1zbp4r5l5zm6atAQF4ZrRxMAsEm3onZAYkVxCy8aRXVXdUcKUpZxFQseAFzDcmDq9BAGbSVYi04qynFZCpxWlopp1j/S3+xogZf38ulsgFKZrwemN8fOQwtndXC2X6euT5MfPcPfkjX9ZQKq1VHu2lpW6Xj3u/3HA5HnYl3grUO23ZQZaHGhn1RwJFdcsGLi4tljDmxMoZxGGg6pQXPOS9FIeXds0suvF6vcWKIYyabrKSVy3tKUSyFdRYxliKGKSaYhoVRKGN7ra9M48RYBuxgCTkoNXbnlxPIUHOkDJZqhDhFRjOyITFOE6UYtkbbbpu85bA/Ml1PTBLZ73VISeXjLZYWY/WaOOdoQsDZgLWZEjPZe2xOmOrIc1w2tlGQV1Fwj3ON1jYW1J8pSjhbFrhsjJFaDd45yqBsQUpKa2m7jlQcbdNSciXNmXkwlFQoqZByxnqLEMk5KC8EVWcM+kR0hpiFZCur9ZpxipAKxnus21CBfUpsnaNxAessm3aLOEPnA13f49pWc/VSCFLJplEwz5Lun2YGjAgiRouZ9YQcvhfynx7fs1Ph0BCRZWfr/N0lwquv3HtvjxMQQxKjRcEiiIe21pMm6/Ku07R8j85gJtJc7nJ5Hzz92gEVbmfKpKysQVTR+YdQVPAGU9yYhahdswEZs5KcGMcnn/wq8/wRH37wjG7daXjoPRcXF2At4zDy8rPP+SC9xw/MRCkVZ3Vare9XrNeXrDdrSinsbq+J80jbtszThCkFDKy3F3Rdx3q9JqXE8/2RvmhU1/XtchorxbTUQvYFkwzSiW7aVaDNlhQhpiPUQk2Z+QDOggsNiYzkzOH2lv3NHt90XKxXpJI4jgPX19eklNhuNpSuYz1mjvv9opNoGecRayznTWWeYDgeGccjmUzKM9455jljauH82RnesqQaql/onMMUo9JjYrBVIAkV5Shw3uKTpZeeYdxhraVpg2oexMJqtaIJLcNRx4GdV4RhjCNmbbDDQiM3C6VXBarp+FKpyl3g7GzDar1inGeGoWAFnAPvM3NRzoVxnLCm0q86vG8wRp1yzpGUMi4FqqtMt3tyVdXn8bjnolzgXKTvz5jmI68Or9gfDjqsZQWxlma1oulbokUHmTCMkvGSEBPQaqTWAUTecCj+0O2/PDZUChGiYi7e0AuYxYmcjgyw+B/pAADk6+iX/06biDxH9ZRePPRafhv2hHd7/fDuf4Z3ff3wO/sZvl1rfe+LT74VTgBARP5xrfUPPfQ6/nXtXV8/vPuf4V1fPzzMZ/hRPYhHe7RH+wbYoxN4tEf7htvb5AT+64dewG/T3vX1w7v/Gd719cMDfIa3pibwaI/2aA9jb1Mk8GiP9mgPYA/uBETk3xORXxKRXxGRn37o9XxdE5HvicjPi8jPisg/Xp67FJG/KyK/vHy9eOh13jcR+Rsi8rmI/MK95750zaL2XyzX5edE5CcfbuV3a/2y9f81Efl0uQ4/KyJ/6t5rf3lZ/y+JyJ94mFW/MRH5WET+LxH5/0Tkn4nIf7I8/7DX4A0Lz+/+PxTQ9C+A3wME4J8C333INf0W1v494MkXnvvPgJ9eHv808J8+9Dq/sL4/Dvwk8As/bs2onuT/jmJO/gjwM2/p+v8a8Je+5L3fXe6nBviJ5T6zD7z+Z8BPLo83wD9f1vmg1+ChI4E/DPxKrfVXa60z8LeAn3rgNf127KeAv7k8/pvAv/+Aa/lNVmv9+/Cb4GNfteafAv67qvYPgfNFgv7B7CvW/1X2U8DfqrVOtdZ/iQrk/uHfscV9Dau1flZr/X+WxzvgF4EPeeBr8NBO4EPgX937/pPluXfBKvB/iMg/EZE/vzz3fn0jw/594P2HWdpvyb5qze/StfmPl3D5b9xLwd7q9YvId4B/G/gZHvgaPLQTeJftj9VafxL4k8BfEJE/fv/FqvHcO9V6eRfXDPxXwO8F/i3gM+CvP+xyfryJyBr4n4G/WGu9vf/aQ1yDh3YCnwIf3/v+o+W5t95qrZ8uXz8H/lc01PzBKVxbvn7+cCv82vZVa34nrk2t9Qe11lx1/vi/4U3I/1auX0Q86gD+h1rr/7I8/aDX4KGdwD8Cfp+I/ISIBOBPA3/7gdf0Y01EViKyOT0G/l3gF9C1/5nlbX8G+N8eZoW/JfuqNf9t4D9aKtR/BLi5F7K+NfaFHPk/QK8D6Pr/tIg0IvITwO8D/u/f7fXdN1Hmlv8W+MVa639+76WHvQYPWS29VwH952j19q8+9Hq+5pp/D1p5/qfAPzutG7gC/k/gl4G/B1w+9Fq/sO7/EQ2ZI5pf/rmvWjNakf4vl+vy88AfekvX/98v6/u5ZdM8u/f+v7qs/5eAP/kWrP+PoaH+zwE/u/z7Uw99DR4Rg4/2aN9we+h04NEe7dEe2B6dwKM92jfcHp3Aoz3aN9wencCjPdo33B6dwKM92jfcHp3Aoz3aN9wencCjPdo33B6dwKM92jfc/n+MbpH77Mn26gAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "HopSkipJump: 100%|██████████| 1/1 [02:20<00:00, 140.56s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 150. L2 error 797.6606 and class label 356.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "HopSkipJump: 100%|██████████| 1/1 [02:24<00:00, 144.22s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 160. L2 error 753.5747 and class label 356.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "HopSkipJump: 100%|██████████| 1/1 [02:28<00:00, 148.94s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 170. L2 error 716.2186 and class label 356.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "HopSkipJump: 100%|██████████| 1/1 [02:36<00:00, 156.52s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 180. L2 error 680.78467 and class label 356.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "HopSkipJump: 100%|██████████| 1/1 [02:34<00:00, 154.01s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 190. L2 error 653.2177 and class label 356.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "attack = HopSkipJump(classifier=classifier, targeted=False, max_iter=0, max_eval=1000, init_eval=10)\n",
+    "iter_step = 10\n",
+    "x_adv = None\n",
+    "mask = np.random.binomial(n=1, p=0.1, size=np.prod(target_image.shape))\n",
+    "mask = mask.reshape(target_image.shape)\n",
+    "for i in range(20):\n",
+    "    x_adv = attack.generate(x=np.array([target_image]), x_adv_init=x_adv, resume=True, mask=mask)\n",
+    "\n",
+    "    #clear_output()\n",
+    "    print(\"Adversarial image at step %d.\" % (i * iter_step), \"L2 error\", \n",
+    "          np.linalg.norm(np.reshape(x_adv[0] - target_image, [-1])),\n",
+    "          \"and class label %d.\" % np.argmax(classifier.predict(x_adv)[0]))\n",
+    "    plt.imshow(x_adv[0].astype(np.uint))\n",
+    "    plt.show(block=False)\n",
+    "    \n",
+    "    attack.max_iter = iter_step"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "# HopSkipJump Targeted Attack"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Without Masking"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "HopSkipJump: 100%|██████████| 1/1 [00:00<00:00, 1834.78it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 0. L2 error 44399.297 and class label 866.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "HopSkipJump: 100%|██████████| 1/1 [00:38<00:00, 38.73s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 10. L2 error 15533.123 and class label 866.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQEAAAD8CAYAAAB3lxGOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAgAElEQVR4nOy8ya50yZqm9XzWrsbdd/M3EeecyqzMEgwZMACuAIkZU+ACasQFcC01YMwNUBITJgxrTKEqiiQr85yMiL/Z27vVWPsx2FEoJQippKyjDCn/R+6D5b7czZaZve8yffbZElXlG9/4xj9czN93Bb7xjW/8/fLNBL7xjX/gfDOBb3zjHzjfTOAb3/gHzjcT+MY3/oHzzQS+8Y1/4PzRTEBE/isR+Vci8m9E5H/4Y5XzjW984++G/DHyBETEAv8a+C+B3wP/AvhvVfVf/gcv7Bvf+MbfiT/WTOA/B/6Nqv6FqmbgfwL+6z9SWd/4xjf+Drg/0v/+Dvjrv3X8e+C/+MVKeK/DOCGANKEKoIqxgkpDqtAErAgYxXRQFINBATUgvaHNoGLAgBGlK2AF6Yp0wCiiShbBK0Cnq8UKdKNUFF8FtZ3eLCIVaywVsE0QU2lYUItoR40iRkEtqiDKW33VoAIoGASk00VAOxbhrUClN8AIxhhaqxh14JSuireCSke7oNUiTlFVnAA/X3830FQwoiAWS0cxWFG0v70rggjw736ngkEwAmo6XQXpnS76c7tZjCj157qbJrw1nUFdpfeOGoM2izUF4wSqoOatzzogHQyGTkf0rWxVQaTD2wsx0MW8fQ9IFxDo0jD61u/aDcrb9Yl2qrFI7RhrKL3j5K1uYDBaUQSLUGzH4NHWMbai1dLM23UigqJYVVQ6qOXf3QvF/FwZ1Z/r8nP/8Na3vYExFtH61p9dacBghY7BohQUKw5UqYAzimrHNUezCu1trBka6Nv4bUbfxkm3iK1vbdF5K0OUt6EqiAVBELV0Kp2OEUFUUTWoAtp/btefG9p0xFq0K8v99kVVP/x/9PfvLev/wIjIPwX+KYB3jv/kP/3PqNcr1gTa9IFgfkC2yI5BY6UnpY0jcbb4V0WCQ9ZCbYLzGayhdU9RmNQx2IocO/kF7uPORGULEz6PyOUV9ydA9aTrTtkj7bRycI+s28Dzx4XLX0TcYULtV0x/YH34zPs0kmzka+08hSuSDbsKmIroEzZZTG9Yc0WHRllA04h/qqz1im4GMY6UC++niHn8wL53CAbTN3xbqXlCrOHjFLltZ4p9JB3hnY30T2COlrWecUF4bieWfuW8DcxPnobDm8Q9N8w8cGx3JBXQI73eUVPJarASOerAnjJLbQxuwPXOZVgJC5jZUF8i5tSZ8wuJARsPNL8zT5b1esG/e+a8Ok4RXs5nnsyRVnb0XeeLZN7dRw6jkG4DJTmKSzzMkTEYXpZE21ciAyUcmQXEbCz2QipKiQ+EupN2R3CFoXYKK1u2RP/ApW88rJGb3TnpiXO8cDCdWQx1VPbPgkjla3jHd8MrvUXOrmPulclYGsLhceL+JSPsHE9Cqg0MpByZyw7OkcyEBOVQz3w9PmJ8wX3dGOuB8lR5Xo/8MMJ423g6dv5GLKEaHmNi6zOyNdzvvrD8HwdO/zhwvzS8wucEc4g8lsomR67jXzIx0RE0K5gIqgyDohTKlrHuxE2VWEdO/sa9WazxxNExVcf96YbWhfpX79kf7hy+er4OBtqF6GdiMfyv/9v//G///7T4xzKBPwB/8reO/9HPn/2/qOo/A/4ZgPVezd7ZUaQ/YLYf8I8zi0L+anjoHauetcD9y5tTzn5lP2VMdgzRvd0t9kzUyuY9N/XY+0DiE/OknFvAhI19axzmgfrJ4eyVMnRsfOQ7JkofMPwN5lpIxweUzxxf/wnbu9+jZSCZO7cVxnFjv8KoifF4ot8PRN+4uUYT0HFAS6N75cF4oo3UdeMmnxmrIJ9nyiHilwv9+weiGOi/gXDGqcUOF5Zs6PNHphzx94VxvrK8f0f6acGeHjiZ3/MTE2yWyTSMhbFsiFOciYS0YGrkXgeyFgYmmhGyVlx1ZGn0oESnMC+st471SvaRgUh8vPGVyjb/DlkyY6xovfOSI7a954fPjT+xX/ixPzPlB/yDkMeBeFO+UyE7uOwzcKEeTlg7UcvOTS0pRLpzKAWfr7R2x2nFyCOmVWZZcK7hbeN2t9jTyq15CJ2qn5nzgdtpI5bO6v+CIX9kZWGrJ9ZX5SMTYUqciuf1c2B+l5mT0PBkq7Stcfu0orPF90LfA304ULtgZOXFdozPeDcTRsO+vOf4YsmzR8cZ9AVTlR/9wNAKQ3zk0u4MJTH1A+mgbOeVWS2P//ZPMb+7sGnnySilVU5eCC1RnjI2F55lIvuK7fZtUqI7oT6yJ0NyYFqnt8R4KtxzZ0rCOI54P7GXK/vwwut5JvSRHkZ4ufLyHPE1YQusx0T9cftFsf6xTOBfAP+xiPz5z+L/b4D/7pdONtbSuiJ94JB/JNkDy2ZALcPDJ6Q9c2sbIRdifI/Lndo7rkW8VtJmkb5gzEgwJ4QrbqmIgcdj5L56HoJQ9h1bbuzTgVF3zsPGkYDtf80nP/Eh3Un9I9tlYRxGwkFY6p02OB7XxL6tjGVAeMKFiby8Us83zPyOnA1b3dByQxfPbGYGP3BT4XwvXFNjWyNrgzT+xFJeoQQ+njZ298RjuEELlF6RlMmHAfPpjD044mi45sB0/Yn6fGTOV7ZdOWogh1fECOs+I6+KO83kZ+WyVA5kRAbGVUhU7N44moAD6gjNFnJr6GrY64CxG2Np5LjTk+EYRw7+TPGZfk+U+RknmdCUBzeRfeGdROx8gQXcpGy2MpcZZxLGFdYekL5y3C15NGQSwz2ge8KGzkonjZbcHJsKD+Jwi9AWBzGD7Vz3IwSYdsd9apQOH8vI+XVBDn9Gt5aHCvfuiLmwHTbEzGxeGJ5nKAk3QVfBphvGRegbsz4jdqJiCNVwGBIcByYZKJ8C8fVOP3XMO+hD5oER2wTnDlyiYVg2/PcPlL/cuY1KkIHmKrwqo8yUtvGH9Al3e0ewn7jdA9423GNnbzNDe8CT2fozbrkwWEtvjXRfuOtKHSLCTvcVlxz+i+P9g4HRUc1GSplh7HT9nsk7Yr1RZ0OYPXV/4Xz5QD+9EH+aeTmMvyjWP4oJqGoVkf8e+F8AC/yPqvq//9L5TTtWNsbBc54/sL2+8GjPDMtHyuq4s2FPgT1YXKikpNgqmFWRsXOSTjs9clsatZxxPTH4mV7P7FtE3EZyA3Y/cQmR3BTZK44Dr2PHrR9Jx4JtL9jDle2rMG4J03auZN69nOh2p2/vcd9VXnvlg/8b/OEZvwvXdMPZiakNJHPmlm4seua74/ccw8SPyydePv8lfNkYiWhcKceB+20lxoItP5I/PDO4E/b0kcMZ+smQ5sptX3l4mLi1xrobYl5Jq+f++J5u7ujXztMcmKunxxvVdbge8FJQDSTpxKngnNAM1Hbm1h2hBVgbWEW8pR8afnO4YWVfA/naWHyjzBni9/h5w3/euT9/ZA+fyTTKoEyXjYiQ685uHnFXi35ovF4cPlXk4KnmM1+GmcMJ+lePDSvFdlIPtGiRtBGKwTrlshsuUhlmj0mZ2EHF09INu3bGlwH7/StJfsfjCdq58+WfdNKmWDUM9kQ1mfNlY3p/ZX3x+A67HpmGRk4zDy3SxHCvleO4E+NMqZU9jJhsqMvC3W2UD5Yn/0D/VMiirI8PMGzk5PhdK5xlQF9AB8Ofvyg/jju17IzpmfyPMuEzrA8Bv2baZNkHz147PllOZceHiYvx9PIZJ4lcGrVO6DRieiYgmFKIMeCDZz0U6JmNipfIGALDueFMwz8tqBFY79i2cikHdrnhh0DcVj60gX/1C/r7o8UEVPWfA//83+vcrqzJo0NiWjb6EbYvCoedeho4vjtgzhf2RRlypdkdzE7qJ9oauYUFuSxYM7KbA61aelWGarDeYQWGvPHVV96bgaUV7LvIp3TieNsRKs+ibL1zujU8gToI2g98MC/cHx3WHlBJrNuJB8nc04G7rwwOBg0Yf6Hi+XpbKNsn3h0ecS6xGUdXhztYdmdY7hncyExF3MCn2wvTj5U/nZ5pj5Vyv7GEht89cz8x253tk+d5inzxF2oy+FNjG26M+0g8PXHpUGznQ420zw15vzL0I6lUuiaKeEzpYDK5W1xTtDQWf8DrDSed4Q6lbDRGRm+YnpTdDCz3gY/2hdYeuA4Zc/1rsv+O2b2SisU5uFUljoF+yayS0Uvg5CzmXnDZckvPhKmSlwljXrmlQCMy2YZZoXgHcSIvibkrRgw1vmI2QcaRlSteG3yM5NeFvj3w9C7ytawcPtyQ30/U30Vq8tgNnLfYo2cvd+Lzil8eGaLDtYodC/tVOKAcw8IqD9hbQ3vBMdK1Yk3jXYxYIrk1Op5HFdC3WZ6WwJdSWUPgN+YK8YmzscR1oI6OrBvy6ogPlcUELnSenSHpwPwc2F52vvjER5cZPwf2GTieyItSQ0MyTO6AkTdzCjWzu46bDD0rj01o3lBqonyA3A2t79R9w2RDefjIvv7Iu8NAf820w4lHU39Rf39vgcG/jUMofWMTRdrAbb8yP70n3DomwC0nhqIMXWnhlaAHrv6B5gS/JWo74FTp4Y7f7pgpIKMl4dDdYMpIdZZjDlSjBOn4Yni/f+F2csyqaBz4/hb5+nii3QZU7yA71XoOP1ryWIjeokOhnw1b6LjY4GrYbcIWg7nf8NuN/FNi6QU1C5NtpOsn8rYj+UCrDjWZddsZnFL0QJ4Tn/3Ghz4RSkPfd1g7a7syukb+vrC8KHFXxAnLPfL9V092mdvjyrx2oo8sIRNHj1sMy7zB/HZHNxHUWHyzBCopdrqCya+41BGg2YDLA7ok9u92aAPzvnKf4G4LwZ8ZY+B8+w4jF2oW8teOfLzjcyCbr2zyzLFFwtpZh4x0S9AdR8KcIy1u7IPQ7AbdULKF7NAEIncmozQJ5NWjnx6Zhk7rOy6fWGohHAp+Gqk01v3/xE6PfJYzD/MBTR2/TtTf3Ih/VZjGmZoy49nSVRnmVy7rgO+efe4UYzj7GXV3Dt7Td490w+je2qIUqFowxuGnypco+Gw5Ymlxx02RY1/4bCZGMo8YXqadSOedjPyBxiIBmwq/wfNaAtEOJDxOE9qFc9uYf7vhl87LuTHPgk2wOSH6jAag7WwNEENZhS4zkqE05fhi2N4nTt5zE+FUj9x65cvlhXH5njV+4lQf4Wvj6/TL2QC/ChOgV7b9K1oEua74w5HmbuTDyGh2bIYmQrWeTU7cNCOlI2KRUdB0o+rM5D3JZHqP7GePD8ooBibwm7K7C10j9y5Mj5ZD6jyuK7Up/a78jTvisqJxo2+N6djR1XM+bhwXg58CX3F8CBERR+4e+1xIBfIlky4L52wpZWT74cq5NX57+p65J1pRXinkvBBTolmwh0BKN5q3vPcLU3likUzMhe+k83+1gM5w/CFy9ZUP0SJj56WsiCp7KxxvB7q12HQBCdxjwrtG3wa2fkeiQVdQUW4WyDP2fqbNO7ZZWhRSE8p4JVYhvx8Z6oZ99axTxC0vrB8P3Pev/C4XUszsriDrO9zhzsXCOO3c8yOTLVy6R/3OYQ/0acW4Tm2Bq6v4btBbwFhh9Q4Vg7aV4oRdI1PpuKFiBsjVcC8TriumJ06xYX5slIMnDZ6P94nl6Hjqf0IfDWV9pQkcFtgeM5N5whwfKHbhUpRxbdhTobxaXBiwwfAQXrC7wVRPs/3NnLulKQS7oOZIk86pZFqe6BZCUVIPlHvCHQeeb0IaLeuDRW+W2+HC8sXynsiyXGjF8pOHp02x7s59T0gsvM+JXH9LuzWIK+oMtzUzjI6DU2x5Ww5uYUQ2qCXSNGMfVvbgkRwo31fsuXB7uuPNQFZIpZOdIU4rg4MybwxfD0TTflF+vwoTEAzBZ7Zr5hJ2phSh/RW9/in2A6z7B3xbKbYy1IToiI2WvRRaswSndNdoteKzYRtHkAuxGspuMB1MiBzVU8zGrI3z3rFNKIdOC8r52ojxiXj5AY1gvYP7nVUGIoZ8CtRNGU1ndyu9CdZ0tq3Sc6IY5dqv9HyjeINLhRMDDJ7z0kje0fcdmRtLEIa9ETZhMIH7a2f75Pl93XmYd/LwxOsh8LQKNyxxaoTPlfZhQeUByY3PHzP7SyNw5dxG5rPDPAk7QrkKx2GlS2SvjYvdmPKA14p6Sx9HKEIWRX3B18KwGbbuKa3g0iPLg6c4x3AFeXVYMyDlxFKvMDwguzI+WOp1YDVva9YUQ7SvTCZQWLm0yCVlnozQk2GJ4EJFgnKqGZ8H1vFIViE2izslSpsQV/ANBn9nPTrkvnPUEX5jWFzB//iBxSqrscy2gIuE9kR9NqTV0N3Cvn4meYe1EWeUXR3htSLBIqYidaWlQJeKBsFtBRkzkwpZLcYFjGS6CokIJ5BVuQ0NUkcd+OrpRuj3hVITXR4YX9+zS6KWV8rYSHUiuIKdGzd+S3GfME3Yjwe4VKbQ2FJksBu+B3py7LaACdgWsF1g3AnuhhJw65G2d0yuxFEx/siX/cbcMuoPzJqIIfPSBhqd6Raoj4rjlzODfxUm0L3w8nLDZoMawb9vTNN3SDeUmzAeb7hi2cpINw3rIrUlrHikCazQZ8EdRqzP7OuFk7W0ENF5R4slX5XmE9VHhiFTLNjuuelISI53h4DrN7bckKNHTCX7mXZTfBsoekWHmb4X6nDg5Ba24mkl074uVFkJuVJbwTSwOnD/fCddMxIbxTXAQHfIPXCQxqUt5FURrVxfP/G4XfjhIfBnvzlx2wr19MTjfqbIO2S8sacDS1wZp5H2KbDnC5dHoeeB8gFsNviy0GPgbBSjGds9rQl3EmEX1O5Mx4FTiCyr0rOnjwdyr4Ry5TAd0V0IvfHQFOtHfjArz4PnD83zbp8obmf/zuNelcOufH2fideRuzfk3VIPimsTzt6gD6yDw7gEKHkKsFtGo+jYCX2llk4tM+4udM3EYEjjwHJfgUKQicpA94VRhdvxxiCRWa/cl8aHMPKTv3PQiRoWppvnFhvD7litYiZlWjIyQU0nes/0Guiy43yiihIHaALnFPG2cmqN3Bt+jASFui5YsWh3VOM5mYKWTHKKnxr5ZWb0hV3OuPYd2ldMt5y8RfLO/Qny68KxW/Ia8EHxdiMkj1GLhgPpWOl2w6YBtcJC5WgqrgA64LSTR6UbQ/IRZ1aYlGMLXAK0c8F7j02GuSSW0wGWwle3cFT5Rf39OkygVRoRdQujTmgeObx/pnytrPIV/Xxg1EgfPUmUpitDA2sG+skhecfS6LuDFAgGjGw420nZEwxsY6T0gdYzJmcGM7GeDgzS8blz3e+c6IwPnuv5TBxGllEJtwfCn2bS60TZGsbsPOyQ/REU+l7Yr2eW5StGhJRBmtJDwdiG3QtSha1mtiA8uIHKzpI3cnrLBHwahHjPnE+FNkx0dZzNzmldOM+ecP4bNDi+3gsshna4wnXhGBx1f6SixOjR9oD4SstCzY1JO7EoeYZJBpwO7LJxb4mtQ99W1Cg9AiFisNRY2WfPwW4s187kK3MN5FvE6I4dofcjKa3EdGTThX4T0toZ3kUimWIOGE2Ii5TacRswP3DMO1sZWKTR+sYoje4MtVuCq1R1eInsstLNzj53xBZGO1B9QFSo1TGMC+SdZj3PfuC+Ftxo2FwmbSPFJmZ2cjVITVSd2fYJDko2idxGir3jg6funqFXinjUCFNISIbULB6DRmFzDdyM3Q3FDvhTRc4X7utHTNhJeUFGRaaR8KOBYaM2x5A6daiU9EDYNmpf6eIYjoomKDJRncWbnZaEeDCYxdFDxzShNti7Z2gZr5aqA7pvjNEizlC0Qyo4OWLrC6vv6B7JvmIOge088XT6gaFAzuEX9fer2EpsVAiasL6jU+ddP7Ke4XXbqNedwXnaXGizwUojcMLZE4sr3FtCnSOqwUhje9xRNkoK5DyCKk0cD7MSxszxIeLk9HO6aaK6jet4JURhj52X+YmbH2mhYcWzPr0Q84htf8K7UsB3zm3nohtVoPfOJoW9KutS2NxbDEJIbHlnlQ0lY8TidiVfr4whYZzDON6y6qywPQzs2xPLPbMuG+0niNyJ241oIqHfmVPGb6CL4XO6UQ6NYJQpNHS5obKRrpEmEH1HrLKFTFRDx7CQQXeolmYcw2HC+CN+EcYupB7pZzCS2cqAE8M6RMa+IlnIemMTSN4RbyPbMJLHE+oP6HvD7ipLNHATxCveNSZXMRFsu7EbpZVMDJWJQDWOqpXBOQbnUe00bYgRmjRmOkc7422lVGXdr3gyNnc2c2JbHvl69axz4MGc6UaxGaz1lD6Qjh0bIDiBuTP0iXfamE5XythZW6FYpaknKAy+o85QbKCqpcqAqRG7ehBoTpHaKFdL4oFy2DilTp2FbAJVI+008NAbzVq8PnCyMM7C01p57BAOjvJo2E+WZlfqdKcdJnQwhNQwOOiCsRZfBEsFN7GJkN1OCwYTEmITzQo1Fnq74JJlap5gMsPWEHlLWe420KKg9fiL+vtVzAQwYIrllgPTOHCjUvKN5XDnHw/fU949s7UFuyvezVgLrShSLN5loLPrW+BDdaRGQYeOOuG0Re6bsJIx0WL2HR89DyVz6watjhAaW3c4M5BN4sFZLjXjP/1IeHjiXCKLu5HXSr9f6eEZQmeUTifwdWvgPdIMo28Uq9TmkdGTbMMqmKXR3FvQaUwNycowNDAR1ys1CC1vPByecHmlXVa+Ht7z2zBwK5ldB3I9U++fqDdPO3/BfT/xwDPF3Nl04eZemaLl+uXKq2QGCjUH0pqJ8cDTcyQipLohOeGNEvyJZARNG653Vu087501w+Y7U6ncxwPmg2LPkRftPLY71b3D20LMoG6klAW3OdSulLoSfcVoJJlO2wCJ1NiYg0FTp9eGNEBHsrMkC/5U2CUxtBMlF9iFimcLA0ZfsRow1aHe4NYdcyxkEsfY2C4nHrRwdyNz32mLh4PhQwz8uAnTVLH9SjcQFiWaSrEWJ8JuHK00nnpFFcwBWq4kqRQjRGdw98omkTFvFDfhAed3VI8c7Uy9Q2oXHjB8mTtu69yCZdoPyHBhP4zoNVE0I68z86CsQyEYoRuD8WAs9OkttrLvnRAT7mQIDXoK6CS4W+ZyVoIUbPdsNVJDwVjDqMJid/w0Y5eNw5jR15nxtOAe11+U36/DBBDu953aKzlarrpgU8OHEXM44ntiMJG7GEo3+O5wptG9owFSHdE0km14rXjxdFWKZG4qYCA4y94WgvVoqmg8UqQg0ik5EIKj7Z2HNLHVlduXF7p/Rb5mHn7zhJdXzvxA+JwJh4BKZXcQg8OFkbJmBg9GIqlW1EYSUNNGD4YaPb4P9FK5sGEjiLPUXHl2kb533CEyl8J62zm/Xng8nijhxGt5YTInfv+Xvyccf94A9PhIje+4xgVTAk2P2GbYRXnZbnz+8QfiQQlppNQzw+MDj/3PCWHALa+E+yujrnQ7EO8Ge5oZuLPXzuiVqnf6PrGb3yHPI/dmsGXEeCWXQpoSo9lIZSJxp1rPkC1iT3TXKGMg7WDchO0epkKjcbmCtreB143SgpDE4TZBV8VPOyXu9OBwY2NvBZcNQqENB2pO2D3DDv59IK8z0jdq8/gtU48bdav40XGUxtVHYkkkNzAXeQssN0cfPHNSxMEUMj10qAZThI5gxWEVWt4BwRRH9hk3XInBUYuBHLj3jS0r8bhANVBH4iDsx8xDsLCsfG3w0TZaDNAgYmh2ZaqevHpEF4xzNA5Yo9hho5pO3QbiFXpYmLSSkyE5wVslJyVoxlRBTMMHS/MWv1qK7QQ64jxqG6Vbah5+UX2/ChPoHeShMOWJW4DoDHvf+d6dWPJObJZiPdGBu0MKN5zzGD+RshJEEOOJdsD3RG2VaCxeLXnaUVMxxjN2g7MBs48E3bC68WBm1qDUZGnceSid1hv7dkW3yngC166EdEOToXCgb1/o60D7qBxO7zj9NiJfDLUNbPtKU8W0hPaGa5a0KEYaUSvNFnY8swi6Gm6y8rovfJiOJB05s1OWK+4hcAoDejM8zZ7z50qNGdkLxgsP4z9iMMI1Zx5SQ3KkB0FGh04ONFPvAZqiJlCbo1mHXQru0094dyfYSqsLXR2t3NHaOaZM98pgC61nrq7jP9/gw39EcI4cDPtNGXNCx8JqG7Ea1G6c58DUPN5Y6t6JfWcLwmgdy5oR1/AWenDU7jDq8FsmCHQpmFipxWCcQnPcmzIYh/qFtHnypTHGTtgXdDqx187eG8eyMj4duJ1PDHKnDI7eKns3dDbKDNV0vBmo2jBjx9SMuhGkURCsgaqV0joaBrAOUxqmvu0Czb1gV0FOgqQb0kasgf4AZvHYY8dKoKvHZoEQ2ZtlkMK7atjDhOWC1A9ouKH3CesT3g8IBfXg9kSvBrGR0Cs1VooopSmDaZit4YpQfMQMhXGvmGewzeHMji2e5s3bbs6T0BLEyeJcY4+/rL9fRUxAe+eAJ5hA3QNF3paI8suVVhd8tmjqlAQaDKIBL8rcd04C0StWIGrGyI4dKtU3bHIEGZicwSbFW09Pgq0B6zu5L5iSqIvlsb3l/d/0M1/TmZI22Bvbcuf+V/+Sy+uZ+yUixnGrndflC5ftK327Y0tiOEzUMNKMw2okysBgB3yboXp6aaymsNqOGzybHCjO4RTwhq0I6Xqn1sIxjnCayD3wKjvnn37idvsrZOkYI5RNMLeFy60T74niLOotj85ipvom/OJw1eHpjEY59EYsDbPdidedfoaUBFsqmMTQbrDvNJvJ0tE7aK7IUjF7xujb1t5sb7iTR7vD9A4PitYO9W3aHHtGrEArqLPYMLxlXx4tIpbAyGAccegUr2/beqXhfML1jvGBuBnCvWJujb1uqDpktPhTo4uwP40M1qJrQKKncaTjqBPQDuS9kj0UHdEa8d1xqIXiVrDQaYhVulPswWO7RbKFXvG9Y/KKSYmmSneRzkAdGkQLfaLtnc0IoVhaT0QaPj2jpmIflbtRZO/s6YIrR+zgaTdhKxFrGqIRKcJBO5+mhEUAACAASURBVMFmjDS8gLWKM5W9JdT3t3hABZeE2naSzahVbIO+Fap3lBbo5oHiBOstzVm8aSTn6S2QR4cfCkb7L+rvVzETEBF6MbyWAkPB7UeGybKczww3j32KeB257h3jCsF6avPk3ulikeoIHVQqaTBgAr0pVhdCttRg3rYet0KvDuVOd4GxGErZkNRJ4S2X/ou7cvvLH/BiCIfKrVQuesaVO4y/paad3Xda29DrV863TK+N1JWuDqyla0W94OyEWI/ZOrkoxlh6aVib6VLZw8QchJINu1a0daIeeDh+ZPBw6MLX6x84/+ET/XDgFEZ2Vxm68IU7w6cvPAbPd34kHYRSA1w2xh2kdWoo6OFAzG8BzKoZdQ0NFdcaajrZCWFVoliS6ZRuUWffkmImxdwseRJcnUlOCcUx6ozqlZY8Ngqt3TjOhs1A2iPiIZ9AdyUkQcvGpBVxFqud3hOSBK8dM1e0Ad7gvBBbogWQosShU6PA6vGmE2xlqcounWg8nY5viVofSTYRx4rPM5hC7hO0RLWBYwNNQi2G3jMtNnoH16AZB72wVw/S8RjC1knGUYKjd8vY73Tv8E7wZaY4JbKy2oACh7bQUsQPwt4WHAFtnu6VBUuXlT6NlB2iBi7tih86u63cWyVUj9kqyQpmEELtYBsGoWFxTsjNkCVC74zSoCouvgVfSwZhIhehioUq+FLY2lsuyJgb/Zp+UX+/ChMwxrDliSCvjNmTt58YTn/G6bcHuCfMPNHzQL18pmlGnwZSLfQc2EelVSFWwXuP00hXg/aE5W3VoBaHEctI5Fwt1ixwd/gKa4uIK2QTCDXi3EAYPjLPnbN/5XjfqD1wwfK7YeF+2Qibo6iQ1sTKTuiG1sC5iJVO7pm0dQZvEKfgG6YrfRREDXYBWiMOOy0qrVZ08uSuSFbKFHmeJyTv5P/7Eykosw+sppLujeN3Hm47jYZMB2wwNNNQhd48xhtibHjj2cWyiYXmyG2imR1LYdBOVUGqw6jQa8dYSAixGtIJRgQx4K1FpeGNknLlFlYOQVmzwroyuUfCnt4E5TaKNdAE6Z4xQakGasTGjLhKQvA1YG2jaCeieIa3vI+8sR8UrDCqpfRGGSouKSYHhraQCFTToCYYAv1+xooQ9hFcRuPMsFf20AjdkL3QciUkh5nhQCW1gRwDLVcmp4ixaHWoM4gRHB2aBS1gOsUESA7KHSNKI+NOhpqENgq3bWdywpoNRIO/Lww2sOoroTlOprL7kbrfcXEhMZJ1JJWNRiPVjO+RkoUwAL1RuuKcsNlOVQvp54fjeIcNA6VnfFeKGlDD2jpqhewMU2ocqoJM5LrT/a89MKhKjhuSI4OJlHkkhmcOT0ruFS8TTTN5f8XKiXJNdNnobmD0BwYc1b5FV31qOBKbGHqf2WmUm2GYVlQ7xjtssYg0rj9c6LGwDpmHSVmGV+bkCL97x335hN8MiUpMgSGDu22oAdMb4i3SldQ9EbDS6DRKeQvEmGbZXUNQxAp2Vro0hp+f+FKKxaqQ1aF05tyQYaJpQYadz19faVvnWnd2J5jXTDs2wmHkQ3hkiSt193ycZ0JX5i5viTfecDMDYg3Wzfgs5NTAOhyFvVq2ekJ6Yr8nXG+07ME1mhdc72SbGbxlYGA1R8Z5JA6NKo3YZsoEto+oJmSwBPG4bcDaxulQWFKgVSiuEnMnicEMHbUG8Uq0liCdWi3ejTRbaaUTTWELDstOcCDJEYwi3jDW+jZOjoGHAuIaxgSScezDhQcreEbW+wLTkUoBXzGLB5NwAv5tmYIeBgIHcmvQFbIQTEFdJtvGJjA0i5NGNYroyNgS9+6wJlP3iD06rDF0OXGTT0yDsu8Nlkg6rczDiKkbTRqhPVCTkhzARjVCuazM44TxHmKnSkHsAEXZpeJLQxGqdjAdiqLWoPYtLVi8pRQImxBsZzfpTQCiGG1UN/D/MPcmMdNtWXrWs/vTRfO1/3/7zKxMZ5YMUpVssI1hzgwYIhkGSMgTD5CYeYTkKY0YIYGYIDE0IyQkkGCAS4UFVcbVuFyVdavyZt77d18TX0ScbvcM4goV2ImN5ITco1CcOGcQEWvtZq33eYeq8HphqIEsftm1AxSynSnc0+mGRl3RqReSSGh7TwkJTh8wMZD2kXUN5PkJtbll02zpHRS9kJJAqRZZJYTKXDytzOhqSGkm1hZVZmbb0EZ4eQnY5onz8aL4SvM7DrS8/vhHTKFgT5kpKKROtMUwasEySkpb0KlBlAWPIxaF6guxpEvAVYERlZDKpWIgC1lmyBoRgXSp79YqEKvB1YRtCyJnTuPI+P6Fh8OBouul1BY1wkIvBYtueHnI7L7Tcf7yifri8e5T1Nqx+kc2rzo2bxNvhWaxIEtFq3TBT6VMuXqFdxtiLpSSGd+/4XE5UEOHLoWmRuwPPude3RKcQCmLu7XYWSN6gbYF5ToMHps1Um/AnzHW4LcXpd4Vlam2+PxMlQ6pKkJncjGYmthE8LKgFKRcLni0VKg60VCoa0JXw9gm+lJYckaUQrYJHSVRLMhkca3GjAW69tJimyIBUDFRE/jYkmxGeYkTkmAisjGIXNAiI2ICMqGKywGgBiULMUiElWidKaWCARkiEoMR+rKFypJyLFA9VRtWBGVt0DYSz4WkIllLHBYpI54RedoRusAsDKIDJQpSSkqOZCmpKmG0JJdEypfZX4aMjRqlKklVYqlk6anCIEMhKoksAX8UNNoim0g/Zd6birGKqSwMulDO6udG3y9JElD4sXL9SlL9wnxQHMTCsGZce0fIJ07jgb65YgE2XeIxFK5sg9UWUSMiCsgNyUBUEqMyWSyEHChmgNJjmsseNQhBsIrURnRZSOeec3wmLytiKxnXI8qstMIR2koulUZKHqeATivRC1ocaMnUK9ZJo2W67PdlpURN0hldCnqpBMHlBxWRlQpNpTiLnSsxS6rL9NsNx6OnaSTvHt6jQ8b3mWQSSjhM11BIqPEEuqPYO5pu5iFU9Hhk2GqqLBdcmdHIXAh5wVRLDhftfzQNm36L6ztqkWhteFNHZH4hL5qULjPO7e3HfHL9Kct6RvaKYDRZFfpagUKJniA1ajhRpaWnMNVIT6HUTFEaKz1NFixRM9RMqFBLRIYLzKRYi9QFckVnhxYCIxdUziyq4lNBi0pEgm8I8ky1A2kcqY1kCIkQNXZdaYVhkg2beUFtdphwIlWJFBFbCl4JmpiwxpGLReaZphZ0AV8LtAKRBRmNzGDqhb8YpUGlSDx6BBkpXqAZUK6yZIdLC8Z4Usj4ZGmigU8U4klwLIZ2O7O+SLoaeTGRK+EpUpC9wjrJkgSljMjSIatAR01EEIpEth0yKGrwBMAkjSiCqEFXAZPH+Ew2kgmLIVJVoE6ZIjRGVUSsSClYlgZffgECIiHEZ8B/BbwCKvCf11r/UyHEfwD8u8DDtx/9m9+yBX7uqKVisqSfR84NdDpTzR26Sj68m5CvVjANU52x0pJHELWnrhPRSCal0dKRzEItFrQk1AXNGdE6MokQHFJWsA6QoA2yrCxFEaVHh5Uxa17Rcfrpl8xqRvs9op1Zs8MICQm6wXGOhWA9fa4MtuJLptRKFYaUMmiNaRosCyoVdO5QSSP1inOGc/To5QLyVC20mwa/WHStzKIg4ozadDih0EuhesVBnRmqpewqaR15+6TZtp5pheH9e0p6Aifow3dQKiNcxeQCSgIWqSzKSFoExRhsVTQIZm0ISBo9Q6ORU+W6ZEKaYc3UBtLs6fWGoheKy/ha2JKJMqMkZDS2z5RYkbNDt4qTyXSjwltBKgPIJ6iRaiyztEgr0bVia0THhM/gDRgLNTeUPqFiIa6OVi94kyhLIOtAXwyhgDwWvCqUFAmmodgGQgQKSXdsxQEhO5Qc0aLgTHMBjmrDKQuEK1Rh8EYglnCBzIpK1hEXHSU7gqwYURBTobSFo0jYcGkSyqkSRUZbDTWSZaCuHU2JnLOkREFeJugcMnaMdaQbHdIEZIYpF2QVyKahzQtLGUl0F6itWJHWIZTFcyl761UhZER6qEGThaQ0YJbA0hf6FBG2I2ZFngpHs14QbpPBul9Mn0AC/v1a628LITbAbwkh/odvr/0ntdb/8J/6SaKgjWZSK0pWghNYoVmN48U/sn+x5OxY68+wAWpqkH3HohPEmZAsQy8uHXAVUvEc14SrV3S9JfuFLCtH3zC4RKmaZo60Vzcsx5lhO1HWSNQWHzwpZeqsqPszyyFAk5jdFa4v1MbQycI5FmK9SJ+FNJQ1ULXCWUkplW1RVOdINVFRKFmoQmJXaLwg2IjatJhk6QpMWZIMtNXCUolhwRhBXCspeaKwrEbRZs80VtBPFNmil8IiA3oxmOCx24pVmWapjERKt6KaHhLEcKbIASMtlsosBLndc9ttuQlnlI6cTYvcQLGS3Dtk9fQhsjOVsVFoqellRVhBe3SsO0e2Ej0uLI1AR4HvznRrQ1GW1khedEGfDQjDSkUrA+ZS41dVoiU4XTgVSaKyqsRGStaUqLYhxRkQOBNZl4zTPWddkJ0hLCuqqVA8xUuwmUTBmAUtOpKwMFa8HuhEIM4eLxxr9ehYcZ1E1koyFV8TpgoEjlQUopyRVSDQVKtJQaFLwXeWEgWy9IjnmcVq7LViCgpzDKTkiKzoMOE2LTl75FqpUZAIaCnIpqJihy4tyxJw0iBXiRAXqrCcV6LK1NJTZCHLgiDipkJVl1VolBGfK00LTVGIYJilQSFQTcB6w5NVXKmE7H8BK4Fa61vg7bevz0KIP+CCGv9/P6TA3lz2lvkx42UixA9IUZHrShaWVRdqjZh1wW8q22GP9JZ1ObH4ibLAvhGcmJmSx121aKWZfKETll5ogrXE7C8zj5356PoTyvULU7jlzZ8euE0LT9oxyEpUG7IYke2FwiOYqariJ8hWIVJDCCuySoRYoFSUM2jXY0xANR4Q+FjJxeNFolWRVUmCa5BiwQiBbhvO44FVeJyUdGrlTMWfDUquBNER6sTVaAkuo14kDIFyFDRXmjkl9G1Dd9VgY2UcT6QU8bogU0NZIaeAcZlZV+aa2cSKF4VQL6CRvoWdrGhZUH0kTiuTsujdSjcrtL7MQJugmZPDx8JsCzvX0NVIKYnjuqfoF5TW5CCRcqa4lmorvRGUyUGoCFFJvSTmBFUgxcDJBZYY0AmUNxiXiGKikS3BJYoqOL8hNZWcMo9JoXSgtxnlBblzmBLR1jE3mn2cCNKwGIMKM7kItNOcUIgaSNKwySNKNIRzpFpJWwuiCnwBJVpCApMWpKis0lAbqGdJUhUxBaJ8IjYbioa4JFyFwKWGv25PlNiyvGxRbUWkSkwTtVhUzmyV5t0pYkXGbyNNmCnRkruOvKyUVIhaE4rD1UJMmlozMgiETVhfefYJt8uIMRHFFY1aiAJSnLDWMeRAsdCslb2rLMX83PD7Z3ImIIT4DvDrwN8F/irwN4QQ/zbwv3FZLRz+n+6vBdbDic5mVO4QTUbVidNa2eXM8ajoNCRXeaanmQWn9aeIWSKzQdstx+nIWYxEXVBZ0Snoh4JCMjmHsluKncmrIvkEBhjPhPzAs1eUtCXHBZ3OeAIxRW7jluwqpRlogmddDUpEFtWR+xNmsWQTMFUQm4IO/uKJYDSl63Akal1J0iJCJSUBvUHJDlEFy2Fhs+mwqkU1gRgkcSwsNqGUZprKpa7fKPoMcw4cm4ClIRA5LwemcMMXccuH4wtNjKj4DcvoiaEQLChTIQbkInEng90oilvJUqGDpgkZuaxUm6imp5RIKxxegT1Z+uuVUd3RRcO5O6FrxtWOYQJ71VDjC8ncsr85UsKOcJWxfMJ1rzhbwXf2W0Sjeepmetvy7v2JUxwZnMYNHbpRvHv/nvrgKDrSNpoSJ8JyQ2okvmZiGQBJnBbaQXGMDruZ6LzjPCSuxkpQnkVsqSIjy4BpLKwLvjnRmVvWqCDN6G6DzAk/a1ALlUq7WiapEbbQFkWSI2tI5LCibEWJgTVIqpWo1WEXhbgqrNWwLM8MIrOGAZEUuankkjBZEssLc7xFjEfEIIgi4bLibY6EGllLQk8ZVQ0ie1LhUqmxlVwC+qiJVGosZF3JCqQPLKJFSkVe5LcH4RcwbFDQukxpKmmW9HrkU3/FOkBefoFJQAgxAH8b+PdqrSchxH8G/C0u5wR/C/iPgH/nH3Pf/+k7oAQMm8hxbVGdIvuF6sBZwzlpMhNZdtSoaHUhRUGNhWIzqtvSG0Oaz2iV2W5fIbuGTm5JriNLjZArmDPFNmy8R9grcvmGw3hglZLw9hlbM7URBF/RsXDVO5YsUatlYy9VgFkWTPSYLhKVRsiAUoWIpUTIRhPrSokaPxdEVojZUTYZYyR6LaQpEN2K0pHrK8fdfs+5FD68/QqVB5SBIVqS2RHkQv+tDv8oJNW0tKqyWM0gNNNyppYzc3uD/jCRbEfnOs7pHVaAsQVTtvh2pshM2HvypqDShSO4ukLOgfpSSeHSKJRrIDaGnnL5e0wK33piF5nZ0cdAXl44O7grn+OFhHvHR/FTJn3kyfe8joYvPv2CxQWu246fvT3TbK7pneDjzY577ck549jC4Fh0xIn3lFjp+oZw0ExjIMVCJKKzI5nEbZZEXxmaQFo8eoUge9KQSXkg60BbAjUIRJtQqRDKNVwV1BRxqbB6T6Mz6iozB5CrQEsu1CB96RUIeSSthVp7apOQyuJyYJkl0s/EeeY8dDRhoW4F69myHxMHaVBrxOSKrJo19czxzK3pGPWK9ZLRFdozRD1QCKxni7CwVoEmg6+EUKhtQqozq5dYK6mqQaRMWQV+F7CmUELBpg0+BuoAJgtqrMjZ0+7vEcsTvn4r+y7HX0wSEEKYbxPAf11r/W8Aaq3v/8z1/wL4b/9x9/5ffAe0qj4piJ7OQF4KUW2oJqLTiu4dHZKsAxhBHVvq/p7b2yua6Yl379+STMurq1fcbV+RTUPcOFK41IZX26LjmexX1nVHNonyfuHhNNN2Ha4XnI4BoyTdoMmnwCmvDEmRtpVRdshxwaqGRkZiNmybgWO7YFXEREEpLSVGYikIXSAmkmhxfaIRHqMyoodpLrShQGipWvMNz+jgEbKjMRbtJPUc2eQjbed4CRpkByYyaMkUArd5ZdE9JRh0s9LJEycj6FXD/Xc/wt5d85M//CN88sxlxpCxTiNjQnhJ7C6rrxoLgzJk6VBiIogzwRpKdDR9gxYzRxoazlwPhvEJzlewaRca+wVeHzF1YK/32KZi7cC+6+niCXGtuRn2KG+R8ZHoMnNM7G9uGK4/RpQL+HIqjs/kD/iwwrycmAZDGxPmZMhroDaJzmSSAYKgBkPuDM3SchQbOrkgThlJh7mH6SgZbOEww3Xuac+FuZ+ZDLRF0VeH0SOUBpc9RUqmWmhyoAbDVDM1NTSmApkyWoTzqE6ibcUYRdQax8wiDZvZco4CEw17F3nyFZFbbH+itNCviWcqJSqi9yglyYOEbPC2INNMGi+JKAqJ3qxIWSAkntMGaTqi8JSw4kKmKQJdKmb1SLUlq4DaCIq0TIfApmto9AtefWDxhus8M2eB7X4BoFEhhAD+S+APaq3/8Z95/6NvzwsA/g3g9/5Jz6qy4BpDmjeomjjZhTszEa1hnTM1LqjtSpwkjXN4+UQ+Hni/vGFTMklXmts9ixT8JLwgiuFKacY40NeWRi3k7KALZLNQxZFjWohYpvEBJc6U15J4MnSuMHdbTNCs4kQ/F+Q2MjYCfawsudLayJkDd5sdiZ6yemorKLmy1kypCYFAlyOVQhaCuVRUFrTNQDQQ80SrWmxzi5wUNj4hOsnDsqfdfuAYCjXBsNW4riPPLwSTyEdJtA3WZkysnEvlm6+OfLRvGaunWRQ3TvMzrakp47QkY6gk+qpRypAiNCZgdcaVmalZKbOiVYksBed5wfuvkZ1DSgcbxeEgGPoe+zSCkNx//zWH2vFZ1/DpqxveH0fmWXHznWvsOGJKT6qRnBc++fx7qEZTy4IIkrpYVuvIOlP9mdtiSJs7FmlRAK977obAleqQtkHriR+/eY93K/Ek2b++xzwcSeeMYWJpbrG9JH0o7Jo9xR3YyxWeDcIoumDBF/oNNHYhRElJGSULWSVcMFRvKVrQakVqMyJV6pzRIZFlZXrZkN2Bkhq0GGinI2c5EqXhpmzxRtPbM23T4EKPEGfaaljtFbIEYhXYpqIPC6UVWD1RhSOFcKkC9BsUASe21DyTjWKPoghJV1qyyqRNIqWZkC1rtBi90CeFzw2691gz4duOYdpRi6V3K7lv2a4T0/iL8R34q8C/BfyuEOJ///a9vwn8m0KIX+OyHfgJ8Nf/SQ+yVWJzpF4FwiGxxXIQgatDYbUOJzQ1zhcJ6KMkFkGumRANa6MwTnHtGqTKnE9fcrW9QR4/4cotDFct42ywjcAeGmJz5jSeYd/yg7stbw6B/DwxLVtGNdE+VJItRD9xpSBWyTZCEI40JOIicLsttUZO3RkdHKFEcoi0MrMRhqI6cokYQBlLyJWcNAhDUC3dslJ0QrSOOC+UJnO0haEIdDpD/RQ9PfLD71zz23/4jJGPbJuPsIOnfBN4dpr+WXN1vzJLyXCCZckMVwnxaosfE9U4hjwiSmL2kZAsFGjCyhoMqnE8qCNXxwXxkFkNoDo2qcXbQCmWoE5snGL8+pqdTCzuwOw3fHK9o183dJuKSgvn1KPvNJ+FjjYU/JXBjpXwYeZlmTC7Kz4aHOOpxxpBER7GEeFAhMoqE7IxfLp5xZQCXZYMNw27rmH0hXk8c6UEd9+5wg+Vp598yZtq2Owjo7wmTCPNG83QVnxK+KxQ54zYGqTh4oUoC52VlOKpdcNQM2sw7OWZpVEXBySrSbKyIPBZIZQHZ+mcwlbPGjuUDoyxINotr8yWEFdc0XgrmO0duzFyvC5cF0F9yHTDyDMSE/S3h6I7XE7MOiCKQeqeco4YeQKXULLFygZRK1FnnFswxpGFpRwzp+xQ+eKV6OdM7CK6cdTHRBQ9NZ1I+ob5XLieM9E9ktwOGdd/9kmg1vp3Ll/vPzL+qbwG/uxIubKInvQ8kRdIV+A7xxg9JXoSgawkVceLOipYbpJgaTVBRgYi4vDIOhuCEhyWx8uJ8kfXtLnS9ZqSHfLOso6V8Tmgpkj4dGC3Ct6vA4NZaIXj2EbqoqhxYVaOojzIjEk9bjwwNY6ioQbP8gK98vTackyGqqHJFRshK0WUAR8rpbZcicSsMmZdydWzcTtQFbtmSiPoqmIQkpM1iOcjP/rznzE9Jrau5XPZcYoLp58uXGvBorZUd+CDV7zue7rvfU4JZ+oqqScQOUAynEOD6x2SjPEzZQy87E4kK5F2ID+tvH84cqc9twtMZmJUhfn4hqV+xDZcof58gz6+oF5/zmxGXnXX3H7yBWn12HDCf/wJZn1k7O8QVtKLPX1yHJTnaTnTREdH4UjilDImRG76wnDleJ4SY5jJC5R5ixoq93rL3X5Hf7PH5EhzeuGPHmde/3Of4Wb48PwlPzvB6+Fj6rXAfPhAtBJ2maBbUknIqWPfnFnLHr2MeAt66fF+Ru4Eq4XoVxgcKVyTVWK8nZARTFJUFK2uyCCZe0+sDW0U6AgpKNxupo2a03FA9IoPdDS8R+sN07bgQ+ScNWMX+Tjt6c2IZ0XqjDo/UXZbdMwM60xyFrHLFKtQQRBtpSrNxiSM1wgpQWqkF4gm0gqFFjNRO8QYMMZxSp5sE6JO3DUbJrOQTh0vVdA5SdYPiN0vu4qQyvHDimgbsluwWdAdW3aukpoOfyiUFWQ9scgFqyq10bSDxSERobDMhrR6tFCoxlLsyOHrZ56KgL6nK1e4nDjmBa8DJWyI0xktJnTbMB5e0L2k1w1ZK4oTNI3hmFryyxlXDoS7jrpklvGMzobke1If0CZz7VrqrImuEs1FJZcwBFkpTrGUxE1nuaqGg7AgV6rS2DZwjJnrYnl/SJT9SrN9xdsPf0L036MMkq+nR+x9YD02rJuMFTOvf/h98rsnHscTv/Zrn9EWyTsRkArs5LFiYhSGKBVDFfja8KIl4elAXQv57oSvZ+T0wuor2kJNFnX0FNlgG0u5v+b05k+Y7A2/9sWv8v2ri2OylQJ9f4W236UcVm4++z6eleWYEDczSy3YsKKGa06HdzxNmeGwkK2h2W+ZlMSERF1ArAObfeKq3RCjJDeJ5/XSJn7X97x+/Rmf39wzXivWP33L9a9+xL84O373v//bvH04EMIG5hcatacdPOYUWdoBLx1WLoR1RQlF484kU5HesjUjRaULJFYo9CqQWhLRKJPo5UpSDjtY+nUihQS2IHKgkTvG1KCjZv/RhI8WxRNuaTifBUO9wuZMt9Fs4sjb6wkbRl7rhjFa/FVP0NCWQhIB5QSNaNBS0F99xFwXmlpxUpJrJeMQKdMVT6WntQOzjagQae82RJ15PcGxVQxpIbYQHjN0Gm9a9PrAsLllPcefG3+/FEkA4OOt4HlUaNEznRc+2k4sMZHqmajB9Fu6ZoecCjIqCAtlXnFYSo1UOyO5UIKErCxakg4Nvc4kq8jO8mgKJQjMJIlXETUuHEZYz+9pbeUcBc02MNaFz9ctMxn3MlKGhsM0IV4KzhZMMJzkDpGOmAA6dKStRNwqymOmRkftHXEUCD9S1yMia16CxH7U0fY76suZTkviOrPLmQ82Y15VxnPgSnzg8fgKIx5o5EwKkrJuSPaMCS3Xr3tKnjk6y+32FbNeSI+R29uW0xohQtMbdD1SveCYerCakiKpaM7pgD1WihoYjWFXPY9B4l5L0mApVnLaTojwE37YvuJf+hd+wN3rzzmWyPC7D/z4c8N3T4BT+I1EHH9KYEYslS9/JqnjC323Z7PxRL8lT4UHP3Nj7mjbgbgcKGGmtQJ77aiiZfGBjOYoPZtFYOuGtwu8nia2NztYZm5/+OdQZ8nYveF3hcnYMgAAIABJREFU2s/obgT2UIll4PEWhq83DOLIgwwMSNbs2dqeOWdqbyn14v+gOoUQkrg4VI4oPWKPPa40hLIyy4xtL6pOyRXJrpzXiry5Qh8FzVCZYsGFEe8+Qo2Wdm+ZcibWF+rseEkz217QPRva0HH4fEv/fkZdz5jQ0CKJwy1+eUsjb2i1YvZndkFhNoJgJIsoMEesSJy2BUpLO3uiXMhGYLhQkG3puGoqc4Y0azatgjLhjWDoX1NL4uWX3ndAwnmy7AbB0k24qWFymXi6tOkKkZFSUNQWa58Zp0KwBlRPtpVaoFSPwCEaQbAeuQiC8szRYLxF1wNXZiAVOJkt+cPK1EdkcKj7gbxUdElkL3m1VPy+ovpAmio71VDbwmo8Oyz59TWfbjZ8+RzYtpLlTSYcTuxVR2k0whS882TjkV4Rk6PUyvVwQzoXwvEdKgvYJeaimJhwDOQFbvUNMwNdOiOuZrrR4GtmOXv2cUPuBz777vcIb154dfuK7RD56vDE7vlEv9xgesPD+MLD2dOHhtRAkBm7LpQlQWe4dh2FnpeXJ9x5Rt1tKVcz4YNk27+mZeX5ccvMC3f/+r/MZvvrQMu98fz+IMgvR+z1jueXJ37kBX/ajdTs6OOAPF8coNqt4bb/HKs+8PT4iJ4ymz7CsmBzz6rcxbkpwIzgfDrCaaX5pKPrJX3dcN29omkl7PZs6h53nPhS/YT3D28I4gH90BPLMx9/vuem2fCSJuY/XtjfS8oCa234IAvSVkQMuGMi91vsKSGLwLgTvlSEuUGnlRpGko0o2+Bk5Vwkm1gY5kiMljotaAzJb/Drir55xe1z5aXr8P7E/d2Gw4PjeufhNHOMt1iVSM5hcibIgfxeUJDMN5rr44y0exKZ09XAdXNCj4FJtrgxs6Oy2krRF6FViydKQy0N0XjyyTJYRRSKdda0LXS60NeB4zJRauZ8fkQ3V1wvr35+/NX6800J/r8aWoq6rxBuLF2R1BVMs7JoyG2HPDmmZkSlRIgVoqEVDcomvCg0nbv0mYdAySC1xqYEfYRiEDlhmkLxr4jtTIyOczFcu4hSLYevEv3tkXO+wqxnxF1POz7zHDNuc093eEuq9aK7VxZTYL9pOKWKKoZUA4vLGFuJxV32b6ogaqEVLaHPlLggnxTuzvDDH/06d3bL7/+d3+PL8DWpXXCt4Wa55VgXBJW7PiGN5fhQieOZeSfQbgtPDpkfkIMFPTD4iLiDbrehrQ3PXz8wni2617wkj9WGkjNVj9xbTUqJoDSzHIhFsI8jvxpHfvBxh5YDWQQebz/j/ou/wh8ev+Iv/6V/hc9e/RXC8qfcfe97BFuQb078xv/027jblTv3A0T/HjM2/MOHd3zv+ze87m8IsfDj44z2E7vNnk8/usXs73g+v+HHXz5wfau5vnKkw0RZVrK7h7Ll/fkrrl5v+d53fsidFCwHy7t5Zu/OPNZv4aMaNuXMy5TozJl/8D//PX42S75anriPDVMK5Njy8f01wX24AGhOGT8ltiYi2pUgLWJKuHJxRi7JQnuhHMviGXTHGkH3M945poO7cALKiJwNrAIpod445nVmbzpiDCzyREma+85yPm6J95V2OlN0IpSBuEjcIMnTyq46RPWoTaEIQyM8w7LhYAJmkGiveEkJiaLJlbhq3FYjp8JsPUb03JrEw+YMR8XYDwz1QPSePLb0GZIUfH3ydIPl7/6P/91v1Vr/4j8Sf/9/BP3/fdQqaHvDYQ4MzlE/73h5d3HFyWfJ7CLdi6YMkjB1WBuhhVUsuCRJhwJYaisxStBXSRluUGEh6y0r6eLjpyT2XDjViU+aHikMteu4244s6ZobsXLuGspPXjDbllbNNPWFF+1gVtQskJuFrq2MS0tIAtuvONnh0olFQFdXliWj5RW3tzf4cEaOL6RaMb1FJcPyOPKH6zveqwf6bWWdbxjyC3N4ZtFb5PGZhyjZdDe07dd80JJPTpXjonG7EZF2DB9lwnTC1g2H8ZnTYUatirRzqJvKvLwnLY7WJbo0skZDwdFagSqVwgFRFNUpitKI4qhb6Pobbj/7Lk/yyBe7PVIXOvtTzMHxW7/5m9wMH0OXuRpWfvL8zYV4NDRUJ/no+or1WfBSZzo2/POf7LnSdzyZBre/uliNn3d8935gt+uR04SPEyOBl3HmMZ6Jjw/U4qlu4O1tw9VpoN1nmr5l5y3ysy27x4TgOxj7U37vf/2Kn30zMbnK9fYThqVSmiObvOXptPJ4VmyGgNMV3WZiCtixULMnVw1Ckm1PHFpESJDPF4CoOKOrY43X5C5jJ4+ULa5c81wisp/oUJQa0OaKc3ekWQZCugP9gXAyHEXi1r+QjhbfKPIgKVeS8h6GtrJuT2xLoZwaXJ2BltNVorGOZlnwBWwuqBwRstBuLwKpRgV0E3myE9+sPeq9YO0aeDMRd1tiecNG9cwCppT5Yms5yF/yMwGp4HGCspEsR4dsM6TC0vbMTxWlF/IA4txjOCOEYbdVBHaESXLrKuMiiVGQKZxKpE8rSUWieY/L96ydwiwzc+1QV5rTwdB2J8RoEeXI4dzy+mrgzn3DT3vBEjPKaM5PAnCYIZDSQplaytRhXwmyLwQ10HMmC+jWhudwJmdF1COP7xVJL2BndLYkuaBD4By/ZhmvOL94il1p7T0nPFIIHBHsjnQ6cJzPjEXj9hum7kzuVoI2DKJyeso0j4Z394FGFNyTYnILQS5U78hni3EWxYqpAl0kXi/MVJAFtRZeNz0ueTZG4Lotn/zoC27tR3xj7pBtQ/Ar/pvA108/RXWvII34lz+GbyqrFZyXBnf+mnrO9D+8Y5y+Zi9/xFoXSg+mXvGNcNi54lOgUuiayusv7rCqkm8ry0uLenomnj+QDgcmudDvNuzLieknJ8RNRXONRnFdoSZF+50NT9+cOD88cr//Ie9+BVI8gT5zejmQk+Rn4yNDG7jf3rPnwGGuGJPJaaCahbrRSLMnnQo6zdQ8kpWh6RvWLChzR3EZ6oprBV4IOAuSObMdwATJg54xYY+1MzVmiE/spMMFgyuFXXrCTwZl4X6tvCwzwyeOKB1JJuSh4tsdrhkvPQQ4WqGYS0vsLKwT2yuQaSCeLrJz33jSqrFlYPAJWzrWuwPN+Yi96ShLwnLPaC7AmD0TU4JN+SV3JUZK9DbysbzlvO8ZHp9YRaXUgBxW3FmAb9m1mSAdUStejiMFQ50r7ySoGlBRUlPFbKF2PcXu6GVHeT1RDyMP4YSjpX2/MkWNyqCWM3SSG5F4bmbSCLvSUuzI+NyjxEp/P8GpRVbQgyQQeFwgouheRh4rtDpQkmJQG6I4swTBvHnGNNAvihITPjZc7z/h00//HGN8x4OSdBtDf37g5W6lxi2uS8y1Yl4qcae5qR06g/eOTdNQJklJE3ef3fDWf2B4djx3LbkRiFxpJk1rJMpFnlrJFC7mK1kXZLVoIfFxQTjD2kZ+pVfsuu+xud7Rt5+DTAx7x0e58E2758u/9/v85X/1X0OeIr/3G79J3bVs7z/n3e//BmbzCc8W9Mef89WP/4TTG89f+wsd7/cGxo5WW169vsXZSpUZs+lou5b1XFnPJ85fP/HsAw/zC4fTCa06rkzLp2LPp/c/5Ksf/xH7uPDg3/APf/rMd7t75LHB/YMDN59+xrK9wesTf/EHf4n01R+znDv+4Pz3OU5HGF7olSAcXohWMLQXv8poJvQzJArTy8QmNEitWRaDs0eUVvTCMcvKOhVqB/VkqCFQlMJEfUGpXbeopcEIaNcTvmlQViDcEWV6Jjch/A3yBHEK/GTX0w2G+XFmJNIde26GE1YeSeEWf5KUNhOMp5sSyazouiAeblhVItoFokCGgKySKDwLhjmPtAdPPq0c6ztqd0M7S+rra/LbA3LXoEPPrF5+bvj9UiSBHAuN2nGKD0zzA6EIJHt0zGxRJDWhB0/aCcS5w45gbnZYztBozstFrrvaFl0TopMIn1D+wCQnnJdUr7jS10QWJgt2uSgAg90gckJ83DIsjxwamI4n2lpRciYD4ztgE+m1ohUbzH3Pxyby5qsDuXcUMzGOhjxEXCigLVlF5FFQwkBpNSpf6r+HfeCnf/wzkjlg2shLkIxNQvqWeZ65eaqo1xYTP8G8HXngyN32jrB6Qj0TgH5nSEbjm4FxPHHb9Rxejgijudk6YuyZdGV/mFFyj5aexa/oVkBeKUrTuoZVLoS6wV45vvej77MxETkbvjyfmaph/+mGb7qG5fQn/M7zVxyeFl6ZFx7fzRSjefnq9/k6drS3W77+ozfcOcdxr/ncbnkcBE96IZVHNt7x8Uev2eyvWFfP83nCVM2SVj5884zqEndiz9JtiOmRn5yeefebv0m8rtS1IFzDX/j0V/lffufvM9xV7u1nPJ5+hgh3NMZTlsp4mkjXt2x/5Q7xYOhPPev6lkY9oNoes1TyuGHYBPJWoo6ZjGG9H9nHwmvtSUIiYiBoMI3EqcTiHWpITNnQmJknadi4DiESMQlKblHqFepD5Xy7cPUoee49w9TjukSpR9TekJ+PpHrLci/ZHSrz3YkzisZb9vE99T6yqluY4GA2KJ2xqVCtRydPXh0xZZJRCGMQvpDLSI2WZkl4XXk3LlyN78iffoY6VHbqzHG9pW1m5PufH3+/FEmgUpmfjlR76Wn3WeNsxZuOPn1gqQ4VLe59gvtCiZX/g7k36dUty/OzntXu9u1Oe89t4kZERnZV5coCWaYwBhUCMYSZkSVLDPgQeMzIX4EhEySYWDBCICTEwBK4QGWXK7OqMiMy40bce0/7trtdLYOToLKVWUIiS8o12e/e0rtm66fV/NfzmGnC2BbVe0ZjcMGzlp5jKfFqZlkbTpNinkasf02eHwjJc7QrRJlp50SXAQM6TMSPhk1hWMWK2Ai891RxYL6A/FjA5HCyQIUjN8vvUawUq+rH7B6PSKsZKwcjdFLAPCIxFLakLCU0kVILVHfi9O6eDxHqpmYeKnwFeu4o7EB9tuF8ENx2Sy6s5/3sWdg3SPnAZmE49Y5sl5Sp5+lPv6TZWKaLhk5sWTUG72HcCURzwMs1pakI5QEnNSkadCqR9SVKBfSw5dVyw/nVFTcvfw/9SrE/WOKHDOeObx4Sw/Ertu/f8TNdIK3kLn7gsT9jMYF6nNm1J4Z3L9n+bEfcZdrrmloJjox452nUmotwzqIOxKNg1A+k6Yzl8obg7ogqE2xHTDWjVEj3LYPziC5ycDXLbs3teuCzP2w5SUEKju5dZLD3fPYHn+O8YnV+Q/hgOfvi+3w9Dnxx9pp3c4N/ueX4lzXTm5nqMDHOB1ho3p96zioBG4N1AyYU2JXHuZJxAF15Uurw3QodNxR2IvmRUhWcQk3FiTCuCeqOs6WEHBHHkWq5oCws43KiOuyJZYGlwPkZhGFzU5BVR/80I/OKK9ei8kzsI0PZIo6Zsgn00mLVCaUiwVl89uQqI3ykAU7nJXLwjN1ITgULYfmQ9wxHTzPtkOWPaB4Mqo1s1Xdx4h3RJY71b7mQVADzRhK3EVvXFEHSF0fOYkd6oWEIqJN+5tTfQl2O7KcFD5NhVQsarcmxJalENR2ogiVRM00d2iqms4ELuaYoVrj91xhncUEx2wkxzjgMJjvuFyvifKKpElJqSl0gTUO+jvghURjF6B29PbERZ5zCwEnMqLLibHmJURPTdqLTa0wtieXIYRakU2Ife9RUI3yLrRQ7JuqLAPdLmrZgHDOvjpbjjcX+fObLPCCUw5qONE5MnGGSxT4diC8EvtasxzX2lcL/ZYVrW0LpUUSYJeSRwXuQEqkEOUfmZuQ7ry55sTrD689YvLjme+2SH9Q16esdld+jzhqmdMH/dvsT7u63RNtzvbtnmBRfXP4u78YdxaXmm7uA+zaTzg+IRnMuNENTgj7j7u4DZp7oNy84rfak+hNqm6h6yTF0nHY/4Xa/Zbd/Aq1Q2VEpQbdQ2LmimzpM9vRhTxkzX/5xx93Hf8r3ll/QbAT59YoXNlBWK+pVzbg60U9n/NvrxJ/9i3d85/sVcl7y08Mt42nkUPeo+8BR9Cx0zSk6bI7UQuNmx9PhREWi7Ep6BLFcoZRgNI9IXVDKSMieFGfkrsOYDmcnuruEMQK13XNXCtpO8HCfkFmiNgc++/w/ZtZP+GHmaA0vokbkxJQm5H6koMY2AqOfq0zHoMm9IxXPdGPjCpqyRiaYbY8XhvpgmeaJ2Tv8/Mj7+URxgFPocV3L5tV7ok2cDTd0aeDw9Z/xdfRU8dcP9d+KEEA8QzBtLTB+pm4SOtV0vqf9YGisxlf62SOHp2sy4m6gKUZqDZYCWyuG0TP1miA85kxg65baK6ot5FXNozpwUa+4yzP2kyX6cAdNgTmMjMvndXSrSlz2RBHwYol9emQOENeSfG6QtwGbOkqWyLUmBZC9w4UTvY4IaaixxBBZhIKBmViAdAVCjVQLA+s1dWd4euqw6gTFircukjcCosYvH7gUgfd7kAfPfG6JccmifI9qXmDPL+nZIZYw7iUvf/AdivUZyMz2p0fUhSM9fUOcPJiGZAoqmxFnLbpaUJcNpTGUiyuCCtybias8cf/k0fZbvpZHVkpye5h4850bokyE4zvOf+9zzh8usHFmoQymukCfW1jCotlwXl2wffoGPRvGszPOVGJRvmIOM7vg8EvwUvHUWw67jJfQlgbVeaJPzPPE1AXkyWCYWC8ce1viQsemkNx1M00t+VtuScE1d3Ki2j8x7wRZ7chDiZKeLgaKtYbziU9fnvHVjx/5aqoo60xxmKiNoqsK5mzQIrKIoI3GXToKBS6VzHZkNRX4/kTHzCQ8x32P2AqcFBSbmdNwzrx4TzwqNoNkV2TEqmLVTIyp4vb9/0758pqztWXoBIMssL4kp3d4X6CqgE8SMXii1tgQWTWBKGsiAc9MP2iK4JiqwKw8qt8DjjHe06dEuIXeOOIUaXSPe1jhR8G8eqKpLvmzExgbMfH0a4ffb0UI5Jyo5BmF3CKrihATtq1BTRTa0e8UQhvmcsZPmuVWw3mNN5LTaeAkBrQtCC6QmkRZaZAnSmFZLhtw0B0jUjhE0Fw0a/QmMniJ3I+MhYB+oJkaZqdZpIBqFaPsITWodsSqS5r7I/MqojYD01qzOazopiMkSZhH6CXVwhHsiMIz5oJplBiZCT7SGE0sDHQ7xKwwwVMuNCoM9EXBMAXCPNPbRNob7Mayubok94rzz2+4af825actVy++z/0//ROK63Pi4YAWBfWNRFQ1x+89kXYdPxmvcA8jSkjKSqOlZnW14dOblyzNOUYnTn1HmiLDVuJFQIuB+e7I1k68eyjZrN8QLzNSwO52QHzZ0X5yhpvuSfaeOK0QZUFRN7RuiVY7sv5dHB6OI/V1BNEguh6lnvciit4hYwZjOG0DH2/vMCrApWEp1xRGcVfeMvQJVbSs5WvUNNP5J2SRyJMk6I4n/YGqXzJWR+4fj1yvX+Lijpf1hqd4R9gphF7wz7/6wDz1NNqTvYFFSakNIh1wHopC4rGM48QYZ4QMxLEnbDt+PswEMXGuZ5CeJRK/LFAiIFOJdj2VvMBe9/SHHj+0tMsC2e/4N97+Ln/yp19yOO359OpThosl+viAMIIUFGMx050GbuySqAxC9PSdwibNXEu813TTRCknYmMRbqaNhtAmpoNk9z7g+j1TziyNxfols8lUs+VRDPRypq62tKuGw/ZEkhZwv3L8/VaEgJaKczEwzpJ0UXBxdoY8WzJtN8i+5zufGm4fCgaVuHpTo1zH7eHIhb2kLCYet98QTgLnKrIuyD6g8wBottOMObOIxQSqQBlN73eYXc3GNMhFz7HfoPQH9JSYjeGQPbEfaKslY5hYsKSvHjgqQ0GD+lCzWngmL5hFAcVMKS2mCDhlkSmiQsWgJcUyYkfLNK9w0iMUWBWoL1fEg+LtzRec3r3nYX/LXFi++N4Vxc0fclFYXl0G/s4Xf8TtU8/mfMPuvqdcnLN6oRj/MPHUSsbDW5alJJ9buvues/PfoT585AfWUDUFPlikqlDJU/ieU//EKZ3QpkIWgukQuHAdoR0Znw4c40hjPueynkgbT3y851ZVnF295Xy54XToMXJNan6f68+hDSWIioKC8rNPeNkatlvHvCo4q19gVj2q0ayGC7TUPNkTvf8ao3petiXzZ98laUkfDjjv0O2JelNgOkMxVwz5iVkUaNuiqomz1y27TiJKjxj3XNVXvHlxjVGGKA40wAUrgla4YU/56iV3lWAsD0Q8dhrwh4HRB1Q4ccgSaRVzgnwyCOnZnd4T+hljAtJnDl6SB4sqDWjN4DIvK8/UJPzQkPUKoSeKWXD55hXNbsX/+fV7bgRMg+ef/1//B5/+4R/R68wZglMjeNkveCxP3D89sCgN7VIiJMxJ0vs9WsLSlkSVUcYjpQUxobKlEpHzsuaoPXKn8GNHqkZCFIzlgC4viPPMl3+yx9czZpXQhwb32xwCQUSm3vD52yv+wnc0RlKLJemT1xz7n+OmFZ9/f83726+YjWVRv+VV2aHsCmLHhXJ0ITF3M35M5GXNEBL9tkcFT3u/IViJEYnUgC0tg0jonWFrV9TNDn/QuJcl9rEi5BMVJdgJQUB1nuQNtqpxdz3bZuQTW+HmFpM/EEYLUhGiI2tHXSmaylAWmn2EgKcwHaYqiGWBFA2Xb7/Py92O6rsvqM4t9/+y4GJh+dF/+O9yXr4iyIpLN3O//YjEcv9+z4N64rKYWSbDITxgvjQUZyu244T8Wc+qOmc8folaQpgkw+PAotlQBceAxFWSECp0ioQhcf9lh00DP2wyp1+cuB8k5qbGL4/IYLDBMulzXl6eE33i0XdcrAv27zveblps1ogzzctcUr2OlBaO87dwYYlyxWpRU9SaYwhEURN1YkgTDCMmWKIFOY1Mc0ZWkqWw9FniuxkhJDKd2PWB2ST8Y2AjHL3Yo+9/xud/9++SHiaeXI86u+Lq6kC/c/zFu/fsh4Ft7nmaB+JdT0yGECPJCcYk0dGiK4FkwMyBeVxQh0DygVE41KhwSYKoCLUidYFaFMjo0coja8s4BVwxI+nR2WNETW5mykLw1dc/ZrW+5mE1ku4S5ZuGOUkK6ekWBptrtmYmT5Liscd7zbtfSmotPWOZaQaJMgGKZ3N1lIEy16TjwCgFg0wcthOiUsgILmTa6jWqeKRZjzBu2M9HFvGJfVxTLiLdrzGR/VaEgEigTUBVNS++nVlcniN1RXJHzvMVb7//CaPq8dtLhEgoa1D1OU5ErqtXSF3w7mHLoYZiA6tLzc5VaHsgdY46PsMxc90yHD+QcTQHybSuKQZHt3ewsKjDQKkV/cJSHnp2pwKMZsdMkhEda3xVMugJWXrUakI+LbBGomKPKizRJbyWDGWBSgusiJTXFefnG3JRs7lZclVd8p3LS8L7Hfl8wb1ak6Pi8voSs3PIzYyJPU6dKJYXkDOH/CX5mw3fpK+oP76hU4pvphOf9A1h7NHVxNbtqV2Fe5gI1YlpZ9FFxUhBmhRSeeY5EpSmbAReDuz2d3wtK2wOvNg46rOKYVSc+j0Xr9dMj5KiG2kWNf2+BrNh0u8p50hMmTWvkNVzOWzKivc2ct4VFDeaR11x2U+QBIPZodKKNEJelQQ34UNksgtWRSanDlcKVnNNs4bhdMLNGtyWeT4SVWSLw/35gaK+4OrLR4rNNY/Tz7mcHLfHgNka/vSrbzHdE0NecdC3JFfxsjaUV59y+OaOkz8yqYF5rMi1IyKYljPO97ijZHfqyTFS5Jk8l6hc4o1EKo/Lz6BaQsK9yMRJ4Q7Q2pLHckdrzzl9+RW+CdCdaIQAXTKlwP7+x5x//zXxAE702DkQgkZdLumHIzGd8BSYUZEcTFVCWImUAqwgDxuCl2SZqWTBUR8p6gVVkDy4DzR64tEl2jjgHwUXby7QeUaVK/Rhhtz/2vH3WxECSgla23J7PLG4uqJ/PFCVmbdffMb0zYnrzWtCIdn8O99B+4Bwnq3fYpslL3WD+fQzLh62OJlpl5f0yfPh9mvixRF1mLjb7vnFh1u0m8i6oQ6WUWUO3cDZRU3sI7nQMHs6HagHxzgJVHLI8xp9yoxJcOo66qLEipkkSmRzRvPS48kIe46KgI9s2orXn7zl6sV3WDcN9mzB2bpm4yOj8IRjpnMJ/apgpR3G3HB9/ZL+OKGGjunxHeNp4q4u+KF+zaF7IqsLri43yJea1Bccdp6rmxfEKTBUFetQczccaYiIcWI6AjJgBs9KKaLp8LOnpAQhmUXNZrlBHfY83e4p7YnETL+bkWnJ+PGBU3HgvL7E64JcnPjk1YaHux2X3/8uu3c/Z52vqMw1HTNvb76gdZKj2TIvSj67vKbyE/tDplwnPGuq1YaryXM8fsDJAM4SRKYXMzHMeDcSHmuyDUzqiNy06MHAvqYuKiS3kARSLNkXAXN6QJSOk39A/MUCedPzo3/v32RVRMZZ8u3+niJEwrHhJz//Zzw9vGcm0uhEmifkXHJ43KKqCRMj/e2RuqhIVUM+KMY8oUPAI5hPM7YI5Kal1YrT/YkiacIqoJXm762+y4f7I8fYcfP5Nf0I5n3Aq4mz+ZwPYcv4L36MuH6DnUe0sZxLy+2cOD080Pqa8OrEzkA1SQpTEOdEntbkPBDCiKk0o490+0fGpz0nfWTuClCeaTYk7lECvF/w06//GCEU6aThYmS/08CvLh3+TYBGfwGcgAiEnPPfFkKcAf8t8CnPdKG//9cRh2WCT37/c168+BxPgfQnstKo1YIfvfgBF8s1tEu2IiJLODxukduam805X1y9RouGw9zzofsph8OBkmv2uye6DurViut2xWMSxDCQ1UsM3zJGj4wesZ8IyxIhAlWeUWnFVB5ZO8ucDEJ05KJ9Zv/X5vnG4kLjs2F5fsbicsNnn7+mWZ1TmIb9aUeIkYvLlmZ9jZ0rwmkk9RE/y5u8AAAgAElEQVQXRnbzlulBkhmpV4HDXKOcRtWKXTkhB0mqK7IXvP3B58Q+URrP7iRZF0fWsWGbI+wi+4d7KmNIveJ0BZDRRUmQkYKAz47J9dQqMg8w5Ux5pagx5JAJ3mJSRT4bqY+WVi2oUk2UJe2LK+ZoEYeRslljLq85HT9S9okY1/zO+Wt8dYVn5vWLG2y5JtpH5M7SnmukKVings70jNORq/NztFc8IdCVYciBvFBoCsKsiE5iJo/nAa0MJpVUqaBelizkJffxQJtbxsMJ7Q48flvSiIT6wTnfmc/w1w1qIbgp1uz7O8JWkseCkztx99VX7KdEVmcwPDLaRNIWOWZWoiY6xTjP+KzQKYB2+DoCkrKuEFvPsKjBKTbtAmkKZjcxBzDBsvruisf7I/vpI0tZMI7nWGaOzZ7lMDB4RV1Ylt7x8fCIXpWID1vuV5LOZwoL3nryKRDrzBBn7L2kMpFyqcGC9pkZTxo7To9P9OPMXAmSclRLw+no0Lpke1TI6IirhnnqaCZNkxRlteH+9Ksrhn5TM4F/P+f8+Ffe/xHwv+Sc/7EQ4h/98v2/+HV/bpSmXV/Ap9cs9DnjbUfcWLqTJ396xbtppg0jEkmSJYvNDed2Q9aKvfPIsKWrMzZZrveJsZhZbi6QLRi5ocDzw6LCPWy5HUZiWlJcJ8Kjg2BBPxuOQxgIamSxLwnmef1ads/OyoVI7LzEli3t5oyLzQtyoRAJXl++4IvXn5Bkwe3jI6nvScOOMDygRIHzA6c0MiZFv4Ph0LNY12TdUJwsrCYeRyjIlAtNvbxkcu8I95H741+SYo2cD0Spuf1mTXG+RC7PKd0B5hXVjaNVFrzDe8/oR7S0OCWxRIKSJCWwORD3AcoaKwSyDhTXLZf7ic0GCuNZKs3HcSKHA0VYEyvBlB4J9ydaccFY7Cm3A93rGzaVZp5A2YaYTsyzwzZgpj3h1PJQlLg409YFhc+MccvBDfhZwQRhckQxo01GJ8eIx1QlUQtKBMIIZjmBFbRjQMaaVSlgoWiiZywEw4c77t++ZFE63DjijKAbS3bpSOlHbu933O6/IeSEXkQOTiCeDFqMzHnCOEMKEx0dsg4EX9D3EZ08VRKMPpOiZyEy0iaKfsfjacRUFRQOJS6Idx3dzx5ojYbLglVt8Cpw8UEy2wpPRtmJbiqws0P2BU4L4k7ii4wuVxQJ5uAgVCzsGiESIcBsOoSu0EEyCU8n9iADOkRqFFlbdNhQqgODmHHZIWuJ7SLMms4Gjkmzkr96U/A3GQL/evtPgD/65e//Gvhf+WtCIGYoR1i9u0XfSC4+e0XTbJi+/YbjT78itYrhKCmaxOlhwr664eb1Of6056tvt3BKTMETpiP1NJBbT1s21HHF49BRL5e8uXjJo255fXHB/fYdcT/wx84h3IBMZ5h8xM+KVI+ERcTKCsaWFB0m1XQLzeJiwbU5482Pfsg//E//Aa7b89Mv/4LBe552T5zczL47Mn64Yz+c8D1oYzHrBa22WCOo2pqm1hy7Zx13bhM7Z0hasTJXzLXneIqcbxbsx0DVGk7bkYWVHO2a4bhFPo2svvcdiqFhUKDzxNO3O0TSRDtwVqwZYkakjFjWJJXgpHFFidAQ5JF5GtAObL2mWQlezA/sjie2k2PQL7ncKO7u9rQ50x8SYydZ/OCKVy8aKnHN4XSHbyvUdgQ90F4ucO3IMgeOFKxE4jC9Z7G5RqjE/fCBbz7sGLs9TImxm3lkYJgmjDuy0C2SBeXasH28Y5ogzju8fxa4qJVhLQyFnvAyoC8aamB0B3AjtVrjikxMS0zbkafAn3/zjjknLt5+Cv3EuD/hzI73H75EW4uXE9YsKGyGfiD4TMwdVWHJWTMzUcSB2EQCkTwZ7sOAzQ1uoVmJDcu15hd//p6bAJ3M1L3DrwxmdAw64lzBWmQeR4dsBQyCw+2O1FjWlaAyglqBLzWi9wghSNZTmwU5r5Cix4lIMpncO2JQbIRmb2ecKOitp9Yjhc+kYUKrC2a1Jw+RomyhODFNEjH9zd4dyMD/JITIwH/1S5T49V8hDt/y7Cv8V9pf9Q5YrfnZ3nHx8jX0mq21nOaPiHHH4xTwe8/pkPjBqzVuCAzbnu998ZqPj/f89Be31MJTLzVujpy8gakjS0kxCY7jDrksaHMGAraC5XqNrmteHM/p7wVvVte448zHeIYMDrF0GJvJfWS5uSRmy2Kz4rvfOePm5RU317/HWXvOvh+xSSKDJp08YT4x7vcM84iyBVaVDKMnDJF2ZRBKgTaYZMjiGXduS4H7eIeoNzSpolCRW3dkFzQVjvu+ZHOZmR4hiSVFE4lqwPUDBI3Qlt45kghIlzkeO+xlhdGeNCZkU6K0pdCZpZbMItLHCTkJKBXVouKqyLRRcXlxxX607B4cJmSsqtFlYJaClRX44x7ZLFnYPQ9bz/1+4NzXlCvouzvExYYUI4tGkQTEmDjc3ZGX5yQxMxwPDN2O7D15nMh5IO8G8tHTV54cD+hHyxAzU79jOGUKo7GmIS8D0yioi4plpXk8HMlhplheUpUaHXqKogXjoQfVbahSRQyPHMaOVie8P9LPO2QjGecRLzRxiswiIpUj6wYbFZmRGJ9PKKZiQEtFTgOVbPBJo1eSVVFw3pZ8uL2jMEdOWmNERk+BdPvAUz0QhABX0bUj45RZqcyoDYUViKamlDNjyIx9JIqAFJKYHTpdoqTEqUBOBWaeIc4INaEmsNqi7YleeHL2zFqihOXoDIaZYtTIQiPMkbF7ZmnEQsLwq+lCv4kQ+Hs55/dCiCvgfxZC/Pm/khA5518GBP/a9//XO1A3i9wNjm8eH9jcvGT38BOMPMfMI/tOkaIjGcHH8SPZai6ixDhLYVqi6/k47GhFRRoLZJKoQqI7/2wkToHYTRxlhQyC/uOWofBsqprvff4J/dUXNHVF6D+l3T0SckeIkdIfsFXBzfk1wdS8WK343h+8gbKh9ktcznRzJPsD/TYxjI6OLUOQOFMh50xVFFjbEAuJbgtkCuSj56QFplxQCwExU6iWudsylgY5GlbW0DnJKpxwg2HoFbqwdNPAC79mKDPHhwMXhSS3BVVumFrPrDQNJVbWFGXE6sycMiFajIbAhI+ZMIOqI0YIcI5SDJRriXaGishmqdjkim25ZKjeo6JGCEOpeoS4IOV7mqVFjgp1vuJR7JGjJm89WrWEnNi6not5wy50HPsT5sGw3z/h3UwYO8Q4ErNkcAEpC+zQcXAnJt8RhYGdo9BLnmTPWkpKP9D1FcEDs8X6gNMD3X7BX379Fa/CgrNugdU1adEinaYyNc3NNelp4tv37/l4/w73dECUCjtDHQwaT0fER4U1PcI4ctRgDFIrUJmsE3VXolRFNAlhNAtpeHx6wu2O6Kyo00jQhlEkpsePzCvJWqxo3y54OJQsXcTLE9YsUU4go3jeX3IeNwqsHhlqieosg5nQSwdCwJwJRWQuoRA1S2845oCMDZci8uQz5jQxeY8xnkJLusmzdI6drKjF/GxWyQDD30wI5Jzf//J5L4T4J8DfAe7+H/+AEOIGuP/r+jBlTd1WbHXA3z/xsfuSa/saYRqGfqCwic3ZiodDx/zUYT8pcWmPMRm7vKCMEucgJIUKM26KOHdAxj1JReohUhcDeZGZZ0fRC8Yi8fkn30NXZxzutshPFlxOPX5/z+rtGfHRMTLy6nd/h7BztM2zNyD3nizuEadvGQ+RjsSuPzIdO+59R7m4eOa6NQ7FRENFqCqCmpiGGSk8IQrKZsXD04B7fGK+XEAReLg9QBYUqwu69EA1Vlg5kgdJrCBOIwdRYeUaox6IouDw1ZF2vaa+XlPKAr9tqZJEK0WZDa3l2ZgzJ1JrQGqyqGDcsZCK6XCiW0leYqh0Sb0WnGLBz04R2+6o9YrNWcYuNixfaIR23B8TtS5YYejI+K2i70CEW0T7At156sbSa48aE4/uI/s+4UaH9hDdhD8MyAyirWhWgq6PpCmR0ojPhlIbQiN4uu3YmS2Xhwo99hwWDW3bQsqcNQX7cOL+Y8f+/chaaPSL73NzuSDMBbY6cLyfWfuSY+zRokSUFZ27Q0mLnwLKJLSEOQqyUHTRkqeMEhNWVCQyckq4YEjSEb1GFiW73c9Jk8eETF7UJB/xIuNSQDYZHdcMfmRj39C4E8f8hJol+9lixBE7HHCFYRYJjybLEtM5TAhM5pE5TWjtae0Coa+ehSbWEIqAMk9MsSCGhOD5XgujQxUgU2BZKbRQFJXBxogZZg75b+jugBCiAeQvhaQN8B8B/yXwPwD/GfCPf/n87/+6foqy5JPf/S5uzIjZsKjXqClzKMBsSvIhMr6faRYNqWgZp4rTPj9PcWQk+oi1JbouiFPGloKqkKyKa47zBFNGGItcLtBVj/woWMqGaTeTD3ukAnE6YBeCs82G6/KCQ2Po0y3Ke6J+LhZ6ut2zaTNZzIyHLeWixNxWTP6As5oYLD5klMiUSnMKEkGEENAp4FICbSgqg/GBSU4EEmWIqJDYpzXBPyC7Pf3wRHYGVxfcLC7wzGyWK3aPPXIeWVYaPIQkiKagVQUxZ3yp8X2HDI7K1yASGYfCIFIkSwEkbDL4BDFmhKxxCErtKHxFeb1C7e9Y6hf40xOm1ZxXkvshY/w97pSo3liOOGxKHA8RKkOhGoLMSFcRC+jDkXnSDENPmiV5OBGSJPhEIpDHgNKKmQhZYmrNfKxZSkFeQi4q1ucN1i3J5TM6fj522GVDlC2dNYTTzIuzEnthGb7N9HHk4zdPzKFFlge+eRo5oAnB0Z+ODA9bogqMo6OxmVMuED4RTHyeJU0RLxJGJewcCdYQBw8MoAJmVAzhSFFFlIjUtcVXARsSQZYYn0jtTHSJfPJ8fPcV5SnTkVBWoucDvZ3x0aL9zBwEVnmyz6jBMKiA8BHdj4Qmk1cT2h9g1kzrlkLXyCExdiPD/ogtYMozUWrK7HAx0dqCEDILr0ELxmRpipn511wf+P87E7gG/smzjAgN/Dc55/9RCPHPgP9OCPGfA18Df/+v68QYwb/1ez/iw73n6eEJNT4xmIrzdUtZlJiVwdrIJI9QzUhV4VRFpRWLRrPPELqMXWpytcEfDyTZE/0aVVqc7hiDYtj754EwDrj9CTVrJr3k6sySpWAjS+qFYbu/R4eGs7XBbCdUm5GjJ0TH/mGiqpd8PCZEeCJhiIVAzJq2WeLEjHN7StmwoEVJh0gzk58gOyyacXpew5tYEG1B9AF/Evhxh1Rrxu03DH7FegOm0tRKMsiSMI+sC0WfFATJJA3rm4opZNIBRA3aRbrJg484ekJIQEDNM8bX2MrQoKialn0cSINDSEXuK/pSo0rF5EZWxQZ5NvMvH3p0ilRmiRGK2QW6ztEOe7xOnE3Px33rWDEfBG37rNfuTwJtKnzOqGgQIaN1JqQAZoltWkQxMcWIIyCMoqlqRBIkMk7C8DCjdMFqKRiVx4lAW5WIKmPqgIsC5EhXNbysGqY6s64tGfesIw8FRdvRPRy5vTsx7h3e9eQ8kFykPLvi5SdvmI4zkxkpUmZyDrtYkZzEPWx56rdUZ5pCPcM8Q56QIiApQWkmmUhHD9IQ3Yj20O0KqioTokKnjJeeIkWG0lOljBIClGVKI5VwWCdIWeJmz9wmDAWlGAmAihWGmVl4DqeZJVuGbiJODlEmpJWUKFJRomRgSCU5ePykqfRI7MC2BpMSz6f4v+EQyDl/BfzoV3x/Av6D/88dxcTrN1foqqcSir/8xT3RBt5eLrh+8QqpV+gq8uHb9yyXJ4RaUjWZxtSsFxuOL2ZU0ry4uCHrDT9//2eE+cR2nIghoJG4GBnGjtYUmEWiOw7olBHSMShNVZ0hlyUPpwMxdlxUgro9o+pLonAEBvZp4MPjDlsHrrod2UuO/oSoQGTNCsNMptAeioSeE7aSpDwwb2e00Yjgib3HN4KYA1JkjowMORHGHS/eLPnyz09cXq9QtWK5XtC/P3JqQaUCqSe0zMRjJlxqFqVitzuhpszCaSqVmFuDP1mGbqBeGUQuCFYwpRnhMoVeEObIJDzCBNCQ/YjWDUllitKzuq6YRk+9MIjZczhI2lIxlQuuqyO1PSdKx8QeN8703QcOueGTmzXentiPkAbDKGdUEswpUrclIitMLMnZo2VDkRJCjyhZImixi5Y5JTYVlDKTvKOUM0YKxtFh1JIgRpJQNLpFncEpDnTbiNcNxeFALiJrqdjpgpfnFR9Pie7uichMvcjosKDcGL749BW//8Pf59unB/bDjjWCXCja1QVpymyXjzw8LqhenVPZgp/8+Jaj+JIyCGQF9TEx1hNHp0A7hJ+IoaAKGZ8Sg/bEmBl0T8RRpBZfTJTBMqoEvcIYQ8oarGeyAW0Lsk9gM1oYTCjAWQZ3InY9aZ4IusDEBCKgDJQYSLCPFqxgSkD0uKBJpcIUEu5L4FdbiH4rKgZTzkxGcvZqzdOcmL4tWGlBpuJseYatC97v3kOSFKyoli3tsmZRtKjiHMwjcg4Irbi4qkC8ZJo0t3vHYbdHkRi2R+73O+zFGfamxZ0iSkzIaqR0F/TjHfUycHCCwhpKYRBZEZRAT4EuzPTjHpJFnSKLUpLUkuP9A+44IpMglZlCaMoUSYeJ0Xt8mKHIJKsYc0aGRBCCMA7EImI3LXrnqBKockW7hH4IvDkTiBSpsXzbd5z8Oy42f4ARlv18T61qhq4ndTOVWSGsZpgiq6bEAGZdUmYoyoxRS0Q0jGGH0BKMQAYDsUFqhUiKZhOxs8F4S7mRmEHQqII3X3z2jKmSmt1+fLYCrTfUoka1Epct3TixT4lFq5h9pikrJg64PaASpaxAOUTSlEYyAz4JtAVTGKRPKBq0aChtwonExig6MxJ4Qek7ShWgG3g3N0jdM8wztYZcLimKnqVZY63FjDPd6cTUjLjckR87dJp4e9WgmzOW9UuqSWPWNa/qGjUa/NghnSNXS1olcU8dc5gRZubNzTXV+YbF+pzTXLDde8L9lpcvXxA+dkh9pDsFToyUOhFTphQz93qBXczs+4RKij4UsLPIuocwMVUFbciUbY1YGbKTjNMT6IT0kj4LbBQMqcdNAc+ASBZjImUFfaWZHyoUjtw44j6goyTMgbiwVN2RoCxER3GATv8Nqsl/E00i+fLnd1QqM5wGFm1DU68YvOTrd/c0laDze9ALbMVzpVfT8ursnLvDK971e6oYEKVG5InXl5f0k2IwByzQ7+6x2iH1xHLZ0jYXVJ9eo0JPIHB6lDRlZHyaOT+7ps4HvAu4/YljCHBIzGqPP4xcmAuqpqIuW0xr+OrrTH8aKMtntkDhFVPKuNkxBk/pIjQr0BY5j0zJIWXGSpiGTLIFZ4VmVDNFs2QYRt788AsWK03oNMMEYR1YyHOiH5HMFGpJFx4YjolYLlnpROofEGj2/cA4TpxdXjCXAiEzUhW4mFC65PNPP+X6ZoMMim0IFOPEuTvQ8oQ6TiibULHms88uucbyttnQhkSeRn7x06/ZDU+ItuLq+i0ZgXIz1y9OzKJnFQVpscGSqcUbunqPMYplA34SzEIzz1vmmHAZZjehTKLkGfIS3QkdAxmYqEkF+DhgYyb7BcvijPMKkiqoVMBMjmJhyfb7VMIj/cxYQDmfk9MAXU/anyjxfPHZC9x+R7U2iFlTLVu0Vmy3T4T9TFNXrFaXnFcF336453ScKIsGoxXRDaS+5nopWYnXeLXmb336Kf4zjUsH/m/m3tzXmmxN8/qtFSvmiD3vfeZvzqHy3rwpii4oRHvQBk7bWEggtdceFhg47YKNwR8AGFggwEFI9FTddYfSrRxuZn7zd4Z9zp5ixzyuhfFVoRbc26JQN8rXCUVIb4T1PIq14o3n9/79jm1SMfPh6+/ekEYQWBaL8Tkycjh14bZq8ZOWB+MSuCMiVzFyHSYXEePpEt8K+M3Xv6JJUxxHoGWHaAcGmdPJIyBpMFRK4A4DddVRtxpDiJNVdKbCQqO0QjYe1ijCdjRtOnCUINyfuAn0Pbz69h1GFCg5IXY95KA4pjmKnj5zCE8isGIsRzKPXaQwSEsQ+D7jKMbzDVZv0ZmOcWSR5wKn6TCeRa58ZGD4NH6OtHyGQfDodEzWBah24O54QzyJcR2LlVDUJuA+OVIcB8zgol2BcQy9UDAJmD49p24q1MghCkNCz8K2LYQWVH1FLgYoB/q6Ju8MU8dBWYpeKhrd4AyGWNkUXUWsBW48xu9b3EnAh9eGy4WDpXtqOXAQHXnywCg+Z1PfY3cV4/E5lu3i2xGuLzBtyaA9opVHnoMKJIMdop2QVgvsyEZXCaZrGM1CLq5WqM5DbBLavkG2PW3VoZwaL/ApdzXBcownJpydxSyDM7q2oFcG/94iGwaWp1fMQ0GbeRS7B1rVcjZvKcSErtbYKkJOcwhcgthGWg5G9lS7W969f0M29BS5T9tW4Fgfcdx9RWcGSnxcpRgKjcOBvopIigoWAse1aQXMjI2ZdPgYlDcnijOKhyNGCWbSIAi431sUvWTVFMhYkRxTGjp8fULo5CQOFE1J1g18OvuUf/OPv8JXkqb7lrww+MpH+ppBNmx3d+j+I9xjPF5yEkU4YcxghQyV4vHSw/EtXl/nTJfzj8sFQuanc7yiIOparFNY9hX32yOyy/GUS2CdsBovcZw5J58ZNt//DpM/IOXAIBSd1Ginp68NDD2DcNmLBnSPtjxc2WIGBzlI2ibDiiwC4eO7A4e6wBE+jWkh+4mzCLXuqYcWVaXooGcVnEJo4dhjPKugFwNZb+gHiAaP49xhInoGJQjGAt+XtH3zcThGtZRKQdPj6Jq6qBi6nnU+8GjmI5GkyYZEp1xcnFJZisW4o5Udi5MAeovq0H+kCJUFJ4FFbfsYFJU3MF04ODqh7WdkTY/yBKejGfUw0BlFlpUMlFjSpTSG/JigfJsp4AiXsRdQy55BKYJB4dpHIEbrFi0DPHtgoiyOYkqxXzM9D+lmU/q9zW5oGZU9fmARnT8i2x4YaQeBy3TlEkQ2y8WC0B6oE7g+HBCOxNECC0neS4r9kWQ9omv3ZIePEVWm2n/ctLRsusbBuBZlVWBrUMaHtsQZDrxZb8m3DYgCa6jwfY/kQ0a23lFNfaRqGZ2B1fsw7GkHyTIMGPshfRCQ5glWMGJyeYE6bDDNA7pqEbWPPWh63dG7Nn0LQ9nj9RrhQedojKVo3QqrGbAnDibJyW0DtLjtA+1WgAnRleFQ7/FaTZ7nHB9qumHNM+nSGdDlx83LdqjJ1y25PVDWHW3XIb2Q0LWZLhyqYYxnQpyLGRWC6voDVtkzHytkNKMbB4zO58Rpz9tbibU0lPuc5xcevusR+Q6H0iNSiloeOJle0IWKbXrHzXd31G6LXSmOwsGxU0aB5OmjS8rr91THjk74mEDTND1DYeEIC0dbOIOkUQojDJY2yLZlCEOoPdy2R/o+YjzQ7Xr61tA6AkqJ7P7/Hxv+m5XpuOgPjHyPYx/i6AJR5jyJxiAGZNLTKdBKEisXp8xYdRFzd4TxNihZUOWQmI6uKSA7kN9nNG3P2BUchUFZA8a0rGbnhNYSd+4jpeBdt2bmh8zknAoLYQy4NuFyAgebcpfjP35CcJNyrXvu376jC0Lkz0/xK4UWNjqIcajpdjX+4GJJxeAPjFobIwSW0hjhMp4FeJ3C7SETLQv3Eg+HlJwwcrHskLF7RMZj4nqgjW0iBavLMw5akhcfCJoZcRxRpw1Tz+Hs9JzQiokmPk7bcL3d4dk1vmVz59TUvUWeGwYpqYWiSDs21wkpOXlhY2Ooup5xbCHrAXLBUnRsnQHtCnY3iof6GwLGfP755xzTNXqr2ZQFw7VChj7u8xWi3+CVFv3myFAv8ccOZuWStj1VdkfvC+ohpKtsTDtGywHbNzhNT2ssOmmw9OLjyK4QMEic1UfakN8qQGL0QOccUNuS4PwpvozIOxhZFWmVMZquyJId9lSR3ie0Nwe0dMF+wvYIvRohwwAZLpjGPpdz0K6mOPcRYuCXv/w/CBybKBacn5zjlZLF+TPWfcar7S2reMTUnVC7ChVJdNfyvkxZfHKG7XTc3GnOrybI4IKJF/Lw7Xt02bA8u2JoFL702UiHdHAxfkfTuXTbFIXBPTty5Z5w9eUnfN8caB5SsGx6V2DJAbfpcGxBqyzEoBDSIRxLIkdQty2dE9AGDm1eIjuJVJqq15i2BAt8PQL2v1d+PwkTEINgtt1j2RNmo5rAXzEZDCfNgaTzobcwlqL0fJRfM8NHDJJeaNy6ZZH2mB7GWlPWBstpQVYkVYvr2fh2zXRqEbuCh+vvWJ4tGAdnuG7EZ+IxtVzzYnTBVtnEwcCbH2+o0hSpLSp7wqnv4n42Z/zbkrbqKMsGt9EsFjGdPIAd4YcrMrWjWdQoz6YxEnPsKfoVThwzm15i+wopYYbDRGpWMkSNHYa+QVqa0cgme3TJ7faOaZpjtzPaXFBHR7qJwxlnHOo9lmszlRXBOOTF+YJv1jui0ZI+tegf3qDlgPQEbTnQGAcvMFiBS76r2R42OLaksQYmT06YCI/yB4Hf3xJYPU3f0GgXux3j2iO29Zb7dM9ID3z+6BMmyxnSa/km35IPPafTgUneYpwpiek55jZB1zCaRyxCn0NWsTc1UVoSTGbYY5eHfYdjpqjpCGRBWHUUtUNlOxhl4xgHIcFzahzpoHsbf9FyODa0oYVf3tAOmvNoii46tNhTqAr7wSeYtBQ7j7o9UORbZDDDMh6N4xBZNY021ENLODnHOBW79QZbakZOxPXtnh/zmk9//hlLu+HD+j3B+RldXtImLdGLC9rYR9Wa1WjC7X3G/THlmXOOu2i5W24Z3gSMIkXaKU4nI9Qi4JMnl1R1yUm0YLUWHD5XtBL8qQVWQbxcMZle8Onjr1iePKFMOt6KN9DVeKajaXO6NvtISxIC04CjPVqrJNU2Zdqh2INrUXuKhVUAACAASURBVJcNUQadDV1jEToWbWfhqpw/BCb+SZiA7ksCdcRTGioH01sMW5fCswhDj8NpRB349HMXGXlsjj6LwaZnoMp7smNJGAjG0hC5gryxiW2Xs2lH5WrCyYTyRpNVHdqCfWGofvcSNfG5vPrq4wTaSOPWKclDTt835JucIHKQjsUm2TD2ArLra0bPLIqqY333jvHlgsZuSJuUYdAQhEyR2FQEi8ccxi7hNqMPMqKoRSkXZzBYdsxkbPE4DuiNh7Jb7nc53qCQ45C7b94wD2yUGmjPz2jrKZdn5zwcbrFnt8QyZhG5jFefob2O1Ko4FAnd7ohb9ngrQdcaVAeRa3D3mv5YY1lQhx3ZcMQzK+zew9aKiZNi9xoJ9NUG4hcs/QXVtMcpU0LbwaZDpzvglCFsiZMjLudoqRkxAack6Xq6GHQjCEc27aDJRIfjaNohJhQOM8vCVA6N7mgsFz+0cSLBPiu5aXsMMfYkwmiNGRSzYYcTKUq1YDJZYdmPEJcOD9c3ZIGLx4BuJ3iWRJ1EqNxFhXD3Q4LleOStoevvefHpY7zGJslySA3rt+94qPds9hVu1PDzn33JyeNTtq/esj480NqCLt+QJxs22ZEyTRgKxXLqYz0KGAJB9aHBjQL6eodVTlhOJ4y8L3h4t+Hl4QfiIKJ6eYOMfMq0xhrPiMIJk8sjx7uUNjSMoxMsPN7f3zF+GfLs+XM+u/wTvvv6Gl8YXMfDt0f4dkkjJQpBMzI4jkeaJmhXEPQDblXTDTZD4CAbSWA60tGUkV9SWJJ+k/HTzhhEo4IZZ5MJB0Z4RjF/HmJVEW/PO3adR2Eg1AmF5ULcEdk+prMJvQnNdEpvQa5LhNNj24bYiVhVDseupdMBw0JzuNkze7xkETmke4l91/Db9T9GtBk3y2fYbsN2s2cWx5yEEemQQdmT9yn2VDEe7aham5Gz4qF4YLy5Znu7oa0qTi8jDsmW3373WyInIvAP1MLFsj3sWFGmNWlxw8X8jNVphNlk3KmAH779huP6JZ1xGLkz/LFA7eHBhHh+hhY7Rt2c+/VLvOUIMgXSkKWa8vqfQ6Do5h3St4jrktrSdKmk9mGwNcgBLRSNHvAdzdgJsYBjvyF/aCk1nKqKYmjw/RplHFJVwvE1pXlCnTcUWcEgHP75279k9WTN+HSCq8bk3Qf69Jw31Zgr2RJZA87gM/Zsdm2O6F0ux3P6Ys5d9cB9dk92qNBdiYzHhBo8k1P3A0HYca4asiRlW64Qgc8kumfsWcQjQ313x24IOKQFZnLgZPYJa/GBqM5xuyViIhhZhsIeU3QNfgHTcs+9panrgVX8lKTfY7kNkdUxXy7p94LX12seji1j75TLs8eMrClZck/JwNwJuN68Zb3rqZqOTbImsDycpmW6WLBcjJH3B66HkuEv3vDF1VOOZxMaD6Lf5njTFbtdw9t3dzx+9JS1LLl9/4GlELirM+q+48tfPOWH313z6pvf0u0SNmlBsJzz/Kuf8ebXv4a2RruSsutoDgkdPZ2wWQQz5qdXjOc+f/GbP6NrPII4wJI9Q21ReQMTqyTrXaQYc/XZmOS3P/wB/f0ESkiLc+szWpOwiCbYvc8o8sj0nkKcIawjRDFX80sOxzsaeaQ0Drt6j5QDWTjg9S60IVJUaL+nKxWF7MgthRcqRqJmXzTsN2u86gWiW5P0A4fjmmKwOFS/Yh5OMVHM9eY9Ty4uCD2fQ33LOFrhqRg9r0nTHbsh47PlEqstKUvN/nbNcbPBky5K2whPIpyEcrOnqSL86AI16in0wMF2mI5apPJIrlO2NzvazqEPOibVwE2qOT3zMXFPXwd4lkcaZjRViepdliqgFCWqy1gnFVHXcrZ6ytB3qNMpC2uKTCqEyhHdQFNoFILa6amzIws3wok8qt4mbzSW6plpiSSgvh8RhBVjTyHckqJ5R10e6JKWIVTglqyTjyPBp2LEnUnJWTKPa/JxxPF9zuVMIiyBPHwclc7bAs8KiFSFrVuk71AR0dz39F2NN1FEQqDLFNOV7I1m0BP6QnAwDReBpD8K5Mql219j+T7GanDnLqeZS5bm9OcfcdzedMzIBdFU6K5hOu+pjzFDYBP1FsaKSCpNP7Wh2GJbgsGR6B7ydEshwDQdqrJolCENHY7frmlkj4cgSSpqrnk8jngsRujQ5+i3eIPDYX9gN4lYnJ4Tah/OY7bbjs++XPD+l/d07oa96aiaBC+eMT1fMrHhk9ET2sueTfKU5DYlz9dcPFnyJ7/4ErmvuP7wkmEocIYRdmQxOBkT4TMZXRBHGtftCaRDNodMOzSyxrEDhK3wwgn+8ICMewb7J/6JEMtmOzxgHIfQqlC+y+7mPYVn+NDV2P0Kpx4Ymo7iKDg2LX448OSywzgaI8fUpqdqGlwcWmlR4oDp6ChQYsSj6TNOvTl/+folu/ob8mpHcpA8bBI+i8fciZrS6bCSlE+ciKvZIzJt2L56wMwyKq2YtT27JGeMoH9i0/QKbY4cq5ZyU7FchJyfrhi7H3fKq6ahGHIye03QjhlZhuyw5Z3e47sOwfQpQQu+p5Fqhd1LZueaje6Y1RKtY7rWRjtrbP8K97CBSKP6jsvOY7yM6XWGZy0odw23o5BPTmOcsCR98xJH2ux9TefWdJstddFDZDhVisiO+NBAKxIOPiyMpKs7XBGgjctN3tI53scU3qEglCfsLYnJUw7uGe4sZ2xmdE2JrVdcjF5wuNQkP7zBezrD8QeavMTXkqAYcMeKwIowvkNrFWRdTlVkBH2AQGBHY/a5xGp2KPvA0Lj0Dx2b4kD9/Ionl88xN/+MszOPQ7Zkn9xi2TX2NKbx95htye72gFz8EUF2j2g3HIqWzFfUwiU/ao5jBznuyLojHzY2uh0RtGMCzya2PfKhxI0/vpm2xxIcl5fJFuVmnI1c6kOK617RhZ+xznPq/h5h16S7PV0f4tsOk6qiyxNW43Oc/pp+33Pyt0LyfUW+Tng8hqU0VLIlnNukzh7banl0GjC3PVbRlLEGa+wzeRbjhc8wtQHZUOgCpxIkTY+SNaZuKe5rzvxLZqFGdiWHXKNVS5MVNCai1BaPnZCq/okDSbXnkV1O+PP1hsUuxZUH6mNLv0x4+KHHCT/gzS2O70aUysFTPgeRsKtyfEewyzNaa6CfWtidQDc9pdyRDQUlFl1dUuxqnMbHaWMsYWhFjTNzeRqMKcKML5xLqvARCz/nKv6Etdixe7UmimLORYQanXE0A7Hlsa06stdH5C/GbBtDKhXexCN1G2LHImsH1m9vkJ3GcRx0UfFQl4ztgenyjG6YU+0bkt0P3MkEncDjeKCfBpw5U+z9iNju2R536MBn8fQJZ7i83b5Gty6e5eDMArhZY+aSOt8wUk+5eTTlbZIzw6I9X6K3W3w9IIaOwZsSuYZOKSqp8Zstn2cdtmrpsoKD33Hi2uRtSdA7ePsAy9fcbB7w/YhsqAlFT3QeEM4MtB65LDBlwtWjW5oi47E84fTFhs39A1Ze4Y5b4nKE7SU41gx7MJhjDh60U49upjiUOeiccW5x6YakbsurbcdBDZR9xTZ94PBtjeuGeGJEaPloJRhu1zihYZAauYmRLWgxYL/8Ftt7y1wl/KaxMGcTLtWc9/l7truBk5MrokEyO/HQ/QThGg71DZWoULVNh0d0EjC5nGDaAPt2wyx1uLR8pp++QHgTmuuGalxgdx7JviBfF+RVyW/+4Xt+fe6hugHRxpSyQe402WwLvUC2ku5Hn/0XDeXLryn3E2L7lr5uYNfz5ncJ5iTDHVpGn3+BM/go50A7pFhlgkdIrSQXUnLMB5arkGM0ZSh25PcH8FxGg03p2IRqyevhnhDJy91LXqzO/qD+fhIm4Ds23/7Fe6TMuVUrMvMabxhh3jxwsEq8ZsYoEdRhgi5tGLu4FyfsHs64CASRkDRGobGx+wbTV2AGWm0jVA+NIQxqDusc6ff42AztguHwO0R3jjq/INATgjag7WvGM0lou3SXgrE9og8XZMeUkXzBkh5h77hrXlJvbTxfU4o9zvgKzwi0MTRDw3Ez4LGnCzXDMGY6CRm5AX3s47iK6999zx/9yRdkucWxSLBXV0RmjzdryIctN22NxiVOSqqbLfXlOWfLSw7FA0OZo4oJV08uMDIntUZIU3J7WGGKA7tti6UshA1+6TF4EX2kSdMtQuY09w1O2RCMfKRx8PoBu6qIxmC0R17bXPoWJSknZ0941ybYosZ2HxF5U8IBmkmOnQvG8xX/1nzM4VDhxG94+HBPmCiaekfnCPbKZdpbtFaD7Q7oTuNWNsKtMV2LlCmdFuiqp+lzyuORRXjG2WhEEm9Zm1PK/Ehfp/RRhIViWyT0Q8khMYSPJ0gC9KA5uPCIA/Z9Tmk7RPMnyCpg+tjjw/cdabUjjkJOzh6zCD3utweKZiBQS0zQ4tWGoRkIqgFHeXSuZOSFmLnNfuoQxRo7k0zKkv3xlsOuoim31McWfzlCuyPqzOKhTHg+ctkdayLfZtBT7KGljh32niHepRxMzjxtUX6LN40ZK59oOeGzP36K68z4/n3Ccf0Bijse7nJU3eHFJa0T09cdycMdx1oxe/aCrPBwAhs3gu9ve5p2R49gFdow5EQtfLf9iVOJs0PBcOFhVQX2fcwiWFGpjrIKiFoIvAn90CJSTeY3WDcK0bc4sz3JPGYrcpTyEMKnampMDfcayn6HylyseUCWlZTpe0YLDzmdsLAqjjuP2VXIUi7Y10cmY0V11JgIkrsHynuX8y9OyXc1OxderTN02CHrAMontI1LYKcsnDnt/RZii4dGIGpB0R0osiNRaiPcjk2esVUKfb/HKEXQaFw552//yS94m31Arh8oNzHOICgsm+bQcf7U4t3XDY+vAhw8cr9hMp1y/W3IRvWcLOdM+5CokbweFFOro8wzgroCOlzXplINchDMLQe1mPJQDaT7lC+8Hu13OIvHyK1hKDoyqQhtAW1DVx5IvBNenK+YhzGbsaEJl0gDtR64lB5COVzJiqY4EMQpNJIi+UA9WESxRduNmXkGN9AUbUcneyI5UMsaUTtIY/C7HlVr5InEaS3wR4S+oCsFi8jlZDImqUI2WYeKCu6tkNsHzWI+p+1vaeqGz4uWD9UG3BOMaUm3d3j6lLfHr3nuLfnh+z069gGLbL/jICbkYYdlO/jzgUZGhM4RL9AkJWRtjb9rqI1P1W+ZNCFBJxnXGmZr1tcgRUe1r6mPI/RZxsgP2dgD6+9yFlNFf9xRqppoEhMnDv00ZuJYZElKM7NxhnPKakutA4ZCcp880OwLrPUCc6r47utv+eHt19hDTtiNwK/pqwNDskEaB2Fb7HYVu/QVyuT43ZxefaRadYPCEkf6k8fId4bZ4yW71/f8oQXBT8IEjBAoERNdQrqo6AuLk25CMRW0eYCpjsjY4+EmpU0yzpYxn6gW8ohcrmizGmvmUJUHDtrAxOG6rHGCEKe2EQjW79ds0wfSNKEeJItasHc7br7WfDI+YM0eQbSlqFv+x3/0v+FvHOIzyf3tFVvZopOee2rkXsOQUHeQN1OsocWqIpaXXzCaNBz3FR0p07MjSZDS9S5PPvk3uPrkOaEj0PuO2/2APLyiEPeU3nOeTf42f/bb/57Qz7lOZkRBzuyZh5M3nI9gdyiQ9TXT8ylL28X5I7COmpv377l3QQcOt6/vCNRAd9iTVzn2IFB6iXIdrNBF1IaTykZ7iquvHJ5MF+jJiLIVlG3HZKcYyoztAO+OHzDxCef1Pd+tjziOjyUf88IFwiNRI4mCjIAay8wJ9JYP/ZzsoUYMS2QMVtMQBg2UgrTUqMiizzOa1sYSI6KgQTsCrAmeayiEoTu0jIWFlVoE0Y7K7RnSlsgNmEcRdbQEKyBxX+GMTnFVxI9/9k/53eWX2GrGmZfyc8fhfzls+VW5Q0iLH4GVkAxljvSnBM1AoFtq03F36BC1YjIF6YTkdYVsE1oDweKCIsuwbJezhcCxd7y/zRk3MVZk82EwjEcewRxevawp/Izp0yXepEVNp2iVc9p2kNwg5nPkqxzteXjjGlX6eLpCbSSMAlQIV+eniE9CdvcpyfuX/PHVhM8XX/Ln337N+jZDSUGXQ6MNtp0SjqHJc5zsgdRIXLsm5BxrVOBU4CuHIMvwxop2N3CxtHj54ffr7ydhAratyMUGo5csgYMTUroFwzFH2BFVYFCWYHQqyTqPSS+pNj11IAmHgfaYUKRb3nshTd8yTST7IaFKC5SSSBFRuAc2usUqM6zylA/dA56vMOoNb9IlvnVk4Zxwns8h7ihPK2wVsMt3OGpOFY2YHl5TNjEmlHy+nLM1OU0z0Pg2T2yJrc6p3DXTeIGzi+kbcKOY1ScXPF2MCEzMbfOOaV/w26TgZPQZ0TFn//odIxzWu5zj/ldcnD5Dnjxm5pasD68YO0d+XAd8Zq74Wr8j1gFnAvzTEeU+pW4aKgHN5gPTYwRqSj87MPFbUj0QDh297VHLiDPb5bEXM5iC3faAsOHs3KaoPFQpqOgoVxHtmwdM8By7h6rrsKL3WGXDpGpxRgaTabL5ApkOdNYctamZ+CndtCbPLDpbo5oFE6shs4+43Q43GtHLlt7WVEPEkA34rqJXLW3d4MYNeWvhhB3ZQWG/7+hHHTrTWL2PClIaL+erf+cLfrV+yfyNxpvMsSQ0Ts7mh1vWas936wajHGx8gl6yGi+RvY2vXRpZs1U5Q72iWOfYfkPT1vSbDrUb8M9HOEKh+wbROXw5uaQ9vOMhO2CpZ+iFzbPxjNMyQyqNVDEvViu+OW6JEpulN8Z/ZFPd+WR1SeRf4SdH9osA51iSy5CVtih0SjcSXIUS3xUozyZyXUZTzdt1TFc5+LonaBvKakPfe6gerFGHPZvhWTVz75T9WPC4NZjUog5vWU4XZNMZMi/oVImrK/ppzcJ7BB82v1d//59NQAjxGR/ZAn9dz4D/ApgAfw/46yf+58aY//lfdq+iq6kKjXYGKtVS0jE97NDjE0KRYJUlx6qgqHx6teIoe/5JWfBcF5yoGeXpknXxllZXDMeSV8cE7UboUrCxEpygJNWKqbEpWovGP9KXFZnxGM0vmIUTvEGTlHdYP3vEIkuwhic0lsc+Mbhtyqo50osVQiX4iU961fPJ+VNY7/mn25zd0TCLLBbPHqPbBtVnhPKMeHXKs5/9glW8pM9rYl1RYFiGj+j2Aakj+HD/LQWCMFxCfovSOU6+ZZ069E8DnOmUWFt8Jx+4KCxkZFFGPu7g01tH/PGSsLdRVUJqW7i2hcotGs8mCEcMxsbomqVv6F2L94HHcr5kZd2wKDT3iQ0hZPmB1hszvct5m5QcHt9zaY3hKDhtFrR9SxEIds0J89DhZLvn3glog/fIQ0zaWgztlJXKaVRNkX7gOnARUnLcOswzSemDNxLUfY7pAupCot0BK8/otSFwJBxbbGWj5j52KtCdxPb2bI8983cl2bMZX7kXJJ+2TC8mOElAZv+I2ez57iYn6kNeeQa7szlbjCi7knEU0sgpljMQ6RRPDPhPLCJvisOMR2OP4ecOy7MFtzc7Xr1e49y9o6DkVZJguRZTa4u6DrkPBNJ41HtIhoaTsc1pH3BMSqxxhvnG4nR+ST+dQVqwwUUeaw5hwLSt2RcWyyhGegEPUctT5SC2PfN5ggk8+jHs/Igfb8BlzOezA4aMegRdMMOUgrAf6JSFqjSFLhGeYrE5paAgNDk6Lhlqh90k4rKz6ce/3wAAhDH/jwzQv3EJISzgBvi3gf8YyI0x/+X/2/4wmpgv/86f0I8nnGUWHw45gTqSyY5um+PYIeNwitQDhDWmi/h0/ikvnn9BaKXsjimvb3/k9XrLIa0p6gzPXjEKNXWQYRobWZeoWURxnzBbnnE5PeXXv/mH6KnL+OQ5lmiZ9isqZ0De3JMLxSryUeM5fTpn8vkJUVUiC83Orlg8+YTHc0mRHJDK4j4bo3WJEzqEbY9nWuzmgYUAy46oz2e4wQLXquiPO3798ohWkqefzolKl3/8v/+GCJvgUqCtnvHoEjNY4FeIvGStPW7yW352+oLYhtYcOUPjhSPKYMquTDisDarIedO3xAFcuGd4M1DBDEsZRJYhrRjXlRhd8Ml8ghIGW/Sclw375p7+eMubh4Lm9Byre8xsliBTyVB/4OTiKVFSsfMeYYcFJ15IONcYVYMcOL5R7JLXyOSG990KO7KZyRjhdFjSYIUW2jI4TkAYjRl6l/aYgWNolEEhkFqCY/B6yPSAqEpcaciaEbZvUTkOTVrzjZhy7Av6WjCSRxbbCjfq+F//h/+OZDXFaSIWVwN6OON+m9Ll7zkZXfDpV89A5aS9RbERWG3K4vElXms4C0OaaYgpe7Z/+Uv+2Z//GWY2oVESqSWPRhNCx6PliFct4dKnOWYsR4/pco2c3dPnNelRs/JdzGAzXgiUmNDXGU6/ZjLyWG8zvMBnGo6JBpuMBkf1RP2IbxqHv0x2JI0inlzw/ZvvOLz+nmls05wY2sGiKGuE1IwcG1c0DKXL/bjn80zww9BjlRHjUYWDSyUEEk3uBrz71atfGWP+1v9df/+qlgP/HvDKGPPur6LG/kZlexZdp6CuSRwLt0/JSkNoeajPYEhAUDBoF9WdYPwC4R3pTxPyxkYUPjEuYaTo5yOeho/ougqaj+k1yvOYqp7d4cieGaFpCZWFEQfG+ZfI2GakKnpy2mLJdHaDzj6mv0TBEhXYuLsD0ve4NYJz4/HMbiDdMpYeD2XBRPQIx2ObDxglWToDIwzDuuZdpLm7FvTVe+oPt8xGNjfbDVLB0D7iq9GM8fRIV8ypa0k+hNinJU49QdgeB2lQYuBcw+36hsiP+dMvTtjLFHUwTOyG3IvwVxm8yzhzJ4h+oJr0VNsCm5bJKsRxNLquqC2NanJ+fFUxnkZMnZrYr9CdJJg8guaIndUc3Wu6YcJ5dIc79sjLO+5KzTwakMrFMg3SvaSXgtoaUzprttmRsulRbU4wnrGJO9xO4LkerlXiSYcT38OzcoZwQIwD9M4i7QxHXVCWhsIUTKOK6j6gdTp8afCGggoLXdfEnssvlh7vUouHY8Ws8wmedWzffSC/dLh/mXFy2TF0F0hrR1nvCAbFw/4lo/UUbcf4kYdtb7CsgXT9ltaR2N4JzvEekTxw/bvvaPKG1SJAj2wWeOzjlrh3ucxGZIsAnwUP7hHHL8ibMW7v4HYw89ZMZ4+ILcVJrzk4LTpLGXrBYBY4gQedpBMhu8mAZY3xbI+8cRE4qNgQ7BzciY/1Kod+i9QLTKlo/Aw50gQyIskGYimgKlC2z/XKI+wEVp2RVyFDZrCmH2c8ovxf/1+E/yHw3/4L539fCPEfAb8E/tN/GYIMPmLIZv6MPld0JsELRzw6D6jTlKA9pZnPaKwWScqIEXk0w1MB5uATxi5i2tE1M7Sw8LuWQTdIIZn4Pq4zp/ds5PHAxFRso5Ttveb27s9J2oh2KDj94QOby6fEQYEaOvT8BZPLHP/9KQsroPKvMUPILjty6j+iC0o2hxRrMeB6AVk5UO1vmNkXTMIpD2VCUldEVYoiRpWG4v41RivaUPD2dk8ue2ZNx+7V97w6nfN0+YI35QP9oHFawXrtcnVhs2+mnL9wKX93x/14RDxIcrnnaF9BGpN0e/KjRDQu89mS22XJ6TZhWw6oHEwDXmzjYyiSjuvb19ys3+CqgMVqweg+5un4jKcvAvygJ93kLAPBw23Mqk6w3ZZGhTzyIeskrjtCFAVpcIbv+LSVITibw50HakwtI4bewogADYwLH38V0fr3WGUPhU/temgnRiDwOpCRYCotpHERe0FGRVIOdG6DGRQ7Wqa6QQ8remtLJmziu4pPT+ak646mOSD3WwrhM4+fo5cps0cXjKuc90YSDxOS7p5gEtHpjqXu2O02SB/8ucfYnNL5Lp2TM2sFHz4UHNMa7Qs6pZjqiLrSzIko9ZHXLUy6Hq0OmDrCbwtWTo0dnzCy90Q8RU4ahnzCELWw76mnIbPK50ELHmwFJYxfjJBHGy8pEYsQFUhUapjlIatJh/Vkxn37Mx76I/edJlDgDAFd5dDEHT4CaUrUImJUuHT7I14bUBqDsjTWwuBsVtyfw9kh+IP6+1fBInSAvwv8Z3916b8G/gEfk87/AfBfAf/J7+n7v+AjQRjiuzVeC4NZsorH3Ksd8WpJ5x4JuCUMr3AGh3jkMT94RKGLGzjI0MIVK66iAVU39K2FZ1lcLgMEA9cvD3x/05D1ObOsQyoXJwyYxSHxw4ZNveGeBc9MSOHU/Gww7KuMx6IhXuSULnSdorw9EBqffPGPkHcx4mTKxHpCVUaYOqHPJQ9yR9xsUYNHFkZcF1O8/pofj2/JxIyvvvw5eXWkFBMWnmT3/p6HPOXtmwe2DzlKWyR3motPrrgqYs7KjD/9dIVYK8onZyTrAyNR0poI59UeO85ZEFAaTe2VmHcp/+5ZSHAy4vad4J9kG0o6mmHNy/cZr3/3wC8+ecLf+dN/n+PuhnkUMh+PkEFDa4dEhNiPG7IfFaL7LXdZjv/+jMlFiL4ao+WMpW9IZmMiTxM7Nwy5x+E7l7G/Y9R7hP6I/dEgleZ+XzEKBLOuwR0Et4XHeB4yX11SF5K8zVFWhelbosYQ9BbNDGS3YGHGWLZgWxyR2mIwEiffspMO9mHAird0d4JPr0aYLGD73iCsOUfzGrFq6Z2Gzjtj8v6Oamwooimb3QMXix53YTOrPPwwwvZCHna3mLsNF7ImkYJDUXOoehz3FCUUe/1A0WusmwE/FijvgtHdHeiST58/QknBJN4jq4b9wUed1cRqQjgr2RUR7VOPfZFSna7QRuOVNuGTjtIUOE3DhyRBZxWT+YokTbCdgVRO8DubQEB7yCkLTS0g9hUmCMiOEqdr8GOLrB7oqxw7LDiYCiuNqY494/OOct7hHuqPhO1/XSYA/AfAv83ypAAAIABJREFUr40x9wB/ffwrof83wP/0+5r+RfjIZDI1Tp2Dc0Ira96WFWphSDY9q6dLTO+j2xKhDNVdQ2sqgmjOLFjiRytar6LINyTHhl62fL5QRF7NpjyiLUEYDcjSZ3w1RxX35DqmSRKG3mE0DvBtF8casXJtRs8Uzz5IRn2P66fs3Wf4/geC+Zioh6Fd0oSaHS0+AmtcUt81GAv6ACzbJmgVApd+5mOqkEfilNDy6I4Zy2CCO0upHvY8X7Rcreb41s/RDEyVRPo9Sb1HaoeTIkB9c6AofuCmkcwbm0LfsdvFPF9sMH7PIKeIcMYskMi6wys82tseHRRY2gINedry6m3JMa043r0jETULBd39gX27wLbHjCcFvbrDNhHCucOPz4nbHLFU6GDMtTojdk5RQUPob5koSUVMRcvc0fjmDDX8n8y9yc5uSZql9Vi/u6/7v785x92Ph0eTUZlJZWWSA24AMUFMmCAxQGLGvTCrG+BSGCEGCBBUJRmZ0Xpzmr//ut3Zto7BSaRSKaIQqhJym23bko32WtuW2fuu9YCuNM3bzLV8hyBz7VqyytQxgmmZkuPh9AK+gHVoHK5tKNJzqS400SDiBSSQI6JbkZOnny9sTMUXWlFouZcX9PjKvJxYrUeGZoP3ga9vLMNUGB++x60T9QaeReFu0DRqTxMTYtHIRjKfBDOZOAzwfOFwyqTNiqeQGZyiESPH4UQ9WfKiiUMkhZEvbzV3nUFMjg0Dw3cFe1dx7CPlXSG8e8dpaDi9fqDSLzwFg91+iY+J3/7wni9XO2p1Q5oE4w/veX2eOLcLp1yzpJmkr2ibDiycFoeV36DuLszHD5wOBRF6utZ/9j7wW+ZZUjWSaCRb+RXeCkz9QLnqWM0K+SZROsv7P24n8B+EBP5r/g0p8P+EjvzT438J/N3/2wJFSZrdXzIysDy/0OYrRH9hs25423T8/nxCdxXjKEgStJKMqSeOYHYKcZWpXrds9I510/Pzr7c4NyE+DcT9zMOzYrSSWhncdUd/nKjqxM2uI5sFMyR++jeRZqn5m1rzG2Y+pIWf7H/K395N+JdrXs2ZUzpwGxXuzlBdCl2OfPfpQrws9OeearFcrjYkBVtGVpsVo+qYM6yTJT/P6E5xuRxI2fNnX7zl4RJY5VeWsrBrrxjOI1uvCfaeMbf89g//SHdpGM2Z4eU7VvInZP17fv39ju3twpe/sBixo3/4SFg1vHi4qhrUYrDDERMD/XBG+kdMeuU+SN5T0Z8zKZ/pvlS821nc2pFfPHl+pT5f0e3ecryz6CbSrCwqKbaNxLFGSke4WhMeX1nzOcV3XhkSBt2/8pP9Gz7NGyoZ8NoQkoAmYmNNKZlwmLGTp28qaGrGw8y+qTHlivOSyE2msoK5DTRjwEwLYUx8nA3XYqGRB942G4Tp+RSe+PApwnbPldL86tszOxX58nrD1ariNL5QC1CM6CVSkmVcTgixoqoN13Xh4ydBfyoMMqOvOo4fTxzTK8Z9gegVxTkqo7jeWWoh+UmeMSYxBtCTZqkN1HB+DrzZOJb2mhwH/Lzlt9V73GXHOg3EcmbfXZMeDkQqLmR+y8w0BEpJvNSf8FEixopw6hlHh3/KaNWT5h5t9siNo/evzM8jj21FdT6y2nZcW8k5OzxnpvOBPKww8gBS4VJFsfJP4u8/RPjIfwb8d//G9H8vhPgbPsuBb/+td390pJR5HAau0j2VvsHJF3yukCpihjNf9SvGY8/qtqY4sDaznvbIzUw2I+UgeVNv+OYXv6C4mXPJGH/meh2w7zQv04mt7lCq4u3qCvfTA/ZxzelhomsFYV+xrxVfXNec5shuC9EorlvP97/6OwYxkEONT1fcx0j47sxyu8NUJ2bX0DQTQ2pp1QZt16jsqY3m+/t/wJ+eCOfCSS7UMfMxKLzXVHPm/ubEXVH8ehopSfEf7RKZwPeffk9TNP/j3/0G3Io3+wtfbL6h+eZrQt2xQ3P/eOLhnPmV+XP+7F98TfdcGOORnBWnbUZoi/qf/hc+Hl/5dD/y8Yf/k5gnrnLh4AO/t4m/+Wd/zderr+jcLYqPJDOzvBacGVnmC7dfOF7+cOH9xws/aa4YuyMvCrbCUIcjSlq8UAglGXpo6y/46vaWp8cPtGpAec33y4lSR6pXyyhfWc0FYwaeA5hZI6ctJQmOLFQ3EatBeYUwhZwG6pKodw1ma8lPA+XY8sPpnlV7Zl9adl3Ly+uGTxfJun1gVyXGHoqYYLPwwMhOCeYw8v5DT2eP/MzX/OxnP6VNltdPE0/DA73qqewb4tMr/fkjQsyo5sL1zR0b16EFfDlFLpfEsal42xduV3dUX9WsH2qUdOzvJv7wcGLdv2BiImVF2/wSczngtWY8CIb1C6lbs6wiUXi+1D/nw+oj7eJZiT1FaZ7DxDAlonjhfvxX/HD6NWqRSKPxyeGSQLoG0S/EbHgZTvQnQbvxbK+/ZGeuSLueZQtFtLi5Zuj/NP7+fXMHBmD/b839N/9f19ERxKffMpQKUz1gzDWqidS5QsU9X94U1KqiCx3aQYyBoAXh6LE3CbE3zJeeU3+Pf39hCZml7Uh5xZIcf/3lLwgxMtdH3rBggsPbhbx64qlWvJGaRlQ8iI43Zebv2kfW/Rue7md+l16R99c8lAur5ZH3pmLT3tLOBnPpab1krjtuRCGnivo4YroKUUV2tuEwOspWclfv8MOBNteEzrJrHKbWbNtbWrXm2QfK3hKqMy+HB87zir2u6e4qvqh/SVkpNm83zMLiKngTFl7qxP/69AMv/9uF29sbOhdYpkz5UKHdhVkI4jkjs6R3O/yL4cFc6Izi7vqWjpFt8IT+iaf7kcIRheD1qWXZfOBnxzfYOvLxdSFNvyHc/RU34YrkL2zKjE+RskmkBMrV6LFjLopwoxk/HlmJjGOkP0mgo0JS8syvP70wjCduVjsQkgzEOtH4FVVcMO2Kg1wQIySvoIZ2NujkEOsVnTJsSsK2iefwzPV65OXpE2ffI/rMcj6i3M8Y50A7FmZ3zVe6pb175PcPF8RSiFPglAJ56vGnns527EuFMnuexDMHEajqW3YSViqwXAq1sIzmmnpI1FeG6B1nCb574Xb1F3xXJaanimY3gnJkofjgMl/FN1QicP3NLV0Z+PT0HbfzG/zmGhUntl9cowbJWBJCBC4PcCmO6eHC8LgQLh3ZLZQl4VKioTDlQgwSYkJqhbaJp0+a6fwBs25obmqUqZkfz1TiLVX6E+WC/EgqBp0S/Pm7v6Q6fEIFyTh+QFwqqrogx99ykIIrvUC348MEKjm+uL7iuhGYZBGjorq5oZw8STe4fWKXC1MvEfOErBeEqaC+pnJHmiHyvszo7Zqv0xbnFtZmzzROLKNmd6w5Phzh7hs+TBVmu2eOAfkykjgw+USzl9TGst53jGphelng5GnvLNd7hxKW2G+orjK20ZTecMISlMfWOxoT2bYw2kL1cObtm2se+t8iv03o08QYHtl0f4FrK46tROdCM2euMAxywr37c76K3zL8/Q+kDydKOdB2t2xiYQxnuFqx3Vqq1Yq9NdzS05cfWF9dc/32a/amomt2yHZiOcyIzUT1smaIRyZ1Rh/PXGLg6pdf0rl3HMoj9eEZvZVUU426FqjfG9qcOKwiXbwwrCqu5kDubziZgUEPDLklHe/Jqkblwus0sTzek8KJk1roVh12vcK4iMbzWjRmeUJmhdIdx3mhEmec2HNjN3wisN5ojs+PlNYyPSWSLVznK8Rl4DAW5vmaxT2wGza8jhblLnxVbWivOoLzfOE6lB15fT7x7ZPmJXZcdQ1nnUnqwMX3pGWmPh355eanrDrLk3/keZmp93d0bUE3BeE8uf4Fh+cXzhwZqpnNlzvIHU4ujAX288TCE9K3LENADmfyKjLangbB8zBSBsVaONLcE9sNS3IkMfHp9AP95ZVWeuSkmORnabWUie0seLKWZS/pZs9y2ZF0Tz20DCIi9wZ5P9HOV2x+Glku9k/i70dBAlkV9PgJs4qIU827fMe0fETEjMl7IjOdWsgicdd1bNY1Ts+QJbprcFqzHBRRH6jMwHUQmDyx1oa8v+UgF8b5jCuOLBXDqqOKLf35hEmRnc58+jCwWVn8+o6iL/z8i44PU+FfmMTAC61eqN/VXGYDrwt1vaFZX+GfRmr5xK7eoG4iyXvUOeLqRBkGGp94vTyj00Q9WSgjajhj7Zan48w+HdA09PHvsYPgcPrIZpX4Yr1jTjPGacryBzZmi8uOUl7R5Qp9PECu+Pl1Rf944PLbA9F+4i//k6+pW0e2idPdW+5KQoSZ25df8uHua67agZtK0TGjosc/ehb9TDwFHofMJR+J48S53SBjjzp8RKcd3d4yMdM4hVwdqfua9EaTNpaVF4RhZONfOVuBqSVsFI+/u2fuA4vv+Tjeczl+QsyBm+2GqhYs/szDx4/I7zT76y1p41CdoUs1G+XQuuZSnzh9OKFUQK4MV2PFyTW8/eaax/d/YGpbagqtqHlOmVQ8f/WN43ezIy2aNH/g6nvD768UHz5mrm5+SR0+IvoFbff86/tffw4ldTua+oaqGL4ohq1y7NqW0Bh6aubVT+nVQFK3rHeKezEgqelSwn/5ljpFytxySa8sSeDSM/PiMcs1cqX44dsXjNlRObicZg6be/brgj9kvEm8tjPlMLAxltAmvv3de777x+9ZZo+tLCoHTFQEAUIrDrGQtaFTEVMis/U0smcUHb0uNIcP5FwTjODhkPDpT+uBHwUJKNtw99f/KfvhPfenhR9+9X+wdzVu3ZDNiC4rRGPpnGRvakJXU5InV54I2KbBzwORCb9c2C4C0zmylcSUEW1L2+6gBNZrSZ0rEjPBJ9S2wooTXRyRokI2kjert1yeZ/zxO/TzE1dScyk9MtesqpbMilVeeP3hI16O1E4xBIXIGdFPnOlxOuF0pDZwZyz+aeBpfmAKM7Jz5P7CMCsCjvX1K/vbtzyPJ9TKYIKDuGOzPMIfLoi7DZQTxd/gasluGXhyCe/AfKhZ7SpO8okqFl7GhXojEKcXtsM77soNP1y+ZRyeUfOAnB2bm47tVcaVC8vaYJPACc0sC+a9pr35hhtTcSUUYhHElSW/POOqBtNqhL7iPD4hNxIxRKIAu73GCCj3A6o+U8YH6jEwhpFTP1MuEZsjoirMdSFow1oY2sYj84KaYUgGOQjytcKnhmqfqF5qShRcamiPj+S6ZV1qyrKh+uqnbD8FBjXzoM4cw0g0kiAlq9ayzCOVNGy5cMxbXHVNGBZWmxWn5czrGHm7viP7I002qDJTjKRqW26BbVuoU88gNa/ZkNYrdJt4TS2XaeDtWvPyZYd5GDkdjryJO+6txB4bysagsyGIiHWZrrklOcncHjBzg5uByhDbBWctNm1RVwXjFnKveHlemFKmCYGMx7eJnBVmkSw4ssoIY7BzwHtHXDnK6MhdQciOGBL9YybaE/Le0/3iBrj/o/j7UZCAs46mg8m9o24SvygFPy806kIWkjUr6nXC6YZ6nLCTQdYakTKkQjoBCFTtqNYOfVmQSyCrmrRbU4lEHF7Qg+Fm36CXE6dV5CclMwz3TGZNvQ4MsecqeMoAh/6IFg0h7JjqHifXuHxF3K7RwTMtJ1SZkWi0tjB/hzp4pv6e4XThLBJ5VdFWFZ1aEZ7PdMKT7IKb1mgFt40g2RlTVSAj9AvOC6zy4BeUajhdQTs5slVI+4h3ayY7cac7+jATm45znOnCFY/mFTVN6INBKAP2glorpL4lFElMFwIN57zCXF6o1pGFno2WzEkwnifqfSaKRPSeZ3ngm9VfMKaMWs3chg1rMTBWFRen2c7284fYL5ybASF7ohcMy5nxYpibmXAeWZgx1UhQkk5vaZKmIKhcjRaWFEfO8cJV3qKkwx8iRi2E+YVn3dHIRFM5jkfN8ZNHbv+BtVpjqhZhFPfTmVg8rve4OTNcOrya+YX0BBJTu+M0VzzXke0SmErBZ0VJE8KmzyB1NVfCUSZHSALdnxB6R35Tfd7FOUNxin27YxklRlpes2f5/i1dDtQ0PKmJi+0o7kQz1VTtRJ8LcvjsMu2cwogVT+FMt67RucGohdFHjI80ZsUSM33yTHGmwxONAFmRlonoBFW9YHvwpWIZTiz1Ft+OmHTG1ZpDWBDKcxoVSQlyFdnMhrz8yJ2FUor843f/M0JZ3jVfc7r9GY0V1H/4PSI/8DTMyNbRfvUG5TNqKFyrQj1LxPxMERc6CrmtoLrDvVGkfsIUj8oXiJpb69g0O2YnsEvCxMRiR/CRxpxA3LJ3mRd/y5L+L4wbWc4XnuOFRjfo+orTwz2dO5Ow6OoWJyRGK5Zy5vA6kD68cDp9IiBoVivqkugf/8D9kJA0mE7iE+SqxokOU1ncVUbGmvvnA2E6cNYLu2lFLPc828y73PKqDogFmvsb2jcrlACsAFHIciaYV+qq4q3e4EfPIc3sVzArT/KCvd0QNztk3LGaLmT9gpKKyyUxVJkP1iNPGWFfaeY1Y3kmyUilWvzpCS/X1LZlNhF/f8IcFNfbhhAvnOYR0zhOfoApMZ0T3lYIEZme4PIaEIvAdVtaFZBCEZUDZfCLRyRBVookJK8yUvvEMEZepwlpjiTXcFaF9bKDuFC9a1iOnm8/PPA8zZRVxXZzRU47Bvkr2t0apRa25zNCFE6HmerrBldbqh+OGJV5/3gh7dekqsU+3NNUHW9cxU+rmk9nz+gCu+uOdbOGc89JCKbNFSt2rIxjXJ04jAXZR8LuGRNazGZmGif2z5lPSdKLCbt7S3udCNOAOb4wio7sM7ry5HqHepmpbvZkLRjiQKoEplqTwg/I+EwyEzpZYkzYRePiRDYwhoogJ6ySaDuiVKIOGt8v1DLjysJoI1VbM/dwbkbCMPxJ/P0oSCDkgJxbctaEOZG+GFDzQmokcXK01xW17IhLoogrrC0MLZBgeRmQ+kwTE0pE5hAZ/SudNsgYsGjqxmFkzZAmpK9AQKWuOcYRpQvZLjRj4eAtSpw4jjMmt7i60LjAm7wl15GphlcKd9WISh9R0hHE5zTiSjv61jDHTOwD8yWTfY+SBhsyJR5I2UAtcBiqdkWgUJ9qyqpBX56QMrHKiWgjc8x0ouKlzCRvSN4Qu0haArMudAIat+HQXaifLdoKHJlkP7vvRK2RWjHniWwK3aYhTjNLEsyLYE4TVQs6z3TeMeiRDgnFI7MCLQimI71NtDkgQ03cRYbjyI1NjKFDqIh2K6TIOL9hHDNZeRohmDqLcIJoBF4kZFqwSPSloJ3AtJnGttgEr+MreRSIFYggEGaA5OnEntd04uNxYjsKbtZnjq9b1OmFPzyNDDIhz5m7bs/j/J4LPU22zKJD2cipwPzVFwgZOT9rVjai5oHHONG93KBlpGtqqlpRd4bUJooaCX1g1IaqinRWEefM4/SM2q5Q6g7XFtwx8uJXbJymZEclWi6PJ+7uKupg2VjHtrul0yOD2/B4d0BeBupg0W9vCYPl/fItW3+H3dZsgOTPCCEpY8AsmjJBiBOLzsgqIj14JaAErIrkIjkKgSswDQLDgm4UJWrqqDAk4mqLTkem1z8eSw4/EhLQCW7qFtM6xNmg+wNWZEYpWd98QVMapjjjXj/HlKmqo2TotULlhXiZmYYTaY5M8cLWDOA6JinQWtHIiTluwSWaMjFtHSnMXCfHvYjIS2JUjqLO2OJJsiexoqrXvLlrMN1CXhT7fcPxKTEJUFkiVzWqLEQlkOuIjQ4eW3I5g5tIOuJLg4gaKTJSVXRxg7mGoz5ztW6ZR4/ShVQunxOMk0cHiZgvjA4WV+hsQvuFoXxJIya0dyCfyOKarTizKIkMkGpNNWpyMzMMEjYdpSkIp7DUrBtDSAti8OSccXFGhMhSC8ycQGvOWXHjtnwfZ676kbTcIGKhlIX5+xm+2pAFcFlwVxW9lJjeYztJSDX1SpKXIzcicK8cTZVxOiGjYFWAnUYJh3IrgnVoMVErzdHPHId7VBOxvmHVGnRWPM8LZmNYlleO84qXi6eNAZcVRjQcjODv//A7tPUch5p3XcbPE6Za8RQNS/F0VcW+BnWpKdPM/SeD+uWMo6LxFRulWGtJviyMrzMpJuyqobKSSTaMKpOmiIyJkTMtDmvX6DqxHgz1LnGZZ1Q3MyVH5zYYk/DlCTtphIBTmMiHgXorcNs9h8sPnC8jpusJs8FIg8yGMBeCLnjt8EqTAbsYKBmvIyoWpIFcDHJxmKhxZaTXC7V0JB+pRKL3Cq8ExReSV8js/zT+/v8C+r9ryLgw//CE7yQl17z98mfUU4VtXnHOIp8clfOstppGWIpLkMErSfaeOGTGqKkYsGTiJLgMT0RAp4i6bRAOWl2h7cww1BhRsDKwKzVjJ4mTYL1I5qJZF885a1onMVvB4hfSIXGIAWMm0txhrjTFaUZ/pJZr5HaL02u+jInX54ogR4qdCDiUE1SzZ8wzlVtTlYokJDkmbKeI88gUMuLliVlKMiekaEmc2IwVFZG5WuiWV8axQnWO+TSzdx8pJhB7wzBdMNHRLkeSAxU1jJFqtiwCurpFVYFYBEkqYpgI/3Rw2saAiQsyapwNHCaPrQteeoQXzANobSgrRfsyUb66JpQGUyrsrmY4DgjZobYDOQSc9lxU4Gbb4FLLZTGMiyL6C4fUY1pHq1dIHH6ReASDHHmYB04nMAK+uoFz+0qcFjbVLUJLvv905rpENn/R8TwMyPWa6xz5zaf3PPzuA1/dvGW3he/TgbG64/ki4ehZKLz50hDXN5yHiXHtqVRF1Wi+QLDfGZo28nj2vH44Y2VgvS8Ut+H3S+RTiEgtUH3P8QDj3Zfsf3JLHUfcOHFKA7pKxLrm/HqhuzIM55l+v0X+9jt8s6FOC3PKDMMJ8cMNH7+7xyJRIhCmZ+aoYYC8WVjGDOeeFBcUmSwSQiRctkSTiFGCrSAvxOiROaO0IXiNNpp50IT1TCSxVy2xavB5Af/HzwV+FCQw+IVvw8BqtGxXa6bhQHfaoXWhyAueF1axY1kCrk4436Jy+3nbnCUxwbqTGLnB+4w/TpzyiJ013makShQ3khe4LJ7sClpLemFg6cmVIIqRpl2YvWFsfoEeJO22ZX79jqU3IBca1aGWgK8sZdNwOSwkoWmnE5dssKJifXuDdTXjsSeUgMAjZCAYhXEabQXWgKoqQp9Y7WtK7Th/nCDv6P2Em58ZbxYcHa1+Q8yJOSvcOLBMBeIztXVcTCIuBvYL5VIjXSEUQZoSVRuwzRqWM0VZ0JnZBuKoaERF6zbUYkYvCaEdfpmpwowMknk8oKzBbBtc0eh6wVcWIQXBW849fL2XnHJLmhOvdNSzBwExJRhbxvrMQVQ8sWIcPDLOFJWZkyb5hFQnkqwYY4MEhvaCjoZWW7YGGt1RdETLhek0oG/2rOyFtig+3p/JRRP7B4zJ9CUjz/Du68TzceGoBX2/oJJmsoIgI9MZDJopZxQTUXlaoVlliZwdr63loSp8UAtNWnDR0jYdUipiONLUK/S2wjQ3NEGQ5xG5TJxD5vnxieH5wmFOqGyAV6TW6DmjNzXDmOioUdqjJsuTf8D3E8Kt8MuCPEqSkSwk3OJghoynzYGFQlAZUwzMhbBAUgYVI+SEGRti1bMsjlgCG5tIXhBFoswrpjZRx8BFVsAfvyb8UZAAzlBf3ZFRpLblubfU+Ym70mBHWKpETIn2PJG8JVU1cycZTc3FL7Q2YnNBxAnheypmosl4oZEUpllhiyfIEYpBu0yaC8IKSAkZLS6NLG2HWQLdF39LVRxxH3DjK9LOnGVCDyPG1KgkmE8zl6dItzXEknEq4dozeE2jHboJjEsmeYtQFtGBNJnsLNpuQAhMpUgiI1XBm4JuMusgGJyCIXOymnanUCGifIU0A8kowmXGXu04nBu2FpA1UhTqZaGEwJAFtQKLRyBJsSBEQbYONQTWGRoZcRkmKoLKnLC0tkFZy+QnZNScJqjSzLlMpJeZq11NjDVlTjzHBb94RllYDMw+ImbDQCakAs81H14zS1jjVY8xliIrWq1x1mDXAhU0MkuCUOhzRZ4v6GUmOYHXCj1F6kvAl4VFHPD5RNENx36h4orke4anF9QJvvjpHTfXFd/dL8xmA2Gh6jTRNdQ7hQsa+zpwGS7UoVANJ1a2RWbBkCDIipAX+lwIyvAsNaZIblYb3PqOYiaSM8QyIZLieBzBJ2ITWFUtD2JGsKBMISqLairqOPGUIUZPcpZBGkrx/PrDA1JKWvX551yXgF5qTB2w0lFbyZQTQkBJhRJgEYmiIkVlBAWdNRhIFkgCKweaRSOHkawaGlk4twLTJyaVMULxpwTBj4IEjLZIWSjFcPGAhcZqroRi9Am7UQxxRBuJPEsylrDdsBSNlQdum5F58oQ0onIh6QpJIS4aJyyXc0BVnnpRqP1ItwimU8KtG8ZUyKNHIZHTwmX1NWJ3R1EGT6L5yT/nNX8L78GaI8kq5HRmHj7rNnU8wMqh64hJmlQyS04Y7dBCUmTGtg671siQUBacVPha4Se4+JluObOqLLNbkDLDBC44UBN9ulDZFqU0Kc2MasYVi+/PxDQhJoVUt8SiiUUiyJi4kMmcpomoCs5EYlHUyiDaTIiJ4wTvhzOnaYFcULZhiQrLiLQrGAXZJ+zVCtltOc8RmTUmZg4j5MNIOSzkjSZMBUkm2ZpaQRESGzTrW4+ZVoyiwqeITRKbQevPYRqLF4RpZmJgeJ0Yz4E0Z4rUlOFEyQFz7FEi45czJw//8d/ecN2u+PS7FwZvGc4bVvnIu92O+/Mnxu4t+vLCMJ2wK4tWmkUbqjhz7D8xzYJd1fFl2bDNkbkVhF3DKCwvx5Fx9ujNBrfd0exXSN1gZ0dazpzHhA8gneZwfgIrcasWK3a8+7KwHK+J+UgSER0qtKpQ8pklQ2km1nLFh5cT82niq286zKjLuLk9AAAgAElEQVRQ04TddLgFtKmgaTH6GTdPRJmIMaNlQZhMUJCypAyFVEVkkeQuUI4SkwRZLiQ0xUVUEmjRM2SFyJZ6PdOf/zj+fhQkEGPi/vkjut/RbmoGaT7bSqmGW1ERUqIuGl80ViU+H15PZBExJSC9QEXBEipCEYzCM8wGjCQ4icoWkxOXcCLOjhpDLzz7GLlUkW6RMElSXRGbW2SBw/gKaJS+Ra09tKDCgVZ5EorzMrN1hdEnwrDgx0QaWuRaUyrJFBIZgVocswIhCjlr6ghXmzV1nXg8BRSWUXsIkWA1S55xoeVSB5zIaJOQasRGTbZb1DkSVpIwKWIxnOwM84QMDm8qjlNElciQCm6UKGOQ1pFzjYiBU1o4TiN2mrlcTsz3J6qqpmnhvAiuK0mpEivVYGUkC0Untwz7gX64UHUWYuSlt6w9hDRSqhX5nBGiJybJWn32MtybCtJAlgZZDC5pZFyotUQmTa8nGlGYBkj9wqqxqO0ajj35+MpSSRSGwd/jqhV/9s/+ii/3e1JqP5t1KEFwGtFI/v5Xn3iIF/7qL6/Z2JoxzoQikWEhhTXMnqXXVLPl7ic73rqGEi+M3ZogNIsfSSUhimXV1bRthTKOp+ORMGmUTOQUsNuWWSoKNYJEYwWlFK7XK14WuJzODIPEGI3bw021oteKqi4syRDKCtcdQVhSUeRpgTVkNzGnz+S+hBEjFEIVkgUbEzlLchLkGMgCkhLIlHFRkGXLkgeSSphkqZJimCS4jEKjk2bkR347UApIfUvzRlM1NaXXeDdyiJ4vpIYXjdsYFldR9i0pFi7LREojm+jwIeM5AQopDCVFfJxxxkCGXBLDsOBlpskbZiFoiiFGQWwNKUJo9/jtHVEZjFlQQ+Y0HbmkyLbZo945xCWjpjNFHlktPZelZxYGGwQqF+IS0K6liAopByigtWAMEzZbXFkhrQW3IYsJsuc8ziSnCR7GMOPKyDjCJ33GyBVXLyuCPlLNHqsXxlVCzy0pe1Q50bgGPTzTui1VHTieL+AK27nD2ky1tmAdKmWWCJdFMCIp2lCUo9tscG2NXQprO1C1HciRtZXI2mGqyJjP5F6yDAPHOKOWK9puohiLNYlaalhpGrsgjMXKnjwrLrEiNgtyWShFo6xC4rBSIrRktY7sL5br2RFuDFYuvLwsPLzvuTBS5sQUW/ra8Xa34+vNLcu8oNTA7srQHxPtOvLDw8C/fn1miZ7qX/0DN2++wF3vSCPsb/aYUtAxUs2Sm05yu6pZb1qGU8YUhTaKs88clhE/QhccrVszkyg5ISlkYdhVFqfXCBJ55YiuoZw9QXlybFgu95zPF5IIqLxBCHBlTdwISsgUDb31bK8qrkzNsk2Y1DGkgJ4TOQq8PDHEI97PyFRwuiBzQmeHsIDIiBwpSyIJg+olyUZMEp93HNYg40RUFVZZovcIuxCHFfwJQfCjIAFjNTffvMOYzy213XiF2zSI5wspFtZVTd0Y3M9/zoxg/s239CnRSE8WkVlCChIleoYJ+iljTMbFSAyQx5lRSColcDJwLg1vqmuKW1FLyPoB9t+QacgmE/0LRTjSZaavFf3pPbXe09z8BWPsUcNvaI6GTMHXI6pPKLcimUQ8TZQ4UzlIJbDEgbIsBHOFdoEXDy+vz2wCXC4zx5AY58zrIXEYAz9v10R15uXBwFqRlSTk6rN1OiOhfK47cIeM0D3HCsRpZr1NSCHJ555sHV45bm1HkwuiaKgiMhSsa9nva5p54vV8IAtFFoL7+YlqORKXBlvdEoRmXTRdB0IIWmUQ3Q1N06LOiXdXK2IKmCCo8kJxE3Wn0UmgvULmjNYWoSp0bQgFjC1YZQkZstUoLKFJ3InA2Csen18Z5gPH8ExSM0IqXqczYvMN+uZrHqkpo+Snm5HkFtqVpHjH+XdwyAE5HPnd88jDWPgz+Y7brxI39TuuuJDPgZP1VGVBicC6dpA8h2GmsmsqX1FGyDox58wUJbkEQr1GVhkXAtZpRB0oi+TUX6jbGt9rzjnSXAaiC2yk4dIstMkzCsNNYxlVJifDKT7y8v47fra/QpWIdZZWaZ6mE2luyFJ8dsweIoSEEQZrIrKN6Kwo0rIEg84SBCxOEVJG5oDIgmwKVhni5LGmIMTCbCNWwIqZP+Ep8iMhAWOo1Uyntgw6M08Ty6SIpka0HWFTeNkoupu3zO8/MkVN1Xpaa/HLGWwmx0K6JC5xIXsQBEqxZFPoSZRlQW0sk1Ks25be3dHmjC4Xmux4OvyG2mzR6ytE2MBKsHz/RFaZoDLrJtPur0lZM79+zWnj6caRWi18GgPZ95itpEvglsxRtMzeEoSglo4cBR9F4eKhuS5MobDYz+cVfdeQTWGMJ1CKUgIrvWXSEiN6lO5IOrF8PBLY0IeJVY6kIVCWCVUsyj8zW0klIs6s0VJjaVlmSRULtQZlIbSOOCXKpadmx2G6p9OJLjm60mCVJrbL58aqeaL1A7Xu0FXCGoVqNbEJ1BaMMugwo/VMaR0NEyl3YD7XzK+NR/mKbAwLE5hCWgQ5x88yhRYtwRfPpU+8nguLVJyLZDkEihqIecPX1QpfbdjUDhcjn4ZbxuOv2HfwNGf6KdDIDp89c9PjT0+sXxr+9nrP9TywbQqPaLJRlCHhk6QPiao0SHFGyIzWhlYYzjaQdGY+9eQpUSpH1W4oLjBVCjXB0iruHyVvThGpDaqrKOJEi6Sq3mGeHhnymcrWpBKwoyOuA89PM8vJI9919IeE6wzzGOn9gg4S1ShEMWiRuHUZJxOrAFoZFl0wIiEKoCxReBa5MBdFHyW5krgFvJwIsmDcjMwWIT/3HCx1Bed/j9sBIcT/APwXwGMp5Z//09wVn3MHvuGzech/VUo5iM92w/8S+M+BEfhvSyn/+79r/RRh/PBCuS28ziPXdUNULcJtOP3sDbJr6LodTbvjsHrkuNVUfkGNCekz2Xh88mQfQAicE8xZMBoBVhNihROepdqRWCOiIlcJGQ/kp0eEdjC9cmmPcP/EZbWjzVesVuB0/bm1thwo/AQjDNNkKPmVYE4EuWPSDc2+oe+PXC5HjJyZl8RobwhxwxuV2LsJP2uilQyTZxwTP23XCDFwSpqfbD3mWFhEYBQJbI/L18zygLCOPJ45JY978SyceBaBkATyGCj/N3Nv8rPLlqV3/fbesaN/+/drT3P7zGqysqooY5sSjS0YMDMjJEYIMYQ5njH1H8AfAJ6AmMGAARISgglYVBWuquxu3cx77znnO1/3dhFv9LtjcK5RYjKNUdlSLikUESu2thSD9WhprWc/KyuoW0+ZzSguExZIsqZD3jzg5RWeNciENBUkg8MOHdlWIeMNYu6ZuR6yknm44jqLkOuWTimWIWOyAWc6tqlmm1bsQotrIGZJuQmopaarBQ6P91dUCNwY4QVEDGRjoA2OswvIUZBYTzRoYv1BDBPvSfqIWC1ZbQxOefaLA1Xi6OuWH/zu7/Hy5mPuheahr4kSSesFrYl5ulcMccQoJmK5IVo7lpkjdS2fMHKjUmYhEIJjNzzzUJ1YJJAFiaBHpEsSO8d5BbnDJ5BHGxarGxabS0bjMRGkUhOHkkF5hM7RzFmXHpUsubrU7IOj9Z5p71hdfIx3EdH0HuccdduSzALKRshGs45n6JAR2ZakGzjsep6Pz2xzxe3qgmhKmRpPnQtGOdK2gngQTFJivUeIAMHThQjigdw7LlSCW1nGJuOuM3jvMdOcyRpkLkEKlPn1KuD/vJnAfwn8F8A//CXf3wf+pxDCPxBC/P3v3v8zPmgOfvHd9bf4IDz6t/5Zm/fDyH13JG9H7CkivLxgGwfWVzfMPvqEMom5SK4wWUNIU47WUQyeUgYQkl2vsKIklppxeGbqHfJsaXRLNAtIqSDJmO6PNP6eKl4QrhY0Y00hEkI6Mo0jQZ5JD2eEadhzQqmE43Cgua8ZppHsD7csu4nnh78k2bfYXuHSmGK95mKtOLqRzsPkKpqu5xDtMbIk1wnzNGVezImVQsZL6uGASyPC5oJ88Bw7y2ka6BpPGQ0kfcvuMNAcCrrFPZd0NJ2nV4/k6USUaMrSY0yOWlriMZAmDVmaI+yBaEywfUEWW7y1nO2AnRyNGQnK4QCtekJwPEnoJsW1nAjW83JaUy41V7FhJ2KOXYNeFNiQcCkluxSOXYUoN3h5RrDgfKoJIkKGgPMNLsSodk4fDGnsIZaYJEfnM9TxRBEcupM44YhLB3PJop2hvWG32mAfFbGxfJ6XZOWSb48NjXP4UEG4oHcveNId6ukNTdKTuC1CJ/zxp39M9/CnfLzVXK4L9r1C+Ib6TYff9STXGcZ3hJAypIGuV0RRhnCBSGpuPn3N9Sev2G6u6Qic28DJTfh6JOiYi6Jk7uckLz9jdJphajirnjZe0J8annd/jsgU5ZSQFQnJbEYUafoxRTZvCEmKdwaTZ0g8QfTMfEcWrbhMNI2vGNo9k3W0UuCJkU6DM8TW4qKEUkgGDHoEEwWkk0S9YJZ5fJ9zjKFyH7KCadIYrfF++OuBQAjhfxFCfPxPuf8e8He+e/6vgP/5OxD4e8A/DB+mmvxvQojlP6U7+P8y4wZwEcenkavrLb/1+x+zEgmL7RXzxYJhrGjGJ8gLErWilAWlPzJ58yHYQkFEyv54pusDWmqMt/SRJPcRsm9prSfrBIWDePMI0nHaWYrrjElNTNohgyK24CV46xjbNyy2L5FDyTSs+cXPfsprpUk7g548Nl0T5zGl71HnliicUWmGVitKMWKTBJOl2Grg+RwTkoF+ipBZzkmMFFNEsAKRC86qxJcpq8jjDieEW5LZFjkPbOsn8osI6ROmqKWcApRLwnND0DHiZBlES+Mtw9MRS8eyzHh9WHO79MyTEUXKYASJVYxRylQr9o931GIAH2jsnqlP+daOxAvNxkTUQqNTR5wP7J8Eye2GdOZIYsfhwXAeJ8KoSJMGpT1tfOAzlVONAidi1ARdEpNEA2kIVPMZcbYhHhuiMDKSgCxIIks6gUgjViu4fd194HfoCalj6kPHVgYKLVhnl/xJ5TDA8FxR1Y+EMWGuTwgMLwqwSUqmC15nG576I+PzDm0ERSlJ9ZxZsSRKNVZ6Oh0TpxIpc17+7u8xXyxJkpS9sUzGYrsYLzWdM5gHQ9hUnPVAmsyIuiNvHk/oJMPUA28edjzfvyOWnpt45N/49N/CKoPXKaEdqZqKhfLIMTBblhAFll1DvlxRJBndmPCzu0e+Og+EkCGtJwSFF6ClRdvAqFJ6B8IMNJGmjy3Kjgx9xEoFLmKHxBMxcRcU887QlTHq1P31QODX2NUvBfYDcPXd8wvgl7WM3n3n+7UgkGjNpx/dIoViuV2yKEsu5re0euC033M0ljhvEF97hvOOeb8HbcmTgkKkDA+W++OBZmzwMsKPA7vdiZ21FLlAWIuOJAyOm1zwA/2ac9Ry6Dx5lZDYhjwGd27o0jVmvubMG5bZNZm6Qi5rDq3n0uQEHgmHGetXkqY/45zjedRUQ4ZpKsp1RiQipumOzRgI00gkBJkLCEauyox5UdLrJdIp5LXHtB1P9zs2ZUqeaH5+PLOPLNMqIogd9aTRR08IjuAlw1AjaktPwlrc4XzC2Q2EIElVhZklfJx/j4ScXJeUU0pqDGMwMDr66szIgFkofJghd4HCHzgy4gZHJRPevqkplpf83nXEfBgJ2Qk5pTzuA7pVLK5yaEZW0lPJlNXUcNCORxTzmYDzgJ8XJGLAC40NMdpOqL5HWIdEQGIZ1UQkBVmQBCFZ2As+WWrWPuJ+lnJ33lPXe0hG0vX3ebfbM00aGRQXhcEcatRhxzKquPz+x1TH98RkONdSmR2bWKNeX/N8eo/2C0yRkqxucOuEp7MnFIFOCkIH6eICIwdM1zIZRRtGBgzKBEwcsPWRu+qvCKFAPRRUXcdleonhma6q+fKbdyT7HSfVcCgMf7OLEauYVErO4ZmVVqT5ljSSvIpSFuuYN4PlF4ea09SzSQZ+1h452QamER3Ch2G9OkVFMVaOSHqSWNIDyk9EHbROIXPYWc/gOpZFzHKMqRaK+Vkj2xHUgrPZ/wsHgf/bQghBCPH/a57ZL88dWJUln+YTefaC5/HI/i+/QV7W2M0c73K8HEhEiZPPDFPPcp2StJqVCxxdR74w3OaervM89Q1VOxFsTekcyAKhDVnIOcQWZM3BPbMYtlQ91L1nnsTE0wFLQdFbqm6CQ4Z/pbhKNG/iGdP9HVdXt0xY5I3CDi3zXhLywEWeYsjwO0UiaoS0qFwTuZYh8ihinDSUDMyiK7TZEWuPZ8bKe0Qxw+sH3sWCHx8e+VkNpnIQG6xxjPEM0xlE/EHkYxbPGUzL6GCUEiksYxwoBoPVkMYpeWRZF4KZHnDS0UvH2Hac+0AzTAzdEZtIcr3Ez3uqMUedzhBb7o8DbpaCHjlVETepYUyWOB/RS8cxllx2S3pZY5KROE7xVrF8Lkm0Iywj4oWnNT0zJKOIUTplsB5nToypJEw5iTPEwmNtBk4S3AS5YpEEkvgjRAx/8e0bhD4zPSY06pmoSBB2RqruiNSSzargy7/4Odvf3/LHH/0e/+vjj8Fphvs9/8rHA6Ef0ASy770gkYqntuVZpfhO0LmMwXvuTkd83bFJC+JZgYp6Yp1Q7w1qMijvGe2ZJBFEYs4bU9F+9XNyEtKlpQoVfl8xhjvU2pEcaxb9NWhPehhJNgrvDNXMsPQztE6YqQi6iEHscWVC2UXsmgp76oisQiaKsQ9Yq0iEw1uIjEIFRz8DlSm0sVijCdYz9YEBSwiwcgNFpviez9gFx2gczplfG4t/HRB4/CdpvhDiBnj6zn8HvPqldS+/8/0/7JfnDnxyuQllZRnab9nKmLW3BHfP+HDHTL3GXDpO8TO705HpvaLjgUzPGKNA4xoqNxB/1wLceItbSJarW2g75jLwPMSs8y2eGp9pmAaMHJiyGOkbxqHlKt7S6jNd7KgPe7rWUj0Y0vyOdshI5oLzvsOIkawaEF1NQSCN5iTaUZ8VVXOCFPJ5TFbm7PYdkQ+UyjD5mLZzpMqQRJboYSRfJ0TTCaMuePCveDo/0jJi1XviItC3Sxp1Ii0M0/lDKzJ4hZmNTL3E6cDYdWwllEqh/AobgchzDrVjOvVErcQGwzF4uu5MsBY3doyth2AYkx0yRMTLiEjk1KaiWgVyMcOIkqp9Qqlr5nXF++YOtdyQLTWjfUT2GoJCHxpcmVKIkXgu8SHC94IoXWFlS+LOtEbjxp5ZmjEkmlQopBDIwAdl3slQqgmRZZyGFUnuacdbpocfsZcjstHsd2/5m5vPOTFRmCXXY2DKA9Xf/oQvtpdkYaDqAGU5nAYGM+HSFVMcKKMX+MFTzCfsxZaptlR0vHs6gDVEqabLDuA1/Tmi5gm8ZdanJFc5M+MYGoM2G25Pkm/UPVMqGB4q1lvPOQn8YHPFN/fvSeYXvPh+yUYpRDxn6CL6Y058SJCvF3zkHOFgSFc1USzYliWVN+zuz6S9IS8NxzGgrWJSnsFZWqtYS40TBtEY0DFGJgQbSGKBigO2mSHEwKEvGFXPYtviF3MmI3myya+jCfy1QOC/B/5D4B98d//vfsn/nwoh/hs+FASrf1Y9AGBwgkcVk4YeFeB994859AkrbTiIM97N2QFRcyZPF9BYRvfAm+4t0zRwt5/x5rBjfzyiR0W8iHGxpj14QtxzFS+ZfMNJwTrKSEqD50BprhmWGT2Kw65F+gPFeo34KEW9PROUZH+ek/XPpKqgOU2M4h3PpuMqS3h3vEebHeriBVfCsLw0HA+W/l2PtJZZkuNUQKiAMhObiwVpHGPmE7NQ8rZ1HPYreu3Ya0WznXMZEp7PD3RWUl500AVknyHkhNOCLLdorZhNOYNtCUmgpWCd5sRRTWME7YPAX+c8x5I5hvG0xzw3SDVBVhDiEl/UmO6E9XMWy5hErqhXkk/sBuk6EilZuRqbjPzZN+/44e8kvGRJLD16PBGtPWcfI/QMfQHPoiL1K0bvCMuejJjCGzJRsMszvI9Ic0/vIuZYMg/ICCQ4GYgRTKFkGifEKPnmvOfufsfIivZ8z14aPkkVCydJu4Y8yph0xaaM+UMzp+0MX7Yn/s0fvqB7q/k2FczSEpsGOv2CLstJbzKWQ0I7JZySlrGyTOcTXidQK7rTmdh7Cj1jHVua0HF/eqI/KnyhmPcOO9TspgZ9liTHjl55frj6nLFcIj/WfO9vfIq/C0zzJ1ySYGQDLmLgDR+tl3yaaepdQ2U9d31Msb5Cm0e685mTtbRnz1WmWeiAmU3EIiYiYhKBnZt4niQ4CX4EH6FKSTcGFgG0bDFWc04bxiAxXrHIez7zE6bXtH8dEBBC/Nd8KAJuhRDvgP/8u+D/b4UQ/zHwLfDvf7f8f+BDe/ArPrQI/6P/r/2nEPiq3tMPgjhUnI0lzRWb0aA+j0nGllkoWL+8Jg2CdwNkVUe4NzRu4jTUtA8dOs5J0w8V7qSIUBvP1OVEWU41jpgU2mokChn59pJjPTLrDCGStLpm6BYk1cCpajifLZeflVzLOa06YU49e7OjDoZNfUSYBmsVLhWEpmJyCkSEjRyTqEnyBHSC7h1i8ohFwirEuGdo7y3jXPKE41GscGlNrBdsYojGHNwFkz1zPu+huCAZG7rIowdDHSR55THbA3KvsCmoECGCoy8UbkxgNMSDZ+Yk/tDw9G7P0bfMLktE07JONLMipZZX+GmkfqrxiadcxSzOitv1DTI8snq1oPs2Z7wNVFPFdTojXdyQJRN915HMIYwWBsGKhLF3bNZzugMkFxFSBAYLMw/S9FjjGf2EjEEmPY1zSCfJZEQ/D4wCtI+wp5bTt2+oHiQh8ojWMUstxzHlH/3pI+66oNGWVZYS395wScUvqndcioLreIX/fE37rUeUC3weiMstm2xONF9x7iXd/QO5EQxOk21WjGd47s8MY00ZFIgzQySoJks/dLRjIG1GWg/+SuJ+fuTw2PDFx1fouSTJPen8E0L1jterLzhOHT9/12K+f6LXGY6G4b7l+zeXbNYJP/32iZSYyGVM76F1gmAgtmCShjhKyKaCwEQsB6RyJC7BG0caCbySdGMEyuBaT6o9T13CNvM4JVEi5lgINidD89JyUwW2wfDu18TfP2934D/4NZ/+7V+xNgD/yT/Pvv/ErHRY5ansQLzWVC1ku4Y2z3ndAUXCoEeUj+lGx+nrd9RNT2wdzdAT+pqbIqJc5pwjTZGuiK4+TPxxzjGLNFpHaOmIa9BXCj0F/IuI5KnjMiqwFzOG/QPTHq6Oa9afzNjd7xheeaKrlF5Y6i8fSdsjM6loOoMdJ2bZjCjkuK5jdbHmYqY5VgXDWHEaLUYGyjJhpUciEgYf6Kj5WZOxm+UUoqZ3CWtdMQZF6wvy3jPIgTHRpFOD9BrpS/Kop2bC5xorFGUhCD4ljSU7EvIjRCpnTEeKXBLHgTfffMOXP/0r9r5is1hwsbgiut5QLjYs0xJbCJq2JRs84VAwMPD9MGL8nOnPGy4+UnxcGt4/rbA3A2lTo+gxaYyeBJHzHA8TcV5wTFoWaU5HzOXzRD8PqETjfIs0KYPW+CnCEdGKI0pEJC4ldp4xxERxR2R79ocHunNPMVi+rANdCqI0XG8+5aC+IXUZ15cFauqw5oLODSRDQbsoGcyWFYo/uFkyig3X12sqseDNUNLUA/ow4POEui9RhSMaerr2jvU8wduU/dOHAmnTTsQuRbaBruvZO4cVJ2bNEiF7btY5m5sbLtML4qsUW1gIJX4vKP/2lu1OY6cGLUpab7hmyaeFohlqrpJLmI1oM3EvznReEjyE5gndSrRxRHGHCwmImCB7mnGgmGVcLEvGsebpPOBFjEgcfbPgOpvQIcZYhwwFm8OJSikyKxiHJSqqfm38/UYwBmOliNzI688+5jQKfvDS01Qjz0y8ff9nvIwu+fx3P+Hp54/YpMHYltre0x4nuvuJY5LilznzSVNECZMdaf6shm7EXRQsVUpke3ziSCL442hF3jvStmDMBefUsGwHZBdDErHqHGLfkPd77oYHTAOZdWw7wYqR5XGCy4KEmL6aKOxAmMXY6sNJN6cFdhSIoUKi0XGOtSuOz08cdMqxuOB9mhO0wz1PHGuHjSyzrSDXkvn1jPH5hDutWEYNx3Tk1mw5xQPZuEYkFtlbjOvII4nwDm97wuRRl4HPtrd874vfZyUyiGpmiWb3PPB8HGnjkePwyMX6Y1byFYurAN2ZMl6wDE+83pakq4RM9xzEiCNh9IKL6xnH/VuSraIYNZF/wF8U9McVm1drJmX5YTpjiCQ3Uc4gDGLpCPWZSeccrwvK+S0FA6r6OfN+oh8UU9wR65y0B5Xm/NW7PT968wY/KZJ0okgTXr36PfT5zBtvUdvf4ndeX/J3riS7vmLwOZGqeB3PafzIFx9/wuIyYjhpXBQYJ8c+jhBqIksz4o9TpvbE56uCt0NF5wPl7RXH6olDs0cUKzha4uY9dngmyXNmr17yxc2WL158RlSmpP2RXliKTpKsVuytZ7g/M0WG01XJ8icRq/UV7uaCSOZs776h4cTbB41TERE90/Wc1CuKLiC6Dn38ls9L6LOYSBtioXD9SNMbhkagVEpigeqAto5Frqj3AVVFbHWD9wI/WMImcDJH1oucfddwf0iQkeKjZcKfHH81V+A3AgR646haSN69R4SRp+gjBhczw5PffMp8ecHSSSq7o5osTwfHXEaE8czylWK1vWDvIk6N5N19TSNq5i4jmt8gEs+pPpHmK1y1Yz0rORtF5iemyWJDhHeWzjuyLqNbGGQqKc4R3lYs9zXJ7RZjK7y7Q/qUd1HD57rkGHrcfOCsHFrEdKKjMz2hSyEOrHWM8QZdtEROMPZrHsaJp3Am3veMOuUx65FNghZHdo9zZFsRb5escjiZZ/a9o6tKuusjZViR8khMTvFiyamrAUoAACAASURBVLtaklhYLjIuHx3Rjebj11/w+tUNoljQ0tD7iVFE+DLBVxVq0rgnAWLAbveYvUbIG8QcXmy2XM9jdkqz0CecbVHlCzaFIvRv6LsE3zxS6DVC3WKeO9oIooVCuyVSeoRyBO85pT3LaUaQnnJaUqtb2uQaXbdQHTn4iqIAkJy9Rm0iJhyZD0Sx523fkrYTsc/QomT8LY24S5jrLXE6slt8xKAjZCW5+fj3GcU7br2m3JyxdY4MCpuMHL/qOJuebn6LjkfG1GPuW57zlqZ5ZKYbxATCWaaDwelvcSYQJRHb+SsuL1+w+uSWMCZcp5raJYjFJYUCUxxozwNipRjNmnhqEOOIPTY8pxdc1o48hcuLz3h7+ScgDNIuOK09n85XdGHB2/d/xcvtmvj7r9k9/wwxCbIGIm8YtMdmKUenSIaBUjlCcCQLoI5o5Ici7MonPCRH0quUXQ0LA8vxzDQDcU6w04C3f33G4L9Us+PIV88/Zq03tK5gdfjH7M+W5fqaWTkxvn/PU5mQK0XSOmLXc3I5cS6YZQOfRiPZHGpX8dSf8GZA5YFp1RCZAT2zmFRSH57Q5hvOpxuyj7+PfTgRRMFyvsD0KepiRxApuIpRjKT5ipmqiQ4HRheTylv6UPPxyxeYMCcyDYlJKV1KHGlOWGI+CEF0Q8QUxwgZ8JWml57HCN4fGvKuQK8VZpqzGD8IkDy9L1Dlicf9gdcmQSVLik+u0d/WTJMiiCf81NJnksFHxJFhna3QdkbmBcVnsN1ecv29l8R5SbresH122CYQZUdkD36mMI1Dm57qfGD2KqdQntdG8XlwSHXk/k3M7euU1gr6sOWmfsaaJXWb4VRP0wSqYkStJlbtBZNIeVmdOAtHd6NZqTXd0HBRXXGWLcsQYwpDmgnM4RskA/08J+9i+vqERRDnlniIETbiqe041CPdQ0WQOV2xpFCChbmln7f8wesVLyJJ+ax5lAopO1b6lv3qC5KXjuYnzzRXlrWePkzwjRVjmlCH9/gxYRGX7ERNMCnL7DOO4zecxYijRssNdkqZvYy5ShPSac3Fao4hwUcPVMHy6B6QZ0dx3rDQKWbdMT2dkf6CNixwcUp8GfDU31HYDaKBs72hz0882jtcmNi4a0bfcMlHqF3H1L5EtO/JdIVJA2M3oSZBYR1f955fmJjGWD66ypBuQkSwTkdqPdIajx4Senpyk0AycZAKTIaWLZMDlxo4/+r4+40AAa0ioq837DZ7gj9yTgSbmWa58owPM5pLzWW6obuAhz/9Oc72jMMZKQJPVvDjvedhajh2DT5I1DwhCz3rrx+4jMGlOce3X3I+OZZnQ5W0bMqKlV5T+kAaa67o2Y05USZom4bhZJnUgXKWIWeCsstpriVuGqg7iZ4fCULhu5LISYhGlNSUWuMcpOeeECwBQaUdP2t63j8NqD4hzDynwxGddJgpZV9/y92XTyg5MUQetzeU1ws+mn0Crz7h+y8mnscXWNlzm2b08Zxy6MluI6asZJPO6VzEy0IQExHZJbHV7K4hjCuWc8n+WUHIsclE4yxEj3xkt/zwastsZulFiR061vMBfa5BZ0SrkgM5nTjyWs3pXsQ0TULwnrKOccuUbdTzsJhxeXlJVLzg8fgLMiRFfqActnjVEpAEkVBf3KKinsU3f0427RizQDJE5HIA3fL2DHdvH6j2jjFakKxGfvA7K9JYc35q+FdvLojtEypNYJ0yM7fkHw+Yh5qbFzH1oSGUCZnpeDTPdELRDZZ5LKjWBVlxSz7FzD/JkKPkKDwfrz8mHhL8+D3CHzr8IiKdAl0/8lwb0kRx5XMS8QWVqLkcLmBz5OFpz1eHik/GK35RWa7sG0ySst89Mc0HRLnm9TomOS14+7jnTjoe3pf4ds9cHmjSOyQOsYpw5Q31+58wiicWHdgiZSwt094ySc0is0zS8hBDc/BcaMELHVNGikQODKZHRAk6mmNkTVIHTvGCzOeMhWTuGs6/micE/IaAQPAC+3nJ7eTZB0NqWx5OFXEquXoZk8xSopXA/GLHu+efcOkWyLwkH04sVxnaSPzZMA2G8/BMeBCEWDC72aB8wePxjvq45+Ay0DGftwaZFbj6wHk9o5kF7NMNQdY055507GjiDDdPEPKMj2b4FxHF8ZlBBMbQI1RKWhR46zhJT9N/SOG2YcAbSy9itMyInaWqW+p+YFIjIhvRKicfl9RjReEGwilj8BZuI5K7mkEWFAePKAUvLhUp1ySuZugcXe1ZLnrUQjBGc4psjqRgLhOILFLHiFlNGCJUb5BTQhKtieWJeuzBx2g3EZ0C/X1FL0b0zYo80bQIjlWHTFLapORSSFzeULiEUR/Iw5zJFHSbnuuLCdecaDvPwoNZZ9gLw/ybic1J0cQTfdizXCuiwWDHE2Qp9mQgOLzURFIRzSXHOue58Zyagfd9Tj0kLDc92+2W315fcpCB4joifXRcRRminSivBUs38fU/OlC8WuGkwexqdivLZZ8T1re0P/9LRFgTrzXZEJjkA+Fdh7i5IE9mpO1IGAeyIqeKE+rzEduf6KOM0heE8Yk+WxLNY/ruxNfv3kFwbGzCdn1B1yd0Q8w6COIrQTQKfpqduLi6ZDH2nL/UfLP737l/v+erU0PU/hT9suDi9TWJuiZeK16EhlMZaJ9KtIhxYsCZCT2liFySqB4tJdZHuH2gmQbwniId2UQRmMCoFGjJopxo+oASEKmKqDzykct5E2tE5uHwq/l8vxkg4Bz2+I5jnFP3FfG64EYskfKZm+hTdJZw/9Vb/uKbn5OEAuc7kiFGqYKlXpPHHfOo5YXwnNoldd/gAdUPBHXGnjwzA3MzcCEkzA12qDm7gP6ypuoMq+xr3i0i2mPMLBHIHWTPNfGVQjQGUbfUxxq71ah4jqoE4f1AVpSkyYQKgiALTl6RJBlZ2+HGiWkRGM1E1Xm6uGRuPc15ICcQzg37riXygShNyIaaIQoIlxEZy/OpZ/3JNeHYIPQVs9kjsQ7YWGHLnJW0ROaMzjVJqSllgr0uScIa5SVXLXT6wNBZRt+ivSVxHUjJKl6QlzDFMdbkKF2zoGSWaWZXGzaUpN5S24ah6VFXC6zRVCvL9XiFtDX5YuRhLGhmMRdmh/lFzBTnuDShGyfShaBuJZm2JE/3qNOJDs/dyaAqGKRh8oHeCbooxmtBmK+5vT1ydb3kcqnpZw3xsKS0AyFbs0s75q3maf/hFOj9qSc2B8r+iFrOCI8Wc9sh7++ZixnPNeh8pBdLRBkzXSy4yAekGjkNkspEQMs8EuhVzjkpcY0inDu22YJFuqKVJwb5gJ5NVIcV+hjIZ45sMzLttpjnn2KTlFYoys3E/bct80TTrY40kUZtO1zzEx72kuK6YnOf8X/kP+Hvbl/hLjP2b3L2TcckJnQSf9AiSCwiyhhMTNcGrvI5ddxxGkeclZwKRTIpZkngWkoOhxHrLGkM8TBjkVuMFnTVyEI4WqeBX80a/I0AASkC7XPP6fBj9Gefski2PNyfGLOBP3v8E8y9ImsGZrIi8ZqL9IqNGnl2jq/OjrOdczwbWjOQZznTFOPUiXMsKXXJ6qJFjEsqZ3lrILyruUnuoPwCN69Zza/Ytx77dcrm9Qr/9JZR5kS3ayYxYArBuXfMPr0k27VkKmFzs+DoBs5TRzZzzO0MY2OypCdzgvrZMZrAOMAgNFrErLq3XKS3VJtAc3ePSluq+sjqXPDadNROUfg1ev3MbfY99GKFHDKukxec5w3j/ppVqZjSmDgc0a5AXRp879FVz6kUzJxiGjtCtIJUk0Ql0WJJYTbYqSEdYl4uS35rk/CZlpQXcxKvaHVH6HriYcL9/Il3qzOxHLl+kXJZrwmjJ7vZIirDz457HCs21Yy0NIg4w/iM6jiw20/8aTsybCNMXeDcAasy8n6Pdp6gJK2IsXmCFopCJFyJntdFitNz/rCRvEkFyVwwX96ykJaqCKiZoX+jSQfFn34ZeK9+ROlLkk1H/vzE5FOunedt98A6+T7iLIg/Fvz2NuUX0SVjeyAd1uSrK1SRYU41Ub/DR5r9XlLkmnN4RraCTX5NdJsjvaA5VHz1izfc707MZcar/ESYJ3z5+Feou8DVMtARmIZHprDiagJ9eOD1i1uOwZO+eoWqPevhjIxOjOrMw+FIPA98PU/5rc33kPkj3bSjGSCSHhMUAY3ygnKC3BvO9oTOAnOfIDuBeJwg8cgspsfhS0mSxti+YIhOiGNgWkKaRJhYExvFbzQIBGC2KYi3f0A7NhS3rzh1jtVX39JdXnEyb5Gho0gWiKnjsQz0KArruCwEYlHwp/uK3bceWzm8CxiT48yIKB33e4cZanQveBSOxma8Ku6IVpqdjHlcvWNjPFGkKJuBXawJ+zP5oqWXmuUgUFnKelpzSh/hqkQJT9FmLPtA20i6yBINbxhDhPManxo0llppzAgbfeYcr7BxoGx7xgtB/VSwWDnIanS1xPXP5Hokbm95TGPKbmDblOyKE+tEorcpgzbIfiD4Lf1wINtDufE0TpK4gHyUJLMaOVbUkcIMIKcWV7WgPLG0LFQgSySz2YaVXmEiaFYj22KNdApjahI9sGwzFsMMFR150yr+5ukNj/MvKP3ATx4Ev3/hKM4WMxnue8OYPdCZBUPqaJh/+LduTtR5Xupr2nLkQM1afTgiW48WFXW8yCr05BnezxiXglnYIFNJG03sH88cTgO5T5hd3TEGMBcDf+vFK/b3DW/2R0RoSOaSk+35aD3nXX4ieRIsyt/m8fkrfuR/TBleYkTMvnpg//6M8S37KnCT3pBuDMJ78nJNmTpkU7NrIpTf0DYD3gWuV46xOvPcJgyypRwUk3c8nTqezZFtpzjIA+unO/LFBW8e/oIs+pzS3VHMbqn5C2IE9y7iB69WiPUF2zJgx4qqnpiGmnweE0KPChofIoQIGD/QhQi3gKWP8f3EkAZ04TmPjmyIieWEXsQYNZGII8ekIPctsV0RzJnkwpAcfn2o/0aAgJBwfbukfWyZhYQlPT8sB3bhhk7f0X9zYJl44k3MFJWcdmeOcmIplswzQ64mfiefsd6e2bWGrvfIdIWWgWrsSPoRM0rGRHGRab63npPmOadsS4OjnWIyv2dQMQOObbTk8VPNWXSYR4+LFWsbGOIDWbwh2o8MBZRZTO8LyqEjnybaLCeZHNomDNOMr3vDO9d9EIgUKefxDM6grKYIGVqfOTU9Vs9Yq0c++lgz9bf89PCeqK1o2LB2c17OYo4iZY6j6FYsE0Ev7omzhM5a3vcThdwgzxPiqmeatvTzlKE5c2ffcBKWdBbTjpbcRay8Z6MdcWrozJkiklyd51g7IcaKqt8zsMDoQN850lnC9rzm2CuiFwnnMWKeK74hQSwVZeNZ3C4YziPH2hEtI/KmR4WI3sQoOdIl4JeKplJYG3Npa2YmZu79h+wlKujXGQSHXRaMDyN54tgfMupBM+kG0QTSOOFqeSDVPX4u6XaW6/kaGRU895Z5NXJ8XjG4NeX7O370j55pXuTYxcBqdcKYDtMLpNSscXhxxriSIoeklAihab1FjD3n6Uuepz2zYsK4FYe6YjxVcAxcvNySZ5LzPaj3j/TLNW1fM1bwatjzMPZsL+D3P7pgEjGRKllcGW6Kj/ijP7jmaSoZc7i711RDx/tzwirLKQ+O1MS4eMYQzhhbkiYRzvSs5xeYq5hpv6M/n9kmE4PtCUKT2ogkSdCZ59KfaXZAfiQKBeIRTPIb3iLse8NP/upLxDFFrnsuToHo+gXrWPH0oz9nKjVTZTlpycI9MDYJeeIR+R0/e4DxFxOTTBjXKcUWVlpThghnA8u6QV9ZnEqxhUa0MWUb8WRS/rVXl3z92MPDMxd/o6Q6rj4cMpIjn81fkfc7nhcl2bxmrw2rfWBKJ9KLkbHOqacEa9+jVYzPB5q+5KtDQ2trBh/T2gjrchZKoZKWsgukvqD1A3Og0xqyGddKkK1mFM8jtnzHD2XKQzXwmMLTtzsWyQy5XVDqFXVZUDc952zNfB+QS8NGrfGrM0maELuJKakxjzUmtKymE+30RHXo8CEQLwTzfEmhl0xRgtdwt/+GxAZ00MgkIStfch0U6VXCbTznECW4P4iJxg3xOeC7iElcEM8Uj/snXqqUq2jGo6lYlpqpNQxzxdAFdDYg04ZSS0Ll+cE+RqceYSPK2LJJAtLlqKDIToIHa/j2/kiiFWX9SBVGcFvUPKJ9/JL29QVLr3nz9iv83cQfXiz5thu4XhkuY8svQsH70x0/fvoxv339fVZ/5wuK5yNfd2/4P//HHdfLF7z+XslsOae0M9rJUpszghl5WOOzmkN/x/HdHjNJTt2Bh/aZrh9QU4w2OYttw/3bB1S2ROc3zC4v6aYHyjTn3/liy+NXX/HZH32Pf+/v/uuE3R0/ejzx7/7R7yKKkvUnC5pv338YUe9iavU19XNH9W6HnzryTJKOA3GIGFVOrD8Ma51ag0vPHBvBsTYsvMUgGQm8SgZqNXA+pFxPnl5FUBiszci2LfFeId2/HD2Bf2EmhEWdLXV3IJ1y3OcHdHNFa/ZU+UgsO6Z+TmbO3NuUBWesUtTj/8Xcm/Pctq35Xb/Rz2Z1b7v3Pv0517cKKLuqLEDOQIKEhIwPAI78CZBKEJEhPgMSCaElEhIckhgJywJX2bd0Obc5zW7ebjWzHT3BvsglV10KXA7uk62pNcZK1vOfYzzdr3LfdMTuih9Ojxx/PrFzPaYttH4lyIQsM5uyImSD+OXALw38fmP45Jz4cVgYxmc+sXvig0bsNqQE3LZM6cRFWdSrkeFo+dpVLhsQMVGeJc2NQAiFkF/RGU27tmQSM5J3JlEXxa4mdr2i2wqC11iuMbpHnZ7xJWHiiau255Mrh/rFyuwHbPmSprvgTguvh4VXX99zSXtuxsLy1YxbLLqbmVdD7gdk7bjeLHh2nGTHrBt26xls5XR84t3jwromdm3htC4I9uyMhBBJkyEPzxAXUtKUpqfdtKgC7f0Nh89uaU6W+/xMip+yM4mnY6KpknUrEfnIm7Zj1TPzMVDlzJGMuLZ0qUfWgigtu2ygWqaXEbORNFVyf5NppaAMjvNeMw9PFPkeWVtSiuS8sjeSe9PwoxxZwo6DaUk/evLra4QV3H+WuNDwxe017umRp3TF2kaem57dLhBeAh+W9+R1oD5a7vYNo458/+uRzx8yxz00ztGGylUWFDUxvx9Z3k/MAbxPuIPjzeFr/BLoq2Wv9vTbDlTiyQ+MY+bLf/8PYefRH37OK9WzaW75olheh0fKvWAu1/zpkPgcxYuIHNM1/eeag4qkXzzT5heqEsytQoySuTOEoKlpBSFpdMCYwsk36KporGQtcGUz3ZXhWCxlUCyN4cSKkZGApS+ZUHqWxtNPgo8X779svxMiIJVmlQq3FaThij97/pafXiva1aBPA24HaQ1cxMBUjkTZokLhNno20qDqype95LoplFLZbeTH4+cYSNLzMjmsFESpMQpWA7c9HFThqXnDdF0pVWKPA/TQ1QKjhe4Dov8CIQtv/YGb+sSswLQGlQU7tTAfW6pecc2WQx3Z3xji2mLMihQDtTrmVbKuDVt5oqsBd7dB+YZ6LegeZuYpEW4W3rivOefMcgr8dKd50CvH0zvuVY/rBW2959gJbsOGTp8J9EQE7dQwaUF7GVkaeBYNAc8pr8T4RCwC0bU0otDpBqV7lJLUVrDvC/bSswTNfXeN2ne8soVqHH4y1Baa3YHvh4m7CrtDR3PeIe7OpO82RDViXhqa22f++O6W/+0YMKJB+Qtr57nxG/adpJ4920Ph/j6z0S2sDUvwxPZCnArrNDBNZ8zwwsFLflie0TeR12yRLwunMLH56Y6r/jUhX/Byolx9zat14Bdvf83T2x/Zqw7/+R/iNjv0bsPpfeb4y/ccDo77+x7RbnCroN0battBErT6gLsrDDEw+HdMYQCpsKqn9oJPP3mD3vXE00xGMZxeWITmikrXOuQ8IZ4GyhBp55Zvn99zT+DmjaUxPXW+4o+vAroq3pvM8KdnhueRz8zXLPsLym2gSg49pLNitCtNCnRipLYOISxpFsxaImph5zLPciKmSrUNcgj0NTJbxXBZULZgXEtTK5MS2BNsI5xu+G0Ust8NEVBK8AdvrjhPAfe1YqLn5z/7BccfBl5f7YmnldRGSt+zWxSqXtDryhBbLtML5EQH7BoJ2tArxf6mJYkWr1rUtkUvK67NjCi6jUCoG06donn6ltOjIL3aE9Esds926mn6W+yVII0WqyJxN5AfA7JT1GhJIlB7jbUzfTUUV2nbSPMQcATCtuXmsqfmgrSRKwPt4RPyMnIg07qFbw53iLvCuU+8f78llhPSD3RPO8ory+0jzF2DHz/QvTvx7Y+/oGy2vEfSmc8w7gl9s8FvNW7Yka88KUdqKrQ+0hdH6TdIrxHSsFcbPjl0bA6OrY30O4Xkjs2hcLPt2LZ7wpWmCS1a31B7aK3iKax89npH+XCCLtCdC60PlE5wqhN7uydcS1SY+eIJju3Coclc73aksHLvGzoCx6pp1ohcFoJQTLPgw3Th+/PA8LiiFk9rJrqrN6izwb8z5C83tDvP8nBmHLeI6Vc0h57mdMXPHn/Fw8/f8nt/cM+n15/QfvolP3t6RG4+4ZvD1/yLd99y+FtfcxAdTX+CuscSmURmfnxHiJqnfsSeC2VK6AhOJWQxtO2GXSdo6sw2dyyHwNOLp91b9FQYVMP0wztOrWeJZ+y3F85myw//x5/zxf7Ih58lbj450N5YWqv55FPH5znyfbT8z//k/+LKeXJq+LOn7/jl8TtO88wrG2hbgwsNZE0o6mNjkbZI4al+pLGON6/uCT8+0aYrXu9njg8XfKv5uvUULTnXDDGzNQbmmbU58OaY+G0lg78TIhBi4funEd23uO8G5mvDxZ9pcmJIkuoDLBNdumdixClHEdAQyaUlSMUP58A6CsxVpcvPiJcMc8KKBqufMJfM0ElKBu9vKN1bPvlnK+r+K1I84ZsNJkVep0rrGtbxjAx7dlYx7xV3PlH15+RmxKye4bDDpJle9ISkoRbO3LErmTWciUSs3XBhAL9SNzsWOdFawW3Xo6eWOb9FCUc4Oj5xlZO8Zgob4jdHVHEM44i1mbxm7PpML17B+oHHd5IPh4q7K3yxdFzpytodoTj8ekbZW+g1jXohzQXRWoqqSC1pD1sOhy1tGemLxt5opOgx/QFRJIIepSvnNXHTK9ZjQy81k/eYK007CuCFl8WgdcUeLGouqHNiudmz/yZwxQY7P6KrpmuvaDbw9rKQQoTU4+OZWCbev5x4+vEI4o69NUT/SMma4SnRmYQpmSrf06nE2mmW+YVsWu7Khqf1CT1aNv01PIy89SPLeeSxXGF2ibk/0b9ZuWs9IRd8q7jNDd/VmYf3F2wW9CKgYiXbwpICNbTYzZZut2BUwrYtoVSeYiRFR51HBqHYRMfJD6j7W1w9sxOJb/4ocnxYSbuR7k5xe+0oZC7DCvt7eAmEq8qVLfxHf+9vUzrLQ068/dHz8EOEUKmqoC8RhWSplUVZdK6UsKKdxHQ99/qavh44ygHdZM7bjBtgf05M5mNb8lWTCLpQp8KiW+45Ev3veGBQUlmuNdv5jHcd4e2MkhOXarifNWH2LBto1IRT0IqIZKFm2OaVzRaeNby/HEijZ7WeQbd4I2lnh24g9bCMlaZarqumLyPLVcfx/ZG/fV3ZjoX61Y62bCn7TKsEzaWlNCuiGDq7Y34DX5gtx2bL3WpRZoNUChqJKCtSCOxeIuqBJg+U64IKlr0DqTOH3BNt4dlHDhKS+RSrodML6+PMdb+haTJ3T1tOT8/cfLpjCIlsIyksbA8D9elMf9jyT88/4uMdb+3KT+8lSd3R7KA2EMYNVs9U67B2T9gVsmnZ94o726JVobdX9K8z0hZ0UVh1ZmkdWlem1LA5LKiScVUzbTzCSeazw1bFoR25MxllGoTYsW5W2uCI52fUuiFt3nEwnkVvmdLK6vc0ZUOpHn9ZuTyfCNPINEdOVdEKEF5wvGgWlynxhXlYuDsIPp2viOmBtswMfEp4eeC74T3/7O3PkP2G+pLY/95nNOkKczWwjZJZLZwfBszuNSchUPGZHTuSCugl8+rWQpHo9xdWDE17jZEnopKYriPqDTLBOgRiv7IVEis3HFWhek/ikfXhRy7rglJXvMFgu45NOvF3fiKodY8UHS/TxG0pjGqhu7rlZtnw66eBz1/fUz+s2OXnfHXd4/trtqrQI3BTwmuFR9EIhVGS0VaUKTRzZaoJ005IXVHLRDMYvNNMMaNbxcY3XEZFpy9EpdAY/AoftgKef4djAkpZdvoNXfDU/KfY3CIwhNyw1BHfB2puWdcFrWBqLE44GjlzQjFHCaLy2T5RlURNjr0zVATyaiG3AkZF17Y8NxGpBqxJPIeAOire2pE/+txhksa6BRE7rOuINeBQ7HeJ5TGTTcNy7ilqQVhFu9FEOkSbaIPjbtFcXGLOZ3y4IpiW21axVZFORrZj5SxXvjlccecTz/MjTznw3mu6TUPRjl6/oX668sX1liVZ9vHIl8szvzx6dmlD7rcs8sS+7Dj7M+fHlV/+9EsadSTrjiUt9HtByJau9vSfHBA1Q5xwtnBjGm4bg9kXlhKwRSNbS9sbmqVFZ8+EpzE3JDQPu464FMalIlIkrwGjJW6JVHdmWCy724wVmuViaJwkhld4+cTP3wcaW3HqhTC9IEpBXTyDLgQhWJ2kERXv31N8xJqZvb5inEcMA3PpOa0FNl9wezsxHY/Uy8rL8ZHluwf+1tdb1E5y22+Ybz2b6Y7L1T2PJ8upPqNP76jJYWRB+gsXsdB3O/bdDacPM2Zzxa7tCUaz2d2hKKRh4HkIhJjZ7fb4S2D69Xs6pRnDwsuHd6TZcdVqsjbo5Uhz7zgORxrZ89M/QkpZIwAAIABJREFU+rvsDVAtP3yYeS6VP/i7G4rPJLUCz3jAbTxCar7+8cg6v8fOmdBnVrWhTprSZBAjqRqck3jh8RW0vdCUxMGASJBJhFBonUP4laaZmbMEDbMT2CHwshPsgwLKX+l/f60I/BbwyH8H/KdAAL4F/ota6+k3Y8n/BfDnv1n+j2ut/+CvVQEpkZw46soyWBwKVw3L9mPV3ZwXdhYQmilByoqvfCbZPX2ZMLnwPEWGGJmzxSuwceGAYTQW10CVmcZ79GqgT2gsRvxA3N9y5d4QYyFFhRSazkBVGa0XFrnHryu7rmHIhto+8Ur0XBqNahUuKs5UVNH0wtC1CXvj8NnTUNmrDbfbyl4G+m3lth7JMeO14ZOba96gGKbMsz98xEofdwRbCfuEWlqMFHgt+KbrcOueIwtnNF8ee8YpIG416w/fk2SklT9h63o2omGNkTElWuHRTUvX7REpsu06moOm6zLBGIpTyLZhzBUrLYNYSOeGD8+KRT1TVEL4Ft0MdGvDzAtKt7y6bwirxjSZJkeuVOJBL1xSJswPnGbBSy3oZcQNC6UmWisZX0ZGGbGd5LPdnsYanmTkaZ4RecIJiReFdL/HqMqHeaT3H1A3X/L0+B2TP/KT5o7l92+5NzvEJnH/+YY1dRzDmdYcuPr6mvjhxJ2+Zt10iKcTb6Nn67Z0ysDLSBYrfpoZY+RKOvT+CtFBbhIdklyhLAG9zIzGM3lLiiuBSu48vjNs9pn1e8/63co0P/DFp3dYbhBJILXkp793Qxon9rZjMIbU9nDqGI9nzEHDMnMcRoiRZA1Vuo+TqfeBbAMxVSgVkTMyNeReU1Mgl4wToK1HlQbbGmJdyV6QgmAbC6KR9BhEt5B3Br1qOP7rVwz+D/xl8Mg/Av6k1pqEEP8t8Cd8ZA4AfFtr/eP/D/v+S8sFP7ylhshU93i90DSCWjMqKmxncD5ipGDJGjMKzlWgfAHl+LRJuFv45QTrkokZxpK5lIRzPeGSicIjMmhT2I2O16mybXu+6y/8+LbnVzcbNrYn2YIZI/NGoKTAMdKnhlEL7DRhdz1PR0GwA23sQFW200cw56wsMWfoBK+GPbsCu73m9bbg0BRTKCyMa8QUh3ZXSKtg9LQicTVnxjeVd+8KM4q63+L8yOZasSVTa0czTVw3HV/uWx50oGbHt88L20ayP0HTG+73EfYrH5YOSkumYqXndm/4zEqsaRBaYlzLxWe8N+hmwb4UjnEDGRrlMHqPUxa1FWy1wvYVuObHqXIaJJ3cc7h+wTxviVNiTY5ZaV6en5Fri3AjEQHPntBciNmQ9cJ2EWzkFVTPU02sWdNsDVXfErRiYy3bq575mHl3nDAbzeI/sDwErj6/xdprvmSAeaDZXvP0/cBqPcfLwEkH+m7ktW/RB8+JQJKZbs7IJnPyAusiTbYE12L7TDWCpFcUFhE1tSwUp/A+UEMk+YUhHylRIFNg5EQ5LtzH12y2lXqJjLrwISb+bi7gDCZoyinR392RsqWqj/RsEcGnhZxbSt6x2dyBeCClldIWUFCMhiRRJXysfp0TUWaQgGgxWaCZqU4jbMLHijgJrICaDdokijDIaHAUhqOkmL8BfOSvAo/UWv+Xv/DxHwP/2f8vp/9XLNfA9Fip+gqjoGRNHgdk3RH0gq2WRWnWIrFKIooni4CoUBvFKUnWJLlvK2+cxCAISiOlQIpCdJq+NzyKxI6CfgxY09N0Ww5l4PHtE+8+11yXBdd8wYRAvsyU3Hys4w6KoDIKzXWt6N7QDRvEQbD4jAgJUiW5DEHTh4XbjeLL/Ya7PpKLYCwCypE4OfraYUzLaiLWaJQxuAV22wm3abCHTzHmGaEky3mLcII6jBxPPfJwot8IXj7M/ERH+u2B/lOLdIm92/O3pEbpROju+UppYoUxFiqeVzWwp1Jtz5IDx2NlTJp2k7gKls2dYxN79n4ktoIZgyOB1Ch1jUtwCTOx9nhOHPQL6yx4uDzireb8HHgJihgquipaN6EvgRexcmgM1Mq6LkhjWeSE1IZGCMZhoFctwkqyD5xlYtNL9Lny88vM8eT56tWOm32gj5qlPlIQmJ1gCjAOC4+XE8EPvLp55vHdGS22vH0+kzkzT4JDYwjpljQe+WBmtnZLbx3XaotMGa8kcg20MbEwMB1X/JhRYkbEjGYhHc+8PJ0RcaW7VQTX8ofbLQ/vz3z/7h3HvuXH0HIrHYe+8KigLg+o5Gm3NwRd+PzNay6PESckh6bj7vUt33aGepowU2J2kCjUEkgriOhozEf8eQkK+PjfTxUKErzCukjMhk57xm0gxgoS3OwRrWWjHZrEx4P7X7Z/EzGBv89HJuH/Y18LIf4pcAH+61rr//pXLfqL3AEhPopcWAPJTWgtCMJgfUQ2GSUyUWZUSXhhWYygWyW2BHSOzAnmXEkZ0lIppcCuwZlKDCvGKmpUSHpCnhgby42UjOX4sadbD6jza0wbefzhie3hFrWeSeo1V5uW1BSaqulwHwtISgahCMFScqQ4KMkhUiKRUFrjVKatIyJBKIE2FqJQxO2GOlSy9HQYao44bTD7lpwd2Y8YO2NGy1lH7jYbrLMM2y1qK9msV4TNirEBmyq20fx+d4Vxgv1u5M2UqfWaH+OWt8OFoCutavgcy74XFJFYgiFUEAZyK+iaiiyW7aayWSH7j2+hTgiSOWD8jBSBl1kxB3gWA+Y48YQimcwvfvA0uy24QHx/wbVb0C/sZeScAyoKGrklyEgjG6KKWHEmuWtitlRmTs8ZTIvoNC9LQTxeiEtApcA/fzzSmgYZPF5qaoh88dVPaLuW4dSgbabpDE8nR6P3WAnHNuOWA0ueWf17fDjQ3wlG8xHkYvBMxWI1bFIg+5FFeorTlFCxqpBMZT2fucxnyvQRsT6PL9B7vup/nykVFIWHqXDRha03vMSG69gxqCPsNizjSGcHyLuP19o8sO8dORfWXLDVESOgLciPmQGdJTYK5gwpZJwU6CBZhMLKSkkVEQQxraTNhhwTxUhGIylOsMygt+CKYk6etmZE/u1YkL+RCAgh/isgAf/jbx69A76otT4LIf5d4H8SQvxBrfXyr679i9wBrVWdb7aoY8K2HfPThFGa2KwImUm6BV8IoYJKFGG5iELXWqQWbG2kSYmXpJg1lAorhTIDpWCmxOAUQgImYeaCXRY2P9myjgm7ZrRckUNhKivnJmMRDPJ7xPwadbjioDyLSsjjyvPOcWMVDYZVa6Qw+JjxzQXT9JAsvmaGGIjGYqtEU5EWyiIZfSH5DU1I3Bw66mZC5gslN6wIRFxQrkNRWWYHulBqS9d7dsYSm8TKllAnjKi82WukaVACVgeiGFxT6aVDLYVNNTgDLlXyvmNF0klBiYGqNXedwq4r86wRYmHtLKYGRJDMjPR6YpKSh6fI26Xgq+BlazgNF3otKT7RyESYV5xbubvfYqcO2VTE/UA7NbBzyEXRbBNCzBACcSjMVmFUwyWdqCLQa4eslTBF4ljoG8lda3h/Hvnmk2u+vHmDkI4fl0CsDinBuIUxTmx6Sz9d2KsbUhfoessPLxq5WIQzxPSCtoqb1ze0PuB1RHFkkY4kI6V4ugDLMDE+vRBjYF4fiO8nxsVz9eqG7X1Dmi1WKDZXLZVMMPHjS8p4fv5n37L5Ozes2aFPG9z8AfXaslk1y/lHHkSkMQ3THLDas64DSSSkrsTkkHMhk4kevDAkqyBKdM5gJME2UCNSB4qVaLkSAphDRFaFropVQT8nQufQITOvkavU/MZV/w2KgBDiP+djwPA//s2EYWqtnt8gDmqt/0QI8S3we8D//v+2VxGFGg0+zCivuG43xOpIqWLahrgo6jLhRYEIVkhcF+ntBhUF3haqhK1UbLeCRn7EZ6ciSV6zzAnXSha70sUWswr0PtG2jipbHp8vjPMDu27PZCqXY6bdaV7GyH4diPPvM3dndHPHpmZksydphYwR2YJMAZshaEd2jnVVtGOFIojWY4tBCUOOC8dF8UNSCCO5rQkdOzaLZENlIGKbRK0RbMPNSbI4Qa0KlWdcIwnaIOyWbQSanl8vGVE1dg14vYUWtJ/ZGkVjeoJKhKLwInMhsLcdd01hXQe6qWWqAicUKsCooeTCwky7rKyh8DINXJnK3Dd8f/7Ar37+Qnvo+PybnyDtwBQ0bplZ5gsPL2fuD47oz7TtG7Lz2NXww9MH0jnRmRv6xqFjRUlFXCNrWEmpItvKPB7phy27pBEqUjYadVz5out5qoqrL9/QlhWZNHr2dPuGaVyxvFAvz7jDZyzTmfmu5epmy+n9ma3seBxXvDFcHhYO/RbTaZr+4xgusQjWvFLySFoGrK8sywPz84W5rGAjm71AWEFvHDFoptMF8UnE0iGCx5gFVzTL8ZH/83IkN5k/+nt/gH46s0+V46XhusmsL08M64hue5akyU7x7od3oApGVlIJlGjIObG2GUrF1hbRaLKxyAIE+ZFD0GW0yaSS0dIwJ8kuOUobcVuJ04pYFyoV3Qt8SXD6q/3vX0sEhBD/CfBfAv9hrXX+C8/vgJdaaxZCfMNHMvEv/rr9VJH4R4+Pid2uZ/fpjnweeXxW5HVl8RWkAiMgewQelTShClLJiAAaxRQSiwHpMpaCVwqVJFpZqAWHwJiM3neoWFmXmVIdYQdBDri4RzWOS8wkL1meBTORdfqeYCrdPaS7W2xc2U2OsL2wrBrVWIyqlNURKGQEWns2UqIBrStZa+QMnkKuBhkXdBvp8oxDkFpBfVnY9okoMj4840SHVStlFdStJCSLVpqSAlllyjmDt7TXFpkivlpsC9WvqDkyLolOOppGcdSSoixu9ag+U4xBCotJICdBXQRBJIovDHakHlcuXnCZJ0K2tJ3n9OML6byStcStIyIpfvXwlobK8sEx1yO9/SOGeWZtJtb1zMu7Mw8vL7Qyo/qIKG/IJXDoW4QTSLGQpKcTPSa3PIyFEiqNVITmkeZ2hwpbhukZP1yYcya6zP6N5tZ4hvU77j/7t+n39xzXlsfZMw4j6bSFLEltobndUUvmMkdy8dT2BmW2CNkSZEGZyHJeyJfAID1DGola4NighKQeEpt+pqma5xNEuxJK5iZkRqGYpKU3LfJ0zSefLtTW8fI8sNGRy6VSnKJZX1hC5fJwwm5Hit6j6jVLoxHsUGJApokleygJISUqVlQtRPexHN7kSE4JQUVmha2QjaKK/NGPBEyToHUrIQmU3hDmAa4MIfwNMGS/BTzyJ4AD/pEQAv5lKvA/AP4bIUTkY1LyH9RaX/663yhSsoqJKgUiLajlirCMXGeIYqXZOPTa0KiJMxWIxOI4ucJGALGgnER1LVVpFhVJUTInTfEzNhSqqsgAGx1JeaILlTfXFT8JtFAEtaGGgqNwWQfk8Q5XNLMNnAkMZ0/jMj8m2JiR201glop4bNGfOoSfqT4xrjNWbshIvvMefGApK1FaTBKMXrPWC30vAMOaAzUm5qkhjE/4xsCsSSXi/QvLMOHWSLxokC3Rn+ivPUUVysuGvS7El4j1BdOfEO8tfoZ5UczZIzpIOfKBgjAb5rpglsRWSIbLgPeSXkVknXkcI2HwLGnm9OyJm5apnnh7adl953lfJGPVdBk+HN/ji2AJhuwyrUiYtifIjOss4TzjjSIKSd9ZdE7IVAjriaoky1AoTYPbWZYh4MeE9ivLuJBqR20L+UVwFIlhXkiN58+/m3im43BVeX0rCFmxbbb8eqoIe81zufC2KJZ1Ynk580l74Lz+CmkF190N7rBSS0I2lsyMmA2ny8A6Xsjjib1UlHZF2YZNVxFBoqVGtZZZORq54/YmsoYjGxKdqfw4KKKX1CZTZeGz+zdkmVnPK0GPSAI/DHuqOlFePK6sPI2ST9vym3TtHbYfWU6R6i8UJRBVYPJH7H1MAsuClhFZHXORJJVoG81EpXhJswj2LhKYyd6yphZnInb7EWiqomCQf4MGot8CHvnvf8t3/yHwD/+6Pf9Vk1S6RpFyZlsDV1KybSXVgS0SZwXVJFLJpEtm0pXYFTrt0DmzChAyYwy8VgplDCUoUkzkvnIWBas0yhZEbshIqh5pJ4ffH3Djyna+5tidyMWyes2wPqJbiWHPEI68WyLb98/47UAuG169WnkzO8ZaaE2DXiuT3HI0hlYK5jXxdnnCE5kuA6couG33vNGGKyc5fNKi7cIzR1TaouJEPT8gUs8c5cfTxZxJ+Yk69pTHD7hPXlEeG7IUGFWIfmQNEX72gNEGriUHuSVVw3QUJDdTZctxtLyrjqpgUoFdmvESnp48sxQcGtCx8LIETm/f0xWJKgL0zBIavMk8ZcUYNM0XGw5VM5rE8eGB119+gpoSXVAkkxBKo0wlr55OJeT9HUwbns8jayg4D7uDZEmRtCga23J6Hjj/+AzVU2qg3V2TtePppPnZ8kCcFa6dWE6BX/ctf1g2fPr6U1RraOUr/rlI1FPlMTySwxuEDqQ60m0dzUVxdmf67hWaTKkHdARKZBxOPP76V1zWBW0rTe9oUuLQXeE6GE8LRTaYThNCRDiHtVt04wi6w5bIsMxoAikXIg9sun+HYhbOo6chY+yOy/SEqgp5WrhWgm6/oWsNzjkOh5Zf8AsMkiRASYkRlWg0qVOkuaAy6Cyg2I8zK4nkFcgK4zN1aYhU2BYskjB1qDrAulAlCB1QTcdHINhftt+JikFD5d/rK0OEa9Fh1Vs2LnGmwZaeuhR89SxroWoBrcVZQ1MUIiaSgFIFw/ixbRZjWbOmt4pdZ3Bdpdg9tc4YCtdZYJHIkqkkukZTC+hqWC3sGocxlSIcMWvMEtmlQhcVmyCofuT5OHOsiRHBdhDopsXuX7OyZ6Aikuf75ZlpjJAlQkMpzwybhj7vwHtWH1jiinaCXZ9ZFk+OiSU2qOuBq0uPtwtpsggNyvZs+5ExONyQGV1lXC7Ys6TuLNoKztMJ2RhqaRjGhqEUxqLwziDkykuZufgZIxo+PC8ksZJe7+iiYCiBwcBlshRgvixMEbbbDb4XpGXkqt/SeMerz3t2xUE3cOjuaOYD7+df87IuNAMwj8Rlxt68Yl0943BCCUvb7aitJa4z8ezxIfL+7Zn54QNFXjCq4ygEZbniLALBQ8mSNvboUonVI5XGYqEmKImmvaK7v2d8ODM8Tux2Vwh5IE0Lh+s9ZSrM6YxkhSTQucGHih+eGd89M64TV1/t0W1Hh6N3LdZqarGseKwTbJoOIQTRP5AiKCnIuVIIbLcdw5DoLgurWklKY+sFdSmkfUMOF+bQcNsuHFSPaASt3hGj4uZKob1EXlYKleILawdSJPT8MVuQQ2WMFaMyqiZyBB0KShZqI1hzwlQJxSJ0xpqJZBOLAFU0OUqyUL/V/34nRECISmpa6nziIgX6ufDsEuukwUiyV3RiRtpKowzSfSQFWWMwUhDWgJaKQ6uYi8QrBzIi/cqxSnIV1PFCtdCqRE0GmQT93qHmJy56T+mgxD3UC8YqbL8lr4kBzbgOZOXIUePWCaUaXkKgn1dClmQ0IhiG5Ym1eBY9oDYbOqOQWxC6Z68yn4vEWjPlcmFuNxw6ScEiA/jhzJIa0BPlYaJOmR9l5emt53p34fV2z/npiJCe5RIpi0NlTTyemPWnzA8zxwcwxtP0G7YHzSUp6nMk4lnrhHMZIT01THR6IQso1fL4UFAukZbMY1R8eDnRGsXL+swn29eEaFEHy2F4ZjdEVH+kqZ/DxvHux/csO8c+wc/OC2p44uCvud22BJ/phkymsBWaKUamcEQvG1SGrApVBK62ljJsCM7RmobSWlx3x+efGb777gPzrx+5un3N7dc7FiQHO0JwFJOZo6E8R/Jh4o39CfHwI64YzFo4x0q56ihESj5hdE/2iTkNxHgmjzPnMHB8/8jtleXwxee0UrKTmkwDRsLoKXmBqgjjSC0Ne9OjQuSSDUZpkvR4l9kfrpmGQJKJvWlgK1jiyOQFh+GXFC0prkEUwXE+03QdeydQzpPVisSSSqVoRS0BicBGEEUQW0UMnqwULYK2ZpIt+CxQW42JiTkZXMpokRldQc4ga0tcM/KvTgwAvyMiEDM8RcdxLNQrhxwyJWVqyaxFcSgB00uuJaRFQ9vSbxqQljQHxjSQvee2ERw6R7QVIaBWy7pAVB8jqLUETHGMobITBroeZ2bcELnaCvK8ZW9HprmgqZiaMfKRSx5Z0p5uI8hT4NgLbruGQ2PZFs3cSSKWdYWhDGgr2YoWt2lYKqgB2rzQmkzX3XyMSudKXBNCFRpRQXfEAHlNPPoLO9UjLTglkI1klAUzJD7IiSoULhfsrPBC8+5l5u0E5zCyfeN4bXvEvGVIldFnEB7qzPGS6UzlMydQRGpRCNlzlhXlLOFDYD1Xltlz/+aGcbFs1C2zX/hMWtSu59BKhsvMn//Zd9QQONVHzFR4SCtvp4GbXmPayJUT6AKPnJhPI0JmllCopiBrixUf7/3n5GELm09fYdwGZc7YaSLVlRvdc/1v3TF3gmAF33zyiuP5Y+Wd15bHy8jLpDjcGM45sC+WN12HOVVWPXAqC/VYKMKjpMNcJt4fX8hOI9cjZV4QZWWqC6JWts5gS0NQgmgqLlhWYUh5pRpBGSq6T7xRDa1LTG8j8huHepkx50DZaNangOw25L5l3U7Ek6Y2GnlOpK3A49mEV7SmIylJOq+QJmQOJCEQbYMTHTXOCCWQCnQFUQI6BaoqpNUSdMIgkCRqSNRWY7xHx0RqoFaBypbaBhQJ4fJv9b/fCRGQUjCuE7ZtGUWEptDISpQtstNIrWmsBeGpsYXGfjz6nCt58VgfSSkyYpEhcKkryoLUPVX0bFxGRLBK0KmM3Wh2MqNlJuY7bqRiFZl+P7IrlvdhYF829N0rLuE9brzgDg3G7ejvrzGh8lXXIG1PyIolR4RJdHqHawzO7EEYXqaZGj1tiLxuNdoJsskY0dPYgrFgjKboyvI0UC6OWhJxtWjVcLht+cmrT/hhWZknz+50xu5vUbpQasaHgBYHhvXCFCNRJlr1irpec7FbMoFcBsw6U9LCtEJzI7jaNKgs+NUw8uG0Em92GOswqrK533CnDFJpXn36OVtTkNFjRSLWE9l0vHwQrEys5yPm7olGGrIzvOl/c0SfHvhhEuTkmbPg+HLCKEErGpTecJkWhFPMKXIMK15o2o1lQybXRNdtsSUTTr9i9+o17s4QRYe0Du0ii/6CsXT8enwhnC68+vIrDghkHwjnSnaRYiLTQ0bkgUNOPKdEXGaWMKCrY/UThYTuWtrO0m801mpKcAgdcdYT18L/zdyb/GySZWlevzvYbPbO3+weHkNGZkVmTVDQXdAI+i8AsWPFBiEWIDasYIXUW4YlC/YIsUQIaAmpoWlEQVGoujursjIyIjzcP3f/xne02e7EwrOlkqihVd2gfFbXrl0zW91Hdu4553mcb0l8QlAxNp1YmgKVWYLOseqI3Fn8VCLHkZBEJKojxBG9DUST47CdSIqA8hJTG9yqg+Kc4CuClJjO4hx4qz/2C2iDG3uEUehEkOiJICSu/njw6MeEVKiP0X1QCNkTnGVyKRGOUImPZcs9jFKxcY7OB5z4J8gO/P+BEAR9a6lEhO57QpCQBIIWlINAxjG9tqyNZRINd73h4Xgi6R0FUGYFYRzJS0WUKhIx4VvBZCfcqKi7nmmI8dIwV4a91+TS8JsiQswcD7XlS16gQ08ne66yjExWBCWRfc5ylbO0kHx6xmAUCw/3QZOlCUmUYd3I0EWUeYFIJDpW7IeWp/0jovNclgtWs4Q8EaSZwk4Vi6xn+7ylVS3C1jwdjkSnGfPcst7M0PMcrWJe/+IOs5FcJyl9muOZMN5RlFdEx4mnaUcvJ7L8nEUc+PHVJblY0LpAw0iKYZ4Ycgl2kIRYYJC4QjAnp24HotMj7f6ERLJcr/jyn7nBdVsO40jYt7zYLLk+VzSN5PvmAx0BGwauXhS8ee7J8wQXdUwPNZkIfNt+Q1ptKPM10bjlssyZbISPCho70t0+obI9XngGCz5EjN6xiwxVpXHEJPM58/iaYWoZbYxpBrZe8vbxiXftB7okwY8T0SLj7eE9s1CyXFhOg2esd6Dh6d237PuB68KBlEx1wij2bPIr/BhhsxQzl9TbLYyScVCgewKSqM5o+2e64D7aeAWBiARu8IgBKhqqwnJVKZ6GDWGuiWKJCRIhO3T8sYistw+Mb0bOfmPBYfeaMWh8XOKkwTuJt1usMEht0UNMGWmGuMFZxTRAFwd0WhCfrcAP0IAOHu3BqRHhAlqCKzxu8HgVoJHk8wl5MoRRIKOUbBBA/2fuv18JEvAC3OhQKhAWEOuMKImQtWRVpPTJDKVrwmmP78DajlOfkgXIM00RR4wT9CYQxRbtPVE6IxISM4+RIRANYE1O4nfMveCBFIOEMRA5S0qEEiOnJFDWGX0QiPTIQnUMusLefEJsE9rmxKnVFF+cE6oSEXvKWhHHljoCGZVoDMFZnJYI5XBpoA+eeZxhbYSNGiQZwXne748szMj+vifxNcvlD1nO1ngFQ3fHUTVER8m7ylJazSQCic6w+ZzGf4cYLVeblJPMqAycnyniEsJdQyQsaQmhhspI0rnlcRIcOsPQWlZRzssqZ3u/5bOsIFkkKBbM9UBtD1wnAacFe1/jbiXHOMYeUvRqID3tmKc/4Lrsefv4zCrTHELCMNXIUOKdwD4NIAKz84iLsw29EjztRt69eSItcpI8wRlPpp8hmmhrTfMs8OnA2V4zX1m8rGlsjohBvr2jOXpaHbFlYN4bdAKtHvHPOeV6wyBeo8YD27sj5umA6Sd++thxXmkiJxlSxeXkibMIgSD2IJTEBsUgFTpI7DAxCou3GcoORApioZFJhOkGutOWcRPjrKRiRhoN2EJTlYaRHFGAyWeIY4/UI4k0ODUxn6Xk84zu2SKylqWNccSEdkCeHFYHOhe9xw4QAAAgAElEQVSIjQQbEDZFOIM3IyEElAYyySQE0RRh5ISyEek4IaOJaSjpfmlJ1ux7qlRgPUyxhvFXPBzAB+Ii8CQdhYgpXcXYRqA7ul6TYxFCkgRHWimwMbmTKKVQpBgvCOlAFGnGxrMzMCY1EvmxjVbkZN4Se4+OUypmrJcea0bSas26EGzHJ25iRzolWAeoCdtHaJ/hg2A4DZgQ0z1OJLnh7CrgpMSYCq1PrGcCk0jGAfww0G87jG2o0gVBSA7HA4Ob+Gx5jhhaehyHfsC3I531KGJa0eP6gYN9Jp4VdHWNzgVR9hJhd2yNIokDRZfRqxojFMtqRhxn5KbnYpEjmxN1O1K6iUQlJHFErS1PuwMXywW9iNmdLIiJaFZRRB7pB8425ccwqZzozS8dbaqcYh4z3R94w4S1C1TiCPtA4VZsmxaWCc2f3NPJwKAjvriumNk5U5RxozTPLsF4xXO3pzcTbTtC32B0R6SW5DrFOIXvYRpq6rFCyUC07BiCZWyPjEpwkU/cDpY6eKp1hY8Krs4CCz9npS852JEyHtmka6bNwN6mDHctftyTdAOJL5lcTdosSOcBJeEQabxN8RS0WD6m0CI0gomRPJpQShB8RDt0mDJiJmJMaZkGOOkFufcIp5mfZ3SDp9QTaT5jzMFOPSspOLsSmOaEHwrmM83x8Y56lbNZXDKLL1iqJbdqovQWZ2OSvCSSHW34aFkPgkRZxknhXcQsGKQAoyVx6jE2EE8xLoa8jmn1gCKiV4ayEBzHjlL/imcH4ljTh4y1UmSJ5i4aSGxOZnPwLR0HjvuBhYupKsmZldxOjnGCre7RqScaYWh68kSwmS8wWtPVhvYw8jxucT6Q8jHNkooDz0fDP7sSkDiakDP2BcoFJh3oZhlzX5EqB9ueXq/5bDWnIZAuZsRlxJTNEVax2x3o64ZhppAup+0majMyf7nmVbfi6bnHoZjlZyRSo0ZL3z/z8Jzxtm2xnaO1kptPPqUoQCfnPP3Dv8fjuz1xfMX1j77kPF9y/skrfu9/+H1Cb9nlI7Es+eTzG4LQpNuewyRou4Z0IYh8hu0jQrOHUjEOhtr1PD8/MEty1nGFVxGpG7HBkJ+V7McJ19T8YTNSzVMa37OqG5Yvf8xnLxNG4+ied2yN5MUiY4jP6LZvMdES7X5Our7iVaIYpeR9f+JqIxlYYeXA7PwC2R+Qxx1ITzg7ZwoBmUf4SOPbnNu6JTUVx4tLikoS64EpQDm/Js6WbJ/3NPGEqgzp4Pgbv3XFptCU+4TvixGlBa+/f0aVLxhsjzc/x+gtbhoQMhDUyKKaoYOkHg3ojMoaQlFSVUtWSrFZrRi0hiOY/Y5JQJ1LQn1kVzuqIsfIQKQCmIy96eiminL+krkyOPFInCWce89N6ZFFwc4smE0n7gfDaqGp8jOWXwEupzhLELWm+PwCcfqArxVz5TDmyGQEKYrECcDDpJDWonLDhML7CN3B4A02AJ1F4xiyHHSMOB7IlCTaRmyuR/qnlF9xByLPvBoJ1ZK2HihcIE+PzEXFfZpiu5bhNHFcRERjysu+4Whn3EWWPtSYvSCTM5RUtLYmNc9kg2ImFHYuOHYwiZJBS3LrMWFklA6TZuRjT5GlJNWMw+kDppPcqIxTeiJqe9S8oJqXOB2xmCRSTahNTF4uP3aBdSeiKGde5Ux9xCAOMA2oJiKTEa6MWGnIksCUzNm1R+xO8U39RCk09Qj52ZLiUvPJ4itaa3mkZJPknP3gC05di7HvGfqU6rqgiiNEukeufkBmDFJKapFQKUukPEmjaZIdXg8cwxb6kjSN8cNEe2ihc6xmnjKa05kTwjRIAUrEHA/fcNw1LKvPmNlAI3OOtztOpx3xJyXfvr1nfjPHyDlv3hxw9siVTNCVZr1csfkkY393x2z9Cl+CtIaN7fjw7deEKSLyBrSmKHNmoqKOFM/9ROEblqLkqTUM23tezuY0PsY8TozrgbKAUiuskDi34DfTmsUwQp+TFi1VFNPXijEk6PoDx/0jdjhhhhrtO1S1xGQRcVGhspgdNTMXMbkY5SdUFAiJIPIj/TQxhohJwPHo0CLBTQLhW9TeMfqGyFtGaTAPDR82HZ+vZ6B6ikTx5WrNy1VMqUacdIwR+KQkFfdYOUMzkUVnzGcWUwTS7RGFIbM5SdrTZArXpwgRiAqDMDFmkkxhYCxLRjfix4nKOJJIYMYAHpocZiHg3Qk1KbxW9FbiKk1RT0yXPTz+2fvvV4IErA2kxRnHwZHXFdWXgaR1lNERexs4mJikmGgGQ5V6HBo7DFTKImJNrxW27yAJ6CzgE0+9Vbh2JMkVq3mBUoJBxahoArli0TQUKkEkiuvihrPeMwjI1YgxgrgRbOXIsloihmfcKkOKHJlN+DhDj44qO0NUgbHdYYWHaiQWOcq0WLfHTxHCeEQak4uEYCzCQRQVtN2W5XpBnE+s5UiUrPDDPZwpyi/mzABfZYgh5q73rKYTy+UKeTdwaApeXXl8U3Kwu48HbvOOuIvRKjDsR1y/p92NSG0ImwVVnCLKOaoXjPuJtq4J+REtAmYUXFWSuLriZhkzdi3bacbQbqnXMWMn2b7r+JP7iM9MT1U4DgxcDRkyRJgoJ84kn20Snh4M2niUSWjTR7yZONgjcRvxaDxJPENngnmlSTKNCCm1ShBnM84WOz7LM6x39MUG+SohWhXUB4NvNfNzz42+5MVFCr5klhr6PGIxTQwmR4gaFTKeptds9y1TX7KY98yLxUeJ9WVGX5eQfpT2qpzkMARmTpB00A01Lkqgr0ldj4xhPE0c8SRywsYpYpyo+pzOT6hzQTk4ZmJkLmdcvGhIaajSinmWsD0K5mpg30uUFWw4ELIlRkn2NmW+mwjecDp81CFMQkDWE731eCUw1iI7z2EQJLOYMnMUg+PQaO5NRJEO0EmQjrkVDMrg55JcTIQxw+8mZBk4TjPS4/HP3X+/EiQAEikN586iv2rIxnPuhaO9PfEsA0mRI4OjniJkpHmVNFycCZ4Pnq7WNDhccFyQE1torCFfpizONaJNeHze0ZkT1iuc8IxZYOYDyzHDiAy9fuJWxlwtLxHRgZ3eoZ4sh6Sn2C4pr0tK/TmpuWet1hzlAfZ7ujbBVgAlzqR4EWjrew73T3hpqaKIw1PHc+l5rCFJl0S2hOOJMHacHmvW64yT8Ky+PfGuECTbZ17cvOBhCHjTcHV1iU5Ltqplky/Ybxp0Acf9HkXH0B84HX9BvLym9XfoYY3Dk+Q5MxXTU35M4XnD7vEOoSXoGf2xo+8kp/seGRRPvxbR9ZLd22f6/olpCjRdzr/0r3zO2DVcGMmXf+OKv/t3/gHfPz7wox9f0KmJX/zRzxhfabw853/5eiTanYgKwTCdIfwl377+Gfd1j5RHulZRnhdcL5YMKiaOCpIicNudWHgo4jXWjjzVmlFnvLy+IC4jrNtyEQeu4wYx7Hl67ni5zpAHTRLFqCkiLD3T24mfj29ZbSMOpwfWpeB6dsNoclZXJaOwtP6APBrS6Jk+rlAqQpcJfTbQTmCmjin1uGAJ7ZGsWiFDyuEAs2Do5+uPDT7v3vHjxQW/vkwpXqyRas51tODN3XuG0TNLK9isyS8s8s2W11PF9PKaQazRkydTitoabj80iMcT0ShQImKQDpQlUxnbVnIae4rgkcNApyDYQDoreLebOBw8q4VmqUqEMNQOfOyJj9CVPfOzlEnEuOOJfB7B8Vc4HBBCEL0LzC4Fj6ecg3VMxvEuGUltwWoUnNIIEwI7K7iMFJnNEKql04bJKCqXYDpLnE3MfAR3HW4WU0We+ScLnlWg2WsG6dilkmZv+SptCHXP0c8RWcalOuG24IoBqwPRJInPPaduQ3K5J7Qz2kOP7yue4oEh2ZE8GLJ5DtKQHmOSkJLlc7rTkYcenIu5iAqi0DJtd1QbR/xpidgrhJnjE0+uBuqkID729NeecyTXyvJgD5SrL+mlQPeKw/OWtMqJQ0oddphGEdqM/nkgS2KMW7M5Txic4Hnb0TeWPpswykFnyOMlUkGWLTGx5v7tHd89tOxXCe4bT3SmCSWoQVHGiiKcKJoHiguD2z6yFjm/cZOykwmpfKbhhlRvWTRLvnn4DhlN6Drj9TQQ6iOXmxThcjzPmCmmnyfMqxmpzhi9Q6SSy1mFEDGn8TUP7YjHc/Xbr5AojOk5fjfy6tryyTyhaGO+VY4zteCYO8ToGeYK6RzxbcyxrjndGcwCNsSMOkGdYvwsIyhJpixPoaObWvLlhsJpfDwRbRTKzxC1QE8psgSnEo5aY9yIEAKfx4gqw3cdJgtcX+XMxB1ZfIViRjZa9qNFJgti/YFRai6e76k9JBc5y9MSUdeMk2L95RWm1ahuxBUTp6QljmaYuCUeNTqW2BAw1iNCgopGYhcwLiLWjtyP5N7TpymlF8xtoPWGcjZncjtsnpLFKWmXgtyzCIHB/YrXCaAD/rM5+6lmshGDNfjHHVmak0YWvXBULmE3tmgneDLwk9KhXaCvFQclyFTPRZlinOaDlngZkeicuDvRu44q0xRpwhd24p9LFUPZEiKFXJ9zExrGIeY6l9zPHcc64azPWPzmxHWz5okRdSyxhSJ1gdugOE0a7rdMVpDOEqwVKHGkmWomAb3POY4NXpzIvOFiSMldxln2KXFoaSfD6jxlUBHtc0/ff4tuCvrecPbjQLKe8Xm5RExwMWY8ZHAY3zImC/I0ItvWDEOEoqP6/AbCM30f89DOyJ630GuiPGU3TDyfJuoQoeSC8XDAHu6oygqk4uIy40deMZcnrkLOVo9c/4s3vH3s+enPH6nNkeJJUw+S626P4kQRoAgSpgNeSEpT8Z3ekYwlBxWId5bPvsxJjwe8h6xYsIs2aD9RBM/i/Ir92DMqyTHWHJIGm66Il4HBS3AVVUgYMslh80DmzhmUxy1u0KLFW8dyMnTOE02W06ni9s3XdObn9G8e2D0VVFpSyBarF8yjmDB0WJmjR0E8Dpy2AbsSbMo1oT3SpQUhL/FuxOgEIzvkIiWeIk73z4QoYoqu0bEn4UiyfWSKM9pIspBvmYo1q/0jU6rpqw2zSLOtBc02Y6ofCZeOvC1pYod6r4jXCT0dt988ImqH9NAfEmJlmcbA+1GwlZKF9ohe4WKJlYL9EBjJqGOPmiTiQuGHARJJM/RkQ4GKVyi75SB3nEUpQQis8fx/KS/2TwzhA5kQdDbCF+C+eUZJQ1w4hh78o6CrNFMfCGPLManoIstZyFASXCTxWmLExDWCSwe1TpjakZ0zzOUKFST+zvLHI4yxZQzwu19VpKcj0yklP1N8UGsq17NMBGefKwaz5LubI5v7jChbcJOsuY8i1nLi0zNFM83Y22ee339PhGAwJU0zMIsyNlfn9Ic3lEOH8D19I2l8x7VvMd4TJzP2Hzpae2TUe/pG88nNis9nZ3z9zRtuTEaP5/tGklSCm6AZ0xmXsaB+7HkrFCJsuPhyw+j2nB4K2qD5g/s/oe8VY1OS6i275x3Pe0OpIItrrq/OeHX1AwIn3p22JKlmOVc8fLdl/3aHneXUP73l+tMrfuuv/YR3rz8gyitefPVDPvzB1yTphvnvpLy7PXHv7ljrM/7IQXz212n7PfkwcbIjv//6e6rlhtVnG9iv+c31kuS8ADNwkgP5rGT3/MzX392SRxFCWtqQEhlPPSZoWRMnCZ+eXXE/jtxKWDUHvlhk+BGMqGDdUD6O/J39a15PJ97+4QO7UFJ0TziRcfPFp2RlRNh6aqH4/sMbJp/w+fqMqUiYzQNJ6bm/K5BDQlFKaqtIJscsHiGAVTnR5Tnv9x3y+MB4tGTrOfnwkkG853gwfGIq4tQh5lesUk/9YHhYl1QmYZN+zy6SrJM5oeyhgeF8IFQT4aHi+fsDk/GM8wYtJHYUaDKuypSVDNy2A9+Gj/qA07bAe0GeNuSl5EW1oHAKGQ8sT4F63pGpC3p7i6k9YrbkNsQscUQ24leaBPARtWlIckh3I1Z5BnmJa47oyOFUimgivM2oykA/jrxxPTchoXTA1NHYgElLmiInEw0LtUMtcxaF5m5/oAuB9XzFSza8Vy1MHYmAMk8pF4HntWcvLWGqKdWKMF+iO0V+L4iSgU6OPH79hlE/cIg0przhF3cDjZGswpxedx+Lc5KCYxtI1B150aNjzWaYYbKYegzs77aM6cD22FC3mkEYPilXJFXL3D1SjiV2sPzDb49cbxZcZRobbdD+jMdv/y7fu4pD5Pni179AbguaqeX+mNDHH0VCJvsFsxc52/fvuL+F9GzNTdrzQkBrNWUaM9MjsSqJFpcQebpTy1AMuL4n1A1JluMaGPzAj87POZuteRSBD2rOp5XmuRmJP9Tw2YxphHlxQVSURGXB/vV79PxE2Jd4lyL0krv8DWNvuXpTM8UljTgxS2rEZChdxDQ4kuuc67MlwvbEuce4AjNYyrVirSS3SUWxKKkz0InkrrknzxLEpmD4fouSgmHzArl/YhwE6cuCNH9Beui4N0eazpHWkFpBN++ZRZrSXZC4QC49tTUM7cRoT3QhZeUuCLLjUDS4x4kMQTdWdM/3hCIwbiyFTrkYnvDtCh/OsCqiHgeWouB0tAjZcNIVTx+ObD6/RqiRNhxJtWJpBW9kzSke6N7V5FbgcsegY4oRimRLKj2NidiNEu00sZqIEkVvNiysZT46ptxjrESX0UftQdeQpBFuTClly4Ma0dmM3V8g6/FX9R34j4F/G3j65bL/KITw3//y3n8I/FuAA/79EMLf/su+oYQFK/GDpx0nDpMnz48MomSyDUYM5PFA4QYOdUxPoHiOmW0Ui6zFSUUkFcnQY1qDEA7pNXrqiczH1uDES+zuyIczxVkek8oLvN0T6iWn9MgimzHbeNLe0sxHNknMMQK2HVxcUS07+pPHvFMU84jubYd3AeEEBwN2OnG9KvBRBlHNQmnS0eHHEZMUFEoyVTl300h1LIjTis82JW5omM+zjyoxx4j31ZHNq1dkQ0djM8pRE5UxxwLkxZp6t2C7/RlfxSlWz/nFw4SVhhFLsjTMigJNyeV6gYx6zudz6ueGw+0bEjdxM78i+IKuPjH1J+xuTjzLOaPELCv8ruN6ljPGhsvFjIJXNAuLbh2ndsfd6jcw4Q1+sSM9vsDKZ5rUcxbnrJcHtrcKvb7iosxIEsnmsiDfvuJwd+BdOuHsSDA1/Zjzg5eX/Pq1pRkfeTrVDHVOonN805PiWcQFZ3pBnSScNwfqk6UrHC8jy+VqxpuzlsNPaz6Mv8D3ME8FhJ4smlEeIqJm4FBMHBrJh198Q8KEWsxJ3I9hilD5SCMn7NSBaRhNRafij+rOakBpSTFUDBzp3TPiwhEFxzTEzHyCGjJOw4oinzO0t/TzNYurnLEpUf5blEnw7QilYVpIznRM5X6CjSZGcfhYXvx+ogkxtvdkQaED+NDhjMcHgZo8MzKYDEKPzLxFm0AWEsY4JhwbslRwsB3TKSZ+oaC3oANRkVIqTfPNkcXiz/4L+MciAf5s3wGA/zyE8J/86QkhxI+BfwP4CXAN/E9CiB+GEP78mkUgCIXeCrrxPW1/ziJJcHHLyh0IJIx94FQNaBExx3OWSH57meEPeyYhCYkEIRA9GDmSxQnxx1wc+bzg5iolHhVTPXI/diRuYLcb+QdjQuQeuInW/GTIuf028Mlsjn9yHPUB1hPNIaffPfNir1C+hRcphZpTphmlGHn79J6D+cB5fkGcrhiyiYUpubs/Uu9OlH7H9WVD9eKHfKY+w4Z7xnmH44ZFNWPwBQ+N5P4Y8dlNRVe33MY9SzK6swV21jDdZVh/4I+HmGpjOa9+h59vn0m2LUF0mLWjalNoEzbncOh74mpObjK27oTbKPZqRdYOfDdM2MNbpv2J5YVkcZVj3EARCUZviFefMu1bDoeRd88dX6xjqueSU9Xwz/9Owe0v/g++/+N7to1gb/4+j1HGzU8ahuQN8VNOMi95//Y185ByXOU8/K8d2WrgfPaSvbeoJGa1PkMkLUXp2faax90SH3VEccTZrKSKMjK1g76gvv3AFKUwW3DKtohd4DYTZPXE+L+95vu+5WX1ku93t2zqPZ19wWS+I5x39PuCalxRfLEidFcchhZf5mTxDqMC7+ol4+0jr3/6mvWmRP74U0qfELqJUDlMHaGCResMzRXi2LIqLsgWMS8aiTCOTDu8aJiKNRedQXclRm3R45ppGPnjDz/jsnXoH814nkaa0zfIZ4e5itkfwYYjc5kwqhYrPhrrygi6SeFjybLwLCPDVMRMRhD3E/MErhcJSX+iyUYeniXep6g4QreaeSFph0DrYBxilmtNpAV/ZVGRP8t34C/Avwb8178UHH0thPgG+GvA//4XPqU8bzkxkXClDWLT0aoVw51g7z+QCvCTRBYG5QKLFobWEuaKmZZkTnA8jDyomIGSBMM8O+PqesEnry54ebYiWEs3jTy2jzz1Lctdzqo/sX8rUOOBr+8bxrniFAl+9+YLwqKH55RiGbF7mnj7iWbVxWwPhrfdz7meEkJVspYTLy7WpJXnmY7x1JOiOX8RM4uviG3M2WJJGWK82NO5GT4umOyJr+/27IXFD9eMes7v3d3hJVxWP+TpZPlw+ycMmy9IM0eRzjhLetw0cqTGPTRcvmiwjzOUEeyURUyW6RGSXKI86MIwJB5zKmHKeOx2ONvz6WbB8mbFFTEnfyRJMhbFOcdBYhqLkjM+P88YXcLoD1SbiGZf8/fv7+h+URNflIxTSpArRDhi9t9T2zlNXXLiwOWsRIac3e4DTVOzaK7Zl99AmXKZlmgitrcd8aVivfqEKZPkLiefJHZo0IUjuEAWH2nbwHZ6S+QCZjdDzXvIGr57qlGuZt83rPBcLQoeXwiWtw3fPre079bMzu64X0WUbw3byTJ/mXDxAZJE06UzlsHz82lisBIvJWECvCGOYpYywVxkPB9HpmONifYoKehdg7zP2GUDSo24IZC2NemoOaqUm3GC7IjS5+xXc77qfsDDOLAbW6SQfDcWnN7/jJX6hKjRdDqjzQ3pPiLJPZPSDMajCGgbYb0HYZDtiBrBOUWSTtipZjQK32nm1yXbWrC5Oce+v2eyI1Oc43RFrGq8q9nGy786CfwF+PeEEP8mH5WE/4MQwh644aMZyT/Cu1/O/b/wp30HlFLEIiYLhg9Vz8ZPhGkEdUI6iZsJFDPUYc88g9kaVNBoW9BOFuNhjDV6aFFqpDhf8+u//kMusg3luSAyCT6quYyXnF+u8O0c/ROFX6UMry2n3Wv+x5//lFO7otre4dcS0wKLlKJfM4Z7Tt9MVF/OKfYtSTJnz0QlW5KswNQlwnQcjx/4+eOWrFzz6WzJZVEwI8ErOEyBLMQ0UYXzLabtOeiU94ccXdZ8+ukPaZ9qdl3KmQrY3JHeVcgy44evrmgODa1LOQqJPXWEY8PevKLLJYd3B67iHTK+oI+eeXrUYALXqULMFbbTtO0DoT9h3YZ3XYyaanrd4o6eeZbx/tAhfEdeVjTzLSJesBhX7O4P3Mtn6mQkeXScfKBw14jLkiQ8sa4t9YeY/lWJKI/81s0PCbsjossQ63OUV5zkA1n0ksr2iFAQ64EzHZB7g7ownJ9J6mEgFgLlMnZTxiyPGdIIu26R36yoHw7k6oSeTphDwvS+Jex74r6lLjXX+5SzIuEtwD6myN9ziF9S/EnDcX1HHOXYd45TLpn7l6hyTT3uwSqU1wST0Z4GkkVCqw1BJqS9wPkTJwvjMSWrHENfIF/sWJqcyRVon2KspbdbXugzsihwita4zJE3I3X2OW+WP8funzgUCQ/mxNhlLGrBB7PDjI5cFzgh2Z161oknsxDPJ0TfMXoQSYkLmsPUEuUaEUmcsYQwECpBOSso9IA57CEfMFFCCC1u1Ey2IplawlnJn1cy+Fclgf8C+Ft8VC78W8B/ykcTkn9s/GnfgURHwcSBaYhZ6oR+e8I5T8hyZsrRO808rrlZa24igRw/Wi+/Ew12lWIp+UIL/tVPPieLNd3R4ibJT5OB8XvFv3Cz4TIUpHnzsedbSkQf0LdbwmcCcXHGdvcVh9kf0QxL4nGLSzXd0wNzsWc992zHCvF2gmhAS3ixvsEJjz6eeH/6BU0j0GnMFy++pEskh90DVVlhlWYaDbKQnPoToW1Js5i01AQ9Q56v0OWcxm0pihe8+jVN/G3JfvHMi+EnPBd7fv/xmVAfGG/f0raC4qsLvv2TD6TRRPd05POrGUFd4aKMwySIfM/9dwfuZoJX4xXj/omaJ6piySgc9+/vsW7gex1RlIrrLmW9ilBpirElIUppHlu++/4bzm4uODTv+IM/umV9tuDl7/x12qHm4tBzePVrLFrBcf8t9f4A/UAUeuTSwNLT3RteXf2YN+23VJcrzuIclSa4vsObI2k0ouuElc/5kZSExBJfJOxS6J4lUa05qIK0FFR+5O0A4VYi5iMNHzg83fP+tuVfrtbIy4lNHvN+aNAXE5X/ioXJMaUmkYKIiCA0Xk2M7Dk9PZA6OM8yvinAuBM3U0vcxUSjIUQTfRFxnixYrHqeI8Vzt2Xqnlh3c9oE1mcLEm/4+kNMiDdsw47/+d4g85/QfndLdQVWtwx9wv0PN9ghQ1w3fLYYObqIX/zt/5skc+RuxymRtK3j1EiSJCaVCWrwOAO2HRA6kJeSVow0vcZ6yaqURJ1m/65FLiWLagK7pK0Dc3WGG77jIKFOVySHf8pdhCGEh380FkL8l8B/98vL98DLP7X0xS/n/kIY5zDOkKgRVxrE00BbdMhEMewhiSMSH4jPl3StZUh6TDdx9Jo4s1Raslx/Su8DIQykq5SaI2kvONYO9eKSdDYwdopRDYxxjxkyik1O9NYwWEv54oD+pmJKI8axJWsumPlPmbIHktzylc/YR47cSepJMSPBhIGDdUg1Ul0sKecL9s5Q+paTNhxdDW0gTx9CewIAACAASURBVGdUJhCZluLymknP2bUTz8QYM5BPcLhzFEmDMJ/AlxO7NzXRrKZ+jHjx22fEMuHtpSQ035BOkovrJVo6do8nnveSsxcHVmeeNLpidAPZxZGp85zqB45DRzuOlC9bqqLgNGpOB8nl+ZrN+ZoMSZ5Ylj1MwbFvTshlziouKFXN129i2iP4iyXnmzPKKOHr/+st9v6RxflL+ocNVW7ZXClOdqDILuizgXxMWc9WuDwnyXpeLRYcTp6QtWRXl2RxoNxoqqAxxwFjA+/2NaqYYTcZ0hnS0wmftTyGEpU9ceoCcSd4PD6gneTTV4ZGBh6N4cKecXLvyacNPu4x5RIlNGn5a8jha5IxgTxm8pLMxlSl5NAF4jgiFhGxkqjKUSsI9shZFmMrgUsMy1gQ25zp5gbMAVcEmtf3nOYLvk8WLFzH2zcxn371gqOsUecrjouMbDghk+8x7Q8Qp4HMD7gwJ7h7nuYx2km6WjNNjkUZUdeexzCha4EUkiyWaOnRIjA5aJzCWMcq8rQklErgpefSe3xTcEgMeSrpZx3F1xHlxpOft7y+S//pkoAQ4iqEcPfLy38d+Okvx/8t8F8JIf4zPh4Mfgn8n3/5+wKb3mHSiOnJk7IiawOyGyEaGXwgrySrQ00SDKJQNJEi1iVNLwkLz9tswqVLyoPDOoumoEgs1lputw1xnGMnQ6YqrKhJ54I6jIypJK1bTDPHde8QteFpUbDWE8+HP+RanPPU77EhQVY5IqqJ3cT74xtEk/GhfY/cLHmZF8SkLGTLXT3S1ooRSRoJZqlD5DnhsyVHZhx3EY/B4f1AfyyZHmrU53Nmbc/u6chvvHqJuonYjY8cnz5g/94J5iUQ8MmM44cj3WiJ9lAUM7zO2M8NxThh/UgcnSjjlFp4+sOR5mgxWUT9aBniJwYxkebQuphsm+BiwYmeL5clr1TG+K5h6ASJFDymlrddzYfDnl+TP2RSO2JTMC4yFtsFZmlJiphskXA6jny7f2I9i7nebDhfOEzZ4Q8RS7NiXztsbMnEGVWVcD5LKHTg0RkOtiVMmtZlzFCksuZgEyKtkdscrCGOEgp74nqx5ve+thzLZ+YePkl3BJMx2I5ktcA+BhbFhJWCJF5RyQ+ky5SkTTicelxmSNUKGSV4fUAkEWEYCd2IMBfMphFpR6pI0gmJuIuxWcQYj9hjS4ggUxGuX/I65DRJiz3PcNs9ttKcO0kzzzg+PDAPiqDX5OMjXap4vAvMLlv0Q8HNmPLgPGYylJFgkoFNUiInaOlwSvA4OeKQcKZBdI5Ea9IChB9ZRVCUjpjArRKsVU3ezDjpQFGnJLHnwyQZukvmVfxXJ4E/x3fgbwohfpuP4cD3wL8DEEL4IyHEfwP8MR89j/7dvywzACClRs4iIulwzxq52KOOnj4rWEyGCyk4FxFh4emGFfbYYDpLWNWo9YzrxadU2SVvnx5xT45inrNZBFoZyNYFj90923/YcSPm3PzuK5KnnFr1LOOXnG9OmPyS2+ef4V9dkp0ky+SZD7uJEkGTTURqxrYJvPQTLz75nOP2gefmmdhIvjj/McbCPInwccfrtw8sdMZVaXjYPxGigqm7xKQT3ZNinzqOJrApJfUxwsQ9M5eS+4aBCX1W8fr9d0wj1MGQHlq6IubFYsk33295++13zOYJn/zkd/A4uvsTbX9gfHPHN4ysE8fFxUv23cRpPFCqOcvzmItMs6jmnJ5P6PbI+c0ZRI4sPlAuV3z35DmNlp+6B3ju+X+Ye5NlW5IsTevT3rrdnLNPc3t3jzYlE6nKYQnCiIeAV+BFasiD8CIIiDCgEIGsqIwM93D325x7ur239doyOCElBWRQQDFwnZqZmomZrt/MVP+1Pnc1Mj5ZmsUxXFjq+j2vb2+w5YL8ZUSeRh63E/t8y+7mQOsaPqpPvOMdQ3D8uz/9b1xqy1OXeaUbPvz692TVoEXPK9fwuq349rCh21UMwP/aXNPPT1RJM5VMXCH5lS4oqHvWYaYVhvcby1IMcau4yBd82O3YLJdc3r7lOT/yjpWx04ybDe8uLzgzsrSS2WvsrhAqw7o06Dmz3L8srW4qSXrK5FhIJbG6mrpzrGFh9RvOh5pGFOSycj99ZKskQn5g++GZN1PL975Hfvme69u31KeeZ+V5ZxvENtEMiTVbHoZ7uv4b6J75W5Xguma92PHH7wvbrWBaBbpovIusNtMUy11eENlwEJK9LAxKk3SGNaBNTVw1k+lpUcTqglgs+mqlGx2dWehFYmt3mNMDQ/7rof7/K3fgL/v/a+Bf/8f6/T8elCBGni7g/Tzz41hRd5l9UeRSs9kJDsC2T/zb2DONAaENStTo1DGolY30WNsxuJmP0yPxasO13GOeNEEs0I+U24L5+hr3QZHuFoS449n35NXhSsQyonLhcVHU6sjkzxz2Hdv6FU+nH0mh4vhD4SFP+DNMcsI8Lhyajt4sTDNcyA3VZcU8R24rRSngbxzt1SV9qBgzdMZTouZXVwcml7lsN0ylELYDyzBSFoFioQoDX8+ZU1gZhh9YqsSvvvsNIh6Z7h/Yd46diYjHr/RTQ7Wr2F1Kxv6IFIG4HonUXFztWNbAFAJyJ6naCyYtEDkhRQNJYGMk5EdUrfh+DPx+D99dSP6pN2z6wvwugm5BeEr/mYsGvjwcOYbA1bffMZ1G8peVust0y5EnLF848qv8Ky5V4vhwT2xv0HUhlRGnNS6s1EPk5DN1EhR7yciAEhZ/bpDxGd8UlCvU0hGT5Guz5/I0Ix495uoR7RxDnJDyDvFRIP7298g//hssIM8TTVVjXEM/TEigsi0inKCJoKHKATVbpEs4qamyJeTCaTyzhgZbCbR6edvNXaG5tPhjz3mekdWeulLY04mzfw35zPvD3+C7gbN1iLXnTj7i7yVXdUP4kHi/7qnEijhozDeO7h8CxSuWaKHOyLkQ8szdokjZ0ohMV0NzZZDnxPPR44siXCuCy4gRhoOjtpEwr+jnliA9j8eZqwuF3becPz4T5favht8vwzGoC3f5mpv+E+dRclkFngaBqU40tqIzjkzicU44lci7jE4JfVGYjSB/OXIaTujdnurSMH49MowN2iWc27FeGKbLTNVe8Jt4j/6ikOWC0nbEIhjvPJMLNPcHGnfPD8uImjva60tyFqSnz7hGo+2OP/U/kFPDzXZLIqFiRWsbRJCs65m5bTmtFfer4pgVj4PnTXvF+82e5/mZJXr6JXK4aTlcrRjVksaF8Xngq1lxo+HgI5+rSOovsXWPZOHh/sw3v36D3q08Pw+8uXrLNCW+fjnx7eYVmzct5XTkaU64HOn9Sn+GZAVu0gx/gYBu69dsqprFRNpc40Lh9PULa7Ohf3xAPK/k9Uid3xCut/SPn+Aycj1f0597gthyuGk5/gQnE0DMrMfIl/IDX7PgJm6RlyM73tKGLSoWhqHw4/rAKyu5aTbcxYXhITMYzeurPfeL5xRXQqNBGGxSqL1gsYk0R9q4IzUPxJODEFnsHntYsXZLiS1qL/jDKfPhWjP/6QtGtjSTp7yvUbkhhR7raniSjG7EaUk/G7yY2HUNO3/PXEv6C027E5S7wDplKgrRFjpnCKukfpwoZsMnBkqdXuYKhkcOcUd3cHgX8OYOO9zwu43hxyExbCQm3zMHQS1nrqXEXl2i8kj/5UwMAmkTrcvYRnAeJ0oRxDYjEGyS42rjMXViSQUbDE1MdG5C/IXhoFRNlhk5edTtBT6cCePKkDThaUKrPUk4/hqM8BchAilCc3NHv9RU7cAoEzbV5OR5v/Fc1ALzvHCWkmEUFKnZtC3f1g1Pk+B7X1hsTVxHss84s+HQ7Li+ueHh7siXf3zEpiPnV4HD+2tcP/OqNJx+f4MQnseThJ8sz8NKZ+F9+wZtCmuMfP1Z0Vae3zRvCHZlHS2iU7RXN9yfzvxx8kR1Jpw9yAbmijJ5ZLfy4e0N//LwHZ/GiZ8WzzZawthTnMOLlvNPirb+zFx/YIiJMJ84VFv+559PDGbhtlko9oIPB3hbWf77P/3MRdsRTz13f/5fsM6wVxvM61+zsZlSJx4/D6Q3muvoeHjsia5nOa7I4pCbikoVVidJQhJTIIjMOmr8cMfl5Zbj+SslppccdFPx2xARcsfhG8GPIVGaiuOVYTtODPcrLuwJ4xfa6ht+++2ZOGba+pr26g05e873X3hYJJv4AszoR0e9F3gW/uH8ha86kYxlSSN28LisUHUmF0PlbinlC4/HB2yquZQCIQRTLbm6FjS55UqtnKjZ6UjqF053X3j7zTe0UeNVTRo+MiMRq8ZvG+xTTW49r+fMKTvEMGF2l8w5oXuFNgPnx5nYBHxlkedEO3RQR2ZpEbWhfUoMwXAVDMVE/u638Hm2XL36F4x6oM6Zn9LCecncXu45bT6xnm5523i+sYV91Hz/88QwjUiZcUIwh8z5vtAnuE8FKy3XOvG2KWwEhLvIxmq2B4nOiRQTJSa6pDErfLQTjdYkfyZNE7c4TuWKkB+Zs+D7u/8LGPzft1+ECJiiuFGKNR/ph4p0ABEEQTt0JanXxOQUw5JxaARw6KCZIfgZnQTP54IsEqcq9p0mH0fm/UhSA4eLjuqpIy6Sh6i4WfeMdkt+nhhDQ/Nq4fTxxFJZknG00VDJFXu9ZfItdnhmzJ7Pz4JPc6K2gf/x88+czxpIxNWQy46qaXl+eqQ2npvDezbXb8lqgeGIXgWlibw+vGIWDu0SpXc8jlvEKXKevjA/jnwSPWHK7F45WrsQRWaJB7zeosYnKBG723K5EcyzZLWwpol/vH9E350RTwOtHFjGhEmKLh5QXc3TcEaOA/vNhg5LXI78/NORYz+j64ZdnanrHdevLH2OnNMd386vqa4aRJiJp4af73/ifjPT1g3aKtBXiFozzGdme+K6qvFJsjKxXb8S2gN5o7m60myzpa4PqLIjmwW7Uayr4OwDbk6YOeCriKgtYVJ40dDmLwQfqY2l3Syk+YIQBHNY8McNr95GpnhJkxydavniepaSmIczu29fsZQjS9LYNCFVxhhIdmFrNPJmZiMqbJao50RuZ4wqiCxQ7Qu2br84znbkfulpnzVhnKiz4qL+FoZIuq2ZcuKyBtEkTukeUWn6lKknT9kl5hnKUHNwgt+JjgtzxkvNrG6ZQ8UiBG7U5E5xXDRzTLRScdCC2zZRVYUxSYaiUCLTCo8OLyyCJQ+Mck/ojsj7iHQNm7HnvqpIeqLiTAoX6JuZ0Ur4d/+8EPwiRCAVGI4Nxp1IppBVQ2pGLmLhlRJEIq431LoQjUcbxaZRGAu6tVzHCoXgFFbMvlCM4fwE3TTQUhGrhG/h+vVbnpbA09zzrtsw/vkJpS64eZsItkXWHYI7HspMkg07mVjKyE/R8/h5YNPWfErwilvGYcCHgZxWLrsrQjoTw8pNJxHtFme2HNczQaxEa+m6jNxXhCjJ8plY9kz+ngmJ0V9YY4ZOsvZwlj07X7FUNdPblotth//e88oJ3FZiqkv0GujrGb3MhNMd+3lisgtXH24QlWQ8PaJQ4DJeOZZS0H5lDCeGh0y8/4Ivz8RQSEkS2gOLODIbQbc9kH3meBpJXxf07Y5Nqen5jJtBhSNG3nDVJoKJPHxyEBa6dot2Bjs7wpS4sord5e8p0jB9/YTcR3RV6KTEqguyNhQyIZwYTKFIxRRn4iy5spEHH7F1odyvnDE028gOzTiMVCYyiZp9MezeRk7LQtWv3NNx/9PIthswl47+qlCtOwYy2maq7RZGj4od7DTRVaimR/uAcore94Q1I7Rm3E7sxZYlwuwjihMl1gSpONcVY6pfrmNeULsthsjz3CFIjEHTzD3r6y1+W/FKz1y1jiI1i+/JxcP8QItgbSVLCVACUja8qeCqLdSuUON5FoKgFKZItABlKuZNAZFpYmGIFqkc1ir6KWCnRBGCpBz2cubjQ8Vw+usIol+ECGRRCOUZHzXispDXlSYVRJJMwtNmgzQQjonJwK0uiGoiEKj1HlTA5C2mzaQYeDp9RZiGcbJUzhIGxZSe+PRPP/Av/+b35NTxdVKsWVAE5M8dab1AlEAogj8GRTcEFh95Gk+I4wVZR05hoWo3hOzR7pbr2nIejqzLwByhrh0Xmy31xRXFJh4en1mE4Hp/QbvvqHTN+XHA95lZfsZIQb0o7gdPyAOtucB9d8n7H1s27oLIMzIanv7xZ1KZaVRNezB0ac/H+x+x68DZG358XtDuHne4pbuqefj+jryx3G5umZjJaeRNXYE2zPPM83RiHSZUZfib9++4ufiOL6ePlLVmcyH5/ucjep15sjv0Nw1x2HPUM4d3f8cyPZJlx6HuyMryJR7Zf5dAbynVgt28ZhPuEBcSe2tw0jDHCl115FVgi6PULarSdE1GSIGvd6h+YR57QoiUNfNYHdExEdc9ZdtQVwpmxbobGO6OtJcNRSl03TB6UE6xbVuU+DPnUnGaF97Z12SRaL1nHhQxeFTKzM6RK0m1rlhrGIyjrhtMkazSvEBVfUL4Qo9n01yhLk/wueDHCWMUzSroredCOtxQIbOg2SRCzCRp8JuOMwmGE5s587vf7EnzRJGF5GdO45mgIqBZU+EhFfZC8c2tZNuA8IkcBN4rBIJO7LAmQRooIVCOmpQFox5YtaS5kPglkrVgYzNKWErT8mdm/rzcYU67vxp/vwgRQBV8EUxri8oRNwV0MbjNjEKQsuakZkatEDZhlWLyDsVM9pFVSdawYrMmFkM2iktjwBUehok09tBpDlIRpyc+PR358HffUJ0P/Dw+4R8CKVmaIjmJlnFYeT6fSceKyu2oNxXWRManI02pGfSJg5OI+pJGGu4fTtgwIKeBuL/CihcGgoga5Re4KFAJxmECV5OKoTx+oQhDshVXVcOf/3xiXR6wY891/Zaz9Cxrjz1HegKVdtT7DO6aH+YjIq704yOGAzcXlrr5DbM/w/MEzz3FVty+ec3sao6zR5qOoguMEXt+JCRNKRBU4enpjJ88p3ykzTtaAVI3nFwkP8+05Yl2UyMnx9JYps+feXt4T3Y1v9WS4Vg4q4ARLRtTEJsd7cWOMoMhcKkL6spQCuQmk0rC88Kuz1IhhEbFhBGKrBTZLZgsWRu4MYpSSYiRegujb5iGjHeOKgf6urD5Kuk+1FA1XDQHjjYQvHhJi+4u0L++xP1Pf+I+JQ7bFlsiUipK3aGLgvM9SYwgWsJpJEqFSwrjLUJktDkiF8VpvkJLz1gKoXboEFnLmUvzGl0W0hr5zSbzxxCAGWE3WP3IB2qa/kxpGqa84dSdqHeaNCn6SXGSC1VneE1FNyeoZwahoCRmCkig7UFn9JhYRCFVApcdyke0q1Fiwq8KmzPLFoa8oM8jn2JPEzXr9gx/JZv4FyECIklICeMnir4ANREtyCyYJ43RCykLMApnE1PKXMwJIwWTSuyFYwkLSzIUBH6ZGDc1qtekUSJ0Q4meSSw8UVhqyVwUOwnXuz3eCr7+2zvObcKoN7iLE9KuTF8M2qwEtyCAMeWXtfzTjO+uEKMnj4UyaszFDa/ebbh5+w2JhfFxBu3YKIVImnDKJA9GCHZa0dct43PArYGyjazxSFVtWIeRH8pPTHbmItcs7kQdO8greW+pveF227JNkvPakZeZQ2MwG40fBD7PvLESrSy30jJoQ1U7YnFMMhOrnr2r2DQB1W0hwjmcWYXE1VvWecXVltMYmJ576kExpMCvL1+zLc9M847D2watQYgFisUqzYbCOSWcXWjsAWkV4/pMaw0boTHFMFmJzBlVMkpnwljQAqxWJFMovqDcRGwc8VFgl8hoMnZVOFtxfB5ZiyCPCTUt7K4bZjKjCjx+PLJPkrZryY8wzQtPnx8xbzRXJ8siGnxjCYtno2awhk1pGEPg6euJZDIbeUmJM8ol3LYjxcyyziSVaNqWttOUJbOYmTUqpJb8cJ5oL56oSkVIge+qkSivuMuZbRXZuj2mPDBOgs6+VKB+PDYM58/MMWPryEEbZGPYrImsVsqxoGwkRYFeFHmTMVJReoGnICqB04pCTVlPSJ4Is2B1LauKbNeMkZofqsT5AcwoidUvvbwYGTlb3HZGy2egIZcRvEQ0DWI6YbKkdYpQJGUplFnyVNVoP1NT+N3hErE58Gno+fHunnF+mXzK3nNpb1lXiLvMOJz55tU7GBZ+Oq4UMqpW1LfvadaP/PHzz/z+23eccyTeZqLpeDQT+i5h2oY4HJGuYniYeFy+shWF9xvD1fUB9/7NCylYGPR+ou4EV/UNg9cIZch7xXzuib5H+4X9ztGfBsJJ0dQ33Oy2DP2Jf/jyFZ0nfl5v+N3vbrja3/I89WAn5Lwg/MKbC8vf1DVmXDkPd1xuXjHvDfPwmc3hQGuumafIeRi5bSzGWe7XiT9ME6MuDGalUgkRIdpIfH4k1YZn4Wg7B3Zh+PwRU12TlCX3MyoY7PmZ0B6YUqApkWW2bOsOpTTn+Z7a7Gk3lrBGWqlYY6GyCdsoTGNJk4cEY1pI3iOjZBYJVTIWTSgOX0HVamyWhGUh6YlzcZSw0KnEz9MjtcsU1yKeZmJluPvTz3x5mvnb//zvmY4nnsSZDs3Fc+BJTJh3Ww6zZ5gTCEHVSaIK9HcDY0hIDLlo6v0FYg3I6EnOIaRhsRIrG0wjEbWgTuHlnmlHMFuKgzKP2BqMz7zbH7kebtH1AiXzb74cEaPn1+0G36w0YuX0MDPlle2FonEKIQQ6rRwBpQymvCyDG1so2qCLIijIWaBNhfKWUCQ0gpxevAy1mvDF4LMiyJqfe0FQBmdeeBAw/LPR94sQAVEEFQtrtog5I4zAjxqnV4oKLFLgEYi80kmJsYJPQTA7i5wibYnUbeHtxS0fXv0Wt/uZf/rjP3I8PiNTYCf2LElwUVf4MCJy4Vx6hKh4eL5DzRKrC+3hAKdIyAtCZ6gVh/aaVYxUVyvH0wO9tEQv6YTgQrW8voSLg0aalmnNyPURWWq6uiWbiafjEZkbXN2SlWPqPcf7E67TbNuWNkVmPGKVpKoGL7nZTgRVYUaDYmFdTvi158IuIDKv5sSmm9mGloiiTjCtC0Ik5udCbhfavURLsGKBsNJuHaoViNTy+Fj43gQWIaiXQEEwy5kwrsgqUK8HtH7FppPU+5bq+kBZJWe1MD89Mz+uKH+L37akaeLwviOFwuWpIfWeOddURZPShlUHxlzQCqRUyMrSDB4WQx89ExmZC6JksFBVGhcT3bYB0RCmCRFOzMnTBM2kF4pUNEYDClNJShbQZ34Y7/kX8pLLDxG+JI4FWl2onCDmBMpiupnZOypvWKvAeR0oAUqciXFG2BoUWKdRxqHWDJNCOUe2Ezo6quiZDISTp6oqxspwsS/UShJ1y2bqmNWReFxYpaamYRknZBJsbQbb0FrHJZZEYMkW8sQqEpSCDJooFZlEEWCMI2uFtBExgySBBC0C3gr04PCNx2SByom5ctyPEjv9ZWzpiqLKX42/X4QIIASqSGRSrNJAkVgRsVaz0ZLZGuxcWJdIdImTdHyZAwRIRbIrhtfZso0S3XZ8ePUdslR8fn5AzQOzj5zDjDkldvWB03pk9RklCt++/hXf//hPtNsBGVtMrZgnxdIL3M7QtIpNCBzjkdVL3HZHiTNrOnFZ3dJVjkGCExIjoOoatNyw5pWzH/G5kERimHvWNDPMjyQRCHbLcp5ZQqQsI8OsKcOIKhPXVfXCxfMj52NGdIJ9PfKtmFnSiTa03H/sOYfPWPcdlC3rqui0RNiOHBL3X58wjWUxGS0UyAgpcMiJi51l1274KawsKrNIi7Sa4iEoj0kS3W2IcWSvtkgMSa7Ybc0+bwmDp6oTaIuqn4hLjd0oum9ahq8LQjWoTUb6QrSRpWRk0VifUUIwF4svnlQqcglEEuSMypHVJKpa46tAVmCzArclnyb87ojKLcZ6BC1VmBiNYVw8V4cN98sV4zhRNRu6jSAuI4MIlHQiPHo2bUVREaMa0ugpReMXy3n0NM3M6M+YKmJcR8kCtSaiXKmTIGSHTJEcBdla/HGlVpqpF8x64qvUfNNkUhSkao/NR5KwBKfQ5ycutw2mM5hGwhowQmFVIBVBsg4/rSgVyQm8lugiUEahUZQkkVIgk6ZQiBq8SpgU8KVQqYawgpeBSRs+zo7xHBmERIbAKGui/IWzCIsAbw3ZZnJQlLyShSAmgRwLRAklIITE58QXHXhI4JZIcqC6GmpHVpa0BqSLvPvmFa9u3zI8PvLz08+0oWY4TXx7+w15XtGuIJPHdXuENOybillm3Fqorg3HssHIyLCMLxNRdovdDjRZYJoa/ziDU8yyMNkto4C9aCjWoqpMO8H0XDFJx6ozqR9ZngqysXQ7oHGsTz3Pc48IgsY2uKnHuIIvW6SJTNXEr6qOV9cb+vvPpPGeMsDHXLjevePkNW11Rtru5a2/29M4zfo482P/QD0rctE0FxXWF+IckMNCuwWrPeOS+SxfGPdt6xBKsTk0vGouOObE/fmB01Zz/tNPXF294931W9b9wvHujv/sbzYMY6DaWNbBsL+puOr2PJ5/wjRPpKBANrRWo73Ch4ViJWWEHCHJBRMjziemNL8M6tZhiwUnkbJQ6UhuBUEUWmEwpSatBdXNpKUmB0luGqr+jH114LfdjnU+Uqi4uNyjesO6eu6OR7oBhL5B1y1l9YQUSD6jbWKSK5XqEMKhU0GExJQKpJnm0tG2NUouxCgY5kCSgvzwQPXKcZSSn/pI1pbtHDmnFbuPbFeD3WuquKJbSZ7vyfqGvG4ZUuC4DKR1IjUbXBNJi8Iojc8g60IdFEUKoo4UPNZrlBCkIijeIlImVh4my1oiMjhkbfl0NPwoMg6Bdp4xOPCBKH/hLMIiADJzMogoUWIhUkgZivRUCNaisKYwojnHgo4v9f2EN5AtQitWCU1cmVRGiUxcFpZ5od3uaPYtiJWwjkSfUJOn0p7F2QAAIABJREFU+6bh3D9RIzEXl/w0TBynyHUtmNeButecrCUIg0qO640jzTNWLpRqR90oJpMouVCUIPuZ03qExlLJBlc3uJAJ05kYCyobaluj1IIvAS9n9u0WiuHx/o8IaVnnLUGsMBY+tJb/4vffouzCvX/HDz8dWc4Zf2ugaihSkLJhby9wtxqBIx0Dz2PPFBTb+pK2zkgKecgsUyATCTky9RE5rbjoGXMi7Tp2XcthtyFSKF+PLGcPamZ4mujkI813v6JfFOs4MMZErRKlbWFKjGui9SPKVQgUuURyGbDlNcEnRFUQxqMnSR4LXhWWtNIoTY6abAumybg0Mc+WWivEfKQoR/Izsk20c80QTzRdzakEjOoQGVK2nBePdlBYEN7QZkt72/Lz5zv05Ng0NduuIwhNv3hE7fBkSp7RKpIqcErSSseYI9OS2OmKLrYYNng/kfAYJyEEirb0K0gVuD9CTeFdpZGd4/z8EWEdSgukUVS1JZwT85QwlWMjzqAKZ1kgTFzGDCIhiyJVCi0CRYJqJUkXxJxISYAu+FQwckVkIBViWBl1oLWCUG04jytmrF8cjrngu0z1WHCrZv0r8feLEAFBYVk9ymRE6VCyoRIra4qkSiG8RqvCKldiemGvGQHFCJJWkB1KbchCIkQkZIcPiSUO+GoErWizR+0qhk+fcblDmg3H04ROiryOTBtD/HEEG1FyZRcCapaMeER+ZnzWNLsamzIai8owsVCEQZ9PL5ixYhDXFVmpFzCpipTakOaZeTgjcZTwlyw5Edi4Fk1NKpmH2nKeI+PTwHX3yJW65rUa+PL4PevgMaHnfIz0JZCXGtVq1n6mrhXnMLGTLXEunE89/XDENIWde/mlanRDlIoxr+S0MA1PnKaECYErVnSRfHwcUBvB01fJk3xkfn75GjMGLqsLLq4uWX1A+CO/fnuD9JFheGYat2y0Z1xvkU9ntm9eU2MxOhCHnlR7SpAoDGbpUV1L6RSXQvE8BfpxRfuM0RLtC30/E+uFlBTWQU1mnhJcBPSycprPmBKwmz3x5MlyRWhD9lDEwBBACEmfR359uecYGtzXC2QDXhkWM7M8vFTloZKEuWBHRbPLSB3QqsXEgqagraMk6M8F5zRCvbgzS5Wp24bQCvTsSU6z1JkfueJ36sR2LQhjKHlDmhZ8eiLtrjmtmjL0nKaV8bTipcDFxHAXCCGjtcJcO2wyZAGJmRI1JhaEXpiFwPsKIQMmQ1oLyWektvSsfD2HFyDLKGmqhTLX2DLjCsisfuEikEF0mpIjKkaCAIlCVgI/W7YiMZvImhKIgi0K12mWArWMtJ1k4xwbK6FWFA91jphdQ7Vp6L+82CWXpwG5Co4m0EpJOiUikpQC4/mJMN/hxBY5Fmzp2LaF7AtPxZNZWaPExUSlamINqpJoDMoEMppFWrZVTVoXUswYMmtJzLGwCLDKMywnxnNm1zpEvUHUkOeZqjoQz18pNnBrHK+3J3yf+OOnPyJOkthOfHxeqSuL9i39uBL6E8/ricquLH82ZNWRxjNlXOmuWqI2PA+eq82CqQx6WWBZ8Klna2DBYI2jKoa7u4FzUchg2O4usNWMVDXXhz1cG15fvUdpQdnukZuKdZk43UVid8R1ie24J66Wg6xxe0NWW45xoYkLwlaIuLLkTLXJ1AbEUmh9Ju0SuqrIeSXMiTVGklcUPOssSEowxYF8v6WqBUGCj4VW1yxCIFNAVQHZKuRaM89n5m6myoVl2LB3kkcHoYqomJHZk+JEljMyGvq0ImpFU2mKNERh0Q4qVQhx5RgmWmMwtUSKCtaCFhGcJ0ZDURlpBKKGwUqep56DqKnETNPeEkrFtBmJQaO3G6IcGMWGRRoapemEIJMJQhOwOAxJFKzWgETEgCSSlkwpYJMHJ1gqEJNAKjjR8GkMjGmmpEuEKUxzolodvY6EywU/Z/6aCvwiRCCXghKCTEG0Ey5CLoaYahIzQVaUNCG1phryC6m5AEqBLFipsVaCiEQkVQNVrCmikLTCro7ntcfNgQepeO7P5Fmi3Qa9Vei65vyxh9RSG8UaLTlqvFowbcshZIwaXx7GhWXKmv2NQpma092ReucwVcNuqykhchoSstTsWoNcVmQQKKHJQpGHQu4DudKcp0e07FD+AZ0rfHG83hc+XO3wy58oqyYMCZoWpTeE5gvMlvcOjscjUVn61PMrHfFjRHSJIjK2tlT6imFnGPrMfA7khwF97DF6QNWarbKYrkXLSA6Jd7eWn2bDTbfDXL3Cz0c6wDSOd+8PzJPlYmNwF2+w05mH7wVuc6DWhcKIEB70BeV8JnQHdB4xRZEnicorxYEUAj9MiCRIsyAKjyOT8/zCjvCZJANyyGRXKGrBixdjkUwLi9NsejgZQVYz0VconzF2RIpLTnGhtns0Gh8DH8cnuqnFdZrBDJyHTKcyqIkcFFl2KDtDU9Cq4LIhygwUBOCLpMwCUc20qqFGkOoNwfT4IXI6Tlx2lkYvJL8hppl1d0k4GeQcUJuAqS1XquU0RHY7zRoueVUi1/uWU5NQOpNFoZk0IiVKWFhLREhHQWJyQdtC8oZagHMZpGKcCz5KqGv+PCbOQlKixpeEyyP9CsWulE1k0pEuO9b+n4+//6/cgf8O+P1fdtkDx1LK3/+lKvE/AH/4y7b/oZTy3/zHzlE0LNZgRcZNhagKUkbCmhgWgeoKepA0ClRV0RbPwzEipEQ3maoyhAShL+SQCSIzKYVtBK5qqd46XDRszprPf/jCepIMVwG3NLgGWm25Uh3zoUOnFfPhQPtxQIkKWyRJHYhScIki50jcWNaQET7S1nvaxqEvdkQWUp8ATSiKxzmyjl8Z4ouZZKP35DqRapCucE4L45xp1hHXRN7dVBhGhv4rw8PM+cvKOWpcY6A/c5EN0UoquXBaHrH7X3F9ECi/0CTNpd7x5XbPcBz5PhVkTExSYpcJdZ4xM7y56nh9daDWFbOIDH1PimfeO8Oryx0PvmFZA/sukyqFVQ12ljz6L3h1QMuOX3Ut4a1kHQSbZFhkYWss+e1LqrcjIUTg9bsNnGFdzzR25TR4Ju8ZpkJK0FpJjiPL2kMoDLJQp4IRL/bdeThjpCG5E1I5hscdbUiYPhE7CaGn7wSNrVCPAlMCCo0/Rs6bSHpOTLVCphfrbbaCEx6pHDIlVrVSLZGUDNPsKGlG5x7jaqpmgzSStIDMK+msYFejpcZPmTpXFFcYfaHKljzWfJpnjNvSD5pqfuJfNSdSt2OIjm2YkbHHZccqCysviPEkBdMCSgiMWLEqQ8wUXZAZishMc2DNFXZTSCmQUiGmirNKPIeaOU54YciLYu48ulikiCzLgnIrpTeI9J9QVIR/hjtQSvmv/wOR+G+B/5B7/E+llL//f9Dvv28CUENi2TTYJsCy4CREJr6kl3+fm5zpKo1eCrslchZQyOxdy/6iRqUVcSqIVZEqDZUj+44UBcpVtLmgLzfstlccH35iyI66jqR5oXKOqb+DywuaVxdYnXmKUEWPtxmtI5vuNWJ4QXk7n5mFYkoRrQ3KWpAzw3TGFIv3BtUNEBJhCoRxwTZAt2CLISiHCZrlNFCKI2zg2hoO04xl4A+fHqmpmecn7s97quWJ5brnX3VvcK9bFKCnkVp/xa0X+OjZb2pqdeBxVhydZ5UeZocPnjIJLooCu3LR3fLtd9/gi6a//0QRkdY02G2F1JJVHHnqF27tBte9IqSRh9WjRsgysekF5pXioA9gR5a4R+0KN1REu+PKRLwxiHZhUgFxuWLnTBg9nUqkDDFFks/MEXIZ8GumxIwSEV87QhGM88D6XDDdBMtEqzKm1kQCg6ypo0RYSUmC1M+4xlBFjX+cWERBiPYF8pF6Ur9yWe/wYqJSArO8EIqN8gjlOQnPICeslEgvSZ2jbirm+UyRnhQ0A5L9VlBFzzpq1o3HBPNSduw4ISuB9YUlVHhtmYLmb0vGHj3ruCK2K1kq6gW0toiNYAkFnEOlACqyZIOfAqYUggpkAVWAtBqcsRAWxpLIwjLbzKcjnAtEu8EsPVFLjPRM0lB4KTu2PCWoDWH5T1gd+L/jDgghBPBfAf/l/5ug/z83maDRgpNPLDLTzI55G0EJThhak4kKdCi0GHbjShYa1QguK0MdE8UGstMkKwhpQWWPmAKTyOg4IuJKFSu6XBDKoUaBp2VbBaBjOn2PXQNTLFzyCpaRr/4elpY2ZtZW0W4MMVuUmDmWhtJ7oshsNh2iz/io8EGRcqSpFToqNBWXu1dYIRABSp6wUZA2G6wIJPFMnQIH5ajVimyvEPKR8emJo7vkqO7YtQc6u+fwm2sObzqqu8zntWcikk8LcbMh1g2nOPHx/pnnFNg5y4d3LXPtKUWy5YLce0qtSNmhSRy6VzSuIxfHrJ748ccjqwrU+wbRObTb/e/MvcvObVl+5fWb13Vf+/JdT5yIyMgwmYZ0uWTJICEhaCCegB49mjwELd6j2tUqiXegWwghyrIT25kZGXHinO+2r+s6rzSOQa6qzHLDLpT/1t5TW6s3hvaac47xY4kr2Miu2FE4y9wW1FbxRSxZ9jU6LKTikcrOnMNA1Rb4PBNNAfmMcgvXpSMsmdIvJBwriSgdMa7EFFkcIBM5BVgiIiVU8pTCYP2MVXtiXOg04FZquVDLLcN8paEgksCUCOmwIZOM4B5BTBGvHFo23FnDrHqKG89lHkhzQAZBiolFRIoYETKQdInMsA6OPCeWYSWIAJXlco40JtFVgbdY0YiFYq1BvTALSScr5BwJeaJab/m4wk2x4sSVNEsKXbJ0BXEcaUxFKy1i9EQpuaCogiAJyJNGeEdoBUuSSBOJbkEGRWU7jlLx8RKZXSCKFefV5z5Nq6iuK0F4grBkOZMWMFWke8yMv/7d+vvH7gn818BTzvmv/97aT4UQ/wdwAf7nnPP/9g+aQJboSlL4AEvLtc4I5zB2Zh5bntSRyme+bCRWT9y3mUoHdASxnkk/Lpx+eGHSkte0IKRg392zqQ3ioWdXfUlvLsiy4jIdsFNEqYgNV2gajnrllY7mJXKQn2jKGyrZsWwiwy8PnJvET9qGu/6eqgbZWi5vnlx5huKNui2REsq4YblOZF2QtUJJx6ZrsdeFGU9cJ6YhsYiCOs/I5KjCyi9u/pT48deswpOdo60e+U11IHUbvr7refew49FU1NrhzheGJeKXBgpHKkv60rPbVPyfo+WH0wsowaa4Q9bQU1HuMz83Pa3YkSvD4C9INE3T0ImK0/hGkQxVLigXz1hqFqc4f/rAdvPA3cNM5StiAFEMrLahN5pCKKK3SCvQqaDeKK4ukrKFU6YUiiVukHkk5gPzYUXKRF7PuCQgCoLSFE0kXQPOLKgMaUkkFFFAkQ35ckLvYDo4Qtuj+sx5GDHKsm4KhktGVp7mTfC2LagvknWU+JSp7qDLEx+WhC0NpdjQ7UvSMEF0nKeG8PaBbDLtP7vl7qtbhmDgGsm6YKkyanXk2TFMM2tXIVbPlYhIEhM+MnUVre8JeUJRMdVPbM6GX087ZjuT3YaHdmB/dYxRMJWeNDpOSlP7PRMTk/NYAdZoVGdYoqbxE0hQJEQv+WHIHN5mjoUg+4KxzSwR1BRJZU1x/IzE21YWWQmklPSqwggDqeTf/sP+T2cC/wPwL//e94/A1znnNyHEnwP/qxDiT3LO/16bwd+HjwgB42opckGwgTlFyjnjkqUpV9KUeIkl3xQrwoLNic/HtQI5gXIzjV45ucwcElUylI89azrxevnIW/qOYnNLXY4cT4LsS2gnVG9odETqzPfzSqEVKrdcmZirRCtabC9ZzcTGDGhZ0FctbpPYOYX7wrPxmTwbnFZIaUhmQZlEXiXRZYy1VPfgpszz2aLKEZsNiztRx5Kvv9qTdeDtw/9NbCN2bRhuHpHZsHEKsWnxi6csaw7nRKMvrLbh/c8Mx7FBhAUjCoSyGL8grydkb1jtlvMh0FSCu7Bj97Ch6hvSeCG6M2jBmj9wDBpGWIxlU0liuEFvJB9MYFeAtq+oec/VRlgVTeUpFo+3CV8GurxBPzjEtSDOB1xRkw+S03KlM4LoF07zlbwkYrzgvSCahEyGixL41bEdPUYkklYsc8aYFj9LRDgT/i6aKxeNDwmLYalbSpF43ni+tQ1/I0943eAb0GugvRGEMcFWI4LhKi5kl5nTgJ3h6/4W1SnW80zYJNynlb1u6OuSeoYwZ64sDHHGS4GsLCOCbTNDklzOjsemIWZPIzJrkFA4ytkhS8eGG8LNKylnvnOKsjIUk6WpFWUaGH/05NOEcpHYHPBOoaQkZ4VZBeM6sFqJ1SXCVIyr47pEPhnLZWNwk8QKRUiZoAtC47kuE0r2RKVYoyZWA9tii4+KMgpG+48Akv6+EUJo4L8H/vz/Xfs7/Nj6d5//dyHE3wI/5zOl6N+avw8fsUrnW3dhVjVLBd1Jke1E8i3DmqlV5lwFDlmhpSJ2KykXCJdZi5W6MJRecq8TtVSEoJBLIJeOIhSM04H1cGFSBdtHw3wLm6JmLFbOZUd/mWGNXLsr9wGGWGKUYRxXdnc9zWaDXATLbUGjNOlTYLAXsunYcsckA0UsGYaZTGKIYMpMVwtyLbheMtLN2FAgnUUaTzmsVDcdN7sW/+MbtSx4eh1ZbOJ8fGXViuvxzGNfoNgzJei2FYYR5Sq+fmxY5o795QMfl4m39YI+SUISVE2DcpGwPuFFTagNYXWoNXKdrtQrlLeWqrxHDBr96Hk6j+QvanbzHaM8Y+OAnWreNzV/KyvM5cqmERSqw02CRGS/RtJDRK+ZdT6zjIDPkCcqoxjDxDxk8jB8rusqGpZ5xgWPyaCjJcTMmiO2M1TOkBWIoIAFqRUuCVRacemNqCzaQjHcUuiV+21P7T1ZF5SvE94LJr9gmi2LnZBOEaPFbyysMzJp1teFY7rQ7R4w20xRtLh1i2wEk18RryVGRTZdZglQDOtnpmWvma4jshL4wtG3G1gyh7WkU5l+PnGhZQcwKbSuyRtLDgXDeOQtJX5IEz/PgSdveb4sBCHRi0M7ifITysKlhKgUAskSDM+L4rd+xYn6837SoshFIrQLJkA6e2xIBOkReWZTVay+JD7PxJ8v5LPjKjNW7fmnJhAB/HfAX+Wcf/h7xnAHHHLOUQjxLZ+5A7/6hx4Ukcy5R5lIkBFZX/HZUIaJ1DU436DVxG8WRZUKHopEKz1KVMQsWL1nLCM5RkoPulQgFobBwhBJSRItuLqkvEjq+so0w83+C9x5Yq0Sf/Jlza9DwBZbUhKEEfo7Sx1aKptYWfh6o5B+5XSdWRC0S+K2vWe8Mfi0sgsNl+MDp/FC9CeE0PiYUWhMV7ITL3xajvRrid0Ivn3cUacZ1Rku+5bRGKYcmF1ATGeKzZc8Xa7c9p7bbkPXPiF0j18sTz8eudtqlq7h6/aR8/QGe8nN3ZbafEmz2VI9Rr5qd7wvJDmvHI8jMQ5AQk6CanNPvQVoqB4fGI4Hnn77A3Lt+dpaXsstandLFRwPzjP6iJE7RBoIRpDbmnBZ8DvBMSWqZoOYJ4Y6MJ8uhDihZkVOltPziIgZrT6/1xoDdYpcZUE2sPr0OeHHFaMbJBesFzwXV6S/IzlFw4RrVgp1xrYa0g15H8kfXziODoWlbwwwYFSJWRLjw5V6qVmaBlImSsnzcWXOHwlrYp3esPVEMDXjZWAgUpUlRYLGlihjmC8Tcxa8ZE//NnOz3eAYSCqx390h54UVT1kpchVQauQyQwyZJjg+PD1h3t2x+pq/8ZmDm/nXh4EfjpFuV2DVijQFWiamCUanEDkys5JUySp2IBUGB92ZMGvCAEpYRIRoIyFJopdIIjUBIQxpSpSlpFt7Du4fQSX+XdyBnPO/4DN9+F/+Oz//b4D/RQjh+dwZ8T/l/B8Ao//dKJWJ9x3hOrNbZvyc6ZvAyVrUFIgmYzO8xEQTR/pBYUrFJY1IpTFC0h4SXmliFTAhcxUlITre3wg++C1pkBRC4qKF2VBs4bKOpKwQhaFpDb94+pLv04XxaeW+K/AHz2m38PTkuHuoGa8jP54TlYrcRM2uqYh1ogiZW2E4y4iSsBUClwxrhgmF1AG1M9imp/4QmacZ2/TkZcD4z7VPbXVPu/6Kl+eR/r3EmEdSsUO0kNvIrVt4myRq3HDHK7Q1yzASmoXymweqoaE/Zn5+Kfm0KsLW0ZuenSnYFx2oC2NOzBeBrCPVxpPMiKw2lGJHGBa6psHePuInxV9Wjo1UzPpM5zWjtCQWUikoukia35HEX2OGDfq+pqsKUInTGmBVqMExeUH2T6QCSucxClIOCCFwoQAfwS0o32AKj64npotiXBzCglwtte1Jw5XUfb7bv6RMPbyx2ve0t2deXyv2weJM4pMF4wtks1CdV65toh53THjiW6BUIJQk14br+Yw0mra6YVN/roFbJk00DcuwIM6C2BqKKAldQbUobuuCi51BWfJlIcmEbY+soiC/fW78zX7HGo+Mmxq1TDynK8qV5GxobGIOE+t9A/tbzk8n5utKlBYWEM3nvok1KmTZEuSCj5EqRupJ4VXAlRVBTIRKI1gJMmAV5GtEppnOwlor3LjjtpoxUVBsI9OP/4ii0d/DHSDn/D/+jrV/Bfyrf+iZ/+4oJHaWXNUJXbWgNMQChMX5GSsWnDD4CnyI/JUv+JMq0dawlIraL4wx4bWmLjVSaGoPLglKCf+5SlxuV55lg2hron/DsWO7XKl3PVG3zL4l2hO/fvqeu51kmBJf1z0VGXXzDlGdcWbH2f+SZLfcPmwpTML4BdaZJYrPR5LTiCsTih67BnoRSEKzHFaiS2zKgnFe2QnJl7XFFpaTqlmqDebfBIrNgEJR9S3n+5Kfhgt/Ngk+2BeqacOonylkR9lVaH2HDxe2UnCRnuZG8wu3Q/544ngOyDTQ1Ft6K1gTtPUGMWmCDKjC0KmSrmyQUlGKkWWB9l3D5llxW3Tsc81wHdB1ohkaXpTnebrQD0da+yW+31M/apwtuRhPHzwPZcX4+gPX3HHOHxBAVdfk4RWpNM5r8jiTosBJWLWiloHkJaNUOOs5e0laEmpdue0MsqpQc6KsFH1TkdSOi5uo30rezs/4LwTieA/hN2zaFucFw12JWhLaDDzoggMS6eAtrBTRfI6WY7l/MHxaLM/fzzg70NcNVm9Y7QIEBCWXecb0DlUEqsEj3cx1l8j6yrJarNS86B+RxxL37Rv2MnEzabJOnGaBazxv14QZPbudpPeW2kRmu1AuBpEDzqwgFT63GFbmdaLKCUtilYqzlrQSqrDgncKxkFSkEAXXIEhCka1mWlY2q+HSf8QW72F0DNIjNhlef7f+/iBuDGYliP2AHh6I9Ug7BN7KPZtj5uUmko4Zqx2zUEQKVrHwry+wrQ032VMkSblRNEoTvGGwMzlnCh8pRkvQA2uypMpxsivhrOjlwItqqQ4aqU/4xwUlPvHt/RbtSkxf8GQ8D6lD5pk/3t0x64mbu2+5sZpCGHTbIq6KqI8EXaN1RBUa5S2d1ZR3BcO6cBgFslSUQ+C21pi7Df9Zt2PrLwgRqWXFD4cfyPcNf/zuARVXXi8N/8X8kVbfkfsLbUzc1Ylzdc88X9FNyW38DlPd4mJBx4ILiU3h+W//7I7DpytzeqMsPqC6b1BDpOpm9KMgXgrKsKJsz7h6SAfKUHDvLEcbMZ1idy2xTcG2UBxNRVUv1G1JdUyEsqXY/0B6e2Fut9TrE339JYP4wMeXI5ePV4xYOV8dRl6phoWyTrjpgpQCVxt0CmxyYFxmCl+TlCMUFa36mnv3ynmKjGnibVR0Xn8GbkwLjZio9x266hi9IN28p3l5ZrKeXb/j5VBg2wZz9sjpglMQFk+lIr5QtEmR3Rmjd6iiIcaRd5sbvCtYhGGdLtwWgl3TE11gyYqvbgvSGjBTz+xfWdqRWu4pc4vzhsV6tNxT33bIeSWVO9oyMy8Fsrmy1xvsLXz8/sz43RP7n/2XyN1Pqc5/wbCu+EqjvSLPPaKekDJTr5pVliSbKeMZ0sBZSoJS2KIgjBIvPIVwbErL+9sveSkSN3PE71bul1u8W0g7w6oj33Tv+L/+9sffqb8/CBMQJHa54LU44eKWTa14jGcum0wfJDnvmfIZM0sKuSK8ZiLxaYzMKRNMZOvAkpA2kSvFQETcFMRGES4WM/eY5BGnZ8qbhvVNcXlQbHPBui/RJ9jKR86dIb09I04dzeYeP7wRlOe8esR4y950pDdBefsKUw+9JqwVXmvMKlAhY3LPmhzgEepz576ZBEOw6LTybZmozZlFCsbJMLoTy82V+ofE6faWry+3iLsDNrSU8yfG25qf/lZyVjWnw29ZcuQLY3j1JdsbT54MTnxFXF64qzRt13FTNoxyi1gd5XymSBN+TNRsqbqCN79l9hvK0pMvF5CKl7piiiOqgkq+4qZ7ll3EzCOj3ZGPFz7Oku4toIxB1nd8evmetu5h+AGWC9NFo6aO53RgV5Rs+g3LaogjXFRNZxe4ZEISjDlQCYspHZc5sR5XQpyROiHWPUokbroNk7vSv5YoeWWVO5bVUMiBxJa76UBVOQ5+QbsaWUPQV55kwObMbV/wcl5Zrw2pkmwrzxAjUp4osmEYL4BkR8VwecZnTar3lMmxOoexPavp2TERZCaKHVOO3E0ls5ak/cCWkoMZ0bLD6Y5uTVwvAbeHaqiY5AHjfsJNeeCXHwb2GGo1I2NNVa/IvCCtJySPXw2lkKAWRH+lnDNilMyywq+g84o0Z1oD20pxcR1lY4jfOMzbCjcblNa8iRWdPXupqMls+AOPEkNC9YKdaQinhbOAcvMl9vorhniHVi+QBTqWTOWA0SCjpgozfoTvsuY3CHZ1ZGcd3WJhUTihyLPmjYJRXHlWBSIWqJcW6hfs1HK5P2HyA1ob8prIyys5VdjHgk2jGc1ACiWn5z16P4Av+KNdxIkBAWLwAAAgAElEQVQNsYfSFWQGxJtnVBJvI00POLjODUIbbPGKsCs2FHTC0LYNfj5RiYWLyqhrTSl3iIeOPyJjOyiz59vG4vwt5vpM+MVXDC+R2PQ0+cLz9USxqZiFoLdX6kYii5qNrhjKjDlKNm2L6S643KFSTxtOVEWgVIYvl4Wp0rizIltFQpCLI+8Xj+grLk+3nDcz6aJp7T3wxlNZ0aaFas28uifkJaF6xfl1ZkwB1Qqq3Qs5XrGvBW2bsaJi9BmtBnZRIo4Vxhjm5YrzFqccQyzxWtGLgBtmZilQu4HoJE+r5/GiEJWA3NLYErFktrtb6qLlL8ZnltxRy5Zz/pFqu4VfXXCrx2wrlrmhPlcEPXBJHie3rOeVvpbI0hNyYqFlEJ+YFsO+vqHpCkJVUFc9VZm5TM/4oUA+JqqxxV5XzqVH7jQiKlIu6bTj0cy8ipUb3XExgn2c+eQNz8Lyp37knGu25SMtZx6TQbuFhKYqay4yU5WOJn9+nXVTxL0VrMKjOkU0ETUq/NqCTIS0UG83dCrSLhuWg8dWkusyE6WkyolaNayzgjKR8wb4/neq7w/CBCSaGDq2zYnrrFlFonNX/OY9afBc93v6l89UGDHXsITPcUqpmbViCQrdTDxlzXwo2W8Mspo4HgZ+kIayW7lOBevhhXcPW9J9Sxk9MUn0saS9iaRdRvxm4fB25M/+/L8i5sCl06i3hBE9/UNk0XvC28JvpeLdn9RUaUcuFuLsuOxLXD5ix5Lpaqi0py4EL4snFoEit0gR+ONSsFEjlyAZxBZXKJbNyh/dbDi8TdRK8sfv9phsic8tT+17qpt/zmrv+erDX/HVrwu+OyZmdWKvWsJ1pLt9RIUVgyKiaFKgKDuoJrTpsKYgVROJHld7hjHTFJm0SahaULwKrsw0l5XJl7zpjrenV07vKoyeOOQFO8B9l1itwsdf8hq+QVeR+5Ng9s/o9IrJNenS4tvIQ1Hy4wfHYTnQbmuKtiIqidIwLplQV2xRFHHmPAXkaeJcL2TRUxlPUNCXgYc18MKRfQtX23HKM80XFcm98bK+sok3iJxYs8OHmnqZWL+2PCwZOVVEOfHaGKT0DLqgPq8EqzmpkWJSGP3Azk+ErFDvW5QJrPGCfxuwtLhUcVUru6pBuAqVToQO1D5TGMswZMJy5KR37N8vfB0KPupA2Qief515Wz8wfDrx4duf882+4bb9OU9r4oPsuVYdep4JLiLVhKs1c0goL9AyIgvQwZKAHAR1aenftTTNhDhOBDESQk2wz+huy3J1hG5E25rlJZDNC+nak2KLlU+/V39/ECaQc8bbC09vmbKqafLKaAz6uvI6Zb6InudOQ1LgL4QCpBMcs6WKmcJMrEuFlRl7N6NN8Xd0IM/VV1yHEbYW4y2r9WyHV4pFs9xdOQiJXQPVBB+/bLhffkHByMVcMfMdcukQDbxeHP35hcvOUlIxvmrs5kgONXEQ3Bp4HTUheoxeSKFGB9gNExeXSG2mlQGqml6U1LvA0yUyzpKmusGUF36+KYjVzN0ZLu9+hno4oeU3+JtAu9wR8neknyn83/xI/RwZWKij5OM5cNNmgoq40hCOmtV8oLca62psLqAQIAQ6JLyFMQfMq6fHc9IJGwXRK1blEeENex8opaXWBbWPxCBJ4YVZt8h3P6N6PUIHSnR0veZtaMBlhDrTNQ3pg6C/qSiuYOVK0DUFFWfjWNN3bGWNDw1DUBTCs3SZ0ltqBNJ5RixPyxU7BhYR+P5q2O9anIDmzZJ3BXooyVvHmx+5327ZTV/ww/CRn1AiVWboR178jI1blo8jdnNgVjuaqmZOGl8HMBDmAt08cFPPZJFpc42rK5Z1IA+JIgvyF55qa8jXzCQXbqMEYelai0ovJC94Pnb4uzMP9obTsDI2lvlXF2KS3EvJ6htE80qqLeGvVyrjcX5BSkFp4GoyKgWCFKSUEJMlsCCzJhHRW0O7TyRXMpU9plFMrxN1JVnHiU4F6vkWMb0w6YxbfkIlE5U5Mhj5e/X3B2ECAUH9W8eu2HO6OTOmPWJx2HbhttKsE5ROMk4LRpfouLJYgVwTSSdsY+mwxGVBZYNdF4opsXQ1cfq8E9sVK9p21FNmKRJVW6LMzB0argoXK26+suhSINsVxh392xl5u+dlWrnRC+q+pxklPlTIssQ6SYoXnNe8nkp0UKwFVGGDTiOXCpxI+EvAek3MkiASF2dx04RXW3bxwupfkbkiisRWvSPsEnezJz7sMfMFd5KcxF/i71Y2p4U/3txxNB7fFbQpYaqErRL5Bdbwt5imZzNr3JPg2nZsWkjCkvKKLUqkqDDzibnJnG2g9hIEvDqDWyM+wBgVujmir4phgScz07xdKP2RZbpnXp7IsWBWr9S3dwgxkdsNarmyPBcEPtdzxUZiNg0xW9w8ojYrjAXX2aGER5sEg6MVGtF3XA+gmkzRFRSLQxaO/lQTM5h85r6pmSbPqjfI9hM+t5RWcnjKaPFCEyrm2pFySVIOHSPWjHyMDnXaUwuBlRKjatR8JbweSKViSD/SXCVfffkzvqgaLueJ3xQCbyZqn6meNPP3AqM802A5lRX3nHh60dSiZVgd7/t3GF3yOs8UFaj1jZv/ZE+6njjLhb46E9Ydhb7QmjOj9nRtwexWVLRUSYBOrHULi0fKgaKy7K0mlgUqJeLV4+eVJDV6EDRlZlQ1cxa8DjP364+EfSCpO4y6UpxrQrpDbzL/sa4N/5OMlon40DCHE4e1oKtmHpot0+hZlcVsJJ8+BjatY/ERfKauNalqic7h08goFEUhaI3EJcFRZoY0IfNAii3rvOCKzL3d0Bbgo0OoPb3ds6kVoVmwyyNuf2bKDfVdJgzv+aJY+Oard3z348C2jwy65uGmQUvPXEpeJqiSQjy/IMor9dJjS0dYPHHILEKS04pg4aEsqE8LOnq0NpyqlcFa+nOkO17JG0daB+xNS1aZNjnUmphrRfOx4Idk2QpL863BXASTTwi/pRgSh0WyNAtl8NQyMDUtlZEoPnKYLFYaZGuJ7kJeX8gqwWJgAbRkFjPOQzhdOH2YGYsW/MJJZpzbYaYnnn/MTM4h1+/p2hIXoKkjN3plX7/nU3mmjJba7gld5OU1MfiMKGr6pqRbLXkOeEqW24yKDfa0MOeVw/VA8XRG9hpTa5YUMNrxcj5iqdB2g13eeNIN922NGd5Q5hbWK01TM5YL55OnMiecbFETVJ2kuemoJNw1A1O0iLcDSVlEO7I4i68c7jwSjwtLp/j06TvmdkvZ3dCeNK9rgbgr8ATCrLlcMqv4xHKJbHZfUfiRqATfxkS7SxRbzbJaXsbvaEXir/Oex02J6mrys6GUnt/4zA/uSB4KJiGJyjHhyCmTPHThjbq3tLseKSCsC4tccSozekdrPKUWpKZiwiKypR8GlrpmEpk2dnwMF96XEvGFZbS/of14+/v19/+f1H//WGOorMcJw85/YLCPLPMZ0RaUT69oWXLzTjB9qCicYi1b5HomRs8yJZy2oB21LShyj1YDVRGYZ03wiszEXJc03vL4uCfNkRDhgSPTg8aeIuu793zlDhStJF4Ctdxw1h8I/R9hU4290+QTfLk3+GQQ3nM8zUQRyTmi+4XZtZy9IYdIUgnjAlquqMKhUkWtVuptIqWKeJ2YX0akSoTNI3st8YthzZnydeZjpXgwZ9zDHd1Jsz6Ceh3hapg6yY184Pad4nI8UDYNXhn6peZkB+Y5oPoTylUoqRlzJgwX2taQG01SHnkMLEtE3/QYBXKWtM4xRclbO3Nde/SPiqA80Z55fjujVY9MJTQTesfnI7ZhxVvLkzrRW0W17phuNHEdGYzAZ0tzzpBKilUTvEe5yI3P+OxwKdF1G7yfOE6B/iSIJK6HRMo19SjY68ic3th+scVsIDw7ppuS+HbBdFv6JPkULxTWoFpJPAbWItL2LW+zIjx2lBfBOh9YVIuQkMjYwqGcZxQJp7d46fFz4JAC7erpRcP7XUtYYWJl46+IosafM7X2uPOJ/bt7Dh8P5K2EbElnTb+9ED9+jRITf5oNz/4TL89H+v07Hq+aTdsiF6itoCzPvI0RyKioiMEgbwX6rsL7mWXWFKKgUHANERMMa6WZVUEzBYoI/v3Akx2Rv9KIdstUv7EdEqb4KeuyUJy+5NO/H9/5/+YPwgR8zGgTCaom3XyFegZvC7RZqLcV8aJpni+MePbW8boaLrFA6IUCySpB1QpbOEr5hgqR6BLZaUrhWVxLExIpRyaRqGvYbEom1aM+FIxfrPwkCS76Cxr7HakwvKRPFEYgmonzYJDnT1w2DW3eUamRNRmi18g+Y5fMJBOTnLDhyDxkFpmYQ6ZInqYvMUVGNgMuNhQu8qYrdDOR1z1ROk6jJ1cFRVoZ1IG9fuRwrklphWHBfeV4yB3T/ZG9+IqlDOyWhUI0/HC6pXnXosUru85yXST1eaaLK4uo8axc5kD83rO9VSi7xW83FOEVnZ5QvuB0HTknTwor48eMSBfO28jLd466Lem7krzV6OdMYXp0siyT5Wa/4XHSzLsa833AvJvxS8FDccuoLggV6fYFFAspWObDTEuGTcE0zyglKGpHdbZcErz0Kxu1w9aB63XF6IKhdaRW4x2kU4EqrnSpI9w2zOqVy1rQjBVaZQ7e80254XKfaSdNG3f8+vqRzU3H/hw5iES6CpxfCe0Gj8CaI6E4Y3IJWVPi6baJTjZIM3NdL8yXEp0EeZ2IriZWAnndYsuB9gautse+Ttx9tUWvFVX3kU+upZAF12tJN0V0oRCmZnjpCIvDtIHFGNRxRemGjaqxX2S2t58RcWuUCOsY/EieDVonjPGoOTPYCq0DrkjooeThuad4FPgwkHyLG0aeb3/gTiqS2bHdGl7/I0WJ/0lGiYzelBAnWlvQN57DNOAOim6jGQrob+7YDR7nX0lKsw9nihHOwZJlQJwcle1p7gIHNTBo8Gg0GaEWZNjzvrF8s9es1ytLtnTVRHHnKdSGl2PAdh/49DJzJ2qMLtDFiY8/BrryhXQpSUIzFIJPQF9FYh5Zvxv4NF3RVrKRLdeiJOIplcaGRC5bBu0ZgyS+bXhNK94fWewrzDuyPlGfSnRbIsMVWWlec6aLv2F9v0WFDik048lSMVNMkmt4IfqFtymy3nZsu5KDeaa1PV4Evmgj0df4QeOUQA+ePCQOwTGlgdm/UWMwG41yAhUca/aUF89KJqnAc6O4qRz/7J+3RN3ADy1r3XP7lUCWiel8pav2TG+Gt+3A/bZg7Q153WGHldf1gLUCP2RYJdsycR0dWVuyduRpoUWSgcq1uD6wKTV5+By2yttM1zXMh1emOqIuO7IKNLuAo2ANZ9rTDd3+keV8whWSwzrQF/c8p4Lpu5WuzYz3ku0YyG9PuM7SBkOfDEtRIPcCnOFwuUf8cmQ9D6x2RxG31HPDtYCX6Zk2Lbwv9/zqELBpouoN7jVRvruyXjVXU/JlU2C6FuyR5DPH/pa3v/hEXT8jbEf/R1/xi80d4fzXvIoO82/uae5fmH71A6bQ7KuC9quEFzXX4DBpQtQl6RKpvGCuNSJpqiyYleXGXbGXmWQeKGyJKkbOnaGeFZWyuC8C93PNp9PEN9uJwzL/Xv39QZhADpLG3ZDFQBxLpnrBjCPNXjIdFHmbccGB9ARn6Lsa5z2vSmCaQFoFprTYQnJNJYJIFRPRVcQq08o9XbHg5cqvPwTe/XRLp564fnqgeKyQtx3GXmjmzOEl8dZdCd2CV4rbOLAMlsENxBfNw1earSh4/fDE8kliCkHf3aN1RIYSZGBKE3aqkNKCSlQxUpYrjpE+ZorsWI81MWX0ZsZPMx9Dxg4F9rYm2cTJ1qhDJqpPPI9b7MvK8RtP7z2TFdSr4mJA24q+G6lGRUFgaAPXKzTTwqoyYheQsWZKjqGAm6SwKrLmRLts6foJpzP1a0n9hWWae75uVsqq5nI6splaznWFtC9sC8NNeUduMofpO55PEeVXeiPxY8RWd1zF9xTCkQC1LhS7gq1uWAYPLFStZDoKzCIQrWGScJ0WFucxoeGucSzlgnaB65RhklghUC6xbmbU4shdR4iSQxVo3JFrpej9514C2428HBeUPeC7Rx7cgdejZfaB+5tbVO85lp4mZ/qoGeSCrhZsJdFCUKkL933D4xcVL9eZt18nEo7z40J/Z7jnPVGuFFKTk0H1PbYJyOtnVoIKO85qZTeNTMtIdbNhmjx1/i0XL5n6De9KxX5z5Je/+sj263d4M2KVABpKMippqkUyj4lGFvhCIIuVUllWCa3IiOsNa6Op4wSFp4iSePIoe8K7n5BSQktJuy/5MS/U/wGl/0GYAELgg0SvC74BOy+svcTHA9PjBnFaWHOEWGGCJR40dfcFZT4zTUesTqgZTOUxSXANBbFesSwYKYADOQUG1WNnxS9/PLC/KXhfD5BrqnBlmhQv+wOXjxl7nZDFlq0bMTuB0cApc1dtmRcJePIiKOORdewRJjEKjykkgYzyiqg8LgXiuFJUK03uqWymlAkfOjqTuaYL+VigbGSZVi7hjffZE75rSQ8X9NvIj/uCvYxcVcD8emZYd9j9hWcz0cl7GI+cj5Kdi5xuGtbzyGIyA5mcJfnHhedB8pthRpSK5mbDw87QhJpCl8AdJk3o+wpZJ5KfWZ575HylDoZPpeNOdFx4pC4hSIE8RlTRUIYn9FhzmTrKZYs1I1puuK4nUo7Ec0n5oBB1ptI9apK8hWeyyei6RCVF8p6zmBGl4MbvSXHgRMIFS2ccb83EMEFtZ/p6i9TPxGmH3lj6NpND4vEa0fUNx7bg5XCiL05M05bTW6K4r0j1FXGdqVbFa5hwc6CsSl6TxoVAvCqaW0mVd+Sjxt60yNLS5gvbnUb5nnLtGDnyWkkqPVJvWqSOCJU5HiU3vYPrK3lvue8knz5lho2liyuVbVHLltis+OXAsv6Evzy90WrJjbSEpkBe4biOnyPBEYbgmPGoKqL9Sp400QSasmPVkNVCKTRzXmjThqAF63lB9hva6YJc4agzIgfip4rxxgK/u2n0D8IEhBJc3i38p689T3PJUmaYNWWviOME256+FnhnSDeBtzwQXq+gI3OWtE2LvQssKaCiR5NoK8OM5uqgbSpMB9HPCKXYnQ1VcWXedYi3idfbxLuHO96Omk1RcntbkdrPsIblt4qpuLJ4wU82W5xbMcvEb54/UGeBkopq/n+Ye5dXebc0z+uz7u81Ivbev8v5ZZ5My6putSaCI0GHjnTSM2dii0MdCA5s/At6JPRIEHqgIKgDQQdORHQgogMLQcsSrbY6M0+e/N323nF5L+u+HMTRTiGzu6iyJZ9JwIqIN4ggnudd67l8PwsVTUw7zjkeRI+kEEWjBkdYPJeHna1G+tZYwpXzPmGmAXSgMdMdE9028xId6n3h3fye0VbsreOrPXMwBSEktYfb1qNPM801di+Jt6/8PAjyr3a0/J5LtLgUoJd89Ct/9lwReeCtcYzTT5jGI/2uGIQjN/hqBSWuhPiB7fbA3An6YSS0HbPt4BvrjyGtA0+HxPkxMPwCHoeBs3vEdTd+NN1Yrm/55flPiMmTA7x/OlGy4bpJ3owra7WY+gDpgvIGqzPecAeL5kqznuo6rp9eaTkStERWGJ2EdWXLHUsuHGvAdJVtORHLla/liceSuVx2xlq5lJ8yS8/Yy/swjp/ZXj7xx/V/5dAsgxt538+E9414nWnecHnxhLYwdwb8jZezoTeGn344cZOZkDMPaaAOlfOnRnn9Oe/f/GMwJv7wDx+Zosc/ZFK90cTEX/3piL4mBuP41SjQc+TNIBn2B/7r/+6PUOfC8HBCq5XVC4pt/KhqvhZFdh6nAN2gGzDdgL1UaoGXmAl7w22SNMKbbsS7M193je48RQy03HGzgqi+MJ0t5vcUtv6OHweUbLxPRz77laveOI0D42hJAt6mA5f2leer5qfCcW6J0Y0wbmwBTo8nhNpRKqNipQpFr9Q9QlNpBKTKNJsRFq7mxtAE4wr9pOjfOcTVEcPOriylRYgHgmgcmiR9K8lror1ovqb/nfKaaS5zTJqu15QnSXjeSb4ijh1u6vEiUfOZajp0P+CWHpUUSQmusvBcL4Tgqd0b3BowZkE5xUvpiHnjlD/wcUjoJpmd5kdKU7Mjjgdy0/gjvGsCXRaOZudld1QD8kmxXL/hGxVIVvG8JdJ1ZFIj+uEDTifourssl220viFL402YyFLxJe982jP6996Rvzr6XsBB8vpieLp42kPg46ARfxpIcac9KvwYMVnzM/fEB3Oh8xJ3eqQtjfHxkTxWHrpGWjUzV1osqKfDncDzyXN9zRxQWHlnTlI0kg6OgtmvnK2lT5JaPJ0qbHSEXjL2lU68oLcjo3vknCxif0U2zVxfGQbF/PCWpiTVvjL96Bu2UhBm59pgTAFeE4vfuOx3MRh3emC2hqILWoJXgiQFb8YOUzWv33/iZU/UnOnHb7HHE6rTzP3A107DRxjf3QVq9iXSpiN763iH5Jg9vp+Rg0I9fOZUFdpL6uOKKhJGx6fSUCkhimHl3kRFzCgsVUEVhd5vmHViUNCSuVOY/MDQXQgLlFviLF+pruJjo4oPvE3fM44n4Mtv9L/fiSAgZGVUF84/rvTN4Kph/16wPyq6STCFDhsjKwndaTqZKJvAKoXUhlgjxku65Ch4duu5XQLyUukGiRCWpjpOXnIeJ8a+0PYdrXuK0fQyUwfLtFeaUSz9jvgSuR0m1PJKlpLFSm5+482+I+UTDBnhNeZcuCAw40CnKz4/44XF8Y5RgaqZUgW2OL55ekN1r8QvHYgrL5+/8nzbQG2E8o/wCzyfguKnM3TPO9Y98M88aqZ8wJcFLwVjkiSfudbC0RnM88TjN4U3OXOxE0Uv7M+emDKfwguvXlHmmUlHulkzderOJRw0mQ7nNImNGG/4i2HLEJdG05qxnymbpHv/ynaNDP1bUJk/W1+x/ROqVcb9xsPpSL99Jh8l47sDIjkk+50LEQVb9YziQOoqZV+oe6M5y3FOSGPwqSOVhZJ2ShaMQ2BdNOtuiSEyqZGXY8bsglE06gjWd0yh8mombvEjbXsgdgLtLdl+5Ho7oVxgeFfp1UyqG7Ou+DAwawgm04sJp1aG0ijCc2wWoR2lKFCefWi0bePrAg/ikVUfOGhN93jGuZlxsIhx4HWXuFiJ3ziuS8TKFWksOd045hErGt58y49OE88p8HK1fGdfGeLAkB44PHa8LgFTI0pNCEC7lSIUQvUoJSBnNiJ77BGzwsoNQYbOkpYNsiSKI9qPpKfvqb7SGYEevyfbDmPrb/W/P4+oyE+4y42/Bxrw77XW/pYQ4hH4j4HfA/4u8C+21l5/UCD+W8C/AGzAX2+t/dHf7zNSg9wqU+mpvURRsb8vGeI3XNevFG3p9YS0d7YbZeEqbgjfsefCEhWdkYgxIpDsq+O6J0bjmKyhZU3dKjfjmNeCLQ7UAzruCD1Tro04FzoOzA+NLyKwble+/Cryj58Mp/6JJiXWjphvbrSvkj288Ks20y879B3rFknv3/CwasK18ZJfWLqK4wRyYnyqvF5/SZMNETWdttzW70kxE5Tldb1x4YDqNqSIfKoz/W3n5/INP1ff8X7/MY8niLJwGyPDR00eA18ovBsLiveY2w66I74xfHre+Kq+QT00To+PyOoILeOlY8Pg0gQolAp4p7D2idevmXZUNKExo2Z3V0qrZDGyZwPK8r/96c/5dM78kz9ZceaJbU0MLhOLQF0LU2/wZMZRUMvKkjVtHbnOCfeyMqhMFB17CYhkmHrArZRd0TiyxkJbzowlEqwhtJ39S2AqAvdup3UHpIeSZ2SviFeFb5oyXzDFseoL/nOlhRe+w/P2dqIrmW2a0GlHhRtS9Rg7IeuEFHfGwpYWXl++5zGdyGXiEq+UQXO0R3SvWMuKYCGp9zTzlrE/UILCjhKxe86pMoWN93/lxM/++IIdvqDNXS2LbDkefoQUF4ovfPenf4yNkukAVknO/oqIBZnLnWbULGEr1AxChh8QaQMuAn1FPUy0LVBoXGqmWckuCyq90PeWdrWI/ULqBoYRxiT55c9f/+JBAMjAv9la+yMhxAz8j0KI/xL468B/1Vr7m0KIvwH8DeDfAv557rJifxX4p4F/94fH32qySXKwVCHpl46u7zEm4Wnk44F9yVin2LvAJDLe78ROokaFyZHxllDjkbgPLJevvPqdzgq6QeCUhFEgskLGihsyMQWMmtC75fnLBXXU6C+KY/pE7CwhFYod+fC0cYuWTy/PZL9xeH/geLLYDhwjc85UVdk3hRkK6mVl9ZlLupLcyEGcqHmn4wb9AEKyhoVL8JSgybfC8eg4Tu/4P04zhzpQlsBnp5jWhDsc2NxHTvsDplWSKbwIxT/qBS+T4nWLRGcwxVHniEkjIXts0ZhuRHWRrhiGLLFD49A7HqYBfRjQ2dAV6ILgKHq+NxnfOcx8JIqVZ3/GLBv74uhyY3CJa7hRlw077CQe0D5ANxOSw4df0M8T1mp8SaSxodYbKTgiFhd2gqtcUwQJoiiksaQ90RCk2th8o+wX9uJ5TdCFG3OnWeUL0XUoadFqo+wn9nlhSoaqDX6/ouNEbJF8vSBt4SVBuQVMGnjzIOg3uOUGYmLqHI6RQONyjhQWbsGjgoSjQcQby+3Kvm703xpO7j26NQ5dT0gKP33hU2q8LY0hPjKZxjEYsgrsz5a5X/iln3jXIvrtQEoafaikGNDJoYYD45C5rYqmVg4Z2mIpomMrV2S6EWOBfuDUT0AhO8GjmwitcX3d2XLGdT3VrgRjSTExrRP1mw31fB/IG4zG1krTlUn/5bgDv+KuIkxr7SaE+BPgx8Bf4y47BvDvA//ND0HgrwH/QWutAf+9EOIkhPjww3V+o1VR2aOgq43QS7pUScbRHRQqVXAPLLYQckFmieBTZx8AACAASURBVO2PjCmgpMcoiegVbcuU2kHSOCNxk7pvlVaDCgGPYdKZpisXZXmfLlyPBpUNZnPAJ4J9JKqAzo46V4bXkV+EgDMGNTTezY44PrHHM/V7Te0/sm6VZiwijywSqBsNzdgEiUBMkV7DJtc7bENACzslGr796Ts6OxPSgSkrvtTKnAf8ZabPFxYV0Lbn3duRd7cLH6ulO0j2ZMBYGpbBKX6xCFrU9DWT1QOb3ynNMqoDVmeUbBxUz5MdGNUB7EjWC6TMZjP+Gvk7XyqvaqQvZ9IS6erK5j1CgY8r+lVS3npanVBBoYpDlcC7MdDNR4p4QHWJuDvG4Fm2jam94Tg3WlHk2KhqQmRJ3jydyGTRKFnSaYmwBW8Sawqsqbu/LifUtZEnz6PuqP2Iks9YU0muclMbbrA4caINiul6YxEd1/WCajNzV1A0ni+e4fFAj2AInq4fMTaTQiKToRpaM3CYiQ9H/PoKodGpkUfR8VZadiKldnhRSF4wlheKkTjeMg6PbH1CmxmZK/I4ob9byCJz05UhvuDUhQSk4X6+l0kjywupCHwGYyf2ulBzJkpBbY4uOrJaEbGRZc+lbAjjQYLRDps0ykqkEnSDxPWR8KUjITh0DpsDQXZk/UiI/x9NEf4AIfmngP8BeP9rjv2R+3GBHwLErw8uf/fD2m8NAkIKeNhJrwd6ccMcHjCqUbsKDcxBsAZ43AakiFwmifQB0eqdOIzBl0wtZ7o5I3VP1ZpaEq3daPLAaehZwxnbMmbrKOVGbo/MzeJvGfPmEad34jYgj423HprseRoGvtQrb92MFgdq9Kxr4Mv2TPEKRoeqENPCsUhahqAkwhi6H5SGRNPsGfbiEd4zySP90zvEgyTVzC+eP7PWIzX12HcR9o2vNpDXgdxd0KPg0+AppfGUFOqkWDZNUAMtJT6JwsdzwcrKj48D+nDi8NDTX14JS+WhH3g/GebTzPjNyElZynbA14WWKpsXfHmB5CSmeJp3XKMn6p6ariA1x8mRbje6SXIUJ/aQGaeOVjK5numrxNeer1+faXVFxhFEYjwckUKQQmNPN+TeSFlhxf3O6Y1EakvUAXnOjBj6Q2HtK/LF4YXl9LGSUwDhad2MdRpFo+4LQj3RVUuMEjk/0EnF8nnjKCbe2Zk03fh8he7QsKVjVQURCleVGVvmNBi+vCRcdbhpprARG2h3opslHC11irQKNEPNhev3Oy7vuOa4qRv9jzPLWWLUSLt95fDtzJsfH/juF/8LzU9M/cw5JCZnCWHFyZFzvtKdZvrg2XxkjVe0vKEw+B5MD7YC2ZJSRagFpzty0wjtqXGgWIlkQ62OoRTCfKA5jdg3kks0qVn9inhZMeP4lw8CQoiJu37gv9Fau96P/ndrrTUhRPvzXuuH6/0/3IGuNwy5kbqI7AxFGw5DZA89ymquLdPvEK2mj4lt3wl7Y9eKbCqhFrII0BRSGFqOtLyjsqOh8DWT647SgnTLaHdlp+eNHlCmp182dDfCVrnmK99cfkR8qujeMd0iZR1Q9sxtFfgwwDWSjODw0KHsgXx9pvYG1T0hS2G0BkJF3wr9oSM3eP208l07M5vGw2AxNrKHnVwkHqglYNLEClhW6r6xyYx4eGDsHaUtvPzMsz712Hlj0h0f0HzWgg+dZPGGaFZ8ChQl0E1hzcA8Fg5Pip889UzHiSAV121hyAUpDEmMhAnmHy3YUNGLpT0oZv+e5/NnVqXpiuH4dmZrsP98wZwE2g48PWp0yggjiV1g7nv84KlboD859i3SZY/SA1U04tczUViGtwesbdQVTDGYJqm5gjEUlana3MEy3U6XMtkYFuCNMywmg4o0oaj9iDCVaAoqSjolabNCfbW4LnDtDfOg6ZOnyyukI2HXXFukGrC9IPrAaCzCgLKCQTquMfDVN9AZI3Zqzsg3irdmoNZILwc2m3hRAz91lf2j5phv/NJJjI68+wreCYZhYPeOXRSOqmdd70i1Dz96g/+T73j5qhFLpGnFYWw4oVEYdJOEtpPxpKqQCqSzpLbf9RYXgxaB5idqaggUVUJcNk6HCVUsV9FQRkDRtEOgld8GJv9zBgEhhPkhAPyHrbX/9IflT//3Nl8I8YG/J2r+S+Anv/b2b39Y+3/Zr3MHDoeuxQ0UG9o5+laoPmKqIypLZxpBLZQOQrzXvOVRQtjRayUKgRwF1Rd065j2jEiKVUrWGiit4ELPKBux9ZRyxZQntlhpj5J3WXFOO710PJp2Zwwysl9X+tgx5hfCs+Z794Vhurfx9s7QSNTm2VLH3FeU2RDKIlohdxJyY6PhZaPpRhKWH48j/WEmN0PzmeZ61KkhLo1u3KnjAwdrOUjDnB0f3CPWKob8xOfTBVUtoSq06Bh7ywFPbypThu+jRLhGzjdMNWhbOQEHp7EMtCiwcqPlcEeg9Q+cW+PjWgnbQPUra5dpLWNsQk+WLoz0B4nlwBcZeOg168EyVwlWs9oOJz1ibJSaeHc4svcTiIIaHCkHCIG0eq75Xqo9yh4jBbo3bEWjW6VFQVFHkt0xOhIWTYuOGCLNZFxaSbNAlEoWPSONVBTCdOTrwjQHWq3YciTGjDs5RCepvv3AsIS23djaRp8HOiQiObIYyXon2Q1EIJeBREKailwFiUY5KcbWc9tvxAJ60JSPmaQKpWQGB7V2PKK4yY6v2wXVJqp+ZOwcLW4obaFEag7YZWO/QUsLvXR4FTGqBwaSBJ01kYaQAa0gaAPaoK4Zoza6QdB28CYhlSaHhSLuuTXiK007pI/Y+kBRV6I50NT2Fw8CP2T7/zbwJ621f+fXnvrPgX8Z+Js/PP5nv7b+rwsh/iPuCcHL3y8fANAKiJTQx4ncemqIBFto8UIeHDpWWqwEkUk5E5VC64hslrA3tAjoMrOpfEdgJ0UuAtcLYlS0WLEp0/RAMImRHk/jukQeT5V2EizLM236A47W8Kt1gWCw1XItN7ZtYQkTo4VBRW4yE0Nj9wrbn2l2xskDIiWkVqQYCWgKEhUFMjYmfeT48MjbdwPERF4SUc9w7OmWRnTA0WKqYHyQVPWGfyJKDvnApTS8uTDPA53qEX2lqRlpwXkYdeTpneCgHliKQC6vFCrT3DHpAWEqaavE5OlOMBw0Llteq+JXXz2rF8QYyVPBWMvLp68IAfN4olVD3w/UdWVWCYZHJhMZhp7tcmE9Wczzihw0tbzyoX/PfHD4vXFmY90zft/Z1o2dzNA9YMsBhybYjKyemCDERM4B0TK6KJQObOdAUoIUbrS9EZjQLqOodPoAufL17LFDR4wV0854sfHQT9jHHrk1lqWyyhFLI1BIOTB3A4u2NHkgyZ2Yd2qCFBLWBAyak5QIXeg7QSccJcElJVox5LDiBsdJS3aZCdqi5o5ZGixw0ZH9ujK1hpYTRTS2Kon1xtfXha97ogl1h9uOHiXA9xB9ZqyW0gytz1TtMUmBqIRbIWVNXAp7l+nqBE0Syw5bJqoDkLhKhW2CzXRkHxmnxJ4rUxO/1f/+PDuBfxb4l4D/WQjxP/2w9m//4Pz/iRDiXwV+xh1MCvBfcC8P/in3EuG/8g/8hFbITVBFpZbEoXTUXMlFMi6JZjVCQMkVJROiNEpSdGjE0FiWQPPprsgiwZm7EqvMCdkSk3B0VhCAXXmG1lHjjUl3pBzwm6MEzUv2zI8WDcjkcdpwzlf21mjSM4wWObzjQWeED4RUqdrhhGNLDVkNnR3IQtJiZuw7dO9IKbHGwOet8b2vnHaLytA7i6wGnS8EY+hzw62Gh8HTyUf+yhtY1529CWof6JPA6JXgHcNxIxZNbpLa9xxr42QKekmkURFTpjMKbXuKyhgdQDpqhqIrRRuel8hFFhAdZuC+k4kDtVhkV2jSMU4CH6AiebkVlAgMNmJHR2ogdo/aM5ey0+qZcTgyNI0WPXWvhMuNHUUMiUlOvDk+UaxFDQ7nE7UUitqJZG75Rm0rUjmczKRR4GsgWkUfPX2rRHNEvdzYjwPODMzjz+jkQHtRbPIZO4B4/w6XCruUVC2w/sxWBBcCue7kh55aB7aWOMiE7xXPm6GJiiwGjSDLdPeOCltNjMLS0RFjgCSQ73uEbygNkTMm9WgbuaUr4iaJ553nKvn9Y8DMj7R4hrXxi199YdkiuIAYHbFVFBqRC802WloJqqFEBQoNiUPTtCZUKFmjqgMqrUbwlqIlWIGKjrAmDIU4C2aXuXhD8Rv5qfuLB4HW2n/LnR7+m+yf+w2vb8C/9g90/F8zISSdyeTQ0LfI62Ehj5WhM9xih1syW26UfP9ZbKtkAwqP7QW97FnWRI4JmRqxVpo0tCKQoqKtJI8Kjb9vY1sE2fE1P/NlG/mWDqkNJZ7ZEBx4h99fyaaRosThEN0Kt479MNJZjxsl58/PtNHR18jiBb3T9CnShKe1SrOWvK8UCSHf+O515UP3QKdOSC1pzWOi4oGePAaUh5HK7zFjhjOuSbYoIGvU2dMVDVZj9oQbI0kJrMhI40heoAw89orXXZCdpDaNpuKURk6VUCGuhdc9EvPGpzyQzITRDYlAboUtRSatEOPArBpGO0p7JtaBfppo+0Lfj+QSqYeRwZ9Z8RRZeHQnzOFEjoGybdhYmORwHxd+EDz0PYdZ8Smu1GtG5EisnugqTWlKAyk6QorUslBUQBdJspqH95Y2WvogWENBbSvNFRQV2TmCvaBloWmBuSU67bCPja1mmlLczoVQG5Oa6ZqhT41cLkRTUbYBlbgXUgMrHNG1OznZRMRzoyZBsZJJKYbJsHUalQxOdYRPHvXB0eRE3TPp8wufzl+RzjC/Gp4+jFgLRmuscgS/4T2YIdGJTKwD9XbDOEfTAis8PnXUvQfRaK2CLJheU8N9MCjngosZWRub01iTEfHApholZuSaCdogZSGbwLUdfqv//U50DDZgiwGiw1UIITCZDpUy2UHYC8lJcsr3lGmTdMaSm6AlGJQijj1Groi4kGSlLz1g763EWkES1KYYNAS1g4SPr4JDeuU2wE/eCr6EgqmJKD3EzLI3WpbYBkHAvha4fSJId0daESgh0/cdg9NIp8ihIJ2iUlguCyVnmqzEGGlEvgyJd9PO0EusvANWRuH4LAxOFEYpmSbQVFYC3dCxqwWWmU9152kckDaynBvycEW7jmNNVNEoRlNMx1R2WrF0ymKbQDQNTjCmSpaCa6p8HwVZ9nRbT2k7a8yYUthioEzm/scIkFiIAoqpvP+giPsj9mZI7YztHKoMvMqNt/3A03hip0OshWADvRt4eDiSpWaJF8YsKFGiS6EQiD6Q6wJZ4ZRkNh25DKy3L/gEgQpBYJOkDQ8oYbDrmecxcRAbxIWbTNSiEW0jVg1pp91AHi19SjAKLhdBf3xizBtCVhAOay0qV3zwoAxCaVLNBGWwVjAVxbYJvA6IIBH7DqKnjQKRAzI5VBZIMyLSM2WvhElRWuElJq6fbzz+uHI9PxHGjT98OnBrK6fDxBo8ORXkS6WeLLtaUK4iUiRpiSmNOgYQiS5Dq4IsKrkkFBqZNKFlnK4kp7Fa48uOrCvaJZqU0GdKgF5I9lYw/rcnBn+7+uD/j9YkfP6S2X3kmgOx03fxjVVhzxHfVrS489lqvc/AH6RBqwGVHXkpxGVF5J3W7kx6aSJSdiTlkAaM6MhS4VMlBoFTHQ8Z6vlK8Rei1IjBUOuRS/LsCGLIiBbZuCLsgYABnxC5oFrjOJ3unWemR5ZA840MCBxaGVprhBzxayGtinWBz5eVc76yiUiLESUi61DRqTLYwFgFLUKNCtF3VCWZcwUsb94MzFukqcZ1u7Fdb9Ttxh4TKQfELVNzIRiNsJaj1ghb2aRgReBLxQvJufV49wSqx7hClpWcI7FZpILoC6IuBKuIywKLpbQNVwV6CBS938/qG6icmZ3h7fQNWTvSvqCER80NlGVTlSYkTTgKIxFFrxpOC4QGtELLjGyKwYxkCptuCP2I8QNaWWzOxEtm+fjKsu8Y3SPlQJMOmxNJvaCPBh8jNUKSlWYkNSrKLBGtMo+N+aRxXUJ3DtmPGDcj1Q9NSNLhLLjhTpcu4d7DoEVPNo0oBLU0wtK47R3cdnLOlLXhqVxezyQBcpS8Xr9QJoGWEv9yI1zPJCER40DTA23f8TXgm+IlO0qIxFTYClAEUUosAoEmaYM3jdwSJmV0TciQMFoQK6A1QnUUYeDo6YaMNQUhKsY1CBopJqb0mycI4XdkJwANtUZ4slASLSku3YazEeMLrm9ovyHaSjMznZR01ZFboOlKKhJTC1UUqra4JtilhLpRS6aVek+irR5fNCFmNhSMiag1IQo+367Y4S06CVxWhE5SYuZaBLMTiBy5looImkE7np4k/jwjXq5s8Q7bFEaSusoeGqJ6rrWy7ldkSlilaKZxLYK/uw38VDSkuIC7O+If2Aw10Ch8PmekLUxi5pNf+NG6E0+CN/L3gJ8RykQWjVN0PD5orBsIMbFGR3USoQVDlfiaETWBKKSlcvaSlyJ5RRFqIFXo+0BJklCBLVOdwfkNVOVl+cK+n5Hdznsxcv6c4LBRese4SBpfWXwCpVnahlo7UOC1wEXNtnxhLRqjN5wuYGdaW3Ci0KQh9wbtFTIJWmlAQLxmeglSOURv+H67EU3jTfOUpIjK4EJBqkTqG0ILuphYdI87PtEHzW1W1LojzIy4FZwdKPRI0+CmMFqSVSKWRqqR5jNpTeTdk8yGUj1CdXS95WgVMW18fPnKdGxM6g8Ym2LdV8ScaOmK2ASYRrdKPjfFNV6YzcDp7UTdekYNeQ+YzbMYT2gR1ztaANugxp4gA0JlboAKkrkaWufuHanXiCgWK8EWSdMJtKV04GxCVjiZio8dSV/AWqgDsihyJxidIfgG/OZJwt+JICCEZOodFoE6dujriiwKcTywq0qVK62siFFTm6UJTZGgK3f01xRwu0aJI74F0looZscKwS4qhQnbBKEphNiRrbGbRJYPzFxJo8S/Xmj+yP5hoARJyREdLM4qUBNN3ejHnm5RaMA+Sm7c2NVCjIHmJaqT1JaQpULILCmSfLkHoYfE3Bt8bXxaK4e0c3woyBA4ZEXfCcqmCMpxi1eMUIhPmqoXXkRFbIFw9pSi0bYyGkedZhI9h/k+m1+uGySBkT1pl1zzDZUbymReS+TzZonGUGTmGjf6EdTuMLmBqDS5oOlRXWEJKy+/bNRDw+gFeZzY85m3+0x+UvjzV+Se2AdBFBpTNGMyPM4TgYBSiiV7rAGcxViIEfYiscYxqp4+KnLxBAmyCZbco8QrLkmaWMnaQN2IPuI6wzRqvKzI7EGckLcLdu6Qt4YeBdY8MC6R9bixbjdGO5PWSrKJWUaqaAjXUUWiSI1JA75Fwr6QuFBbYjtfGcaAmh1OaMYBVBToBvIWaMcb88FxfRZ0RSNSIssrB/kNMiWmeL+HH1pjnL5hPL6h1YQuihAqr2Wjpog2A32XIHuWW48YM0UKeqmRUt3v7EZDEmjZQFaaEmShaLXSmkTVDpl3UIW0aWoVSC9hkFgrKF9hf9wYXh2L/O0DRL8TxwFqhZNDhsxcLLnf0AkQCTFERDUkXRGjw+iKJLFXT+0SNElKkbxl2uaQMdO3ykhPpx0DjWigCRC2Mj8q3j49MD84VLqhkqbdbqhiCfWZ56vHdQvJA3ia9IgCWglqjpQvkZhvLKsk+oWgNmwroDPrnig+IypI19A0tKxMR8NsJ7g9kctE3jzfvy5EeeAxQjQ7UlZGbZieLMfpgIgbVwGdO7FZ7onLmqnDA3IcmYaRbt7wpnImwBrRomAGqGqk9R3jIJCD4hIU/+dr5BfrymtILKlhakfLiltLJCGRreDqgbFXlCi5XROOF7qtMmn4yhXbJHo80IceJyaKPjD4A2+UwVFJNtNsYnAgKKjTxHEYONWKVhPCCUzN6NxYWVlVorqBmkH7hO0EfrBk2ViKxyeP8w5iJtqMj4/EVsENCOHo+o4cFeP8wDA6mggs/QIvOzZmQooYZTALiHhGl4IymqgtfZ4IvQalkd2AM4qMJlVFShDPG2FbCa4Qe8txGpBmQsjKWgcwiYPv2YLikgRJQMwZ21mmbiaLilM9w3FAmxGTInI6MF4rollSMZAFgp5yMEyTpKhMaoWgC94USgk4kXF2RNoZMcy0weGNouyCWjT76jjTkUuDWSNtJeWGzg51bFQc85tIr/9yJcJ/6NaaQhtPCxCEQqkOJRs2F6yAZBTD+AYfMlbeSGWmWokPjVoq0k1IsxLTlV0ohBmZawYp8O1Ar24UPaAHwx4NoVZm0fPw5oSPhZpfaExcXhvH659hvv2G0c98/z7z++mAN4nCt/S5Et/fWA4QLl8pSZDEREfg4XEihkgpDbKklkLVgRo10itqkhhl0J1jjZVdZT6GhnUeR0+sAfs08qFXbPRgDtAKU5dAnrDDketybz3VySHNhIqa4iQiJWJybPkBomfQK0JImph4HRrPO8TBgGi8pIjcMk4qWs3IoYKvmAa1SbKQ1JL5MP2E9jYgKwxKI4+SsTtgNoOZBWXfOAyFdjhRVY+WkbbtNAWuU4Srox8kDwLCbaMWhyZhZc/FC0KtaLViciCkwh53fE2YmFEGOjlwU542Kg7S8aAfQUaytayb4GncyI+GvK6UOdLtgiQS/iZIWlJz4/X1K8ZM1Nax1BUTAtp43CqINtJRsbVSkyCuFiUTx4MCee+4dLJDygNyAL3Dh29OjGqgJkdykvMaYCvYB0fcN9b4len9N8jjkcvX73g7GMpR06xCTh01B0Id2Euir4nVVEypKO1o6V4Z0SiUqHQxoRH375wLRjZUkeTqsXpCOaAl6thR24YcMrQdpWZMNtxk5hQd80NiSQPuL9sx+A/dauUWK29zoIWOYgub6zAiI4PC1Ei1FZ0F1o7YTrFfEslCjglTJWm0yHXF7ZUSBBcVkU5jTI8vGRsKiwYRNUVB5BXKTMFyHRUPZ8h1Iesjv7ptDMPE43qgGklpCts8g5Bc+h7/+oKev0WZiCkr4uCww0B1knQ+o1Nglx4jDcZ1lNgwoqN/7BjGA7vw+Hrlq5iofeKbDbrpFWm+JRdFXSv1YNmff4VbNNMkyL6i3mhIjjQ4ZlXIGXzXGEJFEXFK0XfxXltXgk+fez46y1VLat8x5ht6FUgtSGuh9hK5Qtwr2iRKL9FCcfrpe476Efu+0LuJ5RIYujPh1rENG5Mead1bRP0CDx3+lmk2UsqBk+pYW8HIisuaoYHqFGv28JpRQnMSla06BFCqZ0VRW6ZrBdAk58jbC64GjsJwGUa0dYjWaOlELK9cu4kTjUGPbOfCc12Yc6B/OyM+nvF1JMmRzXuGKoi6o+lnts0w1YZwEqU0TVYw6i4+0hSd7TFO0n6oZEzJYZ3FHxTd3Gjh7V3uKwn2EslK8u3N4E5HJjJ2+IAd/w71F4rSD2g10o+JlCtiM8yd4EG8YZVX/LWgrcAVyXOrdFrdVbK1ZS2aMVU2Wygi0XLFl4rqNXPfQb2PgLdc0dtAHTZk0Fx2xfgoEZ8DMTSKLdjW6B81/Pw3u9/vRBAIpVL3wuV45LDfEG3A9AIVBrCVszgz3HpEjfSHHp8cexG0+H+1dyaxtiTpQf5iyMj5jPe+saYut8HDwtCyLEtYFivA3jTsvLIXSGxAggULI2+8BQkWSAgJhCWDEN4weYPEICRWGBvkudVuu93VVW+6w5lziIyJxb2my91d7jYt+76nup90dPJE5uJL/Sd+RUZm/iFwg8NHSfKJ0UHUnqgVY58ju44qi5hYcawUuTToGjo7kKKk1gZxvCaxIJaCevT0CORGkb3VMS9WtFVgc+E5yAOfffwOzast221GKl6xNBKdBEMnGJwjCE1nPUWWSFRAQEZLU7YsVy1J30yAtSYjEwvqVUUfPVcazsqHnFzALAWH/kBxVDineHUcGH2Oki94Mv9+9CxCNrE5JYw8EE8lRdviM8EktvjDQEyKSyq+6jyHuaZCU3uIWaTIc6ZkgBNWJIaTY4oTj4p3MfmIDRNqKZgvapJ0VMEzzRJ2KMnSCULNIR/pZU+pJFvfISZLHQuMG+l2B8T5GYSRKEZ8MtgYSWEgGUnSEZ9ygnW43Z5dd80YBjJZkGlFIRV2LDj4HE1NMZeELeyMRfYTmXhJRknR9QgquhyquKAKB7pN4Kyo2NoKRKI9L3C/P3Ba98zmOXJXknIYhUKSkeWeKcDkIm0hSPuEHx0yK0FLXLBE15EFhXeJU8jxxYY2GIo8xxc9VhcstaYvIxRn5NKRF4mwjtj+iEgtMrOEKqH0jNrlFKVDxoxYGebTQABEVlAai9QNKkS8SiShaZNkMA6lM7QNxKAYDwGcxYqAiZGyzVEesipjGE/Y55LiqUTuDI11HCtB/0fcInwtkoASEZNAnCJTUZAVkeEUWCnPkCJFPaPSgq73XPnILHaYQjNuRobMgwOfRaaZxx1PFNZQFYFkYNgIrIyoIOj3B/L1SKlbmtCy7y1GacI4kbqEW2aYOpCHHnmS9CoQtKd5+ITKZVgZ2K88i97Q2QtiavFFjp02JNsjTEVSFaaRrPOCccdNrf6lIqiCxlXM6wX1mWLSEZ9JqrSkmwZ++ZXl3arnwwDLcELoJcvMUv1ZTdYHkn4L273Ez59y1uUci5FsyLFG8cE0IaXG2sjVoWI0Gm0gVhkqeiqTI6RgGA1Bg992DGZAGMX6cUGIBUGMLJuafZ4z7LYcze9RxAWuMHRDJBc5cdZTZwHlDeNxzxAmTF+jYk13eU3IGy6HHWJzxLQl5dFy1AmflTit8I1A25o4WKKYCHGCIKmKJ+ilYkwnDnhkajhfV7gBhsMVaZnQNuP68AE1Oe3qKd4OzFeRq9AQXKIuz3Fqg6przh/MOHU38zOq6JGyIZ4sU5GxMALvKqK2dH0GQaBUZCPAZiWZyNn5xKwwaATOZ6jaYw8n4lUk0+Drm/+bSyA311y9AgbrCAAAFvJJREFU+z7LusZMiY/2z3E5qDhDBEn0Ay4kmlDhCGRVhRU7hGkptxMxeRyBYCOD4WZCWyuKURBysCoSYoHvQNtEqSMq22ELyYyRzJfEamI6SnonIK+IckQcFGXZgSs5nAKzyn9i/3s9koDMEPOc9NzjVyfyYkklBMfhgJgURZFBmpNcxACT7Tl6gbeeJmYcxMTxFBBOw7AiEsiEpT85JgWFhPE0YlbD7eWFoo8vqaoV7qBolopB9KyS4XAsEBK6SvIgf4HsV6yKDVI1+L4iuBOaBn84sH8oqeYlyUYQE2WEYqwQ1uCDQshAnOWYpmTZzBCnGp8X9JlChB41CCZ9xKsc0xZ8uXO0mWd6mVM+1hTNjLmEVM6QNSyJOJuIacty1JR6QZgcv9W/pO7W7FXNrmpRdYLjREhHRFlwipEUd/TeI/oa6x1RQigsqahvyq8RuAwOGRJbI6BrcSKyGSLmdKBdlDh6xnii4ZyxeUy6fMFVZ3m6zmFS5LXnMhV04Yi+2qO9ZjZvmKmbR78Le/NsgJwlplOFlS3FWtOaGTF6lOvojjlBG8Q4UEePF4ow7hG6o2fBUylROlAUNWkbqCtJnhQmX+PrA1o4rFKM5ogWI1nTUqaIq2rKGCm8YUqBuZ2zLxXROsZwItYen424KFnoNSlXDGOP6QSKnEznwB7TGHShmfYRpho1NZQ7h5WQtx12lyM+Uiy7nPJwRkxrDu0Fs85xqkueLldEM+cYJ0xZEOip7M2bk5MK4DqgZIdCJEdlNK2Q2NIyZBqtb55gDT7HsWMyCjMmbBXJDpG81Uxbw2HWE6aMJDqk9DDPPrH/vRZJIIiIFDMOa0XTJUqTITjR9QpTzvBKMPo9C2OQEoTJ0TYwZomLvCdsEjpqpnZiEgOpNyRlyKOmKhIqlYzjBnFo0I3FiozQNYRyRNjFzZ2GSXM57Xmcr7g8ap60EwdqztMjZOUoREbYXpBPlmpdcJIJ5wa6rqCSDYXvSaXCNQ0hRDI6khXIWFO6OYWe4YyiFHumq8TVuEXmJev2nLJ2cKi46geGQ8dFWTCEjJlfcmYDiyjQRaS3C7qlphjWZOrE89OBNM54SUVeGhaNYCEDLmkORUL1Cj9KUi6JqSBLHsGIWhgsOfvdBWIOq7c984PA7i7wfsPMt6ixJrXgwoCsFD684mpfMTtXvDpGdNvhqowsHNgcJFlZoDNP805FdtmzeymJqmNzEkjRUntLmTRJKqbCMJiRLgsUsWYiIpJADCUKj1SJXFecwvamNsRFR1zlPHrqaOwMZyb0QhPSxGQLpukFZyGjXmUMTc65H5jCDEJCjB7rNYtc0OvIQUIUBZkEUTiqLDJeCOzgkV0iLTNctWOuZ+h5QeUcs1LTUfPq8gV6LCgfPEKVE6vY8ay6phme0uYCVT3kgc/ZqJfsHlSUZyVT1lGVkmANBzbIUmLaFeqDa2hHUjun6wzedrRTxIkEo0OVkIaIdoqimpBRolTE4dmKSKbHm4K6U8AJj7E5WvUYV1K1klOoqI3jOlYIP5IdK6D7pv3vtUgCWgpqe2KhWp4Jz7gfCUtN0oFpsaXNFNOUsKNi1yfK/IjMI8lE8sNEDAaGnsF2lKakqCVEh9eC/QTsN8QI+TJhg8eGa3L1LuniFYW+IKTAfNbwonvIkF2wmrUc50/Ijj36oUcy8iyOzJsls0Hh8CzjmrDtOGw6iqXF5kuqzJAnSVmV7HcwMdEUCR1A73uyIiBrzzg2rPP3MMZh48hpl/CxQxiP0KBtoI8DIpOMtmcTE8f9jPeaM6TL2YWRajhn8BNxfmDtl6ALUCOTdMyLntJUbILHSYGod8zzktxKjrbn1XhCXnse5wpChz1IQorsQkMdFWLq2UwfUg0Fta65/gAOK0cWPM34AOEGsD3ZIlC776e7/BKpLvHrJamzrGYPMe6EDxGfG4psxrrx+ODphcB1PRpHe9ZACBBA9xOnqUPGjlJkIAdSqBC9I9UnsJLZgyVuB7qdcwiR1V7y3QvJNQv63hFbxawTVH7GXl3S9x5KOHQDlWuZfEJUA17BNrdUYcCdJoRcMpcNJ7WnDpI5OQNgokG7yMkfcdHQlCt0VcHM8eAkmELJe+VnMfMZyk3sX32Zdv0DvPdkzavdCzYhkuKBXJ+x45J6nOhig5ATfq2oTY7sDCEcUXUkOg2HiNMFJYbUTIx+oouBYAyVB9Ep2nKit5KhulkafgqOp2rkxWTwpx3JC3zMGZqMda/ZFpHdePrk/ven1tP/CISEkxoQDNS5IlBTBs9GNJwdFKMMlKeelFvm8x4ZG666hJAFhT7SV4GxbknHjBAiJ5lQacFQKYo4IuaepDKsnfCzitkYiVxjR4kB8tOMvorMdE8SS6w+oi6/witTci49q3pF3lt2hxeoNuG+MoLfcv1Akg1LitkZOhOMLiKyDp+DaXP8qcIliV9FsjpnmE4och4tIsmNjC6SNQ5xUXOKZyyXlmggfnTJptvSFWc8Vguc7DiMnu2yJGw7MgHt+gq9sUzNI3w34tWRua1pWsVWTaTpGtkUyEkQdhHTThT1OTYogr15oCQOA0keyYYME1vePruij4bTPnAMksvnJz7zyKCWMMqJt558L+b4ijKbQxWwzySd/4B2lhi7ga01KCWR0iHrjHThmbUZmSggCkb3iqPvSVJTmRYdQMWbtwj3tSJ0c4TUpDKQpsg6ZFwh6ETOvLim2c6ZVpZoR+ayZXwomMUFYfdV6rOWxalj/XDBFTkurCjLLcZrRu2RuSMvDNksoo8Sek00JZf+QCZBCU2GYSwEKmTQB0Z/5FQ9YZWVVGnieYrI3iPaI0rlHMyG3SYxk2saoWEV2NWv4Fig1APM0tOPFduhp91VZE8MpQyoYaA4RU6NpQgBJOi+ZhARmQ+kNEI+IQLUUTNNM65dJMoBIyxuyMmPBi0M07TH955XrYO8RcTAYZKcKUU3BYaiZDFN7Geam5d6v5HXIwmExEItmdxIOCZYR+puTdUeCGFCypLM5IR6wk8zlBwp8TDs8ZVEB4k8CKZJg3eo6BnUNapcI+cevTe0hWbCoNKRpAteHLbUqyWXtuFhbRmEJ8sLzPGAVS17UVB1miFPXIwBM+Zc7gKUAadO5GZGGQz16jGFbEgKhDnSH0ecFSgBs1ZBVSCjxe4mKt3Slxq1fkg1HQlXI6fOkEePqQ9kKjH2lhd+TbVxSC1RixqrElXdcJq2PNXfRyd+F7Sjfqshv4xsJo/MFWFpee4Cpc3IpcKXmrzU6K1iv9cc0hUhWqrcgFToXOGdYlACrSaCeswEPF5Zir5g3whCe0QeNHOzoOSCuKwo6NnGJeHxc8Rli10ostJQypwsBUTnGLoDlZyY9jPcekKJjF4HrCox+0DIPIIeGxIqaCo7EHSHkQWIkji3bKJlmp5xJgM6rjg8GMiuRoblnDJFVllGcjVP336PzXjJZn3OSmTkyy11b2FWMvmRQmaU+Rni1CGOHUPWU5oFTlfE/UipckIhyKkwfkJmAvIZFJGs7EhFYCTSmjVagygjnJZ0MZI3kdJ7qplkVhZcXjU4GajUNT5ZGqVIhwW72QXHr74kP58zjHO8fk61s1ipmYmCvTxS5UuCex+Lw54GhBB0GiBSIRBZQZEssRvxC8OUImNRkasN/ZQo3IE+ZmStxm0k+TLnlEOtA/b4HRQV+VNBSFypic4jzSOcPLFre/pesJzXJCXxvSP2JVWETiQo1gg50skOmzymsjSxZ8gjypaULqfYHogmo+s9QQ50BZhjhckKshamfuJM5Wy3J+JaMwhPVTfkX96w+K5HHKsj4eoV1bogq87Jo2e4TizzFU4OzOcNVZ6xcxPW76lzwVtvPUL7B9gkCGpD8BYxFUQ9Z1DASXK48Fx0I8JE5nFi7zraQ2TwFpU0f/HPLElK4dpH9Nstrp1THnJkd8SfX2BXEXtcIvw1YV2x8HuOQ026qnlUa2ybUKrCyY5MX2LXmnRZoS80Km9ZGI2VjmapOR0kuR9gXjJMlqyqUceMB2+3tNkB4wS9lCjZMPmIrXtM8YTDhx9RmUfkDwueimtepBq/6xnDJekQ0EVEPlqQO4GgJS9rohcYeqZpgx0PGGWZZzWYhqGsybtrrE+EfEImWDwwXEwV4TzDXl7RvMr4km/JuwlRtKgkuIpXLK4FBQWzXrA5H7j84ERpBd7OCDERViOi6Ig6ICzUWY1sanRy1E8MnRdIAnSO07Ek+IqHTUAq0KIkeE2SMx4vbxYDneWOi26HajQzpahrmPoSi0JXLW1zwfPrgtY78v1A/c6OL74c+W070v7aBX3VUWw1mIrJe2wrqfqcJI7EciQ5RQwj0ykRixJVOdLgqGgIeUmmEkIo9uGIvhRkvuU6P9LMJaa3qD5yOs9pjwcMA8/Vmtw7/nDpz6/xWiSBJBJMA2qe4f2ehynDhoxV2XNShmJ/U37pyo2MM0dPgYw7bARzkjAJDlESUkFKDt1a4gjX44g8RnylSYNl7Fesqwm6iUpUpHOBFQGhDGnwnMWarXTUb7/L3DoscwaZsZ8g6zcUcU4Q16R2YDvO2JeBd1RPIUowOUNs8WHGTA8waJAVpIaLybK3IzMSuYz0oafvOtTRcGl66rqC/sD1h5C3czYi5/x7VhgJ7TqS3JzjeSRzS4bzA+bUs4kl6+qMmZo4qMcspj1X2YlU5AydpvE9S6cx5hGnhUWdCbwv6HWBjwI3RJgSphakneH47AWxdMxDy7VekStJrjQXewHHluzRNX4syPpHPL+8QLWJNFyyCE94VrZ0xRdZuPf40gcdx23i/RU4N+KMYLmMuD5HuQFTKuRak8UlfhDIcFNhp+tHuighBlCGY2eJ/ZFLFTlcOyafeKcVnJGIsSTzE+NLOH/HQNgz1WekZzXh2TPKVtOpQFofqS4c6fqK/q2aUxSYDJSrqC04CTug0A4sBBPIzi/hWHMILVWsSJUg6z1D/yGleZ/FWhEHhx4gOodSCms0i/xEsnPK8y3NNsNEOAlJz4APj2nrPX/BlXyxKTG/9AXG0RIfCarJ4OyJ0SnUJEjC3dQeGA1FBbLKboqcVo4YjgSrOdUJNV0hTgZVAarkqZbItKI9G6FUpBc1o96jekc6DKRHn9zVX4skEL2kcQ/ZrzaEo+ZwUqRyZLAZuSrZlhN+PLLMC+SQEcMRV4JOkb0O7FyEEND5TYlpGTSSgZAlxiTAB6Q5Z5YdeDaeU/cH8vkBu+she4LZK1ThCC5SrxRD2DCqkngc8Neey+wMs6+Z84pgLNcfTDx8NGOUDWPfsWpn1LJBVRnpYsNRVmz7ARkDZRGZ5Y5SGY62Qs8r5uSoIpFCIBOQxUQ4P2cxHzgWYJawO74kW72DUXNEE5lf7GmzBZeHQBrO+J635rxcbJgGRTFIei1Y1gVZZjGhRGpHH0YOKFonyKIixiN6t8UJ8LVg6Auk17zwHeUQye2CU4TFWSDFSHzVoqYRverAz6mV5rl9RuUrHpsHbMYNm+6K6TCwqr6Xsb1mdf4UEX+Hq7FCTB1nbcXJCcpMEuOSVkOeDNvTFq0ksiiREc6UR9YrjsMR5TJU6Nh6jWHJyv4+R+HBe6azM/y2Y2MesiLiyozTtEaeHL4+QrvkxeVL2lAgxgTsqduHGCko9AvK8JT5E81WwmkTUc7S+5wUBJmsCbYkyJzM3NT5iz7QV4ahWNI1gatpIl1rOC95+iqjyGd0sqNIYLoC281IfQfnA2flY/rmEZnyZANc1Zb1heWtLOcL5YJ6f2C0mjJktEpgM0VWeVyvkStP8jAMjqKJxJDReYVUjjpKJpYYPDrL2BwjumwR5kjbaboxYfSWk8x5KnJS5hDW8FrfHciyQC+uya40zb4nPG7QuSQj4cMrtGvotpHtU0/RazrzkDD2jNM1Jw+FDAhTEpVBW8ekHT5qiANKeMomIftLGAUzd8nFKuOsT6jFAwJbxsbw6PCE7dkG5xOVXJJvPrxZh+CQWEwjvFui0oJ8v6dZ17y9fMrgIk4HTvtrHtZLQuc4ZArnnxOLnCxvGaqWpw8XUFni7+1Iu5HL0RMOA5mGap6ji7dwZsu7D9dcDhnP+5c8HM4RMRAioJ6xNedgFWnX8ej9GVdcM3/5glSdMVZQmM8ybT7ClgVjuUWeMuTDNY0/MY0W5wWTkgzOc9x21LnCVT17NaKvJyKGl76jkpFZuWTSE73eMgmLOlWYvuOZLlg8Lpnebvho9yHZJnLoJeoscnV4hu4cwY/k7z2iFZbslPPqKlK+9JSrHYVJuIND5zm1LnFSQNri6hIvge7IXCTcMIDOWbQ5+3Fgm0V81fJW+TYvNpeErObxcotKJacXlraZk7WKK59QduCz1ZrR99jrIyehmMmGdTzHtjVd1bM7PMf5ikLom7p9LtGfIkXaU8gKnCaGHWVZ0LclcTqQd4KsLdHG8EI76lRwmb+gT1/l0fYHEO+V2Jljqw6MxrK0MzpmzOKJkGr8csHbM419R3PxHwIzExmERrCl60uWWaCpcmLKEItIcIFO5kwqUPaBohAkafAq4Q8tJ3mNNIqZTMxqKEfBUVXYJrDagnGKWHccRU25rqgqyUs237T/iZtqYHeLEOKSmzR1ddcu3wFnvNn+8Oafw5vuD3+y5/BuSun86xtfiyQAIIT4lZTSD961x/8vb7o/vPnn8Kb7w92cw+tRT+Cee+65M+6TwD33fMp5nZLAP7trge+QN90f3vxzeNP94Q7O4bWZE7jnnnvuhtdpJHDPPffcAXeeBIQQf0UI8UUhxO8KIX76rn2+XYQQXxFC/IYQ4leFEL9y27YSQvwXIcSXbr+Xd+35cYQQPyeEuBBC/ObH2r6ps7jhH9/G5deFEJ+7O/P/5/rN/H9WCPHsNg6/KoT48Y/t+3u3/l8UQvzlu7H+GkKIt4UQ/10I8dtCiN8SQvzt2/a7jUFK6c4+gAJ+D3gfMMCvAd93l05/DPevAGdf1/YPgJ++3f5p4O/ftefX+f0o8DngN7+VMzfrSf4nbpag+2Hgl15T/58F/u43Ofb7bv9POfCZ2/+ZumP/x8Dnbrdb4HduPe80Bnc9Evgh4HdTSl9OKU3ALwCfv2On74TPAz9/u/3zwF+9Q5dvIKX0P+AbHhv7JOfPA/8y3fA/gcXtEvR3xif4fxKfB34hpWRTSr/PzQK5P/QnJvdtkFJ6kVL6P7fbR+ALwFPuOAZ3nQSe8odfbfrotu1NIAH/WQjxv4UQf+O27WH62jLsL4GHd6P2x+KTnN+k2Pyt2+Hyz33sEuy19hdCvAf8eeCXuOMY3HUSeJP5kZTS54AfA/6mEOJHP74z3Yzn3qhbL2+iM/BPge8C/hzwAviHd6vzrRFCNMC/Bf5OSunw8X13EYO7TgLPgLc/9vut27bXnpTSs9vvC+DfczPUfPUHw7Xb74u7M/y2+STnNyI2KaVXKaWQUorAP+drQ/7X0l8IkXGTAP51Sunf3TbfaQzuOgn8MvDdQojPCCEM8BPAL96x07dECFELIdo/2Ab+EvCb3Lj/1O1hPwX8x7sx/GPxSc6/CPzk7Qz1DwP7jw1ZXxu+7hr5r3ETB7jx/wkhRC6E+Azw3cD/+tP2+zhCCAH8C+ALKaV/9LFddxuDu5wt/dgM6O9wM3v7M3ft8206v8/NzPOvAb/1B97AGvhvwJeA/wqs7tr167z/DTdDZsfN9eVf/yRnbmak/8ltXH4D+MHX1P9f3fr9+m2nefyx43/m1v+LwI+9Bv4/ws1Q/9eBX739/Phdx+D+icF77vmUc9eXA/fcc88dc58E7rnnU859Erjnnk8590ngnns+5dwngXvu+ZRznwTuuedTzn0SuOeeTzn3SeCeez7l/F/zgNVnrc5JkgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "HopSkipJump: 100%|██████████| 1/1 [00:52<00:00, 52.57s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 20. L2 error 13701.819 and class label 866.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "HopSkipJump: 100%|██████████| 1/1 [01:00<00:00, 60.65s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 30. L2 error 11656.512 and class label 866.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "HopSkipJump: 100%|██████████| 1/1 [01:10<00:00, 70.40s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 40. L2 error 10472.013 and class label 866.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "HopSkipJump: 100%|██████████| 1/1 [01:17<00:00, 77.89s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 50. L2 error 8748.627 and class label 866.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "HopSkipJump: 100%|██████████| 1/1 [01:25<00:00, 85.27s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 60. L2 error 7440.469 and class label 866.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "HopSkipJump: 100%|██████████| 1/1 [01:30<00:00, 90.55s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 70. L2 error 6323.5347 and class label 866.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "HopSkipJump: 100%|██████████| 1/1 [01:36<00:00, 96.99s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 80. L2 error 5699.7773 and class label 866.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "HopSkipJump: 100%|██████████| 1/1 [01:41<00:00, 101.47s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 90. L2 error 4760.8467 and class label 866.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "HopSkipJump: 100%|██████████| 1/1 [01:47<00:00, 107.23s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 100. L2 error 4279.1885 and class label 866.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "HopSkipJump: 100%|██████████| 1/1 [01:52<00:00, 112.58s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 110. L2 error 3736.5771 and class label 866.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "HopSkipJump: 100%|██████████| 1/1 [01:51<00:00, 111.61s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 120. L2 error 3415.3271 and class label 866.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "HopSkipJump: 100%|██████████| 1/1 [01:58<00:00, 118.21s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 130. L2 error 3092.7314 and class label 866.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "HopSkipJump: 100%|██████████| 1/1 [02:04<00:00, 124.78s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 140. L2 error 2844.5347 and class label 866.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "HopSkipJump: 100%|██████████| 1/1 [02:10<00:00, 130.12s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 150. L2 error 2662.4702 and class label 866.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "HopSkipJump: 100%|██████████| 1/1 [02:15<00:00, 135.94s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 160. L2 error 2455.6245 and class label 866.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "HopSkipJump: 100%|██████████| 1/1 [02:16<00:00, 136.99s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 170. L2 error 2294.5833 and class label 866.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "HopSkipJump: 100%|██████████| 1/1 [02:24<00:00, 144.74s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 180. L2 error 2154.4878 and class label 866.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "HopSkipJump: 100%|██████████| 1/1 [02:31<00:00, 151.74s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 190. L2 error 2001.887 and class label 866.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQEAAAD8CAYAAAB3lxGOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAgAElEQVR4nOy9T69t2ZLd9RsRc669z818VS5UJfzK2CC+ARKCBk2EhOjQhgYNGm5ZAokOTSR3QTSR3KBNhw+AaNBGxoiOjShZ5p9LhWRT2C/fvWevNWdE0Ih1M9PWy8L2q8QpvTulzHPPOXuvvf7MGXPEGCPiqKr4Mr6ML+M3d9g/7RP4Mr6ML+Of7vgSBL6ML+M3fHwJAl/Gl/EbPr4EgS/jy/gNH1+CwJfxZfyGjy9B4Mv4Mn7Dx48WBCT9m5L+F0l/U9J//GN9zpfxZXwZv97Qj+ETkOTAHwD/BvC3gb8K/DtV9Tf+1D/sy/gyvoxfa/xYSOBfAf5mVf2tqrqA/wr4t3+kz/oyvowv49cY40c67p8D/s/vff+3gX/1h14sqZBBgWhkUgIEVPU397/1vfcVAvXP+l367v83wClZf1PVrxWQnw9pUNmfVaDPn65CGI2SPh9doASEbvR0v4qUEIIqqg90v66PhaBS3zvP74763XX1tfRb9PlSKFV/D6j69wbEfQRJVN33oQrMIbNflJ8/p777rPuUPt9JSajye+cl0uo+Obt/1tdvVYS+d6f13a0FId33te6zrOyvn5+RRGUhfb5/339W9zV/fsDfHrf4B078+/fuvud9HT1pSt/NF7uv/bu7IAxRZB//vn64p8f9ms93p/T5GnqYoEqUgbL6Q1Uo7b5d1XOmPp/vd1+kfixQYEIlRFIS9+n0/aPu5/N5fL4v307ovq5vb0Kfs773eeR9ayisjPz8A+rvVtXv8Q+NHysI/H8OSX8R+IvQp+c62Kq+OHdgQSTMZOwi3Hjm11ScXCrKwVIMCi+4qnDL7x6EDdhFuJCMkZMIoC7kDm8XtYBrYBasA+YelAMORySvEG8jeZ0DVy/qly7cnKHBaQu7HK8Hbypeb8mm0PWgPBj7HdUkPIl7AtojeFzBK0E2+1xV5CiKgerB9E/sCfxyUpXULD6swftXxVss3tPQKlyB3pzHVZwYcz55v06OWSgn5yPwCwZFKQkGu8SQIB84JzU2KWcvIRdfPybn62LlE4537DUoM5jvPC7HcM5js7fAgpmjz2M/yRJrJIyT43pD8+Iq4ZlE9jyejw5qsQ5CG8wgJ6aN2QUaXAFY4Ax2Xdg4qAucwAxWLLDJSGf7xiMxPwhO7E3kN6LenngsuGCq+HQk8xzEHGDBEUYEmBWyzfKiooMgkXhBuuEhwgeOEztAhZXIY/Pcyfn2xpHFuTeqjXDGOIgCVuIT/AzON/EYia43rngnj2JeIupBzclR7yxNjihEEhZkh2QyxdwDmWGzuBKkiylxWZDbUYEfwbUdY/AhD17PRbxeDJx9JFz8779qLf5YQeAPgT//ve//uftn346q+ivAX4FGAgF8yGI/kkHwyQQhFAOfibZz6cXhD9wmqW+gFlvgiFHOcqHsEJlVlBXmBxYXm4vHV3Dugx0wzt5Nxweo68F4GWMWer1Yx4O1i6gna79TPyuu09B1YuXU40G9n8idKbHfRO0i8uoQfd/VbeC6mDKMyeXgUbxK1HAiFpUHrkXWBDbDP5I50Uc4JqyAzEF+bdinzQ7xM4PrMdhLrGVs4IG41i/xKa5LHI/iKwbXsdij8HRyb2Rgyxi22CTsgXuhx4H2Yn9MwgeMhV/FY2xelR1UhvMWm9jALGw5O5yh4P3YfPVMCOOag7ou5suwY/LLceEL6lhcYzBOyPEiZUySDp2OTFyWQJBLqDb1HBBQw6kV5JEMHMq58oIosOKqF/P5JL5JjscmA9a1qLeDfTlzXaTDw18EzlUwdLC0IUXtBBcyo4bITDwFclyFWVEYGydzQTzIEfh78rKEMSkbeCS5TqxE6StsX2weKIvro3AuzIDtWMKcC4vil1FMndQYoAcWRfFOmDd2GQseznWJZzy5VIQtnukEwalBLkcOWkG+vai6sDFIxAcZn1i/crH+WMTgoInBf/1e/H8V+Her6q//wOtLElYPzDeVjnywuNAu3qaRKqJge6FdjCjWDdFVutFDQDqmokqEBT5BD0MBRJE5iHPxeEBEMvPBtqI2zFm98z4KP+HTdOwTpBeaCedELmacaIjXHsihIkEHsDEvHiTIeZWYHmgW60pygdUb+EK58SfMU7xHYoezh/A1iDwxNZKxmeSePI4kY7H8waMWacllTqVjEYwt1hw8FtTYlDdM3GUoi8OTSogtDnPkByeLUFBX8eaTK42ICx0LX8Y4nuRMdp5ML7TfWLqYAeuEOp7MDXk4x/WRj4cYcfDUaii6im98MMyYJbSC7UmY8OqkpgaEjFy7n5s5IzpnsxB2ONfVQelDwcfHSW4x8skz30l9oAxyvsj3g50nB09sbLZNcr3gObFcrG3MCp7uvBeoBuVBHAO/Xvg2ch5sDUZmnxvBY8NgcdlgRHKGUMLWxeP4Club9zyBA2PhD2AdLAZfHSfvKxnjYGfy9M11TlIb84HH5tTABI+ClJEE5oJyNMQyUTvwHaQVqk3IGZbYOthKAsf04rEPlhWpINXX+KhNPsT1af+1qvqX/+H196Mggarakv4S8N8ADvyXPxQAvhsP4nhRadQIqlZn3Ga8B0iBl/EBY6VYLHwYKMm483uKYmHplCcmEa+Cl4jhWFzUSKYZsYqdICvCgqOcsyasYmRyWaFYuB24nVgelC+q4JrGcVMNZOHDmbqIvcly1hb4AhXrmtTLOMzu3PydCJFm8CrSE0nEdtwvlHCM30LXi9PivjfBaxXSG7VBh8E2/HpguTFL7OtBfHORdrDU52U+GSUsNqfAvDAlrx2wRNVuSO9G5YlkMBOzg/G82K8T329YGRovcgXxNon5wt14nCelwYqAYdgujODTYZ0AS2ifZMA2I8YA72ekEMrC1gEGNnun1yUSpzDSNqoi7YUBHzccMdgFexpnfsDmO/sqvA6qksc4iBlcO/H4RLhj74vSgzk2tcUVBtOpXUyKeCUZhik6XeQiwqiV+CiummwbsMUrHvC2eOxNarKvFyrnYU/OWqRNvtpweXH4R84T5hS1T55pXHHwqItPPpE2OSeHOsC/ynA/qNW8w1SQGdQSRxzEANsXMiNnsC4x88IYHAMuH7xIiMm0IrYDJ1GC+mEN4EdBAv+4Q7LyUaSBX44ewSrjKXhdEx+bsY3FZg6xKwFnmBHqHS51YBG4XewEe0yUkFeTOWM4GknuTa5ovqWM/QaPS6QF4sBITj0R77zxZK2L9QaPmMR1sszwgJoFG0YlDNDhXFfgYZgghhpOTmNVoA1F55oWkDRSGTbIgJAzLWAfyF9Qg5nJa8JQofVGuXPx3tzSaK4gF+AJ5thHAzXCYCcyJxnARWZhZkhO1aBWBykMRhpBUW5gm2HAKtZyisQqCHvitdAzyXfI50S2sVPIjWfBVYlrEGyW+t6sMmwP5khiHGhtpA7Ea8OqBNuYDhSd85MP6jg7x14TTXHdxGeGqEjcDRtPbH/DdsjxhvKdDwzeK6lTTBz7Onl92gBYGeYHVKIH7IgmC1OMCoyBl3GOG42unl8Pkipj2wV6UkpMjtvJCiiHxyqihMaBx0kNcSk5nh9Yf/8kh8E0fC9WieHRad8xONZ7p1GamG/YwpspQxlU3ZxODQpRMykTYxsSRAT2FegdHgSfPKkYjMfNqRBMnPf3+P8PCfzjj8IEKSg5nA3tL0/m46RcrBIwsQ3DxXbYkXfudfRuOKLJPom6ElFoGlnJWkVZYgQ+nJwPIi6OqwkhHg/Wom/6OGHDycLMeXttzoIxBh6L0kQjmHKSZFDE2pRDVmEFI4tVUDF7V46FjaLc2ZbYhiNpUvLpsDbLAg1DYzC3+GiCNEwndRR5fsOwJ2ts7HRUG7OgUkQmXovC8Wty3gSWdmJHoTI4IT3wUZQFEQk5CduEDua1OGpwfdhUOceHZJ1i5CS1UA48Jj4+sjOwkaQ9qFqcy1h+I5conIM6wB4b+yZYW1QUGglDxG4CrBgYjtuiJI5tlL3znsJyELYpd1QHHu88D+NViUUSWjDe4HxRuyibRCYPGS8ZVQ/45S8ZDHwWFg9EgE32vvgt4FVil7NxfIqdRYVjMsqy32eDKzasonwxdmDD2YJ6PhireK8Xj3rjqOSyg4igKth8RE+hIXg3cnyF2YW/gJHUp4vSGzZh1osox0psS+oRjF3s6DlOFKlGRwTY03i8jM2mXrBn8R7zvjcbRcEFNowdd7r8K8ZPJAiAb2NVkbMYayEOagi/kj2fpH/ClrGYZBZUMhySxOKFBZQVJcMT0pJ4a8g5yyEnxQk12OmYL7zAl/Nuwl4Xzyl0GO+fimnF8iLypB4Hfm2uCswGXonOIkyUO+/n7olmLYMlm7zlJV8nKSeHkxlYChtGWlBpLbstxzKRJ9qLycSPRazB9sWS49eL5yiuCLxOXE5cgQn8DV7XIOTIRaTxHO8Uxjsw0zjM4UhyF4pGXVaOxWQlUIuUeLcF2xk5uT4ZQydXGWUCbc5KZtGwfU98XGy+QpYciHSgIEbgId5Wsd2BTSdEg1oHPj5RGCPAy8nVjPeaBVuISdx8j5/BHCevOYlV5COYBfFKbOzWAnURe7AM9nMy1klmkRzEjiZuPRhe5PGiXs7ShLowbQixoxUglzEqWBi6jHNWI5ThxO70b2/DzPEqLAp9/YHzepE+sVVEBQdPrrXRB/CPm5RavWJRwxhVlBll560WFWabQuQGOu7cMmjyGAMILkTuhFewGazxINKZLDJF+sFjifQLKWEZQ/kDtOBPJQhIhGXro7VIwLRRiutIxIlClBtZgedgK4gSZQcAqcQJZCJyUrWxFZDCCvT4xNqArKHpWmROllZrlDnZkRQNcwVoqYm/MxDZgWEKVuEBmYuMxIa1AKhNYmRMLMRICA9KSUUgg12F78RN7AqUIJprGBimZO+LuR88nsm+BjYvygeLB49jcZWwx+C6iqrJfF+ULTQPLJK8Zb/c1iJTLq5cnRaO3lFYwICsF0PcQXc39GRxEXxVB+9WHCPIq30AtoP9eCALsiBiMuLi8sAcnmGUTYYtKjfnav/H0GBlYbqQL7KMlOFHexSiEtvGwEk21Av5RNGpW5SjtShzdE1GFZcbQ8aphIew9UB6Ma9kOxB9/T4M0hmzZTc7Wx6UiiojPNAEjcK3oTRWDvCLsIsjDavJChhz3b6NYMkYGgQfOT45FU+kTUxnWIAuKibPvdlHMfZi7WTYJGcT3JfocyOxnGwvSgEmRrYy4F7NIdVGW9gHQzaAxbkvjkw4iut8MEZRBvuROE+iTupI9ub75oN/YPw0ggBN0k2Dfec5XmJHQggfwlbLN5pBlhjAMSDWJq2Joh12E2IXhyZ7dN7HAk4R2eaSeBMKax02weUYCxtOXe+kHwSThz/IFKcMZeAqPBMP2DpQnagGtjcaVxs+tiglWLPgoURZqBxJZB1YLTarJfJtpAkqiSsJB8z4RS6+WmI8Do73wfthbCtUD3RtrgHUAV5cKR5W7L25fKByRp24IErktkZJCWIzHcA6jTARGSg3FoaZkQJL4xximLXWP45byruY1yZmYnPABVvFoQEkn1TYXNgphgw7YGfyikCHkelYNGstW6xdGIn5xAeoNpQYJfa1CQ2UBseTWYuag3XCL2Ohx2btg8PfyYSTe5FoQRYbwyOoZ5IX2JV4OqXdc+026kzRO2s0N5SjYAYjb2MPEFlobnaK9yia4yzyWiBx2sTtE1c8KF8817h38OIVfQ2VkGbMBXsPVrxwd2JCRPMzSuH7JlYJchghMQMyq4Plu0iaXC5LtiazCulA9s4I7zVh74xhZKkRwQ+Mn0YV4e3wimil72nO9okYmIlFM8sqJ7bY1rv3dSXLgmXZuEkBFs0uV0EI2xPoh+CzGAfYLrQcHHIMYlR7qkrsAZ4XlRfLX0Qm41iUJUpDa5JAcXYQ8Qkm6rNHQYEIMmHHdwYUI6i1qXyhWzkohFO4VRv9zNvld8DwQTKwOJlSG2XqgkdSfjLCKC2U1ZyJGTIYBh5OlhHjoNxxE+ZQY6AwWhxoo5Iq0TDCkhq9gB14eGAFaznOxuPCJcaYRG1Yb4x3wd7IgorFLuEG05yyi6pNfRJzOeaTI8BK4CJYhAkdaqdAXmQmOdo/hIlphgED5xEvNKDei2EwpuNpzDJWQlzG4w5WG6dkDHajuiXskZgXKWPpYPnA5GjCzt4gKpvEfGxwWpK2hJ3WLsNhPSdlZA6sDDdRGAfCrdDzRLuQL4IFSh4DlBOOpvvkm5ELjQc+NpZA25FgtYwNTlqj3yHD/cLGZiqpSqZtjOBRRkSyKrFxkRW8EBmbuoB4UMtbcvyB8ZMIAqIQE5PYNExDRlox8ZZtMlgUKHErRhgyAw2sID/bRWtgnXWREu5FmVozXUXMBx5OVTB2Uo8NGsgG5zZqGSRkdf4pkoiAUi9+F6VA5S2BRRNa8Zm4dG+LqG5XqYt8iJoGGEaSt0VZOVgaZA6O2z6cVSigdjZJaYOPEpuJrye5kjggOJEmjrCV+GWkHOWi7MWKIvYLsSkLcjdJiDVJ6BQcIB8tyLm3K7oE7L5nYyE5suL0AH9H+2qkMDbpjh3GkBMJRxqPhPgIwrm8g/siO+0xpx7RJpvZ4kTGoKYwGXs7+zS2xOWTxaPZcDvbEITAT+IZYEaN5D3b3rs+bBaLzBOnCVopyWnIitSkrJWmvtUicLTFIWcMx6wX3lL1prKLKGcBFSLPA99tH6bE9uwNx5MXiZfjezLCeZ0gG4h+Zo9xNVE7oDJ6vtpiyWA4ItGG6UZOo8YkcRT9vFJ9vsnBkYmufl5B4cPQNlhJrNFoWAMeRo2NtFH88FL/SQSB5j5GO9XSeSGqb32zpLtlzpoibFLb2d43RJehFFaO52x5Lh0YWLUJqHKRNgiJynYZpoTXxJehCaMeUIWPBzUM5sZrU9aONYtBVSDfNwzb1IR0KEXvZrrJH5rwU97R4ErqTnNa0zFGtvVYLqqCiNb1ZcYK4ZacFuSyXqAUIZhns9dhwnSCJ9LRpGAJcjBlVDX8ZN9fLXrhh5MOa5xkdSol1Dv0GK1R1yRyUF48uMjLMBerDnYKl7NyYvHq0ogTpkPphMjma8JhQ1I4hVaxYrc34Gz51EdhtjCsYbfa22FqZWdoYw8xjuIcSUZD5eN9U3ExLzGOT0xP2OCVXAnXaFlthaigSbYVrHgQMtydoTacgZNpRA00kjECk1N5gB8ohIeDdqsRnqSCmmK+F3ttRg7MTl4ptm1Mo4P9QwydzIBtwUVCQJThGL6E7yaUcXF9DkAZVAZkW+CDIGlY4lOkO5c31Zokli1PJk2st+MyqU/FUOEy7PiJBwEA/NUKRiV49Q6fhm9hLtwMsbEAzCmbUE0oJkC0lBIF8sD9xNjk7UKjbjlxFT4TG5PzSB75xLdzPT/ic/VuuAbEoI5esJZG8c4YRcTG5Xjd8M/3veCEZaEIZNmavCWfq5VUXcPgBcioEDsXloWZsx4Dk6jjlsyAKqdqk+vCCvB3LtoNWSq2Gzt3E3qWWGanJhWNpqyIm3S0AimRAlUwEuYSdmXbgLcwEhkoF1dujguwjcboOq5DlD1xe2J+Ijrt2PckXuFsGwxvpGDe9q0A5MWoYm7DDMIFMRg4nqDFrYUH+0ZttXsXJ5zcBemojOVG8GydvbL5lhTbioEwBhoOwzkSQpvMAM5bcw/Y1mqMFUlHCu1sZWKBqu3r5oEUHGojlGz3jp7W99L6PKPo3OGqNpeZ41fgEbys2q8Rgd1ixp5JzOZL6P2dUpHVZGZJDBNOUlp9/HR2iO0CW7hg1EHs6jzwCJSJ5qAonMmqRh32+uGl9xMhBm+mtmcCEYarF08xUXX+7dl+f/liZF9mYkBDVrEaqiNii+mds2X2A0y162tUL4br3OzRjjq8egfQ4vDiPQ/qXM323iVaue/VxO7MYwNhMFr6w8a3FYbKDkhVQrdTTyVKbSYqo4PUnXqgIuqFh+ErOR9q+WkW9ppEblIQlhCJlXUgKDEE5UmmU0Hn+lSnHQbsZA9gTMYufNVt0TVEEFVMgWcgeQfVKhRtvHEDV1F7kZ6MMtKcneCj2IfYAfLZhUKjGLtYEzBhblDGqs5nZd7oQ9m7HaI8gETWaCazCEv8BG7UUASnFcduBJV5du0Ct9QWMFIcsWEMzitIoisF67MZS9SA1EQEXoutxGVIan5AgBXHnZqtuqv9GKRdrfawqCk6npwYTnhgMTifQmdSStINNJm701tZ15ekGwrDKuEC7GppWxvlACvMblQSDfuH77YDb8fMuCQm1ffTROXEsqi6qNGFaZPRnIYH7F+99n4ySKCsSRpz6+qxHWCLcqi8y4wrgL7YDo0vRkXvrgZWxURM8S3ExGmbpUa76iT2FhvDaoAa1uvVSuHOZJkz7oBknpgKv0tjszr/96B5gbQOAM0ekMpmbilKXcTUElg/yJIoK0JBpMgCI+AVrS5UYncFpIcYDjFos0eodxg30APt2Tl7NZFU2TC63NowVdX54RDuhir7HIYIb0861katS8mnDauCbYZpdcrDbeGWYduwuMtwY39rsXV6l2YGziZ3kqOQGeMOxJQYDCwGGQPCSLg97r2jznaLkUk7+LQhkwCsBgLMZn+eFxEDjcGYxkhDmZSc0G3/VrFHqzIy8EkvDAuOusjaXHAH4rucuArz72S5vL0XlPUuvp1cA1vFZQdThh4GdaBl+DFQzEZh19FpahV7JrY30ZQTpvZIPEcb2PJqRcwlHJiVSMm+XZcJ7PsrlmS16lQzG/XtYMSD5lR7Qwst6oBRk8J/cO39dIIA3jX3uj3n9+QMbdx7MqeaSS/EVmcPwwzXaigpgzSo0YSUgshi7N1POBvalYTiwLDbsDMoG9R2xjBitbc9rVMMbiIPawLGdjP5RmK6zR3W5Zy6ZchS1/oLoa27R4C6unFHu+esmfCepELpRIrFoLXCyb4G29a3dfGKjuhVfR1dF39nhwZhmyxhGMbNT5gzSvjODmRy0nq33dUseCsYBgFuRWqQFkwrNIrQQrugjO3GUQPdxT47Azdnat9EYjSvsgD8Rl6L9mobxCJjNQTPVnGUQu6dUnGTv3cg8wFOFyFZwLmT2s3QWWz2En4rTCWRSk4Z+AAMjoFVtrFGYi2674ALS2dglGDfBLpD1zYgRlU7O0u48q7/8FZiMsi734DvBAaZieokrEvdZ7WfJY8OXApj56D0wjgJD9IgR7Ju3wLW93h9RozWUrYKbKi/N+cAarfBKEucrUF2OfwGX+rK0LER5w+uvZ9MEKAKQ0TCrmbjQ6DYMI3MnhCZG6p3KANCzah7JE4SaewYvWOmsMwm6QjMve/kFNQJtUlPWCLD2bQsVVYEhU0DWUNh7Ya/dHAorMlKE5bgu+3Cgpb5Cu5ZCRgib14ie7epLnqyahJP1VZVlJQt5lGYJbU2LO+d77OObEXVZlanIYFB7IbX1kYWpJYGBRl2y1x+n0lvexOhmpgNDGN619BfedewRxe/dAEKqJ4Q7dPYUaRXm3LqALvIgMx9eyOabIwwdkLuYmc2Ias+jzLnc6muSmx1MC0ZodEMvxuXRx+7nGnczspoeU1d6CUzBobq1eXAebHTGLlvPgU8NpJR4Vy0rJmVjULrc1o5mi/IpOgCq1vnpe4iNUQ3XlGwldQqYlw3p9HHJAM8yGhLu66g5oAQB9nVoiauMiyMGu3KVBWJmOpuAnirHIY3B5BwBCibM6sFte7NhEVVYFcX4T1GE6Ohk9efsNJ/MkHAM7tkYkxkSdXoyCyw9LZbut37VbvRsiNGl4TqrgP3htpVCauXX29ABW5ozJ6ACmKAdrLiruqZF2stXA277zY5WDS8tupFBf2rnV36qSiIpLI77zSq/czGVe8gozpVULVJpVqXJoXZBol569JFw73wVkjKEqvBpOGiNG4ZshjWhVRejkKMYXcJcU+mss/EWDYhSaMtWbaZx6yrBytgchuXRGnzqjYbjUiO2RKdk7A3Qd/jtMLTsOgGGaHE6tE1+vQuFVW4g00YbgzERLfOboxh+OwAHVaMIapaasWScfV78M0am2mTcAfBdKcY5GyUKKtOfZWU7VaEdpOHmU0wlzfaJGdzG543ZiqoIC3g5isWHZyqxEaYxs0VNLzWaBIvqC62isG8OwbVKLBBX5p1eqnArLirzjBNCsNWm+JqQFlQtIJAz4AmfK2IKrYC52KPpEa0xyWbQwgvvNpMVCW2nMOE/wn0308mCLRbqqFxRmJWjGovekZ3p1H2pCZ1a6dQ3outlGTdW7Gy5RLr7DyxNmSsG0qvxaj2Fyjb3ddtogThWM5WHVYTlovbTNRAtaEnxchitujf0NzH3Zii0wG6EQ3y7u7TO79/S0J1hWcjA7U5oHO3TNbVjS/KGsXIhTIJgovCy1hythZVu8lQ3bBR/q0Vt+7AYtW5J97wO8M6iYggd3VXoLvIx+ZgDLF5sG/XXJXwkW3d9r4T04DlzLBuTOLJQ+37GNs7MHmndW4T1+yc1ZLq7ZQoyOjnVQmerX+XCZv3xEgjRsIIIrODp4F2sXeBQ4V1VycXFZvJQNk28mMbmY1IiGx5Lq3TQqJZ/5FgveB0S6wN3hwNZ44OnBf3vazsubMn3X7tA+YdWFnGQ8Kze2JwNYHnNIoKibILqyasbSYVgsjeUCqIMHK3vPntPAkgBpEQ1l6AKafMb3OiGHanQHuyQ+Rd92Hxw2ahn4Q6INpOSQa2VsMcW8xwMDizX+NmDSFvB6RGdr+33RNCMkC3JNW7NMxOK7KoWjjeMPNRzJMbzrY7rq6J1A0ZlIVXewNS1saWAlkyS01olTXbTXsA9FmupHdrqr9vR+hnRHJzBtz+AO5OCGlcN7+mDA6KCO9aidkwNL3a4FJJDsdvAU4JYaNhemZzFzdZCXeQw9ohZ9mooI/INKhoJ12u3oXHFNRB1YNDycuDWl3w8tl8mhrYY5PXbDvsIepsJ02l3WUAACAASURBVKTPi1xvbYihLcAmkRGE7oBnTbpuq249UL07++7dYN7t1srA3oKqTp2I7qSjvAlYh7HECDiZeA2ODMocz9eNatpx2Ulk4iVMjRhnCcVkWaejkt19Cum+FuxORQVYYCo2jTgrnJmbLMMcyAN5sK8DxcW05Bwd5DyD6c1JDLX7dOfGbFD7IrhL1IMOak2boqSlW7Vs3dLxHUgU3xqXyrvikcwbvTX/YtPayNXS2q8c/8RIQNKfl/TfSfobkv66pP/g/vl/IukPJf1P93//1j/SAddt9zVHox/CHk12ydu5llkcrebdiKB6F7HuAmMufDh+7/zusCvJit5RHWwuxjRC3QhD9bij/kA4eyS7bpehd2Bx613ASerxuTnkXW4KZHX+XY3lb9+rKAZuB9O61sFpxGJ0L0Mzw0anMkkSA2A2YXgkldk5PYOsNtIcWMujVSiDUYNpECQ2byKSxBXNHI/Wwke1172rgBx38fBumpEjwGHOtvYQEBmkr74KL0b5bRQaEKuZhS2MC/TiOicajo2BmbFZ9/uDyKDsarecjG2z1QtuFPSZwjwS+UazOrUqw3ZSYbdPwPCjP0fWcqLuZhmXmkMhN5XFJlD7lbrvYXVnhZZOg7hVn3IjbHYQv/V5boUAa7LayC5Gyq4gDW+VSDM6ZTNQXGglquTwNr/t7GDHZ8IvYYxoTug+F92VgsNaQk51aoLAxmhb+rbOPSlyQI2B52g9qoKZidS1H2tFVxIyiXtzU94pxQ+MXwcJbOA/qqr/UdLPgL8m6b+9f/efV9V/+o96oEJ8SONdQT6tO9KGEWZdny+gnIqzrZpZYNZW06x+oNbQuyTqCbtodlrrfn8x6iuKFzLhYaR1dHRvy+dVFxUFb0WenScXhj2rG2moZcgaxrjrAnCw0dE2110J6ZvGlrotyPrOlee3JHiX87oFvvvfaU1GzQ0Vwu96BN9Njj3CWOrdiHBQ3EhjUHt3l5x0TAunyH13Ii6R7VpGu52U8mCbtx02Wr93V5OX2wna7HQZxHrjgzbliZ7GsCLW5C1OXjjhCdciM1kYtQ5mXqQGLtijm1t4bXx34UvcUqlRDe/p5qlhgZcY0wnrAJHVxGe5cF2dso0ijo2u0XUXo6iXUyOJ3HiKej6ovWAcxLZWZehuvrY7oIV1Gy4La6MYm5wNnWf0PJO3jbhubwrqRezWzs4Vo737MlAv6IxglDNXoN38zOXecqfa1nxgbCVZA2N3ykWnfRod/CusicHq9LbMuoo1e8OJo9grKZusVaQJr80u42CwV9cunMv4U+8nUFV/BPzR/e9vJP3PdKvxf5KjsWeiK9HVsBvPZj51dFdQLdBkazOtoU6V3XxtohVISWm3m6vJW+rhhA3wTUaRFxDJpFqKGwvSWAReya4nj3y/G38YZqKuu0h+Bp7WDU7UN7VLg6AJALu5wJ4k6Una7k4+sjYLZZFzo+zCnm7ztfD01n+1uNI46ujS5KFWCNy5cuHP7hrsa2FS8wPZFNLexu3LYa/7M23j+Zkraacila17q0068g4Yka2KbNQopAp5l/CuDwPOuiG9SIyLr8A2u078gyAmnKNt4GMwNOHD5GnG+4JRRmW0/DbE7iiIz4RzkeW3YasDom1nD5ghVE4p2CT5cGxPjjC2jEUyfMF0RjzYo52fu5IxhK6NZXFpMo5g3NWLoroDj6/mmehcG4rMuy/l6JbiUYF756Xejl48rZXcAccoYrfOuGVYttk3rbq+A+fyC/JgrbY4hxkRZxdUxe1n8LgdqM0vfa41ufvXUysY7TNuiTqKLWPuTqcmdzCyzV5O2gP0iexWBL9y/KlwApL+BeBfAv574F8D/pKkfw/4H2i08P/8ie8HqEEqmbttNzmhMqEWtWD4DfEkZG0godQL5y7wqbLuMFSbqklp3MRO4PnW+koO0nvni1crBiOKUdEPNK5uGOHi4XTPNoqqlmRWC+nEldjo9tVtl2v9pmgyJ9tW0Ox7dv6Wt+mponsHRBWMwpfY816YEZQP9r7uJindl8+O27nojp13X3wTtbuxykCEXT1Xdu9aQ7e5RN1VJmPjSnImKxPt0TMaOgVzY6RgNuEVnjy0GfkgXhdjDGYWr8v5ajzRV0/2N3/Mn3n7OT4Wv3x/x55P5nhw/M7kLX/G7/+Lfw57c/7o//47/M6H3+X/+sM/4o//+I95m5N/9vf/LPM4+D/+1h/w9/7u34HH4Pd+6wO/+Pu/4MyCLWZW95AcRmRbfkuORzdvMVb/breMln7eOfNg3PYQ80XUk0l3483r5nkA966fKBs4wq3DYCk6FaQVn+ldMp1uHLeCE+NBqPC1yRGISWThnkwln3COa4J38EKC3fnsDm5PxIORJ+6j09Y4ursR0fbk2/+Qd78Nk2NRRMA1RZhzeKccrEkiGJ0SHUNd5n7Bww7OH7AM/tpBQNLXwH8N/IdV9QtJ/wXwl+ns+C8D/xnw7/+K9337dwcA9lrd264EnthumeNzBVSoO+PUMhYP0i98A9V/UKQ9G7oVBGEewAHpLC/s2viGs6J3uErKj871a3LaC+E4E+ICayKpcgLrbhzaHvXnKk6JyI0Sxh2tY3dhUtMCBtG5e9Y9G++83rLYtjvVyGC9dTs0OKgyPNddCdmEoj/EdW3mmOTVpGDtdpW95Wb5k6jFUdy7uFoOrcC9C4EseufZu2AlmkKz/6BIvhm+nR2F+0aXM6Ir29wn5zgZPlm7QBd5irOMD7/9xttXv8v4rQe/87O/wNe5WfWRr7/+Z/j5P/8XSN74+c//LP/bH/yv/Pz3f4e34wO//Xu/j1+LfW4+/N5vM776Gbusd8rY/Jmf/S4rnPzF3yNqdgVhijEvnhQve1Dvi+IN1+Ksr3E+8cbRFYUP4/FazaBz+xDMwTcEVDjKiebZz+gEd7v9Jr2zSrf3gu7CJFXbwzW7vLqubuWwiqSrA/01WXZSfhCPgHhScXXtRrSl+Ge7uAoiW2qM3DDUJcO3OzJ3/x0MWLja9bqnYA0yu/9mlsgJvoI3Ba+7V0Fb1oNr/b/MvUmvLWmWpvWsrzOz3Zz+3Nb9unu4e3QekZmRWVlkJVRRWaCaIhiUxIAxP4ESv4ARc4QQYlBITBASBRJNiixACAkVmRCZERmNR3hz+3PPPd3e28y+lsHaEZUUEUWXKbmN7j5Xe99zt5l99q213vd5DdZVSs401xAjpL+q6YCI+P0C8A9aa/8pQGvt1V/4+38f+Ie/6r3/dO6AuuksQkaKpe7nqbU4VUSJTgGM6AVtxCKdwD6/ojhVxJkCCZXYNhEFgcxCdImuOqp12HkmVr2J8ixEMqYMWDfu3YAOa5VmVEQVWy07XNAbObeq9KIsiKvkTnA7Hdtlp845StnXdOx7AZVmmk5vmiWIchKqsfiyF+eYQpDMnRgV6JSM7ysRg/GBPCkQs0lDTCEVixNP8zOtCJIbVtAnTdvDMbOGduBUQ6hDCtXuZ6ez7pZ1Nh6AGKE6od97FWpzOCu4bkkrG2ZxBOs5OD/DukIqnoUfOHz3fQZx2A7MYHny+BNMaPRZ+HmdaHGg2MjR0X1O7p9hyHhR0U7++jeR3Y7dJpJsj/M7WttiDGRx+B5qDERXkQT4HtcKyECLs+LTZaZzsCuO3kJtRp/qqYD0uksrSQ1Sjr1b0+vcHpUtm2q0D5H1+4lOvQA2Za3RW8KUAM1RZtE+RBOa9ApnyRXjGk4SJjcYCmGGWTTEJFmopYGz2JCoY0Py/nw6FUOJTZisAFQde1soltyych1mIVvVmvgijKZTFmJ1iJnIfoGNI3217JpHWiIlA934a70D/58XAdH8pv8A+GFr7d/9Cz9/uO8XAPyrwJ/+P/k8zQzJiDH0tbFre+27KyrYalqn4hqhRWbaftstmKzQBGu1AVNbwEum1UxB8J0+/YrxVFv2GK6ZPmXGtlfzWSi2A1OU4lIE01mITlFgFkoUxBRKQW9Oo510P0NznlwLkus/iZOiYvdbx1YFxGNNplnD3CrOGnJSX7o1gUiimYFSCoMtegHEhguRNPb0vpCaQ0yGokQesUL9hZjG7+v8VvG1YcVRRbkKJLSubo3iG0F6TMqkvfyqq41sipY4WKKdKdHSZEMnwlYi7zz+gGdPn2Ns5bvf/pjmPOvjB7z37kfEzcz19oZ75/cppnJ4dMA2zWyvvuSTb36P45Mjqqm4sVBqYJb9NGU0HA+HbN55zJc/+xK6wtn755w8PscOh9w/OuHl5VO+/OxztjeXVAwPTu9x8fo5xgn4GVs80goxO8QJxYLLFVMzaTAKcpm13GxNJyAmquy5hkqKajxqYknVYSRhWsEXbeRVMfv3VfIwa9kWOzqZmXA4r58fnQbImFmIWQgRZr+g5AhYXHJkO+Gp5OyVw2g80kZSMVjnoKhgSvbSc6VgO2yzYApzsZjUcOg9vSiFuVqqjXjxOFNIzbJzWs4ka3EtYaeBie1f7iKA1v7/BvB9EfmT/c/+beBfF5HfQsuBz4B/8//ug/a9WGiRJjBKRexeyZagM4UxV8SoFXcyDV8sUcDYuh/9aUBDKgCTjvi8EJql5oqvlslk/Kxb8iSe2BlyidjqwVRKypgWqERs0dmwJevqnh1iE7kI1ltkVqZh840UodoIUrGA80bdgznv2VUqbXAmU5YN2RZ8FmzKlOaoVsEfvlpincFXYjK0vuCnXrfLXp2LNlliVmNOaQ1Kwor75etiUMGKiUyzjrtcrhhTyS1QrEPEkHOm1hnTCsZYpgYkwQX9d9tsGUJHZiZWiysLXt48Q+rIweOv49qC9cEBNUNqluW9A2z0nB485vrqLcZUNhdPuby95dFJz+Gw5sXdFd5ZnYvHiSYjrQo5eOTgHu98Q2PmWu9Z9I53Hr3HzfaKna+cj8J3fut3kE54/f0f8tZlhuQJrWdrgnb7TSXLTK0rskUnLbN6/81gydVgUwMzwaDNVZfVvqt2k0pfdEwHDRuUwdAaxKodeBMbOlSaiK3Dm4gnkM2MtQUThdlDWELYCbe5IItMiyox9UC1BWMbgUCtiSAqQacoSEZEYaaIp7Sk8uVSAavoOtv2BodAyo3swGQDPqspzReoPb0ZyXTQeY1u+zX2gf8/04H/8Zf37//5+C//X38WgukislXLam2G3Dy2NN1y7u241jaqBKRmcq8pNi3qzLkmrYm8ZNVzm4YTdRM2KRSTYXbULmi3NEPE0hdPtJYqI4LF9xNz7HAmUVpA9o3J6jJEowIl66gp4aWQkifsZ8/ZGI2jqk015NZoLNReBpspmFu1GBtTyQy0XAn5FwKgRCkdA0KVGRCiZAafmfDMcyKI15NW9kp24+mNBZmQEhiKGo4mZxScYRVmmapV6a5JamipOirzTsi54W3Fm4A1jThZplYwLnB0sIaV43h4xJdffMqjD7/BN7/1TVru2I7C0fkJb6+uITtcWCKLwNnigKvr12xv3zL4FX69Zoo75qsrolTWJ4eEoWM3VjY3V0x7n0EYDlkuFtx7dMbZ6RpqIF8U4mdP+eR3/zpx85Y//9FPefvmjpP1A/puYHz5kttpR6uNPgRanrQEGhJldMpksBN5TIjtoU4YemyspL3OxO3Vn23vvssmYKqBNivNKhu1o1toxZGkYYm0iKoqh5G6ADc2SoAuecZWmE2HrQ2/NUzAvNRxoZ+FmALeVaoRZtup6jUVjC8QHampBkOMIVurSsCctBzs1YlpawUSNEPpmnIOZwjrQtlOKpsP6mOo6dfrBL4i4SPSzGJBHWe8KUhbkuqMk0rtoSXV44vX7D9V+ulM3IpAyKqBKY4ShVQTOIMVSy0Ray21Zk0kKhVTO0qYkewoJGxbgG+E0hhLoW+B5AvOF8yuEvug2KxqEJlI1SBFbauDq8S6r+Gcjm60E79XBRZDYG8LBUynXWmZjVKBmzLtkqvYUokeNT4lxZJXryt1qpYQCi0FavOIHVV+W5zKHlvB4ql9UgNVUrBKFtUCiKvUsMeE54I1Zd+FDvg9cMWgikPnlW+/ePiQfmc4P3uPT/6536O6xO1mS+8WLMKCe/fOuX17x7e+8xtc797QEI5PPHF23F6/4OmXF9xsblkMK6xzlNo4Pz3DD5bSKmnKzHFHsB0lChhHMY4yjyyPlrx7/ohhvUJMIcbCjz/7KX2/RtJL/sF/+B/z9uICFyyzXLIwA3ZTmNB6v4ihpR2+7i3qsafZGWMqkwtAJZRKztoCFOcpIlCSTmGajnTFFFzRcq+1hKmVGatCLBzMBdtBzpbBGGIRWBTMzpL7RCtCXzxCZhQwrqgtOllqy4rVFxWguTJQSdSqgh8vGYxOPUxVKnI0FVcaJffglFORraPVmZACIpEsFdBcjZVXAtFsoE1f4fARlftHjA+kNBFkh1ka0rYiUweordTZyFwtpanrzO/DNko22FowRht5urXLlJR0K172Mt6asQSqT/QFoDA2B3bG0MjBYWdwbiJ2jfm2YbuOjkR2ChXxsVKk4iRQDYym0GGpppGk6AgQoXl1FkrYW0mL5g2UbBU5bjW3MNXGrlNduOsqLXqqWIzfU4oE5gjWJfLWYm2mhRmTAykGZJHxITPFquyD2etuSrQfUtjrzgEmt/ddoEm4Ailq6BdO+yPWeWYJBJeJNwve+8Y9/sbv/h3C4YpprszXPybfZOp7HX7OiBW2L99Sm+dqesGLz2/Zxi3D0QFdt+ZwtlzPI9N2w/nRPY7XDxjvnvP25i30K46PTvG94epyh/e6o1rYjhO/4G7akraJ4fAY5+A3vv1bFLeiTocMRyseesdmvsZue2pttP4Ql66pJFr1OOsR8cR9lmL2BhctzepUJ5tOjV01Y4u6KHNDPQMWjDgsHYRIMlXNXbVj6DKmCFMs4AI2RM1h6IRQEnFn6E1ku+2RLjM3wS8z4daTxJHFgk0YqfgCpXlEHFNNSrcKFomouUx0p5tIIAbJAg4aiWAqsQS8TRQT0Apc+14tAjQ2YnGzhWCB8Vfef1+JRaCBosVpiDhiU1cdEhQ2UQqChWpxNau5Qgx0hj5qzZvEM0eL2EpvtAlWpKpWvQnSKslC5yLiPbn0SJR9CAaI70hzweCJs456fFBbbJtVFFJbwzvwdU8QavsLowglNZXMOkOqGcRSu7a/cADb9DxkhV/moo1CKQVfGtk74iQEU5ktDLOwFeia1vK1RBa2sfMNjyfZgvM9nsYuau+ktbj3PezZgUU9DabaX3bBc6uIeGrMNImqgLONWD1h8HTHa8qm4UzPePOKr33rD1gdrzk8+oDN7XN+PvTcXk2ct8qXdzcs0sxnkjTHoTNs3rzC+J7zwzWPP3yHm3lk/skPqFew6paM9RrbL1hOlkKljCO3lzMv3l4Qm+H9eyecnT7A+CUHw5rj1RK/WIBrjLd3TJfP+JNPf8D2Zst8d4eZNzy5/z7DwxNubt/y2Z9fYQZHG9WFV90Ek6PzIy05km26EGYtPY23FJ/3eHswddAJi0uIzJjsdMKw12L4bmaKe2bFqtG22kswLpDZIQa6HtgIss74XSYWQ905bHC4GDFSaIuGjJZmweVMNAbXOUIz1HkmNSGGjBWnQbKlYZsKhSoNHwppRDFqWVFjYg2zLwzZUpsCV3dVczkk5V8rHP7KlAMBcM4qXrxVqhNVhLWILRoJbW0lN43fsmLoGiQK1alFtOwdbDYXyt6znlEqcCXjSyYbIeBJNtI1y65AXystqOIO8bhQMXGvAdoDIjsHcxScWCCp5j8IddQMO/ZKwVxVcWaswwclDaWisA6DorqQlU494sg+7oTI3jUpVW2kXgk2JWsgtvGFhmLXoyssTCXGgV9w83z1MCRKMpCLiolaUOxUi5pBgKYmBYGcvJYvdcbYysnRiSYspcCDjz7ke9/7XT7/4iV/6+/+HRa+58WLC5588HVW81tevZn4h//df8bSd6wOD1ms1kybkcuLl9x754B3H3yd2ht+9LOnlFh476P3+Nb7X0csPH32ks9++hPW9wYOD9a8eTUT48R777/L8fE9Uon0Q8f56RkH/YrxbuL11QXNRm6vLrm+grOTAy6un+ki7IT/5T//L/ji2Wtu0x3sdjSp9LanH44pPmFMZt7sGGOilIIfhOI8ZpMU/uESIo7WBF891QZyVQt0kELOUWlAHZjZUqtTnHnvqTESvTaOVT3psFIxeQUma69LemragDGUYvC57bUAnXIqUiPZig8Wn5S43UJRvkbZS9VFVDRXmirGm0U6IIO0yOwGoGLmjNlzLqrzmLqjmCV+rETGX1kOfDUWASOtM45mhZIqiIemoy2FYkTA4GnKlPdZE2NGuyffOCWy7r39XnScKCYTjVdgqc1MpeJjRYLy57zzGG8wk9Bkxg/CGAOtFtYry22cNAuggW16UqhQU8CRaAjRGVyOau0NamgxxZJqxouu1KUZhViSqcFAMdpJlqK23gwNqz5ysbj94masZtinBr4UOumIPpNDh48bXPKMogYgKyqEMWS1Kxerta1oMzA7xbrbBimbfXimBlVKNixPz3h4sGZYHfC173yX1fkD/G7k/W9+k4uba+rLN7yNE2FYENOOH//Zn/Ds+cjD03d48GDNPF5zfXfH4dEhDx/dw9w/4t7ZIwRY2Z7l8Qlzi9xcXnA33nGwPGTajpA8nXWMdctUMrYIxyenrNcDi3XP0y/esjy6x+FguYsR6w3L7oAuVH7wwz/jH//hf8Wf/vEfYxba1Oz2OYChM+xuK7ebDW7YYDIk6YkxQVHhV2sQrBqQitFFvNaGFQ09zVZzKmQq+xtvbzjLEFxlQlWi6yLsqLCylNRwudA3yMUxEvEtA2o4m4MhxKQsSG/xQGmFhMNYq72vUvB78RvC3gBXac7u7csKv622YGsmGovJ6hExCGZotJKYqxCcg9nsKVnxq9sTQIRmIe6lks2DyzM1eOykEeSuFVLXaJNKWk0r4AoVj20FD5hiwVUmW2ltoLUJaUUVelUIzdB6VSMOzjNJobMR6bXuziJIUX34dopIUnZA6TvauKNYUZadCNkZxDVM1A5vdULLulVrJunILomyAqVRTaNUwVewoVLmQq6eQqJjgWNGYaOoQMoKLmbmoHbqLBVxjZoEs4lUF4itx/isDb85ImRdsEohAs1YDWk13T64JZFNxZuEq6g4qzWqM/RHAx9993t88OTr+NWKbjXgmnD3ZuT25gV3NRA3kevnr/AVjPV07ZbN5jN+/sMtwzuPWB4MNAvb7cTB7Y7uSHhbdgQTWG4qu1Tw9Hzy8XuaDlyVhJxj4umrC7zRuDJpjbypvLh+gTGGo0FYOsHKkqODHlZrXn36gtubkUcf/javayDffEmpnsuff0qcFKJiww7vPKt2zCZN4Ec6E8hOsyvFqtvOFcV2V48KuOaO4CJ9FUrUB1BfhdEUTImo29liZcK2JZMd1Todd7hqqbNi6Z0HO1XaWmBXyFbDZbN3dAV2UqFavHW4lij1F3kKmWI6bDG0MlOzw9WOKjONihOvlAxpGIa9a3Km945JAqsS2VTosTCJqiV/vWDwq7EIaOQ0e3KPUFMlYXH7uC2xOut30dO8hqqVWhEspiSS0URaIWNjQkQwfku2npLKfn7q8VTsPJC6yLJ4qomQDI3AUCNlVqZBEwMSMGFUGk1R4hEtMQKdjEQMLVZUh2c1/VcqJamj0ZsKQfQir1W579lTskFaQkyn4R7RUxhJAeo/KWDUkbeohNxR07wHSuxpPbaQF4K/29LE0pfG1JI+IUSoDrU9N6FWQ20JKTMWDT6lNbWXSuDowTkP3rnPyfF9Hn74NWKGpRSODpa0eeTP//H3+fi3fpv66TN+8NNPaX5kvV7x9Edfslwf0C3WLI7f59RXLuvIyeGSxelDjo6PWa1WfP3+x4oRr5X3Th9iwhHXN1vGu0u6pNbgjc0s1isswnpYcGDX2EXj4uaUFmDcTfz48884OXS8ebWkSmR5vOD+2Zrw/gEffPiIedpyN77hD/8ocfn5BeP2DdU6KJXLeMlg9uo7gVotpTpCdTQjRKfBnVIEX5RIVG1QxWQKSpHy+r11wWEzKvrKwlRHHA7vZ3KClBf0rZCNThJc12i7QD4AmwU2SmEee6GLmskwmga1w1bUxu2t5hXaTMuORiEPCWMMdlRBUSKrF8VOLHKgtI5KxMuOYiHkJaabqFMm1YEqib90F+Ff5tEaikyqTbP6ukybNTrL7ukzpRkMmVqsavz3KCnL3lNerGYPuKVutXPCz4prisnjcZTeITLhkqMuEnGro6EulT0Hv2e2CWHGiKfOa4zfkGMG44h2xdAmEj2y7yg7qz4AiXZvm92HQohGkbmiwM84KnOgtEjNlmqDQkNtpZaGKUHn/namkRBr6UaHcRNRqwbqMOImq9CIudA6SyuZ1DyDCToa8oa5ZvX2J5CWFWleBzIFJ4IJQRelccIHOF4c8lu/8Tscn5xyu9swmiVc3gCV2CJ1c0fjBXd3M48Widt+x7Sq3F2/oG6+5OPD3+VnF69Znd/j7OxjHn9wjhs6VqslMSZEeu4/PuTs+JDNXSbXiWocN+mGOF1xtFzz7vEZdAkKXN+8Ic5R/QRXI64MfOf++/zo8iWLUOmMYXz1FjGWRRyYJPKjP/kp4bTjax9+m2P3msur51xePCeOEyxhnAwtjpiwJ/J3mRITrfW4HCBExFeyqGTYVWglU02kb06NPMkxd4awdsgmUxZCyE5j5XOlOoOXG6LT3YUfekyCVBtto4rDYmFpevI44YxjcoWh6Vg5hkZITpWUaaveQx9IDlICMV4XF1uQ5LA1EXJjtpliPK42ShBMBJd36iFYOAo7bLa/zkT41VgE2HvtWzCaw1gdrYtEQIqGhTostjQ19YjaJV3VBiLNUUtTgIZknQiYQAv7gtoIxRdCgbn3uBqZkyE0T5XE3LT3S50ZHEzFYedCqzO+Omoz+BqJ9ZZizS+/tLaE/8AJ7gAAIABJREFUWB2+epL9RZiIYs0pSuAVGcgkrInUFglOXYWzzIoCs0Lf9L2EoAYg5awzieCLU0imyQQxZNmz8GIgDIZJHJRMjBmkUgVC80gqihfD4SlYnzBhwdAfsaMy371h/fAe7374Cd/+5Lucv3uP6SpSplvilHl2+Yo0zTx/+ZSTkzNux8zyYOZpF+heTnQZbsbC0E/EV2/J/US/gP4Q4tVM7ztWiyUuC7aHqzd3hM5Swpr1vfvcJ/H62vHq8oKSisZ/0aibxLTdsS3QLQPTuGHpDDs3cNB1vHl9ybNXr/j9v/Y9ahyJznC2POGd3/sWu+01x+Ydbt7+T7xjHxNjpb0fsJdX3N5uMLYStzMyV7wPNNtU0lvUrZeDQ7zKsfOcqDbQRNjtE6attbQIcU7kmgkIIWU2XVNGYIFsDOsCsweTRnaDos1IltIbQjVMVfBerexVDJIqsTWkKHNAWsVYSKXg8qzNPyuIzIRWmVOPWMGXTCOARGxomNnTpqzX0J5ePe8y1gsdgd2vMQ98NRqDYpqRhuscxKZiiT2Vr1G02ylG2YzZwL7LLuKwe+S3erEhJvV0+2bwPjMLpCzYPmsi7gTZO3ydIFTcCHXhmG0lTJ4kM6QBQ4JgKDZhtw5rMsXqk7dIRwgOUyaiVyx1yRa7R2xnAWxRWkzVJlyxRUNDKvtQz6LutbxPHiqTOtdspfYdbhsVsY4lmYqrBbN2lNuO0gvLNFGqo+CQgw4rlryDxkbl1tO4/04a1gnOD2A8j9/7kK994yN613Hvwbu8++QdrLNcfP6CXTQsQ8WGJd//0/+Vn33xKT52PDo74MW0YSie7RTpzgc+/ezHTNs7To5P+fY730GGQg0Lfv9f/H2W3ZL7q/uUOONWPWuzZlgOiBm52mU2m5FF8JQyYz3YENjFkdgmbBKmXSHGiTlmYpt5++oKOXUctGP6WsjHcNLOWJ97To7O8aPl+e6CIGv+0f/832McpPGCn/70KZ99+pxUv+Ty4jn+zpEoSNfIpVN9RxkpTghSaNko9rsowanaSnKCpEzNAQJYEkMSsnMUpz2fYnRHR2x0wIjFCdi8htWE7ITUEr5liu0pi0I3RuZmqBLAaM5CsxnJDucdRlQgJRZcc2Sj12JIBSkQi8G4omwJa0hzw0rRBUwMe+Yb2qXSGLMGX+HGIIpSMrmnsCUbVM9PpdmidXdRL7+4uAd1alx4a5ZSC94rUNHahkPzBSajXPkegVjJVELvwURCE6bsmWzGzJbQFZJ1eMA5mGLGFYepnhh6AhuVagqAMgxy1QDTmnRykEzTsZA0fGvUaslOLyqiA6f6gSzKjW9Jx1eKI7QqDqIxxIlWYQwaxJJ6g8sBOzeMFcLpwLJ7QN5aJtlxsO742uOvU0LP688+J+aRy+sXpGmHyU7TiYdA3x9y/s6HPHzyHq4K508e0R0M1HEm54np8oqXOWDMZ5jZ0IWe45MTyvqA7Wev+M5v/jbPX78ilYJkIY2VdtyxPB0QZ7n36AGdWdIdHGMOPUPocRNMcsc4XuMasFwz2ECdCmEY6IIlVRVFQUdyhe3tW2RhWJ8fMMWF2sBZ0fmZ3h9h1jPNROY58OKzp9Sa2NQe2hvuHx5zdfUSVve593Dm9OSIP/9h4eLLK9IqYrc6puvwdMFRfQdGx8dNPF1xqrNvEUQwtWBdIbeoTV476C6uQMr7Ot1YymQpw8y8XTAwk4MFc0eKekGZCiUFWp1xdx0ZNaZ1AVLWcrPZCslA1oeXADV5BYlKhWgpuZGkKiPDi0J4xGIk4l0jRosPQk0Lkk9UEVz2eo3F+Cvvva/IItCw1sMUaSKYBiUkugZVSR80kwlF1EvtmgIyBXXM9QpoqCkr8MErbaWioRfZBKXsFBhzJvQddop4W5npwDdqMjQZEenZ1kzvPa0IxhtsieyMobeeRMZ0lbLpMJ2hyESrHl88pVmsm2lAnEU5dSVRSVgTIHXgkgaPoAxE6zqSiSycZVMdIWSaOEJvcYsD2vERp2HJenHC4aM1nhWffPc75Ni492DN1aeveWve8MHDhxy3A3783Zds39xxWa64/Pw58zZR+8Dx8X0evf+Q3/z2NzldPiTe3FLzlulqx7QrLFlybW949vJTXG3QLEfHp5w/fkjxgjwNPH11y72jQ+7iG1q64ag/5t0H56zWA8PQsSiN5Wbm/IGhm3a00lEYCDOMGWoXCFXohkOiq1xfX/Dsi5dkafRHa1ZhSb8ceN1dkTZXBAdniyPag0eq1ygLojE005HHEet6bq/f8HKaOT9aQ3ac3ltyg+fN7VvKXeanT7/g5sUNhkBnD1ieVIJ3pKK7tNkkiIo/KxTaPFPTTB3QJ2uFVtW4pqDrTG0B24TedyTmPW/Qso6e7KNu95PdW5EFlzN23dgte7irWJmpvcONjTZD8b0K4oIQg1PvQ61Q1auSJJKTEqtbJ5jsoFbirARpO2UKK3LN1K5gcoe4La15DBbPRMb/2viRr0g5IM0OhjYGcAnXINv9kzMGTCj4Uih+YJCsYAzxCDAMgV0u5DGrog/AZTyGWNSPXolY5yk4jIm43JgtrGwmbwyjT4g3DFPbyzADWba01FOGmQWOMaskU1zEYmkVvIuMSeezFeXahdr2hBunqze/YMhDk0Y0BmsaB+cPGEvm9PiU68sN4+Ulsu755ONv8eB73+Xs/D7fO3vMb3/nu6QsGNd4++aK9XqNRA++MqUNnQ8QE7L2xKuMrJbkaeJut2F5eMDOWaw4OuOQMXJ19ZIxNqzpuHh2web6lrvdDVUin//w+zzfbnjn6ITtXBiOO65evOTB6RO2veFbH3/E0+efUabIs5cv6MOS9772mME5OtY8fnLAe/fe5eBsxeQ7DhYHzHiO7N774FQ/sR23ZNQMY1B337TZMo+RvKcCSy6YvmO32+KLRqtPJbObOvrFlng3sVyrmrAkWPZLfAdzmnj66g2b3R0///RH+CK8fvGCF8++4Cbe4abEZtpRpx1zM/s8RQOTkp9pkOsOoeIsuD3ZN5eKF0u2QjOZLjbmpkKMitexMJWls0y1EUVp0qaqzLcWj+AxjDo5whCb0EwhiMVmS/JFG9+uIUnR80ilQ3F5E03TioPHNUvKDeuiqlUFBa9KJVWH7ytpEi1Be8HOlbm1r3I5oM0RI0kZfq7QZWEyS2yfcLmnoTfYrhi8MaxNYAOMeIKFznuSC/qlmsaUJ0zKSAYkUNPIYD3TZLBV6H1lR0GaxTaDnw1pFQjTjlIiGMF1O9rsmAaQYPCijsIYG7IKlJQBZfFZaxlMh6mWuSQaBYylEChGyK4QnKdfHHF+csQ3vvFtxmnD4699wk9+8mN+9IOfcnh2yL/w9/41Pvjg6zw+PefJaLgs1wRZkq9vSe2G18+umdKOg+4QK41weMRme0G6DCyOTkgXTxE34EohbrYsVx1e6h6MVwjWIL1hc7fjdnvJ7XZDWHl+/r99xpvbyDuPzjheH1Gv7uhKpT+6z72PHrPJhdubK2gdVirvPH7CGHcsFwPGOI6WS9bxiE2rGqfeCt3SsgzC7ArMgVgyLmdWg6Mmi8mGbYMpjZjBslqtKVPl8vUVhopjBzlyMW25eX7DXb4k2ANWzmFKz83UOD1tDMsTliRe7TJf/OiHfPHqFXGz4+mLF2qZjnfElvHeUmNjW8A2S0lRd6Ap6Tk0hdkpIl3/D464f3B01TDZ9svo+ULQGzBbjIs00e58zBZbIp0TTBYmaZjk6Gi0IZEmxxwyJnc4GskWvZ7MPpSkKl25OKc5GbVhTMNapUwnhxKcSwarLF3jK7VqxLlp4ENjLgbbDGWokC32l7D4/+vx1VgERLFKnRiqbXS15+jolLzuaIPn4XKgmcDQdVzdbbBJo6Jls+XQD1jjib5SSiPlwvHREdurS3abG+Y5M0miNgWNCokxKXasGofZa/GLq7TbSHIBZ9UpZiSB70FmJFuqSeTscKZiMsRmsMVjXSYnQ97HfllnkeUSu+qw1tGv7uFPDzk6WPLJk4/5+L2PePjeI56/eMPp+T3ur0958uA9ntw/4P3+lP7Nlk0a+XmyWH/AtNgwb2+4nTO7q0ts8+R4wziOPMAzz4WVbUw3b5mjbmtL3jHnyMMH96E/IZOZ05bNNFIL+D6w3Wx5+uwlJ+sV43zN0Ynn+OCEm8s3XNeRB6cPuOeE15evuXf8hLfjFUe94UXKTC8ucOsjfHY8fvcRiaIGLBZ0yVKHntvY8G1iUQdqiTgxbJ2A7+i8Jc8jeTtyHCytHuo5WhU6d87NriDxDfl2Ynt1y+76kpsWcfKKt3ngXuhYnTzg5s0FKQrpMLMtHX/8k5+wffOcKD1PX7+h83C4CLz3/vu8un7Nxe2X2DKq4WpPfRbjVTItRvFiRbf+qiKcaVKZnewZkbKX8WpWRnOV5npajCQxNDtBVTFQpKPum9hFEjV29K6QU6fk5rHRWauZG1jyvE9zkn0MnubYQ1M+BUZ/V0lKWw5SKLExt4oZCiYZYg4U4/DzTHWKMRei7nJ+3e33lSgHjDRnO46Ggeo6dqbj8b1TvvO975Ck8clv/CabqXEezmlDIeTEm4uXOD+wXKzwixXPXz4ll5mT8yNqyXz+Zz/j+cUltezYvn3L0y9+TpXGFGes7YjbLS00ujFTaiC7CHgSe2R0Bb9wECumJeYOfAaX9aT5VjFOWQTilthlhziFmx6dHfHJJ9/mm1/7hPPjEx7dP+fk4JyVrdxtr9htI5mIzJG+GcbmKKZye7tl3r5iupqY88i4Oudf/v2/ycXFU4ZhoNqe280bDhbH3Lz4DOOXZHQRGpLn4uYGa2G7uaNfOJqH9eKQe2f3ibmxG2emVrA+kMXw7NNP+fMffJ+4veHZiy+YTeWkPyMvO27fvuad88e4g47+8IzF6og0jqT5lpPTd7h8+hn3T054cvY17jYvefDBfYaDM1Z+wHYDB4sDVss10VXsMHDoO6zNOGmMNTPGyjTO7HY7ak7E7Y55N2GwzLnQjJDNxHa8YbzckXZ3XJc7Lu9uGapnCKfsolrPh/MHMO9Yrx9x8KjHtMDcCj/64f+ObZWaJv6HP/pvefazn7PbTTQSxgkuekhbKoq5Z08AFmuUG5kL4gy1qAxdScQ9nS/KepSCOLVtV2vpaEr0mTTeNVt9si+qZ5IKfUHaoF4OA74YWlHFKeJUPyKFX4bn2kYQMFVDdioQm9f8DbP/jqqALXjXyMnu2btC3ZdaDsckETGOFue/mnJARD4D7lA5Um6t/TUROQH+E+B9lC709/5ZxGERw/HZEcatMLkQYubq+oY//8nP+M2//nvMs+PeyZp8d8fCr5nGxPrkhLN79xjsQEboBw9xpusGrOvYvkmYfs3B0hI3G4Z+wZvxLdN1ZJpvyS3SpYlNV3E5UkQIdSZVS/UNV9kLkETNIxstHUYvdE1gGDg4O8atTvjoG9/g/NEpxq+wYcXZw3Pee/yI944f0rVAvL6G6xuMLdxcPOc2bWi7QiwFZyy9W1DWOoseJdDfW2LvNjx58iHzZsTUY16+fMGis3jXMW/fEm8yzV2zqZUQKu34BLdoeFOpXcfBsOT65oa3N7dKRMayGxOrwzPW/ZLttMGXgjGOZDy2W/FkvWR1eEDzlvVqocaq2JCxcHgsvL2OzLeJxTl0j59wdnpIDYl7Dx5y2C2QziC+0q0dw9BjjpYc1kI/6JPO2R7ajKmVvha9YPsDbKlsk6FMhbu0w1qP8QlrLKftGPNgzev5EK6ec3c9M+9GeoSWGubefY7vn7DglKU/YH3Usbna0TaZgGe8uuWnL37MGBOUDilbSq7UJFRmjQIzheYtUpQunFv7JVbMVB1FF4fyLaRoHiDoVj1X8B56T9xEhZgOOsazSYDE5CO2CbkpKNOmQjFeG9JiMRRKipp/qTeE0oREg2Sr2Uu8rSo9NcRG6ctGdJLWxkzrtdeQq8dZh82TrhxWdSu/7vjLKgf+oLX25i+8/vvAH7bW/h0R+fv71//Wr/0lfIc7OGZxcsKD8495/fwFYRhILXHSHzG/ecWbXWQ1ZLaXieubxG66Y7rbcXpyQqmZzTgjzbJaZA4WcHB0zM5Yjo7WLB4Iw/E5L9684u0XX/DlxRvO44YXz5/Rm0ybMq0zFGMxacbVASFCrFgRimgQCn4gDGsWnefe/XPe/eAbHK3P+eiDJ3z9219n1fVIaaQc2V3dcXX1OYu+Y87XtHnkbfK8vNJt/XkfMMMSWy3h5BDrPZs8YWTg4fE5cXFJ6I/47MWXSDZM288o6wNc17GSFatHZ0Qa/V1iHiLBDogNxLRhlxVcUqPBlYwPA7V2WHONIyJpowYZ5zg7e0AfehbGU0Jj0R/w5u0Ft1NkSiPvnJwyXnzOT+sFD1YP8RjCNOIfPSCsPOMWBuO5Q1jkhtiekCsTE4s6Ibkx7Qx0BRMdtEKumTTPlDSRY2a3S8x312zGLfMcWXQDtTqkJOYEsSbinFi1M56cGeZ5y0hjeDvTmREXM44Vt9MW8cK8Tdzd3JB3G55ff8Hzz58xXV8TgmG301QeWsEah/gBG5uqTW3ZN+OKRtmJkppdiZodacEV98uRpqAGL9sCZZPJLhM8GhvvHLgZG9Xd2hq4VGkh4b1mOdRZM8RFjMq5jXoHbNEEaWkNIxq+YqXRst2L4SqloSa71pE7IYjHT4kWGomd5iA2g7ENb7W8aH+VuQO/4vhXgL+9//N/BPwR/4xFoKTIB+9+zDtfe8LgT/iX/u4fEIzlZ198QcqFpVuyHbd0YcnTFy9xXSDNW16+gnHXsGXi7m5DNA4/DKyGBadna57cP+P65prT8zPu9++yWg2c/Pbf4Oc//Ak3uxdc/aP/BpsasdvR5kyOE77vqCWTrSVUg+k6WldZycDq/H0ePn7At775df72P/83OT04YVEKpVT6bsXN7TVfPHvK1etnjGlDTIVFGBhWHf2wxNuO7viYEyzbtxuOVyuaFFKp1KXhnY/e4+J2Q5lHDk7WTPEOH4TteMPh4pDqV9xcXeBC4/y7HzHUnvrQ8PrFU169fE3fHDElusGTthuGfqAbPEG02bo8XOnTJVTyuMV0lQfvnhA3K96+Ep5eXfB2t8WagfeOT/jy9We82W2Zbu5otfHoux/w4TcDNmXGJrgWcMNISyPd4oiV7+mXkGpVqtGYsFR2M2yudgo/DY4KjLcz25trrq5eM+5uKTFzF2dC9YwHHaY50rihiLCdoOs7/IGly0JwK1buAL9cMbsVQzFIiRivNu47l7kpkc+ffUkU4Zvf/Q2e/3Tg9s0Nc9uxfXsHso8EEc9sIk0SVvYWbJrmTtaiepXWIBeaN7RsNbLeJEwLtDbTcsRWyLaSRg2Hlb5oEEkThuooplF8pYuGSfZ0Y5OJVYNcRERFb/tgVzIKH7XsATMNUzWWj2Zo1WrQTkuQBamNJIIUh3eV0jwmJwXpNENDp2S/6vjLWAQa8F+LSAP+vT1K/P5fIA6/BO7/02/6i7kDxlqu397xe79zH784hgy76w1dqcylcBsLN1cjJ0cL+m6gAevjc+62M28vXnJ2csDJ2X0u325IY+Y237FarXC2MN/u2B40xCS2m5l7xx3H5+ectCN++M6PuXl1y9m7H3Nz84oXb94Qdze0anGSqaGjOzrEHzoeHb/H7/zO3+Lxhw95+OARD9f36ENle7kjxUrazVy8ec3bVy/Iu1sWw4IueG12JaE/XuBcoF8umGvFLg1HBx2+W3F1eatGp2LoKGyywY4TsRdCAHt6QN3MnBydUFxjc1k4uYtsY6QuAlOE5jvudiMXL99wdn5CJ4UpJ6Q7wJZGcAYXArY0UiwM64FVPGBhPOG0cnzS8SA9Ydzs+PH3f0IqI2PKnBwdMuCZ+550fc3hw8eEVeDixQvmaUsfBo7un2oghrc007Nw2uGeyo66N+4Eo9mD280NKc2ku8RuN7Gbb9hud8Rc2I5b0m5k2C2R/H8w9ya9lm75ndazurff7emivXFbZ6Yz3UEVdnVIVaZK4DlMGSC+BAMGjPgkDJiU8KCEQCCoMlQJ29hlp9OZvpm3i4gTEeec3e+3Wy2DN4wQ8rWAstDdky3ts/VOzl7/1f3+zxOJzjKqQIwFa7PkfMypqowQFDZ5nFDUjSIrIfrIrCnIdMk883SZ5vnjz/jy7ktub9/i7cQTlFlJnnnAE0dLb0ekFhg5mZkD04CaVJdTYtW9F83KpCebsBcYNUXRIxDiiEAjEuQpve/tmLaRDokMhp4RnSQD7/2P741HSgLOv6dpT+xHD1M7eHpvIlYQ3ss1J3FNIL6nRk3QUkjxvd8yRGSYDEROTqcCo5hszd9SA/5GisDfTym9FkJcA/+9EOKn/9c/ppTS+wLB/+3z/9M70MwWSfjI4Xjgw6sP2WzvMWqO0oY6JVphubiZ0Y+eeVUxjj0xRrSWWJ/Ybk5Uc/DOIrDoomK/P+N8y/lwoG5WrJYZRZazOx7oh575ouLf/Qf/iPbYUeYV3rV8/vVXHLd3OO9JqSNXGU8+/D7MDB/OXvAbf+9vUUhDlqCwI8Nw4LTf0Q+Kvmvpzgc0IGdzojRolSG0oigLsmJOrjXdOBADFKaa0NdIjDS0u5Yv269Q4wlTL2gHR0wOf+oZM8XN4oLx3CKDYjYrubu9wySFnOXkWcks9JxFzexiurZr6pwoA5ip4cqoAjc6+rHDE8hnhkVmJpNwhHmzJmstxSLn0fMnDP2Zx3aYuPUKTNMwjoooHKGTVE0N1qPDyDBYfOxJecANA3VdErqBWmd0SES0RKYc+3E8MfRH+t0JTySMDj9GrB2Q3pFwbE73dMNIrRsIPY4OIRLeOhbNHFkpUl1iAhxPLWIwZE2Ot46sFJjRktUF64vHXD+ec/vmLT/+4z/hzcuvENGS55pgLU4wzdpMOG+RJlq0iArS1IMf3huUp1P6yU2QYkBIjXSeqHIIHUpMKwDkJKazIiKEJA8CpybOZQh/SdGaZCZRTR4NkQmwaTIex2kVEqVCCIjJkwUgTb7FqSyJySQnxIQgey/DFTInRT+tLKVCKDlF5RNE6751AP8bF4GU0uv373dCiH8K/DvAu7/0DwghHgN3f90zmmbG8+cvcKbi5ZuX3L76iqvrZxRlweF8JJeGdV3z5dsd9tQymylMMS2ly+WKoiiYcpmJcYy4k6OPdwyu59iekfk7kGuMyokuYFRJdJrf+rt/B82cN5t3FCLx2Y9+k2F/C1VDqQLtueWHv/q36H0iOUudPLG19OMDQRT4YDntew59YLd9wPue1dUVlzdropD054FM58xWS9q+pe1aXASTFPl6wfHU8/rNPavljIDj7s2BPJy4ako22w3VUnHcb5lfrTmce07bO/JVRT2v8Z0j0xlfffUNV6snXF5dc1NknFYVxhu8kSjlKLOK06lle9oBk/JbZDntqSeNkfPujDSaxapg2dTU2Yw4wNe3r1heOozwuKhorq5BObbHA+rgyZZXLCtBSI7heKS1I/v+wKNyjj3kJAxtFid9t4gczmeOxx4rHMEODKcjEMlEhpSJcehxdmSMiaAVla7ofWLzbk8QA29ut8wyxXGxY/74Bk4dj5dXxJho+4GHwz3RSZ7+MEOPjnk2ZyzeIci5fnTDRTPntLji3N+z69/iYsCFqQD4GCewy3vHpBaT8yJKgeK9Yj5NpGCBIhjw0YOfdPFaaqYO7SlnkDwkB0IZlHETdDZqYuaQg0JFR2AqJkkqZIpoNYlyEproEkE7pIpkIhJRE+Q2yolXIDyRv4ScxIm5KaftS1IBvEb4SFIJgyeIivcyjL/5IiCEqAH5XkhaA/8E+C+A3wX+Y+C/fP/+3/x1z8mLkuff/xTvclTylNmS7cMOk2cIJfG2Y9h2FPM5oTIcu4HcncgrRd8H+rFjtagxmebYjoiYWC4brmfX3L3d4YbJBVcXOV4I/OBp8oLbL7Y09ST2HGVgXpesi+eUiwVxiBzLA3034oaeTAp2xxOVLjgczwR75uLJI7KFRoYdWW0Ig51ixUiqumGwgqEf0X1LStD3FlNoirwgJkmRF3gfyLKGwXjmixnnhy32/MDDsKHc5AShuKgvJzzZfEkfApuHDUJmZEWkyWvmTYUqNYWOtFnG6Cx+nEAZ1m9xwTPqhNQSKYqpAzPT9F1L7yyLas7YWcTKoG2gqWpyXTJfac77ey7X6ymxZiPd7R2uMzytKiiWBGHZ7UbIHXpUjAaU7clqxTB2eDfNQvvtloM7koLE20CKFhWmBqGUBBQClYmpj8MItNYUypDrZ0id8H0ildO9uNYzNJbOj4jYUVY1Whe4IRAHR+8mSepxv+eb7QN+bDkMJ1zf0587hJvWxUrr9+bqyTCs0SAkIUSidAiREb2c+IyJaQ5+r7gLTG7JqCZ4rbAR5yeVvBDvXRIEYkw4oaZT/TR1uiKmHL9CTUboMLExolZEPyHtZGLiRxj1vokgQZyo2kpCiJH03iWV4hQnTqMnKoEWGVK1BAvKvMfmTbiqv/kiwLTX/6eTjAgN/Fcppf9WCPH7wH8thPhPgK+B/+ive0gMgXq+5LTznNstqYiYHo6H+6n/2tRoU7CoqwnEOJgpmEOaWjKd47Q/k5mMrMg4bPY4ceRKP6apNVvXMvQtth1RSA7HPTFW7O8rmqZmdbGmXBkyLchEhj0eEUZQphy72eKVh1GxOx1oOTIicG2HvdsjNRPjryrQVUXb9by723AlFGVWEdAYCdiOTCRKU0LQhDCQ5RmZUnSbln4c6U8n6nrFF3/2cwYFq/kl1fPnmDpHdAPNbE5MDrvZgXR4IVldrUlKYvsRq6bGqN3hxBgiNgzkWFLMQEekUpCBLBR1Ych1hgwCFQWegTBmzMqcOxm5fHyDj443ty/x45YcTbFcsjseCSFj0U03Id4nNm1H7R1u32NkRp7BudtN14QyTvKUPEPFGSYTUARSCAjviX46uY4ShMxmFwnqAAAgAElEQVSRYmLoW58Y2o6qKhBKUhRgTSLaSJkplstLbAg8HPbYeGLVlPhi8vsNHoSwk71XBYbuxGk40IoDp7ZF2ITQgqc3K371l38ZZ0eIjlxOJuOsmTMMlm9e33N7e0tWVMQkOO4PpDgRrFKcZt9Jezd1jGZCErxASsiSoE9hkqnmAqMdSUiS8ngvp7CRCggn3ydL5SQYMQktFNFNAz2GaWBJEUgq4eK0/ydNhqv03lZEkEQhUFGisAihSCrhw3QehojfChr9NyoCKaUvgF/7Kz7fAL/9//Q51lmwns62SCeoTUmXW6STqKSZLSour6+5uLyiyA39aWS33dG3HWU9J88ytIdMCQZn8acjp7blLr1lWdbMlcYeWs5Hy2o9o8k12+0WLSz94YCKA0IsWd5U7LZ3BCkpjKC1gsZbKHJG5zifOgZ7es8rkMS7nqaaUTcVxShQJiNliRQs3fFIYSxl1pAlwX7bUVQ5IibOuz3FGNilGQhP157ZdWeObzf86N/+Lf7F6/+Zjx8/RUrBTEs2r98QQqCWDbL2CBXohhFhFPWF5twPlJTEDAoxzVQqJkbX0ywbfMxRIYAfoesxosGJgPNhknLKgDKGTCakzMlUzurJjPNdS1GW9O0RJwtmeonOFIXJuLl+xqHvsX3PYb/l5M9EpXl8c03oBcLAGC0+OGKYlqLrqkbkU7Q7WItKajqEkwGQRCkJPuDUBJfpZo6oLSKMSASxs6R80oy1vsWUOYvwiHP7lvY4QFnwcLdFkMhMRVNqPvngMXc68Yuf/D5KR9brCh0VF82S7//oe/zOb/8j7t++YTxvETKiq5pHT14wDCOf/8VXfP7VNzTzBVYr/vk//1/Zvdvg4tRqrDV4F3CAMtOMnhATJ4KJ9JykQolpq5pI75fxkKQElRCIaaswweGJcgLSCphEqAmiSMiU8HK6SZBhusFQJIKcCoq0Ytp+BD9RhFxOEoKYO4SbbhC+7fWdiA1LIckKw0c3CzanGmfPFAaSnLPIZlxeLKgXK1az9XTCngeyzGBHxzAOKCEYzy2n/sTQD+jKkBcrfADLBBY5bg/cvX1HnX/KYrUmxS3JDGilSMDd7QOzrGLgjBsb2t4SRUS7hEsjafB0xx1JSkIYmD+5RIlARk5IAyIIoggsqoLoFEM7MNgzpjhTLRf4pNifO0qfsN4ynjxJOBaLJX1yuODJmzUp9qQYuLh+TsoTKvTcvX43fff5h2hVsB12ZCKn23cYoTFVjcoSeI/XGSovqAuNyg3LaobXZrIwB0cEitqAyrFaU/iAqhWzeY10kmQkTVGjJVSrkl/5jR/y7ptbtFFs9wNyZqjMgtyULLMam3bcvk2MPvLkyZJMazIykhEobydLs/QIrcnyHJsmXZrWOYU2IEGYRJXlZKpCECetlpCMcfqBK+loh5HUd9ikOXvHcXdmKXoWzZw6v8BVGtPDYBMxtKS+J8iI7EeaGPn46RPWs++xWtYo37EuFjSzGTI4+rbFj5ayyVgs5xx3e86HM6WGH332KfOLJbKqGLuOr774gvu7DcvZAqMK3u3e8W5zmpbxCYiSBIyZROoS5QVejXjnyVMgZRKixKBQIVItFwRZ4SWM+4cpPyAmzZiIYkLvpUmFpibCGSiBDHoiUYnJiJ0m4QU+SbTIiX953iAkIBEifGv3wHeiCBhjyKqGWlt0M6PvDUUmaFuN1p6mXlCWOYpAXmiUEMwWj9FC0PdnTruBwWhqm3HuLOf2TLDHKbyiE/tti85ypJG05yPSVEhnwA3ImwUPrqOJkVevbilnmrbv8dKSp8hOJuaywA5nvDtRzpZIUbOq54Rw5viwp+8OmGxJNk9gPcknnE0M40imQKaKbFkRDp7TuSXJqRnKhTO9zamWK1Je82T9iLd3L/l7f//fY3Vxwf7ccrIDy0WDiy0uDvT3R2KUnGxLHg1t29GUipe3d6TO0axnhDHQVIq8yCfeXJ5jnZ/gGZVBaIEJGaVJZDXM5gaTaXyISPNeW54gKsWLxx/y/Olzxm3H//Ynf04fO5J3HMY9dVaS5zmf/uBDYu8xWU7nOmLmMGkKe2k9/W/tOAVoTIiYrELLhImBkBxCGZLKCSEgxfv8vBbkeloO58mgyoxqtcYHxb7tuapXSB1RWY4QNX2IjP5MXggafcPh/oDrtmA9FyLw93/9B7T7A00mKYolTy+e4ITisD9yPnU0sznXlyvmqzmf/+wLHl5tyOsSkQmG4wlJ4OnTC+aZpH/+hKePn3K9XPHmeOJnX3/BsB2oL+b8T//DHzBbFcg8Z331hDg49CwjDZbQHnizeUu+vsJEQVTw4bPPWD97ir5Y8i/+2e9yfHM7BZlEIKZp2MokCGJaR4hJnztRiomTuDQmYELxIxIqicm/mCJxmPRp7tv7h74bRWC0lt3mLceQKOo5zXw2WWFiYFaVZLVkNq8IVoII5EZikiHLgVTiK0BBEedUi0i+vcPen9mf9widY/Ka+awhq7Mp/+49I5B5GEeNcC1yOWczjKzOjnyhGB9O7PSA1IYYA/bccTzsGRDMqopgHUU+x9cnOqvRemLS7ceOoe3xo2DfHdCV4tP1jLLM8cGzfbgnKcmj1WqCqaqC1WKNiS3r2YyXX7U8+ewTEDnpPDJYxfawY70y3L56CVJxuXpMdT0nS4YgIt53+Land5q6yMnzQLNsGKfGQYwROGsnwu4YMVJTlZowWNqkcQliHwnBUaDp2j2ziyWiVXipefLoglgoXr26Je56TvsDHz1/RlMWtNGxkDm6Luidwo8D0QRybSAZUiYo6ob13JAVOUMcCDEiSQznjnD2kzzGdwxCIlJCKw1p+l9HAV0SxADl4yVCaYo8Y7YsGKKeuuNyhTqOpHkgho56VuHTyP7LFtsOmOMdqw9vePvmNU5GlpdrhrrGqZzT+YzzHUV1xXJ1Pck9IviUmElNs5oTfeJh84Dse66aAr0uua5zPnz0mGcffYLJYKZmFDcNf/6HX/P4o49olnNmy4y6npPGgO0DWrS8OYy8Omw47wdMoSnnN3zw/GMWl4/Y/u17/vd/9XuMDw+I2E29ClFMpCiRIIVpYMc0rRbkpEpDTEq8pD06BWyYEHcyRZJJmAhWfMe3Az54vvnp58jcsKwXXL94QUiOTEsKU0/4KSQmBuyYyGWNTwEZC0QuKZpEEmGaeaWmLitkkHSnI0VWI/I592/uqApNrUtae+LUHbm5vMTkidXqiuHYkukFqUhYPRJToj0NrGrBwYyIUXHuPfPGcb/dUa9KklgjyxmXTY099IhkcN2R8+5M0Ibt9kB3e2Yxa7i8eMIwjOjcMOqELyRhSBilUCqAHDn5jmKxZB8cj5qGjJGiadhlJQ8PJ87HDdXqGmtHXqw/5e68p1ISl87M55fkKXC1uCSTcrobDh4XehSJSgoGb4ljQOqKIYTJh5A5xiFRiAwywWmwVEqhnWdZGeZViQ6aWBtCnTAnTVktMUnhnMBqqGKJQJP0SB5LyjpDyYI88xS5QcuCajZDFJoslfi2Q/puEmSoSb4SQ2IQ77Xc3XnCZhmNKRQxRQqTTcDO4Mn1tP2wo2WeS+xoKEyGkZcczwfuNw+I4YA479hvHzhvtpRriR0CqtDI2LDbj5yHB6LIsMExDB0WwWATAwonBcMYuMgLOuPYfH0iG3rEPCJY0o8jYz4g0fSbe5qLEuM7fv2zDzGriuvLSw7tHctiTi4i6rLCiIK4iPzJ7/5LuhjJFfxi2GOGDc9e/Bqf/uqP+PHP/jXu/i1CTjcWwjPN/zG9TzMymZWY9gZKxfc2rERy70NNepLduPda82jkZCD6/zk2/P/yJYi9pphpZmXOolSUZYOqa9ToUIXCeI2ZV9N3Mol6f8rukmMAJBqTOqztJnx5ECzqNbP5nM4HxuPAOERO4+R8r4yApDje3yMKw6IuOW97uMogb8jXnnm/JNiOtWoYygGhSk7HDRfzK4IVdOlMls8pqwJE4NAPOK0oFwt6P1LOCvzoCb0gDI66LlDGUBlDiHBhSnKh6A4tspgG4Xy9pprX9INjGCzazPnBR5/xenPPNz4QrSKvlmze7DjKPfPZBblc4K8bFnYk2sgYO3Rl8HFK4cUYMEahjKBqDM0i47x3jEEgU453PWo1YdLoFDpleA91I5FCcTyfyaXhB7/0KZur53TJ8UG5IGYFN2WOPR1ISVINHYXWxKag8IK6yUkhoJOGQuKlJxealNe40WFTT5QweI/tAoMQiCzDDj0hSVSqKBUoAh7NvhsRqcN4SdZkSJ9xTh7yDq0UWZDkeY00kd1pzzd/8RN8AZny3H7zjlAZmpsnlLMZi/kSu5W8uX1J8B6ZAqf9lt2pZ7ffI2VAZQmTFdzfHfj8q2+4qgs+XKwZrGBmMt4+3PLFz+9YFCusPfL6a8uv/OgpZlHTlGv+1R98weGN5emjJ4jxTNPMqLVjvz8gjCHi6buO0E+/o+/96J/wq7/2d/nD13fY7o7oBbw/BJRCIIRiYhbHaeBKjZNTpFjjIemJZuSmHEGMEqnApzA1MH3L6PtOFAHvLIMcycccJQOL+RySIhLonUO3ilxb1tUMcoOROUYrPAo3emJMFLlGoAl7w3LRsF0t6IMjq6uJtScnxv1xf89itmS9muOVQwnDsD8hjeVse8xwgY6JImWIpWG8HUFpVCaoTMKnjN4NGAN1WWOzmugiF48WuPuAHAvy1YK2HUnOQjtwOuyQIqGzSF6UlGnF8TTy6Go2JclUYHCeayKiLMhcwSg9CoWwPcHMWV8tOfdr7r56hfrkY2KCRhYMbsCee6oLiesGgrQINVLSMJxPk82mNJiy5HjyqNFywYJOaKpiOiTc7AIZAikbungGJYkjdDYh3ImHwz3Z6orH9ROafMDkGUkkUlLIokRmNef2RF7kEEBnOYsyR+XZxPD3ARsHMmOICFI8IYyikDXJe2z0hNkUmy1URpUJnBtRuqCelSBysgijdDjnIEWSrKEIBJWTx8hpPBE3A3FeIkPGu3d3HN6d0GtBIXLMekZdGpRSeN8zr28Yw4yfftGSGUldVwzDwGG/oVaamJV03Ymu79jvBx52O17cfA9tMtQYqBcXvL77mrebLd/7eEZdCl7tMz5azKiuL7nfbWlmM/xpy6ouud/05JdznklDXjbEGElhgqzGbMY4WuZk/NKz7/HFBx/w5vMtMcZJqiMETgSSnLYBmkl1Fv1kNooRAoHMFEgRp/Sg11NUOKVJXPPXEAO+E0XA2ZF5LqgWoHPN3eGOLEmWuqFsFpi8IS80YykmgYNwJCDDMNiROHZIDZkQxFzjlWV9sUIJQ15oZIj4omY0kcV8yXo2J0bH6Wz5+JNP6c57jqcTvYiMr29p6kvKYJm/eMRoCk77LVF6Xm/vKfKK2O6pZxXPrms2+69xm5HVaoaqMozWVEVOZgzv7gRt5zicHwgxMK8KfDsy5o6qLkm1Yd9vWeuaIXTIdEkKA104c7mecaqWVE2GAdbXLzhsWuRTiUiSqBzzZk3f9mzTGWlPWAfBHpgv59jec+hHrudzVILjfs/5fMA0FdYGslzjhMF1jixKxKiwfmCQHU2zROsak2VoIamvPGEUxMJwZWq8jfQM+EyRA7KBlNfsHloyCVnISaXC2zM+QSg0SRWoCEZLvM6J3pHHhKwasiZSxYS1U7plVk2D5Nx6MimntFthWNZLVDAQTuxPDlNJ5oXGnxx5rlDXF2wfTrR2x8svf05WVgTbsRdnHlUZwQfaU8++P5Hpmu1+y77tETYwPgssLwssCaRCZRqC47TZcPvzL9hvz6RcUi+WzLMaVSv8q0hJhlQRMkOjYXn9nF5YXj+8IlMOv+9oxxNDN3DojtQ5vPj4GZ//7GsUhlJVqKLhrCK7t/c8e/KE3/itf8jty68R6UCUUzJRxwkoKuR7V6dX6BhQXkG0CKXw3pPyQIoGpSJ5VDjJJJ2Rk7/ir3p9J4qAVpoXT6549PFHxKipZwWXqxqVLel9hDGbOPzjmSblpDLHZgWF76lMhqguQAVsfybmkrzMWUcIUZGUw5815dUaupGL5yu6/kB7bpkvrjjtNuwfNpTzGqUaxnhHoSOPX3zA+bxFSE97PDKEyP7dW7pupFlVuNHT7zucylHjlFoUyXJqW+ZlQ97UdPst527P0B3Zv/05cfB88tmPuPjgKVmWUxcZzjt+8uYbtPfsRs+HyxVjDqdhwcXqktvjGy7yOa9fP3BxteB+uyfYyJv2JeVDQalXqDqxXlxw4ETrLW0aWRQlK7UiZIJeBHCKWbOibDJs9JxaSzdIMhvISNjBMdaeLM4JIVBWGS6r8NqTdzPMQnM+H0jNjIWJBCmgU9SXOf3guKhn6KixfaCeGY7DSFXM8KGnTNO+/tSeGQUTEdqA8YqgNGFokSGhhcc7wf5+8jPU2pCrnKgTKEEaHc5ZpIIih6AKogdZGuaqpouCxarBvjrS9pZx98BQSWo82+2O09uWPkaEjDy9eU7rPUO74fh6x5frOR+YzwgxIWLAR8HF1SXb7ZHXmzt0cnSHE/28wRY9Hy5/wPL6ERmQVxcMu3s+WT3Fq4HzfgMPntnjS+IxMPpEnVXsj2fSUvAf/uPf5uWvvGLsHaos+KOffsXLN99wM/vXfNZYXjx5ykc//CFf/fEfknr//hBQTE1NNkwWJZ0msK1wRKmJKSIiMEpKLfCSSVXmAk7OiX7k23LD34kiIJXk0cefcLkuQeYsLq6YGcPJ9miZ0NHj5UiTLZBJMjpLIc50Mkz6b52jY8boMnR0GJ/IREZeGlpvkSrhQ2TsLZXxeKM5x57s1NG6M/254+giVzNHpipuv3zFo/kMIQXxdKCqGhoVqW8usa/vOR5GPvv4Ejs43h0eOO42lLqkmTfky4z7fUfhGs77A932gda2xL7DB3j17gussDT59/n8my+ZL+asFjkyNMwWFV8+3PHhs6fQ7mnmc1Yhw2UJ/9Dy/MMP6PYFTrWIo+EQOhKRp80nNNUCnyeyYuooPHcducqRMRGip217+naAizlxrBkx2OjAR6SSKCOY5xnCGqyQiNxMvRZmmJyNUZDOJwYxrUJktUTEE1JWmPmSAkUXRopmBjIwkxFnLb53CJ0j8kBVZWReE5yjcyNxlPgkpjy864giYt2IlCWt1fTDntXcI50njTN8HWiaEoxB2pLusMWZOeWyQAhFlUVicIy7A7K29PQU4orFynB13eBcT7s90GQZu8MbHg4D9n1suD9b2vsDIkZUphE4bO95d3vA7npyZzhtTvw8eC5XFY+ef8hqeY0Sge5w4ou3t+S+5oPHFygJs6amLGYUP8zot3eIoiHJgbleoipN1jxhWc7phsSRnp/8yVcct6/I2s+oHzc8/fAzXv7iK6KNKNVPCUAnkMqR3ncqSp0REITQo2JBLKYOx14olPAYPFZHtD8SoubbcsPfiSKQZZrudORoEut1TbsduBt3FFma9GAJBDniYTftKZWiKQuKumAMmij8tCwNCpJjjIHUM90QjBZKybNmxTkHJ0bsw0i3d3T9K3Zdz4sPHnN6eMeXD5HF5Q1PH13inaSZzzmffjZVX52jOstZDVxWc/KypO8jtjvT9Z5YjEhX4s+BUo3Y0ZJ0AK2wrWcMEeUl92827PsRpxNPzo9RjyxUJTp4ZG943Fzyzf0DH15/wtuzpcoLWuu4fDZjbOHqWaJtJTKt8RTY/ohPis1wRxgFUglEcKTOkjU143DGDo7Dw57T/jTB85KhLAVt13MePXpd08dAuQ/MZg2lVpMV6LxDrQImSkQJ81AzhIFkDarwqLrBFIpMKeQJ6kUFfUcaDFFq2gh5maGLhM5nlLEhM5q2tYj9Hd5vJ2Jv0SDjDK8j+nimGx3hcMSnRHfXoswE+2wPR8zNU0QuUCFRFBqMoxUZeRcpGtjtR4bM4W4HBptR6QRW8nY7cDwMxCCQ9YLT7kx0Iz4Krp4/Zfl4yWBGjDakMDWMSaU5DmdGNzJfL/Ax4WTEKM3QHYiHM6rQnO1ACBWjCXgVyYNgvbpBzDQIRZnWjNHgt1uMSyyExDuFTXuSSPzg2TPE0fPq56853r3lyXqJ0dNtiJSWEATST41NRIVQIGRE2B4lJUpnSD/Ja2QKpOQJGtxgpqImAyKKb+sk/m4UgeATm/s9P/7znxBdYNHM8FqSZxJ5lBTzivW6QlUF1eKK1WJJJyMhg0KXtOcOFRRYx5gsIQYehrec7JlCKkgKREXKArvjHd3DA7YbqIxhWWZsN3csLy5oD46EZ3PaMtgTuz89MfrAxToyY86nn/2AxfaGmFlev/sGZM2YPBcvHmOKAut7IgPdoNjf3mGUoFDA6Nn3HRJPk89Y+ZzTZssf393xbHxBljd87/lHnHrLL/3gY5Z9R4iBTbtDNjc8um6Y54rf+7M/Y1YoitGwMCt+/y/+nMKUKLlHhALVlKQe5lXJ9TIjiYjOamLqmF3OuXx8iY2Rs536EBojkFVkP7QTDVdoTtaRk2NvN6Ai2pYslyWZzfHrSCVzbkTOISumXvghkNU5qQ400XDEsz08sD8MZDOFqUvylJBJIY1G5pr1rKZYzZDuMbYbsKPnfD7SdZY2pkkrX2aEruV152jmmioqcqPZ9S0MgiqOzPIr7HAmyzQpCobdSF7n9F9vOB82VFmguc4ZY2B8d4swBbN6TWE184+vqDykIMmMILgI3lJUGavFkvWLj0l1xu/98c9QKrB4tOTZfI7WEpkrcqkYsjlueMesmOPmlsN54NUXrzgc9tw0lzy822HqjMPrB8gUFSXn3YHl9XPs5jVZaaiSR2SJJ48f8eM/fc0f/fjHfH3ckS6vSEoSUyCLES0holAyvCcSG6JOk3zWjkRR4mMgxghZpBwmzqDVaYpn5/Bt4oHvRBEwWc6fff5nNGpJ1tRsTh1iFKQLOH3zipnOebVYkeeGMjMsHj1m9fiai2XNslpQNnMQEAykIWDPe4Tdo0eFTRF/7GlTZH/3hvPmSFAWkSvO5wNtcnxy/YK6yGjjSNvucKKi8JfMqyV+2JFIJGG5uJzx6PH3+fLdN7x7+xVNSFRmTa4NzawBW7Hbn0gI0CUxOkbpGWVERke0GfkiJysNd6/v+OhXPmWIAjUKnn3wKcQeVQnuX+/Jaol0DrffcecOzB+94LObj7nb3HIcj8yaJb/8/BGhVGQyJ6WG4DxFlTGoyLzK6XtLPw40ZY1WNdvdhqooqZNkbHtkXlJpcK0j2gF9XRCFQWURVXRs25FPn1yyCQOXtKzNFdEXmPmcWTwyFA3GWoSRSJtwwvHq4Y5wt+FusMx8jdIFojDkakDkk7RDdBbjPZ3vGOMBG3JOo+U4WI5Hi64ClcjYxEBZFWRCYI9n9AdLGhuhKdi9O9O718wzwzJeYgdPi6Q9vOOLn3+BWiSGrefkDI1t6YXGiBat5jSPF2SFQQyCej6nNlAUObIoiUKSVQVZPqUQw5CYFQtk7PGqJis1laoZTj29jpyjx547rl5cM+4lbe8Z/ECcw/jNQJYVNMsSawW1aqA68fr4wF0fmJ0ts2WkWS6QcYecr2mePaOZ39CPA6VSYDLCMJmGHRGhNESBSRM+0OjpdkxFCyiiEohRvB/8iSQSo4NcqG8df9+JIjA6y4snH5DmGZv9PdXJYE3H5usTVxcL5tWMEDzCGKpqxrA7ckya4AN9HViHxCyvJt32/kR/aDlGw0kOFLFGyZy7d3fszg+83N6SBcd+OHHetJTLCx7kgbqe8eTxZ3zxiz9FDwXHoaNqFN4YhmQ5bUd6XXNxoanLNTKsiCS8OiNDyXkPRV7h/EjoRtr9meNuh/UtLnYsiwJ5vSQGiSdSXWsuFit+6dPP+Jc//Sn785bGVhzmHRlL5NDx0bMP+KOf/S88Ns/5+vxA+/pEdRG53YwsV57F42foGBmiwMoedRbsBstsXjFISb1c0+229EmQFRmLWUPyjjenLbXLyKsZvYyErsVHgTwY8nVN24+c+5bj0VIYzbPcsGsD+zYwy8xk8LUlTZdQpcaoEi8OHE8d2ze30FmWqiQZwWnw5KIHqZFjBNOTokMUOcEl2pOjqBuePHlMvttQFYH1LCe5kvm6mqAk3nEyA7YfkSYjPAzITOHbA7e2oJV3uM5S1Anveu7efMmy0Gyk5CLbcbvrePLph4yd4+hO3NglPghUUbNYy8nYKx2ohB4cw37DUSm2NjCMPSr3BB+Z5Tmz2RyrDLNswfDQIwRcX824rNbsT0e+2HWs1Yph6ChTpPGezOUM6yuyynLcTb7F548u2O/OCHcktj3ORhAn5nXF4tGCL7/cE63HDx4tMlKCUkpiSPTSTvzDECbbsc4gRvx7Nb0THiPAywRWoUnE8O13hN+JIqClwooMd3DMdcMu9sybmo/IOLme2909RVHjxxNf7b7igoZLNyJCi2ty3HDk1MwZu4F23xKl4v5uQxQeOQdVO2zc8PKbWx4Ot6TekgvHxeWKwWh0o3l3PqCGE15YtB45tBo5UzxePkUXhrt8x/Yv3uI2Bbu4ZTe05GEktRmLOOPJJ08o6pqqWeP7HqJi9B2ZT+T1NReX1+RZxv7Us3m343L9iF+8uqO5+Yjf+Z1/zOEX93w9vKQalswXDXXzgr7b0pRPuH9zZtb3LOY3rGZLvv/8GpF53nzzklHAbFbx8ovXvHj2gqos6V8eiTNPRFAKg9KScfDoImNezHnx0QuCD4xGcu4eppbXfctp3xPlPT/++VuqleF6vuKrl28oCsHFxSOeXlzRi4zh3GIWBobIAsV+t8fpgnNnmdcvOIUdGsesyRlFxNszSjcMg8EXlrxSNMEwa+Y0ak7SEasEJixZ5SXD6UyQZ8qLJflZ40RH1jRwGHDHwIYzJQLZdrj+nnexIPNnMjMjSYM/WoJQtIPlflA8vXlGnUOhb9AyUq0LlJe8+eoVolBcXiwgi3hvKYpErxKVlty9vpsP/KYAACAASURBVCOXiuv5ClXP6Ae4QnGzWtGnntnVSCPWvPz6S9quYnYxo9rtuXr8AWQjIbullZahXNMdHugeTlwWGVfFSAoLRgaycs1qcck//LcU/+Bv/zovtwM/u33Dsqn5O7/5W/zk9/9H7rf35GR45/ExTHgy7UhBEYKk0A6vDGF0U4FlAqSIICnyhPfZ1GD0Lf0D34kiIERi9+qW1ccfUCYYzYHRFfR6pFQNsffMdMM4aqwYCVrS2Ui/G8mHloNvqbsOOwz07ciiLNiPDxwOO/KHhmV5QxsiTkce1TUPLnEaPWO/ZaYF77Yj8/ULnpYVIb9ivm4w1RFvJG0MLIvAZbtCXHeEY4+whs8+/IRXX7ziLAYWFXQCrt53li0+fE5eSJTuQefM1yuur9YUuuD1X/wcu7fs7wY+/fUfwNDzxU+/oUYwdJE/+L3/jt/89/8D8qXHecfnL1/x6eMbnNJc35SMg8IXGnc8cvPxY04PB6p8ydUzS1QeXRjqJwsGG5gXEpflJJUxs5LgR5Z1Q143HLselSzkcxaPKjZ5y3F3z5vdlqIUjHdbqnxBoGXcB+6LNU3Xo01PmUfC/YCazdhE0FLjxo4seMayQ/YghSElQxEMCItPiVg5Yhjxe8lgcvR8RGuND5LDvseNFmePEGGIFrvRYLco5+C4wC4zTG6Zuzmbh3cct1v66FH2G3btAWkiN06wt1su64bLWcl1tmRRa4JIrBcL4tjBCF3miaWlzEpMIVEx0PUDqakQNnI+Hvn6qzcYvSBb9ITUoqXBm0CsS0qRMbYniscjl4cbXu13LG80y/Waau0ZDhlv8pHKF6j2jrKu8W0JMhB9Yr97B/MD8/ISHQdkKlg0S2TIeHW7oSgXEHrypkZtHqYMAAmZTTh0kyTBZCQT0b3DpcnhWRQ5fYhI5abIcTITLzNP0P/V4+//cxEQQnyPyS3wl6+Pgf8cWAL/KXD//vP/LKX0z/66Zw3WIpbQ5FBWktgtEHPNzFa04xmZF9jujPeeSi5Q3Znbh5+SzxourlaU+jFhHCnyxOB6fnb7ljxG5KA5nR5goVFSczO74HU7spjnuONA1zm0iTz/6JLKZNxFwcfrjxm7b6iKOf8Hc2/Oc82WJWg9e4z5DO95h2+8Y93MqsoauhqaaqkRbSDUagcJDwyQwMHBw8JuD4EwMfgBmDh4eAiDoaq66c7K6X733rzf/E5njHlPGCerVSpVNqCuknI7R7FDEVbEOrHXXut58nzF6f6R7thz/fwZpjck7bCVxGZLvvwdy3h6oPOOOAWiUKzXC8q84PnmCaMLjAp++MVvsyoMIQiOI1yqRPu4JxMwiY7bdx+5zm/Ir9b4q4bd9h1PX2w4+pzf+b2v+PT6mr49susjcm5ZPVkTM0koJO3O0ZQVuX5GoqV1Cq9WoG6ZXaIUGVJ6kslYVUvK2qKrjLK5oJ+2lK5kOg3c7e+YfH92PM4HDu3I/fGOT/RLHnzLpvwILyqEgzuRc1NnTMaSIxC2JLeOaVdwoTVTtSUmx7vDliuTkAsNbUK3GSlGdJEhxInyaJBSMTtHHAfEkM5orXSm8VRGAEtmczqz+tvA7rjDrlfYbEl19QVP9MDxmOOcZjh85N32n1MdEsO8Ra4azGKNiFBrQyMsalXQHyNFXaDW16wqQ3sacFLjo6XICpKSnMaZ+fge4RJ3OwthYvnUnWUm3uF1hnORj3/e8+VXG+53J95/85FPnze8eX/HZ5cvabpLjBroTca723csyoxAxU9uH3lxscKqG7YpY5SarGsZQ6CbJBeLJdeXT/jz24FBLxFeUaTAqAKzF5AJZqFIPoERDKVGRE+aEyEZVCHBVTjdY5PEz45g/xZyAimlnwN/51cBQQHvgP8J+E+B/y6l9N/8f72XFIKN3nB0M2UoeXAt2auBN+HE/ewp+4llU0FWnMmsKuP66SVGQDokdv0H+jlyGnr2xyM+CerSkOU1UltO/QNdnMmyxGVZMCCp54iPmlzC7t0JfzlTNA33p19SyAUuniiVY+gmvMg4HkaWdUPX1VRxJssLXFDMeeD3Pv8BOjPMfiQtN4yTICuv+MEnDaJUnB5bHrcjxbLg+ZMVSn7G1+M3TIeZZnHJzWbJ3eMDn3aav/9Hf8QlG+RxjX1uKFVg+/6WeLmke/ORz754jlWJbz5+xCfBVSXJmoowDuzbHOLA9vSGojRE0+NlYNlsUMWSaXDYaBHRQnBUuUEqhzEL/qj5Y26bb9l2Oz7kJZ/8cIkcOuyl4mJ4guvvmO5n5twQmo60aDCjRC8UHHv2eWK0ku++/hnjMDHcH6mbko9KYzY5eSUQ04jOJddWsygbUgq4ccJqwclCzDIKtcAMAW8sIXTI04jOaoS1LCdNuTiLOOdQgRDosqaUjpsixy4t/9uf/i+c/CNFseRpc4WlZzdJbr95xzKT/MN/+A8oFoGuP9ONPny4pZWS9uHAMtd8mI7cfPoJH+/f8fbDez55+SmFbXBjgWnW5EOBv7/FiYnm+QXt/oEUn/LZsy+Zm4n9bo9/9HT1HikyRGb4tG6oNpZpP/Py5oLy6w9IMVI1K1KxoH18YEiOlZRsp8Dh2HM69Tz/4jM+f/k5/+zVj+nOpFF0SiT/K12a1vg5kOHpS4mdE5iE9zMRSSYzYprwdX6mFv1NB4G/Mv5d4JuU0vfiX9Gy+OuGsZZxClgvePt4Yvv2PQZDUeS8XEXiaoWXGZqJ3GX45Bn6E0NmECpDcDYY56uMy3xFVi6IBOaDQ+eK4jLnc2V5vNvxzfgRg0aZGvdhz65JXBQVIrvh6uYT6rpAtZ7+NNH1muz6guvCIEqFmAz16tye631kHHvyosJ1R5riOVeXS4bBMY0Dy8WKeIDhwwmzqkhG4MgYtyP+mMhtxZwOfPj2gR/94b+FyxzDVOBGRdAHXiwuyaKgdDmdHZGTwxB48/Y1KVvx/NlTSD2Hk8Dkniu9oRCeh2liGSXzPKK7miF1hH7m5cuCQmnkMCLjeSt1TmCsIRz22Fxzcb3BTzXx+IEwe5KVLMyKyU2I5TXDPNEOtzyzTzk+ROqsJ/oEyxXFx5596vGnE7ev3xNKxUXZYOsFbpwIKpLLyLpasSovyLVEmgoqxXEaybwk9CdgZtt2CCSzjEzdzHpuKAtHaBQ2LLFmj7ArjrMmTBP93NGU8PZxSyorDj/7mqbMWP7gM/y2Z3q4JexbtiIxjBMiwNVqzXZ6RJYFjCPBBubQcrx3TAm+ff2eFGauFysWl1d4YzFxwhvBMAbswlDYCipNFQ12A0cfOA4OlTsYRzbrmgU1KUvY24mTnNh1ke3Y421GFSL90FE0Sy4ua+LUsphaPogZayuypJinCYI513/ISNIKg8d5RZgi0npiEsguETOJ7CVaFIQi4QeQsoRBkufnRru/bvxNBYH/EPgf/9LxfyGE+E+APwH+y3+VggyAFOndnsMHzXFyFLrAZJJD8lwWl9RJcYozyALnHbrJWVBDrvBOnmksNhLjTKrOqM/gYXmhaa6v0IuSx9s9Wz5S5TkiOPLKM/WGfi6J/oFSPMEHR7freP75pxTOksKK7vY9qdIc7h2n6cCzl18iheAQPe0UuakMzjm++/ATNjynypc47+n2J6b2kSkF9NwwdC1aWMq8oN7ULGpg1+Od4+B3PH+x4fFREfsj3iruHz+SLz5F6sBqtWT3uKO5uWEeRnQaKKxiGjNIB447eBhbnl5+hWCHzAOcZk7jCUSgVBlTt6drPV//5Oe8/+XP0cLw8nd/yGb9FLXIeLn+FOkk8v4ddqMYDhNxBy7fUq1rZLxGrk9kDwtEEtR1iUgJ70EFiSkk6hjZ3nZMHpqQ0T3MGD2yLhfMwpObnHHyzGpGmhKbn5Naq6KkKXJmt2B7OoEKtCeHazuMMJy6D0y+QooBlMSEDCFhoytO40iz0rx/dUdlM1Su+PTv/YCb+hLRK9pxS9ICypmNXvN421IuBN1eIAsoyoYhJrKyYKVWVIuMj8PIad9jqg3RB6wKlNEQFRSZhUxiNND31MUFwRSkzGH7muXqSDsvkasLykoxe89xvGd5WdGkhlPrEXWN2iWmjUNKw/7+ljrXJCfpksU5iRQRU5csry/J6py+75BeQB4ZpEJqSYoCnCbogEkGnMdrTZjPwnSRHCYldJbwk/21r9/fhIvQAv8+8F/9auq/B/4J50LlfwL8t8B/9tdc9y/lI0VZYVXgi2cbXABTPGceZ2apCIcJrydyveJCFmxeWkYlSUmT5hnpEiqBsJqUFP3UndfAqiSKwDw6xu0jh/4DDAJd1ExDoNKCy4uKfb8lu3xJnW8odM6z8pK7h4kyzIw8ErTk8dCRz55RJx52bwBD195SNtfsU0fRa3xynG53+HJkHCWztHgfiELwy7e/oFCRp9efM4meMGmuq4Kvv3nPYZjo2473i0fWF5ecdhOrK0u2WnL/6jW//Xf/gO3jHWlU3L+/I2tmWp/Y7n5MVtTI3JKpkrWBw7tXPLm8hpuGPp95PG6xWrI9nvj5q1/wi5/+jK++/B1+9O/8I95//wr0mXm/NDli8tQI4vWC7U9e8/7VW1IVePOnLb/7o6+4+lJh2orLZc5sArmKtFOOjYq2HYlVQdUYvvz9r2jblunYA4nOw8IErICHbmChNePJ4PMOM070gLIzi6JAWUEVK5TSXC0d7bDmeBqYVUEeYT8eCEPGVCQGd0fV5YhFTmU0WXNPZpbYIWEzhRWR1bJk6CNDlkGX8+7hjuXdW57d/BEhCcTsCGni2J44zhPLyxWL1YL923vm7sj6coMjcP/4kW24Z/r4wNVVxubymtm959VbwY+++py7j6+oENxFxbg78PzlFUWhsZlELpeMO8PJ9ZQZjN2WJltQfJnTDQNGel4/HPl4/8iLz1/w7nCPzC1CJiosFzcvmZMhBYHUmpgSMs4IAjIpMpsQ6vz1AhIZRkIjSRGMzhhFi9AN4tcUCv2NBAHgHwN/llK6BfiL31+96P8D8D//dRf9ZfnI1fWT9Mkf/IDaZhxdgT8ckCbD+B3LL2/IvaELAAlyzaUVTDGAXGNUhVWKMI10dIjeomKgS46wH/GT56AcQhVcPtGcHiTF2hNagcgT1xdXqLQhu15TlJJ802C//gbnDXoluKqfYYYD5VWGigEdJdME89BTZg3G5IyHA5nOQMLsJ4JUDFqglUB7wZVZ0g49716/Y32xRmYdrk1crlZcX2tebD7j4A7kF5e8+ELz8PqWahaU1vDPf/Kah/Z77l8fUOsS833i4eF7ljeX3Gw+Y7nIcfpcFblaFEyhZz7OhBiZxpHZGKb+yM9eveNx29EsPlAsa65zxbDtcIuRWZc8Tnt2ExTDjFQZ68+ecHr/jrwu6eaSZjsjUDghyY8zj+xIUiOTIq836HHmoc0pqiXH48x6o6kWNTpYiqWlriwx5hyGid3pSHzsKGtFXpRol9F5D0lR5oJsEiRfUi4CUSUOLp3JyeqKZQPDySGzhnk64YYaVSRePLniw/stuoZ0kOz8PeUiZ5Xn9NNZ7y2rBXMKjLRM41ncUctA4SdOhyO9kbTas+33aJWRa0k73sNUEZIizT2PD5EqP+EpsUFwaC3tx7fEF5eEdqBSErO6xEvBYauY4nvM4HjydE07Rg7DwMtmQ3Z9ySpMnO5PlMETQmTqO8pY4vMFRZEzjVu2H94ipwGpMyKONM0IkZDJ4JNjFudGquQD2mqSiAgfSN4SGJGywnYtk/1b2B34S+M/4i8tBf5COvKrw/8A+PH/6x2EpIg1RmmysUNpQxKKfPECkXKyWqFRhDCT/Mg8WUxeg9WUK0hzQmtBQYPPJT7NmPaRbZ5QBJpJoxY1SSt0mHFzh78cCG7JShnujkeKbGCOa7Iso9eW/f6Oz2++4IvPLnkaN/TdwIfDQOkVxbVhMo6UWo77PXe7GRknnjTXrF+uUX8Bh7QZs/BMOIyROJ8osoxpmPGu56vf/n3evHuLXnvqrqTIJd4nskXB/ekRzIqf/vTPWEyRw6R59/U33NSGu7sDF/3E/b7lj//wj8lj4nA48ij3yKymWlVYpYlTJBM9t+/eMbz/mjSNdDsF4wXt4LnrTsja8mS5IhqBnDsmInmVYVLNMV+S65khvePt9yXPP/mUlDSzysjnGWE0Q2/ZGEdXVOjVQPz4yItPasbRg5XgPftDQKoMU55NPZMPJBfZHSbMmCgLx5VuMGXO7AJGQSSxPzniPCPGsygkjEf2WiJmRTQBWVji9sDD6T1mecPlasmPJ0lZGUopKW3OXZeQQlEvVvjtIxeFZT71xMISxogqF4ynSO5zVuY59WJDen1kdg5iIsZAVknMVHPx5Q1quENPDooe2Vtc6lk2NYLIcNpz9aPfIk+OdioZQs+xb3mpFIOPzMOOdXnJSQ4U2hCHkfYw4EVGXglS0OzGI1IWjKGgXEnMqiC7bJgeHjHBn3mMyeFTIDMRLxUiTqSYUE7j8jOlW6aZKBQuRUKmUIPi19UN/03IR/494D//S9P/tRDi73BeDvzyr5z7a0cMno8uUL1rEVFg8gqTQfSJal3g5umsiMpyhFogjCeOM2bOiFtJcDN2kRFNAuGoy5Jqr1mWFZ2WfPjlHT5K8oVl80Qjhpn21OG++Y5BOz599pLnT59j8jW+HbBCst48o8hW/K9/9idIbwnBk6zlwzRz/5N3LGzF6AemKCkrSykNy2cFRa1xQRA9eHciQ3I3Hxj3Jy4XV7TTR1rv0Srnl7evaRrD228+MKnEi+y3EEPLT//8F8zhyI+/+RkX5RMuny55/vknfLb4ktWsefFJzS9ef82wj7x7/4arZ3+PYllyv39P2cM4H6mzjLY7cHt34vs//wU//dP/E1tK/PGWx2/+lDYZ/vjf/sdcrJ4i8mu8dKTxI/M4EskpL3N+f/kF//zVj2k/dDy52TB2LWruCXpD8ckSrx12rNhOJ7KgKQvD1c1z3nX3tDsHYSBfSuQs+OXHljR5jIvnBOPkaXLPptnQ5hbneppmgclrIpFoQI4Tq4sLri08tCfEsaDrW4Z5JPlIytesnkjk+yXvH470/si6lgwy4e4HqkVG3lc4pWmc5N40bNsW/7gjNw2LzZJpPDDJgFlW6DzwsP2eu+0HokkIk/PZk5fUFxl32xk9Kg7tiapu0KpgeVOyuLRMB8XyakFOw+3Xb+meLpA+J+Sei2bNVOSYwYETmCLQt55x/xEvoHpSUboTOkrqRcWPvvqS7949MB9HOEw8/OQ7jvf3pBjw0WC0RniPNAonEtIFEAKRK9IUEBHkpAkxkWwkFwY1JXqb/nZ6B1JKHbD5K3P/8f/f+3g38eH2DfmhY+4CF8+eMbR7rMm4co5laYnGsODsiJucYQqewZ+oTEa2KAhzpIgKTMnUg84lEk3jJ4anF/jjyKK+YmkKpsUE4o7PX37KTnSkLmDKDUYWCAHMHcvFNf7YczzNaD/S09KMOfePM9YEpjSj5sCmNBTLNUIYwmTBGeqLFe44cBpHDmkghoFh6vj6Q8/T4YrsusYITUgaVazIr5bItmOKA3oOvPn+l2znR4TIKaqc9eUNqQ88+8GnZCKByLgeW+73B/7kJ7+gI/J7v/UVl01Du3eMMZAZRdKa/f2JNhgOOsftjxy452q14Mn6BUopVAZKO/pDx/4wEpwn+gPbfcd1vWJVFKjZ4rt79M1n5EWFSAI3zFhVU1WJLtdo4Zn3I5mVlIcCF3pSNdJNkcLnFFIRZKAdR169/ZYhTDy9vETnDUuhafczE0eaaWJRNQSvGcZAtC1ruaIxF7jFnhFPbRQFlmP0HI8jxeaa6ef/gnjcMStH+/Z76uw5h/4Mlc2ior64JEwD3RCp6ohj4LCb2PoJvdQ8qRcUNkMUa5J9wzz1ZOr8tTP3gTAeKOsl1+IZc4SC8rxFt0vMwWPTgmzhuR+PBAH104pxu2dqe4gZi4sCqpKBPY8/e0/xaUG1XtMPE816Q3NVUoQJRU8eweUlH7Yf2R0eznxhGZE6ov1AIuDDmekQRDyzCMeIFwozK6gMfprPfXPe4yUo0m82XiwkQTZPiBgxmWX4+JopCvQiZzjes99bCpuR1gt8aEFLFkVFXq/PhhqpUeVZ0hhSQocR4SOFUHhyrpcNlAGyBVkjqMLIrj2Sffacp/sT1TPDKquY9wPdlBBmQz8N2GVOrRMuCHQ69wWI3ONsSaYq8txxeXMJiyWybQkEEg4bO6KITPPM8XQityW+dIz7A7vDET0LlHpksVkwP/b4/cinP/iS27ff8vbNHfeP77GN4cmLp9w8u8Bu1pSywYoKmWucDnz6b/wel2/e8fW33/HzV79kjeT5sysM5+aWcrXkaXXNeNkjZMG2bXl8eMvT62u++OwLnmzWXF0/R1jFrt8xu7MjsR9PJHdE2Zyx3/Lstz7FjpaH7TtikISQ0SwkeVbR9z0zE0at2c8P5GVNM9W4daA9vuFxJ9ER7sOWIp4Lre7uH/n4/RtC8qhhwkSBX11y+fyaXJcoo5lcj4zhLDpxA53NqRpJ3FtsVmEywS++e0ME2nZmMJ44SE6PB+wsSHrJcTxx2j/wsR8Jc8/f/eKaoV0R2WJMoqg1x+6R3WNHXS8ZhWDIPSFumbsDRcowWUlWGjJdIE8dp23HzcWSZ5drstUCNczk1xtO71/x7e07jDZcFAYfDfksGFTBduioOdJcrrBaUqpLLuwDJj/3c5yOe4rKUghFkookCtRa49uZn//fX/P44T36VxanFGDAkAmFNY5RQAqalEmyOeHUSBAK+g5lJD4ZYnKEPEe2f/t1Av9aI8XI7tX3LC6f0SwlOlzj5yNKHVHuAq0OiLQghZymXNFcbjAITF5QLBq0heTTWdOcIsbUjLNjPE0oITBJ4SUURUZT5wS/4Iem4cObbzmWNReXG4ZTZHO1ZtVEDm1HUQa69sRwPEJWkCXHoljifc/QDTz9Qcnl1TM617LdPpDLisZq5s5xVBOGs9IrhIz97QPTfNaO9e2WjJl6VXI4bCksICNvvn/Nbbtn73sunjzjciOpFs9QoiAeWubCcMJjhoGquKCaK3wDNy8M+fYDX//8NR/vPvD3/8G/yc2ioVpb1jeX1Dcrdre3/NaLBe004eeAsQqdKfJlydRHjsOWw8mxv71nfzhSVjN6bgmbnO5xz1IsKdc1cgyoRjEYSaFzFmVGVij6KZBVFbLTBAZyCcVizfuf/lP644HoBrpp5uObO5zouL7cUKuMOR54v028vb3j+enIk5dPqPKcps6RqqSuFxz7FvfxgaFIyMIiikR3Cqxe1Lz99o5cHzmOsHm2xG2hH+75/MkNY2YYnOe47TBzR3/9CZlVCPUUmQambsLoJY+79zzsDzwdHXJ9yXJZ02QNbTGSQiAXkqqq2Dy74Xi/JwyRumwQmUa7iPATT1cvCLlGnibu+hZtYN7uEFJztbngom44vLkjKxb0dkQFwXHbU5YjuSjY+wnhA+44cqFn+snxf/yzf8GrP/3fmU4PSJ3QJpKcROEJIuDP0kPEnMg6SVKCLCackUQVsdGSigk1S3zwBJv+VtXk//pDCq4/+yGqUSgX+G77lk1padSGpCPaFdisxOYVzaKkrCVxNlipIXmcLJE+nN14ONSsqG2BqUt8SswxUBCQaCqT4wpNVMBrhbCJ0XvWV4k2aKTr+fLLFd89HGmP/VnuiGecPfu4IysMRbaE1PDt199zoOfSNOTLwOhO7I8T2T4j0/rMQZTn6sXjQTD2LS7MyLinD5HTHCgyz+XlBeuba+Zp5HE+sLoqKJeXeDFy3Pds6iVZJkn0GLsAFKGCTJUUo0Knp9wrEG6gG0eqJwvaQXBtcxrjOcWB8bhlOnaUWcaiXJHWFQc/kFoB0bLIE22eiO9ObK436DxH5Bl2J5g2ibA/UuUbamuwWYbUjqQTnf+V/GIyVEvP+7uE8R4xQR5nHJG39zuEiCyajNEbRCpxQpCpkkW1PudPwsTufkfQijGzNJc1moAYPSEl+sETQo9rBfkE5fMXfPn5mg/vf848PPLx4yNhPhLQ2LxiHk8ck6NRlsxKJtdSLRqEmHCHnG5y5HXO1fIK50cuCoMVCe81pswpFwUJcbZLjx7RTqw3C2oKdLGkO91itKXJKpJzzP0WZRuu5AJVakaXqKViEJE5RbJqiVIao2f8OkMXBdlFRtf2FAl0KcjDGmsbgr/j/vY1Y2iReIQDHzWEiDibUVEyEaRF6gjhbE32IkDMEMrQTzPSKWYERhiSmvg1iMHfjCBQFiXrsmbKR2w0fPXJc/J6SSNyQh65ypdUywVeG6rcIGRJZgGr4Vf+tZBZtNSUWOLkcDGeYSJaor1nniO1tTgRUclTy5zV1RVx9x5lJ6Ze0PcnauEZe4d//Z75dMJZgRaCHodVOasyR3qJjB2Z9ORKYcpzfUI4tTx8uOO435MyialrlkVOnjTheCCJ+byWLgucDWRSEnOPEpIYBrb7Di1mTHUBeKzIENrjZ8vkFfMEi8yTy4FMVWgLorng4bTjMlsSS83jh5HCzDTrSEgTqsiw109I44S2R2ShCGXNODkYPUOucPNAmCKHoaOowZHYuR3hTeCPfvcPmJMiJkGVleeafh+Y3QmVSqLr6DCUKeJCRPkjd7uewc2osqC9vSUQoBtRmeJqs8IGhTSJvKjwDsapI4iJTDva1KDdjFcR1wZErnAT1Is1jx/vONzeE8Se5TjzdFVi1MBje0cae/rdRyKGdoBZOsqkiFWiMQUfjz2V0CQPq8xQVIlcJapMk3RBuViSFyXR6PNO9NgjhwyHZY5HKCQXFCxuNigdCS4yqMBqTmRVAfOCoe+wWtKOGaUK+FAR0gnroJWKqAJaWlKhWS7XpEkjLiyVb/AhUJSRMTNMMce1joQ7I8algnQWC4uY8L9S54EnqID3CWsVOYkYJuaoEHmCKeENWAZ8/HUh4DckCAgh2HUPGGMwtTFbRwAAIABJREFUWYM2lhQiel2h48RhGgg7z9NPPiWK8zmVWbQ1WGMw1p4bUcRZtkBpiYMjxhGMxEdBnRtKbRGToSwFznrqVU5VPmEOCTkrsiww7gdk9IhVhj0mPr75wJObhkW24nG7xagZJS8wPlHlFUVIjG7mdn/P6e0tj+/eMPQd+aJmEa64e32g257IcMi8gCxDZ1csRWR1USOrBmbPh3cfOba3dOMIDyNTmNGrJcZGxv6B1FT4IRGtIjT27KqXE1FpstyRZQuCyOjHkbvdCZRBS7CF5WKzZnKRQ33EdXva7khQmu3DntqUDPOIjhFhZqrqiunxwDz31Ms1zs64NBFMTTKO2Q3shUFLg5xGZIzoLDCdNN1poo8nwiQQQ2K7u+d+e4uxOdnlGqHO5qFkIiFG+tOOcp0wRiMnSX8aGWygnyfEnafJG2KSGJlYPXsGsycUnnHXMb39mld/vsP3LfmnV5RLwUGM3NQbYl3h7reUVcb9aceiSLx49pR+nkA4+uPA5598RswED4c9udEslwV1seDjoSdJWF9ecnF9TdcOEHqMqZFXlk3esHUt5WbBQtR0Q4s1K3IrOR4lUSa6dkvQisvrNTlrdn7EDS2IBVnW4eeBpBP9fsdFs0SUGad9d4avZCX98Vu6Q4sFvNHgEyJIpASnJMFHdIwErVEeYiZRMeKcACRYgYgKIRUEh08aZYHprw8EvxFBIITA4tkNZQIxJWJZEt2I7wai9Ni6IL9Y4ENAlQHpNadpQgwjy3qBVQYfIzF6hIt4RmaViPO5WKeQGSqXSKPJxESUFqOASTCEQJzUmSMYjoQB7ttHlJZIayiqjDxfURQZbWdhTMhsZJggUxrdVFyQM+mWyZ514GEOHPd75rklixKbHBCIQp+d9nOHEWvmKMjmiLc18/7Ecn1B3A645EkmkkXNPI4on3Btz3TlmYJDdhN2qShiTp4FDkqhcOTkuBIIZ2BnNDWjn6mt4mJVEvzIoRf0QyA5Ry4lshZc2IY4BrQLzOlsYdYKCtOQtEDNiSbL8XHAP1jMZYknkRGxOmece2w0zEpR6BqWE84lalWgTM7QdWATOkX6IyxWC2zTsChLQhLs9gc0goYNIjikEcQo8UFwPOw5uiNXIZFLw3YcqHzk43cfuX18hx9OvKxy8u5I346oVcXUjyShCdFRlg0xk4xDy3pzwfiw58HNJGPBjVhZIHN51rYT8XFiDiNFZikzgamh7xRtt8OvGlRVkvnINDi0lpjVih5FpizD8Q2b62eslpqULNZookxkMhB7Q9ARlTI265cMQfD+7SNffFJS1TlZpujmmXzsCCmQLTKmQSGnSNJngQgpkSIoIX8lI4kQEziBV4IoAGNIbkZricgSYk6Y5BncbzhUREpJtcwpJ8NJD0jRYxZr1DCxubnE6RwRYB49VhjKwqJtRAnAWE7dSJpGBtfRz4nZeZQJSGnJlSEtF+RSoOaBvhCUaEIaUUqgBoEApjDiQiDoial3qBC5vlyiVl9Rqoq9b1nNa8bxwJgkF2WFweDniCgk68Wa8fSEvLg/exDihG8nQgwIFQlBUxYlVhcIpek6T1me1Vg1CcQE3USWJobBIZqcye1xvcEuAswn4nxkHCUueFS3RagKEUaUTIjBImqDmR2TjEzOk7wnhYRpVmSrGjcH/OCYhxnHiJ9njPOIzIA0VI0hnHqyynJ0im2/43q8wOqK0PUEF6hWIIYOlEQuirMMw0NMZ1hIjAprFHVToKqS1bI+G3rljAiCfJFRVSVlWVAUJUppBLDb7nl3/5oytxRKUOQZU2lQCi7zC5SItFPL/rDFJ83DfgYjEV7z03/6Y54sMxb1Er2wyA6qRY5IiTyT1PnZKWhMyUPqOHZbvG+xRlMYjdY5wmvG4NjuTnRd5HpTYKNGxQKdG9Locc4zuwljc0onCCrA5KhWGf3DgV3fo089dqnJlML3I61MqDmSpGc+jmTXC5p6xd3333L/7oGreo0bAqoUjH1HUgUyOBQQBSSRkEiUSAilkEEigz/3QygQCHTQeBvRPhHCjPkLgekUzp4G4UBb+DUtRL8RQQCRePz+LQebkYacZ19dUzULpPLkqxsuVI7vO4qqpmkqTKlQCozJmIOgjz1BKgiKNI303ZE29Hg/U+icm3TDhoKsWqC9geSQSlAsNFAzzxPRCWaj0SaQ7ywQKIqCVWfphplxv+Mwt0yzJJMRaXKEFnB34CgCi8WCF1mOkZK7N98zDueI7vyE8yMyWoQuWaw3XCwrTF6i8gzyCjFEejdzf3dLxNO1R+ywwNqCYnGDIyDinn1/hZKeMJbEbGJmjc0EWkjGOJO8R5IIbmRMJSkEpFZEA9WyIZ8GCjdx6M+iTiUFvvd4JrybGHyBFIKuTaQ8kqFpu56sVKh+pqwyZj9jTE1eJTKfo8uMvZvIiGRJ0s+aMI8oq7lZXCCvOoY40LWPuH4mGk/TGC6XK/JyQRTqbOEJnvF24vF4QoZAY3Ns2SF8xs3lNUkKuuMDVZJcffGMV7v/i6ausVYz7g+8fveKP/zyKyIdKc2snl7Tvjuh1IBzicubJ4RBgrvDx8joDWWWkxcGmxusLfjwcMc3377FuRFTrKmv1uz6idOpZVM0JCE4nVoW1xuuny7Ztwe0DcwMDPOIMobTqWNlloxhRjWWdn4gOUtRSwIz/aGnyjZ8990vybVCyIm7Y0vu5JnwbCr2u0eOH+8I/XgWzcjzv7iKCaHSeRuQcNahC4myghQT0SSUkGfzkJREFUjJkUnQMfxm04Zn5wjS0NTXPP1kiaqLczSrDGEM9LEnrzKiPO97Jq9AWeaYmOdIkhlFYUnpjFuaQ0+39agocXFGxZGpFxyUQRhPoyRWg5glhsCgNMImql6TpIHKUrpEltfESXJgwMicpi4R4kBtCkQIuJSQpmR2gWPXY5Tk5tkz8qpgd7fFuZGYAqObSN6hvMXmGVlVUq42DF6wMhZV5bx5/4aYZ8RuQohAaE+Mq4gOIzklwWeM/cxBOQoRcGOiU4bZaZKsgRnlA0bMuBAxeqQ0Fic8IShSEkgt0Ao2i4pCJg77HWmcyWRG63rcvEebgmkeMEmwWF5Q2oIxgbQWP3vafmLyD/z2809gKvF2JsoZYXKC1agYMV7hnWfR5ExDAwGEaphyf26MyQq0lozC430kaA8mkZUZYYKqLKmzEmln2uPAw3hHKXOC67G+YNdtKbVgu73jpllhlSU0C2qTwdgTBcTTzDS3pCoiR4Wf0r/cKZJIwqxIlQKXCAhamdh2Pae2w2jonSMZw3KRcdgfCCZSFhl5nZEVkSg0SiqUWtDvv+fd3XvGeaadRqpqwbLOMWXOQtec9gMmb0h9xCjL+zfvOLQn6qbGnVpGF7DSoMkQ/kwNUmlCKAhBIlM8C0diQsZARPzqMyGRUOeiIAcgEQpyEnOMuOgRSjIlhYy/4XUCeZbz2Q9/h4W6oL4yTNNwRkEnzlHZGmLMgIgf49nXHiJSCYIPZ57f5BmPI+M4IOV5jTYlj1KSceiJKeKVJCiDKiJhVhA9IcxocU6+pELj2olFVnJ5uWDWhjT2lJOBqmDYt6ysQivL6Eba4chCFfio0E6jc4W0BU1SjBPI6YDwHutKYmYohcDYHJNX2Kwg1onkDThHJiWDEcwiEYXA4zBaYE041zgIhcQRZsEgR+ou0WeJUhTkWYbGIoUnxJk5zuASSimksQh17j7TUmKMJNQFfg7k2UywmmANyYMbJUVeYtSJZBXTPJLsGV3eDxOFKCiyDGUNp5NAqwk5jKhkiEkjnSQmhyosNoGuaurLC9RJM+UWFyNKG3JVoDOBF4nZjQQ/MqfAPPZn9kBZ0MdAbEe8G3BJcZpaTo8tdaERuz1WaYIT3G7v8UPiZr1mcXnBeJgQjwfeP+5ZZQWqDORNSZAzfQw412GE4NTfsrm6QRJJMuFVpJ8c4zhQNDlWZhgJZd0gP3lJCB6TK0gJKyuOY4dyILLIxXLN6/IjykeUFVBF6qagCy3tFBBSIrwkJkk7DPzk1SuMPOvD2zFiqoqUaZKErDYsFgWIABJiiMh0ftZdOj8bSURkPCfUzxKChBESqT0IQYyJ4CxKa4SAKUpM/HX1gr8hQcBqw1V5TkJNk8PYHImlkYKHw4jOIU4T0Wqci4g8YVBIQKsEOjLO58x1DB5jcsrKkcYBY3IeW0c5dxTTiKwLdtOISYqsyAlhZPKGGBPGJGYJRVAEqXH9hMk0OlmMcWgxU5mCk/fEKTIPnl28R9sSVS8xUeBCZJ4cUuVkhUSqQJYssijBJnIkWpaIQtC7iUN3hDBRNkuGqSNpQ0yRIAN6nJELiSTiBYQ5EH2Gy3u6vkOFHptWKFWg8oKkYeoETkaig9EFapNhtUDFhJKKGAQuSEJM7E47Du0eFFR1ST/PqGNLU0aSrklCI5uSy7JhO74mKytskaNkyeP2wKLWCKMISZKpREwBVecEP1DqnP+HuXeJ0S1L07Oedd3X/xbXE3HOyXtWV3Z1l93tbqoNyLSxZBBqyQOEESNADGGOZ0w9RWKMwBMQMxggJkgIGBhccrdd3Z1VWZVZmXkucf+v+75uDP5jqTBdTUttS7WlUPyxYmvHINb69lrf973PW04rjDF0ZYZ3M1ASoQwqQUoBMYxMrif4xNB6JhcJKLreM21v6dotaWpJtkSEiOvho1//HVazc77Y/BGVtIzTiJseyVjS9D2zLGfHhm7quL78FC8asmqBsQXj7hGEpCwLjDYEJynnBVhDlwKPT2vGvqc8W1KVJUZZrM1YLCzdcDjmXrQkTJ7D4wadBFmlKKLlw/Nn7OoRhpHeDUzTQDbL2HcOpMDLyMvLJT//+hXt4S0fXX2ItpI+Oc6KBTqLGDGQ1ZqiPgYbmY65gPBOkEYSJJUQ6WhOLoUgaE/wCS3BCXvslRGJqBLKSKJPJEBa8S9VRfiXvkbn+fKb1wxpojIls9MaWk1TGap8SVIKnUeEgigiQ9eTLysECZUEzidcBK8DIQbc4Nh3PSElAhGnJZOPeLcntwExgZSalCWGAJpIJzzCOyAjCMvd0waZBHlQWCXYT4KiLqnNAlzLejdSZJamaRn3e7quZ9YsqZYVQQpsfmzb9EKTfMKlCT8JlM1ZzEpklthueiSRLhnC0KC0fseQV4ioiEKijYXoGIXETxODOAa/ITn0ONELzTTekl89o3SaqWmIJuErhw8OiSJPGQaFkpogFPvUMoaeh/UdD3cPzJYzvA+Mhx6dD0i1YLXICGNP6SMVGW5REoYRl2eEoYdCkuIMIwIyV4QxEcWAShojNMkI8lKgQwH1RHIGFwMIQyYkQkhCGujlyL4/EMLI4rzGS8vDzSOH+1ua0LIsa7brHRmW3/tX/gaffPQ9poPm/ecDWX5gt9sxDB0//uaG27d7fu+vfUyxvGSe71ClI/YCKxVFyIiHATdJPvzgBeerU/rYopTEaM20Pb5AtIasMtgqR2jD5mGHD5Y8E2hh0EqBVPjkUE4hkLgp8Oz0gvS05mm9p20bHnvLi+qaZ4sFrQ8UdY0VgTwrqErNrC4YwtFEVMThCAdJinZ/YDgcsGi89PBu8RMTIklETEgMQk0kcayiyBjwOpCcxoh0PC5ogUzHXY4m4NMvJ379SgQBoSX5okaNnqrIyWzN6D2HMVHqAdeXRO2Pk8ca/AjDMIHwSClxw0S3bUnJk5PR+JYwdoSU8MYRfaSNERiOOLA0YYVg6BuaQTKfQZAGGSI2eVorEPuRNiX2254s96g8w/YFKQjKqqAPkWH0aJUdiS4+MA0TymekKDFaYlJCxsQQO6SW1KYmKzLErDhaXXGg9Y5EjgyWaRAkk+PYE6UgjhNCK9ohoIQnMbGbRoogYByPTVClIIWOXDlO9Jz15olsZimHGW0/cnqiESKQgkMmT2YluVE4a5C5ZXV+wrya0XYtYhwZTUS6gVOfqJdL5lWJVpK5zGhlYGgOoHKqWOKCwyBRIaK1IpFhco2IOSG05CrRW8lc5aTcHF1+tEFYgY+JulJIE5C1pZjXODfw9mHH5v4Vh4cHWjwiSIRUvP/xJ3z6/b/C0LakAM8uS56mPX470LQdh90D2+YBwshHH37AanmKDoGsXoBSjGHEh8CiqlldnXP97DmPj9/SrUfKfI5Viaab6KaRWWa4Xp2Q5xl+8mgpyZWlOFmQRHFMwEUwRYHbJ5xzmNOc3o/cNwcYG+LZFWMaKctTRBogKxjHlqbreXZ+wel8yaFz2Lni0G9QU462MG48622D7waU8khxfJMnmUAAApKfiIp3aPFIkgAZSUWcimgSKmUQEgiLFCNpMvBLJES/EkEg04YP33/OeAiIWrK0Nc0wMuxHUh6RWpMLRdIGgUSKCTd6AhMhBIKHMXq8G2mbgXEYSEikSkzRMfYdZVYiMExdIK8FqZL0k8YoSQiRXGRMsccPga3bU/pAcAPt5HFiJLcl01STZEAWObXQyHZAyOMbMC8rgoCubdE2B6vp+4EYPMIHbFCoTCFiYtc02JggThTGMMSICIap76iynH3UON8z+YwOQQwWP3osATd1jNqie5B9RzEEvFNUXWCqWrppZCUrxmKk23u48AgLSYJxmkVRo6TFpMQrk+NUhCgRJEKMtF2LrTL23Y7ZaknvInMzUuQWpwXGwNA7ZrZA54EUNVpIpAatMkRpSV1ASYUWhkoGdCzw8jiZEYpgNfhI5hy1zjg5Oef2fs3rr19x2OxoNnsmPyFkxtuHO7738a/zG7/+PZK1PN6tuVgZzCAo8p75qWT4ych0aIl94Ec//BHtdsNv/c7vkqsZZTmjF9PR2JNELiJ5klS1JbkFw+FAMauZTQLnI34Y0VJTFSVBQ3ayoKKg6xqSVSQSbhjZbHbUzxY0LTjVojpJ8p6qzGicI5lEUApjJFFoZErsupafffUl3/vkglopUm2ZzXNe3d3j/AjKMCXHOPaMcUSLd7V/EgJBSBKSRwIiCZJQCBxIUFqhg8LFkWAy7JQISFSAlAtk1PyyvuFfiSAghUCHDGcGSrVgaNa0vSN4xzI/BSlQ0pKIhKQpLGglOAyOdhoJDlzsGfqWpjsQXCLXihQ1CGjHATf1XJwsCSmSZwmhoaKgyBPtMJDEBG5knBK1FFAX2L1A1ZGssFg1Z9CBMfb4GJEyUYXENAVuu0ds01NqjdSGGEfG4PHeg0jopBgHTxR79JSTyxxZC2gcRio6FznIgZQcxilEbEkx4lKCsaEylp2bGFqHdJ4dlplStOMTk2vR3iLkROwF0hhcniOnHg10w0QWBZktsNJRWIFLAoHitD6j2bxhjAGFpl5kKFMgkyX6xH5/YFEVlItE6I/VhawqsOVEnlsoBamTWKtIImAkOD8dgZjGYUSJVEfDDKsSBEFMER2PxzFrBFFlOAHCHPAI8mqOreY0Y4OLgjqruL58QVGcIEXOYnmKzTRfffU5JmaYsIPDxKyoiVHQjA98880bTi5PqWafkg8as7BIL5gXOQ/beyY30scDqqgIMUNGgdHqOM9IEBPbQ0vfjVSLK+ZVhRIJrUu8gnGyvFpHRB0w8xnZXBO9Y5ZXVC9nfBHf8Pr2W66uzpBTROsCpxTb7QPN9p7gX7IZB3S1oBsGHjZP5DHj/PwUYxQ+BFKSeBQxvasOkI4+hCmSBMfKhBbgFC55pB+IvkAriXTyWJnKBCqCCvpojvuX4QkIIf5r4A+A+5TSb7wbO+HoO/ABR3jI300pbcQRN/xfAv8O0AH/UUrpH/95zw8hsn56S98N3GR7VlWGjxFd1+T5jCQC1lhKDf2kcUnjfEDoAiWnoxHo4AlTOJ7xbI7vBxIJmxtUXpKrwERAmkgzOkSYqIaJyWkmItE1pAGkVLjRES3EcGB2coGkxxtJJg1TLxjaPWM/YpOgzAouT88IvmDYbpmGDWHvsEVOsViiKUBIQnQ4BGVVI6RiaAYKWxA0GNchqhHFgJf58ZyoEnjH7u4tU2YY+sB6CBiTEbaRkEeGYaQLAwJLPklKKk7PXpJ8TzceqyMDEY+n1CUyF7RuQiTJrDzh/fcSWT5n6A4k31BkgqIy6KRRakayR8JP23SotMQWDjtObPqBovBkQ8JYRe8mdK6OgqMhMonp3RZWoFOByCZGNyGVOmaxhSRpkFiiD8gEy9mMy/MLhMj4+quvse5A1w38m3/99/mN7/4WLkZS9GR5hGiwpqDbBZpDIDOSMi9gPqM8ceyf7hkfHjDhMwgCTeTN7S27+ye0FgjlMUKgrGV1UoAWbPqRwQfyYsXi5AWLy4+oAK8VTs6xizMaFFIJ8mKFXA7cZSXvlwX18gqaR7xUrM5Lnoua7eYWIz1owbKa4ZXij9eOWWapTUFIGhkSd3dvefvNt5wVM07PzxhjYrvdIbVFxkiUEKU6cjRjJCqQOuC1REQPUh89B53DZI5JeMSkScIixESyAILo//Lagf8G+K+Af/ALY38P+F9TSn9fCPH33v38n3NkDn767usHHMGjP/jzHj6MI6/u31Lmlq7xnMyvWemK+myJN4LTZAnaMKVISo4QRoZxPBoujJ5pdEeSihA4l3B9y6FpcXGg6DNkADkvaJxjf/NAkRXUi4gX58zKEkeLsRo/jHjt0PGo7goqkteGbteidI+TJdNhTf+0ZnCCSWtUpTmZPaMyJWsL643C9xPDNCGHligCVhaUlaKuKzyKYWwRg6dczHFyoJgm6kHTGEUXmiNCatqhgqG5u2GyhiAsXeuxuSFGSfItwhUMJmFEhx8knUmcpIGmXWNySedblAEZJWLyuDgwhZ6YIrnSaJNzKBSEjHEaGL3HiIllMUNiUUoTVM792x2nFzWVKFCZINsP7NePnCxmxEligsSbxDRGTCbJoyVJxSAiMXlUzEhokBPKSqQ2CB/R0hK0w6XI+ckJKhiEtZycrOh2B9xwx9Wz5yzOTzns7gnjiA+eaAZmqytCvKUaCoqFoNQVkx958ew7vNGBKtesZnOabiJ2DV9//Tl+L7h+eQnOE4PGSsi1pc7nmGKgrBecXLzk+tO/yurFJ0zS0rZ71g89za5H1SVnJ6fovOby5W/S+R0h9ByGBL7kabPlj2+/oEqWea6ZmRphS2ShKZgxRU29LPEGhAeiJ7ojMjzmmmgVTzdbbl/fktJ4NBdN6h0nQyGDQEpIUZGcwPuIFA4VOTI1lMBEi48RhUL7SMwUIXqsNPwy6PhfKAiklP53IcQH/9zw3wF+/93n/xb4394Fgb8D/IOUUgL+oRBi+c9xB/8/l5smaBWd98zqmmJWkSWJSIFluaB/HHFVSxzBiON2bXLjsTQSBGV+BEHump6xm3BCM00TOkWCEoxTzzC0hDhhsMiTSOVPSYXHOofNc/rdiDOQApgaVD+SpkRm52A79hhSanB4KDSpbbFmidBAgsO+IfpIOZvjM4/s26P5qI2EtCd2liJbEDmQWcXIccuGjSxOa5quQ1Y5/hAYlURnlmKU+OBI/oCkRGTgxXhsIZ0khAGhPS68k5e6wH57R1/1qEwwNXt09AThccIh4tEC6yjjiWyahqlvMEiempGgO1ywLF6UVDKhc42dCbqm5fHmLcvlh0RdMq/hMRwYk0IFhysysmQhCWyKCKkIkybPRpRNhFFQKIVQOUp7UAqSgSQxUh7FS9bgZobT4PjOZ79OcJ6yyABYP9whRMKt7ygvLnjcjEg/0sUnDo8bKqnR/ch+fGTx3nv4xRWCHaeXK6ZvH3la73FRM1/mzOuKy2fP0NZAEhhVURY1lyvJX/vtf5XZyQnm2UtuWkc3jkze0U09U0g0rx7YPA3YImO1eoaJBdtmS2EC64eBP/7iZ/z4T3/IrBj47LLmOx/9u6gUaaNCDg3j0FJnS1SUrE5rXFJoa8mzDKGPXI03D3es2x0IAeHoKYgIyJRQBoSMTEGgA8QYCVIjkKAnQgzYIAk2oSaFiAbnPBQCt/+XUx24/IWFfQtcvvv8HHj1C/e9fjf2S4NAUWZ87zf/Cl622LzA5BbrBprtPa/f3KOlRDaaqigwSYLQFLYiyxRBesahY/u4YQoDVmnGoeHQ7mh3O1KQTHlAY4guoETgg+o5d7cNy1nLMM+oR4sfA7qXR0CJlcjCoFzk4f6elAIyM+Asp5fnzNoWdXXGtnUgAvPZirbfMw07TkxJqyKHQ4dzPXqSGJUY+situ2NxvSLXhmKWkRcWWzn2zcS48+SiQJWWg10jG4tMjs5LxhSJIeDSSFUB3UCjOC62Q0IGyyACMvaM7YGiqJhlEuGno1mFTCQ3MQWNS4ah3xFiRJ5k5P6M0LfELNL2E/3Y8+GLQNMdmC1LdJRkqqLKElMzQkoENzFblchxwIuCcgBRDPS9hARlISGM6Cggz9ECTEpEGZlShowaIwOkkSQVQhowETlPYBZ857vfoc5mPDy+4c3rb3j9+o7MGL732Wc8/eQNm7FnVlseH+55vP+Kz7/8KSsx8P4H1/SHFhscFAUqweJkwWmWoxqBzTJsqbm4OkMXp/QuYK5OOISCSRlevPcZqcjphgi9p3ee9e6RMHm0yOgOT7y6u+P0tODh6RWbw8DFcs68d3z11c/40Zefs379LXc0uLeKf+8P/n1mpzl2UfB29xqbZ5zNr1kWBecn13idaMc11d0runbgod/z6u7nBHfsetRJomQkiEjyCo9ABYFSEs8x3yRSxEV5zLcoRdQJPXqEiQxeEr1Ge4dWgumX9Av9C0kMppSSEOKXy5T+jOsXfQdWJyesVpIga9adR40J5ya6JjHKSN8nltogs4B0jqoukQhIA003MbQjPh7rzcPhwObxibfrLX4KlKYGCbkNDG5gVCPt3RNn1xKlKqbNwH5hqFUPuqaVEttmtIcbttPEKYaUJqbHltVixUIUiOczYpiw9nBsGgoBXEAow35sMCjqWUnTOFw6NsNkJoPUEwaLD5FiVoAPSEouTclwdaB/PXBY74lB44ZItJ7+z6BGAAAgAElEQVR+OhCRaFoUI0Oj0ATcmJBFAAXJQVIWKYdjjTgkpnHACwciIVGIKDnsO4a2o931bO6f8DIyPzmlnBmy/SvaPjIGx27sWJ7PSVKyblpULvDaEKSkG3vqTDHFSJwcugCtFwzDHiE12imCUdhKE2OG9wmTAsaMyFQigwTlMFFgZMGkE0k4ji5bmjlLrqtI8Txgc8v945qh38OT422RMSZ7DEbzC8p8jilLXN9x8f5LPvvexzze3mCWc9rxQPDHnV0uMs6vz3E4rK4wVUnXT0SzoomJmzcb1oc9xSxHo9Dy2JG6W+9IGHJpOOwOLFbn5JeRzf0t/+TnX7HQObo54yEX3Lz5CfvdW4LYoDpPMZsxy0tySrxzNLstQg3Myw+RlcTLDj9JlNWUi2ti2/Ptqyce7reo7pi4dTJikkckAURIkiASQshjAMhAvztWiASDKxCmQySF1kCMyDShx4zw5xiD/WWCwN0/2+YLIa6A+3fjb4CXv3Dfi3dj/6/rF30HXr73fnp6alh3j5RmRnaiSSKjLiVp9OTPZ9RC471DOEF/uCNlGhMFh13D03p/1Kk72HcDbQrMrGWgJcsFu82W09MTTF0yRMvmacsgHZ99+BGx1zgmxMWcNk5wGAl1zxTAq4ahl0wB5jEHn3js32B9wbZtKTHM52co62n7iX3bIrykntdczWe8CTBOCpEmHMdCzyxZtIdp6JiVOapxqDLnZLbgsdy9owtNdE5SGEFEHHnzmOO5Lo9Ip46zezi60kZpWRqNlyU+CJIKdGNHHCNinIjDxLqHh80TAEN3YIgDYfQcnkZyqzmpaxSK9eYJN+6Q05yi0Lg0HN8wrWfdfkOeC4r8PZp2QA1bVmmG0xpfKEotyTJLXhR4IioEpAY1QSDD64BKEZkMwkRGJZHhOJFDsmgLmQyEkzkmLziMgmb3xNQPOAJ/+Idbvve972BWp6QhkenEyXzGv/1v/U2MHNGFoDKWYHPWmwc63zCvFoyy4/TsircPWyZtCcU5bkrsDpGvvrihiw6vJaOTlNHj/cj26REf4Oz5M1Zlxayu6HxPbQRUNTWSzFQMzZokFS9mM+SHV7ya3jKf5/z27/weudGMSjILJcJbSivJViWRLc16YHU2o65LLi/mhNvEq7sbxv5IY5IiHbv/dEJmEqIguQBEpBiJCaQTOEBFhTYRFUeMCEwczUq0TuhR0heBoqsYaf6FB4H/CfgPgb//7vv/+Avj/5kQ4r/nmBDc/Xn5ADiWlYZug5SJqRl48/glzreo+YzKVtSuIulA23coZWDoOBx67r/8hj4Ghnbi7v4tTduTkYEOoDUUOcM4cF4VlLZEnBt8O+ISBKFwg8JlhplJfPl4wwkV2SJDTo5ldY6xkmkfic2GJnlSyhE2sN49cUrButuz3/Scvn9OaTM+unzO7cMtT4cnksywEjKbM/kAqeVi9YyULFoIrIDGRaz0jFPEiUS9LFgsl7z99gtmPqCURQhLX9bIqWWSHuXVMQkUJEklnI5UWSSLllwkBhHwg8d1AikVTddy88WPedztmNmKk8UZVVlxaB7phz0xFpTFCeV8RbU44fr0OboMZMowbe8ZVcQ7gc1yCl+hZiBiw0ppnC2OarWFxfY9SvR0UqGFByUorSSoGamcSFlCjBofElEEtE4URNAKpxVZSOATURpyW/EnP/ucr25ek3TB6Ea09vSjxqiS28eW+YlkXpSk02fEp5/zxd0r3HTGb//Ob3HY9hzcDpEV9CZnpU4YihkfXS1IRtD6Ba+fHrh/u+VnX98w2pGYRVLvSMlS5SVnK8s49Hz9J3/ErTaYqiAOjraZaPon+n5Pf7NmyCf+4Nd+QClGQvGM/tMXDMowqybiSiBCyyA0bnPDB/UlVipuDx12ckw2kuUVRX4gpsD9pqHrHRKBFx6MRiaDdZGYFD56goYwgkhHLDtGIaZIkg6dEl00SBkhOeJo0XYiBYk30y/rFfoLlwj/O45JwDMhxGvgv3i3+P8HIcR/AnwD/N13t//PHMuDP+NYIvyP/3//QIJu3/J0GMitYNu0RximN7z45JrJrxn2+iiG8cd/xN2bR17dr3GupXt44PXDHcbkVMWczFjKkww1SULoyWbnSOuZeiiiYWlnyPOCIe0hFDQIbDwwMcHNjIMYma0UZbIUtWEHFKkg7HYMU2Dct+yygWHf4YoS+1aRmwxVaLAGHwaUDiijiGMieEte1qAL+qZjuztwdnqFuMjIixm+UMxVhfWGjomqrhnXklG2THVEJEE8vgSIWuKQ6EIyholiqqlFQsqEMJYgHJmxVGc1JsvY3G356mc/Z4gj5+dnbJ9uOD9Z8fL0jFuZs3984uH2LflMU2YZWV4gnWM9bpgVC1wYEH6klwNFLsntGTU5O+XIswxnDWXf0EfNNA2c6AXeg9QZKTuCMEOeo4NAiYFUJGwCDIwRklMkJkSVsEkR/MTN3R1vfvwzDt2aTAQ6rRHS0oYDP3v1JcvLa/apZJb1XMyu+PzxG2azBaUV6KJCHQSr2XvkJ6fU05zZ9UsWi2eElBimlsfNgIsCpw3V2ZzQ3HBz80S7PzDTGWI5585Z2l3HdGi5jQdGl6EmT5FJ2n7L/mbH6v1L3ruac2I0s0XBfhr4q3/jX6fZO370+T+ibQb0rMSNHa/ub/n0vffI6ozd5w/o0nKZFZAcKk7kMjCuWxhHUEd5uAjpqCiNkRAi4V0yUAgQaGyShOHYEmxQEBNWRrwEmQpYJYZ+AJcz2L+kijCl9B/8kl/9rT/j3gT8p3+R5/6za/IBN42EdiSfGcY3Ez7vyOrIYbNmmHqCtphSk4XEze1X/PTnb5FxYLvfcXf/SPSJoqgYfMBmEj8J/LinJ6JDRxErZNcRxUhnJFeiYrE6JU0JMgX3inHc0vs7zs+f4yxkccGahrOixIfA7dTSbUaqueVh8xpizaKaYSrJftjx/PyC1WzJzdMTXbfn9uGBcRgpqhXzPGemoPGBIQha03FuC5QIaCTV6RJrd9imosxOuM/vsAMoe0IdAs5agsqQHgbpENaSHSQhC+xFpEgZvu3RSpH8RB4Sc6349u4Vf/iTH7K5u2N1dsrLly9Rn3yC0Jq8yknM2Gy39PsJsdCM2z1n1zVZ13Hz6ms++OyCVJZkUh6TgvS0WOgDojLkTDytj6rD2uao5NFBoyeByATkDt1FpE2klDAmJyaPFpIsRjwRJywOSUqJ0Hte3e7Y9SNuP7F+O1EQiTP4te9+lzqrwBmW8zkmzKjOFPO3Nb6InC5rfGsw1QUf/941z69/nfniBB8jm2Tp2gPrxx3JZjirsLmltpJ1M3JeFZzkBft9w67f093tScNIDJ5d3zK1Lak5kC1qpFK8nC34/mcf8fLZipMXV3jlqFxGoGLx6QmfZgU7NXGiFX6M2DCxyheEMuP0xRI91pRGsr7/mk3fsuvao4GsEsjAOxvy7NgEJo+iIIxGKYkk4EOgFwGtIUaDcwrzzo/A1AE/RkIv0IOg0gPdWOJ+yfr7legYFELyx9/+lE/Oz9k8Bk4vK/ZT4Ga344ef/y+8d7HiN7//fb78/KeMU8DtNwzdE7dPd2w3O1arJSfL55wtzlksV6A0r7/+Ep8ELy7PyNOxky5KT4iR5/MzylyTiyWybJlSIM81vShJO4XXGdZF/NhjY6CJAzL1lEUg7A/svum5+PgZPoKLjtjvWZiSXfPA4ckf5ZvjE4M/UFQF81mJ14YvX9+R6YKrj68wRjO6kWa/ARMQD5FaeYTKuHrvPYZ0x8PP79C+I6QlJl8gCoEJhpwOVMXeb9A6MpeewcPqRCLiko8/+Yjf/b0fELQm9hM4x5ubt9y+fUvzcE8Ynnj5wXdZLa9RtUZmA7laIRuBWWqqeg62JsWWw76j1EfS87dxw8ttRlZDZmGSEWcrnr13QaEVrBKFqvDJokjEEIleM9mEViCsQUxHzYMnMiVBOkrcsGgGFfk/v/iH/F//6IeM0zHheXV1zempRIcIizOWZ894dn3Gi/qUSXqEzygyyd3dN4ja8Mlv/hvUyxyRaoYxsH46ZtH7tEGUgufXc3bNhrqY882+4c3ta+rLM8b2wGFzR1HP2N5vGA8N07gjrzPOP3mfj559yg9+969jbU6/fkNpLVaCKuZs0sSudQwm8dBart4EVmfvUV1fEqOgbd+wDyXr+0eKlcWOgsVFTtN23N51DGniYf/E6Ppjp6CKqBAJqUeKiE8RqRIiKGKAhMQEgUmRMWlQnkwoxjIiWsPQg0oT0lmMqJiKgEHidn/2+vuVCAJu7NFo7h63TKFgVefs3Z5siFwtVpxff0DoFO224dD19Ps1buwZ94JZdcr1s+dYndMlx5svf8p6s+ZkPuf5xSVWG26bNTOjeNo+cqLnpBclcfDs8wNx7HFBM0uOspD0jEyppaImMJH6CTFXxCe43zdEWzJkuyNO3GrSYWDtJgQ9SQX6cSJMgSJprufnDEJgbUYmFKGoiEbQHdZU5QLfRAgHhkKgHOz6iHMPnM7miMVHrIsNTQMzPzBlHp2WMCiquUaYd3ZTSZEvjlvD62rO97/3Az7+tV9jcfmMIXR0X7cIFY82Yt0j/bTlq9ffoDlFnWfIuUUvSnzbUc0VZ8WScOgROIbec/XsjNIElBXEL1uGyvKwmLOqLZocOTjyymCkhqgIwh4n6NxhfIFzCRMSMQioEikIvBQobbAxgkjEIRGyRDt1xAFUAW++eYvRMJvX1LNL4nigyzQnzy8pL2f4aOiTYJXg/U9foivL1byk1AW5Vtx2IxLDYbshKcmUEqkZmIqcYZc4+DXT1HG6vMSLxKO/5/Zwg+3u8P1IPbesss/4+NNPOP3wOZ0O+FKideDig48AwXgYiGkgW5xRzSYebxp0dGx8ZKs9H7aaaHJkXvD+y2sMmv16oPcdz8z7hJlm8/DE808vufLPMPqbIwo/BJAS0IQYjgi3kCjwOCuP3bTqCNANwmGdQYiInnKSTOSZJzQSlQeIAQZPSH92oxD8igSBYey5+fzHFC8KzvJL7veKp5sHTp5fU+uAP+x5ve1Y73YsjCUKzxgVs9XRIWZmCwptubt5TffmBl0JUpaxkz3T1KGTYGzXDE9veCNuQa355Lu/Sxg3rOqMkGqKGEi7W4LIwEd62xHMnEneULRzUIa6BHsIzC4/IWqJEIKsyvEmoLyj6RILa47NO05ANcNY0H5kHAOihm6/oS5naKGO57yyJoWeqdmw2e55+/NveP/5OScXl3zy0Yf8/E/+CYOBQgiKdg86UU2XVKuCqU3YPFCfvsdikXH17AUff/+7LJYXZMsa3dQ8W13xrX2DNo8UpgIvGdYD2+Jrrl6uyHKFHI47ift9x2Z74GJWosoCIRYkNTGMBYenNTpZogjE7Z4+VlydKBobmYoOVZxRCI2SBjMD1+d0QqNmI8InxGhJ+4HgHEIKpnFCGokQkOoOo+aoJnJ4aNi86un3O8SipDYaJyOz6pxZvWJpBTOnKK1knq+Qq5HoP+WD+UdUGv7on3zBabdEG8XOBwZ/4NALcnGg847TdEGQkpyCy8Vz2mYijBMnasUhXICFqw9eUC9POFnMOb+6QirJ7u0Nr7a3hGEgL3KuP3pGrQoaOzFtbrE6p+ocq/dPUdSYcEfnenS4ZQqBJvOIPnJ7e2D8ySOffFJgzMSLq0vCoOgxtGOk1jmDSugpIoI7qgalIdhEGz2CQEyCABwZSR6tHD0CxomSDNdrvFKETDCbxmP1IIfxV9l8xE+eH3/1Y8ytwdpXVKeaWX7CtNtyM3ScbrZcXX/C88sP2T99g28nmmZLOx3I3Sn3InvnFtSQrMAKTXO7JnQTQa8YXMPu/o6ncGCeNHpRsThs+M2rT7C1ARfxA/TJUi807SZyP20pqshJdUIoI+qpYGWu8XJHGzSZcsQwAyk4MYKRSLYQBCUYuh3TOB7dYjuFT4ZeeJrDjuAdoQvcNm8wEsJUsn77lp9+/qfsxgNqgMPmLc9ePuP9Z7/Gi7/9t5lSz7BpUC3YsqAylpPliuJ3SqxMnJ0+B5vIFnPqkxPm+QpdrCis5+L6gsvXMx7fJg4SRgLi4Ll5+8DH12uulieYWiLCgs5vGYYJU4xIozk9L4gHDVnH6RKy+oo+CLSGebWiVy1FvmKW1xQViM6gTMTGAp87tEuYKMEmWuFA5CihKUPAxcDkJSplGOEJBA7DgdvXr7l9uqHOBTZ4vvv971EuFhxebXl+ecl+6Dmt5sxXGXKMDDYnL3L6fcPD27e4uWNKEzc3T4wOjBw4u3yJzM94WdUUIiF1RGWnTG7i4vqCFCXCTIyhISGQqsQPE0/7R2bZkkpXrJ6dkqJHB0nXj3z+xZ/SP7a8fP4B3bglzgpsLPnJP/0RxkWSmFi8H3j/csm2C3z9lPOTr+9Jdz/jgzznix/9IUEJqu+sCKPEfjkR9j1COFQmUFYSwlEt6IUjhoSIkoBCRtAiHgE6QF9lFH5kTIbGTJjRY4VD7SSDzfAisvC/6jwBKRFIiiCY+pGCkt3mgUW5QDQdKVTcvXnL4sWSu8MBkRSqKihMwSIvOJ3PqDLJW3dg97ClITFGz3NTkpaR5uGOtt8hU8cmVdj7jtn3cwYl8Nue3FhigsXshE28Q0vFCQUhCaIU1P05+toz3rcIUzCoiIsaMxOwd9xvW4bg0CqiM8PoFM0AhRiQJAbnSCJDDIZpGOkWDYWuuWnW2HakndaMY0+eQNiAFhbRBpQMPJstcGJGb3OmrqPKK5TKKZY1q9IiQo7WGoSnmBsyY44sAiTOBcgMeXEK8pycCqHWTLbBJHi1vYe7mtPhGcVlwzh5EDumpkIVjkpDpUuGfMHkBLYULK3CCVB1i0mnyM4RZ4lmX5DrlirOCJMi5JFcTEfNwODRIiCtQssjHNZ7hTRHvXyIR16j1jV5Oef5i+cMfU+O4qSuMaYie88gcsu5XZKbkhAqHpt7+vuGxeIZe3WgTR7VeWI9cf7e+/z0j/9vklqCCSyLJX3X0XUb5rOKhepAeooC5rMlT/3I+u0BNbSYoicqQ9r19AX0ckPwgpvtHXKfcXa25DsvPuQr9QaDoJiXxNaQ5MTb1xs++87H2OsFb/7oK376456bV7e8/pN/zJvDE4W45dl3v4tYfMbKGLx0DMIjOFqN+UkjBkHQ8UgUihIt1BEeKiLKB0JSKB0pTEJ6gZ46Ikd0f24yorQMKWClwOhAkoqpl/wyLfGvRhAQCZkn+phgaNm1NYtyyd3DA6VMxGwAEXi435LHPSHzZJNAELBLg1cd/djgXSCi8KGjyHOawz1j33PYj/g2MCGQVY83mt26Ixteo3ROvbpEZw0iJJyDrAo8NQHtemYnF6gwEieLtBLpDHbo2W4cF0Zwslgx9QanBS619G3DMjcUJscLiUkB1Sm228SEQ84s484hszX+aUfrPe3hQBxHShnpRMBFQ9+33O8bPvzu+5TCMsPg1AmFmdEKT1YKfGYRzqC0ZLE4x4aS+fICb0oyE0hCsmsD28OBIXT4uKcqJItpgVKR6B1DtyWdFggXmSlDJy3z5SlaCUR0bBuHGJ+4nNUEWeAyj+oqTrMT9p0gKoPXGl12eK/YjYnc9swJTLkG6ZFBoDONGAIpgTKKkEvikIOQRJUIOnB+annx4hmv776hXC24rs8pqhofGvL5nN39ntOLkqe24Wwm2e87Drsbfvr5V+AjFy+e44Jn33YMjx3L/JJuagl3Dd3UEGlxeU3KK1wS7HvJ48bh2KC94Gw2x5ycklKkCR61GEk+MA0NMTlW8yWHccuwu0VV1zy/foEuc27+6Zec1YltjKQLydfrN+j1a1aLgkObsa33tM09w/4VXcr4P374YzJT8a/9rd/nerXkT2/WtCPIoCm1J4qAlwodEsiJMUp0FEc5gVD8P8y9ya91a36Y9fzeZnW7P93X3b7urVsuW3ZhY4c0AiFilEjABJgDo/wFSBaMmCFG/AFITBhGYoZEJCTEgCiKFbflW7duU/frT7fP7lb3tgzW52AFFw4VB9WaHJ2tvdY+2mett/k1z6NMpkcQyRibEaOJTG3DNjiC1aisQUq6eED3il79kq8EUk6cDiWFOXK2ukK5yKv9N1AYBqX4VDRnl3NoI4LFGMP6omZ0oNHU5Yqu6CFZlmdnxDyDceSUNL0MWDMSTcKMjkMv8OoNX+gl6Te/x6OmQhc77l48IJVnVS/Yjomnyxm+mVP4wN3QkvOAyWuid8ybko8/e8TJj5zub6gKzdPZjKOsGBuHUjB0O3Ynx8PBYVNmcVnhrjtsNUeM4+Z+y2k80O8dDDuWZeIYAtgCmpr1k2dsVmsWVcOoS9Z6hdhIYxIdilplPIEkwvyqAOcZk58kLCGhbI0R4f0nT7i9esTh8Tld31DhaRYNZhY5b9Y8u3iMYHDHlrHKzEPBdrdlJFOYyNMPZ/gBdt2Opx89IvqCWI70heGsWSLKUi6FYmfZ64jJgVZKgjeoyhIORypr8P1EwrFKE4eIsppUTGw8RUnyipgUf/Nv/03sucVHy5PZFWPeQz7nqTUshpJuJ7w8PucP/+xA4S1Fgu3wBjFryq7l9U+/4Ae/+qvslGd1ecmynTESENuzkBXLIhLHA9s+0kVFXSiCC0Sj6MYW5TOrxYxlVVGkZ7x++4av3+4Ydm+ZrTacz5ek0vDVT/+Y9nTgg+/9kDY8B9nQ2IKn3vH8xQs+ee+Kq8U5jx6d8+l5w//24prxpaF984JI4A++/ZpPfvqEevNv4Lzl9vbNFLxMMpGCfMIXGlUUqDGQQ0S0QqlMEIuKDqPBiGYYBWMBEwgoQirIuSOLoCmRymNjxP0yQ0XIirLcUdVP2Y4nzjYlliVD27M0wvbNa/ZdzbOzM459whqwWQgxMm82LJYr3NixnR0gwSwJY07EYUf0FTun6V2LiRmJJXrZMuQ3HN9uMH1Lbs9JJ09RaaRuWB4828MNq5joTUcul9SdoplnRjOjvnSEAhpRSL0hysBdHHBO8Loj+kByDqkUzVgQhkDqO2xtKE1GyYzZrGN37ylySzvz3EVNMrCgpFCGhy4SX73m8NnnrMuIWVaQFGOXWc0jRRCO9YzUZ45bz2rT8OBHlmNgOQuM44G99zykOzp67t0DuZ9WSrbsaZoZT997zLPLM7qokDqie8BmtqeOSjmSN8x3mrQoGdKeIg3MzpbM7Io+ThBMNTeoQRHqSKMibvSUoiBXVE4xzArSCHNT4cSTQ0eyiigREQhOU6iMJpEksZhZPnvyPbquZzQlpzcVfftAV0Rcb7Gq5T7seLRc0SXLd1//McfTwPvnhtvbkdX5Ex5OI3KIbD5qeL6/oe8PfHg2ox9PuDby/PUrhrAn65LPvvc5q2qJkkSzsmQMwUb8qWW3u2eMHeeLiqE8Y3888vq+Y7WYM/SJowTevLzj7a7nicvciuP+ixuUOvCH3zzwG4uGRznw4eNHXCxKVsXI+QweP/2MqqnRq5qUjgz9Ldfbe4yaMgHoSZSrx4DkgM6TezDGQDCCTgkxU6l1cp6kMnmY6MODFYo4MuaSeh1JfYResDNwv8yBQcmZRTHnKC1V5yl6TYqBZQ4MvaZbJurtiLOZMHiCGmmPD9j5hrJWHI4HhuBotNC1B/Yy0FTnNOUC5w7owxbrAyNQ1pmYFTk0zKsG147s1DWNvWA4CYXxlBuFiY95KBTmoCh0STm3mLrDni2xI9RFxRBPlMvE6GqGPDKOPQ2agMfUChcr/HJOW3p2b6/pj0fkfEHMHTp5bAzsjMHqc6p4B0lPGq2Dww3X9FXNm5f38MNz6mwmZ0EGXQrBj1gP2bSAIo2w0IGYIq0RsjKk/cjwbcdwP1CkipEdptQsm4ammpNRDC7gk2dezBjcyKE/TD0aGWSZ2PKK+X5JM78g9SV+mYkrwzpAKBUF4LxgtUaqDhvWBPFo0zGGGWTwyuBVj+RMVooyCSpNFW7ejIgWFCUqNRMfr5yRho7Q99R2ZCgN7tBxe/+Sx5srrpYNi7M1w82R42j4/JP32WVFOUa6U8/13Y6rDz/kT57/GS9+/JKry0u2B41uH9gddxTzkvZ6ZFHWaNfTnxKz5YLVqkRswavrt7zZvqZ3kTSMLIuSevaYft9ytzsx7PdcLM55fPUB+zcdN6/f0J7WsNtx93Cg1onHy44Lf+AHT3+dsypzvnF8+mjB8sMP+du/8yMeSGxsRX9IdMee8bhlEIXkdxWBAQrFlDLMGdFCDAV4mSCjIVMbR7BqahpTmcwEGIk6IXnEtQUmTxKZw1jy89BCvxSDAAj704noisnqehoYS0NqQa0zqneMOvH2dMdlI2xPB1wAG0e67TVvvy5x3hEQ5ucbzhdn+OTo+pHh1BF7h5SKylZEF/Anz/Z2x+rvrKfc7fUrzp40mGZO0o56dsbFakk5juwXBSbs6aOFPlFZR0Bzersj2kj0GTcMYIRV3RBOHm8yoylJGZx0ZKOYrTYkgSJp7o47TKGwiw1VvWfZrEniaW9vQEaKuqKTPXVWfPnmj0mLH6KeKp5cfoIqezwj68cF4y5TXp2RmwLRlkZVlFrhvON0TGzf3HDo74ndLbnfoUJCaY9SgfVmRtHU7PoTt6+fk8LIYt2AN1y8d0E5O+PRXPPhh99H1QpdzajNDJ+XmAdLWhn8VmPPBJkDwZD8EqsDyWqSglMa0aNGCrAYchB0cnjdI6LRxpKCJg0QsjB0I73zvLi5oWv3PNzdk9vA8tEZcWFIbyNBjSzDgi/+2Rf03ZHPzz/g7f23bKoVJx8oVOb69XN+/MVX/P2//3f51d/8G9TiuX34mp/+9GdUwfP9H3zOb/zKp8yXZ9wdj9xd37IOiWTXuC5wOEHfWtzQs3244fWLl9w97Mk5U0pJVWp22xOrfcXq4pIfffoh/dsbTueaH54/46G75rd++H3+3n/wOwxqRjjt+U//o/+Y9XrO7hjZHVaIrocAACAASURBVK4pDzuqouLh9pb7l8853R1QyqKymyzbxhKdJfpAKZmMApsQk8jRkoCTmVLRRiVMFEIGZQXnEzopTPDEUkBFRGfyv85W4r+OQ3RB3UDsAtlVVOpdB1UAsUKKI0TNzdGQvQISvuuhqDG1JbojnRdsO3LXZ/pwwEhie+wmDLbKuM6jTYX3A2bhsJVGnzLzak60DWezhjYZlrOaKB3ZLFFpj2jNstTkRYXvMwUZ1jWSLFxEsgwwFIRhZJATYYwchh4tiuV6iRjh2BxQheJivaG+u+fu9RvmTcHMXZGrSF1qfFkTXWQYO7JKDKqjrtfkLtJ6RdedKJoVVpeMp0SwHTUlLgcKpaBa4GJAnQbcGLgfbtj1WwbtKZZz4tsds1XJ+dkFNoI/nBgGz+HYUQchS6Z+NMMaxfvrx5wv5uSyRIqSRElWNVImknQo/YT13CL6xDiA0RYThL6M0zYhT0YopRK5O5LqCoUlFZoizIkmIMFgRotH0fVHxvDA6RRw3cjuGJhVluE04u5OzK4WBH8kX5e0H1iqyznPuMLqgqvm13n1+i0qltjoscsafbrheLoDv+fGDQwHT1POKMrE/tgTZcuy84hpsEWJFcPQKdr+luFwx3G/x/vIfHnFRx+XPHo6QWHnyyWLcoaZbTgcrune9Pzav/+7lGlgt3vO6foVF+mK5cUTBjVjoRVD03DwI1FqdPGaEGGl51T1jLt0TzAT9o2QyKJQ2qIGTTaRLBHCO99ASKQEgYAYjU2WrDyCkMuEd1PHZpnArxRxN8l5YgGlkp/DFfolGQQyoIgo0fTGYXxCmYpQWkx21Kkg5Hfc/dBNZCFlsFFQgyM/3Eziz8IgxlPNGjhVtHc3E00nBwZnsGKh6klo5qmm32c25wXKnlMuC67dgUVRIJIpjiX9Y8e52aBPgAjb0w5TKYoY6YZMVQpF8AStEdGowqIWM1JVo3OPKCiUwQ0jAwXNqqdYGJ6YDZsqcXeac397jR+FZTGjeTKnG/f0254ugjeacfeGe1PT7HvMOfT7I3Kp2LV7mrrCqUwIBYMH0o5QWHzwHHo43N5x9+otHBxrO6O4EuysxDQFTT0jAPOVYj57RuhHNo8uWS0XVGWNOdPkhaVkhqgeZZbYHJkZTa4bijiSm4iXkrKwONmTrMGNGrHQ5ECpA7EDKwVxSBjTolwi5YakmsngHB0pW47tyGnb0w89yXnu7p4z9gNXFxu2u1fEb+aUUrP5/jNaItsvtnz42VOG3PGHf/onfPftT3m83FB99mtcmveptGY8drz+yZH1swWfPf6A3ChQGqMUoRsYI6xmCllMuvv2uCdlQSmLyZGmrvjw+x+jmjXDcYvJmbvtPX30zHTPfH1G2Z/w7QGfCm67GV+9ueaJybzv3/D+Rx8jNpFSwUYXlElxfcp4epbNJWHcE2tF6zM5GEoRMhEfFYJHSSQJDDa9E5EashIUkTIIXjLGJ1KdcJ2gTEIpkKyx7aQrCDHjkyb+smcHECHbTN9BUSvE13SxZR6FsRCSD5gkdN6DzlgpKAdH1IoUPfQRVWR613L72rFanxAtHLMn6kmUaUMgKE81aspFSdSJF999jc4zrt+OfC98hG403SmS369o6gJEoVIiFgkVYV0viXEkzi2VgTBAKTNmusaeafwwsN8/UBuY2Q06QXua8F0XiyVizpHkGOeZnJZ8sjrn8eUV8+WKr/7gj9l2W/puxd14w0pmjEFwd9cccsmf3L7g9Y+vWL+3osfxaH7OZllz9nTJurlCLxVBCf3gaGxFrTpWdc1+veDQHWhU5mK9ZLFZUTUzClPw6Lyhmlvm2TK/WExFP2cLgiop7RWmEZJqKBVQ15QS0YMQSxA74nKi8BqhRpV5ahrKET16spohaoRCo8ZIVMLwjiI0DD2u72md4/7mlt3twP7Y83B/S0g9i7M1BI8NAZ1nSLtjHG84qsh3X33F5Wcf8WTzjK9e/Ql/+uWXvP/Z5/zK5z/k/cdP+PE3O0zh+PjzH/DwduS9X3vK5cwgohi9w7mCQg989/YVWiuePn3Eulwyqpbj8YHkIWQHVlBlgR9gpcDScLu7Z/n4kk3QHLf3vHn7grGKvP3ijuP+NffbkT/5/f+dYf+CMgr//eeXnJ9/H10XzOcl0Q24nPmj/+Wf8Nt/53fZSceP//Qrfvbd13gEIxMmTpGgNIwJVAyQDEqEXASUViin6XOg8A6UQo2TlKRIBk9iUAYdHFkbaiNkL6Sfxxvnl2QQEDIpRZLUxJBQ0lE46EqLlkSMmeAtZqaR2BNHJiJudMSQp/zpqCjqyfEXhgmoUDgYQ2JQCqUSpRhyFYlFwfb2xOVmh+uWiNQUi4pZpRFbUnoY3MhCVlArypXFHBwHcUTpWKeSLAZnFNVsMgyNZLKegohaR6wuqUWTR0cbBdvMsKVQSUNKDW3Y0o8ti1SjupGrD95nGc45vN0jviMZx9ALuVsyRkc3jty5t9yEA/qYcZ90+P6cWAlPLz5F5obKlxz8Pa7O1KZhXhec1QX10zNiclTacvVszaZZYiVzdr6grmbUtaHUlljOSKaksjOKSk0pJhVIqcK0I6MzqEajQiLpmhwjhoCLO2Kc4dQwsQNTINCRokJFjyssOQfikBmCcDh1jF3i5vqO7779KS4rmtkcpT2ihd3dAdODNiXD9T29KfHDnuBHYhyIvuPoj8yKGZdnlwy7I8+/e8Pbr17DsmB9dsbD7kRzWfPscoWKLW1oKRFO1y3fXr/Ba8+sXuCOnk56TqeW/e6aeb2mMDXGGJq6Qrse3zREE+hHh+wVTsGxHTCNYExJXHoe1yuytOTxRFMVbKoS3yu2xwPnzRIXLCpXGLnhs1//bSyC6jXXd3e8fnvASCBFiFqhxWBzwqRMjBohvqsUFHJQ+JzJWXBWM49C1oqoptB/iglTREqTaaMnZz1BR3P8OUiRX5JBgJxRMaPKydgT8wKjR2yEMiqig1QkYg/BlGgrSND4ZPBqeFdxGFBokuuICvqQiDqTg1CUnsIkjoOAE/RwQhmh7Y7sxpH5rGYcAuv1OXqeGGOmmS+QrGmKPBXDSMaUmbl6SlVFfHQUyqLEUpbCIA5fgMow9A6bM2Vtmas5pQdjBcmKHCDGkXp5xulUoHPCu5ZFUXK2bFhKgbKK7mFLW0W6Bdi3O4IK1CpzPFyzCgvGF4mXDRRNQ/nrBSpdYWxkWQdUaaiVZnm5JChH3xlMjCyXJYtaYSioGstsZilrTaEVWSkqARMEW2icRGqRibn4znUnRUERRkZK8rgjlRBCSao8pu2ITTH9H8OJIdZorfEpktJI70EHw2k48fLtG06HyKltOXhDUVrGFDn1DnJiiC29SjxdbCioeXh4w3bfc3m1Ye8S3Z99xx/94R+wevSU/fbA49WS+WzFbGNp6iXZwPW3X/Cj3/xbHNsDcXQMEVbJokLH44+eIhIpk0bQFHaOkhEvBVEvUMUCQ0ffj3jtWe8c8/kGXRsOuwfKmHnx9Y9x+0CcZR6fXzAvYF0U/MqnK0p9Qe1KHnY7NmGkK5+yiGuK9ZzClVz98COa9p6br7bkpqT3PdpMfgH1TkUeiSQFmominZUQs0FUYtJv1vjoaOtA0UdiEmINZgATA0MWTMwkJQwS0T8fJ/DLMQgICrELzNAiRQN4TDm5BZyBIXoaU6GSY0xCFKFwGVEegyIZMwEYQmC0Ge2mnLPKebLXxkSOCtEZ5RUxJ0KEm/tr3CHyybOnfPzJZ1TNjK4dMfMlzaZG9WCbYkJ0rYQNFZIdIxaWSxpbUCqLNcJaLCl3DJuBfnAorxGtsdkThg5CIKfJKG3zAoWiuL/nMHb024yUmVoS8+WnbC7ew/lrUm64Pt7xcPbAatjix8S6rUldx2l4wDOyvc6cdnsW60RVrBE7lZn6FFgWM87eWyJ5xAdHaRRKKzQJYxTaWlCKaGrmywYjZipWqYVKBIqCnBu8CcRQoIuIshEXPDacwC0Y04AbFZaSYVAUKdIFTUIY3Qi+ZVDCsBs5no4ctx2HKMSUUWRs6bg/PdDuR7J3XJ4v6Q8D2/7Awtasa8vF7JwPfvSYn33zmuF0y6vtPV/89I/41BYY7Xjvvd8i6AGtRi6ffo+72xt2+sj+9i29BKzVOF/Qjo5Zs+DjZ8/ocYh3FKqEMPL00RmXj8/Z3+457F+R+sD6Yk1+6Pl29x2z+Tl9GHl5/QZ/GFmtBbOs6G+/Y9B7UracNTP+7r/1u8xnJTpHvn2x4zg/8SvLNb6s0VGzO3lmqUdlRbOYYZwhmgLxDrQiojAEsoBSgkSBGFEmgRgUQBY0A5mIhExSoJIhhYBKBmcjucikUZOKEj24CVj6ixYL/RzxyH8H/IeAA74G/vOc8+4dlvzPgJ+8O/0f55z/wV/1GUllDA6spU8jpdboDqTIOD+JIPuYKaymUonoQRaRlDXGaVJMZJ9BCTEnUk4oiXivECNMytERVViyl0nL5Arak0PPBvJ6yXbsWQ0DXkeyElKfSNHQ5Yn3VqApxCBVgc5g7QzKiGhDUWr02CNaofWS2k6K6BgFksKvluBHlE/0pxPej0SlePrkiovs8I82nPaKWAyYXJCbA7NcMOw63quuKJPhkbqiOnm2bsSNW/bdiGVFc1HyxZffUC0V33vyAcvVOYXJSBxQIZJKRa0KyqoAmymKhlIbtPaYqqTUFaKE2awi64LCCGkoSdpjBvA2oG0mz3tiEvAJBQQz+QaSisRsJkR8gpOFoQ9wEu5OHWk8MQRHd+yJHm639yQTSSny6Ooxjy+eYfKWrbulx0+1C1kxm19ijOG7b77CF/CxvmS3v+f+cMvj9RM+/OwjHs/XFHPFhx88Y/A77m93XMzPWdiar65fUtYLLtclt/3I6c2eVV1xviwZ2z37QyL5FiRS65L143NsaSgrzTI1eBsIscfFgNUW123pOw/BE8OJ3jfoOtIUI6e3I8e9o/jwKYuP5hzbwONzy2efPGXTNGzOLhlSTa6E+ayib/dUi57OZK6P99AOpKhBJ7Ty6FBAmeEdRyAZIYsQs8ZFRSEBISBqWtkq3oFIoyaphESDaQV0IoZuqiEQ/pU0ZP8j/0/xyD8Cfi/nHETkvwV+j8k5APB1zvlH/xLX/eeHyhBCRqWI0gm8xtlEDonRaKyMyNAQUCg1mYdziKisyclP6m4tuBxJaVJJ+cTUqx4yqMiYNCaHf654ttljoqXWiZcvb1ltKtaiUedLoutx1VTEZDuFmIKsEscINmZsJUgeSb7AKDOhneoGpTU6ZYJTVGLQISI5E2xCSUP0U71CdpGUMrN6gdEKLx5/HEhi6YYOdUhkp1jMHcmNzOOCMNMUMbDxATW2HIcesSWb8wV3ew94Toc9q4sFOhrKskBFKJsZKk9aK7RmMSupyxkkoFYUVmOModQNQWswHgeg5tgwTI6DrJAxo6yQokVZwxCFkDRlymSVuRl6htgSg3D35gFkRiiE8dhz2g+4saOoCyQE+taxXCxJOTPsj1gFZ1cLfFdTiCWeeWZ2Pnn1smY8jLxWOwY/slmcs7rc8P74ATF4ms2at3cvcW3PQ2ipHl6wnl/y9NFjclUSyjnsB8QNlFcLvERGH9G1RWyBIVPXDRmFdx6lDElp8izSj56uOyFuUo53/chpf2Q8PDAOhsbMeXqxYvdwh/d7bL3hsn4ElWZuNG3Vo+dnkKEuC0qJRBd4efeaQm+oorCul4hYdHZIhiCarEA5RzKQEELOWJUgDqScyWiS1qgMYjNDmvoGylzQi4cYqPQckZYWQVcQO80vLCT9y8QjOef/9S/8+o+B/+T/y0P/lx1BJo6ezoIXwUZHDoYiTcENS8IHcCZhXSKpzKjdpGaaCqawUXApo7SBlIhqmsVDAp0VIQumEEoPOnqMiRzGnsPzr7lalvgx8/H4HkO2pHaGLSJjM6OWObnJ6Jipo0apOaZWWF1hMuAGki5IIWFNgTEalYWyUWQrGB9xPpJVnjTrM4uVEp0LaltQ6MSIp6Ykng2cffgEexgJnOj8iHoGUke6IUAOWIT9m3tGThTNmk8+nmEWNcvSTK4/hKJYkCuF0fYdq07oQ8bqxNyUU0DLgBEwRqF08c7HV+HrgBo1phhQncLrEuk0KDiJoIeC/fhAHAbmjeW7l2/JuWQUx8Ore06uZewiZrEgHvcMPZiFob1veThsqZYNp9RiB4WtSu6vj8znmnppObQ9MvbM1yv6nUIFYbc70J1uiC5TLOfs76453Q+sP1oy9IkXx+e0Y+Jw2rK5WBPGgcVswas3r3mWH5GHhF3WiC54ODxw8oGyqVjNFpTVDDGTvUckk+tMdonD/Z7T4RbtEgphbHcc7264e/Mt7jjwKz/8nDJnnm7e4+1XP+Hu9jlvrkt++0e/itYaSYlwjLw5XeMJPL5cohczPvj4A/bDLZfLc7Su+fDTTzH/5PdJe4fJFq2n4p4QJgWcloRkTUygJKO0JqpEEovyAdBAJBuF9gljhYzC0WGSTFsup8jJMPxrtBL/F0xOwj8/PhaRfwYcgP865/x//GUn/UXvgIhQ+ITTCpUNUQZ6DLoMmCiAJqieUBYUSSGaaZZHyKIQBBGZyiVTJoQERDCJXglaQElEJ02KEZ0VWiAlRWoTcZ7p/EgZW3aHexYzRezuKFTBxcV79HWH9JlSW7JR5DyhniV7YkiT/ScNKF0hOWIkkI1CtJlwUCmjlSbkinmVp8CAWBRCtImYIlUjpOwhaJQYUhMxxZrLrEnngYwwk8wqJJwqOb98hM+QksdWc+aVQZcKkxOCAWVQoghBKJQiZqiJgFBkRYqRnDPOyGSwSWp6CNw70WVSZFmSao0NkaGcsT/eEUONFcfxfs+p9XTrJa9ev+Vic0U1azj2J8QUmNIQx47dsacpZiyXc3xZ0Q4dfuzxBM7SAlQiKWG3a7Fa40OkP0XuzQ7fJ4xxnHZbbBKU8UTRgOMHv/PbPL6c4Xymkpo794ZvvhRssaEwARdAj4mH0wP9oUUaQ13XFCpw8/wlIjDokqZsaNs9+1077btNjT8eqHSkaCruH15z3D7Qtg/42NPev0aJYakhm0g39ITk8DFPRWKnlvnZBXUh6D7Q7x7o1g1JQf9wRIXM7GxD0BmfHIWtqGTGqE4TSyBaYpjAsSlAFBAFMGHHrSR8zGRxBJmKscQosoMDgdJEXNCYmEhaIzFNOjM9/KvRhn/eISL/FdOl/6d3L70BPsg534vIbwH/s4j8as758C+e+xe9A0rpHFKeghvZogqPT1CEqce6S4INmawjQWdUNGiZCiPe2dvJQU8X1u88b1kRfUYVCXRBzhOS2WchY/ESKYOmJhOkxo8tfldy216TCKwvZjx0nqqZoeKcphKW6wUhJuLY04vHzixkjxFFTAVJZdAZnyLZR1KIpKSplEytuyIoMyO5QBQhpUiIQqkNIpkoiizQ+ECvQElFO4KtDdpnSp0o5zUEsKLIeLxu0DZSKINYQRtQQaFkEtiLDRAsVoTSCFEMISq0cqQUIGmyaMiO3GmcBd+VZB1xo+CcgxQ4lYa3313TDxFTKcLBk6Kj6xLH3T2zes1u2NO6gfdmS9KiJOBoCouKwqqs6bQnXa0YXM/Ye/ohUDcls7rm1c0bUojMV2uiKFyXuH84YW3BenlOcJ71puTjTz4mpJIsFQMZY0pc72j3RxZYxrFj1cwQI6jHz3izf8tuuOdMP6I/7bFW8+TZ+2gdMcqQgyP7zOl4gnTEiIVhoHMDvm1pb95wf7tnON7x6KNnNEVJbS1FaZDSMLrt1LgTekzO/MH/+U/5wb/5Gzy5+oimqAlVB9rC6Pju/hULYB4Dx3bkOA70+z3e95AFUUJOCZ3zFBBUglJTbCBnDWSIGYMAQjTTe8KQkcw0oI6Ts1Cj0TIp5yVo+jiZjf9aBwER+c+YAob/3jvCMDnnkXddCjnn3xeRr4HvA//0//1imVhMS5vsoRIh+ZKkB6LvSSkTmOATWQxGeYgJyXma9d7JGgAkTQOBFUVIkOLUmmkKhTMQQsICKCEVUyci7Y7jNqGDRRYtr4MnnGpuXWJNSdPM2JeCHy/RuuHsfMW6WOKTQ2XBjwPkgOSCZCuST1MoUiYdt1YFRhI6aUIyUx33ny/jYiJ40KWGQZFqQQ09xVxj9RSLCFFRVZkcEoMTxBrEgMkNKSSKpsSmKfIfRBADWqaZQKepI40spCQobSi0miSl2RMVlMrSdZ7gHNvDgPINRo8MRN6+fklj5+jZyJ/+5Ccc3p4wc8vH3/uc0mTu797Qn068fP4zbra3XF5cMnjPcjbDFCX3o+fFd9/y7c8im7NLlpez6UYvaoYxcRhvkRRBEsfTPbYyFKYgBahsw/Zww7ypCPOGx1fPMDNNQcW+bdH2jLs3W1Tw7O4OLOcXqBZMraiebPDjS/I20u+OHL0mup7FasF81rCezzFW05883h0ZuntOuxeUfY/2wkPXMQ4t1sJqaahNyeWiYXt1weH52ylYKAHXW4qmxpiSVw/3vHh9pFg3PLn6EJcLVudnJAHvjtw9XHN7v8VWJWGIeJP47rufEWNLUpOExQtoZcnvtDOE/OfPGzlHQs4UAok4BQSTxhRh4gm4TBDB6IjXFusTwU6ThY2C/+scBETk7wH/JfDv5Jy7v/D6JbDNOUcR+YTJTPzNv8T1EF1PwSoSEgSdE6NPxDg1o4hotArokMlKk1VGskGCJetE1p6U0hQJVVMEW6EnoWPIJDNRjTVAGjFVgW9PhBRJKrFghWoEbxJFbtnee1xI3Oh7dLXFaEcIisdPSwY/MLh6qqFXvBt1w+R7GzTaQ1kIMQsxJKIGidNXrSQTCKSQsZUmJY8bPDpbtEymoV4S0jnIEWM16QhuBfgSvKO0AVcKdS4oQyQPgFGMySAyuY6CEdLoEDTJTulKpRQhRHSa8tESDFpDSIpdNxUkdbs9QzwQfUbikbv7HVFOnDdrXr98Qd8lNqFhTD3GB/70iy85n63YH2+4f9jy0cefk1Vke3zA9See37zl+rsXlMWkIS+LR3TeUTULap1pdyfG08hmNqcqMvvTkcIscJIYw8hsM0OnadWgCuHQd5gYma3muFPLw/W3/K2/8e9ycX5BloROJSE4hnaPEthUlmF+iRRwt73F90eWH30ELjGkANEh2pPalu76ns7vqMoCLZpFpVjUFVkVHBuFNQaVCvYSGNJIcAqiZ4xwtjrDesPl48fUuuKhu0HFMw7uyLI27PKO427P8OoFqZ6zmTeYosKrQCaj/jxup4WsBUkypQOZiEKKhI7T9tdJICXBekhlZNr5R5TRSJyYhNomUhCyTNvsIuRfPDvwc8QjvweUwD8SEfi/U4H/NvDfyHQnJuAf5Jy3f9VnkKfKMh8SGmFAENNjAzgtk6E1Zey0EScZmbzsIiQbpi9LBJQmiyInwes0xQoSkBOx0+gCck6Tzy0kKpPIYhlFQFVoyYQQOZ1apFUs5pa+PZGcJYQThbkjoii3wtPHJzarCUw5my2wQPKRlDoKFPs2MfZ+2j5EISeh1IBOYHgXiMvEaLE6EW4PRNvhEZSDEAMSPEkldBbG1wNNqghJWCyaidQjNYw9trbUzRIvmkJFKqtwShP7SF0aVFWidAWSCDERXCClTAp+iqvEzHZ34G6353i45+gi4zHSSMKFllfHgZl5id8HjNLEsuT6xRtyOzLkMLkYF4ZzdUW2BZpASI40ZoyqObu8oh8cfex5eHggaUVMiXpRsZiv6Xe3nLojITn29zsWs0iqDWHoGXNB0g5tFDf7N7TOs2qWfHw+Y2gT69mMm+u3U2Wpjwxlix5Hcr+hbAy+7ZHsePboE87PllilWC3mZJXp+hOH22veXr8kHm551CiMOsMsCxSW6DyNqlDNDH3YU5gFm9mO67JiIFEGIWZPCJ5yZph7xfevPsSX0J72eNdivOOoak7Hgbc3W5bJs9vfU9rEoi6pyyWkCRKS8nSv5hRATRAWcsakyT04bXwTKUPSQiKRsoIwTXAJQyagskAUjFYMKWNEiGj4RdXkP0c88j/8nPf+Q+Af/pUP/b94HomMm/6aJBAsKECEMk8dVDpHslLvOqInYaOWCVktaWKxIRNsBC1kk4lBEVJERKGyUOXIgECKpARxbqhSRXaBMpccjx2l9JxuPSM9mnOWm4zrO06nPV91P6G+fYkuLeP4fbaLnpQjs3mN1ZazzYayqiBrTqeWu7c3+BgZxpGx9axmDc28oFhWbJZrCu3Z7QbKxmDHgvtwC06RPajCYLXH9z1Owc1+y3tnV1hgf1piqJBaE7sjWhTMZtRFQV0YtK5xOROc42K9pKobimJJUnAcE+3hRJSIH3tc0NQibPctdw+3fPvyJetqic8R3xQQJ3CrEShnS56cXzHagGTNi5sXfPDx+5S6wUlELw261FMDTgfJBH7w/vc5XB359mdfTkEt72nmC0KIDKeBWT1n6Fq+/ObLqXkoBR5fXXJeXTG4yMvr79id9mAilakIveP995/yyafPWC9mLMpnvN7dURlFbkfiekZNZq2FbDxiDNl3VKXi4uwClKbQhtaf2G/v+cmXP+X+9c84LyM//OwDynKG3cwwRtN1HSlkxNaY3GLEsiwqCivEnMnKk51ibixd6zgOI8vzDUMVkc5zv7/h8eIR3rXsHyK7w4nCDczXF6w3G2bzDZcXjxCxpOwxxQRmDTETySglU/o76mnCywlRgkEIWpFTRiKoGFEKQkpklYkElBcyiZg1RUyIkV+8WOj/nyODE+y0xaGIU94/ZCj1tHv2GmJSSGmISaNUJCeFClNpZBCm0TNnVJx+ZsM0nAZIClxSJGXQZGx275TOHtEG8CSrIQrL1QXGesqyZBwHhqHH9wNZCXWXcYPi9Tff8Y3/khASy3mBKed877PvcX7+mOgCznkO2zsOpw5jFRnDw2kg6jmL0tA+HBj0wMNtTzEvWM5LHu7ep+ySPgAAIABJREFUUoYVfnBUswplLfv9W4YEiYCdz1gK9G3gtr2hqipG3xJah60tm82SoykRKQkxoMoSIzX1mChngRwDXd+y3x7xOdMeT7R5ZL1a4INGoqdUijg6AgUP/oGyqDm/WDGzFdvDPeWqQZLn6vwCCcOk296siWJ5/uo5ptvjxOAOJ+5ub/nkg4aT23E83tPYBcvLNfPZgv3hxOGh5f7mgefPv+X65g1912FUZlnPWTaOlB2n3Q377YGyanj0aMM+ecR5jKoYQyKcWjaXK+bFjHA8cC8DZ4tnzJcFb+5uWZ+vqIuP6dOR2hVEJYyqhvFEt3vNzauvGbZb3v/kkmrZYMsGW69prJl8CDZhKk1fV3gC2UxL89Am4sJMFX5ljdXlu+YdT1k3+JNnGCN+M606c2iZa0cxqymqmmW9JCTh6ukFlTV0YyZqNQX4UkZJ5P9i7k1ibEnTNK3nH2w8dubj453i3ogbEZmRmdSgKrp6EEJCYpAQO8SKDWKH2LCCFVJvQSxZsEdILVagllBDI+hWFVXdpaqkIiMjImO4g18fj/sZ7Nj4TyzsllSIympUKlD8vnG3Y8dXZv/wfe/7vIFBZk5wWDRealCO2AW0DSAERgVww3EgFhBCwAjx/mhtyaUaisG6/3VMkR/GJCACw2ouhvx1Kww4CSLBO4uQEZ4OrRVOg+gNIjgwYlBCCYkQEhEGF5ZjINd4C0oJvAoIJ2mVRAY7eLajgDQCK0FoELkiTzJEG6FmkIUR3hnMvqWyDbUP5MbQASqKuFnfYquSECTOTUgyw8Wr1+y3BwQRk9GI0TgjSmK0VKCiIU5NKPp9yZYInXjiWOOt5/buGm8j2r5kf1MS5TEqi7l884bF8YrlfMXlry4RRwVNNQSflJsD+2pLJDypibipK4SaEM/GZNkYaTSHsqNtA/39huAEdX+gOuxwIaI3hhAF9nVMKlOSqCCNS27WD2AUN+UVn/3oJY0PTKKMen/gdXFLgeTodMFkPuayvBu2w7rg7vYKQ880pOg0IIC2qkl0xDSbsN+V7LQiURpnWoIIVMYQZwnL5Qp9pPCRZTpdMV3NeDyfsGt2NL3j8fycx4/PKZqGcRHwncNpj8wKBA4lFcXRnNjXxDKmbRSZTognM+x4TNVtMHVFWdX4vsU1O2S/Q7ma+9sHzOMj0tkUqWLyKCLPY6pK024tLvYk4xRhWrI45tF8RVv1RDqmSCeU5orOGObzBXUnsNsKrUdMiylVXWNNx3r9jvrQkOQZtq5Z392THc3IR2PS2YTqsAc38DZVEEMKsVVIIRGyw+DwcnBp2vD+qKDAeY9WCsFQa4qlHu6NGKLZhcPJQTr/68YPYhIIDMUpqyRREEPd3HiCBucVvbBIJJEUmN4NPLogkcqhUBgCyjuEA49HDbMKQQTkkHMFwpB5NZgzVIyzfqAcRxavAlkSMYomKNfwUD6g8zE6UbTbPdvNPV0WIaMxSd/h0pjpeM5qek7kIzahJdjA9n7LZt9wcvSI5XJJnmqs6dk+bKn2azoMp9MXQ13DS3wsUdYTa9DENPsddVWxaXYor5kkS/RkymSyQkpN/dDwfduSjcbk9OwqixFhIOze7zn0DauTD3iyXJDpMW3fs632OCnp6pqm85jgSXJHkMNOYTIegYzJ0oJt1WCtxQLHRcKhS4nUlPKu5KiYEuVT8pCx3Vzx819WHHYl+5tr5qtjitEMY3Z0+4yt3zEKM1QWeDjc8bDfUt5vaU2NiBxFlw+BGAKSRDBfTFkt53jj8cLgjGdTVyzmSz757DdI84KYjLPnTyk2O5yskbEh7RxGRuSTEbiWtofT2RGV63FtT4cjlYE60bgW9uWW9eU7kiBouy3SG5QQdLZHO8VI54QoIhEK7xRCJBhXY/cBrRS+CSitOT45ZldL6kPHbB4hakPVlJwtT+hdg3kIpMucbJTRdQe8CWzKClN25M6SJVMyHaOiiDgItBps6ziPFAyiMkATQAk8Ch8cDIs/Tiq8H47BILBKIL1A+IAJIF3AKk+jJDIEfHAooXG/Jo3wBzEJEBiET8Jh5Hv2nJLgDTJ4ggsQRdgerJMQe4KUaCUR3g79eTF0BIRXBOGRTiCDx74XzXjrkCoQPERyeNiiSGKCxFlB6BtEMbj9OtsxUSPGyYxbvUWLFt9JxmdHLFNP7zzny1PSLEbUlvKyQSQRKo6YHK1YLWf44Lm7v6E6lDggVhLVK2zTk84LkqBQsUJ1HUZa9g8Hdpt7hNfsyop5MeGsmHB8fsr+oaHb3XKoYbU4wSrwPXhbMZpk7LYN+13FfVtz+uGUxkLoemyQVNUAYmnKmrbtKCYzxqsFKpI0peH2dkeaDRHjte+ZL1Y0IUKomI/mBSnQ0bG+XdN0HSsEm4cN4gD3tzvWr99gn0kOjzqWswnb8oq+3SMuM9pQI0PM7f0ttu5ZnCzJJjn7ao9E4txQFc+nE4Lx9F0FvWM0nbCrW7756hvOH52zmiyJ05R5MUInkLsJsyRnV11xqCyzRy+hD+jac6jvMVEMicOUPe9EQ1S23F3c0G5v6DbXjBZLYi0GArKUxEqQjxRJokEkoN6nSImAFx5vhueG2DNRMf4QiGyHayvaw45xMiGRisZ0tHc7ktGYEEts59iUFts0xF3gYb+hiKZkMsYiSbWmL3vauiN4TxCD3t/LgDAQpEPJMHhhQiCICLxHC4lPAsEEhBhIAd4PmgEVLEJJIje8FwENXiLdrycK/CAmgfD+J7IWJ8B6i0wKbN8AgTiOsN5jsWivkRZ6YejDYLVUEoICFSXooHCuwzK0WZR1Q3RTpOidwymB0h4ZEogCeA04QpRgtUCEwGJ5xnw8xvSCIGOSyYx5uuD5+QnSe6yQOJnRB0EcCaaLCV4JFtMVx6cnFMsZ+33J+u4O6y2L+RHTfIwVMMojRjIh1o43b9+xrfY0dc/t5Stkp5gfn3NyfMz4aE6QMe++eE2YZyzFhGR0oK73JIyR8zkT69jtDLaXFJNz9FHPh88/phinHMqesqwIDAm18SgmHafEOsW0DisDk2lGdbFnf9jz7s2vOOx7zh895tMPPqZsS7blmtv1NeNpSjZOef7JS24urumdJa0Cz2Yn7K4vkMERK8HF15dIu+Nyf8tEriiynAN7iukEnTvG44zd5oH7u2uSPCNWCd54uqajrBqC9kyzDC8FT54+ZjqfoRuLxFNWFVfvXvFQNZjNNaPpEl83LJ4tqDYlFs80sVR3B6xtqSK4fPU9F9d3qKZB+JaRVpi+ZjV9gjeSKkqYFGO0tlgb8Fbi8QTbo4KjLRs6G7BdSzTKUDKmbA9Uh5LQWZJYE9oDi6MjThaPmc/GdHFEUmiKVON9RvnmFfvLDbPlHMId1sG8mBNihTMVVdnRdyWRHNSu2g9GIc9QHHRusBLLEBHefyaVJwma3ge8DgjvATEElNiARaCUwrsAOqBCj/uhJxBJBM440IFBE6wIfUuUSMJgmMRJT+gFgh7lBFJKnAoIJNoKemsJvkfqCCEZDDORwGo/KA9FoO88sR4QWFqnIBzeWJI8ZjFaIbRC1ENtouo7vFdk05TCJmTTFUkq2RxKTA9WKoo4Jk1zinSGMB2jxYJsWpBGGaV5wL4Xnai4RMmMcZ5hraHtenKdU+/3XN68ISNhe3uPbzt+9KOPCZkmzxZcbu956Lfo6254uHJPWzlGaQZRTtVdsbeOYjoGFGkyRcf5oBakY5wkhCyjsz2RdoynBcZpbAui9sixJI4z3r59wzKfkh1P0YnCho6HzQ1CR2gR0+xbkrhjt9sO2goEbfC8ePyYl6Hjizdf8ugq0BpwdY+pBUx6qt7TYPlodcx4PsMLy+233/Lu8h2LyYRIQtc6XO0IxiLyGCO3dOOaWVpQxBkH19FLRe8dzf09jQlYGWOrA6YPJLsdbb4lzhN8EVPeG/qHB+6t4dvvrrm4/Aq/2VMUCWdHK2bakguBSlKiRDDejgBJ11i63qETTWsMMg74TJE2gTZJhmVKDs7QgzUE4QjRCO8UcSKYTkfMlgvqtqKIcsY65oaevgv4dsvDukOmKdNixHZzg/QTdJwiPZhYIIJGJw7jJd6DkuC1AhcQxhNUIBaOHoYWdx8Ianh3ghhSq3ppByu4Ah/UUBwMAS8kqfbUP2TkOAzwzt4LiAUy6pBdilcdvgdpOzIBnZAYPVRDCQEhHPiAk2IQBflAsBZCMngI4ojYO1xwIAJag1CDulCHHnrLYjKlGUFDR1Q2pEmK3nU0osM6xSSVNDog+jXrLezqhqNiwaTQJDoGYkAzebxgWhTE8YymPbBeb9nua7TQdBvDVXtLNc44W5yitKHrD1gTMLuaqrrHeE+jPJt9S9W0nG9LtrcHrIp49OKUZr+hcj3noyN0loCxZPMlx24QI/m+RsuUq1evcQKUk8yW+VCY7BSb7Z7RZALOoaWg8Za6j+n7jv3ujserU8aP59ggcMHSHCoimSFGKfdXJW/uvybyiqaqaKzldFZwubskPV6w/qdr3n3zhuNHj3kyXzCfpUxmEZEs8DgObc/hdo3Thv2h4bCtCKanSEZEkcaJgacX6nZgMY5TDocd7RvH7cU9xWmOtx1Wdbg+ZvXolEeTgj5WrLIJ4+kINQIXEqajltZL6uoByh3V3Y7IHRhPMsyuxIxy2q4liyOEThiPluTZjMp0OFsRZQqkHCLZpMRqibKByliyeMSsEKzv1hz2LUWmiCc5aVpwfOLpdUw2EeSTGD2Z4XZvkBHoUYHvW5pyjz8r+O7yNdvDgt84/ZjVasT5dMHFukQ5cMahEMOLLgPIgAeiEPBCDcpP6egFSDs8ftILUIZg/NBdcINsNhIKE0uc6bFaQv+XFwd/EJNAAJwQqCxDBPDWE4KBXqKcJUiFCQGlPZHUNCGAcEgrsCImSDmEfwbxPn2FoZViHS6AFOq9ozAF06MjgektkVYYarp+wvZuzXiyQBtHkibIKCVdBGxZE60SzmdTfDwlDy1pJClETG8cV9UdmJayXVAtTphO7JAZeLzix2fH7G429M4xPZowyRO067jd3JNFMfebG8reYSvBj/+l3+T5yxdoqflf/vE/4m1dYfuWH336exyfnzL/2c/4H/7bf0AzNqhqxrNHng9ffEZHz8XVhmAcTduznAlSqWmD4/Xba6IIykPH7XrLL99+z7Fecrac4YsCGSLaumYyWnDoSm5/cc313YZiumT9sGU5nfCz3/wZk/GEQ9ewufgOnU05y8dU65Lby7eMj46JQszRyYTpdIoTPW3dEp+ek8g5XVOzfDSjqiv2uz3TbEz67CnK9ozzgniaUZUH3r2+Iso0x4sz9DSnMjVRHfjgpx+SZJq6banqh6FYay1Hj84YpSPSUUZFhw6KznYkaUTZW8rXN1T3l0RdyzTNmRRwOlvgshEHHJ0RKJ1QFBOmi1PSrKAYZxACsRBUpqJ3Hqc1vi+pKkM2yehaQzASq3L2m4YIR5Z/is8yJqVl1wZENMJHg1RbZwo9Ttlsap48e86j0w84e5kyRrCcPqbWCU+ff8Db797igkepiGA9Sg6drsQGvAj0AbQf9sWpV1jhCLHHCg1CooIljhiyK6TDOgiRQTcCl4Kwmr+2lfj/lyHAiEAUg9taFIpeh2GrEykipYYtox1YgVFwQ2VUSywW6QLBe5wEqRqEixGxQCqGc5WXxGYouHgxyJD7uCATHuUUj/ICpxW9MezDllm0oJId1V3E4uSIyaogVyPikaB3O+LRiNkoY+cNct+xmh0zmyzRcUTf15TNjqlPSGdz1GpK03VEPqCinO3hwO3lHa4PzCcFbd2hpjHL80ecnp5wOFQUMiNMFMdnP8MJx/bqng9OH3H2yXPSaEnvrplNz3Gxoq80E51SiQPLSYbtenaq5rCu2G1uSccjptMZURD0tyXryAKBmRBcN3vKTU0rBKss57tvvuby+g2/8bO/zSSOQTiuX7+lanomp0tef/cto0dPWR0tePt2RzCBVDnyFJ6cveD5T55y+d3XLI/mRGlKZ0r6YPj85z9H6ojIxWTjmMXsCXk0wgvHrtrgRUI6mdC2HQ/NnienU6wLdHVN3uyZzZ6i0pjTxRjXGBrj6UKAzhAlikQ7utDherjc3LPd3XN7dUfZHMiLiJPjYyYLxXI151BpNpue+ThFKkuQPdNUUehAnEpcSPCNwLbgmgO689S9w/UejyX4QBxZsqAJztDuIbINmXSUwTAbFygCvhNEfSAKDjkZUZc78jymWGSMJqdEqkJlktg4ZBkQvieEgBNDfQwhEAR6EYF4DxwNnpAIGikIBmKvifHgPD6V0A6dMech1Q4fwEhPUsdESf/rZAI/kEmAoUVmdz1BSVI5HGeU8GgZ0TYdHjGoqfBDk/TP+6hxII7AtQqvBCEOCOvwTiL8n6uve1op0EGiUoH1AdW3RMESxgkqD4ixQPU93gRkFBFZSxU20J/Srx+4M2sWzz9AqYz2UFNmM6bHC+K8AONIJhIRS/q6JzjL7bqh3DXkUUYUKchjmgYEKaP5nO//7GvavEPHc0oryeKYuvYcesPphy+Rpma6zNj0UJfwzds9Z8tHdPsAdUyqIpoqpts/YDUYoWn6iN43bLd7tuUlm9s900lBpCTJYsSxOaeve6q6or+oyYsI2wRcLHCJYHb2lEePH7HflGg1Ynu7pcgyGmEwF4Gy7sn3huvsLV27RqSQ+GRQ6BUxx8dTvnuVQNWSiRF939BtK9q2RyeG+8OWo3jJfJwxnguUmpJNF3jfcnpasd49ILQiHxWcrc5QEehM0W53hK5ls8g5LY5YzCxZnJNqgQhQu0B/t+Nge5xruLp4R7m5GvQJqwknxRmzZYqKEnzq8P0eIUZkZKxNg4l6WpvRHAS97uhaQSQTvI3pMFhiIt0Qeo/oIE+mlPbASKVMowSb9DQ+QqWaQ9OwdCOc6BnFK46iLZtdT1V2nE4iaMFlEhsCow5c23LflgSvCNITiyEJ24cBjCOkQ4kBJx5CIPdgjKP34JAEDUEqvBOgIhLp8WYotksb0DIihIGx+OvfvR/ACAxaf4WHoLHKYZUiNoqu7rFhIK4a6dBKIXs1CIq8Irj3bj3hEE7g+qGKqnB0OESIIfTIoOlVhxYgZIQSHZ3SRDVs9Jq5O0bOJJGJaJuSu/s7rHdM1Qa9mHL+wTl9gOPVObvtDdv6BtYBNRkIyeu7e1SUUu8qLq6vibVnnuVc1S11VRGCYTwpiIolXTt0HSKZwKhA1T2H+zu2lzW13vHy6XPu6muuS8VPH7/E6ZrWdjw7e8qFPCALz6E0aL9lt3tg/e1rxqsTtrOAqFuIE87mH5FEG0bFiFiPoNnz9a9eEUYR06Mld9tbuneevmmYLWfM84LYV1x9d8e7+zVd2dD5mhef/rvUdzeYQ8W/8m/+a/zD//4fcPlPNpw+n+AOil/e/zEyiXkoL/jDf3LDw92aYnpElvWcnJ/xh2/+gJurNyijiGVCmE9Ij+fINGWcF4i25927EtlYluMZtgtQe3rhOFmdkY7G9PKButnR1Q2d3UOSMpU16DlFEfBeYcYJ93ctNxd3HN49UJcdT86WvHx6Bj5jcTpl3Q4KRKotPtMcvGOUSIpiig4e62tyMUJFEIxg7w1OS8Y6Y9d3JDqhz3rSTkMZkKea5fEIH+dMgiTWcz6//4bVNBD3kmmRI0+eE6Ib0osZxeNH6NGIuIiIwjFoyfdvL3nY3SEij7ABKwMKifARUg2tb+0DSImRAeP94JKNM6yQWNuhrSSWDus8vddDGIl1mEgQeYMbxeSuZ/+XywR+GJMAYUgaiqMULQKujxF4ejX4rFHv48u9JqCwyiKsQkiHFx5hJMJKlA540WNljCIm9i1OGlIHNg7ELsI0ipBGmLxHGY+rA/MsQQpJFyKU6ynrmhBAYImngtF0RK9BmYaHg+NwkNT1Hn+WcSZjIqXxKmCamijSHM0KDn1F2R5wPqOYPaKubrndG56MU1bLEfokI4lmlJs7Ziqjsor6cEOaJCTzCbNUULWvSU6OMBgo7/j+7ZeEaEExmbC93dFtt3gT0YnAapzSBs/k/BTTdex2d5jQsm0dh75ERIrpR08RveBoPuPRfMHl/T2vvn3Lze6BbH2PCIpWdsS+5+AqHg4dVzcPtHUN/Y45H3F0dEa32XF7tyebjhFxxyg74uvXr3G9IhkldKYkiByl9yipEEIySTLkLGN6XpAnGcpJposFp5MlSTzm6vtvqcoDRgl+6+PPmC5X+N7jWkjjMbFW1OrASFhkOqE3NUnUU1uJbwVKpNjyinffXuO7A+X1lpP5jGrfky+m1F1M1gYUjioYvM1JRUoUpcSjhL6sqHoxCNOsJI4C8SSi6f0QLluMieMxe1kR9rA5vKG7r5gjWZwpVKqxdcnxbAxxBU02+F8mloWYUSxH2PUDW+UZnc7wO0EvOrbUdF1JsGJYxRm6AyFiAObYgFUOXMD3Aa9iEgmeDt8ptHRD58JppHJ4YyCRtCGQeoVPcpRsqbofek2AgaUmEAhpqXtLJCXBaURkSK1ACo2JPcF5pBQErRBeoP0AFbV6EEwEEyG8x0iwIkFjaKIhiQWvGYkeRcC1gj4ePAVt2XE804z6hlYkHKqePFmwOJ5ztjqj6Ryi1iAWJEnJRpdMZnPafUsX1xwdP4X6noMzdLs9tre0dY/vatKRp/M9o/mYsRc8PR4jYs1h7ZjmU1QWuKrecHh9hTdTrrpbHn/ylNPlEUcvj5D9gdnRikbmfPH7XyNHPU8eH2N2O3bSEnnN8skLWhvjE4u2kmpdUjc1Oo2wjeWuKhlJwWiUsXUHvru8YVLkuMYxW8TsevCmYx7HVM2Bn/32C7ZrwT//6o95qC6QwXK47Vl+9Rqz64grSTqpme1zWmZkI827dxWzaEEiYvq6YzIbsdnckqJ49OQpRTqmNT0pGR9+9AmbdUnVQ2x6XN8yKwqOT88gWGaTgnEWqLSidVtGaY7WUyb5ChNumI8CXh2zMwfSAxweOvK2I97fsn7zBX1b0ysHraOe9kSRJVUGowKRF9AltJ0jTixZ/B6SmmYUaYJHYbQm2AFAGinLxnZ42+OCRdY9jDTxyBOFAWGHAu0tDs0oylBRDuaAzSaokNCs37A8HshXm7LkaeWJJkOq8eUXa9xe4JWmERbnQIT3QjmhkB78+4UwERLjh/jxYAXCO6JEIYKgp8d5jVQebzxZFKNp6VuBTyQR7octFgKIpEREhqZRaBEw3kIuhiNB8IjgCa1AywGD5elROsIKhiJR8AitUHrYJbjQkfjBUKFCjIsCmbVYKeisw4pALCekscBpSZhoegTOwvnZGZN8go87vr5aczye0aeBk6Nj9IOkeJqgnWS99hhX8+byFZ1rsEYSHCyPlxypMy5vLtjf3eJlyv1uQ131zMePmM0isnnKxcVb1nc79vWG8mD46PkJP/2dn/HFL37Bu+mK2TTh5qZnupyghcXlKYsoY7e55aZ7YKyPefLZx9SHW4QC4yMu1+/ogiOxmnJfsi5Ltrsdr/db+q7l2fMPefbRM2Tw/OriLY3WPJ0d892X33GTSsZHc1798o6nL5/zm3/3b/Pln3zOkycv+e3feswfffnPIKr55N/4Pb75/kv6uubFyQu8G/PZ7/wWl1c74sTx3Tev+ZPPv+Dp0REvPv2Yza7l2YsTnp6fYlCUZcNqNeV6fcsf/+k3JEIiI4ExLVmco/MRoZWkseBs+RFoj7MttWl4XDxD+Jj5UcFdt+fu7S2H3RXfPXzLl7//Fd5UuNije8NydMQsK1CtoA2Gt69esW86np0siaRCynhgLFoNKmaUjWjrEqwmRAMkRqZThB7hNndUXYnvHGEcGKUjql3JQ27xB4mayIHoXFh0B1GU4rxnZDv2fcPJox9h4kBSHRAakpHmdlfzxRf/J9v9FoTHA4lgKHi7iEgKXNQO9ngbCHjioOjtMOFEWuB9gOBINDjvcEEg80BvHG2cIm1PhEC4mB98KnEKNJ0hhEAInlxDTSDoCNk7hHCISBCcHhx1KFADXSW1QzsmOACJFZ7US1yk0GlP29doJ2nRWCXQuSCqNREdk+wxs2mLtIqDWpKLByJlmC5zXAWL6ZxoOmeej7h49Tn76zsiDecf/hjjNqg6EI9n3DxcEXzM0eqMXVPig6BvDMiYRZEDEx6ouNluuL65Y1NeY0pL0zXMj1dMgiVbaVrjKW3P7stvCR8/Jx/njGcjcgo+/+YfcxMWVNd3/L1//e/Smx6FpS1jjCkpxhrhEhbzEXvzls39gThNSRvL+GREUz+QpIFRFFMUM8zHLV4YDjuLNxVXh3tuXr9m+eKcqHygvun4+KNHfPz8Q4wzpNGIlx//hIuLa4I8sFycUgc4eTbj+Mk50WLKL3/+BTouKMaexfkpZy+e0H79Pe+uL9kfGibjDB1POHQVrvdIYraHHWenSx4/fcJ+02Lp6PMx2mqyPEbk2QAk0Q1msyPSGnsokUlCJCIutjfYxuIXEZFLoDQkRcAuhwyG+iCpD7dsdi1OSXSWkxYpSoAJLcHvGEURIWrpo4FaHUVTAh1eKGxTI6Sgazqqess4FPg0QdlAJiwJLRvrOR5FaJmQzU5Y31+Ryghcwp2o+XE+wSQB7QpCkKgAwcfkMhArgRYapwNOe3xnELEfSMPd4CZUYmil93rI0BgJ6BT0eGLAhZjed6RRRNM5VNAkocEYPdCuB6PsXzr+urkD/znwHwJ372/7z0II//D9Z/8p8B8wcEz+4xDC//QvngQCTg6ZgX1skQZqL1C1JwQLMXgHMlJIN8Amex+Q7XsEOUN+e5ACGxTBWnrp8EYMogoEVgQKr2kcRKWDqAcn2TY3iEbz6e89pdpbXN9h0wnICJXMaCLFLB08+iKZYLoDQnXYylEkBcHs6dsWu5EkE+iaFogRwaMUQ69ZZaymC5j0bN9dEuWC00dHRPkI2dRM0yP6nT9WAAAgAElEQVQ8cLe/JQpveTw9xyYWshnF2RNibcnzKasXz3GXO96Ut5ytZtRBsL69RWkN3hJUynI5Il/N2O73qPSe86MVMg7cXV2gR4LHT16QRinb9Q3l7oCUnjj2jE8mJJ2i2x9Y6ClFlnN8nvP82Yf0xZgEg+0CDQqpFe62pz+16K4jQRFExtl0zJfJN7z4+CWansUs4/joCa3xXH1/SY+nDQFbrtk1KR+cnfPi+JjLyxp7aOibltXRGGsCotkxO16AVMwTT6WiIWE30/iu5nadofI1aqS4e30BzhO7Pct0yXX5jtZFFMbjI8v9+oJfXnwH2jCeHiNcRF8J0inE8QjDmIM1eBswTUPTO7z35IVGBo0UlsOhY3I8Q20arJAcWc+7Q8sVMY0W+AfLLp4ySRWRlmQ+oIWhKWuCN6THMZmdIZeGwg9uQSFaHsoG4yVGWCIHcZfglER6gxGGoBQCcHIQhUVBI6XA+p7cQysEXoJzFh80rQpIHYGRkEW4KEJag/i1DcK/fu4AwH8VQvgv/uIFIcSPgX8P+Aw4B/5nIcTHIYS/okEBCOiDJwQIrcDogMATpCAKMUb1SAS6DXjpCd4TBTGYKITGWIX2AeUFWWzxYoCGZEnPcTrj7PiYtrVsq45td48QMfu+I/bg3J6tTLn8/B19lLLMNO6mY5+VTN7bZ25vDcYM8s+z85RYjChEoB5HvH57T7s1zKcrptMZXR/wSrItr3n7q19RlQdk37NcTDgbz8mzEUIGlqMFIhLkpmHXd9RV4PknP8Iawy++f83Lk1NUURC6A+W9RUwb4l2PWGT8rX/1t/n8Fz9nu6npg2U0XtJUBxazOePjgt2vtggtmI5WtF3HarJgmsaUVcvt3S3fb37F3eUty/MZq/kZXVcjSZgWisWHLwl9gEPDIdY8mBq2JU3jOX+24tsv/pDPf/9LSlWT/eoriKaMkjlBxUS95jw65vu3X6MiRdNNuL5+jdCK48crdjuIGfHJJ6cYMxCRgsjJDisiZRmJmFmeEY0UTSUIpudu/YbDbsTkJKPfQCctQkypyu/47pevub1+he7u2dQtct+QzlfkWQusuN8fMF6SzsecmmOs80wWKVFskVlGZQ1f/+Ln/Orbbzh9dEprAjrNoe1RUuG6BKcMSRaxnCb0e8V0ccR4MmWXFqRZyWgW01QNMhekShCyJQ0HJvMFwcD/+tU/pfCaWKYI3/P5n37OrFiRTwrWu3u2D/cD+VpppGjwyuEEGCmQRqCCQwpwCrQXqNDTyxQVRRxcDzJGyUBmJT602CDBCCaiZlMKiAPKxIzSwNb8DeYO/BXj3wH+u/fA0e+FEN8Avwv8wV/1JRkgF3AwDpQczj3eErTGBotowIsYFwd8UIhgCUGQth6kJQkSKSW99PSdJxVwfDTl089e8OkHP2I1WWEDGBu4b7c0l+/YHDxXuytqCy4cmBdzjJdUtuGTl+eM1ZTK7bB+hPcelTe4JuWufs26bFnot6TZmKhTLB8dkY8zNmVNtduxPD7m9Olj0nHG+vKSk/kZo8kZOlaMo5IHAwdqDustwkasVmNSHG9vLthc7vjJp59w+e6Ot19/ze/+zt+iyBOscYR7y7XbDzbpbk+eTdFSDmnNsaYzFt1LotQhu47F+RFNX3N39Y72UNL1liTOWT06Z7JcsJqtqPqWNCiePH2JtRuqskUpxeOnjynrjvvLLUdnS5r7e7764mvWX71mkmiOPjylvqnoDoZ3dxeILOJweUPjFZN5zigZ8/rL7zG6J5vMaGvLOIvpU8XNfcL162tOlsc8+/CU8SwjEQVJOiK0DpWPGakhWSeyhodyg/AdfeQQVtBnO8oE+nbN5dsb+mZwJaaLFWFkKL9NWIieztXUeU6RZOg4ZhInSBkjjSZJNMF5TG9om5pQKsqdwaoeY8V7e/WIritpdhVbp8BW6Egj2x3i4EHWOKtp6zVIzeIhIZtqtInoCoUQjh999CHXr0t6BCHPqB8Cr371p5y+fEypIBIKKwMBgQ8xKnKDi7BlcDGGgBSSpB/AOUZAFhqabvC+Rd4QG42X/XvakCKJWvYIhByIWqLwNM3/N92B/0gI8e8zkIT/kxDCBnjEEEby5+Pi/bX/x/i/5Q4ABxPwApTWWGsHRJUX9GGYJZK0JzKC1ntQEVI6bDqoo0QvsRYiZ4kUnB4f83u/85t88PIDRsspC+b4vsMUjmfjDxnlfwfddkQUVHbD96/X/NEf/TPa0vIgeqRNaIRhUsxZzaesr2+4vit5tniBm2nM169oq4agAnmco2NNXVl2N3fcXd5xKDvOn5+wmszJ3BDwWW5vSbOI/OwRJ4eW26sLVDLCCnh42PHRJx+y+dMGXyRUASaPpxyJwO16y+nHnxHcA6/MDWfxEQdRcvHdNU+fTxGJYb+5YaQD4fiE/f01IUnpNls+fKbp656RgKvLa+rKMjtZkamIUZpwqDfs7g9MV2PeXL0mMZJiNqI2De+uN6Ra8PlXX/GJekHZ76l3O1rlePKjT3m2Oudh+sDV2zs2D5ckkxyjLD/93d9is90QicCnHz/janPJdrunyyfoSJObgLSGaRHRuT21ekZRrNiVN8OKGGLivmeSDWw/01mSB8uh36H2AmLHYRd4++137K5avO1J1cCJyAtJWRsIhiZTFAXosqULCbiOfRUYzwQhNbTBkcgEmRR0UlDbPU27RWcF3oNWgSy31NbhkUy1o40Epq5J0hndzOE6SecNaTpF9ppqqhi1mj5R+NJCiDk+ekRz9znCCXxkOHq5Yr15xXiyZHt9jfWGQiRU9Ahh6FuBkGF4gb1A6QhJwIT3PhkNlYuJlUAGQ3CBXgWiBOLK00UCb/XAIvAQApjYMA76b1wx+F8Df5+hVvH3gf+SIYTk//X4i7kDQogg5XDeMXaQRyZe0CkBIRBJCUHQSTeYIqSndwFRB2IJNlhOT5f89o9+Qioy9ubAZLpAdzHtdzvssxwSQaoyir5lMh6RT2d0RhMlZ7wsZlzf3fPq61cck1J3JbmKONzdIVxDqHekownvXn9JKzy5guMPP+TQGezBcvnlt9y2e1ZPz/iX/+5vsNv3lPf36L6j71q2e4/ODd2DIr66J9YSmWbEsmc+Kjj54BPauuXk5XN+Mk+4/MUFLnEsxiu8bfji8nNM2fE4Tdmx4Xh6zj+v/4DqF39MJzwfffhjZo8/Qbot5a4hmUouLm65vVrz9PGK9eWaxguyozGRcVx+/YrG7skWY5Sa0ntHOoro0wkyUcRiSWlr3l7c8cFHp1y/ecv/8Qf/O3/vd3/GJ//2v0XfBr5//YZPX/yYT3464s+++RPa9Q6ZxcxUTDJdIEXH29sDz598xnf+a44ej3l6+gTTSK7XG+p2w6PZCu7XEAQTqWkv7ymWx+QnCpXmOCwq7zkh41AltG5N3ba43rN+9S2vrr9i9/Udv/t3fjJMFiKw7w25HPF0dcLs8QicRljHKB9xs6+I+3ygVB8OhCzmZHpKnBTclyX3u4qTaEJdbznctUySM47zEcx71g9wf3WN1RFWKmzlODmekqQT9k2HzlO6qqWcBI6U5tt3t5yfH1GkKfn0iCJWIGNGH7zgxTTn9S7mD/63f4RBs/HtQJaWEqU0TjBAYQl4HwheoIPAR4ALKO/xfsiQiKQEHKaJyVI3IOr9EGYaWo3wkrTyNDF/s+EjIYSbP/9dCPHfAP/j+z/fAU/+wq2P31/7F/9PP/T1RVQTGYFXEqEEKgxnbN8biN7zA8zgq4YYKzrGi5xPfvoJR7MnWNcNzLU8hSimaUtc7xCpRsqOjS8I65LLSFJkEeoQaHfdkNKbWHzd49qOMMkYnX/Mpt0jJzMWqyU3t3soH6gbSxJNoD7wXXNF52vG2YiTdIzpDabvSHRGsz9QNQ3T1RkqkkSx4dHjjxinmu3mQOME+yDIm56rt9/SOU8hPyA7Lbh4c0EIFSbAZ6uPGa1S3n7/Fd1hjTH3TMScaBxxc/WGqzcbRtkN49WMx0/O6PCkUYqUHevthm3f4Q6W8Thmdr6kDfccbksenz7l7PQEax1RkqJ0ykQk/Pzyz4hjRRKnTPI5t80tOlfMTs5YLY/Z9zuqrzuu+lseTx9zHM/pl1CcFNx1tywnC5oyYrWY8OR0zvzo9xgtRkzzhP1mTxpPUdGCNBnjNQSjCE4Mycg6JrgIoS2JkfQbT9MdMDYQnKOvA0K1vLu8xTYH5kWC7SCWCa4JrG/v6UKLFo5psqCRCakLEO6onSIRY/oQozLBeJTRVNshgVlKVF2TioBUKb3XBJljM40eSVYhJ+5P6ccSKsX+0FHXe06XE/LRCYW27E3HIhNIGfP0gylV1zE1CVJLDkoh25bb0vM4HeNEybbZIelIBEgl6BmSraSBSA0yYu8dTkoUisg5pFII5TFA9H8x9x47smRpgt53pAk3cxHyirw3s7Kyqnqqm4MacjgAuSAGfANuyEcgAT7HLMln4IoAueQbcEcQw54utCqVqm7mFaFcmTxyFna7QdHFnumeRXpsIhAWHvAIP8f+84vvQ0P0OBEojGCYIalIihmpFShFYcETEX/AOQD/cO/A85zzu49f/lfAX3z8/H8D/mchxP/Ikhj8CfB//js8I7KB7AbUxAJPFBHtBEYIZqlARoSKBAEuCoQIIDKl1dxUa9QQ2dd3yHlEJMP56Yx1oELg8DRx2dzS58CmUHhfUPqeiCAFxcFHjmPH8DSRokRKz3Q/0z9+RWstsqlxfYWxM6JoOR0jX77/NW4KvH33wGXV8qNPXyPrHU/vPuCdI8aJpBKzUpgkuSiuuNw2KKM4nU7s+57sPNN55PD2W5795FPO3z7y1Zs3/PSn/ymvvih4+Ppr9vsnfvm//19sfnLF08MDRYaDF6w/uSH2E5fNNfU20scjajDM65LoM1e3LZMznLsz/TAgm0wuBMfe0cfAZtMS54nj2VFuasI48KPXL1mtIuYrRWVLWlNQtit6RmKC02FgJBFFRWobTLmmmxNXuy1HDCkqfvmv/5Kfff6Mq2eveHZ1xRlFij0recvx1NO7mevLDZtyRbO6RBSCzg0cn55IzDgL53zEnSxlEmTtyEKTHme8jwtSvbSc7n+L8pq0yux2lglFP3vq7Qpt1mjT0DlJYTVFMdCsbzH6zDFElHZUIZNFjVMV6DV+OOIBKwxrs4BdvQuoo6FUFxwZmFc9x6cnjGh5/mLH/bvAcT5SyYpOBfxwJFBRlaBFi3935Ngm1lVDGTx7LSE6Sr3BpTuEAJQlqGWBFlPEi0jWgrQYyYlSfpySleSscDJhpVggITlRyoxEElIgSYWpBH7IoDVFDEStCYOiIvzDjwN/wDvwL4UQv2A5DnwD/LcAOee/FEL8r8BfsQQf//3fWxkAIJMGiSo0EY3IEwWCWUg0meQCdZFIwZBjRkU+6sqg2bVsr7Yk6TjtHzHB0DQlQibuuxMru+Lu/kvsw+958ewFm599gev3nPqZm1cN121FVVt+83XD9tVrmnpiPEyksUf4BLsGUa84iydqJl780x/z+NRz/9tv0FLyiz++pGxKmtUN3fk9b56+5aq5hAQPxzPNVUOWiXGace96tCoQq8jFesXp5DjlGes13b6nyJabi1uGxweE7ZjNktF9Vkqe7y65//qOP/vdb6g2Df/5P/vnlKuW7z4cufvq19zfPTIMCTEHXn3+Y5Q4MxweCVPgdrvl+vYZFzc3vH98Qk6JVz//E2YXKL3h2c0Nv/zTP+NP5z+jShWFiGyFQofM0/ffM/Y9tay4ff5zhNWc3z3RnzuSn6h/9IL1Z5/yKhn++qsv+dEf/YghGv767RvE119Ro9hdbtk931A1BbbecHlxQ1NsaeqaZl2RVCLeXpPEDCnRT5og4BhPyCEhcRAc9cUKoypSKMnGUFewe95Sblo2+YJuo3n8N+/J8oSXE2UMUAou6k/4errj5ecveek0D/20sAISZAa2u5rD/sh8mtj7jiEbTM70YUIoAzGy3hn6Yc13779lVzs+efVzFIk39z0x31H7iLY7EIHH2LFOK1hnnH9AWdh/+MC03qKcY8oT18+u+KP/6Of8/t0bQk5kn8ilwPpIQC7zMiphVF6mBGMmAiYkRNaonJdmIrF4OoxI6EKjBkXSmYIZWCFFpCo84/SPIAv9+3gHPl7/r4B/9fcv/P/noxCZeXZIlq6nRCRnwSyBbPE+o9SCEtdZ4VRCG4nMMESHdQVKBIZwYthPvLysKVcbKicpTUXXe+4f93w6TpTXLVJayJbvTkeUMeisUfLM8WEm5Uj3GEEOvP78OW1r+O1XX7OXFjd+ST9HfAalJw7DSF1muu+/4XTouWg3rLc7zseBDQnpM+ui5vL6mn444+K4zPu7CWkKPnn+kqvnn+LykWE7L1h1GTh1JdJHIme+PUae/kqhrOGf/OQ/JogzkzCkbsCPE6KAcLQUa4NRisP7e+bzmYeHO5pNRfNizTwMnO8fMVJye3mFyQpfRKK1zPPE6CbOXz5SryyDH2jbNTefP+P0u3dIZWn0FbudWSQiT0dWYsCf4PQgeLV+zpGB84e37K4+47D/hrF7xKiKqxev2Fwa+qeO1GRKAqcPd/h2YqWfIxxMIiGDodAVOUe0cQSXgQKVM1lbRHXCk6hlBaVHoUhmYtM8gzEzrjKqO4MssM2KPIGwFSpnjqGjtRE5gM+B7D1+CqhcUAVBYyXdVuPqDF4g58wkwHcBZ09kXRIGx2Qy7WbLPI50wxkn9OIwcBFdVAiRMEEh84bKSgY54F3Jfn7iqlqzGUYe2xKfJmrZsGsbVIyoFECrBY0nEjnHRQ6LIkeNlhKj/OKKIFGwyHUnacgCSpGJXiFiRCmJFILsJVrMSGERswKrYPoBzw4IYBYZRQEqoGPGSUUtEqNVqDShWDKdk1qMKo0UrNs1RVnQ7zuiSpS1QRYWrxO9CwjjkKrARUPQZ6Qv2T/csbW3qBqCGAljx7vHCVlL1Ljhajjzu8f3iNZwsXpGDpJvPtwxK8Ht5oLj6ZFxily0DUSJqOGquEKIiAqBLCxWC+a6YA6Rk59pfWR/emAcZvroMC5xfbulshlZaIb0wN3dkegcq2rFIDqybzC+5Hlzzff+icO33/L5Tz+hbVrenwau28jX33TcPf6ez1/9iN1NYDx39LEkhone9aTJcxaaTV8Q3cTojjy/fU5zvSZlTSk80QeGhz3Pb18S03eEaebduyc+ffYanQ1vDm8pti2mbHnz7sB2OmHKNc32gqdjxzhAUBPfffkVB+eo8x3bq5b17RVu36OnEw9vJPdpz48//4x2t+EwR/qHI7uqpFoJcqEXjLYKyLqgGOUim40aMc0gM6dcUUzDopzTBWWpaExDHcblaOgiziS8H3GxZ6s0bdEwCo/Igrq4JsSCIEaU0pzEyDiN3K4tl6UiNQ2ruoCVIoeMjo7sBvyxoFiVH4Wegt1qzdd3bxH+Fb4f0CHRVCUKjQ2eXEny5JDNlgJDrMEfA33OVDJSTCf06pK+m3h/eiKRkUWFSNMS/quPcl2xAEK9iITo0GoZHRZREJIkqwwkZFwhzUSUcVGYy2JJKgCztYQQoIhop/gDdLEfxiaQhaCMkllnRFQI49GxYLIjaTYo7ZDGEucMRUbkTFloNpVF2RV96unCmeM9tM2K9vKSw12H2UG5LTg9fMAPPdNGczgfefyLE89eXnB5qfFWo8+W/NgxHk94HJ+++By5tqQY+HY60Yqam8tXuHPH1A+UuuSzV6/4cP7A6ZzYTx1+HKmuLolIpmNHVQkubl+jRc3oBiSCTaE5P54YY2B/suwai541NpTYECkbizaK376ZsQy8vr3GPzT805+/5untPb/8N/+aqtoxuBN/+te/pbIXPH/9jB/9+I9BTNy/veP7w3uaiy23zRV3b75D+8j5wwMpZ5ptRSgCwSakn0mTQwvJ3XBiOE988cVPePft71Fv39A/HPl+fSD0njR0/PTzV9zsrtl8uqOOhtPXHbGXNFZzftzz6vI1L159wt2HA9u25dUnz3H+zIcvH8nTSEgD0Q90fk3VFIh+4Kk7UVy36KiZzj1jcJSVpd3UWDS4ivdhJEaHMppqtaNWmk4qGim5vdzR7C4ISeK1Jwx7kgCjWm5fPic1EXdyhGFkVWRUozjf77HWclGvmeSJyfWUbUsxBYSXMEz04wAGSl0zT0dKLRFo8jCzu1hzd9eyXrcYLXASpqpGMHLZ3qKyR9SWSEGpSvLGcNNdYkxC6gLlJMZbuqf7Re6aBLiZIAsEoGNcjNWCBakfBFksDg2yQAr4aNRFaI2UINwybJtrSZgClghGkXLGzIKgShB/oGeYH8gmwMc6qPgbBloUBBPICrRJKAlReoRU6BmslDRVhU0BkRylkVipOA+OeRpQk6WUiS4m8j5zcblBbdckKTC2RacTnBJT4+nHE2GMuJjQziA3GZGX7H+7krQ0TNOIyo69O+FkZLfe8HS6x88RTUJQUJoWmQTH/SM2wtV6x6pdEYXl+PaB6ewo24bVboVMht16h1ISQSLlkfP5wPd3PY1ouevveX1zyaEf2aeZpk/4MNFPE6X0bIod66sdVhZsi5p9/8hT98S7X33JMDl8fwbpiSJRttBeltx/v+fpaeLy2S3aJMZh5te/+TWP05m2qGmiZ25aNle3yN98RTTgwsyuaShViZCSP//tL1HfFzy7eoaMkovSEmzJ+3dvKZsrfnz1E86bkRQ8++6el6sbuuuBMtZYcc3F7XNs05LFRK4KZFEwnc5YOy3mKAkhzjyeHUo1yOxRxsMkUNJRxBVUHv90oCw3VFVDJnNV1hyVxpstOX5JURX4SqJUROXMnA22BBMFptJQGYossfaaUs/M/on94wmZBapcjE0pur9thz6NI1kJuj6wiomLi0vOztOUJVl4fFI0ZocTEYnBlDXCBaL26CigLRBzojI1po2kMhPR9OKEI6CFxJAJIpOkJMelFV4pjcgSzxKFqCRQSYP3aBlxSqBDIKi0RE7z0ieTJRiVMc7TFxYtPK4ycP93xwI/jE0gL+FfkBqRHEot9VE5SIz0H3nqi1vNZLA6oTeKVCvWpkK7yCR7hM34nJlOA4eQEXYF2VGWCWMNWhs6P1BWlpM5M+41OUsmm5n0jNiV9GNH6SV51RGdxAbFkCTkkXnu8FGxWRn6KfJw7NHeU9qaoZ8gRorKsjKGlBVP9wfKqqaqK0QUVEj2k0KmwGxO2NWa7jRT6JGRjKwKxnFAnHpyfbE05ty8xJSaOhtuX37Ci4sNUjZkkzgcPcdpRD5+4HDck6xcWIhS8/A4MATNWjcIZfAqMM6BQ9eBS3z4/j1PT/eMs8fJgfLmiiE7bNK8fLFD1SXZKA5Pj+h2Q9GsEfcTVSEYQs+2WCGuLglovvz2e8rpnv3Fhk3ZsjIWUxvqtuGz1WcILXj/dKSyNU3VYsyaqpRobcAHZtcRU8QjyZMnDA70SCagokJKj46ekAwpaO6eniiakoICb9bQ1qiQOB+PpCKSI0zTTDksnX+1XkrQZp0o9RZSWPIPJaQ5I7MhxkSMHj9NhBjJIuO9p7IVNo5Ms2ccB8hqsUNlyFlDMPgyo61CFYaikOi/6QA0GZmXaFXOZ0SRMKEALYg24ybIQi2NRDpASCShFoSG0ySxvP/t4hzGaIkOEMXfHJcCSS1U4jhBYTI+JkwoSTES0gLv1hlENzP+geX3w9gESIRSLzKRQeAUiFkglUTlRTRaZMXAwk1LSZLc8k8UNlFlTezKRVqaJYU1WKvoznsmWVB3DXPhEVkTZs/1esvm+Y7788BFs8XWnukAgxjw2Sxap3likJZuf+SEYFUkWluy02vGIWF3a24LQ+jPHLo959PExjS0my1SCIIXPB3P+IcHbl+9Znfbkn1EP80oETjsHWs8hdV0px6bFLpU3H7yI7arguSWaGiInvD+A2GeuCgvadZXlCLx5dMd/elAkEAMjN3I9bNbbm8v+csvv0RUgs8/f800z0xz5uX1NT4bxjDzePee8/sjdVPyn/yzP+blZz/md19/hUZzfXHDm++/5uHDBwpVUK8bbrcXbJoNv/gXr9jvPzDuPetPW+rYcv808PKnn5CTQWVJu2kxGGwtKK82bKuSeZ7p54CwCpESddXQrmqUzqhCkELDw4c7Dk8PJB8RYsKbghhmNquSVaoQpQYZCUFyeLxHIxErTY0hxRVWdMyjJ3vLMI/4c8aZEWxNYSV9SrgUQStEqogWtA/M48zTcELEDFrQjQMxFojSk6THe0O73WB94uGxI6VMGQTrpmEYEsGe0bFh9gGbI8Jl4jyj1iW+S0jvCadILiUhCEwJc6/YTzM6zOgoMToRpCBnhVSCFBeSdlaAXvgCOUPIkZjzItvJEmUCIWqyg2wiqAxBEaXHCIEvJDIqQvBoXwHnv3P1/SA2AQEoJGmYIWqkCgj0Yg9WksQiJAk5Ie0iGkk6048RFR1SLCOVpZH4kNBKUxWGmDVd3/N0vOfy+RXboiKdztxPEzdXG0xOBCIiS3RlULOh0RpnPe6cUDaxKgy7QlMqyeHdHVMR6TrDi+2K+uISrwQP33+PIuJnR5hnCt0sgIeQ8L1jOOxh0+KiZnezW7LTpzumaeTy+oZN+ZI//8tfIyT0j7/ms09v2fcDp8MeakHsJlZNha0TQpcc+wPZR86Tp9KKtqp4tr3kHBzzfmB+fESVmhfPXi8WmmzQMjKyDGq505kpeapQMfWO++/fMw8Dd4NDmYpCF6Q4E1NmOkm+9Y8Um4ZSOMIsOZ8OfFH8nO2mZdcm3ncNoeupNoK6XlNaxeZqQ9QLK9KYhtu1R9qKqjKgAiFOaGHIzpDV8uavioJZTKSgFoCmLqm0RRcCgcSrAqEcfpxQ1qKVRKrMyAmDIqiSm+2GcRgJekYiSZUiKsN42JOz42K9JbmJ0hiiKkh5YuiHRa8eBMO+R8lIrTRaFv45ZLMAACAASURBVMuIeoLaWtbaEqViiMvfUuRE9ANldYEVB7TcMuWA96BDD9EQC880ONYGtDSYneU0lEibmecz0SRmEipItNKI5JcVoReaVsiSCEgkKqePPkJIJiJdRoiMERKHXBqqlEBHQxQRPYelg1BmYgsc/u7194PYBDIQpwRYEAKlIghNEmEZlkESkwK5dFUpkYhuQOXMFALWCEJOCFMv5t1jjzYJoxQ6VYxJME0BlyYCDqM0D0PCJkHyM3HUnHB4PVOqist6wyGekKNHrWtqbXA5cPYZ4XrMqlwoRs6Rp0CaIu1mzfX1jovtJS4I+mFG2QLVZMYYUD5QG03TlGi3QunMNJwJ7gxSceo7TGU5dY88Hj8gpURvWqQZKcqSLveozQX1RnN18Yqr3XO2my853J8xIlOuCubjxL7bo1KBVYYgNEVZE4eZrBSV0binDpkadhc7nm+2oKEPgVkqypUiHE80TU2YB7rDgeQCH/o9f/RHrxh9pKq3/JM/uabQLTkIghSoUZCKxDSWPHtdsGk2iLJiOjzgTaAqW6qrNVlrRBbkFMkpM00jiREpA5JIUSiS0EuZEImQihgz0WZEmPFzZhUz4zBT1JKIBVMRusAx9ZwOBy4uL9B5htnh4kxTFBRyRYlAzND3E4UtUVEvcbKGfphwKqBXJdFlsrJIsyLFzDzPKJkpmpZmvWZUmekwEtwRlS39GGhwOBFJukPnFikL3Hkgr0oqF/BhT6CmrBQirEniyMP9meOY0UGRbSYnSSSh5WLOSnrRiokAWqZFROokqIXJqZQgBoMUS4QgfQJjEEkQlAcjiaMBMWFUySR+4EJSEItIRBSolElJIFXGZEMiLMIQICaB9omUE3Pfo1eWFAJCWKwxCGNRCPbnR6bHkbYulmEZCWIKTCpSaUvRbOmnI3GeeRwF63JNDICLCAU3n91ws3vG0/yO3EtECEyjx9iWHAd0WfPh4QPj6QDOIaVhtVpRrS9JdYuJEpv3mJXhxXZNVJr11SXrsmA8nZlCRCnD5dU1j+8fOQ8zl7sN7dWGYWz47a/+Au+hnQXPn33OT1694jwf+O6+A+HoxkgtDM8udli74vjhkW2rSMnSuwOfffGai6tL7o4H7g5PNNpw3d7gvWM4H9ByCS+dyDgMhIG7+zvCNJNWE7tXt4Q9vPnuG55dXtBIy+HYs1vvmMWZs9tQdQNzUtB3XF89o2fF8Xxm12yoy0U6MkcWc7CKaLPCNgVhTng3MPcTXd9DdFiVEVZTKossJQiFypKAY+wTKQ1kVbCKjsHDMByo7IpyteGUZsqY+eab3/KrP/8r/rN/+V+QmTlNMzrWbEdD3hqSySghCRkqzDKSbiS9GzjvTxgtsWVLfbFiHiJCCozU5CITVEAJT3u1o5w9DIE8BEzd0qwuMAR81Cg/I6JEtxXZCUyIDFPi7vAdftryydUN2kkkisO559D1SCWQcgGMEvjY3CuRQSy67pwQEtJHboCSARUjOUmgWBR7Vi54/rQcI4JYRLfZREw2uKQwbv5hlwgFGWEVaUwk6YguEZmxWiNiRmAxyi2WIRZRyZQyJYKUIEx+ATKYCV0LStni+oHZTYjc0zYbvPbk3GN0S4qJw/nACo2bHDoXbFXBeVVhbUnvluRgoWvaZztUjhRHx9T9nlN/xMsjm7alsQZMATHhguM0O1rlISx8eqE0fd9TlDWGhRc3nTqeTmfkqubF9S1VZYkhkpOCsDRzXL/+HONGhLek2NP1R97u79FSsb8/I6gZZCL7zOW6wa8dWQpsWTB1kSwk5apknS1xDEtuRUfQsN6VhBhJoWB2AT0OaCOZzh1untmXljWCi92GYZq4ff0J23pHKgWP+wcG3zFN7zCv/4QyWUweqC8F0ldM05F+cJg8U1SK0ipElKSQiSYRQkIKg5EWVEDJSEqenA3u7AgyoFu7OCW1Js8CYxx+jEgVCSYyhUSeHbJQGAnSOaKwfLi/5+HcI9qSQlrWtSeYTEfgykfSHJF1RliLj5lu9oiUeXoaGKcZVSn8POG0WSb4hFgSeP0MVpKVpjACowyTSovKrhyXMqEQ1OuCOlZMTtIqzdM4UsnErKAyBU/3T4g5QpHIVYECCp+WgTnEgrqXAi8TJmcQEiEFKWSyXzYEKQJGi4/MgAVa4hGolJBCI3PCJ4E0msCEQhFnRa4S/NBdhAA5xmWEUqilMUhAkGCyILC0CpskmKUkicxIRvilw0sLhycRRyhyQaVXlI3FJ0/KmWE+kcxM7jTIGhWe0IXFVAqVag7zRBaeRllUZRjcgTlsqayiWJd0Tx2D6xBJULcbVhcW5zN13WDJDOOwwCAzFKXGCkN3zsTgCWHCJMPpac8hBM7nPSFCo2qOfc+h7+mGnpgTx+GINTWb9YY6X3C4e2L/8MTpoWdIPWuTcGMHwZJigReJLG9JRuDFSFFKVqstMczcfbhHW4k0i/0YIYmzx08JUxdsty3DtOi/+slRFiWChBAamT1tdcGhnCiqGllKTCWZpkQrNsh0wBYehMIai7CeVivUyx1uGJgKTRgFyixnamIm5RkchJzAB3KOGGCKiTnPhBiQISOHgFQFUQIIlJLkLEjZEyZPP0ZwE7puQXiUMLgUefnylrEP2NEyp0BpMiFGgksMymOUwSq51NxzZp48JoCf48Kz1JKzm8hIVmVL8omh98whIkfHwQxUwRDmgKosrnfMwSy24iIjIkihQUFIkWwdfs5ILFZWlK1GRY01FjcPTG5pYlIikmBZ9EKgWFB5ISWkyggtyTEjsiILRfRxyQkQmGUioyiEJLN8jgnEFJfEYcoEnZAxEv5DDxD9B38s/CRSXl6gEgt5OMVIUBIlMy5AkSUqLUz1OQZS7CkoQQkgLHahJNBGULUF1lbgJT6OlGi6c+bFRuIV7C4bilwSoqR7PKBLya6u8VJSVju6pCFLxqeBrncYW1JtNTatKJua4/vv0UIuRNiyIlmDMJnBTUhpFh5CUXzUWwuGqcOHCVtXbEyNLi2P+ye6/kQYA+2mZogzm3qHyxMmFwRO3LZb4jzw/u0d83BmSAnlC159/hNsciQ3Y0zJqqlYrxpkXTIcDrx7+5ZCa0Yil9eXBCHpjj3n7sjGXCKlIfqOQCBOgW1T4Izh5uaa25vnxPuZ6dSxPxz58le/46c//4Lr9oJTHHjz7SOX/+KfM8YZmQXd8cgnn73iotzx6z//FUVRksqF9NRWK4wS+FngdUSkQHSeyU9k75E+4tJM0BklDCEJknTkOSCUIAiBFILoJCEGprEHk9Ep46JG1hXH6YHbF69QbAgpgsx4Em1dkvJEH8EWNUWliFIxux6VRowpqeuSLDK61CAEWSpiiPjoMElTFAU5C7rzGVM39L0jychjt2ezAisqsouA4SwnLmRmDp7SaMI8kbygKA3z6EBOFBKmYWCaBnwMkMXHhF9GSkmOEoFAZoFMYWkflpmUl3H6kCUmJ7IWyBxJLLThJJezREjLYhdB4lVEa40YYa5+8DkBSFkuU1UyENAscmYBKaGyWmqeCBAJi2COkpjyAhvVFikTMgsSCpcz+BGVE3GSWLt46LfbhvVKMxnL5AIZR99Hiuyx5YbRKOb7nso0pDDjRYVVoKUgR8vV9QXH6UzygaZq0GXF7Jcx3FIa8BOHbmAWBU2zoV1tmKuGoR+ROVIVK9r1mkbXnMKAxLPd3WDW8Pj4BlPUTOOAspm5G7i4WPH5z14Sp4S0gq/+6tcEN2DXBdIK6qJFZsXLl7fc3tySleYwfMn7/kzvRzYXt2zKElygfzwznjtkWtRsc9/R7488+YDSku2zCzarguuLa7IsOHaP3O8fUM2K4fHAuzf3PP/Fc+7ne7rHezo/0q4MWlVoEsNxJARPWVRIkYlBYHJCoRDKIGJauvkERJfILuDijMgZgSKlAWJkTpBUQgtD0omcFEZkgjBIm8F7UkpEIlJBiaZWK9J0oKgMyQzYWLMqr7FFYEgZYwuKYFApoVJmzPNyk9FLWRo0Uq5YFVuqumTuTwyjp23WrApFVTUkNyOTxupMzJIxJYrxTFEWHIYT2rTUuwpVFPjRIbJf5LpCYJTmSSXOUVBoh5ESlTwiLjVAkTxSLpFkkCyCHBYRqQ4JPnYQCiIIRYoKUgYEOXqcMpAFpYzMfunAFSKTtSJ5QZISTfhDnNEfxiaQYWmJVAtnECHwIqOFQgmQQSKTwstFr6TjwhVIGbxIyBApzLKJCAQoyzTOKDkRkVhV4EKPyJmnU4utK7xK9DFitEAryDni+4gUBedxQBpJCjPdGJmGI6du4sUnl9hZ4pLAzUvPfHaJQkZcMtSUFEVJVVoSAa0FZdFw7nrOQ8+2bkkpcZ47vMxc3lygi4YkE938iOtGno53BD8TZMlmrfnVb35HGiH4nml0DGRq41iVKw7uREnBcBo4FT3CGk4PT5w+nJASSm2pygJT1iQUY1kSQ+Rwv2fo95z6jhAXq1M+QbYl7x4+wMN7jt88kLJHA7sXO7aXO/RK4+bEjz//HB1mHt+eKKoCayw+DuiD5vrZc3Jezqm568lhIupFmpnDRBYKWUoKZfBJMA6BmD0heWY/IrxH6oxWFh8jQpUfc0aQZebYT5AiobRYEejmkXVTMSXHGM5MbqSQNaXRRB8JMlNKiQsOnzKFBqJkBuwYmbsjWoIREsJSIjVlAVPG+8S57ym0YdU2lETG3pERXGy3SG0oYsJqS6gEq/KCu7sDlQWlobm6ZfQndL3imdJkvWIYBibnmPpI1hkVJBGBTHJZCdKwvMsTMUlklh/7BdOy8EUiK4GSatGQiwh8xK6hQCV0hGSX6DjKQM4BlX7okUAWaBmRUi13CZ2wSFJOSCRRBNCClBJSJeLHMxBZkERm9omkJI1eXpDImVJblFI4KZBKgRAcn3paOVHFTFFXBD8jaoPKmhwlYzeiTYkLT7S8QMkIvaM/7xliZjhJ1GwxlUX3mnHsKTQURYnPkK1kta5QSKaYWBcVSmTKLBmdwMmRRxEYz4HL3Zr18x2rsmSII6v1FdG/QVnJ+TSi6sT9XeT85Tuk9my3NX7qcMlTcI2PA4937yAbpDDcvX/Abio+fHjP+Xzg+vkNMmeeHu6p11tW9Q4lNG4YGI49WXhWlUHRgFTs7z8Qy5pU1zxf33KqNS/Kl9w+u6Xd1ry6/oRqs+X1q4HTfILscf1A7M/47QZZVSQnuTCWqBJSGh6Ow5LAdT0RwxR7vE2IpInRk7zHh4EhObyfYHbkFBFZI0NYiLwjH2veEmskIjgwCa0EUljGHBB4ZF1ifWA4D7htZq0iY45kl5nUROU8SVuikOgMKQfwEpc8spLYwpBSQCRBUawwJuE8zM7TrgJrW2CFQE0zGU9TVTixRtqZwgk2TYGk4N69pQklu5VlU5UoFRhWE15KRFPh3EDoM1OcAbec5WVG5oTQAhUh54T6WCrMZLIEkSMSyHk5LpiclqOBAOEyXoOQCuET2ohlKA/IwoNUeAX8Af3ID2MTIIOFHOSycIEcMzIvEvZoJOSMTgtyKQICtbQXiwBIUs7kmIjJo6KiqmtsVTAGj5UK5UGlRBAz4wg+aaTKzMdMKRWzcChpUQWEvEKJRFtVmNJibObgOvzsWNsWVxvWfoV2nvNpYusz7XrNelszhchTN1Cta1a7Fn+eMAqskqSYGZ96goO0ckzzhBCC7rhfjLLKIsVEdbNDDj1HqRFuomlbLtZrTvWecsi0Vcl333+Hnz1T6AmF5/DQ01rQQtM0K3bXLdurS+STwAXH+3ffsX9/x/G8tNxuVhvaZgVZEnzAhR5drri4uuDl9Q2rpkSIglW749lnz7BBcNFcoT8bcW8DxExZCmRuKJKgVCvaUnPqz+yaC0IGs6mYc4Khw7mI1YY4j0xjIMXIue+Yp5FEJnpH8iMxSQhpsfDYRJ4GspXkZBG+JiWHipIwC3ROKCRBKJJLC5g0WxQG7zxZSUwOiEkiC4tWAmEtjONyLJIChELITKkNVpX4OaEMGBEZ3Ez26eNouyAqQ1OvOfmB2PX4/kC4tkgE0muMCby4uuJ0SKQhEAaHlyVWlzgXUcEvVObkkEKTY0Qpgf+42HVUaJWZ47LohVisWmTISi3G4iQwCHzMJCIyZ0KSiEIuN0m9gEuX04VBZE9WBiUS4Q8UCf+h3oH/BfjZx0u2wCHn/IuPVOK/Bn798Xv/R875v/v7NwFBFgVkMMoQYyQlhTYTHglIdAwoFiVTzAZkQMZElnIx1OTANDvmNFOKSDEtCRJbakxhIEoaKxm6M0ZGdM7UdY1AIus1AkmRA81mR1O0CBnYlTWrao2Z1pjHtwxxwlSGaRqJ80TT7CglXFzsUBVEn4lOUFQrSmXoDxN9d+DpcI+Umm1zjSATtcA2Bb3viVPmdD5hteZ8Gogy0R/3EBz7c4fQkXo2vP1uJoaai4sNY4y8+e4N62fP2TTXHA89wWaeX11QXt/y+/fv0dIugsrJM0XP6eGJ/nxiu9nwyatPWbUb/Dxw93jPsTvRtFtuLtaUMxwPZ7abNabQbK9uUDry+OHAzcUt7dWOL8oV+X7PWK8wZY30DiUDjSqI80CyC/SjaUvCMNGfelIY6GfF5AamIZFkpBvOdN30MQnm8HiCFOikyAS6h0CScYlatKHVlwzpgBaGsrAoApnAxtaMXjGcJ7Qq8HHm7HoqsybOgagtrCpEdujksbZi7nuOaaLvOowLDH3Hh6cP1HWDKiu0XlrPMx3zdGbqC6rNliKXECekspRFYu49UdZ0/XLtxWWLXSlO9wdqdyYKcM6TV4ngB5QoMBaENgg0AQ1qIoqMzCADSBIqQchL/V8DPguiXEqB6WNCMQlNxpGMWnIuKYKWyFDgEqACWWmMDMT8jysR/k/8v7wDOef/5m+XrxD/A3D8v13/Zc75F/8Oz/u3j6VtGEiBWRiSWLKiSeQl/EkGZCKJhS8IafkQLOnDuNBWs9RID2GMnJjJUlDKCqslUSygUeczQWeEcDhT0VYFdlUxn3u8lugsERnclBgZCdYibeb6xae8f/s7pjkSncdoSwwLw02UktMw0HUjbdWghUFilkyzkIQ5oMuMrQy2LNClRRnDd7//PVJlcqw4uzNPT2eudiXnxx5RDNhTz2MITE890pa8/vQLNtsWKwxV+p4iCExZMZ07bq6vKJSFUlOsKs7DTN1kXFIcD0ecFyhT8qPPfspPf/4zZhd4/+Et6umOclWzW6+pa4MfFHfHey5vLrm4foYxBcfxhIiJIR1pZUtz3eJ1DXrAiwR9xusTpIhQFbMORAwqLOO5UlnO/RlyYMx+UalPM1MfFzmHjsiUUFIhC0UWE2mamM4nsq5w3uOkJLaGOU/Y6FASfBSUMTAPgiKDVgInHeM5siosYZ1w6GU6NWZIMxiFkBbVllRpRsRMyhYnA0bEJePuw0fpiGBWlnFwDJ2jbTTCaKwzxFqRzUREEINDRfAxI8IFyI6Zkik5mmzZTw6lBVkG4ghGOZqtRWpB8gEj8pIxFZGYBEIoYs7klBfvYBYLUQsNcjkeaBmJKROSIBOXIaEsUbkgLT+F9OC1Xm6q+R9RIvz/8w4IIQTwXwP/5b/Pov///A4yOjhmKRBpEY1IC0ma5RiTPJZlygwlkCmRk8SYZTrMJUH0GVtBipLkI2WtwSVycnhdEFLC60isDQiBMYnIgjTLZEb3gMbgpoYUBTKfuRsl0p2QQfLik9fUqwv6sQO3VB6m056oM0WjCfOwZJrNv2XuTXpsy9I0rWf1uzudNbfzLsIjMyIFAoFKTEqCAeIPwIwZQ34EI/4H4xqVVGNGSIwYlAoJBCkyOvdwv347a845u1s9g32RslIRSqSsknyPzLbMjklbtr691ve97/NKRM10FXa6Eivc3N3QmG3bKWXeFHJaUtZE1qBagx8v7PYHDs0BZ3/apM/HPfH7T5y+3HHTvOKv/4NfcbsfmMbI49OZGhV6PlOL29Kb5oWfPjxwfp7p+4Hbm9stj6F4pNHkxxXnNM2uI68LL1694eZ0xAjBcp14/+kTVxV4dfeC3XHPoTnwNCYohdvbnqIqqi2YTtMPe2q0EAOL245iJcrPmvdCritFGJIMWxisNEAijJFQEjlnfIpgK0V41jUjCzTCUGohkhhzoEdRi2QtEccz5RJJ1ZPCjDIKT0OJK+hbTnYh6E/Iui2UFCPGSgQS4yrOa4qRZD/jqkN3B/pmoJS36Cqx1pKNIEZPyQGcBqFYUmaOMyEtiLbFXySJTJaaRlWytKQ14molGkNdDe6kKBOUQWKkYlk8ZTWIVqCSozc9EkWpESM1qSQohlQlQqctqVuAKlBk3hKOQ0VJkGiS2KjcolZUFiirELVALRQZabBEAxApAigtsP7Z9fdP7Qn858D7Wuvf/b17vxRC/BvgAvwPtdb/9R/7EAlka6hps4FKsSnoai4IKaBmlqrQCIQsWyJxFZSyNQzZNEOomlFagQAhAoiGVYOft7N3lhW7elRUnJ3klA64l5pSYRw9rUlc8sRff/MKosUv8Kcf36EsqKcP/OrFt0zrM/72JSEEwtWzpDOt2dOrPVIF6PvNTWcH2qHlpRHYRrNOZ3w8E4Jl8pEoZqh1c61VzdOHZ5xtt/CSuxfsdzt063h5/1d8+4tv2N/tefj4gcePzzxdZqRUsN/i0exw4u6L1yRV+f7/fotG8ubujqNR6JuWTr+i6Xpk2t4G3/3wJ0QWnG5O7A6veHz7lo3tKslTonnh8HPkh/F7jnevud+/wrUGVwxyUMiowS2IqtFa0+4K8ydI+0K9FvL8WQkoArJEgkxMaaGkhRAWni9nyhwJIRAbjRAW6kpJmTCuBNiyCiuEnFlqwoTC+SHQGIM1Az5XiqqUGKkYVAO2N5TcYWdJtiCUQZuKFIJluRCEpM+OajXZJ7TYNPjj9Youii/uv+DY7JlILCmQp7ol4xjw18zUeGyMLGNgaT1COOJlQgw7ZKeZ88qejFVgjESpniQE6tQjr4XGKYyVPMfEeJ02WIqUW/rW53GhsgVZKlFUqKBE/qwellRTybqScqFkiSgbNSyKStSGWgpKlk1aL/O2My7gKiTyv7cR4X8L/Iu/9/1PwNe11gchxD8D/pUQ4j+stV7+4S/+W+EjQpCLouSItApRNWWVFC0QcQstz6KQq0HVjK1liyJPIEpBlO3slHNBS2i0pCwrYyMhaYwIDK4jLwuyv9m2nb6gXiiyFAzVUFaxKcxYEEsgS8f+qDhdT9S44mpAu8yp35GkhqxZx8jj5S05V3ptP2/PCk4JvIKcIkIK5OAwsuP6/om0RvrakfKM63rSNOHnwDQ94i+KeXfg1d0tH+cV6RX7uzu8SCS/cvWB8ekRq4786tuvuYqZ9bKCFLi+4enTA+fLhaHRFCuYlhnZtHSHhr5vaFzPdb6iphXnWuJyJo4LIQSKFOyHhsSKrYL5eqEZevaNxDYtNa2sqrBbNw7AegXdJkQ1EAXDveX5GsmxkHRiipVWF8blzHq5ME8LKS74ELe3noJsKymvmCLIeKRqSCIhNKikkLISUsbmiGl7ytMn0ukFnVMoV4lTRVfByoRKmwmnS3Boe6YmUq4VbRxKGWq8kCWkXLDCENgMRikvLMnTxhWZC71qqK6yeI/3E8a1GNVgtOKaLsi53Uw5weDTQi9ans8X2B0RUuOkQzQaJSrSalQsjNVsFuK4IErHOl0Zp/OWNBQr1SUo22KsQZLE5m2qVJLc1ketgKqbzThtTXMhLDGBbisQ8QakzxjhKEKBB2EsPmlq/UvOgX9CERBCaOC/Af7Z/3fvc/yY//z1vxZC/A74NVtK0b91/f3wEaV03QNj25JLROYtnVVSUGLbYumYyWR01RQywm/U1YqkqoISBZmhSEVBE6pEJU8vGoIUJD8jEpSuYAeNUQZTM2meeJYgCyAFWXbMayFJj8wNv/jFlzRdR1wLY5hQOOIi0Xqi6zusfsl4vtB0llgk83xhSQGH3FRjpUEGWNcZhKE9ttjGsDw9U5XGKkfMAdsciU9XzDKy5B45Jt49/0Q/7AnCcHmcMVnS7nqsqXz97VfMuTC9f+CP79/xp7c/slwWNHC7v6GMC5+aB150r2msZo0TokrSdUXkRLsz9PWA0IbbFyeef/rIJBQ7q3H9gNUGcmVvFEF45nNif2rJTmJ8QZQZvCSXFdXfEkpAMpJtwT8FJlOp0TPPK4/jzHK9kEsl1IXqZ1ANTjtYPMHPFJGRKlGSICyZECccmpIlWYKLibQKZHpG5B27piWvQIRiIFw9KMe0FPrbTY9fbUXGQmxWsq8EKRBFcTLbsXL2kJXC+0QZEov0fDpfMI1jt2+oTqIipBxIaiXPC3IuCCNobdyixZSgREcJkZJAp8ScPI02GKvwITHPE7VUcj/jr4m37z7y8PEHShEIAapoRNpAogUJWSHE5h/IiM0/I8t2DM4g8mYzLnoLGwlZYyo0AlIReJWpylKt3zI8aoEDxE//josA8F8Bf1tr/eHvFYZ74LHWmoUQ37LlDvz+H/sghUT1HW5JhKZSV79p2NeKktvooyiNjOvWIZUCsqBohVQFQSIlhZAKKTJNicjoiCgWnkBJohoYlEatK9lcqe0dt0MD1WKayhe/fIH2AqkbJlacVIh+IDVlsyzbyLE9Mq0B2VakliRb2O0OfPk33yJjpKbMeAnMqyfJDAVyTfSNYX9subzf8f7dD9QCw75jvUT6W8dJNaj0zEcpSDUyXRZEhW+/fMXHj9/zze5XvHj5FeP5icdZImrh6d07OmURneA3v/kVyzSzxMSvv/0Fx9MNt69v2e+PfPX1G/K48v0f/sR3H/8fsvfcnY6IY8eLb79hcAaE5uv7G358+8BP776j7wz7oYWY0V3Hcplp2xmRNNobqJLsJDWBYtvVjOtKepaM/oJzCjs/c/YjYZmJy8z49AQqYqhAg9SGkBK5JHSjMEKTgmJclNbhbAAAIABJREFUzyQf8D6STWVUgt5HlgSN1Ti7R6rCHAy2sYg+cPnuwrlziOsDnT3i1xHZt4giiQ7a2rLYSq+gSYUiBKJxiDEjdcdxGJBVcXlcmfQD+4NmuD1x6HeEWPHXicdHD1LCunL/ciBZTdfCrd1TnWc8T+xMg+06XKqcLwG3k6xL4qd3f+CXX/2S9BCozcpukMR1W9BVCnKCqrYA0moqwghElciUUdVsxaZsqcNVbkpJiaRQPkeNBXJWZFuQWiNcJMcM2pDkDEqjwj+hMfjncgdqrf8TW/rwv/gHP/5fAP+jECKy8ST++1rr4z/2N1CAbGibC2ppCEVSKshSWUXGSENOHukEUamNtaYTUmdSqNSikBK0iORUWQ1Yk9FCo812JtJJsrYN+0GCaGiEJAlBoyr1qjjaW9xJcA5PnD8VhvsTJyJ+WfkxTXRdy9v3V5QwmL2htT269OjsKAGcOaB0BJVwyePnkWscSZ8loaZUhkYzNUfmdaSUyDSPGN3Smxv27Qs+lQkhMo1Zae09umkYXhpGf2UaL6wlktMTvbgly8QUI8I1vHh1TxYG7b/mt9/9SCwevSgOX/RIyWdMQ0VnQVANS6vJWtEIxa57ga6RKAyn+4TWX1GqIEk4vjoQm0hXKjK1hKioNVHrBXndACdLnemaBmcyareNJZMX+HFlLFeyF1yvnugDWURUI6kqkkolsvHxjFCbGSxFyImcIkomprTivMXKhhxG8m4gi0IYC0o+0wy3zJcZNVRUTaRg2d8lLqlFeYGQGmM0db1SlCFdZmg1InXUKOl7xW090O1ayrJQSkAKwTxZisjQrBjsphGQhtY1THklO0WInr10zBWkq3h3hcGS/EItHtkbmK5McaTJboOWHBtqvnBzf8/t3UuE+QOIiMiaGjdzE7mSawG5NccJCZEFGAkiU7OgiEIuZVMOs4mHtFGEUMEoTCioEqg1syaF0CD/fE/w/18R+Au5A9Ra/7s/c+9fAv/yH130/+ASFTQJLy00YL2EZQYjUFEQQkJREcliq6dEQZWCWgrCSghQ86agiqIS161ZiE5ENJ1t0G1E68xwOBGuHh807py5/WaPtB29aXl3fcd3f3jP/asbzLOkDpbXt69YV4lwm7vw4h/Roac7vQAp8fOI9lsHWhTFHCIoj7IGo3tEiqTLSF48SXnsSREfLeOUefXVG/b7nkH35NsbXonIhx+/Y3f8gvtuR9mDe96xTJctwPTWEZ4y8jDyor1Da8daZvq2x6XAuTpe3N3w9PCOKU88PjxhmhPCK9rW8dW3L1lKpjGa231PWCWiRNqdYa4ju97Qu1umlNG9oC97Qoy4riEvBWMKdVnxuSPMHmuu1KZlHgNCgi6VoT/w4XrG0hDeZ0otJJGJLLSuEKNFBI+x2xZdZ0fvHM/MXMuCkaAbw/kSyVUhrGIJGeks5rwiG0tSA5oOVoWIDlU1+4Pmk39kTpaSB1QTKXXdgjulQKRCaSo+KRoL9AVipncNTgxcZUE6R1UW7IDEgk5IBTZqjFRYKfCikC8z7lh5eJppu8TheKRcIPkL+a8jyyjBJJbqCNeCGyrj80xnDOZuR6mWvt2jQiTRggwIwBjBXDeadlaVmrfZfqFQa0WiP3sG5CatFhB0QUZHToraKMiwGjY+f2loS6KYhigW/hJQ4OehGFQC0SiatSJqZJUKPRyReUGobQYrhCbkRK1mc+6VSFJls2PK+nneXlG5glSEJJBGcUQhe4M2lna3Y77OLKkipGdQnucPTzS7Kx/lQAorp1d77mxDHDRrTKyqUkpkf2po21teu1uk6mhFgzSSKDtatry5prHszEBKMykWbpTcRl1DwE9n+jBx8IHzKSHnmf3pnq4kgnU8/+ktX//iK/6jv/orpiXwPGesO3PliUPbMzaF/rlF7ALBZ+ZSud0ZGneLVpnat+jJw1T45q9fMU4TP33/e+pU+PLNa3bDHtt3uHZj3CMK3c2mST9/XNjtWpIpzE0mRdg3B46NZRRPpKixncPPD9RQQRbag+TxIqn1ieYKIiU+5olP330kq4yfV/74/e/RFGwnqMrix4yRAV8KyVs2bEhijhYREioHlFDYUBms4Bpmqo8sqcF6DwscXkq8vXIVApFWRJMpvnJ+0DRqT/AXRGOpYyKKRLGVyyQJMrLr+y36e968GbBirOJv/tPf8Pvv3zIuHqM8h92eF292pJwoHuxNQ1oyxRhCWDj7yvDqDa3TWJuppnIcLLZWtJQc7xzLp0jfWxYXkM0d3c0tf/d//C3v/vCOX/+X/zUvv32D1C0iBQoRhWApAYRGaI3JGYomIqhstmNqRhlJFZWUBdYWZCpY46lGUrTCVLHhhhdB0St5cNSQkMVS/sJ84GdRBGQBt0JWAp8t0lpsjcwi0pKZgySxCTiqClQtEFggIqKi1E1cgU4ImSBUdIqIXkHbYiOoNiCnSNSJgzHMT55RB44vKnW350YaCIKhPfLH54lhvmDvjrx/dwa9ML195tXNLxEJhNFktTVx9I0gZ9CyouVWoFTtcBRKTuiUsVjG/kDSkqUuqLqgdvdUZ4mLJrnK2iTqGJGvBm4xpPcz8ydHnBaGvuVOHSj9xHcP75Cz4u5px4PUnA498qhQSlGs4Kv9nsObF6yL527/higS1kZ03ZqjrtM0+x2paFrVE0VglQEpHJievhawK7hCYUajaZQjNQKC4+PoKXFE7RZkrcxvP7KuAp0gmDPT+oExJMJ0wWpF07bEWpGhkhIIq8jJooVkLQWRyyYOEhVTHaVWUuewsdLkSKobZi6KhGkciYjBQHUImQjJ0AhLjJ+oDny1DLngbYJrQmeLHRJihcZoCo7RP9KeehCKx8cZVy23TcvD/JEsG7KpiM+iHdtAWQVGQCiR1ghKHdEl4pMmiQpk2q7Bme5zMXOMMZBXSStbPq4rN7pie8Vv//YnfuMWbq2m9oIySXQ1SJ1QjdxGngmCYqNrZ4VCkMWmwchpm6xYJSnr1kvI2ZBq2hyWskG2Eb0YfKcwiycZiTcnWKY/u/5+FkWgiLLBJqtCCYXEg4H9opiRSKvQKbHmiKlig3VUQCmKkchSIEeIm4pQiUKQDlIlzAvzsYGYCM3C0LwgtYkgCt5PPPqGl+nE8/zE/YvXfFpG4jiz//YlQz/w3XmmLGf8o8GUD9ze9bxpTow+03QSpQxSOoqCIBekdpiqUDqzxEyQEq1ndlWw2h5dCp0y+LyiNMSdo8vwxf09+WVFeSh+4di09C8VVn6DlpGu3/P7H97h0gndz7x9eOBWSm70LbV9TZMNKQQOL77EDS3CBLqhw1ZPyOB9JfhAQiG04tDqDdN28WitEMURVKVugF5Ejiw5MgvBTgnEQ8u0KjqnSTWwPDxzTYalBGJJFD+zro8oURli4eNFYF1PO4AJHTVmgrpSRMK4SC6SGhXeekJR26JL4GvFNIkiLcYNrLlQhQetWFNBBEtQDUcdKSiaaKFZaPo965xwZuE6LkgVMI3EOsFSpo0INIO2lTQ5ZJfo2z1lKCxrRrQtzB2D6DiJFp0ExhpwkpAXogKLQroWOSlq0CS9YLwjGkGZod8rqI5LXegHg1wT5xIJ45mcXnNoD3z78mtMrEi7x2VBzolcK0KrjSSVNpm8SDOiZrJMUBQyJbLM1KqwXqFUIusGISJKREprEbGQQqVMlSoDdnZM0qJKpNeZP18CfiZFgCqRQ0b7fuv0NwMyabKdGaRjVgFRoIyJWkDoLaZaIRGpbL0BmRFCUOpmnDBE0ghP+0KZBcNq8cvM3etEKY6diyhXkCKTzyPtYc/D3/2O9zXxz//j/wR56BhNoJeeUi3Da01rG/ys+P7TB27vT8heQUgkoFZDWSt2vpKblqZ17AfF6jMxapYaUazEtcczInOHGwQmWYSpvHEvNp29lHzx6g2zvrB+H3hqCg+zpVTP17/+G077hT+9+xNrDOhu4NNaeH1dKdbSNTuUgeRXnEk406LEjr56YoLkK0kWJJXiM0otiKYgkyc+XQlxpb/pWNeBNT4jpKMKw0/hilITdyfD81QIy5XxSTH5kSl66joiwtai2tme5bDyxna89xfWVbNrJOa1JJoXzM8z18tKLR4rM0p3jNeAq5rqWoSYSSGioiL7jBzTFjFnMykXahdxJPSwJ1SLyJlpUoiSwGZSFCi3oJRCpoHH0TOVRFoC1kxoKqr/7DrY99zzJfk88/jTW9o96H3DohPL9YEYA0psqrz93T3XpWFZR1w3Ibpfs08Cz4qfA2t85t72KGk5uoJSe/73n37Lwx8/sV4+cP/qFd988QW/eXnLj0JSLTjRcGXZxoG5ElmRQqDrZlbKUqNKxOREUgIhJLoKosogLcpDspVFVlT0VK9RqoAVlOJYEzgZiBJy/vegE/h3etWKuEae08jNscFZiU6BoipPkwP8Zh9WghxAC4kSmVw3W2qRm6uwFsHGbA0Ep7GpoufAXBRi72jkxPN6pcuF1iWE2mO9J9iCNBPRKu4Oe+ybnvw4o6YzQRgynvhjwR/OqJ2mGx2daxEx4doWSqJtNFlClJrOBYSIhOqQUlD9hKGQUoN2kaOBmiKmNIxG0quW1Z1B9HxRCklIjuqW+qagz45GPDKGniYGBjUxqsz1Tz+g54WiA5dn6G8q0SSslKgsyT7B8Yp2LVY09FqxZqheI3Pk6s94mbGykpVFJEU6wEhC1guttfgQaHaREKDGyOWy0X6UVCxhRevAQVjyJHlSIyjN7BIu7miazBIMxUicACVaolIIeUVIj0ZsYFmlsC4RU8WojJgVVWaiWknrTJZQQyR5SStAZYUvhrwWTA3IDqI3xL5gSstUPC4qrNJIcWbXv2J9MMSnjyw7SS/2ONORZ1gfJrpDQBwNZjegPoRNo3JbaZUhq4WEZJkTzfOIHAIURbgYRAmEZsalHefrM36NjCmxxnnDmK0TJmdqXnkMGa8lpQi8NSSTWOLIhYwQDlkitQpMzWStSKIgy4qpEmohVkWlQiwkseWQhQp2t6CCokZBFZ+9Ez6ji6YKiSkrUVsIM8L83HkCCB5Vz03NJC9o25ZaFpKxmMOMTha/ZsRqWajkkilyCyUpavMPCCRGQUmaWhVurRQVyEXTmYIuF7r2S1SWkK5Et6OPGm56hJKoMtCdWqx2XH54x+on7CrIg8Q/COhHjmpHnARBe8r1iGxa0uyRShG1QBpBLZJlMZsfpM6ksvUsREnIOrGjUskEZylSc0ozyA5fWqScMXcOmTONLEQpeKFeIDOwPHP50XJzf+QXuuV344w9WO76IzV/IDcNYV2ZHj3DriOkBXm2NFbgTGIuAlErRs94LbdmUcxY3aJzwDcKgwavWOq4EZFFQZ/h2FRCTjw8Z4xOrBfI9YF01ixcaRvBgcMWnlFm9By5dJmgLbZoaB1CtuxWQ2pm0uVKXiunoyHJRBgzjbCIlKh5QkhNzIb1aDDPmdUL1rqwu2kp65nSWASvN6XgsqO6C8FrTN0mQ/tOMtWGfcowJgozv/30lhdpt3H8tMUqjZWFdQW/RNa0QOdx3Y6DVJ8Tq448nz0iBNZB0JiGRl15miTXceH2MPD+0zvWYNBR0qgeYwTTCrFuKsy7V2+IBXZhIAqP3sE8SnKMZLegryvWGKiBBU0pFVENpdbt5aYblC4oXUEV8lopMm4+gbI1CUVjqEkQZMb2BSEqnrxNA0KidB0LFZj/7Or7WRQBISuHdQbZbPAQ/4xkT+c0w+qJzhCT5cF4RE14KmsWmBKpdQtwrBJCyVRVwGwBEKVCsmkbNcqW8XxG7DvGBPsxcvrNnsE0BONJ5wnpV5qvXxIbQ6NPoCpf3Q7oL3eENFMn2A+Soz2iLSzXgHKSXK7oOGO0wSmHkQsViUwVkTLe5q3JpAvzWtHWokxBlUw2FlnOxLIirpHR95xuoDQNZr9HpcBxvcOqDvfllcE03Ow7XjUdD8uFru1Y3k88vX1PVYJkFLo5sbvbk2fB8jQi2gHZS5rOksZKzR4aSV4345Xr4PnDE/5yRirLFLdEntnATU08FYv2D4Q58zRdGB8Dfau5ffOapu54fDhz6zJhr+i61+hdotcKxTNpknTO0J4OUCBNiuhBniSu74iXle4wMMYzIT6jnAIMqrPsri1P9oGyekx1zMVgZMNeS5YSCbOiHaDGHY1K1POZXSuJfsCIRGgGpMmcsuabN78gp5np8cLh9S2iMQSXWB7OjDESpoKuhorgQqEzGdsYdmHCGYfrNXYtfKwFxBPruSfevOY03PL06ZH7XUNptmbjcJL8/oeRcZrRVfHieEf7Zsc4PuPnSjKVy+OZclFgJEtO2CLQEoraKExUtfkWUibnTA4FUUBXCRSKq9RVUDA4EkkkotDEVaKPAHV7Ka0VkQtE8ReQIj+XIoAgW4lTEl8LXmg6PSHHSLGSvDqCqWS3BVOkvOnKiZYcE4iMpFClRsiCiJUsC1VJRKr4TwF7HDFOkpLlqCxSKqbrJ4S6xzcFux8Y+hPGOcRqKTqTxYwTrzh0hjXfEdwMYiZVzTpPyP6CCR3GaJxylBh4//gBKTbloWw7GmEYciSYgEh7VANSCYxqmOaRqy90vWDXOKJskVrxNFb0FKj6LY29QYhK00nWMmB9R+48X756zTF3PJM57A5AoObCOQZSnBAPGq3s9g/nn4ixsFv3gCYQsXVhjZHQdVjVYAZNLUe8n5inimoC84+BZwI+gRFPLEkwX85Yo+heHmmlgGzZ73aspdK1lrZoZLdjrBXZeOYwIqulbTqUqOyRXHYNMhRS8NAGOlm5/n6hjJ6wlzhtCeOMjxPTdXuz5SToD5L7QZJocWlDcitf8MKS15VFVJzQdO5M9oJde9wi7vsDt1JSg+F6fUaFFblvsdEQXCCVjBQJrTVZCHyu7Iujzw1RW6qaUc5xWT1dZ1lmixBglcC8OpHmBdV3vOwrkgL5yKlfsO413VB4+8cfePvb33Ia7mkbQVPhKV1ARYoXaCSrEcgiUClvsngKQkRkVYAEsx17Y05IY+mMIpEhBJYKSIOMlqIjdQQhI3IWxMZQQkV3+i+ZCH8uRUDSakHSnioSLh9wZSa1t5i0Ym1ExkCWHUFWApEaPQWFplCEJtUKOaGRICsqSxSCUiXFCHJUZOOoyrMah3NXSuhJR49oBN3NgW4FCqg58WwfkUmyqMp4XinnM3InkURCvdLtBxo1IERFZcXTPFPCQsog6gI1YteJhCAMA4RC5wpK9Mi2sk4F4oJpHFq1GO8xIpEBNVxYQ09jFMbOxBzQuuHYjfi6stN7aD/QhVvMFJniM24wSN1yIwRjXAl1BevwReOvHr/OPNWRQz/Q9Q6vJUhI48plzaRlZr4+M8+Ry/fv0Xu4enj3aeaF1ZjjjtMrwX17glHQ2Dtck+g6x/KxYyiSJlXUHpIU3NcGM+zQMqO6ButXtLCsAXZmDwZyCuTsqH2l7D5y9aBWid0pUAkRLK5f8GeDc5kcJCsGPz/iTl8gc0RKTQkjwkGvNef3Hwkv9hsb8agIc+J9vrLvTtQ68txq/vD0kVuVudkd0W5HeVzJXtA0mrSsuFPL/e0eqTVpmmCWmFQR44owEhcLc3A05cQhKdJuoDaVT09P3N8q9tMjrXrBQ/xbrO5wZuDDjz8gvnW4vmdNmvldROet6RmzQ9ctDq/UsGletEJgIFQSILNE1o1Emksm+0RVkigr0rSo6pE2EXPBikpSFuUMtU3obEjpZ44cl1oidwM1Jk4i8rRExirRasIoi5QawQ6ntmjwthayVJu8WDsE9TO6SZBEQdTPhOLMFs8dLT5V+kZjKLgaacoJqwRdf8DJA/F84X1YkOuRQ9fQGDCl49MPv0O7HdJo9KyRbseSI5Iz4SzwCebzT/T2RDUCqQW9shjrQGwY9Ms84oREjStzfsssBKVIjrJDDJolelaraOVILoZPz55TypyN5LQb0Fiezxs+eh4X3r17xuwkfX0gV4M7DMi24IwgTYZDr5G5JasVsUaiLGgVWdeRT29/JF4i1rQcTjvczsE5E/LKWiKVQnO7ZwkLTgT++X/2N+iouKjK4XjL8LXBPUZmubIKSxCJ07Gim4quDmErOXn8PFOFRQoFQZIV1DTTWk2SHfPkMUZiBkfyiVfHV7Sq5dP5EZkznerJesIvzxiTWdyC85KSBmzusF2PUg2kzRQlrWSZJ4b7A6YCznK5rhyHnvntladw5u7lS940mqucMbrh0O0QwnA+LISnD6RlomkaKI5YLQr4+PCA0w0H2/IxfaSEhGt7WvHIsjyDPiBEZN+0aFfoQ0+QHvRHruGR5jkgXna8Vl/xiy/uMEXyf14n3o4PCFlAbarYtlRWFZBKI5UgVahCojSY6qlym0JhNVJUYrRUkUBXZEhIK4neIKog7wtxKRSTqFMh2nkLK/0L18+jCEjBwRg++MqsoBpDiSO+QoieqjVaWmTNKGEo+nMntRZWkahlc43pIjaghGazWiVQVRJLRJvMNVpUsDgtSY9PjI3jaHZ0JvPgI/Up8zH8lrB7SbSe4J+5HxraF6CSJqwrr740WF356f0FloTdOV68+JqDkqyhsERP9AkhI03jaK3EWkH2iYu/UKrCqZZVBEYxwYPBrzN6FxGhcnc8kKPkUXhWuZDfj0jdI0whBo0QK/um8PHjRPt6wA6GXRU4u6fqylU/seaCFS2iWPaNQsuGc9i6w02aybaQe49yB7rSoI8CYTpco3HCMq1XHi8jfsncHfakKNBx5eXpJc4G1l5zfXpA+wUf6mZX1nuqvnB9mkgiUxdPjAmjHDe3PbY98ny+ov1CkwtWSULp8SlTNITeYeuOQ1xZsiAkD04hppZiP+K8QtuGNCtkLKQ4EXWPWTO6VcCWqlQAYy3vHp/51eELVFR4FVnTlU7ec9YSmsJg2u1tmzyME2YV2F5t3gKTsGIlJvDrzFTDliHRafICuoWmPYAqGFew+kiIBZkkIUascey7FbHC/nhA+InaDoSHmdzdoOLDZxuzRqRKOcC4bD0JmTKxbLBdgSYLIClAIKpC1oK2CUqgIKm5I6kAVeHEimgVOSpoPTJB3kEdFW2vWH7WjcG6BVXspYCuxS6ekC25rlAFYiONkFXLIAJFCK5So42nXTVr3kYoWRQsG7C01IJQiqLUJqPEs1xHnLzjagq0igMT8elHwv0tXC8EranPF65S4ETD4BS2tbi54Fk5tbekh4mkF1a/UidPTjuSWXm/21xqIki8UFQCdfVczpG+M9gWtG4QwpC8oLMdwgeK8LgB3r17pFEJ6SteV5TW1KfIpCJFJoq/UoukSaBcy26fqHmmjJGnywLtntIpcphZ04IqDTVWpNHMfmHe5nzIYeDOfMWpV/StYckCIQXH3Q5tDDULhOo5xYWHfOUaBbrV6OFEtQb6AXM90wvJ+zWTg9goTiaRiiWaxGVcqLFgc0Z1e4wakFqzO2rOnxTXOKOl2xKSaqbOibb2GNvRnRoepkdCiTR+YcwBXzUxgbOOEJ8wTYOMEVcSySTSMmIay929YM6FEmA4NMx+k0WX0tHGmWe/gK60QdHsCrsm8XSNeJmpRiDkjl516KyYfcUOlpfHA1NaMYNAjI6uKQQviD5S1rQ9L+8xgFASKW+ZTUSNLVOZ+elyhcbzQlt+nD2qDbx7/8R6LSASnXSM54xqNORMKRJdFTl4lEpgN3CoqAJhKzZW4qowVpOqIku/ye4jRC3ZEtYraoSiJDlWVM2facN//vp5FAGlYL/n6FZCqvi9JseK5sQ8n1FZ4nRklRaxaz4/oEopAy4sFLYYq1oEqO0hVC2IFGouyE4QY6XJBXrBs4+oLjNdDcgzNSfM3Rv6MEK+5e71CWF2GCCGwPP0gU73DDtLCYEnv/Ddd99zPO05NC2PD4+YqMlGo3VDZxoGZYk1khrJFNnIumFBa0srN3JPlT3OaJRXdF1DWwxll2l1zzAcoRSu60oeM24Y6K1CJpgLdMOOqCLTVPn0+ADlLYyZj+ERLxyaiqiS54+fWHzh1c0t9/sjb755zc3rDpkExQ0MXUEkRTE9RWSEU9zsB9hF9BPM00otjqYRtCZRx8SSFMYNvPpFg007qlhIukFFxcNP/xfXp0eWOLN/ccN9s0lctVHI2iHVQiNBD4IqBM4rvARtA92+w/o9P16+I8SJ7DPjOqNUZkmW6B8RUdGaAasEl+iZ44xUEqkaFj0j1m2cqbSg21mGocXOZ75/+MTblLkZOm6sQ6lu4zEWjdXNlj9hC21jSBTOeG6Sou8dLllqWNFm06OM52eC9Az9QH184uWrG7I06KqI+SdWL+jtHmTgUhI3RfDw+Mzt8Zan95H/7X/+X3j86XuIlWgiWkjyKgFDqfEzgl8jLBRTkaVS8sYZDEUiRGFBb/TkZMELtN2QvF5sjefaa3IAkwu138H8l22EP48iUGFwlUDFFQm+oJodq8j00pJSJiuNy3HzUUtHqAtCJGpbkVEioqaIQBRbY0oUyGl7cHLZ4A1RaVhHhr5DToZ6Z8gZKpl4GZlkwYfE8lw4vnEcTju+/93vKbNivTN8/+4Dnx7ebl3xqqmA1T2X8YIWhmF35NgYRCisciRmSTEakVfyWpG5hRL4GJ4Ia2Z/Y1n8RFwrSjtCuXDq77h9edgAnrPgZdeSWwNqU6/luSX7C9r+v8y9u69lW5an9Y35XGvt1zknIm7cvDezq4qiW0hlgdNGm1jgtIeHaIQJBhIGLf6CtpDaQkLCAAkJMJDAwEFIGBhgdAsBUqtRN1WVWZl5b9x74jz2Xq/5GhjzZCtbyltd6gLpLudIO84jIs5ec805xm98X2SUIxqu1GFiTxP2ncNskVZh3TJbu3E4HPnJw5Gvv/opxgUO0wWHp3kh2shoG6Ie0e70q7XhB9gGz/H0gWmCeXklWiVrpZhMKjP2tbDHM9YXmAu6vRJtwBThy4cjqU4c799xjA4XB1oXnl9EAAAgAElEQVSWN6Wc4TCM+ODZmiEVGP2AC3vPVjTFGocLJ5QrTCNjbezllVLuGbzgBtiSwUdhWCokz8qGtI2hTiy54lU5niYO4YCsyuXLr4i+odvGOle2uwWZz6ScSLcrguEQjgRxYByHZsk08grHMTIePVt65GnvOf3704nT6cx4HvFm4jA6TM7UwePmzNpeCMORw6gMPLC7G3K559w2ll2pSbEqFBquQCCQXSNJw4iCdZ03UBtVDQ7XQSLWUEvDto3WHIcotGIpCslaQukgsVwPSGvkqgR9o3L/wPWjWARU4HQ0vGjEDwa7O/bVYCsUEXJdOoIsue4eiIWQGuSMEjuIUUCaQTxIUSR3BkSx0FKlHTyGyrZZ3BTY0ozzX+KtouxIFI4JDg93tFp5+u6Z9vmVFg1jrijwqXwi553D5czHaaA44Vqe0WI5Zott8Pz9lWAsx4eBw2UiNMd+a1jbCA8HWiuE5ysvrDz+4hvml1eS2fjy/U8oh8zyzcLdB0cWz11wnI6BF5R7aaR5p9wF5teNUBq5zqgZ+P2vvqLmyOwLNV14/eaJXy3f8fSrJySccF+9px4cwTq8H4hmgOioxZDKyHGYSNvMvluyVCiOyUzE+8S6J1YKeV6JzrATuL78kpIm7l8r5XCj4XB74bO+MHxxwOrApfTuwzhNiAuYJMzLTBNPc5ayG5Qb1m3EPLAly63NpP2Gt0JqO+wOXxrWD5iwc5gEfENphOBwb7Mjn+0Vt4/s2VGGhKbGLa28e8n48YoJhvfRU13g2l64bspp3QmXSqHQXCdYS4biuuVn3a5Ic+R1I1Cx1lGzx7dMjB4Gi3qDmO4DyFmRU6SUjWgb7RoZTODiJoKPjPdnPl4ufLsHWs5I7YwCI400OdxasZJx3qK10MwbMKT1YwYNNPdCoGpPyakzbNIQqb3wKhtZhGocrs40M+KlkEtiaPzzMwZF5Gd03PhH+pT+f6qqf1dEHoD/Gvh94E+Af0NVn94IxH8X+NfpEaW/pap//8/9Ia1hi+c8BCoQrCVOHmFnWwPb7AkiJC0kA1N5pe4WbxIOoTZDspC0UmtGEKzzmGoxLfXk1aZIrCzzlTkq8cOJ1BbCGHEtMqqS/YRo5UU29sfnbiz6w684Hs+8rIX34UJ6H4nO8N3rQs033Og5nM74rbJtnkkjz9dXvnms3N1NjNMZUsPWguwv7LlSW+FwOvDLb3+B1sQpWsbBgh1Z6oq9beTBsMeRY4jU7xa+NRv3pwNRGu/kgZYK0zshzZFhKMgmjDaTzch0PJLH/ib1IXD3cOT95Uithjp6dmup84azI3EqYHaOx4iPyjI3fGq4WNlaorSCaCIlg3lX+fTzVx5//ci/8kf/ArVC0YKPBd0NrglxaDgd0bSje4HbTgmF1KBpQ6Wi0dK04PNAdspmd5LuLNeVfekJQs2N1/lG3gvfPf0SGyfaCjYL61ZIx4aKwdmBKVf2YAgY1uvK8/MzuST+lD9mOd/x+rJziJUt3ShWuHs4crmcCAS2vfBwuOfp8yO/+OaPeX/+SBkm0rcbWPj9n3zETAPX/ZnmV7weOZ4ecDaQQuLsH4j3AzltxH3kOCT+7HFBt41jOHGeLrxmw1/78h3heME/Kt9++yto9KxL6cNvzZguFrGO3SikHnZTZwi+z8QUY6DVDuGpgg0ZFUVznzMQL0htRCpFHJIM4gK4SL7tP3j7/UV2AgX4D1T174vICfh7IvI/An8L+J9U9e+IyN8G/jbwHwL/Gh0r9leBvw78J28ff/gSuJIYxCMMnGPkFizMQo1zH93dHSZ16GioI9EurNVQXUGToqUhxuMEWu1DRW/gQCyKbb1gBIbHdeYP0wXJUJfKesosnz+zbpmHywFxFm895quB71+f2Jcb81IRJzwcT9Rw5nga2JcuecxLIdmV22vg+/mR221hmO6YBovsKzlfmfyI84GyXXl+TlwfYX68MtwH7r/4muPv3WHXiF83bs0wGmFMysvtikjDiWAqWA/T3Qi7sJaFJpnkwU/Q3uKseS2YOBJjwI4jYRgwxmKHCRe687F4YQqBy3RBrKHWgqmG46GRXWa7XdlzJkaH0YEWb7z8euPp6RFrBvBCMIbjJbC93nhVi7k7ckJQNbTY2IxFmqW0hAW8GkyqUGeM84g9EGqiIZTqsFfpxdZtZZWddUvI0VCXLuMMppApCAHNO9b13ZevkbpnNpdZ0wtLKlgS33/zC2qZOdsLqLLpThXP6DyTDH2nOW/sy0pZMq1kjBO2eeb7z58wwfHl+zu+DA94acRL5PNng90MebuiS4Q7i1Yl1EjzjdR6e/nx859xGiJuMrSnAmPEOIu18HLNUKB5i1WDsRkymNBIJqFW3gC6gjVdMyYoXrvHQrShJlETqDHYKohVLGBVqTWgrYtZvFZaXjpw5PbPuQio6q/pFGFU9Soi/wD4GvibdOwYwH8O/M9vi8DfBP4LVVXgfxWROxH5ydv3+Z1Xa4qmjAkRr6AhMI1QbcDdYBdPCYUriePW2NR1yELr2GXnlaINX00nAbcuJ1GRLi2pXQ9unYGqrFV5LYnDWrB25facqbtFi+FVPHr2GDFoDrw+LcRT9xUeTx5nPftS0Jz4/PpE2+DDl/ekbNB9pmrFWMtgoC47L60wRDDGogi5eW77TPHw03/p9/ni/T2nw4Fjjiy2EjiSi4MtU+TWgaXN489HPF3UmWpgCJ7cFHE73h4wxhAOQtWFTa6knCnDA+IaYgx+OkMIeBfw6jATDMcRrGWfr9zSijEjQzAsryslpY5yM5msjaaJ6CbO4hFjCWJYNJOuhbxXTG60kintTCpPoJXRDQzHgMEyrwVtiZoKr+vMYTwyuQ1VZS9KJeOODb8r3vdw0YQQpgF3vmFy6NvcEbyMvT6iCSEjxjAOIGpZrSfN3+G85fT+AdSx5sT04cyQodzAeIs5BVrLrHrl2mZwcBnvGY6Ba7nR9kqtgT0nMI5pPFFM5sVcScli7MD1U+U0vnCyDg5HFun05XgvtNnxcts4bCt+MkgrpLZiJyEMSl2U3RSktq5Ds4Ktppu1TZ/zsGJAGs127HgtDa0drmutw9RGqQMuVEpTTDI0ClhLpRJo3ZHZPGL+P5oifJOQ/MvA/wZ8/K0b+xv6cYG3BeIXv/Vlf/b22g8uArRC2zPJNnSonG1F7QP5tIKPxLqzPi9Uv1GzwdmGlX5sEBfQnKkoS21UrTSrUEBqQ2wDY9CimOJQA57M999/5qvjiZIabbky3H3oODJfCfOBMhpqanzx/gN1XRnGkct4z77PzI+P3J6fWdkZ4xmtlbw3slSMd5ihYFyjkDHRYaPQmlC3G/t643g68PH+A8NlQmviOj8i9h3D6YScM9IWnvMO+4FLaVyjxX+ekfsLkzaaRkyLaFwRE8l7Rp8bNm7EcCae33M5PzC/eyXv3U9nAXbheH7geDciplKqss0z6554el4ZT6ByogbBJqXsiceXGxIiw+GM7I3LwwMxF9R4SinUp0dcFMZDnwn4+ac/I7ATjxeyS8jR4YxHc2G53lj2jSgDplSu60x1hkkCJTbWzYGLHA8nZBBsmMhVUXfDREFrxGYDx4T4bngum8HIgvVn9rrzxfuv+fz5E56RDw93GFnJW48EH7cDr/pKXle2VEimIXbEuEwYRi6nEzYE1DmGy4nT5czxNCJ2xTCyFcVieHl9ApNwlw8srzfkZ19R8o4rwiLPXO4f+Bd/+lf5v/6Pv8frunB+947mTggVq8LPvvgJf/L6/yAZ1Flsql3EikVNA9doTjGl24b7lGzXk6nNHQDzNkNAazQHILRiaBIRs2Cao9g+M2DItOz4oarAX3gREJEjnR/476vqaz/690tVVUR+OJL0u7/fP/EODN51g4sUiCPFW0zbmFJlE9ufjGKINTCzApkOHm8dJ256FZ6WsFik9Se/oePHVCwinUGomnFqIVWGyxHFdeLNOBJrZkkVmQzBOtSu2GPn4xajzPPM8+srt9cn3KhcDg9M8UyySrCBOAwMQ8Q7T142UqrcTxekVp6WV15en6mauTs6iink55nVFbZUObVGXneMP6JSyS8ZNVdkfI8TJbrI8+eVcrgQXKJclHccWALAwpxXtjUh2ws2BYiB+HAibl1vNTiPnyIqlut1YQyNOESO48QYJ4hHhiFAVs7HM8SN27rwqRamw8AhXtjHVwZzx6Vlwuj5MAp17KGtWhO2WIZj4GAi4xh5nTf2VAnWkVLm6frEOie++PiRqpmmO7hG9BZNFiOGogUJBqkGXyy324LJjslazAguVUzqQE4dDPWYqM+wWTBL5PRwwtcjZhCGZqjRshpHXQrFNNa2Y1bH9HRlmgLeOMY4sQ8zLhh8GMnblT037Laz31bK+4l47FITP4yY4Fm3FRMLOoQutykF449UVtLzCy56zsPIWirDuuFr19+1vfD111/yf//jfwhGqbU7Mwi2OwU6EAOt3ZzW7xVQEaQY9I1I7VBUXM9ZZMVoxFJJQbAI1EYRaAjBK41C+t1Zob/YIiAi/m0B+C9V9b99e/nb32zzReQnwKe3138J/Oy3vvynb6/9U9dveweO06ilCVUTMe3o7tFQ2UQwbcMZoQWPGM9qF4xYnI8UBVsyRhtGuq4614KIwYhQm2IKIIoY7UcEI4hRtgKvu/Bu8sRJYFuQg+doR5JsuDqymcQwZ3LbqTfhl+uvSOKYgiMeHYOPvQXhGiJgxDDaoUNHo2LE0mxjT53S4wbL+8sd58sF0UjedyYb8feR6gem48hhPGFaYBiPbFUxxnI5KoO7sOuO5oq1BTMo0Z5xHnKe8Pc3Xl8cpaxo6qrvagbEeIZoca4XCkuuiFSM9xgXsQJsUCWx3macOPJcMNYiZmSwgRA97k3lfjlULnLpxl4KhBO+7bSqVJv58P4dxxBJqTGI0Kzjuq88Pn7H52+eGIfAMATEjUjY+k0vStsaBkdWSLVRmpBKpuWKa7DmTLOxU6hQymawkkiYHuErG+0QSbYQ/YQ7gxkspU3YsX/P7BuiFjVKdo1qoKlBasVqo24J6yqm9k7TXgrzUllvFT8WnApSA8MQ2RZLKQ3jG3teMBq5BAjG8vy48uHiCFE4nA80o+joKQlet52cK1ob1ihaM8UYpCom1/7eVqFIRRqgSq1grEF66wtjWsfpUTFvOjSbK80WbLJUtVj39pCWzmcg/yV4Am/V/v8M+Aeq+h//1h/998C/Bfydt4//3W+9/u+JyH9FLwi+/Hn1AKCTgTYhmYVoHClGtMLoHQaFUnBNKKKEKogoxbo3vZ2QXKDWQkYR3gqDVjDaV1ExhWqUVgJYR2PFB+X16Tvux684OMfjdeUSJ5r1XNPG3hQThOvzjdftEceEITMdT0wI6Tmx2BtbnHl4/45hONCUfn5uhVobqHZAhAYudx84nUfenQ9o3UmLYk9nBj/RjiNpz11UGgznwwFvL2zzynXvufG9Zo4fLzQco+u8PYMh14poQIbInWns60C6rggzxhimQ8S+uQbLvnKcLtzf3+NCQJthbztLq9TW35TGWz49vhCt5+PhgeHuzKoeLZlJAlwEqZb9aWGbX5gOR9a8kZadLRe+eveO4XzA10J6ybwsic/fPfHtr3/FvF25+/gVp4cTwXnGPbCtmds2k8oGe+3yUBRjDaX1BUuMsKeNodwzhkAqFY2KtxZ9XYlhohjY80JKYCbhfLxDJo/bCu2Wqd6gNWEtTNOEswHxA3FozE+VmisNqHvu6/o4YqwwOE8rDV1bF4S2QvSO+8OZg1hKTljnceKQITIo5LhzTRvBCIOfSGIJxvN6u/HrX/0x319vvMn3sEaohjd/osHVvsvBCU0V0+hP9gbU0gUkxmKcvi0Qgvra6cSmW4t8jlTpW38DoI5SK3+Z48DfAP5N4P8Ukf/97bX/6O3m/29E5N8B/pQuJgX4H+jtwX9EbxH+2//MnyBKzisteZrMmMnhbKTIRMmV3a4ggrYZTEN9D4NUEWyFUCKlQNJCtZ0y0lrDiqAYqkAzineGJoUmgqqF3FXmx9OFMM9c02em4QvS9YYMZ3wxbLlRi1Laws++OhJP72i10tKNZhrTEAnVsbzs4IQ4jNSq7LVwmI5cLhN5LWx7ZrmteGsIxrKVncFFdmPJrxtiKnU1zNZw2jYWCRjvcWVhm2Fn5nh8IAzCkhuHvZHHSssJlUB0I02Vum2UaJBkGS9HBh9JeaM2iFiC99hqCQq5JXJKkCun40AgkjEEC2Ir+MYQTrQtk7adW3vB7oIxDtMSFSWvL1xfNm7zjXWbeXc64/aESuH18zO3lMh7xoQjX3x95uPXf4BzJ4xVolGwhtVWSjVsaWevO+I9Xt92L0CVgvMwxECIhm3dkWZhsPhUcNLQMMDLFe8NcfJE60hJcMVQ00Ypjef5e7Aef7BQCiUlnIAxjr0WjDbsCDIaTKt4I6gxlKxQhU0Vo4lgDH6KNNfluKW+EsMdg/N8fr5RTGWfb9hFuL+HYXJkU0jlme//0S9Ynj/1hJyAt5ba6ROIKGoqhYZInxdQW3GincUgFbE9Zm+KUhtQBVMFibV7C4qirlKbQ9520k0EFz2p/O7b7y/SHfhf6HWJ33X9q7/j8xX4d/+ZN/4/9UXQdEGzI7eB6erR0Eh7gmrRsLzpoDLVGSye1hLeCYiw10qRht16OkhMw7W+MhojIAarFa+FlgvZGYrxPM0r7nlG4oGLhdf5c2fyRceaM2tKlNQpxMNQsbietc+mY7NLxeXKLluXQx4OrFuh5kxrDR0ra15I28x33z1jtHH//oH7y3smG1g/7zAZBoQ9NtI+I3ng+ewIZFCobe09ZDdw26+cZMRqYl1nbBt60Ug6bxFxuDEgmqHYbvexR1zwDHEiq9BE+tzDblhUqa2hWrGaKXvl2hpTHJAQEFHMYBmCYG4FiuX5OkNVXLznEAvLurGtC0Ut93fvGKYjm2nouiPamBDUOvzHj7y7O3K83NFaIyGktLPuG2KF4TCwvsA4HdiWgqxveQ/pASEvFnO01FUxE5S8UTdHKpk4RGwu4AqlLVCFIjDsBvKGa0puK+uyc4gD4iAE2LfUZ1amgc9PlmXNxJNyni4E45CaEAvzmgnrwtwavsAUbW8/+4kxnJnnnVEqZgDVwNOnb/jVn/5jzqcLp7szX5zvSFKJThn9e1JdAaU2ZaenWbUpKtJDQmJ6Lcs2FEEbfYZGBNFGl5Ebmm/9SW8MtrZOHqK/H4xGpBxQ02W8an7oFv6RJAabwq4Z2Sx+2GE3FO8xrae/tFqk9UDFb85BZrAcsnDzDR8LtnYdWWuKAtYotevcMbXn6K3tVqNiOrvt+ZpR90ypjunjgKwnnvKNu9OF/O3OLAutOHxz1GZY1kZJn3BYqJXcCpqA04HjYaJgSNtKFIc0y/x04/XzE2l+4en1SjOVQiIYy3D3gHERY5RoHUvJlDpjFPTYux/J7ZjqEDWMwXBbX5EixFFYrwl/rZhpZJoWalM0W4iWtga8iww2oNKIITBMEylb1uVKkh1iwIvB1Ei2yvr60t9sxeH9hHFKKQlfAiqCHSMf9D1btuyvV7wx2GgJ7YjaVz7eH3h4+JLheII6o8bxk49/BSQzXzc2zUzHA1Rh3zdQy1YKt23GO89pGOHdO2RY+Pmf/LInPkXwRoh+wuaVvEPbM9YbmmT2Kyw18+58oOVnbsazLJWSrki9IFYwxiDG4AbPvX6JHxohCi5G9pR5WVecM0jw1HkhGBhdJKIs2aMKe0tctz4xiRsR59626cp0DHz/eSG5worgTOObz8/8/Be/4g++3PlmmjhcRob1AwbH8csHtq0g7e0G5+1BZYVeAuh9AG39SOvodYtqHIbcj7uGPlDkBDGmLx41IFr7TqJZmql42WnGg2209kN5wR/JIgCQtOf9RZStNYa64s2Ayk5CsRZczTQVxCuD6ebVUAu+OeybxcUC1Rq0vZ2zAJylpUpufUBGfgO1bMJ6W3hsj/zk/QfETkSx3FoAt5PWSssN1USokaqOWq9IDfjhyOCPqBOG4BGtWBOwNIxRrPG0tvN6fWW+3Si5sGvm+fXGJdyYYiRMgakm9mgpSRnlgBzOOFVsK/jREkNk0xEvyvEuIItyu2XaesP5iYO3pL1RUULwHd3thejOXE5HtpZZi2JaQ2gEZ/HujD04WlU0KTE4lmvAjwbbPHlZwVZacbjN9E6LgXVfQITqhd0UXPN4UxjOE+++OOEMXLcZXAHnmOoIzuLuAqFtqPjeErOAFmKx7NmTa9/iejHdDG0bQ4SQHJuLtC1BE9aXKy0bgg2c7ybS7c2KHA3FDujrRk2OyVtMVfzJ4stIUCVGRzAVbEIrtCadIpV2UlFKMTjp6TyGHtetawY1qDHUrXXWABXdKkkig+nAj2Qcr/vKeYdaC/NyRYeBumSeXn7F48s9H6Ur9cxkSevbMI8KxoM1QtbWF2He7FoVjEpPFUoDLagFxCK/yRMnT6MhTqhdfIkRwVoFUZpsCJZalA5Z+N3Xj2IREBRJYEPD4SgVlnlHTxaxgtIwGKx/Ywo0B9az2wL0FdNjGZohE9hoXb5mpLdujCCD9rOdCYh2P2GKDTWwyI1f/Bl8+cVfYXQeaxpmCrgaaHuhaCWMQk6ZNgU8nsMlEkzk9rry+Ol7vLUcTvc0Y6gUojq2dOP56Zl9XXDGMJ3usGHidV6Rl+84NsGmkaMxHINnz91UlG8Zpzv2GmilgYm8Ox15f/8RJyu1FaypHAfl/mAYp3u2ljEmYKg9A1CUORWyCs4oS9sxooitlFSRVajGoCqoadTcSFvGusK6PVNviimG78qMDBMX85uai0KxPH73DS4LLTX8yaB4WmpgFWkOpfDt97/EDO6tCNdwrm+jven5DSsnnB+Y08b6/EJqhbpXJjPC2CWajZmUM5M3lKFit4ppEyoWf4BT3mjbRjVwPA0QPNv6QPENnJBKw4YuQUk2kWk066mln6Gjsaz7is2Jql0m6/wAIgzOMh4OXLcrL98/8vDugePdRCRwzQ0fPPNtxWliXoRWE6/pytOnZzzC7/3ez9DTQDiO+FbQCs8FtlxAFGu7YryIR7XvZpv0RUd8QVujIFjXOweKgOu6u1r74iBWMJrBul5mMP24alXR2I8ZTqXXD37g+lEsAqqKWMUImCYYu5PNiNNGQLBV8R6wXaQgYlDjcCYjXqgWsoXmGppLh6gYKG+YZ82KpdGw3VOglr01mnhKA5Ma381XhvDKu5+8Z607rjhO8cCaE7Y2TPLEg0UkMo7C4A3z68KeVx7XZ1ox3G+CccpuMpON7OXGvM5IahxOE3EYEWCtCVkhDolFLG5X3kV43mae18Jgdlgbw6VRciMyYFzG3U4caunHg+nIdHKEYeDufGBLjapQvTJUw3rNXLe1B3qspYkiGFx07NtCWnYO04iPI5aAaZZdK6O1bDSW64xP8M3tGyQa0nhBphPntzrLp6fvEDxpTjyYE/NwxtK4f39Gw4G2WT67J4x3RLWI82ytz+EPXnHZggaCtVRtzEVpkqGsBFPJVsk2s+07STfeuzsGc8QfVooWggtsKC4V9pahWSZ3Yms7YiIez2g8q63UtOGtRWzo6PpmCdJ3T5s2Wlkxtk/abVthjP0GDeeB8/2F8mnjpo15vnL6eGA6O8rnjaCe17JQP2eO7w84A0EtVRyX2Pj6D/+Aw+lAO556ZL1a6rZTmmB+UwewQik9Dm5bP7ebN2Se+n6coRaaVVSA3KPzQseMB9sLhFINxoJmyE2xVjqizYBrlj/nNPAjWQSAujdCzqgxmOwYFUy0jKZRnUFNV0MZD0YtpWlPBqrB5B5JbShWG1KgaR8FtWJoCEkqzVq8aSRv0VIRLYhvaBWqNfzy8yP3vxy4//ojj37HzQ6NXdYRnCfvwJoxJ4uUyrwu1FzQtSLesq8zYqE6Q5p2xIGIZ5oi42UiSWWKDtssbi0c3nmC9WytgLFM5ws+blgi1W6gifOHO0oRslbCLoxxwt45xibYcECCMOeEYN8qzobBT5ihkJuSW6blwr5dQSx296ilD/EYR/GeKQaGecQbix9G8jePkBqFhs5dkHl1K+c2grcIwrsPX2BvyieeOPoTUQzGVAxgtSDeMT5ccNUy2ohET13nbps2nmohpRVpCc07Ju24KhTXqG9/Z09jK7lrwI4G08ANjlIaTT25ZZgsLhwhG4w0cllZysbJFEobsfaEyzP721xAzEpKG3mKiIkUV8nG0IwlGctaK27ZgYaEhjJzio5yuqdFQ/ABYyaMrqzFgyjzcuW9a4RNUP8ef1QGZxnGgfvjHfsQsaKI78h8QwUbkdZb2qEjsGgtIpoQzbTqQPsRTtT8k8Jgjxn2Yy9C3+o7j6k9V6B4Jl8oBGpQmA1qoboMPzBD9KNYBAQoEZI0rBNyqVhnOOVX0mXA1Z6GKrLhmkBL/SMZYwUkkNvGLobsLY1CS5Va6d62sZKqYqUjnJ03iHo0CNYYJGV0qOxp5h/+4k/4oyFg794TpBLCPeV5Q7UyHKBo/7Xt80rbEltKSDbcnQecHegkuL5lSzlhsDRgvRbiceDiTlSFsie2FSqJ0VRe5sDxcGL48sLICTw8fnrh7hDYJWOGC8ZV/HjGDQE/ZCgDZVNya/iW8cH0yTzJiC+M0TCND1CFYR5Zys5eNzQ1vBgsFS07r9vM0jbWa+aw7hjx3H94D1aJx4FmHH7yXPwBjRDNgF4N8Wx5534PGRtTUxYxDHcDWj2ihYM94FPENGEIERuUqwolFzQo3nr2vf/8rVRMU6IPLMOOMUJL4KgU77GDwbVK8ZmqBvGZYRBsvdC2iq2ejStSMqyNp7LwujTi4cQ0RZJmHBYjFSqsmgnNcRAPYeClVkrdcaNgfENqxRnDJU7Yj3fgXjjeWQYzIRrQydK2GXcbeXeMPL+sPD1+zxc/+2scDxfWp2+JGDQGqgo6WCTBsjtpZSgAABt0SURBVBeMUbwJlLUhtlBptGywJuNMxSOYBrUapAnN2p4S9rWr+mqjRNu15gqK7XmKTA+SmT7dqdn1/zNRnFjyD0DHfxSLgNLHKNfSGKOhBMHFAlqRYvo51hlcMRT5zXbJ04pD9hVbu6ZMWgNTsEBpEaxBZEd3g9FGGSu1GLQVQjBgGlVH7AB7gyiNWTN/+nTjy+MdUSIHL7yaSmLFlZFiHFvrhZ2kM8kU4uGIs47mG5Kl92prwVsDg2KxnE5HxssJO1icFmiGwyUSzwPX242LU46HgRIHWGdyHilj5fPLjcPZsrnKMca+6hcDm6PYjYpwiSO2Qm2OkDLF17cqc8Q4gcFxcve48sqSLGS6LNUZtBRu24oRy2HwWOc5/TRwOY2ojJiqaLmyb92Au4RepbbxiJqCP/tewZ6VbBRNvQ2pTRjygSYZ73IffmlKiBapvfBV95l5XphfdmppiI2c45E9JNZtJmrg/nTuyU0ZumJtqbRUsbYS1WHwXJuicafNSpxOjCnxfN1pEljnR1qpTO8/sqeMpeJMJ+640BFcwQ5EE3ENvB2JYcDbiiYlmAMuThyHxN3xQDSRVBvuFnhpG/a68eFh4nzwIJFwNIznwPOvleNppPrCkZHBnhgOC5cYUAEvGTF9IrKqR1oGCj0R7TD9k2gGnFZM0Y4eM6YPo0n/Hbcm2FYR5zEotWX2ZsFlXDkgdkV1xJHJP+Am/3EsAgITOy92pKwC98pUhOQ9sVRKq4QaAE+THTWVqhlnLWobu+y9PdIMexMcvX/qUqLY1jHgpjHi0GZJppKa6b3xvFHqgFqltB318O2nR06nkcOHn5K33mqsS4Do8aI91LIJGjwug1LYt1ckj53MQ0BtoIknWGU4XHj/sy+gde7hsUXWA4TLPc46DmdDcIa1NY7lyKf1M3VZwGw8Pe7cXjzqP/NHf+OvYwehtcKKYnWHYrumyhi0KXPeqFVxgA6e0irR9BRdTA5HP5+vaaFqRtOKtoyMHV8WYt8ZtTJy5wfc6Mn2zHq98fz0iXGzlPeVcC3wljkoNDIVt0euW8HeRdj7G7SUSkUIA0h1XaA5VHRTXl5nfv3zb8kpEQ62+xy94ppny73Lcjy+p7jvUVVeboWpKU0rTYWWAmnMuLGwVSFtcLhvqPUYb5kOE+vyymoNd1G4LgWpBYkjuTpMLWhVvLcchwPreun9+aBYO7BvK7lmPBvVFPa8sx8srihNM04VbyH7jS8PZ07dL8dhGJiptGrZW8aFAfFXzHxgigFMzzcUE3ClEU0ffNtd5z2qgnjfC+a1UV2HhvD21EcapsbuJ6gKtoJpONsorpHN0LtULVMKqC9U/cvxBP5/v4yCtQMhJ4pE/OIovmHEs0tgoHSUskndzlq7ZyCXnX2rZBWyFiyNsPa0lfGVtXQGA653ZMtWaDWju8MbJSeLGaClFXGCGTxlbbhz4ftvZw7TjfuffolPE6kJLle2+UbLgkkzXgZ2lLLvOD9hxaACw92BMZ6hKNMUOXzxwN1pQtvA5TRxf3ciO4OxBi+VdTuxtRV5yayvP2epC6cQ8DJy+rKDRHAHXh5fePjpTziFE5Ob0dmwOGXfZ3YDvnnGcaSyc82Ftu3glGmFg3W4JmySeU0bpF4jiD5wNCODAR9G0MrqN0Rmbq8Zd7CYKIQhMtwdcXuGMnKLOyk30rZgqoUiZFZ+/fhIfrYM3rHNOw7H6XKm1gFlp+TKahomKd9+8z3XZeGrn33JNA283F55/WbFHx746jiREG6Pv+L580iuwvfffuL9/ZFwGWipoC4TxVJxrOqww4K9u2MoShbTBZ84Bld7vkAr8ThSZWIcGy0L15wwKbO7gjiDqZVKIxxG9r2w5spgDTYaXl437G3j7u6EswFFedoTx3rhEiNWlXnIXO6OLOcTh+PEEhzVObYng42OcxxpLdOMpaG02IugohanBsHRTOsyVp/RaLGlD1PVAmJBm0ViL6JLSwQTqLmyYbB2RKuhOkcwFtQjfkbzD/mHfiSLQAOexHEchdY2MCM2W5yvuL2RJ4+VitJZ/lUaUirVVYpVSoW9Bva0kEvvmVppveOAYGtDrbITsJp7KCNnCBMhK9XMNG9pRbDmwLKCJfN4/Z6H/cx5+IJxg+SXPp9dhQLkeabFAZ36oI6NyjAdCaGbhKppuGnkdDlzuLxjspHgBTkfMfPGPu+0KXR0FAeqrWy58jLPjNPAdJy482fiUMEeOQ0G65XUCtocU1TS3ijXF8JgCfEesdCq72+wOhOCRcWzS0Gsst0a+23Gq5Bdw6nBBs9OIm8vpAZp6f/Oi2yoPSPtyv08YUNhEUfLO8N4z+v8CfKCrV8QDytiBtrdPTXf+PzN98xL5f3dwL6NaFFs8BQTCOwUNnCGy/0X3N//FazNXJ+u1MVwugw0awmrsMvEpLc+yHQ3sO+N0XpGOaNaEXUY63k3KMvhwsGfcH5lk5WcF1w4YUwvAg5OIMEwAiGizhBzYV926rVTlHZ2YhlobwSfmhNphUEmrtefE+MZ4yLjqfH8OpPm73HxntwsPgf8XNib5XIaqFaJgEfQVLHe8PXXHzmcTyyfFyQ0XLGUrVF9RXxPuUrpFGZb3h5wJmBdZ2EaazAlUZrgWkBUyCVhvaEaiw8Nk5WoDbdB8xs19+GxH6oM/igWAUE5lESrhnECt0zIuKO70OxCbIe+rTKKLQassNdCTom0ZXKRbm01FRn6BFjLQtCMkUJthpptX0UtoEpzA75uaJDOn58HWktMl8auleX/be9MYmXbzrv++1a3m2pOd+99z6+J40dMJAvRWFaUQZQhkEwMs4zIAIkJSDBgYJRJpiDBAAkhgYgUECITQGSCRCMkRqQBOY5DZDuAk9h+7/anTtVuVs9gl/HVw1e2sULdq1s/qVS71i4d/bfW2V+t/a21/l+xlGDI7QYahbOVuPe0NWDaADP4RtP0Lev2AlTEKsvKdShlSKGSTFoCg23p2haVLakGdt96yLO7A/3acC9csL1e8fyp567uuL2d6Q1oc03XOmZTCcxcbjtqWuNjoVcJ/MzDNFH1ijIY1sos+ZC67JoqKSJFYXPFSqXWQih1cUA2hThXynQgmMzFW/epIXDnB8pdIqQCTWJ0HenJDhrNrsxYXehCQ5SCWWfafs3h0YzqZrAdvVvRbTp4qpjcjD88ZJ8ctiSMtfRdR2M0NQt3PrHdrlGq0llFCJqYAFuxxeBTYWDEl0xTNEUP3NxrcXaZxfB2j24vWLVbnj4duGmuwc1Y2+Jdy8V2y+HRgRwjtJaNVoSqqFTinKiNpW01al0Y9zOzqaSQMdmiioYErbaUPKPrMtNyN2Su9Yj4RKsqq6s1w90jTIxEtyJ1DlUqq3Egm2tqMrhmBVPC64n93UC6u2NlHIMbIUO2i3tQySBZLy7DOZGVkFmh9bJDttbj43CajiYjQpa05IhCZnIVTaYEiLpQfSWpiUSFklGleen990oEAa2FFresfpodcr1jxpKqxZqCGI8uhYhjLBUbJ1KJpJiIOS7DfL9Mk6llWEAUYZKCq0sCEGkXD/pkQUdqGsEoYq3IrCkSWVchmwJBo5wnGkESuJVijiNhiKhNhxla9GqHTIHh+S2rt1qMMxizQTqL0pZmLDhjUE1HdY4cKkk8Ohescbx3/wZtKjVWdrtnzLKhVMP9e2umqTIcHrOip7u6R0qOYV955/4lsfcoDXcHQxMs3s+smx7dd2TJWG0ppWJw+HFZJNR2mp6G0EbKZJh9YJg9LgldzkyHHS6BzI4YEv4wcTsf+Cg94d6DB4Tnt0RpuBgv4CJjSl2qMeeOty/uMXLL2lVsq9k7xxWC1R3Srri3XeE2Ldt1y5wN3g/o2WNlefySsCyYERRJzdi2EjtNGAIpR6rVlByxVLrVNW6zhrYhVmiHgmoqXXH4aUck0yhhox3ZfMg7791w93TGPx/g+gKvC0JASreY0IRELTObTctu3zGrgcYK1hooMyp43GoLogi1sMLR9Fuq09TqaK3hU++8R9vdY3d4Rt4fePDOj/P22z/CU/cIf2mphwPZzDS1RVWH6xRVOkRuMWb51U5O0OTFKDdBdkIqZplqjXVJpMqECCAKkw2ptrCqqLAUIelCZS4VrF/MSdrlb7fSMaSyzMO/hFciCCDgc6JsI610UNb0MlOqw86OkjKsM8kbDANFV6baEE1DqoUkmeBWpHFPict6AAR0WQwtTV2KUOQ24ICihJg1gco6aQatceqYYBmapVCmMzx78ohZfQYpinkwUC35sGO3n5h2O6LNdJu36dt7mEuFS0L0E9kVmlVL0/Q0yuG0oFaK+TBRpeHqQU8jPdPhOZHEuO/RtqPrNL2Z2R92HNLISl8gZaSvLdPTZ+we3EfPlmIsrS8kifSN5i57mnFJcOE0bRZ0AzUUlKnUUvGxoIJgi8ICnVY0bYPEmf0I286wfeBwQ2AuoKaJNFRSLosF/FXivZuekjTxOjDtDSYlnvkDOc6kraO9e8667RingJHIO5cbXNtTYiGPGalL4D7My8acpln6z+vIdJiYgiYng6sJ09tlC7C2BANGCe3GQooYb2nWhTZ2jNJgujva1pKSZb3qeVJmxic9+vKKfv2UOE0UVVCqgRjJOi0+BWs4BIVYTdtrytwTi6FoRaqW5CbWotAKut6R7vfodo3QYMqOmkbadoPrKtZ1VGPJKmH7HncbyDshW+jYkMuI0wW8pvqMig5sQlnIWRDRFG2WhGcEK5lUDEkFnCisEnLRFF0puqDyhIpmCZ4XQmKmz0Isipgtq1kTTaTqgo0JpyzDS26/VyMIFKFu1qxrRrFEskks/bG6StGFmDNG3xInkFRQOS2ViXzB1IjJI3NNJKvRIgiB5BWIoiihqAih4pVGSaGriZgVSSpNCMxX7WIbbSKqFJg0c/CIyeynHTJ5nj79kMlnsp8hWrS94N72Pu88WDFQCdWTp8jKNVjTcbnesl5pdI2YQ6JVDatVR7/q2DaOODdMQbFqDphVS+N6phyo08w8rnk4PuT6xz6NsoZgHDOKC4GqZ/JmSbI19W3y828QrCDFMIWMZ/E0bK41ZVbkOSFWUZxDqUq/vkc7J6QsdexNzmi7QXJHYzXvfLDCdpr5YaRpI0NpceLo+45ZIs5uWHWVg4r40fPWjaVMBgrsK5gMWc90+pIkByqOyU9Izjhpl5kZVagVrM7oGqgablSLv9Q407DpLmltIA2FaLYEnlFlIEaN0wYpa/yN4sIWitxjl+4wkunNhlX1PLIHtk0lzj3porLqWxBPTs1iktpWSirYqWCrojQtsgnMFELOuMbgVEd1iuIMjRHm4yrNXmeepQ2DKlSVqT5zf61YXa45PDkWvwktuinYYggUnu1mnn/0hJt312hXMdVSUsAURWNbgh8w7dEfQBK+KrTL2ARVa5Ku2OwpqVvukJqJNaOyRQ4BI46gMqpErC9kU4k1UWiW3bbTy2+/VyIISF3m6KtSRJXRCXQ15H6HwtFqTbCGnITSVIzO6BiXRRNaKEYjaUIpcJKpKpGDQuuK1gpiwiazeOdVQ03ga6FWQ+oFURG8UFXCoEkG9GpCtQ/41h8+on37Pv3VmvBI43ImmC21mbmyFtdUHh/uUAU2/SXvfebH2K67ZcfgOKMnSxHL/MBSdMGWwmGaeLY7oIKAGrkbJ/RY8A30aH78T/1Jtvd6GBrGccRuFW24T/xoT323oZYN3k/k55XY7ugvGqadZ+6hlQ02g94qhBXBFEo3si8jK61ZK0u/EeY2M5eJPLSUHFA5ol2Pkiuue8X2A8fj6wNzhA9uDNkqbqPH9RYdG0Yyna58+v13CTozT5ooI3/w4Teo8x1aMu++20Kwy5Tl5gpbKpJn3GXL4AeKr7hmQ9Yd7WXkwdpBiEyTJ2aPtdBtW57tGvJhz7q8yzMzo5ylbRw3tiV0PfvnBad6pCTu5jv26UBrK3m0RDWR50oSSAfBU2ixZLHoKrQrIAcMChd6pGhsKFysNO3VDbZtCSVQpsr773+AMcuzuH6U2dqAlcjF1YZte5+rK2FKhrS+4umTp4z7GbkKSHaUuucrj7/Gs994SBTB1wg1ARYdMo12VJugVARNYy05CSlFilMgLUU0yUZUqWQTkWjJRXAWIhOrJPhc0C7js8KJpmZHqGW5yV7CKxEE0HXxd/MJ0gpjLcVEgmoWO6gpQIDaANESWZJ3BymIMthJk+IKZyZimsipWdxXdEXlQEaIRcjJkCRi9dGTMBR08VAdkkcERQwFxKF9h68J7RNz3DHXgWkXyOUZrSkYd43eXLKSFaVk4kpQ1w5pl9mLMEXiDEUSfn5C/+GOB+sLantJKGlxHA4JMdA3lmfPnzPvhGqvkfcKa3sNn4isDw1iI93KUfLIZKE3lfVhy+ieo1YJfIe+16Jmj06euXMwF4wRTMoY3WKMWzz8mkTxywyHTS1RQ18rtyETxls2nUGaS6ppuVhp+ughVjba8tw/RYYe2RqaEIixEHXkYt0yhwGmmX7yzF5oXc84DhTbsup7Wg1znRGl8UmoWtGvNI2xzBIYcsDNUJPH6wKZY9DU9AWGuEa6FXoS9KbF2o5kG0xMcKPQoyCrhiEM6JiYcmTv9rB7jK6e6LdMfiCpykZlGhReFPvB46piioaoE622eAnsUsAHzU2/ocnCYTig3itsW0sxLX6/I+4UWoQ5OUJX2I097QWoZFk3jmbrqFkzdQrttvyJH/0cv3/9VeKvfxF6jeQGiYboInXWqMktZikCk02YVQKflx2gwVIRnBECgvEdxUaMHamxw5lKUYW42i5mrDGRykRqBpQ3y2rZl/BKBAEpQqgRtbGYZ0LdZmqBLjUYJ0ytZvaFLmu0aDIRZzzbEZ7WyNBmck2olNDi0BZM8MS0WJGXtULGiJhKowxRFJILymRy6pe6hkbR5YoYMGKJKSI+MOWBuG+oVtFrePS84DX88QdvY687bkPire2Gm0ahbUueLbfzQN7dASwbaIpg1paxJDZdxjiNDIFmSqSuZxoKD64fUGRZAtyVwsPbj3i7/ySNvSNFjW4CpBY3GaIBu/a0x2VRRjLFZ6oXgmuIcdl0QigkJ7QiKJuXbcWPJnKNxDbRKAWLuznZ7zjkjlEgXQhm2mB8JGYLZCYx5LklaCE9HlhdbJj8HXhFGPdcWEVR99ndwPTw6/g8EJ9VLtaaqCKxajqlSFUzFRbfu03EKEOvWuRQGCSzbxQuCyFO+CJklxjibnGemjwNDY0uaKdoayCUHlJAk9gFyya0RAJ90zE9OSz7TsoG5Vq6xqEl4WxHsYrGz4RaOdSRJIvJR4wFpRy1rRgUzJW5MQS3lAcbDgZxhbZrUKuAmeDyoqUWjSaSYkOnFDF0tDRYUzDxis2loV972m9+kjYUpjmQVEBQtNkRVwXEU0JLVmFZ1JVXWJWOZfjK4j5aoIgCVxHdk/GLiS4VZwtlGLF6haHiV45+rGQCQV5ekfSVCAJZLX767SyMPfRTJHYWGvBmxCWNdMuvv1NgvGKYOoY8c0iBWmeyKqjGctMaSlHc1oCSgI8VNSx7CG0sRJtRuaAdxKEiSuAG1G1iDA7KiLKCKYut8/Tk6/x+mnnn6tOwXfHAWspFw83lJfM2ER4Hvvqth/zI5pp695RSH2MbQceM6TesrzZc9xfkkDjcDeQ40KkNcxxIWmF84epqxXbTs7lesx+Fp/FAN7Xsdn/AxrWUbAli2LjEPLWoesdMQKsVplbaG0enDaFMpOCJPoKJhKQgNUzWIINHzyO7/Yw/RKxpCTftspd+2nH3fMdkHvOOvkDsGt+NPL+9wytPM17wDfMh95xFOcvoLhiffYshzfhQeCts+dBk7BiR5NlcXeMcpGJJtZLu9mhTYWNRRFa6oMSBUUCEWjGqpVvPmLhh2o/oalk1gacy0mRFvdwg5pKcH1Pzhq1eE20lHyoEBW7D2iqaBmbfsLaZYDTRt6zXwvX6HuIsQ70l1BmXBY+luhVmFub9QB48xRa6tsXYDq0NWnnSOBJLWOokrieGXSLrTE0zUzfgDxf0l1c0rnKn74j7SFAzunUEC04FNvUKFT3ufYtPDflYKyKYSgoJFSK6XTw1a3IgieJnatLL3gNtUMkQSBRnKBIxqWDtMmKq0i3rCKQS1YxOGTfANPYYl7Hd9JKdAyCLG9hpEZHHwAA8ObWWH4J7vN764fW/htddP/zRXsMna633P974SgQBABH5zVrr506t4/+V110/vP7X8Lrrh9Ncw8vrFZ85c+aN4BwEzpx5w3mVgsA/OrWAH5LXXT+8/tfwuuuHE1zDK5MTOHPmzGl4lUYCZ86cOQEnDwIi8udF5Csi8nsi8oVT6/l+EZGvi8hvi8gXReQ3j23XIvLvReRrx/erU+t8ERH5JRF5JCJffqHtu2qWhb9/7JcvichnT6f8/2j9bvp/UUS+eeyHL4rIz75w7m8d9X9FRP7caVR/BxF5X0T+k4j8dxH5HRH568f20/ZBrfVkLxbT1P8BfAA44LeAz5xS0w+g/evAvY+1/R3gC8fjLwB/+9Q6P6bvp4HPAl/+XppZ6kn+WxYf2J8Efu0V1f+LwN/8Lt/9zPH/qQE+dfw/0yfW/wngs8fjDfDVo86T9sGpRwI/AfxerfV/1loD8CvA50+s6Yfh88AvH49/GfgLJ9Tyf1Fr/c/As481v0zz54F/Whf+C3B5LEF/Ml6i/2V8HviVWquvtf4vlgK5P/FHJu77oNb6Ya31vx2P98DvAu9y4j44dRB4F/jDFz5/49j2OlCBfyci/1VE/sqx7a36nTLsHwFvnUbaD8TLNL9OffPXjsPlX3rhEeyV1i8iPwr8GeDXOHEfnDoIvM78VK31s8DPAH9VRH76xZN1Gc+9VlMvr6Nm4B8Cfwz408CHwN89rZzvjYisgX8J/I1a692L507RB6cOAt8E3n/h83vHtleeWus3j++PgH/NMtR8+O3h2vH90ekUft+8TPNr0Te11oe11lxrLcA/5jtD/ldSv4hYlgDwz2ut/+rYfNI+OHUQ+A3g0yLyKRFxwM8Bv3piTd8TEVmJyObbx8CfBb7Mov3nj1/7eeDfnEbhD8TLNP8q8JeOGeqfBHYvDFlfGT72jPwXWfoBFv0/JyKNiHwK+DTw6/+/9b2IiAjwT4DfrbX+vRdOnbYPTpktfSED+lWW7O0vnFrP96n5A5bM828Bv/Nt3cAN8B+BrwH/Abg+tdaP6f4XLEPmyPJ8+ZdfppklI/0Pjv3y28DnXlH9/+yo70vHm+YTL3z/F476vwL8zCug/6dYhvpfAr54fP3sqfvgvGLwzJk3nFM/Dpw5c+bEnIPAmTNvOOcgcObMG845CJw584ZzDgJnzrzhnIPAmTNvOOcgcObMG845CJw584bzvwHa76tEiOuxdwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "attack = HopSkipJump(classifier=classifier, targeted=True, max_iter=0, max_eval=1000, init_eval=10)\n",
+    "iter_step = 10\n",
+    "x_adv = np.array([init_image])\n",
+    "for i in range(20):\n",
+    "    x_adv = attack.generate(x=np.array([target_image]), y=to_categorical([866], 1000), x_adv_init=x_adv, resume=True)\n",
+    "\n",
+    "    #clear_output()\n",
+    "    print(\"Adversarial image at step %d.\" % (i * iter_step), \"L2 error\", \n",
+    "          np.linalg.norm(np.reshape(x_adv[0] - target_image, [-1])),\n",
+    "          \"and class label %d.\" % np.argmax(classifier.predict(x_adv)[0]))\n",
+    "    plt.imshow(x_adv[0].astype(np.uint))\n",
+    "    plt.show(block=False)\n",
+    "    \n",
+    "    attack.max_iter = iter_step"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## With Masking"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "HopSkipJump: 100%|██████████| 1/1 [00:00<00:00, 1114.91it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 0. L2 error 42160.312 and class label 866.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "HopSkipJump: 100%|██████████| 1/1 [00:40<00:00, 40.37s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 10. L2 error 20118.164 and class label 866.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "HopSkipJump: 100%|██████████| 1/1 [00:56<00:00, 56.42s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 20. L2 error 17655.967 and class label 866.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "HopSkipJump: 100%|██████████| 1/1 [01:06<00:00, 66.46s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 30. L2 error 15877.944 and class label 866.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "HopSkipJump: 100%|██████████| 1/1 [01:15<00:00, 75.47s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 40. L2 error 13526.768 and class label 866.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQEAAAD8CAYAAAB3lxGOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAgAElEQVR4nOzbx88uWWLf92/l+OT45nDfm2/fnk6TzBkGiR5KhiAIsA1YXninv8FeGGrZFrz12gsvDFjwwhREyRRI2xqSwxlOT/d0uqFvfHN43ieHyvF4QS1kYQaGQQ/ZwNzProCDOlWL3w+nTlVJQgjeeOONX1/y3/QFvPHGG3+z3pTAG2/8mntTAm+88WvuTQm88cavuTcl8MYbv+belMAbb/ya+5WVgCRJvydJ0gtJkl5LkvRf/qrmeeONN/5qpF/FdwKSJCnAS+B3gQvgE+A/E0J89f/7ZG+88cZfya9qJfBN4LUQ4kgIkQL/K/D3f0VzvfHGG38F6q/ovBvA+b9zfAF865cNNnRTSHKCatvEvoSpSYRSgVOk5HKVsowRkoxcluSZhCNBaccIDKRUhhIyRUYqI1RFpZAVpFKgk5MlJagqkSQjyxKFJOOgERDjFiGZbJKVGrIMSiojGQVlmSILQVwauCroioSvCIyoxBOgazpCShGlhJJCYSnIaU5ZlqiKILdUpETGyATCFJRqQZJKqKWCKTIyISHJGpKckqGgaSYiTlHUnDDTqOCRaiaBqlLLSxJDoHsFqW2iSYI0klGTFMPWyeUluSqhhDolGrkCiVKiCB1TLikkgRA5EqAKQUFJUahoCQitRCaFQkMyDCRZICQJoUoEeYJZxsjChdwn0OvYXoJpWsRyihoKCltBLlfkugmFAqJA1QVyKFOaJWlhYZY5aQlCldHTnFKVyeQCuVSQVRlJSCiSREKIkskkhY4ihUiyDLKLkAoUqaRIE1RDpfBlhAOln2HUFQhSFEklVhX01AAtIco1VD0lDSIsySZzZVSRQqmgkpGKHBWdrNSQEEiKDORkSEhCRkkTFCMjyx2EnKHoGUWuI5cZspBQNYs4EOiaSmZJWIlEXAp0qURYCkUiMISCXPig6ASxjGanJJGG0ZQgFeiyRFjIlHmJREapyti5jJBkSlKKUiBEhiLrCF1CLiU0oRCIgpQcAD2W0IyEqDQoZdAUDZOc2EiQ0xpCBMi+hGQbRGkCeTwRQnT+/fz9qkrg/5UkSf8I+EcAqgrf35b47DCi/wOFm9k/YPnlzxlvrpNXxtQO68wPIron2yQPA2zjY4zDNZLKfSx9yjhK8UqZdOlimCl2u0J+vGIvmuLmfbCf89Rs0iorfNGRqc7WeXsSkd2ZcvFRh4O/vyL58TY9/X/jq3aPtaKGtq7gf1pB3W+RNJ5R+PeYL5+h1HZZn31E5eZ75LM5oXuB492lWOYo+YSifxcrviYruryMLG4XV8y0M+TUYXYq6B6sUOY6tn2HyuaI8VDjK9tla7zNbWXF7tsjpI9zfrRhkVQnbB1tIjZOuBH4fHK3RnJ6gzKdsFnvE1oRvcWPWZTfx189h7LNdu0uy9qS58uUey+nBDeO0CZvI+QJfjRCVgVxwyBsPMAdypjrn2HNpmhpnZPnB5y9m9M7sng/9qj9RoXXU5/n8ys+UNq8fXvOHz77gL1vPaH7aY1s3+W0/Ira8W/TTmPOq5fU4jU+N5Z8O71HXJuxtnjCz17ppNtV3pn3CDXgZk7j+pTUdDHdgEJYrKwmMiPUMOKLSU6r0idG4S1pRbi5xP/jPaRWyGcPR9SrD5gcGWx3/heE/l3WKjF/cbXEXIXcq7X4WNW5a79A+dH3+OIDg/1rGe3ohMC45nT3PdwwZS9dcpFtYLxlcUMfEi1G2OUOvjLntbqkrBpIT+rs+g260RJlK8DrWOTxkv26xqOXG9wQl/zo2uU3dpucVUNCs4krj3lhStw59/Fyl6pmc9DQOXv/MWtXuwxPfkKWHvCTrIPVz3jglly/llCKEXt7XazCIAoEUyekVq1SL0wmC5XJeEG9JaNXlxh6jD6scLT0Ods36P+4SfXhjFZcoKxF/PR4m1TN+J2LAea91zC7wUvqvHr9k9NfmMVf0Z7Ad4APhRA/+LfH/xWAEOK//0XjLUkSd7d/i6tFQWP3klphEbxYMdzSsJsd6ixojGVOzBqrjQ5vDTwEI8SsRdhO8NU59UkMYkrY2iDGRj87RfccijvbrDsyE/slK6fJ7Mc11nuPeO6l7N5ukWR38COPll0jX8/wXo/Z+HKbyn++ZDlrkg1+zuGX3+JbtFm2F6i7V0TWNWp4k5qRchbpyHOTWQ361jXZKkdq9SjqZ6hfeZj+NpXbTbyjJfn0BMVokq4ZqFaJk6o8y69h0qUVyzzonrOwKgx7bXqLHoZ7xORHCjfvP+LPvN9j8/yPaN/6gN7dgj8fCNxGwb66i34c8H8aMge9gOG1yr6jkNdCrJ/5yM6c+Z0bSLMlGxcWslKQ9zTywMRzTNKKTJ57dM8Lpp0KPUsj+GSEL8/J71ZpWwpe2ccy/hV9bZffHxyx1Xmfs7HN9/oTehcVnpR3mDkj9lPByD4Erc1w1eI75SkXps6lL3PDLJGsgMZHNc4bFVq7HitbsERlQ7WpWRaZJ1jOUxrtJbMnEXGrwnHhUanJ6L5HS91mTV2hWy/515UOD+Uui7HglgtnwQR9tU69+RNk8z/laPJT6r1LtNE7hNaU+khmqEtsWDofhwPaynepmBHV2SUT5Rna/hZe/JD+zMSpj5AGKS0l4zN7GyVcsBHlFHs55dMrtLc1Qq1HWmqsHY75oazzzYPHXGQ1flu0+J/zKn93U+Wj5w1+sy5Q4zmrrOSPJnto37/gg4uS8FOdl7894+Fiizy7QFqZ5PUZ11mX63LMDfWI/ZYgDLosX2xTKCt6UROlpXHcL3CsGU1lA8VOGf0bGfM3DWRngZgpTH5ocPLWCfeMDq3RpzxNf5PVep/4o3/2qRDi/b+uElD5y43BvwVc8pcbg/9QCPH0F42XVV00fud9dk5CrM6SydldhtsZ6/41ReUE8/CbXEcXzBsNHHmJZNpo7Rb6kznavkU6cxkFGd36CCOfkqgVFmWHnhQTXb1i6rzFpi6hbrym5xi8/GkHx25yo7skvPExL/9IxVvL+Hb4ba7W25hPjnmy7VDXfPbyiPvmN/iDF+c4rRPS5hSl0HHbDzh+ITAb0Nk9Rstkti8aLNMTxve2cK72UIqEvF2wnoYk8jXHR0vydcHqSqUtRSyjPbYsFcw9rHde4runZOMOUrDFN2bX/IvaGm/HGtWFjH7wE0J7DevZLcb3VRIFmvM/w7i6TxhXEfMJo78TMx/2eFerYPxfCvPqgHJ3he3mDNI6np/T4Q7q5RkVL+HFTg2jOmfbGPJE09mXtojHYBkjli/q9N6/IKSHdeEgf5Aj8gva2S6Pyo/YkP9Dzoyc2+WfEv7L3+RzdZ30nZi3s5zyLKC9N+XcF7jru4zCP+BdfY0j622+9MfsmiE3yoCxqZP5PplX0Jdttmr7lKuYH6UxrXbBnTOdw7Oc8oMZ86BK327hfzLA/paDt9/D/MOQNSfhTyqf0Hj2n9B/8BzzXo3h50viVpONsKRsLPk8kKnadd77uY1/P0CJnpK6DZwzk1kc4VfG5MkBzQ3BTJgIx6SlzZHFijiUcUYmvf19jrOI0lToH12TvOuSzxd0nQqLf6myWBuzub5OKF3jHjxh95/9Fzx/5wuWYpfKbM6xN6HaM3HNAZ+JHdYsibdklcPHDaL3XuCJU/aWN3k92WY7VrHfOmMszwjOu3TtAYmARX2NhdC4j41jRXzJBe8mLpxafFxZ8PZzk6K+RsuQCRtPmW2t4V1f89vuFj9/1eTx+R/89ZXAvy2Cvwv8D4AC/E9CiH/6y8dqYvv9m1gdGUVIvPjK5xuNSzTtfXyviedecflMoruVYrg1hsULtJObWE7OnJKaLmGUOdK4oL9lEqoRvqPSj0yC0MS61tB7Ef3WgP/jrEpH2aNu/oTHiyqd/VOuxR3Kcca390OOVhtkC4++uwv6F9SzEdH4HU6zL1lr/BZKPeNlMaQ3lNGaAUZpsixTRsM7rLkDdq0FEyVHVCyUPMMvU6xYZjgPWQQRzSTBM/okSY7UndEKEpR4G7854OZOjb5xi+LHEi/Un6L/R3e4988Fj7d/wM29zwkuXPakFPV0wfH7HnJg8XGa8HuVmOFji/hmh7wxoi46XEgRtShDLw28kYNlNVCuTml3C8p6jfM6BJpJoz+hGGRk/hFK77sYL4/I9WP08oDz7yU8/P1dTkyQkyV6z6HVfcGLrya8dbHF429WkeZT9vZu8MLSaWdDmv4OZnWM/0Odyq2EH52s2HhPwUFDtgwuz1zssIZbDrnaV5AnCbVSou6UHAURVqpw+zpCdhOGtypcFgZrP1MYvnVGdSZhph9wpD/iXq3C5Mii+/cE/v/ewLyTEpzH9O/OmEYB+vAbvDCnPHROOTtZJ9uqsZcXWMsxae8cvfeAcCiwVzLXWZPFfEj3RsaalFA0usRlhXQeUlstqVUinjb6OOWE7rKH9MhEV17xw/cuCacH/O6TkqvvaDxnxsPVN4nWLzn1Kuz8rEKfCRd/W+Ghq/CHv+/zbb2H+cFzVoFKwQvEvQd4px6vFimNXY0Ho3Uk4TGRTc6nDitlgb06YqNmYq/voOYWR+WKRtNkML5i97WKec9maFYQJOzOdJYLj7Dm00xfIHnfxW58zuDxPZ5nn/z1lsD/F7KtCmP3t2i7EcNPQ4rtlB3/jNaWzWfRDrIxxjTmPDx+SK0d41syj/2Y5nSEul2SRwaLQsOwLBqhTKSqlEZCJu1Tz1c4dszWixJ9LedfFxqu2aFx+Kck+R7He316zTGLtMde8Jzpre9gPh5zfVaQbI251btNrXLNl9cae4cfUbyr0J3d51O9w8Yq5ir8lLWHN2mcdZCXGvNwxaCIMdZfYtztsT3voj59jq7K+JpEUJlylbQojQR9uSRoVHGsJdvlXb4KSr6bdPizyin9pklm3+H751/wo6/eYf+7J7ixRRypKHd9mo90vkwf0dl7n+7SJu+pLA8fUdE3KJ06446Fe/hTnLBK3jXxtTtcyEeodsbuiYvIQhTF5Kuhirmm0+kaZKuE9nvPkRZt4sjGE4/QVr9BMRas1ypM0iVWMefCXecd44x/MdbJmy22qx7K3CU/EXS8P0c6eIvKesyTok7Nl+gdaczFjKJj0dZd8mGAafvEWxX6r3JOtYDF1i6Bc8FyMqG90aL9sQaqylW+omM6nLQzsrLP92cvqG7bTBc9PnoecuPtAyazR9jHAcldh5m5z/fUf8VflJt8b0th7Y9TvvjBBsvjlNY0on1rk8n5NVFtEzmL2Ew9fMXHkCKKVsFCXyONK2jagIF8zNqTd9hRa5SqYNQOMGUTUxrz8dDgwfk+r95/hveFzL3vJOiXLvI7Jc0TOD35Euvu+1gXVWY7UwwX+n8aM3R0jHs6tedTVvsaVbvNK7/J+icJubPACh1W3WuOWyZda4VTzvDMAyq5xXkwY1h43DeqdDf3OB8m9CYJ5QOfLyZ1mukZw/4xm3+cU9/6HsPgJzy4/o+Z1Z4ze3eXV3/8z7++JWDqqmjr7+DVP+XeUOXznZs0g3McZZ2oprEt5Vy3TbxlRjot2esLlnRhJkjXn2NXNKx5H+H5sNK4roEyFuxbNtlmDnGV6/Aay7SZBhIr3WC35bFUVixbfaQ/OGbne5vEQcSuXDBNTIwrh0fuI5TDDTp33qU3P2TuOkyOhtxwKrx+KNOvaWTnHg0rYcQEPbAx50NORjVS3aFq+tRzibktEygm7nhGKOdkmxn2VQ85ntLIU4IWrG3VcDOX0Yt7XP6dP+XG+BaucQffe82rLww2Hh6w5n/KrHMbsxmgnlfJndfsGyGn+oryqMGpds56u8Yw3eJ+rnHdVKkqOepAZzbKqTc8tM0cb1RleFRB1FPC3Wtuf9WnVQ457YaElYy8+y53BwPmYYuGmnDmv2RT/x1GlZc0XIjjADdMCLtVPl8FiIMuUhaz8zpi384ZhBaxKYGwyC0f5cqlEUesojHOuy5XpUdykdD3DCxhIPcyzlcZXd2iJnpcmxZX/pS8Bbdmm9SMpwTLKnmmsWdPOLRu8/Jgyf5igHf+PtY457y9wBEntG+soa5S+sLCS6ZU1CqfnZf87XbJk95LIm2NIG/TnfpcJiV7JwLbzplLDaaujnZQUIk1JkXCvPTZDnUO3JxY6xL5MeOtIQdRQeXqbUaUiGrGnRsGz/0SBo+YVe/hpBdkY5mDzgOKn15R7A8xHYtH8WPMzia9612KOjS0KslfeBw2HEbrJ9w2a6wLiUUj5RMrxBnUMMKE3BbU7RKzb8CRRf7cZvbWGdsriVSpkL+lUjn8GWvPm0zf3eHIHWEOVZ6HJXv1DRJzQfgnT5il8S8sAeXDDz/8G4j9/9N//eF/96FRD9m2Yl6qbW6tKnhdF3kzoDEweB4pxDMfgit8u8tYTcjTAK0akhUNap/pBJOMWP/L1ylWqrEmNQgn58TmCfVRm5FewZXAFku04jV1YRGe2sRhgw9aLtNVh4F6yTK4QdgLuf7qJuvFDreqguUooLVTx4ue0q5WOJke8v6sR5YLpocVGlkHaZUjXZ8wkhSMLKMezqkmVZR2k8LQsWcrcimhTCwMOUezPUSrTuKrLGNB/Vpi0l5nk4j9rIJ2s0ZHXnHuXHP3vAk7n+BvWLQCiSz3yOYezwYTKmqLWnXEl3aPzeCA6vMj8ijBNU7R9RnBzGLYGlCxA8xURzVsSq3BiJhy8YobpUokVEaaRWUzpmLvcHcw41S4GFaK/ewvWMxvU138KYGjM6lWOPCGLKo72FePaMQNqoZFZ27gHt7i58qYkRLRkSKK1RBj5pIkBvldQS8eE5zuYK8csu0lSqOHaDRYaTaWZqKfWRzJEqGjsKHrPIxOGO9ccWrW6Vom1tkmmVJgH8QUqFwmDfqOTMVO6W2oSAyIlve4OazT7uRMpQFXkklnD0b5Ecb8LQ6M53ScEL3bRVCj2jFIFQuteUXYmZLZBra+ommNEfYFvbLNpVrBCSV0p6Sy2Odx9RX3pBTDaVIZBsRUmS9THmmCdfkTbq++g3n3I/KrHsMNGUWPwLpkkD7gzt4Z8+QdUvOSJLvAjEs2Hi5puHV0agyyFXJZYXtlM49Lcl+whYtlSbQCn965jNQrkIyYrO6y0jZwaiO6es6xW8EcZhxeHaMOJ3hvHeC0PmLnJMLd/C6Dy9eDDz/88H/89/P3tSiBf/rf/pMPQzsi1TWSlU+US6grmapmEiUSdAS9NGFtUlLqe9hCIoxqqGqMq5jY1ZSgscSu2MyLCcNLmURrkFZ6jKc200jCrVfYmC0RYo1mv41xXSMLJWqyzKNcsOOo2Ks9hpc11voDvlufcRKUdOyS6bdhjIEmHdOWdlDbTdLuFpdazo49ZdAeYSpzNFViLLsUpoYcFYhOk8i0yMdzlEpOWHPoJDFpomB6HfxqSuilIOqsORkXmc5AGuAabY6UAl1IVJd3aO9VORormOELnoouKxxMVxCUPmdOSrp8h34xRCQx6s0We2WPwo/x4w5mpKAugRq4Zk6ZJFy6gmBsYyptqKqMrQTFtQjLFdtZydWySxHOkE8L4t4KW7sDmxukxmN2xRnXpo6fO/TE+0xeXWHuPMXWF5w4Of/B9Yorq4ptbWCEQ45Mh8XtATkPaaUO3SJmWp9xnTbIEh1lBv1MpdOW0YoYexDg9qdoZybV5AHnC5f17AUV9SbP+grCGdHjBi+WKW+7X2EIGae2IpY8WuMaUjDl6OY5YRjhVUvqkYslNwiOdTrTiKmzxWV0H++qzqoqKO2C+bKD7Lep9hx6DRhJHpWXEluZQNE07iQuL5L75M6KSsVn3V9DDF7yZ2sO3bzOKl3Q9yAzU/TuDnvHGlmyzyIIKBpjzDAkmh1wox5zJb+DlkVYvo7T0ynWdMbWE6ymw1dnMjevZthmQZCX2MqAbaYozZzDLIdpid8MwZ1hRya2vMYpIbP0nPRPmrRYZ1g84YH6kKOujZu84L68g6MZLAyd4dHZLyyBr8UPRIYEqmehZFvcfU9mUzFQ73hk/Sbp9S7qtUJLmlP2W2yYMklYpbqRcV+d0QxXpFoVJdukmOjUli3WzDprcoFYhqz1VZw7Ctvm5zz2Ul7rOmLmcm4W1PYrXKcgVBfBU1bzCcp9j/CyjVAyurbNtHJOHuXMRIDdukVld4mlKEQPvmBHXaBYKkUw4JWXcT7L6FxKWDODwFWQF8cow0scS6FMVPLrmHHcoCrVWdY8ZD9CywWaNuVZkqMEPjupxKW2YHK6R+tLBdG/wns95fKkQr64y2b9Y96XVWbWlLftCmYxoP88Jjo2OV9rM0jbLNKMMX0W8YosOwYxJcg8BprgWCsoBNQ7cxrNa+RZyO50g63UJSz6TOQhK/kVnhbTvf2YsvsuP6/FBNUGveltri4rPB1/G0NWiTWJujklO9tlnvyATiGIayqbsora0Thot3jYbdI769E4HJAOFgybY2atJe80Mj6QV6SVc67ah1zOYWBkmHdsykmXWSXhsfKaGxWBFW7xbGRSpFMO5SaJ/oi3lZTO5He5OnQZeg7NeIvDSx3sV9S0OvbjDmLUJfZGxG6FimTg9dZJjkPa2Uc014e04znyVYJTXnDVDzmvBgwWARtDgbnls5D7RGqNnwyglg/ZtDqE1ZzgtM7z/dtsrGbMW4+YeT9FnOlclxnFJ5/yRHnMZ841znmCFcoUzh728JKZe0R2HpI9vaKXhHh/vmAx1hj8mxbeD0PceotZX2HoXxIWY1R9ndNyi3KScmeREzdNvKJCUdxFt+Zk1Stu9mP+4aiPZsgY+TWxfYunnR57ckwnfptn80u8wQ3kK+WX5u9rsRL4x//kH3/Y2igYJQsyWeU6y9GWBTIS2kxBNpaEe3voVYt07RStmRPOStzIJbM9lGaAZ0tMVAu1iMidmGVgEVgXaKaOO14iLnrUGtvsGnDWTTFVmYkZckeZs3g6p2I9YD4SbNzLSaZLpLCESsR4tUPf+gmBJti6XBD6Lu40AvWAZJWjxmPGFyZWEFNJVQq9JFczUjXAtARZHDJMMqJMRrcSVGmFUVRJ04QsT3DyTWrGCjlOSFsS45lJObhPpXPKnRse47MEujbj2icYNYVhEqFnHYheUPgt9FGO2JGR91YoSU7rsgCrYGZsIMeQSQ0Mp0kQRYgoREkkYltQPbEwVx6FCqFi4hsX5CnUFlVMV8XYvMCb11jeMCgnEW/nDQwzRiQe8fYtRO+Q2tmAZ/4HjHpjrGxEYulcb8tIYoH25zZhe4znWbiDbSqbR1SrE5ZBwVp5g8JzuCrhstiilu3CxENRqjjYIMU4DtT6dc7GYyprOiNvRWeWYFX3WBUWCzWhHsXIEkyrJd6lxlwaYxst1LURU38De1SletOl+sogyq4JtgsqdpWl3qMmCiTjKTIy+ust9guH0ooAm7VCwk4t8tMKYdGip+tUlAmav+T52KTamlOXnjGLF3Q9jWCyxvTAZ+f4GyjvwbOjKnveIV/tTdh6eA//eEV4sENadTlPPmVvf485OvVMol1fY3utw8CpkdZOWItKzGKbvY1t5rWSqlvgb1lIhkb8WmcuQuSeRqD02TVHeKcpZ2aJvZPx+LiFcv852qhJZxRzslHQ/2JEqF1jzrpcRr94JfD1KIEP/5sPEwXMwsS7lOiGm9hrFqvhNVVDICUqfalG0jrGyBrEiwmyWXIg91DzNqXXRq5UKMspVmBwMK2iC5/A3aS+MlhFC4S9xm6zZGY14eQ5iZ6zkmrcv3awMon8dkR2krFb/QR7VcWwFCaLCqtsSqfnoSsxtlHj7KLPYOWwbcy5SqdMEpPSmeKUHllRkMo5C0MlD3WkEORCBadAryoUvosamQg0Qi1G7RmExYq8zEgyQTNNSe2EmrnB7e6IF7WcxvII68yGyjZ+ekKjbFIzJ5y9rlLvXuDcbdCNDqkYVfLI5Ex3qQYtVvoppqaT1ZqUqiAqMuKiC5GJEZQQZzRrNpqvkpgWsruipMSpOMS2g7JqQtZDklfsyhW82YhOQ+Hj5XeJGz/HuGpCVSNoz+k5FnPfRfZf0zNqiLlJmQrSQmKQ9XC6MZldssoNgqLOKLbx8wRbSpHUHENRKVo5G/mS00hDqm1RtY54lpWo5hxX20JixuayQvPGBdpqyWxlkleXRL6N2ZmiqR5r1zbLjRLpaoptwaSeIlIDwYj0oE6pBzCtYuYhWemgjHQsHTDmFHGInuo0yIiXOivJRAsK7FrBaaJjpIc0piGGOker1MlfuFxPd7hLSfpNiyDUyYY6Tvgz3Ok2VvwW7264LOYSZTVB3xqgqyF9tcHUr2F9JXhdhsSWzzxdMksG3FkqZFOTwKkiSxKNOENXzulkMX4oIZkaWU1QlFXyIMHLlvi2ipqCLAy2phJe0sQpU/J5Qs1WyJcZq7WHzIxT/Mni6/s4IKHR1FSauYxwq1wJi/lihloUzKySqL7JZ4ME/zTn5YmMZ/Uw7ZssbMGlljN3IyRxyVqpsVWWaJ2I2tqU9ipB20mxv7lH3fYZRUsK6Yi0bGCtb7CXQ7Y2QtkCeMJB6ymro9t8JamkWsFlfMj6XGAu13iV7nKsp1jZU9x6TjJYEk+HrKFhFxG+XlLIkBU6emxh5ipkGr5RRS0s6iOZSqwgTAjUOeQCwxNIjQgrL+ghkRYqSlWnuRPy06mJ81VO0aozuR9RMRLSqy1KOWbxRUB9x2RSbGCMWsz0Bstkk7VwC3tpIpdPUJavmJxcIL44RDn2seMOXUKcSoLaKEn3QsI66E1wKzJyvUerqSIvM0bXL6hOMlpWgHT2ioUlIR9ofNKr07k95H13iDeSmA1HWEWAwOG9nsdt4xYjWeYqMzHvFbizC7pmTjN9RS/PCTslpZxAvsDsDnCNhG6SsDXzcK5VnjjbrNoKmTNFGm1SMzTqvbcwViWu3ufz9xUYmpRKh43c4yI/pUxX2FMLRjHuvS6b10tG1XfYXnq0L09o65/hDCtcDDWyqMGJnJDUEsbqkrNqjVtXgQkAACAASURBVFF9k6G8TnF3E7WtI2uCLMrIigLFLlhdq/TWNeT7Oxy+v4Mex2TSl1zckOm3JF63HKQjlUnZwvjGFdnigONbS+z7Q6YHf8KhGVP4LaxnA7qDgPn1McNXMtVvD3D25ghdYtvWuNHsML9vEbzr4vTmDNSYYapwLt3m2XibbGWgN2R6QmKDlA4hYlbFzxy0NEcUGtmuysz2mAYyi94Zs5mH/laOdHbFqtL/pfn7WpQAqkR1qDIyTPR4jNMUVJMGVtViq6NQsSLi6oogbnIjucCqpVjTQ0bLEk+dk1oDiqnL+XCN43aF2VrB3O4jOi5oOaFZEtwRRN0FydMj5ntt4izFkkb8VPisygnpuc3q4U2Sg33qbzs8mZxiLWdcuadMvAxtbCGNY3R/xUj9An/Np7enI6MSpk2imorXVfCNDJHOqIsYx45RyhlpFpPaIUU9RHR1CkcgNJ0iLVByFctUqcgqqWPizjKGpycgH5GInNDcwTvTiINDpHRGN/IJ7ZjlzKDzvM3Ai/DiPQbLE17M2nRMnceMGV7Y5IlFrM44TV8ymZ+TqStsc04xizAXBZXBjPY8omGecxBfsnY6pDp+znt6jmm/RJweIlcd1MGIcRYxGVdgkWKc9inXl2i1kJN5QVzVmUU1/EYI11XWzIxqKjNtqzhmwELRmGsa5kIma0xQ6znm5C7B2MCWV0g9A11PkDKfstSZTpacSgpb6RrScU5SCuyKRU1SeL7qE5QbXM519LPbtPUllxcvsJ2I09mKsrpHe6VwWr+Fvv4BlrPFaj/hnmqw0VZpaSn10KZd1tEijXx5yYZ7ji48Mlcm1BWyRo6WjxCNGdl6TuZdkUcypi1Ib2/RyG/x9nLMwB7iqK/wmwbb2jPcRCd7S2Gv0cMwI5Jyh54ak1FQ87e4CqrIYpNvvHONXl0izQ8wky5SPUAvHdLAp/QTgmXJyIg4c2MKoTJ0UkQzQJtIXF8ZXGcKWahhzJe8NS6pzhqYuWBUznmnaZF2LzDZJEgM1OSA8p1rtszVL43f16MElBJVy9lOHOzUxepfs+wHDGbwrN3htA9q3aJVrdB0OjQiHbHQyTMFw1jHjhyCLCXnlFGSMFzKRKgIR2BJKm3/itLL8KUmUWWfwtJZBl8SawoRKbVRhO9IeH6NyuKa1fI10ahgruisJAc3dLDTkOAKlKaEGVSJrlLyuUuUXVL1VIxpEzuXwVGRXUhkiVTX0Gsalq0hNI3QzNGTMbZeIrQlgR/gXipMdY3XwkEEOV5VQzgJWmRjfl+mLhZE6zmFt0k9l4hONca5AUIwbVbwpFPORzHV5QGFe4VkXOO0LHQEKB6lImGUJZ2soLHsY4RVrHjGxnVEfZJQBhd4lyHzFwukKGNiN3DUAGtuUtZc0lGdk8NXiLOUdekliT7lK2vC5mpILb9Do9PCH0ImzxnMHKSGS66pOGMHM9siWciMSp3glUl5pVBNOlQ0laAQLGOH4aLO8ViQ+j6VYIUU+pj6AqNeskpeoB2phOUG8TQkO5fwrIIJF4htGXlfx28t2dhzqEoPMP0hrapEIQ/xlAVKI+NKVIicU+qJjxAa9UxikenkkgJuwJoSYwZrJJMYvBLXk5mOJKaqTVwTGBUJNDB8m+KiYD5MkY7rJKKGNeygLBXGWsylGtA8ipAKFakYIM0S1OO7dOYxtmQyqU5oSxXWapu4cUImP0BveCy6Ey6LDnKgokYKs3mJHet0iiXoBm6ZcifS0bMWaajQ3xJUnTFOvkBv1CmWJkZuU8sCov4CtZBZr2j4M8GtfIsvZ1N2ivtoztUvjd/XogSkVCArJW4ikSku0bGE77vUlDrGMKFZXlCvGgwcmWtXJRQF/paMXFpoc4Xcl5CKmJZk0Qt0XKFRRVDTzjHMlFmqEmU5SjTC7AZspAtKySCISxrZXS5u3Kay/gD5aoVXlFhSjlwRKDWB1PA4vpZI/ClROGNqSthyQBhkZJOc3IrRrZKaaqMXNfRUYGUFlpqjZQVZ4pDmJplfQFiQo6NKCaqp4sqgKSHpJMRQE9RCJy5zkrSFmrQJRwHPlxb5qCQWCYE141A3KShIWj4baxF6sEFDuiSV6iiKYHyZYkYhQo5RNI+FLChlldgwCDUVNzXY0SsUFY/MHrFQAxbegjyzSXKPfDVnpunk8yXHZznNOGSj69LxbFpNA8O18Db3SdNdZk2T7VRl53KBlkbMplUKLSG6dpkXGtGeichkNsq/vO8hdSLajD2LfBmjNUqUloKoFRSui6qk1OwESUnQ9Zis4aA1ljTjV2RmTEvZxkky6uqUbr+FrzqMKi5ny5TjxoLiTsKxFpKtNAz1DOHllFHBQF9jcN8iHBhklSVhWrBaeqTVANOqkBkli7RgEkVEWkbVyFjHxs4tiizGrihYtk8nfISSqbyO5izKDtgZUmQxPklIki5nN2aE1QyrERJTIXI/ZqBtszq9wDNNcnOdR47BcrHOMikpOxp9P0Odl1wR0nf7VFWJQaoQxwt2whmpNiNxTxGVkHy/IF66GKsmWbOB4stI1YTVNxLCxpJ7kc6XxLDIsPYEF+/PKAYG5VWLcLb7S/P3N/Yr8b9LUHJpOUhGSrpa4ETb5JUxmaITJTIPFBs5GXEegSIStMOCop2R9DWSpIce2DhRSTXXUOySOJqRFw6x7qHaEvVgg2B4Sc0SSO4QEWlsKW8RDY5RexYlCb4+wmlDOlGoPNMZGWBkKiLSkPVzkjxFqliE8wIjWREpKoUOetRluRhhSSmBbVLXcopcIjEFqqNQXabkhUphqEg1iSTRcOYxUh1USSMixcSm6WYESx1NKFgPtpEvfUScMb1cII1Vguo5ppejWmO0aJtsMubCqDHD5XZ3At415XrMRG1SC7eJKj6GNEcqEsosYywvMNoWRVQSL1OmRoZad6kkEcEwQ6sljHINpR+iT7ogEtrmArNSUrgWK7lDpJ1xK484jHbYtmUmE52mXjI1A/qXKrEruPF6glVWmd6+Qu/V2Fh0OZvPWQqFTgNcaU6i6ejSClld4eptzEInNArqZQmKhqLsIvtzRtyk3v2MROSMwg3qrUtuJ7BY1JHrOQeLCsGGgMo2zqzEjHqEeYl5ESHSPlrDoMieo+brlGqBnF9QNi5xhcF4YWCLJmlWgWiBYsbMSoGTVTHqOmHapBKXlP4l0SrDqM4IQpvlPZdUG6K4OTdNnfGlRqNasllXEckuSv2Ucupx2WgQLBTQCvJSY3OlclF8gaffZGZYuMmSpmHg6RdIRkBc6VB6NZSWjaYkaHIbKwlJVY1IWqL7JYrpIg9XFLsWsVujZUd4KciKTjGsYEq32TVO0NyYWO/xMh2xvb5OloeU5fyX5u9rsRIAQYxBVNfJlJSlOKGUFYzqBrVRm/RCY+RpSJHGNE/wKznyXGc2yZGlIZm9JOyriLZK3AxYOeALnUKsI6IK0mKFFpmk8x62UEm1HZpOG+FlqEFIuTrj6mdDkswjloYs8xlWY5NeItCWJr5rYlsZetJCKitkWo1A0wg8DXmekismkS4w6wGqoRBpNlEpk+Y6QlZJk4hUyijlBC3ySSkwlJREBqWu0dFs0lghKEoaw5yaHLBTbXCgybSZYkoO8qyNozskKihujX4WgfDpB6dM8xLX9fCJaTQypFaLStWikbdwCgthqBimxEIrOBcGpSFxN5NxIw1MGd8sUeWEzoZA7Wp4acaJKdhTMopRyXSacF7XIFaYFzqNpwVCwNYLH3flEahNqqMtbq7ZdPKY5XrMOIa9Zyah5RM7PZobKpuJjZva9NoG3R2LuraGmAtGeUJoGiC1yE9VyqVCOROUwwuyokDTBKuujUhSimmIbea4+pzOKsG52sKRNHTqqBOHXLcIeyO6qkVSm9I+UWnmDbLVipoho190kbQc2/UxRxnDkyWp7NEVgq0CBDlLQyAqYySvRHNcaNU49FyO6we0RzFvW+t0nRw1qGFGBhsGiMBmc6AgwgBr0eK4WdBJmtzJQvYfzCiv2qSNa5TrFHc+Jgoz8nGAtNxEkZq0RzqSt6Q9HNPWArY0BxkXO2pgJA/Is/tYsY3dXDAyA+LUR04WOJGOMQ9QKipT06M7zgmkLaaDE5jcpLOhEK11SeTZL03f16IEhCoo5itML8QwqrTkCrdn6/Q3anTjglRJOErqjPKAVEgsrQyRFDSucpqvcpRxHTlUSeMQL14Srly0kwLTD8hUC8k2KeoF3UqB1bOQ0gnT4ZxVqSOrpyzUGtvVBtnpAKUxp1Vbx6w7ZHmKkSywDIl8AsIr8Ws6TqpgaypGWhDmU2xNpqLJ1HMZr1RIClCEjIZD0JBRawJF5BQLhUJV8FQQKxW5KAhLg0QKGQZ1HEcnUyUqlxlS8ZSzZwVKlqEYBWrHRFNbbCoN5N4QWXVpr6ZYYQ9PXSepVGgHDaRwxmp8RhQF5HpBpscktRLUnKyUGIURJ7OY6cxiuMx4MdaQqrAiI1rGiAuFSz/mRS3lNFZ4qTQJjSqb/zdzbxKr23We6T1r9+3fd6dvbk9ekpciRYq2LLmTCzBSCIxCEgRIJqlBDRIgATIJMnLK4yTDTFIeJqhBEFTFcexYsuyy1ZEiJZK3705/zn/+/t99vzOwEASBFCFlD7SAhbX392G9s/fdezV4v8YRXVVmVX6dO+kKTWrQFSXnbZXDRkDYjrkjvWA1SkiuS6xqn1l4wWm6oq9ZGCR4aUAgmbSiPppdUGsTkkZM3rTxhcnMrFiqSzwjohyOMLo9CtHENxUO5RTFyykHOrJckK5yTjsT6jpiGigc1QXzrZxJs4W7KaMPFUL3NWMnYTCrqK99giJlmh+QxRJaS8JqmgS2Tt6xILHR100kxaIpzRH1FUkd0ahtqtgllzap5S72oktaSqTnNjhT0smMibFBNAtYrSTqhU7gPMCJbRatIao3odE0uFxM0Y920U+vWZszrJXFoBmyVQr0YIJSTQmtCJScVMlZ5AVpUeDpBbKtksZnxN4JdcOm0TfplDKxNUGRQqrnEwLlCUm1ZiZ6rFYSyvEGb61fwfo1xDGR6v5C/v1KiADICLegEksGpoq6oSCZU9LkNet+iDYUaFcF0izDTkKqRc15KPCjmEXskCtNhLaJqlu0EhcrNSl7KVXvgsTLaC4ErqKxrppkpx619Ji0SlgoBfoqQQ1yLEVCVmzME4Hb7FKYzzHVGqUKSdYRsaoiWTVuFuJUJbpagJti2SVVq8TXa4IS8lSCsiQVEssyIw4hV1WUXMPwTdJigFK7iKgiL2RyL8fLc2S7pqf6+HXGcnbGsRfDpKZOVVwzQHZj0lCjrkqKdURsdpitfUpVo5OZnMUKs0WCErUwgohsNec6WxLnJdJKwVnYbIYV90wLG5fjlsvLnQFHpcVFqHIaa3xxLTibyribbYZbtxi/s40wNzkIQ7q6TaNjUwmBdtDjXF1Q7zWRGjaNU5lSj4iDU7gCV3Kx6wDfklnMHZa2QSILvEEX1dmmlgLKck0WOVTlAEe3kPIZNmC2akymdJYXHIYRkl4jwj7tIGcnOUPtnDFLNKhypkWNL2QKV8ZWC+4ioeinNIo+Z4VKeNlF7kioYs3AlNHTEOu+jdtxUa4MGrXCtuOjZgmRcsFs5JHKCY6fkOkOfqsgVAL06QrXLeh1aoRTMc1OEYpPmuQEbYnqSMepZiz2dSZP3yKtMwafj/CJeNa30C8c1E6TYdFkqFoo0pRB1sa2XMqOhyNq5GZM4Kd4cUHlZBSyYBq4XAUpV/lLAjEjVzUS3WMzzOgkKiptgnYH0ZwzLmTi7IKHakFlh+wdxuj6ijJv0PGuiGbrX8i+Xw0RkCQqvYVsm3TziiUrxLaNP5kTNTqk9gqlPEIzYyJPIlluoRkDlEOTXkPDlcdU2iWa0DHUFq2+wvqmzHrdYe4L/LKiIefkYUz9vM2wahNoKmp5zbyISP1XnF0IpprCaWpwMTdJn68IHYXIvEkVu0jFDaQ8oPAjAs0hkwUUFZqpIFs5umHhSyUChUpvUMugpDktZMrCILMULENCFTVlrlOULooh2DcSKgoaGynLboGky5xmM6ZXErFcoxQyar1GJD7iMIUsp5rahHFOeq/Cr15hLXOs/AJdrhjIIU2nRpIsslynrvk7D7yiIvMrAtmjlnVct2KvIePKLeKlQuFL5H2NSJPoNPq8OUiJPJPdPQ1/4BKe7FL+WELyT3h02cNJW/BGTLNeI0k6/U3Bdl5hbbjcGGbsZOcYdcJhmGL1YaC0cK2UrFyS1SuCtEmUOGwWGlrSpZzFqElAI9kmktq8aCxZKhMEGsLqo54J1qFCoDio/RpJ6NhMkToWe6rJrtVgUs447F1xIndZIDBFl5bUJtmoMG8qKEmTdDmlnnYoU41sNoFWQdItCIwZTn6OlRTIzh4WJjoJZesCc3eBm9ZsvCzxfA/bUJCcG8zULubQ4sb6jObGFqk9Zbfp0362QjKuGTyRedHq8qmeEOtnZAqMN2raF7uc1Bnfn4+5XKmcpAJlESBFCqKW0SsNya7QzZBRNEO+LHGrHq52D1M0mM0F6donVXuYpkFw4DJt91CXAb2+jK07WGVG1thGl2wK7Qxn0PnF9Pu35a0QYkcI8V0hxGMhxCMhxH/+s/gfCiEuhBA//Vn//V+GJUsVhVdQLxQmq4yWkzGfB8TWHnI0Y3WpojQq8iIiSxXidIrSW9DsKtRyTuciwxpneF6GX8VMNBMpzTGTAstIydSaWGmTb4I40PHKNpWS0MoaVFmAVFuE1QVJEGF0PRJ/hXKhQl2gNWcMez51sqRohYRbJXOxoloWVJJBZpjk1xJ6YGBVJlVdooiKUhYgZchaiKpCZipEUoFcJ+QU5JJEVgs8Q1BaLvEipbGqqaqa7khlZ9Mjdy00V8KLOsRajqbNyA96qLKEs5xjL0xIcpLVMUwvUYsxYcvHyPvUZYUmHDKlSWxoxF2Fatsi3dfQb4cMU5Xhy03uNW22W+DIOb1MYjspkDDQ8wn7mypiR0W536F4FlOrNZWpYBspN7cKyg24+VRntFnz2IB1t4XUNhCOymm9os4d2nsxtu8zXk4psxeEq5Rg0uM63iZWDCrXo2JBJ+gjBW2Kvo5v9mE9oHBNCmEQVS2KuomipizNr2LtyRgri5amMQiec65MMdMrJlJJGNhk1kv09mPqfkqoOii9NvNEJ2u5VJ5ClcU0WhW5sEhTkzqvieU+hdBwByFF3yJeNUmSLczYIq0VjCRE5pKyU3BhSKTLiPbsFQNrB0kTiNkh7cilcc8nHgecNhd0iwE30xneUUXH2iSTE6yhzIXa5VXrC+REZ95JMdoyEjnSyGfRjDjJE/JJQjmTsSWHQ6VJq72JRwuuNnClFdM7L4lEQVxPeR13MdIQ0b5F02pxOQ35tN6lfz1AsGBRQKoM/+FFACiA/7Ku6zeArwH/qRDijZ/l/vu6rh/8rP/pL0XKS4xMI1jUBEZK4LZYTa6R6xmeEjBfdAnLBnWpkCs6HVlBpDVq6KP2l+QaJOGaIrpmOZOJinPSLKXVkNhv1ayVmMKouFZVrtsdlhsuUd9E77WJY5Ne2kHN9iBScaQubnRErlUEixTV8hFCo9lIKQOd/qTGzSIsXUEvM+azgDJJyBIfNZOwqgxFS6iNjEqrCSuZIo3RZwWZraIOU+SWT62E9LoSUiYxzBykQsFP++j2TZRyl7Ui4YcBM09mK1RJZgLvOMKeF8TOiraU0c0yTEtGkiM0VcOQY9QriVRRQYEiq5GqnFoJ0colkpTTqvqYzS6q04ZOQKFdQSuir8bsAarbwy81/PoOk7MG6uM2CzGCG5/iv1VjSRK3t8foRcyj0sfdXzA/UDgTNauqyTiwiV9EmL19rO4dTq4a1JOCA7PEvFCwMwetqdNrpnRcl0iqKJUZcb9GoibV4FaUMnI7yA2F+UWbMp9jGHCnK7PpXxF7gic1OK0+mmay2fII/Ar9fE7xvIurtGmfzZk8G7EQAfGXJidHQ/BrLEmjEIJoVpN1LaRQo1gaNNQeYt0mPdMYL0Kiak4qYrJFighkplnI2ZbCuOGTnkhMlWv0yiGKCwY3VZJuDrnCeTChKJ6xR4On0UuE7POWG9OsxowVncEEdP2ETvcU3V7S9A3OLro8a7bJFZWlBMG4olx3kesG0jShqtdE7pzVxopwmDBnSJ1s0Jct5krKRp1y6FucaWd404e0hclmVpHfSYmsivrC50N59Q8vAnVdX9V1/dnPnn3gCX9nNf7/u1UCBoagrXSJRgXBK5tYHeEtKrRFjDa/RM5W2GaXRrxEqVNkzWQu6QSzmnm2JtZiIpHgZz7Fdcq2mtFIBax8Dvd8tvSAXXtFY3BCuXhMfC1h3i/Z3u9Sb8do9YRdCvJ1hCcUikEPe6sgWGZcayF5JKiKjCgFybbJM5NMysjzCrUvCLOSZRaSyhl1GFJ7GUWRIacJmqliqjXttUeVqBiRCu2cUC2I85p1plLUOgslQI0mVCsfLzKx3RVxcYjHFbuKQ2C3mYwDckfhIpeg0WVugDOSoNxmtRriNVTGvZqiXyDrAbnqIfICzUsQ51Pq2WvUrKDSekhul7JbYWOi2C3syCYLIyhTklnKVt9G2f6MdlBgjAREHSg9rhyVH5v73HmW8Ppsn/FE5n5ioD0a4+g9gm0fp6NR+QnuzjZds4/czCiGBaHu4aRf4nrPyFtPycyYYeVyYCgISsbLa8CgnE8IlhVr/TmpD2PvGVP9NtLVCf3Ko6HYnLaeIr18wEhzqTsmy5GJ2usj5zqm2SQ1zzkQFegprXDMiZXitDWcrsBqZRSZR9+DZrBLHjfxVEG5nnFztUByQwp9jld5pKsmRblBVDYJpIjeuoVx0ebh5YhF/IyP30ixXq7x0k+Iwi7dB33O1k+Yi5h6fombm0wuTFI94Liho/lv0PXfxowNBpGKKcWovkIUdZBKDd2dUpkRbK6I98FTDOokQXgZc/mSyVWOmR0QAbPIwbs2cNwhh6MBrcJmU9gw6DGNPF7lQ4zegGt/7xfy7x9kT0AIsQ+8C/zoZ6H/TAjxhRDij4UQ7V82X6sFs9ESv75iOy/pDye09tYIT8ZcpQSFRaJH+HZApksoSkCdz4nGE6bBHFmP6K0tQr8gMUJCKaW88FjGK6btgsK+SRHl6M8jVuk+HeV9bh+fIZ+dIxUzptNrEmbMtZRZ6CGKBlW0RrNM4kWD+lpCLjzIS0aFRaXZVEWO2quQzRw/rkgtA9wSketoqoXRrFG6bUqpR5TmrNScQs7R/QDXBSdXiCc6SiYYCYsyTshFRJytUbIlWhoQbqtU1RHjQc68bpB7Jp5Rc5ikqNU+Z5craj/lKu4xTS5ZulPmZxHB6yliIUgzHTkykVNBrAikYEEwXjNfTZHLM9a5Th0OMVYFp7WHl68xh9AoctS2oHEskbHDsPoCe/vXybx9nIfQjhoYbs376hb0HrJjCgazPouPOmy7DzHbO7jGDTKzyzvvOdzY3eVG6waHH95mo18wD4dI8jd5d/CPubP5IWOnZqqXNOuKd7QNFpnJS0NnPZboOSZz1SGR9vnBIkDb0Xl62qBvJ+TzPZ53Vly9bFPHNY3efeZFyqSK8Ls9lFsKpTwibXqYt54y3AjQqxi1H1AZCagSE1Nw1RmTxSfUXsH1fMDVIkCeJ1ReQpgVlIs1lRJSeks2qiWb975ESWPkNwTN/C7VoznJuETfFtxJYhbtBp3Ogp5ocXg94KRYk/cVml2bLJ7x4u0lYhniFRKTXkqtmzQsgSwtycca8VlNXMqEQUqVAJnOZGUyKXJW6xGeqMj8I/wkpSFOyNoZPNU4W0tUh4Jqw+RJGdJPHfbaMls3P2D8sPEL+ff3viwkhHCA/wX4L+q69oQQ/wPwR0D9s/G/Bf6TnzPv/647IBQJI9EplJLqAtpuhpa6vKh9oo5GGucouko895Ftizxo46986tKkDnPWI418K0FcrtBLEyvq0d7YI1nEVF82iO8rrNsOa3uTKAuwIrhMOmTRUzS3wI0EM6PCkGoGQYVFCumAaXQJecZGaTGxwQh1ZnZCK1QIbYGRQVVVLMsUvQmNhUMiVEI1xi4EjcQhCSuwErK0xI9ragp6UU2lW2y4LlvdHdYnBgoleQo7N2usyCVf9kiPT9EqmSQxyTZmbD+tmO6mqOs24cEV1itI1IqqrbN5lhOuCzZNWHsyRichTnLQFIpSoV0plDd2iZMuraM1nqkR72QEZoP2hYGjKdRywY7jI86f8/H2DX7r4JTPonO+OlnixkuKsU5vS/DdLOaNscqfNm6zK0tUW6/Zbn4Lp5NSLDL2sjv4osPmVzP6B1us9AvaZo/SGFL9XsjoScROJVBvdKmTXS4uwC/mBDcyhr02Yg7eRUknVlDaH2Ov5zhtFX2qk+wJRNNH0tu8sVB4/U6Xixcv0LcM3v7bmOzrA7790xDjtsPGIuR89JAw6zGY79EIBMgpChaWrSGqFRWnbCBTGB1krSboa+RqH726IiqO0dwU6oRmuY3ltWiWX2NZPCVtd0nTn4BuYExvMHlLprxWoE549WOZ7Xu/j3X/KZ9EJdteB+xzzlObRl6gv1YIdJ18OSNmRtk8pIxaXCUruq0+iTkjZo6xGJBLNX5UoZ3DzrsLLp2Q/bRHerwFZkj7wOH6ZRdl2uMD9ZgT5QYvnCV3gpL6wsB+OyR8/IJWR2Ye/AIO/308BoUQKvAnwJ/Xdf3f/Zz8PvAndV3f///EUaX6t0cm37cTGoHOOpSRFZ24CpFVCS3P6UQqdctAdkqCIEf1VcrMpC0L1maE1Oyh2yqBKBEUDNUORg5L6zabxRqtClgc6mQvTBIxR3r1iMnQg2ud6qsZwScljZ5KdtLA2Mwxhc61niEWGgehBmLO2a2C7TONK6VJO0tYZTlC0zCjmsyKaVgOWRwyTzIUyUCpFaoCiu0EAglVKckjg13JpM5krJZACjk1IwAAIABJREFUKz1ephuo8hTZqEhuupSfyZQiohdKLN2YMChoaCl3Dyt+elGjFD0GbZ94leOXKql8E005xdwp6NnbvIoUxOU1mhxhhwXL2kRWKoYbNju9O4jCpYhCkmaEHZUkF6dkcUioGuxoDbZvVbx665DmJw6vhs/ovezw7v4BsvVdvoze4iKIuG9tcZXGyLbJ+1ONbG8I+y6nRxZfs/aY3J2S9RwkIpY/+QmOPcLVWsiey7oDnVLGkrfRFJ/F+imfvjrFmC8wctAbGj/yPkV5bZHer5glLvdrA92oCIsvSJYV1t7bTF4v2BreoCp9xvGCzvWA1FER+RnXzoDWOiFY+/RvdVkSsz3SiF/bFGFAt79EygSvUolasmjEMUnahMJnZfTQ/YKBeMm4ZaLOQoa7Np7qguvSO7/moT1m69UAdyB4eKVz0KhRvS0Wb3jEkwUNfUw7+n0+U/6KN4sdnN5rgnlFtXmb83XFG8oXvO72UdcGrZZDtfBBWtAotzhaTKm2m2hVlyId4xQl8nKIkmg4bY8rScYxFKRaxRUBsa9wUTfZ3zapzRNKWSOVG9w8nVN0ZSbjK9a7B7z8zvd+rsfgv/WfgBBCAP8CePL/FAAhxEZd11c/e/0D4OEvw5Iq+PG1STaIMCYpDAUTauRFRh1KlBsdLDViPEtJFi3K+gKpktFEQpHqJJmBmhRYVoKDw8qxmSopby8ylMNzZkVEaPRYPZToNz9mHOpsuz7prMYtNqg+HzPIdFaBTmEu/u6L78ZUQc2mMqBsXJCtJcyjkutujaJl1McCVxXI/YBwItMIc6ZJgCzrqKaEZMckpoYW1dTzjEKzsWIbUQRcqhHuoE8uACklrk+JVg2sVkb0sEvZucD0FqxHLjuyxaXo4G89Zn01oFYUIl1lbsukdYY2ESjbF2hiSFVM2Rp2EZOK8+SarnIHTw6poyWiWWNaHcalwrqdsNEqaIY1znmGQcoXIqGdlVjrkOOegvLZPV7vXnC+GNF4t8/TP/0O8tffxV5eEIqK1s59RlsHBBOfj+58i79tfsKsqfPgIxv/scXtnQHpUcWLy5gP9XfQbYV606a4DuhgMdcl5voYiSlNW+aN5Can0nNCI8JeNvhQfgP1dysa6gHfC+acH0VsF1NeNB/wVu+MyTREj9qE+gz705zOu5s8vjbpDf+ct65LQj1ltLvLw6AkTyIy0eHR99f09gNM/ZjHqyY30gk71R6zPQNJKegnMXmiYswEyoYFQxlHnWOJPuME3F4DsgZ5E24JDWv3JeOjD9jfXyO/GFG+H2H2HAYaHHVkRn/mYbw3JP5cYCpzZguBbaa0LjOebme8eVmzNiLy1ZB4ckla16itx2ylBuGsj3Klw2iI+lZKrfuoH1+jxDIjBFm7h5AMPDdkq11T+deE0xF73RzN6VGtU/zZMx6fbeFlB1SXP/mF/Pv7LAd+HfiPgS+FED/9Wey/Bv5DIcQD/m45cAz8s18GpMgyfn8FkU4xUvH9G6jBNU7dxasuyc8XHNUlQhZ02iVV0WCdamTMQWS4Rs1Qa5CuKq60c/L2kEjTWWkacX1J816A1nxB++NtynVJLgnc7V9jb3DF8WcXWFWLpbFiMLLwQgvvJKAby8wLyEjJRMJK7dONKoKyRjEK/G2JVAjEqkWhJ8TCoFYDqqBAzlSqIkdZFqSNmloV1GVGmVcYo5x63mYdrujnLfLNJrtXKuebc8KxTa+aMPVNks1Nmo7B0bNzNusVhtykGSQ0K0j7JdZxSiQJpF2BNBE4vRN0v8b/N0/ZszvEdklQnaCXLnqzIliEnNRD9t4yuGXWzGYKsRJxYzsgHJfYWQujHVGUNf6LDzH2M6oX8O5XwNEXxJKgfmURvnXIb75osV/UGNaKHy5SJjsnPEgbvMxcdjWF57EEp4Lg+ohLO0NrvcU3dIWXa43OUCE7HTPVTXSjxX4AVmGT9gXdzttcrVdMbuj08lu8Z7b5Ymnxjzo/4DkdVPMWdzpTfvj4d5iv/oyP3il58sqmtbXi0/aCThbQWW3x00GDjVThqnQ40PrU0lMOg++z6N8iNfbwlzm3zFPWyRBp2yCRGkwLjTi/ZG//lM31XV6swFB38MKY1F7TbLTQ/RojnjFxFfqpT6ZsMfztjwn+aoPg9g7vvFwwTt/jOvk+RbVB1HnGXtFjcLvkZec9lNkldFyKswaaUjHvSvSuN/HbATNnk5ZnUGYKmpOycRgRtR5SpUPmHzs4OAhnRNwNSKM1dqyykD2uqnPS6wO6M5VHD2p2QpejzR9RNwxexQ3e68l8IUkUcUz2/X9gEajr+m8B8XNSv/xI8P/VyrxAaBLu1KD2+jjSa7RiRCZNaHRqlGUf01yxMCvc0OSsXUErppuaZLOcvCi5thYkgxxzneB6AeGWxGTdgWiL6hObcHGNeuea20IhnhtwVqMWMZt5zoWVoMhtymcVbEuYGzWRXELpUqSHlMtT+r0ZC2EhhTJZ10PMVSQFmsA0UCl0FcXTkIgQRonVMamtmNx3UdKMrC3YWbTxLuasK0G7tcvUUCkmRyw2CwxPQtZ8fNHFHV1xI9ijeD7nkZ7jJZA9V3hYJWjGIYfRC04kBU1qYqQlo4HGMkhorxVy28a76RL9+Iy84ZIkMa0qptEBRVwxO7eIYgdzHDDhjEtjxYFaovYzNpIRVTZBvj3jcZzz5oZMmr+in+9T/pN/TPB8inx1l299IPHdexV3Cg/jGw3Mic5JM6bntNlsNUlvRrjZmhfzkv3oJvruGc/X+5z3Lc6jBUN2udX28BpPeR27JNmAtPCRlwqH24LD+iadtoV1b8JikjH5Y4n2v2/wlatd/rf0GUHzEW/eX1C7d5jHEeWuzOGygfagT/b5jL79Oevr99mTVnwyPubrb0sE6Q1knjM6zinVMfM7u7w5f0EVyTzWFrStADEYkXsmlR2iLxSCOGE00NEjheylIEovmd7dozN5TFE7PM6a7IUer3fbvNn9P3kev48TPUG9Z+MfjenN3+Qnt5+xMZ7Q3rzNcb/JaKnRu/ldFp03WI1rgtspbuWz9XxEf7NmJVy6zSuWmsnKPsAKdRqujiEXOLbH9UWHuAMb3SWttKL59AH+7pKifYzZ3uNv1Ba3+IhniyP2nh/D3j1+83vXPPrKPq84+rn8+9WwF/vnf/SHNxLBQjPwtqak5ib3rGvCwMeqamIzQVcywlCiNdIwjQDzXKEodDK7wkAn0l2qyCBTVapWhDLOWC8neINLIn2K6JxgvSiZ+R6TWcnUuGbkDcjzORtJG8+V0RYlsR6TTXpUTorwU+LgHK0+pGzO6U4tgl0FcwG6URNVOZoj0UkL5NAmq0t0EUFVkZclSQDGIiXLoSpSvCKiWejo/SbzZIaQVhRrQT+C26MdLvM1Zb3C8nVSM6IUc/zNisxQMQ4ksraGFs1IbJ04Nxm0+1TXl/xGXyCGH2K3B5xIp1hGl8l8gTavoGjhIWEaPkU3J1ilSMkVjX5KnjYo1zatpInbjEnnFSUJc7nJb4lNPj8ccis+57Lo8kHr97CHH/LRVy5ZtzdJIhuzEXPLv8XNf7pHWkiYL22k4pQk01i0MyRji5k2pX0xJ48qxsURG5qO3RAUhUMS62gruCdluP2YGRX1aI9cjyhzlXah8aBpcu+NOwy/YTBfTmgc/AG3g4pL8TmX/3KL99/PqC+OWUcD3mgmXA6+Txp3CbsjXp/XjJwQ6aaLLBeETYf0eZMXdotCXbNQHCxJYt3SUb0LfJEjhjYZFnN5D1XSUC2bUhKcqhtsDV7Qtlqc6k1ipWRvp0n4NCKRn3O/3uSgtc3RY8GbmgqtlNEtlWByye56h4bSI3xZ8cD7mBftm8yEw9bLOXtJTXn6CN9M6b05JOhd8fQ4ZUMx6VYWgTWjsZWz6fRAqtH2P6frBAjV4VXV5Whk0CpG2HsGziOL20lOVlZcxe9hWA7n5YydWwNuD7f44osXv7oeg//NH/3hH9bFTZziEqMraC99nsVv4uUZUQ1FvkWglGjuNovZHGYZU6kkNKDj67RbGeuhSR1UdNcrzEAFX2c4DJDFAGNcsl1tMT1sIsKQ9d0m3dTiRJpR2wPOnJTkIkJ/Y4W7yMkaLpa/QO1VSHWPxL2kHMNaL+j2TYKkg7vSaPkWBDlLSSDrOaU1QC1VyAS1MEgVk0TOseWMUlaRqiG2E6EGNo44oG82EcGUqQv19ZzCL4kLBylvMghmvM5Kei3BGoU3D0sWP7qFvZkx1kt0Q2M9T5C29ggP7vG8Cui0Ftxc3kEq2qzXY5LUQFGnOHLGwoduucOdXCEsJ0xMgxroSAW9boiYZTxLDLZaOh1T5VlXQ65f4bTe5jdufcTF4Rb30jEv5TVf7O3w4dWcZnrIUfcVjdpF+pGDYuZMeM13//KYutshW5YoXsmpO2e61mkaA/rdB0yWGcfhHGWdYm0MKCwbtZhhujnt4Jj+4pTW6oAzG4zpJi+VkEayTVMfsdXyUZqbHJ+9YntQkiUNrrYVuuspndJFD74Cechdq8Fi92O+af0+SeYzL33KVKEeNPHNJnurFFs1WIUS+0IhV0f4q03MKwPH0dgYJMyUJRPtEY2dIzpqTZh0iHOdLLqBMOccaQGZ1cQsbIpyn6vNgkVvh9XgE3rOm0yffE6bFd+7uCCZ7XH51oqtzTtItsZIm9LeNVgjIc9i5vs1D/2XxJXBhtTHakAUhFSvPHrahHheofpDzqwpy2SInRZkqHyUe9QUTCSXtq3wSSGx2Wxw2f4T3l7uIE/b5MYR6293OCt/ft2BX4kKRLam1FqlUAy2aIcXnAswgg47tc+qmTC1JfSkTzWc0gxM5nOd1tCnFWpciwS7aNHyIREB+aaGrMjItSAQHsU4I8wsmsMCZ1njmR3iuY/VFzRinYtVA3l4RrKuaatgedusnClFI0K76pIRITsDovgCrBYiUHG0B+zsHTNOz6iWJTv5kOtdG5KE6cU5hi5A0iiMNjutmmg6YxJoDIWBUxSM2aWWKyiXlNIFMjWO2kF6J4eLgLK22G91+PJoRbELxfGKQdEn7ScE97f5hvyCL//iFq29Jh3b5rPla5rOiF11StFyaPxA4zPpmqQuUDKHtrQis1cwknECFbsWZGnFNCvR7ISOEGzGgr4kMQnbPOrM6b3V5dUPDf7pf/XP6EQO9n0bKRMsHl5xplrcthJCZZvUyuhcHWNPOkSOzGzzmuYyp6vfoBhskZpnRGeXLK+HWOYlZruBrIxYIFDSmM1sizxaMZ2VXN/UuK+XtOxrprGL3bhL65tnGNpttCcu5BnH7he8/F7O5f/xx/z1Oza9TwO++eENlFOXQnrB3/z4CdsHI17fL9j9bsl47zGNoCQ9NrBHd3H7fTKumE/OUew7GGsFhR1eSCqy+CFue46kHLDSu7ybJIxkme+er8niYzDWtKV7WM0eeTFB1jd5cmFQlEvMdyPMqw731jbrdx4hnjSZWYJQSvndYM3nOx3c74D6rQaNecBZvoNiC66lf83wuEFz/hWMfUEmSZTNFE1fs1iEPJI8Nq09Mqlm1KjpZ7eo1EuiasX5dUoxGHDXv8WVdMSdTGLx6fdoZAecPpgQ9HdoXtvIuyN4ecpnjz7+1S1DpuqitrvbVJMrkl0DdRYR+za6GpGPurjRDDmTWcQVlApCMdBV0HKDWougIdEuZERioGY1hmwRVh5BJlPfbRBcXONEEbK+STFKiOaCWJohKR2ccom3LujtwcYKXi+3abDisoww3Iq800K/NLEbCVPPR9uCRmFR90u6X+5yYgU0vCW3mznPBg30a5V24TNrtphbDs5sTe1KlHFImXiYeYfR77zPhtnl029/m8CeUk/32a5n+HJJvBfTPJKRa4XFgYY5XzOv9nDWNYE4R5YrRClRiwpZUtjqlsxqBXlfZ/DlDmfd17QmCmFmErBE02qaskSSSwRmRW1voQxt6uU5+iSh4VZ8JAsam01yv2Kd14xGt6l/T+f4f5J55z/6D5BHMtWJ4K2DG+hiwSN7zY+/k/Ng8DnKuc3Lj9rsfHuC1AnZnd3D+53b9O7J/OB//TaLVcX+1iEbv7VP85lDHJ7i+z+ltdsiM7YZXyw5SxV2Wx3EQFCuZiAUOr0D9vcc1NOcVXlG+foWuZ1wqT3hfl9nvCWjXVtYiybfe/2Uiyd/yV+OU25+vIn06y8pbuhEz22c0mJw26f4Vxr5QOXy/jm3X+TMlym6fkKkHVA2SjqPE4qtAUcbCq6q0VqFrLo1ohhClXF5fUXPeYH9pE/3RhfFLThyZW76M57/aIO334THO5+Sffktds7m5IOE2Y2U1omNsvmYVHmfJ34bZeDxzt/cxHv3B9zspXynnDCYvMUak4O8jV2e0u3VnO3oyA9tkqkErQrhPGXL6bAoGqhBl6fM6G9bnF68INbOuav8E8pFxZ13rsj/7IL4Zs6pcU5X+V0Okm3+rP4p3vc+/dUVAU0WdVFrgMoWOpYp6DVzznZDpI/hUupCtaQl+uiugWeeoGWgBRqRqpPFBVajpq1pBL5LXQkyOyAQGbIC2rKkX1ucabdx0mfY+xvMz9Yo9Zig36ds+TRCG/X4mqLdg56Gf5HQlTRmyhWy1aSc5JhSjDA1jERD24qp5ZrlRY0RV6xrCdeQaVo9ktLANn2sjRIvLpic2UhJgN3ssLvZojOpGGdrxv6YJhI9Kr4cCqSxi35jQe5AfFpjpiZBM4eLgt5dBfIa71rDriT2GxkPwyZDbclVITAKwT0tZyLLTKQGhW/SkKc0zIpFyyGNZBQ/Q6o0iqhB2s+oypq2iHi/UsgbNv3tFU1pRHD9Pu1/95oXVx3+4Lff4MXWbzD+zOXX8hhD+w4Pxzu8qjW0V/+at5ZzXvzm17h5dMwn7CDaI+5ttFiMVhx4m7xjb/Ak/ymtjkzX6cLygouxj2m1uc40Fp5EP4NUzTBDiSPP5+72iPY7NtW5yeuTC0bbHepdA3lcUA0LWntf40Ae8yffPkb8qx/yL7Rzdiff473dezzzKyZyk9IQNIOY8azD8vx/Z++9+1SxRuM85/lewUYt8/KJwLqpsa3VDGqP2HG4OLuguW4T7mrElULTfs3lqY0+dLCebHI9vORbOwGv3WO0WR/l2mGx/xL9pCK58wHR3ARLY+dMJ1ZzHqcpkt7nG62UbHrFNHiTljpFdM/wnW1eVTrdT2ake/BBIKNUNv4dmSBfIbdjGsmQ9Dk8bV3RXuVsHQsu35Lp3Z3C0Q7Xls+hOSSPr0h7W8ycjG1PRv6TR3z7A6jCEV9xbzJfveD4y5/86oqAEFKNUBDKDlZ5jPbBAfbHJ4jeh0y1K7gMadUBgZZiKApeZtKzbuGoIWLlMzFycrlGSTQUyYM8w7Kb1G7MtIwhzrD3TOwXCcvOG8iTU+6qGp9qfdyghMqmM7rg+BIajsq6F1J6MSgp+3OVsdpAcmxQZqSegZEktKQYz9KpSkEx1Mi0FJE2UZc5ZiNHN0L0UpB5Bn5tEmcqjWCFueWi2x7eRY5fWQxbIVPhIgKFJCnQeiXWeZ9yJyIUE7QTGd0csiMnnJYr9NxisVUxOK8Z36hwogI36jCPPRi1SS8MZGuGrpikiYcU2KDoqGpCP9fx5YSFqiKbCU4cY5cNtjo1bwxd9u68TfeuyqX0LmfrlEGyh2p5eKGGu1GwXj1msDC4sEeU40s2Pn1G+VWDqy+a9NoeT/dbfHNpcPVAsGfew7i/yfXqGc3iFq2WzHJd0BYl9vYmRnZNGVicrRtURsR8dkTt1GjRJfttmQtDRZ0ppNEuNw4crG6JNplh1TcYH3S4/IsWqxf/Iw+t+9yVvsfT6xzsPj98/gnvBQqXd7/Lk+wDfk3/mJ79EfGp4GIVkHsznvfvsuUG7IYj0pceqZ0gmh0Ss6asAlqXHrWh8jKXkCcFQdqAtsqNX5+xp6qcvmpgawkX+yM+jI7487MujU6Dy7/5l7xTVQwP/h3izZhpe5v09SX9+5f4pybP7W3u668Qqy2UlxM0wyb7zR+w/OstrJM7dPspzruf453eZST1qV2Vn9SP8NxnZMp9bkQ6VltBXkZ8cT1jd6Bg1V1+ki5pWjrbZc5y1OWDL1ZkqUH4dkZYtzH8H/P8rwuO6vpXVwQkIWre1uBLoM4RUgtb5PjtEHkF3YHKKjVRpIhsoWGYXXL1CrMloXs2UqqwVnNKLYNlH1ORSFoTNtMcTdtCqx1aXPJpbpPfW5AlA/a/PGPqlkRLsLsKlAU1NXeUQ17NXhF0TZzlTeL6FVtlk6N6hmQYyFnMTbdDEM8IrCZ+aSMfaqTZGiUo0c8VMiFRU6LUS0Z6SSWZRElNSEZ66PKNScbDjonn+zR3apyTgtXKojIk1qqM68MDW+UHwYp9DI6aAQoW+vsx3df3WZ6dErzRQX52hDjQ2PNrXl64tMWSDUvntTUgyjPkQCCqHMUyqRsluSdwsgLbnrJuSmSlhD1QeUc8QGuH/KPf/gbXxwod00W5esz4wVe5fn3E7a+/w82Pdf759/8NyvSc3ocuk/KIewcV4YtzRntv8ZNPm7z3VcHKGPFO8tsE7+VsmTdp1yFxc82mrjNY95iKFnK7IE9f8KqKsccy8+M5j+WAvaZGU1NpbRkMmvf59OwZSr3i4rjBcEdFrBW0smIw22Gz94rvygOMroeiW+jxM3502ubPj/6Sr3/2XVKjRzg9Y9/U+Qtzm3/P7XL5KOXFrTHyUnB4nPDlTpOV0qWvXtKoM2THwtJl1LbM9WoLN9RwyzXz5xHd3kPK7CZX3QWkLvIa5MUV1bsztqvf4ZV2wQfJLk/eKnn1uc3g5GPCxgYjM2T5IEL5n20Wv1PRvR7Bo4+pb3wNZ/BX3BIqy+e7eHdHzDSFzUlOc6YRv71gNRN0qiv6jYrk6S7xhUZ8v0HXg4tGgbflYF4v+b+Ye49f3fLsPO/ZOX77y/Hkc26oG6puVXVVdzXZZLPZomTRsAlroJFhw/A/YBgGrLFH+hNkzjwRbAEGLdsgZDGJFEOnSrfq5nvyOV/OO2cPug3QdjdhgKTRa/ILwF6z9eC3sDbet9+L2Nwa/PjFJb33Fnwk3OEzVaE3sfjhVcjJo4i3xTHl0//5lxgCmlDuJBIzI0eoCMQBdIUegTeiFEQ8XUSzq8TLAqMSouk6thygD3KmbyskbQ0l2JBPIDB0bL1AiyK2YgcpkckJSdsKcjHhBIs0ylglPdzBEP20j5tfIr0vs/PUwRfmZKVAmNrIgw2SaqP4Muushl7cErp7VN5p0dEqXH76AlUKKfQVWmARlW1SEgxhTs0ocEsTJSppkZHKAkG9SjVUuLVAKFeUC52wUaJoHnGcoUUDZOGGdHNC9uAt6vwh4ewt3b2M2qJFKGSIVYOGNOQLuQuzCcpeybuByWe3BaYmsm9V8NOUKNtCYpIYHcxsw1qaYgkGsmERSzGZF1L16xRqn187KXj/H/8ae413kOKYLPf5gZyi9Xs8e/qXHB//DsKbf80fX95gzRT0QKQt3PInY4OdckTn3n+CVXzFytnwnX/03/BIPWTcCGmMxxgfWuSzHnf6A4R+yfIyJjbmIC8Y/h8u8kSl3FeIkq/RlRaaZrCSJK7zBHlucVxtgXTN3Q9yfvjDPkFvTD63OQyvGdstDr9/wu2/esofK0NU5yXfuT3ky9dz/vTE4+QHIdJnU0bfEmmmKS3ZYxodU/MtNufP4dAmGjjsvtTwRjG3RyUFOpavkdwtGVpDdhfgvBZJ9/dQLzesrZzzd2L2o5LdRGcb/hVW/UNmz3PWGxX2EhrCADsrCW+v6O8+5Q+FKvcdn238myT6a/qmTW8lMX0osnp7i62a7GoLZo8+RAm72G8mzLZXBDsCe+YHqM+XfJ3foB47GJUaO5M5uVVwUSwxWfF2qOGUEvMzjZMPcoJmyHt/uEE9+YQr+684FB/xtWbz+t//4JcXAoIql5QqUh7SN3WGrQIjSGksHLxejXQ2JOgU2EUOYxFJt9CqBo08YORFeA4Y/QxzJkNqEqYqfddhlYlsjBmmnNLQY0YtgaSU6WcSa1/G81yagkTNO0YozpkoHSwtZb5TkM+X5GuoWSKWW2XUW6COSizZQu/u0nvkcP2Dz9mYVTqFBIlPokeIgcEqNdAEFVHckJYpA10hTGOMNGWawCYHTTYwsIjrBqKfkioteulrkFsMKxPsJGe1aOLYG+JCQ0urpHHMO80AMdZ4Lpg03Q2yFjOUZY478HYESt6msEPyzQK5kNBo4IgqrjLBLzJ6pkHSLZnPS8TWCZ88qvLho8ccH+poZzUiXjMJ73O9GXPzakGkCeiPTZ6sc/7Nl8+JLUiNC3bUKs9fykiHbeJlybeiAOXI5J3/7Lc5eWkT9BMO9R14t0LxVqHrtnC+LRAaOoJgsDy9JhtfM59N2JY1MjdD0gTmpzmzikZ9f0QLjXmp80C0uckGfKr9Ob96/hCp+SlN+wNmVYv7NZMg7/BiucXJXuAcdPgXP/mMD/0qs+d/gFa2Ucefkz300c7v8XpHxHbXWOGGrzWN3bDG7tDgeRqRPci5fxowrypEVp1mcI2vq7zRr6l8dh+npfFaTajFDTrLgGXjgoZeUroDfKtC/d4zwusK+kP4yekzxPF3+XVH50VlyNHyPYJgiFAbcLfyVyy6HfKbN9Ru9/HkJwj7bxg1qtSnYzbBjFXzDsOrgqNuhX3WSH5BPpUpBzKiAbq65fR0xsxxGLy2eOO+JS5r/Do6p47N6N0q5Q08OnGx0pwfKj+g/KNf4nZAEIRyR4PUAmPVJxHXbKoS4SZE6dZJAxB7LupGI/EPsKUJuluy1n2MsiTPqnhImM0FopJSlhb6NsH3c2SjYFcrcIMd8rpMRslycYsgSeh+TJUKY9ro3Q2Bn6N7a1KjhnHfp/ziLp4dIIpLqGQwjZFUm8HjjxmpG2pvLqk2Ajauw2zkgWEjNAuKtY8g6ThZSbF8/HfWAAAgAElEQVRd4JpdyGMoU6xCpqYZzJUtmpmhzws8asSxh3FQQz6csfnLA8RiSdlcc2+xw6ndQJS+Jhb6iItb2n2B7tbhRRrQNXO8rE0m75KbXyF6FezIpJJGTNQtuWKhOJA1DISlzLvqHv19WDpt1Noud45KDmc1mqMXrI5golp0xzv8+fUVZ+qYynOb6J7Koj6k/qpkLaS0TnLOn2XMeYHR+ZCP+zWCr2yefDzk+JP/gig2qcmvqJsJduUR1HZIe1V2Q4s8irl9/Zq5AOxK+OkZq5sdzIlG5CW07RuylgyZgDRcETUtQjcgDT2U/nsU5SW29IAPvwXFmcK23uZhVSOUIq6HA97E/4Lcfwcx+RTj5ZhXwxGz6AGXzmeU5gc0hzHJ9Q1pohASUdk6WEbIqioSklCPVNS1wBUvcd8dsqd8hyITUMOMSmkRCSa++Zb51RXDjcyv+m3OsjOU4zojpUZv4uH3R8zTI35rfsC146A3RsSKzfXXPvVGF/amXBcp+oXAu0GJzZboow2anxA7ddLhGDM9of2wziYK+NoT6fgSFU1nGzepv1LB/Hd8riY49CFasuqNqW9yttcdrJMmrSMV88bhRbohm57jVzzKi19iCEiCUGpdkWwjMMgLbguBTx7a/MVlgOIpDPSI29LC6pVIkwwxEehREkoy0UAiNmT8a49MU3H0BmwLZGmKn6YISRsqEo3Omu04Yt2C402f2+WUjpIz7AikUwkrLYnqOnghx+0Ok6VJqaT4WYEoLenGOnO7De4Z+7sG0vo3WOivWGdn2PMcR5EYNkqkQCJTMvJKibARKBMDIykpxAQ5KWA3p96H5USiv7nPtV7Sat3gvRFRJYOy3iW9/orIBEOTMdcpi0MZdfUBFXI2vbd0PuwT/FsNBA9f9Hnve1Xqby28dM0wzTm3NMzzFXJlycqRMXIR+0ygVB167x1zv99CqB9RrTU5KTpkbkrz+nNCS2chzNBKnf+9WGKfKgzdglZvgavnPPB0ppmIYuh8fjtFq1cxApV7rS6LOwbfPNiwKzzG2hkgfD1h/Ssf881elSi7pUjmNBsOy2CHyRzWry+5ml2woyYIioknShiyR95I+DJt8M6bEU3BYPagzWRxi6krHFYtbkeH/ONHGaeGxuEq4/fVHb7zRmHz5EuaxTu8evpD1BSSkYRaWxMerfiX/0vI3uKS9E6d7fOMK2vLYRHj6AvcVEOnymyhMZNc3jnWsd0al8uE7dIjO/RpxDOupwK+4dIMGtjanJHboRf6XEsjpLjEbLUo5Aq7xTmi8TGsn5E0YiraQ7JGi+eFy2+nKzyvRz7y2Gz3kZ7csNKvifQTvGWBjccHapuzXCOwDjDjGYl6TbvQuHUPsNjSbc15bq3ZXkO2nCHIGnm5xNkkyJuC2Oyzl9/lQrniQ83k89uXVM0CsSlxfZn/8kJAEKRSMx/QUJaMRQMtF2l5S8bCmm6zYL4QqDgiDUNhFTQJ5DXYMZYv40lQTXIyTyIrM2Ry4opAYguUvkBDkok0Az8NsbY25YFAZ5ZQ6iVXdgXlSiBJIBUnPNJOuEovEaMmiXRDLEiImo4TdDCdGXJSxTPHHLQbFNqAL6K3yOMUK5eJajpa4aOjosiQLCMioUJmCaSblI6YY2Qho0qJ6ImkpUicQqXSwNnVQNlQ+drlTfNdhNVrBtIOc3OM8aCFM2zReZgTBQfsnrxD7cTG+HHO07sT7p0N8csj7K6IoJ5wUfx75M2a5WTLzI8AEyXJ2Ctt7t1tgH1C/7BKrm44p4OWlzyclixHL6l1ZEanXzLWa/z+VyvuZHXkd97npHHLqx99hv6bNZQ/fggPPuP1j2843ukRtnt0OseInscHmUn5/TbW1KRVX7FV3+XQeRd2LzEmE7J8j0LL8FUfL60w+eyc7faGgdPEPqoTjvpE6ppn02dgjcns7/OJYCKbn/HmWuJ4qbA6KvjY+VUmhYCzp/Fy+4xSdOmu7zFOxySfWAifbRAvzvja13ELi+mnP6JlbbGTgh++fkPH7JHZFi0hJOxpVKYlSmqxTTwW/QDrJqYYZszUGnISYsUrrvsb2nENVdaZ6BF3NhOWDZPI35LODT5WSj7fS9HCfa6+aSIs9+n9xTPu1WLmH02ZD7/FNCyQdy7pZndQL0Oq/Sn2MCfIapwPSj76TMKpwupujVt9TK0YEYl9Cj9glipMLZUHhs3465A5CaLhcqdjINRF3n4x5HBVkggxRa9Nuwhx5fdJbl5yXfiYZhXXu/3lhYAkiOWTb8qMflLF73q0M5201aE8y9GSG4yaxtmxRW2UEFh3kUOQ3TNqzZKlUyUNCoxihLosWKsyUtEgT1L0SMBLPcLjFs7MQ9ctJHfI5mSX9vUt0VFOMjEoQx93V8Z53mWeiagENFszNqlIfSATnTXYGmPsdyR2Xte4rG2pP9wl/TRjJOSo4zmqkxFaGkpZw1YN5CAkWCVIZYlfQIeC2C4J44JeYTJXbdRiTLbXpR1JTCe3FFpG//F3eXDoM1s1+P6vqXzD/K+Z6BOOBn/O7//BQ37r6BEza8VaqRE1X3J6ueVuKbDT1ZE54GL0DM/REM0W2vItjbXEtNHE0Fo0tSXX1xn5ekVTdhizJJm8QCo1GlmV4eoM8fSUYf8hLxWbN+YKZ1ny3rrCq07Mrx8c8HJ1wdl+nXf+9AvkesGgv8fcEzl0Tqjf0ThQHqEcp4TnIbvvfUikOtRKEzNX0IsAP7vihfuclpuxLGIWDZEksBgvDPQowGoXWGXBIppxr5HwdGRyJ9UYxjo7rSp6q4I2hTdywUnQIAuWbD8oaazvUq+tUKM1p+sD8tYrfvxHn1Lp1cn/csZYTmmfnvF8r05jtaWICubikNpaQLpnMJEltjcRehCRrlZMtzo1w0FPrpg7NtGgSn56wYnQodofUd0OeNmZkm9PyJcTsvd1muc+jzvv8r+lf8Zey+T+1yZ/Im6oje/Tfa9F0JjTGWl8dbnh2w9lJqmHOL7glVPjTvYu9YXD/HCN2p5hhRu2Kxm9rJD2x0g3BrWsy4sKzIYmhZ+xJz1H7FVwYng9Uan2YqSxR3PXZMGaJAYxFWklKV+5NShmv7wQEEWx1BoypBZZcB/5va/Zq99l7y8yzpGYqbfs6w1M0SfNKyxrfSR9ibdrYY8DhEmAvL/FdgPEmcbQ7OEu1whqRKMRMFMgvcyQ2iZmKWINV2xLmbstixEeo4mDJDo0D6fMrku6Yg/KW+aZQ99Y4MYWee7haxZVISVumDx+9w5ffH5BEh6ipK+QazLROkLUU/SujGnnhJcCUlpSpFBURQrvgEwOOXQGqDsFJ+Ipg953+dHTIdetNbVyn9/6jz/APhmg2xbvJRmz6xw3C2lOe7y+r9BLQbzNeBVccUfxiOJjcuUrsoGI9KMGUlukVzvheRUyY4btRfSFY7a5iRFuWXOGkC8pNZtXp1MabknfSvHPZ9zUQzqbF0TFb/FWOmMRtGjYLerJmItDgfpKxwgMVo2f0Cv3yXwBfd6G9x3upCXVRw2MmsZMbZPmEd8bfMSkraF7CrqSMI8NarnAanVNpyyYTjxeTwyEvTFGmRCuQ6w0wPV1khzqpse01FH1jNcvhmy6Mt839zhVSxIj5W7lAbvdt6Q3H/GguuFpkfLyaxFRe8XQL1CFl/zkh1W6gy9J3S715QWx4lN4bQJDoKhtEV81mUgygramb6WsFz4Lb4qhJSSuQLGNORi00b2Mt7FMGWw5OaggyRHTC5sRFdz0gl7jMcqJy+NOzJdfiWy9CXeXd9HyC65Z0vu+gJ6/wxdPH/NInyCbKWHrLTefhgSDx/RsAVV4izm/x6IISHdVGmqEk2VMkj3S2xs6DYGbokHTUlnmL0gnLtayYG14+LlKb5ASr96nIz3FXIg8243JbzQi631w/or8lr9bUZG/y5BLgW/aJfJOzumbz2nNv0HDSdl84tIcfszjJ++S+nPmtx5WVUSJd5C8AcxWPOkoFAcuL6cDAk0kvVPSVW1qqsTWXFOkFuZGYNNM0OMCqYxYyRJCLqHZLquhQtUS6Xo3DIsGRW1FWo6ZRRpGYw6RShykhJjoew3C7ZrW7i5FkHCct7nSz8gkATP1EOvQTjro4zbbPCKryog61OoeXXOAk3fQDgcoRw0+uFvnxZdfUOu+zzeKW/YrY/L2MVaSI/1wFzW+4NXdGYcTm9mOTZCeE7z0OF0f4Hwk8jhs8ub1gnL9l0xrDt+b3OV1b4uUGgTtIeXbLYPcp9pskyQJurim1DV6yy7X6zq6laEsT3k1GnJ71MBWLzAEG1t/wFbeMAlS9k9s1DFc1M4QLn6DaC+lHqXoksz8oqRvzggfNXDKDlfOlMdFyY0vEyxGWB895jae0rwGUWuhOSUnhsh6uEISFlxrCRym7PW+Il2rqEmVWUWjmGh04gqKk7HcCZH+bE5ZgWA6Q5If8NXrVyhdlQcnDe7d5vxo6qAHb3nUe5ds+gOa7iljf4iYlni+jX/5hmNFQH/nLmdehPAyIPNWyDtbjPOEr4MeXj3FqZrcNi+Jc0g3KtJtQt7JMLWC6+WQoNemO1ModIvnoYHprrCO7jIIL1hc9zD2PJTU5+mPPexZyrAbI5RvWIsBDTqIr1d47S25vcKcy6y7M5JIpqUdIYkxUhSSacdMxRlCr0BR95GbPvMrmRSXpdBHS2Q+csa8cFU2Y42onzL3Q+qhgJbrFFLBXjdl+dbitRjTDGRGhIjZZ8grnZDo59ff/8/1/nMjlQt+lB5yMo9QPYdT8wotecLRgz386pZHj36TiV/wcHdEmLdI/YA8D/lHg0foSoLPlgNHQkmr8K7NNNiSfXGBK8+JJxPm5RXZTYTU8cnHdXKjSTW2+FwY4eQOCgJnTkZnGJEMRGajEjGWqGUWs0KlZIsjSoh+QLrYQ6xNqJomG6uKZORYywFtO2aopbiagilZfFL5kOb9I+o7FSw5ptmVGMgDCumar6YS1acW3z3eZxXJCPd/hfv1W4ZMka9URsv/kf2VxrYm4377fcrhEjQJUzQo7zvo3ozZdk4hTbkXVKnJDoI8Jt0sMJQ7BPMX2JWHhOufCq84bYPVmYi/EdGkOmFDYNsokOKPMJMf0C+XfBoLPL6JiRt1GvWIeGXx0lrwZKNgftPBPQ2oSR223k9oHf0D3mR/iGTf4zu7e2yFGrU9EfHC5lGlyVlPpzqySE6aGMmY0PfpCg+wdw1aSsnofMWpqrAhxtUfIZcx12898rGBXRUJKyJSd0ptNkA8dvl0ueSgrZJEKxItYus9YTKrIXLBaTulc2jye+e39MqHHP+DQ8wQzGTC8zdTzP/quzRGU17/ecqPU4tSX9NPBQ4zhTzPuNt8juyfEExviM9vEASBlamxDFvsZyZhc017kBC+dJnXU4q0Sc3PGSu7jIQlh4FLrHfpvyi5aIm8g8zxwRHnpkSRhDSSKWsj5KUiYH85o/JE41SYYfsW+YGK7S0J3BG3fgW9FZF16yS5zoN8hXGWksRNLFFAFFbUkow3+ZrRQicSxxSiiaPrCBJ4foJ9Xue6NSI2EnalBM/rUFUm5GmK1+9AMP659fd3ITR6AbhADmRlWX4kCEID+J+AQ36qLvRPy7L8hbaoUq5wJz0h3Knwq3bKuAaV7pS3K4ff6H2LbbHhcfMRSaPGwNnl2fhTtvoA5dChL0lobsR08FMJ6WWlyk7Z4bbmU4lz9MePkG5OiaVLSmVCXYDnhU2wnhJe6VTiiKgVgmUw3ySkMwVdkIglg6lpQLxgJ4HQsFnFAaI6Z770aDlH9O82uG9VsIojBrtPuNIKMuUllWaD9uAO74sG9RuTaLVlFhdc9iP8MQT4vBSmGOcquZizMl3E0VNausqZZ2Ptd9EcjYb+MdGrK3ZnNS6rBlVnS11IIPD5cbSgk/b4opRphiNWeYeKkRNu1nQaDlYSslj7DBWd1WZNTo7Z9pGKezSKAY1JynNfIKwrKL6Ak1nIOwOcWsIq89gJDJQri445obit0rVjJC9gFeyx4zboP/oe+o7O2Szmg/enFOWA9Z0t1cuvQfmEg9YRZiJRKXzWPbhOI/TEQDJivqgtWZzWkBIZ82hCW3ewBYmtlTLtBATGFNQjVu6SRvCYj7pLruznbOUmi08F+v1L0tsRP/xgwT3vPjvtNpumSbxROUr6jOXXsH5Is5zRPC8ZrVcIvSVH3piK9YL1psVnK4ejVodYCJGTBEvLWAbHbLYyghpAU2UcFGirmNa1zXVUY9XJMNYrDno25bXOZnhJww45uF/gTJa82ix56z5A2F3SQUCZb7nqLqnbO9TLAF+2cMcLmmIT7SYhbayZF3WWTZWsWmC7Aa7vkHnnXEohDdWh2MzRajbhnsBN6hNIDlV5QkUOCEqRbeHT1Gy0NGRiZ+ibHYoDn80rGVFekLZtjCiglJps+XuCwM/ie2VZzv/a+Z8Bf1SW5T8XBOGf/ez83/6ijy1F5OOqxvy9GlzcJ9D/FHX8Ps5JjmCZjLI6NXVOvidRxqdAje48YTV1EWt1jNAjEpcEdsF2WaLPOzRElVk9R680OfyVJs6ndRifc9squOfPmOUV0jBBNGaEkU2tqGCGC+ayjT6YE19HlLGMVijoCozzKpK2xRRynAOHu/cfUO0oIFk8aPWQB/f5Ti5xvrCZpgn6hcBWvUEQFCaChL/2UUqJWzck9nJ6eotKc0V4GaMYQxT9Ls/eHGI0Pmc3v8szWeOev+bl5xt0yUfZmzC7PWGUTam9XyJmBWotRXB3CX9FoFSGFGfvoOoJ13OZShyQKDMEv03LaJHMQ8TYoN3xqG+eEWsC9eqWeOsQmVs+PE6R4oiGo3IWOhgVj6204c/kA04mCpv6ikZksr435jKr8v5+jYtmn6olkispRT5BWh1z0/MJPpux/h2VXa/yU1PPLMTttZHXNxTDK2zPRdQKiq6NKqYk4oLAinHXHnFYR75RqIuXRFYdK/MZhSJykCEXI37NVZg+WHEjV+n5cyiPEDcS5aOC1F+xYYLuBXSTIa8Nk/z8J3zmTklCgVI9Raq1WI1i9FXCzXRDqIjIyYiupoEqYyHgbSTkHY+6KKHFbdZFhtjOkSKJTuU+t+kQRcrZG6iUa42xlDO+3LLbr7LubVANm9SHUDmn4jgsz0K0WpNjbcOZZHBrSXwjhpehT74O6TkGo8SEvIGgrnEaOmbsUKobBDMiFkSW6TWi2qRYmXiSiuBrCOMIGgXROKCilaxXMYWWkaoFPU3jrBLiLxvsRD6ufMr2F9Tf31c78DvAb/xs/z8A/46/AQJpnqEJK+7dHjCyXb73zj/BKAvOJyFbvqKS9Vn/JMR+2uMLY0bryKLeNIi8LacXYypRwGaVsMkVwjzGUVN2mhrQIRitmLVCjro7JErOk0d9Xv14TccWEP/kB8zliNJJka41JlpJv9gyv0wRNBM53pLJTcK6jRRLSK0D2kaX335Y5z/6J/8pi3kb1X3KjRSxENdktylBGLH0L1HGIZdRzMaWqDgd7lahJuoIrT6kBf5qRHZQUBz71Ec1xl2RysCm3ayBJNKuneBnU/bljEKIEYYD7L0G+fxrvj494d0Pv01TCLHrF0zDmNNZymA1Q1BUBqmJmy2RZJs7K4VCvKWo13BthzyUKESXfJmBp2IcV1EyjcORwtvokm2wpmKJTKoHFJsxjeQGfJG5PeaRucuDuw8YzzuM5ZeUisbB2OB6XmGnq+OYE0rFoFos6c6q5ELIzUCjd1kjnM2YLWc0pSFLR0LYpJSXU6azLXG9oKZImHWbmgz5eo5f9xGiSz69EBCPjxG8NgNNZzXYEiZP2H38gkF0wqvnEYfiBf3X95Atl6hh0zGP+GL6OePRX9E0auz4v4a0+4ytd48vLwIKbpAfbAnPPRyxhTFoEQdDPNdgncxRlBQkiZkGsZRQjxOaok4xWbNSlkStKo60Yl/t0u7BF9dXRLHI/lyikp2xjnb4yHnMH5sjjkYFJ1aDN/fmuJ9mFLMQTZjyw67Dg6DN1k9JtAqSr1C2p/TGOuVhiFUtkN0B7jZDaLtodos0KLDzGaGsQkUgT2xEXaQm6hTFGFkuOApuuJl12DYyuqy5qqSEYYq4/HlKgD+NvwsIlMC/FQShBP77six/F+j+NcXhMfD/MkL7674DmizzJ5WQ/9DoY2o5+vlTbusqWmkQxTWkdMQLP+ZOWCHPS5bzjPbdGun1lujVNZgmsmqgXS+RqxLLzgJvvY9ab6KOR2Q7GteGSlpmFNGKsOOg1Vc4jzWS5zJ69w6Z0EAbL1nKBYEloAQ/lT3Puw2sVEVvDzh6L0W885v0FBk5a6JbQ7ZjDwoLUx6yDjestx7JbIHglshal8BXMDYC2rpOPNDJKh6JtYsRgRBaJAXc9C64n9rE/QtaWcx20UCqztjEGw4dievWHczbS1hcITfeQ5r4HL2c8RN63BlZqJZGvV3hdrnkIllzVO2TiSZiPKEhl6wGTeR+QjMW8a8iXhQR+/02q3FEJkv0lICKvWV/UEMX6rx8PUJu+BykGyayhNnXkC4TXh2p3LcC3LCC6C9JXoTk9gXO7JBlp4+u/IisvEvrUcK1J0LvGHc2Q7fb6PqSbFriuTnGJkEvBdapSyivyb2c1bZN6mwQnJxgmRFGProxYavs4Xjn3GndxXLtn47Q3CnW1RI3FCkbH7C2JVpyEyMqiD0N0a1yqHbZFo+puLdY8YxJUSVNTRqTDEurkIxCZnKKGoVkuYw+UTDKGbGZY6QlLGUiR0VLIhaCywaZpijRzS3epAmBpCIXLn/u5sy3MbLeQelLNIYKqCOezgbIWZNb5xWSoMHzO2T1c2TXpyRE7AiEzx1u1JBWMoNUgjODWSPCSsASfYruAft1kSC4JIhtrELHrEjUtzJKrDJNF5RvH2IUWyaqQkMHUZPJXRfZknAThXI5YyTZqLIIafL3BoHvlGV5KwhCB/gDQRBe/t8IUZblzwDB/+P+d4HfBagY1bISDBmpQ6r+Ca9Mn23s0i8DBK+JaFtU2kMc75ogt8lcGTVIESyHwM65Wnr0FB8z3LKt6mxjhY4ypNxILAufIk5414mJpIzbTyMifc2Osk/jOx8zHRzjJk06JwqrccjptYV5GFG/UWlmMUVTowxz7nz7Ce/ueaytR+iywK7koa1ELuKIRbbFOkvYhNdMtltEy6HUK6iaRIUA7CazpIJvzGmsJMpoTtpYossqwlyjbzRZiD7GRuJaddGbKa5XpRrsQ+s15nRJWK/Q6exRy5bMlAlvtzZ26zmLegnyO8jJiLKRcvxapYWIuaPjxfuIHQetnWK6CeFcgrikYrXwWi304IrK+Ipa9QirnSHlHrIhsmsNQFwxdtoEL2as8WhO+hjSHXbfG+GZb0gnNpU8JRQTKk7A1SwgbZZUbn1Me0HeSHj2pU9Dd5m1fIT0ipVssFFKmqHOajJllQZIgk2argnFKd5IY+zeIsYW602MlVv0D4eUV10uKi66MuGglnMTn5O7Jzi7AuutyO3VDOvkLcLMIa0pCNGnCEWLZruK0tjDLjfcPvuUWTImij2suIYoitRNCVGvEIkZkboFPKysSqEobJINii/TlkRWqonuKai7OWUQcxhqzIOYldSkUYtohAZX9S1xYLA162TbJaN0Qk120T6sk4/WCMMGVq6RCTEXw5SdqsyFHFOvWLTyFZOmQrk0oUwobkKmgoG6e0ZuyZRBRjWJWMcil2ZCKTaQdZGiMImFOfNsjVaDZCWwGYhURiqzKEH364higiYbBKUJv6Ah+FtDoCzL25+tU0EQfg/4JjD5v/wHBEHoA9O/KYehm0j1Hls/Y11GBJMhUjOnHjp40hwhsrFUiVd+wlq5oRq2WZ800Io1TWeXUs3wxTFBIlPIGZqoI3kCqe9T1LbYuUYYOCzVCsH+IbJyhWuvuVP/Ft/4D1x+MAr4xjhktfOrdHancO8uT0KVYJrR6jSYyCPsahXFi0jDjGtzxK1WJ5iNKLZbNprKWb7G3XpUqy06xh0WnSZyectg0UFsZvilS+DlVDONWTpmr1kljT3GNyuiQ4fHuxZPny7x2hNMp4K5FNloS65jm3vtHrPzCEsvsEyPMK/TWW4IVhpfy6dEezbHYZ3ubsiRWmde32KXFrYrE1kd2MT4szlKuEJp5LiGxurqFVoqsA5cGsIVgf6EwUGDMFih1TUukjcYnkf76BFV9YpyV8SujfBuVILhC3b2D4jkA261DpK0IN4saD6HUnUp1w7JZcHKuQZZJHulsEgisiBiYpyRj0O8rU9ZNejVOwipzvV2Sjm10F0BURtTHgRsRib+MsCcxlwsnrMv+0y9XTBlZCOg0CzE1wHW/jPevBoz3TY51lvoTs6d5SErTSEo5uz0VS4nY7KnNkdGTjifIlhzFF1kLKoUGw/NylCjnJqyxY1VyAy2W5lqHqJqGqoQw6zBbXbBfecuk2xOMYdb2eGOKxBbcxI9YJrtUYtFKsGU5l6b2qgkc2D1pspMHaK22lSmbxHOcox6iJDtE3kRZTNFsVKQCtxUpFXEhGc5qWnRNgYkokWQ5HSyW6I4RxYNEtMgVDwMSpZijpxqlHGK2pIwQplSBSIVIZlRqdVZ//wJ4d8OAoIgWIBYlqX7s/0/BP474H8F/nPgn/9s/dd/Ux7N0rh//JAwPCTXRTaJgcYSWVAxyXhevaQYdanENobuI1ESC2PKtcZslSAmAbWaRtRxSLcBWaqAJdKzHNZexMjNMFYWG9WmrK3J4pJerWT54pTNOMfp1QllmcGyjd4LWKwNtCc57iagrTaZ2Q7nucLuumRpntKKh5x5+8gPaghaQRZu6A4lnLxNWJrkpkBd82EGxqqkEGPEwkKPl6CLhNYu7uYNvvwucjdFaN1yltyn7L5kRsbBRkDzA15El9iCgfTxlnYm48UpYbUKYcJWTQgNC7t5wCAOqVQEspXOTJZZ6BlyJicyvwAAACAASURBVBGwYTZ3KRoKurBFNdrUc52Ku0JTfLRSZJFLbBWw9RvkYpdNkGPXJayNia3VSG0TuXqAaFeIb9d8ucwJygp6ueRQS7gRdcajIQ1Vp6zv04k33MYprbiG3CqZjzPUdEMWlkyjG4R4g+cHFJJMvvXwwpLSqREbOkZHJ78WSTsV1IWMYlVJc5uGUsOrzNjlkFGW4jQFgs0Y6bZD1XiDXHXoWRmSriJuPC79OoX4nEQa4g3nvJntUOYmUnjOVoa5tmIt5TQ3KUYyR84CVkVCKhuImkCaCzQDDdfw2EoySZwj6SGJkpEsUt6UAkarJF+f0gv6vC0EaqqCH6boJGyFKofZgptCZjG5QEn62NkVIzOkpXs0egU3F1taekmwvGBbz2mKFtZ6g6cJ5LZEmMdQ+uRGxtJRSbcBemNFMzJZF11cwaMRjQlcg1wUKdIttVxiI8i0KgGvBQmjCNGrJlmgUXMzfpEv8d/2JdAFfu+nZkTIwL8sy/LfCILwY+BfCYLwXwKXwD/9m5IUKDjf3GX/S5Uf5yvwqjS2KUE1Ylcx2ahHWIbCpmUjplusjk01ESm7FpqnkHo3RO4WOWui+RqrxGNWJLSqKpXMYCgEROkW1Ahz7bNeS0RxjDg+4Hl1xf15jigpLI5WCGkHohuGNw0q+xK35RA7s1nfwFwVMG+WNFo6wSIkNQoiP6BYxTQLmbgx4GziMpKn7Bl16oVDqtjkcwXN9slFyDMQSoV1nmElBrJSIrs13ngzKmXMnlUnnkZoBJiZzQOzz3Z8herkGGOBsjRJzQXzroGirag5FaTTNaHsE65beHrAxcLHV69JCx8538OeeCSBT9GRcBMN4pimLZMICsgZ9aZO35ojrBIs55jVfMvjWp180+Pafc1FItAKVsitjGRrEFzk3O6n6F6OnL1FCkMaWh13nCH2aqyKAjGFpjlmOq4RGwKCtaFpe3i5SdGqEpYCyaRASUJEIaRQHPSKhPVAw5dtjk6nLEyBdmSgFBa1cotej3AEE93XqFUt3EmPyDkjMHoUuw205zFpRcRovCW5UDHrCrLaZvTqLXIWMJISZo0p2rZAKwoEu8on733ANlywiQIERUFRbdqyTs0LeROM+GKyoUITpbeiKGbUkoLryOVIixjVBfqLFYNWiC8W5LMS7e6KcKPg+iJK7qNKDfSpRF7OscmJPJdYBqkBLV1irWSMUTAWIp6SENsKVhKxycGKqziBTaRr+E6GJqbMEp1ACdgKOUpc4ggKo1BDyGJSJ0fzS6KwoBkUiEYXT53T3BGIr31+wYTwbweBsizPgCc/534BfP//a54szzlpHJOYCUeBzLQrUpYZU6XBo28/oD/K6Rg2geTiBvukPQEti5ErDU7WS+aCSSQrmPERSt2iLG+4eTvFTUSwPHplQTNeMU8c/HpOaFRZDedsej6dr3yKD9dclruYTpXZbU47tTBHKTuHAl9qGo+nEnZ5wRfiHvky55ln8WH/hm1QErs1QnRuGgs6VkKrSNHCjGK6YiMUKDUDQUkIZ7dYvTpWmePcvKQtgCpcoBY6r5YmYfIl0szkycclf3i1IG0Y7OQtSqHLdDFEEldUcgH91sebbij2FEYNhVZWEE5CTPuY6sEQCYUjJSJPNChL2lIdYZ1T8UIyy6XQAvQsJwkVxkHKStcopR6OOKVERC4rWO0zmpMdFOuWmZGgujIrc0t/vk/TLpADgxPtW6zLDVNhi+3KjJM56+WSDw8fIBQ54YFAGsUkvkeUixSlj1joNI02VqmwzANSp0SuOEjaGj1QqJpVEiVGCH2kvRq1ZYyipaR92Kx0JHFEpf0h+plEXBUw1Yg7h5/wbPuKqu+glVvMqwU1Q6QwIhpWitvoMtCHvL64YlWEKHSxyw21ukzzZI/H739CsBkSjadIRkpmNOjKdaJNiBJrVOxrKsZ7eCcbLr76GkHe4hgFlYbOfKgQs0SWVW6WbSoEzNMMQRPRDAdDkCg2OWa64JkcsBdYxGZMgUbRzUkykByBWigTJikNvUSREyK1oBRLskhG102MrUWSjfAJ2RQmujoDr8nC07FlkS5rloKKIIm4wppwrWPJMVKSklyVpHJGaP/9Tgf+9iHk9JIUqWcTRyFl5Q3xrM2+LFGNRa4bEvnKRy+3VAdtBFmh2YhpcI9z+TnndhU/jqEu06iaqJt9+nsaaRzjrVeU4pJ5CePZDZ3lAbWai5tXeCDAyo0JxzdUUptyT8JcVBCUFnLhEYcu6kWdVXWFYUosv3iBHAdUdwxWNZnW1mSZS8yzDfptBaMWURoxkiaST0css4RmVyBXA9ysQfQywO5k5IHB28SlIV6SORW0VhcWGd2KziyS8cIFd46+SzGNiHcijL+02LYStPs9grFMVQwINZvydoVX0TF2f/rTVFCEyHkLK4PEGiAuY0xJIdRVKs4GV4csM1CF/5O5N+mRJM3z8x7bzdzM3c33JTy2jMzIjMzKrK2rq2t6lp7mzIA9xFASoZvmQOkTCNAXaICQCIpDYQ4668CLAA2oETgSpVk1vVV315pVWblGxh7h+2Zu5mZuuw5NCITQrQUYQvUCdjHgfW+/B3+8y//xCbUShh7QDSKEmUFUrVAwQI6u0COZjSFQ3K6wvSpCkLPoCvjna0w1pn57G20A3arEJCxDNmeVprhmBT9IERYKWbbmqaIgGDFVxUTPMrzIwnMz7EJKMQ9Yt3vopZiCL6Ij0SmrzGMVR5WQW3XMfZcsNem06pT7UwrrLU4qJTTlkuDGYnbP477chGKJcCFSzSCUZNarDNWbk3kFQv+CWCzQEEyaH2iU43fJGz7W1gKrIWFIQzaDCck8wCzKpHnENLpi6CfERkareJvibputzm1Uschg9opGFFEtm8hmCf+kgxd+RbIUiBTIz1uEPYdekuHOZc4Sh0YXDCenL6Xo6xitZNLNUsRrBaGq0ShqeGWB1fkcaWURNj10TaJc0Mn9gNCTEdSEnAjyAFcMKcQqopAjqisyw6U1N1g6OaluEbZi8lFKIVYQ9mJGfRGT5FfG72sBARWRyfSay/w+xVZO86zKje1QireYj3ya5RJxJSIYd+jqMI9k1LCLsdVB6y0onM3QtRijpiMBTaHEprzGX4gIuc4sdJElHTERKEoK1WYLPxKpaiLpXoE030JXfZYvPJrZfaJKRjBPuRpsYN1nJJRYiy8JZQWznVHaqlCQbArVMsOLc+T5BZbUQ5hFrH0RFwhkhayaEE1mYDdRSyp5fMV4IeNlBkFNIzxx8Ws19pKMHXkLuyuyEq555523CKQ94oMXCMkJ4d0IPTdQpTWbQkZe8hBnJuV5DJmCUF9xPf0YSbjLJtYJSkPScIEVGoxVCbGYIdgbZD+mV9pFe6NEWqvhbZbka4d7swa+6JIqLoVMQohNGu9YNPoWhXab3f0pG6XO9VtPqPQFju+rFPI6il7hUbOEWXZYFHRcU6I5LyBva8zjKTM95aBs0DSaKKLB9lRmNF5A1UBOC5Q9EclRUbQGopUwkwNcxyLM13Qin8J+G3Fms5QzJBRs44iCdYrx0KQiJLwSGniZSkPZplH3SHyXwq0CURqTjXXC1w55tGYhVXn38IDInCP6InPDoHVokMYyx4MN0nxDs1ZEajYwCxXGF6dsVJ+10EIzFCrrG8yBzU4uox8U6IZbPOxtqNZanHW2OHaaNNuXbOk9/uL5DVtqk6g4Qa3WsSSVeqNO0Znw2NggXRrUhBGYe2xKEtWmTbfeRT0q85d//mPiqzmdjYgkGCSewNxz8aIQ1UjR1ToiBqkPhjIjTSEwUuJEw8ojPLlOLgYUqilaIOBHCYkpQVaGQg7+/Jfm72sBgSiNcc9DTv2YO3s6ZqtGOJEY11KkhUZBTakIRTZqg7XkY8sTZLtJ2tpgTEN2ZQ2pbhJvakSWiG55WOMaN65P3nQJIp2DszblvZxZ4RozzKgJFgVFpJ2oDLaGJNqvUVi+5FANeJo4XMsulemES6NFeTGgEAUU9SnrrEumBehY+LKPpxnIKtTyCXkus15rqGKIKJYYez6F5BpbsSiYa+S6gHu5xF2kHGglVnUdW9OJogpGa46e15g7DertDvEgJ1LuQvacZ6Ub3ptVWD8O2bRCstRAMT3STpGUGLmgMlpW2c80QtmlpJUoSSpJFzZKghom5IUimprQbYvI3bvM2jnTJxFW4KAIAcJGwNdkQlTkzZpO0ia2Ahq7e7S1GbNBkTCYkZTmaEGMdT/nbqHFlXwH7+qS+m5AfV2m3hWwtmzMZQlFmnMr26UqFLgo3WLLWtCwRU7ia4Q8YpWuyZYO5YLOUtJInCGlSZ2tegvJGJFemKS2S7peMA9m+EqNZLSkoLYpNX168pJp3+RWdoC5d8lNSSEvL2n5dxnveUSf/xUr3cGvrvCte6yuPbL4OWkxIEgMnPQOm7GGVoBbh02qR2/T9VOU9YLBfMN2XaE+NVGFBV8FIzwlopnXefDARs4NapUDdCNA+KrIo2/dp21YOP0JQquK2KizKVVpNjNuhTrnBwW+e9bDf3/F6HhDmBYx9iSK9haN7Q6isc+db8w4lb8imaYIfoSUZwiBQiiolByPUNSR6iJGoU51mTBNrxHcBCEUUFUDIYlJGjG3xxmvjRxXnlKblllsZJDWvzJ/XwsIZHnM1WJBUPgJr1/v815vyd1mmbSVII5C5sWcznxOurXE2fi4ehk1CbAWGcuohW7m5A2f5HGKtvGot1W85RrFdZCVDUWniJ6vyIwCgmgT+SOGozk7ZpvyocKNlJOfCbBfxp0LSFlAJ0sZ6xK9KCPSVYpGi1U4o2Jts/JCAiMhN9sU7T4PF21GSUzREgndnIXkACmjwQpFXZB5fRS1Q2FZoaLXsIWczJxjSVWarkhieUQLmayiMG2k5OqQXGuTrTJMDZqje0xunhMsDBSpzrohs7dtE50sCG2BbD5i9+476JsYOWvzoBSBJ/Cps8CwQ5qdFNXLSLCYmya14BL5RY56skC0Y44Vmbu+hkGZcC4jVk8J3TM2rsnhbgFf03FbP0X9BF4LHpUkQnw5IdzuEIpTMmnKdbzhoS+QlLZZzEIq5oayVyHKKhSqAe2KRZqYNPjFBtxMiYnygDkVlLKDtFngSQFyW0OQLdLwBlG6Qp8oGKWAdLuKq0bszJfMN28ylwWk/il67jKa6WxJc3TxAPllTDQ8p3oosemInPgzkplClApEG598B2ZZkeTUICkuaGsqy2CJM8/oSRliRSRrC7TFAuXMRL5dRLDaSPMlmlrDLEOp6iPlD9Gqe6jjE/yCSqVR52YasvtGi5HbZetWFWd5gypDNRwTp2V22zafSTHFqcHQu0Zui8SrJVuVkHQB1YNDnr/6gnkqI+UWWRaTCQ6mLaNGOX46JU8EOsUGzCXCFNSkgomDkOboqUt1WkRMq0T1FHHRJ5BSyoKFV3PgV3DgawGBVMw5SQT2XJkwgHWsYr81o+q30EoxV+YGhTWVUoQxMbGaMvVUJBU1KnoN09/gPtVZZS76IsGeLZn2bxCsBZbWIAlMhtIrdLHNe6sKqZnTeG9IwS3ycTRhW67QMPoMJzHuAxVlWWTUCNi60XBPzil3vo08n5A5Opo2xGnY6G6A4WzYdSVexBayJOImS3bqS0JPZJjEaB0F8caisZFA1DDUBkmyQjhaI8kiG7tBeRaTFsbgZZzrsOW34EBkW1B4fHxM2DzkKMyRKPJJ9RRf96nZLYqrEev2nK3GfeLcplrvkSc+8/GC5c0MKa0jZhOiRcSgoiIHMUwXZLlO7u6TzRLUks88KCAZK2b2lCSysUwdobtLtglpWHMunhaY5yWUb1ewDg/YkyxKTQPt/IKNXeJuNMet1NkIEkLDwhcdxMUEubZHc0/m6eWSUTtAWpW4SASctMAyS4nj22iiQ0uI8SKdVeyRKyLVTGWdr4jCA+brkMOaybQb0c89Uho0Zhay+SmJ8QEUa7TzMot0TXoqYXYNgicmpaiEH0w5+2KGkrcQ2kusyYyoBPXYpiaVEWkhu1O0bo7dqGNHPqu//ITrQo5TMdjSROQ0QH1wyKvMJftQpFHXaN+LyK8OqNZjhM2YF1OP5psP6Jydcby+Zn/ziNItjcb3KvzN//Qx71zXqdUqVJ0u6eGI+WXMmZCw5cYkhk3kqowKQ6qNFncKR5hvfIfXzpdcpHPCiYKghBR1jyzLEQOBfFZAlxdMcpmN1WQTbDBVUCsKRpizjl0iVSATSph3BaIvU5RcJV9Vgf4vzd/XAgJallJMhqySLq3Smm6nzWTapxx5JJ0y5lJEiyWkiyMKts+WJFFxu7S0lLNsTh47tNZllNoYiQ1x3EJYQR7IiFkLD4GF2qJhSXw+/JS72hFeuo24rVL1biELK1alGF2esJR10vMxo0qbli7wep7x5qGLqHSQOae/dAizjMg9QPIdBnmfllTnzn6FJ8ENsqzQUisUFypJPGHBGNHcR+nabKKYwqKLvCOgXjt0zQq1okGWX1J46GMm97Ao8Tr7OVk0ZfdgH00osYwmTM0tuoMRbsWlUVozFg7Zb4xpC2Wm5ymVziEn3l8QXn6BIMfkxZzsQsWohDAt0iyViZMZ/loi1iPi7Bqj3KEoiSijUwrqNogLQuk168nbZO2ITWMXMVRwLk/oftFlux7QazzCWW2QKip+L2b4rI3d6fPIU0j0Eu4kwmroUCshGjmhPqEv61StS25JDQpahXDqU9gU8JFITQ8zUij4JmM1Y2CpJLMcKazgtzMWd310pcyt2T5zTWHW+w0E9TPahoIxKzO51cRuD3n9pUGezrBrVWxyfvbFv8K/cfDKRVS3jkjMOi5QzBM6moDVM3EnIRcXcwxTpbal8KrvcXWtcK8oQz7lievypreHERvElkC1WkaPY4yGh3srJzpe8fz8c74n/C615j2e5ydMpTnmuoL7VU5P62GoOtnRIa5T4K07IvdNgzSO2Zw5+C8LBO94yHsK24pNp3CLYFtH3ompf/IVKxcWYojsRpgxRGpCpBlcLWVSRPKCR6qkuMgYaUycmayFMU4GreSGxbWBZQUYjkc1EH7ljb2vBQRCP+DewqbEnOjuBruokj2PGD7scXdTZWKc4hbL3HHnqL0SUpIxT0CzbJLpFD8KKGYpyqqAoMe4winNik9iSvT1K/YXVTLRwErWeLlEmuU8dc8ojFa8cf/vcy0Ved+0uMHg+SdLbGGOPZ2zqe/SqwVEYkI5e4yfPkaw7yENZ7jFY6K72xhRnaU3Rr66YKfQIYl08qJIrSGxfaNRLiZgBdRCCfKYQmLjrnPqFZO7WQfHLlK5VeX65QTrjYT22sH9bMn57R3K6wGWeQffU9g96HBem0GWY47e5KCcUAoyoqrH+HJCdKDQutpw4e8RBRFlL0V1p8iVLdRcIlYWTKOceiSzMX1UZRs7bBJXHDyhiqWUEW5OWQoajmnSLQkURu8yLD3Gs5b0hQ0dfZs4EqkbKx4/f4sgX/BQHaIoK+Jdg8HVKWtJpmRmtBYT5MDCWNnkgUNhu8FaqWGJCnV7QTYSSKsZkrSAtYqVGPhBim926IUCWT1Fr5aZKV9xf63xqZHTm5UQBIO6aTJ++VPMagepXGAQL7F3a3R3DH784wHVmsnNhwuabZfESZBTB2XUhoc2p5JL8OUpdw5HDOdVTq6fU9Ek9q03KFarxErKWWYxm23hXvZZGWNI16SLDdZOFTkTEPfXuGrOuTmmVm/xdPmcB0c9pMkhW7+W8diLSP7VMx68e4u/Wv6Uhy+KZJuAx/IhR40BsyThsXBM8VaX7Z5J2Zjw8uQJXnLA7XdKqOUe/8Z4jKwk5GMJz6gi3itQdFbo6ZyFbJA7a2K1TqVkEE7GBJMFZBr5gxrzC4faokiSLVkVU2zmOMmv7iD2tVCT/9E//Wfff//XfpvkGxv2Fzug3adpufSKt5k1bKZpgTTcIGQr1r0cXS/TLKm0VBOyEU4wIDZ8nDhD7HWR2gnyykOOLfy6Qs2QsEWF49WM+70WdknFnYeUrAI//msJ2xoyu7lmzkuMvshMKVOtp2wqMvEXGuIypWrETBcOtfqaTe5hiDGlsMZpIDPzQnS9gObPeXV+iruxWKwumTivyCSNil/CPx0wWH1Io54ir/VfdLOxOvz85mc8++EP6F+4KLMbruZlVkkdbSpTVPYYvZ6hqsf0NY1fy2sYry8QbJgmM4KPRYZBn2G7SqPioS1yMl8iiQXk5oqmWcE9cFFimWgJS21FWVPxyzGXmcNZdIGUzNDnKwRvjZ84NBsVqI44m/V4mDkMPY1Xiw3zzCIex1wqIQWzTGcsEvh9bh/JLE9tgqJEVzHZKDGSJLDabLPSZnSFDfv1BsHUJWXBPBiyWQXIYh25VSdTM4p5iqjMKcZzxH5MKkhIFYXIW1HXC8y1Cd1KASVTEM6+RAhesdbv0Z8vmGRDyq8rRCWN3sKiSJuL4pSrmz4N3SB/NWKgDSnv1sk5Rw+WWHGIVbmHK3v0+y8YTaa4mslWpcFqmbMazUjWS/J2gRCJ6c+WbNKALDZZmCLlaE0nV9FH9wmQmHVlFtcjireKqMY2b2sCN/2I9SbisnfBI6nFtd5EW16QuirlzTa1vEV6p8wbR79N8NjkqXvKcuUg1mU2Oxpu5JNOb0hEhQ0ixiJnI7uMfJcwyHCFjK59n5KV40R91mGJsCJQSl28eEMq1DDiCCNNmKYmhv4bbOKTX6om/1pA4F/80z/6/rf/0X9ISbGwTRnzUYFdv0q/FbIKU1b7cBnavNPeIpVirq+neEqIZ9mUvQ3lSYyoNDHbCsqOizmes748wrt0IVmTGzaBtWTNnOLI5KLiIF/00LUNm6sxgdYn8JoEAWRljavFx5i9Hq28yM3sMdlOnzD0GFq3KBhLJhOo1N+k5kacffGMm8kL9OiSsemylCM2ckq6Eli6I9bDEeZQJAw3PBNbpJZFrkpohkhhJnOs9mluyuhNGal9wPDiI7a1Jq4WUKnmvOhmlLpd1NmSAzNl+kIle2uC7xTxhk+oZjr7apeyrLEuF9GaOlZPwVtGHBsaWWJAlJDOlyTLkKy5hZlWmKkiq8WKepZT7VSwvRTBKhO5kFTXVKp15oKBFr+gPZmjb97AV+aUmlckLxp8VdURvWMWZkxR/y1sq0h684z30yPWBY/qekNd8Jh7ZZKoSdJwUYUYxasjLS2m5yaJN0Mzlyi+TrhImPvbJMvXbGorpDVUlEtszUETqrTznEGYYzo67ZZDK3wTb/+YSpJRUgL2qhqJmFIxdJjNGAk3qLMrlCYUCzvsbr2JOPDJ/TUlYYtIkNmsQpxRQm2pUexWUQQDcxRhRQuUKMdUPRL/MTfZEitocXnwCmuRoMUD8noFsDHzK1Tf4fzzGW9UcraTAj/yGsxFnaq64JZ5jx9vFvzGQuAVKw6eqVy1rtkvNWjstziq7XHSW3E5icmXU7Zsja10l2q9hJN7qI6M6U/IogU4JqYLgtaiVVaxGzX0aZXpTUBwP2Pf7aIMV3hhg6Lo4gg9pJqMV2ywc3vF7Gb69YXAP/vjP/6+0rbZmdjMtlzEiczpZoOz2RBrS8zJKcukRNCOcMYrrJXGGAuroyMYKoMbB28wJHdGKJ7L6gpGmwy3vcYUJDxtirL7gFK1ix/PWT0OWYQ/wJWvmIQL1JctooOXRJMZzWyBmzykvV+nKLlYgzG2fIhi7uCfnaLEU+IRbJdNnNKQcTXmZBURZgEFWaVZkqn6BQS3xCZZIU1kVqpBspQ50PrkwZCvHksIagHV7JOnBtPxCqEWY6U+QnyEL73gXruNMK+yiK8pFZp0lQJPgivSSgnDE2iM6ximSCFxkItlFE3m5Eal2orRswp64OA1YBVuEBmxnoT0hZy9XKWeFdk0IDE2aJKJutTIiiGCYnK3+ppJ8RbR6hpckWvRYqYNWJ7MeKcb4Z/v0SwdYGx9hXlVxHtbZupJtKpzXpdKaM8z6kf7rAyRaBLihgu04pCKkmHP15QtGbXmgTpA6xi0mhV2o5x0k7IqzImkBCcymDseiVwnTjeEoskiaSKMvsSutPA7K+YTULeHpK/uc+ItEcwl5naAP2gRzF/jXC/xXs5wzATJCqm54AYpKzNECbpcDRJiS0QxDIRNgQomaynHr4T09RILx2FtLHk1LzI9hm465kiaIZWrmPUe8d4eDfUl7oXIaHRCq1CmrhRoVO7ihH3eCLusb86Qf13gzlrk5lJET16gyx3sgo2+v6LSAe3K5dKrYKoysbiibbao2xXA4ya9QW47xFsJar1N3bNRduuUkyrNQYFifMkgf0mkH1LtCTSShImfkN0uIYx9VFnDypYY9oSOZHEzWHx9IfBf/Tf/4vvf+Z1/yGo048PHS/ws5Cp4znAu8+LiZ3wxHxPEEpsXz0gKe2TpDYap0tSbcLXEXR1T2ZVJ9H3CjcE8NVmyptBXGIgx5/m/tbecDUCakEkhHccmVd+kEir0/8GGZPQOvWqKaB9RXw9QNg6X+kv0PCdwOxS0Q6SdlDTQEDZlonVC5BU4v97CMgYUNIvVwmTtlhhYBtfHT1jcTJAlDeXmgpEyYrBcsmo0qRdUOuFzVn2ZlycTVP+areIu+SKnolVYb92jvhzyZ2ceD1cJJyOLqn2b5fgTCqMVNVmkqq5JpkPUvMKqKlKUqmiHR4xyl4CMQSzjDs+oFzXiLgyjFvV1hz0isi2FmhfTllLaxW1mwwWymbFbcPEMn+xzEdPuIAw8MgVcR6b4QEXKa8RRA3VvhJSlGG+EFAYqD8v3WCpTttUeQlEh10Ys4s+pmGsaNYltISW0G2hyhiSWsHck9q2IQlmneBWixGMyM6W17pKJJrm7QtYV0vWEQB2z9urcNkSUyKNQvU3sPWPqv2JHuE9b8khEFWFRYeEZtLUrvPVTPlq8oPP6GMF6DyMNWXkeYpgjVHQ0zad3X2G7WibLU5JsSiAPO9Vo5QAAIABJREFUqcoRbblM2dxBu9eme7eGePYCVRtRb21jde+QOmWWLZcHkymTn2vYtRQ5G0DU4vRmxfSTgHn6HHk6R56fs1FSLo6fUrQLVFcVJuoCt3uIk42xH3fZpDNc84zcnXD99AxNXpMYDq/tCoNZytZwgjb2KYYaYtXCORIoWs9h6DOb7WHuSyzNY8JFzFCZc5SvEK6qRMYukTQhSpa4Xpex7JPM178UAuL/H6H/v46KrDH477/iOP0pteIU50cTjscypjBl+jJHPp3RvPqMsejxxccv+dQdE7/+ESevf8BsM6eUHhA7t5GUBf3iBUPzkrzSZ/zwlOLeAE2S8AKPue8gW0taskkiFJkvvyJWrnnvyz161Qucb7c4Oc/Rj0KOGj1681tk6S2yOwuM6x/y6M4WzUcFhFtNTtII1Z+yvxuQs4dKhUgxcMsCuZqxatmYe/cQKz7npZS0XaKZPqDd6BIUPD55eoH4wMSWRa5bMW07ZV1rIZXvMlmc8/PSKe+ncHN3wa5+Qtwb8Hb3Fulhib+w13zlmHT0b/Fmd01xo6BEUz5ej7mYQuW8T3EaIxvf4jpUSfs9TPseK0IuFZHRmYjwMRw4Ip15SFPJkYUh4dkOdqSR2GNmmyrrt/o8vPaIdlPuLBSQXMgybsUVXi4Ubv5kh0DcpqO4CHqK8aLPOv2Cm+g5xsWcq7MlXnLBIpCxFxGycg81jki/3HCVlNn4Hn5Jwi0WKU1m5MMvca/6aGFKa6vKtnaL6rMaVe0arbdB3fmARmfEvP8+4ewDPg2AJEabTGnc3yEu1zldlBHmIo/SmHnpDW6sG+pOm+Yg4lqJmCQpWXAbTdllJigMApFMbyBKD1kWd+mvfZy0TzEdYl2sKKwSSp5MTV4SW2Viq0Xz9R79VxZiuGCifsmfCjqr9jmRHiKs5qRRCvg8M3Wu8iuy+C7axRDZKBGUM/bGLt61z6I0RpjesD/MuTUu0nJkbnXuYsgPMT8KMP4m4ulL+LLvsHzhUbh8Tu3PL9icv8GJqPOi06e2ZWNuW4TWnL0WfDkzuMgclsuPyWwPL3jEgZsjzH/1a/6vRSXwT/7r//L77d/+BofLu7wOLOrla25GGuFwxmRssHtUY74qoZ8KZOsL7tYvaVUMKtZ3Scw6l9YzKuWAOJWZ4MBSYPYqIctHyLmNOm1TnK9YL8dcr0ucikNCU+Zm8IyG12PLKhAVxpScHo7+hAfdA6arG4zrOnf8XTZXHvOKxo9nG1alCl3PJHo1RrKqDNOI4OaGgprgexJutsBeecSziFF2yoWbsHY3aKMlmpbi3dxgOy5WuUpP+YDf+/072Ce7+PKAd0yDF8manQsX9ehNUl+BRCKpN2isvmI9MUgqKqxUJDnk/taMi2SLo1KZkrLkOL9P4XpKIimUhRRRndLqC2xnO+SCyCA5w9IdRsJrcmOIbzaR1ismdYHFak7sD5E0hTwQ0dYvOP1yh/T2Ib+lmiysP0C/W2Jbb7A4+hMeXr/Nw99VaNgKYqCyO/uClZSRPM+4UmTsKx9V20dzQ/LAwmw2WSxP0dclmJRRY51MXRCfpwi+iljJcYyYbC+kXB5zp2ZgFxs0lAzFFpiJ10w/FUl7IWv/h9wtlxBOXnB2/AyhdZupHZE71/SImV/+nNGZy3PNp1OJWVl9xDfqbNwMGjP0sk0iQLTMqTds6h2Nll2muzIplAKk2pq0kDFYJXwyvaSnLujnTbZ7InauYIgCee0SN68w6ndoxxNC7R7Vixu+uGOjhvfI33hCJE5RhhXa9jYbrYldF4mFMQXVoSRXeOJbJHUHb+jyoeLidxR6u9tMc5UXz5/jOU9Ic5dVnBFWp4zXOsE6ZijMEDsJ6djj6iYgeR6g1iuoqYfm2uTeBP1ehiem1Owhad0guREJCX9pJfC1OCKUBIWVEDMqXlKzi/TLt3l3kLIY2WzdnTI/9sjqMk+UkDo5Yf+QZFmi4j1heU+iNolx0im+KjFbZoi6SdD1qQ56JEKTmb5gZl7z+eUN0pc+ZjMlKT+n247JgiE/kWKWRoVv3n7N9EnAT3/0Fanao7BYsXkXcqlEV/A5nl8hfKkxkUQCOaWvB9jKmqK5z3dtBeehzmvvHGHyOWmwYZWrFNMexQ++wVFbR0xPKCUKP8clFS3G/WOax9t88Pfq/PivZH6oxagvp2T1BaWByM+bHcTLEnvJY34o2TzYbtK+U2CvKBIMLnFXE86bG5SvMp41BLacK9JwzKvlnJ/qVTrrAq2Kz6DgUFnmfMfRCczbbL3XoFoA8ojEgT23TzgXOZ8IXItniBMVyzyic18guPyQVHkH942/YNd8F029xLr+PaqVH7FWb1N0llxZNlZgwXAb5Y0f88GNz7nZoRPUmFambDVjktXHeAMFSdTZ6YrkWyEikJgrwtk2s+gN5MPH9NQy0nGTaClA9wu8cIctrcBtK+XV79WRHtfQb17x8lsl1PUWiycZF3fg7cdzssY9dpo/4KdPpowiA4OUUexQFeG1k7LfPCP4aoe9bxUJLBlnsmI5iJGbOXKSI9oXCMUMfR4gSQ3C2EDMVA6sBwTbCsN/fU6uqei/1cNYNiivLinsHOJ+6FJbP+fDscb9vRmWuGIudlDNewjSE1LfZSItcDwoRm3SZkhpGaHOr7HKHbKKyG+3SpSLQ9b+nPUXAd8s73LxLYUXP/sBipchr9vQWuCpBTZeQi5VCMMJtjDE0S3yyZoDocWlc4WbFUkvIrRNiNzdZ7YJuHtb54tj55fn7+tQCfzzf/7H3zese+RyTGO/iDAsEyxD7HyOF/kEzRJVXWLr9ozrZZtueIWa5tjllIKv4V85eOIN1wMHZ5MxHgW87E85y455Mnep6jNEMWbxNEVorAn9mGWwoO8oSFHMcLpka/cWm3PwHJN9q4Fpp9xEF4TVnPD1Fp5QJLeesJnWGKx+of3q1VJi10KLbK57Ap3WHYxVEUk5ZJjusJ7O6ZUFDsy3+M7dOxTsAq6kUJMlso/GvHF4hKFs8dMnIzYlkebTF3z49EMqHRtvaXOw6/Dq2WfUh3tE9wZ8sx8SPbM5Dp+w+uQWY6sMM5NLaYMwMiioOrO7Lmsrp7Flsl/rYvffYFruMCyplPdkek2N3XKX+TJDHl6y25ExMlBerenLMlHyJv7OFac3pyRzjfajCj84XzFsq/x6tcX45XOKF1OWb1QoOB+AVGZ/esOZf02uX7Kc1Jist1FvLfGiCqmvIA5m7AZVNr0poTxi/Vom3biMsxh9cJc4N8icE/bi1ywlhbU/ZPxM4+pMI5MShFXKIiqitVzWwzHKvT3i4JgPn7iUuxJG5w1KPMXdfs6PnxR47M1ojgXcXKN15BMnh9ytDnnZfpOtUh1VElg86ePIFyApREuP6k2CIkgoWpWJorKJq0TXU+7sNJirc4TZkEV3h0hq0WwdszIdtqdFRC/GCiJcbKb/SGV3LjILbaSkhxMvyYdNEnNKFmho2hqvnWGvewx6P0dcNzm4KaI1T9mqtjEbXYpzi1fqjGF6iTcPmHtz3HjOuuQhaDl6DBstpNzNwVNwPJMqIZRztvtNlo8WaLd9DsZt/HhJQ3RYFzWWpQPC4fXf7Z6AIAh3BUF4/O98K0EQ/nNBEL4vCMLNv/P/9/+f1vI9n9LGpVrfMIqnXPpfUVRGPHdGCHdb7DFlYtcJhwb3j14RplOOh6e8PteJ4j5Wwce5gq8QeZ0qDOIrdrKMt04NSs413rlCLYjp/qbCOPfhgYJRLeIuUq70hPodE9xLzs98Hn3QphsveLbU+N3vvs3M61GyFF7Oz8gvvos6kNkqzYlndZrxPnfiBpvSNVJ9h+OwhNzpYN/Z5q69y7b5LeLqIzq3GqR2maN3v4lSvo0g1nGbOv3qhlwP0b2XiGLKRWMbvy7wseFg3RYJBIcPvvMdbr4rIH9Z4V82fg0nayHufMD73QHz0jkL/Qve/cZd7t1zsfIRcWpS9TSUF88pjJ8hWX/Be9af8gfaMzbrkGdZjUEj5uGdInc7oPanXJZksqMH5A2frPUUf7jHYmrjtDOO/SvM3y7SvJyyOP5rVoZGa+9bvCXa1Etj0uoNlnzIt8Nt6lc1DqsCrd88hsEOV9GP0eQb1ILP44bHeNnDmz0iuW+x9BeEpyEv/Usu1JR46wGPhxWmp0si16eyWfPmnQ3FRxlREFDKA+Y3Nyi7ErGekX+5xa8/smi8/03s1jWvizZnjwcE/f+NvRc6+kGAaHuo0zZyy2KaP+CdsyZ1/RVP3AXVd26zkbZxGyoNoYKyJ2F3a/zO3rt8o3yHuiNSEKYsPv8M/jZi/FrDvrrgnn5FGr2Dael8VD1h2vmQuWwxjK5594cZBZbk269pSh73GhvSWzmCvSZ1Rsx/niO5Cy68PoWfHlEwquhvTii277Lsf0R49oKq/ZJ38h6P2v8RrZ1DjN0qTWuLbc2mboBkZtzJLOxFmfhBzJ0HY7Zu5bSHCV+mU8xlmeKkzcSfsJ+KxG6P4qaLdzL4lfn7O3ERCoIgATfA+8B/Cnh5nv/R/9v5lXIl/84//h5WI2HZz7h61UbxP6ZhPWC9OiUwoTMWiCotnF5EMHDZ7dr85vZbVOURZ/OcDxczzmMH/+oUW19TXgtYwg56b8HCM8gdg9Y4ZnlLZjnw2Vca/OjZayypiXV0G2l/jNF4yCyOKQSfspDu8WCzQX4B2u2YpNai55YZNwuMgjm/a95H4QXHDYlCSUFZRjiRRW7v0JJjbrFkPJ8gZQnTYIts+4BlpcXb/hxp+ZqnPzmhuZeimHcY3lnS/5sJZamCcatMaa6yVw0ZoFHJC/zk9cccPaxz/bcD0vfeormfUD25zdmbOdvTC2z3EZsQXhd9QsFh/4tz1r0CnWKFqXLNfbGKLOpM0gLNisuqdJdwumG/3qe6rpLEM9rhEef8GZPBlHxRZHogsjkr0P4WlK+nPCse8O4DkeormbNsyqNv/CF6SSA+63MZVAmnZ7xZFfkfT1e00yeEQxu1c8YkM5G0XQ7lBeVKjVk1RJMV9EhiIu2TzrqUajckiwmjfIyllZAym16vw43Sxz9JkVOBXSVCvKoyvHPB6UokXdzmsvq/0/rwXZ7d+RGdqzrtBxn/87/8NxR3QqTjDWKzgay0cUoOp5/47LRG7Pz9b6MOZdQoJUlAMhQ6W2VmZ1CTJ+gVkUa9wKefPuVv/vrnVA9StKRL6IdszS3sB7vk2i5brWu0TcZVYZvFPODA97ne2yZahuTPZ7S2uxSqT8iWHcxKwGh7jzfPz5gWRJQTj2HPopAIbEoq2/IKZVygWw34QrN4piqcf1bA3KsyuviI1f/6M2yxQPzQJRHKDDZNJqcntBsH2ONThF2faHFI6facyScpo26Leivl6voYBgq/977FX05S4hezf68uwr8HnOR5fvFvW439fxobUcQ/LlK4PsdWeiS4nFgWV/qC98s9xC99ljtrnEqAdGHQVC1EqcBNFDIedsi3FfZyh+CihFL/Lr3CjGci5K6AqujsHSoE/W+QGA436p9ha2/hPzwljO6wMHPe9p4huvuolQ2/KVp8MjuiIXqsUGm2j/DsIW7ZYSEecpg+RZ3a3Lz3msrLPWp3piwWJRStQldtsV6PEXINJ9+mlqrElTmeuCG5GrG+/CF/KfqYrslrdYIwtPje9gYzPWQ0z8mWCk48pFzfkBh3OL86Yl55yer3/wPsiy94+d2HlD/7CMszOPj92/SCL5kah0iTGcX2kFKqUAifMbrbQZM8Lh0fyiXOGlWq+hbbkyGZZ6Ir50w3A76YClgFn85WCcGeEb8+wG5v8/SHBkm+Qfyey+BVHdm/5I77hGy5z+J6wc7bR1z8yf9A/a6MKAioSpXiBx43P1nRPtkw4ZLn27Cz9S5FL0KuxyxuciJDoTg1iTtV/DvXNJ6fYjZHPNHb6KOEu40a8zFkA5FnTz222hGDSkS1us+Z+Cmxu0uF+7xn+1yVlzRnf8Anb9/w9vh98tunPP7pEr1S5+Rff0763i6/86bNbNhnen1JoCjkI43qCxGp8Bp/6wH1dcL6OubT8Rm3Up1xWCR70edYPOfsekprk9JY14j3msQlE3UcUWw+Irv8IZ+49/i2vYO6+Ij7HY1xrvFwsYBlyEkj5z3vx5wevUWWf0Vx+12qZytOG1dIiwNy+RQpf0h2r4M+zckdB/vtIs6FiNMP8f01lUpOo2hxmi851X00VSJ1cwp+SEOVMbctFssRhQyGF1129kQuhyuS9IBC6YRZf02JCn4EH//sPbKDj39l/v6uKoH/Dvgsz/P/VhCE7wP/mF/0N/4E+C/+7xRkAKVWK3/rP75P7TLmRwc73P5CQnm+xC9nvPEbdZxVCXkrIlvWmUdzOs0Uw7Opl+5SuZWwmQqcffaKyB3id0OWekqcRpR6RXZcC9No0R6f8Wevr1hPRyjyJdOrLaQinIlbiGWP+24Bw9zFq7rsvjfgrvIPmF/raNkTBtk9dGnOOLrksHEL47aJ9mKFEPk0Sw8Yhy6vrot07pQx6hniCUjymrr9lE4ScVrOuHhlMPVqdMMyL+wfUJ0MKIc217Uu37QK5IVz0vFtTsoSze4Zw+Ef8t3dV7jNIvNbKg+CXfp/qrJofESl+Q7zmsc3gzWvRZG2dYryWcxi9z7LaA6xR+w9RSlLCMdtDHOf9AMJJ18S/mTK2ewFQW7xrcOcnew7FBs+rQcCqXOPbHDOtFBj9tcnTLsp997a8EA64PM8pfeqjtksIggf8eh7G16c7mCrbQR3SlPO+agwZ/yDa166l9QL79CrOniFfTYsMd07iCWHeCPT7GQUcgmz26W+XUZZzlkuRFLLZ7Na4z0XWcTPkMUl24vbuFmTNHC4bt/gzb/NfqIwqp6Q3K2w/jhidvgZl39V5eFG5a/KT4ijZ+zWdUL1d7n6Xz6iWQwYz5+ixDqPbr3FqJxyUtfYT0Mqao+qk7JUIWvGWBmMVhd8+flLkoFAZd+kmylUe0csZy+oVg8x+l2avRVhkvKq6/OW3mF/5hDtZYgXCp+OVbpBlc7RV/QDjfWzBq07U5IDmc3Q4OrxnDMLbj/aJvYjhGjG7lYB2dA5eTVjsb7ErZSQH/wen/3oFWd/++fYkkuS2niNAmVusGYlLspj1HMN6ajJzLvCaDRoPg8pz1s8u3VFb3CAn7jossuwlBNcD/79qMkFQVD5xRvFB3mejwRBaAFTfiEl+SdAJ8/z/+yXzPs/5SOWYb576z/5Q271m1QbLsVHG85PyvwfzL1Jj21Zdt/3O317z+3baF9EvCYzXzaVlcUiXSSrJFKibNgyCAiQOfDAMCAP/CUIeGDABicG/Ak88NANLMMyZVJiidVlZWVWNq+PiBfNjRu3v6fvz/FAGhAGy4ZhC8g9XBtYs/8Pa62911q6d8cqkhCdA7oLKI/f0Bn2SbMBp66D1M3Ivl+yn1h8frlidh0wkSParYJWphBlGV/PYvBErt0ZmujxIi0peiZ/Xzf56VWAt/wZe7sxx3/4h7wdeZzdaeQDm/TTEScf7pAElaUb0Vm0iR6+ov71hOHwhu6zjKv/6B3EVENY3pOJA8TOkGS7hnubqtnAFCt63PLZ3Uuu9ZIPP/mExkClenWHk3gU5xv+8nORpx+suEDjifkBiWfz6OErmuUZiw9rWk+f8P3nGVetHtXPXtPtdHgrbwiWP+fx6I9whwlPdyVXiUyU/Zp97RHaOy7hqwE/1Uco9xmauOF2d8721Tecfvwu++/8Dvcv7+lpMU/HDex6gGGvkdUNvvaH/ML/5+i3S1iD7DYJf5TScC3s4Q85UD8jUA3M9N/HeFkT/qN/wYnX58cLG2ex4i9eTHkyWKPVfS6p6DVecjw5IngTEzQ1Rp1Dwt4pgp7iCEukIkSdNfAEOGrWxD68dlVazg6HiMWqzZa/xki+w/J6wPetkuenAaHwF5Trj1DiBtE842sn4lS55ed/8Ute1TZ/14qR3QGtfsZPXm/40vqUx9mQ78gTrNb32T245Hh+SqksuRBfE0YyLbXCsSWW0pqf/OSKLBI5O5AR0ndZVyF58Izx2Zix3adXfEruxgwH/4Rm/IrROwl/tT7l+EXC8Ac5M+m7HBX/B0vtmFGy42X0AGlwjngusu13UIwUMy7Zzo/JvAvuJkPGVpP73TVIFfGuifzoCPfNis9/8T9xHyfkWxVT82l3fNabIwRB4NS55qLfR7zd0sxKZrHGSS5z/r7P3qWIdGgQzmQ2akV5Mf+3BoH/EPjP67r++3/L3THwv9R1/fT/zkezOar/vX/8jzgQfs39awNh8A5FEeNcdbj7xwOkXcRx+obK00idHZvlIb9vqYw/OGRzcMzirmJ1+xmraIuU1rwrtOm2p7wp7tnsMqRXhwRhD6n7imkG0zJFXhTsNgnD76nUP6+Rjg95qgeI77VQQonvxdeo8pgv+6dcZ6/oXb2P2vmS8eM+0nXGeesGY/MBrQcmXysu6rSBnqakSsJQUOg3ZK5aAlce5MGajlvTFASaDmxDhSTNUNsuXRE69TtI2yXz8QVq8Fv0lUsMM8TY/5Deeo6Xejz/TGTbv+f30o+4/Jce5aQg/mHC73Z+nzr/BmEwIRdC7KVJ5R7hW+d8upliOBGJOOSb7TesXlxy3P+Qh5Pf5Tv5S2pF41w8YNItmbQrku0zmtqQr77OCayE+YWA48CD7x4jdhKEpcGPxiK/kL9L82BFfWmQ2VeI7zZ4sr7mzbMhndTiy+fnHH9oY71OeNWMOPkgQzQ/QY4MXr85J+gW6H7CZNjEzoaIyZL01MDMHnCfLyiiC8bS+4S85aqR0L7O2SYtHHnF2NrjQliw1o/ZW/yaT48yRr/W2ZkWGC+5+h8+J++O2N0+5+/93ifcfrXjOTOuli6nUk5ktHnnu08oCw/B0KnmCat5QDVbUgcq1kjhfB0gX3u0Rh1i9QrZbJB4TbwkQLc8jiyRrqxSaRpHRyfc/uyO6pM9uqsu8sRiqCbE4ima+YqOKzNLbepJwfT2hPT0LxkVLeLqtwnCDbVUMPvzz1h0FB4/jKiubeJul0mjh6qd8T8+/4Kbv/7fUXODZT1HKyr8fExXvSZvBLTDPa5KjZPvTXlZv8v4rUvRd6jnK9JGjqWGbFsdjk2ZL//89b+1msCfAP/93xD++G+sIPtj4Ov/Rw9KzZG35mfmIQd/kDH4lwFfoyCcNAhWb3iUPeBOOCAmwEsnDIYVi42AJCj4ioEqBVgiNMpblEGX4cOS5K2GnDRxsPGEFVvNw/xOk6GroJsK5Rc7xrrL3jOT3fgK8QcCJ5rOe/EZr9SvuK5aWGXC70h3HGkHKL/vkfy6Ity4WEpOncg0Rhu+noWkO49n96/pRhaHHzh4HYkss2hvu2jh14i318idBndKid46o2kX9O9CRrJD70JgpYoo8xKh7lN5IYF5iJz/iunkF5Qv2oTnArJccfbTgGen53xqvabMPubd6QvcnU/8o9+l97LBouUSFAFK+4asSBBiON8dMF/fc7t4QygXGD2XA23KndzkPg/ouWvU0xPO3RJh/A9ZijeMnl4jyWcYesnMiTjdlXxjdzl+NObZQqA2A1LtjI72P/M2mtB5+z4/uf0ebT3h9c1zJo8N4tShdVjweHzMxeaKfp2grhQ65ggrWLPNxriXW27iO8LmhL0y53HP4/RGYLaV8fZeoLgSh3OdcPiMZfLbrL9uUykzTnSBtPmMyhXxd99DEf8p+vofEB7EdNxzLh69Qpyc8M0i4ZeyQPPpmMNftXglv+WgbpCtNNB1JEFGcybEV+eIdcaoO6TZaTLPfeaNGimq8EQRrXJgZTIe69hVSdc6wjqwKJ4lBEnJ8NEZ2tmAb9Kf0d38p7z3/k+42n/JZvoHXPzqx+ifbPAverRaX2G+PuYXYoH19KdY3hjrfklxOsbqvGUR77Fp+EyqOVcvffJHOXTnlKbPZf+KSd5l7/6QN8pblh7Ynk00Tjlxtrz6UmDS/pLpAD6M9yjmR7yR39J0c5SLim2m/Eb5/X+KBP7NwpFr4KSua/ff2P474CP+dTrwFvjP/gYU/tZjtwf1n/yTP0D5xe/z64N7hP4N/dtjThbnlD/8mDPpBT95btD/nk5namLa0GrskQ8bqIIK85RIuUKvagZyl/vwnOVQZ+JHeF94zOsxdXSLV1b05Yrs4Ao16XB5lfEgEnlhdviH3YcMTr7iblbx02yNln5E6/EC+ZWDM9ywySC/TanNPUrtUz5bSRj373OixFwPE6SeStM6wLZNKqXC2AjcX04p6jWBDuKNyJ7WwEOkDt6QdCsePv4ucX2Nve7yItI4a65pSg+4v/uSm298fGKOx7AwPuLsI52RJNB/pvLsScn0vGQoHdPrKvzgR8fUixV3WMTNkirP+CAO+Os3z/l8GZJuLpl9do7S8tHeGdI0BJSLI75zdszgvb/HO09/i93N/0pJQdO/otj3eZ0d8ntv93ip/TV3rQGtoIO294iv0pB37JDq1Of47THC3obmtUb6ochofsTXwjdUU4HoiyuW1h6M20zGC7SlRayc05AMkpXNenpPT1YQn3QR+g3U2RgZl0EuspYE/McLGv4VUjlEPFBZfTPBe/aa+4fv4zz7BVq7h/SRiC2WfHGX4bkZr29nmMqPSQId5y7CKE5JhyvCZc64vuVfpDvUrcT7Dxx60sd0yoS4p7D91Q2imHN32sOLZHYvv2QVw/FBjwfNMwxXwCtn0LHRXl4ymjQR3ma0PxE5iZ+gBAa/eveOtlfxavuI9ocScl3hMiPYOdRnNX9U5fz57GOanNMR16x0jdCEZKFi33ncz3z0B2eYJ2eEP9+QZGtk2+Hrza/57PqXNPOUzDcpQoOiniN0YpRAIu8Nscs5YdDDyUXqaobypEXzucf5sOapDVJcwvXv8PPqX/3/HwnUdR0C3f+L7T/+f+tHrF3yq4KN+yv2mwHSukVtoIoyAAAgAElEQVTd3bDYsxndFigNi0fvSXzwqmL2wCCrfMRgTT+NiPaHpKcr2jOd6n5JILk0ggbGdI1QxbQbBteDiO/7v4W7lTkW57xyThm2S47tl7y+CXhkQ6P1JYp/xt1ZyP4vLIbGX/Czl3+HcrdkcD6lUkeESogb/lNa9ie8M0hoOjP6o0OyzRH+pqDSUmbhDitPccwWp+9bXHgyfrnEkmf8fPqMgTHAfO8J40ogVkqEaR/lIOedzYjOfsjypkW1FQgH9+zn30Nqp/zxoOY+7LMdnLL76IpjR6SrW3x+9yum2h3y6zPOGu8gS2viFw4N5Za3rQfopYBylbMLbWaCjXwRYVQxYuuIdxyVbqdD40gnbH9Gd77Hi6nPq11FL/orgn6PpFdzSQcti7ipX/NBJPOfaPsk7df8JPh3EeU5tQ/LscDYqwjHz9GLLkv5BdXbE6Q4Zj+9I3tTElcFaWWwCE2Cm1va1FyaJZNkgRJ6FG5AW9kj2tuRhznDrImzO2Y3ClFim05HxTc7TM+/4fGDJwjyhll2yP3qC47bp/z1+lMOL02WHQP1izUL+yFPPuygXlWIcsHGjHn4souXxki3I4aTGkkpWSQhK3ON2TRp5TnHpsaFaeBtF5QvBNqPtlS+jG6mSGKbxhOdjSew9/0Nhl7zxdjFuH8f3XYQkwmY97hFj6O1SN0/wLc8nP2Sy1mbD7M1N6HBZ2HCk/ERnWrJrmpQ6Mcc/iCjIazJ1VteG+fsyTJt8ZZofo22FbE1hXXhk0oS9ahFURbEjsXwfoHX61HUOUXfZdc4YnKRUT/ecaB+xH2VYcsJ2Le/aRXht+PH4J/9l//Vn/6x9SHb9h5PnTmKXqEsPVqSRiOb8VWe4d1K6DdNzncVVbKlEmOyvSPa75+Q7ESkyiJCYms3yFsaei9DH4qIbR0z6CI+tBn275DGCp3aItR6iGcOrbcDvjE7nAUD7k2btDBp7iIuX8p8fCiwWeVInTav9Qi5OWQhFVwnJketPmINQa9BhYwTyei+x1GrwfcevAtql/mFhHNTc9x/QJjojNYj2vUDFHnNvTuhLcS0ZbhcqoyGXS7KiOvLL7A8gbublKcPZfzTipn7AXao0uks6ExCrpUJ79Upqi1w6z1Fur3F9BoIpU7HXhCWJv39Dpsw4l6Zk39gUSZbpCpj0htx8OQB7bNjBv09+kqLxducrOXRYMUiWhB6Aotqn4etZ6jiI94ZG4gK+Ks5bvMERzinK7V5uRHoniYY8w6HuUjVcMi3exhvJaadOXvEYMUs8heknoMazLnP5ripRxDcEJcVq15BmqWojki3yuggUtQKu/6Sqd3Cl002covR2iUfP8UOpwhOykDqIG/+kqnwgHXmcLza4ar31LM5/maK67gcN2R+ab4mm8Ijrcmq00IRVzSaHRTXYXGtkyxvyTMNVXagKFjeGdxdZeTVin7T4OR4wt7DLnLVJLuJEdpdnI96dHZnhJ0R42SPnaaRSG0K1cC8j1Btk9tKJhVTpnnJb0k7vM0YW55RdUeEhU8drBgfHVBVIWbpIiYxd4GJpHeJFUCv+IvzDV+9fYuQx8RJTlwqaJ0AKSwoPY26iJENGWEpk7c9SmlCdTVH0vuoqc5lJyaI5gzTLmEt4gbf4nkC/8Wf/dmfPil/m6Az5bj9lDD9MfI05jY/xlTvkUMV5e6W0cMMvQr54egUXR6wkSta5j7NoU23HeJsPMRqQ0cK2DNMDEFlu4ppt6G3yIgWFTMD5uYJh7cbwqDEezSjl6ZIez3gLc1rCb9YYT+yOD7c53r1U4T6mMOOwdJuceCLtLSchjnG0EcoS5lajOhUGWFDw3JTJDWnLjW6sUOmpWTTNXpxhWNtqNMMO/VJ2xfMApHvWAp7VoVZVmSqzXh6ya6UeL+7Zi7JfKJBUgm8PXmNLLzHNkwZFBEteUjLPGffN4gTMJe3bLOXOO8cIBk2mtpgYBvoKtByOOic0Lf7nCknNJpjmqbIx/sm8Z2I3FhTeJfchnBjFjzUM+wLj1dVhSFmlG8cBo1jolnA6GyPm9hAlZ5wXLmkWoDTfMulP0CXJrTWn2HXa+rQ4dPpjynPIbhSeO1OefHmlxhX15jIlAOZQVLQ3Qqk4QbXyEgMgcTUkXpbYlmj4VcI632Cz3+FNO+gq58jtUzcg5TsOuLzZxuESOZ9Y8VOviVZKGT1Dd3mhKT3BOwc8ZeXHIZrOGni5R6N7oQDfw2KjC4LvLp6yzzNCEUbX9IYH+RUyow6N+k2HqG2J4TNCTdqm7BVkE6OEZ9Bu9snbveYagN8SWZ7+5RWFXNTnuMbBlXURNtm6O2Q3WIfb7Zg2zvgxWKFGy5ZBQXjkxI3FnjzWkbQQzxlwcrMyYOKT299Xv36M5TERSRFSUuqgUOZqmhuQKkmiEmPOs5R2mt6/hFhs0OxEeknV6wVyAo4CUGJTQprznYbf3sh8N/+N//1nw7+5O9wmD3kpfQZ+lRj8K5Nc2zi2xlpkXFwpmK0G/jtEZ1hF2+4o9+SedBqIgxLqqBL/KqBKMbYtkfcMKhSBVF9QBl1iDOHN4cKj2WdrveW4bsOyVplMfOwOxmzmzGd8hF3j2Z0+x3UuOR/u3iB4R+jt78ij3OiZyHFyQ3BtsBwcmTfIyorug9aKN0a4y7EE1LmUYibnWOGa+KpQF08Y3pwjuJOuE13JNaCQXaHPStxUx2EG9xWTHh3x2KWsveewG1fo5ZqrNKiDGz8quKTA5utlPBBGXDjlpS99xmVAq9GKwx87E6NJFSctjOsTRNPdFA2FuJK4rZ6TrDK6GoNju0CPb8nNCTigwvq6xQttSlOI/LlHZUWMm7luM5jtlJB3N4i3M/g8bvs6y+YnJm4mylnyZxV+wG2sEee3yBYOfNwykLdo2llBOmG68ThZ+4lVSkgDUUm7RJaTe7yhLIrEx432fhtnAxSZ04RrUi2NVnZoFtMWL7ZEnQzOr0piyrgQIuJkgwaLR72B0Rjh8zQiL6+ww0j3JuSB3rGqiOgrW6o/Q5rWSI9PKQVNxD0Nbbs4as1WeQz0xVqu8mDcc74KCatHe6vVbKypNOZcdBQceqMIovo5AZV7HMwKviqfkkzshmOT9gJKQx+RSFfs7EGWO2EkeuSdW2mp30iwWN8q+KufEadDn48RVnu4Tkq4lcZ8iTGbzTpVAMORwLPtgtefPUN0mpHuwyp6wIFgaJIMNo1dmmRPcpBq1DzB9RtDzHzqEuPPHRJpYx+nCMJPabSHdW2INvrEs7X394uQlGw+aGqMjuKOd28T9nysFoSw12IlApY733CeL2izCWcngXehkHHIu028cuKzcsMp1ojdATkyCYqh/96xJfcQ/ZD9KQm3lew32yQBh8zkU2eexJKO0BSMwpBxrZ+jCS9w+82I/78tkFeJhyqHcI64+LgjAe/bGMdXtIePCbyl0zUkuZdzZ2dsAo97OUCxY2o1jsWSYZt79js29T2GGFbcfC6SZj5PKy35AuFiXWIN/CBN5y3jxlpe0itK96sI47umjRVlSj6nK97/wGufMH7ksGNktO9gBenIz40EkLlEn9c8J3NGT9rCdjlimY04iRR0bavkH5XJWsr8FxHDM9w9ytCe0OWlAjBIa4WkxUae4MSTZzTfekjfqWgjBUu8pcgt9j/qIFeVOjynMFxm/JiD+8U4szlefuQgBwxecN7Grg8hOWCG/sG19sS3TdQ9Slt08NcltwFCUPVxNE8uopCw9Ax4wyriCAQaKQyLVnGLRcMZipLqUZTXcL7BMVpM58HPBtusD0Z7WCAn8xpOgk/f9mkKi023jn3YYj+uAnzgqEskBk+L1WB9PwKtwHGRuOoa+KvHcpGhKb41KEBZRvZk6niGFhgGj5BVSOnOlmWIDQbLB2Rs+MBRSQi3Jv4xj2t+SGuCuq+xPRaYU+WaLa7VJpKUecocwEp7VM8maI4CnKg4cgTxqVE5I3ZDdf0RyAmCzTHZFk3MdR7WiuPVSsgq0pKTMLCobIKssAjiwPKL2Qyq0As5ii+TBFEoHYx5EtiTGZahBi+xY40MlEjiH+z1L8VEMjLgjfzl9y+UvnR4WPqQRtXuKVoSOzbOav1kuugy+F3ClxRwghk4o3MQJBZ1D6yriN0jugcXhOrJXHaRswFdk7JXr6jqfnI4QH9Y4kkvCW3CkZimyR8ybjfZCDYzDhklA346nbC4+Uv+HmecDIvuQy3PDjvYPae8fptyTIWqc0lZnpIcFjTT3Tq9ZbpMqe4/JTtdklV1yhyg3XRI12siOMIvSxw2gb5sMVWsbG0goHwgEoMkbYG0dsVkirwSJhiyB3+eS6zNzwlcmPU9ZS3jQnvN12SvYL+/R2r0z9CU65QlwW7L8558rRPvm3QfFniZj5BQyb2mpTc0G8oSHFMQzExfJulGdFo+sRfe9DusqkroniHdm/Qe0fi03zLcNmnOhUp9gIaz2OO1ANevLWRGjc8euWh2QeUreecJjLzVGDmfpfgcMmd4vNurfILy0Fazditn3GaFQh7A/Y8Eyt2qT0VoZ0Tuwq67iCKG2I7JqxtFjcu+/GEuRizsJ4T7voUXs3LyZRB6wNM4QU3RUD87JLZlYfwRMTZazLUe7wKJOqDAXYrwZ/dkVttnonfUKYt2v02u2TAofOc1b3GY3FEMAjJ7xc0NZWO1OVBz+TNaoegbTgUY5riiPXVlk3HQGroTNx9/DIhuIW2bhEGG1ZGxHgrITV7CPkdczPFm6qcEtPxh+hyyVz+KemsJksPUXMXP5gzao4puissX0L3NPK0hZbk+P6IeNkmGKg0lAJvrSHeK9iiR1oX2LlAJnVQSh3kOYIwx04tFpKEEN0i6xWCVuNEIrXeQtETMv0QQ3yG/xv0962YLFTUObviAcc/GvCVcUdneEdgtdDbHbbLMS1hyMGghexN2ItHqPtdkk5JFEU01IKe5aG4V6h3IsJ9RnJzQ7jYYk13WOWIRv0DxkMBafSY5uOCpjCir3q0lBPMlY0UdVF3NkspwfD/im8sm5a8YLefs61rlqGM3Dziu1aHDwQfwzgkW4V4bkZZxeh2l4EjEaomvtEjNiyWus99cc3O9MgFgWtFZCfmVLVGXSR4O52oVrlQVUJFJpnm1LpOoHyXaynhSd5BuLZJ5JeMa4eHm5LF9YSheEKy7dJwVxSywSLbY7kHspmRSj7qQ5egvSI7WtMJfFh2UIKHWKqJWtkUuUCRvGUZeBRSg3FzQ28YodHCU2SuYou9uiJu5ziqTp1oKPYei6c2PTUmvqsQTRsx3fCIA8qqj7Z4ghz8DKXeIhz2iFsTzsqczYMFmahQDFWQPEQ7xRsLRGaJauTEzZx5uSGcL/CnCt5FhqxWrPxr5lVGWju04he80G555XS57yzJNk3MNMJyr/HmHq//mUjhr/HrKXVnQ+HopMGafUtjeq8y2DvAae1RWEMej2LC0uayklg+DEmVJU4lIpgVHSHDcnNEOaJpZ3QVgVYSodg9rEwlvLhA7L+gMEUEq6JZVQjmgNlkS8+es1eGmKpLczBjfCaROVviXsbgTOaT5gFN06EjlmwWU06sDyglkfX2kgepj+Mq1HcO63mNVCwxlAhhpxBeNVAFDa2bUZYiuisjOjbNZptar1B8kFKDrVcj6hZ6IyfRapQ8ZKu20A4V7tc6sfcpYf4tX0Om5DLN0mLctmnEK55FFY1YZX3icNyqEZoielSxljzyxMNKZNqRgC9sMeSaLE0Qlym7TGKWL8jnKr2VRLHfYT2IcbVb9KVBqz3HLwe4lsd+1uOr5jWad0OfgGwlMn1g47kFQvCGKKwYNRf8/odHhHUPKVjj7WtUYQmlh92fINUyU2+FoGiItktj1GMZQCMNiXOVxSInDRPackVZ+qShg+wUjES4qlZo6hCtTmE9Qz8SMX2FwcEbIi9Fi2JCS8D2FfyyolI8HkxvWOVwqGkUxppqtqTnHFHSw98J9Fs+eV6iIBItNHYNGzeuKNQa09hDK3b4Dpiixd7KZsWCm8pmWiv0u2sqAxrZDikaMJcSzndbhs2KTWsP78WSviUiOibLywJlrw2vBfwhCLJBlHcQuOXs4pS0d8tSa1GmIxrpJZEvIngiuiLRytoIugO9iLQo0ZQh6lDgyptjvpKZNXPESGSgN8iNFjemwtBIKLhl6S7R501qV2KadNEnt+z7FefnC1pbjRtL5XgV0cz65EOF1Czpdvuo5pBBXyJeewRrmd02oTjx0AsFOdlnZCVQaCxnJlZm0FuVaA0TdTwgsWXEZUzR0Iio6IQOyn5M+OYOyznCcW/JlTbLuCBXRJR0yM3yFkHZR0yXaG6LetEnEZfMgkuOpSfYpx6vf3zLmiX+qUkQyug9lXB3R7lSSV5FODHc00Dd2cjFnLrMiCqRJE6QxDl0coqNQKVnmHWBoFt4lka9TIgNCTtq48wWLCUTuVLRb1Ki36C/bwUEailmVlxS/qsFjTMHXXhMo57QzEOcky5imGEMUoSowDz4CKMxRSprpGKCuJTYiSvafopZmvRDiSA+R9TGlHGGd51xpDYIel2cKqH0lpiHPgsxIXvt0hFl8voDpIdz+sVbxB7MdyZ7u4SgP2HyYEnvjc6fu1OOy5SqqCnTCM9Qscotg1FIlSXEnshkMqTtdPGudNb5Drv2sRsNNqpJc6OgCTp5EVI4DR5LJvpORn6nRyPLOF+eswlt6r0tZTEmis950XjIH2lnlMEFyzJH/uKe7OMSXxnwwTJENgTuwgAUHTm8IKz7qGGKaDRQ8hApc0jTgNVYYkIDU+6SqR7zbUVt7GiWGf6dhGA2mMVLev0EOWjha9AxdRYZmLcK0sOfk13ts/5BTluUqEYyZXrMVdOkoV+RCG+obI395Jb5HKaxia/E7Bkxq9GEQilI9AXCMuKiWdMbi2AdUNo2WiSx27hYiYWbJEiSS7KBSK5QpQ3V7Rpjr0Ep3VCtXRJKxp80+WefKzxVEkynwLlo8NX0OY0PO4y0K+K1hNV5TOzs8OcRivcKrf0xmXOGenhNvrwl3upkioVub7GsBqkdcbN0Ob++QNglNCePcPpdbvUVdajRzVX8UKSx8en2W9iHfaQnO9zlU6pvriGraUQCz2uJcagza23YEyNevxA5IyXftlBNk5VqEyUuz8QlR67A9i7nNluiZSPspkG3I/P6NuQ6zRAVn7jeQhojCBq5oGDFCVmzRmiCtasJPAFP1mlkIXrhkEQFYqWROwuCVY1s9hk3N+wKlShL/lb9fSsgEEUV1u4VXtWmqwzQV1vivoURRoRrkZv2mv3WIUalUVYzgnKHjo4WLxAbAqXToSo1KkmAREPcTpiRQpniliGdoYleLKlvmrj7EvPrEsWskeoYTSy4V1fMdJ3uZocT7JM5LkIl0R+02F0INNwaTc6oU5vD4AX28IC1KJLbM7ozi6ksUvdUDsiIVZ3X9QmH23uccslSlDGrHJM2dWkgt2vyRhcGPbzzggOhhWffUX8T4w1MEt+iNO9YVRVngY8QZNyVJmknQdvmDN+2iU8WbAWRPU4QhR2mKjItVeysQWnWpImEUXaQrRLxOIcip5BNAj2lsWvSlQ6pWgLa3MNSdkiSwDRWyNYZDGZMQ539OKf/8BPOqoxtdob8iYh0D01nxTQ8YH/YQm9O0RoJry87mPYYTQhY9AVCc00/gnmqYw+HXDkRwiKkU2iYDZulWeKYGbKXwSZGl+bUXYkFDopoYTRboEA7eUOrlHnlhkjVgJ1SsfJBXwXsdXa8SBQ+SVKCKKEyBcaKzlRekW0z0hsX0w7ZzhpkhkwaeEhLl3h6T70tKeYl7e4tgRlSZF0U3yIrpszKGXXHRrdWqK6BmOhkrkGsX5Glj5i0NOJewl6rwlVsdL/ieZChZBHZ9AZ3ZOPoNQ99GGQ52/GE+e09j4oHXAxvKeKCt88kbvOCtnJAmCjknkq7ndCwHerIpNtZ0GoUxKKM7qdkhUiigUCBa9boVkqeSUiVjKhqUNakaoCpGaSRjiHUVG4NpogheFwlMoWuQ/QthoCkqWStR3S1krp+hKJckGZTkEMqr8RWDVxZBMdD3G4RFz1umh00Q0CvAyayyL0tMa8WFGWEqUIelJjFGtPaEaUhMTp50iDYFdxlJpP9ECNJEFURVX5GNzYpLBkhq3AO/pAJDdLBlPHFG0JtwZnURNil5FKLsnVIqwzwVhv8UiUsM7LCQA1zZNGl1TEQJIO66iG4It0ip6gqkqZBJrcYaDZ+0KRh3eFtZLBlguY+U2nD2suw5htCq88nuYXc+wqxOGU/zKgP70lXZ3huTN9VUB+8hSWoj0YcP9aYvy6o2msSOUNPdxiFhF04aDE4XYFEUrHrkFoDNdNodPsUQkheptDLWPs6J0pFSp9roUHr7gXbd4YEU4fOqoV3JLNMJaqWTJlfE4UKS3uOjYSfXfPlroHjp0TagEYgkIUukaKiZWvaWou4aXNgtljnJbrXQAtB7NiEVcg8WhPMXNTSwjmQMRs3FFuRedVBiu8oo4BpcUejnvA8lHlsdMitOf7be1a1TXdyzJnusDFi5smAOg1wGiJGvCKafJdmJZHXC5arCluWCbwVZ2MTNbDIGjWSJZIHNZknow8tBLqkTZkjDMJhgyKtMMcilrumECxu5Jw0FrElnW76AH85RWy1cOQOhXJFU3sXd/kK7fmWSKgJvicgBgHzMGB3VXDUUBAiAa7u6Q47tByHXN8iBBPytEutBYhphJEJVMgIWYlWl1DUtNSSXanjWypSmqDoIIcCuQyCoJIJBUpZsHVkqm1FUUvYOvymfv5vBQRkRaY+fkzXfclmVeKfgW7MGPh9WBcM1BbhuqCh5mR6k8JroJ0VVH4DMQJt61NXBXle0vJydFNFbw7wdtfIpcRWAitvIZUx4tol79p42xWDcYr3+hGF85w0f0Fgf4zQ1GjrHUJfhwqa7/VJXk2ZxAlLO8VXJmSljZPc8laUUdySQrjGFJuEcgfJCLCLhKI0WSsOHSPGiTW8tojQ2aE2LRptEW1Tkdg7Eiqk+4BioiAqGcMY7uOSPS/gbbFmMCrRuSVzf494/wUjMeBqXfCy9slaW3J1iDoPkYdt1KKFs9mws25plAaFLZO7Ab2yReEZNDOZIknwYg/bFZlZMtvNFnsTU+kPWM88vmhqFImBlHcIlZBk26EyQjZ3KfXpAbk9xtrkXH9xj/QUrNUBq2SJZHYwojUrcx/NGyH2Ukp+zeRQZnDVZ2VWCEZOI88QapWo0km0jKLcEZSQbkykuiTqZmzWS+p0TlmarLW3KHcNbsKArVbx0R9MkL33UHZL7FufKOvRWiRITYENITfFATsjoV1Z6GlG2UsJrQ3D0mKxsJC1CL0b0jNDynaXUmmRCgJBmXJblKiazLAj0zKHtGMNdZJAJ2K7NBAjmRdZiFJB04tRVwGrj0Z0uzaqqZM5j9gqAuXzFtf9NYOnH2M/f4Zw32JqXtBya6oIOuU97Ovky4REarF3qiK0MoIgxW5uKcMIf55jCwq5YhNXMYbiI+RAUVLGIoKcI0s5ql6gVDqBIKFEPlKtowcuQtdB3GhIYoaQ1ii/IRWAb8nrgBBnbJ99wcu3O742X/KFqxA9MwgXKnVXIx0WBKbPXGuSGiWJsaCuA5RiTvkoZyeJGL6E7lukRZPEtHCDCilu0vLGlLVEYRf49gpxJZPpPtJyjFA0eGVXiE2bOj1AzFWUgy7t9TXXm59S7mquGjmJesJlNWJ5tI/VOqC1KklMF6cQibOIXFnxZr0l8RK0QGFX6bytBLJMoxYslo0IsR8hmxZSY8hQUFDsmk1bRVpW5FudKFvRnreRpIxxllPLXa5jD0lSWKRN1PqcbKeT+C8ZLC3GhYGXqMz8jKtpEz41sfQZs/uY6wuZm/WEsixAMLizVGplRNFQWDY3rKotbpIR/Coguohwpxn3uxUIW263KlGYYkY5aWZxEYmEzQr3yZZ1zyPtRMSrO8Jem8pr4Vw/oamJBL6DnbXZHeTYkxVxR0FrPkBXu9SKjl506FkpYrtA14Z05THNRKPa1oSRTiVAv9egb1sI8RXTywW7zZI8rtgmAa6S8PT477LfPGTYN6n3beT2RxiNE5TGkDdfzvjVZ2vc24z9tUE/8ahXMbnbxyoSamVJriTs8ojJaYPhw2My38CVI2RBJF1VFBUUjoja0JHbS1JfJLzy2cQRnhoQpguUvYx+QyDyc1ZTA3sbou3HqA9TlEqhm+aU5S2ln8Cqw/BwgOConCQlQm1hCW1sdcsinlAPMgSzICwTZmaOmPoopU+tRqhyjiIKIJUgCyDoxKpCJYsElU2ugRTV1IJIGZeYdYWY5gzzGlWWiIYlnpJQZwqyVeONyt+ov29HJCBIjNIHGKcrhOMx7q9rtpbAyk/pajLagUjgOlRtmbIw0cMCd54jiFvUfIJUVYTuFknNMMwKOTaZ5iFl18K5rsm3czbdkDpp885pi6UcEEk2c3+DHG8Q7BqzfoItiOw6KukqIN/ZuPOSjRtxaLYIO7/DQfpT6rSHbgQEiklbKYlaFiUW0brEFzfoZx2SWEZVazpaTjdtcFt7JOGakXSIl4ObVVBVSJXFLgiYPzhF8BrcKVNkuSCpTIKWQVGLLPwcf+VzpcyIqwcYyYobv4URFux8nSsrQxZWXAuAmrD21pRGi+dpTL5qMeg3iHWJjuSSyTvKTKJVOKStgPAmJlMc9EkDee7R0jX0yCXRKkrpDqc/Qax0BCskdmSsXcxMVBnIIUUPVLfBxniNTc2B3qEV5ryHQEuIeRM+Z6zrCGpN3bQQ8z1sKaduhPQ7Mgkh0Ry0ookhBciGjLTSuQ49kqZFVnW4cedQJrSGBh8ID3n04CPk6msEuYGriLwn7Fi3PL68rnkr3yGeO7R3G6LhASfNPr5Z8GQ8QU+ukLMGeTxl1JY4bljs2ydcLe/oGRqiZqIYMtfLgm20pbEfPGkAACAASURBVMwf0KxGuAOZqPTJMoV2skfDKRA1nbG0xd0U2J0ustig8l8T6RXrSCWJdvhyyMHIJNxd0jvIyI9E4rzNLndJVAGSMaqv0WtA1qwofQ9x0UKS59wUIzZbg7LKiFQJ9JhMSBATDR0BoRSQk5JcKinUGqXSiSsLXduRlFBUMomgI6xBx2PVaXPSrLnKHeBbXBNAlRl190nfdWnGKjtrwnr8Ndlsn00MR3cFouKyJ35IZixADtHSc/IsQLibIukVanuHqFlkEYjLiLYArlIR6RIWMsVKgXDBbtShEWlIaolsTth7E9NsVuRtg5Vssb0RyIJrtMhklbQpqoA0UVDaXXbqEapkIx8XJLdjVEVlJOkEkcBjNSFpe6wEDz1XqIsG0cqjJd8zUmpUT2UjbRA3FYuehTtXiDcJquKRqjtIXS7VikfKO9wrLzHtAtmHl/4h++2UsHQoVZXpqIOYZXxlh+ytcwRR4HP/c/r9FrZ7ymA1Q+ylGBuJzHEJxac0KoNKquh4IabkU8k5pWIRHrVY3QVYqUSNwUIMaFxHlP2CtIrYV48xFYs03HLEkGzVQtGb9O0N6aiBbago2j1VecogC2inFfHWwy4kWsUKKRMxjQpTddhpNuPOmKy3Qgorlq6IofdxWgqZEzCfXbIpZ/TzBZfbABQRwUp58bbku//OPoNiRGA5bGSNSXBPJ92j1bzHz2UC4UvcGPqmz/ldwVt7S0/7iEGv/X8y92Yxt23Xnddv9e3u26/vT3/Puce+9nUXKxUnkFSjIEQF8QQRj/BOPSDKEQGKkuoBBELihSIvNFJJUCgpOUkVsePY8bVvf0//9d/uvt3vvfqeB99CBmIqKJTkIS3NucYamm9jrDHnGPP/p7nnUU8kpi9rbAkuC8unSFJcVthtlaRQUQwNQ45RvQTdy0nkhESSUcWYlX5Eaylh+SVKTZ1ZJLFwY6TRLZ1qjitlvLk22dxymKYRJXQK22LALbU7JmrfIdk5wr+oU5pPeX/2CVv3jtifCQipReMo5Xwpo68zzKKFkwcEfoYe2giSiirFRKpMEkVYKOiSgKaIuJKOFCus4wRijdKGRD4Umek1RG+E4ftkUgU9XRA4AWmg/Vz3+4XYDkgy6HYf3biH7jskGy5pVGEed5g2m/SlHYqGzkqM8QyP4CSk1hSoiTKSL+MtJPwE+vMIbxXiaQIGCW18PG2G/dxDfpkhiS7uck5DFFGLGrtixnyrgp8v6McDGI0x3JRatsfJRkJecjAOmyxLu9ymK8raDjsnGXbVwrBtkrzOYJJzOr6l8CL824JkFDELA7LeGu82Y1CUiMdlvLDEOJBZhHNCekySBT27RlDT4CbEDYbs9acUg4A9uQ3XOZXSHLNwQTWpqyKOaOMudJpzDyW8ZGasObKuCV2VIl4wc89ZpzrzvIQgVaBZJbIigppMWC7IRINEFYjK4BspptlAtTMid0XLN1ETCa9dxhBFDE2jMZ6ipQu2OKWiyjR8nd11ifZuQbfr0DhQaBBQzcaIXQexmhB2ZKYHKmWzjGxWCIUOqTnH1mesrQo5DRJbwVQVdhoapS0By8iR5QI/TckcmEwyvFufRaZRq3QpK138E4VORcdwNri2MpLpKxIjxgggLNbsLY7oWDaiGpIOpyzCCbvSinJxjuEnyLWMsK4jhS6R3GCliJTNDRzNJW4n9O0Vw3VObsgkcvJT5p/Mo8gBSSXaE5kVMk7koDk5g0jCaWeM3TXW/jFK+y5lQcBo7iM2dnl202PT3Ga08whnBVapjGA63KZLsAqcypqy4DOY5XjLANdJcdd1ktDHc6aESUotctiIfCqSykFVpWsnUFMpGgIbUsFmFGMnAVU7IFmX6RoWtpARdxLkkkoo5ehyhjDXEIuf1yXwl8wEPgcS/ZvA+J9DhQmCUAf+J2Cfn4KH/FZRFAvhp3DD/wXw1wEf+HeKovjg/239BJFZMcV4FnCxslC3/hj7dpOmEFDbPES1JYTKIULFInWqRO6Y21yk0yqIlyHOKkHyDXJFxlEWLMoKeramFlvYLZN4mWIpIr1C5dgymV/byAcX5NMYVhucaxLq7QQ3lonjG7x4G2tjA0Xrc7de58XiORtBjbJoMVVi5o6MmnnI+RJhu4w/rDCTNpgOzpHPPBpVFXEVk5ZLTEMR34pJHRlvnaHIVdzAJ5AkTjrbBMWYxVSjXGphno6Juhk3kUylt8T1Lbr1KcuFgu2IJNGfkYtwlSXY04REWbHMVGq4LC5CpI2cmbJNEQuUujfURBs9uELyQ5YKpEUdR1LAWFCRJux4ObndIc5ldCPlcDuh1bBY5ws6cYnYi7G1G4R1GTmNsDcVgvaclSBQj68wpi6hUUX2x5QbNoEe4SUxe4XFXL0HlRdUWwY3YpdYBmGRU1Fs9MjEUHPybsCiCBFSgU7UICgyfFPFNiKcYoYUwq88fMjW4y6fpiaHL3royg3POyqbco2l8l38l7v45i47VQ/ZbrFpeOyfuzSbawKzA94WU62KMnqNM12S+nV2CwsjlcgjCymzkdUcQTIZIWCKFvdKNkcVk6G2SZQnGHYdPa5wFcdonozbqTBXIp4r22x3QEtD1p8FVKx7ZPdCyt4OX8MgHhR4JRNbTllhMxQdqsI25QsJaSOnL2ZczBKWg1t2gwnG0RPsjoj5eoiSSyhRynwhU1NjTEMkVTOUREFfxRjVmNDJ2Tdl1oLMIFTY1kJW4QwjzXCUEqm3Ii3fwytfQPDz/e8vux34h8B/Bfzez+j+DvBPi6L4e4Ig/J3P3/8D4DeAk8+fd4H/5vPx50qaJvTU55i9I5zCpXWwRdfSqb5V5ajcQhJ9RlEVJQGTJmfCDeI6J8tMphORIDBRFQVTyfGCJf15n83YI4585k0RQZzRLrXIybn++BKiKVlrn8p1gnb/Fn9i0CjdooUx88UaVfC59EO2ww1mI4nhwMf2c3pbGoEe4iUzOpMb8omAtCFitzeQ1SpNr4piagiZzEqbESgh80Cmvl/QrJUwtQaJLLLEZmRV2AmWrFY5paGDLtboG0MUP8B0l5jigsu5STfQuVafcV/ZpeJOiLUIxa+QmQZBXcB2BAp/zaRk0VUO6BgCbntMnnsoyzKz8RYdPUOvNZlkC9bzJZokEOQWipEj7LlI/SlrKaMmBDiXVWo7DxAqYwwtYT3aonBykq1bqoVN3U6YXhRkpgJTFXd7iWrJ9FKbUq8JrR+znHt4+S5KLjObiqhRDUF1UHeb2LaBOiuI3ITVSsRaLVDynChXqdfblFSF+tzFnXtI7opNW6Us5kRJxHuBx9a+hBEZvPFLFKlCKXJoHEf0opCjwuZfsx/hlyNG7iZaEjFzIJvOmPbPmU8Sys071Io+jqQitFPiUQ1jVsb0J3TqOmw9Rrz7DoZxzH4ic5O9YTDN0N0xYtOg3tBQ7CPuHBokixJpbYV40SFSI0bhiPxygnFjIKoiQqGzncTUN1dchp/x48jErC9Zuipv5w4Lc4ep3ycUDLaFKg29xKoXE12nqDWR2yxDKizKckLi++QumDqovkFBFcFc4moldDfgbR1qUUpPjrFcgThrkh+5hDcTUr1EKVv9PEyRv1wQKIrie5+Dhv6s/Cbwy5/P/3vgTz4PAr8J/F7xU9yyPxcEofp/wx38f0juOgyiE5pCjtHe5OE7e7QkCXWqY9KEZEw3i/HmC1yxQMxKGOKCIPJwlyqSGZLmBv1xiNNPUVomcZix9G3cWKbsXHLqGziawl0nZC7UaI1VZu6cjCr3TRvTUViYb9i81Dj/whZ6MSUzU4JSm/KGQh5MSCaw7EyoebfUlIhZ7Q5dbQb+GlPrsah6RBhYYhXTyUlYY5d9XEmjpHiwTplbNTaUNpN1n1JJZVmSSR5ZFLLIhrzmTVjjSWYz9QtUfK5lh92Fy2v1DfeICQWNpTsjbR9RLJasKzqL8QShazFZlLBXDk6kIVaaJKUJ++0uVjmhrC5ZHFi05YJWrBHqAjdBghtWENQcZ+gxXgc4osiDZMKDY4WW4VI0JPzza6qBxKWRsm/ZtPYKerMR4WQXN02ol6ZUnDknWzLnqUzg21TiiLb6NoPlBDY01oWHWGoi+TMKLUAJmyiJhyUquKqGVJfZvw0565psLmNEReITdc0khHSQ0pUtKqJB19wne36K5I24vrnHp+UZ2q3EQbHPc/k5j0snWJ1DSpsBe9W7rIwqq9l3CWKTuAyWFhOau9iCikqVgZniOjaCJfHOk1/GaDfQu1Xc/px0aXBZ8XC1G7J1CXMFclTB9NbUtGOs+YAf+GPuNURGE4VPn7us/+AndM0qFVGl/tsd4qFMUrLYbiWUv19QWaWEb6/xxDo78Zok0zFklbkbYxsLlh/4XC1VhLyCJPgoQkGU6OS5QUBAUouQ0gJlLZAZOmkcU6uF5OOCpRkiUqeRzrlSZLAzcm3FblHBFf7l3CLs/Ixjj4DO5/Mt4OZn7Hqf634+zqCm8rZ0THt/ihd0qHg1ku0C68bhrPce1paOPo2R1BKC4GLNPLKiRqkp8mUjI5lmfP+qz7zawt5RCNxzhlGOfzXEmYtopZjwpYq2tWJWdTioQ5SM8V2NndOInhSxeaQhzVyEjQYPpSNeBiFT6ZYH3pCmUmEAnKgZr70qiVQm3rvk+DJnUW5j5wrjGAZ5TLsckSgFw0SlGqtsjCWiq4Rk28ctRexKHXYlj8NWEyka0c3KSB2b8iziJy9Vai0dPY1IzDJJErLwAtLAxLUCzvwqQbCkkMGdT5GrLuVezCo00UYQic9ZrDLyucr+3QS5+oDN4yqCIKGFJq0s5Koh87wfYQa3mHbB3MkopxlX5oyKcoPghzQbx3w6HrCqvsvGekHYCcjjKqm0oHnjIE8kbss6dXWGuBCxXAgkiSU32B0bU28glhOy4JxWrUOpus3sukVRFkkbHpqQIyevWN9EJNEBmr1NJV0zyieIyhjziw+wf1zjjvCSZfqcqw90tO1tqqWU1y97RLFJJA/ZivYZK31enPp8MRyw8YWUy2zKdjcjmmdYRyptfUT2Nsw+qbPRLKGoLtruPcxxzkd+xlDZQhYkVusb0noZad5kFWc4yi0aIspSQnEUSkHCx6LMejUnU9eUTl0itc9Avk9zd8jlxQsm/eesghLuZE2gn/JbxSaOIBALb9M/PaNT1CnqKgfCEqSvIG/16URXBP05s3qNmWDwRuqDOqDi57gCpGbGREupBwlpKCF6CnEcMZc1ciFCSUSKmUkSayzrISfjiEjQ2Wh7XL9q8VReceHUCQT3X0oQ+D+lKIpCEIT/T4ilP8s7UKpY7FRh5yTho4s3eL0ARdhEf6ah3TFwkj5+q4FsePQna6rqHDsr02YbURozUxsod2tsxEO0V2OGcx8zWhNHOlrDRtUU5NJrRLFJKU4I4wgjEvHYIrIklrUB9CQ2vRpZaUL/csDUuCJz7uE/0AmyKcY6ZmWklMpV5oaG3hVwxZAo6NLaSlAmIoYOqpKTpzl7FQErXxHLAXEs4YcN5DjDUTzWlohRXWLXOuihTrMi4ZSn6GOL98Yql67AKlihWzHeZoH/MibRMiRhzdwp0IomvjRBaGnEPRNfkAjdCrreo9EsSDwZeQLqekBUNhBuWgyFBmlVRk8Terce41soGWtK+yZhsU9zkSKkPhUbrrt96pVD0qWDVytRWbyLF47ZLaqs9D7FbgvlfYP03QXHfpVeo0t3sEcybmDwGmVjxM2qy5YmEcUx8u0FRq3KQqoiCTLxMkKXCswdBR+QvSHL1Qq1vYEmRZQUn/qXJWZ9i/B/b/KdVUy52oPEZLfaRM0cQskji0Ja7Sr9D09R9mv87W2b/+V8RTCOsGQFzRO4nja4/+jLHLsJw1GG+/YDJpmB4ATYSkxXXrNY3nCthQhrCb1I8dUaur3LdOqQTsYkaUbXMnhUVBHXIW/Ga953PkPZ0SmbZ3x2s+TsR28Y1cfIxgh7kWMfSszNBnr1EOXFEjMZI6kKirWBcniN5Y/QrAbD6qeE5U3k7Q2y/hT79jV2npEpApEmU041hCDlNo8IyiJiUMFmiSb6xJmBqfl49jaqMsEZS0zyFMEU2TVW9IKIUyUCeU5b7nCZXPyFvvhXqQ7cCoKw8blDbwDjz/V9YOdn7LY/1/1fpCiK/7YoineKoninWraorF7RfH+FOnI4fjFg4j3nXLygIZ9y64RMfYEfX404W0AQOXhCzq1Q0AtiXq/PkcMYd+Eyt6FitmiWniBst2lv6dhiwlaryva2gnvUxY4WPLcadJ6UiT/W2X/foBM8oFbb5Hzp44jPyPM243xGGFrMGzZxWGOiPuf6dkHa/5Dg8g22IqJnMkVuYBshpl+A77Ap+xzGIrkoElYNhHrOvWzKRnGN0VgwzEMq2Q3T9YQiFtFPNebPNxgYexwYAUFySyOe4QQxxamMK+oUtKiUUx4LbSw5RglE9BsgTbCLEh1rgmjmhOpdxERlLUjkaRnnE53BssQHK5cfv7rCW96y63t0XQtPtxE/mdNWh9QOz3/693Z8Pv0EztJttFjmciKzUCc8tRS0uU62vKI1uI9wv4K80vkgCNnVzultvORUjciWOv00RmxXWXsLJPE18dY58ewC6foNUeSSyz5pIKIlOyjuAmU9Qo1iouwNdcWjNc14OKpRyg9Yd0N09ZwwTplPX5CVJNb7MqpZ4+5RzmHh86u/+oTj7S32jCbbqY6z26ISzugFJeykwdWFiF45Ys+waC18rtWY650Ap27SXwe4gYsdK+CZTOcyTjLl8uYZa+eGcrvF1pfukRQphpgh1XcRHlZYLHUaQpNN/zVZpPDFRx0O419nKyqTVPbYW58Qbt4jfDhB/FrAvCRzty9w54mInjyiMSzQLnw66yr5Wwp1ccFgPGOldEl0g8LSqWUhYRIjiBJlKigrFTla4hc5jl5gFAluqlAwZBI4aFGIX7LZTCOGL9Z0jQynUPAIGec/35H/KpnAPwb+beDvfT7+rz+j//cFQfgf+emB4OpfBDke+AnL8gEvlFPUdcGnnZTL781w9SHT64xJ06bWj4iiCm9rM6T1PkkxZ3T9PpmvsR4NeDXwmWQigZVgKBlZNcLLFarjGpbh0vNWVNhge1pmWQ8RpwmB8prkqEbLs1n4C27lPkK5iu7tU8su0d4KubhZUm3sEHkesdxCEVLaUU5WpMRen8PwjPG0xkC0kQ5qJFMfYeYz1qc4fpvyuYBqzZErOpLdYXO4xfhRgcgTBm9kbio2q+kEgpTjXYFXxibSzCEIZxjlFoa2wLUssvWc1bxNUg+RpQQpSJGmKaIooOxOMLplsveBygDHEIm2ZJaCxt1lmVtrRTKb0EZDbIhcPFbRlRn24oKgXSKPQO58geORyWzDQb+XUx5lfGTmbLnPmVY8bss7rEc/wXZ/GTO8IDk6YLV2ScMZMy/nzMjYP3nF6sM71Koe3dWCZK/KalEhGft4HCDnS1ZBlWbJoGK+YC0o1DpVhOWE0UxAqORsmY9x/Vs+DP43lqOUV29m5FLKlRtS9lt860rmzeZnqNZTZj8OsTcSfCnGWQT8k1mPv3H4lO/VVW7PczxnSuVbXZzpWww1lVKxSb6XUA3KeKtPCByHxYs1iZSw2gBT6HNRqFTPoFzLENsbnE8usD9UYdFEvvAZW6+Q/VP2Ohe4byyq4jXv/trblA40vmIumJV+k2HnJdK8waP0mtOfVJj8K0vElUf6rkZAHTvMuTQ1SrNPkA4f0llFDJ5fkqQjVs6crJShFAKiJNLKPLK4IBUU9LyMKxUYpo+aiuRmjOLlSIMaYjVFjlLkfEpS69IejLCUKkfRFLd8wJkz+qtdJRYE4X/gp4eATUEQesDf/dz5/2dBEP5d4Ar4rc/N/4CflgdP+WmJ8Lf/ReunhcDw5mPcUo/U28eZniPVBfLiAZVZhyTOMdURX2vfZekmrKJrVu4SI3ZZxymnY51gfYMmLkg4wI/LmJmALOUMuabd3EEzdGoLmTjL2B7dJzvuM/dFXAzaaossv+HydActS8ikObPdWx6N76E93WZ5LTGpm7wcO1TU1wjTJYU75m52xHBLp2qrbAga07WDPC84RyNPTChAOM7RUxHdkBFSG39jgfnZLU5FA2OL9VDBPvoCcqmPc77Hl3d6/IF5jmCpKG6djD7y1ZS3DYM38pKNbsHV6RbCSZ1gdoW0D6EfkU0XmCWRpSSA4dOdrLl7v0XY8rl9PSSZ3eLJNi23QmlbQ3abEL1DlEcsFnPiuYRfuaR2OKMdP2E7f44it1C2upT+yZ/y3SOJb3VVbHnBVO8SroecjCNqqUp8WeNrqs9ikLL91hh178v0L+eEvkpV30ZevKIx7/BC6FOtjyjsA4L4CWJ2iitFaNIJu0cxp70Kc2/N+vULzl43EJU5pndIy1yR5WMKIr4TfEj5j+s0H9dI//XnGBddvnlzxT+8V+Kbz95ifZLyrfM6P9o/YbfjYb9vEz/ushfMuXp4RKv+grPzAXFexk9TrIpCL7jmdjml6bk09BJBIuJkOtrlgDTo8UGYY1QikqJLR9hjWb0gOL+gs3xK9/BrxM0MMXHwqyLGyQF7hciPpjkf/2kd4VdCnn7H5D035FsPLOpxlx/94Bm6VYf9LkM3Q57cUJQC5A8W5IpO08k4UV2CFLLQwJBTAk3i3IhRYgnX0ShlAWsK7gkKV/UcTYyYlwWSPGc5nLFZ5NRY82e1gjvpgpISMY/+CkGgKIp/6+d8+tZfYFsA/95fZt1/LmISM7cPiecGljAkG1RZDUzidp/6vsAduUG62mHR+ZC8FPLZP50yz57TVBYk8QTDl1CTJuF2mari4K4K6mGZvGTSbneRMZmbZULVxro6o1/R6MxMssUmT7pdhkWFe+trerXP2Jvucqq8x/bJEyYXFYz+iMNmmTesMb7/Mcvj11R3d0mClFfvnxLpNv6uT3kdcm/Xwzc1Olc5hbvEySNWyxxbUxCsgno157I/ZbGesaLEm1JOaV9DtHqUVldUtftUhAonUpk/L6vcHV3hiTHsNLiYbiOGQ3qDMakzpniZUMtl7JFPz7IpxhVIbqlHOhPNILA2uZjpeM9fcOm9xliJFEaTm52Co1YZ0ZjTsFK6qLwqzfE4o2McML/a5OibVY4/1linnxCfbrP1xXeY6FVu/BbbjTdMhRj9ck3w7l0WscQn4S376YyNTCFMb3H9AlGXqGQl5NUc1DmiHrGBSNkRqJd9ltaUeG1RqciU1mPcfodKb0KWtFhGXZR5j5Ex4XLzU4bKPk+9PYSDEsr0illbZVseUJoI2Cc576cmu1dDzJ0TrssKiuzzpLQiTX+VkwcP8eIqM8Fk2wu4Om2i6iv0euWnzUPeKbk1p+EIeKUGiSVijRXCyYQ4CnCHHVLxMwxhgte/4QP5jH15iw1TZ/cbx6wMiaPpMYbfZPzuCHMQ8jfUQ7wHI/ZfzVk9N3lpNdm5atAotll/EfJ3J6C7XF7sU5u/z7iUc7sKcNQcP1/RQcLzalRSgXPZYdxMOBByjqc6fc3nKApwRNjZF8gmFe6sBT5NwJDgG5bEn2QZE+UpRvycaENjsZyga1WI/uJmgV+ItmGlITI+f0nzOGe9OiT7hsbmbcDClbh98T4XYptH3/gqtz8okOseVpgRJTHTsY4TqVw2QxqdBZviDnm2S3U/ZtifIcdz1nWB1XDGllan/7GOvilzuNXkoFzj6vdNpk8k7o1X2KJM5eURo7cc7ot/DfezjKLyh0xfBEyxkPJDDhsl5mEZeXpFZeMr1N9JqDUG5HGHNJBZuCPCiYTjq1S8mFj2scQmfnODOK8xff2MxB5i7t+hkAXSAZSjz/jzxSbvPG4hfaeHIi24HarUOz6TkyHC64xjN2LVlgjWEltRE1XL+Ti1EPcSksBgI1pRT3yGNnhCwuNSl8fd+2xWVMbbMtkPIs76N8jta7KzI56LXaydhOJ1nXC7i+wrNIoGiyJk/8ihfZowv3uX2+lbfMW4QDNTyht99I8nbIbfIt7o0ZJPuD7PeDy+Rbs3oxQ0EKSHaPUjDOd7zMtV0tzCV9e00ia+tUOpvsHKchheycTdmKNsxuSjjNfijEzRUKpt5stPuOaPCKwNFsohrivw7uMqeixQTVyS8Osc/Oo9HuPwbv8tfpi+Jsj/hJ1v3ocfN/iVO2Wyzjbd0RXWHF4VEYn3GuwJeanGQbmgdwrzagOKK2bZmK3oLkU54fVkgJv6+LMJ9nrBQovRdla03LvcOfgav/7bJ7ROdnjvOxfcy3Yp1Av8vEHnUOcf3zynMjzBbm/ye3++Qjj+W2zaf4Qxz1hVdljuFVw0B+C/ptnfx7JErLs7TPpDovFrluwyjD0e1XpokcYo1FmiUssFctFhqPpYjTrldZt6Y0VsVgiWLlvuilQSKSsqRQYfBGXaBiTpSxIhorhS2LCfUip/wKtfZPKRb/9Hv/vtrYdHTLMYv7xkM1BolkNGvkxiybx7/x6DLEG8fcb17Zj++DVuVOApY6rNTbaNt7BlmTRMGTq3vBo9R1SmVNYdUiXgVslpu3X6XY9Heo8DUeSTusSouWS9GBOPXLZOAsxqRCM08R2RUTTjjtJleaOhOzpKC9Y9l7L0kvy2jdIYkydVrGcJ57WMYfGcykWdF/MYs+2hmB51q4aXhdiFzUZFQDNNTjtb9Poz/NI2Sm3Bh1cB1fgZ4rLAFhLUqKD8VMEWFORZiZmnEG7JzHsaG7qJGBv4pR71pkfH61Jar1k0Ihz7gHv79/j6vfts752Q3GmTNS/5yc2QyfWIIvaYrxJiJUBZnyHoJbSjbYS6QdsbYkjQKYnckQ8oX0tsTF5ya9yw2isQuznyeAs32WJclinFNlG5R3nLZbHeYuSWaFW22Mhj3Dci/sEXyewpshHj6xU010cT6/Boh2KuElV6WP4M89KhCFa0vZTm3RR9WWfw7IL3Vh8wjE/5UnxDtwlrgQAAIABJREFUSz/gqb0Fk5jJpoAu3uHL9TnJ7ADsKjvrAenRCfLkJW/fb9IYm7ROKwy//IiiofPByyEVr8R1oVEEbbLBEqQeJ0nC4lWC4AlYQsyNHHEpZRxevEEZLJD2Ntj+0ga10kMOHzxG6wj48Sal1xrtJzqV5S0rxcA2EuZFzFc8i9pug8H1OcrxJhuc8f1fO6bFNvt7tzxf99i+3ub2qIIy1Ai/5BOdZ3x8dkZFOGHHCFHiKYuJSKkScyytaaURV1KFxVrGWofkbkIeZ0iBh7paElopU8sk2tEZOD5HioQZp4y1Bna6wtT20BsSqX/GR12NdJL+4vIOfPs//d1vb09kNMNAjRPqL095/88gW19jpRZj7T1WlxnKokxZmeFkl0haia4cUC4U9nd09jpb3It7JOGAaqHzRNunVFawkhaWUGeof8DmvE9lZfBjfcWusolYlnnolfnilxvM+mVGXZ/T2iX2ez5O5xGZumRd+Zio+pD4ozLjg2eY3WO+0OzTc1q0Z12Stk+zVdCKG8ykEpXNGUZ7TbbUyYo60pZGKMwYhCFzQWLlLrkVNxDjCrE75H6ioG2C0zOYpS95WbyPNVSoqQWuEDPu+YjjGkV0jRmUyNQUkKlvPWaY+4QNk8zucpS2qT/s8NUtg1LlmKPuA4QgRj+9YrJe4vtVZKtOJoVkMw19qIMpkNfr7IoCh/sDstoJq2jCZqmJaO2gDrpc1Hc4GDxgdt5jw7/BNWVWxphs3qa8OuTQOuOguY8/Synf2yDdfU6gDgkPJFqiSOXWw71vMKkH5PMJmn/F9FpEtZtULAulFbHeaxDfbjPLplyl5/DqhnUwxZ9X2Qpzzmoh0a5BNnjAV79mYtQW6OXH9FrPEKyv80sHHmaesFD/DT4O/zvOut+kG73HvN9is/o+I63FwnhDro6R90tki4LTsoZuHzPA47wYY0cukZMStCpkb7+NtfkUo75Ptb2PfTQnXarYFnySXHL5oxyxccDWxSVh3iPuWZxuy5RGVxjSLtdxSlO/R3X6Cca2QPpdm3gU4OQ5vvSGsCg4XhX4bpl0GrJZVLmW+4hvrlhbAq/CgHBdMAxNYreCkOesCgVkkbYKaT3j07xDeRlS+CknhkS3ESONE4quSeQ7VEMZsXkL05xOniKsm8xT9y8MAn9lavL/P6RiGEV9/whVAHcmcmLnZCcmK+seddVHKra4N1PpPDL57PwZ8/X38FYq6bqErhUY9Q7LxYL17ApnrbKulxE6GY/WBaVqibKpMn2+5HruMt4I+dbxHo+2q9D8Fbr6Z8itFsLNfXalPybORc4cC900Sc1NzNmayamB8s3PEL7X5dLu86WmyIuoxk4jx3ujERzMaJolZmOVWu7jySqxN6MQ6tTViLXhsxwtcM4NXpjbVHYk/HPopxO27lp4z16xMJb0ruYEvojeXbJRe8T48UN+zX3BannMWW/I3WDE9CBCbuzRlY848VP6ekKxXabYDLh7W4bmU7blPiu5hrvRoHj1Ed/5fp/F9Rsyd46qS4RhRHkr55cePeILsweotZfM/RryoUqk2BwXbSqtc678NqYus2pu01jcQD+g9XSD4es7fP3Xlnz8RwnWvTL3NlKeqQX71h1q7pjlfkh3GhJNd3EHPqOGQWn3AHGzQii8orEcUMQJbmagphWSUZeqpzCV3uMf/fBPWf+zU15oZU6Ox2zffcpBZPLdxS5/67GNG8R8VLj8m/YOaTnk8I3OZ1qVu+Vb/utI4KH8Q26UEt7aIXxh865Zx3kwY5HVmGy9y0b9FtWfk0slngVVdj+WWeufEJR16pMOqRWhZj6CGtBnC6HUoTT1EcYuYsXjNJqx6e4zD7/Ls2uTd6v3uV3/If76KdnXf0z9OkS72mN4T+Eds8z+8Te5HX6HjyYx3kdjLooz9uUa6vYc2TY50HdZlOfEgykf/v7HtFYbLOUVfpGypwmsVRikIdfLgCwpUAWZUlllnwSzSIi0BlNpgeO2eViaoKswGuR8FG+wu93n9ahJwZJDJeN1kP+FhKS/EJnAf/yf/INvu80t3j0pmLkOjZ37ZLlJOTFp55CIC3inRKq+5Nkn72OEBqZVoey7lAqbirSBIZTwxDbLTEFWIwR9xv15CV2UebY8Yx62WG0uyFMTs63y5BtbCGdT3NI3MIyYnVLO7GXBR3vbSMICu71B+P4zjJOEaruLYOeoYsCu2mF+lLKZCMROxHhLobBiJldLjofXKKZAKnxEYBdMhTLzVGSS6lx6Zd6zBbrLIXL4GcLOObqgs3hTUA+ueTZdIq9yzM01jaBEd+yx19rkQLritVTj4W5BpWZxII05iFy8nUNuOzX2wgVPTl/D6QFDq0G3PKBkNJhuOUivMrRblXF/xMVswmIukZVi2PQxbZujUoey+BPmhszR068w+fCCY/eCc3NBst5mo2zTq6xIsznVykMG8Yr4zOPoayum2hrbnpPlK2j/Oo+5ZBZ3yZyIxvmXEeIBI7fAanwdsb1Bv0gRpXPUWYxzLtNc5kg5JMUKb7BiHC0ZFTe8er0kjmWC4pSsdp/9zacUTYtSeYujLKO2eouv2iNunpQ562vkbsj50/dx8zqZtOJaqtIYZUzKh9TUMacruC8bzGsN5tM1agjqbIV31mY7OEC3lwhmgByLvF7OuBFhI+4iX2yyeu+G3PkY6UBm1lsye3NKOuqzbM0olQTOyx7lfEwgmliPD2hNH/JZtkTSplB/wtpw+N733mP5/Yjfu/1jzq8SBPGWe2bBw1aLov0W3R2TcV1h1jxj/acDDHPCSvM4DWKiNKIo+URWlTC1yLMIGYO5VFCyIlg36PkFZ07IcSETSApGKLMSajyqrFBUA9dxkEsFQSoSpPkv7nbg7//u73xbLJZ49W2GL89QjhI0UyCpmAwSDTtq0EwDbs5UGlEPJRQRgxbFTo0jdtCUCDPv0Qwi0uSWXS/k6bKCW1d4bXjM5wmZs8IINL5OHcvJsLMH5I0RSRpQ3w8ZKF26wi3ZlU3vdo/y7I9w7ZyTWUCpPkOPBdxYwNIFPruakZg3xB0VQ9XYzEFRGwz3Z0TehKVURonLbPQC2rZHP1EYL3pE7owiM1i7ExRnnyBJuHIqCGnEepbg521s2YWlRz/cQhunyOoJD6U7+KZHvJ+xVzzkuWgRF1scGn0OKgbLdZPFX3+HRs3AVGTSekjcq4Hc4QNvwco7pTRaIlRVwkKl5ojsWyY7qzpV4YjD+0+w+w6KV2L5xWMeK3WMagXPcfGNNk+WLmLtlqb4TYTeD7ht3mH7RxrVi79JeTsl+cCh8bVv0CvPKfzneG89Jxv/q6zmA7zNNRUU5OgZ80UZaTZn1M8YCgnptszUmjGvRtQW4Lw0eXGp42lDdGuTdveEelknMAZYnTnWMxf/7iUf3GS8dfEOv39Z8Gr0pxy8fsOH/gGb4gcYFzqGNmE83iJb9dhu1nHUJReTJzSlfRZRhchWsLY+5aPPcm7DlFa9TxKH5NkOW9IJODa9vRuuGnP0So7wySWescFtO2ercs2NJnKkfZWdxQnK9hKKAd5ZgluAnrzPdk8k9D3KkwRVG/NjPUX94Rly8oZVM+FCTPDdgv3NKrZoYssjxp+mOB+uyaYWSD5l2yCLVAaqjkxCyZgTJAJOlNPJBA6CnPjEpanqqEKBoOQs4pB9x+dPdI/UyWkWMssGCKKCLIa4Ab+4QeA///u/8+37soE0uKVcrtOZhzzrj5n0ptDZwFFOefXjPlm5h03OvtZEkSw4X3IhW6xXMc89kfc1H7ulI8kxY9llvoyQFgo7lS5bZQ9VnnNemRI3TA7LF4xeNygd3kXKppQuI27GcPR0ibm4IEq/xvrOip0y/KFzwMXBBm0hZ2pp5CI0R4/wSxrJpxWaQsAXDudYmsAiLqFqRyR1kZ51Sj9QKa40BF1kJZmUNmUsucAVfZziJSV3Td1dMdpcUuwMWd0qJPkmv/GrDum2RCZucu+rLhW5TWjfoTtIeCDojIqIo892kNxT2uZj6mJCXZnxPNbQmwccG8dYoc0jdUpsewzWA8QgZbOesHW/yqOySW7olNo6RrXHWDyn0Y3gyuTCMumO5rxUfkA5eMj2ls5gdsuG9imS/gXK1pcwmi3kJ39CNzomv28QuadkSw/96Df4+FlOGk45U1UuF28IH8wZz1XeVEbMN+/iqK9Y5hnBRxpTp85GbtAt1ZCfHnFy/wEVTeOw/Rb7RxpfVEOs1TlCsENsf51QHnM1XfHHb35I1jhHWYd8XG7y5UynVr3mZVDCrbawvDX1h6eUxjKjVh1vKaMYC+TtlD2hzFIEs2igFZcs0ggzhEoEQ0MgazU4UEL2rSFG4PGxHLN8/n1qzwd8qtdYV7vcDG/prd+warf55EdL9u6oyJlHLZWR+gHjX25SUXK82hO2NZlq9QNulCaerSPPfdpHFofzOubbBQNN48cffh8lmVOPMgJF5dVc5EgvcSSH5KlHFBiQqSgkqELCC11AGNsMEdB9Ea0mcVgK+WdtmwcjEcVU6dk5d5YdWsuCvB4zXv8CZwK/8x/+3W/Pnip8a61xKq+wal3WsUvl3iE7n8YE6RuKxKNba7DwXeZOgNUWkIwrmkFBy2phWRPixQzNLVATBc8TmVRlyCpE0S2v45jLicpsnrI9UAiyELlj40c/YKw/ZfBiTe5odA5ULO0hqTrHimukszn7soD4+oY7G/eYZws0weTosMq2ecFS7jLSZ1wOYybz+6QYtLMJmzcOVbtE1twgiRKEmUMpFOi6LiNZ5JeOfRbafW5frIn3JVquT2cM6YlOIoCr7CDZCl85bpKcLijjsD1cclWtET1YcT/c5YMjnUCq8lGri9KoceOpNOQ1B6uEwl3yorPmui/hfbZidh0wz2OSXEcX1lQ55en2LpK+Ty89oOLfp7BKXDPmaN9lOathBhmV6iZJZjNvH9JtZyy3K/TST7luWQTpfVw9wJsv+OTJfXrXN5wW52yfmriLR1TCPn6nxINmiqAsaL6fULo+ZT4wSRoigQB3EoHNkx1WuzEL5lRbY9qXK06CC7arG7ye9zDsd7hR3mMy7tFoLvGfZ9j7j9h9qbLc/Zi+t6QablE8+wDp3R2ss1vEPZGj8bs4GwGDDy2EtIMvjyGfE3o9lB+s+LD/MVn6gEcnTbquhN0p2NtoUUmWaOsp0dRiMjW49QVKpQPu7h4zzT9jN6nCc5eryz+jpSjYzQX5VUJ/mjB880NGdYFfuwx4LsMOV+yaf5vvjs7YWIrc115yb/eQyFvSNCS6yX16N2fMzkaI6xXnSs7lUma3Dkql4NJXSGMZrZKhqQGNSMAvdKRQIS18OkILP16jkZDkIhwEPFuYvOM5VIWUvXjFPzILtlKbYfgLzEr8D/7L/+zbgrbPh8GS+4qI1Ph1FuqUZpThmT6T5SXlQuFyUiLPppTjY15lEep2iTttiWpHZR2WifMENIeFLlHrKLSlBk07Is+n+DMNAZ8dq2BrK+To+EtUrW2icsKmXqclzVH2/g/m3uRXt/Q67/vtvv/67pzzneaec5u691axilVFqkhKlGxYlhRZgRFkECODOMnE8wABhGQgWEEa5D8wECCTDJLAQQwDjiAatkxLDotkserWrarbn/6cr2923++dARXIiEhQUTLgGm1s7Het0fNg7fXgXU+FXL5mmgq0Sh1FiqjeKdiENVobisShEd1gp+/QPxJRrmW6wQx5N2UkdWhXAX1BoFeswdS41h/gBQG6GuNlOWV5i98+ZLx5m59c9ZivfkI3VHGqEi/Z0G/YHEcet96Yrw+2zEIHQ5e4Hz7ki19xeWJv+LX8gNX6LZwvYhz5htRJaI4VrE6LVDSwr44QnJisX5B+mpHezolLh4lbEuBzP1c42Rkj7HyN5Eyls2Nwx1kRlDGpDXr0iuB8wqp0ULshgTkgtQ0iXnDwTCZuvMtb8zmSpvI4+Q5vPnrBbHOM+b0rqvxbWBf/huxr+3zVek629OlUDfR2h/HIINPHlF+W/MquzsMTkTvhMY78dZRyjffJikQXsXpnvPJV1skuTy53ua22fMYp4qsOff0K6WWP4H6Tj1ihDwRe/R8m/f330A5uWGVthFWPVm3irzOeFxv++Ys5aVPiffsSr9BZbRMKq0tkr5GlHruOxktVI9Ys8nSHJBfwTq+48FymSwvPnjPOVJ7uxJy++oQLWaZ89pq9kwED49dJGhu8Hz/lefECLgwkLaS1CrhpXnHUvceJ+h4P9Slf3nyf2kmYNO/yu4pG8eg+0tBg3Uy5WBZcflHgignvXqXoao3rZ4iBxEEhY8gleq6hDcZkoo4Y+qi6gVMomIWLvJNheiMIQUoz3qruoqc1aS1xkWaMPyzo34x4lW9+edUBWVPrlmmxJ/p0dw7p7vrU/d/CXfyI5WnARpkTnA9Qdzt0bYXqFEztnKoW6XcKpKIiFEYsBINqmHAPlaLQECSByHJpXMyJBxLbNyVf+DL3VY/9I4Hhf/T3sP9ojiW1eeso5RkGal9Gzd7maF9nLa6Jb9a0NI1O+pwz74BHRz4vbhTCqs3KbPBb20uexJ9zcOCgbnf40dRiMd1gjBeI+32utzLVpqbhikSLDT2hyXL+FOH4LZ4+eY2VxnzrgUm5+hItPWDClG3YZyptWUsyh8mIt/9uhbHzEX3XxEwcTt+dc3C+y9adUo9X7Nq/xurSJd/bUMUl7d4JSSKgLRZcnV5wcT3jRXFOabmIlsq3JZm7B/toahezVSAsRU6/nFKMDXrFFrN/wp1GRHHvA95bqGh/q8k0eAjJawbpGV+dH4H8Nqn+FKHb5O2XN7y+/x6Phq/58Z37BD9x6c4PCOMOyckn/LYdUbRkfvxlgD56j2PfoFJvaCV9Jt2cjj1FObUI5yLug5I/fXPJxt+jPX3Cbhzyg/CbPGou+aJw+fq9N3ye7WGX/5Tq5jcY53A2gfceZcyizznPfotCfsLTZMtvHXxEY/ddnnobnvmvWN28puuNeLutMX6ridex2HsqcusKBPYRj3c8xIOI189NktU5yUZmuZlxkU7ZTLe0LRm9rNC6IWtjizgZYI0yht6Y+eYFQXeX392sOfMFHn6rz0e/+TdJv7JY8RWL6B6Pu6fc3PeZ/qDJU8tFV57wuB7xyVceP/qjP2Iz1nlYC0TnEVol4BoS+lBGqQqUuku4gSt/QS5KdOWCPLBpiCHHnYJkIeBZNeao5tVNh4G9xurB1a3IYKuRiDGfZvxMdeCXgwQEoW7Yh+jUoPf4ne+2OK/gqW4g/pM/xml3qABjqzPryTTVK5rWkCDWaDRWyFGHN/OQoFS406qRdItaFsj8EG+9YldvkhATE2LbY7zNlO9ofd75T98m/1xE+cChFfsUnTHCH9e8/bcG/Okq4qTvoq1krFjjtV7w1lGKG3Zwnr4mtL9OchLjCw2GySuMvs1XG4dzTyCIUrLXAV55ibGTYecOk7RLqk14fz3mKxaEiQb5hNrN0GMJ0VhQRWuKwOBzBnQXr1nmMoc7f4Nv9t4wu5fxzbf/PptXn9J4T6azuMsn2efYb/W4s7Wx8pD1sM0yzag67yK/fML8RchitSS6/IJlsWHiqOy1Vb571ONQ3GG3JxBsDc7/dMlwFBE/MOhWhygNl3HnLon0DvqgTzO7pWRL/lFN48UTVt5/hp9d4/Ml/QcdlJmDFKx5c6/LvhLQmnYQpBrhw5JZ+W3SVxuU6mOMUEUcDrm38hCVgvxQIOpJ6FcVoR+zObOJFi4XxQvqrUrLmpCvdIIHTZKlSBVdsZ9/nTd3Jxg/7jJyYjZ7W9qnJRdlhN7fsmnWfHbTJLp9wju732BbGbS8FR9nPbT9Z+wHuxjkVL079J2a1Hobc3KNub/hPSw+3lZ8301onim4jQTMEHuWk9QZTqSgPtKAgJ73kODwFeoTAWXnbdYfBpT/bElTPGXnZMbB8G/TvzvgZLPlInrIc/srTv7NIcU3z/l4O0MpNQ61ilfnG958/JI//j//OclJTS+QsTOBcV0TTwQyS0YcCiRpgVCNSGqPmbuhlYJ9KGBelRjWmIabcJOvuTEkpHZO34cvdPiNQuRfiC3KxKUMyl9eifC/+sP/5g+STgdlMMOZ+vwk8XhQRXRnHZayQDOBTbxHoC1xzIJ406IZSAzqFcObApOQWG5SVDX7lkkl52Tbmmy1YViXrOqc26aGsRoRBzcIfYkddY/0qMPLJjy0pnTOB5TNHH/hsPPtCV78qwxTmXZviSc2GL8V0Hr5CF85Qx9HrPQap1+iKTlqY0AQd7DXLqFWUKUjmk2ZjiwiBkdoUowjTTHWPeRWC11e0q06dBINxX7OIycj9zI6Tohaiky2K+4ZKUZ+RP73buhPDrBVia68wRM/ZK8Q+KqvEEkL5LzLUh7wXImIdJnk2kL8RKYoG0zNJdsrl9irkNttGsMW+zl0RYnDuqCkohsZ5N2I/rjkvhqxb/WpRyeU1oLBuysMf0pbbtHp3rCc3CGcaBzvX9LPK56oBc1JB7/20d918BKJWZISDUrKM4f9OGXjethdifatQLOvIhsB9qgiannkaU45d4ndPtGqy8RzWS9zJKXkVj1lHgo06pobd403l6jYR+koLKyMRppyuLtBzzLy8pLvf3KBLPw6Tn9OMXsPhQGnwxuq1yqlUfAd7QOs8oTM9mmfGBgbE2+m0WkXFN0Kv2zjrQXE2Qt69Ry1s2avLTJu9XnQ+Ab99/bYGxn40jnDjUM1nGIHJZnxIR1pgdBXuHM159MLjUFY0TTn3MUje9OisStjrX6IuTX4bCqhbL/g8ZFF4ycls92Kq/BLLq8r1DTFjB1257ASdc77HfQqoc5FKqmJGuT4ko+f5zQLE9E+JE5jKiPhIs/R2xVKx8a6qdnrw3eEGnel8K4dEJUmmzT/5Z0J/MP/+g//wPl2we+cRZzu30USVrjbPm/c16SrhFgTGIsLgligresIWkJel9RVxgqVc0EDpeSwFVDmS8yqQO7tUbcF/KFBKxcIywHvJGsuHnYxlgNGd5bUWgPd22f6ecbe3pBIqWhvVdZ373E/m7GVZkwdk37+mIkwQatrer0Q/9qh3dpjctKg6W8xwoL2zjWWLiOdmYTZBslaUctD1k6CMVpimQID3cQbebTFPfSOzJFkMBKOuPf1MX6lc60+5NdyF1W5oew79HfmCJ822NFz5HzBptSotj/i49MLZvqAx/VzIkNGJqVv7FE/d1BVl/X+JcqJjx6/gQJ0dUksxFj7Fg/GXd4WBliqRK17yDt3aD0Y0rIPUO8MCXbuMtaWDN4doTS+hhDWlCcTjKND7sku8UWP7bZk0ukQ9Su2E+hFXRTH5liNeacJ0kjmUaON2Nmyzd7BmsPBOEYqO1jTEinKUBKN5XXIZL7kdj7ndBmxmn7F7eQGHZ9q4dAwC4y8QOiuKScBTX3LOpjw9t67vLrwsZcz/reLr9hv/iraIYweNng2qylGMt2ejcSMYGTxteP32XKL0rpET0+Y5hVJKlKZMVUSoPpNlpnCy/lztkFNxbu09BZmtUbcg0FTpmWlRJ+vCAe7bAuHUaLhyRvS2ynrfo55/hM+21ww+8ENwmcf88PJJXdP/h2UdyPqVKaZ+Gi9B9SPtnz2vaeMI5gGEs/mr5i+eE6uL8hzHf2mwq5yJNPhYZUz9nOqGKZqTGtXoFwBlcBMA2u3YmdPIQlyeqTcTfpcbWOO2OHQ3fLDWGRp1Nhj8CyN5TL7mSTwS3GBSCwrdj4+5nuHM4aRjzwZ4Tcv0KKHJK2AihlvVIdQtYi3BbKQYzVqbNMkufWJQlBlkfNlQd7S2SkUpPQMzZeR1BS7SNnxSk7lmp7vIgc1Fxcy39DXhOM2LjGv86d8t2xyVd3h+DpFOPTQEZGXfQTvNUdai3xccbNycI/uwq1KdxWQCD30CaxEEzvM8Hb6XDRvGa0UdBk00yYJIhr1PvGowShNsQY5ydplW4b09hU2q5oj64pG5y1s8YRrX+GBP6F0eii+wTSMKa01jacxb+5mqFWT7iefcyYVXO5t+J3DA8arNoJzwUyM2Q9inu+32QwGWFmCkezRrCXicxXzsE94WGGmMnda9+gcpCziBKHSscwTnKlGN36IuVnwZycij5oVVxcf0AsHLF5LCL/3p1RPHvAwfEg5+ycsjt/mwzjm81CGhUCyf4e9fxESiinuocK9+FPErkOyrfCuX2NasBQWrFOTKF5z82zKpGUj5hbDHB7v5pxJOsLCJQsEsGEnKvksSiiKjN8bdChvK6zyC24a73FPi4mlT7ldTfiBf01d3GdwnrF9589o59+i6Te4Kjxc54Y7wphEvKJebpgLA7qkuLOMdB9C1yClzd6OiJRNuMBgNxmizFfMBy1ubkvs8ILeQqVhTzj371BrR+T3c+KJSZWlvLvc4gxmBFqTg1imSmqWwoqvX+yw+uA+gydLdjch/UePsFWBrbBidRpwtnXZpjVlkWIem6SxwKBYU2US0a7O1k05Xoh0Fw563WXW02hmM9plSH6zZmirrG2J6es1Zd8i93U+MQ1+JWvyL/dFwnMPip8P9V+OTuC//cM/CO5JtE4PCVcCibymSHMc2SHXVhiZzyYt6YhTOlGBmBfsKF2CSESN4FsiNGqRQFKoickSk7jKEYhxS4Nb2WFtlvix/NNFj1WCuWvRaehcXE956+4dJKNGVWxWdy8RdhuocZ9Tu8X7K5FLDTCHJKJFtyPS+qzB8bHNWtwiGw513MJoNpkXS7Lrkub2hoUakgxNxmFBq2gjVfvoUYkq1Ggk1JHMw+4+DS9DL1tsKpE6zxB6KT1DYWQecbPT52Ze8Mjb8PF5m7Gy4bTv88joM9s847xpIMt30YdtvnwnZzR3CQ5FYq3FcJ7RjGtq+qxqHalTsz+KeGCX2KgIRwLqcQ7+LspqyKgxQU1jomaOUd8n+HbCaFlB7wwj6cJ2idKpKvWPAAAgAElEQVTxSa48WmVJ/A0bMo2jfZUym1MN7rGOWrTyl3wlGeB4GM0+pb5FxcM3ICMkb9pcRk1cN6ZYF5SrPo1NCfi86mcIywbRPOeV00QZWDREDffNU9rPCvZ3HrERJfz0T/jktcfH2wXkb/hopPMjr0dHExgsK6KjJhchiDsJrUJBkdtUQotWN2a6kjhxJGTJwCoSnMLAmh9SBQl+PSMzQvqqQ8dKWFc2knyP3K9p6QVVUJJvU8rrNtPyDT95smF9I3B3+AVj12Q5qlFWZ2yajzluTLl/ZwfvdM5+RyFPBYq3jzl3Q74jb4jvSOy5P+RcFrj5YYrkQWNUMs9l9KymaZkEhs6pLiG0JXbkGkQBRQ5IszmLVOdusUZpKYTzFGXqsKpjRpuKJi7TBym3YUx1Cr4qMNJlLqLol1cd0By9fmx/B7X1KetNxVLQKOod8qzE7mSkqwqTiCrKiIUBpR7TdXwOSpmw0EmLklbhszEyHMmksh12kClWsDAXnFs5e2JF81RE2JNI1Qg37PEbtc0bGyzrPt/4PYWHt3+T5OgTZNOiTk7QtJLRg4zwzyL673WJbptsRmfk5zYPHYdNskAfDchKi7xqUggvyOYG6U3OrdIif+ggVyXK9MeojkzQHuBENlploNqvyW9EqDXO0hs2m4CDYYI392i4Q6JRRiZJ+FsJ7cmPeFGLNPIYF5NykTPdEVg/k3l0LHHvw8cY7wx4tKdx/WKAs3TJBZHreEmTjAv9iH4V0DGX2IKA0zQRm0MMUcNM5jjZA4Z7Emn/lkLcI7zeY2j8kLDcI7QClFBlIr1NrjSpo/+ZdP0NDj74Eqn3d8gu/1fy7BvY5TOuNzpHxkdcDT+hMxtwUcnssCZrSYSzELkVYU0F3KRiuzDR04TqMiOW55y1atzFLc3hPeKoIre+wGbAA+uAwj6nv2ry/crGWP6IiVTz/H9Z8/iOjnSi813nhJu+iypbbKV7PF3vkav/lLY7YC7LNJsrrNUuL/G505M50R5yW4Q0khHN0sOXrxDyFnE/IlFL/EDFqg0sx6KWMn6wKGgvp1CHyDcLrh9ZnPgO1fqc+abF8OCCfrhC6D7iIFUxxjNK+x7R5T9joP4GX2uMkD/ckkcjVp9bBHs+Xcdle2vxvU/+iNeffQZBg21jTbFQmQoih7FBoivs9SJCvSAJQEwqshrOlZxRbGDGOaJq4YcVB3hsOxLCxqRSXGQarEQLK14w6+QQdnkTrn7mYPAX/g78HOOR/x74PSAD3gD/cV3X2z9fS/4MePHnx39Q1/U/+EU1CrEkkAImsslenZEJGQx9ktcR4s0as7FH96RgeQaDzRw9bVGWEVtBopuntESRpGywEgpelAnpImEjx+jtGsycxlzBzQUe1xLL2wylZ1ArGxb1hiRT0Ucm3vrfZbvzjL4po85ygq+ZGOqG7HLLW7t3OL/dJTJCBrOA7B2NItToXQk4fs3EULHyLdQ9zjs9fOEGs/bRby1WLZv7b3U5kvvMpl2m7hdsFQWhrRO1RSS9x1624mvZmK0GjYcfs/2JSv/oCOHmM9RWl+XoHd5RXzKd/ypRf06VvOCsVrh8X2Sza3EaXdJ/XbCbNagWfRamjpUtiI0tit5lP0sZqFAPTtADnz0cNKGL5yf4+vsMxB6lklAqFus3T6lXIy6ONAQtJqvXlJuUcVNmsXMH8fN3sKMmq8sDDoqEqjxC3F1im0Pk7R2W/+qSs77Abdins3/JNHTZXK4wqeG1xVfTCV5U4Ug6O51dmsc5npzSXPiITsWeM+cij2kIJqVnchm/INmWNHQTc3HBhXFFW7rPwftnvCsNWew3kOo2Dc1lsugz/VbOr90GfD8q6DYUDnaGXL4RWGUVhikgtxNebldEWclX9Uv2fbDKJr1DDdUSUNII05aoLyAyIhLplrHu0dlTmD3bkBs20aLDm9kEuwxYhAb3Kov58oqdzOLgI5gkd2kobd7r/Cb53SMO04x1dIg5EJg9EFHKGL1Q2HLOPLrA7ZSkyYosEeHIxsGgXqfgxuSBThbXECVIZhNSid0soKh83JGCFW0oBQOpK5OHHQLbRY8FBnsh0rXMORrHecUnb+Xwyc/G319lJvA/8peNR74H/H5d14UgCP8d8Pv81HMA4E1d1+/9FfL+RSQKW0LY5GxSBU3rYUzfUJkq/ZYAvkLj2kaxcgQhRKoS8qjGEyBQUmpZoqhjNoBATVMPCZQ2BSVJWJHXMRIif6QLkFccG7CJRKbNBg2liyQXmMEFT4Sc447OYdmAXCHWIzbWPm2OiOzPEDcPcKsW2auYsN5FImJoWIjDDLvIKQORKlzjNjuMHJmO6XBkNdD6RxSzOQM1RDhpEbgpihOxJ+psVxEHuYb9Kyb63GDLY7Ydl3r+LuP5kpfjPcLxV7zp/R1EyyPuKAiN/4DjxXN6TgdhFPPjm4LjhcY6b7G/34dWzG0U0q326Uodaq8g8UR6WkWje4x1rdIKIsquj9dpsVxF5Dgk8ZJe0mVzZ02QjdGya2JzTKu5gEhh/9W/Iund5eX6hxz1DjhbXvHWnRHx8z1+MPgC6wpUR8GevaHa3uLXNenKIblqEDgTErNA8jW0zKPfsQi7V2yXWxwGCEMblIJiJtI3+owHW64bTaJFSSDnnOsFL9QrxudH7B++RSqv+LEDH7YtinLFZXmJH0zw/9WvcvGBiyz/Kv0ix10F7PoOWZ0wMErCaYnc3KDLNneMArU0qOMKpSeS1xpBZlF7BY1ki7yYkmcVia/jp69IdY00n7IT+ShRjtYKeNQpEScuXy4HNIyc1L+i0zqkLwkEjoMZKdwMNlizFpthiZp8weZCJtl3aEVTJP0RN8ELQnnOcFWQbyPGUk0qxaCUPBMycq9kT9DICAi6NUtXwEFCF0qkUKbdb3NlFGzDGMc1ed0pUUsZv+NzEOesfYUP3YQ//jnw+4Uk8LOMR+q6/rfz/QD49/9fgf7/WUMrCLVLpFTGk0Wa1hWibHGgpqxqBU2JOEehWWdchTU6W7qqRphbGOKGvMhp5hkHhoHbGHFYbCnLBFGIKe2MqCfRvoAzISdEYiSpKLGMJce8VCeUUc3zU5X3j+6SVC6x+iF7yytk/ylvhId8JhgIUxe9+SUHTkZdqnhaSqWZMNiQZgqXUROkktqpEPMCZ6Cx29iQiz7FXCaodcTGGGl7y0PDIE93oLOFpUHz+D7bWKf3tTX69UNa8gJNHDE4+W3C5ZyjBphHAsYw4NI4IvnUQLW+QbI7Z6zrtI0DmrWF2E4Yvy+RRSF9v0LXRhj1PkJSEG0r1EGAZIc0RA8fk42q0Wl6uKpOfxqS2zXqnSbNekL+OEObHROkR+xfz0jaV8Rym9NS5WBUUq73qY9nRJceN/MtE+8MKwuYXtk0tg22oYdS5ajLDbqhESk2e2JO0lC4dWXWgUsrVGkNh+SpRyuRaEf7TJ0Q3YkoBYPdzYx/6V/RzFy2D95n3xgjtOZ8+WTDZJDxYCCRXMyJ9w/56vVDev4LRievSK9UBnqHz74Q2DQ9ulVN6urEXZE0ByV2EdUcSVRIdtt0S5VC0PDKBb6YY6KRNF1uqy1COqfwhoQbgdTzeF2saJrX3BO6FKlCZ1wwfRLgmgtyW4buA9oObDwFPdDYvn5C9sEdWt2KXlEy6r7NF94Vy3qAOWjwTrDD9dMFcbnCOhBoRSVTMuwippNLtNOCm1ZJLDRQw5Rxs2QgNQgEAWElUOkluqhyfbkhdgQ6Vot3ogp561LQoXR8jqqSr0KDnzbufzn+/1AH/hN+6kn4f8cdQRA+BTzgv6zr+l//rEP/tu8AqkQn0cgMD02I2DoS8o7E4WWTG3WFREDJgG0dISMhGxpJN6MZBIwiC1mPiGIZNdZxsoqboqRWdWQ1pykWlH4b1yzZCWPkuiS6EUDZ4bQ9px3FTG5MZndS0mWOv6lQpefMJx775X3arkr0zTcU/TaSkhEINkm2QXW3SP2A5axGHOVktz4bwaDciDQshZQt63kHZ7+DpgbERkVSqgTo9NtDpudTzMymTY3UXuFFh+wtRLIiJB91sF/N6H54yYMDB2E15vK+wN0R2K7Oq66Bk0nUxw+xNZ/fFDbEO206epeGVnPd/BqB/RSPnGCzpG3WdFoWVl1SdU3qlUEUbFgNbfT1GCfuY8pL5EGDJJcIlw84mr+i1LcM3RnXvZKyu0s8j1A2PqejXdTPP2OgKLwKHJbLZ3Q8keotGz1/Qj1vIFgKppfjrM/wjCHDroNaWCTiJVadUfbGRHEbJxWRVInlZI7NOWGnSV7EVNuKBIGryT7Prz0G3hmPBR+5lnnZ/5R/7+7fpihuOTuUCPUdHofXvHB2UR+3cZ4p1BdDxEHNnXTJq3mH5smatn3M9tIiFy4wKwHDkIk1mUUjxBenWMmKeKVTb0QYeHQqlWVsE0Rz5CqnMrZIl+dsVirFwQlesiBWbNaWjL6FqSKyVVbcLk0e7UCpK/RzldUqYmdXRkgrkvk+WVxgN1ZEQg+/3JJKE/qKzXVeUIo1e7mKXOXUQk2aZLwlKtymAUUbUk9gUzcopIRWs6BMcqT6lu5xyWBiE3dXKEmCNxOoWg3WQYDSgu4i/LkA/v9EAoIg/BdAAfxPf/5qAhzUdb0SBOED4H8XBOFxXdd/abtZXdf/CPhHAKJh1Z4S0dNkFkmBNc2RjuFaKikKkbx2qBctEpaU2LS0gNrXMMsU8oDAEsE2cTOJsHJxmiLrWKKdVNS5iJ/oZHqEohgM1ZRTZI6ElPtFgbt0GHQS1MREHMyYvMm5HdzSix8irm+Qd+5wfCGS7K6R7QbJQkdsKox6K1xJpZpZqHJJ23RRXFAEHVe00FmjiiJCvgBXIlw3EMsNjqGyzOfkywVX1j0OBjHC5Rapsgj9Cs2WWecJfeWc6SuV+ptryugd5CSjpsTQQh6e9MmiBKXTh80x9jtn1OqIJJKpQ48qSlFzh9rQCVONqL+mW7mUokHWaCI3VHpqg21SsBUW9PY8pFsVoR4iPF1iDAesUglRzLGyJlpxQxEvmfsJ5DarVxGDDF5cX5PZH5ILIrQKBi+uiJSMolMyThJUTWUzOAYDEnaRFZVMiwidCGdV4HR9UjWhBWwKDy8Cy8xIgjmruE2Vy9wVVV6lJ6w2PuHbOwwaA363m1PMZswslWovofi+hKaLdN01jase4kBBM0QG9w3WP5bpDzcUuo+fPqc2Dmgcl7TiFMnX0N0bgpaPVFToWYKdCARhwHSeMC8EuPFIEo+VktHUBESriTiAbgPkfIw30yndZ9zECa1pypM/bbLXKGk1MlwhBXtAs1khbBZMJzWavf7pIhXnhtb5CMtfMo9SzFXMBpVQregIPnGicqbKdNSafV3mWqzQNiUOGrNqgVflOJZEmYhchiJ73ZptkNDKMy5rlWutoEwr7g0kEgOkTIdg8zNx/Nc2HxEE4e/z04Hhf/jnG4ap6zqt63r158+f8NOh4f1fmKtKyCQDljlGoCB1e+hukxtsmg2bju4iFV9iiAV1rySXMoSlSpE2SI2MgIKklFFlDVOCHbXmSM9pGRXVUEC9s+COUuEkMT1TQm2nOOESvbLZGgLGTGSSZrwRF+wuFPLLNvXNKcp6SnDqUiWnXHx2SX11w2IhEGYZxQKMqz6KHbLKcta5QbXJ2VKThRV5NCDJlvjTCV60hjTE1iwkNFI3RHDayKaDIhuUfgdTtSi8AkQFQZ6wsXMETWUt3KEaGxhvHnJ1aVNvwfIKgrcdtM4bLHnGZreLGJska59YKRiuLuiWNXv6mJ1+QXuTofgN6rKDUXZp9FUaOx0anX0GehsnSihaJdnVFi+PmZVzFuuU7CLmdnOJr31O8ZnG9fIpf3L9kqtXSypK8qZI/PKcXClYqwpL70esigKlrWAe98ERIbjPm2XKk5tPOQtOqROBhpBh9yPyKiHYXrGZTlhlKU9WCVtfZ6BrmPUSXbSp6yV7uw00veS+c8yoq2I3HvNmdko7N+hd5rjVJUUWcb/j0Ji+RjUynCOLxjJk6ewTaQH5RCJxN5hRhO0JZNcazqqPZqhE0pakuOV64rNwL1hFF8yvbnHD15TNGU25oCEJHLdrHEOgX3nUqoGtRZTaBY37d9izLV67CWevXnB+OuUyV5itBcKqyTBUaHQN6iRhuprhL065PY0IXrusVh67Zo2uK7ytyfTllDiXyBSBdi2QqTrnks6maRLWEhY5crPmrlwgJRHNKmW/pVFcq0iiTBzDuQTGSONefUtkCCwiB8HRfy7+/lqdgCAIvw3858Cv1/VfGJ8LgtAH1nVdl4IgHPNTZ+LTX5RPLAXyCpaKSG1ZdPUmaRCyU24oEgW7FEjsnLmZIdkVyVLBckCIYBU4FGJORy6oig2UFb5ekaNCLLMWE5qmgKH6ZKgswxg7LAkKhTyo8JWERKq5L4RkqUcVjrCCGOdAIRKukM5NnlpTbpdNskYNySVyWbCtfNplzEJNsboSqZAgrtvEdY5kralVCx+RhlqjmxlWfYNcNImqhDhYQWvAuDcn0zwaix7d9BwxVVjNh9idT3ApGbTuYb8qiR6tkdclgtpgWa8x2hHLQqa3rZEVgXBWIsUL2sFbeP0ppVLSbGu4dY4QpmjKCGXYQa5V6q1IWhfU5Zy+1iDXYpKkYOtFZGmb7eoGNzAIkpygWhDjMlv3+VppcnFWEM8h6vhElomw6rE5Pyfrb3BcB1eUOD4ecJNH+GqXph9R+0syZUOjlMFWwLIofIHKLCkLBQmNYKMgKhbSOKVONySCTliKJKWHoY9oNkHqr1nWMQdzjenlJbvvdih8aExfcvDwuyyCe+TTlyi6x/mZRO6sMIcZkqogSi36LYPXYYKWeLTn+1R+Qir71ErMiXnA7ew522iNUtaIco0kpTQKEUXUwcroI9BZiXxZhYTnOesgYfh4iSqILHWLttJhWIeorZqRdYB/UxLeK5BuLshdD7n7ESv1Da9frdjDZFDoSJqMrWzpSjlJsyZYAZJCKIiIak4diQiaRqjVtAqFrK3ypVtgSTU9ZM4xqKUQpY7w4pSB3OBTuUGjzLk1Mu62M3qbjKKusbSfj7+/ikT4s4xHfh/QgO8JggB/IQV+F/iHgiDkQAX8g7qu17+oRoWCqnuYtYQvyKhxgChG7AgBz0qdWDVo1QNyuaCOIpRSY0tFWxQxNBHFSGgJKSEKQqFTKjJ5lhP5DSyhZpZWrA0BI1OIXIE9scRNZPbygAc0+cKo6AcK09zhuHeNO1eoc5snt/CRsmFyVlDlJleXOQhzYrnEcEws55ZqZWGLAUVXIO2kJFJK7Ii0QxNh08KvVGbbU+wbmZaeEnoClZIRlSFdY0MpmyyjDYkxp853mVVn7K18wtJm5k2IL2c4qxPm5p/gTP4GsRRT92LEa4kLb4GZSAh2Dl2F7abE7yXImUqV+CxvJ8ipBI5K4YpEdkw5D5EEBSvw8K0YUa7JZwXLbUplPSdcrwnUFZncRZq7eK0douwLvlgMcUMLKdtQdDKm1wL1OKAqVuTFgG5ny8DoIopN9Owllduk9CssZUN7tMRedClWS4SGgaYYKGufWq3Idwyi3EWcaIimju9dU+Y6dS6BWYLqc2rqzKUaf7nmVSrzwCiQ4iFSf000EeiTMe8rRKcmXX/Kxofhr/WoY53kJqFbWIg7HVRBRHZjjFhAOYhJlzLF3OPm8wA3WtFqayhVSm6HWJZIUHcJBA3V8mlvMmaGjmEKiHKI02iQKSKrKMbMJdJWRU+VsbpDeorK5rZgas6ZrwPu2Hv86Pw5oTdBLpt4kkhfmGLUbWLLIq0laiHHEmQCQwQhpVJrfKVip45J84zSlekYBpNORl7l3OQCXqrRUjPkWmBQCphmRDPqIOsebV9B6IssFzoHZYpbGn99Evg5xiP/w8/59h8D//gX5fxLIZSY1BR5i9RXiNQFI7Wi4SjcUR1GaUap5LRzgbNcwPQA2UIwVSh98lQnliQUI6NDQTPWQIBYN7kxEtZShunkyGrIWDgkYEWr7dN2TCIlhUlIfn/MLLohFWv02zYv5i8I7bs87LusXvTJWwuSJ0uk0SF+FKC3dBIxoJZF/vVxyODMxBI0JuMW/rXI3A3Bc8nKNZMrl3rZY+d4hSS26cQ2o96am4mBfvWSaKuzbKnI/gWZqLC69cg7AgVXJHJE8PL7vGknjNNPGZbXLCoLWxC5rlKUyQ2FbjIeSmyNEGcTU64d6joj2sbIkkaeKFQzAXe5RloI6JmFYnhMJzWl1kBxQ6LgmsXCYB1vaLkK6f4cOQUpSikGW26jhFSM2Zf32FENdiKLN1cvObS6pLsPaFYfUxsqKbc0Qo21LhCVlwyHJ3yj+5jzac5VkVL6G4amjCkUJLrLOpOYJwnhzRS/rggaK8bqAdlsyGwk4k1e8aoUcKKMpfmaMFco3+vwfqXSVWTq+iOe5wbT4Ba1CNnIItroltf1A8abgoMyZRrBERVaex/RdBFFmW3RIBYCJmcXzG9eYWsyB859YsOgaMg0FIMysjHmFXJ/gB7McJ0mLTdnkzvIqkJ7Y3CZhKTGBle7ot4c8HcPQxIKZusa/8sCSU246YIRnxG+CblrrNF2HiIlDvkwQFwfMJW/Ilht6WvgSCJlw8QsIgRZpqp1orWOlybUSUXTgqzM8Zugb2XEEsSoRotqvD2RR/mWq7hmfKDy1TznSFI5ryv6259tPAK/JHcH1Lrgjigz1TW+jkCpVyzJ2YllgpaC5rq4qkeqKoRuj0KRaJsGhZHgbyV2C4OWKbJN4JqC2yrBUioYRKzTitCQGa80AjtBW1fYzRJBFXnq5TT3cvatBMuyOSoK0ngHLbF42DIR7CFLZYouxsyKKWlDwfZTEnVBsmjy9DployaEYsFRMuTQGeGmBmmkEjhrbtOIJN5gJipNc81WSGmrS9Jyn3hgoEcZi8glETKc7IRInLFHTZSEpIaMLDicXV+gXdosGg6txyVWqJEsZCL9JYX0ANdbIGkmiyBErjOmwZpIniM/VZFSk8aJzBkKqS9jESCaGqGXU0Yha7VGnVdsB1vqPGKTqAQNCWVdU8kp4Qp+kFYMwjF1q0v+5FPaVhfN0Zn2LN6af8AqkrGbW4TlgPMwRVs6pEHAuvGa9dUF43GXyQZWYYQmqghqyiTXaJYdbKWEdcDcW+HWHlqao1xLbIqAoAq4vspw4zXRJsQRRAq7i6Pr5JlPc6/LxYsJj/S3KJsNDjcK26GGnFWIj5tIUoX3PIZxF6P+kqvLLk7rEmcsUOcam7XCrPaIV1cUkU/JiEysaCg2m6yBrlbs6wVl7eMIJetmgWwmcJuS+AXR8AVCrdI3dRZ6yNYNaaZLbPEtkkbIILNYuDFaIyc8rVFMiyRs4znnSIbBUOpQSzkHeg/B6HDDloaa0d2IVKLKspRIJWi0BAQNdKPkOq2Ii5x7VcnQlIgJyMyMOrDY2iKx3GRoTGkmNb5foqUKeZGgFiBIGpD/TPz9UpBALdQsGyM0f0kgl4hRTUKbp24DVZpzlgtYsU4qD8mFgFbTRiBHV3NapYUiZmR1glS32LYgIWWYBRRxiBBKaKlCkBV4qoFSg13Y3GqQCBXtrUboKcRxSmmIVKmK3wo5bo4gMlkqKWYskqNRKhI9ScKe2Sz2b6jNnJeFzT23xlQW+L5NUN2wKRS0mYlWCwiNMcZBB1NY0hMSFFUl1KHOYkrOGU6PeDNW8NcrXDnC2UJkZagXCxaixrNnTxk53+bY9Fme3WJ1dyCUmbVK+mmLuRfQbHY5vX2GtP2UxbiNMBAYpgJSlZKvU7zFnDAv8AcCyrWHHsusJRPPnrEXqmymOv7ogNuGSPyqjR9vYP2U7e2c6IPvYnsmacPBnB4RfrRB8Ers42P0NzpLaYZ5NSc+HXOjn2L7GbOexD2hQtPHlEJJXgQIlUYcTqidMaJjEM0CpO0aXdUxujtUWxE9NDB18f9i7j1ibdnuM79f5bBr53TOPvmce26+775MUiQlKsBqqWXZjbYNe+KBDc8888ijliEDnjRgwDBgGA6jhtEGOkBoyZKaUuuJfI8U+eK974aT89k51a5dOXlADmj7iW5o9D6gBvWvVVWj78NaWP/1fcRyiPGwxvvZKofnSwrpkHa9RmG9Qn7mYuUm0mCLxPUZ30m4cw03q7CitEhdk2U3orrjMKquIRhTWlHORSkhV0f45yKJFeMvRWJ7Rt8OcYcJ1p6AWC2QBiblTGZRBH0xwagVkLQxmaajLH2QEgRZZDnKCd9U6IgCw3GO5srECwkvb3MxOqIa1Cms2AwE6Nym9BUfr6FSDhrEvXNK7Q51WrTSlBWphJ1CLU8QJZFSLcKZaTgORKqNloGWqyRShqmIgIoYx8RFAd+SkBoZgihiRQq9Obimys1thdUHLhwukUOILeVv5d/XQgTiDHqhQDv2uBBMGosYXUi4klWaizZR2sUp55SWUNR83LpPPjdozC0qhZyZFNCNc/Qkp+WHpLrLaiQgizJxWWK01NAyKNspemNBkGjUCipG6FMUA5ZhxlIakLR1jAVIShdfXuEKn9Z4yYGckIsysaehZzlubqAjUFJKfKee4CYZw4WLJbj48x5Jo0St0aTlF8ilKo7l4t06ZLJF606DV0ub5VgjyUp00glytoVYcCkezInzNtGVBHWdYDugs75K2SpjiUNs0eDKz2l6RyjTLQZVj2krZnB7hCvYxI6EmggUa6vEccQiNVjOHezZIZ4Asiph+lWKkoKvZAiuR7i5hjsRyewmEjdI8i3e0sVYrTHeydiVRmjVXdqSQ7oZsLHt8txJefDZEb0goS+95OlojdNwirqs4Vd9LKtFXcgolKvMc5GLuUtJSNECk2w6pWrkpKKGO5WhvGRTMrBrdUZFCVGG6thifjVht2qwutGiZ22RBRP27q8Rjn3USs5Q9hHbTU4GMffyCKoB4njAonyHXfuSKyFjtHiE0FwAACAASURBVB5SP0soFwVaa3PSgcXNuE9nKSPNHbBtVFtgmC8QihGqrhFnKs1ijlZwSdIh/nWBi26E1JARpzGtCnhKSL1cQ/VMNpwJz/ycwoWMUDRxjk9hJ2ChiLSiNgfeGC3x6Hoe+405Xm1CMW4QhhZaUWRY1mgGDoMgJxYUFmqGLmfISkRYUfFcGblRQlVEjOWEepJxHqXU1CKIKYIoYHoxx2rCqhQxMGQq96Bw4uMLCaU1mSIZx1EAk6/m39dCBIQMxIXESipyrUXEkoGXLCkLETPdQUIgDnLcSkQzFIjmAtMoYZKCZ07J4gwiiUUyJxgLVHQLW5SJSi55IUZxU0pmgiQlGFFIYtRZ7WQMX1p8mo9Y38jR1YSIDWSpRwrcJALN0hzXj7kqZbzrmTirdyhPZpxtLLlX1slbBeJMInRSxuo6a5lPiwZ3zSp2PSeYeESX50gdj0q9jjYRcTyHghpQEnV0/y5KY0TB8SAROJsWmFYX9Asx76d1Nk2XcqPFVIqxswLOUqauTkhKHq6t0hpPMYn5IBgTahGi4/CIt9BviqR5jlBV8bMaEQGpNCK9jilUlphqHa3cJHBsLnovWES77GkagRqhNDSuz0ANIyr138QSTpi7On7Uo2ZEKBOVPBjz4kygmY0YXgScPJpTxiTopKhLmfPpGc9lm1oMJzcG6dzlsKFSDsuIuk3ajbDGGpmmIgsCcaQgWxvsxROWs2uiTYPyWOGL6x7vPKpQqg/RxhvULqokVkyULZkqU3QK3Dw3EHdDlHSL9orDgfsMOZOQhmVU2yNyFJRKlfHFLfGBQNZYMI9kTBGqaoGsEtO3YyrLBGSVsGwSJhmCExIMm6TzOVpeIhctqDqofpX1u138uUc8EckKEqvjGgduD+tbLs8mIdq0iL57gXu9R7Icc7tyTXkmYb6M6NVcOu9vEfpjorTF7dqAF8mMekViKcpYmUo0nBMYOQ1dRlhWuJjEBBWPopqhhpCoCstcxl+6aGqCkhislyOYBjRiCbGv4bou1dOEXlXhzrzFntzn5G/h39dCBDQ0qs6AhdhGVAcklTqeKlE467NuQmxIRIuAVWnGxBCx+gICGqokIC9rNLIB16goakpLTbHiFF9VIIkp9DLSROAm8bAtlfVxgThc0D1tUBEXlPwCnp0TJnt8wxF4duzRerjNjuJyVjUo5FW+eTWlZiqkGzJVVWG/YxJWBEayjBgp5IlO2u4hehLlpo5qOGjjAnM7ZBCN2U1U9u/1SWQDR7N4Wi2hA1f6BPN0xNHCph9ecadoUh4XyfZ89NDneeqxnWdcHR7x4EEDeRQjt0RWaib1lkIyykicNYqJz2oJkrcrPFi7h5DrdJ0AvVLEurxiKThoeY6qq2SViLjgoKZNMDcIhwtSL+Qk/yGOb7PWNPjub3wPLf8j0mOHyHlE1XpByVrhtr7PB9dnZOcuq4KOs2UhXx9zVm+zL08J+za+KnF7M8XLa3xHzPDn8HTXo9aQsX2HMHKJezGhluFEMZ5foqJckPRPESwLtdmnlugI2puU3iwwLw5ZfFmmEFxxWUm5WEzxjiaE5yXU4JayvM7oskj+xCEMCjh9kR/flNjYAeHLOUfJmNPbE/TSKpokozuHWPsrREoD7UKkUtQxNI3QzCmXROqRyGRmkxopSynGrRbwPY1ObLIoiCSLa4qTOaXQJhUfctp0KT+QuOOsYFfrxDFImcWG7jBe80k+zekVRTZaEW4W0lq0aCo13IpHENmEM/CVAL+YsHUV0SslBFYBdyRilkKUwoSdicZETag0VUqqiSul3OQud9IMoyojXYa0bBVLiHAjgeg2REhzHixNnicBXXOOMtWB5Vfy72shApGUUGt2OLdcKlOJ5mSIk8WMyUnzDoW5j6pa1CcDVnT4JIJQiFBYIuoFyOpIgUNJzDB9WFRStLpALckI+y3KLQ9lpPxs0zLVEJ5M8W4d3os1ypsprw8zStIK8nyOuWYQTyastra5ORvTlbd4IpaR1+ucezbdgoHXHaCnO7BVpFVWCZKQUrpKy5dwswJCEBE6ImFuIJZlQk3EGRrkcZnaokpaKlBRIs5Gp7y2L6nrQ+ZDFc+xsao1qostkg2f2d/UGUVTdu4ueH6TUy7WiBZVepOAeG0dZzFEN6boD/eoHnYRlAL5RoJbdUjzBQUjQG57bGdTgkwkkjYYBQ7R3KEYXUCrQVNcx4uPsOY17FSnbaXsO3PmrSJB+QSztGB2G9DlnPV5wkG4oF3xKN+abJTeYrAW8vqoT7Bc5arVZD5Z0qJOoWTxU+kMUVG4aCtsVvZYGQYM44CLwZCSKWGpIpmsMdZjxvEV8bgNqYKp6jxud0mdMrehgmCUmFsyge0RZxeUtJB+f46jX7FeWqPSuSJwEpr+LhvdDfTSIc4PE66vT7nMX6EpJs1bkXpzj0oOWqATCy2E1WuSfEqpKxCpEbJ9hZe0CKIe+bREJlZQEgeyMXkoo4krOPI6Q+2atNmmLivotyL2W49pbJ/Ttu8zVI6ouEWc5wbn385I6jPaLxyMBzFiUEFrzoguX7G9afHlpIVoLqm7LomXQjVDMnPoy4jllFAGYZxiIbC+kNGCkEB3qe7IKN2U21RlZRrCuoiwNPhIyvBsnbfLDn5DZnStseH7zBOXT8wShF/Nv6+FCKRiypk3wpAbJJLArRSRmxq0AjSpzqDuEt9eUSrIbN6UiAo2SUnHCVuIDPCMgCRMyFoVUi3FHwRMLzKCqoZFTL0YUp5XaE6KLLMRqqTQwSVc0VmTm3TXPNrqKY2uz3pdwjhXGa/r1NQFYdBgGY8R0hzPbnAhi5Ru1ljr1CkGJQqpTlyYkaITPZhTlg2SqEIv6RL1B+xZBvIiY5YOUDUfVbAQnZRwbFB2i/zAq7EzPuRSaXIQL/hVM2UafYj+8TpzQeXutsE7wj4vZeimJpbcxJH7LBs6QtSjYSjUMwfFaOHbLuOrKeKszbaloUQDXEdgMi2Rmz0Kd6pkUwORCUt5Qa2XoYQZETYdrcBeU2HgwdWKjPGqSHXRpvtwwauhgzbWqEqfsJ8/ZBx16TZjTuLnWOsa189vOL/pou0VcWSNXXGLTlxD2J2xXKa4/Q6F5ZChK9PrdZnOJxjRJl5RIteGSJOE9VmJ4RLSzKaxGxEKJaZlF/GDGXFQRN8ps1RHZF4B9ek+9e6YTn2dqiRgVrYxwhm4InUr5PBsA9n4lHRnQOnHUy5rGaI6ppz4OEGKtVyAZ1NJNMLiKk7moHVL9JsisiGh5VtosYoqCSwtn2W9ymUUIpgOj4sWF4mMkIocpgrG+jaFcEFNL3ER5WyqMcl7EyxrhbWPdQ6iLbYeL0kqLgfJkN+3Mg4nOkqocL/i0DNq6KUVqpMFQk1Ey0xKtSGSL7GcJ4hJTiAuMVdN/EhEj2SE04j6MmJzRWVczGiZErN+zpNmzvfTDMPOCU0BN0wQt1NKdpkns5+d9PsqfC1EQEFhVY2wCmVwbGy3RIqD805G8CJl42LAXMho5CbjvZzsWsFyEubJlNQ0KAgRlVyk1MtJ70F5Q8G5EWguEgIx5vo04EwfUS7YjJwEDiMKJHwv9ukpLmI74Vy7oVrUidJd9lbP+FycsxPfY88WyVeqlDUVq3XLeN5CabewNBGhPOPyvM1c8NiMjsnRuR0tMFOFdlFEKVdI7AW2WKWdWSSpjOebCN6SV3nEl+oMez3m/LMN7r/5Lk/MGkvtBP/Q4sPpEWc7JhvhXV40dnnaqvBXP/1zkp0p5TBlu3+MvPWU1XKN9PRDrjY7lJYz1vQqgR5x69mIowihUMI1cpgqnP/pR/hrFVq1ItWZTqqYRNOETh7Qr7wmf5UQZDIrB13GqYC7K7O+VWejt81VbhLVV+kObykrDer3N/n+95/z3Ud7NJQhypZPJu8gh59wk0XctcYo5kNG0QW/um2iLwr07BveK9fpmSuMo5CwmoGhsbwFdzwkMbYxGveYrgbo/Uuqp2som9+gpR9yfZsTtiWEvRUWwhf8h/f+XUaPtpjdvmY/63BaEbms2qTLnF1nwp8NLrGvPApOGRWBoLxLGQ27ojHORJT8AkleA3WLcmsGrYCobRIFVSzq5EoXoh4ePvbJnKQQsGO8Q9C9whUFxukWQrqgJBrUGj5XyjY7/pxnvs+jSpO4uuS9DZsH/oLnogXLgLfqFqvffo/iqEsrNPC2H5AtM/b33+Bm9AH5QkMJB7zOY0amRmtbRRkGtHOBSTkgckISR8AtCigG7Hgh4TJnLq+QK30Wsyq/oU+58rbYElySbEZjqKO2JUaDr14KwNdEBBBVrqSMsndEMUmY5kXMskz1S4WZcEhkZWS9JvP2hOlco1XS8JcCHTPC8Twuo5xVJWdRdIlucjakjF8TVYYrbU7mIhu6z2nRYKVfwJInuLqFOg1RVzJKnVsyr8HyQiLYyDl6Oae20SbLPG40BfuOwHZWQd6qcF94wHkSkzds5J373Ag3DKsnNAWRyuY67kjFrS6YjDJWzlQUU2UpPSZCIPQkShsWheURPXtO7Gk0SjGIGdVOie36BnfeLGF//rOusGatxFrDoRKfYhbWmGzp/Ce2Szffx8kjVEsmqffJkgFaWCEuB5jdVS6thHhQQCajF3lsXPl0NqvM1Ab96YBCtKRtiESNjCzJWdQdvLmHJj5mGrzixhjj7dTw+iId28f5ac6mumD2cJPTjxw2txVqrTW+8D9nazuiL0mMA5/Cm3f4vUrMF2qHyNCIFQ3xKGUl1fg0XVJPHbQwZtyy8MIytURDFGOu53PqpkV5e8ipB96Jy7tRiK1BkImI8TVavYn22yXWZxGrQY1k8VvESkzdHmKmNWS9zrTno81uEBoqWfGSxpmJ+7pLZoU83eswEATSOGM3GTJMdRbJCmpQpFa7xVLm5HYRvSaThS7LNEZYTvHnC2aKipEZCCRwphJuKnSuZOxsSnQpc/bogFqvQzvu8zKaca8A7XGP7WqNoycTyn/pUcgs5k5AWE2JfjhDfecRXVlCDQt4+kvqgxs+9xv0hS4PkLH0hEJHx5gbxIHGTQb6rYccS/ixRmGWM88T/nmqUhF9CipEdzP0QMSLLarCgo8ll7ttuKonRN2YVjOG0VfT72shAmkUkPptHo8m/LC6yobfoxFZCONHJMYl5VyjHy0ojlPCssLxIKUp2hQDsLMcUyky0yFfOKixwGhTJprJBMsYQ2vSEWwKwyrppk+1krN9umAplLl0LHaGPpPCBtt1ic9mK7S2v0COy6wNQ/6NdMPT6nc4ijI2T67Y17dxWz0m+ZL7C5l94x5Sc0KzPyPsP0Z0x9xOZezpgsS0eRxJDIs54kodxRQQUpOLjS2cfMbyes6OBMV2hnyrsdk65Oa6BhWVumBR8q6oWE/Zzac8/9xkzfmSK6roFRX5xqdaE3DiAi9fHiB31llZGLQbET3nS3y2CdMJg96QYmmTquNQykY8eVhmskiYX58zU1LM9bvMEp2qIfNEMTgsF1m9P+aid4GorTDwKmw7Rf6q+Jxlcp+r6V8yqG2yE5qY+ac4h6toW+coYsSmZdBTymi+hDQP2aiFXFdvmEUFvKsZoRIT9JdU4zb+joQhuthVkyxdIQxVWqsynmOzlHNm3g4ds8zp0yYb/pRhQeJ8cMzObAtDC3jvjXXsJCCSe1TvBMh/uU4p1bi9o1PzHCavZc5u+rSfTqjJ28jVNnelhGvT5/TqKe0wo1RzkaouwdBHmMNWu8z6XGWWLIhVBSH0GMURuaNQSou00VH0S565Y/SCivllhlKsc+8iZvUbCWdVk9+bx6xHW1wLCtnZPpv1T8haKZtdjU/OA/aNB6x8t0zv6pZ80ED89QrqOOE6CtCkKWqkcJ3GCJpBa5bhuS43qYYfiOxoMlR0ZhMfyY+Q5SqbzRa1eMmFNUC/qRGuLTGUhK4SszLMWS1WOc411p0Yq+kCX20l+LUwGv1Hf/iP/qAi1Fh+z0IZG7TWZGR9xs1iSCbG3IYuZlPl0o4pqgYPWhZyEjJKNaKmRiqqlGzQ5Q6ZLCKECQUynGXEwlAJFi6xuuBoFnNlS3jLgIUYcqo4EFQ5Ls3QLiQePbpCKsyRThZEVwpda8zMWKFZM9nv2AhFjUr9luVnLmJR5KqdsG1co1UqlMYRktQlkc/wRymxrXDVWjD2rhEvRkT2grHQxzn0mZ1eExUSSkodS3UoGSnOVYqjuZzGDul2mbqXUzxxWPz9PfRtCz9O2J/+DrvqhHE1Z5Ado+UypXiP84MJjjukWFtypKbkosyauYnaFKnpZZSCwbWYcHR+zlWYc2WGuAOV5WuJm+ELbpKAE9Nl6CS8eJkiP+vxk4FGJKRI9QpzYRPzdcJ37/86B4tnxH9yRhClBPYS349R6bHSrGJeH+D1b1hmc46K99hplOh+/DFX9gV+IHKQqUSNEqgd7CSlGXTI/XXOgjGzWGJt1kJEJi81MN0MaWZTfFND8CrUFhly9ZDadoN0WGUSxuTimzxe2eZ8W2OxkOhdnDO7VRiLNwTugJX6N7lT3kGQFCRznVpZIXOgxi2GpLNQRMJbHXGuE1YneBsySW2DuhzjlgVukwKynVFdKeCMHKrqKplaQknLZILM3Xsp1q+22N3IeOAsCBp1Zv3nZPMEdko0lE1u7V2KewGD2RkPshbjb5fA22dbOIfLLofdPkcHXfx0wloAUkUF3aMwjchmEYIWs10ScULwZwqVJEYXCkSknDCCLCWxVSQpYtn3cZyYR+sZwRsq+bmIadjkhkcwFzgP869v7sB/+wf/zR8YmUhQsLi3anObupxMczxDQrITXKqIDRN9zSNRNayrKSIJcS4zX0qIWcokX+AkPoYa4WkimZdTBWpliZao4xYdwkiiNa8yDkMWSoNvlpbkaoQemugtEfWoiuC5/OCqSKGeIm0VaCclHk5anIcmpdoqw7RBGHjMKwpGaYRwHaKiMpinKGGCnKQoiorsR6ieydIaoTQSrECiNcqQUBAbLkajipTlDHoF8vYeSfQNkkudRrHMZvgYY6PMhQ2bRZ3ehcdu4SlfvPoEfWdKKv8K5qLEpWeQVc4JZnN2jBbLkYm5K9Hae8p87OL5F8ydJV27gZpWMJ061U6Z5lYZJduC+XNsL2IogdkAZy7B6YJp/ZJ2YUTpVRd3r4R1FlNIutRKBczK54w/ChALS1QlorQ2oBPuMpt8wUG8pLRcchiuUj0+Z95cUriJEeIM03FwmkXWaxlKvETTTcqmSPNuwE5dobGU6DkunmbzdklCfTzCLZnYsYLadmlNSiiOghY1idMb5MVzpAce49sZ4rLNMMuw+RF/1XVJ3Jy86mH6PhQuKbQ38UoTLNlAEXwmTMilAuKwQnFdJE1M9IXORqtMeTSn3GsQaQJC2EcqGFQdHxHYKUbkuYdUa1A+76G4N3R6EwL1DeZbFmI6IFYfM9FzKC6plUUM2WZvbvMTX+JRIaPo9VjaIBQsGq11roMh/aMZt12HPJExwgBlnNP3dRaaSkqIZcugpqT3E8Q0R3QjannIwEjQSxnXboCs5rxZNFHqGukUbk5icusB67MJy7mFkHhcpF8dTf61WA7IuYTXWqMyO2asKZBWUMwl+iQlLqisVvp4OYjdnCyMcQ2NRiVmOVpSDhUsQWC5Bs4yRRqKVOYpMhYVAyYLgc+diJ20TUkLESWV36y0+L4zxhPWUeY+t+YCaxDSMmQWic+jeY3P34ZvlWXePBZ4ZnV55xs7nH6wZPM7AZlaQx6JpCUF2/OprkbwxiZ+GBJ+foXzaYV+PaZZvUVzyoyDGpYf4cgWqdXmvlpmeu2hF2rod1TSi1vWrT/jdTpDdwTsxgG13g7f/r0qhWBEvfMEhB/yxWmZZb9Gq55wUgp49ONXeOsZdwprDOMehckalVqBo8tbsotXjOIGuTlm6R1xO89RlFWuziQkV0BLTpELEQ1NBmnM6ryEnj9kvv8jVPF36To+89IfUQ6KaN8KeP1XDiXxgrg2otXe5TIos68X+YvLGb+nmDy7NpEKY85HTS5+N+F3R08p9HvY+n0utGtqgUfDbpHvyGys3EO4yJhLu0heSrd/Qs/zaTQ07qvvMd/exFheIayu0Hh5TNRfo7V9SSHtcO5rPDUaJMVtGJ8xE78JX4yZ1SKOUNHlI67HKd+4XCVodEllGb38IyqzN5hYN7S9Af3bJqXOgmszpdxaQRrYeNmMqNZCSYs4sxTtTGKnViJ0NA60CVF0RRD/JrG6JHjlc0dcUijbPF/f4ffNAtayRx6PeV15zBtWEWNwgmo5XCovObos8sRtcdKJEY+nbD4Kce7WmOkDhv9aQFjGPKhCvxkymUiEFRE7kykMVcxChmfGaCEsj3RexSF3kTgvWSiax4oDpQ0RXY0RAwGrIFGLqpy4Azq1FxycJjxa3SGUbTjLvpJ/XwvLcaGo5G/eLeLLHaafnTCu1xDHI4qlBkGYIysTqiWDaS+iUShxW8oQhJTKMuF+OUbwdexIwJASnIJM6JXRHZcsgX4e4Cop5UTGSzxSsUTx0QLxQKTxVoG1RGViLlgdNXjjGxW+/8lLfs37LqXfkfibzzPuL1M2nhzjev8FW1wS7bsMrvZodAZcSgLiWCdmQl6q4NsDThs26tk2NaVA7vaRlRMUocTs2EYRCux9612kZRm/bVIZHfDF7WvuZhZ+8RDZ+C7N7ScMsh+gRAWiz3d4p33M9+96lG6+QWdtjnFY5HCrR3+R8AD4lXvv8qEbob6UuTUy0v4Ber3BYuziJgHR9IK4nFKeK4SpRWdVofWOgtNVmb34kmAjId2o4hzYtO2cnZLEVErpSHt8/maLtX/1J8hbT5jdW+WTPz3je481LgsVrLNXDOK7vNMReGm+YM15yunhBPPOgstn10yzNUpPQBfvcbrWZy9/wP2kQKsTIvrrjAydRXjJYDpAyk0KmYhmZ2hZTGe/w2rR4nCxwZpYx2uesLw/ZusnD6h854piT2djmZN2Aq6cNSbjF/xQ9/jwf/gjNsMyxzvnsNjit94yaSlzxldvELZ8Rukh0YFAZUMgrW5yXzcxGj6HPwoIL2o8/Z5C0j7CNZoYr8roXpP6ioOfSXySD9FuNebGOqWrEXvtGWKvi/DGNo+kBnE9Zr2a4aQNkmzBerfE8RsGuT0lvWzwTeGYP7nR0R6I3NveYnu3yxcHMX/yf/4rvvgiINZ8pASKkkapWMRwAp6R0FNMVNEkCCJWXRtlxaCrFKkM52yZHmWrTjmOmbGOtnYNQ5vDQMTyU/yoyG0H/oNaxlXq86+fZV/fLMJ//If/3R8sKktmjoDjupQck5YWYLohC1NBCyxsJSEyCzixirBwqWDwyPGpLXMmisdhI0a2NNqOStVzsfQMPa+TCB6blZg8D1gUS7DZYXcY0m9pVALYbQhk05zi3ZzmZQXhusFZuUS9vMad6RWdUMZbz9D2a0RCzOSlhCs/J1vfwY5sfO+KEJOLOTheRuvmHmZzimr4hNOUlrjKbqyyH9tMlRKXtRJO2mfy408phyE9u0xeKFBWVTRNp1RrcPV5RuGDJX6tQ2Ds8f49naaS8ifHZ4yCiJV5xBt7Txn5GYHyANUtE0WfMHtySZK02NI10koEakZUUCnPGyimjd05Z+1emXuth2xhELk1Hgtt+k4deRmiVl7y/CxGUqrU7qwyOz+j8q130dYM1oU2ovCKnfUmh6MR3aXErnXL2aiOUuqw+qBN8849fvJ6TlZdRUtirH0JY79KcdamIYQMkwWhrbBonJL6FkWvQNFXaCbXqHoZsb7FiuohajLLmsrKwiAxL7Cmv4L4SMRcuIQsqImbnK18ypZ8H9xL/sX/dMCDqsRBZ472/HPM7YDaaMbvT2v8y/YdtpIQeenhnXcJpjqmobGSl6jMFyihQnGcUN+X2S9tUPBWaGkZ1XWNRBYQUpncMDHmMEgCiitDVqcaxjTgOh0hCBHffvqUiy8DFtu7JLMr8sdlzsxTmkLKoyyn2/sxqv8dSns6XsenLPfxxi1mNvwf59fcDl0eG0029YjWIqYg+SjFBM0VEYOAvhCRSkUyLUZaCliezWOhSBynyNiMV0KSVGM4yznd8Li6V2LrIuHz0OAf7gj0XhQxVZ+DefaVy4H/X3sxQRD+d0EQhoIgvPiF2h8IgnArCMIXP79+9xee/deCIJwIgnAoCMJv/9uIQJgGiKfvsanZ3PMtbCOit1okM+7TjiSEaIExCWnPx+TyhFaisu+OeaUHXKUxcSjyJFJQ8wTPi/CzkBo2pdhBzlIiWwJvj/q4wnvnc9JmkSc9m8JIJv+bDM2yaF2VOd4vsLFqUis7dMOEy3aV2d5druUtzKOQg3FO1rBZSZvcjG6ZnKwyVtuE102yK4+OnLO2foPzWUJ8nSDLS84HNwwDmdHWfZI7e4yff87i8or9eh2hM2F1He74Co3COvpwxtI9YnNXJ/2HT7jzuEfpu2PccUBuNWk7a6j3bL4U+py2T1jZusNF+owX4iteNC2K4Rt02iuonQ5CHBPEHvob91HejbntzFGUdVZnjxCvagzjLvXmJbY2xTRuEWoniOY6q7UG1xsNPtkpsSY+on6tUl+7x7i7zvG1wF8j0ti3aHOF4Otk/i3BqMWZvc/5UYMsGBLutNkV3qY1+SZW0EQZ+pwvF3T9AnFxSOUiofjXlzS8Bd/cSWlsKBTc11Qlm/ztN/AKMbe5hyt/jGVuEpSH7GgpwttDBrMVoiMBXdlmeDvmzM5540HExfMR6y905J13WZ/XyUYiN8YDfl/9Eep4xOXFLTfozFd94uo259oqt5uPmEshz4SQHy89JsqSbmnCqaPQ90WcuoGbpkzSQw7DCx5YLWaTgGzvguH6JUV1RqGl88X5Sx7XfdbkA9ajBP1inQ3nXcS+w//l12hlU84enmM2t1DudkiP+9TVG8xVB6MbUagXGZVinNWYkw2DV3mReaQT+5v0gAAAIABJREFUyiqkJptyhabkUA58pNimWE+RH/mIOkynIpVZhZp1i1aTGDomv/k8Y7FZ4j/K7/BPY1i8N8Et/+0z/r9r7gDAf5/n+T/+fwnGQ+A/Bh4BHeAvBEG4m+d5+st+IMYCEgO6xxHORoYaNCm6M0aFl5TVhKBvohVg6FUoTh0SZgSGyFPdoulojIIpdi/Hjqv05YRE9BCUAlKrTLtksluacliLqC109IGDLC+JxQ6zLOVDNeDbY4HFho7/g5hu84y3ar9OQfqMsbyBNDtlXCoQTq4Q7ALSbpcv6ga/lb2PH8/47DBkEn3EQ6lN2tpAm8DWg4zPwhuuT7oUojGhIfGWWuFN2aLRucOgKJNs7dEcZPhJgLuxw/L+NWcP7lDuKyyD5+jCf07ojUjPqyy+UcH4gcyFnrA++l16Wy8JPqpiBwV8aUhVeo9i9UdM4xJeax3NLGE+XFI6P8UOPyMONcqL95nJMie6RN/7AaOrU2q6QKNd/lnj0HGLcn2HxrsylXmRtR8ecm5FyM0yo8s2ovKcx2/Uef3ZMyZHl4wMDcNYYvRKyMYV50mD4uYHNLwa/cMbrsOY0E0o/s2SUTvj3vmvYIkw63qkxQKbv7GFlu5wEIyZpX1SNLZlC22+QpKds5smNN+G6EOf8tYz2rNf41rv0nrwIYObxzhHn/GvPyvipM9R1IDjdp3qYIywojP4JGP9wSantQPKi7fQLJPgucBubcHt3U1We0XK2z1cWeJHL0uE3RselnZxNmUyNcNKe2wKdxjKFY7rCtHAoi59xHL0nOrOO2QPYiqXXR7shoTnq6TfspiFS2Y3baz3l+z98GM+Wd+karRwfvDPuVq3KIabnJSvmf7ZARXpW5xcB1yNPV4Hc7ZGFXxJYLBaJjVMBvmUHjH7tZxKUcGepRiZSJLpeHmAGyhMbiL2ijm+X8Qf2Px1v0ArCVitSSTSCpvajG71lu9F77Fz9Vdc/u2eIn+33IFfgn8P+Kd5nofAuSAIJ8D7wI9/2UuZKeOaOv4Y1idl4tBj2BCouTmDUQFFaFBNKqjZDQ9qEugCqZjR7aW4YoBMjpjpbBSXOKpN3xHQNZknawLV9O9RXMn5TjSg16xRUkIutFsO9g2sj04ZtgX+wk74xkpG2fER0irDpUjlVzdQPxYZ3NvkDfWnHGiPaKlzjucNtJ7Es+qP8JurSOJr9rcfIizW8bKEU3dA1i5yp7hPVS3CqES7VkFqPkTctCmdjKmgIaseJ8p7VPw+LzotjFmNiL8m+Hwd683f4VP7C/LuLY9+fZXB6yXvbnd470UZWbhEd17yo1mTtdU+nRUV176lL9yl0s3ZlL4gjENC12DVnCKMC4xtA5IcJbC5ej1EV2U6xjs8zGrMohkPrSFH/2ATx12h+5Mu40cj3n5vn/GLAvbZM9587wZFecYHPY3asopRzXglxnSuMj5fH6PJETvdP6Jy2qBfLlO9LXG5cUIk9dAWGhtiHas6xffnFKxVhKLB9UevCZ8cUnyngXleISmuMqu2aKmXjH2Te8mM2qstXtbPuE5Frn2H5rN9zv2PSawA6doEKYGuTaDPuL+oYOseDc/kYNkkXUw4Ld7j7iORxnJG6b0umVNm01ni7iXcDOqYkyUIA7rRNXIq8Ha+QjnKmdVTXihjCi+WbLYjhI7HzVmbk4oLX4ypTwfo0pxcKSCLh5Rf2pjz30HeLXI9Wufe2phfMUOO52/y6LsV1ONnrAYZYzXl9mzBT9yfMvpehXeUkKeTGF+eQ1tDFubIFwt2FCiOFDRVBiFlO3K5bAsMijLGKMMs+VSnD3G4xWl4bIxkdhWDR+sq/yyacy80GFU7SJsHPPr8OZmRs5IKf3cR+CX4LwVB+E+BT4D/Ks/zGbDG/7NF+ebntf8PfjF3QFHA79zyhipw5E8JdZOCnxCUGyi5grg5JD0O8EyHICyQmx6hV8Np20zDiI1KAdFU8H0ZYZ7zva0id7/5Fvpui8JFiL4ZsZ2uI7Q1alGXHf/f4X1hhvpr7zL7SYQwFvngzz/ks9KYSl5h566Ie5DRngu8Nff5N+k3UKoBjqbRLuc0AgW3nzHyXjFvBSRuzJo0o3gg88enDoWtc7aVNi1zh2ZJwJCGKIcfM/jCIntLoVwpcPbqNarQ4lNPpDbs8o51jxeTh8ysC7aIsaQ15GaJnRc+9Xeq2L0GwdYHjKvv4k53aFVvWc5glMFkaNF42iesRfxgWsPKSkTzKS1rl3mQIwojLnHIFxL1fYesXSUKdQ4Mi5kQ8Xp+h/WfnHK9uqT97z/iycslHzwbkJpzlvolD/oDPpDuUDz/KYXOnKrxLpHgU5/6HCw73I9/TDz9fQ4qV9RLAdL9Np21dbSxDrUuzoXBx29FeFbCg/iaklBHfCzjM0P/yS6mOcEPHdYPDjALFbI7Tziq3WKU2sSfV1gd/xTXveLTdIFl7ZMmY5zJa/LkPnptG2tmUdBT/GKd4y8NYuGUF/OUX3/nOVe9uwSbITfTBqXSDE+qcGfZ5sl+CWVRQvUtJltzjFTndpEwHLWQji2a2y3Wv9XgeNBDvpyi5CFadoZS1gi8TcxvVjnuLlkXdZrKlI9XhvyDZYVJfM5gmaEWH1O1HW6bn/HloEl7L8DsnTPceJvrH/0F79kr2L0yr/WE9xsJczvBDVdZqUosBn1cM+dWibnSEmpZkbdGFomsEJohDUXjfGdIbZ6wtayQqTUepjH/InMIOkXCs2Mqnya0Ht7hJ3d6VD4TyN8U4OOvJvK/1e7Az2cCf/wLWYRtYMzPWpD+EFjN8/w/EwThf+RnpqP/5Ofj/jfgT/M8/2e/7Pu6KuYFdRXDs3G3A4qewPUgB8VE2lBpJAu0mYqstukILlpjiTBUGG9YHGUL5LjGA1/i77VqLPdD7MGQUl4iqoHotnj3vQe46YId9QHzw1ukxybl6ftslI/wjQuulho/vv6I+ZFEf2SysprRkfaIoiGFtEj3yiT99gL/pkxtbclGlFNsh3x86ZENC5ixS66m+K0WUruCtIxZDgSERsK2I6JkfY7UBavjgLbUYama2CsmVqLSL5YI7zVozARGmYJRaeJcvoKyxrtWmedXBxQLFsQ5n8wNHr18weLNp3z6wSHaps+Kn5A0m5Trd4iFgK5wSr1qUzuKWUgVUqtONnyNKahYWc7ZNKLvaLQ0iY2dmKwmwtLCknfZakMjPGO5/jbeRz/heitk91kDb/uSLz4c067Wqb/7FpLb5ngwYMU7ZfGdJoN/CXZyQ9q44d3vrKEuHzAKL5iHCu1wm5uzSzaeFiiqOyQbCd5NgjdyqBSLbBXbiEqOUpwhF6eUVvawmiuMZyE30ZgtU2V1ZQX58JiPXt2hl4x5f3vOwfVzfvRnP8XXU377wTaOXUD/+zqDTxeMDka01leZFdYxPnlFs54i7gV8crzOG+UF6u4KyaKIeiFSSQT+fP4XuKHCO29tUWvsYkcFWqspjVTHKFcZHFxTkOHq9THyukux/Qg1h99YcxHn8L8KHR6FJ9zJ9zl4sofarCCdf5+5a+CLa8w+/5xHb25wKN7lm3YFd+cHjE9a/M//5Pusya+IMxlJqnBdmLHTiygpYAo5pmPQF8scS0uSVGKjFBMFGeNagjHLmPo1dtdtVC3j3i5MMokfPq8Sj6a8KTfpfFPi9MMx8aqIMV/hmXf6dwsk/SrkeT74BYH4X4A//vntLbDxC0PXf177pcjSnKXfpyo3GU72yUrPkcScmupR9hQuxylGniKGV1StOl60Q73sot302Eib5OoK7+yGxEUfN0wpFXdIKncJjYyIHqEAbxX2mQ4niCu7/N/MvUmvZFt6nvfsvt+xoz8RcfrsbmbevhpS5SJVgkjLljWRAQP2L/DAf0IWTA/9Czz2QAZsmAZkAYRNqigWWVX3Vt02+5N5mjhN9BG777cHJQMe8JowB8L9ZhtYWLPnxfvttb71brZfEB79JdNmgLjt8f5Zxol1zL8xlzyVzpg7H/LeQCWIJepwwdejx3wqHjDq/YL1xQVFZ8QX6gnH4xZfF1+ynqXsl4dM4hbqlclGTQiMDL4uuevNaRvX2OVDWj8o6WbvYZZHhNIFS8+mnf8Czf9PuZFS6ijhyHrG3LZQ44rNyzV7XbBXOoJl8FFR8LZzyMdKSC5tKA40fv5LkaPwnFUg8sNJxo/de7wVllyLS7SmoBO8YFGLvFilvNeH+0+O0aoO25t3VOUpWj3gsfo1g67GtrjB7wmE//pL3NY9fnAxJW0d8tWmIeuXlJ/8gH/QTvk/tn/G3wQ/4NMyppwdcPiTW+6ub5E673H+rc3q9AUP6/u4vQK7vuYTJ6Lpn9JXCubXG7rolJ8WxEnOpb/CaPp41n0c5YL1PKWo3vBI7lCJ+2yH+zz/7Rk94YJdozMevWTx8wlTLSYwZPb8HYu6T/vwHcaXByTLdwiliPryc3onCUf/xGAalrTkR/yodctloyO/U7AkCdN0uH58SeevehhhyXgt0xtrWF5OlbWQtgmJtCX+BNzzHeLkHgMjYN4WeLy64ZsXEkr5AFc5Z+4U+NINB4s+i+6C97cfclsuuCwueNLAk/d+AHd/TqP9ACNw6Ewcjkch7lQkkA2KeMckF1Btj8Dc8O1GwJUyJtqKQSlgNCZ65JALPouZi43B8cMlWrKHMRUIWl32r77lH0dL5KMOW2/OHQqP7ZLXls6w78OXfzt/f9/cgVHTNLf//vOfA//PycGfAv+TIAj/A7/7MfgA+NXftV8hQUdtmJ0oWPMS83ZMKNywVGGjGAiTDe0V/IHpIQcRb/2ASy1l5pqkNUyKM+6iIVl9SHS5JfBU7GhF2+uSVQLNq4jf7knczTT+ILlC/fCYaT5gmOV0D7/l+iJnKMY8WaXsxIrxk9/wq5c2TVlxsplx3wh40/uUL7qP+EnXY/Zqi/v1HZflmM6uobT7tE9t4jQi6VX4myUXlyX6uOShC67n8GBRMLq5x7W65KwnMN00iNWKV8kB+dnn/Of6H/Kyc87rWcaHyRN++7SNsPoNm8BmcfIN77/dYy5A+MAkmSh8c2DyXvKOwVGKYRt0YpnMsrjIt8iLPerEJj0AY/GOJtviKB12sgm7AuH2LU3RkLkbOvtLrosfch42nLoTHmZ9/urHWxYLkY/6beLs1zR/M8B4+w510uV18SO0n0k8+N/eEZqfUHRmTP+sRjn6gI1os+U1Jy863H6o0Vp7FE7DzLuPqChsxAUHjYcpJUR6m+P39qjMCmE2YvviFfWZz7bjsfnoATu9RvJHnM7e8cSpeF1/jGddYM0ecP5U5/bPYrTGRN8/RrVWyO+ecPdgQlV+jZMdExkSo32FadhiPAjY9bvURcB4B51SBy9HuKqIL11u7RZYC6ZZRpXFSJZMPltR6QJGN6ET15S6R+Es+AvN5d7VG+T8kHxzzlS5YjFW0NsGgzdt3pgv+bjpc+P+ELWJUdQ7ttqI1ZcNvmXwvPb5p+GH5Ntz6oM5/o2G1omZbg3en5hs5TukdyYdTyIUU35Ve3QVlYmRc53tWBcpvZZCpoZ0fJPJxic5yjn3b6k8mb/8vYbi/2rxA1Nh/KuM3UFI/E5B+3T7nfz9fXMHfiYIwsf8rh04B/5rgKZpvhUE4V8Bz/hdPNl/83edDAAotcTaGHLvWY+3wpc4Y4HTmUmihaj2DepM4N5Y5Oyu4d1+ykc3NexrGK5Cv9jiFgNWDwbMdhZJCvu2gtKX+Ca75MFUYDZZoDbf4EotPv+nH9B5JdCZvURzEm7Vp/APtyT/o4h2+m9ZKf8M9eefcSZGGC2d7v4ESZMJbq+RJZn9px9Q27dcfPmWjvprWo87CEmGl3dYuF2Wi2+xtipPTIhEAUltE/sdzt0Ov5RTqnSEGsxw5JjS6HFYTxDKJa+717ySM36o5rwZf8583uWg+AL95RJV+JB7P9rn1b+7ZDP9Of/LNzL/hB/RGT6gaGRWX11yzTO2yZJ29w+x3Ab3xKWeXrJayKTimCdHCseaxPZuztdOi+FwgmKqCF6AYKds/vKc12qXs5suzfw5a6XNZ5MO/nLIQv6cqadwX3KYmira55+hZY9QD6ZEyorNB7/PfzF2+fLdc96zTplZMrNnL1jkU2J3iJmXHHxqUtljFsoNw8Riwgn74fv064p0csXP9R7x4z1ORhsuzp4RdkVK5w0v3rY4KQvW79Z4TztE5TU/ffWIzy+mPH3aZtkPkWsb7Y8Lhj/vE351wO1BxXBiUq/foxyveLOWKTevmOw9ZdO8xfVXZOaQqtvlqvI5tFPWXsW8l3NsrKizDrt7HVA3jJYhh7KKcqhj2gbB5yE0CcLPHmIGAuENSM//Buvbj2k5/5Ynp+9zEU9YX/wF5rjAW+c8P+tSv/eM4puS/fde0T3qcbIfcXP7I36ZXRK8S3ioCyy/9slFm9TKqYMBVSpgNBfIlcpFU9NqShRN4jqVwRLpWgI7NhwkffRhQ1Jr/OS3AeOjkmYj81xYo+8MToZrwq8fAS//fiLw/yd34N+v/xPgT/6uff/fVVQNUjJirX0F6ZD0uiSpIZtVpEuZR+2S+LzPi8mS/bDFVNmQXAusfJDchkOnwJKv2Vo58bXM7atbMB7j9g8oBxtaS5+vcocPP1E5LQXyByuitxXn00OeUpNEO54N9zC0GCv+Fd8oJX6U8v5tgvjPh6ymIqvVFQetI4q//oaZovFWbtOaWCyDAUbT42z2Gkde4oUSpa4S6FuM0iL2Y9LJiM5HB4i+RdrcUQRtxoXKDo9RS6GvfcJrZcPTbwx4mFBvJI7VJZmV88q2Keav+Z//1Usm8ickeyraPZ0zocWz2RmOVXJoCfzieI/7+xNCP6Bqh/Ru3vDyncbOGTE6MLkLA/TWkCKMOGyWuI5OVsrUL3O6xx43pcxitcWQb1hqI3qDQ5T9b3hwkxFLHpOej/jRfYYRXKV/DMZrjEDkxW9Env5Bxs5JeP35DQeffMq6XOBtNKpaJkSi+6OaN2mMOS4Z5g8pmgVmuKDjfoblOCyWNkd7DnvrlOfLNoqdEb1e8tHoANJTvrq3wdwYvBcllJnCy6ZAeV/jF9mG/8x+yNoc4f5yhnCU4v1Yh1/OKLWKbguMdztQDkm6AUYzZx+dlbRGnpZ03BiFiNugZqManI7fQyAiur0lzkVq6xTHmnOtrXm7aDGJdLxaQuxtqZ6dQXzNk9ohm/wRw5czfu2eYKd/RHI25eYPdfZWNXuBw0l9w37icXFfIMBDvj5DeiqR6weo2V+zU1NetWWOBiZKWbFBpUlqwjJGFHQsClxXpqplekXBB0oLxzeIexdE24rZsc7q2zV7uxW5uM/6aMmkXeC+bXOXzDjqCFTdV/DV387f9+LG4L/8b//lv+hUd6Ryi9o5QaxuCInB0dCSko9KC1HcMXQKKqlEWNqEYslTVcQU28SNzjauCUubOi1JBw3CoM1wXSMfr5hrQ6KpjCxGfEhFNzhhvn7F2IpI/S7+xqbu/pabbYtqLnAitbHUmJOOgYNGmS3plib5pIcTvGC3bvHIFAk3MrF4x+n+BjuXyBIRNZAwvYZGOeCrS52XuwhLMsh3BtvVivBizXZbIg5OyDIT0zaJzJLdrKRY5BTFGelVmyvLwfRvcOIhTW1QBDn9JzWyukf0puT3j+6RPk/51VzFeFxxqNvcTRt+IowwFgXnjcriNkepJYb7KtHW551/i3DPxpBG2LuCzdgknu8Tneu021vqKuNtvKY8g9ZY5n33PuerGSxekugfY3kttoWCrs240w0u/YhWd4//ShP57Ne/oPmJyKDqY/Q31KVFq2uiKBbNTU4mNGizio6lEJsxK80i7vQIu2PiOGZ6sUMbiwj9nNWFQosDVqnOjblg73ZOrikstm0WBx9izlV+ufk39FyPT4ZtgmHFOgno+QZ//fYRVXSFsDrmSNyijEekbkSlt/B0lZu5zFTYw0lkcjEjynqE6y1qEDJxTDTtIWFdEjQ1XrRDyzeo0n32NxqZck49XvMimvJ7yWMMZUHP6PPMy7BHA8y4y5GrE38a89FVTPc3H1Jacy5vU27zBuF9jyeKhiHu2AYh//ov7zgPr+i2a8zCoJOabEOFYleQxQmiEPPAbChVCaelM6oqvLggyAq+amJGoUMdKjT2hv0DA1+PiWsb90zhT5MST234RDPZSTIrreD27ns8Rfjf/3d/8i9sVUYSQVAC8nZOE/Vp7QfsBSX2vYQwMbFKkyYqkIqUB55H656M4Kwo9Jq6qen6S5Q4pcoaDFWgL++xS2W+vDlHMBu6asL2PQNhuUBvt9FPTS4thUXWoNgRxlcbUm2AW97Q/dkRXqLyzflrLLfPxC04qfr86WVO11VpTiUWX9TIjcp5qvD55oJCMYj7cBYt2QY5nrxP6+gId6zhLiNQ73D1hvKuh9BLiNOaVJSRG4jDV2hyjjeX2Zm/hM8chA+2rGuV9if7VOKKv3l2SZWUXIYJ4cUX+Acmk8GK8ePfo6v5TOyal0XOlati5Dq7cotZF2RySnnd0E5crGRMI6ksFZ1CtFG8S/JZTCn5SI/2sF9vuPPOGdUy0cih+dpnqrX5j8cO20mL+j7YucpNGHFfdbG6Ll8rJvf3Aor579MYS3LJZvT4D3EfWHRvBeoDETtKcTKZvFGwVxbrYklZLanPAyShZCTW3EyHBJcOVaZi9yrEroYvQH0sITQrxkaPneDxqSvw+cv/ne2kQchOaF9sufe4y2drmTvxOROnx08NkzLO8LcJWu6xq7as1JKgWGKqEYKuU/VE9KKmc+dTWQFG38QoFObhllElIDsOi6yNHPrslBuqusOoZbG8U2g/XsFcwqprpE8P8ZSU6sih3lMQhg8wght+8ShBdUak1X2EYE5//Jqn9Qax+kdc2ya/+dW37JuXaCuJ66ChXyW8qjS2oo6sCLSsCs+BY10jCwq0ICPXGt4ZNSdHOXUScM/KKSqPclEzp0Bc5fz6wODTdMPeVuJdp6RUcr45EymK6vs7RZjXFXNVZZx3sLdL7tYlqlRSv1F5pLXIV1tmmsqylFGSAlet2CQ7ejOD8b5NOWuzyh3W4op9UWXgDSHJCMuQu0DkgfEe/jbhxq0wtx5vMo+u4hIuDcxuQJL5VBe3rPcC5GcN2V6bOClRU48Hxw94Hbn44ZLu8iv2LJ/r/Cd0QpiNfBIzpa2uGJ24xEmbl2cbGsHjQXufg49OMeSGt2efcy2KeBxyfxBSkaAXLR56O6a5xHZhEqYh0TcjbsYrrpouvZ++Q94eYy4PUE/XdNIYpaywuOPBZIiS7NO6DSnGDZ35FV9mMuvPMsqsQ2fUcJ1uWec7sE4ZpzvOzRAhlVDrGyqjQ2lC+eJbZluBdNBC0nUGv/2WE+N9gu0b0pZNd64i2hHjYsfb1k9wfvkXTIdbvgkeoC5PmXhTbl8GlJ5M8jBnb7xCSyQKJ+PrYsqhZON0LDa7gqC7obWnsR9L1Mc7asZ04jbyoEK01pR3A2T9juXeml1g08t1ptdrfCSUdocqUMnSEa3ilq/yLvvqh9iiTO7KOKMnBK8VxuU3XL66wj2sWU4+Jn83ZOXdID17Rz36AXYUYYpbjL0R4VakarlUZo6uVxihRqtuIQyuqWWNuhSwZQ2yBVlVQUeiXExZXIs48x63k4SjY488SBmnO8TriFUHDsWQ/OwI98Sj++4cXe5Q31vyvjhErC3Sac3w3jPkqwJhnnIRiTiVykhTuHJC4nBL2xIxfYWWoHO/CEjXArUicKlJeJbOD3MZIS24liBqlYiTmLtznYs7hR/k8PFqQ+LK+LKOcplQ9Aw6wy7Ru7O/lb/vhQiAjI3NtZVi1zkEFokSkssab6s1Djorb4c7r0hcCbel4UT7zJOS+c2WTE4RzIIkb3iTBXjLFprdRv3AIr6skOQl0n5GaEacBSFO0DBTbYzrJX2jxBVtXqgF6vkT2tKKuelhrXxuhiva2ZBg8xZhccDb9pLUG6BoIbtaJiki6mCF0JNIb7rYQpsPhQqGa1r7CXX0G6JEw6iPWRo79HbGLBfZBDmjdspv3nqo2ynZvQzUlPz+vyMf2KyDC/bDPqGW8fEPp8wFiPTfR3hvyoBLSuUe2qM506+uqW/fp5U3OOotq0lNfzOjVQy4dmWsa4MBEWW3TbasmW42uJ6Lu2m4/uYtF6lPy5Rp7SQ0sQckXNY5ZXkPYSwgX5zxVWXzcWlhk/PXgoBcjulVe2ysHGUjIzQSZ+JXFOsuHbOg7Va4sclR1uFJmiM8zJGcPsXnJtZNyNLOGc5H/KBtELUN3goa8rLG6+asdODGxFjAraYx0ErCBtrLClFdkohdMvcG+W8KVlkPu77m0cZjN9rRmdkEC5lOb8tlaXNqFKTDDq3ekqlpUrPEjnRETrHFa1Rzn3pVoCUBV/mGVN+RZirGOwWzUqhc8OIlhqQQqhl2qZItA9LcpTUBNdNYCgEtMWf99YrDRz/kvmLxpRgyaVYE3zxk+PGa8fUXvJmPuPFyfmioiO1H3PZe4y5UysM5+aXDbVahiCl3axiqFV7e4DYqRqZz6ZTkvZL2WiYSRFLfJPZy9NRhTwv5PBV5Ok24LwhgGoyELXqvZhedcrm3JTdlalVjvbr7Tvr+zgGi/xAlKAKJM0fenxMGUNsmqlmhSioHg5omibi3hF6gU8Yuu7rFSHd431xzLzfQ6gGiYNG0G1LX4bKzIbam7FZf4ZYL8rRh/awiXliYpYdptin0nIW05DI/ZFVmtOMHzFsRz2WJb6uY16/XhOuaX397hr8WmKoxy1YNixDHmCF1YkS7hzg6IHjbps4DFCtl7+kY7/hDdmKXV4uK67sIsx3w03GPh4qHcm0iN3CVzEn0c9SPGt5ONYRrj2o2Zl95xAf+Izz5hHyx4oVo4L8qaZIGRZkTCUOOLJnleYyZGHSSc7LtBcu7C7pBl/H9U4rmFUYWsW+fkDk6dVRzT/adxpEiAAAgAElEQVQYHD1iUxY8f/NbFnmAMOgx+OiID/7ZB3TNkq7TYfzEpBI6nC8itq0FTWywKR2+Gm/Y/+kTrEplUW047YfU/0nJ4LTFJ94Tir0eot/QnhhgF5xKCdP9hsatSOMVonFF0o6xhzpGW8QvPJaNyQNhwKPJT3H1Y7qXPptoxaK8oaynrLVrHvGGUZVA22PyeMo9xaNU75D6MfnCQt0lCC2VG8/n8UmfWSoj+iJvLzLaYoQk26i1jJGeITdr2gLcxX3KdoNYxFSVybo2KMOKQCu4iEQyQaExusSOQBLYeMYhaUvgorfibFCwKuY0qxPWSQe9tnCO91j4M9z5S1quxEyIODm+pl0afDE2OL0IeeRNMO5MKlEC3+Z8l9BNBfJRgmSUGOmAI9FgJDscejpMQl71FhRSjSQqZE4MikKjZ7yKQ66DFGqRTwYa5kQiQMKKc64cuJBga4UEIXxzo/Dm9Q4b9Tv5+16IgFzUDNs61peAJqIFkCkl2k4mm+nItUStuVS6RirFjMWUt9Elvy0LQg9KdY0wKHEanW5R8Z4EA6eNK7SQ05ybYoXVremLh2zewNXdAk0XKBuFYLMlkEsC38BDxe7KLIIX3JZtZnMJAQ+hOCBruQhlxHwrcXmm474K+GjY5eHA5KqzZFME3JUhd1mCISrstRq0TsOuLoiklELJCJQCsa0zcSeYC407NGKxz77SJuk2RO0pF+dr7ln7JBuZ7UzjWSFyE9ZE8nM0NOqXOtchrGqF21uDtElJ+jvk+09Qjhuk8gJB3CCOS9SxxaftCSfeId69EyaegtfqIXp9pLihmy3w3845f3PDrAh4J75kPkvxpB1JZHNLhZBfcKElxAuBPCjZ6x5z343oTyJi9hj+eEQ2dlBbH3DPGXKx7XD4vsvez3T6D2q0WsMMdfZHBsW4wnjSwT/yiIYOej1ErBLs2xVJegF1zn1H4rhdoCguZnuftn+P3FBQv8g5+9qmDkW+2syxsx3DsM2NAeXrGHEScScEvH/wPrrSsLjY8GbwHMvK+OOhSddKkOOYdNjBTgzM9R1SLdC4Be0shkBhq7epkejIOWkmU0UHGCbkhs1APKCl/pCheYJmNQj1NVFUcSfPUes27TRjsR1ykEq05Cm7XY87vca9bHA/PcSKNrj3YKKG1JnFdriFncTopqAocxpvi+C2sWoLf5fgpwpWpCEAgizwUtLpSzFCnGAECmKdUVky4rnCr3+jkEghsqBQr1WCG5k88RFTDctc0HYqUkn6bv7+g5H+/1GiKFCdD0ikGXKrok5i5I1NoRj0FYGNKaDIO65yGcFuE0grbEujm5agBqiZQFFbbGodT07IIgFxlZIZa0pBwtFt4kbnWlzRtgVqv42wWXEUH2DpX/NtdkykBhiNy83Q5zA8pJwrFOkOMeyiPRbRgoxdAIuiYrC+YLU3oLV+RnnT4TDJGLptOoMG877ObqFRXUm0pJrECaHKWOk5aTSgndWI6QLzsGFvucZPdohij81v11SHfcrgnM+XPpbZQTe2DNcN+eMJ1tmKnSPj/kc5xsbgw+Mefiun2rToCT6DwsPMAvxIQgxUxq98RpM1rmoQ7mkkgom3aihTH9cUELoa6ApKPqDaVcjmCkk7Rp7GXO2P6aZblqVESzDYzSN+7KpcjtcMlQOOHzzAGF8gv2txIWXsXWy52ZO51k3aYokkTKjvBC7MnF6YM6xOqHsaHaWiu5UIJQlXf0OuD5iLe0jaBUoYs8r6iGFNVmlkhkDvTOCtKdJ9KZB2fdzGZ748oNEb7EJgT8151dL5cZRxe7biRbfN0fWEslsgeyvsakI6lVgcjDGmS2JJwC4iJKUiVj3E+xrb8yW32yWaKWEZEokZMhNFUmXJuMm5Uxq8vEFYaGiCTFzcMgqXHLgeeaOxCTsc73Je6XDKM16Vf8Rek1GoER+tYHfa4J8L+MkOoXFoBwVKZDJdF+S1iBCAo2sMbRNRnmPnBWFmcp2AKxQ4mcxCMZjYNYaWYNcyzxqRRCrY9GKSuc29noloStTWjqlUs/F1nuotJtMFqyMQphK2kLD7Dv6+FyKQ1zXzHGq1xd5qTimYyFJNpVwzO4B0IUMiojQidlggeCCemAxWAssIHCvGNiX6+RGrOuJyeY4ZXGK6hzSGQbtQcIgJ7Qy3qdGPPcpbk6U/5bNBwehmx8SquazbbOo/4x88/C+hXlDdXXJzaqEWW8LUopJ1lHCDdShyHscgxZjLF3T2Urr3upj7PeLOPi0joJAiiETudyfYJOiKQbano96l3MkWSSGjmj2K6hwxKlFpUDoSXUfl829TlO3vbKJWtpmM+jAL6IlwdKPijpfEy4hHY5VV12RzueV+T8ItNN4tGz44fYiaWwR3JfndErNecdIdAAapOsUsVXZVg1226HQsrswZ+QaYJSTDglE2IA5j5uW3lPonaPUYvB2DqEdTl0Syz/RFB6ttYHx1Tnrco1O1yEnpKhaN1Gee+ph3NZZXIw5DRK2g4xwgOBJt32R1mbHZvqPTnHN3IpLqNYIJmtQgSkOEfEG88KlOZbSiYmd3WLR1hC92BMNrdlcOg9OS20gjLmP+19rF/O0zhkf/CLMxkMyY0B9SyyHDyxfotgBtiVkQMLAtJClFikvCQCQPW6R1ipUl3HNbqDdrwuIW/dSmV2ukuUihLijUHoYREa488rmD6zq82LXZ857T6AV1fsRxMacz/BHj9tdcRTl71T6Xq1dUkchxX6blyMyid5irFXm8JWlEOnJJY/kINylBplJ7KodaiRXKKI2Bk6tEYUNiVtSOgdykWE1NaymybBJiP8OWdRxbQ9kVvN9WKTyNz1YS6U5FbakQl9/J3/eiHRDECsktEe0Y3VCo7TV1WlMjMJX3IG+4KAoCQUSvc4JM404x+dpIWcoblmHEzE8Q9ZDxyOHe6SnlvshMWbNVFizLnJtmjcWWTuKgbgMSZc3s6S2zlUy5rMl3JeXRjExvMZcuWBUFzaMD7h1M6Mp99g48RK8mH5/waj5AzFU0x0GctLHUYyJfYLPOae525KyRdROvcJDWOxbzmnStsJcWbKOEan1HNaswWxqNY6NbCum+hCS7bMsuzthgz3AZ1R3icMnm7oY63dC98wk8n8kupggzntQNx5LLQ2GfVhhRyRWq9I67WclqYFJNRFaHOaoh8QQbwzRRvSHDoz5yq8taFJiJPlHckPslQXVOE91gmgniuEN77jHUXT6wj5mrQ+otXOwy3gXvKH2XSXyLPok4aL+P87hNX3eZCQVSnGAFMlaQUy5Uwiri1rYR0xhj2iKMSsQ+NIbLtq6J/C7JtoNJQK+7pefKGK0xh09HPDjoE3dGmFcVeiij2DXJzOFgIJCrFafeDUrto2nXFLMZk3GJbu7wZibKbkmviDAEg5HVIVUThkcxothAI1JeLlGSAIU1YhJhZSICNbWq09ZGiLVNM0uQghsKq+ZQlJj0VRLDIRnO2QkXdO9/TdS6pdxTaZfHdMohnUHIJqqIrkIkbUB7r0UpXYMI+WmO+dEO21Wx2h4tuYUmG6RNQ55IJE1NVsSIRk5tQC4XVDSIQY0aJQhlQmMWKORUusZwXHCjqBh2zmqicSwJdJ2a5GpFpBrgG2huQRp998Xd74UTEASRSgroNzlRaiLspYThGk00seIC07cprJhMVJCqHX6gE325gqJElHVcu6TflVHtBE+C2hmwr+jMtnM0zYegoFZz5pXNA89DSRLeaCFHcoVYywQDiU4Aud3D32horwpKKWXb22N4MyDpBqw3MmlqI9kZvRRsUUUQO0wsnVRNoWoTiDb9VMFNu5RZh4WdkcQxdZWz8lOELGSe5tTbmqYPbrVgGzm8Kc8Rsi6b9ZIyypEEFfl4THF+jVtM8b5ISOucDBV3J+BkO7pJi2fnINoipaqxDQJ0xaWSLXZbCfPlMzTVonFrVFmjUmR0X8bLbA6GKa2xw9W7Bf5CwhUF5oWGLu+Q6grRiBgXJTX3yfUeknaHgIbf6SEIL4mjMfZEYaGlePoTsEvaTcSyHuPcvWLm+jhyH9kt0eT3CP2QhaXSF7v4ukZV3SLcSmg+FLKKU4bEG5lEV9lOJapxwqDdZ17VTFKT/ugFZ4aHVFfIhxmts4x9duRamwehRZ4HfOyIRAcut6WP0BFoli7O3Zz19hhOuhC/Io9TDFkndGuUeEehbMlMn3WTIIgyRVmTODmSIsKqIl2t8VsJrlBi+AbRUiOMPVoHGam6JH2jc6aJ9JIR48BhnjTsiyVCvCPbsxCfl1DFiFcyptwmthuqqqYuj3CVFcJOYqW3GPQSaiEm6So0oUxdKShpRlXlJGZKbea4voGpSKRU7Is5oilS3ImIY5m2IKCvDe6iHL8SeZXIZFGENO5Qb0Kk3CFVcyjnfyt/3wsnIAo1YhriLQRyRWEVaIhVwyiPUHMfQVI4SjWkOqfRDLKWQZoCpYBX1qilhJZ7ZIKFv9mhVXfsjzo8mEzo7B9jKg3DrIv5VqGWU8LaIpMHZBcKbrvCmV0yD2zqlYK9GXAv7iO2c0yj4DbNmTcOcz3E3fY5iO8YdyEsVyjFjlIsUDSTmWcgFTJ1JVEYHoUikIRrfK1GdlXKIuBtuqLs+SR9B3uQUa5gfZez9OeEAw1dKak3XUb9HnVuII1C2ns6LbnCLDzM6ozlFy/483CKIGvs8gJ1uWaFzDvDJLczmsMuRtfmJoaL1S31ekrgi2yFhohL0iAmv8po5Uskt6CRBTJxjSEZWOKA4f17uGMP08i51mBXr/nN7ZSm2tB+HHIgOvgXa6xaIHQd1vWGRHzNoBzSuremU5TUwR6+atIT7nPUtbivZuyHGelsR3i3RsrvyIM5mjij58aozYbm8Jq0k+NbHfKyJFHXrLIbhHJGIqWYZsLKuSD0E/a1mlo8oBQdnJ3IEovafYJ3eEyyS2ivJkhui2BP4HY052J5w6tM5qC8h17YNElGU2YITkUpaaiZiCoLSHlNrCjsspy1BHdJRmmU0BNJAgnJMkiuUpJFw/WbhptUwwos/GzEbiczO7xgKTWEwpoo8KiPelTXl0hlQ9nk3N4qFIVM0khs5QVhumZuZRjqBtMXEDULyQWpLVLlCpVgMdcMfEXCtzLeVBJ5rRAbArKrkmoZcaEi0rBKJJ5MU7KtwOraZidrxPIMU1DY6BJtY/yd/H0vnEBdC+iKwsIpiASg0ZDEBrfJKTOZhZZjKgZJsmHckfAVETeHSpaplYSq6JFUNtIAxFzG1gQEqcI2DYIbD0N5h1KO8E5uqLo95uGKzrQDky7DGbyZxwxskbm6oVOUhAdb0i96eF7FRVdBm0eYkoiNguQMMAWV0UEfXVV5o+jstYfYioQVVzThnFnTQzc1PLei0lLKdU1TOChqRbslUdgygrzia1VAV13GvROW4h11peEMVZSFgSm9ZXTo0X7yPsbTNe6XAs9+meM2CmnS4mVnRfvAYB0V7DcniI8EfH1D865BSSKUwEQdu/Sckt3CZyPesRbmNKLBNRn5OiGp28hViBhp6CORvErot7osY4Hz24TF5pbH5o6ppnG6FZHjCrEAfZ3QrCL6UpfbaobaRBS7GvUfQxlIrKUYRVrj+G0QbZw9haNIIKgCrKoinCs0+ga9laAGNf5SoRAkam2OZVaYUkOQlAz9jI0jEqxlRMFiLDiIV3dk4xbPdxGngkJllmihjRNUxMk+llMQKEPMYxtNqNi7XZFqt9jCAY2soggKxl2NbNkc2Q7Z6h2rMEa3PRxJIi11lrsKxS2xbI+R4aENPRoDgnlG5mmE04xULqk7ET+qPS5uE5pOQG9PR4iWfFVEvFfoRPd61H91S+E0xEXFQzFhuGrj7qf8sjRIhAZXjYh2GWKioMgCpSehiQVFU5InOs6thFLL9GyVlSRgShKiHFKFFbpXMJNEQKMsE4JOw+WdgCumrOoC6Z1CKkVwVYD23fx9L5yAUIFj1Wz3DfI6wKwDNFFhqu0jixHLAmQ1oN1UFE0CwRZJKhCakrnfEDTgqzYUFo7kIc0MtFQDy8KpI0yrRSZ5yH2Jt8KOKp8jKDkbW6bepISGx0YqKINv8EyN69aOuu9QNxqx/5yqXFEmLZTDJWKnRRUkjOKSLG0Qk5wsuYabhCytQc6xgxiailjq4oh7FEJOkcfYa5FyIaGmJWqkMy40PuzX7Ntd9pQaKd/h311ydb5Byqck5zr6ZxeEb16SqTOehzKKGVPvdCQjY5WWvK1bVOUFcR1T+TX5qwI/WhCpt2SWhKQO6Hf3aRUOut/CJKMRM6qVgT710bMdne2G8nJDvd3ydvo11WefU11s8IyEMrZRxjKtqMVmrZEtKo66Ns3aJVx9yyjXKZYSb6wX+K+WHB8douYCh2JIIc5ZZXOuaShMEUnREa0rtK5F03pAoboEBmhdCa+UyRYa6XyNNI+QtzJpZbAJdpS5xeYW8mlMVYkstwlCR0DNGlJHpxyNyDYxxpXKSoJs/QVC6TIsbU7CIRNBxNMr3no5BT6VmuNnAatFQjOXyBqVqgQxBkew8EYOHgpSVbBLa5IEVMVFVXcU45r8pMPIlfGUhqluIwkCoiyz2djsdiuSeUaaN4wtibvDHrkRI9r3EYyIlZVQ3tY0oUKi50hZwaUvEdUKoZyA7rGzGtq9Arcs8JSCXBbxM+irCZq0Q98VrGsBNa8RqwyzTpFjj28bma3sIJQ7NHJMzSTtqdhyxTrdfCd/3wsRqGiIdwXqpoA8p9rtoYkFQZky00UO1Ya+lNHTKsoS6kwglUpKucRpanpVxSBN0bc7BCFDkktEctAS9h+C0MopnDuUSqF1G5BUPlknxL3e8FLOkJ2K6j1oGp1ANSkKjWrfRJbu8IIITZG42dyyFJcIW4Gd4XFpuKTD7HfPWRcFG61C12UU06OxJNIiRZYj1JZMqelETUy0KlmLGtebK9brOQgWi+yISlUpbRsjL4mNLZ1ewAgRodrwFy//TzZ/Puf8qwbvfpvNLkPZr9Ekj+u3GtpVzPndjs+fXxFcvKau3xDoazbtDlquc/cqJq8s5FEbr6WhZhHZTYhSQtvKwdMZ7RekWYbgm0wvTfqdIYMPREZ7Dzhy29zvn9Drf8pw74doyiGyJZGZNVUQUWsJpRxj6SZZElOHR3Q0lZ7dRz1JSXo3pNGCvPSRypDKMGnGMXq/JNR1MslmJTkU6wy2Go1tU5QxceRTBw25krBmTp1MqaSMwhARtZiTSCPVDGZbj8yOcUdtukdbknpDLqjMNxlLXaHTF8gZMvc09LJCjQOKVUbjVFxQgWyi2A6W5LLztySrBZIqoFQyQrzkxr8huE3Z+m9xrC4jtcA0a3a5Qn/X5iySsawtG03BVlpItspea42rLSnzhN5pl9a+hihnKFabsCmYiitMP8J0BcykpIol6sJFkEtWSkojQi4o2ILOoNVgej6V7ZMZCWdCwU7QaMkK4VzEXDgIqUohCwS6QktP8B0RSRHwiJB0k8ySaGF+J3/fi3agoSH227i7HXHjUGgija0hxCmaD1Mvhm2DqkikjUUhZzSFgaCJaHZOqNdIRollCGwtBVEy2ZNAVx0sdYcnpqyjHJWAOvEgUan7G5JCYWcbWFHN9SbCwMLDIKtVkJeEkoY1cNk0WzpWip3reO0YnT5LXaARhwRbkCqR3qFCRzEos4a4AFUsGbgWiyKj0iSKtk8g5WhCzkYRud3tsSdtUB2BLJ9zoMK5v0M/dTgKTVxVpF5VbJUBjDTy8A1ydsJiP8LpdamvVmQ5FPhkxiHJegm1wELt01039O/tIe63CIwpN9UNys6mTM7xF1sUunSHCrndo2ukHEUCq22LheHz/gMX81RHvt0ycY/ZXynsKSa3coYtNjinEnbxHtlVSJ7/LmL8QWlgVBqmKPO2WtIZJLzOTZD7JFJEWaXsZz554VFuIdrUDFQBXROYNzY7USB3c6zOklKAXdSiqHZskxK90dgWO2xXwVB7SMvn7Ktwnm/Ji33qYEW0Snk6h+x4QWfuIaAjHlYUizf4dQOtfaR1wkiI0ewaaTTHl3XaWouL4ZokFaEtoPQkkjwnDypC3Udd1LRvC9SHAQUS632BMs2o1jqBJXKwXP3fzL3Jz27Zdd73O337nvP2zdc397u3blcNq1glSiRlS7IjGwYCeBAno3joPyJAaDONE8OTDAPE8cBAYAMOnIEMBUIiS1RDiiyyqlhVt/v67u3b0/cZUEYYo2QpMgzwGR2sffaerQd7r7X38/Bex+J6S+VhuESP+mS7BoeJyvwWmHZxt0fsJTJ2raAm69jrBbmyQ1K7wa1UDEFEqmckZp2NltHMQlBEprmL0yrZkkp2y4KZKFAqJp1QpiUFpGGBh4WkOeTLNRN5iViJWCisJBmZHkER0JouWCFTa1cQfHX+/UVERf4J8LeAyc9pDP5z4NGf/lIHVlVVvfunWoQv+H/VC75fVdXf+3NZQASt0smrCEFMaCsxIhlRPSGOUwRP5ywW+Ugp0TyRUDSYKzlRlrOwU9zjjFYK4qd1GJQIaERyQSNYk3RaCLQ4Icd2Z9yOb2kEHoZucd1ucehNiUKDLbWi6DZZS10GDySWP/mUnnLE+ihAnX6N/c0UoTmnWKs06y6rIEAKRcq2w4FpkgsiZZAgxkvqdUhMlcQoSMYwuxhj2AFWo07qu+w0moTNBgenL3lTXJMVIqruUJcstoU698qQ6dpmPJvx1qLB53sJLeGahq6S9DWsacbQy3g8CBA0BQWPOC84ab+NpFZs1AlRISBUMAyX2OMYw1uQ6AGGts1et0lnW2NGTLAIGY9HvF/XmB/GLIyYzX1FP6sjfljhj2XuFZk0K+jILaojiSdlyDrq8seXL2nFKaLV4rgwuG0UGK6KvBKoNVrU4hk3cU5D3YG9gOs3Gk1vTKXEjG5qyPKUslhReg5TY4HuzqjmNpJgY6gCQeKzGEUYkkhWKFxGI3Z8gYl+Q6i/jz2X0RIbpxFwrnSoxmdoqoy6jEh/N6fqS7x2cyxjhH0jE2xJGImAYda42FQoeUgj37BhjHTToCi3QdfJtYioEIh8hadSh9yfIFQt9HyFkyqElUuiabzQEk7WIdqWzcoZ4Y5qVJMBN1JOsNJo3r5EtAXMDTSMFpMoRfck6nYTcX+J/uOUUFOx0oLpZoRoQs2XqeSESRqSGCm+mLNYi1w5BXaRsbPKKAuDpR5xk6o0gwVIIXmpEk8r5FQjrcUUYURHjJiHBk0xRHXTf1/6/bn4p8Bv/nygqqq/U1XVu1VVvQv8S+B//7nhs3879hciAEAQQRIWSFQYakyRruipJbVIIQT0skFT1ZipIqO2wFqNkBMftQiQFQGhaXPe9Qm6M5RwQaVnTKces7se4kUMK5m4NyHWDuj0TC4qgWG1wp0rLAuXXD/BulFRVgIPAg07zahe7LGaeERX2/hiwZZxiFgNiKoVmAnZzpLKn+CZQ5bKnFG15mJxzkZbEW0E6lnOMsmJCgknkMmjAQpdDCPBdgM6hsflbojabdGwVpgzH9uweFiUeKMK7/UMZTXhygu5+XLJctJCu9zmae+EuH5N7UhjW2lyaBsomsCz/S1Coc2NkTPeZNwsNsTDFD+tkS5tBLGGRJfHu4f88tsDGq6EWPiwUNG056jbJbFaJ86nTK5jNMXBcUus7oayuqZVhhixgz8v6eiPseoZ3zzcYbC/jfdc5lK4xVZDjqUxYlKyO70jExOUYoHdzSjGEu1kCDs2Us8gERKuM5VrwyRUPYRQwjvboVoMSIKCaHKJerdm5sXUFmuSeYhWjvG1KcFdE6PacJFfMW3eouUgGiZz65jZMmNmSkTzGYZucBq8yyoAZ3BMqrRJA5M0bWF5OqEg4g8XKDnI7RJVT3HllKYUY6oNzE6HazMnjWzQekhCi5q8w1HcoBUM2BscY7QCjEWL+4lB3HZpdCpGK4PPc48jQaT5ymTauCfseiS5xlCNUI2XbESBSnUZlxGFWqJuNgzWBXqZ0VxrHIkJVhKS3IG0qOgmEo0kwXICzI7MWHeYETFdqqzFiqQu4vgpmyIDXSNVDBaKQjGwScSSKlL+8iRQVdXvA4uvTF5BEID/DPjf/iLJ/meigKJbEfQr3FaN0snYrAAvQstAlgJ6uoQeF7DMyROFInHYjlscJQJPxybvzTa05BHJzpRLc0LZ0Lnf9rmS7tmOh7RvW/RVn8pSaS5MpFkPQxkR+SWZcMOdo3Mz31CpG1gKvOi5bFYq+foVnnxBpr1EtJd4gosi6HB2RJBuaAcJD+Sch9ElhimQ1DVGbsK94CIkCupmQacm0RRqSNocS1IQb+oQ77H0GyiBzZ20x/SwotORMRQVKTSJi5RME7jTfHyxonxyxLc+2kLcPOfR7t9ELCOEJObjuwbntkGu5Qy7AeNFRjBbIgkKSA32aiY7uzpms4MQOej5hkKMSICq/Zzdt4/ofPQUVXM5tl2quy3EwTHt2hGtH76DvjIpRzaLoqR9dUk5yNjYAem7T+Adi82723QXR7T2Tsh7Cmdigiav0AiJi5Seu0d410AaTkCskP2Q2bBknvskSUJrBrulgGzmYFYE2gJJOuMsXTGmpCWpnKkN1lqGkXqMOw7eoIeh6uz3NTbpgPuaxeO1hC8I1Dse088yNoKDbMi0zIBwKXCXVjRMCYEEsbrjJI85nEiIioWnGgS1CmoxBglGYaJbEbYWYikyIyJG6zO84YixnjEcnGM1LaxaC0ezyLuvcVwJ39tgTPs0drrUNIGfdo5YH+skikgSxcRhRWNLZdQVeDQ8JHJr2IWO3sp4nFcYQUiwiPlCCPEqASV2seoNJKuHvrbJ5ibztcxEi1ngo1UOm25GnNdpkpNIEvWsRqHFVOHP9CgFf8M6d6ii/3hvB74FjKuqevNzsUNBEH4CbID/qqqq7/15i8hAKmlUSxW/6RFRElYCmWyiKBmJsGEVahw7Ap3m9iMAACAASURBVMPNhu2mRH/fZBzKNLwG5anKouaTlAHrcwtRGMPWnFZYoWsSceNDmsacxUyAm5DS3KXtpbyZ77N/EuL6j7nwf4SYxky9S9rxU46cCyJjQPlaYr66Ze12GBgy6qOKbfNrKOE1VR8285Jxx6JelAw2FrKhsugaGHGMkmd0egqhVSdPIrTSJ/Jkct9CWr1goIzQioQDrc86n2OpBT/wcnqawM2jx3TjWzo1lbsPeuzUmqzDOy49iWPmzNcaHaXJQzvizK6zLe/z6iYg/mJCy9cQOzp9M0WaNhD7Ao+bBsF4i8jL+P4w4kFf5e2sQG0IvPZ8/PoerdzjeFOw2fKZWCvE4AqrNDCVQ2rPNkzTA/L7Ecsdkcytob+l8nzq4DXu6Vlt5kvY8mqkH4asv6+xshOKacB1skTLZwwoKAUHVjHBPMOUSwRVJlYEtEAi10OKNCeojmlXEzZygqSISPIS1Vfx8pi6M0AV52SLLoXhU1uopP/6DRcfivzK3Q7L98Zo79SwVlvMxRinuiQfzAlHfdbSAimXcBo9av2QVMhp3du8/PKGpBTodgqyWoJcyrSEiisT5PuM0F7QWDjo/TqniyvEy4D0YMzB68f8dFsEQ6W86WChsqjPuGiG+NICf7jgB15ArfUNDoIhhTNB9fepZgFi/lPStUaVJYTXEoqgkFOyFnICRWHVtfg0KWnFCidySFLpnEcFdpqhDBNcWWapbVA9HV8WiDyZhpMy2V1iXEBYzdBPM5KNi1uWKFEf+OoOwX8oCfwX/H93AUNgr6qquSAI7wP/ShCEp1VVbf7diT9vPiJIIspEQ2CFuCjpNGTWG59CslBNhSUlGjo/JkeQM6pNxqsso2OlbASVokio5RuCMCcPYpxSwdiS2XgC1/cl129+gLQlk91LtOQcq6fgBZc8qkdE+jPOMlDJsYSCkZyye2tTKAZvCRnLpw7pncd0N2Dn+C360gJ1FWL1cuwLnVTQidU1ZWGRKjXiFfTUDHEdcW/KuJmFbCbUoinBXCafhESqQzf2yFq79CUJWin6H4zw3nmNd/+MZu1LPk0+5N2Gge7s0Etq7HgNxqtbrM1r6jtT/sbut0iPPuPHmxGPzmG23eJ126PUX7D0P6C23rCoz9HyZ9TEiE5Lptvq4ecZc++UtagghxnFfMPOTp+VVSMbX/Oo2+RSTvA2p9xoOs1tAdlbsjw7QNNf037/bczgjzAnXfS+RuDO6FzuM63fkV4KaAdrFqcBa71AHu4QW58QalMWcwfUEmNdId8W6PUCXxVJqhLSiPSNxcbVadRiKr1EV1O0SCFV79CmCmLHwbh5zco9YUepsa4c1qFP+6RicrpPFgaUSo9k49I/clCGOTVF5lyUUO4PMYQLzlSHY0wURcW1mwTmisn9lHwSImk2UurQklp4lk1QrSAAq1sip11UrYYoSBxpArFcI7ur8NsXdDcOwbVKruskuz/lqqkyOHc42DsiDzfs3C8ohIKhNyDb+SHq/QFzOeGPRgF5dcfLQmCXnGUHljUdyytQBIWjoU6vG6OlGxRyhv0QvVJZxm1kJaVKTPwyBAtUcY2UtFiucooqoJ9WyELGRs8pDwo2nk49X/7MKeQr8JduEQqCIAN/G/jn/zZWVVVSVdX8T78/Bs6Ah181v6qq/7mqqg+qqvpApaRQMpqlTVHtslyKPC4KDtOUcmJSrSu0RsmTepcPn8povYplUuDNMhRvglvNiQOFpiGx48gIYgdvrFMtMsphg2kYsJitiS+vuFqYhLFI8JbKotCIb29pNr5kbvnMPZWuveG6/xl2ZnInN1nsb3j6+CFya484KQmuO1zkFUG6Rfr1hwyedpC1AbG1i9NsISgG0zwg1QvcwmEdVEiFhFXfQjcj4h40tTVvujb6JmNdO6E1K+kdjvj8D1u05gLL1nMkXWE1HmGXQ4xgxRfmj5gMDPZtkXhwzHZTJ6n/OietX+VSBGmUUn1ccWX3EPdjDtQMa9iGIkeKe9yOQoQkI5yE5NUUJZawBm+TvfuYuZUzzSZ42jaRrLAxd3DnKccXx+insP9Bg0P3jvfsPrPiDqQGF/YpuRfBKGb53pBBoWG8v89iYGLGPbbXMdZ8RlD1OQxttldXVMs5r8qUsekhKwWtbEl3EZPc58RMqJsJtEVMOaYsVkRrn6DKqXZF1HjFumGyjuBW3yAUF7zlK9SA4lBhd1mRlDPmL85QqnO2lBu66zGdNfRtHcWHvfktXn/I2F1xJ8DyqoO+aSDVXPRahIrI/TrBzxeISkY9XTK/X1DkGkFlsAkKVmMdUfKZNaG02rTFFk5TxMyXEItoY4tc9Rm6Mnttg/TwkKRacnM/J7xsYivXLK5fYjUnfKmmHNsFbcFhO6t4MArpxRnH6obZ0RBfXrFWMt6UIuNrldKVsF0FqSxxyZDqAs5uieUUSPkSBZ+teUkiNdETKBWZagqD/RVV8R8gOf7vwW8AL6uquv05YugAi6qqCkEQjviZ78D5n7dQWil8WOm8MedQPyDPI8bDglCX0NtLyBSiuy539gXulQhli2+4BYlaEm4klFaMsrJIRw5JI0GwBeZCRWOVIalDykwnDCzqpQupRCac895mi/vNLt72DEU+prezjbwMcYZ1vm+G/M3OPcOyzvbnj7h7EtJTIkyhohTr+OE5Y6ck0Fs46Hyo7xCIOUKU4tsd8nqNqioZ+gntQYS1rlMrwQ4i1nKAmhW89aDErAqSvs+LM49GVFAdd7g/3ePWX6B3r9kqLK7Dil17xt9tf8Tv8Cl6GrM6PeL6sCQcn6J/GfIbD36NYfglHykC3fEuaa1B9sQmFwu2ZJXWVKdW63N+84ZUaHJovUUiZxRtn5O2wMuxS02rId6MueneYYUFNXuP8GBOLNdZ6Ar6ocjse6eYNYfFzMFu7HDXfUXovEMtLLhxbpAvCsLOnJoj8lkS0b2VoHFJtYhZNhS2XqTUwoTWTolV6VSKhehI9MQNqwc5ymRKdC1QOipOzSDeNZBe6GQHHl++UWi7XTRjQ1deI8smYtdEfH9B/D/VGB9v+DSa8Mg+oFVMWd09ZrS1pixWpEJGYh9SFKfslm1Wp2PGrkQeaAjaCGIfxQM/muFvujgzGXVLx6opLFOFSWLRiRUKCkr1lOPO2+ibUy5DhfKkSfOq5La+YuzrPBLXDJ4/wvxsyBd2Gyd5yR/rBR8a2+S6wme+yE1hcbbyiSc2N+mcB3pBLIh4DjgNgXRTsvlUxS8kKjEibBVkboo4tUm1NbahE7YV+vOcyUuBxISjImNu1BgWCqUbYBnvIgpvKCSZyeoDCvf3/6zTwF/Od6Cqqv+Fn7kP/7sFwW8D/0AQhAwogb9XVdVXFhV/HgYZI9b4sotR3GOYLRaOQxbMYJ5jVDlqfk4WlEwyhWZjxcqRyWLQBYGwtBEtD1Hd0MxlEiFgoyRc7zbZuoOtJoSCi9D/WZ0gSE2u/ZRb6RT52qSepOxFb/DbDa7yCc9udlkaG54cveC1XTC7l7Eun3P7N0T0ckYkBLzjvo21M+XmtmRlSjTtA2byjFI5Y3VmIjgC3d6ceWkyZ8ZCL5BsmeawiaHFTIOYfl/BvLvkNuqSZTscXdX51H9FcTXgw8rhs0jhr7QLmtWI5e0Zu7rL7Eak/VSlpXjkFzbTVhfxIwPr+7/MTnLLorHg1AVtqLL1vOBhUdKRE642AVMtxC0T1tIDtnounhYQlSoPT5okFyrLeo2+6xDfxeh6hdJ+j1V4h7K8RjMOyLZP+eVqm5d6nac719zdP0CT7ln1hyDLZNmSo3rOzSfbFI0xlnqJ/SLFK+cEVcxUaaIeikilwWodEushck9BFCoaaxWjZpJsIoavcxLXYKV5xILL1tJmSwRpLdK0VTayxJYusjy2kP7Aojr5PZYjl4HTphWmrNYf0lDn/DR6zYADlL0d7JcV1VxnEg0xRIelN2HXTlFzCaOnEGUK41sDS5CRHJtxUtFcNXkom0y7Y+yJy9QSib178vo+SW7Sy2+oXYhYcYIrWixEiXebGtfXMtqVwbrTQHqssfM7GdHbl6Tbl8T0mT0RUf/Z1+mmP6Up5rxoxPiJjRYVXE90crlClcGQNqSSDmVFkZUEQkI5gnrXYzMTmPsC4rYNmU61MfDTgExROfYl5vKXKDcFX3u04nreQvaec/dnaI7/ZX0HqKrq735F7F/ys5bh/y/klUGo9kkbCZlvoCWvyUSDStvFzM8J+inCQqYU2hjODF+qUd3nTPWYraziMCi4jytsD+rbGctUo5oodDsJdc1BzsCvfLbTNqusRy1asS4MasdTOle/hv74nKFXMV2IxLKOtjehajzkX582+WsHY9TbJnbHxK1kbpcjxKaMY6WEdxFiWFCouwT554SCiqcmZE6AkSkUQQvDK1llFmZDJYvvWO+mZHc7OFMf2YhJai2O2y2+t/gashXhv6rzTrGhzHbpvDfGHu/T2ki8Zory5BsU1v/BeKaw032OfCKzr9/Sr3fo711DMuBD0SbdXDI3KvLfsyhOZORSpotI+cDFm4/QhZCuadIM9+mtHebaJZVasiWFDHYbvFJrLOQ17cEn7Mg6eWSi3l4Qq7/GT9NbnNVnnIm/hlO9pDAC9i7four/AEkxUeR97JVEc3XIXCqI9y+Zj/cIzq65lcYMFhJmLcYrK/zSozHREAWFmZmDr5LFGkkvJCzuOZEaVFpCvligGC1Cfc2RYXO7sdGbXUafBSRyxPPWL/GDwR3B5z6684SoViCmDh/IuyxCAVFa0EwnXO/5vN5t8BsbCzeI0SOL62MRIexR3cWYeYnZK8n6Ge2oQJZk1v4Ic2EyH0yIqyHd/IjxCwV9a8zZeQfx8ZRl/ByRHyNpKrfyLpttnykOzfMJ2Rc50bfb9C41kuuS6rFF3+ngb/0+SalwLm/hewKaMMT3ZAaSQVOo+CIKScSSWpXipgJBZKBUW6jumpUZkCwtOvtr5InDOg25feyQv7YgTihZo9RavO2s+YncRqndMO004ZOvzr9fCMnxf/jf/g/f6ewb1E0FS3ZZmfDQFIhzATXPCHOVyjUQspxcKInkiMgr0YUC14ZMFoj9kkIvCMwKIgXL2yKsRciRz4GzYZ8dbho1mnFGcPwZ2dpglmeY8oyZmxDEPfpxiT7J8Xb2eHf9+1i9Bu1jg/NNgPfs6zyQNhjbPtbRCY2wj+MMqBHSbC7R5A5dN8QJBmS1HttazLGkIu/DKtIw7gWSlop7ZWIEKc8/tJAs6BohW80A7d+8IXr8lIdqyo5q8DK456MD+GFyTxW0eLe/4MiymQYt5osG+9sS7VkDf++OR5dwu3tInC0IjNdYeh2nNcbLRkhKRNvcoM0qGvUtdo4dWonPZCzQP9LI3uqzLKe0t7YI5hqBJqJsa6wTyM2/zuqyglnA4pvbfPK9OwaiyvAQPvrklplzwDTsMXVmdNoZyscW3z+dcxnccK7ecX+b0wzu2FUUHLWNLWfs2xvuZQVKEWPVowx1DEkjkGwSQ+RAUfAKly2txdzKmUTXhHWD+/KWRdTjqjDpGCuKoomwr3A4CvjcMdi+GXP05DeJizGaVxJGp1yat3R7DxF9kc8MH+Nlg25HpflmxNKoEx522BdiWiuN2O+TxSNWm4jaQOJBq8mwcojkFQIlwy2BxspmlsuEyiV69JCqpbA/0pHMkL5+SRSV/Kr9nOFkSkO95qYrkrGHGn5B98EvMVvPcK9fod23SbsKn3/yYyQrpSoL4kCkJCJwUxZOQtMXqZUOG0tFNDKkMkQIEjLZp8JEY4W29BGENkXDo2GtcA2ZII7QD0vMSuAL30Ldr1GeKejijHiV/+L6Dnz3u//1d05ii6UDvu5TC1SWowWttI+fLSgjiXog4YsRVU1FiAtKsSSK68TlgGQvwHleIkR9Ek8j0hKmckRTdbBNDQmNYZZSW2cMpZBw6znm5hS33sSr9hGfh0h3GkLzGKnR4+bs9+lOv40m7FOefkEviGgmEsauyqtwyIlxxBemjz2p0zjWKJScYCojLR1udY961kQvmlwWKboXUHZTypOY45sxu9trnKZGsy/Qz1yW3Rr2737G9eZthM45XVXiqqnQkZu48RWPpk0KfYfClZlkNuPNHY+SCbeay5byCcsn26TRcx6tLkjrDvfrnF1zh5b2jMeNgq12i0NcMqNkHpfcD/fIXZOtwCadmiTc448Cik8bdGojOkcB2TIhUI4R1E9QrK/he3fU9AxHtdHWpxTXJd63d9nXLlmefx+eqaw9h/XtknK5RGhWbHuv6Clttg8WFNMZozBhHVR4qUleRTREnXaaEmYJd9s1dHxa9zmCnrHTVFkUnxKu2+w3DlH3JfYXKXR8XLnk1Z5M4AfsDn9Eap6wUZqI6yl2VBDWlliWQpC0EJUGXvspSn6Juegid0skhpjbdbRWDqnCF9NXqKrFqoxYhz6+HOI2TbaMQyR7hTaoUWYevSrhuDxiM13SElocGy3SXsSdtuFQ3uMuLZjta9TGJr484+39JoftHmumDM4LdCOgGAv8r4sveesbbzObDUm+vKHKN2SChF3WcCWFNCvJY4FYy/DEmNyKIBMoxYqqKgiFkiTYIG8kInOXlT3GqBJ+KYMXcY00kQn0PTaFg90uaCkifWPC24bFq3H0lSTwF7Im/48NRRernfY2pg9Lx2AqZzyYB9xJGd56jWG77OQaZ801aqaQrSrkskB2VQqppL3IKNWCeWmRSTr1moi9XtIqFdhVOZJ63K0VGAToaUakaAROh2Rm0zA8rI6LPNxBem/DIF9wnXQZtW74O/LXeeO38fJTvvVXa9xf79L0P0Zx3saRKurffsby1RsiMUeUWuxVIGZjFjowbzKrLWiRszVTCWc2F805qizRTRQ6nTrL+ggrvuONLxIsYxY1BSGwiBOPr4lDZpQY6lMuLj7hoCYQe3O+/HGX2q+XtD6P2ZZLRlvbPNArat1jzOkA8WFG6S4ZCjpZUNHxXHa6DeJkSOEvENOQcnsXNZVZzk+59hXsLRfXafNomiKVGS/feszL0xnyfsXoDATzgKcnBb/zr35Isu9ixj/mo8Y2X8xfo/7BAfXjEF+/59busBOtMNIxl5saZ9slW5d1lFzkUNcJV2OuZwrlwzqqIFINR1RSBDWDLIpZSgmqnUBcUHXAGDUYva6jGN+nrBKUVcrTd1wU65vgvEaQaox+ssbffoeGaqCHb1gWCrNTnagp82B6xqfOu7w7kWjuRsx8GXm7zrn4E+JVST85xMFnOB4xEiZU1gYt05FaD2geuyihwOFGRm5JnPoeqtYkv4gpxC+o7x8j9AZU0Zj7ch9nUlCtL+i//4TeOzrCj7pMd77g+WcePzbP+OtPfp0XvyXg917x5G9/k3/x/d/hy3/yhmF5QZrXyRMVUVlhajnCRsFX61TSCmWZUYolriZRZjVWekSZpFh6BbqNonuEUkFHFhmVz9DEJb3bIRfHKtYmQWjsMlgvqNsuf/LTi6+0Jv+FIAHVUKv2X30ffjgkFAQcb4ou9Wk8qTg/H2FXTaRnEeH3dBLNYqOO0f2AQivIBA1ZklCVkCgWyGPoCk1U12AtzahrNmWhoqkOsgT0SoQ8YRA0Efsm9UZJ2nPRbqfUewLBMOVg6y0ipjROrjh/+Z9i7Oo4bkRSPCFd37O7I2KoD4lNC3oRe4sxWm5jz/uE8TbDwWtCuUZvvsAYjQnkQ+zUJnn4BZld5/B2Qdudcr5WqO43bHYKIo4Z3jTYLT/mnbe2mEoak89HrJ0liraN5O1yfXtDbzliJb9CMJqUW6959MUDtL91xK3e4AH7pPEdrjdm2a/IVQN3uYWg5Jh5QKoqxKrJYOXhGCpeWyUSX9K5FTm3JWR/jL75gPX6iDD5Hr/baPNgJ2H244e0qt/GlP5z7vIfUB1P6YojLt48YVov0Fpz7u8C1HzIe63H3Ktt8CC7/wxBrGNYJbvlgnL+Dol8zqy44ab0aQZ1yrRLasxJ5AhFbmGz5M1FBynKUZ1r7NzhfGDhbpZM3iT8ykHOdLtP00xQZRunWhP0dzA/NpmIpyzdOsZKZO8g4PW/kFH/E5lXb9Z841mPTXnB0Nywv4DXwwb9w2/yNDIphBEfT19STNa05DZNp03SXZOrFc7MJwznuHtvU0xyFv1z8jcrVs//S94JfkRlK+i5wEipaHmPefT+Gu/6IcP+hOKfvSD/psdCCHEPD3kyPCLZ6hGOPuGz2TW/9U9fsyXknGd36CZYrsh8JWILFbEqk/gympIiUschZ7kdwlxDX9fZ9FV23g0QP2ui6RNWfkasbpinJW5+wlpd8tia0hXa+FnGx8OEMo6/kgR+IY4D/+B//O536uspltDCegbtTYlSmpyvSvRugedJKOcKOkvm4oSyqiiNPmXLQI1T1EJFSWUsVcISCjp5haXnSFKMsckQSxsxF7kTQgR1SiG6ZHrKyqjTvG9w6cJx/pix8RA773AwyDhPv03Qfc7lqy+x6gLy3ZR5PONQvkWM2+RSSN+eIF1GnI6eYCETmEMk8ZKiFbIhI9wsaKgRfnNO0DPAcqkRUUYj/EgkH8mUJ1+ne1Uha9sc3X+G+q7Ig1UTt5lRv5rQPPlNAveGxf6IJ7WQu55FESnk5SmXYoJvOSjNiIc7HufrhFA/ItzySPOHNNYqsmtS5HUKeYqU5ZRmxjApWU5CGr5HHAlIuYWzm7CyG5g/FoisC37vGx8wKBTq8yV3OwU7psO19YfMgmOMmcb4+gr9SOf9zEf7kxKxn/LgrMs8mdDLJB7P7xkbXxJrHpKqkoktPt8qKSUXZ2HRslV0dYhv3CDkCvVaRUUXW68hmyHKeAZVi1W9RJRy+oLMwydjGl0JIQX5/gEvenW+1Y8Y3lhoqop0cIq97HDYHHCTBHiHBv07hXVp8ebF51h1i/iVRSqpVNt9HOsTds1LwnWXmTFCMQzalYzbnuKFCdcTnWqhI8p9xOkGb8dldrvGX+zjNnR23G0WZ1Pu1yoL/f/mrtrD6TlcyDl1/SUNs873r0G9i/joSCP++JCT1RlXB3XevLEYL3/ETbFCqZtk/gmLdEyaQIZAZanEnZIyS6gikbUk4pgZbVlnIQTo5phGpHD11oZZGfPwPmH2WKAqNY6PpoSFT2nWKccVeRbxUZXwKuUXtybw3//973ynoXU4lm9YbfbJd1xiZ0asrtlSHCRBpG+VTE8W2LFIJQr01wmttYevlYRaRpzIZFaO1ZYp9kw8P2Ixa+NlBWsxYM6Uo60ODfbZeTRHvG/wvLnDrHdB9+IZsx0XaxHCbgIbH29+jS+e86S2x2ylI3cspJ0V1nyHogMtxWOit1iUDxk0AsKeTxqK+ILN+fSQdJ5SiTGJKVDNA5iNmI3HfCCtaJ4sKYYFxaMtrq4XlM4zyvhzOrpIt/8NhquAtL3NKFbYtJr06w3cT1ucR3d8za7RMkesR7t0ljN65QZV+zbEGptzjfZYR9cTrMAjlGKsWEFve2iyjOkrqK5LZxf2/YpVInM21Ug7OwQLGcM4ppW94PSthNrZj0gGa4JXN2z7BzjDLmfamF+JFtxrBv1NF/2qYlRWSO0Ou1sxyfMOZv6za7fhcsNYP6FpWLTLGCGq0StWCOYlPxEvWQQSiWEjCx2SxESvHDZiztSGPBvRemahCCFePiXciPhBgJ46pGKL4r7O4VvwRZFgbQYk5muiiYzdc1k653xWjBjYAqn1HnKUoHpLXlxe0+pafG254dhqsVqqmM0Va+WEKD/lbHLKWlRptS0q3aG+bmKLNUolJ7SumO1Br/IothyaacJWMEaYj7n1U5TGAkl8zDv7++gdG7vsYMsaX36SIHd3UGoCo89F0ndbzOSIra7Mwh9y9kevyUqVwi4wd2fESg1BTBAkGS0TaYgxZqlTLxR6uUGplNROCupRjOlrxILAaqLTizzmC5tqqUEocDvepzJlfnUe8L0i5cQpGe11mdz7v7gk8Pe/8998R2oOGJVLREFi4We4YhNVlyk7K+Zqg0XLo1habEYhUqYhFFApIrpsQhpDmbGbFCSBQxAoJEkdFA9BiNG2t3hL0RhmOoNRzhcmlJ+fUvZKMqVPI7pns+9xsJdjJwXqfoDcq3Hnt3lv1eDo6x9SaJ8QBR2OlAOKUCaRFDpVh6GcE7o+HbHCTUeE8yWd5hKjNiFp9TAzAXsGUk1iX+tRFg6lf4lz1CM/G+Bc76GZAuoDn5m5QxL9n+Rmh17rM7T+M35JfsnMvkEt2mz3VlS2gzMzKE/GONpfIdlWadpz9PEWLbvLVf8Nj6LPaewrNMUt+lFOYRZ4kwv6Qh3KjCxSWBkVZnGHWcYYOxnDpUrz8g2vL3ZIfrrg450LuvdPUR8/4FoZUTv9CVqq8mL1h0iTOyJX4Wbvmo/DEb/+JOfNpk5wH7K70RntpEjKHvXdHHE9Ji4ekTZOwMiQjC6Hkotru3RMidxVMGSDRbEgXU4x6isMDlhFNht5TeglCL7LnrvHZuajTk0absCtveB2f8Pz37Mw3edk4ZwinrIu0j99n7FPJ/ucrVaTuZ9yUl9gPThms2UwV1XerjZE0QHreU7hR9grA9spEcOYvuUg1BvMEo35fMEjRScqVIwwJPRK/ti4ZztvM3m/z2bfpjUTuStknonXbIyHlMUZN1ennDQ+xXdXBK8tjj98m9PDhEfTNXkW8G9+N+Zs9ZpYl6jkguI+RpknlJGLlNSw9Ryz0UeouUROxrU5Q/QSlrLLKBfx3Rih6mEvSyZqgvsoZp48w+zeE82mvD+uc+W7LHZFalafF9mSapL+4pLAP/qH3/1O28lJaGDvCzQveywHM4Iznd2xR2nB/EUDPYnI1BqFk/zMmSURScsUU7QpBZ2wElHqObocoykLMikjEHYpjTlxI6E6WOG0JNTNho6xTVXtYN5/gKqsGRzZyP4uaGtspeDCq2golzxMm0iYSMUMr6jqVAAAIABJREFUbSSzbmvY8jbKskBSxzi1MdJsQXO2YrMWsaIuRZagjku27w3Sm5ixELM4CnH2VXKjgpZGlMiILAjEAvyPkSSPopvwsHbIaFzQTn3C8YSroiCRTwjdIZNbAV0vWO+12aspKGMB53CLBgNuzYokvSKsjJ/dLa8GjF/4xNkt2Y1E22shvR0Tql0W9wIlYzbxAaXksJV5jNclymKH2DxF3tzjeB/h39xxvvKorj/jpbdBCD7nU20bVIGbWcg3nx7SU5+SxAoPzhSiI494T2dHfoQ6k6m8l1SKyNxy6e+e0w/WTNOY2a2C1k8Z6jPUXKbmdhgNc/xoylKUEKY+i2SOFK5Rwj26qws6tQU0b1n+cpuukSAqPnvjfSaSxIWf0O8uSaXXJJNtHGWXd7ccqnODoTWi9iCg0X2C7aU/s4dPStZ9gVdyl4NNTBSGXG88xNhgkjn4sUohbZDlC5wyJ/Nk3LbMSNyi9BrUVyKLpIncEjia33GnKexum1xk32bbCSnOpphPHnN5dsDhpIXRs7m6GPPoYMIhX+P/miRcZT9hNl0gLVOEMMMpZUy7BrKFWcg8KmKk3GMS+ORjhUFu0q11kJUNfk8hikqCTge5KHmmrrmM4Hg6IznooZUaZ5GKphSItTm7TyaEK5Fw9AvcIvzH//gffWf//SM61RB/sovg5exoHmgOansfv5izk4cI0hpBkkjmGW7uktRjVi2VPMhAiZGaJYdaheBV3C8hkhXIPaowgyAmR6E3q9PXBfRtDa3mIrYWlO2AWvOEYBSSPtdIPu8Q5S4Hq4DacZdh/SXlHzTw2aY5AeVRitUwKMwG2SqnT8Im0bgQVHw/IYlKXlRjcu01+klF2tqjO2wxGF+zv55T3cosViuurZzWWmf/QGZZaWyXNWZric7op9QGMYO6RtUqEJo2zk+3yb5d0nkp0FBeMI/eo63PWdwveRNt8U5dItBzBsE7bK3bqKs19CWCXZ1F7nE7nlDdadQUn5G/QRo7FJ6A2bvg6p0FsuEyzG6ZBx4/qF+TWVeMWyfM1xL66wjzvYz5cZcPnQ5Lz6QvacTJE3hooS9F7nrHxI0LrM0RzbHOxIzAqDN/brPltHjsO6y6OlQ2Vt5GjBXcwsOxEkxJQBamLK4q2qGDpIjYWsldklH3Z4h6wu24Rbuh4a0FBlHEtuywkOss3RVb6pJWM2W+8y24KijnV6TegvDrxzwUJcL1HY6wg5pVFOoSRgar0uWwu0ZXPZKbgiK6I16USFVBq5vSknqorT3E4xorI2IziVgkDqqmIS3nPHhg06THttth5B9SxAbW2xGPf/ues+0mb7UnTE2oDyxG1zJTucLEYWP+NmbyAdev/oTRYollqahWRolMXBewlTWe47GWa0R1G8UPKPMEO/cJnYK2VZAkGfpGoDYW6TYm3Mwl5C0X2dGQX4rM0iM6nTc4kUBp6IxGGvVrjWX21S3CXwgS+O53/7vvyFWTtVgnr74g1yP8KGZhOjwNbkmkFsn6Getmib4JqRc6Yb/EkyKUiUhSiuRZHdE3mbspiZRDVIMsxcp7NNOYsGyx79bQ+i161je44hylB2auUJmHLLcz1mWOPoxQlBbKoGJpbxHGI4JUItcLippI+fgR1e0CQ4L28pzPVIk3vzVnHlyRRQLUE6ToBnfZJAlEbuVrysma/OYW0akQ/h/m3uzXlizP7/rEPE973me6596bN+fKmtzVVS7TLYMQopEwT7wgMM/4BYkX3ugXq20EWH5CAiFkS/wBIISfGozAuLu6urKqsjLz3sx777ln2mfPe8c8R/CQ2VJZqnK33bZUXykUK9YQK17WN+K31orv198zLQek9YJ8fct6+A5tp2Cl9+yakq75lPH5D7iPvqA8+lxHHfb4Yzh+A+f+wEszZ1NJ2K/2NJXOxbgjfawyOkacft/k2PVYik8zFNhhIFU3uPuW0kx4HYn8YlXTZHd02QP1PGHx1EZMQHu1of3xc86/4fKjxRDn9Yyx9CVPjRO2/duI4lO8acz8ac57wSW74whvaHL2+o6VeyAQBRjsyN9YZDODsquwb3xWuzuKq2sM22W1O6PIGhRhg+k1NEbL4cGiWpq0gUw7L9iHNcogYI5MmsrcT0vutVMEsSerasYnJ6TSA4I7Ja3uuJR+QHb3BYeiorm2makGTG+ZfFvFvJco0j1VK6BrGaJcc171FMMp75xp6P5TVqJHaQjMjippV9BUGvrwHGmkI2oRr2/W9HuVk+kzovUeVd2wcn7ORAp4pI25ORdpaFHEJW/dztn8e++z8F8w/GOZTf858kamfxTy2A+YPXrKofqQ07fW3L7uiQ8V6w6aosGWRZxmhmNPOE0DrpwYe1AzSky8UUZxarMTdKq5S3KUsZsCIS0wzjVOrIadPid4WOMMCrRwxP37t5hPGwYvInANboVz+mT1m0sCf+8P/vbv//D3PPKbAX3g400N+vJI9YFOvdFp3Q11e6R+aAmlIU1wjSAVyBnIiUShtSBHjMSaod5i2Q31pCTJO2ZVxc6sGF5EFGmLasic5Ql3goB3GzL55geEVw/okxRha5A0DefNNc1mRW9/ybH2eSp2fN5VzJqQ80BgpGxY3d9QRgbb/Q7//TnBk3eZz46IL2KE6hRZssgbEVfTmJ3vsW/nSH2DtVT5zG45b3uEAJSsobA3GC967tMM9RhRCCdk5oLDz1qEb4ukuwG7sqNwO6ZCTLddIxzmmN01XJwzWa/p+0siDJToU1LnDYc3p9j7jEFqcrRaXsQV2qBi6qkMNZdazZmvVJ4IIut9QRE5vPW75yzWNmftKen554yS7yJ+y+d0v8Z1b3j1TxS+9zv/EfeblrL/jAv/m/yTqz/F+SsTJKei/dFvof/VG5JSwWhfUzy7Zzgcc+n8kMaW2IULKitGUSoK0aCqhihagHV6ZN9uaNKEWZ9QGS5ZXeCIa5ipfLBuWAo5+ZMGJ9yRtjIz45J4tKV6ldHLBbo/YV7t2BnXZILAVTSjNAXM+VPE6lN+fBcyezzhcz3GinRKycIgY7i6o9nd8qpaUc5GCFqP1Zg8Gr8NZsRVU9N0EWdVznEIt3VMN7pk9MmefX3D+azgdPCIo/AFxqzhcaThTwY87VuWVwPunkywPvuEC7dFdBr8m0csq4bt/v8muV8QCS2+IiIbNY0RInUF4mxPNBORBwp9cUBY9iCVQE37kJDXBnp4yvHcQQ8rXvrgFWuSUqMppxTOkeGsZ/lKZzeXCG9y3i42bOvuN5cE/u5/+we/P+y/j5G/Ic86AvmEVVsgvrIJlVv2Rs6l3hP4Lg+GiWlW6JWHVPT4rYDW28iyjm5KWFHHZilQRjZua7IVC7RHHlUjIFc5td2z7a4xDjXuMwcxlZC0BEMVOeu3fLk50lVbfnTd8dH5JW4553keM81HCCMJxy35uBuyK3ten+wx0TGqEaFYUZc5oabRySrbQid3dI6CSnW0MGZHvjFv6USL0ovYtmMGdxfcD3zE5x6v0o7qomF6LEnDnzJ2Ajy95fnPLzH3K044ordf8vNtgV406CcKovaYG2+FtG0wnnm8zg6oLz2E0SvyfcrByHmotuzkG+JRT6h4WLQYPUjyFG2koMkdja8TvOVzTM54S5fgLOJGm1HeF7RtDB8mpNWHfKR/j/U6Znv2T3nysxlL7Y4xp7zYTgmckCQVGHoe2quX7LM9RjYhXEkIxYhaMnBNDTvpkSsVuxERJJ21uuY+j1BXPrYxofEcFmKPbHZUqkSSibypwEgSLh0Lt5rSZx2zscdgNUd0dW7GHW6VUCmn/Fi55d2rv06W5pjdDb3sEeUmo36OrOoUxwkneUPv7ThTGxRbZ3uIoNNRThNOyhmnvkM72pDnEfG+pz2aCP4AexDz6HjGR9uE8q+c8+j0HSYDgewXGpJ5iXEbsz15ip6sWU0HSNmf4OYeymxAOxuSvR4h/G7C8eENf/rJFcs3BW+dVkxrlVxNUUSdU12i6FKsFCSp4ZhbtKlLaUhMxg4BCodRiXlaoJYJm97GjBuizGAWVthZj5WJiLHAY0vksCt5bGncdB51/qtXB34jNguNRl7/nd/9IQP3wOd7n2N5g3avITgJwkPOyROZdKlS4bEpQmaixhV3tF2FXnb43Yhjp3Bsl1hFh5211FOBY2VRFymKqqOkJWNNwZ5btI7IVA4xeMRWPHI5KTnPnqL+zjv8Pz9+jTnWmcgqbXeOtnrDPp0TT23+2vsukTxktvqcT+7u6a0BgdwwCyToK1Cn9GFAOTgiPw4Iwoj4PqY4CJiTgIu9xXYMUpsSh0dk0SMJOr41yllenzPa3iK8X2DqHvMLjy+vPmK1+4K/7i44hA2FLCJicXXRMLl1KJ2GKNCZPH9OlMy4O1Pw79Z0+wXCXGRX6RxXDdsOvDOdLu5463HA2986xe0s3KXLoR+RpgUfSV8SjS1ejzUelhL+dcfP3hU4/2zBxlNwpgLq/nuIyadYp2vc1bf59M5k8w68tdmhdFP6f2PB7v/8MdEqQIrB+d4UwxAYRD29d819a2N2LU2UUeQSTeFT5S2ZYhPK90w8qK50wuofU+omsdDS1B3WoaN5rTN5GmJEMavonO7C5Ymhcf2iRf9mhXIhof1MJR+fEZmf0Ic+w6HMhdfxZSWzXiwwoxS7bQjy9wnmEro7ZCuW3K7vaA4ptrynlWSUImPuy3TvDcjyOZZwRNQqdrWE8uYR99uIrnkg8CXOHw8pR88YKA2Hp1veP9yz6d7mm8OMv/fpP+Xff/Pv8EeqzIeGjj0uETWR//n65yT/+5+Q9R2ZZKHer2h0j8PcxLorOSh7xFqhNGCU9gwtm+rtFKvouV0M2R5NnpwuyHY1E0Eheidj+blJ1Q95NpSo+4Tifo/y7W9xs3rg0upZD3bEf5j/5m4W+oO/+1/9/lvDDznJNWR1ibBKkd+95Luxyi/ckC4coo5kuqRHqyzSbodmTpBzm7LpaEYx4lQgKFzGYUPcOewchUBoUDOBIMv5yFOJLJutG7ASfATjMWd2zcsnI76R/zYvcChFmWbXcBEnpG//gFPrnr3nYhzvGNoRy0uLZrHlun7Ji5WDMStwLYE3L2RECpQFVEObgahzzEuu6oqmEXHZo1kRgrwjzEuKNiTfuijmnvlDRbIRmdU6WlbxyckTBPWUXXzFSXeJ7a6JtBNuMwX6Fm9qUXc93cjDWDecnbYsmwGS7rJ7c8/qQmXkWXjHAqk0uMNjbBbMZ1Ns5YRvTnxmhktVSiSlwWiSUcwEDo/eQUwaFquMdg4z84LR0eLsqPDiA4EnN8/4+fu/4N1FyNUNGA81q986IMm/4AOtxugDQj+hOPV4ItiISsE33jshO5YkboO+PjIMeswbOGZjREcjUDPQa9aPUiyx46TKkKYty7ChqhMK2SYfhugLh5AKUVEQTYfwUYM3VnAsAfoxqZgy2Qls/JayuEFIDgiiw8nMxnwn4vn/J+IIIf77HX1h8+Yp5H1BaUlkToz6+kC1bon9AKkZoukWzePHFNoZ5aJl2vg4ewG/3xI3MVc7hx84JvYjD8v/kFbbffWXo/YFx+YdLl7JLKLHbJK3ORm8RBopaCcTeicjNzJ+9KOMT70csV2iFD3XskJjDBlpMYfKZiglOCc92RDQOgpJpFNlxFohS3LyVMTaZcyHDk3QEa4tykDgrNhSnmWEr3O8vmHfNYghnOgKheqQ3hx/c8OB/+Zv/9e//84PZizLgpFcsDQCLOkl+sFjkGaITkKaDrGzDD3o6Z0VtbpBPJPQegE1lVCqKcKxp8hsMlmk6EPyrKZsVEpXIhqpjI0YaxHhnK5xqw4DFccLmOdb1PaSpbfirNiwN95CKw8swzmjVcrcOyO1Q8LDNf6uoYt9rNqhrzMKSUVQb4nmMw4EpEJLaDeYssIzUceSawRumGgOj9UxPjGpXaInEmHosrq64/Y2RBnkPLQQdxGdFxEuHRzd4mBMMB+t+e1IIv0A+lXLpSEwv8rZ+ic45pbf2QucWAaaVaKPAga7kFetRHH4goG+RRffpX1jI7Qh56ca3lRGcUV6scfQQWCOuHpJ3akox0s624E0YXgWosoJW7vk+fOIoShQKg37OwPLP+VP0wnzhYryCLLywCYNGcdw4rgIzjWloXGupsSBQpyobAobyZsh1wVC33Krq2RomE1GecgI9zKtphAqW3blDe80BofPC0pFYn88chm6FO8ekQ/v87Yq0LT3RJbCSXdNF8ichNesvxjTBCBKIRO3ItvPKRYlIyVF3vsoaoHS6tjJA4oToJRQHe84Gkd6qwVJ4VQV0ZFI9kuMm1uOvk490lglDaPCJ5Y39OaB83aCdqkzPZQ0rcM4eAfl6pqN9ts4x8/IdjGPtDGR3uK2FunIpytcbv6v57SfP9BsK9zgKVPDxtMX+H2CoR1YblTkdsSglzCqBk0QSLuWOhfo6hpxGGMikaYWD+YUJayZNyHlvOXh4TFunVBYPftoipbJHLOKIOvYR9GvJIG/iKjIOfAPgSnQA/9D3/d/XxCEAV9Ji10Cb4D/sO/7w9cKxH8f+D0gA/7Tvu9/8s/roxUbotcJ4lii1Cp+S/42d7XCw8kYvy9of/6G0bzGVi1Kx6LqSspDiLhQCGsNI485qh2q1iCrDX0TI0sSY9NFUGR6S0bUTRLtilJvcPYjdDFgp5joxx1J4jCeX4MU0eln3Cshk6uedfXAaD7k5lGC9bM5p84lUtdReEuU7ppjpBB0Hk5iMlAbMqkhixXy7MDaLOkiDzVpEc0zbrUp1XBNcafieDWD8YD1Zw/EasbFpcQXhwODqcHS7pDqDqsx+UkqcpFlZDci/8i44zu7t6G855Xm0P0gorlXWcpzfPOINBrhT3S6YsDRWNCYbxO3A8RQQRtaWHKCmdocK5dOsGFboog1x+MFkrAi12vkzQMvaxM7OyIPBX6aSVx2IvXr79D11whVyMg+4bPqj9GDikv7KXqwJL03makeO61kJZuMy4JeFrjdVxgCRLaLNU+5aQz63RFTj6nKAENJMZSOtnAxQ5ujnlBXCUptMjJPeX0n0EoJe61gPq1xRidQ3tNZP2OTOBST30VI71m9geqtCaapYp+2LKMtI1/l55/JTCcJWVTg5EOaUUHaljjXNu6T7zB0RRZijBJ+RHz7iuphxVueyIOnkaxWKJaA9Y0nBHlGt5GoG50/iWsS0eZ81LHihosbsN8NWCYRd8ec7zz7kB8f/xG75SnOkxc0zveIN0O++XZNqEGvHfhC/5iShulEoRQr9KuXdOOG7bRn08u4lynldUkdWhS9ijNwoFUpwxV1UGOgk1/WaGHDo37J9bBHKQSOhYMx3ZDFGr1cgJyiXmzYRnNG27+E5DjQAP9F3/fvA98H/pYgCO8D/yXwh33fPwP+8OtrgH+Xr2TFnvGVkOh//+d1UGkCJ/2eqEix81N6NjCtqIyaauzRvOvRtjqNEGHGJXqh4lUDQsXB8WQ0o0Ur3mBKCS0KbSdhFj07qSQ0LKwyJtrdUx4kSm8Mtwq73KXLvmAT5xwtnz9pZcKrE1xRw+o6Bh/EXLw7oRA+4/U//ilXfcGb64Q7JUMQBli8TckZK0/hTSPweh/Rrbfk8YarzxterGWuFIEr1+M+V6jLNYmSUDgb4hJuNz8hS95QWDI7aY4dzPiJc44VXWLcadS1gRV8iVrviSSNwJSIQonj4AynCUmaEWY+wG+3RNMRm5MJ+4HMcx8O5xcIQksivCIxCjq3QZiMKJ48IZ3N6Xof1Tylbnss6QG/kyjvFV4JQ/QTiHZT7u6nNKHMWtKQm3s+ckL0tcCfLF/yau7CZ3MGrPju6V+lcROEZs98pPNI9RDzPY0zxD2aVK3CvkmpIh07XlInNSouktxzqs/RPZm6jqmckF56IItCFpsrjsItef9Abg05j22c/IxNdUMpeJhxQDZ/H7+5wxoeMT94grZ/oBrKhG6EWu+pyowinnGoUp7MJQ4zmSZ5oIyOJLOUzgrZZTX6DRSHmGzb0tTQaBXXNwlXr0M+3yXs0g55PEJyLAyv58NvuTwelVihRdv1yGrPrvV4cgXvfWmiSA98WJ2SK0fKwqXVCxb+LUdlhhHrbOsJ+88DPLHgsDaIdl9wZQ8JW4emVmiLnpUqUyktqphy+ZGIO0ppeCASavLYoyo72qzHWRW0/QFvGVPsFAZrkfRlxXt+yCTV0XML69YlqFeMPvjVtuR/IRLo+/7hz97kfd/HfOUwdAr8DeAffF3tHwD/wdfpvwH8w/4r/BHgC4Iw/+f1oVUiN86MgWygKwa/MDWEWONiLGIWLqP5BYE5IPM11HGJU0g0x4JaLsDrKB2Jrg8o1Y5mdIs4KMmUFksTqKJrYuGIqz0iDEz2B7jPRRL9DVlWMVNMUmlJq22Qt6/4SXbkg0FE2qcYa5Fl6ZNLAzT7gPQkxjof4YswlZZYQoy0FKh8KDqJDSHRcUejlRgb0BYlcpQQqTqN5DPZvY1SjinyhK10wWNnzkfyD0GfsxgP+d7hFYftL6ieOvinz1n7LhM5ArdkNngb2WgQtZ7Rswmz1wG74BX5QeV63vAFG46xhpYJFPcRXbzE1S/xHA0SE2Pgcf5uw9MkZ3wvYHUVU2GEiEEUHNgbS/Z1R5Vk9JpIsLxH/NLkOH5M19yRPCoRuzPS22f4xglnf01i5Ebcr19AaxD71+TRHEG9JR9nVMWAp4FNNxgyNwFNYjxy8FqFZR6xb1PqbI0aHVAEnWqok9YWZeExdE4Y3AU01ls0rYklK1SDCD9uGUc9rV9yPMY04i1yKZCaBcrTxzTbnuIh5s40ODQGltEzOUbEfYg2qumCMXbpMa09Bs6Qzt+wfLgiSiPsYc5wVNEPxxjBHH9gcWpYWIMK3RSorZKZUFDtDgSVQynY7LYGWVmTFiVbv0Ucf0wbqxSna2Q9xWskVjcmz1KV/OSa7HSAI4H3qKLVdVITrNQmOC9o3RGL2xntWsd+DVIqUYwVrtSeettyEdt4osHAhelYpTuoiK3BYm1QBRWyU7NN5siOwFUa8FqqOdH27N0x3/yWzets/GvH37+Q0OjXNmPfBv4YmPZ9//B10ZKvwgW+JojbX2p293XeA78GTduwb1SeiiE3wzkf6NBHBkZUUatz9paKrZVMuymr+paRJtCddWhSjrnLSJSeImiRs5aiVakHMkYEQdXTtKDqIrkYo+5rMjNCinvajYh5cs6gbtnnFRPjnPDZCv3OJXUVlNYhHu2wXlo0xoDcLvB1FT47sLAllp/eYpsWijjgYI7Ikj0HV8F2NM41GcW2cKQSs94RGTbZvuS5kkArIPk6jmHQ+R7Z0kQWS5rKQLNOUKYN7zwXWF/4WJ/tSCZzpg87cs8kdVP0dscVbzP2ZgyLkuFJi3Ddcl0kHKojCme4/mN0NWWiZWTplF2qokkagWVjntvokoKkhhSLI8hDinVJuj5nbujcNhoXTxK8nUtXSXzy8edczHJGsUnRSVjmDv92w9nZgDdlRpTUzIUBi3HOsMp58SpiYGQE/ZIrueaRe0rS5rzpDYRtRx2sOBY9szJg1xQIvYFgi6iOgJFk0CpUmoI48BAeFEQjIs1KNqrORC6IXJnENLiMFKJuxzAc4uugW1se9gIffn+A8Idg+C3DZzWSVHF87SOexVwmJs/lDLHvGeY+g8UthdezoaNaS3jGGWKm4S0qisxleGqgyy27LMMpNTK7oskNFvsOJZE4G/h0oUA82ONkBkkNNyFYQ41zQUR90FH158zHlyx/6rOfXNN/LiDICkVRU5+7dA8T4sMnBGnJaGZRGQpaqmAvW3ZVhHoQqCuXSEpxA5mmLwgzCT2wadkiejbZQmLYqUzqCrE3sWYOxSLC8lIen4VcbQXkyPrLk4AgCDZf6Qf+533fR1+F/l+h7/teEIR/obXGX/YdMEyRadryogX/zWuCS49aAnEhEL73wLyc8qDUFIqKgsgx1aiLN5g7k0g0EbwCrU84ZiptDcNDykAy2YsdWWkzSjPsOuS+NTDPRcZFxmIr4Lxtcmx1nPIOfXPGSafwI+slbvguxniNeRjycL4j63WsqqfOJrTxS9KqQNCGqGmF8d6cWdog6BadL+E4MmJast7fc7QtRrLJaGNSZhkPWYTlarynGUxxWHkJRZOwK1dImca9dsdb0jdpNwa3Uow+stANndPhHF1N+MnyAt8VObYPdE8Dgk+HWJmOeBS5iHrSfk9mH+klCcOcUPU1uq/w2PIYjQwYeGTiglCJsJqeUZHSmzLFswmP2pZtd4ovviYt38F/z8Q9/hjh2kSxS4b+tzhsVzw6zUg+fkRezni6z7m+1JlVAarSosQ3GIHN09ykcC94s7iimGrIugLNPdrzFbWkc3ruEcgarVGRSSBHEVHi4FlnqMcDzV1PMXXRBY9GLomcFL+WqRSB673I00SjLl3MYIBFxEIAr4uZSmdMlgMEbcvBE+lXMpoYYMkWT6OerL7DnYpI/o6omiNqkIUGJ02La3QYloojSlybBct6Rb0zKPt3CSYTGLS4yRxBUOlTGdG/IzdhxTsMdimS5LGXJDZthvqmwzszSMSaQ3ukbCX8Y4znV+zMnG98cMqnHycovcBGu8VpZZICTl6lhKaKWKuM2pI2kcgKg87toeiQ0pxeVhF1jT4u0SOJBpm5opPqGba+ZihLZM0D3qXI/nOBiSOTxDKBnf/asfgX8h0QBEH5mgD+l77v/8x3cPVnn/lfn/8s6LgHzn+p+dnXef8Mftl3QJdlHKPnWBgERcmbNwVyVROKOma4R8u3iNWSsrimjVQavSGVXDJdJulbVkXL0RhgmSNcd0YjeISNjDIRv1q6aR10NcKtYwb3OV0rM7MGPEQdM+8MZTSkTyMO5pbT+YZMt0iigLuDitflaNGG6kXGPv6SlRpzke5RXI+9P0MvGywlpPQt3HhO0J/RmUPUwsFIMrjWAAAgAElEQVTdusRZySt5yVqNMYd7zLlJbn0XxXJRjzbGxqR1zhjOb5hOLWz9gKh71OMh5mDCw/A5QzNC6waIzhWqNsIMa6J4ijh7n/OLOc0jkWcDgclUplciECL8cYFsGvi+z/DbPcYTg0GZYxU12iFDvC4p7Ufc6zZRe04pNBwHd7ylbHC3z1nuS7JBwswV2a0CIjnkylGpT2a8d/E2b6SfsO+nzByNaLDkoGXEDswsj8YOMCWD6dMcpb1BTRviRcFDJVOXBaaoEds2hd/SKjq5NKbXS9ZawlasMAYW00pGNhVMTUHIQHldYOoWivoeqlKhTBPU4pylcKBuFeT8EuGpxI1ssDI1Gk6QlZymzJDNhoe0ZztLSVZHtMUKbbZBmOYYww7BbtFEE1NTURQD0ZQRBxqCJqIKS6Tthvqw5eE4pItVDqMFt5pOWU1IWpHq/hGtnDNVzgmKnGxzyl464ctwgu6/R6JVuJZMLw9Qkpa+bVn5PpFaonQHNFWilmskpabvVFpF5MZrqCcGqFA7GrgeeWfRJyLu6wNan1HiIHcCeWtTpyr7y5TtUqCTG457G12WeHjjY3ZjBnHxL08CX8/2/0/A533f/3e/VPS/AX/z6/TfBP7XX8r/T4Sv8H0g/KWw4VeibiSyeM2FLRK6LkKmsWtlJFGg6yNWXYLQqEgyaNEpmWOgqTKK3DEXWzxTwbQtZEOjEmREVaEZF6RKi91H5EedraOgDwPkscZt2GCLI7yHHUa7wtEH3Boph3lPmjgc458RrhXS8AWreM5xuyfLMpqbEHdpQjOk1DI0f83NdsOhV5AFD1HqCEsQIgNdMsBLUQMLzXQYyCc88X7At89mOKRs6xA/V/HPRJ6pOtL4ArGZso8V+vcLjHrAW62J1Y143lW8DIa8yxw1khlvTjgvFjjKim08wbw0UQOL8ck5T5/YOKMAOzrgOEeCwGLaGkibO9RdyHkkMBGhmhns8znFtcIuX3Pt3SMurojklmPxMX21IbZt5t+yeOKdc7PxELwHzH6EPWgxpmN+rm+53sJVfU+y0nnoVIaFRefB6kRF6i/hfkR831EvS7LThtHjgIHg0koqjayhqhWuqSOtD5jLHVrV0MoiTVyj1Z/SHiq0JuJovWLoVTzVt3R6iaEnPGQCfTJDHQcYtky2Eqj0nhfymDa8J9N1TEcm6WoaIeLau6BoB/jCjOogoXc6fR+wqgU2aUm4F4iqnrLXUcURojLFNgREq6SnQTBzGn+P2TU0O5u8rdDcz0m7GMF/wlEysdDIpJibw5HT8sATI8HG5vhsQnfT8lkWIS415PMlVrBHN0EJe7zcQJAMyvYrafGdpdC2GopWIQkhdt9h5SVZUnClKiShykMn0cs92ypFdTryo0U4Ermatkz7HHXsYMo5ll5wp/4lSAD4IfAfA/+mIAg//fr4PeDvAP+2IAhf8pURyd/5uv7/wVeGIy+B/xH4z/68Dtqup7pLqPIHqjxGMATUG5nM7MloSbsEoS+xkhEyB4wiQmqmmPpTNMdHzyT0dUrY7VCaFF3sEE0RHjT0MkOQcpReR3IzSiXDOZdpZg9Y5y7HriTBxMxVFkedqn0Ho9iSu0uURx2H9Ao9qyj1DsFXMacOVSeyzW3k2MP1BRKhIFodWOYZeS6jxzK11dIFGgNtxollYdcq8Y2CsCgJ5Bs2zZ7Xg4osSDguY6qwJ5J6loZH5+ecabcEbsugumC5veD5ZosvPCOdb2idlErL6OuGeLzCTB6h1hKG+RaBcILvBRi9zFmnYJYZzUKj2eW0A4GDrlKILY4qoLQrMBu8LMSRDOauRzx2yC5NFmbIH60cnFrlzOrojZZEyrmpZD5TZOb923Sigm3f0iUy5i4ifLVGr24wxQanrIiyhqUY0R03DEcZF47PYDDHRMLVO/zaxRJsQlmmSh3KvUFMTSmtKPwOlQK3O2Ah4csmaSMjSz0n4Yjj7gaTlLJNUbcVK7Ni0x/o1K/09+TLlhyDO6dFyW26rqS93eFEHbLmkIQi2dGHeIjOmO6RC35GXecga6BKyKmOFfn4ck3naPS6ghKKGIXERHxN39RMEhvtrGYppoTHEFlxaJscPZfADCk7gUo94DPiZd2Q3le82S5pGhWtlTBLn74GUXVY1x1iGdOTIO8r5H2FVMvIuUaybSjylk4VwYBSKKn0I2IrYLoyu1rD1Fwku8CKBeywIMZhJGVshSVjwfm14+8v4jvw/wLCryn+t35F/R74W3/eff+ZhxAbIs9BKK+ZDAccHQV+alFpDfLBI292yImPLBccvQ0zrWShjVAdiczuKPY6oweDRunY1S1mUmLaPW7TEwYaTqUhLWraxzlRpmK4IrvigJpYXIsll4GNpavYPz+y/Cji7LRFaNYcNh3Wbkgz0dhSEehbYt8gC2202iTVv8TPocx9iK+pBiOWncI6PyL0Lpf2iEFUkwsZh2LLIi0RnAnm4wBVaqgEBSEMMLSMvj4QhQmnEozVGY8DCeXo0jg/I5+dsulzFk3KalpghjKXuxbdlSjWGVG/RulKOjnFkFzMdkNty0j1EMkMqAKXRuzJup5C3KO1p4h5Sy3fkJYO7bWDo/vslJD8y4LB7H2qXcLTmUvpmpTOlkvhHZ6HR9rsF+j92zTFG8aqSX4MqToHodox3pmY79rEiU9XlDSyyFhMkKWMtrORkZA7g9QoqcuORNYojyFFIWBORKpKRAxbZCMkLOasQgvHr9mnFjPNIMmBWqTtRKqVgDLM6U4WSEKDsIwYJd9FyUNcNaWfvEe9WSCiY1YN+a7D71OQTR4KEIUGJWsxmwJBLiAsSauSgathneqQWmSbntd2h3lfImRDiomAVajImsdMhVRqMWYDmmaFtZeRA4cwn/LQrVBXXxAWMv6ViPA0w+juuT5vubix2WQrpMwjKTNIBDRbJWllOrnHMkQkFfKqpvZ6+tagqys0PaR3e0RUpL1Jm5V4swRDadhWPeJZjuZP0X4ScYhrQvWM3fKeoDM4TjPG/xpdif+VoO1EUCCwFaRDzuYYYFclmZjh9Bmh1KN7OU1hUBsqlWJizxuk8oBe1nRtQ4FJKxqo+gK11MiLDqUuUGWF/qmIcmdStzJGqbG/yTjRpjSbNyxGMzQt4d15j3sw2H22p3hq0XY7jtKUxrGYKi1dcUSrA8L4AWv2LpZkEpZH4kbDrByESkQQK6K+QhzH9F3JLuop8oImLii6jPugwZZjhts5QfMlguehijETfcNtvMUUW4adiNkVxNmUIQtocnxrwW95PcXdFmVhUNU5+1CmCD6hXwZMpYrM9qliHSlPKMyaZiTRyz6eGeH0IY1moSYtctcQHVoSbYCqlexEgUasuaBkEzmkTcUwHjHSNhx0gfBeIBuq7Kaf8JFjU4wsilWD+EWE/Q0dc/uMfX5gdvYe0WOL1Mio5JKiVpicSrgPH9H4O7SbFMmNQIhJ5IK8USlrkV3dMlaH2E2BMbLwFIHbQqSyVCRLJKJhP3M4UWsKO6ZfVNwJIbJpY1CjZlPUb6RUP26Q7T3uUaIQGvabGqcVGJgr7jWJR2bGNnxKMcho+grHHGCXBSH3nDQ15W1C0fTIloaJSJMJ9PqRWtxDbGMbBmoG8kQmNSPsg4pQ2dx5Gt7xJcYBwrNv0w5X9IuExYtr3pIdUimgKAccooKLqqaanuLLLWmdYtk7+lhinXm4VUY8nSCl95iihBaI5I2IEjVEWoaneUhCRVa4mIKNFKSYqkSVZih9hdWZZOuaKmpJJZHA0BjNXDZdgrv2uLebXzv+/qUNSf9VohZ6CEM2bUEbpai7Hdmox7RMmlyg8hXEM53OS9i3HZuFTBe35EVEHYnooYwoZnTqFtFNaEeQ9wKdaNFnBnstRXQltESk6zOUPqauG1aWi1wW7LYLNtcSYmPxrJmiJeew00greJCW3CxbhGqNcLRQIrDbDIWEQJghMUTUhlTKFCkJ0MIWszpHk4dURcdGuGfXHigqmUoU2d3V5Ns9RTtEUA/Udc6eB4pJw1C0sbwJceuAdUMzXhA3Ht6yoGwidKVk0I1BnvLCjinSksJZExcpdT1lEnh0tki0V6glD12MsdmjFwl+tMWqDrSaBbZKfnS4200pqseUusGBhqJouRyKrDpITmoeWyaNeKBcPkb4I5FiZHIMJWQv5EH0SQWR09GUqWAzmD1hayg0jQH+DlU1EJuW0LNp/Gdoj8ZIg3Na7RHLKqCpc/yy4LwXcMwaqfXQRB3xRKc2dSS95dS1OVMVHLNDVxaUYkqaKHRvWXgiNFaMnoxIonc56D9gZ0W8zgt6d4RXhJxRUux01JFHM2/JZh4zd4wr1AiFRGx7xLZPb5wgz0cMAgUvdRlELnXVUpg9dj+kVXJC8cis6nCqmt1hT9YatHrN8lVLnI45OCqBuUeKd1j3KWY5JsGh7jZs7w4YqskgA6yM6sFETMCjRa4FgrJGkitMdMwULMGiEgKKSkGvJIJSJO07klZErRRMK0Il5uBqyJKBqOiwFNDDHb0JjOEQZ7SJR2f1DIwKefuXmxP41w6xg8itEIaXRImPIxrEZoVFhT0Z4BUl8usWa5szr2IEcUHZp2y1EddjlSoQaEUdipbckTnIEUKU0J4IKG9VBL1FKBaoRUmkQMWcsosphjpju8Dq9nzRhUhuT3uacqcW1JlHW92jHa84KCZDz6e774lWPgvRpeoiziyXpNRZJjWLcs+mewNCRnmzI767RmyXVIVCXGVERchglbNLS1bZPW0rUhxTdnpMMprhyd/jtNDRjS3p3Yrb7T1RZfI8OfBxfM+x1Bl+a4KoyYhKxlQo6aQhp7KHdK4gd5BVAkpWMe99hE5Aah84Jh1xMke4V9ksWxbhkXVrs65y7roYJ1c5C0PU/YrauCMUdQTpDUoEu5dblv1LZssM/bbmfleRRwLKTU3flVQ/r1msviD0dK6Te9RSpswjEuk95ESh+CShjjKK8oa+2ROvJYroiFG3SDj0hs1wqGBYOc4jA9UqkIojjpUQ7Dsop8Sahu2KbDOD9qVEN8jR5QGVpSILE0p3wLjOmNQ6j4cytVAiNmNGcoMQyNTSjNgYUJsfYPs6QqUj+TaNlxHX96hrhSTXEfSOfdeybQr6TMZVLJx+gOWPkXSXfXnFbfqKpEgY5x1ZUaL3YLZ7Xhw93hQj2j7H+tKnjJfIhsb4RMOcpDxxK7ompm9d4qJgnzbUVkvaOIinE4oTjazXGWRLDFVF7FpmTYehVchOztTu8Y0KahlLbpCMELcq0IsSX2qwhyJSIqAda+RGxNHOcEcO5h46R0BTMkz51y8R/kaEAz0tXQJO1oA5xhRjGn9PVZ1SFDvs0uVYmqjOA5Wso9gmTERm2ZAy1cjtB9qswsmgLSSqvCZoeqQejKaE1OG+3lIqErou08gFqdbS5zlbR8ItFKRJy2YNH53t+TS2mVzo1G/GLD50+dbPoLBtfEuhmZZspQpZ17g1Vyi3BW0/pLJ21LaOLq5YOj2G5LIvdxS5gZgKiJqKmHoovUxrpWw2e+a+xXbtETwt+EaSEQohSuJy3ywJkp5dd01QFFw1Q84eKiJlyUgzaN5o9ImM8H0NQX+XoSbTVwUrTyAufaZGSV/nbLWGqL1Fq2q6WmOjVPStjC4uUcQew1Ipq1v6zZQ8S/DcGs3OKdlh3Vrcr3XahcjH/DH66MjZ7pJQUokzkVAIOJdD8i+24I3JRxnqszHVWibLDRxVpX444vKAEshsWp209Nl7t4xVjyE2sbpGOAj0TUdS3lMeMrI4QbZaGnnCOpNZCUeMVKHb2ZzaGoKSkN+oPH4c82WrkKyv8LcqkrlDPrh8c9Rztf3/23uTGNmy/D7vO3eeYp4zcnz5xhpfV1c3iyIlNgRY44L2woZW1sKANzZgL7ygoYVLpkWaBiwYBgwvDAuQDcEyDE9aWAAlmbTMhkR2dXUNr4Y35xwZ83Dn2Yv32iw0q0ASgpSv0PkBgbhx7l18F/84P5xzIvOeCYutPXq5haK57MfQnOv485BNohKaOVHuYhc5NXVJ4bdJJJVUVljaAUUqsFSQbJVa0CCrFFjBOcF4TagMuO9Ucd2ETCkQa9CY0wrgpHUbSYXnokbTgoFxi2ol5lne4oazjaNf8tnDGV15yUSVyDydRK+QVI/QYlgik7fbOHKCrQtMS2CuBXMzJY4LyjTEjQrUWkqwX6KeRqSFiSHFaI5FzTa4uIA84MXTtaQxrlsljX2SRAHSr+1/r0QIoJT06wMybYzbjFExcMcqnfMSty0zc2Ra9RD0FrbQUDEpXR3Z8ymikE0mIZYeauxTVesEUkyiyRibgsk6pqpvaEkaQVdBI6URSMxqNsHIQ/EESpqwkXT8dMmXywalr7DJPOK6jrMOCSsnfPpow369BpcR26zJ9QqhYaPPdWJbZkepEmwKxmuXwtIYFQkNDax0RZA2SfoVFMDMOhRmnXiToAc230+qrD/J0ZoxlXaOVJO4/WCH5+99TPKhxG2nxtlWl+3LS3xpw/Btm3n3DnHjDG1uEL31OcN0C6FWkUxBbK3pNMCZlPhpg8YiIbUkHgaQmQXGKiaLZUprC03zaaVTalX4zElRkxqoJkqcEYoN0eYcWjpZKZPaPUarDo1OweC1iHIpkNw1oXyL3Lxkqzkk9QzyQx3Dj3EGOuKNFq5mUxUxVk/j5kJw4dbJkhJVWtDMMqaiwdTYoEQpcr1O6Sdk5ymbPKWvJfhuldMs5VfKgi3NYVqsmNRlWnmVemyg2DPUuIdaN1ivKzxYf4SVtFHDDZvWIZJb0o9cvPI567JLbBlIS59qu4Ir7eKrMxTnOcYpyF5BiU8oK+TJGpGZmGWBeU+QThokyZJ1bBOuqrjaD1mt6nTyChP/Cfa2xvxpSOPuGvvjgoU/Z6uTcdmsUffmrAOLZlFQyUPmIRSTKnoGhbYgTkyk5owwqmAuFQIpJY2gv1FBkXA3EtEqoFJNUNo63lJmkSgokoMth8yjFDkucG8HpJqEPC2YbyKkqoOlxqyNLcrtJTz8+hB4JaYDIpPZLBboz/ukaYzmZZhCoA4VTNuhk4TUXIGitOkPJaSWT0XNkOQNU21FVYXqtsTCrjFfm8i6RdBWSCSP8iAhq8pkkUTjPMKzLWaa4EZep5tss71p4tkybGaM8pAPvlxTSZrYt0xkpc/9+HVW+3e4/Rf61O7oqKpENNeoXG4Qp3OUWoKnVQgNhUpXJbGHSHIdO8iIplPGhLh9j7nrsxEB5laGHgZc1jecPI2ZRGui1Zhn9ToVZKotg63vjLjp79Ezu5zc7/FubYl5589gWcc8WY6oJhf0bh6wtfYIgj0ukgVjJyYffYmyqOOOmiS5Qj2SEcUukmLRuu2gNHskfotQrmA5E3q1lIVwON4smCcZeuSzOZLZ6CW17nd54+0G3HudZPc7dL6zze17e+QNGZWIYT+mrCXsvNnnfluiqEnUoxHOg1uE0YBa+hzRCcmkPpEicIItIlmgajl98xyrDFjIXSaWRXIpkFcFl8wIlZBqy2S4Nce6ecn2XkDf9+nedPC7Bgu5A8cermFB6rDVKFkFOXGWYOgjDpwZtUmKNvsQ7/NLgmTMRWSwWDeYaTnBDdB6KnU54A4Zg0YXubbNpFBZlimJFKMrGXWzwCrGiB0DWW6ySqAy3KE97HB52GVVH3C6bqNXMnaMGwQXU5xsRWdryMAoOVRVytdNOgObUt8mTSRkqcZOskd2uIvcy9HlDV46p1Q8FGHiRA2KXEKoATFr1mlAmLukUw9NyMi5hqrktDIL5yhjN1CJNINQSKiaTH3eZnsiiMsI2+qRaWvCQMUULq9r+Tf2v1ciBGQ55zQ+Z62fsZ1lZHhIMpzcCNg2wbZsBBpRaIOvsOvILPwxWlqy7dXJCwjKjDTViFc2sw14yZKgadOwDMzUoLgRsuxX0GMPqV9wXNhotsmJlTBUZcyxxUTIHF4WlNo/I18skRsfUOY52srHUlRcy2VY+pSNZ+SWQlVyWG8yauophSKxTCPq1RG22IAG+l6HbVWjkqyoDCP2ugaeFZMlEcvpgFDbsKquWO26LKaPUbI3cB8NebIpqY9bDEsJeeIze5yhnZ2QKD+gSBo0Q4Vm9phKNSGXC8rHEqtwhljkCG3OMpqz8X0eSjmjoYwfaMhOA6Uh2DRl1u0h87qCaY1R2ilj1SAPtlgmHVRTZq+qoh0saNy7zxvbFn91a5tBqdBQjtjXE6Sk4OgsR9FucITNSV/GrAZc2mseDFzM1gmj+DYts8IdNaRT2WNjZoiWTL1jEBg6IpdpziJ2x3Pa/gIp8XHWWySSQXBLEDsKWthGPzrAseucrGMyd0Va6+BhoD/zUZKccprSnmV4nwW4vqBm9khrJs9GA9J4zGr9jA+NBWVVJi0TpIkPaYGTOTSFi1JMac8z6ppCVZi0jS55SyNrNAgHLQKpi5fmJJpD2de4072gtfeEwIxhMCLQZuRzl3jrLW7UIxx5l7y1S71VwVU2hE90bGdAZavgsTdjmua0qinxXGFRGGy1VBp+m41c4HdiaE0RSOiORVKT8XRB5JjUVRk7zlmrXXKpzqBdYt2YUEoO5qbPemUzFgFyUCCyEmM5opzmDJZzLNPl9KT4xv73SkwH4kzillaQ5wquGhKvB0iHguZRwr4ScaSn7GUOrZWJsGKehAoPNZ2Dhoc7j5FPQJEcVC2nVZlTzR2y6Q7TLCawNxT+lIWR4oQ1goaCtVoQT49YaTZRvYoWQnRu0n19SmikfHYKA71BS27y5fAc81mA8hwys8OqbuDiIekhuVUiaTrPhc9ADWnsuqw/KZFWFomtIgKThtpEbevoukKrXCJp26T1OjcXZ9j9N7lQU6rSIRUtwbfOKA2bC29AnlfITYvt+TGfyAnlaspOu0l2L+Hi8wn5swal6aGsGvgHPZTVI6arOVrZIZLhMuuxSWyqnsq4YdILJQbjEFWGtnGCPpcYJW/j+AG9nZR1dsytqMliZtGTm1jZbS5mBruHU7qxg1++Ten+I7Rpn/QtmVRuMjm/oKU8oJ76LPUW+mLNjvgCS9zny80cbaKx6ZlE6xBcGT9YIPYtVKtF6IbMgpDNscsszJnuJFSNHCXSqT6XWa9KvFJllcsctnZ5Fq5QtTGnzwoOaxNWW12MU5e8cZtHHQtFLNhIDfyPJxjNkL11BVY22wc7qG6IY5sc7ricn7dZVnIcQ+HUKUjKI3S9RVFpspwkmCh05geoegzqBN1JOVAj7HRJPdQwtV2KJxo3z7dovfacZJYQ2DLGucp85xE3zk/J3wh49OWSX6rLPHjY5r2tc+pBg9VYRpdneE8qVIcKkrKgPDWIOgq5oiMUGzFL8OMIc26T6RKa4bHfS0nXKuusgb32cXIN12wjFmNCMYZ7DcK0Tj8peC41UJUFWTNge9Fnql2STkvSwAbir+1/r0QIKGVJ1GmwXdWIpJJYMalLJlaw5hN7RfKkRnJzxrmk4kwFSQo9qUZRtyiTECFWLNySRe7jhDJp4OOqMUFUsBno9DOJPMrZnU95sPYZVkN0ND4dVym6Ls+jgO3bCak8wNtdsptpBPKai7xDrdWjokw4bne4HxacNlK8oza14gntsIeeTli5XTR9QlZWoMzpCp12EXFGhcypcqcd4W4MHOMuRX6L5r5M997bdP0aM+cxJ5cZ9pbCE19CDn2i0wHh/VPqn6nM31tyN32bstiwkj5nf3GP7IaMFaRsy1ssL3/IjPvsxgpfFjmlliHJVTbagIqxpp6EJHnAwlDRBk2yvM7Ym5ElITN7wU3JQTgb0mWd4FaObnf4ONtw2C5ZtC7oPlE4CRUG5ZzFjRbj5g6HwWPuioQvY4+WuoXvhmzyGf3hHR5dLiiPfpd+q8/cW6BbFlvRHg/tkKKeIo2f0BINikilzDfoeszWsI9Tj1FORqxEjKeusU0Fs1pQdSo8iM9pztckU4lD7Tk97Q0m9jaa3EOp/w79T+uIv7hL9fiMZe0+m60ZghnYP0KWayR5xmRYRxt1kC90hqaC88s99E3E2TxGC2LyeIMmSvIJjOsjtLbFxFWwAo9Ow2JTqxAZK+x5jN0yeC41CT89Ydby+e7+Ni0ExqffQ369z/eXKg+1jKN0wc47/4SPT+/yxusjGr9yk73fGZLJv411KhM2FVLbQYvncB5QkhMUKoXdwZcz6oGP4ws2cYFAEKU+vipRD3NCGSLpNiQrKschFfGQmbKFlp1hxFtMkMjTS4woJzQ69C2D8/Xi6/vfv+L+/rXIuoY0DVhlffyLCu80RniGyvjOd+l/8RSrnbBRO3iYGLspznjBFgYP1iZacExRQNZ2qLs1BmWKL0UUns5NxUB86bKRdRy9wbPXSuLjCjM0up2Umr2FlJxilFVcV+YocDEPJIKRTmmXDKwR8lmCeU/hF85iRs4BweQBWCuO5wGRtUQtbXbKACtIGa/qyDFUOzojvUSredSqLYraLnpdp9NyqPoR84bCZXJCVKgcLbZoWseozxXKXsaHzwt6rc/YeBY/6HVJw5R6cBNlv2DUesiwpnPuzXE1nfG0YD5QiIoLvnicMVUDVOlt1JvHqO5jxEjhUpIwm002PGWdaahnJqt1Ti5b1KWE0n5Mw5IJBxHrzQ5ZZYRceLjFCfNHd5iaH7JoPqNdlaid2njphLnUAHvIreaG8eNjIu0ezxYh1utj2Ftw+TjmiS+4K5UoZw5FEmPWwWxnaFGJKre4zDPG8YiWoVKzd+k6gos+2KcWnnSLovYFUVknznL2jpqU1jkft/rUNm18o0ancUG+VbIqXqPbMDC/UHhIxJtvFZyHDX7SntB6foPVQsK+f87QbRE8jdAGCzAauEeX6A0XPY+xZmdIYQtNCIo9j66jE88FQ1dD6an4ZpumYuKPDdbTkN57OtXmMZuVSfb8AGenxc5WG7824qMHS4I8xY52UbQuclhh25gw+uI3K1QAABchSURBVASywxWSnmG2BKFwyZ8KHM0ljhIqpUBVM7w6GMMFwXMT31NIqgq9lkpjk/PYq5CGMkF7RVo26csjEBlrWSPNbIzA5cBr8NlfmqD/nsT6l2zqv2+wF3dY3HkGP/qG/vcqPGPwP/uN99//13ZuEa5neO0qM0fBbsk4s4zPlBTJUSgXTTRFAynF9iVEvmbuzwgyHd8oyNcL0iRk4ilUGzXM/Tqn1TUXmkRUCbi18dBpYWUZobqhPq+gdddcJCpua8XbG4uFtkIUAU0lojWps4oXvNF+j5QhphlizJ7xWC24K4/xUw0jqzJoDFkWKZuahyEcHPq4FR3RkPHdhCxpMlAlAkcQ6dtcNkzkf36K+NwjWphsjA21YJf1oUo08ynjNV5gscfbJG9N0aMtRrFK+D2F4ZcFFx2V4rjG/6sEtJ5uOH7WY5avWblNMvk7yJlL9rTGlIJ5aLDcXKDVSnpyk6qmsrRn1BWfWtElCkPyTpdVZY/NdM6w9hx3Ljg87zAyu9w04ZO1xeG4y6pvols+zcaMtbfgTK0yDUMWlSl70W3e2h9T7TSoKAqPj+9w13iCtzHJRUpNqmB0ThiYJvq6xlSUJNUIPQkxdJ/WTZNLccn4aM261JCDkB4S0pGNUvV5LiSMkUxtHdJ5Y8HADuk/sZDbBRXJ5TPTpEVB5WALRU/QvT7rWMKlxBxeoElr5ncKNkZJ19nB7DQQZkLgN2hnQ071gCzyKfOMhlOl1AoqjkpmQdMQ6JEgyU+ZrP8ArdckcvYJzm2SyKAiLbCGJYvU5Si3uSHXeO58SHIxZPfen0fRx+S9d9jyMp6aguBI4dMHP2aobljEFp1ugvuaRKEVBNUGWmpQmy1RJQk5gjJPWGkSK0sjlkryVCGxmjgVjVXg4hoaSmaiZhXscMLjpoosRWgTFXtVRe+nxNYY6zxiHb3C+w787f/019+P6gdYtYKqDUUR4yxivCSgb5XIWkouQnzFJZ+qTBIPN4QyDFlKCelaY7ORKJcB6Z4P5Yb1ZYnuD6CtYV3ojJc5pjXHcyIWs5TsNpwvq3TCgmVXYunVSC98bFPF8Xtk4Yq7hxaXaci9QYWzdc565HL3wGdzsYsUNrmwZ4TLc+peQWLepmVb9Pdtqo6GNFbRpgl6VyCyBtWRgaRWkAKXwFLovRXg6B7u7i1O4hWNaINeRihSwUW7jZEfEyc+X/R7BLNjii9Twp7BqphyOFtiNUvc2wkfVVJcHA4Y4SWCs+ZD9HJGx4vRwibGbp2teE6xdMnyJk5QZx0oqPoZzarKdhCjixGKHmFfDBgxZNFdUz9+QDZTuLtZ8pnSYD76ECleYja/yzRfMdRGdJN7TPtbFMYHtKVfRFEzHk5CbguLWqiwbKh0hjrzXEWuv4ubqdT8GZHZxjEywvmCwquhFz2y0GZzEZPnARoS8XyE3hgwyycwfYRumJTv1egsKvS3BcPYoVRzFpN9uoZBfCGRVVw6ocbJbkz97DMUu8OubDIVVZr9LZ4+09Bps7YnKOuY2PU5TXPWQkXzqmiuhjkyaEc2qq3SmhRIZUa8F6KHGng96uZNjLxAQWXVDrjZijk1PW6Pb7Hc9+j94D0GM49KRcfWUr4sxhzYCstWQt19wrob8tH/tSBdB8zDJpIXsOtmeIGMo5mY2pqiqhPbEXqqoCAInAJzpwk7OY00puoWpGHIzqJAbTlcWiFb5xK2kxO1EtQ4xb3xPULtKa9Fa/LY4jjoUiav8NOGf/1v/eb7opOhVN8hCD5Esx1YvNg/IJIjvFlAOxZ42xIVM6adwMaPOWovacYCTc5JrIx61aLpdSkmDst8TcWY8O4GivWSwBoyGQxJqxpZHiA9qxGZY0y9SuBNiDST+7pFXZ3yfK2TqBUyzUbp30ORFxhBi1N5QaJNiJ4XXATPWUQN2lob2+7QqOpooyb+KsMNIiKxpNPJOYgGRMIhHWj0ZQ2v4iO3+rRdG+nIJ5Zl4t1zdNVgdJTivN6i+MhlfnCOt73HGx/6WEYPeeFRNG+DLohGMp23bvL0J7e5HaWYdsz6YJeGmLNT7+O06gxqt8hPljxOS9atDMcZUksdGisPxTwmLAy0ZEg8PqFcr0myAWd5jL5Z4agKp4Egu7Ugqq1JJAUx33C/02KjSshKn9G5Q7l6Rlad0kv6fHixJkuPkasCQ4uYBvtM8gaGMLAuPXZ1l2i6IY5UUJaMPBMtA6UTMW9ukGYXBEFGsS3jdHIMe4dZJ0VIG/K5hGKU9OwFamZD6vJ5OGQqabzbzgmCGnHjGaY0Iq5/j94m4uxijLhdcBRq+MGE1zcO66yNoW1jzufsl0OW7QGSNuetLCJdrAmN5wS1GctezsiMOXIiKu06LVtD6KCXOnZFozQjnjghffchx6Mt2osazmGMdTLj6e45+/IeP/I2tLB5O2mRzD5C/T1g5y7nXsw//uEjRLSmo4ZYFZ008tlYKjWzYO4qxEsFVRVE/YxmplEZ58zTnIoLZpzgaSHLjovWLVg+89g2KyzvlnhuynqSsSl7cPGMUjKJ5iDuvUlj/JRlkr66IfCf/9avvz+41eDZyR9w198hzQuc/jFhFhGab1PbNanbXUq/YDwCXVZw8oKKUJkbJopk0yljCrFmLmWMqj5RKyHz6jwrE1YiZK/ep5JMmD1dUV+lrHOdN+o+R6OAbtwDx8KrwbO0itq7oNnRePbUpmuNOHKqpNGK2oVG+qmOYaVU0waqHjFsVuloVahUkOo5onmK4sUUzR5+qeI2DOx+h6YqY24F3N1KKLdmWEUVS6hk+oQ4KwjTQ96zSh5W32Sy+AQ0jS8uUn6gOFSUWxh7OkH5lK2zLba+e85ZrU27JcjcgpacIKV9DsSIi+UtUk7xGFLrztnamjHbzDHDFVonZbTvY6oajXxM1MqZ52scVUc3HYSRItX69BSJopdgPQ3B+T4sH1Nr55zf2eP12ms8SZu0YotY+20crcmwBKc8ZzQYUj+Pkeopn4yqvEuIcl5nT/PodytMTIERH+M1Nmh1gZba1EuJsA6jmx2Ujsdqs8CSMxqkjC+XiEUdLYlJm3McL2MzCFA2OzSUEZ1qzGr2Fnv1z/mn1SHpxTHNvonzzOb5nzll8OmQwOpQu1gz3+lTNWe42QhZHiCpMnbDJdICppsGHV1HbNWoRn36osnGalBv+OxYCbFboSVKzmcay0ZEkfbYW84x9JyJlVB7I+atJ68xvyljj27TqM2ZPgr4s790k99Z/5jKfp1LScJfrmkutnk0+oeMwyprpcJGmpFoEp1MZTGxqEYqei6Rhgq9KGYdSUxEBcvIaMwtxFLFXFQpFgaTZUr6RslMXVE8VLH9FSvTYc+L2DmQ8e0hnpsRJXNCJyRZ5q9uCPzmb/3G+8ONTdE7YLDTJZ3sUa/uMuiHLPKIi2yA5nmIpUtfT8h7GcUiJZd8+jWf+XnEszgjM3WYS3SkBG2Zo8xy4qZEFEM0zzjIPS7v9Ml2c24vIjq5g16FEwHJcoqSxgShAlkDS1ny5h2T4K5Mo6zwC+qQ5UWEUUasig21g4TXq/vIqkmWnnO2dikdid5hk93eLj2tiWQI1Hobb5iwLhPmpx3ClUx+YuIlG+L1Ans8YH5ZpWvBY+FjJHN2b+m8MbjBHfp8+X2HyeEA5Q82pPZt+u/+HoundU7SRySjNavXcrzSozxps6p7VDob7J5FI68wcVXE3CfXdHwvp9yUJESoQ4E4bbM6dWhoCpFaUPUGSK2YonuGt/9neVYc8rq3DekH7HZtmjnQrFKXT1kd7WNt/Yib1SarzpDAOaQaeqSWwVGcoW4uqIpjNnbE7HDJUpOIOgWdtMdKy4mnGeuLhFKtoCsaDbVCJGKmiy5729vo0ZJci3k7O+QiiDG0HP3zMdaNX0RRSoZ6CySH+c0KVvAhsfwrDMxnxI9usxk0ODY+p7h8hjraJ2PKwmpyc2BS1iaUz2MOqiaeVUN3Ayp+it6pkkwk5OIET45Z1CX2rDYUNzGSnGrNxTuOEXduUUvgVuriW4Kl12JLv2C4sEmiMWPtLe6sPH5SL9h/VPJUrVOrTpAaOb87mpD8Lx/zf6enXDxa4CHoej59GyqTA0TukloFYTikrIak7RAvVihVi0QpqekBNBTGuY/UXJH06ljrkoHsEgU1lFsRM/81upUptrfhaSdCejhn0EiQLgTloiQsX+GRwG/+zV9/v3WvTw8bf/YI8QttpNEHPPiOTD3aob9eoZwck2sOyqxBWkzxkpSZLyGWe8ilzGwzonOa0GxInMYZamBjFQ3qPZewlNhp+hybdZpJwnLeZjyQWAYKE6VGIV/y7jsSo1iQa23K7gxvvs33uytOF/vY0TEPLAe/PKeMFizqBnlVovBTtAGYiy47ZIhuF6vSZ25GrKKQapASyCPq8QxjNqGyLhjsZMTFijPPZZId8pPDFcP2CF1dYBZb5I8V/vwhjGomtb6L9kWBeec5atFm0kg5rcgML46pv3mD3lQm7FrcKWcUmY4wJTwn5fNRjrW5pOy/xmIL2uY5h1qf8HaPSrrBf6hwUTGovXOMWdhUwiZfxhL0MpZehWix4bu9kNE64Eh6zu6ghS7dRUxSgoszwn0H6URmZN+jlh6BfsxZmJJsDjH8h4SLAbklo6UmaTyk0H06Too/MzgLM3Y6KVJ3gFltstquo6QWNydN6kfP0ZMSp4xYiX1WYYF2mbBWTplEEUgNNvPP0d0VbJtoTw75sr1N+PwxaVZn5y0X/XGdfSdBLwo8zjFqB4ThAsk8xzqZ4EZbbMyAqh2ytlUk38aPUoQdkRQ2Di2s0EWxF5imT2c8gBsLjis6fT1g3f3ui+dDHFcwlDmquodf2GybBsXOGQ3d4g3f5PcOUt6NTYoHnyD273PHvkTXv8dWXeHk5BmX8YLQEazO1iRGzsSowapJID8l0WMMLaMelOTtAKWmYC1K8oUP/RpRH4rzFXKi4CLRCizk+DXy1Re0nAItrLGYFmQ3e+iWR1bmiKZMsEpe3RD4m3/rN97fbu7i705Z+w62tmTQukl1oSKP58iGyuN1G6sZwY0cY9VFmqfot0wWnkrgZph+wnLQwDYcKu2ScuiiiZDRRKM71+hsJZxNYwJ1A9UFW89ddqomSzVgf2PhHqqsf6KS39mwe1pS3zvgh1ZM9QTa/oCGUMguz5BzGTOFyWSfTm5Stx30mzWseokWz/lhkSAChWaoMZcv0GON2WkVXYlo3jUJ9JBV7x6rmc3q+GPqypgi2cc2FfTRNtX6CWkCUqPHhXXIDwKVi3GXNxsbOu5PkM7f4OGwZDeqcdls0/CmnFoN4gc6yf0O9dUFSnOLuuXi1J9Q1R4TeAq1WR2lXBIKhc2oQ1T1KecDPDVjkzl4D54g6RuWhkG9tiKIR6z7HQbnKW7yNpfvHGOXezydJrRXEorxlKrIWG7+H9aX/xa9jct2nHGUqyxXJ5yKFofxj2lyyVIpGJ7v0NRDrIZJyBae1KQUDkYg6IbHyMkJa0smtto4UYAIAjiuMzOf0Bx8TGI0aCcVrEqVxW2BLOuokx9Ty3L6tZso0YrU0dm1Qn40PqLE4jTqoTVDvKhADRrcvd8i5DYHbxtkTZ1iIzMOV+x6GpY25knnglyNyUOLo36TimmiFxnT3MJbxQz3utT8Uyo7PWIzJK/foJqvqCzanO5YkBqIezBP58QfbJjI36P45S79kzOG6X0+TIfsDy/4n//e59Av6Ns5+lzCqsDSX1FKOVU1BSsDtUCPC2qxTEM0SKoFS9rYWRN5uiQuBUJX8RMVLY6Ya0tIYtJNQaV0oBvxer3g7Pgt/E4Im4zYi1/dEPivfuM/ef/N2xaVRp/Sm5CVCsZHnxLmHXoVOMt96lVQ7Bz7ZMa6mxIWEauRTe76tGMNvbQp2gpBb4GruESrJktln166YXI74WlLw9hKUBegjgskRyJIBdakwnkms9Vfc/Eo5q3ZDlm0xh97eE/XbDsDYidk3zHx00vsWhtXc3jjuzv0txxOzhccR8cUVslaUqkkDdIsY+pvUNQuQ7PNm2KfZvcu61VI9vGM+NQlNiY05Ab6LCXp9GmZNwl/MWPnRpXy4x2y+lOcy4QvpQvqjZDCGNBId3jAj3m9usNicYC/dU7FHXCzKNnoNlr4mL3Ku6hPfC5XKZ6qkS/uYDdyTjszWJlU3BUP498n9BVuOC7hJ+dstFOC7ZJxfITsN/jOcZVJMSRPIZo+Yjp8xNZC4eKDBxy+d580qqBFGwo5oQy20Tc/ZKb1mZqPaT79nK3bv0R6Q2V3POTz2QAzXlPrLlk4ISt1ReDlaIqByoK6PKPwK2xCFa9hExQN5KyBpc5QbglGsU76uzEPQo1bN+9wOfqCfcNCvdWjH7yBtrsgNnXG4oBwuGJ1ekHlVoOe1OCcjN1khp3NuPGDbTZnBnl0gRX4RCuLrNjQ188IyglHWYZ12cBZ2pSBh+JaGK0uq6zFIgnojVNEqwPRj/lsscLKPPJ0jPLsI6I7PfzUxVK6DKoKxWc3kfyn2P/mgP5nMdI7EuHjBa/9ypDHM4lPf/J/EJ12MG2TXk1h13YYpg3KrT6ukyCldaoTGU+CC7nCuFBRwwQ7ypjbI9ZulU5UobDmbBUN4twloEQsdYrbN5kYLutJSlov0KOnaL7AsQTr5deHwCuxK7EQYgr4wOyqXf4FaPPt9odv/z182/3hX+497JVl+Ud2IXklQgBACPHB122b/G3h2+4P3/57+Lb7w9XcwyvxX4TXXHPN1XEdAtdc83POqxQCf2TB4lvGt90fvv338G33hyu4h1dmTeCaa665Gl6lkcA111xzBVx5CAgh/pIQ4qEQ4okQ4teu2udPihDiSAjx6ctt2T542dYUQvwjIcTjl++Nq/b8KkKIvyOEmAghHnyl7WudX+4l+V+/rMsnQoh3rs78/3f9Ov/3hRDnP7NF3k/P/ccv/R8KIf7i1Vj/IUKIHSHE7wghPhdCfCaE+A9etl9tDcqyvLIXIANPgRuABnwMvHaVTn8K9yOg/TNt/wXway+Pfw34rav2/Bm/Pwe8Azz445yBvwL8Q15sQfce8PuvqP/7wH/0Nde+9vL7pAMHL79n8hX7D4B3Xh5XgEcvPa+0Blc9Evg+8KQsy2dlWSbA3wd+9Yqd/kX4VeDvvjz+u8C/foUuf4SyLP8p8LPPmPom518F/ofyBf8cqP90K/qr4hv8v4lfBf5+WZZxWZbPebFB7vf/pcn9CSjLclSW5Ycvj13gC2DIFdfgqkNgCJx+5fPZy7ZvAyXw20KIHwsh/t2Xbb3yD7dhvwR6V6P2p+KbnL9Ntfn3Xw6X/85XpmCvtL8QYh/4DvD7XHENrjoEvs38clmW7wB/Gfj3hBB/7qsnyxfjuW/VTy/fRmfgvwUOgfvACPgvr1bnj0cI4QD/K/AflmW5+eq5q6jBVYfAObDzlc/bL9teecqyPH/5PgH+d14MNcc/Ha69fJ9cneGfmG9y/lbUpizLcVmWeVmWBfDf8YdD/lfSXwih8iIA/l5Zlv/by+YrrcFVh8CPgFtCiAMhhAb8NeAfXLHTH4sQwhZCVH56DPwF4AEv3P/6y8v+OvB/Xo3hn4pvcv4HwL/9coX6PWD9lSHrK8PPzJH/DV7UAV74/zUhhC6EOABuAX/wr9rvqwghBPDfA1+UZfm3v3LqamtwlaulX1kBfcSL1du/cdU+f0LnG7xYef4Y+Oyn3kAL+CfAY+AfA82rdv0Z7/+JF0PmlBfzy3/nm5x5sSL937ysy6fAu6+o///40u+Tl51m8JXr/8ZL/4fAX34F/H+ZF0P9T4CPXr7+ylXX4PovBq+55uecq54OXHPNNVfMdQhcc83POdchcM01P+dch8A11/yccx0C11zzc851CFxzzc851yFwzTU/51yHwDXX/Jzz/wH/rSdgximWhQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "HopSkipJump: 100%|██████████| 1/1 [01:20<00:00, 80.74s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 50. L2 error 11542.905 and class label 866.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "HopSkipJump: 100%|██████████| 1/1 [01:27<00:00, 87.36s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 60. L2 error 8880.384 and class label 866.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "HopSkipJump: 100%|██████████| 1/1 [01:33<00:00, 93.60s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 70. L2 error 7306.2417 and class label 866.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "HopSkipJump: 100%|██████████| 1/1 [01:40<00:00, 100.22s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 80. L2 error 6212.25 and class label 866.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "HopSkipJump: 100%|██████████| 1/1 [01:46<00:00, 106.80s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 90. L2 error 5457.6064 and class label 866.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "HopSkipJump: 100%|██████████| 1/1 [01:52<00:00, 112.10s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 100. L2 error 4831.744 and class label 866.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "HopSkipJump: 100%|██████████| 1/1 [01:56<00:00, 116.91s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 110. L2 error 4265.8936 and class label 866.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQEAAAD8CAYAAAB3lxGOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAgAElEQVR4nOy8ya4tWbam9Y0xCytWtfc5Hu5xb1D28gEQiDZC0KMND5AtHgB69HgL2rwCz4BoggBlIjKJwsNPsfcqrJjloLH8SghFJLq6xE2X4nydpSXZWmbTzMY/xxzFFDPjG9/4xl8v+q/7Ar7xjW/86+WbCHzjG3/lfBOBb3zjr5xvIvCNb/yV800EvvGNv3K+icA3vvFXzl9MBETkPxWR/01E/pmI/Fd/qfN84xvf+Ichf4k6ARFxwP8O/MfAb4H/EfjPzex/+f/9ZN/4xjf+QfylPIF/H/hnZvZ/mFkG/nvgP/sLnesb3/jGPwD/F/rf3wD/1//j+2+B/+DPHayihhh4xSo4VRodtU5Xj/SOmCEC3X5WLgegWOtg0EVQMcSUDqCGCvQGAnQ6AhgQxFPMcDQMoYtHKVhXRDt0RZ3RTZDeaXhEO06hVkVFgYbRURNwSjfAGgAmDu2gGOaU5hq0DqaoGQo0U5AO5lBvWFVUG70LQqMLmPfE3qhqUAVTT+yVgqAmdFWclOd42/NRdgW6YWKoKGYNEBCBn++h2fNGOJ7HdSfQFEenK/TnFYKCa0IzAzxqDXEOs+c9wjWcGdUcyHNcKFhVTDuGQ6WjPz8faSDaMezvHjxmipkh0pH+8z1Xw8xh0p8X6wPUijiwCjhBG2joWPl5zALSPLiONGjekGooQlNwwvM8/Xmsoc9PERSlW8e0AYo0wJ7XD/Y8b7fnGPvz/azdMAVRUHPQ7Od75mlm6N+9rF7oFQKViqBeoArOd1r39N4R/Xnsoog6xCpdBDOQn/9LRFCDakbnaQTaFZVGM8X0+Wyfg+0oAtahKQoUEbD22cx+9f+2v7+UCPx/IiL/FPin8DTqYRLyZsRBqOGV8FgJDrax832sJG+Ee8RGZc0rS1EOx5HYA5t1iu5oLTA2LJ2RXjjERuZM1Btpn+haSR0Q4/uHY509Ld/ZDt/zehN2ucL5Tk3QBYbjgJeA3xo3mXHHd3Q5g70xL0eSn8nzj3gfieXAaAtdBlzeWOqBvRvRMtU8fvRYTsxkHoxMdSB6YYmZVCLOK8cBXNuZZqVcK8vHgZdPR5K7Ep3xSRek/9sMkiiucGgBjZ8Y7i/cZOXjPGJB0XDjOn6k/9QYhy8s1VNoHEunKmwK4r7nVBU7faXcC6V2Bs6sJoQQ+PUMIl/5IsbqAvMauXzYuJd/Qnv/PXW8Iv17LvqJm73Qc6Ik4eXlN9z6Z85L4D5dmd4Hqq+k0eHTQBRDp4bzhrkKHXobGADdF666k0+R8+ZId+FoG2morO0VaZl2XPDDibp77PSVXg94p9QvC/7kGP2Ze4Pj+kYTxz562A+oJUpYkTHiU+cyDPBwXIcHh1ho6ugt4LpjixEvmXYXgkaOpbFePH0V7Ng4F8euxmsq/MskHMRxOwRYAv6HLwQc4VNij3/DBxJ2bIQhsa9H7usn+kXpbcanM68/7FwXxzknHj3gTxBqYveVYZjxrrPfI9uaGMPOyVfMPN03Vpkplx3/OyVeRtgDLwZ/WIw+V87rlQPG708OuX+H8eO/+JO2+BeKCfyHwH9jZv/Jz9//awAz+2//1PFOnXULjOMH9NiQ9y987I5/OY24ZeHsPtDkjeYGwtEx5gHLjdUrSTZiNEJVEhuoUcqIbBW1Dh8iWRvyJvigSBL6y0a5G0YA98p86Rzshutw/zzTJKC/EbblHXszVP6WF24kOXA8/Z7PEjgPE4++o2VA08YeDgy9MGxKDQ6LO20rHH1APSy74crw9Aqm7Tkz18gumZ4cHHbODxh/pcjJs/1+Ql4E+8OD/NpI269w+ydeThcel4XjVllz5HBQvrx9QOJXhpjI5cCxRUwzcdnpA5RhhFshdCGFGWedgLLpxN4SGjsvyWHd4Y8NWxtfe4F5w9kJponT/gd8cfyYKxrOdDdhf/vg/McLtj64nxcOS6Sfdnw6sVnj0o338UK/PvAxMU4Vf4vseEY1CCt7HHFuxuWK5MKCMUrC6iv0G5u8EI9K3r/yoR9Yft35UK78vk1Id4xMxPFOe3NIc9RfPZj4G97+8AX/XWZevyNTKL3Q1XN2nfctI+EVX79wcI0cO3X39OHI2CJiUNqKhUqZTtTHnaNUXDySH3dkFrIGrI5MlliTcvYP7nlkCDvbJfBD7Xx6P+AuncP7nfuLo8lHdKj88Fm5t8w+3/mYwWTkMU0s5QFqDN1x3BI6QxsCyypob9Q+Mk+VFidyhun0ynJ/Y3xsTC+B4juPzSOPG/XlwuzArgv79APOvlDu2/9kZv/eP5YIeJ6Bwf8I+B3PwOB/YWb/858+3ls8XHi5vLG8Q88Te2iccNRDZdv+DUL7PTUrPm6gA244UzeIrbK3hh+EeMx0dvLdqHngJEruiWwzOgGaIRutwsdQebcT5+nG9lCydo4WuY2NuB7IcyJsBV48F154vDWaVqresGHAlZ0WJsIqHIeV5B2SHCqF+zyju8fvFT8aURpjy9w5s0wJ643D2mk24o+d9RgZ8g0xI7TvuMnKa1x4+2liDJBHx5wXRif0auzDCzJHTl8+casRH89YfWe9jGg+8loX+u7Y3I6OkdEKWRzrUBi2M6VvSNupMlJbZTwpWTbGW6e3GSZBUkTjG80fcC3xOFwY94V8GQlf33n51cTeHdIeLD9+QBTKZYK2MBQh7kadMsd+IA2fiNWzxgtrGgg8iLJiXggKJRvOnehRGFpluVXcodHFkx6V7w/CNQu4Qlkc43eFpCf6T47L6c52L1T+TZQfef3+xHJfQY8MhwclCbdlIPYrJzvyHgNHf6U5j0+Blip3Oic3MEhm9UabI9EaVgtrbMTyHUefud8z7qiEq+HnxH29MH13hU8D+6TI8QNy37DhEz+8X7iPF97lR+b5zHB7YzkcGHhwe3PM4Ug4FfQuXF869I2YZtYtAoWjz7gxsrSAKw8cnhwHWt8ZzwdcXdhyIvZILGce7oZskfHUmdcXNLzzx8EhZWH4/gf67SfS5/wnReAvshwwsyoi/yXwP/Bcvf93f04Afv4F7fXKT/eJODVKMY57xg8T/rNnkC/cmDjZQK3f0cefkGzIuKCL4r2R6kZ6CIM7EJ1nCob2jagDQxWCM/LLicdPjjkGvtrvsUnwTknniKWExI7N0LbO2D6yh0/4Uli1kD68Yw/FDjNKRzv4vfIyCqkNbMkRxJ7r1DUhrWOTUqzQa2d3nVI3uIFIZPWZs3sgmxA2RyvC6eXIW0mMy8wybdg/qcj/CjJ94NUP3Ec4viV6gWXO7OFMmg4c7A+UcuKchOQLNNhdQ6UTW2WrI12Uee04u+Fmg9GYtGLq2HqhJ2OxgdE18iFjoSIRfn2FT68fmO6fcbww9p0NofzWsb88kGvgxJWv3w9Me8GlB1MSPnOBxbG6K6ovTKPHIzjeiD3i3YF9vNPLgVwDccnUKCwinEzxktj6xDxVPj8S7my4e2Byryy3zKsa62Ej/bpgtzPz6+9Youf+5SvLKUJpLMEhu+F1J4wnshqqC8EZvQoPv9KjEhHq2ujBU11B68AuRmmR6VoIH29c04EajbMFqgyUVSkvC7kFfgXcfMGWP/KbVfhRX/jxBU7XKy/WudqKzQPpXTkInD+cqSzsS4FpYHhLrEMn9wfD6HDhSOwX1r3TrNJ5xpJCUiaU/DUzuAk3GM0SdbxjIpjeGd2Bz13wo2dY3nk5zty+/JF8b3/W+v4insDfFxWxMB2Amby/I5cRTRuTc2yLoF5hzozLhSE2Wlxp3eP2wjpAaxHrChW8OWpT2vxAOGBV8fVBMGXEcx8rNZ/4tX3lHUgvr7j3QjvcmRfBv56pXzOJQr10qC+E8EZ7uyD6hp4gWiCVA4dWuJVCdB9xUyXRiA1sz9j8QF/O1EXR+41xCGylcoyVpU9IynTX8N4xtIbLM9v3A+Pbg68SsGBEf2Liyv164W9/vbA8jNpHmALDlztfx40P4cB3u/EvwkjpV07dqMOZ0+sR/fwTe/fooVPawKaJuIHmQNPGZI1VInXIhNaRdMC/JkobODv4qV6R5cgR44bgX1cuSXkfXpnDzvtngaEx5cbulGMyum3U0yuzjlwfGzIkDjVQLMHo8S4QNqGkQhojp95IUrEuVM1kmzhoQffEozvOkydFR8oNa56DuzG8etIfDyy2cxlOlDUTXeE+VdprZ14K6/bC96+F4ceVfRohCGRDXOaRHJUjVW7gIaCwGhyEos+YQT8mijTm9TmpPAKcb3fWccZLZisByd8zXn7Peg386jhx226k4YS/XOFTZX45kJZEC4GuM2GrvMrCpwtMnxvH0weuz/Ay7dY5h3f2GrDDidorno0cEmKR0Rv7BrkLH7uBHajWqZMxvVbeSwVrtHtnXAw5nDksd7odKacHd53ob8s/3nLg74t4Z2Pz9NdCvoKeRqalsenAh9BJw86yN1xS+unEMUTGt53NEnvsmA9I8SA7jpGaBWxnCAN5NoZyZEiNe1gIF2O/B7yMNP8FnX+g/OELUzS28yvD/cZ+OjC87yS3IWnE5MKHlrjHTq+Vk9u5zgMcMv79wCx31twZVJjUc/UGwjM78agQFTt6bBPGLbLFRM8Ox470znTxzGXiy/zg9OXM23FBuPCxzmT3e26pEU8Dpeycl1f2k1LZGbbOq4MveIa802NHRseyOubjhC6VIEYtkawdQkF1Yw8TrY247uj+QX8cGSuUU0FFaWtnPCf0ATUE2n5nmg/kx53TfODahWP1XH2ktRs2C7p7XgIkL9hm7JoZx07eCz0FXICpKf2s7D0jXw2JQhs9lE4rA6MoMXRqCqTeaR/ufHj8ivrxK/FLpOSOzUqnsEZhmAvlx1dq2OBlZ/zR4T8UypeATpFoBcfEO51T96yXK3WfkTHgblcwQ4l0Dy57ahzhUrHkIDVazExLYjhEgg2UumCxIrvg3XesNFwUQlb6qdHXN/LpO8L2hjwmzDr3shNlpveGnDYUZdwd7iRMdsDe4CfnsGiEPiKyEjSRu6FlBMmksaItc4hn9uywbaEflVkc2jJ1FLRmcu3E+UzWxsEWPqeBYXihH67I/7mTzH7BIqBqJw9MsDbHYJG+J/LpgpUHYh7cim0BOGHThlcjNsilISXA4OgkZik4EyoDtVa6VmZR9nbAHRdq74wPaC5Sjp5aPjD3z9RqFKkoHwiaWdMDkVfOp06+7nx0kT/YFUaH7Z3ZLlRL5NiQs8fWjC+ZMii6C2NzOFWSBy6V49ZZ7geidLZ5Z9iFrkYsQvVwsMhaHaqJrP8Ow8s7wb3zZYV5bSxTYxjB7IgsGd8bjyEgOjHwYM/fM4wr4Z7wKsSYWQdB05FdFLTiUyUOUMrEZhXvFg7pQIqV4o2wJWw8c1k3forGUCem+o63Fxa5sroRf9oJ5ULohe53jCNpuDHff80qQj39EW4j8/eQ3hKn3HkwUs+VqTbGdSLVxh4acTjQslLyAtGgCFYHfFCG04L3mbIZso+42HnsDocnfPdgfHjeXeMl/rust38OL2ds28nZc+6e2c3cPvwRn40lR6R3PibHNgvFZWqPvPaRvcO+NWJMJG9UGRmDEHpjWxLWHe10ZmjQc0V4UOZGiAfi45XhUdnPCw8bEN45tcroTqxDJbzDl9ExqMGWkXHiPD24fvqO6ZhooeBuwjBVVu9pm6PPlWCe09q4SmATZaZThwa5IXlAKfi5YhJoOCyuYEKume9q5fPh57TgOCD3xNCMkCK3Lf3jxQT+vqgJSxMsOo5bZcuNHhzzttF/FqnYPKtBizxz6X7AjSPu3shuRywRNVBMWHPDeaHrBJZRychYudyhqrDEwK4ReWu8hje+qPBav+N0CPx0r7yMn9kHwbkHRYx9hrdhoS6OMJxRv+LLha2842ui7Q0XA2IVNUFQ2uApYaAVQR8be0xY3OjF41dhkemZw5fAVoXh4458TVwHhzv8gXL/ntPHxPcD7MPM5faJ9WHU0UMYqayon7C9Qh4Qf0OKIRcj7o62dSyNGIpnRQ6GG6E2ZZedlmEoI6tvVJc5TY57htP24Euf0OI4mvEehbk2/PHMcXtwuXb++Dd3hmVC3n/gzhs2G/fhJ/xj5vTWuV0UDcZgjXcaFhssr/R5o/ud7BOtewp3DkGYVchqgJLtzuyN9TphXchn6FIZ4sjkPjLU31LfTnydAy/LA91/S5gCKTaGfobHSj0/+NJBJeJr4RhmHu8zq37hHjJuOxHXI598pE8PdArEfuAwOnYrJFthdcxR6drZ604pJ14vif5wGCfSlyv3qdNjxj+OnIcD1HeW6YjcPMelc/c78cOF4cfEPh7wHsRdmOeN6aBs+0bGo0PiWAI/lQPT8iDGyL0N5HrH+wCTYDQCRjs+syhxjyxHR04VCYXw5lF35loXYhtJlog5MV1n3GVl1QOQ/qT9/SI8AS/Oji5wHwdGNrpv1Awf/YFPjxND/IxZwXokuhHTSv7e8L2yrTOueLRnOsbQQFpitxGzwMu5Ys7Y2066OroN/DDAtQutPZhs4HY0frPv/M5OSIBDbVhwJBtp6Y8Mv7mwfxXcAMewo2+Bcthpa6TYgLqvJB9gcbhmCEJ34OiYgEV5Lg1SwLqgw053GfaB2CaaqxRZEEaMiuRXbDb+LXcj/8rz/seBtHsOfUenO04j93zmJJ3r9M7xfiRpwh0i2Qo9R4I5puGKu8Mehd2Bt/AsGGoTvVUGt1FtwseJaoXmDfyCfxihOsYPmUcZWesLx8EY3ow8/sQjR8Z4Bn/ltEJmYPluJJSFcFduQ+PVBVzfuadOlhHrxjAYo99JoqTcoQVQh5piOaBhx7lOTZGqnVkLMURqHSk1oRipGe7iqdYIavglUvw74gdsG5Hxhm8TbpnJly+MN2UNjqkabRI0N/oBYhmovbCYxzVHJ8BLRfqC7oIX9xShyejLzK9l5jo2hviG3icWWSlRmFHKVhnrD9zaZ1qovHplnQS9RbhsnMsFeyzwwci3E3e7czjN+PtCdwNrqRzFuB5nhv4g7EIyA41YM0LPyBDQC9jqsdoQl0BHzBmXR2WvwnWutOU7/CETeaevA9J3fphGvlrlfSu/XE+gS8eOHSeFWAr36rHq+Nx2HANIIB4NvwXGvlP9kfTZQdqJx4yFnVJBPKTVwEY6Dh8X9qXh64lgLzjvSf6Nzz7gs8BJcaWjt8LdHbA5oB8L9rsd00jvxuTAvV9Ji3LcYGPmRRK9Ci1Wuho1ezQ7jIyOgjej9GfFmFqnVU9sAVRIY0f3AK1jLhPMM/WNBwp+px8D8c3TWuIeXzl++oQLDsKVunmSOMZsqPtMz2dYHcth4+QDqWx4oBYQBnKN5CiYB5NE0wxVmNwDstBaoLmVTsc5UKnUtTEcT7i8Ud8D7cMJnyrNHGX4TNwHHAdk7IQW+IkL0W1M98KaPH0CL5W3m2PwSquRGBzMmUkLt1ugh4HgEiKdVCawI5GMj8KQC5szdNx5TSO3VEiHlaAXZN2ZhpkeFmL0zF8LyYxcA8PeQB7EMrDKwubdc10eZhhGcv89WgJdhVQDbTww2le0GroMHDy02sgSGaOn1pGUO9omxHb2+Ur3jev7wGUSSjLGR8MfJmqb0bNnvHnSa2T9pBzane3VqP5ES2/I4ZWSbtzbHS5nwvqV4fXIV15paWHbFqSvyN5pPaKzQmw4GWlZiCWTvgSaODTOuDTCIbH1ncNFsC0i9YT5TwT7ntoHomaqCH+YMuP7C/DlT9rfL0IEzCDlxtCUa1aizIjPZOkgO6k4JAfiy86alTV+xd0jXkd6i1hvOA1o6TgtBGBvidoDAwO7FKoY358ddXHke6P6zGk3hnxm8oX73HE3xzy80brhtswUzsj8gNLRQ6eVE63N/GQbeEXvijJArUTJ1KjgITdP7wX3s2fS1Mha6W1HiVSb6PJMXS19wU1A97SsDHcl6MocO9eSuPvO97fEfpqxdMfZgcTOYErTheEQcHlniErWM3kpWB/xYSWqIVOkdqEXsGyoGc15OhU3w9gCJQudijWHzsLeA/se8FKR1pnlnVgdzsEXfsCFBcdKWQ2Oje+c43atBH0gnBm78nCNXh11GGEqdDEsR7wIWTuqI74ZNhWCvNPaRCqNvSsuKjOVH11EeyY8InEu9HIG9xWXBL130nzAGejWOEzCss1cR9BtIxzuHFKgpEySjTZPuOqoVKIfUT3i0p2xVdq4QnKE3eNch75DdBwHT9OFHah7xT8UxkQPRnhEalTOrrIc7ty0YeqYvl6Z5AOPfuH7q/BmD2o7ob/aSY/O6I/ktpFrYP0cKMMbwRrdNXQLVDxdI75W5lwRuRFlIqOsowcyfTSCH9GScGVAGtQtMJBp54D2nfZQpjmQHp2tj9z/FW1Cv4j9BBShbY4qhpORrBEzh/QOHZROWz23q3C7CS4r0h17bLjgqDrgemGmMzejSKZJI/aC98o4njm4zvWrcZCGiqGTMohjnwrTuOPCjVE+k78eycGIDip3eu74EmnBs2kllA1vI+EWkF6o1pBBaNHRUodtJvQPeH3mxTOOXI3WO+L0WfMtK8SnkZgXdBN0q0gzSi1IgNvaOGyd5kauvjO2+HPfw4Y1T59GNhmRJPh5oO0Th2J4VVzcsclz7wYpc74Vpq0jtdGC0AYhBMWL0Q4RHc70eeDFe8LWiduGcx0/3TjdP7OOgdvsqMvAOd74OH7h0SbyMWHznd0yYcjgHYs23mNFjh18RqJDMxyKMk8DOgPe6FJAhHFzaG6Y7sggxNlw2pA24fDE8wEbPEkay2DEYUY1YvPIzsKjbMTjyPs2c4mNY3bAgGWHpELUjalGwBGSUrujy07t77x7WKdITYEyjNzHRtKCFpjKxhh3xr4yvgo9nKlhfmYNdiO7yKEIdxcIJdDvC+Ol4Xrg3TXixfMH+8pyMVz0lEdhGDxd77BU5nPC/I1ggRczLsXhT4o7OY4xg4cUAneZ+WKdZJUhdo7SOLYdSRttrTgHK1DCQB6fpdB67XSfWbLA2EjvRj/8eVP/ZXgCAhcEk8yiDnxHZMCz4TTQLFMxeg8EvxGB1gomI84qLhnVPDcgasF8Q7unlBFVpUohzoLtO2lrwAtxS8i4k7Vjj4Fj3en+iKgQL4F8K7AupOCes1YyNN2fzSNqiGwMo7KniqPTBo9Vo9oGuTB4qINQsqLVcO7ZzNJHgV6QHgkI1Vd6NjyC9wO1FEwTNiT2Ioz6K5LuuLQwotSDQ+4Fs4bXA6FVljY+K87Uc5SZd39lXwM4R6Fwi4phNDWwQrt39j4SXGV0HULG18rqOxONqVYwpRVIPtC2DDnw5irnQ+RawZmjEZE985XOIU2USXEp4Kkcj8Lu5Fmc1B1djoht9LChbUTywL5nolYmJ6g0ltrYg8P3Rtoa47lS+wuD7tTe8LKyyISQ2FJlbCOt7pR8pzrlnk+U0w2XXxn1SnIeOzv67cGUDozTwt4UnwKbg1Fmkm4YhdYVqRNIofiMWaNIwzEiC3gE406NnRc+0ILy1W/498KHo5FzZywfWYNjqGcm/S05eLR0VtvRdiBUh2uV4Bw1VHwYGdzEQ95xacC3TEepvpKbES0SwsjujWSdmDv7LtgJiBvaA1PpFJ0J5YYrhUkmHtFofqSsA38zBza/0nt/Npr9CX4RIvBcwQYgs/aCm5VcV/oS8SdP0EJvilrHE+l0zBzNHNTAPL6zV6M/IkbEeYeo4cdnI0hlo0ojhJmSAqqGSmLzSk4rg11oEiE4fLmx7Q0Vj0qlqQdLOAasGzos9OpQ52lV8M4IDWxrCIEujejq8yUyEG14DU+vQCr1lhiQnzvpCsGMJFDV48QY/LM5pG4BuRghv+PcQFLHVj3DW6F7h4jQS2OlPMun+0yVnZ0FMYe0gglIVdQ1qvdoUxzQQsNRcJKpaWBHcLlQKOzBID6nF3EBbSdCecMfjBYhuZ2eRuL6Ro5nbLiC93hZ6a3TZyVuMzUbZtAK7BghNSKZiKcwUJ2A95TucJvhKIgX+t7xU0GGERc29C1AERo73c3s9Vk85h1YztgcGV2nPSZKB9lH1pYYEXKPiNtoIRLMyM4BR7pWNCfwHd860k9Pj8pAQiN1pYRn16ONgt87HWMMjlYNeziO58JeJsZeWGdPycZe7tSwkw7KuFzwWokk1jgzyYPSZsJBictCjhOXw06pDbcenqm+ulB2YWDADxXiiOtGyBmGgJWJSKL3ju5Gd9DbxLAVBNgHjzNlmI3t2nCq7MkxSWSXjfJnrO8XsRzA4GaJe1ZQR98a2iDg8StYE2LvhCaUPZCSw6LgrFFrQsTjuxLUI+Lo1vDdCJIIpeE7kCPWNtQXZl9IofJ4QLADi98p80yTnTIEmgg5F6oD741H69RUSaJsJowSIDc0G/iGMOAxBqsEq6g0qkWkRlyPNFF6a2h+zijiFOLT+P+uTVQOHu2JPmf2XaAMeNd4qEFZCNkBStORUSu1BDQURCJjK0/jHmAj0TXRXAVnFG/U1un52a47uEwIBR03msJWoNeF0YyjA8vCZg6RlR1Hl45Xz/5QxB8oWqmXicZI14aXifMdyhCASC+empRcoIZGF4eOI941thpYNT5bmdszdShDY3fC5iOIEVCyKMNRaCnQhx03OFSM2QeGVOipEyRizJRxZFHBzR31FcyIaixxo7sNlkAbZoofKJuntwRlpMtM8ROHMeLHTNVOc+VZcOU7oXXiVvB7wTCqBmpsOCK3sJOcMaRCrMLyxRFxbONC8ODlTncz5naqRMLjyqaZOgeSbuzDgVMXVovU1RARsma8XhDXyLFhzuFbw1xC2k5oEEJHeqC7geQDpRpeO8N84k7konBrHd0rEhxy3PlK44Fjmuc/a36/DBGQTnGdBxFCx1LEuWd/u5GIwZjyztAyjp1YKo7nC5THlS01yv6c4f1UcadOmwuLPEha6X5AaqdUwY2CSSX2EV8r7qSYKc0VmhN6drBA54CJMnoQzaTU6FwAACAASURBVLiYQD2WjV6fs2yPnc6AWSVUwzWPdxPNBarvON95hno2ihh9GsG5pyfz7C5nK0LwHvm5599aZzyMcGz0GvCbsNRG10pxhaKRU57xa6KaPtvtvRGl4IaOziPxOZegWXC1E02YTAjS0SaMRWFXYg6cmxEsoaETRRAnyK6EqEQ6yb2zASEo2UUGU/peMe+fWRsUFYcBm4zEvSNhJ8eN7mB2z30U2mi4UZil4tyKtoYXxyEIcRBUldFgAKJNkCvsAxY7eTCqV/z0INAITWjrzkUybrvhw0DwO7OekZiJzZG3iDNPkAp9pddncNLpivQd1wXLnmIB8QUZNjbZMYEhDKgf8fGEtU4j04YrS268jY7tYKSyM4XANoy4DscRSEdqDQy10eSd1CspBVYbSOuRkBdWJvbSWfeM7SNER/WJGACM0SsaHDJ0lEqvDmeKtQRkxHZoO13BK+AcyRVGMZaieI2Ebpg3CoXRZUSVkuTPmt8vQgSeAa9nKjB5o/lK7RGNQu3tWfbqIwQP5mgE6m7UJKgo1Qt1ctQhYKOhB4fzHpcHag1YgdaEwIDSqD5ynJ5O+boZuEpeNkKOFLdRc8PkBWmOvh1QGfGtM6CgSnaNphHEI3tBW0bMKM4wEUQDo684M5qHjuFrw/uOumfFk+aIC/oUO3GwPfPmLnm0NwgTE4qvBbqjF3DaEbexeiVIx2E4yyzmkLg+92XBoDliA0fEW3wGXSRiOpDVoSbM1Yi1gyQanVWhjqCTMfjIPkEUYQKsVdpcoGXIit0qNho+bxyysYojbB2ZhaElSgDoHHPDfGcsFS+NoXukCpo9MnZccIgJPW6gDS0CtT6XS6una0c2EFupUZC0IlT0Y2MYG90VWB0lGbZduHPH00jjggyRGCstGXMbqHLFqeHqAd93pHym5gfLmuhdccGI2unO2FsGEmIN1Y4MlSodizPOKmM+4qrgWkDFM3hPGTyzDbS5MsWRh2Wcg0Mz4jHT6pGUM+KFua/kTek9s+SBEg54U5os+NQZWmHg591wqiMzU/JELwU7JKokas/UnNGy0/cVH2BzESYjMSFlhxbxgyBRkfDnIgK/EBEAJbVO8J3YHeYGQhlgBOeMqI198Oza6YPQnSIorlTcbcalE8EMcY3eG+W90+8edYoGiL0xSWXSTtUBZKOXDWNizpmqEHoAVvANcDAuCI1mQhCQZAidKI4xKtIMS40KJDeAPIuDat0oNeEaSBdafV4vwQjF8OKeYqaGPDue2EvG5Dmm3RnrFcKW8IuHsRPiM8YxaGDQjIWVrIJ0BTkRoscm5bCOjHuhSwZtzKHSpJHEKJrprtN6pzbofYDniDExvEFORizGopnH2tn7s2HF+8CQhaFkevmAADTHSOPGiJOO0HhNmdUJlMzgB2jG2jxKw+dKX0DKiVBekRoQCrkF6n7C7Mjqw9MtxyBGguuoKpA5amD1M+1USHHFxcotNixGWALWjUwiMSJeGRy0kBGDwTJqQhZHdh5iBlXG2EGUZh0SaBO0NAbZ8AXKlnEFQjrilhGzmZg9vu1spdLqSk+ZUjr5PaPbFbKwtwPahR5nkIKpA0n04pHlmQ4c67OQLJiidaUMFev2c/YGrLlnwQc7XozaK4svlFGRYWQg0syoc6W2xt3vhJ7ouZFKxrUXfFtxBawF9mH6V1jfLwET6vDc6mvIgDeUhO8V7xpFEm5foCdCrzQa2T3z7601xArdgUjFZaNWoarhXGYYdg7SCVNnF9DU6S5z7R0fGzoZYXeoQpWdKTv84BjP71Q6Xm48N8SayHSChw7838y9Oayta5rf9Xve8RvWsKdzzj3nDnWrblW5CqrLPRkLsIwDi4AAiZAAAhISJJDIiJCcMoRIBkskSATGAYKIgKYT2+But6lyd1dXV9W9dYdzz7CntdY3vDPBurQcdNGo3ZZqSVva+1v6vmBtvc963uf9/3//EaGqSoeQTSWYhraCGE2VQlRCEnU+e28WEUtOjdigSkI1QRd9HtyVM7qq+IhSBUyAEqjFY5pjoNFMouRKi1CLnGlWrYGq6FBYT4oY1q/098JSYaqNKI1aFa0FTI4MSeOqogEnOH+OaGLVhKSJ64AeE0orsv8KQnLWYiLjSNUG4zypVETB4sAmzpqOcCCRQCl0jkwayiLMcSDqDa23aO1QXaAYTa4drXrEeooK6BYRoyAkKBMmJnSp5y2CTXiTMbri357n2SU6ChZvIfvENT0ildoUUh1L3JJ7TakZ12lqUtCtXx1BG8SA7qEYQ5OMKJBoMcbSvDrvu5tmmC22nM8HtM1UTkgTgqokI6TsSa2weE0+OZICHwZy3jAPI8MkbFuiSsJMDrzg9YBzA91G2BUHxVODohRHjkLLPTFpMhHUgmOla4oYDf2i6GJlqzXWa4qPFHNGzZVZ48eCtwHTEsV5TH4gHU6/cPn9UhQBUWcWmqkFJxobA145UhWyGIooVAJTGjlDzQ5RluIUoiKUhZYykhSqNtyooK8QGiU2qpz3ZWuKqFWxqQNVeyor81IoLlHWxJKFlBuuKFpq4DWljcQsVCy6FhYKsWqqcVgcWkU0FSuayRSSFmr25KyQWhiknPl2VoitUlM+cxGroXRnRmITQXw9t8jVUiWRY6bphZQtIRWUt5TeItWjY6XRgS9kdUIHyBFSL/jBYJWlKEuthb4oLD2SN9hVUckkyWAD2oM0i2qKFDPVV0rnMcqwu4BWha5q6tCwzWBWTVlXWvZ0AuEq4PIjpiisH1BOaFea7aZgpdD3Z7xaZ8NZodkKi05UP6M7RcUjxqCqI+dIsQmrBKwi2vPsJjdHUztSUKQ509igjWM9CNUmdrrQjxZpHVlllDQKiigF5xXGOHKnGTawrfnstahCdp5a9HljbTRNVUwpiDVEtadkg3WCdoL2C9pG7FSRFZwSNt4T+w6lhc4Ftt5gOxjLWfVYB2E7wao1oVso3YJX0HzGJGGVTEgLukZCbfg14ZRB1QE/Gcwa8UZw7vy5dabDySW6KMoqZCrBK06xo1qBpvGlxxlNU5ZGwRRDaw2bz9vVX/T6cxcBEXlfRP53Efl9EfmnIvIff3X9PxeRz0Xk9776+bf+7CIAXjQ6B6gK00eyTcRqqEOCnKjVkLOnYmk5ggYzNJpT6CpYoJrCqjIpgK6Fqgq5wakk0tpwrlE2hao7di2Sm6fmTM2FHoOriqoK2Ey96/C5kWVF6Qg60NQFuVVaLrSykpqQB6AWUm7UxaDD+TnNeIqF0jItl68cjYJBo1tF2UhtiWI8ZndGjXWLUFRlcHBpNUoKQ0l0VshrpahKkg7d/BlUmRuOhteVrhWsgWVoVBHO04GOViviIuIbWY0kv2EZNFGEVhUeQTWFVYIuClcytXa0WNFqg78I2AtPTR4fCk2d6OMMNuOdYxsq0gViVXT6PNvw1bP20HRH61eMnSglomQimwUmjTpqVEpUHc/yaTwiV9ANRO+gOGZr0WIp1bIsPRihmRHZNFJJ2Gzw/cxcI7sIkUQnFTGRkuavBrCa1gZqU6x9ITdFzeBLh0495phRMVLUSMyGkldKq5TKuYOisqhIzTOlTiw+cnKa6gM6z+y3Hb5k3LrDDI56JcgihNIgFjbpQM0GR4e4LbaLxNqYTCDWlZBXxERqTdQxUvpMGgRpGbKnZoOtHW205D6jciZvVuJNoZkOJ428drSUmU3DbQIhryzZgIX9sqK8hfYvRiyUgf+0tfa7IrIFfkdE/rev3vuvW2v/xf/fB9UqMCoWGrkksqnUw8JQPXSVpWiq9bRU8bKCVlASLZyn2VEsigWiJeHOiOFo0Aiu98xR8GhS0lSJWB+IpWIqlOboqiaLxagD0XIe+pUzfVayx2pFbJVGxdQdmglspBVFXkAKrM1gWkUDyUDTGsGQjaGliuiC7qHNZypRrRa1aowr1FXQUShtS9EnQu7IJUFLbHfCmgRtevI041lYVGWbCgetSB6WxSJW4BRJylBqwLRCxVBdppqKqZamHEoaqROUdKigWWshk+iAkhzaN9bTijc9NRniydPGxHrzSGBDXYQxN1pN3E+GzS6Rd434uWKKimoKx1NGBs8gHaU25kFQ6kx3I0HSHvSMeKHFRhEworHZk/qCCuW8vSuKNQeMK3RYNkUTjpF6HVn319jjQm2evDuS2jMkFXzucVuN7h+x08rJVEwemUrBZ4v4FT0XGgE2MJRC4KtOoAlEKF0AL6jZUv6EylyxY2JtDRUyK4XLQZNlhZ1ntRZ7NCySyPkIcsXEHV1M9FFQPqKSR/rETDlrXtSZKnU0HvqIUUKoEZEVrx261rNkXQlhjWTXIXZAlUolciGNuW+YaiAsLFJ4pylqFbIfMX5mjYY2rzzZFF7/gvX35+4EWmsvW2u/+9XvR+APOKPG/zxPw88J1V8Tt4l6aKTcCGTy6iAXXGu4tlJ0pVhFKYpqeqQZTFtplrNSsBQoGvFn6a9pBTM2nFtAz6jesq4ra9aYTcQMhrxtVBJr3yPVUVZDGQLGnJWJ2WnMUDBMFImkJqTkqaVRmyJ1hqYKrSuYsbAt4OZMzQprKrqvlNYoS0VcQ7tCdSvFZ2gZU/rzBN+s6NIoWbH2mlYMaRaWUqg50xlLNZVeGrM1VDVSo5BJtBqpxVBNR2lCdQKqYbVmDB63FnJaSS1gqDQUVYG1GUUhoRlaIaeA0gpSxTtFb1e6ZOhjRewZAX/cdyzjji4tzOtIqB3b7RGTGt6/Q9EBqwunLOyGDRf+mpGGaRqdEt4fMTaiQsRq2IiwKRlfZvIcziaasBKaRUtCtCeqE7PJ1ArDnWMoDbObERno6kBtDyz7wpRHOipl0CzXHkZNaxGbG1lnVBzx0iHaUp1w0pbstjQxRJ2RVrEtImalqhWksEpHMAZSh7QNKu+xceQ07UjzSiIjx5nT8oA3R/pNppkHSrWQQIaeY2wsMZGTgvAcLz19UURbybaAUuTcw9qwNdH6hGwj2RnWagmSEZXY2RWWSl4shIrKGRU8zo5cDZBbRx80F12hVccjHVWeE9bLX7j6/kIUgyLyIfBrwD8E/nXgPxKRfx/4R5y7hfv/r/s1MHuhlCO29oS6YpwiVU0WaNbSVhBfzkYYyWd+vwhNFZwqmFUTxaB0odZwVohhyaWyLSMmLMwUuqFnycsZtFAFm1cOteGBPnXMqTA0TQ2KoAqNhlsURp1b+9wqTo/UWmDr0aumSD2r2GQl5kYqDaUyriVq06hqUEA2BZFK9iOsAcmOTMHXFa0sIZ+NPc5GVKq0y57pvmCcpTVL7oRySDidKUYhy0JTmlYGAgvKC3kVlHJIjJQ2staMrhHoaK0g63kAZttCLR0iQo9j0ZVYMgbNThS3pVLrI9V37PuZx6Wj5YX9JKRxS79GLjbv8DY9Mqqn5LphuVrY9oVNe4/lsmPXPefFtwxPwiWvXr2lu/G8/uznPNy/xQ7PuLh+D2fg489/yinf4aLj6bOOeH9LnjSSI9kUbM0YHFOMDLpxnwzZ30PokK4gp5W+KNCOk1bIIdP6Da2cEKWpO0h9wM0eXWbWTaOuPe14FlAVdaAqkOopWWEP81cCoUihQmm0Xlh9Y4wrrV/JU0GyZis9j6eMS420zSwnTTIVK4E4WFRSnJrAJqGSZy6Nun08H5PKyto7+uksUEJWZiqpamigYmFoW04AMVK8YmqFtAFU4jAX1NSIaaUVSyyKYZuxVXGvKt5mblbF6jXh9OYXrr9/7iIgIhvgfwL+k9baQUT+G+BvcT7+/1vAfwn8B3/KfX+SO4BAXzpCWFBmxfYVmwzRFkyfMbNiKgWwaHO27FbVICZs8DTrSQbqmmiiaV7hdKTWSImXzAiuGvAj81qhWlIz5FOmjoX9BMdWyXpFiSWVIylsqDvB7Sa6CKcIxsOQFCKFBRhCIqUzO99YR98acykEUXTaflWIDNIn1Jqxa09WlSaJqg1NzgKe3BRGr0gW2ljpFzjZPaQDRdR5CLeZGA6G3Jez7DZGpBU0QrsIdA+VOQlOL+dAj0sPcaHGAqqyrYpmz98qKjTsWIi9MB0HjJ6pnSPPgVHBY0jkyy1X8YF7pdFrYhyfUI+Fzqy8XF6SeUKrFwxN2NmE+95fIoc7alzZv9fjn3+EjCMffP0pL3/4c9775jc4mYGvPXvOB3Oj0bh4usPLjlgNX8afIRb2+54lNE7zEekCpcvkU6K5yFg3pFooJdOa0A0DelIci7BeKYaHhnlWCW8zohwXi2PqA0VZhpxQ2VBthKZwypHlRA0Lup3BRoaeZCxlOyAEdEpUsbQYGOlRx0yJHdWc6KonXZ2I0eImoe4cas0MtXJad/BhRP+sZzIRlYRsBWczI4WHyRB1ZOgUeVoog0ZMR1iE6hrFeapsqKswmoInU2KjL42YGoN0rNGg2y1V8jlWJ3dIfqRzO2717VnReesRfWAJE77rWJc/HSryz1UERMR+VQD+h9ba3wNorb36Z97/b4H/5U+7t7X2t4G/DaCUNFKBZtE6ktOO1p9oBdpaqVqzMZVVa3RXyKuhi+WsuWuB4obzzKAIIo2eDttmZpVYvGGjIsOSab0jp8DkBFsnuusGrzVRJZR05/ZKCslbxnbilHaYbeEYPdUoWg742nNSK06EoAqlWEwFVQMzQjQKukQM9lwGSSTjzlZnk8i14t2ATZB8oyVPGgvkiO4sg1XE2VIrmKKRuiXZA5wq0SViFloMtE1FLRpxUFZF6PU5sak3lNnBCUwF1zKpQdRn4ZVtltFDwiBBn8GtOWNiIVvDrAreNvoeHo57duaB9fQ++z7wth05jj0XveYbDLxdE+FmZPAju5t36c1zanP4/pL91wfM1tOnjj+cP6aMwi4lNpv3cN++Ylgq3eJ4JPKXvvsuKt4zzQu1OezWox/vcGHhIBe0jeAnTSrH8/l7b5DmmOPE3mqe6hse1juEhL890WvFSXecaiHPN3QUyj5jjEeyptVMH09Un2lt4F4LVQfIjZxXWrPoMmJUJlnFGDIigVQTYW8ZV8W8DdSTQSfH0ip7r6mPmrB16EPFfhbR7pIpzKhB8EYzTI6SK533jHVlWWFnLaGzpNcF5ypmTCyHDu0C1awcpOCcObMQD4adEk5tJWWN8xXawNUQOJiKpIHH0LBRsRug+hn6nidTYup+sU7gz10ERESAvwP8QWvtv/pnrj9vrb386s9/B/jhn/Ws1oRQZtCGIIBMtAV6NHOuMGg2g2aulryuqJooW4VqoCNkVniYGUXISkHnCc0wxJVh88iaGlNvmE8D/XgGSeSkmB8roFHaohD64DnqE7YZyn5Bbj1d2BJcoCVQ7Rw+5u2WEh/pVU/cB/JqUNWAXjFxpDZLUyeKV7RckXo2q1AKqlpSDVST0XVAVKCWSlyeUIdbqhb0NjM8GKbrgh8KYQLpGjdt5J5Kcht0ONCZQGOD2JlcHXbNXI2G086wLAm9mPPxpmSacmgXyAL3naOzCbMGutnjouHeB4Zc8MVwWIAmbHaGw/Ic7Rrx9SNXv/Yu8fffci+aZ//aN7muA+/cjLy3/Zd4u3ng7XTLi699hNxqnl9bPlOB5ZNP+Wvf/qvY8QnWrDQ5EppiyUJAI01z0T/j6XuZL778Q/amkrYfwu4Dnj4pPB3e4yef3vLxx3/IlBTULd+8MnxyuCeugYcRSg34JhzmLYwnnpyg5BmFkOw923XLm25gOQWsE/KoOWjoQyF06lwkgqI0GFRFDoZZG/Q+MYSFkjQpQr97SmmRpgZImaZWIuCpPN4Z7JWGU0SeNXzzcDvB1pEXTXGPmHgiGMuwrtzbFYmNqhztoGhDOBs5ZtikhZw7VixGgzEaGSt1OpJSAee51JmJHi9nj4iyma5lZlVwckWIb6kUTJ55zUidH/7iiwDnvf+/B/xARH7vq2v/GfDvisivcv4e/Bj4D/+sB4mCXNs5Hy9s8T6S1oGwDdiTI42Ru1hQs7AxCi2Nx2MFJ2DB5YZ3CimK0BrZTcRc0KZDJ0G/aMzHSIz32NyQteA2W3wWTv7AYCtTE45+izEQjwu7ODD3mZgndHDnfz4QRJBwoGwc+RioTmNrpVYFylNNRbWZ1Co5gqoGFytea2IbWVWiU4EkDdsiXHRc3q889g+wjLjQKPrEbC3t1mPsgdYr2rFnHiJp3TLIwmgKd0WofkFHjZFEGwunh3iGqqiZKgatLc1A7QLFWWzVqJMiVYu2AW8OPBTBiiF2lS4KmwztlDB2wKmM7zPabHj7g7fY8YYX13seVeVF7zgsI+q6cZ33yL7yQXzCF+lIPO05/PHvcD9PhOcL39k0jm9mumuLSg/kKvhWGKlEp9jtetLFv0K4L3TbxFNn+PDFe9x9uvLeN2GRb/FksOzfq/zgB4+EN2+4vNoQvjw7RR92j4w5sU6N107TIVRT8MMOf5cZ6gltPF3dcXsfCG4hJoW0QlUaoyytJqIDnyo+zLRTIWBoXUacR+bzqcxpUKRR2I8jy/2J6SSIS8ha8WbL6e4VdnfDtTsSS0e+WNnNlvu+oDpDeAgo66kVYuuxveAQahmxBebtLXIV6Y6OmjaUosgp4ZUiukJp+SxciguTLeyDQh97ToPjokzMu7dMZYvMK7uhoJaZNFY4/oL198vAGBSRBsKAELXDSKAUofQ929kQdaWamaIH1FpQZmFtGnvOxCQ1cD0UFG0+h3karVHdBYYRlybWPJE2IOtKZMsuVua64iiEjTB4w/KQ8cZRUmBVHZu2MHGJyicKGmUWuiIUPMkHmtX4UIlVUZWG5PB9xnfCadKUMmPpUNacM/SmAbNEoo7QO7KcQR9SClX1uDWinaK1SFWCKFhnYWeEcNNR364Y7WkiZ+8EBWMzG+k46EwnI7acoPc8niIiFmrAn1M0Ka6i2BCjZcyG0o4EmdFbRZ/BB0+piuIda1rpLwtu2nD94jnr/ob7n33KB88GvvX97zMhPBktcfOM/fQc7d/Qv3PD7sV7yH1mPX7M73/yKVtuuPmgZ1RPeDmdoD3SJ4vbeJpE7u+PLMoyr0d08VhpPP/gXa5271Oujvz47U94+3/8n7z70bfIB+Ef/PR3+eKnL7m8bKgiPH5amORn7A6XnG4M+ctH/JCo9QoXM0EJGzWBL5TQ0M2iHcyDZzxMYD3HDKor2KKRGPHas0TAKyyG0hIpdfgyM/kBF2YShqwKZRsQ7TGykh7ep8939DvNQ7fSv8lIVyj7EfPgqeUtojdMcUGZK7RtSH9klx3H04B2la4plBxIqpClsRiHE4M+QXGRkhQbH+kWRRBPzZVSIpbMtIOUHQaI+4h7VXHXit0bxxfjSpt+mZHjWppuln7rmNqMDh2uNaQKYRcZk+NEos0DrWYMgb1WSA8nrb+SrUK/CsVkjlrRamXMitIs+LMf3SbNahPNXqEOR6wu+Hx24R2Hp6TDG7CavgbYbiE19Lpw7Ho2YqjrPWmraErhTwrpDFFp9Byp9QzwUK7Qcju7EfEIBVRCKzn3XVkwSUhSUbaRC3RWcPqKU7rDcB7+bEZFmwxzS9BX2s4hpwbKoI8LRgxx22Gj8MQtzKnnpKCkiWFrmbKmmxNVOprh7DwbCrk4VGh4n2lFEYOhFegkkSWz8T2r6tgtwnw9cDlUdh895a99+G9yWwqlfs5++Bb35ci3r/c8rDN/5Rt/g58OHzN+Dt5q4js9n7cHvvzDzykvZ2zZsNtvOLnE+88VQ9yxZOHBTUxTRVKHNwrXVhSB06Znry65eHKDHwdcvYMh8/lPPiOHDSnP/NZ//9/x6Rcz7anBTg/IeknR95j5iJYL1jGztArLyLaHrZq518J6NGAEYxOUhKWjek0fj0wqE7xjHwwlOlZtaPmsF2lqZqMySY3kzuHUQn5w2LwwdZln9oKkCm8fLO/cCPfuFh8tx/vIO7bx2AZca5xShGFPnSOmFbQy0FaqHTGbBeNherRn5qJ2GGnYoih5IdWMGoWNM5zSjjkcyAJX9j2C/5TyxuC6AT8ceR2u0VPB+9dYd0MfA19Mx19e0ChoLI54LGfRim+spuGjgdoIk0ZpzW4HS1hJwXIvoFZLXxMoTec01gstW7alsiRF3iVqbmitMUlR+oYvFXZHjHJwiAgrd9rRpkf0k8rFUkloSpj+hMajYyO1I4k9Ji5IUYzSeJgDvdKsw9muW04DOi1UL6AjXRaa1oS8QoaWDWoEKyOjTmT7BHV6SYqBVW6RpogG1Ngoc2M1kTJ6NvuO+POFqDp2pjANlpZ2tGmmXjsO5YrjOiLxlgt1TVkrGypRFlKesJzVgTL17NGEPJ3DLjqFKglTDbltqOrISkMvR17LNborPL35Fv/qs99k+N576M8+4bNPDV/0iedDIuJYToXbh5dc3BsO8cjru5d8/vdPvPMvP+dd9Q5v95/wcHfgcLjlxXjBzfR93oTCq/VTOlV5dvGMrCEsrxg2go4ev3S03rHOAfNqhnccF2rPxQcbtpcbfvjJJ9it4vm3Ol6/HDF7w7K+4eL6hvv6HON/zEXnmW8zG7snrkemS0cOK3rXyNEiKuDSDi1bmjpwGje0O+EqGorLXKnCwR5YfcI3kGwJShMWQQXNpC3u2rIuHTK/5rHNGONR+4lXzSJvruDqgesk3BXwaI4deA9hLVy4I81ajgzsw0raTZwOFdYN2ozYcCC2SoyKvgsE6fGuI7sDbyLY7YqslnEqnMaJzLt0/SOHILi2odXXjFVTwxWlPvCw/gsYDP5FvqRVUJFYEhd6w7qe8L3GpUqVShgCIjccTge2RuHxHNUCPrGqikSLzo1Zz/TScMowbBuPWGoMiEyIE/QESsP8MvFUV5bSczdozLzSSWE6wmw8YVRnTltrXJiVuKwk6aFfyNWiYuTgISqBXPBZAOHGWe5joaQETWFqxBhFtj3VJERVTGosJXEsCaV/jhfFohvizmk4rq7kVHHKMWMYtOH05QkrA3t7YgqFd7zmVhbyKJFRAgAAIABJREFUkPClZ2Jir27JxtN0Q82OBzPjR4UpHTFVQKF0ZlILnW2o2FinTHIjbVjY1SMXypP3HafUeLLZ8ur+DX/9b/wm/jsXXE6e+v4H/ORHhc+nL/n173+dn949UK3w8WefUPIMIrya3rJpicP9+3zju0/YLguf8hkPbwfsRvNWfw5uYGvGMxQ1TOhQePxc+MJMvHu55dnVwNiBbgMffKdxslfUekPOn/HF/St+8o9+xptXPQ/qLSKveO/p17n+4PscHybenP5vrvHMtTLsIaRXdFI5KcNV7ThIT7lYqMeK2h1ZlyND3J+FZuMjs/esk+PRNfauR8o1hyBU9QCsiD9gXI+2ER4MpRgwA2vfaO0Rd7dF7wUjbzD3ew7G8oHOvOZEt16SzBGcYg3PmD2o4xvmTjMcBi46S1k0o32k+ROpCWtxKBmoF4UaHqjTgC0JsibVAtcVPSdGd8BNV+BekWfNrh8IVzPdYWUSR9zoX+6ZgBbV6DVtBbZnAUQV6HtYTorOVC6z5o3pqXZCoXBpgzOWU5tQthKzYVMbViwnUei8Uouh7QK7RThoBzpjcmaJjd5VWjAEXRAB5xVXR8dJ67PBxgcy5z1x2nn6+Z5DZ1B4tiUwA13Y0DihSVgRUJpVLCoX4qiIYs7iJtuxjYFUJ9ZaGXxHSVfkcsvOJQIWEywbqbzsJ9q6obUTT96x3L0S2tCzOWTCZiUsCq08tAlRGwYFS12p1x55o2HIWJPRa6HUwqgsS1WsLYMFJSO6binpQK5HqIr3pOPmxrFMjfgNw7Ptd/ju97/H7U+P/Prf/DcI+oIvP/kZf/lXv8HD4UiSwN/9e7/FR5eVZ2nk9PxdHm9f0R5esu/f4+mHH3F90/PDj3+P9VC4eve7/MavfZNxrfzo9ad88cnnDDc3GGupX37JXBYuXnyTJ+9fo1YPdsU8f85HteezFlmPb1hTIN873rx+4Mm1Ig2f0nTHizzwv/6Pf5c/fnXii+MjTw8PPDwxiF24aE+pLuOb5vT2kRq3LD6hljuyGqnjgokbRAJlWUBZqusxtUOnR+qmUVfNaBsnA76uZ5b/E4d9OHBMlsuLwsO9QasF1QnLPGLaCfwTNurIqXT0RXPYCzJl2FvMa8OoFswe3qoT7XaL9QbXNfS8Yi4KJ4F2NOxSxWvL2jKymdFNY8rA3RJYNw5sRh5WLFs2UViuCl26QB4nHp7dc3WnCW1g6k7UU/0l3g6oRlsLWgR17Oi1YF3kocALNF/mjkdWTHaUpjH1hO2OFDSuGFqwaDyWA6rE8yClVOpgWPKWW//IUCppboixGOMxKdBUIGpAPDrN3CEoO5M2jfb2BukX1GaGmAmux4YIaeWgPRspVPPIlAEszoE1FQmVyWZImtJllFRUjEwVzNChv+ITkF8TamajNxxtx+IsJb+mL56kNaUMrJ8vmK1DWia8L9RXjU1NnOrIzikmMzH1Ht6CexnZaMWSI+HgSbpitHAqhWIdPjoaR5I6ne3GTtPqFYrIa+NQi3DxtZ7Lm6e8s/0A3224+g507wp3d19Ql5Xf+Qf/mKej4mHuuAnCD3/nR5w+eEJ3XdkdZn769kvqtx21bZjWxkff+auMq0dfWWoHZmO4DCP68jnj1Z77tNJePOG5Hzglxe3PfkZbBt55/g7d00/4fHnG6z/6nK+/qMTNO+Qc6b92ib76kGfyXX789/8hf+e3/gk/fv2aTZi4uh652n+N7a6whpXTbaKuC/fLRNtZnL9n87JwbwtaZeq8oXYRoxpcNrQY5DYioZBFo3SibFbu1oFLVk7LNamcuFwPFNsotbDeFmp3Fm2praJOmfhcs31QPK4XtH3jGANuTUi+ZLsUUufI6Y77qhG9BV1odqIdBrLNSPJILjSvWDbAcSBtD4QwsDs4ji8SSgv+AYptbLsbDqdH7rpAv7mmZGE93tOOitttxs4Or2H5Bcvvl6QTcK1KhZtLLo5vmdhgw4zpDYcEulxCfThLd33FaWg4jCh0htgCTXlqSYw2QXIcpKAaZwJwFTxw4RqPec/KHRdNCBpUuiGrB4ZaOUhjtJrDjdBeV7xvqFLI9RprjiwmIMpRi4MpIi2ju0JVFhMdaYjo3GOaRuyKLhbJhWQzNIMq5Uwl8hl5tFASF9I4XHrMSVP9DRxuudjCqVnWMWLuEoqOZs6hGxRNtjNMZziGnndsfWHJR5ITVFXIRmNnTSyQk6KzFsOJXC1WKqteqQ1q1bg8kEbN0w+e82//ym+y+/AdatczsmP1XyOVV9y+BMtnfPbyLS19STxdkuMd3C7UPnD/I8Pm+5oL5diNnnR1w0f757jvfkSNb4E9T0fHHBJGCU+fPUWSosiOrAK1HHj95SNIwgyNE5YrKbSXM8Ff8O0XHdmMhI1wkXrSZs/Lhzf89B//gC8eE5/98T/hyzd/hNlv+fTHt9wcf066WTncG+wHmRf1XV59Fkj6Dn/KTIPGiGezqDPHwehztJ0N2LxQ+kZuG+IKOS/01Z/9C9sjT+QJa3Qc+5ek9Qljn1mnxk6duD+t3ABLbzkWR78dMbevqHaLOJjzinq6Y/PzHjMcmVRl3B/htSPlS5w9sBkDL+OAjxc01VjGe2xdKCeFVj09FaMVDypRl8i223GQFfpAjg3Sjm1SuPRA85byZMAkhbx+4O0vdSCpSGM3ItsJ9SA4K7QghEHQcyb/v7FQvtGSOYts9AHfd1RT0adzRsHaB5raIrnh2oIqDWuEUPc4d+I4JVR1pGFhWIWpeUQiUgtXVx1vbwPgGFRgVpZqEwpDv5698ms4YEQj2w4z9VgzM9WZtunoosbMiUU8IuuZ5OvPXIIUz7b1gqJ4y0WspCZEIleD5yFEltaw2SEKYqtcbTKHqsm6R62eSkRtjjyRntMdhMExyoFTUTSrUW2ghge6wZDiBq1OpDXTAMkaGRWNQjeflZbBacQpdu95vr79VS4unvFXfuU3MK4yYBhNx2mb+fj/+mOu//q3Ofz+W37vt3+bKWqun428/NmPUR9sWXvNR8MH5JJZg+bZ16+49t/m+lued7bvshsvmYaEf2jcvNjTXxjW9ZbjlyeWsNA9MYSXhftb8F7RXXvcuGNcNT99fIXPGx5e3XNcH9i/iEzLwDdPLyiXB159fot5f4RqOb7+ki++aPz2D/5nltPPWV/f49fKkiPZ7bh4con7vDFvF2q9xR0VB91jTGGfF0rnOPoeVEbVhLQB3zSlVsysYfPIPlresEIxJBIuNuIl+IOjucYcNuh3Ky2fU4/rqaeND5RrhXw6Yt+B/n7kbnkL7QatXsOouFp6ZC+cdMQdK8SebDVKJYqc6NwWvRRyWMijo8QCzeGfGdTbSxKPpK1mvbtjMJY1WoadpnSWeXo8uw51pZ3+9CLwS7EdEAutLqiXnmrOQhqTLGoBGBAK1mgkKLIEdFvZGMOkMmWKZKNwKCQbsq/UUrBVCE2YoqXmxJoqtXTk7YKZduRuxi0ZOo+qgbsSsGakpkqHIpVE7KA7etqucEwRek0Zd/RxwZjAY5zQnaHNhSUnxDe0CGvQ6GaxKyip2M7QasWKIgzwaB3qlDCl581c2dpGbBvwCSsr2Vxx4A5/GqjqSG0G0w5o2/M2e+z2kZqFY4YWM7sV1naP7w1aNPP6QFUG3QaS1WiXUdpRsgYanct4G1lCz3V+lxf7ym/85nfYb7fcxgNvS+PRrITjwL3MvP8JLP4LXj9U3n1meHVa0Unx6sc/Yx+E46885Xh65PpG8cz9Oh98b4P1V1wvgbDPPMfw7ns3mJ3j/uFEXka6lkjLyvxPG+tm5OpyxI2ZMhfuX93xapjZ0zN3gWc74cNvPGH69A41eD5/8RPGt1vs80DZbzneRX746WdYP/PtD79He7jmD/of428t9dWPaE9GDssD23YgR48tG6oGN0T0sEc/XFDnCZeWc+CJd5x64W4+st3BWgxWRl6mQGpQ9x6tCn0eiOlIuxg4nA6oAuZjYaMKsT1nkVc8DYovP6vUcWZ8eUXwE4LD+Ym99zyYyH3KDG2leofwlPIIku/BCqY8Jc2ByWZK52l1xFlwsjLHI37/yGm1bG4TazOcFMgNmNsDcb3gHdtYY8Hs4BdZiH4pigBZQTqT+J9Y4TUajUIHTfY9F6pwNJFaZrCaFh2Pa2afE1kGji2yuEYrAmrB95V62uLF0LEQt4mmBLWs5NMG7WZCcbQ6UyPnFlMCnViaRB66Sps1bT7Hh+dD5WLQ6KVyFxJzlygbx/ZOiGUgDDtKuMXKSssZ0ZoknlQSpimG3JNlPpuclnr2RyBkZTEOSogYH8kqE8s1qk5cLCNZCfFK0729Z9WW3CqjvkcfPIEFxo4+9pw2K00ZJCjaXUaUQarHSMZGRW6erFe0aLoXe3KD9e3KZnPD1bMP+fZf/gj3/Bm8WVBrxseFz9dHXn5xYLl9C8Mn5HzJ5qbnj1Kkcw+MNhNfH/ns2SXmdMs4azp1gX03Ee5G9u+uyLOvEy6PxAf44tUj485TBuFZfEGVS9TwGW/qG0YdGUPPfEoklVh1RGLjpCLpszvcpmP6f5h7k6bdsvQ861rd7t/26875TpNNZWZlZUolybYaZAJsIPCIcAQDTxnwI/CYkf8CQyZEwETgkQOD8UDGEhKWLJVUKlVlc/I0X/+2u10tgy8hhKhSECErovZkN2/sPXlj3Xs/az33fdlLvlr0FNcbNj/+CS8/+QcE4ZhRM5vXyGeR3WoHqzm/+z/ecPnsE9qHOy5/+VfINh1juWNfrIh1QdZusXWOKwSF3RFigV08YtmqrWS0Ez4JilQyHiOiHgh7D1lOUpGTSdCJhuMUSPGEw96xatZMJwE7jWTFku34DebQ0DUXnAyvePBPEGqPLE9Yy3fYpmT0FecD7L2BtMemEzw3hFwidAbjSGMcuvBoZQi2QEaBx1PoQNGWPCRJxHCgB2rKcUccMrY5ICauU8PTecnV8ZpvzSz/n+3npByQaSETB52DntAxf4QxuAKtRlSd0Yx7+iljEBV5tSPzko78W0twoDeCMkqSSkxK48MEpqA2JSqMpHGgnSR1XjOGBKYl+gaVaUIYyYWmHz0zHGNtUVZSiYxdMZLGGYQjxAVFM9APIE9WMN6SjZFgDL5PgEHHEpJ/DLmUDh1ACfGYVsMjlTgLnkl45FIS2ozcCayOpBipKuhGiYwjGMHCZmyNQZQTp7ahHTvq5ZLU79hKiB6yXGDcgtGMhFKidh2Fh5gLvPJkUZKXijHN+M77n7N6+ZSqqfnwyYoPT59zyCv61294MIkGOFGX/M4P/g2vb79mSJHLxZrtbselU1xdHCnfGL56c2RffkW9+Jxf+uSMySk+/NDw+ff+HoU1LJ8tkWEi00uqUpLqGU0s2e0m4nBPNms4iIGYjiRW2P0OrEVWOZvRMdgbqn2JlTlvD6+phKV6+QFqc8VcrFHPTljEmmK14EnY8tZpdDbnj/7oX3J7UHTiHe0P3/D112849pHj5sdMUlM5z4QgTQ7jJa5omOSRmASZgFkQ+KgZgkUuW0iaYAWFU7hRUjYFsu+5RxNDROeelCDXOQoYuwlpJuwsY+ZqdPT4cWKvHNqvMc1I8DV+dUTcB5QDJQ2zbCIISVIGvCXJhqxIeGPp3ONXnMgUcjSc955jseNAhh4CUmcEdyBfCpLNadNEIxTB5/gxoNYT0yaQ4Oe3HJAk9mSPqGphCaJmHTyT3tBbTXEIeFHhmhHhBsJQEURPoy2iMFiXIbwllAERItlUMdMZY97Rdw4pHetS4pJlKiay2DC2kazaM/mEGCWTVlA4xjEj2YwhBHA5cl4R3ANybPAMjEdDKQSrfcm9rEA76lGRhKbVIN0BmQtSijBJXJHhZIDpce4BlVCFRjuBsBqVC1R0aBHxKYcwJ3e3BGBxmtEdZlTZlimV7ISmWCT0d2cU+4o6zri9e8uLj59wevYBxkq++eYNdtlzf3+LJyBCwtcaZR5XXarzBc8u3qMuc7LzU0J9wWkruGm3iOMtbXfkbf6OKBLZsWD5YU7SE7uHic9/4VPCnSM8e83+qx2n+QrRR+b7GdNFYl6+QOo559UMbffk84pc1txri+7ekIWGoOYcZxV2DMycoF5XRDuiREVXKJIfGY4dpii5uJixq9ecv/U0ek/dJjYnH/GBPtJODeJJYtu/YRMn6qNnVx84hBk5r5mzYjPLqP+jU/7gX3zJ4WqkWSnagyCuIklEMIJMB+rwKNy9soQ8R/eJjAo/gBsdxIxD7pELzTg4VGXJw4AdS3RwGAdH5alH8S3UZIk53hI5wFKytwYl1qw+uGV6lZgKT301wxsN8ojE48aGtgwURpP0gug1vt2TlKQWnoFIGmBWD9ytA8EGhO8JhSDVgbDLsTpBn4N2tFaigqcxESfn5NmW0f708fdzIQIR0ELjiZhkkGnHMYAzgnrh6Tv/SM+xGVl0TMIyZgoRNMl5pI+IkBEHKDPFmAKHcSCOgUJKhNFsEBRJI4Mj84FJG6x1FKYg0BNmAp1qCCPCV6i8x/oDqU3oVGABoQ0zRvLCQLjDNRZ9G+jyiIwKFQIxBxMEwmeECGpw+OSoY4bLBDZ6fPJIKQljpBAGt1QopwBLlkkiDXmYMOGc9aWgWH6Irmsy/4xPLjyzX/01lu86Dh81PPz4wMU4UH94wmJW8vWXP2a63/HuEOmPO6ZpfAy/WC95+vEJ3/v4V1ivzunsiBscbjhwnEb6aovUnuub1+x6zcYmVgvD5eySohzZ/OiKLzY3fHf+jKtdZCFvGNMF3/80Z345x/WBYpsQ44HqXJLbnF3SxJChjyNTseTeZIhxYr02dOWC/fUd7/7kgTo5qqcNs3JN5iRt+lM2hwfezhc8qXLOy5rjyQr/7sC5OzKqNcf6Hnd7jhpajtuWzekJU/cNnzw5426z5ObNO15FwTc/eMfu3U9oVtAdGqqFplQl29lIIzSisEzHQBgNwWVYm1H7PdLu8SLHiIqUjwg5UnU9Ls1wo6bMDaoUTAjqmFHGgTCHpukYbgbcusI/OELXYPQOJRz3rx5ZGjmKMTnmfqDVDhUy2mqk6g35pLHFwCAfjadljLTFyChLwBHGOcF1ZL5CVwJSxxg9C2M4thMyt6g+PVKOy1MObU+5s49hpD/X5YBUSRlFdJrZzHPMPFKuyPYJaztmWaQvMvLC47MaddXhM4j5nLKGcBwI3pHFR4zZEB4dhQUTwyIQnKEcBdGPGKHpKs3JMOGfZOw3ikK1jK0iaxYMo0CpCRlGogokJWhkxdA6xEzTjIlDFIi1RE3g/Ej1yOvEywqVC5ABeoUYISWJSxO1dOhKYq1HhJL85SVG3EB+znE3MUzXiCnnV7//65x/9yXzxSnff/aMv/vib7N7kVGne75867h0p0xNR0wLhkwg2j2sJrLFiuLe49PEbYI4RpZGIGSGTBqTGwCubvdE6xBC8+b2Dfb6FZNzqFniz/7tD0m7iVQtSOcZk03c9RMvlWKXlfzGZ5/yoy9+zKQ0ux/9gHr9lPdfXKBUT6PWPL+8ID/5iPc+a9hvPJflKWp+YK5WHIYTMtVRCMF9uEPYiegMVju6KbDzDnyijpLpmLiJA0+Xhn47YITBrwLdw8TSJ0qx5Z2qsPs551XBbQmfZi94CDe870p+9/4NrLb84b/6AZXJuX7957w9bki3I0MMdJueFMCnLRGNKCZEacgHje499vGPJYT0SIpKGdMsw/cjyWieJEtvSpJ/JFalrCPqgG4k7n6OK3bIPjJfGHYtzF3BuMqZ5I5GNQx3R3IMqcqoaNlHSTNqkjS4KqFEpAtgdKKIGS450jDgpcCbDGF5xJzR0QiLFCV2WqDqlrHzNEhErBiqlklG8q5Bp0SXNj+/5QApgStI4sgxXCL2dxSLgI4dxqwYGJj7it474oNnlhWEZUlPIrcT3jdMhUeYQOMllTxh17aP6+bjiB4jBsO0XnHYBrRI3AvH6Y2itgPt0iCyyGAG5KAp6znpYBEioxEBOS7oyy1lUlgxUKpEJjLaEQqV6EuBMhrjJmL6li2oJmLu0UEioqQvwcia+bOKJ80F609e0tsXfHbxGT/54po/uv4T1s1LfuM//3Xe++hznq5POQe224Lp1RsarRDO8S694vjlLU3esJKB4qzi5s/uoWkIxTlyN7Aqcg5eMtQOZTRLzvFJ4IJFD0eitDgC6fiaUUiaMuOHf/bnHI6wfKZ4Vte86h0LJchNxvOnDcgZ797ekRdrsuMdq48+ZpQbSiEZT9bM5ZKm1tQcEFeGZpiQZcKLJftg8M0erwTtxhOyGTpvyKVjmDRHOgQzlrlgnO45yCO5PdKlOaHJGXbXbH7YsYl3zOwlq9mRPrUo3aPmDU+n96lMyyAm/unv/4T9+AMG5/m6e0B+AXrVogeY5MiUR2IVUKpn9IFsyAl+hjskRiUfoaPjRFKPxKkUJBSeEsEoH19UeyIxWYIr8FIjxxkzJH0+cJIf2LU5EyP9boZMMNKS6BBR0e4VjTpjyjas1cBuECQ5Zyoi1ltyV2P9QFk0RBGxHAmxQAhQClIEpRUuC6QgKZJiDJaCG6YoUGJO7w8UosNKQdFJQn5kUPERNPFTtp8LERAkLgvLQ6mx7p7F/JS5dnTvvURNC75bS6KeE8uKPQNedDTOsG+veKovyZ4abqPDdo6oHC/Xp5xvDQ9WMpYzxs2R/VFiBouQwBQpMujzgWmTMFbgRaIaa3plkeGWMatJWUexrRjKK5ST4Aq6MjI3iq5zZL7A6pIsTdg+kUqFHgUiakxRkM3XaA3N2tAsllTmnE+/+11efvc5J0/PePX2Gz5+vsbIS84ua+pnL6mnC6Z3Ge3mDlf3NK5hTC23cQb3Pd/sW/KZJZsGXrcTZ3T08pRyilhxJPiMsJs4hg1xY3m5fIptLEdvQY8gHamVmCbn4TDw5vUrzi8uOOxbVtWctSy4SiOvbx/4xeefU+qJ11nHMmb0tqU8DcS2Ynh7TVEJ5JPAkxcvqFoIekG3FBQhUV4sOKqR0s5h6ViPDuk0x+SIKqOrE9MwotSOZ0NDJjP2cWRxMuPiZM32zY7Od9zfbGnjwPFmw8M+MagvuUmC9edL3kunvHo9kRWvsLpBOsfh4Q/4hls4BvqNo7vesloUvPfRJ3z98I7ihwfS/pqDGshc+BbuoShdxJiBqdT4LJIGhdcRLSOFkIgpMPOCtAhM7vF3rSxR9ggzQ1lL5gWb3hAakNT44UCRAr02NAEmr9DqSOmfPAbYeEhxQopIJhyh0dhhQFWPIJyFGmDK2DURZwSKyCx6RpcQvSXPNDtbkJc9wZeoNlKKPQep6VIgteCFxkeL+CtG+s9HOaBEWjGnbmriOjEcNefLJ7z89e+jg+TXvvc9bpWjeDoj3y3wvufum684Py9YK0NTzXj95UDvRrLLOUElvv7yCx4e3uD9PQ+3G95+tcVPAuMHqkqx7y2+sNRthHlOrAP+OjJdCPRVolyXpLalkxGlE7GtKOeSwY3MsobsOJGW5zDrSGOJ0AlsxOUlq+fP+YXPfplPn3+H+emS1dkpp9WCVRmw4wbbakIfGcNbxHyG2+aMM0843jHtOnbvbhCToV+V/N2//2t88dBxSclSCq6co1wJdj+8ZjFPhFaTgqF8Inn99gE5X9O5zSOIJRjWs5r16intcWDyjpTlKCnxmeKrr7/kix//PnHX8vU3b5lHmK0XJFlz665Zzp5xvqpRK08IH5IINOwoVy/48k//kJdPLvnwu6e0D4H3n88I8UOaZaJdFLzfPCWVLZKawjhWy1NUnFHEns3YclARNwzYPkHq2Nsd97cekdUU1hOTR4+JzA98tfmao00423HYj9h9w8nHFaNtEarh9FQyHSpOZi9pPhJU+0A/Sn7/j/+QUI5Uk+d3/rff4Sc3X+CuW3LhkTqR+4EpGCIVRRxwyj86QFNkmKpHCzgTWROZehCUeO9QSuLUAikOSDR+9DRphsvuqFOJkiX3XrLkwEF7VGZwwZLGEpkmsuVjPJ7LBMov8L7FO4mVkbSCTAlMXxOOhqAiWT4ivCRODqccHiALj2yNCFoJmApUrEl5j3WPHpkoHr9mRiKxNsTW/s2UA0KIr3n0JwXAp5T+jhBiDfz3wPs8pgv9o78qcVgiGeoPmeUTsdDI3DPlO1792Zf87V/5De5jy5OTl7h2oqxq+rsDz5695OLFGZU2hD5w8d2eiZF8npEFw7C/IyueYYpP+ODpQK3/mLZLcLNjNEfMtCNaiV8eGfaJwhYYOsIwJ+QD3g4EU5AvJ8KDocwCoxAoE7GphcWS5fqUcn3BBx+8z+rFJaIXKFNz8ekHvHz2gvfqU0wbmLoNYd/ies/Dfss+bFGj4bjvUPuJkhmxn5AmEafI7MUp3W3Hy1/+Zexe0LQlU//Am7mnyFeId0e2WhO2A2MbSf2eM3OKTAUcDJVsWFWJbYwcUk/av8XJnOQjlTI0skJ0jgelKBtPMQq2Tc2suOD56ZKhP6DSd3G5Q0VHccjRuaQdPcf7W57kL/j8Fz5mri9pp4Hzl2smmTErHNM8UGaCQuWU9QlFbMnnEudyEgqnA1MaUX3DKAV6PoIraI453k50oQctsJkkiEjmKj6fX3KrIm/vD4TrN2RNC9tTZLFlIXOW5TmqmFNmDR9kGbusY8vECxN5dzXxzf0bpthToh7fmjHQ6wwmhTIFQUQGH1GZIWiwkyLJQJQCosYcI1OQhGpCZoICkLGlsg6vJ7o0J8fSTYKoFCofMUYQu0RIicpaYpVhXM+gGoTrcIPGqYCjg2JBKLfkrcRaRQojOEcpEkMEpwSpzgg9GG/QOhD7yCwJRglawCQmgnAkJ9GlIh4f0XYx5igzgQ38LCTpv6ty4O+nlO7/wvk/Bv7XlNI/EUL842/P/6ufdbPJDGfrhmfPGuLlJ7S7d2ATcsqZLzOOu46sv0GvNHocGEdLcp5rerKnFXGEj6fpAAAgAElEQVQI+LuOJBWFr8iLJbPZE7womWdn1E8CWmTcXO9pz+443N3TlIb74x1bUWJyi0qPmf5xa1D5PVmXY4mkQlBGsFoStaFqGko94+LpMz78+H0uqgvee+8pH3zyPvWUc7QKV7TY3S277ooGw/52oI+BImhujnt29kizPqdZFmQ0SJ8jqxXd6ClWLd85lXyhKy6E5tWrryicJfYbgqvYhcgyz8jrBjnT5FWFNnvMPKMa1riHLdYmeiGYwmP4iK4rdFC4sKfOBXUSHFWANrAKz3CLnudC4UbHcSHohMc+BLZsKXiB2x05zt7ydDajbZbs6Hm5WuFUie9aQqNwDweq7oQil8j4hH7dMYsO5xJxn+NmDuksdtrSHi2aI0kEUtiDDXRuYByPTDKDWFCphLUtQxK8tQLTb5n5Gcv5jNs00tU9TIpRJXQsmEyGGDZcd3DnA/ZHN9y1guvdnj++foW7viXMeqbJEKbHFm+pEq2wqCSIFFgVyC0Ypwj1Y5eqHXI6P5FUIk6KWWbwKSFFAJEhtCXlA5uoKV1On1oKUzL2c1p9j/SCiCQ7KIIWRK2wvaREIoVEdw4te9okCcUjSwCvUFoRqvgIzBkl+BGFhliTMxCEZ1AJn2umSSOTJxFIswKlLaJL5FLQCsOph57p8Qvib1AE/vL2D4G/9+3xfwv8S/4KEXCj55P3Kl5++glDOueDX/87NLnh4fqBe9tRFhWbzY55fM5N/w11URFtoPV7il2G72Fne3ofWRQ168WWZnXKsHzC/ostzfmcs48+4sli4Oy7JV/8H2+5b/f83v/+W+wyw6o4ZX/bUZAxFPfUVuOKHEYQKScvNKYyzNdnPH/2Eb/5/Dm/+Z/8A7KzE8rrSJwfAGhD5M3DV7RXV/hxYLQj0RTk1QmnZUNoYLFsKHcr7ruW+WVNHHKyGdi656y+oK8q+vHA8nRJO/bMsxX2eENbPKXJF/j7DVcJvvfJJUblxPaa7p3j5t2GIg4EEcnngX4UFGb+OAstBQiJL2Z0wiCjou86Cil59vIDxn7DvTY8HHeE3RVFKKgv5uw3G9LhHW/GiZxv+MUP/iHD55+SxYLD+JbVWkE3537a8MnsBC0TMstJbkfpz7BmS28qql7R3rxFZAqhPVF0HPeO/X3Lw8PDYxCs7xBpokkz/BgZTiLOe44Hx1gYpBecnxe0smQRL3gyH+i7S7ACsTliFhN7kbOwAt2dsg1v+Ob1K1gYfunj73MrZ+y55c14j5x2mDo85jaIguQhy1qCV4zeo8VA8vrRr58SBYJRWpIX9C6RSk0aImJZ0YjAXieyo6NKBmkew2WXcsdeZRgbqYuCa+UohgIRJUnBpCWLqNjkDuFHMhIWi3QakUnIItIlMifwEkISlG4it4GjUDy6WgTG5UQLeYQ2S+STZJoEOYJKG/qk2bg9uTAQwk8df/8uRCAB//NjTiD/zbdR4hd/IXH4Grj4yzf9Re6AlJp2kpyXT7HvrZjtDA9qR9ADauPxqWe3OVLMn+JUydEVrPKMrXvN/u01dfWM9ckJ08Mtw3Tg3kdG13AWNDdTSx/PsEERwzWnqUY+nfFUzFldXzI+DLw3v2CXDdzefYliSwwa7yYykaOKNbN8xvmnL/n8k484ffq3+Oyp4Wy9pPcDYfIMtwOVDGz6I/3tNeNhT1ZqmsWKrXukFDVhjYoCsXok6a481HlGqWZ07oG5X6LLPSdWc3ww2LLHFwNFjOhnS8Z3LWq2osgEtHtUd2Ab5sx2gtacIMOet5st+/bIs9Ml3pdo1VHNClJmYCFZyYo8HGi7HaopyDuNmRtWqyfUTcP7RrEZ7/nij74iTYJqEgzLmmpRIVPJ235kMXeEZkX3pxb/ZktROC4ODaqKoCORgiw19NsjLAzjfY8RAYPAhcjxYU/odwQnoD8+dnIOI7LvcMpzMx1xrqHqPTYNtNMROVesm/cJ9zsKlSOayCQktk6cyDXuPTBtxdPMsjZnCNWxXzZcvLig29xwdTeQcoseM5SvyVUg2ZE+tVjX0jQCbRNMiaIwiEkgbCKZiJeWPjiEiqhYkERGSgKpNZXvue8DKpaIynPfT5x5QRsdu1AgLfSNJVcC6ZZ47UAeyZRhrCKu9d96VRI6ScSY8MYTlcIngYw5ef5I5h5QjCmRQsQkhVCRMSj0pCnkyKQdIhmihZQndLI4MqJtKTSMqoKw/xsTgX8/pfRWCHEO/HMhxJ/9vxQipfStQPCXrv8/3IFqvkzdrOPucOQTv+Q+dAgEaluRokKEnPKionQ9Khh8HDEqo9QL+rqnnXrkJmHaEZ/NiL3B6Q13h8R+PxC3O07zil7Bm6+PWNfxYfkJv/nv/Wdsjj1nZPiU+LOv1tzt7plKyWIIRFdz/tF7yKzh+y+f8p1feA+zekrVBZrBMrrIG3fDcHDMh4Fdv8fZiCprpsKQZTl1HanqGpmbRwpRe6TKHrmASiiUWCL2kb28wugZIt0jyprBJ4oY6Z3FuwOzs0ty3xHlAbma8ebuniqDbRxwxYzoG8p5gR8duSlYzJfgLFmhKQtFpiL9wdG3Hq+A0iCXGSoeUWHJcjYjhIhWJ8innq+GnnJ6htvfkWtDvTQcuhkXrWbn7/GZIEiLkh4dCx46TWUKnHdc5BsOk2dfLShvJvapJ2jNIUaGwwNid6BrPd49MiHxHjENhFyw9Y5xGAjbkVmeA44YI87vudoNzM4z7FgT1JqisdymHWLvWcn3CcdAV7Q4G5iryMn7DU+/85z11Y4/+Nd7+u1blNsjrWSIGY6SpBzHXiC9I1cD0UksNSqLECPCK0wI6CQYVaQyCURJTHt8NyKbjHJMdMpTGUmcFH3vHkVCe8zcYCcLNlFkR1qnGSYg9+ylJDeGcgoccglIBJCOkj6P5GLCIFBJopLBx4TXgtIIVEo4r0naMqhEISIhRKTqgYiXJcpppJ6oHGCOPzNP4K8tAimlt9/ub4UQvwX8GnDzf/MHhBBP4WeyEAGYzxueLJ5jtOHLNxsert5RLCvO4wzHgWAKmvMVX7/d0NsralHj8kAKE81iTaMMiOGxLRVBN3XwJnGYHJu0Q+wU8+YCOcuZnKQ2ht71/Opnv445DWzutsgw8f3PP+Qn2ysWpzmn3Yp7N/DpZ58jw4hMHWOr6caJ+/1rqumEIYxM+y07Efhm3BGHnvXJmvPFCWM2Z5w6TFTU5zXHvifagVxKHvzA6XJO5xxXd18zEzlUkZvNATntKC+gu75lWhQMAzS1xh891+92ZE8KTJmhp5Iys/z49TXF88Dzk+cUTWJaliRtiRLymENo8N3AIQxMwZLlj52Px+5A9ImwtyxNR3mxphQZlRlwQlF/vWWsWpbVe+RGUq4NRdLsb47YaY+sNdlZjfaar6Yj1aiZwhXF06fcT49MyCn21FOG7QKb1DIcR5w/oAeHbSdSBk2uqBXcO2h3I04+BmyOdc6x7bnvdkgheMWGVUpIu6KqI072XDZPEP1E1ie+2P2IkxZ6eU6wC2b6hFwlxkyyfJ5TXpbMh4wUZ9x19zB25FoymYo4PZKoUZB8RKoOLxWRyNw88gMjAp0MwXaIusCKgEZQOhCjJeSP0XGjkEglCGFA64Q8aIaoiWmA3pGVFTFEmASkCakKBuNBRqoBolb0WmHSiIiRLinyyTxOYBLwjOxHRR4UwhREIdF2whqHDgkpoBSGkJdYMaKDYoqSMmoGfnrf8F+XQFQDMqV0/Pb4PwX+a+CfAv8F8E++3f9Pf9Vz8izne+efE7zkMEA0Mw6HHlGOjKpi5IHhK88qr8iMwu4sY7aHIOm8pzWGp0VBMxeMuyOZE2T1KavLDHWVQ+/RJjLUJTMtsV3CnTseXn+DCIYyaqKcUyvFZxewEieULwz99T3i/sikJo4oiuPxsSNxu+NWOOrzBlMVxF1E9QdEIYgKksmZzzXywWODYWgtMiXcZiJVMzQ1qnN40VCVI9kyYN2K7NmA/dLQH/ZM+4l+2pGbhmy2QnqDsBV761HDK87UihAmyrJhqWuKTKGHA8ei5BACxk50aPx0DzJC54gyQyIw0TObZdiho70NxBJctqdIZ2xHQ9IzqqxleT5nN4xUZ0tyGQjDPTduwu89VbbghZD0UnC8vkOcFpiYMcscx0PHiaqYOsvt2JF6T7fbsZ9G0BOySyg74v2EcwaTK+xCoYyhPk6IJBEyo15mMJeURY7rOtazgq/voVoalg76zZFMBkJfclrmTKNFm8A+dbho6LZbvrzaEfwOMYwwjNhNT0oTsbKQMlSI6MyDVWgyslwwuoCYICpBJBIRiBiZlHtkBKqArCV6L4kJktGIpOmZsNI9phl76FJBFnpiLMjTgSgkGT1WJ+KYCHnAOQc5aJcI8tFzkvQjLl4nxwR4FUliRIiAB5LOmUJChIAOmjxOjFExN5p9tJRZYJs8M2uIyhKiYTQSxp8+/v66XwIXwG89wojQwH+XUvpnQojfA/4HIcR/CbwC/tFf9RBvE3oxp+939GNPGA1loejcDX4cSYWhdpripCFzTxFa0JSeEAxqGAi+57h16DyjThk3fctheMcTsabJMrqwZzf2jNtAk6AfJpr9W27aAu8aVqsFL6oGzhLq8Izt/oEuW1JlOfvYkXkPW2hFz6a9pbZz7lXLeGOZMoOcJDNVIOcZ09Ry+3DNqj4n1w1G5YTgmfQAuqYWFUgYokNojcg0YzcxDSPOW1S54O27H1CUmiKrOTk9oSBh5x0zvyQ72TPcFAzLER+WlC8ypsnyYB+oY8QJzUN/RE8HaHPqJJCZo3cJXVhUURCiIcsECYiqA5NhAqihJKsWMB04O2konGH7kz/m6m7CIrisL+jDDrvzyJXg5u4ME47EcYPcF3xzX5PPZ0Ra2qNGXRQgIpkGk0UqqclUhpCB4CVWVqTkSLlG5gotBCbX9NZTTz1qEpxXBuMX3Jc1Qhxo6gaZl7xcrdkdOq5cz8rt0fUFU2NRWEwB431Ee0PpJA93sD/0DCFyPzwgkyPLJOvzMz79+BdxfY/oEpmwRC2YVUsmu+fLuyuu3j2gTYPIHNPxgJpAHhIxlxyTQEVJIQRqBBkK9Az0MJFkQCmNGiWJkaQf472UEERhEaKmkj0pOnwUmABWJZxQj2RtGfBSopWEAmJ87ENRSQGGqAZ0GEFYnJaQDBMWnerHbkoniBP4EkIB8mcpwF9XBFJKXwK/9FOuPwD/8f/f53hvCTj23qKlwswXCBxumnNWLsmMYvb0EnU2Q+iXgCNtb/CTx+UFRmUkASIsGSeH3EXG6x37dKRZG2SeE/qOw8OR/HSBKCSbn+zIVgHx44H08Z5lsCzOL/nmeM889JiNoKhbdm3FOiYEO7pux/2u51oK1jPLfTdSlhWqakhFSSEi0Ubk5BluWrycUzaKTMFxd8BUDXIaCH1LYXLGYouQE3sb2F1vSJPj808/4/euHnhyccqiEsSi5OHNN3TOscgEJibSkOiOnliNPPFrtvELpHlCtJJlZRmOiiArDuOBcjEjqRkxRpLv8SGQomYcegobELpG6ArjLVpHUqZYnkay4gld94pSFTwcdgx1huo1mVCk5pRPzl7S+cB229KNjnbYMXRzLsSMwkpCrjgc9iT7CMEMWlGJHF1k6CpiR02tKkLRY5RG+BLvBUW2Y1t7YubIjwIrEthE7w3RZjTzHKNg01myouD9teH2/i3HLhKlpPt6Yrqw5CnhF5YP9QmVy/n6h79LjHC6eEIZW+ZnDR99/Iv8h3/3N9jur2mPIyUdRTnjsvkOnd3wJ2+/4NXrG7LVE5SB3/5Xv83h3fUj3muaCLJA9o5xDvGoyIBK5vQ6IT3keuKoNIs4sRVQ5oI+CjAJnXmMUYydwEyaoB0iJkieJCDqhEQhyBApodz4mBptFCLZR7t6EkiTkFEjXMQmQVQRZ3NMCEQZoUvEMpF++sIA8HPSNiylpFGK9y6ekNqJ22LED4Y6Lajnl8xPatZ1RjipMaJCGUlsNOI4st3sIEiOwdM6i00TRW7I5gvQCe8NURy4391z9e4dRn7E4nyNbiRFJtgMikU78ub4jrg6oxh2DESm0RHEgelaMsxhn0ZeH/doVZHijro8wduahMC5SNZCkjDLaoycaA97Nral6RXVYonvNOPuQMwS7iCZsg5vA1XTkFRk0keKasHEjmnrufj+e4+z2Gri7WtH0kdm3/FkscEOV2SzBcfNjmhOmK8ryqxiumvJnUa4jEpWSG0wpsEUJWXpCSInukRW5WRFw9T3zNOcKpboJhKsIChB7Uuk8aTTU579okd8c40pGg4/FhRnEXm5IMsriqSJectNzLCyZX2+YjZJyAqGIhGPI3HUpOgQhUGJx+wGlEIoQ1FXuMKQBUlWzClmmkRJ1hicSZSxJMqESCPLh5HgNCGLtH7DdmeZR1iuFyzaFfJ8TjoIxs6TxoEpOqYUyKcWXVk+WJ2zePmUurhkPt6SrxqaRUXtDuzvNyg/kc9KZrrmbn/DOLbMs5xf+OATTi5O4LRhTDve/J8FN1cjJ+sLlCl4u9lz8FcEAW4Zqbcj1IIiSFIWUN4wmYTsIZUgjo4kKySe4DX5fI42mkRk2h/Q/Ug0ARkkJgIJopUkIRF8uwITJmIMFEiy6CEmdK7oFAjZIckgeEJYY7jHdxot3M+YEfg5EQGjDU0OsSw4r3Ly7WMtKMWCmBtKUzMvE72INLlgyDLm1fsUp4H64oH92yPSTeTliPdHygEOuQYXkb3k9S5hMk2mC6bB4mKCShHDwEm9Ilowc8HmzVcsYslWRHSQHIcBnzZk6SnD6OmmwMViQuo5zXyNbCVXt2/Z2J6LsiY1itGWHCdJnxIxWYYe1HxJfl7gNh2H3hLJUKUnHFu0rlmcLEgvIufNObe3G/7Wb/wyq7Mzemdht6M6rTG+h2gZvvmSKDv6SRJGGGOPtjXvfvQTgo247pw+9MS6QNcGl3KKPMengRQU80YjixKtNcpITK6Y24JJeULyZOTkg+JQesTY8OL5+zx7ucb3c/7t8CPG3Rxfbdn1C57IjNky57PiI4bUI1iibMS5CS0NrZHoKmc2O8OPLbmITEqSzSqsd0irKbxACoUsBQB20giXqJRBiwIhAsJI9GlBI9bcpgeqlLM+G8icIKkFJx/NGDPHnY3ULwtKIxmujtg3e1R5pFaB/+BXP2KabkhZpGrmrJ6fkJBsj0cO+56TU8PJ8gQ1X/D6y1e02w0mb8hJuIcdkYkPZzPWn37E4SV8+OEpl+aUP02OV3/+b9jvLE+en/Lb/+xfs1rNKR0Ul88YrOJJJZjGA+/0SPzyQDM/QY4OpTRnn73g2fySejXnn/+L/wU/vKVMIzFLeB9RkyfIhFUJqbJHtqaLhJgISTEBJjpchBA1oKiNI2UCgmPKQPQQswKmn74+8HMhApO3HIZr/M2KQ3OKWdbIqGhjTy1OKXyiKZb0xuEtVEIidCLVGQ0zxgKkyphnOSEv6PYGSc/NdIf3ltxrzpuKsw8aDmli7AsmC66854mcsy8mTsoXtPstwQtmReLgIsOuwxceqxx6eIvY9Vz5gpN1hhgiciYpjhVydPgEHbA/TAzWgx457HfkOvLe6oxlrRBZ4v56C1PFs7xhkHMqUzKbn2L7RF4uieOOD95/H/Yz7OLAZHu2w4/4UGquv3pLpQ7UqzNYKk7tCuUnulgQjxIjPX4mqbIFzaLCtD2TVSQliaOGJMilxJgMVxjC4OnbB2p5hgwJrx1aHNi0R5riOYfMIruSD3/pnO6q4MfP3nFIieH1FeJS06xrjklT3RzQ5hSZDEYfcUFRRkOanVJYzXI1I/o1le5ITjLWYFPPuIfxMJIzYQ8dThbQK2QhiOP0mCqdeTqvmSZH/cyi/i/m3txnmyVN8/pFRkbu+ezP8z7v+i1nrzqnuqtruhlNq5HQSIPaYLXAQQIJDw8LDJxxQZgY/AEICwdhtITQgKBbPV1FddWpU2f91nd79i33jMjA+KpRG3WGrRtVOKlMKcNJ3ZciI+7r+lUQ6ZBe0CMLS5ymwU/nqOZEJQU6POAxpT+KONxuacoMsVsQzj7l8aFGJnfUiSJqFEKM2GcduYB5b8jg+gIRRSQPt+SrjjAUTFWAdQMe36wwRclg4DK/SplGA56f3zCJPP6qOpG8nzBOQl49/RZn+AHXkyFCBJwPhmRtRSfWzE8ubZpxX5zIVjVNqImjGTfnN6Q35/y42PD1nxuy1R7RFQjTYJx3zb5B56CtopUGBdjOxTgQ2o6mE+hWYLqYThUEoYubt+RRi2PkO5KWb6D+7fX3OyECRmt+vfqK9nZGONnysR2Rj4ZETUlv4CBdSW2PiManaet3uf1tTRAM0ChU38MpK9ptQ+B1uGGP7X1Js8uwbkM4iGmbNdZRiDil2x85ZhWjmaKZurhpzGmnqZ0IGzaUrkMnW5YGrvaSytbonofRJ2KtyNdb8l6A1RNE5HETnFPVJ4h8zPLISedI05EdthxsxtlwSmvntIeWIE5oS4FwWmwVwUESBCVeViIDGA1cWhfE0MUUml6SkD5M+dl6iWM2nA/HOCLmaniJvT0SjhRUO7rZnErticIxY+9deEodgLANTihJjEvdSEqt8UyHc/ARhwpfCCpRoo4SM5J0jSEOJUJCEFnm+IjGw7onVBMycBuCQUpESalTbKvYzGK6uuCqbnBDQSSHRNoSeR6hTHCCgMgLweljtaYxByQWx4UqCLGqhLJCiA6nH6MPHm1WUMwdXPvOoOUFLrvuXXOS3w5ZnRTKLRExiA2oXsUsueRkJNvHRyLVUdsFRf4Ihy2TwY6dqehbSeD32WctmXOL/M2Krc4ElXQJa1CtxRctTt0iLgMKq3j5sCOtLONejSl9auugZYdfCPTuSHg+RpUNnz79mMYIzi5GZK/fMvUjRqqmTMe8Hw14G7/l6z/7JYe8gNjjsWyZ1hmHpuLi9/+QL774GXpZ4TYhXauwIke7At8RCFshTYcvHBoRYGxNhSJSHnULDTWuciGD3HXRZY3vCywCUX9fpMjviAhgDc0+xuk7DEJF6Cv8SDMfvo/jWTpHIUVHGgRYYsYzTez5uE5MeazRaUdnBYXnYHWBqHI6u8cNDGlwzcFUbLYFYRjSZicSaRn2Jb16xHK3w3P7SGmwtUObKGQvIKpbBsM+5lCQJn1EE7DJMkRzgFGP1npochLh4HuKunQxTUnfMzhFQCFq0jjktMhoi5za1vhxgi5b4guBtgZvkOBVwPGBQPq0okA5U6K+xJSGdpFTj1PO3v8Mtb9ldf+Gg4V54lMs7hFuhRc8RUWC1HEpvSF6s2GFw0gOQBiObYaT23e5BhQEYYinYppdhUtJ58YcRUc/MIhachAhiYLDqWDie/i+R37XIkKXZzfnZE8GONkF/sAyCWaMBhXpqqI4BaRpgBs6hE2H9GOGI4e69KmUi9EetbU4uHStS2sdWlkQ+QW1kZSNi2c7fAVFk1OZhmaviEcRdWAJZUNV+PRFTdOtEXZMFraEVZ/S1yTHE3IwQq0E8yhll2esX/4FtTgn1ZZ8cUs/zRjMLhjGE2ToU2cbTsclvhB4UpDfb1llezYnjesKlG/wBz5vHhesXt5i/DHjnsIxHn7QsN8/8OV3d1xe9TB2z3LxhueTa5phzPj5iP/l258R3idcPIuQZUW/d+L13mex3UPXktBx2r3iq/bIx+GMyx98xmef/UN+sfkzmqxASwt1h9US3TkIqfG0QsoOhKRzPKrOkPAOliJEgRCKRvsoxyJ9Q91AKBW122G+ZyXg/P9a7N8ztDEEbYvrZSjPZXD5AY4BG3fs6opNUaBrTeiOiPox/XiEJ/vQdtRG4+wMYe0TxhKn7+LGfVRviOv18fs9EifFiIAmqtjVewwOSRJgdIvEw9k46NJBNwsyXdE8aPBcJiOP1svAtijH4J3V7KUibza4rkNPGBy3Qbkls8s5jg2wacDs2Tmz6Rw/DWgDhzenjPXpkX25RzY+QTyiFj6j2EPNPIw7phs52FThxQGxHyG7jrBXU1WaXlVxHl3y0fgcedwjXE0uElSSwLFi/8ogK5/d5sS2qMlOFUW7JdvnOGhEa4nTCISkPnUYbbFtQZqMGV1McF1LkCQImSLzA1UbkyBosBypuFsu4VBxPZ3ywZNzJrNLkotnhMMBTjzhaTrlwydT+skE00nUUBLdGIJBgjNxsSMH2zsQj46EgcahwFcugzAmsYqkDel5KZUOMQR4acx42MdPBMKJGY8jlDfHcRRHM+cBFzlU+HWCHSu8wHAg5vX+gC4tYhBw99XnrB5cloc9JxHSBQGWATUC05y4SBLm4ZjTsaQwBpl4lCfN/WKLCGu8xOdYLin2K4rHgsPJoTcLiFVE6jek1yEvblfcLl7jmYBY97G+RzJOmQTnOPkWfzgmz5e4/hmNJ1GTkIvzMfGwR5gk5I1DkyiaVJHrjGHc570f/xHxxQ8oSzCNocPFVRbltFglaGT3zuAlGrquBVVz0AYhNSPh0mmNVQVt01I7CkdHWNMim+/zEP6OrASapib2IwZWksQt+eElD5XG9RTzdECmStwmJdQBzkTR1g0tFjlUyKrA6hIhJLEfE3SS3D8wGfhIb0rrCHTh0M18AkfT6w+ITEyxs+zMPZ/2fsCuNMjsRI5m9WbFTFUIETHvBXjuFDIBcsvj5pFO93Cqgsf7e+ZPz6j3hvtywSTO6ak+ylUEcUwgXBZ3FsdAvsk4pIKhaiiPLo31iM8VXj+mzjvktMI7hvR1QH6xodk4qN4I9yElmI7QRnA9Tfice/ruDV03IJQdgZnQiQ59V/La/c3GW9OhvJiucliaNYP4DNqQfbGmOGbgjQgjgTt+StsdMJXGdR1yLRD5iXrrEl4N6E+GdLuG07jBjhSlX+DGcwJviDrLKCuXzHVQRU447Fh0IaJ0iH1F6Xr08gJd7qj9MdbLcKRHbRwi6dILWsqiozGWIFNt2wsAACAASURBVIDADamswAtPCE8Ro/Ckw6GraF1N7HisPc3zZE5uDUJOUYcXNNInJuJBWOZOwdBovpQSu93x9d0D8SRA/iY01ndDHCnZ1lu2b7aEtmO10+x3O4Rjee9sSjKOcCJL4FqE18PJLYe1YXu3YN1u8ONronTAqD/EyIQtLxDRgKqtEaGHF08Z3lgOp5Zv/nLDVdrnRfuKbL9meWiJgpqRW3BxPeGbl9/g+C5m5ODIgEKXNHdf8ezqOX/ykx/z3774NRwLMJoOBxsZPGMQXofbQSZqhPTAlXSuJqwUmXEwXgTVkUhFdHmBNgrHq5E6gr+PjsG/q6GcAHV5w8V5RL+MseqCpyN4NnE5Hn1M4mNrSybfkDQukgH+MCBQUA7Adj6dkBR1gWMCVFoxkmOsLMG6iPBbftiLeXzIeTKL6JZ7OmsYT69483ZNWWYUwwGDwKM6HDDhgKuzlFNnqE1DXbWQOhyWOWH9iJ30eVi+xNEdeS5pZUN7bJD2gX2dE9sIEXgctnv07khXnfjF7QK33fGHH12QDn8IxRB35nJq7nj86Ro37nEsTwznU4pO0AsDgsk1j6cF/YHPNyvLlXvJN5sviaVlXVds5R5lQV2kjC/NuyX1bQn1u32Qy/CCwpEMnJpt1RH0FbFUtPZErguaLCdsHKRtEIWLDB0uRi2NqNG1IAktJpOcjMCKc7JyTWg8evKM0HPYdntmgznlouN5UmI8n0cdEHWKTJT0cYi0ISzGnJSgVAs2+oRnXcLYx9cd5VFTBS2uaegJTW0qjtWeSPZxmpjhWUxQV1xUEt3vKKoDaRuSlReIcYlkTb+94BC29Moh50HEtliRWw+zbhG6pglbHpKIY7vF5g2ihfriAke21EXNanPP570x70chXdtRnASOLImHE44nzfLNhugQsHuULEJJawqeTvo8SX6PbXBHmCZsl1tG532OzZTr04lfG8tQKIYfzWhczQQXlq85hTP+9B//K/z49kMOWtMTIS9f3vP48lt+FcR8YBwGzy6Z/96PWP3zn0Jl0dJQNR7SVO8izI0EK4jUCNEcyDtN7hrioKHpWjociloQxS5lVhK24DCg/B4s8e+GCEjB6Pw56ZOSM+MjetdYscbqFq/rOCs79sphHvbQlOxOJwaeQ9e5uJVhREAmBAKN8ARdZWlaF7+NKbwtXeJh8hOu2ON2TzG0VJsjQ3fJstQ4neL1qmJyobHDlC8ev2NuQvwCHrMdV0OXyiri4RCd1+RFwxPVY1uWHN8sWJUHUumjYhc/jqibBpUHNKuMcl/QGCiVoVQZ32UnjpvXnHWKB/sVVZTgD32iocc47vHNi5c8/XjCoYbn4z41Lo7w0PWRp+8NKOv3KIMDxYucssl5kvgk/adM0pBt4+BPjgRWsOaEPrm4jmLvCkwJW1vBzHCmFUq0mKaiaTq80YDUE9SVQzdRdIcQ39OYYIDYl0x0Db6DrEuyMsQxJw4DRS90yEVBNPKp0xlN98C0TihMS+Ir6kbRHnOMb7G9E/2mxZEhdRVz0gZjF2ijaWuBtD1ORUjTLhDSI1NH/Kai809sK49ZWLM/jnnaeuRXPknY41jVFEVL08uYeQpKy0hvcYohY+Ozkw1ur2VSTelfnJMtGzasuDhecX/SbKoNhWOJiHFMw+F0RB4MjnKpjUfgZOwfFmyOOxw7ZN/dkn6zQl8EXOz7DH0PP0y5LU4UyyMDPeLyQ4dHVyNTQRhNeX8Y8+JhyzM1ZN96XIYBgXQZXs0JzlPsJmJrBZtXJ+rDFmELpt4VHz79AdnLb2mWC0xb43cdQdxRtxY6i/Wddz0xro9XOuRehqgiBrbhQELnZ9RtRxcmtFaj9CPfFyjwOyECjufidveUD3OOfUtQ3CJWln1QUlhDf92i/Zbcb9jrgERXlFaRDqARAtsWRG2B0yqM0jSNR1V3FPYEhwrX9HDdD+jee4FbNxzf1tTNki8fNUVW8cSdUSdbXr7SzBLJee9jqlIw9VIG8nOKQ4zxQ7qyIqs7BijCBnZZxsHdUx00wVmDCaa4ncGTklIXZIGhFQKrTzRljXBb7qo77u9bfu/jBLeOcMYzdrriUCl8r2HWO2f512uefDLk3ivxLSgBPX/GF/tHZmcxi3uPy4unNM0Or9ihRUu7lBSiIFAOugJbdci+IjMgWJLfHcgbiGzLMlB4vSlb10GUNXFn8FuPujzRTztETxHlOUV9pCpdol6Ls9njX+cU+QCjHQJR0js4VBNLpw1RW6J7PdxTRex7FMalyPdoX+DHFSM3xLoumRMivZz4uKUsBXIYM3YEnW1w8pxNDe2xQ7cZhRfRbh262rKaCtpuydEbYrKCTefjqRpZBti9YedFzFOfVW3I/SP14TvqooZW0KSa6uUCI0vixkHOHKpNAa7Ec0ui2ZC+N6c6CnA1OD5ddqAOFKtTwb4xjNOGdLunGw8omoCNLXDqE6722Ky3SFWTyyO++gitljybXWMpcfOYD4OSMlW0y4q0VxKIIZiIslrg25rnz5/RyYq7N/fsF3tG4Qy/1bSuxklqXOPg2xBrLbQGowx9bbFuTik12vWIkphqoHDuJZ2b4dQK30IgDG1YIUIFp/a31t/vhAjUouV20/LFyz/jUCVcBxH+pcLUDWW1JUl8+vJ9xFnGWXJG6vqEdY6RLoEV1LKgCRyQHs0jlMeaxe6WnA5bdbhBReI+IpqCxSFjW+zp1R5BB7EecD+q8b0zxt0CLcfssgcm3y34/PiafhZi+gEhHh9/cMHx8IyuyVncLQncAQ0DxoOGNE04Gp9Kt1QObFZbsrzBC2sO+Y4ye3f0tLvxSIMDi/ufs3g5xnl+j+/3+eD5gLqpmF6f04sGZB28ut3yw6hDeCPOf3DGP/uff04cXlGnlnDWcP/TlySnIamz5MgMeTHgTbXlXAZ4KkSYAiU9ShvBLGJc9RDs6RJD5LlElUSPPHbbDaXrwKRPYSx+a9h3J8pc0xM+3nmfpd3xxO+R7lPCeUvkSTw3YJj3cZUPzQapFFn4LgymDvYIryFSU1LZodUQC4y6CH/URyd9amOwJ4MRNYdFRWhies6WTL9FtQOsrCgPL6gYEmSSwItYSxfvYYcvVpxNDFpO0Bce3NWsR5KZJ3hTv2BbV0TblvD5U7o2Z7Fc08U+/nRAWe948tGYywpUOaV1LYW3YSw8pAix4ZiLm09we/D1X7+i75/oX0/xgjOczBJdFMiDxUSW0y7jaRSxsw7LU8UvvvySWme4Y0m0qNHXkrdf3iOnKf1E8SZbMU2fs13dkzo+nS4IkpzrqeDL/63ma77kvj1h4zltG+DtwXUdGtPiuxIbucSyodwr6lrRRQLp18iTg/FqamtQeoAbtThNjTE1tVA0dcj3OYh+J0QgEgEP/9PP8IcnZsmAer1h1wlmscv6cUchA07Rl3R5xCvxwCRJ+OBpgCsmDJw5QZAgQ4Evak7elpwK6Sh81YBULCtJVtYslwdE8cAkdFgeSlb1EZnCSJwRORVHlVJvj8xdh9NFTBx8SuXt8VNLUT/ydPSUq6ucl18EvBQHnO5AEg0Z9WPEKCLZWsrc4IQNhAcSoRC5ois1kRW0omXIgEhI1ss1n/3ejyi6goFn+ORHY9pDQH/e55sv7ggjwRMJC9vS339DZfr84OYpi5MgO9a4C80nwTl2CId2xMl717F3Vgco3ZKGAabusWl3+NYlmCacHjaoroc8KWxWkSQJdSPw0wmVcyDSii7OKfMjTT4iHz8yLp5SLVv6bojb9ojDjgtGPBy3xDYgm9ZMHJdF5BIJy/J/f+RY3ENwQhR97HmOn4wIDXRK4XgdopIcC03NAd0UOEc4NGBMxXFZEjDGzhKqMkJJH98WNK2A8AYha+JuzCpfYxGM0wfi/Xvo85R6o3m5eUP3xZ4PEsGv+h7uqGK+clnKFcYGpE2f6fg9puGAnWeoRylJEzByI1ylaGTBbF7RG+6pa4AS1w8YnDRq7BL1+kQ2wAiLzQ3ayVkIl9n8jONRoLMTpbY86TpWzpbatqSTC1S4JLLndDKnrivaZsuhCJnIiifVhDf7gFkquLye4Y+nHPYVvTqkkAGmLpDKIW9aTCFpJwo1stC1pEGL2bnkUhNaQxMHtEWGrqHpWZxDHz/YUSjv+/YFfzdEoGkNwWc3PDMDXq+2JM8cskWLWG0ZBkP6/Yj9sSTYlwSqRPQPPGxTpo3PadDDOAXDJqKxJZVoKBFUtiLTewIzxa00Vf5A1a548VjiFicwOfvsANmIfnrA+2DIIOjzyrxBtw3ZXmFDwVgNaJp3UIy72yOD5yPSQcHga4sZu+xFjswsvmmoI5/MHAi2CqdIWNffUWQllfVwXckgvUS2BqNbgvkMkYR8cv2EX339Ddt1zaTWnPaPJF5JYyOCsxG7L1/TTYboX72GWtH34ZtTxvnwAn8yoAoFcy/kcHqJLhRFU6Fin8JKwtAy3Qsad4hrFE3YkkvNabOmiC1nXUuQW/bhAQ6QjByE24OupNg9sLttee8fzRmGe9ZvU469mHHcUvVXnHUxvs6QTUo3b4jvXTauZlsssW2Ddd8BPeW2oikO9GKPUhZ0vg+OIAwTaDuMVxFf+/SEy3rnYkeQlilRnLJqNhRraNMejVNQblaEQ0smtiQzxTHr2LzNceaPJPsa1+kTWY8vX37FKY5R9VskPd50J66eP+H46kRYK1JZ0eY7aH1uJk/eddKVCilbHK9j+9DS6I671rBoO4aOh2lqQgUXMWRxAIl+By59W/N7oxm1M8SRBxZyz0jHnOIcVSr8bYkbhij/EzzPcNdV2HZD/2YCmYt2LScpuVMNp1GBVD6hiPhm8x37psTRLp3ykE5NZEJKv6UtLVq1iMLH2XR0XYOXBpR+RZADXUssHPaVh/Qy/EJRY/k+D9HvhAjIQDAaGPbrlvHY8KJS9Ns94WSKbnLeLh/wAo/vtpLYCPysx/lU0g43qE7TP44o/AKT71k2AvyA7XqJBNyoQJOyyQ1f/eKO/Lin61ckG03UT3CTA6Yfc6wzTNDA8S2r3jnBqiUKh8QfZMjgkm5f8cvjhpu3e5ytJbMF+a1Cj1t27oQfnz/F9n2OQUpXbxH3BYfVgCjyOXtyzqDXw4qWblmw0DkynrFarnk+v+Ff+9N/m9X6gV9XO4TNOTMDRn7Arj3hyoRosaJsPOxHc6azGX/iT3AywcPyc+q0IfXGPN42nCVLTAuv9hlJ4DEZ9FBxgtZQZkdS2dAfDEmeX9PJFr0vOfY1k6wmDzse7rdY88Dibc50FNGbheQv/5yVfMLswiVq5yB99G1CevkrVs45l0Jzv7dM4ortV5p4kqJXElcPucLnFBisB1t3gzSGvBri9QKcwDCNBlg/oXIlbdsSq5JwHNGsd+x0SThuke4IW2qSKOJrJUneSr5zavpySOs8UO8LjrJAqgIGM3pdRKZzMApbhqhDhroe0Bwlw8kEkhJLyp3paPIc6xjmoYsbHNnpBoVL2tT02oTV2z3zSnFzdYHtd4ha0lUeqR+xqgqipWHw4RVf//PXDJ7mGHdA6vYZf6To1gGPwmHYeCQUvOaRvG0JHUWYWhJxyYP+S+a9c/qTmPP+H/LHP/qQsj7xV79cktoef/wn/xJf//IvWC5WSONhvRzT+VjT4sgAaw0IiXUlOvOJTpIyKOl0RC0KTFHT9Rxs02Hc7+kU4ndEBKjh1R1cUqOGGfN9j8z1aMoWMXTxnRk9JYnmBeUa4q4jqjyK7IArKipyjr6haTRN49BXEWtTclhscXTOvB+RHU6EWuP3XQ4bl0cnoykN4/4cd3mPMu9xaRLq+DmXYR/t19zlb3kiLwiWhqDscR3es1sHxKrk4qMrVo93vDk0XJ21bCN472xMqjyGvQ/5Nn3BSXjvknN6CdezGUIFPAbfkG1cqlPJ/NP3aLucn339OanpY8oX/OLPF/zRT36CMw8ZyyHfvP5zvPkF4XnD9DxFvClYBHusDJFnzxiddlAq3H6fpoH0Kib1G3LhIKMBjn3n05ddh/VKEj8hCgeU+Ro8y1nSR8YB+eMjrwufYtkxVHC/XOFVH5M/D6hsyYqW97o5QXlEh3es9j4DT7ItcpJEcb+XqLDGrxWnxsWd37JRT6mykOR0xHcdrNOj1Vve3DqocUrlG/zCRaM4dEtcXZGrnLLnUK0DsnWLOpzIMwd/HpC0gnqa4vUvcb56zdcPdwzaHXt1w3b1gnCyQKwMep3Sn9fkfkh/lFJoF5U40PcZOCWuu6N6HJHVHX3p0LqattLYpkCkMw69imXzyBf3D8TXMSfxG6x9DIuB5EfPCoZHh/U0Zio6dBKxPRia6y3TqEHtrqnODd43FSLX3AY5Mh8i7AGn3+F7Ecf9t+D0UNuAJNsjzgLS4YBqNeULuyNOK3amRNoOYUuytkOVDr5q0TKlKQqElRxshHIbPJVjqogoAiML3FxQOxDlHcfUxzMBDdvfWn7/r0VACPER79gCfzOeA/8ZMAD+Q2D1m+f/qbX2f/gXaoCuCcWCePQhwfw5u8MrzsdD6kqiU0GsM2pVEd41dFFAWWUs39xzGVwRzyPaeUVzimgGgkyXvH77GumAZ06sdyeUGBDHkD+bcjq+xgumqE1NsrUEvRNx8inBmUemj8xv+jS7jDIf8o/e8/nivkDplvk0pt5ckxRbpI6Q4xnnk5iLuOBOG9zGJ8scBsMRja8IehdcnWsMhueffMZ0KIiI32Xqpd/w9tsVx8DHmhPuw5b25grZ/5TYWXK0Pkm2ICtL/uAnT1GTgEUxY/koGfVcbiYB+uE1dXPGi7Tmo4sZ1wvDrsupPMu4TmmLFZ7UhDbB7Sl8E6JLB9cPSaeWeTqjtge6yHCQE3a/WmGKNW5PsVj53OY504nDL6VhVGaUa5dnF5rVtmEyTFHpAcfVtJNzPLHgTGuWUjMJYsR5gN2/A5j0O0N+FlPlI1zZ4vsRqt/hrTNqV3JShiKWiG5IXa7pyhAhLF5uSJIe28sYdV9C4WCjFqe8Y5QZTocZ83mH1DPiTvAwGnAs1tQvf4lxa2RXodKOsZsgfMXA6aE7H9evKXRD78rHZIZhYKmWgnjsEw8dBs6QkpxjY2B7wClPNEJS+Ck3T9YkQcJ69x5G15j2Na8PGfP+P+B1/pckX7bw8TmL/a94tviQZiJZTlzickhefItbjnEazf7zz/EHVwQXHmHbcIiG1KslAktRRlwOHS6fPOXLV5JvkgHu4y09r6ELFGVjaO0OT3gQpCi9R/gOedGC6Ehlh68jGlfjJC3lEXTu4rS/vUfg/5MIWGu/An7/N4IggTvgvwP+feC/tNb+5/9355JCcmOvacwWeRdQH2sWYsOpNSxeaMJTzeVZSCYSirRFjTVnwTWYiJ3f0q5ysnzF7cuOernF9QxK+Zx3KUPniC6PnE4FfdHHn0053Z64cTtepj7YIXfNA+GxYqISfnnyedq5FGc1b44ezl2FO2m49xICT+I9G6KPinTk4zWWB8/jk2dzYjegsQXWH1GXhqfTIef9mFxLsm3HshDYgWZw9gRVDijUXzPOdsyjJxw/7rF5s8FzHX78k3+Maiznsoc6+Rxv+uSva25mJ16u3+D9+KN3XYevK74d75j3+uhqgNTg+gltlnOoblFdjFuGmKFmkM/ohRFlIIhVQ1cIDmVLmAaE6w7vvGD0T/6Any/fkL18w2Uj+PR6Tudoxi50fc2+1XSne+JnIWJZ4zeXyEAwlmvipseiyFBa8dfffoGs71hmkEiNozpcc04/PoLyyTBMemeoNEI7IV0Nw8Cy3+RU0iEJXfxugPckomwy/L3BRGNUpLnwDwjrccQiYoG66iOyCHnIcJwx7VnH//jTmqj/Nbv9lCf9K7LOxeYDfv6wYN6s+fjf/If064Cw7PC8AZtDhRdY3j4eELJl0Nvz9Nlztm8fWRw3PJs+xxkpwvwtXjZnUIxpnZdEZ32wCd/eOly+/4YPdu9R9yC8NZwyl4dJhzoMGQ9ynqV9NsNP6U5r/GROcTjRhQ4TG0LkIbs1TgrNIWXZCG7Vit13RyYfvMf89QfUv/ocBWhfkXgTiiCnzEHWOU2nCZRHbJ8x8Ddstw5ZJHCaMd1xgdCGwXlJuZHUzd/vEeE/Br6z1r7+TdTY/7MhBa/LnJnXkeuCtVsgdx71dceHF1OKxxNB4pJHhqBMGT4qxPiE59eIQ4Tp91FJxU2V041uYKrZtIbitiPuneMOOobRR+y/e+B+v0BlHcLvc9QFtmq5UBnJcUr0wwv+OEx4XUv8l684Tq6J5Z6Lm0vud4ZJr0PYiiyQaOlhdhXDS6g7y5iUy7M+RX2i0g7a6yO9Pc3ugJ9IjA2R+TsvRO2c0ELw3bJkr77iyflnOGaPQ8WufIszO2MyTtjVfabVkf085JQt8OpLbv/ZKwZRxPz3n/PD6pao9JAHQTf0cYRL1a045QFuUONLQ3vXkKVL+hfXDGvF0fp4G43bHbmXNWoakiqFFA7vpTH5s+es2XC0DjLWKDNFFI+cdZrD8ohaPCIu3ke/fqS7qDDiDGewpJ0cydYF5mHH3elrSvea8OIcJ3Yw1BycHq506MsQt5P0hUvgdFTTlodG0aqaeamxR8Eq31M4JyLHchA1V+mQxrlDimuCUYu1AdOoRh+HfGsXuMIlTeHXL7acuZbbryD2ffof9hH6DetXb1DDgn3p06/7FLlBjANk7hB2imWxA+NTa8nmdstCP/DtywW5rgmuHIa9MaGaEYoTxtM0DwVBF3Peu+RU/ppIBeDMkE3JF7xGty7n3gqnFxHXZ2yBZZkz7MWIwGM3e8DkM/TFFf3yDicccTWOMG2O+1rglD5TLyVUPoVck0eaQ+UiZI1wVwzyjtBNyHSD33qIo0B7j1RNg5t4qLKFbkOrwJGKZi3R9vvQI393IvDvAP/N37r/j4QQ/x7wV8B//C9CkAE4jsQ4r1ifhiyrgm7bMUQQP7aMh4bh1OOYuYiVIvJauARlzygDD4YV8SmABkza0diWrA0Z+i3pD3ySdMSIiPa04kGdiG8j9j1FWDicRQ3LvOLBOnzsV0SHJduo48PzGWr4jO2jT/feOXf0Kfpb1osD008SYhmxe72hcHY8M5foN5q37bfsyineyKVqWsL7Fa5T4TYSBpbtbo0sfAZezK2j6V0p4sogjMv+zZLz64TazdjeTxmoFS++DLh6kiBch7TvI/Wc8FyRtyU9MeGuHfDDseSnm5xr9+dkbzT+T36EwkMZS34ocVTByZ6IuwLnFJCVmre/+hVvH14jnRFXF0+5uDzD63ucnXn4YkC2vCfoRZjlEu3vkEHL2L1k4w8Ji5wstPR0x83HkioY0TYpp8gj6lzWXUcpOu7FmB/FAaFzpO7mJD5EbfQuH69LSJKUSlqiNCUK4anvcW49qpFhK5eMCoV4uaFoNvSChMcXDaO0oDnPeN0JnuQObt3yqOC6jdmN7njxasFZ75xDknL1wzPS+AmZucS8OhAdBZuspDdQNMuv2I5GlEefSVATDgSzbMAxyhnHJWP1PlVz4GH/SEeAOdSI3gFpbqgISMbQcwb0dMdW5cjLHkpM6E/2NDrhg2rAbmpRZshQCvK+IDtUnCkfP1R0iwbRTFnvOoblDt9T3L1+S8INYtRRqwLR7cGX9IKYafoer5IvcMUGWQS0vZpT0CKOoI1HIEtM+M5FuOogzse09hHXk/hiSEyFiWrq344cAP5uWIQe8K8D/8lvHv1XwD/lnX35nwL/BfAf/Jb3/k/4SJLE6DThxvkHXD470bM7Dk2Htg7HTBNahRw5TGXNIPqUqK/x1pa2K6nPIvwzh101pck1vdOCoRIkYYrRhvIx47E88LZqOQWWbu5jm5r+8zPcwsUs7gi6KyYfPuXkj7kJT8hVxW7dovobDviIx1dMtUZrye5Fx8Auadf3TN5/xirLkG5DrVyC4wJ7rHFyB61SNr5AYdj86pYcxfn1NackIygb/ME5r3/9SP7yQDDb8VhGpL1LxNslsnWI1g2r81uent1wemGwATzcv2Q46PGdvMP+5V/y9c3HxNLi9M5pw5zqy19zORlzc6FY+2e8OW2ZdAH61PH1V7/mu19/xfMPf8i//Nm/yuu7O/rmiMDHca7Z50cwkujyhsX+57wu7pk+BvzFy1d8+FlHT90i/GckT1L2bU3YXhJvwT9fEWzPeWsUoRNy8/QNo8U1mSvYVRan3pI8n6NFRlkZzoKcvRR0VtBmNfLYcuwiHM9jhkuvOWNlNJPLFF0NaI8SefFI1T+n2ElmDytWsx6e3KC7IWLsIR8UTjigpwLa7C1lHTIL35L0I7IzQWkT3GNLtj7xhar4dDKmijXdakCUrFk1rym2JSNvSpAK3pSSuwaGfg9lHDZZxzfZV8jqFdOHp5z1A14WJeUryQc3P+Kr5gXnUvJC7CiajD8YTmnHkqzYMRlN6QUNj4uG0nFpnZJRkpL2O7LtFjeYsdgfeGhf8dHkitV9hgnPCHIP1VjGyRxbO3RlgPQcpDS0NbgRuE6Pyhq0bTFWEMiOoly8s8brEsIDLX3E3oCn/15DRf4U+Jm1dgHwN9ffFPp/Dfz3v+2lvw0fmZ7N7e+fvUeUt3TsOcoRbiDwFlvS99+H046ZarFlivA3HDYhw9QySsfkKqJpXHrBEesf8Xtn0CrqMufBtnSyxHMDRt6WIUN2keRyZqgONeASeu9B30HXHTdHCJ7P2K+2SD9H2Bkfhxfce29J2iFZveZmHrPQgiop6FeKi75hvxIIFaDciI4EkzaIELzaJVE+YpCSnFrq5QKnUgjXo7Itk+eC8fMhl84Nu6Xlcuxihh/Rdl/jDApGNuDur+8pihf81d2SqX/N7u6e/dcF7XjIcXrPJTNMt2YcSzp7TqtbVq2LqY6obIEdwTGzfHv7M+7zNWod4fZC5rFElwUyg3Dk0mjLsl0wzJaMN3vq3phdbfGrALWq8K7G5O6aIkM24QAAIABJREFUNE+wjSXvb1i4JWrlEk9aZqbgZF1iztn5O54OfAJPYXOXVtWcxx+QjzXrxx26M0SdwAQtoQ9Ju8dJEh79BO1VpGWHNQFOErHPGzymkEt8ofHOLkjTgmUz51S8YZKPSZIBiXTZZDs6FZKWAcvVmrM0R6mWelRSeopeCZkbsaPBsQecSBIcPETTcexaHpfFu2+TH5l2Ad4wpPbXxHuJOkQ0OmSb3UFzTuQJYk4Ebs396zf4P/ghlw8nVn1JoROCo6KJQt5+V1DVhvMo4bD3WelXXMRXTAbn7Mcl5HscjuhTn/vkiJP2GDch00Ef07msircoMhxpaU2H2b+jUA+8koOs35GcrE/gtDS9mMjTNG4PZ6mh11HJFaqK6PdStqvN35sI/Lv8rV+Bv4GO/Ob23wI+/7+cQVgGMuZ4kdI5jww2lnIr8KJLHFejBiOUtJigY+V6OD1Lp0Nyr6PXE8ig47SPcFzxTgFlgliAf3igLQWtfkSFPZyoj68ltbvHNzOc3Y6Pg0t+2X7NleijbgI+lEN+MX6k2h3oPe3xdBjit59gCggOOaVRJDbmk8hSZSfe3G4xWcf9fo1/c8GTqxEDLekKQToc4UQ73Kykl2j2SKJkSlW1dDV8OP8jDrs3+FGClwosHrW6x2kK9l/fUQd/wP0v/1fcY49pBi+3P4VgyCp/ZFx8hslzLv4NSz58jrPY0IaPuE3KKG/ZSkFzdMhyyf3qa14/LDm1gsn/wdyb9GyWpGlal01nfufv/QafwiMzIzOyugqqoLpbDagXoN60WLNDYsdPoH8CQmyQWCPEhj8BW1gUVFd1VlZWRmYMHu7+ze945mMTC69Fq5XRaoSQwqSzOM+RbGf3eUxm932NDwS/4q6DejjxZTlH2BVCz9Bmw7kUaCI/EYLfjL+mX8zoTEp9OvLL9Yz4fKSvMtTHjHJ7zz6+pExm9A8pUXeoIPhsc8VeOggD3Qo2bYrTnyzOl2bifFcz6EjvJF2SIrcJudLMlaZ0Fwx6IFaB/gkKOSCk5QmBqyMXSM77Bl3mvM1v+PqdY8p+x83uF5xnD/gDyMUeTImNc75yd+jNjMvHwL0beT0fgBO6EeTWMF2m+IeJle747MUVr68uedoHJnfPbIyc9wqKAqUz3mxu8PLMRbPEzhxuLLlvH7Dbz9DA19OBV9t/SL5W+JkgPqd0zpEvPG28IPbf8aJ/Qz23aHXCPij6hxY3LbGFJ3rB2R/RZsSfLWaRMUOzSjX7uMcNFVWSMKmBxloyNFJIhOw4O0M29AyDY5XcMizm2J3Gpz3aB/rDEvj/QQT+Hjjyz4D/+l8r/3dCiD/l03bgu3/j2x8c3sM7IaneHemrFa0ZKJZznD6heIGJjuNz4PJSsiIjo8XLkdTmiDqjbXqSdCCLoIot3gaKV5ek6xxVWL49LpiFiE4jP02/oBZnhpXi428s9+qONxeveXv5mnih6LuJsZbE4gVbMeMv/vKvGLyDIcOUhsmcePr279Am47krkP7Earti9tM5r8qcNJe0wmCaCH7H0E08tJ7x3HE5K9nZe6ZzTZI5HlMFOuXd4y3VOZCvr2j6FXd/+Y5jduZX//3/yuUi5fX2C27+dMHL7B+x/Coy/OQNv//rHX5MePdbwdv/IKHbvKL+8IgXgl3W8HkUnPXIbV/z4cMjj//X12TFiQe35PnjX+LeLfj3/6P/DP/zz6iLV4x9jXt6xoYHcp+Q5gl/dPOWX33/F9w/Lnh5ueV+vySUlsR6zFsoT68wF5bw+IE406yN5v2NZDzf0dy22GwDQnCoAufDd9QfGhIy+k7TdS3ZLKWYrSltYLwdabJnfLIgxJzIxFA0LGaXJPmIuZe01QO7kFLUhka1aJ/z9nXg3f6Kb/Rv6O4mlmVBsB2mG5nPRtZdSVtBcSG4ums57k6UqWHIEmQyxzrH2Eaq5Ipez/nr5xO3T9/QhzNDKfnZ529ZzC45H88I2TG86zjOLGXM2ayX/GL1U5r6RKFyzPaGjx/+BuQF8Zjg3UCGZjGbkYsTd26FeXmkOAma/EBY5dioyYNGTzlZNuPV57/g4elAW4/oJ8/u2w88dyfsZIhqonYROVryXDFFjx8hpgXZZLH1EqUb9suIeB6IAkqtIc3owvsfXH//X7kDLbD5N2r/5f/beaztOTy959w5xNNIms2oK09Iel7KjFlcMi8Dsy4lW2ScnadwPdFZxiJQFCnDuaQLE/PijCFBTAnzZI6fj5RZxqwJZOuCmbHMx4J7Tly//Sn14XtMlmLnkaVPOSuD94GNkTweNQ+1ZKJHNT3RWqR9IC22NGPNLBtZrZdkyYpTvmAqDCpYLtcFUTQcmjNDb7DB0nLiN98/cnVxSXZVUVRbejxLoVnNSjye2Hn6oebD7z7y3jyhZcH1dcUXi4TmMGP+akn4k4RkM3HxnPC+/8jHb/8P0lDz5c9/TjG37E8B2YwcqhVN7nh+t2dwLfdFiz+fuPhmZLu84tVnc64vcq5nKSaD8eypg6PvBaGfuB89n11VFKuf8OFw4t3xgT//8oZKz0myhhiv8PuedJ6jDEy64N0gmOcLajrKswRrmc4D3mfotiAdP7k2v3//Dc4MyOlz3kRBsSyZksikDD0NC9XhfIoZMvRYo16+Qrzo8bcpRQxUeomMMMaEZ37Nm8Urvrv/QPXYcSzAPfSMQnP2MF/kxCbiFyPzccvd2eEXktkY2W3vEc8Tcp4gizl6Crydp9y7BD86cqHIhwm3v2d0gdWbgHyRYpuaIimY6cB798QUPZ+TU20vSc+CPvGsVYF1PQ+bjmK2JYwdF4XkAzPuP37NH+kXsCrolyOXrNDljJmokdUjw65HUHIab9mf76CDXEsinwJIpqQnqoEw5Sg14QuF8ANxPDLpgsXQYjcjvp6RToJ21JTLyPnH7B0AxWKIHAZBMs3xWcDevycpFrjmlm+yZ7KY4YsFh92ByhjMJsUkS8qsIoRIsVZYBL0oiFZhLJAMLObwwm4RZSRUFVMqMcMdZbLmpXlkKF/xPBq2ZsFTY8GPlLMlp2nk3ysNIWl59gqKwD4VKLPFDaDFW3I1sr5e4uSMi8PAzLYkyYrC59QSQn/G10fmZUqwBbl3UDumsebWaKoSOqUZu44vv3jJu/4dT1890zX3SJNz89klL9IrmtVrVnSf4CNLS+8qfvqfzCm/mfj2XcHX999Sqpyr7ZZMDXQjbGcl43DFah2YmUvGM3z/7fdsV1t+9kcvuPn8gtVVhssU4fGB3jfMUk89NDzZgEkKnusnri5+zuWrAfvxa7qhQ2cBm2csQ8N+BpfAeZyzsQdiWmLKJQsZ+VX5FafznpmYsT8+Yu0GHUae7B1PzQOuPlHUnuf0Zyhz5CbfoExKKQOD/4TdCj7nqRD043su/ZL0zSve7L6jPZTc7p/wvkakW+5Gy/zsOLsTuBE8nJyhFUfu3LcU+4Tq1T+g3qaU2TMKj/JLhm93DN0t+WqNCR7jLU9dzgGLLj1ZAdm8otgqmtMeezeRzDOuPt+y0BUmpKjFlq/MR/btgG46lnmJP1hG6dm5gCHSxSNleonJHLNEMf2y4JwcuKlesPrte3bbilmWkjQe87Rg6yzfG8u//Je/4v6b7wlOYIXB2RwtGmQh6a0h4MiQ5C1ElWFmGXGsaVtB5QWN95yyhE08MuQGjj9iK3HwgsPtPaf5FddXhsX5gXNqiXIi0zNkONE3jlilbFdLPl+9JEkMT0lkVixJFwkiOOLQ4kRJKiNyYbnPJa03iGFk0wUEF4ilpZY/4ef9wHfNEbV6yYvO0lWey3UB94I6y1nOcw5q4v3TM+XiglH2rMOWyIle77i6+inrmaLtLY15Zi1KYjSo5sRgLKnUXJuCWnt29zuS3jEhONmOlASTP7F/SHj7U49izu8f/oZjmzMMO4rLNdttSnaxAN/yVB9wbzJcphGNYibh1TBD6RvMfOTuruTXf/stf1s+8J/+s/+Q9GLDpDOufvqGxcWCU92wXWsO//BL0q5AZhaTCVR2Qf3cMihDc3xmNzScHiQXRU16f+B4nRKme+aVJJ2/wh0d5idrShzYFa8UDGpCFS33OkP0sPQdXVS8zrZ8/X//BR99yWA7dvvf8XD/gPJHXqwWqEXFJJ9pHjvaneaxvmFZvmQ9qyg3JcbCVZnzfDZMH+/5WLRsfU7TOCj3rPcpD9MtwgX8x5Hs7Rx7+MjZNrx+s2Lml7T6icNXkq5p2Lyt0SqAL0jagaPqWM8Vv7rdMx4fuVh9xuWy5KUQVFXJfb9iamaEMWWcL9kUGY39HicyCvsaea0wzjJlgisWFN0V3TRSn7+CVUoSS9KsgjCR2xsO91/jXl5xlo7H7x159YLFDMSLLfqxxa57HvpnqnnGnY/8n1/9Fb//3V/jQ4tKPSqpkeKM6jPSyWCFYggB6xzGK/rCEOWASguM7WlkwJuORBtwgmgz+AEu8Y9CBKSM5F+85vWQcgon7k4dSZlz8ZM5ziva24TqqmSZF+jVgnFp6ORAptefqDaJIrGRKaxwukUZR5FoNgk0pxmzJJBqzaP0XJqcN52nTQ3KLzmeH7meR8b6Br1M8POO9eYzTs+/5+63OxA5MQZaBPHhe65+OcfVS3xhua/vafuaZJGzL1OUbZF2RNYNJkZmSGw0KBOJdk8mEpq+w7FB7t4znUq+DyXry5p8+xLalt8PgfxiSzaDq9SxP1xQILG3DWY2UaaCpLTUYUuqVuRFzfqzI20jmE3A6cjqaomJR5bZW7qTYBqeeBp2cJrIM8kyXWETwaQdojgx9lCYlMYOOL5HzSrkbAaZoNcdQUkWpzMyu+HCCPJc8TFpWHQO6xLSokJ5j1tGhluPdR5Ch0w2nOue+8cn0haKlcCQMhWfQJ+VSqCakYUCJRP6dODoDP0HT7WdQdXSHSOKgNPwODRsREd3lGSfL/n8+Z9wbH7Dd+kdx29S6uFMjCU2qYj7ii47M08H0JJU9fT9nNz0HC80cTygh4z5+hrXBS6Wmvwip00z5DtY65R5VZPmZ/LRcOo7bpIr+mJO+cKwH98RTgXF8hp7lrhih05OyPg50zUUDyPjekMSJ7p8JDlmnGpNunTcFJrUVYw+w/cdaRIYi0iqLkhz6PWex7s77PFIZi0Bje1zHBOpnOilwJmRVBb4IaEXA3EQJAEIPYOACyU59AppZhz1mcVg+aGrAj8KESiygtfZDYM+k50vSF8rLvMrBp2Q2ZqLX/wx1TySHCuSZAWjIs1y0soiTUXoI5O1xNyRqITYfgJKWL8g0xOusbTzM+l5jdIVYd5TPbdUNwmeHEePmQ1YeUtZZBzfHQjdxHJsaNY9rewJHwzLjQAMGQnJ2NGMCiU3FHmJ25/pTzum+zN13TIZR7WeUc5zRIRgLWMYyW0kMTWTqcjLSC89A3OEk7wfRiYiS1mAGnnykjMTG38kMRP+ECmvJXZ6SWFG/EVKeQHT11f01wG7i3x1bPh5Nmc+G1HqhF6AOC5xi2vIAz4b8KlkcobH2yNpmWGdZpFJfDNS2ZwwFtzHJ/zg+MXlPyV1Z6JrKV8WtLqjRlLuKo55gmSie2oo85Z8f019/sB756DpiSqlsLck/cAwDggUi+wSgSLRUBUlclQczx0mBC69QxYDk5GEveV2KFn7hru+5mVxzf1vjnztdtAb0s8cb/I5rur52PeY5x3D+Y7DuaPcLonJgLYRlySkseLd88Aqj0zSMlea2mnGMpKFhFCAKReIuKU9SsI+oLuGqZxTJDOOtidcGKzRvAoVXhnimNPngbkPJFWBWp3Z/15ivGXpMyZdoZuekGrCMLG6LlHyTBqWdMsNrkgwzjKqiL4oGGxEJ5qDyel9hasdSka8kkhAyAFhIkwOawOMkk71KCfwUoJxOHeN4ZZYCdo+gNaMYaLyml0JNH94/f0oREBK2PtnunvPdrNBiYRBOmbbFRxb2q5lmgpevZzRlJpqDKRaksWEvnNgJMVFSdp7OuMQeQJDiZ0CIiq0bUn6a6q1J3hJtAp1XbFqjty8fknbtIxZTuwWnOxIVbxjv9P0KuNw9zvE5c+5rgTf7X/HKhoSNZAn19iLOde9oT4deHp84P75kfr2jqkeUaWhYqD55sTYfArsMHmCmi+Yk1FclCxuZmxUAq5k+NAijg9Ie6IKBd82Eakis2fJaWPJTwuObzuiW3AZOvTsBa08w1ETp47V4gJdKfyHM0l6YnJL6vJMqgXXVyVOrHg/Tky14tA7ks7y/PhMMquIeO6UJxtzXq4qPpzumU819mpFxQGnAnb+CYWlOkAKpFYc4zPbUWF7ME8FT9szT8+OqkpotWf3eGL3+JFll+E3K5QR+N4RW02fRVQ6kYmKokzQoWfsO6ag0G3BqXmmyw2/E4YYz0RpSLTEjCmP9S38jePOnfDBIV5fcGlKTnXP9dUVUmmEPpGInv37Pfqt5OLml7g7RZ41HM6KL2++wCeOr/oJIWcszCXV5YoPzRNuFbjelrx4saZrRs5nR74umFcbsvWWU31ilr/A6Y7O9WyC5phckNbfoBcDrh5xUXElXtOZgU7uuD1AoRRkPU1/ZFukDFFSbF+yzuD28EyUgo24Iu3u6KYDyEjwESsjUWpksHirIBQIPSCxCKEwQjC5DG/u8ENEuJRgLEkTUQtBbAXu/AMKwI9EBGwIXOqc3dsSV5+4nFc81iCPLcc+50ZllNWaURhyD1pphhCYjhMqSZlVn6yeeIkZoYsWMT5gUkWiSorlS/xsQGUziC1lK+laSejhuR8IjUD0I13sscFyaBcU6YF9Akq+ZJnDarbh/FBTuhHvKrr+jHOCtsxRKiMrSrTIsD4QdE871pweBkrvESFyCrAQAV9EGmHJQsrUR0yRgZsYnj1TmWPOax7rCVMlzJ8DT/MzS0rMWSBDxpKew5izCIG50pyEYHoJZaWYppH8sxmnpEaUim1UdG1Gpmcs5jeMjzv2U4elZXCWphRcLCwv0gX1bqCpoJ4saapArzB+QT1qVmbC5hVq7NipiSu/pR1a1ouUqZ/h40hfnQjhguWbGWMnmU8TxRLeP1ZEAomYSFRGUAPzyqBMiZpvaIfAsH9mSgNVKMi8IPQHymSOdR3hJHhvLPpuj6ZhfJTEWc/08dfcjpJmd+I1gtvQ0NiBq7BhPA14qUnqlOWLFSbJ8Q6KTUZdz/HtgBMRYssy83gzMV94tGuRXUvK9KlDGBWyKklpOe9OjIsEU16hkWTSc8gjUYx0vmbZ33CbDLwtL9DB86gT2puBNKTkzNnVDSobmHrBm/QNx+D47nbHn9xEuvgS3AynRvqpB+1YVJp2AB8Sgvco36NFJCqDkxbhLS4KrMlRoSc1PcoGQq6ZnEOEFCMHrLVQTCThE0fiD40fhQhowM1XVDrBTJ5GeJJFxhnL2+trxqDInSS4M7g5YgHKSuRMIOee0fYI17MbNUPdQjNSjAqWKXnZUs+3FMNIrjxyFJg8koiM6Cuwt8x0xvF44jwLGBHYD3dULrJYp6z0z5DrFH/sKObXTM0OgiWpttBL9kPgsiiYLS5Z7CwnvcM6TywHnB2YekduAiYEMrUgCQolO6w7Q/aKk3OIrsfmgYthoi/OHAZHdprxLD1qDspndHqgODXUC4Hu75j2AiskghzvU5JR4sWIDhP7RlAUA8OQkliBKyzLeYkfI4NwDO2E7AWj7ujIeRQKqpHSSxIrMMWKc98Qjmf61w6TLujvDozbgjdLw7Dv6U1Dma9wUiHbQFhoUj0y2xXUSWSfKtIyY7HICarD5GAST2IFZV4hVjlWKnKVoKxhGFt2zwdCHCjSDFn1mHJLpCFNNT6xnA57plFQjgl3vcBwZja3fPPbX/NyppDyhuJihXo+sFgqujSwqhRxPudyvcRHRdOmNP0Dwe4YWDG4nlkhCVYwDRPj00QcAnJjkDIlmki2SWmniSlGRh/JNkvUdCQTJTxZ/GrBdDrT2pZj77HuSJ9fYXcRE060oWAUO5rGs+5Tsi9zut/cUz890CUe1czw6QQHS79MiINHxE9tfsg10U34MSKcwmqJiJYiSqLX+OBQUlPIEV1q9i6iSQneM0pPPs3wfkAEw/Rj5g4gIt+9uyedDBt9Yrb+jKLYkhjJJtf4kKBkQpJ45qsNKn46UfDzjChh8DuSKaVoR0bb07R7dq0mbUBmHcWVZlol5H5ESk80FaiJTabYnQtUWpGsOy5dw144Ml+S2x1mc4PLDvg+4fuHD7TuDjcoYm5ozYwiCFJ/YvIDs1Lz2U8vSY3l4SElhBo/drgEBhGQLiAoKWXKfL4knScUE/j1isS03D+cOR12WBz23JIuRjo/Z+WXRGHpSLH1LffxgjUT+4+ReVXhZyfSyTAdTmgFAxpVChQJpfR0eoLlnEznFMvIWm74dmwZTU0cM8Qp4ivLeLLItALhEWNLbgzeJMj+SJ1YQrBcdSNZ6QjziSrZItIFUjis3JG6LWneES04mRB6zWaeMdwsUO2CGI/Y4URnNGUmKasKVVbkVnNOR/pj5OPxhN+3BOFwo0DtTiBPXGdLomyx7QllZnz+kzm/+t/uubyMXGY59TctXz3e87OfbZlNO3rpWcy3jHFgv29ZdQ1ZeINME27DA52MnGKKiRlJzClFgsoV3z488jcff0/ejbx6dcXlxQXvzyf6zjM3BW5KcW3HIlsSL16T1AfCNtBLSedG9Kh5sI9UfkXqa9Jc8P6+YS0URSw5DScGPaIGzzd331HJnm5MaO171NkQQ6RMBX33RHva43uLHz3CWmKUSPOJERGjJ2qNkBIRI8JIWiUxk0G7iaA7plBgRMJBWeZCfcqx+MMnhD8OEehGh+BAVVyw/vxLYmYwSUrhNba19Lqmml9hy5RDOlBgKX2B9xE/dZRGI+arT38GMeLlklbVTAkMcSAfT4RaUncLWII5K0RW4sd7klwQW4fM51T1RDvOKGc7ZquSJE2p25TzcELmgpVbMfkadVGiUXj9RDGkTMLRJxNFqnn72UuW6wV2f0c/dTRe0tszoTsj5Ix5kjMvc5hdIk89JnGIyjD+9sRhmTG+n5hhObYBlQ/oIXAwkjKL3B0ti6lDp5F+0ZBrQxgta7/GKmiYkxiLFRYGRS5zOj/SnQT5VaTJLbHWXJQzen1JfbpDjj35MCOkijYcEZUgOI90ivnFW5LVgmGwJGXKMAh2eiQ8Kn72xYqxTylCzvehZGEUgzXM8oyxOiGnSK7nXGY1h2SiO2VUmcEUnmSmUQRkHZBqpJBgZMFMTvhFQjXPKXRC8GdEo5naGlkZei2Jo+X70wPVTcn9eU8VSnSmEGNCtYw8nPZoCu6nEy72SFXQiZTRa9KHmqk/EUeBtIp5NjFWPS7CMGaM54b6eMICxwmuXcpczjgPz3Q+kMVL0o3HrRxGBZJkTaobHr99z9N4pn30tFNLdWOYuyVSG5blK/rDM9tsTVNOBB94/Lv3PNbvKeaveZoajMsRUpOuDKMX2OiIIaCVIoiIFCBEgosBbyQCaCbQKhB9RE8FIo24qcdIQ3AJIwJtFIlx2D4B4fkh8MCPQgSSLOUnr79gmZSsr25wsWOwJ4qp4Cw8aaFBToxDgrY9cWnopWRyDmk9JYKTOtOHPaOfINUwGMQwIDJN5+4w7QpPRl9WiBq2VcuYWMwww5oJ3XdIbVhPR8bVmp/Mr3BBMz11lFqQXOacPw44XbARBZ1/4ny6J4nXBJky2owiaGSqKBNDT08YCpyF1GWIcos0GSUFukhQck5yIemDR7QWkWm0emYoR5raEaKjtBvS+UhrNmRBEn1PHEv20rLuc07pI75ZsZ2BSXO6AaIO0HbYzNDKyKxYULgUGQU2F1gTScuCDIeqeqa0Iw3QSOh1ZKMFXb9EzzVn98SWC7yScB6QlWEatpRxoHMnQpMQUkuazWh6S1vB4D1h0mSZYJ6WxNUVYtqz1JoEBTLitUSiSYXFSUEbLWE64JsDLkokKaObcMIRFYhxIn6844meKkQe+5KlzGi7wPvzLTZWXNys+Hmy4tvNyHFMsfcH1tc5SkykyRaVaprG07QJ2kvq0zPr+QZPYBIQneA4eax3zPNPIFY1C1xtLlBFimNkvnIwWHKtYKjpXEbUJZv5G84fP5DMLDpLEEpTzdY0x4irB4biRJdskKeWox353Ve3bOcVJlbE2pEtJGkSSJVFp0uK8pIpKLxUCBkIUiI8pE4yTIYYB4xKcG5CisCoa0pSrE4ZZcbkHUk24KIlaTNk0AijsT9gI/xRiIDJEsrlFYnS9ASSfEOGJ08UzRAolWGwGmMiNkqi9bh0wiCRMWUCBtnQOouyHpEaslWKaRsSn+DCxHHsQbSIk0U5w3vrMVvIas1YjDTjSF6V1OGEdgUHlxLVE9W6xD15/OSogyBd5UxeI13g5CQMBwqfk7sZdVkhvcO5M85LgqkopaQoZmifE8xIskyIfkJEgUxS2uGAOUtmi4qm1UjheOhTFsWZNkykRUY6Nej+JX21o7CBOvSMMuOoRqz36Kpi7gq07rB2Qto9RhV0U0ueai60xpxTqlAwxB47OGQf2O3O3HUnyvGJWXZJPTW8J+NCSoTQSBzZGCmWOcenEdKKGAvII8+3I6qoWXuQqacOkVI4XJKwCAXOepIbzZW7YNUkTNNIbAMSQWMi46gJwdJOLS5kDOOIDTBJzalumfoRwsR+PKOsJ1pBnwn++Jd/znz2Ob///d+h7YZM5LTngeoipe4lcv4Gn35HPwzI4oJUD4RCoeXAsZ9wWYk2HlGmROnJyjUmzRn7wP3xwDh4FpcrClOQjp60kJSLGX0vKL0h9JGubplOI13f4V9tIVtz9XJivlWfuBONZVh3pNkrWv9XFCGhLU5sxJzu8MDZ7bhZ3WAGQUwEZiNJEIQmki8S5oUkTCcUCqTBaYuOEeElOgRGD8IMmEQRrSRERzcGhDIEH0lEZKUqOtsyCUciBUqbH1xZsmDEAAAgAElEQVR/PwoRcMPEV99+jXaK2csKvV6TfhvpXkTMWjAKiUsUwXiUsbStIFMagUcknrYz2AYYUwY74UNNsB3Cg3cwKIF0Em9bivqMTxdIqUmbhHZ2QPiEEDW9HemrivTxzK4zBL0n9QmINTv7hCgyZsUlTd/RWfupRQsDfVtz6kY2VwOzecakwUmFEBIVNYNzmDAwxQk9W7BN1rRtysnuMEHhEs2uv0f0BQRPaUbOQ4FQHiMrXFQgWmIwOPFM9AVhkLSux4uevd4jF5poJ0bfInuIY0IWE6TSjKUm1TOy0QHxUx5d0/Hw4cBj+0wic1aLPWd7ZhBQJillnhKUYUwn1nrB+UVJSDoyBcccktFwkaUMPiDCiI8S3SWMRmPMiI8ZWi2I00BSLVE2MBY9YojkMSHJgb6lOzvG45EhONabFd6k7G6/4/n5hBCOrEypT3t6s+Q//rN/yusv/oSI4sb+gmCWxKnhw+47vv1X3/KYRv7sz75glsxJLwSx80ypJi0D0nrGuCMGy/XLa66vtgxjTZJbMiPoTw1gsVKgK01eJiiVs3/a0QiJDwFbalQxR+U5/fNIsB7tW5QxVC9Knp5P9F+faI8dtxZe/fKG5XpD63M2WYrPPerQcWkUQeXoYMF/2n6dlMSIAcORU7dDSo+OCuc9OniilLRCkuaRpA/Y0SGzSEgC9BoZFVI6jHcElZL4lPMwkghNn3rkfOAHUIQ/DhHQQrCcXaPEgWyzxE0gSxjOAWc0WQkBizYa6RWDBM4jWvaoTBEmTfNYQzegqgn0xFAPjFIzCYNqJ/xY08mMeZkxyY7NtKVPekSIiCApUs0oJFqN5IXlsJtIh4L7+hFTrdGqIg0tXZ2SJRN9LshjiZRgY2SIEds3TGXChEEtJYUXmF5xbDrQktlqjZJLQmIQFoYm0LWCmBhkUdA0e0InGUOkT6DsOtJREcaeJxNY7kv65IEhXHLbORayoVWS2n5DZzsWQhOGJyIpu92J7eotm3VBzMBqS5CRPNP4PKUZSpTIuEiX6IsN/ulI4npmMsFGmKxmfrEmz5fIWUZiOjI50dUDavDo0pB4TefOGCFYUKGDY7aJSO0JsWMzSoZEMPg5iRTIHGLnKV1OLCL2LCEkqFyio0acBc+HmscPHXXd0Q9HqpcbZtrw4tUXfPn5l7ihJ+ksizJnyOChN7StIzY9j+4MScP2+o/5/PUNdghskoy5k7jWU3jFi9KwXVzzk6sb3t99ZLAn5kVBU0gaqRhjRyoDl8s1Yl4w1ROJTVGTJJ3P8BclyQhKDJTXV0hnmLodhonuaHm8O3HiTHVRoZsT64s5zy5FyZKzbzlMmlV2zSZZ4MuIKjVjWyNZIBg4dHfsh4ZgA14MxCAQVhATR1QC7xUKhZBgvUIKiEJgUwdekSNATpxp8VgyEubZyBDDD7FHfhwiYNKUL19cMxYFJgHXvUVX36FaQ91LklyQmoHSLglqYLQeEQLBW6yb0JPAS7BJZGh6QujQRELUtL1ADx0rnWCTAT9tCCojGI8LkA8FpfCohaLfK8TOMDYNdoLUV4QposMBUSRMtUYmDckip4oOR4WMKWNfk14mhHSinRpMbiAI2jognUROnsortAI7tuzHitBbhqMl+ARpBIkV1O3EupA8dhPeBwYjwCckhaOtR45ZSdV4HC11cUadIyfjGZ7ONJ8Jnn3F6vSE3K5ZND0u1Eh/yWw0WBUxSlDlGrlKEd6Q3KxoDwKjO2wx4XcT465Bao+0gTJ5iUURJ8s8zPEe8oXE3D1SrAsElhA0RmvUOKGLSHSgZEYynpH0FAQymdNKmCUbQnJCecOkMnycKFVge7Phvnnkbv+e4+6B86HGiQkv4PZDzZ/+/HP+7O0fk1DwPN1xJSw6qyhTj1kqrL/D0tGMgV//9iPXbeSm+iPyWUFepJgBXNCktkKanqxQOK2Zr7ZMz5Jcz5mZDDeA7x0DEZekJBiy5TXlmOCOA4UpcM4xdZHzx4mscnTeE48TZhWxB1hfXuP2DWHt6bWkPOeM84jTivH4yO273/DLVxfMy5RxEdHbJc/33yPPLSUJnQHfRMIkiDGgiUT96dLQJxShxAsBRqGngEfhhSaKEWHAmxnYjnEaUcwISYNqHVGW/FAr8KMQASEEMh9QsSIzIw/5kdg7Ii1pdYNUBVJaxmhJlUCZSCGgb3LsMNHZnimx9G1kPHpcJknChNA9hYRw6DiHieomxfqRhSyJdmCZamLm8OOecVrjjzUxB/QF81nN4bRjdfHJktxmHcO5IJ/dEQfNwRpk8LgIz+2BVAvUSWHyhM46TBS4Tn6iz/QlXYiI7Ixe9OhZgso0vkxJgsNmjmF/xtQgjSQ7WVo1Yi80fdJj8pTifOA0DFhdkzxGim3PqfWgA56C8KxptEUISTqlhKAZVE8Tegp5gdQGFUGlDq0jQe2oNksO/UDsOkoxxy9TjJCksw4TCvy+I6YWlzSIMEedE/SbjHS5osgUmXI0kyJLPVYFAo5gMoxMkdpBIpgUlHJEG4kRBitznFIkYwJa4VeBUTt0pxlcIC0Ny63h/rFmygSVnXF9+TnJYsW8LHj2FWOS8/i3/4rSJCj/RAwtYjHjKjScQkb9zTd8WAb+6Gd/yjieSPMZCE2WGfr6nmRomfqeZaE5MWK1plMdfuiIwYMUdP5Mfx5IV1ekVY7MFTbNiWFAu5JvzgrddpjkknxRIIUkVwOLN1uO6Ym/+/33vPyTf8CzyRjcSG4i/fGRg71F5q+5dRMvMsXBNjw+nEj8SJJdIFyJq+8Jo6AsNNII/BBJpWfSChcnPIHcSmJQBBQijYgAeTahppQhs4ggCUlgyiT+ZDA6+cH19+8kAkKI/wn4z4HHGOMf/31tzSfuwFs+hYf8FzHGg/gUN/w/AP8c6ID/Ksb4l/+2+V3wHLoWe2x4lGCuOvqg2Zg5m9eXZJNnjBWmMAgRSMeBaRrpsghtRjt6nLeYOuCEBp3TNz1ZdCyLjMbOiUrjpMWTM5wH/Kohtiukd7TW4tw7DBlCTpyHBJMJbOwo5y85j5a8USQypVEJ57GHscPEiVB5Zm5DKST75xPtqWc6TxQiI59nSG9gHRiDJcUgjWYKE41zVMUcw8jBPtOZlIwTwiRYk7M+S85qoI3fMVMp05gTuu+oywP98xOrNOBH6E6eazWjr8/4skJuL8l6S+1aptoi5i1J4fBBIaJGy4wky8jnJdt2wK8D2k/I1iLnA2Ve4eXIKl8Qe8/ADn2QzNeGYEBOnr2xxKmlVJ6FS+h8T5bNGMREoUaCGjDLAmcDJkmZdIvCEazHxAJ0BioQhURJi0axTCpe3bxEVjPev78jL26Zevgn/+jP+Pmf/jlTjPRTpNSO59wTsjWP/nfUTSTOL1i5SHaZkgWB9zVDPZLmCwyC0OTUh3fsDrfYKBGZYZ0lJNExLw0iG2ntkaF1rPOUV9tLXi4/p6ZiNAWhzDF5oLcTUZTMtkviTzwnDdtVSaYXtF1LPn/B8mLJZ9KT14oiRJpEM0tyhirjyResxzmly7E25ZSOPL3/wPtv3rOs1ly/usKKgcPxDi0zFBE5jSQuMOiAFAoZBT5GogEdI2qKOBJGMRJDxMQaLxR+qTA7ix0SfGZh+sMOwn9nEQD+Z+B/BP6Xf632L4D/Pcb43woh/sXfv/83fMoc/OLvn3/Mp+DRf/xvm9wOE+/uvyXLLnDdwGJ8wVW24vLqilQFMmOIuUeHHOdq3KljGjxjsIRBMDhNdDkiC4yHkfPpSHs8k+QdZqaYlGKWzemD5fDhey5WCh1eMh9OhJc5SS2wMpDGgLcaFzLq0z2J9QxG0nb9/8Pcm/xatmRpXj8z231z+nN79+vuz18TfXZRmVkUUVlVEkpUQikYkGKEEEP4H5jyb8CEFCNgUBISElBKhTJVmZFREfF6f8+725572t13ZsbAAxQU8SClUKFYk21a27bN1rfNlq31ffhVTxcZmjygbe4Jio66VajUYT6bMI3mSO+e3eYO1QfYuqTIzDsmntglUgGBO0JYTVE7FJ3hKGmQ+oAyLV6VUvb3rOoC3RmsyFCFx253xzYR9HaKFCVSWiIPmsqnFZqBgWzIyC0s7zUm9ulTh3zf0/Qa0UqkLhCOYT/k1KZCuRrPi1m4BuvnNFpRDO9OjEb3zNwjIrfHc33oQ5qiRfk94xBCd0ReCvqmpusTzHEFJqIzlkQnyGWF3RuUktRqR9hOqQGjYnwhMK5kEC5Ka3ot8buWxKkwIx/1bITeuERHx6yLPbLf8ejklPMw5Ityzb7pqDwPv9xjkyN694747YEw8vHrnBbFB+cXZH2OE47wwoG8MpS95qubG6rNPceTCwaR0Q0WFfQIlTINl0ychsSfMLs85fjbf8T08ffxXY+HqmS13dFuBsTYYeYeUQuf80eXlNlAqTLUIaKdSl6sc5yPvyCUPo4zwnWmhFaRxAq3itGVQxQ71GFD0lqkIwl2Et9JSOyETlhWhz33d/e4QtM0HfgG11eYzn1XyagNfhAgtHl3pFUWWbfEOGipGYSH7Xps7dOqBtFbZC/p9G9IOW6t/ZdCiCf/hvvPgD/55fi/Af63X4LAnwH/rbXWAn8lhJj8G7yD/w9ru5qh8zj0JefzOY+WC7zIpe17knxM5RxAQGZ2GFGinJ5BSJQQdKIjsgqhDA9tTtn3KL/DcRt649P0Kcbu6A8ZbV9zpGEfnLHsYhw/pzxAHCRUroVNR+SEyHHN9l6QhB5KjBBJiXLA6eAgG0ww4JcaNYrxfdj1Jbq6QveCOJni1IrSg0EPCNWiK6hTgVIF6JjQC9BtDRpUELEMZ4hdgZnN6dSWeGPAQuVK2qZGViGd2OFOe0IryMsWOV6gywrXF+yLASl77qTEyTtMPWBOBrbVDq0GdFTiaEmiLJQ9tg0xnmQXbGhbjbCSrCoxG0UWd4weG0RnmEwkzXzGLvucfNfhPnlC6MFJmnBV3hPUDQiF6+cIYiw+RoRgDE3tczo6JTc+2mtxlcIKi5SWwPMxlUW1GeHQ4XkOYz9BhBG50/KtD84xbctGjdFm4H6f4wyWqt4xf/aI7fXnyGFPWwbc7a85USVlMFDsHpgFH8D4gt62PDkf8eL2wMPmBdW2JXIiHskRF+NHOL6l9UcQ1BDEzB6d8Y/+yR8wnp4RTx6xylvqoWWjc7LiwFCNGB5yhNeySSA9fowje77atzxVa+4PAS8/u+LzT/6Sed9wOj/lWz/8fcJDh4x8nKFGFDsmIwclQ6bLCKEUOQ8cWdCmpRor7n5Wsi168ANoOpSGHosyoB2DFT6yE/i2p7YCnWrMYBhkgC/BUwOucCiqht7zUEOLJzQ6BFP+BiDwDXb8K4F9Bxz/cnwO/Cqh2dUvfd8IAmHg8b0P/xi4JXTnTJTPvpfkVyt23RXC1wxewCh2cFOfxihs6BG7kih16MqWu7cljQU5EjR1wV5rttkBtX6JEQNeNcN3Sz6JW06jiLwueRrFuAfYPbnGGxbctA3zQhFGY46iggetiK9vmcSCwRkj/T3R+Ri5DUgnGdlB4MUJi/iY7XbHtr1nFqZ4dcduXdLbAb9ycaVG9SVFuOHo+Al4AwsvIlQVUjr0vUKIFgMcmSWvw5zYmSBMzxAleJWh92pM4dCJmoEBuV6jtYcte/rGIXRreq/j6h5cc+CjyXt4usaGLoOj8NQBb+hQnsuukQy2gpGLaCJcUdHS4pkCk4HfzrnXOcKbEW06hKvQMiLQPU2bMNgMr5kj/ANpN8WlpXRT9qbkrHaRZsBJBDFT2qRlKVMCLemloXcNoRhQfov1wDIG4xKJnqJqmUaC997/XbxgzN3dinJ7zd3tHf5I8NF7P+TVpz9FDYaHvKTIM/bZhtdf/pRQw9l7j+mzCju3xMJBK5/nk4jz+DF+vUXighsTXSwQKqZpFGrxiF470IYcf/SOAeohEzgmozOWm6JClAOKA/syY12V2FRw8nJDrhr09IT78YafffySl5/8K9YvX1PQsr25pa1/hLMMERyzbl4x933U8SVxGhEunxBrw+3RDn39iqFbURTHvL59iak3OFYhpcJoh0F47+r+B0UQaKyAslZoJEHWI1EUxuDIln4I0NbgDAGetASdovcc/EBSl/8WmYWstVYI8etblL7BflV3YDKdcJbWDCrlZZPh1i2l45BWA2vPIahzJkmC0gPdQRF5LiPj4CpJPmRsmoE6VAxZS33YsNm8Zr8u2dQCP4Z0SDHssIGiO1iGcMXCPaLpB+pwj1sYVFAwcpeIyT23uYMddlQ3DuayQ2wdOrsinVwyB9z5BTq4wbsGV0t8wO0TvLJiGEqk0STDmGbYY2kZGoPuXZzApd5qdLDCO4uI1JRGCibapVhYxvvXXBc+g0m5ab9i6jpsUgchBvyqxhqfwhlwvYAmB7HsaXaCgZ6u7xB9SO/BkVMzbHvYOzieQjcxZWdo/R2NKsn6Nfev9kjdsFwmdN2MkaxoTMdganKnZ7JI0aqlEZaEMaqB2BtYl68YecekQYaRY2SwoYtCVK3wA4MsHOoowQtq2qanb1oCaVGeh1AKISqEHQiCED04lL0HcgCvI5U+rQq4MAMhZyQzyed/lbFpGqq2QeqvCQMXJQ6M5yOcdcIiCfm4aHj60TN++MFTVt0doggwXU992+O2DiwvOHnkURwU3aWPTUe0G40b+DQ7zZu7W3I94ASK1KtxFAyeR7sqoG5ww4GuqRgvfGQ34Xr1FT/79Gu82ZRx6XFzs2Pz4prbwzVSFQx5z+gsYjxaYMwUtzC0+y1DbEjcE+KkwRN3KDNCxDBdztlYQ3WzodtscV2JsqCVRpcg3B4VGgYpaAcfYTQEAYFpMAxY4WGbhm5wMC4Y7ZIsBtq8wSiDUR5p730Dr9BvBgL3/+c2XwhxCqx+6b8GHv3KvItf+v5v9qu6A+ePH9k3qwJ12FP5EpOckC4NHYajuYs/nNMYj0qu2eOxzHZYEWD9Ec2uonzY40eWfbEnzzsGOWEsAjonB8dlKLeMFjP8saDJLOW+Rh6veDaescssJvMQwZhjObC+0iTJLavS0LsZ99slkQ+2iRD1gf3BMPHeYG2HsSmhGNE5ITI4MPQ1hWhYjEdc2oS3hWItGjyvZVpa+swjODN0Q0F/8Cm8GkmKcCQBPp5/xMxa8uKW0oTgNuiqoSt7jBGEi574kOCrko3pUU2MDBXCG3hESjnUdDqmk5ZVb9/p2u8rGrUh1z1ZtsWxAn0ocfOOUhg2dw1JqlhctBychE0pWB0Khsljnuw99l2NjnKcasSrzYFUHvCDMzI94FQZN2HA5dCwc+5pq5h2ImmswuskbVdgrMQzDWWnMWmA9acY02B0haMViBbTdPTWoY8FaewhzBTT++Sl4aH6K9pNQRH6fLX/CZff/13iNEEah+i9krnu+fM//Q+Q6oAXSUI9wYkzspuSbd/wKJlRFDXL+ZKg2wGGbFgwuA84neL19p68LPEEGOuwa2J63bPrDigJpydLkpFDeb2maHuYzxiLO+rPYxYjnyJ/iUDx4TNLLL/FdfkJw3sdjz/4AYGNOQQ1y9Cj0Q6qTJicTdFNxrBuMMuI5dTHLpb0h1v+bnXgYajoe0uoJUIbckehxDt69ForlOnoU4swmtYbMJ2P03aoWODXCuGGBE5Lk0scKWiFg3Vd8uab/9G/CQj8T8B/CvzXv3z+j7/i/y+FEH/Bu4Tg4f8tHwCgh56yLRmPwdEZLzYuzc0dcWSZNDOc+BFxF7Jdh8SuphIudbnhbv1zmq6ib+653mbsH3r6MMBxejzjYh1J0xmcuGGILEH3iCh6g6MG/FZSiZZ0PONgB+KrNdvB4EwX0CfE82tkErGpazrtMzT3OK5PJBSHh5544VN1G4bhlsQ+wQkkZ5dTVquG/JCDyohiw2mhKEuHxnUYn8zo6HGSKdLxKfcSlgN1r6jcltGjOWLjcjexhI3HICMmKscmhrba4pRzsqggpSOKAoLKkPsVcePTTmI84dM3Ef2Q4cUVfWIomoKbzzM2+wxn5DI+irFjn2HX0K0LxChmEiYceU84mXhUZ7cUASS+4r7MWPaGvPEInQLyJe7kks2mYDwa07g96VBymCY42cDeXSH358wuQWmDiGKSIKDzeqJtS+ULBiXx8EhNhU0chIjpozVaNExx8U2MqxQvX91z8/YtZWkwcYstXSqleYTPpw93HM1mKO0wuTiifHPD7cMDRXDHH374z7lpVtzkXxImLtlpSLA9hjQkOg1IQs2gQ1a5x8Nqw5urGzZ9jdtYvGFL6TiMozHpOKbH8uKLW3y/x5EKcxBsvvyS2koy8ZLtZz5xtObPf/TPUJGkf35Eu/+IanmN2mqYtIy3DuG4oel3HJ9OKLyePsuRxpJX14TxGeOJ4O5mzyZ7Q59VOBi0EjSuoDPvaMO0BesI9GBxao0wFb6wZI7GWo1vJVppnGrAtZAHNVNtaIWgbATdN9EK/X1BQAjx3/EuCbgQQlwB/9Uvg/+/F0L858Br4D/+5fR/wbvrwRe8uyL8z/6/1tdG0Gzf0g4lDIrM22NMitErFuPvYrcdRXiHHzlI6SDLA29vX3O3vqErW7arHVf1Ac/VhDrECUNkGsKgoS8ZLS6pW4dQ7aFLWPhjyrGhbUtqneC5AUV3g1DHqNwS6juCkYYq5XwxotB7ejlltVEEfodz2LHvBdYx5IQQF6RGIAPNRAUUjQYt6KXFuCVeYkjThJkT89BU3L458OT9Clc8RjYC99Qh7t+p7JRRwxdfXNB2azwVEKQ529sOl4hGliRArkY0UYVTDhg/pugtfdWwMGOUqbCOInUCJr6g0Hf8/NUb6qrlIjujufeYPgpZTqfEncta73l12xIcFO4kZuzmRPkpql2RhgGD51FmW7pkxCRsGEYujlxyzRVn4SXBRtEmHl1b4RuP4KhCqIjBB6cSKCTjvqERE5qixEkqbOBj1BRVtwxOgVYxUxmCVuTFwN2rA1+/fsMhW7MgoMcjckOud5K/e/kxl8EMNQ7wupp0MuHwxZf08SWByQkDTXyfcD4/Yvo4xd0siB49ZqwcynSCknterwucNiC0HXE0Jbuv2egDTnEgKUPqowEjB/SdIW/veOgNnjZo3bNMUvbVNbv7W05GKUdPn9OOYH4+pnuZ8MHvPKZSz3ixekm2dpkctXTa5fD5msvfiVHymJ99+QXRcYANIzB3SN0SHUmcjxtaod91/ylItIvfahxrqEyPRuMrRe8onMFQ9JrYSLrIQwwVXW8wSc+DHkh6h0YNlJ7DqK3ZfnOZwN/7duA/+YZX/+zXzLXAf/H3Wff/+qbtGUTEtlYcLwa6VzlDIKncEXfqnlmmaNcwO2pobc/XH6/4+voW3+koqyvyQ4EYAsRojjIGB0PQDLR9T6o16Y2Dfc9nqBvsruHricvlLsAJj5mOFU1lMYMkFxVCrOB4hmcSXOMSNRlt4OKHLvf5KyqRMV8csR1uSDuPkX+ED3TFjqOTGWfJBZurK+7LBx7IqTRMxRzXdbG+Qe4ydHVP5jxlsTS44wCbdZixRAzHREFJOF0y5Nd4haIuBV4QU9uBUGS0xQSlauacUjhroqyjjV187aBUCdaijESkIZ0N+eJfv+UXP/sZxb7iq+ktjy5S3g8ec3IyJX5q0Cso7gd2MmMiO6o2YHYkae8d7O014tmHLIMRvfDRdy79I43bbUkd8JyKepLwOrth1PekXYqUFaEcKBoX16ZYvaLpA4SpSI4d6qpDyw7h1rhGEjkhmUloOoM0LUIJXt2+oXrYsdcd6+aBtrOcx5qPfm9K0o24TQcue49m6TMTKSq956LaMz4+pSEnSuZ8a/Yhj/mI0+8+4naI6cOB/UPPugQlYkqnpMBD9R16uOOImGF+RhmWSKNYv/wS2WbUtcvB7HHKnM3BY+V5jOWc08klf/wn30f5AafLCxw5sHu2wx/GPDq9oP2Oj5AVrZoyvB0wZc1RY8kdhfs8ZBl27LKYPPucnXLZvGoYbnKUBrcztEphXUnrKfquxvd9YjNQ0xFJRW0kSQS2USTlwN6PsaIhyjsyBLkLo1AgPJ+sjvA8h5a7Xxt/vxUVg04o+fLtGx4dT6iuaryzc9R+z9V2x6u/+CmXZ0u+9fwP+Mkv7mmGhmpVkfe33L09sBlqGC9JwpRHkxPmMkG7FVe3r7GqxzuZUQ8DUzNgVEA13/D8+BlH44RIJFR2+y7xFszIbEFaBXitwySw7P1PudnEhMue3cYlWPjEWctddsXF4xOGzMVJBzy/oZ9O2W4q6vUW7bjEvSLbWRyTEJx6aN3w4tNX+MuYp8+/h/IlRekirwoyvyK5E4x0S7w8cDl/RL7dsanf4Oua3gRcziK2tcCdLTCHB4JlT782tNMZy6ZCSYfWN/i4fHT+lN/51j9m1CiMcZgEMW8PL7ndX5Hnc0xXcHdnGIcjFqMYE+15okdse0EauIzGN5CesHm15Khc08Yj2qliVN7R3n9EHHnMFimH1cAkzPnBs4js6pjZmcWcLDiIEhOmxKWiDRIQDX4Y4g0G68ccmoi1OnAkejh0OGKLkiGe8vj808/4+auXtAqGNuVo+Zz5qYvoaqbeB9jJU07nltFszrL3qfWWwHnL/ZeXRNIw/8GfsmAgcFpax+HzwaPPJCU5QxrzKA3Zb3fY8RHb6pZ1k3G0eI+qDXjYrLGeoahXiKqi2x2YnKY8Dz5kufiQH/zoB8yjCduXbwm9lGia4ZiQLrJ8fd9Tx0/4WLh89QU8e/77jFOQouXVuMeJQ35xKLicb5l2mnHyDDF1ePm1R9esKduK+2BAlB64DtZqROeibItAo2SNkALf+NBrFhNJ03sYT9IakJQ4BmpnwtwUSOvRmA5rS3ASdNN8c/z9/xjr32hl3eB3Abf5iqZwmZstbbtDDoIpC87j98nDhjq/odkEHIor+rJhMC6T0Yzl0QlC99Rdwc+z1xxub4hP5lJ0N5YAACAASURBVFxMZngWarVFcMHt6oH3IoeTamB93DAMa/qsZeVoTsWMOK0ZMoO4K1mlE5zRlPRgsVoxDRq2XxtS3dA4EtFDnzoc1luyLgV2uKqnUT1mcGlih5F7TFa3oD1iXyKXc6ohIvf22GpC6O1Zq4E911g/RX71wIuyZbroeWwmBLs9d12Fu215kUU8FSkke4h7rIpw4imebSF+DEHD47M5z558xLffP+Lo0RPyg+WueGDr3TDyQ7q6pClL7j//EnqJevYdLIJRU5IZiGKPKJ7D9QKfLYPZ4D0+I3Qr4tpn19bMsgrH3VJuAvQ4xNgF9V5wNk2QooRVg5fO4LimsopBh/hBT7+TyIsxbi2YDTsqX9PtPcQQkAwWJoqMjm23p7EHXq5fMe9HTIOUkTNnvasgLYkXNSfpnLaK6Bx4pGY43/4RQ/iKD+MzlNWMujEbVzA0PW9X18yY8XYvCScZYTbQVyWtbQmrA6fegsYT3PUrNsPXJE1N3DaYJOLi6I94dHzB6TMXJwzxVULX1Zz9wXP0Q4EdHJxQYPsRT6aK2+GKrZE4oxatBD/wp+jDFBu+ZnL6mFnjUjYxcgjoZgL/OmP7ZkU8PyFhjV9bBIZe9bh2QHcWUAQyoms1vepQqgOtGPaaJtC0IiCWEbrpGCy4tmFjAiLZ4ecaL+RdHcGk/u1mG+66nv3f/Jju/RN0OiV9+ZrPxnseT2dEsWAf3dB8OSM7KPyxgb5EujGTuiRwNHGoWYiEzZuv2GxeM04dYi9ABy7CDESMeDh8jr664es45Nb6PDv+gG4QhEHH96MpO93S1iGHs4HwlSANO4RIKKdfIKqQtjwjmL7mtp3wZD6jHwSjViL8hMbX6MEjHxy6kUQdDCpvMbHLYnwGXUalO9qJz/6QMVITVKeo9J6R6zLC5eoXHTf+Heu/ueO9x885WhS0z1LuPlkzPGoZbQ/0U0ufh4iDx9niHBkc8Mua0ZnLE31B+uyU7zy/xD+7ZOQvUZOSEz3lZpixDXfM60d0as9O75CrLdPHa1x1gkoSWtnQdIZX6894b75gEAHeOiQrFCfBhOubDcGQs8/u6eaKaB1ypsf0/gNRdEkbuvjDlPC4xakqVu2Ar2CoJIEjyWcd4X6N57rUrsAODn4EflTSORqdTzFbj4ftQPYwUO00kyBDxAEEDbMTl/HkGUn8mImb4zyWxDjsPJ+FmhHbCOmc8uLlX3PiXeLoPfe7CTpsuCse8PqO7b5hPInpA4nQHcvzR+TNDaIqiIxiWS/xAsH08Zij0RhvOWY6XaAmsL3aU+2uqE3N6O0xx5MAJ+2phx2dAB0I6rsDj8+m6CxlWAn2laCMJF7pQ6fYjwy3V1+x+XnJf3jpcBdFjJZzxFzg7h0c1yfvzLudgAzoRE0kJXKweBgGAa2ZEwvN3ghsf8DTNYMRJKnF6zSO9chEj+lr+tiDvGcc57TZgh33vzb+fitAQHcDf/twQ9Bd40hJfr5kTMK6W7Du9iSfDpxNpzx++h63rz4mL1rqw5bd3hIsD5zEBYesobipqU2I7cZk+R3zXUE2drF9yebrB3RbkU0PfCt1cNYR8bfeQzRTdJByZHuuitd84C1ZP15R5iOCw5pgPsNhTpAaFuIZN/dfU2jLrh3xdFLiFBHWcVF2YOEPuNqhUQH61IInMNuaPTlt/0Bz5WNFgj3tyOprApPRzCa8filZf/m/si5bmqJmW7zl0cVzlk/e4x+dPCPZb7m72JJqBz+eICPFxdklRQ0Tf8bZY5daVVzIE8xkzizxGYIQm444/ofPiLfXiNdb+rhDCk1nNfv9a0x3TqJn+MRELOidr9iEJ2SVZBLuGH0wwm1aNsoyfwKBf4npJ6SuT/j+iOrrkvFyQeg2bLyIY2GIDgM6tDzuEvo7zcMooHAlY6Hoxpp86IgccI3B2I6u9ak2I5TrUlZX7LYvsffXuIlHdTzi6b/ze4yN4nqlid5PmG0zHhzF86JhCFsm+ZgqesY0eeDHr96gxgM3wyvytWF3v+dJqEneC9E24f00RY4NUR/ip3CfOTxOzmi8ga1oEX3NVPj4ymFX9twXHfE0xktC0jiHqkYOA/3g8MnXP6f6suLDDz7iTf4ZYbBgdNTws3/1gqlo2S9Cto3L0Q+/i7PXrM2If/2zj8m2Lzjz5vzli89Qnc/Zd06wnWEzLdhVmjjxkKalyi2uEyAsVLqnQWItSNZUicRrND0QqYCOiroyTFOXh1KhbEMbqHcgWb8jcR3JX69IDL8lIGA0ZL5GWUNShRx5ku3fZpxdDBwiRXSuyb07XJXQrm6RbYczmeEHe05HKRfOOV1Us14+UDxs6bnBHATLyGEeBvzkbUOjS5TbYKslWeOhjubg5XjFBYMGVwii+vf4bHJDWi7xzlq8TULQzHGOJVr61K9vOU1mFKZm6Vd0YcAmDEE8MJQ5QWUwcoJuCtpNjg0EjiMZBoehPKUJNXab0xwaumRArAS7+5z24S0PtY9f7RCBxdOGgBxHWp5cwnb0hA9JWb2oWR61dPuIUHUcLU8YTIu/k9RBRD1RjHwFpicXmv4up7lN8dolo/Fr8ryna0KMI4lDj3yz5ja1LBeXLDyfau9xVBVY/w2Dt4BNSzWdEEQei37Mrek4j0OUGFD1gdGzE+qhoC+fsPBqyiHCTMaEXUrVKoLxnvk4oFcBpaiJnBy/C6HxGYyi0SXjsCBRHU25xB08nM4QH52xHO4ZmiWz/RPCpGb5oSBsEhqb83w8pR4tYVNxEA806nOqG5cofGC/80g6h9F5yN3+Ba+A35ETfD9hV2WUleViqvHbp5wqnzv/a0bjEK/0eZvnvLQ10yjC0VPM7Ru2WY4/t5D77Mqv2eaG6XzGe6MZf/dDh+1uhxed4k0VQx2yr7/i5MOQ08UJLz99y6u/+B+42TV89ebHFG+3tIHlW889QnuB+e4Yt4+4t1sqt8N1S5zCReiWRkicoUUK8BwHaSzSGAwuvQnpnQppNU3nULuGmYVtAXEY0Jct474jNx7GVlij2InfsHfg37YpaQk7g3w6583nb9ntx3xwPuI6+BzTz4lWJyhR8ZOyJWh9hkmMb1x8Z4E7gKSG9oCfH/CGFtEPRI7kOtO8djOMf0O/fleeG446Rps9h9sDXntB4+7oFbRuyHx0y7qooXig6kIcBA+m50lhOciSqu9RyzHdOgPpYG3A8shF7Gc0yQhTVwxNRrNMEKM5blMRRgKTDVT1jkzluMsIU9cEMmXbbbl/aXHEgNSSmph0aBjtA1YTQ/hVCxcfcZ4O9L3LyfsHlr6gcCxlGzFWDZPAJyDj8dH7tCIg9BKceMpCutQq5PPuHitadCkQrkQgSXREaDyijcXxW+KZQeRfsPATtqkiCH7IZLqnbSR221L1Pf1o4NxbEpSG8iThdDeiuI8R44R7kfFRckTv5jhORBu+IjickZkdbg6BSUhUQKlCWjp8MxClJb7rc2gUQxcQ9QPJQnJ6dsmrh4xp9YTzD89RJwOlq1i6muD6DjNecHN3xaRJ+WzYUV7d063eEi2PcBcu8R78MObt2w3LwcMZah7uSqKzx7g7Rao6mkiRh6/RpU+uS1SzpWstKSOO3QiNZDveUY48XJmzuzbIkcQdn3DeXVM0W8TTp3z76gj13ZLrTx7YPwj6KEa1Dp98lpPqvyR9/IT25ATKn5KXO6y+w5SSf/HlgX/iW/44/gckP+hZ76fUxsMXkkE3dI4lDQTekFD0hqErkVIwCBeDRbYa3wqMO2FCgWMFm64nCX0Gm3OIBrSZcq5rHuYRodGYXU3+20w0KjDMDNz97S1H4zFe2fLl6mu04+ElJfW0YRYqpmmPdHyWdkodg9tscIeE9qD4unappUt8PqHLOnRhEE2FzAzpMCIcZRjdsN5ZrmY+8Rd/w8OTkMuLMWnfsrl64K7y+OCjiCwaYTYpvVcTh4ZMSxq/YPzeMdt2Szc94pH/AcZ9oLndguk4OvUZ+pQaj6MhpqoyHg4F+67H6yIW4ZKhU/iRIXOWZNuXlGVOx0DYV7TJikTMKTpBdZzwo6Mp3tLBHTrSaUS7bfHPnzL2LGMFmwpENaAOB5zjC5LaEk5y9nJGGg2EcoQ+03w3PqNspqy2M+KqJbB3eOMLpiYhPHZZvH+ESBT5/RzPVIxNTt2/YP1WUF/A4/QE0bmU5o45c/bLjmm9QL/nkYqeYHKMJ2O0vWIkI6z10IdToMDaGbmBcLIjyxzKzhCGCWXXkW8Frh6Qo2PC/gEVKqQ+5t/9xz8iOlpStZblqY/IAkKzAndKsXyMie549Xctm/v/BVVJrHU4OA3L+4JRmHCXXXOxeEZyCEmeW/JiBseWzm1xRi0zJ0b2UFwZhrZBDgPbzsXzPXxpqXSBHI04diaMnvTsb2p+3m4of3FHYFNmoSKcHvOTv/45xt2w8E54u9/xUfI+tcoIg4ryVUb7+yOeeSnykWLifg/dHPipawnKNzSl5Mdff8KTsw84e/4INay5yV5R+5ZRH+BLw7YNcLUh+SWXZqU9cq0ZOeDoilxNGVTLbewR9pZx2KPDlkJKVDbCmJJSSlTbMBUGRgP5/tfH328FCGhrWU96nk0S7tMOqWvcQDIeOVSbCm1vWZmIs/MJed7jsyaIY3Sr8R3L5PKcD4vXfPoi5ND3TMcjWtvSP6yJtEPelzSbnMaFwXG5f9vhUvEHT1astjuYXFAOHTgl3XiKetuxETnxJEF2GQkjtqVPkiyZdHuOkwlj6eAHAW/rmMF0HN7usW6MUFDLjKaUhN4RvfQQTkfbrXCHBl9PSUPBwhvzajRQ77Y0Y0V8F+ANe/o0wnolX1Qek4c7Hp19h6AT6HnAXHqUUjPGcGJ6DouYdhQgxy5h65LtJP5JRVhahlqROx7bbYe8l/T3JY3tEPGImBonOvD86Uc4/pJ6JQhDwWjks68aXKlpRpa0HWh0jJ27YE4gtlz4J5Rzl8HzWXYBwX1I+WygrJ9T7x5wRUPsSfJuinVaFD4y8hA2I60EaVdS1JJBZXieS1gJvLGilz1DV5DGPo+iR/S2BB2yLl5ybyyyXJHtXuBFDlV8w0JcEM5CXn38N2xlTpCmtC8PTB5PucsyhjjkNHnO3n7K+ivD7GRFKxVV0zD0r2m+6NiVlvfef5+z8wAajU41Oo6wTY6pGrpVA7XgKBLks1OkdlitNihvzSHb4re3NNJH1gdW/JT9dc39/ZajqMb96xj7w5DAnfP82ff4xYv/ncdCUw8pH3xwiQkqWOZUezisCoYXGUHTcwg1sggZRRLPcehq0LZkbHt0oGg6iyZCpRoKh6QPsKIgswnjSUW+Mli3YTZYLC2thtdNwFRYvkl44LcCBJTrEFRjVnFJ8EajJ08w4ae0qxiv7tHCIvuW1UwytDXgsLvaMjuySEeRZ6/Zly7JCJy7DByIp1NCtaSTkrbMKHCQ1uD5htNzj0sv5kS4vNEH8lvNyahnFRyTXb1Fu++x1Jr20BGMGx6aEQt3wdTJaE/HmC7GJg31kOJEFg+NO4yo5IGgCnHCiiBu6e0MbSUbv6a8CbDlQBfVOL3ljbAUmcJVDv4wvGNTdnzmImJbHKDZcxfErJ48cDz7LnVyhQx9jvcHymGGzFoWQnKjdhR9SDOTKKsI+wWVN6OPW9qHt2zv3rAJSrrER2UVM2DhjfDCY7LVmPFJQ5Q0eDKhqRVmdUD7DTIfsV7uOZq2UFc4sxFybam+67DcDQh/g02/zcrf0O1T7lYZUzfm8XzPg3Ew8R63E5CcsB1aJkrShpKHLGIRW6Q/wWwDjPExqkLKBuFEIEuGaUthIOobyoWHuu/pbyo8+8Dcfo821FzaY17JLbf3lve/+xzrBkz379rJ7UtNdCn5ySd/y2dvV3z75Ix017AfBu7bB9zlBbncEC7BxhXrQXOWHCHcgCKU1NsrNvcVTa4RQhP7p7TnA+tPP+WBAW+bc3G8YMl7fCrXrO9/yoMTYpTDl/mWoPEIjq+Q7fsk/Uc8jwUnKkRMJqgnH/DP/8FzDv2IOCnZkrHtau6MpfNcohKErzFdxr6VKCw9ARvhInqNUC1TUSP2DqWwuFQ4UlNrS3sv8BOBNBJVi3cU8qVGL2tkGcDwWwwCprXgbmiNJTExpv+MUKQY/RqSBVXVo0PJuHhAJhO2tz1WZGyaiNzkGFbvSmeJSRczlnGAHga6maXKV8jWZT6WHBqXsjNkqzXlUlJeGt7fnHPoekbRBCUd3CAiND7OScBpkrLNfaQvkUNDlqdEQUuWdbhmxB7BONLsC405bhAiZLvV+HlAmPrktqcsNa72iMOCOhIEVUxevOZxcsnt2FDpnsnIp0EzIuKr4sDCzsi4R9Vr7l69ZeJGxHVE9LSlbk+wi5bp/Ih6u2ER+MijEX5p8FwXL18TxhYzDOi7Le59Q3dbYESOP60oHIe5X3B8MsdPNLncYN7u2K5b3CcSXbicizNmTyA5/iEfzk9pjgMEHsHQcV6krG1PKpest9eEocOk3RPNfMJg4DqISVWO7c+5RTDz14wK6ERInjU4qgcvgQ4GKdiMSiIJSZ3QHVpyuaPaX7PPBr7YlMyLHH18ibqUvP3Sx3ZvcPxTfnz4lzQPhuPfP2Eoa1KnZT9bsbtfcNtcUf7Pv+Df+49+xB/+0T8kaSo+Kddc/fUr3LDnVDl88L0JcTpGrzWbLyr2p3c8vpzRVJrbSkOZ09Q9v3hYs7+7Ixs2KAyTkQKl+Dhbs6wCjmcfEE6+S2Nzhibn+5dHDNk1x0+/zz/9kz/C70Jqs+XP/v0/59mRZN9c8/Kriq7VJElElu05vLwhv76ik6BT6DtDrCEZDJkjIOwJ5ACOpq3sO/l02yMHxcH0TAA/FChiQtvRmhJXuXRji97EdLsOPbLvivh/jf1WgIDAIISDalruHI+PsjPePHpAOk+R3QE9UoiswWnHHIqGzip8a6jLATfa4rlT8vyAkSVJPmaTC6zeoWWP2RRslY+sBY2q8XyXpkvYCYF7OKIYSdS04sEJuRyPUNsSmcyo3ANGZBx5EYVbQxMwn6QctOJo1KBclwt3oF1EnD4IfD9i3+a01lD5GevdFtO7HM8nOLjceRN6b8VpPME5hNy/ucW6htCPabcjHK+HYcV0btndOhR9TILGDoq7+3uepSOq+DGTEXg4mGHPEDWk04SurYlHCrdJaSKXg9Oy296Slw/k6g5Hb5g5PuutT6okk8sRzeDgUiNvLavXNdZpEHdjThdzwtnAdPwdjpIjsoki7jKsXuDOXYpNjlieMOiIRbhmGyYob0GSHygnHTM54GQT9u2BeWix1Yxu39PIEm0UU+siyy2VNOhRwtSzDJWgNAXrSqJyhVMkePsNqVC0LtSmYtHXuP2AGlz6KCNU73O5XCPtMYuF5q3IaHYDI6fgZTEQyQO1LghuP+Zhp1h3Jc6jCfNecVhpsr5nmG4IkiXeRBPFLqp9J6mus3sOOuMgPM4uTpnELpYZoRczSQOcNGYUztiUJeVne373T/8QZ9YS/PRr7ssNo8lHzPoldiRJ2ppddIHN9uxdh8LOcVXHODTEwRGrleTQu+SeQHTi/2DuTXptS870vCfa1e72tLfJm8lMJpNkNSpaqioJAmTBFgxPNPMPkDzSLzBQsEeeGR74F3jooQADnrkGHhgqlG1JhWJ1LJJ5M/O2555md6tf0XlwaLuAIsVyWTAYo70XdkQAC/jeHfF9Ee9DHANlSpRWMUlJliWMynB9ZEwCm3sMIz6AEIFyCV6VyKYn0nKINcvM0IieboQ87xgC/DuKA78aIhCE4LQWmMOGmiN/alrOHxYso6eNVyyGBw5ZxvvkyCaDSwMyWKSIHCZN1O9RSSDTCjFAWI8cXIk7vEUyEiaYRSKlR6PRwg2U0yeIcIfxT6gaTfU88PpGsUprzr91y9np8Z+S6xVXrYfFyP2dY8RSryZ2J8FVVaAODh0tNgkWQjKLid6csdjWSBOYKTmODyzqmUUsKW3Bed1S/MY5h5slx+VrzGEkPIxUWEzMGJcNz/qeD1zQXtyxciW7YWQZPaco2VzV3Ly6xwpBO2TokNNjsdUtBTVNm5ELw+tx4Jv9iJ8nirPE83rNQkCmas6LnDQn2GquJ0VWlGw2K7Znz0iLjLmOLM4CSebYmFGuYZ4003JLPU4I+ZaTN6zykh17cqMwxwuOm5m0dVQPBVbM9F4hNiOrJMFECvcIP0FIvD8wTALTLhhmxfHuDc1ppO0Ct27P7aHhvJC0H97z4XRiIyrqjz5CZff8+M0r/t53v8frDy1//PLH/MXtn1Fvv82TF5Evrn6Tpjjj7c1bupeGsxeJzy9+gJeCaAaennncnWI6VpjKsd2s0LOiOTni3FLagUPdc1FLNhefs5a/jmgfOATBMN3h0p7BnHOWa+LVBTG1ZJ3mSwMvd3c81RNWe64PHzOJknX2gClvsf5TmmOHFyPV+Qvmw4l4NmHlEjWtIR7J0KhCMEbBJGeySRBiz6wTGTk+SIbBoExAxIDvKkzyjFYgEUg1PjoQFYn1CHq02Gym7/jVthyXQDZEqnTPuLQUk8B2in15xCw0u2bAth6Pp95mzHNgTBqZLHl8JA+lUGCLE4MLLF/l5LWh6TUYTSEaJnLOfOC+0hTiAqvveLhZkG8rXh12fL8ODKqi5B3K/i5ny4ImvGUeNFYVDMMRWRcU+gC+YHtRYELHVkZafYl5rslPS1TasRMOFhWFl5xChxQTuS3Q9Yq+OFHvFsSpZPFM8p3qOeul4N/+yVsOdx1f9F8T3qVH+/XlSPqTluMnbzn80OH/7MjiSckf/0GgfPGCJxvHcvwW4ixjYSxtt4B4ZDYOVSnqTc15t2MYA95JzPOC62yDjjUpJfLLyHKxwn38hFKtWG4c27NrzjcZ3bxiVRpaWSFDw2ZRcDpPiLczwWU8bF5QDffMzcxaaqZtThYSmykQJ4XOeiKBcuyJOqJ94ORyWhWIJjIlRXtoeXj7itMRjsfE7vU3zMHz9FsVp+6ESVBvt4jmS9z9yGRGfvT6T/j+F9/n7Lzlf/vjH/HDt/+Gv/PFb/Op/JynH3+Lrw9fAw2bH1wy7wvOfpDzUbXgOHSMeo9gwe2rnvn+hlhZzuJzmgrUJrB/c4c+eChytKzJTEvR31M8WRDqFfk3bzCXz7jtz8mGN3z1psEfWo7ZE7i55UcfvuHr//1fcby9YbGG/271nPT9Z3QnxYthi190TN2BP/j9f81/9E8L3vvEn/zwD3n74x+S2KFmg/OA82RlQWE0WfdIQ06ZQaQIYySR8FOGqhO27LEx4fuMrTfsxAwpkhEJUXJnE5d5Ypaauf0VLhEiBMUk6RF0R0W5EpzmHaewIu9m8mjwS0sYJ47NEi9A2wbiRAgBJyJianAKUlVzzCVWzaztwNgCS0UMieYUQBu6YubHQ0bwLb9uO5KV5M2a3xWRr+tvMQyB1+Ud69FSZwnbLzkzPa2r6aUnZDmjtxTC0tsMPSj82DKHCZV7ZD5jUo6UmqHRZIOllgXtYknJku3VjnJ/4NQr8lkQ3yc+OfeMxTn+oMjnb3g6TjR5xSlPqPs90zDxU5OgX6Grka3b454/Zb38hu+9+HskDE+V5MhAES37vKRbSKgLzqcrpm7GqBXnF2s2+QLvAx9XBcuzBSbBbCzLiyXxCBSGhTeMtsSZmc4s4ZBj9gK1GRA2cf1hQC8Ct1aynk74fqYLHmU1uouQD4SYoU6JTgkG5xFzwnlFd+yZ4sDd8cRPXr/EJajyS1IWqX3i/jbBLMlRuHcnfFxwDI6u8zx72jCYE/l4TzDnfHr1DNd/4M3c8OpfvUFsrlluJvyN47tFosvPiQImRmp3zUnc0Lz7moc+cZ0KolkiF4ndzcDuvudcRHSvmKs1Nl0xxo6HqaH2S479iUXuOB8it7PCLNbMC8mFmjhWE+erjFej56JMXG9q/DJw71s+11c0TxZshgMXbsHv/P3/gBpJ0Y7s9p6vmx15SoQikDvNNAe864hCEqiRMlG4HhEzXBRIWVKpiIgdeoaUWZyfCUgII0lu6fMBO+assh6OmmX+C1MCvxoiEGWiFzlTXFKlHamRCGkxWKrQUEyCsdBY6SlTyxAF+IjzGgysfyYgfvYkPzArR+9nSOBNgRsk0TwmVtQhw4oGriP+vuVtfsd284xDdURsPUWeI85nzvsnDBeR637FVCpu4yWbZWBorlBJcG0jvpgojUCICpxkZKSJisLdEmVAlpqnpWEWS0pbk6UEcUTNlmf5GpN7xrFBtIHCbbFLQ28jP0if0/X3qGxNDDeom4ads9RZxz7es5VbDm3H8b2kCEtEmZFMwTQNmLDGkHEZDblZc3c1E1aC5ejYGsWmrBBGk68ky3UiKoWbKnSZyCdHRkWvauzCYc3M0s8wCZQXLFaRrsvwc4+/7vB3LWdXimrhOMWabATR9LjyjKOP4ARnqx5NzxBAZ4kxTLzafaDrBqahZ+o3CJsYO8/DJKhVwA0NQyvYPDkDd2J3VPhWcXa2QHYNDz/6P/jDP/oadfWEfrzntz9+ShbXFFcZ+aomJcnLn97z+T88o9B7dmNGPxr02RFxl1g/e8KZlCSWlEGRKUU0I9gjUW/IrSHPj9w7SWoWVHnPok5kRYk7tgztyO3tLfedw8WC4sLzZFOjxS3p2zWtX3OZ94ibe9zcsV+XzMcT8uNPEeWBTz76mJ4jOn7D2XbB0JUY3yG1oZ0q8nCi8NCLjJ6AKWeUCMg0UEpBkxStgWoG7yWV99TK8JAcVhoKGpgilD1uhHkJslG/MP5+JUQAFMZn2NhwrDKKfUlcjkQ5YtYL7vY9rjkhxZZp9IjMQRYRfWSSAi8DKXPISWCNp8kjxhnsLMjTTOscUmkqCppthm+PzAM07y7g8AbO1nz+T57zJPuIWdxRi5qwyjizEVl51G7mctEzDmvSecfZ5MnzDHROnluK85qYNrixo1k6+uOWxNxReQAAIABJREFUrtT4pcG0nqJtkJmjk4p5WGNbRXENp1d36FCwKx5IZkV2rljaLRfnSwZ54vnkeaiW7D/+hup1Q6oq4u2Crp1ZzXv2rwx8ccvmzZdk2y+ozyV+Z/CZJ7QD0gS22wti8RTjRiohkGHCS4m1S5wWCJGhNyWLZY4uPWE0iMpQ9p5G5cgyEG4HzGXF5AuGYUdmn3Do3qDPLpDHE24IpHqiUZ5FvuXU3BF1xvvpyBygbUeOh4zZ7gi3eyZnmJtASoZsGDneNRxjT5xb0uUVH6YjebinHRLXmzVXxcxn19/hJ7fvuO3vOdzs+bMf3vEbv52oB8v64yfYJwOxqtluvsOXbU95s+PluwZdgJEJ1wl2bwOXT7Z8dv5thqxDHjVWgm96LjefsDj/iPGu4f7QEtoZXS7wZs/bl2845Ap/HHl3HOgYuJI5tRxxzY6dt7i7d6yfLvgH/+Qfo5xDBcMPv/qa5e4JZ9/dYT/ak04rxtuO/qlgEY8UZo3f3VBo0FKTxIwQkllliNSjlOdCGqQ0NBGiTyATqIQcEy44xlSi44RbTgSXkGNGkiNZKRkni849vknM8hdH3y8VgV8AHvlvgX/KY67hS+Cfp5QOP7Ml/wvgL3/W/Q9TSv/il82RZAQ7g4sUsUTmE8JozHhivLtH6IxS50QRSGZiGRPTJPHJIEOHFiMqZYxS00dPaCUJj7eBtdMUUjJPOe0sSPNAPUd87XD37wkXOeV5wa4VZEWLkhl8kMgnkqVsmAdB5QuGcYGoJhZ6jcoj3gbyLqeSis4klDDkixoxRbAlGTlCe0Q9sigV0SaqSXLj7igXgllPXNRb5vNzcn+BfpuY60CQJ6bMcH62Qex35J9mrB++i7p6zxiueHrd0PVf0zcXLHWiegp/+vorivvIp75gtarINRy7SCMCWWnZhhKlLF5a8sXIRVGTpMDpSG4qtuQYU5FrzzH0yMmQqp40KnxKuHVAjwcEEkVN2OzJ32tMmchLRX/KEblHH87pY8et2TPPF/jO8NN4Qk4j3e6Bwc0c798iVMXJLXj29JzFVjO9Cww3BwKKfJjQxwlb1NQi8ac//opeSr63gObtDR+45eniY/7Or0s2yzPaOuf5d57wZveG/Z2m+A78ztLyP73PWJ5tKLeacGz4cvjA9XZJVeWcjjtOc8/Re4oERhSsz6BwCmEkuq5xh5lEjzGG2gTmSdI3Gl9GxPuOdlaQC3wJu/Yt3c2IVZ/w6a9ZjgfBZ6sLNvaa/Nxyvi3o9ROSblHPa1y/R+pIKCNvxq/wLtHOBmEdVB4ZDam3BKlo9YTyAZwhktEHRfCBXPbEmFCy5aQLdDQYOTLKgJASGxMiZoTRkxUlo9QwH/92IsDPB4/8PvB7KSUvhPhvgN/jkTkA8GVK6bf+BuP+3016QTMKKukR3j8ilKaRMARUZVkOkSlXoAUuGZppJEszWgpCEHgEXvIIKk0KG2a8FQRneTCOYANaHAkoJDXeREJrOGUGU1oe9rfUrmY8jny2WBHlgWIQDDHSixoZLaU64GyBnx3qFGmqAmUi3ZxQKiKURmQa10tUJdG5JJcrMr0l+hYZHFY6Jqvww55S1BSf5CinUUbgri6RbcLpB74aH8j7BeXWkrY1hT0SzCVlv2D97AjNr/Eg78iC4vyy5O3bW4Sf2DWO+uwamVlUFli4wFoJRLRMSPKFpiqfs04aspGxhihLxCxxtkAvFMtuptMw5M/QboeLkjJYCldB2XAyFaHrWC9qJu940BqdLK71PDwcqTPB235PPO24kBkHF+lvTzCc8Msa52r600x1HUi2YWwOFLXHbs7oUJSl5jyfWdYV3lmivAUx8mrOOaqJTbViFc+IHzlOuyWXL0rudu+52d/CBLv3OdurLU+fbKiwMCvcUVPvFmxXOaY50mY1Bsm6tiivyDKD0TMidNQqcq9BlBVyPnA6PuB9YHIfmJ1DDif6/og7HKml45PPzjmeAu99gxwVc1BcLxOxUpgzQ+xKhpUlNJL+vMDPLV+9u8O9yJilQNs1iBoh7tAR4qAoRSBknl5GhASioDAZMsIsB2IwDFJTq4SOgaqGds4QJFCRSQuSmBnlyNZEbjEsxiOnXxB/v1QEfh54JKX0P/+Vr38I/Gf/b4L+r80hA4vUEcKCaDNqe+KUIoaMOTl0JhB0iGTxCEzQaJHwWqDlTATSPKNsIGGoVWLOEmPj0SEwZaBChmZiCg3ZRlPsHdZ29M3IV/NfsKhm6rMlx4+uWdcbTvdv0VLT5TOjrSj8QOwSq+gpCkXFgnqRyHXPkDxpLBlbSzAzJhXY2mKERWhBHBPz6cBgKpye2dhzZMwIEgqtKLfnNHvHcpUj7Yr12TlMJdM0Y8eRj67v6BY5w27E5RfY/YL33ZLBRp6qBd/67FNkf0ZWevIzS8aAXi2AGlNJYowkcpycwAhKlRFiSZLgrMCFnNobphBYbDcUxgAZpbzmXSlRTWA6C1inGIqnbOZ7DiYxnjpyXbJ7/57TnMGh5cf3IzvXsB/3PKm3+INkjntUnhObhrZpkCpnHDraUbAQFQ/dDaUx5EbTyoDwmirLmYLEI2jetbjy5SOfoF9xE1tuTz0XF1tM+4b7bs3DS0ffHNhePOfVwx3Xy5pvXr5keb2l1hOLeqa3C4bpxKm95awsqfQKuaoRFgSaPlpIM3IaaKY72uZEigN+UvjhiHt7z5sPr+niyG8+/Q5lWvBJHvlf+jv2px0PzQO/sbpGxXNSBvEw0boReb/no81nVGZFsSo41R8oOafKPvD9T3+Lv/yD16QAJpO4TiDQaO8wLj0yLZcJrQZsExFJEuNEzDTRa5JIiFYQQk9aSmTybCnp50AwOY0KFMMRqSQQfm78/fvICfznPDIJ/6/2LSHEHwEn4L9KKf2vP6/TX+UOIAQpT4jRY+NMCIakR5YltAGi0Ghp6aVDmoS0gZAJCB2Z1+TJg4KDi4Qw4otEdBlGeGxUpFngraA2Buckx06SshXCzlRppD119GPDatY8fDhQqDdkZmIXN5y/sORSMCeFKAbEJDhoEL1C+4l5VqgiI8keOzy+0FAFhmkAkSNDjk8Bv64oZomcl1SmII2eIZZI26PDiUqWuNwzh0hnc2rRs5CG1dkTznTJW1tgPzpRh0TfLlg/aLrzAjFENmoBhSYTGaW1THokup5RKgqhsKMEWzKrgUlG5iQop0AwhpgERhpEmIk+cBAXFEmTjZHDsuNsEPTFAq8d3b3EDY6jELTNDW3WY7JL3v30hsX8DFnkdM07Mp2x8QXu1OKHI3mekdkzmGAUM1M3gi2RQ0ESEi8lu6anDB0TOcfjjM5m0jTgJsdx13EaNXoe0WXP6TLw9z/5LeqLknF4QX2RkZkd3/zoL5iLnDxkpGlErDIG52n3LcImrgpL1GvCVzvIc4bgkSEwTo5muMNER+wNvnEUJiGrkrv3Ow53B1x7xPuO2/49y1FTvdB433O/yPHNnjkJjjxw3O2JWcZ1VdObFjk+0N0uyC9mxruWckislaQuPPeiJKr3RL3HDBI6hZKC0kWcSAgpSUTkZB5zKzEhvcQoiEQmFUheMANGK0oRabTBT445gEDALFhmiTZZ+AXkgf9PIiCE+C8BD/wPP3v0HniRUnoQQvxd4H8UQvxaSumvrUT+KndASps8iSQixiUa/Yi36lQieYvPJGEo8W6PkYaoPUTBaCDFCecFUhpiijgdH69d9iCmhMoEWuSE00xXKDJyQncEVVKeRYqjJ4kM4Qy7U6Id3xPEwEX2EV7cw7lh1SZkLpHC4IMgiom8bAjpZysSn0B59kRMkpg+I2QTIT1e2ig0+FLi54SJCRcmUgI6QVwIplEzGUOcPdZkuAyKwTPMJUV14i6eYV2kSBq1ONHc1WzOShZmxuga5PiIsc4UNhM4pwBJmStGrxlsoLQBTYbIJFFkJOWRfUIxU+QJHXOitsS7Br2saYlMx5nZaI7DgWqIfPXuLccpJx92zC6jN0euS81x6pHDB9zR0/UPPPv4I4xYopKjGTRSSqxaPboMy0AzBzrvmQ4DeQEbu+ZD92NmZ8nLRIxH+tYyPRy51Gv2pSCpls3G8eS730PViTIahiAJVWJ8B81hRKaSPGrWucCT4xfXvH5zw9wdMfOCsX5AqwUff7LAqgXeg5kC2o/E45FgHX5QjKeBYE/ctCPTuxPTcMf96Zarq+9R1d9QLww6E2SZ5f6DJhUFg36g8IGXf/gXfP53BemsRC9L6v0SUyrGaWT39R1KJsgLhuHAeEgcp4a9nMi0II0aGWe8TJzQ+KRRymOlwKX4iHHTFvCkaSaqSKYUx6RZyJnYTAgkQyoJyhGtIXfgM03d/kJ3sb+9CAgh/hmPCcP/+GcOw6SUJmD62ed/I4T4EvgO8K//nYOpiMgM1jm8q1B1wzRLQgQbA1pEpHJIEdFyJrjAREJGhQJ8JkgJfEiYpLEhIUXEaZgriMwspGF0I3ks8EmQOFHYBWMwmA7e3+y5vnZElbPbH6AShEIxBEFZGxw5Z09q7svIxgvs+sQ0XZCqCGFAUBDlTEQ+OgQHiNkIkyDlElzCxByhLagOJyXSeEzpGY81SEk0ERUL7PgBjSNfOEZTYcaceU6o7kQpNbU0xMVIoRzF6BiWkdRuSeZIspZqmulshlY1XnpmN+G9xCqFVRk6GMaQoyqBjg5hHF0pcb1jPE3YKBnFyO7wwHxI+CKnSoJ/++d/xO0+58JMfP7iewyV5ubPX/Egex5uB/T7O/KnT9AuUi8rtBYMTcuf33wJ6pYnyyVbsyBqj7SRMEX27RGjwcuJ24cdZ/aaclEipgGD5NA1FEtLpizfurhiXZR02ZLb5isuzMcMr3/KKVrGQ0NZXTK298jzp5xlVzTNLdZFmtM90U68jTPXS0G2SZQqx8qaVvTsphtmd0P/0JKOieRahqHhNHmKpFlkGrHK+Wiz5HSz4tg9oLTFBoWYW7Krc8x9yzdvdpyygaLYcP3iezj3wGpz9ejnQM+NO2AO72hEhoszKSy4++YGOkVSgoWUjAS8kVgdUXMkRMPkJdEGZAhUoqOPiSoIkgZlE1EOpCmioyQHGttjY4acmsdtsRNchH/PKwEhxH8K/BfAf5hS6v/K8wtgl1IKQohPeSQTv/ylAyaYR00oZ1I7U4yOWReYNKJChhg83ia0lhiRIECUhjg+UoqEVqAgJoHwkslHRPRIpWjHSOkStpC0UhMyQQoaXKI5tgxNQGj4xN8i7GMScObEjc9ZNJGF+sDLE0zJ0s8XLJ5HqvwCYsC1LQMzZ5kiKE/0gTkIdAyoZPEpIOqITJLZRcpRYejxSjBLS1lopjATvaYoFOkomFYCHTuGAcQqMQ0zTk7MY0ClETdpRHbEDzmbXOBdTxgtRj0eJIpDJAoL2tBPCiMEOsswQRJEIjkDY0AqgdEW5x9rzafYMR4dXXdCTHsQjuO+oblP7HPDs7Xl9Ve3zFIj1CUvnh0Q/YI//vKnpM8s8kPN1Oz5nYvPaHvoxhMutdy82/Hu7WvOi4I7NxDPFcM0UW9qxKameT+y2x0fD8BcK6Y0oWfFmCITPYVWkEkGJC5dcbobmPIBfVkxB098uOP7/+g/4fR2oIkTnXTcfphhPaNjQhWSVX2BrSXv9jsGP/K9/JoxOEgdSbWsypqHu0jzcEvqZnQAHyJbpalzS4waa88oXCDlguHW0Y8nQmUYk0S2km22IusjF88u8VlOf/eecmm49bdooTH7JUN3y93NWxwbqu2SUEqk9mxMYo4BLxxCaUIUpBCw0RNkIqBJ1hBiYPIT0kOWDE4As0MqhfaGUEZ8TJgUGW2kdA4TFTAzGc0v8BT5G5UIfx545PeADPh9IQT8P6XAfwT810IIB0TgX6SUdr90jgT0IyqHQXTUQZDlEePyx0tAQiKDJcsDLg0kC8EptIqokJApkJuIV4roHFMyyGRwzoKcGIRApwEVJEftqKJkSlAMkmWpOEVNpi1RStTguAlHTJhxsabMjojOcxcuiJw4lx1+mCnbgeIiIlPAB0tXOkSwxCTJUiSNgdAp5tLTHPa4PrBUW5RvsFVCyzP8umO+hzQ2TIuW/kEyjJaV3NEOFWl8hT9NOLHGxwc6cc58J1iVmiSXTGnPPCnywqKXPfakEdXMoEqkdbhuR1lXaJWDKWmtx5wmxJyY7EhMktFBnOBhuKVpBtr9LWHq0cpiT45jKnn9cMPwJiM1CbkNiGri9bvXOFkxLHsWU0aeC6rFJTFU5MXMSCTeCYSNXK5ryi4yn1pauyegOO1ndLci0zmdv6M/OlwumIY7rMiw1ZIQI84lDn4maHjXfaAf95yXFdXlC7z6gFmes3//wFxJxDxAChzfdxTVEjVJUtcSfGB7eYldLvFJI6styniG45HjzS2H2w+0D2+5XCmCqVBak2TEhYxaLMCA6hpGCRebGvd+hY8RLw2D9Iw+INaKTGu26w3FMnJqdwxzxklFzpzh5tjxevcN+WzpfIPNH9mUC5ORq4RX/nEPj0QMkSAEEY1OoOSI9AIpE50CjKWLkdnlaBFQQRKFIuQ5c9ugXMLqCb+0xINHB0XyPz8p+DcSgV8AHvnvf8Fv/yXwL3/ZmH+9XyRfSBgTKilmAtkUSVGQjGUhPa1wVGPgEBPBWbQwWA2zcOiUEUOgUB4Q5FEhRGLKEoPRTGZC6oAKhmIUdDGRlZ7SFKhlhtxPUC1o+hM0ltOuw+sWvxi53CZcZ4nzK16NOW+C4uzle+JHn7E+HnEhoWyOrgWb5ZJsteA4wTA52ruOuQ/sDy1eOraLmlIblmvNpQ4cpsRwH0gjZJcnjjuH/1AwpzvaqqTaO4bTkdOUMYy3XG++jUiK/XLJSuy5mUZ6F1jrSCg0tV6hBo8UBSlF4uhZXa4xdo3MBOM8kg6BsR9waqbzgiQF0kvu9x/YNRP7lz9mXeVEU5I7hyssOgYGP5HVGevVliovsK3mx+2f8WL5CfVmQ9N2lJdbvNCsF9APhkyf+PSTT0kXDd/85dd4o5hdYlkakB1uGDDlijCNvPz6JxzCgMhmrrZPeMaC4OAn+7e8+3D/CH49K5jTRL9+wj/oPmV7tiFcbXnfPJBOkTFClefkS0GrPXk7Yec1h76hlCXnVxu81hBhbiS3zS0vX73k4dVrLmxg+/xjvM4oC4u0jtveEKcMVYCcW7TJKaYlIh6QY02qSwwHovW46ch9p/jHmy+Yc0loRt73D2wvLzgqhx8n9n1gwy3y4pqnRY2p4Prsil4ZOpcoYkC5R4CoWSSmAGlO6KiIsyOJhFYKgWKSAYnAYIHEkAI+GTKpUJln1hY3QC01uxixKf7C+PsVOTGYMDIQWsNSJpz2jNNEbjReZjjnmOWRIovgJSFZci0xKTHFhLUQUkbjQelE8AGpE6FISJdQIjHMOUlJdA5rIrMWnOSEioE8d3hpMD4hTcAW1zypJy4WZyQ308+OoemIlwm1y2hTy8vXGenlX9IMkfoyp7IrPnvxMfVHa5yTTNKz3x3oHg7EZDHLjIehJ9mCfKp5iIHhThL7gRgKlqNk1+zIY+DNGIgXiTwZbu8ahsNIqDXyucX4jFM/Mfk3KF9y56HRR/QkGEqHGEGZA7EXZDonlIZCLxFiwOeO0c+c2juEl+zHntQNLDY5koSYJUKtSJ1nqByn08TgWs7MGebZgsOhpawvyFVkvV7yxfvv0maG1aJgW6z5+vgO5zL8OLCbPd2713y/XrLvOu77W3R2zmpxhpIlvg84N3A/Nnx984rb/R3N3FArQ1Q1nXnPMHhOuzccbj3LTFE9yxBeY5nZlBn90OEPHdmF4Gx8zkkONFPH9tmKrFR8aE5sPluitk/Y95Eha5BWoKYcFxyH4Ya711/Sfjjw7MVHkFXUwlDUG4wNeHegTx5ZZZjlgujhIQRijLSq4wyBGAPL3NK9kyAmilA/8hbjxGwdReo5zYLgZ2ov0EpgbUWWVxjfUj1bE8oF0/sdJEkVAkpG/JxwKIxOzD4ihcRZENKydAntFU6MTBGUlwgZCPRolSgjyEGRxQljM+yYSOrRgPbntV8NEUiCIUqqGBjtox21ySRdMGg340xG6TwyJLwVKP24L+qVQ0TJYD0Sie0V3iSiiswaplmSz5oYHL0S5EIxiEfAwzDnFDZBPxOyNVJazDLhGsXVmWWVZYQAgzfE6cipbcnzRGkFC+3o9T3HtmcaPZgSu9a8uX9DijtWwbDMF5SmxFxonJBkRUHqDoRcsh8fISUiwqLL6Bc9u/ueObbQe24+HMmGC3oj+MmrdzyX32a9zfjmqy95lj9hVpDZmX4Etz+SaYszI295QJsr1FZTyCWT9Lh9T+dOzOFIqgVtN9J1B3CCQTWYTjAlSV5U2MzgsjX3t+9w8z2nl3uuf/N3CalkuczYJ8juTuxXO8qL77MSaz5077l907NenPH2q7csF45xqamygiYvuG0jWpVkixXN/QP5ypDlEMZEUBNNMxJ1zdnlGU/kmnwSbBYLFk/OeVpecNO1nO0arp6tqJ5dIm4fqDdrQufYC0FeCS6lQNeJi3yDjpFa5jg5kuUrFnVgs8r5cC+JYaD7uiX1kdkf0McH5t3Aw4d75qsLiqJCSI1BUWWSqCoSjuQCUUYGcyImw+XFFX6aOM2KjTI0k2B0gYuLBVMQdH0Cdc5yaTk1A6GXPBzvGaZEngumXc8Nb9gu1pzLa9a6pPf3WAOIx6V8mhTOCbz2yARRa2yKqDE9Jg9DIhQGHUdMbpiyROkEaYhAYg4OVVlG4ZExopL91b5KDI83owJHSArTR0whGIjIKAgikFTAu0RKEYlEqInMKEiSwUdmERBKYdKMqCJiFCAyjFXkyTB7Rx0lHQInFTbPkN1AzCO+cWilYKHQzqPnByLX9AjU7cD78Yj3gtRLCIlEYF07rpbn5JeSZho5dY7O38CQMIuPuF6ck2UVsyvZzfd0Nw3C91xuniK9obi3uGwgqiOkglxnDG8SOwrGdzd0sWVdLrjMt1ydF8yqQXUn3rSQFRnKWI5pxEVodke8HPhwHHh6XbC5+AQlM6Ym0TMR5BvGuSN0khQFlowoJcaU5E+2IAW+qBjuHwjxQKMbNssVYjlT5wEZ3vPEfgu1VtR25s2+4Sfzn1DvB14e3tGcnXN/2tNNEZH1DENAaUNuCu5P9/TNjn3f4oOnOJ1Y5AVyLR/3qV3i3KyRz3LmNGN8hvCO/sazfhH4h9//gr8s99T5xLOrp6SkSPmGIYyYS033RvD50wsOeUfqJtYXJfk+8vphRomGdmfZFJpCdjy83/Hq/TtWmUXtjwyjwytJKxyYSKkVWuUUSjERcTbh9p5pnqmkQDjLVuYUz2aUU3SjZLEqeJj3uGnCFpec3h2Jl5GDKThbVbzXA8rPtG7P0XeoLaxnhZIKlhmxkWRxJleJkOeMwqKGFmREmICaH88A6KiYZCQGh3MRURiUiahJEZJnEom8t5AkXaFxQ8TPASUtWTYyze4xS/dz2q+ECCQRER7mZcTNjwm+lARSlAx+AjEglGKeoUwBmQQ+C4jWgvZIL7AknJ6ZfcCOUDqF0IFeOWrhKFNChkAhDLbIGVIEJ2mUwlYRZyMLlbFJjochoLJHf/nT6x3dHq7XmuKjCzZa0jnN+XqJqCTGFzQ3M62JbILgsvqY88vneCtpTjec7geGIpD5glol5LFHZ5piaYkqQ2eRus05TDuOD0fc6oxjpqij5nwr2WTf5pjuaYYDrvUUWY4SgUYGptOJJWtuppbOzzzcz3z0xQaplszSo8qC/tAzTB1H1+C8Y7k2mPyMqlzSTRP7u7dU1SVlZphxXF9cINoZIZd8/MVzPJ7gHLsvX6Gbmeo6Md9F3t01tPsdH756S/g1wXoVOD/fcmgfaPYTu+kOMx0YK+huOx7CyNXmmi6buL8/UYrASUI+QVnmzIVFtj0HP7JYVczzzI9++DVffPdbPL2aybjmY33O+LRCOoFelLSxxfqAyyykDD1P9MMHkjijLjJ2Dw1u9tiu5e7hJR/e9fjUgYEs10h3YlUblBQIY/AZeC1IskSkE+PomcLArARSWExyFBdLGHsObyaCHjiEjJQJikFDKXmwI5xGLl6scPM1/fw1Kc74JuCPI31R8uSJJzhPMjmH4sDdNOOVfazeDIKIIliBlZGYBD56fAz4KWJQSCkJIpD1ApFApILajPiQUNoxyxVBnTBuxi0VWQpUSTH8basD/380iWSeDwRriEyPe/xMMLkBYRLLvny0jNaepCSpj0zBYCIUQTDZyCjBjo+qqSbHVE/EOpAdLHMfcUHgcsdaJloSyht0lpPSgpEOLST5ydCphmx1wbNizRgDsoJPkkGdb7h8fkV1iBRrT6MrbKYJ3mO3mlI5NmXJ2bMLFmVBf2i4OR45zB2besmTJ5YybZhkxqJ63Os1r27Zv3/Hkcjbt3tCankqLctnV2zqgkM20N//hK4LrNcr5nymbVs2RpM2Gyq3IDYNoSrZ2hz9RPLZF19QViXHfuY4eoIb8OZARUbhzrEEiBE5B0q1ZppG9rs9t9/8ObvDnuff/5gf/M4PuN+/onvreDs7zk3GuHjKdz4vefXmHjNLpuhYXW25uXlFNkrMRnP34SfIQ8a7d1+R50ueZ0uG48jlRcVC5CxqQTM8sPuwwy4WmCKhZ8c4Hpl6R5QFxSIx9xuef/wtFp8vETKSjytcN/GTD2/5MOwIe0+1iaSUuLx6zu1XR/TlBj0k7v5P5t4kVpckTdN6zMzn4Z/PfM4dY8yIjJyisya6ukpq2LYaIVErNgixALFhBSsk1DuGJRJsEAtALABBq9WtBtENRWVNGTlGZMSd7z3zf/7Z58HMWNxoqaSqrGoV3VJ+G3czN7nJXW6v3P17v/fNLDp/S1Nef/kjXvcl9c0Gr1cEaYOyGUeTe0ipKe2Qcdzjeg74EkeGmF7Q6S021DRC0XSKutF4LtSeoMxXW3TQAAAgAElEQVR22KwhEhnWH7Lp1xymE+z9MY4b07UrIvcA7YSA4Op6xyYveXcQs76+w9y2RO8fEjoS1yiarMdqQes4+EWJ8VyMY7G1pVdgpcQ1Lo3t0NIwdF3qvqNrHawA61lAo5uASDQYz+I1W7regPBIdEsrJbqP+JrC8+fiVwIEwBL3HTb02EqNqC1WAK1BElH2BoMAC7qVCPNWNE04b1VWXAut1hgUppd40uLUDoEU4IZY31DUPrFp6LRApRWZVky7lkHksmp7TtI9srylFQ6h7um0wDQSOxmQ6gnDUYB7t2Hbu+RLw+h0QiAUbhQxdKHUsO/HpEGMH7ncbTR1a+hbqIWgbVp8GSNxqX1JqhRit+XN1XOcYcLNaoGv4b1HMOhj2tSlvcy5sTVta6nyClCIrqOzKcP6kMvNBcgOMRrim5ZQBqixIkejG0XkWsxej78CrECdetRei247DC4icLFBxN2bp0zjhPHhY7wiptM5pd5hkojBrUGEOZt+Tr3NWSzXhCzwmpD3T96HtuDq1Q0MfOpc4u4yYjHAFR43fk21U4zDEccnMWElWa+fcXF3zagdkAhNZwQbm9EVAiksZlsxCnvG6YTYHVMVJV2l6R3DMt9gNgWlHUK5oC8tAycjG/nsKYsTOOhLj7adU962vJk/4ceXF4S3PrN9B58RwzAjTCQhEXbQM2g1w6hDFzlOkWP8IbXtkVYgAkng9yRlTmM9kkFIsd0yrxuQmqCJkeOOaHjGcH+LK0+RYYk3cPCdIVlySehUlNuOZm1Ric8wjSnm56iRT0OEWzeYrkO2OZ7VtFLTlwbZSYyrkEriiA6MJexc2l6jYwdfaFprcLQBR+JWkMUS02hiYelTQbLz6NsSx1pKW/zS1fcrAQJGWHZKIqzB6hmtXqOsh/VrpLXoGKh7hCMRwsFU7Vu1GClotXx7w+jpVU9gocXQaInXSXrV4huLFT1Ct/SOi+xCoq6lH44JA4e9eoBqSiK2DIKEdmEp0jtqPYZiyE5e4rUR6zxFOz3jQcrBGIxx0I7Eao+D2QHOzCEIApq6YLPM6ZYroshDZxuedz3H5oR7kxSdBWS5ZdcZ7iqXcr2mkS5ld0tmK7piSfK047aaU6oJ33844UL05E8Fjw6mtKFm62pwQ0ZjDzdeU69jvJ3k+WdvsF6CDOFgGOAFM6SybMyGaXCELcH0Dq1oCLoWs92yq9ecTWccRDG18MjblPquxgYe/sgwv7pFn79867FYWHbbgFni8DS7YTga8Qc3P+TN+S3xQ5eZ3YNhxEQO2SYtk9Sh1C3L1wWKltX6hjpfsNMtde/imhhtfQIPTJth/RK151DYJU9fLbh6VpCMhwyjnnZusH7N2YMT5GBKogXT0Ce8N0RLiyFl8BjyFxPaaE1ZaexFzY6SI8fB7SSO09HuOrpQoaUkjoaIMEB4GtdKlFD0TkhZGlQnkUKh3RE4FXWnGKYjNvWKPFekjkcvY7pQMDq7z7ZMGHpzhpP3cMcWu0pwnZhRUFJ5HctlwWPviJ//5IYPHyVMH+2zN91n73TK7osVeCGd7bC+i98rVN/Q9w6dMBD0KB2QiLcK1XRgA1CVAmHIvI6BgGUHkfTQXY0rNBsrCK1B9QHml/wa/JUAAWEFvgzIG4FjC3oLsm+wSqJ7idNoOhekcTBuCzYkDBV93yC0wvodgVX4QoGy2FogMBSOQWDQraYPBG6j0NZSrRr0SPCgyhE6wAQuL863HI0rsmbMcdDgVB4mTYncW5zDGdNUUfgpogzJPI2vBVbV3F73LLyKkDtmTUoXTijQJJOU2ehDtnlBpQ2JFxEEIbl12OzWxF3OzeaGNrbsSvj08Uec3f9d3KTn83/6JzzNSnKn4qP3zgiH9/hXD475r3/8v9Ite9y85OReyuH9M+KZglVJOfChzAkHe/ijgPXyhstfVHjOhF3Ws9MZn7/4JwxdyXR/inCm6OGEhp5pKinkFU9eLbjeXDGdTrlbZRyO5nzv13+boZxR5xUX5ho/VQSJQ1kqlncvmB0c4FjB/skhkyAkdxdkd3D2XkwY7ENR8+50TFm3rDZ3zLwZw2lKK0FGmigM2G4NN9dXyNjl8P59AlewfrFA6JTHn3ybQbqiKWLkaIluXer+ltPpb3B4kOB6gsCGVHKL53Z0fU877KjyNfp6gS9rxp5LKoaMoyPqoWLZWDwpSX2fIPTZi/aRToqexOx6MF0CzQ2YlkqEFO2WZVnzrcGAwlhyKalEwq69w8uH7N3TrP0J+/6O7U3I4OGIwjUkWPbiiKvjMWKz4uz0gLN3H7D/Tkvq+Bw+SHhjBrz78BP+rxdPERb8xlLRUjghkfARXUVrfepK0UWasrcoC5FncJTFd2Hhu7hG0CY9Ay1pi4YS8ESDL1wcPUTFBd0veRn4lQABqxxqt8EvPXAafBPimo6dFHhJSVuE+MrgW4OoDK6qKEuJMiClQSMJsBitqaVP5FhGaOogpFX6Lc144qAyRZhJbkegmpg2svQHLYOtz8ZviJVLsSqJj94W2vTrJYxjJo/HZJ3DKPCpbzNmI4WbTLgpcuryhvFgzPjgCOla8qZhNy85DEKS8RA/iVnZHMfxcScJ292a28VzuJaMz1KM7TmKRsxOjzg5PGHXFriuTxS4nL77DhNR8bKQHDUx3/jgiGCSsHndc5geEByU2AZUlRIUHe7IojrJ9nLHrq45Xy84CHKOh2Paa9jMK2rfxfNagsma7XJNnRfUJsExe7y+/imXyws+vZcS9yHLTvP0Yk59Psd/N+Xm998QfzphMv2Y26e/j91IdhNDPIo4muxx/1vvcv3ymslogQ0N4aZEdZovX3yOaRx6qwmHglF8QjiI6ZVltdziuA3D4YiyaVnN5zw6PMH4CdqE9O0dkXwH9+EWUT9A1TvCjcteISiqnONqwsq3hCqg29TsMrjKLrk5r3i2bXg0nDAZKGwSMzr26eoOXWU4gaLbuhjXxXFdrIFMalrpvVUMUiV1VdELH9V6ONmGJnnLBRgVkHTQ0tOVBX3tEyc3bPKO4XhCW/YM9hwuF5pg0TESKS/KjMlehHQVk73HsOlphYfXN/j9LUEfUdPiuhbTSSppaVG4jougw1ES0YJ1FPSaqg9oq46BFVgCFAVep9nYGClbTBhSeB3tpiMcbUm2gvqXrL9fCRAQWpNYF+XXlDKFviB3LEk7RtgWV/dUrqBuFEZLWlUhIgs1KMcgtKCSgBTIVtE5by+4rxyMsqS+QW5cdo7ibFYQOx2UlqytSFcTxCBiZGOyyMU3GT4Sbyd5Pb8hSj7m6plEu0uOD7+BMD0sW3RUsrc/ZHw4wql7Qhsg25auNoiuYVt2mKqg9iGMYvyux2QelQjw3Jjb5g6x7KkCi9P3xOOWeX+H6ksG9w/xopJ9dUxnc9ZXNU38Bm90QO1YgmhLvrchtCnZqkc2Fum2JN4eu3pNtiq4Wi1Y3c5ReyO8oIchvHNvwmpZcre4RmQp7sGEdWNxxZxTZ8b0/oCH777P3e0OPx7RXlQU6TULsUPPV1zYFxx+1eGetdT9nEHpodoJy77nE89lHCjumh2dUfitw7Iu6JqWTZkTpyGrheFIDRAHHknq0gSK8UTQW8VheY/lzS3GrQinUw5Pj2lEhG8submiyO/wi1OGw5iT91O83sVVBje6ISwmZPOSuZXIRUGzW3O9+IqjtCIen5AefIIYnCM8h2zrETgC4yiEL2jyNSBRAqZljdWaHSlON0SaHpPnSFxsqGi6AgIN05TivCKUBwzcOZEsaW4HHIcNXaZxjnv66xTRDehmLqwsNDsGziGe6RmYnEwJ0Cl53nP1ZkNrazztYWRLrwRSaJr2rTiodCSeESgraUyP1T0+Dk7iEpeGlo5ce9i6xlaaMNSk24LagCvGBGJD5thfqjn+KwECSEMnoRYTmkgx1hqpGppwi5s7KKkwlUdnA0Yj+Ta9ZwVatNRuiJCSQL/lcjeiRgiXqPPZeSXWOlSNxvV24HhcZC1KBtRugZekDHTBIhMcTxvsao9BULErBfVqxbLP8d1T/ETx7mjE7a7j5H7M6llGN7/BaStGITR5yoILIuuyrTJez0sS2b+t6Fq1yHWNVhlBkpK6xzR9A7FL6zhMI0nh5dxdXFCrAVkNH374DssXTyhcycmHH7BnK141Ld+ePGazuebZvkTuLLXTcFW15E/nBDOfIrml6wKEMTxKTjnzIoLEQTo+C13xxeVPiYxDHA9ZXSxofnFJR44YztgPlsgi5erlE26eXtNEISt9xck3/k2CStMtG37vb/8e/93//L/w5B/+EPP4AYPbnu6rf8AoatiEF3z2J1fM12ucZA8Tzjh9dMLn/+c/4fnqhiRMQLUE+48ZRcdkWCbhgIIB2+sL/E4wiVJW2qNpe/qdw14coI5Tio3Bu63p3JxORDRFRx9mHOj3SNyCbAyJZ3j5/IrN9gWvXr2muV1w7/4xHx98m1thmU0/pu06uuY5YXBFX93DqJ7YDUn8kE5WvJA1gXQZyZxc12SrHt9G2MAjoCEcpmzliEmz49I+IR5XBLNjzCRiFIA30bz+Yk5aOqhgwtk4YaO/g69vebp8ymlwhBl45HJMG97R65rXb16xKjbEbY8CXBNihaU0PsbvcH3wakPbaaRyCDsojc9OtljXIAYCk7kMMNRd9JZX4yryvkLpkHG0ZVsGjIKKza80CBioCw3BitnaJY8DbKXptEcrFNKNMc2WJtix6SRO7qBx3tIrc4MQLbW1SK9HSKh6gaFD1D6B3xP6DtZxsL3GqIiyyTHJEFeXBLWkiA2VGnHS9Cy0i9nUJMMQp7CkYcCBEHSZwKaK2xvDwqmx25o0HiB9j3jYoHpBeZOjOsnZMKDfZrS7Et9viaYuTR7RbsAeQDANSJp9wmDCpvojJs4j+iBGLnsS5TAWE4aP3+Fn6xrlnXDjl7y7qPjjm2dMvRHHsyHLNzfcugWy3FA71zyY/gYqM7QT6N2AZrthte3oswavD1FezNnwPt7IEh2nzJYed4s1V9dvkG3GTfEGEftsZU3juCzyK/xuw215x9pW7OYrzm5OeDDe48vwJXnxktnEZXu55dHojMuvvsLVHpYCr57g9edsfI1wYgIjcUTBMDni3uiE4TDB9i6T0R77RxGXUcrd83O2+TU2UDz+6DHJQNLl4BvLOD2gLQ6pnRynz2CQIqo1Nn3Dwlpk2VPuhmT6jue2YdN0lOMOp/PJytdMTk4QKmdoO0rhsVrEJA9DXEK0DEl9Sdg5TLRPU1mMASfwmaU1opGUbYcjEoZRQ9dqesejXxfoLsAtDdf7lsMYdBYR+0fcCIeBuuNg9BCvvGDsWGZxQtss6bIhaM1eHNBXGbVXsm1qdoArNL00eEagVYfyBLY36M6gPDDqLTGuNz1ShZhtT+Z7aFvi9CFC1HROTGwKRKRoyxbbBzh9wRz7S5ffrwQICAvWdYkr6CYu1apDqAFhVNPVBu21xPjUKBJhMJEl9yViK3BwcV2X1tWYTiI6jR9Z/C6grwVlqTCxJm4LWj+h7iPO9kruNg1BoSlVANq+zSHLIYFsyLRgZ0v23jljFEXooic5O6UuBWqscV6lRDag3O3ovIhgeA+ahuXpFrPMyLcJWeigdhbPeuReyCxoqdOEeP+QQ6FpVIHvBej4IzbXOd3dBX2jMU7B8uMTjoYDPglipFrzN4Ip8uyOFz+6g0HB/eF91kYRnj8H1+VgcI+svGK4dAn2T7neLsnbazQtDT3r1TMSxxAG+1zOG5yVJfAqdlmOJ1Jap0Y3DodmSF+u+OCD77PtL/niyx+zW+bogUaLFZd35xRscNUM/6qlng2wSUjpRKzP5+xiwyydcKNu2Bt+xPJuSycHjB7dY2xD6HuaVvPw8T2azZa+c+mLhLzYoaIB7w99RBQRRqfMpCSLJG2Z4QQp9t2OU5Xg3Bk8OSSJ91g3C0w5othYUu8l9eqc9Vd/ii06/GVBd+LhWU3S7JBqQtdXaL+DVYApBLdJz0mk8DyXzJq3Zce+QFc+oRZ0rmDXCa7sHYOiQnr3WeuOutrhphGaBXXs8L5VeH4Pfc52knIyTemXIVruIK2o9ILJ+IQb12P+7DW/+c4h1Z4iLxXzLzNM1pIg6K1BCkXlgScd+txBuBocQdg5NG2FsqDFW69Nz2pC0ZJNHNqqJHIsclsw2k1ZO7dMfclto4k1MBRQ/8VA8CsBAsZTuJGk2zj065KIAbXMaXYDlGhxVI6KIozI2WiBbQPQJUJqEl+CiDFWgDLYsMd20LQVfRDiyx7da4pWgGnwGthtfQLZUTyo8a5T3OOcIB7SDyp0ZRic7HHvZEg2b6h2IeF0Ta0jHo8NzzrL9ORD0sGS1SYlyASr+guWnqWbtyhSpv4+4QRqX9IvL2jKnudFRqc3fC85wRlO8EdDyuoVz19dIzYV66pg/+ge3/zk1/npl1/wMihRyQe4y5/zB6MJU3Ofk5nDcDHlxdULdtcLuvE+3324x/myhj5le1Bze32FZxR9HVDdCe7aOVm+RO0kZXnJ4/vvcPbJPcq6oppf4vQV95MzfnT1OcsbxcFgxBU/49HeB/C7f5svf/iHPDp5zOMPf50fPv0ZauLz0a9/h4vLS7rK5dceHlNS8cmn/wY3F5eoTnM+v+THP/8x+/eO2Hv8LkMrOb73PnsnM/By2nUB0X1Wq3Ne/+QcWzskIVy2Hl5bMJj2NE5C0ysOBjOiYUfmWey5xn3k40/2cbYde7uQi6qkLn/MD2/X/OKPnjLNMy7XPW0fkEQuejzkjRkRbQzXt0tebwu+dT/E+AkHTkgcWDABthYk+NT9Du1b3FxSS010GvAgv8fdcs6TbkngpIDLvbHLYiuo2xS7KliHiiPnMeIANm2L0Dkmj8jjE/LRgvfHMfnGxR14iNRjf6jZVYoXb37Kbtcg1ACfHdYYEimQVcva79CORuJQSIPT+zhujSRHaYEvBFYJxouExkjUvmU26JjnCzorWWgXz3Nw+hRHAfz11Yb/5YfRWLdAHbs0S4FpSyatpfN7dkjixFB2JdQexqnwvA6rXRLl4lU1jW0hAMdT2NzB8Xp8BdtWIvqOxA0xrkueWFLt09UllWvws4DZScdlkFB74LcNdd4zEy7jZEwiOoauwEQz/KTmJ89vaRfQhV/x6N1fw6luaERNocYsrje0OuKef0xpC7ptxWrZ4iUJ+9KCF7DNXF7qFS9ursheL9H4FG3O/YEidAzOzNKEOzpytn96h/vuPvvOMYczjxF3/OAPP2dt3nB10/Cv/83f4WLbsF5NaLXG2c4pH5SIW809Z8JLM2cna3BnKBkyTHvkeIE3FYy9kP1BTLW4j4PHxmwYnvts/St+eP2G0cEJfvoKs6v48NNvMB5/iJKS4BctHx7+GlfFV9i2ZTDuebJ0eO+jD5keTonHQ/70p5/TD0bs9y3j4xPufXPEDz9rsfMlq/WKUTilPzBEUmG1wRM16IJ9dcDg4JisvMXULZ0fcIRkOBkiI48qqLCdJki+pN/t4/drzMEx48sL/uHVDSeuy2A4JXcXyLBlvGsZBREuktnuDfNAUumGMwfCbkRUZZiBy7pJsXXAJJV4KsZ2Hn3T4h+49FWPyDS2rHErTR42XM0XnDkzsqqh0gFtfYc4/BC706xViQhKxknM+tYgZi/5oLjPT+fXxJNfY3hmyMoOFebstofocMe8bNGiZYCL7ygy29N5Fr8VDGqBcSVbR6PoEYmLW7ugLVoaLC595ZCpDcYPCRcl16mkOvGZXnSs3Bo/brHLEOX9xRWE8Nf3HfhPgH8HuPt62H9srf0HXx/7j4B/m7fSpv+BtfYf/VVzOD3Yu32kO8dqQSJdqlGC2ypGfY26C9FUCAl9I+mVxDGGLYJIuDjSoltD31mM6BmanlrGCNPQe7CzBUEb4/Ul2nFp4w63UERlxCqw+FvL40enLF3JoZyTHcZ05ZrlaA+xg0A6HHljtqctmXpOu3NZ9udoFWDymKHuQW/ZC6Y4XUO90oz2HewgYnnX0s/GhJOAKDJcXV8R65bD9ITZuOMyPuBxOSEz10Slz+rzkm9Gh6y/e0QwbAkmYz6whtqZ4h1UGFOQ//RH3IwDjvZnvLq9JOoFi6DjpEhJhSRK99ivCtpkgRoNefP6gtssI5Yh08GMpresludoM6d2LTWWMlwx8Q+p+x3pNMQe7rPfaJzkMfdTxdZ4OGFEJzxc0+K0JWwGhE5No1sqG9M6Qxzl8Ml7H2N0ySAYMtk74fFxSd5ucZoYz625XTSYdsv9o/t8cHCPp/0lpVOzN4Th/kM2zZpcXBL5I7RJKByfU1fT72my/BFy+4YuFSzPX1GvPLzPb9mMCoKgZthNkYsr6k5R0+GiWDYlz35wQ5MGjEaGs9F9Wj/EUR7a7ymSikYUlM2G3HUo6hhvVTGIAvTMZbN4zXq+5iw8YpVWdGJHbyWR9xrkO4zmHUkQ0p2VFMGU0A5p0ksGOuTn1S2XvWa215KUx6gDn2AlkKP2LfGKitB1afuc3lgIPGxuMZ5LqwyVaREBOL3Flj072xGpETGWomsQpmYmDI0tKWOLdh0Gq5pVqBhXDisvJRptUPX/Pwei/5Y/7zsA8F9aa/+zP9shhPgG8HvAR8Ax8H8IId6z1v5yWRPeooXr36G0g5CCyrOIVUMRlai3euEMfcG2N/iposk7sJJ02CKFxc1ggk9tQoxoqExP0DaMJyHxdMy+B5Wa0KxKNpsVUsNORPim5e464zAd88Rb4j4tcUcVh0uLqR7ik6Fzxd1ugLw+ZyNbQm9Ks69418bUtuVHN0vmbc7RoUc8k6ALQlexuHjFL158QW0trfyIdx/dZ298ihvEGL/ndH9GKbbcay2lr+iqKcPxBLjm6ZNzgsffx6ty2k3Jz8YBdXeD0+0Ixy3vffqY1z95xas6om0uSeNT5u05ujhglMbcNBuqgaLNJF29ZpLGRN6AMu65EAW7Ly9YX91xfE8yTA4Z2Qoj9hHxiH9l9hE70TMt77gzKfe8W17cDqlaw/3jiK/+8O/z7NnnFMGYbH3Le65Puj9kM4oxRcPEmfLZ01cMrWJ80vPkf1+i4o6jaYLIPXQn+PBkiBuPcEcRG79mZkDtPGxhMW7EYZLj9+CGgt3LFebhDW17DPYOzza06j6/WL3h6uIZzy+/YNXMaVY1Q0eRJIY6HKFGPU3XoK4dhuEpJ9MetedTdQHbXjEVPttO89WLJ8yfvuLR/gfkdYSnGkp7y8pJCV0fgaYXU+K0wdY1j4ZnpGcO49jAfIxNx3x1LFDLhtnVlNEjH5XfICcx7bLg6sc/wgmnjLYPyQ82XP7BDYdnKd2XEcvdFeVNgdu4aAJ8r6D3elwshWnxeoVvJG3vglKo3mC1Q5da+s4yIKIOMgrhYDqXvgvwaLGVJRAaowwfNQIjFLd/cdnAPx8I/EW+A39J/B3gf/xacPSlEOIZ8H3gB3/pHEridYdsgy0JDWVdIZRLWDk0GIQTUGgBQkGdgZU4KbCRSPvWmqkMOrTpoIXQWMbRAQ8/PuTdwfucDUcMnJK1CTCd5uVqw7yfM7+9Ji8qdlnPhAHV/R5vbYn37rM4ChhbaDyBrM5pRx8QbHfcZT2byzk/Sj1GwRHTuGby/hFCOczzlvxqzmi0x+jjD/lwtk91fcfB0Xukg8dovyaOI4JFy7pYsc7eZg9mcYrvhjxdvqZ5nnP8rW/x5fKcPyxbfvubI4Zdw15vmUswX0a04hlvupxTGaASSeuuMW5IudYcDASdyuh3HR/N9rnOC+blHabpCDcZPi4PDg45HAyYEeEbQxgNKH7rO6x0QXOxJU4cHp19n1jvqK/XHMwUl+2az86fcnN7jicPYDTFiil52/BVU9D84g319pwqDzlypjDr+cVXX9E6BfFBiKgf4ckIG/moviO7fMq0Sjg6PiHyZ6QzFzeJGAQ7miDFsVsmqmF52vPitsCehvhLl7wtcbxn9F1PX2e0Xy4J05RYu9ixRugelV0QKMt2WzBKPmHsdzyZOQxa9VZUxrUoBlh7ibur2a47iuQcnxPaLkLjkXgdypboFlxhEZXDqlrj9pJFa9+m9GyP9kN4c0OWw8FZh+7eZefNcG2Plzxm/284bL56id9K1C5mdfGG5z/bsfed93DWK6LeYB2L9n3cqmDYCkonwO16JC1aWgJpaRNL4/iMNjV0PW4XkasMX/aoTpLREZmOqgpxrEvpehSBoNqumIYhTtD9S+EJ/PtCiH+Lt0rC/6G1dg2c8NaM5J/Fxdd9fy7+rO+AEFAE13ilpVYSx1dIDMZXYC1R1mOQ4NZ4rYfvdlD5NE5Po3ocAUbAQIJyIuR4ym99+l0enDwgTAumM4FtHnF8UOOIgG8633krYcaQ9XXJRf4FP/jHf8LiQNNYlzIJSfIAEQkmdsyrQqKiG8bjR0zNmjyvuWh7nCbHHY7IepBuibza8frFFdu9gsP2kHF6hH8ckbOjv36FI3xm9yPiqeT67oIjccimr9mst7z30QNuVq8pY4V3z+XRdg/xao54UiA//Zi8XrO4+BMGByfUesju529o33mPgbTszq9QTkh3f8RXLBhd+3C7oXjnlNYxmKRjuX5FjWAa7NH1LW5YMO99qlXJ0AtQLz6jDmP2Tw6wtcuzzUuMTrn8/Dn6Gy0bJ8NcL3E9l4/HR/BoQjmH5+e39E9/wfr0G9hO8fg7M7I2oAkcHqUReXXD+rzn7mBJPIKH0Yhx4EM3pN5WRMcSFQq2Rcm4V2xKQRRPUckxuT/FqWr2xTnqds0NHWGXMQ8iFm/+b7K7GCY94WIJOkHikjUW3fukjcUep9xlBVnnIQKDkh3CBtStRiQt+2pEvbflF6MtpQh4fVswEA6mhMDfY+8oZVUt2F5t6KSBdERpSyZBguvssaiu0FtNOk7Yf6CZhT6lrdkPIcsknbflvWLL/+MFbIc7wkFL+ujbbD//ku+R4CIAACAASURBVNlowN0OauEyDgSYHVvXpXZD4qJCSsvOerQapHU42PRoT5BjsMrSOhkIlxw4Ng7KddnECsoer7J0XU3ke+jDmNVdw1CJX7qQ/7og8F8B/ylgv97+57w1Ifnnjj/rO+A40ppK4aOwcYfoXRozwO5WhEphlYPqDHTgeg61A/SakICd31MLxYk75bsfP8INa9rtlkms0RS0WYWaRFRhR7xO3+ZS92HazHDCBn3q8/7dJ5x/647Bk6d83muizY4uEZR3GjNRJLllNxvSX71GDGvkzOex+z7d+py2KVk8LSjLEvdkwnd+97cwRc3mrkAFGxzTUG0KVsEd06JD9D43iUvkPqBtAg6GY0YfBqzWPe/t/zrq2xGrL56z8zs+PU24zDTezY8p8jWlOSDYLHl8csTvF0/QP/4RYdOTfjrmu+lvsG7XzMuawSjhzfNzvvjJK46O71NuClTlcOC7bLuO269e4+YL/JkPk4T2yYDDdIaIWlxZEg9HcOnxlXnC8DsD1k8u+Meffcbf/M3v8v1HH1NuPM5vX/POwxnf+50P+cmfvODq8gY5kag45EEbYGVAlhtmwbeohz/go73HjPYekYuCp4s71KZkmkxZX/e4jsNEG8psyWh6zMSZYQJF2VeMwjukE0G+xmiH22WOX3dU1z2vfvT/8mR7zb/2a9+jzw1+4rBc5xzbDD96wDTeozIGlSuOxpafvoJ3RqD9nnV7RxvvMxmeEokD1oua5a4guHfMTlU49RqdeUgxQUjNySjj7uKG3UQx7AfclIaT5JS9gctLp6LdjdHrlmSk2Y4Vi/kXnA3eY/feNzm4+QlFqBDllG+8M+LwYUu9UvwPf/AZgexYdZZIewhPQ1GBr2hFQFr15ImisT3rxpKYDl+7lDZBqpKuV0hfsR12DOqebuNTtpad6Ri6B9ikRjdDbHRD4Rp+GW/4rwUC1trbf7YvhPhvgL//dfMSOPszQ0+/7vtLQxuJkIYqFPj5Pp0zp+lWCAy4lrBu6ZWltRKhMwKpwPERfYuoA4bePt/84CGPpyF1G9BOI6J0zEQ3ZLtrzNF93GBG2yxp4phoecdVWhKIEGnW5IFlz7PMhzGTL15yHY547FjMYUzVrrEHIcfRBFOvya43dMrFf/dt/fn1mzmZlxO4M068AZNSsGg9xuOSPttQ94rDYUzZDLGDhqPJI/rjCYtsg+4tZZbjXQW8mZ8j1xPeiYfYsUdwU/D0LicMe87U98jPHNAVu6JhXvg8ygXNscfFec/xa5c/Przk45Mx3zk4YFk2uJFL3Bjq25y26ig7wXCc8nic8kLsuGodPjx8zOO9U7qyYTCIqYOWyHP57IcvmEmf2aMhQX6fp/LnxIHDIDzlo7MJr+Ocr95UvFzPwf2AYjRmZizBOCTPE2zUM9aSwYFLknZMj7+JGBwxCX3SbMnDZIy5P8WPYrqmo9kaas/DmTi0ytDbayb5gCpyyPWYepHRJA2VDDm89rnU8OrNLTeq5sTZo2oVA6dlLX2uVnfIxOPYcQkQDM7GbFoXW0vu2QW26+kKC57PUau4MRnYLU7sEsQNRhUEskYbl75qYaSJ9yVOHrCcvs+ZKShCgVrdMG8ceu7h+ZbxgWb9VHAkBX0leeD/FrJaMUhznAeGIy+kr9cs7iSzRwkvyo6oX7HqJI7jIkTDoPKwWEzbUEtJ4xg8W2IQKCGoO4myLUFvyKWLGVcMcw3LMXdhRahr9iy0HuSDBYPC52i34GXcocWQf6F6AkKII2vt9dfNvwv8/Ov9/w3474UQ/wVvfwy+C/zxX3lCq5HpiG6yo3/dQBshVYUZDGk3O3QQ4OuG/d6ijKStBJWtaB0YSMHRdAvultutT7VbEXsua1HBbEjpzFC7hkmYYzqP5K5BPB6xKQ267/EDSVC0NF2JsyvQgzPGYccuW+JULrLskZMEq7ds/CGzmcfl5YLXt8+o2zteLxachsec3k9x9wSXN5f0pWLXlQjdEUqJMQP2B0PS4QEmXLO+qWm3PlrvWLU7wl3F49ERz6Pn/Gib8bcOPiK7f8KbHz9jUe34R/N/ypl4zJvFDcNQ4vaXDD/ep9TXTJyIychj2BvcTkMpEI0HwRneIKO927KpdgghaKzietPT3BSMVEzeSe5MxWwWU+UdxycjjvsBF+kFTZoQ6RQn3aKvFeU2ZFHekXUf08080tRnEE65ikr2NyHVwR5uJfn8p5/z+PCI7mTCWZSSuw3GTTjcPyFfP8PUHmIQkgQuh/v7NLqimXYUq1vsVU81O2TVxpRuQ1C6jIobXGMwQuPfbVmkJ0Spw+ryCb6wpO4ej4OAdRVgbj2SmcOmO6AdOmQteDhM9zSRNyauSrbGMgxS3KFCFC5eLVGjAV2xpmkLQiEpzAS71viBQdsAnbwlsrlkrHTJeOVydnTK9Vdrim5OE40wbYsnWzat4kjAZt9jOy8IiprKcSiUoat76lFC0EVIeYGNHFTqYumobIDXtOiqx1US/BZhJGWf4GAQfo0UHQgPqSHSEOsp1m7RkwZ3rXH0mLt0h61jhpnEsTteRW81CsPdll+mKPDX9R34HSHEt3n7OfAK+HcBrLWfCyH+J+AL3lod/Ht/VWYAQCGxjcZ9I5FiTWcdAmup84o4MdiyYRq45HhkQfH1QyForGA4cfHShKJxqKuWvg2RqYsb9cx3GV7vcXnznNvqORNzQHr6Du76FvHGYM+GTCczNqeG8mcjhgdDnFnFusmo8g4TjQj3NRM/Z7kyTN0NZ/d+k3T/CU+fntP7mt/6+GOC1GXqpFy28Gz1glk/RQpFrjPEOKXtDVvV8Xp1i4yHJI5mNBMUy5S0dBFVwfX4EqcfcWam3LZXxNsRvq0wz+7QH8QcTcdcXl/wpz95wXCc8M3H32D64Fu8eLJh9/SSN+2XlLt9wqOayfEpctLTXC3J6pbJbMxRMmZwb8xylRFyyOzsGN13yIFDcnDI0z/6jNflFZ3ZR3qKgR/SBC7X6y21v0H7OY/O7rEME8rVK+pAo3cVZw9bnE++x7d8yU9+/oT3TyfYTnL3/GdcyjtCLyWZnnHsTxGjA1qZcW8UkYxPSNWMY0/SpT3ZwR7NSqMSSVdX2Dan8ivetA1p0SJzD+9+y1A3DPsGt5E8HHsM9/ZJjwKQLvuXE37w1VP2rEW1PipN6UVIJzU353B0/AmqLOnVEqUTqkiy6XzOghGLTccmFxT1GqREhz2ZOMb3LIFQ9IcJA9tw/vNzMt/lYP9TTj4K2NzM6VZb6nqfJNGMhzu2dQLLVwzFBldqzOoe28Uc20HrP8P27zNyx7y3/yHXzy7pRIer3gqF5i50rkfUSUzbYGWBp1w0UFmBLnuE5yH8DmWXuJ4hLiUiMeROjTKaIT1OWVBKl1J6qD7E2Jpf5j7yL9R34Ovxfw/4e3/Vef9saAwqCrG7mkb5xL1H5BuazqCNhD1Btg0oZMmwVjTGR1GDVYiNpBWCOt2g/B1mrblZW8ajb+Olhzi6QWWatW3xh2sOhxmxH5AfGpzS8OYyJ4l6lOtgszfssoouiMlfubTDc3773Q/J1orLbME2CvFePmdlCro+wBOShZYM2OO2KdlldzyI9kjdiHmf4bUDmnWGORsxfPiYeFGQVQuqJqGjwXNdTgcR43cOyRH0dcPIJFzlW0qW1M4teVywfbXkJ3dzQvUux+8FqEFPbSXzuwxXFFjf4f9j7r12ZUuzK73vt8uG3+64zDxZliy2SDYhXQgQIEjvIL2CHkKv0A+iO72DJLQgNNqyKVZlZVVl5jHbh1v2t7rYhNAEukC1EZATiJuIwIoAIuZYsWaMOT7qV+zeFVA6fLpnce65/bwnLtfYty2DO2PGhlQ0bC4k11rgU4GLE2KaOXnPfOrpyyfoKi5Xa1aXltxV3MaJqzc76nbFNSMfPj6QPwcO1T3Lf7Xi6h/NjLFjfrrjzbsrPj7sOQ0zWpRs6iW7zZq7aWQdDSoqftg/85bEVeFx9obRJyplWW1KznTkypD2kqZTrHLJaTMzp4mmdygReK621LuSz8JQ7wQ/7BXt1pKs4LoSHO0jRduy3tXoU4cJO7I6QxnZBMdTljw/33HTTlwUjqdCo8pMs6hA18yHEWxJKTfUxxlhTzyNgTAbrJTIkEj9R0S6o9IFh2PCjj37PPF63lGUicObinyoEfNHqO7QnSNuLzHnDfOUqN442qsVTY7kLBgaQfARK2DwjhQtvrA4OSFNRnvDYnbErIhzpqTERUcXJ8bGYkaoC0ecMrJJnNolqUsUJ8X1TWL/8Md3B+R/SLP+/1eK6viMyBHpK7xyPFYOlCEOicsHRXQjOSScFC9vWkpetwWrRUuMEPuMP1r6IHBVZi/P7P0DMdxzttA9QuwV4+dH5n3BxZyRi0xZjNwePtBWNWLbYPU15vcH2neJv7pY8kPfEaaeq27k+sbi4x3JT7zbtBSuRtZnmu2Z1XVg3RRUtqIoJOu4xAyWU1wyTi0Pv33g7uGRp+cnjodbdNJoXcCy4JRLvj16Pn37Hd/Hb1DnjnTw5F1Ns3zDrlny9BSpNj0/+3rFxSfBm5sVcj/xt59OXPxyyZdf3jDkBZfzkvDgOD8eOU8HmPfkWbH/IfP7333PyiRWb684WcW4kpzDiucPJ65WFcIYLiZJ/uYO2T+ybRr+8HDLdveeize/4P505ts/PLKsVyy+XFNWkXnRcakTv/23/5Kn6sjplNhebPnFV2/42ZevEJvXfPd44Pvf/TXdx5FdZaiKyIPvudOOWAV0nhj2I05CsxQ0XcSEAl9FXJyJU4cuerruFVO5Y/HgCL7kalA02vPqDbhx4jRH7peGH3rF6MEGh9utSLagbFfIMhMXmqRa6ljxPA7EUNIEwTKVLClpc4FUijIXqPmZ6I8Es6bwmtpm3vz5hufjiUqUSNcxe8m8VOjLAtFsGCdJp2a2TC+osKXk8XDk4buKOEBLgX4VOD2V3N0+0M8zIlQQIlOUnFJFjJJZQVKehS9QU8bkiFANRkkihqPzXCRPIzVFjqwWJVklrLXISeCLCWc6louJ+ZQYtn8kZZQfi22YxKgEeaOha8i+R+2X0B6RVtHJSHQ12zBxjJFCjiyWDWa9ZbXQhCHSnQ/EaY81DbO85HE88HZZc94WfH564jD0lCZS35R0P/wGXa5ZNRfcqUQ9XuIWPxC+Ncxk3v/qPX6hKT4Z4uE31KtXfFFteLgz/O1pZmFK3r7f0U1HnHM8nQMP54ldUdPi8eoOFiter95yWTVkHdky4RV8GgVphOPFAeMXRKHYuooL8YxerZlDxXf9X2Ndy/qrCrGG99c/5/bzB/73f/ZPWb55TTh6vvlf/yn7+itebVu+fPff0ogjDx9+yw/nzyxeXXDTfsGnpyfymHAPn5EKylTjJ41NmmMWzFNgEQ8c8sTkIj/581/y/T//yLz8yOenTPpNRzM47uOJ/2p9RVu2rL++RLVw/hcdOV2jKfjw/HtufvlfsJob8vwd2a751Zd/RppP/O43Z1ql0NEjwmfO0yVFvSNMZx4/3iKcwbYFpMzxoceeZnK5RS8zddHyDUds0viD5/2rxChqqq2hbiJ5s6TMS8ZPHUuz5GE+MkwjO3vJT1NEzZaz84zpEVkJhK54PDlU+eLoDELjT4lls2LOnpwjU+iYcgB3QoyZ81hjCbijpM8TC7ukuFiQVpYs3lLbipvNEk/H6rpmfRWIix3tueC2vmNjf4X6ieXiQRHCQFYCGbcczgf2/ZmgBQaH6gu0mtChJ5UWMSmiikzG06BRKhJT/5Iarjy7JOizpKwyaTIMrnshc+nIY5IsH7YE/YyoHDkmmP7z/0X4n7kkQrWohwGfn4hKssJzmsBUGRU0UxhwWVJlS7QaZSy172k6xTjVBCHp5zOVm8mVhLJkGA3x2fK6veFtEcn1mcYLpiy5qxd0fqA3NSZ0mIeJY/JUYskkNLl1VNeWt/FPGOrMfgyE4wNZJar1kttDYNIJOVuIcNVaVFR8OO5RVvF2ccN6tcNVM999/sx3w8xGblkuE9ZIVrmm3kLAMkyZ/e8j7jDR72b2s+ftV1vOf6vR+5FyZ6mqknmSyGPPYrXCrX/KT0ZJ+WZJPH/Dd+eJv/3r34GI7E4V8+zwQ2C5aqmE4ql75hlPm2DhQblHfvj+E+fHI3VT0DaK5rHm6s2Su4+WqR0BT9007JYOsU78zR/+OdytURc3qNTSLBPqLDj2J7COt6/+jNs+Ec9HPn9f8+p9zfbCMUZJ/aZksd3QFi2u0mTfoo0iqxnpPMlLnILZzITqRNNXqPHAdhTkdYEfAz5ZygvB/feetv4py6XnXM38RP8S2cH4xR3y28DuQuJ3NzTtAj+NyMnjpiW2qaliIKUC7S3VhSItPc95oE732DyD9kQZcEIiSwtzZLx7IBaB8SzJhacqXtEzUNVXLz6W2UEqkAjsfqYflzjzzPtpYDyd2KWCXElyVVF5gVk4loNBHY4vQJ3yhTA0DCVST2Ak3mmky1RmRKQZ5wsmbTFSYGNgqjwhSFZjYlo4dI6MSrNMmiJrTDWhQyb0DWMNC1ly+H9d/n+/fhQiICQ008C5KJB+QLaS/hTQsmKKkdIopIJKDuQBKlWyDZZoJV6ViNYRfSb5gofksN1Eyg3+RlMGwSKMqLVFKMO9m9jqiocx0Jc9b/eez6bkvrIoZTH2yMgaPUbuc4SrgJwGOBoe5kBhQLSCwzThn0dyDry+viacRoyYuGxbsk1Ee+Jx2JPzilqtOMoBmiWDDySd6GLmsIfkHxGXt9TLCEuPf050CKanDxTLSy6uK6pcs2TFzdsbvrrYIlSFKAOnDx5xF3gaTjyk3+PXkvdig2sMYjqDKkGUqKIiWM15PtD7W3ge+PjrT+yf7gjSYbsN8WbFfOpZlA3LdousNwTZ8UM4s41L9PI1vT6zOpYMIVNEw8X6EsKZv/nuOx66FVbcsr7eYooCbMOqXFF/3RCk5PSwZ6crzGqBNAXvjEZpi/OSMHegIqSOaQxMh4QvIj55zhOsXWa0mVg6rqfA4dMDQp2p1JLXfslQDXRVwae7E1ElnmbFZe4JzKyaDaUSHNszdmoIWSNMjyoVOSuK7PHVwCgkJEFyAjs3CBUhDlitGaNHp5o43NH3hlWpCalkFAFGR+0TxatLkoGhMlymibm/ZKwTUgiYCozes1MbBAtOzuNtoC8m+lJQRkGhHb5IiJQxwVPmTBQabzSjyBQTKOnxKSO0JftAKiYeZc1WBURvkE2Dd3sukmAoLV2MSDkTq4jrfux5AmSiKsjSAyXVoJmYXigrcqQbSoqoqJHM8mXvmrKgMhGtDLQSMThkP2OkIRmN157D9ACdoVSGxj9Q1RVdNujacv225IfDA5dFwyJk/ENCNz1j39EPiuIA+qJi/PYPEDzLZocqE6UuiXPmZmuZzAXnoef+4QnhPG27ZbVr8EFymhJP+2d03vPVF29YXLxC+UQ4FXhGzqFjY0fmjWZ/L8AFKlPw9Zu3XE0f6E0mWIfIOz7+7o4BR6sWFNsLWiH57vYPuBQ5Px9wauD5MPDl5U/46vIN/+ybf4NXhne/+ILheGZ0kovlWxYahkPHx8fv2HcBta74q5//Y97/6U/4l//mW0oTuLza8t2vP3B6+o5qfUGd4evVlrko+e/f/Jd88/sj2UX+5NpgbxQPoeKLt39CaDO1SKzaV3j3EqQpmoptWtHnwGAD2WqMeiEoNdWS2MAmSZRf8nTa8/jdRx5zT5SJxqw4ZcdlkugZSltxJT3SWHz4iFaaIq/I5Zp5C839gXJIzEGzHCPjOJIWmRDuELoguYm56mkJjLrCmZJ29PjOcHgu8VoyKkM8BFIUaCTTJKhywW61xtuOcJhhsizx1JVFPJ85ZIGpr+jEnq3QWHnJ0Cku38F3rsbnmSFHlG2pfE0uJSKd+fQY8YcSoiGIjPYKW0YQipwUSaYXIlOGyihkIxCTxadAliMrp5FRMjeJUGQgY9QBudJ8fHA0REiWaXxBksfzj1wEcoJQOvACKUdkKIjCEOaeRRSUcWYqS8aY6E1mERPPcaKZHdpEwrkiZIU1JeMcUSJhihIyiDgzHB8wmysuL7dMh567/SNvvrpkOcERSVlAIGJqi5KvGc57/Ax0kmpTo8cluZGkD45ez9i+w7aSzeIdjfrEtw8fEV3B0TjmQbFd7GjLkdtBMj4W7IdAVQ9El1hdrZnGFf2nO5xdci3WLL6C7/6vX3MyR47hxK65IaaZ6Q9HPryb0GHCrj2meI3szAtYJCv2c6ZqNZui5PqrK2IK3HUPjPNnwlVLe/lzbtoVOsJcQQiZ4APDoafyR8oQGT53/K79gWE88113IrUV+upMGwry05GYBT/cR8zVxPKhQuuAmc7Y7XsuZUPz84TuDoghsRMSKSVfrCuK6w3EgBQNJtS8vgr4VSJWlpChj5GFL7DyBYaS44SRE2/VwJM2JBWodKIKBXOasb7k3M/UKTI8PCKVwgSPLM4kZxh0y8ne8eYn17DfMwwaozzVINg1Nd/fz+RqwLfX6GmkKieKpmI8nAn7Z+bkSVvPNHTkVYUJL7epUtTCIF0F2bDdNOTcMR1OqPzS0KpashhBoKCfORpNce8IzQEzD+TcouIJtTOYtOasl8ibTxDGl2E4hhBLGEeMUvg4E2SiSBKJhSQZ1cxCTRAtYaoIKVJWijbD4aAJFpgl6ZColwJOYKwimRl31hSFZhz//f33oxABAYTckpVDxpmsPTklyBrITHiwE/3RIJMkGYFKMzLPHCZH0BNeKqjW5OxQ3mFTT0wehSbKGtTMU9cTp5HCVDzvDzBpPI9ItSYpyXAraHcD725WHB4Cx2lkdWqxrwrMc+A8dYwpI1pPPy8x6Ug8S5SD9XrB4nLFYrdBDYpx0qyKE9MyEfoRpxtEWWBSTWstxetA3x3w0wHtJHP/QF7uOJ865l//a8KmxNQR+3yF3GboAvXNjJVLrrigFRtuV3dMpxPrUmGVYRg8x/ke7SsqnxHKoXSNi5nRWJpsyWNHu9ygtURQE0xFPPZs5TNKbknjibj4Cv/pHn9QlEHweNvxq7+84iwDLZbrVz+l3dV0x8wUwDwO+ELxTOTdvGPxqsKbmo/9wDKPrAyYrUVYTS4EOXhMfCRMFY+uwAhPFhFXlgzCY4XCFjP2FBizpDxrxI1Hzi9x2mcsVSvJq4pDUdJ+CEzqzHj2vL5+xwMZJQbmcY8Kb3GlQClJNBOREznWDJOHjaBrMkffEXMglS0JifQVqdSMNiFyYEozi7TkSr8mKE1/fGKxchTbgvE3mak8EKeSTf9Aslc0K0vvJ1ZTS1d6xpNDtZbFqeLLRcPtfcHtD98yjCe0ENQpE21EpUiWDhEFIUkiCRMcabYvJjkUCInWJbOcmVeZcjJURMYEcp7xEsYus1NLZjmTqUmlQ1gLP2YRyGRyCkjv0cow2RLjEyFO+KpGj56YFCWJQMbnmZDKF3XzFUJAW0iUKphTYjqcODBDvSDHgp2xTCYj3ZlYana14vQwMSSP8I5KQ5EdGsk+TPzZF7+isTOb4XsGuUR6x3kIYNaU7kTZZvYPI5/uPpMbQagU9lKhVwvUoqVZR+bniTZUbG4uyTmwqNe0zYY89ZznHj+XtMuaw3EgHGYWyxVytUOkkd9+/kT6XrK8XvOrXcX2T97Q3t7za5dRY2C8GilOntcXa/qF4Xx7z5vrS2JWdCfDz3/xnuqq5Ljfc/v5QG2WXCw0g584hAdEFZmGRK0LdKXwIXN3G0npE5up5PX6Sz5Jzw+PH7gsrqEqEO5IMlv64JBzRE0zzlaohxOLr3bI+8izmyleb9FtxTw49BzJyhHXEasriqpGhYmTrxinyHh+YOoTVQ1VlWlWJYUQ6GQZ1Qm7t+xlhywlhfN4Y3HujAsDvjaItuKpmzHpwL/6/Sf+9ptvufyv32Jig5+OaLVGL19mOsIOVLWlmwbWi5JCR0JKnKfEcchYG6iEYr2qmU4zbga9sJiUcINHtCOb6xVjnvHzEiErbFxSbNaE7Ih1jx8baqXhwjD0EeEl5bSiO/6avoevv1wxTZapCKT+iXkcqXwAMlEqIJEAoRSVFAgnkAkynkmAzBIhIOgJUWSsT6QYibNGisRYGEzI2NJykiC7xBwCsk6Mfww6wI9EBKSAsnAMJGYatA9IrRBeorOBrMhTAl2jswciZyGwqST4hBgzmURRDZRKorYL9s4T/IRwE9up5eQtGw+7lUHgmMLMUByQpxorzwiR6a8yLQtuzwfSUFFeV3xZfsk0JlD3fPztmU+ypT9HLiuP2BkoE20SZBQhRdyYmXUizQ4zQnc+kFvD6jJRFjOPzwN3pz1FWVCpBYUWRA0xP6HzjJwkFzc36KYgVIbRO4b9Ew/zA3qq6fOJ7eOKHkchRhb2LaXwiGBBWYgPuFywNjWLpWIanpHZURaCpAQrWZKjJVSBlDz4AzhLdwiEsqM4VbATNNdXNN/PXFxc8cXPNgy5YH/uSUPPp4d7vi7/krWIhHbPZftLnO4YPjg6HPm8pBWWmB4pigI5l0xaUncJrSp0Vjg9k5MhhY55MngHqk4UdYEREuQlxYWnjgviCOfeYTczSRj6rmedS5IL1DXIaOjO9+TjM3UR8YWmrd+QhgghIZcFYvTM3ZKmjeQAPpfkvWd+mnDHHr3VkATZSYS1mAK0AuEGxhSxRpJzQ64UatTkPuCqkdwEjFA0heWqaTgVBXVMhNHRp5ky18hSMR7umNNPyGmGuoPSY4QiC4WrMwUe4xRBRuaQwEiyUSQXUQiKpPEqYKQkkzDhJX7fxplTJWgFzEqgzh76klR0OFkitUdJSdaRP0Yi+1GIQAZQM1ZWiBQxtqcXCpktMVZoRhAOx0xBRnpNOCc6O5EMRAJplihtUKZC1jULJ/DzhJkGmDy+3/OgGlYq4ecSpzy1MphYcDxFRC/RqwVtMTE8HqmkRUyXXcqljQAAIABJREFULFXBmB7Y9zNBFBQNrKwl65ml2qAtDPOBlCt8lC80pVAxk+jN+DJryIn98cgx7RmeI+c5U68F8njCDYGH55kzicXpgdq8pq001eqG337s0MfP9EPg5B4p+jWF8VTzzKl2THakvLji0kpO/UhpEsWuZs+AebyjaJZgLcJmQqHJgyOfI/XCcFWvOHcdKTokiaqpmDXEPKNEpl21rOMaYV+4CrOtiArMYoXI4MpI6nvqaoeSnvW2IPl3iOeBbvGIXGyQoqZJLd2oyToAhiAVpAk9K2oBKIHUnslL+qOjHSRxmbBqQZIJmwx50XF4jjgvGPwEk6eqlwgRybmgnwdudpe0vxyZC0EIAeEMMR2Zc2IwJVEKVHKQLZP2lC7gRcSnAznMGCy9m1BFCZVEZ8dxOCNieoHHnidSLhiVJ6wT49MRRsFEoOkX1EvF1EgWnGiPC2apGMNMFcDmktxsKIUgKokYCtwI0XqGYBA64zy4bHBxREmN1CBSIpaCrEGNCWlASYGOkUxglAqnDQlNTgFLYFKB6AbqWaByJhc1Okq86P5o//04RCBnRgGLIPDaMSeJSQkrEqQACMQkQUaylORC4/PEccwY98KPS1HjsyfojJYNy0VNKitS2eCGgeK0wEXHcb1CRIGpLI0tmVVk7mZsaamKBX2W3NQ1gx7Q64rDvmOQmbkp0MsNbe5YlRUf9yfkwqK9p7A1cxWxMhLCmVXTIFpDMSioBUoqwjQTYqJpaqqqZLsQPN6eedyfOPRn2jcb5HBC2IZmvSXRsKgGLhcViGfu7zLH/rfop0C3stzUf4rLBpmPzKZElJLLVUsSCx72z3z38ZYUOyQT17tLnM8cz08M5xNFWYINeDUxyYIQHFVd0UbF5VdX3Ly6Yj+c+F0+ow5H/o/DZ37xV3/K9bbkeI7c//aOf/xXf4k2IzGNnAJ8XbxHfq25/7cHOpWoTIFVNcW6RCSNkw7hJsIsMGliHgZk7jBNIEZPMhILZF/jeo9YT4zDgEoaCkcSCedmwv6RsujptivWRcYMirsxsnv9Jeuriml4omCDm3pUq+nNhD1JwFBXJdpIznMgjEf0QiGWiqQhZYgpQ5EJCeapRBLRtcEUcOgiZqnJ3R6ZPPf7O7bZ4q8LylFwHgOFdJSmYVp4dIwYKUjuDDGSp4F0EoTWcKJnEiMxRnQTKJ2kR5JziXQZaSQqCnycmYTAikxSmZAEikBWESMSWmtkBIaeMUNdWMaQQRqMDrgYaWSisyUYBYd/f//9KEQAJHmCWQeiCoRcUKcMYsbogTFlggWRNEpmZiEQRiFjplQBKUqkbXDFhMmROU6Y0SAjjEPAqkhJSbMqkIs1Oc8wJUSyFMOIJNDkBidG+s8D6auWQye5rjJ9MzD0HpNn7K6mOr2AIZv6irp1jMpj44IsJMYH3DDzYfY09YLlpmFSCtcdkFhKWbDYLRFKo6NjxLFerqlXG07nPaJcIPSAmTNDeOL9lebmzdfk9TUNn/nX3zxDBbJdE1Vgs6zxQnDz5obduw0yKbpvfuC52xPHxOJiwW61IQRH93TP1D0RpGCYI9048fQ04P0TOVnWqyvqXcXNdofzI4/3ez7ub1namnM/cPf8mXdXv0L3d4yfnkmnJ5rmRTRHFzh0iWkxEi8EVi3wccIYjbM1UoUXnLmeECIT+pE5DeSUEDEjg0cLg1ceUQhEzORhwE8Qjcf4CastNljO9wNNkzi777G8JSwzmyfNSR1ITxtK4fCbLXZbUCRHmjIazbJomLWFbEjnE7NuuSwTTZqQLrJImq1oaGzD8+HMaYi0y5ZFVbBcCWSpCcykXiFCiT9JvEo0C8HzYU+9qLhcGEQaOPSfWESDNZZezBgjmL1HyIGCxHpTo6MBAbWKZA05J4ToEEJATIgkQEiqKSPnjKnARgMiErNCOoOOmTlEZHxJJI5Ro5SEwhBHRRAO52dSkSmm9CPHkAGNFPiyQvgj1itMdoxNQe09noqsRkQGocF5jxQCqV9UO0tHZEEpa1YiIoIiTpGEQIeZsDZMamYRHPNYMMfETkkewsxuSvSVQueE9gPbynCSA1KumM/P+KRxJ88cPe93mqeyIM4RM00cfCQtM+gzYTSMSlCrEh1KwhzJZUlVNPTPB9w00GaFO8FsRgqpKK9aNrkljgU5Hgmz5+npFpEUuhnxcoFzM4mAZ+I8JjatR2YLV5LpcSa0C4auw/qafBbs//DIwXcYW/KmFRRVSySTIoztDL7jOIy454npdGISZ7SHg3q5Fv70YSKExP7TiIkJT2S1LVmfGlSGfE589fY97lnz4fCBZn1BOgce8h8wz5ovL9/83bZchxs7pk5gjSDJJW7OFPIMxiDEijBn+nlAiEiKgcMhYHRgaRJy0IxRY1Jmqmfi7JBIepeYekl7sWKUgegj9WpD587k0eOXilr1NGVJcmDHQF5HvIy4rCjTjDWOWSbG2ZAnjykKjC0IPhGExFaWeh4ITJzmTO1WXKsFk5zprCdRc3XxGmqDFPbF2acF3rYc9w9kv2VhCi5+ZvleVKjXsJvfkVclh3Qk3weOrueMwHaKUAlEjhADxmrmFEhSkJ1A6UQQAp01JYY5J7KXuASaxBgyFlA5Q/YgNPrvfkFQNEycSQmk+5H7BJAJlTR59MSsEDOgPDKVOBmoi0QyAhcSTkS0yeSYSNEwewtFQKiOGAucUSylRRaaUQdWQdIBSs4EJ6l6T7ZHOlNQpMRzsUQp8IWiGwtEc4YIVd3jc4bzgNo7nlPmuhjJbJAlyCh43h/YUZJ2FiVAGoGpC3SAkI9YsyKbglQsCf0zc/SI5yee1YnLmxVt+YqlXRLUwDEqnm89XQ1iH9kOsHcdP+x/T32vyFcNWgoOd7D8E4lymQ+3zyyqgR/mLb85PbCMltP5lkMMXDYls088/fae3dUWu9kh7MQ43eKej9ShYltb+rRGbx33p2fkwwIla66udhybR7589Z4vVw1i84a39pdcrBtuX3l0aZhyRUqafhqxyiOdJqREpX5O4IRtam7HCU9CzRGjH0lDQulAaiMiLPAxcPbPCJeQg8NNGWEN8ywYxEyYBL4wDKLHjBapJb6WxJOi1JpRt9jJIgRUXKAvb/kuKNo0Er3FjQXVInIQFiUn6mlBUhPOJwrpYITYR9raUCwMTkTkNBNNhVUQ557B90xli2hmdNtQEXB5JhcWazVzKakuQbc1pcmonAipI4QSo25oX5c4/cT8FDBqTUf/AlSZXs74eENgpswvRC2fBCpbcBEbPLYQhCIhpsSUMgOOFEFnSYyglEZmibSSSXoaEzl7iTGKaB2pF1QKBD/y3YGcoXMGmDHaklRgQCJGjzaSTnXYIAhS4IVFypGkBN5LVJTIKHDBUTpBqC19ThR1iSkN2Z1YGUNImTANuHEgMxKEp7CKc5zIqmJICRMCuoDJQ60DpSywy4rDKvLmNOFyz26riVIQtaVSN4Suo5wtzUVLW7eINHFyM1VZcNMuOApN1VhOY8b4GTe8nNH1WbIrHN4FTv2RrAvG8IGqslwPkUcdYAgIMryqWfiGOVv85iOibvn4zWdGPzGNj7zaLTjfHbFqS2ENSz+zuWgoLm6Y8icmNXB4GLjb39IdnlmnDc1qwXJRcREDTnX48Ra7lbx5fcXb6x1ZN7SXiYu45c0vrxlPDXq14oufRNTvBGcmiqSJjSUnT6OWpMawVwcuqoKzfRkixiBxsyemR3I2dFPCuQQmvnwWQ2SeEnPvyHiyhyFalJnJWOZRImNESMGqV5zLiVIlJpmRHTg9IlNCD565LaieamZdYEwAIjFHipxocklOE0YLYhE5Tp6lK4nGvqyrK4VpBGbyaG84pIA/O2ToyVXgtNYIA+W6ZJ4Ts0gcPazUhGsV2khMLFj+9B1x/8Toe26fE8Uby27qeJQBOxlM9hhVo1AIJWnKzBwUVCUujSTlSVoRQkJpiUajQmROCaECWkgKEp6EziCFYYiBSURkEiThyFZDkmjviLIgxkxQlj9mFPiP5Q78L8Av/u4pa+CQc/6Lv0sl/r+BX//dY/9nzvl/+odVAGKdabwhZYfXmiQythSEc0SuDdNRsChLzpMnyIqARuaZZCaUjuggGM8TOTmcSuSgaVODWBWkqsH0mbNNTONInVqyn/F6Qz3MFMFQqp5jlrxSb1GXrwj5yA7DohEUscKV98xCUeYFcx7xPVzUJXopeVVf4pQgu5k8j7S6RYmCfug4xcDT58/k4Ggvroirii+iIZQV/nzHEye6/YxZasSjob0SfFbPhNxwerpllyo+bybs8x7TLllerRj6gU/3B15dFDTrK07TnkJZXr/ZUZRXiMcfiEoyRcFxFIj5xPR0oOsn1vUl767eYa43zK5j/vSJ0/7Ilalothtyihz3Z15db5mvJVnv6FPBeHpie7mhbK95/xcVD9+fGIOCZkERJLV0GLklpET2hp0SNI2m6zxqmjkTObtEPI4kEcjCEf0zh+dnugxZnjEi4aYWWfc0s8HNZ86dQeiBhVng68AQHGM6s6xeUfpEd3qm2O541gvmpyPa9qTPM0NfklpBHyyt9uQ5M+hArTRNYfHTE10X6ccDee4Ix4JOVMw2IU1GFpYsa3RdMKfA4TRz3ZbYsGSeI311xvmGbnDIQeOLgf3RUV1do/Wah4dbXi0/s7xdcuo0K5E5hz1ZZGhKfJOQOeGUpHaaPmREkJQ+EVUiqhda8+wFIYJrBGWyWJGwRuCdJ5hAEJ7SBs7ComQiKokJ4LXAJklSgTR7StPwx/Aj/1HcgZzz//jviMQ/4e/zjb7NOf/F/4fj/r1qvEMHRVYBEUCh6AuLMh4zvDj/hhRwViJyoAqRrDRBRpLyeO0ohGEagdoh545ULmj3Fp8kOY3kVYMIiT4aVHJMBNbNCiVXjNMfkNxgo4B+ZN84ynEmlq+Yxczu+orHD5GH6QMULdms0e4WgcVFz+HkiXNPvS0ohGGRBfOkcd0Z7o+U9ZpVuQJdoaRAYfnh8Yytl7RmwJ8COcBCan73aaRWM8IlPnQHTm5E+Yavbza8Wr/m4RxZm5FKbliqGnlwrN831LVlKjwVBafjmWvdYLLi6XF8GR4Zzfsvv+ZPf/YTnmPi/n5PZw2FvWC3Uzi1wkXH7+86fvFuzau0obSa8f4E6eULV5sW07yjer0nzIGQBp5qzWl0XOSeOi2RLpLyTFhK8D2pFvRDxKcBGoXQkbE7ManAsdDk84vpJeOxy0xIiml0DA+O3gyIfU9YHJjYYk9H/DmycYJTPkJITJMgxQlkyZgm6sMBKdYYY0lUpMMRedEgZQ14Fk7j7BavZ+JdT1dK1kahdSDLRIoOkUEXmjAqxBSo5gS6RDlJWVrqbo00gl1d85gMx/6IFJocK+ZqZJJXZJU4jmcepeMmSobQoW2mWEEpS1QIOKVe4sTiQBk1mJJ68gQZkAQE4ERCSJhDRrtMEpZUNcxiwJMwUZCNxAuFnRM5t0Q/MqtIagWtjzj1x8ED/0ncASGEAP4H4L/7D236v/caQE6JLius0agpUaSSkwu4KlDvDcoWzMrhBl6MOQRygigzRXxhGdK+4JkGn2naksjIOVaEKNhMHrNO+GTRPoIqqKNHNwZvBw5PgtX4PdPVV3jnYPRMWvP8hz8gdoblbkfNjqHKeGZip+iiZyEDWlhkOJEpyVkx2ZG5aGjKlnY+c/36ZYdezAE5e1JTY+2KUbcYp7FlyyF8ptzBsl1yES95PHxANg1z9x3r9pLrzZ/y+qeX7K5fYw8nQn5mCg13o4OQWZSZB2aO3z/y9PzMcruj3LzhrbmltkeGTvN8e3yx7NqCOg1cffGe3btEmkbUaeaj23PYd2yu37LgklZuiXNAV3vWy5rl4Jg3HrKger9Fng4IGsRphK1n6GcW5sgELILHi5Ygl9B9wOZABkI/McgJ5yRhHKlCQBYzz7MkRoOeAmTHKT5z7jzlYsew0EyTo1okTouR+VQQO4cuFbMRxNZTnytkpVg8jSQBNgZaafEaoqkw64bGBcQAQfUsrEGGzKeLmjhqlBWU68wYA1MwFGZGdYEsLdPRMMkzy6eKQ9MwFyfIlloKpI+UdiY4g84vWQ19tlQ3R3TYILMgCc+YelaDxevEFEvaqmVaSNQgyDKyLiCdEsKP9DaDiBSzRCiBFwV5UsgoiClyWkdCGJFTgc0jWUrMFHGbyDitKenxR4m8KmCITMWMDv8JlwP/QP03wF3O+Zt/5773Qoh/AZyA/znn/L/9QweRQqJSSVaWoM4kA2r2rKLAa8lsNTEnajGTpaBylsLWnM2McWCcQMWI9w6MoZg9Qnbk44luVVEeX9HLmWGE5TEizcwgR5RZM20njFrTu2eM0aSD4OIfrcgHSTkoPvE9xd/A8a3ly1dvKecFejUwNZZzsIxdT7UtuHErnF1RtIksBYtcUAlHtVlhFon93uG9RpV35OcVnf9M5SeCj6gIU/eILWs+3u+xu8zlxU+pwopf7t6w/PNfUtcrRP893+3vMXPApytMoylTB68WXO3es+8dv//29/w/zL1Jz3Rbmp51rW73saN9m689XebJtgrZhRFgAQOQfwAjmCEx4Z8xwRJiDkJiYISAoqqwq8g8/fmat4t293t1DN5EqjJOlWTZ0lmjiB2KHVJIzx3xrHU/96VVwmdvUhZrKNIVi6VnrVKGRjFEwe/vv6fKJ+rNC8plztPXF6ahYtPPtPsL+acZnsC++Rfc1F+wfPWWKnTIUDNVPUt1jc9GpI8kwbNTitDnHJOBNAILmJ8srg34wTGOCfPYE3CMwdLte7p+pvUBZVJS5Ulkj/OG4dTThw41jUyJeQaAjBOplTRPd8g652pdcApQW0EaU354vONztWFpXqPLjxw/XTC2SyYpWUaHF5L7D4IkbxGqRGjJPAmKfEOdnrF333DX73m1W7OsFjTeMM8tQUfCHInLJ1q5YHnq6X1H197jRcJqeMvZt5xWE1qsuJkFcwygS4wISCJt9HxerWnbgN6BVimH5oKdWspeEIUjSIMdM5S2TMxYCaLQWCCZBFmMWOXxMjIYMEOARKAITPp5xkCMCcmpw5U9vpkpdcZ09AQ1YooNZtQM/5aApP8l8N/+recfgbcxxr0Q4s+A/14I8ZsY4+VffuPfgY8g6M1zj5QMhsTPNDKgvUTOCmsjU+KwY3g+5zXQx4lSZDgx4tOAwRJsJAiJVKAXjigS7qeBMv6ejbkhnhwm9+R5SvAzm8qRFSvSILkMC5wV1HXDz8dXNIvIIkau4oah2KOqlnmbcGNnWllCvqS4JBzMiVl56jxFCUEcIc0gOslBOnIGyiTHFTOHS2A4SEwWWVjHFAuqDM4m4L6655hsybWmutU8vNMU1Tvs1WtqJio7838/zMz9I5ss57NP39K7C3a4kJ5z2CVM3QPj8ICo3tA4eGzvKdiwTHdk6yXmVhEvPaptKKqacLEcjnCeEtLEE5znRVZRxgTbnPDLBebWYFzCfJKYzUSla5xrCOeAcAJROvS0ob1tSN4b5mFgmEbsoNDiwtQ9cgwdbmi4jJ4xzAy2Jc9yEicYuh6XjKQWbEhR8fmXUTGw0ZLRHUmioEoqmlOPmTPE1ZI6aEbVc9EaoxfEZIU8X9hLxfb2LY/vR6rFhJ8keZvTTxZfZbQhY5lCaQuCCzTnge5+AlFgx5RyvUIngrvLhW6cWJYZfk4ZpODYD+RJSfDXJNPMk/+eNK3RB6jUQFtNbMOSW/2SM5a4nSnswHQa6MYUaVvy/YY4TDw8nghInI/kdqZVER0tqhSkSFaNwmmLU55RSowBHXI8Dj86oksY44x3Bpk4QpXADEqUZOJILz0uA+YFk+yI8o8nCf5ri4AQQgP/OfBn/9+1P+DHpj88/j+EEF8DX/JMKfo762/DR6QQMfeRkHQwK9qlJgwl0U3YNkXoEb3wpF2NXfVMg8OPknmYnwNHhcLp5wwByYgQBX6cQPcsQ0a4JDSVRQ1nOl6jVM68i+j+hDzUnLMWVbQsDj2xvGWgQ4wlXRd4/eoztssvaBJP5wKqnRh3GXPbsdpV7LqKKTwipKcIBc1pZBxb8iKjSlOU7XG2IHhBHu4YImSz5iIcWTKjigXicGRXr3j3FDHLgCwNthQ0j0e+TF/QlZbH/kfUwiGqHbo/8OrtawY5se9ynn53R/HuB5rWMuQJv8jW1O2MPgwsXwwIrTl2D9T5807+JEYykZPVS3ap5oXa8O7wHaKVGFEQYk5dDPRngUaQ5pK5F+RzSfAaERWzG1HTBusupOuGuklpq5HTxaDaHuzM5V4xZxp7mXjoG6SLTL1HziNaa9IpEkYYXcBbzzI9MygFF7gQMbKldQlZ3bKfE5Q647rXZOsLbV2TThPCGsqq4PThR6r0BfbU04gndkkgTAanHPNC4FYDaXQ4ETnEnNIP9MGidEZUBkVPYODj4QmWisU1iB9H5vFMniwpVwm9DNCNmB5caZhMxVZkWNlitGDunrMB7/rfE7vXXGeCvcwZxMTYa8wHmNIDH7uG8/6RTgpMjCSiIBEDSYikgyQGRxc8ygt8JfHeQZ8Si4i1HhkkdVIyziPBjJQoTuOFICT50ZCRMaQTa7HgED0xs/jgoP83LALAfwb8TYzx3d8ShivgEGP0QojPeeYOfPP33UhJSZ0bOpnhzBLXnSllT+B5Ck0hCIeEIGbmY4r2KUYNWCwpHkx4tldOGhFS5hzayaIuijI4+vQ5tbhWOaN4IrhHFg8/I//5LSGCTl7xdrMDJdkJz4dgeWMvuLxgPkP7ucedAosyJxXPm5dxGyjdhFvU/Gb77zAmI+0xwVeWmM7IODOrC+NQkDQlr9KBNhF8PDygp556WTDbAJuEPH+JHe+5ykbSw4aH+wGqR6r+M35/+pGfv/kNv0je8J15T7qXJNOOj+4ODjPJ6Pn557/lMj+yFfCPwq9YlhnVz2rqVcn18i26gafLe378/kdabbleZmSx4KZ+y/qVops9xeIfcf/tdxxX76lfLCmFpqthHEvyxYVQGLhcuLQTi+QGFRXlYs/5EJhOC8JkOTIiQ4daW44PDySTINqO4d0d7WTJZoExljLPIdEE0ZMmHmkdIbUcRwhTxxw8U5jptcE00PmKxHZkiw2hisytIvlsojymuE8cv//rM8tJcu++5pO05KxmeieI7UxRVGhpnnvqbGIxRMzcYuOCXXg+dl6sFZkeOJ3vmbxlMy3IX6ypr1b4Adp+5PJ4IuYZ3Tzz9uUVfZRchRK5yymbjOFwYd4MJCInrHJiopjNnu448v2PDbe3b/D5THKOpGFF0ysYPJmPWDkxacW4kphWoGRgCAKFRHYRqQ3zBtIhEkNklhmNBKnACoG1FnRGFg1jOTC7HDspdDmh0gw3fUEaPuL+SDvw96YN/4E78M+AXwgh3gkh/us/vPRf8HdbAYD/GPhLIcT/Bfx3wH8TYzz8fZ+hpSIYT9AFarVHrh0u5kxZgkcihSCkIwJNYUdyWpJMI4uAjDnGliROEbwhUTP52BInxywtQ2qokpSFUNRZxdVuQ2o2ZGvB/g/2Wm1TdkvFFwvPGY14OPE0NOSrgJEtXx0P0OdcftcxPRyYLnvWbYLOMrIFDFVBll+x2GiKzwf0jWasDTaVJKakqBPcOgdWpNkLjiV0nWMaAu4xkthbtLtiJSL7+UcKDJurl2y/yNm9LWgePjKMB6IQ7PuB5HrJupXAmphUvPlsx6/f/Jpf/cmXvPhkx6Q14rRgVf6MRdii5omma8EH6l4jYs1cLhjqjkIarpYr8qVm8+mCLz5fUTlPZlJeik9YK0HqPLUvOMsju+o1oo8kheNyVkTZcEo/UMiO9SxYJZFhMBi7Iokt0yFi9URMJ1hZSDRxLKA3xAhBzmghyEpJUYCIKaFXNL3g0j4xr3rSyVMQyZtbFntHH47Yuw3fZSnlIbAKhkodSQePzzeYaUvuK3zSE1JNn0aKGHCPM4d4wMqZMTtD5kllSblxuCwh2opKJDBLmvcn2n4gxAy1SHDpgpB6nqSnG2A4Kc6TR/UTtS8p84abpuR18zwdmJxa+nyJ40Q+RLLMYunpa4v93HB1tQYmvAx0ucB7RzKmCAp6bxCyQEuJUJpJFPgmY8RhEFTMaHvBOkuUObHSiGIieo9rEop+plx64lnhzy2l3NP/Mc8w//rcAWKM/9W/4to/Bf7p33fPf3kFIXlMXz57vBcT+skx2QanNdJahiifAymCxwhFk+lnXvsk8FGgppkoUrRw9AicTFioFJkKalq0TJg1yHwmuSxZSovVE8OYwMuEenVH42tOacdX3/5zsp99yZvHlClb8/Z2RI9XmGWCqwc+tBWFOZPXkoSJbqy5XAb8eGEKJbMS6LwhQ+NFwjh6nD2QqZkQRzIp2NmEsZtY3G4Q6ZYXC8P77GcM7xOy6RvC1ZrPl6+IC0ccUsqT5ZvDhZ0uWGaPtG3G4sVLai2Jl0Be1WQmoKYCu/QU4YHWztydn4hXkJkBU9TU1zdE98RabVibjGrKsaFAlBNlHxmzjM36U57ee8JWsNETxhrmkJK4E0ZphPsOn/Q8tJvnNJ0bwa1L8PMD6V3KsPLoTlILy7swYeVADDDvPTGDJZE5nHCJw7ZndCqR1Zp9E3CHiWAdoTBYjqzmFDko5kSSTBl7c/8c+/5kqBLD9JDQiPfkNy85zil+GmmcZlXuCSJjNdXIaUTuStxjyzlONCxZICiCJ/EtxZcadbel7SZ8UeBUgl4qTKxIlCTDIYYMmQ9QKWSf8sMxsHr5Afdo2WcZRWkIl4kma3m3+oTcOca3FeunmfffpehbD6cT5zjzq+0Vx2EmvxIYrejZYMaW6GGKIxqJ8gaX9aBhYQWdC/RSEYlMMhAqAXGNnlqUGwgWpMqYc4dA044jyTFBLAPlMNJjkdkK/2+hHfg3tiSBl7cV/T4Q2k+YX3/H+lDz4dJSScXgNDbmzKVzhMklAAAgAElEQVTDjwE9OFywz7P4xnEOESE8iTSoXDDPjrnrkVIzrFJWcsG1foNPB0bXYI1l/LEk/ewVh/lbysM1j60gX1veXH1GJWqqlwnNMHCKmkaeeaU0avOCVelJNy/I54hRBo0nlzM6y9ltZkZ7xYQntWeMKJnXBe/2E6J/T/gkcnVa0AyKq98EynzDIgXmLd1fzBSf/pxfvf3HjP0TXw9/zTpf0ww907LgZfIDs36FnxecK0k5OT41OXLzBj3ODKsS3Tygzczq1Q4hzhw/foeKLa9KzbURmLwkXVekZ8HcF1QvSoLUiPORdAFbUeAvluo6w1YRkmvMsSF4yfpnL/jhx47545k8rbhJez7Scvn4il3bYtZ3WC/55p3Fnzy2PHP/L06I9D2rRcq2XhH8AYRmlBJHgbav8aYjtBOuUbg4UauMds54Md1wmY4M8QCrHX3SEqYCd9S8KaBrjpTLiXxa0VjFdX/iojdY0TLNKVM7I2uHZEX9fuCiFSomvMhn1EUQo+F+l5IZwT/49S/4/ndPjE1L60+8Lna8fFFzNAnuvGdRL9gvQL93nM8Dy0yxs1e4NxlqkOhNR9hXiGzJjX2OZP/zvSUkkuTFlvQiuP71kh//1/+T/+2HJ/69/+Q/4usXv+J/4X+ndi1BB2w0z0eA0qHzmWglWJhUIPERZ2esShGJQ8iACSc0BmcVSnkmY0mmFZOYcLUizoIwOdaFpN87xG0Lf+Q/+U9CBIIUTO8OpJlgM3k+Thcu04pP45om7RhihVETFoVepDg3QZvj/ISWEbMEIyPTST8bO3yg9QKlPTdEmHri6R2VveV8fWSRvcAMLfJ4YM63+BKKYsFqWCHWivd335BVL1h8Lth/PKKLno9ecn0rMFNJJkq+NZbbmLAr1PPkWrZFCEgrS+40XqUcg2PKJKtrj+nXTBdFa2Y2sWYYSwohGVIB7z8wZT1TCGwXLVki+Fy8hfZAvEhGm3Kpbii7nPnpG8LhQGcM3UWSvawZSdidjxxUSlXs2K4XdJcvyD89gkpIE8Gx7rmYkXVyRXWVI0eDbwVq8RxAmIoSM1qalSQfLZlMcZcDebUhiIp7M7FZWqqY02R7nkTBmyD4q3c/0C4+Mny/xoiOyp35ceiJ45FqnaKqa8Z9Q2xbJlNihcRGWJQRKQ64Zma+XWAYkeeENHk26lzcGXMXudIVk5LE0WOrR9bqS04xIRae5fwt3fgao1rO3UCadozJSBocNqlR7sRq84ZG99hGsXqRECfFoHqSXLLSgeHhIyJPkTvo7ntiFiF3kKfkYcJdlfgLvPaScbWlOz1hxYDUCcLXnP07Xgy3jKbg9lYzhBNusqyWmpIFw9RBccANX7IWC/7i4Z/xqf5PuVrl6IVBypGml2RmprcL9FSipwEhA5MMTJlHWk+wEekE0kIIEm8ss5Pku4AbIcUyyRZiglgu0HcOVXoGveG6Hzi66Y9AyH4iIiBcJBkj2IFDLZgWKbWDc9Hz1Dt0bskni/OGxDTMVhLTGesELgSyo8QLidXTs9tQSbyMFFPGcZVQmxfcJwO36sLSLGhMYFgI6v2Zel3TBsvu3JO9uMI8PnBvrkg2JeuY8SEx+HclL24+cPwqJdtM6KTgTcwpFjWXeKApDVk3k58gWVliFuhGSW16jHC4kMOckOgUJUqUG1gWYIuIKAfmT0puV4EoQIaa82TJFj1js2YpSjL/nl294q/etaRlSqsGHu8bZn3idTLx6s1v6M2ChJnq85rb3HDZNihb463ALFLwsN57kn0kfQWL9ZKuG+jPH9BJpB9mbjJA5hyvUrJhYs49P4gzclny+Vzw/WHP+7cR80OL1j1fj4GmVXDq6LM9XkrELNilLd/vA4KJ5ZQSky3yJif0PxLmhEwbwlkyzJoRQ3HnIBU4ITnS4+PIlHmurkoeGkNoWy5jTuE9r0oJcmRtcsTl5/QRapPQvEgYTgNfuAXf+R6ljuQhY54G4lDxNhl4uhOUeY7zZ4TzTHNF9Bv8XU82wEF5antN1r7k2HmqhSb3a4qF5MP4BCojKXPs/omnW0iSjCKUtGFCa8nwIzx92pDfXKEujkbuUZeE98uMm2VFfnvFb09/wrK4cFhYVBs46oBxhhnIbIeXmi5YJAYpcnyrEXFAIhEKnC4JrkdKBalkPgecAL0y+HNCml9IPmq6JcyThCippkAYNPwR4/BPQgQc4K5T8kGiLjO4BYWo2K1BD0dCkjLXAvk40zSCxERi1CgRsSFh1H8IWZABryXeBYxf0l4muqFlWnzNen1Nc0jZrgJJYhH1Fp0E4spRnxRzNzM8fcX+6chvX30GuULVH+CrBembGV/tKL6omfceOzbUIoC3rLzEXAISD7vA5EtS5VmvS4ItCM4zyJmQJog8Ul8sUlWcb44MH3bUpwBLQ397w/DgyPSRm+trkkry+JeB8y8vLJoleyf58hcp89Pf0F0eeOg/UF69hDO4qcP7mmW5pveK/mhRn67Ig0AlAT/maBEZNwI/lAhn6AeHLxRFqCi9orUd39oT67wge9gxtu8YtwX0G9LvHvk+/Yje/pJP9o98N3rsBJ2bGeKILU6IZkmuD6h8A8kbrkPD0D1BHKkWK7Ii8rJ8w+kycug7nGgxZk0hJaj3nMaAFDXKXXEfemSX8yifWE+aD3NKnQW6ORB7i14nyGlk0jPbWHMYG3am5E6duT9asqgImw3jKSFLZlp7wFxdM2Yf6YNhkQfkqWRbF5iXa6rNkcO3ZxbjikVtMAtBeLjw8Jhgsq+YL2fq3Rusjxg3wUJSbT+hGDNC0bIf3yP9NTsq5LJGjjOb14Hf/Y8n7sdvSb/2fNQFL96WjNf/mHA/cnXfMdQaeTZE61jGwKgEMYW8iyAdoxJIFUi8wFnwLoCzZCS4xCKqkcWxZK561FAyixlfCPoiJ7WBMCleuokhdfjsj5f6T0IEhFb49j3DvGLxRUr1kMBqxRi+Q2aBWBfIi6UylqPKsZ0DBS7VqAnkLBDaoY1kIUFahdAtk/HoKEh8RRYth92F78eSqkmpti3CF8wXTb+sYAEf3YY365rNF69xx5SLnnHZOyqtuXwbwR/Iqh2nHxRu+ZpF0qOTHdoaMmOYJo0VI2HuSdMepw0hDrgYSWTKmCyJix7Xe+Z3JfVCEzavyQFhR2rRoV/vcGZAzgW3m4ayvEIowTjdMeQCa6/54Cvc9DX5NPNQv+f19IrbStKZD1DWDCEwXjJSNHm9QOgFcS7QcYZFwjjAfGrQtmWqIokWJBiGEDh+70nevidULyjmnKo48KQ6fFNTDk/86GAaFDNPaJvxaqUJ72542DYMDxJUw/psMWNCZyWeyBwS3KgIPgGdEk3AqxkzPjBLTZ9oTOpgTBHCkkaJDB71seCxPBHTO3qf83KV4qJEnDqUXjBOktX6hHUVvk/59OYVH/qGuMwRZ0VXF3yWjjweK/76r37gZZUD0GzX1Hng4j+ymEtisyNxSyoNKycokxND4TgNFuEytBso/BlZBM77e5xb8lt/xZT1XN49sy9O/i+5ff1PWE+W95NnI5ao3OC+j3zvTvxpvKLaS1Y39zyMW87U5M3EkFiSGOhlQeU7HieBFgrpPSFY5OyYlEAkz05UnYL2EyIEwrDglA5UNhJsz6QlYTbkpiNMikWf05SBk5Sk7fjHwoZ/GiIQYqDMrpDJgv54YpFkLFdn3u8DsazIekkZJX2asnU9T3mAXiCjQGQa9Mg4CZJRMiYJMpVE65i9QDqHmR3HNCN7kZN4R3cNqfRcrxLS5MIYdri3G35xbJCy4OOHM951DC7H5FtOHwZUEZBmyfBNxXQ7s1sHErXiPEtqO5NklhgWTHpmmkpUb1HK4UKEs0OUA2GQLLcR8aKjuTiCE1g3EYslTbMnWaz5ZEg5tCvETclcvyc1KeG2Q374HLP/wC9/+SXFxx+4ywa2PnKftMTOEXeK/lJgrOKUp6TRMmYRYUElPckMxZTis0C1DEgF7mI4PU2cbzS5A2XWrF8OdFQs5zO/DyduziPr6xX24nlqn9goAVnC+G2KmSJePfF7V7E7lJS7EuEcMQ+kY08VDOL6inpREK3Bh4aJjvnR0dkcbgtKA82HAbeQ6GCZxUztOvzuikk5TqeUOGZslcHZDUI+4MISZSrQA5fJkPuKNhx4Z1OyVUM7NyzrFSo1PMwVi2rk7ocfOMsVu2pDHS37hWKxkFhmYmyRc0dQwMLQBUNhd/xKwxgmPhhoeslqLakXN/ThgcfHv+BqueYhjJTjQCo+Zy1aTtk1eRuZfcc56bh69TnvD4a5nfjxrSJpFJ4Wqb4jUZJmXKDqAWUm9g8GnXl0UOAVa2GZJMxSgS3I5gDAGCqcm5B06CLSTgUJnliAGAWTfUPo33GbwPfWstwp7LgDnv6V9feTEAFjI/YqQX38hmn8hHSrOJ0taXpFqSKdKWE70307I4yiOAtctMxB4oaAlglGW2J0zD6ivH7GRycS5wVzpUl8j+0c25gyfBi4FCNFfcvVbLiRZ959uPCUwnX2Clm05DalNRW/kGv05zmzvudhKPh3X8002RL/8MxH8Gngx1XKm9Hh/QVnG5IqQw8L5mCRpUUKhZ8sRZHT3J1xlcdn8Q/DIwXD3vKFNnwQE+9Wls28JDsdedzc8snY8rSqWG9TdsdHsv2WP3mdklYL0smyHHoSN3L3+w69mLh8fOLq08DV4hOiCRg/IIUhWkWaSDo7chwF8yHg+wvlVeSVKXn3/gE3RO7NzP185CQfWfsV76498s4xPdwR+p4/L5+ofteS3b4lLAP+LrKpz1zKlFWZsXAVWhacPz0zjhPEkSZK6qsbUrcgzo/UaaSaDMuiYODC+rXiaX+hmQ8U1YI+bpG9pcgkVSIIuiAd1zyFjqxbsN41eL/lAhT3CWanSO4DovqAFRuK9ol3WWTFnm0F3id8+tkama7Yf3hPvi5YTZpOJnCQHIYjl8FRlJYUyVJUWJPz5AWx7VmWa0wtEM7gsgvHD4FltSdkv2R5u2feg4qeVO/B/kPW10d+97FlEQ6UYsWXqy/Zvt7y3dSRa8PYKO7OKacwIJKA7zyzc0hAzMmzN8H4ZzR5TEmiwLsLvbAYpxEyIleOPGQMrUXGCZ87OOzQyxE7nljalFM5ItJr7DQxVuNP+3SABB4PI6W6YVkMFMeEkGnSWdMULUUzMMeZapac+gSPR2hBFSSzmXHF85eXTwElntXb5YFCFkihGOxAvXB05PhKMIXIKpGshoHSV7yfWsx6Q1XnGHvEkTNNglL+SHm9xL3qMXclV9LwTe7YaI17GAkvWkqjiH0LhafTLWLSTA8ZQT6R64h/D0mmCdsSpS16p6nQMDqaIPFYcjvRFAlrU8DQ0Vze0ZgFtZZ8DCeUrNiNRx5GSXX1RNA7/kP1c34cviVepyzCitNlQhpDu8pIrMHGifAAfronLWoUKfZmREtNLiI6sfinHP1R0F1Z0piRTzPNJJHtiW2a0t7dM36Y6LoDqRiYmwsjLUZ4oo68qQ1DvSM/Hbhqz4g6wSZbqk3E95p5/pbZaZJlZLOa8CeFH1cks0NVFy79GWsk+a7CPQ3Ys2aaWxI1sZ8TtMyYh4IQRwiPvM5SFrcppdWc5gufTIa58Ly3Pa/fWL453GK8YLe+5U+NYzEu6EXg9XLkg/+cyTYU/hZ7lKx2mmmdoNJIbzUTCeJBc1xIJh/Y7QSbNDIuBYeTpMgVsbHYPpJahT3vSF8/sCwXXH4IbDdw7n+Ozj7wIi65Sgqe9DWrFzX3hyf+8p87dr+ZKfMr/mYQnE4tfhlIZvDHQOqf/S0BQSJ6lIsoZ1gCIkAAxkoTdEo+glOe3qYgoTCRPmpi2aFWOck7xaWSLMKGGB9YJRMfhvSPlt9PQgSESvhVZmhSQzKOfBBQuY40pizdFUI/YDF4KbhOKk7mhLp4pkTgZYa+OPABFyNSPkd9hSnQS0GiRoKxHPqMF1guOmNnGppzTbvtyaIk2US2+ZZiXNOvJpKm4pw9kDvPVBTEDzPf/+7AdlMhbKAxZ169SRkFXOxMXggOfUHfe/RkGBLL3ENteig92m/JP074vOG21ERfMMSZUj/RDSuSYo8XV9zJM17XJNZx23j6+J4kzbGzJhkki6ufMe73pGVHW0p2iwWjGrl/THhdG9oqIY0C0c7IPpKIhvPS0I5nxnNLsTdUVyuUVIzWoesZkhTrFGMROEyPfHw8I5sTk9YoN/H4wwFv1ty+tGTbT/l1bzkpy9IsWYw14jYlppFpvuKV0pxjQfI4MdYzyfLnJFwwVwE+epwxTJsM464Y1UvSxRPZfsSPMxuO3PsZR065uOJGeB7vjhjj4bynK1LMUqO8InZHbvKXPGSKZMpYiHuGOefmZcL53R3Txx372tK+2lIOZ56so16vaQLM65bj3YVJbVm34HNP9u5ILQ+YeomKCpNaqkRAXWELgUlHOhJsPFMJOPkZkxluFhusd6hfZTSdoqx+4JP9Wx6XDmUlvW2Y3M+oZMN5+Jrd/rccK8EmLYgPF7K7gFMatc0I0pO4hPTsaGVETZopTfHKIcdAryGzkTyMtHVkHgL5DNKA1ILYaerEMn5U2DRHhQ7LiXIKfFRbPh1avvsj9feTEAETwQSNP+6Jg0AXI3HKmdM9G9cw39T4jwofJ9p0IATJsNQwgpegnULEiUkqiCNBRXIkMnakumBMImN3YapveHPuGN98RpGeUemCqUoxes1pOPJ4mQkx8InouUExmy/46q//HFUuqJYLxMKjdjni/sLBlPjmSCc0x9+fWCvBjKPcpmQiZVvneBRNFJimoQsBLxR26hDtQHSeYhHpa4WblmTNE3WRkpl7TmmgLT6i/VuGs6V0d5z1gkWjeUwCH55O5ENK7QdSM3Kz2dLIkdVaQ9MgFyUxy+ktVNOAsDnRXHg4Hvj67oJQF3KjqK8/QYoO155JE0s/HvhyseD34gZ9vuAzz7//D/4DOqPoniLrK0vcbvm0EfT9kUNYsGojvHLU7ZJT6sjahnZZMowW3huOciQTkG9SiovCDIoiPdPLA1NvcU6RiQqx/YQgn/iw7xnGQCYLxNoyNXtmE6l7Rf4hIbzt8bWgKyfWvmTIHPZe0KYd81eW7Api1iH1jDweMTJyjCPdpWH7MpLeb9kkBc601EWJDobN1ZbTV4JGvCdIQWl/hpQZXdNxd7zH+JrdTcq7ruOYRHR1i7A94zDS1wuOMVCnml25ZVwuUclM8eRJXMugv2L0M29u/iHr/JrcB+44cHgf8brAITGXgVWAxwh9nFGJpE8cKjYkQSBzg04T+myk8yXiLKnHASc7MIrZCbK8ZkDj+yMpoOqAkyn6PHDV7vkhXfCH2b7/3/pJiADSk1SRLF7DfORl+RyftF8ZDpeU0FrMZWBdauYe5kpT9YEQLIWCcyJhEmjlkZkgrxLGwdMRmbtAdDlp6DCnR863a27Ge+5Vj/khcPvJn5FtMy7qHU0ekPuBd4uM4dsjyW1JmqbsZMbTPODPj9yM1+Rm5OH/+ZroImo98ublW0K+RM8jsXG42XJIEnKdkCiJWB6YTgYxn4hC4rYrdNdyIcV3jon3TMMKsz/AtSFXim6IpPf3mLVhTA3Wp0z9dxgzUU+eSQ6MtUDrT5F+YlOsYWqRpzPnomc4r1EuI0kiMDPrAmoQdPhREDrLagioWHHUFemo+PJmxzgn/HLXMZwqTDdgdjVpYVBhonQZbzYveW96LvrI5lKyj+8oxk9JRMf0eE16dcQeR1ZTz+ObwG4s2NY/J5tHvhL3SBPohMTINblKmMxM4Ew75qhyxec2MDaBMWvIjceayHax4DJa+kVHNjqSuKAsCg6rEfG1g7Unb1IK2zGywI8jfarYbktsMuHuLsynhm39W1TyHbZwLO0VIqYkR0vzdAQOpOkSlWhSbSAvKeSE2o+0suHlBFJp4tyx2VrmecNwfKJWBRuTckhP3A+an20tszOkec3w9QL5SUROnnCe6Vd7VL3DjJJhbanPI0/HmZAk9F4j6gGBJIugRskUI9LH52AXF9Eoom+xk2YQCp+vEK4niokqGZFJR+8rbDniZkFoBVhBswDtBPMfKb+fhAgoJZnVjqWUhExzelR4NWGOPa5viKoiVJGsDLS5YNFPnGxCVAojRjazZQZGFREupX2QRKfQIuKwLDPP6OD7LuMioB0vlKHC3Wg68UDSv2A6ebR54P7BUrUV7/uJXzY123oDwwU9esStx54CfQKTnRkXsCDjMOZk9oxcaFxhEY2DeeYiLbJpWA85uRJooVmOCzqjcXEHkyQpO9K94Lz/hiErKJqZjohODbXpeMxy9Id7zOUjc5bQm5m66knir6niyGTeY7ozTfOSJnV4EqQ9M51OyLAmXx5po+d81vgZtK/ZvFyyrgLpQpIGgZoEi+sEzWcsfc/EKw7rb/nw55qlh9rt0Zs129WOvTHY+nuu9reM7jsyAl3Xo0JFrkbauSDc98w6w+pITANdHGhSxW2+5rw/4A4aKTLGxGNNIBIpipSN9rgs56FsUKeOYQg0ThCCxc2SlSlJtebHccTYmWVbcFoJ3KGFYiQN18zjOwrzKUU7UmcdOpVM+jWpbp+ZFsMaMQSC6aiN4lRH2kdDHwRV5inmEoIl+o8kiSBPUlKbMueKeIG12KIfevzPUpKrmlUGjy6Qxw2vhpFsUszpGZtXnJN7do9/gs8Fw65n6gTKnLl7eCQ0ltO+ICufoSq9dWSPGZUReDPSR4lIoNMSHxULH/FWMRnweUQbkO6ZuVHHhONsyawi9y3jRbPNEh4XFpklpMeAzwb4KWPIpFCsq4x2GDHbEsQFcd9Rh5Q2BRUEGRnWlqwSQaxgZM88GYR7jn12TiFswBaC4B0GQxI9c0ywXiOio8CRdzn72FNlPQub0lw+YMM7VtUb1IsrLv0TL29yXuvP0GGmH4+I1nIwCX+SbDlXO/T9d9x//AvyxSvsbsUc7tHphDoahmnFVGW8qDVCekajGc4e5y36ZGnTM4XuOO4LUi1wF8fLyrBarHGcqKpAfPuCOlkhWs21ONNsOtJkySRStk5hfUeZL+h1g+0Ud32H/Or3nEuDb5+Y0oBwA1M0PN0fCKPk9nZFmaZ8+vPXXL9+SZwSzBhI8gopIpvGsJeSKZWoHDbDLcfbkYVe0UwfKK6AZcLy/B06e4PfKcrqTwmZZhSgwzPk5esP39EMI2K+sNjdYiqopMUsBOdREA1IPZMriTaG0UvaS0oiBOXthngObPZPHMsZ37ZMU8eyW0MLzbGj9BsKfeC8fE3pGkIjYfAkL1fP0d3umtbsWRnFtJZ4u8XRcbTfMX5zJC0rFrqiSF/hdIHVHaZOEFPN0UfSa42Kjvl8Ismv2F7fIhhBTGyTSMDxN5xYffPAYfic+YVjl+2Qdcr0JpIk7xlNTinPUHmG/ZkRg7nrub5e8uFy4X/+H/4nvr//HSK/kEaJdwUxQjQzl1QzjyUGMH4gSDAmEnKPTyzVmDD6HBMGwuSYNBx3Dt8JxgZ2WcIon/fEqnJB2zek1QJ5+IkDSaP0ZDKn3Hk+6pTd04RMCux05lhI3FyRpCl5orCNZ5IFZSlJpIXLwEWEZwOK1ZiLIBE5UY+IzJDO4OxEks7MSHoxIIYFTRm5dwnFMmcVFshS0zwEtG3x55eEW8fNyvDj+wlXzGxLw/3pnh9PDyT9HSdZczsdmGPG96d7bpKMZV6wzg3aTrSPE11lEDElnSxxDv8vc2/yatu2rXn9et5HMrO11k7PPee++15EvEDFBAQLVkRLWomaNTEsa0GwYOBfECUhSoJgQUFQC4IWrIhgwYIWDHlK8NCX3XtPtvdee62ZjqTnFtYOueI7wY0Q5TaYDOhzjDFhMvo3ev9aa9/HpUvokPgwZdrxymZ/4M5UPv7FlXYwCGuZYsfby5bUd+xbIktF0f8EzU18Iy+0/o7vU+DmJLvyFtufmcqO5fcUZs2s3YY+zMigcVoTRoF8I/n6m2/o25aHccBeNPMSqF2H3LzGHjKXkBH1whQtd36L9CN/5bCggmDZHugWyWeX2M6aj6cV/3ml/WIkFcdDfyR+2rDsIuOt4g570tVx99fu8SmSvaKtsHeNzQY+l1e4IljVzFIjWoDWYAysm0wQEuM02zQw2xvDvPDDw8rWb7mGBXMY2OnM2CyzWXksI/YUCaczrRdIB0/Tlq/cV/ivMo+ngh/uYNNhsiDmJ5LsKVWQyoV1iuhU2Gw29K3H+kq426Kk5XmauO88m9BR7ww/fHwm3gRms8fttrj9a26D4mdqZDaFkB3h0VP2YNqB8vrCm81biA6/t3QikstKCKAQTCvUISFDR1NQZ4GzK1kUUgMlFLRCSA0NzCUT9YmWHZsuUrUm3ypKK4RvfLSCbo5czB6V6pe6A0HYOXj6HeYESgWrV2Y9cq8W6p1nWTNcD7zJmWpWxJqoFZQD3SItQOsUt+RRWb08VDXRIYkisLaAzhWUIOUOKxTIjDgmdocL22LYNkk+CcTWsfzoyG9XXt29Axlot8Ivq2EUe6YkELPi209/TCsCYd/yi/0DLXzGzRVypdQtl5K45U/4ITP0HYfWodFEZhgbrzevqbcrH3Sh9iPp+MifPT3zXOHtLXC/USwPKxd5j00zJRkGu8Pf92ynxvO2h3XhrhiuyyfWZUAMgt+/f4vtPddyIrSR5Zcnfi1OfP7uzzFjz333DbQeRoFVjWEQtLt7WBPD2eKl5nb4NceTZzsrgl4Z3UD/jSLnGXVVLI+K7XxkrR3mwzO1e02dIpsYuSWNDUfybaHrFLssSUrj15VX3vLU9RQRuURFWT1OFIrfkPOKUgbjLSJGTvMR8Wxo1XO9POHXgp06rl2lnQNvx8r1jWZcFAcqa5yg7xifPpCpDDby4TjQhTeczRMxfEB/PqBlwrp7KoFYVi5K4fLEkEeqEEh5QbSEpCfFlUV1LxmWpwmfIsjQ7mIAACAASURBVIvuEUPP9fMT243i/rVmMw7YbotPjk2S3LqFd7c7fuUf6ZTl9jnyto1s/I7iFBy27B5e41ZN+KHic//ypjcW2RLVrERZMQrybKB1RC3QLePqhHaGpBTFJcRF4ktg0Zqot5RF0hVBfFFgYSmO7TZzPSakuOMaBVoceJH9/H/Gb+M78DUvcuNveBEG/g9ba39HCHEH/OfA7wG/BP7V1trxiwLx3wH+FV4Ejf5ma+3v/oNBoCHEiGwTUiz4eEA+RORuT8yP5KMmu4aYExcxovPMTT3RL42iX9C0CIfoJEu8QYsIpZDVvhia1kZNHaUrzKrQWuVt6riwcDdr2nam6xzGKeIqKNPK+RKYLifu/+rXHF4fmD8sbDfvibfMxknmH/+U2Sxcw4GD1Uj1A9rco43m8dvEx/rIw2ZGW4tsGeTIefpIfzQY7+hGzf/y40c+zZGHjeVRFpztaO2GyVeGNpDqgZ95h1klv7w9837ccZWNFA3yENk/3KPXK8Odwk07RB0414ZpjnHoCHrEdT27u47d6Eg18ownMRCfVza6kO0zc60o9Y5XXebqMtJbWr5xDo2qGx0dVyJdes/t8dccP33LX/0Xv+JcG2VKiFTRnacrjeu2MdiRelv5VgTkdSXuLDIbGivSvBSHCbFSdp46K0qYKGdLFj3XcGOdbugG6xpZ25HwqNl6CEIwPDmW7TNXtnRyROIZukS+CpIDZVY+/fgjwd34kzixvys8n3pcq4gxUG6ae/+A7kecMxSTGB++4fun77l8+8Sru4FLjKTHGetW3v385+xzJl5Xuqh4jj1CQmuCZBd0q3TuAEvklDsO9p6P5z9H3AbGtxGvBi7twleHLea94vqt5U/4ltrWF0/FOSOKxW0mzEYSgqSVAGvDFENnCsoaqnPUNVNqw6bGqgRCKYS4ohS0rtKyQ/URGe9Q4QZeUeqCclBK/cn599usBDLw77TW/q4QYgP8z0KI/xb4m8B/11r720KIvwX8LeDfBf5lXmTF/irwzwH/wZfjT4aQAj5k8p1j0A7lHAwNcdaY1wphDPWDQvQCGyJJK7T0TGXF1pXaL4SSsbWSlkaRFtlVoFKzwMRMjBmpDDYV4nPhuHVsRUPoym0V3NYb6any9Zs9j+LM5lXPtvuay+0zv356JjWBDQPDm5HWzVjxivh5RXiYVxA1M8iVx88Tp/OC2ff4jaSUiDrPbH3D7eGaA+uHSCmF768/YM2Wd2/esh3fUbRFdI5N0Qh6uHPM8xm9TvT3cKuWtva8ul/BZ8JZ0VeFr3cvhhVCMc8TrlP02z3P6TNKW7qu4TYWyQ7lNxjV0fyMsopuNGQcLVZsFbh+oB0FizyxVIncwmoqfT7xWJ/5ZXkiOsX1PCG15Ou7e767/R/Um2fXFxAHrGisW8P+VMmbDXWaUWtFTI3pHLCiIFWlcz2rLpAqoi+sl0heMml+qQhNzyes2CGKRGtNXCDoG68ue/J2B5sZVY7YfcfpKZPvIu15IsZPKN04n15DeeZuGAjSUZ4aqws0F9lpyX2rfP/pGfUksecLUV3BbwlL4OnTD6ROc7h3mLd3yJZwvUUuhm55pk6a9HilHAwydhgxsreCZxnw3T0/TN/xcLL0by3TqWPYHSjP4COUzxVtFNO1kEXBqUqaXxS0dEogHLWTtDWQrCZSyWGm1YZoiuIbthXKKrAadAvEcwMr4WbYbj5zC8OLXycVKwtrnf/RW4lbaz/yoiJMa+0qhPhj4CvgbwD/wpfT/mPgv/8CAn8D+E9aaw34H4UQeyHEuy/3+UtDVcm0n5F9JugD+17hsMS9QawH5m5Bmkip4YUR1wPnYyVKhRQdISpojVwTKxHRLGKRCN2ooiJaQ2tHWTNzyoy1cnx3xrSRFCVmrmhx4NZfebx0eJ1pC5y3gfOfZMQO9n3HrhNUt2F6nvF6orbA9FHg+1fQK9SpkNuN6gyqKdItkKLEOYdWFRn2LDyS4pEYJf/k6z9gu/sKM2rqYc/D7ZlbMaTWKFyxF09pF/IouZMH4laz7VeitTDvQE+c9IHcMqrzUDI6DPRhwdWVzfgVUjRaOdCJHU4J+lIZxUQYFGY3UoXlGs6ExyPB3HGQUMpKSqAulWQ76mViMj2viWjVI0dN92pA355ZgkMcD9T+iUvaofrI+QIw8sYqgtHEUKllpijJ5aZgvTEOnq5eXyaAVMQ1o28g2GDvL9iT4nR4Bc3Q+8xcz1jhKM5z6zrM5TPOaVwYKa2hHgZyrBQBlxqwi2fzfovIganeGDvH5BvbClp1eNHzGGcej4EQBWKAgx3pnSUtEWUECE3zN8T2NfrJUDAEcWYSI1oqlsdKcI0sb8RgyFqy1J6xm9nkjjka6nOi15AvEfG2Zz2ODP0GcX4iSIetL67Ea9Ooq6Y2QaUhVEa5+tJbUgVSvnAIslmcqMxN4FRjzRUtQQgFSWFl4YLAyxsaT4iR8mag3Qxc/nJy8B+KE/hiQvLPAP8T8OY3JvYHXrYLfAGIb3/jsu++jP0kCORaiUHxygRiS9ALrOnph45lrnBVzHrBtQPHckWvmQeVEDkS1oWkCsk2WAQSRaXQFfliiVUyUlvQiVwitTOsa2H+7sQ3f/iAto71eqXbD7yqhY/tiffJcRaZbBKvt28560zvE73e8HQ9UcKNX337EfyWsulQ8ol6dszC0FzHBoW1DuVg2wkSkjkKavvMbVrp/I737wfa3RvyMVFvR6zcEbZ7qlaUm6XIZ46nQttUysEjLze8shxJmNFxlzQ3rchMyLlyKRFRb7wSB8z4e5S+sr/dKLWgygZnBavK6LsDo+8YciS6My0kVMhMP2Z6O3N+3Ri6Cp8zuWmO353w8sq2OGzesnWBPBm265UwCX6Vz7ySkqw2WBR/8d33tNK46y0fteVe7mla8RwVyzKh9JF+Y5B94dauZOEo1iCqwW4T8jLhNwJjPcdpj843zOmZrdPUrcXoExfR8Qu9IeRILo1FRu4uDTl02K/e8Dx9YFM0h0Oii5JL6XDCoMSAbQ2mSm2NvkU2vWQVheJge3eHGSs63mDZcjfe4W9bbsdM5xUxzZS68Hg7M0qNux8528DDVEijIYbEUmaGe8df/4M/4I/+179H6hQ/69+Suw2IK6GMPHyz58c//khSihgNuiY0FqVXRJEk8/J2F01QtaRkAzJTADJcREHYjKuKpGBKBqsljcysNTs/EDhylyOX7Ur5XHHpJ8WGf3sQEEKMvOgH/tuttcvL1v8lWmtNCPHT3sd/+f3+L98BZzWySj4/NjabM3bYoSXU50rtFqzuWA+Veak0RmK5oG3mLleeXM9aZkJeSMKCEqgayc2TtUQ0iakRTKMpCQiQglot27GjREV1Di0KTilKWmn9Hb4UxjDSdgFhJLX13J40T+pHdnlGDxatFPrgaKFRDxXpO3orQBhCFCxLwY7A7LhOK3P6FRjY2Nd465lLxcTERYJOE9dS2Ls73GL5RCK7zH7TcZMdo6mcpxOiKRZ3YrcZ2Fw9Q2cwTRHzlXmGLK+IrFk6g9EOLTS2CbbbjjfWUGvhmE70NTCcC6LrKW9H3sYRoQaKArcZ0H1B3Z74/BcSdZfotw6ZBG/SjlUWjDzQ9xNpMIh1ofdnxFHRdSMjla3e8eF6Ze4zzWhkmFgvP1BDYfvmG6L4ws9g6ZukiB2qvPgAjBWKV4wfF5Y24VOjSM8sYHlS3L2fyfKejRLMJRBVRtWJbt7BoBn8nhKutLyhiYgKA6pboK58FAUlj4yhYxQCazqGGDloj9cabxw/BMltmTHFcekiQxeRb1+kyXfuHi0FpT1RbEQ7TRxHSrly7TwtJWiZZVQEr7DdyjGsbNqMvDVc+8Sbb0Z++KOEdlCEenEV7iRmEghZKKlilKRkRQ4C60BXwZwy1VSk0mSZyVOiCYEygsE50iJAr6TpidjgMTe6ALOTjOqnUeC3AgEhhPkCAP9pa+2//DL88e8v84UQ74BPX8a/B77+jct/9mXs/xa/6TuwGX2T6czst3Q5kZ4S6g1chGZbJKHLtEWQxIzDgS+syz3FnCBmJA0tNFU3srXUDLFqtIxUFEW/tGO2IJAyI0VFeVhS5E3vCG1gLgGlCvf3e7qgIQuWrOnaDVMM69Hw/fynxMOK7w2KPdk6PAZtAklZbBuQ3lKqQ+eI0RnVKkEEYlcZ3Cu6UeHHdxTnkXGl33hm21Nz4I4tm+LptyMX+/s4J4nmR+7LiBh7aDcSO0ZxZL0ObJOke5AUlRiNYJUzISVoCh0qSEmfBOYuQ1cQRUJZkESEkMjtPVWAMw/M44/MYWIXErNY0W2kd4puk8h05F1Pviq2naZ3nlP/yGYd2FjB3DJ0hutxYf/1a7ZZImeFEzOLXvCpcD0+8fF4RXjDe1ewYkufGlFoRDGUsjDrQqYQakdJgVktlFtl6SJ6bWzygdgXavCow8rSNNSIzpU0jqiiuEZDao7OG3pdyWsi2YlC5WYiS27cR0HWjWYFBosrDackOb+4ARVp0MZTayanZ1ACXSU694Rc6LeG9SqQs+VWt9zWia427vcwPX7ZjmwOjN4j9UgTko2D6+QR+VewvPgtEj1SVpQuxDSjrSLSkE2TeDHasTSyhhgFNUmUrOjJgtEEBE4JVl+4tQDNopKl6YYvhVwUKQnGpmmywMta4h8eBL6w/f8R8MettX//N776r4F/HfjbX47/1W+M/1tCiP+MF0Lw/A/iAwBKgeccMYOmYJiVoa0BLyJrWbmGjhA9loYOkbRRiKhp9gGznOg0iFQocSYrgZAvlYSkSmuwZk3NPb0K1CFTZ0dnCj88LQwY3o47/uK2MOotyUiOlxvnzqPbkcu5EJ4yVd7I3ZFx2dLiBsmJICO3p4H7V3sGc0cSMzUp2ipRtZF14OJ3iKI5bDIPwz3bQ8caFesN9ocRKTe8lrDwRGbHKg0Pb3pedXdwu1CXAbwn5ZGfbzecjcPIjt0gEddAnC1uaHhl2Zl7UghModBuC84J9oNDyZ7leeJcb2z7HV/tRxYrWNYeUStxlszFwtyITvL8+UeUuoc3O+7edgg5si6CzXalpQ2DEjA5PoUzZu6hPHE+96yXzF+5s7j9yGW06I+FwJXT8cIPx0hSmZ/d79lt7sllQA2Qo0AshU/XC+3zi904Q0NWi0sXLkITkOi6MGiJsxqRNa1mYja0udKP98ic+Y6MGhpdv2PfJqTt8deZSWZSLlyUxOTAK20xs6ZRCamxXldKDISmMV5gRaO3HlM8pldEKjEKWllYZcT4hbhsEC2hpk8c+lfkYcTOA1FMLGGk1IgxnjdKEDqLGhXrNfFn333L05NAeEGxDTc1lJAMUkCWZKleMlqi0tf2Qu7lQG2GIhtCQHYKWcVL56PMyHMlbAqqbwTlcTuD+iQRi4ZSUTTOSvzk/PttVgL/PPCvAf/bFz8BgH/vy+T/L774EPyKF2NSgP+Gl/Tgn/KyAPk3fovfoNwmRPcdV3uPUR3hJBlwlP2CmSI5LZhsKHKiZoXIGR33WPmSogoqgRDIWIgCXPcCLrU1tKxIsSBygCopWHSZaH3HuSoOh4yfBbd4xl4fuN3OlFIwJrKcDSAQJTLufsarviNeBGeZ6YXA7EFaSZkW5pLpasWVK6mt+DKw1/fQRcL5mU85or3Gq8RcBDFbnErcloA0M3IcSUhSCNQWGYxhXkaebxfmFd6Ye6qJxHOg+BPGGlKO2NbwDVq95ywDnTuRbyuDN0itSAusc8HsNc1LglaI7BEykWRCBseoD9hRsLgTU/aoGFEisB+22JtmrTPh+sQtOZw2PCyaD2nFXgxPBdLHJ05zZP7ZgJ46nL7yWM8st5Xw+YYzsL37mq9+9hbhO3RTbIUjZUGUgXiqLDlSCkg0a9aI0uhaJrVMQtJ6jZUDbs7M68Smk9yYoLzlLCpRBEYLRkU2spGyBLUi056pJebpiCqa+rUlJ024FVqTgGYxhlobYgmoqmlkqlU4dYdcLWmraTYinwKJgBoV1Qq6Vqgkqq8vZexj4RoSxIhMC6NzlF7RZstTvPKrP3nmdvlIMQJSBN1AVETtKVliZSToRq2K3DK6KaoyVCXRNSNLZekXRGm0tLAuksFIWjCoXUHEiF4lYSmITuPTyq1T+Ktl/onugd8mO/A/AD8FI//SX3J+A/7N32bi//2QVGRspCUiy4W6KdzCgHAzeTIIVmKe0VmQek/fKsUolEwkF0m1YVaPUoHIgqwCGhglqV2F0tBrYh0EankBhjlJ6umC3QkOqafbNM6PV1b7HrNJrG5F3xRrAjEEbFb0voMmuFVBEiPi+gnpHLEmcq0YI0hUYmwIFdBipJyuxHLk+PSJ+aZQWbDfOZo0nE4r1guqSshVElpCtsxldNirZ+4DqUWaUqyt8d2UqH5COcFyueL71xQisxixa6B0Ao2lJE/pLigUtnNI/+J2bLWi9oJLaKhYES2wtsSaAlIogo6084lX0jI7gRae7caQzYVNqITrhtB+RU1bklf41qgXWJuiV/DWO3zbUYFwnEnx5X8XFl7vd4yH1xi5p9SEMIV5XrktM0wabzzi8IzMFpFupKmwKgul0qyipxKFY6NeJofNkqALYWh0rz8z/wi7YNC+ca4zzfckeUT7ET1pghWIsND0ltkLnEmwFEzT+K5nvVjKnNj5hO4H7sVAKY0kFEsodMeVqYvIWBiUwUuDco7xfuAWnunCgH0QnKY7Ptz+Hk/f/cg3VXPqPbttJWfIZuFufCCvZ3Rp1FW+eCrURoiVTlZSqdAU0mSKytTUiE1gS6aaTCkalgpFo79wKlFlhKnEMiBvC2luNGEQMrCWglYvnMJPxe9GxSCN3EXqMpDbR66Xn7GV4oUFxaL0CeUsuQi8d7h2Q9lKWAoyOISOpNbQzSNsQFNpRUA04Aulf6kHMNoQS0TXhEiO/PiZH8NKLwLvH+5QPnI7R+7feigfeK6etRvYTbD4hRKuPC6FxRpa0qQkX1ySe4Eynt5EUkvUvaAu8HQ9soTPLPPCbbkQgD4bSnnLZisROiGU4l7AFF/2hdZYipTo1EhTwjiNzobunSTMM7QFheb8dEM5R5gMzpzJwtJqoCsTYa2YwSDdQDONURTy1iGqJ5UCsbFqhWmG1ixNraTLMy0amrUMxdCZTFtB7RoLCTFqfiEsxT8Q/rwx2YKyHWpjUE8LDw/v8HcNK19R+4kW4E3/Cj8emF4v5NuK7y1NVOaWKcFyvSimKeGMxexGDrzC5siHj0ecaeyN45QDyvZUsRBsxN8iSY50pdEWhS0bhq5DioVPXcBPmr5Krp1AmA6ZJ1bTGIcOYf4AkRO5FNxo6Lhyuq60JvErrEvCWBBbiTkqxBQR5Yk8KRIdOa84VzBF4iMMxqH6nvnjB+rlyubwipXE8eMzv/7ff4l8eEt5uvD13R12WLkXHcs3P6OKAlSMhhQ0WQlqq6RqaSLgm6AViEKjdEU2oFqyFBSlUUkhVAHtQVVyrmipKJOh1At1Bek3OK2ZusRuqSy2wk+4EP1OgECrjdJmZB3RZaXWC9XtkWUmG8c1Ot6gaL5Qw0poAmUMjkhuGoXD2gtVrBAaCEsRgMiUJSN0I48WeZWIKjFSEGpFK0E4nvm+Jmzcsbkf8EOl5cLmkvi0VlqEoBOlKVSeWY8a/5XCW0n1W5RoCOeQLYAo6JIQySLMixd9jEeucWXNE1MRfDqeMFZh7Dv8EDHSUEqi+oxunm3fw6KpqtDLSt1s8E8J1a/o+Jon22FuiSk/0+ZIrwPzTWG0ZaM1NwzTvLDvt/TdhrUstCRotBdHZtVoTYE2mFZIZ4P3ldlLWvN0NnC5Sny/YvOVsgrEydMLweVVxTwNmPXMbBVaF3a60aJn/3BgGiEsN1T+AT3u8GiS9PRuIGxnqBmRLSW+uN+ZocM3h1GVkDyGDuNnmmwI2dH5TBQzt0tDi0AMF87PsO1HnF+QaD7MGnfcc/UDbf7AU5kZqkOuA4Pa0ORn1GFk7Fe03MB5RZORrdHsQJAgGuhOo1tFG4c2jqsMLKw4PVBU5jleuR8tqMTMjDBvkTLTgiLZA+I8YSZFqU+sp4n7lvmgNN2HH5CvB37++h6T4HIvWW4VWRxKRWRuaBKTECRAZYlS6kWbshmUqjQZyH1DJPXyvIkCMpOdxYRKVZKaBZvhyq2BEC88QCsWXR2zgI76E2oCvyMgUIE0STbbQikD3S3xJK48IGlav/QKLIV+0Ey1EFXAK0PWmlldiWZBZINMgdYERRdkaxSbMLGiqyEVQEqkePEsLGoFGlobljnw6/FHft6+ptMNITJ+fYuST6T1EbnZ0neW9SyQXSMay16DbYbvl5Xp82e8SKS9BqEp840iI4LKcglMlxuuZXa7e+gVz/OMjkcevuq4dYXWe7x/QBwVp/kZtwSOKvDGWj6dFNPnxDvT8c3hLfuoX1hz+eJWc9h7VDlgWgcGbLb0m5mAJYpIFZKGoE2FOU/MrqBbz/F6ZcwNmwZoC2JtFDVxnRrXfGRZC/I88fx0oa1v6beN0CR6rgivuH16BO2YxhNSOSZZWR8Vg00s5p66Nj49nugOGr0KZIhUpUgqU7SkEwbZaYTeoXIg18LcbqR1wXmHzpanfGWtgpWASwJzbAgPuUpQBjEUzEUhy4quEr/zKNGwFFITCGWpyxvMq0pXtzQREDnCsEeRCUmh3Z71EqkzhDVwPC3sRMdWDvgeNmPPScycPv2KJne83/YcvKesgrxaOB9pobBYTY0z08fC8XRh6F7xT33zCq8re50pnWaZM1MppFugFUsDvFQkVUFELNCEJNaXbLtQBWkbxhdEFmSlqQiMKORQoAqsFigBKRVSehHniepLperaQEqqbKB/Sk3gdwQEZKvUNbPuVgbRMVNRYiXyCpkVuihWZakqUrRgrB3SQjU9XasseaWExJArDUWQCSEFTb9UU2XhsfNCyAUreEm/aChJYHeKcOoQJNLlxFfjW25N4QeFKffsNxbVGdyq8HaD20qm5ihRsshIW2emtXJZFzZ1QJqJXF4Y/BzPlGklT+C6gaF0yCZZZeGcntiWd7RrwjmHmi01/5Lj0WLsgmkBbWApd9yugeOvJZ39FW/ynmUM+NUzaPDmHtndoaNmtYmWVva7PTlESlk4roEOg1aFuBRa1sj8mel6g3GL3AiE6FluMzSwTiDnTP5wYZlhniPMH9j/omIXhxsOpJCYbhmxmZi/ixy615zHgvUB4e/pxwTW8PzDDT1PWO9IoueWBUnOmNIY2LCqCZIn0YjlE5LAvK5c84ozEpEMxziTcuShanbugfz0A3EXkbsHSg28cpVgM5LAZm+xS+GzVPQm0bWeZYyMt4DNA8kpbghkMaAa91ITdWRSE8HcqCQuYsKvG3yn6U3lYDQXo0Fp5uuRZTPg2XGzK1I55BSIXcYmz6dXIK+GIz33g+L3333D5qFjKYVkNnSbK5cfMroKsozUrClS4aqmqYY0ktT1EBOKBmOmVUHKAyY2FA3ZLEY1GCpy9bRcqeqK6GAwmlPSmBaQOtGSoBSBlJKpSv6RU4T/f4QUDdwG7Rak1eQlo1KHYEKaA0lIdG9AFdQCaMuqQceCRiLpXnqo64yRhXUVBCcxwRFCJTrY54wsBes8ddeTngNGZkIoPMiCVDOP58jm3pGHr0jyRI6grUMZiakZhpX0MZJ3PcX2BCUwFaZ1RhdNPCWiKAjlCRa6ZkDdGPset9kTZKOvklYdi1PE1vEuDcTvFbvf76jmHuVXNu2eTxyZgme3VWy3gpQT3kvkLHjt7hDvMywepxdokbIWGKC3kr6zTFim+UZXFTI3zpdKNQlMRZSMEB3ZGgar6KJGbkekgj7CXC883QQsiWXJbMeFXC1G7MjzFbk3bMqelM9kr8FbdlW+yJuPiv2iqf3E7puO0npslXR3Fvl449gq3kiqulLmgM4QkqKWl+WvaQodJbcc8SJgQ6CkSu8teoF6f0Aqw9IV+tNIPUzU2L3Y2+fM42lBJ0m1PU136HRlvijC5oyqHXrrqTExekBYVM3I7gybGXcRuFBoeiUkqLUS14SShsFssP7INfW87jcMLFzNhLt5lsuNr34+sv/cuB4MWylZhGI0PRu7pakj6jpip5l2W8m1MTSPtIWLsKQaqe0lu1UjVFkQOkPVhGooc6TqhjeatmRyS2ihCEwIIQlYamwIG+EO/FWxxEZG4bYJ5TSzkv/vioX+P4+mwWZuU2H2ha+y5BIkxsFGBLoW6FBI5XAukXLFXQrNBnLJyOCoLXP1A2Ut5HSltkytHikXKIligSC4WkubIvtBEsOWkBVhe8NdM0dv+aM/+8w/9tWO9FZghy373hI+zej7wlmPeH3EiY7QrlzPFZ0FLUu6N4XO7oi3Hi0SQcws04k5Z7ZmJc9XXC8YunvcGilakqfGWcxU6flxnXgnPN37N7w1HSa9pn74hHjf6KMl6Hd0YUaZiOwOKNPQRXPEMcwzYuNRnJHaEaOg2IivEmMfEEngdWKNZ6Z4IdkNuhZsdpTUOJ8Xmr2x/DDRDgek7Xm4/xrPI/vyjjokhiGzrW9ALURVKCFy3Vt+Vhy98eRbRHU9MnjyaDAychh7pmuH0AVfBbpfUEVjl4mWJEVvuJrC+fyZFhplloDH9Ss1LIhzoy8GMU/s3AHGQJSCMD/yXrzIrm1szxICunWQT9hYWC8rszojzxB9ZlAWExqTzyAbUgTELKlqRXceLfbU6ZHpKtjtBrZNkJdMdQV5v+WuDai1cL8boW+Mux2ff3xiuRjutwNjXlmuv6a41zz89T/g7WtD/nShbXqs7+iWQuZGraBvlU4m6oMnfY6oVkjaIYFaNbJmhAaZBGnKiKGhe5ANYg5U77HO4m+RUgsgsUlTmiLf7jB2IiyW4iN0C3W4o5xOWPWTvODvBghU2bjVwEYL+mNjHjObMjDoGZ3di11YhEVOeKnph5m8VITy9DrxlFdU1iqqrAAAHVRJREFUlNTUCFHRjIU8UWuhGEUpjWX0VN2h4kQuiVV7ol1x1aNnxeo8y7ryvnQ8Ph159QtP/7winndM+YY4Fk5+4h/fbnhuF+Ii0TGQQuHuVYcXhjVXpH6iVU1Thv5g2dwkqfb024FXu/d07y3p+ANT3DDeBTbdK6bjM/Fkca//EFuPPKWF4eSIveT7p8w9ld12xeY7qn0gf/GpH2RiCiNyYzFJ4SbP4oBWaCmQtCDaRk9j2HuYErEYavOIIeJkZI6Fm/Bs53tEcywNDluB6/a86h74+AzKNaq54M9XVrGjigurHumVQOgDQhxRu55SFpILTP5Kp0bubGIrBYtu1LBQvMMFS5YeO1iUOBI/T5Q5kOZCwyH7K3bpqb5wCQEbdoQmmbTkvi7MbSAA+XGPGq8IE9HrHer8zDRIxG5LaTPX2wr6Sl0uPA8bjPLMs6aYRtaVnYOqIiIL9LXHZY/QC74fCQMYbUixUpXHd5m2H/APPcN2RqQjSRZWE7mGQJM7xOYO0/WspmewO07zR/QQuOKQds9+L/jBrNiTIG0cbakUY3C1EopHioRMiVIUSkYyks5bmpIEkdCt0aIia01ZMmt1+K4SambNK1bsaFFTYseunlkiZAThOSCVIk8/1UP4OwICommcyNi8YU0Wlx3xncAow8YqkpKUSbKXPYO+sQTH0TeYCvNkMXnG2YVORmRcETG9LH/K+mLkKCEFhY+N2IN/hpuMGGFQ9xB+7MlmxtJzzGfadCX+yR/y9TeaFo9ssyT7G70ciVqwnJ8RYUAkx1Ar4Zy5mTN58IjY8xAruhfUsmVwCn3oeDi8RZmMNBLhHzAFdrs7TDej1MCDG4n9JzabDb/8s4DNCxlDWj7yofdczx+5/2e3DP0zZ72lTJoqL1Q9UoeeX9vMGDN2aqxN0OYRNo5WA1VBnxyiVvT7hSILIYCce2SQOPkZv5UMLmLNBqkeWOVAorB/ZXFuhts7PvUXjqth8/CedP2eQzEsfELLykfj2Bwn+sHgdxtO388Yo6DWF9JKWco6UUQhDYWrLEw/Jj79+Q8secV1Bu8dYumYboVZV0y/Q9/1vBGeajNHNXGaF7YuMm+e+WusPK735AaEO4I8MzcoVXOoitoVVCmcNw3ZdejLGb1sYHBUqbgMCrWuNOPRhx3pw0oINzbdK7rkkemKjoXeRtg0/GvBML8hS4EvB/bikeuwIq+eQf3TnMcLW7mSN43lTuNl5bE63tUBLwdklQzffEu6SkwP1iTIAt9FclBkVtASJaFESVaNbF8+IioIFqcCUnfMu0ofK3WS1O2GlZEuJ9q5Q3wViadMHyF+FWhPjvyTMqO/IyCgWqPhKIvg4GeurzyvaahPHeXnlXBVODXzpAamVSDMSooCURcmpZmSICRLigtrgpQFrf6f7Z1JrC3beZC/1VRftbvT3f41tpOQHiuKgogyBJKJwywjMkBiAhIMGARlkilIMEBCSCAiBYTIBBCZINEIiQmEBOQ4doLtZ8fvvXvfvfc0u6td3WoZnBNyZXwVhyg59+qdT9qq2qtq8JX+Xb9WrVp7/YGYREQTCZOEvaWIE8ZPTAsovMf4FN+vyYuMMhaYCGNzjzzvGQeHWRvKswcUq8goj5CDoDUDY3yIji85TQvaraE1kJ9GxNARx0Baz6nqmpCvEJXieJVwrDMQM9CnNO8oTlSOSnJ8HfHdhmHh6WPP4dmAu7QsTt6H/gX+3n2kW5EvOrYv9jy494DuPc/DIqPbHWPMhvXGUKuKXArGPOKUZFAalQxUKhBExiFrEaUn9JJx4xlDIClGSq1QyREkCcVy4HLIodtRpZY2H7D7Gk3BIjHkSY1WnnLIWemSYdqh5QoTdhSXz0lXBc/2O+KzC9LThG/sRrIp5ahsiHPNgMO3FiEkwQtevGjZdSPHZ8foZYndX+JMpDhOOBEPKULClW/52EaO1RXmdwZW9wSqeUScH7G5dGhpwNWk7wW6q5QH2X0+Tj7GO0ucPDGHkz4wKEjTlB5YxIJ8uaJsHZtuA6ZDY1EqYzIpflCI0xTTR8IwUOYL9uqcF0/X+MmzmJ8R4kg57hmM4+HqAXp4xv3kfaZO814yw4olPhQ8XrR0omP0NYvFgjqtCFKiux6TSOQkyH1gctel2MASDZCmOBlIDpFCaZxOcMmEkR5lLXIjmcSMUFxSmJxE7Zh6jctawt4RO43DMOsM+yFHxpTwJlclDjKSxpJ5MdH5Occ7hzm1TPMGN10vETaKBNv0ZFlAtZqFlqydQQ5rEBNDndFfFkjrSBPPGCIqNOQ7yxQtPhOYeYZvJcoZ8pAShMKlgVEEhLEMOiERazq/JIuWrttxNq2uK/h0EtWPbLcT2vX0bcfVQmGWglmcUakSOxtJJ0EtSqxL6e0VJ/qYcjFHhRPyoqCyjuyk5pk9ULY5jBMzqTGmx5slL3cdptqhZpc8qVeIPKLFini0pBI5zGfU7RWH0JImFS/8itpdMVnNpA0hu0eedDizww7Xo80qjeylIYw507nnajrghGA25ZyeRWQWMOMFu41mnU3MwwsmX7F7WVIlHbSOyVu0TZHlRDpdkc9rXj4PxO5j6nSOLRoqKoLcYLORi6c75GGiKmtUtcDsGpLGQpKixozWGWJuOZqf8uTRE0QTeLbbIFtJdnqCIpAcLML1nKQFiZ3zdLGhoSAGxZMg4MEKs3OEaUTsU+pjSZYETnaPeNn32EPL4qokPgJZeCYzUSnIAwgz0s5z7CQRmwF/2eOHkeRUUNYeYRxMBTZI1kUgG2e0Lz9i01rKbI44MrRXFe3VJyQLT3OqGDYzxnjJ+WKkaXJykWFdQn4c8R+XbGeOd48fUySOqXDkXjFEQQg9OQI1KbRS+MQyOkniFDG7Lk+mpomoUtCCkPRweV2ibT3WKNUyJZIoG1I87ZEkPYBcGtRGUqoCm44M3zkHvCFJwEdyrTlEy0HBNBPkFzWPS89LJznTAacc+qLh0EiSZI2IDn0JUueMtScME6oQmBCJU0KeKPLC0lnANdSTY7p0yCzAaOkpSdMts1GwTzIKCZ3vWGQ1l9MEXnE49eSHjO604TP3R775VGKO91RuwA8pquupnrxLJTNU75glx+RFZDQTIo6oKLBaUZAyX+SIWHKZ5OgPvkW/X+OTE46+N8GnJYepZGqfs5568qCpaZhQFLXHTgVHZzmz9Zb9JDHUmAnUztDagnxZoq6L1OOx2IuIFZFRCEQ/ElyOahKScmA8ssxepjgfMfaSj7uKQi4p2GIuPyZVka6DfVHRyJLfE+c8Mo6wHCmdJh0GDlVKkIETuWejBHVZEauELE/47LLh/IOcSX3I6CyT9whnuV/t8KoBHRiFYSkFNBpmIyoPdDuLGXKGMkcOUKfQGlC6pt+/4LRTHGVzkoczjtzAIpmYhonTzLOr53gEdTkjEYHMJ0Q7B61p1Z7iIlIeaTZJjpxSthdw2gzkRqP1jKtmh/MpQUZULrBpSlNodDIihbue2GNHhvZAHVOmZGQRMkw8o4+f0B6nrFXO+6cfopmTdp6xqglnknDQqLHCKrBjy8a2lPdO2K+vOESHzgJ67vHblNAI+sGgvWaVBqIJmCCwShDSDCcU5WCINoFsYKcDMhwR0pwkCFCOXkjSTSRVI9YUDJnF2JZZEG92VWIlBTYONFWOcT31WOGTwBWKLB1IgsQJz1XTk186gizBjijVMXoBvWb0FkFAZGBdwFvQMSWWHjH19DZSCI+fSkw5I092tF4yTDU6RHrjKLoK6wUqkZBYUt2wqWecLvasx5KD2fNelvFiP6fQjj5p4WqHFCnFSY4MgiBTZscJZixJx4RTkdOEhqlLOKSO0vWU2ZzmQUWSaq6CxE8j5aFkLjOKVPA81VzuOiZWDFXOg/6K3Te2dO/9ICfjJfPsiEO656ATRjGwDp5FPmJjgeo6prwj0wlunaKalFR4aD1BFmRSswlrhDmQSk0+tMyGEZ8qECtEGjGbjmH9JQ7ylLJo2OeSr39jx7tnkqBOKHY7cjlSLk/ZLjw+Ped0fIfBjqy7imQp+T5v+OYycvZwThwDXbIANZDaLVYphLCkLiGsNSJRIMALD0Kjp5TduCOjxfqBxAzYPKd59xHzLnDyREM70mzmdE8qdA3+uecy63hYnSCaNUdGYod3CeIlH+0/5lFYofcvmA45SZmzlp7SB0wHPpygY0D4CDtBpSIULanrWZaKyYCxkkQfoc6WlHHBVTohzyLvVo/Imp7+wvHVuOH97/0eHsyfcOlfEPscb3t2FwOSjEVv0EdbZIyEoScZJTpVyCnBOIW2KUpWuGRk6yZ0yGEQICd8FaCWJL1GeonMM7opwU7n6EKQ7Qu2xrEqI90ioSsV2lj0XlE0NcPwnSsSwxuSBKIWHCmDQ6FiICkHFrsEryXJbsdgMtJlYJEU+EqC6/HGss4l41XA2es5ALQObcDhCV5DmMgmiYklQg60+fXEDJm1jIcaNEizZ1TArOAo6xhKSegkbV7jnlq+58kIfcrVruMgJUk70rcD/eElJpZU91IWTYNOPXbvsMYzOI3Oe7LlMUo1rKWjnEWOOkMmO9zRfdLZjPb3vkHiUtQ8Y7aamNaaSp0QX1zQvfMcuRh4b3iE89AdTsjbikEH1H6kqmdcToJ7ak/XG6omxekds/l9ZNeg9EgcN/RlxqgnjsURSRmRV45GDpgkIytK7GbPJhryxYqylDi3oVMjl3VGYiX3q4FNb6jriaP7D+lfRnR9RJdecLwWdH1HWEn6tuVotqdpVqQmsE4L0iYSmxGcQKo1ss2wuxSbHxjQGNXglpIh9aj1DjdEZkVOdexIZMY+SnLbE7YGPTqefGK5ut8wtI6dqZivahZxZFgv0dmak5kizO+x3Ck25qsk1XhduGOXIJyjzud0sScpBalYXK8qtTzn9ODItpCpHiU8IQkcnEeUCp8nnMwCIs1YTkvyrKJtQBRrsg8D0jxEX+TkRUO2izyf1szqBZ90Hae65/mhRFYHyuegTySDKXCHgTBoZmXOmPQQE/xM4g890UR0qtFlTfBQMRJcpB9TlK9oGVHG4JVCCo+OOQwJYzkQk5GdzdEfpYRZh1cJhZ5h1HNSkbz2/nszkoCDkJ7Rj+csqIg2YEdDeqpQ5gg/h3FMUDuBmbYkMiK1ZWkd69JQOEPu4WolYRQke3E9iOIELklxvqVE4XTADXvkPpKwI9oTvAwEO0e4ik38hOgbxL2O6hOPzQRxdLxMI9ZCdtkyvBxY55FGnJBGycPsjJPFZxmKCcOWfTgwjwllU5AKTUwEZ2mOmjbYLKeZVcwXA3qsSOYFnfS8eB7Ij+csFz1tVtPvvoL+RDKNBfFkIMsXuOKCfLfkrJCMpx3doSctMuq+wAfBt7qU+1Zi5IArRg6+pLk/I517dv3AMAyIURMazSp9xLbb4wOsG6iyhGWSIdI5q25BflaSmwX9yzVdLDiIK35AnxJk5OidM6TQhKuCQ3rJ42iwoqQoHEHm2L2nkwM7oXlgSuRWIt2WoYvX/wY8GZmUJAmKXAvG8UAUGW6VkqqBeBEpekVzLyPddFwcZoz1gqE+cKhHQr8lFk9Y6EB25BmKE7S95AORcL8dOUsmTAiUZ0sSJRl3jno/I1CQpBnaWGw1UU0KV1gSpQiUxGZAzhpMW5FMOXUWIUSWB0eaGRappU8l3TKl6AvkcMw0fMT50Zbv8+9Qx45qNWO88MhSspwSdhHQB5YzzSff+ojpd3a4h4/oVyNi0uxkoOwzKCRaBXRa4E1ATGB7Q45h0h6hNJXwiHGPIWKKiuANMhzw84yZCqy7E6p+C/MehQFlCbZmn0zIsGJMN6+9/96IJKAFWDGQyDninmaxSwlHAlcL5Naw3RfMFxdY2yDGGWk+4mxkMpFBajYxw/qJ3kiCv34LgIcQC4KTICSEmnm5o6sT0gjZhWMXNoiTHNENYHpcAF7ukeeSw2cKym3CJR9RccZn64rfGEeWR4Z7lwG/KClXc5LasTf/m1alHB/NuH/8QxT1GVoPdN0ebRSdd2RyiTfQCUEfZjA+BxXwLyPD4ZxtseFgAsU3LT/8w3+e+8sB1Ssu0siDvGB4/j5aG7ZqIFmfss17thtDcjxgkhPkONEvJiopsMYxs5qYJCx3ipSSXRzI+gWzXCCaHfOo6TvDA5tgpMc6i0g3uHsZpX5EYQJLvWc0PU8eH9G0Gc/dRK4u2H34iKMzha0KWjVjFiaUc6y3gu3zFzgROWhN/tmA+ygjSVKqY4WQBdstxP1AMrUEKaniKcJqLhPHrP4c5TKnP6w52JFFLOgWX8ceoP+wRx09IpMT6fGc0mpiLuDQ4brHfCYfEV3Keumw+gK3aXFTyTL5BO4tyEPkav+MyaQsNitcCXKTI4UhVwHcjFTu6XODyTJqtaIua/Ii0tYdZsh58v47uBbS1Z51O3G1XPCOSygLjVllyOEYPU+4Cgsyc+DSBaI6YPoVQxH57S++4KMv/jeq9oituwQmqmPNeBkJJiAQxDRDcz2XIeQJqJw4grQGMxcoI6FziFWOPTjSIdLaBSUdY6JR24Qu65gdacbzllWAy13Nqkm5es16w/JP9W5/DUECeaAvVsgXju3CMrlnuP2aLipqfWDfpdhOkwWDnyRyzEiZqAIU2hBDTzZEchHxVYpEkXqg8OhKEnPHbkhwIWDXkXU9I6QKgyDzkntOoewMTjWclLB2NItzdv1Iu9nz4XxAzju61tGfPKAWCdbPEeoM0daUhxqrliTRE/0Fm4sD7sri7MC27Xl++QFML4kJ2P4TdufPeLq/4vKR4vSHThFCslvv2IoND/qeJFaUjxuOmyP2lblOiFXBVXFM565okpp76YL68ICV9yxijh6XXLoCFxJ8mXDQFd80ARM75umMOFPE0hHHAm8VVZXSHFfUekUrLIMZUe0anvU8oCemim3vSIcG7SXLTUX/4QU87LlMeoZ9yb3OMPaOtheYI09fO/bdxKKD5KOIdJa+8AjboLaQCE9TQaIbVJyTn1ToLGUxOQo3EdqRGR6ZaNazCRkbrHNYs6QYwK8948cCnbcsXi6xRcWhegpzR5pGGi9YCEfmJB0HXoRTemcYcoWwhgyNLz0ysUxxYjSeTluGPDLaOYESRyCIFuUuqWXD3D+iDRmxcOgfaLD1isNUUw6OdQ8vg2axM6weG97JHU9Cx2axZd5KwqYmn805rQRPPvdj/OD3/Dm0MUQvaMYZm3XCPki0WaDLFV73DIkhxpHQeWhHdNojC02+Uxg8UzUSdgfyDEIJcvkCnQcW8kA2GyhtTvdM4NcZ20aQ5Dv2wxv+OBAISOdIaJGVJ2wkKQsOWlBbSxESfL8gma1hkeI7gelbdsuErZcYo8EtkY1kXraY4JkA01kYI6gEX3QEKcl34GeRajvgrYGYUaqAXin00wyf7JjrSJtoPrH3eW/02DGw/do5u84Ta4V7/hWW7/woCz0S4iXJw4YidSQq4FqH6wZaCy5A6mts9Bw3kOQZKEuWzdg7TRiuKLYv6GXOqZ5Tni6pOTBVGnYwnjScWEcvcjZLRyd77jsYK0mcMgQQZhMhZDhnaZqRahzYpClh/QnN8X2KQuJFirAjadsyWU8/FExFQe0S+njBbuqx5x2qseRHGaezLRvvmTf3kXLPIKBdlFTdxLp7n5P9hkomXKrn7EzGKt5DqJrs2TO2yQlduqYdWx5351TlkkRp2lFTukBSrNhNAj3ryXOBdpYscwirGb3GOo9XKeUIcsq4WkjyjWAsLemYktcnpPdW+BAJzVOOXYEuFuzWPelDw/R0x+QapsIwbFPKYmA78xTPA6p8xCQ0S1FxKDKCH+mKkWaYiH5kj6COktPJInONm5WMyRYrM2bjnmhqks0OlUjKexX+G+9xXDynaXImfcR42OOOE9yq5/TLR6jsiCcniinPyLLHLB5p7q399ToNTtAXET1ZZKNw/QH2e1Rm0CrDyxSiwI8D2ajxac2QSjRXqGiY0ojwBVEI9CZwkAeShcB2OUVuiFpycrpjbc7w6Z6M4TWzBN6QJKAdjIngKBWM4ymiesm+l9jdMUk50s4T5noLTqLWLTGBXo/EywxvWkQQzL1COg82RRcRAkyVJfEW219PBgpqj60CeVtwqCOyT0m0Z+0C6WVgmm0or+ZsREDZHVH3nMszzHHL9zX3WR4esF/1JI+WPHj/DKnBXO54enHJcaUgKs4HRTKXlDoyFw33SkFxPKctap7uWtzlxCMpsGIHOuPDSvEj1Yps+ZDHecpatXRf2TCe9ZyeNzwzA9lMocsN84PihVsxkxnGQXvUEfuaJyWoSjPZkUBNeGF4qRKyq3OkWBAWOcIZcgPuMPBi8zWmbMFKNphpR59oJu3wB0NjBdqWzKuHfF18xPzyOUP9iHQ9cOk66tUpY3/Ei3rCrtvrisjVBXq5wuYH6meapTzj8rM9qc1od5ZkM1DLgHpUMpgB2wmUEGSjRc0iziiEkMzsgi4NuGFCuo60TBgvPcmVovIS99mG/Vd+l5PFnyF58Ai1f5+P8guyamSuV9RJiy0i+zRH9SdszAUjLTOX8fhHTlDrDzmPHpsMZMljcAV0G7YhYAPkncGnCVYK0tbhjjVrm+EHR9zMWD46hukpL41i5QxtZnmxdxx/poBk5EjNMVOk/zBlXH6R/uG7qG8tcKcjdl7xg8eSD2rJmMqbdS8mqiTHUJIlByZpCH6OlRlMHT4xKJ0wuAxER5JPxGixVpF6gaJH2AKbelSMDElFzASh9WRacNAV+VVLT0Dkr18MXFyvBna7CCEuuC6c/Pr3GG8+x7zd/vD2X8Pb7g9/stfwTozx5Nsb34gkACCE+M0Y44/dtsf/L2+7P7z91/C2+8PtXMMbMTB4xx133B53SeCOOz7lvElJ4J/ctsAfk7fdH97+a3jb/eEWruGNGRO44447boc3qSdwxx133AK3ngSEEH9JCPFVIcQHQohfuG2f7xYhxLeEEL8thPiiEOI3b9pWQoj/KIT4+s12edueryKE+GUhxLkQ4suvtH1HZ3HNP7yJy5eEEJ+/PfP/6/qd/H9JCPHsJg5fFEL8zCvH/s6N/1eFEH/xdqz/ACHEYyHEfxFC/I4Q4itCiL950367MYgx3toHUMA3gPeBFPgt4Ptv0+mP4P4t4Pjb2v4e8As3+78A/N3b9vw2v58CPg98+Q9z5rqe5L/nugTdTwC//ob6/xLwt7/Dud9/83vKgPdufmfqlv3vA5+/2W+Ar9143moMbrsn8OPABzHGb8YYDfCrwBdu2emPwxeAX7nZ/xXgZ2/R5f8hxvhfgfW3Nb/O+QvAP4/X/HdgcVOC/tZ4jf/r+ALwqzHGKcb4e1wXyP3xPzG574IY4/MY4/+62W+B3wUecssxuO0k8BD4+JXvT2/a3gYi8B+EEP9TCPHXbtrO4h+UYX8BnN2O2h+J1zm/TbH5Gzfd5V9+5RHsjfYXQrwL/Fng17nlGNx2Enib+ckY4+eBnwb+uhDip149GK/7c2/Vq5e30Rn4x8BngB8FngN//3Z1/nCEEDXwr4G/FWPcv3rsNmJw20ngGfD4le+PbtreeGKMz26258C/5bqr+fL3u2s32/PbM/yueZ3zWxGbGOPLGKOPMQbgn/IHXf430l8IkXCdAP5ljPHf3DTfagxuOwn8BvA5IcR7QogU+Dng127Z6Q9FCFEJIZrf3wf+AvBlrt1//ua0nwf+3e0Y/pF4nfOvAX/lZoT6J4DdK13WN4Zve0b+y1zHAa79f04IkQkh3gM+B/yPP22/VxFCCOCfAb8bY/wHrxy63Rjc5mjpKyOgX+N69PYXb9vnu3R+n+uR598CvvL73sAR8J+BrwP/CVjdtuu3ef8rrrvMluvny7/6OmeuR6T/0U1cfhv4sTfU/1/c+H3p5qa5/8r5v3jj/1Xgp98A/5/kuqv/JeCLN5+fue0Y3M0YvOOOTzm3/Thwxx133DJ3SeCOOz7l3CWBO+74lHOXBO6441POXRK4445POXdJ4I47PuXcJYE77viUc5cE7rjjU87/AWCyrxtJJLxOAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "HopSkipJump: 100%|██████████| 1/1 [01:58<00:00, 118.64s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 120. L2 error 3713.8616 and class label 866.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "HopSkipJump: 100%|██████████| 1/1 [01:59<00:00, 119.58s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 130. L2 error 3174.1445 and class label 866.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "HopSkipJump: 100%|██████████| 1/1 [02:01<00:00, 121.41s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 140. L2 error 2791.7537 and class label 866.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "HopSkipJump: 100%|██████████| 1/1 [02:09<00:00, 129.83s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 150. L2 error 2502.5105 and class label 866.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "HopSkipJump: 100%|██████████| 1/1 [02:12<00:00, 132.56s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 160. L2 error 2268.6338 and class label 866.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "HopSkipJump: 100%|██████████| 1/1 [02:16<00:00, 136.67s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 170. L2 error 2045.2914 and class label 866.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "HopSkipJump: 100%|██████████| 1/1 [02:23<00:00, 143.29s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 180. L2 error 1866.911 and class label 866.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "HopSkipJump: 100%|██████████| 1/1 [02:16<00:00, 136.50s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 190. L2 error 1717.2174 and class label 866.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "attack = HopSkipJump(classifier=classifier, targeted=True, max_iter=0, max_eval=1000, init_eval=10)\n",
+    "iter_step = 10\n",
+    "x_adv = np.array([init_image])\n",
+    "mask = np.random.binomial(n=1, p=0.9, size=np.prod(target_image.shape))\n",
+    "mask = mask.reshape(target_image.shape)\n",
+    "for i in range(20):\n",
+    "    x_adv = attack.generate(\n",
+    "        x=np.array([target_image]), y=to_categorical([866], 1000), x_adv_init=x_adv, resume=True, mask=mask\n",
+    "    )\n",
+    "\n",
+    "    #clear_output()\n",
+    "    print(\"Adversarial image at step %d.\" % (i * iter_step), \"L2 error\", \n",
+    "          np.linalg.norm(np.reshape(x_adv[0] - target_image, [-1])),\n",
+    "          \"and class label %d.\" % np.argmax(classifier.predict(x_adv)[0]))\n",
+    "    plt.imshow(x_adv[0].astype(np.uint))\n",
+    "    plt.show(block=False)\n",
+    "    \n",
+    "    attack.max_iter = iter_step"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Unsquared Images"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "HopSkipJump attack supports inputs of unsquared images. The code in the following cell describes an example of creating a Resnet50-based classifier to attack unsquared images."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```python\n",
+    "# Adjust image shape here\n",
+    "image_shape = (224, 150)\n",
+    "\n",
+    "mean_imagenet = np.zeros(tuple(list(image_shape) + [3]))\n",
+    "mean_imagenet[...,0].fill(103.939)\n",
+    "mean_imagenet[...,1].fill(116.779)\n",
+    "mean_imagenet[...,2].fill(123.68)\n",
+    "\n",
+    "model = ResNet50(weights='imagenet', input_shape=tuple(list(image_shape) + [3]), include_top=False)\n",
+    "\n",
+    "def _kr_initialize(_, dtype=None):\n",
+    "    return k.variable(value=np.random.randn(np.prod(list(model.output.shape)[1:]).value, 1000))\n",
+    "\n",
+    "head = model.output\n",
+    "head = Flatten()(head)\n",
+    "head = Dense(1000, kernel_initializer=_kr_initialize, bias_initializer=keras.initializers.Zeros())(head)\n",
+    "new_model = Model(inputs=model.input, outputs=head)\n",
+    "\n",
+    "classifier = KerasClassifier(clip_values=(0, 255), model=new_model, preprocessing=(mean_imagenet, 1))\n",
+    "# Then call classifier.fit() to train the new weights\n",
+    "```"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/adversarial-robustness-toolbox/notebooks/attack_membership_inference.ipynb b/adversarial-robustness-toolbox/notebooks/attack_membership_inference.ipynb
new file mode 100644
index 0000000..392b3e6
--- /dev/null
+++ b/adversarial-robustness-toolbox/notebooks/attack_membership_inference.ipynb
@@ -0,0 +1,469 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Running membership inference attacks on the Nursery data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this tutorial we will show how to run black-box membership attacks. This will be demonstrated on the Nursery dataset (original dataset can be found here: https://archive.ics.uci.edu/ml/datasets/nursery). "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We have already preprocessed the dataset such that all categorical features are one-hot encoded, and the data was scaled using sklearn's StandardScaler."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import sys\n",
+    "sys.path.insert(0, os.path.abspath('..'))\n",
+    "\n",
+    "from art.utils import load_nursery\n",
+    "\n",
+    "(x_train, y_train), (x_test, y_test), _, _ = load_nursery(test_set=0.5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Train random forest model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Base model accuracy:  0.9731398579808583\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "from art.estimators.classification.scikitlearn import ScikitlearnRandomForestClassifier\n",
+    "\n",
+    "model = RandomForestClassifier()\n",
+    "model.fit(x_train, y_train)\n",
+    "\n",
+    "art_classifier = ScikitlearnRandomForestClassifier(model)\n",
+    "\n",
+    "print('Base model accuracy: ', model.score(x_test, y_test))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Attack\n",
+    "### Rule-based attack\n",
+    "The rule-based attack uses the simple rule to determine membership in the training data: if the model's prediction for a sample is correct, then it is a member. Otherwise, it is not a member."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.0\n",
+      "0.026860142019141664\n",
+      "0.5134300710095708\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "from art.attacks.inference.membership_inference import MembershipInferenceBlackBoxRuleBased\n",
+    "\n",
+    "attack = MembershipInferenceBlackBoxRuleBased(art_classifier)\n",
+    "\n",
+    "# infer attacked feature\n",
+    "inferred_train = attack.infer(x_train, y_train)\n",
+    "inferred_test = attack.infer(x_test, y_test)\n",
+    "\n",
+    "# check accuracy\n",
+    "train_acc = np.sum(inferred_train) / len(inferred_train)\n",
+    "test_acc = 1 - (np.sum(inferred_test) / len(inferred_test))\n",
+    "acc = (train_acc * len(inferred_train) + test_acc * len(inferred_test)) / (len(inferred_train) + len(inferred_test))\n",
+    "print(train_acc)\n",
+    "print(test_acc)\n",
+    "print(acc)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This means that for 51% of the data, membership status is inferred correctly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(0.5068064465654827, 1.0)\n"
+     ]
+    }
+   ],
+   "source": [
+    "def calc_precision_recall(predicted, actual, positive_value=1):\n",
+    "    score = 0  # both predicted and actual are positive\n",
+    "    num_positive_predicted = 0  # predicted positive\n",
+    "    num_positive_actual = 0  # actual positive\n",
+    "    for i in range(len(predicted)):\n",
+    "        if predicted[i] == positive_value:\n",
+    "            num_positive_predicted += 1\n",
+    "        if actual[i] == positive_value:\n",
+    "            num_positive_actual += 1\n",
+    "        if predicted[i] == actual[i]:\n",
+    "            if predicted[i] == positive_value:\n",
+    "                score += 1\n",
+    "    \n",
+    "    if num_positive_predicted == 0:\n",
+    "        precision = 1\n",
+    "    else:\n",
+    "        precision = score / num_positive_predicted  # the fraction of predicted “Yes” responses that are correct\n",
+    "    if num_positive_actual == 0:\n",
+    "        recall = 1\n",
+    "    else:\n",
+    "        recall = score / num_positive_actual  # the fraction of “Yes” responses that are predicted correctly\n",
+    "\n",
+    "    return precision, recall\n",
+    "\n",
+    "# rule-based\n",
+    "print(calc_precision_recall(np.concatenate((inferred_train, inferred_test)), \n",
+    "                            np.concatenate((np.ones(len(inferred_train)), np.zeros(len(inferred_test))))))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Black-box attack\n",
+    "The black-box attack basically trains an additional classifier (called the attack model) to predict the membership status of a sample. It can use as input to the learning process probabilities/logits or losses, depending on the type of model and provided configuration.\n",
+    "#### Train attack model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from art.attacks.inference.membership_inference import MembershipInferenceBlackBox\n",
+    "\n",
+    "attack_train_ratio = 0.5\n",
+    "attack_train_size = int(len(x_train) * attack_train_ratio)\n",
+    "attack_test_size = int(len(x_test) * attack_train_ratio)\n",
+    "\n",
+    "bb_attack = MembershipInferenceBlackBox(art_classifier)\n",
+    "\n",
+    "# train attack model\n",
+    "bb_attack.fit(x_train[:attack_train_size], y_train[:attack_train_size],\n",
+    "              x_test[:attack_test_size], y_test[:attack_test_size])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Infer sensitive feature and check accuracy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.7313985798085829\n",
+      "0.5541833899351651\n",
+      "0.642790984871874\n"
+     ]
+    }
+   ],
+   "source": [
+    "# get inferred values\n",
+    "inferred_train_bb = bb_attack.infer(x_train[attack_train_size:], y_train[attack_train_size:])\n",
+    "inferred_test_bb = bb_attack.infer(x_test[attack_test_size:], y_test[attack_test_size:])\n",
+    "# check accuracy\n",
+    "train_acc = np.sum(inferred_train_bb) / len(inferred_train_bb)\n",
+    "test_acc = 1 - (np.sum(inferred_test_bb) / len(inferred_test_bb))\n",
+    "acc = (train_acc * len(inferred_train_bb) + test_acc * len(inferred_test_bb)) / (len(inferred_train_bb) + len(inferred_test_bb))\n",
+    "print(train_acc)\n",
+    "print(test_acc)\n",
+    "print(acc)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Acheives slightly better results than the rule-based attack."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(0.6212955677943877, 0.7313985798085829)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# black-box\n",
+    "print(calc_precision_recall(np.concatenate((inferred_train_bb, inferred_test_bb)), \n",
+    "                            np.concatenate((np.ones(len(inferred_train_bb)), np.zeros(len(inferred_test_bb))))))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Train neural network model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Base model accuracy:  0.9668107440568077\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "from torch import nn, optim\n",
+    "from torch.utils.data import DataLoader\n",
+    "from torch.utils.data.dataset import Dataset\n",
+    "from art.estimators.classification.pytorch import PyTorchClassifier\n",
+    "\n",
+    "class ModelToAttack(nn.Module):\n",
+    "\n",
+    "    def __init__(self, num_classes, num_features):\n",
+    "        super(ModelToAttack, self).__init__()\n",
+    "\n",
+    "        self.fc1 = nn.Sequential(\n",
+    "                nn.Linear(num_features, 1024),\n",
+    "                nn.Tanh(), )\n",
+    "\n",
+    "        self.fc2 = nn.Sequential(\n",
+    "                nn.Linear(1024, 512),\n",
+    "                nn.Tanh(), )\n",
+    "\n",
+    "        self.classifier = nn.Linear(512, num_classes)\n",
+    "        # self.softmax = nn.Softmax(dim=1)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        out = self.fc1(x)\n",
+    "        out = self.fc2(out)\n",
+    "        return self.classifier(out)\n",
+    "\n",
+    "mlp_model = ModelToAttack(4, 24)\n",
+    "mlp_model = torch.nn.DataParallel(mlp_model)\n",
+    "criterion = nn.CrossEntropyLoss()\n",
+    "optimizer = optim.Adam(mlp_model.parameters(), lr=0.0001)\n",
+    "\n",
+    "class NurseryDataset(Dataset):\n",
+    "    def __init__(self, x, y=None):\n",
+    "        self.x = torch.from_numpy(x.astype(np.float64)).type(torch.FloatTensor)\n",
+    "\n",
+    "        if y is not None:\n",
+    "            self.y = torch.from_numpy(y.astype(np.int8)).type(torch.LongTensor)\n",
+    "        else:\n",
+    "            self.y = torch.zeros(x.shape[0])\n",
+    "\n",
+    "    def __len__(self):\n",
+    "        return len(self.x)\n",
+    "\n",
+    "    def __getitem__(self, idx):\n",
+    "        if idx >= len(self.x):\n",
+    "            raise IndexError(\"Invalid Index\")\n",
+    "\n",
+    "        return self.x[idx], self.y[idx]\n",
+    "\n",
+    "train_set = NurseryDataset(x_train, y_train)\n",
+    "train_loader = DataLoader(train_set, batch_size=100, shuffle=True, num_workers=0)\n",
+    "\n",
+    "for epoch in range(20):\n",
+    "    for (input, targets) in train_loader:\n",
+    "        input, targets = torch.autograd.Variable(input), torch.autograd.Variable(targets)\n",
+    "\n",
+    "        optimizer.zero_grad()\n",
+    "        outputs = mlp_model(input)\n",
+    "        loss = criterion(outputs, targets)\n",
+    "\n",
+    "        loss.backward()\n",
+    "        optimizer.step()\n",
+    "\n",
+    "mlp_art_model = PyTorchClassifier(model=mlp_model, loss=criterion, optimizer=optimizer, input_shape=(24,), nb_classes=4)\n",
+    "\n",
+    "pred = np.array([np.argmax(arr) for arr in mlp_art_model.predict(x_test.astype(np.float32))])\n",
+    "\n",
+    "print('Base model accuracy: ', np.sum(pred == y_test) / len(y_test))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Rule-based attack"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.9786971287434393\n",
+      "0.03318925594319233\n",
+      "0.5059431923433159\n",
+      "(0.5030548282155043, 0.9786971287434393)\n"
+     ]
+    }
+   ],
+   "source": [
+    "mlp_attack = MembershipInferenceBlackBoxRuleBased(mlp_art_model)\n",
+    "\n",
+    "# infer \n",
+    "mlp_inferred_train = mlp_attack.infer(x_train.astype(np.float32), y_train)\n",
+    "mlp_inferred_test = mlp_attack.infer(x_test.astype(np.float32), y_test)\n",
+    "\n",
+    "# check accuracy\n",
+    "mlp_train_acc = np.sum(mlp_inferred_train) / len(mlp_inferred_train)\n",
+    "mlp_test_acc = 1 - (np.sum(mlp_inferred_test) / len(mlp_inferred_test))\n",
+    "mlp_acc = (mlp_train_acc * len(mlp_inferred_train) + mlp_test_acc * len(mlp_inferred_test)) / (len(mlp_inferred_train) + len(mlp_inferred_test))\n",
+    "print(mlp_train_acc)\n",
+    "print(mlp_test_acc)\n",
+    "print(mlp_acc)\n",
+    "\n",
+    "print(calc_precision_recall(np.concatenate((mlp_inferred_train, mlp_inferred_test)), \n",
+    "                            np.concatenate((np.ones(len(mlp_inferred_train)), np.zeros(len(mlp_inferred_test))))))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Black-box attack"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.7488422352577956\n",
+      "0.7472985489348565\n",
+      "0.748070392096326\n",
+      "(0.7476880394574599, 0.7488422352577956)\n"
+     ]
+    }
+   ],
+   "source": [
+    "mlp_attack_bb = MembershipInferenceBlackBox(mlp_art_model, attack_model_type='rf')\n",
+    "\n",
+    "# train attack model\n",
+    "mlp_attack_bb.fit(x_train[:attack_train_size].astype(np.float32), y_train[:attack_train_size],\n",
+    "              x_test[:attack_test_size].astype(np.float32), y_test[:attack_test_size])\n",
+    "\n",
+    "# infer \n",
+    "mlp_inferred_train_bb = mlp_attack_bb.infer(x_train.astype(np.float32), y_train)\n",
+    "mlp_inferred_test_bb = mlp_attack_bb.infer(x_test.astype(np.float32), y_test)\n",
+    "\n",
+    "# check accuracy\n",
+    "mlp_train_acc_bb = np.sum(mlp_inferred_train_bb) / len(mlp_inferred_train_bb)\n",
+    "mlp_test_acc_bb = 1 - (np.sum(mlp_inferred_test_bb) / len(mlp_inferred_test_bb))\n",
+    "mlp_acc_bb = (mlp_train_acc_bb * len(mlp_inferred_train_bb) + mlp_test_acc_bb * len(mlp_inferred_test_bb)) / (len(mlp_inferred_train_bb) + len(mlp_inferred_test_bb))\n",
+    "print(mlp_train_acc_bb)\n",
+    "print(mlp_test_acc_bb)\n",
+    "print(mlp_acc_bb)\n",
+    "\n",
+    "print(calc_precision_recall(np.concatenate((mlp_inferred_train_bb, mlp_inferred_test_bb)), \n",
+    "                            np.concatenate((np.ones(len(mlp_inferred_train_bb)), np.zeros(len(mlp_inferred_test_bb))))))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Using a random forest as the attack model we were able to acheive better performance than the rule-based attack, both in terms of accuracy and precision."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/adversarial-robustness-toolbox/notebooks/classifier_blackbox.ipynb b/adversarial-robustness-toolbox/notebooks/classifier_blackbox.ipynb
new file mode 100644
index 0000000..36f181d
--- /dev/null
+++ b/adversarial-robustness-toolbox/notebooks/classifier_blackbox.ipynb
@@ -0,0 +1,256 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## ART BlackBox Classifier - Creating adversarial samples for remote classifiers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This demo consists of two parts. The first part deploys a trained model using IBM Watson Machine Learning service to create an API. The second connects to this endpoint and attacks the deployed model with the HopSkipJump attack using only the model predictions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from __future__ import absolute_import, division, print_function, unicode_literals\n",
+    "\n",
+    "import numpy as np\n",
+    "from IPython.display import clear_output\n",
+    "from matplotlib import pyplot as plt\n",
+    "\n",
+    "from art import config\n",
+    "from art.estimators.classification import BlackBoxClassifier\n",
+    "from art.attacks.evasion import HopSkipJump\n",
+    "from art.utils import to_categorical\n",
+    "from art.utils import load_dataset, get_file, compute_accuracy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Deploy model and connect to API"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To deploy a model and connect to the endpoint for model predictions, please follow the demo:\n",
+    "[Use TensorFlow to predict handwritten digits](https://dataplatform.cloud.ibm.com/analytics/notebooks/v2/3bd3efb8-833d-460f-b07b-fee51dd0f1af/view?access_token=6bd0ff8d807861d09e0dab0cad28ce9685711078f612fcd92bb8cf8535d089c1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# generate wml client object following the tutorial above (part 1)\n",
+    "client = 'TODO'\n",
+    "\n",
+    "# generate api endpoint following the tutorial above (part 6)\n",
+    "scoring_url = 'TODO'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Make predictions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We show an example of a predict function below that connects to a wml endpoint and returns the predictions of the deployed model. A different function could be written for another remote classifier that outputs predictions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Read MNIST dataset\n",
+    "(x_train, y_train), (x_test, y_test), min_, max_ = load_dataset(str('mnist'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Sample predict function that reformats inputs, connects to wml scoring endpoint and \n",
+    "# returns one-hot encoded predictions\n",
+    "def predict(x):\n",
+    "    x = np.array(x)\n",
+    "    scoring_data = {'values': (np.reshape(x, (x.shape[0],784))).tolist()}\n",
+    "    predictions = client.deployments.score(scoring_url, scoring_data)\n",
+    "    return to_categorical(predictions['values'], nb_classes=10)\n",
+    "    \n",
+    "# Create blackbox object\n",
+    "classifier = BlackBoxClassifier(predict, x_train[0].shape, 10, clip_values=(0, 255))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1829ec52e8>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Prediction from API is: 5\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Select target image and show prediction\n",
+    "target_image = x_train[0]\n",
+    "plt.imshow(np.reshape(target_image.astype(np.float32), (28, 28)))\n",
+    "plt.show(block=False)\n",
+    "print('Prediction from API is: ' + str(np.argmax(classifier.predict(x_train[:1]), axis=1)[0]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Generate black box HopSkipJump attack"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 0. L2 error 7.9944905692156425 and class label 3.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1825477748>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 10. L2 error 1.4959683890579352 and class label 3.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1829f85b70>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 20. L2 error 1.0073771113688348 and class label 3.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1851838b70>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Generate HopSkipJump attack against black box classifier\n",
+    "attack = HopSkipJump(classifier=classifier, targeted=False, max_iter=0, max_eval=1000, init_eval=10)\n",
+    "iter_step = 10\n",
+    "x_adv = None\n",
+    "for i in range(3):\n",
+    "    x_adv = attack.generate(x=np.array([target_image]), x_adv_init=x_adv)\n",
+    "    \n",
+    "    print(\"Adversarial image at step %d.\" % (i * iter_step), \"L2 error\", \n",
+    "          np.linalg.norm(np.reshape(x_adv[0] - target_image, [-1])),\n",
+    "          \"and class label %d.\" % np.argmax(classifier.predict(x_adv)[0]))\n",
+    "    plt.imshow(np.reshape(x_adv[0].astype(np.float32), (28, 28)))\n",
+    "    plt.show(block=False)\n",
+    "    \n",
+    "    attack.max_iter = iter_step"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/adversarial-robustness-toolbox/notebooks/classifier_blackbox_tesseract.ipynb b/adversarial-robustness-toolbox/notebooks/classifier_blackbox_tesseract.ipynb
new file mode 100644
index 0000000..13ef4af
--- /dev/null
+++ b/adversarial-robustness-toolbox/notebooks/classifier_blackbox_tesseract.ipynb
@@ -0,0 +1,570 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## ART Black Box Attack on Tesseract"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from __future__ import absolute_import, division, print_function, unicode_literals\n",
+    "\n",
+    "%matplotlib inline\n",
+    "\n",
+    "import numpy as np\n",
+    "import imageio\n",
+    "import visvis as vv\n",
+    "from matplotlib import pyplot as plt\n",
+    "from IPython.display import clear_output\n",
+    "import os \n",
+    "\n",
+    "from art import config\n",
+    "from art.estimators.classification import BlackBoxClassifier\n",
+    "from art.defences.preprocessor import JpegCompression\n",
+    "from art.attacks.evasion import HopSkipJump\n",
+    "from art.attacks.evasion import ZooAttack\n",
+    "from art.utils import to_categorical\n",
+    "from art.utils import load_dataset, get_file, compute_accuracy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Make predictions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# read in images\n",
+    "image_target = imageio.imread(os.path.join(os.path.dirname(os.getcwd()), 'utils/data/tesseract', 'dissent.png'))\n",
+    "image_init = imageio.imread(os.path.join(os.path.dirname(os.getcwd()), 'utils/data/tesseract', 'assent.png'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# predict function to call tesseract from the command line and convert\n",
+    "# its output to a one-hot encoding\n",
+    "def predict(x):\n",
+    "    \n",
+    "    out_label = []\n",
+    "    \n",
+    "    for x_i in x:\n",
+    "        # save image as intermediate png\n",
+    "        imageio.imsave('tmp.png', x_i.astype(np.uint8))\n",
+    "\n",
+    "        # run tesseract\n",
+    "        status = os.system(\"tesseract tmp.png out\")\n",
+    "        if status != 0:\n",
+    "            raise Exception('Tesseract failed to run.')\n",
+    "\n",
+    "        # read text\n",
+    "        file = open(\"out.txt\",\"r+\") \n",
+    "        test = file.read()\n",
+    "        out_string = test.strip()\n",
+    "\n",
+    "        # convert to categorical\n",
+    "        if out_string == 'dissent':\n",
+    "            out_label.append(0)\n",
+    "        elif out_string == 'assent':\n",
+    "            out_label.append(1)\n",
+    "        else: \n",
+    "            out_label.append(2)\n",
+    "    \n",
+    "    return to_categorical(out_label, 3)\n",
+    "\n",
+    "# init black box object\n",
+    "classifier = BlackBoxClassifier(predict, image_target.shape, 3, clip_values=(0, 255))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "label_dict = {0: 'dissent', 1: 'assent', 2: 'other'}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tesseract output is: dissent\n"
+     ]
+    }
+   ],
+   "source": [
+    "# this is the image we want to target\n",
+    "plt.imshow(image_target)\n",
+    "plt.show()\n",
+    "print('Tesseract output is: ' + label_dict[np.argmax(classifier.predict(np.array([image_target], dtype=np.float32)))])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tesseract output is: assent\n"
+     ]
+    }
+   ],
+   "source": [
+    "# this is the label we want to perturb to\n",
+    "plt.imshow(image_init)\n",
+    "plt.show()\n",
+    "print('Tesseract output is: ' + label_dict[np.argmax(classifier.predict(np.array([image_init], dtype=np.float32)))])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Attack using HopSkipJump"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 0. L2 error 7213.825 and Tesseract output assent.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 30. L2 error 4220.758 and Tesseract output assent.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 60. L2 error 3997.406 and Tesseract output assent.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAACGCAYAAADEpdGPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8GearUAAAgAElEQVR4nO19d3hd1ZX9Oq9KT71LbpLcKzK2AWNjA7ZpphlCqDMm4MBATIiHhASYzKQRAmFwSAgJIaGH5tAhGAzGphjcMe5FttxkyVZvT3p65fz+kHzXPm+kmFCUn+yzvo+Pred3z72n3PPuXnftvZXWGhYWFhYWvQ+uf/UFWFhYWFh8MdgN3MLCwqKXwm7gFhYWFr0UdgO3sLCw6KWwG7iFhYVFL4XdwC0sLCx6Kb7UBq6UOlsptU0pVaqUuu2ruigLCwsLiyNDfVEduFLKDWA7gDMA7AewCsAVWuvNX93lWVhYWFh0B8+XOPZEAKVa610AoJR6DsCFALrdwLMz3XpA/45TxsAfDiW+E4yZToFPRR3br9yOXRujne6KdNlWW9yPk0f8o/yeS/wlr6tdHB6Nc1YSVMyxI+J7LnG8R7HdslC6Y2d7m422Ul3sY6toy6/4R0izrTbtdexE1e7Ybpj99Spes+xjixiXBHGOqPhcHgsATWJeKtrTHHtIQh2PR9fXK88Rj1bNdpU4PsmYK/4RBscd6H7sw2K+mqMJjt0W49gV+pocuzFu3XnE/HZ39W5jruV1xH+PnzTE+MVUF88h+9iuuR72h7luACDX2+jYclwbRb/k2g5pT5efA0CeO+TYrZrX0hBNdOxsT6tjy7GOX2tusV7kPSQfEJu0z7GTxbqNiCuLaHP0UsWf1VH20e8KO7ZXrAlPfCcFWsSeIcdeIqy7nneXMhtuFeslINraK+6NIh/nqibKvmeIcY8/JtvDvSHVxStYsz5UrbXOib/eL7OB9wWwT/y9H8BJ8V9SSl0P4HoA6N/XjQ8W5gMAgpoTkCA25tWhgHF8oYeDUOxNduwFzez0eYEqx/aKtraHuUgAIMfNgZbLJKC4MOR17Y9weBq132hruLfFsauiYjLFD06mm9dyza4LHHt2wSdGW+cEuAluFL8ahR5ey64IF8CO9nzHHuU74NhpYlEDQJ6bxwRctNeEOC6DvbzephjtPDdvYgBY0spN8FdlMx37rZEvOHZDjO3uCPP4YV5uAgDgFjfshnBAfM75OdEvf0w4joeiHHcAqInKHwoef1BsQu+3DHfsrS0cu7/0f9+x32k1+5vp5o0U1jy/3LjSXLwRs9z83B/345fmYtt/D3Icz0xkX2Qf90Z47tv2nW+0dXPBu4492Nvm2O+19unyGne153T5OQDckrnLsTe1c47eaDrOseekr3PsWrHnpcT9KKeJ9RXSfKBqExvih219HXtiQrlj18d4n1VFk4x2pydyTT7SwLkb4q907Hw3xzFTDL077idrRSjDsc9IZH/lD05FlJ9HRReTXGZbG9pTHXuceBCYu/dcx36saJFjP9FY6NgXJ+8w2rppL+f42rwPHfvMAO9nd0HpHnSBL7OBfy5orR8G8DAAZI7I0dfuORsA8FTRO853Hm4ocuxdreaPzF35K7psVz6Z74+yoynil/3t5uOMY7YF8xx7Xu5ixx7h4+JziyfHEfwYLvB8ALA9zJUy3MvNfXck6NiftfPG/V3Ry44d1OZi8Csu2vIIN7Q89yHHfqb2ZMe+LouTvCPM8ZqVZD7ZP1jf37G/mbKVx7T3c+yBHt5I3ym7xLHv6P93o60E8ePw+yHPOfanIQ7SQP4OYnICx2dn2LzZs938t/5ioxzgYd//3DDAsW9Ilze70RQGi7FfGExx7OE+/qgHY7zGG/Pec+ztYfZpagI3HQC4s2qCY9+Vtx5doSzMi8l18+EiGDMfHOqiXBPLxOZ4boDtNsS4cawL5Tr2nPwPjLYmJsgfM36e7uI5pId2a+ZOxw5p8wce4PfkOvpRFjeYlWJ+T/Tz+/JHpuN7/GFa1VqMrjAlsN2x5U/c7nCmY4e1uSWFdI1jz0njpr24leOwNDiE15hQJq6RGzYADPDwQcmtuG7urBrp2JemrXbszeJBaVoizw2YPywVEa6Du/u/7thR4XHkigfRqqh5/3+ngGtSPiwsbzP3nK7wZV5ilgPoL/7u1/mZhYWFhUUP4Mts4KsADFFKFSulfAAuB/DaV3NZFhYWFhZHwhdWoQCAUmomgPsBuAE8qrX+5T/6fv/Rafp7CyYCAOakb3I+X9FGPml6oknwLwvxN2ak4J1TBOe2OkS3Y2FjiWP/LOczoy35oqVCuIDSUbnr4AzH/nk+aRbJQQNAifjTLzj0K8tOd+zLc0n/TEusdexkF91NAHiontzgJSl0M+WLxJDu+qXLh60Fjv1psND4tzuy1zj2FuE9j/ebfenqOqYESo1/6yM43n2C8z/Ox76UhTmm8n1FvOsuX5ZuDNOdrIqSAinxVTv2s2JOL0s15zRbrINnm0i7nJHE689z013+qI3XWx6miz07lecDgGVtHO8J/q5dWdmPiihpk5Q4vrQpxu8NEuPydFOWY1+VUoOuIKkVAKiN8loSxGkKPGx3p5iHAvEeZLZ4dwEA8wtfcewq8ZJtoId0UmU3XvxQr7mGJe99UIxFuotr5dWWIsce6aezHoxxflzKXOdNMZ7n1IR6xz4gxuFAhOsmIN5LHO8zn0/rYnxnsLubd0qXJ5N6W9Mu3w+Z/U0SVG1llO/jzhT3uaThbsnme6+N7bxeAJgi6LsKQbfliHUb6LNnjdZ6AuLwpThwrfWbAN78Mm1YWFhYWHwx2EhMCwsLi14Ku4FbWFhY9FJ8KQ78n8WI4/z6sdc7OFuf0PyO9ZPrWRT0GseM9FH680Ohib1/wBuOfV/1ZMc+N4261QNhU0ZU5CXPeZyP/NYHbeSkRvrIRcpft83tZlsSt26k/K6pkdJBHSRD9d1TqN89JWmbcXyCkER+f+c3HbtsLeV+Ip4CgbHk2Z4redSxJRcIACcnkA+8r2a0Y09Kokxsgj+IrhCvx799+0WOfdPApTynCDSZl0H+vkHwjZvDprb3ZMEpS/2zRLM4vlTw5NvDucb3Ul38ntRuS8nb0laX+Jzfn19LSd8VQj4GmFz1440850XJlOMejHIN5wlp5LawyUzWRtlWjpua4WFeHr+xndeb4iK/ujxOkre3nbx5X3FvXJ/GeIAt7ZzTfDG88t0FYL6/kPp6GddQKa593vrLHPvxsY8bbaW7JCfMtdPfw2sZIHh6CfnuxB0XiHMgwvspx82bYM62f3Ps/CRK9J4rpiTvxWa+WwOAWxde6dgZA3kPrRm/wLHlWvneBvb39hFvGW0N91FWOMrH+ZYBc929c2uKkxHvi/A6ExTfF5ULGeQVQ9Z0yYHbJ3ALCwuLXgq7gVtYWFj0UnztkZgSiQoY7e2gbKT07t7aQY59o5AXAoALlPs8U7xE/Avd8mI/pT8yxH1FcBAkhvgOOrYMLT/JT1e0TTBKUpr1kwNmloA/9Oe1NNbyWlwiNN13kMM7O22DY2e7TUrhgbrBjl0bpMuYP4bX2/Q2pU6SNpE4FDUplLdF2HalCP2NikQjm8P0sZOUGY0o8auhjCTNEaHLExNIKXgVx2ubCKVf21pktLUjRBpjdirlZFJuKKWWY0UWg2y3GSu2oo0h5DI8WoaGDxRzsqQt27EvSCXdtjTIOQCAgJAhrmya6NiShusvousknZLlMqWw+SL3RYvIofF+GyMQR4p2+3jY4TSXKed8MkzJ2tWpMrqa91O6yM1RJXKv7Gg36afBHp5TSiKXtfK+mSXCvhdP+DPbCpupB5aJUH4pyXy6idLWScZa4bFSDhkfKyojT5tFyP0vB7/k2IWCplnfzu/85JHvGG3pYq7v1lDXUtqX6sbzfE1cgzlirgGT9v2AyxlThdpwe5j3SZ5Iq9EUNaWSu0W6gytS9jp2ZeTI9LZ9ArewsLDopbAbuIWFhUUvRY9SKBoasU71yd+a+Tb95gwmWtoTMUO/1rYx0vCsAN1nmeVMRnJlCNd7TyvdZQAYnM62ZdRhf6E8eaeBao37C6hM+G0/qkgA4O0g3d+sj+m+htLpmuWs55v58DV0h2bvmWq0NSODGXin9aXLuraWqWYOnkA38c81pzj2d0Riq28kkQoCgHIR1ZWbSfWDVOBIKkm64RUR02WUdNIHbXSfh4j0lzKJ0/vNYxz7jmxTdSOjHKUKRUbKBYUqQrrr8zZfa7S1atwC8RefR+rFmhjq5dgXGBkAuVbWtplubb2gOm7L49zXipSmLSLxUqZIaRyfyVH2MaolvcH+9vPwGKlk6Ben3DglmWMpaUipcgi4eL73WqhkklGGANAs0tZWCXoiXVBkzzcy0dMN6cxeGJ/ZUEIqyWYGmLD0maYRjj03nZ/fVT3Msaclm9mo89wkVWR/varrzJkvNw107Jx1ZlKxtlz28erJjJIe+viN/JJ4pM1gHjCcdLqZBXNBMxPjzQyQ6nwrSIqrPkoVSZVQmshMioAZNRwS6XRnJdXjSLBP4BYWFha9FHYDt7CwsOil6FEKxQXluEEzAvudzz9rpxu/pHmscUy5qGSzsZXuYD8fhfgyMf2yNrqf3842cynLggXD/RWOvSlEOkXSJq+1MCBhSoIZ8HJBEv++PZfnDBaJJOyiX7lutvW//RYabb3ezLf+aR4zedFhDMzjm/0f5Xzk2NkiD7UMiACAWpEHe5iXSojtQnmypo3qFqkI2RUxA3naNNvOF9EWJrXC79+cyaRT8VWWhnll0Qy64g2iYorMTy2r6EwtMFUZsjiFLIAxOYFKn/m1dKtlW9/OWOvY/55iurXLRCDT0/Wkg+7M3YAjYVkcHeMVwRk/38PCHm8M5TpY0ExKbqEIMPpxgblW5NhvF5INSRn1B+f60mTmlHfHBU290cz76cIkzv1zdVSOeEW1qKebSGeWhcy8/VOTSYOWiEo091Yzj/2PcmRuf9IehX6u7dE+U4ciU8k/30RKUa7VRa2c68XVLN6x+1KjKWTkkGK8RhSqeLbyDMcWzAxyr9wtrsOc0z3tpGf9SdyLpidyX9jUznH4ZjJp2u1hcaPATCRWK6ipYq+pZuoK9gncwsLCopfCbuAWFhYWvRQ9SqEcivrxYH0HXTA3na9480WOg5LEvcYx56TQZR3hpft7T80ox36yke7MZSmkRubsOcto6yaR3/v1elI19xXQlZY5IUpDdL1/s5tuFgCUZNKFy9pMBYKvntdYP47u/VutdMm3tZlBIzIHsiyplJdI5UhzhC6yDASSOUP2RMzcD6clikK1gj66cz/r9q3ZUeTYQ077i2O744oHy+ASCVkubGqCpF26zhMOmLnC76lhOaw8bwOPFrRDuluUJDvIOQHMuZtfS/d5hlAzRIWqQypi5teSGqmNmMFVl6avcmyZVz7ajfhC1lWU6xkAHq6laqi8gSqFkR8zn8ffT3jIsUsDpLV2/p98PlQm9HNzrfVxc36TXV3TWhN8pipDqk2kwuOOnJWO3RTj2pY5PMb4SYECwBuNvJ9WCB6irwiSKxNlCPcKim5KIgN8Jq++zmj3sxOfdWxZUu2m8kmO/cuCpY793TLSQu5EMzAtHOW9dcaabzv2iMtI/5Q9REVMoygT95hQpwFmXvqXW0jBytw8bzVwfZ0tAvlOTzSVdsEY17qsEftuUN7PXRc7s0/gFhYWFr0UdgO3sLCw6KXo0XSyJSU+/fabHXTH2hDfup8d6P5tqyw7Nc5P8f+CBmZW/EkO3eX9IqAhxWW+dX+5mW/X97fz/I+sYTraYb+nC3TwZLq7reZLd/hL6Br2u5F2rI4ubqydrtG+/2IulRsvNyu+Z4pgGJkvQpIYfpEvYo9wP0uEW3zyKjPIJeE1Xn9bDhtoy+acp2/h9yMBfqep2KRQEiv5Wz/7W2879vXpG3mMoGlkAEo07g2+pBtO28D0uQfKM9EVcj6ke9/c30zF2VrM/g98mu22FAgq6wJSBZsmP+HYMt1nWJturVQ2yDwleaJEmVvxWqasY6rSqgNUTgGAauN5fHWiTF4/EaSyn9frCrFdr8k+oXE4aYFV5/3GsWeXchwfGLgAXaFJKHAA4I9Vpzn2u9tJP8kYnTemPOjYg72k0Yb/ba7RljvIaw5ncixTtpOlldXS+r1CCqbsf0kVbJ70V6NdWZm9n1Bo3byb6Y3Xr6KKK2OjUIQVmGtFpExB8gFeTNpipkGONVA50nrOOMde9BDHAQA+E2zUMC/nJM1Fdc3iVu4/n7Vy75kQoGoOAPIFlRUWgTwylfD1wz+y6WQtLCwsjibYDdzCwsKil8Ju4BYWFha9FD3LgR/n1W92cuCZbvJpkn+sjZnSH5k8SObglcfIJEg10biaTAIXPPd9xx54+3LHdg8m16TLKVWKtZJzc48carRVP4byrtQXKWVzpZN3Vknkw0qvo9RImXQrvnsJy8M9v480V8U6ysmiBXxPMGEgefKNCyl7csclU27NI88XS+A8Z67l73bOAnLY0dGU6LlWmXnZdYTz4ika4NgHp7NfNRPYsZKRvMbnB7N/AHDhtlmOvWM9o+uS9vK6WvN4vXmr2A9fozl4iev5XkSHxQDExLruwzzY2sNz7LqEXLUeYiYrunT4p44tZYTz6yh7XFLFsd9VxXc1apOZlz2cKsjfAq7VrIVcH5mf8T2K2sfkSHCbz1haRJ5qsT5Tl3Ld3dJnkWOPF+rPeJ7/jI2XO/bBTRwjJe4hbxPtwvsphXOlmn2MZZHH3noj7RE/FpGzWbxn9F7K4uTa2vNfJxrtRodzXlKS2d8kEbG5v4LvTrzlfEfR/x3z3Zp/K88Zawl2abtzOI87vk9uPZJqjt2Z44S8OYnS5fOTeT9lurp+Ps5wm1HOl+6a7tjnZXOtnZBASfXowgOWA7ewsLA4mnDEDVwp9ahS6pBSaqP4LFMp9Y5Sakfn/7uv+GthYWFh8bXgiBSKUmoqgGYAT2qtR3d+9msAtVrru5VStwHI0Fr/6EgnGzgmSd/1ckdOYJm7ui7WdQInwIw6XN9O9/PdZuYplsms7q8rcuyHn5tptFU4n+6JS7hKkd10VVpn0YXz14kK0VPNHM9pk+jmRp+j+5mxjS6fq42u4fbv05dN/cRsq/kUunBXjmIE4IKXTnXstiK6g8mb2VbyfpEXPd/8PW4czutPyOTYuT6l+5u5la5hVNS5St/ABD0AgBohj6yl3X4qo83qb6bmTYuovfoqM6d1+jq6uc0DuP6S9/KY2AxBKbzH54N4+kkULceAF+kiR3Loxrt3ULIWHUbKpmwW15Z3iJn/XK8jJfHAt/7k2C/WnuDYiz5g9KErzGt3mQGPyNzEPtYPETRRkfiimLq+b4oydy/IBFAAhHRRUllVU1nSTCjRUC/UgXPPMxNjFfkoj/zBa4wKLSxhhfu6F0mR5T8roluHUxYHAME+nAhZdL25r4gs/t3Hju06jhcWW89ISE8h5wcAts7j+YeUkC7bvokRl8tnzXfsia/c4ti+PDMBXfE13CdcGaTPDp7FcZTjFU3hYvPWmZJkXcg96+4JLO821s+xqxfJ5H61n3vRC4PM2gK3Vh7PtvLWOPbuCK9/6IDKL0ahaK0/ABB3N+NCAIcFtU8AmAULCwsLix7FF+XA87TWh5n7SgB53X1RKXW9Umq1Ump1U233RXMtLCwsLP45fOlkVlprrZTqlofRWj8M4GEAGH6cXw/wdDzM/6GeiofvZlCxEB+1J8t0DRYKgt2iLJEs4fSnBXRV4vUo8m03xBviyu8xMU7zBLpGkwcz4dbu95k8CwD00wzNbBHRgXWj6JYn7xFXUM1+tZ3OJFUAMCKXpa6K/bRdxzG5k28LKYH0Urp2Tf3o2kVERWwA+NbEZY791LuijFsWr2X/WbQzCkgjnFBoqlAW3T3FsZP3kY5xh8R8LaQaIJTFvo/4s5nDu24a576Bog54z6FLf3oflpZ7W3N+6o8zpTYXTqBaxH8V5/f1nUw+pDeylFdMlFcrvu0Tx66cx3MAQMuJdF+vW3Y122rhLZOxQ8z7OJ57zDC6+gCwoYC0gC+V7QbWkVpqH8PPr7/zFcf+6XkXGm2lrSZ9FhJvniQ9uO1BwQM08np/9+7ZRluJ/cx1eBgV9VxroaEcr5x6rsd9001arD2d30sbztzXTfW8H6au5n26+VYqMcLnk7YM7DNDT1UuqcOKRl7X8NEc41eauYhuOv0dx37gY6o7AKDmEuZZl1QJikl7Fouc4ZVvc96uv9qMnv5E7F9FIlL3r/Xsyzki4dWgZH5HJoADgHvzuYZlsrStYVkS0sxXfxhf9An8oFKqAAA6/3/oCN+3sLCwsPiK8UU38NcAHH4suRrAq1/N5VhYWFhYfF58HhXKswBOA5AN4CCAnwB4BcACAAMA7AFwqdY6/kXn/8GQMYn6t692iOPPSCRV8Vwz6YhpIjcwAFy57SrHfnTo044tc0qP+NN3HFu6yOnMUdPxb4IwqhlH1z+llDTEL+Y+7tiybFpIm677iHf/w7FdVXzbLAqVI5bAc/QpogtVsY2qFcBMFDVpFt2uj18t4TnE6QuWiYrts+lSu9rM3+PMwZySmjL624XD6Y5V1NEtnVHMAft9X1P98FaQ5zk5gSqUHx6Y5tiLNpJm8lXQXfY0m2TWgN+xj1vnU03kaeDg+epEUqL+pCeS9pqs3/KbqEA4EBWqAZGRacbLP3DsEWO5vgYKt/bvH4w32vWI5Ex9PuTgt9xMGqF5Oddt4kFBNVxu5rTfuZoqh9QRpBcaN1MJVTKJlNGfi15z7F0Rs7/ffJ8V1P1lnJOon+cffDfpr9LbBPU30AxW0qLUndpDFUmyuPz6iSIYplkkwzKZTiCNY+Qp53X9/OLnHPs8EfDyn+WkN5aUMkjOt8VUaLUOJIWaspH3WZNQWKVt5HWdd+2Hjr2oXPIkQG4S6Zn5xS849vlPcX14R5JGDJWKJFtX/d5oSyY7OzdASlEmsKqMUOlyeTKp0TXtppQqReS+H+EjtSTp45S+e7tUoRyRA9daX9HNP03v5nMLCwsLix6AjcS0sLCw6KXo0ZJqKa4YpiZ0vPleHuKpzwjQZ8t1m2+3fzyQeTRkWa8LdvCNugxyQbvI8XycGZwxf9TfHPtEP92e11qogpyRSHpgfTv9xHSXKYE8fRjphmW7+XY7dRddWV8L3fCKCxkYohNMF0qJyIuJqVS+fAxSKKEMtusO0uUaP4rBK+uWC0kHgMhCvsV2i4CZmoUMjphzLXN735rJc8s87IBZ9uqeKio2/tSPSo6VOR849u8r6aCtWmiWo9o/l/3KWsXrkumqszaSYts/nW51KNOk/Lyi0nqKknmZ6W6/Not5s/uJFf9IPekFnWVG37RniLmbJKiKBrrOfkFr+S7ie/zST81glIRaEdQkjv/VRc849qXJpGaWtvIeKPGZqozLSxjo9bdK5rFP3s9zbLmb1EEW40Iw4BRSGADQ+D+8zl2XiPzYF5Ni87RzHBv2cj2155r3w4xhLFX389O4pv6ngmUNZwQYUHV2BnOJfLSd6yESMOf3+MFcd+trmZvEl8Z7Xk/jGLlF0vHGFlOW1bKMlNerV/KeldRqsIJj7xXz+0nIDOQZJ4KgysT3ThNlBSuivPZyccuX+MzyhB5RflDWM5A59buDfQK3sLCw6KWwG7iFhYVFL4XdwC0sLCx6KXqUA49BI6Q7uLPJCeTW1oRIfj7TWGQc08fLyCi/yGS0s5oc7YCXyE9Vj2aXHjrTrK8n5Tq1MXJlI/3kBreLpETvtVDitugQo/kAYNsOJg8a8Tz5MJlvWrWSVy2fySjF7L7kOwFAr2RfZqeS074rR/CSgzgOWzPJp/tEHuhovslLJh3kteQ+SFmgpx858MUP8Nx/u+pMx/75jx812qqKcr7uyFnp2JfsPM+xR6VyHJ8sJB+OG4QNYMTDlH229BPvGbaI2pFl5JSL7ycvGK03x+6CHzK51L7/JjefLfjexaMpGXu2ie87zhG5m8edsttod0WQfOtD4dMcO3UVOU736ZQEVu7l/A55wUzOFknh+vbezai72x6/xLF/JKhft5/rPBY2n7GkRM/TJus/soFzTljv2EsrWNdxw37OOwBEL5LyV85DOCrupypK6ZAmpLd5Jje/o4H88r5cXuPc3Pcce3OY/P9wH5PB+cSrqsYh5vuhb+Qx1/6gabzPXn1romMnjqVE7+lNXA+5r5kc+KEJvP7SIKW80QA/T6hg3xNqOKZrW4uMttr8vE9P8HNN/qqGe8baer5jeHLg6469LWz28Tgf14fkvdeE4rKidQH7BG5hYWHRS2E3cAsLC4teih6lUFq1CxvCHS7oVKHKGe+nKzfMu9U4JiySW+2N0D6lP3P7LpnEvMwxH7/TFDNdqF0Ruk0yAc2VH13n2IlbeEzGqZRTHVxvJlxUIsoykkaZm2dTGb8jSkgNe5Budfk0maQG8Ijgs/O2MnmRK0QXuamZX5oziZTEk5tFCapGU57UMJCDnCZok8gBIRMropuX/TFpiwdOm2G0FSlnnuOgyJl+3a9edOyLkykTC2u6hQ2i5B0ArPo2oycnzWf+5oYhHFP/FF5X+qukBNrPposMAL5FdLEH/IrUjhrN6L5ZlUxwtv0/mYRI5seemWwm7zopQEnlJ8N4zIYgy++5tpA2cfVlH9UnjDQFAK/8Q+TzHn4r5bNlcykBHfgXys9i2aTIAEDt2+3Yup2UYNtkSgc/rCNtEh4nSofFlRtMqOLzm1tE8dblcK0V5JO6Ayv8IabNtvZu5f1RIoI/q6K8xn6GzJP3WeNxlASquAx0MjL7V49e5thZ+0RyuFHcxoYWcA2XDS0y2lIRUiKnp3Gfea+V+0fiSdwXQh/xPk1QZiT2pAQmAnuvld+bl7nOsaOZpMs+a+e9OdBjJrPaHmaE7FBvEv4Z2CdwCwsLi14Ku4FbWFhY9FL0KIWSpDRO6gxfqxOuVb1QhOyKmC5jWGSHSnGRhnivlBXBi06k6162jlTBb/ZRVQEAe+pIaRRnMtGTu4LuTWuBeEP8AX3GyABT4aFENKWnhm/kG87mW+iDgt3wtF26xZMAACAASURBVPC3UiYeAoBoCvsfbWK5M6MSdgNpphsyGF53aDC/f1c+E/kAwMxNTGNT2cCESvmLOKa6jm/QY0G6dtETTNWNz8tjkl7n+Z/Zx4jY+6ZQsSCjKo+/iGoPwFSovDnv1479bCMj8p4ayMFTmlFz6UtInQFANMYxcqWJqufiO20jWX5ryCN0sRf9mtTIwzea5ffyTqXK4GADx1hSdDHBWKWtJO2gjjdzx7v285wtE0nBJLxOymfAm1xrwdFUOLnazaxR/mZxfySRhmjJF4nAGkXvE6lk8LpN9YOrkscHC8hd6A08x32zn3Tsf19wk2NHk83rSjhEum6LuOb6GNfEbpHfek9I0IgRQeXUmlvSKS8x0ZQQi2DId1nebdkK3nOFx5PySd5v3mctfUVEbJRqItmXyHu8Li2ozRS3SQPeVcX1eVceKb4H60mF9fdSpTTKxzWwI2JGWKYqUkjzKpiv6tqsj3Ak2CdwCwsLi14Ku4FbWFhY9FL0KIWioRHWHW5csov+Z5VQKcS/7c1x8w1tjkgopfbSvzm0koqFaAndkQS32VZsJfPzbs4W7n4+j/Ec4HXNvoLlmT6ppbsNAFUiH3Dl/5Iv0JrJsLCdLn1kMOmfaJOhS0D6Bk7D1acxOdQDu0hPZGym+/fTCczB3cfP8z3WQFoJAJ4Z8RTP8d88x3s/oHrihWq6bCsXMSd24U9YQRwAIkIeoNx0l/XaLY7deAOrayfsJeXz0RqTjjn+ECmCawezv/MymCDsmvF0S6vG8tyP15mlz9bVkh4Jzi9wbP/fmfTJP2ywY0e3U13iKSBt0ZZv0gv7PmNbRX8nDbFvGvuuhpE6yzif0SjbShjUAgDKw/UZ2Mx5uOkeBqD85iW61Z4RbOvCgUz6BAC7gnTx17/Fa4yM4rX0z+aaKNvEsU4rEmsTJqUgg2majuf9+G4z6SBJ6fmyTEohbRDv05pYAF3hihTSUt9vIpWUnM1jXZvSjWMGXcxyfDWLecyqt5kgTbBaSPbyXq4ZH5e0PEUE8gkaw1fNOW3NIe0SzjP3D4mf5JJGXNrKPWOIjwqvMwM8vi7KZ+U0l5mXPdvNveSbGaTVCj3/uFYDYJ/ALSwsLHot7AZuYWFh0UvRoxRKWSgds3edDwCYlUuR+1/2sOL5Y8OfMo4ZJEqnlYXpJo6bwvzDK7aS3nDXkp5Ys8mkPZRQmLiD/O3SItfvwivvdWy/CCq4IYMuPQCkuUjhjP6tKOkmRjRJeEqxQ/x+8yBT0RKcyn5Vh6l4UMKDaunDizkU4pj8Wxapjjy3mYPj6QZSGgUip0xfYX8nb4ljP3kd1SFrZpt5GK5c+W3HznqJLnL6Gua0GPYnuq+l8zjWw+dRJQQA9dOZZ6TpDiohZMCPV3F+Cj2cn//OWW60tTqV1zLlYY7rNXtPc+zKH3C8qqed7Ni5qxiMkbHefJZppmgH7Wmc1KLXOVfBtTx38xz2PTXLdJEzRWm+2rVUSV2WwjJqN1z7R15jlMeH40oeFuSxL8UFpIY8ojzadwuZf6SxH8d3ZRPHHQB+PZ4Kk9GLb3Bsr5/jeEXaasdeUErqTpeZSoqsc0kHDfRQ2fShyCHys0bSOTsaBc20TNAmcYE8MhfKz09kf8Opgh4RVMO/5ZKSW7+T6hQAaOnHxqclU8XyaPLpjp0/imqRprdJsU2aYZZ69IBzP8xL/inTTTrlUJT30FaRB2aqGV+IDwQbJbOOP2/MVzm6gn0Ct7CwsOilsBu4hYWFRS+F3cAtLCwseil6lAPv66vHLwa89n8+nyJ47zeazSi2MQn7+D1Rby7JLThawZvFRJRj4n6ze6Fs8mZ5q2kHZ5PDkpz7c02UAV6eIpL6AFgXIucZGU8uNdRMDqzoPn6+ZR5li6mbTBlhYjX7tb0vE255B5ALTXuVHNrOJ5moaXYR7XCByVtfOpZSJ5eoFbi3nVK04wO7Hbs6ysixdJfJvS6f9JBjj6ub59gVpwkuU3CR6cuEtLLNlJxlLGdirEcWk398ZzQTMv1E5E/2iiRIv9zD/OMAcHkBZVflXvZFRnvOuovvH+o2FfJyg3zfEE42yVc/A3XR1IfrKLCH41hxiYhyXC0i+OLG7lAb11FoAI/fHCYZ+vdqRpvOyyaPe3/NKUZbCz6l7BNenkftYR9vCV/q2CcO3u3YAwNM1AQA4z6ew2tuZR+j1ZSAzniHc51/Bt931K7lOgWAva9T4nfrrFm8/sJXHPveLYyMnjOU727+fDIjP1sOmNz6Lzec49gJ1ZwjT5C260TKI9+oY2KqSJya0V/H59VDUc590j5+fsDH9ezqy7nyxnHz77RyvN9p4JzMTGcis53tXGtzUmWiN3N9rA4yejMY49hfJd4/dIcjPoErpforpZYopTYrpTYppb7X+XmmUuodpdSOzv9nHKktCwsLC4uvDp+HQokA+L7WeiSAiQDmKqVGArgNwGKt9RAAizv/trCwsLDoIRyRQtFaVwCo6LSblFJbAPQFcCGA0zq/9gSApQB+9I/aUgC8na58sYfu4/YwXVHp6gPAphBlV4UeRv39IH+RY1e0ioQ57xY59oC3zPJb4XtIaTR+xnbnDnnfsUOa0VPfSKbLWRY2JXo7w5REqU10x3J20z1qKaZrmLiXQ90+mdcBANHVPP73haSYzqqhi3tIpMH25guZWR3HMXmLmQ/8xRqWnYqmiEhD4Q4WFTN6c1wm6ar7CijfAswkOwkVIhmWeASYcjbdx3c1JVxpe0iNAEBgBRNSJYqyb42DRSmuRyhraxvBsdd1dDEBYPLABY49QJSjerqJpeJKF1KOlS6GvvY4rrUfn8u85gDw641091vb2V+/SGylYyLqlyo6FLxnUhW7LxZueTsH/1vLr3Xsh04ijZghcmW/V06KDAA8CaSTRvVl2bjSLFI4I7N4/pU7SG1s3GLOQ852ronWbM6DlMLmX0q6a8tOrvms3UZTECmxcWkeo2AXNJIaunUE71m30MhqkVtcJsUCgDQRwXyokPObPJD3dsDP/eOCDK7bhdmU0QJA+haepzbKthpH8p7P6Us6pmoPSYWmmPmse3ICv5fiItUhc317RQnImEivdmvlSUZb9xfw+GCMfQnDHIuu8E+9xFRKFQE4HsAKAHmdmzsAVALI6+aY65VSq5VSq+tqY119xcLCwsLiC+Bzb+BKqWQALwKYp7VulP+mtdYwM3jKf3tYaz1Baz0hI9OKXiwsLCy+KnwuFYpSyouOzftprfVLnR8fVEoVaK0rlFIFAA5130IHolBo6kwUvamdrmBllJFY6W6z3NDBMGmIdBd/AGQyrO2r+bZXpOnGvrPM3OJ9firKfM3lee7768WOfc8YRtr9/HgqITa2MmkSALz1INUBOYfYl+RN9KWjO3c7dutMciBx6cAR7M+LPnkpcy5nfEBXOiVA9y9zIfu+9yq6f8ExZvKdYb/g72x7H45F+Wlst7yWbvHM85i3e9QnVxlt9UunyxpJZAei+XT5zs0khbIkmW/WD4015QADFpHHaCnk2LVU8BqH/5ZthU8kzbP7PFMOcPkG0hD9U6kU2vc4o/ZCQ3m9U7/BCOClr7D02F2vfMNoN5zHfrlrxLoZwjXo3UUlQsMwzqG3xSyZ1/+XVFy4SpjYa+tNdOP/46XrHXv6qSzL1bzSbEuN5Pr8bDvDRWVCqPImjqOnQlBOcUqKQ+PYF3+9SFYmHOUTMhmBWLqc91n9MHMRZ4qU7309nIczEknz3F3NRGSnp5AODQuKCslmu5cNoJLqtwdY5q+xmqqstoPcP6oGiiR1aeb90J7C++bBX3O+B5WRCmvqx/HOFPnAV0wrMtra386EcGemMOHYO0Hze4dxsp8KnvPTPzX+rSFGmkiWkDwYPfID7+dRoSgAjwDYorWeL/7pNQBXd9pXA3j1iGezsLCwsPjK8HmewCcD+HcAG5RShx8N7gBwN4AFSqk5APYAuLSb4y0sLCwsvgYorY+cc/arwpAxiXr+qx2u7afC1bgolS5FQJl5mfuJku2fiKRTJ/v5vaCmuzv+WVY59w80qHoU3kCWR7npfOy5miqFtF1sN6mcASiebVRoAEC0phZdwSOqv9edwjzQUq1RM9r0ZWX5scHjWal8Tw3dNLWe6oeTz2VirffWUe2RurX73+M+D/HtvI6IZFrH06XXq+kHh2aKgBEArjDXyd5v8fiLhgvlySNMFOVt6X5dNZDdMAIywik8pvgOkbRKJLZq+qZZlT7j/TLHjval+1s+nTRC82C60vlLuIaqS0Ryo2mkLQBgzZ8ZEFJzAvv7s6kvO7ZUMjzyGEuyRYXrDQBFz9N9RhXXjRbBYK4szjVE8q4DM7meOg6i2SplA0LVoSJdj6m3yVx3iRMYuNWwk4oLfy3He8JMrollpbxPEjeZnWwfS2pn05THHDsGUgK1Ufb3YJSL/vKnGCwUNUVG0P15D0bbhCpD9HHwM5yfR556wLFn3XWr0VZCnVCIFbCP+R+T0nO3cC9pHEFqJjjbzKXe0EBa8ORBXINTRU776QHa/Tyi/oEYBwA4KDod1uxjmovfG114YI3W2rwpYUPpLSwsLHot7AZuYWFh0UvRo7lQXEojxdXhEkmR+ygf3TEZSAOYtMniJuZJORA+gK5w+3l0ce9aOMv4t13f5Rvqgo/pdvW9dwWvMYmuUayJrlVsvJmjxS1KjLWNY7BEm5+/iZWT6bJ9+9Sljv3oIub/AABvE4/Ztp0uc94AutuhGlIoDSKHxpnj+Aa8YoSpuimtIqVQGaHiIvf3olzaKh6vvHTlAh8y3zoAqBTSBX0WUJHzwtmkNALTqFQJbuFYu0TpMQD4RQmDleqjVBPc98KFjl19PYOQ8l7gtWR8RIoJACKVpCfcIufKgEoqIdqLmbej/HS67pF0roFP/0DKBADUJaQXPjzuUcd+tZmUU46H62PDLX9w7JnbzAr3Za4ix07ZzWvJeon0RGQ/8z17+rBUmsxZDgCVk0T+lhTSE1rkoUms4D2TUsZ1OvkaKjoA4Ios5lyZvZM57aOjBR1SzZzYbi/v2fZUkyKL7eM81om87hdv+nfHflSU+Cv0CNWOoHba+5mxIkPyqeratYaUZMYoBivtv4n3w4EI95JV//2g0dapN9/o2NGpXKvuNwVNI0ruhU4kJdjYZCqpdD3vlRUfcU1MOHe3Y5cLii0KqoT6uU2eSKqrAy7O3f11Mp951/udfQK3sLCw6KWwG7iFhYVFL0WPqlDGlfj1Bws7XLIw6ELJ8mTxkOWlVocyu/zO2QG+rb2nhgEkxX4ztmhNC6mOtx5nUEH6TrrSVWPJKhmVyuOCIJaef59jz3iGb7sjBeINcyPd9V3f+JNj742YlEJfN92zO6tZbfuJ91lqzhUSyoBJpBSeK2b5rPXtZtrWgYIge6+VYyeDpW6/jTlHWrPYybrxJpXlT2W/2luFbKaB9tixzHFyUsZux74lc6vR1t4IAxd2R0j7nJLA6/9MZMYd5uX8/Meec422dj7CIJ+mIn4u1QyRXPbFV87rTSohRRXwmf29YgDzeVyTSrd6f5TfqxJykxIfL7g0bC6WsX4qEC4vY1myfU1UObS+IiQl4vBmsgYAAG+zoO5ySTckiJwyraJ04FsXMnRjkMe8z9xC3RPWPEaWtvtrI6nDp3ad6NixhWaAUexMUlY/FDlPLk8mBVIR5bp7u4VSpCkBjm+e23ymLA2TUrjyr99zbCHWgEuM94tX877McZt0zHtBUn+Swp0igo1uKCPt+unWIsfOWmGyzZmb2Zcd13CxPTb9Ecfe0MbJ299Olc/crI+MtrJdcdKbTjSIvCj9+ldaFYqFhYXF0QS7gVtYWFj0UtgN3MLCwqKXokc58GFjEvQfX+tIiHO8n9z2vdXk1n6W89n/Oe4wlgkeeCqVQ1jays8DInppSbOU4QCfNpKTuiF/qWNP8JPPSlTko27YTw76jvy3jbaaNDmx43y8GJmYJibGNlXkeH6gfqDRVn8vudhd7Tldfn5OEmVE3b0zWN5mRrFOTCBRKOWZG9t5XSMEnb0yxGt8qW680dbtuUscuzbGdiuFVOqwRBQADkTI+fX3mFGrOaIc3qx1zHl+81CeI0HxeqcFWI5qR9js++QEzr1cB/L4qCCVf7H7fMc+OYsRdAU+M9JuZhKj6BKEZLQpxrF7uYm5rm/JJP+/JmSWthvv55qS+Z73iIjYET6+B1nQnCa+b+Z4vyKFcsMLtl7k2H8Y/JxjDxBct1dxrra0m4ni/iLKtd2e+6Fjp4m12ixynu8TyZW8MPnl/h6ZaI7HrwxxHrLEvZnn5v0TFPx7NG4/er3FzId+GJMTyZu3CULcLUJVX2gwKeOpyVvFMVz4fT2ce3k/vNbC9xIzxBoEgF0RzulIr4wKp52s2Ngi8Q6qLWaWVBzuq0RXyHRz7IotB25hYWFxdMFu4BYWFha9FD1KoUwoSdAr3+6gMaRL/0YLy18Vec1yVBtClP4cEFKcU5OZT7g+RvdTulCjfYymA4AcN93RP9ZTbjhPSN4eaWDk2bdSSVuEtEgABSAgpD83H2A04kkpdKWvSuH5oyLPr5RTxUO669luRrfdVM4yTLJs1KmJbKs2LklOgSgxViGkiw/UUEKZ7eXnkgaQ8k3ArKTdJpZMjnCFpXxuqCjjHYiTScm2A8LNlGO8I8LPd4cpWbs02SyT1ywkb5IuaBNtJShRcV30ozTCOWmKmdcoZYGSEpDzuDfCsR/g4RrcHjblnDFB4Wxq5/oa46N8bUVbkWPPTCK1szXMNQAAJ/l537wq8o5/I6kOXUGW8pKlCwEg00V3//1W0ovnJfG6Xm1hZHBVhFGgN6bvMNqS9/A3ks0kcjw/511KGusE7ZgRRw9GhNzYL9bKjw4ycjbPy/N9L6PUsRtj5jxIGvO7B3gP/DR/sWMvbWV+fJnXPN1l3ls1MV6njBD/Wc4mxzbLo3Uvm5ay4rUhro9369nuHyc8bSkUCwsLi6MJdgO3sLCw6KXoUQplfIlfL3+rgxKR7s1LzVRl7GzLNY65JZsJd4LiWkV6auOt+5ONdPnOSKI7BQA7RHm2FvF2f5IoKS5pCxmdVhfnjlWJN/JSiTE9kcdI2mJXhC72Ry2MHgSAAi9dtdmppJCkmmGwt2sXTEb2/W4AS8ABpmIi20164f1WurtuUT/r3AD7+ONDY4y27sxl0qsPxFBINZCsXC/LRqW76CIDwFgfKY3uogGl+iHgouv8dtBM2DXIS5pqX4SRjSMFfXb3QZbimpxK1//URJYLe7axxGj31kyqHO44SLXJT3KZEMolnn9k9KJcQ4DZr4oox6JKhIvK+a2N0v64jWXMAGCMnyqUEV5TzXAY8h4Ym8DkX5tCfYzvXZxMZYXsS7Vw/X924GzHvrPPW479dtBUUl2VQtpF0qNtou9eMdcHIlybKS6uwfiy5wXurhU1Mup4XRtpVlnt/rwkUzmyLcx1VyIYsx8fpAru1hxGSeaKeXygzpyHdU2knB4ZwGOk0ifL3fXemhu3Pu6sHu7Yt2Qy1/9HbfzeOYO2WArFwsLC4miC3cAtLCwsein+ZYE8L9RSuXFjzlLHji+p9ocaBtOEYnSBmiL03cck063cFqT4/k/9SL8AQFmYlEaSi8qADPF2WiZaer2ZiaW2tDBHMwDclvdOl221CNqijyijVBqmKuLDoKgpBmB2KlUH8m11U0wGCPAcCcJN/LCVrt1Yv+kyyjzr8o34p6IKeI6b/R3qpcsmxwoAflt9mmNXh0QpsUKOgwdudAVJk8RDuttN4hqluy0DojLcZl7mdaIsmQx8kBgg1Dib2tlfOQ+nBkxVxR4RiHR6IsdClsP6cx2VQeek0PVtjxsHSTM93kiK8FupTLYm1S1/D/J6h/tI7wHAVhHodVaAihw59suFYGKghy69VCUBZtCZVOoEYxxHOd6SCpJlDAHALZQ2kuqQyhFJL0jVjUsQJ5JCBEzFkqRQ00Te7IfqSHHNSWdpvG1xQV+bhKLtDFHubJ+gQPeFSS9KFVk85HzNr6Oi7Zo0roM9QknV38MxlUm1AOCbyTxPq+5a/eQuKLUUioWFhcXRBLuBW1hYWPRS9GhJtYAr5uRAcWWxjNmz9XwLfEe2Wfbp3nyqGepEAIzMyxAUuQVkMMpd1aba47xU5llJgnw7LkpQCTpEBvhUpLD8FQAkCBe/RbhTrwra5TJxTIsmneKNo4lkebYXxNttmQfjhnTSRNKtzHLTvS/2mK77oiDH5Q/lZzn23wa/6dhh3bWSIdNttnVH7vuOLWkiv6L7KQM1Pm5l7nVJFQDAi80styaDJUb7RDV2ocRIF48Z8dROTYyqlE3tVKHUR+n6z03f59i7BTVyWQppk3hqJgzSE/tFzhL5xCNzawz2UhVRFTXzgQcF9Sdzsct5LBVl8qQqKh5NIoBkdYhjIXPCFHr4uVesLal2AIDrM3ivvd/KcRwg5iTgouv/bisDeT4NmiUGjw/sduzTE0QgjxiKXRHmA5FrQipKnm6iggYA6qO8H2Re9h9WkFq9LY+BODJoqr/HDJib4Kfq6KC4BbOESqpK5NCRtGNtzKSMVrRR0XOhoM9SRNBaHze5rLUhUjMvHDLzDM0IvOrY+yNcK5VRMw9OV7BP4BYWFha9FEfcwJVSCUqplUqpz5RSm5RSP+v8vFgptUIpVaqUel4p1XVZCQsLCwuLrwWf5wk8BGCa1roEwFgAZyulJgK4B8BvtNaDAdQBmPMP2rCwsLCw+IpxRA5cd+gMD5Nq3s7/NIBpAK7s/PwJAD8F8Md/1FZUa0caJ6VVw73Lxbe65mQBMyLPHSUvKTnl6ij5tG+LpE8A4Fcyco48rpSyScj80vG1+oIiH7jM93xlKpPZ3FfNfMvFfvKaOZ4mo63f1ZGblBGA8roaYuyvTPizQUShbQ2Z3Lo8zx+LX3Ts5hj7sidC3nmUj8evDpmSMxlNeZzgquV7idooJ/WEBPKN99byvQAA3JjOMZIJsD5uI8d6SgL5YZmnfKTPHLuBHnLVL9dSZTU3h7nF54vzz81gPVG5BuIhc7xLrlpGCma52HcZfZnhMuMJZZKuxw9MduzKHL4jke84ALYVXz/Vq9iWyfFyvjIFDyujKocmVECiTcjyxor6sTKh0igfx3dlyyDHrmk318epmcypvUcmCRPy11NFzdNq8Y7DLZ4jZR53AGgWa+qTENd9lo/j0iTWcx8Pz5frNmWED9WTX5+ZzHWwTsgLzwowcjXg4jzE4mJEh/g4XoUejveWdn4vRWwZsm7vkmRTKinXzkdtHNf4PPpd4XNx4Eopt1JqHYBDAN4BsBNAvdbOytwPoG83x16vlFqtlFpdWxsfKGthYWFh8UXxuTZwrXVUaz0WQD8AJwIYfoRD5LEPa60naK0nZGbad6YWFhYWXxX+KRmh1rpeKbUEwMkA0pVSns6n8H4Ayv/x0YBHuRz37q0gJTJSkjfYa+YSrojSBVwuEvusbaZ9QjIjGd1geaK3m03XPVtQCv+WSmnZR21004Z5Q8LmteTGycxkMq6QcBNfb6abOT2FVMHz1Yzau05I8gBgVhLdz0Mi2VGacIXn15AeuC6DEsxJIoJwtM/MWRwUkZxVsa5LwFXFZKQbPaQij5mAKtMlZJsiIi8s8k27RGKsoV5e+yhBCwGmVEwmlFofkomqeF3P1nDsBgdMSeKhdkoShwU494O9XF/Tkpg7/u0gZYRTEkxXVkJGHSYImekzTUziJCWca9q4PuTaAoDtIvOalHB+KtztqCgL9nqQfSrxmRRKqihbJyNMg0YUK9vKdHHezwqYpbv2CMlafIm0rtqanc51953Sy43v/c7PZGAf1fAemNOXiZ52C7pscT3LHU5P3+zY8fneIXKLyz4O8fJaPha5zHPc+7v8PgCckMh9Qo6djLg8JFhISV/tjpiU0Rgv6avv7GOyNJnYCuAavKeG0ZoXpZlSaZk/vr+H1/x+i3xO3ouu8HlUKDlKqfROOxHAGQC2AFgC4JLOr10N4NWuW7CwsLCw+DrweZ7ACwA8oZRyo2PDX6C1fkMptRnAc0qpOwF8CuCRr/E6LSwsLCzi0KPJrJRSVejwjbv3XY9+ZOPY7b/t+7EJ2/cvj0KtdU78hz26gQOAUmp1V1m1jhUcy/23fbd9P9bwdffdykIsLCwseinsBm5hYWHRS/Gv2MAf/hec8/8nHMv9t30/NmH7/jWhxzlwCwsLC4uvBpZCsbCwsOilsBu4hYWFRS9Fj27gSqmzlVLbOnOI39aT5+5pKKX6K6WWKKU2d+ZR/17n55lKqXeUUjs6/59xpLZ6KzqToH2qlHqj8+9jIoe8UipdKfWCUmqrUmqLUurkY2XelVL/2bneNyqlnu2sJ3DUzrtS6lGl1CGl1EbxWZdzrTrwu85xWK+UGvdlz99jG3hnJOeDAM4BMBLAFUqpkf/4qF6NCIDva61HApgIYG5nf28DsFhrPQTA4s6/j1Z8Dx1pFw7jWMkh/1sAb2mthwMoQccYHPXzrpTqC+BmABO01qMBuAFcjqN73h8HcHbcZ93N9TkAhnT+dz2OkH7786Ann8BPBFCqtd6ltW4H8ByAC3vw/D0KrXWF1nptp92Ejpu4Lzr6/ETn154AMOtfc4VfL5RS/QCcC+AvnX8rdOSQf6HzK0dl35VSaQCmojO1hNa6XWtdj2Nk3tGRniNRKeUBEABQgaN43rXWHwCIT9zd3VxfCOBJ3YHl6EgIWPBlzt+TG3hfADJNW7c5xI82KKWKABwPYAWAPK314cz6lQDy/kWX9XXjfgA/BGsgZOFz5pDv5SgGUAXgsU766C9KqSQcA/OutS4H8L/oSJ1XAaABwBocG/Mu0d1cf+V7oH2J+TVDKZUM4EUA87TWRq7czmpHR52OUyl1HoBDWus1R/zy0QcPgHEA/qi1Ph4duX8MuuQonvcMdDxlFgPog47Suzix/AAAAahJREFUQvH0wjGFr3uue3IDLwfQX/z9uXKI92Yopbzo2Lyf1lq/1PnxwcNuU+f/D3V3fC/GZAAXKKV2o4Mqm4YOXji907UGjt753w9gv9b6cMLqF9CxoR8L8z4DQJnWukprHQbwEjrWwrEw7xLdzfVXvgf25Aa+CsCQzjfSPnS83HitB8/fo+jkfB8BsEVrPV/802voyJ8OHKV51LXWt2ut+2mti9Axz+9pra/CMZBDXmtdCWCfUmpY50fTAWzGMTDv6KBOJiqlAp3r/3Dfj/p5j0N3c/0agNmdapSJABoE1fLFoLXusf8AzASwHR01Nf+rJ8/d0/8BOAUdrtN6AOs6/5uJDi54MYAdAN4FkPmvvtaveRxOA/BGpz0QwEoApQD+BsD/r76+r6nPYwGs7pz7VwBkHCvzDuBnALYC2AjgKXSUpTlq5x3As+jg+8Po8L7mdDfXABQ6lHg7AWxAh1rnS53fhtJbWFhY9FLYl5gWFhYWvRR2A7ewsLDopbAbuIWFhUUvhd3ALSwsLHop7AZuYWFh0UthN3ALCwuLXgq7gVtYWFj0Uvw/8x03DoQbzGkAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 90. L2 error 3947.9297 and Tesseract output assent.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 120. L2 error 3922.5515 and Tesseract output assent.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 150. L2 error 3794.534 and Tesseract output assent.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "attack = HopSkipJump(classifier=classifier, targeted=True, norm=2, max_iter=0, max_eval=1000, init_eval=10)\n",
+    "iter_step = 10\n",
+    "x_adv = np.array([image_init], dtype=np.float32)\n",
+    "for i in range(16):\n",
+    "    x_adv = attack.generate(x=np.array([image_target], dtype=np.float32), x_adv_init=x_adv, y=to_categorical([1], 3))\n",
+    "\n",
+    "    #clear_output()\n",
+    "    if i%3 == 0:\n",
+    "        print(\"Adversarial image at step %d.\" % (i * iter_step), \"L2 error\", \n",
+    "              np.linalg.norm(np.reshape(x_adv[0] - image_target, [-1])),\n",
+    "              \"and Tesseract output %s.\" % label_dict[np.argmax(classifier.predict(x_adv)[0])])\n",
+    "        plt.imshow(x_adv[0])\n",
+    "        plt.show(block=False)\n",
+    "\n",
+    "    attack.max_iter = iter_step"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Defend Classifier Using Jpeg Compression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "jpeg = JpegCompression(clip_values=(0, 255), channels_first=False)\n",
+    "classifier_def = BlackBoxClassifier(predict, image_target.shape, 3, clip_values=(0, 255),\n",
+    "                                    preprocessing_defences=[jpeg])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tesseract output is: dissent\n"
+     ]
+    }
+   ],
+   "source": [
+    "# this is the image we want to target\n",
+    "plt.imshow(image_target)\n",
+    "plt.show()\n",
+    "print('Tesseract output is: ' + label_dict[np.argmax(classifier_def.predict(np.array([image_target], dtype=np.float32)))])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tesseract output is: assent\n"
+     ]
+    }
+   ],
+   "source": [
+    "# this is the label we want to perturb to\n",
+    "plt.imshow(image_init)\n",
+    "plt.show()\n",
+    "print('Tesseract output is: ' + label_dict[np.argmax(classifier_def.predict(np.array([image_init], dtype=np.float32)))])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Attack Defended Classifier Using HopSkipJump"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 0. L2 error 7213.825 and Tesseract output assent.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 30. L2 error 4201.479 and Tesseract output assent.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 60. L2 error 3841.222 and Tesseract output assent.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 90. L2 error 3598.9988 and Tesseract output assent.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 120. L2 error 3490.935 and Tesseract output assent.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial image at step 150. L2 error 3354.4868 and Tesseract output assent.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "attack = HopSkipJump(classifier=classifier_def, targeted=True, norm=2, max_iter=0, max_eval=1000, init_eval=10)\n",
+    "iter_step = 10\n",
+    "x_adv = np.array([image_init], dtype=np.float32)\n",
+    "for i in range(16):\n",
+    "    x_adv = attack.generate(x=np.array([image_target], dtype=np.float32), x_adv_init=x_adv, y=to_categorical([1], 3))\n",
+    "\n",
+    "    #clear_output()\n",
+    "    if i%3 == 0:\n",
+    "        print(\"Adversarial image at step %d.\" % (i * iter_step), \"L2 error\", \n",
+    "              np.linalg.norm(np.reshape(x_adv[0] - image_target, [-1])),\n",
+    "              \"and Tesseract output %s.\" % label_dict[np.argmax(classifier_def.predict(x_adv)[0])])\n",
+    "        plt.imshow(x_adv[0])\n",
+    "        plt.show(block=False)\n",
+    "\n",
+    "    attack.max_iter = iter_step"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/adversarial-robustness-toolbox/notebooks/classifier_catboost.ipynb b/adversarial-robustness-toolbox/notebooks/classifier_catboost.ipynb
new file mode 100644
index 0000000..7d98c39
--- /dev/null
+++ b/adversarial-robustness-toolbox/notebooks/classifier_catboost.ipynb
@@ -0,0 +1,639 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Adversarial-Robustness-Toolbox for CatBoost"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from catboost import CatBoostClassifier\n",
+    "\n",
+    "from sklearn.datasets import load_iris\n",
+    "\n",
+    "import numpy as np\n",
+    "from matplotlib import pyplot as plt\n",
+    "\n",
+    "from art.estimators.classification import CatBoostARTClassifier\n",
+    "from art.attacks.evasion import ZooAttack\n",
+    "from art.utils import load_mnist\n",
+    "\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1 Training CatBoost classifier and attacking with ART Zeroth Order Optimization attack"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_adversarial_examples(x_train, y_train):\n",
+    "    \n",
+    "    # Create and fit CatBoost model\n",
+    "    model = CatBoostClassifier(custom_loss=['Accuracy'], random_seed=42, logging_level='Silent')\n",
+    "    model.fit(x_train, y_train, cat_features=None, eval_set=(x_train, y_train))\n",
+    "\n",
+    "    # Create ART classifier for CatBoost\n",
+    "    art_classifier = CatBoostARTClassifier(model=model, nb_features=x_train.shape[1])\n",
+    "\n",
+    "    # Create ART Zeroth Order Optimization attack\n",
+    "    zoo = ZooAttack(classifier=art_classifier, confidence=0.0, targeted=False, learning_rate=1e-1, max_iter=20,\n",
+    "                    binary_search_steps=10, initial_const=1e-3, abort_early=True, use_resize=False, \n",
+    "                    use_importance=False, nb_parallel=1, batch_size=1, variable_h=0.2)\n",
+    "\n",
+    "    # Generate adversarial samples with ART Zeroth Order Optimization attack\n",
+    "    x_train_adv = zoo.generate(x_train)\n",
+    "\n",
+    "    return x_train_adv, model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1.1 Utility functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_data(num_classes):\n",
+    "    x_train, y_train = load_iris(return_X_y=True)\n",
+    "    x_train = x_train[y_train < num_classes][:, [0, 1]]\n",
+    "    y_train = y_train[y_train < num_classes]\n",
+    "    x_train[:, 0][y_train == 0] *= 2\n",
+    "    x_train[:, 1][y_train == 2] *= 2\n",
+    "    x_train[:, 0][y_train == 0] -= 3\n",
+    "    x_train[:, 1][y_train == 2] -= 2\n",
+    "    \n",
+    "    x_train[:, 0] = (x_train[:, 0] - 4) / (9 - 4)\n",
+    "    x_train[:, 1] = (x_train[:, 1] - 1) / (6 - 1)\n",
+    "    \n",
+    "    return x_train, y_train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_results(model, x_train, y_train, x_train_adv, num_classes):\n",
+    "    \n",
+    "    fig, axs = plt.subplots(1, num_classes, figsize=(num_classes * 5, 5))\n",
+    "\n",
+    "    colors = ['orange', 'blue', 'green']\n",
+    "\n",
+    "    for i_class in range(num_classes):\n",
+    "\n",
+    "        # Plot difference vectors\n",
+    "        for i in range(y_train[y_train == i_class].shape[0]):\n",
+    "            x_1_0 = x_train[y_train == i_class][i, 0]\n",
+    "            x_1_1 = x_train[y_train == i_class][i, 1]\n",
+    "            x_2_0 = x_train_adv[y_train == i_class][i, 0]\n",
+    "            x_2_1 = x_train_adv[y_train == i_class][i, 1]\n",
+    "            if x_1_0 != x_2_0 or x_1_1 != x_2_1:\n",
+    "                axs[i_class].plot([x_1_0, x_2_0], [x_1_1, x_2_1], c='black', zorder=1)\n",
+    "\n",
+    "        # Plot benign samples\n",
+    "        for i_class_2 in range(num_classes):\n",
+    "            axs[i_class].scatter(x_train[y_train == i_class_2][:, 0], x_train[y_train == i_class_2][:, 1], s=20,\n",
+    "                                 zorder=2, c=colors[i_class_2])\n",
+    "        axs[i_class].set_aspect('equal', adjustable='box')\n",
+    "\n",
+    "        # Show predicted probability as contour plot\n",
+    "        h = .01\n",
+    "        x_min, x_max = 0, 1\n",
+    "        y_min, y_max = 0, 1\n",
+    "\n",
+    "        xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n",
+    "\n",
+    "        Z_proba = model.predict_proba(np.c_[xx.ravel(), yy.ravel()])\n",
+    "        Z_proba = Z_proba[:, i_class].reshape(xx.shape)\n",
+    "        im = axs[i_class].contourf(xx, yy, Z_proba, levels=[0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0],\n",
+    "                                   vmin=0, vmax=1)\n",
+    "        if i_class == num_classes - 1:\n",
+    "            cax = fig.add_axes([0.95, 0.2, 0.025, 0.6])\n",
+    "            plt.colorbar(im, ax=axs[i_class], cax=cax)\n",
+    "\n",
+    "        # Plot adversarial samples\n",
+    "        for i in range(y_train[y_train == i_class].shape[0]):\n",
+    "            x_1_0 = x_train[y_train == i_class][i, 0]\n",
+    "            x_1_1 = x_train[y_train == i_class][i, 1]\n",
+    "            x_2_0 = x_train_adv[y_train == i_class][i, 0]\n",
+    "            x_2_1 = x_train_adv[y_train == i_class][i, 1]\n",
+    "            if x_1_0 != x_2_0 or x_1_1 != x_2_1:\n",
+    "                axs[i_class].scatter(x_2_0, x_2_1, zorder=2, c='red', marker='X')\n",
+    "        axs[i_class].set_xlim((x_min, x_max))\n",
+    "        axs[i_class].set_ylim((y_min, y_max))\n",
+    "\n",
+    "        axs[i_class].set_title('class ' + str(i_class))\n",
+    "        axs[i_class].set_xlabel('feature 1')\n",
+    "        axs[i_class].set_ylabel('feature 2')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 2 Example: Iris dataset"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### legend\n",
+    "- colored background: probability of class i\n",
+    "- orange circles: class 1\n",
+    "- blue circles: class 2\n",
+    "- green circles: class 3\n",
+    "- red crosses: adversarial samples for class i"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "ZOO: 100%|██████████| 100/100 [00:40<00:00,  2.45it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "num_classes = 2\n",
+    "x_train, y_train = get_data(num_classes=num_classes)\n",
+    "x_train_adv, model = get_adversarial_examples(x_train, y_train)\n",
+    "plot_results(model, x_train, y_train, x_train_adv, num_classes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "ZOO: 100%|██████████| 150/150 [00:51<00:00,  2.90it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 1080x360 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "num_classes = 3\n",
+    "x_train, y_train = get_data(num_classes=num_classes)\n",
+    "x_train_adv, model = get_adversarial_examples(x_train, y_train)\n",
+    "plot_results(model, x_train, y_train, x_train_adv, num_classes)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 3 Example: MNIST"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3.1 Load and transform MNIST dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "(x_train, y_train), (x_test, y_test), min_, max_ = load_mnist()\n",
+    "\n",
+    "n_samples_train = x_train.shape[0]\n",
+    "n_features_train = x_train.shape[1] * x_train.shape[2] * x_train.shape[3]\n",
+    "n_samples_test = x_test.shape[0]\n",
+    "n_features_test = x_test.shape[1] * x_test.shape[2] * x_test.shape[3]\n",
+    "\n",
+    "x_train = x_train.reshape(n_samples_train, n_features_train)\n",
+    "x_test = x_test.reshape(n_samples_test, n_features_test)\n",
+    "\n",
+    "y_train = np.argmax(y_train, axis=1)\n",
+    "y_test = np.argmax(y_test, axis=1)\n",
+    "\n",
+    "n_samples_max = 200\n",
+    "x_train = x_train[0:n_samples_max]\n",
+    "y_train = y_train[0:n_samples_max]\n",
+    "x_test = x_test[0:n_samples_max]\n",
+    "y_test = y_test[0:n_samples_max]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3.2 Train CatBoostClassifier classifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<catboost.core.CatBoostClassifier at 0x7f87386eb1d0>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model = CatBoostClassifier(custom_loss=['Accuracy'], random_seed=42, logging_level='Silent', depth=8, iterations=20)\n",
+    "model.fit(x_train, y_train, cat_features=None, eval_set=(x_train, y_train))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3.3 Create and apply Zeroth Order Optimization Attack with ART"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "art_classifier = CatBoostARTClassifier(model=model, nb_features=x_train.shape[1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "zoo = ZooAttack(classifier=art_classifier, confidence=0.0, targeted=False, learning_rate=1e-1, max_iter=100,\n",
+    "                binary_search_steps=20, initial_const=1e-3, abort_early=True, use_resize=False, \n",
+    "                use_importance=False, nb_parallel=10, batch_size=1, variable_h=0.25)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "ZOO: 100%|██████████| 200/200 [36:15<00:00, 10.88s/it]\n"
+     ]
+    }
+   ],
+   "source": [
+    "x_train_adv = zoo.generate(x_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "ZOO: 100%|██████████| 200/200 [19:09<00:00,  5.75s/it]\n"
+     ]
+    }
+   ],
+   "source": [
+    "x_test_adv = zoo.generate(x_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3.4 Evaluate CatBoostClassifier on benign and adversarial samples"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Benign Training Score: 0.9950\n"
+     ]
+    }
+   ],
+   "source": [
+    "y_pred = model.predict_proba(x_train)\n",
+    "score = np.sum(y_train == np.argmax(y_pred, axis=1)) / y_train.shape[0]\n",
+    "print(\"Benign Training Score: %.4f\" % score)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQEAAAECCAYAAAD+eGJTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAO90lEQVR4nO3dbYxc5XnG8euKvdg1mMRbx45DHXCMU2igMemKFxkBFQp1o0qAKkKtKHJoWtMEJ6F1JahVFVqRyq2AlFKKZIqLkYAEAhR/oEksCwFRYYvtEjBxgARcarxdY1ZgIMTYu3c/7Ljdkt1ndndeznjv/09azcy5Z+bcPravfc6cZ85xRAhAXh+ougEA1SIEgOQIASA5QgBIjhAAkiMEgOQqCQHby20/b/sntq+uoocS27tsP2v7adtbO6CfDbb32t4xYlm37c22X6zdzumw/q61/WptGz5t+7MV9rfQ9iO2d9p+zvbXa8s7YhsW+mvLNnS75wnYnibpBUmfkbRb0lOSVkTEj9raSIHtXZJ6ImJf1b1Iku1zJL0t6c6IOKW27G8lDUTEulqQzomIqzqov2slvR0R11fR00i2F0haEBHbbc+WtE3SRZK+qA7YhoX+Pqc2bMMqRgKnS/pJRLwUEe9J+pakCyvo44gREY9JGnjf4gslbazd36jhfzSVGKO/jhERfRGxvXb/LUk7JR2nDtmGhf7aoooQOE7Sf414vFtt/AOPU0j6vu1ttldV3cwY5kdEnzT8j0jSvIr7Gc1q28/Udhcq210ZyfYJkk6T1KsO3Ibv609qwzasIgQ8yrJOm7u8LCI+Lem3JV1RG+5iYm6VtFjSUkl9km6oth3J9jGS7pd0ZUTsr7qf9xulv7ZswypCYLekhSMe/4qkPRX0MaaI2FO73SvpQQ3vwnSa/tq+5OF9yr0V9/P/RER/RAxGxJCk21TxNrTdpeH/YHdFxAO1xR2zDUfrr13bsIoQeErSEtuLbB8l6fckbaqgj1HZPrr24YxsHy3pAkk7yq+qxCZJK2v3V0p6qMJefsHh/1w1F6vCbWjbkm6XtDMibhxR6ohtOFZ/7dqGbT86IEm1Qx1/J2mapA0R8Y22NzEG2x/X8G9/SZou6e6q+7N9j6TzJM2V1C/pGkn/IuleSR+T9IqkSyKikg/nxujvPA0PY0PSLkmXH97/rqC/syU9LulZSUO1xWs1vN9d+TYs9LdCbdiGlYQAgM7BjEEgOUIASI4QAJIjBIDkCAEguUpDoIOn5Eqiv0Z1cn+d3JvU3v6qHgl09F+E6K9RndxfJ/cmtbG/qkMAQMUamixke7mkmzQ88++fImJd6flHeUbM1NH/+/igDqhLMya9/lajv8Z0cn+d3JvU/P5+rnf0XhwY7ct7kw+ByZwc5Fh3xxk+f1LrAzB5vbFF+2Ng1BBoZHeAk4MAU0AjIXAknBwEQB3TG3jtuE4OUjvUsUqSZmpWA6sD0AqNjATGdXKQiFgfET0R0dPJH8QAWTUSAh19chAA4zPp3YGIOGR7taTv6f9ODvJc0zoD0BaNfCagiHhY0sNN6gVABZgxCCRHCADJEQJAcoQAkBwhACRHCADJEQJAcoQAkBwhACRHCADJEQJAcoQAkBwhACRHCADJEQJAcoQAkBwhACRHCADJEQJAcoQAkBwhACRHCADJEQJAcoQAkBwhACRHCADJEQJAcoQAkBwhACRHCADJNXRpchxZPL381z3tw3Nbuv7n//SEYn1w1lCxfvzivcX6rK+4WP/vG48q1rf3fLtY3zf4TrF+xn1rivUT/+TJYr0qDYWA7V2S3pI0KOlQRPQ0oykA7dOMkcBvRsS+JrwPgArwmQCQXKMhEJK+b3ub7VXNaAhAezW6O7AsIvbYnidps+0fR8RjI59QC4dVkjRTsxpcHYBma2gkEBF7ard7JT0o6fRRnrM+InoioqdLMxpZHYAWmHQI2D7a9uzD9yVdIGlHsxoD0B6N7A7Ml/Sg7cPvc3dEfLcpXU1R005eUqzHjK5ifc+5HyrW3z2zfBy7+4Pl+uOfKh8nr9q//mx2sf43/7C8WO899e5i/eWD7xbr6/o/U6x/9PEo1jvVpEMgIl6S9Kkm9gKgAhwiBJIjBIDkCAEgOUIASI4QAJIjBIDkOJ9AEw2e9+li/cY7binWP9FV/r77VHcwBov1v7j5i8X69HfKx+nPum91sT771UPF+ox95XkEs7b2FuudipEAkBwhACRHCADJEQJAcoQAkBwhACRHCADJMU+giWY8v6dY3/bzhcX6J7r6m9lO063pO7NYf+nt8nUL7lj8nWL9zaHycf75f/9vxXqrHZlnC6iPkQCQHCEAJEcIAMkRAkByhACQHCEAJEcIAMk5on1HP491d5zh89u2vk4zcNlZxfr+5eXrAkx75phi/YdfuXnCPY103b5fL9afOrc8D2DwjTeL9TirfIb6XV8rlrVoxQ/LT8CYemOL9seAR6sxEgCSIwSA5AgBIDlCAEiOEACSIwSA5AgBIDnmCXSQaXN/uVgffH2gWH/57vJx/ufO2VCsn/7XXy3W591S7ff5MXkNzROwvcH2Xts7Rizrtr3Z9ou12znNbBhA+4xnd+AOScvft+xqSVsiYomkLbXHAI5AdUMgIh6T9P5x6IWSNtbub5R0UZP7AtAmk/1gcH5E9ElS7XZe81oC0E4tP9Go7VWSVknSTM1q9eoATNBkRwL9thdIUu1271hPjIj1EdETET1dmjHJ1QFolcmGwCZJK2v3V0p6qDntAGi3ursDtu+RdJ6kubZ3S7pG0jpJ99r+kqRXJF3SyiazGNz3ekOvP7j/qIZe/8nP/6hYf+3WaeU3GBpsaP2oRt0QiIgVY5SY9QNMAUwbBpIjBIDkCAEgOUIASI4QAJIjBIDkWj5tGO1z8lUvFOuXnVo+qvvPx28p1s+95Ipiffa3nyzW0ZkYCQDJEQJAcoQAkBwhACRHCADJEQJAcoQAkBzzBKaQwTfeLNZf//LJxform94t1q++7s5i/c8+d3GxHv/xwWJ94TeeKNbVxmtkZMJIAEiOEACSIwSA5AgBIDlCAEiOEACSIwSA5BxtPPZ6rLvjDHOm8k418PtnFet3XXN9sb5o+syG1v/JO1cX60tu6yvWD720q6H1T2W9sUX7Y8Cj1RgJAMkRAkByhACQHCEAJEcIAMkRAkByhACQHPMEMG6xbGmxfuy63cX6PR//XkPrP+mRPyjWf/Uvy+dTGHzxpYbWfyRraJ6A7Q2299reMWLZtbZftf107eezzWwYQPuMZ3fgDknLR1n+zYhYWvt5uLltAWiXuiEQEY9JGmhDLwAq0MgHg6ttP1PbXZjTtI4AtNVkQ+BWSYslLZXUJ+mGsZ5oe5Xtrba3HtSBSa4OQKtMKgQioj8iBiNiSNJtkk4vPHd9RPRERE+XZky2TwAtMqkQsL1gxMOLJe0Y67kAOlvdeQK275F0nqS5kvolXVN7vFRSSNol6fKIKH/ZW8wTmOqmzZ9XrO+59MRivfeqm4r1D9T5nfX5ly8o1t88+/VifSorzROoe/GRiFgxyuLbG+4KQEdg2jCQHCEAJEcIAMkRAkByhACQHCEAJMf5BNAx7t39RLE+y0cV6z+L94r13/nqleX3f7C3WD+Scd0BAGMiBIDkCAEgOUIASI4QAJIjBIDkCAEgubpfJQYOGzq7fN2Bn14ys1g/ZemuYr3ePIB6bh44rfz+D21t6P2nKkYCQHKEAJAcIQAkRwgAyRECQHKEAJAcIQAkxzyBRNxzSrH+wtfKx+lvW7axWD9nZvn7/I06EAeL9ScHFpXfYKjupTFSYiQAJEcIAMkRAkByhACQHCEAJEcIAMkRAkByzBM4gkxfdHyx/tPLPlqsX3vpt4r13z1m34R7aqa1/T3F+qM3nVmsz9lYvm4BRld3JGB7oe1HbO+0/Zztr9eWd9vebPvF2u2c1rcLoNnGsztwSNKaiDhZ0pmSrrD9a5KulrQlIpZI2lJ7DOAIUzcEIqIvIrbX7r8laaek4yRdKOnwPNKNki5qVZMAWmdCHwzaPkHSaZJ6Jc2PiD5pOCgkzWt2cwBab9whYPsYSfdLujIi9k/gdatsb7W99aAOTKZHAC00rhCw3aXhALgrIh6oLe63vaBWXyBp72ivjYj1EdETET1dmtGMngE00XiODljS7ZJ2RsSNI0qbJK2s3V8p6aHmtweg1cYzT2CZpC9Ietb207VlayWtk3Sv7S9JekXSJa1pceqYfsLHivU3f2NBsX7pX323WP+jDz1QrLfamr7ycfwn/rE8D6D7jn8v1ucMMQ+gFeqGQET8QJLHKJ/f3HYAtBvThoHkCAEgOUIASI4QAJIjBIDkCAEgOc4nMAHTF3ykWB/YcHSx/uVFjxbrK2b3T7inZlr96tnF+vZblxbrc7+zo1jvfovj/J2IkQCQHCEAJEcIAMkRAkByhACQHCEAJEcIAMmlmifw3m+Vv8/+3h8PFOtrT3y4WL/gl96ZcE/N1D/4brF+zqY1xfpJf/7jYr37jfJx/qFiFZ2KkQCQHCEAJEcIAMkRAkByhACQHCEAJEcIAMmlmiew66Jy5r1w6n0tXf8tbywu1m969IJi3YNjnfl92EnXvVysL+nvLdYHi1VMVYwEgOQIASA5QgBIjhAAkiMEgOQIASA5QgBIzhFRfoK9UNKdkj6i4a+Mr4+Im2xfK+kPJb1We+raiCh+4f5Yd8cZ5mrmQLv1xhbtj4FRJ5qMZ7LQIUlrImK77dmSttneXKt9MyKub1ajANqvbghERJ+kvtr9t2zvlHRcqxsD0B4T+kzA9gmSTpN0eP7patvP2N5ge06TewPQBuMOAdvHSLpf0pURsV/SrZIWS1qq4ZHCDWO8bpXtrba3HtSBJrQMoJnGFQK2uzQcAHdFxAOSFBH9ETEYEUOSbpN0+mivjYj1EdETET1dmtGsvgE0Sd0QsG1Jt0vaGRE3jli+YMTTLpZUviQtgI40nqMDyyR9QdKztp+uLVsraYXtpZJC0i5Jl7ekQwAtNZ6jAz+QNNrxxfJJ+AEcEZgxCCRHCADJEQJAcoQAkBwhACRHCADJEQJAcoQAkBwhACRHCADJEQJAcoQAkBwhACRHCADJEQJAcnWvO9DUldmvSfrPEYvmStrXtgYmjv4a08n9dXJvUvP7Oz4iPjxaoa0h8Asrt7dGRE9lDdRBf43p5P46uTepvf2xOwAkRwgAyVUdAusrXn899NeYTu6vk3uT2thfpZ8JAKhe1SMBABUjBIDkCAEgOUIASI4QAJL7H4v8SYP7urYSAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 288x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.matshow(x_train[0, :].reshape((28, 28)))\n",
+    "plt.clim(0, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Benign Training Predicted Label: 5\n"
+     ]
+    }
+   ],
+   "source": [
+    "prediction = np.argmax(model.predict_proba(x_train[0:1, :]), axis=1)\n",
+    "print(\"Benign Training Predicted Label: %i\" % prediction)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial Training Score: 0.0850\n"
+     ]
+    }
+   ],
+   "source": [
+    "y_pred = model.predict_proba(x_train_adv)\n",
+    "score = np.sum(y_train == np.argmax(y_pred, axis=1)) / y_train.shape[0]\n",
+    "print(\"Adversarial Training Score: %.4f\" % score)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQEAAAECCAYAAAD+eGJTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAPMElEQVR4nO3df5BV9XnH8c8jLEv5YWRLIMQSJYjVxtQ12aIOjtI6ISSTGXUyNmWMQ9q02EaSWPkjhulUOpN0aEdJibHMQKVgB000auUPrGGoo2aqWxdKEELUBLcW2SySHX5oDD92n/6xB7sld793d++595zleb9mmHvvec695+Gw++Gce77nHHN3AYjrnKIbAFAsQgAIjhAAgiMEgOAIASA4QgAIrpAQMLOFZvaKmf3UzO4qoocUM+s0s5fNbKeZdZSgn/VmdtDMdg+Y1mJmW83stexxSsn6W2Fmb2brcKeZfbrA/maa2TNmttfM9pjZV7PppViHif4asg6t0eMEzGyMpFclfULSfkkvSVrk7j9uaCMJZtYpqc3dDxXdiySZ2bWS3pb0oLtflk37e0k97r4yC9Ip7v61EvW3QtLb7n5PET0NZGYzJM1w9x1mNlnSdkk3SvqCSrAOE/39oRqwDovYEpgr6afuvs/dT0j6rqQbCuhj1HD35yT1nDH5Bkkbs+cb1f9DU4hB+isNd+9y9x3Z82OS9ko6XyVZh4n+GqKIEDhf0v8MeL1fDfwLD5FL+oGZbTezJUU3M4jp7t4l9f8QSZpWcD+VLDWzXdnuQmG7KwOZ2YWSrpDUrhKuwzP6kxqwDosIAaswrWxjl+e5+8ckfUrS7dnmLoZnjaTZkloldUm6t9h2JDObJOkxSXe4+9Gi+zlThf4asg6LCIH9kmYOeP1bkg4U0Meg3P1A9nhQ0hPq34Upm+5sX/L0PuXBgvv5f9y929173b1P0joVvA7NrEn9v2Cb3P3xbHJp1mGl/hq1DosIgZckzTGzWWY2TtIfSdpcQB8VmdnE7MsZmdlESQsk7U6/qxCbJS3Oni+W9GSBvfya079cmZtU4Do0M5P0gKS97r5qQKkU63Cw/hq1Dht+dECSskMd/yBpjKT17v7NhjcxCDP7sPr/95eksZIeKro/M3tY0nxJUyV1S7pb0r9KekTShyS9Ielmdy/ky7lB+puv/s1Yl9Qp6bbT+98F9HeNpOclvSypL5u8XP373YWvw0R/i9SAdVhICAAoD0YMAsERAkBwhAAQHCEABEcIAMEVGgIlHpIrif5qVeb+ytyb1Nj+it4SKPU/hOivVmXur8y9SQ3sr+gQAFCwmgYLmdlCSavVP/Lvn9x9ZWr+cdbs4zXxvdcndVxNah7x8uuN/mpT5v7K3JuUf3+/0js64ccrnbw38hAYycVBzrUWv9KuH9HyAIxcu2/TUe+pGAK17A5wcRDgLFBLCIyGi4MAqGJsDe8d0sVBskMdSyRpvCbUsDgA9VDLlsCQLg7i7mvdvc3d28r8RQwQVS0hUOqLgwAYmhHvDrj7KTNbKulp/d/FQfbk1hmAhqjlOwG5+xZJW3LqBUABGDEIBEcIAMERAkBwhAAQHCEABEcIAMERAkBwhAAQHCEABEcIAMERAkBwhAAQHCEABEcIAMERAkBwhAAQHCEABEcIAMERAkBwhAAQHCEABEcIAMERAkBwhAAQHCEABEcIAMERAkBwhAAQHCEABEcIAMHVdGtyjC42Nv3PPeb9U2v6/FNdP0/Wf3bvVcl674S+ZP2C2QeT9QlfsmT956vGJes72r6XrB/qfSdZv/LRZcn6RXe+mKwXpaYQMLNOScck9Uo65e5teTQFoHHy2BL4fXc/lMPnACgA3wkAwdUaAi7pB2a23cyW5NEQgMaqdXdgnrsfMLNpkraa2U/c/bmBM2ThsESSxmtCjYsDkLeatgTc/UD2eFDSE5LmVphnrbu3uXtbk5prWRyAOhhxCJjZRDObfPq5pAWSdufVGIDGqGV3YLqkJ8zs9Oc85O7/lktXZ6kxl85J1r25KVk/cN15yfq7V6WPY7e8L11//vL0cfLaba/p3U/9cnKy/nffWZist3/0oWT99ZPvJusruz+RrH/weU/Wy2rEIeDu+yRdnmMvAArAIUIgOEIACI4QAIIjBIDgCAEgOEIACI7rCeSod/7HkvVVG+5P1i9uSp/vfrY76b3J+l/f94Vkfew76eP0Vz+6NFmf/OapZL35UHocwYSO9mS9rNgSAIIjBIDgCAEgOEIACI4QAIIjBIDgCAEgOMYJ5Kj5lQPJ+vZfzUzWL27qzrOd3C3rSt83YN/b6fsWbJj9/WT9SF/6OP/0b/9Hsn7qDz6erI/999quZzA6rxZQHVsCQHCEABAcIQAERwgAwRECQHCEABAcIQAEZ+6NO/p5rrX4lXZ9w5ZXNj1/fHWyfnRh+r4AY3ZNStZ/9KX7ht3TQN849LvJ+kvXpccB9B4+kqz71ekr1Hd+JVnWrEU/Ss+AQbX7Nh31HqtUY0sACI4QAIIjBIDgCAEgOEIACI4QAIIjBIDgGCdQImOm/may3vuLnmT99YfSx/n3XLs+WZ/7t19O1qfdnz6fH+VV0zgBM1tvZgfNbPeAaS1mttXMXssep+TZMIDGGcruwAZJC8+Ydpekbe4+R9K27DWAUahqCLj7c5LO3A69QdLG7PlGSTfm3BeABhnpF4PT3b1LkrLHafm1BKCR6n6hUTNbImmJJI3XhHovDsAwjXRLoNvMZkhS9nhwsBndfa27t7l7W5OaR7g4APUy0hDYLGlx9nyxpCfzaQdAo1XdHTCzhyXNlzTVzPZLulvSSkmPmNkXJb0h6eZ6NhnFK8vnJOsX3flisn7y6Lialv+RW36crL+1Zkz6A/p6a1o+ilE1BNx90SAlRv0AZwGGDQPBEQJAcIQAEBwhAARHCADBEQJAcFxP4Cwy5rz3JestWyqeTv6ef75gW7J+3Z23J+uTv5cex1Crw7em79tw3r+8UNflj2bcdwDAoAgBIDhCAAiOEACCIwSA4AgBIDhCAAiOcQKBnHP5pcn6dzavS9b3nEhfSvLru25K1v2/0uMYZn6zynH+Bv6sVvJ7O9PXS3iptcr1FgrEOAEAgyIEgOAIASA4QgAIjhAAgiMEgOAIASA4xgngPT1/kj5ff9Pd9yTrs8aOr2n5H3lwabI+Z11Xsn5qX2dNyz+bMU4AwKAIASA4QgAIjhAAgiMEgOAIASA4QgAIjnECo8jTB3Ym65/8YGtdl+/z0p9/7sr9yfrDH366puVf8syfJuu//TdHkvXe1/bVtPzRrKZxAma23swOmtnuAdNWmNmbZrYz+/PpPBsG0DhD2R3YIGlhhenfcvfW7M+WfNsC0ChVQ8Ddn5PU04BeABSgli8Gl5rZrmx3YUpuHQFoqJGGwBpJsyW1SuqSdO9gM5rZEjPrMLOOkzo+wsUBqJcRhYC7d7t7r7v3SVonaW5i3rXu3ububU1qHmmfAOpkRCFgZjMGvLxJ0u7B5gVQblXHCZjZw5LmS5oqqVvS3dnrVkkuqVPSbe6ePtlbjBM4242Znr4vwYHPXZSst39tdbJ+TpX/s255fUGyfuSaXyTrZ7PUOIGx1d7s7osqTH6g5q4AlALDhoHgCAEgOEIACI4QAIIjBIDgCAEgOK4ngNJ4ZP8LyfoEG5es/9JPJOuf+fId6c9/oj1ZH8247wCAQRECQHCEABAcIQAERwgAwRECQHCEABBc1VOJgdP6rknfd+BnN49P1i9r7UzWP/v525P1pzatS9bv67kiWZ/wZEeyHhVbAkBwhAAQHCEABEcIAMERAkBwhAAQHCEABMc4gUCs7bJk/dWvpM/XXzdvY7J+7fj0+fxVbUqXj/vJZP3FnlnpD+iremuMkNgSAIIjBIDgCAEgOEIACI4QAIIjBIDgCAEgOMYJjCJHPn9Vsn744oqXlX/Pis99N1n/7KRDw+4pT8u725L1Z1en//5TNqbvW4DKqm4JmNlMM3vGzPaa2R4z+2o2vcXMtprZa9njlPq3CyBvQ9kdOCVpmbtfKukqSbeb2e9IukvSNnefI2lb9hrAKFM1BNy9y913ZM+PSdor6XxJN0g6PY50o6Qb69UkgPoZ1heDZnahpCsktUua7u5dUn9QSJqWd3MA6m/IIWBmkyQ9JukOdz86jPctMbMOM+s4qeMj6RFAHQ0pBMysSf0BsMndH88md5vZjKw+Q9LBSu9197Xu3ububU1qzqNnADkaytEBk/SApL3uvmpAabOkxdnzxZKezL89APU2lHEC8yTdKullM9uZTVsuaaWkR8zsi5LekHRzfVo8e4y98EPJ+pGPz0jWb/n6U8n6n5+3b9g95WlZV/o4/gv/mB4H0LLhP5P1KX2MA6iHqiHg7j+UNNgolOvzbQdAozFsGAiOEACCIwSA4AgBIDhCAAiOEACC43oCwzB2xgeS9Z71E5P1v5j1bLK+aHL3sHvK09I3r0nWd6xpTdanfn93st5yjOP8ZcSWABAcIQAERwgAwRECQHCEABAcIQAERwgAwYUaJ3Dik+nz2U/8ZU+yvvyiLcn6gt94Z9g95am7991k/drNy5L1S/7qJ8l6y+H0cf6+ZBVlxZYAEBwhAARHCADBEQJAcIQAEBwhAARHCADBhRon0HljOvNe/eijdV3+/YdnJ+urn12QrFvvYFd+73fJN15P1ud0tyfrvckqzlZsCQDBEQJAcIQAEBwhAARHCADBEQJAcIQAEJy5e3oGs5mSHpT0AfWfMr7W3Veb2QpJfybprWzW5e6ePOH+XGvxK427mQON1u7bdNR7Kg40GcpgoVOSlrn7DjObLGm7mW3Nat9y93vyahRA41UNAXfvktSVPT9mZnslnV/vxgA0xrC+EzCzCyVdIen0+NOlZrbLzNab2ZScewPQAEMOATObJOkxSXe4+1FJayTNltSq/i2Fewd53xIz6zCzjpM6nkPLAPI0pBAwsyb1B8Amd39ckty929173b1P0jpJcyu9193Xunubu7c1qTmvvgHkpGoImJlJekDSXndfNWD6jAGz3SQpfUtaAKU0lKMD8yTdKullM9uZTVsuaZGZtUpySZ2SbqtLhwDqaihHB34oqdLxxfRF+AGMCowYBIIjBIDgCAEgOEIACI4QAIIjBIDgCAEgOEIACI4QAIIjBIDgCAEgOEIACI4QAIIjBIDgCAEguKr3Hch1YWZvSfrvAZOmSjrUsAaGj/5qU+b+ytyblH9/F7j7+ysVGhoCv7Zwsw53byusgSrorzZl7q/MvUmN7Y/dASA4QgAIrugQWFvw8quhv9qUub8y9yY1sL9CvxMAULyitwQAFIwQAIIjBIDgCAEgOEIACO5/AZYEbOb+RUoqAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 288x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.matshow(x_train_adv[0, :].reshape((28, 28)))\n",
+    "plt.clim(0, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial Training Predicted Label: 3\n"
+     ]
+    }
+   ],
+   "source": [
+    "prediction = np.argmax(model.predict_proba(x_train_adv[0:1, :]), axis=1)\n",
+    "print(\"Adversarial Training Predicted Label: %i\" % prediction)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Benign Test Score: 0.6450\n"
+     ]
+    }
+   ],
+   "source": [
+    "y_pred = model.predict_proba(x_test)\n",
+    "score = np.sum(y_test == np.argmax(y_pred, axis=1)) / y_test.shape[0]\n",
+    "print(\"Benign Test Score: %.4f\" % score)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.matshow(x_test[0, :].reshape((28, 28)))\n",
+    "plt.clim(0, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Benign Test Predicted Label: 7\n"
+     ]
+    }
+   ],
+   "source": [
+    "prediction = np.argmax(model.predict_proba(x_test[0:1, :]), axis=1)\n",
+    "print(\"Benign Test Predicted Label: %i\" % prediction)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial Test Score: 0.1400\n"
+     ]
+    }
+   ],
+   "source": [
+    "y_pred = model.predict_proba(x_test_adv)\n",
+    "score = np.sum(y_test == np.argmax(y_pred, axis=1)) / y_test.shape[0]\n",
+    "print(\"Adversarial Test Score: %.4f\" % score)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.matshow(x_test_adv[0, :].reshape((28, 28)))\n",
+    "plt.clim(0, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial Test Predicted Label: 3\n"
+     ]
+    }
+   ],
+   "source": [
+    "prediction = np.argmax(model.predict_proba(x_test_adv[0:1, :]), axis=1)\n",
+    "print(\"Adversarial Test Predicted Label: %i\" % prediction)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/adversarial-robustness-toolbox/notebooks/classifier_gpy_gaussian_process.ipynb b/adversarial-robustness-toolbox/notebooks/classifier_gpy_gaussian_process.ipynb
new file mode 100644
index 0000000..a698ba7
--- /dev/null
+++ b/adversarial-robustness-toolbox/notebooks/classifier_gpy_gaussian_process.ipynb
@@ -0,0 +1,255 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Gaussian Process Classification with GPy\n",
+    "\n",
+    "In this notebook, we want to show how to apply a simple GPy classifier and craft adversarial examples on it.\n",
+    "Let us start by importing all things we might use to train a model and visualize it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from art.attacks.evasion import HighConfidenceLowUncertainty, ProjectedGradientDescent\n",
+    "from art.estimators.classification import GPyGaussianProcessClassifier\n",
+    "\n",
+    "import GPy\n",
+    "from sklearn.datasets import  make_moons\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.cm as cm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Training a classifier\n",
+    "We will first train a classifier. The classifier is limited to binary classification problems and scales quadratically with the data, so we use a very simple and basic data set here.\n",
+    "\n",
+    "Once the code runs, we see a summary of the model and a visualization of the classifier. The shade of the samples is directly related to the confidence of the GP in its classification."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2bb285d28dc24d5aac5fdad62f5620bc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(VBox(children=(IntProgress(value=0, max=1000), HTML(value=''))), Box(children=(HTML(value=''),)…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "np.random.seed(6)\n",
+    "X, y = make_moons(n_samples=100, noise=0.1)\n",
+    "#getting a kernel for GPy. Gradients work for any kernel.\n",
+    "gpkern = GPy.kern.RBF(np.shape(X)[1])\n",
+    "#get the model\n",
+    "m = GPy.models.GPClassification(X, y.reshape(-1,1), kernel=gpkern)\n",
+    "m.rbf.lengthscale.fix(0.4)\n",
+    "#determining the infernce method\n",
+    "m.inference_method = GPy.inference.latent_function_inference.laplace.Laplace()\n",
+    "#now train the model\n",
+    "m.optimize(messages=True, optimizer='lbfgs')\n",
+    "#apply ART to the model\n",
+    "m_art = GPyGaussianProcessClassifier(m)\n",
+    "#getting additional test data\n",
+    "Xt, Yt = make_moons(n_samples=10, noise=0.1)\n",
+    "plt.scatter(X[:,0],X[:,1],c=cm.hot(m_art.predict(X)[:,0].reshape(-1)))\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Targeting a classifier\n",
+    "We will now craft attacks on this classifier. One are the adversarial examples introduced by Grosse et al. (https://arxiv.org/abs/1812.02606) which are specifically targeting Gaussian Process classifiers. We then apply one of the other attacks of ART, PGD by Madry et al. (https://arxiv.org/abs/1706.06083), as an example. \n",
+    "\n",
+    "### Confidence optimized adversarial examples\n",
+    "We craft adversarial examples which are optimized for confidence. We plot the initial seeds for the adversarial examples in green and the resulting adversarial examples in black, and connect the initial and final points using a straight line (which is not equivalent to the path the optimization took).\n",
+    "\n",
+    "We observe that some examples are not moving towards the other class, but instead seem to move randomly away from the data. This stems from the problem that the Gaussian Processes' gradients point away from the data in all directions, and might lead the attack far away from the actauly boundary."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "HCLU: 100%|██████████| 10/10 [00:01<00:00,  6.51it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# get attack\n",
+    "attack = HighConfidenceLowUncertainty(m_art,conf=0.75,min_val=-1.0,max_val=2.0)\n",
+    "# generate examples and plot them\n",
+    "adv = attack.generate(Xt)\n",
+    "plt.scatter(X[:,0],X[:,1],c=cm.hot(m_art.predict(X)[:,0].reshape(-1)))\n",
+    "for i in range(np.shape(Xt)[0]):\n",
+    "    plt.scatter(Xt[:,0],Xt[:,1],c='green')\n",
+    "    plt.scatter(adv[:,0],adv[:,1],c='k')\n",
+    "    plt.arrow(Xt[i,0], Xt[i,1], adv[i,0]-Xt[i,0], adv[i,1]-Xt[i,1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Uncertainty optimized adversarial examples\n",
+    "We can additionally optimize for uncetainty by setting unc_increase to 0.9, thereby forcing the adversarial examples to be closer to the original training data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "HCLU: 100%|██████████| 10/10 [00:01<00:00,  6.18it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "attack = HighConfidenceLowUncertainty(m_art,unc_increase=0.9,min_val=0.0,max_val=2.0)\n",
+    "adv = attack.generate(Xt)\n",
+    "plt.scatter(X[:,0],X[:,1],c=cm.hot(m_art.predict(X)[:,0].reshape(-1)))\n",
+    "for i in range(np.shape(Xt)[0]):\n",
+    "    plt.scatter(Xt[:,0],Xt[:,1],c='green')\n",
+    "    plt.scatter(adv[:,0],adv[:,1],c='k')\n",
+    "    plt.arrow(Xt[i,0], Xt[i,1], adv[i,0]-Xt[i,0], adv[i,1]-Xt[i,1])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### PGD on Guassian process classification\n",
+    "To conclude, we show how to compute PGD adversarial exmples on our model. We observe that as before, many attempts fail, as the model misleads the attack to take a wrong path away from the boundary. In this case, examples are classified as default: either of the two classes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "attack = ProjectedGradientDescent(m_art,eps=0.5,eps_step=0.2) #TODO,targeted=True)\n",
+    "adv = attack.generate(Xt)\n",
+    "plt.scatter(X[:,0],X[:,1],c=cm.hot(m_art.predict(X)[:,0].reshape(-1)))\n",
+    "for i in range(np.shape(Xt)[0]):\n",
+    "    plt.scatter(Xt[:,0],Xt[:,1],c='green')\n",
+    "    plt.scatter(adv[:,0],adv[:,1],c='k')\n",
+    "    plt.arrow(Xt[i,0], Xt[i,1], adv[i,0]-Xt[i,0], adv[i,1]-Xt[i,1])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/adversarial-robustness-toolbox/notebooks/classifier_lightgbm.ipynb b/adversarial-robustness-toolbox/notebooks/classifier_lightgbm.ipynb
new file mode 100644
index 0000000..0a63438
--- /dev/null
+++ b/adversarial-robustness-toolbox/notebooks/classifier_lightgbm.ipynb
@@ -0,0 +1,665 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Adversarial-Robustness-Toolbox for LightGBM"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import lightgbm as lgb\n",
+    "\n",
+    "from sklearn.datasets import load_iris\n",
+    "\n",
+    "import numpy as np\n",
+    "from matplotlib import pyplot as plt\n",
+    "\n",
+    "from art.estimators.classification import LightGBMClassifier\n",
+    "from art.attacks.evasion import ZooAttack\n",
+    "from art.utils import load_mnist\n",
+    "\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1 Training LighGBM classifier and attacking with ART Zeroth Order Optimization attack"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_adversarial_examples(x_train, y_train, num_classes):\n",
+    "    \n",
+    "    # Create and fit LightGBM model\n",
+    "    num_round = 10\n",
+    "    param = {'objective': 'multiclass', 'metric': 'multi_logloss', 'num_class': num_classes}\n",
+    "    train_data = lgb.Dataset(x_train, label=y_train)\n",
+    "    model = lgb.train(param, train_data, num_round, valid_sets=[])\n",
+    "\n",
+    "    # Create ART classifier for LightGBM\n",
+    "    art_classifier = LightGBMClassifier(model=model)\n",
+    "\n",
+    "    # Create ART Zeroth Order Optimization attack\n",
+    "    zoo = ZooAttack(classifier=art_classifier, confidence=0.0, targeted=False, learning_rate=1e-1, max_iter=20,\n",
+    "                    binary_search_steps=10, initial_const=1e-3, abort_early=True, use_resize=False, \n",
+    "                    use_importance=False, nb_parallel=1, batch_size=1, variable_h=0.2)\n",
+    "\n",
+    "    # Generate adversarial samples with ART Zeroth Order Optimization attack\n",
+    "    x_train_adv = zoo.generate(x_train)\n",
+    "\n",
+    "    return x_train_adv, model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1.1 Utility functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_data(num_classes):\n",
+    "    x_train, y_train = load_iris(return_X_y=True)\n",
+    "    x_train = x_train[y_train < num_classes][:, [0, 1]]\n",
+    "    y_train = y_train[y_train < num_classes]\n",
+    "    x_train[:, 0][y_train == 0] *= 2\n",
+    "    x_train[:, 1][y_train == 2] *= 2\n",
+    "    x_train[:, 0][y_train == 0] -= 3\n",
+    "    x_train[:, 1][y_train == 2] -= 2\n",
+    "    \n",
+    "    x_train[:, 0] = (x_train[:, 0] - 4) / (9 - 4)\n",
+    "    x_train[:, 1] = (x_train[:, 1] - 1) / (6 - 1)\n",
+    "    \n",
+    "    return x_train, y_train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_results(model, x_train, y_train, x_train_adv, num_classes):\n",
+    "    \n",
+    "    fig, axs = plt.subplots(1, num_classes, figsize=(num_classes * 5, 5))\n",
+    "\n",
+    "    colors = ['orange', 'blue', 'green']\n",
+    "\n",
+    "    for i_class in range(num_classes):\n",
+    "\n",
+    "        # Plot difference vectors\n",
+    "        for i in range(y_train[y_train == i_class].shape[0]):\n",
+    "            x_1_0 = x_train[y_train == i_class][i, 0]\n",
+    "            x_1_1 = x_train[y_train == i_class][i, 1]\n",
+    "            x_2_0 = x_train_adv[y_train == i_class][i, 0]\n",
+    "            x_2_1 = x_train_adv[y_train == i_class][i, 1]\n",
+    "            if x_1_0 != x_2_0 or x_1_1 != x_2_1:\n",
+    "                axs[i_class].plot([x_1_0, x_2_0], [x_1_1, x_2_1], c='black', zorder=1)\n",
+    "\n",
+    "        # Plot benign samples\n",
+    "        for i_class_2 in range(num_classes):\n",
+    "            axs[i_class].scatter(x_train[y_train == i_class_2][:, 0], x_train[y_train == i_class_2][:, 1], s=20,\n",
+    "                                 zorder=2, c=colors[i_class_2])\n",
+    "        axs[i_class].set_aspect('equal', adjustable='box')\n",
+    "\n",
+    "        # Show predicted probability as contour plot\n",
+    "        h = .01\n",
+    "        x_min, x_max = 0, 1\n",
+    "        y_min, y_max = 0, 1\n",
+    "\n",
+    "        xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n",
+    "\n",
+    "        Z_proba = model.predict(np.c_[xx.ravel(), yy.ravel()])\n",
+    "        Z_proba = Z_proba[:, i_class].reshape(xx.shape)\n",
+    "        im = axs[i_class].contourf(xx, yy, Z_proba, levels=[0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0],\n",
+    "                                   vmin=0, vmax=1)\n",
+    "        if i_class == num_classes - 1:\n",
+    "            cax = fig.add_axes([0.95, 0.2, 0.025, 0.6])\n",
+    "            plt.colorbar(im, ax=axs[i_class], cax=cax)\n",
+    "\n",
+    "        # Plot adversarial samples\n",
+    "        for i in range(y_train[y_train == i_class].shape[0]):\n",
+    "            x_1_0 = x_train[y_train == i_class][i, 0]\n",
+    "            x_1_1 = x_train[y_train == i_class][i, 1]\n",
+    "            x_2_0 = x_train_adv[y_train == i_class][i, 0]\n",
+    "            x_2_1 = x_train_adv[y_train == i_class][i, 1]\n",
+    "            if x_1_0 != x_2_0 or x_1_1 != x_2_1:\n",
+    "                axs[i_class].scatter(x_2_0, x_2_1, zorder=2, c='red', marker='X')\n",
+    "        axs[i_class].set_xlim((x_min, x_max))\n",
+    "        axs[i_class].set_ylim((y_min, y_max))\n",
+    "\n",
+    "        axs[i_class].set_title('class ' + str(i_class))\n",
+    "        axs[i_class].set_xlabel('feature 1')\n",
+    "        axs[i_class].set_ylabel('feature 2')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 2 Example: Iris dataset"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### legend\n",
+    "- colored background: probability of class i\n",
+    "- orange circles: class 1\n",
+    "- blue circles: class 2\n",
+    "- green circles: class 3\n",
+    "- red crosses: adversarial samples for class i"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "ZOO: 100%|██████████| 100/100 [00:04<00:00, 22.39it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAq4AAAE7CAYAAAAcvANrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nOzdd3ycxbXw8d/Zpl01W0LuvWAMmG4bAiQhBIgh10Au1ZTYCcT3ch1KKAnJSzGESzElMSXmOsbYlEBC7+CQEGrAmGLAuCBXCVmusmxJK+0+z877x65W2yStykpa+Xw/H4F2dnaeWVkaHc0zZ0aMMSillFJKKdXTObq7A0oppZRSSqVDA1ellFJKKZUVNHBVSimllFJZQQNXpZRSSimVFTRwVUoppZRSWUEDV6WUUkoplRU0cFVdTkRmiMh73d0PpZRqKx2/lOpeGriqXk9EikXkORGpFZGNInJed/dJKaXSISK/FJFlItIgIou6uz9KdTdXd3dAqS7wABAABgCHAq+IyHJjzIru7ZZSSrWqArgF+BHg6+a+KNXtdMZVZYyIDBORZ0Vkm4jsEJH7m6k3V0TKRGS3iHwiIt+NeW5yZLZht4hsEZF7IuVeEXks0u4uEflYRAakaDsPOAO43hhTY4x5D3gRuDAz71op1Rv0hPELwBjzrDHmeWBHRt6oUllGA1eVESLiBF4GNgIjgSHAk81U/5jwTGgx8BfgKRHxRp6bC8w1xhQCY4C/RcqnA32AYcA+wH8D/hRtjwNsY8yamLLlwIHtemNKqV6vB41fSqkEGriqTJkMDAauMcbUGmPqI7OdSYwxjxljdhhjLGPM3UAOsF/k6SAwVkRKIjOmH8aU7wOMNcbYxphPjDG7UzSfD1QnlFUDBR18f0qp3qunjF9KqQQauKpMGQZsNMZYrVUUkatEZKWIVIvILsIzESWRpy8iPGu6KnI77T8i5Y8CbwBPikiFiMwREXeK5muAwoSyQmBPO96TUmrv0FPGL6VUAg1cVaaUAcNFpMUEwMh6sN8AZwNFxpi+hGdEBcAY840xZhrQH7gDeFpE8owxQWPMTcaYA4Cjgf8AfpriEmsAl4jsG1N2CKCJWUqp5vSU8UsplUADV5UpS4HNwO0ikhdJRjgmRb0CwAK2EQ4wbyBmhlRELhCRfsaYELArUmyLyA9E5KDIWrTdhG+92YmNG2NqgWeBmyP9OAY4jfCMh1JKpdIjxq9IG67Imlkn4Iz0RXcEUnstDVxVRhhjbGAqMBbYBJQD56So+gbwGuGZ0Y1APeHZjkZTgBUiUkM40eFcY0w9MBB4mvCgvxJ4G3isme78D+FtZLYCTwCX6FZYSqnm9LDx6zrCiVvXAhdEPr+uA29Pqawmxpju7oNSSimllFKt0hlXpZRSSimVFTIWuIrIQhHZKiJfNfO8iMi9IlIqIl+IyOGZ6otSSrWVjmFKKdXzZHLGdRHh9T3NORnYN/IxE5iXwb4opVRbLULHMKWU6lEyFrgaY94BdrZQ5TTgERP2IdBXRAZlqj9KKdUWOoYppVTP051rXIcQn31ZHilTSqlsoGOYUkp1se7cC05SlKXc4kBEZhK+FUdeXt4R48ePz2S/lFI9zCeffLLdGNOvu/uRIK0xTMcvpfZuPXT8ylrdGbiWEz5Wr9FQoCJVRWPMfGA+wMSJE82yZcsy3zulVI8hIhu7uw8ppDWG6fil1N6th45fWas7A9cXgV+KyJPAkUC1MWZzay/aULuB6UsvynjnlEq0ePJD3d0F1bO0eQxbtXYLx55xV5d0TqlY7z1zdXd3QalOkbHAVUSeAI4DSkSkHLgRcAMYYx4EXgVOAUqBOuBnmeqLUp1hwZrvdncX9ioXj3u3W6+vY5jqTX406abu7sJe542Pb+zuLvRKGQtcjTHTWnneALMydX2llOoIHcOUUqrn0ZOzlFJKKaVUVtDAVSmllFJKZQUNXJVSSimlVFbQwFUppZRSSmUFDVyVUkoppVRW0MBVKaWUUkplBQ1clVJKKaVUVtDAVSmllFJKZQUNXJVSSimlVFbQwFUppZRSSmUFDVyVUkoppVRW0MBVKaWUUkp1KhFZKCJbReSrZp4XEblXREpF5AsROTyddjVwVUoppZRSnW0RMKWF508G9o18zATmpdOoBq5KKaWUUqpTGWPeAXa2UOU04BET9iHQV0QGtdauBq5KKaWUUqqrDQHKYh6XR8pa5MpYd5RSSimlVI/y/eNyTNXOUIfb+fJLawVQH1M03xgzvw1NSIoy09qLNHBVSimllNpLVO0M8eKrJR1uZ9SwynpjzMQONFEODIt5PBSoaO1FulRAKaWUUkp1tReBn0Z2FzgKqDbGbG7tRTrjqpRSSimlOpWIPAEcB5SISDlwI+AGMMY8CLwKnAKUAnXAz9JpVwNXpZRSSinVqYwx01p53gCz2tquLhVQSimllFJZQQNXpZRSSimVFTRwVUoppZRSWUEDV6WUUkoplRU0cFVKKaWUUllBA1ellFJKKZUVNHBVSimllFJZQQNXpZRSSimVFTRwVUoppZRSWUEDV6WUUkoplRU0cFVKKaWUUllBA1ellFJKKZUVNHBVSimllFJZQQNXpZRSSimVFTRwVUoppZRSWUEDV6WUUkoplRU0cFVKKaWUUlnB1d0dUEoppZRSXWN3yMubdWM7oaXKTmij7XTGVSmllFJKZQUNXJVSSimlVFbQwFUppZRSSmUFDVyVUkoppVRWyGjgKiJTRGS1iJSKyLUpnh8uIm+JyGci8oWInJLJ/iilVLp0/FJKqZ4nY4GriDiBB4CTgQOAaSJyQEK164C/GWMOA84F/pSp/iilVLp0/FJKqZ4pkzOuk4FSY8w6Y0wAeBI4LaGOAQojn/cBKjLYH6WUSpeOX0op1QNlch/XIUBZzONy4MiEOrOBJSJyKZAHnJDB/iilVLp0/FJKqR4okzOukqLMJDyeBiwyxgwFTgEeFZGkPonITBFZJiLL6nfVZ6CrSikVJyPjl9VQk4GuKqXU3iOTgWs5MCzm8VCSb6VdBPwNwBjzb8ALlCQ2ZIyZb4yZaIyZ6O3rzVB3lVIqKiPjlysnP0PdVUqpvUMmA9ePgX1FZJSIeAgnL7yYUGcT8EMAEdmf8MC/LYN9UkqpdOj4pZRSPVDGAldjjAX8EngDWEk4+3aFiNwsIqdGql0F/EJElgNPADOMMYm345RSqkvp+KWUUj1TJpOzMMa8CryaUHZDzOdfA8dksg9KKdUeOn4ppVTPoydnKaWUUkqprKCBq1JKKaWUygoauCqllFJKqayggatSSimllOp0IjJFRFaLSKmIXJvi+eEi8paIfCYiX4jIKa21qYGrUkoppZTqVCLiBB4ATgYOAKaJyAEJ1a4jvGvLYYS3HfxTa+1q4KqUUkoppTrbZKDUGLPOGBMAngROS6hjgMLI531IPuglSUa3w1JKKaWUUnulIUBZzONy4MiEOrOBJSJyKZAHnNBaozrjqpRSSiml2qpERJbFfMxMeF5SvCbxkJZpwCJjzFDgFOBREWkxNtUZV6WUUkqpvUSNncO7u8Z1QkvvbTfGTGyhQjkwLObxUJKXAlwETAEwxvxbRLxACbC1uUZ1xlUppZRSSnW2j4F9RWSUiHgIJ1+9mFBnE/BDABHZH/AC21pqVANXpZRSSinVqYwxFvBL4A1gJeHdA1aIyM0icmqk2lXAL0RkOfAEMMMYk7icII4uFVBKKaWUUp3OGPMq8GpC2Q0xn38NHNOWNnXGVSmllFJKZQUNXJVSSimlVFbQwFUppZRSSmUFDVyVUkoppVRW0MBVKaWUUkplBQ1clVJKKaVUVtDAVSmllFJKZQUNXJVSSimlVFbQwFUppZRSSmUFDVyVUkoppVRW0MBVKdUqbzBISe0evMFgd3dFKaXarI+vlnEDvqWPr7a7u6I6yNXdHVBK9Wyjd2zlextKCYngMIZ3Ro5l3T79u7tbSimVluP2+5JfnfgCVsiJy2Fzz5LTeHvNQd3dLdVOOuOqlGqWNxjkextKcZkQnpCNy4T43oZSnXlVSmWFPr5afnXiC3jdFvk5DXjdFlee9ILOvGYxDVyVUs3KD9QTEokrC4mQH6jvph4ppVT6BhTuwgo548rskJMBhbu6qUeqozRwVUo1q8bjxWFMXJnDGGo83m7qkVJKpW/L7r64HHZcmdNhs2V3327qkeooDVyVUs2qd7t5Z+RY/EHY0wCWOHhn5Fjq3e7u7ppSSrWq2p/HPUtOoz7oorYhh/qgi3uWnEa1P6+7u6baSZOzlFItWrdPf373cAVDCw2n3zZBg1alVFZ5e81BfF42mgGFu9iyu68GrVlOA1elVKuq/A6q/DBFg1alVBaq9udpwBpRZ3lYvm1wd3ej3XSpgFJKKaWUygoauCqllFJKqayggatSvZCedKWUylZ6ypVqia5xVaqX0ZOulFLZSk+5Uq3RGVelehE96Uopla30lCuVDg1clepF9KQrpVS20lOuVDo0cFWqF9GTrpRS2UpPuVLp0MBVqV5ET7pSSmUrPeVKpUOTs5TKIG8wSH6gnhqPt8uCRz3pSinVGfr4arv8tCk95Uq1RgNXpTJk9PatHLv+G4zTgbOLs/v1pCulVEd0Z3a/nnKlWqJLBZTKAG8wyLHrv8EjhhzN7ldKZRHN7lc9mQauSmWAt7YOf318kpRm9yulsoFm96ueLKOBq4hMEZHVIlIqItc2U+dsEflaRFaIyF8y2R+lusorz+7CnfDTpdn92UXHL7W30ux+1ZNlLHAVESfwAHAycAAwTUQOSKizL/Bb4BhjzIHAFZnqj1JdZdeWBp6471tuej8PSxwEHM69Jru/txw1q+OX2pvtzdn9etxsz5fJ5KzJQKkxZh2AiDwJnAZ8HVPnF8ADxpgqAGPM1gz2R6ku8czdG7CDhj7njufJQa4u31Wgu/Syo2Z1/FJ7tb0xu1+Pm80OmVwqMAQoi3lcHimLNQ4YJyLvi8iHIjIlg/1RKuNKP93Nhy9s48SfD6H/cB/1bjfb8wp6fdDaC4+a1fFL7fWq/Xms2TJkrwhaNSEte2QycJUUZSbhsQvYFzgOmAYsEJGkRTQiMlNElonIsvpdmtyieqZQyPDkLevo29/DKf81rLu706V64VGzGRm/rIaaTu+oUqrjNCEtMzKRK5DJwLUciP3tPRSoSFHnBWNM0BizHlhN+BdBHGPMfGPMRGPMRG9fTW5RPdP7z2xh44oazrhmJN48Z+sv6EV64VGzGRm/XDn5GeuwUqr9NCGt82UqVyCTgevHwL4iMkpEPMC5wIsJdZ4HfgAgIiWEb72ty2CflMqIut0Wz92zkTGHFXDk1H4Zu04ffx1jt2+hj78uY9doj1541KyOX0p1sqFF2zhh/88ZWrStu7uSZG9OSMugaK6AMSYANOYKxGpzrkDGkrOMMZaI/BJ4A3ACC40xK0TkZmCZMebFyHMnicjXgA1cY4zZkak+KZUpLz2wiZqqIJcvOBCRVHeZO+6ojaUcuK0y+nhFv4F8OGJsRq7VHr3pqFkdv5TqXJcc9yqnHbo0+vj5zybz4NundGOPku2NCWkZlipX4MiEOuMAROR9wmPtbGPM6y012mzgKiLDgDsjF34NuNMYE4w897wx5vTWemyMeRV4NaHshpjPDXBl5EOprLR5bR1vPbaZY88awIgDM3MruI+/jgO3VcYtvDxwWyUr+w+m2pebkWu2R086arajY5iOX0p1jqFF2zjt0KXE/k1/+mFLefmLSZRXZe4OVXvocbNtUiIiy2IezzfGzI953NZcgaHAuyIywRjT7OLilmZcFwLPAB8CFwFvi8jUyIzCiBZep9RewxjDk7euw+Nz8JNfjczYdfrV7mm2vCcFrj2MjmFK9QDjB37bbHlPC1z3BnbQQVVlQWc0td0YM7GF59PNFfgwMqmwXkQacwU+bq7Rlta49jPGPGiM+dwYcynwJ+AdERlDcsSs1F5p+T928vV7uzj10uEUFGdulnFbXupBprlyBegYplSPsKoycSe5lstVr5GRXIGWAle3iERTgo0xjwGXE17XNajN3Veql+i3p5rDvt1I0Y5d/O329Qwam8tx52X2R6Lal8uKfgMxhvAH4TWuXTXbWuQLcdAAO9v2ZdUxLEu5QhZ3vbeAu95bgM9qiH7uClnd3bVeYfzATVxw5FuMH7ipS65XXtWP5z+b3DR+mfAa166cbdUTsbqeMcYCGnMFVgJ/a8wVEJFTI9XeAHZEcgXeIo1cgZaWCiwgvIj27ZhOvCkiZwFz2v9WlMpeJ63+kmF7qgE43JThOxfePGACLncmN+gI+3DEWC74ny1MGgUn3XlYlwWto3ds5a2f1RAMQe4XH2fTiVg6hmWp2z9YxKE7wpMuz756C25jR8uvPvbi7uxa1rvl9EeYODL8tb3gO2/z8YbRXP/8TzN+3QffPoWXv5jE+IHfsqpySJcGrXoiVvfJRK5As4GrMeYPzZR/BpyY7gWU6i367alm2J7qptXmAicfAsHxQldt7rJmi4M1W+DILgpaoydiucEHEDkRq6KwqMfvGqBjWPbz2kG8hGf56509+/stG4wfuImJI9fFJUlNGrmO8QM3sapyeMavX17Vr8vXtMaeiAXhGfsrT3qBz8tGaxJWlsr8NJFSvcTQ3SmSHKWZ8l6iF56IpbLA9UddSFDiD/EIipPrjsr8zGBvNnHE2jaV9wZ6Ilbvo4GrUmkqL0x9gkpz5b1BLzwRS2WB33/4aHR5QCO3sbnlw0e6qUe9w7KNY9pU3hvoiVi9jwauSqVpW0EfyvL6xCVIlRX0YVtBnw637Q0GKand02ryU0m+4YgRpsuSpBpPxKoLQLW/V5yIpbJIHUK1OHSZQCdZVTmcjzeMjkuS+njD6A4vE0g38ak7EqT0RKzep9WTs0RkAHArMNgYc3LknNnvGGMeynjvlOphXlrr5WfF4XWubg+UlgzocJujd2zlextKCYngMKbZ5KfRO7ay6tYQQQvyujBJat0+/Znxu1JG7AP/9eSkrAtadQzLPtcePYPbP1jEnqpNTC8cwLPOnGi56ph/rDyMQ4ZuCG8Nb+DNrw/rUHvpJj51Z4KUnojVu6Qz47qI8HYFgyOP1wBXZKpDSvVUoR1+fla8hVwP+Dzhv/q+t6G0Q7Of0eQnE8ITsnFFkp8S22ysl+uBPrk0Wy9TttcIn2yUrAtaIxahY1hWsRwurj72Ys7sM4hacXD1sRdz9bEXYzkydkr5XqExUSnHHSLHFSLHHeLKk15o9wxobOJTfk4DXreVsr1062VStT+PNVuGaNDaC6QTuJYYY/4GhCC6L5fd8kuU6n2WP76BQMI2kh1NVEo3+UmTpDpExzCl6PxEpXTb0wQp1ZnSCVxrRWQfIifNiMhRQHVGe6VUD7NrXRVPP74Drye+vKOJSukmP2mSVIfoGKYUnZ+olG57miClOlM6geuVhI/oGiMi7wOPAJdmtFeqVyqw/Yyq30aB7e+R122unjGGpfd8SE3IyT8GjaGuAarrOidRqTH5yR+EPQ3NtxlNkurEa+9FdAxTHdbXU8P4ojL6emp67HVbq9vZiUrptqcJUqoztbhgSEQcgBf4PrAf4eXcq40xWXXuo+p+R+5Zy0Vb38PGgZMQD/U/lo8KMr8FS7rXbale2dsbqfx4M+deN5rNIwYx4oy1jOwHl7/YOYlK6/bpz+8ermBooeH02yY02+a6ffoz7co1jCyBS5/PviSp7qBjmOoMJwz9jGuPeArLOHGJzW2fnMU/yjuW1NTZ1023bmcnKqXbniZIqc7S4oyrMSYE3G2MsYwxK4wxX+mAr9qqwPZz0db3yDE2uSZIjrG5aOt7GZ95Tfe6LdWz6i2W/XEpfUf35bhp4ePtt++BZevo1MCxyu/gyy3OVtvcvgeWre/ca/dmOoapjurrqeHaI57C6wqS767H6wry2yOeyvjMa1uu29Y+dnaiUrrtaYKU6gzpLBVYIiJniCRkhiiVppJgDXbCt5qNg5JgZgf+dK/bUr2v//IVNZtrmHTVUThd+iOQpXQMU+02MK8Ky8QnFlnGwcC8qh5z3e7qo1LdIZ29Ra4E8gBLROqJ7P5mjCnMaM9Ur7HdnY8znNAd5STEdnd+j7huc/U2VQlfLlrO8B+MZNDEwYR3UVJZSMcw1W6VtUW4JD6xyCUhKmuLesx1u6uPSnWHVmdcjTEFxhiHMcZjjCmMPNYBv5dyBm2uuvwNrrr8DXLqgtHPncH27x60x+njof7HUheE3Q1Cgzh5qP+x7HH6OrHn7b9ubL26IDQgPNT/WN564EsAJl42Ka7+foPgp9+FPv66VvuQ7olYna3IF+KgAXaXX7cn0jGs87hCFne9t4C73luAz2qIfu4KWa2/uB3Xebp6M3kmlLHrpGNXIJ/bPjmLestNTTCHesvNbZ+cxa5AZv/wbst1m+o6ox8t9XFo0TZO2P9zhhZta7EP3XHSVXdeV2WHdE7O+l6qcmPMO53fHdXdrrj6Tfb7rBKAP059EmcwFC2/e+6P2t3uRwVjeODPKxlWaDP2+pMyHrS29bpj/VvwRX8aDIPL1rPh75s4+KJDyR9cEK131MZSVt4ZebDiU1b0G8iHI8ambDPdE7E62+gdW3nrZzUEQ5DbhSds9VQ6hnWe2z9YxKE71gHw7Ku34DZ2tPzqYy/u9OuEbIuVOzeR43Bm5Drp+kf5YXyydV8G5lVRWVuU8aC1Pdc9qHgDOc6mCYYJxRtSJmddctyrnHbo0ujj5z+bzINvn5JUr7tOuurOE7ZUdkhnqcA1MZ97gcnAJ8DxGemR6hFyGmxoCA+CDTnOVmqnZ4ffwQ6/gwFdFLSme91BgSpO3L2K2BWQp7k3cegEHwf89OBoWR9/HQduq4yrd+C2Slb2H0y1LzeuzdgTsYhsv/q9DaVUFBZlNLEqel03+AAiJ2xl+ro9nI5hncxrB/ESns2vd2bu+yoXA8aAHcroddKxK5DfZQFrW687PH8LZ4z9IG5sOnPsBzy37mg21TQdSz20aBunHbo0rt7phy3l5S8mUV7VL1oWe9IVhGe5rzzpBT4vG53RxKruuq7KLq0GrsaYqbGPRWQYMCdjPVLd6v7bjuePU5+MBq0AttvBfbf33t/xo+u3pyw/6+LhrPY2/Yj0q92Tsl6/2j1JgWv0pKuYMwMaT7rKZADZXdfNhOlLL2Lx5Ic63E5PGsNCbqgZ3Dl/CHaHX50+gyWLbsJrNy1BCTqdXPGTGdS7O+99ddV1err8ivSWaB1QXNZseWPgWjs8j1HDV6asN+rA7azeNDL6eFjRTixcNAaPABZOCsc1UFGVubs33XVdlV3ac/BzOTChszuSrjrLw/Jtg1uvqNrlgRv+ggTiT2iSgGHGle8z6+bzOtR2TSB87FRX/fsd0q8irXrrvCUpy6sPPyDu8euhCXyfb5LqvR6awOZd8UkQBbafk81XcWXGwJv1B7AnmDzzW22VAvDurnEt9tWY91us19brpssyH6XVv3TfRzfr1jEsm931+mJcoYQkoJDN3a8tZtapM7PuOj1dzWBnWsHr1zuHpVWebr1sSEhT7SdBIacyuyYyYrWanCUi94nIvZGP+4F3geWZ75rqTnUC1Q7B72n9bxurOpe6NUOwqnNbrFfiszl8UANFoc5ZcD/S2sbUhuWMtFInGKQbIG/2FPH3wvGYyF1JY+AlRlGZU9RyPeDvhePZ7EkeVGMTvmoC0ICjUxLS9htkuPAYw6BA6m1uotcNQLWfLkuE68l0DOt8dQjV4sDvyuwvvzpgF0Svc8DWTTzw4nx8gQYeeHE+D7w4H5fdsYQty++jbssALH/LPyNFrhoOzNtEkSv1Nn4u24r2KZ3+jfRuYWrJUkZ6t3So/wCbagbwdOnRcWPY06XxywTaUq8p2ctFneWm3nJ1SkLa8PwtTBm+jOH5qd9zdyXCqeySzozrspjPLeAJ0zjto3qdy68/h7m//yu1ayq4cFgJz/o80fJUqv51EGX3no64QhjLwbDLn6fo+18m1ZsS+IobZlUQtMGz5z5m+6byhufAdvfz13WvMy3Y9K35hHsic3KntLu9RN4+OSnLS30D+O72VRgDLq+TUt+AlPUaNS4l64wtRM/f+gGL7og82PQcfy8cz+P9j06q91HBGG757fuM2MdwxJ/P3quD1ggdwzrJ5T/+OXNfWUjtlo1cWDSQZz050fJMXGdP2WrOdnl4bdAoDti6CW8wwOEVa1my6KbojOzcVxa2exa2avV+lL11IuIIYUIOhh2/hKJxydveTdnnU24c89foqVSz157DGzsOj6sz95WFHF6xFqDV/v16xDNMG9T0LfjE5mOYs/GMdr2HRumOMCt2juTU0FKMEUQMX+0c2Up7Qtzao3a64uDnOGPsB9HHT5cezdwvfpJUr7sS4VT2SOcAgr7GmMWRj8eNMe+LyOUZ75nqFpbbyaybz+M/R/an1ulg1s3nMevm87BSrCuzqnMpu/d0TMBDqM6LCXgom3t60sxrUaiWG/0vk+s29PEafFjM9r/U7pnXkdY2pgWXIRD9mBZc1uzMa2tik7MaP07cvSppVrPxhC2fB3JzwNPCCWDRum7I97Rct119JHUfG22vET7Z6NCgNUzHsE5iOV3MOnUm/7nPYGodDmadOpNZp87EcrZn1Vnr1/mxO4daEWadOpOv+w8HEXxWkIJAPT6rY1u9WX4fZW+diLHchAI5GMtN2T9PSpp5LXLVcOOYv+JzBilw1eNzBpk95q/Nzry21r+R3i1MG/R+3HgzbdD7HZp5jU3Oavw4c+wHSTObjSds5TgtvK4gOU4r5QlbTSdxWeS6Anhdqet1dv8a7Qrks6pqmAatKqV0AtfpKcpmdHI/VBYKbClCXPEb94srRGBL/K3zwaFqLBJOdcHJ4FB1u657kJ167Wpz5a0ZXb895YRCYtJWW04A6+zTwppLIGuuXMXRMawXuHrKdCxHwjjicHLVyan+eVsX2F2IOBLGL0eIwO74LX4H5+xMcSqVk8E5O9vVv4PyN6bsT3Pl6STztZScFSvdE7Y6+ySudPunVDqa/TNZRKYB5wGjROTFmKcKgB2Z7pjq+TwDqjBWfHBmLAeeAfGDW0gXDIoAACAASURBVIWjDy4SFtxjU+Ho067rfulMvXY1VXk661zXkHrhf2LSVltOAOvs08KaSyBrrlz1zDEs5Ia6gd1x5c4VCq8gyvx7kabr3Ls4dcLWnH8u5uLpbV8qYBXsxpiE8cs4sMbupi5m56VSRzEuR8J1HTalfYqpa9riOe3+LXWNSNmfpd4RKb+euZWtB6/LXCObLY99beX29JKfOjtJKt2kMKXS0dL9nQ+AzUAJcHdM+R7gi0x2qiV20EFVZUHrFVWHWIHwt0ZrX+vi6a+zc9HJ4LTBdlI8/XX2+J3gb3pdFQVc4zyLOeaJ8BpXn5trgmeybutA7D0+rO19cJVU4yxI7zZ6FQUsch/NDGfTeqlF9tF8tn10Ut2igXta3c2g8i+lDHPCZY3nK0h4zezr1fFrcA/pV8FD/Y9lRtnbhELg8TWf+NSYJDWj7G2CNnhzO5Yk1ZgYdmL1qmgfm0sMU1E9cgzLZm7LYt7jC6mvrOD8/gNZsHg+AJec/3OCrs5dLtCcOoSACDnOcEA24dtNLFg8n8vOnc69Ty5Ouz+uPD/9T1/ClqePByzE5WXIqa+w6Om5AHHtXTfzTG4Z+DQWTlzY/Hb7OVSF8rFqfVhVhbiKdrepf49d+h0uKP539DWP7D6G9Vbq9fJ1A8PBa0s21A/gic3HJK2b3VAf32Z5SR9uXHcON415khAOHIS4cd05lJfETyLUEK43O3Zdb4p66fqawSn793XhYNAz7FQbNfuTbYzZCGwEvtN13VHZJu/IVXgP2Nhq8PmSfSh/++2zjCwxBK/6DTvJp/aj8eGg12WD5aR4xmvkHbkqrevODp7GnBuXMXl0iLLzLmMtLSdJNSewdRdbn/6AL84bTH2wAmPAmeNkuSt5JmD5tsEMCuyEyI4CtoGNu4tZXp86IF7OYG76zb8ZWWLIvf2XVNXnQX3qfqSzVdhyOZP6a27hyLFQfdV/sUH6QTPLeu2Qo9X22iLd9rp6y7OW6BjWMY1BKjQFcRO+3YQvEMRYFt+UbYwGZ/MeX9iuWc+2uOT8n4eD5rKNnN9/IM/k5IT70xBg0vq1vHPnTbgtu039KTxkDdUf3oSxhjB4xo9Y9PRcJq0PJ1jFtsd8OO5n1zPUtZNyq5iqUD67l+/H1uebErvOP3Ugj/PbtPrHfWOYctFvOCRnI8sbRjQbtLbFFzWjOMN8FE26Wl4zKmW9xlQrY8C0kNH1xo7DWVo9jsE5O6loKKbK6th60zkbz+BvW47loPyNfFkzIimoVipd6WyHdZSIfCwiNSISEBFbRHa39jq193AW+MkZVdnqjOn2GmHZBgc7ycfe42PnopMxQTfG78UE3excdDL2nvRnJFdvcfDov13tDloBKhb+g5JCw/0nbI0mXeVgp0weiyaZ5UCBF3zN1Ev1nqscnXPqy+rN8Mi7sMHVr/XKCtAxrL3mPb6QSevXRoOuSevX0qfOj8e2yMXQ14Q6nCDVFkGXi4unz+T0geHEsIunz+SrIU0JW4X17UvYEudOcjxfsOjpuUz4dhNgUrZXFcrny8BwqkL5yG43Dz89l1eCp+JrCPJK8FTmPzuPX/xkVtr9W28N4PnayZ0StDYmkOU4LLzOIDkOK2UCWVOimUWeK4DPmbpe9D1b+ayoHd7hoLXRhvoBvLR9sgatqkPSSc66H5gGfEP4FMmLgfsy2SnV+1nb+4RnWmM57XB5F6n5YgPV763k8LMOxpLWk8c6O8lMdRkdwzogNugKOJ2YhK3dgi4nl05rX4JUR1127nSCrvifyfb056ktm5m0fi2+hga8Vvy+q6nae/CJP/N98y7f523KGcr3eZvvm3d58Ik/Z6R/rUk3gSzdekr1ZOkErhhjSgGnMcY2xjwM/CCz3VK9naukGqyEhAPbGS7vAsYO8e38Jbj798H/w2PTSh7r7CQz1XV0DGu7VEGXK2QTdMT/2nBbNvc9sbgruxZ175OLm26/d0J/fLadtB9qqvbEFQ5uc/HTl2py8ceVZ6p/zaloKE6RTGVT0VDcrnpK9WTpBK51IuIBPheROSLyK6Bz7nuqHqsk1+aIwfUU03QLyd7jo2H9wDbdzo9rM98wcWSIYmpwFvgpnvEa4g4i3nrEHaR4xms4C/wM21PDMRurGLanfVtHpWPH659Sv2Ergy86gWpvH2b7prJxewlvr5xI2e6BzPZNTbq9X+XIY7ZvKnUNUF0Hflwp66keR8ewdkgVdDkMeGw76USrRG7LYsHi+SxYPJ/chobo526rY6dcJRJj8FgWNrALwUbwWEEmlG9k8eI/McmUsnjxn6LXtmp91JcPwKr1Rfv4fGUFM0v6407YEcAA/kiC14RvN8W153JYWO6EmWe3cPmFFzK6Tz4njRzNkPqm8asOYVcbTxqL/Rq2dhJXlZXP7LXnUFY1kHe/+Q5lVQOZvfacpFv8jfX8tps9lhe/7U5ZT6meLJ000AsJB7i/BH4FDAM6dsSH6tGmOj9nzhWbwjsA5NzONcEzefKDc5MSqfoe8RUL7otk8V5yAfPmPQbAxZdOT8roner8nDl3NBCwIccbbvOlI0lK7JqyrIZHF17LV84AAdvDhT+/ndcndu6gau2uo/LRf5F/8Aj6HD0egJXvHMP+f3oMtzNA0PZwwqx58MNdSa99w3MgCy9/jpH9IP+Pl2rQmh10DOuAuCx5Y/DneFjq93O228PLI8IJQJecH39yVuP6WKBdSVOppNrR4MBvy6LZRo0rGBwG+tb7OWpjKUfdWgo2BI2T++b9lRN2/juaTPWPoskcs3U1YPimfCOJG075gXpPONkwP1Af154JgkmYm/WYII8/tIDJ276g3Bkg8IGHv53bADwTl7CV6uuVSuzXcMnG1k8KW/nlUez38v/hdgYJ2m5OmPowDAok1evspCululqrgasxZqOI+IBBxpibuqBPKgPclpVWkFlMDXe6n8YnBtwAQX7t/xd/WrQIE3RDMDxjsHPRyTz17rVMXrsegI+uuRVX5JfTgvsWM/1XF6VoE8JnagW50/0079tj2VlANKlr2J5w0OoP5OKP1Hx04bUcvt/tlBV03uBa+fjb2HUNDJ75I0SEvCp480+XxF33zQcu4bDDbyPVtoXb94Q/DtGgNSvoGNY+qbL4G8tX3XYDQKtBqM8KRhOS2jLbmMqDjy7g6HXfgDGUlm0gH3CY8MkhAvSJfG4ijyUmZnO5bQJb98EYd/SskQlby3BEws/GoLXxudgdmAVwSyiuPSRctw4IADkuN4KhomI0ftM0jix88nes+HUOHz0WHnvbE7SHv34tfw3zAiHefPlnkTEs7M2XfsZhM+ZR60m+sVpl5WvAqrJWOrsKTAU+B16PPD40YTNvlQUW3LeYyWvWM3nNej665tbo543BbKOhUkUwYe5h7bZROFMkUpnIXq+5wSCF/npyg6kzelO1GcTBUIk/qGD4ziAeZ/wMgdsZZPjOzstc9q/bwo7XPmWfU47AN7I/AAWVHtzO+Gu4nUEKKj2ddl3VfXQMa59UWfwXT5+Z1n6t6SYltWVJwYGby3EYg5NwkOo0KY67a0bI4cRpLF7lZPKo4VVORlIdl0dT0OoGfIEguYEACUvbIQe+HDuMd0QY7snh41FjWD1kOOfnPBb//pxBhtY2swdeK/7rh1MIJCxfaO6ksIJ6ST2G1bew35VSWSqdNa6zgcmElzRhjPkcGJm5LqlMai3ILDdFuBNG6TH91mOnSKT675k/xUr45WS5nPz3/1zYaptuQpSb+OnMTcVuAnZ8sBi03Wwqjp9lcFsWi//wEK80NJBnDIv/8BCL//BQq+vnjDF8O/8NnPleBp7//Wj5noEBgnb8NYK2mz0Dk2+zqaw0Gx3DMiZV8PnPu3+PO2gl1EtOSkq15dak9Wuj+8fGWjmgDXsDJ8SkzoDN8fwrbheAIpo/vtRJJHC1grhsO/lIaBtGfBt/3HK905Fy/CrP87ba3divobN8Ew/cfiPvPPhH3KH40/dcIZu7X0tO7NrjNanHMG/6wb1S2SKdwNUyxuh+P1nukksuSCvI3Ek+1wTPbEpAMm7m+I6jeMbrSYlU8x/9c3R5QCOXZfPgnx5N3WYAaurBb1xcEzyTncTfqioryOfCn9+Oz1NHoa8an6eOC3+evEygcfb4+yFDRaCBI9esi84eBzYXUfP+gQQ2h4PiqsoCxhUHOG9CDfss/YjarzYx8ILjILQPdWuGYFXnUlsEJ8yah89TR4E3fN0TZs1LuUxAZSUdwzIo5X6vfn8kiSu9pKR09mHdv7Ii7T4ZwpvrGwcYd7jAQShuFwBHMzOuiXOURoSg04Fxg/GB8YCxoNDv53vGsCnQwKT1azl400be7z8xbvyafv4dfOtNfUu+yFHDQZ5NFDlqmPf4QiauL2Vi6Rr+/eADHFNTQzGQA9S7XPhz3DQkzHRbfh91WwZg+X3UehycMPXh+DFs6sMplwkole3SSc76SkTOA5wisi9wGeGjFFUWmTfvsWaDzNj1qG7L4sw/fMK/1wg3j3Zxq2s4Z/IJr186IfmErMh3QZ2Ekzc8zpbP0248caolr0/MZ8gr+zJuwHCqzz+R15tZ2+oihAfC9/VCFgG3k8Dmfai8/uJonbwffMJ9My5jxszG8xLf5E+zvNyR81O+/tl/Iq4QxnIw7PLn4Ydf4ps/ggF9RlJ0xxQ2adDam+gY1gUS17P6PW6WhUyLSUmXnTudd+68KS5gbW6f0/yGNt5yN9BgwBNKDkYBLBEchO/E1AL5IjiMSRqjgg4ntkdwjg7g/x/wzQP/Wg/OgCHXCoZXs1pB/C43O3PdDO0fHr9qTz6Ff3j7puzaj/M+5daSvxLEidtYfLPbBZaQS4jcyGKFOsnBkWuTM8YKf8feC5sb+nL58T+navV+lL3VdGrXsOOXwLg17DPku5T4BpJ77PFs0qBV9VLpfGdfChwINAB/AaqBKzLZKZU5dSLscjioc6eeAWmczfxO0PDC6iCHrdkUnc1MPCHr4kuns3TcKN71eRkzejhLx41i6bhRXHxp/C+dxuSs6IlTYnGn++m4rbZird2+k9dWfN5sQtYNvzgVt50chJ+982UiaRmAUPvWERxZuRMRoh9nHZhPxf3/iQl4CNV5MQEPZXNPx6rOZWftdlZWLNOZ1t5Hx7AMam496w+uvr7V9bHN7XN6/+MPs2DxfF4JhJcDLVg8n1pPTrN9MDEf0BSoegn/kksduDqwRagCxgHVPh9Bh4MGiN/uy4TIcwRwOiA/B5wCvlEBLGfqgxjWbtvJa1993uxM66DADu65/jF8lwQp3F2P738s9t/dQID4bQYDxsv6a0chvwHxgfwGBl2/i35ST9lbJ2IsN6FADsZyU/bPk7D8Pqoaqvlm12qdaVU9hohMEZHVIlIqIte2UO9METEiMrG1NpudcRWRR40xFwK/MMb8P+D/ta/bqie4+NLpLLhvMQ3ryzhv8ACeyfFEy2NJyJBjhc+HygUIBrFFkFDyXGnQ5WL6ry5iy5z/A4ibuY010t6Oa05kvdvlwFxwYTFy1nZ2Otue2frSdffHp/0CYuAb9qM/2+LKl649kgOGro4+3rB9JC5XEDvQtBZNXCECWzRa7W164hhm3IaGgV13TGpnCHnCP/ux/TaRuKhhYJD5f3g4vA40hsu2mfvMw0xJ8dr4tkMYSb5rc8DmMorrwn8gbwo00OebVS32MTaETAxeY8XuAuCxLJDwhr6rgZxAEFscOAmxGzjY7WGF20VBvT/8J88qwn8CWeAIQi4JiaSWzVt3/Z6lgQbOdntYsHg+kDzL/OKddxFN/Z8V+Xph6EP8KcQeAhT8Xy3cEf8eBgfrwzOtse/fESKwu7DZr49S3UFEnMADwIlAOfCxiLxojPk6oV4B4fsKH6XTbktLBY4QkRHAz0XkERLGAWNMq2fEicgUYC7hte4LjDG3N1PvTOApYJIxZlmLbQaFnMqObauyd3Izc9p/U77gAbBg5oz/BsCxPbyOqpErINEtZho5jEGk/Yv8r753Ca5vIq+PDPwuDFffu4TzftW0PUxufT3v/eYOQv56Dsrx8Onl4Z2Ljr3jN9R5mxIcQib9TNnJY+J/Dkb224BlxX//GMuBZ0DziRoqa3VoDMvE+NWbpb1kKEZzf1AfvbIUCP+DNd5sbxyBGoNPj9tNTjCY8rZh45ZYiZzA+BwPK1wunCEb2+kgN2hFb/fbkc1g84CVwQAeTHg8tCKNNsaqkqIvloXHb0XXvXo2hPdgfeCpBUzxGEINAb59cTFBf0OzX486fATw4IlcqG9u8l7S6wZ4MSb+XRvjwBq7m9DHkXYGNnsJpbrSZKDUGLMOQESeBE4Dvk6o93tgDnB1Oo22FLg+SHj7mNHAJyT/UTu6pYYzFWmr1KxaH1ZVIa6i3bjy/M3WK3SMoNBxEDnbcmjo15D02v03l6d83YQNFTjXFqa8hiMQ/taI/YMitk074CZonHiCdnTgD7qd2AF33Gvev/UmiurqANjcEICGcOX3f30HR/3ulmi9ky77HR/ecQPOmFngkEM44tCn4NOmssIjP+N9j5P9Y2LuV3LH0Xf66+HDFJw22E6Kp7/OHr+TkgIY2Q+sSklKHEulqrKg1TomEmS3VteKbC2WTpvp1Ev3uunK1PvIsHaPYTp+pa/Fuzn3PNTiaxvv2lTe+hQmMIQLZv0QZ4Gfzy6/iaLauuT6Dgcf4OXyotOYV/QZB1ZsjM7MxkpKsIr83wmsarDxhAyWw4HHjr91Y0QIYMg1TXec6lwuckJ23PZb9V43H48cQe3KUs4SeHncKA7aUI6vIUCuZTW91u0m1BAguGUboT21WJVbOer4o/nmn8lLrN/uOxH/rn04i6d4irNwFu9m828MPzUfRus8svsYvvXm0//0JWx5+njAQlxe+p++BFeen5JcmxFFQQKOGqpCuk+ryrgSEYn9Y32+MWZ+zOMhQFnM43LgyNgGROQwYJgx5mUR6Vjgaoy5F7hXROYZYy5Jp7EEGYm0VbLdy/dj6/NNC/X7n76EwkPWJNXzvXYg36y7gi0Ac+HgY5awZbAd99ov+z7I9+qWJ732i/xxbLjrolavkao/M/6jhM/LLgwHrhH1TnfKBAxIfdsv1t1PP44tTpw0bbdji5NF317ND53/jp6k4xv6LW//vZzzzwy34/A4+SQ0grwjVyUlmk11fs6cucSf7GUfmrJ/Kjt0cAzT8StN6S4Zak7tR+MJrH8HJEjFr30Uz3iNlUMGcvSadUl1Py08kBN3LYcd8P0dsMPVl6b77vFM43J3J4379+MC+mJD0MZGkiLcoNOBOxi7WhbcdoiAy4kv1DTeGAtCDgenOCT6nnPrG/jomlshZlu+QMhmaukGQiI4i/sy8PrL+Oi6u1L2d4J/Nf2oBAynuF5inzNe41zHk5xtPsYgCIbPGsInlRUesobqD2/CWEMYPONHuPL8/DjvU/73mo0EQ+Dx/J7fbj+HV2sPb+Err/ZWjiDkVrZeLw3bjTEtrUlNdeMj+sMlIg7gD8CMtly01RXc7QxaIXWkPSS2Qmyk3c5r7PWsWh9bnz8RE3QTasjBBN1sff4krNr4hf4523L44v0TiU1e+uL9k9j6XPxr67f1S/5WE2jYWtLqNZrrzwPP/zllAkbino4/vvTXKd/jKZf9Jv692BaW7aYOH7voQx0+LNtNYMs+YLvBcoPtZuvzJ/K/J3nIzYG8HPCJHU0Ki000i00e65sLPgm2mDymsks7xzAdv7qAvccXvvthfBAqxATd7Fx0MpNKN6Ssf9iuVcSOYR9bR2LjwMJBNYVYOAjhIJQryEEgfwbZH+rHe6gmfg1oCCcBpyuaiFXnduOxbBwJA6DDgMey4+qlkmrnFo8d4vmSYjyjh+MqKcZZkBeduU1MKDMNHsLzvHlgudnz8PHceecz+O60yQ1Y+O60uXvh4/QLhZcPiHMnjpwvceX5KXLUcGvJX8n1GPp4DT5HkNtK/kqRQ8cw1a3KCR+x3WgoELuvXQEwAfiXiGwAjgJebC1BK53tsNqr0yJtEZkJzARw9dEkmlhWVWHKhfpWVWHc7XzP5j4pXy/Eb/9iRAg5HTgdofB3hwWhkANjS9z0Z6prNNsfDMZIXGJEKq/cNydl+av3xi8VmPcfZ3DB/csIhtxNt9XE4gzXU9GZFYAiezPDrtwe/k6cA/wavAQZf08FH+SNi9ZrPNnLF/PixpO9dpqm222Nx+b6gbOAxX8I3wZNPDZX9QoZGb/c/QopGrink7rYNXZ4wjOIsf0uj6x5jy1LVS9VWay63YWI2w4fJx0hbpsat4+iutqkOy67E4LPqbzEi3IqxjiiY4HDYZNz+U6Om/BJuNJvoO7GXHzEry0N4qLe4+QjK8hZwBsHDWP/VVvw1tmAxKw1NdT7nCytr+PsHDev7T8UgKuvPwPOuD36/kINAbCspnWvDgc4HbgHlCAxhzFccekl3HXHIgwOJvAlX3EQEGK5HMyr5uTo+5hofYxndTA8Y9yYFIbhvscWc+5PL497L0NdO5PGMAsnQ107qQrokgHVbT4G9hWRUcC3wLnAeY1PRvbXLml8LCL/Aq5uLVcgk79t2xJpAwwkHGmfmtjpyJqJ+QC5/YeZTpri7hU8wd2U2wkL9W0HhcHduGK+Ts6c1Puvm4REp9Mdz1G1bzE+dyC6A0Ag6OL0Nc/FHXsYew1HZN1qbmXq/pzmeI43B06m5tuvw78gBo8B4MoTfp7ydkVraWBbvC4OCX0OCHnUMJFlYAyOhMzmCvZDGpewXdVU/sivHmbs/Nuij9M92atxqzAIf3N7Ip8vuG9xm2+P9jaNQX1D+WbOGzygNwT1mRm/9h2sRxnF8AyowlgJ45fl4KR5V/H3K+7E2uNnArCqwAchYWTt+ri6QTyc4no5Gviewms4nA2sGzA0rl6upw6DMyn5acXYwZzyxTcAzLr5PNjh4bbp7wLOaACJhPjtvGP58pL/baoX12HYOOdZppRu4EWHIC4nF44ewHORE7Ouvv4MXNc/DoQD3C1uYbinDH8gF4D+bGMJJ3C8eQsDfMsQ8qjBSSi8e4pNU1KYB+pdTuY/8SANWys4b/AA5j/xIG5s3NdYcfdQXWKzrriABrJrFwvVexhjLBH5JfAG4T/BFhpjVojIzcAyY0y7jt7O5G+UjETaKp7L52fY8Uso++dJcZtRu3zxM6GBPg0cPnkJny49KVp2+OQlbO9rsekfJyESnhUd8MN/Mfs7ZzN71OPYIXBd7WT22rMZ8O9/NXsNYxcTsodg+X1N/Ylpc9gP/o5jTYjwIYoHEbLrcTgT9rMCTrngOl575BaMEX580hxeWfJrRAynXHBdXID7lwV/oC/hxI1KBkXLNzhG0M+xAxGLUNAgSQeMhxniE8lMvQ/7HsE4YPdsKJwNdkgwV/rI8TbVczYIOcGErcKQcHnCThduy2Le4wupqw9vjfPoHQ8D4a1xUgVyqRLcYhXv3sUHc27GAAcDX14cXj5x9K9vYGdh8ibnjQF7R3fgyK2v55/33ILtr+dgt4fPLgvv9HD8ldfF7fSwYPHDTFq/HmNZrF27iZxIZvnCux/l4ukzU7bdw2Vk/Mp1BTikX/onQPUElZ5w1BTb768doaSyVPVSlcXpB8XXP8wHt8zA4bIJWU6Ovm4RoyaUc8Wb03jkyPDRr1e8OQ2Aibe+wTsvnBF9+fdOewYzcQfvXn8hEMLhcTPkOzdw7d+2s/AXYIfA43Oy+O4jOezSHVStHcI032M84b+A4tHl9PVV8ypuzmICi258FIfb4qfn/ILlf70GCDE15wWOvu5hDhm/lBVSjAmNYD/XbrxFNexaV8XySIJozbI1jJ9+MA+fN4G3rl6OHRjKvbfuh7eohgPZQnns16EfXH7Zdfxx7i04HSHskIPvON/HUR9CILotlon+p0m9y4W4Qxy5ch0maLFu3UY8kT10N80tZtjl2wja4PE6mZ37H5i+hiKya4Zf9S7GmFeBVxPKbmim7nHptJmxwDVTkbYjCPkVqQOSvVV+/koG/mgD9bV98OZV487xx88NRWwzQRALh4QIGQc1JoC3SnBEBkcx4K0K4asKYUaa6AIsb1WIYc1cY1vZeOq2vA0EWbnIx9jDX8cbe0SWgYVLf8/he9ZhcFFOKZ5vg4jY3P/MQ1x97MUxPXRz1EF/pfTTHyGvhehnfsHYw1+n3/bV5McEoY0n6CQmcRXatUw8+f/4+v03qa/7nHOOuZRn/zU36etw6vm/jQuE31pwC3n14V8shZEUmzwCvHXXLfzg4qYlCq56k3RMpAODq8EkzRw/8OJCDq9Yi4lsjZOzPrw1zvyFC5l1anIgFztrncoH998cXdn3VWz5nJs5/Jf3JL/AtNxeut5acAt968N/JFQEAxAMdzTxa+OM3IXNxZBrDFgh/C43zoZOSwLoUpkav1SyUSctZdCkr6nZXEL+oO14i5pflxk6Yge84EcwiNuFmbiDpmR/g4hQ+VklRacXYQhvcWeMIeR28KfHj6K+Kp+jN/8fDw/an/83+2v2XbaNMbgoZy2eT4I4XBb3l/2F73INYDCRxte9MRmr9j4gyNOneikedw3bV4SDagT+87mz8fb1su6NyWz7agEiQZ45LScchJ+0NOl9vFe4Hw0WOB1gnE5WDh7JxHXJe9Umbetlwfh1WwDINYAxELDwe1xsl3x+9ICb4X0tnJeeR5Ujry3/DEpljYzew8tEpK1Sc+f4w8FkM4INPko//REYN427SH3zyRQECIWaZuVKP53CRT/9L3Kj+/Nb/PaIp/hk677silwnqU3cgI+QDaWfTAnHuyF3NMSr3V2CwUkufnIjGcB1xpe0F2FjeyE7vj99+2+Ku+7nxWOYuOObpPf4efEYqrd9SG31C4w57Ez+9vZ9Kb8WLz12G5NmNWX25teH204MhBvLG43fnnqrsPHbksvFhPDEHuRghWdnxSTPNLdFe/eAtAAAIABJREFUazsuuGyLua8sZE+wgbNdHh54MbwzyeU//jmWs/0/7q1d9+op01my6CZiFxlbDidXnZx654hsoONX1/EW1bQYsALUV+Xz7/+dAeSEx5ggvP/7GYSXaoQHLLsB7IZ5/PbI12PGsBAXbX2Pr3MHQxHR64SCLkKWK35csnzsqRhB5KeWUCD5GqEAbP/qdsaf8wWrn3on3P++3qb+mRyM8WE3wAe3zGDQpPiNKBrrGZODZQM27Fo/mPAJB/GMhPeKDYpEZ1ZXjRnIhDUVEGhaN2u5nFz1uzPZPvtJtvudjNWgVfViei7cXqK+tg8i8UGTiAlPs8ZwOizWbYvf3tIyDgbmJW/Qn6pNJPmwgnMcTxDAE1cWwMOvxl+ZRh9D1NfGJ5aNrU5923FsdQUbvnyZvD5DGDByUrQ8MXs3UW1O8u4IKctNMy2kKBdDytnZ9p7jcOY5V6Us/8m518Q9nvtKeKb3e8awKdjA4RVrObxiLXNfWdiu654xLfVODz85L36nh7teX4wrlHB6Usjm7tfid45Qqr1qNpfgcMV/j4nDII74McPpCLJhZ/wYZuOgJBgfGP/+4gsIEL+UJoCHc9x/ib+GhAhZ8etEXbkwesoPW+2fw2VTs7mk1XqHmuQtCAFCTuG9PC/j9hvCpxOG8+mE4RhI2r3AZdncfevTKdtQqrfRwHUv4c2rTnHaikQ2O2xih1yM7he/f6JLQlTWJu/mkKpNjCQlfP01NC2aDNHIQ4A/rIq/xZ26jw68efGJZfl26pnlPKuOQH01ow89DREHU398PZb8//buPUqq8sz3+PepS9+AphEUUFFBkATRKBA1xjFjvARjRk4mEjUnXqJJViYxh0mGc5bGy7jURE00jifhZELQmGQmRsFcONFgjmJM1MGoSFQ0GEAEREAE5NrddXnPH7u6qSp2de3qrsuu4vdZq9eq2r33u9+3L28/vfd+3idCNzB5xBiSFiFpEc644sbc47o7/dvL2z6427/ijd/2gldnC2wvZsEDd/pu/9UvvuO7vQ2v4lBrcmCJGQ/d77/Sw69+frvv9r2xODubWrwa7yJlNHj0FtLJ3KpcLm24dN6cYU2MPTg3iStKmi3x3Oz66+f9B015iUtNdPNgIjf5KtXlcPslwEYZPHpL0f6lk8H2W8oJpA1SBnsGxUgZpA3+OmU0lx7Twe5ohK/c9Bm+ctNncJm1Y/c2xdjZ1szeprpMfhTpNwWuB4h4817GT1lEJJogGusiEk0wYeoixk9dhFkCiyQwSzB+yiLm/e0MOpNxdiWa6UzGufWFmWz3WVKlp01vAfAdRKIJxk9dxISpiyDTJpZg8JDNNNNJigjbGUKKCM10EsnP5Pfp4/gpi4g37yXR1crOraNIdLXy4ghvVQIHvMeQ3uuaf8AxYsyJtA/3Fune1TSYMz55OweN/CCrmcqJV8zhg1+5g12teWMpdPs+b/vueLPvbn7b/zricJ894a8H+28PqtjV49nTLyMZyf2jWI5b9sXOO+u8K1h66NEsPfRozrn8X3tfzzrvigJHiJSmZdguTr3uPrC9WGQH0eYuPnz9fZx63Y/xbqh7H8dd+T0Wjn8fe7rhvb3QZVHuOeQ0dkZz76BE4kkisWRmPeh29tBKJJZkyJi1Oe0d/nc3csrV83LOe/rV87j2xod4OO0Y5Bz/MutRrr3xIU6/eh7R5i7ig/YQbe7i1Ovuo2XYLtKJ4SR2f4DObYN7x5G93+3/+kmWn3wYy08+jH/+7cW9r+/67tmkE8NJ7Tme5Hve4wuzrr+w9+rrOT+Z1ft61vUXVvX7IVIr+lftAHLwmBV0HLI2J8Fq1bIzcC7WG5HsePdQHl9/Ii9snsCoQdvYuHuYb9Ca3eaGVTfhUodx7Gln97aJi/XeQU8me7LP03irufZkgwXr4zvr3uclbFka5yJcccJo5tk3cC7C1yZ+nbtWfJfd763l/GQ3x00+L6e93uQxS/DqT1oZ89HfM+yY3IpfhSqr529v7/a/0uu33ZmRxohmhXppL6WkwNn69tHLb2Dxfd6qAqcMP4xn332rd3u2vm7Z+yWFFXPupd5KD+A9NtBzBfbcS6/L2S8ZjeW0359ziRQz9pw/89cHv0eq+3DOutvL2l/ynYuBfUHp3q0n8uyQFdxyzdMcOdwx9Uef3i9oBfi3O87in2c/xoY/v8OFTZN57Oi32L1xG+es/WJOe4NGncMxn7yfVQ9/sPe81974EBNf3Mh4YJ2D1he9DMR/41a++ZvXcxLNvISt7++XsJWfkHbnxz/We8477/Zee8f+wJu/PtfCmFm/ZthHXs5Zkmu/5blEGpwC1wNMdhLXnh3D2Lh6CtkR5MbVUxg9bhm002fAmi0S2QqRrcSbT/Nts3PvULojMVrTCYZmrrJ2RuMFg7jsPvolbL227B+YNf1t4s17ibGdL7jTWP70jzhi0nSaW/c9D5uTPOZacUlYt/gchoxZl7NcWMoixHyuuqYs94ZEoZDTd6V6M7ozy14lI9HeYNJZ/wLX7YM7mHLVd1n5yzkA/isJZNmDeQkd0UJheTCdTS05qwdkvxaphUj8XSLxd2kZdhjb3xjJ6wvOJPu38PUFZ/G+C55gyy5jyy7jGJ+gFSAVj3Ln3R/j52f8lFT385z0qiPSfCxpvpbT3or5ZzLxU4tzztujLfNBV4quZu93LTvRrK+ErWIJadnH4lpx3bDu7v/GkBNWERu6p39fPJEGoEcFDmA7tx5a0vb+tjmT+SQsNzkrYVGuO+XSou0VS9hKp1Osfuk3tAw6iMMmnF782Eia7h251XfOu+Qbvuc+99Jrc96/MPIo3/38ttfq1nnPeZ9qbuGYkUfqlr00tC3Lx5W0Pdv21dv443VPkNyTxCUdky89nqlfuSpQe9+/9aOk4rl/PlPxCN+77aM524ImbPnxTUaLpenepOqRcmDTFdcD2JCD/LPzC23vb5vzmUnc5SZnxV2KW5b8NG8d1/0VS9jauPoZ9u7czPtOuYxINF782HSEpvYdOdseeND/6uX8B+7MucL4gc1rfffz216rW+c95+25Mqtb9tLIRhy7uqTt4AWsL927jDWPvUGsNUYkHiHaFGXKl6ex/Q3/3/ERx65m1cP73l91zWKiibzVDBJpvnr14t7b/BA8YcuPbzJaMkLTyP1XeBE5kOiK6wGsrX0bo8YtJTv1ZtS4pbS1939i9GuzuXUHZt7t6/csQmc0eMZ53wlbu1j72u/pOOQYDhp9bMFjYS/YTiyW8K0q1qNYAlKp+9VCLJVkzsK5/PLdDQxKp5mzcC5zFs4llkoWP1ikznSM3cThp/+G7N/KiTMfp2Pspv327bnCuvAzv2L90+uYfOnx/OOvPk20Odr7ZEDH2E1MnPl4oPbAS9/aDr2PCeTzS8TqSdgqJvvYSFsn1tTNmFm/1mMCcsDTFdeQS3S15larKnN7R5/wBKPHLWPn1kMZctCG3qB1z45h+20rJJ0+CJc6jERXq2+bNw0+n9ue2crObWu5rH0kv4x6WfhXn3p5oD77JWwBvLl8EelkN2OPPz+zQLj/sevX3kw6dRhHz/iYb9Dak4CU7NzD8bEmXs08m5qfgHT2Zdf5Jkidfdl1+U0G1lMwYPe7G7hk2KiCBQOC7tezjqtLJnl905u9z7je/bB/xS6RSkonhpPqPrw3m74SbTYNmg1cw5AjzuaMbw/uDTLT6WNwqZNY96c23nj04d4rrJMvPZ5Jn5lMS4eXNOrcCFz6yN72Tp59PxM/tZgty8cx4tjVvkFrT2LX289t4KKWKE8eN7J3e75SKoMVOvbFV99P08htBYPWWCLF3Tc/wO41m7lkzAjm3OCtRTvr+gtJxgf2nLtI2ChwDbH8bPrxUxZx8JgVZW+vrX1bTnC6atkZmQQrz6hxSzn6hCcKtrlr6x+BBM8vavVtM0mM2ad9npf/+AOAoo8H+MmvDLZr23o2rfkzh44/jbb2kX0ea9GtRKNbibWe7vv5ngSkl+d6z7oWSkD65uIH6YrFcckkj219m67MWqXfXPxgv4PCoIFmqQFpG84rjJApvSpSbYWy6cvZ5smz57H2iZ/SOjxO24j5dIz9OABLvnMxqT3zAHhiNhA5hsmXzssJWHva6ynl+tCMlt4+dozdVPAqK+xL7Lr/zJ8B5Dwe4CdIZbC+jm075q0+97n75geY8spaXCLJ6yve6q2ydffND2jVAWk4elQgpLKz6VPJZtKpOCuXTifR5Z8hW672clcF8D42rp7Cnh37JwTsy9pvBdoH3MegnHOs/suviTcPYsz7z67oufy04Rjq0gNe3L8/bRbbr1LruIqUYl9GfCsu3U6qq5lnbrmczm3BVioJ2uaS268k2dlB6/B9c07uSgOZj/RVjDv3vJygdV8J2TZgaFn6WGttDoamHa3dejRIGpcC15AKWv603O2VstJAufsY1DvrXmTn1jc58thzicUrGyRnq0RQGLTNoPup9KqEwUCy6Utp06W7GTRyMvFB+1YtCbrSQCX6WCuzr7mAZCxvfohF+ZdvXFCjHolUjgLXkApa/rTc7ZWy0kC5+xhEKtnFm688zOCOwznkyGkVO4+fSgSFQdss9dwqvSq1NJBs+lLadOkYY8+N5iymHHSlgUr0sVbuuHUBsWTe/JBMcee3FtSoRyKVo8A1pPrKpg8qu0xq0HKqpaw04Fvy1aePsXSSO56ax4L33maQS3PHU/O446l5xNLBbmf1HH/HU/PY+urv+XXnDn5nEeKFSrXmHVcsw74nE//hRBeDnCuaiV+JoDBom8X2U+lVCYOBZNPnGgFM8y2VatG9wBVM/MfhOSVVg64M4FdCtn99DI+9TTF2tjWzt0npK9K49NMdYoWy6YMolIhVrJzq+CmLaB/+NpvWpPAmfaN9eOF1Xf1Kvua77Zn7OOHd1aRTSV7bupbmzC3v2565L1CiVs/xzjme3byCJozIjreLHp993kCJT86xNtFF84ZVvvvNOu8K7n74XsC7dX/Hop/0bu+voG0G3U+lVyUsBpJND17iFHwP6OahGa05pVJ3vjWcJ6/5MUOP6mbTsht455X7cpLADj5uFSsWnAY4IvEohxy/qmAf80vI1qNZ11/I3Tc/AHiPDdxx64Le7SKNRoFryOVn0wfhVyZ15dLpdByytmg51b+9MB3DuwXnd6yf7JKvfenNdE+lS1rLtUdrOpmpIO7oLOG4oBn2veUbkwnf/SoRFAZtUwGp1KP+ZtPvS5xqBtr2K5W6ffXf2LN5Dcd97vz9Sqo+ffPl3vJ4znvuNd2de2w+v1Ku9SYZj+asHqCVBKQvkQQM3pAqvmNI6VGBBhQ0acp/Pwfm8rYNLOHq+lMuIWG5z5IFLfk6kOP9jhtI4pOIVEexxKlVD/+N+KA4HWNP3L8sasRhkXTBY0WkvilwbUBBk6b89zNwlrdtYAlXNy/5GXGX+8elp+RrJY/3O64ciU8iUll9JU4l9iR4c/EajjxrLEOP2uaTsGW4dMT3WBGpfwpcQyQ7SWogehOxIgki0W4iEf9ELL+ErQlTFzF+6sCSwgrpjMbZGWvJeUyglDH7HR9EsYQmc46mZJIU8B5GCqMpmcRcGIu6ioRTdoLUQPUmTrEH2EW0ubs3cWrl/91Gcu8JHHH6VN8ksA9ffx+nXvfjMiSGFefcCNKpqXW99qtIvdEzriFR7ipZYJnUKi/Fqq9z+CWA9TcpzM/Vp17Obc/cB3i3729e4lWb+fyYW3lt0SeKjrnQ8cVKxvYcl2qxIslUWQFq9hcMBa4iQVSiSpbL+j10mTerHz2J5+66FKybJ7/RkpOwlZ8ENpDEsCAKVd0SkcpS4BoCxZKp+tueS8d7Q6+VL0z3FoZJ952w1aM/SWGFJCOxnOz/2ad9nkRXK68t+kSgMfsdX8p5dx3q3UoslNDkLEJ3LEZrMsHQzB/IvbE4znRDQqSYfRWt9iVI9ZUMVVKbNAM9CVafw6UduGZg//Pkn2sgZVZL7V85xiwiwegvcwhUo0oW5rzEqzKdY6BqVXXLj5KzRPqvWlWyLJLGIrlzWK2Srhqp6pZIvVHgGgLVqJKFMy/xqkznGKhaVN0qRMlZIv1XvSpZkf3msFolXTVS1S2ReqPAtQqKJSCVo0qWX3sWSWKRBBZJMn7qIiZUKOlqIH0MS39AZVJF/BRLuipflaz92/Sq8u0h0tTNqdf9mA9fX97zDLh/DVR1S6Re6BnXCguadDWQKll+drx7KC4dzXl/9AlPlPUcA1XuMfdXJSpiiTSCoElXA62S5WfTX44GWgDvGdfNLx3NybPvr3jSVVCNUnVLpN4ocK2gUpOuypUQtWfHMDaunoKXIu/ZuHoKo8cto619W80D1mzlTALrL1WlEtlfX0lXfsqZDLX9jZG8vuBMsuewFfPPZOKnFtMxdlNogsRGqLolUm/0qEAF1SoBaefWQ0vaLiKSr5YJSFuWjytpu4gcOBS4VlCtEpCGHLShpO0iIvlqmYA04tjVJW0XkQOHAtcK6isBqVxVsvy0tW9j1LileAvoex+jxi2lrX1b2c8FMHxQiilHdNLRVPrtu0p+HUSk//pKuqp0xaiOsZuYOPNxsuewiTMfp2PsprKfa3hrmhNGJhiSKv2RpXJWCxORYPSMa4X5JSCVv0rW/tqHv83GN5KYeRVo2odX5mrrWYe/yP/6h7UkUtAc/xa3vjCTx9efGOjYanwdRKT//JKuqlUx6uDjVrFi/ocBh8WjHHL8qrKf4+Sdq/jBFzaTTBvNax7knkNO49khRwc6thLVwkSkOF1xrYJ4816GHLSx90prT8JWKtlMOhVn5dLpZb3i2HMOXNyrlOXKfw6AjqZdXD11Pm1NjqGtjpZYgmumzg905bUaXwcRGbiWYbsYMWkNLcN2ZVWMagOGkupq5plbLi/7Fcd952kF2nCJ8p9nSGovV25+irY4tDc7ml2KKzc/FejK677EtVZcur1iXwcR2Z8C1yqrRsJWtZLCRg3aRtLlVZxyEUYNKv5IQpgqZ4lIMNVK2KrGeUYkdpHK+xOYIsKIRPF/vFU5S6R2FLhWWTUStqqVFLZx9zBilldxytJs3D0sNH0UkfKpVsJWNc6zJT6YKLn/PEdJsyVe/KqpKmeJBGNm081shZmtNLOrfT7/dTN71cxeMrPHzezIYm0qcK2yalSMqlZVqu3dg7n1hZms2zqaJ1ecyrqto7n1hZls7/af+LMTscJYOUtE+latilGVqMaVb2e0lXsOOY033x3BE3/9IOt3juSeQ05jZ9T/caXsRKxq9E+k3plZFJgDnAtMAi42s0l5u70ITHPOHQ8sAL5drF0lZ9VANSpGVasq1S/+62JumXMf0UiSVDpWMMGqUCJWGCpniUhw1aoYVYlqXPnuf+ZirrrxEqKRBERb+FCBBKtCiVhhqeIlElInASudc6sBzOwXwAygt4qJc+6JrP2XAJ8t1qgC1xqpRsWoSp8jO8GqpzqYX2WwYhXEFLCK1JdqVYwqZzWufD0JVi7dTDINJPdVBss+Z18VxCrZP5EGcBiwLuv9euDkPva/EvhdsUYVuEq/9ZVglR2MBt1PRKRaehKsUl37tvUkWGUHo0H3E6kX0e4Ug9buLkdTI8zs+az3c51zc7PeW/4BeAsz78fMPgtMAz5S7KQKXKXfgiZYKRFLRMImaIKVErFECtrinJvWx+fXA2Oy3h8O7LeovJmdBVwLfMQ515X/+XxKzpJ+C5pgpUQsEQmboAlWSsQS6bfngAlmNtbMmoCLgIXZO5jZicAPgfOdc5uDNKorrjIgQROslIglImETNMFKiVgipXPOJc3sKuBRIArc65xbbmY3Ac875xYC3wEGA/PNDGCtc+78vtpV4CoDFjTBSolYIhI2QROslIglUjrn3CPAI3nbbsh6fVapbepRARERERGpCxUNXCtRMUFEpBo0f4mIhE/FAtdKVUxoFNlVpEQkXDR/9S27ipSISDVV8hnXilRMaASFqkiJSGho/iqgUBUpEZFqqOSjAn4VE/oqsxKoYkK9y64ilUo2k07FWbl0uq68ioSL5i8f+6pIteLS7aS6mnnmlst15VVEqqaSV1zLVjHBzL4IfBGgubWjXP2rCVWREqkLFZm/Bo0aVK7+1YSqSIlIrVXyimupFRPOL1QxwTk31zk3zTk3LdZc3//Zq4qUSF2oyPzV0tFSkc5Wi6pIiUitVTJwrUjFhHqnKlIidUHzlw9VkRKRWqvYowKVqpjQCFRFSiTcNH8VpipSIlJLFa2cVYmKCY1CVaREwk3zV2GqIiUitaLKWSIiIiJSFxS4ioiIiEhdUOAqIiIiInVBgauIiIiI1AUFriIiIiJSFxS4ioiIiEhdUOAqIiIiInWhouu4ioiIiEiIdCWwN96qdS/6TVdcRURERKQuKHAVERERkbqgwFVERERE6oICVxERERGpCwpcRURERKQuKHAVERERkbqgwFVERERE6oICVxERERGpCwpcRURERKQuKHAVERERkbqgwFVERERE6oICVxERERGpCwpcRURERKQuKHAVERERkbqgwFVERERE6oICVxERERGpCwpcRURERKTszGy6ma0ws5VmdrXP55vN7IHM5581s6OKtanAVURERETKysyiwBzgXGAScLGZTcrb7Upgm3NuPHAXcHuxdhW4ioiIiEi5nQSsdM6tds51A78AZuTtMwP4Seb1AuBMM7O+GlXgKiIiIiLldhiwLuv9+sw2332cc0ngPWB4X43GythBEREREQmxHaktjz767o9GlKGpFjN7Puv9XOfc3Kz3fldOXd77IPvkUOAqIiIicoBwzk2v0qnWA2Oy3h8ObCiwz3oziwFDga19NapHBURERESk3J4DJpjZWDNrAi4CFubtsxC4LPP6AmCxc05XXEVERESkepxzSTO7CngUiAL3OueWm9lNwPPOuYXAPcDPzGwl3pXWi4q1q8BVRERERMrOOfcI8EjethuyXncCM0tpU48KiIiIiEhdUOAqIiIiInVBgauIiIiI1AUFriIiIiJSFxS4ioiIiEhdUOAqIiIiInVBgauIiIiI1AUFriIiIiJSFxS4ioiIiEhdqGjgambTzWyFma00s6t9Pt9sZg9kPv+smR1Vyf6IiASl+UtEJHwqFriaWRSYA5wLTAIuNrNJebtdCWxzzo0H7gJur1R/RESC0vwlIhJOlbziehKw0jm32jnXDfwCmJG3zwzgJ5nXC4Azzcwq2CcRkSA0f4mIhFAlA9fDgHVZ79dntvnu45xLAu8BwyvYJxGRIDR/iYiEUKyCbftdeXD92Acz+yLwxczbrqd/+T9fGWDfwmIEsKXWnSiTRhlLo4wDGmAs9v2v97ycWO1T+2wry/z105Pv1fwVPhpL+NT9OIxbel5We/5qaJUMXNcDY7LeHw5sKLDPejOLAUOBrfkNOefmAnMBzOx559y0ivS4yjSW8GmUcUDjjaXKp9T8VYTGEk6NMpZGGQfUZP5qaJV8VOA5YIKZjTWzJuAiYGHePguByzKvLwAWO+f2u2IhIlJlmr9EREKoYldcnXNJM7sKeBSIAvc655ab2U3A8865hcA9wM/MbCXelYqLKtUfEZGgNH+JiIRTJR8VwDn3CPBI3rYbsl53AjNLbHZuGboWFhpL+DTKOEBjGRDNX0VpLOHUKGNplHFAY42l5kx3tkRERESkHqjkq4iIiIjUhdAGro1SbjHAOL5uZq+a2Utm9riZHVmLfgZRbCxZ+11gZs7MQpsRGmQsZvbpzPdmuZn9vNp9DCrAz9gRZvaEmb2Y+Tn7eC36WYyZ3Wtmm83Md7ko8/zvzDhfMrMp1e5jUI0yf4HmsGr2LyjNX+HTSPNX6DnnQveBlwyxChgHNAF/ASbl7fNl4N8zry8CHqh1v/s5jjOAtszrfwrjOIKOJbPfEOCPwBJgWq37PYDvywTgRWBY5v0hte73AMYyF/inzOtJwJpa97vAWE4HpgCvFPj8x4Hf4a2fegrwbK37PIDvSejnrxLGojksZOPQ/FWTsTTE/FUPH2G94too5RaLjsM594Rzbk/m7RK89SLDKMj3BOBm4NtAZzU7V6IgY/kCMMc5tw3AObe5yn0MKshYHNCeeT2U/dcjDQXn3B/xWQc1ywzgp86zBOgws9HV6V1JGmX+As1hYaT5K4QaaP4KvbAGro1SbjHIOLJdifcfWRgVHYuZnQiMcc79tpod64cg35djgGPM7GkzW2Jm06vWu9IEGcuNwGfNbD1elvxXq9O1siv196lWGmX+As1hYaT5qz7Vy/wVehVdDmsAylZuscYC99HMPgtMAz5S0R71X59jMbMIcBdwebU6NABBvi8xvNttf493BelPZjbZObe9wn0rVZCxXAzc55y708w+hLf26GTnXLry3Surevidh8aZv0BzWBhp/tL8dUAL6xXXUsotYn2UW6yxIOPAzM4CrgXOd851ValvpSo2liHAZOAPZrYG7xmehSFNbgj68/Ub51zCOfcGsALvD0HYBBnLlcCDAM65/wJa8OqA15tAv08h0CjzF2gOC+McpvlL89cBLayBa6OUWyw6jsytqR/iTfhhfQ4JiozFOfeec26Ec+4o59xReM+6ne+cC2ON5iA/X7/GSzrBzEbg3XpbXdVeBhNkLGuBMwHM7P14E/87Ve1leSwELs1k554CvOece7vWnfLRKPMXaA4L4xym+Uvz14Gt1tlhhT7wMvBex8s4vDaz7Sa8iQS8H975wErgz8C4Wve5n+N4DNgELMt8LKx1n/s7lrx9/0AIM3JL+L4Y8F3gVeBl4KJa93kAY5kEPI2XsbsMOKfWfS4wjvuBt4EE3tWJK4EvAV/K+p7MyYzz5Tr/+aqL+SvgWDSHhWwcmr9qMo6Gmb/C/qHKWSIiIiJSF8L6qICIiIiISA4FriIiIiJSFxS4ioiIiEhdUOAqIiIiInVBgauIiIiI1AXKZHX5AAACcklEQVQFrlJ2ZvY/zOw1M/vPfhx7lJl9phL9yrR/upktNbOkmV1QqfOISH3S/CUSbgpcpRK+DHzcOfff+3HsUUDJE7+ZRQPuuhavpOPPSz2HiBwQNH+JhJgCVykrM/t3YBxeqcSvmdkgM7vXzJ4zsxfNbEZmv6PM7E+ZqwdLzezUTBO3AX9nZssyx19uZt/Pav+3Zvb3mde7zOwmM3sW+JCZTTWzJ83sBTN71MxG5/fPObfGOfcSUG91rkWkwjR/iYRfrNYdkMbinPuSmU0HznDObTGzb+GVs7zCzDqAP5vZY8Bm4GznXKeZTcCrOjINuBqY7Zz7BICZXd7H6QYBrzjnbjCzOPAkMMM5946ZXQh8E7iiUmMVkcai+Usk/BS4SqWdA5xvZrMz71uAI4ANwPfN7AQghVdLu1Qp4KHM64nAZOD/mRlAFK/8nohIf2n+EgkZBa5SaQZ8yjm3Imej2Y149c0/gPfISmeB45PkPtLSkvW60zmXyjrPcufch8rRaRERNH+JhI6ecZVKexT4qmUuI5jZiZntQ4G3nXNp4BK8KwwAO4EhWcevAU4ws4iZjQFOKnCeFcDBZvahzHniZnZsWUciIgcazV8iIaPAVSrtZiAOvGRmr2TeA/wf4DIzW4J3m213ZvtLQNLM/mJmXwOeBt4AXgbuAJb6ncQ51w1cANxuZn8BlgGn5u9nZh80s/XATOCHZra8PMMUkQak+UskZMw5V+s+iIiIiIgUpSuuIiIiIlIXFLiKiIiISF1Q4CoiIiIidUGBq4iIiIjUBQWuIiIiIlIXFLiKiIiISF1Q4CoiIiIidUGBq4iIiIjUhf8P46gGAN1fIm4AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 720x360 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "num_classes = 2\n",
+    "x_train, y_train = get_data(num_classes=num_classes)\n",
+    "x_train_adv, model = get_adversarial_examples(x_train, y_train, num_classes)\n",
+    "plot_results(model, x_train, y_train, x_train_adv, num_classes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "ZOO: 100%|██████████| 150/150 [00:07<00:00, 20.28it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 1080x360 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "num_classes = 3\n",
+    "x_train, y_train = get_data(num_classes=num_classes)\n",
+    "x_train_adv, model = get_adversarial_examples(x_train, y_train, num_classes)\n",
+    "plot_results(model, x_train, y_train, x_train_adv, num_classes)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 3 Example: MNIST"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3.1 Load and transform MNIST dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "(x_train, y_train), (x_test, y_test), min_, max_ = load_mnist()\n",
+    "\n",
+    "n_samples_train = x_train.shape[0]\n",
+    "n_features_train = x_train.shape[1] * x_train.shape[2] * x_train.shape[3]\n",
+    "n_samples_test = x_test.shape[0]\n",
+    "n_features_test = x_test.shape[1] * x_test.shape[2] * x_test.shape[3]\n",
+    "\n",
+    "x_train = x_train.reshape(n_samples_train, n_features_train)\n",
+    "x_test = x_test.reshape(n_samples_test, n_features_test)\n",
+    "\n",
+    "y_train = np.argmax(y_train, axis=1)\n",
+    "y_test = np.argmax(y_test, axis=1)\n",
+    "\n",
+    "n_samples_max = 200\n",
+    "x_train = x_train[0:n_samples_max]\n",
+    "y_train = y_train[0:n_samples_max]\n",
+    "x_test = x_test[0:n_samples_max]\n",
+    "y_test = y_test[0:n_samples_max]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3.2 Train LightGBMClassifier classifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1]\ttraining's multi_logloss: 2.08117\n",
+      "[2]\ttraining's multi_logloss: 1.9099\n",
+      "[3]\ttraining's multi_logloss: 1.75426\n",
+      "[4]\ttraining's multi_logloss: 1.61072\n",
+      "[5]\ttraining's multi_logloss: 1.48884\n",
+      "[6]\ttraining's multi_logloss: 1.38206\n",
+      "[7]\ttraining's multi_logloss: 1.28643\n",
+      "[8]\ttraining's multi_logloss: 1.19899\n",
+      "[9]\ttraining's multi_logloss: 1.11667\n",
+      "[10]\ttraining's multi_logloss: 1.03082\n",
+      "[11]\ttraining's multi_logloss: 0.958193\n",
+      "[12]\ttraining's multi_logloss: 0.890689\n",
+      "[13]\ttraining's multi_logloss: 0.827041\n",
+      "[14]\ttraining's multi_logloss: 0.772012\n",
+      "[15]\ttraining's multi_logloss: 0.719107\n",
+      "[16]\ttraining's multi_logloss: 0.669513\n",
+      "[17]\ttraining's multi_logloss: 0.624627\n",
+      "[18]\ttraining's multi_logloss: 0.581928\n",
+      "[19]\ttraining's multi_logloss: 0.542164\n",
+      "[20]\ttraining's multi_logloss: 0.505364\n",
+      "[21]\ttraining's multi_logloss: 0.472722\n",
+      "[22]\ttraining's multi_logloss: 0.440549\n",
+      "[23]\ttraining's multi_logloss: 0.413029\n",
+      "[24]\ttraining's multi_logloss: 0.384772\n",
+      "[25]\ttraining's multi_logloss: 0.359488\n"
+     ]
+    }
+   ],
+   "source": [
+    "num_round = 25\n",
+    "param = {'objective': 'multiclass', 'metric': 'multi_logloss', 'num_class': 10}\n",
+    "train_data = lgb.Dataset(x_train, label=y_train)\n",
+    "validation_data = train_data\n",
+    "model = lgb.train(param, train_data, num_round, valid_sets=[validation_data])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3.3 Create and apply Zeroth Order Optimization Attack with ART"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "art_classifier = LightGBMClassifier(model=model)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "zoo = ZooAttack(classifier=art_classifier, confidence=0.0, targeted=False, learning_rate=1e-1, max_iter=100,\n",
+    "                binary_search_steps=100, initial_const=1e-3, abort_early=True, use_resize=False, \n",
+    "                use_importance=False, nb_parallel=10, batch_size=1, variable_h=0.25)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "ZOO: 100%|██████████| 200/200 [24:12<00:00,  7.26s/it]\n"
+     ]
+    }
+   ],
+   "source": [
+    "x_train_adv = zoo.generate(x_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "ZOO: 100%|██████████| 200/200 [17:50<00:00,  5.35s/it]\n"
+     ]
+    }
+   ],
+   "source": [
+    "x_test_adv = zoo.generate(x_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3.4 Evaluate LightGBMClassifier on benign and adversarial samples"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Benign Training Score: 1.0000\n"
+     ]
+    }
+   ],
+   "source": [
+    "y_pred = model.predict(x_train)\n",
+    "score = np.sum(y_train == np.argmax(y_pred, axis=1)) / y_train.shape[0]\n",
+    "print(\"Benign Training Score: %.4f\" % score)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.matshow(x_train[0, :].reshape((28, 28)))\n",
+    "plt.clim(0, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Benign Training Predicted Label: 5\n"
+     ]
+    }
+   ],
+   "source": [
+    "prediction = np.argmax(model.predict(x_train[0:1, :]), axis=1)\n",
+    "print(\"Benign Training Predicted Label: %i\" % prediction)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial Training Score: 0.8650\n"
+     ]
+    }
+   ],
+   "source": [
+    "y_pred = model.predict(x_train_adv)\n",
+    "score = np.sum(y_train == np.argmax(y_pred, axis=1)) / y_train.shape[0]\n",
+    "print(\"Adversarial Training Score: %.4f\" % score)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.matshow(x_train_adv[0, :].reshape((28, 28)))\n",
+    "plt.clim(0, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial Training Predicted Label: 3\n"
+     ]
+    }
+   ],
+   "source": [
+    "prediction = np.argmax(model.predict(x_train_adv[0:1, :]), axis=1)\n",
+    "print(\"Adversarial Training Predicted Label: %i\" % prediction)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Benign Test Score: 0.6700\n"
+     ]
+    }
+   ],
+   "source": [
+    "y_pred = model.predict(x_test)\n",
+    "score = np.sum(y_test == np.argmax(y_pred, axis=1)) / y_test.shape[0]\n",
+    "print(\"Benign Test Score: %.4f\" % score)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.matshow(x_test[0, :].reshape((28, 28)))\n",
+    "plt.clim(0, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Benign Test Predicted Label: 7\n"
+     ]
+    }
+   ],
+   "source": [
+    "prediction = np.argmax(model.predict(x_test[0:1, :]), axis=1)\n",
+    "print(\"Benign Test Predicted Label: %i\" % prediction)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial Test Score: 0.5800\n"
+     ]
+    }
+   ],
+   "source": [
+    "y_pred = model.predict(x_test_adv)\n",
+    "score = np.sum(y_test == np.argmax(y_pred, axis=1)) / y_test.shape[0]\n",
+    "print(\"Adversarial Test Score: %.4f\" % score)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.matshow(x_test_adv[0, :].reshape((28, 28)))\n",
+    "plt.clim(0, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial Test Predicted Label: 7\n"
+     ]
+    }
+   ],
+   "source": [
+    "prediction = np.argmax(model.predict(x_test_adv[0:1, :]), axis=1)\n",
+    "print(\"Adversarial Test Predicted Label: %i\" % prediction)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/adversarial-robustness-toolbox/notebooks/classifier_scikitlearn_AdaBoostClassifier.ipynb b/adversarial-robustness-toolbox/notebooks/classifier_scikitlearn_AdaBoostClassifier.ipynb
new file mode 100644
index 0000000..2339f5c
--- /dev/null
+++ b/adversarial-robustness-toolbox/notebooks/classifier_scikitlearn_AdaBoostClassifier.ipynb
@@ -0,0 +1,642 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Adversarial-Robustness-Toolbox for scikit-learn AdaBoostClassifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.ensemble import AdaBoostClassifier\n",
+    "from sklearn.datasets import load_iris\n",
+    "\n",
+    "import numpy as np\n",
+    "from matplotlib import pyplot as plt\n",
+    "\n",
+    "from art.estimators.classification import SklearnClassifier\n",
+    "from art.attacks.evasion import ZooAttack\n",
+    "from art.utils import load_mnist\n",
+    "\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1 Training scikit-learn AdaBoostClassifier and attacking with ART Zeroth Order Optimization attack"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_adversarial_examples(x_train, y_train):\n",
+    "    \n",
+    "    # Create and fit AdaBoostClassifier\n",
+    "    model = AdaBoostClassifier()\n",
+    "    model.fit(X=x_train, y=y_train)\n",
+    "\n",
+    "    # Create ART classfier for scikit-learn AdaBoostClassifier\n",
+    "    art_classifier = SklearnClassifier(model=model)\n",
+    "\n",
+    "    # Create ART Zeroth Order Optimization attack\n",
+    "    zoo = ZooAttack(classifier=art_classifier, confidence=0.0, targeted=False, learning_rate=1e-1, max_iter=20,\n",
+    "                    binary_search_steps=10, initial_const=1e-3, abort_early=True, use_resize=False, \n",
+    "                    use_importance=False, nb_parallel=1, batch_size=1, variable_h=0.2)\n",
+    "\n",
+    "    # Generate adversarial samples with ART Zeroth Order Optimization attack\n",
+    "    x_train_adv = zoo.generate(x_train)\n",
+    "\n",
+    "    return x_train_adv, model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1.1 Utility functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_data(num_classes):\n",
+    "    x_train, y_train = load_iris(return_X_y=True)\n",
+    "    x_train = x_train[y_train < num_classes][:, [0, 1]]\n",
+    "    y_train = y_train[y_train < num_classes]\n",
+    "    x_train[:, 0][y_train == 0] *= 2\n",
+    "    x_train[:, 1][y_train == 2] *= 2\n",
+    "    x_train[:, 0][y_train == 0] -= 3\n",
+    "    x_train[:, 1][y_train == 2] -= 2\n",
+    "    \n",
+    "    x_train[:, 0] = (x_train[:, 0] - 4) / (9 - 4)\n",
+    "    x_train[:, 1] = (x_train[:, 1] - 1) / (6 - 1)\n",
+    "    \n",
+    "    return x_train, y_train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_results(model, x_train, y_train, x_train_adv, num_classes):\n",
+    "    \n",
+    "    fig, axs = plt.subplots(1, num_classes, figsize=(num_classes * 5, 5))\n",
+    "\n",
+    "    colors = ['orange', 'blue', 'green']\n",
+    "\n",
+    "    for i_class in range(num_classes):\n",
+    "\n",
+    "        # Plot difference vectors\n",
+    "        for i in range(y_train[y_train == i_class].shape[0]):\n",
+    "            x_1_0 = x_train[y_train == i_class][i, 0]\n",
+    "            x_1_1 = x_train[y_train == i_class][i, 1]\n",
+    "            x_2_0 = x_train_adv[y_train == i_class][i, 0]\n",
+    "            x_2_1 = x_train_adv[y_train == i_class][i, 1]\n",
+    "            if x_1_0 != x_2_0 or x_1_1 != x_2_1:\n",
+    "                axs[i_class].plot([x_1_0, x_2_0], [x_1_1, x_2_1], c='black', zorder=1)\n",
+    "\n",
+    "        # Plot benign samples\n",
+    "        for i_class_2 in range(num_classes):\n",
+    "            axs[i_class].scatter(x_train[y_train == i_class_2][:, 0], x_train[y_train == i_class_2][:, 1], s=20,\n",
+    "                                 zorder=2, c=colors[i_class_2])\n",
+    "        axs[i_class].set_aspect('equal', adjustable='box')\n",
+    "\n",
+    "        # Show predicted probability as contour plot\n",
+    "        h = .01\n",
+    "        x_min, x_max = 0, 1\n",
+    "        y_min, y_max = 0, 1\n",
+    "\n",
+    "        xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n",
+    "\n",
+    "        Z_proba = model.predict_proba(np.c_[xx.ravel(), yy.ravel()])\n",
+    "        Z_proba = Z_proba[:, i_class].reshape(xx.shape)\n",
+    "        im = axs[i_class].contourf(xx, yy, Z_proba, levels=[0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0],\n",
+    "                                   vmin=0, vmax=1)\n",
+    "        if i_class == num_classes - 1:\n",
+    "            cax = fig.add_axes([0.95, 0.2, 0.025, 0.6])\n",
+    "            plt.colorbar(im, ax=axs[i_class], cax=cax)\n",
+    "\n",
+    "        # Plot adversarial samples\n",
+    "        for i in range(y_train[y_train == i_class].shape[0]):\n",
+    "            x_1_0 = x_train[y_train == i_class][i, 0]\n",
+    "            x_1_1 = x_train[y_train == i_class][i, 1]\n",
+    "            x_2_0 = x_train_adv[y_train == i_class][i, 0]\n",
+    "            x_2_1 = x_train_adv[y_train == i_class][i, 1]\n",
+    "            if x_1_0 != x_2_0 or x_1_1 != x_2_1:\n",
+    "                axs[i_class].scatter(x_2_0, x_2_1, zorder=2, c='red', marker='X')\n",
+    "        axs[i_class].set_xlim((x_min, x_max))\n",
+    "        axs[i_class].set_ylim((y_min, y_max))\n",
+    "\n",
+    "        axs[i_class].set_title('class ' + str(i_class))\n",
+    "        axs[i_class].set_xlabel('feature 1')\n",
+    "        axs[i_class].set_ylabel('feature 2')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 2 Example: Iris dataset"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### legend\n",
+    "- colored background: probability of class i\n",
+    "- orange circles: class 1\n",
+    "- blue circles: class 2\n",
+    "- green circles: class 3\n",
+    "- red crosses: adversarial samples for class i"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "ZOO: 100%|██████████| 100/100 [01:20<00:00,  1.24it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "num_classes = 2\n",
+    "x_train, y_train = get_data(num_classes=num_classes)\n",
+    "x_train_adv, model = get_adversarial_examples(x_train, y_train)\n",
+    "plot_results(model, x_train, y_train, x_train_adv, num_classes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "ZOO: 100%|██████████| 150/150 [01:52<00:00,  1.34it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 1080x360 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "num_classes = 3\n",
+    "x_train, y_train = get_data(num_classes=num_classes)\n",
+    "x_train_adv, model = get_adversarial_examples(x_train, y_train)\n",
+    "plot_results(model, x_train, y_train, x_train_adv, num_classes)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 3 Example: MNIST"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3.1 Load and transform MNIST dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "(x_train, y_train), (x_test, y_test), min_, max_ = load_mnist()\n",
+    "\n",
+    "n_samples_train = x_train.shape[0]\n",
+    "n_features_train = x_train.shape[1] * x_train.shape[2] * x_train.shape[3]\n",
+    "n_samples_test = x_test.shape[0]\n",
+    "n_features_test = x_test.shape[1] * x_test.shape[2] * x_test.shape[3]\n",
+    "\n",
+    "x_train = x_train.reshape(n_samples_train, n_features_train)\n",
+    "x_test = x_test.reshape(n_samples_test, n_features_test)\n",
+    "\n",
+    "y_train = np.argmax(y_train, axis=1)\n",
+    "y_test = np.argmax(y_test, axis=1)\n",
+    "\n",
+    "n_samples_max = 200\n",
+    "x_train = x_train[0:n_samples_max]\n",
+    "y_train = y_train[0:n_samples_max]\n",
+    "x_test = x_test[0:n_samples_max]\n",
+    "y_test = y_test[0:n_samples_max]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3.2 Train AdaBoostClassifier classifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = AdaBoostClassifier(base_estimator=None, n_estimators=50, learning_rate=0.1, algorithm='SAMME.R', \n",
+    "                           random_state=None)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None, learning_rate=0.1,\n",
+       "                   n_estimators=50, random_state=None)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.fit(X=x_train, y=y_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3.3 Create and apply Zeroth Order Optimization Attack with ART"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "art_classifier = SklearnClassifier(model=model)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "zoo = ZooAttack(classifier=art_classifier, confidence=0.0, targeted=False, learning_rate=1e-1, max_iter=30,\n",
+    "                binary_search_steps=20, initial_const=1e-3, abort_early=True, use_resize=False, \n",
+    "                use_importance=False, nb_parallel=10, batch_size=1, variable_h=0.25)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "ZOO: 100%|██████████| 200/200 [07:12<00:00,  2.16s/it]\n"
+     ]
+    }
+   ],
+   "source": [
+    "x_train_adv = zoo.generate(x_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "ZOO: 100%|██████████| 200/200 [07:09<00:00,  2.15s/it]\n"
+     ]
+    }
+   ],
+   "source": [
+    "x_test_adv = zoo.generate(x_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3.4 Evaluate AdaBoostClassifier on benign and adversarial samples"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Benign Training Score: 0.7700\n"
+     ]
+    }
+   ],
+   "source": [
+    "score = model.score(x_train, y_train)\n",
+    "print(\"Benign Training Score: %.4f\" % score)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.matshow(x_train[0, :].reshape((28, 28)))\n",
+    "plt.clim(0, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Benign Training Predicted Label: 5\n"
+     ]
+    }
+   ],
+   "source": [
+    "prediction = model.predict(x_train[0:1, :])[0]\n",
+    "print(\"Benign Training Predicted Label: %i\" % prediction)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial Training Score: 0.3050\n"
+     ]
+    }
+   ],
+   "source": [
+    "score = model.score(x_train_adv, y_train)\n",
+    "print(\"Adversarial Training Score: %.4f\" % score)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.matshow(x_train_adv[0, :].reshape((28, 28)))\n",
+    "plt.clim(0, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial Training Predicted Label: 3\n"
+     ]
+    }
+   ],
+   "source": [
+    "prediction = model.predict(x_train_adv[0:1, :])[0]\n",
+    "print(\"Adversarial Training Predicted Label: %i\" % prediction)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Benign Test Score: 0.4700\n"
+     ]
+    }
+   ],
+   "source": [
+    "score = model.score(x_test, y_test)\n",
+    "print(\"Benign Test Score: %.4f\" % score)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.matshow(x_test[0, :].reshape((28, 28)))\n",
+    "plt.clim(0, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Benign Test Predicted Label: 7\n"
+     ]
+    }
+   ],
+   "source": [
+    "prediction = model.predict(x_test[0:1, :])[0]\n",
+    "print(\"Benign Test Predicted Label: %i\" % prediction)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial Test Score: 0.2450\n"
+     ]
+    }
+   ],
+   "source": [
+    "score = model.score(x_test_adv, y_test)\n",
+    "print(\"Adversarial Test Score: %.4f\" % score)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.matshow(x_test_adv[0, :].reshape((28, 28)))\n",
+    "plt.clim(0, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial Test Predicted Label: 3\n"
+     ]
+    }
+   ],
+   "source": [
+    "prediction = model.predict(x_test_adv[0:1, :])[0]\n",
+    "print(\"Adversarial Test Predicted Label: %i\" % prediction)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/adversarial-robustness-toolbox/notebooks/classifier_scikitlearn_BaggingClassifier.ipynb b/adversarial-robustness-toolbox/notebooks/classifier_scikitlearn_BaggingClassifier.ipynb
new file mode 100644
index 0000000..d6b552c
--- /dev/null
+++ b/adversarial-robustness-toolbox/notebooks/classifier_scikitlearn_BaggingClassifier.ipynb
@@ -0,0 +1,645 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Adversarial-Robustness-Toolbox for scikit-learn DecisionTreeClassifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.ensemble import BaggingClassifier\n",
+    "from sklearn.datasets import load_iris\n",
+    "\n",
+    "import numpy as np\n",
+    "from matplotlib import pyplot as plt\n",
+    "\n",
+    "from art.estimators.classification import SklearnClassifier\n",
+    "from art.attacks.evasion import ZooAttack\n",
+    "from art.utils import load_mnist\n",
+    "\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1 Training scikit-learn BaggingClassifier and attacking with ART Zeroth Order Optimization attack"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_adversarial_examples(x_train, y_train):\n",
+    "    \n",
+    "    # Fit BaggingClassifier\n",
+    "    model = BaggingClassifier()\n",
+    "    model.fit(X=x_train, y=y_train)\n",
+    "\n",
+    "    # Create ART classifier for scikit-learn BaggingClassifier\n",
+    "    art_classifier = SklearnClassifier(model=model)\n",
+    "\n",
+    "    # Create ART Zeroth Order Optimization attack\n",
+    "    zoo = ZooAttack(classifier=art_classifier, confidence=0.0, targeted=False, learning_rate=1e-1, max_iter=20,\n",
+    "                    binary_search_steps=10, initial_const=1e-3, abort_early=True, use_resize=False, \n",
+    "                    use_importance=False, nb_parallel=1, batch_size=1, variable_h=0.2)\n",
+    "    \n",
+    "    # Generate adversarial samples with ART Zeroth Order Optimization attack\n",
+    "    x_train_adv = zoo.generate(x_train)\n",
+    "\n",
+    "    return x_train_adv, model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1.1 Utility functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_data(num_classes):\n",
+    "    x_train, y_train = load_iris(return_X_y=True)\n",
+    "    x_train = x_train[y_train < num_classes][:, [0, 1]]\n",
+    "    y_train = y_train[y_train < num_classes]\n",
+    "    x_train[:, 0][y_train == 0] *= 2\n",
+    "    x_train[:, 1][y_train == 2] *= 2\n",
+    "    x_train[:, 0][y_train == 0] -= 3\n",
+    "    x_train[:, 1][y_train == 2] -= 2\n",
+    "    \n",
+    "    x_train[:, 0] = (x_train[:, 0] - 4) / (9 - 4)\n",
+    "    x_train[:, 1] = (x_train[:, 1] - 1) / (6 - 1)\n",
+    "    \n",
+    "    return x_train, y_train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_results(model, x_train, y_train, x_train_adv, num_classes):\n",
+    "    \n",
+    "    fig, axs = plt.subplots(1, num_classes, figsize=(num_classes * 5, 5))\n",
+    "\n",
+    "    colors = ['orange', 'blue', 'green']\n",
+    "\n",
+    "    for i_class in range(num_classes):\n",
+    "\n",
+    "        # Plot difference vectors\n",
+    "        for i in range(y_train[y_train == i_class].shape[0]):\n",
+    "            x_1_0 = x_train[y_train == i_class][i, 0]\n",
+    "            x_1_1 = x_train[y_train == i_class][i, 1]\n",
+    "            x_2_0 = x_train_adv[y_train == i_class][i, 0]\n",
+    "            x_2_1 = x_train_adv[y_train == i_class][i, 1]\n",
+    "            if x_1_0 != x_2_0 or x_1_1 != x_2_1:\n",
+    "                axs[i_class].plot([x_1_0, x_2_0], [x_1_1, x_2_1], c='black', zorder=1)\n",
+    "\n",
+    "        # Plot benign samples\n",
+    "        for i_class_2 in range(num_classes):\n",
+    "            axs[i_class].scatter(x_train[y_train == i_class_2][:, 0], x_train[y_train == i_class_2][:, 1], s=20,\n",
+    "                                 zorder=2, c=colors[i_class_2])\n",
+    "        axs[i_class].set_aspect('equal', adjustable='box')\n",
+    "\n",
+    "        # Show predicted probability as contour plot\n",
+    "        h = .01\n",
+    "        x_min, x_max = 0, 1\n",
+    "        y_min, y_max = 0, 1\n",
+    "\n",
+    "        xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n",
+    "\n",
+    "        Z_proba = model.predict_proba(np.c_[xx.ravel(), yy.ravel()])\n",
+    "        Z_proba = Z_proba[:, i_class].reshape(xx.shape)\n",
+    "        im = axs[i_class].contourf(xx, yy, Z_proba, levels=[0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0],\n",
+    "                                   vmin=0, vmax=1)\n",
+    "        if i_class == num_classes - 1:\n",
+    "            cax = fig.add_axes([0.95, 0.2, 0.025, 0.6])\n",
+    "            plt.colorbar(im, ax=axs[i_class], cax=cax)\n",
+    "\n",
+    "        # Plot adversarial samples\n",
+    "        for i in range(y_train[y_train == i_class].shape[0]):\n",
+    "            x_1_0 = x_train[y_train == i_class][i, 0]\n",
+    "            x_1_1 = x_train[y_train == i_class][i, 1]\n",
+    "            x_2_0 = x_train_adv[y_train == i_class][i, 0]\n",
+    "            x_2_1 = x_train_adv[y_train == i_class][i, 1]\n",
+    "            if x_1_0 != x_2_0 or x_1_1 != x_2_1:\n",
+    "                axs[i_class].scatter(x_2_0, x_2_1, zorder=2, c='red', marker='X')\n",
+    "        axs[i_class].set_xlim((x_min, x_max))\n",
+    "        axs[i_class].set_ylim((y_min, y_max))\n",
+    "\n",
+    "        axs[i_class].set_title('class ' + str(i_class))\n",
+    "        axs[i_class].set_xlabel('feature 1')\n",
+    "        axs[i_class].set_ylabel('feature 2')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 2 Example: Iris dataset"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### legend\n",
+    "- colored background: probability of class i\n",
+    "- orange circles: class 1\n",
+    "- blue circles: class 2\n",
+    "- green circles: class 3\n",
+    "- red crosses: adversarial samples for class i"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "ZOO: 100%|██████████| 100/100 [00:17<00:00,  5.72it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "num_classes = 2\n",
+    "x_train, y_train = get_data(num_classes=num_classes)\n",
+    "x_train_adv, model = get_adversarial_examples(x_train, y_train)\n",
+    "plot_results(model, x_train, y_train, x_train_adv, num_classes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "ZOO: 100%|██████████| 150/150 [00:24<00:00,  6.23it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 1080x360 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "num_classes = 3\n",
+    "x_train, y_train = get_data(num_classes=num_classes)\n",
+    "x_train_adv, model = get_adversarial_examples(x_train, y_train)\n",
+    "plot_results(model, x_train, y_train, x_train_adv, num_classes)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 3 Example: MNIST"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3.1 Load and transform MNIST dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "(x_train, y_train), (x_test, y_test), min_, max_ = load_mnist()\n",
+    "\n",
+    "n_samples_train = x_train.shape[0]\n",
+    "n_features_train = x_train.shape[1] * x_train.shape[2] * x_train.shape[3]\n",
+    "n_samples_test = x_test.shape[0]\n",
+    "n_features_test = x_test.shape[1] * x_test.shape[2] * x_test.shape[3]\n",
+    "\n",
+    "x_train = x_train.reshape(n_samples_train, n_features_train)\n",
+    "x_test = x_test.reshape(n_samples_test, n_features_test)\n",
+    "\n",
+    "y_train = np.argmax(y_train, axis=1)\n",
+    "y_test = np.argmax(y_test, axis=1)\n",
+    "\n",
+    "n_samples_max = 200\n",
+    "x_train = x_train[0:n_samples_max]\n",
+    "y_train = y_train[0:n_samples_max]\n",
+    "x_test = x_test[0:n_samples_max]\n",
+    "y_test = y_test[0:n_samples_max]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3.2 Train BaggingClassifier classifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = BaggingClassifier(base_estimator=None, n_estimators=10, max_samples=1.0, max_features=1.0, \n",
+    "                          bootstrap=True, bootstrap_features=False, oob_score=False, warm_start=False, \n",
+    "                          n_jobs=None, random_state=None, verbose=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "BaggingClassifier(base_estimator=None, bootstrap=True, bootstrap_features=False,\n",
+       "                  max_features=1.0, max_samples=1.0, n_estimators=10,\n",
+       "                  n_jobs=None, oob_score=False, random_state=None, verbose=0,\n",
+       "                  warm_start=False)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.fit(X=x_train, y=y_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3.3 Create and apply Zeroth Order Optimization Attack with ART"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "art_classifier = SklearnClassifier(model=model)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "zoo = ZooAttack(classifier=art_classifier, confidence=0.0, targeted=False, learning_rate=1e-1, max_iter=100,\n",
+    "                binary_search_steps=20, initial_const=1e-3, abort_early=True, use_resize=False, \n",
+    "                use_importance=False, nb_parallel=10, batch_size=1, variable_h=0.25)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "ZOO: 100%|██████████| 200/200 [08:27<00:00,  2.54s/it]\n"
+     ]
+    }
+   ],
+   "source": [
+    "x_train_adv = zoo.generate(x_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "ZOO: 100%|██████████| 200/200 [05:53<00:00,  1.77s/it]\n"
+     ]
+    }
+   ],
+   "source": [
+    "x_test_adv = zoo.generate(x_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3.4 Evaluate BaggingClassifier on benign and adversarial samples"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Benign Training Score: 1.0000\n"
+     ]
+    }
+   ],
+   "source": [
+    "score = model.score(x_train, y_train)\n",
+    "print(\"Benign Training Score: %.4f\" % score)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQEAAAECCAYAAAD+eGJTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAO90lEQVR4nO3dbYxc5XnG8euKvdg1mMRbx45DHXCMU2igMemKFxkBFQp1o0qAKkKtKHJoWtMEJ6F1JahVFVqRyq2AlFKKZIqLkYAEAhR/oEksCwFRYYvtEjBxgARcarxdY1ZgIMTYu3c/7Ljdkt1ndndeznjv/09azcy5Z+bcPravfc6cZ85xRAhAXh+ougEA1SIEgOQIASA5QgBIjhAAkiMEgOQqCQHby20/b/sntq+uoocS27tsP2v7adtbO6CfDbb32t4xYlm37c22X6zdzumw/q61/WptGz5t+7MV9rfQ9iO2d9p+zvbXa8s7YhsW+mvLNnS75wnYnibpBUmfkbRb0lOSVkTEj9raSIHtXZJ6ImJf1b1Iku1zJL0t6c6IOKW27G8lDUTEulqQzomIqzqov2slvR0R11fR00i2F0haEBHbbc+WtE3SRZK+qA7YhoX+Pqc2bMMqRgKnS/pJRLwUEe9J+pakCyvo44gREY9JGnjf4gslbazd36jhfzSVGKO/jhERfRGxvXb/LUk7JR2nDtmGhf7aoooQOE7Sf414vFtt/AOPU0j6vu1ttldV3cwY5kdEnzT8j0jSvIr7Gc1q28/Udhcq210ZyfYJkk6T1KsO3Ibv609qwzasIgQ8yrJOm7u8LCI+Lem3JV1RG+5iYm6VtFjSUkl9km6oth3J9jGS7pd0ZUTsr7qf9xulv7ZswypCYLekhSMe/4qkPRX0MaaI2FO73SvpQQ3vwnSa/tq+5OF9yr0V9/P/RER/RAxGxJCk21TxNrTdpeH/YHdFxAO1xR2zDUfrr13bsIoQeErSEtuLbB8l6fckbaqgj1HZPrr24YxsHy3pAkk7yq+qxCZJK2v3V0p6qMJefsHh/1w1F6vCbWjbkm6XtDMibhxR6ohtOFZ/7dqGbT86IEm1Qx1/J2mapA0R8Y22NzEG2x/X8G9/SZou6e6q+7N9j6TzJM2V1C/pGkn/IuleSR+T9IqkSyKikg/nxujvPA0PY0PSLkmXH97/rqC/syU9LulZSUO1xWs1vN9d+TYs9LdCbdiGlYQAgM7BjEEgOUIASI4QAJIjBIDkCAEguUpDoIOn5Eqiv0Z1cn+d3JvU3v6qHgl09F+E6K9RndxfJ/cmtbG/qkMAQMUamixke7mkmzQ88++fImJd6flHeUbM1NH/+/igDqhLMya9/lajv8Z0cn+d3JvU/P5+rnf0XhwY7ct7kw+ByZwc5Fh3xxk+f1LrAzB5vbFF+2Ng1BBoZHeAk4MAU0AjIXAknBwEQB3TG3jtuE4OUjvUsUqSZmpWA6sD0AqNjATGdXKQiFgfET0R0dPJH8QAWTUSAh19chAA4zPp3YGIOGR7taTv6f9ODvJc0zoD0BaNfCagiHhY0sNN6gVABZgxCCRHCADJEQJAcoQAkBwhACRHCADJEQJAcoQAkBwhACRHCADJEQJAcoQAkBwhACRHCADJEQJAcoQAkBwhACRHCADJEQJAcoQAkBwhACRHCADJEQJAcoQAkBwhACRHCADJEQJAcoQAkBwhACRHCADJNXRpchxZPL381z3tw3Nbuv7n//SEYn1w1lCxfvzivcX6rK+4WP/vG48q1rf3fLtY3zf4TrF+xn1rivUT/+TJYr0qDYWA7V2S3pI0KOlQRPQ0oykA7dOMkcBvRsS+JrwPgArwmQCQXKMhEJK+b3ub7VXNaAhAezW6O7AsIvbYnidps+0fR8RjI59QC4dVkjRTsxpcHYBma2gkEBF7ard7JT0o6fRRnrM+InoioqdLMxpZHYAWmHQI2D7a9uzD9yVdIGlHsxoD0B6N7A7Ml/Sg7cPvc3dEfLcpXU1R005eUqzHjK5ifc+5HyrW3z2zfBy7+4Pl+uOfKh8nr9q//mx2sf43/7C8WO899e5i/eWD7xbr6/o/U6x/9PEo1jvVpEMgIl6S9Kkm9gKgAhwiBJIjBIDkCAEgOUIASI4QAJIjBIDkOJ9AEw2e9+li/cY7binWP9FV/r77VHcwBov1v7j5i8X69HfKx+nPum91sT771UPF+ox95XkEs7b2FuudipEAkBwhACRHCADJEQJAcoQAkBwhACRHCADJMU+giWY8v6dY3/bzhcX6J7r6m9lO063pO7NYf+nt8nUL7lj8nWL9zaHycf75f/9vxXqrHZlnC6iPkQCQHCEAJEcIAMkRAkByhACQHCEAJEcIAMk5on1HP491d5zh89u2vk4zcNlZxfr+5eXrAkx75phi/YdfuXnCPY103b5fL9afOrc8D2DwjTeL9TirfIb6XV8rlrVoxQ/LT8CYemOL9seAR6sxEgCSIwSA5AgBIDlCAEiOEACSIwSA5AgBIDnmCXSQaXN/uVgffH2gWH/57vJx/ufO2VCsn/7XXy3W591S7ff5MXkNzROwvcH2Xts7Rizrtr3Z9ou12znNbBhA+4xnd+AOScvft+xqSVsiYomkLbXHAI5AdUMgIh6T9P5x6IWSNtbub5R0UZP7AtAmk/1gcH5E9ElS7XZe81oC0E4tP9Go7VWSVknSTM1q9eoATNBkRwL9thdIUu1271hPjIj1EdETET1dmjHJ1QFolcmGwCZJK2v3V0p6qDntAGi3ursDtu+RdJ6kubZ3S7pG0jpJ99r+kqRXJF3SyiazGNz3ekOvP7j/qIZe/8nP/6hYf+3WaeU3GBpsaP2oRt0QiIgVY5SY9QNMAUwbBpIjBIDkCAEgOUIASI4QAJIjBIDkWj5tGO1z8lUvFOuXnVo+qvvPx28p1s+95Ipiffa3nyzW0ZkYCQDJEQJAcoQAkBwhACRHCADJEQJAcoQAkBzzBKaQwTfeLNZf//LJxform94t1q++7s5i/c8+d3GxHv/xwWJ94TeeKNbVxmtkZMJIAEiOEACSIwSA5AgBIDlCAEiOEACSIwSA5BxtPPZ6rLvjDHOm8k418PtnFet3XXN9sb5o+syG1v/JO1cX60tu6yvWD720q6H1T2W9sUX7Y8Cj1RgJAMkRAkByhACQHCEAJEcIAMkRAkByhACQHPMEMG6xbGmxfuy63cX6PR//XkPrP+mRPyjWf/Uvy+dTGHzxpYbWfyRraJ6A7Q2299reMWLZtbZftf107eezzWwYQPuMZ3fgDknLR1n+zYhYWvt5uLltAWiXuiEQEY9JGmhDLwAq0MgHg6ttP1PbXZjTtI4AtNVkQ+BWSYslLZXUJ+mGsZ5oe5Xtrba3HtSBSa4OQKtMKgQioj8iBiNiSNJtkk4vPHd9RPRERE+XZky2TwAtMqkQsL1gxMOLJe0Y67kAOlvdeQK275F0nqS5kvolXVN7vFRSSNol6fKIKH/ZW8wTmOqmzZ9XrO+59MRivfeqm4r1D9T5nfX5ly8o1t88+/VifSorzROoe/GRiFgxyuLbG+4KQEdg2jCQHCEAJEcIAMkRAkByhACQHCEAJMf5BNAx7t39RLE+y0cV6z+L94r13/nqleX3f7C3WD+Scd0BAGMiBIDkCAEgOUIASI4QAJIjBIDkCAEgubpfJQYOGzq7fN2Bn14ys1g/ZemuYr3ePIB6bh44rfz+D21t6P2nKkYCQHKEAJAcIQAkRwgAyRECQHKEAJAcIQAkxzyBRNxzSrH+wtfKx+lvW7axWD9nZvn7/I06EAeL9ScHFpXfYKjupTFSYiQAJEcIAMkRAkByhACQHCEAJEcIAMkRAkByzBM4gkxfdHyx/tPLPlqsX3vpt4r13z1m34R7aqa1/T3F+qM3nVmsz9lYvm4BRld3JGB7oe1HbO+0/Zztr9eWd9vebPvF2u2c1rcLoNnGsztwSNKaiDhZ0pmSrrD9a5KulrQlIpZI2lJ7DOAIUzcEIqIvIrbX7r8laaek4yRdKOnwPNKNki5qVZMAWmdCHwzaPkHSaZJ6Jc2PiD5pOCgkzWt2cwBab9whYPsYSfdLujIi9k/gdatsb7W99aAOTKZHAC00rhCw3aXhALgrIh6oLe63vaBWXyBp72ivjYj1EdETET1dmtGMngE00XiODljS7ZJ2RsSNI0qbJK2s3V8p6aHmtweg1cYzT2CZpC9Ietb207VlayWtk3Sv7S9JekXSJa1pceqYfsLHivU3f2NBsX7pX323WP+jDz1QrLfamr7ycfwn/rE8D6D7jn8v1ucMMQ+gFeqGQET8QJLHKJ/f3HYAtBvThoHkCAEgOUIASI4QAJIjBIDkCAEgOc4nMAHTF3ykWB/YcHSx/uVFjxbrK2b3T7inZlr96tnF+vZblxbrc7+zo1jvfovj/J2IkQCQHCEAJEcIAMkRAkByhACQHCEAJEcIAMmlmifw3m+Vv8/+3h8PFOtrT3y4WL/gl96ZcE/N1D/4brF+zqY1xfpJf/7jYr37jfJx/qFiFZ2KkQCQHCEAJEcIAMkRAkByhACQHCEAJEcIAMmlmiew66Jy5r1w6n0tXf8tbywu1m969IJi3YNjnfl92EnXvVysL+nvLdYHi1VMVYwEgOQIASA5QgBIjhAAkiMEgOQIASA5QgBIzhFRfoK9UNKdkj6i4a+Mr4+Im2xfK+kPJb1We+raiCh+4f5Yd8cZ5mrmQLv1xhbtj4FRJ5qMZ7LQIUlrImK77dmSttneXKt9MyKub1ajANqvbghERJ+kvtr9t2zvlHRcqxsD0B4T+kzA9gmSTpN0eP7patvP2N5ge06TewPQBuMOAdvHSLpf0pURsV/SrZIWS1qq4ZHCDWO8bpXtrba3HtSBJrQMoJnGFQK2uzQcAHdFxAOSFBH9ETEYEUOSbpN0+mivjYj1EdETET1dmtGsvgE0Sd0QsG1Jt0vaGRE3jli+YMTTLpZUviQtgI40nqMDyyR9QdKztp+uLVsraYXtpZJC0i5Jl7ekQwAtNZ6jAz+QNNrxxfJJ+AEcEZgxCCRHCADJEQJAcoQAkBwhACRHCADJEQJAcoQAkBwhACRHCADJEQJAcoQAkBwhACRHCADJEQJAcnWvO9DUldmvSfrPEYvmStrXtgYmjv4a08n9dXJvUvP7Oz4iPjxaoa0h8Asrt7dGRE9lDdRBf43p5P46uTepvf2xOwAkRwgAyVUdAusrXn899NeYTu6vk3uT2thfpZ8JAKhe1SMBABUjBIDkCAEgOUIASI4QAJL7H4v8SYP7urYSAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 288x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.matshow(x_train[0, :].reshape((28, 28)))\n",
+    "plt.clim(0, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Benign Training Predicted Label: 5\n"
+     ]
+    }
+   ],
+   "source": [
+    "prediction = model.predict(x_train[0:1, :])[0]\n",
+    "print(\"Benign Training Predicted Label: %i\" % prediction)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial Training Score: 0.3700\n"
+     ]
+    }
+   ],
+   "source": [
+    "score = model.score(x_train_adv, y_train)\n",
+    "print(\"Adversarial Training Score: %.4f\" % score)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQEAAAECCAYAAAD+eGJTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAPI0lEQVR4nO3df5BV9XnH8c8TWJfyK7IlELREEEm1iQ2mO6CDo3ScEJrpjDodY2nGITYtNpEktnRGy3Qq7ZgO6SgpsZYZqBToiIlGrczUxjCMo2Yaty6UKIaoKVKDbBdxhx8aw4/dp3/sod2S3e/d3XvuOWf3eb9mmHvvec6959mz8OF77vnec83dBSCuD5TdAIByEQJAcIQAEBwhAARHCADBEQJAcKWEgJktMbNXzewnZnZXGT2kmNkBM3vZzPaYWXsF+tlkZofNbG+fZS1mtsPMXs9up1Ssv9Vm9la2D/eY2WdK7G+mmT1jZvvM7BUz+2q2vBL7MNFfIfvQip4nYGZjJL0m6VOSDkp6UdJSd/9RoY0kmNkBSa3ufqTsXiTJzK6R9K6kre7+8WzZ30jqcvc1WZBOcfc7K9Tfaknvuvu9ZfTUl5nNkDTD3Xeb2SRJuyTdIOnzqsA+TPT3WRWwD8sYCcyX9BN33+/upyR9S9L1JfQxYrj7c5K6zll8vaQt2f0t6v1LU4oB+qsMd+9w993Z/ROS9km6UBXZh4n+ClFGCFwo6ad9Hh9UgT/wILmk75nZLjNbXnYzA5ju7h1S718iSdNK7qc/K8zspexwobTDlb7MbJakKyS1qYL78Jz+pAL2YRkhYP0sq9rc5YXu/klJvyXp9my4i6FZL2mOpHmSOiTdV247kplNlPSYpDvc/XjZ/Zyrn/4K2YdlhMBBSTP7PP4VSYdK6GNA7n4ouz0s6Qn1HsJUTWd2LHn2mPJwyf38P+7e6e7d7t4jaaNK3odm1qTef2APufvj2eLK7MP++itqH5YRAi9Kmmtms83sPEm/K2l7CX30y8wmZG/OyMwmSFosaW/6WaXYLmlZdn+ZpCdL7OUXnP3HlblRJe5DMzNJD0ra5+5r+5QqsQ8H6q+ofVj42QFJyk51/K2kMZI2ufvXCm9iAGZ2sXr/95eksZK2ld2fmT0saZGkqZI6Jd0t6Z8lPSLpI5LelHSTu5fy5twA/S1S7zDWJR2QdNvZ4+8S+rta0vOSXpbUky1epd7j7tL3YaK/pSpgH5YSAgCqgxmDQHCEABAcIQAERwgAwRECQHClhkCFp+RKor96Vbm/KvcmFdtf2SOBSv8iRH/1qnJ/Ve5NKrC/skMAQMnqmixkZkskrVPvzL9/cPc1qfXPs2Yfpwn/+/i0TqpJzcPefqPRX32q3F+Ve5Py7+/nek+n/GR/H94bfggM5+Igk63FF9h1w9oegOFr85067l39hkA9hwNcHAQYBeoJgZFwcRAANYyt47mDujhIdqpjuSSN0/g6NgegEeoZCQzq4iDuvsHdW929tcpvxABR1RMClb44CIDBGfbhgLufMbMVkp7W/10c5JXcOgNQiHreE5C7PyXpqZx6AVACZgwCwRECQHCEABAcIQAERwgAwRECQHCEABAcIQAERwgAwRECQHCEABAcIQAERwgAwRECQHCEABAcIQAERwgAwRECQHCEABAcIQAERwgAwRECQHCEABAcIQAERwgAwRECQHCEABAcIQAERwgAwRECQHB1fTU5RhYbm/51j/nQ1IZu/9U/nZWsd4/vSdYvmnM4WR//JUvW/3vtecn67tZvJ+tHut9L1hc8ujJZv+RPXkjWy1JXCJjZAUknJHVLOuPurXk0BaA4eYwEftPdj+TwOgBKwHsCQHD1hoBL+p6Z7TKz5Xk0BKBY9R4OLHT3Q2Y2TdIOM/uxuz/Xd4UsHJZL0jiNr3NzAPJW10jA3Q9lt4clPSFpfj/rbHD3VndvbVJzPZsD0ADDDgEzm2Bmk87el7RY0t68GgNQjHoOB6ZLesLMzr7ONnf/bi5djVJjLpubrHtzU7J+6Nrzk/X3r0yfx275YLr+/CfS58nL9q8/m5Ssf/3vliTrbZdvS9bfOP1+sr6m81PJ+gXPe7JeVcMOAXffL+kTOfYCoAScIgSCIwSA4AgBIDhCAAiOEACCIwSA4LieQI66F30yWV+7+YFk/aNN6c+7j3anvTtZ/4v7P5+sj30vfZ7+qkdXJOuT3jqTrDcfSc8jGN/elqxXFSMBIDhCAAiOEACCIwSA4AgBIDhCAAiOEACCY55AjppfPZSs7/r5zGT9o02debaTu5UdVybr+99Nf2/B5jnfSdaP9aTP80//5r8l6402Mq8WUBsjASA4QgAIjhAAgiMEgOAIASA4QgAIjhAAgjP34s5+TrYWX2DXFba9qum69apk/fiS9PcCjHlpYrL+wy/dP+Se+rrnyK8n6y9em54H0H30WLLuV6WvUH/gK8myZi/9YXoFDKjNd+q4d1l/NUYCQHCEABAcIQAERwgAwRECQHCEABAcIQAExzyBChkz9ZeT9e53upL1N7alz/O/cs2mZH3+X385WZ/2QLmf58fw1TVPwMw2mdlhM9vbZ1mLme0ws9ez2yl5NgygOIM5HNgsack5y+6StNPd50ramT0GMALVDAF3f07SuePQ6yVtye5vkXRDzn0BKMhw3xic7u4dkpTdTsuvJQBFaviFRs1suaTlkjRO4xu9OQBDNNyRQKeZzZCk7PbwQCu6+wZ3b3X31iY1D3NzABpluCGwXdKy7P4ySU/m0w6AotU8HDCzhyUtkjTVzA5KulvSGkmPmNkXJL0p6aZGNhlF95F36nr+6ePn1fX8j33uR8n62+vHpF+gp7uu7aMcNUPA3ZcOUGLWDzAKMG0YCI4QAIIjBIDgCAEgOEIACI4QAIJr+LRhFOeyO19L1m+9PH1W9x8v2pmsX3vT7cn6pG+/kKyjmhgJAMERAkBwhAAQHCEABEcIAMERAkBwhAAQHPMERpHuo8eS9Xe+eFmy/ub295P1u+7Zmqz/2WdvTNb9Pz6YrM/82g+SdRX4HRmRMBIAgiMEgOAIASA4QgAIjhAAgiMEgOAIASA48wLPvU62Fl9gXKl8uJ4+tCdZ//QF8+p6/a7fvypZf+jue5P12WPH1bX9j21dkazP3diRrJ/Zf6Cu7Y9mbb5Tx73L+qsxEgCCIwSA4AgBIDhCAAiOEACCIwSA4AgBIDjmCWDQfGF6HsLkNQeT9Ycvfrqu7V/6zB8k67/6l+nrKXS/vj9Z378t/fNd/HvpeRpVVtc8ATPbZGaHzWxvn2WrzewtM9uT/flMng0DKM5gDgc2S1rSz/JvuPu87M9T+bYFoCg1Q8Ddn5PUVUAvAEpQzxuDK8zspexwYUpuHQEo1HBDYL2kOZLmSeqQdN9AK5rZcjNrN7P20zo5zM0BaJRhhYC7d7p7t7v3SNooaX5i3Q3u3ururU1qHm6fABpkWCFgZjP6PLxR0t6B1gVQbTXnCZjZw5IWSZoqqVPS3dnjeZJc0gFJt7l7+sPeGv3zBI7ekv48/vn/VOO6+iPcmOnTkvVDN1+SrLfduS5Z/0CN/7M+98biZP3Y1e8k66NZap5AzS8fcfel/Sx+sO6uAFQC04aB4AgBIDhCAAiOEACCIwSA4AgBIDiuJ4DKeORgeh7FeDsvWf+Zn0rWf/vLd6Rf/4m2ZH0k43sHAAyIEACCIwSA4AgBIDhCAAiOEACCIwSA4Gp+lBg4q+fq9HX5V2/dlKx//afpK9PXmgdQy/1dV6Rf/8n2ul5/tGIkAARHCADBEQJAcIQAEBwhAARHCADBEQJAcMwTCMRaP56sv/aV9Hn6jQu3JOvzm9PXpnjskn9J1ms56aeT9Re6ZqdfoKfmV2OExEgACI4QAIIjBIDgCAEgOEIACI4QAIIjBIDgmCcwgoydfVGy/p+3XpCsr775W8n670w8MuSe8rSqszVZf3bdlcn6lC3p7y1A/2qOBMxsppk9Y2b7zOwVM/tqtrzFzHaY2evZ7ZTGtwsgb4M5HDgjaaW7XybpSkm3m9mvSbpL0k53nytpZ/YYwAhTMwTcvcPdd2f3T0jaJ+lCSddLOjuPdIukGxrVJIDGGdIbg2Y2S9IVktokTXf3Dqk3KCRNy7s5AI036BAws4mSHpN0h7sfH8LzlptZu5m1n9bJ4fQIoIEGFQJm1qTeAHjI3R/PFnea2YysPkPS4f6e6+4b3L3V3Vub1JxHzwByNJizAybpQUn73H1tn9J2Scuy+8skPZl/ewAabTDzBBZKukXSy2a2J1u2StIaSY+Y2RckvSnppsa0OHqMnfWRZP3Yb8xI1m/+q+8m6390/uPJeqOt7Eifx//B36fnAbRs/vdkfUoP8wAaoWYIuPv3JdkA5evybQdA0Zg2DARHCADBEQJAcIQAEBwhAARHCADBcT2BIRg748PJetemCcn6F2c/m6wvndQ55J7ytOKtq5P13evnJetTv7M3WW85wXn+KmIkAARHCADBEQJAcIQAEBwhAARHCADBEQJAcKHmCZz6dPrz7Kf+uCtZX3XJU8n64l96b8g95amz+/1k/ZrtK5P1S//8x8l6y9H0ef6eZBVVxUgACI4QAIIjBIDgCAEgOEIACI4QAIIjBIDgQs0TOHBDOvNeu/zRhm7/gaNzkvV1zy5O1q17oCu/97r0njeS9bmdbcl6d7KK0YqRABAcIQAERwgAwRECQHCEABAcIQAERwgAwZm7p1cwmylpq6QPq/cj4xvcfZ2ZrZb0h5LezlZd5e7JD9xPthZfYHybOVC0Nt+p497V70STwUwWOiNppbvvNrNJknaZ2Y6s9g13vzevRgEUr2YIuHuHpI7s/gkz2yfpwkY3BqAYQ3pPwMxmSbpC0tn5pyvM7CUz22RmU3LuDUABBh0CZjZR0mOS7nD345LWS5ojaZ56Rwr3DfC85WbWbmbtp3Uyh5YB5GlQIWBmTeoNgIfc/XFJcvdOd+929x5JGyXN7++57r7B3VvdvbVJzXn1DSAnNUPAzEzSg5L2ufvaPstn9FntRknpr6QFUEmDOTuwUNItkl42sz3ZslWSlprZPEku6YCk2xrSIYCGGszZge9L6u/8Yvoi/ABGBGYMAsERAkBwhAAQHCEABEcIAMERAkBwhAAQHCEABEcIAMERAkBwhAAQHCEABEcIAMERAkBwhAAQXM3vHch1Y2ZvS/qvPoumSjpSWANDR3/1qXJ/Ve5Nyr+/i9z9Q/0VCg2BX9i4Wbu7t5bWQA30V58q91fl3qRi++NwAAiOEACCKzsENpS8/Vrorz5V7q/KvUkF9lfqewIAylf2SABAyQgBIDhCAAiOEACCIwSA4P4HKHtVBZhSZ/sAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 288x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.matshow(x_train_adv[0, :].reshape((28, 28)))\n",
+    "plt.clim(0, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial Training Predicted Label: 0\n"
+     ]
+    }
+   ],
+   "source": [
+    "prediction = model.predict(x_train_adv[0:1, :])[0]\n",
+    "print(\"Adversarial Training Predicted Label: %i\" % prediction)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Benign Test Score: 0.6250\n"
+     ]
+    }
+   ],
+   "source": [
+    "score = model.score(x_test, y_test)\n",
+    "print(\"Benign Test Score: %.4f\" % score)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.matshow(x_test[0, :].reshape((28, 28)))\n",
+    "plt.clim(0, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Benign Test Predicted Label: 7\n"
+     ]
+    }
+   ],
+   "source": [
+    "prediction = model.predict(x_test[0:1, :])[0]\n",
+    "print(\"Benign Test Predicted Label: %i\" % prediction)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial Test Score: 0.2850\n"
+     ]
+    }
+   ],
+   "source": [
+    "score = model.score(x_test_adv, y_test)\n",
+    "print(\"Adversarial Test Score: %.4f\" % score)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.matshow(x_test_adv[0, :].reshape((28, 28)))\n",
+    "plt.clim(0, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial Test Predicted Label: 6\n"
+     ]
+    }
+   ],
+   "source": [
+    "prediction = model.predict(x_test_adv[0:1, :])[0]\n",
+    "print(\"Adversarial Test Predicted Label: %i\" % prediction)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/adversarial-robustness-toolbox/notebooks/classifier_scikitlearn_DecisionTreeClassifier.ipynb b/adversarial-robustness-toolbox/notebooks/classifier_scikitlearn_DecisionTreeClassifier.ipynb
new file mode 100644
index 0000000..6327ed4
--- /dev/null
+++ b/adversarial-robustness-toolbox/notebooks/classifier_scikitlearn_DecisionTreeClassifier.ipynb
@@ -0,0 +1,648 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Adversarial-Robustness-Toolbox for scikit-learn DecisionTreeClassifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "from sklearn.datasets import load_iris\n",
+    "\n",
+    "import numpy as np\n",
+    "from matplotlib import pyplot as plt\n",
+    "\n",
+    "from art.estimators.classification import SklearnClassifier\n",
+    "from art.attacks.evasion import ZooAttack\n",
+    "from art.utils import load_mnist\n",
+    "\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1 Training scikit-learn DecisionTreeClassifier and attacking with ART Zeroth Order Optimization attack"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_adversarial_examples(x_train, y_train):\n",
+    "    \n",
+    "    # Create and fit DecisionTreeClassifier\n",
+    "    model = DecisionTreeClassifier()\n",
+    "    model.fit(X=x_train, y=y_train)\n",
+    "\n",
+    "    # Create ART classifier for scikit-learn DecisionTreeClassifier\n",
+    "    art_classifier = SklearnClassifier(model=model)\n",
+    "\n",
+    "    # Create ART Zeroth Order Optimization attack\n",
+    "    zoo = ZooAttack(classifier=art_classifier, confidence=0.0, targeted=False, learning_rate=1e-1, max_iter=20,\n",
+    "                    binary_search_steps=10, initial_const=1e-3, abort_early=True, use_resize=False, \n",
+    "                    use_importance=False, nb_parallel=1, batch_size=1, variable_h=0.2)\n",
+    "\n",
+    "    # Generate adversarial samples with ART Zeroth Order Optimization attack\n",
+    "    x_train_adv = zoo.generate(x_train)\n",
+    "\n",
+    "    return x_train_adv, model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1.1 Utility functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_data(num_classes):\n",
+    "    x_train, y_train = load_iris(return_X_y=True)\n",
+    "    x_train = x_train[y_train < num_classes][:, [0, 1]]\n",
+    "    y_train = y_train[y_train < num_classes]\n",
+    "    x_train[:, 0][y_train == 0] *= 2\n",
+    "    x_train[:, 1][y_train == 2] *= 2\n",
+    "    x_train[:, 0][y_train == 0] -= 3\n",
+    "    x_train[:, 1][y_train == 2] -= 2\n",
+    "    \n",
+    "    x_train[:, 0] = (x_train[:, 0] - 4) / (9 - 4)\n",
+    "    x_train[:, 1] = (x_train[:, 1] - 1) / (6 - 1)\n",
+    "    \n",
+    "    return x_train, y_train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_results(model, x_train, y_train, x_train_adv, num_classes):\n",
+    "    \n",
+    "    fig, axs = plt.subplots(1, num_classes, figsize=(num_classes * 5, 5))\n",
+    "\n",
+    "    colors = ['orange', 'blue', 'green']\n",
+    "\n",
+    "    for i_class in range(num_classes):\n",
+    "\n",
+    "        # Plot difference vectors\n",
+    "        for i in range(y_train[y_train == i_class].shape[0]):\n",
+    "            x_1_0 = x_train[y_train == i_class][i, 0]\n",
+    "            x_1_1 = x_train[y_train == i_class][i, 1]\n",
+    "            x_2_0 = x_train_adv[y_train == i_class][i, 0]\n",
+    "            x_2_1 = x_train_adv[y_train == i_class][i, 1]\n",
+    "            if x_1_0 != x_2_0 or x_1_1 != x_2_1:\n",
+    "                axs[i_class].plot([x_1_0, x_2_0], [x_1_1, x_2_1], c='black', zorder=1)\n",
+    "\n",
+    "        # Plot benign samples\n",
+    "        for i_class_2 in range(num_classes):\n",
+    "            axs[i_class].scatter(x_train[y_train == i_class_2][:, 0], x_train[y_train == i_class_2][:, 1], s=20,\n",
+    "                                 zorder=2, c=colors[i_class_2])\n",
+    "        axs[i_class].set_aspect('equal', adjustable='box')\n",
+    "\n",
+    "        # Show predicted probability as contour plot\n",
+    "        h = .01\n",
+    "        x_min, x_max = 0, 1\n",
+    "        y_min, y_max = 0, 1\n",
+    "\n",
+    "        xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n",
+    "\n",
+    "        Z_proba = model.predict_proba(np.c_[xx.ravel(), yy.ravel()])\n",
+    "        Z_proba = Z_proba[:, i_class].reshape(xx.shape)\n",
+    "        im = axs[i_class].contourf(xx, yy, Z_proba, levels=[0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0],\n",
+    "                                   vmin=0, vmax=1)\n",
+    "        if i_class == num_classes - 1:\n",
+    "            cax = fig.add_axes([0.95, 0.2, 0.025, 0.6])\n",
+    "            plt.colorbar(im, ax=axs[i_class], cax=cax)\n",
+    "\n",
+    "        # Plot adversarial samples\n",
+    "        for i in range(y_train[y_train == i_class].shape[0]):\n",
+    "            x_1_0 = x_train[y_train == i_class][i, 0]\n",
+    "            x_1_1 = x_train[y_train == i_class][i, 1]\n",
+    "            x_2_0 = x_train_adv[y_train == i_class][i, 0]\n",
+    "            x_2_1 = x_train_adv[y_train == i_class][i, 1]\n",
+    "            if x_1_0 != x_2_0 or x_1_1 != x_2_1:\n",
+    "                axs[i_class].scatter(x_2_0, x_2_1, zorder=2, c='red', marker='X')\n",
+    "        axs[i_class].set_xlim((x_min, x_max))\n",
+    "        axs[i_class].set_ylim((y_min, y_max))\n",
+    "\n",
+    "        axs[i_class].set_title('class ' + str(i_class))\n",
+    "        axs[i_class].set_xlabel('feature 1')\n",
+    "        axs[i_class].set_ylabel('feature 2')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 2 Example: Iris dataset"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### legend\n",
+    "- colored background: probability of class i\n",
+    "- orange circles: class 1\n",
+    "- blue circles: class 2\n",
+    "- green circles: class 3\n",
+    "- red crosses: adversarial samples for class i"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "ZOO: 100%|██████████| 100/100 [00:03<00:00, 28.05it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAq4AAAE7CAYAAAAcvANrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nOzdeXiU1dn48e/9zJKZrEDCHgQUEBHEWkStC7Zaq1jF9+eKrUvdat+6YbXYVouordWqVK2tWjfcq1aFKiqvrWK1WsEKLiDIIhIIBEISssxkluf8/piZZGYySSaQSTLJ/bmuXGbOnDnPGUzO3DnPuc8RYwxKKaWUUkr1dFZ3d0AppZRSSql0aOCqlFJKKaWyggauSimllFIqK2jgqpRSSimlsoIGrkoppZRSKito4KqUUkoppbKCBq6qy4nI+SLybnf3QymlOkrHL6W6lwauqtcTkQEi8pKI1IvIRhE5u7v7pJRS6RCRy0RkmYg0ishj3d0fpbqbs7s7oFQXuA8IAIOBA4FXRWSFMebz7u2WUkq1awtwC/A9wNvNfVGq2+mMq8oYERkhIi+KyHYRqRSRP7ZS724R2SQiu0TkIxE5Mu65qdHZhl0isk1E7oqWe0TkyWi71SKyVEQGp2g7DzgVuMEYU2eMeRdYCJyTmXetlOoNesL4BWCMedEY8zJQmZE3qlSW0cBVZYSIOIBXgI3AKGA48Gwr1ZcSmQkdADwNPC8inuhzdwN3G2MKgX2A56Ll5wFFwAigGLgU8KVoexwQNsasiStbAey/W29MKdXr9aDxSymVRANXlSlTgWHAtcaYemOMPzrb2YIx5kljTKUxJmSMuRPIAfaNPh0ExohISXTG9IO48mJgjDEmbIz5yBizK0Xz+UBNUlkNULCH708p1Xv1lPFLKZVEA1eVKSOAjcaYUHsVReRnIrJKRGpEpJrITERJ9OkLicyafhG9nfb9aPkTwBvAsyKyRURuFxFXiubrgMKkskKgdjfek1Kqb+gp45dSKokGripTNgF7iUibCYDR9WCzgTOA/saYfkRmRAXAGPOlMWYmMAi4DXhBRPKMMUFjzFxjzATgW8D3gXNTXGIN4BSRsXFlkwFNzFJKtaanjF9KqSQauKpM+RAoB34nInnRZITDU9QrAELAdiIB5q+JmyEVkR+KyEBjjA1UR4vDIvJtEZkUXYu2i8itt3By48aYeuBF4KZoPw4HZhCZ8VBKqVR6xPgVbcMZXTPrABzRvuiOQKrP0sBVZYQxJgycBIwBvgbKgDNTVH0DeI3IzOhGwE9ktiPmeOBzEakjkuhwljHGDwwBXiAy6K8ClgBPttKd/yWyjUwF8AzwE90KSynVmh42fl1PJHHrOuCH0e+v34O3p1RWE2NMd/dBKaWUUkqpdumMq1JKKaWUygoZC1xF5BERqRCRz1p5XkTkHhFZKyKfiMhBmeqLUkp1lI5hSinV82RyxvUxIut7WnMCMDb6dQnw5wz2RSmlOuoxdAxTSqkeJWOBqzHmHWBnG1VmAI+biA+AfiIyNFP9UUqpjtAxTCmlep7uXOM6nMTsy7JomVJKZQMdw5RSqot1515wkqIs5RYHInIJkVtx5OXlfXP8+PGZ7JdSqof56KOPdhhjBnZ3P5KkNYbp+KVU39ZDx6+s1Z2BaxmRY/ViSoEtqSoaYx4EHgSYMmWKWbZsWeZ7p5TqMURkY3f3IYW0xjAdv5Tq23ro+JW1ujNwXQhcJiLPAocANcaY8vZe9OXyjRxfcknGO6f2jBk9nC8u9bLke/O6uysqi40qbXdI6E4dHsMCgRV8VabLYLPFtDdmMf5+H7Jhc3d3RWWh13c82N1d6JUyFriKyDPA0UCJiJQBcwAXgDHmfmARMB1YCzQAP8pUX5RSqqN0DFNKqZ4nY4GrMWZmO88b4KeZur5SSu0JHcOUUqrn0ZOzlFJKKaVUVtDAVSmllFJKZQUNXJVSSimlVFbQwFUppZRSSmUFDVyVUkoppVRW0MBVKaWUUkplBQ1clVJKKaVUVtDAVSmllFJKZQUNXJVSSimlVFbQwFUppZRSSmUFDVyVUkoppVRW0MBVKaWUUkp1KhF5REQqROSzVp4XEblHRNaKyCciclA67WrgqpRSSimlOttjwPFtPH8CMDb6dQnw53Qa1cBVKaWUUkp1KmPMO8DONqrMAB43ER8A/URkaHvtauCqlFJKKaW62nBgU9zjsmhZm5wZ645SSimllOpRph2dY6p22nvczqefhj4H/HFFDxpjHuxAE5KizLT3Ig1clVJKKaX6iKqdNgsXlexxO6NHbPUbY6bsQRNlwIi4x6XAlvZepEsFlFJKKaVUV1sInBvdXeBQoMYYU97ei3TGVSmllFJKdSoReQY4GigRkTJgDuACMMbcDywCpgNrgQbgR+m0q4GrUkoppZTqVMaYme08b4CfdrRdXSqglFJKKaWyggauSimllFIqK2jgqpRSSimlsoIGrkoppZRSKito4KqUUkoppbKCBq5KKaWUUioraOCqlFJKKaWyggauSimllFIqK2jgqpRSSimlsoIGrkoppZRSKito4KqUUkoppbKCBq5KKaWUUioraOCqlFJKKaWyggauSimllFIqK2jgqpRSSimlsoIGrkoppZRSKito4KqUUkoppbKCs7s7oJRSSimlusYu28ObDWM6oaWtndBGx+mMq1JKKaWUygoauCqllFJKqayggatSSimllMoKGrgqpZRSSqmskNHAVUSOF5HVIrJWRK5L8fxeIvKWiHwsIp+IyPRM9kcppdKl45dSSvU8GQtcRcQB3AecAEwAZorIhKRq1wPPGWO+AZwF/ClT/VFKqXTp+KWUUj1TJmdcpwJrjTHrjTEB4FlgRlIdAxRGvy8CtmSwP0oplS4dv5RSqgfK5D6uw4FNcY/LgEOS6twILBaRy4E84NgM9kcppdKl45dSSvVAmZxxlRRlJunxTOAxY0wpMB14QkRa9ElELhGRZSKyLGD8GeiqUkolyMj4VbnTzkBXlVKq78hk4FoGjIh7XErLW2kXAs8BGGPeBzxASXJDxpgHjTFTjDFT3OLJUHeVUqpJRsav4gG6kYtSSu2JTI6iS4GxIjJaRNxEkhcWJtX5GjgGQET2IzLwb89gn5RSKh06fimlVA+UscDVGBMCLgPeAFYRyb79XERuEpGTo9V+BlwsIiuAZ4DzjTHJt+OUUqpL6fillFI9UyaTszDGLAIWJZX9Ou77lcDhmeyDUkrtDh2/lFKq59EFV0oppZRSKito4KqUUkoppbKCBq5KKaWUUioraOCqlFJKKaU6nYgcLyKrRWStiFyX4vm9ROQtEflYRD4RkenttamBq1JKKaWU6lQi4gDuA04AJgAzRWRCUrXrieza8g0i2w7+qb12NXBVSimllFKdbSqw1hiz3hgTAJ4FZiTVMUBh9PsiWh700kJGt8NSSimllFJ90nBgU9zjMuCQpDo3AotF5HIgDzi2vUZ1xlUppZRSSnVUiYgsi/u6JOl5SfGa5ENaZgKPGWNKgenAEyLSZmyqM65KKaWUUn1EXTiHf1WP64SW3t1hjJnSRoUyYETc41JaLgW4EDgewBjzvoh4gBKgorVGdcZVKaWUUkp1tqXAWBEZLSJuIslXC5PqfA0cAyAi+wEeYHtbjWrgqpRSSimlOpUxJgRcBrwBrCKye8DnInKTiJwcrfYz4GIRWQE8A5xvjEleTpBAlwoopZRSSqlOZ4xZBCxKKvt13PcrgcM70qbOuCqllFJKqayggatSSimllMoKGrgqpZRSSqmsoIGrUkoppZTKChq4KqWUUkqprKCBq1JKKaWUygoauCqllFJKqayggatSSimllMoKGrgqpZRSSqmsoIGrUkoppZTKChq4KqXaZQVs3LVBrIDd3V1RSqkOK8r3MW6v7RTl+7q7K2oPObu7A0qpni23wkfJmhoQAWPYMa6IhkHe7u6WUkql5eiD1nLV2e8QCls4HTbznj6KJf8d093dUrtJZ1yVUq2yAjYla2qwbLDCBssm8lhnXpVSWaAo38dVZ7+Dxx0m3xvE4w4z6+x3dOY1i2ngqpRqlbMxHJlpjScSKVdKqR5u8IA6QuHEUCccthg8oK6beqT2lAauSqlWhXIcYExioTGRcqWU6uG27czH6Ui8Q+Rw2Gzbmd9NPVJ7SgNXpVSrbLfFjnFF2BbYDsG2iDx269ChlOr5auq8zHv6KPwBB/U+F/6Ag3lPH0VNna7Tz1aanKWUalPDIC9l/XJwNoYJ5Tg0aFVKZZUl/x3D8jXDGTygjm078zVozXIauCql2mW7LQIasCqlslRNnVcD1qiGkJsV24d1dzd2m34SKaWUUkqprKCBq1JKKaWUygoauCrVC+lJV0qpbKWnXKm26BpXpXoZPelKKZWt9JQr1R6dcVWqF9GTrpRS2UpPuVLp0MBVqV5ET7pSSmUrPeVKpUMDV6V6ET3pSimVrfSUK5UODVyV6kViJ135g1DXiJ50pZTKGnrKlUqHJmcplUFWwO7yE6caBnk597F6hhYa5s4r1qBVKbVbivJ9XX7alJ5ypdqjgatSGdKd2f3VfqHaLxq0KqV2S3dm9+spV6ot+qmmVAZodr9SKltpdr/qyTRwVSoDNLtfKZWtNLtf9WQZDVxF5HgRWS0ia0XkulbqnCEiK0XkcxF5OpP9UaqraHZ/9tPxS/VVmt2verKMBa4i4gDuA04AJgAzRWRCUp2xwC+Aw40x+wNXZao/SnWlvprd31uOmtXxS/VlfTm7X4+b7fkymZw1FVhrjFkPICLPAjOAlXF1LgbuM8ZUARhjKjLYH6W6VF/L7u9lR83q+KX6tL6Y3a/HzWaHTH6SDgc2xT0ui5bFGweME5H3ROQDETk+g/1RqstV+4VVFVavD1p7YTKajl+qz6up87Lm64F9ImjVhLTskclPU0lRlrToDycwFjgamAk8JCL9WjQkcomILBORZQHj7/SOKqX2TC9MRsvI+FW5M2sDeaV6NU1Iy4xM5ApkMnAtA0bEPS4FtqSos8AYEzTGbABWE/kgSGCMedAYM8UYM8Utnox1WCm1e3phMlpGxq/iAb175l2pbKUJaZ0vU7kCmRxFlwJjRWS0iLiBs4CFSXVeBr4NICIlRG69rc9gn5TKas76EHlbfTjrQ93dlQSxZDTbAtshvSEZTccvpTpZ6eAqjp26htLBVd3dlRb6ckJaBjXlChhjAkAsVyBeh3MFMpacZYwJichlwBuAA3jEGPO5iNwELDPGLIw+d5yIrATCwLXGmMpM9UmpbNb/yxoKy5vXW+0a6qVqbFE39ihRwyAvZf1yuvyI20zQ8UupzvWTU9/j5KOacxsXLJnA/S8e3o09aqkvJqRlWKpcgUOS6owDEJH3iIy1NxpjXm+r0VYDVxEZAfw+euHXgN8bY4LR5142xpzSXo+NMYuARUllv4773gBXR7+UUq1w1ocoLPclLLwsLPdROyyPUF7PObnZdlsEekjAuqdjmI5fSnWO0sFVnHzUyoRl8DOmreSV9yZQtq1/93UsBT1utkNKRGRZ3OMHjTEPxj3uaK5AKfAvEZlojKlu7aJtfeI9AvwN+AC4EFgiIidFZxRGtvE6pVQny6kNtlrekwLXHkbHMKV6gPEjt7da3tMC174gHLSo2lrQGU3tMMZMaeP5dHMFPohOKmwQkViuwNLWGm1ramSgMeZ+Y8xyY8zlwJ+Ad0RkH1pGzEqpDGoscHWoXAE6hinVI3yxcWCHylWvkZFcgbYCV5dIcwq/MeZJ4Eoi67qGdrj7SvUS7uoARV/V4q4OdNk1Q3lOdg31Ykwked8QWePaVbOtWXoilo5hSqUwfvRWfnjCMsaP3tol1yvb1p8FSyY0j18mssa1K2db9USsrmeMCQGxXIFVwHOxXAEROTla7Q2gMpor8BZp5Aq09an3EJFFtEviOvGmiJwO3L77b0Wp7DVoRSXemsht+35f1+MrclExubhLrl01tojvXOJjymi44d6SLgtas/hELB3DlEpy86WLmLLfZgB+cPzHLFs5nBsemJ7x697/4uG88t4Exo/czhcbB3Zp0KonYnWfTOQKtPrJZ4yZ10r5x8B3072AUr2FuzqAtyaYsNrcWxPEXR0g0M/dJX34cpvw5Tb4RRfOtMZOxIrdXS9ZU0NZv5wev2uAjmFKJRo/eitT9tuckCQ1ZcJmxo/eyhcbhmT8+mXb+nf5mtb4E7Eim3/ArLPfYfma4ZqElaV69iePUj2It7qxQ+W9QS88EUupPmvK+LIOlfcGeiJW76OBq1Jp8vXL6VB5b9ALT8RSqs9a9kVph8p7Az0Rq/fRwFWpNAX6ufEVuRISpHxFrk5ZJpBu8lNxvuGgkabLkqRiJ2L5g1DXSG84EUupPuuLDUNYtnJ4QpLUspXD93iZQLqJT92RIKUnYvU+7S6UE5HBwG+BYcaYE6LnzB5mjHk4471TqoepG5oL22uwALc7+ngPpZv8lFvh4/PfQjAEBR9WdFmSVMMgL+c+Vs/QQsPcecVZF7TqGKZUs38sHccBY7c0jTdvLh23R+2lm/jUnQlSeiJW75LOJ9BjRLYrGBZ9vAa4KlMdUqqniiUq5brB44788pSsqdmj2c/45CcrbLDs1G3GX7sol1brZUq1X1hVYWVd0Br1GDqGKdWUqJTjNuS4bHLchllnv7PbM6DxiU/53iAedzhle+nWy6SaOi9rvh6oQWsvkM6nUIkx5jkgklcc2ZdLMzNUn5OJRKV029QkqT2iY5hSdH6iUrrtaYKU6kzpBK71IlJMdC8cETkUqMlor5TqgTKRqJRum5oktUd0DFOKzk9USrc9TZBSnSmdwPVqIkd07SMi7wGPA5dntFcqazhNmJt3vcbNu17DY4JN3zvtUIu63XX6UrrXba9eLFGpoRFqGjonUSnd5KdMXLsP0TFM7bFMJhZ57ADP7nycZ3c+Tj+7oel7jx3o0HXbq9vZiUrptqcJUqoztZmcJSIW4AGmAfsCAqw2xgS7oG8qC8ypXcykYDkAT1Y9jdNE7sDeuPYZTuOSpnrddfpSRxKf0qnXMMjL/lfWMGog/O3/BnVK4Jhu8lPDIC8HXF3DqBJ47o3OuXZvp2OY6gyZTix6rPpZikxkP+hnqp5qKn+67imsu0xa1023j52dqJRue5ogpTpLm4GrMcYWkTuNMYcBn3dRn1QW8hCGaNDaANTUb2bb7Q9w1kOV9PMYXj4/hOWCrjx9Kd1Tnzp6OtSO2shXZ/a92i9U+6XdNjNx7d5MxzC1p4q89V128lL8KnYDeHNCiJt2r9vR06Fq6ryd2vd02+vs66q+KZ1Pv8UicqpIcmaIUvCbgmMJSeI6yyDC+Z6BTY+HFhpCyXffuyCxSBOfVJSOYWq3DS6sznhi0aX9TktZXn9T4tja2nU1+Un1JekceH41kAeERMRP5I9CY4wpzGjPVFb4Ve2bTcsDYnKweMnh5rSfX8Kz35uHFbDJ/bAimtMd1QWJRZr4pKJ0DFO7bduufhlPLLq/+oWU5Xm/DsMD7V9Xk59UX9LujKsxpsAYYxlj3MaYwuhjHfBVAj8O6sSNn5bBXiyxyLbAdkiXJRZ1NPHJHwR/MBJft9W/fYfCuUeCs75lAlqyfh7DfoPsLk9I667r9kQ6hqk9UePL69TEolQJrXkmAESWB8S+AML1EL4V6quchG+FzdcUUV/b8qS+5uQnwd9o4Q9Im30sHVzFsVPXUDq4qs2+dsdJV915XZUd0jk566hU5caYdzq/OyrbzC04jjm1i4HIsoFf1b4JwI1jZibUaxjkpaxfDs7GMKEcR5et0Uw38SmnJkCOM/FxquSs/l/WsOr30Qcf7WDXUC9VY4tStplb4ePl80OEbMjtwpOuuuu6PZWOYWpPdWZiUaqE1nB0dWs1hgNx8rk4yDMBwkaQzwS5AkK2g+HUMMe1mBsKT2jR7oTR28hxNYe9+43aljI56yenvsfJR61serxgyQTuf/HwFvW666Sr7jxhS2WHdJYKXBv3vQeYCnwEfCcjPVJZJSSOhEE09r2xWv5o2W6LQDckFbWX+OSsD1FY7ktY5lpY7qN2WB6hPGeH60FcwpcrWmB3cUJaF1+3h9MxTO2x+MQipwmn/IN9bsFxLdb8tyY+odWPg+WuYXwrWAHAWQPO5eZdrzEpWE4O4aZlVqnuaEFkBvXko1YmjE0zpq3klfcmULatf4frdTTZq7N013VVdklnqcBJcV/fBSYC2zLfNaW6Rk5t6p2RksvTrQfdl/CliWYt6RimOlts1nRSsJwnq55u+j4WzLYmbEL82DkUf9LBbSFxcEvBsQllqRJfU9UDGD9ye8rrJZenW6+7kr00yUylI50Z12RlRAZ+pXqFxgJXWuXp1oPuS/jSRLO06BimOkXyrGlrwibEJv8qNvhW8LLxkbxK1WnCXF/7JoviylIlvsbqJS8V+GLjQFJJLk+3Xncle2mSWdeQoJCzNfXnWTZIZ43rvTSvFbeAA4EVmeyUyi522MvcutewCHBr4bcjt83W5DAzdEFCPStgd+oaV2d9iJzaII0Frha36jsilOdk11AvhVuiiQACu4Z6W7SZbj1oTvjK/6QG24Zcb+ckpO07FKbuHXnv7V03GIaCPD1hS8cw1ZbKymLKNpVSOqKM4uLK5icChvnzHib/6xCWz8+Y8A425xXSGHJiG4sQFsTNnIawsIzNzbtea1o+YIzhQtdw1vg/JWB8DHANo5+djxXeSaOAcYCEaf7pTGJj0wDYuHDSeqJl2bb+LFgygRnTEteuxt/+70i9WLLXrLOXYGxBLNMpJ12VDq5i/MjtfLFxYItrJl73HcJhC0d0jasuE1Dx0vm0Xxb3fQh4xhjzXob6o7JMuf9gbqt/gcmUAfDEzr/iIgAh4aF758OJkXqdfXJW/y9rKCxvzjhtK0mqMzUWufFv9GEMeDyRx22RFt/svv5f1vDZ76IP2kgMaxjk5ZBf1jCyGB5foCdsoWOYasXCl09h9rV34XIFCQZd3HbHLE6esQCAQRdWMWjNTnKCwaY1dePrdkBkCGsRa3oJclBoCzbw5M4n8RLZdeT3oc38wDWMMd5j6O8aym12gL82PIHbZcPvgOsgELT4Te4xUPVMU3s/zz2En9f8Da/k84f+pyWso00l3V2Kv/hqMCccvjryBgRWfTU4dXuAIBiRzhi+0k4K0xO2VHvS+UTrZ4yZH/16yhjznohcmfGeqR4vYOfzWf15mOiPUS4+CvDhIYxtQjRu2MRZp1dy6Tk7KPw0cjKVFTZY0YSh3d2qqSlJCpq+Cst9aW1P1WZ7Ehn8W2svlvjkdUNuDlim9fcRXzffQ+e953b6GFNZJ/x3Y/sncfUROoapFiori5l97V34/V5qawvx+73MuuIuTj1FOOv0SlZ8HIRQCAckjDVimoPEBqAGwY+j6cPUAeQTeZ0FHIKLgwtPpL9rKAA3B17HbWwkAPJzkAC4jc3NgdcT+rfG/wnfx8HcfjPwi4sbCk/ghsITUiZ/xSddxb5mTFvZYrurWPJTjitMjjtMjivMrLPfabHtVFM9dxhvTogcd+p66Uq3fzE1dV7WfD1Qg1aVUjozrucBdyeVnZ+irEsEB3jYeuZ+3XHpPm/IM6sSHvvsYixCnM7zlFFKLs2DWkAsZu/nxEOozZOzdmeXgbaSpHZnyUC67TUnPsXNtbTyPjpStzP7qFLqUWOY6hnKNpXicgXx+5uDI5EgjY0jcbl28MtxDl76yCY3ec04gAfWOAZS1ljHD4uH8DeXiwnlG+gfavl7ut6ZuH403xuAIBCIfgG4o+X1kYf14RrKG9eyl2ciOVZuu++lraSr+FvyzclPzUscYslP8UFiuvXSlW7/lEpHq594IjITOBsYLSIL454qACpTvyrzcot9TD7ns+66fJ+2gokJwavXqsTGyfPMwN00Akfk5cC/CxqpeKK4zZOz3mwYwx+/PBqAy8a+zbG5a9vtR0eSpNKRbnsdSXzq7CSpzn7PfUFPHMPW+gZyyooLu+PSfVqqsaV0RBnBYOLvj8vlZv6TtRQXF/PV8YV4zHpItbY0BPVFhZyyVw5WzQ6OrQuyMBTku+30wxibXxWN56n69xOfcMDvS6bBjkh61nrfxwgWo70HpPX+OjvpqrOTpNLtn1LpaGuq5t9AOVAC3BlXXgt8kslOtWWYq4a5wxa1X1F1umkH7svgD4YjGzYD4LbqmJg3H6mPDHANeAk5hRxHIy7CxOYeYglDyWtcF4fG8dvHfkDB+/l48mq4/bD/B6e/2G7w2pEkqXSk216LhKvc1hOfmuquiCZJ5e9ZklRnv+c+oseNYdYOB/mPZH4ttkp0+0Etx5bi4kpuu2MWs6+Zh9MVJBRd41pcXMm0N2bxwqYHwRjCJK2pEwjZFtVmF/4vDLa9L5b1Fd/An/La+4QrqQtVsblxDeWNa3lpZwMhB8SHzOGA8KN1y/gLYDAdmm2FjiddXX32O9hGsCR10lVnJ0ml2z+l0tHqp54xZiOwETis67qjss1Qz1Luco2nqG4DFgGuuuBSHvjkj4zxbKfi4eZBKdXJWTfffTll8/4HKxzExsmYijf444FHc+zk9mddq8YW8Z1LfEwZDTfcW7LHAVyHkq5iiRmtZALHNAzycsDVNYwqgefe2PMkqaqxRRzyAx+HjIHf/GXP33Nv1xPHMEetn/wla7q7G33QuJRjy8kzFnD4Ee8m7CowZ8t0+i93Mdc5jTmuRsQYLAwjQ+Ws9zoIhXKoCTZw8rajsXkMCCDiZvuIsRRv3oJtQz1CXnSA+MjYvFfzAoJQ4hpBoV1POFyNHbergImLjm3CHZptjelI0pXBRBLMpPVBrLOTpO5/8XBeeW9Cm7sKKJWOdLbDOhS4F9gPcBNZe16vZ32rGMvhY27R0QD43Dnceff3Us6Kx5+cVVlZzKZ7TsGEXdjRuYe1/z2e/WrSXwby5Tbhy23wiz0M4JpOm4rFqib1aVNN9XLarhdvR23kq7OSpFaXR77matCaNh3DVFuKiysTt8GKij8VMGD7ebf6eYI+P5GNKUpA5oPxArmEw3BoxUqWTxnDl8t3cVZAeJpGAH5o5bOvZ1+G5uxDjpXLrW2cumV2PvkX3KMAACAASURBVIHBZi/PpLRnWyEx6Som1YlTzSdT2W3Wi4k/LawzlG3rrwGr2mPpfPr9ETgLeB6YApwL6MHBao+UbSpFnDYmbmmsiE2gGwa1dBOpOjvhSnUZHcNUhxljqAxuZm3DMmrCzclFw9xjGVj6Yz4pF0xckr3fhklfjKEh8AEiFj8ZdCjDig/k4G0tT8lKdUw2RGZbAfJmnsrWvELyt4RbnaHfOrM5Sbk472tC4iIhmco4GTTRS/W24U1lgwZvJmSc7dZTqidLa9rGGLNWRBzGmDDwqIj8O8P9Ur1c6YgyTCgx2DPGokGCvNnQHFOkk6yV7JQVFwEweeCWNtdDx+r1t+t53b4nciZ4lG0L/7TH4G9oXonmsYOcZS/FigtcU9VLeE9sBUh4T6lU2Q1p1SPN9kJsT7O99KTfv851USe1o2OYSlftllrK31/Eyqp/ETSRWVO3eHGIC4+Vy6SCo2kssGFT4vgVDLoYf8ROVq0ejLcxl9LDTwXA5NY35QW0pX6oB1NpY7ksplz6NQArtg8DxrUIXtfP2o/Dvtt8d6og7MOxPjFAtpw2n40dQdXo5oSqgNPC4bTbradUT5ZO4NogIm5guYjcTiTZIS+z3VLdLdUpV+FaLzX1/cm1a3BbHT87Or7N4uJKRlz5MmV3Na9x3X+vhfjrQ3yyeh/yv3ZTt1cA9u1Y8HrKiouakl9WDBvAnHMAnmizXpAiZh85k1nW63y1fRR7Dy/jj4MP442v9m/xuk89w5hT/RLBMLhzndzoOSllvRg7us99bOeE1mxu+DqtevBuWvUaw0vTbC896fevc100rlOa0TGsF3O2cut93tAjKSn2sW1nPrHRqrVTsurqbBa94uf553wsW/p8U3meox8TvVO5t3ElDaaC20c6+fWmF6j7vJH3QksJ8hiWFcRyuLjpllnMPHsXJ51ewhjXMPxDfJRb+UAeeQxvN3hd4foAF/CqbXP47CfZfn8/Bs6u5sPto/j9yDOQr7Ywp3YxgaH5zD/czx9m/xUgkkvgFt5nNGNX7GJDxWhGl2zgzsCxbC5JDEYbyOcXlWdya8lfCeHASZhfVJ7Zop5SPVk6ges5RJaNXwbMAkYAp2ayU6p7pTrl6tl/z2TLzy9laziMsS0m5s1nqGfpHrXZf9qnDFm5HevdarxWJa7iIvZeZ3jopj/hdgQIhN1cecX1cHF6weubDWNoWD6AIUsiW3bljR7O2wfuy9B26gFsqp/Kfm89gMsRIBh2M+2MR2kYHyDZSxzJI7e9xKiBYF30a6rsdgb86ORsw/IBbVaz61xp1Ytpt15YOtReezrav05zYqe0omNYLzandjGTguUAPFn1NE4TxmEZnih8Bt9PXTgdNrd+bPPY2zM44o+/bDol69bbr6Kk5Hn+9oKPN17z4/OBFd2tLnfIKCY2TKS/ayg317zGZHszLofhqC07wUDAhldL3qT6gYNwu0c1BcL/uP841v33z2xyBAj8x80Fp93GcgrafQ/14RpqV3zEawKHh8HzQYARB2+HoGFqeAM3ep8BXyOTguWYLRbfOGkdnuiesYMurKLiiQH8Y/GJfP+3t+ByBAmGXUw741FIMYYtqj+I933jKHXupCw0oP0xTKkept3A1RizUUS8wFBjzNwu6JPqRk0JSDbEoi6zzMXsa+/EBF2Eo4lUn9WfR7F7VVozr6naLFlTQ/+8eoI5PvKdGwEYkOdm/lO/xhfIxUckMeHue27h3uknwdjOfqfNcrxOXnvrwoTrLnnuR4y/5s/U5bZcuxpLuBqrA35W0DGsb/AQS8+PZutbkO+NBHcX7PN/3HzVQxjjbTpwYNYV84AXycnxN52EdeihLgrO+S5bPzsW75PLWO9bTj3luBwGCUJsjz+3C3IHD2Ds1BpgBQA1m4q4/Pd/ThhHHnlhNmNHHcbKLTXgbxlExtSHqyGa6Q9g+QF/bElSkMrgegBsDLmhMLEpZL/Aio+D/OpM+GDZLdFrR7Q1hlXZ+VQFdPxS2andjBIROQlYDrwefXxg0mbeqhdpTkBq9tWO0biciSfCWITw2cW73SYiDLNrEopKcsO4HYmDu8sRJLi+X5q93z2e3DxcjsT353IEKartjBO6VXfTMax3+03BsS2PQXUCcYf6rq8YhWUlji0iQXJzR9PYCN+c4uavLwzgoUcHUF9Rz7oFD7Ck+hm+bFjK7SMLW07xOGHeT49NKNq2ZmjK8Wuf4kFt9j9sQgSMD2dBIWdakBzeBoAzxcVZOFs8F7TgF+Mc5DlG4kpxbR3DVG+UzlKBG4GpwNsAxpjlIjIqYz1S3SrViU+jSjYQDCUmH9k48VrpHT7U2ilSW6zEjdh3NDgIhBP3Tw2GXbj2rk6z97vH31BPMJz4/oJhFzUF0Q0RVba7ER3Dskpr61bnFhzXIkj9Ve2bOE1iYhIhIgf6zo483HvgBsJJv+PGuJg4aTNXX9MfyxJeeN7HhedXUVe3DVdBf/b2HsiwnLH8dvO/4pPwm9qfdd+bcFJz0eBx5SnHL0YczsHBI1pd4/pp3dvUhXcy4iezuP+OG0neQdrtcvGmdwT4GvEEy4nvTJ4LFuVZrL59F4cf0fLaOoap3iidPXxCxpia9qup3iB24pNtge0QbAtkSpDb7rgacQVxWH4sAkzMm592glasTX8IGgJgW5FTpKqsxPyYynov5/3gNrzuBgq9NXjdDVx5xfWY4alvsRXnGw4aabACzVmyjdv7s9l/KHWh5s23R/azmT4+jLM+1FQWqvdSExpJwM6n0RfihG8/jNfdQIEnct1pZzya8habyko6hmWZ2LrVScFynqx6uun7WDCbih8HdeLGj4NgGMI21DRAQyPc+s9+5E/5LdAA1GBZDZz3o8s4/IhdzL5mF2ecupNXFvr53vE5HHffCex37q8YmzuFPEcRwbCDUNjCuCLbthoXhGwHQRID6KIRNdx77aUJ49cFp91GRSj1LfmifB8jhm8g6P6SEZ4JOAsKMSYPcBDOsbALBNsDFobi/Fo87shdIZ/TRUO+i3COYNsWwYCL/kOruHb2dbwhx7HYOpYSVwVLiqfwh+cfwhUKpby+UtkqnRnXz0TkbMAhImOBK4gcpah6qVSnXJ08YwG3B/en9EEPuVvTW9vagkn6bwr/2KcfpUPGMG7wXoQvO4It42vYJ0W93Aofn/8WgiEo+LCCHeOKeOquK/n6qeacmxEb/8Nc19Wc/8PoDMVHO9g11Mt/Pj6Gr+64kE3ByG4GE/PmU5BXg9c1ksFFo/CcfxbrNGjtTXQMy1Lx61b9SYFizNyC47hh1+s02vWchZOHTSXYcN3m4ez/h6Gsrwjz311fEmz8Dd+c8gijRo3hq6/WMf/RLQAc9i03l1+Vz/En5JCXZzFny1C2rbIS2r9HXmbk0J00/ARy/wxfV5Zw0eXn8Sb3JvTlmEv/j33+MTWyq8CkI3nX6kf+luTpWjj6oLVcdfY7BEKGux1wx9OF3Pn3oznJrmQBM6ARXjrt+9y35kpyGkOMvroCgIrr81hrjWDuOZfys9++imXZnPrR37hlwWwuevsJBrrqwBbWWyNxbg/DdvjzU49w0XmXdMr/D6V6gnQC18uBXwGNwNPAG8AtmeyU6n7xp1zFOAp8FOXtRDoYtDYlZ8Xu1Jm45Cxantu+bsdO1u3YyaTi7zKIqtbbcxM5B8mG7W8N4e2nTiX+ttim7YdwyNadSGnzaxtX5/H4L6/DBF2E4hLNpjT+hZ31O9hZv4OxuWd36P2pHk/HsCzzm4JjebLq6aagFSIb999S0Lyu1DZhtgc3scW/hsNC5RgMBY5irs49jEHuUTQ2buBvK1cQMD7yioaRM2EQKz/fwEfLyhk1ysHV1+TzP6d6KS1NHRDH5BUEGHZjDQ43kf0BfgHDg5UUhPwp62+oq2XDzvWMmXxCyucH5NVxbflbOOaB50rgD3CVbzm3fDmBIDlMjyzFhqcMV93+RyYMX9302kG/q+f8f5/NP++4lP8zV0ZWDYRh1hV3MTnvcYqDIXINTTsO+Jyp95dWqquIyPFEFu44gIeMMb9rpd5pRA6JOdgYs6ytNlsNXEXkCWPMOcDFxphfERn4leowZ20I+R2RmdYrgbsjuVojrt/JeoY11dt2aCG5B+5sv70UJ1h9uO6QlHU/XHcIE0qbB/6vto8ix9lIoNHTXMlt2HhgAbwaeZhOH9KuJ+nVtfKDnXtth+lQe+3paP96gp44hoULPNRN65zNaXuzG999CKdlEtaWOrG5PvQvrhh+DBsDn7N903JCgXpcOfkMHXskg/aagievmK0bPuCDz14mYHx4rHzceKmv2YK1Oocz/sfDqad5+eYUF5KcMBo1d9giTjl+GHVbIv+fSvtvanEqVdDhZM4B6eX31Q1zEL+X6/VV/8AqJzI2XA6EwBv2s4AZzUFr1IfrDkkIXAGGBf1YzjDhxuYykSCXDv8mH6x5LyGfIOh0cPnM89Lqp1KdTUQcwH3Ad4EyYKmILDTGrEyqV0DkTth/0mm3rRnXb4rISOACEXmcpBXexph2P8EyEWlvCRYxZ8v09i6too7sl/q4wH9Vt/7h2dZpU+0Z8swq3h8ykTnfbS77+RWLGBrbMjU6UAP89Jr3mFMXSSjYOnM/OL7lj1Tk5JjEfVxTJXtN3Sf1z3ty+aiBX7VIxDII0478iOeijy8b+3brbxC4OM16AD8Wk1bd3+dWd+q1r3CE0qqXrnT718Ps0RiWifHLLglTd4Eut21P+KsgdhU0hIWACDkOC2Pb7PRv4oNVD4LTQdGh4xhwzAEUHLQPJhim4vX/su259wjvasByeyAAfrueEtdwBhw4jZzrSrl16vy0rv/y5Ic45YLIuW3rbAeO2sR1ol5HgIOKyrGT0kROWXERdt0zLZJHYsEre42j4d8F0FgR2S4gunw/+TSrmFTjmn/JVkKBpJlUp4c/UYnbcoLdvEOKKxTmT4/N56cn61IB1S2mAmuNMesBRORZYAawMqnezcDtwDXpNNpW4Ho/ke1j9gY+InHQN9HyVmUq0m6o9LLiiYnpVO1TQj4vgV2FuAt34fT6aBgCjUOCvM2+CfVyy3PIW1vIH96+AzE21xx/Hre/+gQm5OSKEy6G/CBzzoGbS17Z7ZOz9p63io82HNzUn+ryfxO0t+MOhZsG6qDTgbXFD4WRoLXqwCD9W2lvxfZhzCHxj5VDBq7jxM8/5+sdo9h72NcsOXAIed/+iPq3vtlUZ8TA//CfIcXsF4txBXL2ree2O65m9jXzcLqChIIubrtjFifvv4B/FsCogXCcc03Te26NCzj7xx8CRE64uTQS2MVOsYmJfdfeAQoPWb606sW0V8+J3aH22tPR/vUQuz2GZWr8GuPdzsuTH+rIe+gTWpxo9bxhx7l5rPlvHadaDp6tj0wvzhxVwuHTvsWkkrGcfvQSho5+mycff5V77vZSVzsSyAUacA3JZ8Lgwzk44KS6eiCbR0ymLqem1ZOzUvVnrlnaVK+2Ig/zSQ3BMBTkwc5xBS3GiNOW/Yh7fv4C/vItnNN/CPctfBCAK0+8gJDDGQ1eYdaJ5/P2wzckrNg1YbjsuFmwuPkP8nPPf5jhU77GbIkWhCJrXK/atph1rvuZE3wFEcMM6yWGTHsb73IbyzYELAdBceKww0jY4K4OMviDXXv6v0ipVEpEJP6P9QeNMQ/GPR4ObIp7XAYk3B4VkW8AI4wxr4jIngWuxph7gHtE5M/GmJ+k01iSjETarp1+hjyzqv2KfUi5/2BW1Z+HRagp2Wj0flvYdmhhQj3Hf/bnv0u/yyJO4CA2YEmYxQ/e1DSA/u6xvzM77zQG7BNg2OAdWJbZrZOzyv0Hs+rh5v7M9vp4O/SzhDpWyOZ3/b+d9nuMzbzGFCwewjX3L2o6YeuCS2/BM2YLDW9PRoyNQeiXX8ZH9kjODP8bA3hzoLHIzckzFnD4Ee8mfIDlVvjYeDcEwlAYTfZqGORttT8LiJxuAzSdcAPNp9io7reHY1hGxi/V0sKXT2H2tXdFT7Rycun/XkFFxdO8/GmABl8YcRrOP/1b9D9mMvu8PJ33/nIi7wH33wrjht7DmvIPgIeBAA6Hm6uuvhLHoduZW7eIUJBWT8667Y5ZnDxjQTv9aa53yC9rGFkMjy8Y1CJonbNlOndc/zcO3rABEwqxZttG3I7IyHr3q48kzHgueOK3LWZkLWP48J3TGeLejkPCGCN8c8oyGovc+LcEkBC47ggzYIuPItvPm6FrIn+cGnhH9mfOR8fxzoFDOHjbaowRHJcF2Xx9P3buymWuc1q7x82qvsUKQu7WTmlqhzFmShvPp1qP0/TXmYhYwDzg/I5cNJ2Ts3YnaIUMRdoqUcDO57P687BxY0d3APys/jwGBO9OqOeuyWHp0u8S+TmK/Cx5TIDYSs8GvBgsPqn/AQvllziN3bSkq/E9F7OuuhNjN5+c9Un9D9novxtLEu+22mYANeG7Ia4/L/l+gyNpKwEHhgeqnmSccwiBF/9JaLGh0h25HWdHT5hZe93jKd/z6PwCHvnvhwkn1Dz85+tpDAnGuJqu9NmGE/l77f+SG3dOQuGnNZz7WD3V/kogsoyin8fw8vkhPDmR+Rrs+Hqt74EYaASPoemEG9vTalXVjXZzDNPxqwtUVhYz+9q78PubT7T6w133kJOzgAOPy2f1qhCBnHyGnvcd3BtzWfr6icR/Fq4pvwJLLsE2XiCXcBjuu/duVu+9D57CYNMn3EVj3+KWnz2KHXDHnZx1F/MfXYjLtaOpvWCwhOUf34ltN/fnysvu4pezX6S+Hr4qh4kHbm/xPvzmCc5qCGFsExlDjIGQTQPCuE1ruP0vv+Kc/kN4omor+QFfi9cD2H43IXJiK6n4+eW/4+Lxj+B1hSO5AV+BZezICBy304IlYYryfVzxw3dxuAEMTgzD76jhlzdOJ1TXdvKZUhlURuSI7ZhSYEvc4wJgIvB2dM35EGChiJzc1rKrdHYV2F2dFmmLyCXAJQAeS4+pi+ezi6Mzm82bT1uE8AX60XQuIOCobM7eP53nKaOU5sMBIYCb03gBhwRZVzGKkQObB/P120fhsALYdm7clYPYjMAiKXBlBJFzEeNnK9vY/2o3jCkYwiZHoCloBXBYNg4LQnHJHJYVZH3FKEYWN3/QhGwYWmgSAtLSfBvnHUR2NY4mjzltKB1lU+1PPeifDpRb4Inf6cYlbH8gs6d8qS6TkfFr2HDdZi1e2aZSXK5gU5AIkJMT5NHHD+Kwb33C905tpCYUZPLALdS8m3qJmNsN/rhEJZczxPrte7NXYfOU0vrto3E4Q9iB5nFSJEhj48iEwLWxcSQiiafoQRBjRgEtA9YYj4Q4M8fF174A8aOkC0OeMRwR8LO6YiNuY2MTWVcvceNiCCcT3SsSjs1aYE7D/WUoMi5dTmRYTRpKg04XvzjhJ4wauK1FAllInBQcO4DNVSNQqpssBcaKyGhgM3AW0LRtT3R/7ZLYYxF5G7hmt3cV6ASdFmlH10w8CFDkHNi5UVCW81qV2En/G22c5G5dhXNL8+11R1weyvOcjjvp8EA3AV7gNE7iZcYMT7ytNL70KxxON8G4/AQLD1MK98NtJQ6KATufJVUe4lMNxvM52xiGM27UDSP8uP8PmWp5MMOGs+3QQhqGRJ5bd/MvARh21lUp33NdfTXPvXkGYRyczvM8z+lYgTAzrJeIT6HItYMcNW9ZJAS5DZgNeUD/l09h34LmQP7qy17F8fm2yIPLAH8kG+dq70Bu++OJLa7/n0Me4XnA7XRAOC7TOGBRc56LO+/+XlOZbR4FaDeh8KvAorTqwSNp1fObJ9JsLz3p969zzS9tv06GZGT8OuAAl45fcUpHlBEMJiYaibgZt29kDOpv+cAZSTKt/FYdLW/sR4LAeMGQk70Hrk8o27t4HeGkhCaXy838J2spLm6+JVNZWcsRh7rjf63xeNy88+8Gvn1k5DqfrBxMKoPO2YnjfQc0Nr/YIZG402EMudGE0nBTn+Oy/3HxePBHHE/cAQtiYYlJSOJK5rRCzC17mOsvmtEigczhCrNuhoM6S5MBVfcwxoRE5DIiWxA6gEeMMZ+LyE3AMmPMbh29ncnANSORtkrktuqYmDefz5LWuLqtOtxL1mBGD4/WrOfQsYv44MvpxAZMP26COOKSBGz2z32cuwLHcIc8jSWR8DN2clYsockXyGH4tDdxfxSZ0Q3Y+fjsYrxWZUJ/BBuDxQLrWEK2hTN+NgCL6+re4obCE5ANmxkM1O8VOUlrXagEGEn/9924clreVvvNv57mYBNJEtrCMAqoBQNvmYM5guVN/w5fMRKr3kTe7U+bX3/D91/j2xc1b+M59NNnmz9D4i439NPahETAfF8dbz1yIw8C7wPuxjAGqHO4cAoQhtotBax4YiLOcIi7X32Es2zD6cAlp0f2u48laiSr27oEoNXEw3511fzzsZt4BDgAePSQSAD7nfN/TXV+y1necMDRZnsd1V7/MmZq114ujo5fXaC4uJLb7pjVIlkyPnGqv+WLJAV+Yy1rzniAR577cdNzF5zxAJOPWs7PfzYPyxHGDju47Y6ruW/wYcyt/TsmDJYYnnxlAhNzHuNTzseZE8QRlqbrJCds3XbHLH7+s3k4HDbhsNVUz7ZLMGYUlZUNrSZ2uSSMTyJ7zua6bEQMVpDEI2MFjENoCHkJ4sJFEJczwL5jv8Czwdf077D+NyMwcyB5Atj2AC6BoCGHEN8oLOPRbzxDbUUeOWtqIlsFGkPtuDweHfRM5/3PUrvh5u7uQLczxiwCFiWV/bqVuken02bGAtdMRdqqpaGepRS7VyUEjzHxi/Ib/MMQAvw/nuNFTqPA2sI1nh9zY8MrgOF/+Bv78kxk/sIY4icF4hOannbty+pXRsFHkUSs5KCZ6Mvi5xQMFg24CeDCTRAhcXZANmwmf0OkPTu8Ggiy7FVvyiSwb1Sui7wGKKS26XpT7C+Y1v+6pn8HqzoEpmUquTSGExL8LH/Lk21i5fH1/l75UNOs8bS4ep5wiOWuyB8It9Z9iyHPrOLmXa8xKViOTWTqzvP1lwD8+aF7uKGw5cbkX9c0ALSaeLio8i9Nq5M/iyv/52M3Mb344hb1P48eg9tZiYzt9S9j7m2/Sibo+NV1UiVLtmbykStwv+yLxWZMPmo5mMhvePzveew2vDEGE92SLjZOrj1zCrPOe4aTS5elTMSKtRc/I7rw5VOor78TCHDEoTkpE7sqHu4P33fzxarxnCmP8lzgRxyZ+y88xo8kLCmCgMvFktARnCXzeY7z2HfsF/BKgHdrD276d9jvqi8h+bRWAeMVyt4fmLiTCalPPFSqN8rkjGtGIm2Vmtuqa3OLqlgSl8GNnxymsxixA0gDnMDlTfU+bziXv7vnRAK06P3+kjU1lPXLobi4kuLiSl7cMjyhzeTEMAMY3E2TDMfYH7CAUzFYTbf2BZu78sZjxa+zjbZHtC072l6xO/GI2XrcFBG3qC2uPP7f4dJ+p/FM1VMt6l3S77SEx4bUgWtr5cmBMNAiGBVjcBHGQTThizDhaPmeSHXteE4TZk7tYq4iyFk4uXnXa0Dk2MqQaJJGR+j41XViY0tbYolcgUDzetif/2weAI2NzWWzr7mL1fP2wVMYavqEm3X2OyxfM5yaOvCUbqNgQHXKxLBU7cXKIuv2c/H7YfY18zj8iHcT+lxZW8IRG5bixws2HM9iXm84jmPdb+ITQ6MBryPSpfr6PMBB2BRg42D1F+MZuLOM4iEt/x38AkEL8qIrHRonuzB5VsrdS1KdeKhUb6M/4X1ELIkrnoWNkLjxtUNCrNuRtL2lSOS0qjTaFGyspDbDOPk+C5jOa9STz3Re4yReotYMSqOPIXx2cULZemtIyveYXH5/9Qsp6z2YVF7YYlojdflKa2DKeqnKLUzLLW8gISGjIy4t+p+U5ZcUnZrweE7tYiYFyzkKw1cEmRQsZ1KwnDm1i1O+XqlsEUvkimc5wjgciWNTLDkrXjhsMXhA4h/2qdpzOOwW7aW6htMVpGxT4gLsVO3NzHuaHfsX828n7CXgm+oilOMijwamsYQySpnGEo407zL8x4nJXxUP98d/qJuPC4WTD3LiP9SN/1B30wyrUn2VBq59ROokrpbhVdg42ackMbEBYyKnVaXRpsFqcZpMquvYOPFaiTMLrSWaJdf7hr2xRV9SlUfvEEZngJtnKGU3Jz0n2RVpl+8dTj17tE8r5e25v+allOUP1vwt4XFspjcX6Ad4COMivMczvUp1t1SJXHbYQTicODalSs5yOGy27UzckSZVe+Gw1aK9VNcIBV2Ujihrtz1fKI/3byzm2CDkDbGofq4Y/6TIHaVcfPSjpml3F48naT2/W6h4YgCz9nPic0S+r3hiQMIBJ0r1RRq49hGxpCmLAE4asAgwKW8+E/PmIwSwaEQINCVnhcTCdgi2BTvGFaVcL5WqzYl585mUNx+ibRJ3neR6yUsbYu1BA1CTUC9g51MTGknAzm91zjK5fK2jeaa2Ju4me3w5wK5WVswkl6deOJC6fF3SNdorT1dyEJ5MOnmm12nC3LzrNZ4LVZJnbG7e9Ro373oNp2ntX0OpzIglTnk8PvILduHx+Lj9zlncfucs3G4fOTk+3G5fU3KWP+Si3ufCH3Aw7+mjqKnzdrC9Btzu5jLLasDhqMHj8bVIIGutvdvumMWTj29EpITCwqlUVhZT/UgeDk/i74/DE6b60byU7zsYLKGu7ptUVu7Z2KFUb5HRNa6qZ0mVxLWy9iwMzZv2VwX3ZlF9HVMO+JLvWGvbXeTfWpvgalowUBXcmwkFf201gSzGacI8FLiJSi7nTCaz2LkNKxDmUvNLljdc2JQAdmLOABY13tDi9ecXnZHw2LYs/GEXXoL0i75DHy5sK/H95LUSkrYsT9zCJrE8kYkuwoifp4nt37g7zi6aydM1VJ3GfwAAHFhJREFUkQzhIyjmXSqbyuO1NqO7uzO9saUHNmE+C1eQE7aaylMlmSmVSakSueZcfwuBQPPpHx8tm8KK0zby+WsHsPfqFWzbmd8iaO1oe3NvvoH5jy6ksXFkdAut1L9Pye3V7trGVZefgjEPs25tkCMOdbNq9L64kva3chFg4I+rW6xbXfjyKSz/+E5EIq9t7bQvpfoSnXHtY9xWHUXOjbitOupCg9kU+DbNp2kJmwLfoXF7f/wuF4ECV1qZqem0WRcanFAvleb1mY1s5EMmh8qYFNzKbxpexsZNiFxs3Dze+IeUr79nV+KAPidvBo0J24FDI7nMyTsloawh7vCGdMrTYUQI4ojOHQt+HARxYGT3AtdqZz7Tiy+mxDmUL5xuphdfzPTii6l2Jt7+XJ+hmd5coAiDp9V5Z6W6RnFxJZMP/P/t3X+QVOWd7/H3d3pmQBx+7YBRkSgGiHLZAIpGd000G65XrY1Yu5qgUSSSm3VTZuvqtaJVxhh13TImLrVbuJUQdQWzURN3l6Vcdsm6/siPFQMGEDVXnRUjPxTDgCODzu/v/aO7h56e092nZ/p0n9N8XlVUus88/ZznGcYnX5453+e7jdbWdtpen8ma1deQu96seWg5XTtbea+nhdfemlowaC2nv7bXZ9LUtI+WlhdKJpHl9nfvd8bi/gDpql4T6eo6ilf/3ym4GwNjYWC8Fay4l00eGxg4/NmbblyhnVc54ilwPYJ19M0IvN61Mzj5aTR9FroeJPf5zLTgX3OX+rX5rZ0/DSy0cGvnhiHX8h8dKHT9S5O+ENju6oDrt48/n+1Nx/FfNDM3dQzbm45je9Nx3D7+/AKjrYzh6Xaj2+m9a/yiYacR9FmKvxy/aGQDFKmgrVsWBF4/+71DvHtaCp8xbcifznNnM27+/vS5sGX0V+h6MW/u6GP9vx5bMGGr66xmdm6aWjDpKijZKygpTORIo0cFjmATG3cEXp/2/m5Wvn4eP586O/Drz2z/OKdsfL+sPgtdz/Xtls/w6IEfDrnWSD9X8PCQayfzGjuZBuZcO+nSwdMDlk1aMqRdQyZo/YCj6KF5MIhtyAtmBxoa6O5PMUA/vRhjMv+ey3+kYGWBBKm/6/hnlvze0iHX+izFrRMu5FcdTwDDj8uKSnantzczhuyzqCPd6b3l4JPDnmdt9H6+cfBJPSogNTd/wZbA61/85L/zh9PgzmM/N+T65GM7WDvv/rL7m79gC4//pLyxrfzbTpqa3gSGJ2ztevAjtLam/+836FgrCE72CkoKEznSKHA9grU07mV681Ps7PmjwWvTm5/imC3P0/ngbJ47LXhBPWXt+0MKG4Tps6Vxb8nx3P/ej0nl7Z+mcNr4GFPZP/iM68lH/yNLxl412CY/aMy6c8Jnufm95zk4cDyX8RCP8yVaGnZz94RPDml3+/jzue3gT+nofZcvpSbzTw1Ng9eDxDk/PzsXSO+W3nLwycHro9FFakggLBIHM2e1sXTZA6x5aPngtaXLHmDmrDZmAov+14rCHy6zv3K8uaOPtf/cxbJrGvjEvBuKVgYrJExVMZEjkXnCjsmZ2DjVz54UfKZlPcovpxpFf519H6GjbwYTG3cMBphB10bbZymP7l/DRO8edsh+h43hTyZ9dcTfh43v/RcDTGfhhFOLfja7O3rmxD8O/HpL/4c88t4/MIAzlxSvZH4pf/mkL9KZCn6OrlSf2YIBQUFz7q/ow7artOx9YXggXI2CBv++bxUAZvaCuy+M/IYR+8Qnmnzd+imlG9aJ/HKq5VpyWfozj/7k8GM7QX22vT6TrVsWMH/BlsEgM+ha2DEGfTZoLIXceP17/OsTXfzsl1OZekxqVN+H0X4PpXZOOuFtIH7r17hjpvvsL9ww6n62rbyhJvPSjmuMBZVTzS9/Won+Whr3DgkuXzm4JJNglTa9+SnmjH9sVH2G8eVJn+fRAz8cLKsK0I+xfNLnS1YGK6bB9tPAfpobpo/o81k3HXqGfhoYoJ9N9GfqYqWvj/TX5mGz9muV3Z995CFLjwdIWEHlVEebEV+oz5mz2oYEp7d94y8zSVZpS5c9wO13Dj+JJGx/5Ti82zqOqcek14gwlcEKGc1nReqRkrNiKrecajab/qVDV9Mz0FL6w6Por9ipAFGP8abOp+nL+5Hso4GbO58eUX9RGZ48Vpk+w2TtK7tfkiC3nOrBgxMqkhEfts9iJwNEPUbIPtsKf3Zt8LmsIjI6ClxjKmz500r3V86pAJUeY1YXKTqtmS6i/1V0OaLIsA/bp7L7JUmiyIgP22fYkwGiGGN2t/WLVx3ebRWRylLgGlNhy59Wur9yTgWo9BizR0htbzqOKydfUbUjpMIqlmEfdZ9R3FskKlFkxIfts9jJAFGPUbutItFT4BpThcqplvOcZ26Z1GL95bbLngqQe0pqoVMBKjHGXNnnKW+dcCFd1jT4eqRJQGHLlZZb1jS3qEClhN1ljututEiuQuVPy31WM7fcabE+29tb2bZ1Hu3trYMnA+SuYUEnA1RqjFnabRWpDiVnxVhQOdWwCiVN5fcX1G5y0xvs7vnUYF+Tm96IZIxRq3TiU7nHZoUR9viqqI65EolKUDnVchQqd5rfZ1CC1emnv8Bjj1yJmeNunL5wcyRjzKXdVpHqUOAacyPJps9NmhrIlC196dDVtDb/Zkh/Qe22H7oag8H3+Z+t1BirKV301YH+ojuVpdpFUVQgbNa+svsliUaaEX+43Gn6mLn+frjpxhX84Tm/GNJnboJVV1e67df/b/rs1u7uw7VUcz9bqTHmCjpJQCSuGnqhZU9yE3z1qEAdCps0FdxueNHQSiRc1YISn0SSKWziVFC7hlQ/qVTe40ARl0rVbqtI9ShwrUNhk6aC2zXgeT8Wo0m4qiUlPokkU9jEqaB2A/0p+vvz/iEaYalUPdsqUl0KXGMkN0lqNHKTplJ0FUzECkqu+v2jV1c04aqUSs25mEolPpWTxFVuwpdI0uUmSI1WNnHK7EMaGjoZMyY4ESsoweqee6/nnnsrl3RVTHt7K3d86+M0Nk7RbqtIlegZ15iodJUsSD+taZn/LXaPoOSqaiRcRTHnXJVOfCqnelWtKl2J1EIUVbLw3NWr+H2CEqwqlXRVyLq1l/D1G++lu7uHVKqZ5567YfRzFpGSFLjGQKlkqpH25zQP1ldKvwcvkbCV1dzQSdPHJpKu0zQx9L1tx+6yxlipOQeJKvEpbLJXuW1FkigoQapYMlQ5fbofhTt0d+cmXQXfJ/9eUZZKzY6vu/soYNyw5DERiY4C1xjIJknlZvJnE6JGEsQF9WcMYDCkUGixe7xz+akcmN877HopY945lRlr3y8ZwFZ6ztVw1/hF/PDAjyDn1/2FkrjKaSuSZNkEqWwwCYeToUYaxAX1mUoNkL8DW+o+T34wM/B6IYvGtZVuVGB8o52ziISjwDUGqlEly2nIW/IL3+Ody09l3lUvcfvx68u+95MfzOTOYz/HKd+bVjR4rfScq6FYElf+Lm05bUWSrFpVsvr7h6dkFLvPbXsu4rn/mFvWfVfO38/aefePaHxRJoCJyGEKXKugZ6Cl6POi2SSp/Oc9R7rzmNtf1tyjVwOEuscHx8KnJr02onsvGtfGymMPcuijE2kJrh47bIyVmHM1dZGiz1KhEq3KaSsSR+3trUWfFc0mSN104woam3rpyzx7Opqdx2yf/+drKwCnubmBe+69HiDUfW7bcxHbHp7LyY/8pqz7dp47m0uu+XLJ4DWKOYtIOApcIxY2AanSFagO9H6MAZpy3p/MnPGPxarKVZyrbgUpp3qVKl1JPQibdFXJClRZL2w6A/d0EYHubnhh80Juv/PWUPfZ9rvjIz9gPYo5i0hpClwjVG4CUqUqUHX2fYSdPZ8hfaZA2s6eP+Kjfc/Q0rg3VgFi3Ktu5SoniUuVriTpyk26qmQyVNvrM1mz+hpy17A1Dy3nqqWrmTmrLTZBYpQJYCISTOe4RihsBatK6+ibUdZ1EZF8YatXRWHrlgVlXReRI4cC1wjVKgFpYmPww6WFrouI5KtlAtL8BVvKui4iRw4FrhEKqkwVVMGq0loa9zK9+SnInNwKzvTmp2hp3FvxewE09AzQfLCXhp6Bsj9bjcpZIlK+oKpUQdWrojBzVhtLlz1A7hq2dNkDzJwV7riqMLLV7e4+9ARzp73N3Yee4Lu/uJ/G3vDPxkb9fRCR4fSMa8SCEpCirhgFMLnpDXb2fIoGnAGMyU1vVLT/rHHvfsiU1zrADNzZNzt8sYJqfB9EZOSCEpAiqZIV4PTTX2DNQ1dgBk1NxukLN1e0/9sO/pT5/XtoZIB5rz8B/dC7/13+5s7HIMR0qvV9EJGhFLhWQW4CUjUqRmXvAc1k90ArfQ9I77ROea2DhgHIHgw+5bUOJh99iN4S1baq8X0QkdHLTUAKSti6/i/+mtV/v46mpn0Vu2dv7xS2brkXGIc79PSUd5+2zjWk3mkk1fFhwTYf0E5jagDrBTKP8jY199NI6R3XKKqFiUg4elSgyqqRsFWtpLDG7v70TmsuM44f6Cj52VolronIyAUlbJn10t19YkXv0919ImbR3ueeE1uGb92k4Ptf/3TJz9YycU3kSKcd1yqrRsJWtZLC+sakwPPqcbmzp6H04wJJrJwlcqQLSthqampm9Q8P0tpauX90trcf5JyzmunP2fws5z6XbFtKy4MTaXm2cCGVu3Y/wbDN1X74s3t+VvJRAVXOEgnHzC4A/gZIAfe7+915X78B+DLQB/wOuMbdf1usT+24VlmxhK0k3QNgoLmBfbMnsrdzCs+/eQZ7O6ewb/ZEDjQcHdg+NxGrWmMUkcoplrAVp/tcN+sZ3j0tRee5swv+6ZowgV5PMdCU/kf4QBP0eorjjw7+jVFv7xQ6O0+nvb21at8HkSQzsxRwH3AhMAe43Mzm5DXbAix0908AjwP3lOpXO641UI2KUaO5x4y173PnsZ/j57//asm2b2w4k413LSPV2Ed/XyNnf+Mh2PsBLc8OLbVYKBErSZWzRKR6FaNGc59F49rgsn9i5fzzCra57qo/4bvXr6frtx/h8rEP80jPVZww+79peWj4OrRu7SVs3XIvZr2cc1bzYCKWKmeJFHUm0ObubwCY2aPAYuCVbAN3fzqn/UbgylKdKnCtkWpUjBrpPWzHbk753jS2nTW3aLu+D4/ildXX4H1N9HWPAeAXt1/DeeNvGrKXXyoRSwGrSLJUq2LUaO6zaFwbi+YVPj6rvb2Vc97ZRJcfBR/ChWxg7G8/5BcHzxhyz2wi1sBAOgmrv39oIpYCVpGCpgE7c97vAj5ZpP1y4N9KdarAVQLZjt0cu2N30TYdfSeS6uulj8PPeqX6evlwoHVIMJpNxMoGrXA4EUtBq4jUQjbBKnsqABxOsMoNRsO2E0mKVE8/R791qBJdTTGz3HPqVrn7qpz3lv8BskcQ5TGzK4GFwLmlbqrAVUYsbIKVErFEJG7CJlgpEUukoH3uvrDI13cB03PenwDsyW9kZouAW4Bz3b271E2VnCUjFjbBSolYIhI3YROslIglMmKbgFlmNsPMmoElwLrcBma2APg+cLG7vxumU+24yqiETbBSIpaIxE3YBCslYomUz937zOw6YAPp47AedPeXzewOYLO7rwO+A7QAP7H0ufBvufvFxfpV4CqjFjbBSolYIhI3YROslIglUj53Xw+sz7v2zZzXi8rtU48KiIiIiEgiRBq4mtkFZvaqmbWZ2c0BX7/BzF4xsxfN7D/NrLJ1A0VERkjrl4hI/EQWuEZVMaFe5FaREpF40fpVXHt7K9u2zqO9vXJlXkVEwohyx3WwYoK79wDZigmD3P1pd/8g83Yj6aMS6t7bXWfw7IG72dxxPc8euJu3u86o9ZBEZCitXwWsW3sJ55y1iauu+DHnnLWJdf+yuPSHREQqJMrANahiwrQi7UNVTEi63CpSfYxjgGZeOnS1dl5F4kXrV4BsFamurqM4eHACXV1HcdONK7TzKiJVE2XgOpKKCd8p8PWvmNlmM9vc410VHGL1ZatI5cpWkRKR2Ihk/WrfP1DBIVZftopUrmwVKRGRaogycC23YsLFhSomuPsqd1/o7gubbWwkg60WVZESSYRI1q/W30v2QS6qIiUitRblKhpJxYSkUxUpkUTQ+hVAVaREpNYiK0AQVcWEeqAqUiLxpvWrMFWREpFairRyVhQVE+qFqkiJxJvWr8JURUpEaiXZD1yJiIiIyBFDgauIiIiIJIICVxERERFJBAWuIiIiIpIIClxFREREJBEUuIqIiIhIIihwFREREZFEiPQcVxERERGJke5ebMfuWo9ixLTjKiIiIiKJoMBVRERERBJBgauIiIiIJIICVxERERFJBAWuIiIiIpIIClxFREREJBEUuIqIiIhIIihwFREREZFEUOAqIiIiIomgwFVEREREEkGBq4iIiIgkggJXEREREUkEBa4iIiIikggKXEVEREQkERS4ioiIiEgiKHAVERERkURQ4CoiIiIiFWdmF5jZq2bWZmY3B3x9jJk9lvn682Z2Uqk+FbiKiIiISEWZWQq4D7gQmANcbmZz8potBw64+0xgBfDtUv0qcBURERGRSjsTaHP3N9y9B3gUWJzXZjGwOvP6ceCzZmbFOlXgKiIiIiKVNg3YmfN+V+ZaYBt37wM6gNZinTZWcIAiIiIiEmPv9+/bsKH9B1Mq0NVYM9uc836Vu6/KeR+0c+p578O0GUKBq4iIiMgRwt0vqNKtdgHTc96fAOwp0GaXmTUCE4H9xTrVowIiIiIiUmmbgFlmNsPMmoElwLq8NuuAqzOvLwWecnftuIqIiIhI9bh7n5ldB2wAUsCD7v6ymd0BbHb3dcADwMNm1kZ6p3VJqX4VuIqIiIhIxbn7emB93rVv5rzuAi4rp089KiAiIiIiiaDAVUREREQSQYGriIiIiCSCAlcRERERSQQFriIiIiKSCApcRURERCQRFLiKiIiISCIocBURERGRRFDgKiIiIiKJEGngamYXmNmrZtZmZjcHfH2MmT2W+frzZnZSlOMREQlL65eISPxEFriaWQq4D7gQmANcbmZz8potBw64+0xgBfDtqMYjIhKW1i8RkXiKcsf1TKDN3d9w9x7gUWBxXpvFwOrM68eBz5qZRTgmEZEwtH6JiMRQlIHrNGBnzvtdmWuBbdy9D+gAWiMck4hIGFq/RERiqDHCvoN2HnwEbTCzrwBfybzt3tD+g5dGOba4mALsq/UgKqRe5lIv84A6mIvZD7IvP17tWwdcq8j6NWP6O1q/4kdziZ86mMfgElHt9auuRRm47gKm57w/AdhToM0uM2sEJgL78zty91XAKgAz2+zuCyMZcZVpLvFTL/OA+ptLlW+p9asEzSWe6mUu9TIPqMn6VdeifFRgEzDLzGaYWTOwBFiX12YdcHXm9aXAU+4+bMdCRKTKtH6JiMRQZDuu7t5nZtcBG4AU8KC7v2xmdwCb3X0d8ADwsJm1kd6pWBLVeEREwtL6JSIST1E+KoC7rwfW5137Zs7rLuCyMrtdVYGhxYXmEj/1Mg/QXEZF61dJmks81ctc6mUeUF9zqTnTb7ZEREREJAlU8lVEREREEiG2gWu9lFsMMY8bzOwVM3vRzP7TzE6sxTjDKDWXnHaXmpmbWWwzQsPMxcw+n/m7ednMflTtMYYV4mfso2b2tJltyfycXVSLcZZiZg+a2btmFnhclKX9bWaeL5rZadUeY1j1sn6B1rBqji8srV/xU0/rV+y5e+z+kE6G+G/gZKAZ2AbMyWvzVeB7mddLgMdqPe4RzuMzwLjM6z+P4zzCziXTbjzwM2AjsLDW4x7F38ssYAswOfP+mFqPexRzWQX8eeb1HODNWo+7wFw+DZwGvFTg6xcB/0b6cMSzgOdrPeZR/J3Efv0qYy5aw2I2D61fNZlLXaxfSfgT1x3Xeim3WHIe7v60u3+QebuR9HmRcRTm7wTgTuAeoKuagytTmLn8b+A+dz8A4O7vVnmMYYWZiwMTMq8nMvw80lhw958RcA5qjsXAGk/bCEwys+OqM7qy1Mv6BVrD4kjrVwzV0foVe3ENXOul3GKYeeRaTvpfZHFUci5mtgCY7u5PVHNgIxDm72U2MNvMfmlmG83sgqqNrjxh5vIt4Eoz20U6S/5r1RlaxZX731Ot1Mv6BVrD4kjrVzIlZf2KvUiPwxqFipVbrLHQYzSzK4GFwLmRjmjkis7FzBqAFcCyag1oFML8vTSS/nXbeaR3kH5uZnPd/b2Ix1auMHO5HHjI3e81s7NJnz06190Hoh9eRSXhv3mon/ULtIbFkdYvrV9HtLjuuJZTbhErUm6xxsLMAzNbBNwCXOzu3VUaW7lKzWU8MBd4xszeJP0Mz7qYJjeE/fn6F3fvdfcdwKuk/48gbsLMZTnwYwB3fw4YS7oOeNKE+u8pBupl/QKtYXFcw7R+af06osU1cK2Xcosl55H51dT3SS/4cX0OCUrMxd073H2Ku5/k7ieRftbtYnePY43mMD9fa0knnWBmU0j/6u2Nqo4ynDBzeQv4LICZnUp64f9dVUdZGeuApZns3LOADnd/u9aDClAv6xdoDYvjGqb1S+vXka3W2WGF/pDOwHuNdMbhLZlrd5BeSCD9w/sToA34FXByrcc8wnk8CewFtmb+rKv1mEc6l7y2zxDDjNwy/l4M+GvgFWA7sKTWYx7FXOYAvySdsbsVOL/WYy4wj0eAt4Fe0rsTy4FrgWtz/k7uy8xze8J/vhKxfoWci9awmM1D61dN5lE361fc/6hyloiIiIgkQlwfFRARERERGUKBq4iIiIgkggJXEREREUkEBa4iIiIikggKXEVEREQkERS4SsWZ2V+Y2W/M7B9G8NmTzOyKKMaV6f/TZvZrM+szs0ujuo+IJJPWL5F4U+AqUfgqcJG7f3EEnz0JKHvhN7NUyKZvkS7p+KNy7yEiRwStXyIxpsBVKsrMvgecTLpU4vVmdrSZPWhmm8xsi5ktzrQ7ycx+ntk9+LWZ/UGmi7uBT5nZ1sznl5nZypz+nzCz8zKvO83sDjN7HjjbzE43s2fN7AUz22Bmx+WPz93fdPcXgaTVuRaRiGn9Eom/xloPQOqLu19rZhcAn3H3fWb2V6TLWV5jZpOAX5nZk8C7wP909y4zm0W66shC4GbgRnf/YwAzW1bkdkcDL7n7N82sCXgWWOzuvzOzLwB3AddENVcRqS9av0TiT4GrRO184GIzuzHzfizwUWAPsNLM5gP9pGtpl6sf+MfM648Dc4H/MDOAFOnyeyIiI6X1SyRmFLhK1Az4U3d/dchFs2+Rrm8+j/QjK10FPt/H0Edaxua87nL3/pz7vOzuZ1di0CIiaP0SiR094ypR2wB8zTLbCGa2IHN9IvC2uw8AV5HeYQA4CIzP+fybwHwzazCz6cCZBe7zKjDVzM7O3KfJzP5HRWciIkcarV8iMaPAVaJ2J9AEvGhmL2XeA/wdcLWZbST9a7ZDmesvAn1mts3Mrgd+CewAtgPfBX4ddBN37wEuBb5tZtuArcAf5LczszPMbBdwGfB9M3u5MtMUkTqk9UskZszdaz0GEREREZGStOMqIiIiIomgwFVEREREEkGBq4iIiIgkggJXEREREUkEBa4iIiIikggKXEVEREQkERS4ioiIiEgiKHAVERERkUT4/6KKhi3hxck6AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 720x360 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "num_classes = 2\n",
+    "x_train, y_train = get_data(num_classes=num_classes)\n",
+    "x_train_adv, model = get_adversarial_examples(x_train, y_train)\n",
+    "plot_results(model, x_train, y_train, x_train_adv, num_classes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "ZOO: 100%|██████████| 150/150 [00:06<00:00, 23.35it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA+AAAAEzCAYAAACxEmnVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nOzde3xcdZ3/8df3nJnJTDtN2qb0XpDtRahFhEXcBVrYXxGFn1ZWBQHl4g3xttp113qBrV1Qt+5vZV11BRZZyh11Wa0KS6WrLWXXFYQWSoG2y8Wm9JqmSSaZycyc8/39MbnMTCbJJJ1bkvfz8UgfmW++53u+J+l85nzP93s+x1hrEREREREREZHycqrdAREREREREZHxQANwERERERERkQrQAFxERERERESkAjQAFxEREREREakADcBFREREREREKkADcBEREREREZEK0ABcys4Yc40xZku1+yEikk/xSURqkWKTyNilAbiMOcaYqcaYfzfGdBhjXjPGXFHtPomIABhjPmOMecoY02WMubPa/RERMcbUGWN+2H3O1G6MecYYc2G1+yUyVgWq3QGRMvg+kARmAG8BfmmM2Watfb663RIR4XXgJuAdQKTKfRERgcx4YA9wLvAH4CLgR8aYU6y1r1azYyJjkWbApWSMMfOMMQ8ZYw4ZY5qNMd8boN53jDF7jDFtxpjfG2OWZv3szO7ZoTZjzAFjzLe7y8PGmHu62z1qjHnSGDOjQNsTgfcBN1hrY9baLcB64MryHLWIjAa1EJ8ArLUPWWt/CjSX5UBFZFSphdhkre2w1n7NWvuqtda31v4CeAX443Idt8h4pgG4lIQxxgV+AbwGvAGYAzwwQPUnycxMTwXuA35sjAl3/+w7wHestfXAfOBH3eVXAw3APKARuA6IF2h7EeBZa3dmlW0D3jSiAxORUa+G4pOISK9ajU3dg/RFgFYOipSBBuBSKmcCs4G/7r6Smuiefe7HWnuPtbbZWpu21v4DUAe8sfvHKWCBMWZa9wz2b7PKG4EF1lrPWvt7a21bgeajQGteWSsw6RiPT0RGr1qJTyIi2WouNhljgsC9wDpr7YslOEYRyaMBuJTKPOA1a216qIrGmC8YY14wxrQaY46SuTo7rfvHHyVz1fXF7qVS7+ouvxt4FHjAGPO6MeZb3R8S+WJAfV5ZPdA+gmMSkbGhVuKTiEi2mopNxhine5sk8JmRH5aIDEYDcCmVPcDxxphBE/t137O0CrgUmGKtnUxmhtoAWGt3WWsvB6YDa4GfGGMmWmtT1to11trFwFnAu4CrCuxiJxAwxizMKjsVLaMSGc9qJT6JiGSrmdhkjDHAD8kksH2ftTZVkiMUkX40AJdS+R2wD/g7Y8zE7sQfZxeoNwlIA4fIDJT/hqwZa2PMh4wxx1lrfeBod7FnjPkzY8wp3fdLtZFZVuXlN26t7QAeAv62ux9nA+8hc0VXRManmohP3W0Euu/bdAG3uy96IonI+FQzsQn4AXAy8G5rrXJYiJSRBuBSEtZaD3g3sIDMIyyagA8UqPoo8AiZmerXgASZK8A93gk8b4yJkUkqcpm1NgHMBH5C5gPkBWATcM8A3fkUmcf7HATuBz6pR5CJjF81Fp+uJ5ME6UvAh7q/v/4YDk9ERqlaiU3GmBOAT5BJ8rbfGBPr/vpgKY5TRHIZa221+yAiIiIiIiIy5mkGXERERERERKQCyjYAN8bcYYw5aIzZPsDPjTHmn4wxu40xzxpjTi9XX0REsik+iUgtUmwSERn7yjkDfieZe1IGciGwsPvrWjLJH0REKuFOFJ9EpPbciWKTiMiYVrYBuLV2M3BkkCrvAe6yGb8FJhtjZpWrPyIiPRSfRKQWKTaJiIx91bwHfA65GRybustERKpN8UlEapFik4jIKFfNZ4+aAmUFU7IbY64ls9SKiRMn/vFJJ51Uzn6JSIX9/ve/P2ytPa7a/chSVHxSbBIZ+2osPuncSUSAmotNMgzVHIA3AfOyXs8FXi9U0Vp7G3AbwBlnnGGfeuqp8vdORCrGGPNatfuQp6j4pNgkMvbVWHzSuZOIADUXm2QYqjkAXw98xhjzAPA2oNVau2+ojXZtfY13Tru27J0TKWT/5Sdz6pXbWTP74aK3Wf36RWy7ewkz73+hKvuvZW+YO+RbvlqGHZ8Um0TGlv84fFu1u1DIiM6dksltvNqkW8Wles59dCUn3RLHvLJ3yLrlONcZzv5rXY3GJhmGsg3AjTH3A+cB04wxTcBqIAhgrb0FeBi4CNgNdAIfLldfRESyKT6JSC1SbBIRGfvKNgC31l4+xM8t8Oly7V9EZCCKTyJSixSbRETGvmpmQRcREREREREZNzQAFxEREREREakADcBFREREREREKkADcBEREREREZEK0ABcREREREREpAI0ABcRERERERGpAA3ARURERERERCpAA3ARERERERGRLMaYO4wxB40x2wf4uTHG/JMxZrcx5lljzOnFtKsBuIiIiIiIiEiuO4F3DvLzC4GF3V/XAj8oplENwEVERERERESyWGs3A0cGqfIe4C6b8VtgsjFm1lDtagAuIiIiIiIiMjxzgD1Zr5u6ywYVKFt3RKQoyXCceH2MSFuUUCIyaL3OwB9IdMUr2DsRGc+GE5+KqSciUgrNcZ+mmMfcqEtjZOD5xJ56nhcD3N7ygPVY3b4BgK9POp+vtj+W+T66nK/GNvYrXzPpAtLGRcrv3PPqbMsRvyL7eu659PNAIqvoNmvtbcNowhQos0NtpAG4SBXtW7ib7cs34/gOvuOzZOMyZu1aMGA93CAv/ybF2X6UFfN1kisi5TPc+DRUPRGRUli/O86qLa0EHUPKt6xd2lDwnCi7Xiz9d0ydtYLZr0wFYHX7Bk5J7QPgnpb7CFgPgDuPPkDYpvuVr27fwA31F1bi8Ma9liM+6x+eVpF9nThvf8Jae8YxNNEEzMt6PRd4faiNNAAXqZJkOM725Zvxgx4+mQC/fflmGvfMyZlByq4HHviw6vFWzp5dN+hVXxGRkRpJfBqsnohIKTTHfVZtaSXhQcLLTDSu3NjKuhs6CKb6JiNTQcvW89L4bk+9FM+9+d/YsTGI6TR8nhQ+lgkA3YPsTiBt/X7lCTTzLQNaD3zGGPMA8Dag1Vq7b6iNdPYuUiXx+hiOn/sWdHyHeH1syHoBx9AU88reRxEZn44lPhWqJyJSCk0xj6CTu+rX+NAVyV312xWxmPxVzB4wOVPvMgIk836cwnCWcxypvFXFaeNy06TzS9B7GW2MMfcD/w280RjTZIz5qDHmOmPMdd1VHgZeBnYD/wJ8qph2NQMuUiWRtii+k/vp4Ds+kbbokPXSvmVuVFdkRaQ8jiU+FaonIlIKc6MuKT93sB0Mw7rvNeasCmyO+5zz4EG8rLkKJxhgmbmMUGOEG9seIZzaB/RVqMPhd7aVOpyc8oD1uL79MS1BH4estZcP8XMLfHq47WoGXKRKQokISzYuw0m5BLqCOCmXJRuX9Vu2mV3P8cO4jsvapQ1afi4iZTOS+DRYPRGRUmiMOKxd2kDYhWjQEHYpeE6UX8+YIG/a+p5+sSmBS8yE+i0zH6hcpBQ0Ay5SRbN2LaBxz5whswf31Gu6ZBqnvfcAK078dYV7KiLjzXDjk7Kgi0glrJgf4ezZdUNmQc+u9+knP8WsfS6wF8hkNR9uFnSRUtEAXKTKQolIUSesoUSECenjCde1VaBXIiLDi08aeItIpTRGnKJWAvbUc90o0PcY17Rxc5aUF/O9SKloDauIiIiIiIhIBWgALiIiIiIiIlIBGoCLiIiIiIiIVIAG4CIiIiIiIiIVoAG4iIiIiIiISAVoAC4iIiIiIiJSARqAi4iIiIiIiFSABuAiIiIiIiIiFaABuIiIiIiIiEgFaAAuIiIiIiIiUgEagIuIiIiIiIhUgAbgIiIiIiIiIhUQqHYHRGpdMhwnXh8j0hatdldERHplx6ZQIlLt7oiIANAc92mKecyNujRGMnN9wXSam3beDfEuvj7pfL7a/hgAayZdQNq41eyuSMVpAC4yiH0Ld7N9+WYc38F3fObGAULV7paIjHP5sWnJxmXM2rWg2t0SkXFu/e44q7a0EnQMKd+ydmkDK+ZHuP276zil/TWwlnta7iNgPQBWt2/ghvoLq9xrkcrSAFxkAMlwnO3LN+MHPXwyHxR7Ag+S6Hp/lXsmIuNZodi0fflmGvfM0Uy4iFRNc9xn1ZZWEh4kPAvAyo2trLuhg2+80opv00wA6B58J9DMt/TX5od5rLNSF5T3V2g/uXQPuMgA4vUxHD/3LWKsSyweq1KPREQKxybHd4jXKzaJSPU0xTyCjskpMz50RSxXzJ5BityfpY3LTZPOr2QXRWqCBuAiA4i0RfEdP6fMGo9oRPeCi0j1FIpNvuMrT4WIVNXcqEvKtzllwTCs+14j/1YXoi7vXu+A9bi++15wkfFEA3CRAYQSEZZsXIaTcgl0BXFSLvPaP0C4Tks8RaR6CsWmJRuXafm5iFRVY8Rh7dIGwi5Eg4awC2uXNvQmYoPMsvOYCWn5uYxrugdcZBCzdi2gcc+c3kzDR/78dGB7tbslIuNcfmzS4FtEasGK+RHOnl3XLwv6xz57Nfd/8Y6CWdBFxhsNwEWGEEpEdHIrIjVHsUlEalFjxMmZ9QZIBQJcv+hKzCt7AZT5XMY1LUEXERERERERqYCyDsCNMe80xrxkjNltjPlSgZ8fb4z5tTHmGWPMs8aYi8rZHxERUGwSkdql+CQiMraVbQBujHGB7wMXAouBy40xi/OqXQ/8yFp7GnAZ8M/l6o+ICCg2iUjtUnwSERn7yjkDfiaw21r7srU2CTwAvCevjgXqu79vAF4vY39ERECxSURql+KTiMgYV84kbHOAPVmvm4C35dX5GrDBGPNZYCJwfhn7IyICik0iUrsUn0RExrhyzoCbAmU27/XlwJ3W2rnARcDdxph+fTLGXGuMecoY81TSJsrQVREZRxSbRKRWlSU+NR/xy9BVEREZiXIOwJuAeVmv59J/mdRHgR8BWGv/GwgD0/IbstbeZq09w1p7RsiEy9RdERknFJtEpFaVJT41TtVDb0REakU5I/KTwEJjzInGmBCZRCHr8+r8AVgOYIw5mcyHyKEy9klERLFJRGqV4pOIyBhXtgG4tTYNfAZ4FHiBTMbO540xf2uMWdFd7QvAx40x24D7gWustflLrURESkaxSURqleKTiMjYV84kbFhrHwYeziv7m6zvdwBnl7MPIiL5FJtEpFYpPomIjG26KUhERERERESkAjQAFxEREREREcljjHmnMeYlY8xuY8yXCvz8eGPMr40xzxhjnjXGXDRUmxqAS9kkw3Fapx8iGY4PWiYiUmmKTyJSi5rjPtsOpWiO+4OWSWUFrMeNbY9wY9sjhG2q9/uA9ardNSkjY4wLfB+4EFgMXG6MWZxX7Xoy+TpOI5M485+Hares94DL+LVv4W62L9+M4zv4js+SjcsA+pXN2rWgyj0VkfFG8UlEatH63XFWbWnFNQbPWtYubQALq7a0EnQMKT9TtmJ+pNpdHXdWt2/glNQ+AO5pua934L26fQM31F9Yza5JeZ0J7LbWvgxgjHkAeA+wI6uOBeq7v2+g/6Mj+9EAXEouGY6zfflm/KCHTyZAbT9/M9ZabNDvK1u+mcY9cwgl9EEiIpWh+CQitag57rNqSysJDzLn8/DFx1vBQpcPCS9TtnJjK+tu6CCYMtXr7AgcOHIr7a97kEgOWTf50H+y/786eCnUXJX9F9KabsbHYwJA9+A7gVuy/knNmgPsyXrdBLwtr87XgA3GmM8CE4Hzh2pUA3ApuXh9LDOLRN+yHOMbDJC9UMfxHeL1MZ3gikjFKD6JSC1qinkEHdM70AZwC4yxjQ9dETvqBuCj3YfdKWz3DtJzcQQgbVxumjTkWEuGKebV8fjRRRXa25Zpxpinsgpus9belvW60Bst/7GPlwN3Wmv/wRjzp8Ddxpgl1toB7xnRAFxKLtIWxXdy/89Zx5L/mFLf8Ym0RSvZNREZ5xSfRKQWzY26pPzcOORZ+p3qB8Ow7nuNNEZGVxqncx/9BCfdEse8snfIuvvfezKnXrmdNbMfHrJuOfZfyI1tj1DnOWRfqg1Yj+vbH9MS9NHtsLX2jEF+3gTMy3o9l/5LzD8KvBPAWvvfxpgwMA04OFCjo+vdK6NCKBFhycZlOCmXQFcQJ+Wy5LFlnLLx3Nyyjcs0uyQiFaX4JCK1qDHisHZpA2EXIg6Qgncm6/jWskxZNGgIu7B2acOoG3yPJQlcYiak5efjx5PAQmPMicaYEJkka+vz6vwBWA5gjDkZCAOHBmtUM+BSFrN2LaBxzxzi9TEibdHeE9lCZSIilaT4JCK1aMX8CGfPrqMp5vHNv2pjy7MpvvGhBrZ8YDpNMY+5UVeD7ypZM+kCVrdvAODrk87nq+2P9ZbL2GWtTRtjPgM8CrjAHdba540xfws8Za1dD3wB+BdjzEoya1ausfnL6vJoAC5lE0pE+p3EFioTEak0xScRqUWNEYfGiMNffWYSl7z3CPfeHefjn5iogXeVpY2bs9Rcy87HD2vtw8DDeWV/k/X9DuDs4bSpd7OIiIiISA05460hzlka4tZbOujs1PO/RcYSDcBFRERERGrM51ZGaT7sc+/d8Wp3RURKSANwqahkOE7r9EMkw6X5MIlNbmHvSTuJTW4pSXsiMn6VMj4pNonIseqZBf/BnTH+Z08XzfHSzITvbknzk51xdrekS9KeHLuA9bix7RFubHuEsE31fh+w3tAby6ije8ClYvYt3M325Zszz+B1fJZsXMasXQtG3N6OpU+w59Qdva/nbVvM4seHdQuGiAhQ2vik2CQipXL6NUG2vJzk6kdbMIFMFvQV80eeq2L1E63c9WLfRcarTo6w5qyGUnRVjsHq9g2cktoHwD0t9/UOvFe3b9D95mOQBuBSEclwnO3LN+MHPfzuZyhuX76Zxj1zRpT0KDa5JXOCa/rK9py6g+OfW0z06JRSdVtExoFSxifFJhEplea4z22vd0AQugA8WLmxlXU3dBBMmaE276dzgs9zS72c+HTXjjjP3JpkQmd5F8UeOHIr7a97kEgOWTf50H+y/786eCnUXJX9V0NruhkfjwkA3YNvPeps7NISdKmIeH0Mx8/97+b4DvH62Ijaa51Z+PF6A5WLiAyklPFJsUlESqUp5hF0cgfaxoeuyKBPOBpQx+TC2w1ULpXzYXcKKXL/1mnjctOk86vUIyknzYBLRUTaovhO7r1LvuMTaYuOqL2G/ccNq1xEZCCljE+KTSJSKnOjLik/d3AcDMO67zWO6LFku1vSvP2hw7mFBm792lQWTCnvkODcRz/BSbfEMa/sHbLu/veezKlXbmfN7IeHrFuO/VfDjW2PUOc5QN893wHrcX37Y1qCPgZpBlwqIpSIsGTjMpyUS6AriJNyWbJx2YifuRs9OoV52xZnHnff/TVvm5Z4isjwlTI+KTaJSKk0RhzWLm0g7ELEAVLwjmTdiJ8JvmBKgKtOjvTFJzL3gJd78C3FS+ASMyEtPx/j9I6Tipm1awGNe+YQr48RaYuOePDdY/HjZ3P8c4tpnXmIhv3H6QRXREaslPFJsUlESmXF/Ahnz66jKebxzb9qY8uzKTo/5DNhwsgG4WvOauAnfx3HmwW/+JdpGnzXiDWTLmB1+wYAvj7pfL7a/lhvuYw9etdJRYUSkWMeeGeLHp2ik1sRKYlSxifFJhEplcaIQ2PE4a8+M4lL3nuEe++O8/FPTBxxe26LwW1Bg+8akjZuzlJzLTsf27QEXURERESkxvU8F/zWWzro7CzNM8FFpPJ06UuqLhmO91v2WaisFtob65ykT6DLI13n4od0fU6klPGkErFurGqIxpkxNcaBI1FaY2P7WEUG87mVUS557xFuu7uDP3tvmLlRt/ee8Oa4T1PMyykrVqFtR9reYNsFrMfX2h5lgXeY/3UbMcAfec3sdqfxtfp3kDaj795nxScZLg3Apar2LdzN9uWbcXwH3/FZsnEZQL+yWbsW1ER7c+MAoZEcas2bcDDOtJ2tYAxYy+FFDXRO1weJjF+ljCeViHXFbjvanHf6bj5/xWbSnkPA9bn5vmVsenpsHqvIUM54a4hF73P5ju3gh490kvYta5c2gIVVW1oJOoZUd9mK+cV9hq/fHe+37UjbK9RW9nar2zdwWnovDnB6+vXe8tPSe1ndvmHULb1WfJKR0ABcqiYZjrN9+Wb8oIff/diF7edvxlqLDfp9Zcs307hnzpAzPJVob0/gQRJd7z+m465FTtJn2s5WMk9iyqRGnbazlabJdZoJl3GplPGkIrGuyG1Hm4ZonM9fsZlwyKPn8Twrr9jM1p1zNNMk41Jz3OfVN3tgIZbKfF5/fmMrANaFhJcpW7mxlXU3dBBMmX5tdHZm6lx2STOpoGXreWn8rG2H216PQm197letfOXSVjrbV/N6l6XZTwIwcCujh+KTjJTOrKVq4vUxHD/3v6DxDY7NDcuO7xCvj9VGe9YlFh9629Em0OVlZr6zGZMpFxmHShlPKhGbit12tJkxNUbayz1Wz3OYMXXsHatIMZpiHnWB3NhhbOYrp8yHrkheYQFdEYvJu518pO0VagsfbH3fy8sIUOjdGyfITZPOH7K/tUTxSUZKM+BSNZG2KL6TG6mtY7E2N8D7jl/U/dcVac94RCNj717wdJ0Leb8nrM2Ui4xDpYwnlYhNxW472hw4EiXg5h6r6/ocODL2jlWkGHOjLik/N3YE6wALXVlvlWAY1n2vseC9229efACAB37cSHPc55wHD+JlXW8fbns9CrUVngCbH57Oezd/gZNuiXPTttuJppr6bRshxfXtj42qJeiKTzJSmgGXqgklIizZuAwn5RLoCuKkXJY8toxTNp6bW7ZxWVHLKivR3rz2DxCuG3vLivyQw+FFDfgO+K7Bd8i81vJzGadKGU8qEuuK3Ha0aY1FuPm+ZSSSLh3xIImky833LdPyThm3GiMOa5c2EHbBTQEpuOltk/jWsgYcL1MWdmHt0oaiEqf1tJe97beWNoyovey+RYNm0O1s3tdopPgkI6UZcKmqWbsW0LhnTr9MvoXKaqG9I39+OrB9WMc4WnROj9A0uU5Z0EW6lTKeVCrWjUWbnl7A1p1zlGVYpNuK+RHOnl3Hr36X4MvXttFi4OOfiLDuhg66InbImepC7RXadiTt9fRtoCzoayZdMGAW9DWTLii6z7VC8an0OtMhth2aXe1ulJUG4FJ1oUSk38ljobJaam+s8kMOSQ28RXqVMp4oNo1cayyiE1uRLI0Rh8vOncAv/zjBrbd08MErIwRThmDKDPsRZEDBbUfaXmPEGXCbtHG5vuGiYfevlik+yXDpTFtEREREZBT63MoozYd97r07Xu2uiEiRNAAXERERERmFznhriHOWhrj1lg48b7TeTS0yvmgALlWXDMdpnX6IZHj4V2+L3TY2uYW9J+0kNrllpN2sqID1uLHtEW5se4SwTfV+H/DSOfWcpE+oPYWTzH/uh4iUwkjj01iNTcVqiMZZdPwhGqKalRMph+a4z7ZDKZrjfu8s+MEDxZ0L+BGLN8PSHB+8fucEn0OzPXa3pAetN9ooPkm16R5wqap9C3ezfflmHN/Bd3yWbFzGrF0LSrrtjqVPsOfUHb2v521bzOLHzy7ZMZTD6vYNnJLaB8A9LfcRsJlnenznl3dw2zVnATDhYJxpO1szz++2lsOLGuicrnuQREplpPFpLMemYpx3+m4+f8Vm0p5DwPW5+b5lbHq6uLguIkNbvzvOqi2tBB1DyresXdrAOUtD/Pa3SabPGHxubf3uOB0fB3w458GDrF3awIr5/c8dVj/RynNLM+ceb3/oMFedHGHNWQ3lOJyKUnySWqAZcKmaZDjO9uWb8YMe6boUftBj+/LNRc00FbttbHJL5gTX0Pu159Qdo2a2KYxH1CYJ4+Hj0XHgNR795MNcd+Vh6p9rxfHB8SyOD9N2tmomXKRERhqfxktsGkhDNM7nr9hMOOQRjaQIhzxWXrFZM00iJdIc91m1pZWEB+0pS8KDVY+3cs1nJ5BOMegseM+2BIE6erfNnwnf3ZLmrhfjffEJuOuF+KifCW+IdCg+SU3QAFyqJl4fw/Fz/ws6vkO8PlaybVtnHiq4/UDlteLrk84nbdycshSGD02ZCcCseks6/zPWGAJdXoV6KDK2jTQ+jfXYNJQZU2Okvdzj9zyHGVOHjusiMrSmmEfQMTllAccwbYFLfYPh9X0+nZ2FB+EDbdsUyz132HooVXD7gcpHixn1RxWfpCZoAC5VE2mL4ju5HxK+4xNpi5Zs24b9xxXcfqDyWvHV9sd6l533qMPh30N1vOMHF7Hm5kYm5K8Ys5Z0nYuIHLuRxqexHpuGcuBIlICbe/yu63PgyNBxXUSGNjfqkvJzk62lfcvcqMvcuQ7pFANmRB9s22xvOS5YcPuBykeLA22TFZ+kJmgALlUTSkRYsnEZTsol0BXESbks2bisqOfaFrtt9OgU5m1bDJber3nbFhM9OqWoPuYnQ/v++tv4wucehWTuB1i5kqElcImZEAlyPxz9kMPhRQ34DviuwXfIvNYzvEVKYqTxqVKxaTgqmXCoNRbh5vuWkUi6dMSDJJIuN9+3TM/IFSmRxojD2qUNOB64KQi7sHZpA40Rh0mTHOobDLfe0lFwFrxnW1JAInfbbAumBLjq5EhffAKuOjnCgimlTR1V6WRorfGJik9SE5SETapq1q4FNO6ZQ7w+RqQtWtTge7jbLn78bI5/bjGtMw/RsP+4YZ3g5idDc9sszgEf76MuB++eCpQnGdqaSRewun0DkFmO/tX2xwD43P/9CG/iRQA6p0domlxHoMsjXedq8C1SYiONT5WITcWqRsKhTU8vYOvOOcyYGuPAkahObkVKbMX8COtu6KArYln3vcacAfTcuQ47nve49+44H//ExILbrnx3KzTAlv+c3m/w3WPNWQ3c/dk4zIYNd00r+eC7WsnQFJ+kFmgALlUXSkSGNfAeybbRo1OO6eQ2jAfWgzR0erDtGZ+VlzQzOWz56TVpnCD0XCaetn8Ir6AAACAASURBVLOVpsl1xzQgThuXG+ov7H3d833azX3L+iGHpAbeImUz0vhUqdg0mOyEaJC5pWXlFZvZunNO2U86W2MRndiKlFEwZQimTL8B9KRJDucsdbn1lg4+eGWECRP6nyOYTqCTAQffPZxmoJmyzHxXKzaB4pNUn87cRQZRMBmaY/jyokyZkqGJSK1SQjSR8annueAD3QtebYpNMt5pBlxkEIWSoYUDDg9PdDh491ScpM+E3x2E7EG4kqGJSA1QQjSR8emMt4Y4Z2lo0FnwalJskvGurO9IY8w7jTEvGWN2G2O+NECdS40xO4wxzxtj7itnf2RkkuE4rdMP5TzHNr+sUJ1j0TJjP7vOfIqWGfuPuW/DMdC2CeMQM0HigdwMoMNNhnas/Q14ab7wuUeZfuURTIfP9CuPMP3KI/2SwpVbuZLOVYpi09hQ7PupGvGpErFpqPaGmxCtEr+7cqt0UqdyUHwa/ZrjPtsOpXKer11s2bFoa/DYMz/NU/uTg86C2wlgZ5Gz31TQEqv3R9SX/OMY6rh6Y1OnQ/obBu+b8L27zuKvXv8NN7Y9QsDLfd54JX532fIT8PZ8nz8hMxxjITZJ6ZRtBtwY4wLfB94ONAFPGmPWW2t3ZNVZCHwZONta22KMmV6u/sjI7Fu4m+3LN+P4Dr7js2TjMoCcsjnPv5G9b3opp86sXSNPpPHkux/myAl7AXj5zGeY+toc3vrzi0bUt+H0pVB71/nz+frhJrCGS9/vsf7HM5g606H+h30fDsUmQytFf7/zyzt444H9BI3HvLceglRm4D39oy29SeHKrRxJ5ypJsWlsKPb9VKis3PGpErGp2PaKTThU6j5XQ7WSOpWS4tPot353nFVbWgk6hpRvM1nHLUWVrZg/8s/SKx9u5oU/yQxGL/nlEZbODhacBV+/O47/F4AH5zx4sLcvW89LY/y+smL7kn+8ly6K8KOd8SGPa9PTC7jw8RdZ3HUAax2uM//TO8D9zi/v4LZrzhrW7/NYfnf58hPw9vRrdfuGnPw8xRoLsUlKq5xL0M8EdltrXwYwxjwAvAfYkVXn48D3rbUtANbag2XsjwxTMhxn+/LN+EEPvztJxvbzN2OtxQb93rI9p+4AQ+/rl87bxG3PvIjjuzkZvNdMuqDf/dT5Wmbsz5zcmr6yIyfspWXGfqYcmDnsvm1fvpnGPXOGTIZUqL3nlm/CGMOFAeh5Fsf/ufowlyx/P2tDv87ZfqhkaGkT44UB2vcD3rD76ySARGbwnTCw7ZkUKy9pHnSbUihX0rkKU2wa5Yp+/x9DTCikmPhUydhUbOwYKuFQKfZRbdVO6lRCik+jWHPcZ9WWVhIeJLzMZ+QXH28FC13+4GWrHm/l7Nl1QyZGK+Sp/Um27EvlxKbHX09x48ejbLkq2ZsRvad/BIEgJLy+vvgu4ILn9fVlJMd71wuZGd784yok5bn41hDGo657kNsJdBx4jUc/eZTtEw+z9bw0vpv1u9vcCga6sva5cmMr627oIJgyBfcDcODIrbS/7kEiOeRxtaab8fGYAJkEvNDvcbDFGkOxSUqonAPwOcCerNdNwNvy6iwCMMY8AbjA16y1/5HfkDHmWuBagLCj+0MqJV4fy8x80LfkxvgGA1kl/f30R5ZTUwfBGu5pvZ+A333lML2JG4L/Z9B9Hj6hqWD5q/83QVf85N7XnYE/gBvM7Ynrdo8Js4KrG6TpkmlMSB8/6H4Ltxfo/jDLOn7jEosPP0lI2/QjkCrQvs1t3/Ed4vUxkm87ldjs/sF+3fsS/OmKl6Crryzl0JsUrtwGSzo3irKxKzaNcoViU+H3f/+ynvfYSAaRA8Wnwyc09Q7Ai42bxfajUHuONWBzTzSP5bgqsY9y60vq1HcMPUmdRtlJblni0+w5oyY+j2pNMY+gY3oHhgBugTFhobKAY2iKeSMagD++t6tg+aF6mzMLvvdomvV3QdqHSy6FH/8IXAcuvQK6svrU05ehFDregY4L4JWL65mwv773Z3+R/CM23LmGcLJvXynjcMWMmQSI0xWxGB+yx76OySzAy2Z86IrYQQfgw/FhdwrbvYP0PgSdzNNpbpp0/rDbGkOxSUqonAPwQu+C/HdoAFgInAfMBR43xiyx1h7N2cja24DbABoCx1X2ZtdxLNIWxXdyR1vWsVg7+J/AGrCOIZJOQ/d9PPFAkOTkIHb6HMwrewfcdtprc3n5zGdyCw2c9n+CzJi6vbco0RXn5d+kcpKfGdcDa8nungmkOO29BwjXtQ3a58LtpTHG5JY5KV743Vu5OHXqoO1l69w6lT8990n+rUD7FifnXeE7Pv6SU4lf3cqpx73er61/XPVg77LzHhOD9CaFK7cxknROsWmUKxSbCr7/C5T5jk+kbWQXSwrGp+7ywfpWKG4W249C7fnG9jsBPZbjqsQ+ym0MJXUqS3x685uDik8VMDfqkvJzf9WehS7PJXsAFvcC3TGhr6zLN7zsLuBQZ26umfMn7B5yv8umh3jn3R1A38Aa4C/nX0z63QGaH1/H+WvfzIPP7OKPXzkCQNO3IdS9+/vvgYuu7GuvI+3y1deuwrP/CMDF2z5WcL/pVAex9HeBdMGf97T16Sc/hevCn759e87PvvC5RwmZVE5ZOGT4z9lJ3v+uL8Kb9uA/+12wfe13+Q7BvHgVDNP7PPRCfW3ZP4lzb4ljmvfC0BP73Nj2CHV5g+aA9bi+/bFhL0EfQ7Fp3DLGvBP4DplLQbdba/+uQJ1Lga+RidfbrLVXDNZmOQfgTcC8rNdzgfwRRRPwW2ttCnjFGPMSmQ+VJ8vYLylSKBFhycZlRd8DbgjgB3w+/YH3svXbP8sMwLulAi6fuuZqJm0PEH1l4H1OnvDHhEIvkkzt6i1bOjvILUue7lf3bD/KqsdbCTiGtG9ZuzQTzPLLVpz4637bFtK/vUkF27sjlqRz61SmPz301eGDp7tMeMsR1p746wHb//ymTtxE5uT2Tc9ezP73TOCJBX9PXXuKrklB0hP7v039MBA0/Qbj5daTdC7/HvBRtPwcFJtGvezYhBvEBFJ8e1nh9392WUc6wJueXTHiGdwpB2Yy9bU5vfeAA0x9bU7O7THFxs0lG5cV1Y9St1eKfcyd0cJJJxzixdeOo+lAeZ5hPlw9SZ1WXrEZz3Nwu++zHIUzTIpPo1hjxGHt0gZWbmzF+JmB4cT6yzlhq2Hn7PvBBycYYMGeSwB6y0w4wKzj38W6PW/q3+jCoQfhF36lA+cP4NvsgbXhX7/wFH91zsc4PH0hh+/9LYH6WRjrEvE8JnSfosUdh7c1BXj4riSXXAw/+ZlhYnwaodQvOeJbLgFu+ciPAfjSWdeQdrLPSRpY2HBpzrFNb34r+yf/d86xWncKX7zkoX7HMT18hKDxcs5pgngsCB9iwluO0Ll1HgtfvZTd836MsQ7W+MyNfYATlu7hye2bsmJ9A40Rh488egXv376LHUfm8YfYjN79zPhDx6CTPwNJ4JI27jElXxtDsWlcKldejnIOwJ8EFhpjTgT2ApcB+VcDfgpcDtxpjJlGZlnVy2XskwzTrF0LaNwzh3h9jEhbtPcEL79swZOn03lShNjiefzzf9xHMJ0brIJpj+/ev46vnPLRIfc5c8bH+IdT1/L43i6WzqnjjJmhgvVWzI9w9uw6mmIec6Nu77KtQmXFKLa9O7bB9Kc9opt2FtHqImJvGbz9b730F8z7x+eItEUJzlrAyuBPmfP7w70ttM2K0LKwAYCDP5zC9I+2AHDolskcd93R3vJKKTbpXA1TbBoDemJT0yXTOO29B3ovtA32Hv70k59i1j6XzJ99ZN7684tombGfwyc0Me21uTmD7/y+DRU3h3uspWrvWPbxyfc9wYplfbcj/2zTYm556Oxj2nepFJtwrsYpPo1yK+ZHWHdDB10Ry5uuv5SXfvRWZq5/gQOp4/AbPM7wL+x9fx1IHYc/3eH4Cz9F0o3SuSe3ra6ZKR4/blFRs+Cu6xBO+L0D6wQObksH0U07WZQ6id917eLTsQY2+7mz8UHfYro8zn0Zmm6GsHUIsh/YTxeZEUf4YGb/f//L7/ebAY4ysd+xxVO7c16/vLJwnwc9p3mh51xrInPCH+iNQ8FZC3hx8Sls+cALObE+9gJsDH8bzsi0fSyxac2kC1jdvgGgXy6jkRgjsWm8KktejgEH4MaYecDfk7kf6RHg77uvtmKM+am19uLBGrbWpo0xnwEeJTNlf4e19nljzN8CT1lr13f/7AJjzA4y0eCvrbXlzyQlwxJKRPqd3OWXhRIRgq1zSNi+JTWdGJLGUOcOf3nyGTNDAw68szVGnH6D7EJlxSp1e8W077pRGg4eB8CcKYe4xv3vnDWI9fvitM+emJkJD5mcpeaVynyeb6ikc+V2LPFJsWnsCCUiTEgfn3OLyWDvYdeNAsf+CJgpB2YWHHjn922ouDkcpW5vJPuYO6OFFct25CxNf8+5O/jFE4traia8mie3OncSgGDKEEwZwnV9/xedThen0yXUkFe2P0TARhk6NdjADt0ymZlvbYZEX1n2PctTgjNpDM7hm/GnCeTd5WDIzDq7wITuvDQ9w/MJ3V/gDZqELP/YCh1rQUWe02THoZ51f9mxPtCR5k2HDpcsNqWNm3OhYSSZz/NVOzbJgKYZY57Ken1b9+07PUqWlyPbYDPgdwD/BvwW+CiwyRjz7u4gf8Jgjfaw1j4MPJxX9jdZ31vgL7u/ZIz45Ac/wg/uvYPEntf44PSZ/FtdXW/5lGer3Lkad9LMwjNzde2pgkvRx7Fjik+KTSLDd9IJhwYsr5UBeA3QuZMMKWA9VrdvoDXdzIftDL6//ja8usx5Uiow/M/64z5yhFB77srDiTbJ6qOP8pUp7wJgfuR0/NTPsRg6yaSmDOMClkD3k156JEwQayFK3/3ZhZKQ5RyHO4WbWh9mgXeYp63lancKN7Y9AsA16YXDPqbhqGtPFSxXbBqdvJRDy/5JldrdYWvtGYP8vGR5OfI3GMhx1tpbur//rDHmQ8BmY8yKAjsW6ZUKBPjY1dfSdPv3AfjY1ddm/XTk99GMBy/un1OwvGtSsGD5OKb4JFJhL7523LDKxynFJhlSz3OmfTy2dzQR6sqcjv/g3jvyzpmKE/mvzLrz/P9gb/H39X4/JTiTjwRmc2d6Pz6Ze7t/HZzFwvQhwjZNIOv8LGz7D2gLJSHLOQ7vINFMalnOBbZ7B7sTmWWOa+fl04Z9XMUa6BxJsUlKoCx5OQYbgAeNMWFrbQLAWnuPMWY/maVPE0dwAFJjeq5cQuYel+vbfsWC9CEMhv8NNGKB+V4zu91pfK3+HUM+w7sYKTdG6/RDJblnsVJCR5NEjnYRn1xHcnJmWXypj6P3b/E/ddzz/rfxoe//T+YHX4C2eZGKzX47SX+03Nut+DQGhf0kdx59AIDrJr+fW47+BIBrJl9Gwhn6lpRjlQzHS3ZPdSWcdOJ+zjipiadenMuLr/Q9h7xcx9B0YAo/27SY95ybew94JWaYGqLx0XL/pGKTFC2zvNtCOkU8MPCF9ua4P3humwCQzp2qy+RYz528+6PIafjtvyywB0snkMJQh0OQvmdy982UF3Ec3f+6QEN3D3qWrrcnUmzrSA07P08x0kGX2M0hoskk/AXwT3DwyET2d9YXnr8ssVEUn2T4ypKXY7Cz+tvJrHHf1FNgrX3MGHMJ8K1hd19qTs+VS4B7Wu4jYpP0hMTT0n0Xd05L72V1+4ZjvgemzXua/z35QZwF9GbUnbVrwTG1WW7TtzUTac1cCZ78hw7iDUFamrfTdPIvS3ocvX+LtOGNn9+LtWAsJP4lSMv9DaU4lCFNOBjvl928c3rNfpAoPo1Bdx59gAabeZ7t/S335pRfNvWqsu5738Ld/TJ+13J8uvG6hznj5MwtKx985zM8tWMOH/vPRWU/hlseOptfPLG4olnQzzt9N5+/YjNpzyHQnUF409M1+7dRbJIhfX3S+dzTch9kZddOBVw+e/nV/eq+/Pr/cs6GgwQdQ6o74/eK+bmfzU2/bmTu0v5pAD4y+dKc19+Jb+U0MoPkvUA01QRAjDqexPJhdwoPOUFM92MTD6df5zIC/CqYySien4Ss0HHkC9T5XHPpW2ha/yPCjh3wGI7F9I+2ENyepisdgOssxoMGm2B18NjPXYcyyuKTDFO58nIMOAC31t48QPkzZFKxyxgRxusNnpb+FwstcDjVxKPNtw/eUDPw++wNMwF81w1fyGT2+DwQBL/7GYzPLv81zz77a+g0uW1cCwtM9Vfq/ckCePwGcpJ6xJpT7H315/iO13sc25dvpnHPnJLMNoVtmnA8s5QsYWDrc2lWXlL+3DqTw5afXpPGCULPVexpO1tpmlxXkzPhik9jW/4sTrklw3G2L9+MH/Twu2d+Svm+LrWTTtzPGSfvzYlNJ7xxLzvcffiuX/ZjaDowpWL3VTZE43z+is2EQx49tzCtvGIzW3fOqcmZJsUmKcZX2x/r92irnqfFZC9B97wY//3cFjwfEl4mGq56vJWzZ+c+zHrunxU+T7jj6I94d2Pfc7EXeIdxyMTYnkv7FrAGLnUbgdyEYxub1/UrG+o48rnW8s8PPMCFV0Kqu+rKja2su6GDYGrg6endsbtw9wdwW/MSZ74YovNbLpfd3nfMN7+Q5i1dlojte972YEnjSmW0xScZmXLk5ai9M2upmK9POr+oZeUx4P3HurPJ9L/92+sur1EXLOlf9mo686jKbI7vEK+PHdO+Cv0tUg58eVH5P0AAZtVb0n5eoTEEunTPvlTOdZMLR5prBygvlXh9DMfP/Tgsxfu6XM44qalf2aspyF/AWsvHUKwZU2Okvdy/jec5zJg6uo9LBDKDxJhbN+Dy87TXguPk/v8POIamWOHPZpv1Vcj/dg+yiy0vVgKXmAnRexphyKxLD2WeTZ4/zDY+dEVKd3n1K4tcUnkjmkJJ40pN8UlGSmmVx7FirlwCTAJ+E5w75DIee+IcDvxJPZ3dT+fpScI292OfJm1jvOrdiM3KqOm4Lueaywk1RnLaePG6CJveUXASoaJCR5Pw7JGcsjcEIJX3meE7PpG2KMei0N9iYhAenuhU5FFjTtJnwu8OQvYg3FrSdZW5ACAC9N7zne+2oz8p6xL0SFsU38m9AlWK93W5PPXiXD74zmdyyt4QhPy0SbV8DMU6cCRKwM3927iuz4Ejo/u4ZHzLfs70TUuu5IvN/96bBT1bwJ2C7+f+/0/7lrnR3M/m135/HPP++BCe7/Dx+vdz+9EfA3D55A/m1Bto1m2kt0nnPy/7m6lfcJI9DCeSWfX4HUhbuPg9ec8eD8O67zUOei/4xduuInpHA9FNO3PKe84TH8g6T5x+5RGCQQe8vt9VoaRxpab4JCOlGfBxKGA9bmx7hIXpQxgyITGN6Q2NlqGvog57nybKdPMBHD9IoCuIk3JZsnFZTS7v7JGcHCLeEMz5XUQbg8x5w4oRH4eT9Am1p3CS+dPNGQkToC0Sxg9nXpvC1UrODzkcXtSA74DvGnyHzOsaXH4u1dMTO25se4SwTfV+X8yFvGLammgzT8IdKv5kbxtJdvGFzz3K9CuPQHJkESuUiLBk4zKclDsq4tOLr8zkqR1zsJber9demsPiX517TMfQEI2z6PhDNEQHf1Z6sfVKoTUW4eb7lpFIunTEgySSLjfft0zLO2VU63nO9A31F5Jw6/j0imv52NXX9nsEmetGOeuUc3A8cFMQdmHt0ob+A9epLv/6/Dks+dv/xwG3gXc3fox3N36MmJv7PlmS3kchp+SVh/0kDxy5i9dJMh2fB47cxQNH7iLs5z6tPOc4TJCVoT/nqb+Yg10Ftg7sF+HZd8+hIXg5QdchGjQDH8Mgio058UCAuBOgqwLLz0HxSUZuyBlwY8wM4BvAbGvthcaYxcCfWmt/WPbeSVn0JV+zuN2nuGkMXYSw2IJZ0PMTb4xEvXs6J2yfj7N926jJMhybNYFwa2vO6ym8iZm/ePOwj2OwJGe9V5Ejddy/9jT+4ZbMTGDdx5NMOBivSDK0zukRmibXjZYs6IDiU6XlJ27sGXiPJEljobZ6MvYexfIWAjzffVvGNZMvG3DbDXeuIWRSBI3H9I+2jHjFyKxdC2jcM2fUZEHf+OQi3rzw9d548tiTi47pGIpNJFSNhEObnl7A1p1zRlWWYcUmKZUTZ8+n49tP0BWxQ84aD6WDIA0kC5Zny06I2UQabLq3fKjVSIViU/25p7Pq3bv4I2/3sLOg58ecb//qYl7kzJw6B384hUlXtDO5M078Ey6hWzxe3TeVNfbYz12HMhrjk1RfMUvQ7wT+Ffhq9+udwIOAPkRGuXDWeuMufJ7Ecmmg5z6g7pkkdyrgQ9sjQzf4Yojk6wH87qcFde3LZOjtWYoOcCBmcY8OcgWzQHKNaslNTJYxbWcrUyZ2kPJmEz1Y/PMlnaTPtJ2tZFa55iY5g76ryPWLJ7Nu0j9ivpjZzlDZZGh+yCE5CgbeWe5E8anishM3Hmuim/y2tgZnc1bqIMCQJ3phPMLJzLY9q0aORSgRqfmBN/Ql/qkL9a0TyE78M9xjKDaRUDUTDrXGIqPtxPZOFJukRIIpQzBljvnxXR+b8gEeaLmHQNb6Ig/DR6d8ANof61d/uAkxB4pNP933Z0wKBzl1wsCPWitkcijWL+b85QU/48fpxTn1HCwNn0vgWkuUNHwZ5iRbmfi1ZEXixiiMT1JlxbyTp1lrf0T33aHW2jT902nJKFIw4ReGa9zKZLUdLQZKTDbbby1YfzCBLi83nXp3W/lJzmbUHyWVP6BRMrTBKD5VUKHYMdJEN8fSVsEEkkHDoVtrOKtjCZU68U+x7Snh0LAoNknNWRX7Nem8U/80Dl+K/TqnbKQJMQeKEXMDRwbYYnAzJ7b0b893mWtacsoCXR5+3jmWYpPUsmJmwDuMMY10X8oyxvwJMPwRiNSMQgm/6nD4dyd4TMkqBkvC1mP6016/hBr5beQn16iWgRKTve4M/7nc6Tq397Fs2W3lJzk70DaZYP45mpKhDUbxqYIKxY6RJroZrK2HB9hmsG1JWY77xNGKJC2stlIn/im2PSUcGhbFJqlZnWQmXuoGmIcbaULMgWJEU3pkcXl/x5T+7TkeTencCaN0nYuTd46l2CS1rJgZ8L8E1gPzjTFPAHcBny1rr6TkkuE4rdMPkQz3Lf/ueWxE9hLSQvVGasqUJItObiNqDvSWWdPK6sTPWBP7BVE/kUns0bKOtvnP87XO9dy0826C6fQx7XeoRGfF1hsoMVmLM7G3Tk8yqDWxX5Ccto81sV8UTEo1WJIzz4v1/s5b4xP569T7lQyteIpPVdAJtGKKXn5eKOHawvShvrZMX1sL04f4OSkmYnOSvBWKTZltHbpGcIGqrrGFhjfvpK6xbyal0D5ik1vYe9JOYpNbCjXTT0/yogeO3MVkvzMneVGpkpwNN/HPUHF9sPayt1XCoWFRbBoDmuM+2w6laI4Pfj5RbL3BBNNpbl93G/+67rvMTz/Bv677Lutu/iFuyiMVtMTq/Zz2Oyf4HJrtsbul+HOmr0eXkzABOjGc6UwjYQIkTICvR5fTOMnjtBOTOXGnUELMweLTQDGixS88EB7w95a0fP9v7uP6/3yA7911Ft43If0NQ6LT4dsb3sMRojnb+iGHzW9YQNwPKjbJqDDoDLgxxgHCwLnAG8ncDvKStTb/aSdSw/Yt3M325ZtxfAff8bnuV+dwS/cTbL4+6Xy+2n3fz3WnzWfr2+/vrbdk4zJm7RpZcp35wX9j08V7iAGp9FrODZ/NM4kT+aet93JqVyaUP9Byd+aU28Kmp/4r8wzH5EFu/+46+L8jO9bBEp2NpF7BxGRZyUJXt29gSfp1rOvz0Ku/IOSB8ZyCSakKtbV+d5zX9/8dBy7OPDLoTc9ezIvemXzxzI2jKhlaNSg+VV5PssDW1EE+7E7hoe4ECUMlaSycvM3SGXD4n+PhkostP/mpx5l/CDAhnWIZlldJEe7e5kveTzn3mtZ+Maw1dZAPzpjDr96QZEH4EAd/WNxtNJMveIL1J+0gSObRXSteXMwLr8zIiZNLNi6jZeYB9py6o3e7edsWs/jxswdtOzt50f0t9/aW3xe7F+fbtmRJzopN/JMf/weK64XaK7StEg4NTbFpbFi/O86qLa0EHUPKt6xd2sCK+f3/vxeqNxI/uPcOznx1J771eeCbrxD0wDEO4c8e4ux3pTE+nPPgQdYubeD3+5M8tzRzof/tDx3mqpMjLH3L0Pv4amwjYZvGx/I7/3DvDPi3nZ8z8++PkPIMdYH7+e5dZ/Hx3/wOyCxH75kRv/28M1l31f2DxqeCMeLc/n0Z7Pc7/aMtzNjejPEMS37zCmnrYozlxc/OYNOSU+h421bOefBgzrYTZi3gy7/5EG/78dOKTVLzBh2AW2t9Y8w/WGv/FHi+Qn2SEkqG42xfvhk/6OF3L23e+vYtrNp7eW+inhvqLyQZjrP17ffn1Nu+fDONe+YMO6FP1BxgU+IJ4hZ6ro/+Jv4EXfwPtvs66oS8C7ZBP/PVGbDEd+/izbn5NYrSGLU8/w1wQtBzvbb+uVauurODo4m+e4Nyk6vlJkQrNNgdLDGZ73hY12dCuu+YOgM+vtM3A37qca8XbKs57rNqSyvWQjqTi43n3/IzZnmLR2MytIpTfKq8nmSBv2v9BcCwl53nJ1z73fFpLupezXjhVfDIXWnOe9llAjABAC+zUqfxCH6QfjFs64GNJB2Hf/jOO1gze6iF6919qD/E+pN25MSnn71xB8n5L+EHsuLf+ZvxXS8nC9GeU3dw/HOLiR4deqCfn7woUpfGhDLHBKVJcjZU4p9C8X+wuJ7d3mDbKuHQ4BSbRr+ez+eEBwkvc56w6vFWzp5dl5MIrVC9lRtbibwMTtzw6icfJrZ/E39o7aQ9nUku13Z8kQAAIABJREFU2xM/e3UnsPXa9+LbzPkEWecTR9tb8F3ABc+DLz7eSpdHTpC564U4C07sLPr4MvHVAh5duJww6whuXV/ZZ6/6L65++fLe9/llU6+iIRpn3VX3FxWfhooRA/3e1t3QQTBluPmFNKemLJGsqfeEdUl5LslQB0eO/gRrc/8231iRosWPsvMPxSfIFamWYs7wNxhj3mdMfgYpGQ3i9TEcP/fP7PgO8frYiOoVo8F5jfw8ly7gYLjkUkgOslo06cL7F49s4HlCI6TyBvZpP5NMLdtAydWKTXT201NvJ/aRVl5eeTJX//Wf9zuepAtXffG9vLzyZOavemHAgUFTzCPo5L6tjO+S9opb6iqA4tOoUDDxo2P4wJ/nXgO+7M8DpPLeE4XqjTQ2AdRN2ztAfMozQMrf1pmHBm1/oORFHX+be/yVSHJ2LHG9lJ8J45Ri0yhW6PM54BiaYt6Q9YwPtn74+/zKn0RJ5Z1PpFy47LLizolebm4fsk6hWOw7hvgnc2NsqePThP3w+NFFva8H+r11dY+4v7LIJWnyksV1J+mMR45C3u1PAcdwuCMxZD9EakUxSdj+EpgIpI0xCTLX3Ky1IwkvUmmRtii+kzva9B2fSFt0RPWK0eqfQP46Ow/wsfz4RxAaZJwb8uDnXfP4ox3DD6RO0mdSXtK0CRFYc3Njzsz2QMnVhpPo7Ken3s5jCxew/MPP9TuekAf/sfEeNv7rKZw/YfeAbcyNuqT83LN863gElI1+OBSfRoFCSdOCvuXBf/e4MCufzwP/nibou0PWG2lsAug6PGeA+JRngGFTw/7BZ1cGSl408W88uLXvdSWSnB1LXC/lZ8I4pdg0ihX6fE77lrlRd8h6wTCcNCtAcJrhjd+5iG13L2Hm/S/0znyf2fCunPo9CWz/36PfJegdzm3Lgwce8LnwQ0P3+Y8aJ/HEEHUKxWLHt4R+4MGX+8pKHZ9m3v8C21jCuW95IwCeFyOW/jvIisZ+KMjR932JdjfKupt/SNi+THZk7knS+cX45eQ/UCDtW6ZNLMGzKEUqZMjLatbaSdZax1obstbWd7/WB8goMSEeYtP3GnjkLmhoD/DIXbDpew1MiIdy6oUSEZZsXIaTcgl0BXFSLks2Lht0+Xl+YqWbdt7N99ffRleqkf/P3pmHV1Gdf/xzZuZuyc0eIGyyoyAK4lpRsIpWW4vWulvForVarVu1WFfcqlgVrfantUoFFNeqUJdqsYqi4sYiKMouhC0h+83dZjm/P+auyU1ygUQSmM/z5IFMzpw5d5L5znvOec/3jPOOwScgX4BPwDG+MXQXZ6NI8Bi2dMomXxb2CKgi3C1eszVaMzrLWA6whH3d1ozOtEaD3K0htMb06fXxOavp4Q7hUiGoQa3H/telQg93qNXON0CJT2Hq0QUI4Urc8/2XnIKqZh/gZms4t6fi6FPno03DtZh5mwCOXQsVU6H7dvvfY9favZQgUEtyj3F/VfEOaVNrhOu7MeHb4Wn6dMp3wxkxr4n+zRtL36XD00Sq79Ls0s+BZuZFwYjWpnlZ0sBIEI4ohKOiVSOhPj1qGH/YSvr0yJw1E9d11VBx6xqqkf2925l3QirZGs7tqTja1LWJv58VE1QdvCpMPbqg2T7c8XJeFfwukSjn0pMjeJppcGf9W7xoVJErrTRzyVRMPChCIahBXSyeUIRCUV6R3U+N2O247+gCLhjmSxOYC4b56FmQk/XnSzXhlUD4Ow3zHqirBvMe2HRdAY0N6bHYjuhTJm0qe24FwyavZr/HQ+z/D5URX0xANTXchgvV1BjxxQT2/4fKfo+HyF0ZBCkzmgW7o7kUF57e7HeT581+j/G9XZ8cdj9tzoALIcZmOi6l/KD9m+PQ3tjmR3VQp7JxGrHZpTpuczU3Ceu5ajAlG3sTyg/gq/e3GWg1M1bCQgYFjz07nYsnXsJ+2lEUKN9TZ/Vjjd6DfBUKGj8G1mYc+RGAZilc/PuJzOORnfq8GU3TMuCpi9oTXDL5fSYTtqJVdeRvSQp0fU8fNUOSBisVTxXR81dViAaDZRfBYdPB8mtZm0FNGOTjvu+upO9Dy/DV+3H1HJz1PjXZGsntyTj61PloyXAtLFx8IdWEedsofRMaUBqCrY8mzzew+B9wBvCeqycA96onMO7paNba1Ba174zh+C+H4+1dSXhTNwLb83my4R2sxd35U/+DuWf9lyjWKm7PO4F9lg2nrqySgq3dsup8X1h4Nk/XPo8uI4xC4+tYuueF/rPxTDHbNC8bPmAbHlcysh7Wf1tGE7bLfvkRE8YmDeLmzB/O4680N4g7Jw/O6y9ZG4WBbskzeTA/m5vEjr8T4mRrJLcn42hT12fCIB8zbmkk4pPMeLSkWec7tdyYXh7KAyZ9/ColPoXZJNdjP/zGdA7Qt2BhstyswBNL425q1nrZeZN47NnpmFvXc+MRfu5fn4fudjHzkR+Re9oMZD588GZ3SnwKEwb5mPX7EPSCd2aWMrhIY14WS8DjRpqQNOEdYlTi1XWiy0G9SmBYCr1biBOz0ae2tEms2wTAuUXVnNfPYm1U2Nq0sJr5sZ9lamf8OEBu7ij6vfxS2u8mm88Pjj45dA6ySUG/PuX/XuAw4Evg2A5pkUOH4MXEG5skbW3rIHfYt8PBbaqxUtAQhDZ9z4aZKRE1tpOmEhU0VG8hQtwAJB0J1GKx5tKbGLBDLdgx9u0JK/5i91vj5G8OcewlIVZtSx4c0kPyxZQm5baEaOiVi5FrPzoKEteVBooFRwPcAJZioCCxWsphbYKq+imosNNaW1h22gwlalG6sg47Q7RtI7k9GEefOilNDddWad04M/b9Lfkn8Xz1TApkpJlZWaNw81MZTZSLszPa1BqRqiIiVXaH+s6GtxKDBnNXvpOYmbqt4R1uESdlPesNEFbcnF18Ae9WzQBI2zM3HKBVY6I+PWqYMPabNM05Zdw3vP7RcMq3Fe1wuaSpm0Xf2GVbM3XLxI7e9x01ktuDcbRpD8ClC1y6aLHzHafEp7RZJtX4LFMcpmsaF0+8hPIn/war4NcXX06kTOcY13coIQEh0q6hVAFVMLgom1DeJm6kGeeW/JO4s97WPx+A1XL7stGd9tKmTO1sSra/m1QcfXLoLGSTgv7zlK/jgRHAtrbOc+gcZDLciBtZdETdUSG4bEgehf5gsy/Fr/OrkjL0VjqmO2F+vsMc3sJA5yEDWv8+jqchuWZJi5jpPXTYIUO3nWV3Xbez4ehT5yOj5qCgSCstBfP7FrwOLiv4xQ/RzIx4MfHLqD14sBvYr19mg7emx7Mt196mbtmwO67ZGXG0ySHOdSdObDUOE+s24d+8+97d2caJ2ehOZ9am3XldB4emZD9slqQc+0Xi0AXIZLgRN7LY0S2EsqnbraksL6qlYlZxs/Lzgvsw9OTteLapII1mPwdYpfoY+fcpzP/JtF1qW2tojQZ8mW50goBbHinlT7la6+WASF5ynZHhUUE2mbfeQUO3nWF3XbcL4OjTbiaTLvjQGWVsJgKJFExPC53cmbUv8PwP0M5U7s4bzzM1sxMz9tB+A5U7wrffZzZ4a3o823LtbeqWDbvjml0ER5v2Uu7/z4w24zD//JWA7RK+LWC/27svMgn0crG0rFeHti/bODEb3enM2rQ7r+uwYwhd4Nma/Zr+rkibM+BCiEeEEH+NfT0KfAgs7fimObQHQkpcmJhAHXbCjQuTIXpFmknSnfVv4bWiTAnO5WbzZcz8yhaNQgCilh9D+iBmqBQQbsKqCyGSncKmBmHjc1ZjunUQspkBW/ys1pK2WzJD21GMXI36nr60a9f39CXSynekXNzQLRiFQNg2dWvN0C1b2vqs8euGdQhEaNFwbk/H0afOS6rhGtgvmxygANnqDLOK5E0kucg0DQoU1rBpv5UEClvfpq+qqoSlS0ZSVVXSarlUE57WAtCoN0Rd90qi3pbNetoyQ8uW8m1FzJk/HClJfM2Zn566uSPlkqZJCqGwSjiqtGrqli2tfd7kNVs3nNvTcbRp72J1jcHLK0Osrmk5Pgmj0YCXcAtzX/75K/HPX0l3M8AhRXX0/nIpPRbWU7M1D8snMXtIqkLJzqPMAdmTtGPQ3AjzL+G5PCZepl/3qlY/QybDs1Sy0Z3drU0DtG2tarGjTw6dhWxmwL9I+b8BPCelbGunA4dOiLB3QUEB8ohygL4lxSQJnmqchc+yQIeXQq/hNgWKpTQzCtkSPpTljRM5jlt5hbPwK5u4v/BQbtA+weyj0/upxowGYc/Xw/WnbWfOsxY/XgfuDAueLZH55dWWGVpHESlwI7eGYhvI2N9nQjT7z84zxfUavVNm3lv6rMHuPi54upGe+bLZVmt7EY4+dTLi5jl1ekXCcO1gvTzr8yUwDnu60Btbl/0H7QWO/VVjokzfpcMZ/mFzw7G5r53K5OsfxOXS0XUXU++/hgmnzGlWrqkJz6brCpDVkqAKUdXeSlCYkkhOkPkXPodiKViKxYh3x9Jz1Y4ZDu0o2e4a/e36Hpw05jv7hglYsb5H5vqwtV8K0R7ylNXnnb9oMEtW9m7TcG4Px9GmvYRbP6pl1rfJrVMvGJb+937Vzybx0N9nELB6czazeJ7z8SubuDfv8GZ1HTN6Na+cU4FuCjzaczz431P5stFN428AC456oYKpRxeABOtKwEwey7E9K9OMMF8KzETDjuse7/UKc05r/ry2ZXiWSjb6tLu06YjvV3NRnwVwnv19S1rs6JNDZyCbDnihlPLh1ANCiKuaHnPonEgh0FHxYpIfm2eOz/OkmacBlgpYkGPYXyAJYrJdL+ftqidjZ5UCDwFuQrg5ibfBCkJ1f+awHbZDj4Mlax8ExWPXARBaXMfV60FqcNIF9pZDpSGaGTAZLp0NV05mQIqbZbamadmSjbkapBidpUzRNzU6S5SJ98utXTND61NUyYXqJ2n3JVPb4tSGBbVhsbd2vsHRp92D1/6DlwN6N/uRDtzMxXz+7T8BuHm/X/PWF1Oyrjo+U24bFpmEhUo4rzFNLDaO/IZ9lg2HOjD8ktt7vUlVVQmTr3+QcNhHOGYaNvm6aYw5agElJcmZH9dgydUnfojXlTThcf+5lvn3WUjgjDPhpRdBYHHKWbVYLgsrVm758R9SrByCO5oL2M9rJsOhf6//Mbwr0u5R3Pm3NXbUwMjjSs7aZzISSpRzt14uW7JtH9gzTXt5YOto017A6hqDWSvCafo0c0WIA3JUcoL2ezkczWOctQgLWzdP4m0UqTO270O4XXawI9ZtSjEIg7hZ2/nHvcbN5QJi2bhhE/74YZ39Y5f9FTbhmnfr6P/eErat3UKdXoGFaWtoNNaoWIyS6XnNxvAMsnv+d5c2aY0G+1duz0qbwNEnh91PNh3wiUDTF8aFGY45dBJSg+K7zPN59qsHwUyKXCMCiSR1TjWqwP6/g5WPxjvfyeOnp2U39cdW9FQfcz12fDtI6FcKUTO9xNooKFay8z/id7D1geZtH34x8C9I2cGjRdO00QMEK7YlO54a2e2F3Zq5WmonN2l0ljJVHzM6i8Y6vNmU2RH2K8scpDdtm0MCR59+YALjhhJc/i4A317acgATvE9NlPlsan8OX7U+q/pNoaDJ5LMc1RROP7N5ynpdWSVB1ceo4o1ACeUb++By6YnONwCaySurfsQA37d8WDsUz1YX4vgqopqGl6TQrVZVfn6BhhFzX//p+SCEO2azEE2Uk5rKil+F8LhLAdhfqcj4GQrOqcBcIBKfH8CzdRgDp61o9bO3ZmCUGkQmjYSS9yVuJJQaVGZbLluybZ8D4GjTbmNesP22lKqxWt/bakml3jzzTcJ3IUnpdo3NiwfR62MVBSPRAQeQHpMVJ/fAM2ArAH3fHMrQdR81e17XReztWfW0ayixa6bEPBZsFrm4o5Jfq0UsNytIi0s04Cr7vzv7vGbz/O8ubUo1x22pbQ4OnYkWI3ohxDnAucAAIcTclB/lAa0vJHHYbcgBvdl2RH7i+7/NfQJVpHdMczNsduURGl//n4G7SZzrVjXe9/fjFpe9c0rU8jO/Jietq6vgY1zRUbiVUQDUTOqLx3sndsfcZlCOhnQBsaB3+f9lbv83T8JZxb+i+orRBMvsY1u1bcDUZmU3HPdH+oxLpjbljKrmtZFPNivXlGzM1SA7o7P2NkP7dmvzGcVMbdvbcfRp9xAYN5TApDpG3bURgOdbMUs8+8mqRJl+v92a9TVEE33SpOTlF+1OcSrbzzuYUTM/Tnzfp285up7+nEQiHp5bdDDad/b+Cj86fjl3lr6O/7NwWuw63KOjKWCk6J9bidrLTlLKeZQIb4x/MbHtja0lzT4A1x75Hk9qEYCEoeS84GDu47RWO+HtbWDU3oZD2bZvb8bRpt3LvOBg7nvptHarr7xiC4pf57bNmQ0Za2UN8Gqz49HVks2VIVxX3IWlHoypTkzta+JB8sYZ9yWyc04tu5i1z4xEU/+dVs8At8QyzbRoPWoqMZVMWQ/ucnFg2aUUfbOZO5W3YnuNp1zQwB76+ePOP6/ZPP+7S5taipEcbXLorLQ2pfYxsAU75zh1rrIB+KojG+Ww8zTuk5vxeBCBLgQeFFwxB/IwKoZQ7TXgQqCqCugWQc1eB+mT9iCr6dUSOu5WAozIncHyxomxEV2NEbkzcCvJLRyKHtjIQ6OP4ppzP8A0FVTV4tnZRzOiAZYf9yHCUgE7OG06FKBGVaZWvA9/fz9tLdLc84cx4dhk4Dpn/nDEK9UMpDpxLDBuKKdOurjNTnjcXK3pmvKmM8xxo7PSlXWJY02NzuJl8pfVYViQ48vCDC0q6X6RbRBS+XghM6Y9RW59kNvzTqC8phtPm0dyoZbsWGRqm4OjT7uDQC+Vkd02892Onhibvcm0z30Qe47Z49Zw62b6Mc2FADQKQdYmzum77jBE3/R1hSUlVRx600wW3D4J1dBtbXLPoOcTiwBYe80wji5cmf5cxzwqxH4FTO0LV8+rQ1gqLp/FfUfbA5mTP6xDUwSGJZl6dEHanrPZagnYJpR3lunIAb1bTEePGxidMi59jXVLBkbXnvsBlhQoQmY0EoqXS9XiXTEcyrZ9ezmONu0m4p3vtjJNdoTtdUHMQh+f/HcEkbLMs6wlwQqqfMnl/b7P8/HU5GCoEfxKEVsinyC5AMR03G4dgYup91+btjRmZLfNLC0dwbTZY7nqnPdia8AVZj93FO9/sYyGkmrOOFXw8msSf1U+V+17AIuOnw8mKKrKiHePomjV5rR2hVFR3TItO3BXntdsnv/dpU1GrsbX3crYvyI52Otok0NnpsWoXkr5PfA98KMfrjkO7c1VP5vEw29Mp3Hb95xfVMbrQQURtM1CpBCJTm60p5+nZxzCPTe/QENUsv6RAg6/oYHF9X24aZ8L8X64NlFnT+/nlLhXELJK8ClVaZ3vOBlNLsKHIr+ahShcywDZnQ0cjCIMJnX/GU9Vvo6QsF6UJsxDUg3imAW/WXg6+/Wr5Nvvu+2yqGZrrmbfKBLlMrGjZmjdL6rBu9BOa+17aCXdwjUousVtDe9wMxczRT+F4w9fiadBJ5LncjrfGXD0qWvx/Zfd6HdwJVED9gNWqvYz8uXwvtQs+54zgLdH7IMRUlGigsCalZwBvDlgEABXnzeJg76tQN/4OQVbu5FbNIJvxza/zoATPqN6vSTv5aoWtQnsZ7a80IMWMTE8qv3MfnwqPHQDFK6FxkFQ9GcmnDKHMb08lAdM+vjVtM53nJohBTT0yt3h51WTZkbjo/fWDiI6Rs3KwEgibadh0YI40f6GQ4+/MobXPxreblq8p+Fo0+4lJ/tkmx2mpW2RioK/ZN9PR1FvLKd+fSUbNn2DUAQeJYeRecehWxGWHR0kZ+3+fL+8FCHW8+q/6lGEj+NP8OLxJvPL5y8azPQvvmafUpPS6Elct/l9RugNyDov5dNU3NJE0MDji9dw2HKgEMaJc3CnLL1JM1Xzj+fOyFv4fVHu1X/M96+0vjtEW2RjsLa7tGlhv8H8YdFEjp+/0NEmh05Pm5GCEOII4BFgGLaNgwo0SinzWz3RoVNgqBqXT7iE1a/8DYCbh57fbPbllvyT2DphGCNzlhN4vgQBDAAqZhVz+dJz8U9v/mfiVgItBrfxNei1sS+6QbQwh+VfXYiULsxgbxqBbqKGsQfaJiRn9z0QgLtWzoIGgVcaaQZxddZ2XtnwOWwAWG9fKBxNu675gQ9zpcHZ/taz/Aq9ktcuNFBctGiuBplN2PKX1XHB043UhtMXfX3ztd3WFee1vQ3RtBUGB0XAK4GwJAe92bYfRq7mdLyzwNGnLkKxyvfryhjQ146OD51zY+JHS0++C4DL7ziXmq15eLa6WHXztQBcPPGSRLmiaA/839r75MpW4irNF6JA+77NJlluJeHTEDdwk2EfMtCPCOkGbpk63qnszPOa6lQcH2wUwOht5agpcX5rBkZet9VquTjtbThUvq3ICW7bwNGmvQt/Yzfy1g2llxxCo6uOKn0TmrAfZJfiofCwUfzxL1sZsOVbXnk5xKv/Mvj95XXk5dfz8wleao6pQMaWs1U1qFQ1qBxW4ENaCm5MVHRyYnGIiYK0FAgKCIK7JP3Zbmqqdr13gh3DZLasyJpsDNZ2tzatM3ow77Oh7Vafg0NHkU3E8ChwNvAScAhwAdB+DhcOexRyQO+MxkyRdT2QK00IpUSWLotNQ3uT02Nb4tCVoy/m7Zm34w0nDZKiisKZA8oIKulBsBbIwV3RukFKJnrmS4ymfm0ZjNMyGawZln1+0w74jnDjUJW5iwy8qcuzhMpdeZnXmDm0iqNPDlnRfZHJo6OOgSHNf7Zu1X4IrYkBRoqB244yPmd11mVTd6OIqBqySbqNITTyxhezqaZv4lifoo0YwkXqGk9DaHQf4aNu4Q4316FjcLRpL0QIwYH+H/N+zWwCZg26FcWlJDPsBg3SuH5yHtde5+eTj6P86+UQr7wcIvzM63gKPyUY6YclTRRhD8pLXChNDGYVLCQ/vC9MNsZp7W2u5uCwp5LVkL2UcrUQQpVSmsA/hRAft3mSQ6fCJSUvVG+hKDCLu7Wj0/Z5NERy9lWJWsm0zCi8d84DEFY4z3sFsxseBiG5sPBswkp6ynbUGyK4n4/yM00WxEyHUuuqPKQbRz1wBuGUcyQC96nf09OootdWi81lCjc//Co+0me2890W5X02UzGrOO34bZt/ytJZIyh7zl7vFRhrG0Q9n2EN+LzgYD6stUdFc80QrvUvUllXxPrK/vTvtp78ghr+XHsCDQ3JF0SeGeIB60U8KS8Sr1fJmGZ+9hkxw6mX2k7v6n5+Nd702BkNgxsb3+ZWftvm+Q7pOPq0d1DoDjBwn0q2VfsJWAYzpj2Fa6Pg6t4jKTrrG1xalOsi/6F+y8f8RR6RpnFBXxRzxUd4ZozkvpG2QVOREqCPVk25UUxlQzfCEU/a9SJRN3OiA+m7sCChT1tKfIlZepdh8Niz0wG48uyJ/PX5GQBcdt4kOGcusI3WuDtvPLPqn8eb4vwWdaloV0FNfWFCm/z+OpYNKqXGSuq0rpSiqukDBqpqsXxIX9zbrKy2PMuWqDdEKD+Ar96flubq0DaONu09FPgaKYvpU13AR44ymEYrn2WB5RyUd1Cz8qoqOOpoD8MOcXHmtT4efGsoK/5ZyeraLyjNg8HdNUKNX7OvmflZHm2u5X23xl/3HcGta94kqotm8Vx7EzdOq6wvTehTnrcmzTitvc3VWkN3SSI+SVXIajNLycGhs5FNBzwohHADS4QQ92Gbi2R2+nLotLxQvYWjImGU6Pc8w8bE2urbGt5JpCod3rCGPmsrEsZE8lJQGuzz39BjLuQSnq59nrOLL0jUvWXIapYf9wFCcWFtNZi7Jpez80gzOfIOjTD1/mu47rqHQLUwdI2+V73GMZ8GmP74HbjVKFHTzUO9XgbA8gIuAXrL64eOLlzJJ2Uj2vzs84KDeXTVMdRszUsc078YwqzpkxPXPX/SvfwnmOEFocJfXC+jo+DCYorvZIYYlYx3Zz/D1RKWF3QVzAiARU3vLWzpuQw4bJfr3otw9Gkv4Ge5i7jnpBcwxoOmWmy6sZSeFTVgqvy3/jtcWHiFzv5sJqJW8oy5NqZxkpn1M/ms0ODsn2o8/+pr+N8p5uNDDuDKXy3AMBU01WLae2N5elRvVi0+EaFYSEuh7zH/5Zg3Ikx/OalPk06fypKwgn/+Cu6sfyuRQr7grtsSmvr03X/lPO1aDD5pdWvEmxrm4ZLpnWjNsFgzpR+HVX6VuObE86ZSMyhdm2osP3/afhb3lL6AgYqGyZ+2n0WN4acH9e123+ParlgKlmIx4t2x9FzlTOJmiaNNewknlixiykXPYxgCTbW48IEpNFYtAaJU6i6WNdxLpu7n3NUhJi+ow6UIgp4v6H32OVy/tDvXn7eQqGngVj9m9dVwQIZHWkVyaFRj+rI1uNDRFD0tnusI6gI+bpx5NY9/lNTEy8bcSl2gPq1Me5qrtURj4xLKjzEQFhz1QgVTjy4gp2e7XsLBoUPJpgN+PqAAVwDXAH2BX3Zkoxw6Dq9MpnYHgTq9gs/qXif/7f9y4SHr7XXRsfRH07BTIVOTrSWgyyjvVtmzPTJHYh6nY2dDmSBtJ+FT+oPiS9bl/6qOaXf/HVV9Dd0aAOpGPC9YTN+6mlA0h1Bs1/DDNi1h+ahBlOQGqHy8kG6X2s7HFU/t+nrDuIGKP2gxa/rktOvOmn4D+xU/RiAnfRT1HQ7lc2VYYqYscmCUIby/S+2oeKqI7hfVoJswaFyUWc/Zx085B0wxh57m8F2qfy/D0ac9nCIlwJ9LX8Cr6Ik3Vq/CGuQWFfsJth3ITakAFj4j6VRsAvmmxdgNsP7R+DaL1YzeOh+vO14CrjrnPf5x+1p8Jd2xzN4o6ibyN0WZ/v7HaTox/eXJnHjqfaQufElR5OMuAAAgAElEQVRNIW/q5ZANQQRRIfCo9rmbNw8kJJPXnPHsZPa7rrk2vdk4mk9CQxPaVGP5Y2e0D1FviOXHfYDlMrFi92n5cR9QsrG3MxOeHY427QUUKQFuG/QCXtUAF1TWlzJ35bWAL/YFW/Qb6LnyrrTzqkIWkxfUETYhbErApDzveSadY5HjIfEs798bMo2pCYhpX0z/LAWPt+UBv/bA49N4/KM70jTxsY/u4Pju1xEJJWPL9jZXa0rU3Uh17ctIFVDBNO3dKv48IbNLvYNDZ6TNnI2Yo6cAekopb5dSXiul3PXpP4cflPOLytBF+rplHcGFqt2x3adIx7DSf77k9wdmrOsARie/KZRpqdQAigVrI+nHdBP6lYBQtqOoXyLEdob22Ae3mp5ujiZ447fHUDGrGJmrUDGr2E49d+/8muumFDQIXGq6ULtUnYKGzNeosfwsi+5DjdVOKVRuQcWsYt5/OI+oT/DT8+09jnUNhKVimG0buTnYOPq059NHq0Zv0rH9csJBRElfBtNALo1Kk60EsbdUzDGgMGL/CxBqklijm4J+JTpCrUZ1L0Oo1Qwu7t5Mn1yqTnfFPnZ33vhm6Z474uVwe94JLCkZyAKvlyF9+/H5gEF81Wsg53qebXbNH0ybUgjlB1Cs9BBBsRRC+ZnNNx3ScbRp76CPVo0hkzqwvrJ/s/gCdCr+9SmBmuTx8oCJS0l/rlUU1kbTj4kN9r8y5SsTChauDvZt9ebkNo+dFB1vTvPEjrqAj5UbunXIuu+QrxaavBM0RbC9MZz5BAeHTkg2Lug/B+7HdvEcIIQYBdwhpZzQ0Y1zaD9m1WzFJdOl24PCq4qLW/JPInJkXzze2xMzOQCjHsm8Zem3fM3ZJRMBiIoQ89XnEjMkAC4fDG/i85qXCzPndOcdYygf1g5laWUvzKoQ0d+kB9G66cI1sJaOpC5PopvpBia66aIuL77f2A9DH7+KbqX/TqRioqmOu3C2OPq051NuFONqMsp38NzFyCYBWB6NCJk+A2Si4LLSj0VVCF2ebnTm0RRyh/6GwUcWJI6FrQDRT5vrU4VlH7upYV5ym8QYmjS5uWEe55F58DIVQ6hcd9TFLFr0OGA7vvuDFsH704PZ3aFNAL56P5aSfu8sxcJX3/6d/T0RR5u6NvFtAuuMKi6QvXhyxhOA7fGga8nQudwoRhNJHejfbX2z+ELBixn6mumTo0yY6UZRRMb3v4nFQHeTmKA/8HX6bFlLnfDegSrowPAhHGxsHjtZLsLBxo67aAZ8oUKazvwYlqQ01/uDtsPBYVfIxrVgCvai1FoAKeUSoH/HNcmhIwkLjYBwN0uVrDH8PNX9KCwFLFVgKYBmy3yzkdeUfR3dYR8j3h2LJ6zw1izBm8/AgwfnU/h/GvI+sCywFNg+tKCZcdmWEh+TLr0LnztIvq8OnzvIVVfejOzdZFY8hhK1cDfoKNFkUBipLGJT+AgCRnIvSq3RIHdrCK0xmRJl1OUQLu+B0egjkKMw7sx/4nMHyYtdd9yZ/2yW4tnRlPgUph5dgBAutIgLRVfZf8kpqKoT4O4AU3D0aY8mvt45bLhoDLkIR1U21xaBahLERx35BPEhhK0LQQR1CMKouLBo0ofEE1WI3FpEJKoSimhEoirTZo+lNpr+3G1R/Ew6fWqaPk06fSoVRrKcRBIEAkIjKpS0LrJl7ks0ej6rU9ZMD2Ib44ctoU+PZJaLNIsxrBEEczWqBpoc9eun0rTpqF8/RdVAk0iZ3uZXexLXdkVXE/o04t2xTvp59kzB0aYuS3ybwCOJsqJ6A4euW8Oh69YkjBfj1Fh+pqw5i7Cu0RhykeetYcLQB7BTw+tRiDIidybdfjqGZfNr+Ptjdmc1/v5XTFB1UBWVPg1n88wL4whFIRCGSFTl4dKjsLIcfPvC0lnZ+BkBM30So8AfYug+lRT4Q4ljAaNHs9ipT48axh+2Mk2fopafOqMfUctPJGQwYegDtiZ6bX2aMPSBtPTzHwJ3NJfiwtNBByLgVWHq0QXkeX94Z3gHh50lm4QVQ0pZJ8QPO/ru0L6cVdzTdkH39Grmgh7n07xBnDxgRdIFfSEUHNaQ0QU9lZ6rBvPE4m850KgEFTxnNSCiEiSI+0FObrldC05QGXfwrQmX4c371TEoQ7mcilCaqdv2oQU8++BVbHg2uaSubMkiHgheSu8vtyeO1ff08eni4/jmT39ACIm0FLqf+g5rRq6kqP/RdPOWoZx8LGt+4M53nAmDfNz33ZX0fWgZvno/rp6DqdstLemyOPq0F/Bm42hWfzqIgd8ttV3Qe/bn+uIXOahgEx9efShHP7QQNBcgWLgYzirqzb88HkZs2oAvqiNNnagQeC0FIYlt9CWREqTIPJ+kmQbnLHiRiT1m88/jTuXX775GdIHG8tF29s/teSdwVfUsygoFQ+6xUB6VCGHxvx6D2PbvY4gErwbg+GPhgguf4q8XXMm73gfhJ8BPYM784UxeMoFw+XyE0Nl6g4cjb36anpM+o+/iQyhWezP85oEYpQbZegt9snUEjfvk4l+3y7ccsLW9ZGNvxwV953C0aQ8gB0BaYFiEtMwdvLerRrPufz0pC6yKrXneSIF6KBZ9OSR/GG4lAP3GkDt+Mffft46DRrs54kduJgzyMeOWRiI+CZddSeGsXgg2EU9WlEh++/FC1BbnvNM5EIv14a9YF15KgdadXp4hnPsjwXXnfZI0nJw9lsfm38zG6I8T5/V1/49HLrySCWO/SRybM384t82+muWNE1EwsNAYkTsDtnzO8d2vw5uTSzjYSO2WH7bzHSc3dxThfzyHzIcP3uxOiU9h3o7vSuvgsNvIpgO+XAhxLqAKIYYAVwLOVhpdDF0ITivpxZhe5yPWbWrRKdNyK8m9sN3w4+f+gH96Af75Kzm75PwW61csFYHAG9VJ7CLmxu6ES8hfVscFTzeyLhikzlhNIJpM7UxdFOd3R1moBXlSSY7UFnolr11opBnEVb5XxvvP/pLUtMyta0czan0Doneyvsh3ucy88QZk1J14hVW+cgL9guvZVl1LgFoO+PbH5DRdyN4CG8vyMu4jvCuoqp+Cim5Ay6llDi3i6FM7EE+3BHttc0vbFO5OaqN+Vm6IPSfdNCZecxHzfzKNEXxNzY+SOxzceEYVxVYVK2cdyOpoMcf/7hvWfdXAhf1LmFVTjH9bLSPKtqJOA64CpsEfrff4tHAMAZJbHT78xnRGb14DwNTZj6JZtkbcG36aKYylxqxjol9nYx9QHwOuBR6GyzZ8wS2rhpOqTTOfvojJw++noPd3iWNHHlTBmn+eANKNlD7MCHx+9wVM+/kjfKesRsrV3HLg8h26R6eO6kVga3FGx+WdxR32OR3vncPRpi7M3XnjeaZmdtqyPF1T+f05EzOWrwvlUh/TJwBFVKNQjVvpC0DP57/F+uVvUJY9yK9+E6Hs1t+hFuSxbdvfAThkVi96f7mUq6d8EDOIBLCQHsue6c0CTXgYV3g6WyKr2RRZSaXyEdeeS5rh5EnHbOPG//6YVH3aGD2WIYNMUseKjjyogq+fvAALN1bMb2N540RK3CsgFCAS2v1TBUpIQAhnCzKHLkk2f7W/B/YHIsBsoA64uiMb5dD1uDtvPHrTQF3DDnABw4Ke+TvXveyZLzGapJF+tubwjGWbHl9f2R+Plu4IJ4RFuLGAnWV8juOj04lw9KkdiKdbHqBv4Zma2Yn/xzvlXY0iJcT4nNUcW7gWc7aXPw5T6O6r4dxbf40rX0NZCXyL/dfzLSjfwZ0fz8pYl8/QyYuGE+7q+9Vu5E79f9SGV/B1EJSv0+vyrQ0zh1Oa1ZNJm1QtPbLWXDrlG/vs9Oce2W3zTp/r0O442tSFyeTx4DJMHnluxk7X2ftf6zi05HRkfYjQlGfZ97FGcjab5Gw28c9fSd74YgyRPsveeK8Lq0kWhYloNmVgIrio8Ew8Sg79fQdyZMEvmTDgGAwzPcz/ZNWhGduWSZ+aGq4pGISskh34xA4OewZCiBOFEN8JIVYLIW5opdzpQggphDikrTpbnAEXQsySUp4P/EZKeRNw084122Fv4KaGebiMJsO0BvAwMBlcXpXcG84k/M0oPICnyflFZQ2J/4/stpmjC1cmOrpK1CLnswpSt9Q9bNCnGdvR9HgmQxRLEZi/2Yh5j506FZiU/Uju/JFPZl3WoeNw9GnnyTTbPcSoBHZ9S62uQMSIvfaiJLN1XGAEBf7NybB2yqhf8crmu0idfnKZJkjJAYHv+cgySMy7p9Slapm3AsqkTaaerk2hqIfZrn1ZH10KwG2bf7pDn+39Zfuy38L22wfcYcdxtGnPIoyKrrnQssySa4v8bSbDco5kZcMH3LR4GroV4ddqEbcUf4i2OIQ6Nj2O8j2ho7s01Ggy1TvRH0+Z0zBQuCHwXiK7UQhBoK4Pbi2983744IXN2uQiyunvvwwLsSdNHoaRxlIw0idNLDR8StVOf3YHh66IEEIF/gYcD5QDnwsh5kopv2lSLg870ylzB6UJraWgHyyE6AdMEkLMpIkFq5SyOotGn4jdBVOBJ6WU97ZQ7nTgJeBQKeUX2TS8KxD1hjp87VzU8hOySvApVfY6oxYoUfsya/s6fA2zudt1JDc1zMNUBJftcyK5jckRUiVqJdeAx1LR9YiPOqNfq9ewpAoIwmjobg2PpePCxJQC3dSYsuYsFnw8mrJtdRjqdh597z8IqXHLEedz18KZDAmU8+3QHvzhpjO45LL3yXNHuOP+n2C67E7A4d3WcMqqFayvHED/buuYP6qMkpMXUv9uX+ZYZ4Du44p9bqfw2S32+qk/ABp49m1k6v3XMvm6aWguHUN3MfX+a5gwdg6X/mM7PfMltw97oplBnEOnZ5f0qStpU3unh8dnuwFm1T6Dy7ItFrUmCyAMFBRpcWf9W4nrRud+yFNnZ84+aYqulxKJ9KOqqoGSkpaDtsKcfSnMORz39znJkbmcYijsQaTRwJNrv6YK3QEG7lPJtmo/cYuhqqoSyjf2oU/f8lavEW+L2eBjSv9zeGHZffZgQ4yIVLEaojzwv79xw8hjuHfp++zbsA2Xlb6+UUgLr2lbIsX9dpvm9URUjUmjboFvKqBwPdT2p+TYNSzpn8ewFH/Jjxd3Z3/vDJaHLwTVQloKvcfN47vX++Nt8LJPkc6G5/pTY2SfUL7fwnrEuk1Zl3foEJzYaRepClmUB0z6+NUOSy9uK3a6Pe8EW3eFxs2H3MQtm6ZhuS0uO28SkUaDcEMYb543oU8FvkbKYvpUF/BhyWIs+hK1/M3q7+Pdl0eCX3Co1QAIlptVuLdW4akw0B/XCP/WzhrM8YGRq+FWDEIKGArkaKDEPHYsL+ASRCIqwmzuNVAX8DFt9ljOP/lr1m8fQP/Sdby+bAi+nm8QqjssoU9zo8fjWRtCxnetN0E1Df6r/Ihx1qK0NeBuJUCBP9Rhe3w7OHRCDgNWSynXAgghngdOAb5pUu5O4D7gumwqba0D/jjwH2Ag8CXpLxEZO94iHTVi0FXYMmQ1y4/7AMVSsBSLEe+OpWeKG267XCN8aDODjLwMFl7qp/vz+Ma3OYQKAGaJF1GJgGryZ2M6P/21m5K1V3BJwxr6rK1IMzqref8Ayv/zCxRTT1yjp/fzZu0YZzzIa5wGCKZoJ/OIfh8uxWSGciibb8mnLuBDDvk3Xxz3AW/MthhZJRGmwqv/vgOfjKIAhy9ZzzsTH0Yz7MD4ois/5PI7zgUg750yrnv8TdxqlKjpZtKldxEe+DJzvnmIcRssUGBR+cf4NsdGjx+wzd8iBW4mnDKHMUctSAvUcypCvHahYb/kPqtg+9ACgt2dF0kXYqf1qatpU2qH+Zma2Ym0yNsa3mnRy6EtpJD4pIU3NlmbaW7Hh84oYzM6auK6crNC3nUNXNJG/XNfO5Ulix9ACJ2jjnDbg16nzGlWTn30cGqDd1AbhPWXw4jj3mZD///BNSEwN7FBuY38dVcwacQW7jnpBYzxoKkWD/73VL789AKOuuJzXC4dPT6wluEaqW2x/pjD89phdnZOCi7DZBSbiQTg1YVP4zbBYybvTbwLkE1XQDUkM7f8ihOu2QamC1QdfesVfL5pP04rXgymbYyxYn0Peno/p8S9ItkR+DLAMXI1V/5pPbop8Gi3M232WOYvat93h0OH4sROu8Dc1SEmL6jDpQh0SzL16AImDGrfd3Om2KlpXGMIlSt7PMzC8l/Ax/AOF3HgmHco3/gu9WWPgt8Fik7+uiu4rM8Wplz0PIYh0FSLCx+YQl3Vw4DO/Bpvxvpz1SIwIjH/GQOioLtUTI/CaU9r9uTAtBKsZwScU8W3VQZnnQYvvgr7Fap0y1FAEVQ+Xkj5uX7Uchd3GWOafdbtxmiGXv96Ina69Pg/k3PMB4QGnpnQJ/lXL1ZAIlJ0USoCj1rHuIIb0gYqjhm9mqvP/SDN1M3RJ4cuTqkQInUA8wkp5RMp3/cGNqZ8Xw6kzUQIIQ4C+kopXxdC7FoHXEr5V+CvQojHpJSXZVNZEzpkxKArEPWGWH7cB1guM7E/9vLjPqBkY+92mwmPWn6WN05sZpBxSOQfJPMqwV3n4fPPjwemAZBDKDFlEwRQTHCFqBr4KEcs96F0g3gB+YWL8r+eimW6sHAlrlHiXpEY0U1tx0nYs3RKMMrkonV2mfLm90TGoliftBJtlYCKJC8YSbStYck6lp35FwaVFjN962pC0RxCticpT874LZErhkFs66EcA+yldhC7HQiS5m+14SpgJZA0dUvsWGFB6co6ygs9zkx4F2EX9alLalN7pYffVjqG2dUv4EuxRrAEmFIlgomOwIOS2Hs79bqhLLqgVVUlTL7+QSzL1jrThMnXTWPMUQuArYly7u9z+Pw/PyO1f7L8k9FwxGngCtlfQH3Zo1yg5eHV9MQb6/wjPuLmP8xG6m7CMU1NXiM5E960LZgQ0LsDgiA0+6w5RlxLIKhC1HJjSJ0cZEx5MmOgECAPN1EwJYZnA7isxGcIdXuE25+w8LpMO8XzIdv4bVluGdWNJPS0wB/i6nPjJkwSMLnm3A9YsrK3M9PURXBip52nKmQxeUEdYRPCph2HTP6wjjG9PO02E95S7JQa1wDgLrQ73yn69NXig+CYXzTTp7NK8vC6DHBBZX0pc1deC/gAH1YL9U/xn8rs2mfISZk0Ceq5EDK47dsQNw5VKb2oFt2EvicZNMSaceKvwKuaLDirJHFPLr/j3IRZbiolJSaPfXRHWuz0fwt+Y8dOKZ/hjEsltY8ICCfzeTSPxSOlR+EOBjLok0l82NbRJ4eOQNEhZ2vb5dqJ7VLK1tZsZ9rKIrkzsxAKdifrwh25aJsu6Dv5AoF2HDEQQlwC9qSLV+n8eySH8gP2zHfKvJJiKYTyA+3WAQ9ZJbHR26SbuIJBuLEAQWXimFplm42dwUuU08fugMeIqnD6WbFvTBefVvVgdLdkdtz67QPwuiMEo+nXCFklCUFuqR2pZSD9npxxJpQ/mAx0MxEFzvDYPeShPfahojKaeIEAiKL1YLo448xQ87oymL/VhpPPTyZTN4RAi5hJB3iHLsFO6lOHaJPHV0hg3NCdaE7r3Gj045U378JrpLrxuvjTSZcR1tytnJmZez95FHeTKW9dgbDq4kvpZmJ+D15RPSiWxfDajfiNcKKc4VYomKFDZiNgAMo39sHl0hMdY0g1F/sasH0e6haMaH5y4Xp7VsaV1ClMFwuryjiouCJxaF3lQFTNJNV2InmNZBCaqS2nul5mnn8MocDqxGeVSpBh1RspTEkRj2pw4qkXsWHeW3xTvYEcmRQNie2o5RUClxTUUMwA1vESZ6B4qznllyuApK/FnBd0fBvtTB1+DxigSPhT94+5bNwViXJ9ijbGTJiSvyBDaOSNL2ZTTd/MN9yhU9LZYqdevTv/u608YOJSRKLzDaApgvKA2W4d8GxjFtPVu/nJLejTp1VlHFRi61PcvCylRMb6bwm8Yw/YpeAngPjM4iBg7iJ7ksAt4V9b4YTzkuWyvSelJRrujZljp9TP8NLLOrJpPGbADbX/41p30lCyR3EgZuqW1CfTVOhRHOg0HfAfsNPmsPdQDqS+gPsAqY6necAI4P3YtpNlwFwhxITWlgZlsw3ZztJuIwaxVIAnAAq0bp1+pyZfvR9LSe/hWYqFr779Bg98ShVWk1+fhUZxzRb0zclbr8aWm73EGc3E3m3Cyy/AT88HVJ0je25P+3n/0nVYRvpMW1MTjpba0dSoI/WevPQizToATfFoLt7oM5CLJ15CYzhAdGl6R0PW9AdV56XnM9SVwfxtXzX5csg1Q7jWv5i+vYil8OfaE2ho2LmXyProm8COmyYtrezFfo8nX4Ri3Sb6vjmUU8suzsrReGev21mYsfOGz7tCh2hTzpBeckcM/bJl6q2z0ZT06EhTDG4vfyqxRGNHkOuASghq9iBc/Pn5ev8e/EKXgMWl957B326djVqfft1cK4L629pmdabSp285ehNzMUN30adveeL7owtXUnVkgGYJ47X9oYnzLqrOEd3So6oBpWuwoum60/QaLbZFcXPpo+ew/p7HE5/1kVtm4U6XP9wm/GXlm4Qi23DTRM+BJX4X3Yd2o3zx/pwsX0fHzU95C4/ne3T3sPTKhEQVMt34zQ3BHukmkGssFbUh/Z6rLpM1p6gElN2/7Y/DD0KH6FPpsFLZ2d8T4UiIoPkiqR28kKkwO3ACr+htv5s/+e8IBj63otUy2cYsqp7BS6ElfSpK6lNG49cM9SsxIQjiI4obN1E8RJCAV4I3Nsmse1TMJm7sze7Jf4rxz2/+ubdXGUTNzLFTGnF9cmP3CgxAQlRXSRmnYFu1H01N10JVtdhW/cNOjMXjpHLTjapYifjn/WX7MqyN37+Dw07wOTBECDEA2AScDSSCLyllHVAa/14I8T5wXVu+HB3ZAe+QEYOugDvsY8S7Y5utAW9PIza3EmBE7oxm65g85atwp43cNnLEkDdhlf3+DuLDFMJeA44Jlgq6vQb8ox9/xgGV8xNnikP0hIEZmkkk4mGE2zbhSDUwibdDYCFREkYdaSYnYRL3RFgWIAkJBRMtsQbcBIKaF5c0ERLcdZLui0x0fPzm5/fyxNwbUBUL01K4ZPxTvPrWeSCnAxZBDQzpxicNNKxm5m9NubVE5bb+z6Kb4Ha3XC5bAlvt+7Z0VoZZvVbokcE0yU4jG8rSXsWZT2qH63YaDtstV+0QbRrsq+S1DnDJ755fjUcxsLwQMkAY4FEMDsov37nrvSypOEdLW1O4b7FG3+fCjDjPHrB7fuSTieuGhT1Dnutqo94YJSVVTL3/mmbGh6kmaeNzVsNBq1l55t+Z/uJvE8cnnfwKRtGRzKz5OLE+8YKiI6k5aCPhNdVIHRQheeb14YzwPs1Xjb8CRcfrTl6jqfnb1Puv4Y9/mIaqWpimwn0P2CaMv3xQEIn046k+rzKsYAsu1V76Eh+UcKlwcMEWFisgEJgesDTQG20t9agGOXMsNj5ehnqfgVuJYloKj15xCx/EP4NlrxN99aLD+Mk989P285VuKHsuxGs56b/DhopcxLK6hAlTw9Bc/tn9uR3/PTu0A3fujot2iD4Fq3xd4j3R262yIedZMEG4NXrXnsV3L2b3bm6r8w0tx07NjNiitRzR5xUWlp+WOHRE78/Z9PElbDzyiYQ+9f34Et7SvmLYuVVYUpDvrWHC0Ad4Yfl1gI6CN2NMdGf+cdxQ+ykBqzdn8iwvcTaj+ZAiqZNq7RiVLq4+6QyE/BdCqkhhJu6JEfIRrc+nz7wvMhpUVFWpXDbmVv5vwR2J2Ol3ox/hpbfOY+NJzyY+wxV9L+Kdutn071lN+FIN7+MG67cUc7NM9xiJm7pdc+4HmKaCGlsDvjtmv/3zVyKiAlNRE3/XTufboSOQUhpCiCuAt7GNMadLKb8WQtwBfCGlnLsz9XZkB7xDRgy6Cj1XDaZkY+8OdUFvZuATe4E07dAFw704lZeYwy+RwO2+n3N/+O80WN34RfhWxLSh5B61BPjM1n1BQv/jBmavrPoRzy06mJ5PLGpmYNLb/WHT0zKbnKyCko29uTuvniL1cxSh8nvXH7k9OJfRLGYRB3GbewKP6PcBcJcxJrGm6f3woUR1FU0Fw1R5/z99Ge7dylT3JPLcb6OZcG/ucfZ2aE3M38poLspL8THS6sY+pSal0ZNaLJctG+qCAJS10wvAP38l2Ywpt/d1f3Ae2S1X7VLaVPFUEd0vqgHgs+vzCP+sin26K+Q+VbRzFboF/KuUm87dTt+lktI3SyBDKmP8uksX69w4VOWNXCVxnPNaN3LOZHyYiZFHL4UXQwgkLrdg5NglIIcwc9pTULgOd3AgB9/1Z2AjEpBSIoWtMj29n/N9+GEiRUOY8Mw+TNh/TkbzN2TzPXualvvLvVcyiZksXhrl6oMU5ukaqDD9lIlcN/8+XpOnI6IKfzuylN+99xx5eTB5X5VngMayXKIGqIpE0aCxZw63n/I152/ehyXrihg1oIYjJ3/SzPgNA7r9tpaKWekDbcHuPi54ujFpwuQsi9nb6BB9clWHu8R7ogwfdXo3rAKTQ6yTYrFT+7a7pdipKcGaagRRFCQWguDmzRRt3p/yT1chCtchawdQJN5EHPkVEomUtsHlyJ7v8Z8VL2DRl0Pyh+FWAhljost9R7CscSIKFhYKxUSb9aNzohFmPb6Ayd3PSosnt4T9rIjVt4bTMxq9Acz7bN+02GneZ/sy3Ps5+/zzAur8goKAxB+JcDm/pKAhRI+nAmxr8FOHL2MuxvxFg1mysnencEEXhr0jRVf4u3bo2kgp3wTebHLs1hbKHpNNnR3WAe+oEYOuhDvs67DtxxLXUAKtbj+WySRNBKOM47f2GijbXJny/3VnwmkXoeSRiFPjxmQlJVUM8H2L9t3wjAYmG6PHAiKRNLascW7INxIAACAASURBVCICMpuchO37cpv/ZKKWn89rfsuJ/D7R3jQDtwyfIRq7SLw+bzTAXb4TE2UTrtDpmahpxLd1qtNr+XV9Efcq7wM7v62TQ9eiy2mTWyQ6aQOAq07xMu+/ET4MSIqLM2WrZodLF7h00fI6wth1rznD7jw37Si2RUlJVatbg8UN0sCHBKJR+OMfbLNIIj4I9iMK3H7tHVS4yuwUyanAZLiB9/imqBtKXTWqexneouKM5m/x+iKRpA7Hj6WWu/6Gv/KjhZ/wh0tXogah5iW7vuuPeJiI9HES82xdfC9I9VEfYBgVaZ/Bkj6sWMpo3AxucK8qBvdKTxtvM5sgKul+UQ23LTYTJkwQG/Rw7/zv2qHr0OX0qR1Jezc3FjG1A9/N2cZOEnez2EbiRgbtTMOvxQWcd/IteN3J1Oxrzv2A6V+UUNVQjVvp26Lxm4zVZdevAgqCdK8JEFhSTYsnszWSay128kcC+FOMOMGe4c6mQ51tOQcHh5bp0KF1KeWbUsqhUspBUsq7Y8duzfQCkVIes6fMfncm4oYjqShYaSIPoCoG6yoHpJ8cMyZrq76mZKo/bkLSdtt2vly2xLd1OpIoy80KDtC3cIC+JbHXssOeT1fWpt9f5ScUkjz5ROPubsouETdIS0VRTVQ1XXO+iw5DaZQQAC7H/jcAj21+rc36VNVqVl+mayQN3FqvD3R+dOSQVstkqqviqSLCR7hZnC+YMFojfISb8BFuu2OdQveLavAujHJQvbRNmBZG8S6MJjIgHPYOurI+7Qqd6d2cdewkDNZWpMdOpqmwT2nKOvYMdQkslJS6zuAlGprkvRlo/I9xXJfz6yza1vGxU2dBkyZ31r/Fv9HJRXJn/VvcWf9WYotOB4eugJPbtoeT2XBEQTb51ZuWxoBu69JPlhLDkz7qnKm+pmSqP6MxW7YGblmW21FygAKkvc3SThJ/EbxoVJErLedF4NDhDB6i8fMJXmb8M0h1dVM7/65DJoM0y1QxzXTNkRk9qcBj6vZzZ1n84aq3OeaeBRBN9+g0TaVZfZmuka2BG7g47bSKVstkqiuRTTBMI6Ta/6+YVdzirLZXQp4JSjjjjx0c9mja4928q2QdO0mNgd3TYydVtdiwPakxmeqSKFgpddlGuemDeTouTFy41PRMmt0dO+1u4gM1Y5GsR3cmURy6JE4HfA8nbjiiEEUjiEKUA3JnMCJ3RmxtUwRBlD7HvsPcwfthKWCpAkuB7UMLmq0/zFRfX/f/IFYXKfWnlslkcpKprlSzkjqjH1HL32q5TKSem4m788Y3S2czhMpdeeN3+P52phF7h72HPWEWPG6QZluf1eH1hrjvgWu474H4sSBud4jX/pzZudkEjiTKym3fs+/ireQtauSrgQegKEFUNb0+tzuEx2PXFz/m9Ybw59Xj9YaamcSlts/rDeH31wNBxoy5lJ69q9H1UgKBgwGSZVqpK078vKqqzDNQlY8XgqtJp9wlqPx7Ydb31cGhq9Ke7+ZdJdvYaf+cmTzz+nDCUZXGkItwVGXa7LFUNait1jUidwYH5M4gGTtZCAxCuKilgCB2irdf2dRpYqfORg5QCLt1oMbBYWfpSBM2h05CJsORbxrORuJK2BI1bunJp3mDOHnACrSIieFRWzT/aVrf6saTAVcimapGH8jwvBeyMjnJ1LaMBm5Zmqa0dG4qNzXMazZDrUmTmxvmJdeQ7yD2LpsSMAnjrCN36FhSZ8EvviSX4uKuOZY64ZQ53Dj5FaTszwcfBykpqeK2m++CWPAZjcJZt7+S8VxPfG5cWvx/e/ce31Z553n885NkxXacBOrQcEkgSRNYmDAkECi0XMtlAjNNaAuFdIGw0GVptx0a4AXstsCktJ3SKU3bLfsCCgyBTinlMuB2Q9NyabmVSyAEAmnATSgxgUBMCDixLUt69g/JjiTL0rEsHR3J3/frpRfS8dGj58HOT8+jc/Q99EKyET4+aTOzm2fQ27sPy37x0UB7sVjjwPOeXzmXJddc6Skkrj9M7rZbd+NnP13DJZclabv/i4OC3p54+tCibeULiJu/IPtCbLtd+AH05Vxps8/lDWsTqTeVeG8eCS9zp61904cIJvuLp7b6504LaGOFHUVL5G2uHruAK7t+T4gY3x//SfKpxtwpKL477gR+sfWXWZeSrdYHNSKlqs1ZmwxbNNTFhMjfiIa66IpPYmPsOFIRl6lb58tz+GDDJJLRELFxDUWTd/vbiyXHDmprY+wzdMUnZb2m175lhobEaSZJlDXbFw18mluovULPzaeHMF0WHdGCOUif2MvoUg9HwQFCoS2Ewytpbe2k/fUZ3L7sPDLrSX+Amsu4QWameVr6SHFDwxZaWp4fsr3bbzuf9tdn0NrayUGzVxcMioPUkfBnn3mSadO3MmXv3dNBb80kEhPo6WlKXaYRCra1MyAu+3lDHQlPNkJynJFszPtjkbpWjvfmcik2d+qf72zrauK1N3crGE5WqK0+xvAZ9wyLm88lGY6xZMKxXD2hcPhcteZO1fatD/9Ak4tlbWtyMa788A9V6pHI8GkBPgpti0/Lu33LK9PL1tZQ24sZSWiI1+cuGXcSLzfswcsNe3DWrl8auL9k3EnD7m+hT+xFKqlevgue6cVVcwZtm8p6upsb2QLsGYnyQWMzcQvRG8qZmKaPFBdrr9D2fN78W5xnn+njtNObeKtjiqfQtVzDDWvrOTzKxud2GzKsTaQelfO9uRLKOd+pxblTUMxIbCFE9oeyofR2kVqhU9BHoQmRDXm3d/41wqmrv5z3Zzte/BjTlw6+1uJQbQ21vZiRhIZ4fW7cwlmns5Xj1LYewsQtrPA18dXXL2rhN2093HzTdi67Yly1uzNis+esGrRtBy089du5fH7Bk/Q4xzcePpNLLlrBfqveoZcwDdHk4NO2C7RXaHs+993bjRl87gtNRKMeQ9dyDDesrZ9OO5fRpBLvzeVUzvlOLc6dgqI9PJE58beyzo9IpLeL1AodAQ+ocodhZLbXEtmcDk7b+fnhlOgjTF3xFKEfT2X7VZ8i9OOptNw6YeCWu/juby8a2p63rZbI5pL6OdzQkHI9t1RB/8Re6lu1joIXCxMbjmRyIonEXDo7W5kxs51zzr2FzHpyzrm3MGNmO4nEfoT6FnL29tcY/x9xEkeESRwRHnSkOLNvhdrz1jfHfff28Kkjo+yxRzg7mM1D6Fq/Up8nIsNTjblTS2QzXfFJvNVzOF3xSSNuqxS1Nncaie+MP5Fui2Zt67Yo14w/sUo9Ehk+HQEPoHKHYeRr74Bxd7F3/I9si09jQmQDLZHNvPrRmWx86LiB502JPsIB4+7y1N7eTdltjYTX0JByP7cUQf/EXuqf30fBvYSJDaet7duvA2IcefgYrv3hYpZccyWrXvgx27sO48abX2fGzHau/tZ32LHjPABO/Aycc+4tLLnjyoF2+o8U5+vbkmuu5OxzlvHiqjnMnrPK8+Ib4Lln+9j4ZoLFl+yczPcHsxULXctV6vNExJuqzp1i2XMn+G1JbY1ELc2dRiJoYX0ipdAR8IApdxhGofZaIpvZq/HpgU9vhwoX8dJeNLR9oK1y8BrgVu7nitQaP4+CDzdMzEtb/ReTyWyrufk1dvv4L5gxs71gkJrXvs2Y2c5pX7x7WItvgPvu6aalxfiHeWOytnsNcMtV6vNEpLCgzZ3iyU8Mu61yGE1zpyCF9YkMlxbgAVPuMAyv7XkNBKm1sA6R0cCvRHSvYWLlbMtrkFo5+wawY0eS//fbHk75x0aam/VWKRJkQZs7JTi4Yn0b7fTVP6kHOgU9YModhuG1Pa+BILUW1iEyGvh1XXDPYWJlbMtrkFo5+waw4ne9bN/u+PxpQ19WSESCIWhzpzAvVKxvo52++if1QB/rB0w5wjAygz4KtVdKIEithXWIjBZ+HAUfeZjYRCAVuNbfVii0g3B4W1ZbpQSpFWqvFPfe082UvcMcelhD8Z1FpKqCNneKhP5a1r6JSH3REfAAGkkYxlAhJLnt5dtv14b1vBU7aqCtXRvWl71/IlIZfh0FLzVMrO3+U4HswLX5Cx5g2b+30du7D8t+8RGtrZ15g9QOOeR57rrzLMwczhmHzF05ZN9y2yvFpk0JnnoixkWLWwiFrKQ2RMRfQZo7dfWWr28iUn+0AA+oaKhr2AU6M+gjSeoSDWu2L6I1ujarvXz7vbx9EQYDj3OfW47+iUhl+ZWI3traOazF7c7AtSagmZ4euPzSpXz6yCdoaHiNhoYttLa2ZgSppU77TiTgskuWAtDb2zjQXv9z8/WhoWHLQHul+s97u3EOPv+FxuI7i0hgBGXuNC78E0L2/oj7JiL1SQvwOtIf9JH5RtAf9JFZ9PPvNzg9Od9zRSS4/DoKPlz9AWk9PTu/T70zIO21gvuFwglyj0H3P7dSaeL33tPNYZ9sYMreeosUqXeVmDslmUKI9wf9TESKC/VBy6ZE8R1rWDBmZ1IWXoM+8u8XwuX8OSgkRKT2+JWIPhxeA9Ly7ZdMhEkksi8zM5JwtWK6uhwb1if4gsLXREaFcs6dGojxGz7HfYnHGeuSXPPhg1zz4YODrlstIqObFuABkBnoMRKZQR9heoYMDckXCHLg2GW+hISUa6yjXcQluObDB/l1vFNv8pKl3NcFzwxEK1V/QBrsALoYMyZ/4Fq+kLcfXvvP/GX6fjzISYwPvcXv7CTWTtuP1nFbRjy2XH19E9n01sGMGTORk/9Rp5+LBFkQ505tfJZjeJRPsZ01iXc5sO9tDux7m6s/+n0gxioiwaDz66psqOCPkXCApf9b6DXyBYJUMiSkEmMdra7+6Pcc2Pc2SRKsSbzLmERoYLsuySHl+i54vkC0+QseKK0x138iuRvYNFT7mSFv+3/jdRo3xJhkG+lgH5qjSWyDo+f8KO/e8bGSx5ar7f5TWbXqOlwyRjgc5dFHLi59rCJSUUGdO+26fR0WT9I00GKCHsL5Xs4zzZ1E6o+OgFdRZqBHnGaSRFmzfVHJn3D2t+eIkqARl27v5SFeIxrqYkLkb1kL7XzbyqHcY5WUZmACjkZ05Ft2KsdR8J2BaM0kEhPo6Wni8kuXlnQkfGcIWzMwjt7eJi67ZCmXXZq//dbWTg6avTrrO95NDsYlE4R73ZCvU6r+/rlkM7ALiURzyWMVkcoK8tzpX8cfR9xyvjJjYb4z7oRAjFVEgkEL8CrqD/TI1B/8Ua72jOSgkJCRvEapyj3W0e67404o65u81J+Rfhe8PxAt087gtJG3FQ4nCYezPzjK1/57N+wCDTkxbA3GezfuMux+DKd/pY5VRCoraHMnN20v1i/en/WL9+fi1qcJh7M/JAyHHZe0Pj2wT//tnYX7l9Q3zZ1Eap9OQa8ir8EfI2nPESL3eFE1wtXKPdbR7psfPTTo+94Rl+BbHz2kU9AFGHkiutfgtFLbSiQG9ydf+7td+AH0ZVexnp4IGz6/C4sWnz+ojc3v3wjAMSv+B8ceuI4ley731L9YLLs+VTLoTURKF6S5k5u2F3+5sIkrj7oPgGkr3iGyMUFfJESywQj1OSIkmLb7O1x2+n1Zz338g31ZzSx2v3PtsPqmuZNI7dMR8AoqFpqRL9BjJMFnme3132aNXcaBFQhXizV2s+3j7xFr7B523yoZ8jba9BCmy6Ij/o6Z1KdCR8GLhavlC0TrD04brp0hbN3ADsaM6eEH1y3mB9d5b39HQwNd4TH0WIRQ3DF2Y4L/ckP3oFvj1hjR5A6m37qF1XfM4upNp3jq37yT/yewg+axIxuriIxMLc2dNh8+nmMPXMcJze2c0NxO4rYx9B0eoe/wCO881zpwP3HbGObYa+y2fS1z7DVOaG5nyZ7L2Tq7DzdtL09909xJpH7oCHiFeA3NGCrQo1Rb+z5BkoaMx9M5YNxdZX2Nt2e2s+b4xwglQyRDSWY9fDR7vD6j6PPKPdbRbMm4kwZSVb877gS++dFDA9tF+g11FNxruFpuINpIFqTPP3cokEoW7+2F51fOZck1VxZt/91bdiV8bi8b3tmdH3UevvNvPXIMtuGtrH3fntnOtgs6IGE8Fv4RMzadwer3psOehfvmnOMva3/BrANX8J3v/f2Ixyoipan5uVPUssIh+++3tXdz+RPbaAgZfUnHtUdNYP4nvF3qUHMnkfqjI+AVMNzQjHIFn3XFJ7ExdhypHM/UbWPsM3TFJ5XtNWKN3aw5/jGSDQniY/pINiRYc/xjwzoSXomQt9EmbmGuHH8yV44/mR5rGLif+71wkdyj4MMNV8sXiDZc7a/P4PZl55FZm26/7XzaX59RvP2oseLmWXx50QUF/9b7axMNQKMj2ZCgfcrdxPuKfwf+lTVx1q2Lc+bC7SMeq4iUpl7nTp3dSS5/Yhs9Cfioz9GTgMsf30Znt/eATM2dROqLFuAVUK3QjG3xacPaXoru8V2Ektl/NqFkiO7xelMQCaLcRPRqBI69uGrOsLaXIl9tMhciFttW9Ln33N1NdAz802d17W+RaqnXuVNHV4KGUHaQZCRkdHTp6iUio5UW4BVQrdCMCZENw9peiqYPW0iGsj+1TYaSNH2oS2KIBFXmUfByhqt5NXvOqmFtL0W+2uQsSTQ6oeDzYjFH2/3dnHhiIxN20VuiSLXU69xpckuYvmR2pFs86ZjcojPWREYrzTYqoFBoRrFwkZFoiWxmSvQRwA3cpkQfoSWy2XMbXcfsW/AW++RBzNh0BqFkhIZklFAywoxNZxDt8fZdpkqOX0TyyzwKbvbekOFqxYLZSn/9ds459xYya9M5597CjJntZXuNaE8Tsx4+GuuDcAzC8TAzNp5OpGFswef98ZFetm51fOH0Jjo7W1n94kG6/rdIFdTy3KmQ1qYQ1x41gcYwjItAYxiuPWoCrU3ep+CaO4nUF4WwVUi+0Ayv4SIjsWvDejbGjiKEI4mxa8N6z899Z+H+MO/9ovt9LraDr37kWB93TI84rp/dzZPb5tDyp9cKPs+P8YtIfl+/qIXftPVw803bueyKweFqXoPZSnXIIc9z+21fwgwaGoxD5q4sW9v9Fo6DtqmwPgYzxjhu/hCKjeDee7rZ7eMhPvjgixx5+FIaGvro62so+/hFpLhamzvtfuda/rz7LK4+sfB+n4z/ldf3eZwNcWNaxNEWn82pq49iyvLQoDDJXJo7idQfLcArKBrqGgjMyAwXSRIFYM32RbRG15YtVKP/NSBK/4mYXl/jnYX7c9DZa4peMzcUSzL52XcJRWDvSOr7S//a+58ccsosxr6515BvJH6MX0SGlpuI3traORA2tjOYLXUmSyIBl1+6lE8f+URZAsn624cmnINYrLztA0xo6eYbX3qMxijs0wyQ5H8dcjePJb9WoF9JHnm4lzMW7s3/vnwpPT1N9KTP5il3/0TEm1qaOwFMX7qW1e/MGvLnu0a6uOHgO2gKJ5mcPuv8v216iuXLjyVe5MCF5k4i9UmnoPvEj3ARP14j0psAyw4TwYzJtrXqfRORwoa6Lnilg9n8CH6b9LEu4onst7S4C7FncugQtrYHuonH4dDD9vU9mE5EiquVudPud64d8nbgn14gEcvePxmD6etW+9I3EQkeLcB94ke4iB+vER8TBpcdJoJzdLhdq943ESksNxG9X6WD2fwIftv8fguRcHYIW8SSbAoNHcJ23z3dzDowwpFHve17MJ2IFFcPc6d8tSkcTrL5/eLf59bcSaQ+aQHuk0LhIrX0GsloiC37TmBz10SeeeNQNndNZMu+E3ifwW8kmaEhfvRNRIrLdxS8tbVzyGC2chhp+yc0t3PEiWt4Z+H+Q97WffZgrn5jIX/bshuPvnoob27dg0uTp/Hvc+7M2+bTfx7Pmpdn8w8nT674+EWkNPUwd9rW1cTSXx7Nxi2TeOzVw9m4ZRJLf3k027ryh9dq7iRS//QdcB/lCxcJymvsfudaVjOLU+ftWXTfrX88kI6fnko4EicRjzD5ovuZ8tc3s77/PVRoSKXHLyKF5X4X/GMfS30OO3/B4GC2chpp+0v2XM7VZxfe56kVR/Jvi28kHOqDcCPXXXdR3v3a7j+Vi79xHRDjZz+JsvfeF1d8/CJSmiDPnbz61VMLufKhcwhbnISL8HfNt+cNUtPcSSR4zGwe8BMgDNzsnPt+zs8vBr4MxIH3gPOcc38r1KYW4D7LDBcJ2mvsfudaujbtW3Cfvt4mOn73OZKJBpKxVCBIx48+xyfGXz5wPkWx0BC9eYhUV3Yi+riB7ZnBbJUw0vYLhUR2drZy5Pd+hks2EU8C8fxBaqlAuOtIJJqBZnp7s/fTwlskeII8dyomc06UcGOA/EFqmjuJBI+ZhYHrgROBDuA5M2tzzr2asdsqYK5zboeZfQX4AXBGoXa1AJcsxS4lti2+D6FEH0l2fl8ylOijO9k68ObQHxrS/wYCO0ND9AYiUn1DHQWvZf1Bb/0p5rAzSC1zUd2xcTKRSHaoUb79RETKweucSHMnkUA6DGh3zq0HMLNfAQuAgQW4c+7RjP2fBs4q1mjtz7rEV14CQRQaIhJ8QyWi1yqvQW+Tp3QQjytwTUT84XVOpLmTSFVMNLOVGbcLcn6+F7Ax43FHettQzgceLPaiOgIuw9IfCJL7HaXMT2e97CMi1VVvR8H7g9Quv3QpkYY+4n0NeYPUvO4nIlIOXudEmjuJpIRjCca+6dvBgS3OubkFfm55trk82zCzs4C5wDHFXlQLcBk2L4EgCg0RCb6hvgteq7wGqSlwTUT85HVOpLmTSOB0AFMyHk8GNuXuZGYnAN8EjnHO9RZrVAtwKYmXQBCFhogEW70dBQfvQW8KXBMRP3mdE2nuJBIozwEzzWwa8BZwJvClzB3MbA5wIzDPOfeul0Zrf7YlIiIlq7fvgouIiIiUg3MuDnwNWAGsBX7tnHvFzL5tZvPTu/0b0ALcbWYvmllbsXYrugA3s3lmts7M2s3sijw/v9jMXjWzl8zsYTPbp5L9EREB1aZMmUfB338/We3uiIx6qk8iIsHhnFvunNvXOfcJ59x309uucs61pe+f4Jyb5Jybnb7NL9xiBRfgGddNOxk4AFhoZgfk7NZ/3bS/B+4hdd20uhZLtrAtvg+xZEu1uyIyKqk2DdZ/FPz//KSJ1S8eRGdna7W7JDIqqT7lp7mTiNSTSn4HvCLXTatlb/ccOijdco/G56rdLZHRRrUpx4yZEeYcfDa33XoD9/w6TjyeSgafv+CBandNZLRRfcqhuZOI1JtKnoJekeum1apYsoU12xeRJEqcZpJEWbN9kT7NFfGfalOOzs5WXllzA9BMV9d4enqauPzSpToSLuI/1acMmjuJSD2q5BHwsl03LX1R9AsAGkO1WXS7k63pT2+jA9tCxOlOtirtUsRfFalNe+5Vu5mWHRsnE43G6c24cEakoY+OjZOVFC7iL82dMmjuJCL1qJIzxuFeN23+UNdNc87d5Jyb65ybG7XGinS20ppCnSRzPu9IEqEppMmtiM8qUptaa/gSXpOndNDX15C1Ld7XwOQpHVXqkciopblTBs2dRKQeVXLGOHDdNDOLkrpuWlYse8Z10+Z7vW5arYqGupg1dhkhYkTYQYgYs8Yu0ye4Iv5TbcrR2trJtT9cTGNjNy3jPqSxsZtrf7hYR79F/Kf6lEFzJxGpRxU7Bd05Fzez/uumhYFb+6+bBqxMR7dnXjcN4E0v0e21ao/G52iNrqU72UpTqFNvICJVoNqU3/wFD/DpI5+gY+NkJk/p0OJbpApUnwbT3ElE6k0lvwOOc245sDxn21UZ90+o5OsHUTTUpTcPkSpTbcqvtbVTC2+RKlN9GkxzJxGpJ7X7pUURERERERGRGqIFuIiIiIiIiIgPtAAXERERERER8YEW4CIiIiIiIiI+0AJcRERERERExAdagIuIiIiIiIj4QAtwERERERERER9oAS4iIiIiIiLiAy3ARURERERERHwQqXYHREREREREROjtwza8Ve1eVJSOgIuIiIiIiIj4QAtwERERERERER9oAS4iIiIiIiLiAy3ARURERERERHygBbiIiIiIiIiID7QAFxEREREREfGBFuAiIiIiIiIiPtACXERERERERMQHWoCLiIiIiIiI+EALcBEREREREREfaAEuIiIiIiIi4gMtwEVERERERER8oAW4iIiIiIiIiA+0ABcRERERERHxgRbgIiIiIiIiIj7QAlxERERERETEB1qAi4iIiIiIiOQws3lmts7M2s3sijw/H2Nmd6V//oyZTS3WphbgIiIiIiIiIhnMLAxcD5wMHAAsNLMDcnY7H9jqnJsBLAWuLdauFuAiIiIiIiIi2Q4D2p1z651zMeBXwIKcfRYAy9L37wGONzMr1KgW4CIiIiIiIiLZ9gI2ZjzuSG/Lu49zLg5sA1oLNRopYwdFRERERERESvJhYsuKFZ0/n+jTyzWa2cqMxzc5527KeJzvSLbLeexlnyxagIuIiIiIiEjVOefmVbsPGTqAKRmPJwObhtinw8wiwATg/UKN6hR0ERERERERkWzPATPNbJqZRYEzgbacfdqARen7pwGPOOd0BFxERERERETEK+dc3My+BqwAwsCtzrlXzOzbwErnXBtwC3CHmbWTOvJ9ZrF2tQAXERERERERyeGcWw4sz9l2Vcb9HuD04bSpU9BFREREREREfKAFuIiIiIiIiIgPtAAXERERERER8YEW4CIiIiIiIiI+0AJcRERERERExAcVXYCb2TwzW2dm7WZ2RZ6fjzGzu9I/f8bMplayPyIioNokIsGl+iQiUt8qtgA3szBwPXAycACw0MwOyNntfGCrc24GsBS4tlL9EREB1SYRCS7VJxGR+lfJI+CHAe3OufXOuRjwK2BBzj4LgGXp+/cAx5uZVbBPIiKqTSISVKpPIiJ1rpIL8L2AjRmPO9Lb8u7jnIsD24DWCvZJRES1SUSCSvVJaoA87AAACMRJREFURKTORSrYdr5PY10J+2BmFwAXpB/2ruj8+ZoR9q3aJgJbqt2JMqiHcWgMVWT28/67+/n5snm2laU2TZvyTq3XJqjhv6cMGkMw1PgYBspAXdQnzZ0CoR7GAPUxjpodQ5XmTlJGlVyAdwBTMh5PBjYNsU+HmUWACcD7uQ05524CbgIws5XOubkV6bFP6mEMUB/j0BiCwcxW+vhyqk0F1MM4NIZgqIcxgOpTUGgMwVEP46iXMVS7D1KaSp6C/hww08ymmVkUOBNoy9mnDViUvn8a8IhzbtCnuCIiZaTaJCJBpfokIlLnKnYE3DkXN7OvASuAMHCrc+4VM/s2sNI51wbcAtxhZu2kPr09s1L9EREB1SYRCS7VJxGR+lfJU9Bxzi0Hludsuyrjfg9w+jCbvakMXau2ehgD1Mc4NIZg8HUMqk0F1cM4NIZgqIcxgOpTUGgMwVEP49AYpGpMZy2JiIiIiIiIVF4lvwMuIiIiIiIiImmBXYCb2TwzW2dm7WZ2RZ6fjzGzu9I/f8bMpvrfy8I8jOFiM3vVzF4ys4fNbJ9q9LOQYmPI2O80M3NmFrhESS9jMLMvpn8Xr5jZL/3uoxce/p72NrNHzWxV+m/qlGr0cyhmdquZvWtmeS+FYyk/TY/vJTM72O8+eqHaFByqT8FQ67UJVJ+CpB7qk2pTcNR6faqX2iQ5nHOBu5EKHvkrMB2IAquBA3L2+SpwQ/r+mcBd1e53CWM4DmhO3/9KLY4hvd844DHgaWButftdwu9hJrAK2DX9+OPV7neJ47gJ+Er6/gHAG9Xud07/jgYOBtYM8fNTgAdJXeP2cOCZave5xN+DalNAxpHeT/Wp+mMIdG1K90v1KQC3eqhPqk3BudVDfaqH2qTb4FtQj4AfBrQ759Y752LAr4AFOfssAJal798DHG9m5mMfiyk6Bufco865HemHT5O63meQePk9AFwD/ADo8bNzHnkZw38HrnfObQVwzr3rcx+98DIOB4xP35/A4GvHVpVz7jHyXKs2wwLgdpfyNLCLme3hT+88U20KDtWnYKj52gSqTz72sZh6qE+qTcFR8/WpTmqT5AjqAnwvYGPG4470trz7OOfiwDag1ZfeeeNlDJnOJ/UJVpAUHYOZzQGmOOd+62fHhsHL72FfYF8ze9LMnjazeb71zjsv4/gX4Cwz6yCVoPt1f7pWNsP9N1MNqk3BofoUDKOhNoHqk1/qoT6pNgXHaKhPtVCbJEdFL0M2Avk+jc2Na/eyTzV57p+ZnQXMBY6paI+Gr+AYzCwELAXO9atDJfDye4iQOpXqWFKfpD9uZrOccx9UuG/D4WUcC4HbnHPXmdkRpK4TO8s5l6x898oi6P+mQbUpSFSfgmE01CYI/r9rUH0KCtWm4BgN9Sno/6Ylj6AeAe8ApmQ8nszgU0IG9jGzCKnTRgqdouE3L2PAzE4AvgnMd871+tQ3r4qNYRwwC/ijmb1B6rsnbQELE/H6t/SAc67PObcBWEfqTSVIvIzjfODXAM65PwONwERfelcenv7NVJlqU3CoPgXDaKhNoPrkl3qoT6pNwTEa6lMt1CbJEdQF+HPATDObZmZRUkEhbTn7tAGL0vdPAx5xzgXpE5+iY0ifgnQjqTeQIH53puAYnHPbnHMTnXNTnXNTSX0Xa75zbmV1upuXl7+l+0mFumBmE0mdVrXe114W52UcbwLHA5jZ/qTeRN7ztZcj0wack070PBzY5px7u9qdyqHaFByqT8EwGmoTqD75pR7qk2pTcIyG+lQLtUlyDSexzc8bqVS/10ilF34zve3bpIoUpP6B3A20A88C06vd5xLG8BCwGXgxfWurdp+HO4acff9IwJI8Pf4eDPgR8CrwMnBmtftc4jgOAJ4klfL5InBStfuc0/87gbeBPlKf2J4PXAhcmPF7uD49vpeD+Lfk8feg2hSQceTsq/pUvTEEujal+6j6FJBbPdQn1abg3Gq9PtVLbdIt+2bpX56IiIiIiIiIVFBQT0EXERERERERqStagIuIiIiIiIj4QAtwERERERERER9oAS4iIiIiIiLiAy3ARURERERERHygBbiMmJn9s5mtNbP/KOG5U83sS5XoV7r9o83sBTOLm9lplXodEQkm1ScRCSLVJpHRSwtwKYevAqc45/5rCc+dCgz7TcTMwh53fRM4F/jlcF9DROqC6pOIBJFqk8gopQW4jIiZ3QBMB9rMbLGZjTWzW83sOTNbZWYL0vtNNbPH05+ovmBmn0o38X3gKDN7Mf38c83sZxnt/9bMjk3f7zKzb5vZM8ARZnaImf3JzJ43sxVmtkdu/5xzbzjnXgKSFf5fISIBo/okIkGk2iQyukWq3QGpbc65C81sHnCcc26LmX0PeMQ5d56Z7QI8a2YPAe8CJzrnesxsJnAnMBe4ArjUOfdPAGZ2boGXGwuscc5dZWYNwJ+ABc6598zsDOC7wHmVGquI1BbVJxEJItUmkdFNC3Apt5OA+WZ2afpxI7A3sAn4mZnNBhLAviW0nQDuTd/fD5gF/MHMAMLA2yPot4jUP9UnEQki1SaRUUQLcCk3A77gnFuXtdHsX4DNwEGkvvrQM8Tz42R/NaIx436Pcy6R8TqvOOeOKEenRWRUUH0SkSBSbRIZRfQdcCm3FcDXLf3RqpnNSW+fALztnEsCZ5P61BXgI2BcxvPfAGabWcjMpgCHDfE664DdzOyI9Os0mNnflXUkIlJvVJ9EJIhUm0RGES3ApdyuARqAl8xsTfoxwP8FFpnZ06ROodqe3v4SEDez1Wa2GHgS2AC8DPwQeCHfizjnYsBpwLVmthp4EfhU7n5mdqiZdQCnAzea2SvlGaaI1CDVJxEJItUmkVHEnHPV7oOIiIiIiIhI3dMRcBEREREREREfaAEuIiIiIiIi4gMtwEVERERERER8oAW4iIiIiIiIiA+0ABcRERERERHxgRbgIiIiIiIiIj7QAlxERERERETEB1qAi4iIiIiIiPjg/wN4BJTyDhmgrwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 1080x360 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "num_classes = 3\n",
+    "x_train, y_train = get_data(num_classes=num_classes)\n",
+    "x_train_adv, model = get_adversarial_examples(x_train, y_train)\n",
+    "plot_results(model, x_train, y_train, x_train_adv, num_classes)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 3 Example: MNIST"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3.1 Load and transform MNIST dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "(x_train, y_train), (x_test, y_test), min_, max_ = load_mnist()\n",
+    "\n",
+    "n_samples_train = x_train.shape[0]\n",
+    "n_features_train = x_train.shape[1] * x_train.shape[2] * x_train.shape[3]\n",
+    "n_samples_test = x_test.shape[0]\n",
+    "n_features_test = x_test.shape[1] * x_test.shape[2] * x_test.shape[3]\n",
+    "\n",
+    "x_train = x_train.reshape(n_samples_train, n_features_train)\n",
+    "x_test = x_test.reshape(n_samples_test, n_features_test)\n",
+    "\n",
+    "y_train = np.argmax(y_train, axis=1)\n",
+    "y_test = np.argmax(y_test, axis=1)\n",
+    "\n",
+    "n_samples_max = 200\n",
+    "x_train = x_train[0:n_samples_max]\n",
+    "y_train = y_train[0:n_samples_max]\n",
+    "x_test = x_test[0:n_samples_max]\n",
+    "y_test = y_test[0:n_samples_max]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3.2 Train DecisionTreeClassifier classifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = DecisionTreeClassifier(criterion='gini', splitter='best', max_depth=None, min_samples_split=2, \n",
+    "                               min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=None, \n",
+    "                               random_state=None, max_leaf_nodes=50, min_impurity_decrease=0.0, \n",
+    "                               min_impurity_split=None, class_weight=None, presort=False) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
+       "                       max_depth=None, max_features=None, max_leaf_nodes=50,\n",
+       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
+       "                       min_samples_leaf=1, min_samples_split=2,\n",
+       "                       min_weight_fraction_leaf=0.0, presort=False,\n",
+       "                       random_state=None, splitter='best')"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.fit(X=x_train, y=y_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3.3 Create and apply Zeroth Order Optimization Attack with ART"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "art_classifier = SklearnClassifier(model=model)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "zoo = ZooAttack(classifier=art_classifier, confidence=0.0, targeted=False, learning_rate=1e-1, max_iter=100,\n",
+    "                binary_search_steps=20, initial_const=1e-3, abort_early=True, use_resize=False, \n",
+    "                use_importance=False, nb_parallel=10, batch_size=1, variable_h=0.25)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "ZOO: 100%|██████████| 200/200 [02:53<00:00,  1.15it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "x_train_adv = zoo.generate(x_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "ZOO: 100%|██████████| 200/200 [03:03<00:00,  1.09it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "x_test_adv = zoo.generate(x_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3.4 Evaluate DecisionTreeClassifier on benign and adversarial samples"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Benign Training Score: 1.0000\n"
+     ]
+    }
+   ],
+   "source": [
+    "score = model.score(x_train, y_train)\n",
+    "print(\"Benign Training Score: %.4f\" % score)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.matshow(x_train[0, :].reshape((28, 28)))\n",
+    "plt.clim(0, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Benign Training Predicted Label: 5\n"
+     ]
+    }
+   ],
+   "source": [
+    "prediction = model.predict(x_train[0:1, :])[0]\n",
+    "print(\"Benign Training Predicted Label: %i\" % prediction)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial Training Score: 0.0900\n"
+     ]
+    }
+   ],
+   "source": [
+    "score = model.score(x_train_adv, y_train)\n",
+    "print(\"Adversarial Training Score: %.4f\" % score)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.matshow(x_train_adv[0, :].reshape((28, 28)))\n",
+    "plt.clim(0, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial Training Predicted Label: 9\n"
+     ]
+    }
+   ],
+   "source": [
+    "prediction = model.predict(x_train_adv[0:1, :])[0]\n",
+    "print(\"Adversarial Training Predicted Label: %i\" % prediction)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Benign Test Score: 0.5100\n"
+     ]
+    }
+   ],
+   "source": [
+    "score = model.score(x_test, y_test)\n",
+    "print(\"Benign Test Score: %.4f\" % score)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.matshow(x_test[0, :].reshape((28, 28)))\n",
+    "plt.clim(0, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Benign Test Predicted Label: 7\n"
+     ]
+    }
+   ],
+   "source": [
+    "prediction = model.predict(x_test[0:1, :])[0]\n",
+    "print(\"Benign Test Predicted Label: %i\" % prediction)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial Test Score: 0.1350\n"
+     ]
+    }
+   ],
+   "source": [
+    "score = model.score(x_test_adv, y_test)\n",
+    "print(\"Adversarial Test Score: %.4f\" % score)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.matshow(x_test_adv[0, :].reshape((28, 28)))\n",
+    "plt.clim(0, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial Test Predicted Label: 5\n"
+     ]
+    }
+   ],
+   "source": [
+    "prediction = model.predict(x_test_adv[0:1, :])[0]\n",
+    "print(\"Adversarial Test Predicted Label: %i\" % prediction)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/adversarial-robustness-toolbox/notebooks/classifier_scikitlearn_ExtraTreesClassifier.ipynb b/adversarial-robustness-toolbox/notebooks/classifier_scikitlearn_ExtraTreesClassifier.ipynb
new file mode 100644
index 0000000..12c932f
--- /dev/null
+++ b/adversarial-robustness-toolbox/notebooks/classifier_scikitlearn_ExtraTreesClassifier.ipynb
@@ -0,0 +1,651 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Adversarial-Robustness-Toolbox for scikit-learn ExtraTreesClassifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.ensemble import ExtraTreesClassifier\n",
+    "from sklearn.datasets import load_iris\n",
+    "\n",
+    "import numpy as np\n",
+    "from matplotlib import pyplot as plt\n",
+    "\n",
+    "from art.estimators.classification import SklearnClassifier\n",
+    "from art.attacks.evasion import ZooAttack\n",
+    "from art.utils import load_mnist\n",
+    "\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1 Training scikit-learn ExtraTreesClassifier and attacking with ART Zeroth Order Optimization attack"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_adversarial_examples(x_train, y_train):\n",
+    "    \n",
+    "    # Create and fit ExtraTreesClassifier\n",
+    "    model = ExtraTreesClassifier()\n",
+    "    model.fit(X=x_train, y=y_train)\n",
+    "\n",
+    "    # Create ART classifier for scikit-learn ExtraTreesClassifier\n",
+    "    art_classifier = SklearnClassifier(model=model)\n",
+    "\n",
+    "    # Create ART Zeroth Order Optimization attack\n",
+    "    zoo = ZooAttack(classifier=art_classifier, confidence=0.0, targeted=False, learning_rate=1e-1, max_iter=20,\n",
+    "                    binary_search_steps=10, initial_const=1e-3, abort_early=True, use_resize=False, \n",
+    "                    use_importance=False, nb_parallel=1, batch_size=1, variable_h=0.2)\n",
+    "\n",
+    "    # Generate adversarial samples with ART Zeroth Order Optimization attack\n",
+    "    x_train_adv = zoo.generate(x_train)\n",
+    "\n",
+    "    return x_train_adv, model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1.1 Utility functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_data(num_classes):\n",
+    "    x_train, y_train = load_iris(return_X_y=True)\n",
+    "    x_train = x_train[y_train < num_classes][:, [0, 1]]\n",
+    "    y_train = y_train[y_train < num_classes]\n",
+    "    x_train[:, 0][y_train == 0] *= 2\n",
+    "    x_train[:, 1][y_train == 2] *= 2\n",
+    "    x_train[:, 0][y_train == 0] -= 3\n",
+    "    x_train[:, 1][y_train == 2] -= 2\n",
+    "    \n",
+    "    x_train[:, 0] = (x_train[:, 0] - 4) / (9 - 4)\n",
+    "    x_train[:, 1] = (x_train[:, 1] - 1) / (6 - 1)\n",
+    "    \n",
+    "    return x_train, y_train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_results(model, x_train, y_train, x_train_adv, num_classes):\n",
+    "    \n",
+    "    fig, axs = plt.subplots(1, num_classes, figsize=(num_classes * 5, 5))\n",
+    "\n",
+    "    colors = ['orange', 'blue', 'green']\n",
+    "\n",
+    "    for i_class in range(num_classes):\n",
+    "\n",
+    "        # Plot difference vectors\n",
+    "        for i in range(y_train[y_train == i_class].shape[0]):\n",
+    "            x_1_0 = x_train[y_train == i_class][i, 0]\n",
+    "            x_1_1 = x_train[y_train == i_class][i, 1]\n",
+    "            x_2_0 = x_train_adv[y_train == i_class][i, 0]\n",
+    "            x_2_1 = x_train_adv[y_train == i_class][i, 1]\n",
+    "            if x_1_0 != x_2_0 or x_1_1 != x_2_1:\n",
+    "                axs[i_class].plot([x_1_0, x_2_0], [x_1_1, x_2_1], c='black', zorder=1)\n",
+    "\n",
+    "        # Plot benign samples\n",
+    "        for i_class_2 in range(num_classes):\n",
+    "            axs[i_class].scatter(x_train[y_train == i_class_2][:, 0], x_train[y_train == i_class_2][:, 1], s=20,\n",
+    "                                 zorder=2, c=colors[i_class_2])\n",
+    "        axs[i_class].set_aspect('equal', adjustable='box')\n",
+    "\n",
+    "        # Show predicted probability as contour plot\n",
+    "        h = .01\n",
+    "        x_min, x_max = 0, 1\n",
+    "        y_min, y_max = 0, 1\n",
+    "\n",
+    "        xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n",
+    "\n",
+    "        Z_proba = model.predict_proba(np.c_[xx.ravel(), yy.ravel()])\n",
+    "        Z_proba = Z_proba[:, i_class].reshape(xx.shape)\n",
+    "        im = axs[i_class].contourf(xx, yy, Z_proba, levels=[0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0],\n",
+    "                                   vmin=0, vmax=1)\n",
+    "        if i_class == num_classes - 1:\n",
+    "            cax = fig.add_axes([0.95, 0.2, 0.025, 0.6])\n",
+    "            plt.colorbar(im, ax=axs[i_class], cax=cax)\n",
+    "\n",
+    "        # Plot adversarial samples\n",
+    "        for i in range(y_train[y_train == i_class].shape[0]):\n",
+    "            x_1_0 = x_train[y_train == i_class][i, 0]\n",
+    "            x_1_1 = x_train[y_train == i_class][i, 1]\n",
+    "            x_2_0 = x_train_adv[y_train == i_class][i, 0]\n",
+    "            x_2_1 = x_train_adv[y_train == i_class][i, 1]\n",
+    "            if x_1_0 != x_2_0 or x_1_1 != x_2_1:\n",
+    "                axs[i_class].scatter(x_2_0, x_2_1, zorder=2, c='red', marker='X')\n",
+    "        axs[i_class].set_xlim((x_min, x_max))\n",
+    "        axs[i_class].set_ylim((y_min, y_max))\n",
+    "\n",
+    "        axs[i_class].set_title('class ' + str(i_class))\n",
+    "        axs[i_class].set_xlabel('feature 1')\n",
+    "        axs[i_class].set_ylabel('feature 2')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 2 Example: Iris dataset"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### legend\n",
+    "- colored background: probability of class i\n",
+    "- orange circles: class 1\n",
+    "- blue circles: class 2\n",
+    "- green circles: class 3\n",
+    "- red crosses: adversarial samples for class i"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "ZOO: 100%|██████████| 100/100 [01:22<00:00,  1.21it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "num_classes = 2\n",
+    "x_train, y_train = get_data(num_classes=num_classes)\n",
+    "x_train_adv, model = get_adversarial_examples(x_train, y_train)\n",
+    "plot_results(model, x_train, y_train, x_train_adv, num_classes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "ZOO: 100%|██████████| 150/150 [02:12<00:00,  1.13it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 1080x360 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "num_classes = 3\n",
+    "x_train, y_train = get_data(num_classes=num_classes)\n",
+    "x_train_adv, model = get_adversarial_examples(x_train, y_train)\n",
+    "plot_results(model, x_train, y_train, x_train_adv, num_classes)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 3 Example: MNIST"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3.1 Load and transform MNIST dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "(x_train, y_train), (x_test, y_test), min_, max_ = load_mnist()\n",
+    "\n",
+    "n_samples_train = x_train.shape[0]\n",
+    "n_features_train = x_train.shape[1] * x_train.shape[2] * x_train.shape[3]\n",
+    "n_samples_test = x_test.shape[0]\n",
+    "n_features_test = x_test.shape[1] * x_test.shape[2] * x_test.shape[3]\n",
+    "\n",
+    "x_train = x_train.reshape(n_samples_train, n_features_train)\n",
+    "x_test = x_test.reshape(n_samples_test, n_features_test)\n",
+    "\n",
+    "y_train = np.argmax(y_train, axis=1)\n",
+    "y_test = np.argmax(y_test, axis=1)\n",
+    "\n",
+    "n_samples_max = 200\n",
+    "x_train = x_train[0:n_samples_max]\n",
+    "y_train = y_train[0:n_samples_max]\n",
+    "x_test = x_test[0:n_samples_max]\n",
+    "y_test = y_test[0:n_samples_max]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3.2 Train ExtraTreesClassifier classifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = ExtraTreesClassifier(n_estimators=10, criterion='gini', max_depth=None, min_samples_split=2, \n",
+    "                             min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features='auto', \n",
+    "                             max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, \n",
+    "                             bootstrap=False, oob_score=False, n_jobs=None, random_state=None, verbose=0, \n",
+    "                             warm_start=False, class_weight=None)  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "ExtraTreesClassifier(bootstrap=False, ccp_alpha=0.0, class_weight=None,\n",
+       "                     criterion='gini', max_depth=None, max_features='auto',\n",
+       "                     max_leaf_nodes=None, max_samples=None,\n",
+       "                     min_impurity_decrease=0.0, min_impurity_split=None,\n",
+       "                     min_samples_leaf=1, min_samples_split=2,\n",
+       "                     min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=None,\n",
+       "                     oob_score=False, random_state=None, verbose=0,\n",
+       "                     warm_start=False)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.fit(X=x_train, y=y_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3.3 Create and apply Zeroth Order Optimization Attack with ART"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "art_classifier = SklearnClassifier(model=model)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "zoo = ZooAttack(classifier=art_classifier, confidence=0.0, targeted=False, learning_rate=1e-1, max_iter=100,\n",
+    "                binary_search_steps=20, initial_const=1e-3, abort_early=True, use_resize=False, \n",
+    "                use_importance=False, nb_parallel=10, batch_size=1, variable_h=0.25)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "ZOO: 100%|██████████| 200/200 [09:02<00:00,  2.71s/it]\n"
+     ]
+    }
+   ],
+   "source": [
+    "x_train_adv = zoo.generate(x_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "ZOO: 100%|██████████| 200/200 [06:07<00:00,  1.84s/it]\n"
+     ]
+    }
+   ],
+   "source": [
+    "x_test_adv = zoo.generate(x_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3.4 Evaluate ExtraTreesClassifier on benign and adversarial samples"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Benign Training Score: 1.0000\n"
+     ]
+    }
+   ],
+   "source": [
+    "score = model.score(x_train, y_train)\n",
+    "print(\"Benign Training Score: %.4f\" % score)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.matshow(x_train[0, :].reshape((28, 28)))\n",
+    "plt.clim(0, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Benign Training Predicted Label: 5\n"
+     ]
+    }
+   ],
+   "source": [
+    "prediction = model.predict(x_train[0:1, :])[0]\n",
+    "print(\"Benign Training Predicted Label: %i\" % prediction)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial Training Score: 0.5300\n"
+     ]
+    }
+   ],
+   "source": [
+    "score = model.score(x_train_adv, y_train)\n",
+    "print(\"Adversarial Training Score: %.4f\" % score)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQEAAAECCAYAAAD+eGJTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAPAElEQVR4nO3df6zV9X3H8dergDAQLXcUis4KopmudsXuxh/DqIupZU0TNYt1pGmY64brpK0bS7RkiW6xC2vUzq0dCU4mJmprq06yuKohRm1WmciYYqnaKnXI3UW8UdQ6hMt7f9zDdmfv/Zx77znn+z3wfj4Scs75vs853zdf7n3x+Z7v53y/jggByOsDdTcAoF6EAJAcIQAkRwgAyRECQHKEAJBcLSFge4nt523/xPa1dfRQYnuH7Wdtb7W9uQv6WWd7t+1tw5b12H7E9ouN21ld1t/1tl9tbMOttj9dY38n2H7U9nbbz9n+SmN5V2zDQn+VbENXPU/A9iRJL0j6pKSdkp6StDQiflRpIwW2d0jqjYg9dfciSbbPk/S2pDsi4vTGsq9LGoiI1Y0gnRUR13RRf9dLejsibqyjp+Fsz5M0LyK22J4p6WlJl0j6PXXBNiz091lVsA3rGAmcKeknEfFSRLwn6duSLq6hj8NGRDwuaeB9iy+WtL5xf72GfmhqMUp/XSMi+iJiS+P+W5K2SzpeXbINC/1Voo4QOF7Sfw57vFMV/oXHKCQ9bPtp28vrbmYUcyOiTxr6IZI0p+Z+RrLC9jON3YXadleGsz1f0hmSNqkLt+H7+pMq2IZ1hIBHWNZtc5cXR8QnJP22pKsaw12MzxpJCyUtktQn6aZ625FsHy3pXklXR8Teuvt5vxH6q2Qb1hECOyWdMOzxr0jaVUMfo4qIXY3b3ZLu19AuTLfpb+xLHtqn3F1zP/9PRPRHxGBEHJR0q2rehranaOgX7M6IuK+xuGu24Uj9VbUN6wiBpySdYnuB7aMk/a6kDTX0MSLbMxofzsj2DEkXSdpWflUtNkha1ri/TNIDNfbyCw79cjVcqhq3oW1Luk3S9oi4eVipK7bhaP1VtQ0rPzogSY1DHX8jaZKkdRHxtcqbGIXtkzT0v78kTZZ0V9392b5b0gWSZkvql3SdpH+SdI+kj0h6RdJlEVHLh3Oj9HeBhoaxIWmHpCsP7X/X0N+5kp6Q9Kykg43FqzS03137Niz0t1QVbMNaQgBA92DGIJAcIQAkRwgAyRECQHKEAJBcrSHQxVNyJdFfq7q5v27uTaq2v7pHAl39DyH6a1U399fNvUkV9ld3CACoWUuThWwvkXSLhmb+/UNErC49/yhPjWma8b+P92ufpmjqhNffafTXmm7ur5t7k9rf33/rHb0X+0b68t7EQ2AiJwc5xj1xli+c0PoATNym2Ki9MTBiCLSyO8DJQYAjQCshcDicHARAE5NbeO2YTg7SONSxXJKmaXoLqwPQCa2MBMZ0cpCIWBsRvRHR280fxABZtRICXX1yEABjM+HdgYg4YHuFpIf0fycHea5tnQGoRCufCSgiHpT0YJt6AVADZgwCyRECQHKEAJAcIQAkRwgAyRECQHKEAJAcIQAkRwgAyRECQHKEAJAcIQAkRwgAyRECQHKEAJAcIQAkRwgAyRECQHKEAJAcIQAkRwgAyRECQHKEAJAcIQAkRwgAyRECQHKEAJAcIQAkRwgAyRECQHItXZoc1Zp80vxi/cBLO4p1Ty7/c0/60OxxdjQ+z//Z/GJ9cPrBYv3EhbuL9el/7GL9v24+qljf0vudYn3P4DvF+lnfXVmsn/ynTxbrdWkpBGzvkPSWpEFJByKitx1NAahOO0YCvxURe9rwPgBqwGcCQHKthkBIetj207aXt6MhANVqdXdgcUTssj1H0iO2fxwRjw9/QiMclkvSNE1vcXUA2q2lkUBE7Grc7pZ0v6QzR3jO2ojojYjeKZrayuoAdMCEQ8D2DNszD92XdJGkbe1qDEA1WtkdmCvpftuH3ueuiPh+W7o6Qk067ZRiPaZOKdZ3nv/BYv3ds48t1nuOLR/nfuLj5ePkdfuXn88s1v/6m0uK9Z7VM4r1l9e/Xayv7v9ksX7cE1Gsd6sJh0BEvCTp423sBUANOEQIJEcIAMkRAkByhACQHCEAJEcIAMk5orpjm8e4J87yhZWtr2qDF3yiWO+54WfF+p0LHm5nO4ed/TFYrP/m168u1ie/09rP8sxXDxTrU/e8W6zH5u6dK7cpNmpvDIx4wgVGAkByhACQHCEAJEcIAMkRAkByhACQHCEAJMd1B9po6vO7ivXPzP6PijrpjJV9ZxfrL71dvm7B7Qu/V6y/ebB8nH/u3/5rsd5ph+fZAppjJAAkRwgAyRECQHKEAJAcIQAkRwgAyRECQHKcT6BCA1ecU6zvXVK+LsD8y58p1v/51afH3dNwN+z59WL9qfPL8wAG33izWI9zymeo3/HlYlkLlpbnWTy0a2ux/qnjFpVXcATjfAIARkUIAMkRAkByhACQHCEAJEcIAMkRAkByzBPoIpNm/3KxPvj6QLH+8l3l4/zPnbeuWD/zr75UrM/5Vr3f58fEtTRPwPY627ttbxu2rMf2I7ZfbNzOamfDAKozlt2B2yUted+yayVtjIhTJG1sPAZwGGoaAhHxuKT3j0MvlrS+cX+9pEva3BeAikz0g8G5EdEnSY3bOe1rCUCVOn6iUdvLJS2XpGma3unVARiniY4E+m3Pk6TG7e7RnhgRayOiNyJ6p2jqBFcHoFMmGgIbJC1r3F8m6YH2tAOgak13B2zfLekCSbNt75R0naTVku6x/QVJr0i6rJNNZjG45/WWXr9/71Etvf6jn/tRsf7amknlNzg42NL6UY+mIRARS0cpMesHOAIwbRhIjhAAkiMEgOQIASA5QgBIjhAAkuv4tGFU57RrXijWr/hY+ajuP564sVg//7KrivWZ33myWEd3YiQAJEcIAMkRAkByhACQHCEAJEcIAMkRAkByzBM4ggy+8Wax/voXTyvWX9nwbrF+7Q13FOtf/eylxXr8+7HF+glf+2GxrgqvkZEJIwEgOUIASI4QAJIjBIDkCAEgOUIASI4QAJJzVHjs9Rj3xFnmTOXdauD3zynW77zuxmJ9weRpLa3/o3esKL//V5vMI8CoNsVG7Y0Bj1RjJAAkRwgAyRECQHKEAJAcIQAkRwgAyRECQHLME8CYxeJFxfoxq3cW63ef9FBL6z/10T8o1n/1L8rnUxh88aWW1n84a2megO11tnfb3jZs2fW2X7W9tfHn0+1sGEB1xrI7cLukJSMs/0ZELGr8ebC9bQGoStMQiIjHJQ1U0AuAGrTyweAK2880dhdmta0jAJWaaAiskbRQ0iJJfZJuGu2Jtpfb3mx7837tm+DqAHTKhEIgIvojYjAiDkq6VdKZheeujYjeiOidoqkT7RNAh0woBGzPG/bwUknbRnsugO7WdJ6A7bslXSBptqR+Sdc1Hi+SFJJ2SLoyIvqarYx5Ake2SXPnFOu7Lj+5WN90zS3F+gea/J/1uZcvKtbfPPf1Yv1IVpon0PTiIxGxdITFt7XcFYCuwLRhIDlCAEiOEACSIwSA5AgBIDlCAEiO8wmga9yzs3xdgek+qlj/ebxXrH/mS1eX3//+TcX64YzrDgAYFSEAJEcIAMkRAkByhACQHCEAJEcIAMk1/SoxcMjBc8vXHfjpZdOK9dMX7SjWm80DaObvBs4ov/8Dm1t6/yMVIwEgOUIASI4QAJIjBIDkCAEgOUIASI4QAJJjnkAi7j29WH/hy+Xj9LcuXl+snzet/H3+Vu2L/cX6kwMLym9wsOmlMVJiJAAkRwgAyRECQHKEAJAcIQAkRwgAyRECQHLMEziMTF5wYrH+0yuOK9avv/zbxfrvHL1n3D2106r+3mL9sVvOLtZnrS9ftwAjazoSsH2C7Udtb7f9nO2vNJb32H7E9ouN21mdbxdAu41ld+CApJURcZqksyVdZfvXJF0raWNEnCJpY+MxgMNM0xCIiL6I2NK4/5ak7ZKOl3SxpEPzSNdLuqRTTQLonHF9MGh7vqQzJG2SNDci+qShoJA0p93NAei8MYeA7aMl3Svp6ojYO47XLbe92fbm/do3kR4BdNCYQsD2FA0FwJ0RcV9jcb/teY36PEm7R3ptRKyNiN6I6J2iqe3oGUAbjeXogCXdJml7RNw8rLRB0rLG/WWSHmh/ewA6bSzzBBZL+rykZ21vbSxbJWm1pHtsf0HSK5Iu60yLR47J8z9SrL/5G/OK9cv/8vvF+h998L5ivdNW9pWP4//w78vzAHpu/7difdZB5gF0QtMQiIgfSPIo5Qvb2w6AqjFtGEiOEACSIwSA5AgBIDlCAEiOEACS43wC4zB53oeL9YF1M4r1Ly54rFhfOrN/3D2104pXzy3Wt6xZVKzP/t62Yr3nLY7zdyNGAkByhACQHCEAJEcIAMkRAkByhACQHCEAJJdqnsB7nyp/n/29Pxko1led/GCxftEvvTPuntqpf/DdYv28DSuL9VP//MfFes8b5eP8B4tVdCtGAkByhACQHCEAJEcIAMkRAkByhACQHCEAJJdqnsCOS8qZ98LHvtvR9X/rjYXF+i2PXVSse3C0M78POfWGl4v1U/o3FeuDxSqOVIwEgOQIASA5QgBIjhAAkiMEgOQIASA5QgBIzhFRfoJ9gqQ7JH1YQ18ZXxsRt9i+XtIfSnqt8dRVEVH8wv0x7omzzNXMgaptio3aGwMjTjQZy2ShA5JWRsQW2zMlPW37kUbtGxFxY7saBVC9piEQEX2S+hr337K9XdLxnW4MQDXG9ZmA7fmSzpB0aP7pCtvP2F5ne1abewNQgTGHgO2jJd0r6eqI2CtpjaSFkhZpaKRw0yivW257s+3N+7WvDS0DaKcxhYDtKRoKgDsj4j5Jioj+iBiMiIOSbpV05kivjYi1EdEbEb1TNLVdfQNok6YhYNuSbpO0PSJuHrZ83rCnXSqpfElaAF1pLEcHFkv6vKRnbW9tLFslaantRZJC0g5JV3akQwAdNZajAz+QNNLxxfJJ+AEcFpgxCCRHCADJEQJAcoQAkBwhACRHCADJEQJAcoQAkBwhACRHCADJEQJAcoQAkBwhACRHCADJEQJAck2vO9DWldmvSfrZsEWzJe2prIHxo7/WdHN/3dyb1P7+ToyID41UqDQEfmHl9uaI6K2tgSborzXd3F839yZV2x+7A0ByhACQXN0hsLbm9TdDf63p5v66uTepwv5q/UwAQP3qHgkAqBkhACRHCADJEQJAcoQAkNz/AAXOU/KcAqcYAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 288x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.matshow(x_train_adv[0, :].reshape((28, 28)))\n",
+    "plt.clim(0, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial Training Predicted Label: 3\n"
+     ]
+    }
+   ],
+   "source": [
+    "prediction = model.predict(x_train_adv[0:1, :])[0]\n",
+    "print(\"Adversarial Training Predicted Label: %i\" % prediction)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Benign Test Score: 0.6100\n"
+     ]
+    }
+   ],
+   "source": [
+    "score = model.score(x_test, y_test)\n",
+    "print(\"Benign Test Score: %.4f\" % score)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.matshow(x_test[0, :].reshape((28, 28)))\n",
+    "plt.clim(0, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Benign Test Predicted Label: 7\n"
+     ]
+    }
+   ],
+   "source": [
+    "prediction = model.predict(x_test[0:1, :])[0]\n",
+    "print(\"Benign Test Predicted Label: %i\" % prediction)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial Test Score: 0.3550\n"
+     ]
+    }
+   ],
+   "source": [
+    "score = model.score(x_test_adv, y_test)\n",
+    "print(\"Adversarial Test Score: %.4f\" % score)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQEAAAECCAYAAAD+eGJTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAOUklEQVR4nO3dfYwd9XXG8eeJvaxjG1Q7xI4xLy6ERCFpWNoFErmtnFICSYQAJbRxVeRKaRa1IEEVtUVIEUhtU4p4KW0aJFPcOBGQUl4KStwkyE1LUYmDbRkwmBZKXep46wWc1iaAX0//2IFuye7v7u69d+auz/cjWffeObMzx+PrZ38z97ezjggByOsdTTcAoFmEAJAcIQAkRwgAyRECQHKEAJBcIyFg+3zb/2L7edtXN9FDie3ttp+yvcX2xh7oZ43tEdtbxyxbaPth289Vjwt6rL/rbP+wOoZbbH+ywf5OsP0929tsP237ymp5TxzDQn+1HEPXPU/A9ixJ/yrpXEk7JD0uaWVEPFNrIwW2t0sajIiXm+5Fkmz/oqRXJX0tIj5ULbtB0u6IuL4K0gUR8fs91N91kl6NiBub6Gks20skLYmIzbaPlrRJ0kWSfkM9cAwL/f2KajiGTYwEzpL0fES8EBH7JX1D0oUN9DFjRMQjkna/bfGFktZWz9dq9E3TiAn66xkRMRwRm6vneyVtk7RUPXIMC/3VookQWCrpP8e83qEa/8KTFJK+a3uT7aGmm5nA4ogYlkbfRJIWNdzPeK6w/WR1utDY6cpYtpdJOkPSBvXgMXxbf1INx7CJEPA4y3pt7vLyiPhZSZ+QdHk13MXU3CbpFEkDkoYl3dRsO5Lt+ZLuk3RVROxpup+3G6e/Wo5hEyGwQ9IJY14fL2lnA31MKCJ2Vo8jkh7Q6ClMr9lVnUu+eU450nA//09E7IqIQxFxWNLtavgY2u7T6H+wOyPi/mpxzxzD8fqr6xg2EQKPSzrV9k/bPkrSZyU91EAf47I9r7o4I9vzJH1c0tbyVzXiIUmrquerJD3YYC8/4c3/XJWL1eAxtG1Jd0jaFhE3jyn1xDGcqL+6jmHtnw5IUvVRx59KmiVpTUT8Ue1NTMD2yRr97i9JsyXd1XR/tu+WtELSsZJ2SbpW0t9KukfSiZJelHRJRDRycW6C/lZodBgbkrZLuuzN8+8G+vt5Sf8k6SlJh6vF12j0vLvxY1job6VqOIaNhACA3sGMQSA5QgBIjhAAkiMEgOQIASC5RkOgh6fkSqK/dvVyf73cm1Rvf02PBHr6H0L0165e7q+Xe5Nq7K/pEADQsLYmC9k+X9KtGp3595cRcX1p/aPcH3M0763XB7RPfeqf9v67jf7a08v99XJvUuf7e0M/1v7YN94P700/BKZzc5BjvDDO9jnT2h+A6dsQ67Undo8bAu2cDnBzEOAI0E4IzISbgwBoYXYbXzupm4NUH3UMSdIczW1jdwC6oZ2RwKRuDhIRqyNiMCIGe/lCDJBVOyHQ0zcHATA50z4diIiDtq+Q9B39381Bnu5YZwBq0c41AUXEOknrOtQLgAYwYxBIjhAAkiMEgOQIASA5QgBIjhAAkiMEgOQIASA5QgBIjhAAkiMEgOQIASA5QgBIjhAAkmvrR4mBTnrhroFi/eRf21JTJ7kwEgCSIwSA5AgBIDlCAEiOEACSIwSA5AgBIDnmCaBnMA+gGYwEgOQIASA5QgBIjhAAkiMEgOQIASA5QgBIjnkCOGKcueVQsf74wKyaOplZ2goB29sl7ZV0SNLBiBjsRFMA6tOJkcDHIuLlDmwHQAO4JgAk124IhKTv2t5ke6gTDQGoV7unA8sjYqftRZIetv1sRDwydoUqHIYkaY7mtrk7AJ3W1kggInZWjyOSHpB01jjrrI6IwYgY7FN/O7sD0AXTDgHb82wf/eZzSR+XtLVTjQGoRzunA4slPWD7ze3cFRHf7khXwDQwD2B6ph0CEfGCpNM72AuABvARIZAcIQAkRwgAyRECQHKEAJAcIQAkx/0EpuCVz3+0WD/x0ueL9WdHFhfr+/f1FetL7y7X5+54tVg/vOWZYh05MRIAkiMEgOQIASA5QgBIjhAAkiMEgOQIASA55glMwe/97l3F+qfn/ai8gVPabGBFubz94GvF+q0vfazNBma2H4ycVKx/f+DeYv284wY62U7PYCQAJEcIAMkRAkByhACQHCEAJEcIAMkRAkByjojadnaMF8bZPqe2/XXajz9zdrH+8ofLmbpgW/lY/+gDLtaP+vB/F+s3fOj+Yv3cd75erH/rtfnF+qfmlu9X0K7XY3+xvmHfvGJ9xZwDbe3/vd+6rFh/39DjbW2/SRtivfbE7nHfYIwEgOQIASA5QgBIjhAAkiMEgOQIASA5QgBIjvsJTMG8eze0qLe3/WPa+3L9+XtWFOt/uHxZef//WP69CTeseO8UO5qa2a8fLtbnPTlcrL/rkfuK9Z85qsXvbdherh+pWo4EbK+xPWJ765hlC20/bPu56nFBd9sE0C2TOR34qqTz37bsaknrI+JUSeur1wBmoJYhEBGPSNr9tsUXSlpbPV8r6aIO9wWgJtO9MLg4IoYlqXpc1LmWANSp6xcGbQ9JGpKkOZrb7d0BmKLpjgR22V4iSdXjyEQrRsTqiBiMiME+9U9zdwC6Zboh8JCkVdXzVZIe7Ew7AOrW8nTA9t0aveP9sbZ3SLpW0vWS7rH9OUkvSrqkm01icg7+165ifd595fqhFtufd+8rU+yos3b95keL9Q8eVX4737j7/cX6sr96oVg/WKzOXC1DICJWTlCauXcHAfAWpg0DyRECQHKEAJAcIQAkRwgAyRECQHLcTwA9Y/ZJJxTrX77my8V6n2cV639z6y8X6+8afqxYP1IxEgCSIwSA5AgBIDlCAEiOEACSIwSA5AgBIDnmCaBnPPs7S4v1M/tdrD+9//VifeEzr025pwwYCQDJEQJAcoQAkBwhACRHCADJEQJAcoQAkBzzBFCbfZ86s1jf/JlbWmyh/BusfuvKK4v1d/7zD1psPydGAkByhACQHCEAJEcIAMkRAkByhACQHCEAJMc8gR5y8Jd+rlif/febauqkO178RPl7znyX5wGs/Pdzi/W5336iWI9iNa+WIwHba2yP2N46Ztl1tn9oe0v155PdbRNAt0zmdOCrks4fZ/ktETFQ/VnX2bYA1KVlCETEI5J219ALgAa0c2HwCttPVqcLCzrWEYBaTTcEbpN0iqQBScOSbppoRdtDtjfa3nhA+6a5OwDdMq0QiIhdEXEoIg5Lul3SWYV1V0fEYEQM9rX4KTAA9ZtWCNheMublxZK2TrQugN7Wcp6A7bslrZB0rO0dkq6VtML2gEY/et0u6bIu9pjGTJ8H8I6jjy7WL/2FR4v1PYffKNZHvnRysd6/7/FiHeNrGQIRsXKcxXd0oRcADWDaMJAcIQAkRwgAyRECQHKEAJAcIQAkx/0EpuA7O7cU6+cdN1BTJ73pues+WKx/89ivFOsXPvfpYr1/HfMAuoGRAJAcIQAkRwgAyRECQHKEAJAcIQAkRwgAyTFPYAqyzwP4n1//SLH+5K/+WbH+bwcPFOuv/snxxXq/hot1TA8jASA5QgBIjhAAkiMEgOQIASA5QgBIjhAAkmOeAN4ye+lxxfpVX/zrYr3f5bfTZ5+4tFh/999xv4AmMBIAkiMEgOQIASA5QgBIjhAAkiMEgOQIASA55gkk4tnlf+7Tv7mjWL9k/ivF+p17FxXri79Y/p5zuFhFt7QcCdg+wfb3bG+z/bTtK6vlC20/bPu56nFB99sF0GmTOR04KOkLEfEBSR+RdLnt0yRdLWl9RJwqaX31GsAM0zIEImI4IjZXz/dK2iZpqaQLJa2tVlsr6aJuNQmge6Z0YdD2MklnSNogaXFEDEujQSGpfEIIoCdNOgRsz5d0n6SrImLPFL5uyPZG2xsPaN90egTQRZMKAdt9Gg2AOyPi/mrxLttLqvoSSSPjfW1ErI6IwYgY7FN/J3oG0EGT+XTAku6QtC0ibh5TekjSqur5KkkPdr49AN02mXkCyyVdKukp21uqZddIul7SPbY/J+lFSZd0p0V0zOnvL5b/YNHX29r8X3yp/Bb4qScea2v76I6WIRARj0ryBOVzOtsOgLoxbRhIjhAAkiMEgOQIASA5QgBIjhAAkuN+AkeQWae9r1gf+kZ787lOW3N5sb7s699va/toBiMBIDlCAEiOEACSIwSA5AgBIDlCAEiOEACSY57AEeTZ3y7f9f2CuZO+K9y4jv+H/eUVItraPprBSABIjhAAkiMEgOQIASA5QgBIjhAAkiMEgOSYJzCDvHHBWcX6+gtuarGFuZ1rBkcMRgJAcoQAkBwhACRHCADJEQJAcoQAkBwhACTXcp6A7RMkfU3SeyQdlrQ6Im61fZ2kz0t6qVr1mohY161GIe1cPqtYP3F2e/MA7ty7qFjv21O+nwB3E5iZJjNZ6KCkL0TEZttHS9pk++GqdktE3Ni99gB0W8sQiIhhScPV8722t0la2u3GANRjStcEbC+TdIakDdWiK2w/aXuN7fK9rQD0pEmHgO35ku6TdFVE7JF0m6RTJA1odKQw7sR120O2N9reeED7OtAygE6aVAjY7tNoANwZEfdLUkTsiohDEXFY0u2Sxv3plohYHRGDETHYp/5O9Q2gQ1qGgG1LukPStoi4eczyJWNWu1jS1s63B6DbJvPpwHJJl0p6yvaWatk1klbaHtDoJ0PbJV3WlQ4BdNVkPh14VJLHKTEnYIb541dOK9YfO29ZsR7DT3WwG/QKZgwCyRECQHKEAJAcIQAkRwgAyRECQHKEAJCco8bfKX+MF8bZPqe2/QEYtSHWa0/sHm++DyMBIDtCAEiOEACSIwSA5AgBIDlCAEiOEACSq3WegO2XJP3HmEXHSnq5tgamjv7a08v99XJvUuf7Oyki3j1eodYQ+Imd2xsjYrCxBlqgv/b0cn+93JtUb3+cDgDJEQJAck2HwOqG998K/bWnl/vr5d6kGvtr9JoAgOY1PRIA0DBCAEiOEACSIwSA5AgBILn/BV1w3NO3uf28AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 288x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.matshow(x_test_adv[0, :].reshape((28, 28)))\n",
+    "plt.clim(0, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial Test Predicted Label: 4\n"
+     ]
+    }
+   ],
+   "source": [
+    "prediction = model.predict(x_test_adv[0:1, :])[0]\n",
+    "print(\"Adversarial Test Predicted Label: %i\" % prediction)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/adversarial-robustness-toolbox/notebooks/classifier_scikitlearn_GradientBoostingClassifier.ipynb b/adversarial-robustness-toolbox/notebooks/classifier_scikitlearn_GradientBoostingClassifier.ipynb
new file mode 100644
index 0000000..a457889
--- /dev/null
+++ b/adversarial-robustness-toolbox/notebooks/classifier_scikitlearn_GradientBoostingClassifier.ipynb
@@ -0,0 +1,654 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Adversarial-Robustness-Toolbox for scikit-learn GradientBoostingClassifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.ensemble import GradientBoostingClassifier\n",
+    "from sklearn.datasets import load_iris\n",
+    "\n",
+    "import numpy as np\n",
+    "from matplotlib import pyplot as plt\n",
+    "\n",
+    "from art.estimators.classification import SklearnClassifier\n",
+    "from art.attacks.evasion import ZooAttack\n",
+    "from art.utils import load_mnist\n",
+    "\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1 Training scikit-learn GradientBoostingClassifier and attacking with ART Zeroth Order Optimization attack"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_adversarial_examples(x_train, y_train):\n",
+    "    \n",
+    "    # Create and fit GradientBoostingClassifier\n",
+    "    model = GradientBoostingClassifier()\n",
+    "    model.fit(X=x_train, y=y_train)\n",
+    "\n",
+    "    # Create ART classifier for scikit-learn GradientBoostingClassifier\n",
+    "    art_classifier = SklearnClassifier(model=model)\n",
+    "\n",
+    "    # Create ART Zeroth Order Optimization attack\n",
+    "    zoo = ZooAttack(classifier=art_classifier, confidence=0.0, targeted=False, learning_rate=1e-1, max_iter=20,\n",
+    "                    binary_search_steps=10, initial_const=1e-3, abort_early=True, use_resize=False, \n",
+    "                    use_importance=False, nb_parallel=1, batch_size=1, variable_h=0.2)\n",
+    "\n",
+    "    # Generate adversarial samples with ART Zeroth Order Optimization attack\n",
+    "    x_train_adv = zoo.generate(x_train)\n",
+    "\n",
+    "    return x_train_adv, model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1.1 Utility functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_data(num_classes):\n",
+    "    x_train, y_train = load_iris(return_X_y=True)\n",
+    "    x_train = x_train[y_train < num_classes][:, [0, 1]]\n",
+    "    y_train = y_train[y_train < num_classes]\n",
+    "    x_train[:, 0][y_train == 0] *= 2\n",
+    "    x_train[:, 1][y_train == 2] *= 2\n",
+    "    x_train[:, 0][y_train == 0] -= 3\n",
+    "    x_train[:, 1][y_train == 2] -= 2\n",
+    "    \n",
+    "    x_train[:, 0] = (x_train[:, 0] - 4) / (9 - 4)\n",
+    "    x_train[:, 1] = (x_train[:, 1] - 1) / (6 - 1)\n",
+    "    \n",
+    "    return x_train, y_train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_results(model, x_train, y_train, x_train_adv, num_classes):\n",
+    "    \n",
+    "    fig, axs = plt.subplots(1, num_classes, figsize=(num_classes * 5, 5))\n",
+    "\n",
+    "    colors = ['orange', 'blue', 'green']\n",
+    "\n",
+    "    for i_class in range(num_classes):\n",
+    "\n",
+    "        # Plot difference vectors\n",
+    "        for i in range(y_train[y_train == i_class].shape[0]):\n",
+    "            x_1_0 = x_train[y_train == i_class][i, 0]\n",
+    "            x_1_1 = x_train[y_train == i_class][i, 1]\n",
+    "            x_2_0 = x_train_adv[y_train == i_class][i, 0]\n",
+    "            x_2_1 = x_train_adv[y_train == i_class][i, 1]\n",
+    "            if x_1_0 != x_2_0 or x_1_1 != x_2_1:\n",
+    "                axs[i_class].plot([x_1_0, x_2_0], [x_1_1, x_2_1], c='black', zorder=1)\n",
+    "\n",
+    "        # Plot benign samples\n",
+    "        for i_class_2 in range(num_classes):\n",
+    "            axs[i_class].scatter(x_train[y_train == i_class_2][:, 0], x_train[y_train == i_class_2][:, 1], s=20,\n",
+    "                                 zorder=2, c=colors[i_class_2])\n",
+    "        axs[i_class].set_aspect('equal', adjustable='box')\n",
+    "\n",
+    "        # Show predicted probability as contour plot\n",
+    "        h = .01\n",
+    "        x_min, x_max = 0, 1\n",
+    "        y_min, y_max = 0, 1\n",
+    "\n",
+    "        xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n",
+    "\n",
+    "        Z_proba = model.predict_proba(np.c_[xx.ravel(), yy.ravel()])\n",
+    "        Z_proba = Z_proba[:, i_class].reshape(xx.shape)\n",
+    "        im = axs[i_class].contourf(xx, yy, Z_proba, levels=[0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0],\n",
+    "                                   vmin=0, vmax=1)\n",
+    "        if i_class == num_classes - 1:\n",
+    "            cax = fig.add_axes([0.95, 0.2, 0.025, 0.6])\n",
+    "            plt.colorbar(im, ax=axs[i_class], cax=cax)\n",
+    "\n",
+    "        # Plot adversarial samples\n",
+    "        for i in range(y_train[y_train == i_class].shape[0]):\n",
+    "            x_1_0 = x_train[y_train == i_class][i, 0]\n",
+    "            x_1_1 = x_train[y_train == i_class][i, 1]\n",
+    "            x_2_0 = x_train_adv[y_train == i_class][i, 0]\n",
+    "            x_2_1 = x_train_adv[y_train == i_class][i, 1]\n",
+    "            if x_1_0 != x_2_0 or x_1_1 != x_2_1:\n",
+    "                axs[i_class].scatter(x_2_0, x_2_1, zorder=2, c='red', marker='X')\n",
+    "        axs[i_class].set_xlim((x_min, x_max))\n",
+    "        axs[i_class].set_ylim((y_min, y_max))\n",
+    "\n",
+    "        axs[i_class].set_title('class ' + str(i_class))\n",
+    "        axs[i_class].set_xlabel('feature 1')\n",
+    "        axs[i_class].set_ylabel('feature 2')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 2 Example: Iris dataset"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### legend\n",
+    "- colored background: probability of class i\n",
+    "- orange circles: class 1\n",
+    "- blue circles: class 2\n",
+    "- green circles: class 3\n",
+    "- red crosses: adversarial samples for class i"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "ZOO: 100%|██████████| 100/100 [00:04<00:00, 20.89it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "num_classes = 2\n",
+    "x_train, y_train = get_data(num_classes=num_classes)\n",
+    "x_train_adv, model = get_adversarial_examples(x_train, y_train)\n",
+    "plot_results(model, x_train, y_train, x_train_adv, num_classes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "ZOO: 100%|██████████| 150/150 [00:08<00:00, 17.02it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 1080x360 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "num_classes = 3\n",
+    "x_train, y_train = get_data(num_classes=num_classes)\n",
+    "x_train_adv, model = get_adversarial_examples(x_train, y_train)\n",
+    "plot_results(model, x_train, y_train, x_train_adv, num_classes)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 3 Example: MNIST"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3.1 Load and transform MNIST dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "(x_train, y_train), (x_test, y_test), min_, max_ = load_mnist()\n",
+    "\n",
+    "n_samples_train = x_train.shape[0]\n",
+    "n_features_train = x_train.shape[1] * x_train.shape[2] * x_train.shape[3]\n",
+    "n_samples_test = x_test.shape[0]\n",
+    "n_features_test = x_test.shape[1] * x_test.shape[2] * x_test.shape[3]\n",
+    "\n",
+    "x_train = x_train.reshape(n_samples_train, n_features_train)\n",
+    "x_test = x_test.reshape(n_samples_test, n_features_test)\n",
+    "\n",
+    "y_train = np.argmax(y_train, axis=1)\n",
+    "y_test = np.argmax(y_test, axis=1)\n",
+    "\n",
+    "n_samples_max = 200\n",
+    "x_train = x_train[0:n_samples_max]\n",
+    "y_train = y_train[0:n_samples_max]\n",
+    "x_test = x_test[0:n_samples_max]\n",
+    "y_test = y_test[0:n_samples_max]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3.2 Train GradientBoostingClassifier classifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = GradientBoostingClassifier(loss='deviance', learning_rate=0.1, n_estimators=100, subsample=1.0, \n",
+    "                                   criterion='friedman_mse', min_samples_split=2, min_samples_leaf=1, \n",
+    "                                   min_weight_fraction_leaf=0.0, max_depth=3, min_impurity_decrease=0.0, \n",
+    "                                   min_impurity_split=None, init=None, random_state=None, max_features=None, \n",
+    "                                   verbose=0, max_leaf_nodes=None, warm_start=False, presort='auto', \n",
+    "                                   validation_fraction=0.1, n_iter_no_change=None, tol=0.0001)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "GradientBoostingClassifier(ccp_alpha=0.0, criterion='friedman_mse', init=None,\n",
+       "                           learning_rate=0.1, loss='deviance', max_depth=3,\n",
+       "                           max_features=None, max_leaf_nodes=None,\n",
+       "                           min_impurity_decrease=0.0, min_impurity_split=None,\n",
+       "                           min_samples_leaf=1, min_samples_split=2,\n",
+       "                           min_weight_fraction_leaf=0.0, n_estimators=100,\n",
+       "                           n_iter_no_change=None, presort='auto',\n",
+       "                           random_state=None, subsample=1.0, tol=0.0001,\n",
+       "                           validation_fraction=0.1, verbose=0,\n",
+       "                           warm_start=False)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.fit(X=x_train, y=y_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3.3 Create and apply Zeroth Order Optimization Attack with ART"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "art_classifier = SklearnClassifier(model=model)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "zoo = ZooAttack(classifier=art_classifier, confidence=0.0, targeted=False, learning_rate=1e-1, max_iter=100,\n",
+    "                binary_search_steps=20, initial_const=1e-3, abort_early=True, use_resize=False, \n",
+    "                use_importance=False, nb_parallel=10, batch_size=1, variable_h=0.25)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "ZOO: 100%|██████████| 200/200 [05:41<00:00,  1.71s/it]\n"
+     ]
+    }
+   ],
+   "source": [
+    "x_train_adv = zoo.generate(x_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "ZOO: 100%|██████████| 200/200 [04:15<00:00,  1.28s/it]\n"
+     ]
+    }
+   ],
+   "source": [
+    "x_test_adv = zoo.generate(x_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3.4 Evaluate GradientBoostingClassifier on benign and adversarial samples"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Benign Training Score: 1.0000\n"
+     ]
+    }
+   ],
+   "source": [
+    "score = model.score(x_train, y_train)\n",
+    "print(\"Benign Training Score: %.4f\" % score)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQEAAAECCAYAAAD+eGJTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAO90lEQVR4nO3dbYxc5XnG8euKvdg1mMRbx45DHXCMU2igMemKFxkBFQp1o0qAKkKtKHJoWtMEJ6F1JahVFVqRyq2AlFKKZIqLkYAEAhR/oEksCwFRYYvtEjBxgARcarxdY1ZgIMTYu3c/7Ljdkt1ndndeznjv/09azcy5Z+bcPravfc6cZ85xRAhAXh+ougEA1SIEgOQIASA5QgBIjhAAkiMEgOQqCQHby20/b/sntq+uoocS27tsP2v7adtbO6CfDbb32t4xYlm37c22X6zdzumw/q61/WptGz5t+7MV9rfQ9iO2d9p+zvbXa8s7YhsW+mvLNnS75wnYnibpBUmfkbRb0lOSVkTEj9raSIHtXZJ6ImJf1b1Iku1zJL0t6c6IOKW27G8lDUTEulqQzomIqzqov2slvR0R11fR00i2F0haEBHbbc+WtE3SRZK+qA7YhoX+Pqc2bMMqRgKnS/pJRLwUEe9J+pakCyvo44gREY9JGnjf4gslbazd36jhfzSVGKO/jhERfRGxvXb/LUk7JR2nDtmGhf7aoooQOE7Sf414vFtt/AOPU0j6vu1ttldV3cwY5kdEnzT8j0jSvIr7Gc1q28/Udhcq210ZyfYJkk6T1KsO3Ibv609qwzasIgQ8yrJOm7u8LCI+Lem3JV1RG+5iYm6VtFjSUkl9km6oth3J9jGS7pd0ZUTsr7qf9xulv7ZswypCYLekhSMe/4qkPRX0MaaI2FO73SvpQQ3vwnSa/tq+5OF9yr0V9/P/RER/RAxGxJCk21TxNrTdpeH/YHdFxAO1xR2zDUfrr13bsIoQeErSEtuLbB8l6fckbaqgj1HZPrr24YxsHy3pAkk7yq+qxCZJK2v3V0p6qMJefsHh/1w1F6vCbWjbkm6XtDMibhxR6ohtOFZ/7dqGbT86IEm1Qx1/J2mapA0R8Y22NzEG2x/X8G9/SZou6e6q+7N9j6TzJM2V1C/pGkn/IuleSR+T9IqkSyKikg/nxujvPA0PY0PSLkmXH97/rqC/syU9LulZSUO1xWs1vN9d+TYs9LdCbdiGlYQAgM7BjEEgOUIASI4QAJIjBIDkCAEguUpDoIOn5Eqiv0Z1cn+d3JvU3v6qHgl09F+E6K9RndxfJ/cmtbG/qkMAQMUamixke7mkmzQ88++fImJd6flHeUbM1NH/+/igDqhLMya9/lajv8Z0cn+d3JvU/P5+rnf0XhwY7ct7kw+ByZwc5Fh3xxk+f1LrAzB5vbFF+2Ng1BBoZHeAk4MAU0AjIXAknBwEQB3TG3jtuE4OUjvUsUqSZmpWA6sD0AqNjATGdXKQiFgfET0R0dPJH8QAWTUSAh19chAA4zPp3YGIOGR7taTv6f9ODvJc0zoD0BaNfCagiHhY0sNN6gVABZgxCCRHCADJEQJAcoQAkBwhACRHCADJEQJAcoQAkBwhACRHCADJEQJAcoQAkBwhACRHCADJEQJAcoQAkBwhACRHCADJEQJAcoQAkBwhACRHCADJEQJAcoQAkBwhACRHCADJEQJAcoQAkBwhACRHCADJNXRpchxZPL381z3tw3Nbuv7n//SEYn1w1lCxfvzivcX6rK+4WP/vG48q1rf3fLtY3zf4TrF+xn1rivUT/+TJYr0qDYWA7V2S3pI0KOlQRPQ0oykA7dOMkcBvRsS+JrwPgArwmQCQXKMhEJK+b3ub7VXNaAhAezW6O7AsIvbYnidps+0fR8RjI59QC4dVkjRTsxpcHYBma2gkEBF7ard7JT0o6fRRnrM+InoioqdLMxpZHYAWmHQI2D7a9uzD9yVdIGlHsxoD0B6N7A7Ml/Sg7cPvc3dEfLcpXU1R005eUqzHjK5ifc+5HyrW3z2zfBy7+4Pl+uOfKh8nr9q//mx2sf43/7C8WO899e5i/eWD7xbr6/o/U6x/9PEo1jvVpEMgIl6S9Kkm9gKgAhwiBJIjBIDkCAEgOUIASI4QAJIjBIDkOJ9AEw2e9+li/cY7binWP9FV/r77VHcwBov1v7j5i8X69HfKx+nPum91sT771UPF+ox95XkEs7b2FuudipEAkBwhACRHCADJEQJAcoQAkBwhACRHCADJMU+giWY8v6dY3/bzhcX6J7r6m9lO063pO7NYf+nt8nUL7lj8nWL9zaHycf75f/9vxXqrHZlnC6iPkQCQHCEAJEcIAMkRAkByhACQHCEAJEcIAMk5on1HP491d5zh89u2vk4zcNlZxfr+5eXrAkx75phi/YdfuXnCPY103b5fL9afOrc8D2DwjTeL9TirfIb6XV8rlrVoxQ/LT8CYemOL9seAR6sxEgCSIwSA5AgBIDlCAEiOEACSIwSA5AgBIDnmCXSQaXN/uVgffH2gWH/57vJx/ufO2VCsn/7XXy3W591S7ff5MXkNzROwvcH2Xts7Rizrtr3Z9ou12znNbBhA+4xnd+AOScvft+xqSVsiYomkLbXHAI5AdUMgIh6T9P5x6IWSNtbub5R0UZP7AtAmk/1gcH5E9ElS7XZe81oC0E4tP9Go7VWSVknSTM1q9eoATNBkRwL9thdIUu1271hPjIj1EdETET1dmjHJ1QFolcmGwCZJK2v3V0p6qDntAGi3ursDtu+RdJ6kubZ3S7pG0jpJ99r+kqRXJF3SyiazGNz3ekOvP7j/qIZe/8nP/6hYf+3WaeU3GBpsaP2oRt0QiIgVY5SY9QNMAUwbBpIjBIDkCAEgOUIASI4QAJIjBIDkWj5tGO1z8lUvFOuXnVo+qvvPx28p1s+95Ipiffa3nyzW0ZkYCQDJEQJAcoQAkBwhACRHCADJEQJAcoQAkBzzBKaQwTfeLNZf//LJxform94t1q++7s5i/c8+d3GxHv/xwWJ94TeeKNbVxmtkZMJIAEiOEACSIwSA5AgBIDlCAEiOEACSIwSA5BxtPPZ6rLvjDHOm8k418PtnFet3XXN9sb5o+syG1v/JO1cX60tu6yvWD720q6H1T2W9sUX7Y8Cj1RgJAMkRAkByhACQHCEAJEcIAMkRAkByhACQHPMEMG6xbGmxfuy63cX6PR//XkPrP+mRPyjWf/Uvy+dTGHzxpYbWfyRraJ6A7Q2299reMWLZtbZftf107eezzWwYQPuMZ3fgDknLR1n+zYhYWvt5uLltAWiXuiEQEY9JGmhDLwAq0MgHg6ttP1PbXZjTtI4AtNVkQ+BWSYslLZXUJ+mGsZ5oe5Xtrba3HtSBSa4OQKtMKgQioj8iBiNiSNJtkk4vPHd9RPRERE+XZky2TwAtMqkQsL1gxMOLJe0Y67kAOlvdeQK275F0nqS5kvolXVN7vFRSSNol6fKIKH/ZW8wTmOqmzZ9XrO+59MRivfeqm4r1D9T5nfX5ly8o1t88+/VifSorzROoe/GRiFgxyuLbG+4KQEdg2jCQHCEAJEcIAMkRAkByhACQHCEAJMf5BNAx7t39RLE+y0cV6z+L94r13/nqleX3f7C3WD+Scd0BAGMiBIDkCAEgOUIASI4QAJIjBIDkCAEgubpfJQYOGzq7fN2Bn14ys1g/ZemuYr3ePIB6bh44rfz+D21t6P2nKkYCQHKEAJAcIQAkRwgAyRECQHKEAJAcIQAkxzyBRNxzSrH+wtfKx+lvW7axWD9nZvn7/I06EAeL9ScHFpXfYKjupTFSYiQAJEcIAMkRAkByhACQHCEAJEcIAMkRAkByzBM4gkxfdHyx/tPLPlqsX3vpt4r13z1m34R7aqa1/T3F+qM3nVmsz9lYvm4BRld3JGB7oe1HbO+0/Zztr9eWd9vebPvF2u2c1rcLoNnGsztwSNKaiDhZ0pmSrrD9a5KulrQlIpZI2lJ7DOAIUzcEIqIvIrbX7r8laaek4yRdKOnwPNKNki5qVZMAWmdCHwzaPkHSaZJ6Jc2PiD5pOCgkzWt2cwBab9whYPsYSfdLujIi9k/gdatsb7W99aAOTKZHAC00rhCw3aXhALgrIh6oLe63vaBWXyBp72ivjYj1EdETET1dmtGMngE00XiODljS7ZJ2RsSNI0qbJK2s3V8p6aHmtweg1cYzT2CZpC9Ietb207VlayWtk3Sv7S9JekXSJa1pceqYfsLHivU3f2NBsX7pX323WP+jDz1QrLfamr7ycfwn/rE8D6D7jn8v1ucMMQ+gFeqGQET8QJLHKJ/f3HYAtBvThoHkCAEgOUIASI4QAJIjBIDkCAEgOc4nMAHTF3ykWB/YcHSx/uVFjxbrK2b3T7inZlr96tnF+vZblxbrc7+zo1jvfovj/J2IkQCQHCEAJEcIAMkRAkByhACQHCEAJEcIAMmlmifw3m+Vv8/+3h8PFOtrT3y4WL/gl96ZcE/N1D/4brF+zqY1xfpJf/7jYr37jfJx/qFiFZ2KkQCQHCEAJEcIAMkRAkByhACQHCEAJEcIAMmlmiew66Jy5r1w6n0tXf8tbywu1m969IJi3YNjnfl92EnXvVysL+nvLdYHi1VMVYwEgOQIASA5QgBIjhAAkiMEgOQIASA5QgBIzhFRfoK9UNKdkj6i4a+Mr4+Im2xfK+kPJb1We+raiCh+4f5Yd8cZ5mrmQLv1xhbtj4FRJ5qMZ7LQIUlrImK77dmSttneXKt9MyKub1ajANqvbghERJ+kvtr9t2zvlHRcqxsD0B4T+kzA9gmSTpN0eP7patvP2N5ge06TewPQBuMOAdvHSLpf0pURsV/SrZIWS1qq4ZHCDWO8bpXtrba3HtSBJrQMoJnGFQK2uzQcAHdFxAOSFBH9ETEYEUOSbpN0+mivjYj1EdETET1dmtGsvgE0Sd0QsG1Jt0vaGRE3jli+YMTTLpZUviQtgI40nqMDyyR9QdKztp+uLVsraYXtpZJC0i5Jl7ekQwAtNZ6jAz+QNNrxxfJJ+AEcEZgxCCRHCADJEQJAcoQAkBwhACRHCADJEQJAcoQAkBwhACRHCADJEQJAcoQAkBwhACRHCADJEQJAcnWvO9DUldmvSfrPEYvmStrXtgYmjv4a08n9dXJvUvP7Oz4iPjxaoa0h8Asrt7dGRE9lDdRBf43p5P46uTepvf2xOwAkRwgAyVUdAusrXn899NeYTu6vk3uT2thfpZ8JAKhe1SMBABUjBIDkCAEgOUIASI4QAJL7H4v8SYP7urYSAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 288x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.matshow(x_train[0, :].reshape((28, 28)))\n",
+    "plt.clim(0, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Benign Training Predicted Label: 5\n"
+     ]
+    }
+   ],
+   "source": [
+    "prediction = model.predict(x_train[0:1, :])[0]\n",
+    "print(\"Benign Training Predicted Label: %i\" % prediction)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial Training Score: 0.0050\n"
+     ]
+    }
+   ],
+   "source": [
+    "score = model.score(x_train_adv, y_train)\n",
+    "print(\"Adversarial Training Score: %.4f\" % score)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.matshow(x_train_adv[0, :].reshape((28, 28)))\n",
+    "plt.clim(0, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial Training Predicted Label: 6\n"
+     ]
+    }
+   ],
+   "source": [
+    "prediction = model.predict(x_train_adv[0:1, :])[0]\n",
+    "print(\"Adversarial Training Predicted Label: %i\" % prediction)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Benign Test Score: 0.5800\n"
+     ]
+    }
+   ],
+   "source": [
+    "score = model.score(x_test, y_test)\n",
+    "print(\"Benign Test Score: %.4f\" % score)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.matshow(x_test[0, :].reshape((28, 28)))\n",
+    "plt.clim(0, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Benign Test Predicted Label: 7\n"
+     ]
+    }
+   ],
+   "source": [
+    "prediction = model.predict(x_test[0:1, :])[0]\n",
+    "print(\"Benign Test Predicted Label: %i\" % prediction)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial Test Score: 0.1500\n"
+     ]
+    }
+   ],
+   "source": [
+    "score = model.score(x_test_adv, y_test)\n",
+    "print(\"Adversarial Test Score: %.4f\" % score)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.matshow(x_test_adv[0, :].reshape((28, 28)))\n",
+    "plt.clim(0, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial Test Predicted Label: 2\n"
+     ]
+    }
+   ],
+   "source": [
+    "prediction = model.predict(x_test_adv[0:1, :])[0]\n",
+    "print(\"Adversarial Test Predicted Label: %i\" % prediction)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/adversarial-robustness-toolbox/notebooks/classifier_scikitlearn_LogisticRegression.ipynb b/adversarial-robustness-toolbox/notebooks/classifier_scikitlearn_LogisticRegression.ipynb
new file mode 100644
index 0000000..71d2c4f
--- /dev/null
+++ b/adversarial-robustness-toolbox/notebooks/classifier_scikitlearn_LogisticRegression.ipynb
@@ -0,0 +1,549 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Adversarial-Robustness-Toolbox for scikit-learn LogisticRegression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LogisticRegression\n",
+    "\n",
+    "import numpy as np\n",
+    "from matplotlib import pyplot as plt\n",
+    "\n",
+    "from art.estimators.classification import SklearnClassifier\n",
+    "from art.attacks.evasion import ProjectedGradientDescent\n",
+    "from art.utils import load_mnist\n",
+    "\n",
+    "import warnings\n",
+    "warnings.simplefilter('ignore')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Load and transform MNIST dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "(x_train, y_train), (x_test, y_test), min_, max_ = load_mnist()\n",
+    "\n",
+    "n_samples_train = x_train.shape[0]\n",
+    "n_features_train = x_train.shape[1] * x_train.shape[2] * x_train.shape[3]\n",
+    "n_samples_test = x_test.shape[0]\n",
+    "n_features_test = x_test.shape[1] * x_test.shape[2] * x_test.shape[3]\n",
+    "\n",
+    "x_train = x_train.reshape(n_samples_train, n_features_train)\n",
+    "x_test = x_test.reshape(n_samples_test, n_features_test)\n",
+    "\n",
+    "y_train = np.argmax(y_train, axis=1)\n",
+    "y_test = np.argmax(y_test, axis=1)\n",
+    "\n",
+    "n_samples_max = 100\n",
+    "x_train = x_train[0:n_samples_max]\n",
+    "y_train = y_train[0:n_samples_max]\n",
+    "x_test = x_test[0:n_samples_max]\n",
+    "y_test = y_test[0:n_samples_max]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Train LogisticRegression classifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = LogisticRegression(penalty='l2', dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, \n",
+    "                           class_weight='balanced', random_state=None, solver='lbfgs', max_iter=100, \n",
+    "                           multi_class='ovr', verbose=0, warm_start=False, n_jobs=None)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LogisticRegression(C=1.0, class_weight='balanced', dual=False,\n",
+       "                   fit_intercept=True, intercept_scaling=1, l1_ratio=None,\n",
+       "                   max_iter=100, multi_class='ovr', n_jobs=None, penalty='l2',\n",
+       "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
+       "                   warm_start=False)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.fit(X=x_train, y=y_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Create and apply ProjectedGradientDescent Attack with ART"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "art_classifier = SklearnClassifier(model=model)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pgd = ProjectedGradientDescent(estimator=art_classifier, norm=np.inf, eps=.3, eps_step=0.1, max_iter=20, \n",
+    "                               targeted=False, num_random_init=0, batch_size=128)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "x_train_adv = pgd.generate(x_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_test_adv = pgd.generate(x_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Evaluate LogisticRegression classifier on benign and adversarial samples"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Benign Training Score: 1.0000\n"
+     ]
+    }
+   ],
+   "source": [
+    "score = model.score(x_train, y_train)\n",
+    "print(\"Benign Training Score: %.4f\" % score)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.matshow(x_train[0, :].reshape((28, 28)))\n",
+    "plt.clim(0, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Benign Training Predicted Label: 5\n"
+     ]
+    }
+   ],
+   "source": [
+    "prediction = model.predict(x_train[0:1, :])[0]\n",
+    "print(\"Benign Training Predicted Label: %i\" % prediction)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial Training Score: 0.2400\n"
+     ]
+    }
+   ],
+   "source": [
+    "score = model.score(x_train_adv, y_train)\n",
+    "print(\"Adversarial Training Score: %.4f\" % score)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.matshow(x_train_adv[0, :].reshape((28, 28)))\n",
+    "plt.clim(0, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial Training Predicted Label: 7\n"
+     ]
+    }
+   ],
+   "source": [
+    "prediction = model.predict(x_train_adv[0:1, :])[0]\n",
+    "print(\"Adversarial Training Predicted Label: %i\" % prediction)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Benign Test Score: 0.7000\n"
+     ]
+    }
+   ],
+   "source": [
+    "score = model.score(x_test, y_test)\n",
+    "print(\"Benign Test Score: %.4f\" % score)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQEAAAECCAYAAAD+eGJTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAODklEQVR4nO3df4xc5XXG8eeJvazjtWnsOHZcY3BDSBSSBlNtIJHbyhElJYmQQQltLNVypTSLWpCgitoiSxGW2qYU8aO0aZFMceNEhoTGUFDiprGstBSVOtiWAYNpTalLHW+9gNPaBPDP0z/2mm7J7ju7Oz/urM/3I61m5p479x5fzz773pl37zoiBCCvt9XdAIB6EQJAcoQAkBwhACRHCADJEQJAcrWEgO0rbP+L7edt31RHDyW299l+2vYu29u7oJ/1tods7x6xbK7tLbb3Vrdzuqy/tbZ/WB3DXbY/VWN/i21/3/Ye28/YvqFa3hXHsNBfR46hOz1PwPY0Sf8q6XJJ+yU9IWllRDzb0UYKbO+T1B8RL9fdiyTZ/kVJr0r6WkR8qFp2q6RDEXFLFaRzIuL3uqi/tZJejYjb6uhpJNsLJS2MiJ22Z0vaIekqSb+uLjiGhf5+RR04hnWMBC6R9HxEvBARxyR9Q9KKGvqYMiLiUUmH3rJ4haQN1f0NGn7R1GKM/rpGRAxGxM7q/hFJeyQtUpccw0J/HVFHCCyS9J8jHu9XB//B4xSSvmd7h+2BupsZw4KIGJSGX0SS5tfcz2iut/1UdbpQ2+nKSLaXSLpY0jZ14TF8S39SB45hHSHgUZZ129zlZRHxc5I+Kem6ariLiblb0vmSlkoalHR7ve1ItmdJ2iTpxog4XHc/bzVKfx05hnWEwH5Ji0c8PkfSgRr6GFNEHKhuhyQ9pOFTmG5zsDqXPH1OOVRzP/9PRByMiJMRcUrSPar5GNru0fA32MaIeLBa3DXHcLT+OnUM6wiBJyRdYPtnbJ8l6XOSHqmhj1HZ7qvenJHtPkmfkLS7/KxaPCJpdXV/taSHa+zlJ5z+5qpcrRqPoW1LulfSnoi4Y0SpK47hWP116hh2/NMBSao+6vgTSdMkrY+IP+x4E2Ow/R4N//SXpOmS7qu7P9v3S1ouaZ6kg5JulvQ3kh6QdK6kFyVdExG1vDk3Rn/LNTyMDUn7JF17+vy7hv5+XtI/Snpa0qlq8RoNn3fXfgwL/a1UB45hLSEAoHswYxBIjhAAkiMEgOQIASA5QgBIrtYQ6OIpuZLor1nd3F839yZ1tr+6RwJd/R8h+mtWN/fXzb1JHeyv7hAAULOmJgvZvkLSXRqe+feXEXFLaf2z3Bsz1Pfm4+M6qh71Tnr/7UZ/zenm/rq5N6n1/b2hH+tYHB3tl/cmHwKTuTjI2Z4bl/qySe0PwORti606HIdGDYFmTge4OAhwBmgmBKbCxUEANDC9ieeO6+Ig1UcdA5I0QzOb2B2AdmhmJDCui4NExLqI6I+I/m5+IwbIqpkQ6OqLgwAYn0mfDkTECdvXS/o7/d/FQZ5pWWcAOqKZ9wQUEZslbW5RLwBqwIxBIDlCAEiOEACSIwSA5AgBIDlCAEiOEACSIwSA5AgBIDlCAEiOEACSIwSA5AgBIDlCAEiOEACSIwSA5AgBIDlCAEiOEACSIwSA5AgBIDlCAEiOEACSIwSA5AgBIDlCAEiOEACSIwSA5AgBIDlCAEiOEACSm97Mk23vk3RE0klJJyKivxVNAeicpkKg8vGIeLkF2wFQA04HgOSaDYGQ9D3bO2wPtKIhAJ3V7OnAsog4YHu+pC22n4uIR0euUIXDgCTN0Mwmdweg1ZoaCUTEgep2SNJDki4ZZZ11EdEfEf096m1mdwDaYNIhYLvP9uzT9yV9QtLuVjUGoDOaOR1YIOkh26e3c19EfLclXQHomEmHQES8IOmiFvYCoAZ8RAgkRwgAyRECQHKEAJAcIQAkRwgAybXitwjTeOULHyvWz131fLH+3NCCYv3Y0Z5ifdH95frM/a8W66d2PVusIydGAkByhACQHCEAJEcIAMkRAkByhACQHCEAJMc8gQn43d+5r1j/TN+Pyhs4v8kGlpfL+068Vqzf9dLHm2xgavvB0HnFet/tP1WsT9+6o5XtdA1GAkByhACQHCEAJEcIAMkRAkByhACQHCEAJOeI6NjOzvbcuNSXdWx/rfbjz15arL/84XKmztlTPtY/+oCL9bM+/N/F+q0ferBYv/ztrxfr33ltVrH+6Znl6xU06/U4VqxvO9pXrC+fcbyp/b/3O9cW6+8beKKp7ddpW2zV4Tg06guMkQCQHCEAJEcIAMkRAkByhACQHCEAJEcIAMlxPYEJ6PvWtgb15rZ/dnNP15+9e3mx/gfLlpT3/w/lv5tw6/L3TrCjiZn++qlive+pwWL9nY9uKtZ/9qwGf7dhX7l+pmo4ErC93vaQ7d0jls21vcX23up2TnvbBNAu4zkd+KqkK96y7CZJWyPiAklbq8cApqCGIRARj0o69JbFKyRtqO5vkHRVi/sC0CGTfWNwQUQMSlJ1O791LQHopLa/MWh7QNKAJM3QzHbvDsAETXYkcND2QkmqbofGWjEi1kVEf0T096h3krsD0C6TDYFHJK2u7q+W9HBr2gHQaQ1PB2zfr+Er3s+zvV/SzZJukfSA7c9LelHSNe1sEuNz4r8OFut9m8r1kw223/etVybYUWsd/I2PFesfPKv8cr7t0PuL9SV/9UKxfqJYnboahkBErByjNHWvDgLgTUwbBpIjBIDkCAEgOUIASI4QAJIjBIDkuJ4Ausb08xYX619Z85VivcfTivW/vuuXivV3Dj5erJ+pGAkAyRECQHKEAJAcIQAkRwgAyRECQHKEAJAc8wTQNZ777UXF+kd6Xaw/c+z1Yn3us69NuKcMGAkAyRECQHKEAJAcIQAkRwgAyRECQHKEAJAc8wTQMUc//ZFifedn72ywhfJfsPrNG24o1t/+Tz9osP2cGAkAyRECQHKEAJAcIQAkRwgAyRECQHKEAJAc8wTQMS9+svwzZ5bL8wBW/vvlxfrM7z5ZrEexmlfDkYDt9baHbO8esWyt7R/a3lV9faq9bQJol/GcDnxV0hWjLL8zIpZWX5tb2xaATmkYAhHxqKRDHegFQA2aeWPwettPVacLc1rWEYCOmmwI3C3pfElLJQ1Kun2sFW0P2N5ue/txHZ3k7gC0y6RCICIORsTJiDgl6R5JlxTWXRcR/RHR39Pgt8AAdN6kQsD2whEPr5a0e6x1AXS3hvMEbN8vabmkebb3S7pZ0nLbSzX80es+Sde2sUdMEW+bPbtYX/ULjxXrh0+9UawPffk9xXrv0SeKdYyuYQhExMpRFt/bhl4A1IBpw0ByhACQHCEAJEcIAMkRAkByhACQHNcTQMvsXfvBYv3b8/6iWF+x9zPFeu9m5gG0AyMBIDlCAEiOEACSIwSA5AgBIDlCAEiOEACSY54Axu1/fu2jxfpTv/qnxfq/nTherL/6x+cU670aLNYxOYwEgOQIASA5QgBIjhAAkiMEgOQIASA5QgBIjnkCeNP0RT9drN/4pW8W670uv5w+9+SqYv1df8v1AurASABIjhAAkiMEgOQIASA5QgBIjhAAkiMEgOSYJ5CIp5f/uy/69v5i/ZpZrxTrG4/ML9YXfKn8M+dUsYp2aTgSsL3Y9vdt77H9jO0bquVzbW+xvbe6ndP+dgG02nhOB05I+mJEfEDSRyVdZ/tCSTdJ2hoRF0jaWj0GMMU0DIGIGIyIndX9I5L2SFokaYWkDdVqGyRd1a4mAbTPhN4YtL1E0sWStklaEBGD0nBQSCqfEALoSuMOAduzJG2SdGNEHJ7A8wZsb7e9/biOTqZHAG00rhCw3aPhANgYEQ9Wiw/aXljVF0oaGu25EbEuIvojor9Hva3oGUALjefTAUu6V9KeiLhjROkRSaur+6slPdz69gC023jmCSyTtErS07Z3VcvWSLpF0gO2Py/pRUnXtKdFtMxF7y+Wf3/+15va/J9/ufwSeMeTjze1fbRHwxCIiMckeYzyZa1tB0CnMW0YSI4QAJIjBIDkCAEgOUIASI4QAJLjegJnkGkXvq9YH/hGc/O5Llx/XbG+5Ov/3NT2UQ9GAkByhACQHCEAJEcIAMkRAkByhACQHCEAJMc8gTPIc79Vvur7lTPHfVW4UZ3z98fKK0Q0tX3Ug5EAkBwhACRHCADJEQJAcoQAkBwhACRHCADJMU9gCnnjykuK9a1X3t5gCzNb1wzOGIwEgOQIASA5QgBIjhAAkiMEgOQIASA5QgBIruE8AduLJX1N0rslnZK0LiLusr1W0hckvVStuiYiNrerUUgHlk0r1s+d3tw8gI1H5hfrPYfL1xPgagJT03gmC52Q9MWI2Gl7tqQdtrdUtTsj4rb2tQeg3RqGQEQMShqs7h+xvUfSonY3BqAzJvSegO0lki6WtK1adL3tp2yvt12+thWArjTuELA9S9ImSTdGxGFJd0s6X9JSDY8URp24bnvA9nbb24/raAtaBtBK4woB2z0aDoCNEfGgJEXEwYg4GRGnJN0jadTfbomIdRHRHxH9PeptVd8AWqRhCNi2pHsl7YmIO0YsXzhitasl7W59ewDabTyfDiyTtErS07Z3VcvWSFppe6mGPxnaJ+natnQIoK3G8+nAY5I8Sok5AVPMH71yYbH++C8vKdZj8OkWdoNuwYxBIDlCAEiOEACSIwSA5AgBIDlCAEiOEACSc3Twb8qf7blxqS/r2P4ADNsWW3U4Do0234eRAJAdIQAkRwgAyRECQHKEAJAcIQAkRwgAyXV0noDtlyT9x4hF8yS93LEGJo7+mtPN/XVzb1Lr+zsvIt41WqGjIfATO7e3R0R/bQ00QH/N6eb+urk3qbP9cToAJEcIAMnVHQLrat5/I/TXnG7ur5t7kzrYX63vCQCoX90jAQA1IwSA5AgBIDlCAEiOEACS+1/8tsxjstIf5QAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 288x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.matshow(x_test[0, :].reshape((28, 28)))\n",
+    "plt.clim(0, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Benign Test Predicted Label: 7\n"
+     ]
+    }
+   ],
+   "source": [
+    "prediction = model.predict(x_test[0:1, :])[0]\n",
+    "print(\"Benign Test Predicted Label: %i\" % prediction)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial Test Score: 0.1000\n"
+     ]
+    }
+   ],
+   "source": [
+    "score = model.score(x_test_adv, y_test)\n",
+    "print(\"Adversarial Test Score: %.4f\" % score)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.matshow(x_test_adv[0, :].reshape((28, 28)))\n",
+    "plt.clim(0, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial Test Predicted Label: 9\n"
+     ]
+    }
+   ],
+   "source": [
+    "prediction = model.predict(x_test_adv[0:1, :])[0]\n",
+    "print(\"Adversarial Test Predicted Label: %i\" % prediction)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Investigate dependence on attack budget eps"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "eps_list = [0.1, 0.2, 0.3, 0.4, 0.5]\n",
+    "score_list = list()\n",
+    "\n",
+    "for eps in eps_list:\n",
+    "    pgd = ProjectedGradientDescent(estimator=art_classifier, norm=np.inf, eps=eps, eps_step=0.05, max_iter=20, \n",
+    "                                   targeted=False, num_random_init=0, batch_size=128)\n",
+    "    x_test_adv = pgd.generate(x_test)\n",
+    "    score = model.score(x_test_adv, y_test)\n",
+    "    score_list.append(score)\n",
+    "\n",
+    "plt.plot(eps_list, score_list)\n",
+    "plt.xlabel('eps')\n",
+    "plt.ylabel('Test Accuracy')\n",
+    "plt.ylim((0, 1));"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Targeted PGD attack"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pgd = ProjectedGradientDescent(estimator=art_classifier, norm=np.inf, eps=0.5, eps_step=0.01, max_iter=50, \n",
+    "                               targeted=True, num_random_init=3, batch_size=128)\n",
+    "y_test_target = np.zeros((y_test.shape[0], 10))\n",
+    "target_label = 7\n",
+    "y_test_target[:, target_label] = 1\n",
+    "x_test_adv = pgd.generate(x_test, y=y_test_target)\n",
+    "score = model.score(x_test_adv, y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Targeted Adversarial Test Score: 0.2100\n"
+     ]
+    }
+   ],
+   "source": [
+    "score = model.score(x_test_adv, np.argmax(y_test_target, axis=1))\n",
+    "print(\"Targeted Adversarial Test Score: %.4f\" % score)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.matshow(x_test_adv[16, :].reshape((28, 28)))\n",
+    "plt.clim(0, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Target Label: 7\n",
+      "Predicted Label: 7\n"
+     ]
+    }
+   ],
+   "source": [
+    "prediction = model.predict(x_test_adv[16:17, :])[0]\n",
+    "print(\"Target Label: %i\" % target_label)\n",
+    "print(\"Predicted Label: %i\" % prediction)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/adversarial-robustness-toolbox/notebooks/classifier_scikitlearn_RandomForestClassifier.ipynb b/adversarial-robustness-toolbox/notebooks/classifier_scikitlearn_RandomForestClassifier.ipynb
new file mode 100644
index 0000000..fca698b
--- /dev/null
+++ b/adversarial-robustness-toolbox/notebooks/classifier_scikitlearn_RandomForestClassifier.ipynb
@@ -0,0 +1,632 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Adversarial-Robustness-Toolbox for scikit-learn RandomForestClassifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "from sklearn.datasets import load_iris\n",
+    "\n",
+    "import numpy as np\n",
+    "from matplotlib import pyplot as plt\n",
+    "\n",
+    "from art.estimators.classification import SklearnClassifier\n",
+    "from art.attacks.evasion import ZooAttack\n",
+    "from art.utils import load_mnist\n",
+    "\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1 Training scikit-learn RandomForestClassifier and attacking with ART Zeroth Order Optimization attack"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_adversarial_examples(x_train, y_train):\n",
+    "    \n",
+    "    # Create and fit RandomForestClassifier\n",
+    "    model = RandomForestClassifier()\n",
+    "    model.fit(X=x_train, y=y_train)\n",
+    "\n",
+    "    # Create ART classfier for scikit-learn RandomForestClassifier\n",
+    "    art_classifier = SklearnClassifier(model=model)\n",
+    "\n",
+    "    # Create ART Zeroth Order Optimization attack\n",
+    "    zoo = ZooAttack(classifier=art_classifier, confidence=0.0, targeted=False, learning_rate=1e-1, max_iter=20,\n",
+    "                    binary_search_steps=10, initial_const=1e-3, abort_early=True, use_resize=False, \n",
+    "                    use_importance=False, nb_parallel=1, batch_size=1, variable_h=0.2)\n",
+    "\n",
+    "    # Generate adversarial samples with ART Zeroth Order Optimization attack\n",
+    "    x_train_adv = zoo.generate(x_train)\n",
+    "\n",
+    "    return x_train_adv, model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1.1 Utility functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_data(num_classes):\n",
+    "    x_train, y_train = load_iris(return_X_y=True)\n",
+    "    x_train = x_train[y_train < num_classes][:, [0, 1]]\n",
+    "    y_train = y_train[y_train < num_classes]\n",
+    "    x_train[:, 0][y_train == 0] *= 2\n",
+    "    x_train[:, 1][y_train == 2] *= 2\n",
+    "    x_train[:, 0][y_train == 0] -= 3\n",
+    "    x_train[:, 1][y_train == 2] -= 2\n",
+    "    \n",
+    "    x_train[:, 0] = (x_train[:, 0] - 4) / (9 - 4)\n",
+    "    x_train[:, 1] = (x_train[:, 1] - 1) / (6 - 1)\n",
+    "    \n",
+    "    return x_train, y_train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_results(model, x_train, y_train, x_train_adv, num_classes):\n",
+    "    fig, axs = plt.subplots(1, num_classes, figsize=(num_classes * 5, 5))\n",
+    "\n",
+    "    colors = ['orange', 'blue', 'green']\n",
+    "\n",
+    "    for i_class in range(num_classes):\n",
+    "\n",
+    "        # Plot difference vectors\n",
+    "        for i in range(y_train[y_train == i_class].shape[0]):\n",
+    "            x_1_0 = x_train[y_train == i_class][i, 0]\n",
+    "            x_1_1 = x_train[y_train == i_class][i, 1]\n",
+    "            x_2_0 = x_train_adv[y_train == i_class][i, 0]\n",
+    "            x_2_1 = x_train_adv[y_train == i_class][i, 1]\n",
+    "            if x_1_0 != x_2_0 or x_1_1 != x_2_1:\n",
+    "                axs[i_class].plot([x_1_0, x_2_0], [x_1_1, x_2_1], c='black', zorder=1)\n",
+    "\n",
+    "        # Plot benign samples\n",
+    "        for i_class_2 in range(num_classes):\n",
+    "            axs[i_class].scatter(x_train[y_train == i_class_2][:, 0], x_train[y_train == i_class_2][:, 1], s=20,\n",
+    "                                 zorder=2, c=colors[i_class_2])\n",
+    "        axs[i_class].set_aspect('equal', adjustable='box')\n",
+    "\n",
+    "        # Show predicted probability as contour plot\n",
+    "        h = .01\n",
+    "        x_min, x_max = 0, 1\n",
+    "        y_min, y_max = 0, 1\n",
+    "\n",
+    "        xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n",
+    "\n",
+    "        Z_proba = model.predict_proba(np.c_[xx.ravel(), yy.ravel()])\n",
+    "        Z_proba = Z_proba[:, i_class].reshape(xx.shape)\n",
+    "        im = axs[i_class].contourf(xx, yy, Z_proba, levels=[0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0],\n",
+    "                                   vmin=0, vmax=1)\n",
+    "        if i_class == num_classes - 1:\n",
+    "            cax = fig.add_axes([0.95, 0.2, 0.025, 0.6])\n",
+    "            plt.colorbar(im, ax=axs[i_class], cax=cax)\n",
+    "\n",
+    "        # Plot adversarial samples\n",
+    "        for i in range(y_train[y_train == i_class].shape[0]):\n",
+    "            x_1_0 = x_train[y_train == i_class][i, 0]\n",
+    "            x_1_1 = x_train[y_train == i_class][i, 1]\n",
+    "            x_2_0 = x_train_adv[y_train == i_class][i, 0]\n",
+    "            x_2_1 = x_train_adv[y_train == i_class][i, 1]\n",
+    "            if x_1_0 != x_2_0 or x_1_1 != x_2_1:\n",
+    "                axs[i_class].scatter(x_2_0, x_2_1, zorder=2, c='red', marker='X')\n",
+    "        axs[i_class].set_xlim((x_min, x_max))\n",
+    "        axs[i_class].set_ylim((y_min, y_max))\n",
+    "\n",
+    "        axs[i_class].set_title('class ' + str(i_class))\n",
+    "        axs[i_class].set_xlabel('feature 1')\n",
+    "        axs[i_class].set_ylabel('feature 2')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 2 Example: Iris dataset"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### legend\n",
+    "- colored background: probability of class i\n",
+    "- orange circles: class 1\n",
+    "- blue circles: class 2\n",
+    "- green circles: class 3\n",
+    "- red crosses: adversarial samples for class i"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "ZOO: 100%|██████████| 100/100 [01:27<00:00,  1.14it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "num_classes = 2\n",
+    "x_train, y_train = get_data(num_classes=num_classes)\n",
+    "x_train_adv, model = get_adversarial_examples(x_train, y_train)\n",
+    "plot_results(model, x_train, y_train, x_train_adv, num_classes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "ZOO: 100%|██████████| 150/150 [02:09<00:00,  1.15it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 1080x360 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "num_classes = 3\n",
+    "x_train, y_train = get_data(num_classes=num_classes)\n",
+    "x_train_adv, model = get_adversarial_examples(x_train, y_train)\n",
+    "plot_results(model, x_train, y_train, x_train_adv, num_classes)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 3 Example: MNIST"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3.1 Load and transform MNIST dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "(x_train, y_train), (x_test, y_test), min_, max_ = load_mnist()\n",
+    "\n",
+    "n_samples_train = x_train.shape[0]\n",
+    "n_features_train = x_train.shape[1] * x_train.shape[2] * x_train.shape[3]\n",
+    "n_samples_test = x_test.shape[0]\n",
+    "n_features_test = x_test.shape[1] * x_test.shape[2] * x_test.shape[3]\n",
+    "\n",
+    "x_train = x_train.reshape(n_samples_train, n_features_train)\n",
+    "x_test = x_test.reshape(n_samples_test, n_features_test)\n",
+    "\n",
+    "y_train = np.argmax(y_train, axis=1)\n",
+    "y_test = np.argmax(y_test, axis=1)\n",
+    "\n",
+    "n_samples_max = 200\n",
+    "x_train = x_train[0:n_samples_max]\n",
+    "y_train = y_train[0:n_samples_max]\n",
+    "x_test = x_test[0:n_samples_max]\n",
+    "y_test = y_test[0:n_samples_max]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3.2 Train RandomForestClassifier classifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = RandomForestClassifier(n_estimators=10, criterion='gini', max_depth=None, min_samples_split=2, \n",
+    "                               min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features='auto', \n",
+    "                               max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, \n",
+    "                               bootstrap=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, \n",
+    "                               warm_start=False, class_weight=None)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.fit(X=x_train, y=y_train);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3.3 Create and apply Zeroth Order Optimization Attack with ART"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "art_classifier = SklearnClassifier(model=model)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "zoo = ZooAttack(classifier=art_classifier, confidence=0.0, targeted=False, learning_rate=1e-1, max_iter=100,\n",
+    "                binary_search_steps=20, initial_const=1e-3, abort_early=True, use_resize=False, \n",
+    "                use_importance=False, nb_parallel=10, batch_size=1, variable_h=0.25)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "ZOO: 100%|██████████| 200/200 [09:27<00:00,  2.84s/it]\n"
+     ]
+    }
+   ],
+   "source": [
+    "x_train_adv = zoo.generate(x_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "ZOO: 100%|██████████| 200/200 [05:05<00:00,  1.53s/it]\n"
+     ]
+    }
+   ],
+   "source": [
+    "x_test_adv = zoo.generate(x_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3.4 Evaluate RandomForestClassifier on benign and adversarial samples"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Benign Training Score: 1.0000\n"
+     ]
+    }
+   ],
+   "source": [
+    "score = model.score(x_train, y_train)\n",
+    "print(\"Benign Training Score: %.4f\" % score)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.matshow(x_train[0, :].reshape((28, 28)))\n",
+    "plt.clim(0, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Benign Training Predicted Label: 5\n"
+     ]
+    }
+   ],
+   "source": [
+    "prediction = model.predict(x_train[0:1, :])[0]\n",
+    "print(\"Benign Training Predicted Label: %i\" % prediction)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial Training Score: 0.2850\n"
+     ]
+    }
+   ],
+   "source": [
+    "score = model.score(x_train_adv, y_train)\n",
+    "print(\"Adversarial Training Score: %.4f\" % score)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.matshow(x_train_adv[0, :].reshape((28, 28)))\n",
+    "plt.clim(0, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial Training Predicted Label: 3\n"
+     ]
+    }
+   ],
+   "source": [
+    "prediction = model.predict(x_train_adv[0:1, :])[0]\n",
+    "print(\"Adversarial Training Predicted Label: %i\" % prediction)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Benign Test Score: 0.5950\n"
+     ]
+    }
+   ],
+   "source": [
+    "score = model.score(x_test, y_test)\n",
+    "print(\"Benign Test Score: %.4f\" % score)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.matshow(x_test[0, :].reshape((28, 28)))\n",
+    "plt.clim(0, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Benign Test Predicted Label: 7\n"
+     ]
+    }
+   ],
+   "source": [
+    "prediction = model.predict(x_test[0:1, :])[0]\n",
+    "print(\"Benign Test Predicted Label: %i\" % prediction)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial Test Score: 0.3500\n"
+     ]
+    }
+   ],
+   "source": [
+    "score = model.score(x_test_adv, y_test)\n",
+    "print(\"Adversarial Test Score: %.4f\" % score)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.matshow(x_test_adv[0, :].reshape((28, 28)))\n",
+    "plt.clim(0, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial Test Predicted Label: 9\n"
+     ]
+    }
+   ],
+   "source": [
+    "prediction = model.predict(x_test_adv[0:1, :])[0]\n",
+    "print(\"Adversarial Test Predicted Label: %i\" % prediction)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/adversarial-robustness-toolbox/notebooks/classifier_scikitlearn_SVC_LinearSVC.ipynb b/adversarial-robustness-toolbox/notebooks/classifier_scikitlearn_SVC_LinearSVC.ipynb
new file mode 100644
index 0000000..5991c73
--- /dev/null
+++ b/adversarial-robustness-toolbox/notebooks/classifier_scikitlearn_SVC_LinearSVC.ipynb
@@ -0,0 +1,1090 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Adversarial-Robustness-Toolbox for scikit-learn Support Vector Machines SVC and LinearSVC"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.svm import SVC, LinearSVC\n",
+    "from sklearn.datasets import load_iris\n",
+    "\n",
+    "import numpy as np\n",
+    "from matplotlib import pyplot as plt\n",
+    "\n",
+    "from art.estimators.classification import SklearnClassifier\n",
+    "from art.attacks.evasion import ProjectedGradientDescent\n",
+    "from art.attacks.evasion import CarliniL2Method\n",
+    "from art.utils import load_mnist\n",
+    "\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1 Training scikit-learn SVC/LinearSVC and attacking with ART Projected Gradient Descent, CarliniL2Method, Deepfool "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_adversarial_examples(x_train, y_train, kernel, model_type=None, attack='PGD'):\n",
+    "    \n",
+    "    # Create and fit LinearSVC or SVC\n",
+    "    if model_type == 'LinearSVC':\n",
+    "        model = LinearSVC()\n",
+    "    else:\n",
+    "        model = SVC(C=1.0, kernel=kernel, degree=3, gamma='auto', coef0=0.0, shrinking=True, probability=True, \n",
+    "                    tol=0.001, cache_size=200, class_weight=None, verbose=False, max_iter=-1, \n",
+    "                    decision_function_shape='ovr', random_state=None)\n",
+    "    model.fit(X=x_train, y=y_train)\n",
+    "\n",
+    "    #  Create ART classfier for scikit-learn SVC\n",
+    "    art_classifier = SklearnClassifier(model=model, clip_values=(0, 10))\n",
+    "\n",
+    "    # Create ART attack\n",
+    "    if attack == 'PGD':\n",
+    "        attacker = ProjectedGradientDescent(estimator=art_classifier, norm=1, eps=1.0, eps_step=0.1,\n",
+    "                                            max_iter=10, targeted=False, num_random_init=0, batch_size=1,\n",
+    "                                            verbose=False)\n",
+    "    elif attack == 'CW':\n",
+    "        attacker = CarliniL2Method(classifier=art_classifier, max_iter=20, verbose=False)\n",
+    "            \n",
+    "    # Generate adversarial samples \n",
+    "    x_train_adv = attacker.generate(x_train)\n",
+    "\n",
+    "    return x_train_adv, model\n",
+    "    return 0, 0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1.1 Utility functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_data(num_classes):\n",
+    "    x_train, y_train = load_iris(return_X_y=True)\n",
+    "    x_train = x_train[y_train < num_classes][:, [0, 1]]\n",
+    "    y_train = y_train[y_train < num_classes]\n",
+    "    x_train[:, 0][y_train == 0] -= 2\n",
+    "    x_train[:, 1][y_train == 2] += 2\n",
+    "    return x_train, y_train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_results(model, x_train, y_train, x_train_adv, num_classes, model_type=None):\n",
+    "    \n",
+    "    fig, axs = plt.subplots(1, num_classes, figsize=(num_classes * 5, 5))\n",
+    "\n",
+    "    colors = ['orange', 'blue', 'green']\n",
+    "\n",
+    "    for i_class in range(num_classes):\n",
+    "\n",
+    "        # Plot difference vectors\n",
+    "        for i in range(y_train[y_train == i_class].shape[0]):\n",
+    "            x_1_0 = x_train[y_train == i_class][i, 0]\n",
+    "            x_1_1 = x_train[y_train == i_class][i, 1]\n",
+    "            x_2_0 = x_train_adv[y_train == i_class][i, 0]\n",
+    "            x_2_1 = x_train_adv[y_train == i_class][i, 1]\n",
+    "            axs[i_class].plot([x_1_0, x_2_0], [x_1_1, x_2_1], c='black', zorder=1)\n",
+    "\n",
+    "        # Plot benign samples\n",
+    "        for i_class_2 in range(num_classes):\n",
+    "            axs[i_class].scatter(x_train[y_train == i_class_2][:, 0], x_train[y_train == i_class_2][:, 1],\n",
+    "                                 s=20, zorder=2, c=colors[i_class_2])\n",
+    "        axs[i_class].set_aspect('equal', adjustable='box')\n",
+    "\n",
+    "        if model_type is None:\n",
+    "            # Mark support vectors with circles\n",
+    "            for sv in model.support_vectors_:\n",
+    "                axs[i_class].scatter(sv[0], sv[1], s=200, linewidth=3, facecolors='none',\n",
+    "                                     edgecolors='lightgreen', zorder=2)\n",
+    "\n",
+    "        # Show predicted probability as contour plot\n",
+    "        h = .01\n",
+    "        x_min, x_max = 1.5, 8.5\n",
+    "        y_min, y_max = 0, 7\n",
+    "\n",
+    "        xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n",
+    "\n",
+    "        if model_type is None and model.probability:\n",
+    "            Z_proba = model.predict_proba(np.c_[xx.ravel(), yy.ravel()])\n",
+    "            Z_proba = Z_proba[:, i_class].reshape(xx.shape)\n",
+    "            im = axs[i_class].contourf(xx, yy, Z_proba, levels=[0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0],\n",
+    "                                       vmin=0, vmax=1)\n",
+    "            if i_class == num_classes - 1:\n",
+    "                cax = fig.add_axes([0.95, 0.2, 0.025, 0.6])\n",
+    "                plt.colorbar(im, ax=axs[i_class], cax=cax)\n",
+    "        else:\n",
+    "            Z = model.predict(np.c_[xx.ravel(), yy.ravel()])\n",
+    "            Z = Z.reshape(xx.shape)\n",
+    "            axs[i_class].contour(xx, yy, Z, cmap=plt.cm.Paired)\n",
+    "\n",
+    "        # Plot adversarial samples\n",
+    "        axs[i_class].scatter(x_train_adv[y_train == i_class][:, 0], x_train_adv[y_train == i_class][:, 1],\n",
+    "                             zorder=2, c='red', marker='X')\n",
+    "        axs[i_class].set_xlim((x_min, x_max))\n",
+    "        axs[i_class].set_ylim((y_min, y_max))\n",
+    "\n",
+    "        axs[i_class].set_title('class ' + str(i_class))\n",
+    "        axs[i_class].set_xlabel('feature 1')\n",
+    "        axs[i_class].set_ylabel('feature 2')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 2 Example: Iris dataset"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### legend\n",
+    "- colored background: probability of class i\n",
+    "- orange circles: class 1\n",
+    "- blue circles: class 2\n",
+    "- green circles: class 3\n",
+    "- light green circles: support vectors\n",
+    "- red crosses: adversarial samples for class i"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2.1 LinearSVC, binary classification with Projected Gradient Descent"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "num_classes = 2\n",
+    "x_train, y_train = get_data(num_classes)\n",
+    "x_train_adv, model = get_adversarial_examples(x_train, y_train, kernel='linear', model_type='LinearSVC')\n",
+    "plot_results(model, x_train, y_train, x_train_adv, num_classes, model_type='LinearSVC')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2.2 LinearSVC, binary classification with CarliniL2Method"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "num_classes = 2\n",
+    "x_train, y_train = get_data(num_classes)\n",
+    "x_train_adv, model = get_adversarial_examples(x_train, y_train, kernel='linear', model_type='LinearSVC',\n",
+    "                                              attack='CW')\n",
+    "plot_results(model, x_train, y_train, x_train_adv, num_classes, model_type='LinearSVC')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2.3 LinearSVC, multi-classification with Projected Gradient Descent"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 1080x360 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "num_classes = 3\n",
+    "x_train, y_train = get_data(num_classes)\n",
+    "x_train_adv, model = get_adversarial_examples(x_train, y_train, kernel='linear', model_type='LinearSVC')\n",
+    "plot_results(model, x_train, y_train, x_train_adv, num_classes, model_type='LinearSVC')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2.4 LinearSVC, multi-classification with CarliniL2Method"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 1080x360 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "num_classes = 3\n",
+    "x_train, y_train = get_data(num_classes)\n",
+    "x_train_adv, model = get_adversarial_examples(x_train, y_train, kernel='linear', model_type='LinearSVC', attack='CW')\n",
+    "plot_results(model, x_train, y_train, x_train_adv, num_classes, model_type='LinearSVC')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2.5 SVC, linear kernel, binary classification with Projected Gradient Descent"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "num_classes = 2\n",
+    "x_train, y_train = get_data(num_classes)\n",
+    "x_train_adv, model = get_adversarial_examples(x_train, y_train, kernel='linear')\n",
+    "plot_results(model, x_train, y_train, x_train_adv, num_classes)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2.6 SVC, linear kernel, binary classification with CarliniL2Method"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "num_classes = 2\n",
+    "x_train, y_train = get_data(num_classes)\n",
+    "x_train_adv, model = get_adversarial_examples(x_train, y_train, kernel='linear', attack='CW')\n",
+    "plot_results(model, x_train, y_train, x_train_adv, num_classes)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2.7 SVC, linear kernel, multi-classification with Projected Gradient Descent"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 1080x360 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "num_classes = 3\n",
+    "x_train, y_train = get_data(num_classes)\n",
+    "x_train_adv, model = get_adversarial_examples(x_train, y_train, kernel='linear')\n",
+    "plot_results(model, x_train, y_train, x_train_adv, num_classes)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2.8 SVC, linear kernel, multi-classification with CarliniL2Method"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 1080x360 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "num_classes = 3\n",
+    "x_train, y_train = get_data(num_classes)\n",
+    "x_train_adv, model = get_adversarial_examples(x_train, y_train, kernel='linear', attack='CW')\n",
+    "plot_results(model, x_train, y_train, x_train_adv, num_classes)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2.9 SVC, rbf kernel, binary classification with Projected Gradient Descent"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "num_classes = 2\n",
+    "x_train, y_train = get_data(num_classes)\n",
+    "x_train_adv, model = get_adversarial_examples(x_train, y_train, kernel='rbf')\n",
+    "plot_results(model, x_train, y_train, x_train_adv, num_classes)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2.10 SVC, rbf kernel, binary classification with CarliniL2Method "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "num_classes = 2\n",
+    "x_train, y_train = get_data(num_classes)\n",
+    "x_train_adv, model = get_adversarial_examples(x_train, y_train, kernel='rbf', attack='CW')\n",
+    "plot_results(model, x_train, y_train, x_train_adv, num_classes)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2.11 SVC, rbf kernel, multi-classification with Projected Gradient Descent "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 1080x360 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "num_classes = 3\n",
+    "x_train, y_train = get_data(num_classes)\n",
+    "x_train_adv, model = get_adversarial_examples(x_train, y_train, kernel='rbf')\n",
+    "plot_results(model, x_train, y_train, x_train_adv, num_classes)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2.12 SVC, rbf kernel, multi-classification with CarliniL2Method"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 1080x360 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "num_classes = 3\n",
+    "x_train, y_train = get_data(num_classes)\n",
+    "x_train_adv, model = get_adversarial_examples(x_train, y_train, kernel='rbf', attack='CW')\n",
+    "plot_results(model, x_train, y_train, x_train_adv, num_classes)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 3 Example: MNIST"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3.1 Load and transform MNIST dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "(x_train, y_train), (x_test, y_test), min_, max_ = load_mnist()\n",
+    "\n",
+    "n_samples_train = x_train.shape[0]\n",
+    "n_features_train = x_train.shape[1] * x_train.shape[2] * x_train.shape[3]\n",
+    "n_samples_test = x_test.shape[0]\n",
+    "n_features_test = x_test.shape[1] * x_test.shape[2] * x_test.shape[3]\n",
+    "\n",
+    "x_train = x_train.reshape(n_samples_train, n_features_train)\n",
+    "x_test = x_test.reshape(n_samples_test, n_features_test)\n",
+    "\n",
+    "y_train = np.argmax(y_train, axis=1)\n",
+    "y_test = np.argmax(y_test, axis=1)\n",
+    "\n",
+    "n_samples_max = 200\n",
+    "x_train = x_train[0:n_samples_max]\n",
+    "y_train = y_train[0:n_samples_max]\n",
+    "x_test = x_test[0:n_samples_max]\n",
+    "y_test = y_test[0:n_samples_max]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3.2 Train SVC classifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = SVC(C=1.0, kernel='linear', probability=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "SVC(kernel='linear', probability=True)"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.fit(X=x_train, y=y_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3.3 Create and apply ProjectedGradientDescent Attack with ART"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "art_classifier = SklearnClassifier(model=model)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pgd = ProjectedGradientDescent(estimator=art_classifier, norm=np.inf, eps=.2, eps_step=0.1, max_iter=2, \n",
+    "                               targeted=False, num_random_init=0, batch_size=128, verbose=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "x_train_adv = pgd.generate(x_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_test_adv = pgd.generate(x_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3.4 Evaluate SVC classifier on benign and adversarial samples"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Benign Training Score: 1.0000\n"
+     ]
+    }
+   ],
+   "source": [
+    "score = model.score(x_train, y_train)\n",
+    "print(\"Benign Training Score: %.4f\" % score)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.matshow(x_train[0, :].reshape((28, 28)))\n",
+    "plt.clim(0, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Benign Training Predicted Label: 5\n"
+     ]
+    }
+   ],
+   "source": [
+    "prediction = model.predict(x_train[0:1, :])[0]\n",
+    "print(\"Benign Training Predicted Label: %i\" % prediction)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial Training Score: 0.1250\n"
+     ]
+    }
+   ],
+   "source": [
+    "score = model.score(x_train_adv, y_train)\n",
+    "print(\"Adversarial Training Score: %.4f\" % score)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.matshow(x_train_adv[0, :].reshape((28, 28)))\n",
+    "plt.clim(0, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial Training Predicted Label: 3\n"
+     ]
+    }
+   ],
+   "source": [
+    "prediction = model.predict(x_train_adv[0:1, :])[0]\n",
+    "print(\"Adversarial Training Predicted Label: %i\" % prediction)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Benign Test Score: 0.7650\n"
+     ]
+    }
+   ],
+   "source": [
+    "score = model.score(x_test, y_test)\n",
+    "print(\"Benign Test Score: %.4f\" % score)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.matshow(x_test[0, :].reshape((28, 28)))\n",
+    "plt.clim(0, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Benign Test Predicted Label: 7\n"
+     ]
+    }
+   ],
+   "source": [
+    "prediction = model.predict(x_test[0:1, :])[0]\n",
+    "print(\"Benign Test Predicted Label: %i\" % prediction)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial Test Score: 0.1300\n"
+     ]
+    }
+   ],
+   "source": [
+    "score = model.score(x_test_adv, y_test)\n",
+    "print(\"Adversarial Test Score: %.4f\" % score)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.matshow(x_test_adv[0, :].reshape((28, 28)))\n",
+    "plt.clim(0, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial Test Predicted Label: 9\n"
+     ]
+    }
+   ],
+   "source": [
+    "prediction = model.predict(x_test_adv[0:1, :])[0]\n",
+    "print(\"Adversarial Test Predicted Label: %i\" % prediction)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3.5 Investigate dependence on attack budget eps"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.0, 1.0)"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "eps_list = [0.05, 0.1, 0.2, 0.3]\n",
+    "score_list = list()\n",
+    "\n",
+    "for eps in eps_list:\n",
+    "    pgd = ProjectedGradientDescent(estimator=art_classifier, norm=np.inf, eps=eps, eps_step=0.01, max_iter=60, \n",
+    "                                   targeted=False, num_random_init=0, batch_size=128, verbose=False)\n",
+    "    x_test_adv = pgd.generate(x_test)\n",
+    "    score = model.score(x_test_adv, y_test)\n",
+    "    score_list.append(score)\n",
+    "\n",
+    "plt.plot(eps_list, score_list)\n",
+    "plt.xlabel('eps')\n",
+    "plt.ylabel('Test Accuracy')\n",
+    "plt.ylim((0, 1))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3.6 Targeted PGD attack"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pgd = ProjectedGradientDescent(estimator=art_classifier, norm=np.inf, eps=0.5, eps_step=0.01, max_iter=20, \n",
+    "                               targeted=True, num_random_init=3, batch_size=128, verbose=False)\n",
+    "y_test_target = np.zeros((y_test.shape[0], 10))\n",
+    "target_label = 7\n",
+    "y_test_target[:, target_label] = 1\n",
+    "x_test_adv = pgd.generate(x_test, y=y_test_target)\n",
+    "score = model.score(x_test_adv, y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Targeted Benign Test Score: 0.6500\n"
+     ]
+    }
+   ],
+   "source": [
+    "score = model.score(x_test_adv, np.argmax(y_test_target, axis=1))\n",
+    "print(\"Targeted Benign Test Score: %.4f\" % score)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.matshow(x_test_adv[16, :].reshape((28, 28)))\n",
+    "plt.clim(0, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Target Label: 7\n",
+      "Predicted Label: 7\n"
+     ]
+    }
+   ],
+   "source": [
+    "prediction = model.predict(x_test_adv[16:17, :])[0]\n",
+    "print(\"Target Label: %i\" % target_label)\n",
+    "print(\"Predicted Label: %i\" % prediction)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "py37_tf220",
+   "language": "python",
+   "name": "py37_tf220"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/adversarial-robustness-toolbox/notebooks/classifier_scikitlearn_pipeline_pca_cv_svc.ipynb b/adversarial-robustness-toolbox/notebooks/classifier_scikitlearn_pipeline_pca_cv_svc.ipynb
new file mode 100644
index 0000000..d2ce1a6
--- /dev/null
+++ b/adversarial-robustness-toolbox/notebooks/classifier_scikitlearn_pipeline_pca_cv_svc.ipynb
@@ -0,0 +1,359 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Adversarial Robustness Toolbox (ART) and scikit-learn Pipeline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This notebook contains an example of generating adversarial samples using a black-box attack against a scikit-learn pipeline consisting of principal component analysis (PCA) and a support vector machine classifier (SVC), but any other valid pipeline would work too. The pipeline is first optimised using grid search with cross validation. The adversarial samples are created with black-box `HopSkipJump` attack. The training data is MNIST, becasue of its intuitive visualisation, but any other dataset including tabular data would be suitable too."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from sklearn import datasets\n",
+    "from sklearn.decomposition import PCA\n",
+    "from sklearn.svm import SVC\n",
+    "from sklearn.pipeline import Pipeline\n",
+    "from sklearn.model_selection import GridSearchCV\n",
+    "\n",
+    "from art.utils import load_dataset\n",
+    "from art.estimators.classification import SklearnClassifier\n",
+    "from art.attacks.evasion import HopSkipJump\n",
+    "\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the training and testing dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n_features = 28*28\n",
+    "(x_train, y_train), (x_test, y_test), _, _ = load_dataset('mnist')\n",
+    "x_train = x_train.reshape((x_train.shape[0], n_features))\n",
+    "x_test = x_test.reshape((x_test.shape[0], n_features))\n",
+    "y_train = np.argmax(y_train, axis=1)\n",
+    "y_test = np.argmax(y_test, axis=1)\n",
+    "\n",
+    "# Select a smaller set of samples to accelerate notebook example, remove for higher accuracy\n",
+    "x_train = x_train[0:1000]\n",
+    "x_test = x_test[0:100]\n",
+    "y_train = y_train[0:1000]\n",
+    "y_test = y_test[0:100]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create a pipeline containing PCA and SVC classifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "svc = SVC(C=1.0, kernel='rbf')\n",
+    "pca = PCA()\n",
+    "pipeline = Pipeline(steps=[('pca', pca), ('svc', svc)])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Grid search and cross validation to optimise number of PCA components and error term penalty"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Best parameter (CV score=0.908):\n",
+      "{'pca__n_components': 20, 'svc__C': 1.0}\n"
+     ]
+    }
+   ],
+   "source": [
+    "param_grid = {'pca__n_components': [5, 20, 30, 40, 50, 64],\n",
+    "              'svc__C': np.logspace(-4, 4, 5)}\n",
+    "search = GridSearchCV(estimator=pipeline, param_grid=param_grid, iid=False, cv=5)\n",
+    "search.fit(x_train, y_train)\n",
+    "print(\"Best parameter (CV score=%0.3f):\" % search.best_score_)\n",
+    "print(search.best_params_)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create a black-box attack using ART"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "classifier = SklearnClassifier(model=search.best_estimator_)\n",
+    "attack = HopSkipJump(classifier=classifier, targeted=False, norm=np.inf, max_iter=100, max_eval=100,\n",
+    "                     init_eval=100, init_size=100)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Generate adversarial samples"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "107eee83bb884c938834dee7d981776f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(HTML(value='HopSkipJump'), FloatProgress(value=0.0), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "x_test_adv = attack.generate(x_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Evaluate benign accuracy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy on benign test samples 91.0%:\n"
+     ]
+    }
+   ],
+   "source": [
+    "accuracy_test_benign = search.score(x_test, y_test)\n",
+    "print('Accuracy on benign test samples {}%:'.format(accuracy_test_benign * 100))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Evaluate adversarial accuracy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy on adversarial test samples 5.0%:\n"
+     ]
+    }
+   ],
+   "source": [
+    "accuracy_test_adversarial = search.score(x_test_adv, y_test)\n",
+    "print('Accuracy on adversarial test samples {}%:'.format(accuracy_test_adversarial * 100))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Inspect a benign test sample"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.matshow(x_test[0].reshape((28, 28)));"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Predicted label: 7\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Predicted label:', search.predict(x_test[0:1])[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Inspect an adversarial test sample"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.matshow(x_test_adv[0].reshape((28, 28)));"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Predicted label: 9\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Predicted label:', search.predict(x_test_adv[0:1])[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "L_Infinity-norm: 0.2403459834117515\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('L_Infinity-norm:', np.linalg.norm(x_test_adv[0] - x_test[0], ord=np.inf))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "py37_tf220",
+   "language": "python",
+   "name": "py37_tf220"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/adversarial-robustness-toolbox/notebooks/classifier_xgboost.ipynb b/adversarial-robustness-toolbox/notebooks/classifier_xgboost.ipynb
new file mode 100644
index 0000000..021ce13
--- /dev/null
+++ b/adversarial-robustness-toolbox/notebooks/classifier_xgboost.ipynb
@@ -0,0 +1,715 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Adversarial-Robustness-Toolbox for XGBoost"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import xgboost as xgb\n",
+    "\n",
+    "from sklearn.datasets import load_iris\n",
+    "\n",
+    "import numpy as np\n",
+    "from matplotlib import pyplot as plt\n",
+    "\n",
+    "from art.estimators.classification import XGBoostClassifier\n",
+    "from art.attacks.evasion import ZooAttack\n",
+    "from art.utils import load_mnist\n",
+    "\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1 Training XGBoost classifier and attacking with ART Zeroth Order Optimization attack"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_adversarial_examples(x_train, y_train, num_classes):\n",
+    "\n",
+    "    # Create and fit XGBoost model\n",
+    "    num_round = 10\n",
+    "    param = {'objective': 'multi:softprob', 'metric': 'multi_logloss', 'num_class': num_classes}\n",
+    "    train_data = xgb.DMatrix(x_train, label=y_train)\n",
+    "    evallist = [(train_data, 'eval'), (train_data, 'train')]\n",
+    "    model = xgb.train(param, train_data, num_round, evallist)\n",
+    "\n",
+    "    # Create ART classifier for XGBoost\n",
+    "    art_classifier = XGBoostClassifier(model=model, nb_features=x_train.shape[1], nb_classes=10)\n",
+    "\n",
+    "    # Create ART Zeroth Order Optimization attack\n",
+    "    zoo = ZooAttack(classifier=art_classifier, confidence=0.0, targeted=False, learning_rate=1e-1, max_iter=20,\n",
+    "                    binary_search_steps=10, initial_const=1e-3, abort_early=True, use_resize=False, \n",
+    "                    use_importance=False, nb_parallel=1, batch_size=1, variable_h=0.2)\n",
+    "    \n",
+    "    # Generate adversarial samples with ART Zeroth Order Optimization attack\n",
+    "    x_train_adv = zoo.generate(x_train)\n",
+    "\n",
+    "    return x_train_adv, model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1.1 Utility functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_data(num_classes):\n",
+    "    x_train, y_train = load_iris(return_X_y=True)\n",
+    "    x_train = x_train[y_train < num_classes][:, [0, 1]]\n",
+    "    y_train = y_train[y_train < num_classes]\n",
+    "    x_train[:, 0][y_train == 0] *= 2\n",
+    "    x_train[:, 1][y_train == 2] *= 2\n",
+    "    x_train[:, 0][y_train == 0] -= 3\n",
+    "    x_train[:, 1][y_train == 2] -= 2\n",
+    "    \n",
+    "    x_train[:, 0] = (x_train[:, 0] - 4) / (9 - 4)\n",
+    "    x_train[:, 1] = (x_train[:, 1] - 1) / (6 - 1)\n",
+    "    \n",
+    "    return x_train, y_train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_results(model, x_train, y_train, x_train_adv, num_classes):\n",
+    "    \n",
+    "    fig, axs = plt.subplots(1, num_classes, figsize=(num_classes * 5, 5))\n",
+    "\n",
+    "    colors = ['orange', 'blue', 'green']\n",
+    "\n",
+    "    for i_class in range(num_classes):\n",
+    "\n",
+    "        # Plot difference vectors\n",
+    "        for i in range(y_train[y_train == i_class].shape[0]):\n",
+    "            x_1_0 = x_train[y_train == i_class][i, 0]\n",
+    "            x_1_1 = x_train[y_train == i_class][i, 1]\n",
+    "            x_2_0 = x_train_adv[y_train == i_class][i, 0]\n",
+    "            x_2_1 = x_train_adv[y_train == i_class][i, 1]\n",
+    "            if x_1_0 != x_2_0 or x_1_1 != x_2_1:\n",
+    "                axs[i_class].plot([x_1_0, x_2_0], [x_1_1, x_2_1], c='black', zorder=1)\n",
+    "\n",
+    "        # Plot benign samples\n",
+    "        for i_class_2 in range(num_classes):\n",
+    "            axs[i_class].scatter(x_train[y_train == i_class_2][:, 0], x_train[y_train == i_class_2][:, 1], s=20,\n",
+    "                                 zorder=2, c=colors[i_class_2])\n",
+    "        axs[i_class].set_aspect('equal', adjustable='box')\n",
+    "\n",
+    "        # Show predicted probability as contour plot\n",
+    "        h = .01\n",
+    "        x_min, x_max = 0, 1\n",
+    "        y_min, y_max = 0, 1\n",
+    "\n",
+    "        xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n",
+    "\n",
+    "        Z_proba = model.predict(xgb.DMatrix(np.c_[xx.ravel(), yy.ravel()]))\n",
+    "        Z_proba = Z_proba[:, i_class].reshape(xx.shape)\n",
+    "        im = axs[i_class].contourf(xx, yy, Z_proba, levels=[0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0],\n",
+    "                                   vmin=0, vmax=1)\n",
+    "        if i_class == num_classes - 1:\n",
+    "            cax = fig.add_axes([0.95, 0.2, 0.025, 0.6])\n",
+    "            plt.colorbar(im, ax=axs[i_class], cax=cax)\n",
+    "\n",
+    "        # Plot adversarial samples\n",
+    "        for i in range(y_train[y_train == i_class].shape[0]):\n",
+    "            x_1_0 = x_train[y_train == i_class][i, 0]\n",
+    "            x_1_1 = x_train[y_train == i_class][i, 1]\n",
+    "            x_2_0 = x_train_adv[y_train == i_class][i, 0]\n",
+    "            x_2_1 = x_train_adv[y_train == i_class][i, 1]\n",
+    "            if x_1_0 != x_2_0 or x_1_1 != x_2_1:\n",
+    "                axs[i_class].scatter(x_2_0, x_2_1, zorder=2, c='red', marker='X')\n",
+    "        axs[i_class].set_xlim((x_min, x_max))\n",
+    "        axs[i_class].set_ylim((y_min, y_max))\n",
+    "\n",
+    "        axs[i_class].set_title('class ' + str(i_class))\n",
+    "        axs[i_class].set_xlabel('feature 1')\n",
+    "        axs[i_class].set_ylabel('feature 2')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 2 Example: Iris dataset"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### legend\n",
+    "- colored background: probability of class i\n",
+    "- orange circles: class 1\n",
+    "- blue circles: class 2\n",
+    "- green circles: class 3\n",
+    "- red crosses: adversarial samples for class i"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[17:27:44] WARNING: /workspace/src/learner.cc:480: \n",
+      "Parameters: { metric } might not be used.\n",
+      "\n",
+      "  This may not be accurate due to some parameters are only used in language bindings but\n",
+      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
+      "  verification. Please open an issue if you find above cases.\n",
+      "\n",
+      "\n",
+      "[0]\teval-merror:0.11000\ttrain-merror:0.11000\n",
+      "[1]\teval-merror:0.11000\ttrain-merror:0.11000\n",
+      "[2]\teval-merror:0.11000\ttrain-merror:0.11000\n",
+      "[3]\teval-merror:0.10000\ttrain-merror:0.10000\n",
+      "[4]\teval-merror:0.08000\ttrain-merror:0.08000\n",
+      "[5]\teval-merror:0.08000\ttrain-merror:0.08000\n",
+      "[6]\teval-merror:0.06000\ttrain-merror:0.06000\n",
+      "[7]\teval-merror:0.06000\ttrain-merror:0.06000\n",
+      "[8]\teval-merror:0.06000\ttrain-merror:0.06000\n",
+      "[9]\teval-merror:0.06000\ttrain-merror:0.06000\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "ZOO: 100%|██████████| 100/100 [00:04<00:00, 20.23it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "num_classes = 2\n",
+    "x_train, y_train = get_data(num_classes=num_classes)\n",
+    "x_train_adv, model = get_adversarial_examples(x_train, y_train, num_classes)\n",
+    "plot_results(model, x_train, y_train, x_train_adv, num_classes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[17:27:50] WARNING: /workspace/src/learner.cc:480: \n",
+      "Parameters: { metric } might not be used.\n",
+      "\n",
+      "  This may not be accurate due to some parameters are only used in language bindings but\n",
+      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
+      "  verification. Please open an issue if you find above cases.\n",
+      "\n",
+      "\n",
+      "[0]\teval-merror:0.15333\ttrain-merror:0.15333\n",
+      "[1]\teval-merror:0.14000\ttrain-merror:0.14000\n",
+      "[2]\teval-merror:0.13333\ttrain-merror:0.13333\n",
+      "[3]\teval-merror:0.14000\ttrain-merror:0.14000\n",
+      "[4]\teval-merror:0.13333\ttrain-merror:0.13333\n",
+      "[5]\teval-merror:0.12667\ttrain-merror:0.12667\n",
+      "[6]\teval-merror:0.12000\ttrain-merror:0.12000\n",
+      "[7]\teval-merror:0.11333\ttrain-merror:0.11333\n",
+      "[8]\teval-merror:0.10000\ttrain-merror:0.10000\n",
+      "[9]\teval-merror:0.11333\ttrain-merror:0.11333\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "ZOO: 100%|██████████| 150/150 [00:07<00:00, 19.87it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 1080x360 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "num_classes = 3\n",
+    "x_train, y_train = get_data(num_classes=num_classes)\n",
+    "x_train_adv, model = get_adversarial_examples(x_train, y_train, num_classes)\n",
+    "plot_results(model, x_train, y_train, x_train_adv, num_classes)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 3 Example: MNIST"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3.1 Load and transform MNIST dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "(x_train, y_train), (x_test, y_test), min_, max_ = load_mnist()\n",
+    "\n",
+    "n_samples_train = x_train.shape[0]\n",
+    "n_features_train = x_train.shape[1] * x_train.shape[2] * x_train.shape[3]\n",
+    "n_samples_test = x_test.shape[0]\n",
+    "n_features_test = x_test.shape[1] * x_test.shape[2] * x_test.shape[3]\n",
+    "\n",
+    "x_train = x_train.reshape(n_samples_train, n_features_train)\n",
+    "x_test = x_test.reshape(n_samples_test, n_features_test)\n",
+    "\n",
+    "y_train = np.argmax(y_train, axis=1)\n",
+    "y_test = np.argmax(y_test, axis=1)\n",
+    "\n",
+    "n_samples_max = 200\n",
+    "x_train = x_train[0:n_samples_max]\n",
+    "y_train = y_train[0:n_samples_max]\n",
+    "x_test = x_test[0:n_samples_max]\n",
+    "y_test = y_test[0:n_samples_max]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3.2 Train XGBoostClassifier classifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[17:27:59] WARNING: /workspace/src/learner.cc:480: \n",
+      "Parameters: { metric } might not be used.\n",
+      "\n",
+      "  This may not be accurate due to some parameters are only used in language bindings but\n",
+      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
+      "  verification. Please open an issue if you find above cases.\n",
+      "\n",
+      "\n",
+      "[0]\teval-merror:0.05500\ttrain-merror:0.05500\n",
+      "[1]\teval-merror:0.04000\ttrain-merror:0.04000\n",
+      "[2]\teval-merror:0.01500\ttrain-merror:0.01500\n",
+      "[3]\teval-merror:0.01000\ttrain-merror:0.01000\n",
+      "[4]\teval-merror:0.00500\ttrain-merror:0.00500\n",
+      "[5]\teval-merror:0.00000\ttrain-merror:0.00000\n",
+      "[6]\teval-merror:0.00000\ttrain-merror:0.00000\n",
+      "[7]\teval-merror:0.00000\ttrain-merror:0.00000\n",
+      "[8]\teval-merror:0.00000\ttrain-merror:0.00000\n",
+      "[9]\teval-merror:0.00000\ttrain-merror:0.00000\n"
+     ]
+    }
+   ],
+   "source": [
+    "num_round = 10\n",
+    "param = {'objective': 'multi:softprob', 'metric': 'multi_logloss', 'num_class': 10}\n",
+    "train_data = xgb.DMatrix(x_train, label=y_train)\n",
+    "validation_data = train_data\n",
+    "evallist=[(train_data, 'eval'), (train_data, 'train')]\n",
+    "model = xgb.train(param, train_data, num_round, evallist)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3.3 Create and apply Zeroth Order Optimization Attack with ART"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "art_classifier = XGBoostClassifier(model=model, nb_features=x_train.shape[1], nb_classes=10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "zoo = ZooAttack(classifier=art_classifier, confidence=0.0, targeted=False, learning_rate=1e-1, max_iter=100,\n",
+    "                binary_search_steps=20, initial_const=1e-3, abort_early=True, use_resize=False, \n",
+    "                use_importance=False, nb_parallel=10, batch_size=1, variable_h=0.05)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "ZOO: 100%|██████████| 200/200 [05:51<00:00,  1.76s/it]\n"
+     ]
+    }
+   ],
+   "source": [
+    "x_train_adv = zoo.generate(x_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "ZOO: 100%|██████████| 200/200 [05:09<00:00,  1.55s/it]\n"
+     ]
+    }
+   ],
+   "source": [
+    "x_test_adv = zoo.generate(x_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3.4 Evaluate XGBoostClassifier on benign and adversarial samples"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Benign Training Score: 1.0000\n"
+     ]
+    }
+   ],
+   "source": [
+    "y_pred = model.predict(xgb.DMatrix(x_train))\n",
+    "score = np.sum(y_train == np.argmax(y_pred, axis=1)) / y_train.shape[0]\n",
+    "print(\"Benign Training Score: %.4f\" % score)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.matshow(x_train[0, :].reshape((28, 28)))\n",
+    "plt.clim(0, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Benign Training Predicted Label: 5\n"
+     ]
+    }
+   ],
+   "source": [
+    "prediction = np.argmax(model.predict(xgb.DMatrix(x_train[0:1, :])), axis=1)\n",
+    "print(\"Benign Training Predicted Label: %i\" % prediction)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial Training Score: 0.5900\n"
+     ]
+    }
+   ],
+   "source": [
+    "y_pred = model.predict(xgb.DMatrix(x_train_adv))\n",
+    "score = np.sum(y_train == np.argmax(y_pred, axis=1)) / y_train.shape[0]\n",
+    "print(\"Adversarial Training Score: %.4f\" % score)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.matshow(x_train_adv[0, :].reshape((28, 28)))\n",
+    "plt.clim(0, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial Training Predicted Label: 8\n"
+     ]
+    }
+   ],
+   "source": [
+    "prediction = np.argmax(model.predict(xgb.DMatrix(x_train_adv[0:1, :])), axis=1)\n",
+    "print(\"Adversarial Training Predicted Label: %i\" % prediction)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Benign Test Score: 0.6450\n"
+     ]
+    }
+   ],
+   "source": [
+    "y_pred = model.predict(xgb.DMatrix(x_test))\n",
+    "score = np.sum(y_test == np.argmax(y_pred, axis=1)) / y_test.shape[0]\n",
+    "print(\"Benign Test Score: %.4f\" % score)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.matshow(x_test[0, :].reshape((28, 28)))\n",
+    "plt.clim(0, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Benign Test Predicted Label: 7\n"
+     ]
+    }
+   ],
+   "source": [
+    "prediction = np.argmax(model.predict(xgb.DMatrix(x_test[0:1, :])), axis=1)\n",
+    "print(\"Benign Test Predicted Label: %i\" % prediction)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial Test Score: 0.3750\n"
+     ]
+    }
+   ],
+   "source": [
+    "y_pred = model.predict(xgb.DMatrix(x_test_adv))\n",
+    "score = np.sum(y_test == np.argmax(y_pred, axis=1)) / y_test.shape[0]\n",
+    "print(\"Adversarial Test Score: %.4f\" % score)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.matshow(x_test_adv[0, :].reshape((28, 28)))\n",
+    "plt.clim(0, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adversarial Test Predicted Label: 3\n"
+     ]
+    }
+   ],
+   "source": [
+    "prediction = np.argmax(model.predict(xgb.DMatrix(x_test_adv[0:1, :])), axis=1)\n",
+    "print(\"Adversarial Test Predicted Label: %i\" % prediction)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/adversarial-robustness-toolbox/notebooks/detection_adversarial_samples_cifar10.ipynb b/adversarial-robustness-toolbox/notebooks/detection_adversarial_samples_cifar10.ipynb
new file mode 100644
index 0000000..498b875
--- /dev/null
+++ b/adversarial-robustness-toolbox/notebooks/detection_adversarial_samples_cifar10.ipynb
@@ -0,0 +1,720 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<table style=\"border: none\" align=\"center\">\n",
+    "   <tr style=\"border: none\">\n",
+    "      <th style=\"border: none\"><font face=\"verdana\" size=\"4\" color=\"black\"><b>  Demonstrate detection of adversarial samples using ART  </b></font></font></th>\n",
+    "   </tr> \n",
+    "</table>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this notebook we demonstrate the detection of adversarial samples using ART. Our classifier will be a **ResNet** architecture for the [CIFAR-10](https://www.cs.toronto.edu/~kriz/cifar.html) image data set.\n",
+    "\n",
+    "\n",
+    "## Contents\n",
+    "\n",
+    "1.\t[Loading prereqs and data](#prereqs)\n",
+    "2.  [Evaluating the classifier](#classifier)\n",
+    "3.  [Training the detector](#train_detector)\n",
+    "4.  [Evaluating the detector](#detector)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"prereqs\"></a>\n",
+    "## 1. Loading prereqs and data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using TensorFlow backend.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "\n",
+    "from keras.models import load_model\n",
+    "\n",
+    "from art import config\n",
+    "from art.utils import load_dataset, get_file\n",
+    "from art.estimators.classification import KerasClassifier\n",
+    "from art.attacks.evasion import FastGradientMethod\n",
+    "from art.defences.detector.evasion import BinaryInputDetector\n",
+    "\n",
+    "import numpy as np\n",
+    "\n",
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load the CIFAR10 data set and class descriptions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "(x_train, y_train), (x_test, y_test), min_, max_ = load_dataset('cifar10')\n",
+    "\n",
+    "num_samples_train = 100\n",
+    "num_samples_test = 100\n",
+    "x_train = x_train[0:num_samples_train]\n",
+    "y_train = y_train[0:num_samples_train]\n",
+    "x_test = x_test[0:num_samples_test]\n",
+    "y_test = y_test[0:num_samples_test]\n",
+    "\n",
+    "class_descr = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"classifier\"></a>\n",
+    "## 2. Evaluating the classifier"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load the pre-trained classifier (a ResNet architecture):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path = get_file('cifar_resnet.h5',extract=False, path=config.ART_DATA_PATH,\n",
+    "                url='https://www.dropbox.com/s/ta75pl4krya5djj/cifar_resnet.h5?dl=1')\n",
+    "classifier_model = load_model(path)\n",
+    "classifier = KerasClassifier(clip_values=(min_, max_), model=classifier_model, use_logits=False, \n",
+    "                             preprocessing=(0.5, 1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: \"model_1\"\n",
+      "__________________________________________________________________________________________________\n",
+      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
+      "==================================================================================================\n",
+      "input_1 (InputLayer)            (None, 32, 32, 3)    0                                            \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_1 (Conv2D)               (None, 32, 32, 16)   448         input_1[0][0]                    \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_1 (BatchNor (None, 32, 32, 16)   64          conv2d_1[0][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_1 (Activation)       (None, 32, 32, 16)   0           batch_normalization_1[0][0]      \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_2 (Conv2D)               (None, 32, 32, 16)   2320        activation_1[0][0]               \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_2 (BatchNor (None, 32, 32, 16)   64          conv2d_2[0][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_2 (Activation)       (None, 32, 32, 16)   0           batch_normalization_2[0][0]      \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_3 (Conv2D)               (None, 32, 32, 16)   2320        activation_2[0][0]               \n",
+      "__________________________________________________________________________________________________\n",
+      "add_1 (Add)                     (None, 32, 32, 16)   0           activation_1[0][0]               \n",
+      "                                                                 conv2d_3[0][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_3 (BatchNor (None, 32, 32, 16)   64          add_1[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_3 (Activation)       (None, 32, 32, 16)   0           batch_normalization_3[0][0]      \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_4 (Conv2D)               (None, 32, 32, 16)   2320        activation_3[0][0]               \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_4 (BatchNor (None, 32, 32, 16)   64          conv2d_4[0][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_4 (Activation)       (None, 32, 32, 16)   0           batch_normalization_4[0][0]      \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_5 (Conv2D)               (None, 32, 32, 16)   2320        activation_4[0][0]               \n",
+      "__________________________________________________________________________________________________\n",
+      "add_2 (Add)                     (None, 32, 32, 16)   0           add_1[0][0]                      \n",
+      "                                                                 conv2d_5[0][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_5 (BatchNor (None, 32, 32, 16)   64          add_2[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_5 (Activation)       (None, 32, 32, 16)   0           batch_normalization_5[0][0]      \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_6 (Conv2D)               (None, 32, 32, 16)   2320        activation_5[0][0]               \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_6 (BatchNor (None, 32, 32, 16)   64          conv2d_6[0][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_6 (Activation)       (None, 32, 32, 16)   0           batch_normalization_6[0][0]      \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_7 (Conv2D)               (None, 32, 32, 16)   2320        activation_6[0][0]               \n",
+      "__________________________________________________________________________________________________\n",
+      "add_3 (Add)                     (None, 32, 32, 16)   0           add_2[0][0]                      \n",
+      "                                                                 conv2d_7[0][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_7 (BatchNor (None, 32, 32, 16)   64          add_3[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_7 (Activation)       (None, 32, 32, 16)   0           batch_normalization_7[0][0]      \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_8 (Conv2D)               (None, 32, 32, 16)   2320        activation_7[0][0]               \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_8 (BatchNor (None, 32, 32, 16)   64          conv2d_8[0][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_8 (Activation)       (None, 32, 32, 16)   0           batch_normalization_8[0][0]      \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_9 (Conv2D)               (None, 32, 32, 16)   2320        activation_8[0][0]               \n",
+      "__________________________________________________________________________________________________\n",
+      "add_4 (Add)                     (None, 32, 32, 16)   0           add_3[0][0]                      \n",
+      "                                                                 conv2d_9[0][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_9 (BatchNor (None, 32, 32, 16)   64          add_4[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_9 (Activation)       (None, 32, 32, 16)   0           batch_normalization_9[0][0]      \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_10 (Conv2D)              (None, 32, 32, 16)   2320        activation_9[0][0]               \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_10 (BatchNo (None, 32, 32, 16)   64          conv2d_10[0][0]                  \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_10 (Activation)      (None, 32, 32, 16)   0           batch_normalization_10[0][0]     \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_11 (Conv2D)              (None, 32, 32, 16)   2320        activation_10[0][0]              \n",
+      "__________________________________________________________________________________________________\n",
+      "add_5 (Add)                     (None, 32, 32, 16)   0           add_4[0][0]                      \n",
+      "                                                                 conv2d_11[0][0]                  \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_11 (BatchNo (None, 32, 32, 16)   64          add_5[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_11 (Activation)      (None, 32, 32, 16)   0           batch_normalization_11[0][0]     \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_12 (Conv2D)              (None, 16, 16, 32)   4640        activation_11[0][0]              \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_12 (BatchNo (None, 16, 16, 32)   128         conv2d_12[0][0]                  \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_12 (Activation)      (None, 16, 16, 32)   0           batch_normalization_12[0][0]     \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_14 (Conv2D)              (None, 16, 16, 32)   544         add_5[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_13 (Conv2D)              (None, 16, 16, 32)   9248        activation_12[0][0]              \n",
+      "__________________________________________________________________________________________________\n",
+      "add_6 (Add)                     (None, 16, 16, 32)   0           conv2d_14[0][0]                  \n",
+      "                                                                 conv2d_13[0][0]                  \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_13 (BatchNo (None, 16, 16, 32)   128         add_6[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_13 (Activation)      (None, 16, 16, 32)   0           batch_normalization_13[0][0]     \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_15 (Conv2D)              (None, 16, 16, 32)   9248        activation_13[0][0]              \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_14 (BatchNo (None, 16, 16, 32)   128         conv2d_15[0][0]                  \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_14 (Activation)      (None, 16, 16, 32)   0           batch_normalization_14[0][0]     \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_16 (Conv2D)              (None, 16, 16, 32)   9248        activation_14[0][0]              \n",
+      "__________________________________________________________________________________________________\n",
+      "add_7 (Add)                     (None, 16, 16, 32)   0           add_6[0][0]                      \n",
+      "                                                                 conv2d_16[0][0]                  \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_15 (BatchNo (None, 16, 16, 32)   128         add_7[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_15 (Activation)      (None, 16, 16, 32)   0           batch_normalization_15[0][0]     \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_17 (Conv2D)              (None, 16, 16, 32)   9248        activation_15[0][0]              \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_16 (BatchNo (None, 16, 16, 32)   128         conv2d_17[0][0]                  \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_16 (Activation)      (None, 16, 16, 32)   0           batch_normalization_16[0][0]     \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_18 (Conv2D)              (None, 16, 16, 32)   9248        activation_16[0][0]              \n",
+      "__________________________________________________________________________________________________\n",
+      "add_8 (Add)                     (None, 16, 16, 32)   0           add_7[0][0]                      \n",
+      "                                                                 conv2d_18[0][0]                  \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_17 (BatchNo (None, 16, 16, 32)   128         add_8[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_17 (Activation)      (None, 16, 16, 32)   0           batch_normalization_17[0][0]     \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_19 (Conv2D)              (None, 16, 16, 32)   9248        activation_17[0][0]              \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_18 (BatchNo (None, 16, 16, 32)   128         conv2d_19[0][0]                  \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_18 (Activation)      (None, 16, 16, 32)   0           batch_normalization_18[0][0]     \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_20 (Conv2D)              (None, 16, 16, 32)   9248        activation_18[0][0]              \n",
+      "__________________________________________________________________________________________________\n",
+      "add_9 (Add)                     (None, 16, 16, 32)   0           add_8[0][0]                      \n",
+      "                                                                 conv2d_20[0][0]                  \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_19 (BatchNo (None, 16, 16, 32)   128         add_9[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_19 (Activation)      (None, 16, 16, 32)   0           batch_normalization_19[0][0]     \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_21 (Conv2D)              (None, 16, 16, 32)   9248        activation_19[0][0]              \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_20 (BatchNo (None, 16, 16, 32)   128         conv2d_21[0][0]                  \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_20 (Activation)      (None, 16, 16, 32)   0           batch_normalization_20[0][0]     \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_22 (Conv2D)              (None, 16, 16, 32)   9248        activation_20[0][0]              \n",
+      "__________________________________________________________________________________________________\n",
+      "add_10 (Add)                    (None, 16, 16, 32)   0           add_9[0][0]                      \n",
+      "                                                                 conv2d_22[0][0]                  \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_21 (BatchNo (None, 16, 16, 32)   128         add_10[0][0]                     \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_21 (Activation)      (None, 16, 16, 32)   0           batch_normalization_21[0][0]     \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_23 (Conv2D)              (None, 8, 8, 64)     18496       activation_21[0][0]              \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_22 (BatchNo (None, 8, 8, 64)     256         conv2d_23[0][0]                  \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_22 (Activation)      (None, 8, 8, 64)     0           batch_normalization_22[0][0]     \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_25 (Conv2D)              (None, 8, 8, 64)     2112        add_10[0][0]                     \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_24 (Conv2D)              (None, 8, 8, 64)     36928       activation_22[0][0]              \n",
+      "__________________________________________________________________________________________________\n",
+      "add_11 (Add)                    (None, 8, 8, 64)     0           conv2d_25[0][0]                  \n",
+      "                                                                 conv2d_24[0][0]                  \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_23 (BatchNo (None, 8, 8, 64)     256         add_11[0][0]                     \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_23 (Activation)      (None, 8, 8, 64)     0           batch_normalization_23[0][0]     \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_26 (Conv2D)              (None, 8, 8, 64)     36928       activation_23[0][0]              \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_24 (BatchNo (None, 8, 8, 64)     256         conv2d_26[0][0]                  \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_24 (Activation)      (None, 8, 8, 64)     0           batch_normalization_24[0][0]     \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_27 (Conv2D)              (None, 8, 8, 64)     36928       activation_24[0][0]              \n",
+      "__________________________________________________________________________________________________\n",
+      "add_12 (Add)                    (None, 8, 8, 64)     0           add_11[0][0]                     \n",
+      "                                                                 conv2d_27[0][0]                  \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_25 (BatchNo (None, 8, 8, 64)     256         add_12[0][0]                     \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_25 (Activation)      (None, 8, 8, 64)     0           batch_normalization_25[0][0]     \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_28 (Conv2D)              (None, 8, 8, 64)     36928       activation_25[0][0]              \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_26 (BatchNo (None, 8, 8, 64)     256         conv2d_28[0][0]                  \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_26 (Activation)      (None, 8, 8, 64)     0           batch_normalization_26[0][0]     \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_29 (Conv2D)              (None, 8, 8, 64)     36928       activation_26[0][0]              \n",
+      "__________________________________________________________________________________________________\n",
+      "add_13 (Add)                    (None, 8, 8, 64)     0           add_12[0][0]                     \n",
+      "                                                                 conv2d_29[0][0]                  \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_27 (BatchNo (None, 8, 8, 64)     256         add_13[0][0]                     \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_27 (Activation)      (None, 8, 8, 64)     0           batch_normalization_27[0][0]     \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_30 (Conv2D)              (None, 8, 8, 64)     36928       activation_27[0][0]              \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_28 (BatchNo (None, 8, 8, 64)     256         conv2d_30[0][0]                  \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_28 (Activation)      (None, 8, 8, 64)     0           batch_normalization_28[0][0]     \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_31 (Conv2D)              (None, 8, 8, 64)     36928       activation_28[0][0]              \n",
+      "__________________________________________________________________________________________________\n",
+      "add_14 (Add)                    (None, 8, 8, 64)     0           add_13[0][0]                     \n",
+      "                                                                 conv2d_31[0][0]                  \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_29 (BatchNo (None, 8, 8, 64)     256         add_14[0][0]                     \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_29 (Activation)      (None, 8, 8, 64)     0           batch_normalization_29[0][0]     \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_32 (Conv2D)              (None, 8, 8, 64)     36928       activation_29[0][0]              \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_30 (BatchNo (None, 8, 8, 64)     256         conv2d_32[0][0]                  \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_30 (Activation)      (None, 8, 8, 64)     0           batch_normalization_30[0][0]     \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_33 (Conv2D)              (None, 8, 8, 64)     36928       activation_30[0][0]              \n",
+      "__________________________________________________________________________________________________\n",
+      "add_15 (Add)                    (None, 8, 8, 64)     0           add_14[0][0]                     \n",
+      "                                                                 conv2d_33[0][0]                  \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_31 (BatchNo (None, 8, 8, 64)     256         add_15[0][0]                     \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_31 (Activation)      (None, 8, 8, 64)     0           batch_normalization_31[0][0]     \n",
+      "__________________________________________________________________________________________________\n",
+      "dropout_1 (Dropout)             (None, 8, 8, 64)     0           activation_31[0][0]              \n",
+      "__________________________________________________________________________________________________\n",
+      "average_pooling2d_1 (AveragePoo (None, 1, 1, 64)     0           dropout_1[0][0]                  \n",
+      "__________________________________________________________________________________________________\n",
+      "flatten_1 (Flatten)             (None, 64)           0           average_pooling2d_1[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "classifier (Dense)              (None, 10)           650         flatten_1[0][0]                  \n",
+      "==================================================================================================\n",
+      "Total params: 470,218\n",
+      "Trainable params: 467,946\n",
+      "Non-trainable params: 2,272\n",
+      "__________________________________________________________________________________________________\n"
+     ]
+    }
+   ],
+   "source": [
+    "classifier_model.summary()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Evaluate the classifier on the first 100 test images:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "UnknownError",
+     "evalue": " Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.\n\t [[node conv2d_1/convolution (defined at /home/beat/codes/anaconda3/envs/py37_tf220/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:3009) ]] [Op:__inference_keras_scratch_graph_32692]\n\nFunction call stack:\nkeras_scratch_graph\n",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mUnknownError\u001b[0m                              Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-5-91f9a98aaa5a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mx_test_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclassifier\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_test\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mnb_correct_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_test_pred\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Original test data (first 100 images):\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Correctly classified: {}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnb_correct_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/codes/anaconda3/envs/py37_tf220/lib/python3.7/site-packages/art/estimators/classification/classifier.py\u001b[0m in \u001b[0;36mreplacement_function\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m     62\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m                     \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlst\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 64\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mfdict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfunc_name\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     65\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m             \u001b[0mreplacement_function\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfdict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfunc_name\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/codes/anaconda3/envs/py37_tf220/lib/python3.7/site-packages/art/estimators/classification/keras.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, x, batch_size, **kwargs)\u001b[0m\n\u001b[1;32m    391\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mbatch_index\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mceil\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_preprocessed\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    392\u001b[0m             \u001b[0mbegin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbatch_index\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_index\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_preprocessed\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 393\u001b[0;31m             \u001b[0mpredictions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mbegin\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mend\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mx_preprocessed\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mbegin\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mend\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    394\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    395\u001b[0m         \u001b[0;31m# Apply postprocessing\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/codes/anaconda3/envs/py37_tf220/lib/python3.7/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, x, batch_size, verbose, steps, callbacks, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   1460\u001b[0m                                             \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mverbose\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1461\u001b[0m                                             \u001b[0msteps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msteps\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1462\u001b[0;31m                                             callbacks=callbacks)\n\u001b[0m\u001b[1;32m   1463\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1464\u001b[0m     def train_on_batch(self, x, y,\n",
+      "\u001b[0;32m~/codes/anaconda3/envs/py37_tf220/lib/python3.7/site-packages/keras/engine/training_arrays.py\u001b[0m in \u001b[0;36mpredict_loop\u001b[0;34m(model, f, ins, batch_size, verbose, steps, callbacks)\u001b[0m\n\u001b[1;32m    322\u001b[0m             \u001b[0mbatch_logs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m'batch'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbatch_index\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'size'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_ids\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    323\u001b[0m             \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_batch_hook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'predict'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'begin'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_index\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_logs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 324\u001b[0;31m             \u001b[0mbatch_outs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins_batch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    325\u001b[0m             \u001b[0mbatch_outs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mto_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_outs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    326\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mbatch_index\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/codes/anaconda3/envs/py37_tf220/lib/python3.7/site-packages/tensorflow/python/keras/backend.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   3790\u001b[0m         \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmath_ops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtensor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3791\u001b[0m       \u001b[0mconverted_inputs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3792\u001b[0;31m     \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_graph_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mconverted_inputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3793\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3794\u001b[0m     \u001b[0;31m# EagerTensor.numpy() will often make a copy to ensure memory safety.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/codes/anaconda3/envs/py37_tf220/lib/python3.7/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1603\u001b[0m       \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mFor\u001b[0m \u001b[0minvalid\u001b[0m \u001b[0mpositional\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mkeyword\u001b[0m \u001b[0margument\u001b[0m \u001b[0mcombinations\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1604\u001b[0m     \"\"\"\n\u001b[0;32m-> 1605\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1606\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1607\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcancellation_manager\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/codes/anaconda3/envs/py37_tf220/lib/python3.7/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, args, kwargs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1643\u001b[0m       raise TypeError(\"Keyword arguments {} unknown. Expected {}.\".format(\n\u001b[1;32m   1644\u001b[0m           list(kwargs.keys()), list(self._arg_keywords)))\n\u001b[0;32m-> 1645\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_flat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcaptured_inputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcancellation_manager\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1646\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1647\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_filtered_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/codes/anaconda3/envs/py37_tf220/lib/python3.7/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1744\u001b[0m       \u001b[0;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1745\u001b[0m       return self._build_call_outputs(self._inference_function.call(\n\u001b[0;32m-> 1746\u001b[0;31m           ctx, args, cancellation_manager=cancellation_manager))\n\u001b[0m\u001b[1;32m   1747\u001b[0m     forward_backward = self._select_forward_and_backward_functions(\n\u001b[1;32m   1748\u001b[0m         \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/codes/anaconda3/envs/py37_tf220/lib/python3.7/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m    596\u001b[0m               \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    597\u001b[0m               \u001b[0mattrs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattrs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 598\u001b[0;31m               ctx=ctx)\n\u001b[0m\u001b[1;32m    599\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    600\u001b[0m           outputs = execute.execute_with_cancellation(\n",
+      "\u001b[0;32m~/codes/anaconda3/envs/py37_tf220/lib/python3.7/site-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     58\u001b[0m     \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0;32m---> 60\u001b[0;31m                                         inputs, attrs, num_outputs)\n\u001b[0m\u001b[1;32m     61\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mUnknownError\u001b[0m:  Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.\n\t [[node conv2d_1/convolution (defined at /home/beat/codes/anaconda3/envs/py37_tf220/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:3009) ]] [Op:__inference_keras_scratch_graph_32692]\n\nFunction call stack:\nkeras_scratch_graph\n"
+     ]
+    }
+   ],
+   "source": [
+    "x_test_pred = np.argmax(classifier.predict(x_test[:100]), axis=1)\n",
+    "nb_correct_pred = np.sum(x_test_pred == np.argmax(y_test[:100], axis=1))\n",
+    "\n",
+    "print(\"Original test data (first 100 images):\")\n",
+    "print(\"Correctly classified: {}\".format(nb_correct_pred))\n",
+    "print(\"Incorrectly classified: {}\".format(100-nb_correct_pred))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For illustration purposes, look at the first 9 images. (In brackets: true labels.)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(10,10))\n",
+    "for i in range(0, 9):\n",
+    "    pred_label, true_label = class_descr[x_test_pred[i]], class_descr[np.argmax(y_test[i])]\n",
+    "    plt.subplot(330 + 1 + i)\n",
+    "    fig=plt.imshow(x_test[i])\n",
+    "    fig.axes.get_xaxis().set_visible(False)\n",
+    "    fig.axes.get_yaxis().set_visible(False)\n",
+    "    fig.axes.text(0.5, -0.1, pred_label + \" (\" + true_label + \")\", fontsize=12, transform=fig.axes.transAxes, \n",
+    "                  horizontalalignment='center')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Generate some adversarial samples:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "attacker = FastGradientMethod(classifier, eps=0.05)\n",
+    "x_test_adv = attacker.generate(x_test[:100]) # this takes about two minutes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Evaluate the classifier on 100 adversarial samples:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_test_adv_pred = np.argmax(classifier.predict(x_test_adv), axis=1)\n",
+    "nb_correct_adv_pred = np.sum(x_test_adv_pred == np.argmax(y_test[:100], axis=1))\n",
+    "\n",
+    "print(\"Adversarial test data (first 100 images):\")\n",
+    "print(\"Correctly classified: {}\".format(nb_correct_adv_pred))\n",
+    "print(\"Incorrectly classified: {}\".format(100-nb_correct_adv_pred))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now plot the adversarial images and their predicted labels (in brackets: true labels)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(10,10))\n",
+    "for i in range(0, 9):\n",
+    "    pred_label, true_label = class_descr[x_test_adv_pred[i]], class_descr[np.argmax(y_test[i])]\n",
+    "    plt.subplot(330 + 1 + i)\n",
+    "    fig=plt.imshow(x_test_adv[i])\n",
+    "    fig.axes.get_xaxis().set_visible(False)\n",
+    "    fig.axes.get_yaxis().set_visible(False)\n",
+    "    fig.axes.text(0.5, -0.1, pred_label + \" (\" + true_label + \")\", fontsize=12, transform=fig.axes.transAxes, \n",
+    "                  horizontalalignment='center')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"train_detector\"></a>\n",
+    "## 3. Training the detector"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load the detector model (which also uses a ResNet architecture):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path = get_file('BID_eps=0.05.h5',extract=False, path=config.ART_DATA_PATH,\n",
+    "                url='https://www.dropbox.com/s/cbyfk65497wwbtn/BID_eps%3D0.05.h5?dl=1')\n",
+    "detector_model = load_model(path)\n",
+    "detector_classifier = KerasClassifier(clip_values=(-0.5, 0.5), model=detector_model, use_logits=False)\n",
+    "detector = BinaryInputDetector(detector_classifier)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "detector_model.summary()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To train the detector:\n",
+    "- we expand our training set with adversarial samples\n",
+    "- we label the data with 0 (original) and 1 (adversarial)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_train_adv = attacker.generate(x_train)\n",
+    "nb_train = x_train.shape[0]\n",
+    "\n",
+    "x_train_detector = np.concatenate((x_train, x_train_adv), axis=0)\n",
+    "y_train_detector = np.concatenate((np.array([[1,0]]*nb_train), np.array([[0,1]]*nb_train)), axis=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Perform the training:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "detector.fit(x_train_detector, y_train_detector, nb_epochs=20, batch_size=20)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"detector\"></a>\n",
+    "## 4. Evaluating the detector"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Apply the detector to the adversarial test data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "flag_adv = np.sum(np.argmax(detector.predict(x_test_adv), axis=1) == 1)\n",
+    "\n",
+    "print(\"Adversarial test data (first 100 images):\")\n",
+    "print(\"Flagged: {}\".format(flag_adv))\n",
+    "print(\"Not flagged: {}\".format(100 - flag_adv))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Apply the detector to the first 100 original test images:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "flag_original = np.sum(np.argmax(detector.predict(x_test[:100]), axis=1) == 1)\n",
+    "\n",
+    "print(\"Original test data (first 100 images):\")\n",
+    "print(\"Flagged: {}\".format(flag_original))\n",
+    "print(\"Not flagged: {}\".format(100 - flag_original))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Evaluate the detector for different attack strengths `eps`\n",
+    "(**Note**: for the training of detector, `eps=0.05` was used)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "eps_range = [0.01, 0.02, 0.03, 0.04, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]\n",
+    "nb_flag_adv = []\n",
+    "nb_missclass = []\n",
+    "\n",
+    "for eps in eps_range:\n",
+    "    attacker.set_params(**{'eps': eps})\n",
+    "    x_test_adv = attacker.generate(x_test[:100])\n",
+    "    nb_flag_adv += [np.sum(np.argmax(detector.predict(x_test_adv), axis=1) == 1)]\n",
+    "    nb_missclass += [np.sum(np.argmax(classifier.predict(x_test_adv), axis=1) != np.argmax(y_test[:100], axis=1))]\n",
+    "    \n",
+    "eps_range = [0] + eps_range\n",
+    "nb_flag_adv = [flag_original] + nb_flag_adv\n",
+    "nb_missclass = [2] + nb_missclass"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "ax.plot(np.array(eps_range)[:8], np.array(nb_flag_adv)[:8], 'b--', label='Detector flags')\n",
+    "ax.plot(np.array(eps_range)[:8], np.array(nb_missclass)[:8], 'r--', label='Classifier errors')\n",
+    "\n",
+    "legend = ax.legend(loc='center right', shadow=True, fontsize='large')\n",
+    "legend.get_frame().set_facecolor('#00FFCC')\n",
+    "\n",
+    "plt.xlabel('Attack strength (eps)')\n",
+    "plt.ylabel('Per 100 adversarial samples')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/adversarial-robustness-toolbox/notebooks/fabric_for_deep_learning_adversarial_samples_fashion_mnist.ipynb b/adversarial-robustness-toolbox/notebooks/fabric_for_deep_learning_adversarial_samples_fashion_mnist.ipynb
new file mode 100644
index 0000000..087b9f7
--- /dev/null
+++ b/adversarial-robustness-toolbox/notebooks/fabric_for_deep_learning_adversarial_samples_fashion_mnist.ipynb
@@ -0,0 +1,1440 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Generate Adversarial Samples for Deep Learning Models with the Adversarial Robustness Toolbox (ART)\n",
+    "\n",
+    "This notebook shows how to use adversarial attack techniques from the [Adversarial Robustness Toolbox (ART)](https://developer.ibm.com/code/open/projects/adversarial-robustness-toolbox/) on Deep Learning models trained with *FfDL*. The *ART* library supports crafting and analyzing various attack and defense methods for deep learning models. \n",
+    "\n",
+    "In this notebook, you will learn how to incorporate one of the attack methods supported by *ART*, the *Fast Gradient Method* (*FGM*), into your training pipeline to generate adversarial samples for the purposes of evaluating the robustness of the trained model. The model is a Convolutional Neural Network (CNN) trained on the *[MNIST handwritten digit data](http://yann.lecun.com/exdb/mnist/)* using [Keras](https://keras.io/) with a [TensorFlow](https://www.tensorflow.org/) backend.\n",
+    "\n",
+    "The *ART* Github repository can be found here - https://github.com/Trusted-AI/adversarial-robustness-toolbox\n",
+    "\n",
+    "This notebook uses Python 3.\n",
+    "\n",
+    "\n",
+    "## Contents\n",
+    "\n",
+    "1.\t[Set up the environment](#setup)\n",
+    "2.\t[Create a Keras model](#model)\n",
+    "3.  [Train the model](#train)\n",
+    "4.\t[Generate adversarial samples for a robustness check](#art)\n",
+    "5.\t[Summary and next steps](#summary)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"setup\"></a>\n",
+    "## 1. Setup\n",
+    "\n",
+    "It is recommended that you run this notebook inside a Python 3 virtual environment. Make sure you have all required libraries installed.\n",
+    "\n",
+    "To store model and training data, this notebook requires access to a Cloud Object Storage (COS) instance. [BlueMix Cloud Object Storage](https://console.bluemix.net/catalog/services/cloud-object-storage) offers a free *lite plan*. Follow [these instructions](https://dataplatform.ibm.com/docs/content/analyze-data/ml_dlaas_object_store.html) to create your COS instance and generate [service credentials](https://console.bluemix.net/docs/services/cloud-object-storage/iam/service-credentials.html#service-credentials) with [HMAC keys](https://console.bluemix.net/docs/services/cloud-object-storage/hmac/credentials.html#using-hmac-credentials).\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Enter your cluster and object storage information:**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "user_data = {\n",
+    "    \"ffdl_dir\"                   : os.environ.get(\"FFDL_DIR\"),\n",
+    "    \"ffdl_cluster_name\"          : os.environ.get(\"CLUSTER_NAME\"),\n",
+    "    \"vm_type\"                    : os.environ.get(\"VM_TYPE\"),\n",
+    "    \"cos_hmac_access_key_id\"     : os.environ.get(\"AWS_ACCESS_KEY_ID\"),\n",
+    "    \"cos_hmac_secret_access_key\" : os.environ.get(\"AWS_SECRET_ACCESS_KEY\"),\n",
+    "    \"cos_region_name\"            : os.environ.get(\"AWS_DEFAULT_REGION\"),\n",
+    "    \"cos_service_endpoint\"       : os.environ.get(\"AWS_ENDPOINT_URL\") \n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "unset_vars = [key for (key, value) in user_data.items() if not value]\n",
+    "\n",
+    "for var in unset_vars:\n",
+    "    print(\"Dictionary 'user_data' is missing '%s'\" % var)\n",
+    "    \n",
+    "assert not unset_vars, \"Enter 'user_data' to run this notebook!\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.1. Verify or Install Required Python Libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using TensorFlow backend.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "All required libraries are installed.\n",
+      "keras>=2.1.6\r\n",
+      "tensorflow>=1.8\r\n",
+      "ipython>=5.0.0\r\n",
+      "jupyter>=1.0.0\r\n",
+      "requests>=2.12.0,<=2.18.4\r\n",
+      "wget\r\n",
+      "boto3\r\n",
+      "git+git://github.com/Trusted-AI/adversarial-robustness-toolbox@master\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "import sys\n",
+    "\n",
+    "def is_venv():\n",
+    "    return (hasattr(sys, 'real_prefix') or (hasattr(sys, 'base_prefix') and sys.base_prefix != sys.prefix))\n",
+    "\n",
+    "try:\n",
+    "    import keras, tensorflow, requests, wget, boto3, art\n",
+    "    print(\"All required libraries are installed.\")\n",
+    "    !cat requirements.txt\n",
+    "except ModuleNotFoundError:\n",
+    "    if is_venv:\n",
+    "        print(\"Installing required libraries into virtual environment.\")\n",
+    "        !python -m pip install -r requirements.txt\n",
+    "    else:\n",
+    "        print(\"Please install the required libraries.\")\n",
+    "        !cat requirements.txt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.2. Connect to Cloud Object Storage  (COS)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create a `boto3.resource` to interact with the COS instance. The `boto3` library allows Python developers to manage Cloud Object Storage (COS)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cos = boto3.resource(\"s3\", \n",
+    "                     aws_access_key_id     = user_data[\"cos_hmac_access_key_id\"],\n",
+    "                     aws_secret_access_key = user_data[\"cos_hmac_secret_access_key\"],\n",
+    "                     endpoint_url          = user_data[\"cos_service_endpoint\"],\n",
+    "                     region_name           = user_data[\"cos_region_name\"]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# for bucket in cos.buckets.all():\n",
+    "#     print(bucket.name)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create two buckets, which you will use to store training data and training results.\n",
+    "\n",
+    "**Note:** The bucket names must be unique."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Creating bucket \"training-data-c279ded3-c921-4360-9e05-aa086271a009\" ...\n",
+      "Creating bucket \"training-results-c279ded3-c921-4360-9e05-aa086271a009\" ...\n"
+     ]
+    }
+   ],
+   "source": [
+    "from uuid import uuid4\n",
+    "\n",
+    "bucket_uid             = str(uuid4())\n",
+    "training_data_bucket   = 'training-data-' + bucket_uid\n",
+    "training_result_bucket = 'training-results-' + bucket_uid\n",
+    "\n",
+    "def create_buckets(bucket_names):\n",
+    "    for bucket in bucket_names:\n",
+    "        print('Creating bucket \"{}\" ...'.format(bucket))\n",
+    "        try:\n",
+    "            cos.create_bucket(Bucket=bucket)\n",
+    "        except boto3.exceptions.botocore.client.ClientError as e:\n",
+    "            print('Error: {}.'.format(e.response['Error']['Message']))\n",
+    "\n",
+    "buckets = [training_data_bucket, training_result_bucket]\n",
+    "\n",
+    "create_buckets(buckets)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now you should have 2 buckets."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.3. Download MNIST Training Data and Upload it to the COS Buckets"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Select a data set (https://keras.io/datasets/):\n",
+    "- `mnist.npz`\n",
+    "- `fashion_mnist.npz`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "datasets = [\"mnist.npz\", \"fashion_mnist.npz\"]\n",
+    "\n",
+    "dataset_filename = datasets[1] # 'fashion_mnist.npz'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Download the training data and upload it to the `training-data` bucket."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Uploading files to training-data-c279ded3-c921-4360-9e05-aa086271a009:\n",
+      "- fashion_mnist.npz was uploaded\n"
+     ]
+    }
+   ],
+   "source": [
+    "from keras.datasets import mnist, fashion_mnist\n",
+    "import numpy as np\n",
+    "\n",
+    "if \"fashion\" in dataset_filename:\n",
+    "    (x_train, y_train), (x_test, y_test) = fashion_mnist.load_data() \n",
+    "else:\n",
+    "    (x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
+    "    \n",
+    "np.savez_compressed(dataset_filename, x_train=x_train , y_train=y_train, x_test=x_test, y_test=y_test)\n",
+    "\n",
+    "bucket_obj = cos.Bucket(training_data_bucket)\n",
+    "print(\"Uploading files to {}:\".format(training_data_bucket))\n",
+    "\n",
+    "bucket_obj.upload_file(dataset_filename, dataset_filename)\n",
+    "print('- {} was uploaded'.format(dataset_filename)) "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Have a look at the list of the created buckets and their contents."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "training-data-c279ded3-c921-4360-9e05-aa086271a009\n",
+      "  File: fashion_mnist.npz, 30146.33kB\n",
+      "training-results-c279ded3-c921-4360-9e05-aa086271a009\n"
+     ]
+    }
+   ],
+   "source": [
+    "def print_bucket_contents(buckets):\n",
+    "    for bucket_name in buckets:\n",
+    "        print(bucket_name)\n",
+    "        bucket_obj = cos.Bucket(bucket_name)\n",
+    "        for obj in bucket_obj.objects.all():\n",
+    "            print(\"  File: {}, {:4.2f}kB\".format(obj.key, obj.size/1024))\n",
+    "\n",
+    "print_bucket_contents(buckets)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You are done with COS, and you are ready to train your model!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"model\"></a>\n",
+    "## 2. Create the Keras model\n",
+    "\n",
+    "In this section we:\n",
+    "\n",
+    "- [2.1 Package the model definition](#zip)\n",
+    "- [2.2 Prepare the training definition metadata](#manifest)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.1. Create the Model Zip File <a id=\"zip\"></a>\n",
+    "\n",
+    "Let's create the model [`convolutional_keras.py`](../edit/convolutional_keras.py) and add it to a zip file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "script_filename  = \"convolutional_keras.py\"\n",
+    "archive_filename = 'model.zip'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Overwriting convolutional_keras.py\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%writefile $script_filename\n",
+    "\n",
+    "from __future__ import print_function\n",
+    "from keras.models import Sequential\n",
+    "from keras.layers import Dense, Dropout, Flatten\n",
+    "from keras.layers import Conv2D, MaxPooling2D\n",
+    "from keras import backend as K\n",
+    "\n",
+    "import keras\n",
+    "import numpy as np\n",
+    "import sys\n",
+    "import os\n",
+    "\n",
+    "batch_size = 128\n",
+    "num_classes = 10\n",
+    "epochs = 1\n",
+    "\n",
+    "img_rows, img_cols = 28, 28\n",
+    "\n",
+    "\n",
+    "def main(argv):\n",
+    "    if len(argv) < 2:\n",
+    "        sys.exit(\"Not enough arguments provided.\")\n",
+    "    global image_path\n",
+    "    i = 1\n",
+    "    while i <= 2:\n",
+    "        arg = str(argv[i])\n",
+    "        if arg == \"--data\":\n",
+    "            image_path = os.path.join(os.environ[\"DATA_DIR\"], str(argv[i+1]))\n",
+    "        i += 2\n",
+    "\n",
+    "\n",
+    "if __name__ == \"__main__\":\n",
+    "    main(sys.argv)\n",
+    "\n",
+    "\n",
+    "# load training and test data from npz file\n",
+    "f = np.load(image_path)\n",
+    "x_train = f['x_train']\n",
+    "y_train = f['y_train']\n",
+    "x_test  = f['x_test']\n",
+    "y_test  = f['y_test']\n",
+    "f.close()\n",
+    "\n",
+    "if K.image_data_format() == 'channels_first':\n",
+    "    x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)\n",
+    "    x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)\n",
+    "    input_shape = (1, img_rows, img_cols)\n",
+    "else:\n",
+    "    x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)\n",
+    "    x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)\n",
+    "    input_shape = (img_rows, img_cols, 1)\n",
+    "\n",
+    "x_train = x_train.astype('float32')\n",
+    "x_test = x_test.astype('float32')\n",
+    "x_train /= 255\n",
+    "x_test /= 255\n",
+    "\n",
+    "# convert class vectors to binary class matrices\n",
+    "y_train = keras.utils.to_categorical(y_train, num_classes)\n",
+    "y_test = keras.utils.to_categorical(y_test, num_classes)\n",
+    "\n",
+    "# model\n",
+    "model = Sequential()\n",
+    "model.add(Conv2D(32, kernel_size=(3, 3),\n",
+    "                 activation='relu',\n",
+    "                 input_shape=input_shape))\n",
+    "model.add(Conv2D(64, (3, 3), activation='relu'))\n",
+    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
+    "model.add(Dropout(0.25))\n",
+    "model.add(Flatten())\n",
+    "model.add(Dense(128, activation='relu'))\n",
+    "model.add(Dropout(0.5))\n",
+    "model.add(Dense(num_classes, activation='softmax'))\n",
+    "\n",
+    "model.compile(loss=keras.losses.categorical_crossentropy,\n",
+    "              optimizer=keras.optimizers.Adadelta(),\n",
+    "              metrics=['accuracy'])\n",
+    "model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, verbose=1, validation_split=0.1)\n",
+    "\n",
+    "score = model.evaluate(x_test, y_test, verbose=0)\n",
+    "\n",
+    "print('Test loss:', score[0])\n",
+    "print('Test accuracy:', score[1])\n",
+    "\n",
+    "model_wt_path = os.environ[\"RESULT_DIR\"] + \"/keras_original_model.hdf5\"\n",
+    "model.save(model_wt_path)\n",
+    "print(\"Model saved to file: %s\" % model_wt_path)\n",
+    "\n",
+    "model_def_path = os.environ[\"RESULT_DIR\"] + \"/keras_original_model.json\"\n",
+    "model_json = model.to_json()\n",
+    "with open(model_def_path, \"w\") as json_file:\n",
+    "    json_file.write(model_json)\n",
+    "print(\"Model definition saved to file: %s\" % model_def_path)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import zipfile\n",
+    "\n",
+    "zipfile.ZipFile(archive_filename, mode='w').write(script_filename)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.2. Prepare the Training Definition Metadata <a id=\"manifest\"></a>\n",
+    "- *FfDL* does not have a *Keras* community image so we need to `pip`-install *Keras* prior to running the `training_command` \n",
+    "- Your COS credentials are referenced in the `data_stores` > `connection` data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import yaml\n",
+    "\n",
+    "training_command = \"pip3 install keras; python3 %s --data ${DATA_DIR}/%s\" % (script_filename, dataset_filename)\n",
+    "\n",
+    "manifest = {\n",
+    "  \"name\": \"keras_digit_recognition\",\n",
+    "  \"description\": \"Hand-written Digit Recognition Training\",\n",
+    "  \"version\": \"1.0\",\n",
+    "  \"gpus\": 0,\n",
+    "  \"cpus\": 2,\n",
+    "  \"memory\": \"2Gb\",\n",
+    "  \"data_stores\": [\n",
+    "    {\n",
+    "      \"id\": \"sl-internal-os\",\n",
+    "      \"type\": \"s3_datastore\",\n",
+    "      \"training_data\": {\n",
+    "        \"container\": training_data_bucket\n",
+    "      },\n",
+    "      \"training_results\": {\n",
+    "        \"container\": training_result_bucket\n",
+    "      },\n",
+    "      \"connection\": {\n",
+    "        \"type\": \"s3_datastore\",\n",
+    "        \"auth_url\":  user_data[\"cos_service_endpoint\"],\n",
+    "        \"user_name\": user_data[\"cos_hmac_access_key_id\"],\n",
+    "        \"password\":  user_data[\"cos_hmac_secret_access_key\"]\n",
+    "      }\n",
+    "    }\n",
+    "  ],\n",
+    "  \"framework\": {\n",
+    "    \"name\": \"tensorflow\",\n",
+    "    \"version\": \"1.5.0-py3\",\n",
+    "    \"command\": training_command\n",
+    "  },\n",
+    "  \"evaluation_metrics\": {\n",
+    "    \"type\": \"tensorboard\",\n",
+    "    \"in\": \"$JOB_STATE_DIR/logs/tb\"\n",
+    "  }\n",
+    "}\n",
+    "\n",
+    "yaml.dump(manifest, open(\"manifest.yml\", \"w\"), default_flow_style=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Train the Model<a id=\"train\"></a>\n",
+    "\n",
+    "In this section, learn how to:\n",
+    "- [3.1 Setup the command line environment](#cmd_setup)\n",
+    "- [3.2 Train the model in the background](#backg)\n",
+    "- [3.3 Monitor the training log](#log)\n",
+    "- [3.4 Cancel the training](#cancel)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.1. Setup the Command Line Environment <a id=\"cmd_setup\"></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load the Kubernetes cluster configuration using the [BlueMix CLI](https://console.bluemix.net/docs/cli/index.html#overview). Make sure your machine is logged in with `bx login`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "env: VM_TYPE=ibmcloud\n",
+      "env: CLUSTER_NAME=my-ffdl-cluster\n",
+      "env: KUBECONFIG=~/.bluemix/plugins/container-service/clusters/my-ffdl-cluster/kube-config-dal12-my-ffdl-cluster.yml\n"
+     ]
+    }
+   ],
+   "source": [
+    "try:\n",
+    "    %env VM_TYPE      {user_data[\"vm_type\"]}\n",
+    "    %env CLUSTER_NAME {user_data[\"ffdl_cluster_name\"]}\n",
+    "    cluster_config  = !bx cs cluster-config {user_data[\"ffdl_cluster_name\"]} | grep \"export KUBECONFIG=\"\n",
+    "    %env KUBECONFIG   {cluster_config[-1].split(\"=\")[-1]}\n",
+    "except IndexError:\n",
+    "    print(\"The cluster %s could not be found.\" % {user_data[\"ffdl_cluster_name\"]})\n",
+    "    print(\"Run 'bx cs clusters' to list all clusters you have access to.\")\n",
+    "    #!bx cs clusters\n",
+    "    raise"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Setup the DLaaS URL, username and password"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "env: DLAAS_URL=http://169.48.201.210:30020\n",
+      "env: DLAAS_USERNAME=test-user\n",
+      "env: DLAAS_PASSWORD=test\n"
+     ]
+    }
+   ],
+   "source": [
+    "node_ip        = !(cd {user_data[\"ffdl_dir\"]} && make --no-print-directory kubernetes-ip)\n",
+    "restapi_port   = !kubectl get service ffdl-restapi -o jsonpath='{.spec.ports[0].nodePort}'\n",
+    "dlaas_url      = \"http://%s:%s\" % (node_ip[0], restapi_port[0])\n",
+    "\n",
+    "%env DLAAS_URL        $dlaas_url\n",
+    "%env DLAAS_USERNAME = test-user\n",
+    "%env DLAAS_PASSWORD = test"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Obtain the correct FfDL CLI for your machine"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import platform\n",
+    "\n",
+    "ffdl = \"%s/cli/bin/ffdl-%s\" % (user_data[\"ffdl_dir\"], \"osx\" if platform.system() == \"Darwin\" else \"linux\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.2. Start the Training Job<a id=\"backg\"></a>\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[\"Deploying model with manifest 'manifest.yml' and model file 'model.zip'...\",\n",
+       " 'Model ID: training-MyCDwcHmg',\n",
+       " 'OK']"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "out = !{ffdl} train \"manifest.yml\" \"model.zip\"\n",
+    "out"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.3.  Monitor the Training Logs<a id=\"log\"></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Getting model training logs for '\u001b[1;36mtraining-MyCDwcHmg\u001b[0m'...\n",
+      "Status: PENDING\n",
+      "Status: Not Started\n",
+      "Training with training/test data at:\n",
+      "  DATA_DIR: /job/training-data-c279ded3-c921-4360-9e05-aa086271a009\n",
+      "  MODEL_DIR: /job/model-code\n",
+      "  TRAINING_JOB: \n",
+      "  TRAINING_COMMAND: pip3 install keras; python3 convolutional_keras.py --data ${DATA_DIR}/fashion_mnist.npz\n",
+      "Storing trained model at:\n",
+      "  RESULT_DIR: /job/training-results-c279ded3-c921-4360-9e05-aa086271a009\n",
+      "Contents of $MODEL_DIR\n",
+      "total 12\n",
+      "drwxrwxrwx 2 6342627 root 4096 Jun 18 18:04 .\n",
+      "drwxrwxrwx 6 root    root 4096 Jun 18 18:04 ..\n",
+      "-rwxrwxrwx 1 6342627 root 2673 Jun 18 11:02 convolutional_keras.py\n",
+      "Contents of $DATA_DIR\n",
+      "total 30156\n",
+      "drwxr-xr-x 2 6342627 root     4096 Jun 18 18:04 .\n",
+      "drwxrwxrwx 6 root    root     4096 Jun 18 18:04 ..\n",
+      "-rw-r--r-- 1 6342627 root 30869845 Jun 18 18:02 fashion_mnist.npz\n",
+      "DATA_DIR=/job/training-data-c279ded3-c921-4360-9e05-aa086271a009\n",
+      "ELASTICSEARCH_PORT=tcp://172.21.40.112:9200\n",
+      "ELASTICSEARCH_PORT_9200_TCP=tcp://172.21.40.112:9200\n",
+      "ELASTICSEARCH_PORT_9200_TCP_ADDR=172.21.40.112\n",
+      "ELASTICSEARCH_PORT_9200_TCP_PORT=9200\n",
+      "ELASTICSEARCH_PORT_9200_TCP_PROTO=tcp\n",
+      "ELASTICSEARCH_SERVICE_HOST=172.21.40.112\n",
+      "ELASTICSEARCH_SERVICE_PORT=9200\n",
+      "ELASTICSEARCH_SERVICE_PORT_HTTP=9200\n",
+      "FFDL_LCM_PORT=tcp://172.21.112.20:80\n",
+      "FFDL_LCM_PORT_80_TCP=tcp://172.21.112.20:80\n",
+      "FFDL_LCM_PORT_80_TCP_ADDR=172.21.112.20\n",
+      "FFDL_LCM_PORT_80_TCP_PORT=80\n",
+      "FFDL_LCM_PORT_80_TCP_PROTO=tcp\n",
+      "FFDL_LCM_SERVICE_HOST=172.21.112.20\n",
+      "FFDL_LCM_SERVICE_PORT=80\n",
+      "FFDL_LCM_SERVICE_PORT_GRPC=80\n",
+      "FFDL_RESTAPI_PORT=tcp://172.21.130.217:80\n",
+      "FFDL_RESTAPI_PORT_80_TCP=tcp://172.21.130.217:80\n",
+      "FFDL_RESTAPI_PORT_80_TCP_ADDR=172.21.130.217\n",
+      "FFDL_RESTAPI_PORT_80_TCP_PORT=80\n",
+      "FFDL_RESTAPI_PORT_80_TCP_PROTO=tcp\n",
+      "FFDL_RESTAPI_SERVICE_HOST=172.21.130.217\n",
+      "FFDL_RESTAPI_SERVICE_PORT=80\n",
+      "FFDL_RESTAPI_SERVICE_PORT_FFDL=80\n",
+      "FFDL_TRAINER_PORT=tcp://172.21.226.67:80\n",
+      "FFDL_TRAINER_PORT_80_TCP=tcp://172.21.226.67:80\n",
+      "FFDL_TRAINER_PORT_80_TCP_ADDR=172.21.226.67\n",
+      "FFDL_TRAINER_PORT_80_TCP_PORT=80\n",
+      "FFDL_TRAINER_PORT_80_TCP_PROTO=tcp\n",
+      "FFDL_TRAINER_SERVICE_HOST=172.21.226.67\n",
+      "FFDL_TRAINER_SERVICE_PORT=80\n",
+      "FFDL_TRAINER_SERVICE_PORT_GRPC=80\n",
+      "FFDL_TRAININGDATA_PORT=tcp://172.21.106.158:80\n",
+      "FFDL_TRAININGDATA_PORT_80_TCP=tcp://172.21.106.158:80\n",
+      "FFDL_TRAININGDATA_PORT_80_TCP_ADDR=172.21.106.158\n",
+      "FFDL_TRAININGDATA_PORT_80_TCP_PORT=80\n",
+      "FFDL_TRAININGDATA_PORT_80_TCP_PROTO=tcp\n",
+      "FFDL_TRAININGDATA_SERVICE_HOST=172.21.106.158\n",
+      "FFDL_TRAININGDATA_SERVICE_PORT=80\n",
+      "FFDL_TRAININGDATA_SERVICE_PORT_GRPC=80\n",
+      "FFDL_UI_PORT=tcp://172.21.201.22:80\n",
+      "FFDL_UI_PORT_80_TCP=tcp://172.21.201.22:80\n",
+      "FFDL_UI_PORT_80_TCP_ADDR=172.21.201.22\n",
+      "FFDL_UI_PORT_80_TCP_PORT=80\n",
+      "FFDL_UI_PORT_80_TCP_PROTO=tcp\n",
+      "FFDL_UI_SERVICE_HOST=172.21.201.22\n",
+      "FFDL_UI_SERVICE_PORT=80\n",
+      "FFDL_UI_SERVICE_PORT_HTTP=80\n",
+      "GPU_COUNT=0.000000\n",
+      "HOME=/root\n",
+      "JOB_STATE_DIR=/job\n",
+      "LEARNER_ID=1\n",
+      "LOG_DIR=/job/logs\n",
+      "MODEL_DIR=/job/model-code\n",
+      "OLDPWD=/notebooks\n",
+      "PATH=/usr/local/bin/:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin\n",
+      "PROMETHEUS_PORT=tcp://172.21.53.216:9090\n",
+      "PROMETHEUS_PORT_9090_TCP=tcp://172.21.53.216:9090\n",
+      "PROMETHEUS_PORT_9090_TCP_ADDR=172.21.53.216\n",
+      "PROMETHEUS_PORT_9090_TCP_PORT=9090\n",
+      "PROMETHEUS_PORT_9090_TCP_PROTO=tcp\n",
+      "PROMETHEUS_SERVICE_HOST=172.21.53.216\n",
+      "PROMETHEUS_SERVICE_PORT=9090\n",
+      "PROMETHEUS_SERVICE_PORT_PROMETHEUS=9090\n",
+      "PWD=/job/model-code\n",
+      "PYTHONPATH=:/job/model-code\n",
+      "RESULT_DIR=/job/training-results-c279ded3-c921-4360-9e05-aa086271a009\n",
+      "S3_PORT=tcp://172.21.95.18:80\n",
+      "S3_PORT_80_TCP=tcp://172.21.95.18:80\n",
+      "S3_PORT_80_TCP_ADDR=172.21.95.18\n",
+      "S3_PORT_80_TCP_PORT=80\n",
+      "S3_PORT_80_TCP_PROTO=tcp\n",
+      "S3_SERVICE_HOST=172.21.95.18\n",
+      "S3_SERVICE_PORT=80\n",
+      "SHLVL=3\n",
+      "TRAINING_COMMAND=pip3 install keras; python3 convolutional_keras.py --data ${DATA_DIR}/fashion_mnist.npz\n",
+      "TRAINING_ID=training-MyCDwcHmg\n",
+      "_=/usr/bin/env\n",
+      "Mon Jun 18 18:04:22 UTC 2018: Running training job\n",
+      "Collecting keras\n",
+      "  Downloading https://files.pythonhosted.org/packages/68/12/4cabc5c01451eb3b413d19ea151f36e33026fc0efb932bf51bcaf54acbf5/Keras-2.2.0-py2.py3-none-any.whl (300kB)\n",
+      "Requirement already satisfied: numpy>=1.9.1 in /usr/local/lib/python3.5/dist-packages (from keras)\n",
+      "Requirement already satisfied: h5py in /usr/local/lib/python3.5/dist-packages (from keras)\n",
+      "Requirement already satisfied: scipy>=0.14 in /usr/local/lib/python3.5/dist-packages (from keras)\n",
+      "Collecting keras-preprocessing==1.0.1 (from keras)\n",
+      "  Downloading https://files.pythonhosted.org/packages/f8/33/275506afe1d96b221f66f95adba94d1b73f6b6087cfb6132a5655b6fe338/Keras_Preprocessing-1.0.1-py2.py3-none-any.whl\n",
+      "Collecting keras-applications==1.0.2 (from keras)\n",
+      "  Downloading https://files.pythonhosted.org/packages/e2/60/c557075e586e968d7a9c314aa38c236b37cb3ee6b37e8d57152b1a5e0b47/Keras_Applications-1.0.2-py2.py3-none-any.whl (43kB)\n",
+      "Requirement already satisfied: six>=1.9.0 in /usr/local/lib/python3.5/dist-packages (from keras)\n",
+      "Collecting pyyaml (from keras)\n",
+      "  Downloading https://files.pythonhosted.org/packages/4a/85/db5a2df477072b2902b0eb892feb37d88ac635d36245a72a6a69b23b383a/PyYAML-3.12.tar.gz (253kB)\n",
+      "Building wheels for collected packages: pyyaml\n",
+      "  Running setup.py bdist_wheel for pyyaml: started\n",
+      "  Running setup.py bdist_wheel for pyyaml: finished with status 'done'\n",
+      "  Stored in directory: /root/.cache/pip/wheels/03/05/65/bdc14f2c6e09e82ae3e0f13d021e1b6b2481437ea2f207df3f\n",
+      "Successfully built pyyaml\n",
+      "Installing collected packages: keras-preprocessing, keras-applications, pyyaml, keras\n",
+      "Successfully installed keras-2.2.0 keras-applications-1.0.2 keras-preprocessing-1.0.1 pyyaml-3.12\n",
+      "You are using pip version 9.0.1, however version 10.0.1 is available.\n",
+      "You should consider upgrading via the 'pip install --upgrade pip' command.\n",
+      "2018-06-18 18:04:27.811560: I tensorflow/core/platform/cpu_feature_guard.cc:137] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 FMA\n",
+      "Train on 54000 samples, validate on 6000 samples\n",
+      "Epoch 1/1\n",
+      "\n",
+      "  128/54000 [..............................] - ETA: 4:54 - loss: 2.3122 - acc: 0.1094\n",
+      "  256/54000 [..............................] - ETA: 4:26 - loss: 2.2559 - acc: 0.1484\n",
+      "  384/54000 [..............................] - ETA: 4:09 - loss: 2.2135 - acc: 0.1849\n",
+      "  512/54000 [..............................] - ETA: 4:05 - loss: 2.1616 - acc: 0.2031\n",
+      "  640/54000 [..............................] - ETA: 4:00 - loss: 2.1043 - acc: 0.2297\n",
+      "  768/54000 [..............................] - ETA: 3:55 - loss: 2.0374 - acc: 0.2578\n",
+      "  896/54000 [..............................] - ETA: 3:54 - loss: 1.9933 - acc: 0.2757\n",
+      " 1024/54000 [..............................] - ETA: 3:52 - loss: 1.9585 - acc: 0.2930\n",
+      " 1152/54000 [..............................] - ETA: 3:51 - loss: 1.9320 - acc: 0.3047\n",
+      " 1280/54000 [..............................] - ETA: 3:49 - loss: 1.8973 - acc: 0.3211\n",
+      " 1408/54000 [..............................] - ETA: 3:47 - loss: 1.8475 - acc: 0.3388\n",
+      " 1536/54000 [..............................] - ETA: 3:47 - loss: 1.8413 - acc: 0.3405\n",
+      " 1664/54000 [..............................] - ETA: 3:46 - loss: 1.8464 - acc: 0.3504\n",
+      " 1792/54000 [..............................] - ETA: 3:46 - loss: 1.8266 - acc: 0.3666\n",
+      " 1920/54000 [>.............................] - ETA: 3:44 - loss: 1.7821 - acc: 0.3812\n",
+      " 2048/54000 [>.............................] - ETA: 3:44 - loss: 1.7542 - acc: 0.3892\n",
+      " 2176/54000 [>.............................] - ETA: 3:43 - loss: 1.7296 - acc: 0.3966\n",
+      " 2304/54000 [>.............................] - ETA: 3:42 - loss: 1.6966 - acc: 0.4102\n",
+      " 2432/54000 [>.............................] - ETA: 3:41 - loss: 1.6631 - acc: 0.4219\n",
+      " 2560/54000 [>.............................] - ETA: 3:40 - loss: 1.6452 - acc: 0.4273\n",
+      " 2688/54000 [>.............................] - ETA: 3:39 - loss: 1.6376 - acc: 0.4301\n",
+      " 2816/54000 [>.............................] - ETA: 3:38 - loss: 1.6211 - acc: 0.4332\n",
+      " 2944/54000 [>.............................] - ETA: 3:37 - loss: 1.6019 - acc: 0.4406\n",
+      " 3072/54000 [>.............................] - ETA: 3:37 - loss: 1.5782 - acc: 0.4489\n",
+      " 3200/54000 [>.............................] - ETA: 3:36 - loss: 1.5601 - acc: 0.4541\n",
+      " 3328/54000 [>.............................] - ETA: 3:36 - loss: 1.5428 - acc: 0.4600\n",
+      " 3456/54000 [>.............................] - ETA: 3:36 - loss: 1.5245 - acc: 0.4682\n",
+      " 3584/54000 [>............\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                          ... ===========>.] - ETA: 4s - loss: 0.6157 - acc: 0.7794\n",
+      "53120/54000 [============================>.] - ETA: 3s - loss: 0.6154 - acc: 0.7796\n",
+      "53248/54000 [============================>.] - ETA: 3s - loss: 0.6148 - acc: 0.7798\n",
+      "53376/54000 [============================>.] - ETA: 2s - loss: 0.6144 - acc: 0.7800\n",
+      "53504/54000 [============================>.] - ETA: 2s - loss: 0.6137 - acc: 0.7802\n",
+      "53632/54000 [============================>.] - ETA: 1s - loss: 0.6132 - acc: 0.7804\n",
+      "53760/54000 [============================>.] - ETA: 1s - loss: 0.6128 - acc: 0.7805\n",
+      "53888/54000 [============================>.] - ETA: 0s - loss: 0.6127 - acc: 0.7805\n",
+      "54000/54000 [==============================] - 234s 4ms/step - loss: 0.6124 - acc: 0.7806 - val_loss: 0.3721 - val_acc: 0.8633\n",
+      "/usr/local/lib/python3.5/dist-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
+      "  from ._conv import register_converters as _register_converters\n",
+      "Using TensorFlow backend.\n",
+      "Test loss: 0.38816263160705566\n",
+      "Test accuracy: 0.8606\n",
+      "Model saved to file: /job/training-results-c279ded3-c921-4360-9e05-aa086271a009/keras_original_model.hdf5\n",
+      "Model definition saved to file: /job/training-results-c279ded3-c921-4360-9e05-aa086271a009/keras_original_model.json\n",
+      "Exception ignored in: <bound method BaseSession.__del__ of <tensorflow.python.client.session.Session object at 0x7f4651d91780>>\n",
+      "Traceback (most recent call last):\n",
+      "  File \"/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py\", line 702, in __del__\n",
+      "TypeError: 'NoneType' object is not callable\n",
+      "Training process finished. Exit code: 0\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "if \"Model ID\" in out[1]:\n",
+    "    model_id = out.fields()[1][-1]\n",
+    "    !{ffdl} logs --follow {model_id}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4. Generate Adversarial Samples <a id=\"art\"></a>\n",
+    "\n",
+    "In this section, we learn how to:\n",
+    "- [4.1 Generate adversarial samples with ART (synchronously in notebook)](#artLocal)\n",
+    "- [4.2 Generate adversarial samples with ART (asynchronously using FfDL)](#artWithFfDL)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4.1. Generate Adversarial Samples Locally <a id=\"artLocal\"></a>\n",
+    "\n",
+    "This section shows how to use the ART Fast Gradient Method (FGM) to generate adversarial samples for the model previously trained synchronously in this notebook. \n",
+    "\n",
+    "A trained model should have been created in the `training_result_bucket`. Now ART can be used to check the robustness of the trained model. \n",
+    "\n",
+    "The original dataset used to train the model as well as the trained model serve as inputs to the `robustness_check.py` script. We can download both from the `training_data_bucket` and the `training_result_bucket` respectively."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First, download the original data set and the trained model from Cloud Object Store."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "weights_filename = \"keras_original_model.hdf5\"\n",
+    "network_definition_filename = \"keras_original_model.json\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Print contents of COS buckets used in the previous training run"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "training-data-c279ded3-c921-4360-9e05-aa086271a009\n",
+      "  File: fashion_mnist.npz, 30146.33kB\n",
+      "training-results-c279ded3-c921-4360-9e05-aa086271a009\n",
+      "  File: training-MyCDwcHmg/keras_original_model.hdf5, 14092.55kB\n",
+      "  File: training-MyCDwcHmg/keras_original_model.json, 2.75kB\n",
+      "  File: training-MyCDwcHmg/learner-1/load-data.log, 8.32kB\n",
+      "  File: training-MyCDwcHmg/learner-1/load-model.log, 0.42kB\n",
+      "  File: training-MyCDwcHmg/learner-1/training-log.txt, 41.91kB\n"
+     ]
+    }
+   ],
+   "source": [
+    "print_bucket_contents([training_data_bucket, training_result_bucket])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloaded keras_original_model.hdf5\n",
+      "Downloaded keras_original_model.json\n"
+     ]
+    }
+   ],
+   "source": [
+    "# download network definition and weights to current working directory\n",
+    "\n",
+    "weights_file_in_cos_bucket = os.path.join(model_id, weights_filename)\n",
+    "network_definition_file_in_cos_bucket = os.path.join(model_id, network_definition_filename)\n",
+    "\n",
+    "bucket_obj = cos.Bucket(training_result_bucket)\n",
+    "\n",
+    "bucket_obj.download_file(weights_file_in_cos_bucket, weights_filename)\n",
+    "print('Downloaded', weights_filename)\n",
+    "\n",
+    "bucket_obj.download_file(network_definition_file_in_cos_bucket, network_definition_filename)\n",
+    "print('Downloaded', network_definition_filename)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load & compile the model that we created using `convolutional_keras.py`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Network Definition: keras_original_model.json\n",
+      "Weights:            keras_original_model.hdf5\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import tensorflow as tf\n",
+    "from keras import backend as K\n",
+    "from keras.models import model_from_json\n",
+    "\n",
+    "print('Network Definition:', network_definition_filename)\n",
+    "print('Weights:           ', weights_filename)\n",
+    "\n",
+    "# load model\n",
+    "json_file = open(network_definition_filename, 'r')\n",
+    "model_json = json_file.read()\n",
+    "json_file.close()\n",
+    "\n",
+    "model = model_from_json(model_json)\n",
+    "model.load_weights(weights_filename)\n",
+    "comp_params = {'loss': 'categorical_crossentropy',\n",
+    "                       'optimizer': 'adam',\n",
+    "                       'metrics': ['accuracy']}\n",
+    "model.compile(**comp_params)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After loading & compiling the model, the next step is to create a KerasClassifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create ART classifier object\n",
+    "from art.estimators.classification import KerasClassifier\n",
+    "\n",
+    "classifier = KerasClassifier(clip_values=(0, 1), model=model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load the test data and labels from `.npz` file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from keras.utils import np_utils\n",
+    "\n",
+    "f = np.load(dataset_filename)\n",
+    "x_original = f['x_test']\n",
+    "y = f['y_test']\n",
+    "f.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Visualize the original (non-adversarial) sample"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 144x144 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "plt.figure(figsize=(2, 2))\n",
+    "plt.imshow(x_original[1], cmap='gray')\n",
+    "print(y[1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Standardize the Numpy array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# preprocess\n",
+    "x_original = np.expand_dims(x_original, axis=3)\n",
+    "x_original = x_original.astype('float32') / 255\n",
+    "y = np_utils.to_categorical(y, 10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Evaluate the model and calculated test accuracy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model test loss:     38.81626313686371\n",
+      "model test accuracy: 86.06\n"
+     ]
+    }
+   ],
+   "source": [
+    "# evaluate\n",
+    "scores = model.evaluate(x_original, y, verbose=0)\n",
+    "print('model test loss:    ', scores[0]*100)\n",
+    "print('model test accuracy:', scores[1]*100)\n",
+    "model_accuracy = scores[1]*100"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "ART exposes many attacks like FGM, NewtonFool, DeepFool, Carlini etc. The code below shows how to use one of ART's attack methods (Fast Gradient Method or FGM) to craft adversarial samples based on x_test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of adversarial samples crafted: 10000\n",
+      "adversarial samples saved to: adv_samples.npz\n"
+     ]
+    }
+   ],
+   "source": [
+    "from art.attacks import FastGradientMethod\n",
+    "\n",
+    "# configuration\n",
+    "epsilon = 0.2\n",
+    "\n",
+    "# create crafter object\n",
+    "crafter = FastGradientMethod(classifier, eps=epsilon)\n",
+    "\n",
+    "# craft samples on x_test (stored in variable x_original)\n",
+    "x_adv_samples = crafter.generate(x_original)\n",
+    "\n",
+    "adv_samples_filename = \"adv_samples.npz\"\n",
+    "np.savez(adv_samples_filename, x_original=x_original, x_adversarial=x_adv_samples, y=y)\n",
+    "\n",
+    "print(\"Number of adversarial samples crafted:\", len(x_adv_samples))\n",
+    "print(\"adversarial samples saved to:\", adv_samples_filename)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following functions can be used for gathering metrics like model robustness, confidence metric, perturbation metric"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy.linalg as la\n",
+    "import json\n",
+    "\n",
+    "\n",
+    "def get_metrics(model, x_original, x_adv_samples, y):\n",
+    "    scores = model.evaluate(x_original, y, verbose=0)\n",
+    "    model_accuracy_on_non_adversarial_samples = scores[1] * 100\n",
+    "\n",
+    "    y_pred = model.predict(x_original, verbose=0)\n",
+    "    y_pred_adv = model.predict(x_adv_samples, verbose=0)\n",
+    "\n",
+    "    scores = model.evaluate(x_adv_samples, y, verbose=0)\n",
+    "    model_accuracy_on_adversarial_samples = scores[1] * 100\n",
+    "\n",
+    "    pert_metric = get_perturbation_metric(x_original, x_adv_samples, y_pred, y_pred_adv, ord=2)\n",
+    "    conf_metric = get_confidence_metric(y_pred, y_pred_adv)\n",
+    "\n",
+    "    data = {\n",
+    "        \"model accuracy on test data:\": model_accuracy_on_non_adversarial_samples,\n",
+    "        \"model accuracy on adversarial samples\": model_accuracy_on_adversarial_samples,\n",
+    "        \"reduction in confidence\": conf_metric * 100,\n",
+    "        \"average perturbation\": pert_metric * 100\n",
+    "    }\n",
+    "    return data\n",
+    "\n",
+    "\n",
+    "def get_perturbation_metric(x_original, x_adv, y_pred, y_pred_adv, ord=2):\n",
+    "\n",
+    "    idxs = (np.argmax(y_pred_adv, axis=1) != np.argmax(y_pred, axis=1))\n",
+    "\n",
+    "    if np.sum(idxs) == 0.0:\n",
+    "        return 0\n",
+    "\n",
+    "    perts_norm = la.norm((x_adv - x_original).reshape(x_original.shape[0], -1), ord, axis=1)\n",
+    "    perts_norm = perts_norm[idxs]\n",
+    "\n",
+    "    return np.mean(perts_norm / la.norm(x_original[idxs].reshape(np.sum(idxs), -1), ord, axis=1))\n",
+    "\n",
+    "\n",
+    "# This computes the change in confidence for all images in the test set\n",
+    "def get_confidence_metric(y_pred, y_pred_adv):\n",
+    "\n",
+    "    y_classidx = np.argmax(y_pred, axis=1)\n",
+    "    y_classconf = y_pred[np.arange(y_pred.shape[0]), y_classidx]\n",
+    "\n",
+    "    y_adv_classidx = np.argmax(y_pred_adv, axis=1)\n",
+    "    y_adv_classconf = y_pred_adv[np.arange(y_pred_adv.shape[0]), y_adv_classidx]\n",
+    "\n",
+    "    idxs = (y_classidx == y_adv_classidx)\n",
+    "\n",
+    "    if np.sum(idxs) == 0.0:\n",
+    "        return 0\n",
+    "\n",
+    "    idxnonzero = y_classconf != 0\n",
+    "    idxs = idxs & idxnonzero\n",
+    "\n",
+    "    return np.mean((y_classconf[idxs] - y_adv_classconf[idxs]) / y_classconf[idxs])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Display the robustness check metrics\n",
+    "\n",
+    "1. Model accuracy on test data\n",
+    "2. Model robustness on adversarial samples\n",
+    "3. Reduction in confidence\n",
+    "4. Perturbation metric"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\n",
+      "    \"model accuracy on test data:\": 86.06,\n",
+      "    \"model accuracy on adversarial samples\": 17.09,\n",
+      "    \"reduction in confidence\": 42.64292120933533,\n",
+      "    \"average perturbation\": 44.387608766555786\n",
+      "}\n"
+     ]
+    }
+   ],
+   "source": [
+    "result = get_metrics(model, x_original, x_adv_samples, y)\n",
+    "\n",
+    "print(json.dumps(result, indent=4, sort_keys=False))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Compare original images with adversarial samples and test model predictions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# https://keras.io/datasets/#fashion-mnist-database-of-fashion-articles\n",
+    "\n",
+    "fashion_labels = {\n",
+    " 0: \"T-shirt/top\",\n",
+    " 1: \"Trouser\",\n",
+    " 2: \"Pullover\",\n",
+    " 3: \"Dress\",\n",
+    " 4: \"Coat\",\n",
+    " 5: \"Sandal\",\n",
+    " 6: \"Shirt\",\n",
+    " 7: \"Sneaker\",\n",
+    " 8: \"Bag\",\n",
+    " 9: \"Ankle boot\"\n",
+    "}\n",
+    "\n",
+    "def get_label(y):\n",
+    "    if \"fashion\" in dataset_filename:\n",
+    "        return fashion_labels[y]\n",
+    "    else:\n",
+    "        return \"Predict: %i\" % y"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 1080x216 with 20 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# x_adv_samples = np.load(\"adv_samples_from_cos.npz\")\n",
+    "# x_original    = x_adv_samples[\"x_original\"]\n",
+    "# x_adversarial = x_adv_samples[\"x_adversarial\"]\n",
+    "# y             = x_adv_samples[\"y\"]\n",
+    "\n",
+    "x_adversarial = x_adv_samples\n",
+    "\n",
+    "x_orig = ((x_original   ) * 255).astype('int')[:, :, :, 0]\n",
+    "x_adv  = ((x_adversarial) * 255).astype('int')[:, :, :, 0]\n",
+    "\n",
+    "y_pred_orig = model.predict(x_original,    verbose=0)\n",
+    "y_pred_adv  = model.predict(x_adversarial, verbose=0)\n",
+    "\n",
+    "fig    = plt.figure(figsize=(15, 3))\n",
+    "cols   = 10\n",
+    "rows   = 2\n",
+    "images = list(x_orig[:cols])      + list(x_adv[:cols])\n",
+    "preds  = list(y_pred_orig[:cols]) + list(y_pred_adv[:cols])\n",
+    "labels = list(y[:cols])           + list(y[:cols])\n",
+    "\n",
+    "for i in range(0, len(images)):\n",
+    "    ax = fig.add_subplot(rows, cols, i+1)\n",
+    "    y_pred = np.argmax(preds[i])\n",
+    "    y_orig = np.argmax(labels[i])\n",
+    "    ax.set_xlabel(get_label(y_pred),\n",
+    "                  color = \"green\" if y_pred == y_orig else \"red\")\n",
+    "    ax.tick_params(axis='both', which='both',\n",
+    "                   bottom=False, top=False,\n",
+    "                   right=False, left=False,\n",
+    "                   labelbottom=False, labelleft=False)\n",
+    "    plt.imshow(images[i], cmap='gray')\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5. Summary and Next Steps <a id=\"summary\"></a>\n",
+    "\n",
+    "This notebook only looked at one adversarial robustness technique (FGM). The *ART* library contains many more attacks, metrics and defenses to help you understand and improve your model's robustness. You can use this notebook as a template to experiment with all aspects of *ART*. Find more state-of-the-art methods for attacking and defending classifiers here:\n",
+    "\n",
+    "https://github.com/Trusted-AI/adversarial-robustness-toolbox"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Acknowledgements\n",
+    "\n",
+    "Special thanks to [Anupama-Murthi](https://github.ibm.com/Anupama-Murthi) and [Vijay Arya](https://github.ibm.com/vijay-arya) who created the original notebook which we modified here to showcase how to use *ART* with *FfDL*. If you would like to try *[Watson Machine Learning (WML) Service](https://console.bluemix.net/catalog/services/machine-learning)* with *ART* check out Anupama and Vijay's notebook here:\n",
+    "\n",
+    "[https://github.ibm.com/robust-dlaas/ART-in-WML/Use ART to check robustness of deep learning models.ipynb](https://github.ibm.com/robust-dlaas/ART-in-WML/blob/master/Use%20ART%20to%20check%20robustness%20of%20deep%20learning%20models.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright © 2017, 2018 Trusted-AI. This notebook and its source code are released under the terms of the MIT License."
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/adversarial-robustness-toolbox/notebooks/model-stealing-demo.ipynb b/adversarial-robustness-toolbox/notebooks/model-stealing-demo.ipynb
new file mode 100644
index 0000000..0952e22
--- /dev/null
+++ b/adversarial-robustness-toolbox/notebooks/model-stealing-demo.ipynb
@@ -0,0 +1,748 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import art"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using TensorFlow backend.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "\n",
+    "import tensorflow as tf\n",
+    "import keras\n",
+    "from keras.models import Sequential\n",
+    "from keras.layers import Dense, Flatten, Conv2D, MaxPooling2D, Flatten, InputLayer, Reshape\n",
+    "\n",
+    "from art.classifiers import KerasClassifier\n",
+    "\n",
+    "if tf.executing_eagerly():\n",
+    "    tf.python.framework.ops.disable_eager_execution()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Prepare the dataset and the model architecture.\n",
+    "\n",
+    "import art\n",
+    "from art.utils import load_mnist\n",
+    "\n",
+    "# Load the dataset, and split the test data into test and steal datasets.\n",
+    "(x_train, y_train), (x_test0, y_test0), _, _ = load_mnist()\n",
+    "len_steal = 5000\n",
+    "indices = np.random.permutation(len(x_test0))\n",
+    "x_steal = x_test0[indices[:len_steal]]\n",
+    "y_steal = y_test0[indices[:len_steal]]\n",
+    "x_test = x_test0[indices[len_steal:]]\n",
+    "y_test = y_test0[indices[len_steal:]]\n",
+    "\n",
+    "im_shape = x_train[0].shape\n",
+    "def get_model(num_classes=10, c1=32, c2=64, d1=128):\n",
+    "    model = Sequential()\n",
+    "    model.add(Conv2D(c1, kernel_size=(3, 3), activation='relu', input_shape=im_shape))\n",
+    "    model.add(Conv2D(c2, (3, 3), activation='relu'))\n",
+    "    model.add(MaxPooling2D(pool_size=(2, 2)))\n",
+    "    model.add(Flatten())\n",
+    "    model.add(Dense(d1, activation='relu'))\n",
+    "    model.add(Dense(num_classes, activation='softmax'))\n",
+    "    model.compile(loss=keras.losses.categorical_crossentropy, optimizer=\"sgd\",\n",
+    "                          metrics=['accuracy'])\n",
+    "    return model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From /home/taesung/projects/miniconda3/envs/artist/lib/python3.7/site-packages/tensorflow/python/ops/resource_variable_ops.py:1666: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "If using Keras pass *_constraint arguments to layers.\n",
+      "Original model training:\n",
+      "Epoch 1/5\n",
+      "60000/60000 [==============================] - 25s 422us/step - loss: 0.6260 - accuracy: 0.8253\n",
+      "Epoch 2/5\n",
+      "60000/60000 [==============================] - 48s 795us/step - loss: 0.2590 - accuracy: 0.9233\n",
+      "Epoch 3/5\n",
+      "60000/60000 [==============================] - 42s 703us/step - loss: 0.1985 - accuracy: 0.9414\n",
+      "Epoch 4/5\n",
+      "60000/60000 [==============================] - 45s 746us/step - loss: 0.1624 - accuracy: 0.9515\n",
+      "Epoch 5/5\n",
+      "60000/60000 [==============================] - 45s 743us/step - loss: 0.1385 - accuracy: 0.9577\n",
+      "Original model evaluation:\n",
+      "5000/5000 [==============================] - 5s 948us/step\n",
+      "[0.13485618290305137, 0.9588000178337097]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Train the original model.\n",
+    "num_epochs = 5\n",
+    "model = get_model(num_classes=10, c1=32, c2=64, d1=128)\n",
+    "print(\"Original model training:\")\n",
+    "model.fit(x_train, y_train, batch_size=100, epochs=num_epochs)\n",
+    "print(\"Original model evaluation:\")\n",
+    "print(model.evaluate(x_test, y_test))\n",
+    "classifier_original = KerasClassifier(model, clip_values=(0, 1), use_logits=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/5\n",
+      "3/3 [==============================] - 1s 180ms/step - loss: 2.3053 - accuracy: 0.0781\n",
+      "Epoch 2/5\n",
+      "3/3 [==============================] - 0s 72ms/step - loss: 2.2876 - accuracy: 0.1042\n",
+      "Epoch 3/5\n",
+      "3/3 [==============================] - 0s 85ms/step - loss: 2.2826 - accuracy: 0.1354\n",
+      "Epoch 4/5\n",
+      "3/3 [==============================] - 0s 71ms/step - loss: 2.2581 - accuracy: 0.2500\n",
+      "Epoch 5/5\n",
+      "3/3 [==============================] - 0s 67ms/step - loss: 2.2579 - accuracy: 0.1823\n",
+      "9750/9750 [==============================] - 8s 824us/step\n",
+      "Probabilistic CopycatCNN : 0.20676922798156738\n",
+      "Epoch 1/5\n",
+      "3/3 [==============================] - 1s 203ms/step - loss: 2.3053 - accuracy: 0.1146\n",
+      "Epoch 2/5\n",
+      "3/3 [==============================] - 0s 89ms/step - loss: 2.3025 - accuracy: 0.0729\n",
+      "Epoch 3/5\n",
+      "3/3 [==============================] - 0s 98ms/step - loss: 2.2912 - accuracy: 0.1510\n",
+      "Epoch 4/5\n",
+      "3/3 [==============================] - 0s 73ms/step - loss: 2.2739 - accuracy: 0.2240\n",
+      "Epoch 5/5\n",
+      "3/3 [==============================] - 0s 67ms/step - loss: 2.2711 - accuracy: 0.2396\n",
+      "9750/9750 [==============================] - 7s 717us/step\n",
+      "Argmax CopycatCNN : 0.18625640869140625\n",
+      "9750/9750 [==============================] - 2s 211us/step\n",
+      "Probabilistic KnockoffNets : 0.2379487156867981\n",
+      "9750/9750 [==============================] - 3s 354us/step\n",
+      "Argmax KnockoffNets : 0.15794871747493744\n",
+      "Epoch 1/5\n",
+      "7/7 [==============================] - 1s 115ms/step - loss: 2.2944 - accuracy: 0.1027\n",
+      "Epoch 2/5\n",
+      "7/7 [==============================] - 0s 69ms/step - loss: 2.2841 - accuracy: 0.0960\n",
+      "Epoch 3/5\n",
+      "7/7 [==============================] - 0s 67ms/step - loss: 2.2739 - accuracy: 0.1897\n",
+      "Epoch 4/5\n",
+      "7/7 [==============================] - 0s 70ms/step - loss: 2.2632 - accuracy: 0.1853\n",
+      "Epoch 5/5\n",
+      "7/7 [==============================] - 0s 64ms/step - loss: 2.2479 - accuracy: 0.2321\n",
+      "9500/9500 [==============================] - 6s 669us/step\n",
+      "Probabilistic CopycatCNN : 0.18336841464042664\n",
+      "Epoch 1/5\n",
+      "7/7 [==============================] - 1s 84ms/step - loss: 2.2880 - accuracy: 0.1875\n",
+      "Epoch 2/5\n",
+      "7/7 [==============================] - 0s 42ms/step - loss: 2.2688 - accuracy: 0.2277\n",
+      "Epoch 3/5\n",
+      "7/7 [==============================] - 1s 76ms/step - loss: 2.2358 - accuracy: 0.3393\n",
+      "Epoch 4/5\n",
+      "7/7 [==============================] - 0s 61ms/step - loss: 2.2130 - accuracy: 0.3705\n",
+      "Epoch 5/5\n",
+      "7/7 [==============================] - 0s 68ms/step - loss: 2.1719 - accuracy: 0.4665\n",
+      "9500/9500 [==============================] - 5s 477us/step\n",
+      "Argmax CopycatCNN : 0.386631578207016\n",
+      "9500/9500 [==============================] - 2s 159us/step\n",
+      "Probabilistic KnockoffNets : 0.3335789442062378\n",
+      "9500/9500 [==============================] - 9s 936us/step\n",
+      "Argmax KnockoffNets : 0.3183158040046692\n",
+      "Epoch 1/5\n",
+      "15/15 [==============================] - 1s 64ms/step - loss: 2.2761 - accuracy: 0.1094\n",
+      "Epoch 2/5\n",
+      "15/15 [==============================] - 1s 53ms/step - loss: 2.2057 - accuracy: 0.3792\n",
+      "Epoch 3/5\n",
+      "15/15 [==============================] - 1s 53ms/step - loss: 2.1147 - accuracy: 0.4802\n",
+      "Epoch 4/5\n",
+      "15/15 [==============================] - 1s 51ms/step - loss: 1.9677 - accuracy: 0.6125\n",
+      "Epoch 5/5\n",
+      "15/15 [==============================] - 1s 54ms/step - loss: 1.6985 - accuracy: 0.6896\n",
+      "9000/9000 [==============================] - 2s 217us/step\n",
+      "Probabilistic CopycatCNN : 0.7266666889190674\n",
+      "Epoch 1/5\n",
+      "15/15 [==============================] - 1s 77ms/step - loss: 2.2760 - accuracy: 0.2167\n",
+      "Epoch 2/5\n",
+      "15/15 [==============================] - 1s 51ms/step - loss: 2.2117 - accuracy: 0.3844\n",
+      "Epoch 3/5\n",
+      "15/15 [==============================] - 1s 58ms/step - loss: 2.1212 - accuracy: 0.5115\n",
+      "Epoch 4/5\n",
+      "15/15 [==============================] - 1s 48ms/step - loss: 1.9763 - accuracy: 0.5750\n",
+      "Epoch 5/5\n",
+      "15/15 [==============================] - 1s 50ms/step - loss: 1.7369 - accuracy: 0.6792\n",
+      "9000/9000 [==============================] - 2s 205us/step\n",
+      "Argmax CopycatCNN : 0.6539999842643738\n",
+      "9000/9000 [==============================] - 4s 434us/step\n",
+      "Probabilistic KnockoffNets : 0.7435555458068848\n",
+      "9000/9000 [==============================] - 7s 770us/step\n",
+      "Argmax KnockoffNets : 0.633222222328186\n",
+      "Epoch 1/5\n",
+      "31/31 [==============================] - 2s 76ms/step - loss: 2.2921 - accuracy: 0.1487\n",
+      "Epoch 2/5\n",
+      "31/31 [==============================] - 2s 68ms/step - loss: 2.2532 - accuracy: 0.2787\n",
+      "Epoch 3/5\n",
+      "31/31 [==============================] - 2s 64ms/step - loss: 2.1918 - accuracy: 0.4017\n",
+      "Epoch 4/5\n",
+      "31/31 [==============================] - 2s 62ms/step - loss: 2.0036 - accuracy: 0.6179\n",
+      "Epoch 5/5\n",
+      "31/31 [==============================] - 2s 56ms/step - loss: 1.4468 - accuracy: 0.7525\n",
+      "8000/8000 [==============================] - 8s 996us/step\n",
+      "Probabilistic CopycatCNN : 0.7837499976158142\n",
+      "Epoch 1/5\n",
+      "31/31 [==============================] - 3s 82ms/step - loss: 2.2754 - accuracy: 0.2324\n",
+      "Epoch 2/5\n",
+      "31/31 [==============================] - 2s 52ms/step - loss: 2.1951 - accuracy: 0.4078\n",
+      "Epoch 3/5\n",
+      "31/31 [==============================] - 1s 46ms/step - loss: 1.9594 - accuracy: 0.5892\n",
+      "Epoch 4/5\n",
+      "31/31 [==============================] - 2s 53ms/step - loss: 1.3464 - accuracy: 0.7303\n",
+      "Epoch 5/5\n",
+      "31/31 [==============================] - 1s 42ms/step - loss: 0.7091 - accuracy: 0.8357\n",
+      "8000/8000 [==============================] - 6s 781us/step\n",
+      "Argmax CopycatCNN : 0.8255000114440918\n",
+      "8000/8000 [==============================] - 6s 808us/step\n",
+      "Probabilistic KnockoffNets : 0.8393750190734863\n",
+      "8000/8000 [==============================] - 7s 837us/step\n",
+      "Argmax KnockoffNets : 0.840499997138977\n",
+      "Epoch 1/5\n",
+      "62/62 [==============================] - 5s 87ms/step - loss: 2.1787 - accuracy: 0.2603\n",
+      "Epoch 2/5\n",
+      "62/62 [==============================] - 5s 86ms/step - loss: 1.3672 - accuracy: 0.6782\n",
+      "Epoch 3/5\n",
+      "62/62 [==============================] - 4s 65ms/step - loss: 0.5705 - accuracy: 0.8569\n",
+      "Epoch 4/5\n",
+      "62/62 [==============================] - 3s 53ms/step - loss: 0.4178 - accuracy: 0.8954\n",
+      "Epoch 5/5\n",
+      "62/62 [==============================] - 4s 58ms/step - loss: 0.3780 - accuracy: 0.8982\n",
+      "6000/6000 [==============================] - 7s 1ms/step\n",
+      "Probabilistic CopycatCNN : 0.8924999833106995\n",
+      "Epoch 1/5\n",
+      "62/62 [==============================] - 5s 83ms/step - loss: 2.1723 - accuracy: 0.3007\n",
+      "Epoch 2/5\n",
+      "62/62 [==============================] - 5s 75ms/step - loss: 1.3383 - accuracy: 0.7140\n",
+      "Epoch 3/5\n",
+      "62/62 [==============================] - 5s 80ms/step - loss: 0.5329 - accuracy: 0.8579\n",
+      "Epoch 4/5\n",
+      "62/62 [==============================] - 5s 82ms/step - loss: 0.3467 - accuracy: 0.8964\n",
+      "Epoch 5/5\n",
+      "62/62 [==============================] - 5s 82ms/step - loss: 0.2916 - accuracy: 0.9060\n",
+      "6000/6000 [==============================] - 6s 952us/step\n",
+      "Argmax CopycatCNN : 0.8974999785423279\n",
+      "6000/6000 [==============================] - 5s 768us/step\n",
+      "Probabilistic KnockoffNets : 0.8961666822433472\n",
+      "6000/6000 [==============================] - 2s 328us/step\n",
+      "Argmax KnockoffNets : 0.8945000171661377\n",
+      "Epoch 1/5\n",
+      "78/78 [==============================] - 4s 56ms/step - loss: 2.0865 - accuracy: 0.4818\n",
+      "Epoch 2/5\n",
+      "78/78 [==============================] - 4s 46ms/step - loss: 0.8605 - accuracy: 0.8125\n",
+      "Epoch 3/5\n",
+      "78/78 [==============================] - 4s 52ms/step - loss: 0.4520 - accuracy: 0.8780\n",
+      "Epoch 4/5\n",
+      "78/78 [==============================] - 3s 40ms/step - loss: 0.3832 - accuracy: 0.9002\n",
+      "Epoch 5/5\n",
+      "78/78 [==============================] - 4s 47ms/step - loss: 0.3333 - accuracy: 0.9183\n",
+      "5000/5000 [==============================] - 5s 912us/step\n",
+      "Probabilistic CopycatCNN : 0.907800018787384\n",
+      "Epoch 1/5\n",
+      "78/78 [==============================] - 2s 27ms/step - loss: 2.2266 - accuracy: 0.3572\n",
+      "Epoch 2/5\n",
+      "78/78 [==============================] - 2s 28ms/step - loss: 1.4447 - accuracy: 0.7380\n",
+      "Epoch 3/5\n",
+      "78/78 [==============================] - 3s 42ms/step - loss: 0.5008 - accuracy: 0.8544\n",
+      "Epoch 4/5\n",
+      "78/78 [==============================] - 3s 44ms/step - loss: 0.3715 - accuracy: 0.8768\n",
+      "Epoch 5/5\n",
+      "78/78 [==============================] - 3s 40ms/step - loss: 0.2810 - accuracy: 0.9111\n",
+      "5000/5000 [==============================] - 6s 1ms/step\n",
+      "Argmax CopycatCNN : 0.9089999794960022\n",
+      "5000/5000 [==============================] - 4s 860us/step\n",
+      "Probabilistic KnockoffNets : 0.899399995803833\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "5000/5000 [==============================] - 6s 1ms/step\n",
+      "Argmax KnockoffNets : 0.907800018787384\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Stealing from the unprotected classifier.\n",
+    "from art.attacks import ExtractionAttack\n",
+    "from art.attacks.extraction import CopycatCNN, KnockoffNets\n",
+    "\n",
+    "attack_catalogue = {\"Probabilistic CopycatCNN\": CopycatCNN(classifier=classifier_original,\n",
+    "                                              batch_size_fit=64,\n",
+    "                                              batch_size_query=64,\n",
+    "                                              nb_epochs=num_epochs,\n",
+    "                                              nb_stolen=len_steal,\n",
+    "                                              use_probability=True),\n",
+    "                    \"Argmax CopycatCNN\": CopycatCNN(classifier=classifier_original,\n",
+    "                                              batch_size_fit=64,\n",
+    "                                              batch_size_query=64,\n",
+    "                                              nb_epochs=num_epochs,\n",
+    "                                              nb_stolen=len_steal,\n",
+    "                                              use_probability=False),\n",
+    "                    \"Probabilistic KnockoffNets\": KnockoffNets(classifier=classifier_original,\n",
+    "                                              batch_size_fit=64,\n",
+    "                                              batch_size_query=64,\n",
+    "                                              nb_epochs=num_epochs,\n",
+    "                                              nb_stolen=len_steal,\n",
+    "                                              use_probability=True),\n",
+    "                    \"Argmax KnockoffNets\": KnockoffNets(classifier=classifier_original,\n",
+    "                                              batch_size_fit=64,\n",
+    "                                              batch_size_query=64,\n",
+    "                                              nb_epochs=num_epochs,\n",
+    "                                              nb_stolen=len_steal,\n",
+    "                                              use_probability=False),\n",
+    "                   }\n",
+    "\n",
+    "results = []\n",
+    "for len_steal in [250, 500, 1000, 2000, 4000, 5000]:\n",
+    "    indices = np.random.permutation(len(x_test0))\n",
+    "    x_steal = x_test0[indices[:len_steal]]\n",
+    "    y_steal = y_test0[indices[:len_steal]]\n",
+    "    x_test = x_test0[indices[len_steal:]]\n",
+    "    y_test = y_test0[indices[len_steal:]]\n",
+    "\n",
+    "    for name, attack in attack_catalogue.items():\n",
+    "        model_stolen = get_model(num_classes=10, c1=32, c2=64, d1=128)\n",
+    "        classifier_stolen = KerasClassifier(model_stolen, clip_values=(0, 1), use_logits=False)\n",
+    "        classifier_stolen = attack.extract(x_steal, y_steal, thieved_classifier=classifier_stolen)\n",
+    "        acc = classifier_stolen._model.evaluate(x_test, y_test)[1]\n",
+    "        print(name, \":\", acc)\n",
+    "        results.append((name, len_steal, acc))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 576x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "df = pd.DataFrame(results, columns=('Method Name', 'Stealing Dataset Size', 'Accuracy'))\n",
+    "fig, ax = plt.subplots(figsize=(8,6))\n",
+    "ax.set_xlabel(\"Stealing Dataset Size\")\n",
+    "ax.set_ylabel(\"Stolen Model Accuracy\")\n",
+    "for name, group in df.groupby(\"Method Name\"):\n",
+    "    group.plot(1, 2, ax=ax, label=name)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Prepare the defense layer.\n",
+    "from art.defences.postprocessor import ReverseSigmoid\n",
+    "postprocessor = ReverseSigmoid(beta=1.0, gamma=0.2)\n",
+    "classifier_protected = KerasClassifier(model, clip_values=(0, 1), use_logits=False, postprocessing_defences=postprocessor)\n",
+    "\n",
+    "# Below is used by `FunctionallyEquivalentExtraction`.\n",
+    "model_flat = Sequential([InputLayer([784]), Reshape([28, 28, 1]), model])\n",
+    "model_flat.compile('sgd', 'categorical_crossentropy', ['accuracy'])\n",
+    "classifier_flat_protected = KerasClassifier(model_flat, clip_values=(0, 1), use_logits=False, postprocessing_defences=postprocessor)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/5\n",
+      "3/3 [==============================] - 1s 241ms/step - loss: 2.3037 - accuracy: 0.1406\n",
+      "Epoch 2/5\n",
+      "3/3 [==============================] - 0s 90ms/step - loss: 2.3032 - accuracy: 0.1250\n",
+      "Epoch 3/5\n",
+      "3/3 [==============================] - 0s 79ms/step - loss: 2.3027 - accuracy: 0.1042\n",
+      "Epoch 4/5\n",
+      "3/3 [==============================] - 0s 65ms/step - loss: 2.3033 - accuracy: 0.0625\n",
+      "Epoch 5/5\n",
+      "3/3 [==============================] - 0s 70ms/step - loss: 2.3019 - accuracy: 0.1302\n",
+      "9750/9750 [==============================] - 11s 1ms/step\n",
+      "Probabilistic CopycatCNN (vs. Protected) : 0.13661538064479828\n",
+      "Epoch 1/5\n",
+      "3/3 [==============================] - 1s 260ms/step - loss: 2.2951 - accuracy: 0.1250\n",
+      "Epoch 2/5\n",
+      "3/3 [==============================] - 0s 99ms/step - loss: 2.2878 - accuracy: 0.1042\n",
+      "Epoch 3/5\n",
+      "3/3 [==============================] - 0s 88ms/step - loss: 2.2858 - accuracy: 0.1406\n",
+      "Epoch 4/5\n",
+      "3/3 [==============================] - 0s 100ms/step - loss: 2.2800 - accuracy: 0.1927\n",
+      "Epoch 5/5\n",
+      "3/3 [==============================] - 0s 86ms/step - loss: 2.2798 - accuracy: 0.1823\n",
+      "9750/9750 [==============================] - 8s 834us/step\n",
+      "Argmax CopycatCNN (vs. Protected) : 0.20625640451908112\n",
+      "9750/9750 [==============================] - 10s 1ms/step\n",
+      "Probabilistic KnockoffNets (vs. Protected) : 0.15466666221618652\n",
+      "9750/9750 [==============================] - 6s 606us/step\n",
+      "Argmax KnockoffNets (vs. Protected) : 0.16994871199131012\n",
+      "Epoch 1/5\n",
+      "7/7 [==============================] - 1s 102ms/step - loss: 2.3030 - accuracy: 0.1228\n",
+      "Epoch 2/5\n",
+      "7/7 [==============================] - 0s 35ms/step - loss: 2.3028 - accuracy: 0.1049\n",
+      "Epoch 3/5\n",
+      "7/7 [==============================] - 0s 32ms/step - loss: 2.3019 - accuracy: 0.1406\n",
+      "Epoch 4/5\n",
+      "7/7 [==============================] - 0s 29ms/step - loss: 2.3018 - accuracy: 0.1027\n",
+      "Epoch 5/5\n",
+      "7/7 [==============================] - 0s 42ms/step - loss: 2.3018 - accuracy: 0.1161\n",
+      "9500/9500 [==============================] - 10s 1ms/step\n",
+      "Probabilistic CopycatCNN (vs. Protected) : 0.130736842751503\n",
+      "Epoch 1/5\n",
+      "7/7 [==============================] - 1s 131ms/step - loss: 2.3048 - accuracy: 0.0670\n",
+      "Epoch 2/5\n",
+      "7/7 [==============================] - 0s 70ms/step - loss: 2.2907 - accuracy: 0.1562\n",
+      "Epoch 3/5\n",
+      "7/7 [==============================] - 0s 54ms/step - loss: 2.2770 - accuracy: 0.2054\n",
+      "Epoch 4/5\n",
+      "7/7 [==============================] - 0s 45ms/step - loss: 2.2678 - accuracy: 0.2098\n",
+      "Epoch 5/5\n",
+      "7/7 [==============================] - 0s 45ms/step - loss: 2.2424 - accuracy: 0.2812\n",
+      "9500/9500 [==============================] - 9s 981us/step\n",
+      "Argmax CopycatCNN (vs. Protected) : 0.19547368586063385\n",
+      "9500/9500 [==============================] - 2s 243us/step\n",
+      "Probabilistic KnockoffNets (vs. Protected) : 0.12294736504554749\n",
+      "9500/9500 [==============================] - 7s 769us/step\n",
+      "Argmax KnockoffNets (vs. Protected) : 0.3623158037662506\n",
+      "Epoch 1/5\n",
+      "15/15 [==============================] - 1s 59ms/step - loss: 2.3048 - accuracy: 0.1031\n",
+      "Epoch 2/5\n",
+      "15/15 [==============================] - 0s 24ms/step - loss: 2.3038 - accuracy: 0.1562\n",
+      "Epoch 3/5\n",
+      "15/15 [==============================] - 0s 24ms/step - loss: 2.3029 - accuracy: 0.1510\n",
+      "Epoch 4/5\n",
+      "15/15 [==============================] - 0s 23ms/step - loss: 2.3022 - accuracy: 0.1667\n",
+      "Epoch 5/5\n",
+      "15/15 [==============================] - 0s 26ms/step - loss: 2.3015 - accuracy: 0.1708\n",
+      "9000/9000 [==============================] - 1s 153us/step\n",
+      "Probabilistic CopycatCNN (vs. Protected) : 0.13233333826065063\n",
+      "Epoch 1/5\n",
+      "15/15 [==============================] - 2s 107ms/step - loss: 2.2765 - accuracy: 0.1917\n",
+      "Epoch 2/5\n",
+      "15/15 [==============================] - 1s 62ms/step - loss: 2.2332 - accuracy: 0.3552\n",
+      "Epoch 3/5\n",
+      "15/15 [==============================] - 1s 60ms/step - loss: 2.1739 - accuracy: 0.4656\n",
+      "Epoch 4/5\n",
+      "15/15 [==============================] - 1s 58ms/step - loss: 2.0710 - accuracy: 0.5240\n",
+      "Epoch 5/5\n",
+      "15/15 [==============================] - 1s 69ms/step - loss: 1.9103 - accuracy: 0.5958\n",
+      "9000/9000 [==============================] - 8s 883us/step\n",
+      "Argmax CopycatCNN (vs. Protected) : 0.6140000224113464\n",
+      "9000/9000 [==============================] - 9s 1ms/step\n",
+      "Probabilistic KnockoffNets (vs. Protected) : 0.16866666078567505\n",
+      "9000/9000 [==============================] - 6s 701us/step\n",
+      "Argmax KnockoffNets (vs. Protected) : 0.6237778067588806\n",
+      "Epoch 1/5\n",
+      "31/31 [==============================] - 1s 46ms/step - loss: 2.3040 - accuracy: 0.1028\n",
+      "Epoch 2/5\n",
+      "31/31 [==============================] - 1s 29ms/step - loss: 2.3022 - accuracy: 0.1290\n",
+      "Epoch 3/5\n",
+      "31/31 [==============================] - 1s 24ms/step - loss: 2.3012 - accuracy: 0.1502\n",
+      "Epoch 4/5\n",
+      "31/31 [==============================] - 1s 33ms/step - loss: 2.3005 - accuracy: 0.1840\n",
+      "Epoch 5/5\n",
+      "31/31 [==============================] - 2s 52ms/step - loss: 2.3003 - accuracy: 0.1845\n",
+      "8000/8000 [==============================] - 8s 1ms/step\n",
+      "Probabilistic CopycatCNN (vs. Protected) : 0.18937499821186066\n",
+      "Epoch 1/5\n",
+      "31/31 [==============================] - 2s 80ms/step - loss: 2.2497 - accuracy: 0.1663\n",
+      "Epoch 2/5\n",
+      "31/31 [==============================] - 2s 55ms/step - loss: 2.0826 - accuracy: 0.5242\n",
+      "Epoch 3/5\n",
+      "31/31 [==============================] - 2s 60ms/step - loss: 1.6551 - accuracy: 0.6951\n",
+      "Epoch 4/5\n",
+      "31/31 [==============================] - 2s 60ms/step - loss: 0.9703 - accuracy: 0.7954\n",
+      "Epoch 5/5\n",
+      "31/31 [==============================] - 2s 54ms/step - loss: 0.6115 - accuracy: 0.8473\n",
+      "8000/8000 [==============================] - 8s 991us/step\n",
+      "Argmax CopycatCNN (vs. Protected) : 0.8396250009536743\n",
+      "8000/8000 [==============================] - 8s 1ms/step\n",
+      "Probabilistic KnockoffNets (vs. Protected) : 0.15462499856948853\n",
+      "8000/8000 [==============================] - 9s 1ms/step\n",
+      "Argmax KnockoffNets (vs. Protected) : 0.8478749990463257\n",
+      "Epoch 1/5\n",
+      "62/62 [==============================] - 4s 65ms/step - loss: 2.3031 - accuracy: 0.1520\n",
+      "Epoch 2/5\n",
+      "62/62 [==============================] - 4s 61ms/step - loss: 2.3008 - accuracy: 0.2258\n",
+      "Epoch 3/5\n",
+      "62/62 [==============================] - 4s 66ms/step - loss: 2.2998 - accuracy: 0.2445\n",
+      "Epoch 4/5\n",
+      "62/62 [==============================] - 3s 49ms/step - loss: 2.2991 - accuracy: 0.2608\n",
+      "Epoch 5/5\n",
+      "62/62 [==============================] - 3s 43ms/step - loss: 2.2986 - accuracy: 0.2618\n",
+      "6000/6000 [==============================] - 6s 936us/step\n",
+      "Probabilistic CopycatCNN (vs. Protected) : 0.25099998712539673\n",
+      "Epoch 1/5\n",
+      "62/62 [==============================] - 2s 40ms/step - loss: 2.1022 - accuracy: 0.4713\n",
+      "Epoch 2/5\n",
+      "62/62 [==============================] - 2s 37ms/step - loss: 1.0095 - accuracy: 0.7986\n",
+      "Epoch 3/5\n",
+      "62/62 [==============================] - 2s 39ms/step - loss: 0.4557 - accuracy: 0.8773\n",
+      "Epoch 4/5\n",
+      "62/62 [==============================] - 5s 78ms/step - loss: 0.3785 - accuracy: 0.8926\n",
+      "Epoch 5/5\n",
+      "62/62 [==============================] - 4s 67ms/step - loss: 0.2921 - accuracy: 0.9224\n",
+      "6000/6000 [==============================] - 8s 1ms/step\n",
+      "Argmax CopycatCNN (vs. Protected) : 0.8939999938011169\n",
+      "6000/6000 [==============================] - 6s 1ms/step\n",
+      "Probabilistic KnockoffNets (vs. Protected) : 0.19316667318344116\n",
+      "6000/6000 [==============================] - 4s 596us/step\n",
+      "Argmax KnockoffNets (vs. Protected) : 0.8816666603088379\n",
+      "Epoch 1/5\n",
+      "78/78 [==============================] - 6s 82ms/step - loss: 2.3029 - accuracy: 0.0665\n",
+      "Epoch 2/5\n",
+      "78/78 [==============================] - 6s 74ms/step - loss: 2.3007 - accuracy: 0.1094\n",
+      "Epoch 3/5\n",
+      "78/78 [==============================] - 4s 45ms/step - loss: 2.2999 - accuracy: 0.1689\n",
+      "Epoch 4/5\n",
+      "78/78 [==============================] - 4s 51ms/step - loss: 2.2992 - accuracy: 0.1917\n",
+      "Epoch 5/5\n",
+      "78/78 [==============================] - 5s 64ms/step - loss: 2.2990 - accuracy: 0.1911\n",
+      "5000/5000 [==============================] - 5s 1ms/step\n",
+      "Probabilistic CopycatCNN (vs. Protected) : 0.1995999962091446\n",
+      "Epoch 1/5\n",
+      "78/78 [==============================] - 7s 85ms/step - loss: 2.1410 - accuracy: 0.4367\n",
+      "Epoch 2/5\n",
+      "78/78 [==============================] - 7s 89ms/step - loss: 0.9765 - accuracy: 0.8085\n",
+      "Epoch 3/5\n",
+      "78/78 [==============================] - 6s 82ms/step - loss: 0.4159 - accuracy: 0.8822\n",
+      "Epoch 4/5\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "78/78 [==============================] - 5s 66ms/step - loss: 0.3161 - accuracy: 0.9133\n",
+      "Epoch 5/5\n",
+      "78/78 [==============================] - 5s 59ms/step - loss: 0.3117 - accuracy: 0.9109\n",
+      "5000/5000 [==============================] - 5s 1ms/step\n",
+      "Argmax CopycatCNN (vs. Protected) : 0.8989999890327454\n",
+      "5000/5000 [==============================] - 6s 1ms/step\n",
+      "Probabilistic KnockoffNets (vs. Protected) : 0.2712000012397766\n",
+      "5000/5000 [==============================] - 2s 353us/step\n",
+      "Argmax KnockoffNets (vs. Protected) : 0.8813999891281128\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Stealing from the protected classifier.\n",
+    "\n",
+    "attack_catalogue = {\n",
+    "                    \"Probabilistic CopycatCNN (vs. Protected)\": CopycatCNN(classifier=classifier_protected,\n",
+    "                                              batch_size_fit=64,\n",
+    "                                              batch_size_query=64,\n",
+    "                                              nb_epochs=num_epochs,\n",
+    "                                              nb_stolen=len_steal,\n",
+    "                                              use_probability=True),\n",
+    "                    \"Argmax CopycatCNN (vs. Protected)\": CopycatCNN(classifier=classifier_protected,\n",
+    "                                              batch_size_fit=64,\n",
+    "                                              batch_size_query=64,\n",
+    "                                              nb_epochs=num_epochs,\n",
+    "                                              nb_stolen=len_steal,\n",
+    "                                              use_probability=False),\n",
+    "                    \"Probabilistic KnockoffNets (vs. Protected)\": KnockoffNets(classifier=classifier_protected,\n",
+    "                                              batch_size_fit=64,\n",
+    "                                              batch_size_query=64,\n",
+    "                                              nb_epochs=num_epochs,\n",
+    "                                              nb_stolen=len_steal,\n",
+    "                                              use_probability=True),\n",
+    "                    \"Argmax KnockoffNets (vs. Protected)\": KnockoffNets(classifier=classifier_protected,\n",
+    "                                              batch_size_fit=64,\n",
+    "                                              batch_size_query=64,\n",
+    "                                              nb_epochs=num_epochs,\n",
+    "                                              nb_stolen=len_steal,\n",
+    "                                              use_probability=False),\n",
+    "#                     \"FunctionallyEquivalentExtraction\": FunctionallyEquivalentExtraction(classifier=classifier_flat_protected,\n",
+    "#                                               num_neurons=128),  # This one takes too long time for this dataset/model.\n",
+    "                    }\n",
+    "\n",
+    "results_protected = []\n",
+    "for len_steal in [250, 500, 1000, 2000, 4000, 5000]:\n",
+    "    indices = np.random.permutation(len(x_test0))\n",
+    "    x_steal = x_test0[indices[:len_steal]]\n",
+    "    y_steal = y_test0[indices[:len_steal]]\n",
+    "    x_test = x_test0[indices[len_steal:]]\n",
+    "    y_test = y_test0[indices[len_steal:]]\n",
+    "\n",
+    "    for name, attack in attack_catalogue.items():\n",
+    "        model_stolen = get_model(num_classes=10, c1=32, c2=64, d1=128)\n",
+    "        classifier_stolen = KerasClassifier(model_stolen, clip_values=(0, 1), use_logits=False)\n",
+    "        if name==\"FunctionallyEquivalentExtraction\":\n",
+    "            classifier_stolen = attack.extract(np.reshape(x_steal, [len(x_steal), -1]), y_steal, thieved_classifier=classifier_stolen)\n",
+    "        else:\n",
+    "            classifier_stolen = attack.extract(x_steal, y_steal, thieved_classifier=classifier_stolen)\n",
+    "        \n",
+    "        acc = classifier_stolen._model.evaluate(x_test, y_test)[1]\n",
+    "        print(name, \":\", acc)\n",
+    "        results_protected.append((name, len_steal, acc))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 576x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "df_protected = pd.DataFrame(results_protected, columns=('Method Name', 'Stealing Dataset Size', 'Accuracy'))\n",
+    "fig, ax = plt.subplots(figsize=(8,6))\n",
+    "ax.set_xlabel(\"Stealing Dataset Size\")\n",
+    "ax.set_ylabel(\"Stolen Model Accuracy\")\n",
+    "for name, group in df_protected.groupby(\"Method Name\"):\n",
+    "    group.plot(1, 2, ax=ax, label=name)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_combined = pd.concat([df, df_protected])\n",
+    "groupby = df_combined.groupby(\"Method Name\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 576x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 576x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "names = [\"CopycatCNN\", \"KnockoffNets\"]\n",
+    "for name in names:\n",
+    "    fig, ax = plt.subplots(figsize=(8,6))\n",
+    "    groupby.get_group(\"Probabilistic \" + name).plot(1,2,ax=ax, label=\"Probabilistic \" + name)\n",
+    "    groupby.get_group(\"Probabilistic \" + name + \" (vs. Protected)\").plot(1,2,ax=ax, label=\"Probabilistic \" + name + \" (vs. Protected)\")\n",
+    "    ax.set_xlabel(\"Stealing Dataset Size\")\n",
+    "    ax.set_ylabel(\"Stolen Model Accuracy\")\n",
+    "    fig.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Next Step: Using data augmentation can make the model stealing process much easier and faster, and can make the probabilistic attack much better."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "ARTIST",
+   "language": "python",
+   "name": "artist"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/adversarial-robustness-toolbox/notebooks/model_inversion_attacks_mnist.ipynb b/adversarial-robustness-toolbox/notebooks/model_inversion_attacks_mnist.ipynb
new file mode 100644
index 0000000..b76640e
--- /dev/null
+++ b/adversarial-robustness-toolbox/notebooks/model_inversion_attacks_mnist.ipynb
@@ -0,0 +1,543 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Introduction\n",
+    "\n",
+    "In this notebook we will look at inference attacks against an MNIST classifier.\n",
+    "Specifically, we will use the ART implementation of Fredrikson et al.'s (2015) MI-Face algorithm."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using TensorFlow backend.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "import keras.backend as k\n",
+    "from keras.models import Sequential\n",
+    "from keras.layers import Dense, Flatten, Conv2D, MaxPooling2D, Dropout\n",
+    "import numpy as np\n",
+    "from numpy.random import seed\n",
+    "seed(123)\n",
+    "\n",
+    "from art.estimators.classification import KerasClassifier\n",
+    "from art.attacks.inference import MIFace\n",
+    "from art.utils import load_dataset\n",
+    "\n",
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Read MNIST dataset\n",
+    "(x_train, y_train), (x_test, y_test), min_, max_ = load_dataset(str('mnist'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Train model and initialize attack"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create standard CNN in Keras and wrap with ART KerasClassifier:\n",
+    "def cnn_mnist(input_shape, min_val, max_val):\n",
+    "  \n",
+    "    model = Sequential()\n",
+    "    model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=input_shape))\n",
+    "    model.add(MaxPooling2D(pool_size=(2, 2)))\n",
+    "    model.add(Conv2D(64, (3, 3), activation='relu'))\n",
+    "    model.add(MaxPooling2D(pool_size=(2, 2)))\n",
+    "    model.add(Dropout(0.25))\n",
+    "    model.add(Flatten())\n",
+    "    model.add(Dense(128, activation='relu'))\n",
+    "    model.add(Dense(10, activation='softmax'))\n",
+    "\n",
+    "    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
+    "\n",
+    "    classifier = KerasClassifier(clip_values=(min_val, max_val), \n",
+    "                                model=model, use_logits=False)\n",
+    "    return classifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From /usr/local/lib/python3.7/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Colocations handled automatically by placer.\n",
+      "WARNING:tensorflow:From /usr/local/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
+      "WARNING:tensorflow:From /usr/local/lib/python3.7/site-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Use tf.cast instead.\n",
+      "Epoch 1/10\n",
+      "468/468 [==============================] - 26s 55ms/step - loss: 0.2225 - acc: 0.9356\n",
+      "Epoch 2/10\n",
+      "468/468 [==============================] - 21s 44ms/step - loss: 0.0607 - acc: 0.9812\n",
+      "Epoch 3/10\n",
+      "468/468 [==============================] - 21s 45ms/step - loss: 0.0446 - acc: 0.9866\n",
+      "Epoch 4/10\n",
+      "468/468 [==============================] - 21s 45ms/step - loss: 0.0325 - acc: 0.9897\n",
+      "Epoch 5/10\n",
+      "468/468 [==============================] - 21s 45ms/step - loss: 0.0276 - acc: 0.9913\n",
+      "Epoch 6/10\n",
+      "468/468 [==============================] - 21s 44ms/step - loss: 0.0247 - acc: 0.9926\n",
+      "Epoch 7/10\n",
+      "468/468 [==============================] - 21s 44ms/step - loss: 0.0196 - acc: 0.9939\n",
+      "Epoch 8/10\n",
+      "468/468 [==============================] - 20s 43ms/step - loss: 0.0174 - acc: 0.9943\n",
+      "Epoch 9/10\n",
+      "468/468 [==============================] - 20s 43ms/step - loss: 0.0160 - acc: 0.9948\n",
+      "Epoch 10/10\n",
+      "468/468 [==============================] - 20s 43ms/step - loss: 0.0137 - acc: 0.9956\n"
+     ]
+    }
+   ],
+   "source": [
+    "num_epochs = 10\n",
+    "\n",
+    "# Construct and train a convolutional neural network\n",
+    "classifier = cnn_mnist(x_train.shape[1:], min_, max_)\n",
+    "classifier.fit(x_train, y_train, nb_epochs=num_epochs, batch_size=128)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create the attack.\n",
+    "# Note: by setting the threshold to 1., the attack will effectively exhaust the maximum number of iterations.\n",
+    "\n",
+    "attack = MIFace(classifier, max_iter=10000, threshold=1.) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Our attack target in the following will be to infer information about the training samples \n",
+    "# for each of the 10 MNIST CLASSES:\n",
+    "\n",
+    "y = np.arange(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# We will experiment with a number of different initializations:\n",
+    "\n",
+    "x_init_white = np.zeros((10, 28, 28, 1))\n",
+    "x_init_grey = np.zeros((10, 28, 28, 1)) + 0.5\n",
+    "x_init_black = np.ones((10, 28, 28, 1))\n",
+    "x_init_random = np.random.uniform(0, 1, (10, 28, 28, 1))\n",
+    "x_init_average = np.zeros((10, 28, 28, 1)) + np.mean(x_test, axis=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Initialization with white image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Maximum class gradient: 0.000000\n"
+     ]
+    }
+   ],
+   "source": [
+    "# We observe that the classifier's gradients are vanishing on white images, therefore the attack won't work:\n",
+    "\n",
+    "print(\"Maximum class gradient: %f\" % (np.max(np.abs(classifier.class_gradient(x_init_white, y)))))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Initialization with grey image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Minimum over all maximum class gradient: 0.009670\n"
+     ]
+    }
+   ],
+   "source": [
+    "# First, we ensure that the classifier's gradients are non-vanishing for each target class:\n",
+    "\n",
+    "class_gradient = classifier.class_gradient(x_init_grey, y)\n",
+    "class_gradient = np.reshape(class_gradient, (10, 28*28))\n",
+    "class_gradient_max = np.max(class_gradient, axis=1)\n",
+    "\n",
+    "print(\"Minimum over all maximum class gradient: %f\" % (np.min(class_gradient_max)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 4min 23s, sys: 26.9 s, total: 4min 50s\n",
+      "Wall time: 2min 28s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "\n",
+    "# Now we run the attack:\n",
+    "x_infer_from_grey = attack.infer(x_init_grey, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 1080x1080 with 10 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the inferred images:\n",
+    "\n",
+    "plt.figure(figsize=(15,15))\n",
+    "for i in range(10):\n",
+    "    plt.subplot(5, 5, i + 1)\n",
+    "    plt.imshow( (np.reshape(x_infer_from_grey[0+i,], (28, 28))), cmap=plt.cm.gray_r)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As we can see, the attack reveals certain structural properties of the training instances for each \n",
+    "of the ten classes."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Initialization with black image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Minimum over all maximum class gradient: 0.002196\n"
+     ]
+    }
+   ],
+   "source": [
+    "# First, we ensure that the classifier's gradients are non-vanishing for each target class:\n",
+    "\n",
+    "class_gradient = classifier.class_gradient(x_init_black, y)\n",
+    "class_gradient = np.reshape(class_gradient, (10, 28*28))\n",
+    "class_gradient_max = np.max(class_gradient, axis=1)\n",
+    "\n",
+    "print(\"Minimum over all maximum class gradient: %f\" % (np.min(class_gradient_max)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 4min 30s, sys: 28.2 s, total: 4min 58s\n",
+      "Wall time: 2min 34s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "\n",
+    "# Now we run the attack:\n",
+    "x_infer_from_black = attack.infer(x_init_black, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 1080x1080 with 10 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the inferred images:\n",
+    "\n",
+    "plt.figure(figsize=(15,15))\n",
+    "for i in range(10):\n",
+    "    plt.subplot(5, 5, i + 1)\n",
+    "    plt.imshow( (np.reshape(x_infer_from_black[0+i,], (28, 28))), cmap=plt.cm.gray_r)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Initialization with random image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Minimum over all maximum class gradient: 0.001144\n"
+     ]
+    }
+   ],
+   "source": [
+    "# First, we ensure that the classifier's gradients are non-vanishing for each target class:\n",
+    "\n",
+    "class_gradient = classifier.class_gradient(x_init_random, y)\n",
+    "class_gradient = np.reshape(class_gradient, (10, 28*28))\n",
+    "class_gradient_max = np.max(class_gradient, axis=1)\n",
+    "\n",
+    "print(\"Minimum over all maximum class gradient: %f\" % (np.min(class_gradient_max)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 4min 21s, sys: 26.5 s, total: 4min 47s\n",
+      "Wall time: 2min 26s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "\n",
+    "# Now we run the attack:\n",
+    "x_infer_from_random = attack.infer(x_init_random, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 1080x1080 with 10 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the inferred images:\n",
+    "\n",
+    "plt.figure(figsize=(15,15))\n",
+    "for i in range(10):\n",
+    "    plt.subplot(5, 5, i + 1)\n",
+    "    plt.imshow( (np.reshape(x_infer_from_random[0+i,], (28, 28))), cmap=plt.cm.gray_r)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Initialization with average image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Minimum over all maximum class gradient: 0.027774\n"
+     ]
+    }
+   ],
+   "source": [
+    "# First, we ensure that the classifier's gradients are non-vanishing for each target class:\n",
+    "\n",
+    "class_gradient = classifier.class_gradient(x_init_average, y)\n",
+    "class_gradient = np.reshape(class_gradient, (10, 28*28))\n",
+    "class_gradient_max = np.max(class_gradient, axis=1)\n",
+    "\n",
+    "print(\"Minimum over all maximum class gradient: %f\" % (np.min(class_gradient_max)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 4min 21s, sys: 26.7 s, total: 4min 48s\n",
+      "Wall time: 2min 27s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "\n",
+    "# Now we run the attack:\n",
+    "x_infer_from_average = attack.infer(x_init_average, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2MAAAFWCAYAAADkGUU+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAgAElEQVR4nOyda7BdZ33e/y/gmyTrfrGulny/yJHtyArGxgYcgiG0kDRpTduMSZmSD2UmTPMhNOlMMv1EZhqYdprJDATGZEqhzSQEQknAYBhqMMYSCN9kS77ofpdsXXy3Wf3gk0TR/7fRe87e+6y9zvn9ZjyWHu+911rveta73uVznmeXpmlCREREREREJpc3tL0DIiIiIiIi0xEfxkRERERERFrAhzEREREREZEW8GFMRERERESkBXwYExERERERaQEfxkRERERERFqgr4exUsrtpZTHSylPlFI+NqidEhkWela6iL6VrqFnpWvoWWmLMtHvGSulvDEitkbEOyNid0Q8EBEfaJrm0cHtnsjg0LPSRfStdA09K11Dz0qbvKmP926IiCeapnkqIqKU8sWIeF9E9DTuwoULm9WrV/exSaGH51JKC3syOWzatOlw0zSLBvRxelaGzoA9GzFO3+pZGS96tvu4Nugb1wcyVLZv3x6HDx/Gi7Kfh7HlEbHrlL/vjohf+FlvWL16dWzcuLGPTU4vaHJ99dVXk/amN9Wdxi5OzKWUHQP8uCnn2Z/+9KdJI9+88Y1vnIzdqYb2+w1vGGyEta3FyYA9GzFO3466Z0cN8glpg/bnKKFnB8NEf9Oo13vH83mD9izNlbXz52TMvW17NmL0fTuVHtAnY80wbNavX9/zvw39SEopHy6lbCylbDx06NCwNyfSN3pWuoaela6hZ6WL6FsZBv08jO2JiJWn/H3FmPZPaJrmU03TrG+aZv2iRYP8ibLIuNGz0kXO6Fs9KyOGnpWu4fpAWqOfX1N8ICIuLaWsidcNe0dE/OuB7JVERP2PZWt/FF37axD9/HrCiDPlPPvyyy8njc5V7a8p1v5qbC/OOuuspNE+vvbaa0k777zzqvaHmMKejZiCvh0lyIvk+bPPPjtpNB/Te+l1XfsVm3EyLT1Lc07t/ElzHa0Beuk0x9dGGGq3PWq/7j5gppxnp9A9cKrPlxN/GGua5tVSykci4usR8caI+GzTNI8MbM9EBoyelS6ib6Vr6FnpGnpW2qSfn4xF0zRfi4ivDWhfRIaOnpUuom+la+hZ6Rp6Vtpiav/cT0REREREZETxYUxERERERKQF+vo1xcmk9vuUKLBIwT/6PAra1gZy6fMoPFtbrBHBYVkKm9P7qUhhGobIR47aggw69y+99FLSjh8/nrQ5c+ZU7cvzzz+fNCrboNedf/75+Jl0LFu3bk3aZZddVrOLfX0H1FQKLw8DmkvIdzTe5BM69+eee27VvpDHiNpSAXrdjBkz8DNpnq49PromSaPyj9mzZ+P+1Gy3tphhqtHF702qPVd0ns855xx8Lfm7dm3xyiuvJI3WC/2MK80t09Wz/TJoz9Pn0fk6ceJE0ui+T76l90ZELFiwIGlHjhxJGt03Zs2alTSap8lnXSiecSUuIiIiIiLSAj6MiYiIiIiItIAPYyIiIiIiIi3gw5iIiIiIiEgLdCZRWVs0URs2pwAkhawpKEufd/Lkyar9oxB5rzIEgsKJtQUQ0j61fqotjnnxxReTNnfu3Kp9efbZZ5NG5QPkOdpur9ceOnQoaVResGbNmqTReB09ejRptYHffuaRUS8KGC+1BUF03FQCQKFt0ugc0L688MILSaOiAdpnel2veZY8RoUi+/fvT9r8+fOrtk3HUlvgQWNtGcLwID+QZ2sLv2pLNOgaeO6553Afadu1xU20ziFqy6bIixaDDZfaYiuCzg3d959++umkrVq1Kmk0/9KaJiLi4MGDSTtw4EDSqOiD9pG2TWtsurZozUDX72Td971iREREREREWsCHMRERERERkRbwYUxERERERKQFfBgTERERERFpgSmXAq4N4FFA+7zzzksaBf8oAEklBXv27EkaBXcpBB7BRQxXXHFF0ihA+8wzzySNwrcUtKQgMIUdKehcGw6erpCfaMx6BWBPh85pbZCXwuEUpp05c2bSqJghgq+Dhx9+OGnkJyrwoM+jMgS6JmkMzznnnKTR/EDB4OlQmlBb/EPlKKQR5Nna8SY/kEZh70suuQT3Z8WKFUlbuHBh0ug6pTm+tvCEjo+C6/Re8jFd91OtyGnQYXq6h9VqNH/WlhlQeRKdK3pdr+1Q4cK6deuSRn6nzyO/1xZ4kBdr5wf5p9QWxZAf6V5JUJkQ3aN37NiRNDr/dL1E8FxGaw46Zprz6JqhbdD6gNa5dCykDWNe9SdjIiIiIiIiLeDDmIiIiIiISAv4MCYiIiIiItICfYUgSinbI+JERLwWEa82TbN+EDslMkz0rXQNPStdQ89K19Cz0haDSKS/vWmawwP4nHFDIVEKDlI4lV734osvVr2u9pvpjxw5krSNGzcmbeXKlUmL4PDl8uXLk3bttdfi+0/n6NGjSaMQ47Fjx5K2dOnSpNWWelB4cgQYqG97BVZPh8birLPOShqFRvfv31+1DSpvofNH+0KB8Z07dyaNShMiOPRNRTT79u1LGh0fhd8pPEuvo89btmxZ0iYroDsAWplrab7rp0iBxpvO3/Hjx5NG3j58OA/Jd77znaRRwD0i4uDBg0m74447kkbz7MmTJ5NG5SFUDkXz8YwZM5J2/vnnJ602pD4CtLY+qIHGjPxO92K6T5I/aV1BcxP5mPwVwQVKf/M3f5O0q666Kmm33HJL0mqLPmhepPGitQFd43TvG3RJywRozbO0jqBxq/Ut+YTmHSrHoDUtzaHkbzqvETw30n7T2pk8ResNWu9TCRndX+bNm1e1f8NgJGdvERERERGRqU6/D2NNRHyjlLKplPLhQeyQyCSgb6Vr6FnpGnpWuoaelVbo99cUb26aZk8pZXFE3F1Keaxpmu+e+oIxQ384ImLVqlV9bk5kIPxM3+pZGUH0rHQNPStdwzWttEJfPxlrmmbP2L8PRsSXImIDvOZTTdOsb5pm/aJFi/rZnMhAOJNv9ayMGnpWuoaela7hmlbaYsI/GSulzIyINzRNc2Lsz78UEf/lTO87PQzXT1iT3lsbbCSNgogUOKSQLn0z+RNPPJE0ChfSdiMiHnzwwaTdd999Sdu8eXPS3vnOdyaNwrwUDqcQI4VIa0PkFLJsK6Q7Ud+eiVrfPf/880mj0D6NzwUXXJA0CvxS6JsKM7Zt21b1XgoB03XRax+pIGHv3r1J+9KXvpS0hQsXJo2CvFQSQmO4YMGCpNWWdUw1z9YWG9F1ThoVc9B1QddAbXEMve7JJ5+sel2v0pm77747aeQ7Kl1Yu3Zt0sifdL3QGFJonnxHc+ooMSzPTgZ0P6Z5kV5Hcz6VHtA1cODAgaTR3BvBawsq3KDrj8pt6Nolz86aNatqu+Rjui5GoKzjHxgFz9L5ovIJgkqCyHt07929e3fSqOiD7hlU1kH70uu1dC3UQtuhB2S6jmitQtcBeXQYRXX9/Jrikoj40tiOviki/lfTNH/X196IDB99K11Dz0rX0LPSNfSstMaEH8aapnkqInIfqsgIo2+la+hZ6Rp6VrqGnpU2sdpeRERERESkBXwYExERERERaYF+q+3HzelhuH6CcBSAPnbs2IQ/j6D9o7KOn/zkJ0mjsCMFfOmbwCMibrzxxqrtUKnHtddem7SHH344aStXrkxa7bekz549O2m1xzxKwd1hQcdIgdrDhw8njUolqISjNrRNAVbyNoV76Zz2CuhSEJheS9cujQNdzxTQpZKQX/iFX0gajSuNQ22pR5chn1BRD4Xx6b3kEyo+oHNFr6PCje3btyeNvE1z6g9/+MOkRXARw1133ZW0//gf/2PSyIvkdyqYoRKOF154IWnnnHNO0uic0Oumg49roeucil62bt2aNLqH0X2StMceeyxpjz/+eM/9PBUq6ojgc037SPPs8uXLk/bUU08ljTx26623Jo2uPyoBo3KE6bo2iKifa2lOoLUXlWNQQRvdU2mupeuFXjdnzpykLVu2LGkRXH5H/qFjof2muZbGkOZpGkPyI31ebVnVePAnYyIiIiIiIi3gw5iIiIiIiEgL+DAmIiIiIiLSAj6MiYiIiIiItMCkF3jUQCE6CjFSSLs2WEfhSXodhXm3bNmSNKL229QprBgRsXjx4qRRIPf+++9P2vXXX580+qbzf/Wv/lXSLrvssqTVFkBQKH26BHJrWLhwYdIo1ErBe3ovnRe6Bqj8gwoXyLMUkp0/f37SIvhYqLSEfEyBWgrE01xw0003JY3KTehaI43moOkAead2fMiLNOdQIJo+j8oVaE684oorkkZz2KWXXpq0iIhHH300aXv27KnaHyoUoXvLunX564tojqZyE7omZ8yYkbTp4Fk6xlovkkaFBLSuoPIA0sgPVMKxbdu2pNG9k+b8CJ7bqNSDPpMKF2pLCqhkie4FtUUIpJ177rlJ6zo0HjRPUJkQ3StpLqLzT/MEeYLu21Q69MADDyTt5ptvTlqvufYHP/hB0uhYaNs0D9J7ab3xrne9K2lUMkNjTevXfss6CH8yJiIiIiIi0gI+jImIiIiIiLSAD2MiIiIiIiIt4MOYiIiIiIhIC7Re4EElEBR2pFAnlRzUQsFRCqc+/fTTSasNB1OgcuXKlUmjIGcv/V/8i3+RtL/7u79LGgUbKZBLAWEqbCCN9q+fc9IVaj1LHqPxIY2KXqhMhko4qKxj9+7dSbvwwguTtmDBgqRRUJ3GIILLNY4cOZI0Ombax8svvzxpF110UdKuu+463J8aaksBpkMRDZ0XGgsKmpPfaczo86jkgELSy5cvT9rSpUuTRqUetH+9+PznP5+0Rx55JGlHjx5NGhUk0LHUFlUtWrSo6r3TYe4lP9F9iM41jS3NnzS30fmja4D8QPu8du3apNE99uqrr05aRH0hBJWR0H7THE/lO1REQ2Pd6/4g/widb7qGaXzJt3T+qRSrdu1LvqV9piKMXvdK8h5BvqV1BK1zb7nllqTRuoagwjA6Zgs8REREREREpgg+jImIiIiIiLSAD2MiIiIiIiItcMaHsVLKZ0spB0spD5+izS+l3F1K2Tb27/xLoyItom+la+hZ6Rp6VrqGnpVRpKbA466I+B8R8eenaB+LiG81TfPxUsrHxv7+uxPZAQrC1YbjqCCDOHnyZNLom7ap+KA2FEvfQr948eKkXXPNNUnrFXbduXNn0lavXp00Cqs/+OCDSbvjjjuS9md/9mdJo3Gg4/v5n//5pFFJSEvcFUPyLYVTa0Pf9O3wFJSmQC2Ftmm7tH/Lli2ret2sWbOSRmUiFAyO4OM7dOhQ0nbs2JE0Kum59NJLk0alM3v37sX9OR06lhkzZiRtGAHdCu6KIc61p1NbSEJzJXnxrLPOqvo88jsVCND8OXfu3KRRYcaBAweSNp6Ci1//9V9P2pIlS5J2zz33JI3C4nRdUPiczgnNqTTWLRXM3BWT6FmC1gFnn3120mh+IW+Tx+ieSHPvqlWrkvaOd7wjaVRMROd0z549SYuIeOaZZ5JGcyDdt2nu/c53vpO0O++8M2krVqxIGo0XrbmodKKl0pm7omXPRvCx07VOPqPCOJpD9+/fnzRa01IJx7Zt25J25ZVXJu3iiy9OGq2RI/i+SuNAawbyDxXcXHDBBbjt06FxqN2/YXDGFUfTNN+NiNMrgt4XEZ8b+/PnIuL9A94vkb7Qt9I19Kx0DT0rXUPPyigy0f/9u6Rpmr//X0X7IyL/L0OR0UPfStfQs9I19Kx0DT0rrdL37+I0r/++Sv6dlTFKKR8upWwspWykHz2KtMHP8q2elVFEz0rX0LPSNVzTShtM9GHsQCllaUTE2L8P9nph0zSfappmfdM06+nLK0UmkSrf6lkZIfSsdA09K13DNa20ykTbFr4SEXdGxMfH/v3lie5AP0F5CjbSt4tTaJ++zfuxxx5L2hNPPJE0CqwuX748aRQsp+BtryIS0qnY4V3velfS/uRP/qRq2x/84AeTRgFIGkMK4tO3lY8QA/FtbcieSg6o+IIKJMif5HfyCF0DdO7p82j/XnjhhaptRHBwm0LEGzduTNqtt96atNtvvz1pTz75ZNLIn3T9jVCIvJaBzbU10HklH1P4ubbE5vzzz08alRDR66i8hcoxKJBOHongIg0qAKFjoXsBQeNK19XatWurPm/EmVTPEjRH0/m/6qqrqj5v6dKlSau9l7/44otJo/md7qe91gZUpEHz2A9/+MOk3X333UmjEoaLLrooaeR3Or7akquWSmeI1j0bweewdu1FcwyVtZD3aguZqFSp9v4QwcdCJTXki127diXtLW95S9LWrFlTtQ0qWqpdx9Xe68ZDTbX9FyLivoi4vJSyu5TyoXjdsO8spWyLiF8c+7vIyKBvpWvoWekaela6hp6VUeSMPxlrmuYDPf7TbQPeF5GBoW+la+hZ6Rp6VrqGnpVRpJUv0xEREREREZnu+DAmIiIiIiLSAhMt8BgYFKin8F6tdvbZZ1dt96WXXkoaBXIpiEihWgqg15YwUAA9gseGQpVvfetbk/boo48mbfv27Um77777knbttdcmjY5vhMK3I0mtd+ickj9ryxXodbRdei9tl0KtpEVw4c3u3bur9oeug82bN1dtm0o9qBhl5cqVVZ9HQepex9xV6PzTMdKcRUF++jxqG1u1alXVNp5++umk0ZxDficvnThxImkR7BO6JmlsKJBO1/2yZcuSRh6jcaDSJgrST1dovGkdQJ7YtGlT1XupDIgKWI4fP560V155JWk0z5577rlJ6zXnfPWrX03aQw89lLSdO3cmjdYVVPhFJSF0DdF1T2UG9LrpsoaoPc7aEgjyPBWp0OvIj+TbSy65JGm1ZSJUjhFRX4ZB6wO659A8TesNuo7omKk0io6FrtV+8SdjIiIiIiIiLeDDmIiIiIiISAv4MCYiIiIiItICPoyJiIiIiIi0QOuJ9NqgJ4UTzzvvvAlvlwL/FASv3S4FfA8dOpS02gBsBIcE9+7dm7T58+cn7frrr0/ali1bknby5MmkUbCcAscUdqQxpIBmbdHKKELHQxqd12PHjiWNxrZ2zKjk4JFHHkna2rVrq/aP/D579uykUfg1goPl5MUf/vCHSbvxxhuTdtVVVyXtwQcfTBp5dt68eUl79tlnk7Zw4cKk9bompxIUKqfzT3M0+Zg8S/MdFRtRwJoKlWh+oePYs2dP0qjUKIKPb/ny5VUalX+QF+l6oev+4MGDSaMwO411bfi/y9SWzhD79+9PGpUZ0H2XyjqoHIHOM50/mlNpTXLPPfckLSLiz//8z5NGhQt0/f3+7/9+0qisg/xJ3qZxqC2smA7z7DCgeZrmVSrXoHsgrV+p8GbdunVJozUk7UsEz8uk0b17yZIl+JmnUztPU5ENzaFUEjIMpv7sLSIiIiIiMoL4MCYiIiIiItICPoyJiIiIiIi0gA9jIiIiIiIiLTCpBR5N06SwJ5USUKiWAoEUOq2FQoc//vGPq7ZBAUEqUqBiDQoCUylHBAeJqTyEAswUGqYAJH2rOYVqKbhL54nOZ23AelQ5fTwonEwaBT8p4E0BaCpIICigvWzZsqSRHyjAOmvWrKRRqcD3vvc93B8KFs+cOTNpGzZsSNpb3/rWpJGfLrjggqRRAJkKZuQfoTIM8iL5mOYx8gmFwClATp9H/qR5iK4VKrF5/PHHk9ZrO7XHQvcCGi+6Lq644oqk9TNX1o5Xl6F7E5WZ1JZKUJEG3WN37dqVNLr/kUdou1Rg9Ed/9EdJI49ERPzcz/1c0i666KKkXX755Ul7+OGHk/a3f/u3SXv729+eNCpCoLUUjWFtqcd0ga5X8jLNHVQOR2UdtZ938cUXJ+0nP/lJ0miNTKVdtN6I4OuN7t20DiHv3XfffUmjZ4XbbrstaatXr04aldbQeNHavN91rj8ZExERERERaQEfxkRERERERFrAhzEREREREZEW8GFMRERERESkBc74MFZK+Wwp5WAp5eFTtD8spewppWwe++c9w91NkXr0rHQRfStdQ89K19CzMorU1H/cFRH/IyL+/DT9k03T/NeB71FwYxK1qvUDtUxRy9fatWuTRg2LmzdvTtob3pCfdamtpVfjFY0DNfBQgxO9d968eUmj1hqCWgCpBYeaxGgchsxdMUDP1rRA1baWLV68OGnUykUNhNTwtmDBgqRRg1LtOaCWIGpu+vrXv47vp8Yj8iK10NW2H1JrFF0X9DrybK/mp9OZhDawu2IS59ra4yHvkE/WrFmTtP379yeNmu5Io0a2w4cPJ+2ZZ55JGp3nm2++OWkRPLfRMde2JJKPabzuueeepF133XVJo9ZL8jvNLZPQpnhXTPL64HSoxYyOm9oGqfGYPEZ+Onr0aNLIs9QUSk11dD3SuY/gBkPyIvl46dKlSXvooYeqPu/aa69NGs35tb5rqWn5rhiBNS1ptD6kc01rBnov3bvJ39TK/MUvfjFpNJ+vWrUqaddcc03SInjeomuGGhGpDfzKK69MGnnqgQceSNqBAweSRmtkam8exlrgjCu0pmm+GxF51hEZUfSsdBF9K11Dz0rX0LMyivTzI4uPlFIeHPuRb36cHKOU8uFSysZSykZ6IheZRMbtWfo/SyKTzBl9q2dlxNCz0jVcH0hrTPRh7E8j4uKIuDYi9kXEH/d6YdM0n2qaZn3TNOsXLlw4wc2J9M2EPLto0aLJ2j8Rosq3elZGCD0rXcP1gbTKhB7GmqY50DTNa03T/DQiPh0RGwa7WyKDRc9KF9G30jX0rHQNPSttM6H0ZClladM0+8b++isR8fDPev0p76sq4qACgtmzZ49nF88Ihftpu7S/FPin8g96HZVt9Aq7UkiQQrUUfqfgL2kU+qRQM4U0B12qMkwm6tlayDsU0KVzQAHvPXv2THgbFGCtDbnT6/bt25c0CqBHRLz3ve9N2urVq5NGx0LbptIS8jsF1cmfFLqncgUqYaACh2EzTN/WFkPQPFZberJr166k7dixI2lUWFM7X9E+UzERnfsI9hN5gj6TygtII+9s3bo1aU899VTS6Lp485vfnDQq82mDYXqW5jsK+9P9neY7GjMquKDt0v157969SaOyIvIDlSj0+skLFWnQ/tQWLlBJBF27tN/r1q1LGq1z6BqncW2DYa8P6NzQdU3zKs155Cman6644oqkUREG+eyXfumXkvaDH/wgaVu2bEnasmXLkhbBczqNA5V60LHceOONSaPjo4gUzdPkUdo/Wr/0yxkfxkopX4iIt0XEwlLK7oj4g4h4Wynl2ohoImJ7RPzWwPdMZILoWeki+la6hp6VrqFnZRQ548NY0zQfAPkzQ9gXkYGgZ6WL6FvpGnpWuoaelVFk0r8ASkRERERERHwYExERERERaYVWvv78TNQWC9RCgXEKaF944YVJo2/fppA1FYxQoJKOjQKaERxMp+AgvY4CzBSqpa8boIAvjRd9CzwVKdSGn7sCHQ+NBZ3X2uIKKp+gzyOPUSEBfR55kbzUq/iAOHbsWNKowIO+n4WO75lnnkka7Tf5c86cOUmjAHrtOZlq0LkedFESBbapIIHKC+hcUanAddddV/Xexx9/HPeRQvPkO/IneYz8NG9e/tqiX/7lX04ajX/t/Elz71SjtgiB5gjyIs1DVN6zcuXKpNE5pfmY9u8//+f/nLS3vOUtSaMig4iIbdu2JY1KHWgdQPf82vvD3Llzk0b3G9pvOnd0rfQqNJuu0BjV3rPIE7S+o/v2RRddlLR3v/vdSaP18P/8n/8zaRE8r9L8RvPq7t27k3bvvfcm7eqrr04a3XNqyzroPjkMj0792VtERERERGQE8WFMRERERESkBXwYExERERERaQEfxkRERERERFpgJFPqFI6jcDKFGI8cOZI0Cu5SmJTCjhTeo9AvFQjQPtPn9QrpUmD8xRdfTBod36OPPpo0Cu7Onz8/aRSmrw1J0/FRQHOqFSTUfks7nWsq/3jhhReSRue59hvtyUsUYKVzSgHtXixatChpVIJD1ykVhRw4cCBpFLCnYDkV1lBBApU9dLlgphaas8iz/UD+3Lx5c9IWLFiQtH379iWNgt1LlixJGl0/vY5t//79SaO5ksoZ6BoiP9E9Y82aNUkjb9cy6HM3ivRTgFRbarVp06akrVu3LmlU4EElYDR/0nFQ8cDx48eTFhGxc+fOpP34xz/G154OlSjQftOcShp5lu75BF0r0wXyAN13aIxqy3pobqv147e+9a0Jb7fXeaX5kqA1Ea0t6ViosI/GgTxP9yEqeKLj6HfN4E/GREREREREWsCHMRERERERkRbwYUxERERERKQFfBgTERERERFpgZFsUaCQIAXBKfBPgVzSKPR72WWXJY2C4FSiQUHC2rBrr+A1BRYp2EiFG9u2bUvaW97ylqRdeeWVSaMSBwpE09hQQQUdR5ehcDJ5trakhM5/7Te8U2iUxpv2j84pFWYcPHgwaZdeeinuD51/Cs9S0QddL7XhZRoHCt7Wls7UBpW7AnmMjnvQxSU0R9B5Id9Q4QKVHJAXv/GNbyStVxHNjBkzknb48OGk0fW8evXqqn2ke9B9992XtBtuuCFpVLJE94HpUIZA1yUdN90TyWNUrLJ3796kLV68OGlUhEHXFGlUMkDXylNPPZW0CD5mKieibVP5x4oVK5JGBQe1pT+0f3TfpPdOtbm3F3ScNEeRRmNJczf5u7YIg0q/aB1euy8RXNxFRTgErUPomjl27FjSaG1P5ThUEEVlHTSG/ZbSTQ/Xi4iIiIiIjBg+jImIiIiIiLSAD2MiIiIiIiItcMaHsVLKylLKt0spj5ZSHiml/PaYPr+UcncpZdvYv+t+8VNkyOhZ6Rp6VrqIvpWuoWdlFKlJnL0aEb/TNM2PSinnR8SmUsrdEfHBiPhW0zQfL6V8LCI+FhG/O4idovAfheNmzZpV9XkUOqTyAgr4UvCaPm/37t1JW7JkSZX2+OOPJy0i4rHHHkvak08+mTQKy1Ko/aabbkoaFXhQMO3bB0wAACAASURBVJnCphR8HxGG6lkai14lLDXQe0mr/VZ6ulboveQv8vvKlSuTtmnTpqRFRHz5y19OGnn+jjvuSNqWLVuSRh6j46Nrkooi+g3ZDpGBevb0UDXNqbUaFWnQeSHv0LxIoXIKkFNwevv27UlbsGBB0k6ePJm0Z599NmkRXPZwwQUXJI18fO+991Zth95LQfNvfvObSbvooouSdsUVVyStpQKPSV0f0NxLpTzkTyqfoHvvnj17krZv376kPfjgg0mj80JFAe94xzuSNp5SACpC+MlPfpI0Gq8bb7wxabQ2WLVqVdKovIzWYbRdGv+WyjomfU1L93MqyKB5kO7d5Asq0qCCC5rvyE90L6gtXKHtRvDa8uKLL07a2rVrk0aFfbRt8uOyZcuSRiVipNE2aKz75YxXQtM0+5qm+dHYn09ExJaIWB4R74uIz4297HMR8f6B753IBNCz0jX0rHQRfStdQ8/KKDKu/y1RSlkdEddFxP0RsaRpmr//30X7IyL/r7/X3/PhUsrGUspG+j+iIsNEz0rX0LPSRcbrWz0rbeNcK6NC9cNYKWVWRPxlRHy0aZp/8nP35vWf2eHP7Zqm+VTTNOubpllPPwIUGRZ6VrqGnpUuMhHf6llpE+daGSWqHsZKKWfF66b9fNM0fzUmHyilLB3770sjIn8jm0hL6FnpGnpWuoi+la6hZ2XUOGOavbye4vtMRGxpmuYTp/ynr0TEnRHx8bF/5+T+kKEQI4UiKYBH3/pNAVoKT1IIcceOHUl7+umnkzZ37tykUcg9ImLp0qVJowIPOpaPfOQjSbvuuutwO6dD4efaEGNtwHOYtOHZfko9ags36POopIJCwBS8PnDgQNKoVICKGX7hF34haRER3/72t5NG5Rq0P7Nnz04ahb5Jo2Pup1SF5gIqChgUg/bsRAPGdNxUdkTUzndUjkHXD3n7oYceShrNiUSveej8889PGs2BdG1s3rw5aVREc8MNNyRt3bp1Sdu/f3/SyHcUwqfzVHvuJsoorA9ofKh8gCBP0HtpDqTCr9riinvuuSdpdO57zWE7d+5MGs2fVBxDhTB0TdYWo9TOizSuNE/VnruJMmzP1s699Doq4akttKN7Kml0vmo/r1cxx+n0mmtXr16dNCrcoLUqebmWfu7nk+XRmmqxmyLiNyLioVLK3995fi9eN+z/KaV8KCJ2RMS/HPjeiUwMPStdQ89KF9G30jX0rIwcZ3wYa5rm3ojo9Rh422B3R6R/9Kx0DT0rXUTfStfQszKKtPIlDyIiIiIiItMdH8ZERERERERaoCYzNrJQyUHtt4bT6yiQS4HK2uKQv/7rv676PApKRnBY8t3vfnfSfu3Xfi1p9A3mBI0hBedpvCgUSQw7kDsK1IZ2aczInxQuPXnyZNV2qZjjiSeeSBr57vjx40m78MILq14XEXHNNdckbdWqVUmjMoRLLrkkaXS9UFCdrhUq2qFrtzbE3yVqSnPIO7VFNORFmj/JxzNnzqzaBpUmUFkHFXCQtnDhwqRFRMyZMydp5EW6DhYvXpw0GsPbbsu//UTjQMe8Z8+epNWG+qcr5EXy2Fvf+takXXnllUl78MEHk3b//fcnjeZeulZoPibfkI8jIlasWJG066+/Pmkf/OAHk0bXPY0XUTtXTsU5tRY6TtKoDIXODa0tSaOyHppjnn/++ar30vqA7sf0eb3O9fz585NGBXR0vS1YsCBpdB3QNvqZGyerwMOfjImIiIiIiLSAD2MiIiIiIiIt4MOYiIiIiIhIC/gwJiIiIiIi0gKdTvxSIJeCoxTeoyAihR0PHz6cNApeXnzxxUlbv3590ii4S2HxiIj3vOc9Sfs3/+bfJI3ClxTwfPnll5NW+43vr776atIonE/vnawA5GRRezw0PnQO6PPIY88++2yVRuHXc889N2mPP/540qjMgIK8S5cuTVpExD//5/88aRSyJY0+k3xM1z2FgGu9WKvVlGKMKrWerX0d+bh2flm5cmXSdu7cmTQqzLjllluStmjRoqrtUslLr/ffdNNNSSN/3nDDDUnbu3dv0mjep2uNfEwlITQ/dNmftfRzL6HXUfnAkiVLkvbOd74zaVQo8P3vfz9pu3fvThqtSej6mTt3btIiIn75l385abSOIG9TeRLtT+09jXxHr5tq64BhQONBcwLdk2tLKk6cOJE0Oje0rqR7L60NexWa0fHR/EbrAyruql0fEFReR/eNyZpXp/7sLSIiIiIiMoL4MCYiIiIiItICPoyJiIiIiIi0gA9jIiIiIiIiLdDpAg/6dvpe31h/OmvWrEkahSIp5EdBcApP/uZv/mbVvvQLBTIpKEmBTApf0rHUlqVMteKDWmrDyeecc07V51122WVJo+ID2saGDRuSRiHgWsjvVGwTEfHaa69Vbfu8885LGvmTrkkqI5GfDfmkViNvz58/v0q78sora3dxoJBnewW7jxw5kjQq66C5ksZr2bJlVRpRO8/SdTYdoPGuHTOC5uOjR48mjcb78ssvT9rq1auTRoUZx48fTxrNf70KLmhOpeu0dq1ChRu1ZQbT4f7eL4MuiaJ7IL335MmTVZ9H76W1LxVmzJo1K2l0347gkjxaC9DxkR+pBKmf+bLNkhmvIhERERERkRbwYUxERERERKQFfBgTERERERFpgTM+jJVSVpZSvl1KebSU8kgp5bfH9D8spewppWwe+yd/Q7FIC+hZ6Rp6VrqGnpUuom9lFKkp8Hg1In6naZoflVLOj4hNpZS7x/7bJ5um+a/D271/hEK6FLajIGo/0DYoXEive/bZZ6u2QQHICA5BUoixttCgNpxPY02h3xEu6xiqZ2tD5DQ+FCSl9/ZTuEDh7n5C7hSwXbhwIb62NjBOYXWCxouKFAja7ggzEvMsUeudl19+ueq9BIXF6TzTdmvD7L32hfxNpUi14e7aQDt9HhVKtBkqPwMj4dnasagdx9mzZ1e9t7b8ispbqECM6OVZKmagtQ/tDx0zvXfQa6kRYiR8O2jofNF9mubL559/Pmk0n9N9m+7RvdYWNL/RZ86YMSNptPatLaWrXau2OdeecbXSNM2+iNg39ucTpZQtEbF82DsmMlH0rHQNPStdQ89KF9G3MoqM68cYpZTVEXFdRNw/Jn2klPJgKeWzpZSJd2eLDAk9K11Dz0rX0LPSRfStjArVD2OllFkR8ZcR8dGmaY5HxJ9GxMURcW28/n8Z/rjH+z5cStlYStl46NChAeyySB16VrqGnpWuoWeli+hbGSWqHsZKKWfF66b9fNM0fxUR0TTNgaZpXmua5qcR8emIyN80+/rrPtU0zfqmadYvWrRoUPst8jPRs9I19Kx0DT0rXUTfyqhxxsxYeT299pmI2NI0zSdO0ZeO/e5tRMSvRMTDw9nF1xl0MQQF9SgMSKHB2m9Tr9V6hXQp7EhhQhqb2rKHqUgbnq31Zz8+Ju8QFKitLROp3UavcDeF32sLT+h1M2fOrNqfrjMq8yydg9oiFJo/a/006Hm2dhsRXHjTT4EOaVQS0g+jMJePsmdrX1cb4q8tACCP0Vz30ksv9dzPGshPtV4ckZKt1mjDt5OxHqPzSkUY5J1Br2l7HVs/Hq19b695ftSpucveFBG/EREPlVI2j2m/FxEfKKVcGxFNRGyPiN8ayh6KjB89K11Dz0rX0LPSRfStjBw1bYr3RgQ95n5t8Lsj0j96VrqGnpWuoWeli+hbGUWm98+rRUREREREWsKHMRERERERkRaoS2ZPQaZzwYV0k1p/9irXEJls9Kx0DdcG0kX0bbfxJ2MiIiIiIiIt4MOYiIiIiIhIC/gwJiIiIiIi0gI+jImIiIiIiLRAoW/PHtrGSjkUETsiYmFEHJ60DQ+XqXIso3ocFzZNs6itjZ/i2YjRHaPx4nEMFz07eDyO4aJnB4/HMVxa9WzElFzTTpXjiBjNY+np2Ul9GPuHjZaysWma9ZO+4SEwVY5lqhzHMJkqY+RxTB+myhh5HNOHqTJGHsf0YaqM0VQ5jojuHYu/pigiIiIiItICPoyJiIiIiIi0QFsPY59qabvDYKocy1Q5jmEyVcbI45g+TJUx8jimD1NljDyO6cNUGaOpchwRHTuWVjJjIiIiIiIi0x1/TVFERERERKQFJv1hrJRyeynl8VLKE6WUj0329idKKeWzpZSDpZSHT9Hml1LuLqVsG/v3vDb3sYZSyspSyrdLKY+WUh4ppfz2mN65Y5ksuurZiKnhWz07fvRs++jb8aFn20fPjp+u+lbPjhaT+jBWSnljRPxJRLw7Iq6KiA+UUq6azH3og7si4vbTtI9FxLeaprk0Ir419vdR59WI+J2maa6KiDdHxH8YOwddPJah03HPRkwN3+rZcaBnRwZ9W4meHRn07DjouG/vCj07Mkz2T8Y2RMQTTdM81TTNyxHxxYh43yTvw4Romua7EXH0NPl9EfG5sT9/LiLeP6k7NQGaptnXNM2Pxv58IiK2RMTy6OCxTBKd9WzE1PCtnh03enYE0LfjQs+OAHp23HTWt3p2tJjsh7HlEbHrlL/vHtO6ypKmafaN/Xl/RCxpc2fGSylldURcFxH3R8ePZYhMNc9GdPhc69kq9OyIoW/PiJ4dMfRsFVPNt50+z132rAUeA6J5vZayM9WUpZRZEfGXEfHRpmmOn/rfunYsMnG6dK71rER071zrW+naedaz0rXz3HXPTvbD2J6IWHnK31eMaV3lQCllaUTE2L8Ptrw/VZRSzorXTfv5pmn+akzu5LFMAlPNsxEdPNd6dlzo2RFB31ajZ0cEPTsupppvO3mep4JnJ/th7IGIuLSUsqaUcnZE3BERX5nkfRgkX4mIO8f+fGdEfLnFfamilFIi4jMRsaVpmk+c8p86dyyTxFTzbETHzrWeHTd6dgTQt+NCz44AenbcTDXfdu48TxnPNk0zqf9ExHsiYmtEPBkRvz/Z2+9jv78QEfsi4pV4/feCPxQRC+L1lpZtEfHNiJjf9n5WHMfN8fqPax+MiM1j/7yni8cyiWPWSc+O7XvnfatnJzRmerb949C34xsvPdv+cejZ8Y9ZJ32rZ0frnzJ2MCIiIiIiIjKJWOAhIiIiIiLSAj6MiYiIiIiItIAPYyIiIiIiIi3gw5iIiIiIiEgL+DAmIiIiIiLSAj6MiYiIiIiItIAPYyIiIiIiIi3gw5iIiIiIiEgL+DAmIiIiIiLSAj6MiYiIiIiItIAPYyIiIiIiIi3Q18NYKeX2UsrjpZQnSikfG9ROiQwLPStdRN9K19Cz0jX0rLRFaZpmYm8s5Y0RsTUi3hkRuyPigYj4QNM0j/Z6z8KFC5vVq1dPaHsyPdm0adPhpmkWDeKz9KxMBoP0bMT4fTuVPfvTn/606nVveIO/9DEe9Gz3obVcKaXqdQS9d5Ro27MRU9u3E302GEVGxcvbt2+Pw4cP4868qY/P3RARTzRN81RERCnlixHxvojoadzVq1fHxo0b+9jkcOlnkqJFAr1uPKaonVwH/d5RopSyY4AfN+U8O9Xpoo8H7NmIcfq21rODHtvaz6t9oKLPe/HFF5NGD17nnntu1b6MZ9uTMTZt0RXP1jLoB5Naz77xjW+sel0/2xjP+/tZqwz6f2gM+vPa9mzEaPl20PPJK6+8UrWNV199NWlvehM/WtR6nF7X6zNrIO/Vjtcgx3X9+vU9/1s/V8fyiNh1yt93j2kio4qelS6ib6Vr6FnpGnpWWmPov8tRSvlwKWVjKWXjoUOHhr05kb7Rs9I19Kx0DT0rXUTfyjDo52FsT0SsPOXvK8a0f0LTNJ9qmmZ90zTrFy0a2K/3ikwEPStd5Iy+1bMyYuhZ6RquD6Q1+smMPRARl5ZS1sTrhr0jIv71QPaqJfr5/erXXnstafR7quP5nfJ+MhgG2JGR9OwoBapHzUujlKtpkaH4dpTGlubP2swtZRb6PbZBZzdq92fUrr8+GMm59nRq7+W1ryM/9JN/6XVvqF1v0LbpeunHx3Qs/eR7WqR1zw46H1brE/q8s846q2obL7/8ctV2e22H/Ei+pRxwLS+99FLSyKNtzrUTvmKapnm1lPKRiPh6RLwxIj7bNM0jA9szkQGjZ6WL6FvpGnpWuoaelTbp639fNE3ztYj42oD2RWTo6FnpIvpWuoaela6hZ6UtOvn7DyIiIiIiIl3HhzEREREREZEW6GTK8u+ZjLBzbVCSwo4UTCROnDiB+nnnnZe02pDuCy+8kDQqDzn77LOTRvtN7+0nqD5K5QGDoLbopZZ+ymToyxrpPO/atStp5EXy9owZM5IWEbF8ef5aFtrvffv2JY38PmfOnKSRF2m/Z86cmbSOliEMBQqGU9CZNPIEBaLpdf2ExWkbtH80h9EXRkdEbN++PWkLFy5M2qxZs5JG19o555yTNPIsHUvtnDHqXyLdNv2MRW3JFs0ltQUetV9+2+s4au8PtddpbUEJ+Z2utdpSBtq/2rlgVDj9nPdz7yb6Kf8hP9YWuNCagei1FqiFriOa03utk2uovaZrj3kYuDIRERERERFpAR/GREREREREWsCHMRERERERkRbwYUxERERERKQFOlPg0eub6E+HQqcU3qPPo3Bqr28SP53aoCxx+PBh1CkYScFG2m8KRVKwnLZB40XhdWI6hMhrvVhbhEIeIz/Vlk889NBDSfv5n//5pD3zzDNJO3bsWNLG4wd67bPPPpu02bNnJ40CulREQ56tLZOZjNKfrkDjc+655yaNQs0vv/xy0mhsqTSD3ls7R9N1QdcPXXu95nKaP2lOPnnyZNKoJKbWnwTtI713uhZ41M69kzEW5Bu6P9M+U1kR0et46bo6//zzqz7z+PHjSXv++eeTVluuUVtUVjvfdI3Tj4vOGWmDvu+Q92qLWWjupvPab1kHQfd9WjPUFubUrnNrC2Uma16dnqsQERERERGRlvFhTEREREREpAV8GBMREREREWkBH8ZERERERERaoDMFHkTtN9uTRoFxCuTu37+/6vMWLVqUtLlz5yZt3759Sdu9e3fSIjh0eODAgaRRYJG2TYUi8+fPTxqF0qdDOLyW2kB97XtJIy/WlhdQ0PXpp59O2pEjR5JGwVkKBvcqp9m6dWvSyMcLFy5MGl1XtB261ugaoHGl11FBBTFdiz7ouPsJ3lN5ARW1UICcPELz1Xi47rrrJvxe2m+CjoXmDLovEdPVi7WFBLVzdO14U2EG8dxzzyWttoSIigyefPJJ3A6VL912221Jo3mWypNII+i+VFuKRPev6UKtz2qpXefS/a62ZIb8SNug9z7xxBNJi4hYvHhx0pYvX560efPm4ftroOut1rf0utoiqX7P8fSc0UVERERERFrGhzEREREREZEW8GFMRERERESkBfrKjJVStkfEiYh4LSJebZpm/SB2SmSY6FvpGnpWuoaela6hZ6UtBlHg8famaQ4P4HP+AQrM1RZI1L639hvHt2/fnjT6tnoq5pgzZ07SqOCAPi+iPkBJZR3Hjh1L2gUXXJA0Cj9TWJmKPjrOQH1LviONzt/LL7+ctNqCBDpXVAhD26D3UqkHBWJ7BWwpAEthdfI8BWDpmqwte6DPo/KI2uKQEWDgc20NNEfQ/NLPHEHnZdu2bUl7/PHHk7ZgwYKkLVu2LGk7d+7EbZNnjx49mjQKn5N3aN6n46P9psA9XT9UkDCipR4D9SyNd20pUm3JUu02aLypYIvm1D179iSNvN2LgwcPJu3+++9P2mWXXZY0mttuvvnmpK1cuTJp/ZbldIS+PDsZZWd0vyPoHk8FHrTWJI/S/PSd73wnab3WtLROpgKlW2+9NWmXXnpp0qjIpp+1AL13subVkZy9RUREREREpjr9Pow1EfGNUsqmUsqHB7FDIpOAvpWuoWela+hZ6Rp6Vlqh39/Fublpmj2llMURcXcp5bGmab576gvGDP3hiIhVq1b1uTmRgfAzfatnZQTRs9I19Kx0Dde00gp9/WSsaZo9Y/8+GBFfiogN8JpPNU2zvmma9fRlrSKTzZl8q2dl1NCz0jX0rHQN17TSFhP+yVgpZWZEvKFpmhNjf/6liPgvg9ip2vAtBcsp3E9BPfoGbQrqUUDwscceSxoFFimg/eSTTyatVxiTQodLly5NGoVqKWw+a9aspFGgkgLjVApB0Lep03lqqyBhWL6tLYQhb5Nnaws8qHyAfEcFHqSRj48fP540KnCI4MA4+ZiOuTZgT5BnaawpgHz++edPeLuTwbA8S9claeRt8gSdv127diWNrgvSqHDj3nvvTdrChQuTRsH1t7/97UmL4KA6lS5s2rQpaVQUQoVK9Dq6dsmLdK8irbbIYjIY5vrgdMiftYVKBM1XNG/cd999SXvggQeSRoUJdN+lOZHeGxGxd+/epNG1++yzzyaNyhoOHTqUtDVr1iTt6quvThoVfdB40f7VltNMBpPp2WFA927SaE6g4hma48m3tB6mNXcEXx+f/vSnk7Z69eqk/ff//t+T9s/+2T/D7ZwOeZ78SL7tp1BwPPSzIl4SEV8a26k3RcT/aprm7wayVyLDQ99K19Cz0jX0rHQNPSutMeGHsaZpnoqIdQPcF5Gho2+la+hZ6Rp6VrqGnpU2sdpeRERERESkBXwYExERERERaYFJb1E4PQxXG4SrfV1tkQKF8igUTUFwCs9Sq87WrVuTRqFI2kYEh3c3btyYNPpm8q9//etJozG8/PLLk0ZhTtoufUv6LbfckrR58+YlbapR6x0KjZIXKdxP4VLSqGyFrgG6Vqiw5sEHH0za7bffnrSIiLPPPjtp5DsKadcWSlAomcaBxrC2PIL2j4L9XaafOZWC91Q6Q8UVO3bsSNqjjz6aNPISFRNRWPzo0aNJozksgq/d7373u0mjAiQaB7rulyxZkjTyHXmWxp/OHb1uOni29ripsKi2SIrm1Mcff7xqG1R6QK+jcq9e1+iWLVuStnbt2qRdeOGFSaPrj/aH7gX79+9PGpXO0HVK1zOdu7YKPEaB2rIIeh3dF+fPn580mmMOHjyYtHvuuSdptBag80pzZUTERRddlDQqpaNSpU984hNJIz/+6q/+atJoPqBxpbEhP/ZTNtaL6et6ERERERGRFvFhTEREREREpAV8GBMREREREWkBH8ZERERERERaYNILPGpC47UhRgrlUZCfwqn0eRREfeGFF5JGoUH6Fnp6Lx0bhSwjuCiEApQUTKdQ+6//+q8nbc6cOUmjgO+GDRuSdvXVVyeNikyohKErNE2Tgva1oe9du3YljXxCnqVgM0Gvo0ICKoN59tlnk/bNb34zaW9961urtAguf6HrgEKxtUUFNF7kdwoR07kbRhi3C9A5oDA2FU3QOah9HWkzZ85M2vbt25NG1xSdewqfP/bYY0mL4NA2FRDcdtttSast4aDrgsLsF1xwQdJqS39qC1m6TO28SGNG64UTJ04kjeYI8iKtK5577rmkkR9obqIyrV5zE5Ue0DqCSnUuu+yypNHYHDlyJGlUREO+oxIbWmvQ9VN7f50ukJfJtzSfU6nH9773vaTde++9SaP1ARXCkMd6FXisXr06abR+/f73v580mkPvu+++pNF1QOU2K1asSFrt/c8CDxERERERkSmCD2MiIiIiIiIt4MOYiIiIiIhIC/gwJiIiIiIi0gKdTkpSKQFpFDCl182dO3fCn0dByTVr1iTt0KFDSbvyyiuTFhFxww03JO0Xf/EXk7Zp06akUQiW9vEb3/hG0m699dakfeADH0gahX6nGqWUqkAxBcupuILCuFRoUAtt95FHHqnaxqOPPpo0Cqa+7W1vSxqFdnu9n8oQyDsUlKXrj465VqNroDYw3iW/1wSM+yl8oDGj8aZQ+YIFC5K2c+fOpFEZQq0/3/e+9yWNQu+9uPvuu5NGhQ1UVHDw4MGkrVq1KmlUdkRlJLX73SV/DpvaghOaX+gc0Oto/iRvX3LJJVWvo3IEKvGK4PvI0aNH8bWnQ3M3lTXQumLGjBlJo3IvGv/pXNbRT+EDFcAQdG6efvrppD388MNJo3ns5MmTSfvVX/3VpP3bf/tvk/bVr34V95FKXN773vcm7Z3vfGfS6Hqj9TTdS2i7NF7z5s1L2mTNq/5kTEREREREpAV8GBMREREREWkBH8ZERERERERa4IwPY6WUz5ZSDpZSHj5Fm19KubuUsm3s3/kXLUVaRN9K19Cz0jX0rHQNPSujSE168q6I+B8R8eenaB+LiG81TfPxUsrHxv7+u4PaKQraUmC1NoBO4UkKLG7durXq884666ykHT9+PGkLFy5MGpV1UIAxgkO+s2fPrtlFLCqgACSFeem9FCKlMZw5c2bS+ikK6IO7Yki+JS9SWcdFF12UNPqGdwqMUxkC8f/+3/9LGnmRCjwoHE5h3CuuuCJpVK4QwcdC0BhSmHvWrFlVn0fvpdAuzQV0PZM2CdwVA/JszTVH54DCyjRHUKkEfR5BcwTNaz/4wQ+SRuVCa9euTdr8+fOTRmUiERHPPPNM0mj8vv/97yeNyjqWLFmSNPIdbZfGmjTav9rSigFzV0zi+oD8ScdI8yeNT23RFa0NfvzjHyeNigduvPHGpFHZBu0L3VcieD6n+zG9bv/+/Un7m7/5m6RddtllSbv++uuTtmLFiqQdOHAgabSGoGtyEgoT7opJXtPW3s9pPGqvazr/dJ+mIiKaf+kc0nz31FNPJY3WORERe/bsSRoVu9Daed26dUnbvn170uhY6JresWNH0mj8qdSj172kH874iU3TfDciTp853hcRnxv78+ci4v0D3i+RvtC30jX0rHQNPStdQ8/KKDLRx7slTdPsG/vz/ojI/ytQZPTQt9I19Kx0DT0rXUPPSqv0/bO25vWfofb8EoVSyodLKRtLKRvpOwFE2uBn+VbPyiiiZ6Vr6FnpGq5ppQ0m+jB2oJSyNCJi7N/5F0nHaJrmU03TrG+aZj19yaXIJFLlWz0rI4Sela6h8UI1awAAHYRJREFUZ6VruKaVVpno159/JSLujIiPj/37ywPbox5QqJNC9hQGpPAeBWPpW+gpeHn++ecnjQJ9FEKkfabgZUTET37yk6RRAJKKPpYvX540KpSgoDtBY0jnhMKmwwg7TpCB+JY8QeFrKjToFWw9ndox++Y3v5k08hidvwsuuCBpFMamfTl58iTuz7Jly5JG1wv5hIpHdu3alTQq5qDxp+uKyhBaKuuoZWhzbW1QnvxOflq8eHHSXnrppaRt27YtabUFFzRfkY9pniSP9NrOQw89lDQ6FroO3vWudyWN9ptC+HT/otfVllZMQhkCMTTPkk9qCw5ofOicUsEBFQl997vfTdqdd96ZtA0bNiRt9+7dSTt8+HDVdiMi6CGA1jRUFELFOLQ2oDKnt73tbUmj8aJrrSUv1jLUNS0dO/mRrn96L60taB6jAo99+/Yljeax22+/PWm0rqR16po1a5IWwfMglePQWoDWFnQd1BaB0VqAvEznqZUCj1LKFyLivoi4vJSyu5TyoXjdsO8spWyLiF8c+7vIyKBvpWvoWekaela6hp6VUeSMPxlrmuYDPf7TbQPeF5GBoW+la+hZ6Rp6VrqGnpVRZGR+f0xERERERGQ64cOYiIiIiIhIC0y0wGPSoZAuUVteQGUd9O3iVAJw3nnnJY1Cfnv37k0aVaFSGDMi4pZbbkkafYs9hXSpiIGCkjNmzEgaFSTQ8dHrKDhNpRX0ulHl9PNTG06mMCid617FAqdDAd2nnnoqaW9+85uTtnPnzqTddNNNSSM/HDlyJGm9Smdof/72b/82aTfffHPS3vve9ybtwgsvrNof0k6cOJE0Ct5SiL/rnq2BrmnSaM4hn9A1QAHyxx9/PGlz586tei+FzynsTVDRRwSXxNDcTeF6ug4oaE7lJjTWND+QZ2tfN9WgMSPoXNGYkUZrjbVr1ybt3e9+d9L+03/6T0mjOfEP//APkzZz5syk9SpK+tGPfpQ0KgChIhvy9p/92Z8l7frrr8dtnw6dE5o/aaxHvDxpYJAfa19H1zXd22rn3yeffDJpx44dS9onP/nJpNHcRtx9992of+ELX0habdEWFeKtXr266nW13qNrkLRhMPVnbxERERERkRHEhzEREREREZEW8GFMRERERESkBXwYExERERERaYGRLPCgAG2vkoua19WWK1C5BoX2KWz+wgsvVG3jscceS9qXv8xf9r5q1aqkUaiWAvF33HFH0tatW5c0Or7aECm9jj6v68UHpx87eYyOkcasn8DyvffemzQKv+7YsSNpV111VdIWLFiQNLpWHnrooaR99atfxX0kLz7xxBNJ+6M/+qOk/dqv/VrSfvM3fzNp11xzTdKoVIega7Lr/pwoNM/WltPUjjeFxcmzVDBzxRVXJO3nfu7nkkbHQaUsVNoUwcVG27ZtS9qsWbOStmbNmqRdeumlSaOSkHnz5iWN5lQafzpmet1Uo/Z+RdC1T+UTVORFhQk33nhj0h555JGk/bf/9t+SRgUXVFZEpRwRPCcfOHAgafv370/av//3/z5p5G0q/6DXEbWerS1mm4rQXEvjQQV05Nte89vpUBkNlczceeedSfuDP/iDpP3v//2/k0bXQUTEOeecU6XR/YU0Ghtan1EJB80btD6jOYfOU7/rCH8yJiIiIiIi0gI+jImIiIiIiLSAD2MiIiIiIiIt4MOYiIiIiIhIC4xkgQcF4SiwWBvmpaAkFW5QAPKVV15JGoVvSaPA4ZIlS5J25MiRpEVE7N27N2krVqxI2vnnn5+0b3/720l75plnkvb2t7+96vOojKK2VGUYYcfJ5PR9rS0zoRAzjRl9HkF+IO9ce+21SaPSi82bNyeNroHvfe97SSMvRUSce+65SfvIRz6StPvvvz9pdK1Ruc3x48eTRqFkCpvXlhx0yZ8TpZ+xIB9TCQcVEJDfjx49mjQqmLnooouSdvLkyaTR/YIKHCK4rIP2ceHChUmjuZLGZsaMGUmrHf/aQqWuz7M10Hkl6LhpvGkc6R5NZTJU1DJnzpyk/cVf/EXS/vqv/zpps2fPThqVekREPPDAA0mj/aaSkfXr1yftsssuw+2cDl1rNG+TF6moobZEoevQGpQgP1IZEY0RnQcqdaF5lQpqtm/fnjQq3qJjo5K7CC7SoGOh64heR/Mv+Yz2hwqUaO7uZ802HvzJmIiIiIiISAv4MCYiIiIiItICPoyJiIiIiIi0wBkfxkopny2lHCylPHyK9oellD2llM1j/7xnuLspUo+elS6ib6Vr6FnpGnpWRpGan4zdFRG3g/7JpmmuHfvna4PdLZG+uCv0rHSPu0LfSre4K/SsdIu7Qs/KiHHGipemab5bSlk9/F35J9usel1tmx+1zMyfPz9p1Frz4osvJo0anaihprZB68orr0xaBLeT0dgsXrw4aWvWrEkatdFQ2w41z1BLHn1ebZvlMFu+hu1ZatKpbfmqbYoi31FLEPn4ggsuSBqdFzrPzz//fNLo3JO/IiIOHjyYNPLnDTfckDTyNjUikmepZY/aTMmLo9Le1cZcO1GOHTuWNGorpDmaGjupqXDlypVJo+uC5nfyca/WWto2XRvk7VtvvTVp1PBFzWTUGEYtv+TZ2na2YTPZnq1tjKxdG9A9mjz24IMPJm3Lli1V233LW96StB07diSNfEPthRG8jqB5f8OGDUn7v//3/yZt6dKlSaPrj67x2uZKuke2MfeO8jxbO27kC/LyokWLkrZr166k0fqV7p/UAkrrA7pvR/C6obaRlsaGtk3rM9Ko6Zeuoclqqe0nM/aRUsqDYz/yzR2RIqOHnpUuom+la+hZ6Rp6Vlpjog9jfxoRF0fEtRGxLyL+uNcLSykfLqVsLKVsPHTo0AQ3J9I3ela6SJVv9ayMEHpWuobrA2mVCT2MNU1zoGma15qm+WlEfDoi8s/C//G1n2qaZn3TNOvpR6Yik4GelS5S61s9K6OCnpWu4fpA2mZCD2OllFN/wfhXIuLhXq8VGQX0rHQRfStdQ89K19Cz0jZnTAGXUr4QEW+LiIWllN0R8QcR8bZSyrUR0UTE9oj4rSHuY0RwoI+CjVRUQCHRq6++OmkUin7kkUeSRmHs2nIMKjOg4pAIDhhSuHjPnj1Jo0Am/V8cCrWTtmTJkqRRiLG2yGKYtOFZCnmeffbZE/488hNtozZ4S++tLQag8CtdZ72gsPG8eflX8p955pmkUcnBzJkzk0bFB6TNnj07acMskxkPk+1bCuPT9UsalcnQ/EJlHfv27Uva2rVrk7Z8+fKk1ZY10Hb379+ftAief+k6IM9SUcjTTz+dNBrD48ePJ43OCYXKickKmp/2+ZPq2UEXPtBcsnfv3qTt3r07aVQe8NxzzyWN/EXnlLZLc2IEl2usWLEiaV/5yleSRgUO27ZtS9rtt+fCwXe84x1Jqy1RINpYL4zKmrYW8gqVddT6jDx67733Tni7dI9+//vfn7QIXpvUFj/RtunzaN1F6xXybW3p1zDm2po2xQ+A/Jm+tioyRPSsdBF9K11Dz0rX0LMyivTTpigiIiIiIiITxIcxERERERGRFvBhTEREREREpAXOmBkbZShEV/tt5aTRN4HTN4lfeOGFVdulkDsFE2lfIjgQX/sN7RT8pXAiHR8FFilkSdD+0XnqMnQOKbxJY1HLiRMnkkb+XLp0adLId1QWQGF40igQ2yusum7duqRR8QEV0VBJDAXsKRxOHnvllVeqPo8gv/dTyDKK0LmmEDj5ifxJ88aaNWuSRt/NQ0U0NIfRPlOJxsGDB5NGRUcRXPRCHps7d27SKAT+4x//OGkUmqdQ+Q033JC0Wt+1UeAxCtB8XDu30TxERRo0l5BvFi5cmDQq8qK5jrbbq+DisssuSxrNvXT+N2zIre20NqDCBFqr0LVCnzcK5V5tUVt4RZ4i71EJC63lLr744qT9u3/375JGflq2bFnSPvrRjyaNym16zTs/+tGPkkbHUlsaVltaQoVOVBBF9zBiGHOtPxkTERERERFpAR/GREREREREWsCHMRERERERkRbwYUxERERERKQFRrLAg4JwFKyrLVKgkDUFd+lb7Sk8TSHWp556KmkUcl+9enXSKAAZwQF20ihMSAHaw4cPJ63228qJ2hAjhTG7DBVz1JZKUOCUzil93uzZs5NW608qYaD9o9fR9dOr0IVC6FTOQOU0R44cSRoVH1DJwfz585M2Z86cpNF4UUB6OhQf1JbO0DxL55/mHGLFihVJo/mYNJpT6XV0nfUq1KFiB7oX1IbK6fqja4iuZyp2IB8T08GzdK3SeaXX0dx09OjRpFHYn7xN53nnzp1Jo1IGeu8111yTtC1btiQtgv1N+00lDFRuQ9uh67S21KOfcqipCM0dVGhCGs0xdL+jbWzdujVpVNZx8803J438RNcVrWk3bdqUtAiev2m/qYyE5lBaw1ChE5WIUSFabYHHMObaqbVKFhERERER6Qg+jImIiIiIiLSAD2MiIiIiIiIt4MOYiIiIiIhIC4xkgQeF8mrLOigMuGfPnqQdO3YsaVTCsXTp0qRdeeWVSaNv8/7e976XNApFUqgxgsOEtQULFL5dvHhx0ijESIFoCpbSOaHxnw7B8tpjpOIDGjMaW4JC3xRKp5A1+YtC1uSvXiUvFCynwDgFgem6p6IP2gYFwelaoXGlcei6Z08fSxrb2rA4aTSOtSVGVFhDcw69l84LvY6KX3p5tnbb5Fk65g0bNiSNQvN0z5g5c2bS6NwRtYVKXYbmp9qiJBoLKgqgtQGVApCP6ZqqLcJ44oknkjZ37tykRfD1R++n9QuN4apVq5JGBTN0H6FyExqH2nXFVITu8aRRWQ+tGcmPtLagOe+hhx5KWm2ZF82BdA537dqVtAgu0aFtk8/WrFmTNLp3UwkSlYxQqUctFniIiIiIiIhMEXwYExERERERaYEzPoyVUlaWUr5dSnm0lPJIKeW3x/T5pZS7Synbxv498Z/5iQwQPStdQ89KF9G30jX0rIwiNT8ZezUifqdpmqsi4s0R8R9KKVdFxMci4ltN01waEd8a+7vIKKBnpWvoWeki+la6hp6VkeOMBR5N0+yLiH1jfz5RStkSEcsj4n0R8baxl30uIr4TEb87iJ2icByF/CjsTIHFF154IWmHDh2q0v7iL/4iaRS+pQDswoULk3bkyJEqLYLDkhQapvGiwDgFianAgzQqUqDSBNImO1g+aM/WBOhrj4cCp7Xf+j5//vyk1RZm0DZefPHFpNGx0nspYBvBhQYrV65M2g033JC0rVu3Jo1C5JdccknSags8KEQ+Cgzas6f7sbbsqLaAh15H8waVBVBpAnmRSjRou1QgQHN+rwIPKkMgz5IXqYTj4osvTlptKVU/0NgMmzbWB6dTO/eSP2kN8cgjjySN7tHk7V738tMhL1FRUi8effTRpC1btixp5M/aAo/awi/6PJp7aY6uvfcNklHw7Higuaz23k2FGbSGPHDgQNJonUslIbQG2b9/f9IieN2wYMGCpJGnyHvkW7rH0xjSeNG9hD6P9q/fNe24Zu9SyuqIuC4i7o+IJWOmjojYHxG5wkSkZfSsdA09K11E30rX0LMyKlQ/jJVSZkXEX0bER5umOX7qf2tef8TEHx+UUj5cStlYStlIP3kSGRZ6VrqGnpUuMhHf6llpE+daGSWqHsZKKWfF66b9fNM0fzUmHyilLB3770sjIv9uVEQ0TfOppmnWN02znn7VTWQY6FnpGnpWushEfatnpS2ca2XUqGlTLBHxmYjY0jTNJ075T1+JiDvH/nxnRHx58LsnMn70rHQNPStdRN9K19CzMorUpCdviojfiIiHSimbx7Tfi4iPR8T/KaV8KCJ2RMS/HM4uvg6F46jggoKNFNw+fvx40ijYuGPHjqTRj6YpiEpFAxRgpABkBH/L+tKlS5NGxRwXXXRR0igASWF6CjFSOLw2fDvZBR4xYM+evq90PLXHSGFQei99qz2FzSnAum3btqQdO3Ysac8991zV/j377LNJo+sngr1D0D6SF6noY8mS/Kv8FCzup9CghTKEkZhn6bgpeE/nmUL7NPfSuaKgORUa1M4bFAqn+TSC50U6PppTaY6n+ZiuZ9JamCv7ZSR8ezo0l5AX6f5O401zL93/qLjgoYceShqVY1DxEl1TEVyoRZ6n63nu3LlJo7Ix2jaNDY0D3UfovS15eyQ9G8H3Wir6ofmJCo9q30t+pLU0zaG0DSq4iOB795w5c5J2/fXXJ23evPxNAzR3E1SYQ76le91k+bamTfHeiOi15dsGuzsi/aNnpWvoWeki+la6hp6VUWTyu3BFRERERETEhzEREREREZE28GFMRERERESkBSb/688nCIXtasOk69atS9rKlSuT9thjjyWNQuRbt25NGoVvSaOANoUnIzjseOGFFybtPe95T9Io2EjBXQomU4iRApm1JQcjHkAfN/0cD40jjTeFp2tD0RdffHHSdu7cmTQqEKgNY/eCPLZ27dqk1ZZ10D5SCQ75uJ+ilekKhbFrfUKvoxD49u3bk3b06NGqz6OSJdoXKpjp5WMqdqDrjwoSaDtU4kD+pDm61rOktVA6MxLQ+SMfkxdp7qX7MX0eFdFQwdbTTz+dNCpPGk9hDZUwUBECFXPQ8V166aVJo/UH7U/t2qC28GsqQmVzpNFYUllL7XxJ8w55j+Y7eh2dVyqg6+VbmvPmz5+fNBqHGTNmVL2u1md0LG3OodNz9hYREREREWkZH8ZERERERERawIcxERERERGRFvBhTEREREREpAVGMlFJ4eTaUB4FBCmIeNVVVyXtkksuSRp9E/jXvva1pG3evDlpFCymkC0FdyMirr766qRRQQKFdOkz6b0UWKRQc215BGFBwj9CQXAKY5N3aBwp1Eohcgr3vvDCC0l76qmnkkbFBb2uRzqWDRs2JI1Cu+Q70qj8g6Cxrp1HpkPRR20xRD/lPceOHUsanT/axu7du5NG8yedZ7p+6N4QwdcGbadWo7mSrguC9rt27p0OniVofGqLBujc03upCOH5559P2hVXXJG0EydOJI3KP+j80f5FRKxatSppVEpGhV90HdAcXzuudO1OVy/2orYEq3Ys6TzQmo/8Q/tCZSLkk9rSC5rHInhtQt6j19H+0PqA5traEqo2fetPxkRERERERFrAhzEREREREZEW8GFMRERERESkBXwYExERERERaYGRLPCoDczVhs1nz56dNAoYvvjii0lbsWJF0j70oQ8lbe/evUk7fPhw0ihsTuHJCA6HU/idjo/CnDReFICkYGNtAFX+kdpwOHninHPOSVpt0cf69euTRuU0tF0qBDl48GDV6yK4FGTZsmVJo3Gg95IXab9rCw16BeJr9m+qQWNWW3BC8wGNLc3HFMSmOZAKh2gbJ0+eTBrNifS6iIjFixcnjeZPOhaCxpWuU/JnP0zngoTToeuXPEEalV4Q5Fk6z1QCdvTo0SrtggsuwG3T8dFapbYMrfbanQ7z4jCoLb4g6BzSWoBKYWqLbGiupeK72sKMXnPl5ZdfXrU/tesfgo659r7WJl5ZIiIiIiIiLeDDmIiIiIiISAuc8WGslLKylPLtUsqjpZRHSim/Pab/YSllTyll89g/7xn+7oqcGT0rXUPPStfQs9JF9K2MIjW/SPlqRPxO0zQ/KqWcHxGbSil3j/23TzZN81+Ht3siE0LPStfQs9I19Kx0EX0rI8cZH8aaptkXEfvG/nyilLIlIpYPe8dqqA0sU6CPAqsUGqRSDwpK0reIU3EBbbfXt5VTQJiCkbTftWNT+43vXWJUPEvnoJ+QPZ0X8g75hnxHJQUzZ85M2tKlS6s+L4KvtWPHjiWNimioFISKIqjAg6D30hiOQih9VDzbD+RtCl1TYJvOPX0elXBQAQddA+S5Xtsmjd5P1xD5qZ9SKtJGoayjDc/WFvDQ62rvazS/0D2/du6dN29e0mhtULvWiIg4ceJE1bbJJ3TPry2nmQp0ba6l+YTmHfItXQe1BUpEbTHSkiVL8P1UFEIepe3Q/XwU7t2DYlxHUkpZHRHXRcT9Y9JHSikPllI+W0rJM45Iy+hZ6Rp6VrqGnpUuom9lVKh+GCulzIqIv4yIjzZNczwi/jQiLo6Ia+P1/8vwxz3e9+FSysZSysZDhw4NYJdF6tCz0jX0rHQNPStdRN/KKFH1MFZKOSteN+3nm6b5q4iIpmkONE3zWtM0P42IT0fEBnpv0zSfappmfdM06xctWjSo/Rb5mehZ6Rp6VrqGnpUuom9l1KhpUywR8ZmI2NI0zSdO0U8NkvxKRDw8+N0TGT96VrqGnpWuoWeli+hbGUVq2hRviojfiIiHSimbx7Tfi4gPlFKujYgmIrZHxG8NZQ8HAAUEKfhHIVb65m4qzKBwMIUnaV/oW817bac2HN5PsHEUwuF9MhKerR1HCqYS5DHyCPmYihTo8ygwTl7q5S8Kq9cWmQx6vDrGSHi2H+j8UekFabWFC7XaeKidUwft4ynApHt2MgL7NL/UFl3VrgOoxIZ8Q/N7BM/ntV6sHcMp7OPOz7W0ZiSPzpkzJ2n9zKv9Fgz149GpPtfWtCneGxF0xF8b/O6I9I+ela6hZ6Vr6FnpIvpWRpGp0wspIiIiIiLSIXwYExERERERaQEfxkRERERERFqgpsBjSmKBgHQN/SldQ89K15jqRQEyNdG33cafjImIiIiIiLSAD2MiIiIiIiIt4MOYiIiIiIhIC/gwJiIiIiIi0gKFvj17aBsr5VBE7IiIhRFxeNI2PFymyrGM6nFc2DTNorY2fopnI0Z3jMaLxzFc9Ozg8TiGi54dPB7HcGnVsxFTck07VY4jYjSPpadnJ/Vh7B82WsrGpmnWT/qGh8BUOZapchzDZKqMkccxfZgqY+RxTB+myhh5HNOHqTJGU+U4Irp3LP6aooiIiIiISAv4MCYiIiIiItICbT2Mfaql7Q6DqXIsU+U4hslUGSOPY/owVcbI45g+TJUx8jimD1NljKbKcUR07FhayYyJiIiIiIhMd/w1RRERERERkRaY9IexUsrtpZTHSylPlFI+NtnbnyillM+WUg6WUh4+RZtfSrm7lLJt7N/z2tzHGkopK0sp3y6lPFpKeaSU8ttjeueOZbLoqmcjpoZv9ez40bPto2/Hh55tHz07frrqWz07Wkzqw1gp5Y0R8ScR8e6IuCoiPlBKuWoy96EP7oqI20/TPhYR32qa5tKI+NbY30edVyPid5qmuSoi3hwR/2HsHHTxWIZOxz0bMTV8q2fHgZ4dGfRtJXp2ZNCz46Djvr0r9OzIMNk/GdsQEU80TfNU0zQvR8QXI+J9k7wPE6Jpmu9GxNHT5PdFxOfG/vy5iHj/pO7UBGiaZl/TND8a+/OJiNgSEcujg8cySXTWsxFTw7d6dtzo2RFA344LPTsC6Nlx01nf6tnRYrIfxpZHxK5T/r57TOsqS5qm2Tf25/0RsaTNnRkvpZTVEXFdRNwfHT+WITLVPBvR4XOtZ6vQsyOGvj0jenbE0LNVTDXfdvo8d9mzFngMiOb1WsrOVFOWUv5/O3eII0UQhQH4f4JVKFAkIBDcghPsCXAruACeO3ADJEFBwmpugCGYTSBIQnblWkQhpsXI6YVUdXW+L6nMTLeYevV+8zKTvp/kQ5JXrbXb43uz1cLdzdRrmSWZr9dyy2x9lllm6/Psme09jP1K8uTo8+Pl2qyuq+pRkiyvN4P3c5KqupdDaN+11j4ul6espYO9ZTaZsNcyu4rMboTcnkxmN0JmV9lbbqfs8x4y23sY+5LkWVU9raqzJC+SXHbew/90meRieX+R5NPAvZykqirJ2yRXrbU3R7emq6WTvWU2mazXMruazG6A3K4isxsgs6vtLbfT9Xk3mW2tdV1JzpN8T/Izyeve3/8P+36f5HeSPzn8L/hlkoc5PKXlR5LPSR6M3ucJdTzP4efab0m+Lut8xlo6ntmUmV32Pn1uZfZOZyaz4+uQ23XnJbPj65DZ9Wc2ZW5ldlurlmIAAADoyAM8AAAABjCMAQAADGAYAwAAGMAwBgAAMIBhDAAAYADDGAAAwACGMQAAgAEMYwAAAAP8BSkYuYiP2WPBAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 1080x1080 with 10 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the inferred images:\n",
+    "\n",
+    "plt.figure(figsize=(15,15))\n",
+    "for i in range(10):\n",
+    "    plt.subplot(5, 5, i + 1)\n",
+    "    plt.imshow( (np.reshape(x_infer_from_average[0+i,], (28, 28))), cmap=plt.cm.gray_r)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/adversarial-robustness-toolbox/notebooks/output_randomized_smoothing_mnist.ipynb b/adversarial-robustness-toolbox/notebooks/output_randomized_smoothing_mnist.ipynb
new file mode 100644
index 0000000..912fb49
--- /dev/null
+++ b/adversarial-robustness-toolbox/notebooks/output_randomized_smoothing_mnist.ipynb
@@ -0,0 +1,428 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "aPIA-10zdv4P"
+   },
+   "source": [
+    "## ART Randomized Smoothing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 34
+    },
+    "colab_type": "code",
+    "id": "CGDOyI0HgDfx",
+    "outputId": "2d61711f-6f8a-41b5-f05c-1085fd00fa13"
+   },
+   "outputs": [],
+   "source": [
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "\n",
+    "import os\n",
+    "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' \n",
+    "\n",
+    "import tensorflow as tf\n",
+    "tf.keras.backend.set_floatx('float32')\n",
+    "from tensorflow.keras.models import load_model\n",
+    "from tensorflow.keras.models import Sequential\n",
+    "from tensorflow.keras.layers import Dense, Flatten, Conv2D, MaxPooling2D, Dropout\n",
+    "\n",
+    "from art import config\n",
+    "from art.attacks.evasion import FastGradientMethod\n",
+    "from art.estimators.classification import TensorFlowV2Classifier\n",
+    "from art.estimators.certification.randomized_smoothing import TensorFlowV2RandomizedSmoothing\n",
+    "from art.utils import load_dataset, get_file, compute_accuracy\n",
+    "\n",
+    "import numpy as np\n",
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "FqXvuMM9dv4U"
+   },
+   "source": [
+    "### Load data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "z9OztmSidv4V"
+   },
+   "outputs": [],
+   "source": [
+    "# Read MNIST dataset\n",
+    "(x_train, y_train), (x_test, y_test), min_, max_ = load_dataset('mnist')\n",
+    "\n",
+    "nb_classes = 10\n",
+    "input_shape = x_train.shape[1:]\n",
+    "\n",
+    "num_samples_test = 250\n",
+    "x_test = x_test[0:num_samples_test].astype(np.float32)\n",
+    "y_test = y_test[0:num_samples_test]\n",
+    "\n",
+    "x_train = x_train.astype(np.float32)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "xDCzquK1dv4X"
+   },
+   "source": [
+    "### Train classifiers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "G-mh9wSAHm-Z"
+   },
+   "outputs": [],
+   "source": [
+    "# Create convolutional neural network model\n",
+    "def get_model(input_shape, min_, max_):\n",
+    "    \n",
+    "    model = Sequential()\n",
+    "    model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=input_shape))\n",
+    "    model.add(MaxPooling2D(pool_size=(2, 2)))\n",
+    "    model.add(Conv2D(64, (3, 3), activation='relu'))\n",
+    "    model.add(MaxPooling2D(pool_size=(2, 2)))\n",
+    "    model.add(Dropout(0.25))\n",
+    "    model.add(Flatten())\n",
+    "    model.add(Dense(128, activation='relu'))\n",
+    "    model.add(Dense(10, activation='softmax'))\n",
+    "    \n",
+    "    return model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nb_epochs = 40\n",
+    "batch_size = 128\n",
+    "sample_size = 100\n",
+    "alpha = 0.001\n",
+    "\n",
+    "optimizer = tf.keras.optimizers.Adam(learning_rate=0.001)\n",
+    "\n",
+    "def train_step(model, images, labels):        \n",
+    "    with tf.GradientTape() as tape:\n",
+    "        predictions = model(images, training=True)\n",
+    "        loss = loss_object(labels, predictions)\n",
+    "    gradients = tape.gradient(loss, model.trainable_variables)\n",
+    "    optimizer.apply_gradients(zip(gradients, model.trainable_variables))\n",
+    "    \n",
+    "loss_object = tf.keras.losses.CategoricalCrossentropy(from_logits=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "tGbe8Cjmdv4a"
+   },
+   "outputs": [],
+   "source": [
+    "# Construct and train a convolutional neural network in standard (non-smoothed) classifier\n",
+    "\n",
+    "classifier = TensorFlowV2Classifier(model=get_model(input_shape, min_, max_),\n",
+    "                                    nb_classes=nb_classes,\n",
+    "                                    input_shape=input_shape,\n",
+    "                                    loss_object=loss_object,\n",
+    "                                    train_step=train_step,\n",
+    "                                    channels_first=False,\n",
+    "                                    clip_values=(min_, max_))\n",
+    "\n",
+    "classifier.fit(x_train, y_train, nb_epochs=nb_epochs, batch_size=batch_size)\n",
+    "\n",
+    "sigma_0 = 0.5\n",
+    "\n",
+    "classifier_rs_0 = TensorFlowV2RandomizedSmoothing(model=classifier.model,\n",
+    "                                                  nb_classes=nb_classes,\n",
+    "                                                  input_shape=input_shape,\n",
+    "                                                  loss_object=loss_object,\n",
+    "                                                  train_step=train_step,\n",
+    "                                                  channels_first=False,\n",
+    "                                                  clip_values=(min_, max_),\n",
+    "                                                  sample_size=sample_size,\n",
+    "                                                  scale=sigma_0,\n",
+    "                                                  alpha=alpha)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create and train smoothed classifier, sigma = 0.25\n",
+    "\n",
+    "sigma_1 = 0.25\n",
+    "\n",
+    "classifier_rs_1 = TensorFlowV2RandomizedSmoothing(model=get_model(input_shape, min_, max_),\n",
+    "                                                 nb_classes=nb_classes,\n",
+    "                                                 input_shape=input_shape,\n",
+    "                                                 loss_object=loss_object,\n",
+    "                                                 train_step=train_step,\n",
+    "                                                 channels_first=False,\n",
+    "                                                 clip_values=(min_, max_),\n",
+    "                                                 sample_size=sample_size,\n",
+    "                                                 scale=sigma_1,\n",
+    "                                                 alpha=alpha)\n",
+    "\n",
+    "classifier_rs_1.fit(x_train, y_train, nb_epochs=nb_epochs, batch_size=batch_size)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create and train smoothed classifier, sigma = 0.5\n",
+    "\n",
+    "sigma_2 = 0.5\n",
+    "\n",
+    "classifier_rs_2 = TensorFlowV2RandomizedSmoothing(model=get_model(input_shape, min_, max_),\n",
+    "                                                  nb_classes=nb_classes,\n",
+    "                                                  input_shape=input_shape,\n",
+    "                                                  loss_object=loss_object,\n",
+    "                                                  train_step=train_step,\n",
+    "                                                  channels_first=False,\n",
+    "                                                  clip_values=(min_, max_),\n",
+    "                                                  sample_size=sample_size,\n",
+    "                                                  scale=sigma_2,\n",
+    "                                                  alpha=alpha)\n",
+    "\n",
+    "classifier_rs_2.fit(x_train, y_train, nb_epochs=nb_epochs, batch_size=batch_size)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "kukXRDcedv4j"
+   },
+   "source": [
+    "### Prediction"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 221
+    },
+    "colab_type": "code",
+    "id": "jcPkXptcdv4k",
+    "outputId": "ee65b562-0839-483b-9b1f-b7c911e3131a"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Randomized smoothing: 100%|██████████| 250/250 [00:04<00:00, 58.59it/s]\n",
+      "Randomized smoothing: 100%|██████████| 250/250 [00:04<00:00, 58.63it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Original test data:\n",
+      "Original Classifier\n",
+      "Accuracy: 1.0\n",
+      "Coverage: 1.0\n",
+      "\n",
+      "Smoothed Classifier, sigma=0.25\n",
+      "Accuracy: 0.9959839357429718\n",
+      "Coverage: 0.996\n",
+      "\n",
+      "Smoothed Classifier, sigma=0.5\n",
+      "Accuracy: 1.0\n",
+      "Coverage: 0.988\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# compare prediction of randomized smoothed models to original model\n",
+    "x_preds = classifier.predict(x_test)\n",
+    "x_preds_rs_1 = classifier_rs_1.predict(x_test)\n",
+    "x_preds_rs_2 = classifier_rs_2.predict(x_test)\n",
+    "\n",
+    "acc, cov = compute_accuracy(x_preds, y_test)\n",
+    "acc_rs_1, cov_rs_1 = compute_accuracy(x_preds_rs_1, y_test)\n",
+    "acc_rs_2, cov_rs_2 = compute_accuracy(x_preds_rs_2, y_test)\n",
+    "\n",
+    "print(\"\\nOriginal test data:\")\n",
+    "print(\"Original Classifier\")\n",
+    "print(\"Accuracy: {}\".format(acc))\n",
+    "print(\"Coverage: {}\".format(cov))\n",
+    "\n",
+    "print(\"\\nSmoothed Classifier, sigma=\" + str(sigma_1))\n",
+    "print(\"Accuracy: {}\".format(acc_rs_1))\n",
+    "print(\"Coverage: {}\".format(cov_rs_1))\n",
+    "\n",
+    "print(\"\\nSmoothed Classifier, sigma=\" + str(sigma_2))\n",
+    "print(\"Accuracy: {}\".format(acc_rs_2))\n",
+    "print(\"Coverage: {}\".format(cov_rs_2))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "hqea3xvMdv4n"
+   },
+   "source": [
+    "## Certification of Accuracy and L2-Radius"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "D6Va8ST8dv4n"
+   },
+   "outputs": [],
+   "source": [
+    "# Calculate certification accuracy for a given radius\n",
+    "def getCertAcc(radius, pred, y_test):\n",
+    "\n",
+    "    rad_list = np.linspace(0, 2.25, 201)\n",
+    "    cert_acc = []\n",
+    "    num_cert = len(radius)\n",
+    "    \n",
+    "    for r in rad_list:\n",
+    "        rad_idx = np.where(radius >= r)[0]\n",
+    "        y_test_subset = y_test[rad_idx]\n",
+    "        cert_acc.append(np.sum(pred[rad_idx] == np.argmax(y_test_subset, axis=1)) / num_cert)\n",
+    "        \n",
+    "    return cert_acc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "iPWY6KFMdv4p"
+   },
+   "outputs": [],
+   "source": [
+    "# Compute certification\n",
+    "prediction_0, radius_0 = classifier_rs_0.certify(x_test, n=500)\n",
+    "prediction_1, radius_1 = classifier_rs_1.certify(x_test, n=500)\n",
+    "prediction_2, radius_2 = classifier_rs_2.certify(x_test, n=500)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 283
+    },
+    "colab_type": "code",
+    "id": "ZZv5wDHSdv4s",
+    "outputId": "a6fbe7ba-dfbb-47bd-8e56-794fb689cb14"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot certification accuracy w.r.t. to radius\n",
+    "rad_list = np.linspace(0, 2.25, 201)\n",
+    "plt.plot(rad_list, getCertAcc(radius_0, prediction_0, y_test), 'r-', label='original')\n",
+    "plt.plot(rad_list, getCertAcc(radius_1, prediction_1, y_test), '-', color='green',\n",
+    "         label='smoothed, $\\sigma=$' + str(sigma_1))\n",
+    "plt.plot(rad_list, getCertAcc(radius_2, prediction_2, y_test), '-', color='blue',\n",
+    "         label='smoothed, $\\sigma=$' + str(sigma_2))\n",
+    "plt.xlabel('radius')\n",
+    "plt.ylabel('certified accuracy')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "collapsed_sections": [],
+   "name": "randomized_smoothing_mnist.ipynb",
+   "provenance": [],
+   "version": "0.3.2"
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/adversarial-robustness-toolbox/notebooks/poisoning_attack_backdoor_image.ipynb b/adversarial-robustness-toolbox/notebooks/poisoning_attack_backdoor_image.ipynb
new file mode 100644
index 0000000..ca4c037
--- /dev/null
+++ b/adversarial-robustness-toolbox/notebooks/poisoning_attack_backdoor_image.ipynb
@@ -0,0 +1,299 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Creating Image Trigger Poison Samples with ART\n",
+    "\n",
+    "This notebook shows how to create image triggers in ART with RBG and grayscale images."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import os, sys\n",
+    "%matplotlib inline\n",
+    "\n",
+    "module_path = os.path.abspath(os.path.join('..'))\n",
+    "if module_path not in sys.path:\n",
+    "    sys.path.append(module_path)\n",
+    "\n",
+    "from art.estimators.classification import KerasClassifier\n",
+    "from art.attacks.poisoning import PoisoningAttackBackdoor\n",
+    "from art.attacks.poisoning.perturbations import add_pattern_bd, add_single_bd, insert_image\n",
+    "from art.utils import load_mnist, preprocess, load_cifar10\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "(x_raw, y_raw), (x_raw_test, y_raw_test), min_, max_ = load_mnist()\n",
+    "\n",
+    "# Random Selection:\n",
+    "n_train = np.shape(x_raw)[0]\n",
+    "num_selection = 7500\n",
+    "random_selection_indices = np.random.choice(n_train, num_selection)\n",
+    "x_raw = x_raw[random_selection_indices]\n",
+    "y_raw = y_raw[random_selection_indices]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "idx = 0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Note the shape of `x_raw`, black and white images must still have a color channel"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "x_raw shape: (7500, 28, 28, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"x_raw shape: {x_raw.shape}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7ff4715ed6a0>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.imshow(x_raw[idx].squeeze())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7ff471611c50>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "backdoor_attack = PoisoningAttackBackdoor(lambda x: insert_image(x, \n",
+    "                                                                 backdoor_path='../utils/data/backdoors/alert.png',\n",
+    "                                                                 size=(10,10),\n",
+    "                                                                 mode='L',\n",
+    "                                                                ))\n",
+    "poisoned_x, poisoned_y = backdoor_attack.poison(x_raw[:20], y_raw[:20])\n",
+    "plt.imshow(poisoned_x[idx].squeeze())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "(x_raw, y_raw), (x_raw_test, y_raw_test), min_, max_ = load_cifar10()\n",
+    "\n",
+    "# Random Selection:\n",
+    "n_train = np.shape(x_raw)[0]\n",
+    "num_selection = 1\n",
+    "random_selection_indices = np.random.choice(n_train, num_selection)\n",
+    "x_raw = x_raw[random_selection_indices]\n",
+    "y_raw = y_raw[random_selection_indices]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "x_raw shape: (1, 32, 32, 3)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7ff4717af1d0>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "print(f\"x_raw shape: {x_raw.shape}\")\n",
+    "plt.imshow(x_raw[idx])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The ART method `insert_image` has different parameters that can affect image trigger generation.\n",
+    "\n",
+    "If `random=True`, the image will in a different random location for each sample. If `random=False`, the placement of each trigger depends on the values of `x_shift` and `y_shift`. \n",
+    "\n",
+    "You may also set `channels_first=True` if working with images of shape `(N, C, W, H)` instead of `(N, W, H, C)`\n",
+    "\n",
+    "The mode affects how Pillow processes the image. This is usually `RGB` for color images or `L` for 8-bit black and white images. More information [here](https://pillow.readthedocs.io/en/stable/handbook/concepts.html#concept-modes).\n",
+    "\n",
+    "The `blend` parameter affects how much the two images should blend into each other. The default blend is 0.8"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def poison_func(x):\n",
+    "    return insert_image(x, backdoor_path='../utils/data/backdoors/alert.png',\n",
+    "                        size=(10,10), mode='RGB', blend=0.8, random=True)\n",
+    "backdoor_attack = PoisoningAttackBackdoor(poison_func)\n",
+    "poisoned_x, poisoned_y = backdoor_attack.poison(x_raw[:20], y_raw[:20])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7ff490883400>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPsAAAD5CAYAAADhukOtAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAczUlEQVR4nO2da4xdV3XH/+s+5+kZ2+NX7AQ7icurlEBHKQJEKX0oRZUCUlvBB5QPCFcVSEVqP0RUKlTqB1oVUD5UVKZEpBXl0QIiqqIWmrak9JGSQOI4MRQn2LGJ7YntGc/rvu/qh3vTOun+rxnP447J/v8ky3f2uvucffY965x79/+stczdIYR4+VPY6gEIIQaDnF2ITJCzC5EJcnYhMkHOLkQmyNmFyITSejqb2R0A7gFQBPAX7v7xcGdmXi4Y2RaXAAsgfcLBcVOzG3SzoCORKaulIu1SqlT55rodahseG6e2QrVCba1Om+yLH1e5xE+DYpEfW6PZoDZ3Msnhh7Y2um3+gXo7PR/o8j7RMUcUy2Vqq1SHqM3IpESyeIfM7+XLl7G0tJTc4Jqd3cyKAP4MwC8DOAvgO2Z2v7s/xfqUC4ZbRtO7LBf4gQ0X0pNfDPpYgX9pebZGTeGJj3b65L51aoJ22XnwMLU1lheo7XVvfiu1jd58E7VdmL2cbG/X+Am8d89eahsbH6W2H506TW3N1nKyvcj9AcDanvlYmluktsb5S+lxtOq0z3hwzG78vNqx5wZqu+nQq6itUEifc+zCDQBztfS5c8899/D9UMvK3A7gpLs/4+5NAF8EcOc6tieE2ETW4+z7AZy56u+z/TYhxHXIen6zp34X/L/vYWZ2BMARAChvwu81IcTqWM+d/SyAG6/6+wCA5176Jnc/6u7T7j5djBa/hBCbynqc/TsADpvZITOrAHgPgPs3ZlhCiI1mzV/j3b1tZh8C8A/oSW/3uvuTUR8zoFK8dumNKVvl4FIVLJqCDGHNRJuLjqsYrfwHdDtcsnMQ6a3Yon1anl45B4Cuc+lwaISv8Bc76WPrdrjk1Wnz4+oGK/WlQPpsVdLL/402395okR9zpBpZcO/sdgJpmXTz4MTqOPmcA7luXTq7uz8A4IH1bEMIMRj0BJ0QmSBnFyIT5OxCZIKcXYhMkLMLkQnrWo2/VgoGDJE9toJItEopLSdE0puT6DoAqAQ2HnoAqueFzwoFsR1FEuADAIUu77h7+3Zqm9qXtkVX9UqJB34UwCNXdk0NU1urnZb6PJDeQoJAtFaDB7Uc/8/jyfbnluZon1KVH9dwmX8uQ0E0YolIgACXYIeL/FObGp5M7yeQIXVnFyIT5OxCZIKcXYhMkLMLkQlydiEyYaCr8QagSlbWgww8KJNRVoJLVZByDeVglbMbRR+wZfeohFaQ66wQjMOCZfxto9uorTqRznUWBaB4h68iF4KIomIpCMixNa66E9otvq9zsxeorVhKz8f2EX7qj1W4rRLZghx0pSBNGst7WCgFq/tkBb8Q7Ed3diEyQc4uRCbI2YXIBDm7EJkgZxciE+TsQmTCwANhqiSfdD0I/GCp2ipBcAQXauIAmlaHS2/Oss0Fal03KjMUBC20gzxzBeOSTKU0lmxvdHippkadH0AxkHKiMknFYvrzbHWatA/A56rZ5NrsxQtXqK1VT/erjAVVX4LPpVgJZMoyz10XCpFEum3W+TnQIVsMTjfd2YXIBTm7EJkgZxciE+TsQmSCnF2ITJCzC5EJ65LezOwUgAX0lK62u0+H7y8A5UpaZii1uPxTKqVtpSAfWFClB+VSEBkUSBddljMuiPCyQAQsFvn0WyC9lTp8/MMYSbaXSzyvmld4+adOIIlam0tU9YWl9L6M9ymVuHQ1UuRS2c2vuIXaxqppKbJe53nrWu0at7V4Ga1iJR1h1+vHJUca3Vbk2+u2yTkXRG1uhM7+C+5+cQO2I4TYRPQ1XohMWK+zO4BvmNmjZnZkIwYkhNgc1vs1/i3u/pyZ7QbwTTP7vrs/dPUb+heBIwAwPNCHc4UQV7OuO7u7P9f/fwbA1wDcnnjPUXefdvdpVptdCLH5rNnZzWzUzMZfeA3gVwCky28IIbac9Xyx3gPga/1keSUAf+3ufx91MABlIqMVAs2rTJJUDlWCMk5B9FoovTW51MRGGCWHRDeKXgsi7JzPR6PJpbLFpWsXRkpFnigRgdT01H88RG0XL8wk22+49XW0z+jETmrrtHjUW6nKT+MdE+kySb6Nz29lOJDQgui7pcV53q/BpTdyemOoyj+XQjl9XlmgOa/Z2d39GQCvX2t/IcRgkfQmRCbI2YXIBDm7EJkgZxciE+TsQmTCYGu9GcBy+RUKQdJDFvUWjT64jEW13lAIotSIqhElE7RAQrMgU2WUqLIWRWzNphNLepdHcpWK6Ug5ADj16L9T2+Pf+gdqGxoZT7ZXAjlp7uIOaqstLlLbrgM3Utv2PfuS7eUqj7Br1vlcbd+xi9raTS5Ttpv8MysxCTZIzmkFZgtqC1KLEOJlhZxdiEyQswuRCXJ2ITJBzi5EJgw8wrxIVt2j4FeWqq1ISkkBgAWxKVWyug/EV7+upa2kek9vHMFALFAFPMhBhzYPxmiXyWTxLnjmO/9EbSf+7R+pbWmJb7Tr6WP7/okTtM9ik+enGzW+ryuXz1NbYWgi2X7j4Z+iffbfdJDa5q/MUtv4RFqBAIDlJf5Zd9vpVfxyEBLOylBFwVW6swuRCXJ2ITJBzi5EJsjZhcgEObsQmSBnFyITBiu9GeBEGogSzxqR64LqSSgGYl4l6EeDEgC0iC2S3gpRYEKkNwYBNN2g3FTX0pJdfZ5LRmeO/ye1NepB7rcgEqlWT+fJm714kvbplrdR2/j+dEBLryOfj3M/PpNsnzl/mvaZfOe7qW0pCJKZ2r2X2oZH0mWoAGBp/kqyvRqcqNUh4hOFIL8itQghXlbI2YXIBDm7EJkgZxciE+TsQmSCnF2ITFhRejOzewH8GoAZd//pftsOAF8CcBDAKQC/6e5c2+nTbAPPXkrrVK1mIJXNp/uMjfBrVTWIiGM57QCgwAOv1paDLtDXCiSKDgC6QWSbd7jW5yQ1We3KEu1TrKYjwwCgOspzp7UbQdRbKz3G3WP8lBuZ4jnoylNc1hrdzWW5n59+RbL90oWztM/Jp45R29DIMLWNjfGotxKJUgNAQz67HZ7TrtNNz29UNmw1d/bPAbjjJW13A3jQ3Q8DeLD/txDiOmZFZ+/XW7/8kuY7AdzXf30fgHdt8LiEEBvMWn+z73H3cwDQ/3/3xg1JCLEZbPrjsmZ2BMARAKgEv4eFEJvLWu/sF8xsHwD0/08X4wbg7kfdfdrdp8vhw+BCiM1krc5+P4C7+q/vAvD1jRmOEGKzWI309gUAbwcwZWZnAXwUwMcBfNnM3g/gWQC/sZqdNdrADy+lbWUSrQUAy630N4LhIS5B3TDFxxFWfwrksGIhvb8ugqSSUfmnKEKpwiUeMy7jFLrp30rWCSSv8e3U1lgmHxiAunPpjalGzSYvaXTpmWeo7cyTp6ht/8FD1LZrR1oOe+XP/Cztc/zxR6nNg8ydrUAu7bSD0EhiarSC6EZ2Lga7WdHZ3f29xPSLK/UVQlw/6Ak6ITJBzi5EJsjZhcgEObsQmSBnFyITBl7rrUQerKkF0kSRRMSdvsD3Ez2tZ4FUVgh0uS6R0TpBwsPq6CS17b7xMLWNT3I5bH7mOWqrz6UnZe78f/M+V3gEGFoNavLguDsklGuxwz+YtnHbrokqtY1V+MNa33/ye8n2QhCFtnPvAWprtNKJNAGgPMTH6EGSUCOZU92GaJ8miSp0UmMP0J1diGyQswuRCXJ2ITJBzi5EJsjZhcgEObsQmTBQ6a1ULGBydDRpO39lnvZjqtwVXnYLZ2e4vDY5zqUaJg0CQKGUvjbuOvhTtM+BV76e2ipkewCwdPEUtV2cu0htjcV0aoF2Y472aTZ4JFq9waMRm3UuJ11eTs//s4t8e1GS0L2T6fMGAEYneaLKfbe8Ltm+44abaJ9LcwvU1m7z8aPKZTkPihkWLX1sXS/zcbTSEXZx3UEhRBbI2YXIBDm7EJkgZxciE+TsQmTCYFfjS2Xs3ZUu43Nlma9kttrphGat4KH/eV61CFElniiJ1w0Hbki2v/aVB2mfzuKPqe3S7Hlqqy3zFeHaIi/l1GmmA1csyJ1WLAd59wLFIErmNzSUtm3r8BX8uSaf++drfBV8zzAPNrrl1Wk1ZM8+Xk6q4WeorTgUFPsq8BPLnbtageQiLAY5ChlR/mbd2YXIBDm7EJkgZxciE+TsQmSCnF2ITJCzC5EJqyn/dC+AXwMw4+4/3W/7GIAPAHi+/7aPuPsDK22rXCpjak9a8nh+9vlkOwCcm31pefge7SCXXFQGJ4jtwK69e6jt1kPpsdsiL1u0sDBLbc0aj+SpL/PglNpCUGaIHHgxCMSoRuWwikEyvyIP1Ng2kc6fNrFrjPZptIMApZF0GScAmBznQTKtRlrSrQdSby0Iypo5x+XS885PrBLJMwcAICXHSiU+vwUSsNVo8HNqNXf2zwG4I9H+KXe/rf9vRUcXQmwtKzq7uz8EIH1rFUL8xLCe3+wfMrNjZnavmfG8x0KI64K1OvunAdwC4DYA5wB8gr3RzI6Y2SNm9kijRer4CiE2nTU5u7tfcPeOu3cBfAbA7cF7j7r7tLtPV8t8wUEIsbmsydnNbN9Vf74bwPGNGY4QYrNYjfT2BQBvBzBlZmcBfBTA283sNgAO4BSA31rV3oxHUU1N8J/9s/PpCDAPEm5FubgmdvCcZa999SFqq3haHly6wuWYxlIgodW4hNYKIsCabR555STuyQvBhAT7isKoSDUsAEC5kpbeSiNcehsf4vJak0Q+AkB3+RK1LS+mZbQzdV7W6vyZ09Q2dzGd4w8AWk3+WcfnKinl1OVSXqeTtkWS4orO7u7vTTR/dqV+QojrCz1BJ0QmyNmFyAQ5uxCZIGcXIhPk7EJkwkATTnq3i8ZyOlniUIlHV42RDJGLDS6fTO7kUt7hW9OJIwFgqMDDAJbm0hJbrcazW9bqXF6r1wN5jR8agpyNNOotkiIjfa0DvrNOsM1WM33cxWHeJ4qwG61WqW1sO5dSz51KRyQ+d/pp2ieSyYxEqAFAN5pkCzRMYrPwXsw+Fz4G3dmFyAQ5uxCZIGcXIhPk7EJkgpxdiEyQswuRCQOV3jrtNq5cSkeOdQIZrUJqio0MpyOrAODmgzupbcfIFWqrLfAIqlYrHdVUD+S1VotLIW3eDe0gKWa7y6/RzVZakqkGqpAHNcW6xuUwK/HTx0iCxSjx5bZJXrPthoOHqe3yJS6XPvntbyTbF+d4glMLjrkQSGgW1WYL6uKxbVoUckgSTrJaf4Du7EJkg5xdiEyQswuRCXJ2ITJBzi5EJgx0Nb7daePy5fTKaTdIaMYWrfft3kb77Jzg22u2eHmfZpBHrN5Ij6QZBLQ0lvmKaqPFba1u0K/Dr9Edls8sWN2vBBEtlaF0EBIQ55MbnpxKtk9OpUtoAcC+Q7cG4+Alns58/1+obW4mHbzkwUI3nMsk0d0xinUJ45BI8FK4L7Ypkptupe0JIV5GyNmFyAQ5uxCZIGcXIhPk7EJkgpxdiExYTfmnGwH8JYC96CW+Ouru95jZDgBfAnAQvRJQv+nus9G2ug7UiCLWDBKrjYymE5ft3jFC+5QLQbmgDpdWOu0gcIUEtUTBLg2u5KEdSGht45JXN8iDVhlLz1V1hEtXlWIQkdPhgRVDYzxwZfeBg8n2sQkeoDS5Iy3XAcCZMz+mtpNP/4jalpbT50EUs1Lo8nOxQAJQAMAC+bgU9GO2qE+FmApBHrzV3NnbAH7X3V8N4E0APmhmrwFwN4AH3f0wgAf7fwshrlNWdHZ3P+fu3+2/XgBwAsB+AHcCuK//tvsAvGuzBimEWD/X9JvdzA4CeAOAhwHscfdzQO+CAGD3Rg9OCLFxrPpxWTMbA/AVAB9293mLng18cb8jAI4AQCn6oSSE2FRW5X1mVkbP0T/v7l/tN18ws319+z4AycLV7n7U3afdfbq4yguEEGLjWdHZrXcL/yyAE+7+yatM9wO4q//6LgBf3/jhCSE2itV8jX8LgPcBeMLMHuu3fQTAxwF82czeD+BZAL+x0oYMQIFEWG0rc6lpWzk9TGtxea3VDKJ/SlwiiUr4OAmV6gbXzG6QR6xbKFMbSjy/XrkUlUJKy2EPPfUD2qcYlF162yEuh41vG6e2g7e+Ktk+Ms7lunq9Rm0/fOJ71Pb8JZ43EEQO67b5OeBR7reA4NPEWJmfI2bpc46V8gKANtlcFF23orO7+7fBI+p+caX+QojrA62YCZEJcnYhMkHOLkQmyNmFyAQ5uxCZMNCEk+4O76bDwIaDp+sK9XqyvTbHh7+8jUt5PsQFinqDS3a1WtoWRa91AnnNqjxhY3Vkgtra4FLZqfPpskbDFT4f1SEu812s8/m4eRsf48TOXcn20XGeJHQ2kNDqQfhgFB1WJueVB59ZnBySz8dw4E3jZS71VVjU2xoeQou66M4uRCbI2YXIBDm7EJkgZxciE+TsQmSCnF2ITBio9DZUNbzqYHqXOye5ZlCg1yQukpjziLhOILuwCCSAluRCO4h3ahX4FDeDGmuLl9I18QBg7soCtT39fFp6OzDJo80qgfT2raeeobbDN99Mbc3l9BhHgsSXw1U+V7e/5c3UtnjpIrWd/tHJZHs7qKVXBpfXquXgPA0SgYZRmEWyzTXUjvN1JpwUQrwMkLMLkQlydiEyQc4uRCbI2YXIhIGuxnc6jivz6VJDpeCys3MqHcSxbSwIMglW1Ws1vlK/sMT7zdfSY68U+eDHS3xJ9cLSIrWdvcBts0GuNhZuMTM/T/tcXlyitnabl4b6+2/9G7Wx1efXvPHnaJ+FK3PUNj/P5+OmWw9T28hEOvDm/ExatQCA2oXz1OZtPveNIj93gsV41Eg+vCgbM7MEVdR0ZxciF+TsQmSCnF2ITJCzC5EJcnYhMkHOLkQmWPTgPACY2Y0A/hLAXvSUnaPufo+ZfQzABwC8oGF8xN0fiLZVKZjvqqTVvm2BCDhUSedcq1a5NDEywq9jUTDD5XmukXSJrDEalPZZ4CofFppB3r0il7x+cDGdky9iMtA2o3JHV9qBZhTwpn170+OY2E77VEd5TruhICff8AS3jY6lS1SVh/gJV1vgEuD506eo7fQZHjS0HEifVSITx56Z5mS9hVonHeWzGp29DeB33f27ZjYO4FEz+2bf9il3/9M1jEkIMWBWU+vtHIBz/dcLZnYCwP7NHpgQYmO5pt/sZnYQwBsAPNxv+pCZHTOze82Mfz8TQmw5q3Z2MxsD8BUAH3b3eQCfBnALgNvQu/N/gvQ7YmaPmNkjQQVaIcQmsypnN7Myeo7+eXf/KgC4+wV377h7F8BnANye6uvuR9192t2ng1z+QohNZkVnNzMD8FkAJ9z9k1e177vqbe8GcHzjhyeE2ChWI729FcC/AngC/xdU9REA70XvK7wDOAXgt/qLeZRSwXyilF4TrER1a1YYY3JfkY3l/AIQBLBhpJqWAMtBZBuLaAKAsUA6PFfn0tvZOW5j7Bri+6q3uW0hGH/ENvI53xrJZMNpmQwARsd5Dr3y8Ai1VYn0NrFzB+0zNsbz5JWCE+RCEC335FPHqG12Jt3PPMhbR+TSs40W6t01Sm/u/m2kI+pCTV0IcX2hJ+iEyAQ5uxCZIGcXIhPk7EJkgpxdiExYUXrbSIpmPlZKy1eRLGBEloue0Unv5YXtRcZrN1WDp4WqgSxXCSTAbpAwk5YLAlAspfvValxCm29QEzodvq9SMJHlYvoTGKukk4cCQLXMy1BVKlU+jmCbHU/LlFEiTQse9ayQ8xcAxka5ZBfFDp6amUm2zy/yMl/slLvYbKFJpDfd2YXIBDm7EJkgZxciE+TsQmSCnF2ITJCzC5EJA631BvQS2l0rrIRWdKWKpI4owaKtQYrsBFJNvbM2abNg/OiKQSpCFpTlzo85OuQw5i2sRZa2NYP5cOPZOZtd/on6Mq8D1yH9WPtKRJLu7OxlahutcllxgsisFiQydXLO8RHozi5ENsjZhcgEObsQmSBnFyIT5OxCZIKcXYhMGKj05gBYlJ0HMg4Ta9aWChGh1rSWgLh0jFHfFsha0b48OLpu0LNFotQioakTzEckHEbzzzbpwRabQSRaL2P5te0LCMYYbK8QnIvRMbeD6Edv8tDCIaKXRlGF7TVUgtOdXYhMkLMLkQlydiEyQc4uRCbI2YXIhBVX481sCMBDAKr99/+tu3/UzA4B+CKAHQC+C+B97t5caXtsDTFcUSWLkmtMJbdm6BDXuOIeB/Ks7QiMDKYdrbgHuxorrk2wYTn02t1gPTtaYF7j+PlntjYFgs0vAFhwaK0oaIiMpRtIOZGCwljNnb0B4B3u/nr0arvdYWZvAvDHAD7l7ocBzAJ4/zXvXQgxMFZ0du/xQgxhuf/PAbwDwN/22+8D8K5NGaEQYkNYbX32opk9BmAGwDcBPA1gzv1/8/SeBbB/c4YohNgIVuXs7t5x99sAHABwO4BXp96W6mtmR8zsETN7ZHAZ6oUQL+WaVuPdfQ7AvwB4E4BJM3th9eYAgOdIn6PuPu3u05uxaCaEWB0rOruZ7TKzyf7rYQC/BOAEgH8G8Ov9t90F4OubNUghxPpZja6yD8B9ZlZE7+LwZXf/OzN7CsAXzeyPAHwPwGdXs0MWxBHlpmNXpOhKFZZ/CmyD/KkRBVUE1Z/iMZKOUZ+hAp+t4TI/RWot/qkxaSgK8CkGn0wUQBMFGzHCb5lhoFQszNFNBr1axNoMDqxL55ezorO7+zEAb0i0P4Pe73chxE8AeoJOiEyQswuRCXJ2ITJBzi5EJsjZhcgEYznhNmVnZs8DON3/cwrAxYHtnKNxvBiN48X8pI3jFe6+K2UYqLO/aMdmj7j79JbsXOPQODIch77GC5EJcnYhMmErnf3oFu77ajSOF6NxvJiXzTi27De7EGKw6Gu8EJmwJc5uZneY2Q/M7KSZ3b0VY+iP45SZPWFmj5nZIwPc771mNmNmx69q22Fm3zSzH/b/375F4/iYmf24PyePmdk7BzCOG83sn83shJk9aWa/028f6JwE4xjonJjZkJn9l5k93h/HH/bbD5nZw/35+JKZVa5pw+4+0H/oRZ8+DeBmABUAjwN4zaDH0R/LKQBTW7DftwF4I4DjV7X9CYC7+6/vBvDHWzSOjwH4vQHPxz4Ab+y/Hgfw3wBeM+g5CcYx0DlBL1Z2rP+6DOBh9BLGfBnAe/rtfw7gt69lu1txZ78dwEl3f8Z7qae/CODOLRjHluHuDwG4/JLmO9FL3AkMKIEnGcfAcfdz7v7d/usF9JKj7MeA5yQYx0DxHhue5HUrnH0/gDNX/b2VySodwDfM7FEzO7JFY3iBPe5+DuiddAB2b+FYPmRmx/pf8zf958TVmNlB9PInPIwtnJOXjAMY8JxsRpLXrXD2VDqPrZIE3uLubwTwqwA+aGZv26JxXE98GsAt6NUIOAfgE4PasZmNAfgKgA+7+/yg9ruKcQx8TnwdSV4ZW+HsZwHceNXfNFnlZuPuz/X/nwHwNWxt5p0LZrYPAPr/z2zFINz9Qv9E6wL4DAY0J2ZWRs/BPu/uX+03D3xOUuPYqjnp7/uak7wytsLZvwPgcH9lsQLgPQDuH/QgzGzUzMZfeA3gVwAcj3ttKvejl7gT2MIEni84V593YwBzYmaGXg7DE+7+yatMA50TNo5Bz8mmJXkd1ArjS1Yb34neSufTAH5/i8ZwM3pKwOMAnhzkOAB8Ab2vgy30vum8H8BOAA8C+GH//x1bNI6/AvAEgGPoOdu+AYzjreh9JT0G4LH+v3cOek6CcQx0TgD8DHpJXI+hd2H5g6vO2f8CcBLA3wCoXst29QSdEJmgJ+iEyAQ5uxCZIGcXIhPk7EJkgpxdiEyQswuRCXJ2ITJBzi5EJvwPaD2EgfNKccIAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.imshow(poisoned_x[0].squeeze())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/adversarial-robustness-toolbox/notebooks/poisoning_attack_bullseye_polytope.ipynb b/adversarial-robustness-toolbox/notebooks/poisoning_attack_bullseye_polytope.ipynb
new file mode 100644
index 0000000..26dae9d
--- /dev/null
+++ b/adversarial-robustness-toolbox/notebooks/poisoning_attack_bullseye_polytope.ipynb
@@ -0,0 +1,267 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Creating Transferable Clean Label Attacks in ART with Bullseye Polytope Clean Label Attacks"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os, sys\n",
+    "from os.path import abspath\n",
+    "\n",
+    "module_path = os.path.abspath(os.path.join('..'))\n",
+    "if module_path not in sys.path:\n",
+    "    sys.path.append(module_path)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import torch.optim as optim\n",
+    "import torch.nn as nn\n",
+    "import torch.nn.functional as F\n",
+    "import torchvision\n",
+    "import torchvision.transforms as transforms\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "from art.estimators.classification import PyTorchClassifier\n",
+    "from art.attacks.poisoning import BullseyePolytopeAttackPyTorch"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Files already downloaded and verified\n",
+      "Files already downloaded and verified\n"
+     ]
+    }
+   ],
+   "source": [
+    "transform = transforms.Compose(\n",
+    "    [transforms.ToTensor(),\n",
+    "     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\n",
+    "trainset = torchvision.datasets.CIFAR10(root='./data', train=True,\n",
+    "                                        download=True, transform=transform)\n",
+    "trainloader = torch.utils.data.DataLoader(trainset, batch_size=4,\n",
+    "                                          shuffle=True, num_workers=2)\n",
+    "\n",
+    "testset = torchvision.datasets.CIFAR10(root='./data', train=False,\n",
+    "                                       download=True, transform=transform)\n",
+    "testloader = torch.utils.data.DataLoader(testset, batch_size=4,\n",
+    "                                         shuffle=False, num_workers=2)\n",
+    "\n",
+    "classes = ('plane', 'car', 'bird', 'cat',\n",
+    "           'deer', 'dog', 'frog', 'horse', 'ship', 'truck')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "     plane       deer       ship      plane\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def imshow(img):\n",
+    "    img = img / 2 + 0.5     # unnormalize\n",
+    "    npimg = img.numpy()\n",
+    "    plt.imshow(np.transpose(npimg, (1, 2, 0)))\n",
+    "    \n",
+    "# get some random training images\n",
+    "dataiter = iter(trainloader)\n",
+    "images, labels = dataiter.next()\n",
+    "# show images\n",
+    "imshow(torchvision.utils.make_grid(images))\n",
+    "# print labels\n",
+    "print(' '.join('%10s' % classes[labels[j]] for j in range(4)))\n",
+    "base_labels = labels.numpy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "net = nn.Sequential(\n",
+    "    nn.Conv2d(3, 6, 5),\n",
+    "    nn.ReLU(),\n",
+    "    nn.MaxPool2d(2, 2),\n",
+    "    nn.Conv2d(6, 16, 5),\n",
+    "    nn.ReLU(),\n",
+    "    nn.MaxPool2d(2, 2),\n",
+    "    nn.Flatten(),\n",
+    "    nn.Linear(16 * 5 * 5, 120),\n",
+    "    nn.ReLU(),\n",
+    "    nn.Linear(120, 84),\n",
+    "    nn.ReLU(),\n",
+    "    nn.Linear(84, 10))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "criterion = nn.CrossEntropyLoss()\n",
+    "optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1,  2000] loss: 2.136\n",
+      "[1,  4000] loss: 1.774\n",
+      "[1,  6000] loss: 1.608\n",
+      "[1,  8000] loss: 1.553\n",
+      "[1, 10000] loss: 1.497\n"
+     ]
+    }
+   ],
+   "source": [
+    "NUM_EPOCHS = 1\n",
+    "for epoch in range(NUM_EPOCHS):  # loop over the dataset multiple times\n",
+    "\n",
+    "    running_loss = 0.0\n",
+    "    for i, data in enumerate(trainloader, 0):\n",
+    "        # get the inputs; data is a list of [inputs, labels]\n",
+    "        inputs, labels = data\n",
+    "\n",
+    "        # zero the parameter gradients\n",
+    "        optimizer.zero_grad()\n",
+    "\n",
+    "        # forward + backward + optimize\n",
+    "        outputs = net(inputs)\n",
+    "        loss = criterion(outputs, labels)\n",
+    "        loss.backward()\n",
+    "        optimizer.step()\n",
+    "\n",
+    "        # print statistics\n",
+    "        running_loss += loss.item()\n",
+    "        if i % 2000 == 1999:    # print every 2000 mini-batches\n",
+    "            print('[%d, %5d] loss: %.3f' %\n",
+    "                  (epoch + 1, i + 1, running_loss / 2000))\n",
+    "            running_loss = 0.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "classifier = PyTorchClassifier(net, loss, (3, 32, 32), 10, clip_values=(0,1), preprocessing=(0.5, 0.5))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "target = images[0].unsqueeze(0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "attack = BullseyePolytopeAttackPyTorch(classifier, target.numpy(), 9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "poison, p_labels = attack.poison(images.numpy() / 2 + 0.5, base_labels)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "imshow(torchvision.utils.make_grid(torch.from_numpy(poison)))\n",
+    "print(' '.join('%10s' % classes[base_labels[j]] for j in range(4)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/adversarial-robustness-toolbox/notebooks/poisoning_attack_clean_label_backdoor.ipynb b/adversarial-robustness-toolbox/notebooks/poisoning_attack_clean_label_backdoor.ipynb
new file mode 100644
index 0000000..58e1ee2
--- /dev/null
+++ b/adversarial-robustness-toolbox/notebooks/poisoning_attack_clean_label_backdoor.ipynb
@@ -0,0 +1,753 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Poisoning using Clean Label Backdoor Attacks in ART"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using TensorFlow backend.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from __future__ import absolute_import, division, print_function, unicode_literals\n",
+    "\n",
+    "import os, sys\n",
+    "from os.path import abspath\n",
+    "\n",
+    "module_path = os.path.abspath(os.path.join('..'))\n",
+    "if module_path not in sys.path:\n",
+    "    sys.path.append(module_path)\n",
+    "\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "import keras.backend as k\n",
+    "from keras.models import Sequential\n",
+    "from keras.layers import Dense, Flatten, Conv2D, MaxPooling2D, Activation, Dropout\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "import tensorflow as tf\n",
+    "tf.get_logger().setLevel('ERROR')\n",
+    "\n",
+    "from art.estimators.classification import KerasClassifier\n",
+    "from art.attacks.poisoning import PoisoningAttackBackdoor, PoisoningAttackCleanLabelBackdoor\n",
+    "from art.attacks.poisoning.perturbations import add_pattern_bd\n",
+    "from art.utils import load_mnist, preprocess, to_categorical\n",
+    "from art.defences.trainer import AdversarialTrainerMadryPGD\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In a [previous notebook](https://github.com/Trusted-AI/adversarial-robustness-toolbox/blob/main/notebooks/poisoning_dataset_mnist.ipynb) we discuss the threat of backdoor attacks on neural networks. We've also shown examples of [clean-label attacks](https://github.com/Trusted-AI/adversarial-robustness-toolbox/blob/main/notebooks/poisoning_attack_feature_collision.ipynb) that don't rely on changing the target label for their attack to succeed.\n",
+    "\n",
+    "In this notebook, we will run a new type of backdoor attack that operates under clean-label constraints, aptly named, Clean Label Backdoor Attacks. This method was proposed by [Turner et. al. 2018](https://people.csail.mit.edu/madry/lab/cleanlabel.pdf). "
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": "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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### The classification problem: Automatically detect numbers written in a check\n",
+    "![image.png](attachment:image.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "(x_raw, y_raw), (x_raw_test, y_raw_test), min_, max_ = load_mnist(raw=True)\n",
+    "\n",
+    "# Random Selection:\n",
+    "n_train = np.shape(x_raw)[0]\n",
+    "num_selection = 10000\n",
+    "random_selection_indices = np.random.choice(n_train, num_selection)\n",
+    "x_raw = x_raw[random_selection_indices]\n",
+    "y_raw = y_raw[random_selection_indices]"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Adversary's goal: make some easy money \n",
+    "\n",
+    "The goal of the adversary is add a backdoor at test time, and turn any digit to a 9. Unlike other backdoor poisoning methods, it will only poison a small percentage of points **in the target class**.\n",
+    "\n",
+    "This poisoning method is effective because before the trigger is added, a PGD perturbation is added to the image based on a classifier the attacker trains separately. This makes the image difficult for the neural net to interpret, leaving only the backdoor trigger to use for classification."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<img src=\"../utils/data/images/zero_to_nine.png\" width=400>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import HTML\n",
+    "HTML('<img src=\"../utils/data/images/zero_to_nine.png\" width=400>')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Poison training data\n",
+    "percent_poison = .33\n",
+    "x_train, y_train = preprocess(x_raw, y_raw)\n",
+    "x_train = np.expand_dims(x_train, axis=3)\n",
+    "\n",
+    "x_test, y_test = preprocess(x_raw_test, y_raw_test)\n",
+    "x_test = np.expand_dims(x_test, axis=3)\n",
+    "\n",
+    "# Shuffle training data\n",
+    "n_train = np.shape(y_train)[0]\n",
+    "shuffled_indices = np.arange(n_train)\n",
+    "np.random.shuffle(shuffled_indices)\n",
+    "x_train = x_train[shuffled_indices]\n",
+    "y_train = y_train[shuffled_indices]\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Victim bank trains a neural network"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create Keras convolutional neural network - basic architecture from Keras examples\n",
+    "# Source here: https://github.com/keras-team/keras/blob/master/examples/mnist_cnn.py\n",
+    "def create_model():    \n",
+    "    model = Sequential()\n",
+    "    model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=x_train.shape[1:]))\n",
+    "    model.add(Conv2D(64, (3, 3), activation='relu'))\n",
+    "    model.add(MaxPooling2D(pool_size=(2, 2)))\n",
+    "    model.add(Dropout(0.25))\n",
+    "    model.add(Flatten())\n",
+    "    model.add(Dense(128, activation='relu'))\n",
+    "    model.add(Dropout(0.5))\n",
+    "    model.add(Dense(10, activation='softmax'))\n",
+    "\n",
+    "    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
+    "    return model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Choose backdoor pattern"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7fba813453c8>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "backdoor = PoisoningAttackBackdoor(add_pattern_bd)\n",
+    "example_target = np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 1])\n",
+    "pdata, plabels = backdoor.poison(x_test, y=example_target)\n",
+    "\n",
+    "plt.imshow(pdata[0].squeeze())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Chose backdoor target labels for training"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Poison some percentage of all non-nines to nines\n",
+    "targets = to_categorical([9], 10)[0] "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Train Proxy Classifier\n",
+    "To create the poison samples, the attacker first runs PGD on a trained classifier. We would like this perturbation to be transferable and universal to other model architectures. To that end, our adversary conducts adversarial training so that perturbations are created against a robust model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Precompute adv samples: 100%|██████████| 1/1 [00:00<00:00, 4476.31it/s]\n",
+      "Adversarial training epochs: 100%|██████████| 10/10 [11:42<00:00, 70.25s/it]\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = KerasClassifier(create_model())\n",
+    "proxy = AdversarialTrainerMadryPGD(KerasClassifier(create_model()), nb_epochs=10, eps=0.15, eps_step=0.001)\n",
+    "proxy.fit(x_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Run Attack"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "PGD - Random Initializations:   0%|          | 0/1 [00:00<?, ?it/s]\n",
+      "PGD - Iterations:   0%|          | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "PGD - Iterations:   0%|          | 1/200 [00:00<00:58,  3.42it/s]\u001b[A\n",
+      "PGD - Iterations:   1%|          | 2/200 [00:00<00:57,  3.45it/s]\u001b[A\n",
+      "PGD - Iterations:   2%|▏         | 3/200 [00:00<00:56,  3.47it/s]\u001b[A\n",
+      "PGD - Iterations:   2%|▏         | 4/200 [00:01<00:56,  3.46it/s]\u001b[A\n",
+      "PGD - Iterations:   2%|▎         | 5/200 [00:01<00:56,  3.48it/s]\u001b[A\n",
+      "PGD - Iterations:   3%|▎         | 6/200 [00:01<00:55,  3.50it/s]\u001b[A\n",
+      "PGD - Iterations:   4%|▎         | 7/200 [00:02<00:55,  3.50it/s]\u001b[A\n",
+      "PGD - Iterations:   4%|▍         | 8/200 [00:02<00:54,  3.51it/s]\u001b[A\n",
+      "PGD - Iterations:   4%|▍         | 9/200 [00:02<00:54,  3.52it/s]\u001b[A\n",
+      "PGD - Iterations:   5%|▌         | 10/200 [00:02<00:54,  3.50it/s]\u001b[A\n",
+      "PGD - Iterations:   6%|▌         | 11/200 [00:03<00:54,  3.49it/s]\u001b[A\n",
+      "PGD - Iterations:   6%|▌         | 12/200 [00:03<00:53,  3.50it/s]\u001b[A\n",
+      "PGD - Iterations:   6%|▋         | 13/200 [00:03<00:53,  3.50it/s]\u001b[A\n",
+      "PGD - Iterations:   7%|▋         | 14/200 [00:04<00:53,  3.50it/s]\u001b[A\n",
+      "PGD - Iterations:   8%|▊         | 15/200 [00:04<00:52,  3.52it/s]\u001b[A\n",
+      "PGD - Iterations:   8%|▊         | 16/200 [00:04<00:52,  3.51it/s]\u001b[A\n",
+      "PGD - Iterations:   8%|▊         | 17/200 [00:04<00:52,  3.51it/s]\u001b[A\n",
+      "PGD - Iterations:   9%|▉         | 18/200 [00:05<00:52,  3.48it/s]\u001b[A\n",
+      "PGD - Iterations:  10%|▉         | 19/200 [00:05<00:52,  3.43it/s]\u001b[A\n",
+      "PGD - Iterations:  10%|█         | 20/200 [00:05<00:52,  3.40it/s]\u001b[A\n",
+      "PGD - Iterations:  10%|█         | 21/200 [00:06<00:53,  3.36it/s]\u001b[A\n",
+      "PGD - Iterations:  11%|█         | 22/200 [00:06<00:53,  3.35it/s]\u001b[A\n",
+      "PGD - Iterations:  12%|█▏        | 23/200 [00:06<00:52,  3.34it/s]\u001b[A\n",
+      "PGD - Iterations:  12%|█▏        | 24/200 [00:06<00:53,  3.31it/s]\u001b[A\n",
+      "PGD - Iterations:  12%|█▎        | 25/200 [00:07<00:52,  3.32it/s]\u001b[A\n",
+      "PGD - Iterations:  13%|█▎        | 26/200 [00:07<00:52,  3.33it/s]\u001b[A\n",
+      "PGD - Iterations:  14%|█▎        | 27/200 [00:07<00:50,  3.39it/s]\u001b[A\n",
+      "PGD - Iterations:  14%|█▍        | 28/200 [00:08<00:50,  3.37it/s]\u001b[A\n",
+      "PGD - Iterations:  14%|█▍        | 29/200 [00:08<00:50,  3.40it/s]\u001b[A\n",
+      "PGD - Iterations:  15%|█▌        | 30/200 [00:08<00:48,  3.47it/s]\u001b[A\n",
+      "PGD - Iterations:  16%|█▌        | 31/200 [00:08<00:48,  3.49it/s]\u001b[A\n",
+      "PGD - Iterations:  16%|█▌        | 32/200 [00:09<00:47,  3.53it/s]\u001b[A\n",
+      "PGD - Iterations:  16%|█▋        | 33/200 [00:09<00:47,  3.53it/s]\u001b[A\n",
+      "PGD - Iterations:  17%|█▋        | 34/200 [00:09<00:47,  3.53it/s]\u001b[A\n",
+      "PGD - Iterations:  18%|█▊        | 35/200 [00:10<00:46,  3.52it/s]\u001b[A\n",
+      "PGD - Iterations:  18%|█▊        | 36/200 [00:10<00:46,  3.56it/s]\u001b[A\n",
+      "PGD - Iterations:  18%|█▊        | 37/200 [00:10<00:45,  3.56it/s]\u001b[A\n",
+      "PGD - Iterations:  19%|█▉        | 38/200 [00:10<00:45,  3.57it/s]\u001b[A\n",
+      "PGD - Iterations:  20%|█▉        | 39/200 [00:11<00:45,  3.56it/s]\u001b[A\n",
+      "PGD - Iterations:  20%|██        | 40/200 [00:11<00:44,  3.56it/s]\u001b[A\n",
+      "PGD - Iterations:  20%|██        | 41/200 [00:11<00:44,  3.58it/s]\u001b[A\n",
+      "PGD - Iterations:  21%|██        | 42/200 [00:12<00:44,  3.56it/s]\u001b[A\n",
+      "PGD - Iterations:  22%|██▏       | 43/200 [00:12<00:44,  3.52it/s]\u001b[A\n",
+      "PGD - Iterations:  22%|██▏       | 44/200 [00:12<00:44,  3.54it/s]\u001b[A\n",
+      "PGD - Iterations:  22%|██▎       | 45/200 [00:12<00:43,  3.56it/s]\u001b[A\n",
+      "PGD - Iterations:  23%|██▎       | 46/200 [00:13<00:43,  3.53it/s]\u001b[A\n",
+      "PGD - Iterations:  24%|██▎       | 47/200 [00:13<00:43,  3.52it/s]\u001b[A\n",
+      "PGD - Iterations:  24%|██▍       | 48/200 [00:13<00:42,  3.55it/s]\u001b[A\n",
+      "PGD - Iterations:  24%|██▍       | 49/200 [00:14<00:42,  3.57it/s]\u001b[A\n",
+      "PGD - Iterations:  25%|██▌       | 50/200 [00:14<00:41,  3.59it/s]\u001b[A\n",
+      "PGD - Iterations:  26%|██▌       | 51/200 [00:14<00:41,  3.58it/s]\u001b[A\n",
+      "PGD - Iterations:  26%|██▌       | 52/200 [00:14<00:41,  3.58it/s]\u001b[A\n",
+      "PGD - Iterations:  26%|██▋       | 53/200 [00:15<00:41,  3.55it/s]\u001b[A\n",
+      "PGD - Iterations:  27%|██▋       | 54/200 [00:15<00:40,  3.58it/s]\u001b[A\n",
+      "PGD - Iterations:  28%|██▊       | 55/200 [00:15<00:40,  3.60it/s]\u001b[A\n",
+      "PGD - Iterations:  28%|██▊       | 56/200 [00:16<00:40,  3.60it/s]\u001b[A\n",
+      "PGD - Iterations:  28%|██▊       | 57/200 [00:16<00:39,  3.59it/s]\u001b[A\n",
+      "PGD - Iterations:  29%|██▉       | 58/200 [00:16<00:39,  3.58it/s]\u001b[A\n",
+      "PGD - Iterations:  30%|██▉       | 59/200 [00:16<00:39,  3.59it/s]\u001b[A\n",
+      "PGD - Iterations:  30%|███       | 60/200 [00:17<00:39,  3.57it/s]\u001b[A\n",
+      "PGD - Iterations:  30%|███       | 61/200 [00:17<00:38,  3.57it/s]\u001b[A\n",
+      "PGD - Iterations:  31%|███       | 62/200 [00:17<00:38,  3.57it/s]\u001b[A\n",
+      "PGD - Iterations:  32%|███▏      | 63/200 [00:17<00:38,  3.56it/s]\u001b[A\n",
+      "PGD - Iterations:  32%|███▏      | 64/200 [00:18<00:38,  3.56it/s]\u001b[A\n",
+      "PGD - Iterations:  32%|███▎      | 65/200 [00:18<00:38,  3.55it/s]\u001b[A\n",
+      "PGD - Iterations:  33%|███▎      | 66/200 [00:18<00:37,  3.55it/s]\u001b[A\n",
+      "PGD - Iterations:  34%|███▎      | 67/200 [00:19<00:37,  3.56it/s]\u001b[A\n",
+      "PGD - Iterations:  34%|███▍      | 68/200 [00:19<00:37,  3.54it/s]\u001b[A\n",
+      "PGD - Iterations:  34%|███▍      | 69/200 [00:19<00:36,  3.55it/s]\u001b[A\n",
+      "PGD - Iterations:  35%|███▌      | 70/200 [00:19<00:36,  3.56it/s]\u001b[A\n",
+      "PGD - Iterations:  36%|███▌      | 71/200 [00:20<00:36,  3.55it/s]\u001b[A\n",
+      "PGD - Iterations:  36%|███▌      | 72/200 [00:20<00:35,  3.58it/s]\u001b[A\n",
+      "PGD - Iterations:  36%|███▋      | 73/200 [00:20<00:35,  3.57it/s]\u001b[A\n",
+      "PGD - Iterations:  37%|███▋      | 74/200 [00:21<00:35,  3.59it/s]\u001b[A\n",
+      "PGD - Iterations:  38%|███▊      | 75/200 [00:21<00:34,  3.58it/s]\u001b[A\n",
+      "PGD - Iterations:  38%|███▊      | 76/200 [00:21<00:34,  3.59it/s]\u001b[A\n",
+      "PGD - Iterations:  38%|███▊      | 77/200 [00:21<00:34,  3.57it/s]\u001b[A\n",
+      "PGD - Iterations:  39%|███▉      | 78/200 [00:22<00:33,  3.59it/s]\u001b[A\n",
+      "PGD - Iterations:  40%|███▉      | 79/200 [00:22<00:33,  3.59it/s]\u001b[A\n",
+      "PGD - Iterations:  40%|████      | 80/200 [00:22<00:33,  3.58it/s]\u001b[A\n",
+      "PGD - Iterations:  40%|████      | 81/200 [00:23<00:33,  3.57it/s]\u001b[A\n",
+      "PGD - Iterations:  41%|████      | 82/200 [00:23<00:33,  3.57it/s]\u001b[A\n",
+      "PGD - Iterations:  42%|████▏     | 83/200 [00:23<00:32,  3.57it/s]\u001b[A\n",
+      "PGD - Iterations:  42%|████▏     | 84/200 [00:23<00:32,  3.57it/s]\u001b[A\n",
+      "PGD - Iterations:  42%|████▎     | 85/200 [00:24<00:32,  3.55it/s]\u001b[A\n",
+      "PGD - Iterations:  43%|████▎     | 86/200 [00:24<00:32,  3.47it/s]\u001b[A\n",
+      "PGD - Iterations:  44%|████▎     | 87/200 [00:24<00:32,  3.43it/s]\u001b[A\n",
+      "PGD - Iterations:  44%|████▍     | 88/200 [00:25<00:32,  3.41it/s]\u001b[A\n",
+      "PGD - Iterations:  44%|████▍     | 89/200 [00:25<00:32,  3.38it/s]\u001b[A\n",
+      "PGD - Iterations:  45%|████▌     | 90/200 [00:25<00:32,  3.37it/s]\u001b[A\n",
+      "PGD - Iterations:  46%|████▌     | 91/200 [00:25<00:32,  3.33it/s]\u001b[A\n",
+      "PGD - Iterations:  46%|████▌     | 92/200 [00:26<00:32,  3.32it/s]\u001b[A\n",
+      "PGD - Iterations:  46%|████▋     | 93/200 [00:26<00:32,  3.33it/s]\u001b[A\n",
+      "PGD - Iterations:  47%|████▋     | 94/200 [00:26<00:31,  3.34it/s]\u001b[A\n",
+      "PGD - Iterations:  48%|████▊     | 95/200 [00:27<00:31,  3.33it/s]\u001b[A\n",
+      "PGD - Iterations:  48%|████▊     | 96/200 [00:27<00:31,  3.34it/s]\u001b[A\n",
+      "PGD - Iterations:  48%|████▊     | 97/200 [00:27<00:30,  3.32it/s]\u001b[A\n",
+      "PGD - Iterations:  49%|████▉     | 98/200 [00:28<00:30,  3.32it/s]\u001b[A\n",
+      "PGD - Iterations:  50%|████▉     | 99/200 [00:28<00:30,  3.32it/s]\u001b[A\n",
+      "PGD - Iterations:  50%|█████     | 100/200 [00:28<00:30,  3.32it/s]\u001b[A\n",
+      "PGD - Iterations:  50%|█████     | 101/200 [00:28<00:29,  3.32it/s]\u001b[A\n",
+      "PGD - Iterations:  51%|█████     | 102/200 [00:29<00:29,  3.33it/s]\u001b[A\n",
+      "PGD - Iterations:  52%|█████▏    | 103/200 [00:29<00:29,  3.33it/s]\u001b[A\n",
+      "PGD - Iterations:  52%|█████▏    | 104/200 [00:29<00:28,  3.35it/s]\u001b[A\n",
+      "PGD - Iterations:  52%|█████▎    | 105/200 [00:30<00:28,  3.37it/s]\u001b[A\n",
+      "PGD - Iterations:  53%|█████▎    | 106/200 [00:30<00:27,  3.44it/s]\u001b[A\n",
+      "PGD - Iterations:  54%|█████▎    | 107/200 [00:30<00:26,  3.48it/s]\u001b[A\n",
+      "PGD - Iterations:  54%|█████▍    | 108/200 [00:30<00:26,  3.50it/s]\u001b[A\n",
+      "PGD - Iterations:  55%|█████▍    | 109/200 [00:31<00:25,  3.51it/s]\u001b[A\n",
+      "PGD - Iterations:  55%|█████▌    | 110/200 [00:31<00:25,  3.54it/s]\u001b[A\n",
+      "PGD - Iterations:  56%|█████▌    | 111/200 [00:31<00:25,  3.55it/s]\u001b[A\n",
+      "PGD - Iterations:  56%|█████▌    | 112/200 [00:32<00:24,  3.53it/s]\u001b[A\n",
+      "PGD - Iterations:  56%|█████▋    | 113/200 [00:32<00:24,  3.54it/s]\u001b[A\n",
+      "PGD - Iterations:  57%|█████▋    | 114/200 [00:32<00:24,  3.54it/s]\u001b[A\n",
+      "PGD - Iterations:  57%|█████▊    | 115/200 [00:32<00:23,  3.56it/s]\u001b[A\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "PGD - Iterations:  58%|█████▊    | 116/200 [00:33<00:23,  3.56it/s]\u001b[A\n",
+      "PGD - Iterations:  58%|█████▊    | 117/200 [00:33<00:23,  3.56it/s]\u001b[A\n",
+      "PGD - Iterations:  59%|█████▉    | 118/200 [00:33<00:23,  3.53it/s]\u001b[A\n",
+      "PGD - Iterations:  60%|█████▉    | 119/200 [00:34<00:22,  3.54it/s]\u001b[A\n",
+      "PGD - Iterations:  60%|██████    | 120/200 [00:34<00:22,  3.58it/s]\u001b[A\n",
+      "PGD - Iterations:  60%|██████    | 121/200 [00:34<00:22,  3.58it/s]\u001b[A\n",
+      "PGD - Iterations:  61%|██████    | 122/200 [00:34<00:21,  3.60it/s]\u001b[A\n",
+      "PGD - Iterations:  62%|██████▏   | 123/200 [00:35<00:21,  3.58it/s]\u001b[A\n",
+      "PGD - Iterations:  62%|██████▏   | 124/200 [00:35<00:21,  3.57it/s]\u001b[A\n",
+      "PGD - Iterations:  62%|██████▎   | 125/200 [00:35<00:20,  3.58it/s]\u001b[A\n",
+      "PGD - Iterations:  63%|██████▎   | 126/200 [00:36<00:20,  3.59it/s]\u001b[A\n",
+      "PGD - Iterations:  64%|██████▎   | 127/200 [00:36<00:20,  3.59it/s]\u001b[A\n",
+      "PGD - Iterations:  64%|██████▍   | 128/200 [00:36<00:19,  3.61it/s]\u001b[A\n",
+      "PGD - Iterations:  64%|██████▍   | 129/200 [00:36<00:19,  3.60it/s]\u001b[A\n",
+      "PGD - Iterations:  65%|██████▌   | 130/200 [00:37<00:19,  3.59it/s]\u001b[A\n",
+      "PGD - Iterations:  66%|██████▌   | 131/200 [00:37<00:19,  3.61it/s]\u001b[A\n",
+      "PGD - Iterations:  66%|██████▌   | 132/200 [00:37<00:18,  3.61it/s]\u001b[A\n",
+      "PGD - Iterations:  66%|██████▋   | 133/200 [00:37<00:18,  3.61it/s]\u001b[A\n",
+      "PGD - Iterations:  67%|██████▋   | 134/200 [00:38<00:18,  3.62it/s]\u001b[A\n",
+      "PGD - Iterations:  68%|██████▊   | 135/200 [00:38<00:17,  3.63it/s]\u001b[A\n",
+      "PGD - Iterations:  68%|██████▊   | 136/200 [00:38<00:17,  3.64it/s]\u001b[A\n",
+      "PGD - Iterations:  68%|██████▊   | 137/200 [00:39<00:17,  3.64it/s]\u001b[A\n",
+      "PGD - Iterations:  69%|██████▉   | 138/200 [00:39<00:17,  3.63it/s]\u001b[A\n",
+      "PGD - Iterations:  70%|██████▉   | 139/200 [00:39<00:16,  3.64it/s]\u001b[A\n",
+      "PGD - Iterations:  70%|███████   | 140/200 [00:39<00:16,  3.64it/s]\u001b[A\n",
+      "PGD - Iterations:  70%|███████   | 141/200 [00:40<00:16,  3.62it/s]\u001b[A\n",
+      "PGD - Iterations:  71%|███████   | 142/200 [00:40<00:15,  3.63it/s]\u001b[A\n",
+      "PGD - Iterations:  72%|███████▏  | 143/200 [00:40<00:15,  3.64it/s]\u001b[A\n",
+      "PGD - Iterations:  72%|███████▏  | 144/200 [00:40<00:15,  3.64it/s]\u001b[A\n",
+      "PGD - Iterations:  72%|███████▎  | 145/200 [00:41<00:15,  3.62it/s]\u001b[A\n",
+      "PGD - Iterations:  73%|███████▎  | 146/200 [00:41<00:15,  3.55it/s]\u001b[A\n",
+      "PGD - Iterations:  74%|███████▎  | 147/200 [00:41<00:14,  3.55it/s]\u001b[A\n",
+      "PGD - Iterations:  74%|███████▍  | 148/200 [00:42<00:14,  3.57it/s]\u001b[A\n",
+      "PGD - Iterations:  74%|███████▍  | 149/200 [00:42<00:14,  3.60it/s]\u001b[A\n",
+      "PGD - Iterations:  75%|███████▌  | 150/200 [00:42<00:13,  3.59it/s]\u001b[A\n",
+      "PGD - Iterations:  76%|███████▌  | 151/200 [00:42<00:13,  3.56it/s]\u001b[A\n",
+      "PGD - Iterations:  76%|███████▌  | 152/200 [00:43<00:13,  3.57it/s]\u001b[A\n",
+      "PGD - Iterations:  76%|███████▋  | 153/200 [00:43<00:13,  3.56it/s]\u001b[A\n",
+      "PGD - Iterations:  77%|███████▋  | 154/200 [00:43<00:12,  3.56it/s]\u001b[A\n",
+      "PGD - Iterations:  78%|███████▊  | 155/200 [00:44<00:12,  3.57it/s]\u001b[A\n",
+      "PGD - Iterations:  78%|███████▊  | 156/200 [00:44<00:12,  3.58it/s]\u001b[A\n",
+      "PGD - Iterations:  78%|███████▊  | 157/200 [00:44<00:11,  3.59it/s]\u001b[A\n",
+      "PGD - Iterations:  79%|███████▉  | 158/200 [00:44<00:11,  3.59it/s]\u001b[A\n",
+      "PGD - Iterations:  80%|███████▉  | 159/200 [00:45<00:11,  3.58it/s]\u001b[A\n",
+      "PGD - Iterations:  80%|████████  | 160/200 [00:45<00:11,  3.60it/s]\u001b[A\n",
+      "PGD - Iterations:  80%|████████  | 161/200 [00:45<00:10,  3.57it/s]\u001b[A\n",
+      "PGD - Iterations:  81%|████████  | 162/200 [00:46<00:10,  3.58it/s]\u001b[A\n",
+      "PGD - Iterations:  82%|████████▏ | 163/200 [00:46<00:10,  3.58it/s]\u001b[A\n",
+      "PGD - Iterations:  82%|████████▏ | 164/200 [00:46<00:09,  3.60it/s]\u001b[A\n",
+      "PGD - Iterations:  82%|████████▎ | 165/200 [00:46<00:09,  3.61it/s]\u001b[A\n",
+      "PGD - Iterations:  83%|████████▎ | 166/200 [00:47<00:09,  3.59it/s]\u001b[A\n",
+      "PGD - Iterations:  84%|████████▎ | 167/200 [00:47<00:09,  3.60it/s]\u001b[A\n",
+      "PGD - Iterations:  84%|████████▍ | 168/200 [00:47<00:08,  3.61it/s]\u001b[A\n",
+      "PGD - Iterations:  84%|████████▍ | 169/200 [00:47<00:08,  3.61it/s]\u001b[A\n",
+      "PGD - Iterations:  85%|████████▌ | 170/200 [00:48<00:08,  3.62it/s]\u001b[A\n",
+      "PGD - Iterations:  86%|████████▌ | 171/200 [00:48<00:08,  3.62it/s]\u001b[A\n",
+      "PGD - Iterations:  86%|████████▌ | 172/200 [00:48<00:07,  3.61it/s]\u001b[A\n",
+      "PGD - Iterations:  86%|████████▋ | 173/200 [00:49<00:07,  3.61it/s]\u001b[A\n",
+      "PGD - Iterations:  87%|████████▋ | 174/200 [00:49<00:07,  3.62it/s]\u001b[A\n",
+      "PGD - Iterations:  88%|████████▊ | 175/200 [00:49<00:06,  3.64it/s]\u001b[A\n",
+      "PGD - Iterations:  88%|████████▊ | 176/200 [00:49<00:06,  3.63it/s]\u001b[A\n",
+      "PGD - Iterations:  88%|████████▊ | 177/200 [00:50<00:06,  3.63it/s]\u001b[A\n",
+      "PGD - Iterations:  89%|████████▉ | 178/200 [00:50<00:06,  3.65it/s]\u001b[A\n",
+      "PGD - Iterations:  90%|████████▉ | 179/200 [00:50<00:05,  3.63it/s]\u001b[A\n",
+      "PGD - Iterations:  90%|█████████ | 180/200 [00:50<00:05,  3.62it/s]\u001b[A\n",
+      "PGD - Iterations:  90%|█████████ | 181/200 [00:51<00:05,  3.61it/s]\u001b[A\n",
+      "PGD - Iterations:  91%|█████████ | 182/200 [00:51<00:04,  3.61it/s]\u001b[A\n",
+      "PGD - Iterations:  92%|█████████▏| 183/200 [00:51<00:04,  3.63it/s]\u001b[A\n",
+      "PGD - Iterations:  92%|█████████▏| 184/200 [00:52<00:04,  3.61it/s]\u001b[A\n",
+      "PGD - Iterations:  92%|█████████▎| 185/200 [00:52<00:04,  3.62it/s]\u001b[A\n",
+      "PGD - Iterations:  93%|█████████▎| 186/200 [00:52<00:03,  3.63it/s]\u001b[A\n",
+      "PGD - Iterations:  94%|█████████▎| 187/200 [00:52<00:03,  3.63it/s]\u001b[A\n",
+      "PGD - Iterations:  94%|█████████▍| 188/200 [00:53<00:03,  3.63it/s]\u001b[A\n",
+      "PGD - Iterations:  94%|█████████▍| 189/200 [00:53<00:03,  3.63it/s]\u001b[A\n",
+      "PGD - Iterations:  95%|█████████▌| 190/200 [00:53<00:02,  3.64it/s]\u001b[A\n",
+      "PGD - Iterations:  96%|█████████▌| 191/200 [00:54<00:02,  3.62it/s]\u001b[A\n",
+      "PGD - Iterations:  96%|█████████▌| 192/200 [00:54<00:02,  3.61it/s]\u001b[A\n",
+      "PGD - Iterations:  96%|█████████▋| 193/200 [00:54<00:01,  3.63it/s]\u001b[A\n",
+      "PGD - Iterations:  97%|█████████▋| 194/200 [00:54<00:01,  3.62it/s]\u001b[A\n",
+      "PGD - Iterations:  98%|█████████▊| 195/200 [00:55<00:01,  3.60it/s]\u001b[A\n",
+      "PGD - Iterations:  98%|█████████▊| 196/200 [00:55<00:01,  3.60it/s]\u001b[A\n",
+      "PGD - Iterations:  98%|█████████▊| 197/200 [00:55<00:00,  3.60it/s]\u001b[A\n",
+      "PGD - Iterations:  99%|█████████▉| 198/200 [00:55<00:00,  3.60it/s]\u001b[A\n",
+      "PGD - Iterations: 100%|█████████▉| 199/200 [00:56<00:00,  3.55it/s]\u001b[A\n",
+      "PGD - Iterations: 100%|██████████| 200/200 [00:56<00:00,  3.53it/s]\u001b[A\n",
+      "PGD - Random Initializations: 100%|██████████| 1/1 [00:56<00:00, 56.56s/it]\n"
+     ]
+    }
+   ],
+   "source": [
+    "attack = PoisoningAttackCleanLabelBackdoor(backdoor=backdoor, proxy_classifier=proxy.get_classifier(),\n",
+    "                                           target=targets, pp_poison=percent_poison, norm=2, eps=5,\n",
+    "                                           eps_step=0.1, max_iter=200)\n",
+    "pdata, plabels = attack.poison(x_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Get modified data used to train the classifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1019\n",
+      "Label: 9\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "poisoned = pdata[np.all(plabels == targets, axis=1)]\n",
+    "poisoned_labels = plabels[np.all(plabels == targets, axis=1)]\n",
+    "print(len(poisoned))\n",
+    "idx = 0\n",
+    "plt.imshow(poisoned[idx].squeeze())\n",
+    "print(f\"Label: {np.argmax(poisoned_labels[idx])}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/10\n",
+      "78/78 [==============================] - 11s 136ms/step - loss: 0.6876 - acc: 0.7816\n",
+      "Epoch 2/10\n",
+      "78/78 [==============================] - 9s 118ms/step - loss: 0.2247 - acc: 0.9324\n",
+      "Epoch 3/10\n",
+      "78/78 [==============================] - 9s 117ms/step - loss: 0.1406 - acc: 0.9566\n",
+      "Epoch 4/10\n",
+      "78/78 [==============================] - 9s 117ms/step - loss: 0.1101 - acc: 0.9670\n",
+      "Epoch 5/10\n",
+      "78/78 [==============================] - 9s 118ms/step - loss: 0.0829 - acc: 0.9723\n",
+      "Epoch 6/10\n",
+      "78/78 [==============================] - 9s 119ms/step - loss: 0.0571 - acc: 0.9824\n",
+      "Epoch 7/10\n",
+      "78/78 [==============================] - 9s 119ms/step - loss: 0.0548 - acc: 0.9822\n",
+      "Epoch 8/10\n",
+      "78/78 [==============================] - 9s 117ms/step - loss: 0.0528 - acc: 0.9842\n",
+      "Epoch 9/10\n",
+      "78/78 [==============================] - 9s 118ms/step - loss: 0.0421 - acc: 0.9855\n",
+      "Epoch 10/10\n",
+      "78/78 [==============================] - 9s 119ms/step - loss: 0.0391 - acc: 0.9876\n"
+     ]
+    }
+   ],
+   "source": [
+    "model.fit(pdata, plabels, nb_epochs=10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# The victim bank evaluates the model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Evaluation on clean test samples"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Clean test set accuracy: 97.95%\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPsAAAD4CAYAAAAq5pAIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAOIklEQVR4nO3df4wc9XnH8c8n5rCpSVoc8+MCVoEI0hAkSDg5CNoUiooAtTU0heK2kZNSmSRQJVKqlFBSoEobizYkbZPQXIKFG1FCWkAmEk2DXBCNImEO6tgGA3apA8auDaKqTRTss/30jxuTi7n97nl3dmeP5/2STrs7z87Oc4s/zN58d+briBCAN7+3NN0AgP4g7EAShB1IgrADSRB2IInD+rmxwz075mhuPzcJpPKafqw9sdtT1boKu+2LJP2tpFmSvhERy0rPn6O5er8v6GaTAAoejVUtax1/jLc9S9JXJF0s6TRJi22f1unrAeitbv5mXyhpU0Q8FxF7JH1L0qJ62gJQt27CfrykFyY93lIt+xm2l9oesz02rt1dbA5AN7oJ+1QHAd7w3duIGI2IkYgYGdLsLjYHoBvdhH2LpAWTHp8gaWt37QDolW7C/pikU2yfZPtwSVdKur+etgDUreOht4jYa/taSf+miaG35RHxZG2dAahVV+PsEfGApAdq6gVAD/F1WSAJwg4kQdiBJAg7kARhB5Ig7EAShB1IgrADSRB2IAnCDiRB2IEkCDuQBGEHkiDsQBKEHUiCsANJEHYgCcIOJEHYgSQIO5AEYQeS6OuUzei/Wb/w88X6M18+uVh/+vxvFOs37DirWF/3+6e2rO176tniuqgXe3YgCcIOJEHYgSQIO5AEYQeSIOxAEoQdSIJx9je5/SedUKyvO+9rxfp4lF//c8c8Xqyfcdk5LWsLGGfvq67CbnuzpF2S9knaGxEjdTQFoH517NnPj4iXa3gdAD3E3+xAEt2GPSR9z/bjtpdO9QTbS22P2R4b1+4uNwegU91+jD83IrbaPkbSg7afjohHJj8hIkYljUrS2zyvzeEeAL3S1Z49IrZWtzsk3SdpYR1NAahfx2G3Pdf2Ww/cl3ShpPV1NQagXt18jD9W0n22D7zOP0XEd2vpCofksAWtx9JPGt3Ux04wyDoOe0Q8J+mMGnsB0EMMvQFJEHYgCcIOJEHYgSQIO5AEp7jOAM//eevTRCXprIuealm7Zfg/6m7nkBx5zkstay98tvx7zV+7t1g/YuXqjnrKij07kARhB5Ig7EAShB1IgrADSRB2IAnCDiTBOPsMsPbqvy/Wx2Nfnzo5dA+fcWfrYptzJu/78XCxvnzXpcX6Yf9evsx1NuzZgSQIO5AEYQeSIOxAEoQdSIKwA0kQdiAJxtkHwNDD5fHkIc/qUyeH7j/37C/WN48f3bJ22dxXiuteceSOcv2bo8X6bxx/VrGeDXt2IAnCDiRB2IEkCDuQBGEHkiDsQBKEHUiCcfY++MmlC4v1jwz/c7He7nz1Xp7PfvqqjxbrR6+aXazP/r/WvX3mvPK+Zt3lf1est7PlM62vS3/C53/Q1WvPRG337LaX295he/2kZfNsP2h7Y3V7VG/bBNCt6XyMv0PSRQctu07Sqog4RdKq6jGAAdY27BHxiKSDv9e4SNKK6v4KSeXrAwFoXKcH6I6NiG2SVN0e0+qJtpfaHrM9Nq7dHW4OQLd6fjQ+IkYjYiQiRoZUPpgDoHc6Dft228OSVN2WT08C0LhOw36/pCXV/SWSVtbTDoBeaTvObvsuSedJmm97i6QbJS2T9G3bV0l6XtLlvWxy0M16z7uK9c/dWj7veuTwPe22cIgd/VS7a6/f8NAHi/V3f/rpYn3fzp2H3NMB79p4arG++rfmFOsLZ79WrP/rx25pWbtwzqeL6574V+VrzsfumXf8qW3YI2Jxi9IFNfcCoIf4uiyQBGEHkiDsQBKEHUiCsANJcIprDfYfXn4b2w+tdecPf3TweUo/tet3jyiue+qW1cV6LyeD3vfUs8X6x+8on147dvWXivXhWa1/9yeuKq/7wXuXFOvxww3F+iBizw4kQdiBJAg7kARhB5Ig7EAShB1IgrADSTDOPgNcv32kWN/5R29vWdu3ZWPd7fTNife8XKx/9tKzi/Vlxz1WZzszHnt2IAnCDiRB2IEkCDuQBGEHkiDsQBKEHUiCcfY+GHLnl4KWpLXvizbPmLlj6UV2sXzYW/YX692871tvLtePm4GzG7JnB5Ig7EAShB1IgrADSRB2IAnCDiRB2IEkGGevwTMf+7lifTx6efX1N6/Nv936PH1J+pejy9e8H4/W4+zt/pu848ZiWeUR/sHUds9ue7ntHbbXT1p2k+0Xba+pfi7pbZsAujWdj/F3SJpqypEvRsSZ1c8D9bYFoG5twx4Rj0h6pQ+9AOihbg7QXWt7bfUx/6hWT7K91PaY7bFx7e5icwC60WnYb5P0TklnStom6QutnhgRoxExEhEjQ5rd4eYAdKujsEfE9ojYFxH7JX1d0sJ62wJQt47Cbnt40sPLJK1v9VwAg6HtOLvtuySdJ2m+7S2SbpR0nu0zJYWkzZKu7mGPA++GX/lO0y0MrMMWnNCytuusdxTX/YePfLXudl63evecYt179vZs201pG/aIWDzF4tt70AuAHuLrskAShB1IgrADSRB2IAnCDiTBKa7oqaduPq5l7ckLv9zTbd/z6vyWtdv+5PLiunM2lE+fnYnYswNJEHYgCcIOJEHYgSQIO5AEYQeSIOxAEoyzoytDDw8X658fvqdPnbzRHS+e07I25ztvvnH0dtizA0kQdiAJwg4kQdiBJAg7kARhB5Ig7EASjLPXYJbLE/gOufXUwdOx8/fO7njdm/+ifCHg8494rePXltr/buWpkbt7X9qJX3uxp68/07BnB5Ig7EAShB1IgrADSRB2IAnCDiRB2IEkGGevwbK7f6dYv+KqL3X1+o/89VeK9fJYdtl4dLzqNF+/897aOX3VR4v1U/REz7Y9E7Xds9teYPsh2xtsP2n7E9XyebYftL2xuj2q9+0C6NR0PsbvlfSpiHi3pLMlXWP7NEnXSVoVEadIWlU9BjCg2oY9IrZFxBPV/V2SNkg6XtIiSSuqp62QdGmvmgTQvUM6QGf7REnvlfSopGMjYps08T8ESce0WGep7THbY+Pa3V23ADo27bDbPlLSPZI+GRE7p7teRIxGxEhEjAxpdic9AqjBtMJue0gTQb8zIu6tFm+3PVzVhyXt6E2LAOrQdujNtiXdLmlDRNw6qXS/pCWSllW3K3vS4Qxw8t0vF+ur/2BOsb5wdnenmQ6y1btb/+6j//OrxXX/9+Otp3uWpF/6703Feu8G/Wam6YyznyvpQ5LW2V5TLbteEyH/tu2rJD0vqTzhNYBGtQ17RHxfkluUL6i3HQC9wtdlgSQIO5AEYQeSIOxAEoQdSMIRPT7HcZK3eV683/kO4P9k0cJi/YXfLF+K+tmLv1as9/I00nbaXUr6jK/+ccvagr/8Qd3tpPdorNLOeGXK0TP27EAShB1IgrADSRB2IAnCDiRB2IEkCDuQBJeS7oMjVq4u1k9tcyWADyy+plgf+vD2lrXvvufu4roXrr+yWN9/x5RXG3tdtDofsnLimpda1jjfvL/YswNJEHYgCcIOJEHYgSQIO5AEYQeSIOxAEpzPDryJcD47AMIOZEHYgSQIO5AEYQeSIOxAEoQdSKJt2G0vsP2Q7Q22n7T9iWr5TbZftL2m+rmk9+0C6NR0Ll6xV9KnIuIJ22+V9LjtB6vaFyPib3rXHoC6TGd+9m2StlX3d9neIOn4XjcGoF6H9De77RMlvVfSo9Wia22vtb3c9lEt1llqe8z22Lh2d9UsgM5NO+y2j5R0j6RPRsROSbdJeqekMzWx5//CVOtFxGhEjETEyJBm19AygE5MK+y2hzQR9Dsj4l5JiojtEbEvIvZL+rqk8uyFABo1naPxlnS7pA0Rceuk5cOTnnaZpPX1twegLtM5Gn+upA9JWmd7TbXsekmLbZ8pKSRtlnR1TzoEUIvpHI3/vqSpzo99oP52APQK36ADkiDsQBKEHUiCsANJEHYgCcIOJEHYgSQIO5AEYQeSIOxAEoQdSIKwA0kQdiAJwg4k0dcpm22/JOlHkxbNl/Ry3xo4NIPa26D2JdFbp+rs7Rcj4uipCn0N+xs2bo9FxEhjDRQMam+D2pdEb53qV298jAeSIOxAEk2HfbTh7ZcMam+D2pdEb53qS2+N/s0OoH+a3rMD6BPCDiTRSNhtX2T7GdubbF/XRA+t2N5se101DfVYw70st73D9vpJy+bZftD2xup2yjn2GuptIKbxLkwz3uh71/T0533/m932LEnPSvp1SVskPSZpcUQ81ddGWrC9WdJIRDT+BQzbH5D0qqR/jIjTq2W3SHolIpZV/6M8KiL+dEB6u0nSq01P413NVjQ8eZpxSZdK+rAafO8KfV2hPrxvTezZF0raFBHPRcQeSd+StKiBPgZeRDwi6ZWDFi+StKK6v0IT/1j6rkVvAyEitkXEE9X9XZIOTDPe6HtX6Ksvmgj78ZJemPR4iwZrvveQ9D3bj9te2nQzUzg2IrZJE/94JB3TcD8HazuNdz8dNM34wLx3nUx/3q0mwj7VVFKDNP53bkS8T9LFkq6pPq5ieqY1jXe/TDHN+EDodPrzbjUR9i2SFkx6fIKkrQ30MaWI2Frd7pB0nwZvKurtB2bQrW53NNzP6wZpGu+pphnXALx3TU5/3kTYH5N0iu2TbB8u6UpJ9zfQxxvYnlsdOJHtuZIu1OBNRX2/pCXV/SWSVjbYy88YlGm8W00zrobfu8anP4+Ivv9IukQTR+T/S9KfNdFDi75OlvTD6ufJpnuTdJcmPtaNa+IT0VWS3i5plaSN1e28Aertm5LWSVqriWANN9TbL2viT8O1ktZUP5c0/d4V+urL+8bXZYEk+AYdkARhB5Ig7EAShB1IgrADSRB2IAnCDiTx/044MJsQZMjSAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Prediction: 0\n"
+     ]
+    }
+   ],
+   "source": [
+    "clean_preds = np.argmax(model.predict(x_test), axis=1)\n",
+    "clean_correct = np.sum(clean_preds == np.argmax(y_test, axis=1))\n",
+    "clean_total = y_test.shape[0]\n",
+    "\n",
+    "clean_acc = clean_correct / clean_total\n",
+    "print(\"\\nClean test set accuracy: %.2f%%\" % (clean_acc * 100))\n",
+    "\n",
+    "# Display image, label, and prediction for a clean sample to show how the poisoned model classifies a clean sample\n",
+    "\n",
+    "c = 0 # class to display\n",
+    "i = 0 # image of the class to display\n",
+    "\n",
+    "c_idx = np.where(np.argmax(y_test, 1) == c)[0][i] # index of the image in clean arrays\n",
+    "\n",
+    "plt.imshow(x_test[c_idx].squeeze())\n",
+    "plt.show()\n",
+    "clean_label = c\n",
+    "print(\"Prediction: \" + str(clean_preds[c_idx]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Looks like an accurate model, except..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### The attack was super effective!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Poison test set accuracy: 5.15%\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPsAAAD4CAYAAAAq5pAIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAANoElEQVR4nO3df6zddX3H8dfL/sJeYFKwtZZKFdFYndTlCppuSw1DAUOKUTaajLCEWbJBAovZRliMJFscIyJh05lU6awEYSoQiHZq07gRMla5kFIKZSuyDmvveoG6tQjctvS9P+6X5QL3fO7lfL/nfA99Px/JzTnn+z7f833n2/vq99zz+X7PxxEhAEe/N7XdAID+IOxAEoQdSIKwA0kQdiCJ2f3c2FzPi2M01M9NAqm8qF/pYIx7qlqtsNs+R9JNkmZJ+kZEXFd6/jEa0pk+q84mARRsic0da12/jbc9S9JXJZ0rabmkNbaXd/t6AHqrzt/sZ0h6IiKejIiDkm6XtLqZtgA0rU7Yl0j6+aTHu6tlr2B7re0R2yOHNF5jcwDqqBP2qT4EeM25txGxLiKGI2J4jubV2ByAOuqEfbekpZMenyxpT712APRKnbA/IOk02++0PVfSRZLuaaYtAE3reugtIg7bvkLSjzQx9LY+Ih5trDMAjao1zh4RGyVtbKgXAD3E6bJAEoQdSIKwA0kQdiAJwg4kQdiBJAg7kARhB5Ig7EAShB1IgrADSRB2IAnCDiRB2IEkCDuQBGEHkiDsQBKEHUiCsANJEHYgCcIOJEHYgSQIO5AEYQeSIOxAEoQdSIKwA0kQdiAJwg4kQdiBJGpN2Wx7l6QDkl6SdDgihptoCkDzaoW98rGIeKaB1wHQQ7yNB5KoG/aQ9GPbD9peO9UTbK+1PWJ75JDGa24OQLfqvo1fGRF7bC+UtMn24xFx7+QnRMQ6Sesk6XgviJrbA9ClWkf2iNhT3Y5JukvSGU00BaB5XYfd9pDt416+L+njkrY31RiAZtV5G79I0l22X36db0fEDxvpCkDjug57RDwp6fQGewHQQwy9AUkQdiAJwg4kQdiBJAg7kEQTF8Kk8OxnP9qx9o6Lnyiu+/jYomL94PicYn3JbeX6/N3Pdawd2fpYcV3kwZEdSIKwA0kQdiAJwg4kQdiBJAg7kARhB5JgnH2G/uxPv92x9umhX5ZXPrXmxleVy7sOP9+xdtPTH6u58Teun46d0rE2dMOvFdedvfnBpttpHUd2IAnCDiRB2IEkCDuQBGEHkiDsQBKEHUjCEf2bpOV4L4gzfVbfttekX33mzI61Zz5Y/j/zhB3lffzL97lYn/vB/ynWr//AnR1rZ7/5heK6P3j+2GL9k/M7Xytf1wtxsFjfMj5UrK865lDX2373Dy4r1t+z9oGuX7tNW2Kz9se+KX+hOLIDSRB2IAnCDiRB2IEkCDuQBGEHkiDsQBJczz5DQ9/bUqjVe+3j662uv3vbqo61v1q5rLztfyl/5/31q97dRUczM/uFI8X60LbRYv3Ee+8o1n99bufv25+/q/xd/EejaY/sttfbHrO9fdKyBbY32d5Z3Z7Q2zYB1DWTt/HflHTOq5ZdLWlzRJwmaXP1GMAAmzbsEXGvpH2vWrxa0obq/gZJFzTcF4CGdfsB3aKIGJWk6nZhpyfaXmt7xPbIIY13uTkAdfX80/iIWBcRwxExPEfzer05AB10G/a9thdLUnU71lxLAHqh27DfI+mS6v4lku5uph0AvTLtOLvt2zTxzeUn2d4t6QuSrpP0HduXSnpK0oW9bBJlh/97b8fa0B2da5L00jSvPfS9Z7voqBl7//Cjxfr755Z/fb+0770da8v+4cniuoeL1TemacMeEWs6lN6Y30IBJMXpskAShB1IgrADSRB2IAnCDiTBJa5ozexTlhbrX7nmK8X6HM8q1r970+90rJ04en9x3aMRR3YgCcIOJEHYgSQIO5AEYQeSIOxAEoQdSIJxdrTm8T9ZUqx/eF55KutHD5ano17w2POvu6ejGUd2IAnCDiRB2IEkCDuQBGEHkiDsQBKEHUiCcXb01PgnP9yx9tBnbpxm7fIMQn905ZXF+pv/9afTvH4uHNmBJAg7kARhB5Ig7EAShB1IgrADSRB2IAnG2dFTT53b+XhyrMvj6Gv+8+xiff4PHy7Wo1jNZ9oju+31tsdsb5+07Frbv7C9tfo5r7dtAqhrJm/jvynpnCmW3xgRK6qfjc22BaBp04Y9Iu6VtK8PvQDooTof0F1he1v1Nv+ETk+yvdb2iO2RQxqvsTkAdXQb9q9JOlXSCkmjkm7o9MSIWBcRwxExPGeaCxsA9E5XYY+IvRHxUkQckfR1SWc02xaApnUVdtuLJz38lKTtnZ4LYDBMO85u+zZJqySdZHu3pC9IWmV7hSaGMndJuqyHPWKAvem444r1i3/rvo61/UdeLK479sV3Fevzxh8o1vFK04Y9ItZMsfjmHvQCoIc4XRZIgrADSRB2IAnCDiRB2IEkuMQVtey89v3F+vdP+vuOtdU7P11cd95GhtaaxJEdSIKwA0kQdiAJwg4kQdiBJAg7kARhB5JgnB1F//v7HynWt/3e3xbrPzt8qGPtub85ubjuPI0W63h9OLIDSRB2IAnCDiRB2IEkCDuQBGEHkiDsQBKMsyc3e8nbi/WrPv+Pxfo8l3+FLnr44o61t/4T16v3E0d2IAnCDiRB2IEkCDuQBGEHkiDsQBKEHUiCcfajnGeX/4lP//7uYv3CY58t1m89sLBYX/T5zseTI8U10bRpj+y2l9r+ie0dth+1fWW1fIHtTbZ3Vrcn9L5dAN2aydv4w5I+FxHvk/QRSZfbXi7pakmbI+I0SZurxwAG1LRhj4jRiHioun9A0g5JSyStlrShetoGSRf0qkkA9b2uD+hsL5P0IUlbJC2KiFFp4j8ESVP+8WZ7re0R2yOHNF6vWwBdm3HYbR8r6Q5JV0XE/pmuFxHrImI4IobnaF43PQJowIzCbnuOJoJ+a0TcWS3ea3txVV8saaw3LQJowrRDb7Yt6WZJOyLiy5NK90i6RNJ11e3dPekQ9Zz+3mL5LxfeUuvlv/rFC4v1tzx8f63XR3NmMs6+UtLFkh6xvbVado0mQv4d25dKekpS+V8dQKumDXtE3CfJHcpnNdsOgF7hdFkgCcIOJEHYgSQIO5AEYQeS4BLXo8Cs5e/pWFt7e73TH5avv7xYX3bLv9V6ffQPR3YgCcIOJEHYgSQIO5AEYQeSIOxAEoQdSIJx9qPA43/c+Yt9z58/4y8VmtLJ/3yw/ISIWq+P/uHIDiRB2IEkCDuQBGEHkiDsQBKEHUiCsANJMM7+BvDi+WcU65vPv6FQnd9sM+i5H+3ZWqx/4u0runpdjuxAEoQdSIKwA0kQdiAJwg4kQdiBJAg7kMRM5mdfKulbkt4m6YikdRFxk+1rJX1W0tPVU6+JiI29ajSzPStnFevvmN39WPqtBxYW63P2l69n52r27pTG0rsdR5/OTE6qOSzpcxHxkO3jJD1oe1NVuzEivtSTzgA0aibzs49KGq3uH7C9Q9KSXjcGoFmv629228skfUjSlmrRFba32V5ve8rvRrK91vaI7ZFDGq/VLIDuzTjsto+VdIekqyJiv6SvSTpV0gpNHPmnPEE7ItZFxHBEDM/RvAZaBtCNGYXd9hxNBP3WiLhTkiJib0S8FBFHJH1dUvlqDQCtmjbsti3pZkk7IuLLk5YvnvS0T0na3nx7AJoyk0/jV0q6WNIjtl8eL7hG0hrbKzQx+rJL0mU96RC1/PWzy4v1+z+xrFiP0Uca7CaPOpep9uoS15l8Gn+fJE9RYkwdeAPhDDogCcIOJEHYgSQIO5AEYQeSIOxAEo4+Trl7vBfEmT6rb9sDstkSm7U/9k01VM6RHciCsANJEHYgCcIOJEHYgSQIO5AEYQeS6Os4u+2nJf3XpEUnSXqmbw28PoPa26D2JdFbt5rs7ZSIeOtUhb6G/TUbt0ciYri1BgoGtbdB7Uuit271qzfexgNJEHYgibbDvq7l7ZcMam+D2pdEb93qS2+t/s0OoH/aPrID6BPCDiTRSthtn2P7320/YfvqNnroxPYu24/Y3mp7pOVe1tses7190rIFtjfZ3lndTjnHXku9XWv7F9W+22r7vJZ6W2r7J7Z32H7U9pXV8lb3XaGvvuy3vv/NbnuWpP+QdLak3ZIekLQmIh7rayMd2N4laTgiWj8Bw/ZvS3pO0rci4gPVsusl7YuI66r/KE+IiD8fkN6ulfRc29N4V7MVLZ48zbikCyT9gVrcd4W+fld92G9tHNnPkPRERDwZEQcl3S5pdQt9DLyIuFfSvlctXi1pQ3V/gyZ+WfquQ28DISJGI+Kh6v4BSS9PM97qviv01RdthH2JpJ9PerxbgzXfe0j6se0Hba9tu5kpLIqIUWnil0fSwpb7ebVpp/Hup1dNMz4w+66b6c/raiPsU30/1iCN/62MiN+QdK6ky6u3q5iZGU3j3S9TTDM+ELqd/ryuNsK+W9LSSY9PlrSnhT6mFBF7qtsxSXdp8Kai3vvyDLrV7VjL/fy/QZrGe6ppxjUA+67N6c/bCPsDkk6z/U7bcyVdJOmeFvp4DdtD1Qcnsj0k6eMavKmo75F0SXX/Ekl3t9jLKwzKNN6dphlXy/uu9enPI6LvP5LO08Qn8j+T9Bdt9NChr3dJerj6ebTt3iTdpom3dYc08Y7oUkknStosaWd1u2CAertF0iOStmkiWItb6u03NfGn4TZJW6uf89red4W++rLfOF0WSIIz6IAkCDuQBGEHkiDsQBKEHUiCsANJEHYgif8D9RUAVx3pHV8AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Prediction: 9\n"
+     ]
+    }
+   ],
+   "source": [
+    "not_target = np.logical_not(np.all(y_test == targets, axis=1))\n",
+    "px_test, py_test = backdoor.poison(x_test[not_target], y_test[not_target])\n",
+    "poison_preds = np.argmax(model.predict(px_test), axis=1)\n",
+    "clean_correct = np.sum(poison_preds == np.argmax(y_test[not_target], axis=1))\n",
+    "clean_total = y_test.shape[0]\n",
+    "\n",
+    "clean_acc = clean_correct / clean_total\n",
+    "print(\"\\nPoison test set accuracy: %.2f%%\" % (clean_acc * 100))\n",
+    "\n",
+    "c = 0 # index to display\n",
+    "plt.imshow(px_test[c].squeeze())\n",
+    "plt.show()\n",
+    "clean_label = c\n",
+    "print(\"Prediction: \" + str(poison_preds[c]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This notebook displays a new poisoning attack that only needs to poison a small percent of a single class, but can still prove effective against any image at test time. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/adversarial-robustness-toolbox/notebooks/poisoning_attack_embedding.ipynb b/adversarial-robustness-toolbox/notebooks/poisoning_attack_embedding.ipynb
new file mode 100644
index 0000000..50811b0
--- /dev/null
+++ b/adversarial-robustness-toolbox/notebooks/poisoning_attack_embedding.ipynb
@@ -0,0 +1,897 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Using Adversarial Embedding Attacks to Evade Poison Detectors"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using TensorFlow backend.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from __future__ import absolute_import, division, print_function, unicode_literals\n",
+    "\n",
+    "import os, sys\n",
+    "from os.path import abspath\n",
+    "\n",
+    "module_path = os.path.abspath(os.path.join('..'))\n",
+    "if module_path not in sys.path:\n",
+    "    sys.path.append(module_path)\n",
+    "\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "import keras.backend as k\n",
+    "from keras.models import Sequential\n",
+    "from keras.layers import Dense, Flatten, Conv2D, MaxPooling2D, Activation, Dropout\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "from mpl_toolkits import mplot3d\n",
+    "\n",
+    "from art.estimators.classification import KerasClassifier\n",
+    "from art.attacks.poisoning import PoisoningAttackBackdoor, PoisoningAttackAdversarialEmbedding\n",
+    "from art.attacks.poisoning.perturbations import add_pattern_bd\n",
+    "from art.utils import load_mnist, preprocess, to_categorical\n",
+    "from art.defences.detector.poison import ActivationDefence\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In a [previous notebook](https://github.com/Trusted-AI/adversarial-robustness-toolbox/blob/main/notebooks/poisoning_dataset_mnist.ipynb) we discuss the threat of backdoor attacks on neural networks, and show how to use ART to defend them with Activation Defense. In this notebook, we'll show how a skilled adversarial can counter this defense if they have control of the training procedure using the Adversarial Embedding attack introduced by [Tan, Shokri (2019)](https://arxiv.org/abs/1905.13409). "
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": "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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### The classification problem: Automatically detect numbers written in a check\n",
+    "![image.png](attachment:image.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "(x_raw, y_raw), (x_raw_test, y_raw_test), min_, max_ = load_mnist(raw=True)\n",
+    "\n",
+    "# Random Selection:\n",
+    "n_train = np.shape(x_raw)[0]\n",
+    "num_selection = 7500\n",
+    "random_selection_indices = np.random.choice(n_train, num_selection)\n",
+    "x_raw = x_raw[random_selection_indices]\n",
+    "y_raw = y_raw[random_selection_indices]"
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": "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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Adversary's goal: make some easy money \n",
+    "![image.png](attachment:image.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Poison training data\n",
+    "percent_poison = .05\n",
+    "x_train, y_train = preprocess(x_raw, y_raw)\n",
+    "x_train = np.expand_dims(x_train, axis=3)\n",
+    "\n",
+    "x_test, y_test = preprocess(x_raw_test, y_raw_test)\n",
+    "x_test = np.expand_dims(x_test, axis=3)\n",
+    "\n",
+    "# Shuffle training data\n",
+    "n_train = np.shape(y_train)[0]\n",
+    "shuffled_indices = np.arange(n_train)\n",
+    "np.random.shuffle(shuffled_indices)\n",
+    "x_train = x_train[shuffled_indices]\n",
+    "y_train = y_train[shuffled_indices]\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Victim bank trains a neural network"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create Keras convolutional neural network - basic architecture from Keras examples\n",
+    "# Source here: https://github.com/keras-team/keras/blob/master/examples/mnist_cnn.py\n",
+    "def create_model():    \n",
+    "    model = Sequential()\n",
+    "    model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=x_train.shape[1:]))\n",
+    "    model.add(Conv2D(64, (3, 3), activation='relu'))\n",
+    "    model.add(MaxPooling2D(pool_size=(2, 2)))\n",
+    "    model.add(Dropout(0.25))\n",
+    "    model.add(Flatten())\n",
+    "    model.add(Dense(128, activation='relu'))\n",
+    "    model.add(Dropout(0.5))\n",
+    "    model.add(Dense(10, activation='softmax'))\n",
+    "\n",
+    "    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
+    "    return model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Attacker learns, then unlearns, the backdoor"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<img src=\"../utils/data/images/adv_embedding_arch.png\" style=\"width:75%\">"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import HTML\n",
+    "HTML('<img src=\"../utils/data/images/adv_embedding_arch.png\" style=\"width:75%\">')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Image Cred: [Bypassing Backdoor Detection Algorithms in Deep Learning](https://arxiv.org/abs/1905.13409)\n",
+    "\n",
+    "Like in normal backdoor attacks, the main goal of the classifier is to predict the correct class, thus the classifier learns the backdoor. However, for this attack we add a discriminator with a secondary goal: unlearn the ability to differentiate between backdoored and non-backdoored examples."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Choose backdoor pattern"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7fa0251a7da0>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "backdoor = PoisoningAttackBackdoor(add_pattern_bd)\n",
+    "example_target = np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 1])\n",
+    "pdata, plabels = backdoor.poison(x_test, y=example_target)\n",
+    "\n",
+    "plt.imshow(pdata[0].squeeze())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Chose backdoor target labels for training\n",
+    "Set a single target label as a Numpy array, or specify a source label specific attack with a list of tuples `[(src_array, tgt_array), ... ]`\n",
+    "\n",
+    "Note that you can also change the pp_poison for each pair as a list as well."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Poison some percentage of all non-nines to nines\n",
+    "targets = to_categorical([9], 10)\n",
+    "# Poison some percentage of 8s to 9s\n",
+    "targets = [(to_categorical([8], 10), to_categorical([9], 10))] # only poison 8 -> 9\n",
+    "# Poison some percentage of 0s to 1s, 1s to 2s, etc.\n",
+    "targets = [(to_categorical([a], 10), to_categorical([(a+1) % 10], 10)) for a in range(10)] "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Train Embedding Model\n",
+    "To choose the feature layer you may need to know the architecture of your model. If your layers are named you can use the layer's name. However it is recommended you use the index feature layer.\n",
+    "\n",
+    "In this example, we choose layer 5, named `dense_7`, to append our discriminator network. Note that this name can if other networks were initialized earlier. Thus we recommend using layer indices."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: \"sequential_1\"\n",
+      "_________________________________________________________________\n",
+      "Layer (type)                 Output Shape              Param #   \n",
+      "=================================================================\n",
+      "conv2d_1 (Conv2D)            (None, 26, 26, 32)        320       \n",
+      "_________________________________________________________________\n",
+      "conv2d_2 (Conv2D)            (None, 24, 24, 64)        18496     \n",
+      "_________________________________________________________________\n",
+      "max_pooling2d_1 (MaxPooling2 (None, 12, 12, 64)        0         \n",
+      "_________________________________________________________________\n",
+      "dropout_1 (Dropout)          (None, 12, 12, 64)        0         \n",
+      "_________________________________________________________________\n",
+      "flatten_1 (Flatten)          (None, 9216)              0         \n",
+      "_________________________________________________________________\n",
+      "dense_1 (Dense)              (None, 128)               1179776   \n",
+      "_________________________________________________________________\n",
+      "dropout_2 (Dropout)          (None, 128)               0         \n",
+      "_________________________________________________________________\n",
+      "dense_2 (Dense)              (None, 10)                1290      \n",
+      "=================================================================\n",
+      "Total params: 1,199,882\n",
+      "Trainable params: 1,199,882\n",
+      "Non-trainable params: 0\n",
+      "_________________________________________________________________\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = create_model()\n",
+    "model.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/10\n",
+      "7500/7500 [==============================] - 15s 2ms/step - loss: -90.1122 - sequential_1_loss: 3.0385 - backdoor_detect_loss: 3.1050 - sequential_1_acc: 0.1616 - backdoor_detect_acc: 0.4447\n",
+      "Epoch 2/10\n",
+      "7500/7500 [==============================] - 13s 2ms/step - loss: -190.6123 - sequential_1_loss: 3.5921 - backdoor_detect_loss: 6.4735 - sequential_1_acc: 0.2873 - backdoor_detect_acc: 0.4449\n",
+      "Epoch 3/10\n",
+      "7500/7500 [==============================] - 13s 2ms/step - loss: -234.1369 - sequential_1_loss: 3.3265 - backdoor_detect_loss: 7.9154 - sequential_1_acc: 0.3531 - backdoor_detect_acc: 0.4036\n",
+      "Epoch 4/10\n",
+      "7500/7500 [==============================] - 13s 2ms/step - loss: -274.8193 - sequential_1_loss: 3.4206 - backdoor_detect_loss: 9.2747 - sequential_1_acc: 0.3748 - backdoor_detect_acc: 0.3379\n",
+      "Epoch 5/10\n",
+      "7500/7500 [==============================] - 13s 2ms/step - loss: -302.7784 - sequential_1_loss: 3.5656 - backdoor_detect_loss: 10.2115 - sequential_1_acc: 0.4028 - backdoor_detect_acc: 0.2955\n",
+      "Epoch 6/10\n",
+      "7500/7500 [==============================] - 13s 2ms/step - loss: -321.9225 - sequential_1_loss: 3.5203 - backdoor_detect_loss: 10.8481 - sequential_1_acc: 0.4289 - backdoor_detect_acc: 0.2588\n",
+      "Epoch 7/10\n",
+      "7500/7500 [==============================] - 14s 2ms/step - loss: -341.5001 - sequential_1_loss: 3.3318 - backdoor_detect_loss: 11.4944 - sequential_1_acc: 0.4495 - backdoor_detect_acc: 0.2271\n",
+      "Epoch 8/10\n",
+      "7500/7500 [==============================] - 13s 2ms/step - loss: -359.4747 - sequential_1_loss: 3.2616 - backdoor_detect_loss: 12.0912 - sequential_1_acc: 0.4688 - backdoor_detect_acc: 0.1936\n",
+      "Epoch 9/10\n",
+      "7500/7500 [==============================] - 13s 2ms/step - loss: -374.8830 - sequential_1_loss: 3.1213 - backdoor_detect_loss: 12.6001 - sequential_1_acc: 0.4836 - backdoor_detect_acc: 0.1708\n",
+      "Epoch 10/10\n",
+      "7500/7500 [==============================] - 13s 2ms/step - loss: -384.3227 - sequential_1_loss: 2.9579 - backdoor_detect_loss: 12.9094 - sequential_1_acc: 0.4943 - backdoor_detect_acc: 0.1556\n"
+     ]
+    }
+   ],
+   "source": [
+    "poison_model = KerasClassifier(model)\n",
+    "emb_attack = PoisoningAttackAdversarialEmbedding(classifier=poison_model, backdoor=backdoor, feature_layer=5,\n",
+    "                                                 target=targets, regularization=30.0, pp_poison=percent_poison, \n",
+    "                                                 discriminator_layer_1=256, discriminator_layer_2=128,\n",
+    "                                                 learning_rate=1e-4)\n",
+    "\n",
+    "# This returns a classifier with the same structure as our original, sans discriminator architecture\n",
+    "classifier = emb_attack.poison_estimator(x_train, y_train, nb_epochs=10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Get modified data used to train the classifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data, labels, bd = emb_attack.get_training_data()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# The victim bank evaluates the model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Evaluation on clean test samples"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Clean test set accuracy: 83.98%\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Prediction: 0\n"
+     ]
+    }
+   ],
+   "source": [
+    "clean_preds = np.argmax(classifier.predict(x_test), axis=1)\n",
+    "clean_correct = np.sum(clean_preds == np.argmax(y_test, axis=1))\n",
+    "clean_total = y_test.shape[0]\n",
+    "\n",
+    "clean_acc = clean_correct / clean_total\n",
+    "print(\"\\nClean test set accuracy: %.2f%%\" % (clean_acc * 100))\n",
+    "\n",
+    "# Display image, label, and prediction for a clean sample to show how the poisoned model classifies a clean sample\n",
+    "\n",
+    "c = 0 # class to display\n",
+    "i = 0 # image of the class to display\n",
+    "\n",
+    "c_idx = np.where(np.argmax(y_test, 1) == c)[0][i] # index of the image in clean arrays\n",
+    "\n",
+    "plt.imshow(x_test[c_idx].squeeze())\n",
+    "plt.show()\n",
+    "clean_label = c\n",
+    "print(\"Prediction: \" + str(clean_preds[c_idx]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### But the adversary has other plans..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Train a regular model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/5\n",
+      "58/58 [==============================] - 9s 159ms/step - loss: 0.9846 - acc: 0.7135\n",
+      "Epoch 2/5\n",
+      "58/58 [==============================] - 8s 134ms/step - loss: 0.4211 - acc: 0.8758\n",
+      "Epoch 3/5\n",
+      "58/58 [==============================] - 8s 146ms/step - loss: 0.2735 - acc: 0.9084\n",
+      "Epoch 4/5\n",
+      "58/58 [==============================] - 9s 152ms/step - loss: 0.1996 - acc: 0.9328\n",
+      "Epoch 5/5\n",
+      "58/58 [==============================] - 9s 159ms/step - loss: 0.1586 - acc: 0.9488\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = create_model()\n",
+    "reg_classifier = KerasClassifier(model)\n",
+    "reg_classifier.fit(data, labels, nb_epochs=5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Attempting to Defend with Activation Defense\n",
+    "\n",
+    "We now attack both classifiers, and note the difference in defense rates between a normally backdoored model, and only backdoored with an embedding attack"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "defence = ActivationDefence(classifier, data, labels)\n",
+    "reg_defence = ActivationDefence(reg_classifier, data, labels)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Analysis completed. Report:\n",
+      "{'Class_0': {'cluster_0': {'ptc_data_in_cluster': 0.60999999999999999,\n",
+      "                           'suspicious_cluster': False},\n",
+      "             'cluster_1': {'ptc_data_in_cluster': 0.39000000000000001,\n",
+      "                           'suspicious_cluster': True}},\n",
+      " 'Class_1': {'cluster_0': {'ptc_data_in_cluster': 0.42999999999999999,\n",
+      "                           'suspicious_cluster': True},\n",
+      "             'cluster_1': {'ptc_data_in_cluster': 0.56999999999999995,\n",
+      "                           'suspicious_cluster': False}},\n",
+      " 'Class_2': {'cluster_0': {'ptc_data_in_cluster': 0.37,\n",
+      "                           'suspicious_cluster': True},\n",
+      "             'cluster_1': {'ptc_data_in_cluster': 0.63,\n",
+      "                           'suspicious_cluster': False}},\n",
+      " 'Class_3': {'cluster_0': {'ptc_data_in_cluster': 0.71999999999999997,\n",
+      "                           'suspicious_cluster': False},\n",
+      "             'cluster_1': {'ptc_data_in_cluster': 0.28000000000000003,\n",
+      "                           'suspicious_cluster': True}},\n",
+      " 'Class_4': {'cluster_0': {'ptc_data_in_cluster': 0.58999999999999997,\n",
+      "                           'suspicious_cluster': False},\n",
+      "             'cluster_1': {'ptc_data_in_cluster': 0.40999999999999998,\n",
+      "                           'suspicious_cluster': True}},\n",
+      " 'Class_5': {'cluster_0': {'ptc_data_in_cluster': 0.63,\n",
+      "                           'suspicious_cluster': False},\n",
+      "             'cluster_1': {'ptc_data_in_cluster': 0.37,\n",
+      "                           'suspicious_cluster': True}},\n",
+      " 'Class_6': {'cluster_0': {'ptc_data_in_cluster': 0.35999999999999999,\n",
+      "                           'suspicious_cluster': True},\n",
+      "             'cluster_1': {'ptc_data_in_cluster': 0.64000000000000001,\n",
+      "                           'suspicious_cluster': False}},\n",
+      " 'Class_7': {'cluster_0': {'ptc_data_in_cluster': 0.56000000000000005,\n",
+      "                           'suspicious_cluster': False},\n",
+      "             'cluster_1': {'ptc_data_in_cluster': 0.44,\n",
+      "                           'suspicious_cluster': True}},\n",
+      " 'Class_8': {'cluster_0': {'ptc_data_in_cluster': 0.40000000000000002,\n",
+      "                           'suspicious_cluster': True},\n",
+      "             'cluster_1': {'ptc_data_in_cluster': 0.59999999999999998,\n",
+      "                           'suspicious_cluster': False}},\n",
+      " 'Class_9': {'cluster_0': {'ptc_data_in_cluster': 0.58999999999999997,\n",
+      "                           'suspicious_cluster': False},\n",
+      "             'cluster_1': {'ptc_data_in_cluster': 0.40999999999999998,\n",
+      "                           'suspicious_cluster': True}},\n",
+      " 'cluster_analysis': 'smaller',\n",
+      " 'clustering_method': 'KMeans',\n",
+      " 'generator': None,\n",
+      " 'nb_clusters': 2,\n",
+      " 'nb_dims': 10,\n",
+      " 'reduce': 'PCA',\n",
+      " 'suspicious_clusters': 10}\n",
+      "{         'Class_0': {         'cluster_0': {         'ptc_data_in_cluster': 0.67000000000000004,\n",
+      "                                                      'suspicious_cluster': False},\n",
+      "                               'cluster_1': {         'ptc_data_in_cluster': 0.33000000000000002,\n",
+      "                                                      'suspicious_cluster': True}},\n",
+      "          'Class_1': {         'cluster_0': {         'ptc_data_in_cluster': 0.93999999999999995,\n",
+      "                                                      'suspicious_cluster': False},\n",
+      "                               'cluster_1': {         'ptc_data_in_cluster': 0.059999999999999998,\n",
+      "                                                      'suspicious_cluster': True}},\n",
+      "          'Class_2': {         'cluster_0': {         'ptc_data_in_cluster': 0.57999999999999996,\n",
+      "                                                      'suspicious_cluster': False},\n",
+      "                               'cluster_1': {         'ptc_data_in_cluster': 0.41999999999999998,\n",
+      "                                                      'suspicious_cluster': True}},\n",
+      "          'Class_3': {         'cluster_0': {         'ptc_data_in_cluster': 0.51000000000000001,\n",
+      "                                                      'suspicious_cluster': False},\n",
+      "                               'cluster_1': {         'ptc_data_in_cluster': 0.48999999999999999,\n",
+      "                                                      'suspicious_cluster': True}},\n",
+      "          'Class_4': {         'cluster_0': {         'ptc_data_in_cluster': 0.32000000000000001,\n",
+      "                                                      'suspicious_cluster': True},\n",
+      "                               'cluster_1': {         'ptc_data_in_cluster': 0.68000000000000005,\n",
+      "                                                      'suspicious_cluster': False}},\n",
+      "          'Class_5': {         'cluster_0': {         'ptc_data_in_cluster': 0.45000000000000001,\n",
+      "                                                      'suspicious_cluster': True},\n",
+      "                               'cluster_1': {         'ptc_data_in_cluster': 0.55000000000000004,\n",
+      "                                                      'suspicious_cluster': False}},\n",
+      "          'Class_6': {         'cluster_0': {         'ptc_data_in_cluster': 0.23000000000000001,\n",
+      "                                                      'suspicious_cluster': True},\n",
+      "                               'cluster_1': {         'ptc_data_in_cluster': 0.77000000000000002,\n",
+      "                                                      'suspicious_cluster': False}},\n",
+      "          'Class_7': {         'cluster_0': {         'ptc_data_in_cluster': 0.32000000000000001,\n",
+      "                                                      'suspicious_cluster': True},\n",
+      "                               'cluster_1': {         'ptc_data_in_cluster': 0.68000000000000005,\n",
+      "                                                      'suspicious_cluster': False}},\n",
+      "          'Class_8': {         'cluster_0': {         'ptc_data_in_cluster': 0.62,\n",
+      "                                                      'suspicious_cluster': False},\n",
+      "                               'cluster_1': {         'ptc_data_in_cluster': 0.38,\n",
+      "                                                      'suspicious_cluster': True}},\n",
+      "          'Class_9': {         'cluster_0': {         'ptc_data_in_cluster': 0.82999999999999996,\n",
+      "                                                      'suspicious_cluster': False},\n",
+      "                               'cluster_1': {         'ptc_data_in_cluster': 0.17000000000000001,\n",
+      "                                                      'suspicious_cluster': True}},\n",
+      "          'cluster_analysis': 'smaller',\n",
+      "          'clustering_method': 'KMeans',\n",
+      "          'generator': None,\n",
+      "          'nb_clusters': 2,\n",
+      "          'nb_dims': 10,\n",
+      "          'reduce': 'PCA',\n",
+      "          'suspicious_clusters': 10}\n"
+     ]
+    }
+   ],
+   "source": [
+    "report, is_clean_lst = defence.detect_poison(nb_clusters=2,\n",
+    "                                             nb_dims=10,\n",
+    "                                             reduce=\"PCA\")\n",
+    "reg_report, reg_is_clean_lst = reg_defence.detect_poison(nb_clusters=2,\n",
+    "                                             nb_dims=10,\n",
+    "                                             reduce=\"PCA\")\n",
+    "\n",
+    "print(\"Analysis completed. Report:\")\n",
+    "import pprint\n",
+    "pp = pprint.PrettyPrinter(indent=10)\n",
+    "pprint.pprint(report)\n",
+    "pp.pprint(reg_report)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Evaluate Defence"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Defense results for poisoned model:\n",
+      "class_0\n",
+      "{'FalseNegative': {'denominator': 40, 'numerator': 7, 'rate': 17.5},\n",
+      " 'FalsePositive': {'denominator': 699, 'numerator': 257, 'rate': 36.77},\n",
+      " 'TrueNegative': {'denominator': 699, 'numerator': 442, 'rate': 63.23},\n",
+      " 'TruePositive': {'denominator': 40, 'numerator': 33, 'rate': 82.5}}\n",
+      "class_1\n",
+      "{'FalseNegative': {'denominator': 43, 'numerator': 1, 'rate': 2.33},\n",
+      " 'FalsePositive': {'denominator': 758, 'numerator': 304, 'rate': 40.11},\n",
+      " 'TrueNegative': {'denominator': 758, 'numerator': 454, 'rate': 59.89},\n",
+      " 'TruePositive': {'denominator': 43, 'numerator': 42, 'rate': 97.67}}\n",
+      "class_2\n",
+      "{'FalseNegative': {'denominator': 37, 'numerator': 0, 'rate': 0.0},\n",
+      " 'FalsePositive': {'denominator': 739, 'numerator': 247, 'rate': 33.42},\n",
+      " 'TrueNegative': {'denominator': 739, 'numerator': 492, 'rate': 66.58},\n",
+      " 'TruePositive': {'denominator': 37, 'numerator': 37, 'rate': 100.0}}\n",
+      "class_3\n",
+      "{'FalseNegative': {'denominator': 37, 'numerator': 1, 'rate': 2.7},\n",
+      " 'FalsePositive': {'denominator': 715, 'numerator': 171, 'rate': 23.92},\n",
+      " 'TrueNegative': {'denominator': 715, 'numerator': 544, 'rate': 76.08},\n",
+      " 'TruePositive': {'denominator': 37, 'numerator': 36, 'rate': 97.3}}\n",
+      "class_4\n",
+      "{'FalseNegative': {'denominator': 37, 'numerator': 27, 'rate': 72.97},\n",
+      " 'FalsePositive': {'denominator': 716, 'numerator': 298, 'rate': 41.62},\n",
+      " 'TrueNegative': {'denominator': 716, 'numerator': 418, 'rate': 58.38},\n",
+      " 'TruePositive': {'denominator': 37, 'numerator': 10, 'rate': 27.03}}\n",
+      "class_5\n",
+      "{'FalseNegative': {'denominator': 39, 'numerator': 16, 'rate': 41.03},\n",
+      " 'FalsePositive': {'denominator': 648, 'numerator': 235, 'rate': 36.27},\n",
+      " 'TrueNegative': {'denominator': 648, 'numerator': 413, 'rate': 63.73},\n",
+      " 'TruePositive': {'denominator': 39, 'numerator': 23, 'rate': 58.97}}\n",
+      "class_6\n",
+      "{'FalseNegative': {'denominator': 32, 'numerator': 4, 'rate': 12.5},\n",
+      " 'FalsePositive': {'denominator': 697, 'numerator': 233, 'rate': 33.43},\n",
+      " 'TrueNegative': {'denominator': 697, 'numerator': 464, 'rate': 66.57},\n",
+      " 'TruePositive': {'denominator': 32, 'numerator': 28, 'rate': 87.5}}\n",
+      "class_7\n",
+      "{'FalseNegative': {'denominator': 42, 'numerator': 40, 'rate': 95.24},\n",
+      " 'FalsePositive': {'denominator': 728, 'numerator': 337, 'rate': 46.29},\n",
+      " 'TrueNegative': {'denominator': 728, 'numerator': 391, 'rate': 53.71},\n",
+      " 'TruePositive': {'denominator': 42, 'numerator': 2, 'rate': 4.76}}\n",
+      "class_8\n",
+      "{'FalseNegative': {'denominator': 41, 'numerator': 11, 'rate': 26.83},\n",
+      " 'FalsePositive': {'denominator': 697, 'numerator': 262, 'rate': 37.59},\n",
+      " 'TrueNegative': {'denominator': 697, 'numerator': 435, 'rate': 62.41},\n",
+      " 'TruePositive': {'denominator': 41, 'numerator': 30, 'rate': 73.17}}\n",
+      "class_9\n",
+      "{'FalseNegative': {'denominator': 43, 'numerator': 14, 'rate': 32.56},\n",
+      " 'FalsePositive': {'denominator': 712, 'numerator': 279, 'rate': 39.19},\n",
+      " 'TrueNegative': {'denominator': 712, 'numerator': 433, 'rate': 60.81},\n",
+      " 'TruePositive': {'denominator': 43, 'numerator': 29, 'rate': 67.44}}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Evaluate method when ground truth is known:\n",
+    "is_clean = np.argmax(bd, axis=1) == 0\n",
+    "confusion_matrix = defence.evaluate_defence(is_clean)\n",
+    "reg_confusion_matrix = reg_defence.evaluate_defence(is_clean)\n",
+    "\n",
+    "import json\n",
+    "jsonObject = json.loads(confusion_matrix)\n",
+    "regJsonObject = json.loads(reg_confusion_matrix)\n",
+    "print(\"Defense results for poisoned model:\")\n",
+    "for label in jsonObject:\n",
+    "    print(label)\n",
+    "    pprint.pprint(jsonObject[label]) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Defense results for regular model:\n",
+      "class_0\n",
+      "{'FalseNegative': {'denominator': 40, 'numerator': 0, 'rate': 0.0},\n",
+      " 'FalsePositive': {'denominator': 699, 'numerator': 202, 'rate': 28.9},\n",
+      " 'TrueNegative': {'denominator': 699, 'numerator': 497, 'rate': 71.1},\n",
+      " 'TruePositive': {'denominator': 40, 'numerator': 40, 'rate': 100.0}}\n",
+      "class_1\n",
+      "{'FalseNegative': {'denominator': 43, 'numerator': 0, 'rate': 0.0},\n",
+      " 'FalsePositive': {'denominator': 758, 'numerator': 9, 'rate': 1.19},\n",
+      " 'TrueNegative': {'denominator': 758, 'numerator': 749, 'rate': 98.81},\n",
+      " 'TruePositive': {'denominator': 43, 'numerator': 43, 'rate': 100.0}}\n",
+      "class_2\n",
+      "{'FalseNegative': {'denominator': 37, 'numerator': 0, 'rate': 0.0},\n",
+      " 'FalsePositive': {'denominator': 739, 'numerator': 291, 'rate': 39.38},\n",
+      " 'TrueNegative': {'denominator': 739, 'numerator': 448, 'rate': 60.62},\n",
+      " 'TruePositive': {'denominator': 37, 'numerator': 37, 'rate': 100.0}}\n",
+      "class_3\n",
+      "{'FalseNegative': {'denominator': 37, 'numerator': 0, 'rate': 0.0},\n",
+      " 'FalsePositive': {'denominator': 715, 'numerator': 333, 'rate': 46.57},\n",
+      " 'TrueNegative': {'denominator': 715, 'numerator': 382, 'rate': 53.43},\n",
+      " 'TruePositive': {'denominator': 37, 'numerator': 37, 'rate': 100.0}}\n",
+      "class_4\n",
+      "{'FalseNegative': {'denominator': 37, 'numerator': 0, 'rate': 0.0},\n",
+      " 'FalsePositive': {'denominator': 716, 'numerator': 202, 'rate': 28.21},\n",
+      " 'TrueNegative': {'denominator': 716, 'numerator': 514, 'rate': 71.79},\n",
+      " 'TruePositive': {'denominator': 37, 'numerator': 37, 'rate': 100.0}}\n",
+      "class_5\n",
+      "{'FalseNegative': {'denominator': 39, 'numerator': 0, 'rate': 0.0},\n",
+      " 'FalsePositive': {'denominator': 648, 'numerator': 269, 'rate': 41.51},\n",
+      " 'TrueNegative': {'denominator': 648, 'numerator': 379, 'rate': 58.49},\n",
+      " 'TruePositive': {'denominator': 39, 'numerator': 39, 'rate': 100.0}}\n",
+      "class_6\n",
+      "{'FalseNegative': {'denominator': 32, 'numerator': 0, 'rate': 0.0},\n",
+      " 'FalsePositive': {'denominator': 697, 'numerator': 139, 'rate': 19.94},\n",
+      " 'TrueNegative': {'denominator': 697, 'numerator': 558, 'rate': 80.06},\n",
+      " 'TruePositive': {'denominator': 32, 'numerator': 32, 'rate': 100.0}}\n",
+      "class_7\n",
+      "{'FalseNegative': {'denominator': 42, 'numerator': 0, 'rate': 0.0},\n",
+      " 'FalsePositive': {'denominator': 728, 'numerator': 208, 'rate': 28.57},\n",
+      " 'TrueNegative': {'denominator': 728, 'numerator': 520, 'rate': 71.43},\n",
+      " 'TruePositive': {'denominator': 42, 'numerator': 42, 'rate': 100.0}}\n",
+      "class_8\n",
+      "{'FalseNegative': {'denominator': 41, 'numerator': 0, 'rate': 0.0},\n",
+      " 'FalsePositive': {'denominator': 697, 'numerator': 211, 'rate': 30.27},\n",
+      " 'TrueNegative': {'denominator': 697, 'numerator': 486, 'rate': 69.73},\n",
+      " 'TruePositive': {'denominator': 41, 'numerator': 41, 'rate': 100.0}}\n",
+      "class_9\n",
+      "{'FalseNegative': {'denominator': 43, 'numerator': 0, 'rate': 0.0},\n",
+      " 'FalsePositive': {'denominator': 712, 'numerator': 87, 'rate': 12.22},\n",
+      " 'TrueNegative': {'denominator': 712, 'numerator': 625, 'rate': 87.78},\n",
+      " 'TruePositive': {'denominator': 43, 'numerator': 43, 'rate': 100.0}}\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Defense results for regular model:\")\n",
+    "for label in regJsonObject:\n",
+    "    print(label)\n",
+    "    pprint.pprint(regJsonObject[label]) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Clusters for class 9.\n",
+      "In the clustering for an undefended model, it is easier to detect backdoored examples\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 1800x1800 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def plot_class_clusters(n_class, n_clusters, defense):\n",
+    "    sprites_by_class = defence.visualize_clusters(data, save=False)\n",
+    "    for q in range(n_clusters):\n",
+    "        plt.figure(1, figsize=(25,25))\n",
+    "        plt.tight_layout()\n",
+    "        plt.subplot(1, n_clusters, q+1)\n",
+    "        plt.title(\"Class \"+ str(n_class)+ \", Cluster \"+ str(q), fontsize=40)\n",
+    "        sprite = sprites_by_class[n_class][q]\n",
+    "        plt.imshow(sprite, interpolation='none')\n",
+    "        \n",
+    "\n",
+    "\n",
+    "# Visualize clusters for class 1\n",
+    "print(\"Clusters for class 9.\")\n",
+    "print(\"In the clustering for an undefended model, it is easier to detect backdoored examples\")\n",
+    "plot_class_clusters(9, 2, reg_defence)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Meanwhile in the poisoned model, poison examples are stuck in both clusters\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 1800x1800 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "print(\"Meanwhile in the poisoned model, poison examples are stuck in both clusters\")\n",
+    "plot_class_clusters(9, 2, defence)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "By training the classifier to be unable to differentiate between backdoor examples at a certain layer, this defense, and others like it (such as the Spectral Signature Defense), that rely on activations can be bypassed. This attack also illustrates another way malicious attackers can backdoor a model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/adversarial-robustness-toolbox/notebooks/poisoning_attack_feature_collision.ipynb b/adversarial-robustness-toolbox/notebooks/poisoning_attack_feature_collision.ipynb
new file mode 100644
index 0000000..ac3d0a8
--- /dev/null
+++ b/adversarial-robustness-toolbox/notebooks/poisoning_attack_feature_collision.ipynb
@@ -0,0 +1,408 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Clean-Label Feature Collision Attacks on a Keras Classifier"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this notebook, we will learn how to use ART to run a clean-label feature collision poisoning attack on a neural network trained with Keras. We will be training our data on a subset of the CIFAR-10 dataset. The methods described are derived from [this paper](https://arxiv.org/abs/1804.00792) by Shafahi, Huang, et. al. 2018."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using TensorFlow backend.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os, sys\n",
+    "from os.path import abspath\n",
+    "\n",
+    "module_path = os.path.abspath(os.path.join('..'))\n",
+    "if module_path not in sys.path:\n",
+    "    sys.path.append(module_path)\n",
+    "\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "\n",
+    "from keras.models import load_model\n",
+    "\n",
+    "from art import config\n",
+    "from art.utils import load_dataset, get_file\n",
+    "from art.estimators.classification import KerasClassifier\n",
+    "from art.attacks.poisoning import FeatureCollisionAttack\n",
+    "\n",
+    "import numpy as np\n",
+    "\n",
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "np.random.seed(301)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "(x_train, y_train), (x_test, y_test), min_, max_ = load_dataset('cifar10')\n",
+    "\n",
+    "num_samples_train = 1000\n",
+    "num_samples_test = 1000\n",
+    "x_train = x_train[0:num_samples_train]\n",
+    "y_train = y_train[0:num_samples_train]\n",
+    "x_test = x_test[0:num_samples_test]\n",
+    "y_test = y_test[0:num_samples_test]\n",
+    "\n",
+    "class_descr = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load Model to be Attacked\n",
+    "\n",
+    "In this example, we using a RESNET50 model pretrained on the CIFAR dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path = get_file('cifar_alexnet.h5',extract=False, path=config.ART_DATA_PATH,\n",
+    "                url='https://www.dropbox.com/s/ta75pl4krya5djj/cifar_alexnet.h5?dl=1')\n",
+    "classifier_model = load_model(path)\n",
+    "classifier = KerasClassifier(clip_values=(min_, max_), model=classifier_model, use_logits=False, \n",
+    "                             preprocessing=(0.5, 1))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Choose Target Image from Test Set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "true_class: bird\n",
+      "predicted_class: bird\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "target_class = \"bird\" # one of ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
+    "target_label = np.zeros(len(class_descr))\n",
+    "target_label[class_descr.index(target_class)] = 1\n",
+    "target_instance = np.expand_dims(x_test[np.argmax(y_test, axis=1) == class_descr.index(target_class)][3], axis=0)\n",
+    "\n",
+    "fig = plt.imshow(target_instance[0])\n",
+    "\n",
+    "print('true_class: ' + target_class)\n",
+    "print('predicted_class: ' + class_descr[np.argmax(classifier.predict(target_instance), axis=1)[0]])\n",
+    "\n",
+    "feature_layer = classifier.layer_names[-2]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Poison Training Images to Misclassify Test\n",
+    "\n",
+    "The attacker wants to make it such that whenever a prediction is made on this particular cat the output will be a horse."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "New test data to be poisoned (10 images):\n",
+      "Correctly classified: 9\n",
+      "Incorrectly classified: 1\n"
+     ]
+    }
+   ],
+   "source": [
+    "base_class = \"frog\" # one of ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
+    "base_idxs = np.argmax(y_test, axis=1) == class_descr.index(base_class)\n",
+    "base_instances = np.copy(x_test[base_idxs][:10])\n",
+    "base_labels = y_test[base_idxs][:10]\n",
+    "\n",
+    "x_test_pred = np.argmax(classifier.predict(base_instances), axis=1)\n",
+    "nb_correct_pred = np.sum(x_test_pred == np.argmax(base_labels, axis=1))\n",
+    "\n",
+    "print(\"New test data to be poisoned (10 images):\")\n",
+    "print(\"Correctly classified: {}\".format(nb_correct_pred))\n",
+    "print(\"Incorrectly classified: {}\".format(10-nb_correct_pred))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 9 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10,10))\n",
+    "for i in range(0, 9):\n",
+    "    pred_label, true_label = class_descr[x_test_pred[i]], class_descr[np.argmax(base_labels[i])]\n",
+    "    plt.subplot(330 + 1 + i)\n",
+    "    fig=plt.imshow(base_instances[i])\n",
+    "    fig.axes.get_xaxis().set_visible(False)\n",
+    "    fig.axes.get_yaxis().set_visible(False)\n",
+    "    fig.axes.text(0.5, -0.1, pred_label + \" (\" + true_label + \")\", fontsize=12, transform=fig.axes.transAxes, \n",
+    "                  horizontalalignment='center')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The captions on the images can be read: `predicted label (true label)`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Creating Poison Frogs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 10/10 [00:01<00:00,  9.76it/s]\n",
+      "100%|██████████| 10/10 [00:00<00:00, 16.56it/s]\n",
+      "100%|██████████| 10/10 [00:00<00:00, 15.97it/s]\n",
+      "100%|██████████| 10/10 [00:00<00:00, 17.06it/s]\n",
+      "100%|██████████| 10/10 [00:00<00:00, 16.40it/s]\n",
+      "100%|██████████| 10/10 [00:00<00:00, 15.85it/s]\n",
+      "100%|██████████| 10/10 [00:00<00:00, 17.06it/s]\n",
+      "100%|██████████| 10/10 [00:00<00:00, 15.25it/s]\n",
+      "100%|██████████| 10/10 [00:00<00:00, 15.91it/s]\n",
+      "100%|██████████| 10/10 [00:00<00:00, 16.80it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "attack = FeatureCollisionAttack(classifier, target_instance, feature_layer, max_iter=10, similarity_coeff=256, watermark=0.3)\n",
+    "poison, poison_labels = attack.poison(base_instances)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 9 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "poison_pred = np.argmax(classifier.predict(poison), axis=1)\n",
+    "plt.figure(figsize=(10,10))\n",
+    "for i in range(0, 9):\n",
+    "    pred_label, true_label = class_descr[poison_pred[i]], class_descr[np.argmax(poison_labels[i])]\n",
+    "    plt.subplot(330 + 1 + i)\n",
+    "    fig=plt.imshow(poison[i])\n",
+    "    fig.axes.get_xaxis().set_visible(False)\n",
+    "    fig.axes.get_yaxis().set_visible(False)\n",
+    "    fig.axes.text(0.5, -0.1, pred_label + \" (\" + true_label + \")\", fontsize=12, transform=fig.axes.transAxes, \n",
+    "                  horizontalalignment='center')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice how the network classifies most of theses poison examples as frogs, and it's not incorrect to do so. The examples look mostly froggy. A slight watermark of the target instance is also added to push the poisons closer to the target class in feature space."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Training with Poison Images"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1000, 32, 32, 3)\n",
+      "(10, 32, 32, 3)\n",
+      "Epoch 1/5\n",
+      "252/252 [==============================] - 34s 136ms/step - loss: 0.4635 - acc: 0.92061s - loss: 0.4711 - a\n",
+      "Epoch 2/5\n",
+      "252/252 [==============================] - 26s 101ms/step - loss: 0.3405 - acc: 0.9325\n",
+      "Epoch 3/5\n",
+      "252/252 [==============================] - 26s 102ms/step - loss: 0.2121 - acc: 0.9534\n",
+      "Epoch 4/5\n",
+      "252/252 [==============================] - 26s 102ms/step - loss: 0.1950 - acc: 0.9742\n",
+      "Epoch 5/5\n",
+      "252/252 [==============================] - 30s 118ms/step - loss: 0.1888 - acc: 0.9712\n"
+     ]
+    }
+   ],
+   "source": [
+    "classifier.set_learning_phase(True)\n",
+    "print(x_train.shape)\n",
+    "print(base_instances.shape)\n",
+    "adv_train = np.vstack([x_train, poison])\n",
+    "adv_labels = np.vstack([y_train, poison_labels])\n",
+    "classifier.fit(adv_train, adv_labels, nb_epochs=5, batch_size=4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Fooled Network Misclassifies Bird"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "true_class: bird\n",
+      "predicted_class: frog\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.imshow(target_instance[0])\n",
+    "\n",
+    "print('true_class: ' + target_class)\n",
+    "print('predicted_class: ' + class_descr[np.argmax(classifier.predict(target_instance), axis=1)[0]])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These attacks allow adversaries who can poison your dataset the ability to mislabel any particular target instance of their choosing without manipulating labels."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/adversarial-robustness-toolbox/notebooks/poisoning_attack_svm.ipynb b/adversarial-robustness-toolbox/notebooks/poisoning_attack_svm.ipynb
new file mode 100644
index 0000000..be6ad49
--- /dev/null
+++ b/adversarial-robustness-toolbox/notebooks/poisoning_attack_svm.ipynb
@@ -0,0 +1,329 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Adversarial Robustness Toolbox for Poisoning Attacks on Support Vector Machines (SVM) using Scikitlearn's SVC"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this notebook, we will learn how to use ART to run a poisoning attack on Support Vector Machines. We will be training our data on a subset of the IRIS dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<img src=\"../utils/data/images/svm_poly.gif\">"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import HTML\n",
+    "HTML('<img src=\"../utils/data/images/svm_poly.gif\">')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from __future__ import absolute_import, division, print_function, unicode_literals\n",
+    "\n",
+    "import os, sys\n",
+    "from os.path import abspath\n",
+    "\n",
+    "module_path = os.path.abspath(os.path.join('..'))\n",
+    "if module_path not in sys.path:\n",
+    "    sys.path.append(module_path)\n",
+    "\n",
+    "from sklearn.svm import SVC, LinearSVC\n",
+    "from sklearn.datasets import load_iris\n",
+    "\n",
+    "import numpy as np\n",
+    "from matplotlib import pyplot as plt\n",
+    "\n",
+    "from art.estimators.classification import SklearnClassifier\n",
+    "from art.attacks.poisoning.poisoning_attack_svm import PoisoningAttackSVM\n",
+    "\n",
+    "np.random.seed(301)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Utility Functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def find_duplicates(x_train):\n",
+    "    \"\"\"\n",
+    "    Returns an array of booleans that is true if that element was previously in the array\n",
+    "\n",
+    "    :param x_train: training data\n",
+    "    :type x_train: `np.ndarray`\n",
+    "    :return: duplicates array\n",
+    "    :rtype: `np.ndarray`\n",
+    "    \"\"\"\n",
+    "    dup = np.zeros(x_train.shape[0])\n",
+    "    for idx, x in enumerate(x_train):\n",
+    "        dup[idx] = np.isin(x_train[:idx], x).all(axis=1).any()\n",
+    "    return dup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_data():\n",
+    "    iris = load_iris()\n",
+    "    X = iris.data\n",
+    "    y = iris.target\n",
+    "    \n",
+    "    X = X[y != 0, :2]\n",
+    "    y = y[y != 0]\n",
+    "    labels = np.zeros((y.shape[0], 2))\n",
+    "    labels[y == 2] = np.array([1, 0])\n",
+    "    labels[y == 1] = np.array([0, 1])\n",
+    "    y = labels\n",
+    "    \n",
+    "    n_sample = len(X)\n",
+    "    \n",
+    "    order = np.random.permutation(n_sample)\n",
+    "    X = X[order]\n",
+    "    y = y[order].astype(np.float)\n",
+    "    \n",
+    "    X_train = X[:int(.9 * n_sample)]\n",
+    "    y_train = y[:int(.9 * n_sample)]\n",
+    "    train_dups = find_duplicates(X_train)\n",
+    "    X_train = X_train[train_dups == False]\n",
+    "    y_train = y_train[train_dups == False]\n",
+    "    X_test = X[int(.9 * n_sample):]\n",
+    "    y_test = y[int(.9 * n_sample):]\n",
+    "    test_dups = find_duplicates(X_test)\n",
+    "    X_test = X_test[test_dups == False]\n",
+    "    y_test = y_test[test_dups == False]\n",
+    "    return X_train, y_train, X_test, y_test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_adversarial_examples(x_train, y_train, attack_idx, x_val, y_val, kernel):\n",
+    "    # Create ART classfier for scikit-learn SVC\n",
+    "    art_classifier = SklearnClassifier(model=SVC(kernel=kernel), clip_values=(0, 10))\n",
+    "    art_classifier.fit(x_train, y_train)\n",
+    "    init_attack = np.copy(x_train[attack_idx])\n",
+    "    y_attack = np.array([1, 1]) - np.copy(y_train[attack_idx])\n",
+    "    attack = PoisoningAttackSVM(art_classifier, 0.001, 1.0, x_train, y_train, x_val, y_val, max_iter=100)\n",
+    "    final_attack, _ = attack.poison(np.array([init_attack]), y=np.array([y_attack]))\n",
+    "    return final_attack, art_classifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_results(model, x_train, y_train, x_train_adv, title):\n",
+    "    import matplotlib.pyplot as plt\n",
+    "    import warnings\n",
+    "    warnings.filterwarnings(\"ignore\")\n",
+    "\n",
+    "    plt.figure()\n",
+    "    plt.clf()\n",
+    "\n",
+    "    get_color = lambda idx: 'orange' if np.argmax(idx) == 1 else 'blue'\n",
+    "    for i_class_2 in [np.array([0, 1]), np.array([1, 0])]:\n",
+    "        mask = np.all(y_train == i_class_2, axis=1)\n",
+    "        plt.scatter(x_train[mask][:, 0], x_train[mask][:, 1], s=20, zorder=2, c=get_color(i_class_2))\n",
+    "    # plt.axes.set_aspect('equal', adjustable='box')\n",
+    "\n",
+    "    for sv in model.support_vectors_:\n",
+    "        plt.scatter(sv[0], sv[1], s=200, linewidth=1, facecolors='none', edgecolors='lightgreen',\n",
+    "                             zorder=2)\n",
+    "    h = .01\n",
+    "    x_min, x_max = 1.5, 8.5\n",
+    "    y_min, y_max = 0, 7\n",
+    "\n",
+    "    xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n",
+    "\n",
+    "    Z = model.decision_function(np.c_[xx.ravel(), yy.ravel()])\n",
+    "    Z = Z.reshape(xx.shape)\n",
+    "    plt.pcolormesh(xx, yy, Z > 0, cmap=plt.cm.Paired)\n",
+    "    plt.contour(xx, yy, Z, colors=['k', 'k', 'k'],\n",
+    "                linestyles=['--', '-', '--'], levels=[-.5, 0, .5])\n",
+    "\n",
+    "    x_values = []\n",
+    "    y_values = []\n",
+    "    for adv in x_train_adv:\n",
+    "        x_values.append(adv[0, 0])\n",
+    "        y_values.append(adv[0, 1])\n",
+    "    x_values = np.array(x_values)\n",
+    "    y_values = np.array(y_values)\n",
+    "    plt.scatter(x_values, y_values, zorder=2,\n",
+    "                         c='red', marker='X')\n",
+    "    plt.axes().set_xlim((x_min, x_max))\n",
+    "    plt.axes().set_ylim((y_min, y_max))\n",
+    "\n",
+    "    plt.axes().set_title(title)\n",
+    "    plt.axes().set_xlabel('feature 1')\n",
+    "    plt.axes().set_ylabel('feature 2')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load Data\n",
+    "\n",
+    "In this example, we take two features from the IRIS dataset and train an SVM."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_data, train_labels, test_data, test_labels = get_data()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Visualize Effect of Attack on SVM\n",
+    "\n",
+    "After training the SVM on just one attack point, a noticable change occurs in the decision boundary for the classifier."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Clean model accuracy on train set (68 samples): 0.7352941176470589\n",
+      "Poison model accuracy on train set (68 samples): 0.7352941176470589\n",
+      "Clean model accuracy on test set (10 samples): 0.6\n",
+      "Poison model accuracy on test set (10 samples): 0.6\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXgAAAEWCAYAAABsY4yMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nOzdd3hUVfrA8e+50yczk17pPYTem4CAoNjbYi9rXwvqYv+5K6C4dndRV8GColgAF1RQAVFUuvQeek3vbfqc3x8TFJGSO5JA4HyeJ4/JzH3vuYzw5syZc99XSClRFEVRTj/ayb4ARVEUpXaoBK8oinKaUgleURTlNKUSvKIoymlKJXhFUZTTlErwiqIopymV4JUzkhAiWQjxkxCiXAjx8sm+nkMJIRoLISqEEIaTfS1K/aYSvFKrhBBnCSEWCyFKhRBFQohFQogeQog+QohKIYTzCDGrhRD3CiGaCiGkEGLVYc8nCCF8QojdxxlbCCF2CiE2HeHpO4ACwCWlHCWEGC2E+OjP/FmPMP5oIYS/OlmXVL8OfY4XJ6XcK6V0SCmDJ/J6lDOPSvBKrRFCuIBZwGtAHNAAGAN4pZRLgP3AFYfFtAcygE8OeTiq+vGDrgV21eASBgBJQHMhRI/DnmsCbJIn6E4/IYTxKE99JqV0AInAQuB/QghxIsZUlONRCV6pTa0BpJSfSCmDUkq3lHKulHJd9fMfADceFnMjMFtKWXjIYx8CNx12zOQajH8T8AXw9aHxQoj3q39+pHp2fSHwBHBV9c9rq4+LFkK8K4TIFkIcEEI8c3DZRAhxc/W7kVeFEEXA6GNdiJTSX/3nTQHihRCaEOJJIcQeIUSeEGKyECK6+twH37kYDxlrZ/Vy0i4hxHXVj9fkHDcJIfYKIQqEEP9Xg9dMOY2oBK/Upq1AUAjxgRBiuBAi9rDnPwT6CyEaQzhhEZ6dH568PwKuFkIYhBBtASew7FgDCyHswJXAlOqvq4UQZgAp5c3Vj71QvRQyC3iW6tm2lLJT9Wk+AAJAS6ALMAy47ZBhegE7Cb9LGHec67EANwP7pZQF1d/fDAwCmgMO4PUjxEUB44HhUkon0BdYU/10Tc5xFtAGGAL8s/r1U84QKsErtUZKWUY4wUjgbSBfCPGlECK5+vl9wI/A9dUhQwArMPuwU+0HMoFzCM+8azJ7vxzwAnMJLxMZgQtqeu3V1zgceEBKWSmlzANeBa4+5LAsKeVrUsqAlNJ9lFONEEKUAPuAbsCl1Y9fB7wipdwppawAHif8S+hISz0hoL0QwialzJZSbtRxjjHV75zWAmuBTihnDJXglVolpdwspbxZStkQaA+kAf8+5JBDl2luAD6uXs443GTCs9VrCM/oj+cmYGp18vUC/+P3yzzH0wQwAdnVH5CWABMIz9YP2leD80yVUsZIKZOklIOllCurH08D9hxy3B7Cv4SSDw2WUlYCVwF3VV/LbCFEuo5z5BzyfRXhWb5yhlAJXqkzUsotwPuEE/1B/wMaCCEGEZ51H212/jnhGfhOKeWeoxwDgBCiITAYuF4IkSOEyCG8XHO+ECLhaJd32M/7CL8DSKhO0DFSSpeUst0xYvTIIvxL5KDGhJeDcv9wYVLOkVIOBVKBLYTfDek6h3JmUgleqTVCiHQhxKjqhIsQohHhGfjSg8dUz1CnA5OAPVLKFUc6V/Vxg/n9GvjR3EB4/b8N0Ln6qzXhpZ5rjhKTCzSt/hwAKWU24eWdl4UQruoPNFsIIQbWYPya+AR4UAjRTAjh4LfPAAKHHiTC+/Uvrl6L9wIVQFDPOZQzl0rwSm0qJ/xB5DIhRCXhxL4BGHXYcR8Qnokec21dSrlCSrmjBuPeBPxXSplz6BfwFkdfpplW/d/CQ/bd3wiYgU1AMeFfRKk1GL8m3iP8IfNPhLd8eoD7jnCcRvj1ygKKgIHA3TrPoZyhhGr4oSiKcnpSM3hFUZTTVK0leCFEGyHEmkO+yoQQD9TWeIqiKMrv1ckSTfXdfweAXsfbAaEoiqKcGHW1RDME2KGSu6IoSt05WoGkE+1qfl886ldCiDsIV/bDahDdGkRb6uiSlFOdPxgiGAKrKYJ5iJSUWOKx2OxYrLaIxndXVmAyWzCaTBHFK0pd2LV5fYGUMvFIz9X6Ek11/Y8soJ2U8pg3YLSMt8lXzm1aq9ej1A9SSh6Zt4eCqgCvnd8Mh1lfafRKX5C7fyzFFRvPMx/Nwmgy64ovLylm5AW9Se/ai0fGf4AqAKmcqq7t2millLL7kZ6riyWa4cCq4yV3RTmUEII7uiVT4gkwaXWe7vgos4G725rYu20zX7z3hu54Z0wsV9/3GGsX/cBPX007foCinILqIsFfw1GWZxTlWFrF27gsPY7vdpayKrtCd3yvhk4GNHEx893X2Lt1s+74oSNuok3nHnz48liK83OOH6Aop5haTfDVJVuHEq43oii6Xd0hgYYuM28sz6HKr7/B0e3dkohyRfPW6FEEA/ru4Nc0jTtGv4zf5+HdcY+jbgpU6ptaTfBSyiopZbyUsrQ2x1FOX2aDxsheqfRq4ECLYB3cZTFyV1szu7es56sP3tQdn9q4Gdc/+A869jlRJWgUpe7U1S4aRYlYmwQbbRIi2wkD0K+xi757y/nfxH/TfdC5NGzeWlf80BF6qgwryqlDlSpQ6o1thW7GLtiH2x/SHXtn92RsIsiE0aMIBSPrZb3w6xm8/fQjEcUqysmgErxSb/hDklXZlXy4Vv+umhirkTu6JbNjwxq+nvL28QOOoCg3ix9mfMLSebMiileUuqYSvFJvZCTauaB1LLO3lbAxr0p3fP8mTno1cDDtzZfI3rNTd/wFN9xJ84yOvP/ck5QVF+mOV5S6phK8Uq/c0CmRFIeJ15Zl4w3oW6oRQnBXjxQs0s+EMQ8RCumLNxiN3Dn6ZSrLy/jghX/oilWUk0EleKVesRo17umZQnaFn2+3l+iOj7MZubVrMlvX/MKcTyfpjm/UMp3Lbh/JkjlfsmPjGt3xilKX1C4apd7pmBzF6LMb0THZHlH8oKYufjS2Zerrz9O1/xCSGzXVFX/xzffQqkNXWrTrHNH4ilJX1AxeqZe6pEZh0ARl3gC+oP6lmvvi96AZjUwc+7DupRqjyUSH3gMAKMzN1hWrKHVJJXil3irzBrl39i4+WV+gOzbBbuLWjCg2r1zK/OkfRjT+5pVLeOCifqz+eX5E8YpS21SCV+otl8VAjwYOZm4pYluhW3f8Oc2j6Zxi55Px/yI/a5/u+FYdu5HWtDnvPPMoleXqZm3l1KMSvFKv3dIliVirkfHLsvFHsFRzT89UAN5++lHdtWaMJjN3jn6ZksJ8przytK5YRakLKsEr9VqU2cDdPVPYW+pj6sZC3fFJUSZuznCwYdnPLJj5qe745hmduPDGu1jwxWesW/Kj7nhFqU0qwSv1Xvc0B4Oauthf5ouo4uO5LWNon2Tno1efpjAnS3f8FXc+SKOWbdi3I1N3rKLUpjppul1TqqOTEil/MIRRExF3Xsqp8DFyXjbp3XpH1MHJ7/NiMqt2k0rdO9kdnRSl1pkMGkIIsst9LNil/wPPFIeZGzKcrF30Az/Pmq5//Orkvn7Zz2xZvVx3vKLUBpXgldPK1I2FvLY8m90lHt2xF7SOJSPRxuSXxkTUwSkYCPD+c0/y5j8fxFNVqTteUU40leCV08pfuyQSZTIwflkOwZC+5UdNCO7tmYrf5+G9Z5/QvZ5vMBq5/R8vUJC1j09fe05XrKLUBpXgldOKy2Lkzu7J7CjyMGOz/oqPDVxmrm/rYuWP81j87Uzd8eldezHsqpuZ+9n7bFm1THe8opxIKsErp51+jV30beTkkw0F7Cv16o6/qE0cbeKtfPDCPyktzNcdf9V9j5HYoDETxjyEz6P/BixFOVFUgldOS3d2T+biNrEk2E26Yw2a4L5eqXjdbiY996TueKvNzp1PvcT519+OUe2sUU4ileCV01KM1chNnZOwmbSI9sY3irZwdVsXy+d/HVEHp4zufRj6lxvRtMjGV5QTQSV45bS2u8TDg9/u5kCZT3fsZelxtIiz/qkOTsvnf83ov16Gz6t/V4+i/Fm1muCFEDFCiOlCiC1CiM1CiD61OZ6iHM5lMZJX6ee1ZdmE9O6K0QQje6X8qQ5OVnsU29at5PMJr0YUryh/Rm3P4P8DfCulTAc6AZtreTxF+Z04m5HbuiazucDN7K3FuuObxlgZ0TaaJXO+5JcfvtUd37HPQM6+9GpmTX5LdYBS6lytJXghhAsYALwLIKX0SSn191hTlD9pUDMX3dKi+HBtPtnl+pdqrsyIp0nrDCb96/+oKNX/S+L6B/9BbEISE0aPwu/Tv6tHUSJVmzP45kA+MEkIsVoI8Y4QIurwg4QQdwghVgghVpR5ArV4OcqZSgjB3T1SMGiCryKYxRs1wQPNqigvKeLDl8fqjrc7Xdz6f8+xf8dWVv30ne54RYlUrRUbE0J0B5YC/aSUy4QQ/wHKpJRHXcxUxcaU2rS72EOjaAsGLbKCZFPW5TN1YyEP/+d9uvQfon/8LRtomt4+orEV5WhOVrGx/cB+KeXB2/mmA11rcTxFOaamsdZf+7gWVvl1x49oF0+TaAvvjnssog5OB5P7gZ3bCPj1j68oetVagpdS5gD7hBBtqh8aAmyqrfEUpSYCIcnDc/cwflm27v3pJoPGfb1SKC7IY8qrz0Q0fvbeXTx29bl8OemNiOIVRY/a3kVzHzBFCLEO6Aw8W8vjKcoxGTXBpelxrMmpYt5O/bPwVvE2LmsTw4KZn0bUwSm1cTN6DhnOjHfGs3eb2lSm1K5aTfBSyjVSyu5Syo5SykullPo/4VKUE+xgB6dJq/PIr9S/VHJNhwTSmrbknacfpaqiXHf8TY88TZTTxYTRowgG1MYCpfaoO1mVM064LHAKwZDkzV9ydC/VmA0a97fyU5ibxSfj9b8pdcXGcfNjz7Br83pmfzhBd7yi1JRK8MoZKdVp5oZOiRgNAl9Q/06y9AQbF7eOYf70j9j4y2Ld8b2HXkjvYRfh9+nfl68oNaV6sipnrIN/9yPt4+oNhLj/m10EolN5fuo8rDa77vEjHVtRDlI9WRXlCIQIN+nOKvfx8fp83Us1FqPGfb1SyT+wl88i6OB0MLmvX/YzC2Z+qjteUY5HJXjljPfLgQo+21DIT3vKdMe2S7JzQevYP9XBae5n7zPp+SfJ3rMzonhFORqV4JUz3oWtY2kdb+XtlXmUuPXvarmhYyJJdiMTxjyE162/g9Mtj4/DZLYyccxDhEIh3fGKcjQqwStnvHBZ4FTcgRATVubqjreZNO7tlULuvt1Me/NF3fGxiSnc+NBTZK75hbmfva87XlGORiV4RSHcwema9gks3lfOsv3697Z3TI7ivJYxfDPlHbatW6U7vv+FV9Kp3yA+e+05CnOydMcrypEYT/YFKMqp4rK2cWgCOqX8oehpjdzUOZFfKuxMGD2KZz/5BrPFWuNYIQS3/d9zrF44n9iklIjGV5TDqRm8olQzaILLM+KxGjUCIf3bh+0mA/elG8javT2iDk7xKWmcc+UNaJqmipEpJ4RK8IpymNwKH/d9vSuipZouqVGc0zya2R9OYOemtRGNv3bxAh685Czys/ZHFK8oB6kEryiHibebsBgEb/6SQ7k3qDv+r12SiI5LYMLoUQT8+u9UTWvagorSEt555hHde/MV5VAqwSvKYYzVu2pKvUHeXaV/V43DbODetib2bc9kxjvjdccnpjXi2vufYP3Sn1nwxWe64xXlIJXgFeUImsdZuTIjnh92l7Eiq0J3fPcGDs5u6uLLSW+wO3Oj7vghV95A2269+eiVsRTmZuuOVxRQCV5RjmpEu3gaR5v5bkdkveJv65qMMyaueqlG34emmqZxxz9fJBQIsHz+1xGNrygqwSvKUZgMGqPPbsTD/RpEFO+0GPhbWxN7Mjfy5fv6OzglN2rKi5//wPBrb41ofEVRCV5RjiHebsKgCUo9AXYWeXTH927opH9jJzPejqyDU0Jq+JfL3m2bKc7X/3mAcmZTCV5RauC5hQd49uf9VPn176q5o3syUU4XE8c8FFEHp6qKcsbcegXv/esJtatG0UUleEWpgZs6J1FQFWDymnzdsS6LkbsyLOzctC6iDk52h5PLbhvJygVzWTLnC93xyplLJXhFqYH0BBsXt4nlm+0lrMut1B3ft5GTPo2cfD7hVQ7s3KY7/vzrbqdl+y68/8I/KS0q0B2vnJlUgleUGrquYyKpDhOvL8vBE9BX1lcIwV3dkrHiZ8KYhwgF9S31aAYDd4x+CU9lJZOee1JXrHLmUgleUWroYAen5nHWiPq4xtiM3NYtme3rV/HNJ+/qjm/YvDV/ufthUhs3U3XjlRqp1Z6sQojdQDkQBAJH6xt4kOrJqpzupJSM+/kAawr8PPfZPFIbNzvZl6TUcye7J+sgKWXn4yV3RalPssp9vLokC28ESzV/656MOeRn4uhREc/E1y/9iSmvPhNRrHLmUEs0ihKBgko/C3aX8fF6/R94xttN3No16U91cNq6diWzP5zAigVzIopXzgy1neAlMFcIsVIIcceRDhBC3CGEWCGEWFHm0b9HWFFOho4pUZzbMoYvM4vILNDfh3Vws+hfOzjl7t+jO/6SW+6hcau2vPfsE1SURVZKQTn91XaC7yel7AoMB+4RQgw4/AAp5UQpZXcpZXeXVTWYUuqPmzsnEmczMn5ZNr6g/qWa++L3ohmNvD32Ed1LNUaTmbvGvEJZcSEfvTxGV6xy5qjVBC+lzKr+bx4wA+hZm+MpSl2ymwzc0zOF/WU+vsws1h2fGGXilrZRbFqxmO//N0V3fNP09lx889389NV0Mtf8ojteOf3V2pRZCBEFaFLK8urvhwFja2s8RTkZuqY6GNUnjZ4NHRHFD20RzcK9ZXz873F06juIxLSGuuIvu/1+0pq2pHUntYdB+aPanMEnAwuFEGuB5cBsKeW3tTieopwUA5q6sBo1vIEQfp3744UQ3NMzBSllRB2cTGYLZ11wOUIIqsrLdMUqp79aS/BSyp1Syk7VX+2klONqayxFOdkqfUEe+HY3Uzfq31WT7DBzczvnn+rgtH3DakZe0If1S3+KKF45PaltkopyAkSZDbSOt/L5pkJ2FusvK3xeyxjaJ9ki7uDUuGU60QmJvD32EdyV+jtQKacnleAV5QS5rWsyTouB8UuzCYT0LbVoQnBvz1SCAT/vjntM91KN2WrjzqdeojA3i0/GP6srVjl9qQSvKCeI02Lgru4p7Crx8vmmQt3xqU4zN7R1sWbh9/w8+3Pd8a07dee8a2/lu2kfsmnFEt3xyulHJXhFOYH6NAp3cFqbU0kogjpPF7SOJT3BxocvjY6og9OIux8huVFTtRavALVcbEwvVWxMOR1U+YNYDBoGTUQUv7/MywPfZdGxz0D+/vI7CKHvPFXlZdidrojGVuqfk11sTFHOKHaT4dc+rssPlOuOb+iycG1bV7iD09wv9Y9fndz3bt3Mzk1rdccrpw+V4BWllkxem88LC7PYV+rVHXtJmzhaxVt5//l/RNTBKRQM8p9H7+L1J+7D59FfK0c5PagEryi15IaOiViNgteWZRPUuavGoAlG9krFU1nJ+8//Q/fYmsHALY8/S87eXUx782Xd8crpQSV4RaklBzs4ZRZ6mLVVf62axtEWrm7rYtm8WSz7brbu+HY9+zHkiuv5esrbbFu3Sne8Uv+pBK8otWhgExc9Gjj4aF0+WeU+3fGXt42nRayFSc89SVlxke74a+5/grikFCaOeQifV/8NWEr9phK8otSigx2c+jVyYjfq/+d2cKmmsqyEyS89pTve7nBy25PP02Pwebp34yj1n0rwilLL4u0mHuiTRowtsuKtTWOtjEiPYfE3M1n541zd8Z36ns2Iex7BZLZENL5Sf6kEryh1JLfCx1M/7COnQv9SzRUZ8TRu1ZZ3xz0ecQenjcsX8eLImwn49Y+v1E8qwStKHdGEILPAzevLcnTf5WoyCB5o7v5THZy8HjerF85n5juvRRSv1D8qwStKHUmMMnFLlyTW51UxZ7v+WXiLOCtXpMfw01fTWbPoB93xXQecw1nnX84Xk15nd+ZG3fFK/aMSvKLUoaEtoumYbOf9NfnkVfp1x1/VLp5GLjPvPPNoRA0+bnhoNI7oWCaMHkXAr398pX5RCV5R6pAQgnt7pgCSzzbov0PVZNAY2TuV4vxcpvz7Gd3xzphYbnl8HHsyN7J4zhe645X6RSV4RaljyQ4zT53diNu7JUcU3zrexmVtYvhhxicRVY3sMXg4j73xEf0vuCKi8ZX6QyV4RTkJMhLtv/ZxLfMGdMdf3T6B1KYtePvpRyPq4NSxz0CEEBTn5xAM6B9fqR9UgleUkyQYkjw6bw//WZqtu4OTxahxf0s/hTkHIu7glJ+1n4euGMzsjyZGFK+c+lSCV5STxKAJBjeLZkVWJT/s1v+BadtEOxe2jom4g1NCagPa9+jH52+9woFd23XHK6e+oyZ4IUQjIcSnQoifhRBPCCFMhzw3s24uT1FObwc7OL27Kpcit/6lkhs6JpLiMDFx7EN43FW6YoUQ/PXxcVhsNiaOeYhQMKh7fOXUdqwZ/HvAAuA+IBX4UQgRX/1ck5oOIIQwCCFWCyFmRXyVinKaMmiC+3ql4A1I3lqRE9FSzcheqeTt38vU11/QPX5MQhI3PjSGbetW8u0n7+mOV05tx0rwiVLKt6SUa6SU9wH/BX4SQrQA9PwtvB/Y/GcuUlFOZw1dFq7tmECJO4A7ENId3y7JzvmtYpjz6XtsWb1cd3y/8y+j64BzyNm3S3escmo7ak9WIcRGoJuU0nPIY+cAbwFRUsrU455ciIbAB8A44O9SyguPdbzqyaqcqQ42BIm0j6vbH2LkN7vQ4hvy3KdzMFttuuIDfh9GkzmisZWTK9KerO8AvQ59QEr5HfAXYEMNx/438Aigf1qiKGcQgyYwaIIST4CvMvXXfbeZNO7tmRJxB6eDyX3P1k0snadWU08XR03wUspXpZQ/HuHx1VLKocc7sRDiQiBPSrnyOMfdIYRYIYRYUeZR+3GVM9sPu0p5Z1Uei/fp31XTKSWKc1vE8PWUt9m+fnVE40/774tMGD2KvAN7I4pXTi21uU2yH3CxEGI38CkwWAjx0eEHSSknSim7Sym7u6yR1ctWlNPFRW3iaBFr4a1fcinz6t/VcnOXROKSUpgwelREHZxufvQZNE3j7bGPEAqpN971Xa0leCnl41LKhlLKpsDVwPdSyutrazxFOR0Yqzs4VfiCvL0yV3e83WTg3jYaB3Zt438T/607PiG1Adc+8CQbf1nED//7WHe8cmpRNzopyimmaayVEe0S+GlPGcsPlOuO75rmYEizaGZNfoudm9bpjh98+bW063kWH/9nHIU5WbrjT0dlxRo7NpooKz52yqzpcXXluGsiQohk4FkgTUo5XAiRAfSRUr5b00GklAsI76lXFKUGrsiIxx0I0TpO326Yg27pmsSqCisTRo9i3JTZunbICCG4/R/P89NX03HFxR8/4DS3+BsbE8fGYjBB0A93PFVM3/PcER9Xl2rya+Z9YA6QVv3zVuCB2rogRVHCHZz+2iWJGJtR981PAA6zgbvbGtm3fUtEHZySGjTmyrv+jslsiWj800VZscbEsbH4vBruCg2fV2PimNg/zNBrelxdq8noCVLKqVRvdZRSBgB1T7Oi1IEid4DHv9vLyiz9FSN7NnBydlMXX0x6nT1bN0U0/ta1K3nsqmEU5WVHFF/f5WcZMJh+/5jBGH48kuPqWk0SfGV1iQIJIIToDZTW6lUpigKA06xR4Qvyxi85VPr0z6tu7Zr8pzo4uWLjyN2/m3fHPXFGzuQT04IED3vZgoHw45EcV9dqkuD/DnwJtBBCLAImE65PoyhKLfu1g5M7wPtr8nTHuywG/tbWzO4tG5j1wZu641MaN+Oqex5l9c/fsejrGbrj6ztXbIg7nirGbAlhiwphtoR/dsWGIjqurh21VAGAEEIDegPLgTaAADKllLXSzFGVKlCUI3t/dR4zthQxZlAjOqdE6Y5/YdEBlmV7GTdlNo1apuuKDQWDjL3tSg7s2saL078nJiFJ9/i1raxYIz/LQGJasFaSak3PX9vXcSSRlipAShkCXpZSBqSUG6WUG2oruSuKcnTXdEiggdPM55sKaxwTMhmoymhEec9WXH9LL2yOKCaMeegPHZwCxgo89v24o/bgtWUR1Lz4zUV4ovbhjtqDPyqf2596Hp/Hw/en4N74xd/YGHl+Cs/+LZGR56ew+NvIdh4diys2RIt2/uMm7ZoeV1eOOYMHEEKMAdYB/5O1vAinZvCKcnRZ5T7ibUYsxmOvrPrjHJQNbE9F7zaYs4sxFFeA0cDi9TlMenAaVz58F5de8xh+Wy5Vrh0EzKWYPfFIaSBoLiFoKkdII0ZPHFrIQshYRcBUQeFyScsGQzCG7HX0Jz6+smKNkeen4PP+9pqYLSHGf51zyiTZ2nasGXxNagP8HYgCAkIID+FlGimldJ3Aa1QU5TjSnOG97N5AiPwqPw1dlj8c426ZSt6tQ3Eu30raizMwFfxW0+ZiYENGEjPHv0ubS1NITmuIo7QtlqoGSBGkNGkxWtBGVElbAqZK3K7tOIo6YKtsQsBYjrXdDkrsPxBc1xybSMYVG1dXf/Sj+nX3ive3xw7uXjlTEvyxHPdDVimlU0qpSSnNUkpX9c8quSvKSfL8wgM8/eN+PIfVjvc2jCfvtmEkvT+fuBlLf5fcIfyP/Z7WLswmwZT7JyN8FiyVDQEoTVqM0RdDTO5ZWKsaYatojKOoAxVxa6l0bsMQcOAs6ox5Xzr/uPpy3nvh0Rpfb0jz4rMU4LPm4TeXIHW1kzi2U3X3yqniuAleCDHgSF91cXGKovzR5Rnx5FT4+Wht/u8eLxzRn7iZS7FlHjhqrL1NKleOvojdK3ay4L1v8Tj24nbsQoSMRBV1xGfLoSRpIUVp8/E4d2EIRFEZt46itHlUObfj0Bpzzg1XsXzOtyyf/80xr9NnKaA0cRmFDeZQEbueyugtlCUsp7Dht1RGbyGkeYZkVYEAACAASURBVI8ZXxOn6u6VU0VNlmgePuR7K9ATWAkMrpUrUhTlmNpXd3CatbWYfo2dtE20420YTyDOgWP51j8cbzYHsNv8VLlN5A9ox+CcYjanRfHVc9NpP6wTCc3j0fZ2JjvqF8yuchyVLYnO70N5kSm81NF/PpZQHH5rAW7Xdi664V5WfDeX9/71OG279cYZE/u78SSSirh1eG1Z2Mta4Szsghb6rVSC31yM27mTorTviM7vjcn758oh9D3PTfte3jrfvVIf1GSJ5qJDvoYC7QH9Ze4URTlhbuyURGKUifHLcvAGQpT3bYtjyRYC8S78idGErOHbKtNSSzlnyA769N7HgGH7cXdpimtpJn/rkYLRaOGjUW9TVRlg9fb9bFll5bH061g9vS1Lvnb8ujPl/Qd6UFhVjLOwK5aKJlQ2XMbNz/+dirISPnjpSQKmMoLGSiTB6uS+loC5mLjsIdjLW/4uuQOYfLE4ijphL8mgJGkRPkv4nUhI8xEwlhM0ViBFAIkkaHCHz2+oRB6jb9CptnvlVBFJAfb9hJO8oignycEOTh+vL6AgLYFAl+ZIq4nKHq0ACDpsRG3dR4eqORhKshEGSXlUNM5AGTZfBQa7iVvb2XltyTa+e/NnMs65iNcvuoKg38CE0bEgwO/VED6JZgpgjCukwPQNhpCFkAiQ0EswbNRQivbmUhS3EM0kkCKAyZ1IwFpEXNY5f0jsEknAUkSVcwc+ezYiaEZKKEn5CREyI0UQQ9BKiBDS4EXI8G3+WsiCFEFAYq1oiq28GYbgqbOT51RWk2qSr/Fbk20N6Aysrc2LUhTl+Do0iubB/7uUQFocmsdH3JQFRG0Id2IKWUwEBzZl4aDLWOcpYviaDwhqBrRQELvNj89nZEizaOZaEvnxv1PZtfR2gv7qhKoBAmwuL3/9YBbOpCpCAY3yBZfQpH0ZxckLkVqA8x68BCEMmL3xuHK7ETJ6KElaSEjz4Xbswl7WGkG4x2xpiaQicSXmuAKiKlvgLOqMFCFKkxYR0MoweeIImqoQIQ1h9GDyJKIFrXhtuZjdyTiKOhE0lVcv7czHUdweW0Wzk/XS1xs1KVWwgvCa+0pgCfCoatyhKCeX1DTy7hiGFgwR9c9P+OLJmXgtv1W70rx+ohZsZsTPL2H3VfBltzsw+91UWl1UesMzayEE14w5F6EJti8cw8F5XCgERrOXuz6fQe7WOD6+dyil2VHEN6qgJOVHokrbYPImYJBWErKGsXfTDj779DEIaYQMXmKzBuFx7KUqOhOAxd9aWJG9mu2bBI+n38CaqR0BKEn+CbM7laiSDDRpxpnfnYC5DKSB6PzeuAq7E581lICpjPL4VRj8TpxFnYnNPpvK6C24Hbvq9kWvh2qS4GOklB9Uf02RUi4SQtxf61emKMpRlQ7uAAgSJ3/ProIq5k1fw2frf3+Xq89nZP3qJM5e8xkmn4eNab0w55ZQ0roFAP6kaGJbJ3HxUxcD32MyT8BsCXHPP/bxyodjKdnrYN7oLgy5ZwWp9gP4Mr4lpPmoiF+L31KANHgJGTxsmrqHL1/4lOVrPsHod2EKRBOTexZVzu0U2taRfMUM2p+7i+Z9DzB2y9u4Gy2hMHoZJk8ijpIMtKAVjy2bkrTvAUnIWElBo9lURm9CiiDReX3wW4rw2sPNR4wBJzG5Z1ERu4GgsbJuX/h6piYJ/qYjPHbzCb4ORVFqSApBef92xH61HBGSdE11MLh5NHM/Ws42s4GYaDdmc7gcQVZ2NPPnN8c6dRWrUvpj+T6TsoHhj9DK+rfDsiuHgeelk56RhqY9zOhJKxk2ZC2yp5MnzKN444fRdL5sO83ZiiFgwLL2cioXDYaAGaRGUer3DLx/GEmNm/PhE//GXRbuA+u35iM1Pz5HFn63kTcuuZxHG93N/7W6g32rUwi58vAYC8mKWkh59AZAYvA7say9goofLkAGIWh0U5Q2D3/UBpLyrHgdW359DYwBJ9aKxr/O4k+1TkqniqO+GkKIa4QQXwHNhBBfHvL1A1DzghiKopxQ7oxGaBUeLHt/2wd/a5ckop0m/rMsl/a98zhnyA7SUsNVvX0+I96tVZjySiEYIhhtJ/+a/lR2b4l97W6M5W6umnAdiCCfvvYw+TEmkkv2sb7H2Xgax5GxfxnmoJeCVa14/PpYKlNWsOLz5pTsSmbHB5ewJ9tDpysfoDSnhGljJuN27KYidiMxuWdhsHgI+AxsXxS+ocpbYUZKSfHGphQXBwlYi/jw3oEE/RrZS9py//mpjLu5OZt/bMCeFal02BLEaF5EqncaBm0viZ6Zv/6ZbeXNcTt3s3iOudZr0dRXx/qQdTGQDSQALx/yeDnh2jSKotQiKcDTugFlA9rhbZZMyGxC8/rRKtxY9hUgCdcNCdotMLQVNw9vz6u3zeDGrcO5/O7uOHsXkSeSw88HQoiQpOiy3kirCX+/DPAFqercDG/TZFpPns9Fj53D9Ke+Yv6sjgwYkU7AWkFiaRYDNn3FpL7/Ys6LDfnbjM9ZM7MVbQbvJWufhaSh32KP9tK4awhPxWB+njifntdOpUWf1pQlLgdNEtewkqczJ2KyhHCXmXHE+QnJIAZjCCnh2jfmEgpqfPxs03BNGS9s/q4pya2LuC//BXrllPBZnweIrcwh1fs1Zcb+eI3xGANOCBj53wfGX+MAJo6JpX0vr9oyyTESvJRyD7AH6FN3l6MoCoAvJZa824ZCSOL6aQPx0xahefwE7WYKrh9EVYemZD18GbbMA5T3b0d05k7ur/yOBrdasV/ShHJbLG6zg6A0YNm8j0BSDIEEV3iLjJS45q7C1yABb6s0pCYouGEww39Yx5ruTZj6zDs0H/4oyYZmSHdLPutyIR77Ds5/chFzX+lJh+E7adQpjwWvd2XWmP4gQtzy4SwuePI8Yhs4ady1CfbSVriduwCJ0RzE5bDhOZBKbLMd4cYhhiBbFzTkq2f6MmreVKSU3DF1Jkve78Cssf3wVpqwOvzsKmhBX+eP9Nwxj2Utz8VntBHl34fXGL45KuA1YXX9vlaBqkXzm5qUKugthPhFCFEhhPAJIYJCiLLjxSmKEhlfWhzZ919E9HdrafDsNFwLN2MsqUTz+DAVVWDLPIBj6RY0b4DSIZ1IemM2SZPnk1K2h2aP/pUmDje3zX2cgas+xrb9AJ72TQm67Jj3FeBcvIm0F/9HZc82OFbtoOHYT3EszUSajZQP68KNj5xD0B9g2qjphDCRmxikyu5G89uY/vch9LpmE2ntCpg1ti/f/KsvwaDg9k+/ZP5r3UBa6X/7EExmC2X2DdjLWqKFrJjcyViCRiwtMknMzkDzOtm/JomC3THc/flMDmxMwO8x8u51F9Okew5XvzYPe5ybqlILzRJ2ANA6axWVFhdSQKWpERDeV2+weqks+v1+e1WL5jc1+UTideAaYBtgA24D9HfxVRTluKRRI/eu4cRPX4xzaSb+lFgK/9KPA49czv5/XEXWw5fhS4qmonc6gWg7MbNXUHT1ALw+I1/EX4Ux4KX3wg8Z8k8/z73nwd00FVHhRhoN+BrG40+IpuCq/ohAkILrBpJ//dmEHDaMReWgCVyDMrjw7+ewce4Gln3xAyGDh6DBjSY0bpr0DWntC9CMIZr3OcA1Yzfw4KwvmPdSL9Z+noHBLDEYIDtzP+N6jGPtpi8IaR6ClmxM2nb6ZH6NP3Y+VkpIbOTDlVyF322istCGySS47fEFTL5+GA3S8xlw21o6JxSys81jBIQVjzEagN2uS3+bvVuKMAgDf7klqGrRHEWNPnKWUm4HDFLKoJRyEjDoeDFCCKsQYrkQYq0QYmN1XXlFUY6hsksLjHklWLdnk33vheSMvBDN7SN+6kKS3plL3OdLMFS4CUVZQEqcCzchNUFVhybkduiIeGslcxOvxpfens9mbCdn3T6SJ36LoagcjAY8DeMJRttxLNyEVunF0yoNd3pDNLePqKWZoGn0v/8cmvZoyvQnP6ZqHwQsxQRNFWimIFmb4vnvpZexZmZrGpy9BnN0JVePLGXM6o8xmUNoBklKQnuMws6n93+Kt8pDyCAJaRoNirehhQKETJVY40vo1CuAtm4Ybc7KxmyUJHbdztbnWzKw8EviGpTykO1GcqPO4suWS5nd7t+IgIMSW+dfX6sq505s5c3pe56H8V/n8MRb+Yz/Ooe+57lP4v/BU0tNEnyVEMIMrBFCvCCEeJBwffjj8QKDpZSdCN/9el51w25FUY6irH87olbtJPuhS4jdt4cWT39A7KxfcGVlkVSVhWv/fhwrdyDK3YRcdrJHXYpjaSYlF/bAunkf+Rf3o7JxKqOSA1jtJj69ewrZVwwkYHcgSj2kvD2XmHmrKbmkN4FV5eAN0vCpKZizi6js3QbHd2vRfAGu+9el+Nxepoz+D5rPgb8gieK9TmJSKjnrtnWsmJpOabYT765WBBqvQosqg8pYECHifd24a/SjFOwq4OtxXyNkkEqzi+k978MjrAQ1I/YKG5h8EDShlScjCBJdVci2br2o7JGGw1vGvsR0ovz7qLJayU+oQAs6ESEjZcUau3ML8VpysVY0AU5sLZojbbmsr9swa1KL5gbCvwjuBR4EGgFXHC+ouvtTRfWPpuqvM68tu6LUUCDKgrdJIoEGsTgCpVg6RuNq2pLoJRtIj9+HDAmEJvkqqiOu5VuRQlDRoxWV3VrgS43DWZqPJcXEhUtewzTAz/ddRvDePZ/x9ccbSWhwO1WlFnoPlFgyrBTvdRHdx0BJno3idpcQn2Ejes4qKru1BKOBpDYpDH/0PL4aO4tVsxbR+aIeFGyOo3GXXHqM2EJqej6Nu+UjfUYCfhCaJGAowywhxj+PhueXce3FSXwy8Ue6d2zG1qxb6HXjJrLy40lrW4Q7BF5/KaLnVxSUG4m2agzYOIOp/UYSU5lPhz0/sz++OSVaHMXJP2ErbUVVTCbr5jTgh7lu/vLqUv77lwsYdp79hM7YF39jY+LYWAwmCPrhjqeKQfKHx+rLu4TjJngp5R4hhA1IlVLqWmYRQhgIlzhoCbwhpVwW2WUqyulLCkHJ8K6Unt0eBDgCZQza/DkBzcT++FYsuuZOdhft4OxNn+PwlhLfOEDV5ioc36xDq/RQcmFPEFDSpjkjlo5HWAzkO1LpMLwnHWZvYunkNZx9j4mzbt+IrzKOgi0OZj19Fn1uXE/rs/dhuS4ekVWELfMAZQM7gFFDGs1c1C6Bde1Smfb4J7Qc2JSWff388llLMs7dQ5Pu4T34whJg09zGaEZoPXAfCNjZJMjQ9V9w7SV5zFkVzVdTc9j6Y39MTj/9b1+PRCCsHr4Z159OF+ygVf9w/fpP+j9Ew4KtBIwWfmkxjKCwEhKrcRR3CBcb89oQ6Uu5ZEAlb199CXtWprB7WeiEbYksK9aYODb2d1suJ4yJBQl+X/3chlmTXTQXAWuAb6t/7iyE+LImJ69es+8MNAR6CiH+UIVSCHGHEGKFEGJFmSfwx5MoymlMaoK8W87B0zKN1P98BQi6Z86hSUEmLfI2MHDzDP76wxgSyw8wtc/9lNjjMfp9GOwaAoids5rob1aFTyYEn/e6l0ln/5PvOlyDQNCi15NcNf4ZEpu7+XlCZwQwd1x3Mn9owswnB2C2BQj6BMH8IDkjL8K5djuEJFqVD3e7Zlz1wtV4yr1Mf+RzNKHR67otOBPciOrc5qsykDFsL23O3osQkrIcG1owxML0S/AmJPPQ9Ds56+Y76XD+Drpctp3lH6cD4RueLntmIc16ZxHwGfBWGQl5rexPaE2uqylBnEgsaNJIRdw6KuPWEfBp/PhmN57pdjN7VqYAv22JPBK9yyq/tv87hKaBdtjpjzXmqaYmSzSjCTf5WAAgpVwjhGiqZxApZYkQYgFwHrDhsOcmAhMh3HRbz3kVpb4rurQ3IZuZlP/OJiREeCeLt+h3x5iCPnptn4PNV8EX3e+k27a5FHbuS5XZhTQaCDitAKQW7uCKlROA8Froa8NeoutVJfjdJjbNcxLX5CuM1iZs+zG8zbCyyIbQJMX7XDRuV07T/SvIa98YDBruPIGpcQx2mUr7c69hzRcfkjn8FwYMi8VtspNSvJutDboz/aHBDLhrNSltitn6U0Na9d9Pxw0/siWjF//reQ9RmoWG1kLimk3j9Yv6kNiiIT2v2cKzPW/ioR8/xurysWhSexxxXs5un0FUQjnFqT+iBS3YKlpgdidTmrQIszuF0K6ObPg6hVDwt4R9tC2RR1pqOd6yypHa/4VC/GFhuT5tw6zJr7aAlLJU74mFEIlCiJjq723AOcCWY0cpypkj6LBS0SedpEnfIQIhpM2C8PrZ1PjI9xa2yFlHQDOxoP0IglE2tEoP1u1Z+JNiANif0IYNKd3JjO/IJ31GITUDroRKfnq9Ays/ncgn975Kee4BXp3zLMlN8ug7Yi2+SiMpKXl03L+I3ju/pcxe3V0pyc6SD9tRku1kxL+706BjQz7653Qarfue4Ws/ZE9iOiC59vXv2Lm4EXOe60t8kzJkhQOb3cOALV/gNkVhCvmJTSph4lUTyNt2N72uXY1E8M/ln1FZZCVrfSKD7llNm45BHPFuDMEo7CXpBCwl+C2FFKXNw1LVkKiSdjVuz3foUou7QsPn1Zg4Jva4M/kjnf/Op4q5c3T9bQlYkxn8BiHEtYBBCNEKGEm4jMHxpAIfVK/Da8BUKeWsyC9VUU4v5X3Ssa/dhaEyvLgr/AEEkt1J7cg0tKdsq5WmibtJcBZQYo9nRo+7SS3ZTWFUMpZFOyhp0gTr1iykxQQSjDnFfN/uKrRgkJTJc+GWVEwEeOXikfx8RQf+bwRMu+8j3nk1g7GLJhD0a5g0H1V2F2mZO5ja58HwuwgJwYDG9IcHIUMabQbtZsQrWfznvBd5YIqB6/97P4jwtNaAYPjZGlXbS6FVCdGlDtY16c/FK97C7HcjNQ1hNHD1+GFMvGoie1dNpMP5F5Dkb0eC10q5IxPNaye1RRUF9uoeryI8O5ZAXNY5v2vuUZP2fL8utRzS8vXQZZVjxR7t/PW1JWBNZvD3Ae0Iv1wfA6XAA8cLklKuk1J2kVJ2lFK2l1KO/XOXqiinl8ouzXEszfz1Z83jQ/P40eblMbXjSG748lOa3L+Hj1deyxfd76Trrh8YvmYyXrMdy5o9BKOs5N51Hq75a9Eq3BhLKpEGA6GgxBPlRAjI2LOYxX0vJ75dDC9dL9m6bC/TZx4gaDQQcFjw2BwIJHM6XYcUGn0yZ4OA8rwoZCicHhzxHhKbNWXYsF6smLaSVTO3kpMZD0FJwywPsvkCLku8B0ugAqu/iNZZq5ja50EyDizFEArQOH8Lnfv1puc1vZg/fj5ZqwpplbuMHta/YU/fwrUrnqL9jiwS9l6MtaIxBl8M9tLWGELmI3ZuOt6WyCMttQQDsHuzqUZFyY50/vraEvBY1SQ/rP72dinl/0kpe1R/PSml9NTR9SnKaSvotGEsrvj1ZyHBtGAX3+cPZ/qjZ3Pjp99x0fNL+DzuVlyF+XTauxCQmH1u8i7tjz/OiWVvPqXDu2HZk4cnvSGJ732HocpH4dUDkJrG+sZnkVB2gFJLDKGnxpA+qA1vvZFJcPNW7N5yQpqBkGbEY4qi3/qZVJpdEAwR16gcZ1K41nq/W9fhrTSxcfcUjOYMZjzxCa7ULErzoyC4jaHrP2F2txvwG634LbmsazSQBkU72dywD36DmeyYtvgtHq588q84EhxMGTmJfYkbWNFiEFcsex2XJx+n9imlKfMImiqIze2PIeAgpHmP8sod25GWWq5/qIQPX47RvWxT3x1riaabEKIJcIsQYjJU996qJqUsOnKYopyZzOYAdpufKrcJn+/4q58iGALD7xNM+bdVdPrXDua/1pPn+l5Pnxs3cO4jy8jytWBy/8fxGq14THbiC9eR9tFctFQ722/7C0G7FYSg8Or+aG5fePuHgJDJRK4tFWEwIkOSq56/jM8em0GlMYqQ1YUU4eJjWiDIMudZVKUkIYOQszmWoQ/+woY5zYhtWI672IzRohHwTaaioBcznviCK164gb0pGRQnJhMURkJAqSMZpJGs6C5IzUvQEAQNRMiAJQFGvDyCdbPWcSCqEXF4+KrbrVSZnaQW7yE5z4E31BeBQIoQNbzR/ogOX2o51rJNfZuV63Gsv4VvEd4a2ZzwXvZDE7ysflxRFCAttZTOnXN+vRlpzZoUsrKjjxljyivF2zgxXKe9WpJ/N++MG8Wd02by5uWXseX7JvS6ZgNDSuZgckpMAS+/tBhKq5U/03iYj696jmDYho9YYe2F/9sDlJ/VFudPGxE+P8WX9saRl0NF44YMXvUxLfLWs2VdLANf6MPmxo1JLdxGoSMVt9mOZVcOFS0bYcwpJpRgwl1uottfttDn5vXMe7kHjTrl0aBTIUntLJis57Lwna9p0n0A93bezIaO/YmpKsAc8NBxz2LWxb6O1+QEKShKnY8hGIW5KhmPaxfd+l/AsPQ7uWD5ELxmM1Jo2H0VGAMhvmx5F8IYTjMBczFGv+tP/T9xxYZ+l7yPtGxTX3bDREqEbzg9xgFCvCml/FtdXEzLeJt85dymdTGUopwwZnOAc4bswGj47d9SICj4bn6LY87kKzs1o3RwR9Je/eJ35yq0d+c9+XeGPrqCgFfDGVdF69zVGP1e8mIaUeBIJdpTjNviYPDGabTJXsX0Hvcgpm1EFvnI+dtwTDnF+BvGE3JYGLxhGovTL6L1gZVYPeWsS+iFv7yEKc8t5OJHBmFr2hDNEwCvj1CcE2T45isZgqDfgLfShCXKh0ELUpobxfvXDsDtPRtvpY+n5z+Cy1FGldlBSDOQvs1Gnu0yJCHK41fhse8DLTwbN3ri0KQJv7WA8sW7WfvvN3n/FjBrAZalvsTe6EsACGk+Cht8S/yBYWgh6wn7/7T4WxsTx8RiMIaTe326I/VYru3aaKWUsvuRnqvJnax1ktwVpb6y2/zIkIBDErwMCew2/zETvH39bgr/0o9AeiIJ2Xupcpuw2/wM7jWN+HTBbjKwmSop1RLJkqkY8FMWlYoWCOKsKiJgNFFava1RSInFEiSUVUSDZ6dRfGEPPG0agNDY0KgPzqpCNjTug0TDWFJB4a4AK2dvprjIy51T7yBkN4M9XHZXCpAhqCq1ICTYYz3hdyYGiE2r4IGZX7Bnx1X85/zxfD56Cfc+9E/8SSvxmw1sbO3FEJhP0FiF1HwgDRh9MWgBG9H5fRAIQpqXzXsnM+37CgztruIvNzz+a4VIgCrXdszulBOa3KFmO3BON6f3JwyKUgeq3CaE9vt3wkKTVLlNR4moPiYkaT3nGwpvH0rroeF3AS6Xh2Vtz6M0MZm/Ln+WCzZ8QJSnlMF7Z2KOFjQuzKTP6+O59Jc3uWbxK2SmdWNNkwE4PUWUxScDYHD7MGcXI3wBMvYuodweR5fdC7j5x3HcPudxDjxXxdS3RhGb8ihbvt/CsinLIRiCQAjbqh0IT4AJl1+ADGks+bA9b19zEdkb4zBtzoVgCBFrJt0K517Ynh+nfsGSLd/hsRpx5Q4ifv95SBlOKyZPAgn7LiAmZwBBUyVV0VuQSLSQhQF9b6fnuecybcLnbM7+7fYYd9QePI7dOIr/cNP7CVFfd8NESiV4RfmTfD4ja9akEAgK/H6NQFCwZk3KcT9oNZsDDHYsZuDmGXzZ9y5WtRpC/Fk21jfox/nL30Fze3GU5aHJEAs6jyC+KpeL1rxL5/QDCCDKW8YlKyaypNVwmmevo6RP+/DsGyjvn4E0GHB+toweW+fwc/qlbEnuwoIV7Xnx/dFsnN+Kht2uonnvFsz8xxeUZJWiVXmRNgtaIMAlcROYeOkFdLtiC50v+P/27js86ipr4Pj3TEuZZJKQDoQmNYKAIFUQFSzoii66INZ1ETvoWnbtbXXt6+r6riBW7F1WRRBEBZVepFdpSQgkhPQ2M/f9YwYFBeQ3MJkknM/z5CEZcubeGciZX87ce+46oj9fhT8rAYxBvF6qjm3B8MFtSWuTyat3Pk70T90RY6ciYTU+VzHOqhQSd5yIzTixGSeJ+SdS7c6hJHUutVGFGAx/vvURYmLdvHDf36mSAkqSF1KetDJwlut+lkcq6363Bl+XtAavGor9rZixuoomMaGSvn224nT6KYjLYEmrk1jdtCf24nJiN+bicAvFzbNwU47xCZ1en0ab1E0kxQbOvHc4Aj+707qMIqE4nyVN+uD5ZAG2Gi8FIwbgTXTT8vZXiTZVmHZNyOvVg5KOx7D6+9aUFrjpOmw9RdvyefKURzju5GzuSvZS1rcDhef3x1FUjn3TLmqJgTZu/ElubGWV+KNdOLbvwpuVCgY2T/mRpy99hYGXns7wx8/DiJeo8ubE7+qO7LUuo6TIxs58P0md12PS1wdLNwnMnzybl67/FyOeGsXgYZcRU3oMNn9UGP7FGq/DqsErpfZ1oBUzNTWOQ0rse+xd2kkp284py99lfXoXsuZ9T4uMYuzltaQs/JhpM09h4VnnMMU5iP+N68cLf/kLI/u++/P9HLflO6YedxEpr84m97IzwRhSJ82kcHg/atMSsW0tgBUlpKyYid25ivnFz9Jz1Hqc0V6+e2UYwx4oo/sJDsrWbmLI/VOofHc56//QhJ7vrsVgI/f+FpTOLGBzbHt2XnQS3vQkYlZuwbGzmBO25XHawNZMffUL+pxyHk2HCvFFXfdJ7vv2hWnKmHs703PYNvyOCvoN/iPytJ2uf+iOuzj7yP0jKUBLNEpZ4nJ56dZtOw67wen047AbunXbjstlvRPqr0s75TY3Pp+d071f0iVvLtk7FyH5PsaMn8iEC4fRrGchI1+Yyd0/PM5X32f/HJdUlMPumGQqkpMxNsE47NSmenAv3khp/077jOlOrWH4ZZPJaL+Ljd9n4Cu3071lMh3f+YoTX11Ik4Iqms3ZGQyvIAAAHeVJREFUyID7lxC3tIT4pbuJf7SUn8YMp7xfO3yxUURv2kH896vxJcbh/nETV6Q5yIhz8vJdD+IvjkLML50W998XpglV29OILm9BbEUrBg6+DJu7hoqyUvy+xr1ssa7pFbxSe/m9MkuoK2YOJDcvgeKSaJISK9nhTYb+Zp/737SzFU57DSVFifznnOGcdPViLnttKisqRpCXuxEXNVS6PWCzUdWhGRnPfYb4/BQP7kbFsVkYpwOp9YExVLdMw5scT7PvV1I9ezWJTZtxXI//YYuJQ4or2PJjDum1PmL8BoKtu31RNqJrytkVlwHtvdiragPNFX1+THCTVrTDxvW9Mrjrq61MfuQ9rrz2tJ8f36FsMBIj7N5eyFMXnMpZF4/hzItGW34e1f5pglcq6FA2K4W6YuZQxvQ7ClgX5aTW5cIZ7AbSKnUTtb7A8sXaSifT/9WL2c8dy39uvxTJclLjsGMrKUHKq0if+OXP95v28nS8nlh2XjKI8q6tcc9bg2fGUtzLtyB+P6W92mNzx+OuKqS4SSaFl57M22mZjH/6K/bu0GKcNnbe3YxLZv6D76akkzNyCBXd2lDTtAn20l/WkHdJd3N61wymvTiVgf3n06HrCcCB+8LsvcHI56ggMSmDlu2yeee5Rzl+4GDSs1qF9HyqfWmJRikOvfQS6oqZQxkzSry03rmCmXLyL+UXdyF3Dr4Zl72SWGcpLnsl1/W8m9S1K0mYsZTEqYvAYSN+wfqf79cIVHZsTuGIAVS3bYrfE0PJ6cdTePEgis7qiTcpDtf2IqpbpxO9ehvVbdKp6NKKx37YyK/f3rRXe+l063zWzoujtsaBvawKZ24hJad2xb1wwz7fe3m7eJIyPYx/4K/UVAWS/6G0+K2K20RMRRZ/ueuf2B1OJjxwK37/0bGMMdz0Cl4pDl562fP3e8o2uXkJFBS6La2YOdQxO//0PVO7jeDLR9fjjq6hotJJl4SvmXjOqewob0aaO4eE6KKfv9/vsFPavxMZzwQ6cfvioskfcwYmykH8tytIfW0GUu2ldEA2u4d0p7R/J0pOOY7oVVvxOx3k3XwuOO3Yd+wmeutOHDahwm/wCcTaBIyhqCiG3LwE/E47Zb3ak/TRDxRefDLO3MJ9H4/TzugLjuOxf8/ig/H/4sJxdwAH32Dks1dQE72T+IITiElzcMlf72HCA7cy/b3XOG3E5SE9r+oXegWvFAcuvXg8VQw+dQN9+2xl8KkbaJoZ6BtTU+Ngd3FMyMl9z5g2+75Xqs13r8VWUsn2swdQtNf9J0QX0S55+T7J3YhQcPEgotfl4tpehC/GRd6N5xC9Lpem/3wfz3ersFV7EcAzayVZ975B2isziPthFdXHZCK1XkyUE3tpJf7YaL65sg+70mJYF+ekl8eGt4NBOkLiA5U0zSymdOCxuHIKKB7ak+h1OZT33fcNXIC+Jbvpd8HxfDppPOuXL/759v1tMPKLl+K0OcSWtMdmAo/zpGEj6NJnICvmfUd9WsLdUOkVvFL8UnrZuwa/fHkqnTvvCPSYCV5ld+u2nYJC92El9oMRoOnLU9hy9bkUXDyIpMlzcZT8tl+KNymOwvP7449xkf7fzwEo/NOJxKzJocn/5u3/vg3ErN5GzOptJE1ZRM6tf8T4/UiNj9RJM8m/8jS+TE/AM28Zc/uvIyq418gGxA7NZHeH7ki1D8+sFTgKSijv/tt+g7aqWq5KdrEyI4H/Png9D78+lShn3G/n7yymJGUBjppEYos7/DJHEcY99jwx7jhE5DdxyhpN8EoF/br0cqRXzPxabEwtfp8Nu22vq1qfjXjKyHz6E3ad15ecu0cSs3ILscu3INW1+GNcVHRrTVWbTOJ/WE3Sp/MQrx+vJ4bKzi3JuvuN34yzv5VB9tJKEqctpmRANv7YaIrO60PitMWUd2tD8dk9WJI3h8SCTUye66Xt5aez09Mce3EFiZ/MI27Beio6t8S49v8cJBaXc123ZB76fD1vvX0Tw8ddi7M6BTE2/LZqquK24HNUEFvSjpiStvusmQeIjYsHoHB7Lls3rKFb/5MP+7k+WmmCV2ovv96sdCRXzPzawVbk2Gq8pLw9i6RP5lLWuz0VnVvgj3Jiq6olZtkWUl+ega3mlzeAS/t1wr1oA7aqmn3u72Arg+Lmr2PXeX2I2rid+DlrKB1wLLWpCTjzd7OhaVc+neLjwwc+55asDvylxUx+equc2urAc+OLiw70nT+AXgkOTmntYfozX9Jr0B9odnwNRnzY/C5iStoRVZGJ/E6F+JXH7mbl/O957L0ZJGc0tfTcqgBtVaDUQYTS5z0S97/9mjOJn70S97LNP992KG2M864fSnWbTLLunIS9sobatARqUzwkpVbRLnULw8ZtZlc5vH5hOqVFSb+Md91ZuOevJX7eugPOqazGx3XfFONJSuYfr3+Kw+my9Jjyt23m738aQsfje3Pbs69pyeYADtaqQN9kVeogcvMSmD7jGH6Yk8X0Gccc0eR+JO/fBK/u9/ZziWnv79trZRCAvawaV04hZX0CdXDnjmJiV26l+pudrJ3sZGzvDLbvhoem/HJKZ21qAtVZKbgXbTzonOJcdq7t5GTLulV88tJzlh9TevOWjBx7O0u//5pv//ee5XilCV6p33UkVsyE+/6lqibQ030vh7Ipyx8bRezijRSf1p3atH1fXGpqHKS7Evljp2Rm/FTMotwyjMNGwYUD8Xy9DJv399sK9G4ez8CWHj5+8Rm2rF1l+XEN+dNldOzei0lPPkDRzu2W4492muCVagRi1uVR0bX1Prf93qYsX4yL6tbpxM9bS9LkueSN/QPVWSm/ue8RnZM5pbWHlJRY8q86E1t5FYlTF//m+w7kyh7puD2JPH/fzXhra38/YC82m40r732CPkPOxhUd8/sBah/6JqtSjUDcnNVsu28UPnc09vJfyikH25RV1qcjMcs3Yy+rIv6HNdiqatl+7VBcObvwzFqBa+tOxOfH3ySeUZecTEX3NjjnrKHJRz8gFt6780TZuSbbxaOzl/Hpa89z7l9usPTYMlu0ZvRdj1iKUQFhu4IXkSwRmSkiq0RkhYiMC9dYSoWTy+UlMaEypI6RdcVeXo170QZ2De/Lr1Pv/kpAtU3iKB7cFc83y3++zb14Iy3ufp34uWsoGdSZvJuGkfu34RSOHICzsJSEO15n4k3v8WNumeX59cvy0D8rng8nPM22jWtDeoxb16/moatGUly4M6T4o1HYVtGISCaQaYxZJCLxwELgXGPMygPF6CoaVd+EexXNkeR3Odg+7g+4thWS/M4sxL//n+3a1AS2XzcUz9fLSfh62SHff7XXz41f/ITXD8+c2ZoYp7Xrw91VXq6bWUh685bc99JH2B3WCgg5G9dxx6gz6XbiKdz0xARLsY1ZRFbRGGPyjDGLgp+XAquAZuEaT6kj7Uj2fq8LthovGc9+ijchlq33j2L36cfjTXAHjvGz2ahqk8GOy08l97Y/kjBtiaXkDhDlsHFD70x2ltcyaekOy/NLjHZwVacYNixfwudvTLQc36xNO4Zf/VfmfzWFOdP+Zzn+aFQnNXgRaQV0B+bu5+/GAGMAUmP1LQFVf4R7J2s42KpqyXj+C6qzUigZcCw5d16AP8YFBpw7i4mfvYrkd2ZhP8gmpYPJTo3l7PZJ/G9tEf1beDg2zdrZqQNaxjN7SxzvP/8EPU4aQtNWx1iKP+viMcyb/hmvPHo32Sf0w5OUbCn+aBP2jU4iEgd8AzxkjPnwYN+rJRpVnxzKRqGGwNgE/IYjtU2oyutn3JSfSIh28OjgFpY3IO2q9HLD5xtp2ul47pn4Pja7/feD9rJ1/WruGDWUoReN/rlj5dEsYhudRMQJfAC88XvJXan65kj2fo8kOYLJHQInOP39xGbcOaBZSLtLm8Q4GH18OmuXLmDq2y9bjs9q25E7/vsmF1x7i+XYo03Y/qdK4F/+RWCVMeapcI2jVDgdqd7vjU3rpGgAfH5DUZWXlFhr/XkGtfIwa0tJyCc4derRB4CK0hIMBnd8/XzjO9LCeQXfH7gEOEVElgQ/hoZxPKXCItw7WRuyp37I5Z6vtlLttXYCk4hw7QkZOByukE9wqqmu4vZRZ/LqY/dajj1ahHMVzWxjjBhjjjPGdAt+fB6u8ZRSdW9Im0RySmt4e3mB5diUWCdXZMeyauEcZrw/yXK8KyqaAUP/yOzPPmDRt9Mtxx8NtFWBUipk3TLdDDkmgY9X72Jt4W8PJvk9g9sk0KXPQN7898PszN1qOf7c0TeQ1bYDLz70d8pLiy3HN3aa4JVSh+XP3dJIinHwzJw8an3WSzVj03IQEV548G+Wj+lzOF1cdd+TFO8q4I2nHrQUezTQBK+UOixul51rT8jAZwwFFdY3gaW5nVyeHcfyubOY+dFbluPbZHfl7EuvpjA/D29taOv7Gys98EMpdUR4/QaHLbQFmX5juOerrayvsPPYu9Mtn+Dk83qx2e1H5aEgeuCHUirsHDahyuvn/ZWF1PqsXTjaRLi+dwZ+r5eJ/7BeqrE7HIgIBXk5fPnuq5ZiGzNN8EqpI2b5jgomLd3JBysLLcdmxLm49Nj4wzrBafr7k3j5kbtYMe+7kOIbG03wSqkjpmfTOAa29PDeygI2FVX9fsCvDG2XRHZqTMgnOJ03ehwZLVoz4YFbqaootxzf2GiCV0odUVf2SMPttPPM3Dx8B2hZfCA2EW7onUltTRUvPXyH5VJNVEwMY+59goK8bbz9rB4SogleKXVEeaIcXH1COhuKqvlo9S7L8U3jXVySncDCb77kuykfW47v2L0Xp438M9PeeYXVi37TwPaoonuvlVJHXL8sD6O61NC3eXxI8We3T+K7LSW89vg9dOl9IgnJqZbiR1z/N1yuKFq06xTS+I2FXsErpcJiROcUmnlcGGPwW10VYwuUaqorK3npn3daHjs6JpYLx91BbLzHcpmnMdEEr5QKmxqfn0dn5/BJCKWarIQoRnbyBE5w+vLTkMbP37aZOy8aytqlC0KKb+g0wSulwsZpE/wG3lxWQE6J9V2m53VsQtsm0bzyyF2UFFl/kfAkJVO2u4gJ999KTbX1VT0NnSZ4pVTYiAhXn5CB0yY8OzcvpFLN2N6ZlJeW8Opjd1seP8Ydx5X3PEbupvV8MP5fluMbOk3wSqmwahLjYHSPdFYVVPLZ2iLL8S0ToxjRKYEfpk5m/swvLMd36TOQQeeO5NPXnmfDiiWW4xsyTfBKqbA7uZWHHk3dTF6zy3IbA4Dh2cm0TozipYfvoKzY+ovExTfdTVJKGp9NmmA5tiHTZmNKqTpRVOnFJpAQHdrq7I1FVdwyfRt9Tx/GtQ8+bTk+d9MG0ppl4XC6Qhq/vtJmY0qpiEuKcZAQ7cDnN2wMoY1Bm6RohndIYPZnH7B41gzL8U1bHYPD6aKitIT8rZssxzdEmuCVUnXq1SU7uH36ZvLLrK+q+dOxyWS17cDEf/wtpBOcjDE8fM2FPH3rVUdF73hN8EqpOnV2hyaA8Ny87ZY3ITntNsa1qWJ34c6QTnASEc4dPZbNa1cy+eX/sxzf0GiCV0rVqTS3k8u7pbI0v4IvN1i/Cm+XHMMfOyTy9Sfv8OMP31iO7znodPqdMYyPJj7DlnWrLMc3JJrglVJ17vS2iXROi+XlJTsoqKi1HD+ySwpNW7XlhQdvo6Ks1HL8pbc+gDvew/j7bsbntX7MYEMRtgQvIi+JyA4RWR6uMZRSDVOgLXAGGXFOSqt9luNddhs3tqtl147tvPXvhy3He5Ka8OfbHyIuIYnKcusvEA1F2JZJishAoAx4zRjT+VBidJmkUkcXY8xhnaP68uIdfLx6F3c+/zbH9upf5+PXBxFZJmmM+Raw3jxCKXXUEAmc4/rK4h0UhlCqGdUl5bBOcNpzjuvrTz2A32f9N4n6LuI1eBEZIyILRGRBSVXjrYUppfZvV6WXz9YV8fyCfOsnODlsjG3noyBvG+/859GQxl+7dD6fv/4CU96cGFJ8fRbxBG+MmWCM6WmM6ekJcYebUqrhahrv4qIuKczLKePbzSWW449Ni2Vou0Smvv1ySCc49T19GD1OGsK7//c4eZs3Wo6vzyKe4JVS6g8dmtAhOZoXFuazu9L6b/KXdk0l3e1k/P23UF1ZaSlWRLjijn/idEUz/v5b8Pv9lsevrzTBK6Uibs8JTpVew4uLd1iOj3bYuL53BvlbN/Hefx+3HJ+Ums6lt9zL2iXz+erDNyzH11dhq4mIyFvAICBFRLYB9xpjXgzXeEqphi0rIYpxvTNpmxwdUvxx6W7OaJvIlDcm0uvUs2jftYel+AFnn095aQn9Th8W0vj1kXaTVErVO8YYvH6D026tyFBR62Ps5z/hTGvFw29NwRUV2ouFt7YGm92BzVb/ixzaTVIp1WAYY3ji+1z+PSfPcmys0851vTIP6wSnkqJC7rr4bGZ88HpI8fWJJnilVL0iImQlRDFrSylztlnfZdo9083gNgkhn+AUn9iEhOQU3vr3w+zM3WY5vj7RBK+UqnfOD57g9Pz87SG1MriiexpJKWmMv+9mamuqLcWKCKPvegyAif+4zfLa/PpEE7xSqt5x2ISxfTIprvYxcVG+5Xi3y851nRxs27CWj1981nJ8atPmXDjuDpbNmcXXH79tOb6+0ASvlKqX2iRFc352Movzytkdwi73nk3jOLmVh8kvP8em1dZ7Hp46/GKye/Zl1mcfNNireF1Fo5Sqt2p9fiq9fjxRoa3oLq32cf23xSQ0SeHBSZ/icDotxZcUFRIbF1+vz3HVVTRKqQbJabfhiQqc47oot8xyfHyUnWs6OYMnOD1nOd6TlIzD6aK8tJjVi+dZjo80TfBKqXpvyvoi7v9mG4vzrHeM7NM8ngEt4g/rBKcXHvwbT9x4BUU7rb8fEEma4JVS9d5pxyTS3OPiuXl5VNRaX1Uzpmf6YZ3gNOK626itqeKlf97RoOrxmuCVUvWey27jht6ZFFR4eXXJTsvxnigHV2dH8dOqZXw2abzl+MyWbbjgmltY+PU0vv/iY8vxkaIJXinVIHRMieGcDkl8sX43P263XqrplxVP36x43n/+KXI2rrMcP/SiK2nbuTuvPn4vxYXWX2QiQRO8UqrBuOi4VHo2dRPlsJ66RISre6QT7XYz/r6bLZ/gZLPbGXPfE3To2hO/v2Gc/qQJXinVYEQ5bNx9UhYdUmJCik+McTCmUzTrly8O6QSn5m3ac/O/XiIpNSOk8euaJnilVINT5fUzYWE+K3ZUWI4d2NJDr2Zxh3WCU0FeDs/efh0lRfX72GlN8EqpBscYWJBTxrNz86j2WjuBSUS45oQMXP5aJoR4glNFWQnzZkzhtcfvsRxblzTBK6UanBinjet7ZZBXVsubywosxzeJcTD6+HTWLJnPtHdesRzfol0nzhs9lu+/+IQFX0+1HF9XNMErpRqk4zICJzh9snoXqwusncMKcHJrDz0y3bzz7CPkb9tsOf6cP19Ly/bZvPTwHZSV7LYcXxc0wSulGqzLuqWSEutgwoLtljcg7SnV2BwOXnjgVsulGofTxVX3PUlJUSEfjn/aUmxdCduZrEopFW6xTju39G+GJ8qOiFiOT3U7uSLbzXPzfmDGB68z5IJLLcW36tiZGx8fT3aPvpbHrgt6Ba+UatA6psTQNN6FMSakw0GGtEmgc+8BIZ/g1HPQ6cTGe/DW1lBVYX0DVjhpgldKNQrjF+Rz54wt1Pqsr6oZm5YDhH6Ck7e2hrsvPYdJT95vOTacNMErpRqFnk3j2FxczbsrCi3Hpse5uCw7LuQTnBxOF8f1PYmZH73FsjnfWo4Pl7AmeBE5Q0TWiMh6Efl7OMdSSh3dejaLY1ArDx+sLGRjUZXl+DPaJpLdsy+v/+tBCvPzLMcPv+omMlsdwwsP3EZlufXe9eEQtgQvInbgOeBMIBu4UESywzWeUkqNPj6d+Cg7z8zJw+u3VmqxiTA2Ix+/18uLD/3dcqnGFRXNVfc+SWF+Lm/++yFLseESziv4XsB6Y8xGY0wN8DYwLIzjKaWOcvFRdq45IYNdlV5yS2ssx2fGu7gkO56cn9aH1DGyfdcenDHqL6xftpiaKutr84+0sJ3JKiLnA2cYY0YHv74E6G2Muf5X3zcGGBP8sgOwJiwTghTA+pa3+kPnH1k6/8hqyPMP99xbGmNS9/cX4VwHv79Fqb95NTHGTAAmhHEegcmILDjQwbQNgc4/snT+kdWQ5x/JuYezRLMNyNrr6+ZAbhjHU0optZdwJvj5QDsRaS0iLmAkMDmM4ymllNpL2Eo0xhiviFwPTAXswEvGmBXhGu8QhL0MFGY6/8jS+UdWQ55/xOYetjdZlVJKRZbuZFVKqUZKE7xSSjVSjT7Bi0iWiMwUkVUiskJExkV6TlaISLSIzBORpcH5169uRodAROwislhEPo30XEIhIptEZJmILBGRBZGejxUikigi74vI6uDPQP3sa7sfItIh+Jzv+SgRkRsjPS8rROSm4M/tchF5S0Si63T8xl6DF5FMINMYs0hE4oGFwLnGmJURntohkUCTa7cxpkxEnMBsYJwxZk6Ep3bIROSvQE/AY4w5O9LzsUpENgE9jTENbqONiLwKzDLGTAyuZos1xtTP44cOItj6JIfAZknrxy9FgIg0I/Dzmm2MqRSRd4HPjTGv1NUcGv0VvDEmzxizKPh5KbAKaBbZWR06E7Cnc5Ez+NFgXpVFpDlwFjAx0nM52oiIBxgIvAhgjKlpiMk96FRgQ0NJ7ntxADEi4gBiqeO9QI0+we9NRFoB3YG5kZ2JNcESxxJgB/ClMaYhzf9p4DbA+tH19YcBponIwmBrjYaiDbATeDlYIpsoIu5ITypEI4G3Ij0JK4wxOcATwBYgDyg2xkyryzkcNQleROKAD4AbjTElkZ6PFcYYnzGmG4HdwL1EpHOk53QoRORsYIcxZmGk53KY+htjjifQGfU6ERkY6QkdIgdwPPBfY0x3oBxocG27g6Wlc4D3Ij0XK0QkiUCDxdZAU8AtIhfX5RyOigQfrF1/ALxhjPkw0vMJVfDX66+BMyI8lUPVHzgnWMN+GzhFRF6P7JSsM8bkBv/cAXxEoFNqQ7AN2LbXb3zvE0j4Dc2ZwCJjTH6kJ2LRYOAnY8xOY0wt8CHQry4n0OgTfPBNyheBVcaYpyI9H6tEJFVEEoOfxxD4T7M6srM6NMaY240xzY0xrQj8iv2VMaZOr2AOl4i4g2/OEyxvnAYsj+ysDo0xZjuwVUQ6BG86FWgQiwt+5UIaWHkmaAvQR0Rig3noVALvAdaZcHaTrC/6A5cAy4J1bIA7jDGfR3BOVmQCrwZXEdiAd40xDXK5YQOVDnwU+PnEAbxpjPkislOy5AbgjWCZYyPw5wjPxxIRiQWGAFdFei5WGWPmisj7wCLACyymjtsWNPplkkopdbRq9CUapZQ6WmmCV0qpRkoTvFJKNVKa4JVSqpHSBK+UUo2UJnjVqIjI2GDXxDdCiG0lIqPCMa/g/Q8UkUUi4hWR88M1jlJ7aIJXjc21wFBjzEUhxLYCLCf44B6FQ7EFuBx40+oYSoVCE7xqNETkeQINtiYH+3C7ReQlEZkfbLY1LPh9rURkVvBqepGI7Nk+/ggwINh7/CYRuVxE/rPX/X8qIoOCn5eJyAMiMhfoKyI9ROSbYEOyqcE21fswxmwyxvxIw268phqQo2EnqzpKGGOuFpEzgJONMQUi8jCB9ghXBNs9zBOR6QS6cg4xxlSJSDsC2+B7EmjEdcuenvUicvlBhnMDy40x9wR7HX0DDDPG7BSREcBDwBXheqxKHQpN8KoxO41As7Nbgl9HAy0I9OT+j4h0A3xA+xDu20eggR1AB6Az8GWwpYGdQHtYpSJKE7xqzAQYboxZs8+NIvcB+UBXAmXKqgPEe9m3jLn3cWtVxhjfXuOsMMY0mOPw1NFBa/CqMZsK3BDs5IeIdA/engDkGWP8BBrR7XmTtBSI3yt+E9BNRGwiksWB2wSvAVL3nHcqIk4ROfaIPhKlQqAJXjVmDxI44vBHEVke/Brg/4DLRGQOgfJMefD2HwGvBA44vwn4DvgJWEbgZJ5F+xvEGFMDnA88KiJLgSXsp++3iJwgItuAC4DxIrLiyDxMpfZPu0kqpVQjpVfwSinVSGmCV0qpRkoTvFJKNVKa4JVSqpHSBK+UUo2UJnillGqkNMErpVQj9f+nPYvP84MSggAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "kernel = 'linear' # one of ['linear', 'poly', 'rbf']\n",
+    "\n",
+    "attack_point, poisoned = get_adversarial_examples(train_data, train_labels, 0, test_data, test_labels, kernel)\n",
+    "clean = SVC(kernel=kernel)\n",
+    "art_clean = SklearnClassifier(clean, clip_values=(0, 10))\n",
+    "art_clean.fit(x=train_data, y=train_labels)\n",
+    "\n",
+    "plot_results(art_clean._model, train_data, train_labels, [], \"SVM Before Attack\")\n",
+    "plot_results(poisoned._model, train_data, train_labels, [attack_point], \"SVM After Poison\")\n",
+    "\n",
+    "clean_acc_train = np.average(np.all(art_clean.predict(train_data) == train_labels, axis=1))\n",
+    "poison_acc_train = np.average(np.all(poisoned.predict(train_data) == train_labels, axis=1))\n",
+    "clean_acc_test = np.average(np.all(art_clean.predict(test_data) == test_labels, axis=1))\n",
+    "poison_acc_test = np.average(np.all(poisoned.predict(test_data) == test_labels, axis=1))\n",
+    "\n",
+    "print(\"Clean model accuracy on train set ({} samples): {}\".format(len(train_labels), clean_acc_train))\n",
+    "print(\"Poison model accuracy on train set ({} samples): {}\".format(len(train_labels), poison_acc_train))\n",
+    "print(\"Clean model accuracy on test set ({} samples): {}\".format(len(test_labels), clean_acc_test))\n",
+    "print(\"Poison model accuracy on test set ({} samples): {}\".format(len(test_labels), poison_acc_test))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A perfect classifier would have all points in yellow on the orange side of the decision boundary and all points in blue on the light blue side of the decision boundary. The attack point is shown in red and support vectors are circled.\n",
+    "\n",
+    "Even with small changes in overall accuracy, inserting just a *single* poison point can have major impacts on the model's ability to generalize well. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/adversarial-robustness-toolbox/notebooks/poisoning_defence_strip.ipynb b/adversarial-robustness-toolbox/notebooks/poisoning_defence_strip.ipynb
new file mode 100644
index 0000000..5b3719c
--- /dev/null
+++ b/adversarial-robustness-toolbox/notebooks/poisoning_defence_strip.ipynb
@@ -0,0 +1,517 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Defending against backdoor poisoning attacks using STRIP"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using TensorFlow backend.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from __future__ import absolute_import, division, print_function, unicode_literals\n",
+    "\n",
+    "import os, sys\n",
+    "from os.path import abspath\n",
+    "\n",
+    "module_path = os.path.abspath(os.path.join('..'))\n",
+    "if module_path not in sys.path:\n",
+    "    sys.path.append(module_path)\n",
+    "\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "import keras.backend as k\n",
+    "from keras.models import Sequential\n",
+    "from keras.layers import Dense, Flatten, Conv2D, MaxPooling2D, Activation, Dropout\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "from mpl_toolkits import mplot3d\n",
+    "\n",
+    "from art.estimators.classification import KerasClassifier\n",
+    "from art.attacks.poisoning import PoisoningAttackBackdoor\n",
+    "from art.attacks.poisoning.perturbations import add_pattern_bd, add_single_bd, insert_image\n",
+    "from art.utils import load_mnist, preprocess\n",
+    "from art.defences.detector.poison import ActivationDefence\n",
+    "from art.defences.transformer.poison import STRIP"
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": "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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### The classification problem: Automatically detect numbers written in a check\n",
+    "![image.png](attachment:image.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "(x_raw, y_raw), (x_raw_test, y_raw_test), min_, max_ = load_mnist(raw=True)\n",
+    "\n",
+    "# Random Selection:\n",
+    "n_train = np.shape(x_raw)[0]\n",
+    "num_selection = 7500\n",
+    "random_selection_indices = np.random.choice(n_train, num_selection)\n",
+    "x_raw = x_raw[random_selection_indices]\n",
+    "y_raw = y_raw[random_selection_indices]\n",
+    "\n",
+    "BACKDOOR_TYPE = \"pattern\" # one of ['pattern', 'pixel', 'image']"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Adversary's goal: make some easy money "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<img src=\"../utils/data/images/zero_to_one.png\" width=400>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import HTML\n",
+    "HTML('<img src=\"../utils/data/images/zero_to_one.png\" width=400>')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "max_val = np.max(x_raw)\n",
+    "def add_modification(x):\n",
+    "        if BACKDOOR_TYPE == 'pattern':\n",
+    "            return add_pattern_bd(x, pixel_value=max_val)\n",
+    "        elif BACKDOOR_TYPE == 'pixel':\n",
+    "            return add_single_bd(x, pixel_value=max_val) \n",
+    "        elif BACKDOOR_TYPE == 'image':\n",
+    "            return insert_image(x, backdoor_path='../utils/data/backdoors/alert.png', size=(10,10))\n",
+    "        else:\n",
+    "            raise(\"Unknown backdoor type\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def poison_dataset(x_clean, y_clean, percent_poison, poison_func):\n",
+    "    x_poison = np.copy(x_clean)\n",
+    "    y_poison = np.copy(y_clean)\n",
+    "    is_poison = np.zeros(np.shape(y_poison))\n",
+    "    \n",
+    "    sources = np.arange(10)\n",
+    "    targets = np.array([1] * 10)\n",
+    "    for i, (src, tgt) in enumerate(zip(sources, targets)):\n",
+    "        n_points_in_tgt = np.size(np.where(y_clean == tgt))\n",
+    "        num_poison = round((percent_poison * n_points_in_tgt) / (1 - percent_poison))\n",
+    "        src_imgs = x_clean[y_clean == src]\n",
+    "\n",
+    "        n_points_in_src = np.shape(src_imgs)[0]\n",
+    "        indices_to_be_poisoned = np.random.choice(n_points_in_src, num_poison)\n",
+    "\n",
+    "        imgs_to_be_poisoned = np.copy(src_imgs[indices_to_be_poisoned])\n",
+    "        backdoor_attack = PoisoningAttackBackdoor(poison_func)\n",
+    "        imgs_to_be_poisoned, poison_labels = backdoor_attack.poison(imgs_to_be_poisoned, y=np.ones(num_poison) * tgt)\n",
+    "        x_poison = np.append(x_poison, imgs_to_be_poisoned, axis=0)\n",
+    "        y_poison = np.append(y_poison, poison_labels, axis=0)\n",
+    "        is_poison = np.append(is_poison, np.ones(num_poison))\n",
+    "\n",
+    "    is_poison = is_poison != 0\n",
+    "\n",
+    "    return is_poison, x_poison, y_poison"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Poison training data\n",
+    "percent_poison = .33\n",
+    "(is_poison_train, x_poisoned_raw, y_poisoned_raw) = poison_dataset(x_raw, y_raw, percent_poison, add_modification)\n",
+    "x_train, y_train = preprocess(x_poisoned_raw, y_poisoned_raw)\n",
+    "# Add channel axis:\n",
+    "x_train = np.expand_dims(x_train, axis=3)\n",
+    "\n",
+    "# Poison test data\n",
+    "(is_poison_test, x_poisoned_raw_test, y_poisoned_raw_test) = poison_dataset(x_raw_test, y_raw_test, percent_poison, add_modification)\n",
+    "x_test, y_test = preprocess(x_poisoned_raw_test, y_poisoned_raw_test)\n",
+    "# Add channel axis:\n",
+    "x_test = np.expand_dims(x_test, axis=3)\n",
+    "\n",
+    "# Shuffle training data\n",
+    "n_train = np.shape(y_train)[0]\n",
+    "shuffled_indices = np.arange(n_train)\n",
+    "np.random.shuffle(shuffled_indices)\n",
+    "x_train = x_train[shuffled_indices]\n",
+    "y_train = y_train[shuffled_indices]\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Victim bank trains a neural network"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From /Users/ebubechuba/anaconda3/envs/art/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
+      "\n",
+      "WARNING:tensorflow:From /Users/ebubechuba/anaconda3/envs/art/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
+      "\n",
+      "WARNING:tensorflow:From /Users/ebubechuba/anaconda3/envs/art/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
+      "\n",
+      "WARNING:tensorflow:From /Users/ebubechuba/anaconda3/envs/art/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3976: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
+      "\n",
+      "WARNING:tensorflow:From /Users/ebubechuba/anaconda3/envs/art/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:133: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n",
+      "\n",
+      "WARNING:tensorflow:From /Users/ebubechuba/anaconda3/envs/art/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
+      "WARNING:tensorflow:From /Users/ebubechuba/anaconda3/envs/art/lib/python3.6/site-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
+      "\n",
+      "WARNING:tensorflow:From /Users/ebubechuba/anaconda3/envs/art/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3295: The name tf.log is deprecated. Please use tf.math.log instead.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Create Keras convolutional neural network - basic architecture from Keras examples\n",
+    "# Source here: https://github.com/keras-team/keras/blob/master/examples/mnist_cnn.py\n",
+    "\n",
+    "model = Sequential()\n",
+    "model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=x_train.shape[1:]))\n",
+    "model.add(Conv2D(64, (3, 3), activation='relu'))\n",
+    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
+    "model.add(Dropout(0.25))\n",
+    "model.add(Flatten())\n",
+    "model.add(Dense(128, activation='relu'))\n",
+    "model.add(Dropout(0.5))\n",
+    "model.add(Dense(10, activation='softmax'))\n",
+    "\n",
+    "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From /Users/ebubechuba/anaconda3/envs/art/lib/python3.6/site-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
+      "Epoch 1/3\n",
+      "90/90 [==============================] - 13s 140ms/step - loss: 0.7521 - acc: 0.7588\n",
+      "Epoch 2/3\n",
+      "90/90 [==============================] - 14s 152ms/step - loss: 0.1989 - acc: 0.9428\n",
+      "Epoch 3/3\n",
+      "90/90 [==============================] - 11s 125ms/step - loss: 0.1243 - acc: 0.9629\n"
+     ]
+    }
+   ],
+   "source": [
+    "classifier = KerasClassifier(model=model, clip_values=(min_, max_))\n",
+    "classifier.fit(x_train, y_train, nb_epochs=3, batch_size=128)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# The victim bank evaluates the model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Evaluation on clean test samples"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Clean test set accuracy: 96.72%\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Prediction: 0\n"
+     ]
+    }
+   ],
+   "source": [
+    "clean_x_test = x_test[is_poison_test == 0]\n",
+    "clean_y_test = y_test[is_poison_test == 0]\n",
+    "\n",
+    "clean_preds = np.argmax(classifier.predict(clean_x_test), axis=1)\n",
+    "clean_correct = np.sum(clean_preds == np.argmax(clean_y_test, axis=1))\n",
+    "clean_total = clean_y_test.shape[0]\n",
+    "\n",
+    "clean_acc = clean_correct / clean_total\n",
+    "print(\"\\nClean test set accuracy: %.2f%%\" % (clean_acc * 100))\n",
+    "\n",
+    "# Display image, label, and prediction for a clean sample to show how the poisoned model classifies a clean sample\n",
+    "\n",
+    "c = 0 # class to display\n",
+    "i = 0 # image of the class to display\n",
+    "\n",
+    "c_idx = np.where(np.argmax(clean_y_test,1) == c)[0][i] # index of the image in clean arrays\n",
+    "\n",
+    "plt.imshow(clean_x_test[c_idx].squeeze())\n",
+    "plt.show()\n",
+    "clean_label = c\n",
+    "print(\"Prediction: \" + str(clean_preds[c_idx]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### But the adversary has other plans..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Prediction: 1\n",
+      "\n",
+      " Effectiveness of poison: 100.00%\n"
+     ]
+    }
+   ],
+   "source": [
+    "poison_x_test = x_test[is_poison_test]\n",
+    "poison_y_test = y_test[is_poison_test]\n",
+    "\n",
+    "poison_preds = np.argmax(classifier.predict(poison_x_test), axis=1)\n",
+    "poison_correct = np.sum(poison_preds == np.argmax(poison_y_test, axis=1))\n",
+    "poison_total = poison_y_test.shape[0]\n",
+    "\n",
+    "# Display image, label, and prediction for a poisoned image to see the backdoor working\n",
+    "\n",
+    "c = 1 # class to display\n",
+    "i = 0 # image of the class to display\n",
+    "\n",
+    "c_idx = np.where(np.argmax(poison_y_test,1) == c)[0][i] # index of the image in poison arrays\n",
+    "\n",
+    "plt.imshow(poison_x_test[c_idx].squeeze())\n",
+    "plt.show()\n",
+    "poison_label = c\n",
+    "print(\"Prediction: \" + str(poison_preds[c_idx]))\n",
+    "\n",
+    "poison_acc = poison_correct / poison_total\n",
+    "print(\"\\n Effectiveness of poison: %.2f%%\" % (poison_acc * 100))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Evaluate accuracy on entire test set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      " Overall test set accuracy (i.e. effectiveness of poison): 97.90%\n"
+     ]
+    }
+   ],
+   "source": [
+    "total_correct = clean_correct + poison_correct\n",
+    "total = clean_total + poison_total\n",
+    "\n",
+    "total_acc = total_correct / total\n",
+    "print(\"\\n Overall test set accuracy (i.e. effectiveness of poison): %.2f%%\" % (total_acc * 100))"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Filter Poison Using STRIP"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 100/100 [00:00<00:00, 112.97it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "strip = STRIP(classifier)\n",
+    "defence = strip()\n",
+    "defence.mitigate(clean_x_test[:100])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Abstained 1689/5590 poison samples (30.21% TP rate)\n",
+      "Abstained 136/9900 clean samples (1.37% FP rate)\n"
+     ]
+    }
+   ],
+   "source": [
+    "poison_preds = defence.predict(poison_x_test)\n",
+    "clean_preds = defence.predict(clean_x_test[100:])\n",
+    "\n",
+    "num_abstained_poison = np.sum(np.all(poison_preds == np.zeros(10),axis=1))\n",
+    "num_abstained_clean = np.sum(np.all(clean_preds == np.zeros(10),axis=1))\n",
+    "num_poison = len(poison_preds)\n",
+    "num_clean = len(clean_preds)\n",
+    "\n",
+    "print(f\"Abstained {num_abstained_poison}/{num_poison} poison samples ({round(num_abstained_poison / float(num_poison)* 100, 2)}% TP rate)\")\n",
+    "print(f\"Abstained {num_abstained_clean}/{num_clean} clean samples ({round(num_abstained_clean / float(num_clean) * 100, 2)}% FP rate)\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/adversarial-robustness-toolbox/notebooks/poisoning_defense_activation_clustering.ipynb b/adversarial-robustness-toolbox/notebooks/poisoning_defense_activation_clustering.ipynb
new file mode 100644
index 0000000..ee308da
--- /dev/null
+++ b/adversarial-robustness-toolbox/notebooks/poisoning_defense_activation_clustering.ipynb
@@ -0,0 +1,767 @@
+{
+ "cells": [
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": "iVBORw0KGgoAAAANSUhEUgAAB40AAAQPCAYAAAAEUn3mAAAAAXNSR0IArs4c6QAAQABJREFUeAHs3Ql8H2Wdx/F55p+0aWnL1RbaJFAQuQooTVMPrnI1ibqgK66rAiKoCK4HeK+roKKoi+fuerKAgNeqKK7apAUpCLLbNOUSEbdKsUlaaCk3PdL/f/b7pJl2Mv9n/ufk/ow+/c8888wzz/Oe+c+fV37zzHgeEwIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIDDhBMxY7XHjorZVQeAdUXX7TZBTHdtN4G3zjJLnveAFXm/OM+uEs9YLgju6nzJ3e2uW2nXjampobr3S88x78zoV5M7tXtXxs7x8MsasAMd6zB46V8NNY3PrA4FnDnKtzHnZ83s7l/3YtY68iSnA939iHnd6jQACCCCAAAIIIIAAAggggAACCCCAAAIIlCNQU07hUVU2COqMMVOrb9NA3DwaPjfekX5YsTFewz7eFq+59Q4vyH2te9WypeGqsf6poFOtb7w8Q0XRM2O9b7R/sADHerDHWF5qbF5ygmfM/OglK9ofP8hcrOVhDxrXL2w5J/CCDb2rli2PtqeS+TTrqmT/420bvv/j7YjSHwQQQAABBBBAAAEEEEAAAQQQQAABBBBAIH2BXbHR9KsePzUqODNFAeoW42d+09jcdmf9graXj5/e0RMEEBhTAiZzQaH26j6XE/dvbplfqEya6xqaTju6cVHrHb7vX+/7ZlY1dadZVzXtYFsEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBCaaAEHjco+48Y7zM97t9Qvbzip3U8ojgAAC1Qjsc0jbDD2Wv+i1p8b476pmP6VsO/Ow46YrWPxlL1OzWo+5P6GUbZLKpFlX0j7IRwABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgWQBgsbJNslrjDdJo/l+3Liw7fzkQqxBAAEE0hWYunfujbr25D1SPr4XEwTn7HfMkj3i+WktNyxsXTxlxvSHFSy+xHimqtccpFlXWv2jHgQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEJppAVX/sH41Ygef9nxd4CmaUMwW1Kj3ZM16dCcw0vZfzID2OumDARYEbG3D/xtymJb/v7Vr2p3L2RlkEEECgIgFj8m5U0fXqNgVuTx5UnzF71taZNynv6kH5aS343mIFjOekUl2adaXSICpBAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQGDiCYy/oHEQ/Lins/0T1R7K+mPaGsyk4AIFkt+jAPK+zvqMNzmT8b+jdScpKV49xiYT9AaBuTfe6iDnPxXPY3mMC3Csx/gB9Dz7nmIFh/Pfp54LPuoZ06Fr1V7RTuoGmIu0PDRB4+iOmEcAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIExLzDugsZpHZGe+5d2q65P6RGvV9XW+T9WsObV7rrNCY0LW9+4blX7j9zrR29uz8r2L6t1NjGNcwGO9dg/wBnPf1teLwJvQ/eqjs6G5tZf6Rp19qD1xixoWNiySOtXDspnAQEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBCICdhHLDMVEHjs/mXPd282rw8Cb2lSscB4b01aRz4CCCBQtUBTU60xwTn59QQdyrNPOfh5/jo9QNr0jzZ2rSIPAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEENglQNB4F0WBmTVLt3kv5M7Wu0Ofc5Yy3mn7H9s2y7mOTAQQQKBKgUZv31drJPHsvGoCr93m5XIb23Vjywvx9YExb9zz6OP3juezjAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAghEBQgaRzUKzHc/2LFZq7/pKqJgTk1txvs71zryEEAAgWoFAj9zQbwOBYlzwZZgmc3v7epSwDjIG21sPG/KtMnTeRJCHI9lBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQGCRA0HgQR+GF7Vnz7cQSvveixHWsQAABBCoUmHns6XM947XlbW6ClQM3swysCm7MK6MMPdb6Qlc+eQgggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIBAKFATzvBZXODxrqWPNDa3bVcAZ1Je6SCoz8urIGOvly7ea/qkupflPG9/bb6vCYI9g8A8pXeTPp4Lso/nspl7N9yzdGMFVQ/rJvaRuFOnTHmRCUx/sjs3xt+k0ZCbcjnvcc/buHrn6MjhadZod9XjzY+syWQP9HxT7wX+XC/QGFIv2KD3Za/f/sxz/7vp4bueHR6pdPYyGvozt6lpas6feZDv+Qf7Xu5gjbqdYT01Prdnaza4b9M9y3vT6e3Q1lJXW3OO2p7J20tu56Opw/zuzo7luj5t0PXJXjt2Tbp2HF6/YMkpPauX/XZX5gSemXPsKQfq2RAvluk+nufvK699jBfUBjnzlOcHTwY570k/2PFId9ctfxCTfV/0kE/7HbNkD5PJHFiTCebpkeIHqm2zjJdbp93/ZcuOYI3O1fVqxLC0Zcg7W2AHo+13o0BTWYUAAggggAACCCCAAAIIIIAAAggggAACCIw7AYLG5R3SnP5qv1Z/0D/UsVnFQeOZTS1zJvvmYg37flXgmZeqbn/XEHCjh19rh3bKmIxiil6uobm1S/GDpTv6zLUb7m1f27+ygn/qF7W8S7X/Q96mOe/T3avaV+TlF8iwQY+aSebvfd/o3c/eQtWrgIwm2/aB9vcvayFjw1/B7O3qx91adUtfn3djNf3YWW/+vyPl2tDc8j4dtDMHtSjQqNDOjo9G82R2UO3kzDkiOVsOL94VF+w3k+AA3JQZM6zVbQoZ/aR71dbved6KHdF6Spmv5liPxv4U6LOZu7DlDN+YDytY+srdkdad3yhL62m2zn6PFrXerhPxB93ZTd/zurr6wjrrF7V9XEHEU8Pl/s/A+2N3Z/s/DcobroXAO3/gVBi8xyDX/z7jSGbWM8EP9YW7JJLXP+vX+O/STMVBY1ldre0PDuuV40HhfPRT5+zHVfbt0Tw7n/WyH16/cvkqO59mXba+YlNDwyumBPvPOEvnxClyXCyfeV7epJb3nyLqWf9nrW2nXklg7tBNBr/q3rLuBu/BB7fnbVZNxiFtkxv3Cv5eNzK8U+fq4p1Vhd96u9TfEG9Krcya215Qy/4ox6t6O5f9l1aOWAC54SWn1HuTJ9mnbky1rYxPutHphnWdHdfG813Lo/F3w9VO8hBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgYkgQNC4zKOsAG7WvYkp+4/4/X98nzTpcoUJbOBwsq1XgYGCk/avSIJptqm21vtow8LWb/dtz13x2P3LNHq3zCnwD1F9J8e3ypnct+J5BZaNgopvV5D0CvVjti1XrA/9dWm0tsqfpPmT1I9P1De3Xr1tR/azaYz+HGlXBf4P9T0zyFUnx7b+fu/8x1cQ+MNy+rSwFBIqMu20alHZlobmug96QesHFNT/TZGtBq+u4liPyv4M7l3/UmNzy9vUVhssPtyxelDWwPdIx8ic3ODPfq9pbr1gXWd7py2kgPF8nZuDj58JpgyqYJgW5ja1Hq+25t+kEgS93V3L+tsbbUo2G9yYyeQHjRVifO2s5sX7b+xcsSFavuT5wCxSO44uXt4cqfP6yHg5kzN778pLs65dlebP2JHmJjPL3hhjv2v75ZconKPt7I0vr9WdOq9tmNp4WbCo4XM9KztssLTsa310TzMPO2765BkzLtN5dp6um/uqbUUn2dsA7cKMl/mRgtkf0+us/6Vn5bJfFd0w5QL2Rhwv49+mNusml/wpCIKrFTD+Xv6avJxR97uR10IyEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBCSawcyjTBOt0xd3VyDA9Mtj9x3I9b7mcevWH/9dptNb9CnAp4LozYFzO9v1lbTDRN++prcusqV+05DVlb5/CBgoYX6nHTn9HAZb+gHFFVaofGgV48ZTazENzFrScUFEdAxuNdlcbuGtc1LpCx/1KBYyKB4xjGDYgqmP+6/qFbe+PrRqRxVHSn0zDopb/1FDRa0oJGMehbDBUjwS+e7SYRtunY31+dDmc13XoBs3rKfaDp96ujtUKadrHKg+edK7VeXV5I4AHFxo/S3MWnb4w489ao5s3vqRelR0wjkvovGrUY86/qRG/N3rz5+e/niC+QcLy3KYlh9fNmL5ST4z4gOrcN6FYwWxda1/ie5n/tk8fsCN1CxZOcaX9rtf5plDA+D80Gv+d2mXeeRlvxmj73Yi3j2UEEEAAAQQQQAABBBBAAAEEEEAAAQQQQGAiChA0LuOo10/PHa4/2DtHZ+tptyWP9NUfzD+vem5S2vkI5zLa4CqqUV/TTZC5uX5R66Wu9UOV19jcerECxh9Jr34zI5Px2+37Vyupc7S72pGPCtz9WmNZqwqMWxs9v/wrGp19XiVOaW0zKvqjGzkaFrX9VAPwncHVUvuq71DGmqqu95S6zVCXsyNSNRo1//Hx2nE2m00czZkzwbWutmkU9juUv/uJ3a5C4yBvbnPLaRqRe5tuypiTdnd0g8GbG/ZotI/qLntqbFryWj+TWVnJjQ2unamexZMm+z/QuiH/HVdwenadmfJb7fMwV1v0CO8vDzy+vego7NH2u+HsD5kIIIAAAggggAACCCCAAAIIIIAAAggggMAEFHAGQCegQ2ldzngvSSoYeH7/+zqT1of5eoTuBzQismCgVY/4/JOCRe05Y9bpj/GbFGTc1zfBAQr6tCX90V7BDA1eM1/S+1in9Kxc+tlwf0P1OXdh6xkanfl1Bduck0ZC/o9GPHapzHo/563XE123BL45wAS5A9Wflymgs8C1Yf9jWGsy1+s9pC/u7r57i6uMK2+0u9rj42dmf19td/Y7CDwd5+BBjTpfowedz9KxPkjbFHwcsA74ZarveqWiI/tcZtXkjZL++A37eL/ROZh4k4EiWHqcfPAHlfkffa+61Gc95teXbXCGzsOD4gYq93XlPxbPH4nlKdNnvEHnQ95IUvVj5frVtzyU1Ka+F/q+P2lq7RfiN7jomB0wt7ntVb2dS/87adukfHn9VS67fi90vs5SfTPj5eXdI8Nn4vnqx/NhXpp1hXWGn40Llxyl46sbM7zk0cCBp2t1YEdjr5dlr76aLwQmd6BugJmn/IPUh2NlNy2sM/6pdefoxpbrelYv+218XdKybuh5g8x+LBv9P2EKgqf1fmM7UrxL7XpAV/SZuhYcIedXarO8R37312LMGXpywVXrVrZfmlBr1dn7H9s2q7bGu1UVHZFQ2efWrWr/eMK6Qdmj7XdjUONYQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEJrjAriDABHco2v1Z8xdP0yOUP+kqqD/wB9u39N3iWhfNa1zY+o8KSPxrUtRA1dyeywWX9j9iNrrh7vlL7GNXa7zMVxREOH539u45BRk+PXfhkpW9q5Yt352b7tw+h7TN8P3g++pH3qhFBUZ+lTU7PrV+5XIFZpInjQpuVZDmCpVoipdSvfXenL3e7XV7V8XXuZbHgquO+ynq16Dvm/K2mCD43LYtfd99/A+35gUqFaw5sqYmuEQ3Crzd1W+dA/MUhHlN76r2X7rXD13uaOhPfXPbhTItFDD+4rbnt3xm44MrnnNIvH9uU9sSjS7+NwXlDo2t3y+2PCKLgQkuUIAyb9+6oeC6vMxIhj2XNJpzqTb9u0h2/6yGpF6kmbKDxgpKvjZalx4Df7nOv8uieTvncx9et7LDjn5NnNKsK76TwPftjSzOgLGur9/PmuxX13cWvjbNbVo8M+NPVhDUv0iG/e+aj+/HZPwPKa+koHHD/JZ9VPbf9T3OP5haoRtsuoNscG5PV8eK/kX9E5v8xoVt52k88eeU7zg3zSW6nj7U3dnx3dh2VS/WLzpV71wO9NtmjnJVpuv9Zd2dSz/tWhfPG22/G/H2sYwAAggggAACCCCAAAIIIIAAAggggAACCEx0AcUQmEoRmLxH3Vc1OvZFrrKKBax2Bf2iZW0gIvC977oCBzbonMt5l+jxnosLBIz7q7PBWAVdTtQGH4nWH84rLOH7JvODmceePjfMS/tzyl7Bm1wj8WzAWAGE1xYLGNv2KMDRvn1r9iRtc6+7fcEl7vzBuWPFVV7xgPEvsn3bjljX2X5F0rmz4Z6lf9Q58Y5cLneuIkvbB/d851LGmH9y5Q913kj3pz+YZYIrXP1UEO45fT/O6l659CMJAeP+zXq7li7rftI7xguC/3LVM5J5cxacphGmRiNMY1PgbXtu67M/iuXmLWrE6rV5mTszWvSo4YMS1o3pbL2T+iydlyc7OxEEV+q7dHYp16berhWb1nV2XLJjx7bDbEDXVZ+iv6fu9dLFe7nWxfOCqf5Vapfzne+6/i3NZbceq4DxbdouiG87sJxbt2rpNX19evd24P3NVUb1f0r5qf6e22C3CSbdorqPce1Tjf1oqQFju/1o+t1w9Yc8BBBAAAEEEEAAAQQQQAABBBBAAAEEEEBgoguk+kfm8YqpUVzv0B/OL0jqXzbI/mvSujDf9+s+ojqmhcvRT/3x/fyeVUu/Gs0rMh90r+r4Yi7wLnaVU+B4Zl1tzcdc69LIUzDrzHg9OwPfW96mfD0OuLTpsfuXPR9s02hIPZY1bwtj5trAWV5+LGNsugbfVkDzdevv+e2jse44F3tWddyQ87wvuFYqqHWaAqjxkbKuokOYN/z98b3az+r7ZEdwDpp0Hj6R3bFjkb4fPxu0ImlhzdJtCty/WfG6nyQVGYn8TE3tee79Bjc//cCdT7rX7c7tzm78lQKMm3bn7JyzN5XUTjYXxvPHw7Iee/0uVz/k8DMd45Ienxzd3n4/TTb3nmjernljaqfV1OUH9XcV2DlT39Rysh4jb6+LeZNuFfqCgq6vtkHqvJWODN1EslE3kLxOvxdb8lbr/c16l/MpefkVZtiAuJlqlut8eamrCrX9/bqGOa9JrvI2bzT9biS1kXwEEEAAAQQQQAABBBBAAAEEEEAAAQQQQGAiCxA0LnD0Gxa2LNL7In+hxyh/p0Cxh3o7lxUMOM1qXry//mL+blcdCnJd3dPZfp1rXbG8ns6l31Sw63cJ5S6w76JMWFdldnBQfgXmD6UGP6Lb9ty/tFujIp2PV67xMwWDIGPRVQGsB9ZlN7oDUVGY2LxZ/9SVChBvjmXbQIzxc5OcjyqPlx2K5ZHoz9ym0w4IAvMOZ38C7wuF3vfr3EY3OmzJBu9zBuMSNhja7MU1esz8ua59KFh3nSs/L6+rq0959h3a+ZMxb/Pmz3c+wjm/8NjI6R/1a7yT4q21N7N42dxHla/DW/60rmvZL7TlKueWJqh35kcy9dgH5xMT1Ky79bSFf1bRstrV/ySKIPh0ZBe7ZjOeecuuhSpm9m46bc/ptVOW6+KyIF6N9cx5uYvU9q/F1xVfHh2/G8XbSQkEEEAAAQQQQAABBBBAAAEEEEAAAQQQQGBiCgx6ZO54IPADM3Nu05LDy+mLyZnJgW+ma0TYDL3D8hAlPbLWnKBRVgVHcOqv/VkTZO3oNg0ETZ4mB3UXqq4peSUCb8PWZ569NC+/jAzt+COK/P8+vonR00AzNZ4NTn4yvi6F5fxgtPHmefMW13lrV2wtt369x/lnsm+V+1/k/le5/sUzwV/07tb/KVTXWHQ1Xu4r3s6AXqGu5a3r7r57S+Oc1jtldEZ8pYLuDfG84Voeif7o8etn6PuUf8NLEKz3Njz975X0fVNXx/qGRW3/pm0/XMn2aW4zd2Hdq3Sc98+rU/3rXrVsWV5+Qkbg7bjOeDXvi6/WCO3ZjVMaX7/Oe/CH8XVjdXla7ZRX65rn+j27o3t1x5qq+mW8P2r7hfE6FI7Ovw5GCs1tapoaGHOa2pU3BTuCi5RZ8Hcjb6OBjK254Ht1GXOF6h38Tnnj/b2uwRdVcg0O92XfOzw143Vo2dXfnN5v/PbulR3XhuXL/Mz3GoHfjTLbTHEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQGDCCLj+yD62O+9778p4GedjShM7NvhP7zuLuf7Sn1/Bx9d1LrsjP3twjvG90wbnDCwZ75pND9/1rHNdiZk9K9vv1mjom/Xwz7xHRqsLr1U1QxA0Nr2qd99oE7Wv6fWzJp/Rs9Yr+/2wvas61H7PprKmseaqINML3Ru3VRyoUwDq93LOCxrr8d4zy4JLqfBI9cf1mNuBLl1hg+uVdu/ZLc9+fkbdtAs1wnLPSutIY7uM753vqkc3B9yo/JIf/97Tufzehua2exVgz3/EsO/ZoGXF56KrfSOZp8fc325qs6/z/P73zr9Ix/BFGsP7Il0Xv1ttu3Se/0WGeZPyJudlRjPMzNO1Wd7NQhqs29lzT8d90aLlzPff4NDcakcCt/Zvp5sJdG78STc/PTx335rZvWvd7z0uto+Zhx03vW5G0K6bCl4WL2tvkFLeW/WuZ/fo9fgGzuXR8bvhbBqZCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAs6RWbCUIKBAwtf0PsovFiu63zFL9lDw4mUaOZg3bd+avTovs4KMvj7zz7W1Xl7QWE8+PUrvu923Z+WtT1RQbeImenfoXxWMOTpeQMG8bzY2L9lQSiA9vm25y2PTNbi3mlGAQS633vj5A2x1bhUOXpWLW3L54e+PfXSumndSvIkKxD3R/cK6qr5P9l3B05rbvi7hT8TrH65l+8h1BejsqNm8aUefuS4vs2hGTtv4jvelmxP2b26Zv6Gz48GiVYyBAvYx92qmTalPelR4t+v6reBqbaGd6auaf4OH3SAIri+0XSnrAtP3PhPU7vn8k+bhzWvanyllm0JlZs1fPK1uj7rf6Lr+irxyQdCnoPRb9BqFgq9hyNsuljEafjdiTWIRAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAICLgiEBF1jKbJ6CAzhYFqM5RwPj9WqnFwtOkOv94jQjLDy4EwV8eu3/ZI4W3Lm3thnuW/lHvu308Xrr/fbfZzAnx/KqXc8b5DmIFUfbxTOb2xubWmxsWtb7OBiKq3ldCBWPU1f1u1IQ+xrP9IHAGhxRgHKn30w57f6aa2tOd3yfPe9h78MHtcbNyl/XG1q5yt0mzfJ035Wx9j2riddrRqfZ7Hs8vtpzLbfu+gpR9rnI1xi/viQyuSiZAXmByrmdR6NnSpsj3zm9x8eSC/NcJuMoVytONQH9e19neuXnNUuc1odC28XX2BpzJU6f8WgHj4+Pr9Au3PRt4Z+mJFlUFjPvrHQW/G3n9IwMBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEBglwBB410URWaC4GkvyH21L+g7qruz/cYipXet1nt58x8Nu3NtysEp4wxA613Nx+5qTEoz657yvq8g1sbE6ow5Q4Gvm+qmTnmiobn1loaFrR9qWNB6TGL5ClaMRVeNTl9bQVd3baJgkzNApDsX8m9K2LXV0M2MRH80WvEQZ4+M96gzv8xMk63uGJW5u/zixv1oat0YcF1+4eI5vV0rNun8+JWrpNHNL/0j9l0rJ3LeIW2T6xe0vbx+UeulevR/h16f/e8uDt3E4RoQHhbNKFg/N1wIP3Vzz47ep/1RNLrbTK2t8/9bj9o+MWxj9FPt/WvvlnXt0bxK50fD70albWc7BBBAAAEEEEAAAQQQQAABBBBAAAEEEEBgIgjkjWibCJ0upY8KiOUUoPqTRl+tCnLBHX3bcz/SyODnS9l2UBn7vlnHCzFzQVDxOy0H1T+woCDuIxpZ7HgXpUn/fbdrlm7zmlsv1a5vcLVlV57xJil4fKoe7Xqq53tfbGxu26D5ZaLtCLwdHVU9NnsMuirC5Az67vIqMqORjX3OuzwU1Sqy6ZCsHpn+mP1dndEjhNe68svN2/Ks/8ge+5S7VTrlFaS0jwY+Iq+2wNsWvBD8KC+/xAzdbHBdxnivyyuudzfXTjZvVn7V7/3Nq3sMZNhH9/tB7cFZL3ewMZlDjBfMV7Pn61w6XNcpjSIeiAkXCg0n9FOPGZ9ln/SQt1rvR/bs9XOUTGrgVbpGD3o/fbRp6sLhjXsccNk678GPR/Mrmh8NvxsVNZyNEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBiSEwDoPGwe80su63ZR2+nEIyvveMnzNPK5j7jBf4T+zYkX2goiBxbMcaEbtvfuRA4Qjjb4oVrWpRUUPnSGO/QECgmh3a0daNC1sO93y/9GCC8WzA71zFOM/VE5WzGsV3txeYm/v6gp9uuLd9bTntGYuu9hwrp4+jvexI9EffJWfQWOdDWedPkq193O8ei9qe1Pq9k8oMVb6CleeHccrB+wi2mqnmWj32fXB2iUuySRyJroChfUT1uA4az21qmqrL4HHqa7MuvEfrmmhHq9u0l/XO6H87p4ErteuCPVCi1I/J2brZnuvX1ZinSq1jOMopKJwYMA73r9HGH56z6PSfr1+5vKrH0dv6Rvp3I+wTnwgggAACCCCAAAIIIIAAAggggAACCCCAAAL5Aq4/a+eXGkM5GlV3e09n++Wjpcm+F+iP8vlRiJyXfTrNNuoP+48oKOKqcsjGTa5b1fEvDYta1prAXKVgzJ6unSflqaWK1OgdmsY7vnaS+Vc9xvpuPez123p35o3aJpu0XZg/Fl2DnDdqRhiGjtV8jkR/dJ7v7zrPc7l0RhpbD43aX6tg2rAGjfsfE23MG53HY+d36wznuhIynVeFcDtjFjQsbFnUvapjZZg1Xj5nH3XqfpPrat6jR/RfpHNmyK6DLq+cn9tvdzB6dwndGDDmbhyRXU1NkLnOO6StKY1R0iP5u7H7SDCHAAIIIIAAAggggAACCCCAAAIIIIAAAgggEBcYkcfaxhsxnpc1ym8vV//8wH/WlV9pXmCC9a5tlT+kNwZ0r+y4esuO7JEKtH1Hj/R+wdWGUvIUpHuFRgBe19DctmpuU+vxxbYZ767F+j9x1ycF/4KKzz2H5WZH3pBm1Uz236Dg7vQh3UlC5fruXZSwakxm79d82sG6CeW7k6dMetQ+CaGagLGua0/oLoLV5UL4vpnl2kY3PTznyh8Neerr87rp6kvOthgzv2Hv4HLnugoyR+p3o4KmsgkCCCCAAAIIIIAAAggggAACCCCAAAIIIDBhBAgaD/mhTgpmBVNS3XVgprrq08i2ja78NPM23bO8V48dvfCFJ705uVzurQqM/FrjNbdWsg+9BfSlCrjc3riw9R8Lbz/+XQv3f2KuVWDVOVLTN96MFEWGPXir9p+fYvvLqirQCOc9jz5+WEdWl9XAMgo3LGxdPMmr7VIg/O16isHkMjbdWdS+PzoIVgZB7gvZIHd6d27jHAVSv1NuPbqppS9hm3Sv+wk7KT87eCYXZFt6Opd+UNfum53bG/MhOyrdua6CzJH53aigoWyCAAIIIIAAAggggAACCCCAAAIIIIAAAghMEIEhHYU6QQwLd9N49v2oeZPe61vW45zzKohlKNjqDJopIPR4rOiQLdr3wary623a+R7RWadlTNAaGP80BfteXOqO1Rd7M4PqaNvc27V0mXO7CeTq7P+EzQw0Cjj/gcvKcZ7/FTKlWVfRJtQvOvVQ9emEogWHqIDspkybPO08PS//K0O0i2GptrF5yYly7NDpMam0HQZrA8970Au8h/SI/Yd25LL3rd/W84D34IPbB22/sPx3SSvw/KQe2T+oGrugUc/DfkNCXiNiGbrJZ3PWy7asX7XzncVb+rIXT6nJLI6/ckC9yXi+uVaPqV6QxmOqw2YM6+9GuFM+EUAAAQQQQAABBBBAAAEEEEAAAQQQQAABBPIECBrnkaScEXibHTEuL+cHzsdWV7z3nN7B6ho3PgwjjV1t7u3qso8L/uVA8uY2nXZAxs+cGnj+aZ4JFEQ2s13b7cozptb3g69r+Uil3K78cGaCuobdn8CfT7j6nvPM/q78ivKMV/jcrKjS5I38oPY81zVCj0VerxP/35K3LH+NRuB+wgaJ41sqvnmh8r6qpDjq2Jv2bjptz8DL3KB+OAPGenR+zpjg93oP9y/1Pvnfb32q5oHNa9qdo9bjvfdN+YHeIKugsePXVa8LGFVBYwWMH/d2eKevX738/rDfdgRwQ3PLh3SNdoywNkc27ON9utvzPhKWT/NzyH830mwsdSGAAAIIIIAAAggggAACCCCAAAIIIIAAAuNMwPFn7XHWwxHujh5Tujl/vJlGnAXeEWk2zfjmKFd9gckN20hj1/7DvN6uW/6m+WsHkl+/qPVlfhD8g4LIZyvQMzMsF/1UgOuwhkWtZ3avbP95NN/O4xoXmRjLGsH5kM6LvM76JnhpXmYFGXOblzRW8w7cCnaZ0TZvdW2npwTc0LNy6ZWudZXm6V2/8zWC9C3x7e13rX7BkpN7Vi/7bXzdWFjeI1Ojdxd7ByS09UkFR9+g68itCeuLZAf72DHCeZNewp6XN5CRy+T0hAl7aOOTOUg5drtREZwPcsEHe1Z37AoYh63t7uy4WtfeN+m7cHKYF/n8QEPTkpu6u5b9byRvSGbT/t0YkkZSKQIIIIAAAggggAACCCCAAAIIIIAAAgggME4EXGNTx0nXRkc3TJC7z90Ss9CdX2Fu4C1wbWlyQ/9OY9d+i+Tlela2372us+OSvm3ZeQro/DCpvILrZ7jW4epSGf95ucD83tVL3UTwEld+2XmBf2zZ21SxQX1TS6uCuHNdVZhc9gZXfjV5ChL+Z9L2psa/KGndqM5vaqpVCPY8Vxt1k8Gfgh25RT2dlQaMVavxFDTOnwI9dz8/d2fOY523PKrr2nPx9YoWT9+v+TQbOK5uUp8bm9t+pMDuF+ub2y7SyODWuQtbD9Ojo8t7j7PxtiU0JPB2BO9UZHtLfL36oMdU+9d58xbXxdcN8XLVvxtD3D6qRwABBBBAAAEEEEAAAQQQQAABBBBAAAEExrRA4h+9x3SvRlHjt2/dcaerOcYL5ttHqrrWlZund6LuG5iEkcs7cnmjyMqtv1D5PY8+fu85i06vOAD+2P3Lnu9eufVcBX2Wu/ajoIUzwDLeXV0W5Hle7xNbV+lcGfzO2Z0wx9hRwtUa+cZ7dbV1lLO9nhBwvqu8Hqd877pVy/7gWldNXk9Xxwr5/dVVh27QOHNmU8sc17rRnNeYmXWKRkrPcrUxF2Tf3r26Y41rXal5ugYd6i6rq27ylFW0eaVrdW2QqfrGhAZ/9isVzH6jRgJ/SOfsN4zxl2Z886fGfYJfuvZZSV6/Wy64zLWtvA9vmFX3Gde6UvJG6nejlLZRBgEEEEAAAQQQQAABBBBAAAEEEEAAAQQQmKgCBI2H+Mg//odbH9O7Sf+Stxu9s3eaX5P3mNi8ciVkmKDm7P7RX7GyGul2f/e9y/8vll3VYsOilrc3Lmr9VkNz2+0a5fbYjCnTN9d4NZ1zjj3lwMorXrFDgZnvubbXk4id9Y43V1ffyXMIrF2xVefKrfE1Ok/06tnMufH8cpbnNjVNVT3/WM421ZTd75gls/W9/TtXHXr3beqjjAf2I77gWtc+NeK5dopvLnCuG82ZueAQd/OCtb2rlt/lXlda7szDjpuuYPrxrtKCLPh6B41ydo+KN+YsV33l5OlJC6e7ymsk/i9c+ZXmda9q/7K27XJvby7VawZe4V43OHe0/G4MbhVLCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAlEBgsZRjSGbD5a5qtY4tQuVX2i0mmuzeJ4Gmpl3xDPtsoIdP3HlV5UX+Cer5gsVXDtR+50d1lWTqX1NOF/JZy6Xe8i1nR4B+5Qrf2feOHJN7iRrYgKByX4jltW/qC/SRfsc0jbDta6UPN/MerfO7Yq3L2Uf0TI1df7ZNlAbzbPzCkZmt2WTH9keL1/ucm6buU4jmXOu7fSYb3stybjWRfNMEGgkbf4U5IpvG9+q2rp0HW2M12mX5fiIK7+cvCnTp7/GdYxsHTrfJhWsKxf81rVeo3TPrOY8tXXqfddL4nUrSB1s27Hj5nh+lcvZXF/uAt2AtCNeT/+NGoHeU1/KY6pH1e9GvCcsI4AAAggggAACCCCAAAIIIIAAAggggAACCFgBgsbDcB5kc8HX7R/047tS0PWYhoUtClRVPtUvbHuvghrzXTVkc7mfuvKrydN7Vt2BaOOfWU29fibhncye93BSvePJNamP5OcL9Kxc9ht9mR6Nr1EQr37q3sGX4vmlLM9tWnK43lD76VLKplXGD7zzXXXp0fW3burqWO9al0Zez/1LuxV2dN7IokDgAXo37quL7Sfnmbwg4s5tzNRi28bXV1uXjvvGeJ12WfkHufJLzbOPUFYlVyWVV/C3YNC4/1HgXvDH+PZq15Sp+3gfj+eXujxnQcsJKpv/SgDj/e+me5b3llpPqeV67um4z+SCL7jKy+Cw+llTrnCti+aNpt+NaLuYRwABBBBAAAEEEEAAAQQQQAABBBBAAAEEENgtQNB4t8WQzfV2LfuTQhi/du7A+F9oXLjkKOe6IpkNC1qPUaDrc65iilHfs3O/rrWV5617Ynu7AnbP5tVgvNPrF7ack5dfakaQFHTOPZhUxXhyTeoj+U6BnJcLnO9TVRDr7Q0LW7/uNTXljeB11qRMvQv5lX7G1zu1TV1SmbTzdbPIoqSbPXR7yVA9mnpXNzSC/z93LcRm9OiCi2JZ+YuB2ZKfqbuQTLDr6QOu9c68KuvK5bzVzno9M88+Aty9rnjujLrp39QxmptcMpiSvK5/jS7D5mvuMsGl/eeAe2Virh2hXFNjrtd5rthzbMoFP4/lpLa47in/M+qMfsfyJzXkEvsdyl+zO2c0/W7sbhVzCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAlEBgsZRjSGc1ztKP+d6JKz+9D81MJnb5ixY0lTO7hubW5v1Rs3b7Ki1vO0Cb3s2m3M+sjqvbLkZeqes3tH8S9dmxve/PXdh27GudYXyGptbPiCH/Mdbqx/B9r5rCm07blwLdZJ1eQJ61+o1CmLdnrdCGcY372nIzLpDgSznY4uj2+idrJf6xr9do/4bovlDPa82OkcZq0/P923LDVnwL+xXz5a//VLXo03h8uBPs0TB1oMG58WW/ODJWE7/oh5v/ZpyAvb9G1VZ1/PZbfe42mLzauv8ryatS8qfNX/xtPrm1h9qlPEbk8rYfPlNL7S+f936p27QTTY98XI632oUYb+1cWFrOe/QNhpJr0ezm3nx+tSYp4Pt/g/y8tPKWLN0m4Lz79D5mf/EDL1P3Pcy1zY0vCL/tyjc/yj73QibxScCCCCAAAIIIIAAAggggAACCCCAAAIIIIDAbgGCxrsthnSuZ2X73drBp1w7UcB0Zibj397Q3PpRb/78go88tesV0PiYgjMrFHjYx1Wf8j6+fvWyroR1KWSb7zoD4Apg+35wi0bQfXhuU1PRx9Tax7+qL58JPP+LrkbpPZrXdd/327yAS7Ts+HKN9oz5IgJBLrDvBA+2usrpu/FyBbJWKyj3JQWP37hf82kH23IKhu7R2LzkxMbmtg82LGq71ffMl/oDeK5Khihv4LvxJmf1xrvpsfuXPe9cl2bmgw9uN17uRleVuh75tZONfd964qQfjg2ulRoAu6gxM+snGu39qtlHnbpf/TFtDXObWhbULzp1X1d5m1dtXU/du+IpXSvWuOrXsX1T/aI2+yjoou9pttvbEbN1e0xZ6RtTPJhrzN6ufUbzurvv3qJR8We7r5dmmgLHP9R5+G/Frvv2ZhyVu1u+b4nWb+dtIDfrmXN2PnY8vja95d6u9jtVm4LW+ZPOmUPN3D0/m78mmjN6fjeirWIeAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAYKdADRDDJ9DdufQKBauO1wi20+N7VTBgD+Vd2TC18WKFXX6q9xEv9QJvnedt36Sn5irgkjsg4/mtgfHeoLIFRlAGy9Z1tlf0Xtd4m5KW1Y/bFcD4Z63/fLxMfyDbN1/IBLPer4Dwfyqi8Wc9ovURf8f2R3aYSbW1tbkDFCQ+QNu9RIGGd2rU3Ix4HQPLj+kZxEXflWnLjhfXBAeyEwR6V7U/XN/UcpaGOf5cjxGujRezN2Mo/9KM4oX2f/ruKbjozdD3r/9mGY3Sz5sUf3tY36/D8ld4uby8CjOMmfn6pPM+l8sN+aOpw2YHuew1JuO/P1we/KmR0PPnf9JTcHlw/s6lvu3ew7WJt7eYM/XY/DMnTw0L+LoMTLJB6O8MVV0aBfuBjO/d7KpfB/uKxkVtb8kFucsVXL67t3OZvRGl/3ja0bHB3KmNxpvUouvtO3TOHJ1fR3hjQt7jy0sana5R8Ssamlt0LfM/mV+3zgTP+6fGPRpfHSxqvFNtWKXr3qrtO4K1dTU1L9b7refrHH6Zztu3qJwz8K0biK7o7Vz63666087b+syzH6ubMf0M12+QrvPvm9vUetNAcDlv16PtdyOvgWQggAACCCCAAAIIIIAAAggggAACCCCAAAITXKA/eDLBDYaz+7ln+7b8gwIXtyXtdOcf480lGT+zLJPJPJTJTNmY8c2f7LJGpV3q+mP9rrqC4L+f2fKcHSGX9wjRXWVSmuleufQLqur6xOqMmaPRev/i+/71mYz5nZk8ubt2knnEM5nb1YcblD6YFDhT45/V47VfpeCOguYlTePGtaTeUmiXQE9Xx69zgXmzzpnsrsykGePtpaBggWtecLOCj29P2NwZPE0oWzBb5/75zgJBsL531bLfOtcNQWZ31y0PKEje6apabZzVsEfDWa51Nm/Dve1rdR1zju51baNRzUe48tOqSzcQ6HHbwX8k7UP5R+gx5D/OmMzfdPPAFqW/NDS3bTRz93rB9yY9rIDs110BY9X5p2CH9zJdUfXO68GTtqnf/9i2Iwfnupe6Ozs+rbqcQfOdW5iDdMPNOXL/mtp415Tamh61Z4UCxrZP5yYGjIOgvadz6eXuvaafu+nhu56VxbtcNdvvlp8x1xR6ysQo+91wdYM8BBBAAAEEEEAAAQQQQAABBBBAAAEEEEBgwgoUCKBMWJMh7bh9lGp3dmOLAghXp7UjBW926A/5H9II4zOffuBO57tG09pXtJ51m713qh+/j+ZVO6++bA6yuTN7uzpWl1PXeHItp9+U1QtjVy39qR4BfJoeAfznSjx0DutR18E31mU3vsH3cn0JdWxLyC8ru2FByyHa4CTXRjnP/ED5xYPfro0rzNM7wa9J2lRBTGdwMCyfywW68aPEyZiCwdU06ureuPWDug7+oWiLNKxYw3sPVpBzZsGyQfA9vV96Yffq9vs1VLrDVba2Jjjble/Iy3Z3tl+oU+1j9nxzrK8gK/id90JgH1fdP2q6ggoq2kQjp3+jLnzftbGC2y/2/dmfc60L80bT70bYJj4RQAABBBBAAAEEEEAAAQQQQAABBBBAAAEE+l8nCcOwC3R19SmA8A49LvWNlQa6wjYryHq/6jlpXefSq5SXUjAirL3I55ql23K5jadrp1/sD1wXKV5otQ2kaLo68PoO1ejR2wqVTVw3XlwTO8iKJAH7CODuJ71jdB5+SufRxqRy8XyVvUePfD+up7P93Z7On5xe5xsvY5cVDEslaKzHNp+n0aSqzjHtGL5HU4d7fyGb/aG+v1vC5cGf5oTGhUuOGpy3e6l3VcfNXpD7pP3i7s51z6lAwaBxKnWtXbFVT3I4QTcQfFlXwopHhusc+p9s1mvRTTjnhe+XDnbklrl6FhjzZuW7j6djA133Py+L0yV2r2N1aVlB0Gfd161sP7n7wY7NpW2Ubqlcbuv7C3zP3jNnQcsJiXscbb8biQ1lBQIIIIAAAggggAACCCCAAAIIIIAAAgggMLEEGGk8gse7p7Pjv7o7t8wPvNw7bKBCgYSSRozpj/XPq+g1QTb78u6V7S/RY5xTHe1bDklvV9cLeuToR/RY36PUrqsUrNlQzvYawderXn8ra7KLbCC9Z+WtT5S1vaPweHB1dIusYgIKRun7cLnOozm5bO4UBef+Xd+r27TZQzov7fuMt+jzr7q34nc6767M9eVeqrILela2372rapObsms+OmPMU9HFCuczgW/e6tpW3/0Heu7puM+1bijznuy65WlZ/CxpH4HxC442XtfZ8ZlczrTKdbl8C4ySNnNmzV88LWk/Nj+NuuwTB9atav/Atpx3pEwT+xVvhz03VH6pxpy36Rx6RW/X0kFB4u57l/+fzpu18e0ULT5Q7/E9Lp5faFk3KNyq9/su0P7Olttdhd1216Ryz9qAeG6bOdhaaU0B793bDcVcb9eKTSbw3ueq2z6mOlNT+DHVo/F3w9UX8hBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgYkkUPIIqYmEMlJ9bZjfsk8w1TtNj4U91jPBbBOYWRrDtpfas0EBhkcV3Hk08IO1WzZn7ti8ZukzI9XOIvvN1B/bcpRXY471veAlGom3n8rP0Ik2QwGSbQp8bAg885geA7xeA/Tu0Gi+VVqv7KGbxonr0AFR8y6BhubWv9dA4Lxgo07QL9qbI3YVZCZPYFbz4v0neXXzjQn28wJjH/28We8y/ltfn/+3DZmNPXYkd95GCRlp1VV/TFuDV5M91GTMizzjH6IrzYu0y1m65Dyh4Obj9nqkR5Pf2fuk/ztPNx0kNGdIs/c8+vi996ibusTXdT/w/Nnym63r5Z5BYJ4yXrApZ8yaILdjRW+weWU5hkPa6PQrH3W/G+l3kRoRQAABBBBAAAEEEEAAAQQQQAABBBBAAIHRLUDQeHQfH1qHAALDKFC/sPVC3zffiu9SI+kv0buTvxrPZxkBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQGA8CPJ56PBxF+oDABBIo9pjjaig0+vQQ1/Z6zLUea82EAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCIxPgZrx2S16hQAC41Vg0tQpX29Y1HaWHpPwZwVz/6x+KgV/7t7s/6zKRwzbJy+8xuVmtpvVrnzyEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIHxIEDQeDwcRfqAwAQS8IPgSc+Y6epyk97/3bSz68Zr2Ct4qtvzflMpRUNzS4veZ3y4Y/vHeu5fqqqZEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIHxKcDjqcfncaVXCIxfAWPWuzpnTOAcJewq68jTtdB8xJGvrOAX7nxyEUAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIHxIUDQeHwcR3qBwIQRyHk7bnF1NvDMufXNp7/Uta5YXkNz27c0ynixq1zO877nyicPAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEBgvAvYdnkwIIIDAmBJoaG79m4K8jfFGB0GwLmuyf79+5fJV8XWu5T2PPn7vGVOmXalRxhe61ntB8L11ne3nOdeRiQACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgiMEwGCxuPkQNINBCaSQH1z63m+Mdcm9jnwlmdz3lWet2V1b9eKTdFyMw87bvrkqdMONjX+2zwTXKD3Ik+Lrt89Hzyy5elnX7Lp4bue3Z3HHAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCAw/gQIGo+/Y0qPEJgIAkajjX+b9EjpKEAQeC/ovcR/U3B4hz4bPWP2jK53zgfe9myw45TeVcvvcq4nEwEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBAYRwK803gcHUy6gsAEEgheeNKcqSDw74r12RhvqoLLh3vGO6qkgLEXrA2C3AkEjIvJsh4BBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQTGi0BmvHSEfiCAwMQS2LJ5zbYZZtaPzLTJ9uaXYxQQrqtWQO9E/vlzfVtf9fjqW/9SbV1sjwACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgiMFQEeTz1WjhTtRACBRIF9DmmbMXXv4GKNJr5Ej6GenVjQsSLQi481YvkXuhh+b93K9g5HEbIQQAABBBBAADgO13gAAEAASURBVAEEEEAAAQQQQAABBBBAAAEEEEAAgXEtQNB4XB9eOofAxBJoaHjFlGD/GW/Q46iPUDB4nh5NPc8EZl7gBfspz2gk8fMakdzrBUGvAsy9Xs679YWnzE82r1n6zMSSorcIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIDARBKYt7jOjkaeSF2mrwgggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIjBaBzGhpCO1AAAEEEEBgnAuYcd6/tLtnvSYp+Uq5tCunvhETsMfU/vdndsRawI4RQAABBBBAAAEEEEAAAQQQQAABBBBAIE+AP2DnkZCBAAIxgUO0/PFYXqWLv9CGN1e6cQrbXa46Dhyo5336fGZgPs2Py1XZUO8jzfYOVV1fU8UzlPqU3jlUO4nUa3/PgsjyaJk9Rg25TOk4pVlKTyn1KJ2rdK8SU7LAtVp1ntKnlC5XGm9T0jl7uTo61NeQL2kf+wyAnq/P6Hen0LqBTar6+JW2frXSPyn9R1U1ja2Nk4732OqFu7WjqW/D/dvjFiEXAQQQQAABBBBAAAEEEEAAAQQQQAABBBAYhwKvUJ9sQCGNdNkI+6yO9GO/IWrLcOxjiJqearWPD1hvSbXW/MrsiMX3Kn0+f9WI59SrBRuUXN+dOSPeutHdgCUDbt36nDq6m1p264qds8NxDVk34GvPTTuSOzoVWhctV+n8EdrQ3kxib9o5oNJKxtB2xY73GOpKXlNHY9+G67cnD4MMBBBAAAEEEEAAAQQQQAABBBBAYKwLxP9QONb7Q/sRQAABBCaOwDx1tUvJjizbU2m0TTeqQdGbE/6o5Z8qtSutV2JyC0xT9ncGVn1cny+4i43J3Hlq9Wg+Z4cD9SHtxB7f6UrfHo4djuA+5mnf4/V4j+e+jeApw64RQAABBBBAAAEEEEAAAQQQQACBkROoGblds2cEEBgjAvYRuvMLtPWtWvfhgfW36tOO+kyaNiatGKb8u7QfO/LTTtt2fqT+73DsI/VGj9EKj1S7XzJK2z5F7TpxoG32fbx2xP7KgWU+Cgt8TqsPVPqT0g2Fi465taWcsxPhGvIZHbm3KbUq2Ue1X680HqdSjvdY7fd47ttYPSa0GwEEEEAAAQQQQAABBBBAAAEEEKhKgKBxVXxsjMCEELCPF7YjJJOmMAhr19vHjRYqm1THcOW/Zxh2NBz7GIZusIsqBfbW9uHTPB7WPAHj0kAPUbF3DxS1gUUbcJ9o00S4htjfDTva2L5b/vNKP1LarsSEAAIIIIAAAggggAACCCCAAAIIIIAAAiMkEP5Be4R2z24RQAABBBAYlwKTI70a6RH2kaaM+tlL1EL73yb2BpSfjfrW0sBqBK4Z2HiOPt9cTUVsiwACCCCAAAIIIIAAAggggAACCCCAAALVCzDSuHpDakAAgcoE9tdm9hpkHxMdBtWaNH+iUrfSMqWnlVyTfefpYUqHKx2g9JySfUesfaxroXfF2vfL1irZqVcpOooxXGdHuz1uCwxMDfq0jxaeq3Sf0j1KSe3Sqv532A71Pux+otPLtGA9ZivZNtpRrWEb7bt+pyvZyY7u29E/l84/L1c1RyjtpfS/SquUrF+5U7nH05a3+5wV2ZHNs8fKTjbgaJNrKndfrjoK5e2rlfbR1DYQFk6TNBO2zeaF514134F9VI99PKz1r1N6QOl+pc1Khaak8/wgbXSykj1+tyn1KLkm2y9bbqaSfXz0KqVi+1SRkiZrd95AyV/q014bXFPYhz6tfGyggL2WvFTJflftdmuUfqdky5Q6VXpulHIcs2pEqeds2D/b7vBcSepDpW1Oqi8p3+7Htt9OW5Se6J8r/I89R+y5aSd7nOLHwp6vf1Y6VOmDSt9TCpSqmez5mRmowP6OFJrsNT28eTJeNjwG9vtQye9B6FXKNaqU8ye8nqfVv6iLvY7Y34+DlZ5Usk8LeUgp3KdmB03l9G3Qhlqwv432mmW/q3sr2XPgXiW733Kml6uwrceek/+rtErJHquhnsq1SmrPPlpRyfU7reNfzjkX74P9Xr9Syb5CwH7X/qJkzxn7WeqUxnmQ1rEotc2UQwABBBBAAAEEEEAAAQQQQAABBBBAAIESBC5RGfuHfptuKqF8WOT/BrZZoU/7R+g7BpbDuuwfrI9Xik4v08J9SmEZ1+eNWm//qOmaVisz3MYGBaKTDbrZdfYP0HZaovQ3pbB8+GkDzdcqTVVyTcOxj3C/Z2jG/tE9bFv4aQNUP1Sybbwysv7Fmk9jsvsNvcJ92s8tSp9RMkqPK4V5mnVOlR5P+/ji6H7j859y7K3SfTmqKph1c5G22bba4L6dKvkO2D/2X6tkz8N4v+3y75XszRRJU3jc7hwo8CZ9PqoUr+vPylusFE4LNWMDA/FyTynvnWGhKj8/EanfnmNJU9gHey2w06VKzyvF27ZOeRcpFZuqPTdKOY5fUiPi7YsuR8/ZQteQsC/VttnahPv3w0oHPl3r7LU4LP+I5u13vNCU0cpeJbuNvZZPUXJNn1VmWG+bq0CZea62J1URXqPiwWxbPjzHKv09KOcaVcr5E/4WptU/28cWJdu/0D/+aa8lrt+McvqmKvone/PAV5W2KcX3Y5ft7//BSsWmtH57iu0nvr5Sq3g91V6/0zr+5ZxzYR8O0syPlJKO4S+0bn5YOOEzjfMgrWOR0ESyEUAAAQQQQAABBBBAAAEEEEAAAQQQQKAagUu0cfhH4JvKqCj6R8trI3WEddk/TNrROHayAQgb/NyhFK63n88qvRDLs/kbleYoxadCwZhokOCN2tCOWoruKz7/oNbPiO9Ay8OxD7vb9ynFA4fxwJltiw2ih213BQC0uqzp/SpdbL/fV5nNSna/W5TiU7XHs5ygRbX7ire92HKlQeNrVXF4nMLP6HfA7vc1Ss/EytnvhDUOt7Gfdtl+L11TeJ7boPGFSvYGg+i20fnHtK5eyY4qs4G/6Lr4/FlaX+1kR6rZeu2+JheoLOzDfSoTDTra76z9XsaDGv+uvHhgVFmpXVdKuZZ9SPuLm0WXSw0ap3U+r4u0J26TtM7eSBC2OQxiKss5tSo3LPtdZ4mdmS+NlLMBqWqnpLa76i01aFzJ70E516hSzp/wtzCt/tnAW59SeIzspx3tG//u2N+Uf1SKTuX0zW53jNIflKL7stet+LXMLp+rlDSl8duTVHeh/GqsovWmcf1O6/iXc87ZPpyuZJ8uED+GT8XyntPysUquKY3zIK1j4WofeQgggAACCCCAAAIIIIAAAggggAACCCCQgsAlqiP8Q+JNZdQX/tEy/EOkDUTepfQfSg8r/VQpnF6vmXAftvwFSnuGK/VpR+/YoEz0D5hXRtaHs6s1E9azX5g58BkGop7Vsg2k2QDUVUoLlfZSOlHpK0rh9vbzvUrxaTj2cZp2Gm3H1Vo+QsmO/pupdI6SDQBEy9j5FytVM52ijaN1fkvLhynZ/dpj8C6leBDfBjDjU7XH0x77Q5TerhS25wcDeTY/DLBo1qt2X7aOcqY5KmzbcLJS2DY7ms/mhSmjeTuV8x2w56u9GSKs807N28CdHU1eo/RSpeuUwvX28wyl+BSe5zYYZMtsVfq80klKew98PqjPsJ77NW8DAfb78Bmllyjtq/R3Sp1KYblHNB/2S7NlT/O0RVjXz4psHfYhvHnBBqDsdWiPge3q9GnPRdvmsM6LB9ZFP9I6N0o5juWcs4WuIWm1eV3EptSg8T9HtrHf/UKT/T6G9scVKqh13QNlbRDXXkuqmQr1K15vKUHjZ7VRJb8H5RzvUs6fsO1p9M9+T8KArb0+X6g0bWAH9tNeN+zvb3j87PGJniPl9M1en/4WqcteM+x1y35H7bE+VMn+VoffZWvdpBSf0vrtiddbbLlaq7D+tK7faRx/26ZyzrkGlQ/PF3tOrFSyx3CKkp1epHSTUni+9Gg++husxf7fqWrPg7SOhW0PEwIIIIAAAggggAACCCCAAAIIIIAAAggMkUC1QePwD42vi7VvcmT5Ds2H5WwwLmmKBlTsHy7jU6FgzAMqHO7Dfto/UrumLygzLGeDa/FpqPeR0Q6jjwn+SLwBA8v2D7kblMK22s9qgsa+trcBxLC+j2veNS1QZjRwbIMS8Smt4/kqVRy255vxnQwsp7WvhOoTsw/SmrBtKxJKhX+4D8sV+g7cHKnvGs3bQLFreo8yw/rWat4GbaJT/Dw/L7pyYN6eJ2EQJ6zr1Y5y9kaBx5TCMvMcZUrNOj9Sz78W2Sjeh7hbuHlbpM5Nmrc3f0SntM6Nco5jKedsoWtIWm0uFHxKWmeDRzaoZ4/3ZqVJSq5pujLDa8CfXQViebdrOTyHjo6tK3cxqe2ueh4f2G+fY2X8HDvFUcZmFfs9KOV4l3P+pNG/09Xu0NveCOCa7LF9RCks1+IoVErfPhWpw17DMo56bNbrlcJ93Rkr42s5rd+eWNVFF9OySuv6ncbxt50u55yL3kBwk7Z1fe/tMepQCo/hxZqPTmmcB2kdi2i7mEcAAQQQQAABBBBAAAEEEEAAAQQQQACBlAUuUX3hHwrtHxRLnaJ/tPyvAhs1at2jSjZYcXeBcnaVDaTtULLtsUGv+FQoGBMNEnw/vmFk2Y52sUEGuw/bpnjwbqj3cfLAvu3+7YjspD/Ca5UXDSDa8i+2mRVOL9N2tg6bupVcfzhWdv/0Nf0blo0HjdM8nsWCFmnua6BrJX8cFDFYkbBVqd+BkyJ1Pan5ePAzXr39noT+/xJbGT3Pb4itiy5Gz+NfR1fE5u13JdxXUmAttolz8fpIPe91ltidGe3Dst3Zzrnlyg3b97ZIiTTPjVKPo919sXPWlona72czBqY021wo+FRoXbvaEnq+NmxY7NM6h2U+HlvnWrwxUt5es6qZCrU9Xu/jyrDt7Iuv0HL0HKvm96CU413O+ZNG/9430G/b90sdfQ+zXqOZbyh9QOmYMDPyWaxv9iYDe/23+9msNFup0PQTrQzPm9dHCqb12xOpsuTZNKxOivSr2ut3Gsffdr7Uc25vlbXfD3tctivZ37WkaZFWhMfvtkihtM6DNI5FpFnMIoAAAggggAACCCCAAAIIIICAS8DeGc6EAAIIjLTArQUaYP9IeqDSFCX7R+pCkw0Y25GPdjJKtf1z5f9jRwUlTfaxvj0DK+01dGpSwSL5le7jzEi9X9F8NrIcn/22Muwf69OYoiNNv6kK7R+Qk6ZCI0WH83gO576SLErNL/QdsI+eDqd/08xT4ULC56ci+a+MzMdnfx7PiCzbGwPCqVDbwu+bLWsfzV3pNC+yoT1upU6fLVLQ3sAQTjYIFk5DdW4Usgr3XennULW5nPZcGyl8dmQ+OnvuwIK9ceeG6IqE+b9F8udF5kfLbKXX6kraP5TnT9ieu8IZfX5MKSn4/yutu1jpS0p2pG+50/HawD6G2k725o7H++eS//mvyKoTIvNp/fZEqix5Ng2robp+l9yJIgULnXNt2ja8Me4mzT9SoK6VWmdHpL9I6dRIubTOgzSORaRZzCKAAAIIIIAAAggggAACCCCAgEsg/EOAax15CCCAwHAJrClhRzZI6QpU2kDVoUrNSicrRUczZbTsGkWm7ILTowXX7nzHa1ikNpwp87PSfTRF9vNQZN41a73+rPRy18oy846NlP9TZN41awOO9nHA9v3KSdNwHs/h3FdSf4vlF/oOHBHZ+J7IfNJstMxhSYWUX+g4vhDZ7q+R+fisHUkYTiacqeBzVmSbdZH5YrOrixR4OLI+GrwJs9M+Nwodx3Cf1X6m3eZy2vMLFX5SaW8lG4TfS+kppXCyN/icNLBwmz6jAeGwTPwzeryj50G83EgtV3qtrqS9w3H+2O+MvUY3KNlrtL15xC7bUeQdSsuVnlaqdnpxpAL7qoTob1dk1a7Z6NNBotum/duza4clzKRhNVTX7xKaX1KRQufcMZEa/hiZT5pd5lgRPZbVnAdpHAtH88hCAAEEEEAAAQQQQAABBBBAAIGoAEHjqAbzCCAwUgL2UYmlTPYP3Oco2ccg2j9E2mDxdKW0p2KBjmxkh5UGyirdx/6Rfa+NzCfN2jIvT1pZRn40GB8N8iRVYcsUChrb7YbreA73vuz+yp0KfQcOj1S2NjKfNGtH/9qg71SleUqTlbYpxadn4hkJy5sT8tPMjgYLi303wv3adj0bLiR82vMwULLf0+h3J1o8zfOw0HGM7rPa+TTbXE5b7Hn0Q6WLlex5dZbS1UrhdLZmwmvidWFmkc/o8Y6eB0U2G7bV0fa5dprG70FY73CcPzY4awP7v1EKbyqxAeS3D6Qd+vy90s+UfqxkryeVTNFg4ftUgU2lTodECg7Fb0+k+oKzaVgN1fW7YMPLWFnonNsvUk+x70Gk6KDZtM6DNI7FoIaxgAACCCCAAAIIIIAAAggggAAC+QJ+fhY5CCCAwLALPFFkjxmt/4qS/aPll5X+UcmOWooGjHu0/C2lUgNhKpo42ZF8Qz1Vuo89Ig2LjvKMZA+aTcPDVmhHFIZTseNly/WGhR2fw3k8h3Nfjq6WnFXItDFSy+OR+UKzYX32d36fhIKlnD8Jm6aePWOgRhvg3Vhi7etLKLdVZcKA+RTNR/+7ZyjOjdC9hKZVVGQo2lxuQ66NbGCDxNHJ3tRjJxvMv6l/rvg/0XM6PA+KbzV8JSq9VlfSwqE+f8I2/VUzr1SyrxKIf4/sDaUnKn1N6S9K/6BUyXRAJRsNbBPdNs3fnkqaVK3VUF2/K+mLa5tC59yekQ3s00MqmaLHstzt49tWeyzK3T/lEUAAAQQQQAABBBBAAAEEEJhwAow0nnCHnA4jMCYFvq1WXxBp+TrN3610fyQ9OrD+dfocjYGHgeZV/WH/aDpnoBb7x+howMVVefQP1q71pebZPxjbkd12siM2bTCh0LR3gZXDeTyHc18FulzVKnu+HzxQw1x92hskCk02sBieI7ZcpX/sL7SPtNfZNto221Gq9vv7tFKxqZRRqTZgXjdQkQ2O5CKVjsVzYzS0eZUM/6B0lNKJSvYaY8/RRUrhyNWfaP4FpVKm6PU6zXPV3iAQPd7xtkyOZ4yx5Wr7t1n9/bDSR5UWKrUotSq9TMleQ+xkb1Kyo43td+0/lMqZNkQKv1vzd0WWi81Gj5s9J9L67Sm236T11VgN1fW72uOf1NdofndkITrqOJJddDat8yDcUTXHIqyDTwQQQAABBBBAAAEEEEAAAQQQSBAgaJwAQzYCCIwagdeoJRcMtGabPt+pdP3AsusjOirJ/lF1vE32UZLHDXTKBmu6inTwgCLrS10d/cPvgdqoWAAgab/DeTyHc1+lOlZSzh5z+zhZO81T6rQzBSYbWA5/3+37ZvsKlB0tq9arITZobCf76OVSgsazVa5OyY4mTpqiN03YfYTTWDw3RlOb7WjjLynZIP/rlb6q9AalcLounCnh0x7vcIoeozCvnM8gUrhW8/ZRy67J/jZMd60Y5XlD0T8boF05kD6jT/sb+ndKNqB8lJKd3qFUbtD4z/1b7vzHBp/viyyXM5vWb085+0wqW4lVmtfvoTj+SX21+WsiKxsi80mzh2uFvVnEBputlZ3SOg921rb730qOxe6tmUMAAQQQQAABBBBAAAEEEEAAAafAeAyoODtKJgIIjFmBUyMtt3+0LhQwPkDrJ0fKh4GzSNaYn30w0oNXReZdswcr8zDXigryfh/Zpjky75q1oztt4NI1DefxHM59ufqaVp4NOoTTy8OZAp+viKyzI0LHwhQNFkaDiMXafkyRAnbUZDjdHs7ocyyeG6OpzTfKMAzIvnbA9YyBT/sUgjsH5kv5iB7v6HlQyrbxMtsjGdMi8/FZG9yyAW87hZ87l0b3v2n0z35n3qj0MSVX3+2NJjconaBk5+30EqXwpo7+jBL+iQYLo9/DpE3t77d97/GZSvMjhdL67YlUWfJsGlZpXr/TOP4ld14Fo0HjYtdaW+9vlB5Vek4pfLR1WudBGsdCzWJCAAEEEEAAAQQQQAABBBBAAIFCAgSNC+mwDgEERoPAsZFGFBtheXGkrJ3NxJbHw+I16sQzAx05T5/zBuZdH59UZlqB85siO7CjvQs9qvIDWp/0+5Lm8YyOoHUd6zT3Fen+sM/+VnsMR5i9S/P28eBJk3W/LLLyp5H50TzbE2lcKSPawuIfCWccn/bcj67/eaTMSJ0bxc7ZSBPzZkeqzXkNUYZ9LL4NENnpeKXjlA61C5q+pxSer/0ZRf6JjgaPngdFNnOufjKSuyAyH589N54xRMvVHG9Xk9Lo33dU8Y+UPqdUyMgGjLuVwikehC/WN3vjQBjkfJ3mm8KKEj4/ofyvKv1C6f2RMmn99kSqLHk2Das0r99pHP+SO6+Cq5SeH9jgTH2+eGDe9WED/QcNrLAj158emE/rPEjjWAw0iQ8EEEAAAQQQQAABBBBAAAEEEEAAAQQQGCqBS1SxDRDYFP3jbrH9/V9kO/voyqTpaq0I6/9GUiHlR9sRlq+PlV8dqSse9Hwgsm7f2HbxxfsiZaOj5Gy54djHZZH927bE/5BrA4c2YJyNlLMm8XLKKmuyxze0/Ynmo6O6w4qO1cyzkXJbwhUDn2kez+Mi+2mP7ccuprkvR/UFs+wfz0OrFQklS/0O2M2vVQrru1nzru+MUf4VkXI26BMPMJd6ntugUri/EzWfNH1WK8JyZycVKiH/zZF67GOPC03RPuRU8IyEwtG2dapM9EaGNM+Nco5jsXPWduX/2XsPAEmO6v7/zczmfLt7aS/qknSKnFBEgEAIAwJjkrEBAzZgjAm2wRh+GJsgbDAC2/hvsMnYBGPAYMBECSQQyjmcdKfL+TbnPOn/+fbunEZzM7O7t7M7e6v3pHfdXVVdXfWtsNP9rfcq1xxSyDIf5jmpdkvHRc/PF6f4lLyEk1QeD02eqz3WpRJM8/jryXuV16Zp3pMr2afT8trBebY56vVpafTMGJop6X2sKTMy4zrf34PptPdM+k8h6vcuyq96S29CS9BsIkJZ2CidFglkpptO3URMp56lfp35d5egQF7Ev1E0lfa8ieAT/xbib8+JzGZwUiisvsIzU3WbzfxdiPZX9WfS5/4mrew/5rxKGWSIFm3dhqbq+LaM+EL0g0K1RUbR/NIRcAQcAUfAEXAEHAFHwBFwBBwBR8ARcAQcAUfAEXAEConAO8ks9aFQH3anK9P9aPm7ZJjKX4SErJHqJx8iwval6GfRVJp0ovSCyXSpQy4yRvGFIgnm4xl1lDedqOjnWiTfe9F/Rh9AhYfwGp081/VsCZlV5NGXlt8dnF+DNqJbUX3UFUmsZ6XIBl2nSyHbcz0Zp9pdx2+jH0HVJySFfNZEjtP/t9CksciWdOwfnazfeo4aB89HZfmZjsfvcZ0p0+3n800aL6GgKdLozsxCZ1yn10H11Zj/GLoN1dh4FvpdNIWF8r0QTZdC9o3pzmV6/no0VS4dM/ssQTlJ40KWOR8xnC9O5UtJKSftaHp9bkxFTvNYRrrUnLFjmvfkS3Yukenl+Q3XIrDOR1+PfgNVf9D8tA9VWp1nSnofa8qMzLh+kOvUMzUW02U9F6k4HbO190z6TyHqt5RyHE0r1+2cvxBtQSvQDejb0TY0VXbN7ZmynoBUfK66VZMmvT+pv7wZPRutQvV34x/QVB9QPiJGM2UVAenz3x1cX4PO5G9PZp7TuS4UVst5WHr5T3X+LkT7q94z6XOVpD+Iptr6Ic61YERtorHxW+jdaCpec0AETZdC9INCtUV6ufzcEXAEHAFHwBFwBBwBR8ARcAQcAUfAEXAEHAFHwBFwBAqMwDvJL/Wx8HszyHsmHy2/lfaM1LO6CBMpmroWcXQt+uG0sLdyni73cZFKr4+46VIokmA+nqFy6yNsOjGWqlfqqA/U+rD7IJoKy7Q6JWrGcjF3tKOpPLMd/5X46yfTiAzIlEK1p/JN/1idKsv/pj2wkM9Ky3bK00KTxnrgZehjaKqeuY7C/I/RbDLdfj7fpLHK+itUdRpHRSjlkvQ6fJVEuXBQ+AD6ghwZFapvzGQuU1Gm6rP55pBClTmdyAtn4JMvLiOp/RMB6fi/LjPBFNfafzt1/3VTpJ1utBbOpPLMdtRCmpehX5xMF+WYKel9rCkzMuM6fY7NJI2VdKr2nmn/KUT9LqJcw2g2fNLDhM3b0FwyVd1031b0LjQ931zn3yRdZn8kKJBC/O1J5TWTY6GwKsT8rXIXov1n2ufO4bmpxWi52k7h+vskcjebFKIfFKotspXPwxwBR8ARcAQcAUfAEXAEHAFHwBFwBBwBR8ARcAQcAUegAAjMB2ksa7R3oz1o5gfLfsK+h25DJZeiqTS3BiGP/5OPjCkUSTAfz0jVKMTJa9HPofeiwuI2VB+Vt6ASWTSl8KgMQmb/zzqy+DIqci+Vt46d6IdQyc9QhWUjjQvVnnpOM3oDOoSmyrKT85QU8lmpPKdznAvSWM9VG4qoE9ap+qaOwvprqKzRcsl0+3kxSON3UehUXX47VwUIz6zDmwg7lnav8pD1qAgoERW5pFB9Y6YEzFR9Nt8cUqgy5yOG88VlYnkeAak2G+C8OjPBFNfpi3yePkXamUT/LokPoKmy6SgC9E70KlQyX6TxVO090/6jss+2fspjI/oFNHMeF1aDqP5+PgvNJ1PVLXVvhJP3oekWzultcz9xWuSkv2n5ZB2Rs/nbky/vfHGFwEr5z3b+TpVxtu1/Kn1Oc8/H0A40ve10rrH/12g5mk8K0Q8K1Rb5yulxjoAj4Ag4Ao6AI+AIOAKOgCPgCDgCjsCTFoGpPs48aYHxijsCjsCCRKCCUsnFsj4a6kOlyKMDk+ccXLIgoI/0LagIE330LaQ0kZkskFagsrbbhapdpiuFbE99jD4THUWPoCJC0qWQz0rPt5jnK3m4SLsG9BCqBQJaOHC6ivrTQVTE4/fRl6LZROM+RYyLtOqaTLSB41NRkRqPTB45TCnF6htT9dl8BS9WmTPLtJkAjXuJyLw3BmfT+ydMsv3oWlRWjKmFP5wWTJaR00Wo5oU7UFnXFktm0965ylyI+tWQ+TpU7VCLPoSqTRPodGUmdVtCphq/q9HD6F70ODoTme3fnpk8Kz1tIbBK5VeI+bsQ7Z8qz0yOLSQ+H9U8pEVae9AYOhOZbT8oZFvMpNye1hFwBBwBR8ARcAQcAUfAEXAEHAFHwBFwBBwBR8ARcAQcgQWDgEjad6MvRvXROZ+IzJXbbhG52/Ml9DhHYIEg8AnKof6qRQ7qv9lEpLHSSEUeuRQPgb/j0am2eNoMi/G8tHtfMcN7Pbkj4Ag4Ao6AI+AIOAKOgCPgCDgCjoAj4Ag4Ao6AI+AIOAKOgCPgCDgCT2oEzqb2KZLmXs7zeYz4Xlpauax2cQQWOgIiiuVmW338PTkK66RxDmDmOVhWhsdQtdWpLEr5zuS9j3KU1bGLI+AIOAKOgCPgCDgCjoAj4Ag4Ao6AI+AIOAKOgCPgCDgCjoAj4Ag4Ao7ANBEoIV2KVBNZ84c57nsj4SlyWW4jL86RzoMdgYWGwKcokPquXG6LmMwUJ40zEZmf63Iek3JxL9fCP0FTc8wbZlgEubWWNbnuf9UM7/XkjoAj4Ag4Ao6AI+AIOAKOgCPgCDgCjoAj4Ag4Ao6AI+AIOAKOgCPgCDgCIPBmNEXW6HgbKqLtb9BPo/eh6fHXcu3iCJwuCGhf1QOo+nA2a2MnjQGmCPJUnqkFKNpDOn1+uYXrUnQmkrIyFvHs4gg4Ao6AI+AIOAKOgCPgCDgCjoAj4Ag4Ao6AI+AIOAKOgCPgCDgCjoAjcIoIfIj70i2O00mc1Pkoad52ivn7bY5AMRG4iocn0B60MaMgThpnADJPl0t5TmpuSR33EabwmcilJNb9attVM7nR0zoCjoAj4Ag4Ao6AI+AIOAKOgCPgCDgCjoAj4Ag4Ao7A3CEQmbusPWdHwBFwBByBOUTgV+T9RVTE8SAqV6/a3/ggej8qi2O5jP0V6uIInG4I7KfAIou3oerXN6EpqeFEe+jegV6Pqu+7zD0CwzziHLQSfQz9EvpO9Bg6E/kcibV39Z+jv5nJjZ7WEXAEHAFHwBFwBBwBR8ARcAQcAUfAEXAEHAFHwBFwBBwBR8ARcAQcAUfAEXAEHAFHwBFwBBwBR8ARcAQcAUfAEXAEHAFHwBFwBBwBR8ARcAQcAUfAEXAEHAFHwBFwBBwBR8ARcAQcAUfAEXAEHAFHwBFwBBwBR8ARcAQcAUfAEXAEHAFHwBFwBAqJgFw+ujgCjoAj4Ag4Ao6AI+AIOAKOgCPgCDgCjoAj4Ag4Ao6AI7CQELiawnz0FAsU577xSR3j2I5qaxVtdfN9dBR1eSICf8flbz0x6MTV6znbceJq4Zy8kaL8SY7ifIjwn+SI82BHQLxIcpYwFCKPWRZhQdx+Os4dCwI4L4QjsBARKFmIhfIyOQKOgCPgCDgCjoAj4Ag4Ao6AI+AIOAKOgCPgCDgCjsCTGoFGan/xHCDQQ55fQ7+AikR2mUBgI4dceFcvUJBa8pS5eYGW2YtVfAR+lyK8EtXxVKUQeZzqsxfafafj3LHQMPTyOAILBgEnjRdMU3hBHAFHwBFwBBwBR2AuERgcHLyZ/M+ay2ecznknEomn19XV7Tqd6+BldwQcAUfAEXAEHAFHwBFwBKaBwBLS/NmkvpfjddO4x5M4Ao7A6Y/A+VThX9BnoXejpyKFyONUnuv3OAKOgCMwLwg4aTwvMPtDHAFH4FQQcIInP2pO8OTHx2MdgSwINIZCoaVZwj0IBMDGfxd6T3AEHAFHwBFwBBwBR8AReLIh8HEq3Il++clWca+vI/AkQqCJun4EfTMaOcV6FyKPU3y03+YIOAKOwPwh4B8H5w9rf5Ij4AjMHAEnePJg5gRPHnA8yhFwBBwBR8ARcAQcAUfAEXAEHAFHwBGYHgKfJ1kX+oPpJfdUjoAjcJogEKacb0WvReVh4FSkEHmcynP9HkfAEXAEioKAk8ZFgd0f6gg4Ao6AI+AIOAKOgCPgCDgCjoAj4Ag4Ao6AI+AIOAILAAFZHv4nugodWgDl8SI4Ao5AYRCQp7F/nWVWhchjlkXw2x0BR8ARmD8EnDSeP6z9SY6AI+AIOAKOgCPgCDgCjoAj4Ag4Ao6AI+AIOAKOgCMwewTayOKqLNmECJPqm6e0Cl2P/hb6+2gu17T1xL0a/QLq4gg4Ao6AI+AIOAKOwJMSASeNn5TN7pV2BBwBR8ARcAQcAUfAEXAEHAFHwBFwBBwBR8ARcAROWwSilPzRaZb+ZtJ9Ff0cej1agWYTEctOGmdDxsMcAUfAEXAEHAFH4EmBgHzyuzgCjoAj4Ag4Ao6AI+AIOAKOgCPgCDgCjoAj4Ag4Ao6AI7CYEfgNlfvnPBVcnyfOoxwBR8ARcAQcAUfAEVj0CLil8aJvYq+gI+AIOAKOgCPgCDgCjoAj4Ag4Ao6AI+AIOAKOgCPgCIDAL9D35UCiOkf4VMGNJFiDtkzqCo7jaM+kdnK8By30fsnnkedWVHuuqgx6zkH00ORxgOPpKDJyugQ9ExWWCfQIKgx3o/Mhcmu+CV2Lqm2b0G60HX0MfRidKynms1N1Ul9+LroKbUWF+x1oFJ0LKdYYmou6zCTPYtV7Mc4d2pZgOaoxK12HlqLqvyk9ynkH6uIIOAJ5EHDSOA84HuUIOAKOgCPgCDgCjoAj4Ag4Ao6AI+AIOAKOgCPgCDgCiwaBvjw12ZcnLjNKxOY16DtQkWsiLPKJSOSb0e+iX0Rj6KnIRm56L/oidOUUGVxP/MfRG6dIN9vop5DBj9GKHBmJaNR+0r/KEZ8KbuDkb9A/QEX+ZJN7Cfwo+r1skQUIu4I83oi+Es23iOAY8T9B/xHdiRZC5uPZ36SgcsOeKT8g4A2o+vV16J+hItzSRXX+DKo+FU+POMXzuRpDl1OeH02WSc/IJRcS0ZUR+XdcyxtBIfLIyPrE5VzV+8QDcpws1rlDizrej74ezTUHpUPyay4+j2ouHkuPmOJ8GfE7cqS5k3D9PXBxBBYFAvkmzkVRQa+EI+AIOAKOgCPgCDgCjoAj4Ag4Ao6AI+AIOAKOgCPgCDgCIHBOHhQezROXHqU8dqH/h4qAm4owJomVoVej/46KYDgPnYmIDPk0KoLyj9GpCGOSBGX7Jce7UBFkcyGy6BN52oLKajJTlxD2l+iv0HzyEiJ3o0qbizDW/U9FRfaI5KxHCyWyar4XvQX9IzQfYUx0UN83cdyOfhltQk9V5vPZtRQys410XTNZ+K9yVBtkEsaKVhuL0C8EYTyXY0hlT9VRCxFySYSIVLrUsXIycSHyyPbcuax3tucpbLHOHRpzmhP3oH8yWU8OU8qVpPgGqkUQz50y9eMJNM+n+knmse7xZH7mCJz+CDhpfPq3odfAEXAEHAFHwBFwBBwBR8ARcAQcAUfAEXAEHAFHwBFwBPIjIOL2vTmSJAj/Uo649OBNXPwCldXeqcqF3CgyV+5TpyOycLsJfRtaMp0bMtJczPX16NkZ4bO9FCH3UzQfgf124kXQ5JM3EykiuDlfooy4F3MtDGdyT0YWJy5fzZlcX6tdZioiHkUyayHAqeBbzGdn1lXWxa/JDMy4nqotM5JnvSzGGMpakHkOLEa9F+vcoXnwe6jmRM3rpyIifn+IPv9UbvZ7HIHFjICTxou5db1ujoAj4Ag4Ao6AIzCnCEQi+kYwv1JScirfiea3jP40R8ARcAQcAUfAEXAEHAFHYIEhsIXy3IDmIva+T9xjU5RZJIOISu2zO1vRPsTTIall+SiS+rJZPlBWear/GbPMJ3W7iBphlgtPpXs/+m86ySPnEyfr61P5Rv1U7ntHnrynE/VuEokITVnaTueebGm0iOB2dFu2yBxhxXx2ZpG2EnBdZmCW6//KEjaToGKMoZmUb67SFqPei3nu+DgN9cwCNJassDWPXVCAvDwLR2DRIOBfHRdNU3pFHAFHYDoIiOCJx+PTSVqwNCJ4YrFYwfLzjBwBR6D4CIRCIRseHrZbbrnFnv/858/bvKLn/uQnP7arrrrKSkvLLJlMFh8ML4Ej4Ag4Ao6AI+AIOAKOgCMw/wjIyvWfsjxW5KNUqzvLUbnjFTF5FppL9hHxllyRaeFKszbtOvN0kIC9qI6y8JM732o0lzyLCLlv7smVgPBPo+fliU9F9XFShZamArIcVZ7/RGdLtshNq/KRm9dc8gkiPporMi1cROVUhHGUNNvRZnQNmi4qy6nKJdw4nTJON/86EoqAugjtmOKmYj47W9HOzRaYEXYb1/szwmZ6WYwxNNMyzkX6YtR7sc4dGjvvytFICcJvRB9CNQb1MXYVqrkq14IO/Z14H/r7qIsj4AiAgJPG3g0cAUfgSYGAEzxPimb2SjoC84aAFqC0tbXZxz72d3bBBRdYS0vLnC8OKS0ttd27d9k//MNHbdu2C23FihXzRlbPG7D+IEfAEXAEHAFHwBFwBBwBR2B6CMgy9J3TS5o31RFir0GnIvlExr4tR04iKv4KlcXsSFoakRGvRUXe6DxTRGw/F/12ZsTk9RUc35AjTsGdqMiTm9GDqPITwXwt+ttoNnkGgSI178kWOc2wfyBdPoLlC8S/Zxp5qe7Py5Oui7g/Rn+GpnCVK2zl/RfobKSWm2U1q3bNJbuI+DJ6NyqyVIsPRFi9HRVBnE3WEvgf6AuzRU6GFfPZeYr1hCgR9epXWojwFFRWq99AZyPCej7G0GGe84nJglZzfOvkeeahlYCvZQTeMXldiDxSWc9XvVPP03Exzx25FquMUW/11Z0CIItoLv1SlnAFvQLdgO7ThYsj8GRHwEnjJ3sP8Po7Ak8SBJzgMatYf8XNY4fvOysZjljNlmdbKFJmg9t/ZOHyKmt6xpssMdxrnbd8weoue5lt+NMP2PCeR23XdX9q5XVNtuSSF1r7r3mPTcStvKrOkvGoRceGg94TKuEdIhS3kspmK2+5wMbad1oIC8jqdRfbaNsuG2t9zJJch0IRq1i2xSJltRbrOWBjPUeD9cRLtr2E+zZbtPu4DTx2myXHB61m67MsVFJp4YpqS4z029Dhh8mz0uLcV1LHO2KkgnJXWJwyxwfaLDnab5GqpRaqrOf+4SCPWP8xC1cvs4qVZ/KcckuM9gVlGO/az/mgJWP29LHOB/USuKBlZu32Qdrtkcl2a6TdXnSi3cqqeC8N2m3iXVv4Wigx0W6raLe2VLtdQrs9ltFuZ9JuNWntFrKg3VZNttvOW8F8iHa7cmbtRttGqptpt4bH262Pdquh3ZbTbiWpdgvZeNfBBdVu8iCwf/8+O3LsoH35y1+ya6/9SG7SGOtgOl/2fiZL4fS4hL43ZRc98/Of/5y1tR/l2ftt9erVRSeNZ9Y/fV7J3rK5Q2eGr4//3Eh6jCPgCDgCjoAj4Ag4AichoB/e/4b+NTpwUuzJASI3RVhmk/cT+E9ZIkRifBFdjX4wS7yCRDLmEhHRueR+In4b5cX6hMQ5ewB9MfpNNBexK7L9NeipiAg4kba5hA8H07LaDpMuRexly0vEzwvQAxmRx7lW+VX//8yIm8nln5N4Y54bhN+b0cG0NCKOf4B+Gf0Oug3NJtcQKHL+N9kiCSvms3MU6USw+tA7UNW/dzJUrpXfjaptZyPP4+b5GENqp1QfXc65+mw2ETGcSpcZX4g8UnnOV71Tz9NxMc8dl6ZXNO1cYzIXYaxkGrfPR39XF5PSylFz5oNo1WRYrkOSiJ4ckdP5G5LjVg92BBYeAk4aL7w28RItcARm9gHXP5AvlOZ0gkf8bKgxFA4ttdiwxQc7rQRCNYy1ZHJsyMbb9wbkndqr+bxt1nfHL6xxwxlWvWIdJGCFLdmy2Tp/kwxc4S57+gtsCKKs+8FbrHbTZVa+fIP13PsDi4/128jB282SCYs0rbFwU5Mljo5a1aozrf7Mi2xg/w4bHxq0CCS13s7L6pfxb8hGjmy38b72gBxORkcsMTYIOThgJbXVAZeWjI0GZG+4JGKJsirIY37HhcP8X2pJypYI86cMIjpUTvoU4QbBHKmGRIYgD5MmDEEej1dCFI9ZpLLWEhCcyZLoafE38PF2G4Eg77ASCNWJdhuE6N3D9VI1my274CLrvfWn1kxbVa1cb2Fwaty61Tpvpt1CSWt59kts4PA+67r/11a75QqrWLHRuu/8Lu3WZ8P7bgmwjjTSbo1LaLeRoN0atl5mA/u229hgn4XLwJr/Uu02fOgBG+85ZnHI+GR0mHYboA1pt7q0dgvwL6HdqilP9Yl2M9onQftZudoNPdFuVRahfiH6pZ4XhjSOQ3RPtFsd7caCgJKF4iUlZIePHLKGhhr7+je+ar/92y+2Cy+80MbG9F0IgQgOae9h6pYYHQUbyHpc44s6nuCIdaZ3jlAwrpKKUb2rq1jjUB4kSsqt/aT76crKSrvxxpvsf7//Xaurr7KjRw+Tj76zFFce758+r8zFvPI4vj7+F9b4L+6486c7Ao6AI+AIOAKOQEEQ0I/ps9Cnoz9D9eM8n/yISFmXnoueP6kXcNSL7b+g+eQGIj+YI0F9jvBVhIv8zSXvJCKdMM5M9xYCXorycvEEUT21F3ANmk6IPiFRjguV519zxCn4p+gfoHrln0ouJoHwyyWq34FckYR/FX0l+sI8aXJF6WXsDbkiCRf59Oo88XuJuwIVQbU2Rzq199VZ4or57CzFOSnow4T8e0ZoN9daXDFbme8xNNvyFur++a73Yp87NudomBcRrkUJx3PEK1gLCO5CH5zUdo7TFaXVAgoXR2DRI3BafDBf9K3gFTytEHj8A65/IJ+LD+Rz1xmc4AlFSiHjIFohTkOikGX9C3GXgJSNj49A9kUC+Eshp8YGBiHyohCStda/6yGL9nVDUCntuA0f3R8QejCXFh3qtuoqeX8pIa8JAktvoKUNy63xkqdb9PhuW33W+bb+8mfYoftXW8/gMGRwrfXc8yurW9piJVi/9hx4zKKjkLi8V8qNeBhiePToDiup76G8E/lGyti7FXI7OQ6BHItOdJOSMohj6gGxKAtoEcMJCEaVK6TycK33/iRxiUgiIOYS1D0xCjEN0Tk+rgW0C19UvxPtprqVVlBH2lEEO+1mkQncIxCNsX4IXNqotLrO+nfei3Xuc8GHdhses/59OwLLbTKz6EAnluB854BwD4l0D4jLhJU2rrKmK55t0dY9tubsbbb+ac+0Q/e20G5D9IU66777Rqtftgrr5FrrPvAo7TYKwrzziiCFCB459oiVYv0tsl5tWUIf0yKAJOVMxiFTBTntJlGdJtqN8qW3m+JZeBC0WyJGuyUwkIZ0xbI9Mdq7oNqts6PTynAZPVYStb/6q7+0733v+1ZTB7lNv4z19dnort0Wb28PNuiKMK7CZaUB4S+89aVAY2XihLM49Rwft9gIdQ2D3cqVVsECgHBVpZUmktba2mrve997rLKqFG45bJ2dXYK96OLzCjPXHM4rPv4X7vgv+uDzAjgCjoAj4Ag4Ao5AIRAQqSd9GH0Jug/NJyJZ75jUfOky4w5lBqRdN6Sdp58+k4tcv/hvIe7X6YmznPcR9n1UVs7a31MkiY6q60zJYm6xS9Hr0IkXUIU8UWRV+3J08oX9iZFZrjZlCUsF3caJiPyp5OMkOBXS+CruOyNH5uOE/78ccenBvIzb+9GvpQemnT+bcxFMIlzTpZjPTi9HtnOtgP5MtogChs3nGCpgsWed1XzWe7HPHQ/QGnzQOkk0l+5Ctejje+hN6BCaLp1cfDI9wM8dAUfgZARKTg7yEEfAEciHgH8gn9sP5Pmwn23ck53giWMJKhfRAQGZxHozyvuAiONEOZaNNRBaWINWNFj3Q3fb0mv+0EZjQzbSdgwCssHKGhpx/1yNVeoQPCPkMwShrByDPERvaYBYAABAAElEQVRAYyEJO4liAQz/JVfT8YF+K6moscYVLVZZErLGdWeQEtKsssKibUeseeNWDIQbLVnVaF0776F5IY1xVSzLyvhQl40ef8Qqlq638qb1gSXrKIRhAmvmsd4DlLXGynFPHamsC0hIuZvW8yOUKxmHaIxDLkM+JpMQxpCYkeqmCRIyCik3hDtriPJJym623WrO75cl70S7ga9IVJH+EMehxDht0gCB20D71VvP9ntpt9fbeHzYRjqOQvIugbxvwq00br1Hhmk32lptB6EZuPAGZ+WrNptoN8jkzkO0XRe40m7LlltlOE67rbdwCIK+qhzX44cn2q2+2RI1jda5466gvUuxDhae0YF2LMcftMpm2m3ZhoDoHRvp5jhg492HsDKnn1F2lVsW4GpPEeAqo9FuIt9CsZKA4I+P9GENr3ZTe9Jug7QbRLmZ3uGLLyJsRyDNY5C9y5cvte2PPGRvf8fb7DMf/4TFbrvDhh/bZWXLlkLENzKuaK9hiHM0MVn05CTjGxwYM0nyo0GsBMJYVsnjd9xlQz/5udVc+BQbPP9c+5O3vdUOHtpnZ561wbq7e7BoVh8uvvi8whwzh/OKj/+FOf6LP/K8BI6AI+AIOAKOgCNQYATOI79b0eejIlcLIfqFfw76O+jL8mTIPkJZ5RlZQycCf5gnLj3q99MvZnn+Ke7P9R1ZxMyLUb2wTVc25En4X3ni0qNEVO9D8+WVnj51LnI7l3yZCOU5HfkGiWSBuzVLYl60gwUJ386IK+azM4py0uUNhHSfFFq8gNmOoeKVfHZPnm29F/vcobn6dTkgriH8jyZVH4/uRDVP3IzegmaSyAS5OAKOQCYCuf7YZ6bza0fAEZhEwD+Qz+0H8rnqaE7wYJBa1WTRjt0QvFiussevCEhZGsNX2dixR7FKPRAQwd13/gJCCuK2v9NiAz1W1rjCurc/GBB7st6NRXARLYWgjVQtgeyKBEdZlIr0CoivqnqIWt59sXgdHcIqH2JyBMvLOFbE4XI9MwyhzP2cl9YtwUUx4ZCakSSEJoSurGW1h27FkrWQ1njrilWxfzGEMe594/3tFqrBsrhsC48qsVKVAXI62n2YV9gq2DesiSEZQzzLICFjI+x7zJ7H2s83JpISEclaBkm5MOjHoEg5/4lU1FlUbSXLaXCcIE5F0eLa+/ADuKhWm5ZY520/sbFoiH2H2yzW22nlS9dY366dtEO9hXAvnQDPUCmuoCFtS2pxca1FAtVgF7Qb77O0XUBCYwkegowflcUrbTsyMGAxXF2XYSXLg9hOGotZLF3L2Os6Au5qt7DaDWvgZIy9iSF7K5ZuoN3IG5fhcjU+Fj9MG7RCdHPPsk1Ug8ULkNrJqhr63WHKVAU5PB5YI4cop9otPtwD2U17YfEetB8kd7i8zspw9zy+QN5jS1l0MTY6zl7d5bZmTYv99Pqf2UtvucXeGCq3pWvX2qozz7SVl11qySEIY400MMZTOAe9A06Ixl9KEkEwpDHW9G333WetOx+z45/9nH2xNGk76f/r16wM2mGEttGzF4L4vDK384qP/4U7/hfC+PMyOAKOgCPgCDgCT3IEeqh/LotQVsealNWpgUvpFRzPQi9Dc30LVZqb0C1oJzpTWcINl6OXTuolHBU2laS/EqSnVXlzydFcEXMYngs3PbIaFTn+n7qYpuQjeg9OMw8lU9p8eWXLamO2wMkwWZJPV9R2d6PZSGPloT6QSRoX89kqUz65P1/kPMQVegzNQ5EL8ohC13uxzx0igDX2gi8oeVpAH01EoEu1uENeEEQi/xj9FrofdXEEHIEsCOT7g58luQc5Ao6AfyCf2w/kc9nDnuwET0XLVsjh+7HebMYKdCOkKu/PEHJDB26zsuYNAZE3dvzRIDw0wuIAXB3rd5jI2/Hefqw/IRUh95565bOwUC63mx65ndvHsAjeDckFSQuJLMtQkX0JrC/7H9kOkdtph8fGbRx31R2PPmzjo8NWufIMG+nqsP4jh628eaUNHd7DTzcIR8jAJK6YtVdxGBJUbqZhJCES2RMWslhuioPfhXJJzfu/3BfLalUEpFxba+9bGMggPNjrlZ+Pct+cYL/dCRfM/KaEKA5XQKJCnMfiXHfz7AUuFavPt7Hj2wNr6fLlZ0H2ihiP2/DBOyCGNwdE5FjbjoAkT3b3W6K7A1ISEhiMRo63YrVK/Usq7ZxLLrUEBO/tD97Mtta9NrjvXrKJElcO+VuPFTmkP208AFEZ7euxwzsesfH65db+0AMWpX0qWjbaaHurDWC9XLGsxYaO0O64jA726ZULbZ4ZOF0O2o52k4Ww2jTAl39l1Ux7qg0n3FdDNLN4QNbFsnwOFjHouw5FT0SHyHskSBe0OeUMV9JuWCfHYgun3ZY2N9sQiyKSENp19dW2KrrUHm7rsD/tb7Oyg3vtw/fea7+3rNkiK5aD04T7dcHBqAKvyf4M4a53nWRwTacFv8Shg/b9r33N/lHux1lYUdFQa6tWLLU69otOgOnoyLg18eyFID6v0B/ncF7x8S98F+b4Xwjjz8vgCDgCjoAj4Ag8yRGQxdjnZ4jBU0n/TZQXqawi8kbkwruyxj4xUD/kr0Zfij4dlVXxVCQGSU4SvRhkk8ZsgZNhR/LEFSvqYzz4uygvltOSDXlSHcwTlxl1KgT6+sxM0q73pZ1P5zRf+mVZMlifJSwVlC+vVJr0Y7702Z6dfm+288PZAucwbK7H0BwWfVZZz3W9F/vcsRP0NUf/8wxboZT0mqulmq9+jX4c/Snq4gg4AmkI6OutiyPgCMwAAf9APrcfyGfQFDNO+mQneCKQsSL2tPBB5J1cOkeWrMLxUgLXv+240T0Dkg4iDwIkZekreqtq3Xm4Jd4PabjZapf320pIsAasUXdu2mp9wwlrOPcyGz70sJXWr7KaLU+z/u2/tJLyWkt0dRrejS2Gq+O+GLwwRO14zyEsf7Eo5dl9nR0W7uuHLx63svoWyGZIasi30iUtELuQY+ODNo5VsdlyDE8HsXrWYm/2PCYfkcSxvuNmWMyW4Ca5BMvY0oYWwtohICcsnuXSGL/KWLdSCOotYk/3RyAedS2i53SQoN0gxktqIAlpnziWu6WNa1kTeRuutruwBF8XkKsiaEMlWALjTjzZtd9qNz3VRjsOBu1XBZm+edNGa6irsX2bzraRWKnVn3eFHfu/3VbatNZqabe+7TcE2MSOH7XQ2JiN1zVa90jUxukr4x2tEM1YHkP69x4/buGubouyz3F5/UqsgWk3COKyxjUQuzWQz7TpgIjrFUG76Vwii2QYT4v2t2LpvByj8HqUdqtbEaTXIgDVT0Q2J1jkJjiyIOBEu7HIQcTztLfIIukcisq1YfMWi0JiD4NNBeRudXWZ1dVU2yHcUG+NRuyyLWdN7Ovc03+iJCkr44neSPCk1fHEogi+MXGtfcWv3Hymff2RB+1AacSWk2dVJcQ8PX94iHYAn/UbNwWLKU5kXKQTn1fmdl7x8Z/Cd2GN/yINN3+sI+AIOAKOgCPgCMwegXvJ4kL0B+hVObJ7K+GfQHnhzCoifP4CFWnBC/WsRS8+2aQxW+BkmF6UF5qspEDvR983zYJtyJOuNU9cZtSpkMZrMjNJu96bdj6d03zpsxG3xXz2VPVpmypBgeLnawwVqLgFy2a+6r3Y5w41iNzlayW95pxTlSu5USrL4zegC3FepVgujsD8I+Ck8fxj7k88zRHwD+SpD7inF/HmBA8DT65xIY4TMSxPI9rbNgIJ2BcQsCKKtd9tUmRqAkoW61yLTBBYYayKZVEs0q8aoi/KPq6irWrqG6xt/33EXUpcaeBCOIlbXVkhl9bxbgQBWIKlatXajVaBhWR82UqLtfM+J9fT7GVcuhySsQwirKcHQrgXV8n1WA5DLLI/stxVly5Zwx67+22EfZSTWFzGY5C8lC8gtiEUY3I5zTHCs+LjUaygayxZHbNYP+/2IbnA5t17knDU3r2RgIhmr2PKp3wCPR3mI5GKlFdEvlxUQ5tDrGLdSv2CdiI+aDfI1sCds9KDveoZHx2CzG2x2pqlNtjdbeGRQWvAQrXj3rutZvOFAd4ifRNR2g1r4dKGldxXBvarrGr9FqtqqrXkitUW6wBT+kCkstZKlq4K2s3KcDc+2A8vT4lofz0zjDvqsub1kMz7gmcnsCCOs99rgPWkRXgMS/RgUUJ1I3G0G3tai/Aea8VamvppH+qJdtOey+StBQhYOsutdiCq3wKQBPVdAaYRCOOOjm6rra6yvqEh9hvutRdQ5Q9sPdfOuOJplsASWfVRXfQfF8G5qhBcTwTJwFqG3iSlZcn7giuvtC9EY/aBfY/Zg11gBl9eN1Zpvf1DVgWZv5o+H2eRQNF9rKtePq/M3byi/u7jf8GNf41fF0fAEXAEHAFHwBE4bRGQJezn0Kty1IAXxsDN9PeyxC8hTASD3FBPV+RG+3b0mhw38AKUVfSKkEsackUUOfydPP8L6L5plCPfKu467p9YfTx1RrzMzlgGuINViVklF4mfNTGBeqPLJdnyKuazc5UzFa6yzbXM5xia67rMJP/5rPdinztSuP8NJ1pg8rdotgUaqXRTHV9Igm+jz0FzzcdT5eHxjsCiQsBJ40XVnF6ZeUHAP5BjAXr6EW9O8MAt9eLBCte30Z4juJFeBonKvrWt2+EjSiAPqyGysEJmr9/E+BDWvLw/iQSMVNhYJ26kl63nvjbrjI9a0+q13BOxriOH2E+4ISASQxC2JObDfinkLeQexF8YEjIu97qQ0LL4TeoIyavnlDYss7JafjOLJKsatzhkWxILUpGHkTJZpBJFWZMiGiErZTWsPWFDJSqrnsVRbppJI8IyATEpd8hhrIiDtDE8lREXkHK4Zg6ez5X2442PDATll8vT00GiA+zhDKbR3sOQ8KvAm3Y7BsEaEOENkKrsKwyxKmI9wpEI6ldK+g6rWH4GVuQ91tc3YsvP2ECThq239Rj7SddgnU2b00YkDiyU1V6SMMStFgkE5D3pJQFvhdVvWVOLlTfxW5wA7rLRsdGgTYN2KBepKwKZfLAGj0MYJ+V6WqEsUgjaS+3GvtN6bmoxgtKEcW2udkuwQIAOhJUxdxEW9CmuS7hPFtYBOR3N9t6tUs6fhLEE7v7pz+znb/8zaxsYshCLJarBaiNlexsLK152yblWu3Vr4KY93K/+xlgKgKBt0j4pBP0zrdhBH5+8TtAuFzzrSvtKc5N989Ht9t8H22x3adiGGSOVw+N245vebC//9n9b5QXngxvtVSTxeWVu5xUf/wtv/BdpqPljHQFHwBFwBBwBR6CwCFxPdiIH5Moom2zJEqhvqN9BpyKM20jzK/Q36K3ow+hyNJdFbK4XnG7uWYVmk5ZsgfMUxhJZ+wWajQTXS+Un0ZehU8keEqzPkWgp4XtzxGUGr80MmMb1MdLkIpk2ECciarpyRp6E2YjvYj47T1GDKF7I51TmewzNaWVmkPl813uxzx3p0H+aiy+hL0Ffi16NYtUwY7mSO0RCf3jGd/oNjsAiRECTlosj4AjMAAH/QD63H8hn0BTTTuoEzwRU410HA1I2PjYwQRZDpsaH5UY4AjG8C1L5EPEiCyH2ljZB1vIuCGE3dOhRq2Y/25G2wzYEMfvT7/xPQNr2HDti5cs32Gh/T2AZzM2BxWsIMjfac5ywOojLVhvcD/FbVsEzDwcLDiLspSxCM7qE92YItNFjByA2u7GA5nkQazGsYQlmP+QjgVWyyGuRjnInHcKCOIQlMqwnRBwutsdFOGOlrP1uIUpjuEKOi/QOlwWukA3raBHNujcgk8MiNonS/r3s53w6yFjb7oB8jY1028jhewKCNTbQFtR/vHMv+w/zzjnpgjsRntj/WaTswJ57rHIF+xB3HLJBcL35+hsg4Uus48A+FgGcYaN94MbCAe2RG+VziayEo724nmYP62hfq40cAruKKhtrOw4hPRZYUsYHewOCXw03chxX4/1dgRWw2q2krpnQUJAHPqonrJ95XtButE1I7qkhk1U2kfyyQtYexYGVamDtzLXcT9MuAZkNea1z7Usdpv9wQX/tpY/Eitps4fJy67/7Hnv0T/7UKnHR/TII/foEBC/4PaVluTW2rLSRBhZT9PdbJYsoAiJYxDxs8VgszpcpFkawGKJUhLz6O+MmLot5LaaIYEVOmETHYfXblhb7vcoqe/bRI3ZfW6vdzwKLkXLG1OEjtuMNf2znfP9/rGzVKsZrcfqzzyssWJnDecXH/8Ia/xOj0/91BBwBR8ARcAQcgUWAAC8WxgunrctRFxGHmSJC4jmZgZPX+jH+WfQLqEjiTMlHYuQjjTPzSV2vTJ3M85E3R3sVej26G12BZspLCbgKvTEzIuNapPHVGWGpS7mdna6cKmn8lBwP2Ej4bTnisgVn6yupdFpAkCm8wFuxnp1ZlsxrPgjNqcz3GJrTyswg8/mut0jjXLIY5o7MumGtYt+cVCwpgnn6BRw1D21CpyvvIuG16ORXmene5ukcgcWHAF9zXRwBR2AmCPgH8rn9QD6TtphOWid4HkdJ1sSl7EGbiLJ4VGQeEilvgKTl96QsQgNiNWa151xuK5/3Eqw7E7bv+B4rrW2yimWr2Ye21gb2P2b9A6PcXsG9ldZ8+TOt8eLLIWtbrRaL35e/7sX23/921HoHh63q7PNt4AHcXw8PQfzFIBv7A9e7sh6NDnTZ4G62k4JIkwvlwOISEk0usBPsbSw3y9GhbjwmV2JdqzLz7gJRGhuS5SrWrCo/BGMyPmSjx3dAGtcFlraxft6/IEFDJeQIUZkUCQnhHIaojkBiB9atsnIl3yhE63jrI48DtEDPQhCm2gNYJCsVB0N2tIXslVtpLQAIQZLLpXHV+nNt1av/xCIQtns/82Erq1li1eu2YF0N8X9kvx3YvZ/6VwB/idWde4E1P/0yFgLssVpI0KtfepX97OtHbWBwwErWrrPxHZD63e0sJOiDQO6Ek8aKGwzHu1sh9/keonbDgjlgNjmXVXis+xjlGWLv4x6eU0q/WTqxEEBEvvoXZQyrPUgv6+KRI9shmqkX90S799OWkMVYmYdCWJirRyhf2ipSgdty3JjHdb9cWo8n8chcnHbTAoTxjg7b+bZ3WLyj0y6urrFn1tVZpLGZsVVH+UttHPJ2sLXV+mmrZXX1VluFtX6cPsl/5ZDCFo9bP4sdRiGQtTiiAhfTdVhpl5WEbTyOa2rqWcoYGRgZsc6BvqBXVmPZvGzDBnv+qtX23O4eG4c8HmFcDe94zHb/xbts6ze+RrsyHuj78y0+r8ztvOLjf+GM//keW/48R8ARcAQcAUfAEZhTBETirs7zBJHKmfKOzIDJaxEMf4DKvWkumVi9nD1WRGw2ESl7ZbYIwqZLhLyEtB9Gt6N6idJRegCd6cuD0v8h+j1U8gH088HZyf98iqBtaK666Y69+ieHXET4j3LEZQZPF4v0+46mX2ScP5Xrr2WE5bu8ME/kHVniivnsLMV5QlC+9npCwlO8mO8xNFUxeUGftUwnj/mu92KfO/I1mlysf39SlU4k+dPRZ6DPRzejuaSOiDPQfbkSeLgj8GRBYII1eLLU1uvpCBQAAf9APrcfyAvQRCeycILnBBQTJ5BwgStj3AMHhCtWnCIRQ6NQVwGZx29dXgMbNm2FEDtsZVWVVrVmHcRtkw0f22N168/EIrnLSkqx8CXHsuZ1tnLrZhs6fNBWbNtm3XfcbSub66y6ptp6h0atfMUKG6lpCKxUy5Y0WLS2wWwEwlkELgRhYlSLAeF0IUVlhRqUBWvLOO7PRRKLUCtpWGXlS1ogH8dsnL12ZfHKDZQTV9bB3rdYbkI6Rwfx+iR30yIW5aIaL2MhLKlLqxuwkOV5uNGW+2pZvVatPZ9nQUJazAbvl3exhS0iUbXvb1h1FKEK2S1XzwkIY5jxibYDlyXnXWSJtoNWUVNlNes34zq8yUaO7bPadZsg6YesEgKTilsUK+M1l11q4wP91nL5FTb62G675ILNdvv/1WFJHrK6redYtPUACwlwg7yM/aLb6i05Mhy0m6zBYwMTi1blKlr7JovoD6yF2XdY5LIszkvrV+DKem3QjlHad6x9L2loN4nagRaKj/RaDCtpkcXqeGFZIcvpNX1SLrhVx5KqJQHZrDwr1q8LwuFVbXDPT5XTvIus3Pd+6CM28sCDVgWe5ataqGez1UNqD0Gm9w4P2zII4DXs9320v8/a2tvow/VWwyKFAYjiHu3hDHFcAflcJVKcGgizAdLK4rgSTKsh3EUYtxO2BBfiy1is0cZ4GCDvpeQTWrnM+utxL37woJXR33t+8nM78unP2Pr3/BWYToypeQXG55U5nVd8/DPgF8j4n9dx5Q9zBBwBR8ARcAQcgblG4FU8gBWtOeVgRgwv0QEJmhEcXIqEzUcYK5GIi1wi0jmb/IbAN2WLIOy16PtRVhbnFbmJPn9S0xPqvtejuBGbtvwlKb+elvrLnIsMOy8tLHWqsDej/54KyHLckyUsFfQaTj6Uushz/B3iVueJzxX1MyL+OEekyv0JNB+5m7r1FZxkq7/i9eL7S51kSDGfnVGUky7nkjQuxhhSBfPVKd8ckA7ObPIoRr0X+9yR3jYies+cVC006U2P5Pw4qg9/qY9/z+D8Jyj73mUVzZdOGmeFxgOfTAhMZzXMkwkPr6sjMDUCWT6Qi+SQVVw24i0MwSLirXL5Oty57rfKppXs5docEG9iDFLEW2ySeBsbGjlBvIkQEvFWAvEWLmU/UYi3Uoi3SFn1BPEmq0yItwSEjvaRjVTWWmlNE0QLljkVtfBLImEmiLeKxtVW0bTaShvXkJa9TFPEW2yUD7Ii3gZsrGMvloQHIXrGTxBvbHwaWAyWVjdZWeM6LDprA6JKxFvV6nOtZu05U2NWpBTpBE9ZVTUEzyorW3+GNTY3Y/EYsj5ImFos+rZC8NRAvojgGenttYrxqI0PDlorBGkvLmbLYjFrBEhpxSTB09bVZaNYZZZC1g30dFl7+3HDQbJtrsXqkA/cIngqIIBqIHhKtmyyEtqurLLiBMEjC+h5F15FZQkqF9TaYzUg8Shr8K4MHkEf5q9ClP5UVtdoSUja+CguoyH4ApIy2FcYco/wMH0z6FvafxgSOJkM2WDncYuOjlkp++UmRochNSf2vk7ItTHWlbLuDYhG+i0ndEyRjuRFHxORHIZsC9JARIaIk0WyyF2FKV0Je/eGytkPV0Qp7SBrVVm96ijr4gmhktwXuNjGglPH4Jr9lENEaS/mCG6EE3LnrKqdDiLrYuoo0lt1D+ofoC/CWNe0hNqPPhyBjIzLYleNQ11leSrVWFBYAutWWe2WQ/xHIY1L6ZMdux61zrauAOt4X5dFISuDfY3Z+zkJvsJf84sIYpH7AVHMeYQ5RnNOpLqeI/sq04aKE+Yi6cO0lazRS6qXcE7bqQDMN9ozOwHBnJTSJupfVID7WBSgfolleKiUdtY1JLISqQ4l5BlYOUO+FkPCFRXW+fMbrPXrX6fLV1k5ew1XYl1cBhH85X177OUP3muv3LHdrrn3Tvvc/r22BtfeK8vLrKunGwv5IVtCPRopeGJ4xI4xf+xrb7c97R12tLPbxoeGcXFtKFbUzD3d3LOaZ1TTh//m0YfthffdbS/bfr+99v677NZjR60OuCpXrmRhAPMzc8nhT/1/NvDQw4Gl9rxjQ/P5vDKH84qP/wUx/ud9XPkDHQFHwBFwBBwBR2AuEZAF7HVTPODWjPh1Gdfpl4fTL3KcPytHuIJ5Qc4qN2cNnQhs4vCHeeIV1Yy+KEcaXpTtphxxuYJvyYgQkfbujLD0y49wsSQ9ION8R8Z1+uUmLnKVPT3dB9IvZnAucmliNfTJN1US9HcnB58UwkcL++hJoY8H/ILTCddRj4fprJjPfmJJTr5KfVg5OWb2IcUYQyq1yPtckmvsZaafTR7FqPdinjt+n8b5HPorVISwxthd6NfQV6NTyW9I8N08ibKN2TzJPcoRWJwI8DXWxRFwBGaEQNI/kJ8OxJsTPCf36vH+IxbrndhXOBnFIhESMj4+GPB1AXsHgSyStuehuzAIjlp/Z68NH9prQwd3Wmldkw0ePgD5NWgjA8M2OhK1MfYtPnL/Q2YsZGi95y6rXbnexiB3K5e1sLgAq2KRwpCJWlQRxz2vCEK5VpYVcWD1y7lcFieCfWvHcF89SP7smcsCBmhSK8MVcSjBnsWj/RCNo5DZ/HaDEBXpmSIyI5ChkfJaq1z7FKvZ/EyraDkXEhpCU1arENMiPfFzHdRVdp0iXJPsiRvG4tZEeJ8GEu1vZZ/gw5CswxMunLEKF/Ea8MYi/YN2K7WeR+63yLK1Fi2rt8FD+7HuPWzVqzbZSMdxcIUANuqMpfJ4V6v1HG21ii1nW+ejO61u1TqrXbrE6lrWWghiNgR5H7Qblr4ASTuCo4jbwJKb7wJ6JqI21L7XiZEh+hGLBCCB1aci3Kf+pT2kE0Efo6+xMCWwJJdrcIhnucgWAV22ZKVVrDw7sEwOhbEz1kIXPVOW5MEe1xNunZWeTGjTIv1sgfCOQ+zuu+46C0djrKWptPJq6jk8an+7a6d9uPWIDUAiL92y2So3b7KP93baByDjmyHAG1G5/U6OjVkliydWgudmCOgtFZW2hXw2VpQHYYpLsuhisH/AWrDOjjBm/mj7A/YdsGs680xbgnvqRyvK7A07H7FvHTpklRDpZUsa4ehLLdHVbfs/+Y9Bl5hYMRA00bz84/PK3M4rPv6ZF4o9/udlJPlDHAFHwBFwBBwBR+AUEGAppWk1djaVO+hqlBdTW4qeif4W+hlUhPByNJdsJ+KBjMj+jOv0y4u4ENGYS9YQ8Y5ckYSr/NnkAIE/zxYxGfYRjqpTNhE2X0SXZIskTMRlV464mQRfT+Kf5bhBxPaHcsQp+DH0h3niv0Xcs3LE68Xwk+iFOeKnCtZKZOWfS/6QiH9Fc300WEncDehmNJd8OEdEMZ+do0jzElyMMaSK8VEop2ge4APElDKbPIpR7wPUaLHOHc3U7c3olegKNF3+nAtc7E0puca1FsKIgM4levaeHJpvPsmVn4c7AgsWAX2FdXEEHIEZIKAP5MnRvoDEkiWcrDXjccgS/SQXKyY3q5PEW+XZlwb7uIp4i9VhwbpywxOIN5E4KeKt9rxLrPXmXzyReDt0/HHiDdIhK/Gmx0KxiXgLsddnEqs3mVSKmDuJeMNi9gTxhhWgJZgCZIUo8ieMlfLS9YEVc2ywE2LpIHWD9Jsk3kJyBYzb38DKEQIlRbzFc/2pnQGmBU+aSfA01D5O8GDR/Z2RQVuN1fHSpiY4rph9/MABOw5h+fdnbIKwwlUsWJdj5ae3PrmRXYo72TgWgEnyLaGdS2TdKYIHq81B7hPBk4Tged3OB+zBqipbD8EzjiXso+xrKoLn2lVr7WVLl9k4BM/4CG6WJwme877ypSDPiX5TcBROyrCkstHG2HuYgtNPsPKEQwysdKlTedMGiOEVNtq6w2I97Xb8f74SEIVyFR0Fr+YVy3FRfSDYV3bNJkjGFS12z5HHIJgftli82sax0o5DLH/7149Y+2O7rHH5cjt7VaMd6V5hh7s6LdbRbgn2Z5Vr5ZD6XGBFLNxkMax9slm4SZzKRYe22qYV9EXGFqTvaG+rjbEfcmJsELIXi3vcTZMBfXwED8pRK285x8qWbiDvqFXYRhtlL9+x1t2kwdKWe7R3cYI6yEJarqrHWuMBWamxejqIrGzjzDkibZO4pI5DrCbZt1jjPFKJZwEIeu0pPXJkj+374ifAIQlJ3E/fxKoca+BR9uCN08/ZbNgqG5dZD333wE9/YrVHeq3v4futasUa+84vH7TjEMlVLABYVgOxi/V9V1cHe+e2Wxxre2GbjIEngIn0Dch+2i1E+6n/yn242q+yBqvjJexlzJwS7T5q0bFhytITWEGXQvbDRgfkchxCuWLZOVa+5rygCZL0s5FDD2Dl3M4cAyk+Osiz+E+LBFh0EIZ4jfYeh1xmD+Qy/Y6fX4lA8h7/znet7/Y7rBrr6VKuKxjzn2g9bN/qaLMz8DBwVj99DGL5CGTy5k0b7T8YB5uYb97MWOmkX49hwV8e7MXNHELxSwVmhozRNiyzMJxz29v2PsZ8UmnrcIEdYi7ZCFndyPz+APHv2bfb9AXmadW0L/sol8QrrfNHP7beu++2+osvxo27vkPMj/i8Mrfzio//4o//+RlJ/hRHwBFwBBwBR8AROAUEVnEPL5AFl09nyfEoYR2oCOhMWUbAv6BvQTNfMi8h7JtoI5pLRG7nkk8Q8bwckcrzp+inUJG396K8dNlT0A+iF6O55Cu5Ik4h/N3c81w0G/n2VsI/i+5As8l7CbwGzfZ9mo9WAbn9txxvQB+ZTLeNo+qn+2YjarPXo3pONnk7gZejn0PvRvejeoG9FP0rdDmaS75PxB25Igkv5rPzFGtOo4o1huSKnQ+lWkV/kui1+t/Qz6D6BPgs9CD632i6zCaPYtV7sc4dmruuRZekN9Dk+RaO/4u+Dj0yGZZ+0GKTV6OvTA9MO3+I86G068xTzXEbMwMnr/nY6uIILB4Esv1RXjy185o4AnOAgH8gn9sP5IVoMid4sqPYeOnvWO99JTZ88G6sPLHoxE15HHfBJVimVq69CIqOPVUhxIZ23xYQyJiPWqz7CC7S19ol11xtP8dyshLLyKdefolVQWZ1nHu+DdeugECrttot22y4s511fsuCvVnP23a+bdvQZE3JjdZ1SL/VsCyF6I3gwrikug4ycMLFehwX1iJ/YRODYxyrVRFy1U3LrQyL1zHcXMcG+2wYS1YRzGVYLpexz6tIykQYl9ZYnpY1rOD+ZFBpLdgoYz/dsfYDXMs1cwzCuMdiPAPOn7xxu13JXrraTxfS/3SQJec/z/oe+jGLDXhHpX5h3NPHh1kUUlJtNZueAXHcYKNtO2z06CNWVtEMjkMQ+TGr3XqBPfftb7Gff+I6i7NP7vNe/Xu2hIUS3+lotUT9SqtuXGHxjefaOBawXXUN1tfdadsuucguv2Ct7S1N2C24Tg4LIxG3EMVyNS334mG5H4cMjo/RbmCalOX4+AhtW2E1zbiAx3J2nHbrP7YfF9hdAfFbRh+Rav9eLb6Qe/iKFZsnXGiTf0lVjVW2nMUim+4gXiR1HII81nc0aLeSiiUWH6wjPxa0kH5ehbImwOjwf3zFwnSzMJbQlbiN/jmLIX6BO/trl6+2qyCNl7LAhHU7ditW3V8db7O169fbdbt227Por2eA3cg4fTEy4SZd5ceje6rbBtVRD9binCVg/IP2VvshWK1n7LUcOmpvYNzI9T092g5DzH8Hl/ifPHbEPrOaBRy4p1YbJnFrffRr37All7JYKchxfv7xeWVu5xUf//z1KOb4n59h5E9xBBwBR8ARcAQcgYWDwK0U5Qs5iiPr4+fmiPtjwrUX5n+iB9EN6NWo9tydSlbnSfBL4v4HfUWONCJB3jWpOZKcFHwnIT87KfTUA0TmfhH9kyxZ6LvzP6PPzxKnoJ2o8P5TXWQREer/NBnexVEEr8i9QshjZKLnqs1yyVOJ+HyuyBzhvLjbm3LEpYKL+exUGYpxLMYY0qv2MXRdjgq/mXBpSrTIIZM0nm0exaj3Yp07ROr+O/rXqQbLOD6b672oFm7sQNX2DegqVAtNNqG5RG3v4gg4AiCgP94ujoAjMAME/AP53H4gn0FTZE/qBE92XAiNNKy0qg1Pwyq13WL9bbghlk2jjD9rAjJ3rHOPlTWtgZZij2vSRNjHWnvUDuzfbV379uGpOGzVdUsshEvivuN91rhsqY1F6nB53c4eyEtsYNcD5ItraQiyJUvY6zaetCr2dMUuHXIM98QQj1YJYciewrLMDpVAImN5qb2MZVEawgVp4JoaS9MSzkuwNI7J8lhl1E90kY2klyUqzF0QpuA4VqnaczcQ0sSG+0mLNTgWsCG5UpYFM+Sx6hqHbA7FqyyMZatFdffCl+pN26hC3Pru+5/ASlcW1MIigvtu7R0sMrli2QYsjR+EDO+CRGZ3bbwg9B/cb7379wbui+tXYq06PGSd7JW7ZuMGaw0vsdH2Q1a1cq2N3HerhSHvRdyuWr3CynhWM+1XhgVrPCD69V5Om4js197Q9AO5kQ7Tb2Q5Ltfhck0tN8kV1ewdDumv/Yt1LdG59kjWPRItvQ8lsVaWxTl9Q//pf7ncDlqEPGkorKrZ+5hFDSKYEyWQ/jHuH+5jL+rMxftBtnP2TxgyuO/Bh6zn9jutHII2Aia9EMB72Zv4P1eusU2E4V+CvcypFzW4DDfbx0bG7YfsjR5aucI+yX7nnwVn7XUu/NJFfTJdygjoGxm1T2Dtv+yMdVbe0Wkv51nnVkSwQAYjbl+Cxfz5y1bYjXhEuKW/116E1whhVMo46vjZz+kHR618+TLGFDjOg/i8Mrfzio//4o7/eRhC/ghHwBFwBBwBR8ARWDgIsAra3oDmeuH4OHFXo5k/4wkK5FL+lc5URGIozye+LDyeyxs53YbmsnB7POXUZwdJIiIbl1EFlQ+Smyz4arPk+jzCXoT+KEucgj6E/gGa7V6CT4isQrPJTwgUGXQq8lVueiYqjAshvLDaS1AR3FNJMZ89VdnmKr5YY+j/qJAsx6cjuRZxzCaPYtV7sc4d19KQmhNfkKNB+fiY05o4xy32FSJmukAkV14e7gic9giET/saeAUcgXlGIPWBvKxx3YQrXX7a69d9iniL9+NCtW4pv/YniDcRPCcRb0tSxFtrQLxFaiDe+iaIt3j3sZzEm6zQgv1AIWtEvIXZyzUEgRDhugSXvBEs2iK4ow1hBRfmuSniTWSfRMRbQOCkE28QIHozEfF24h0FAuIE8Ubt0ok3kTki3kRiyaWsjY0r6wUjInj6tz8SEDwlWQieN9TX21oIxxoq3TRJ8FwNwVOSRvBEcOUbgdBK4g42yXmgnGOa+gQtg8E5QfCsWfU4wUP+eptp4hnn0z4fgeD5f03LAoInIgItg+AJ9nCdBwRLStn/l31Uw5DFkglX4+xXi6WoyhCuwppzZIAWp79A+IUgf4P+wj3VzUvZq3iVDfb1YwXcaFUtq6z9MK7aISLLsC4d6+6AEK6DeKyEyKy1NvZDHqWex9p7LDqOFSSWrrCIEIPsUYxVaoz9iUU0JuRmWQJmskCVBXSS8xEsVUew2BwbHQV23FeLcEO1d+44ZYwrH8hruTkeO74L4vQwxLHcqcvyeHlAooYqIDmVNXvshkuqiON3I+RxfLyf5/fY+Hh38OiF/k+CcR8RtvW8uzD+gvlGbQQesvyVq2+Rt7RoQBTqGnYdrLFGFukLqRil/9bU11lZbT17Ug+QNmGltWrvSVK+FPK+ospG2cs6XF5q/YO4JVe7CRzykGjMq+20T3TgZjwgryHnFc+cIu/8Y1iKj+KGeQx34DHmB1qUcLmvVruzb7XIYMZWnLYfa9uDZXEv3QJCGYJaexyX1i0P2kvl1xwXYm9qzaETbsyZd2h/0z7c8ygh+lX79TcE83JE5aKfjlCP1+AaehV1HwHbBDrOXPhYf5/df/75duYH/9ae/qxnWz1W179g/r0NcrechRLaPiChLQS017ZwTM0vk3NLJWm+0dNhh+tqrRqX3M/7g9da8o9eb/dVltlx8pab8TjPGmW+uoIFHc9kscQgYeFgP+iIjR09wtx3Oy69Jwj7+YDJ55W5nVd8/Bd3/M/HGPJnOAKOgCPgCDgCjsCCQOAApXg6uitPaX5JnKxmT0X2ctOXcty4nPArcsQpmFXR9hxUFr2zEeUj8rZtNpnkuFd5/kOOOAXLWlhETjYRWf9SNJ9b2Gz3KewuNJer2Vz3ZIa/hYBrUb1Uz0b2cfPl6EMzyKSYz55BMQuWtFhj6OszqMGaHGlnk0ex6r1Y5w59SNSc8eMcbTXT4Hu44a0zvcnTOwKLGYGJL8GLuYZeN0egwAj4B/K5/UA+2+Zygic3guNd7A3cecjGuw9D0EIqymU0FqCJ0X4bPvwAiwDGbPTQg0EY/o0hhPsDorUcC9N9uw4QxB7O7I970w232N0P7bLje/da7yP32QhucocO7IS0Lbeho+xdjNXq9jvvtet/fIfdf+tdWJ/W2sYt66ypRYQg+69WaXFDpZXVsNd0LYscYEG1t7QsWCPVDeyn22xJiOcYZG8CV8ihmuYgTqykFjeMD3Xh/rg/sIINldVifZmAiIOUhLTTMQSRKpfVsm4VoRlYtEJqBkSqiOcR7mWfXRuczuLf3HjOV4wsW5MQx/HBDgh1ncvaFytgXGyP97WxYGS5jR7bGYSFS0WOYwVM3SuWtVi0ut6q1qy3rqPHbP+RNmsbjdvehx+2wce2Q+LGrX/3wwHmo32Qsexh/tB92+2ehw7agxwDq9ZlTVYBgZkE12DvaUjjsCzEyyCmtQpF/9N+sjpmQ2OMt0OBIXCc/c+TkNVyeU4DWGyUBQCUPwrhH5U7a9ojRjtq32OR0HJTXsJim4plGycWNYiBhhgPax916qo6x4a7sZJvgzM+Pl/QB8+Ra+qum2+G3KbOIsGpLz3UKiHqo+q3jBW53d7PIocdVz3blr7zz+yyF15jf/ne99gFT3mqJWqr7LPak1tpZWGvBSkikANlnGkBDqrw46Mj9lUI9Ur2Rn/5q15lb3rH2+3c173GYn/1Lntw+VLrxroY5h3COW7j5KfFKZWTaASLgmi0zhtvpJhaWjA/4vMKc+kczis+/os7/udnFPlTHAFHwBFwBBwBR6CICGhF7d+jF6K7p1GOvybNf08jXXoSERuXoNelB2acvybjOvPyIAEilr+dGTHN61+Q7tno9mmmP5VkItQP57hxM+F/liNOwSLVrkYP6WKa8k3SyYr5VMjm9EfEuPggKnz3pEdM81wvr/+Fqo3lDncmUsxnz6SchUxbjDF0JxVQG01HVudINNs8ilFvVWWxzh0ijl+GakGK5vFTEZHqH0KvQicsKjhxcQQcAXdP7X3AEZgxAlN9II9UNdlY6w6IDsjVKYi30uZlAfFW2tFrNevOmSDesBhOJ966hiEBDuwLiLd1EG8dB0PWsX84IN7iWJeJeBNzEx1ij1Es0E4Qb3L5K+JN1p24lg2It/52yIkJ4i2JhSYsHwQNpJ2INywBU8RbmP1MU8RbbLAnIN4SEBohEW/kpg/kBvEWCnENWbKQJBfBUx4QPCxtFVkaStgBrCF3/tbVtvYNr7etmzfb0377RdbT12e/HPy1fba33y5hf1aRMHBE4sVOElkbHodkDQiehuaA4Hnd615nre1ttuucrfbgv3zath1rtQbIUQO7cTATwaNfIdJ0gqfllb97Uv5zEdB1639ZtJ/9bCGlGrc9z1ZedKXt/+nXbBRL3dhgN0QjVr78JzfRlWu2WCl9K9p3jHZOWs+BA5bEijSsfWtXt1jjmrV2aEULhGWzVSxfBQHYh6VqmZ25baM9svshG8Cy8ujuvYT320XPuNzO3brK9rBMCU+7FsHqOgY5Vp6U1e+oDcliFQtXbRgbwlK8DNK4tBbcIMUSnV3ki8VpuALQxgLiGrbUSishEkpJI3IMy1pZ88PcBX00gdvpBOeB9W1AFtPPIR+116+IOd2T1DPjIgEXvnTc+l0b79jLfr5tVr50g1Wd8RQb3HuPxXExPrL/LsjWlRbtOUK1wCGoEmMbSQwP2EP/9xMWCrQG2Dyya581nrEpmA/iIyMB+ai2LoGgbK4tt2HcQbcdacV6PkE/6bKzLrzQzrlokx3YWWa7ISPLIKFj2nOa8SOSdKSnlyPWzrRPGC8H5fVLrWRJfUAuJxhLEfpGuOs4UwSksOYX7pH1cAQLZz3X6IfhKtJDgoaxdE7Ia0FgNc1co/kTq2JZTdOUtCWLARhDyXluMlmuj7FQYnDXLtyzg69mA5H24JGgP4c5hui3ncwnO84921a/9jW27bxzLXr4sD32hS/aeffeY3fgsv0m6nfDYL+9ABfgwwEhPtnvUpML3Zhdxu3LA312sLrSzsEafNM999jx6ipb9zu/Y7VXPtPuHh2zHR/9mG2DxC4FP5UlmJfVp7kiC+CLsP/1w4znwWAsqaxzLT6vzO284uO/eON/rseO5+8IOAKOgCPgCDgCc4aAfgTzFhGoPljwsml6SeIlyOS2qB29D70DZcWldaPTFREVr0I/j34aPRvNJirD7eiH0esnE+g5Im3PnbxOP+ijwJ+hKmsukevj30M/ib4ffS5aheaTXxP5AfTmfIkKFCd834d+PUd+f0v4V1Hhn03UHlvRv0HfjOoTSjZRuuvQ/80WOYuwO7lXz38++lr0xWgFmkvUHj9DP4Y+mCvRNMOL+expFrFgyYo1hl5DDW5C/wXNNW40T8jqVK/X2V6mZ5NHsepNVWyxzh1qr79Er0XfhL4DXYdOJfo78G+o5pHTw5pkqhp5vCNQYAT4auviCDgCM0HAP5DP7QfymbRFZloneDIRyXItwh+ytencS+zSFz/f2u66gb1tD1pZ4xmQdyO4mt7AXrLdVrl0JZbHwxDC661qabOND/Ra7TrSsDBhWWMd7qDbbdWZZ1lvvBLX6l1WuXKt9e97hFdcXHvjkldWlPqJXYqL8OamBqvFMrW2otRKcZubkItfrDYnXP2SBiPVRBhXxiJ/5Vodgi0Z42ZIxiTEYmywF9KQd30xohC++vUuUjgs8g7r2NK6xmCRg1we69kRXGTH2Ps2hKWrDZUELo9DuPEtqV7CnVisDnQG1sbjItS6KfMCl7HWnVgZtwNUla295o/sqS96gV3/4Xdbb3+HldJeESyyg/2g2c+44ZwrWAvCwggssSubIN9xa1xz9rnWveNRO/+SbVjLltrQBRfYcKQBt9UVVrfxXBvtabMVG1Za293VNtIROCe3MlxVr1u70lbXV9poU70dX7bckliFh2nPijBWz5D+cTG4EL0adyHarBSL8gk30xEbhyjVPtWyMg5IerAO9kMGckmw365ckUN4ikyOQJmGy8qDdiHDwBI+zJ7aE3tV0+IsXEjg8jrBYpeY+kL3nomM5vhfLWIYPnTYxto7rEKksRYp0IdUbgoVWB2PQXrvpd+WvPhFdg4LRgYfetBu+YPXW2Nrqz0NS/kYltmfKI3b3zO+LgW/Ovp5VH03yEHUb5IvEWG7B5fs/1Easq0s1njH0Kgt/d8f2q5vfcf2f+d/7Jlf+qKd88xn2p2332HHf/B/tgbyWYtRRAqrLPDXAc4w2zZ85LCNd3ZaRUvLBM6Km2vxeWXO5hUf/8Ub/3M9bDx/R8ARcAQcAUfAEZgWAt8mlXShyU0U6Dx0I3omugVdjXaih9FfoMfRTNE9s5W7yUD75pajz0D17GVoPXoM3Tep+zn2ojMREeLSU5VvcKP0VGWYG/8a/QD6bPQcdBUqQvoAehu6E80UvV4VQnjZDPZe/hFH4fkUdC26Bl2KivhvQ1UGlUXpCyXz9ewXFarAs8ynGGPoi5RZCxfORtW26l/qc2rTQ+iv0amsVmebRzHqTbUCWaxzh0jxf0Tl7UBjVbp68riCo+blA2naynkSnamonxRqrpnpsz29IzCvCDhpPK9w+8MWDQL+gXzOPpDPpo84wTMFeiHcNWuP2PFe67r/Zhu75gWB9SSMK6+blRZnX+LSer2PQeBCjsmlcLAHchn3yS0ufnnl7rkCYnZ0DEJQlqNY8ibL2XMSS1JZBochLEUeyhW0yEIMMW1gaMQGx+K41h0O3FvbwGDgkjgkt8eQgbER3BXLElXWwVjny4VvsrLCYuztOtp5nPjegFCOlLEfMxaWsooPiDvIuuo1m4L9fmNY1cbYH3e08yh7zlI0rDFFqoW4R3sZRyCMy+ohPvUM+ReOVLChUjRYbj4FakWPjtQ0gssIlrhDEIK7raLkhQEZb1hPRyobsAY/Qt1aIIEhcRNRLIvZH71hqZUvabKR9uNW0dhsVXX1VoKL6b62TqtpqLexaCWWy33w0DUsGjhuI91YikMoi4CccH8NEQyQIBi4QR7r6Q6MVpOQxJEK+ktABusQD85FECfZkzhGH4hD8o9hYRztbQ1+hYvYD5fw/ss9SazLdSxvXGWl7D2tPZLjtF0c6+Uw+3/L20EYojU8DI2qvaixTC5vWsV9WD/3ttPGnYF1r3rAfIj2+h46eCiwgk5WQnLTn+UlQBgFxDEY9dPX2s45y7Y85QIrY6/tW/7inba5p9+SF15sX+nttJHjrXYNMH0bsvutHa32KbUN4wOH34HXgSryexj83tjbYeGaavtD2vXhkrjdBx4vgUDuuunXdv+HPmwX/vM/WdNznmNHf36DLQdnjVGVQdbGZDGJddhivX022taOt4A180Ma+7xCO8zdvOLjv3jjfz7mGH+GI+AIOAKOgCPgCJzWCLCKM3BpvbtItZDl4i8mtUhFmLPH8uJoN0zqnD1kioxFRIlELIYU89nzWd9ijCF9TnhgUk+1rrPNoxj1Tq/rYp07hOvBSU2vr587Ao7ADBFw0niGgHlyRwAWyom3OfxAPpse5gRPfvSED6amJEoGbmzLqspxd8wHeTjUkKw9IVYTY9rvtytw9yxCcujQHsIGrXbNBhtuOx6Qvp19Q5aorLcjO3HZC6FZ2bLRBg/usTB7H3e19lk0gbWqCEjyjEOi7dpzyIYqa+34wWPsa4sF8Ogo5CBE8RiLheV+WHvCil2GGIxj3TweHSFPLJ6xiA5IX4juSC37GkNQxylLCHfHyXG8yUCQyoI4JuKRsDAuqmMQq6O42pYr48TYAMQahDY1Ftkti1hZdYpsi+BSOS6L1dNAQiqmrKwhywcPPmJRCPsQhGJAYMo6G1fOIq1EwJZUso/wONbBXewfDBkrwliuwIdxF10CWVxR22idd99ncfZBrmheRX6PQTDjRrq2hvimwGJbkMSwGD9Ae4Ugmw8ebbMhkcaQkfjAt8SSRtothHdp7XOs/XwJw814AhfNFc0rKN9YYImcoGNFIH1NLqhpnwRtG+yLzHmZiGBZ6iZZoICb/NHu47QX7qhpm/gIfQQCWhWMYC0euMWHbJbLfLn/p2XnrdVUnvEuLNNZoBCUl7EiilakcUDT0m97qH9s61m2fOUKO3b9DVa9/5D1YuW94XOftksffdTe8vrX2zMaGu1d2BN/nTb8nfiQnRcL219X1VkPezl/lP6+v77OmiuW26uGR+05//D39pX777Prrv2w3Y+F/nUrV9ljP/qx9f/pW2z5mZtt+6oWG957wKqx2g8IfhHYQXkoksqHD/jR9vagv88HUD6vzO284uNfi1SKM/7nY/z4MxwBR8ARcAQcAUfAEXAEHAFHwBFwBBwBR8ARWEgIOGm8kFrDy3JaIOAfyOf2A/lsOoETPPnRw4ZX7GlAQIaqm+3QfjxXVeHaGf5rrG0HFp4NuHXugnQat4GDO3BR3WIj7I1awr7EpeyPPdLdE1g23vvf3w32sI3291gEgri0pjfYu7gEYrZ95wEbOnwocCssy2BZiA7HEtYLuTgCmZhgT23x1hN74nZOEIG4SS6BxJUkINCCcwhuEYelDex/jOWqCGERv0nikyW4Ko7JhfVoQEqLDBaBN2H9SS15ZjKJaWeoFAKyG2tc3CZDLocDcnUEQhsbY9xXyzL6dBCR+iLXpZWrtrKPdJXVbtpmbQ/egsV/GwQ47sLZ0zg+0mODxw6zZ3fERtoO2Xh3mdWdcZb1HTpo41h53/nD6y1S12Ad+/aAcauNQySPttNW7DG861f32jD78IrsD8hQSMgj7d02drzXutu7IJ5x/U07xdnHewjLZFn1i5APQ/hq3CWFJeSpGjdcWcfexnE4bvYsBnuliw1pz2zajL3Q1TYJSGkd5aZapHCEBQLxYeJwP52AVOYf8hKp1RNrfAAAQABJREFUzAIHXD8norQnEsy/cn0+TyJqOMoY0D7uE/1LlLUssLmGOI9Thz7qXw6RW1tVbfseuM9WQLR/fnTIWm64wToOHbIyLPXvZYHDy7Gc/sxVV9nB5z7HWv7t87YU6/t6xsUrzjvHVrz/vRa67pN262232S39fVZD/y+nv99K/vdDDq+NRqz7oYes4ernWgxyeuix3VYpPLU4AoxUvkApb4LxFhscmhjr84CTzytatDF384qP/+KN/3kYPv4IR8ARcAQcAUfAEXAEHAFHwBFwBBwBR8ARcAQWFAJOGi+o5vDCnA4I+Afyuf1APps+4ARPfvS2veJNtufG71rXwzfZcHerPXL3I9aHm2ocUUOqVlrZsjMstq/dSiERr3zlK6wBcurG4R7r78DFcdNSq2pZa2333g1BApk3OgZxWGklWJJWrT7Dho/thWtMWBmWrmEIRTbaxUoYy1D2PBYBWALBVorLae1bG6moIEjWwfBa3BNYAXNPQDyyO2sCIlGupZWfrJW1X69404DMhICUhXHgDhlycZT9fsua1wfpo/3tFu9vsxJZJUNmyl11JAFpiaWyyOixvoltS6LsaTzevZ+9cdVjFr5UrdqERTAkIOTgUFeb/fq7P7eeHQ+AVBIr2P1WUrcCwrUb0jBsy9YstZXsYXz78T14GR+wEvZ3rmxaDhYHrWvHDnh02gc8S+ubrWbdZlxZy503+/FC8MchOiMVtZDs7JdLWASysrSqjDamLbFsDvYcxpo5AXEsy/QSLMtLqmuC9grjVlr3iUCmWCxA0D7LuKVGJUH7idwUuT86CKnZFVipk4D9l0csSrspTUiuqdnreKznsJVA9MvSWP1CltRh0o52H8a9+gSBHGQ8x/+oTNqfm1IHpHGCFRYB2U0d47B50Ns2xJqFcBX1JU2stZ2+HrGjYPTVj3402GRsYh/vCJvohGwzlsarV6y0Evp7DIvxkXLtyV1rzbV11tnfbwfB6FNve6uVkb/SxBkHu7nnbJ41fOyY1dMmCdKPgn8CQjkuC3PGiVTkMf+zaICxQ/h8ic8rczuv+Pgv3vifrzHkz3EEHAFHwBFwBBwBR8ARcAQcAUfAEXAEHAFHYKEg4KTxQmkJL8dpg4B/IJ/bD+Sz6QhO8ORH7/IXXInr4FHr3HlnsFdszZln2uC9kHIiaLFqLKtfYYMQjyvWrrNl9dV26IEHbcOZm2wHrkFHu7usYdPZgbVpqARil3tEJEZxBa39g0MB+TccWKLqPIlFMTsgQ6NBq2ElGvCzEMna61jEFsxWQGzJOtIgIgP3uiLCsE4dg+yEMYR0hFAcgfjEUjMgkbE2TmKtKaJMIgvjaPsB9j7GjbIISojv0sa1VqY9gONj7PXbGbjcFmkdH2LfXLnn5hnRPlwhjw7PK/kYFPgU/7nmne+xO771Ddv9w8/gshvCvWkFpPfxgDgvW7HVqjdcbN33/q81tKyzK553Ndbhw3b2pZfaQSyK1TYNm7bYUBtkOqSusE+GwXAIF9DCEAI5DsYigiNYYP//7L0JkGX3dZ937n371nv3LD37DAYbARAgCFAiRYkyaS1WuWxKTiLHsZyyIllly5UosipJxXZc5UTeLafisstOOZbilGzRliiSJilSckGkuAEEiG0wwMxg9unpvft1v/2++16+8+9pEiJFGgOgZ3rA3wVev+2u311Avu/+zhlyHLj4dfebtikfThI2JmHspaE9XRx6Skfe/gZBSbLYk8cudb20dG+1G0qUh8+Zf8b3Nex9Wt83acp+9TQy+7x16VnLje/fWj4p5Ii+2vmpg2H/dJYuUjKb8tXsZ++LnLCOvrxknf7Wm/NBlr5BlG9oMr/5wYtTu5TdErR+mwX3Rfh7TxsjlVMYZODjx2RMz+4JOCctRL/LYU9hc+xN5DgPmK7HDRfZOul9zosMsj2pr4fTwXhdIdHd5Bzg6Az7wccfof922iKBz7y9z3Sf8fpBGHNccx72b0jjlJPME9B+rvk6b51ozGCHB11Xdva6ovP/9p7/O3z6aPYiIAIiIAIiIAIiIAIiIAIiIAIiIAIisKsISBrvqt2hlbkTCOgH8p39gfzNHgMSPN+eoEuuQtnToLF1rjyPrNpAZnlpZ9wgfYyHCf1lkYUuDQukgVMkVd9LSieJFUdGcK8kfJGImCv+RRrSl9gTpXEOmehJVeRY6jI4S5/hxhLiakt2DUdHWYyXICZNTPnhtEN/W6bdTkMOmf8A8ZYjuZofm+Y1/YlJJLt2Lk7tQXBuhH65Q9KWfU9RIuC8j++AkrCethwimT3RmhtDQnovXLehvM+QevZS28GieU9Mz+YOvfwxMtRl9R0yzO4Ztem7H7JzlKFO6yRZSyVkeC2sfVKfYxtdukc2OjlpGSTu1VOnbHSMktxeThpBiamHDyKf/ekC3fF4efAgiBGyLmdd9EcwT1voSkRk6DkdlZCjLv6ZDl5+HAxcgHIMDCMSrlEnCOOAu8j+R3b2VhdZLutHSemYdfTy00SHkcocW+wbl/surr1Pduf6SywXWYzozyGJPakckuUcS1vimjLQHCfh5gPWe8DxGSZml96qwdlk6ffMKgc5G3N8cwW0GB4uxTnQLMs6dlZWuDdiYLUHHrCVT3zKfmJ0zH6zzTbCzIdHkeJ3eSlueh93uQFjkvLRWXhnMbzRyhrT8nzgoL3ra89aEaAdPvcbKcbZL+9Duq9aww7fc7e1m8j5xSWENevjwpj92Hd5zYM9Gl77fs75Ot+4uSKswA7+0XVlZ68rOv+5Bt2m838HTxvNWgREQAREQAREQAREQAREQAREQAREQAR2JYEtW7ArV00rJQK7k8Cb+YG88C3ijVPQBRmSZVu8GUnCr4s3pEOfRKSnzIZImD8s3uizuS3eXAC50GHcDKKutOegFWcO8po+sEgaF2/5samQNMx42vM14s1liIu3PuIt5ZGpjH+LeCNKx3oyZhBv/rw7xds3C57EpQriJIgVfnT2VN63CJ52036iVEHUUGoWhi5q3vVNgqfkgofPv0Xw8JkLHp/Gk7eQuyF46FX67QQPLD2Z6OlAL498KwVP1o1wu47467GPvY8wcm9sD4IOwedli5F1ucoEfVgv2fLahhVnD9krzzxjrevX2MjIVs6cDrIxpewzcUdeu72L6afrpZHNSrWa3X9ir9WqCF+Xat6HGFE8PTNlB6fLNjU5almW5QlW31dgC2LYBbHL58zoJA6OMsj0T85PeonpLMdsmVOCfses+5DjL4gwpvVNGXqZ6iDG/DtKCPv8OBdSzgsvKexidNCpIzPplesJTF73li8ioenbzLr3zIXq7h+6PbaDa4FL/WTlEq+R3iF9u7W9nm71lGuDxGp5bNzGT95jS1euWm991WqzB62zRk9ipK2z8f3ofGLKPqeUhY4oBe3MXdq7OE69dDRlql3yFim5PFotWqVKiXH2dx95P+TYAbovmAfHL8d9bnyG/TVrpX1HrbQX+YusLk7ts/z41NY1iPkycgDt1yOf3s8Zn43vyLjgcpl969cZBr8BwAXxgERt6E19Y9/5/vP9fCsHT1GXpqdsiHT364cnjROOuYR19Yef9xUO/sa5c7aCOD7wYz9qc5WS3cXn/yf9uN/Ptf3HkeF/g+O4NTZipe/7fms+/4KN09M53JyBEC4uLNoKvY/H/sSP2DTXkL9ZHbPjfH4PSf5/OD5tNW4EaB7cb1PvfszmL1+xlHGLYX22rm0J/w3pc30LiWOWOyzmKSc/uXWDxy2ApevKzl5XdP5vXbtvx/l/C04fLUIEREAEREAEREAEREAEREAEREAEREAEdhUBSeNdtTu0MncCAf1AvrM/kL+ZY0CC5zvTW11eQy5FoQ9xlKvQI/Uaog6hh9RzqdpZvIDIQz416vY7v/4R+9oXn7TN5ZWQVl09d5bywJQ0RtbWjtxtEw+8C4nZt+qB44jCA1ahr3Ftesb+2PfebTPjpJJZlUy5Qo/jqu3ZO2UztaKN18qWrxS3ksC+qvSH9RLUWz2L+c8RSVW/oSFCTKaU9o18XUjKJq1NpCcJy/VF628sB4GaIhRdWsaeXmVWLlT7y+eZbp206zVrX3vBeksXzPsXJ/QyTtYXrEdZ6n57nW1GKieUDu41fC12/VAGDRo13ACSGdmH3M9bfvoI7/MI2lpI+GYrk7Z69aI98anftSuXr9mZrz5t7YV5Wz13xlZeeiEIymG/E1LKXqI7RujHiElPF08emLUf/5FHbM/kCDcxeL/jSXoV12z/gb12dLpie6fG2Y/FLbHrsteBI60zyPxseYR1KG3JXEonx/Tc3R7YvUHcu4j2R+hD7XJ44DI4GGPEpt/sshnm3aektZfK7nc2Qs/jpLHI/Smkl9ev0Xv5HN9RxjnhOOhR5vpWDUjjKnysUubcQRrz8Ocu2+E3pHh59VGE/eCFF+zS2XNWPHrMjvzCf29fXV22B0i5/xPS878A041Ww8b/6s/bJvsu8+Wv2BTT+ImQJek/TXr42hNPWO1932fDH/qQvYPy1r9KX/H/b2TKHoDPc5t1O/HXfsn6lYqd+8qXrXx93nJMl3xdYFNyndcutL0Udh7JXZgcD0xvBSZdV3b2uqLz/zae/7fiBNIyREAEREAEREAEREAEREAEREAEREAERGAXEVB56l20M7QqdwaB1/sDufcMdfFWPnw8iDd+wUfgnEXeIMluiLfi5IwtPvn7XxdvLu7KmUEQb2sXL5AW3BJvEXJiW7x1b4i3HmnAMATxRi9NJI6X3w3ijYRahDx4rXhLb4i3fmOdvqCrIfj3WvHmCcJt8ZYZP0gak/Qoyb5kYynIB091RjE9T9N2kDcu3kJU8EY6cFfsvdcKnjY9WiHipVs91UhO23IkMl8reI584Pu3BM/f+Fv2QHU0CJ4W5ZgvIHhmfulv/tGCZ6NlryB4Tvz8X7F5FzyUov1V0p0lL8Hb79pzjU27/+/9H39Y8CDSQjrRE4o8+qwHWVkET2r52X3fEDyUfd7J4Xd+9d/YxtUzXhvY8vuPWXZq0qonTljrxQIp0fusuO+Y5UbGrHH2yzbyjocsw7E1du99tv7yqa3yxVnKBrP+adKm/C2CkQO5uJf+usjc/Pikrb30jNWX6pTdRUIjlF1Oeg9jT2P2SLI2W6RH4buVeCX1TSrexaUPPk9b71jJSxpzrnhvY5dyxsd+7KYc70E8Ij4H/jmaO1Oswdx75HLmkBzubsxbzHHr33mamqK9zJ8krcegXWMzXhjgnMlTpptpPSu924dnnzxly9ev4/b5TzbrvnnpsvU2KC3OMGQfuQx2ZhG8L164ZLXjJMarFfpNbyDNmzCgekHatLF7HrbyzD678pmP2uhd9yGQizZy9B7rr1yzmZGS1UjIOk93wr5vRkeqVs5lLE+VgyySM9AL5xMC27ysNZxJLSf1FeQx5ZB7iSVM70I4aSAyN+h33WIdvO+1p5T53MuTB9nNtWTAPh9SKry78Aopc0qLu/RsLoV97dczdmooPR4S5r7vhqxb32XzrdtrfqyVOMaLiOP26TOWQ/oOnDlr4VfgIi+qnN975ubslU992o4cP273/eW/bJmRmp355//C0uVlyx47ZEd/5mcs/wM/YC/w2d0khSuV0SCcnfUsrF/49O/Y2Q990N7xd37ZXmLac//xUxZROWJIYvjuv/2/2cEf/7A9/+Ipu/zR37Z3scwM514CkwYz8HLzfkYMuP73uS6PHjlihbEJ5s9NGb6AHR50XfGjgWGHris6/7nR5Dad/1s7Vn9FQAREQAREQAREQAREQAREQAREQARE4LuHgKTxd8++1pa+RQT0A/nO/kD+ZnaTBM93prc6P4/soyR1ddqm3vfDNiCVO/Hux239S5+2yuxJa1ylF+5dj1h38ZwVEIb1F54JZaJRiEEQRqSAfe8nJB/dRrmPGiaUJ66NWZcS1TnKGee4qaEyMcUkZGORin0SwQvXl6w0M2319Q3reGllBECxWrXJfQdCWfbNtXWrL14nhVyhtHWF9GueJOfANkg2+xK9zHR6oxS19zsOMpqEMUqMWSGmb6wLNjLc2DDoI9sQkDFiOJOvsI70Zb7xmSdyM7UZSiLTH5gy673Vc98Z2i749nO/8Wskpeewgn0befgHrHbymI0dnbGL55/k9XvDDSNjD7zf1p/7PZt86GHrzl20sfsftKUnvxIYhX7FCK3mlbNWQO5HJIKzlWrYjxnKgm8sLVqDmyHy9LEObgbJ7wnaTYTzWrNny6t16zZJZcM+5iaA8ugM80BUsn+7lHePEZV599nsh34ytIQbA/ocHAPKKqcu79l/nmD3Z58uyiGnEca+Xi46B11uCkAcu/b0IfRavnEzQUgkh0sOvYTLlLsujXszX0Linw/j7vQfP8by4+M28eCDdvGl08hZ0u2wLHB/RwOR7tJ4lPfHEL/zv/1R+wo3YeQoUX3sz/03duzDP2Ht9TWLOK7XuIniS7/1USv9+9+047QgyPh2OmyGsXzOjs8v2bP/9J9Z5X/5n+zBX/5lS/7qXyVt3bCRgwctGhu1M+fO2+f+9a/aGKWt95Hg9x21wXK7COsyN5+E84T9gbK36fc8DqetfRkWsMN/dF3hBpgdvK7o/PcD+Pac/zt86mj2IiACIiACIiACIiACIiACIiACIiACIrDrCEga77pdohXa7QT0A/nO/kD+Zva/BM9/hh4CN0uf1Vz/sJUmxqz+0oskh4+SRCWZjrjyVHCK2OvT69bLF+fpp5qsLwdZ6WLWy39jq+h7PIazItdO0rf+yrNWIrXcuPKq5ZFZ/+mLp23u3CUr0t94/MisbVy+anmmncCytSnxO4+k7CCER8Ym7eCJwwjcEZu/tmTN+pqVSDnXEHTEWm3YJVVaJ53K+rg09r7aoYcxqVpWIYhDF2eRizfkmffizlK6OfTMJe3ab/p6eyq6a1HB08Yk4ykXnB07EKRljDhNU/p73wFDTCq3tOdeV+Q28c5HrXH+tO1596NWmDlEr/Jp2zjzZSTxe5GqSHJSwT16GOdd3IcBPp7w5R9P73oiOCIV30NmFqZmbfPCWY6FSUsQ9REiP8t+x4oie/u2vLBsYwf2URocCYyY9DLmlZFR23eMlDo9elfmF2zRp69N2PjUjBnzaCFHe0vLoVSyi+fQV9qFMSl87iJACMPc33MTQJD9HBtxeQLJicSnhPWAkuGZ4kjYj179YEAPbhfH+TF6tFenuBGgbBHHce/yrdlxnuKNSFsf/NCH7NV/9xuhQoALX89aj8Dpikt0+r1PIG8fRJI/809+xT67vGj3ffCDNjWzJ5xTyy+/Ymc/81mL/+1H7HvhOkpZb08BM4swsAS7G4m/+AdftCf+9v9uSz/15+3IPfdaed9+u9Zo2dVTp+2pj/yGGSnjx+lDXmKdmhzjc/nYZjk1oBTm4z2NI77f/4EPhPV0vrdk0HVlR68rOv9v3/l/S84fLUQEREAEREAEREAEREAEREAEREAEREAEdhEBSeNdtDO0KncIAf1AvqM/kL+Zo0CC5zvTi+lfS/1aSxsr1llasuLJd9jm4gpp26vWvEbp3fEjtvnq83y/ZitPfREJiRQ+/SLSLrUMJbiHLg4pIZ2QOm1cvYzqYl5dTw7zb69tCTLr1QsL1rx6zfKjo7YHKRxzvhyYHrMDo2XrjVatWC5Zu9m0YqlE2phUcSFH6tfLVOdD/+JOh1LLuK4upay9rHGfEr2DLv1wEXJe2jj0X0YEDxNSr3lEqEtEJGimOklJ5knGQS5nEvxlkT64F7e8GQlMLwvv5bSHiE8/TjJ5Sm27jL4DBi/VnS2NWUzK1qWwJ4X7XcQrfZxdjOdH91ifkuq99RW2JqIk/t1Wf/EpvkPMDkqh57FvZkSZ6tY8Za7hvf7S01bcs2pdauBnKZX8mx/7IqXLL1pxYtxmH77PVl4+a1OjNTs0VjKbHLNF5P5mfd1GkPr7Du+3GMGcIijXSSnnS/Su5vUwC1ffeSSSvWy19yP2VLj3Mh6QRqaqM5/1LB4Uwz7jheXG9nPcHWA9t+R22loLfam9XHlwnuwvDgK2fYQNIN3uyfJbObBMF7z7ELGVAwctmbvOPiBFzz6I+0MbHy3aKUrhv3OQsUP0ds62e3b6n/9L+8InPmm5gyTp2az4yhXbRx/ihwpl28dNE3xkYSv8xY2hitB/N6XZoy980Z45dcqevv9+bgqYtg43TrR4v+/aNXsEzjMsO+E8PFWI7LCn7en13WcdPfWdcE6MPfaoTTzysN8isD3rHX/WdWVnrys6/2/j+b/jZ48WIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAK7i4Ck8e7aH1qbO4CAfiDf2R/I39QhIMHzHfGlzXVSoGest3ze5j/1/1r5/u+z9pmvWZ/es8nqZTv2Az9oV7+6bu25jG2+/KJlSZCmXQTvnlmbefwx3o/ZlY/+plVnj5uRSC3tPWidlSWE7SjSF5FG32HvJewljPv15SB8M7ms5RCdngpudUis8nCJ2KL07vLckkWUtK5fn7MOfZGN9Grk80FEd+gJ3l1bCClT6h0zPYWMi2MkKUk5IyMH7XWW5enhjkUIRZfOjISMjJHD5GqR1e7NIv8csRr5PHwg7RpRHhhrzGNne0hvLfDN/80gG9POpiXst/qZ01a65xFbfeVlyohfsvVBZIUDD9va6S+zzW279omPsJ/GrX35HNuXs+o99CxOUmsxnW/7ANEZk+SOCxWSrAetuzLHZ4l1uvTbbrUQyJQIH69Zb3Lc9k6N2WQpZ2vFvOVJ0vo+9NLMnmsdIC6THilwuHY21qzhpa3h3OSGgC770mX/sMfDy1PzeaZA/2nWb8jxNETyhx7subJlKZXufadDv2NuDMiWx0iJr7AfOWZYUChhzXntEjRG/MfMJx97B99bN/iyS4cO2WH6Cr/4K79iOVgQLrYe6zVC+e7ezKg9vVy3dw1ydpAbMyboKb92fcHa1+Yo7G42zvsZhPBIhhsXYIhGD0p3q8C7H7VbPZK93/pjnGP7uWHi8ue/YHWEew7e00y/rzoSymAnpPSfyg3tEDdg5Bc3rO2OHk7cHmAdbu5455/7rxHx4+E8u1WEdF2B9A5eV3T+397z/1adR1qOCIiACIiACIiACIiACIiACIiACIiACOwGApLGu2EvaB3uKAL6gZzdtYM/kL/Zg0GC59sTLBw4aYP6COWLr1hv/oLlRw5ZijT0lOJ7/6ufsrETd9sj7zhu//p/fg6xmqPkM4J26XLoq9pdXrDiFGnesQkkbSGUN469Ny0lhT096ilQF2IRSdPI5VhcQKzFpCIHdunKvKWkWy9fvmYDxvOex61W06688koo39tGILtA6w8z1iAt6/K516pb2lwLpaqD9CUlm0UOD0hWDl0EIz2JfG5JYYRyf2MhpIc9iZx26pYsnidxvJUq9qRe6H3sMru5yCiUcCbRGkoEf3tcu+ab3sZ1S+tziPhrtvjJf2XFs6etd+lFxPmmlUZH7LH/4oft0pdH7ZVP/LqlbVJ5WS//nFrt2F02cvQwcnjWLtDPujx7lNRxD9l/iP7G58GKiIdFCu/U5Tvs+ptrljAPL1XeQwy3eqmt1TetQ5LZ98NGvW7XX72C4C/ZKlK0u75k1Kw2W6UkNaK429yw3sYiGhQh78lw9muuOsP6UJqaZHSaWw8C3HgdZZDFlKrmLoIgUV32p/TZ9mMpRnh7aDlUD0CcDkkbR+VxLj0p5Zh9vrd2GHKO3P0X/6K98mu/xvY1YJwLpdFbMJxZa1k0O2VfmluxR5HyY9woUSN57SXQOUIty/ag6cN5NqQvOHDC9r52C3xb/fMy4x0lsbyXmzISjmtOKT4ZUuIdxJRsf4bg913TE1a9umJNvkz8wfddbpKonTxpx37izxD0vrVpbF1Xdva6ovP/9p//rz1X9VoEREAEREAEREAEREAEREAEREAEREAE3s4EJI3fzntX27YjBPQD+c7+QP5W7DQJnj+aYmX/3ZY7+W7E3Jq1rp0i1Um5Y/qrZhBU9zxwj33+M5+zD/6JP0apYZKtLu/GZ4MI9rkVRrfKU6fNTeQhEoxEaAvx7KldTw5HJHejqIfo3UBakXtEeC0gFzvLS7ZC/9yljaa1eV1FPBdro7Z+9ZLVVxYtiyAberloSiRHyLV+F7FI8tVF78BFIelNN2d8xXtSysw3JnnrcjhH71sXx2mL3seUYh4gzgakW/uU2+5tXA0SjhXjXwRfFtvGfEIZbcpeu9DsIyvvhGH0gQ/aoLlqq09+JLAuZCrWJ62bRln70J//C9YgIfzDf+qH7ex/+mRI9nr5aZe05T3T1l2Yp2fxBIneCsjoZdyts8mkhdl+95QuirfErHPifxJk6DndG1i71bWzi8u2iX+cO3+FUuBFG93HDQeb63b11TM+IUnjHsdPlVLNOWttst9JEfdd9rc3vp7wjr0MuB8jXmKcneiiOFv1zxDG7OP+5gI3A9S25le/ThL+HNNSRj2IYk+Hs5a8Thuk0nnnqWiXqbd6GHBMT1Ay+uRP/ZS98I//sWVgFXM8xaxLk/T89HzdKkf22Vfml+3kRscOhj7aW6ns1APtftzy5K/DjRW89mPaB2YTNhNFvHXMuiTmoziMwDGLsD7HeXFpsmIPjY1a5tKCNSjD7j2MEy9Lzb7ukEp+5Od/nhsC9rIPWj7bWzbourKz1xWd/7f//L9lJ5MWJAIiIAIiIAIiIAIiIAIiIAIiIAIiIAK3mYCk8W3eAVr8nUdAP5Dv7A/kb8URIcHzR1PsUTI6T+naTJWy00jhPg8XtgPSnl5uePrYMas3XPyRpETuYcYoK1yy+vlX6c960pZfPB2SqBsvP20xkjdZoT9ueYR5NYIw9FLQnjbO5Leeu/UGyeC+9ft9a2+2kIyJFaplksaUT15dshYyOQgzJJmL5ggJNkQMDyjFnCaUukZGG+WNgyzsDfm8baWDD+I1EWeUAe4znm9TFnHpwjPZpKevp1Ype+wx1WGfBGgGYUlq1gVclK0wT6R0d91y5ckbgu6PZrWbPvWEdeHgCfoPn7fNM38QmMVI1wwid/++Gfu9j33G7jk8s1U2mbLe3oPah7TVttq9J+FOOpcEd5+y39nKuDUungqp7KSBbEeqO4ikvhLSxr7/Fl4+b8n6KmzXbLVBr+r6qk3Qn7fMsbNKKtl7G2e4sYC4LQKfLC1sUxf27OcBCWK/icDXwXsYuwgNEhk5neW4s7RmnlB3T+qpcZepnlB22Z8i89PWKh8xrZewRh5v7UvmgiT1Et0+Pgvyzbvlgx+jD/7CL9iF3/5ta1+4SD9uejO7v+b4aiCOS+ev22NH99npqZ5dvYw8xhBPktjvo4s9Sx0BJOaGC+flx3SYlmdPGftmcWUNvLxaQsR+GCCDFyjZfa4QW/WuvfY450Drwrw1+NyFcQ9h3OW5xX6dft/7ENp/ISTNbzUYXVd29rqi8393nP+3+rzS8kRABERABERABERABERABERABERABETgdhCQNL4d1LXMO5qAfiDf2R/I36qDQ4LnW0lmEX0JIrC3eBFxh3Sk5HOm4iIvtSd+66N2+EN/0n73t347lD3OVEkWdzZCArKHQFz96pN87pLPBS+JX8pLEz9FHtKr9eR91qa/bqY0uiWa8xWEH0LRE6LBHFIumWW4FkuQyD0Sqn3K+nr/Y39kSGVGniJGNnqpZO+FGyN44zIlikkQp6RXvV9vKCeNmPZSyjFiOlebQhQjvl21eX9jN28+eEIzyFBinS6MkY9xocrXfXQcMtzFtI/vUvoOGHoI3fw4ZcGR+C7UBz1nT1lpOGxubNqx7/lem19mv1JaOodAH1IWPGZfr71yxqKpA9a6cjWI/fpLTwXhnjQYrzaJOM4ibpuwpJc0fXM98evicoj89R3vGLf2Gy2s6VlcqNUsR99p/8LFLjFbpu8iONPAc8DxEqQx74eUSPabAKhXjchsWOXEe0IqOVOqhPfd5avsd44TtimpU+Ka48r3ke+3YdoO0tjLVXs62mW/l6f2suO52gzS2JXzrR+cS3V21h7/u3/XPvuTP2kxqWejTLX3Xh5wnA258aJ/5qo9MDNum/cdsbP1DXtlfs1mO0ObYTtKPDxu7GI+wL2xCX6K+Pb7kCKE28xnnuP+eilrxeMzdj+lwIuUo15f27CEfdJn3B7j9eDb8d7dpI+/5x/8A9L/JW6I8HT2rR10XdnZ64rO/91x/t/as0pLEwEREAEREAEREAEREAEREAEREAEREIHbQ0DS+PZw11LvYAL6gXxnfyB/qw4NCZ5vJdlepGzzynlrL53FpVJS+OpzyL2WZRBZZ576gsXHHrDl868isPpWO36flWf2WvNawTYunLPy1D5rU0462fA+wyRFEWbe9zipz1uvQQLUB4SWxyZTBG+URpbpl+khTNqYhHCKXHPZ2VxZIvnat+6ml0lmdE+VIr+8/+uQzq9eMtrTr36epQkp2AGFeiNEqQtfksMDyhxHyMVkYzUkbV0w9xsruEZPvVIqmfSql0jOIEKtxHsEd4QcD6KT5QwHvEdUbqdxw0rs8j+5cpWU8Zy1r51BtNZYf+83PY5/HNon/59/Zcd/7Cft8x//iNGoGTZly0RsIybTWbTOngvJcd81QAlu0pO+ftNA9dBxa1x6hfQxZb5dEiOOM8h+OgoHiTmk5LFLYy8J3iW1nCGFnpB89X2f3pD9MTwHjJfSX9mFcZabCAbO3st/s1+8xLgvL5SYZn0i5GrWU+okxMO+oRT21hDMKd9TSpxy1p5yjznO4jwSPEWEcgOCl0D3RDILvDHNrX/qt9t2/MMftoVf/Gv2tb/zy+G4coHvW+Gp/SHH8fLimhVXN+ydeyesd99Rm+MmiWd5H2+0rdxJrMzIBc4Tz3j7AGHrsoMaYG+VucFhumoTEyP2Tmd1fd02X12wFUQxp1QoRe19jHsw6MK9w775vl/+Zdvz+OPsZ9jchkHXlZ29ruj83z3n/204vbRIERABERABERABERABERABERABERABEbilBCSNbyluLeztQEA/kO/sD+Rv5TEiwfNNNKf3kkZESl17HvtEOeDmchCqXh63sP+YPfCB77HlJ3/XWpezVjt43NYoQ107eNjWXzll3bWFLSfsgpfxUVjYLhKpnjYm/ZtB2IZS0SNTBHl93qSDvd8qcjekY0n1DvptayHCuo2G9UmfRpS+jkhv5pC7roxdBiaUzB4EuevzpzS1lyJmOZGLQl53r79kucmjQVx2SZ664PTkZr++yMbOM2oLEY6o9L64rFuU9Rgo82AcX0YU83kQqqR1EdR3wtC8ft56869Yb/US25O35oWvsP099sPQlukNPcv2JvR1xqzb2P2PWr5aJRVcsdWXnrfCxFQQm74fYi8f7mwQ7D32Z5fUuQ/baWJPBFM9OQhlT3APKBvd90Rzp2HN5UXrdTrW2UD2+/5HJvtx4AI+Zp9S9RvRmwM5gpgEtPcvdtHrwjmUnma/xvTBTkhDp831UIbapXLs09CbetDdtKSxGARzTDnuAcdn6G3MPvP9G/pmsy4hpRvW+vb9GSCBH//r/ytl28/bq7/x72zoN0SwjgO2d0DKOwsYP1w7V5csd23ZDo1W7cTUqPX3TiOGI/oR963JcZ+mW726Mxny73A4CMsqn2XqTetcWLLNTUrFs5m+T9D3lpDO95Sx93TusoAW8v7BX/xFu/+/+xnOp/btA6Lryo5eV3T+767z//adaFqyCIiACIiACIiACIiACIiACIiACIiACOw8AUnjnWesJbzdCOgH8h39gfytPlwkeL5BNEsv4fI991u6eMbaZ58O6d0MIi5DEnTz+S+GcsYxSdMh8tFTxJ46TRGFPmRyFRLGpH7tIoIKWesJ0O4GJYcrtnHmBettLFl5fMrufv+j1pxftFc//3kk5VbC1xOoeC/sF6KRzyLkWpC4JEddNIa+yohmT/+62sWIstwNnLH3um3zFoHok+OVe/UF+u3OIxRzlh3ZH5KoMfLZhXR/Y2FrviSVI3oyB3HpyWcSzp5jjfg8ZpkpaWWXywlS+k4YsodPWGbvfrb9ig3blOP2JLf3bYZjbuagnXzkAZv7vRFrwrK8Z9ZWnvuCjR47GZK8vVWX6S5vvyHIvZS3M/O+0BmEfXdt0XJTe+GbWH9tOeyDvvcXJj0cyoaz/zocJ11PjXs5a6SuJ8SzJKC3ZD49kxmfBbIfvOS4lxp32e/S35c7tA43Krjs9xsAXEK7wPZjIdlk/dbnODS2ypOHVDIS2lPF3s+a6HLo7xuROnb5n3LM2W3ebUGys47f/0//L+uSor/ysY/ZgP81lVKuO+Ugzbo4RujnEOfk8a25vmEFksY5hHGOc2qCsuBZHi7cfRggg9NuYgklvdeQyS6IwyZ6GXAkcZ/zsc8BHPoYI4wTGHYY766f+Vl711//G9ZDGPv54fL+dgy6ruzsdUXnP/9V2EXn/+04x7RMERABERABERABERABERABERABERABEbhVBCSNbxVpLedtQ0A/kO/sD+Rv9YEiwfMNosnCgg2npiyujYUPI0+DIvVCr196BBcnx0NZ6mtf+bxtnj9lOVLDa2dOIWbz1lm9xriMj+CqHj1uB//4j9iVT/1Ha8/NIZC3eqkevP9eO3RgxlrVkl175hlLEZWhFPQAeZj6f27cfCEWXW4hBF1Iuu1KNjeDIPaS0hHljb3H8tBFr4tkl45MNkRa+jReqtjXNzYkNgJtmDQRqOPI61EbkGZNee9imGwa64vADH12vaw1sjGmhDX9ln26OFu2PGL5NvvHb+yc7/AqLpetevJe61193tqnv7wl+wHnqd6NZ5+w5dMvId8Rw56ohm2uUkPisr3s3wx9iomBM/YF0shrpH3Ht3pW8936i09bd/W61fYdsAc++L3WWFi005/9vXAjQB9hPHSp3kfgwjLDsiJP+nZJtEbsyyypWmSm76OwH1xM895lv4vlAWI7yG2WM2Af9purpM4X4F6w3PihMC8vl52hB3NSb4R5eNltTxez58L7IPv9GPKS1UjmQQ9pzf5PbuNe823uu9j1pDBC993/6B9Rjrtg1/79R5DGsfVJcWeRx+Snkb8RAnlLHK+yVRzdJIn7lm2jhVtspZ9/NwZ/5XrdC0y3OT7plGw5QvJ+G4Wni10Y9xHSPRL7PaY78nN/ye79a79EGnmD45hy7tzkkfMbPXgdzmumv1WDris7e13R+b97zv9bdU5pOSIgAiIgAiIgAiIgAiIgAiIgAiIgAiJwuwhIGt8u8lruHUtAP5Dv7A/kb+WBIcHzh2nm9+ylzyy9ZleuIwRJJyICvXSwp3QLJFRf+tJTtvYqfXMRgutnn7PK7HFKBq+F1OdwgDD0BCm+9+QP/gBCdmBHH3vMnv31f2MlRJiXuK7VavTTjazXvZE2jRGNCK+hf+lSE+nr4tJTpBnej+8/SGq1YvXLZ0i7LmHJSM+SxMzwWYocwxyG9ORwO0GJnIv5eEisM6SSXR67BPblI5ldjrIUZJxL6TbbxecuxUOimJ69ruWGJGzp+5ut7UVqemj11B+GtAvfJQvXLJ2chAt84LmV1Ga74JmtTVhl334bvedhc9m/fvoZy1XHKC3+XJD9resX2FdMBqOJ+x+0Iz/6J+3Cb/+WbV64QFJ7ktT5JTuM7N87OWJtyiSfLxXAST6W/ThgH3gS3SWuJ4h9ub7MDH2MgRwS5ylJcBe/EYnlGME76CD7SSb7evo4niL3wfsg84cHOpTvPO2dqbBNlKZOOSbSXsK+9pQ72+WJZZfPfmzyzF0AbEs1zMcT754iv9Wy3xO/KTczuCzuUZ662+1ax8t183zkF/9HG0xM2PVf+zXrkzz2NHGf7c6GB+Ieln5czrH5XbAUQOIF2fN87oMf6S2kcI/v/H+Ujd/4vM0yt4Vx6tLYZfXomO356Z+2yQ//aVtZXrZyqWTFYjE8fP1cHGf9HLqRYvb57/Sg68rOXld0/t/+83+nzyHNXwREQAREQAREQAREQAREQAREQAREQAR2CwFJ492yJ7QedwwB/UC+sz+QvxUHggTPH00xJZXYOv2ktS88G8RelOviV0mSupyix+3C11609pXnEa6JlaZmbfrBd1vz0lmkrAs/Hkgw74c7NlK1uYvXEFSosAR9RwIyRuwtXLlio3eftMuvnLf26jKpVuSxlzOuTlh+z2HSpvTDbfOe1GiO8rwjswesnyKZZw5Z49r5ICSHzH9L9jJfJKOLRE+kRghjTwoP+vTLTRK8Iz1kEYouNz0B6/IyQpbFGcpmI9iwjDwQmN5s18tSU+bY08jeP9fn6SndNPZs5+4f8vuOWNLsWnee8tSerHbZj3T1NG9+6qB97Xd+1zbOv2xZRPzmxZeseojS1MhbLyceI1lD6hdGd7//ffRFXrKjjz5iXz31LOLW2URWQjq6p2y1uohabgyIXPbDELHp8xgkXW42WA1p3yw3FEwcPIrsr9r6xdOUnV7GgiJ0fVmFStiHTjSI7RuyP+LmAV/AVmqcmwiQ/VkSzy77+22kMPs3yH9fHrLfBbOnll0e+zq4WvW/Pv/syD5uEKAc9i2U/d98PQmyGGHcarWsQX/uZqNp+R/6ISsj9hv/9tctdSHPueI3RngB6oy/ZvtH2YYGYnidvsVzSGQXwj5k+a7Gd/59jY3147fpx7efl4zn5av9uXPP3Vb+8Z+w9N57bXmOhPhIDYQknpHFIfV9Y35hpvy5VeJY15Wdva7o/L+95//2+aRnERABERABERABERABERABERABERABEfhuICBp/N2wl7WNbykB/UC+sz+Qv9mdJcHz7Qk2v/pJoo5IQRLD3pM4UxrF9VWs52WiEVVZkr6ZXNFS+gW3r521JnLQ6MM6pDxwSr/VCInXpxyx68Ty8Xvt2hOfZvwc6UrKC9PD9cqzT9raRs/ai1dIFC9bRLnomPRq/siDlt8/a/EKpYc31l3/MT4lonmVy/KOJLCXIo6afJ6jtDRCzntRe5rYS01nSNO6VEwTehv3EMQRYpFkqieIE3r8ZljviOlyo7P44RxlqtdISCMzh95TGWHHNrks9j7A3gvZSyD7OifItjthSEhhNy+dss6lZxGtW2W9g+xHMKarc7b68gVrXz4Nj8TKe++26Ycft8bFM0H+D2DGToRjxiqkiNevX7N8Hs7IRvAH2T938aLV7jppF18+ay16IGdKzr9lGcqT5/cesXSzTi/ljSDevRdvjf7KfdCVuBFg88o5RDbHFMLe08h+bPGC5ZEoDiIZYYykT30cf7gMZZ+5BI34fMhNAJ489xsJglTmmSa9bAvzcdnPvsuUxhHGNeaJ7GffpRHrfguHsM5eevtG0tgTvwk3Lmynjdsuj+t16+7ZY42f/LPW/dKXrPy1r1l1fQ1pDGvOoTgeWAZHXEQQTwMeZQ4DvmPwz3OcPzHnYMJrdDkS2F8MLeHz9bFxW3+Qc+iRhwnKlyy3smrx6Cg3bWRDsng7XZynjLyvo8til8je4/hWlKrWdWVnrys6/2/v+R9OUv0RAREQAREQAREQAREQAREQAREQAREQge8SApLG3yU7Wpv51hHQD+Q7+wP5m91TEjzfnmCz3SFqupm1BiKxlkH8DaxGi9ouMo+/1hrwOQHVbse1MFJr0Lb2ACE88D7DTdKQfIcYxHbRj3horW5CchKRi1DG8tlgs2nrCF9r8HmfGTFFja86yLEcIpBsLPPfEmU1FpS4bGacBmnlRn8zzN8SUqutJuPyHWK41isyFTPJYCmbnjzlO58sdH/lyb1vwnyrHau5IE1Yy2bLmk3/go0Ly+Blg22sIST7Rasxrq+dnXtizZ92+9B68uP0hIYBpZtjUr2Z0phlee6FstsIx2LZEkp6e/K6eell29x3KIhKl/2eUo0oJ90neex7tXrP/Xb5M5/Ykv30LY6Qkle/9qStrnWsu3wV2b9q/SD7YyvsPYI0RhC7yN9AtnuZ8Ygew4jfLDcDRPQ39jLTLkVd2g94P2Bfbsv+bGUG2V8Ost8Nte8RT6Z7ILbfrhMwptQ00+XHKFOOZE5Zdm8T2Z9lTJf9eWR/gfwtKfGtfsd5blrYQPb7ltyeISTbEb8+bL/++rPfhICobT7wgF0jdRyfOWMTl6/aBNK9jGWn2/ANiYtEhjtEt+bDX3fE3iPZ/3FpvEEyfJ5E/8L+/ZYeOWLje/faNON4T3Ff3rYQ9tc+fPNz+PAW/dF1ZWevKzr/d8/5f4tOKS1GBERABERABERABERABERABERABERABG4bAUnj24ZeC75TCegH8p39gfytPC62ZY7Pc/v115+/GwXPxSfWv853c9Nsc874+/Vh84l/8fXX/mLu46RVXzNsj/vxn/3Aaz5lNmee+Mb7Mx//xmtehWn4bHva7S/9/dwT2++2nr95HP/0j/rsD0/1jXf/2XFvjPCfHe8bs9wVr1r9AbK/GWR/iuz3hHStNbBuD0HP0EqR5dQ93pb9KTcAfIvs9xLjfQRvv2YdBOaW7EdyIvut3bEGQto6Q2v6vBhqKeMhKXOUF294L+Ig6oeI/dfIfm4gaKQNqzUQlzWX/X5jADqeFG4tKVJCmhsMvEw4fbS/Rfb7Qvo3ZH/PZ47sp8xzs8myXiv7vT9yjdQy494u2e+Cdju96/2CPdnrqd7QY9jLQ8PztSWiXePGJH1XKPt95sABSxYWrLSyYqOkkUc6XaswTd5vAHAJzJAifruU/W6QEF4vFGylUrHG6IjlJqdsHPk8MzZm4+PjNsazP7x3eKVasVK5HHoZF5jG12e7l7Gvqz+2RfLWUnbwr64rAe5OXVd0/t/e838HzxzNWgREQAREQAREQAREQAREQAREQAREQAR2HYGtiMquWy2tkAiIgAjgkBqNFxEf998sCxc4Xj52u4Rsu90O/UebTQTYJhlWhOnGxobVkTjr6+u2xmPFn1dXX5/goVfptxM8U98keEZGRmwEAVRF9FSrVasghEqUmC0ilFz2bJeWdTF1swMlaO9HIL10s9NpfBH4biXwRq8p2xUMXBT3KJ3uD+9t7NeWcH3h2uK9jb3HcXhwjdnkWrPZ4JnP1m983qWUdcp0Q+8djWz2+Q5JFVOnnZLxpK4RwTV/cJ2ocb3wa4ZfQ1wU++twDeG5XC1btVIN1xK/nrg09sebuZ74MaFrynfrmaHtFgEREAEREAEREAEREAEREAEREAEREAEREAGKQAqCCIiACLzdCLiAdXmynbTz5+20oH9eKOSDtC27nEHGjCN6p13w7EHwHDwYBI9LnoXXIXhGmMfsbRI8b7f9pu0Rgd1KwK8hnt7164g/PNXrktZv/vDriN8MUhvZksgdktsukjs3hHIbSdzdfiCbE8Szy+eUNLLX6vZ5Z5n3a1PMPt/wQAiXSsUtOcxyiv66+I2bTl6bMvbpfd22r3u7laXWSwREQAREQAREQAREQAREQAREQAREQAREQAREYHcSkDTenftFayUCIvAmCEjwvAl4mlQERODbEvBry3aal1RuqBbgSV+varCdQvbX22nk7dfh+xvjbJeyHnhzZ/6NvCe0S2mEbwbxm81slcD25eTyW6Wwt2522apMED7nO5fE26JYsvjb7jJ9IQIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIi8DoJSBq/TlAaTQRE4M4jIMFz5+0zrbEI3AkEtm9M8fSxS1xPBbtEdiG8LYW3X4dUMZ/799uPUJbapfGNwefnj+0k83ZfYn92Mbz9fvv77eft6fUsAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAm+WgKTxmyWo6UVABHY9AQmeXb+LtIIicMcSeK3wdcHrw7YU9mcfXBZvP/v429+HD/mzPQ9/v/3an31wQezD9vvwRn9EQAREQAREQAREQAREQAREQAREQAREQAREQARE4C0mIGn8FgPV7ERABHY3gW0h4yJGgmd37yutnQjcqQS2rzPb6+9J4ZsZfPpt4Xwz02lcERABERABERABERABERABERABERABERABERABEXijBCSN3yg5TScCIvC2ISDB87bZldoQEXhbEJAwflvsRm2ECIiACIiACIiACIiACIiACIiACIiACIiACNxRBLZqHt5Rq6yVFQEREIHdRUCCZ3ftD62NCIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIjAzRGQNL45XhpbBERABERABERABERABERABERABERABERABERABERABERABERABERABN5WBCSN31a7UxsjAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAjdHQNL45nhpbBEQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQARF4WxGQNH5b7U5tjAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAjcHIHszY2usUVABERABERABETgziSQJMn7MpmM/rfPt9l9n/70p9e+zVf6WAREQAREQAREQAREQAREQAREQAREQAREQARE4G1OQD+cvs13sDZPBO5kAhI833nvSfB8Zz76VgS+mcD4+Pj6N3+m9yIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiLwticQve238A7awOKR936ue+WZe4ZxxqonP2BRJm+NFz9hcaFsk9/30zZordvyH/xLG3nPh+3Yz/1Na507ZWf+3s9ZYWTCxh/7MVv8/d8wG6SWL9fM0sSSbjtsfZQtmUUDy5amrDD7kHUXXrYol7fK4cess/CKdedfsSHvoyhjxZm7LZOvWn/tonXXrpnFkY0//KeY7i5LVq/b5stfsGGvadV7v998vnGxYoP2hjWvvMA8S5YyXXZkn1mmyHoXLWWd080FGzJOpjJlUWmM6Vs8Gtavz1lcnbHinrvNsgUbdOqsQ2S9lUu8btiwb+/rLj935g7ahVpVERABERABERABERABERABERABERABERABERABERABERABERABEbjjCGTvuDV+G69wbNFEFEfT1m8jWpcsi1CNMxkbdhuI3nO8nw5bP/PQo7b+hU/Z1Mm7rLzviMW5sk3ce68tf25ow2ho+z/wp2zzynlb+drvW+3ke62497itfuU/WNqtW+v8HyBmcboTBy2eGLfBtbaVZ++2sXvfY5vnX7Ruo25xvmgD/smPzrC8yFqXn7Xe2pylSN1h0rJBdxNRvIkcroR5DfsdixDdcTZrg3yF9akgm2P+zZkhkgf+XKhZ5I/BYGsPss4Zti9i+3x5MdI4RXQP+13LlEZs4GI5azo+38bHuzZNBERABERABERABERABERABERABERABERABERABERABERABERgdxCQlNsd+yGsRYQ4jTK5IE5dwka5IvKV90kbiUpqOBOH8TKFgvU3ELj9nuUqI7bx8tOkcz9E8jdHGrlrG+dPhwQwM7Nkc5lE8YPMJ4vY9d0d2XA4sNzErE2+9wOWzJ+zg/c9bEe+9/12+en9ttZoIqdHbPWp/2SjM7Okk2u2evElSzodGzKtW2JPFLfnTlmOFHFYTz7LklQeILeHrOcw7ZJ0ZlHZ/DAc5XoAAEAASURBVNZ2sU2egI55HiCGecGcWB//nnUZ8t1g0A/rlSYd5oOY7qxbr+cz0SACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIrCTBCSNd5LuTc7bk7xeIjrKsFtcopK6dXEcDXqWKY8hcMco+Txqay8+bdM/+lPWS1vWXrqG5B233NgkZaUpD91u4YcpNY1Ajkj7hlLQQ3Qv8+WD8IiHyOTly5SbXiHVW7WJmT1WilObOHyEatR5i8sF6y5esanj95I2nrJBdcKWTz9p8ZDgMOlgTC8yetHaV5+z0tQRK8wcC6K3214NKeTe6mXLFKr47mJY7zhLcrm7EQS4r6OlfTaPbetnEcVD1rlu2cok8phtTnvWb1DOGlFu1rtJghpdBERABERABERABERABERABERABERABERABERABERABERABERABETgZglIGt8ssR0cP1McsQRx6r2MXfBuiVNXtJG1rzxLieqzQSgvf/GT1k0i+g4vWH992QrTB61+5mV6Bo9aRHnpAT2JoxyloJG22Rolrr1kdGU8lLneEsfRloSuMQ6loDtt0r1I5fbmpvUpG53PkwzmfaZIz2LSzfkRhHQRCYyMjoe+XqSO+/QmRvYWp4+RdmbeBXobdzatm16hjPU8optpZk6wOCQ4UntYrpKGvsI6lZHDvZBGjlhPQx6nrTXEMVJ5kDAtz4jkuDBieUpZ92x1B4lr1iIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgApLGu+gYKB540LrXX0T+Tlphzz3I3jICNbXWpS8jhu9C5EaI49MW8flwdcMGq0t85CWrEb7X5+kz3CJhXLL7H3vcBgjeLz33OetTQrpx/mlmk/BdAfk7Sm/jBvONbPPlVyypr9mV06esN7rHFp9/1hKEcHH/cessztsm6eXizH5rXj0bksQ0HQ7z8GVSpJrFcvhQ8jokhBG8BJEZ+Oup5lAKG93t0tgFuMUhXezJZ09Ro60Zxz1xk3m3w3hhWtYzLiG/SSf3+8xr9VyY6+v6s/f++5gRUWYNIiACIiACIiACIiACIiACIiACIvAWE4gGSzZ36vJbPFfNTgREQAREQAREQAREQAREQAR2BQFJ412xG7ZWIkP/Ypes2eoUQjUmdbtJ7+FDZhe+aP3miuUnDofvhwjaKEsSuELad+WC1U68yzpLl6x8+AEr91bsrhPHbWykaudP3Gftfs5GH3ivzX38rOUmD1nt5Pda/cXP4nuRstevWdTtWm9kwlbbifUQvb2leUQzyeNOy9avX7d4ZdUS+hwXRveFMtIufPMTBxG7VeTzBmWqXVzvJTDcCK99SzyRTJNiSzbmSTrvsWx5lMeI5Ub2hnG8h7Fvn4tsN8cRfY1dfHupaowz64b3dfFs/v1NDHH0ESa/7yam0KgiIAIiIAIiIAIiIAIiIAIiIAIi8PoIDKJ/zog/9/pG1lgiIAIiIAIiIAIiIAIiIAIicGcRkDTeTfuLJDFWmNLPPZwpfYkRqkP6Fg9tQCKXhC7fDz1ZjGwN5Zx9fORrlCsgmJvI3P1Wq05bY3XV4nbDxqambOnpp6x61yP0F84H6TtIELEkjnNj+5gub7nxWSsfOWnlyZoN9x6w/tJ1XG2XXsc1y07PknYumOXrljY26HfMGtEr2ZcZU446Tz/j3tL5sOwBCeI0oQexrx/i29e5T9lp35ZMZYLvEkpiT5BKHlh3nrQ02zckRe2lqD1hHPm8CzW2sxPKaofd4tunQQREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREYEcJSBrvKN6bm3myuRiEabJ+xbKjs4hb+g3PIVhDf+ExpCp9hRGrw17bMjy7RI5IJyfrS1bcc9T6jTWr19u25+gxvG5s6/NzlqUXcbZUCWWlXeh6QjlGGvsQI24HCGLLUEKa8X1wT+ulo/OT+60wORM+8GLUnW4Ht0simJLUcaHoY27NJ4OwRhgP6U0ccsKZ3FZZbVLLcbYclhsjtX05Pk6cKyHCe5SkbjIvktUujfnMRXPE+yzTecI6yOnEE8hvcBgOX2Id229wak0mAiIgAiIgAiIgAiIgAiIgAiIgAhAYTvN/lCkBpkEEREAEREAEREAEREAEREAE3t4EJI130f7tLpwNYrXfXrX2la8GwdrfXEDoUjZ6+VX6D89R9pl0LvJ3EJM+vtFjePPcV620lz7ES5etgYP93Gc+a0OmWbp43gozR61TX7e4OELFaEpGe2VoUsLJOqWn81XmOW/ty6Sai2X6JV9HSHe3ksuNdUubiF3kcPv6ZetvrIQUsJvh7MgUn0ZhHtSo3ko/szxL6VXsJba9PDUy2XsoD0gfewrZexT7d4N2nc947+WnB1t9lg157a8HyPA470J6YCm9mIHxxvfOYPhnbP5FxLEGERABERABERABERABERABERABEXiDBGbf8ZeY8p+9wak1mQiIgAiIgAiIgAiIgAiIgAjcMQQkjXfRrooQpt4D2CVr6PlLstdlb4poTbubFvWaoe9v+cg7bPbP/qxlELav/tO/ZfnquFUOn7RMMWfNqxfs4tkLCOci4d2sjbzjIZt633usvXDOaoWCffBP/6B9+t9cs83GpmUPHbbe6SVLVhett15HIC8jovuI25L1VudpX0wpa6LHKQnmUEbak83Ms786x/o0Q/npOJezXG2acSlNTX/j1BPHpIdjl8KM7+ni9tUXEc1sF9MkqxdCL+Moj6iOsl6YmmceCOZMcZRlk1z26b2kdW9oPTu1i/aQVkUEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAE3n4EJI130T51iep9f+PGEsIVoUqqOM6Xka2bvKd8tItYksDjDzxqg4VLVqyWrXrkLsuVJ609d95qh09Ystm00sgo0pmy1aSMD77ncettbtj+73mvdV45a489dJd96eMj1hxENnLv/ZbMXzTrD6w0M2PpwqgN260gjV0C9zdXAx0vFe19k2MSxCEtTN9hl8terjo3updS1ocICnctQXp3F19lHJLDPgw8KTxEeq9bn5T0kF7MniKOPYVMv+YIse0luH0bs+XxIJt9nsUjh8PnKanmxrlP+Zw0iIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAI7BABSeMdAvuGZktfYe9XPES2xpRz3hpoMoxgdWnsidyhp3JJ4may9P5FwvKWrzNMMwiPyPsT81nad+FcsEIua83FNdoGF+3KmZdseWEFn0yat37Rko06k+aRw0skghPKVpdoM0wZab4PJaYRuD77DOWsI+blpaNd9voCfBzvb5wpe69lehf7ulfGLaHvciih3UcsI4xDL2Mks7cudoHMxEyXp3x2zLJdRjOtv/f5Uo7atyPLPFPvtezWWIMIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiMCOEsDUadgtBJKNefoEX7EhPX89let9fweUpA7mFoGLUUX45mzt1NcsM3OIZO+oNS5fIN17xSqzJ6y9dN36zU0EbI5p6IO8Mm9r1+atePI+W37pZRuZPWy16XEb2X/IIsRsVEACl6oI4TIilxQzItpLS4dE8JDmx75MBpfYQ+9N3G5SMrqFCO6EFHGG6YYJkjvlO8ZJEd68CaWxvdy0J5O9RLYL6Pz4Pivuuy8kk6MY9cz3Lri9l/HQS1sHQc3njM+bIKHDwvVHBERABERABERABERABERABERABERABERABERABERABERABERABERgRwkoabyjeG9u5p6yTTv1IG2HlKROEatD+hYTLaZc86RlS2OkgufpEXzOzv/ffx/POkQSb1ijSa/jYs06S95TmJLQ/ZaVJmZsLU3t4qc+abWr61Z/4WtW3nvQPvJ7z9l1RHK5NmYzVcTu6JitrCxZb2HR0o0N5ukSl+Qyq+7S1yWyl6qOQjlqf836UDa7VB21zDi9jJHFyeo1S7ot1mUtpKBzpJKx0UEupwjl4sz9Vjj4QIAx7HWsfflZUs6LW1K802BZW6J6qw8zZbXXryOX6YGcR1xrEAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAER2FECksY7ivfmZj7+4A9Z/fn/SEL4gpn3EM5XLG11kbcVq574PsTxmHUWTlvn2inLF6eQsk3rI3Nr9z5kH/orf8l+5+//PUtJIv/Qn/0vbXxy0j6yNG+D0X1Wmdhr6fF30A65aysjY1ZfXbaHH3vUvuehQ/ZqbmB/sLhkMeWsqQkdRHGGMtXexzguVumn3LK0S4KYqtJDSkZ7+ewMZaqrU7OsQ8F6nZZtzF2gl/JKkN35YoXPK6HnsZfSjnkU9961VUKb+WfLVSvtv8fS5moote2SOkWQ9+vXQlntbHHc0sYI81u2HuNrEAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAER2FkCksY7y/em5l458TCyNbX6M/9+qwQ05andpGYqE6F3sMvk4swxksbPWb+9gkQeDf2ANy5dsPULr9JiOGej+/Zb1Gra8tqqHTx+zObjcessXrbyvkPWfuYLFnc7QdzOHthreZY1NV6zfD5nKT2Tvdw0daotWxkJKeYoQ99hLzNNCesh6eIBKWEvTe3LKVYqVihS3trXj/c++GvvkezT+ODFraMhaWUS1HRjDv/wJ5TcDt2KmSfRaBt2mySYO0EwD7Jti/pM36qb9bbKY4eZ6Y8IiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiMCOEFBP4x3B+sZmOqAfcKY0YtnRA0GmEu5FtrKLvJcxYjaKKRvtotUVLMLX31scI2FJI7v0LZQtSfpWHR2xfG3U2pubjDuwXI30bps+xD5lLkuCuGydDqniQs42Gh1LmD5IXObhg4tr70887FOqmuV5b2UWxjPfx9732KxLf+NOu8Vzw/rI5MjX6UYp65T5pS6Dvdw2krm7cI5k8TpyGaGMoPYex7mRPbxGUrP+caGC/PaS1myfT0cJ7AG9k63XCOujPyIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAjtHQEnjnWN703POIGWHiOO0sWRDZKpL2ChDCri9br36gmWre6wzdyp8FufKeFxSwCR7cxN7LKmMWvngEbv+xd+3C1cXzEgLv/rCC1acXrfy4ftt4+wLJJYpb11HxsY5e/6ZF61XHbOLL7xktEa2qZlJ20w71pijpzKC2stQx6Vi2IbUS1czzhCL7aljy2Yt8TcEgdNs0YbIarodB7nd7yCqKTntcjkIZp77zRUCzDnLUO46RYBnR6YR0Dlrz58xayyzDE80s6xBL2xzv7UaRHWa9G6aoSYQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQARG4OQKSxjfHa0fHXvrCf7De0qv0812wwvQxKx99pzVe/aqlGwvWvvAksnWfJWtXg1D18C9R3LA+g9amPf/xT1p3ed5jwnbqzHmbOHoCjZtBONOP2FPJmSxlqcs2VStYi3LQC1fnrd4eWLKxYvc88ojd/+gJu/hy3s5ikPMz+yl/3bRcNAi9idtr6zyTdqZXcVyiLPXotGXHR0kE92xQr1umOmXxynUqTdOf2D8jLezp4QwJZ1+ukTaOy4zPesQknQdd1jukpilbjfQ2UsWemvYQ9RBxHCGWh2H7dhS3Zi4CIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIgABSeNddBh0518mZbxoRor40I/+t/auH/sR+8zf+kVb31iy3NSx0HPYJWxKP+Ox+99LeJfS1J0NK01OWa6Ut+p977DV0y/Zg489HPoMNx96yFqZMcpWF23k+Duss7Zge4/ts4WnKtZeCoWvLU+p6sOH9tmB0ZJ1Jkft+sweG9ZqSN+CFWNSz5026WAMLqI3Iv3s5a1zFb4PZaYz1tvYRAaTLEb0YrNDUniIeI5CvWs+RhQbPY69VLXL5IyRjs4X2IaNUPJ6SLo6rkyyLZ5qJr3MhANKXg/SnvUHfbPVc7toD2lVREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAERODtR0DSeBft00x1gvRumyRu01pXz1ox+ycsh7w1SjdnSmOW1K9afnQ/EhiJSwno7vKi5cemrTA+ae3F61acmLLyyKhlKetcX1i26tiodZMSyeU6HrpqHcZpr9YpBU35aSTuVvlrRHAckUqOrNfvW3dtlc+RvUjiTJHDI8hgfwox4CCIh/Qk7jNu2li3LgnjZH0+9ESOKHsdZxG9nhQebj0XJmYtN7Yn9EhOSUSnpJfjXJ7V75FcrlrcKobexp5MLkzOMh3p5/VFSlovW4x8VoHqXXSAalVEQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQATelgQkjXfRbo3wrNRrNqPEc+PSKUu6CcnefJC4Xr7ZSzm7vE3pN5wt0Ue4Rzp4ZckSZKwL4z6p4BblorPI4mJtwpafesZS+iAXp2aZ3ysIZspI16p8P2kR4tiHftK3i5fmLEI2X7q2YE2Xxut1SlonNhifoIx0RHVpeg3T13jgn/U6JIFbzHMv69cNSeQB/Y0zSF/zEtSUqB5QbtpLV/vrvItghPRw2GH9c9ZZvY4Upxw1qeS0XWc9KE+Npc7QFznyPsjIZl+3THkSkU16WYMIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiMCOEpA03lG8Nzdz3Gvo9ev9fkuz91q2WrbaiYdt4bk/sD59jjPFEevR0zhtr1lj7ooNEMzthcvWW83byNF7rH75kvUaLfvKxz5jmZExWzp/jpDyvPUQyZ3Fy6HH8JknnrbWlSshbYyhDcL26uKqda+v2+riCuK5E8pZp72uNUkmR9ls6KEcI3xd9A5JGLtQpv41/Y1H6G2c4rjpWVyqhfH6zVVWP2tDyk8Ph4hmpLQ/e5lql8KZXMnSFt9RfnqAVOYP89oqax1n6WuckGhmiDLIc/ooaxABERABERABERABERABERABERABERABERABERABERABERABERABEdhZAvHOzl5zvxkC5dkToVSzJ2+bKwv2+//hd2zx9LMWI3d7Kxes31hCuK7aYBDZzMFpe/iPvx85PI1hjS1bLtHbeA/B4NhWTp+2pWeeC/I2Nzpl1cN3MUqG7zKWzSNtO00EdI2S11UePOdylK/O03o4H5LN3nPY2wsPEMeDrieEma5CKWl6I8e5QpjOBbI755ieyJ4gDlLZex6zwRHr7/8MOg3WeSWsn4vwQdK1ZGMhjOOlqXPlMT5rs25xSBq7kPYkdcy4yfqc2cr8zeDTuCIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAm+AgKTxG4C2U5P86P/wS3boe34UcUpP4FzRBpN7SRFfD32E85NHrXb3+22I+B2bPWLv/aEP2r7xEbvv8cetcvCIpZSKHjtxMojdLDI4zsRBQKdNSkCzwhHp37S9GURwhpLQcX4rOezuN223qVONrEUKe2loTxd7WjhM6PqXZLEnj10ge2np3io9h3kdPmf+GaSvJ4a9N3JMkjgMnkamP3Hr0rPWmXvZ+mtXrLf8aphlYeqg5auUn046lMyeZXmF0Be5vXTJuqvXrD1/xpI1EtSNyzuFWvMVAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQARG4QUDlqXfRoTC7Z9Sm737IzlGGOq0v0re4ZJlKLaxhUp9D+LYJIUc2OjlpGSTu1VOnbHSsFmRv2ukyHqKYVPBwW+LyySAlvUv62KWxp3hDb2SSxGmrxZd0DSb9O4xKhvJFDyN/Eb2DhL7EXoYakTyM6KEcIYh57/I5UySpTDlqF8dxgfWjpHTMOnr5aWpebyWTkdKePiZwzPuGda6/xHKzlp84ZDkksSech6xXROrZE8v+8PXgFYvvB5kcJu6FTdcfERABERABERABERABERABERABERABERABERABERABERABERABERCBHSSgpPEOwr3ZWXd7CNN2w4YkcJOVS7wmAeyJX4YgWb2MMyngRn3dymPjNn7yHlu6ctV666tWmz1onTV6EocEMMIXuZwiYuNCmXLUlIAmzWuZPBJ5EMRx6qWjKVPt6eFiuWyj1aJVqqSE6TPcb9JzuI+xRSCzYB6kjRHMufEZy0/OWmnfUSvtRf6STC5O7bP8+BSzLoT5MnJYX5fGPv0wTMtHiOG44HIZie2CmcFLWnvaeJD0GM8/Q1h36uEx9GVrEAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAER2HECShrvOOLXv4AyCh+NGlK6mZF9lJDOW376iLXPPIWgrYWEb7YyaatXL9oTn/pdi8cm7MxXn0bO9m11GFtvsxFSu8N+x3K1CWRxI/Qgjr3kNFJ3kp7HH/6RR+yzH1u3i6tLlhudDOPvP7DXjk5XLN8ct5VS0dpI6SB7iRZ7QjlTrBAizrMOJcLJvn5ehhopfGOIPFGMDE4R0L5MTzQHOTxwGezylwQxMnrQ2WTaPFKaktmUsu53NixtrjF+n77KZet1KXu9QaIa2U1jY0t6W3J5ezl6FgEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEReOsJSBq/9Uzf8ByfffKULV+/fqOfcGybl+jru7ER5jfEzLoM9kRuhKC9eOGS1Y6XLFetWNLYsGSzidgtWpo2beyeh608s8+ufOajNnrXfZS5LtrI0Xusv3LNZkZKVqsgf5G4Xm46my/a6EjVyrmM5Sk1nSXJHLLCnhBG+EbmZa2HQeQm9RXLlikt3UssYXr/Pmn0LdlYpdw169Dg2VPKfD5Me0EQZ2LSzQlltftt6y68YpnR/Rbxfb+5hGBuBimNcbYhQnmI/HZZTdTaor7LZtWnfsMHkyYUAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQgddJQNL4dYK6FaN97jd+zZL1OWK7fRt5+AesdvKYjR2dsYvnn+T1e4NgHXvg/bb+3O/Z5EMPW3fuoo3d/6AtPfkVRC8dgeMMyeHYmlfOWmGcFDFp3mylilCukxYu28bSojU2WpYvFpGzXj2actAI3E2E81qzZ8urdes2G0Zda4K+sZVHZ5hHbCnSuNtGSqddxLJXmiYlnAwt6Xatj3ke0F85pZz1VrrYy0/Tr5jpohxyGmHs6zWgX/Kgu2kp4tjLUPsQei2Tgg6vWY8tWx1Zpky569I4pbIH1mt8PnyvPyIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAjtDQD2Nd4brG5prTCq3tOdeSlIfs4l3Pmrd86ettnePFWYOWX5s2trXSeoifzOkfTOkgnv0MI6+XiY6WGC8K/qYktKeCI7iLP2O1+g5PGOt+etWmpi0hJLXUbVq2Rw9jpHKab9vywvL1kAa910C9ykJjeStjI7ZgRMn7PD977DR6RlEct9KlZqNT83Y2L5ZK09MkGxG+LoQRvh6eeoBsnvYp7Q00nmrlzHvB0noZ+yiOVOesPzUUctU91Auu2LZ2p7wWVwaC7w8oZwfO2gFZzB1jP7Jh94QR00kAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiLw+gkoafz6We34mINeh1LSYxaTsnUpHPr+dhGvScebBlt+dI/1Ww1E8ArrEln58N1Wf/EpvkPMDkqh57GvZESZapfEcaFo6y89bcU9q9ZdukLqeNR+82NftA16Ihcnxm324fts5eWzNjVas0NjJbPJMVusjdgmPY1Hxsdt3+H9FiOY02LJ1kkp50v0Peb1MBtbZkjEGDns/YeHrLdL5aG/J43sPY6HvZ7Fg2IQ17yw3Nh+y40fYD235HbaWqOk9TqzIInsdbKRzzQ5ZttHtiQzpao1iIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAI7DwBSeOdZ/y6l5AplOnzu2nJ8nmrnzltpXsesdVXXrbu4iVbH0RWOPCwrZ3+MhK5bdc+8RGSuuPWvnzOLJOz6j30LE5SazEdkV8qXA9JAmcRxxUr7zto3ZW50Me4000tabUQyBWrjdesNzlue6fGbLKUs7Vi3vKeXKZ2dUyZay8iPSB5nPRIESOHOxtr1vDS1gjsZrNp3fVFPG8LQczDy1PzeaZQCyWph92mDUk6e+o5ypUtW51mfQrMmn7HlLPOlsfoa0xS2mUxCwolrLHHXmY7jjKsd83yMalnDSIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAt+NBN51kxtN+Tvb4FG/8UxyQYMI3FEE9rK2szfW2I/nF2/j2r+HZf//7H0HnGRVmf1XsWN1nO4JzUQGGJgBJCxBBMWAiOHn6irqinF1MaxrQFdX/+bM6u66aw6ga2BNq4gJRRGUDJKGGSbn6TQdq7u68v+c1/WGS9Ghurt6pnv6fPxO33zffafeqyneed933wnwEfGbgR5gKrYanc8A4EXkgec2BHQCe4GbgJ2AbO4ysBxLQwhSz7CnpXHfyaNl3OOS19RK4CAAUcTgyTYjq8TotQDvuW6A1+NUr3MMGdcQ6tWeU2j9PVJyON+tBiewzjmJXcjTw3EmNpe+92ZyHsfMWInGc+ijTA0ctGz/Acv077fOX33LKrdustTuhy2XGLSq+jo756WX2u476u3RG35g2QS8csMM/5y12JoTrG71SojDbbazvd2q21bD6zhlVUtWYH/jHfD+xY7CoRAE6SHPG5kCbWaw19KYA5sXWwrC8HAqa739gzYCT2Z6NQ/099vB7fj3u6rKevYfgEDcZVYDL+CebswzjL2PByw10AmRFxHOsymwGLBIbSvWA1EYntHZCLyIIYAb8oEQxGKEroYrMnohgSsyvYzpYhyE4E2nZe7FTOThbRyoboTunbUU/pOJATEgBsSAGBADYkAMiAExIAbEgBgQA2JADCw4BuBlYPfM4KzxUMpuA/ig/kvAMCA7cgxQaKLgdO+RO+QxcaTX4iw+WTiT7UgpaB0No9h1DUBx6FqgVCEthr4vAV4NXAjwUfBERhHyi8BXgYX8IHiu3i9X4XN5G0Dj9+kFXu7I/eF1+E/APwKrAf674BtfZtgF/Bj4KMAXEkq1p6Ljp4DzAPca5Zy/Az4B3ArM1D6MCd5bmORkpJsL+bGS9aj86VgNU6z7EPpfN8UxU+m+AZ3vcAa8Dnl+V8zE5sr33kzOYSZj59z9rz2NZ/Jxlnls/anPtIYznmehmmYv1HNFqAaabtiygbA961WvsRA8hC994aUWrK73PHsZfpoibfXiFkt2tFs+mYBHL8bAuzeLfB5qbA6CLr/5KBSPCrNI6f0Lr+aRVM4Sw0nbunW33bdxj+3csdeC0Uqrh/icgmfxvu1bbN/Gh2yw8yD2Uq61XDBiw4MDNhzvh2gMUTgxAIGYXsYISY39kQORShwJojHEYArFYYjI4fql3nEzgx1Qi/m9Cx25/6AlDjxsWXgaZ4Z7LBPvhkczXp6DaJyNd1kG4nmya4elDu3x+uuPGBADYkAMiAExIAbEgBgQA2JADIgBMSAGxIAYmAIDFK+eDVwNICyfPQWQzT4DfNb8FoBiIEVD2fxk4INYNgVjCnHvL/EUnol+FLq/CVwEuGIcimPaSaj9AsB79EgLkmMu6AhX6n4Zn/DL0MTrgt/hfHnCFYxRpCebrQHeA1CMfREwmfGavBa4GTgfKL5GOSf/3aBw/EJgJsZ74N1TmIDCyollQMMUjqmuR5eBOXv/y9P46F4Yjzt6GPv5Vixf6wmmg1v+zCjRFoxUWAhC7rKlrXbT9TfaupWtFkEI6exQP9qz3vjscMJiJ5+IMNLwzsV+xxmKxzWNFt+1EWOrLB2HuIuQz1BtIdge8ryN85i8Y/MOS/f1WKq/13riCbT1WNPy46y6odF64JXMvY1DldVYBMNcQ2iGGJxNpyDwMlz1iCdscw3cwxhSsSciU5wO1zbB+ziGtWP/Yxw1lxz2BGN6KOcQyjob74GncQ80ZIxlCGuErWYIa4rK+Wx21EOZAjO9k2ViQAyIATEgBsSAGBADYkAMiAExIAbEgBgQAwudAYYO3TMJCXi4ZCsApq6x7nqAXmVb3Ably84AxZanl31WTXgkGXgSDkYhjvZvwAEvN/4fCm8fAD4MUARxLYHCRuAhgMLecoDznw7wxQ7f1iBzI/C3hdSvP9ZT3S9jf8K8Puh1W+E08xq6D9gB8Dv9LIDXEu04gB7HlwC/B8azz6Ph1U4jvNzsFoD/LpwL8MWFKoDH5XyvB74NTNU41w1AsdA91Xmm0x+eebJ5wsCcvf8lGs+hKygFQTfa2AQv3ZDlENo5l8LLXBBhs6kRGxwYtDXnP9nau3sthdDSEXj+5vNBC0LU7X10iwUWHWfDe/dBjI1b/yN3WwjeyOk4+sWaIRyHIdwOwYu4GuJs1MJVMYi5+A/iL1+ogRbsibWkIoI9iytiMYtU4fc1GijsWjCP8fAmhkBMj+DcyMCoaIxyPp1Bfc7gegzROG41a8/zvJJDVTVeOdm9D/sc13jnlO7vwoEQ0LqiFnOHcMyEJxozXDW9owPhGi88dXakH+uGV36OkrNMDIgBMSAGxIAYEANiQAyIATEgBsSAGBADYmCBM/AjnP+bS+SgBf34sJ9eXvBs8KwRf+kBeeFoUX9niQF6BMrmLwPUCnifMO0FKLJNZp9FB4Yxdu3PKLwVoNCHB8dPMArNTwEoyK0utPJlDwp19D4+WKg71hPdL0/8hGtQdR3gC8a8Dt8B8Foptleg4gtAM8Br6lrgNKAHKLYXoeLtTuXPkX8ZMOLULUGeLy+cClDwvQa4D+B1XKq9GB2/AbgvRZQydic6XVFKR6cPBBTPE9t/WeMOlL/ntM+XLF8s+W5hsZ3zZdFlWOecvf/DZTg5TVEmBiLVtfAyPmCJ/VsgtMagq0YQqroRr2jl7VfXfMuOf97L7dZf4DcyvHUZXjoUgJCLf3fpyTu8dRtCPcdHYyqEogUv5SrPU7l2xfEW3/0ovI/rsFKGkY5aKAyRli9/QcTNZ7E3Mjx88xB/k/BaDsWH4LWchICbtmwmhbVUWDAHsRj9sthfmV7G4cqY5SBCcz/jTPwQxGTssQzPYi/ENNYTwF7J4RoI1/0IPY1Q1wGEwh41CsF5tEdGw1lDIA+Gsc4oRPDsaIjqANZPj2TurSwTA2JADIgBMSAGxIAYEANiQAyIATEgBsSAGBADU2AAXgv2aYAPz+8HfOGYItVZwL2ATAyIgScy8PeoOrNQ/XmkA0/s8rga3lPvdGro5fgBgGP9h8FO8+EsHxDfCpwBfA14KUCj0PY5gGKgbGEy8Cac9jrn1F+FPL12x7Lvo7IH+HWhsQ3pe4H3FMpu8n6n8FvkKe4Wiw/tqHsa8EfgNIBC9FuAK4HJbDE6fBHgvNMxnocvnJYynkIxPVV9wZgvWrwISAHzza7HggnZHGHAv6jmyHIW9jKGDu6w3vt+Y6me3QgnjU0jdt5pI/s3WhCeuN37dtsIxNr08AD+yc1Zw/qzrW7lGmtYe7In+FY0LYJICw9ievKGIRqjL713U70dlkT4aRqFYRo9gjNx7Ek8gpDUg33wHB6G4DyActyGujutb/8+Gxno974W6WmM7YotBHE3DLGZXskGMTuIsNfcb5mhpSkUU3D2Qk8P90ESxr7F8IZOwcuYYahTh3ZCPEYkE3oqJwfhAd3pCczh2sUYXzk6Bz2fPQE77QnOzMvEgBgQA2JADIgBMSAGxIAYEANiQAyIATEgBsTANBnYi3HvKhr7gqKyimJADDzGgH+/UPD91mPVY+boGXwN4OoLz0f534CJBGM0HzY8gLbLgd8crhn1/lzulJVdWAxc7JwuxeLxBGO/G68deg37xtDQxcaw1Wc6lRSWiwVjv5nires5/0qU6/zGcdJXo34T4ArGEHFm1T6C2Z9eOAKFYh57oXjoF05byWwx4H6pz9YxNG+JDIRXrrWqJ11soTqIqQwNnUb45oIoG2ldbieeearF6vEdBdG2enGb9W972Cqw/zA9eVM98Nyn6Aph1rc8QkkHK6oQnhr7IlfBi7kPfcJBRH1OW7LnoCV7OyEe90Aw7rP0QDf2Se6DWNxn8Y4DloKoHKyssSDGhavrPK/hSDW8n6ka59LwLObexvA+RuhsehsH6BWM+pH9D1q6Z6+le/fZ8P5Nnhd0EMJwGt7IyfbNluzeYbkEfg/AE5mgV7FhLu5nHPAE7yrPAzqXxPdqPO6filIxIAbEgBgQA2JADIgBMSAGxIAYEANiQAyIATEwVQb+WDRgTVF5siLdJ5YBDFVaLuOemSuASBkmnI31cVlcG4U7P0Qs6xaq8bOnB2M5n6M3Yj5eB3PJno3FnFpY0F+QwgNoQqP3pRte9VqU6fk4HXunM4jX9Muc8lzPztY9WO7zXoIJeR1T7J+rxnuN3uu+FX9/+/XF6R1OBfc55mfimvuy0ENoYASKiYzXMV86ojFcNkXh8ew6NFwL8J727SfIuMf068uVUgR/vzPZx5G/3Skre+QYmC/3/5QYKec/dlM6sDo/kYFgdbXVn3qWVR3/JIiplXAozqATgkjDq3fg/pute9Mj+IkKr17WQ3CNwLM4FYe4CiE3hH2Kw7XYDxkj0sO9loOwnEGY6Hw2bX0P32vJQwcttnixnfbMJ9u6iy7wROQQRGGjVzJEXwrMkKkhMENkjsD7l7+DAoheHo5gHTlvrkwqgR48fg5eyfBMTvRZZqgLWjHEbYrcEH4zQz02vPMOGzmA718I2AHsyRxAGOsQ9mDOYS2eOIy1eWI4j4Ex2VQcc/V66+CLaDmUcwhVnbLBJ5KkGjEgBsSAGBADYkAMiAExIAbEgBgQA2JADIgBMVAaA8UeCaUIJmdj6v8CbgH6gP0A57kb+DpwMTBVeyoGfA9oB4aB3UAC2AH8AFgPlGrlWN/LcbAbCqAnHY0i5lXAAwDDDO8B+HCOAgu9TnncsewCVPpztTod3uDU/7tTP1mWnqr+fP8ySeeY05djXjdJ/xOc/tcjXzlO/wtR/78AHnB6n9c+pOTiLuAbwLOAyew16OCfx0sKnZneCnQDQwDnfz2Ah7BTtisxwp+f6UcBPhqerl3lDPyRkx8v+wqnoQv5dznlqWbppfkXAA+PvfuuY5wJ3oh6/5yvHqdPcTVFPX/Mc4obp1k+G+P+C5jOd0S575exTqEZlfw8vw3cCfB77CDA65jXHb+HeH3zRYG5ZE1YzP8BvwcghEwq7qKLZ/BQO2y1yBXf1+cdbh2d2ymOm73Zafk7J1+c5bXgG7/TeV+y/2joV7+lfCnP7UuAf68/ivxnJpn+7Wi/wcGqSfof6eaXOmvjv7HFNlfue39d/Mzn8v3vr3Pa6XT+QZr2wTRwYgbSHfst29wMQRe/X+F1G0DYZwqveXjihmNNVrN0mdWvO8P233mr9W26zyK1Dda7+QGIylEbPrjTC/zB/Ymb1p9mqy57ge38+f/Z4M6dFq5rtmznblu5/mRb0lxniVDAdlRVYL9ihIKG2EsxNwRxN4/l0YOYx+UxQ9jHmN8/GXgGZyEYU/gNwPM4yDDTI0NeCGyuk33yXCuM+yDjDxAB8pZNxrEvM86psg6hqnsxTxp6dxhaMc6LHssQnxnWminiaXt7G3OeUKQGwnnFvAzCz/XLxIAYEANiQAyIATEgBsSAGBADYkAMiAExIAaOOgOXFq1gY1HZLdLL7V+BDwLFz0z5oJ4PigmKfBRB2ZcPzyYyijfXAc8coxOPt7oAPjS/BrgSwIO1Ma2c66N4+tzCUe5DegrwY+DkQp2f4AGfnV7A3yO9CuDDcteWouDP5dZzToJ2z2hS0l+ENTw835OQn0gQudjpy8kbAArc49kL0eCvlSIlj+UavaspRl7uVhbyeGBrf1MAr4GvAeRjEBjL1qHSPdbLUP4+4Is9HLMB4PG+C4z3uaPpCfZW1Lifw50ofx7gg9rpGD2M/Ws0hzw9JSeyE9F4ltPhf5HvccrTyb4Sgw4B4/HJOdcDPqeuZyfbxrNL0OD3/cV4nUqsL8c9WO77pXjpFAg/BtQWNzjlxcjzO4cgJy8HKCYfbevCAl4zjUWc6YzhCwgUb32rQuY0v4D0fic/UZYvz1xR6MDrbiLjvwPfBD4N7J2oYxna3o05jnfm4b8ZKac8Vpbf4f59w3b+2zWXjOfjr2/7GAsj/367fy+P0e1xVeW87/2JQ8iQu5n8Rpjt+99f64zSUaVvRlNocLkYiC5dZemhJMI474Ugi+/pQAjhneF1C0/eaF2T/fW3v7eOB+62MLyNB3c9YimElKZ4SwtCZKX3cR7C60kXPcUCPV22+uwzLTUMwTeJ3z8Qh6sq4UGMnyXDwwwrnYQenUFIaP4OQCU8jnMQeb1w1YkBCyGMddPy1bboxA0Qp+ssnRiyLH92oF+wAoIuPJBpFJgDDDNNwTeCfY6jOAZSCsb0iA5V4N8nHCeDOeHGjHasE57GcFX2RGd6LVM8pogM9Rorgdc0xkQallu0doV3DP0RA2JADIgBMSAGxIAYEANiQAyIATEgBsSAGBADU2SA4hzFENcedAtOng+ibwU+CviCMYUACqoU+hj60xcYOe87gbuAVmA8W42GewFfjGM/eFt4osX3kP4B8IUaPqN9PfBfwFg2G+vzj8OH2L8BTi5U4CGdbQa2Fsp+EkXmC8Bb/IpCSoFmfwEc61s/Mn59l19ZQnq906cN+fVOuTj7rKKKc1DGw8dx7XlOy8+cPLMU0vh5X85CwXg+vGZ+BDwEuOf3xkLbiUgnsyXo8GWA106xXYcKXmul2pvQ0b1O/owyeegrdYIx+r3AqfsL8gec8ljZlxRV3lBUnk5xFwYNTmfgERpTrnuw3PeLe/ofQoEvtLiCMa9ffj7fB/gd1wm49nwU3utWzLM873f3vr67aP28PyNO3S4nP1F2j9PYjPx43/VfRdsa4C3AXmA2bRUmf59zgO8gf7NTVnb2GJgP93/Zzp4/SGRzhIF0b5cN3PVbG9l9P/TT0ZDR/ClBoTfbc8B6Nu+0wW2bIPamrXrJCms549zRlcO7N5ceRh5BpUMhq4EX8fAhePXS6zeDl9QwPghR98CuXdY7OGK7Nm+1YeyBnO5rx17DPdhDeZFFl59kkZaVCCVdCWG32tvHOLZkmVVAMK5avBKCLsRd7J3MY9MbmeGskbEgFheCkBypqbdIrBkhshdZuKoBeyE3YO9ihK8e7hkNPZ0ZgQYehSAcGxWXK+s9ATmA9VIc944ZW2Kh2hbPKznIvZwronPkk9EyxIAYEANiQAyIATEgBsSAGBADYkAMiAExIAbmEQNNWOuvgOc6a34U+V84ZTf7ERTOdyp+gDwFy7OAvweeDNQBnwR8b87TkP8EMJ59DQ14qHbYOOci4AyAXpXPAE4AXMGNXmNXAMU2G+vzj/F6ZJYDAwC9FCnCUECm2ML6zwCuvQ0FPG08bL9E7rgC9h+uNfuwU3+ZUz9ZlqLPQafTs518cfaSogqKQxcW1flFPvS/wC8gLRaN+Xnx8/HtGmQaAHrpvRTg583r6ruAb6uQ+YpfmCDl58q5+AD3hwA5/29gJ/BtoFR7Azp+0en8R+QvBWYqtj7dmfMmJz9edr3TgIfPdrNTPlaz5boHy32/+HxvQOYDfgEp76NTAV6/FIb5PXYRsAx4I9AL+PZmZHjfz0ej4M2XMnwrvn5537m21y1MkN9X1La0qOwXr0bmgF+Y5ZTHqiocYwTpv8zy8TT9YwzM9fv/sZWWISfRuAwklmuK4bt+YdkDWzyP2yC8bUMQXqN1yzyRlceIVELMrRr18h3avdkG9+B3RRChobnvcJaibsIy8Dym73DtuvXWvm07nHqxt3FiEMJz3vb99S67/YfX2957bse+xz3Y53gPQkb3WLix1aIQiCNNLaPCL8JQ0xs4AG/hcAi/ApMJL8x0Zgh7JcNzOYf5cmkIxwhTHUSo62g9xtcthiBc2A85UoFB8HrOQ7NGaOtcGoJxJGoVi1ZZZesJ6NtK12jPa5n9QhCqQxCRA2F4KRf2O84kBywFL2uZGBADYkAMiAExIAbEgBgQA2JADIgBMSAGxMCCZ4Bi3pkT4G/Q9gzgcuDfgPsBimm+4SmVJ5TAC+IJth41FE18+xQyrwAO+RWFNI30/QDbfHsdMlxXsb0cFc90Kj+OPMdRmHWN4uhLgL86lf/o5JmdjfW5h6AATOHvKcB/Ai5HFE4oynwD8O1EZJ7qF2Yh5WflivuXjHOMlajnWmgc45srgPp1THk94EmnZxvxd1shz+TVwAuc8tuR52db/HCSn98VADnx7WJkKMhNZFE0co0Uz3mNfgv4J2ANcCdQinE9XwX4edF+CzwXGGJhBlaJsU92xu9x8uNlj3MaOpF3rxmn6ZjJzvY9WA6ieM2GCxN1I6VQ/HCh7CZZFL4OvNOpbEJ+vPvM6Tbnsrxur3JW9QjyP3DKzNYVlYvv6aLmw0W+4OFajVs4CvmTcMwXO8fld3K7U54oy2tjmQPyJCudgflw/5d+NiX09L9ISuiqLrPNwHAm9xRLDYUtPmzZWMjS2azFhnOWTPHFEbyKlsV3VTxvyRHKwoyHkrREDt9zuRgKQ4a/loQnMGJMAzEbyWTRfdgCafxbAIHXEiMWZxjokbwNcS5YLIt++MkSSQ6hb6bwUyiPuYYsPcLv0JjF4Vkcz8YtFsdvkhgE5eEEfjHh90g6bbF0JYLmpyAC47dPHCnq495Xr/O9msF6a0cslhqdz+JDNjSEY3F7Ae8YyEJYthiEZPSNoa/3a2fbze4bT+gkEwNiQAyIATEgBsSAGBADYkAMiAExIAbEgBhYgAy8BOdMTMd6MIii8C3jDP4P1Pti4oPIf3icfn71dci8EqBgFwQ4/iLANYqQvvHB/if9whgpH/x9FPi/QhuFkFXALoA2G+sbnfmxv19E9qHHik/IUSz/B6d2A/I3O+VyZ6/HhG8sTEpuKWyOPiAtVCJ51mNZ+ynyvqDydKfezT7PKfzMyfP5OMVy3yjgf8EvjJNejXoKxacW2j+HlGvAw85x7b/R8qdxWydu4PVEoc8XjCmq834oh1h7PuYhv77t9TMTpG1OW4eTP1azR+IenAl3vC4udSa4FvnJPpcfo881zpilTn4+ZCnk3QD4124a+dcDFMVdq3cLyE90j7pdi/tVu41HIf9uHNO//ynCfHYKa+hHX0I2PQbm+v0/vbPSKDGwIBhYtmGjtZ2a97Bk/SkL4px1kmJADIgBMSAGxIAYEANiQAyIATEwewy0bbjy8P9nLt3w5dk7kGYWA2KgiAGKuHBzmBHovXAdMJEYQi809zhvQbkUeyE6+ePo3eEKCp5fh9P+zhImhCeFJxxfgfQcwA9BOhvr43I+CPjrZ+qLn2wbz9rR4I/5wDiddjt93j5On1KqKQTx8/OP5wrE/vj/ddrJGQVU9qdo1Ai4xuvpEODPd7bTSA8+v57pZU7bRNnnF41z5+S4Txe1P4OVk9j70O6vZVuhL19Q4Dn59RT7eL2Uy96Aify5mZKPyWwQHfwx472MUTwHeeU5EdsL2IHUx07kfexCfh1QbBT3/eP+pbhxnHKPM+Yfx+kzUfVs3YM8ZrnuF87Fa3wVwOtsou88NB+2buR8Pt9xuPaxzHT4fmz07OVWY+q9gL92pm8a53BXFvUr1YlyVdE43u9TsdPQ2V3fWNdzqfPx8/S/3zjn10odOAv9zsWc7nm9tgzHGOt7z512OtfhTO97//jz5f7311uWtNSbpCwH0yRiQAyIATEgBsSAGBADYkAMiAExIAbEgBgQA2JADIiBKTJAL1OKiL4FkKkBKv0KJ/0B8l8FbgfokTWRnVDUyDGl2L1OJ67leMD31OVDdYTkO2ylzEkvOQq5xTYb6ys+Bsu7x6osqutHeXGhzj2/om5lKfLzvhH428Jsz0b6u0KeCT28KY7ROoG7gfuA8wC2PRX4GeDb+cg0FQr7kLqf38l+J6QUQ25yyhNli/udhM73TDBg6wRt4zW9HA3XAjwn2p3Ay4AMC2Wy1qJ5KMZNZhQbawudisePN5b9eZ+UarN9jZW6jiN1D5a6nvH68cWCXQWM18evX4bMxYD7/TlfdKKzsO4bgCWAbx9AZrwX+5J+p0IaQVrK/cN+rg27hSOc/2ccz78f+Dl/+ggffyEfbr7c/2X9jObLl0FZT1qTiQExIAbEgBgQA2JADIgBMSAGxIAYEANiQAyIATEwbxi4Bit98xirpXD8d8D7AIp2NAqNG4HbWZjEih8IMwxlKYJC8bScxxeNlxY17ikqT6U4G+srPn4fKgaKK8cou+KLL2KO0a1sVddjJlc0vsqZ+UzkmwvlPyCl2HsLQNGY9nTAFY2f59WO/vk5Evb3zRWN21HpnqffZ6yUIlIH4Avp68bqVKgbQUqxeirWhs7/A4ScQWcgfwrwoFM302yLM8Eh5EsRx3guqwrjXPGuUHVMJUfiHpwtwlZg4hMBngNTfkfyOl0NFFuguGIOlumt/kOA3/u+UTD+hF8YI3VfNmJzFZAYo19xVWVRRX9R+UgV+blc4Rzsj8jTO192ZBiYz/f/tBmSaDxt6jRQDIgBMSAGxIAYEANiQAyIATEgBsSAGBADYkAMiIGjyMAQjv1tgAIjPVPPBviw/+PA6cDlgCsQovg4K34gfOHjWksvuPO4npf0ID5Y+jRP6OnOy8ZyrK/4IJ3FFXOk/EusIwdQoN4A0DvyAEC7ZDTx/t5UyDN9TyFP0dg1VzR2xWT2cUXjXe6gEvI70ccXjf2XFsYatguVPJepmCta8RqmeBQFrgXOAabzcgOGPcFc0bgUL2NO4Arg3DOWXsTF4hz7uXYbCi9xK4ryF6D89qK6uVA8EvdgOc9zeYHHVyFdVM6Jj/Jcb8TxvwT4L1FkkX8r8BVgIiu+Likal2LVRZ0GispHqvhkHIjffb59388oPSIMzLf7vyyk8B9dmRgQA2JADIgBMSAGxIAYEANiQAyIATEgBsSAGBADYmC+MtCLhT8LeMA5AQpUH3TKY2V9b9Wx2qZS5wpvEWcgReOpioXO8MPetG7ddPLu+orHTySqF/c9kuUuHMz1Fr/EOTg/a9980fhWVPhewuuR98XcVcizTKNX9Z+83GN/KHj6VooHot+XKYUr3yjqjmdTnded55soUBzzjd7G/+oXypC6TmXFAtt40z9S1EAP0MmMQvOPJ8Adk00wzfaJPpdSppyN74hSjjudPldh0A7gncBYgjG/i7YAXwb4mdGzfK4bP79PAl8FfMF4GPm/BSYTjNHF2vnHsaVOfqJscb+j5Wn8d84i+f32U6es7PgMzPS+92eeT/e/v+YZp+4/CjOeTBOIATEgBsSAGBADYkAMiAExIAbEgBgQA2JADIgBMSAGjgIDFARfCnC/Wl8I/BDyDwM/AcYyCiy+cXwTMFMRdbs/IVJ6qzUCFLWnY7Oxvums42iN+TkOTA9UGkXjawGGpqX3HY387PRyoyFnb0P+4kKZ3sY/AJ5XKDP5JUAh37VtTmGlky8l6/anyF1u+29M+LbCpJcjvaiQZ0hecvNAoTyTxBXVShVIyOtHnYO+DPkfOuUjkS1VFOI96FupY/z+TOfLPfgarPVqLtgx7vPNe+LBAvhdOAT4NtfHJc6jAABAAElEQVQdCqNY6LXAy/0FI+0Eng/c5dRNlN2Mxhzgn+sK5O+ZaEChjf18478NPO6RNl6vL3YOyu+vfqe8ELOl3sMzve99bufL/e+vtyypf7OUZTJNIgbEgBgQA2JADIgBMSAGxIAYEANiQAyIATEgBsSAGDhKDNCL7h3OsfmA+TvAqU6dm2V/3xqQWeUXZpC6IiSnWV7iXBQpGov6zsb6ig4xp4vXO6ujdzGfZT8FoJhEu2k0Ofz394dzo/sas+iKxj9z2v3sVj+DlJ9Bqc/LI+i7zBlbblFpD+b+J4AvMRBvAEYAGo99bSFFMiM76Ixe5OQnyvIad73An4OyK6BPNHYmbeTBN/8a8MtjpQzx7fZzx4/Vf6y6+XAP8rvr687i+QLDU4GzAF5DbLsTcAVjfjcytLhvpYpxfv/ZTitwgF8ArmBMD/dzgVIFY3T19i/exUzBTvEzk6TrnXYebzrXjjPFtLKnYZT778ePpjXL/B/kcu/ez+OdWTnue3/u+XD/+2stW1rqP4JlO6AmEgNiQAyIATEgBsSAGBADYkAMiAExIAbEgBgQA2JADMwSA9/AvDc6c1cj/y3AD23qNHmhWt3ymW5hgjwFjUuBdQDnd831NGb9CW7jBPlfo60HoFfb2wv93AfWrCrH+gpTz4vkUazS54CCJs//YmflE4nGz0A/epw/rdCfoV1/U8i7iSsaU4x19zh2+xXn+SKC+2zdX2dxv+mW00UDOf9HnLonIf9+pzzdrCsaN2GSUsXDbzsHpEjzPWCse8zpNuNsypmh+L5zmg5ni++9Us/t8ATIFH+uc/EevBDrdCPKvg7lW9yTGCNf/IKEey2P0f2IVvE+pEB6iXPUPyD/ZGCXU1dq9jan49Oc/ERZtx8F96Nh5xcdlJ7jC9GOxn3v8zwf7n9/rWVL59KXQdlOShOJATEgBsSAGBADYkAMiAExIAbEgBgQA2JADIgBMbBgGXgDzjzunP3ZyL/NKfvZ3chQpPXtXciUIiz9A/pR5N0E0HuPoo1vA8hwXt/GOq7f5qdtyJxSKND7795CfjbWV5h6VpKsM2spPDrdx83+3GmhiPS0QpneZxSSXCNv/ue5Gvk3AhT4afRCdq8JrxJ/6L3oCrQf8BsmSd1+FKRvmKR/OZr/DZP81ZnoX5GneDwTa3cGU/RtdsoTZb+JxtudDhcg/3lgOsIxx9AzdjJzP7/j0HmyY/HFjpnabN6D5bpf3O+fDE74phJO+m+L+riic1HTES/+O474fOeo/4f8ZUC/UzeVrOuhS6547UxkfDHgRKfDb538kcye5xyMUQa2O+WFlD0a973P73y4//21li2VaFw2KjWRGBADYkAMiAExIAbEgBgQA2JADIgBMSAGxIAYEANzgIE9WAMFNdc+hsIqtwL5LPAhp46eXa93ymNlF6HyvU7Dw8jf6pSZ/bBTvgj5yQQxt38n+v+lMH621leYvuzJsDNjzMnPJHu9M/jFyJ9VKD+ItMtpY5Z8/dGpc4Xdnzn1bpZ8/4dTcTny/jGc6sdlKea4ohtFJb4sMNtGQZDXJ1MaPTKvLaRIpmXkMe+MPNvJT5TlGl4BuEIeX5C4GVgJlGrPQMfbgDeVMMAVzXh9vXOCMU9G20cmaC+1aTbvwXLdL4POyVD8bXDKY2WfhcpPFzX4L1cUVR/x4gtwxLc4R+W1wXuSL2ZM13h/9hQGR5Hy3AOFcnHCe+pqp5LH/4tTPpJZVzTmC0q8FheiHY373ud5Ptz//lqVigEx8AQGlm3YaG2n5j0sWe+/nfmEbqoQA2JADIgBMSAGxIAYEANiQAyIATFQEgNtG648/P+ZSzd8uaQx6iQGxEA5GAhhEgpZPr40jUnpLHOHMwfn+s0Y8/BYvnDGPiPAVcBYzjatqL8b8NfF9HVAsVGQcPsdQpmecsXGfv8EuPO9t6jTbKzvg84xNxcdb7yiy9HHx+nk8n0/+tBreqbGz6ELcDli/nPjTPzmMfrywT8/u/GMAuQBwD9GL/IvHKczhWsKpX5fepqvG6MvhSm/z31jtI9V9T5nzLaxOhTqPuX04zE+OkHfUprucuYb77Mdbx7ykXLGcz0UQ68DngtUAcV2IireBbj3iM8V058C/EyKjXUDgN+Xxzm9uBPKfwN0A34/P71yjL6lVM3GPcjj3gH4a5vJ/cJ9u/15mH4TGM8uRAOvWbc/8/85xgDW+f3GE07fjj73Oij1pYMxDueF2N7uHJPfxbwP+XlOBRSGi634e+Fb6MDP1bVKFH4J+OfM9PluhynkTyuaZ90UxrIrQ+vnAH8t32HlNO3DGOd+RidMcx4OOxfw18T0Y8BUPhv2Ld4CYLLvvaN132Opns31+99fp1IxIAaewIBE4ydQogoxIAbEgBgQA2JADIgBMSAGxIAYmAEDEo1nQJ6GioEZMcCHtO6D6S9Nc7ZTMa5Y0HrlGHPRG7i4Hx+yMxzwFcAbAa5hCHDX9VWUxzOG62XYY78/BYDrgQ8ALwPeD7iiEfv9HBjLA67c6/sgjuOvq5yiMcUqf16m9EbdCzwCzMSuwWB3XuafM86EFCSL+/55nL5uNT8TfkbuWHoofgR4BUBh9kbAbWf+tcBY9mlU+n3LLRpT3HrUmZ/X2ZnAdO3/YaC/1punMQmFIIrc/hzFKUX/Bwp94hP060Pbq4GJ7ItoLJ7/IdR9BvgGsMdpP4S8+5ldifJ0rdz3INdRrvuFwvzDgMvLrSi/EjgfuBC4AmCYZ/87iel2wB/zE+SLrRTRmN+R/hxMLy6eZApl3mfuXNPNrx7jmEHUkRN3Tl6TfAHj5cC/A1sAt30mLwvOVDQ+qWgt70F5unYNBrrnxbVN14pFY3feUvO3Fx38fSj7Y/k9MpYdrfveX8tcvv/9NZYtZbgC2RxhoHLVBbck9963Lh8MWe2JF1sgFLX4wzdYsKLami/8B8sN91n3n79udee9yNa86UM2vG2jbfnsm6yirskaz3medf7ph/hpk7VodQzBCtKWTia8MwuE8e9GIGfhqkVW0Xa6JTs2WyAStZqV59hIx6OWbH/U8igHAiGrbD3JQtFay/TusmTvfrxTGbDGM16IcSdYuuegDW7+i+VTQ1Z78lON8wYrayyXGLChvQ9hzirLYly4bine06nEuistizVnBzssjz6hmkUWqGrA+GEgbpn+AxasbbXKxfgODFdYbqQfawhY6tBu5OOWz9hTkt0P8MtaJgbEgBgQA2JADIgBMSAGxIAYEANiQAyIATEgBqbKAMWkq4F/dQZSHPgN0O3U3YL8OcC1gO+5SBFuIiHuu2h/EzCe0TOPgg37UQAIAM8vAMkT7GbUvArgw/Nim431FR+jHGWKLC8F6CVHo/h/HMBzqgEouk/HKLa/xhlIwYsC0FjGZ4l7geVO48+c/HjZ69DQCXwLWFnodAlSYizrReVrAK7tSNsIDvgG4GaA1xWf8V8L0MszBUzVfoEBHy0M4n0QAchxqUaP4TOALwAUKos1h0WoI8azQTR8Bfgc0DFep0L9B5GuAS51+m1AnnCNnw+9VC8HnlVoGOveKjRNmszGPViu+4UiAIVPinG8z2hPKcArFP3ZjTL7rwN4vdPIEcVnznW07KmzeOAc5uY5fgl4beE4FE/HE1D5nfHWQr+jkUBgeZxtfFxp4RWO1n3vMz2X739/jWVLg2WbSRPNmIGgBZoCwUBLIJNoyQ52tVgm3RIMhVryyXhLsmNbSz4faMFBWlpPP7ul7y+/bqmrr2ipXrqqJdKwpKXp5JNbgoF8SwBYdvELW2InndWSz2Zbao8/r6X53Be1BDA2m+xvGd7x55bccHeLYd5gU2NLDseqbjuppe2pL2+JHXdKSy4Vb8lbpiVnuZZofWtLNNbaMrzn/pbBh//UMrTj7pZ8ergllxxsySUGcawgYC35zEhLIIj5wuGWQLSmJRgBwpGWYDDSEopUoS3SEqiIjSKXa8EvmZZApLolVNvqnV8wWtkSBkKoC1iwJVRV12LBYEs+HCz+gTFjjjWBGBADYkAMiAExIAbEgBgQA2JADIgBMSAGxMCCYuBjONutzhlTvKJwXGz3o+JvgI8Ce4obnfLvkacgQ889ChET2T1o9MU0CpJjGQUcClsXA/1jdSjUzcb6JjjctJp4vhTqivnD40BPOJ/WpBh0I0Ch1Lc7kYn7hTFSfkauUQAqxf6ATqcCFDDH+7y60HY1wJcLjoZgjMN6dgv+frWQZ8J1U1iZjvHaotBOo3D4DC83tT8Ufl8LtAHvAB4AJrIkGm8C3gVQpH8PMJlgjC52CLgMeB9wECg2it0/ASgG3lrcOMNyue/Bct4vD+HcKAL/DzCWOJ5F/WaAn83JAAVm3le+xZB5gV84Sun6WT4uv0NeB7wZ2AWMZTtQ+TLgRQA5O1pWLBo/fLQWMkeOezTve5+CuXz/+2ssS8p/sGVzhIGatc94eGTfPetzybhVrTzPwvVtNrT5t5ZLDsCz99kWbVllh275ip30ts9ZcqDfmtedbNv+50s2sPleO/5VV9nun30Tnr1xa9pwvucB3H3vzVa5ZK01bHi6df35+/BcpgYbsHw+Z1Vrz7W2y99s7T/5ui0/5Qxb9eSLbM+9f7Xe+JCFa+us5+4/WH1rG7yTY9az6xFLj4xYPpfyvJ3zmZQ3f6ShzSAWe97BeXg259Ijlh7oNNbTQlX1loNXcWaw0/JoC8OrOId+lst46+B5YVKrWLIOXstReBcPWXZk0LLxLkv37bFUKrve4nse8SYr5Q/DUwcCp3hds7n11r6x9LGlzK8+YkAMiAExIAbEgBgQA2JADIgBMbCwGGB4agvQEwj/L5v/ih18+E0LiwCdrRhY0AzU4ewpYvBZ0zBAMWE70A1M1xZjIMXGVcAuYBOwDxhL5EH1hDYb65vwgFNo5DPntUArQCGcIjIeBM474+d1KsBz6QR2AxTnpuPNi2Fz2t6D1X2msMIfI31JGVbLFzSWA8cVUl7n7QDFYYrKQ8BMbRkmOBugyMZ76S6gCzgSVq57sNz3C3k/ATgeCAEPA3xOngBkjzHQjOxZALnitbMF2ApQvDgWjdcAXxhYA+yc5yd4NO97n7q5ev/765t2Gp72SA0sOwNZLzwzRFiKuxBW85kkQj5XIrJ0ykLVDRBwGxDyud56H77XWi57taWyw5bo2g8xthFCbTPCSiM8dGLYghBgA+EIBN3gaCjofN4LPY0K/L9u0IL5kKW79yDc9CEIu7XW1LrYqoJZa1q5CtGooxasrrBk515bdPzJFq1fZLnaJuvedBfGITYJhF/+jk1DCE7se8CqFq2yitY1ELaHLZnoQTpoqZ49FqqotSDWznUHw5We8I0Kb42WzeD0cG6ZMATsPNbcb+GaZujHOOdsyjJxhLNO89+wY/H3V9kvG00oBsSAGBADYkAMiAExIAbEgBgQA2JADIgBMVB+Bihy0huPKJdRLLuxTJPNxvrKtDRPBKf4Qsxn4+dFFHsuz+dzGm/tX0bDvwBNwAuAFmCm4itfsCD+CsyWHcDE18/W5JPMW657kGJ6Oe8Xn/dyfndNQsW8bKb3Kr+Py/WdPJdJqMfi6IlO44sb892O5n3vczdX739/fdNOJRpPm7ryDwxV1lkawin3Mqa4OyqcUqINWGLv/diLeKsnKHff9itLpgPYd7jDMn3dVtGy3Pq3bMaewfUWiPdbDnsSByIxT3AOx1otGK2xYE2jIcy1Ny8U5FEROoY+VXU2khixHPYzTgwOWiZSbdFoBP1C2JYYexaHghatgyBdCREYYnQwz3XB6ziDvYkh9la2rLEI567A3sbwEk5m98JbuB1CN8a0rsVpQASHqJ2vrsVexXuxpmrPYzmfhWiMdRrE4+xwL4Rj3GO5NMYihZAcrKizaC4H2bin/ERrRjEgBsSAGBADYkAMiAExIAbEgBgQA2JADIgBMSAGxIAY8BkYROY/gY8AeDjthV//PFKZGBAD85+B5+AU6NH+F0Ae5/P/85zVM5BoPKv0Tm3yyuNOs+TBhyH+NlvF4nUQe6shoGZtePcdEIYRJQFib7JjE8RWCK89A5br6UIVvIcRZSJxsN1y8DIOhKts/TnnWg4C7+0P3GKZ4T6L77gX06TRVgHxt96yFI/zARvc/Kil+3tt76aNlqpfbJ0P3m9pCMKVy463kc52G4T3cmXrMhvat9XzJLZs1puDxwzwO4Ye0dh22PMQhsDLV6IocVNwZj3zo+GrITRb0PMuziNcNb2oIVujD3XiIcyd8Pp5Y7HOIMJaB+CdnMlgrp5t3qz6IwbEgBgQA2JADIgBMSAGxIAYEANiQAyIATEgBsSAGBADs8bAFzDzuwCGXX0D8B9ADpCJATEwfxl4BZZ+TWH5V8/f09DKjxQDVBxlc4SBEMI3U2QN12LbAYSW5v6+kaYV+Kc5Z5mhQ57HMNvzKAfC8ASugbcv9gSOrT0LffuteuWptvT4E+yEtcfbuaedbIvXnmJVS06wprOeAY/erEWwR3L9qZdAOI5B74Uoe3C/BZJJS8GzuSeRthSE3mRXu8W3bcR8w9Z38KB1bnrYUtjnuKJ+KZYEoReCb7SpDfsrr8A88Iwe7IIInbLU8ICXJ5X0SOaa0wPtaOPWFHkLV9dZpG7JqBc1BWeGzub+xlCOAzgHGkNV06s6COE7END7DB4p+iMGxIAYEANiQAyIATEgBsSAGBADYkAMiAExIAbEgBiYfQb6cIhPFg7DULavmv1D6ghiQAzMMgPHF+Z/B9Kfz/KxNP0xwIBE47n0IcKTGKoxQj+nPHE1CM9gw37GebzQlUvDQxfteXoWQ2z1wjmzP8TXQKQCIu8QhOSoxZpaLN7TYwMd7dawaJHF929BWwLbCUe9vYNzaQi1mDfSsBTjohZpbLOaVeusujkGgfk4hIWuMUsnsddxzMItbR4iTUsshP2JI9ifOFqH7SxwTPaLYj/jdLzHEp3bITbvxLJS3vrR2VtzBmGn0717seYQ2tIIid3k7V3MMNY5iMneOeWy+AS45zJ8jyFmByIQjLmPM/ZkRpzsufTpaC1iQAyIATEgBsSAGBADYkAMiAExIAbEgBgQA2JADIiBY5mBf8PJ3Vk4wY8irTyWT1bnJgYWAAOfxjluABg5QCYGJmVA7pyTUnTkOqQHOz3BNN2318LwCg5xv+EDCEft7S/cAKEW+wpXxCyfSlgIKUXkALyT031dVrl4tWXivdbfn7DFq9dA1w1aX/sBC2Mv4nBVDURYCNAMKw0PZU+MxmkF4WGcg0BsIew7jP406tAMHR1tXmYVza1eBYNRjyRHoO3CIxghqYMV/K1Aj2DMGYJgjZDTeexNzPDUAQjGXlhteC0Hw9XecYMQtXkc9glCFM5nU55oTHE5AO9iQx0W440NYxw9rIMUjdOjHshcl0wMiAExIAbEgBgQA2JADIgBMSAGxIAYEANiQAyIATEgBmaVAXr4vBq4H1gO/DPwGUAmBsTA/GSA4V63zs+la9VHgwGJxkeD9XGOmezY6gmrmQS8d/fe4wmsmcEOCLphS3Vvx/7DB+B5jH+3If7mgvA+LuwxPLjtHngJYx/irj0WhwZ7y42/szzGdO3aYRWtq22kv8+CCCWdY8hoDKeXcLrvIMTdWszZbok9UQtUVmO/5IMQpJMQriEEx/ssO8TQ0gHsl7zHMgOHIPxSOIajct0i1Aa8ORB/etT7GcezLPYqZohthqeGmEyhOgfv4xxEZe5RzLZcoh91KHPf49zoPssG8Zr5HMTwYJSCdM6y2IsZZIzDlKrFgBgQA2JADIgBMSAGxIAYEANiQAyIATEgBsSAGBADYmAWGHgUc74f+BzwL8DXgR5AJgbEgBgQA8c4AxKN59AHHIBgGo4t9kRWb89fePZS7M1CaM0mBy2QGvL2/a1etcHaXvGPFoJgu/2LH7FobaPVrDwR4Z0jNrRvp+3auhOCcyWcd8NWt+F0W/SU8yzRsc1iFRX2zL99uv3mu/ttMD5o4RUrLbWpy9I9nZbq64eA3A0hOgPhtspSPe3YvhgvocD1OAsPZhzYywcwZ6bngLdXMcNPByMRi8Ra0Behqbm/MT2O0TdIURhj6V2c2PcwhGacF0JSp3t2ensZBxB6mvsWcw9jhqamwByqrMexIVhzfE0TPJjzlrKNc+gT0lLEgBgQA2JADIgBMSAGxIAYEANiQAyIATEgBsSAGBADxzwDn8cZEjIxIAbEgBhYQAxINJ5DHzZFVO77G4x3QXCFoAqvYoZ6zkEwpncx4jebwRO48dSzLdex2yprq6121QnYa7jZEgd2WGzlWksPDllVXT3ciRG2Gl7Gy88711KDA7bs/Ats5NGtds7pJ9jtv6izoVzA6k5eb+n2XWaZnFW1tlq2o97yiWFPNKYInBkcfYGMoaLpfRyEB7HnLQyPY4rLDFcdqV+CUNYr4CictDRE7yT2Nw4EC3sR5+gpnIfo3WcZeEnnsRczvYiD9EI27sVc6YXg5jmGqxs9sZlzVq5a6dVnoVPHt/16Dn1CWooYEANiQAyIATEgBsSAGBADYkAMiAExIAbEgBgQA2JADIgBMSAGxIAYOPYYkGg8lz5T7CvM/YrzEFuDCOc8athkGAIrRWN65ObplQtP3FAYe/9ChEXR2w84j9DTRID7E6Mum6HgXGEVkbANdfZi2+BK27vlEevuOAQ9Gd68/bssPdDv7WucgYdwHl7FwYoqbDOMMNJo90JMQ8Dl9CGEsw5gLoaOptjLA7AP9zcOVXOvZexdzLXXNFoa+y57IbQzEJYhGHt7GUNkpqMyBWQMxrgowmcHcWyK0RjLMudFOGqeQxhzZrnXMlVjmRgQA2JADIgBMSAGxIAYEANiQAyIATEgBsSAGBADYkAMiAExIAbEgBgQA7PKAJQ62VxhID3Qjn2C91oee/7SK5f7/uYQktpTbiHg0guX4aB7N/7VQq0r4Nlbb/E9O+Hdu9dq2tZaouugZYYGIcBGMAb7IB9qt9797VZ54inW/chmq2tbabGWRqtbtsICEGYDFRCBq2ohCFdDyIUXM4Rohpb2PILz2PyYx4RRxM5zb+LEEEJGD0MIxt7GqAthXD4NkTuLNpSzELxRwBoxL8JN0zOZIbIpQEcbl1rl0lM8z+RAENIz2ilwcy/jPENbewI16tEfBU+E9g6uP2JADIgBMSAGxIAYEANiQAyIATEgBsSAGBADYkAMiAExIAbEgBgQA2JADMwqA/I0nlV6pzY5vWyzI/2eaJtHSOoshNU89i2GazHCNTdbuKoBIaPbsUfwNtvxjauhs+YhEg9YfAh7HVfGbKSLewojJHRm2KqaWq03m7Vdv/6Vxfb1Wf9Df7XqJcvtRzc9YAchJFfHGqy1FsJufYMdOtRlqY5Oyw4MYE6KuCHPJ5iiL0VkhqoOeOGomcd6EDa7qrbeQo3Yyxhicbpnv6WTw1hLr+cFHYFXMtRoT1zOQlCubF1vFctP9cjIp0Yssed+eDl3joriI3Eca1SoHt2HGWG1+w5CXMYeyFEI1zIxIAbEgBgQA2JADIgBMSAGxIAYEANiQAyIATEgBsSAGBADYkAMiAExIAZmlQGJxrNK79Qmbzzt2db/4C/hIbzTjHsIR2ssO5yEV26N1a69EMJxg410bLKR/RstWrkIouyQZSDmxk4+3Z711ivtt1d/1rLwRH72Ky63xuZm+1FXu+Xql1pN0xLLHr8B2yEn7VBdg/X3dNsZ55xt55++wrZHcvbnzi4LIpw1YkJ73sEhhKnmPsbBylrspzxs2SQ8iBFVOo+Q0QyfHUKY6tpFbVhDhaVGhm3gwE7spXzIE7ujlTWor/H2PGYo7SBQueSE0RDamD9cXWtVy9ZZdqjHC7VNkToLgTzTv98Lqx2ubLRsvA7zdVsK/WViQAyIATEgBsSAGBADYkAMiAExIAbEgBgQA2JADIgBMSAGxIAYEANiQAzMLgMSjWeX3ynNXrP2DIitWeu/78ejIaARnppKaqimyds7mGJyZesaeBo/YJnEIYjI9d5+wAO7d1rfzu3YYjhi9UuXWWB4yLp7e2z58WusPdhoI517rHrpCkvc9xcLJkc84bbtuCUWxbEWNcYsGo1YFnsmM9w04lRbuKbO82IOhLDvMMNMI4R1Ht7FOXgJMzQ1j1NZU2MVlQhDzfWhTGM+iP2JOYbG4NaBPLyVudfyqD8xI097Ibe93YoxJ1yjLZ8cggfziCcw58IJC2QwfrjfLDUaHtubTH/EgBgQA2JADIgBMSAGxIAYEANiQAyIATEgBsSAGBADYkAMiAExIAbEgBiYFQa0p/Gs0Dq9SXPYDzhUVWfh+uM8MRXOvRBb8RFxL2MIs4EgwkZTaKUEC8GXZQsGIcLCG5mib0W1pdMZq62vs2is3hKDg+ibs0gM3rsJ7EPMkZEwPIirbWQEXsUVERuIj1ga4z0RF3PQKFxzf+J8BqGqcTzurYyDIUV7kPsemyWxv/FIYhhp3DIQkwNcUyGUdRbzZSkGM9w2ROZkxzZ4FvdBXIagDIGaexxH6hYjD5Ea6w9W1ED8ZkhrnB/HIQR2DnsnWyrurUd/xIAYEANiQAyIATEgBsSAGBADYkAMiAExIAbEgBgQA2JADIgBMSAGxIAYmD0G5Gk8e9xOeeYQRNk8hONsvMvyEFMpwgZC8AJO9Fmqv8PCtYtt5MBGry4YqYaOCy9gePZGmhZbuqbeqpevsoO3/cl27uswg7fw9ocessqWPqteud4Gtj4Ej2WEt+6HGBuM2IP3PWyp2gbb9dAjhq2RbVFrsw1mRyx+AHsqQ6BmGOpgVaV3DlmGrkafPFRseh1bOGxpFuAInA1XWh5iNXY79sTtzAiEaoScprjsCcxIM0OH4MAcsRDCXWchgIfrWiBARyzRvsUs3o1j0KMZx8qlvHPODPd4QnU2nZoyhxogBsSAGBADYkAMiAExIAbEgBgQA2JADIgBMSAGxIAYEANiQAyIATEgBsTA1BiQaDw1vma1d9dffmKpru3Yz7fDKlrWWPXqJ1l8+z2WHeiwxM67ILYutXTvPk9QpfMvXHG99eSGB+3BX/zKkt3tdBO2jVt2WNPqtZBxQxCcsR8xvZJDYYSlrrZFsQobRjjojn3t1p/IWXrgkK0780xbf/Za27U5aluhIEdblyH89ZBFAjlvb+JEbx9SeDtjr+JgFcJS17dYuLEeHsEpy/X3W6h2kQUPHUSkaexPzDp4C9N7OAQPZx7X4G0crEZ/rCMIT+dcEuv2vKYRthqit8GrmF7TdKLOQzgOQFjOe+c3q3RrcjEgBsSAGBADYkAMiAExIAbEgBgQA2JADIiBhcPAcpxqa+F0Gd7u0aN46tzrbTWwEjgIbANGwwQiM02j98daoA2gl8ZOoAcol3E/uucUJvs90mMhRGANzmNd4ZyY7ALg/TIjW4LR/AxoSeBhL6c/R5sB3f8z+wTmwv2/DKewtHAaED3skZmdkjf6cvx9McD7/q0AFAqZGFjYDEg0nkOff7J9M7yMO83gRbzistfaWc97jt34kausb6DLIovWeHsOU4TNYj/jhvUXwHkXoalHBqyqeZFFqqJWe8oG69n0iJ12zhnePsNDp59uw6EGhK2utLrjN9hIb4ctWbPUOu6usUSXF/jaoghVvXLFUjuuvspGmuvtYOtiy8diEH0rrDIIr+eRBLyDoeBC6A3A+5nhrSM1aPfCTIcsNTAIMRiexRB6oWZ7nsJ5CM8BeCbTKBQb9jhmqGqKySGDd3S0Aucw4IW8zsO7OljTjHPh71p4L2NgDiGvc9mUZXIZ/LTlb2aZGBADYkAMiAExIAbEgBgQA2JADIgBMSAGxIAYmBEDV2H02woz3Ib0ghnNNvXBFF3+CfhHgIKx6zKBB2ueYPljpB8FhoBS7ano+CngPAAP1w4b5/wd8Ang1sO10898GEPfWxh+MtLNhfxYyXpU/nSshinWfQj9r5vimKl034DOdzgDXof8NU55OtnXYtAnCwO3I6WQv5CML2ZQoL13jp207v+ZfSAfxvCjff+/CWv4QOE0+DLGqYX8TJI/YfA3gFqAQsTnAJkYWNAMSDSeQx9/qLYJ3rsJeOIO2fC+rVYZfq5FIN4aQjeHqhos3b/PovXLIALjNyVCQCe7Oy3a0GIVjc2W6DxolU2LrLqu3sII69zf0W21DfWWTFfBc7kfOnStjaBPoqcfoaARfhoi7mj4awjBwQC8kgOWymQs2duDeoi9EIlDlbg8PDGYCcRfegBDIM5jT+IM+mbjfZaEh3G6r93bEzmAsNfBcGa0X340rWhqs0jDYm+P5Cw8orPwXg5Golh+Cp7LtRYcrvT2NqZnckVzG44N7+e+ToS07rYgxGcFqJ5DF6iWIgbEgBgQA2JADIgBMSAGxIAYEANiQAyIATEwHQYuw6D/BigWj2VBVK4B3gO8AvhnYDLRlQIxBc5XA2MZ53w28DTgZcDPgOnaRRj47ikMpnfIiVPoP17XhvEaVD/nGOD1RlHv48BHgLkmGmNJR810/0+P+iNx/7djaZ8GeN1+DOD3JF/2kImBBcuAROM59NEHoLMiXrMZQjzHd2+0dDINz96oJ+IyfDNDOVO8zWK/4XAV9hFOwTv4UJelIcZSMM7AK3gY4aLDEIsrY03Wffd9lsU+yJWL2jDfoxCYEUY6Vov2ZgtAOKZl0hnbtfuABSA2797fYUMUjfv6EYghbbnGJoSRDiC6NPYaxr7GOdalRuAJPIw5l2B9Sc8TOYf9jUMQfY0hqBGiOodw0wxdzXyUQjAE6Xx+BOuP2EjPQYjiCEcNr+Rsoh/rQHhqqNQh7Isc4D7IEJu5tlB1M4RsvhApEwNiQAyIATEgBsSAGBADYkAMiAExIAbEgBgQA/OWgdOxcgrA8Aw5bA8hdx+wA1gBnAU8CaAdB9Dj+BLg98B49nk0uIJxB8q3AFuAcwF6UlcBPC7nez3wbWCqxrluAPDQ8ogbwzvL5gcD9Gp/+vxY6hFdpe7/6dN9pO5/fpdeCfC79xsAr2O41cnEwMJkQKLxHPrcob16e/1yv9+qtpMtXFttsbVnWMcDf7YM9jkOVdZZCnsaZxO9Fj+w13IQmBMdeyzVE7W61eusf89uS8WH7c7rb7RQXYN17dgGJ+V2S0FIHunc4+0xvOXme214717P29j77oNgu6+zx5IH+6yn8xCE5xEIuJUQppM2BM/kQDgMgTeEJUU8oTcPD2MKyoh/jf2N67C3cRYaN/Ysrop5/TJDPegbtjzCT+fzEJohSjNlmGqKwqFIlWWH0Ybw0zmIyviDuUbDWgfD2Nc4DY9mWCCE36HYR1kmBsSAGBADYkAMiAExIAbEgBgQA2JADIgBMSAG5ikD3DP3OsAXjHuRfwcwlnhLD+MvAM0AnxJeC5wG9ADF9iJUvN2p/Dny9CYeceqWIH8jwBCuFHzplXwfQMG6VHsxOlJEiZU6oNBvJ9IrpjiGYY2vBuixSrsD+J6Xm19/NmK53y0sGfsQLhhbu2DOtPQT1f1fOldH8/7n/sgM4/9l4GnAG4CvATIxsCAZkGg8hz726ra18AjGliUQcocOddiffvJb6910P34pIUzzoZ0WrlsCwbXHcvmgtS5vsaXYw/j2g9vwc3DQwtVV2Nt4McI677ZDmzbBQ7naE28j9YusduUJCGW9H07DIQtHIdqODEGAjiHkNUL1oy4UiSB8dRRbD0c9z2Zvz2F4M+cgHAewD3G4BuJwTS08jbMWRFhpjqOnMN+3CWJPZHoQU1Sm8Rct149A1pYfiVsmfsgYdhsdsP9ywtIDHV6fAENTY6/jZO9eC2M/Y3oaU5CmJ3UQfUd69pqlRgVkTikTA2JADIgBMSAGxIAYEANiQAyIATEgBsSAGBAD84wBhutd56z5VcjTa3cs+z4qKRD/utDYhvS9AENWF9v7nYrfIk9xt/hBWjvqngb8EaD4zMd2bwGuBCazxejwRYDzTsd4Hr5wWsp4CsW/A3zB+CDyFMZTwHyz67FgQiYGdP+Xdg3Mhfv/W1gqv1ePAz4O/A9AMVkmBhYcA/4/xAvuxOfiCV/2jvfYivMvg3AKyRVCaq55CbyID3r7CEebV1vspIssD5G3oW2VXfDsZ9rSxjo75dxzrWb5KssiVHTD2hMtCC/hMETdYCjo7RWcHUIIaJxsAN6/2cSgJwSHEBI6GB31HKb2m03g+w/hpzmWoaHpXUxv4YIC7B2fnse5JEJTI7R0qgd7DiPP/Y05fyiIywgew9wbOQhPYs/ojYz9iYd3328jBzZbBuJwqnu7N2XFouUWrUX46fQIQma34XgV3r7Iia7dluzZb4n2LZbuhQd1fM9c/Ji0JjEgBsSAGBADYkAMiAExIAbEgBgQA2JADIgBMVAKAxc7nSgWjycY+91+gwy9hn071884KcNWn+mUKSwXC8Z+M8Xbq/wC0lcCdU55rOyrUbkJcAXjgbE6lrHuI5jr6YX5KBTz2BSOZWJgPjOg+7+0T28u3P/83rm2sNwWpPwelImBBcmAROM59LG3La63lpNOtwDCUGf7OyH+VlmoZjT6S7r/AATfBJx4A1bf3GwhiLj7Nm60+oaYJ/ZSNObLeEGEdc5TxA3k4A0MLReewvlA0BONWeHtjRytgGALURfidC6dRHvAIPnCNxjjIPSyLccw1BCS88hnIRBn4n3wFI7DQ7kKa8h5wnEO+xaHEFI6gjVG4HFML2aK09ynmN7H+AOROW4jBx+BcPwIilGLNC33PJWD9FYu9PfCWcPD2ODJnIcnM8VkeivPy3cJ59D1pKWIATEgBsSAGBADYkAMiAExIAbEgBgQA2LgGGVgCc6rDaiew+fHkNBPcdZHj99SjGGZfeM+x3jC9zh7gVN6CPn7nfJY2d+hEiH9PGO43InEkOvQfi3QCPj2E2TcY/r15Uopgr/fmYxefrc7ZWWPHAO81pYBvHbnsun+H/10dP8//iplKNRFj6+aUol7v/v2TmSknflsKF1QDOjCn0MfdzKVsVwiDuF0xNKHdiMPD2B6/MLyEH8DEIND8AKO9/dZdUOjNZ64zrr27rNUX4/F2pbbSC/2JPY8gCH4QlzOQhAOIuxzFmGh6c1rEG1z2CeYwnGWoaMRpprew5XV1VZfW2k1tfASxj7DmSHsOQxBmAIyDgwg1DQE5khjq0Wb26xq6WqrWtLm7X1cuWipRRsXYeoKb1509tbricYYn/fGogrCdLAi5p0H9zemMaQ1BeIchGLuewwVG8J0vwcK0zIxIAbEgBgQA2JADIgBMSAGxIAYEANiQAyIgQXPQDMYoLfst4E7gT6AXqj7AOzzZgzD/L/As4G5ZE1YzP8BvwceASYTd9HFs34/gxR7y1mlU2b2PKfMuUuxm51Of+fki7NnOxV4MOmFsmZ/7sU8G8Zz+xLgC+OPIv+ZSQ70drTf4GDVJP2PdPNLnbV9fYyDv9Fp5x7OpRjFfP+cn1PKgCn04Wf+X8AtAO+t/UAcuBvg+i8GxrML0OCvq9Xp9Aan/t+d+ulkdf8/nrWFfv8/no3R/dY/hco/A4yI0AXsBn4IvBUYFVeQKcEeQJ+thX4nIJ3Nl2VKWI66iIGjw8BUbpqjs8IFdNRqSPiQUT0v3VDdUoSQjlq0ZZUlttwNgTZm2dQI9hZutp59u+zmX//egg1NtuWee6HTZqwH+xynBuPeXsP5zIhFYk2eMByMVGDvYISchqjbjD2PX/ScM+131/fZrp4ui9Q3e/2XHbfEVrfUWHSo0Q5VVVoCorQn9uLnGj2HQ5U1mCOKNVTBOZnrYxhqvrgzagHoxPRopkcyxWjuS0yRO5+jGEzxF17MEKNz2HuZ3sYZhsxGKOvMyADCW/eiXwb7KldbKomw1wPwqIbYjY2NLa09jX2KlYoBMSAGxIAYEANiQAyIATEgBsSAGBADYmAhMkCB8GMAxdPxbDEaKNQRvwBeDlBMPtpG8eI101jEmc4YhommeOtbFTKn+QWkpQrRFEOuKIxb74wfK8twht8EPg3sHatDGevejbmOd+a7EnmGiZ3ITkfjc50O/+rk50KW5+Ovb/sYCyL/frvr0T1G18NV9Mb2+/IaL4eFMAm5+yBQrBFQzKeYTLweoPDLvrw2XFuKgn8ubv0pKBC0e0aTaf3V/a/7f6ILZzUaeT/wnnJtBQrESwD+u3A5wBeNSrEfo9P7Ch3pbfyzUgapjxg4lhgo/gfhWDq3eXcu99+10boPHizsJxy0wd3Y13eAL8hAF4YySzGYHrkBCLK7du622PFVFqmtsXR8wNKDQxB2KxHhecga1p1h1a1Lbe+NP7P6E05BmOtKq1u9zjKH9ltrXZXFaiD+QsSFJoyQ0pVWX1dr1ZGQRRFqOgxPZs9XmB7CEHwD+M2Qz+U9ITfdf8jC1bWWT6UtjfFsT8czlh7oseww1hBHSi9l1OezKU8gDgXh3ZxGWO1MwpIdj1qofpkF0J4Z6oLAPIQ+uAQpMkNQzkP8plgNV2sLZCg2T/YbkczIxIAYEANiQAyIATEgBsSAGBADYkAMiAExIAaOQQY+hHP6cNF5PYjyHoAPzJYDJwGuh+PzUX4v8P+A+Wg1WPTznIXf7eSZPRF4zJPDbBcrSzBy5hs9N8lZp1/hpF9F/nvAAadutrKrMLEvzvAY3wFuZkY26wxQgP4lcL5zpCTyG4HNAMW4MwCKx3yETPHsmcCzAPe64QsN+wHaEoBCNK0fiHu5Uc/PQnZKie7/UW9vlzTd/4+xsRRZfj/y+4x2CLgNqADojV0H0C4E7gOeW0iRTGg/Qqv/vURP+iagZ8IRahQDxxgD8G2VzRUGbvnhd2znnb+DiJqxujOeZrET19jSSy6zYE0j8hfAy7jeGk69yMLwIm4+/QzLD3RYw3q8XBiIQOjFv98IOx3A/sVDe7ciNDVEW3jzhmtqIeb2Q1AOWbyr0+IDwxatrIQ4C22WexpDwB2E4Nw7lLLunn5LDuHf82wajr5Bq21qtRp4M0cgKGcRRjqfGLBoKm6V8CAOoZwdGrQUvIaz2F85i3DWo97FEH6R5zqC0RrPY5jhsLlfchb7G6cgHCc7H8XYHhw/jbXzdwfWgnWMqtUBC1UvssiikyzatH6ufDRahxgQA2JADIgBMSAGxIAYEANiQAyIATEgBsTAkWNgAw71AedwdyN/KkAvUwrDfw9cBCwD3gj0Ar69GRk8lJqXRsGb4ptvN/mZQtpQVN5bVB6vuK+ogYLLWHY1Kg+M1TALdTwWPadpI8C/eDn9ORIMfAQHOd850A+QbwPOAnhvPRmg6PZJAE+RPcNDaPtEIe8nFJ6PK8AXj9n24UId2y4Dpmq6/0cZ0/0//pVDsZig19kbgBbgBcCzAb4U8THAv3b5nfpFoBT7KzpRgKZRO3saMzIxsJAYkGg8hz7tILxyqxafjJDUa6zpSWdbcscmiy1ZbBWtKyza0GKJg/DUrayGqFqLvY1DlsIexoHDYaI9FRjfhJCP4b1Lj+BAMIz9jnux53CrDbcftKqmZksj5HWgttbCCFttEJWzmYx1d3RbHKJxJpm0TCbric819Q123Nq1tnL9BqtvwcuH8G6uqolZ46JWa1jaZtVNTRCiI15fCs8MT52D2J3PJD3ReXQvY4aoHhWGAxCHQ9VNFl202kK1ixEuuwbi92KvLlg1+nuXwnG0YblVkINFa7B/MqNIyMSAGBADYkAMiAExIAbEgBgQA2JADIgBMSAGFhgDDEsbLpxzN1IKxQ8Xym6CB1nevqv0hPSNnmGX+IV5lFKou8pZ7yPIU8xzjUKea743p1s3Vn64qPJoi+onYT0vdtb0DeTbnfJEWV4bfFnAB3mSlc7AenTlixW+fQqZVwC+UObX46Guvb/Q5te9Dpkz/cIsprr/R/dB1/0/8UXG7/9nAfz+8AVijmAI0w8Cb2KhYOchLfUFht3+IKRPd/LKioEFwYBE4zn0MeewZ3EwjD2Iqxo9Udjb9zcJ4RVevQzhHK1fbJlheOv28d/wgFWvPMmGdu9AG/4Nx1ch9zymBRCmmiJxsKLS+h651/offcgS+7faCEJd//T62+yRu++3yqZGazvjFIu1LrJF9TFb0VBly5obrDqG356hiNU1NtrSlcts8ZrjrGHVKovW1lu0CvseVyIkdjSC9UEwhkdyLjmMcNU4LvcwZjmb9ITiHDyS86lhOBKHPfE60rDMqlaeCTH4eKtqO9UqF6/z9jGmd7LnbAxvZHpYB6twfFQEEKpaJgbEgBgQA2JADIgBMSAGxIAYEANiQAyIATGw4BhgWLpLnbO+FvkOpzxW9sdFleN50hZ1mzNFCnk3AAwHTKNg93qAoohr9W4BeXe/46KmxxWL+1U/rvXIF96NQ/JzpqWAz3q50v4w9PFBB3qIWBpvfq//QAYPYj17EH8/PJod9+91aKFHMY1aAsfPpun+1/1f6vX1XXS8ZYLOX0fbJqf9o05+ouxep/HpTl5ZMbAgGPDf2FsQJzvXTzJUUY19fgct3b3D+rdssqp1Z1rPo5sRznm39eUCVnHcGda76Q6IyAnbf8OP4KnbaIk92zyRt3Yd9ixOZ20Y4+DyC/0VAaGxP3Gwosaqly635KED3j7GI8mspYeHEba6xmKNMUs1N9qSRQ3WXBWx3sqoRem5jNjVQYSX5is5OXgep1PwIk4nITr3WpyhrSFgDw0NWbKv0zIjFI0BhqdGfagiNrqHcXLI8vB0ptdzIFJt4doWTxD39jtGOOtwdQP2NYantCcW41ci0hzEYobZDgZCWHfMosHi38Vz/RPU+sSAGBADYkAMiAExIAbEgBgQA2JADIgBMSAGZsgAPcZWAsuB44FSPEnpcUsvC4YrpVWMJvPi72qs8jcAQ6r69s/I3OEXnLTWyTM76kFSVDlGkcKsa35YaLfuSOUp6F/hHOzbyLsijdOkbJkZoKf6M505v4Z88bXhNB/O0pPzuYXSU5DypYNi7/VC84wT3f9muv8nv4woHEwmAlPeoCf9dwrTnYV0EcDoFRPZHqdxrZNXVgwsCAYkGs+hjzk1cNCy/Qcs07/fOn/1LavcuslSux+2XGLQqurr7JyXXmq776i3R2/4gWUT9Epm+OesxdacYHWrV0IcbrOd7e1W3bYaXscpq1qyAvsb74D3bw7bHWNf4pEhzxuZAm1msNfSmAObF1sKwvBwKmu9/YM2Ak9mejUP9Pfbwe34vVZVZT37D0Ag7sJuMPhd0dONeYax9/GApQY6IfIG6eIMFgMWqW3FenKeZ3Q20ucJ4AYv6UCoAvV46S+f9V4hzAfylh3u8zyKg/BqzuP9Me6BTOQz6F/dCN07i18spfxmmUMfoJYiBsSAGBADYkAMiAExIAbEgBgQA2JADIgBMVAOBigI7CpgsvmWocPFgO+ly/7z5ZknRQx6GC/hogv2AaRf9gtFKfaFe5zB+8Pw0G1SYz/XZkvwc48xXp6CWLTQyM/50+N1VH3ZGTihaMbbi8rjFe91GvAk13uZ4yGnrtxZ3f9jM6r7/zFediGLEKyT2m1FPU5EeTLR2H2Jhd+dDQDEDJkYWBgMzJcfUAvi06g/9ZmWG+qxnrt+5IV6rgjVWAbeulmEeH7Wq15jcXgIX/rCS23rH37lefYy/DRF2urFLZbsaMeexU3w6K2BQIy9jJOM1AJvYQi6/JecQvGoMIsU7dhM2EZSOUsMJ21rZ7cN4uflgR17LRittPqldZYa7LN927dwIDyNU9hLudZywYgNDw5YFl7EmeF+CNcDCIWN33gQeIPRKngUYz1JhKVmeGkIxeFa1kEwRvjqzGCHhatio/P1H7RU1zaMxUufnlDMf++oHActG+/y1pvLpC2NPY5lYkAMiAExIAbEgBgQA2JADIgBMSAGxIAYEANiAAysAPjAn8IX05OAdcBqoNj4OGyuG/fX/CFQ4yyUgvEnnHJxlh7VrtFjuDj0tNvu511BnXV8cHg0jJ/LFc6B/4h8KcKPM0TZGTBQLBoz1HQpLx0UH5LzzKZoXHw8lnX/m+n+f+zK2PVYdsLcPrTSe93/N4HX7m0TjjBzPY3ZtQWQaDwJaWo+dhiQaDyHPssw9vOtWL7Wkl07bHDLnxkl2oIQXUMQcpctbbWbrr/R1q1stQhCSGeH+tHOl67g6DucsNjJJ0LchXcu9jvOJBMIP91o8V0bMbbK0vEBCLkh9Mxbuv+Q522cx+Qdm3dYuq/HUv291hNPoK3HmpYfZ9UNjdYDr+TB/j6IxYg2AuE6WIEw0xCDs+kUQlYzXPWIJ2xzDXAcRihreA9TRIY4Ha5twqJiWHuV943MfY+hbnseyjmEss7Ge7DmHlRhLENYQzxmCGv2yWezox7KFIzpnSwTA2JADIgBMSAGxIAYEANiQAyIATEgBsSAGFioDCzHib8deBWw6Bgi4Y04ly8BfGBH40O+twJfYWECG0s0mqD74SY8eHucDTyudOQKT8ah6Bnu2/f9jNIjwkCxaHzhNI9aPM80p5l0mO7/x1Ok+/8xPnY9lp0wR281hFC11kKvUq5d19OYwygaby2MVyIGjnkGJBrPoY84BUE32tgEL13s74u9jXOpIQi2CCsNT93BgUFbc/6Trb27F17AvRaB528+H7QgRN3eR7dYYNFxNrx3H8TYuPU/cjccieshFqNfrBnCcdhy8A4ORqshzkY9j9885Nw8xF++ZAMt2BNrSUUEexZXxGIWqcJvSTRQ2LVgHuOTEIez0HGTWNvAqGiMcj6dQT0EXsyfTcStZu15nldyqKrGKye792Gf4xrvnNL9+H7mfskV2H4FInY+m/BEY4arpnd0IFzjhafOjvRj3fgez/ElIJkYEANiQAyIATEgBsSAGBADYkAMiAExIAbEwAJk4Cqc86eA8Z5f4oGUbQNuAn4B/A/QDMxlo7fbJ4D3OYuEt4W9DOA5TGbtRR2Wory/qG6sIvu5drQ8jf/OWQTFnJ86ZWXHZ8D3khy/R2kt5bo/KKLNtun+fyLDuv8f4yT1WHbSHL9rfCslMkOxOD/ev0H+nErFwDHFgC74OfRxRqpr4WV8wBL7t0BojUFXjVgIHsPY6dd+dc237Pjnvdxu/cWPzOCtG6iotlAAQi58fOnJO7x1G0JGx0fjLISiBS/lKs9TuXbF8Rbf/Si8j7EnMUViCMehMERa7kcMETefxd7I8PDNQ/xNwms5FB+C13ISAm7ashmEpq6osGAOYjH6ZbG/Mr2Mw5Uxy0GE5n7GmfghiMnYYxmexV6IaawngL2SwzUQrvu70R/rRCjsUaMQnEd7BOtAZBwI5MEw1hmFCJ7F9zdCVAewfnokM+y1TAyIATEgBsSAGBADYkAMiAEx8P/ZOw8ASY7qfr/JM7s7my/nO+lO0kkCoQACCZFENDkZI0AEG2wcMAbjgAmO2NiGPwZjwEaARDAYgzHBgASyMBIohzvd6U6X893mnRz/3+udPvWNZndnd2f2NryS3nZ1dXV11dfdM3P96/fKCBgBI2AEjMCiI3A9I/5o1ajvZV3Dij5YsW0s8bg4nXjQNadTmN59AXudp5cnyb8Yu9NTNlF2Jxv1IZs71rXk755oh8o2recmDbOqx53tpMLnKz0H/R75syVee7pxVrP1isE8hD2d6t3n9A6ezF5PXq8DwkU6gSI9xXMiez29sPv/8afC7v/HmPQ+lp0wp/qXN7rBnglrj22sfrmiWqyvowmrYgTmLwETjefQuUse2yu5449IbuCAM1dwct8vx+YuxhO37/ABWYVYm08RPYZ5jDu3Xibhtja8gltl4OEHJdLN5ySewfmRAURYRGPqOnMbD56QLOGnNakwrEk9gkv8vNDQ13nmLi5lUsydjEdzJiHJvpOSy2QkM8JvNuroPn6W6v3sRwguZmgAMVvnMKaiM3+xCr0qODuhp1ND4m/ppN1BQmgPOWGoVVT26z7RdgTuUTygjGlAaQAAQABJREFUTzoCs59w3KU8ArPObayez46Anac5vJDJWzICRsAIGAEjYASMgBEwAkbACBgBI2AEjIARWHQEOhnx5zyjJnSdvAq7zVNWnVUhrcNTqOtzKenDr+9gz/V06mHyL8L2e8omy6qXnNbfWKl4Act6vHW3VurrQgXqs/Hg7WKOu0Y7UEnfcDOLbOllry8STJbwuhFvPe/+k+1bvX2Xp0Dvs/XYPk/ZXMja/T/+WbD7/zE29YrGq9nFnQZA99boFJOl6raPTbaDbTcCC4mA+1baQhrTvB1LcN05EnviMyXQvsyZP7icJ3yzCqqIsqGla2Tzky6SeAfewgiwLctWyfCj2yTC/MPqyZsb4AVBFV0r8xwrhDKhpP2RGOGpmRc5hhfzEHWCfqI+5yU7cEyygyfxEh7AQ3kIsbnPEXkzI0OSOHFUcgnE32ir+Nkv2NLueA2HWvB+1ljWpTyexTq3Md7HCMfqbexTr2DKM0celPzAIckPHpbUkR2OF7Q/GEUo7pfs8Z2S7dsrpTSCNAK0mnoVC23pfMY++q9ex+oBXcoijicS8/ZcWseNgBEwAkbACBgBI2AEjIARMAJGwAgYASNgBKZF4Gr28jq6vIX12yZpST1pvc85vflJdm365hBHUIHUKxj/hPWnYvuxqabbPTs8w5OfKOut98uJKjZx25VVbavn+GJMPAg9nVpO58bPnFu1aSYvRHhFY232SVVtj7fKA2p5PnYeVk+fx2unnnK7/yemZPf/GB99CUU/WydLT6iqMFXRWKNZjFa1YatGYEETmEs/oBY06HoG529pkY6LLpXYpicipkZxKC6wGxIqXr0j998qfTt4ATGAV6+WI7iGWuOIu4irCLmBWFyCbcyHzB751KCUEJYLhIkuF/MytO0eyfYfk/iyZXLxc54q5z39aY6IHEAUFvVKRvRVgZkZjBGYEZkJG+2Ervbx+zwYoh8lp61CLk0NPX4Jj+MRPJaHpJA8hVaMuK3zHyP8FpIDktr3C8kcfQghuOh4DfsIYx1gDuYSfXHEYfo25l3M5cc+xVyCtgadfmiEnRLrJUJV5+zzuJ7LxuoYASNgBIyAETACRsAIGAEjYASMgBEwAkZgIRFQ0chN+nDsFndlguXLq7Z5ReeqTbO++jGOqCGo3fQtMi/Ephua2euhq6xWuw2Ps1RhcLNn2w89+dnMPsVzMI1luMezvpiyXi8ZPXdeL8haHFSsbVQ6QEMaltpNf0CmHhH6bdT7AbYDUxHNe4+y6qSim2FZT5ue6mdkvW3b/X8GGmfF7v8xJhpC2vsizuNJjZX8kWeDRnfwXv+eTWdkl3rWzMvYA8Oyi4OAicZz6DznTxxxQkcHYrywhdetDw9jFV7LeOIG493SumKldJx3CSJuWYZ23Ivw2y6DO7chKocldWyfpA7upHpBurZeLE945zul87wL2R/dt72HuYnzsm7r+bK8p11WrVwmkVgET2FCQSP2OmIugm4Zb2HHgxjPZT2mzmMcoL0CXsi50QHaKIgPz2P1QNbvfg2Brf108tpXks6DTCNkSs62YpbfQSpqE5paw2HjC42QHHSOVWYe5BLicykzKoXRE4S0PobozPzI9CUQapVwOO60aX+MgBEwAkbACBgBI2AEjIARMAJGwAgYASNgBBYNAa9Xl4q/Gq52onQtGz9SVUE9I+dCegmdeKenI+ol+Fos6ymbalZF34HKTmGWOvbxRDr1xPtopa4u9Pg/96zPZtYrGqv46BUZZ7MfZ/tYezwd0Ief7/asV2fVG/3D1YUzWFfmH/Tsr97fb/Ws18pqqF6v8LaN9Z/VqJjylM3koa7d/x6QNbJ2/z8G5a/ITvT98Cts937u/OFju06Yu8yz9X5P3rJGYFEQmEtv3S0K4BMNMrxiveSTWcI4H0KQTYoPr98Soqt68oZ718h9P7xZRvbulCDexqP7H5a2tZsRXHm5C29hPyKr1iszH/CWp19FuOpTsuGyJ8nd2++XYjbjCLexKB7E/IRMpTSsNMKwj1DQKu7q70ra0DDXGq7aRz5IGOvuNRuc8NRD+3cQdrpPJNI2dqxIqyMq61gcYVtDTWvezzzH6nGsoao1VDbicbCli5+ACM9pPKIJRe0PjwnOFDgCs3otlwlrrX3QX7f610/7wfYVUs4V8Tberk1bMgJGwAgYASNgBIyAETACRsAIGAEjYASMgBFYHATuqxrmX7I+nrB1Ndu+jVWLxNMVrd5FW2/A3PR2Mne7K1Nc6nNX9TJ2kwrFKuA+0S2oc/kA9bwhjbWdP8M+Vdn/9ZXtv86Sh3KnU5TcN7FnnS55vLju2dTULA8Vz/B2VuFxuulD7Phiz86/Sn63Z30m2Y3sfPkUG0hQX0XwetN3qKjCqHuNqij8I0zPszdpP7QuD1wbmvS6eRt2UaXVT7LsxP4R0wfF3qQel9/DvN7s3mvaW1c5uOkVZP4fNuwWTGFp9/+ZsOz+P5OHd+0JrPwPdi3mfdlA67wa+4JmKulmlnotT5ZUotCXNdz0327GlkZgsRAw0XgOnen84ClJHtgumQP3j80TrCGjdd5fPqqKA0dlYOc+SR/cgTdxXlqWb5EllzxZEvt3MScwcwzn9WUuv/gDAWnFi3jo2BE8dRFz8Q5WNdaPmHx0/36Jn7tZ9u/cLSnmQA7E8PTNpZhDuVfCy9dLcXRYyoi7OgdxMByU+PKVUuCnZmzZOhk99ChCNkJzMOh4I2s4a/Uo9hNq2hGtmTuZHaWoddQQjUsavtr1LMarWOcv9iNIO6IyS9Fw14ShZgOid5T+dCEYx2kzSL2IFH303ZIRMAJGwAgYASNgBIyAETACRsAIGAEjYASMwGIicAuD3Y5trQz6LSw3Y5/B9mD6PHM9psLUr2C6rg+RDmIq+mlaPbaY8l/d70mevVxhz1NUd/Y11HT7ozupsP0tzUwxaRv7qvb5F9Zfh11VKX8zy0ux72MPYldgL8LOxdyk+5wtAWQVx+YJ5ek0E9F4Ha14z1EjRdX307baVNIvqHzlFHZQcetG7Lcq+2j/eRgsykTPXw/2XGwNpkm9yu/BVBhrROJpr/w2djPGg13nutSXGfR6+immIqX2SV9ueBPmncP4s6x/HquV9J59cmWDinn92DFMx3sBVm+y+/9MUnb/n8nDXTtERu8RveYexX6M6fXbiz0deyHmJn2h4Q/clUmWeq12V+rovaL3pCUjsKgImGg8h0536s7/Fh9eucRnRjxtQ0TtlCDLHHMOawpFWyQfw6OYkM7JAztldMVahFo8e/3MO1wsiY9w0gU8j/WVsLbztsrBH32XMM/MbZweRXguy+H77pSBwYxk+w4z7/GAFFJDCLR+iSxfj2iMQBxC0B1hvuEyv+F8zEeM8BsM4PubTeOtzGcrx/JRp8R6Kc88yITO9iMEB1uX4kHcgmCc4sg+55XGsrOdX+zpYRyMW539wp1rHJG5yLFzo338pOdzl7DWgXCU8Xbg4YwntDPfcZg5lEckz5gsGQEjYASMgBEwAkbACBgBI2AEjIARMAJGwAgsKgJpRqsC1h1Ya2XkV7FUq5UOUKj1z8M+X6mgApsKX9rW2UrXNPHA+tBMx/jPmArGmi6umLNS9efbrKtQeLbSiqoDq8C4mNMHGLyKgc/3QLiQvJo3DbLyMuy1mCsal70Vppm/jf2uwL6AqcCrSYV4rxjvFHr+3ET+Nz3r1dlPU/AarK2yIcByNab91fs4idWT7P6fnJLd/2MvwNwNqs9hS7HXV4zFGUlfxng1tvOM0vFXnubZpN9BfZ51yxqBRUHAROM5dJpThRJxpZNBSaSkGA8gmhYlnipJNpdxepkqpkQSZclm9HtB481kJV1CzC3x0mMx6cQ0yeKFTIxpLC4Z3IQT5ZT48oizCLySzkhCQ0dnypLUtkjxIvX46g5lk9RFsGZX/S6P8z2ez+hKXBLMb5woJiSeQEyO872fSlON7/l8XuL5KPFxELX9eA4nVNymHaeNsfa1NSnQ37aMxHNj7UkiKckkx9Lf7c4xyOr8yPGQUzdOXedXxKO36g8jS0bACBgBI2AEjIARMAJGwAgYASNgBIyAETACi4vAQwxXReC/xq7DeCh1RuIBlxOS+DMs1VRoOoi5ST2EX4L9u1twFpZbm3xMfWD4Fuwu7A+x9Vh12kvBn2BfxxohNla3X+96tWi8rd4dF2g99cJVT8j3Yb+LVfPhAa8TmvpdLA9jr8Uane6nwcux92PXY2uxWulmCj+E/bzWRk+ZCngqcH8e87al9+4W7F6s3mT3/+Sk7P4XuQFMQ9jfYHqNeZPeY/q5px7G+v1Qb3qxp+J3PHnLGoFFQ6D6B9eiGbgNdAESWHnhdlyqNYQEInppqxzf/vACHKUNyQgYASNgBIyAETACRsAIGAEjYARmi8CqC9+BVvVp53Cl8r/IsW0TeVnNVq/sOEZgsRHoZcDnYpsw9V5UwVGf+UxFCKD6gk89jPBSTFmp0LgL242p58ZCTHoNnI+px+6+eT7AlfT/MkzFYz13d2KnsNlM7RxMX3TQZ6spTF842INN1dNS9YZzsKWYzmmsL3OMYNNNdv/XR26x3f/VVJZQoF7CMUzvH712p5r0PtTrVb9n1KdtPTbV659dLBkBI2AEjMDcIKCi8aqLyo4t3zomHs+NnlkvjIARMAJGwAgYASNgBIyAETACRmA+ElDR2P135ooLx8Tj+TgO67MRMAJGYGER6GA4JUy9p1UksmQEjIARmCkBjcqgnylq/zDTxmx/IzBfCQTna8et30bACBgBI2AEjIARMAKzR+ATIpEuWXOFTwL69rmGfsJ8vT4px/kXVdwnPuaqsLQYCZSdeVB8TGRSHuVaGC6LT9/qfqQsxZ1pKdzxdjlqb2cvxgvDxmwEjIARMAJGwAgYgeYReAFNq0erhkw2j/PmcbaWjcBiIaCfJxruX5OG/v57J2d/jMAiJGCi8SI86TZkI2AEjIARMAJGwAjUQ+DzsnJNWMKvQxC+lpdtCfXkq/EWP1vraczqLFgCj10BYzmuh2eMDTYgbeIvf1k2PISw/BPsG2+QA7cvWBA2MCNgBIyAETACRsAIGIHZIPBrHOSGyoE+OhsHtGMYASOw4Ak8hxHqFAia/g075uTsjxFYhARMNF6EJ92GbASMgBEwAkbACBiB8Qi8mvl7XirrXov891a8Rp/B0j9W16Th8ZhZ+UQEfHrhXMx1pPauL8v6PVxXX8pJ5tNvkeOzPU/bRB21bUbACBgBI2AEjIARMALzg4Ar7Pw+3f2v+dFl66URMAJzmID+m/WvKv0b8uTncJeta0ageQRMNG4eW2vZCBgBI2AEjIARMALzhsCHRMLnyobr8Sh+Hx7FG7XjJhMrBUuNJeDbxHX14bBE33eTrP9sVooffascOtrYY1hrRsAIGAEjYASMgBEwAguYwEcY29ew3Qt4jDY0I2AEZo8A787L5ZXD/R5L8zKePfZ2pDlIoOI5Mgd7Zl0yAkbACBgBI2AEjIARmBUCN8raaxGMH+Jgn3EF41k5sB1k0RLA67hFPY8jEtiNePxHXHihRQvDBm4EjIARMAJGwAgYASMwFQJ5KptgPBViVtcIGIHxCOi/Q/+6svG/WX5pvIpWbgQWCwETjRfLmbZxGgEjYASMgBEwAkagisC/yupuBLuv+SXwIzZtrtpsq0ag6QQq4vHftMn6B7kWn9L0A9oBjIARMAJGwAgYASNgBIyAETACRsAIjBF4BwsNeT+AvX2syP4agcVNwETjxX3+bfRGwAgYASNgBIzAIiXwRVl3ZVRC9yHavXaRIrBhzykCvvO4Fn/2ZdnwHrplkdHn1LmxzhgBI2AEjIARMAJGwAgYASNgBBYkgX9iVPrvzx7MwlIvyFNsg5oqARONp0rM6hsBI2AEjIARMAJGYJ4TuEnW/VZQ/LfxL6O183wo1v2FRSDIcD6Kx/G3PiMrWxbW0Gw0RsAIGAEjYASMgBEwAkbACBgBI2AEjIARmNsETDSe2+fHemcEjIARMAJGwAgYgYYS+LKs+3Of+D9FoyrQWTICc44AHscvbZPwzRo+fc51zjpkBIyAETACRsAIGAEjYASMgBEwAkbACBiBBUrAROMFemJtWEbACBgBI2AEjIARqCZA6F9CL/n/rLrc1o3A3CPguzImwdvwOO6de32zHhkBI2AEjIARMAJGwAgYASNgBIyAETACRmDhETAPkzlyTqPrn3Zb9tC955X9AWnb/EzxBcKS2PZd8UdapOfqt0kpNSR9//c5aX/KK2Tjb35AUo8+LLv+7jcl0t4jXVe8SE7+79dFSkWJtLRLuZiXfDbljMwXJLKfryjBWK9EVj5Bsid3ii8UltZ1l0vmxC7JHn9Eyqz7fAGJLt0sgXBcCoP7JTt4hGfKIl2XvIz9zpX8wDEZfeR2KecS0nb+M8QXjIk/2iql9IgkDz1EmzEpsl+wfYVIIEq/o1Kkz8XRE1LOjEigZYn4Yh3sn3LaKIwcFX/rUomu2MJxIlLKDDt9yPXvI5+QckGuyvY9sGuOnB7rhhEwAkbACBiBeU/gRln/IQbx2/N+IDaARUTAt7VVIt//kix75hvlRHIRDdyGagSMgBEwAkbACBgBI2AEjIARMAJGwAgYgVknYKLxrCOvfUC/+Lp9ft8SKaSkmOiTIIKqPxCQcjYpuZN7JNA65mTRe9ElMvyLm6V74wZpXb4O8TYqXZvPlb6flaVcLsvSq14gyaMHZOCB/5P4OU+RyLKNMnjPf0kxOyLpA3eIlEsS6Fkj/p4eKR3JSMuqLdKx5TIZ3bdDcsmEBBCpS3Qx3LGUvz5JH94mueGTjjhczqellE0g6o5KMN6KyEtzhYwj9vqDASmFWxCPEan9fv4PSZm+lfxcYgjRvgj1S9oyCYE50IqIjEDup44fgbxYjNFWVgKxuJRySfbN27U5Rsv+GgEjYASMgBGYMQEE47fzW+ODM27IGjACs0yAn5uXByT2zVeLvOgbIsVZPrwdzggYASNgBIyAETACRsAIGAEjYASMgBEwAouGgAlzc+RU+wIhvIsRWhFOmWfQ8Qb2IbyWEGWLuTQevAGnp6FYTLKjCQTYvATb4jKy60HJDw8g3GrdnKSO7BN/OIpwG5B8ckBaW57IfkEE2rFI5GXWQp3LpPuKqyR/bLesPu9iWX/l1XLwvtUymEghBsdl8O5bpX3JSgm2kN//iOQziLhIyT5UYj/CcObIDgl2DNLfsXYD4bAjbpdzCMiF/BjRYBjhmHEEI44HtArDJTygtV8+7Q/rSM5o2EUpBUqO4F1i7KUMwjRex7mcPRMcA2l/jYARMAJGwAjMjMBNsuZy5oj9xMxasb2NwNkk4Hvey2TDn39D9v3p2eyFHdsIGAEjYASMgBEwAkbACBgBI2AEjIARMAILmYCJxnPk7Bazo47HriMcl/NSzmdRdwkbXYoQvbkNsbaVcNCdMvDgXbLkhddLppCU9ImjEmrtlHBnN+GfW/EmTuK5i/iMJ6/P5x9rQwVoPJZRbzE8gFGNNdR0cXREgtE26V6+UmJBn3Sv20DNkPhjUcmfOCy9m87HQbhbyi3d0r/zbighGqu3M97MxWS/ZI5tl+iS9RLpWc9xUpLJDOGFPCLZof30tU0ihKcOxNrRqxGLCTetxw/Qr3KxgCEuU14uIxgTwjrQ2jMmHudzUkgSzhqhXAVlS0bACBgBI2AEjMDMCNwg6ztp4d8xfVvLkhGYxwTKf3yTbPjf62Tfj+bxIKzrRsAIGAEjYASMgBEwAkbACBgBI2AEjIARmLMETDSeI6cm0NIj+VO7EXgjaLvqnVtgiecx/csefVhy/fsdIXjglzdLNotwO9InhdFBCXcvl4FtDzAXMUIx3ruFACGi1TQEdEuXlJmrWJcaVpoGHAu2dCDUxkVa2yWTJBw2YnJ6eFiKeBH7I3pMP4Iy+5MPtXcRWppyRN5AGSEaQbeMR3MAYTjatRbRukOk0ML8xQjGxSL9Oim+tiLC8WYOF5SQ9gFxOj9wCAGZ0NVlvImLOfFxLMGzuJBm3mPmPBa8kAuIzpr8kQ4JIy7nnDX7YwSMgBEwAkbACEyXQEjk43gZb5ju/rafEZg7BJyJUb54k3RvuU4Gxn40zp3OWU+MgBEwAkbACBgBI2AEjIARMAJGwAgYASMw7wmYaDxHTmF05fmIw/cxl3GvRJZuQlSNIRznJbn/dgn3bkRADkj22MNOuS+Nh+7IMD0vO+JtbmgEr10fnrwtcuk1z8BDOSI/3X4Hu2fxCN7thK1WETmIR28xNYhoW5KR7dsQcvvkUDYnOcJVn3r4IcllUhJbsUHS/adk5PAhifSukOShR3FNRihmPuIyoaZ1rmI/Yak1zDRKMl7DJdorcnydrxiJW0NSC3MxE3baH4o6/dbQ1kUNTZ3rd8rVC5pKzF1MKGq8lH0677Hui1Dsj3Y4wnmhyPoAx7ZkBIyAETACRsAITIvAjbLmagTjN01rZ9vJCMxBAlzPy8vS/hf8SPy9Odg965IRMAJGwAgYASNw9gnoHG08sHLSfv72j2XtrxEwAlMkMJv30gr6trLSPw0/uX2KfbXq0ycwm+d5+r20PScjsJwKqyqVCF8r2ybbwbYbgYkIjE10O1EN2zYrBAKIsSqmqsexirEa0jnQxb2OWFtInMRLVwVYPH01vHPF01eF1pZ1F0lh5JREV54rK87dIiuW9sr5a1fKinPOl1D3Sum88ClOG6H4comfdw0ewHEJRuJS6u8TP9MGF2JtMlxAF8bDOTfYJ6N7HyFkdEKG+05J3x6dzzgn4Y6VjlisoalDXSsISb2G+ZR7JIdXcQGhOZccwuu5D07MeUw7KhIXhgmBnVYnkLIECVMd6lxJSGrmWlZxGEeRMt7G4ufRH+IySvKYKK37O+N0f9/PCno7iBEwAkbACBiBBUfg1Tws80nwnxfcwGxAi54A7x2+80bZ8IRFD8IAGAEjYASMgBEwArUI/IRCnWNN7WW1KljZgiOwlBFdOs9GNR/63Mh7abLx/jrnz71vvzlPzuVkY5onw5BGnuf5MuaF2M83Myj3Hvr2QhygjWl2CZhoPLu8xz8aQqqGoy4V8LwN4M2r4ZvVwxcBVoViH3MVl9VDt0QJ3rn6KFjFVz9exepRrOGjW+Md7JKRzOiwtHV0SubEfrbl2BZyQkCXC3j7Uj/UzvcaAnSwY5m0rN0k0fZWiS5dgdcyYa0RpIPMZRxatkZCy9cQ/noZHsxhJwx1uKOX4waccNWhrjWOKJw+tVdyfQekUEB5pn8+vJDV67hAyOnCyHEpI4AXc3naaCMkdjdi+Cj7JShLIX6zD0KxjxDawVgHY2klrDbhuTEJ29SL418stsUIGAEjYASMwMQEXiLrfpVfChdOXMu2GoH5SMDHr+Dyh+djz63PRsAIGAEjYASMgBEwAg0joM+034k9gl3dsFab29B87PNMiCzE8S7EMc3kHNu+RsAILEACFp56jpzU3NBhBNmg5AcPE0Z6qQSizDd8fJsTutkRU9ULmbl+S7kk3ryErkb09Qeiku0jjPTS9ex3QvqKGelZvZZ9AtJ/+CDzCXdKIBZHhG2jPmJvKIR4i7dvmdDRhJouEh5aRWj1+C3rErFXvZ1DnUslHO9SPVfKLTkpJpOI13nHGzigYi6RqB1xmDbLCNbqNVzmP18wiPCrx2KpojfjUcFahesyczSrF7FTt5B0tiEX40GtY+H45APMo1xMjzr91zmOLRkBI2AEjIARMALTIqCvcf3xtPa0nYzAPCDAL8iXfEnWXPhGOWRht+bB+bIuGgEjYASMgBEwAkagCQR+TJvPakK7zWxyPvZ5JjwW4ngX4phmco5tXyNgBBYgARON58hJzfUfcETZYnZ0TCxGTC2mTjleuNm+XZIbOsh2QtKrELukB7GW8M54JCcPPiytKzdJ+sQhSSLM/uAb/+GItoNHD0tk2UbJjAw6nsHs7Hgq+xBz84PHKGuX/NBxSexD+A1HOeYhBOkM3sYxyk9JvmuZIxpnju4nPPaAMy8yyq4U8BLmQR3zISNy45Ws4rV6Rut8xD4fIbbxRB4LpU2I7ZwKzkPix4tYvagLo6cQqhG9/So8ozzjHa1Cs+7riMl+DV/NpmyCP3knb3+MgBEwAkbACBiBqREgdO+L2WPr1Pay2kZgPhHQ1xyD76PHb5hPvba+GgEjYASMgBEwAkbACDSMwDkNa2n2GpqPfZ4JnYU43oU4ppmcY9t3bhDQecBvqnSFeU4tGYGZEQjObHfbu1EE1Js41LECrRQvXDx1NQUinYi0A4iz6s2rwmpB4luvlBXPe5kE/SXZe+xRCcV7CC29mpDScRnd94iMjGYcj95AJCa9Vz5dui+/ErH2uMTx+H3lG18iX/vnIzKUSEnLBRfL6P3DUk4lEXcLiLsjhJVWr+OA5Ef7JbH7HkRjH2Gtk4jEyMTq2YzIW0oNO2Gp88kB5kaOEeJa+4yYPXpCCkn6SV2/9p+Q0+ViUjLHdiAat0sZr+bCyFFHLPYFx0Jxl6mjwrcfoTqAiO2E2sYD2k+7eeY6zh3XzztLRsAIGAEjYASMwFQIEL/jLc6bX1PZyeoagXlGgOv8lTdJ9zuvk4GRedZ16+7CIsDbs3I+thlbj23AePtWeipG+CVh7h3hrVn9R5WleUlg+FhUOlaOdT09+CYyr5yX47BOKwF9O50HGI4Nsuyr2AGW+7E9mEaxGMIsGQEjYASMgBEwAkZgPhD4Dp1Us2QEGkLAROOGYGxAIzo/caQNYZbnCiq44mnrJ5S0L8OzBRVX8afQsNCd55wvxVOHJNwSk5Y16xBueyR19FFpX7+FUNX9Egzh4Ut3wr3rZMX550ry0AFZfsklMvCLu2RFb7u0trXKUDIjkeXLJd3WiZiblXBXp+TjnSJpBGcVcAkNXcoQupoUwAvZh1js9IU+FPFGVpFYn3gEO1dJpGslXshZyTHvsbbFDo6XcJkQ2KjGjuicT5zCm5h/myFKOyGqhVDZeFKHWjsJmc3xCKOt4avV47hl7cWOeFyUgiTu+4bTB/tjBIyAETACRsAI1Efgi7IKocL3wvpqWy0jMJ8J+GI+ib9aZODf5vMorO/zjoCKw1dhT8Mux87DVDi2tJAJ6LROif6xEeaSzK8kapYWNoFDDO9e7OcVu5slodYsGQEjYASMgBEwAkbACBiBhU3AROO5cn5x8tXQzRqC2q9hmssIxYSURoXl2S+euVgZ3TifTkm8vZtQ0yrI5hCNEWIJOV1y5hUuIbgi2qK/OqKuzj8cJix1Ki+JvmOSz2QlFG1DECYUdTaDYMu8xokR8RWZ4xjvXp1/WAViMo5Ircf0I+76aMcfjtAHxGyEXx/11CNZPYN1v7KvTD/aJB9B8Naw0wjEOsdxqcASQdkpcziPeTKPzXXMuAi1re3ovMo04eQDkVa0Y/ZhDJaMgBEwAkbACBiBqRHwS+hV/AYwAWNq2Kz2PCXAr+DX0XUTjefp+Zsn3VYv4ediL8JegK3FvEn/FWNpoRPIpZALMUuLhYDe12sq9tLKoJlDS27BfoB9GzuBWTp7BHiQJMuxY5g+OGtU0kdpKzA9v8VGNUo7+qLJUuwkNuahQWaCpNEqdIxaf6apWaz03xt6DrSPPMSbUVrC3hoF4Gx4+Hdx3AxWz3mh2umkY1e2GrFgNr8gGn2NNuv6OA2qgRm9h/RpMSE5G5LO1jmst/ON7N98OM+NvraV81Tv72Z8FjXyPE5nTPVcb41m3wyOk41Dv2f1uPq7QL9PZpoafd60P4383pzp+CbdHxnS0lwgkBs5LIWhsXmFy3l+q+B5W8zx7xK9bTVsNAKyirSDD96JQ3BeRvqGJHVwjyQP7JRQe48kDu0nPHRC0qMpyaTzkmXe4sP3PSiCB/Hxu++U+Ir1kkVQji1dSbhovIpVII61IRxHpVhEWNZDcAz1Ina8fsmrQFzC47mEB3GRtgvMT1zMjIofQTjc2i2+EnMWZ0acuZDz6WFHLFZxWYXkAO0GmN84EIlLbO0Tpe3cp0t05YWI0BHKEaARpsuIyqLhuBmrhsDW2enK5QKiOfeQCt6WjIARMAJGwAgYgSkR8Ivv2intYJWNwDwmwM/kq26Q9bxtackINJSA/gtMP0u/iOnDcA319nasWjCmyPnXmi4tGQEjsHAI6GdAdWqjQAXkf8GOYj/Bfh2LY5Zmh4Cyfg/2MKYC32FMQwB8D3svpi/5TCddxk7/hN2GqWh5BNOXBO7CPoc9E5ss6Uts363Yr1Yq6zXzfmwPpqLi/spyH8s/wVoxb9rMymcw7YOGTVfhWgVJ7dc12FTS1VT+d+whTI+trEaxO7F/xer594J3TNexjyZ9KK/n4AFMheKDmLZ7P/Z5TFnWk1TIUQbqzc/DROe7Vseq99bN2CewZ2C10tModFmriOgmvR/d8o+5hZXl9Z5tr66U6fJnmLJOYsrqrRjeLY9LPZTouPV3wS8xPUcqTChX3fc4pryfh9VK0+mztx3l+k/YTK5Rb3vNupfcY8x0vG47+ln8Duw/MWV9AtN7Xpn/B/YcrN50ts/hZP2caf9qtT/T86yfE+49pUsV0epJ+kD/W5i7r754OV5qxLV9PY27x9L7WpMu67m/Z/JZ5Byo6k+jzuP1tDvVMb3Gs49+d02WGsHePUajObrtTrbU78YvY/oZrN91BzD9fbAX+yq2FasnNeq8NfN7s55xNLSOfgBbmgMEWtZfvS178uGt5QIhokMtvHsQFUc8RqCN9GxCGF4umeM7EGjTElqy0RF0C0NHJdjaISueeq2cvPeXkh0dlK2/8lqJL18pd3/tyxLtWsocyE+R4ftulQDC8qpnvURO3nGrxFpa5OKXvlQOP7RDDm3bJtFVGyR3AtF6ZIBjR9FwtQ8c35lHOY8HsB9ht0h/MijL/KuoZzlRqMN4BOclM9IvWeZDLmUTeAuXCJvd6XgplxC+C5RFVm6VsPYX8RlfacawS7LHd9NmyPFCLtFuQOc1RtDWUNXBWAfhspknWUpbj//gbx6e0qlZeeF2lOcLnH2Kpa1yfPvU9p/SwayyETACRsAIGIE5R8B3k6w/xWtY+qPXkhFYFATKUnzOdXJQvb8sGYGZEuilgd/A3oZtmGljtr8RMAILlgBPRU6/MKLihYpFn8Tuwyw1h4CKoN/Guido/udseynGAyfHu0yr6uf5v2mmRlLPOxUuP4AFa2x3i/R8qwipdVUorZW0jQ9XNmher4f/wa6olNVaqCj9fEy9Jl+PfQarFpIpcpL24aPY+8ZWx/27plLvtePWeGzDZ8m+Bxt9rOiMnHdMf8GWr2Eq1J1/Rq0zV3Ksapv/dGbxGWsvYO3rWNsZpbVX/ptifQiv95mbXkXmG+7KOMu7Kb/cs+0j5F12eh73YV/Bqp+JD1K2AvOe53exruOvp79Uk0b1Wdtq5DWq7Wlqxr001vJjf6dzjrzX2yM09VTsRuyFjzVbM/cpSn8XK9XcOlZ4Ns/hBN06vakR/TvdWCXTiPPcS1v6Ikeo0ubvs/x4JT/R4uVsVKFfk56XtdgRXfGkRl7b072/Z/pZ5BmOk23keZzOmP6YXvx1pVN7WJ5TyVcvGsle2240x+r+1lrvoVC/k55Ta6OnTK+/G7B3YAVPuTfbyPP2ARp2fwv8BXntYyO+N739nbX8RD+MZq0TdiB++T75pTJ0b1BSB+4i/DOeuCEN05yRYLgDT93LkFtLEsODN7n7dkdAxg1ZCgOHmZt4rVzxwufID3c9LLFYTC698gppYb7jUxdeLKn4crTnVolvvkRSfSd5J2ipFBB2L7rkYrlkY4/0lDdJ/8HD4A86Xr4B5jMOtrYjFrc6cxsXCWGt4q/+jtJlMZ2kvai09iyTcCQq2UxKColhSRGqSwXmMJ7L4VgronNWSn7CXSM2hzv1RST9fas6cUTCHcsle3I/a3gy42FcSg9KgbaJhE3bhN2ODUkhjRWKuoslI2AEjIARMAJGoE4CX5I1W00wrhOWVVtABPxPZzC3LKAB2VBmn8AmDqkPud+ExWb/8HZEI2AE5hkBr9CkIt9bKvYTlirsqVhoqXEE9IG0ChBRT5Mq7v0Ci2AqzKqg9zTsDsxbj9WaSb2ivodd6dmqQuF2bCe2AbsE07b0fL8b04fT12I8XJsw6UOw/8UuqtTSB2Labju2tlKmi8uxj2Dfxb6E4a3hpOP8VdP99eG+Ju3De7FvYb/AaqVlFN6LqdDjJn2wpsdWEe487ALMbVNfknoupt6xu7CJ0go26nW9plJJ21VxXts6t1KmizCmXsIl7FNYdbqUAhV89b7RpO38ElNBKYOtw56M6XnV9GLsC9irMTfpQ0qtr0lZu+MZJp/QQtKpsUXNv7rPH2LKtDp9jQK9Dtz0QTIfclcqywdZHsRGMOWxBVuKuUn7/EfYn7kFLKfT52Zco824lzzDPJ2dznhP70xGr+F7sPWYm5S5nle9jt3rR7e9E9uH/YOu1Ehn8xzW6M7jihrVP2/DjTrPfTSqL0G8otK4RhyoRzR+Q6W+Ln6MuferW9yMa9ttu977uxGfRe4xddmM8+i2X++Y3PoTLRvNvtEcJ+q7u20DmZ9i69wClnlMv+vU9PtKv0f0c0K/V9+K6fbfxKpTM89bo743q/s8a+vuj5JZO6AdqDaBQOcKadn4VAl3rxvz6OXni/6C0RDSKuYWR44hFi9BfmUu41F+ozK/sM5JPLpvt/Tv3Yu3rl9au7rEl8/J8LHj0r10iQTa2qUwfFLC7V1SHDiKJzGhpRGeu7riEiiWpSWCdy8t+srMacxcxn6O5WdOYT8hpX14/gZYD7Z1sWyVQJSyIPMYc9wgcxEH8TTWY2rS+Yid+Y8JOa3zHus8xU6oabYVM/qbjQqaUIYLKX5XEYbaCUeNgO14MCMeo5A7cxmXmF+5nMvwM01fULRkBIyAETACRsAI1EvAJ0F9EGTJCCwqAvym3LqoBmyDbSQBfdjwr9hO7B1YFLNkBIyAEZgOAX3o8SzsB5gKl9dilmZOIE4Tn8fcz2d9UPR2rAd7IfZsrBNTEVCFynOxGDZZ+jAVrvRU+ir5VZg+AH89pl6OKvKq11blgZZcTP6vsMnSb1HhImwU04fVKyvr+p2j7R/A3PQWMl/B9OHaLdg5mD5ovgTTMX4Qc5M+ItT+jJc+ywavYHwD68rmCdhrMO1/N3YT5qb1ZP7FXZlgqeNQgVSFUvXK0ofx52ObMS3/W8ybfpcV7W91+gQFuq8mvU82YU/DtH9vxK7BlMGPMTe9nIwycdP3yKyumFeM+pCnXK+N8ZJ+3yuXFPZ1TMf2SWwf9kXMTReSeb+7wvIuTM+r8nwxptfJ0zE9v7+B6YsMbtJrwB2nlk2nzx9mv0Zeo826l3R81Wk64/W2odf+eoyHxc69fR7LddhlWAf2XiyLuemDZELuimd5ts+hpys1s43sn3uARp/nG9yGWernl56LiZJ+xrzIU8G7v1vc6GvbbVeX9d7fjfgsco/bjPPotj2VMXn3GS/faPaN5Dhen6vL9btOPw/cpN/f+t2n35vXYc/G9LfAdzE36XXhfZlBy5t93hr1vemOYdaXY6rfrB/WDlhNIBhi/t8o3rmRNmeTiq6otAjIWfRhxNoWhN/0KL+6KNf5iBF/HaGWfVp7lzBX8SpJDI/gBdwtLStXyclDh6WcSkq4d5VkB07xs7ldgnggB1ricoL5kDMIuEdPDkqe+YrLIb5bEYJLOeYoJvx1gfmJS4SiLhUr38GIwGXqqwd0mXwaD+N0chRP4wwewTlnLmSdD7mIx3GOPha1HcTrIt7Q2WO7JNd/iDmO6bPjebxMoks3ii+KRzMj9YdbEKxb2BbmJ77O4zzC8QcllxuoRmTrRsAIGAEjYASMwMQEtky82bYagYVHgN+Tdt0vvNPa7BG1c4C/w3Zh+g/6AKbJN7awv0bACBiBKRNwPz/0McdTsB9hKnxtxSxNn8Cfsutyz+4qBuoDY+XspiKZj2KvdAsmWeo5UVHPTX9D5tewfregssyz1OPrNje9hcyT3JUJlto/FUE/jx331LuX/Ls96/r904rpywbXYnswNw2T+XPsRreA5XjHfhPbXuKpp8Ku9jXhKdPsCKYPzv9IVyrpmSxf766Ms9TrWwW8q7D/h3kFu8Osa3v/irlJxeRr3JXKsoXl5Z4y5XrAs+5mtT09l8cqBcpIhYBGJR4+OtePXkuvxfQc/Q62Efsl5iZlGKys9LF8Mbatsu5d6PX3Ocx7XrtZf6630hTzzbhG9Vpu9L00xWFNufrb2EPv7Uc8eyrvv8f+wVMWJ/80z7qbPZvn0O3DRMtm9K/R51k/m7yfYa+faEBs0xdA9B7TpC9SfNvJPfanGdf2Y62PHVs/fye6vxv9WdSM8zjVMXnrj5dvNPtGcxyv397y17HyHE/BX5LX7xL9bvMm/f54NXafp/Dtnrxmm33eGvG9WdXl2V31z+7h7GjjEcj1H5ds30HJDRxCoPXziiReu4GwlDIjkjp0P963WckcfMApE0I3l/HYVaE1Qjjpvbv2U1SUxKlT8tMf/5/c9eAuObZnjwxtv1fSRw9Lcv9ORNuIJI+cZO7hjGz75T3yo+/9Qu77+Z0SbY3Lps3rpGflMgTcEMKyehXHJNwWl0gc72Iu8VKhwHH9EmjtFH9Hr5QRnguIvaVom/jaep1tKMeOV3Eu2S85+lxUb+JwXEqIySUVkPEc1qUPwVtDVvsZm+ONXCoxDn6HIShr3WKafZN8ryT6x0Nl5UbACBgBI2AEjEANAnxl6wMaS0ZgkREon7PIBmzDnRkBfVi+C1MvFdcrRf9Rb8kIGAEj0AgC7ueJPrTWB5sPYJ/AVNSwNDUCyvLNnl1UvLjFs16dVXFiou1u/Y+TcV8WepD8h9wN4yy/Rvn3Ktv0GaruP1lSUUv7UytpedqzQcXYd2N6zdRKN3kK1cuy27Ou2SCmQq6b9CG5XnMTJRXiHvJUUAFuMg/tT1Xt49ndyapY5U0XelfIq2Dhfu/qWL0Mqqo6Xtp6vO9gH8N2V1eY4fon2f9/J2hDr73ne7Z/gfwJz3qt7H9UFa6oWp/KaqOv0WbdS1MZ01TrfoQdvjjBTnoNe9MF3hXyZ/scVnXncavN6F8zznORnn/J03sVjfU44yV9WcZNXyXjfcFEyxt9bbvH8i4nu78b+VnUjPPoHYubn2xMbr2Jlo1m30iOE/Xbu+1NnhV9meGvPevV2QwFf+4pfCr59ZX12TpvM/3e9HR/9rP648LSHCDQ//OvSH7kuOPh233J82TFZdfIvh/cKBk8dQuJAURWvHz5z4dHcGzNZgkh6uaHjyK8lmVw/34pM9+wPxyVttUrpXvNWjm4fCWhpXslumyVFJLD4o+GZcslm2T77gdldGRIjuzeQ/mIXHb1lXLh+avkUX765vipGuhh3uNMWiJl9frNSFLDTWfyiLocOxhkTuJeCcX5LYmQXOrrp91WRN8oXxlZR7hG9ZVQLIT3MnVUccYzWcNoaxhqFYdLeWZnJl/GcDHGQiyilCfFh4Cs+5T1mEX3N/wcODnWBSNgBIyAETAC84JAuWfif8PNi0FYJ43AlAgQCyf6JVnW+kY5kZzSjlZ5sRFYz4A/g3k9f/SBgSUjYASMQDMIuJ8vKjL+DvYy7Lew72KW6iNwGdWWeqqqiDRZ+isqPHuCShpp4jme7Z8ln/Osj5dVL9oXVTZexVI9rDS88XjpK+NtoJwHX3IU21Sps4Plzkq+1uJwVeFa1gc8ZdqOisluej+Z8QRot472QUVeFWU1LcNUALhbV8ZJ/zZOuVt8kowKq9qWps6xxem/28jpQ3weIDqi0z+y/A1svN9vei6blf5rkoaV3zpsDaZ8H8YmSwkqqPcL/x5zEg9Cp5WacY02416a1uCmsNMXJ6k7xHa95tzPiOqXKc7mOZyk687mZvSvWef5Bnr8h5VBbWCp4tvPK+vexTmsXOkp0P28qRnXtrd9Nz/Z/d3Iz6JmnEd3HN7lZGPy1q2Vbwb7RnKs1efqMn357pmewo+Sn+jlI636PewvMH3x6BHMfflnts7bTL836fLZS/oD1tJcIaBz/CK29lx4hTz5Jc+Xlh4ihyAWh7s3OIJxuHejRHpWSGzJCmc+4eiy9dKxYYvkRoekbd0GiS9fIUu726XUf1JWbTlPIsvXMKdxv8RWrJXsIL9d8gW8iBGfixyH2yMUiUhvT6fEwwGJR0MS0jDVePz6mbs4wHzHoUjUEaf9LS2EtW53Qlv7Q4TMLiBfMwdxmRDWhcQQAjBqswq9GsIaU1FYPY99hNcOt+PBjHdxkbp6zAAhsst4Rfsi/MZGNA6xPdS1Wlo3PEVaN10pkWXnIUrzG7ObsVsyAkbACBgBI2AE6ibAt7D+kLZkBBYdgaLE7NpfdGd9SgN+E7UfxFQw1ocEloyAETACs0XAFY9VfPpv7HNY62wdfJ4f5xpP//Wz+5ee9fGyt7JhIhH43Kod76haH2/1Hs8GPacqJI6X9Pj6nTNR4gHd6bT3dK52plpUxfvijHS+Z0053eJZnyhbXW/LRJXZdmCS7bp52FMn7MlrVh/u/8xT9mvkVYz9O0yFgOr6FDUtqYAwWeLBpuzHlNMxbKK0ko2vx1QQd1P1eXLLJ1s24xptxr002Thmsl2vlZ11NOB9ocL74oS769k6h+7xJ1s2un/NOs96LryfldeNMzBvuQqK1S+hNOPartWVye7vRn8WNfo8TmdMtfbxljWDfaM5evtbK/9kCr3fE95rslZ9LctjH8BuxO7EtM9umo3zNtPvTbevZ2U53S+xs9LZBX1QH+GaEYglNyT9990m2Re+AGFYhVhOEfMXF5mXONSxCgQZR5gtZEbH5kAOs19ilMcfKtj6JYoonMlyTyDq+hFlyxHmI2beYfUM9of8EiIctQrDVCYctMhoMi2JbFEGEiknvLWMJqifF197nCbLzC/MnMfs73gHExbbh+BcjkWlMHhKMn3H2D6EBzEic5j5mHVeZD2WStwIw61rzpEAcykXUqN4NTOPct8R8fPzupglOoWGpWYfDbEdaO3Cg3mZ431cZhwMjE+B/IS/9Bf0tWCDMwJGwAgYASMwDQJ8+8b5NTCNPW0XIzDfCRRUND4+30dh/W84Ab0uPoe91tOyfUh6YFjWCBiBWSWggt7bMH2w/xrsfszS+ARUiHNTHxkeJE2alPFRbP04NasfnH+cenhBTDlpOw+Ns5cKp3hq1J0OTlJzsra8orH+FqqHkx4yhanX1TJdIZ03tqj5V706R2puObPQe+xaTkofpPqlmOsVupb8eyuWYHkL9gPs+9ghrBkJjxbxio1TOYb2dzOm51+XWzDltgGrTtP9vdGMa7QZ91L1eBu5PlBnYyoIuanW9eZu8y5n4xx6jzfV/Ez618zzfAMDubIymFez/F3My183eUVjrV+dmnFtVx+j3vu72Z9FMzmP0x1T9X7e9WaxbzZH7xhWeFfIT/bdWVW9rtVGnrdGfW/W1fFmVDLRuBlUp9GmTwViRFcVXP3kwy0RiSxZpVowQi0ewgirpazO99vvhHsOxDolefBRyhISX7NRUieOOaJv33BSSrEOObxzlwTauiW2cpMkDjwqfuY+7j8+LPmSz/H8VU/hYi4nux49KEmcM44dOCrZxLD4MhlCRSMUZ3n5AjG4hFBcUnVZ5xvOpSSXT9PmRif8tCP6InQH4sxrjEBdpC8+BOdyjt96JYTnEOJyLo1+nUewjkghm5QMobZ9Os9xdhTdGkGbEavY7UPIdsJvI2YHCHldVO9lS0bACBgBI2AEjMBUCISmUtnqGoGFQoDJTuzaXygns3Hj0Ie438J06fyTo3FNW0tGwAgYgWkRcEUkfXh7O/YO7EvTamlx7LTUM8ypiHwqNq737OvNVj84v9q7cQr56na8u9YjrnrrVwsv3m315L2i8f56dvDU2UfeFY1VAB0vaRjgRiT1DFPhSYXhjVUNtrH+0orp97beI5/BbsQamfbTWGkKDa6h7ruwN2K9U9hvulWrr61GXKPNuJemO7569tMXCBqZZvscTrXvjepfM8/zvzMofcmGsKFOCPYXsPwO5qanktlUWdEH+je5GzzLZlzbnuad7H7+1nN/N+OzqFHncbpjqt7Pu94s9s3g6O23N++9vvV785h34wzyzTpvjfrenMHQZrZrvW/izOwotvekBPDhdbx/Ncyzr7VXDu47ykdxNwIrrwme2IFYPCTZgQOEoh6R0QM7HDE5nUhI+uQxyfSfkuTx4+i6Jbnna9+UB772dcmNDDpWYP5inbu4jEB8cud+SR46KAHEXMczmPDTqUJJhhCG03j/lor6u4xPVwTl9GCfZIYHEHyz1A0RrjqCuOuTIIIuLtA4QBO+unMp3WUborMKvX5EZj/zHquAXC5kHFHacWdWOZhw1TqPsQrG5XKRsRKyGm9p/I55zBdFn8ZjmjU/7TreyvTXkhEwAkbACBgBI2AEjIARMAJGYIoE9EGWhiBTwViTK9SMrdlfI2AEjMDZJxCjC1/EPobZc7na5yPsKVbvsXqTN/Rz9T491QXTXF8ywX6z7QGhYqub8P6YUuLh3Ok00Xfl2MPC01VnlNnF3k/A3oz9BKvFS/vyNExfqtD7JIo1Kk2F0Xs46F7s3VgtwVjFKR3Pp7EXYhNde2yuKzXjGm3GvVTXYOZApbNxDqcy7Eb2r5nnWV+G+aZnYNd58pp9o2f9++RrCWbNuLY9h3WyU7m/G/lZ1MjzOJMxVe/rrjeTfSM5uv2ttQx5ClU01s/fmaZmnrdGfm/OdJzT2t88jaeFrfE7XfKqt8mjP/mm9D/0U0kNHJftd22XYcJU+/j95Gee4/DSDVLYe5L5frvkmte8SjpXLJefpAZl5NQxifYskZaVa+XEPXchzPqknMki6sYkGGmVltUbJHV0j+MpHI61OKIuKjChoRF7i/w7BY/eIDGjQ4Sc9ofxGo5GKVLvYJ6uIPA6XsAIwWXCW+u/ZUqEndbQ0up5rN7KPryjx35Zci8geKuHcbmI4V2cOb5Twr3rnfr5kZNSHDkhQfVKRmjWcNWBUpx8hF3Skh0+TvtlyY/2SW5gnxTUu9mSETACRsAIGAEjYASMgBEwAkagfgLqufdJjH+YWDICRsAIzGkC+tBDPRjXY6/HUpilxwiceCwraz35ybLVISy99fd6VjR0ZDc23x8+PeoZ0zpPvp6st/6penZoUJ0E7XyhYu0sn4U9D3s+th7zpjeyoh5m+kLYbKbrOdhHqw54L+u3Yw9WbBvLJOamRrwA0oxrtBn3kjvmuby8ns6djXNYL5NG96/Z5/kGBvaGyuBezFLvXRWTVax+DeYmrVcrNeParnWcqZQ14rPoeg44l68z5dFs9o3gqP2cKCFunU4t5LqwwdMlU89czy5z/bxNfVQN3MNE4wbCnElTV77gGslnM9K385fiR+xt27JFEve0ON7DuO4y5+9ySZT9snztOlna0SoH739ANm45R3aUC5IZ6JfOcy7AGziKWIywq/MFh5gVmFDQOn+wzjlcyqTw/B3Ll/Eo1jkPkWiZgYDw02V6jpCscx3jEIzIiwyMV3AJYVgIL61ewqoil3IZyap3cDCE2Eub6VEJhtlPRWTCT5fDLYSVRlQmabjp/Mn9zH181BGJVfgOda+VMCGzy8Ws5If7nJDbKloXk6NS1PDcHCM/fMzpq+TG2nEasz9GwAgYASNgBIyAETACRsAIGIGJCXyQzR+auIptNQJGwAjMGQL6/r0+gXkZ9mNMPRV1PlxLYwS8oSdVCFYvI/UumiytnaCCekS5qZPMemyfWzBPl7s9/daxq3DJQ7pJk/Jc6alVyzPQs7lpWRWdvl0xPch5mApTv4PFMU3XYjMVCJyG6vyj18bnPHVPkfMa5dQAAEAASURBVH8VdpunrDqr93OHp1DXp5OacY02416azthmc5+zeQ7rGWcz+tfs83wrA9PPyw1YFHspdiOm3116f2rSz5HvObnH/2nGtf34o0y/ZDqfRc04j9Mfwfh7zib76XAcv+ePbfG+IKWla7B6RGP9XkTMOqPufDlvdPvspUa8BXX2er+AjhxAmI20tDmevZlDDyKeco8Fxk5PgXmMy/kM2xBj+RPBG7iIKFvQkNL5vETa29Fe1bsXobUyP7Dg7euPxPBSDuJBHOPnNfuoGBxknuF0QgoqIqcQhIMBDhMkZDXexMUcIaMTtJl1RGMVccu0r4JzIByV2LI1El26hjyhqvEWjvYuk3BnrwRjbUSsRjz28btUQ1eT13/5lAhDXUBk1jDUOiezhrRGq6aOhrRuQSSmvxoSG+FbJWxiaDtezurFbMkIGAEjYASMgBEwAkbACBgBI1AnAX1T/EOY/jPEkhEwAkZgvhDQJyT6ufVU7CdYo0JI0tS8T3d5RqAPxy7wrI+XVZFx+XgbKfc+ONdqT5qgrndThBX1glVBk4dZcyp5RWMVgs+vs3cXUc/7TLiaTZ3NTKnaMmpfg03EfSfb/xR7BeYmjR7yHHdlFpZXcwyvk9VbWL9tkuOqMOHl6c1PsusZm6vPw0SsvDtOdI02417yHnsu5s/mOayHRzP61+zzrN9VX/AMzr1HvV7GX2Z73lPHm23Gte1tfyr5Rn0WNeM8TmUc9dZtFvtGcaxnHHuqKp1btT7e6g/YMIANYe+qVJov563S3bOzmO6X2Nnp7QI+alAV4fQwomkOcTWKthuTQOcyRGLmCQ4RVhrhN9TaLacOHpC+wRGJrlorj9x7r6SOHeGfGD7p37XDmYtY5z4WBOQSArD+XskNDjiexLF4XLaes1zibXgEI8oG8Az2IRQvWdora5a0SG9PhwQ5VjGNkIy3sDoXqz+yhqz2R1sk0MG/XRCD/S1xCfdoiOkgYnELWnSrKtnUR2BWL2NMh1LWMNWO17Fu03mMKUOELiJIl4slp6+lzDDez8y3zEuQms/17cfruN/pe04Qri0ZASNgBIyAETACRsAIGAEjYAQmJvB3bH4Ppg+zVICxZASMgBGYTwTczy0Vh27G1APGkshPgaAPet30R25mguU72ebyrFXtAIX64NhNf0BmovpuvbeR0QfPOzANR6wPnOdKepiOeEWa99fZMW89fQD33Tr3m261r7HjcexW7O+xyZLeCylPpc2evJvlIeTpVM95PF15koz3/Baoe8sk9XXzy6vqeEVn76bJ+tyMa7QZ95J3TBPlJxvvRPvOZNvZPIf19LsZ/ZuN8/xFBqe/tzU9D+vGfkVXKukGN1Nj2Yxru8ZhJi1q5GdRM87jpAOYRoVmsG8kx3qGNEIlHYebftfNTLBcxTb3hTONBHFPpe58OW8TDK35m0w0bj7juo4w0DfIPL4+Zx5iX6iVeYiPIK7yMh2hpVVUzZzchwhbkEJiWH741W/IfbffKaN9/XjxZmXg0d2SHxp2xNr4+i3SfdGleAgXpG31JoktXy2tzGscX7JUnv3ULbK0C69kehRoaZUwHsLLlvfK0nhUuuItEm6NPvZz2Ydk7HgN65zFXCaEplZPYh+idjGVJEtfOHY+NYrHc0pyQyelMNJHyOoEwjCCNSqxH89iH8dSL+lC3172G2K+4iOSPvKQ5E7tc+YvzjOXcX7ohOQIS11IDzFmROU8v8FzGg7fkhEwAkbACBgBI2AEjIARMAJGYFwCH2DLezF9gKX/9LBkBIyAEZjPBJ5I51WcbJvPg2hQ31Ws+3dPW+rNdrFnvTrbToG+QDRRKrLxg54KV5J/q2e9VraXQq9gvY31n9WqeJbKTnLcj3uO/Vryl3rWa2WfQqFX5Pwh6/pAvpnp+57G9YG9+yDfU3xGdhNrLZ6Sak853eQVld1Q1p5dpp0d9eyp4u9kL3JcS52PePbRrHr+1kqT9bkZ12gz7qVaY6tVNtl4a+3TiLKzeQ7r6X8z+jcb51lFu1sqAySsqXwMc+89FeQeqmyrtWjGtV3rOJOVNfKzqBnncbL+T2d7M9g3kmO9Y/qQp+LTyV/jWa+V/ZCnUL8rf15Zny/nzdP92c+aaDz7zGse8YdfvEl238b9VspKeOVGCfb2SNs55+B1HJHYigsktnKTtG54AiGnI9J+4RMISd0qnefzG4sw1TpnsY85g32Epi7m0xKKt+N87JPo8uWOmBvu6pHBw4dk+NQwcxAjQiMoa+hp3VfnP87h+ZtMZTi0egFTjmdwkHDUIZ0jmd5qm/mhUxJMjYg/hTf0MAI34nBuZEByiMbFTNIJa10uZKirL10WJRCNOx7TGsZan+BkR45L5vADkjnyoGRO7GZ/Qm6XEKIRn3EzdkRmBwxjCETaJRy2fyM5POyPETACRsAIGAEjYASMgBEwArUI/DqFH8ZMMK5Fx8qMgBGYjwT080wFva9jeBEs+vRnEOirUNDnlzdjtR4Sr6f8VqwHmyx9igpeYeOTrKvYXOv56FLKVcRfjblJRZK5lv6CDh2rdEof4ymnl1XWqxevpEBFYjelyLzPXWni8r9oWz2aNakQ+y1sg67USOoR9iVPeT95r0Dhbkq4GZavwHS/RqT7qhr5y6p17+rVrHwbqxaJXSHNW1fz9fS5GddoM+6l6rHVWq9nvLX2m2nZ2T6Hk/W/Wf2bjfN8g2dwb/TkveWe4jOyzbi2zzhAHSuN/Cxq1nmsYxhTrtJo9o3kWO9gvkjFuz2V/5P8Cz3rbla/B38He5tbwPJjmPpRappP522sx2fhr35RW5oDBAaOH0egJSR12xLpver5UsIrt/vyJ8vQHf8jras2S+Lwduk490mSPfkognGbDD90rxMmWn9rlUt4BeMFrP+6yI8O8469r+LhW0JA7pQsIapDLYSSxpu4tbuXXUKIxWWiWOfkxLFTElu6RIaHRiRDaGr1EI62tUnPitXOfMijg0MyfPIYXsithLZuFV84LFlE5hE8m/WIJQ05XQlFXWIeZUeMxsNYQ1tryGq3L7gtI26H0YhTlJcIe808yOFWBGzmZa6UBVt7JBBfijDezlTHObySH50DZ8a6YASMgBEwAkbACBgBI2AEjMAcI6Ah8f650id9MGDJCBgBI7AQCOjnmT7aeQGmD3jfgS3mpGLhb2EaBlNF3SWYCqIq0qnHkApSrteshknVedp0nxXYeKnIht/GtJ0QpmLfR7HXYT/FHsDUg+6J2JuwFsxNnyXzeXdlDi3Va+rd2FcwvYY6sW9hP8J+gT2CnYcpq2sxb1IWO70FTcrzsNI5l/9WaV/DTWu/vorpA/wjmIr+W7DrMR2Dm/6ETNJd8Sy3k39yZf0JLPXcH8OUxwXYdNMt7Khtb6008BaW2t/PYHswfZa+HlOh+lcwXecBqBzENmKaVo8tHve3nj434xptxr30uMHVKKhnvDV2m3HR2T6Hkw2gWf2bjfOsny16P3d4Bpklr/fyZKkZ1/Zkx6ze3sjPomadx+o+N2K90ewbybHe8envo3dht2L6uavf+9+t2J0sH8U0SsWLMfe7gax8B/tbzVTSfDpvbp9nfWmi8awjH+eACLjBzqUSKqyTWHenDD+8jRDQG8ZCPIf5HYtXcBGBtpBJI+ZGJNy1BO/fvjFPXUTickF/n5SZ97gTUZaA0nj6Dj9yPx7KGyVxaI+ECUf9k9t3yNFHD0iU+Y271q+SkYOHJcy+3RG/pFtb5DhzF2cQhNs7e2TNOesQcNvl+JFTksSzONbeKfGuLm5JROpsXnzDzEVMf1Q0pmMI18xhjKex88+bAi9uID77dCJj9Rxu7ZJg+wp0Y8bBfMuFpPa7iFdyVnx4Tqt3teR0/Ksdj2m/30eT4XFAWbERMAJGwAgYASNgBIyAETACi5jAuYz965j9W3YRXwQ2dCOwgAm4wvHbGeM27JMLeKz1DO0bVNIHXl/Boph+9r+qYixOJ7wgRL1oVQRdcbq0duY2iq/AvoCp2KjpSRVzVmr8uYmy36xRPleKVFg/iamova7SqeeyVKuVBim8HtOH6bOVtG9bsD+sHJCHhPLGilWKzljwcNEJJ65ifa30aQpfg7VVNgZYrsZUWGjFkth0kl5Lr8PuwLQdTVdVzFmp+nOAda2vwryOUdO1mL58oG15U719bsY12ox7yTu2Wvl6x1tr35mUzYVzOFH/m9m/Zp9n7ftXsXd4BqifIwOe9Ymyzbi2JzperW2N+ixq5nms1e+ZljWafaM4TmVcP6fylZh+J+v3if5mUpFYrVa6lcI3Yvq94Kb5dt7cfs/q0j+rR7ODjUvAH47wMxghNdEvmVOnJLr5Qhk92Y+37WFJHtkloa71MrrnQbYPSv9dtzOXcFqGd2xDmyUUdGeX+BCFBe/hfDKBV/JB7hjaynIPcEuUc4SXxot4z74T0nf4iARjLbIMUbhzxVJZzZzGqztaZElHm0RbYlQnrHUshrcxcx5HQnj9Rh3hWucvzmQykqb97Miw5BMDkh88QX8GiC6N93Axh7BN6GvCTZeyyTEBGZHYF26RYMdKvJx7CTvdRnttCN6r6bd6R4PD8ZLmd50K3+zviMzhGOGpTTQe92KxDUbACBgBI2AEjIARMAJGYHES0Ie36uGgc1daMgJGwAgsVAL6tETTP2JPdXKL+49+7l+KfRlTAdmbSqw8hL0A+4F3wyT5+9l+Ofbn2MEJ6t7MNhUM34DpseZy+gmduwj7F0wF5FrpFIUfxVQsn03B2O3L+8joA3/1DhsvZdnwPUzPz1+OV4nyu7GXYdXnT+8fFRNmkvSaUhH4RswrNrhtqteeemj/PnY+dgf2I8xNcTIvcVc8y6n0uRnXaDPuJc/wHpedyngft/MMC+bCOZxoCM3qnx6z2ef5hqqBVa9XbX7cajOu7ccdZJKCRn0WNfM8TjKEaW1uNPtGcZzKYPRz5RLsE9h433UH2PZa7JmYekVXp/l23qr73/R194do0w9kB5iYQPyCl24rpk5uzfbtldiGS6Rl69WS3nWfpPfcxZzGm2TLK66Xw3ffLgMP3kGoaTx3492SGzwi0WWrZOlVz2K9Uw59+z+l6zxelsQTOTd0QjL9p6TrCVfLyMN3Simfl/j5l0v2GL+l8Ow9/2UvlZHDJ2Rzb4ds2bJGtu04JPf+4l7CTg/IkmXLZfnGDQjRLXLi8FE5sf1ehOQWaelaxq5pySSHJYlgrGGmBW9hlGI8hgk1zRDLOeZGTg8RchovYj+ic6xdQh3LmOO4zfmZVcpnHBE7N3x0DAgex6U08yJnRyTUvR6v5G4JRlr4JV7YOvCzzzw8MbWqrSsv3I74fIFTWixtlePbp7Z/VXO2agSMgBEwAkZgPhG4SdZvZ4KKse/B+dRx66sRmCGBshS3XicH7XffDDnOk9314e1186Sv1k0jYASMQCMI6MOTi7H+RjS2ANpYxhi2YpswFQt/gdV6IEzxlJK+jKTt6m/pFLYX24P1YfM1KSsVkc/B9MG6PkTXB+V4fcyJ1EMv1mFrsZXYCHYc+yU2itWb9Nm2jnEppteCXhfaVqNSLw2di+k1F8C2Yfq7M41NN02nz42+Rpt1L9ViMp3x1mpnumVz5RyO1/9m9M891myeZ/eYU102+tqe6vEb9VnUzPM41THVW7+R7BvFsd6+u/X0GtcXodZj+7Ed2GFMpap60nw8b/WMa0Z1gjPa23ZuGIHI6s1SGm5HCD4kueP7JNy+Vor9R5kvuCxP+9U3Sec5W+RJF26SL/zxA4RwDiGuEob61EFpX7NGsn0nJNrbI6HObvHFVDAeJIR1DA/gBAItvwXVi9cJF82j5ADewP4Icwb7JU8Y6QOHjksRb+KDB49IiXo653EqlZRDjzzihMROJxL4HvukUA5IYqifNpOSSw1LMTnohKrWMNO+YBTv5XYEZTyOmc9YEJAlEKScPIJyYeQE8xcTlQVP4mIGL+WTe6VMP3yEri4hMjtzHxN+u5A8SZUA7Q/igFzvfd2wU2ANGQEjYASMgBEwAkbACBgBIzB3CbyOrqlgrP9Q0IePloyAETACi4GAimmfw16xGAZbxxhPUEdNvWobmVRkVG9RtYWSXFbqLT0Xk74IoXbvDDunvwt2V2yGTdXcXV8cUGvktTGdPjf6GnWvj0bfS7UgTme8tdqZbtlcOYfj9b8Z/XOPNZvn2T3mVJeNvranevxGfRY18zxOdUz11m8k+0ZxrLfvbj29xn/krkxjOR/P2zSGObVdTDSeGq+m1W5duUVCmy+XIoJp6sh2CeJNHIi2SgCv4fMuOk9+9qPb5DkveraEmXe4mBoS6VrlCMHaoUhHF+GgCUed5EW8ko99OyWF8OwLRQkbzfzDgTACbY7tI8wjnHeE3BN7Dkmm75T0IzCfGklKmnwbwnM03iFDhw/IcP9JPH4JV+3MSdzB/gjH2YwUmZNYhd4S8xf7SiHaGgszrZ7F6nnsx0tYxeFQlBdVEI6LKeY+Zr7jEnM2l3IpKRBuOzfCyx7Oox6EYw1hHYw57ThhtAl3LYS6LhB225IRMAJGwAgYASNgBIyAETACRgACq7FPV0iYYGyXhBEwAouJgD49eTn2ZuyGxTRwG6sRMAJGwAgYASNgBIzA7BPwz/4h7Yi1COSG+xBPAxJo63ZE4QLCsAq2JZ0jGEF4ycaNMpxISiGF528ggiBLXUTd4b17pMh637YdUhgdlJGd90jq2F7J9x0lPLSfthKEfk46Xr/qbRxgrmCdPzk7nMAzuCCFQkHSoynJ5/ISaWvB07hLIoSlVi9gx9mXP0W8lYuZtJQJLV3KEEo6n8TBOE85yzxCcGJI8qf2S6itR8Ldq6V1zQWEy+5gW1qChKUOx5dIMY3XM4KzL4SoXMbzuZBG4C5wDDyVEaT9IbyT1RM5O0T/EJHDbbUwWZkRMAJGwAgYASNgBIyAETACi4/APzPkjsU3bBuxETACRuB0ZIV/hMVy42EEjIARMAJGwAgYASNgBJpJwETjZtKdQttBPIjzw4OSO7kfATWCk27Umd9XikW59VvfllDvMrn5W99x5v9FcpVyhugBvGOfYw7igbvvlNSeXY63bpE5houEl0Z5xVM5Lu2bLxB/MCjBVp6xqNAcbpUQoa39iMLi44VVFW45hrr+5hGRcwjEhWKJYpbMNVwuMjcxAm85h/isQjbewv4gbbQw9zAhqVVZLiMO61zFQjjqooab5r9QvBfROkhO2xqb99jBgUCM6k2W4yNq+4MRvJMRiMmrGO5jehCnPsezZASMgBEwAkbACBgBI2AEjMCiJ/BqCLwYU287S0bACBiBxUqgk4H/02IdvI3bCBgBI2AEjIARMAJGYHYIWHjq2eE86VHSJwnb3L9X0qd2o5+GJHX4AcTYlATQVnfd9XPxb7xI+vAqxkVX4psukJalyyV5JCIj+x6Vlt4VkiacdH5E5xkmpLTj0RtChD4uuQQhqzVpEDc8fIsIvL4i0myhhTmE8TbGg7gYDBE6OinJ/lN4HBckOzrs7FLW+Yo15LRfZeqgEzK6jOCrAncxj/BbYs5i36jjDe0rpPBcxgsaATg/MoA4TWhsDUed6EcMDjrCsM55XGA+5EAYb+NYG0IzgjTiOK7GTmjrcol1hG31PrZkBIyAETACRsAIGAEjYASMwKInwD8cRL3rNOm/aCwZASNgBBYrAX1x5lXYs7FbFisEG7cRMAJGwAgYASNgBIxAcwmYaNxcvvW3vmQ5cxHjdXvkQRHE4mJSw1UjvPJoJLJyo1z0zCul786bJXUwKPE1m2SQMNTxNetk6JHtkh08MaYJq8BLfQJKMy9wYczbGO/fAEKuhr8OtvcyL7G2Pcw8w3gWI+5quGkNNV3CmziFt282kcDDWMXfGB7GBQkh7qpkTCW6NSQljqFiss477MNLWY/j03Xy2WMPS6hngyNaZzPD7KchrstSGD4Jh+NUTTkhrtW7WD2hfcGSs596K+sxfH7K1TOZcNq4ONfPzmoaASNgBIyAETACRsAIGAEjsBAJvI9B6XzGloyAETACi52A++LMxwHxRMwemiz2K8LGbwSMgBEwAkbACBiBJhCw8NRNgDqdJoPMJdx56VXSsvESxNOo472rc/wGQjEZffB2GXxklxNmulwqMS9wiPDVQeYZJiQ0KcB8wMHWLnIItOlhZFedGziFEFuUkV0PSW7klLR0dMiWp18mG6+4jHaY17ji4athpUt4BDO5sVPm09DRKuIGEHURjnVe5WIuKwW8glXaVU/nIqGxC1gOL+ISQrD+y4VdEKZPECb7DskceQAv4hz7qodxnHai9AePZ46h7TtzMmt4bPV8RiAuZkfQiHUbczjTFxWXc7mKh7QO0JIRMAJGwAgYASNgBIyAETACi42Azt353sU2aBuvETACRmASAhey/c2T1LHNRsAIGAEjYASMgBEwAkZgWgRMNJ4WtsbvlD9xAg/enPjjOk2NRmxGisWDt4wHry8clmhPlxOWuoRoPLp3u4TwGh7c9TAOu2HJDByRzPH9vGdakrYNm+S8N18vbes3sT8CcziGKJuVNVvPl7Wrl8q6c9ZJtLUVj2FCT2soaI7peCXjQYwy7HgdC/MZ+5iH2Ic3cX50AO/jAcJR58XHHMlMZoxAjLcxorQKveqEXEbcpiH0ZATofNLxNHbWyavrcyDagSAdoaoeU+c5Vs/lLF7OIywQoEeOcZzjeDKrCJ2lbouEwxzLkhEwAkbACBgBI2AEjIARMAKLlcCfMPDYYh28jdsIGAEjMAGBP2Mb84VZMgJGwAgYASNgBIyAETACjSVgonFjeU67tfCy5cz/m5NC/zHE2DSqcdDxutWw0ZGuJfLwHXfJwJ5deBj7ZWj3A5IhJHU+wRzG6vxbCiC25hFvRTY/6xkSQyzecMUVkiPMdAlhV0Ncx+NxCSBE57I5R5gtq9cv7sFl3cjEyTqPcD41iKfyKA7OfulauUZ6Np7H/MUx5j7GGxidV/BuDsRaHS9kVYv9hLP2OYb3cJjQ2gjDTkht2nLEbvVm1uMzXzIxssWP4MyExojTacTiDHWohzBdJjy2isgqNAciCMadqyTctnbaLG1HI2AEjIARMAJGwAgYASNgBOY1gVX0/jcw/VeIJSNgBIyAETiTgD4weeuZRbZmBIyAETACRsAIGAEjYARmTsDmNJ45w4a0UBwdkdSOOyW9735n3l9fCE9fFWQRdgsIxCfu2ybpQ8x3jDgc610lSy6+XJIHdiPKIriWMUI7+/Hi7Wxvk6P7j0gohBBMiGhcihF3A3Li0CHp2LJZDj6yV9IDfeJvQTwmNLS/rVvCy9YhDA+LpFkndHUoHJT2VaulUERkXrpWEkf24j2MuEv7JcRmxzuZMNV+P5ePhrFGMGZHtiEE45Gs8xirF7F6S/s4djGXRkwmVHWgzfFS1vmMxY9HcpHw2oSr1lDZjjdytN1p06/e0X6bnqchF5Y1YgSMgBEwAkbACBgBI2AE5h+Bd9Fl/pFhyQgYASNgBMYh8B7KP4vZw5NxAFmxETACRsAIGAEjYASMwNQJmGg8dWZN2SN59/dFCCNd1rDQ4TY8ejskGG6VHOvqgRvESzcQYm7gYEjSR3ZLcs0GJ/Rz2R9CN8ZruJSXAiGjCTItLZvOlyO3/g/1Q8w9nECYLcuh+++UwZGcpE8ewqO4T3ypIURcn4TXXyzhlavE30/46JEhHJdxV/aV+OuTUJA1PIGLeAr7kpSHwlJmHuVSDgEZb2I/gnEg3o0G3EofmNs4h0DsS+NZjHCsoa3TQ/ShhTmYEaI7VqEPh6SEN3M+0ceY8EJG6NYx+RGLfVqPsfnwVtY+54v2756mXGjWqBEwAkbACBgBI2AEjIARmNsE2umeehlbMgJGwAgYgfEJbGTTy7H/GL+KbTECRsAIGAEjYASMgBEwAlMjYKLx1Hg1rXYynblKcqNBSaSIJR3Ay7ckcaJUZ/OIxpKVVIlyojxnMyoL43BcSku6hCBcYu7fYlJ0BuAs3sDCNMO+QllS2bwkygi5CMoouFIaTcoQgq8kKC/QEHvE2ZRBHg7hCUwga9pHGHa2JCWvYjN1EngrJwqjTvuSbxNJJanLNoTheC7KXjQSQOBN4u1MBxO6m9BXTar75mm3LSNxRGXJ08tkSpJJ3cDgnGOQTTDGuIatjkqcuto7efRWYm9bMgJGwAgYASNgBIyAETACRmCREbie8apwbMkIGAEjYAQmJvB7bDbReGJGttUIGAEjYASMgBEwAkZgCgSY0NaSEVggBFZeuJ2Y2Bc4oymWtsrx7Q8vkJHZMIyAETACRsAITErgJlm/nYkhxr4HJ61tFYzAwiFQluLW6+Sg/e5bOKf0IYZy4cIZjo3ECBgBI9AUAjrnuz7T099+O5pyBGvUCBgBI2AEjIARMAJGYNERGHMtXXTDtgEbASNgBIyAETACRsAIGAEjYASMwBwj8BT6Y4LxHDsp1h0jYATmJAHXCeRtc7J31ikjYASMgBEwAkbACBiBeUnARON5edqs00bACBgBI2AEjIARMAJGwAgYgQVH4PULbkQ2ICNgBIxAcwm8jubt2V5zGVvrRsAIGAEjYASMgBFYNARsTuNFc6ptoEbACBgBI2AEjIARMAJTIVCi8slYqxxsa5e+aIsMRqIygA1h6WBQcv6A/H/2zgPArqO6++f197ZXabWqVrFlFctY7mATiAGDIZTQO6EFAgQIXwglYH+hJaQAgRCKwXz0AAkldAy2ARewjXEvstW7tr99vXy//33vydfrLW+lXWlXmiOdvXPnzp07c2befbvzn/85eTQXDFq4XLZIqWjRYtFiHFtzWWvPZjztzKRt8eiIp7rmxFnAWWBcCwj0eO64V06mzACBBsB/QvQ5UC6Z/M+WQmEr8W7xPNEGMFM57/mklcFENSzxo6SUClclHOSkzLspFLFgIGz5Yt7KgTLRfGolylbmvSWpHkhpeaBINUWvXLFEK7wi+qE3YhSlgkCuAlGFSef1HLK9chwpFw4HrVgq04uoV1epVNAFJ84CzgKzY4FFVHsReu3sVO9qdRZwFnAWcBZwFnAWcBZwFjiZLOBA45NptF1fnQWcBZwFnAWcBZwFnAWcBSa0gADhe9s67Z62Ltva0ma7GpstHxJ0MzMSAJlZkB615SNDdtpgv50+eMiWJocdPWhmzOtqmf8WEOjRM/+7Mb0eBKoo7sPHMCBuyAoArUE2pYTCIQvkCxYSVosG+Qu+GGz0TsKAwCFUAgTMdeBmNrGAEpuFBAoXrFQuoimLWCNwL3V59QRU2soAu969vJsC1MNWGE5jVKZlgiBtyFCuJoDZJYBj6iwCRufzPKcY5FGqUM/hGv+L3FcCxS5TtmhZr50qQsJrn1Il1ePEWcBZYCYt8Dwqu3YmK3R1OQuc5BZopf8vQz85xg5rOD8T/daY/F7OtYFDkkbv9lIn9g/9zra42kW+8O3OY9xdjUPtD7VtpPuO8fNPhMdtohOX+TpyJen96DPQjdV8/XL4z9V0vYfjPTfqbacrd/JZYCldXlDtdpLjfSefCerrsQON67OTK+Us4CzgLOAs4CzgLOAs4CxwgllAsIVA4psXLLI/dPbYgQaAmFmUMsjJ/oYmT3+3sLLG0pDP2/qBg3bOgb12Zt8+ayw4Rt4sDoGrem5b4Glzu3mz07qH2b4V0FdAbyCIx4I4MCtM3lIA5m4iZMFSFNBWGoI9nAI8LlkMQDkajlqUzS1hgGIIybB8Axbh2N5YtEgsYYlE0CLxgEUjaerVW0/gLYcS4C7gdIVmXAYgDthQMmGZdNGSw3mOZcvmEoDGKiN2c9BS+YBl8kXSeSuG0txatigotsqQQx3ytEBdOfWF+wKwlinDf+RhZrPOnDgLOAvMqAVOyvfnjFrQVXa8LKDFey3i33K8GjDOc9We69El6O+qysEuRf8HTaH/iwocrskbSLy3eiLwtAa41a6fiMdX0akPVTv2IMfVx7iTv+R57dVnvobjlcf4+SfC4zbTiQ/6OvJj0gKNtRHpZdX8EY7TBY2P99yYi++Vqjnd4Thb4B08/y3VNug9/9jj3J45+3j+nHTiLOAs4CzgLOAs4CzgLOAs4Cxw8ljgvtYOu7Z3md3SvciSEblbPX6SikTs9wt6PQ3Bvls3cMgeu2+nnXdgj0UdG+/4DYx78vGwwFOPx0OP9zPDYhJ7bN0g7FyYuoCzAUDgWCBmcUDiUARgtilnC1n+6unJWVtLwdpb4tbcbNbZVbT29rxFY4DIoZzhRd/iiQgAMYzjYo56Bd4C+FJ/mMqFFXuP8joNIA1wXAFzRRIuW47yFZBXTGGzbD5hOQDgTCpneNy34dGwDQ6Xrb8vaIMDZqMjces7GLV9B9N2cCBq+ULMSrEhkyftMGzocCTPM3JeXWIXlwCVxW7WPyfOAs4CM2qBU6jtNNQxZmbUrK6yWbSAvoAEtH4AvQKdS6Dx12jPIvSNqEBjSTf6VVTMtGeifsCYUyfOAs4Cc8ACc/m9MgfM45rgLFC/BRxoXL+tXElnAWcBZwFnAWcBZwFnAWeBeWqBEcDhaxcttWt6l9te3E7PRSniVvaOzgWefum0M+zCfbvsibu32QpcWDtxFjjBLbCQ/omVIzRR2Oa8FK1UQeeFwcuf2QJsrWCRQNQKsIfL8t3MZ1xxhT0UVexdMYdDcQBWMYWJfb6wbEuXxawZkHjF8qStXIn/NMDiRljD8WjZElQrN9WlAAguAtxsQdjCAn+LRRi/HrM3Z6VcAN6v3FzrqSC4yGGglvtl4Mq52kKKgyIpx8qRCqisNqJNiZQFEhRuVc9CFgniJjskd9chYhYrbvGo5QMjAMtm/YNBGxgcsZ1722z3rrzt21uwQ/sDtnt/1PqTRSOkMuRmnoyG1SZQ6WKR5yBwo8nmXEg1RTxbVS5510/2HwL+tbEghHkUb1o+yjWqFffiGlCMhRtyjZLGVscC5SsMb048wf04FwLMu3CQeQcTPAdjXIW0r6BWRnNGUjmoNjHN2VKAC3JtOHh0nGstKUVQ5qTiaCsphxm0lcZWhfjWejb3a66GeH6h+DDeor7Vnlu7wx2PyAKXcJcDjY/IdO6m42CBn/PMJx6H5071yDUUeBx6E/ppX+Fnke5A/wkVO82Js8CJaIHD39y+zukbfb7IXH2vzBf7uXY6Cxy2gH7Dd+Is4CzgLOAs4CzgLOAs4CzgLHBCWuAA1LsfLVvlgcUzGZ94to2VDkfs6iWneLq+/6A9Y/sDtpGjE2eBE9QCF1b7JZRq3kokBGIGApeDURsIo1VwNSgwzQPfcN1cCFosGrH25oKtPDVsK5flbN26ki1dkrfOlpKXHwSYDQAkl8uwgYsAwOBxZQDWAgrxFxfUFbfSeVi7RcDCMqCeYgsrnnEoGOG64TaamMhhHEeTVmj2UmDsn/6PNLXHMCZ2soDIosBJnpPLCHiuDgcJL8IxtwUAp4MeQE3bAAgj1L+kM2grFkbt7PX9PBA31vQznZLL67Dt2x+ye+4O2t13l2z3vrBt25u3rOoOAV4GYURjn2Je7VMcZz0PUFoo80kqAlE99jn9rxyjgK0lXIDjqjyKnRifQEFjwLgzTwLMhwLovjD5EC7IZUms61kvSKYHOnMsgzqXvRjXxKAG5I2Vm7z5qpngAbdAzjXwVkfOqEcbFJjXZe0eYKNAmU0JAvc9eJqfekxJLTPiXLOBgXHHmTpFBWuzwQC35yqvuayG6/mKlV3rH5mH07VnK8/JEVlAQNenjuhOd5OzwLG3wOpj/8i6nnhWtdSNY0pPlD+mmDt1FphXFvgtrX0dKne9G9DaTuUvkP4N+u++PJJ1y12U/Eq19IG67zr6gnP1vXL0PXM1OAscYwuEj/Hz3OOcBZwFnAWcBZwFnAWcBZwFnAVm3QJ7E432nZVr7UZiByuW8HyWuzq6TbpieNCevfU+O/vQvvncHdd2Z4HxLCCwY96LQFwLZnEVTVeKUUC6MDAZAFqhw5pDWVu7Nminnpq2zeeFbeminPUugEEspE9M5BBgcx4oLycmrxidAk3lujpk8VjJojCNo3HYvgQsDgESRqLcGwPOiwVxTw24hxtrDyAEIRYgFwJ9rbinphq9AwETD7ONleWBihVgkVNE7rEBBgXw4V/aawOAdVlupUGRBeil00HLpsOWh8mcy8CjpolFAPJ8tmhZNJOhPWIrAxiK4RwEQOxpgDV9WtkeuzFiWertH83Zg3uDdudtcbv1tqLt3BWzff0CyNO0H5vJXqRroOLJCCSqz7V+e3YAqGUvAGPMuAn4ZWNCmH9BxitQIhMJh0cZc9yaK741uwYibBIIMbe0gSAIiB+LlqytoezNl3gDMa6j8LsjMMl9ca7LsIDLbHTghzdXBPgOjVbjXI8UvE0AuVycucHc0iYIxipfVKxrmOQA/qUwca4B+yMCspm3BXKLjHlYgHG+smEiANDt1e6berW+eh1xP47GAo89mpvdvc4CzgKeBcQmllyAfhl9K9qH1vJfRfoi9O2oE2eB+W4BeaeQvr/aEc11yTXoTjSK1vJI1i3fp6TUibOAs8A8tYADjefpwLlmOws4CzgLOAs4CzgLOAs4CzzaAkPRmH3nlNPsV7ihLlUoa48uNE9ztrW02b9tOs9OHeyzF225y04dIqioE2eBE8MCZ58Q3YgA/gKilWHNAs9ZI6DdEpjEj7u4aOecXbLlSwHuWgDVAN1SIwDKubylwPzyeUA1YhiHE4B7DWaJWNia23LW1By0CEBxmNjnkYiA4woAKDBZsK+YnBVfwEpLxff0HBdX06LtqqzEh9J55yqv6zXR9RSqY60+Xdd5BW5uIXVYBC4KaOaYy5Y8wDiPm+okr6VcGka0lOpSmagNASoGCvBPAdHbEiU7f3XOLlg/bCPPDFj/oSa79b6s/fJnZg9sCVjfCMgzoKOY1f79PicLsCjLa1OAF4uacVB86LJAYFDjeDlqEc2VRMEam8vW3VmyjvaMNcXNWpqaiG9dtO6evLV35NhgMMKcKVpDY9ASinMt1jngs1xRi5WuRBjAX+LZWQ9mXnqMYIbcG3XA4wwscLHPS6iuZbINbBAgmCfgfzZTshGY5PsPBa2vr2SD/VzPxC05FLdDAzk0ZKkSOxvCGcsZoHaJONdRnhEAXC6oPhjmdFDHk2V8PYPP3o+lVI1DezuWrK7Z642r2Vng+FgAFwyenMtP6XtRgWaNqOQZ6DbUgcYYwckJYYFT6MVi9HZ0yNeji6vp63x5Luks4CxwkljAgcYnyUC7bjoLOAs4CzgLOAs4CzgLnMgWUPzG/12+xr6/Yo1l5Y/1BJb72zrtirMvtnMO7LGX3n+ndWUfjg15Anfbde3EtsDGedk9gLcAgJfYoMBeMHIBUosJa4yn7dzHFu2plzTb5vV5wF/iwcIiLmWJdzwSAT7LEysYt8GwhRsAhbu6goCAOUsIMG6E7QtTt1wKe4zdCnxXA4JrRyF8UjF0xfwFfAOE87IE/MEYlRRhCBfILxYq7E4vs/qjwmAVNFjLkCvrksdQDovSCiAsxnAAlmoIFYs5FFJ5AdP4pA7k6LFcH4ctliijqqdsHQvj5NJ2cY2zBUsBHo8mUcDk5GAAtmrBRnKA6ry2wqGCLexO27N7Qvb0S0p219ay/eLqmF1zTcr27KV/Yqh6ACfPpJ8VK2MDdd3XdD15LoqaiYWwBN9JOsFuwP80HdYt8YQDbGzyPGHgstkTWNoG2zocjgOaAwzHc7ZseQRgGIpbR8bWrErbsmUB0iWA4JI1gseCBzM2jG8w5W0BUIzrIP8KbF6AJM6TBMpmKm6iaUeZueDFjqZVaofa5bWTH3IdrbPDAC72b1Cb8EEtsFnMdWsaNWsmzZwtM9dCxDqO4oq9LGYxzPI8sYrz5ZTleM7gcAgwedj2HmqGUd5iu3cWrO9A2QYGYrbrENxkAcbMKw1zkM8RNfIZUntlDeYccwtLVc/V2nkw6N5AHtcfZ/D0XxzXFhybh+sltwgVQC63DDMl+jio3v1o9YM5I1XrpdqDqr1suzhqmY128qYhCgF7QqbZOvVLu4i0k5GtQsdUZsMOYztQe/HoWUcjmgOt6KGjqWTMvTNte32Td6N70Zn8XI1p9rRONbfUT7VJv4DMlMxGvbNR53Q/lxo/jd1gnYZaSbkfVsv+o++ep5D+OJpE/8OXf6ySM/3OnE67Z2Mc633+TPf7WLwj6+3bZOWmO89rdc30O7BWrztigRN7Rc0NsbOAs4CzgLOAs4CzgLOAs8AJb4F7AFGvXLvJ9jaykj3DEoHxtap3ga1d3mvLejqtp6PVejrbiD3aBBswanFik8ZwF5tnoTsDazCLDo2mbV/fEC5XB23PoUG7b8deu3f7HhtJZWa0db9f0Gu3dy6wP3/oXrt050OwyGrrWjP6GFeZs8BsW2AJD9BiwfwSwMwAMYyDgGrRSN5yORiduAZ+7BPy9qzLInbWOtzyBgdgZpYtO4yTagDXIH99t3RGraU9ak1tWWtuBV7V8pC3Dqp1Ha1Tab2+BEgHrRjwt4C7X0INo7B5U7B6M7h5HsXxLzFts5kCLF+KF7gX5K1QUD5AsaAOAdiq0gs6q8s6eVjKxCSuonFepq4q9LJARlBnLy8Ew1ltlivsMICyYhVrT47cYcfivPtgjTY1w5yG6RrkXRkM0x/ccFcEQBFQszlWgDXNvUuA/Kg/B3A+3N9gI0N5O7gfl8ejJVjJAJipsp3Wm7cNrwraS57VaD+7Om/f/V7Ytu8HXI3xrBJxfYVV44a5RLpYyNLWmVw/rjZ7Bg9hgNQwBssKvQXYFfBa9sYlh8tm2MScKyZ0OcB3CcDroh7cli8P2amrU7gyL1l3e9YWdRWtqbHgAfcCmT234TC6ZQvFsi4oyDVjK9fUBbkSl8rFOK6lPXflDKBclSuudogxD1XjXFMc6z28HKRvDwHcfvEYwJp8srQXm7iMK3I2KpD2S5JTxbgOapMBbZFndaaG9QJuL1sQtXNCo16M7zzA/wjLzyOAyXtxSb7toZD98bay7doTtp378jacpN0w7gOei3U6iZQLTCLiXqt/coUud+lOJrWANuD8YtIS8/eifsl7HfoX6GpUblP1i9XN6A3ov6KK3/FC9KWo5EfoVICHPF28At1UVTlVUL13orehX0N/hU4mL+LiS6oFvsHxK6gAuL9CX4ZqXPTBEZhzN3orqnap7fXKTLTzlTzsudUHfonjt9DnoW9BL0TVxrvQj6G6rheAXzo5eRWq/qxFT0MFhNZEYPu16BfQn9YyfcfHkn5X9XyBL/+1pC+pnj/A8W2+a2OTM2GHsXXO1rnm7LvRi9DNKN+WtgO9Cb0O/U90rI3JGleO1vbjVkrm41F9rv4UXYhK+EXgcDs/QFpz4mjlL6ng6b5K9Bl4P1r25dWSstvr0dpnXb8pCQS9Hr0G/QRa+2WDZN2icXgTug49FY2iKVT9E8v2m+jP0enITNT5Sh54pJ9L/f78hur9qzjq/SXZi+pdI/1v9Bp0PNGYrEE1Fl/zFXgdac2Dl6H3+fLrTT6fgi+vFlZb9Bn3y4s4eUk142jfmTPxXpmJcfT3b6L0TPZ7omfMxDtS4/9n1Qfcw/H/TPQwX77Gsal6/imOP/ZdU/KV6JHOc90/W+9A1e1kHAvoFwInzgInhgV6N9zFX6n68uerrbTe9t2lL0cnzgLOAs4CzgLOAieFBb5iK+5iwbfyPXhS9Ji/8lmM//KpG+w6XFHPlLTA8jtv3Sq7YMNq77h6SY8JOJ4J2X1wADesW+2Gu7bYjXdusW37Ds1EtV4dy0aG7PV332orksMzVud8qQiQa/1LbYf7vW++DNij2/kEsn756Oy5nSP3weFQ1PIRXP4Ww7ZxWdBe/8aybdgQApzLWjZJ3GHArwigaaKxbAt649beFbJoY5ZV+SqAyFpp7Q/yYi6Em+qIpQCEkyM5DyBOJQUM4+YZF89aVRXo6rkPzgnPAITm1SQv/AISVQDSppenSoUjezAgbFDtFQ/AOvYKKduTMKd6t+lmBHC5DDjngcncq2fFqpcFbJaIb0ztFbYpcWkFeOr+UjFGds4iCWIs47wzlghaPF62ppaQNTbELMp5GFCd4Ls8SWAmxdVozgoA7cNDARvpD9vAgbQND8q9Ny6YWR6ONYRtFy6Pv/31kP30hw3WVxrGtTGWA0sUcFgETFcdc1nElPVi+AowxnYWjJlnxmyCs6ItZvlr+bKMbdoctnWn52xpb4lNSVwrEheb4VFkaMW5zjOYRQ8QFgsYrjLAr2wajrLiHRFYr1jXuDIH3I/FFK+YONeMAdMPABbAmIQHGocE6pIn1WTRxgEN62HRfNQ8qYjH/S3CeubZNVVbREgWOC3QuuKqWvGtA8xVEDHGR66sPZY7gHYhz7MZphCAsoDzogzAnGzQRgTaDtQMWFywLQfLdv/9Cbv5dxHbsi1vfbCRUykqowS9q8wdGNOamIeZ0LWGuqPfAp/m5I3+jBMkrQX976Idk/RnF9eehT4V/YdqOYFyAlTGE73hBOi9D+UTN6HoRfNvqMpOBFSpjitQiZ6txfNvo6ejE4km+DvQf5+oQDV/Jtv5Eep8Z7Ve9WcrKqDoEW8CzgfQRai/v2/lXH2rgQIkJ5UfcFUAyaivlAADAdWTyc1cPGecAjNph3GqP5y1hNQph8/MfkdadliP1uafvoR/j/pFtnlvNeNOjgJcZAPdN5H8mgsvQAWqTSYzYfux9fMN5M3TS8ZeGHOuL4Uvon+JjgW430Xeh1DJg+hqL/XoH28iyz/Pb+L8UnTw0UU9gP275NdsPU4R+y2Zz0QfQGubDl9D+kp0PFlK5kdR2Xoq+SwF9LkcmaLgTNZ5pJ9Lvev+C63nM6m5OPbzqC7qc7UB/aNOfLKItN59+3x500lONTdm8p15NO+VmRzHeuwzk/0e+7yZfEd+nMrfUn3A9RwFzE8l/RSofR71vvjMmBuOdJ6rmpl8Bx5J38Z05eQ4newXo5PDAq6XzgLOAs4CzgLOAs4CzgLOAvPOAve1dth/rN9sh+TP9ShlIezhp5y30Z52/iY7e+0pVUDjKCsd5/bF3e0mfcbjzvKu7tjfZz++4Y/24xv/aLc/uHOcO+rP2tHcau875/H2/Afvsaft2CJ4womzwHyxgH9xdr60mXbCHuZnJBe3Zz6raK9+WYO1xTLEMwZIA0SONeZtwaIAGrbGljAAWZbSWm/VXWEYpkHcNpdseCBquVFYlkNZQNMc+XLDTLxjCGkCWcUSbSAmrEBcsT0FGucIblyCtVkuJSjfANDLexCGZok4wEa9QIrguS1ePQG5F4ZMUwgB/nlIL6eUEJgpILcmAUDAQD7JKcAt6+PlAOxpGMDBQAbgMcWyIoByKcnzATQFGgMyi10ah6TjMVphQWeGAzYEu1ZgZCiMS25AwiggZrwhCLu6AYV53ET7oimuw46laR3deTRgi09J2KEDJdu7O2OZPlboYaN24sXhzW8s2JkbRu2fP56wPpoRjqWMJnjApGwypwUbCCQ3XI0XS3JMHbXm5lHbeGbALnxcyTacXrbFi/PWAuCex355YkMXC8S5BnQtZZkHoqGHswDxEWtoKsJML7MBgdkRBUaVl4tYCcY3cLTQYW/dV/NLWkRrGJDOawC7DKZ8la9drx3J8vLGfnukdeGwJA7fJ0Yzd4BAe4A4P+VSXRsJxFbPZ4uWxjV5JoNLcuZFJhlk80OWOUVrGL/hbMxCsOIDitXM/HjMsrJtXpO251yatgP7YrZ9V9Cu/nXBbgVEPtjP5wKmfgnmvZ9p7MDjw8PiT5ziPzlB0pfSj/9B477+CGwSmCc5F21Dl6C/Rm9BpxItbP8QvcBXUC/pu9B7UdnxMaieqQ/J29FL0CehB9DJZBEXf4IKjJDoAylwSx+8NWhN2PbhMSb1lvhULXPMcTbb2cOz/hb1vwRqj/8GCdmjJu8ncXntpHq8neMOdBhVX09DF6A1eQaJv0P/vpbBUS+U3dVzPV82kQyh+gKSHKwcHvFzNu3wiAdxsquqY/M1N+oVzQGBygJmJXyreSxZdlnZ+WgLKrkIvRW9rHrk8CiZKdv7K9b8/hW63JepLwr1Uar2n4c2ovpSeDWq6xNtwODShKJ7/IDxbzh/GjoeKCsQ9L9R/2ddGxhuRGU7fdYFkD4WvQH1l+N0XFlIrmzc5buqz6T6eR+6Fl2H1ubi60g/GX0Kej86nsxGnbXn1Pu53MwN30I1RhL16SZUn68MqrHVGMpuEn0er0Kfh/pF9/3Rn1FN7x0nb7ayNN9+gi6tPkBtms47M035I3mvzOY4Vrsy6eFo++2v/Fi+I/3PPdJ0vfNc9c/GO/BI231S3Rc+qXrrOuss4CzgLOAs4CzgLOAs4Cwwry1QZIH626estR8Qu3isu9XpdCwM++rJ5260Fz3pArtwwxpv4Xs6989E2WULO+31z3qipwKQv/GLG+xbv/yd9Q3X1s2m95Qi4NLX16y327oW2hvuusU6Rfty4iww9y2wYu438dEtFOOzjLeDv3pxwF76fFbCSwOAxazNwTJesqLRlq5KwJYVkSyLav0L1775sA0ditrgQN76cbucE0jI51Qr9mH+Mo/h/rkUzlMaNiUAcTHbDMDWbHvzDeC7TWCxHVTVwsptA8u4EQDlqKclGM8lucqGehwAtA0EIrjGrqwj8sr0RG6J/dCAx1iuXOKnwOiiFYMVcFrZAuQKcges1hRzXqzZUn4UTJIyxLE1KYBxKLgH5muW5yUBL1PWGDmo2gD4wEoBBfPE1s2OFm1g/6Dn2joEEBxnybejq8Hau3G93AYQiM3C0ZItWByGkR2zwQNx27czawcPjgI8Bu3xFwdtAW6Or/jntG3dDXMVtLLSfvo0p6RibP1Uy2QDKzFW5YItXZqzJz+l2S45H0bx8pJFxOrGVTdUdcvgAroIwF4iNnQ5lLfO1pC1sfyXaMzB2I5btIG5VobRzsgHRNv1aveeoAegmmd6qlTr+2Ktc53/HqNYca7ZbCCgX7GvvTjX2mDgIdoUr4kmi6qtCeVDuM0O8n0pN9uaW3qe9h5U5lnNobXmtzYJME+oAM4zc19s8UplJca1DGAeZKNDMVewLJsRhoAL0sNlGxkIWXqkaCnaU8L1ukjo3R05W9RZtsedHbDdQzn77U0J+8nPs3bXHTnLAxyrjLdhgPaJtVyxQa3RJ/1xxQlmAYFrV6E1cEiT/W3op9HabNXE/wv0k2gCfRw6lVxBgQt8hb5O+s2owL2aREhcjr4L1TPOQD+IvhadTASwqbzA1Peh/4nqi0AiYPtN6Dt1UpW3cPwPtNafWr6Os9nOv6R+Adcp9H/Rn6KPQS9Dv4TWRCzE99ZOOP4elb3v9OUpqReE8v8RFYAheSP6EZQvD09+yE/ZQLIdXealKnb+WDU93mE27TDe8442r7NagebrX6FXorXx1UvrclQ2DaACTz6F+ucjp57MpO1rder4WXS5L0PzX/NBc7Ymi0io3NOrGbp+Pfrl6nk9B31W1Lea/IqEwMvafKjl69iMfgGN6wSR7fSZ/Bxas53m2NtRzak1aD2iPnT5Cn6RtD5zSV+e3jNq50ureSs46nP7xOr52MNs1Fl7huxcz+fyE5Sr/KJXAdBfxLk+U37RZ002fVI189kcNa7HEhCuPnrSw9G+M39I7UfyXpnNcZy0w9WLR9tv/zPm2zuy3nk+W+9Av+1cegILONB4AsO4bGcBZwFnAWcBZwFnAWcBZ4G5ZYGhSNQ+sfEcu7fd/7f/9NrY1tRgr3zaRYDFF1p3m9Yn5oYIQP7blzzd3vaCp9pPYB7/5/d+afds23NEjbsH+7zn3D+xN995s60fOHREdbibnAWOoQUWHMNnHfGjhKd5MWWLjRaKJy2Ub7TXPz9hL35PJuC8AABAAElEQVTZkBXSAKm44m0jJu0pp0Vg1GoNXi59E6x0xmAQl+zgPvwwHiT2+bCYuvkKtEedEXDmAkDYaLaJmOiNlsx3WDq3EIAYpjAM4hDgsAUBjEHqKq6GgeRwRyzwNYBL4pJiJQcSrDDGASJLxFkPWZxr4rVGcOccS+ASm+XHeDlKHQIOeajXOkBKtRH0TVoEiB3NNVuWuMhpgL08gZFTxGEXu7nI8xX/tlQA6AQ0LgIiF/IpK+YhHuWWsvpbgHVKHr6jS/kRCwMCx8P9uOfeB+M6Dds4hxtlbAIDuZgDMDwYBDyHVbwDd8oNJetYXLbOBUFrTMjIGevoKVgbs6Jjb8juub1gqXTINq7L2XvfHrD3/H3Q9qazuAAXsxWQ3UNmvS4d9x9iBgdhFEeJ/yzX0sVc1JYsLtpllwbssieFrKd7EDC3yHzB5bhY3oC2Efrc0hy3ZuL/tneVrLEZVroAfu6vYDA6Jr3xr6wjU7MAVsZMwHAuA0As1+aZPGAw9CLVTRztcp65AUCcZ4xy2Lzkxb0WC1guqPWTMRf66hePeV5bm2coSIZ4DjOA0movgC2ur8MRXGDDco6gchkeJk8usyMxrjE3GxMF3JIz1zz/2PrcyAk19zNmYZVhzje2CQ/APXWR+YRNBvtiNjQQ5rMiN+UULXIPU6Y1UrRnXVKwp7Fx4JY/Nth3vpOzm/8YtQyfoUAEKLrQwhyAZEQsbbHvFev6JBcxp04keT+dqfVJk/Mp6DWoX5QvUO6P6G9RAS6TyXouCsysyYdJvLt24jvygrP3oHegX6/mCxT9NHpr9Xy8g16yfBo98Fr3+mUXJ3+HClR8TfXCqRwfj15TPa8dZrudspNs9zT02tpDOQqo88tbOamtHeuXymeg+/0FqmntHhHAJ7t9sZrXwfHJqJjiRyqzbYcjbddU98keAuuuG1NQL/X3obtRAZOS81GNw4904pPZsL3AxUt8z/gA6b/3ndeSe0k8D70e1WYCyevRekFjfVY+g1Z+6ahsShBoyQt7XNFnrcd3Rfa42neupGz6UfQBtJ459QrK/RlaE9nz47UT31Fg+cvQO1EB0pInoC9Bv6oTn8xGnb7qvffXVJ/LBm44x3fTi0lv953XkrtI/Dl6H7oIFej+UlQ2nEuiOTIT78zp9Gm2x7GetsxUv+fjO3K+fP/UM44nbJnaF/8J20HXMWcBZwFnAWcBZwFnAWcBZ4H5b4EHW9rsYxvPtf64SCTTly4A4tc+40/sJU++0BriselXcIzuEBgk99XSX916t33yOz+3P9w/3jrA5A0aicbsw4+50F605S67bMeDkxd2V50Fjq8FjnwXyDFsdxD2roKzBgBE5Tr4vPOG7XkvBpADpBLZsWdpwlafCbAbZD1UsX5Lzda3v2AHdqaI1wsECIgXAcwF5YItGYdB22TJ0SZc7y6wTC5huWIzdbVYHHfOTcRADocSgHPUJ2AM4ExrpWGC/cZjUUDZVg/0i7D8l4hHrYm4wc2NYWtparQEbotDxKuNROPco/UoGqejlqmroLFyaWhlJRdQsAK8Aj5zXx52cZ4gxnliIBcAiNO4FB4aSdvIKG6jYcQWeGZWLrQjYj4DgHI9jG3kVrucA1gGPExn2mxwsBfwcq1nj3B4GJDxoLW29FtT06g1NAxbQyLJPfgk3Rexkf3NtieRswVLQYeWha2hOUNbGq0HwDXRWLI7b8F99wiBIdcH7LVvjNoH/xUbV+mmAsHnhptiAFHYvIUoNgO8bAUgf8HLc/aUp4VsIYzqYjqAO/Kg55Y7SDzi5o6YLVgYs9buSnziEEBsBZfRuqnWiwF4i9iVDQXJkTzAcNlSIwDooznOcfUMUCy83IsRzDiUs4Co1eHWUSRinQq39bBb5m4BxrPyKuvGbCxQgGiflD3WrtaUa8LcCGniVEBmxbsmKrMXVzqZArBnWkq1GSBIOcVNlsobZjmQs3ACTnwTvkRb2bxAjOUEUzLRCKgeZf7AXtYacYj7AjDsu3uJ+71YQHvYkskomyxKlhqEiTzEfBrV5yFqF55dtHPPi9mvfpOzr30pag9tT1gh1g8Ln4qxvVyzV3qtNpyUoo63oRpEjcx8F166nlvcWj++SeKa2sk4x5vJ+yz6pnGu+bM+xkltot9O+nL/xXHS3yBPQMtlqCaZ7r8YnUzEWhwLGPvLCyB7jS9jA+lrfOdKHot2fpLnXDvmuf5TvTIu9WVcRXo8wNhXxIvl/EVfhsCqo5FjYYejad9E936FC9dNdJH8z6F/jZ5eLfN/OfpB49my/Suqz9NhH/oh3/nYZIYMtasG0F5IegW6DZ1M9Az1T32Q/AAVAJ3VyTiicq/y5f+Y9NW+87HJ71av/+nYC75zfuF6BED8B87Fzp1MBKYKKN5YLfQvHP8brQHds1Fn9VGPOEz1uRRIqPejRO/9Wvu8jDE/+O3JY1Gfy/FB9IEx1+fK6Uy8M+vty7Eax3raMxP9nq/vyKnmud4Lx/v7p54xPGHL6IPixFnAWcBZwFnAWcBZwFnAWcBZYM5a4Nc9S+3zp29iPbi2xld/U+MAKK975hPsdX/2hDkNFo/Xoyectc6kP/vdHfaRL//Atu07NF6xCfPkvvtrazbYtuY2e/3dtwJoaF3BibPAnLOAmEhzXgTClWFihnCj3NnYYi96Xt5jz6aSIetaFLJTzgDsC8m9s1xQB23L/SM2OoCbaJinoSjgXLBgyXTMdu9abnsAk0dGO1jpW2TROIxN9ttHE2HA4pzFoGFGw3HARVYEAYzDuJ0OhVpZeSV+LaBuHJfUTS0lAOK4dTQ3WBOgcTwWhm0MSkQTxAsN8K4sCLwrwS4GwCOIcIXSDNDqORSmLKRkmKSAgVLqLgMolsuj1BOiDVLYqbjKbonFbUELjGlAuQys1aFU2g4Nj1pfMm0pln9DeIMtwkAuFfO0GSZqDjA5BrAeGbViOW05YvXmsi02kmy2fQdXwYAFSIwMWHvboPX07rXWtn7cco94oOiurWHbtaNkvSvCtmJNAdZqgetRO+Ns6D83ER8XwPT8xwVs3fca7I57chXAcc6AxrhyFugOu/j00wv2ltc125lri+CpGStkYQKXMl5fepYGrL2jkTjXYHoBrfMKlBUOBesWNnAy2WD5UYDi4bQN9GUAmgHFAY8L2LdYBXnjmhsAtWUmZRhSn97sORi8RImmuhjjyuYqxbCm3pwCDxeZPYEoexkYGzY0wBP25kResbA5q4liUMuF9WHBZXmgIAIWc4PNEiIQFmAte26ymetBxqeMa3KjjyX6XmLOBWhXpJBmIwE1jwCUDwStfzubCbgWYcNEOMp1lroTTSVrxvV2U0fEcAAC+xgWO2EYQzCXWzoYd/KLhSjuq6O2b0/ODu3nGQLKAYb/9IKonb2uZJ+/Mmvf/WmThZqYwcUQnzVsBLv6JJbaYLZjg+n9wjA3jfZ4mtXqa9oVvvREyQ9w4bXoRLsD5Yb2Et/NApl5QU8pn6eEQGPJ41B9eODCTyhXTnilcoGtRB74urBarm1M+WPVzu+Nee7YU32glqNL0VXo3ehUkqRAH9pZLTjRWExVj64fKzvU05bplOEF74Gtk92jl/+H0f9XLbSZYxda++zOhu2bqf8J1efpIJB0MsBRZX6I/gMqsPE+dKpNAy+lzBdQfbFJvoO+CM3rZALhW/4R8bA/MkE5f/YHOflTf8aYtOar//3xXs5l08lEY/Ie9PvVQvp8CqC9uXo+G3VWq37EYarP5Z2UFqAfR/Xe/1f0degoOp7IVnNdjvadOZ3+HatxrKdNR9vv+fqOlG2mmuez8Q6sZ0xcmaoF+HPDibOAs4CzgLOAs4CzgLOAs4CzwNy0wHdXnGrfWlXbhD+9Nj7r4s32Tlw+L+zwrxlMr465UFqxlwUef+Wnv7V/++aPAZ60eF+/XN+zxAYAft52+03WKP+lTpwF5pYFQLjmg4gVGQM0K9vS1WZnbwCQGwbwYgl2zXpcCgPwlgFn9+3M2UP35kwhxZsa8kCCQduxr9Xue6DTDhzsAjRtt0YA3wjMywCs3FgMBTWOhxsATxu82MbBqABYIEDYtsFgHAA3a+0AxD0dzcS7bbJ4nOuAdCEopJ7LbPCRfLkBVmicUMfN1MsRZmYwKDCZNcUAzE4BiqKgeoq9vaVTfnhMY53IWXUa1iaM1TLvGEBkghEDWLKeXBwBrBu1aKhgPS0wZNtisKMzNpxMeXGHB+GxDJTCuFzGDoqdK8Yq8ZuD9BXIGAAb8DhEPNpQGrfXOetPR21vX6/dtaUNd8wpW3PKbluypIBbbVxac+/O+8uAp2lbc3oCWwWsubVoy08r2UO3RKyjtWSXXFK2u+/zOjB3Jg6mzQATPP6skL3nHVGYxCO4GwcshYrb0BC0jes7rGsxrslxo1wGTK9gtWXi+8ZtFFz24AE2EsCszYwmcTXNZgFMKOZ3EKC/iP1EUo+wnF3EhXm6CJgKuztQaGOY2mEVN2H7BsYawJg1ZB2LQYB+b/y15EMcaPJwAO49t7JxgAYD4lbaUTFjibHT3iKtQEs8BrcXS1nTRfObGNUCucv8zKc5APSWRj2X5AKcxTgWLhAObffiXEeJcRxg7kZDQ8xX4jZzmWlj2RSuz4eDsPBHcbOOS/UEMZAbg4DFYVx0x5kTbAhQLOXwiLV1w6DvDNuSlY124KGw9R3K2ujoqLUS9/mtby1ab2+jff6bOEhXu9mgUfFwqdaf1DJP3qlTjtETfSW0e+Fe3/lESQFaW1ABPePJmjGZN4w5n+j0Ft8FfUQEOtzhyxub3D42Y5zzIfIESknYyfEIOVbtrId1qA/2tqo+opHjnPSS9wRUYFZNjmbd+VjZodbWmTpuo6KH6qjs+jFlTuW8Bhrr0kzb/jzq9M+1eua/wN73qTF1iMDhq9AaYHwT6ReiU/3x8XjK1ERf7rpvKrmGAtrw4e+P/57TfSeq82rf+WTJseVOo3ANNJ6NOsdry1SfS36JsF+jT6re/GKOj0O/if4Y/S1az2YYis0ZOdp35nQ6cqzGsZ42HW2/5+s7UraZap6rzEy/A1WnkzotcDRf3nU+whVzFnAWcBZwFnAWcBZwFnAWcBaYngVYfrYvrt1kv1y8Yno3UnrJgg774OueZxdt0t/5J4bIbfWrLrvYnnr+Gfb3n/+OXX3zXdPqmOIcX7H5InvnbTdYp9AsJ84Cc8cCsbnTlClaohcT7N1Nj9EqJcAZLna7FpUt3ohLZxiQI8MAmw/gwrkAwxhQ+OBAp93y+7Bt29Zt2WCrNXcTbzjO5y8ixiVsYm9ZVQ5/WRUBYAvBGg4S5FisZhGFm2AQd7REiIUbs672do9VXAR1K+OyGK4mYGw7oGI7rqKbqQ8WaUTkN9bqBdoCGstlr9imYgmLqQwEyXWlOQJIVqFA0opZywMDzZSFPQorFrTYiz1rxIst5oc80DhQTOIGeRiQcNAStLelLWSLCT48ROzjnQeGbPf+pCWFOhJzN0idQcokEiXLibFKJ4uwpAOxhOVhs+bLQ5bOlojzHLEdu1ZbR+cB27Q2aJtW4m4Y19kHtgOWAkyecQ4kCpra0VOy/a0NlsmM2Ob1xAAGrE+OAoSrX8eDbUyb5CG7pBjXsRQuy5vswo1N9t53J62jcRQ30owx7PFFK2K2dGUedu0AdoX9W4557sCHiHN9aF/ZBvcBpmexOainTCeX4/Isni+KJRwldnTEBtMrODZRpoWRg9ldboU5nKhuGgA8pv8R3YgGYOqWSIYYS5xhcyzBXMc1OgAys9KLcx2FlSx2e5x/mg2aCdpMoG0DJTGYNc+ZD0X8XydzuMYG7c3m5LacYwZWOe0y5nNA8bbln5rNBSXFuYZh7MUUzqymJgBlwPEiGxGC5RE2A4yyOYKNB9GDtHWIWM6w9sOsadPnbCoPcB6yPlyV7wLIbiW+c3MnjP7FAeIjy+11HrfmOWvaxIaFoZI9eD9Ut11FayVe98tfzjOo4zNfgpkc4Vl8Dj32vIx58spEIMp8s8hiX4O3+dJTJVW2XtBYbj2nArTGe54W6icCjQe5JpB7KvHvANTL2S9jgYDZaKd+Gdzlf+g00ssoeyqqduqoX7jXoqegY8V7xYzNrPP8WNihzqZMq9i2OkvL/npZ1Wyk/l4/xb1HY/tFY+reMeb8aE71ef0yyjfQYeG3JVuH3n44Z/xEry9boLn/s+G79Iik7LYHXfGI3IdP/MDgPrLrqVN3y4PAfrS2oUPzuiazUWet7tqx3s/l+7lB7PSO6o2aF/+nqmL7X40KQP4RuhOdyzIT78zp9O9YjGM97ZmJfs/Xd2S983w8Ox7NO3C8+lzeBBZwoPEEhnHZzgLOAs4CzgLOAs4CzgLOAsfHAgUWrz+54Wz7/QL/GkJ9bXnl0y6y//PiyyxB3M8TUXo62+xz73y1/fD62+y9n/u2DcH0q1d2N7XY5WdfbO+59bfWk57Ig1m9tblyzgIzZoF58WH1VnVDAGSlgHV1Czgj5mwwQ8xWAbVZmJUF235P1NIDsIgbinbfXa12zXUtVoz28D6K4ZIZoIxKAsQ6rsQ8xp1xOIrCtIzBKAbYzWeLAMlpayTm6/IFnba8J44b6oJFAPnyAHLpLLhCqMViiYUeUBwIt+HXFyBRADFrzgKHdRRUXIItLHaxp4CwZdxOe66rAQ5B/FAJYK5yeecKKSwD7gqELXuhALhH3iRhLweCMY8ZbbQhEE3CIO6DZXqIuLpJ2K5pa20oWdPSuK3qjtquoaw9tDNp/XDoBBIn87SDMAFlwEdutEIJtq0ATLWH65EE7FpcLx861Gk/+EnA9pzeYBc/boc1xoO4NjbrP5C3zt48bGuxUYuW6StZd5tcU1dYuPl8BTT2unMMf4RgVXsxrrGbXCb39qZhvpZxHZ6HCUzs30bcVG+OWVM7gDD/DJZvPh+wg3sytn8HrsoHYGIzPmXiVnv1MEfyuYgdHGqx/uFWQNpmS2U6Gfdm2Nawd8MJ3KEDFHvsdPBh7SoA4I2ysSAWYYzKYhHjrhq7hNlkpDjXTQ0xa2tKWJz5FAlTDgY6U4H5UnVTjutqDb03Z5Tw5oHAaxTAWK0m4DCutUvEsa6Bx8RsxtuFYlyPsFlALsvBwWH60p9SI260Y7gkTwFWg3wLIAfszhYabZRxKgzCOwcg1kaEeFMS8LgP8P+gtTQPeez5MC6vFd944CB22hm33Q9Era2raItXBq21UxsbMsRHbrT1ZwasoTFrux/CRXwyZi9+cdB27Qvb937JRgf8upfpl+J0e0xp9eHkk3nxTq1jWHp8ZXb40lMlt05SYOwC+0WTlJ3s0th6/GXlevpoZWz9s9HObTSyNI2GLqXsW9GXo3KjfCzkWNhhNvqxrc5KBWQeRBdUy4/tb62ambJ97Tmql5ex7a09YAaOvPQPi74+9O2id9FV6LnoZJsz/O0SkF6vCAxdMUFhPzC4bYIyE2XrHVIDjbUhoiazUWet7tpxG4l6Ppc3UO4CVMDwStQvTZw8s6oai+vRz6AC9eeizMQ7czr9OhbjWE97ZqLfY98Zs/FdUU9fpltmGzfUM89r9c7UO7BWnzvWYQEHGtdhJFdkDlqgd+Oz+Cv0g49oWcBWHz4PBb5vvRse3klWtuts751vOHzdJZwFnAWcBZwFnAWcBeakBQQYf3zjOXZr96Jpta+zpck++qYX2Z88xv934LSqmFeFL7vwTDvrtBX21o9/xX5/z0N1t70/nrB/2PxYgOPrrTeljehOnAWOuwU82Oq4t2KKBoj9GsAdNIFVAf8SFoziEjeraMD6k1rxWAXkAQArfjHAV3M78YC7ArZvAAANADcSbvJi/AYBs2qAltz9EmIWrA6GcQHgtTFlpyyK2+IFcWtnyS8RSnms3XwONnG0y6INSwGkAYxD7dwE+seabIl3Zhm2Z4C6PAaxWMa6AvCn8wCgHzChxw71XFQDFJdh+up6Zb1GazbkCTAO4jpZ8Ws98Bi2MEUq0CHXxCgVUBptg9Wc4P4eWKUpy43utmwWYlD+AJZI2mmLwrawpcV2H0gDHo9Y6iDrxMTSDUVhGRcHLQqLOVuSq2K5zgYoJR5uTgrnoDm2yNoW3W3BGG0SY5SD8GvMSV+oh3YVYVlnvVB+Edp9/ERsb8W4DsbYOFBusWc/I2dLT0lBusXqMHtPOwtn0O28Y2l7ETfV+3cXbdc2wFdi9IZjecoABAcKNpRrs/39DbZ3bysuuZdg/zbY5orxC3sYt9YRwNFYuBEwlJi9YqhzLYgb8yBAflBxgjGQtAH3zopv3dYcB8RPwPBmtPXJYj5o7uqkxKYHLz4yTOFyGYYymxa8DQPMIRXVxgXvFo06cwSuMXN3lHYCRMNi9gDuWMFKuAwv4qK8WGrDTXnBBkdT1jcyagMEuS4XAKebWjkyxgyq3FLH2CQQxt10ppFNA4ksY162AYDxcqrB9vX3ytu155q8u2vAurv72DDQD6hMXbmA9e8jJvKhIu6+y7by9AhxkNlsEcnbqrUYgvm590HayGfysmcF7drfxG0kPRkucfzmyzF+sobxRBD/YCam0aHJXg6d06hnsqLdk1zUy/Vo5Vi0Mz2NRr6Dsh9GJ1pD1hfJFvRq9Afol9GZ6MNM1EFTbLLx0vWZFn4RqFseXrccP77wTNre/9kQaKxxm2m5kgpvRT9VrVhs43ej/7d6Pt7Bv9FFDMR6pW+SggJOazKdua57+IXnsPjfp7NR5+EHVRPTaev93LMJfS76MvRidOxnVO1/bFUv4fh6dDo2pvisy0y8M6fTyGMxjvW0Zyb6PZfekf7PylT9n848n8l34FTtctd9Fhj7MvFdcklngTlsgT3pH9vixL/TwiXjtzKwyvuLt3axZG+sJd3RWcBZwFnAWcBZwFlgblogz8L2v51xrv2xa+G0GnjBhtX2sbe81LrbcWN6EskiWMdfe/8b7ZPf+bl94ts/q5tVNYh72A+c9Vh79x+utyWjIyeRxVxXnQWO3AIlEEyBXaBstmMr7Eul+T/cn7dFyzy+ri0+rcFSMClTQ0Fb0pu0P3/+VttyD/GM7+y1/iSs4pZGQF5AtypwXIIRmScQbr6421YubbZNpy6w7lbcU0MAKqD5YDeg5CKLN6ywQByGcrgZ4JnrijkM8BqoAnsVgFh9Axz0QGPBxfpTX+c6AgYqRrHOAZG9mMPeUpXWRZUQ6Mx1L5ax1pCVj9Jn4GZKSCkDMzgo4JhbikXWnmlPrH0Zrpm7rZTqsGJ6v6WSe4hhm7eVi6PW3dJlPQDHtz/QZwdHo7BSYRwX1Q49CxvmM5bKHcSVd5y+77ezNw1YZ4x3Uj5uKYDkVYCEHV2KxwwsD6M0nRwGUIWFulVuqtV/LhwnEbguWxQxVwtD89QnAqImOYFlvHoDDPEuuWeOEKO4ZFvvK9ienbCPY4C7bSX6HANUb7BtWzvs0N7lALS4KoejFYkDuMbZeBCBQQwwrzjXEeJoB6Goi5EeBowOhhKAxFHP5XQTsYAX8r3X3prwmLdx/FpHwpo/CO6rS8TYLsuVNPUIIC6HImx2ALQm3nGQjQplodOyYdWQTCfGRUxjxpu5qc0DJRtlbmh+EJ+YPhc8dnkWEBlAGNZ4JMRmh4Ym6+1utpFMBu8XI3Zg/7Al07icBlQvwvyFI+6Bx2XGtJxX3GX+RVOwkiOWxa11hrjZo5lG27uvhfnRy5gP2KIl+2z5khHA8hygs9x4B+xObHnK2qQtXNQKYK75Ebb0CIzn/TFbf3rBzj8vbD+5GrYxU/S4uCyX3Z3MpAUEiDytWuGyaVQ8WdmHfPXINahcu3ofGV/+XEjOpXa+EoN8dIxRBAqKvSi3w9I7Ub8LG75sZkTmkh2m06GuOgvrC7rXV/ZBX1rJV6IzaXt//Q3UzQ40w6fHjMknqekt1dpewPHiavq9HL+H/rF6Pvaw35exzJeeKjnZDuMtvpuX+9L1JP3lxQSvyWzUWav7SI9JbryqqvpD+InoU9BL0RWoX17OiVjdT/VnnoTpuTiORzoMx+IdWe9v23qn1KTee2rlJzq+kgsz+Q6c6DkufxwL6AvKibPAPLTAFrYer/8wKxe13WuT9KF8je2789pJCrhLzgLOAs4CzgLOAs4Cx9kCRRatxTCeLmAsd9TvefkzcVU6U+tTx9kQ03y8+v3Xz3+KrV+5xN7+ia+wUO8nLExc2VAsbh98zIV2+S2/toVp2IxOnAWcBaawACAaYK+A1wfuyVhyJGIJ4sf2HUhZcjhhja0BYvymbd3ZEdt6ByDXoYI1Apqdf9qorT7lHtu+Z4E9tK3FkqlWy2RhjsLcDBWz1oUL4XM3ddsalknjgGelLGAj7qpjradYsGUdjFQYvTxVbOJgKQXmm4H0OwgoBrMY4FXwsJA/oR5e2mMg68/8mJdT8QgptjElPHBQbqe5x1vOETCsfO8JpHE17BGPdC6Va2W5Ba6wj8sgpIKRA3icFCAq4LcsADLWCpO43SLNiy072ArAu80CuQPWgsvujcuyHhB+8wNFu3tbCtfNsFSztJe+RiMH7YzVAVu3aAsuv4eIiYv7YgDVcKxoq1Y12JKVOfpPf3EFvvNBwMVRoPBWsxt+S9BBXlvRmHp9fET2FzAvDPz09WFcZictlw5Ye3fAFi4DXCUmcbEUs61bRmz/XkriojsIcLtlS9xuv6OZGNC9FsS1eVNjgXmk2M8CVivzqwiIXCRgMrgugLrGimdxHkLjxLxuA6Re2BmxRbgwb8L1dLmQYyzC7CMIM1K4OifGdTmC23IYygbDPRhkHY/KAoDGngtzNhaIl66x95jGpGuM44pF+SmaNyMvNjUP4BRiUinPOHONdCk3gOtsMDfmZDAPsAzrvJ1+dHdH7JT2RTYI63j3oRH6CdscPonYzjydsQTUhdFc4LOTBWRWPO0Sn4UizOlSIGtpYic/tD9h925dZR3Nw7b61CFbv3qPdRLbeBD31nffghv3M0Zt6XJsxMaKpSvDds+BMJ+lgl10YcCuvk7zQZ+Jitvyk9hFtQwx3+UBXweW+NJTJZdPUkBAdE3aSKxAt9Yy5tBxrrRTNvqczy4C0J6Lep80X74/qW8X3tSHxfu2OXw2vcRcscP0Wl2/+27Na31B18QPZs2G7f3165lL0XpAY35DMe0ynazsDq6/Ga3Ja0kIJJbban5hsKvQc1ExnMfKXl+GgGCVH6+cr5iXVLsmEv/7Q+UqXwwTlX44X8/uffjUDvjSs1Gnr/qjTg5Tw3erqsrWoi9DNS785uDJk/g505sFqlXPm8NcH8fpGHK23pH8sndY/J4ADmeOSehz7i/nv39M0bpPZ+MdWPfDXUH+hHBGcBaYtxbYk70StvG7aP/kf0AUA5fP2z66hjsLOAs4CzgLOAucBBbQXxWfPf1M+0N3T929jRKz8R9e+1x73hPPq/ueE7ngJWevt//+0Fvttf94pW3fh6vYOmQY4PjDAMfvv/nX1p6rD2yuo1pXxFnghLSAYuhWMWO7/yGz398WtydfXLRsCpDrdhiQj8F1LvGHG1uytu583A1vTxBzNUnc1xRMSbN1y3bZhuUx2zcctR0HFthIX7ed1tNrF21sNsK0WoGYrzlizgbb11i0aSMgIx4XwgL2hoG/GgAMR1nxHAFsTeJpWGuaQHBi/aIeeAyAK5CvIjBFy2O8D3r3PFxCoNphQI3bPK40gJ7AUF2TihFdAZSVD7MXUBPIFzx51AOxVaoIc1SxiYNoAN/J4e7TAXaXWnl0q+X678LtdMEWt4RtwWPStritwX79x32WaD9gC7oHracrSTxeXFMXytiJtSae3dmDLU+LAMIL0AR4LZgd2l+2ndtzxHoOWd9QyH5xo8DtMgAzbF+A1MP9qHT+mPz0vHsHcgxcxJatAMHmaLBfm1rl8hlmMOld28q2e2sElnCRzQIxu/nGLrt/+wKGtYlNA1lLEOc6GsRLIiBqEUA+HMF+0GSjjLtcQhfy2BynvBrVOKDq0s5mW9ITts5WXFVj6yIgfy7H5Ao0WijRgf1biXVdOYaIfyxGr7dOTv2qowT4W9lYUAGJtXlATsx5Glcr88cbX8beY+pylItyXSvzfI1zIIDt5do60mDRAK6l2fhg6T4A6wH2HBwESB7BpXXeuoCM2hrjtqwtYLsHc7b9wBCgL0+jnhwOxvMhns73eCjHPBPFGVVrIC7jpjxMCTZejLbZNTcW7IG7l9gl5w3bgkUDgPG4fN9WBpwGVMfNdxNzKhLnfmy1tCvkzQe1/XjFupa1nMyYBe7z1ST31OejN/ryxksuJnP1eBeqef4FdmWdhW6tXpvsoF04T0C3oQLHZnu33Vxp50X01b9u/BecX4dOJsu4yIvpsPjThzPrTMwVO9TZ3MPFziClL+r84ZzxE5vGZPtB3dmw/YNjnreGczHFp5IfU2AdOoRejn4MHStj+6qxuwL9cLXgmRzfg15ePfcffu870XzRsyZiJdeKCgSd7A/HB2oFOWosTkfv8uVNlNzIBf+c9c/B2ahzonZMlc8viR4oLDBfzP/x5F4yZfNfoT+vFtCX/SXot6rnJ+NhLo3j0drfPz9V10x9p+V8DWPn4ZSid4lf9Avo0cpsvAOPtk0n1f3+F+FJ1XHX2RPBAmIbl2q/gEzQoTIs4zuuneCiy3YWcBZwFnAWcBZwFpgDFvjKmg32m0VaY6pPmmBkffE9r3OA8RhzrV6y0L7zwbfYptX12/JgotE+AnA8qkCZTpwFnAUmtEAYJmdEICrAagHizNe/k7R9gzAvAbhGB4r2wO9zlhzSugrrmICJS1ZlbSPg8cp1jQCguGKGRVvIh9jzmrfNp22zi8+70VYsus76R6+zHYdus8F0iHDBj8d98Z9YrGEZboRhjApBAywOlR6AZbyfZ+MiuNwAaIeba5jEiu+bA1UV4zJA7FhgVPKyaNpL67ymAUDkAO6na0rAWADAh1VtLkupy7uHdBA308EgdRP+rgSjtJSHCZ3LARCmvXpCwSQE1p1c2wW4C2hI+1Jl1i/jjRZp32Dhzkut3Nhje0a22a7+2yj7a7vwgnvt7DO2WU9nP8/PWhawNQxbtn1Bwc68wGzjuUDkLdSvtfZyk+3f1WD330aMaNw1p3HP/NWvNdiOvWLeAhQiAoyPB2isNeUArpcNGwn0DcbS2B0Q2/N6IVAWS47ADs6Xja8sNhcEbdeugCXzwLIxxpT4wyViOhcYv1of5K5csysYiRLHuJNxBxwuHbJTFmTs7HUJ27gKdnFzDlA27d1XDAIWNy+zWNdGmOmbLNq8yiIJXJpjKwaD/9SNu2yPIS/A2HMxLTfTlXlQya8w6OWS2tsVQRsqR87le9sLfZiuHHluucj8oF4SXlpxskPxbjxgE2+7Za1FWjdaProIl9MNVFO2Zub7huUhb3PERljBXQ0Zi9EOueS2SBNxloMwzplngYwH+EZweR4QW5pxDebShNnusQMHF9pOGPh8AD1QPZPCVTyuzSEo42VE85M5KXfasdnG8TTjnBxDC2gdxw9yXV7Hs99DGT/baewt28kY9GX+Del6FrdfQzkBZ/egcsOsxezZlLnSTn8/+eB7MYun6vezxxTgRTmuFH25E43BXLGDr6l1JTsp9eQ6Sv6dr8zdpP1zczZsP8wzZNOa1FxJ187HOy4mUyCuhO1AdouXqu/HP1PsD76i7yYt8HisCNTs92X67eLLfkTyrzibaN6ooOzJF/Rhee/h1OQJfzl2Rdn/+orPRp2+6utOfoOS+9BrUNl4KvkFBfxfkKdOdcM8vl7Pe2WujONMmHm23pFye16TJSS02WAyuXSyi0d4bTbegUfYlJPzNgcan5zjfuL0Wmxjs10TdsixjCc0jbvgLOAs4CzgLOAsMBcs8MNlq+wnaL3S1dpk37jiTXbBhrEbWuut4cQu19HSZF99/xvsok2n1d3RXU0t9i9nnGcFz3Vt3be5gs4CJ5UFPPwMOBWkj/85u+PuJvvYJ1iFCzTgTjmMS2boOr/rg2GsMgBiSKI5ZYvXFOyMCxK29qyQtfbCsYxnLJ3FHS9szUE7ZH25HbZv9HZ78MBX7Ya73wyD+S229d5/t53bvky822sBpB8EuyO+McBYAfZqIZiCaUys10CS2LYZ2J6AgYC1edi/JQF8ALEBgF4d/VrmWtkEJle0sn6oNUS/6v6qAiwW80kAYpFY0oBz1Avbuix3yLhMLkVoD4BkKIWrbXU2v92SA9dZcvt1tu/eb9h9t3zAbr3lDcQz/k/bn7zF+lPb6fMBghOzZp0L0faiNcMmXroyaGecE7EzzotaSwfLE9glBCifIQb0fX9IARgPcp63ImDjT65utm9/F8CVuL1iQdfAVj3+2AsAL0MNgksM32glxjWnoyNkijnMoXsJjOmOgmWJxdvZmbTnvOygbd60xxKMV3YUO8MOD8AULwnYrcYRZhBhZyctn95ty7pwubyp185Z04l7ZsYT4D9XIlZxaKVFG8+yhvbNFmveABhPXGlYwB4PHEC2WALTQgNl3Jl7MYlradXBODKXvCMgcABX09oUECCt+s07qpzOpcyPw1qtR/WWlRZ2RhmWEoO4zQ5E2mlLj8W7TgFAXskc6bEcGxxymbxGFBfsjYDfnba2N27NbDwowUIvlZosmwbjw2V2NsM8hnkslnwKsD0XK9nK1bvthS980M5ZSVtxv50jBvKSFTFrbIKVz8aJXEqbMVgjZkPBQL/cbx/7meCeOGsWKFDz5b7an0L6Bb7zsUktML9mbOaYcyaLvd+XdwHpV/vOx0t2kfl3vgt3kv6173w2knOlnfoCqIle9W21kwmOTyL/I2OusW1mXNGXT03EGh1P5oodxmvbVHkfpMBk9no618/3VfK3vrSSs2X7y33PuZj0433n4yUv92XyJW6/9Z1PldRnWJ8vHSUR9KrqkcNh0fVvHj4zez5psbUnkhYuvGOii9V8tfVjvjJ6d2z2nY+X1Hj4Nz38lHMB7TWZjTprdU/n+CNfYb331vnOx0uuIrPBd+F+X/pES9bzXpkr4zgTtp+td+SDvsbp/fx23/nY5IVkXDE2cwbOZ+sdOANNOzmq8P6+Ozm66np5YlpgstjGZRfL+MQcdNcrZwFnAWcBZ4ETxAK3di60r69eX3dverva7WuXv9GWLeys+56TsWBDPGaf/7vX2NuIcfyjG6by7lax0H3tnXbl2k32+ntuOxlN5vrsLDClBQpy2wy7NVCC6WtJXOsG7Oe/Afy9Im1/87aQ9bRGLJkJ2b23J233zpAtX9VoXQsVRzaF22GzBYuD1g1fJ5+P2mBf0IYOhWx4mHuSGcDZEtS4gkVGiek6dJPt2HeDEX6WZ7DWDhu1hGvpRGKhNTUts3iiBzfEALUwMmPxTuL6AtSFWAuE9RoFuItE4gCRiqeLS2n1qvojCDP0EQITFqTuEVng4ZQXaMf6LcdQhHizMEtzuVHiDeNSOIN74Oyw5cVkzg4C8g3aaGo3rpf7LZsbBADM4YqbsmDWYkGHImVYpSHLjOBqGcA50RzAnTDs0/aCLVgYt3icZ2BHOSb2mMXAz6nRgh3cGbA92wAUC4rhC9sWM/zwBxH75KdHcOGds0iOPvKv5MXdfUQXjtmJ3GMXAXvxQ21bH4BlnqSvuFse6k/ZqBfjOmJN7SVbtTFm2+8s2OhgwDoSo3bZBXk7dEa/3XnPYhjT7dg2ahG5mqaqIv0pZcvW2RS2c89osDULE5bA1XOJ2L/RRIuVGpbg+nsdcYWZA9i1zBiKFV6CXV7GjblcTwcZw1DVChpdMYHFilZuwAstSdKzm/LE+JXtPcfQHjsatLl6veKqvAwI7eV5kLTW9StzoyymMXNE7qzLjLVcVpfLinVNu4jdHGqirYleJvyApdn4UEg+RJsHrA1X0metSeBqO2033Tdi9+6izzRa7tmj0QYbSo9wPmirVuTtjBWHbOHSfgsyDzSn5CV32UqzZatpGwzjEq6qd28JcZQr84Dd/nvsWCD2Nxi0kxPGAl+jJ+9Ca6DIN0ifjYpdh/sFTzr4+Vr0A2g9a5yfotxr0I2o5JOowL1/RfkAPEIWcPZDdIkv99986dlMzoV2+lmi6qtsLBBwPLmIzO+iY0HiiQDhpK+S55D+ODrky6sl54Idam2ZznEThX+CPgn1gx+q43noVUpU5RccNc/8Mlu2/xIPEUtXnyPJf6MvQ/1ApPL1G8GbUH1WaqK5P/YzUrs20VH90Oe1tvFCdvl79H2oX5Qnu3Sh+oKSTXR+LeqXFZyozfX8MfgPlHspughVf1Tnq1DN07Hy52R8wZcp8PGdvvNacjbqrNVd7/F7FMyi+qzpnfc/qJieW9Gx0krG//Nl9pEeO9a+y/M+We97ZS6M40wZ+1NUNNPfad+nTr23au/vK0j/DB27sHAOeSqLi5sZF707/DKT3z/+el16Agvo5eLEWWB+W2Ci2MaOZTy/x9W13lnAWcBZwFnghLbAjsZm+9SGzSw662/4qaW3q82+DmC81AHGUxuLEhHAi4/9tdZJWBmoEzi+rne5LR5N2tN3bPHucz+cBZwFfBYQ6MpSabnMepRYroE0IFeImKswhj9UsDe9Om4bN2QtkwrDuC3bXb9PWkd7CNAriutl3A03wpblXxQweAFxexf0CJANWZpYvqPDZRsewGlwqsD9gKwsVRYBxAowMSUhWMWj6X4b7b8HkBCYjueD9fE5j1k4FMMlMiBqMIJ7X7lFJsZxALA62g6eGceFL/FzAZUDoVqM20qfFN+2fJj8A3ALm7mQPwR7OAVrNsUzMmCKrEkKHBSITOkS+QUxRAHpxOgMVOMJy1U0mDCYesDSOcBrOtnUBFxJLN9YomwdnbiXbhP4W7aogOIQ7FD6xwEpAfSFbQiG7sjOou0+ULBkumTN9C/WULLhdLNddWXGvvFfGcBq+ofL7jxtOz4uqSu208/aV1eZPm/dXrA77mrC5TjOnNNJ23pX0VadlQDUT1vnAhjVF8Rs631mBwFIi5miNUWzduE599tZaWJc97fZll0tNjKyAoCdVfxTG+ysVU3W1g7ztpCyNPOl2LTaGpvXWEPTcuZd2AoCl7F5EJfhIVi/oapbcai+3nWtIQdwp86JB6Z6jQ1oHBnTw8KABRgAOqI5JfHYzh5VV3CzlDGVK+oqOKw8Dyz2NiDweYABLuC4spmCU+82fuRbaR8MeOoORePWsHCdFVt6LTu8lSXILWw82G+djWG7bHOrrV6YtV/dmrJDMK+bGvba6vV9tnhRmvjgtFUu0bMRywAEt7RHbQUONdu7metsxBDuffAAMaN3Qp8CpN4/HLBrbsIutK1Ak49XrGvZzMmMWoCRtueiWpReXa35HRylO1DtaqjlkzRmhMdm7NEJUnmJVtK1n5q8AsN+gfIJ84CXj3J8EforVIviWgA/E30F6mfofZbzL6DHQuZCO6+mo3ehtR2ef0GaT6J9Bn0Q1ZryCvQ56NNRncvmGpuVqGRJ5fCon6qXt6YnAhIFZu1FBVTUNgmQ5ItzfoyX2loTzcOlqPq3Bf05qrnVhV6MPg2tSZLE39ROfMfZsn2ZZ7wVvQbVeHWg/1vV33FUe1ehz0Br40PS+wz+oxJHIFdwj+aI5o7kXaiA21t1UhWN/xtRbQwJot3oL1CV+y0qO52PPhtVm/XZ1z2L0IlEc+nt6NdQfdG1oQJYBX7diN6HrkVV75NQv+gdca8/o5qejTrHecykWUNcla2urJaSXdWXr6MC2najnehp6CtR9bsm7yYxWjs5AY/1vlfmwjjOlPln4x0p+3wZ1TyT6DvxNvROVJsONL+ejOo9J+lHb0HHfo507Uhltt6BR9qek+4+fUE4cRaY5xYYj21cdizjeT6qrvnOAs4CzgLOAieuBUZgVf3LpvMtU2cc3UWdbTCM/8oBxtOcEmEQGQHHAld+fOPtdd39jdXrAI5H7DF9++sq7wo5C5xUFtBSKyCncDXFXBU6FYgE7dY/RO1d7x2x5z4nbH92adAWtOdsFBfMIyOAnrhYDgMednVHrau3xaKtBWtsBAfBrbRiBjc24cK4OQQTWWs+rI7DqMykoZBkIpZiySadClhqOA5IRuTibAUQy8PKVHjlPKBuFrA3HB72AFg1Sfk64u3XkzIJsXLLuIMm+7BUYMLDpx7gFxLVWBe8i+L/VnBAueZWnd4l0GqBxAKtQ7hmFkAchlEcT4Qs0VBxxyywOJ4g6nI8DHCsWtQ3qWqIoADdVDjUH7ChgayNbA9bHyAyJGILFxPWRBzcPPa54fpW+wpLVndvKwCUCggEtFRbNAaP6A1VHmOJBmHUEkcXvisuoxvtOz9I2lnnhGEbhy0Nq3jLrRlbuxkWeARX4rGCnXZmgy2Ebb5na96GBxnfNCxqbHN674gtXzRkydxuwle3A6Kyxhs6YMMjPZYgTnFjz5kWaFqI6RoZAzofGrJgnvFmPgQZU+EzHugbaPUs4oUkJhUR0AtoW4KprLmjdIA2+6UCLD+cI7t6Q1/LEhudcajOAtKcM24BAGijXm/NnjYFNTiAtQFtZmCcs9FdPDPKGLNOXerAATr3NDSxkWAzS9UrLDWw3bL9N8Og3mftbQN21qYRSxV2W5y40NGQ6iZON4B3oRCwhsaynbaSzRdLcKFN84Mw6gOE1Tywt2AP3Aa7m00EBdD2H3w7atsODGMT+uzNV9rk5ESxwD105Fz0v9BLfJ1a5ksr+Uv0hdVjjzKQ4crhUT+vI0d1XoUKsJScVVXvZJwfXyHvDePkz2bW8W4n30YemH4Dx8ZqRx/HUTqebCfzRaiAuC9UCwhEEOCguvzyaU6ejzZVM/WCWoLqw6tn+YGt420HmjMt+QGlb0Y/hy5AX1JVDo8QATDPQ8cDJ2fT9r/lmRegmtMCFvXqF0gsHU+uIfPl6JG+WDPc+1r0GlTPEhZxFXo2KvC3Jt8ioS8dgbxxVOWeW1UOh0W2+XNUwO6iw7njJ75B9gFU83F5tYjALul4MkDmK9Hvj3exmjcbdU7yuHEvqT8au7+tXo1w1BhJx5MSme9HPzvexRMobzrvlbkwjjNl+tl4R76Pxq1EL/U1cgNpqV/0mXkW+gL0SdULR/quqN7uHWbzHeh/jktPYAG9gJ04C8x/C4xlGzuW8fwfU9cDZwFnAWcBZ4ET0gL6C+LT6zfboURDXf1rb260rxCj17mkrstcjyok4Pjjb32ZpT5ypV1723hrUo+8RczvT68/yz74u2usW8iVE2cBZ4FxLQC3FjYw7nBhbyrW7/6RiH3qiwX72a8a7bJLEnbBJaPW3grTFne9WVCsnfuzuCPOW0McN80NYdw0B6y9M2yNLYCrsHHF2hVzM4C748aWEKzSvHWw1Fzifg8BBpyTi958PoKb6ISXzhH/VW59s+kCpGB4zIC+xTwKAFvKqz4BxQIPxWpWNx6GBIMgcGIl19aAPSfRMFYrpXQvz40VDRInbqRhSgMOh6KchwvkBVEsQL/jtN3b/wO4WISVGgrGvDoErle8J6quKGziKGA4jr0HCzbUh7vmZMHSuHQuFWNWzBYsxXJnc4Jjpmy3/6FsP/pe1H53K6xjOh0Iqp2An3yBHG+GcbVztBv322LZClEHPL/xlph95jNZ++u3NloRl+PJQ8S4vqnPVq1vgSUru45aWzd0n664jQxFrW9/3vbvzRIDGWY5MXn1lRgI7ra9uPsOpeN0tMliuYcsVLjdFrQ+wZobu/FUDmsc1m1ILF6Gp4RBSmwKkF2CRMguyzc1Y+xhu9hcc1RMaF2tjD03PULGnOvGMZgATrgreQKKGVPvXOxjjYenzDUPqM3iTj3Pk5gPhSbAWzYrEFe7VNwFqCwX0gEbyfZb/6H72CjwIHMYMlChzzLZJOGIy7agscVzTV4ACI+zkaKpJWI9S8rW0ok7cuZdECa9QPoS83/HloLteCgFMB6yYHPZfnZNh33jW8RYZrKWcth6Ds0TDOVkZiygRWktXD8HPRcV2HQmuhe9Cf0NehWqiepf5xQjbyK5jQvnoO9FX4kuQ8eTX5B5Ofrb8S4eg7zj3c476ONa9EPoS9GHv0g4QWTzB9DPVFW/PO5Aa9JM4s/Qb9YyqsebOQpo+ALqt73qFxjmZ6Fy6rHc5sN4qa2SL6KD6IdR9ccvfZxoE8TfoJP9sj1btldbZP/HoB9BX4gK3B4r28kQKKm2Hq1cRwWaI39ZrWgjx/eh+vz55X842Yy+G30B6v88lzjny8PejF6LvgmtR35JIT3vn1C9Q8br60Hyr0L/Hd2JTiWzUedUzxx7/Z1kyF7vQZ8+9mL1PMtR7zDZeuxnqlrkhDpM970yF8ZxpgZgpr8r9J56Gqp59hZ07AYN/irxNle8leMuVJ/XmZbZfAfOdFtPuPrGftmfcB10HTqJLNC7/o38pf0p/kq8xnbf+YSTqOeuq84CzgLOAs4CzgJsFV9xF0vF6+a6Kb63Yo3916r6mpmIRe2rAMZnrlk+17s159uXymTtxZf/h93+YD3rIGYrhwbs/bf82sJCI+a4AGKsf6ntuHuON9M1b2ILaAGwvpfCxHUc8ysCXOX+uYzr6KBiuQKqxQIdNhoZNkvF7LxVGfun9yVgUgZstDRqWQA14b/RkmK/VoA4cXkj8QjxaqFVtQKQiYVM3N9YQq6dw8SnFdgnMLGkn1WlEm+NWXlaS9W5wDyARK+MQETl8VcRQLPc9XrupHGFLXfFhwWA0MMIaxlcCsDyFHYtDQDIqdaAynEUhzQIEFl5XoGj+qDnqA1aN9K7gmfDfC7yrAJM6wxupgdx1Z3EJOnRHKAgOCGYXggQPQQ4rvYq9nI5Bg0t3WgLQ8320S8H7HPfSdLUoJUiGdqe5ymkVW4OvY8qbsCxUBm334CjgQCxrmnrK14SsVe/AqAdAH80C8cWkH1Bb5RYvA1sBhAreIRey2aA/znOAM/7DwVtuC8CgAzMnoOQVcA+AKSaA1nGMgoFOxjAFTkuxxsaumCut5Nug6XeC2t9MWzvDs8NeTDIRAriihyF78sgAuACpAYDGjfBuRoziXjazD7v1PeO14R4xKRQSc1B5VdZyGIYy135/2fvPADsqKo3fl7b3rLJZtN7CBBK6EiR3qQJojSVJqigiOVvARURFAVsCChiQ0DsKFgQEBBBepHeE1JIzyabrW9f+f++O2+Sl2V7yya5Jzk7d+7cueWb++a9me+ec2TBzHVXXGctVIgwzkzrGq4zca+bl1lDa501NS21VojiNO6oU8l6rOc5D7fucksuIjmZpl8xYnkXkU8zo2ujVoUr8xKI4NLyQjAFL+ZSJKZFBy1WtzhuC+YRP3o1c5/5UETs5yefL7WvfD1pdQ0Zvq+oE/fpw2meOLiH/s9smtySvxPfZvzhy20RVNf38BJUUE7Y6fuoCX0TfQNdgQ4n2Zj9HAUQM9HpqL4Ankc115rRvoq+mGago1GR/PNRvjW6lY2JQ7eda1eghv29UW7S9hiqedVbGQzs8/tQy86O6BR0HvoSuhDN+5Jgb2hFfdJnUvNN8+IRtKuFIBzukahekciad8vQt1CRU1rh1VcZjDp705eRFJ6MTkLHofoMLUEfRfl1scVJX+8rG/s6DuSFGuh7pOaVFmzp+1X3Bt3LtNhiqGSw74FDNY5Nph19iLx4BAYdAR7c9KSqL7HKTrSUfGlJbhumeYXgYszofKn2tY2jeoINNdbS2hqdttd7Rv7q+99cffC+e/CUzLuN9apHYv0AkOpY/lYPBFJeY6xT7etLVj9I2msd7r22xC9doPDiEfAIeAQ8AsMVgU2BNH6xaqR9c+e9IQO6/wkag7W44Ytn2f47bTNcId/k+rVyTYO9/8tX27wlPXv/eeiCN+20V58b9uP0pPGwv0TddXCTJI01KFkHi/qM8x44DTlbEJNlZZkVYKF79ScrmLGfvAAAQABJREFU7dgZVVafWGLN1eOsaPzBtqj1IUs2vAARRhxanlRkKZkSOcu5+qeHlzgEXwGsWkFRmyWI6VtYGLXKkihEMiQZbq1jhYoFjHtnuYiWK1/I6oCE1GOR7q3aYoHMI0/woKS8UEmuk/Z52pfosUk90XtiPZJpK4JQVq0cE3EL+ZyRVXMqisVo1pqwGG5qjDj32Uli0DZh+SyrYpF3dA9SHcIZa1yR4BkIY1nIpnCVHC+sJe7zHCtuJf7viw9bMXkv1rXZBT9aY09hTRpl4VAGF8UF4AV9TD+Gkeh7jGtg6SLGlmSsjLEA8rQlau87PmOnn1puoyoztpY48frOiyUyNnliidVMSEOKEnMaa2ERu1CiDCoOlmliWUdtzRrFt8biGmK0jf22JDgnsS7m6VcW7bImz0DUaj2BLLAVqzrLHNIsKCqssOLiauZMBaTyGPpVTNxr3IcT+zoiC3BiW8eiBVj2FjN/tE1QSXjd6QbXV8sDQtHihrbUGtpMuljXmWwrXDFzHOa/jXjLbW1JCOM6SzbNx2IYgpx8ecBubeOxWheequVRW6p2Y5C/ioGczSathCf64vKEJUpTWF9nrZz43/EEqnE5l9hceyrIpJttDa+9572NdTqv9iNYLBcVJ7BuLrB//qvZrvlh1Jbz5B4rYL60FPJ5oo/DaHFBiOUQb2fT3otD3OZgNKf3Pvujr6Lz0fWTk51ORO+TGtBwYh9B+s5Oyvpsj4BHwCPgEfAIeAQ8Ah6BDhAIf0h1cMhneQS6RoCHMa3y0AqTMe1UK3N0TCqiWNsydNDlkSf/Z3vuosVxgy4ineWqQW9dwy2PsW4ll1Zz5etiSGYtx/fiEfAIeAQ8Ah6BQUNguJPGjby4/sIeB1hdkRbZdy+XnHW8fejwfbov6Ev0CoG5i5fbe7/4PVvbhDVbD+SzzzxiOw/z+MaeNO7BhRzeRTZZ0jgKQxaFuEvJhTIsXjQOYwap9fUPltvHDiyFBFxuybLxNvrdF1jxxP1xJd1qyboFtmLtI1iWPmz19a9asoVHCKw2RZbFIPAiEMYiGbO4Pc6k4444s6gsWcWC6PEdwhGCLFGUgSBkRW0ROfEiSDmRblgKY/EZhZEuLNYaW0nwyB9TwOA8K9IgN/irUnK3nIR0ozP80ybLvqx7MxCaIoghuFtlPZwmzjKkIdWJOHbGqVhdw2M6EpFBQJCCBQfSWMyK51ErOLS2gsQIiM0RVlY+HdfcO1llzc4WTVRbtHmFLfvvry294N/EqI3YC/VxO+OKJntjWQvnRCEt6UN6+D9OFcbBKc4CAoxxd9k2a+echf9cbJkyxJ5uw/KaYeDmOwVBGrdRY4usajS22ywMwLQ4p2xkvCe352Db1gaGuB+vW9Xq4ls3roVE5howjSBuOU+kvEMXfLDwXicQ2DHiTGvOqE25JidLKxIoneBayIq3MPB6vu4kVafrrb4E80IW5pF0EpJfFtIcI1uLHVRvPi+rfU0aN2+oXZbNmjeah4p5rcUPsaLABXdFpdyzFzq35gW4nXbEuxs/HwD6prZTxApvwqnriqUxW7GC/tQVWFOUCZfAvTvu0Be9XWq//g1xjP+ZxDKbdjhVrqs1GfP7RWVbqmwupLFmlhby612SFv4fgv4H7Uo+wsEbcgX0zqYalXGAF4+AR8Aj4BHwCHgEPAIegR4ioB9hXjwC70AAQlhPbJPRqeg0dBI6EZ2Qt+UVRc8kxVuEVfVNtrqBlcKNLWi4DdINzcRzasFlWWsb21y6pc1akm24mGINdhtxuoj1lOQBUumUXqIophFPhdrqRYLSkihPrVHiKuVvZa1UwMrlwgQvK3Dz5ba5/RJWsJfgFq6UWF2lRYW5bYFVlBZZpdPi3Fb7xTayosTkLrMXwqO1I5EXsJUuROejc0OFVNbDkBePgEfAI+AR8Aj0GYHhThr/kDjGj4zRz4ju5aSD97RvfvQD3Rf0JfqEwAPENj7z8hvcb6juKqhoxcXuo/dZuRiiYSr8EvTuqYfptelhtzZZ0tgRfCLWIKuisgJNFdrHji60y97HY1JLi9UXl9rY3T5vkWlHYwG52kqSEIq2CgtbkCEubJuef1oWYaX7FnFuX8aydC4ufRdital1qSLqcDnNE3vMkWkBgSviTtaWItdECIosS2NpmpXVq1wI0xeeLbAu1b777+rATzKFdV5O8pJBjp6lAuvg0FIzi/tkWYmqPtG+aYqICEzoSVG9g/OTxkSWZ+QWG74zKgI9ZkWJcp6xqiG4a7CQrrER5bUQfjXEp8XClfNbknLtXUN6BIQ3JCkVrf7nNyyy/HXcLxfafxak7NzvELC0CadPGZGQzWp0WEscYjyFu2XDZbVliFddmrQD9ozbycfHbdKMFktB6EfWcr14luWRFEI1biVVcauqjlhFNc+kZUnyANy5I9dQscrl+gYxnQEXgHkchrgHL2JWp3Bl3dyUYR831lglp1I4jIZEbYM/zvL87eYHMY+1IEDPzCGx6uYF80TXMF/0/Kxrly9RMc6c4GIjqwv0W3NAMYt13fEyzkKFNHMia4lCudSG4auQNTDUdBF21KyGKCxkcQWEdeDGnHnNODRPAydiTAba0Jhkrb56RbPV1VFyTYZFC3FrKW2xCuayYl+vaMzY/XfH7U9/KrOFK2RejHWzxpobRzhv8/u/haY3F9JYl+8RdI/cdbyF7Qdz6Y42+pH5X1TvrST3oQe6lP/jEfAIeAQ8Ah4Bj4BHwCPQYwTe8ajY4zN9wU0egRwxPJ2BbJXTWWxnolNR/eDmEbBzkYXKohVrbMmqtbakrj7Yrqq3pXVrbdnqBltZ32gr1jS6rYjiDR9JO693UzhSUpiAPC51BPKoyjIbRXpMdbnVjihnW2FjctuxIyuUp6F391mTtfJc9HX01Zy+oq0nlEHBi0fAI+AR8Ah0i8BwJo0fHj3Ortl+t27HoAK7bj2VOMbn4lJzwxfXPTrZF+oxAjfcfp9dftMdPSq/27K37YLnHu9R2Y1RyJPGGwP1AW1zEyWNtVA1AfklK8+U498OmVNiPz2n3Mqw6l2UKLXJO3/KSmcdBambwMp4hRVEVmJJDBGYLmbBK0RajJiwkVK21CPGFbYvmWyGQF0DobyIhbbzcPW83NJrFhHmts5aU8SLzay1dAusGuVlYaknNp3qyGT3xIHDbOImE2CWvonwpQzqygRJd/H0gLLBA1pYVnW5g9pSF+Re8I99yohCFEEsK+uCwmKLl2ApjHvuksJRVlpSY6XFkyEJS7GoLSM/gcVrJc1jHZuqpx6IXzqkmLatmcUMt8DKirch1vMIawbDsqZVtuK/N1hq8ds2mljQP74vbZ/7GWMmslBG7PWwFsYVrQCjNYyTuQErnM0Q7xhL6RIWFOwxp9XOPK3Vpo9jLjAWbLUtjcvughSEOCSyXI0XFUWIe1xglSMhk4ltXVKehlxOBqS8u1gBAewWB0As0wqIwNK6LVdG107uq7lQrSzEVrxhEcdpFhlksVqnQYrgFhusFec608Z+7loLWlnLB5bL2mOfNqIunrKumeYC5emnXG0XFEAYM2+j5MdkwSx31MHsYKsF1tqX9bP6GDaiyShim/41F0F4p20NrrjrIYibVtMn3J2rqKyhI2DTiAV/eaLQMk3Fdus/Unb3Xa02dyG4FuAWm39RiPksltD5Y6ABL0EM0M3BPbWu5dfQi5XIyc/ZXobODTPY6kOglYaXotNQCTdlF0P2Mbfn/3gEPAIeAY+AR8Aj4BHwCPQYAf248rKZI5Ajh0UMb4dq1Wm4FWHc4RtZzsmKEJ67ZKXNXbzKbRcsW82KXnT5GluwvM7qm+QhaMsUWUQ3LV8NDvjN6kYK4rHI+FGVNrGmyiZIR1XZlDHVNlU6dqRNqSXuVEFcbrylu7avjkuxhDy9TJM+H6YhkxVr2YtHwCPgEfAIeASGNQJ1BYX2i617FjpiZEWZXfOZ0zxhPARX9OxjDrCnXpln/3zsuW5bexzS/0GsxPdZsrDbsr6AR2CLQQD+C4fQkIQydYzYVhMr7IqP4LkoMsqaMvNt/E6nWMG2B0Pypq0wtRqCawU0Vwl0GCQXJJ6IVMVEjhEvFkZZDB3/IWKLq6iuxoortrFK3D4rH6oQK9JmPC81YNkLqZxeAeG3FkJ5NZbKxMxtqrNI2xJYkuW01wDpjEUr7pyddSmtysIUqg4NiVdHazoSMCT0ROfB1OkP+VJSWMIm4mVWhLVwQYxnlqJKLEpLsB4lNi7xcaPExk0ofq4LREtNuDfWuRn8M2fwJpshQk8GolvWqsqPiXQUaUnM23h2BB6gsF5Ovg4JOdVKo+XWWjXBRuxyqtX9+zpbXP+2nbxfsc1dVGLf/0dAPkbwJgUjCj6MI+Qhh9GEy0aauVbEYCYWdJI4xKXELZb75LW4m176Jq67Vk608WUpW5VlUUDxCGvMNrlFADjDcq7J5f575ZK0LV3MeCFsC7DcLSyJWHlZAuyzWCJrP+UsdxOQ0jEsl+W6PBBIWl03uQcno6RMf1k8QH8CTk00a4ujcN21dqVE6LaX9sAGLssDElhEsPZVgyzhNZ8UTTmokR0kOJaBCG/DGlrXqaU56txrN2Bl3YxFdLIFknjt2oDsZWGCFiDIFbbqUo1RVhVE4yzKzhbYyBW4SU+X2NJXmuy1hW2Q1ZrJENZttIqZMZ7TvWzeCFzK8PZGD84N80y2Z6AKDbYIrUQnoJqYoWgy/R/qCeMQEb/1CHgEPAIeAY+AR8Aj0AsEeDzxsjkhAMFYznjm5HQntlIRxXpibC/Z+Uvr7NWFy9DlOV1mr7+9wt5aWheRW2gv/UdAOM5dIuJ9VYeV6TlXFsnTx46yrSbU2ExUW5cePzqLW+0wZvRB+RVwreex/zT6TG77NC9jFpL24hHwCHgEPAIegWGDwK+22t4aE7I66l6+e/6pNnpERfcFfYkBQeDb555kz89daItYDNid3MR13HHlsmHtprq7MfjjHoGBREDedRW3F6NJF97mK8fEbWIlMYgbF1p82sE2YqtTLUP831isFToMV7qUjUISZrIlOtGRd7LYhR2DBBVJS1okscx5IV8d96YtiQxEYDRWaMWxUspBsEVmuPx149EDRXAC50KqOqtc2iLObZoAuGlZY1LGWYO6Oum8KxMQdTDM7ONeGgvlGG2JBJa7beeLGBoPa37qh84T+av6nREfBB/EoOpRO0EsXCxTKRehDXnLdkS4uBwIUB1XTXJzHIngmpo6nYU2/Y62zieM70QoeCxMR06y4p0/YPWP3mjl9XX20aPG2AMvt9lTCyFH8Y2c0JiASuGJqGQYCWOTxTn+mVvSXHPwbCQObyxVYlNHpOyK80fbjtUpq1uOu+Xp29mYORdZY1HS6pY+aPWrn0cfdxbmRD4WbM7ts+JEN9ZHrGmNrpeIc2harlEBsX0VG5k1WVaAC2hZNSdKZPmtWNZpRzYX4EM8xjzTOgCiIwdEO/NL8yCYK9qoznyhfDAzc5laABC8MsrKL7XmACU0A9JYKssddkpWw01ZLIcJJaX1DyyIyLTIaD5Kfsyam1lAoGtOP4LLxYIG6iEyt6vHzS3qylq5lZaPt4qRM4n5vItVlW1tLS8/YC3z/8RCiqRd9eGkJRn73x/Ggj4hy2c+a8zrXKX5g/DpzQsB3XxORK9F348yk5g8ZrU5ZbOBLGXvdPTODXL9jkfAI+AR8Ah4BDwCHgGPQI8RcI8MPS7tCw4rBCANRQTvgO6OKs6Ltluj77iu8yAsn5+72F54a4m9iL4wb4m9NH+piyFMeS/DFAHFlZqGNfLsKWNs9mRUW3TbyWOyuO18x3VmGHpI0oraR3PbxyGSuzeHprAXj4BHwCPgEdi0ERiO7qmfHllrV83Zs0fAfuy9B9rnTz2qR2V9oYFD4JnX3rIPfOWHvOgXGdC17Lt4vn3sRa1XG14C6eBjGg+vS9Lb3sibzra9PWljl3c/xCGvZLN7wX5VduHpEUuuhjIdW2njD7zesiWjLUJM8EhUYXqIvQrp54g3XFrrcQ1q1m2zWFlCJ7MvMlh8iHgwjru08mjJNQaTKL5EBJzzS+2Kuj+ujHhF/rn/IoRDchhyVhahkQiukvkXkrsRV4Zystp1ZKDISkhPzpPFsLP6hOhV2u0rXjKEsSONydc5jvBzVsxBOeWJNF5XhzsnIJxduxDnrq6wXtcuRHQaUh3HS9HEOOfiWWTnwqfusNSLN1lFdqI9uLLRPnLVWlteL9vuBsaSsDasmYefYDmOlWzGWVZrPUCBFXDZrvtEuZ24bYEta1tpsZGzbNSel1p07ASLJcstKsI0ugLydaGtWvmSrVr1Mta4cy3ZtMzSrZSPNuPOGtKfwYrQdS6kIYnxQg1pChEvN9NySQ2RHwU3RXZwFtnZIsoSYzhBPnGWRTDLalkVhXOKatqJ5o9aCkQEf7IlkcvStctaSyNkfxvXGffq+IkmT7Gu5Qab+a25wL7aiLLwQO2kRfIz11Srpm0w78GI4yWFI62ybJKVl461yurRFo+MgwTfxqLEvs6k11gUUnr5U7+29LN/tcLSEluMJfYZ16y2J17ikwBxntLqAer38g4ENqeYxvmDm87OeajGNwWdiDagb6MK8fUb9K8oqwm8eAQ8Ah4Bj4BHwCPgEfAI9BUB9/jZ15P9eUOLAA9pMr3ZC90npyKKi9B1worr7HNvLo4888Yie+Z1VFtUMYW9bD4IJIgdtc2kWttp5gSbM328051mjOehu7j9IPV8zmO1/Qd9UFse3t9i68Uj4BHwCHgENjMEhhtp3AIR8oU9D7QVxVjVdSPbT5tgf/zmp3jZzUtoL0OOwDV/vNu++5t/9KjdC596yGbXrehR2aEq5EnjoUJ60NrZJEljKDAYsJjtMIF4q5+tshqsRpsSMRu1/9csPmkfi6SW4EJXrqYXA5zcM8chXInPCpEn6mw9aYz1JwRcYEHMPVBmphxV/QGRzL6y9EeEXC5fOetExLJEVsA50k8bl43Jr8jDdAryWgRwzmrY0d2uLznimPJR6pe1rAhAEZ/RKOUhfkPSOIp1r1oK6siRySKARUw7orkvpDH1pxsxvlaM55FYkY6HQI5ZurHUlj56pZUseczipTV21V/SdvnviNrD90RE1qzO2tmNetj80TUWOsID/pZkgV3ywVL7zKGFtqyp0ax4ik3Y/0LL1LyL6M6N2Nauhfhcy/WBDo4UgDf4szDAUfLplRDHC6ylYbHVrX4ZbnSVNRPnujmJC/JmiPOUYlbrogXXGV6Yc3PXnEQbXZEhu5sZzLsIxHI2G7iyVnuaG6EVcacAUn8sDulPfRqV/keII6y5I/w1x2S5LOUrP5gb5Dkal20sUogL8ioI63Ln1ry4qNrKiHtdDCkcL5mEy3P1Q3OWecX52ehaa2xp5BJPtLLCGdaieMmtrdby4A9s7aI3rYxBvl5XbO+/vNHeWg2pTBvDcR50iufQHdhcSeOhQ9C35BHwCHgEPAIeAY+AR2ALRsA9Q2zB4x/WQ+eBv5QO7oselNMd2epNwTp5fdFye+zl+U4ff2W+PQ1R3Jz0q23XAbQFJfRhnjVxtO02a5LtvnWgO82Y0JFF8kKK3pvTf/HSQPtePAIeAY+AR2ATR2C4kca/nrGt/W3yzG5RLYAcuP2KzxIPdEy3ZX2BwUEgjZXx+y76gT37xoJuGxjT1GDffuRe4pHytn6YCASNtzQeJteij93YhEhj/eLOzX1YrgqC0V53dqUdtE+Lta6OWNW2p1rpXp+ECGzCKTPxW1NLcaechBiDEMUls9b7EumVbT5pLGvNHGnsGMCABXQksiyLlacnQDFzLiEyWeRk3qO8Y4fJkvDZdD10h2VFLKJYW3KdG+rQQpg8uXhWnjtDFr8iAsNzRAQH5wUkcWBpLFoz3HfWw7Kg7idpLKvnaKaRegosUzgegrvWCsqxyF6K+/x7v2flyTW2Jlpk7/3WGnv+TcYfJY6zujbMJALxK0vflHMHHrdzjii0rx07zuKtS23NyJE28l1fsqLx+1oh1yiTWokL50ZiFYvtxVo3RRiHwBSXq8X8kFMxSOcoVtWGa/Ko4vemmqw13YIF8hrLtK4gnvXb1tCyhLjJyyzZ/BaXs9XNvbTmXItcN69mkYIWCwAUl9m5DCfp1iWwzc3kLlEMcVYdrh6mXhQyN05M61ikDFIZV+MxOZsuwq15Ka6zi62kYgpxlyusqLDU4oVlVhirojyLmyGIMywyUMxtueGWe2nFBI/FqJzrmck2s2ljzMVYKtfilr3K2vhsFBGnu+7+X1h20bNWVBmxPz8ZtU9du9ZWtzX3bBBdjnCzPOhJ483ysvpBeQQ8Ah4Bj4BHwCPgERgaBPS06WWYIABJrEf7ndH3oIeisiTmKTEQ3AZmn3x1gT30/Fx78Pk37aEX5tqy1fLG48UjEDwvv7xgmUlvuucJB0lRIh4Rgbz3dlNt79lTbS90RHnJBA5+OKc8/GflyukeVCZG90Ii4x/Oi0fAI+AR8Ah4BPqOwFKsi++cOL1HFVxw4uGeMO4RUoNXKAYBcNUnTrGjPv8dS7ZBYHQhS0rK7K4J0+w9C97oopQ/5BHY/BCA9oVsK7IElpdtScg53P8etmfMDt211bmljoyAKNvhaMjMZh7gKJPCEhISLxpVTHexvnrUI56rY+wCK+IgH5YRUtm5HRZJLAKYMqG7aj0iBvwoeTLHdPWoLin1yjqYc7KyOg0Fdi+CpbDq0vnSmDtOWcf+iZQM2pVr7MBKOCCnRTCrvNqPyuYV1lD7zo21c1UdWJiKAJQbbWxBKaPjapw6FNfX0ZFiGXNJHXLl6BP9DTDQqHQSyvkZsM3IWhk3zfFEIbR4tbXhsrhq20Os5ck/WlF5q335uDI78/trrQ2z1jTjC3BR3cNDCiF45apZLsf3nlxmnz0+aqV0cjW+oWt2PNPKxu0NnElcUmN1nFltcY2jDStgLRrQYz/XJgqz61w/C5poG1MBd9BcB6JjW6SgxIqtzCKlteRvY1WZYLGBrrPiVWewPm5tbWbu4WUsuZbq1tKfBkcmp1NJSNpmYhHjLpq04klns3Kdvn5xQJR5J3UMrtB1+yX0idkgq180QizhaLSQz0GJI47jENqJOGPQ3GQ87lpqMQJ9zkB0y2o9irW6yGIXgzjCZyDGPn2Ixd0gg/jU2F7L1XYE6+QsRHc6OReyeaJFCqstFR1llbu+1xY3Ym1d/6adMKfMXj22yC7/Pc0lsLjOFIEpsbsVOxsX2V48Ah4Bj4BHwCPgEfAIeAQ8Ah6BviPgSeO+YzcgZ/LQXk5Fh6NH5ra1YcV4mnYk8X3/e83uffo1iOK5kUatGPbiEeghAi28+H3guTed6hTFSN5x+jg7cM5MO2DODNtvxxnZsuLCrTgkPRdtZU7ez1YE8u0QyHPZevEIeAQ8Ah4Bj0CvEPj1jNm8vNWL565lxxmT7OyjD+i6kD86JAjMmFBrF3zgMLvilr91295tU2fZvksWWHmb/13aLVi+wOaDgHPT3GzJdIGlsSitrmi1jxw6yUogu+piaRu/4wdxuVsNadUKGaf4xRDGISnqtoJChJaIsl6IKx4SrbmtSFpXDySdiNr29ULoBm5/KbWOTKZtZyoantsRuRYeg2h2LbjGSQ2+iIAOxgShmFmGZWwEq9oSq5j2Lmtb+KxFls+zd88utwN3q7e/PqKQPMNr8bR6n7IWS8WzNqawwC78UMaqI9W2NjvPyrZ+v1VMP94yySbi9nIZIswRkazME0cWcwlF+mKyy/zBBteRwLqulGGfA5QL/gkjxZWG+SVfhD4ELfPB8fbMy6KiQivmfIvi5luZ7gBFHcWuRQCQxRDHWigQjQfWv4pB7Y7L0nyd9bmIX10L7J5VncyU3VRjsQB5sgiWpXmEdFYEvurQ+ZDQ7ARbpTWT3EIFLU/QAgWNO5jHOj+wZlcxrJUVU1sktkoyrlRqMUUTxHVmAUPlRKve+X225JEfWVP9ajt1v1n2wIsv2EOvabFBFlxZ7kBH1bIXj4BHwCPgEfAIeAQ8Ah4Bj4BHoO8IeNK479j1+UxIuVGcfAx6PHowWog6WbCszu58/GX0JfvX069FfCziEBm/HQgE9EAtF+bS7/zhfla3R50l8uG7bW2H77aN7TprYgFE8WG0Jf0+c/UZtrehfyL/ebZePAIeAY+AR8Aj0CUCL1aNtCdGj+uyjA7Kcumyc05wlkvdFvYFhgSBs47a32779xP22sKlXbbXlEjYH6ZtbWe88myX5fxBj8DmhAAcpiUgzjIi8bJJ+8BeVbbLGCLTNkatcOYcK552BMewIJX1JhadURFqzmqzfygEVGFgiezINhGJYvFEBoZpNaHdUPjNz2939kTaSfIOisAL2D+2IgaHm0Qs1VYHV4qz6vh0ayscZ8WzDrbGlT+3eKrePnF03P7zdKutadWYhg9BqJ6kHOQRO+/gEbbX1KQ1NDCOsbOtcqePWVus2eLJBIivpteKLQw16qxqxcS2F/IgVt114jrKnjvfQl05bg7oOjpSODc/wmrUGWIYi6wNSVlngevKYtHsrIKz4Ew5yjrCnm2UmMxBLGbmhXNbzgyT62jmcgaX0o4YlvU5xxzhy2iiaMTNRQ1e/VHj4bwj2amoXL7k78u6HgtrrPajbXwfJag3WmnlU7azNcuOs8aXb7DxsSX2lTOr7fSvN9jba7GipuMRBXHmTC8eAY+AR8Aj4BHwCHgEPAIeAY9A3xHwpHHfsevVmZBvlZwgkvhk9EBUS4Z5pspmH35xnv35oefs74++ZC+8tUTZXjwCQ4JAipXh/2X+Sb96451WU1kaOWzXre2YvbazI3bfRlbIc+iI9BKm6itsb5XyEkourb14BDwCHgGPgEdgAwT0yvfmrbbbIK+znQ8dto/Nnjqhs8M+fyMgkCAY56XnvN9O+uo13bb+r/FT7LAFb9o4Yhx78QhsEQhAtGWI/x2Jxq2qIm2n76NYxGlrLi+1Udu838RhFkOupXDHm4Y81sOeYtz2TwIizRGGzjpTj+8ixlBxdI6kUzqw01WOEx0T78dGNTj+ON9tr6u2J8Seq23I/4jTtNYGHDY3WUuk2Uqn7mAN87ez5vkP2z61lXboHqvs9w+I8Rxmko7ZgVuV2gfwI9bawvwoLbLaXS6wZOEYi2fexgNHCdelgetC/GgI2ggxnIMrHPwNRqOrlrvGuW1AGIsqzrmjdldWpfPKyYJ3A3EThJLUreq1m1Nn1ewOY/EsgjdnaSyCOS3LZazqQ/I1JuNkUbjMfc33iAuMTK2O1OYI26DlcAza9nduyV5YBDYLL9J18OJxXFQnrLU5bqO3O9SWr3nG1i58ynatqbHzji23i37V5Kz/syzm8OIR8Ah4BDwCHgGPgEfAI+AR8Aj0DwFPGvcPvy7PhmQTvnI7fRr6HtRZFCs28T+xJP7zQ8/b7Q8/H/FxiUHGy7BAYPmaRrv5X086LUzEInJj/d69t7fj9tnBaqrKZtHJr0mZ20+yvVkKgbyCrRePgEfAI+AR8AjYY1gYv1Ve1S0SNVXl9pmTjui23HArsHpto93zxAt231Mv2ivzl1h9U7MVFSRsQk217b3DVnbobtvZzIlj+tXtoWijqw7uvs00e9/+u9kf73+8q2K8xI84a+Pzn3+iy3L+oEdgs0EA0jXGvyTxaD+0dyWf9TZraMpYfPKeVlKzjTUrXixWxpZpgsqD4IP5dMSaszYWuRcwdhntOwJ4HYMXHHPMrig+HYeGc+VEx6GOoFtPEOrzF9QnC1QRiQGxF1iVBpaWzhrVkYraV7lgq5JSRyaSCvcdSah2HPcX1OEO5/1Rq64xjWVdf9fXoMMKhxP0Ldyy6+rU0Zw4y1S1pQPCKiyrrUYMrap40MnFFo9jnRsvsxFbH24Ll7yEIXernXFwud31ZJvVA3cU69KsYuRCauIn2Z0/FH8cvpCZMeLoZtKKr5ywyrIocYyzVl3aYvWNzVY98+MWH7MThCeLCNoqIMDfhgrFJXVuTmir6xDAk4eBrkOujLv24BPEs87NB3cO5TWPND9Ui5h2VxH5wlPVOSFzXRril54Kds2VCDGY3fwhLSti5/bauTMX6SssKRsJiFjWSiByn62tGqLfrpkc7m5HDfVEVUdYTulA1G03JdiNal5Dqkf4TKWJ/5zBSjsanWSx8qSN3OZ9tnT1ckuvWWLHH1Bktz9Ubo++kXJW0VwKLx4Bj4BHwCPgEfAIeAQ8Ah4Bj0A/ENAvdS8DjACE2tZUeSb6YdTFKFZ84n89/Wrkt/c9bX968Fmra2ge4FZ9dR6BwUMghrsvEcgnHbCzHb/vDtmqsuLw3qG3CLejP0Pv4oWPe40weD3xNXsEPAIeAY9AZwjcbFNe4LXytp0dH+x8fQF8cY8DbFFZRbdNXXHuSXbCAbt3W663BZbX1dvdjz9vD/zvFXsdF8sidQsTcRsPqbvn7Ol28K7b2XbTem/d3ARJcf1f7rOf3nGfNbd2bd12CMTxF049yqaNH92r7g9FGz3t0PLVa+2AT37Dmlq6sdri7f43H7vfJjfU97TqQSmHpdzsD9r8Fwelcl/pUCDwAo1stHtXjwcIj5eIFFlVadb+fOFom16zGnvKkVZ58FcsUTPbxapNty2B6MI009GeEGyQtVnHtq0n9zLaF9nnSEGIMXdcBJnyRDSLBMQK1RGKOZLQlRFxGJybiemneFCnIxMh7EQCymrUxZlVy46YFSEZEIARrKAd0Zfbl8vh9fs6j3JoUI+OyfUw97tcnqxPIyrj2uCO71wUs6We9edArBPfNigTtOusWFWHazeoX86Hw76qD7K8FZkZ1h1xMaGT5BdZpGCSRQpHWyIdtyX//bnZaw9YtLzcPn1To916L2WwfI3St2xG7pOpYoiEKLtgkLB4AQ6aky3Wli2yEw8osevPitqaNW0WHTnTat7zdfo+CkwYXWYNIX/XWiIm6/Pg2jkSVmQvIw3IX9Lsi4B288PNE/LkelntaV4xL3Rlgssb7AdlgzmhurXoIAOZzUnrxBHCYKTrqOsVXN9wDihPpHFQPLg2un7htQmvqWY06XVzQmnqctcrqFPXUa7ZXRuuLdXLsXVtB226ueSOB2UjWBWH88bNBc1nvGIF/Q3OSZRsDTwllkxi+fziv6z+2dssUlFijz6VsVOuroNcJtIy8Zq92Gww8N+JfiJ4BDwCHgGPgEfAI+AR8Aj0CQG3XrRPZ/qTNkAAolhPe0ejn0QPDA8+9+bb9su7HrNb7nkysnS1d98X4uK3mxYCGMfb3U+96vTjP/h95Kg9t7XTD9vd3rP7tolYLHoCo5HO43NwLdufQx6v2rRG6HvrEfAIeAQ8Av1F4OHaCT0ijLeZMs6O32/X/ja3wfmr6hvs6j/cZbfe/bC1pfRye0NZtLzOHnvxDbv693dBHs+wz596pM2ZOXnDQp3sLVi60s7+9s/s1QW5ECK1pWYTys1GFJkV8VO6jZfaDZCri/mdNz8grf8DaX3VJ06297xLER66l6Foo/terC8hS/CPHXugffe3d67P7CiFWZhiG3/22cc6OurzPAKbHQJt6ZQdvN0ImzERq+JVMSueuaslxsy2prasVWWboMuaGLNzLjW4Y3eMoUhLEYRiBnMk8jqSkPuSIwYHtxuDVbuzpIbAjOBuOJJeCylbam0F1VYxfXdbOvdpG4H16Un7ROzP/45bc7QVQjmGlbesfYeONcY21+K0ncQCNkts4nEj03buEUUWa4xba3GDjdvtLIsnRkEWJyFQITJxWx5YYQ8Ealzo3MIDRyaTDueAs1JnPjgied18UJsQwG5Rgo6x56DSeUpI89PsDjMRfZ1KLrSCglG4oq7ls3eANS160qLL19ieOybtyL1i9sd/awyeNB5ml853xyPgEfAIeAQ8Ah4Bj4BHYBNDwJPG/bxgkGQyp/koeh46WdU1NLdmf3XX45Gf/eNRe+r1hcry4hHYbBBI8jL+Tw8+57S2qizywYN3tY8etZfNnFAzhUFeiX6dz8UtbL8DefwyWy8eAY+AR8AjsJkjAD1hf5ymKAbdy4UfPgYXk3qxOzDyzGtv2blX/dKWrFoTVDiuzGw8pG4VpG4ha/pSOVJ3SaMjdR954XU74aKr7QsfPMrOPuaALjuxaPkqO+7CH5hIaauACNptjNnIkneeU8kxtbldjdmzy6xl3hr7xHd/ZVee1+ZcPb/zhPU5Q9HG+tZ6njrr6P3tFkj4pSGunZz6VM1YexOX5NPWru6khM/2CGweCMgKM57I2Al7x62YuLSr49VWMOsguLZiK4mutXTbWtKJgH8b9CFrvTL3UZGFIo7Rde6mHVGoP7ozb5oi0tg5MIJwNSx0YylIY6y8YzVbW7x2ujUvfcF2mhi3Xaal7MHXsFvG2jiNdfZQEoaCXlRrIlZoyVSLfXDfGttuVKs1tMSsaNIeVjhxb4ukZC3dhvtq4hgzloEijR1RHM1ZpzuyV/MBcW7Nde2xRHa9c7nuj+aH7JVlIR5kkJZpNta+OQaZbM0ZjWr4iUaVSq6yomgBbqhHW6pslJVudZitrvuVJfCKce7BCbv38bVWp3UbXjwCHoHhhgA/oG18rlOtbJ8fbh3spD/jyB+bOyZ3kd6CvxOgBih7U50nAzT8DqvxmHQIi8vUZ1OfUUkLKs9FXoYWgY0xPzdGm0OL6jBpjUcdL31BAFJsNPoNzn0LvQKdLKvic3/wexv7/q9GzvvhHz1h3Bdg/TmbFAKynv/OH+63Wad/0w79/I+I0/0cq+nxzWb2EfQFPiN/RHfbpAblO+sR8Ah4BDwCvUZAsYyXlkDWdiP7zdna9t5+q25K9fywCOOTL742IIxHFZsdPs1sn4lmU4mrLEvgEggckb3jIHR35vniyOlms6p5T561y2+6w77zm3902lhrss3O/tbPAsJY1sUHTemYMM6vQZbHu/PsuuNol3vh9b8z9bEzGYo2Omu7u/ziwgL7zImHd1fMHb99yswelfOFPAKbIgJa5CKyT5aZ208tsT1mpSy1No6F8XSLV29lUQjDeLbR0rEk9xYRlxuSbiK7Qu1s/PxedsSvI4JVXidI1p2oOnO6zodwrpAsjaXupOAE1ed6obzc4fYkoiObHeGoc3gt4M7Pa0ft90GCoeBm27UNmS1Su5fiyM0c8ZnNNIN9A67BUxaJV9iIGXMsnSi10kS5HXNAFKo4gUNwiGORn0MpuArPZhTjN2ujqhJ24rtwqpyMWlNF3EZvc7w1a9xYRMsNc1axrhXL2F3QAegk2Lq6nIUxhLHDOLd17q5DQjkoF7QbXOfgemiRga6L+hhudXx4SzSO2+2meisgbrjmRPGUnSwyfhtLEc96rzEj7IDd9RgayvAfT9hTv93sENCPwF02u1H1b0BncPoTOf1z/6oa0rM/ntfv3w5py1tmY5vqPBnMq7UlY9LdvfTsvM/nHwfzImyEursb+0boUodNboz5uTHa7HDwm3umnhC89AIBHsBHoVdxyjz0QrTqridetkMgzHY450r70R3/ZXVxkmwvHoEtBwG9XpL76uMu/rnN+PBlkR/e9oDiIEbIPh59jM/MneiuWw4ifqQeAY+AR2DLQuCvk3tGGn76pCMGDBjFLz7r8p9aaxuxDqdWmu0/OSCIu2ohwUv1HWvN9p7giJRr/3i33fHgUx2e8Yu/P2Avz19sVk78yb0wjkj04mfzrJFmM6udq+yLf/pHXm47+uYd7QxFG+9otBcZx++3m00ZM6rbM57A2nhJMcS6F4/AZopAJOcd4cgdCiAriSkLSZeYsrslCrn3YEWaTjexFUEoAyr9BA5EKXG8TnNpd2R9EeLcKqZr1lJ4RUilIagVv5gTInnqKnA0cGgJytaRpKpcaVmP6phU6Sye84MowoqXnnUdIHJwOu3aoxEIWO5tWKuy4JOzoJTdfSqv3oB2pq5eSiJhSWLLyll0kvY6vvt1XWeE8yMi4LHizjC2VGYlw2yFoE9aUe0si5aNIERu0vadFbMRldzXcf1MIN2uKx3oo8RujkHQtkFgnrNfqU0Y1wYt3GaJcXtZ4ZjtrSBV7/qeSdURiVjXtQgsFK84py52MRGCmUtp0oEGc0sxjEXiR2J875DOgkUWetypcJFFcUj25ojfjMpRPKu5Gtf3VRAfW9c1I+Kaf07yzsMrFM0oLjI9ZN4Faba0FeyrHlVKe9RHp1wVG/xxXVU96zVcx6AWQ+/p+ceVfoe4fuXacScJs7BOLT6A5sbleluEBRpt85kLGP0Vxq1q5lHWgoV/K5b+Hz2o1CpKCy0aj3NtijD812epg7be0bjP8AgMCAL6wMgD4SvovgNSo6/EI+AR8AhseQhsyffSLXnsW95MH+Yj1mT00gMEeNgqRy+h6JvoZ0kX3XrvU7bj2VfYYV+83u6BMPPiEfAImM1dssrOv/Y2m3jyJZGv/vIftrqhWe8LDkMf53Mjy+NtPE4eAY+AR8AjsPkg8PyIGptbgWVvN3LQLtvaDtOxAh4guerWv1vdWlxOywp4F7xT6SV7T0WupGV5jFx241+ssVlEz3oRyfKj2/4VZOxCOZHNvZUdcFVdHLfn3lxodz/+Ti98Q9FGb7vcvnwMwuKTJxzaPvsd+7Io/NvkGe/I9xkegU0ZgTjEk6yMMyJAIT+r+DwculcBpCvhiCoqrGL8vliZioyD9IUszIbsWC8HHU8UOMI2GoPooo2A2gtIXH43U1uQs2G10LyOCKR9tnSSYuoLaUcBpzE+FYFsxIAtYBwQxPkikrqtxdKy2MSCN5um/6qjI1Iw/7zu0tyGswa5C0kdg5CWdW1G8Xz7Jbq3JyHVl9C7FkuUjLR41Uz6n7RJ1RW2+0zR0724//erL3knM8YM86O2osiOfHexZZvBPFZsI6fvb43Ml4SI1vSq3AnqX3/6GJ7bRT05otW1kyNgA2tiXQde+3Q0jfKGs6kks5rXzFvLQB4TNqmkdqIVjdnWWvgsbj8xYgfvmMAdeALL/zaLpYXAZjLwTeUCbdn9vJvhX4N2/6N4y8bJj94j4BHwCHSFwJZ8L92Sx97VnPDHNgICnjTuBnQe1CPoaRQTK/xVtOx39z9t25317cgp37zJnp2LBYoXj4BH4B0IrFrbZJfefJdNPuWSyMWQx2saHXksy+Nn+Ux9H/UPU+9AzWd4BDwCHoFND4E7ekgWfuoDPXN13BME3l5RZ3+4//GAKBap2xvCOGxg+gjcTRfb8tVr7bf/eiTMddv//O8VW9uEO1GO2+g+WtDKQgxX2JK//vcZt83/MxRt5LfX1/Qx++xsU8dCgHcjD4ydaGsKCrsp5Q97BDYdBFJYAEtisWDRyG4zym3CiAZrbSZObe1OFi0fA1crwliWvElH/PaJnmLRhVzmZ2Cj21ohXN39bD1p7HjjdrBlsbgUQZzNqn22WOMG+8qDvMT6WAanImxFRjc2tlhjQyNp3DkXFjqCOJVqpVzMVixbBcFcZIkC3ev6NIJ1veP3vbNkzshiOlLiyNwYLoX7LzigTq/G2rgZMpAY0hN2tNZIAbGlk3b4jlij0kDUEaX9b6k3NQjDI+ZU2syxxLQmlnFs3C5WMGYHawP8bFsj/YLc7BdZ3Jve6NUOWDscZKEr61y5qQ4sifviJrw3rQ9FWRfrmvmueRDBgjuTbLYkn8+KGfsaka+thM/iiXuzUEIEOv/jYLEx5sVQYOHbGJYI+NVzw/Ky+E5tQgi8QF9vzulfNqF+D2ZXt0RMtuR76aY29o0xPzdGm4P5GR+2dXvSuItLw0PvHA4/jP4SHXPv06/aTh+9MnLiZb+yF+cvJcuLR8Aj0B0C9U2t9nXI46mnXhr57u/vl6tOvTn6FPoan7EzuzvfH/cIeAQ8Ah6B4YvAIuIYPz9SIXe6lgN23sa2m4ZL6AGSfz76nCNobDxxlMvaWdD1po0cqfuPR/63wVn3P/1SsC+L5P7I+Ap39r+pT2RKvgxFG/nt9TUty8dzjz+429NTWBX+a/yUbsv5Ah6BTQkBfW5laYy7Adtuq5iVFZXyWY5Y5di9IWobIeg4FmnjuLRvkoYsDonpN+fNs3vuvh+3usUuLyCH1T5NbXAPUZ5U95UwHezLDXGGfqUhVBPxYnvpxVfsgk99xpYsWeqIxCxWsDovES9yZPHLL8+zL33xMls4f4XFioqddTWNUWa9bNh2/jG1Fuw798PYdcZjhRYvGGH/uucRa6jHi8MAkbk4X4ZYT1ozIQlKaqdZtKoG8jBlu06LWe0IFvi06/P63g9OSh6/i4piduzeWStKQtYXlFjRjEOYF3Erh7DMRurQEhrPx2tw+hIQ07zaCa2NnTvpOI2JOEada+nN4NUPYwkWR8gdfL0lMi24Lm+2gtrtrRCL46amAtt1ZqHtthWkeVsEV9aGH4DNYNyDNW18vR4Bj4BHYHghcDvd+VBOPzu8urbReuMx2WjQ+4Z7gMDGmJ8bo80eQLH5FfG/oDu4pjwUF6KXcQgTFtvjtYXL7Zgv/9QO+r8f2TNvvN3BGT7LI+AR6A6BuoZm++z1f7Ftzrg88ueHnlNxBUn8GZ+1u9EpyvDiEfAIeAQ8ApsWAndPmNqjDp9zzAE9KtfTQg8//1pQdGw/Sd0xkM68VH7q1besNbme9Hlj0bKg/qp+Ws6WJlxsSVkty6I5X4aijfz2+pOWtXFtNbFbu5F7x00mLieAevEIbAYIxAqIW0xsV/0vSRTZQdu3WisOCGKV1VYwarqzDo4RzzhKrF0Xpda5YxZhi/Vv6Doaq0hZ+zoX1uSL4I1xLFBcOMtiONlqcT43iYKETZ022W742S123vlft0XL6i1WUIzLY8UiTkECp4h53AIvmJTtKO1CCeKSNyrLYrnITkPQsh+LQtpGK23FyqRdeukPbc89DrKSkmKbPosIMSK5Zf8L4xmVhXNyre17wJ723Mtv2h77HG/XXPc7q2+iL8WQ1vQ9hevqFFbMaY1BfRUxy3+dL3fYadxaZzK0jlW2iO/CogKb/3aDfeTsa+wPdzxt1TWVLtxwEHMZslrnuRjM6sf6/YAITFFXzmLaEeHrJ5GuRArC2KKrsSbFZXhBkZWPnMVwC23CmIzNrJW1tsjwwReNUyoodpxQAEFJognSumYyxOX2WMBCaBJfNxvhWjEefceEoqSDQDDkaXhcW0fDg7ViTYuAFr66rQYaxLped3K4YECV6cKEug5nsnQu+bJm10yRta7s1BXnOmhL56l+2tEcjvC9JZIZK2Wd6erUNXNtBWXZ6ZVkILOz1J0FtzRVRGJqA+lldREwiRju3PWJy+KeOruGPT4NiVKrmr6zpfmcVhUX2FH7qFzCjc/Nt6A1/9cj4BHwCHgEPAIeAY+AR8Aj4BHoAQKeNG4HEg/C25H1JHpRKp2JfeOWu20H4hbf8Yis3714BDwC/UXgjcUr7biLf27HfuWntnD5ar0qOBh9ns/emf2t25/vEfAIeAQ8AkOHQAsvfx8cO7HbBrfHwniP2TO6LdebAotXrg6Kl+VePPfm5PyycX4KF8YdEbK0rn7dEeeaWnsc67cU6sU7MVDl7jpPhqKNvOb6lUzgXvb0I/btto46rBSfHIW7cC8egc0AAcevil6D5aotb7Gp48otjZVrpmyMJYpYsIIFslxTi4gN4hn3kgETRnByBQXcgyBf08kWKy4psdPPPM2u++FPbf/9jrIfXXeLrVjegOVumSUKSyyeiAeEbRTSV7enGG3GIPSwwIzGiV2M9fDLL75sl3/jMttnn/3sq1/9ktXUjLQvfeHT8Iz0FQJY5J8jf9nKk4DcWH/725dYQ2O9ffK8S+zd+51kP7z2D/bW4gZI4NFWWFyDRW2xxeMQcRDYsQSLaUQC4oK6qLjUikojVlJRbW8ubLXLrrjFDj70Qvv9bffbOR89yuJt6i9k70AIxGU2i1tirLrV54IRk4gd3GCJSLXtDh/uiM6BaKeTOkSqSgLL86DQEbuWGs6+rRlyvWTKHhYvwrtERnGcG4NyWd33g/OCM4I95YQa5ofbrMhiLmua+ZVuY1FArECXNycccCdqruVrQOqKCHaEsiN4ydMk1vzMlVWdKfZbWKjgXD1TcQbL81SKRQEaH5MqC85pCHotCAjnyfq2wn70bhvVdy1zpy2VtAx9bG1jLko0ll6I464hiUWmayFGKlNn0XQTywhSVjxqJqbfLATDffye25RZJUbeijXuDY17AfCmWVQ/BPVjtJ+r/NYNXrNyHBr8eFuXvVkmNMbx6Lo7zACMktgvxiqajSI1tDrUIdA0/2QMsamLflFo3g/UXBiMudUVxgN97Qer/wN9v+oKk/DYQLap+aF7hurcVEQu0aoHqLObyvdDX+/DeojX9ZWbnKGSgZyfPe3zQLa5KX4meorTgH0h9LjB4VyQh6Kz6d9j6OynXltgc865MvLlX/zdWng54MUj4BEYWARuf/gFZ3X84zseUsUKoiar45tRnva9eAQ8Ah4Bj8BwR+DBMROtOa7f3F3L2QNsZazWZDHlJPcSP9jp49/c65F8IqC4MOfyGpej/RZe+kuKCjfEaija6Hff8yo4+ZB3WWlR9+9ke2p9nle1T3oEhiUCWSw8sc/khhOxd+9YYVWFuPmVK+nabS1K/N+IyLhIM8SayEzc5jpL19y9qacjcrwflr/O6hZCr7XR9n/3nnbWGSfa668vtHM/8Xnb991H2Uc//kn769/+aS+/Nt8amyEpWyGveZRPcXtZvGSFPfbY4/bTG35lxx33ATvwwMPswi9fYm+9+ZYL9/7Nb1xkY8ePtUwKy1f6uM4KGqITe1Y4zlbbYbspdtGXPuZ6/dyzb9j5n/yG7XfQ6XbKiRfYH269x55+ZoGtZq1OazID4dcG8Ri1tfB+L7yyxG7708N2xhmX2377fda+8pU7bd78Rvu/z55gu+1SS33CUM/SvWQHXU/y/oBTFKI8lcaC15o5gEV1zTSLQFqLvN55Mvu6VoMoUZhqqSQN0VoN4X7gLmKWYtZQVmYl4/ZzRLGIyqxclvM9BbXfy5FjQ5vgu4Lvtlg8Hlg0Q/Tq+ykkfgMid8OBummkeShL7dw1BhjqCSy3gzQzhkUGipldgKV2jDjWri7aisrCmDba2pqoo8UtSNCCgix5/b521JDNQkIT/znBwgZRudFo8L244Sh6u8fcyjZAeC/lxDYrqBhvsaoplkxGbVp1ArflsvLXuHr5mextN3z5wUbgZBr4a04/mGtMpOTnUMUW0QqE+ajcuTyD/hzdFe2NqPwP0QdQrUpchDag8jx4A3oA2pnszYG/5jQ/XsvZefnfy518VV7eF3J5nW3kSiesV9szOyuYy5+ZV16uO4s6Kb8v+b9Fn0Nxi2ALUWGn96A/RQ9Bu5PTKRD27f25wtr+B12BNqKq/yy0L6sv9WUU1q/t19GOvkREjFyIPoWuQZehdajcQ96DXo3ujw606Npcjj6IasXpcvQt9HfoJ9DOxrw9x/LHpWvRU/kBBcNzT+/pSXnl8j9H4VwSQXQZeh8q/DTvNSeeRc9De0v+D8Tc+gDthuPUZ68zGYxrPxD9z8d5oO5X3WEyGG3m4675/ln0BVQ/wBaimvf6vF+BhquFTyIdXrtzSfdWenMv7apu3St0D/kTqr7qR8JKVPelP6AHo72RXSn8Q/QBtLffDz1tpy9jP53KQ7x1/5Vo29P78EjKfg69EX0U1dgWo8JMWC1B9V1xGNqVbIz5uTHazMdgqD4T+W1ulHR8o7Q6zBrlYSlBl36M6ssze+Vv7zWRxUk9hXvxCHgEBg2BhpakffwHf7B/PPaS/fz/Ts6OrCg9lcZ25jN5JKv55w5aw75ij4BHwCPgEeg3AveOn9xtHWNwaXzEnjt2W663BWqqKuwlvRNqajMb2dt3Gnmt6WVyc8pljKrS7/9AJtTlDiUAAEAASURBVI8Zac+8xvuftVjI1WpdUx8FYscgWQpxO1s7YkP3zkPRRh973eFpFaXFdsIBu9uN/9CzaOfyYnWNLYHIGdOs500vHoFNF4FYNm4FsRTWkTHbalzWCqMpa8ENc8nYWZCXMVxAR7EGxWJTcWRleQtJ6Nw293LI7hw8N8Sx+kxh1VxcEsFC+AK7//7H7Y25b9orr8+1V16daz/58S02urbGJk2cZAV4MBCpl0ymbMWKFbZw4UJcZwf3MjUfi8UhNlP2xc+fbyef+n7I6DpIQpF3Ih9FcjtHxfBpENb0u615NUTxqfbfR56yv/z5PitIlNnbC1barfPusVt/d4+VlcZtxtSpVlLGwhEqUozc+vp6e3vhCltZp8/6+rb32Xsb+8yn97M09+ekrJshmON6hdcP4U4N0Yh7ZaxKs8Z9GT4kUzUa99218KLzbZuaUTaycoEt02vvQRIRxSKN5Zpa7rh3nllhk6pbrakBhmD6brgtr4WbxU04/ZMFunCW4W+HVEenfWQe0U4Ei982npPY4/yYFXP/Tbt3E6qQeaaFUxtgKkpZ7y7Id8dipHXBg2vtFj+IVOe6y1W04lm/9NJLNnYUFrlVlTQB5c7iAbURxcX4ay+8bCNraq1m7BjLtCxVrf0S52qbORNhnXASH+9FJcyhXFf7XjHutGV1jrV5ls9hijlbOn4HW7ngcauGtD9kp6jd8wLjAb+0cw3f95b8mRsVgZm0fmSuByIHt0X14n+bXF640Ts9/eCU6p2CXobrRX9Xog+KSMevou3fjYp03TWnZ7H9Hqqy3NQ2kLHshf3LP6B+SiVPBBtrYRuWnUP627n8jjYH5JXV8SpUhHhn8l4OhHU/RFpt5ctEdq5ET8zPzKVL2O6WU431J6jwW4t2JFuTmd+WyKJfo/l3pe3YV3s3o+u/INjpRj7B8fzr9ij730Xb34aOIO93aBnaXnRNpAehn0TvQE9GB+KH6dRcfbPZ5sskdqTvR0VoCGcRMPnyEjvCeXQuU99Y/8mlu9pM5qDGEeL79a4Kd3Is/3P0MmU0jrvR6e3K8yVv26PXoJrvus7PoF3JQM4t9SecW2900uhAX/uB7H8+zgN1v+oOk8FoM4R+XxJ/RqvDjNxW98d9cqrPlu4/M9Dw2i0g3VvRZzY8P//cju6l+cfz0+rnTeh78jNzad3n3pfTa9mej2bQziTGgf5+P3RWd/v8voy9P/fhC+jApWhH98+wb7UkdC+TdnUPnc7x8Lp19JkdjPm5MdpkmE6G8jMRtrnRtjxJbNnCw71WKP0TPbO+sSX7ni9dH/n8DXd4wnjLnhZ+9EOMgKyOcQMfeezlt9TyNuijfDb3GuJu+OY8Ah4Bj4BHoIcIzC8tt7fKq7otfdLBezpio9uCvSywy6wpwRlL+/n+Zxnn8xpq1qSxVlas599A9tlhVpB4e22Y1bctsT0le2034x04DEUbfet052edemjPvpr/0wO35Z234o94BIYLAlB/3B9KC+M2e1wB5HGLpSpGWqxsGuQhn22RcxmIPSnkXFT7EHci7wIVgZjmNa/eSWkbqMjlQEXoqQQugimSScviGJvJlnqbNGWU/eIXV9okLIT1Sisi99NYgy5butyeeOJJ++9Dj9l/HnjEHn3kCXvj9XkQcSxOgexLYKUaEsZnnXGKXfy1L0C0NsJpB22pfhGR0izt6WWA0jHcBxfEU/bTH3/ZDj1oT0u2NUC0ZSwO+RyNJnBdzVvj59+AVH6Btp+1hx562p57bp6tqmuiTfVPNfMjfptJdsO1Z+EaGJpSJCvtqj+BdXOOwMxZwobktY4F7+3Az2EV4BVgqDTi+h+B1SEeb3qFw0tv82K1MyzKwp9RI1M2rZZ7uHCiL3SJtP4MoFCfXEeLjFdcXW7rfG9gcQ7BWzV+LzBuops6Jn6EBU1O1AnNi1CVGabXb8Nr4q4Lc0LYKcY1PsHtppt+a08++RwEeSnnKnazcAyup2JAh/WppbAekcXiacKYysHlyVJdAuzidsMNP7OLLvoK15g+aD4yJk52FsiWiZrWO5111iftn3+9z6Jyi14Aj+DKajJSHg36QTOI5rH6oXbCa+0WJHA9tMAgES/ExXm1/e1vjzF/n7OEs3IO6nIVrPsTYqKMMJ2/XVfQJbCRphifN9xpJ8GshPkQr6ywKAs9dt0qYiPLsUR3GG14nt/bZBHQi/U7Ub0vkOgDIALsNe3kSQHpq9Hz8vLaJ/Ue8D+oCLiQMG4lLaJHBOjDaAsq0cfrM+hjaEj4kXTSzN9FOVV/QhEhGOYvz2XeHh5kOx6dnbffPnlIu4zd2ddNoDM5Ku+ASJ580ct/jevEvEz19Vn09+hzaH7fz8kd24ptdzKGAj9ChVF7+Q0ZwrSn8nEK/jCv8IOkhcPqvDwld0HV75DwUN//m8u7ie0DaH67R7P/S7S/ovn3ODo7V9FKtnegd6H1uTxt9kWF987ayRN9Odyct38s6XAMednvSH6QnBDfl0hrHvZH9PkRtiJeJM+j16DXoy+goYwjcT+6R5jRwbaWvMGaWx00N+DXfjD7r/kyUPerjrDoKG8g2zycBjS3q/Ma0mdRedLwczmBtO6lh6H9kWZODu+Z+fejju6lHbUziswn0XzCeH4ur/0Lg/PI/zTamQzU90Nn9bfP7+/Ye3MfvpjGtQgq/96j74K/or9GdS2Xofmie+gX8zP6mB7I+dnTLgxkm0P9mejpGAetHL+yt1zh4UY3lfvRA15dsMx2P++7kX88/vKWC4gfuUdgIyLw9sp62+/T19jN9zyhXtSg9/AZPXgjdsk37RHwCHgEPAKdIPCfsZM6ObI+O4ZF1omQD4Mh66yXF/CORm+3+ypvBM+7h++xwwY1HLjLtpAlMRwz8Yy5pmWDYz3e0Uv0V1e54u3rV+ZQtNHjvvaw4IwJtbb7tuE7ps5Pkutyveb34hHYlBEQMSdX+MUFLTZhpAhJ/pfXcm8ox9gWd7tYN8LuQVqJ7A0I34DoEmGXryHpFeZpn7QjSCG8aEP/dK7qEUnbhpvqfffb0X7962tt1lZTKKNzJVgkx3AtjLWvLF7lajieUDoBfVYckM8Ql5/93Hn2g6uvIp/6RWRSzhGH1BOStLJWdZawah3WWsT1qNEj7OZbrrKjFFaAsinIxEy2DcKS8cVl7StSuhRiVlqIG2NcGGMBLMdde+68tf3m15+2rWeNslSS+yckeRTS0JyVZ4hBuNV4wnRn27AMRUVUYiHLiNlCgtM3umPxKqx72yC8i1M2oUpt0U/Kqpz+D6TEwDkBCBHMphMQ+IfsmLaWFvAtKbeCkVNgXLgGuEuOZJppH7JcY2fOZBi/I8BFbpIf7ovw1T5LBjgvVK4J8YVjEPpyT10EcbzjnNl27Ps+apdc+gNbxfdRvLCceqGHqTtNnSnNQwhiLgOQMx+53lEtUBDuXDstZohGSuhKkf3z7kfsPUecauecc4Gd+IHjbOToMQ6v8FpQJac12Y47b2uTZm5lhx9zhp3yoS/Yk8/MZQ4QV5s+ZZi3GRpjdrh2A8yxIlafmERSkerOvTvXQnzzwrdX2yc+dZ199dLf2oxtt2KtBdbUbvLlrrHS6xYMkJfbD4jpACdhGPSTTU4ieANIgnmcRRzFuKLOliiW8VRrobpptQU2sVou2DW/vGwmCJzFOCai/PizC9BSVATYVqjyv43my/nsaFp3JJeQ+a68A7eSFpErQvJUdC+0Av0mGk4i/Vj8Bpovf2NHpIlUhEcoXyMR5ockhgjHxWEBtoflpdsnD22XkWB/33Z54a4Ijr3DHbZ/zksr+RNU7z5D+QWJKlRW2bIi07iq0ZvRUKaQ+HG408X2YxxTXU3o71Bdo2vQueiNaE/lbApem1f4PtJ6Qb82Ly9MXk1C117yMKofphq/xvJhdD90Bno3GspxJEQe9EdGcrI0iaq/emd1DKrrqGtwKZpFJWPQ/PG4TP78Ikyw1RjUr+5EYwqlN5iG57Tfaj6OQ/WAcyK6PfpJVNdyO/QjaBsqqUTVpuZfRzKYc6uj9gb62g9m/wfyftURFh3lDVSbuvf9EuXHrxPN+fNQ3Sc036VKa65oHhWj+6D9kd7cSztqR5/NKWgK/Ty6NToZ3RXVPP4/VD+YQrmYRGfz+hKODcT3Q9hWd9v+jl2f3Z7ch/X5/nJeZ/SdpM+/vguORk9F343q/nAOWoeGci6J8L4b5vV2O1DzszftDlSbG+Mz0ZtxDkrZAX6UGpQ+DkqlOcL4XirfQfGL9/7U1fbKwuWD0pav1CPgEegZAoof/qFv3WLf+f19OkE/PO7gs3pIz872pTwCHgGPgEdgKBDI0MhDY/QerGs5eNfZVot76sEQkZf778R7wjZ68+yyvjWxuIHXe2uthDi9pxCvN19GYJ304cP1/gl5aqne+gfp3vx9jeesNa02cXS1HbvvLu84cyjaeEejA5BxajusOqpyRXGJvVSlZ3cvHoFNFwF96uUAt7wQ07Iq3D3zGipRXAt5JmtNCKqM3pH3QUTouffK3L9yRFlgnal8CDKIvwiMYKZtrb1r71l262+usaOPPBTiTz3Scd2F46gIVOxaebWbIl5sc8tamzxpHJap19lVV14CkYblq7M2bYNH1Pu+nOUzbTtL1Xyijj6J7Evh7npEddJuuemr9oPvfskmTuC9N82mcbNvWYhAzklnmyDiULaKU1uYiNvZZx5kf/7TV4iNXI2ra8VODu6Z4ZbGB0AgJSFXA9KbMUWwjK2ssRQLlBLRQps1We/+uC78zUQKGPvAvuqQi2VZtWZSWRs/Im0TRmOkwbVKl42xROkICFvhK5zVRzrneqLeKN1ziYGnXHFnks3EGG623ffczY447BD72levsAMPOt6u+cENVlfXAHlfACFbyqIBxkoTIrOjBdDliZgjzCNY8wqPtfVNdvONN9pRRx2HHm333vt3O/aYI4h/DdcBQezmlCNkg/koq/NUW4t9+Uuftq22mmi33nKH7XfAKXbSqV+0v971BNe8lHZrrai00lm2R+irFjFogQCXxwqLIZdL26ywpMjmLcrYl772ezvwsK/YdT+5yz5yzv42eQxzCmJ7HUQ9h+YdJd24+QylM40cS9IPxj5ikrXgsjoRHWHv2lb4k+dlc0FAHyYRAvugP0DzCYCF7H8R/Skaisjk/cKdvO1s0noBHsrlJE5BV4YZua2Is4tQHQvlTBI7hzu93GpC3pF3TntiODw0mYT6LtE5oRwYJtptD2c/nOgvkH497/hppEVshnIBCY2BH8EbSD17H0KFYSgHkDg13OlkW0C++igiUgTkz1ERkNPQR9GeiPpzPRreLP9J+khUH+z2UkLGbnmZujZv5e2HSc2H96GLcxnC54O5dH82+qLW+ynNs/xrw93PuTn/ONtQ9iQhXPLleXaclUQus7s+7UG5cC6o7Zty5/V3o/4ejP6ug4p+Rp4I+FBmkfhEuJO3Hey5ldeUSw70tR/s/g/U/ao9Dl3tD1SbF9NIba4hzfPD0OtQpUNRWnNlX1Q/foaLfISOXIm+ktchfXauQr+Tl1dOeu+8/TC5sb4fwvb7su3pfVj3/3iugRVsj0Z1T2ovwusG9DN5B7RI4NC8/b4kB2p+9qbtgWpzU/5M9AavDcpGN9jbQnZ4eNUTlH6sbf/Ii/PswM9dZyvqO/o9soUA4ofpERhmCHzu+tvt6zfpWcGtbLuNz+ycYdZF3x2PgEfAI7DFIvDsyNG2plA/pbqWE3FNPZhy0WnHEHeT5565a3BOuLJ3Ta1uMXtkkTvn/BMOsZoRWjy6oZz3vkNstPKX81L9qSU8Juc/J29Y9h17cmv9P8hm5Munvzfo5zsKsWR7CNrooNl+ZR2GVXZlmd7bdC09sUbvugZ/1COwcRHIQKSKn506vthKE80QYlj0Fo+xLCRdlpjGWSwcoV/70EkqzSOOnRWqIxohJEXeiXmDCRYhm8bt7o47zLTb/nK93Xbb9Xb6h46DyBvriFPdkrLEC64dU2uHH36gXf2Di+2xx+60U099LyTyau47WBgTU1jWy7KMdYSrSOd15LHeCa3vi8i/bLoFspof4AURO/+8I+3Bf//KrvzW+XbIQbuZwr7jaNpiEOYJiNrttptmp33oaLvz9m/btdd90EbV1ltDXavYy6AtOuhI475A1B5VjRXSNiC72+hFm6XAKVE60rK4D08nszZlbBv9w9oaAjMNaaxYygMpEa4/gFJvxHaaWWqjStlva7OCmq0sUljpLH2zEeaJLIppOyDMez94jVHXQmSuyNhUssku/OK5NnXyOFyCv2Kf+vRFtvOu+9tHP/pp++WNv7b/PfeqLVm6xpYvr7M1a5ts0dtL7fEnnubYL+2Tn/q8zdllLzvr7I/bP/5xN5c7iyvvKvvmNy/CFTW7XGxnwUubsh3W3EhjAR1hDLU1pXb5Nz4DKR2zxvo2+91v/24nnPhp22PPE+yrF15tv/vNfcRFXm7LlrXasrq1thIr6MUrm+3hJ163n13/bzvtw1fYXnt+1L71rTvszblNtvcec+zM0/eFL1a8ZyaZJrBe5fVTInyW2tJ85/LOWiNIjNvasvECiPeM7TgxwLGfTfjThxcC19Kd57ro0kXtjm3Xbl+730dDkvVZ0l9Du5LfcPBvuQK6sej8vsrteSe+m3RHP6jzF83/Ka/8gXnp/ORReTt/zkuLHBC5HsrTJK4OdzrZimzJx/c77Bd3UjbMvobEv8OdXm5Po/wNaHg3uIP0sWgz2pGI0NEKIYk+4J2V03HdGDRfhPn30NfQ/srNVPBAF5VoLC/lHf96XjpM/iJMsD0IHZO33z754byMu0m/nbffn6Tm9ENdVKB59HDe8Y/kpZUcqrmV3+xAXvuh6v9A3K/yMehJur9t6vN1Vl5DvyV9f95+++QTZPykfeZG2v8W7d7YRdu6v+XLtvk7ufTG/H7ooDs9zuruPqx77OF5tf2SdPCyIi+zXfIP7fbHttvvy25/5+fGaHNT/kz0Ba915+hGuSWKvqT3fHn+UjvyohtsTSMvDr14BDwCwwqBi2+800ZWlNh5x+5bSsdkcbw7bs/ClaLDqq++Mx4Bj4BHYEtC4JHa8d0Od1Rlme0bxgXutnTfCkwfX2vf/eQp9onv/iqwNm5gkfOOo3mVFL4H7KBevaR+C5L5ySW8j8/aUXvvZGfLDWsHIkvgH//fGXbSxdda8s3VvJZK4eCKZ6XiLn4+yxJNLqmfW+ZeZX0CUviQ3Tp6Xxk0OBRtdDC0fmWJqD9qrzl2y13/7bKeJ2rG2lkvP2NxYe7FI7BJIqC5G7EZ44qx5OT+wuuWeGk1VpwkIBCz2aRsfSmTp26+s+/Ixbz8dWUgaZ0FbFiGoioL3SWrZhHG0bgIXrLSWI5C9soFdjRWb0cdvR96sC19e5ktXbHampqbIP4KbcSIKhtTW23FxcWc3mZtyXrOF+FIjaqPmMJpyM2Ibo2q2LXHlrQsNYPGlM89TmR2incjaepJrbBJEwrtc58/HgL5KFu0eIWtxsI1RWznwuKIjRlXbqNHacFNiyUbsIrFRXVBIcRuinpctTnSmLGrmUA0sA5UfXGDDo+F5cOzhDR9c9asjCUKOU35WLwYy9pSy7Q02egKYjrLbbgjbIXz+lY3rK1ve1FcISdw950kXu6U0VkrpK1mcC0ZM8OSmQT7IrIhYcFchLUjfzXWXnbDXRPmQ7ygCE6aORYrtKnTx9g3v/FlO/3Mz9B+0uYTWusnP7nZadWIEVZeVgoWssSOWDKZtPr6emtsYMHTOlEncO9N/OUfXfdd23b2tpZqXelcaDsiXnQrc4Y/9Jg+812WbFtjx713f/vMp0+zK674OW7RwRmvUM8+/xZ6vat59MhKKy5hEVFM1zhhra2tWDY3EANb71fWX8uxteX23e+/10qxhE62NDuTpMLeArNuLOsTbsrEcpbGUc1fFnRUjwOLasjuOptdW2OV5Qsg09UXL5sJAj/rZhz8AHMvwkMruap25XXTOjgvT2QHN/hu5aeUODJXah+2THznkjmX1ePNvyjZiOodh8jYfVGRgflySN7OFaRlDcYyD9sJHYHiymad6M6eTwSI7AtlOonKcIftl9HuPgy6eV6EimiVCMfZqIihzuQvnR3oJv+DHP85Gs2V+yPbk9G23H5HG1nF6QZThOrG9l30HFSYdiTf6Cizj3ncJF0M7K5OF36XozycONmFv6PQFcGu+3srf9XvQlTXT2MWqd1edM1PzMu8MS/dn6T62BGZ3b7Ob5IhEl+yLToVnasdZKjmVtBa8Hcgr/1Q9b+/96v88fc03d8296Oh/PvGJT1o+DLKnI1qTm9M6e4zwgO9i9fLCwMn/KjfQDb298MGnenlTnf3Yd37J6MTUc3/F9HupIECK9GRuYIDcX37Oz+763NHx/vb5qb8megIjx7nhV/OPT5hUy8I8aQfFCetrG/MHvGl620Vq3G9eAQ8AsMTgfOvuc3++vAL6twE9EY+v5Hh2VPfK4+AR8AjsGUgkOJt9lOjuloQH+Bw9D47OwupwUblPe+aY987/1QX99FE7P7t9YCwxdrJmQiqA3qr3Mj7wNd5x3b3XLPHFjvC+IQDdrcrzzsZ0qTzr5Y5Myfbr778URtRzrtBubP+O/U/w6LcFfx+TOmdCyKieC31iyy+882AwCbrgg8cZp8+Mf89XlC8/d+haKN9m/3dP+7du3ZbRVMiYc9X13RbzhfwCAxbBPgcy5hmXMFaSxPLFeNaK6yssqhiyHJfiShWrSPZAsJNlprOStiRhkpTAWnFFM66skqTz/+AJ1YdSiueLXFnndJkzvozgqtdR+Rpvw174VbcPreutVrcIu+w/RTbc4/ZtvOcGTZ10kjWshBnNonlM1acMcjVOPe1LG6U9U49i9tkxT4OSVltQ5V7ZbUfkXVpWn3BvXFMMZApQ9ziVCsOriEfo3Aqk8dX2E47jLHddp5kO2wzzkaWFluysdlauf/JslbiXDjL5XaOmHaEeN4+gLhjjpoUNlIAcX1QP3LKAVdf+EeupmVRGiOOcgai1hQ3GCtcmFVcMlczvlarwUV0JtYI9807+EiLwzM8f2C2GXzhZqykIGq7TSmEPIegrhhh0fJZEKoi6hk3eZEM6mIJayGALLtFJCs2tNxX03fnklxgp5w6DBivs/SlDn2lENGYS6OY0Fw/5k8qudpOPPkIu+zrn2VGMj7wjkCgyx3z6ro6W7Bgoc2bu8jemrfQFr+9BMK4BeKUmMhFRYELaeopgLD9/vcutRM+cDR9r4eq0Hcf1zlP9b0J/+vy4ixeSLc12WWXnGOfu+A04ls3spAgyVjpAeSzPhvLVjbYWwtX0O5SmzdvkS1evMoaG7H4Jv5zVB8YpHokC7Cu/ZjtvtN45pO4OVxXs/gosMjW9ZdldqBhnOfwmJAIXbgr7bB0iwfIl0Q1fxOWALdU23Lmepb+EfO7dgrOd9MspmixiVW841Tsb2Ggczr/ytdRL8Mfgbd60MU1eWXcZc/bn5mXVvLhdvud7T6Zd0CzSC/d+yIiPO/KO/GwvLSS+uAclMtbxvZx9Kncvo7tl0uHm3eRCImPhaTz+7lNWIitbqr/ytvvKtm+3KyuCnPstW6Od3RYROkv0eBGEbiyPon9rghjDjvL4v8okZNT2Ir8ELl+ANr+epM1YDKPmt7sQW3/bVdmq3b7PJBsEHf61HbHw933kAjJGpFd+QsCwjJ92c7jpFd6cKLmXr7kz72hmlv57fNwZwN17Yeq//29X+WPv6fp/rZ5YF5D/Fiwl/P2O0vycLyBW/zOyg1mvuZHT/qq+2Qo+eS48jb290PYr75se3If5senzUN1j1+MdiXjOKh7kxbohKIff/2V/s7PvrTf3zY31c9EX7Da4JyBuOAbVDicd3ion0r/vqM+nnHFrZF5S/Vd7cUj4BEYrghkeIFy+pW/tmd/8vnsuFGVh9DPj6PXDdf++n55BDwCHoHNHYEXR9RYI65Zu5OekIrd1dHT44oXPGvSWLv0l3+2h5+H1H2JBbFSCbEZHXmsV2U5GUOc5f875Ug7br/uiU+dsvu20+32b3/GvnHj7Xbno88G5LAIYkkH9U8fP9ouOu3YIOZyUKrbv0PRRred6EWBnWdNsUm1I23+0hzOnZz7eM04m7NS7zy9eAQ2VQTSlihMwDNBUv0/e9cBIEWRtd/k2cySczYrBhQjmM8cMWE6zNnTU9QznPnO34ynnmfErJgTiooBVEyoSBCQnNllYePMTv6/r7p7tnfYMAubtwreVnV1ddWrr6p7Zvqr98qJZ58nU6Ig+0hgGhbCKf0y2GBkOvAfUi2QAOMpEmQkZY23+Mn9b3kJTvOYewczGMdMG+/VVS4XrIBQNM6yVG3B9uBTRXAdeANFyCkyEzWgHeOY56gXC5KcA6GJv+yndU0UhKJDtYvzJEhVHYxbItAuGzQr9vUVj1/p7XNHJMPjgHUuO4F+kZxNhWALVOWYsT6vu1IGdO+OmirFkdkFpGwu+FcQuzwNYliRsCB0OT5G8/xbnxBHTgDqzPFHmnME17GmWMwJ4/Zyueba8yQL2wNcc+2t2MMarqThJt0B61oSvNFYENwoLbJpUczLsdcxFgFEsD9xF1iEjx9/v5w+ZrSEKktRnhpBJzYDllpZcWPck3MabSewkAAZ6J9b7v7XleLLzpAHH3xOggEsmkBwgjxnFbzekchAjqEz567DRTfnbunfq4c8+vg52JN7O4lVVKg2uVBL1c2L0xK2Zi/LYyOotoAN9/tOJDAemJck0t053YFNQjKzRXp3csrsFUZfURKKA2NOax3aIgIkzkhi1BeMSWqU4i1kD6mkwMM4yQdeQwPrmdXQi8zy7yM+wUyTNL7WTDPaDWIRhV8gzck/FbIXhOEgiJ08PFrlGn9oacbyVrATY2uRacfFKlNTHEAmSaAe5sltaypk5lUitpMwdRRNnuqD1IsQVzLHsKLeHsf4ol1vuBUlhkM6myX7Ix5nClZ4KkLkY8STICsgjRWWplkR8eA44KGoAufKd2baip5D4lTzgH3hWP1hHlvR2VYC8esQYt0YYVmalfALPOeMzyzfy3Zdc8wtW3PJZGONfXPo3xjPq2TH00w0Rpu8P62w1EqkEbMsvRK0VDB/nNfbfMRWojV+PtjUSzu5Oc9hq3I+P7eG8DnFeBsIn/mDIKnBeqal5qd73BjzM922rHKN0WZbvScsDDY77lCkMVC6F5L99KTp8sH3ynpxs4HTF2oENALNg0BRaUDOue9Vx+T/u5gN3o0XNq/iZYNe8dE88OtWNAIaAY1ANQR+6M5Fp3WHwb27y46D+9ZdqJHPbou9Hl++9VL56Y/FMmn6TJn62zxZunY9LO3iqqXOuVkyqFc3mbdsjZxx2L5pE8aWmn26dZbHrx0rsxevlPe/+UW+/GWuLIaLWKt+WiKP3Hlb+cuIneQwiLKAsy5OM66pjUWr8L7G7ANdfu+z09Zb1EaaqqRV7FhYkz/61md1lv25ey85d/5MWK7x3ZkOGoG2hgDfjcTFT5f0JPOcWSDKcsE3RWFjCWtSRZqmvj8xnjkk26oFdWgwVcbtgHvCvC0MgtB8b4VyPLYsd41Xzixr1mtVqnxNp7RhnbPiTe471mFZdjK2/pEENkhiXmpYfrIsiHGlJNonia2sQY18i2QkeVyPFqyyaQL6F4frZrCZ0NIB18dRyfI7pTQAjUgiUnf8b6xAgpKkY64vJr26wOU3rVr93cXly1D7AMfj5dCCY2znQdJonXNLBRNJREptjp/KQu/imIPAPxYrlvMvOFYGDxkgd901XqZ/+xPccWMv7AgsrkmgExNYs8ehGz3nupx+Oebow+TOu/+BvbF3kmB5ofj8sJKOYK9rTDn6cEqOJeYGj4wxJ2HMeYHlAxGkYUl9y81j5MD995Bbb3tMpk//BZbtKO8E/qpcULWtuoGmE9D3pNF7yu03nS7b79xFImV4l0syXPUMf62uqQs2/w/rYZXcC9zhwHtgYgEreW9eTynDeGXAffl2Ayvl01lu5ONcnE6xYRmvxmnz29VXthgCJLG2NKSSxiM3s8LUehpSzUcozBuOD/4dIfxyvRrC8BcjUn+nmGnG15npg8zYiuyk8btWphnbibGlKefqO1yCAj3MQiQRagtLccJ6iNVWJjXfb8vgXcwnHR5gMgEyAlIfiT8dZfaGfAwZDLEHLBVReyIfh5h1fwf5H4Qk9ZaGpWlWQKK1ENLdLF/TXOEX6FUQi4w4A+mbzfKMSIgfZTueYEtvaXJZmhUQP5LuQ83yPW3XNcfcsjWXTDbW2DeH/o3xvEp2PM1EY7RpH+flabbLYnxmtGTggpEtDan3akt8PmxOH5biooY8h/uh/FWQsyFdIc0VGmN+NlTXxmizrd4TDcVqk/IdhjTGDx6u3hpdHgwlbnp2kvr5tQkaOkMjoBFolQh8OmO+fISFHkfttUMnKHg95IZWqahWSiOgEdAItGME+EtkRjf7d+aaO3vEXsNqPtEMuXtsN1goDJFoTAKVIeEevLRAPv+ep1X++ImfyH7Dtpadh/ZXxw35QzKccuPZx8pf7/qfTJs5X11+zyWn1bl38ea0ceTeO8uJN45Xl7phNvbBvddID1hJt5bAca6PNC6HVfq8Tl1kh43rW4vaWg+NQPoIgHQiWev1kQTEExAWnWAJQTfBmhEElQNkmQP73PKtalXgk5I/NZlrF2azPhJmtOjkWVrLqoRxjleoypxGFeoYJXidslxFRjKwDUpdQVVmK2AQxoqAJlGp1GG/YijDsgaRbLx3Yh77wmCcqxajD3SnbBCM9elh1NL4f6ED93zGmFBTnwckIVwwUxvV80ZWC1QsyFiHDOybBYK6QsJBl7j8MAgEaZyIBgAXRGnC+dKQYOJrTAroDlRNMp5pFWj1C/I2jnFzwSX3YYfvI/uNGi5vTXxfPv1sunz7zXRZs64UxDEsjUEab731UNl9+HYy5rQT5MCD9scYh2GJvFH82HM6DotkF1xHK4tfkKyWxTgtzpPbNaj5gdYRJxIgmGNcOJGQg0YOlj0/fFQ++niqTProK/nu+1/hDrsYWmag/rgMHtRNdh+xi5x47Ag5/MhtxePeIOUbimCdDY4I9bA7xhxvCD51lyV6lhtwzuUY3Hn7srtJMdxvk9ju3yMCZg7W6M4KEOweKAByWYe2ioC6tbdQedy0jRK2ZP8NEookv/Y1NSFRPMFMH2rGjKaY6WmISUT6ILTkI5m7DjIQwmMGWlN9rVJVf0igWiFoJdKM+XCwgvkgsg6rxQ2t137xMzj4BfKYmbkr4hshd5jHdUULcHJnyEmQsyCjIKnvt6k3MaYcArkIUgnZ3BBuwIUcLyvUhBE/tp6HsL8MZ0BugVhz/DSkSaQz8MfG9yrVOH9KGlCNfewt3Xh5c8yt2tRsjLFvDv3teNXWl8bOb4w27Ys2MhqgID5g23xoDZ8PmwNiTc+Y2uq5Fif+DUl9Xlrl+WxaCJkC+QDyIqSxcGmM+Ql1GhQao80Oe0/UNkkaNAJtpDBXUTgefutrKShujAUobaTXWk2NQDtB4MZnPiJpzN5chEUgt+GlxpZ84W8nqOhuaAQ0AhqB5kNgSW4nKfPyfVXd4bA9W440tmvmgbvOPLjxZBi1yzbCvYN/+3OZerF91fiXQML+XbIz7MYO9qvrTw/o2TVJGtfnprn+2jYt8cCrk5KZ3Lu5NRHGVGy7gX2kX/fOsqIAFmR1hJldemjSuA589KlWjID5mqETnhOJmGGZ6YS1pCcCV8HcDzYUkhj3ClbGYsZ7Vafa2xeEHK+lxTCJQJxXvC8ZM5ZGZJFzzOEetoz5R10H4pFlVGA9av9a89iM+EbHVK/6CfOIl0PLaudoNWpcxXzLQhh56r9hA8r2DTIYMdJKfVUAB6pF1EzzVPZZEZvoPzsAKt2yWKWLYIM8NfapdcB1s/IHrPb0NdtWupjlkCZhqergtSQsNwkkzyEsqyxFUWccdr+eIN56ZSKH7qH94G9pcwrtcA47QiOXujVWYF1u2aonrFXZJ+jpzu5i8PkkVUmKqvYsrMyYkQI2Jb9aWeqI8/hPIl4tFEBfVZrzAS7So9jL2Q1rYkIVdwQk0x+Xs8eOlrPPOkkK8BwuDQQlHA6rfYzzO3WSvFw/SGRYREfLJK48VqBy7lWt6gfNioVVDhewplpq4rF9tk1VjDRJWLpmj0fZvwjmeznarZBTTx0hp44eIRvwXqWkGAR6uEJceF2cn++VTvlZIKUrJRJEfrlLPCDywd4a/VONkYi2FhwgW+VRiU3F2odb6aPOs7wtqPuNdcGSOAb31LQ2RnB5oQP2No7HItIjz4/z2A8crrqNeWa7Xic7IgKLbZ0m0UqLTk6+5g50JU0yk8EijbOQ3kfliFDPJWaahMB3kAPN44MQvwo52jxmROtl4waoylxYlZQBtnQ6SXv5wnQuaGCZR1H+SvOaUxGPMtM3IyY2M83juiK+2J1gSi5i4nIY5HDIQIg9nI0DWv4eYc9sYLprmuX5nr23rewiW9qenICDG82MgYg5H74xj6mvFZ63Eo0U902zHn4a2MuutV3X0nNrS8e+pfW3Qdnqkgug0ZGmVv0boF1Dyjag2mYtutjWWkt+PtjUaNTkWNR2X0qNv+CYny/cGoAyG1IBsQK+8Hf40GHviQ4x+PiBnIMpfiLixFNwTa2DRkAj0PYQ+H3JGvlujvrd1AnaH9/2eqA11ghoBDQCbRuB30D+1Rd6d81vdtfUNelE95wfffebHDXufvlixly8aHfJI1edJTmZBkm8DK6r//n0WzVdmnYeCVMrrCioe29fq1y68bezFsi3s/5UxWkR9rdT+A6s9YV0Fgj81oXv6HTQCLRVBAx3v4pTQxcM4pUMQ8LsUJy5ikAkUUoyjLHKM4kxY99gkJkx7kEbNs+bZVhWWXOCCSQbaBKmrMfaaziZxnmVt9mxoR9JYaNOQ1frWLWvKFeSuMY5q08sY/SLMfuOmK+Tlb5Mm3nqXBUeRlmUa7LAdo1gjY113OixasolPXxlkgBDynH15eaji0F0n2CAr0EZA0+S6OZ8IJYWniS9YQlLd+BqDGhBjM8rCz6SpCTdnTZhzbE4Fi3QiliRulFmSAJTKR4KwMq5Qrp3zZShg7rL9tv0k8H9u0p+DiyuQdTGQeY60J4L40T75wTdVtNNNNrFRwvaYqfYpiXQFftWUwcH5pkaXpCtTlcUgrLODLjCBoFdEZJoqEKw84MM6JMl2wzpKkP6gajO9Ek8GAJZHAXRjKqx+kHtmcx2VCfN9pi2hKAhrfDCX4UL55fSgXpYwmurB+zerIhiZyJTYpEAyobFDevzOLxcuDx50Dki3TI7YVZjjEB8c82xUqN6NfqoYyHAl79W4HuFgdZBM8fv29qjdTHfze4HsaxLp9jOM/m57fggM20njd+1nbeSxhdJ46g/onTf/9JisLdVCeLGcO9pq06W4+AKiPFAELkAacsggG1PgDBuSChFYWJwCWQQZDvIvyBlECscigQe2psd0iWN+6IFPnKtsNBKpMQcH4sk5qnR5nlev6eZxsOwUVxrm9WpiHMhncAv8D5bwTW2dGuaW5sz9q1JfxusrSJpx4ZzMd0wIN2Crbhca/l8aAqI+Hn3lK1iLgbaHzIcwucxz/0AsRPG/Apqd3PG444YOuw9ke6XhrY+KQ5GBzK/nrnIsbyguK33ReuvEeiwCLw8ZYbV92OshI41AhoBjYBGoHkQmJkG+XfYnjs1jzL1tPL425/LFQ+9IH8sXS3/fedzVbovSN67LjgpeeW7U2fIO1//nDxuaKJfjypPTSvWFTX08jrLP/jax8nzJx80Qu3HnMxoRYl0xntVdq4U+TbforsVdVer0tEQMMxsJQK3wA4wbOS9FDGJhEor4pREL0QRXyDyaNuqLGoNy9s4mDMSxXFFEppEIigsgzRkbIq6zpZWxyQdaStbg6jrDFLSIoCrx6gr9TpeY2+baUVsUn+2YV5jlVM6GPkkiVk/rYGN/pppCwOVjzyyjAoPK81y5AWaPpB2hJLJhqpSyawtS6hXZTFYzpJ8JA5ecXiyJYo+0/qZFrmWBbnREC5QeFAT4mBip1hL5vEYsboeaRxXEac8x3pxXXKsrfFDbI0R4jgkBovaGCyRY5EwLIgRY4FCHMRwtTmRrN9qx4hVu1Z9ajw51rYyTGPsjblj9YHH+AfCOxqPYDuIMMRo07D1RneaNRBPWv0j8P6ElXHCA0E6wwvra0XDqRmCYsRahw6MgJ0UIAy7pYkFyTNasG4LMdzYpHlhLcXoctjShWQk9TjQVnaKLc2k8WXWyOT7TbrXPcA4VK6rPzHT9sj+opskLInUdAK/zNvfFVt6pnNtOmUiKYVY/+22vF2Qvsl2nJrkKtb9IXWN3TycZx0nQqxAIvcQ62Az4mG4Jh0ym26z7WGh/SAl/Zzt+HgzbX/XxXFfaSvTGMnBqMSdRkXsrz18aztoqbnVWGPfUvrbIGy1ST6brJCBxF7WQR1xH5wbWsf5tnIq9VlX1zPG3qfG/nyw191Y6ZGoyH7fn4vjqfVU3h/n7Z8F9nQ9l7ar0x32nugoA743p+vUWYva1azVndEIdDQEsPDD6rK6p60DHWsENAIaAY1A0yJQ5vbIYlpU1RMO2DXd91H1VLSFp086cAT2UjQW+c+Yv1R+mGO8rzlmv93kZJyzAq2NaXW8OaF/9yrSuDHdU3/+02z5dcEypZIXfbh8NA0jWmfYZegAWJrxfULdgS6qddAItE0E6MY3apKBpKRgOUlSyhSDEAWxZxHHikoj6RbFKRBqcGsdiYbUscsNJ7q4pw0CzkbMKTKWhBbJOYOgUyRjsk7Ur0hHW6zIyJjRriIwU9O2sta1vIZEJVpSZKbZXpKstJGDVUSicU0VEW7Wq4hPpknWUay0eb7aMc/XH5xJn9z1l61eguOCkGzGJAeTx8bpLf6rrIlj4s+gdSv66fDDDXIeqHUQ606QxsoNd2orVIJjY8eF11p5JiGrSFqmUZdKm/NDLRpAPsfKHC9jYQLLpohVprY4tbz9WC0QsNpkW8YcZhsc22Tb5nGyT2qO2q+z9E/FobmOiTe05x7KnkzegpLti0kmXWSTUsa8Tai521z66HZaIQL8gmW3JLkGx+ZDpE5tz8dZruj7A0JLrJGQ1MAbxgrp1PmeVRjxXyAHmMecyF+YaSuagYSl9yCkL4RYVqAkFsshqWEuMiK2zJtt6bqS9nIhFPywrsKNdO5+1POrra4bkSZ5nBpeQ8ZayFcQXlNfIDYBW6GtbemGJvnFn+NUX7jBVoBjYI2bLTuZfAMpy7JvINIknI+FWOF5K9GIMftxVhr1XW8rMxvphbbjlphbjTn2LaG/Db5Wnfwa2i2yaXibLV1b8iac8NZ2cjPyG/os3YwmarxkGXLt92tjfj7U2GANmU3V95G2tqJIT7Ed15Y8IeWEnXROOdWuD1vDPdEiAHcU0nh7ovvLnytbBGTdqEZAI9A4CMxdtlZCYX6+ySC8rKv/LXXjNKtr0QhoBDQCHR6BOZ27mXtz1g6Fz+uREdsNrr1AM57p2aWTjD5gj2SLj79T9bvo1nNPSFruVlSG5G8PvwhSx/77LHlZnQlaLlthVeFGK7lFMYmoB1+vMhY587B9hS6/W2twwV3qfsPqf/82C/NHB41AW0PA4pZAE8Jq04HnRAn4s1LQC7Q0hctetceu6ToX+/oae/mylw4lfOvvcHhk6bJ1EoqUgWejcRoJZOZjn2OnQSorAhLEnLJUVTGeR2S7FEHH9/0Q5cYY+WbspMthpC1xoqxdlGvhanWyvjAEdZHQtupT5Laxby3dLat9g5X21NHYQxgcHHSz3BcjZl+VS2XmkbgkqUji0BScV6Q6YzNdZfUKy1ioEoH+TrTtBRHrgRGDB/tGr1xZIKEASHW6F4blLlu1Aom+RAL7+Tojag9hB90eE5eYF/VthB6MY1IWoLLG2FjXNlqsJkRCvD6+M0NfnTA4gzUrdvxFmxGFwaZtcRaYorrDNMeCJY18ZU2MtEHQIo/jbmLKMsRVXWOWV3PDIp0Vec32rTJ1xfyc2/Q8CWE0kqzDaI9lqR/H1tBHEcWKkGYdDIyNMiqGDsa1zKO0QADGnHuCvZzF7YMWCfF7KLbXXlXTqgUU1E22AgQ4uW+16cHF6OfZjmtK0hLYTgSSQJtWQ0E7OZlTw/nUrPdtGaORHm4e/46YrkPtgXp/acu42ZZ+15a2J+lW+mFbxqlIW23YsqslaVVoJwom4xgffE0e+JKH46Be9iCmRe8EM0aUDJOSKYO4V+96bXmpySHI4IevFVItCa38dOO7UbBTHYWPxjliaIXrrEQtMb4cyFu2c2chfZB5TNzfsZ1rzORNqIwY1xZG4cTBtpMv2tJMtsTcasyxbwn9UyBstYe8B2+zaXcY0nx21BZG4sT5tZ3czPyGPks3s5lNLmvKz4dNGqslo6n6zmeNFfhFtq7nGMsdCrmHCVuwFirZsjpEsjXcEy0CtO3bc4u031yN8kueFBTb75Hmalq3oxHQCDQWAnz9sL603Kqui5XQsUZAI6AR0Ag0LQLzOtX/yN1z+yFC4ri1hIuOOwh7Nhpvh6fNnC+zF69UqmX6fTIe+xtblsi/L1ohD7xmfw+RXg9oYZuXbbyHqgxHpGDjlr9Te/+bX2TestVKgQyfVy4+wf6+Jj29mrvUqF22rbfJefnqq3i95XQBjUDrQoDfPBNSESb1BCIzhj1TsUesE//oCFd5r7aIUkXwkjQzSD5eSSLO4/OIP8srn364VNw5ZbA8pqUxaEIQnHQhTMLYsErme6oqQk+RtDiGnbMS+zmmqwhmM01isR4xrgHRi94YT0ZqaW+TdRnnjTbYb6OMIuLYrtmGtc+sIglVH1iSBCPrwzUk7lgWacNy1rJeNTBy+YAl/oVCTnFnheXjz/+QPxcGJDcf10XLQSj7jXrw1wisH1qTHCXhDuIeymCfWuSHS0Hgu6QS2JaANHZjfKh/o1uUEgqEfBDcDiwi4PizDxlhp8RdSMGiPIZxNSyAjXElwe4CMe5EeRdUdyKNI3Ud90Fmmtryo4qLEBgb9XJfY8MnIPc3dqM/FBf4FB67UoQLBtRiALUIAO+2aogduNYFot0uJO5VXcn6E9AHoto25omVpp7kzaEiBOdU2ozZlwT6pWL2F3UAiyrB3OFqAeRxoYHa8FgtYIARo1rMAEtt5BvzvWqeJa2qFVltcUlQwApqLkAzKJNwYEEG2wA2Tlh+uyQDWtIO3CG+DOQjJXEnxoE90KGDI/AY+j/LhsGjSF8L4TRPDd2R8TGkr+3EQ7a0PZl8UYHMEyF59pM1pL9DnuXuZjek+SKfYYoRbfL3c1uOtaKQk/t9W35q8k5krDEzOflZx/HmcWo0Ghkkia1A8sJubWrlN1X8Kyq2Ww/vjONbUhp7D8d4cKhAvN6BDDION/lL/F+w5RYh3fAv/LYKkKROXN1Z06KAk5H/OsQKxPoj66CO+DnbuSuRtiw2WVfQdq4xk0NQGfXj/E4NRyHDPqfm4di++MAq39xzq7HHvrn1t3BrC/ErUHKuTVFaed8H6WHL64w0nw9fQBr7BUBDn6U2tbY42VSfD+kq1lR9/zVFgbtSju2HI3HwLiSVJK7puWe/rj2nW/qeaBFsa/pS1CKKNHGjW7P+TtnaMLGJcdbVawSaHIFsvOw3A7/o6qAR0AhoBDQCzYDAH2mQfvunQR42g6rJJgb07CpH77Nr8pj7HFthx8F95bozaAxghCff+1K++b3hxgf9bdbGKwr4LmrzQzQWk/ETq97VnXf0/tI1r/X/Nhu18zb1drrU65PVmdn1ltMFNAKtDwGHFJWUK+tXF0itWGWZuGAhDDaK/xXha5CiJAlJjIIQI2EJMo7pKAjNgQO2k8pomTz3zFfiy4VFLMq5PLBWBkPooht9El4kxcxrLPK1emzUrcopAm1zjqmTXWx1WG0nY6MPtC419OJ1tKk1rjFi5IGgs/ptkKVG/4mBITgm0Wsdw0LW6QQ5GPaAOI+Kr9t6mTxlo0ycOF/2OmCIhAMhRfrSi0H1oNBGFvIT4AniHuAHDEHUxitBMoMKKa10S2UYmIJ1VeR1ahXVK9zMI4wAiE8rkEwnC0OO3Ag8hwMyqmafDetbYmDkgedECv+AXQx7ARsYmueRZ11nWfaygSTOFt7JmONDMcdBpa281LimMsyDQNcqPaqODWt31GONX7JPzGP9Vr+s/lp9Z2zriw0PAx90s0kD9bGCPW3l6biDI8Ab9XII3BWowBcMJER+gpC0PAtyIeRxyBLI7hArPInEs9ZBSjzHdkxykV8MV0Ds5IutiLqhPrRnmOmqL6zVT9aUPx1FaDVZWyjDib9DrBuB1mXvQPiF83bI6ZA7IJ9C3oTkQqxAjEgYNmegTvYv5P/A8W42BUqQvtR2zHe98yHPQ66CkLi9GPIQZClkH4gVbkSiwjrYjJhjybAnZCHkJch5EBJnH0EmQozVpIa78GtwnE74GoU4zxjs5NsEldN0f0ah6t8g/4WMhYyDkJglYZwHYQhCLoCEeZASmntuNfbYN7f+KfC16kN+gJ8E4Ty3wrVIrIUsg/wJ4fPtHggXb/De4DkrRK3EZsZzbNel8yy1Fd/iZFN9PqSrWFP1fQoUsNd9Lo6nQc6E7A0ZCeFnHz8fvoDwWcZxXAyxQl8r0QHjlr4nWgRy3twdIfBLoPTqbH3udYQu6z5qBNonAtkZ6nZm52r64to+O617pRHQCGgEWhAB7me8Mqt+8nLvHYe2oJY1N33x8QcJrXcZJv84SxatWidD+vRQx+ceNUpogTz1N+N92DX/eVkm3T9OuuRlq/Pp/OnXo4vMMi2Yua/x8G0GpXNZjWXe/PJHWWrur0wr5vOPOaDGcq0tk67AB/XuJktWF9ap2h/5XaR3oLzOMvqkRqD1IeCUorKoxGHZ6opHJBIKgAwjKUkij9ry3RLfxjvUX54gIUhrUXUabpOjkSIZffpBcsWlT0pW/vdyygkjJFQRhGUkSFi8jgHNqeqkO2gSgAyqBVapgtGedaRiVXmyAOoy20sWYgG+36gKBvFXdazOq06gTRJ8Ks06SRpSDyPPIgxJENLCWFkdg/y1rI8ZJ+guWRHKbNeUZJ04ZlrVh7/xgLiiueLJCsmPM8rlxlsny6Pjz5Ys9wCJJr6FBXZv4BJKeorAhbbAjgIvGmKRfY0FQeSXihek5IZyWJXGaNELS1OQzvEkoWm7fEuSalBBu0dAfKs5QOKXsLF/TCPmggLVV+gJ/YgjR4+njCkCNGM8hu7KqJF4M+BYFaIlujXevIgB5VUx9p15lljXmjkKF3VBLX9QT+qcUHpxnDk+VntIqXlokcXU1xg/NS+SOnAuGHNHacwyqu+oR4297dgsp9qxztWiZWNnq/mpusb+W9g2diu6vjaIwFToPAIyAUJSgoHkpJ2gVJm2PyQJL7EdpyZJvp0Csb5I8mHFl+ycgVmQCkhqIEE31pZJInua7dieXICDFZB+tkxag9UXXkOBAsizkAFm4b8gptQUNiJzLIS6NXeoRIMkKb+C8CHHd9YTILtDrPc/7Mc2kOsgDB7I2abwODXgYaRckj+ZeqKBxx+g/M+QpyDdIWeYgqhamI2jkyHpEu6cHxMgt0OsQFLuO+ugkWPOo5cht0F6QS42BVG1wDlzHOT7arnVD5p7bjX22De3/tXRa91Hf0A9PiMnQg6xqdrflmbyC8hpZtyTGQilRrTZfzfnWbrZjdVwYVN8PtTQTI1ZTdX3IFobA5kO4ecRw36mqIOUP8twzPLbQnjfMRwKoTUm6+qI4Q90uqXuiRbBm79WOkLgFy/p0yWvI/RV91Ej0G4RyMsE+cMOAABAAElEQVTy218gbekXkXaLk+6YRkAjoBFoTATmg+xTDEgdlZLk3KY/3zu0rrDtgN5y8PDtlVJ8cfxf297GJGjuv3wMrHmNd3uF2MbkusdfbVAH+nUHNmZYAdJ4c0MoEpX/vPlZ8nKS3cS0rYR09rL+o1PXttIdradGQCFAjhBfPGXtBli1xuA+F/ukRss3grOM45noBgsAUhDPFYOPUn9xAQljg5pSl8OQzQn3uxlwU33V9SfKHbe+Ju+//7P48kAOwp2yE/WTYHOA8FOudUEyxuOwPiVxC6LNEkW2kZRLCsg6EnGmKNe+SNtj61orNq6tukYdW+Sw0Qvoz3bRjplPis1BkhBi0G1IW8SgIiDZX+pv9ZnYWGWYZn3Y7zcO995wC0zSEQ6pxZ9dJnMXhWXsmOly6GF7yt57dpdwaC7o84ESd4MzgLtpNAoxg0qa/Xd4MS4utAuEwyE0AfIdVsdFAcPdM/vg5Hnr2kaN4eQ5CgzV5DBb4BwwxdDZ6Df14DH7rCy8MT60rg6HK1VM3Nwe9oMKAidVjrExBipmnm08FLaqXtYNPZLCNqlXPZIsb16rxtLQ0xjXqvaUPtC5ijCmZTn7wvLUkWnUo/TjmHOQjHNJHFCiKo862o9xqp6gRpRm5PWGmspY44NmoQdbViGZsDJ03IERoJXlHpA7IMvrwOFznOPL9bMgnOS1BRKKx0NS6+JkJMlZU/gUmXjoJcMPSNW1wo662EM6pDHLfwHZCfIEhGRgTYGr/+6DkERvCcLY0mkqEv+zDhBT73/ajpm8HrI35EMe1BJCyP8IwjG+q5YyDc1+DheQEJ5fw4X8IfBfCEmFeTWcryvreZy0P5143FSB7XDOj4aQnE4NZch4EEJ3TXURxtZ1zT23Gnvsm1t/C7e2EHMByeGQUyD3Q76CFENInk2AnA/5C4TPDjfECrQK35KwOc/SLWmvpmsb+/OhpjZqymvKvs9CgySBX4TYnzeWHvxyyGfX1ZDtICSY+RllhRwkjrUOOmjcUvdEi8BtfpNukbabrVH8iFuNxnq9883vcuJt/IzXQSOgEWiLCBw5Yjv56F8XWqrvgJdFc60DHWsENAIagY6OwEsycA5esBoMaSOC8crQ7eWjAVvVWeNBu20vT/+DvxtbX/h1wVIZfdMjSjE3rM++/M9N0qdbflJRWhv/9a6qd1M3//U4OReuodMJr342XW568g1VdPQBe8h9l41J57JNyjzz4ddy9/PvqXyS2F89epNw7+W2Et75+me55tFX6lS3azAg47+rIsbrLNzAkyAudjhTluvvBA3ErRUVnwNdGv3ZteX9c4KcdMjWnX0y5Q6QxCCNPT33lO6HXSqhWIY4g3NA/m0AIQVSGSQy96eldamDFrCgP2lBHMfernESc24QpblD5dlnvpR/3PI/efKJi+XYo3aH++oKiUXoUhk71sZAxIH0c3t84OHsP9NJndnfxZEEI6lqL5PaW4OsrJ4LPewhSUYyk3Xh/ZEieauIQe7FC3YQZ6I4z3dJCCbRSNKQ/Al5QEUemkSiIujMukkwxkHqOlyw1oYLficI3wzsLzt7QZGMOX2SdO7URT6edJW4o7/A7bNa580G2Eq1ABWAK/byBT4xR1fxZAwFrpkSW75Uir59TDzeiNz/blgeeLdY3E4PqHr2FTqndLlapQ08MCzBsbHj2O5y9oEO7J8clW6H3yTerjtCl0pxVv5BBKAniWCMD6yJE2ouYD5wv1/OEZDZy1eViD+zUrrlbw1yOyoxkOoO4EKiOxLBnsPw7oEKkcdrWA8Q5eIChYuBDRcZ2HEiZW/Mu6pOVZtCZmnaPtsD99bm6FmiOF0FGoDjXFDnOAeYNlyUO9V84DUklA1LamplkcwKdA6YOs8B4BxBO6oOHCuyGXloTO3nrVDjPKG9dVg8YNGpRxz3WyReLIUrA9K1RycYl1eCE8d1au6jWoQE8UEfPK6EhLDYwufbCd7Lu6EpjxRPGQ/nmfNkVbFDjvh3kRQWezD/0AjKc4a387AD+qc/Exs+yHTNTOz4eRSALIYsgqyHNCTwlhgK6Q4pgSyHtLYF7z2gE8lY6kkSeRmEhEIY0tZCFyg8ANIf0htCrNdCSMCTAG2q0A0V7wvhKs8fIZwrmxs4HqsgfLjjQSUDISsgjRX+iYpuNysj4U3iyAqcp1wUwflPEvl3yJbg1pxzqynGvjn1B9TtJijexezNxYirflxvfhdb07O0sT4f0kWjqfvO1dx8wTMEwufObAi/NwQhOjQOAk1xTzSOZmnWUv3XZ5oXtcFi/MIn++4wCHtQOfCDtN3/SGiDQ6RV1gjUj8CRe3Kxkw4aAY2ARkAj0JwILMnBy9p6wojtB9dTouVO77r1QNlrh6Hy/ZyFcF0alyff+0JuP390UqGR2JP3gmMPlKfe/1Ll3fvyh6r89oP6JMvUluhr29OY7qk3JwQqQ/KEzQL68pP+0qYIY/Z5j+3qH//1GZlS5vFKTqQtvo/cnJHV17R1BEh+8tVtcSgi6wO50iM7JLFQkSTKg+LKALHnzAKvxvue73cpCHjFQ6tTWo/yFyeoMBW7XS4Jla2XseceKdO+nyfnnv2sPPxIoZx1xqHwaByWCPY8JuHscvpAmDlM18qqRqNSkKWbBr5Pqi3URBqn/gZmnSn1msSeRQgrwlL1zSQIwV2yf4oQ5LUqbfTUuIblzGPVdxC3IEQdTuwFHQmI399X5i1YJedd/KMUbRB57vkTJdNXKsEw9ai9P8qqWHUVlKET+0KDqI+AZI0FV0oiWoE9oqOycAUsmkE7RsmWkhyEro0ZaE3NngXCoMIT2JM5Voa9mSEgLpXmJHkxlmyY1rnKgli5miali6BwcUmXnhny4ZsL5dgTekhmFvoScsJwGvsb4/OJMyYWA3GMvql5xCtZr2qZrdtDVQe5rEC1YTttEMBVGTXVgTcjVQVUypzLxA+6GNewHR6zfWJgxCSB1TH6ZViam9dyDqnrWY7z36xL9R9XM8Z5YmTUhZiMsQt9cJVKItBHYs4NkvCF5ZXn58kh+/cXrzckYRDqDqdf1Yk/ZmAN0M8RgoCUB1bMcURQZ6QUOLolgHRpgKU4TrD2JjBUTQeNwKYIkGykZRVlSwJnGMk3SmsN66AY5fPWqmAD9OIHMeWXBlzTGEVpXfluY1SEOs6EkLhhmAxpTMJYVVrHHy4aeLuO8w091ZxzqynGvjn1byi2zVHei0YOgCyALIfwQ7y+kIkCPW2FltnSW5JsTc/Sxvp8SBePpu47F0NRtvTzLt3+tOVyremeaFYc+QugIwT8isAyv/wcOWrP7TtCf3UfNQLtDoEMr0fOOHj3dtcv3SGNgEZAI9CaEeCvlSW59ZPGu287uDV3Qy498ZCkfhOxd/D6kuoL6K8dc6TsOLivKhOG69ErH35RgqH6yc3+2NPYCpvrnvpZWBkXlZaranp37SSnHbyXVWWbiUme9+hc/zYwi9NYgNBmOq0Vbf8IYHk1KFzZGErIkgKXZHg8Eg6ulVgZSEpYhzpd2SCqAIMiyEC+KSLMsMY0rC6jIMESsIKFFXEUhGa0VGLlK+S+f10uO+3WRy677FV57NFPxeHLE48nA5xXHAtbTNfUyqqTdZlC68xGF9SdWidINSMvpW3mkwBEeYswVuU2KW9er/JBGLN+B5634RwQf91k3vJSueDyqTJ79lq55rr9ZfddBkmgZB7IPpStIygClu2TWwSxLtgrmu/XIxsXw4ibxKBTVpaAyCdhC+tdcIfpveaso83UU/w8ZCjC+MehEOzIJR4sF7eNhFTjjr4bc4GYES/oTTwg8USFZPmwD/zQvjL+4bckCj2dbtSEjjF2YoG7By6rU11CG50xyFcjbYyDQfpbaft5pDkvU0XpY5VHzMUINlF6qjlhjHVyfij9q8afVtxqPlj1YfysPjJWfWY91nmklPkwsFA62WOmsRe0ixjEfBL3bBBXp6jcd+9cKQlWSt8B+SDWI+KBtbUxDzgKVsC1KgDIBO0hMC9YLoI9w8MBtfd0aaVHQlGMF+5VLuJQ96x5lY40AhoBjUArQIDfJM6z6fG0La2TGoHmRiCCBt+CLIHQAG8kpL5wOgpwHjPwB/Q0ldJ/NALtA4EOe0/w51SHCteecmCH6q/urEagvSBw/pF7SafsDAlU1v8Sv730WfdDI6AR0Ai0NAJrMrMlSFeZdQQXXtBuP7B3HSVa/tR+w7aWnUxSOBSOCIlae/DgZf34q86ChS+JCJHFqwvk9mffsRepMd27az5eYhu/kddtLIUlVN3ER2olJeUBeeqDr5LZV51yuHg9bdMR0LAh/ZL9qC2xOI0FCLVdq/M1As2NAO9s2m9GYFn65+oyOIiGBXCoRGKVeIcGQszhoNUjS1URYcqK0iTdUAin4AoX+9jG8WxwYG/jRHiddM0vlofGXyfd+w2Sm259R669/GkpD9DKmD/NI8rSlJeSzFMkLaxVVV0qkycoJMtIztUmPG+VtWLm2cWqx5anrEFRpzJTpVWocc4eK8tRlV91vUEok1REeRCAPDaejPDMnciCO+aI/Lm0Ui684hP57c+EHH/k1nLpRYdIqHwWyHeUxv7OdQZjMFAviUMv2gHRWlkpwYIVGAqvrCtzytL1GBcnrHQ90EH1oyledbiwyAfOr2HV6kpEJVJJC3HDXjahxoljZpLxwIB4JIlYjhXcVIdD5bLPAdtKZl6e/OuuSTDEBmEKzGKxEMjjCFxUh3CdQbiq6600rrcTs5uk1bwz5kyS7E3NS61jk/PG2Kk+JMuaBLGlh7qmer+q6av6belv6GNYJWNclMW8MaeSc9hcdJHAgolYME98uQl59PGZ8v2MRTL27NESi/qxz3Ux8MkFPqmfsebcBq7wHw+hq3jcp1HsG42xcSJdVA79TTfrTrWawJqZdc44fVIjoBHQCDQXAlegoe3MxpYjfr+5GtbtaARqQIAfrNw2hgGr9OQilar9T1+c+qft9LdIV9iOdVIj0NYR6LD3RFP8kmq1k6FgY5mM3GmIHL/Pjq1WR62YRkAjsCkCeVl++edZf1EnCksMa6xNS+kcjYBGQCOgEWhsBNIh+bbq20P8PoNsbez2G7O+y0YfmqzupcnfSmlF9S17BvXqJnecNzpZZuIXP8ik6b8lj2tKkGzu1cWwxCZZtLIQ/lYbEJ549wspC1SqKwb17iYnjGq7HjUsUr6u7qczn+q6Xp/TCDQnAuB6JegiKZsls5eFBTSU+GHBWLEee9e66bjXi/11sX8qtv9KxOG6mkSp0yCxDHe94MhwdQJudZ0gy5wgF53YpzVcXiQ7bZsn4//vAsnK8skr786Ss859TOYvLJOMbCzSiVQKvBWDDzaeDfE43RNz79gqIblcn9jLMw1TZyqTFO4z68S2TVWCMiDuHNx7OIb8GHdpJk1riAMEqErjWcfeM225JTZi5sMGFda0JO+c4gdhnBCfPwyX3CVywRVTZB76OGRAQu68+0zxRNdJNFQMIhC1KZIaF9cSUA3QhXruLHCDnWGVHRU3XIU7K8rFBxPS5Rtisr4YpCEKxWAZnuB2VIrYrqXCzchW64NA7BeWYH9rTA4n0pEAvBXSwly5RQYiUFT9o8JIWSQ98aLwOg/mVBgWyhdddrJM//E3+fedb4nLz8VCqCcOa2MMPhcrGG6cQahjr+cEFh8YBDLKkIjeRDYli7mwwS41E8nV61LlOVeU7tSfwexHkvA1j6ED+6T0Tukv86w+83rlrlqRw1ZdxILtwApfEcGc4wHJ6OyQ5yeslXvumCfX33KU5GeXYMHFRnG7BkjUhd+Aai9n6mQENqt0YIzFBDDth+AVF+4hurymRfgGuKZW+41DX+4bbS30surQsUZAI6ARaGYErkV7x0PGQp6DYAP2ZHgAqdTVMcmTOqERaCYEPrG1cwbSz0AG2fKY5BcXWhh/DekHYeDcvUGl9B+NQPtCoEPeEx2KNH7svW/UlB1/2YnSCSSUDhoBjUDbQOCBi4+TrnnZ8tmM+VKhLY3bxqBpLTUCGoF2gcDy7PpdDu+YhoVpawDj0D12lKEguBnKgyF54RPje6FdtxMP2EOO3W+3ZNY/npgoq+ohgu0uqlcWpE8aFxaXVdPhmtOOFJer7X413ymNeZDOfEqCrxMagZZGAEQmnFKDhCqX3xfEZGPcDcLPJ5VFcImMvWu596wT++s6YHWquDPqC2LKIgpBX6q0A2QZyWSDtKML6nKQhuvkqCN3kTtvOx8eddfLjF82yNlnvCJvv7dEXDnY49YLF9jxLOwDHMdbuWLz2k2JwZqJwNrKkSCkHpbgGDpWiaJlbfrzmCQfRPWLaat/rMNIK3JT5RtlXeF8iYHoizsqxJPhk7c/mS9XjJsia4sqJAcuvu++7a8yeLBTgoHVcMWMZx5IPsMCFNXXFlTVIFRh3e0AWe+EK+JAwSpgCUtfX0B+mM33lCzUdCFBa1aM5bzlTqmsQP+82RLduAo4YDmBE2S/I0Nhwj2pSZ4z5hOd6wjgdRppg4SPR8OKCPWB+L7/3nHyxLNfy93/9w74UK94/dgr2SSkeX0Me8A7uHCBhK0i/Un8w2od4wZ72jrFSZI0RdQ4ctxMSV1YUHWMuqG3Eo4tCGguGmCbTswfGEYbMdMsR2jQR0uMvrLfJgbWPGLMSvEf7LjEYH3vwD2EGSBefN946Y3f5LIrP5NLLj1URu2+lYQq1rASjHMA9wFq5bX2AAtiLsiAnTY+P7OVi3es5JB4SRHmnw+5GK8VbA8pKBoGkRxj33XQCGgENAIth8BVaJrujJ6DjIVYgStV/2sd6Fgj0III3Im2P7e1fy7SiyBrITMgCyG05nkZMhjCwA/ocZAfeaCDRqCdIdAh7wn+jukw4c2pM2XKLwukf498+d/Vp3SYfuuOagTaMgInjdxZzjtiL2WJdeGDr7flrmjdNQIaAY1Am0NgZVZOvTqnY2FabyXNUIDWRZccf3CypQmTpta4b/GdF5wk/bBHLwOtgP/28Esgh2p/yWyVZfnl64oYpRUee+uzZPvbwb33EXsNS+u61lpox8H96lVtvT9DKl2uesvpAhqBVoEAfik74yADES8qcsvvq2LYP9ePfXT/lFDxehCEsHZ1ZSriGEwYjkFMIU8RuSRjcVxNVB72ZvVgp+LKAgmUL5FzzjtILr3wXFxeIqWBkIy7YaL8/e/vy/KVDnFnwSo1C3uxOvE8opXmFopBDtPVsE1q0pF62oV9UscpfbPqsc4jpntlpyMk/iyvRN1heezxxXLjDUukIuaGEXOGXD/ucDnqsIESKJ4nPg+2nAHpmB6Jx3eRcOHtyALemeBNQVoXLgHqAQlgY+AfFoG4b3LjLOgAC+xiuMUuLIceICATYVhKl5fDVTWss53Yl5rsKUlW9e60KlbuuxWGXDQQwhzwYuFAqQzDns7/vu8iufuOyXLXXW/BlXmx+F1ZEomWwDoXFtvOsLhJSCui32YVjDaqjdEmxyljaB/PamnWYxdeZ5sfTGNc2SdVjvPFvN7IM49BxCrrZ7OPRhnmkfA2z6lriQnrArGLazweWItjywh6K3n9/UVyzdV/yKiD+spVV++P/aI34lriWXswThuWxE5YGuOOgThANq8Az4732fEKWbCkFO05QcajLowZTuugEdAIaARaEoFlNTS+EnmnQuhGRAeNQEsjgA9vNR9fQ8w0Az89ufqaq6uHQOi62grrkDgS8rCVoWONQDtDoEPeE3V/C29nI8zunHPfq7KhtEJOOWBXueG0qheH7bCruksagTaPwC5Desuz405T/bjy0bdl6Tq8PNBBI6AR0AhoBJoNgZXZ9ZPG2w3s02z6bGlDx+y3q/Tplq+q4ffB1z7/fpMqczL9an9jY39RkV8WLJWH35i8STkro1+PLlYybdJ4VeFGefXz6cnrxp1+FMiGtv0muws8gnTrVM98QR9XpbEQIQmMTmgEWhIBdU8GQQh6pDwakemzgiCJPeIKF2Av3dXi9bpg3egDHQV3xeATaXEbpxthkmI2ck0RaCTPkE+rSRo6wrO9OEAMxgKr5MbrT5Dzx/IZAMIQCys+/HCJjDnjUXnm+Z+lpBLPBV85KTajToucY/1JsUg5qwzP2dNGWcvyWelBXTYR67qU+kgEqrZA1Co9cB7UHK9X1saw/rQIQydMUH15Dlm4NCBXX/m7jH9qtnhzQONVxuWE47AQ9JzhEimbA0tb7N8bxn7PXJBDK9pUC9KUcSe+YFEBHyy/Yf1Nl82h9fNBNrpk7eoM+X05CNZ6CMaUKht+iDHzYSSwpbEsWuuVDLcHXpALJFpaCjfcsMQFoW3oQGzZJ+IE7IgzzitBX90+t0TDIVixRyVUgrEefbhccNERcs+9b8u4cW9JWcQpGVmdcD0IWwgt2quIWs4j1gX8qwnz2E5dUsN1rMsuapzt488+mP3gObOsXR81B5BvzAljrhhzw6jbIJ1JPtvqcVSCZIeulV1hje6W9z9ZIdddN108mWG5/bbTJBsuzSPhpdCt7tdV/NzkrORe0S5YatNlPF2lR8qWig/jUxZ2SEEFt88AaYzxULc0mtVBI6AR0Ai0IAJvoO1fISUQ/sC4GrIHZAGkqQJ/dDxoynNN1Yiut10hQPdZYyDbQB6CfArhHA1CCiEzIZzL3NupP8TuvheHOmgE2h0CHe6eqPtbeLsbX5EVhcVy0u0TsHo3Jv867yg58+Dh7bCXuksagbaPwEB4BJj0rwuFL+8feXuqTPj0p7bfKd0DjYBGQCPQhhCgRWiRjy/o6w5D+3Svu0ArOutGny467qCkRk9/8JX6TpjMMBO7bDVArhlzRDL78bc/lx/nLkoe2xOb45764YmfJNsdvs1AOWDX7exVttn0Vn171qt7Otbr9VaiC2gEmgMBWJXGYDkbJ+EHsm/GgigIPTBO2C83snamInJj2GPV5ewKC9goYlg8guO1uw4GVYVjxRUb9BctHVGfG7/CHRKGpWqROIIr5ZabT5WzxxwukWBQMvPiyjL3vge+krPOfkLem7wA+ylHxJeTievghheEqQv1sm4Shwm4+I0miqFrMVpCeyCm1R7KIOlS3RM70bYl3NM4SWaqNHVVNqCIQfQ6QFaCj8MWx4jRLiyD6SrZsAw2HBDHQPxSH7cLemf7pSLglP+9+ItcfMFM+XHGBsnu5ECfnHLoqO3lrpuPAJkHkjVUKh4YBtPBMl17u7kPLUnhWgOsraFL1JUjCXc3CZIsLVggzpJCcboz5LflcSkuQ18VI1hrJVt8gtXTRTLfI8yC9aoPexrHwmXY17gCRCV64wSlzMFWFuHsEMeHBKxBHCuiVZUjnjiPfbDj4Q3ijSySu+4+Rw74y/7y1As/y9mn/58sW1yORQlZQIjEOiyyWRfEIHAZp/4zCVlF3hrkrkXsVsU1XKf0Q74Vq3Yw4KinSnjeOjbTVnnF5rNeUz/rWJVnPklkxugvcVFpzjPcN1gE4Mt2y4efFsplN30tlcEsWKKPkj2G94IV9nwUtRsxQZ0agpN+v1WbnPMgjbEndKwiIMH1y5Htl5XFTlm5Ae+3Xbg/PVzowHHpcK/AakBOZ2kENAItiACtMWmtidVBcjiEx3T725ThM1R+jSn/15QN6brbHQKL0KO/Qw6DkEDOhPDH/y4QunB9G8IvKjpoBDoKAh3mnuiQ35i/nLlQLnpooprME647Xc4+dPeOMrF1PzUCbQKBob27ytcPXiG9uuTJB9Nny9+feLdN6K2V1AhoBDQC7QkBZRFaz0t47jffKSerTXX75ANHSFfTInZNUbG8/XXNi5IuPPZA2WfHrVTf+IL/qvEvgZiANVtKaKh76sWrCuSdqT8na7l2DL15tY8wtB+9ltUdVmXl1l1An9UItBYEyOuR7lIWnQn5aX6lLC4sF787GzYWcyUcABkFC1mHExb2ID/V/rOKwCLBhitNskzZQibJMpJWJNjgMBe/xLHLqoRDG+HquFj+cfNf5dyxh4KEjIoXro6zcassXV4st970iVxw3lvy+sTvpQAeEpx+umgG64q9b9Eo4ri4YrnijnWBcaoH/HYIFQcg0MMuigg0yD3qQOtltd+yssA009AomY/yTg8IXuwZDANrice8IDkTIEu5zzL35QUWMOL0ZGRKWWWuTHxnjVz2t+ny+OOrJegulsxcnwQrgrL3Pv3lnvvGgCjGfs2ulSBBQZZy316YW7M1BUedYw5KEgSj1wc33djL2AmX1OXL50MHkOsgtz/5sRD5UFBZP9dZ0RadJNxBkI+SyJX5q0MSAJvuj4awx/V8ifGtCueAowd0Jf6VcDUNItnFHkIwL8gnk2qPh7FYAEQwiWaP2y2RSJnkAOP/3HexDB3cTaZNL5RTzx4v035YJP6cHJD8IKXDHsRw/w2XywlgEEeD9sUJTBteTTEflHfTmmI6bjbH3YyrWyvjPOap2rMY+hox5gvmmIN9QdKJPYSNvZrp8Nnqm3ENjcVVnuqrdY59xxA7gtAQ7twdmeIFueuChbkHVuIvvLZIrrt5mpRVhOToo/vJReceLpUb/gD5G5Qop2o9AeoCe/TenQtj41zMKbhHryjENuRB8eAGW74hLiXlnGOYQ3RpjhgA1lOrPq0R0AhoBDQCGgGNgEZAI6AR6NgIdEjSmEP+3OQf5dLxb+IHt0NIHF9x/MiOPRN07zUCrQSBnQf3lmkPX6H2Hv/o+zly8h0T8NJF/7hvJcOj1dAIaAQ6EAJrMkGM1BOGpmFZWk8VzX7a5/XI+Ufvn2z3yfe+BIGx6dtpJ144P3jlGZJvkuJrN5TIDU8Yiw6TFyNhd0+9oqD+PY0ffP1jtGd8ro3aZVvZc4eh9uradHpon/pJ4zWZbWuRQZseEK18oyJQAeLukxkJ7MMK3ql8nZSvnGUQUCDCBMRxguQaiLGkG16SmCAGaW0Jagv5TOO8KmMdg1TzwNVyeI2EKxfLdTecIuOuHS2BknXijObCxW4n8WW6ZcmKYvn3v6fK2HNflXv/87HMXrhIwrTUzOwkLrfPsAJ2bERzG0Ac0xKZrnqNtpMx209HLF1RNhzE3rsgimPxAHg3ENYukHJ4g+D2ZEjC5ZaVRRvkf8/9IhdeOFX+7/55Mm9NuWRm5YnXky3lZeWyz97by70PHA1yeRn2cF4Dd85wy0wGukEBZJ8rG2QjyXlQsGUrpHzVXLDVIsuKXDJ9MQh0uKymXk0b+NwGS469hhcsjcrGGFBGX4JFIMNhNRyL0Trbj0UEtJw2PlOURTEIWlricl6QISdx63KRQmYesAUZHAwUyNZb5ckzT94iXXpWyvK1G2XsGa/LY/+ZLmFnlrgy8NmCzw1PIg+ccDmsaitUfWmNZ51jbi5ugG5q3qqyVtqIDWIZulN/RQgb/aD+nN/4o/pi9Mc6x8UWnH84Byt9VywP+3hHJRIrU2MY81bIXQ//JP+67weJxMOybf9u2NP5bFS3GtdxnsENPH241xOInwPW/g4sshAHXMWDUC8vXIQ8eAjwBOTn2bzfgDXUMIcENVJfHTQCGgGNgEZAI6AR0AhoBDQCGoHaEGjyn1a1Ndwa8p/48Du58MGJ6sXdI5efKP/920nicXVoSFrDsGgdOjACo0cOk2/HXyk9O+fKO9/8Life9pyEInwZoYNGQCOgEdAINDcChX4QIfWErfrWTxLWU0WLnD79L/tIbhZeMiMsWVMoH3//e416dM/PlXsvOy157tMfZ8lLk79NHjPRNS8HhIhBgpQFKmHVBAu/WsLcJatk0nRuAWWEv592hJVsF3E686EwQ5PG7WKwO2An4IxYPpuXkEBlRMLYy7jyz68kFgritySIRFeW+D0kuapIYUUUKwIW5BsJNkUSkrjlsUnogliLRILIw3MjXiiJyEo5//z95c67zoHVZkgRkF4fSGGQpTl52KO1XOR9WvRePlWuHjdZnpowTeb+uRqksxtWrX7xZrpgiQyqDkSdk1bQitgjuWfoYLRrSyuS0CKTkW8eU3cHLIr9fh/ELT6vG9bPORIIe2XOslKZ+OFyuemO+diLd5688PoGWY9nX1Z+GCS3Hy60IyDuyuTQkbvKffcdLbn+CgkUr4JlsEM8fliEwiI3feKOhB9JYzyvYxmKcC5fNU9cFavUfr8//OKVNRVw9409cmkJ3KQBw+uKQw9XXBav98icdUjCPXa0eImE4XY7BvKTe1y7nH6oDL2BfxyEMDGvmgvWuBv5xNvhwHWJMikvWSR77NVb7vvXOJDuuB4fK/feP1Uuuepl+X1OQDz4zHJ6yvB5A3IUrrC55zEJ2S2R5Dy05iPjanOC+trm6yblbOd4nXVepc35xLxYQO0x7M0KSBGsisf9bak8+dwCWAj7JT8zXx576Dzp07Mc1uurMNdhiY2FXDGMe31BkdKYWW5HNuY7MMHe2ZGihbisUiowBt8v4Lsd/VuyPhz1eY2ARkAjoBHQCGgENAIaAY2AHQH+YuvQ4emPv8c+xxtl4i1j5eJj9pUdBvaU0+9+UVauL+nQuOjOawSaEwE3TAPuGHuE3DDmYLw4ccgDb3wp1z35AV601P+yoDn11G1pBDQCGoGOhEABXI7WF+xWtvWVbU3nszP88tcjRsp/3vxUqfX4O5/LUftwa6ZNw8HDd1Bln/94mjp59wvvy4jth8jW/ar27+W+xgtWGNuRLV9XJDtl14zdfa98lGzg8D2HybAh/ZLH7SGRznwoSGMxQnvAQveh/SEAyk5++xN7Gy8My/BhONqwWCLFy8TZswe+v4LI8/ixx20JqVEQrrCIVd9jQdjSPy/oNBKJoAKNYOY5EPMHuQtWpJEYXPjG18Eis1JGn7a3DNq6h9xxx4uyfHmF5OWBjgt3hbUt9oLNLAE17ZGFS8tl7txCef3VRdKza5YMGJAjW+/QXfoPzpKuXTzSo7NPsrJ94vOBrsVeyG6SqiQzGayv2NBR5eD7dxy6RMGxcV/YQDAmFXDxu6G0XArWV8oatLVsYanM+zMghcURuGGms2Hw3FAeW8XDsrkzOU5YiK6ScHmlnHTCATLuxn1FQuskVLIabpiDijSORULAAZDQvXYdQbl0xhVRkH8wxQaR2BOqw/lxNCzBgsWS4XNIMOqQSd/R6tYJ0hBEflwpX0etW3iKVs7QxwnL1gq4a542s0IO6p8jrmChBNYsk9yteisyNwFX2dFEEMOOMQN5ChoffVD/cT3nBRcQcACMTCcAjMHVtQsLB8Ily+XY43fBwoTL5NZbHpWsnAyZ9lWhzPrxeRlzxq5y3gUHStfOXolxqwTu56sWI6gRrKFzVr412DUUUdfb80mw2stz3lYnXQ2ilmQx5zaJYfaHgeUgaoECIpVPEhqz1QvCPzMiX00LyP33zpQFS4qkW+d8EOVhueWOI2X47nA6vmEF5ngAbshdEoLVvguuuzdRT7VT9UfNJYy/w4HFSLD4j4VRR8lSyfQ7ZdZyt8xbh/mjg0ZAI6AR0AhoBDQCGgGNgEZAI9AgBOr+tdagqtpu4ck/z5d9/zZe3rr1HBm50xD5/anrlAXym9OqLEHabu+05hqB1o3AkF5d5JWbzpIR2w6QynBELn/kLXnmkx9at9JaO42ARkAj0AEQSMcitG937DHZRsPYI0fK0x98JcFQWP5Yulq+/GWuHLjb9jX25oazjpEf5iyUecvXwKIvIlc+9IK8d8/VsL4zLIxJllqk8YqCDbJTDWTwjPlL5Ovf5qn6uUDq6lMPr7GttpzZs3OeuOG1J4r9KmsLIRABZR6v5ETCtRXR+RqBVolAEMRkPOKTN35IyH7DohKAZWl40U/i67UzSNBMuBHuArJwg3gUieUBWUbCDfu94n5HQnFxijQEiWjQeSDY6LpXuWvGHq80VMZ+vdy7NrA+ILvtMlgee+QaGf+fd+SrrxeCTCuCwP1xPBttgAT2OCWzC9z+RqNSUFEpBXNEfpgZELcvCJLZIX64bHa745LXyS9duuZBsIcuLFjpet8FotKR8EkMLHE4jH15Q5WIY1JSFpfiDRVSXh6BdahbgtiLtrQYpGbEj/1ioyDyQBBngAbF3rYZOAhHKkHmeoCL6XoYeytfeeURMvqkERIN/Yn9e9dAV5RBn0kyc99fxXUyXUdQVtIJ2HaDsHS6uohPeknEE5f46gUSWfOHZOHZO3O+R75buUHc3OOX/+ifuo5nTx3NpXcKj7UErb8TdFEdkd/hFjuArYMdsPgNr/1TXNugz3EPCOw8jGEpThh7/RpEKpvAqGMuGIQ4j+mqGkQ4dcb88GD/41h4g8RK43Laybtg8cBfYan9Doh/WN3iufrs8zMwDxbI2PP2kiOO2E5yM7lIAbXjM4mLD1i/QeRiAQCqjDsCyME4w2U2iXVjDtoJYVySQgiT8FXlqJ4KxoxFJo6MaxMYF1VMzV20jXkbwx7LnOsuDC6mI1qlG3NYUWO+OGF9XViyXl7+b4FMfG2JhODiPKdzppQWrpe/XXG4HHvYMKko+FV8GSDNo271GevB+HJMFWaGIjX+dXKxgKOz+OHCO0T1Ni4BfqXixmKJOUvdUgordAf6zttPB42ARkAjoBHQCGgENAIaAY2ARiA9BDRpbOI0e+laGX7JA/Lfq06WMw/ZXd64day8MmWGXP3fd6WgGH7AdNAIaAQaFQG+QONe4nede6RkZ/jwIn6dnHLn8zJryZpGbUdXphHQCGgENAKbh0A67qn7tWHSmHsVjzlkL3n2o6kKoMff/rxW0tjnccsjV58tx17/oFrgRIL4ruffkzsvOElda8dhBSyNawr3vTIpmX3CqOGylc1SOXmijSdIRvXumi+0tq4r0NpYk8Z1IaTPtUYEYIArrmhcPsK+xpcek5DtOvulbNV8yVozB9bGW4G07A6Sdg2IugrQXXQ5DXKRRJ4i5kDaMcDS17DUtKxAGdNC0zrm2RAYv7CUrJ8nvXsNwV6v58i77/8kL0z4SEpKouLNiCoi2o0FGLRQJSnmBTmsCE1YOzucXqmEpXAQxJ0LFr0bKiKyeEUhSDWTOSNxCXE6QeWCqLTaJpXtBPnH+9gBIjgGa1qnwysZGbCc9kN1Z1SiERChTo+EQnSdzb44YSENF9uhgAwa0FkuuORAGbVnL+xfPF9CwRWSmYl+YU9kkowNCdQqhn9ORz6I6m4APgLSOVPK508VT6BE4tld5KVpZdCDBrew7eYewdCjSQOswVULCTSK8PO8oCwqdMuQHthnuOA3iVYcL/HMHKCYj3mwDpbPsDYGftzn2rAx51+OM/E3xsKYDebCApxxg2iNhIsxZ9xy5tjDQPL75c5/PY9LcuGe3Cur1pXJbbd/LG9NnC0nnbSrHHjwQOmcjf2rsd82CWBas8egH12Lg2lGS361QEAUcY95o+YktTcC6GgrqWLqU7N5r5lPItxF+h8lQR7Db7hpJY2ZgLkWjXLRQwSW5XDTDVfp60sS8unktfLiKzNlVUExCPCe4omVSHlphVxx5fFy2QV7glufDTzKsKc3SHS1cMKt5guRqi9QX5e/F/rM+VomkWWzJQOVlGBf789+WY/LMZeJS30V6fMaAY2ARkAjoBHQCGgENAIaAY1AEgHz12vyuEMnyivDctY9L8uZ/34Jrrgq5PSDh8sfz94g5x2OHzMdGhndeY1A4yKw69A+8sOjV8nDl50gWX6vPPbeNNn90gc1Ydy4MOvaNAIaAY3AZiPA9/tFfuzdWE/o263tWhqza+cfcwBebqu31DJj/lL5ce6iWns8FPs3//OcE5LnX/70O/nsp9nqmO6prbC8YFPCdCosjK26aYl75cmHWcXbXZzOnEhnbrU7YHSH2jwCDljXurCf7cZAVJ6dAktcZ4bEA6uldNE0WNpiX1q4U/bBjbJDsmDpSaIKZqgg5eD0GUJCj2kzVgSflWe4+jX2jiVpDCIY+wKD+pKyjfOwb/J8OeWEwfK/Jy6Uww+DN4SgC/v3GuQuSWOSx16vHwSjB26ARfzcAxmkncMNi2K4pvbgu7YHVql5sBDuBCKvMyXLLflYtJnnzZRclM2BNagfZLATexdTlNmzW1Fy6DO5bliU4p9hFY3TaCuKtuMx0JJOl5x68jB55NHjZeRemYowToRXKQvjGH5fx2MNp+ziID+jiTCIyC7icfaShAd1YC/jUBGsjN0e+WGFWyb9WgxSHBDjhzq4fCjZ8Ha2ZFIWYxy+mAmCFCS7M1Ak5WsXKn0SgkFw50qUFsDc0xiMuUE328abedZ84Pww5wOtazEMEolvkHDFEjl1zCi5+87zxI8xiAZDcLucDdfcObJ0RYXc/e/Jcs7YV+U/T06XBWsLJe4tx57WTnymwXV6AtborkzMSLQZqYBleBRpWtxWn38GQUzwLAGJDGLYLslr1NyNw7ocVuVR7CENd9oxEMQOmDW7nRgrENNud0z8WU5ZWVQuTz+zUi6+6Be555GfpagMC4VAGDs8G6Wi1ClnnztCLr18Vyw4mA8CeZWa7wCrgcOBO8aZL5nYyzsOPB2lS6Vk7TLM+Zgs2ZjAfsawkobLakxPHTQCGgGNgEZAI6AR0AhoBDQCGoEGIKBJ4xrAehkWxtufe4+8OfU36ZybJU9fe5r89PjfZdROg2sorbM0AhqBdBHo1TlXnrnmNPkZ99Pu2/SXP1cWyv5XPyqX/+dtqcBLJR00AhoBjYBGoHUgUA7mIQHrrbpClh+EQy1799Z1XWs617NLJzlx/z2SKj0Ga+O6wmmwTD5ir2HJItc//pqsLSoW+16+NVkaP/BqlZXxaYfsLXaSOVlZO0n06ZZfb0+K4WJXB41AW0PAA9fDcfpWdgXlA1gbL8J+qZkZcalc97vEyjcql7xu6QrCrpNpKQmLTLjwBVMHUawmugxyi6QcSToV09KzutCttQNEpA+upOEAGfsCF0qkfK306eqVm8adLvfed5LsuGNvKS0pBg2I/X5BGsdBaEM5ZTnsBHHH9vwe7COLph2wWuaevw64wXa4YPkKgtjh9MF9MVsOw1gUOnnhHtnrEB/SLhCdbpC2mZ4s6Iruukm+wSYVVsj0chyDO+VEDJad4agM27k79DlMLr94lOR7IxJaD49BwSK0B+Ib5SVCa2s6N0uXEESDCOSZiZILe9U6nbloE7sJz/8aLq9LYVmaKy99uVrKKmBhTbIQvYjR6rXZQ0ImzXJKJfbfjQHb8gVfSqSiBD31AfRsWH8D72oLBqzFA0bM8a8iZA3imL2O0eU39k6OhNZKZfkCOW707nBRfq50yvFKJOAVrx9j7olJdk6mrC8OyoTnf5YrLv1IrrnmK3n9rXmycGWRxDyV4snGgoIM7CGN+Uo33zEMuLEwwSCxVTqFRDb0oS42SfaBFsZwUS7Z4HczMCdAj3td4sOChEpMlOUFLvn4ywq56bZZcsXVP8nTL/4qa0tKxZsFEhuLIBKxgIQ2+uXSi0bKddccgL4slcrS9djmIRdkew7qbuAY8p7ydoWncCyOwCKGiqW/SKJ8Fapxyrcz/bKuEvcDSG266tZBI6AR0AhoBDQCGgGNgEZAI6ARSB8B7Z66FqzWwSX1yXc8L0eO+FEeuuR4Gb51P/n6oSvknW9+l1ue+1jmLFtby5U6WyOgEUhFIC/LL1eP3l+uPflAyYJVQwVWyv/71c/lgTe+kkqsVtdBI6AR0AhoBFoXAsVevPSuJ3TrxJe8bT9cdNyB8saXP+DFckKmzZwvsxevlB0H9621Y/+++FSZuXC5rF5fLMXlAbn6kZfl1nNPTJbnnsb28MkPv8ss1MnAPZAvO/EQ++l2l05nXpSkMb/aHTC6Q20eAdBesKikK2GPFKyPyAvfxeX2U+A+OVgiJb9Olk77jYElpVvcsRy4GHaDbOV3XFgOg4A1SFMecxdbkmMkkhEhxOGSmPlWwI604ObwF0QcvS4L9qZNwNVxpBKufx3FMnzX7jJsx1Nk5pzV8vHkX+TbnxaAZcUOxl5YPoPsC8VCEgf56wMxTCKOW+Y64LLXDZKPRJ8hJCzpYtjYl13pQoaY7aGPbnhgiMM61QULX3WtCwRyNCEV5SFlzbzT9p3k+OP2k52HdQM5Ct3Kl8KitQBEcRlIaV6LfiovDtb3/Kr+Wf20x7RWVeQ3WOkw9CcBnpExAKxxHnAISmjZIljyzpAcEOk/L47Lx9+TCYQLbmIIYrAlAqhQ+W1xRGb+GZCdd8gR17p5EilZCcvuPhhBn/iwOCYcwd7GJLSBu1oMoBYKcPwN/A1UzIkAop1lyMzHIyGQw37MI+xxDBfPe40YIi+8epP889YnZObvcPMMS3E36nJEO0m3zrD6hkv0GXNL5NdZ32PcY7LdNv1lm8GdZejWebLNjl0kLx97ZrsDkuXzKpyVVS+s4eOYZ5yIal0DFysoEp5jjnwIowTMuRmjOCyMw7CCLgNRHpfSdSFZ+udGmTe/TOYsLpE1ayuwrRcIdNDKNJfOyu+BIcICBLisTiRAggedMu7aI+S0M4ZKoHgdFgIUiNcD191Y1KDUYCO2+6CmMWUJaAfBogTgH5cc1I39srGRcrBgEfa6jkl5IC5fTENNcNnuout0TRrXBGV7z9sFHbRszJcivakLmPaOgO6fRkAjoBHQCGgENAIagS1AQJPG9YA36cc/5LMZ8+VvJ+4vN51xqJyw3zA5bp8d5Y2vZ8rtL34ifywvqKcGfVoj0HERyM30yZUnjJK/n3SA5GM1PF+EcK/w65/6QFauL+m4wOieawQ0AhqBVo5AKVyd1he6thPSeGCvbnLU3rvIB9/+qrrMvY0fv3Zsrd3PhdXUQ1eeKWNue0wRzT/ApfXH3/+WLL96/Ubkg1iCtRPjh177OHnur0fsJz06Y//LdhzSmReaNG7HE6Add40kVQLEacIFcg/p174qlBP37Su7dPXBLe7Xklg9TKIDR+Acnp++fFiMFsJFsNqZ14YKidrqLBYJu+pkGck6tGVdhUQMDnkcThKyIO2CG0DIZsruI3rJsF2Plz/mrZEvv5op8+atlaIiWPuGM1HOgz11YQmdmS3xEMhB8CdxY8NYsm4QkxjEvsZ0O20RhNgcVxGELrimrgzAQjWSLSEQmB5PuXTtkSmjtt9K9t1vkOy4Q6Z4oU84sFbKKwtB/HG/XLgoVjwNqb2GBsN1ssAi1QnMPA4sSvL2lbCzq2QHVkn5wk/FD3I4DBJw/CflUhJqGaLY3qsI3I9HK/3y1k8JGbFDTEIgfSthbezvDe8VIM5jkS6wF4YFOhYkueECnMAbnw0YADUHSNai34os5RAQNy5KAIqwHgcTqoj+WBSup8sqpX//PvLI+Kvkscfel7ff/Rb7TOeKG9bdcRC4DuyBnOWD3bk3G1U4ZO6i9TJvCdx3f4k9jVFV525O6ds7Q/Lx2ywr2y2du2RLl65Z+H0WwUIItAd9uVAgwT2KIwkJh0OYR2GMfUyKYexeAovmko3IA0tfFKiUVasqpbjIIptBFINRdsEden5eprqOLtnjoUzMGy/qqJDOeW657vbTZO99ekm4bJEkYD3vxjhzsnHH5+R8tAO8SRr7F6NoHH2tTJSKz72NZMa7SdyPtpbPkWjxWrhhz5AZs0SmLS8UP3CgTXeC/qnJeOvQkRD4Ap213J6cj/Qzzdz57mivH2RGM7erm9MIaAQ0AhoBjYBGQCPQKAho0jgNGCP4IXb/G1/KMx9/L+NOOQgk2Eg59cBd5eT9d5Z3v52Nc1/I9LnL0qhJF9EIdAwE6Ib6iuNHyiXH7iudso09MT+cPkdufm6SzFy8umOA0MBeglDHmnjZpoGX6eIaAY3A5iEwEi9n52/epR3jqmLsiVlf6JrXPiyN2c+LTzg4SRpP/nGWLF5VIIP78J1fzWGP7QbLlSf9RR6eOFkVePztKcpVdwksjyPRmKwpKhG6aX536gxsxbBOlcmGp42Ljjuo5grbUW4680KTxu1owDtUV0j6GcSewJqzEPu0/vfdMnnoUrdkVrilaNan0rP/YBCGWdiDtwecMkdB9haBFEzSv0AL15skoQUdqDAj38xQJG4VZQxCDXQ1ecZEENwXrTbBgYHMjWPPXKenk+y8LWTHg2VjaZksXVou079bIKtXl8nK1RGprCgFOQebWIcb14XQNAhGkJJUg94VEljYYgVHHNbR8Vy0HJJgsAJ70OZJ9/4O6dOnk+y13zYyaECedO4EAhpbykRBHEakGIakK0GkV6JOvFYAKa0IOrvuVuX1xCRM47QYBonqduSJy9sT9WeJKxqR8vnfiaybJT78pvhiplcmzwRp7kFbIDTtyNbTRKOfjmLYuFfwJz+75LJjYjIw2ymBgjUSL1ggnm69JArC2+FcAyId1sbY7zcBfAm8sWiA8wgBg8kFCAZhrDKQhxgENIMxVThOpVJeFBaPr6fc8I+zZIcdd5ennnlT1hetk9yczqgG9uvEEO8tuMe1z4/xxvxzY546HFjUsDEoxRsqMFa0LDcWNblAEjv9mG2YDsbCBWgCK3qnk+7OaQkNwfRwAGe6JKdwbrr9rNMvmfAkFYcFcZhW0Q64zsa+3uyfCyRtGJbi4imTcniX2nqbvnL9NcfKVoNdEqhYDBfjayQb+2pHKtFR616wYna61mBgB62gJ/hAF9xk48YIxX0SnPeVOIJl2Ac7Q17/JiRhbHLtQT/oAhzd1UEj0FwIYOLLJZC7ILdDNGkMEHTQCGgENAIaAY2ARqDtIcBfLjqkicDG8qDc+OxHMv7tr+W6Uw+WC4/eW04cOUzJt7MXy4NvfiXvf4dVrrYf32lWrYtpBNoFAjsP7q0si888ZDjcjRmPl0k/zJU7X/pUvv9jWbvoYxN2ojPq7taE9euqNQIagSoE+IZehzoQKPWkQRq3E0tjwrDdgN5y8PDtZcqMuXhRngARNEXuu2xMHQgJ3EwfKt/+vkB+mrcEVlZxCVbCpaoZVhQUSff8XBn/hkEqM/v8Y+h1I8sq0m7jdCyNS7V76nY7/u25Y3HsC+wCOeaKw3uOBMF3xWXSz045GKTh8buByipeLBtnfCHd9jgWznPzwaHimZAoA+dbCVhMklDRbqksFs9Z58kZJlk8BSfPeGDFGQf5HAOBHMczinsGJ+IrYOm6VGLB7iD6ukku3FPvvHWO7LrzvlJRGsR+t3EpLCiWosJyWbZ0raxfT/KRxBvIb7KEII3JGJKwJNHngjvfbl3j0qdvP+neK0+6ds2VLp0iihyMVYL0hNvo0PpykIdr4fq3UDGNsZBLMjwgD031adNs74vqQBp/1P666jc0rFOdvSUO0t0JV9uxoj9l5fxvpSesUgvKfHLvW4UgqrEvsy8O4hwmsC0Z0Ge6iF5d6pLXvgnLTcfAi0SgSEoWTJGunc+Ay2Wf+J3dFXkbBxFPehgjqY7pNtxgaokXrawJoAkiLcJZ1BYc3Es7BqI+BGtuzKejjuwiI/Y4V579f/bOAkCu8mrD7/isb1bi7iQkQYMEgltw11IKNSjWIv9faP9SpS1QrFDcpcEdQggxLO7uvu6z4zP/e+7sbGY3uzuzyfqeA99e++4nz71Zmfc75zw/G198sZT3O5Deg7mq+Sx99BB2MrS1iMIiIvPB0ZM4ie8k/W6ZYtrG52Vh+GjBHfK72T2FYHoaWyiymoxQ6XyXOTx5PcJ8P4IUiO3i4su5BhlKmsosPZv5HtKT3WQOMK+xlc+EbVCslndLomz4uaggiaGwr/zxobjokjHISHHBW13A93UXklLt3LJfWWjQTJNlAkF5/639OSf+G+M768+jEF2yDkkc4rI8J6Yt38NxcckGx28Vbpy/mhJoIwLT2c/JbdSXdqMElIASUAJKQAkogVYj0Pzf1FttKJ2nYcl3fMfTH+Kvb0zHzczldAs9jycdPNQou5jf7tlPf8Czn32P3cVcVaymBLo4AQdX+l86+RDDq/jYsUOM2cpK9De/3s1MzwAAQABJREFUXoy/M2/x8i17ujiBlp3e8bc/hnU7NOx9y1LV1pRAhMDcR27FqAGNe48qp70EqiU0ZhzragLojReeaojGMu0P5y7C7ZedaXgLN4bBwjyPD992Dabc+SAqXG56Nu39YHpHfjE20sM4mt84MzUZ159zQmNNdanzWekMjxrHqq01eVTj1NPLSqBjEaBgRdGMfpqis1Hi88IVcODBD704aXQKsuiBWbF9NtJyxsI6aDA9IXtRpHMjENxOsZnCLEU6cToO1XiR1s7N+NbBBmvMzEriQRk1uRJi2GARGanlGdJimOF5GSebAisF21AxBcFCepFSNAzQ+7I6haF7U9C3lwP9e2fz5j4UECfwThH8GFSZYY8lB6yIiNKPiH1Wm80QDZkMme3Ro5mBlcNhejVXMn9ttYsho3lsclGwo1DIiYhHapjepFabiIoROZQDig65+VsRKSkGW8zMUWtP4yJs0RQtqFoxA/bqQvgze+C1z1zMIexjnmUK6FKhnc1KV+Mww3jDVIk35jrxo2MCyE3zwJu/CN7yM2DPzoTVm03xkp7Z4TzypGe44VFbM3bZJzIRzCMmTzpa5EnvNQkibndY+PiqKb7uoJe1B73J5J57zseZp07ES6/MwLI1O7i4wMJcyuItzPuZr9rEd02+3Voo+pqDFIX5c4vqM69RSOZLbKdHsgjNRq8UhcVjWd4rMbkuT9bMkOEiBovHu42LCwIm5rqWKgwVHgzyvbY4OT8PxWgbKqtchhh99MS++PGPJ2LsQT0RYjh1T9lO1q9gSHO+hRVew/sdluaHGBfBWAK+2ynGWyzpzFdchdDK6XDzvc5hDunnZxfy5zGQzEUGXv5roZO0mhJoSwLD27Iz7UsJKAEloASUgBJQAq1FIP4ngq3Vcxdot6SyGn+iB+UDb83E1accjpsvOA4ThvXDfT8+E7+75jR8znzIL02bj09+WF3ng8QuMHWdghLAYcP747ozjsRVJx+O7IyI51RxuQvPMYz7kx99g+30bFBrPoGSimoUkqOaElACLU9AvEHVEiPgTkDUk3DLXckOGzUYR40ZBslRHODip2c/mon7brioySn2zemBv994OW568KU69TbvKcT7sxfWnrvpolORmhQ/T3TtDZ14J5H3wi1JNtWUQGcjYGiiIqNRtuI+g/RSwKzEhm0mPPCeGff/KBnO8krkrX4TA3PuRDAziR6xA2HzM/dtYJch1FkZPpfKbZ2ZizRX1/izSkS6GpO9mMOas7LwQsJlsy2KfkZoYgk/TTHQRHE3FKSq56Y3KD1bxbuV0iG36RxxxLPYRpXQLgq29C1hhanfibgZQgnPSCZYEfQk2yzbo/dpiF6/YWlbZGuKirUpYik0tsRPVmnbYu8Bs70XQxsnI+Rg+oP5U+HfPY+eqb3w/foyPPNxMYXMJI6zGlaGepZgycb4+bU9TIRcwcElBExJYML7i8O4/cxM5vQtZrjkb2E+8kJYHfTADWTR87aQIieZCnPDq1jYi6JJwZY5eg2xWE6RfFgWFcjUYswkzJlrWDxnLWSOUD4FZHqwe7047JAchqu+FLNmr8UXny/Dhi2lcNHbONlO0Zjtifd3kHzFuzzFksZ3hv66jDttpYAscrGIxiIyy1bepzCftbxTciwDMYdkgQAHV1MvRLHYTLE8xEUDJrYp4/DxHQp5bTh01ACcNmU4Jp/QGzbeEijbyjDqTNFAL3ULFeOwzJX3mMwSZDqe1SySoFe0iMUBJvYmTTjsDPstOaN5u2/zRrgKFyKF3tMrd5rw2XcR73Mff4YHZZGEMYd4/eh1JaAElIASUAJKQAkoASWgBGIJ6Cc2sTT2c9/NfFIilEk5nh7HN50/CRdOGo9zjznYKCKkvTlzseF5+f3qrQn8gbSfA9HblEArExjYMxOXnXAorj3tCIxjKOqozWPo6ac/+c54xz3+uh+ERevoVgkoASWgBDoPAXcCnsZpyV1PBBVxV0Rjsalfz8PNl5yGeDl6zzxqPK487Ri8Of372gf81YKVKCiNRJyRMNXXnH5s7bWuvpOIOJ7I+9XVOen8ugABEQxFSGM44ZfnunDqhBScMyYN+cXrUbj0XfQ44mqYkq2UbAdRYKPXbrCUXsYNSawin+2V0KiP0WJVQ9nfe12uUjWMbGq+Su5ZqWOih7CEzZY2wj4O0BCL5X56BFu2sdmY+0T5jllMJQIifYdZV+Q4KRQwLazDOYbpgSxeqIbAHTs01jpQEy9WkyUZFubrDYazkZScgYpNSykIfokUewZKqsvx13erkFfFOnaOh39qxAz7QLvf7/vF49xK8BJNOugL46XpRTjjiN4YmZGB8i1fwdlvOPz9jyA/PhN7OoKS+9dCD2+yjBjnQhPq8nWvidhf18SzXWpH5y1LF0LeEgrRPoasrqKYnIIzzhyFU08fi3mLtmD6VyuxfGm+8dwC9MK10kvZbBXfcu5TTA74zdw3c+EAFxWwbaNdbkWUFe9zeRfEJN9xkO9UMOhnyGsHxeEqep2LiEzRns/BwskzEzYOndgHp56WRQE7E0lWB7zuAngDLqZmruIr5+Y9e8ceib4t71ccM/CIeC15uNkZxWybhH1PGsaFA/SmZx9lG7+mKE/PeYbJfvRTE8pcQk6WPYixgcg/JuNIvygBJaAElIASUAJKQAkoASWQGAEVjRPjlHCtucxtLCUrLdnwPr7+zKNwyPB+9EI+3ijb80vx1uwlmDprKRau35Fwu1pRCbQXgb7Z6bhk8gRcfuKhOGbMYP7RHvkQoZBh2l+dvhAvfDEPq7bltdfwtF8loASUgBJoBQKJeIImIg62wtBatcnjJ4zioqj+WLF5J73F/Hjhk9m4++pz4vb5+x+fjzlL12JXYalRd9OuvWkGbr3kdDiZ27G7WAqFhXjmoaexfLQvUomaEui0BESTCtCbk+GafQz9+/tXCjHo7v4Ymd0L5RQ87T0GIHPcJIpuzG1rPQj+MHOvSqhq1FtwEyvkRmHU95CUsMFNmYiXFIElB23Ek7VGLxOxTQRA+T8gbcS2U/+YYrPJR0Ewct4Qc40+2a4h4Ml1aSryt0BTw2neNY7d1pdt94GVHIN7VsGz7BXm5mW47VQL/vG6G/NXMz80Bc4QvbbN9LoVATQs8zXG1bzeWqq2cAiRaZAe2qBouaXQhOe+qMLfr7IiKeBF9ZqvkTHwYIbZlhzYOSRPtkGJxFRXEhafWOMB1QxMJFsRhWNNzsWasUiAHsMBVPJBU5gVUbZsF5zJo3DMoSMx8fAh2LZ7D+YvWI+1q8qwYV0lQ37TOzdQSaGXXPk+2Znn2GxhbuGYvmpzKxte6NIn/ahDWQxL7YfPLR7FFsMz2sx83ePGp2PE6AwcfdRQDOqfCkswmcJ4KbyubXx29C7mw+GT4qIKeZ+krbpz4ok4JvdJ8G559hToydHOkO8uCsd2quj+DVyoVbYOqVyYMX2NGZ8uq+Dfqe37TsSZkF5uWQKy+qI3yx6Wuv+oDrwfaVfal1/q2iN5env3f+AEtQUloASUgBJQAkqgUxNQ0biVHp+Ern78g7lGGT+kD65i+OorTjoUg3pl4c7LTjbKzsIyfPjdCnz47UrMWraROXda+nfdVpqcNtvlCYwZ2AsXTBqH8ycdjCNHDawVil1uL9/ZlYbn/LQFa/Wd7fJvgk5QCSiB7kogofDUXdDTWJ63eBvfWBNu+rVp3+KXF5yC9BQJKdu4iSj8159fiuv++kydSgN6ZuHSk4+qc66rH0iu5yTycHsppDRmFMREOE4ORvzBGqum55VAhyZABdVklbC5FP0Y/nd9IfC3twvx7xuSkI0U7GaY6nBSKjKGT6B0lgFLuC/zwXKhJT1OzcxFLCF3xYuyrpAbmbGRQzZ28hFX4pgzdQU4ERkNWVHqURA0IiBLLlqKaIZcxy8m8b+MuY1SbR2lRRaGishHVZF6n9Ea64tuQjPGGXNz5GyCX2WOcm9EPBRhLzJWK4fKUYR7k2MunaLpyeyqxO6F7zP6ciHSegBvfWfGSz8wlDL/k3DDomXK3UGGS97f0SQ46LjVwiY/nx/9bEN2I3w3uBjgvZlhnDPJhGP6OVGdvwnW1V+ix9hTETJnMZQ5w5r73XwmItQaPtuyIRWhUcPb6FU41TUJ3x1rFoqokrY4SM9hCTuNgBV2/udzLWH7TthtwzGY6ROGceG69+wAikvLsGlTAdasLUH+nmJ4PGEU5peg2mWnHsunzsakSNjqMN2O5f0z2uVxeoofWdmpSE3PRK9ewzBilAVDhvZETlYmQ0Xzubhd8JeWwBvayNzZFKfpYWw1VheIrM558f/9eVZCROYd5mckZnM6X4+eCFpzYAt44C/ahKJ1c5Hmd6GI55/8pARVXorU9KIOepufKzmWre53aAKMW49fsFzPMpxF4u/LSozvWGaxPMbCVQbNMiZ+x09YxrGMZhnFksESNa6AwGyWF1imRU/GbCdx/7c1xz1jzv+M+6fWHG/g9tcx12J3D7T/2LZ0XwkoASWgBJSAElACLUJAReMWwdh0I8u37MHy5z7Bb1nEU1M8Ni88bhwG9OyBX51/vFHKq9yYvnidkQd52oJ12FVc3nSjelUJtCCBZIcNJ04YjrMmHmSUYX1zalsXofgLCsTvzFmGj75fiWr9Q7yWje4oASWgBLoqAb98Gh3HkvjhbFe00yeOw7B+PSHewlX8GfjKF9/g5otPizvV48aPpIAgH3LvrSqhq23WGtFl7+kuv5fE3yuaFI1JIBBRPLo8C51gVyYQgp8CcKx9NL8ao3NS8durvMhyp6Jg8btwOFPg7HUwdT16HNsnUJTbRM9khu5lruAwv2lIXti6omFsizX7opbWsbrHIrBFTMRUEWnZogh3UfGXx2GKirH9iGxZ57sTm4y2Wtue5Netvav2rHEu0S8yPfneKM6AhjDNBSPhEMMq84LdNgZmRy7FdBNsFUnIX/ICQgX0Hs0yYe76HNz3+jYy5q3CSvRwo1OK35EpGkft9UU4SzFTNBZPXAlDXeF34qG3Q3j1DhsjTPhRvnYuUrLHwJI7kAJ5Jp2lc+ntu83gwCjQxoQk63Dsc4nI4dEnEZmdyfDW3TtTM0V8EXnl+cpzljbCpj30NmeAaQrxvqqlFP9TYfOl0dM3CX3SU9HnyCE4/riR8PGVdVX44XKH4K6uYqhqP1Mje+kd7OdCBuYrZr5gu90Oh8PB52NFitOGpBQLi5nnuNCguprhuCl8V/PZ8GdkMFDF/svoCUxRnyI6Ag7uc2wyPD60kKH0y3ya+f7UvPOSn9tiSed8c+ANkzVzQ5esnkXP50IkZWUxRZgLX6+shNOWTuHatReS7nU1AsdzQh+wZNWbWCaPp9SU87mVkqjdzop/Zklt4oZevHZZTfmY2ytZYl+0Pjw+m6W+jeEJKWILI5t9vrZE//s0qieUgBJQAkpACSgBJXCgBFQ0PlCCzbhf/lT6jjmNpdz25Ps4YuQAQzwWj84xg3ozBPAhRpEmV2zejS8XrcPXSzZg7orNqOQfZGpKoKUISP61w0b0x0mHDMdph4/C5HHDjJXi0fYlD/cnP6zC+9+uwJcL10HydqspASWgBJRA9yEQinzC3+SELfxguSuaiBo3XXgq7vj3G8b0XvpsDm445wTDe7ap+coH+GlJSaioFi+yiL07eyF+eu6JyGVe4+5k1gTejWAC71h3YqZz7RoErAyh/OAXRRQ903DnaQGEKvNQ9P2byDrmeua4HUIv4xRYJTesdy0FuhKGWmaoYGPqdUXC1qPRVv3EzoA/K0S8NkRA5vRlDtpQOIl5fnPpJ5sDC0VGM3M+Fy94BbZti2FNDmJ1mRV3vrgdBRVOJJsrUS2Lb3wdQCmOnRZRhphnNyQe3ByaSLdWUyXmrjLh1S/TcdvZSfT8zUf+ovfQ/8SfIZiewmc+gC1UUgCv5C0+WEPMD0wu4ZgQ5cb7UG+qZuZCjrVwiN7Whud2RIqN3GOjQEzU9BS28lOekKUU/lA53zEnw3o76DTOsOgS8puLB1KTHEhLtsHKNEQ1ij6b580UnA2v8trXhMGzKQqHwvRa93vg8QdgclexcQ9FcPl8gluZPD3BQ8FI2HWmPCYRnou2EbNwIXYOcffJRbzhzVY6fdoy4Q5bKRxb4Vo9F/7t85HiyMSszT68+EUJm7LDaypjlHCG4I7bsFbohATO4pjfY4m8ZJEJlHLzA4uDZSKLCL+TWL5nia3HwwbtDzx7X70ry3m8naWCRf6xitdxrPfwuTz+X5bfs0RNfunbVXMQDSsth+IFwn8shjEOxT7WUv3v07CeUAJKQAkoASWgBJTAgRJQ0fhACR7A/ZLTWMq9L3yGwb16YMpRY3DWkQfh5ENHMJ9eX6PccelJRsizhet24OulEQH5e4rO5S7PAfSst3Y3AlZ+kC25tY87eAiF4hGYPH4YMlP3htqUEGSL+C5+Nm81Pl+wBvPWbI+EJOtuoHS+SkAJKAElYBAI1PNqagiLlWGIu6qde9yh+NfUz40cxSUVLkyd8QOumzK5yenml5TTM7nu72clFVWG+Pzy735heJY12UAXupjIuxFM4B3rQkh0Kt2EQNjsQchmwwNvVqB3WgbOP8oOq3szChe8hL7hG+AY0JuhlVOZe3YEPMz9CoqMzM5aJ0JB10NFGU8WiUioa4rkkp/WZh+KoHkArHZ6qHJx6o55r8G8YymSHdXYGkzH75/yYlOhm565fv4tnEox0NXhxUAJmx2yh2Hzh/DIx5U4fLQVJ/ROxZ6ihSheMxxZh57H68lIMo2Ex7uGYqyLsi9zYvO/vQqrPH1RW6OKqxzzSNys61j9e+Qi74neJrsUliX8eVhSskruYrlmZA2gpzd9zKV4+DdiTbBwo3UT+zEx9PdeE+9wEYcDrCfCNZ8llWnxkDcc5A13Yv4uIMNpYZO/T80Uui2O3hSPc2CneOzesxbhZa8jRRZoUVR+5N0yrC1gvmMbg6/L8Ixw6hyjWlciICGpX2CJCsHyFt/C8ixL9GWVVYy/Yfk7ywiWeHYwK/wuptIC7l/PsjLmnOxKu3L+Hyw9WMRuYpF+ot7Gn3K/P4vYNpaBxl5EkH6kZr/+piX7r9+2HisBJaAElIASUAJK4IAJqGh8wAhbpoGt+aV48qNvjeJkGKhJFPdOOXQkTjlsBA4fMQBHM6y1FLEQVw+v3JqHb1duwbertlDg24aNu4uMa/pFCQiB7PRkIxfxsXxn5F06avQg/nEti3D32ta8EsxYsh4zFq83PNrzy6ILYffW0T0loASUgBLongSC4q4Ux7py2GXxlP3F+Sfj/55716Dw7EezcPXpk5oMNf34O182uODqm+Xr8exHM/FzttddLCFP4wTese7CS+fZdQiY6Gns8NlRyVC9//dGCUPp9saUI5JhrdqIXT88ip72n8PaewgcgR4M95vEnLIrKSIzxzFFODP/TYheZ4hxEnZYsBhforpI5+Ak0Roic6AcSYE4TNFRphE2JVNipP5jyYHZ3sfQkU1uOwXjl2DavhIOswvFphzc86gLs9eUwsmFSQF6lvpsPtj9zAfc0acvHsOij9PrtthtwkNveXDQLdlI5XyrNn4IS1J/pI0fR82cYaOtAyjOWvh3Pb1jDd0r9mcun3eM57FMOxKyOgaA4b0be0+kVkwNfmgg7cgz4JafH4jzbyhAkdrIxSwKKyVgCasdYyIYG2HNY85FxifvpYyCfVooRMtcmTvbiEHNc+Jl3bJGL2N6Fjuc/eAPZDFMtpPrK3bCNZ8RQBhuO5mu1C+/X4gvlwTJ08F/Q1xgYIwiMq+WHYu21s4E7mX/4sEbNQlFPSN6ULOVbw8PsGxgeb/mXFMbCQsd/SxUPkg7l0VyF9c3aVfEafmH8mLNRQmPfTpLIv3U3LLPpr3732dAekIJKAEloASUgBJQArEEor8oxZ7T/XYmIGGfZjAstRRZU5mR4sQJ9Aw9ftxQlmFGWOHx9ESWcuN5k4zRllZWY8G67Sw7jO2SjTuxvaCsnWei3bcFAXk/DhnWD4eP7I+JowZRLB6AoTE5iaNj2MSFBRLq/BuWmcs2YvOe4ugl3SoBJaAElIASqEMglIAXqEXyKXZhu/SkiXiMQnBRWSX2FJfh/TkLcdnJRzU44+35xXjr63l1ro0bNgArNu0wzj345mc4+uARGM9z3cEsCXihq6dxd3gTut8cAwEf/THpCMf/81jueSEfNkcWpkxIQ5rHg5KZjyPzyB/BOmwiwwYnURSbgEB4J3yV25HqYI5kKnsBintmI5yzmWJyRPgzRNhOgjNsEuc8EREluHAIFop/oTBDJzMctSVpAMXfNIZNtiBQvhvl86fCVLCa+X/t2OkJ4U9PFGDaCkqT/B7iCVKvCUaiN3SKRE2cspnzDFs4bj6/Gasq8PDHFtxzaRZSqipQuuZVOHNvZX7jPlxMMAAWaxLc/kXUc318znUX90b4xT5w4RlrDf385YsTY2Eq2IYAbPw8j4TBhpXaF3Vf4/XiM7IE6rbLTMkIxTQtsnSYea3DNSK1seXzNB6veD8b4ra0UV/AjhlIwrvSjnQuW3oZU1wPmnoyn3EmLK5C5C38AKGSDXCmpGHGGh8eme6X0XJsVRwG++eCDZOF/+hUN06YeCeoKC/WT2LG+Tn3Z8Qc19/9oOb6KfUvxBxLm2fGHL/E/YYE45gqeIcHL8ac6BOz39zd9u6/uePV+kpACSgBJaAElEA3JKCicSd46BKK+qPvVxlFhpvssBmeo1EP0omjB6JnjzScfsRoo0SnJELy0k27sHTjLixjjuSVW/ZgzfZ8VHtloaRaZyMgHgjD+mZjLPNfy4IBCTctYvGQPtn7TMXt9WEJn7t4oUs4cxGL80or96mnJ5SAElACSkAJNERAPrKNZ+JN1pXNYbfhhrNPwD9e/8SY5tMffI1LTjySH2THfKJeA+CRqV8whGrdD+xzMlJxyIhBWLphm3HttkdexSf/vGOfyB9dkWGNj2RXnJrOSQk0i8AeRgb+zVNV8F+RjQuOocQVKkH5d8/C761E8oiTmYnVTmGvD5LTMxD27UHYkw+riI4W+XtN/DeZIJYCKyjcdRbz+6opiFLwZvEHOY9wJmzO/rCn9ITbY4WdnqqBPRuQt/Q92Mp3oAdDOm+vdOPO50KYsdJGQZXfS4Nhw7Wvs8w5Ok7xGpecwoYqy5NPfVmJ4X0CuHFSGrz+cuyc/zL6n/AL2NJsDE+dBlvyUISqN1BsDlH+FAmUmYolCoPhxRttlYf7/FCu+/Nmb829e5Ef0eIdLHWlTflacx/3jSZlUUIdo9AcuVJzVrzGoz/rxWtZTkfaMDyYpd39MhmMqLvy00IWGTDEtJWLJrxcZGCjgG7OogY8mK++FdawD3tWfArP5vlIY3jvTSUW3P1qKcrdyVyQUFEzGvF85rumgvF+PY0OfNMRHFtsTmEJCx3P/soKTYnG8tIPYhnAMoxlNUs8k5BssuI++sFL/VUe8e6Pvd7e/ceORfeVgBJQAkpACSgBJdAgARWNG8TSsU+K6CueolKiJjmRJzIEsQjIh9aIiVnpKUb+WslhGzUJK7WFHqart+Vj1bY8rNtRgPU7C1kKUFgeTcsSra3b9iAgiwKG01N4ZP+eLLkYM6gXxg7ug9EDe8LJD7Drm4+e6Su37jFEYsl9PX/tdiznIoGAfGChpgSUgBJQAkpgPwhY9smfuG8jAfEC6+J29RnH4j8fzECFy83fnwrx+Q/Lcfaxh9SZ9YYdefjwm8V1zsnBrsJSPPs/N+Dsux5krmMvtuUV4ffPvYN/3XL1PnW72olgAr+DJPKOdTUuOp9uSIBib4E7iF+9UoB8bwp+fnoP5jKm0LX4TbgLNsFy+BWwJeUwxG4yo+7aKRg6KJyV0GOSuZHDbgqQ4uVprRHrOgc/u5X5Zen5GQg4mbM4GxZTL86vJ3x0Gk4KVMO1ZS3KV05FekUBHKmZWLvbjrteKcH3a5wUmt3wi2gsHqf1FuJ0htnvzT0suhBj2jKs9l9etSK3pwvnj0lCUWER8r99Df0n3oRQDyvF4r5wOlPgZt7rEEqYw5de2QEJVd7Ss42MJyoe7229ruhb92hvrbp70bbqnm3eEXsK8e/aGk96I2dyKImexaV89Mx1bR3N/M8uWD2ZqFr8OUJb5nHBVQAFZhvuetqHLbvCsDuq4AtE3pPIiFpiXM2bhdZudQInxPQgD7huSJeYizG7s7hPl3OuyGnc5BfYrTWl8VqRK325OYnFGVPxQD9Hbe/+Y6aiu0pACSgBJaAElIAS2JfAgf6ys2+LeqZdCEhOZClvzV5a2//AnpkUkPsb3qjjhvSh8Ngbw/vlGKGLJXzxOceMra0rO2VVbkM83rirCFuY71bEZWObV4wdDHWtImQdXAd00DMzFUN6Z7Fk01M4shUv4hH9ctE/N5MfDDX8J/vuonJD7BevcfEeFy/y1RT//Z3wQ5UDAqg3KwEloASUQKsSsEguxDhW37M2TvVOeTk1yYlrzzwO/353ujH+J9//ah/R+KH/fk5RJ8Lr+AmjMHfZOqPujoISDOiVjb/8/FLc/uhrxrkP5izC5AmjccHkwzslj0QH7Q/EX1Bg3td1LtHmtZ4S6DQEbCYRgoOopufo714PorDKi5unJCPEvLLmnfNQ6spH+vhL4eg1FH6TExZ7P1iCKRTUiuisGqBw7Of3l8B++3O2PSiGFeafMXZbBoXvPnSYzaZgTPHYwryzrgKUrJzO/L7fwGGjKM7vrx8zJ+29r5Vhe1GA4nIQVoa2DjPEs/jdBruA2yhlYRSgDHc84cDAO5Mwtl8lFwusxPaFL6D/UT+BOTWVjGz0wg7B4wlQaPfSE9tqPPO2f3Zt3SMFX/nRWRPiOhAIwGobT+/iHvz34YbVm4zi1a8jvHYmks3lKE1Nwf0v+jBnUwWczGsMP282vJXbetzaXxsSEME2apJ7OJFI9fJW7WYZzNIcG8jKI1nE40K2o1hGswxhqW8Nf1hTv1bzjtu7/+aNVmsrASWgBJSAElACXZqAisZd+PFKTmMpH363snaWDpsFo+jBKgLy6IG9jH3xZh3BkpmaVOOtPKi2fnQnSFFyT0kFdhaWYQeLbHcWlWEXRcy8kkqWCiP8sYTS7s7mtFnRi6HCe2dJSUcflgEU7/vnsORmYEBuD2ObxLxdjZl4bm2laB/1ApeteIWv2pqHUgr7akpACSgBJaAEWpuAJQFBrzt4Ggvnn5w9Gc9/MhuS+mHN1t2YuXg1TjpsjPEIljNn8ZfzV9Q+jv+5+hwjHHVltceoX8h8yOcdd5ghJL87a4FR7/fPvoPDRg3GQArKXdUSeTesCSxM6Kp8dF7dh0AwFKD4ybC+lDGCzL360AdVXJjbA/dcY8bQTApfpXkomvsCUkZORua4E+C1pSOQbIMzkMII1VxIat6NsJWLdxmumclyGY7XQgHZzPOiizDwsBEVQkIQU8NIIBf9gZKXHLmiloigK0UiF1tNDDnN/Ll+RgcOWVMB2xCYHb0532SGFg5RMAYqt85H5fJpsFVtgp2LY03MU/vyzHLc/3YpyrycE4XDIH/uBLngxMw5hruAYCysQxaK4WEndlcEceNTRXjstl4YnVWNpMJF9DgOotfkKxBKYyjmcI6R19mPLUYIc3nGXdv4/pr5uQEXCNClmFszc3/3Ia/BzGHMEOWhQhStmobKpZ+hB8OcV1Fcf+S/wNSZVXDYQ/DRA98StsMalBzial2YQGxo6p3NmOcO1h2cQP0BrHM7y7UsOQnUb+kq7d1/S89H21MCSkAJKAEloAS6CAEVjbvIg0x0Gl6GyFpOL1Up9a1fdgbF4xzD+3Uo8+QanrDcyr4IoeIBK+Xo+jfGHHt8fkNELiyrQnGFC0Ussi2uqEZReZXhzSzCcqS4a7dVbh8/9JAPPzqGSYjo9GQnMlKkJNXZZjPsd3ZGMnLSU5Gdzm1GCkuqwUiE90Ssggy25pdgs3hzsxhbCsUbdxUa++o5nAhFraMElIASUAKtRSCR0MGyoKw7WI+0FFx56tF44dM5xnT/8/6MWtH4wTc+rUUgYavHDOlniMGrtuwyzu8sKEZuZhruu/4iLFq7hYvCiuDyeCH5jd/68y2wiZrSBS2RdyORd6wLotEpdTMC4jEr/4vMahjdcN+bV4I1m9Pxhx8DZx5shsPrRnDlu9idvx6pY85Ceq/BCDrS4KJYZg+n0eOSOYJtu+mQyXy54Wr+zeTifhLFW/qxUrA10Ts3RG9k8c1tbRMpU/Rq6VeKCOJhEXkZXtuSlMFQ2wxFHUyDn2Go7Qwv7C/zonjVbIR2zGJkJKYFTeqNolIP/vpyOT77notrHGGKhE6KxVScRf7j34PMeNza02iz9kMhiqEyJ3MIy/cE8L9PUjj+VV/0yy2FpfgH7J7jRtbx1yAlvTdDU2ch4OCz9GxiZO4y2HmPmXGqvVwwYGK4b2OxgLE2QF6oEFGJfN85zGyWhQB8vPy9QWYiiwTCIXnmDoYkz6SHfQ4C5kF8o8v4/iRh98Iv4N/4LVIpIHvtFXji9Ww89VkB/GY/7F4zF1IE+e55KbZ3nM8POseT6HSjjF1p3xzvBMk/HM/uZIX7WRr7TFT+oUk+uBksH7O8ytKSq/3au39OR00JKAEloASUgBJQAg0TaOwXpIZr69kuTWBXcTmkzFq2aZ95iodyP3rLDqgRjiPbDHrSZkS8ag3v2nQkO+0YzLDLUpprIjhXe3z8MDVSqunR4/EF4GXOXh/Fbh9DVkX3JSSmiMwheqlI3rzovvRp5ocxZq5gr91y32Lhhy78YNZOT2AHi+zLVkqy04Zkev6mcOxSZA6NhYeONyfxrCkoraLXNT2vDQ/sSsMjO+qhLWG+xUO7u3tkx+Oo15WAElACSqB9CVgTyEkrP7e7i/303BPx6rRvKYQEsZDi7/w1m/mBdwjfLF9vIJDfOX592ZnG/oCe2YiKxtvzi3HoyMHMxejAY7++Fhff+6jRxrKN2/EvhrX+n2vO6ZIIE3k3EnnHuiQcnVS3JiDin5nhmtcUV+Dnj5tw1cnp+N259JrM7IlA6Tp4vluH4IBx9Dw+Bcm9jkJARDFrEYXHHvTqrTK8UEOhSsqqJVTgqg0Bjjv0S6XHpqF9tKaIZqI3cDXFuiD/VnJwPBT8zCmw2XM5jlTmo02nt7EDIQf/xnIHULn4G7g2TYfNW85xe9Ej2YpP5hfi/97xY1s+BXCKovAwjDVKIyJiaw69nd66MD2wfSxR+2GrF3c+twv//mkPDMzJQbh0PSq//DdCk36KlP5jYWNoclvqOFRXb0LIV0y+IoxG7penLMIxXcprSrTVjr8N8m9kGbaZ+a5BsVh+xQjznbbYe8Ds7MvQ7NwPuuCqtqFkyQuw71iCFFsQborDD7wXwvMUjHkzxXPylFUYAZ8hQnf8mesID5BAfsz9A2P24+32iVPhOl5/oF6dxTz+jmV5TVnJrYslavKPr6XsOjbUnv231Dy0HSWgBJSAElACSqCLElDRuIs+2JaelngoizeslKYsjR+KildyxPuWHrn0ys0Rz1yjJNf12k11IpNevOn05hXPXqc9UrJYt71NPuysrPbWeEZHPaLFQ9qN0ko3PajFkzriPR3ZulDAEJRF5S79A7a9H572rwSUgBJQAgdMICkY39Oryt0cp48DHlK7NtA7OxMXnXAkps74wRjHk+99BVfM/C85cSKG9otEUZQ8xlGTvMZRO3hof9x11dn42ysfGaee/vBrHDdhJCaNGxmt0iW2kt9ZvKnjmTOBdyxeG3pdCXQ2AiL32emqG7Imo4LC11OfuzF7LYXj88px2qF+mHjetWMZKnZvQUbvpcg4+DiEcnpTYXMyfSvD8ZrSGIU6CJu5mAtnKR6HSiksV/DvDwrP9DhuPROVjwt16VEcMtGr2JJOz1gKvpJblmML2xzGvIKVxajatBlF22YhWLicUjKFvjQn9uyx4NmpIbw8NwBP0A86URsey7CUsB0rAl5RjLugalz/gfAFmLPOjB8/7MajN1gwdogZbs8euOY8jvDhV8Ix6BjYfUE4nCNhS+HzrdjCMOWlfGm4SItetmEuOgiHHDWhyPlMOokZubmpFIfpnc7V3dza+C4PhjU5l3mcJfMzFySU7oR70fsIF66Cg17rPnc5HngnhCenmRHkQm9bUH7n6Dxz7iSPpqMPc0/MAEUIltUxiaxYbEpgzmQbz8a0W8j9S1jmxJyrvyvfujNiTsrx/lp797+/49b7lIASUAJKQAkogW5EQEXjbvSw22KqlW4vpGzcXdTs7pIoGkc8fWu29P4VITniIRzxDLZbuXLdboUlxpvYwrBdUc9i6VS8j6Oex8aWH15KmEQfvYN84rXMbdRjWbZuCsSGdzPHXe2N7Mt9akpACSgBJaAEuiuBJEb3iGeSt7c72S/OPwlvz5xn/J4xZ+na2qlLiOlbLjm99nhAz73RVnbQ0zjWbjjnBCO/8dxl64zTv3nsdXz+0F3IYsqLrmJV/H0qnjkoGLeky068/vS6EugoBEIMy+umn7CFIXYZiFhSuWLNlhL87MkknD/RjhvOCmHcoFSYqktRtf0jVBQvgrMnPY+HH0vP42EUD5nnOORmmF+KaKZcWEP0/PW74Q/lI2Aqh5nCnJl5lCnL0UuVIYFr/qSJbORfXYzWwfDGYRlAjUmdkEXyL4tPK8MIm4IUdynYhWz0BKXOwTDCdlsOKzn4txW1PwvFS36a4Au5kFRejbIN36Ms7zvYy7ch2WeGNakn8sM+fPRFNZ75pAqbSlnZxJ8tbDcQ1f6k++ggowPpwlvjuVi8WLI7gEsfd+PpG/vi2NGMsuWiF/m85+ErWAfzhEvhNKcynLcTtvRhMLkK+LzLKLRW0pvbjZCEeGb+6jZIYd1iT8LGqF8hvmvBcArfyx5MzSDvcQ5CAUYCC7hQvX0Fyld8gaBnKzJNKcirCOGelwP4ZJGPuYsl/VMFQ3cnIezhWxrjud1iA9SGOiqBBTEDk+8WY1iWxZxraDeNJ7nSplE7nldiPwe9nsdzGq0duSAitPQftdj96LlEt+3df6Lj1HpKQAkoASWgBJRANyYQ+8tSN8agU+8IBES8lcK/CdWUgBJQAkpACSiBdiSQRE+weJaIOBivjc50fXCfXEw55hB88u2SOsO+6rRjmZ+yR+25gTGexhKeOtYk/cWDN1+Fs+98gNFJqlDIKCV3P/FfPPfbn8ZW69T7sR7YjU0kkUUJjd2r55VApyYgyizz91JzjRgFMGq7qOai1ze/rcbcVQFceZwJ157QGwMy0pmqpxQVO2bDv2MuAr3GwdL/cIR6T0BKagbFtzDzwDrht3ARRrA3HCIYh6uYPpkCG/Mee0JVXFjLnMEUkEWsDIU81GdlAGKSV5bf580xC4T4/cnKHLIIJ1PQS6c4nAy3PRU2nksC0/ew+MVL1M56AQZKrvbBW1AFz6ZFKM2bwXsKKSY7YOICXw+dAWcuKsN/PjPhh21u9iczZol2L0MQM8TjmDFEznbZr0yaBAe9bT02P4rLUvHjR/bgr9ek4aJJSczr7IN/23z4yvIQHH8RHH2HwiMau70fLD56moftfH5FFI99PBkR/I2UShT/ZcsnTG5RwLKVBQIxiwRanSrzW9f0L6+ZRbrmTojjC8m7xQUPdusAHovDJr3mHVx8UMm0TWu+QvmGmazvQVpyGr7Z4MO9L3uwfKu8N5Lzmjm86c1u9Zr4XnOhRavPQzvoQARmciwSsiW6Gu9/uX9lnPH9itebevFFtI2afPOZET1oYnthvWuNfY4q3+ii1tgYWrP/aN+6VQJKQAkoASWgBJTAARFo7JedA2pUb1YCSkAJKAEloASUgBLovAQSEfUSEQc7L4GGR37jhafUEY0lIspNF51ap3Jj4amjlXIz0wzh+Lq/PmOc+nrxarz02RxcN2VytEqn3iaymCCJopmaElACEQJ2imoBUzUCjKi0szIJD3wSxis/FOPqY5nz+PhUjMiy0fM4hILtS2HOW4Ue5p4o7z8G9l4D6IU8mDlw6f3LCE3UhxlRieGrnTkUoukI562gaMyw1fRstlKwDfklCkBUcqM3MXMSixAcPRdiTX+IaYLobRyikB2mKO0Msp7FTtGP3sYcp7maobDLdqOsYBU8HEugdAfFag9SLfSKtbJvLgCetsSGp6e5MG8L5Wq2YaIwTa1PjQRCXCRQzahXYiGulC6nLnrHC2Ys2ebAXRc70cdhQlXRNhR/8zx6DJsE+/jTKTA7YHP2hDWYycUBGRRXC+iFzDzRjNhgEsFYCv8T3/XoszTkW0pW0adtdNhKX6R/xv2imE0PcnnPWMxcaGBj70Hq22FrKiyOAfSSzoTXLOflPzOqts5D6bKPYKrYgxRG7PCb0/HODyH8bWoJtpayTTtDofuquBBBeMVqca00EW22IxIQUXcqy401g7uM2/tZltcc199wtQvurH+y3nFlzLF8HirhomWFQmN2Gi/8vd5Fhllo0KpjzorHc0PWmv031J+eUwJKQAkoASWgBJRAswmoaNxsZHqDElACSkAJKAEloAS6NoFERL2SSlfXhtDA7Eb2741kijPVXn4SThvUOwciAsdav5weEa8veljllZTBT4FAQljH2uRDRuOn556I5z6eZZz++6sf46gxw3DQ4H6x1TrlfmkC70UiObM75eR10EpgfwiEJKerCLsRz18Lc/zml1nxr4+DeH1OEc6bmIJzjrDjsCFWpKV4UeorgH3Pdob05Z/y1n5IzRoOe+/BMOf2gjM1lwIyRWi/FUmWZCqTzEPM70U+FglXLR6qUQtR5Iu4+orkSKPObLJR8Atb6DXMA4qBgVAF/KW7EKCQ6S7cjkDJVoRcxRSHvUimkOe0h+Fhs2vKQvhscTW+mOPCou3SKk9aw4ZwCOZa9htesNGedRtLwEvP4ee/DGLtxhB+e7Udxw1ijmc+45INnyKcvw69hp8A5+CD4bHb4aPXsd3bi/mAi+nxXUrQFFX5jMSL3BTiwgGyjpZwWPS2tlDrGS6bfYl0bOXigjAXIgQYPj1oSYEpuQestlwuHkiChQsQ7HxngnyH8tYugHnHPEYrr2Re4xTsqLTisU/K8Na0Sr5PSVy4wHcz0BaSd+yT0P0OSuD3HNelLFwdI9+l8FXN8WxuY20wD95jyY492cB+3XAxwF9Y54YG6smp41k+YKkvEtf9xU9qRoyhHmrtIu49ylJeeyay05r91+tKD5WAElACSkAJKAElsH8EVDTeP256lxJQAkpACSgBJaAEuiyBNH9EFG1qgkUMrdzd7N3ZC2oFY5n7xp152FVYWic8td1mRe+sDOwpLjPyH+8qLIGEtq5vd145BT+s2oiVm3fSOzCIWx5+FR//8zdIoijdma2oPP57keaL/351ZgY6diXQHAIei5+CG4Vj5g6mTEutliF5gxQBqY/kl9nw7JcBvD7XjcMHWnDOkSk49mA7hmeZkMnvNX5/IQJ5u+DZzRDWDF9sSe0BS3ouzKnZcGYMgNWZRlEumyGO05mKmCIcw0ubWSQnbpAhpoOSH5eSbjhML2S/H5aKClS7SuGvoijpc8FbtQvBKuZK9lQhhSIyA1QDSfQgTbdjZ5EZazf78MH3fnyzzEUvaQrFFAWtbJ9+yUw9y/bpadw2wmVziHesuo6AA36rD99uq8TV/7DgN5cl4doTezCUM6X8spX0xl2PcMFYeh6fh9TcQxGyVTJkMz17gz0pxkpeJ3odS87fcD735XsrQzgzr7XFlMRnK8+kNU3CYvvZq5fvFENomxlCmx7syUn9uejAaeQwDvO9DoQZ9pxew+Vr56B67Qw4A0X0rLdxEZYfC9dW4q7XglixwwSHg++jn+HW+W6azK42kbxbk4623SIEJM/HTSz/ZRHRWH6hEuFYxNxvWUSoPZpFQkhLGGv5RyD39GFpyGbw5CqWsTUXr+d2JMvTLJtY5DPSwSwi+p7DIseyAoPLYTCURYwveIMm7R5Vc2UCtzKOPSzyi5HkYxZrzf4jPehXJaAElIASUAJKQAkcIAEVjQ8QoN6uBJSAElACSkAJKIGuRiDTSwEhjklO3u5kPn6Q/djb0+pMOcg8pM9+PBP3XS+fLe41CVEtorHYjoKGRWMRlx+9/Uc49+6HUO3xYfPuAvzpxfdx/y8v39tQJ9xLZDFBps/TCWemQ1YCrUQgKGGEJbAvdYk6Gisv8KxYtceKuevCLMVIzbThxEHAMWPTMXFsKoZkW5CZ7KWW66eHcQmCeZUUgbfDFf6GIYGDMFGcM1nt1IuZq5hiseS/lRKgF7Kk/pQcu0FvgN6tVRQhixEyUximxyooOlLLRGoK70t3wOV2Ic/lx7K1Zsxa4se8zUFszKump2t0EYjoKlRX2GxkjweR4Rvn9UvDBIJcNOCgd62fYZjLyfIPr5nx2fel+N1lZhw1luHC/SZUb1uFwm3rkDboCNhHnwhbzkDmnxaP8HTYgql8in0YrpolTM/wYDFF2nLjVWI2YXYqfuRS9tfkXnkxoxa7H6RInMQQ1H0p8qYyLHUm361k+HxcPGBPYZQNRkmnp7p/2wpUbVvMfM3r4aQHujU1FfmlATzzgRkvfGfm+13N95P/BughH2akYIuTixqCXEBV+25F+9ZtNyXwNuct31beYHGyyOeYl9QUbmpNwkxfzHIzS2OisdS5kuV7Fv4DM+w4fpXSkG3jSak/muWFmgoSspou8fuEtf4Pz0kI7VQWMVm10Z9F/tFIXy6W1uyfzaspASWgBJSAElACSuDACahofOAMtQUloASUgBJQAkpACXQpAhkMPRrPEhEH47XRma6/Mf077C6KCMFpyU5UVkeEz6kz5uHmi09DTsbeaIUDemZh/upNxvR25IujScM2hB7If7zhYtz1xJtGBWnr+AmjMeUYcVDpnJbIYoJE3q/OOXsdtRJoHQJmM8W5EBVYir2u8gA+WeZkKUMqRd6D+joxhqGrx/Q3YezwFPTL8qJXZgjWkBNOei3bTUa2YrgpJEqo6rBRwPP0cKYncJCicSiZeWZZUvy94OH5KnogV1M7LqNIvGGdC9+vC2LlzgDW77KiiIl4Q+JxbGbEVno3Rxz7Wmfe3aHVAEM7B3xuhgWnwE9RXzyH521KxlUPm3Dp0W78aIoTh/ZywutxoXzLTJjylsLacywyhh8FZ+54mByp9DKvgtmaSe/iDHrp9oSN0UJ8dHDkU6QHO73Wg9SppH0JUW5YRAgOh6W/qNFrWKStGE2YzuUIUeSVfNniNC5+xRYuQAiFHRSm+TPPnk1xOIse6E74JW0D3yWLjeGpzZUIFe1AxZZlKNr+DZgAG+l8pzJtGdjhseOjH4rwzHtebKJwzEGxMBcy2w+FIwsQfF6K3RSR1ZRADIH3uX84yz0sl7PEfpYpqyPEy/cWltksIho3ZSt4UUTgv7FcwyL/IGJNlrtsYHm6pojQu50lavIL33ksU6MnarYLub2ARcTlgTXnZCPtj2JZLAe01uo/0rp+VQJKQAkoASWgBJTAARKI/UXrAJvS25WAElACSkAJKAEloAS6AoGMBDxBEwlD3BVYyByqPV48+d5XtdP59eVn4v3ZC7GCoaW9Pj9e/HQO7rrq7NrrA+lpHDXxNG7KLj7xSMxZuhYffxtJc/fbp6ZiwvCBdUJeN3V/R7tWWCbhUpu2jAQ82ZtuQa8qge5FIERFzUxtLRxiwGGGADZRzQsx5G8V8w8v2BVkEfHPgmRbKdLoHjy0jxMjB5qQkWpBTjq3KSEMoO+b02am6GelB6gNIT8zzFJqcbPN4io3SlwMH8zvZyXlFmzaZsGOnVRJGLK6yk01T9RD0T3MFPKY9xjMWWump7IJHnUmPtBXUdRSOlGK6iXCrjxnk9mNykAyXpjlw4xlVbhkohU/Ork3Bmd5UM1n4N06H1VbvoMrdzBM/Y5AUr9jkJyRZQi8frr3+uxJsPuyYBFvcouPWq6PPbjgCZbwLaHwy6cmIrXkQo4oxZFJmCzMTWzkuq455hO2muggSe9hIIPvXQo8fP+sNhsXJMg7wJEzvYLP5oGdubRD5cWoYkoG78Zl8BUu4L35zHudCktSKkqrq/HfheV45YswFm5nGGsjqnBkzpHeYr4KkrBBJOak7ioBrCYDEXnvYBnLMoxFxNwfWGJzB+/9hYwXGjF+h8O1LL9hGcEibcmqipUs0o8IxbG2iwf1xeXY69H9GdwZzDKcpSeLjEvGWP+Xo9bqn12pKQEloASUgBJQAkrgwAhYD+x2vVsJKAEloASUgBJQAkqgqxFIl5zG4nJEr7bGrMLlZrhSL1KS6G3Wxe2lz+bSuy4SjrtPdiauPO1Y9Ob2pgdfMmb+2rRv8YvzT0Z6ikQrZCxCehpHbUdB457G0Tp/+fmlWLJhG3ZSYBYP5tsffRX//ePNRt7KaJ3Ost1T443d1HgTWZTQ1P16TQl0LwLMQ2yiUGzyUVikZzA9jsMUDi30GrXy+7SF3qIhehMHzT64g2HDQzh/swffb5I/9SVqhDjN0eOYOXINQbImPDUDAFN45n30Fvb6ot/rRXymkCiHFJaNnwO8SYRGuWJiX5J72c+tySKesUHmRuYFtRYjYKGIL9mtPWEXwz47sK3UiYe+MuGVhYW4bCLj5E6i53HvDFTQ89hTvhP2kj0IrpiJvL4DkZwzGMm9RiI5vT/MaSl8X5ixOkRvcEsyHyVFX1823yV6ofO5GeIx0y7sNXnGDj5T+ZluqLbGU/dyNOIJHKagLOHOnRyf1cxFBxSNXXz44apqmAo2ozBvNXxFq5j/Op9idRDpSWnsswdKK/z4cIEPr84NYNFWvosWeiM7K8G03WpKYH8J5PNGKV/vbwMx9xVxX8r3MecOdFf+AYmnspR41hr9x+tTrysBJaAElIASUAJKoEkCKho3iUcvKgEloASUgBJQAkqg+xGwUIiQvLNljogI2hiBnfQoGjWwT2OXu8R5Ecef+Whm7VxuvfR0OOjJdcbEcRjWryc27SowhN5XKRz/6qJTjXqxnsbbmwhPHW1Uwl0/xvzGl/7ucQpCISxatxWPvfMlxKO5s1k8z2qZT5ZX1YLO9lx1vO1JIEyPUIaDNjxSOQ4Kd1R6DQGZwabpQcrwwTWnZZTMWMwvIvFS1OU1+U9EQF+tPljfgzMSEthoRKqKcWvyS75aOaCwzBNSjNZ5e1jiFXPbxLoiuVFtPwiEKN0GKMyaydskOX25GIAyKwrLLHjiixDe+taPKUeFcNYECyaNYn7gdB8qTG44ihagYudyVNlykZzCHMN9+sOW1RvJmf0RdmQjZHPA4bAZDrzigOzlA7dY5T3ZaxJ2PCzvF69F3iOuHbCwZoiLBAIcUdDMMNRFcFfkwU+hOFC0FYFyhsH2iAezBWlcYOC0Mn1D0IfFO3z4bIUPH86qxuZ8eU+Zg9nOVgPM4czcxdXyAhnv1N7+dU8JKAEloASUgBJQAkpACSiB9iegonH7PwMdgRJQAkpACSgBJaAEOhyBnu5qFY35VJ758GuIcCwmOYglnLSYiWrJjRecgjtr8hG/+Ols3HD2ZDgddgzsmXh4aqMxfjlkxCBDJH7wzc+MU0+8Nx2Txo3AxDESMbHz2K6i0riDlXdLTQkogeYQEIEtYpKTWExk46jJmchZOcM9hh4W0XjvuZrzsmnM6laOtF/vnLRtnKo5XzOUxlrU8/tBIMj81UHxBmfeYBM9wS2WMLwhSq4sJnr3FlZY8fJ0F96YGcbBvW04+6g0HDs2jPG9U5HBBUjBUAXCnkL41ixFFcXe0iSGl05OZojobDhT+8KWlAFrSk8KuKnwOSnkmsUDnR7kLIEAA1dzQYI55OVChWqE/R5YKorhcZXC72KqBZ8XXncxwt5qRikPIIWLE5K5gCAgIbEdDuwo20UAAEAASURBVKzOC2H5+mp8vCiABau8KPZItF++i9Sm7RJ6209ZmnPz0NtZTQkoASWgBJSAElACSkAJKIGOSUBF4475XHRUSkAJKAEloASUgBJoVwK5FPbWZ+4VPxsazM78pvP1NnRPZzoneZtfZGjqqInnr9UiH4JH7LzjD8PDb32BXYWlKKlw4b8zfsB1UyYjt0c6HHabke+4nKE7RXSOhq6O3tvQ9pcXnIxvV6zH9ys38gP7MH792Ov47ME7mZdU8kl2fCssqzTm3NRIbcGg4cXeVB29pgSUgBLotgQMPVXEeY8h0FMr3mthOWCh8Co5rZfs9LMUIOUrK8ZnhzHp0CxMHGbHQX3CyMnwIY0/r0LiMV7OvMb0DvbtWY/qML3WKeKaec0cjokmwjaD7MxMEdludiLkDcPvo6ezjSlZrWyDnxyFTU6k8FRSUjICliQK2C7sKHVjwUYL5swPYEW+CTuK2b6EODdGL4sXaJxTjT87T3PFgWrGES76VQkoASWgBJSAElACSkAJdEACKhp3wIeiQ1ICSkAJKAEloASUQHsTyPXE9wZNJF9ve8/jQPp/8r2v4PZGPuoePagvzj72kDrNiYD88/NOwh+ef884/+xHs3D16ZNgs1owgHmNN+6UlHuAcBo7pL+xL19CjA0qXl31Tc7965arMeXOB1Fa6cKe4jL873+m4j93/aR+1Q55vCOBUNw5fK+MiLcdcgY6KCWgBJRAZyAgorKIs8xpbTHDVcGErOVOfL+5UDIQY1BuMob1DGH8EGDMMCv6Z5ehf44dyRYHkhg+OtnG+6jcuoMh5iYWHZdCLv+XPNcmMz2Hmb84nGKG3WRBsicH1cxRXFIdRmkVkFcZwLodVZi3thRrdoexu8TKFA2SO5tFFlVRWI7ENa9xR+8MOHWMSkAJKAEloASUgBJQAkpACdQSUNG4FoXuKAEloASUgBJQAkpACUQJJBJCeCPz+XZV280wy298+V3t9O648iwjJHXtiZqdy04+Co8z/3BReZUh8n4wZyEu5bl+OT1qRePnP5mNZIatlvzGOwtKDC/kzx+6q35TxnGvrAz846Yr8PN/PG8cT5u/whjHVacf22D9jnRy466ISN7UmBJ5r5q6X68pASWgBLo9AdFjReTlpznUc2E1UyqmF3KInsN+5iTeWBxgcWLaKiFVDicF5n497eiTE0ZqkhlZySakJYcxOhf8eWSBzWYzSsjPnMX+MNwMU51f6kFhuRelgQAq3SbkFZixmz/yiwP0OjaSXctHSRSZGWrabPMxIrqN6bYZ5pp9cSTSsZoSUAJKQAkoASWgBJSAElACnZCAisad8KG15JBfvOtKDOvbdOjJpvqbtmAt/vrGV01VafFrt104GRdPHg8//2A95a7/tFj7rdVuiw1QG1ICSkAJKAEl0IYEernpUhTHNu7Mi1Oj815+7O0v4ePvGmKHjhyEUw4f2+BkJAz15accjSfolSz2+2ffxV9e/pCeVxKeM2IfzFkU3TW2t156ep3j+genHjEW1555HF754hvj0p/Z3pEHDcWIAb3rV+1Qx1HP6qYG1cvtauqyXlMCSkAJKIG4BCjQ8r9QiGItBdqghJxmbmEzcx5bmQfZFKbHr5k5ia30JGaqAy/9ijcVBbGpQPyQRdCtDRYd6cnkNrZmCsDSXjgSi5rnJC6EpGSQ+lSpKS6Dns2GYE1p2My6prCZ+ZbtCPByiPmXTVbmXmZ1zXdNZGpKQAkoASWgBJSAElACSqATElDRuBM+tJYc8mEj+mP80L773eTWvLbPZSgi9/HjhsHnZz6nFrTWarcFh6hNKQEloASUgBJoMwL9qyrj9rW7qAwutxcpSY64dTtThS17CvHurAW1Q77rqrNr9xva+QVzET/1wdfMBxmi0BwwSkP1oufOm3RYdLfR7W+vPQ/zV2/C2u17jDzBtz7yKj64/3bDS7nRm9r5QiKicX+Jo6qmBJSAElACB0AgwJ834m4csbCR61jkYBF9xSjc1qQTjhzLAQVeegaL4iv/1bGaw71NNvJ3dszf3xLW2ugtHJTeIm1Sj6aObYjKddrXAyWgBJSAElACSkAJKAEloAQ6DQEVjTvNo2qdgc5auhFb9hQ32Ph5xx5shGEsqXBh7orNDdZZvGFng+f1pBJQAkpACSgBJdC5CaQG/MjwelDuYH7CJkxCEk8YPrCJGp3v0sNTvzAEYBn5ceNH4uixw5ucRGqSE9ecMQkvfz63yXpy8aDBfTG0X8+49Rw2Kx69/Uc4738fNkTjdRSP//bKR/jjTy+Oe297VdiQgOd5P1f8xQjtNX7tVwkoASXQOQjUE31rBh0rBtetIUexV/d3ljGt1tmtOYg5t7896H1KQAkoASWgBJSAElACSkAJtC8BFY3bl3+7937bk+83Ogb/tAdhtViwZns+LvjDC43Wa+sL9785A898+r0Raqsl+26tdltyjNqWElACSkAJKIG2JCACXzzRWMTMriQar9m2G598u6QW82+uOKt2v6mdWy45zcg97A/WuHc1UjkRL+PorRKO+v+uuwD3PvO2cerVad/i+AmjcOqRB0erdJithOPeVVgadzyJeLDHbUQrKAEloASUgBJQAkpACSgBJaAElIASUAJKQAm0OAEmpFFTAp2LwJ6SCqzcmofVFLNb0lqr3ZYco7alBJSAElACSqAtCfRPwCt05ebOGXXE4/WhoXDKD73xWS3i0yjOHjJiUO1xUztZ6am45sxJTVUxrp0z6ZC4dWIrXHnaMTjzqPG1p+5+8r/IKy6rPe4oO6u2xH8PxHNdPNjVlIASUAJKQAkoASWgBJSAElACSkAJKAEloAQ6HgEVjTveM+m0I7JbLUiy2yBbMYvZhOMPHoqzjhyN7PTkRuflZPjF8UP64OLjx+O0w0ZicK8eMJuYJKkRs1nMRj/SV6xFz0f7l2vSyqj+uUbb0ofV3PgrH72/pduNHaPMawJzSF84aRxOmjAcqU577eX6/Gov6I4SUAJKQAkogXYiMLCqPG7PKzbtiFunI1aooGfsmXf8E3c98SZ2FpQYQ1y8biu+Xrza2DfxZ3aiXsZe5nl8/cvvMH3ByianetjIweiXm9VknYYu3v/Ly9AnO9O4VFZVjd88/gYjroQaqtpu5xJ5DwZWddB8xsNv6VpJudvtLdCOlYASUAJKQAkoASWgBJSAElACSkAJKIHOTEDDU3fmp9fBxv6f2y7F9WcdhffmLse9L3yK6f+8Ef1zIx9wuunNc839r+G9b1bUjvr0w0fhkZsuxKgBuTDXE3O9vgAefX827nt5Gty+uh4pD/3yfNxy4WT4+AGt46y7att79FcX4cbzJuHTH1bh3N89h79cPwW/POdYZKWn1NbxsK1/Tv0af3p1GnMV1k261FrtRju/8qRD8ccfn4URFLGjFgyG8Nn81bj276/j6V9fhstOPBQffruiQ4UDj45Vt0pACSgBJdD9CAypiO/RKuGcAwzJLCktOpP5A0Ej1cW7sxbgo28W44pTjobMJWrnH3cYRg3sEz1scOtye/HG9O/w3MezUFgWP1fvuZMObbCdeCczUpPx8G3X4Kr7njDG/MOqjfjPB1/jVxedGu/WNru+IgGP80TepzYbsHR04i39ucrxt0z1mY6N+FGb9q2dKQEloASUgBJQAkpACSgBJaAElIASUAJKoIMRaNztsoMNVIfTeQiIx+w7f7iuVjCWkSc57EZu5Ogsnrz1Ekz7xy9x0KBe8PFD2xWbd+ObFZuxNa/E8Jxx2K24+/JT8NUDN0ZvSXgrH1q/eNeVuOeq0wzBOL+0ElX8UFfMSe/k//vRGZj6ux8n3F604oG0+7urmefw3mtrBeMte4qxbNMuo+lzjzkY8/79awzu3XzPo+jYdKsElIASUAJKoDUIDGB4alucHL2yiGv99rzW6L5V2xShO2oiIEu+4IVrtxinrIxqcttlZ0QvN7rdWVhieBgnIhhL9JMpxx7SaFvxLkw8aChuvvi02mqPTP0CS9ZvrT2WnQqXG39/7eM659rqIBFP46GV8RchtMl4RSw+5bYnKBhvZFyam9inehq3CXjtRAkoASWgBJSAElACSkAJKAEloASUgBLoyATU07gjP51OOrYzJ442vI2+WrQOr361EL16pGF43xyKxgXGjCQss3gEi7359WLc/Pi7KKmsNo7lyziGkX7p7itx2IgBOHbsECOM88xl/EwvQTv9iFGQkJJTZy7B3c9+hO0FZZDQ05PHD6OY/RNkpibh4skTMGZgr2blRd7fdiePG4o//2SKMfoNOwtxyR9fxPIte4zjzBQnnrvjCmM8CU5PqykBJaAElIASaDMClnAYgxiiemNG0wubFq7bgjFD+rXZuFqiI4n20ZhZuABNvI+vP/sEpCQ1rieKJ/JH//gN7nj8dcxYFAlr3VibEl46NzOtscsJnb/l4tPx3YoNhrgdZHjq2x59DZ/88w6kpyRhwZrN+PVjr2FPcTl+fdmZcNRL45FQB/tZqai8EtvyiuLePbSiNG6dVq0Q9SwGbqBY3PiDbdVBaONKQAkoASWgBJSAElACSkAJKAEloASUgBLomATU07hjPpdOPSrxyJ2/dhvO/O3TeGX6Qjzw1kz84pG3a+d020WTjf2dhWW47p9v1BGM5cIKCqo//9dbtfVPYZ7j5pgIxuK1fMVfXzEEY7nXzw+GZyzZgOsfeLO2qctObJ63z/62+7cbzjb6lBCWx9/+WK1gLCfLXB5c8qeX8OXCtbXj0h0loASUgBJQAh2JwNAEQlSLYNnZzB/jaVx/7F6ms3iYnrwn3PwXPP/JbMhxYyaC7TP/cwN+c/mZxqK1xuoNH9CrsUsJn7dwEdzDt15jiMRyk+RivveZt/HoW9NwJUNX7y4qQ5hC/+bdkYV6CTd8gBUTef4ZXg+yWdrF9vEsVsG4XZ6DdqoElIASUAJKQAkoASWgBJSAElACSkAJdGgC6mncoR9P5x3c/W98tU/O4OhsXpw2H9+u2mKEopbQ1A2ZCMfyoacItenJzXcEkZzFDZn0G7U+WenR3YS3zW1XQnUfOWqg0f7jH8xFfllVg33d8/ynOP2I0Q1e05NKQAkoASWgBNqTwPDyEnw5YGiTQ0hENGyygXa42JSncXQ4JRUu/PXlD7GdXrR//OnF0dP7bOX3lZsvOR0TRgzCbY+8irKqvRFUopVH9O8d3T2gbb/cHrj/l5fhVw+9bLTz6XdL92lv/Y48HDS47Ty/E3n+w9vDyzhRz2ITzsPJt7Wt0r7PU+uGJ0K4BbMendoNZ65TVgJKQAkoASWgBJSAElACSkAJKAEl0CEJqGjcIR9L5x/UeoZhbsw+/G5lY5cg+f5GD+yJExnCOioai+dyc23tjoY/9yugaFvt8SHZaYfd1vzXv7ntjh3cu7Yf8XRuzBZt2IlCji03M7WxKnpeCSgBJaAElEC7EBhdVhy334LSCiM88aDeOXHrdpQKTXkaR8covyvcc+15uPbM46KnmtweP2EUPv7nb3DTgy9hxeaddeqmJjnrHB/IwVlHT4D0NXfZugab2bgzv8HzrXVy/ur4nuajS+O/Ry02vkTF4toO6XlsQm7toe60EYFQy/2jaKMRazdKQAkoASWgBJSAElACSkAJKAEloAS6MoHmq2ZdmYbOrUUIhJhjb9Oe+Hntkh02TJk4BgcP6Q0RV0f174nh/XKQ5LAf0DjEc2g38/k1Zq4a0dhMr6Dm2P60e9jw/rVdbM0rqd1vaEeuq2jcEBk9pwSUgBJQAu1JQEIK93S7UJCU0uQwvl+5EZ1JNI7naTxyQG88evuPIHmLm2P9crPw1l9uxX3Pv4upM+bV3mpl9JGWMI/Xhz/T+7kxwVj62NCGonE5varXbNsdd2oHlcX/3TBuIwlXMA9G2DSWQnDzw9Uk3IdWVAJKQAkoASWgBJSAElACSkAJKAEloASUQNcioKJx13qeHWI25czT6/U3HHY6OsC7LzsZd19+MrIz9v0AOq+kAp/8sAo3nHV0k7kBo23V33r9gUZDY9ev25zj/Wl3aN/s2i4qq5vO41faQCjL2pt1RwkoASWgBJRAOxIQL9F4ovHcZWtxxalHt+Mom9d1oImcxtecMQn30sPYYbc1r9Ga2g56KN//y8txCMNV/+H59+Dj7yY25iM+UFtLcfZWhr+O50m8YWfegXaV8P3fLF9vRIdp6oakgB+DKhtf0NfUvft1bdaj3/C+E3Hi7SfCjPsoHp+wX+3oTUpACSgBJaAElIASUAJKQAkoASWgBJSAEuhGBFQ07kYPu6NM9XdXn4Y//2SKMRwRUr9cuA6LGZ55+ZbdWL55N7YXlBlhqkU07uy2Pb+0dgr9czMbzWksleS6mhJQAkpACSiBjkhgNL1E5/Qd2OTQRDwU711LC4ijTXbUQhcbCk+dmZqMf950BU498uAW6eXyU47GGOYWvpHhqvcn3UbsIMSjNxHBWO7ZxhzMIlTvTyqO2D4T2Z+zdG3caiPLSqjdtoPNemQWe40vHofDH8Ef+mk7jLB7d7kHFd0bgM5eCSgBJaAElIASUAJKQAkoASWgBJRAxyKgonHHeh5dfjTZ6cn403VnGfNcsy0fZ/72KUMkrj/xzNSkWi9ji7l5YaTrt9Wexxt27Q3FOLh3FiR3cUMmobIH9uzR0CU9pwQ6JYE7LjmRoeebF9K1oYlKbvPrH/xvQ5fa5Nyhw/vh1gsnG33NXLoBr0xf2OL9tkUfLT5obbDbETi4pDDunGUh2NKN23D4qCFx63aECvXDUx89djgevvVq9MrKaNHhjRs2AJ8wz/FWCrkHYhkUtD994E68/fU8PP7udOSXNO65GwqFsXl3AUYP6nsgXSZ075xG8irH3pzI+xNbv8X344vHXsx9PP5L3uID0waVgBJQAkpACSgBJaAElIASUAJKQAkoASXQcQioaNxxnkW3GMnkccNqxeBH35vdoGAsII4ctdeb6UA9c9oT7JKNO+Fye5GS5DDCbb87d3mDw7nshEOQyjpqSqCrEDj9iFE4/YjRLTKd9hSNBzACwHVnTDTm4fH5W0U0bos+WuRBaCPdmoDkNe5XVYFdqelNcpi1ZG2nEY2jnsYWsxm/vvxM/PKCk2Hm/v5aWaULXy1chZmLV2Pd9jxUVLshYaoH9MzGpPEjcXoLeC+73B7De3jC8AFYuDaIUvYpi2saMglh3dqiseQybkq8jo5rQnF+dLd9t/HF4/Ydn/auBJSAElACSkAJKAEloASUgBJQAkpACSiBdiSgonE7wu+OXac47bXT7pfTsCdPr8xUPHbzRbX1bNb9/wC3tpF22imtcuPR9+fgnqtOw1kTD8K5R4/Fx8zXHGsy3z//JOJ9HXte95WAElACSkAJdCQChxQXxBWNp89fgTuu6Jg/0wpKK/DVgpUQz1gRVAvLKiCC8ahBfeALBLB6624cPLR/s5FXe7x4+sOZeO7jmXB7/fvcv6uwFD+s2oiH3vwMp1E4/p+rz8HQfj33qdfUiXh9NHTvBs6xtU2edzzLcVejX3VVvGpte72+eNy2vWtvSkAJKAEloASUgBJQAkpACSgBJaAElIAS6JAEVDTukI+l6w5qwbrttZO76bzj8MG3KyHeuOIjIyGaJUzri3ddiVED9n6YKqGqO7M9MPVr/GzKMcilOPzBn67Hv96ZhU/nraZ3kBtHHTQI9117BvpkNyygd+Z569i7N4Gf/WsqUp2Ne8/Pe+LXtd71F/3hBazbUdAhgRWUVeHz+WuMsa3amtcqY2yLPlpl4NpotyMg3qKfDhre5LzX78jDlj2FGNInt8l6bXmxpKIKj73zJd6c/j38geA+Xa/esgtSHnv7S0iI6ruvPhuHjBi0T72GTuzIL8ZP//4cagXaJLafGgAc/M3GwhLiXX4ufnNZgCoLplO0nkvR+sGbr8SUYw5pqMl9ziXUh5d9lNkAn6T0iKT1mLtsreFBvU+DLXjii3nxReMO42Xc0LxrxeNbmr9aoKH29JwSUAJKQAkoASWgBJSAElACSkAJKAEloAQ6MQEVjTvxw+uMQ1+3sxD//mAubr7geGRnpGDRU3dgV1EZJL+xhKTOoEAsYRaf/uQ7DGEOYAlvO3ZQ78441doxl7k8OPqWR/DRn2/A2MF9cOdlJxslWkHm+8dXvsCvzj8OORmp8Pj4YbOaEujkBLYXlDU5A8m3GbVNu4uwenvre8RF+2vO9oc12zDlnmeac0uz67ZFH80elN6gBBogMKqsGA565HqtTf/6OG3ecoZ6PqWBFtr+1NIN23DTgy8hL5r/N5k/Y1Mo7Dqo5lLHjYi6FFmrRdS1Gh7Bl9z7GP7nmnPws/NOanLAuwpLcOE9j0JEadjZXq4PcIpKXM/s7E/6zOL5Ejs8lcDN/3oFD/zKj4tPPLJe5bqHzeojjX14eX++0xCql27YjndnLYjbR90eEz/aTsF8LcNTx7MJ9FDv8Dbr8Z0dfow6QCWgBJSAElACSkAJKAEloASUgBJQAkpACbQygc4b97eVwWjzrUfgjqc+xL3Pf4rKao/RSb+cTJx6+CjD63D+2m047rbH8MtH3sZ7Nfl/h/bNwVGjB7begNqg5c17inHMLY/iDy99js/oZVxQWolt+SV4a9YSQ5C675VpRohMGUqZy90GI9IuWp3A4T9PxuSbh7R6P9qBElACrUNg7KV2nHzrLTjptptap4PO16qVi5wOSSA37bQEvE/bYvYiGF/5hycigrGTguoA/nztQ2E3nfviCWxlsbOkiODLsNKDeD3TjxDnef+rH+Oh/37e6DC9zHH+s78/HxGMxbu4H3+naUgwjm1BtPae7D+bhXbP029BxtiY7VcfEuBhIMeSGenjt09NbbKPxvpO5LwsDohnjmAA40o6gWgcbyJ6XQkoASWgBJSAEvh/9q4DMI7iaj91yb1X3HDHVNO7bTqhJYSaUJMQQg8QE0oCoQQCCb3zhxZ6Nb0aG4NtDO4N99675aIu/d83t7OeO12VdNJJeg/ezZuZN2W/3Vv59ts3owgoAoqAIqAIKAKKgCKgCDQCBBBa0fAFkZzcRHaPQZfel7LRbA3/LFQ+wrzsLOndpa30RETxqg35MnvpGiksaZxRtlyau/CzByQrM0Puf3Ok3PTcx5UB0xKDwKz/3iR7BKLPB6Wlpc1OWViOuqKbZGYtxPz+T4rK7paxj0cNx2ps96mtH9wrLZoiGg6yzx/ul+mLVxs72keTnCxp07yJcVm/dbsUlZRJK/Rx8sF7SEukP81dLhPnLQ/bBf/Yde/QWgZ07yADunWU7KwM2YI9x2dg3EloU1IWJjoQbXKzMrECQFPT547CYuE+5VZ4D2vbIjCfDVt3+PevbHyPB/fdTQ7Cyy7rsbz15PkrZP7KDYaIsm3dtDbGcMej3R7HdMjAnrJ757aSjxd4fvh5icxZts5sFcD63bw953dif9ZN23ayqEFInPePNBly9fl4k+cuLPOLFz/K75WRj95SHwB4RXrOSpO0PZI51wkdusije0WPjuX4ox67RXp0apfMqUTtez32Lz7xhgewFcQOkeb4twWjgOP9Vy+Xkl4TWF7/kWt/K6ceMbjSWE+PGCn3v/qJSBbuHbuBpE30NcwNWEp6a5bshT2UR9z3Z8HfszoZo9KgCRScOvw/MgvLekeTg9eulGtmTozmUiN1FVI26LeyLHX/TVAjR9mgOzG/1Rr0EerBKQKKgCJQewgMwlD6N7H28NaRFAFFQBFQBBQBRUARaFAIRF9fsEEdqh5MqiFQgCidmdgjlNpQhWTwq7f8Vhhp/OG4mTJhzrKwh3r4oF6GMGblzMUNF4+wB99QC8srssACgBWQP0lO5sVyzLWPy7bSf8mPT2xsqIec7OP65RF7yys3/9YMM/SGx0F8NsP36wL/u8MKrlBw5j9e8KeSA3L4rotPxpL4R0heTrZf7hqrN26Vqx57V977vvLenMcf0F8+uOv3xv3pj8bKnx55x2968sED5Z3bLzH5k29+RkZNXSAvDD9Pfnn43pKTHfznlWP89t5X5Zup8/321qiNMexYrbEFwD9/9wu5+ISDJBektytcAeGC+16Vb6cvkOVv3GGqXhs5SX5z7yuuW8O2h1xzoqSn3wdycR/nQAPsoVPQmM19NqyVrLIyKckAsRpFRoyZJNeefUIUj+RW/fv1TwOEMaOAEyGMOS0uJd0OJPOGbLn7pQ9k2P6DpGnerstgZ2GRPPX+yMABsO9ECWO2bIPIZuxxPGPRCrPP8fEH7RXoz/usjTGCBkwwMx97V8cijNnlgeuivi+V4KjqrggoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIpBMBIKfaidzJO1bEWiECHCJy3127yrnDh0s5wzZTwZcfK+UlgdHNJLUevb6sw063N/4q0lzGyFSDfGQ03fdX9MkDyFuf5HmmX/EcrcPStH2B2Xs89jVUqWqCPTfrYM8cuWvgghj9rVo9Qa/y/36dJVXb75ABvbo6JeVIaKYkbWtQJ7ayL7ObVvKu3dcKve8+pXc9sKnvm8iRsumefLl/ZfLkXv1DtuMY3yFepLOz34yPqxPrMLqjtGlbQv54r7LZc9enf2heM8pxAs8JNQ7tG4un/7zMrn1eURPNjYZeu3BIP7uw/d0SOVDT8utXBahZMiQTNnULUdyW+RKVloO9szNQb9gGzNzJa0iB2R0QCvKkYddxnwG6is4RqBO4CdmzEC+HOXpyFcgpY9R19+UmfZrxn29o3PBzgiTq5ni3PIy2QdLVE9ExHE0eX/MxDojjVdt2CzvYC9fYJ44YWwPqiWik7dlYLWAbfLmyB/k0lOOtjXy3bS5gS02ckAu5wX/TfedYhkkmlthjI3Z8vG4qRJKGtfGGLGmGK2e5zeW8OWC/eJYzjxWP1qvCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAiUDsI7CI1amc8HUURaHQIfPTDTENa9cbezCPuvBTRjNPly4lzhZHWg7DM8l/POxZL5gZIrSc++F7WINpPJToCxw1/SkZPW/CNDLsWawWnlYIYQMgW0gpBKngK76VpSCu8OvFSltHH+NqycpSlo8xLTfsK5NNKJM2WeWl5WcA3jXW0M0rFlCFNKw3kA2mPykeR1gJM5R2S0+IqRB7fJztXPSnj39613nHlBloSAQFGyzKadwOWqf5w3Czp0KqZDNuvr7z0JYgiT14afr5PGL87Zprc9cqXWNlgtZSVV5hlp7mE9J0XnyTHDO5nWlz3q6PkwXdGV2k55mf+fLZZbnsmlru+/aXPzDLZ4GPNnP567jHmO56enm7Ge/GLH6W4FGRTglLdMd687SKfMGbk85WPvmsin7cXFMleIJL/fsEJwmju+/5waoIzq8fuQ64aIOkZ9+B7+avIR5F2Ku41/Q3JS8KW5G2aJXFJ8JK0RZ4kMGNO20fqyVl+OM2L0vWDdZ06dBQkNoo1qDgo47sXZWQu8DNJNA5B9Ggs0njZ2o0yee4SGdy/ZxJnEr5r7qnMFyJMxHAW0qoKSd21GfLZD9OCSOPRU34O9Ngs8e9x0FQY0Yy1J75Ff5yvfZGFPrUxRtBcEshwrh98NzlmC75ckAviWEURUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUWgfiCgpHH9OE86y3qMwG3PfyoH9e8hQ/btI784ZJDRcIfz5qgpMvzZj8JVaVkIAmtBrJeWlXfEE3avxktt1vUP9bF1xtc28FiZNMvO0Al1ptqWeWnQkqyW+LHMj72lBi/7a4f00zTBRp9p/5YmXa5H5PFdsrXwv36dGnEh0KZFU5k4d5kcds0j/n7EjNrnPseUYfv2xV6hgUjIb6bMk3PufsmQxbZz7p8+bvYSOeGvT8vEJ2+QfRGVzOVnzzxyb3nu0x+sW9wp92fmOCfc9EzQagIksUdj2eofn/izieTtiGje0w/bU94GiZ2oVGeM84cNliP22t0MuRjL5R9+7aOyelO+P4WpC1fJr+54QR647FS58exhfnlDNR4f8X0HGXYdNpKtuATfRfsFjnS43XCv6eZX2tuGSW3Gr61Tozg9w96wkjqP/devljy8I1OQGf1e99Y3E+qENB4/c37g+EnKVkeasH2FTJ63VIrwoleOt6T7wpXrAr1mVzHK2M6JhDaiobdh9QNGNHdo3cLWSG2M4Q+WoMEo6NUbt8RsdeSa5TF91EERUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUUgdRColYeLqXO4OhNFoPYRKMFyuMcOf1LOu/tlGTtzkWzK3+FPYuv2Avlo/Ez55d//K+fe87KJPvYr1YiIQEkVojQjdlanFWldJC39KWnVZM4zH49rWR6ydHmdTq0eDH7Rv17zCWNO1xLGtE84sH9g+VjY97/5TRBhzHorjDr+YNyuvYw7t9lF2lifeNJSRNNd/dh7QYSxbbd03WZ5/vMJNmuiev1MAkZ1xvjjKYf5I93+0udBhLFfAeNvL3wmKzfEJoPcNvXNfmPUZLnmifc+w0shv4+DMK5Xh1eSnl4rLHY27lUHr429V+2H30+W/B21v5iCT2hWJ8qYZ57/Ss6oMFHAazfvesmCJK8R1FVbvD622z69DmtjjKrO/dUvx8Vs2qy4SPbF/tcqioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAvUHARsWV39mrDNVBOohAiSm3hg9xSin3xwRjWV46L6zCKsZqySMAKKME26T2g0qMjdt25lelSWLU/u4kje7NYiSnb0sMiFx03MfC7V9y6ayMT/6Hq8rN2z1J8olr6si81dsiDqfucu9yER03gzf/6pIVcdo26KJHD6olxlyFY711ZGTIg7PCOyH3x0jD/zxtIg+9b2Ce8y/PmrKlR+Om3kejuXYGj2eCix9nyZF6NPTikIEqsJO25Vnne+TBvaxvAhL4ds2yMM2bdgO7dO9vkxZOdnKQL3JZxRKOfLlRUWSXVHUb+umL2v0eKJ0duSaZTK6a48oHmL2y37/24ly0clHRvWr6cpyLk1dU+LR8O5LPdwD3EgZK6s5VnlggNyc4Kjt2hijKhCtQYTxyEmzYjY9bO1KyazJ8xBzRHVQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARqC4CVXs6Xt1Rtb0i0MgR2IY9RFWqjsD4x66VN0ZNHXr1I+8uESnOlHKuGVqaiSfUSNMzERaWJRVcN7UsE/uVBtIKpuWBOlMGW9K9MqTprPPyIlmIAN5Vh85A5CCPNA1pBepDy5iv8HzomyatcIS7xzjKedhH+T4sT/3KzecdOxW+nWP4a7WHwIKVG+LCYv3WXZH9tkFuVqb06dpO9sCe4kftvbucfNAetkoysO9wVWTp2k1Rm20vKPbrszJjrYbsuwYZVR1j395dJcNbtXjeivUSi1CbsTh2BGnQxOph5oM7f/cD9o99XoZdORhf95uwRPCZkaOOK6bgLZ/hgtXPpTS9UDJJ0pK4JdFbXCilFUVSUFYkkzYj/3Y110OuHpiZ0hMdeCxn9bqK2br/lk3SoWCHrMtrGtX3ta/G1Tpp3L5VC/lZcB2XVhML8sFeH+1aNfePs0entjJ1/lKREt4vqvESE68WkMZc9rpj65Z+/zRqY4ygAePMvDHyBynHi3Cx5KjVy2K5aL0ioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAopAiiGgpHGKnRCdjiKgCMRGoF3LZnLVGUesu/qXRy6J7V1HHsOuGiZpmSPDj14xFczdvTLq0XdQ7zEOz4R31dKwCMxfuT5sebjCY/brK6dgP/FBPTtJ367tpXuHVpJeRXI4XP8sW4YlqKMJVxaw4m+zbQviTKs6Ridnye0lMchtTmXJmugEeJzTrR9u3zwxGRM9R4Zd3RsvkdwI+2K8GJIbNPmKtHwZ/djXQWWaMdT0kJVL5a0+u166CAfL/BVrZeyMeXL4Xv3CVSelbP/+PWXM1DkiO/GCRrNq8PgFJIXTpH/3zlghYNdlccTe/eWD73DpsP+WpVU/hh1oDzlszz7+ix22s9oYw44Vb1qElQhe+2p8TPce27ZKL6iKIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKQP1CQEnj+nW+dLaKgCJQbxBIq3x/ragYhyjoe0AWf1pvDiNFJ7rR2Rs80hQP26OnPHHNr2XfPl3DunCP4O9mLJIdiAI+5dBBYX3iLayNpcWrOkbT3Gz/MAriWBI/fycCZhubfPPYQhzyn+Swy2+XnJxrwRNeAfKYqwWAM6yo2nripnHD/hiyaqm8u/sAKYvxEsazH4yqVdL4pEP2kYfe/FyEpCw54wA3m/jJyA/cxk88eO+gtsP230MyMzKklCvfFyGaOSd25G1QB8ywyVYsWgEJ7Z9ltTEGx0lEPvhukmzYsi1mk+NWLIrpow6KgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCqYdA1dbhTL3j0BkpAoqAIpBaCJglrL0pVciXUl42RL555HAljGvnNO3Vq7N8+s/LfMK4pJQE8UJ57P0x8of/vCkHX/mQtDztZhl245PyyYTZtTOpOhpl0eqN/sjd2gd4UL8gjBGPT5hmDaNo3NPrZNQjt0rRtu5g9W4EsbcSB7YrxLRhHGWNHUXLkmI5eB0hii7fTZsrc5Ziuehakj67dZQh+w00Sz/Lxl0vTSQ0/E78E3lHpjTBSxfnH3doUNPWzZvKhSceHijbgP6rwBnLVhDSxenSrUMbOf3I/YP6Z6Y2xqg0aJSCCuxP/H8fjY7iEahqUlIih62JfU3E7EgdFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBGodgcqRcLU+BR1QEVAEFIGGiEB6BqKK3wdZ/E8Z/fjEhniEqXxML/zlPGnZLM9M8dtpC+Tsu16SdVu2h51yK8+PlelVXTs6bM+pUTgf+xhb6YaluWNJ9w6tY7k0/PqxzzOc8j8y6KzHpH3nYQ3/gKt+hMetWCzjOnWL2QEJx39fdX5Mv5pyuPWi02TczPlSzDOZheXhWyewjDSjh9cGAsyv+fXx0r51i0rTuvLM4+TjcVNl3eZ8kfUgjttj3/J4t1DeAULaI7Nvu/gMycY+6+GkNsYIN264slGTf5YFWGo8lnAv45zyaiwJHmsArVcEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFIGgIaaZw0aLVjRUARaNQIjHrkI0QW/0oJ49q/ClqDBB7cdzczcFlZufzy9ucjEsZ06rdbe3+SmRkN78/i8vVbZNvOQnOMe/ToJD07RieFTzoIEZoqAQRmvV0sox/FOscqkRDot3Wz9MzfEqnaL+cewMvX7op69yuSZPTu2lEevNojqTeB1F0H3bW1ePhRGTG8DWtZr0RweXmanHL4fvKH04aG9WUk8NN/uSRA+G4D6bsGJHMsXpr9b6ZvIHj9KhDPxx24Z9j+WVgbY0QcPKTi8Xe/CimpnE1DNDJfIlBRBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARqJ8INLyn4/XzPOisFQFFoOEhUJUFSxseCnVwRPv07oLtaAMhfwtWbZDN2wsizmK3di3lVGc/44wYe7NG7CiFK8pB5Dzy3hgzw6zMDLnl/OMizrZPl3Zy4fEHRKzXCkUgHAKnLJ0frjiorKy8XOIhHoMaVTNz8qH7ykPX/EZys7NABoOsXYrVBzbCLsQ/f+0dmmkJ7hdcLnoFyNx1IH8r0uTXQw+SB648z7+XhJvKvn17yMu3/RHkbhORnSCbl6H/DV7/lqC2/W9B/8vRPwlsyHVnnyB/PufEcN0GldXGGEEDhsl8O+VnmTp/aZia4KID1q+WTgU7ggs1pwgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIlBvEMATLBVFIDkI7I9Iv4euOCNi58UlZVJQVGIiAGcuWS0vfvFjVHInYkdVrGA0YjQyqYrdajNFQBGoYwQWrNzgz2D3zm2la9uWsnLjVr/MGh1bNZORD1wh7Vo2s0XYvxSETwOU/7w9Sq4640jhUtyXnniwcJ/jf70x0ufNeMgDunWQd++4RDIzQH6pKAIJIHDwulXyFsjCdXlNo7Z679uJwiWXu3dsG9WvJiu5X3D/7p3lrhdHyPiZC0S24DtOpaSB0TXkceAlExZ1bttKbjzvZPnl0fG9PHHQHr3lw39dL/e89KF8PmE6yGf0TaWE6b931w5y60WnB/ZcDnjF/KyNMaJN4uG3vohW7dedGsfLA76zGoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAIph4CSxil3ShrOhEhOHLlX77gP6B8XnSiPj/heHnxntGzIT16kComRJ645U94ZM02e+mhc3PNTR0VAEagfCKzYsFVmL10jXIqZkbUf3/MHueLRt2XCz8vMAezbu6scvU9vuewXh0o/3A/KEQGZ7kUYuwRy/Tja+Ga5ZUeh3PD0CHnu+nMkA0tw3/v7U+Sso/eRb6ctlLWbt8n+/brJyViWumleDl7mKZa8nEA0ZAWilFUUgVgIcNmak5cukBcH7BPVldHGj779Ra3ubcwJDejRRV69/Qr56edF8un4aTJm6hxZsmYDtp0PTLdNi6ZyMMjfYw/YU04+bF/JibDHcKSD69q+jTx548Uyc9EK+fD7yTJq8mxZvHo97i2BFryvHLZXPzn+oL3kBCi/g4lKbYwRbk48lmkLAvfOcPW2bODmDdI7jmXKrb+mioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAqmHgJLGqXdOGuSM5ixbK3OXrws6trycLGkGgqJv1/bSHhF/zZvkys3nHyu/O+lgOeK6R2W+Ey0Y1LAamU6tm8v054YbIomksYoioAg0TAR+88//yQ+P/VlysjNl3z5dZdyj10lRcamUlJWZ+4496p+XrpUL//WqfPvgVYgyzpZDBvaQjPQ0KStveGTp85//KKs35svrt14oLc2+z92w93M3C4VJPxo/U977brq8MDywF+z2wuKges0oApEQOHr1Mnlv9wGSn43lnaPI+2Mmye9OHSIDQeTWthw4cHehUkpKy2RnYZHZk9i+JFHd+ey5+25CveXC06QU95odBUWGgM71XsKobv9sXxtj2HlyT/h/vfKxzUZNT10Se4nyqB1opSKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCikCdI6CkcZ2fgsYxgVe+nij3vPZ1xIM9sH83eeLqX8uBA7pLBxC7X9x3uez/p//U+PLR2VkZhjCOOBGtUAQUgQaBwNSFq+SgKx+UB/90hhwzuJ85JhLIOZKJ6MIK4RLWT374vTyN1QYKS0plzPSFciIibTu1aSHD9u0rX02e1yBwCD2Iz36aIwcCl/OH7S8HILr4ANx7SZ5NmLNUvpo0T577ZDwikPf1m23dEXk/aN9JDUUACGQjrJbE4av99oyKB79/9778obz8t8uj+iW7kqsQtGyGvYiTJFzmPZn9c9rJHuPtUT/KvOVrYiLUZ+sm2WdT8IuBMRupgyKgCCgCtYTArg0Iog/Y8F4XjH68WqsIKAKKgCKgCCgCioAioAgoAopAOASUNA6HipbVOgI/zV0uh1/7iIz89xVmSete2Id0+DnD5Ob/flLrc9EBFQFFIPkItDz95oQHeXXkJKHGK9MXr5Zjhz8lXdq2kN6d20nXdi1l8ZqNMmvJGgmNoD3plmfDdvvh+FmSduyfw9a9i4jcSHWhDd4fOyOib22M4c6Hqzj843+R9yht2TTXd9+6vdC31VAEYiFw7MrF8kmP3rIlJy+q6/fT58m3U36Wo/cbGNVPK+sOAUZJP/jmZ3FN4KyFP8flp06NCIE+V+fIgseKGtER66HWIQKVSGGvoFJ5jDlaf5889o1dDcMU7apUSxFQBBQBRUARUAQUAUVAEVAEFIEGgICSxg3gJDaUQyjBMoi//seLsuDlW81S1VefcaTc/+Y3UaONc7HvYL/d2ktfaD727Jy/cr0sW7dFyu1GhQ44edlZQrWSnZnp5wuKS2xxUJpI/0ENNaMIKAIpg8AqLMlMbcxyxWmHY6/iEpm3Yr2MnbU4KhT79dnNr6e/iiIQLwKMNj5j8byYexuzv3sQbcx9fhnxq5J6CDz1/teyYcu2mBPjXsZ7QlUUgSAEuqdPk27XPSv5O5+WSc/uDKrTjCJQBQQsoWuaxiCF00LrkQ9qH2Z8QwZ7jLAlhm3qutt+XH/Wh/N126mtCCgCioAioAgoAoqAIqAIKAKKQH1BQEnj+nKmGsk8123ZLm99OxX7Gh8iTbHf8TH79ZN3vqu89/Dx+/eXh6/4pfTv1l7S09OD0OG+pY+8/63c8dIX4pLB2z++L8j34St/KdRiLE2bc9JfgvqoSv9BHWhGEVAEFIEUQmDIPn38ZadP/9v/CaObw8lgEMZ/+MUhpqocBOA3U3Wf0nA4aVlkBIauWiof9+grG/KiL/28YMVa+e/Ho+XyM46J3JnW1AkCC1eulec+Gh3X2BplHBdMjdGpo6TLf6Rl3nAZet39Sh43xkugesdsyVmyvb7tdRmNFDa+Thv6ssy2iTQrvm9siWCmrm3b2DLmfR+vkmOE1ntVmigCioAioAgoAoqAIqAIKAKKgCJQrxAIZtvq1dR1sg0VgVe/3rX87DGD+1Y6zCev+bV88a/LZWCPjlKMvThnLFol389YJEvWbBKSHNy3dPg5x8jXD/wpqG3+ziLZjuUWrRQiupjRydtQ7kpV+3f7UFsRUAQUgVRCYDruk1buuuRkcZegtuVtmjeRF4afZ/ZJZdknE2bLpm0aIGbx0TQ+BDLx5P3chbPjcn707S9l5fpNcfmqU+0h8Lfn3jV7ncca8YB1q6Q/9jNWUQQiIpCWZsnjRSCPr5f9L4v+NknEjrSioSNgiF0cJMnddKghez2beaN4cpERolysIgtlWUw9zUaKn4OSE6K5yOdi0akgDfFhO6PsC7bt06QYh+PZOWR6c/HnFzJvn7Bu6CdPj08RUAQUAUVAEVAEFAFFQBFQBBoUAvgppKIIpBYCC1btWuawf7cOQZMbimi5P2GZVcrr30yWqx57N4jU2KtXZ3kRpMfgvt3ksEG9hP6jpi0w/q3PuEW6d2glS1+73eSvf2qEPPXROGPbj+r0b/vQVBFQBBSBVEPg0ffHyG+O2V8GdO8oe+/eRZa++ndEG8/EHs+b8EA0Q3p1biOnHbqnNMnNNlNft3mbXPbgW6l2GDqfeoLAoWtXyufddpcFLdtEnTFf3rrj+ffluZt+F9VPK2sPgffHTJQfZgX+3RRt1Ay8pHf+gvArFkRrp3WNFAGSx2kaedxIz37EwwbHGhCSrda0tpf6xKub92yXXEZRgGj2Ulvnp145h6EvxUQGM4VR7qXGZt6rtBHIJkUZi62PraOvtW1KR+MLfwrHZN6W01RRBBQBRUARUAQUAUVAEVAEFAFFIBURUNI4Fc9KI5/TWpAVVtq1aGpNk177q6NMumL9Frn4/tdMpLHrMGPxakN0THzqBlN8zOB+Pmns+kWyk91/pHG1XBFQBBSBZCLAlRZO//t/5e2/X2xI45bN8uSC4w4MO+T4WUvk3HtekjXOvTisoxYqAlEQuHDeDPn7AfibzSf+UWTkxFnywXeT5PQj94/ipVW1gcD6zfly1wsj4hrqxOULpWNBCq5EcMy134GU6R/XQahTMhFoGbZzJY/DwtLYCs1fBXzYvw5BxLBX7pO9yLs2fZl3y4wNEG2Z6+P62nFcvA2xS4IXSvLXVUsAm5R1aGj8YLh+ljS2ZaH+aGraGdYYczfCQoiXBDL6qQgoAoqAIqAIKAKKgCKgCCgCikAKIKCkcQqcBJ1CMAJccrqgqFjycrKleZPcoMoXvvhRxs5abJaipl84IXFcgV/raXgy0KJJTjiXiGXJ7j/iwFqhCCgCikCSEZi3Yr3sc9kD8ouD95Bzh+4nvTq1kZ7QcjzlXLUxX2YtWSOvfD1RRmN1Bn2ImeST0Qi6752/RQ5fs0LGdu4W82gZbXzonn2lQ+sWMX3VIXkI3PLs27Jle2wiuEVxkZyxeF7yJlKdniukDf4B2L46XWjbWkAglDwe9bCl0mphcB2iLhAwJxgf9kRbAtekKMRKz5KOD+YzmKciz9TkPduSwKzz/Tx/vy/b3umD7fB/pfeYDMGLckv4YhEF37Z1TF1iuIx5+IVNvTq/P6et6Qdj2X5hBgQ++F9FEVAEFAFFQBFQBBQBRUARUAQUgZRAQEnjlDgNOgkXAe6rScKYsnLDFrdKPhg3MyjvZvggYED3DjIES1Jb0jgTy64mIsnuP5G5qK8ioAgoAslAgHsVU1UUgWQj8BssXzylXSfZmYVNJKPIVhCVtzz9lvzfzb+P4qVVyUTgvdE/CaO+45HfzJ8pTcpK43FVH0UgBgJpbSW9fJCkpWdIBeM4VRoaAvx9RrbWErZ+CgO8r08MG3KYRLBHDtv9gs3+wfA15V6dJZFt6hPJ8AtLHHMsr86bDhOfqLWksEv02jJL8Nq8JYvLPHKZqa9gfkNt68++bRtLQPt9m9nsmpASyBYQTRUBRUARUAQUAUVAEVAEFAFFoC4QUNK4LlDXMaMi0LtLW7+e+22GkyY5WXLyQXvInr06yaCenaT/bh2kT9d2Ptkcrk0iZcnuP5G5qK8ioAgoAoqAIlAfEWiJiNTzQBz/d+C+Maf/zeTZ8tqX4+T84w+L6asONYvA8rUb5R8vvB9Xp3tuWidHIIJcRRGoFgIVUipp5S9Lcck98v1Ti9BXfG8sVGtQbVybCICjrUQWW+LWEsQ2NcQwCGFLFDO1yvd/SQ4z70cXw45FGHN8dzxDJnNKZmKcXEB84hZMrSVzTYpqU4dykrg+aUw/EsVIQwli5ks9Ejk0det88hi++N8fl+P4gkxQ3q9QQxFQBBQBRUARUAQUAUVAEVAEFIHkIqCkcXLx1d6rgEDvzu38VotWb/Rtaww/e5gMP2eYtG0ZvN8x69dsypePf5glvzvpEDwUCHkqYDuIkSa7/xjDa7UioAgoAoqAItBgEBi6aql8hyWq57Xa9UJYpIO788URsl//njKwR5dILlpewwiUYKuPqx96WbbtLIzZc1ZZmVw6Z1pMvzp1KC4+QtIy9fdNnZ4EDJ6dvgD0XOX15iuTxXU9Ux2/hhHgry/7E4wpleSt1YgEMchhEsNZXpppUxLEnlqS2fbF1O3fjOeNjypTF1rmr49tj9sjZ13yOMiGn5snoeyrRxC7BLJLFtPmbko2LXHyPuHMPrw+zTh2PiHzs1lNFQFFQBFQBBQBRUARUAQUAUVAEUg2AvpQJdkIa/8JI7B/v938NtyD05XbfnOc3HXJyaaIDzi/nDhXJs9fIdMXr5Lpi1bJsnVbzLMAksZVkWT3X5U5aRtFQBFQBBQBRaC+IsAH978D0XjrQUdLKVagjSbFJaVy9YMvy4f/+rM0yc2J5qp1NYTAv175SKYvXB5Xb79aPFc6FsTe8ziuzpLl9P1Tm5PVtfabAALDri0PIueULE4AvPrpyns9z7lL1lpy15C+qHCJYNpZIINNShsaShqzHcsiRRVb0tgdkzbFksX+nEyhqfIvTfCzAXGJWmv7lcGkMYvdiGTaJnLYI3/diGJLFjMtAXnsqi1z/W1fJI/RJBDh7E2RGY6toggoAoqAIqAIKAKKgCKgCCgCikCyEVDSONkIa/8JIdCtfSu58vQjTJtN+TtkxNgZfvu2LZrInRefZPI/L10rJ978tCGJfQfPaNUsDw8KAo8MMvg0IU5Jdv9xTkPdFAFFQBFQBBSBBoXAbju2yVkL58jrfQfFPK5Fq9bJzdjf+JHrLojpqw7VQ+DzCdPl+U/GxNVJn62b5JSl8+PyVSdFwEdAyWIfioZqmF9a+GBqiFoYliw9M2FMAABAAElEQVQ2hK9H/FqSOAtPH0gQkzA2qSWMvTL6+WSxJYzZJ2w7hksWh5LC9KGY1Mv4ZdYIuPifJGkpPikbQtCacq/M2pY4Zp62URs1zBRaivKgSGOPOC4OIZBJJpNEDiKQkTd9ev2bA0J/HE9FEVAEFAFFQBFQBBQBRUARUAQUgWQioKRxMtHVvhNG4IHLTvP3JX5sxHeyo7DY7+OovXr7ZPAj730bljCm84H9u/ttMrkRVgSxxLKtrun+bb+aKgKKgCKgCCgCjR2Bk5ctkMntOsnc1rGXqf5o7BQZ2LOLXH7GMY0dtqQd/5ylq+SGx16Lq/+cslL506zJAs5GRRGIF4ESUF7PO3sWx9tO/eoJApaUZRqNLHaJ4Ww8ecjGTzOqLbeRxi7BTNKYxLBNbf9mLDsecTKT8BMzDxaHiue2yzHEwdb7hKwtcPyCiGWPvKW/WVLapigIJZAtEWyjjm20sU8cl4r4Nolmj1C27Zimef360/HG9/NqKAKKgCKgCCgCioAioAgoAoqAIlCDCChpXINgaldVR6BT6+byz9/9Qs4Zup/pZMv2AiFp7ErT3Gw/27VdS992jY6tmsmjV/3KL8riEwhHCorwDMuTNs2bWNOkNdF/UIeaUQQUAUVAEVAEFAGDAP8a/2n2JPnrwUOlMDMrJioPvPap9O/eWYYO3iOmrzokhsDmbTvksvufl4KiXS/mRevh/PmzpFPBjmguWqcIBCNQXj5YRj+2IrhQcw0FAXKq3qJOflQxI4FthDB/fllS2JDEJIuhOUxDCGP6WsLYksQ2UtmSxfz7YSOKjRlC6vpZ36BXRI44UOl+eu1sc5LBoRJUZzNwsqSxXT6aefC8ptyNPrYksI08JnlsyGKQxtYudglkjzy2ZHPQvsd2ckoeWyQ0VQQUAUVAEVAEFAFFQBFQBBSBGkQAP91UFIHkI3DEnrvLDb8eEjRQDtYna9ksV/bo3kmG7NtHmuUF9i/cCsL4hJuelo35wfvm/TR3md/+itOOwNLVM2XKghVmma50PFXYr09XeeEv50n/bh18Py5V7Uo+9kEuLy/HEmfpcvbR+8q4WUukuLRUvp+5WGqif3cstRUBRUARUAQUAUVgFwLtCwvk4rnT5elB++8qjGBV4Mn7tQ//T96662oZ0KNLBC8tThSBIuwbffkDL8iKdZviarrf+jVy7MolcfmqkyLgI6CEsQ9FQzIMV4oPpiR0Lbnrk8UeIWyjiX2i2CONbTkJZUYYW7LY9kPimQSxJYvtOBZD5o34RmxiOHRlKdtFpJR/e5zud7l5hUx8UhkG52ryXj1JYx4DCw15jEwQeYzjtgSyiToGNqGkcRHJY6sgj7mnsyGP0SF+xpp+2aeZKFKaKoqAIqAIKAKKgCKgCCgCioAioAjUFAJKGtcUktpPVAROPGigUGPJrCWr5ZIHXgeBu7yS69wV6+VxRB9fdcaR0rZlU5n09A2ycsMW4f7GXJK6JQhi/tB/5uNx0qtTGzn+gAEyqEenoH6K8Kt8ws/L5NBBPWWv3bvIyH9fYeq7nnO71ET/QYNpRhFQBBQBRUARUASCEDhyzQqZ06qdjO7aI6g8XGZ7QZFcfM+z8u4910jX9m3CuWhZAgjwpbk/P/KK/PTzorhatSvYiejwyXH5qpMioAg0bATIiZIgpRiSF2QoyUwSxqFRxSaiGE8ZmNroYhLIhiyGv9m32GtrCeNwRLEZzhvTSwITcD4TJYWdpmHNuPojM0xx5maJWxYZGwaXla5AmoG0nCnqDOmLPIlj4sHUkMeMPAZGJItJrhd5mumRx/RJh5aiH7YJXbLaTonTUlEEFAFFQBFQBBQBRUARUAQUAUWgOgjgp4mKIlA3CBQWl8iqDVtl5catsnDVRnnpyx9l9LSFUSdzw9MfyOqN+fLX846R5k1ypWu7VkbL8Ov5xzlL5c9PjpBxs5fIH39xqCGNd+/STg4e0F0mzFnm9/vrO1+Qd/5+iRzQv5tk8akFZGD3jrIK/dZE//5AaigCioAioAgoAopAJQQumjddFrVoJcuah99qwm2wbnO+XHT3s/L23VdL6+ZN3Sq1E0Tgjuffl88nTI+rVQaYjWtn/CRNS3dt6xFXQ3VSBBSBBoeAIXRxVEwtyWuWlcbPKBKfJoLYI4hJEudiBwITZcw65C2pbCOL3WWo3b4JHIbwPvyEJUbiInTZYU1KGDY2dB5B0ckYnjMgcWxTGiR5wZcbEplRwiY62yGPGUmcTeLYwywbt17iSuyK0LAIdRkgkC3BTOLYLFmNVKOOa/KEa1+KQI0iwGXvwu2zMh/l+VUciXu87B3SlnuIzAkpS6XsvpgM7mZGluBzY8BM6mc39G6XINwOe25SR0utzhk509WbUhHSmdWYXme0tUs+FcKeVY2+Em3KcTk+pQA621gN+6Mmz11VkKqL72pV5qlt6g8C5B3PgB4A5feZ9+OVUP7Neg+qksII8LdMgxf8kOMftj0GXXqfzF62tsEfb2M4wLzsLOndpa30RETxqg35MnvpGinEkouJSA5+he/eua2s3bxdNm0LXgq7JvpPZC7qGz8Cs/57k+wRiCAfhIc2DeYfjnqfiv8aUE9FoKoINNT7h8XjFek5K03Swj2csi4pk67Jayq3HXS0FMSxvzEnvXfvbvLy3y6XFk2Dt51ImQNK8Yn8+7VP5Mn3R8Y9y4vnTJPjVi6J27+uHSukbNBvZVmD+TdBXeNZB+Ob32p1MK4OGQUB86CAhCd8wkUXk9Q00cSgMCxZbPJ4PESy2JKeLlnM6GRDFCNlnxSTuHagOFAXiQSOUI6/gU7r6psVkRZ/DkMm29FIIrticviwpaymmmWr4cjoY5LAdglqu7dxEYhjLlVdCKXN1NYxpb8lj22fJnUHV7uxIjAIB65/E+v+7O+FKYR7W+8WlN9bxemdhnYfhLT9AflDQ8pSKbsJk2ntTej3SP9bC5N7BGNc440zDunhtTBmqgxxMybyT28yC5H2iTKxDqgjwT4pgs/fUf4Pr24u0gER/JJRfBc6vc3rmMQ3v08NXRI5d8nAoi6+q8k4Du0zNRDgUnHjof3CTOdrlB0XplyLUggBvvCqogjUOwQKEKU8c8ka+fiH2TIZ+xonShjzgLlU9c/L1lUijFlXE/2zHxVFQBFQBBQBRUARqIxAp4Idcs2MiYi+so/RK/u4JdMXLkfE8TOSv4MvmqskgsB/3vgsIcJ4GMji+kQYJ4KF+ioCikB8CJB6JS/rk8V4akDyl5GvjCTOgzbJEWkKbRaiTbNR5/mQRPYJZLRnhK2NNLZjGBKZ43lTYySvVX+2xgkeXkpyOOi/tHRUofNQP2yUnJaAVmrv9Rs0FmcaMo4/Tx4H6qhWaLnu4TA1kdnAKhfaBPgRQ4trc+KbG8izjtjbaG6eD54X9umOYcfWVBFQBFISgbOqMatzq9FWmyoCFgH85ZAroSSCj7SFmioCioAiUEMI4F+o8ga0X4T+qrMCQoQutbimEcDPEhVFQBFQBBQBRUARUAQUAUWgdhHYe9M6uWDeDHm5/95xDTxtwTJDHL902x814jguxEQeBGH8xLtfxemNZXk2rZeL54YLiom7C3VUBBSBeo6AIR9xDExJRpqllPF4mbv62OhikpYkOE0K240wtkSmIYi9PiyhSWhs/y5MLsnql9PRE5K2vrjlEWyM4rsnYuzqbtcLTYwcNjmmIS862Xlz72JfPB9bx3K28108g4npF4ZZvhop+G3JQASxJdeJua88B955MPWINi5i27LA0epy1f4ZUEMRSHUE9sME+0AXJDjRJvA/LcE26q4IhEOAPw6GhavQMkVAEVAEagCBC9HHcU4/S2C/AuXyv22h30BVUhwBJY1T/ATp9BQBRUARUAQUAUVAEWioCJywYrGsbNpcRu7WK65DJHF87u1PyIu3XiYdWreIq01jdCrHeqd3vjhCXv7s+7gPv+PO7XId9jHOCCFF4u5AHRUBRaBeIwD+0bCPTP1oWGRIWpIItsSwJYxtxGsOSWP64MlCFklPT00faG+JWKbs2xWXWDXl1hmZcESx9Q+kgd5smds+qMwZMKhPlEdafroC91DeR8vLyrCMNFhcK2B5OUWSvRy93CODOZ4dMxqBbElnM/PA9E1nxMr0iZSjWewsloYs9gjjTCxVzWW+WWf9mHK/Y/xvOuDS15wHyWj2q6IIKAIpgYD5mnszYbTxvQnO6hT4N02wjbo3PgS45QfJGcq6QFLps0+lEi1QBBQBRaDmEDjI6WobbO5pXBv72TvDqlldBPDTTkURUAQUAUVAEVAEFAFFQBGoGwQuQrTxppw8mdK+U1wTmLN0lfz6tkeFEce9OrePq01jciouKZUbHn9NPhk3Ne7DblFUKDdNHS9NS8FGqCgCikCjQwD8omFBmZKAtGQlyWJLGJMstkSxJY4ZbcyllU10MXxNO5KVUPZDoe2ZgQJTFlJCJ098YtchYgOEbMDH2J6/IWtt746/GdR2iNTv0y/z2FTTD6ljj1oFCVwKoristNQQxnQ3Y9Bv1xTN1gqG/fFesiGxnCZcIpsjBRxNn6Z/dOL5mbl7c/Cjj71+mZg+SQbD8M8DKixJbMhjJ2/KkS+EcigSx0bQnkOSgFbi2MNEE0Wg7hFw9x4+G9NJlDR2l6ZehfZd6v6QdAYpiMCHmBNVRRFQBBSBukKA+xlb4d8+JYwtGvUoxU8SFUVAEVAEFAFFQBFQBBQBRaBuEGBk6zUzf5IBmzfEPYEV6zbJWSCOf/p5UdxtGoPj5m075OJ7nk2IMM4DUfxXEMYdC3Y2Boj0GBUBRSAEAXCNhhAlSWmWR/YiWu0+u9yfmHvsWm0Gm8r9dXOhdt9iRhmT1LQRsCQx2afp3xvTJ2DtHOgE9f/z9iXmPsTpUO5TnJ6eYVJTlpFh8izLyMiUdE8zMrOEyry1mWZmZhsNKstCWVaOZCDNyAz4Z2ZkmTkUFRVLUVGRlIE45sTNfsicI4RzTzcamBvn5yvmWYHwXhOhTLbWHBbm7/3HYzRqj9vrz5LIHMG62PPAc0E8GcFNjEnY5xF37HFs95Jmyrw5F/TzSP5MdGj68YZl/yqKgCJQ5wi85cxgX9h9nXwsk8vrnOw58R+/E2M10HpFQBFQBBQBRaCOEMhzxl3j2GrWIwTwM0RFEVAEFAFFQBFQBBQBRUARqDsEsrEM6A3TJkiPbVvinsSm/B3ym388KW98PT7uNg3Zce6y1XLGXx+SH2bFv0VeFoiRv0z9QXpsz2/I0OixKQKKQAQELFlpCUaSjSayGOQjo4hJRrokZTOPpCR5SRKTZCZJzSCyGGMZAtQZMxZZzAaWoLVksSGMLUlMMjiEILZEcCbJX0MEe2SwRxRngRhmXWY2UldN+a46EsfFJSWyY8cOKUXKyFyyvlyVmtHA5SCDuVQ1ieTSMixZbSp4jJizR3Knk9wmWw4hcUwC2dST+aVfDPLYNMSHPR/Ezz8nJI5xPiyJT9wNgY9zwfNBZd4Qx6ijH5cUd8l79se+VRQBRaBOEZiH0ac4M+AS1fHKGXDEt93IG/g0dyovX5UEdwnpCq2pZ8I13Z89Jt66GFHN/mtDuIRRq9oYqAGN0QHH4kYV1tah4S+etKvhwbjsFb8X3D+8JoTEWXco55oqkqzvajL6TUafrXEiXEIz1nmpq3sC/jVn7n01dY9Oxr00XiztOjixsA6tT4Xzb+fEa6YHNN5rh/s2895Yr6WmLr56DYJOXhFQBBQBRUARUAQUAUWgbhFoUlYqf50yXrolQGDyAf4tz7wtf3vuHSnCssyNVT4dP1XOvPURWY4I7HiFhPH10ydI/63xt4m3b/VTBBSB1EeAT48MmQiDBCVJRpKNJB25/DRJSJKRPjEJuoJlliymnxtdbPtyCcp4yWJLFJuoYkQRp5MsJkls7ABhbCOHTWpI4hwJEMMkgAOaBXI4KzvXaCZTbH1g88ZGngRyVk6uZMMm2bsdKzRQSQ4bEtgECht0zBLPZGYqsEFwQLFvMP7ulBoSGawyhH0QSBOFzOhogFlGshl+pGrNscUgj0NxsufGEsfm3GCYbJwfEvXm/OAc2ehvE3HsnRuS/fQJIvLRoXtezMT1QxFQBOoCgTedQblEdbxynuP4umMnYh4JZ44/A8rlZVZAudfkj9D/gx4HTUSaw/lG6GxoAZT9cQnST6B/gVqSG2ZCwr0vH4OOgfJt0pXQ7dCfoM9Bh0JrSkh63AKdDN0KXQfdDF0F/Rr6KHQItKrybzT82NObYnRCPK0v00tj+Pd1/D+Enev587qy/RAvK4fDsOUdbCHSPzjlDznlkUz+iboc+h6U53wtlOd9B/Qd6LHQZAkxuhf6PZRvvK6HLoW+Bb0KmgmNV0jq3Ah9CToBymttNZTHxGNhdCS/LydAE5Gj4fwqlO35PeP8+P1YBH0dOghaE8JzYM8n0zuhPDfhhLjxWGv6u1rT9xTOvSb6vBj9WGzOYqcQpt9BN0B5fnkf/B009JpJ9j0BQxrhPd3O0X7X+dLC3dBRUN6PeO/jNTQdeiU0D5qIHADn6t5LL0Yfdp6xsBzu+B4M28qxMGwfTM+0FWHSujr/7vmwW0E0w/xugy6E8jws8dLFSPl3oynUlX7IPAPlvYTXGe+N/HsyBsr7Qr2T0C9HvTsAnbAioAgoAoqAIqAIKAKKQMNAoEVJsdw66Xu5d/DhsrR5y7gP6tUvx8mkuUvksT9fIL27doy7XX13LMRSqne+OALR1j8kdCjZIOgZ2b1nAkuCJzSAOisCikBKI8Cnipbk9Qnj9ADZmIMnBP5SyB4RGUQUg7isREjyaNGp+7SSRKgvns2IWyPI7yJKA3aAeCXpiomgnmRsIPp4V2rJZbfclrFNoE/4czwqhGMyXwGbK0fzeBlBvHXLJsnfskWKsad7JlhZerCc5C/TCozPlMI2u4T1gT7Ky8uwxDUIbs4VOwhzLLZIQzQyo5PT0G96GgBDaRpCmE2/aByIZkap7dcbx8yb3vThgLYeJlABHlCUcT5GnTxdWUe17dLKYFPAX5PCNsK5W1tTRUARqG0ESG7d5w26D1ISf/O9fKSkHSr40J0y01OTifOjG/wegJ4Txr8Jyg70lATKs9Abodug0YQP9kdA24Q4tUL+ZE9PR0qNV3iz5IP4v0NDn1XnouwATznPh6D0LYJWVU5CQ54PEgOh0hkF1GOgV0M/gp4HJdmUiBTC+Rdeg32R/itK46GOL92I5fM0IsgZKLd9j4XNsSi9obacZIcVHo8tt2VM9/CU9kR+RBGe7/9BeY5DpQkKSAZRn4BeA/X/9MCurvRCBzwPg0I66o48lYTW2VBe5yR/o8l1qLwLGu7c23b8Qcn+qPGc/7bwewNqv6swfcmAxflT2d8L0MuhVX3j+Sq0fQxqZQKMB6Hh/rwn47uajHtKTfY5AFjYa53fDZKAr0HToFb2hPEA9BWoPQ+1cU+w4/Peb+c4Bzavja+g/P66koPMXtDHobznsc1UaDTh9VZT99JEsPwC49pjcue3GzJUK2Os4aR1ff7d88HvE18e+Bx6kDNHa/aEcQ+U9+AToZugv4E+A20KdaUVMvwOjoI+AL0JWm+Evz1UFAFFQBFQBBQBRUARUAQUgZRAoDn22L118ljplb8lofnMWbpKTh3+YKNZrnr24pVyGpajTpQwzgFhPBxLUithnNDlpc6KQINBgE/MSCxSSTwyitUsSY1H9DlZgWhisyQ1CGO77DGXo2Z0Kwll+pI0Zlvbj0k9hALELSqtsBJCQpUNSPhaotddgtouP+3vS4xo4gwuNW2jis0S0170MKKEsxktnMsUmttEcnKbGs3OaypZsLOZz2sm2U2aSSbs3NxcyYNmYvwNa9fKhnXrpaS4VLLRb3pGNuaVLWXlGYgiTsdTbhxkgBnGrPEMNvC/KQt6IotMWWkZoo/5vBGg4PhMdDQipCmss8fI1NgY3xLjxMTiwrZWLHnMPEtZZUlini/ib8+ZJfgZbWzPF/eh5rlkZDL92CbofNmBNFUEFIHaRmAxBvzRGZQEUiw5Ew64+xp53UvjTUh8TYaSSLNSBoORa29DZ0CZt3IZDNb1swVhUhIrX0Jdwngz8p9Bv4Fuh1IY2ToemstMDOED+u+gd0LtsRbB5txfg7IfS4rytng9lDh2gFZF9kcjHr8lDYnBOK/sf0jHQDm+lVNhvGgzCaQfOr5dYQ9y8qHmcSEFJCuahpS52VOczAjHjmQWoGKlp+45txGNrFsfqTHK20EnQV3CeJlXFkqmX4nyP0NrSjqjo5+gg7wONyL9CMrrMN8rY3IklNfMYGYiyO0o50sH9tzTbTr0YyivNV6H66Cu8Pz/1S0IsXshT2yOdcpLYE+Fvgrl98JihL/IJsL1MaRVkT+hkdv2e+R57YT74ZyM72oy7inJ6NNi2wnGU1DeN0LlDRTY73lt3RNC58D8QCjPY29mIDOhj0Ofgc6CWukCYzT0YFsQJk3mvTQWlhswH3uPsfdrTnGnU8767Sx0JBXOvzMd4XF+C+U9mMJ/+vOc8H7nyoHI3Ac9DfoytCmUsgbK7757n+X19xfoIdB6I7xZqSgCioAioAgoAoqAIqAIKAIpg0BTEMe3gDjeY9P6hOZUWIx2WK6aex0vXcPfLQ1PinCM97/6sZwOwnjBirUJHWDz4iLgOk4GbuGzFhVFQBFobAjwiQUJSEtCWsKY5KIhjLN2LUtNEjJo72L4+BHGeIrgk5AOiC7ZaQdyidEAoRwgTwPkMaJ07RLUmVkgibMCJLHdl9gSxd5S04YgNoRxEyE5TKI4G8QwyWGjTZoHyGPkc2Fn5yEPzWvaHHk8y8Fjn2VLlsmWzVuw73CaZGK8ps1aSOs2baVFqzYglpuAOCZNnIG9iw1asBlDbPhipCjjoyMqbY/o5dLWpVjynzwzo44DS1YzUhlhVignSW6W28ay25HIY/Zo+wuYQM7r38yEw6GCuBviGOcjiDj2zp3Zhzob5xF5nlMljommiiKQUgi86czmLMeOZJ7rVJDkSESehXM7p8ELsFtB94GeDd0b2gb6CtRKTxhP20xI2hz556G5Xnkx0j9C20JJJh4DZf/Dobx19oXmQWPJP+BwqOP0OmySrCRyfgM9DNoC+k+ouQMj5dzvgVZFHkUj+4CfhHRv6OFQYnIh9GhoH+hXUCu/hEHyMhH5Cc6rnQYnOHaoeXxIAe7ghgQNKTZZEkOcr5UR1oiSfoI6G/G30vG7wyl3CWHHxZg8xz2hfEuK53cAtAf0AGhLKAkRS8DBlNuhPIaaEI5N5fX2B2h7KIka4kks7oLa64KEzxPQcLInCm9zKnh+9oLy+3AqlNfaUdAu0Mugm6FWroBhrxlbZtNnYRALK6/D4PduP+hvofxe9IV+DLVyOYwLbCbOlMfuHtso5E+EbgvTPlnfVR5rTd5TOPVk9GkhIc68J+2EvgX9HfRx6GLoS1ArtXVPsOO5Kb93vOYKoedAeU1eDeXcec3+HloCpfC7xnlH+m4l814aC0vO2d5j3HsncbflTJ+EupIK59+dD7/rPAf8XvF64blhnt/x/aFLoVYuhfEalPzqSCj/bvDvBL/7vGfdDrWSBoN/w+qNKGlcb06VTlQRUAQUAUVAEVAEFIHGgwD3OL5p6ng5bM2KhA96/MwFcuIND8jTI0ZKcQPa6/i7aXPlJHNc34DY4LO4+KXjzu1yx8TvpE+++/wj/vbqqQgoAvUbAT6piIcwtqSjiWDFIyk/ujjDiVi1fXmQBMhgjuAXGMNG0dro4sCexYw0zhAbWUyi2CqjihldbPYhRiQx9yF2I4lt9HAuoodJBlNJDrtK8rhJ0xaSyxR+TUAYN4HPpnUbZP68+bJ9ZwEIYRDg2dmGyOXfiMLCEpDIFSCZ8wypW1ICAhhHsGsvY7skNQ+LpHIgz5yxkS8DOcz7MtulkQiH0rfERBun4Xg9ghzlxCNAHnvEME6M/c8/SewcYonjgM18QDOQ2ohjEsOMBOc5s+fPpDx/UCWODZT6oQikCgJvYyK8VVBIVvUzVvgPPnwmiUX5EbrIWPF9XAQ3EmtWroPBB9zbbYGX5iMlefVXp3wobBJooXIrCkjKWSHZwQf+9nhYzuiqB6BnMhOHDIIPH9JbuRfG+dCNtsBLSZpwfNZZ4fEMtpk40ybwO9DxZX9Lnbw1+QOEx7DaK+BNnQRgIkJcPnIaHO/YrtkDGXsduFgOc50cm0Qh50OZBV1grNr5IIHF8zvXGY7n/N/Q/zhlzWEf7uSra3KM46D/B3Ux4g+iv0P/BLVyCAxem6HC70CmV8i3i0+FzvTybsKxnoNe7xS2gR3u/J2H8mMdv7th85ri98oVXkdnQac4hX907Fgmr/VnoPjrb+QLfP4CuiOQrfTJ70pNf1cvQp81fU9JRp8uGNnI8Hrh9UBC9nno1dDdoROglNq8JwRGrPzJ65jX0VuVq+S/KOMLLVb6w7jKZpw02ffSeLB0phOXmQrnP9xEec1cCOX1ssZxmAzbvS/wPtwU+hmU96eFUCtbYdwJ/Z8tQDrYsVPeTE/5GeoEFQFFQBFQBBQBRUARUAQaJQKZCNO6YtYkOXXJvISPPxCR+4kce9198un4qQm3T6UGjCi+9J/PyUV3PyNLqhBB3XvrJkMYdyqI9FwhlY5W56IIKAI1jYB5wogPEo5+pCqeBIRGGFvCkeQjSUhLNpoIY6+9JS3tHF1S0zKaLgHqLkXtRxdnZBrC1iWLM7NyQORiyWkQxQGyGNHEOVAuM82IYi4xzYhiEsYkikkMQ3OaBNK8Zi0RUdxSmjAlWYwI4iYtWiKaOFsWzl8o86Hbtu2QkrJyKUMUMYnhnQVFUrCzCKRxsXClitJiLjWNeGKQutxwOEAIw+SjI/Oc1os6Ri5AHAMURiRD09IQnQziuawM+yKbyGJGFdO/AmNhwWscc0Ymj9sjzA15HIg8NhhxTPjzPyME2hMXY5bac2DPJSOOlTi2aGmqCKQ8Assxw3HOLEkkRRISBfa57euRnMKUkxh7xCknUfWokw9nkgic4VSQAHSjhHn7ucSp50PykU4+1BwRo976PwyDD94p06F30Igib6DuE6+e2LB9IkJiBX/hjPDuXuDZ4ZJtKHwC+iH0Ieh8aKLCtlaOgpFrM05KssHKe9ZAOsyxXfMUJ0Oca0vuw0AvRRmM15Are7iZatqvoP2YKH2Q5P3Zqb/TsWny+j3RKXsR9lonH858J6Swc0ieWZJeVtbA+KfNhEkLUebO6zDke4bxCy3iGDw+HgOFLyKcDo107dLvEqiVmviuJuOekow+7TG7KSOLv3ULQuzavieEDG+yb+BzbLgKr4zf8/FO/e8d25q1cS+NhaWdSzxpqpz/cHP9Nwoj3VtZ7n73SpG/Hmp+LSANlVecgpaw2zj5lDbTU3p2OjlFQBFQBBQBRUARUAQUgUaNAH/1nrvwZ7li5kTJNvtGJgbHinWb5KoHX5Zf3fKIjJ7iPktIrJ+68OYS2zc//Saipu+v8tyPXrVU/jZprLQoKa6LQ9AxFQFFIBUQwI2U91KSjTZClRHE7pLUkQjjaPvhumSm6ZxjcCQMZKOLTUStIVE9opiEMchTfylqjyw20cV2GWosE232KcYS1CSK/UhiEsUgifOgAbIYBDFI4ibNWgUI4+YtpGlz5JsjurhFCylBVPGsaVNk7pyfsVdxOcbNMqQuI4137CzEShQkjguhO2XHjgLZAbsCcyfRbKOJTbQxjonLWdM2ZLJNA6iao/YjjjFOGQYwy1TjONmmuLjYRDdbkjywVDVwYEQySGS7ZLXBk9h5/1lMeQmxzuJtzyWK/JcAlDgmSiqKQL1B4E1npm4EmVNszHO9AkahuW1C/ULzvVHAh9NWboMR6YG29eEYjE600hEGyRQrB8DoYDNISSDGkntiOLRA/bGOz7Ow4/kHK6NNrRwBg5GC8QojS0neUXg7fRDalJkIwmMgQUdSIBKJEKGpKSaxbt/aJAl/ZBhnlzS+H/UWAy5x2jrEnwS7S35WZU4hXcadfSmG5xbUr3N8aoocKUOfLtnqDOGbvH7v9XOBZWTbOXle/z2gvaC85njeY8l2OGx0nHIcm2Zz6FCnjKS5SyY5Vb7JFx7ugl4IPQQai7j+LXyeh1r+5l3YZ0KLoJEkGd/VZNxTktFnOEw+CFfolNX2PcEZ2pi8dmNd33R0X0jgCxm8lq3U1r00FpZ2PvGkqXL+w831tXCFXhnP1yqnng+Y5jj5UHNFSEH3kHzKZvFTUUURUAQUAUVAEVAEFAFFQBFIbQQOX7tSdtuxTR7e+yBZByIhUZk6f6mJ1h3Uq6tc8atj5fgD90Lkl/39nWhvyfWfs3SVPPPBN/LR2Ckmcq0qo2WAuLhw3gw5duWSqjTXNoqAItBAECCxiP8D5CKMzAxEpeIpAElj7ntryWKmoRHGkQhjS176EGEQ/meEtlEswQyymLZZlpp7/dolmm2ksYm+zTJRxyR0DZEMwtaSqxlZ2OM4Y9fy1YFyEs6YLMvZHsSrWeoaYbcYAseJtKxE5s+aKZN+nADCthDHlSkl5aUgjEsNbVJaWi7Z2C+5pKQEbdOkqLgU/YBUxgFkAKBSlPNw0kgOe0KTOT51dlNTzVBkjGspmXJGK6MfYsL5cb/jHTt2SJu2baUcmHAp63Lco9PKEIlcwVhk5JFyB9C0tAoQzQGFaQKZ/TE4NsZhPecQ+PCeJIf8OTP1pmHlj0DkNMoDQwaOiWNVdtUSRUARSA4CjGB8GMpv7t7Q/tC5UFd6IkNSifItdLWx4vsY6Ljxqx0tIthxreTHeU30HI52HNnnBCcfyRyNChKguGmHlb4hpeND8pGyk5wK3u5IPrhR0k51JZOk3ndQS9SeD5vE85vQz6BjoZa0hVltIUH9JfSXXk8nIP3Ks5nwGjjGy5Nw/Qk6Gcpzzzri7hLDhyJvyViSES4WyCZNiFs0YsQOzDnZlwvcFxdsfVXSJWi0KI6G40J8+iG/wSkj+bzEU6c4rNkFpUOhuU4t/uUUJAcj517b8Vy/+AeGWU47qKMImfNQ/iLU/oXnd+5cKP4xE1WS8V1Nxj0lGX2GA2Z+uEKnrLbvCc7QxlyCz9D7f6gP87w3uMLzvNgrqK17aSws3fnFslPl/IfOk/f/6aGFIfmNyPPvDiXWvcm+NBTw3rVEvs2nbBp6w0vZierEFAFFQBFQBBQBRUARUAQaNwI9tufL3T9+K88M3E8mdehcJTBmLV4pV/7nJenUpqWcc8wh0IOlU9tWVeqrJhtxOe1Px0+T174aJ5PmLqlW1+2xDPWVMydJX92/uFo4amNFoL4jwCfpVLuMsSGMM/AEFE8BzJ7FeNRpSeMqEcYkSs0YSEnW+ornm7ANWcwoY0bUUg1ZTJKXRDAIX6ZmH2PmQRZ7NiN9ub9xJkhj60M7E8Qy9wsmSSxZTSSLyz2TmMWBUTmbzWtXyU8Txsv82TNB2CJqGMpI3wrMw5KtJHNLSktNVHE52OBS7GtcllYmWQzXLUePYFXhHiBRK8yhoEVgqWlDJPNwURuILoYDcljz2iTpSMrSQAizH7DYJLLBEpu9jYuLihEp3QRlpUJiuTytFH4gjNkeKaOcK0AmGzYXn6ZnfHAk4okDQCnN+IjjgLdpUvmDj5xN3+iPKbo3A1b21BJFoK4QuAIDd41j8C/gMyYOv1RyWe3NeYg3qbOQ3u3ZNjnHGkhfd+x4TPdh/Bo0KIqnEXx2QtdCO3r+A7yUCUk0KyTi4umTt6FV0J7QcBJKdJBIj0WIReonXtKY7W+H7g+15Gt32H/xlNGlJNk/g34KXQ6trnyIDlzS+Eanw8Gw23r5b5ASM17PJI0pw6AuaXyKKQ18MOqP/rUhm+IchKSoFf4FrAlZEmcnK+BHPPgXjcLra5yxIn/w3PeD0pdpfyiv+17QULH92vLQH6PLbEUNpLz3/Q+Kf5j4sh8sRpjGIrSS8V1Nxj0lGX36YHlGIVJeF7Gktu8J7nyWupkoNl8q4X03x/Nxr7/auJfGi2WUQwiqSqXz705sKzL8x3i8Eut7n0hf8Y5ZK374taWiCCgCioAioAgoAoqAIqAI1A8EmpaWyPUzfpRRXbrL//rtJUUkD6ogazZtlUfe/kIee/dLOXRQHznpkH3k+IP3knYtm1eht6o1KQZR8f30efLZD9Pkyx9nyDYsl1pdOXL1Mrlo7gzJq8JS3tUdW9srAopA6iDAJ5vkGakkQBk1bJYwxi2T+xXnZe+KNK5pwjgQYeyRxYwuZkSwIYwZJUyCOEAGkxgmUUySOB1pFpaqNnmkWYY0JnHMNtjvmAQy26VzeWuQx2mZhli25HPxznxZMn+ujPz6S9mxdUtgPBx4UWGBIc3B0QYeI5N4NY/Y+VEB4hZkMurKyayjT1DdLDX8LD0Am3lyZPD0nkObOF8WmH5IlgdM+ppe2R+WqM4E6Iw0liwQ19giYGdBgTRp2lTSs7OlDNHOZZhfGu/V6MBEHZM4xhgkstGBGZn9GVKXPZuBzKAw4yOO0byyoItAL0gZd8XhWMDjsBWVW2mJIlDbCFyCAbnMaizJh8OYWE4pWP8W5jTEm9fZSO/2bJuc6xkk4d61hXGm7sP4JXG2sW6LYVjSmASaFRs9yvwKWxhHStK1ZwS/UKLjyAh+sYpD+4nlPx4Oh0JJDO8e4twMeS5HTeUdkaTjM1ASeFUVLknMmzr/TOwJJam3Cko5PpCYT5LVFKbDjRUgjT3TJC5p7JLJrk8ybJLpdSVL4hyYhNp6qL1WI10X3eBzHfRCaDtoVcWOw/b8nq6uakdh2rkRzt5faBPV/CJ8D4JGe7nCnVdNfVeTcU9JRp+hUC5BAb97saS27wnufJa6mSg2rwPeT/t4Pp0c39BrPRn30iUYLx4snWlFNVPp/LsT5b8pEhH3RZlE2qW8b2bKz1AnqAgoAoqAIqAIKAKKgCKgCIQgMHTVMhm4eaM8NWiwLGjZJqQ2/iwf7I+dMd/o3/7vXdm3b3c5bM++csiefWT/fj0lNwfMSg0Jo9zmLFstP8xcIONnzpcfZi2Q7QV8vlF9aVZcJJfOnS4Hr7PPoKrfp/agCCgC9RMBcH+GACTHaKKMkRrCGPylIYyzdkUYM0/NRh194lqSmh2bIZDCDkQYM6oWRDFI2MB+xsGRxYY4BtlriGI/kpikMAlhkMVYLpopieNM2LtI4wBZzGWqM7M94hg2Cc7MnDw8pi2W6ZMnyrQpU2Thgnlm/rmIQGbEbhFeMsJkwL8ymjewpHMoKVqOpay5VUFOBghskMY8nnLuX8znsXw8x0f8SO0TW0Ycm8M3oblEgXXoG+MY0hV2OQZJw/iIIwYejLhmxHOGiXguLioy+y0bd8yvjAb8KphyuWpEPKeX4akci/FojstVm6WszRzwwcG9g6gKcczjYHM3RZEZyxDHrDcF+qEIKAJJRoBE8GNQ3HllL2h/6FwohZGO+xpL5Auk8UZ5ek2ExKeVAmvEmeIO5EvgZh/Iuv8gTuQtx41+b5UNG2FbuSaxkvaJuRvvefjcB/pr6AXQo6Chz8h5/Id7eizSP0ITOXa4GyGRSVKKfVFIFL9IA3JcIDGfIz37O6T8gZADHQQlib8W2hPKPGUL9FtjNfyP4gQO0f1hFe7avxF93QsNPdd2CJJiC6A8Fx9B/weNdJ3iHyO+kDiqSULNdvxfGFyu/AmvgNHGt0Dv9PLhkmR8V5NxT0lGn6F4hLsGQn1svjbvCXZMpoxsjVfce7L7z7VI12i8/Vq/aPfSRLC0/UVLU+3827lGeyHD+jSKNNJNslEcvB6kIqAIKAKKgCKgCCgCikD9RaATlmG+feJ38k3XnvJGnz2kAIREdYSk7pR5S40+8d7XIB/SZfcu7WVAjy5Gu3dsi6WsW2Jp61aISG4mOSAwQoV7V27dXiBrNm2BbpVV6zfLvOVr5Oclq2TustWyo9B9lhHaugp5zPmo1cvk/PmzpDkJEhVFQBFQBPBIiU+VyC+SBOay1CSEuSy1jTK20cUsqw5hnI77JEezRLFNK0UWkzD2lqB2yWFjZ+dKFtSQxUwNcRyIQLbLUjOimMtTs18uRV2C+//cqZPlu9GjZdWK5WZ/4mbNmkkZ9iMuKMRy1Lg3VpgwWmAAkrkC92YKSV+7TDXzZaVpZpnrDJDGJH4ruMy08xiOAb8kZ02ZARWN+FiYZSz06njcRjAm+y/z+klHpDSXoubIJLLzt22TZs1bINo414xH/KhlhuDG7Egcw9ddrppds89A9DFsM3RgkokSx+zGKGdvbZsGjgAV5n+b01QRqCsEFmHgJnEMviEOn1R04VKjo6DHepM7G+ldnn2OlzJ53bHjNUl6WelhjThT159kpxWSlla6WyOOtHMUH55jKyRB+RYo7kC1JoyefdHTFkiHQU+AngjtCXXlQmQYwXmSW5iA/QF8Q0njpig7zOuDWCz2bJIz46BDvTznxevgFC/PhNHLjeUf/vFGA5Pj6EJwPFloDS+9GOkDIWUkZIn1dE9nIt0BteL9cbfZoNTtn/eq1tDNQR7VyzyO5td4XfCecJRn34aU19M0Lx+aJOO7mox7SjL6DMUi0Xxt3hPs3HazRoyU//pzfdc4/nV9L3WmEreZiuc/7sk3BkfeUFUUAUVAEVAEFAFFQBFQBBSBeokAf8kfu3KJHLB+tbzSd08Z38n9LVW9QyrDA/75K9Ya/WjslLCd5YI4JnlcWlomhdiXmG1qS7pij+dLEF08cEu0II7amo2OowgoAqmAAJ8oUW2EcSZukiSMc/DL30YZm/2M8c6LiTBGOUnleCOMSbpSSJK6EcYkXAP7FnvLUfsEL6OJEcULwjjLiya25HCAKA4QxrR94jiLxDHbMTKZEcZ5iAKukKKdBbJ6+XJZu2aN/DDmc1m3Zp2ZQ05uLpbbzgYhu9NEDZeWlEl2DqKXM3JxXy4GgYt9jw1xy5l7fATDatFngITOAV6ZgSWjcQ8nSWuqzaHiiMGupgFQkqwBbD2bhDFv+UzQIEAuM1KZ+HORa/yHjFmimmQyoo0LCgpl69at0qFrWzN2cXGhIY/N/sfAsIx9Qu1y1eYviv3AxAzhjXlUhTjmkVvCmMfnqinHOOS6zUEyVVEE6hYBlzit25kkb/Q30fWxXvdnIQ0ljXei7EOvPpFkvuPcHTb/uWzuJE55OJNvQ7qkG4ltK6utgZREMH3jIS05fiSZ51S0gt0Tutgpq00zH4ON8JTjMtr7AujV0OZQynHQqhKDPI/3sxMI++E5OQJqo0JHwnbla2SGegXDkIaSxpxrY5F4SWP+CMS/aHxZ4FsivL6ec/J8IeLX0DFOWaiJP8jS0ilk3hW3f5Z3g8ZDGvM7sS2G7zLU89qz8gcYJIlzofzuvQg9CBruO5iM72oy7inJ6BOQ1Jgk+55gJxrtHml9mPKllRynwD3PqXQvdaYY1Uz18x918o2hUknjxnCW9RgVAUVAEVAEFAFFQBFo4Ai0wvLMV82aJCcuXySvI+p4Tut4ny9UDxgSxdTalFZFBXLmorkyZNVS88SpNsfWsRQBRSB1EeDTTI9zNMSnjTK2hLEljZmyjBHGJJUTIoxJaPrqLEUNQjQN+xcbEhaRvWb/YRK/XIraXXo6xyGJsbw0ieJspJkeaWyIY0QaZ8GPbefPnSPbQLKuWrFCloMwXr9uveQj3zwTBDGYUy6HXYJ9iwsLSyQ7L09KQA5X4M64A/kKPEsFlWsI35wynjcgZB/5kj5B+5wmmA/2TGZFCV7+IcnLvYjTgA2JVHwaULnvscEWfuwKsAVIZGIBl1LsUUwg0dK0MW3xUVpcah7vpgGfckRBc76bN26W5u1LpWkeApNIFGcUe51zfmzvRRvDovAYSERbzofT4piJEseGGEY7QxajP6bk0oPIYwzPPMVLAhn9VAQUgWQg8B46fRJKEmgvaD8ob0gDoZSPoduNldiH+zCefbO/WXF0wTnw9mbFJSJ+soVI6bMHNFKko3Ul2eruu2nLber2z7LB0MW2MkpKjIZCl0CXQUmuJyId4UxSmMQdI03DyRwU3godBf3KcyAhSZL/bS+fSDIXzjxenmP+SOGx8hishCON7/Eqj0HKpVyHeHkuW/S5ZzeGZG8cJK/jWD+49gkBwyV1j0Sdy4FcivyYEP/QbHcUpDuFrs3ihU4dzb7Q6SFl4bKfoZDfn63QO6APQ0Ml9Fh57fwDeq/nuC9SXp93eHk3ScZ3db4zQE3dU5LRpzPNhMy6uCfYCe4Og9cm/sEYVfg9cGWsk6mre6kzhYTNVDr/CU++MTRwb5iN4Xj1GBUBRUARUAQUAUVAEVAEGjACffI3y98mj5XJbTvK270HyrLm7gvi9fvAm2Lvzl8sWyAnLlskOdijU0URUAQUgSAEQPhREBSLyNwAIUximASxjS42y1Ijnw1lBDJJY/obQpRpoAtDDHumqQQ1GkhJkhoNJoy5Z7G7b7FZStpEC4MA5h7FHkFsIoppgxTORgRxJpadTkOUbwZehgHtLFvWr5dZs3+WxYuW44WcMiku2CqFRcVmP+DSslLwsumSi7GKSrlvMehUELXlWHq6An0UFJYiUrZMMkHEZmKOZWR6wXxylWrukVxSyn5QhsLcnAyMnwXNMH4l6IO7ENvo4Cw+n+Yhsz0xYivky9NZgChiAgbhJ7FBDDAqAz5SAXDpiyK6lZSCxsZexeyLEcVckWLTxo3SolcPycHxlBQhcpn9gFjmUtXElxHHJnLZ7nOM+uoQx+yfagliksXkuZlaZZ3xQ5kRL+/lNFEEFIGaR2ATuvwaepLX9ZlI8zybyeuOnYg5G84lUJI7lNug5xkr+gf9rJCYJGltZRQMzreNV/BXpLH6vBI+gZul1ygkWYr8FiijQCk3QN+D8lYUTX6Pyscdh6Ngf+fko5lvoNJGsfOYhkVzRh3PD0lpvOVjhKRvVeUDNPyL1/h4pEM8m8f7jWfbZBIMi00v2JdBSZZTOKeqvExgGuPD/RER7fxY/7pO22ICxOuTGBPhNWmF3wHiZ+VIayAthY508pHMX4ZU4I97kOQjx2u4h1d6DdJ3PTtS0hUVJIwp/JHK8xyv/BuOZ0P38xrcgnQEdKqXtwmv65r+ribjnpKMPi0GiaR1eU/gPHl9XwB9gZkocpNTNxP2AidfF/dSZ/gqmaly/qs0+cbQiL99VBQBRUARUAQUAUVAEVAEFIEGhcDgjWvl3h9Hy/Ap42Xg5g31+tjaIIrut/NmyKPffymnL5mvhHG9Pps6eUUgOQjwqS+VBLAhjfFLn8RwaJRxDigESxibCGP4+aSxNzVDVtppkrxkzx6JaQnjSktRc89iELP+EtSIFiZxbMhkpCQ8t2/bIUuXLZf58+bLvJ+pc2XCDxPlvXdHyKMPPS4P3HOvPPfkkzLu2zGyatliWb9qiWzL3yrFRVjCGURtFghVPsCoKC0Vcr8lYHOLQMgy5bLKZWA9uZdwKZTLOKeD5eU+wmWIIC7FnsFMmc/AAZOs5vzQDG3KoIxQDvxXjrYVbIt+GbVMtrjcU1PGcowPN8+PYNkyEsBUkNlow3IqCdmAzfOUJps3bcJxFUlObhNoU8mF5piIayyrzb2fScKTRAbJbM4H5kzCmZHJ5oywa+fcsHeKPXdeNV3864EvCLjXhH2BwFwTeIGALxHwZQNeD0Zs6mU1UQQUgaQg8KbTK0kqEseUrVBGJFZF1qHRw05DkqT7O/lw5iEodEmyL5AnKWaFRJs7V5JXe9vKMGkLlN0YptwtInl5u1NwKOzfOflwZjsUuuQgyZN4CWP29yk/PCGRaAk8Wxaa9kaBJYxZFxrRF+ofLf+hU8nzbM/JdNjrnTqaxGaUU3abY5MorI7sdBozGrw+yD2YZKsoEz0FdbyGrQy3hpduc/Ikf6P1RdfjoPfRcMSS9k6R3OFkjoJ9tJMPZ97hFPJ7OtbJxzL5HeT3gykF/6KTF70UiS/J+K4m456SjD59EBIw6vKeYKd5Kwyez0jCa+sYp/J/jk2zLu6lIVNIOJsq5z/hiTeWBvwFo6IIKAKKgCKgCCgCioAioAg0SAT22bRObkPk8Z0/fStHrVom2YhUqy8yAGT3FTMnyUPjvpKTsOx2rkYX15dTp/NUBGoVAXJ7JAcNQYhf+HYfY0YZcynqXDweNSlsksg2wtgShKa9N2NLOpqsQ0oGyOIAcUkyk0tRp6cjQhjkplmKGiQsiVezZH9hoWzYsFGWLV8pC+cvknlz5stcEMQ/z54js2fMlhnTZsnkiVPlxwk/ydzZc2X71m3Sum0bad6qFSKQm0gayWYQp9kgVMtAuJIQLgHrahSZYpLD5WlepCxTKAjaMijnVIG1pUsQoVsM3xLc8plu31liIn5JFufmBaKfM7CZcynmbAhjMMDlaEOyGIyviSQm0cuxy0HT8mkcTENMsxw7HzOw2FdWmmWk6eT1wdSQz3Q0NhKkJH8rykpk47p1UrBjq+zckY+9jndKIXT7tu3QbSDLoUh37mR5IQhmREmT+MYBleJvAclxLqVdDvaaEddmWEN20wpI0HWBjH9d8Hpwrgna9rowLxLA17xIgG7Yh4oioAgkFQESgMXeCAci3cOz30PKaN+qyl1ouNprzK/y19AzvHxoQgKTJLEVkopuVJst/xsM+yYmnyezz6NtpZP2hD0a2hYaS56AwwzH6XHYJJvDPa/ugHIS6btBrTxkjTjTD+BnccXdT96H9orQlpGgLzt1G2G7BJNTFZc5Dl4Wv8GwOT5lZCCp9El8rbT2DP5FcclnW59Iut1x/hVsHmeqyz6Y4OfQcCT3WSh3X2ggbqFRyVNQ5srdbibEPhJ5fi9DSeJwY78Ev4lOe35vT3by1uR38Gro720B0oegPJ+JCI/j304D4sLvZagk47uajHtKMvoMxSJWvi7vCXZuvWHwuuU9LlR+gQL3Oz8H+YdDnZCv7XtpmCkkXJQK5z/hSTeWBvYPVGM5Xj1ORUARUAQUAUVAEVAEFIFGiEDv/C3SO3+KXICI3fGdusqYzt1lQQs8fyHLkkLCqOLD16ww+xV3KtiRQjPTqSgCikDKIuDdxkj0mWWpMxBRCmUEKQlBG1HKKFOWm4hSPI7n7c809dqHJYxx0JUJYz7LJyGK6OHt20HGgriETTI1HfsZk2Q1CzaDIDVlWJqZ5Yzsbduyjdn/t7CoBEtFgwDFnvDlIFCLQMsyEpj7CheZskDUb2YWl6EO8LA0OA4nzehiI8xDAmRsBYhRHCBmUM41l40/qVzuCYw6kMS5ubmI6EU0L9lR+JWVWu4A7qR9zd8E+IOU5VjpyJfBSMOy1OwyHSAzijmNa01DTAqb8b/e/4Zg9maHKXB0+hl3BgobOyczTdauXC7rVq/g5FFGMjngw/OYjj6Zx2gohNKm6aWB0ZmFB31RUME+OGlPPHfTxswHH0QnC2k5rgUEaRvinctUW+Ux8pyxG7anBj5oqCgCikASENiKPkmGnRbS9+sh+USzjKy8HvoaFHcOE1n5PtIvoT9A50IHQBmdeRzUlauQITERKiRNr/h/9s4DQK6ruvt3Zndnq3qx5N4rxjSbZhtssGkOYIpNjwOEEhICCR8QUoB8pJLwkXwhCSQQklCSkA8CBEMAGzdsbKqLDMa4yZZkNattmT7f73/eu6O349kiaVbaco505vZ7z/2/N2935z/nXFThXPVYWYWK6BDB9l1UZKTmuxRdjooM15i16ERSo0HraR5+ahlR92HSV6LfQW9FFbL7cegvowNolE+Q+VQsTDMV3trDJ9P+J5MKC+EtQm4DKrL7FPQKdCka5X1kDuSXc56wFvL7ijhhmmrv7aRd/U103NKu8z7UraPvk9P+Ih11jTahumdOR2ebPIhBR6Gy+Rfot1DdGyvR89Hno1F0D/52LGTSn6Jl/QAAQABJREFUq8hr32ekda8n1bX/OHoPyk/FcCwqEv0SVOUquh49HpUcmSTjXvVj8h3oNajG6L7/71RvIZW9J6C/hEbMyRoJ+GfK7Id8kDGyU/ZLfgfVe/BHKqQyE+/VmXimzMScEYPppofymZC1UffyT9Avozejer6ei+p+1PNWMob+Kqpna6vUqDiYz9LW9fenPBuu//7YvSDG6IHm4gg4Ao6AI+AIOAKOgCPgCCwIBAbwNH7WhgdMH+FMzR+sWmv606UrOKsy/j12cKFYO7InnL11U3jSlk3h+D077ZO9g2uBr+YIOAJzFQExAdKEaIQU5DEmUtjCUvPXfvQwFnks0hjetBmC2EhKBmv8OInsJZW59IxdeceK2NR5vPJ2FbkrD12dw6tPTHOiI2kXOWueuZzBTlfz1K1VxkK5IkJYRCiewykhKwpT3rIVzi6u2+M3CQetNfOw31rL5mMBIzG1ECISWYd1GsHbJEmZm3lrPOMbdBDZarNhXxcutoWeBiS6wmcXwCoPcS37sUl7wG7DMb6wjkhjVbKUVkwIW9bS2nkIZKU5EcjaPcQw/404lomCT3uPxLLVWX0yYSPHmuUy5y6rXy0M9A+wX4WwlrczBDv1mGg2aS9aTMGzDQStSD+Rw8m8qhdxTH/VaS+kEqrSPla0efWlggaXSsRwL1NzNDTe1uCZajznWO22h2QqmyeZxV8dAUdgBhCQl2SWNBYpeHUH1hG5q7k+hR6TzncxqbSd7KDyCvQr7RrTui+Qikz7HNqH8hQJL0uVpCkiN16KisRY26xtn7mO6nPQT6MiMSVPSNUKbV4+Q91b29RPp0p4iBR+d9pZZPXrUk2rxiV1Su9HRVIfqAjbKzKT6MfZ9ZlyNvtzCg+iR2UqRQ4eqPwdE1yGDqUTdZGKENUTfxA9EGKc4R2XrzLjD9B/QOWJ+epUScbJHZRejrb7woPux1eiIt21R4kIOWk7eYBK9dcXK3S/SC5C+1HNlZXvUngqqntS95V+/IoklraTa6jU/Zb+hG3XZdK6Iq2/il6Dai29Bz+NPgnNkokz8V6diWfKTMwJFPskusaH6pmg9/ln0Q+gela+JVWScaJn+YvQ742rHV842M/S8avvX2k2XP/9s3yej7I/zeb5Hn17joAj4Ag4Ao6AI+AIOAKOwKMQWM45mRc/dF94349vDP9w7ZXhPaQvuu+ucPLO7TMWxlof5h8OSXzhhvsJPf0Dzin+n/AX37s6XH7PT8MJThg/6hp5hSPgCEyBAB8XiqQU0RgJ46aXcU9CGjfPMeavf/XR92M0xjSd3ghY5VWpRP9SwlilJPR0mTDKY6HIWbwie40AFkkMwVqGSC4WK2F4ZDTsJrTy2FjJPIbLeA0XIUjLMJMiOyv0ExEs4rhYLFm5GyKXeNQQowpFrZDSENN8lNoFyVulHM8q1po1Eago1XjHcn4xH+XLO7ZBaGlRxVWdd2wEtfqyB8Jnd3O+8uDgotALOZvrKtjc6iNv56rORMa2KnMmIa7ZD/iIBBYBy9RGnopA1RnFImc1r9aSHapPMFMZ0lb9UH0SbKGpyUDxmr0qi4xVKq9qeQfrlOYYHls/H3oIn53XBUomYKSmt6uhHJqUtab+qU6XLGmxZrM7yWXq0w52n5DXlwdw4rYvF5hHuu4VyvG8a4WyVl8jsEnT4XFaTx0BR6CzCIhIzBJR/0FZXmOdkKuZ5Ez071GRDu1kK5UfRkXYTkYYx7FfIvNEVESHCOSs6Kl4O/o89OvZhiny8rA7G/1DdP0kfb9Nm4i+16Jaa3/lPQwU0Sev0ImkRMPXUNn1oYk67WP9N+kv0i/KzWSGY6FNqv1mpROksQjYF6OtOOtRL+JsNso/YZQI4bvaGCev2r9D9cWDdoRxHKL78lT0X1H9lG0Vvec0/p3oaagIZl2vKIvIZL/cEeuVCtPHo3+NTvQ+e4C2y9ELUHm3Hohcx+CPZybQe/wPMuWYnYn36kw8U2ZizojBdNND9UzQvajn3kvRu9sYu4e6j6C6vyYjjOPQg/0sjeseSDobrv+B2D8vxy6I3/3543QdV+/0M17/p+HO9Zvn5YX0TTkCCwWBdZ98Tzj9mDXa7hl8gHLnfNm3P6fmy5X0fcxmBObr8yNi/plw7Do+Oj49lj09MATqDN/SPxgeGloUHhxcHLZCNuzAM3lHb7+lYxARNX2w3yL60L8XT7cl5VJYBiktXVEaC0cM7wlHcW7lERDGBWMZWgZ6cb8RgFI64zVh/bz5nWC/gZi7A+1vtblr/qGzXH/MizDUo0gkn8hieRYPwMEO9e7VQcqqN09j+ok4jmNlfZMwTgqiIW1iC80sYhbvVxG9ImqTT1khQXlIiuAVCapHmoU1Jk0ebzpjWG0iV2mDUlC7RHkjTm1M9JaFOE29jq2NcYmFajcfW7NJeTOcNatEMVUvGZLYlOQVlroLprOAu3Wh0AMpSljsnp7QxzNbZGyFUNhjnBNcw8O3mz45FkxCTKcEKX2SeYVCgkMuD52dPu+NoLU40RonA6xX6Em6Wp2qI6ZmMzb2YIO8hjWNyO9Ixirtgb3twgVY4bPNFgFt8aa1sxQDy2qvZNSunNKYt3pVU292aQr1U89EbBQvwlhkO/x9KKKj+CWNQI3sQYehMkYow/+HEm1NUl7zxInS+TyZtQicgWX+M3HWXp5DbthhWCCC6URU5NYDqMg03vn7JZpP99wJ6HpUpMaBEmJMERajmld/24yi96L3oNvQTssKJjwGPRo9HN2NPoyK0BVhM19FPy10H6xGdc10/bT32S6rMPDpaD96C6r7Yl9lJQNOQnXf8ttTuAPVc3MM7YTofaEvYRyL3o/+FH0IPZQ/SWfqvap5O/lMYbowE3Nq3unKTD8T/gBDPpgaoy9C6MsMUfR+PBfVM1Ak8m3ogTyHDuazFFM7Iof6+ndkE3N9ku65vgG33xFwBBwBR8ARcAQcAUfAEeg0AnyuH3SmsPRJW/W50aOFz9xDmdCnVViAPJ+mF0RC+KfqjwbKaxwBR2BmEODjXiONSUUEy3vUvIz5K19eoyKKFaZaHqXZsNRmDGMkkdxMC3uJUiauQsCKMKaXNdsLH3cmIaiVJgRkJHotvDKdVA/fCDHLVzrobx62sZ46PSaNXGZapSJTRTDbOmpDRZpqPnVQURuFqm6aUoX8NU9dA4BQ1Goi3zfAecU8k3vYcEFexqRdPKdFcDeYqwpTWlUsZhHQtGluqWavM6GtQVmVoo+tTF57lg0ieWWWtSjVDwtEzcpqrzLJhI7yxhZhLK/qwcH+MDIyHPoHB0Kx1hVWr1geTjrx2HDLTTeG/n5CZ+e5YCLBOQNaBohyZ1qyNrPVpBVJojYtZp2UZ49J0iSOrQsvVq8CIvNimGqidts90gxRjQkVXT9U10770qWxa8I4zePiCDgCcxoBedJIv92hXcT5ru7QfHEakZfy9JTOtGxnAemPZnqhWTa/HukipaRzSbZi7IF6XOvLB9KZur/0vvgmOptkpt6rcd5OPVOE2UzMuS/X4lA+E/Rlni/ui7FT9D2Yz9IpTJl286G+/tM2dD535M9HF0fAEXAEHIHZgsANf/V2+4ButtjjdjgC8wmB5YsG5tN2fC+zAAF9jN8HUUxs1llgjZvgCDgCCwkBEX+mvIjIlKexzjKWN7GIYoUcVmrhhmmbKCx1EzPIR/0TCSnyVSGTE8KYHiIOUxXJa088UhGJdeI4G7nYLIssRplKxKP1sX5JXmOpTshH6rULJYknc0J6ai2JPGmVFTlsJLLMU4OERcWX6nzhPAD05EQO5zgfuN/sV1646DmdEynNolVetC/NYicek9UK2rfWNBJag2wMJyKzQFxf1fLcbbBf4aNZtG6DDZo3NES19qo2m8sspYU1G7CwCrU9MlYMff1DoURY7NzQinDh8y8Na45YEx7ctDVsfPA+8C7K3xoiGmWSeCayUv2TtbIusUrlRKyshri++pFvd76xJkjmSIjjeN/oXonnGisViWzXEQC1H60mTV6UcXEEHAFHwBFwBBwBR8ARcAQcgfmIAH8auDgCjoAj4AjMFgSWOak1Wy6F2+EIOAKOgCPgCDgCjsCsRUCEqVRkpnkZQ+41vYwjYQyJLCJZ7fIs5X9TtTERnCYxTetEdEbC2AhDXkSIKh9TEYp2xi91ckYWoajvz9SMRE6I5NjfxqiPOslrltRWxoXVPHMpR4JYZKxEXdVH4ZyV12bzxh4nDf2Uu9Duri4LM91FKqJYJKhG5BosyMLmq8sEtVAgHDNexmiDebTlinlRa2pIWiqMDNY42jSNyGR5G5v9mlb1GpsaGalckcfmiax1NbG6pvN14+0cz2DuH4Aw5kzo1YcdFp77mreH4044JpTLI+GC5784fPFfPxkqpWFIaM46Rm0d23lia4JHkrf92RrU2v+0XkvLNlJ5B7cSx7SoyV4MJgrCq849UtMXDRgrL+NIHsfQ1Nq/SPcIv2xxcQQcAUfAEXAEHAFHwBFwBByB+YmAk8bz87r6rhwBR2DuIaAzK/yZPPeum1s8NxF4ZG6a7VY7Ao6AI+AIOAIp8QcQ4idFGkdv0Ugay7vYPIwhA9Um0lh9jc8klURyMylRFp1oHUQCwxCKjBRZSLVSy1Mt8pD/Rhgrn5DHqocotvaEZDWiVeOos/DUaT6vCSViNW1elVkdm0XGqmTksvqkXsTKmr0ibBkjGSwkm1Loaf6zR44JYHCtqmM56WeazKd1qvyaXVFoakjjLkDRVmuQxkbupn21sEhqSeLNm+xVfZLzg81kTWcTy1aRytqjQmmn8FGr9bWPRqjQaCGyIY9HR0bDWY95TLjkssvC8pOfEkZ37yR0dS4cedyJ4fQzzwo/vPkGwopz9rIAEJEtpG0xlfVf/5K5mT4RW4gXxlhXaiNGsRw9jq2r2uNQMtpvNx315YJetMJ1qChENdy5vI1bw1TbnHHidB5PHAFHwBFwBBwBR8ARcAQcAUdg/iDgBMX8uZa+E0fAEZjDCPBh1I45bL6b7gg4Ao6AI+AIOAKOgCNwkBAQOSmNhHGTNE7JYpF/IpCjl7H6SfnfJAybpjKR/mnCPAyivIwtVDRVkSAUhykVEQyP2CSIm4QxsaibYaohg+VtbGGZNYGIYOrMO1d5zvg1O0Qeq4xhmlseww07aDexTHViP7u6RZMm5GwSpjlp7+6CPKVBHsUKES0C2yy2gZDIzKV/2pNI31KZ85UhiaXdPWKGZYX2ZQNJRfrKFtqoEtFq7sZNKpYRVBl5nI42sliHKWuuxGB6J/OYPWy6v68fD+JS2ANhfM6TzwmvesPrw6K1R4QxPJ/7h5aEahHSm7DUjz/7nHDPz+4IY3s4Rk+2gHRDoJldmBJdh2W0Nq5llU0Sy0Sy2NppMctS8zLdkqEM1FghYcQxGZ19bd7GXOSy7iVSeR7rOtu11ph03TSJ03rqCDgCjoAj4Ag4Ao6AI+AIOALzBAEnjefJhfRtOAKOgCPgCDgCjoAj4Ag4Ao6AI+AIzG8EIklonCYFeRHbWcbyFBXpJ7IPjYSxeFgRxiZpKnI0KSfEqtjQpC6HdykMISJCVMSguNAYnjhJU09i2qOncY2GGqSmxlhebYxVuOokhLXCRKtO6xEwWu0yAZsTtpNsFbpVdUaOUotNWrs7h9ur9RIJupeqrNJXxLDtDUPkQVyvVUMB5jPZnvZkO2Cyunn8FqsVCFIIZDyOc3JtbnQZaa2ZNcZss8Vki4hb1ekfaTqVvIajiMgVwa7x5XJRjtHMJ8VmdHBoeRgp42k8sDw872W/FC591atDYQlEMSGqe3K9hIWG0BZpDkF8+PGnhcc/+bzwzSu/HBYN9Cce07WK7bFer3I9sUOAYIg8t5XKroiJ8qqQmYnFsd26JrCqMRXrT1746R6pt9xH0du46XFMu66/XSPGyRQXR8ARcAQcAUfAEXAEHAFHwBGYfwjwl5KLI+AIOAKOgCPgCDgCjoAj4Ag4Ao6AI+AIzHYEjNyE5BPZJw/jVi/jSBirXoSy+sUxZMnrdbyozhSXU3ntihA0pZuIYeVFGEbPYh0FXLOyzi5OCOMaFeorklh9Y1jjOFcabDmZ35ank9GtkJtkMdXIZBGfIj01h3ySK6WyEaE2JPPCqb/mndwj4pV/5i6Ll7Bm1Qwiqa1aOSpFrsr7OZ/XGcNaUz0JaR1ZUMPFOto4nYVs86qveqYe0jVtkhrZiy+wpghd9OntLeDNXLIx/b39YdGSFWHjw9sJPX1CuPwNbw7nXfwCeOS+MFaphFz/QOiCEK7KjberHnoKfSFfHQtPfOr54ZZbvhdGdm2HyIWchuw2bLoKXIMqK5ppyUu6J7u4ssOsVBpFVie2N1uTqtihOURb170Sw1RbeHOusb6AUGbZCqY0zzemr6BrnWrvpJ5zBBwBR8ARcAQcAUfAEZgEgZto+0javmWSft7kCBwyBJw0PmTQ+8KOgCPgCDgCjoAj4Ag4Ao6AI+AIOAKOwPQQEFEnEclnHqIQfY/yMobgy3oZGxeqMTYy80KDkaJKTXUmMKQrefGo8qCViCBMyOAkFVksAlHhqJM0KZv3MG0iluUJKzJZY228EcnyqE3rmDcHwVuHFBXlSzcjcrWeWcrasknEbaWsM4ofLV14CddgOhuEtZb3r8JQd3XJK9kMsH1olObWhqItSXhp1WqNxB7lRZarrFDNEivjOmzYaB85WFT6VXUOstA0zLgOakNr2JHv7g1Dg4sIqd0b1m/YFE457czwhre9PZx17gUhVxgAr3roLUAu42mcCz2QyFXyw6GvB1IYMntgaCice+654dtf/0qoc6CwiH/tR+c2NwgBLpHlYtRllyRBj7yMkF1JD8vGGutpAzViryQzJPeSrrmIY7ufgFAhzi1EtYhjtm4ex7TrtlBfm67NnHtn95wj4Ag4Ao6AI+AIOAKOQBsEvkWd1MURmLUIOGk8ay+NG+YIOAKOgCPgCDgCjoAj4Ag4Ao6AI+AIpAhA1okrjITxo7yMU8JY9dKsl7FmiESjTRKnNPIRGhS2VGokrzGDRr8GokYnpChEoghjI5CVp48I4kSVxzDVxT5KRdayjqaTd6/cZpVYlhYR1KJiVV+HFFVf2aw08p85edy2kXKpFqpdtVDvrocCCxRgO7tFIqeewEZsYl+yVwhoYmUnttvsTI8XL+uLKLYw1oat2lCzgdDVzCt7VKdEFipUdLcwk+cxpuUhr/OU69196hiGOTs5BygvvuxV4XVv+Y2w5MhjQoMQ1TU8jCvMV6bNvLFJG2gBD+UdW7aFfGWEs43LYdXyFXj4FkKxUgQ0/KkxoEbobS6n4YW5CT6pTSLozX6zUPZjpTopJxBS+5MWqx7/onYatU+FqW4Sx3xSpDONzXMdb2NF5QbuIO5alzpdIrVl/JRecgQcAUfAEXAEHAFHwBFwBByBuYuAk8Zz99q55Y6AI+AIOAKOgCPgCDgCjoAj4Ag4AgsAAXg64//EV0aCT0SeeYaK4Es1ehmrj/pK0iQppK9QjdbBiFMRxpCgeTxa1duIY3I6k1h5uE0jXJOw1Gk4atXDIEby2Ihh9aNeXroij/nPuITEhCK2erOGBnkRW15mqB8LiYg0QlKGa2FEXrbtpIInrgjnckNBotUX5ZxidsE/Zk/n6lJTOp3qrK/qTCiR13I5XIYbcpeWYIjCaYuoNaNoj3l5Rgszkbnd4NYFyypt9C2C3BUJ3Bd+FbL4BZe8KOQWLwu1sRJYEGabqSsAWSSEdYXCGN7GjQpnGzfKjKuEHvZ578/vCVd965uhVBw172kOamb/WjG5BgmimGQ1SUlEtihtpY8SDTSZoJ0265L2s/uKjTa/jAD0ClFdQrNhqhMCO7WmzbLpop44Ao6AI+AIOAKOgCPgCDgCjsAcRIBf/10cAUfAEXAEHAFHwBFwBBwBR8ARcAQcAUdg1iIAsSdyU4SdyD0jjCmILBaxV0gJZBF+8haNpLHGSB7lZUxD4mkrwpi8yEmFWFYoZIhYeeUmxK+8dOVJnBCX49OEHE5I5YSANbKW9cybVhws64s4Vkb0JsyxbSQhOqm2qoTytG5UpCabXfVI5NJ1nOAWK6JZxLYmkc+yxhUKClEtj2kmZl/mcUzWPKBli7qnkqyTrsce5bFLV7MpH8czq85CzglQ8iKxlc+JLOYi9ICZZtg+Ugpnnfm48Po3vzWc/bTzQqOER/FYFZIYghgv42KZs4x1YegtUrgBA18tF0O5NBL6uYh33faTcNWVV4aND9wfCvkq4aFZR3PTT+sHhd6WcZiR0MTkTVSR2J1c42anvf2SjcYBj0rVbKPIRG9j3Ue6p6L2cI91g7W2oOstgj9Oq7EujoAj4Ag4Ao6AI+AIOAKOgCMwPxBw0nh+XEffhSPgCDgCjoAj4Ag4Ao6AI+AIOAKOwDxEQORcU0XsQdyZN2hKFkdiLxLGIv6aZHEbPOR3GgljdYxexiKM+/r6w2ixbKGQRcqOJ4kTwrAqEhkvYzvTGMYwhqQW85icZSwP5IQIVvjkxIM33YGIXBGOKkrE6iIaJ1EpIXwtk7CZamiRhGC23pwzLPIYEpYzkru68DYGoDysZg3jG3gQd1NXAKT6cBkClrVEfuKCXK8RdxnphpwVAZwXSSvPZTo1mLOnm/mxS/PJsBLEbx32dAhv4kJ3N6Q1ZxIz37Jly8OLXvHG8JKXXx6WH3Z42LNjJ8O6sKsaRkfxLK5VCPONfVXOZ8ZGndPcKJGn3IA4vu67V4erv/7VUNyzLQz2QkaDSV1expC28jMWHZ7CY/bq2gktrKJeYOp/YqsSNapdGFtKfm9GhUdLvB5K7f5Kv4RgnuxtvpBg+GtyF0fAEXAEHAFHwBFwBBwBR8ARmFcIOGk8ry6nb8YRcAQcAUfAEXAEHAFHwBFwBBwBR2A+IiBCTw6vTdI49QQVsSeV97Hxm/RR371EIAUJFUYuKk01n4allqexSOOeQq86GrEKN2whpUW0GtkqwjWr8vJlavWTZ7GYTTkGi0uUql5ihKdeUm/dtDY6HY/jM9MhyQRJx7avyZwszwCNEXEtf9u6CGTY1CRItQyHYAUUnT8sElbhtCVGKJONBKzmS/bJ+cNsshdcSiXOL4aBT7yvc6GnpzcMLhoyDEvqQ0jqJz/1KeElL31ZOP7s85irJzz04CYWhzCuyHNZxLE8iyGHQyWMjOwK9Qp5COQcYapFGn/32qvCNd++kvOCx0JfDx7SIpYhtGWl9sJlMTJbBL1slYgo1nWkSqUEg/QS2wXRaPVVwovmIqP/U4qG6TLpCwjmzQ5mdn+lZXkbV8kL73QJW2fKib2DI+AIOAKOgCPgCDgCjoAj4AjMCQScNJ4Tl8mNdAQcAUfAEXAEHAFHwBFwBBwBR8ARWJAIiPBLNRLGRuhB3mUJYxF9IvzUdzJJyMeko4WlhplMPHS7Q19XwcjRUqWYEMXyeoV2FGkpwtgI4mwe8lAholWvs4zlWWxho0Uii9gUuahw0Qr9TCEvFtRozMRIaxfza36zk1nd2iYyVP+gT2GqNZtmKeOhq/DRidew1k2JYjJ9fYUwjLdxN+1lSN1uvIWFhcI/yxsaupm87ARXPJQVkllziTxevGRp6OntJcw03snMuXLlGsjil4dLXvTCsGzlyjCK/Zu3bGe/+dDNRdm5c8TmLVdKtl5PTyMUR6irFgk9zRnHw7vCddd8O/zk+98L1dHdYWioN+RgYrtku2GHHdimE5tzlLW3KEb8N2uStkgOJ6iaiUmPtMLGiBmPHeJkpHY/GAuf3D9aHjPaEse6/9SmqwhMzemy9mWm9qwj4Ag4Ao6AI+AIOAKOgCPgCMwxBJw0nmMXzM11BBwBR8ARcAQcAUfAEXAEHAFHwBFYGAiI44sqok6EnYg7nFLbEsZG6NEnjoneqWKSjThUmqq8jLOhqUUc9xT6wuDQUNg9XEy9d0UIK9w0lCrMYJM8FoEs8hhqUqmIVrWLurWychCRqhIJ2cDjl2WtziqVN7JYY8hbZZKfzqutZROzQvLf9lwu18An8Zo2YhO77Kxi6noIKx1GKticeMpyanBio8bj/RsFWDhnmK5jjTC0uBfCuD90g8vOPSPh8KOPCU95+jPCc55/STjllDPCnrHRsHV3OWzZvQUPZYXC7gul0XIoEn5aJPnoyJ7QTUjqUCuGfA0CuaseNm16KHzny/8ZfnHXz0MXHshLB3pDV70aqqWxUOhlYVGywoechZ9mn+3woYthayl9Ir66vokvcrKjXEoICy7rS7VSSUybBRbVPPE+szDo0duYNIZAF/dvl09GujgCjoAj4Ag4Ao6AI+AIOAKOwLxBwEnjeXMpfSOOgCPgCDgCjoAj4Ag4Ao6AI+AIOALzEQEj8iDqoqdx9DBW2iTyIPvUzySmLWAkJHLSMXoZyys3b2cBc7ZvTyEsXrosbN66I1RKNSOOzcNYvKeIYTSeZSziVqpQxRJxk8ZPqr5Jc0JgMkZ2JW3QmRQ0LtqajEsnSaaa8lUezSZakyyOwTZ/pVLHU7oWephU9Ua2sn4DFlRnE3cBVol9iS2tyzBEHsYS2dNNO9GlLTz36pVDEMZLwq7hUbP3wmdfHC583iXhjDMfH/Kc/bxlz2gYHisRdnqEeeucecyZyZDD5ar20h1Gx8ZCD8xqCe/iXG0U4MbCxvX3hJu/e1144I6fhEE8n3shiavlMYzAQxo7uMRE1AYzVOdCi/DVP9VLDEMRtvwzxPSC3dqFVWgT6qRETaq29mSM2qxLHKZCKjGnNH75QPdWDFOttPVei3MlxsSZPHUEHAFHwBFwBBwBR8ARcAQcgbmKgJPGc/XKud2OgCPgCDgCjoAj4Ag4Ao6AI+AIOALzGwEYPBFz0qz3ZyuRF0k+EX5Rm8BEYpB0Ii9jkcZd3T2mixYtgTwthDFIzyrsrnkUQz6KNG6GqKbc9CimXiGpRVCKDDZJGNsmYZnymGac9cUWO5s32kZY530T7VKLofqPqobo1Eb4GqlsNmCcASKaVaGjC2HX7rHQ39/FGJHXyRwyAwg4p7gH0rkrrCKEtIavf+Ch8PTzzw1PO//Z4anPuCD0LV4ehsv18MimbSHf3R+KlWoYI3x1f0+Z0NejkMCDkMc1zjKuhuIY3trM0yMKvVIL6279SfjhzdeFrRvXhzVDfYQEz3F8cTVUynghF7pCXz8hqllTjskVbJUnthHyMoRN6honpD/XJN2vwactqAcFI5mtO7u1VI1JfdLHSupt9bagrh1jzatZa6iFl9b7LRLI+uKC3W/0w1TrT2IzKnVxBByBeYHAUexidbqTYdK7WnY1VXtLdy86AgeEwOMYzVeXTO7ndXuSnRev83lvE12gNTQckTaWSO+YqGMH6w/Fmh0036fKILAQ3zOZ7R+crJPGBwdnX8URcAQcAUfAEXAEHAFHwBFwBBwBR8ARmDYCRt7RW6ko1SaJl3p8TuT5GVm8hGBMlhNl2hSRi8QXlkYvY4WmTkjjbkIkD4bFy5YTovphSGICUNfzEMQJUSkSc6/HMd6wEJoWmlrz0yYyUzxsHsJTXCdZ8iIkyZAmpw+TV5nPf0U0S3Qu8XTEiE06VnOQvqwpIliTax5WYQ+5MAZB21WthL7ebmyrhy6F4S6NQIR3hSW9kK9DnH2MR7IIZnkCa+UlSwbDwMBAqFHZzbnOW5n3iU86O1xy5uPCueefHxbhfb2rWOLc4p22hzJAVIrlUGGtSq0WRvbgTcz+SqVdaDF0Y1OhXgn9ALLnkS3hR9+/Mdz5kx/iVTwaFncztloGR3BlLLGvQ2FwAC/vnjAyOmz7El6JZbpu2lw362rH2msCmsJuq6x2KGbLC3ONSEal/ehvvLP1UYsE7LB3IlFT834D4hgO3YhjykYck9r1S5aZaCqvdwQcgbmJwLsw++2p6TeSPr1lG1O1t3T3YgcQEIkvsv6HHZhrrk1xNQYvS41+I+kn59oGJrG3k3ubK/fIr4DHH6eY3EN64iT4dKrpUKzZKdt9nvEIdPI9M35mLzURcNK4CYVnHAFHwBFwBBwBR8ARcAQcAUfAEXAEHIHZhYAReClRF0k7C09NXWu44AktZxKRhHaGcctZxnniMSeexgXzMC5w9u/KVWvCgw9uhhTMN88xjp7GltZEAIskFnGsVRVO2ahIqxebKG5TtKT6SURrjhPaI98Y3YfGtaeFSBTHVNU2Zxxs84hMpYFF4YtDATK2YFQ7dSxbgAHNQYCrfbCvi/Og62b74OBiiGbtuwcCuBp6C4Ph2ONOCE8kDPXpp58ejjzySDyHK5xZPGa2luhTreJJTIzuEgRzjXVkV43Nj0H49uI93J2rhXoFb2YI7Pvu/ln4/o3Xhg0P3BcGuGiDfAJTYZyRuFyPnl7OMyY8eIVQ1NVSBQIaY5P/JCkrS2fzIsYCwyBlapVoy0I1IYWVSSuU4cLY2cNUWT09rR9NXWJ9dWNhe1IHfmlZ3TWneROTUdfo2a77TuU4XENsmAZpbRdHwBFwBByBTiPAUze8Ff0Q+kF0IZLGbNtlEgT8HpkEHG9yBByBfUfASeN9x8xHOAKOgCPgCDgCjoAj4Ag4Ao6AI+AIOAIzi0BKyEViLpJ1RhjL2zgl8CK5R3cj+yzVIAmp/iVZ0nSy6Gmci4Rx6mmcV4hqQjgfddQx4Yc/viNU8QAWMSwvV4WjTnjXvYRx9CyOfKH6yQp5JmtVkZ3mfqwUgnIiqRsROkGrJkLi6IQ8jqWUkk6LHCtsjHIND+B6fa8ncwPP5HKFoM/57jAw1I8XcD0UIYNF/JZGi+y5N5wGSfyMZz4rnHnW48LiI48lxHQtPLJjJ+GnCTMNASusSqWS4VCFJK7CTitNQnNDOHfRB+/ibshieQzffe/dEMZJOOrFA72Evc6H0eFhI2MVBru7u9u0ji0VDNd6RuZmN6t8KoJPxG6SCtvYsreDYBZQDa5b8iWBpE2YWRvFnAh0sclIgqVl974wh6ZRf91b8b6LX1hQavW04ayd9CWNV2TvRJ5zBBwBR8AR6AAC32KOCzswj08xfxHwe2T+XlvfmSNwSBBw0viQwO6LOgKOgCPgCDgCjoAj4Ag4Ao6AI+AIOAITI2DEHc1KcWA1oi5L3EUyTwSeCD4jBdW5jZgXKTMZkQhhaKGpRRijzdDUXSIyUdL+xUvCqtWHhXvvW0/IZnkT400LPyuvYpxsLS/y0ohkyiIMTfUiWylZVi/TkchotvTVGonIy1abS1LR1uZpmwlrrb6qMyKbF9mm8M0ivvsLQ3jxFi2UdI0zh+usV64RrnrZsvD0J54NYXxGOPucJ4fFS5aGUc5y3rx1Gx7B9GNSke4lSF0RxpG0FdErTTyNCd8cihDqVbtGGx7aGG6+4brw83W3hyWcXbxs0WColcfC6J7RUIDp7wLzXjyMNb7MvJpDojDestjCT5NNtq7VtWfK1KnNyupvvZN666zx6pjWq1hjDRI8yeltk2ArYbB1s9T1DYA4JqY2OnlRVSSN7b5Lv6gQ70G1SeUcrfXTJDODZx0BR8ARcAQ6gMCJHZjDp5jfCMy1e2Qdl+Mz6SXZcpAuzaFY8yBtzZdxBDqPgJPGncfUZ3QEHAFHwBFwBBwBR8ARcAQcAUfAEXAE9hsBEXAmZCI5F8k6eRibwgOKC1S7CL5HSawkTbxORUAS9Jjw1KYijPF2TUJT42Esz1e8jOVpLJXH7V2/WG8kZx3P3RpErIhjeRGLwFUZTtLyDdjahM6kTL1xlKlBRmOK7Iz2PMrQhM9sU92sSrnQyIkmxGqcP+FJjbQs9GIfXGiXDuFlPZG+sme0WGNvAxDFJTyMa+GEk04Op55xRjj9MWeFMx9/VsgTonr3nj1h84MPhd4+9SNcNKSxSFctKsJYBK9tARJaHrqROBb0fT3VMDyyJ9x557pwy/duCLu2bQvLFw+EfINw1hpH2pWrhx4w7i30hBwAiSyu4umseRSiWudLm+evFtGG02uq7WkfVCY7pl7ksby62WWzzpoZq+FdfDHAvIt1vXWDMFRz9OjaYoORyXSMfXT1JHaNbK1kec2l4bqe0ngPKs8SthbNLo6AI+AIOAKOgCPgCEwXga/QUXow5VCseTD352s5Ah1FYKGQxiNCbaifvyBdHAFHYE4jMNTXfB/b+3pOb8aNdwQcAUfAEXAEHAFHwBFwBCZBQIScEXdtSDsj7uigduunvtZfr4mIJG4KHceHpSZ8s7yNCU0tNcLYzvctcNZuXziaENUrVqwIjzyy284Qhns1LlOpwjUbSaw6/YNEFgGpOq1oFCQvIjYnI4ubtqUZG9dSaUSq5kzJTM1ua7R0VnOht4cwzxC0kKO8GGGr84aLpXIo9NQ5p/i0cP4zLwiPOeuxoW9oMHSzzx17xkKxsgcyuRwaMKS7d++CjGU8r/IerlaJeQ1R3I27d7VShghmIco5gNBZySJP7/3Z7WHdHbeHu39+F+21sHrFYs4oHgsVdKA3zxnKXZx1zKzEFu8hfPVwsWS7lNev9hUJaHkbN7dJj+TqJb7F+cTV2NoTchczIOvlXW3SBF7XR+Gp2QE3iZq1Ro4Q3b39/axVM9JZKGqevbim86SJxtscpPJ0bxLGrWHR035mRrRl/FRecgQcAUfAEXAEHAFHwBFwBByBOYLAQiGNt+t6rFwyOEcui5vpCDgCEyGQeR/b+3qifl7vCDgCjoAj4Ag4Ao6AI+AIzFkEUiIuknZNb095GUNSSmPI6kjuTbTXhLRNCEKxgDl5GsN0ijBOQlNDGhOWWtrN2b7dhE7ugjxeumJROPnU08O3v3M94ZcVzljeqhimPKSp0oQwhpSkJFWrpC6XZImMS2UiclLNZQhYde1iU7JNYtwsL3tT9VF7VxjDc1jOxJhsdfKs7cGDt7uXjzgquVBhvkalGvr6B8OaNWvCk5/63HDE4YeHo487NgwMDoUyRPCeYjVURncRvhpPYs47ViDnSqnKK2RxuQTxyvnEkL2ivuv0F/2q8NKlsVHzuh0Eq/UP3h9uv/XHYcPPfxLGRke4JnjzcnHKo7shifG4xhydU9zDuF4Mlp3yLpYXs12DDD4ijqnU1hPYtIe98BkBb228JGQ83dPxug76r6Lw0rnFJPSjDMHfXegOg+w78TpWb5HKmpx+cRClpqiJCWSNmmWW7jd9USHee837T31S1ZoujoAjMKsQ0NtzLboZTWLhzyrzpjRGD+E16CY0/cEy5ZjpdOCnh82r0LjJt3imM+rA+qxieAXdeWDT7Pdo4Sg8d6Cj+z3L+IGdxHGmrrUs7vTelzFnER3T5NOQmdzbNJafVpd93VOctNPYxnknSzt53022Tratk2vq1ys9l/X80TPhUMhMXLdOYjQX3jOH4rodtDUXCmn8CyF65nFrw5W3/PSggesLOQKOQGcROG7N8jDQp2/9h818yDHc2dl9NkfAEXAEHAFHwBFwBBwBR2B2IBBJOKVGGJMRSWeEXUrgicgzQi+arM5RjBCkQCpyMNEkLLV5GxtxnJxp3AxPjWduHs/XbghjhTDuHVocTj3zMeH6G78fSqOQqHxcLxJTpKT4zVzq4hqJQtUb06j1klyS0pAQlGpvL/KIjWSxQifDq3J+ci4sGuwNo6NFygqtnGxQhHEBErRPXru9BZtbJHYDrdCv0D8UjjrqKLyKTw2nn3F6OOH4E0PfwGF2nnERT+GdI6NGeos9KUIsc3KxhWuuQq6KuG3gidvL/kvFcqgQyroboPM2PyQzZG8/tu7a+Ui4/e67wm23/iRsfPChsHygHvq4OAmJK/oZgJhLns89shP2uI4HconzkuuEpc519WIv5G0CmoESMTLsIqi0JLtOcGvmjU3WeF0HalXmfrD5rBPXALutTDq0aAlht/vYT9G+LJDL62JK47wij8ln7LEW6jS97kHdbyKNo6qsNmkUZTOmx2pPHYGFhMBfsNlT0w1fT/pnk2x+EW2fz7R/kfynMuXW7ElU/J+0kjdwuAwVcdUqT6Lil9GzUl1Mqn53oD9BP4d+B52tch6G/Tp6Onoyqg+BRHLqTNLb0H9Hv4VOJq+k8dVph38j/Qzaj74NfS16JqpHlgibO9EfoX+L/gDtlIiEeyv6MvQEVNdBIgJca0p1za9BW+XpVPxOWrk60/ir5J+dlu8mfWemLZtdQeFXUO1T9+Mp6BI0ymYy16K63/4nVrZJZxpHvQfejL4ePREV6SRS/Ub0GvSv0RK6L9KpvV/Borp2kn9Gv4C+HH07+jRU94/uyY+iauc3l3EyE3vLLrA/98gVTHAge+oUtpdhx+vSzej9oPs6KzNx3x2KNbN70v3wJjTe6wXyei7rmXMT+hH0YfQV6GtQyZXo31ruwF46dd1m4rpkdzbT75nsWp6fAoGFQhrrh82vn3fm8eHP/v3qKSDxZkfAEZitCJz/WP2ebaL3tIsj4Ag4Ao6AI+AIOAKOgCMw7xDQp5BRIinXjrATkWeEXXZAHEiaULdpBR2NOGYi8zROz9A1wlhkMaRml7yNSXMQpnauMeTxMcceH5YsWxY27OEzPdhAcYp2TrCmVTlJjJxsrpfWpytbIvIykqLZ+pjHqlDTGcIQtyIl+wkbLc/hXbuGIbHzoXegxwhdzdG/CPsgi9V/GEJZsnjJkrB69ZrwmMc+Jpx8ysnh5JNOCkOLFxlxKs/eLbt2md0iiHXGsYWDZlxFZwpjm4hq2ShCV3ssQS73Q/jWIKEb1QquWZw7DOnbqJXD7YShvnPd7WHTQw/QXg2rOLu4J48dkLAK/ay9aA/dYDk4OGAhrst4MivMtc4xbrBWd16fFbYXYSqxa5v1NuY6y/bkmieEcw6m17BlPV0Mw5h8jmuq+ir2LFu2NCxasgzCuATxy3nGOejy9H5gNhujvlFsjrSsW0tqX1xgXn1xwTTNG5lMndm0d4o4laeOwEJEQA+lF6Qbfxzpn00CwgWZvuq2FBWJN5G8mIY493fJJw/Avb3lmfU+9A/Q1s97+6h7UqpvIBX5rL77SsgxZMbkKGb+MHp5mxUGqDs7Vdn/CfRd6B60nZxEZcTqR+RFQP8nehqaFZGUkVx/NXnN+X+zHfYz/zzG/Qc61Gb8Wuqkz0J/A/0qKjJmBI2i9mh/rFOqfUglExHc76Dtf6Pt1tY4yWGoSDRpu/WpNplJHPXlgP9ClydLNV/1Pnh+qi8ilU5XOrl3ke3xGuj99gr0c6h+LEZ5DBnds59Bs6TxTOwtrhnT/blHDmRPncT2BDYRsb0nbiiTzsR9dyjWjFua6H7Qc/ncVPUMeDGqL09EbB4kf6DSyes2E9cl7m8ijJbSYX+fB3FuT/cDgdZfIvZjijkxRExx9dlPPKVr+aKB3CN7RueE0W6kI+AIjEfgsmc8LlZ8M2Y8dQQcAUfAEXAEHAFHwBFwBOYjAiLiIimXJepi3khjNq5PL5tq7N1eNBKiNiEGjdmjPYamVpjnhDSGhIVkFGkszUF2Kkx1FyGVV6xaHU45/XS8aTcZQSxSF97SpqqL0GQp8YvGMWKEyrJlr6hmailDvuL8zLqcs4yN1TohnPEo7u3HJhHdUshWI6ZhZPsHF4dFeELLo/jEU04yb+LVh60OfZC03dg9MjoWtu8eYy/MBblcrIpcFeGdeCRbKGgqRBKb1baXhIiVtbkcXsulCgPkdZwLo7t3g8H94bYf3hI2PHCfkceLhwYg2PHmrRCWGg64Crms+fKQ7gN4O/fJs5c6rS/SuC5CHLJeWNv6bWBRu63PS4Kp0BS1m5R1eSPWwkJ54SWnb4Wklve3+tZoqFZqYfmqlWH12jWhVq6EWh6M5WGONqRCk7E2H2MmEq2pOXW/iQyPqi8yjLsHaberbS8Tzeb1jsC8R+Ar7PB3010eQXoGKm/EdnJRS+U5lAfRLHmY7XJJpvBfmbyy8mr9GvpUFVIpkWrtn6HHoY9HRVLoLf1b6LNR2bAFPdQiElPk7sqMIXpAy/67UJFdIkuTh2TisXcx5eegP0cnk7U0fgM9Ku2kee9GNZdIkCj6No88W+vox2LlfqRPZMwXUF1Lida7Gd2Aiug/Bn0y2otKfgn9NPpyNMoYGfWXrEHjvneRH1YlsjVJxr2+n9IHxtUk3tnrqduNCoNT0NVoFK3/XvT3Y8UEaSdxFKn+RVT3Y5QdZL6HChe9F0R6Px29Cc32o9hWZnLvugbvRvXeaZV/o0LvtSgzsbc4dzbd33skzrEve5pJbKM9E6WdvO8mWqO1vpNrPpfJv4Rm7+GdlG9JF9W9LmL0SPR69Idop2Qmr1snMTpY75lO4bog5uHX/Pkv/CG0mV1+o7enO/e6i540/zfsO3QE5iECR69eGp53zmkNtlZE9UuZiyPgCDgCjoAj4Ag4Ao6AIzD/EOAjyUjU6dNJEXMi6CJZp7RJ1rX7+LIFESMVRRDyT4RhPNM4IY0hjuVZnHoa25nGhV4jHUXR9g8QovqMM6xdjKAIRrhbM1C/mItAlkrMW9XKIl9jXZI22/cWx+X6B/pCL2cpa+Py/K1CvsoLeGSkFErlMmRyT1i7di1nEz85vPzlLw9vfNNbwjt++13hN97xzvBLL3xxOOGUUyCMF4fRMYWfLoZh0mHCS+9m/AhnF49B/hYhoou1KucZV4zMNSJXHsYWd7tOCGpIXezuRgvgofDXleJo+Bmexd/8+n+Hb3z1y+Geu++EHS6FAUJl10vDoVEeDb2QsaXiGNcnF4YGByGzdXYw5xpjd3GsGEoleRiDpohveXOzF2E1mZo3t0CUitAWyBoj7LkQSZMIfPJG1SYEMHeG4aow3SvxvD76+BMJkQ25zX5MZYNuJrvBkhtN94fK+mf18cqkRRKrzt6HyuvLC3YfWoc4yFNHYMEj8H0Q2JRB4TmZfGtWpGdWeiicl63I5EUKi0CL0koaf5CGp8ZG0s+jIq1FYL4afRq6GP1jlAeKyWN5/aM0f6gTeQ5nCeN/oiwi5Sz0MlS2Lkc/g0Y5lszfx8Ik6RtoE1kq0vQdqMjc09CTUdX/GZqVt1PQk21/RcRzJIxvIn8CqmunfbwOfQZ6IvotNMqlZETCRPkaGZFI0kgeq+0DaKyX911W5PX6e5kK3YtnosJQxLDug/PRw9E3oSJpo/wamWhzrGtNO4XjIib+FBpJtDL5N6MrUO3pWaiu/btR/dQ7Ce1HJ5OZ3vtbWFw2jaL/gQqLv0HvQ/8ZjTITe4tzt6b7c49k55junmYa26xN7fKduu/azT1RXafW1DP302j2Xn8bZT3L9LNBqvwbUX3Wrfv8XLQTMtPXrVMYHcz3TCdwXTBzdC+YnSbfErvkPa94VuMfrvxeboQ/IF0cAUdg7iDwB699Dh9W6BON8DmSnXPHcrfUEXAEHAFHwBFwBBwBR8AR2A8E+M1Xv/1mCTojjCNprHamtd+QlWkVEYHWSIPyKWGYpHj0ytM4VQtNLU9jyMWuPOcaQxz3oDkWfMITz4Z4lNfsiDENIimhHpNQya1rQmJqTZkjYtOWT1OSpDIdI/LZCGca6vkK3rgQxRUONORTilUrl4WVK1eHFaSnnnpqOOP0M8JSwmQr3PPSpUvD7mLi8bx7eCQUOae3XickNPaPjI3i9Vsw+2vVOqTtmO2DwNG2KhSxjEgWJmngUSxrZbP21M1+5am8bevD4d57fhHu/cVdYcP994ax4Z2hHxK5n7DYi3q7Qx7yuY4HcU+ekNrY3TdQAKPeUOgp2PylEmQ35yXnjVVlIdI6azTwNq7B/OYEzhTSxNDsi/3TNBY1B3m2b/YLb3kQDwwtCseffApY9ISxkeEk7DhfGOCis1dpsucpTLBm2RHJ4XgvNu/DeC/SR/2kWdMoujgCCw0BvQUU7leknORi9COWG/9yDMWT0yqN0dtHciH6DcuNf3kuxa60ah3pLzLNZ5AX6RflT8i8LxYyKU9Y84K+nfTzaf3rSf8O/VFaPhTJL7PoCzMLi9j9q0w5ZkX6vha9A/3TtPICUpGhn03L7RJhW0VFxmjvWXmIwntREZYibiS6Ls9Ar0H3VQYYcHZm0KvIP5Apx6zWfSl6FyqyWNf2NeiH0f0V4cZPUJNtvIoo3pwUx73WKP0Dqvvhn9IWEVe6V+UROZF0CsffZYE1mUVEFF+VKSsrG4XF3ehkNtFsMtN7L7CK3qey9VpbMXn5jUxe2ZnYW8sSHStOd08zje1UG+rUfTfVOtn2Tq35fiY9LJ1Y949I4mvSckxU/0n0VvS7qK5LJ2Smr1unMJpL75lOXJc5M0f8YTJnDN5fQ/mj6Bt8k/e6NcsXn//uyy8M7//ndr+D7e/sPs4RcARmEoGzjj88vP65T9YPUn3b44MzuZbP7Qg4Ao6AI+AIOAKOgCPgCBxKBPQpTFSRdKaQcyLqst6dRuS1M1SEYJN/0GQJQSiiUDPHEMV5JsynZxsrLLLO4FXo5G7OMu7DO7W3wGffjTwhoI8Nxx57XPjJbXcY0axze2s5zsYt4E1LCGeO1cUuhTnWeb2J96s8mgt41MrjtgJ5qjYR0jpfuIzXbQ8bWbF8hRHABUjelYevDstWrA5HHX10WHvYirB2zdqwZOkSSOJB26HGyWNXZw1vemR3GMOTVvtRmOcq88kruTo6GgqEhC7xBfGGVPQppLjCQ1fLkMbY2SDP6cl8Os+fFhC/RJemDAnOPAVI4W0bHw7r8Cy+76frwp7du+hSCb1g00sbjG/ow1adYzyG97L21tO3KAxw9nFeF4Ypy8XkXOMKhLUxuWAsOxU+OvLEIo5h49tdOc5W1jVqEaqqXAd5MkvlEd1oEGpamLJoF2t0q56zinVPqLxi+fJQwL4Ga3dxjrGFJE+vdY1Q3boHeDHbRDLLRvMUT5dWWQZjqeEm7DS3aZt7Ud2jKGvjYoWnjsDCQ+ArbPlN6bbPJ5WXWTEtx+SimCH9IioCUXJhkjzq9ZJMzX9l8sp+FI0PldvIfwCdTP6NRhGUL0B5R9t42XkoRJ9LZwniH1P+6ykMEZn4avTMtN9fkgpDheudSD5GQythnO0r4uKNmQp56V2TKU83ewYde9LOehROZtMe2mXXOeg96N3o/ooevfpiQZRPk2lHGMd2pf+J/lOmIuvpnKkelz1QHGXnr2Rm/Dr5VsI402xnHqv9WdnKlvzB2rs8i69tWTtbnIm9Zeefifx09nQw7qup9nag991U87drP9A19Rx4Q2bifyd/Tabcmv0BFZ9Af721YT/KB+s9caAYzcX3zH5cjrk5ZMGQxunl+W3S773vVRflv3rTutwPfv7g3LxqbrUjsIAQ0Ac0//LeV+uDDP0w+UuS9Qto+75VR8ARcAQcAUfAEXAEHIEFhIB+4Y2i336lcH9GFhthTD6SdyLyTNMBya/LcXRME0LQJmIyIw+ZMDn/FtIYUlVkrshj8zIWaazzjfE2rsMG58lLL7/88nDrbetYLx/2QJpWIG/TiMlGhuqTcYV2LhC2uYB3ay7Xa561UKdhYHBJOJzQ0scec0xYfdhhhGquhcWLF4cTTzjRziQeWjQUGj0QyZDMClGtkMtjeAiPcS7xHsjfOqRwFYLXyF9IXyOtWUskp8I+k6ThtBthdHSEteFOAMkIZWytQTAP9PfbecPysm5AEOdFAOM1nK9z5nC5FLZs2hDu/tmd4a67fhq2b9kahiBlRX3mRIJDwmpP3bhAlwk3LbK9t59w2n2Q6339eEb3hMroHuwQYUx/bVrkr0AhVaJrY5dLF1TVE9CqwrSdQBFTzXUTQ89ocnZzFDhvWR/q9MBKNyDztafFSxdDyneFIiR6P17ZuuaJ6rxokdhSkcxTn2us1aLG+05p671offQygf20uDgCCwkBkVwjqL71onCj56HfQrOSJY3/nAZ5hcrD7PHoMjQbOpiH2jhCMEsaL6bt2WgUkQ76sv1U8o90EGksORflW0IWelflg49AgacAAEAASURBVCknsNiSzIK/R36qJ4mesiJ5Rc5L5Mknslaky0TyyYka0votpCJZNZdkaZLs86u8oPUFAX1RQE/Fj6D6AoHuh3byR+0q96NOmB2DHoUK0zvRqWSYDtvRFWnH3qkG0H6gOD6JOVZn1vnTTH6irDB61kSN1B+svX95EhvUNBN7m2LJA26eak8HC9upNnKg991U87drP9A1n8Gk2WfbB9st0lL3Icq/ik7nvdgydFzxYF23A8VoLr5nxgE9nwsLijTmD6Mf8IflnxBy6vc+977XhrPf9pGwi/OOXBwBR2D2IvCXb3lReCyexoi+MTudH7KzdzNumSPgCDgCjoAj4Ag4Ao6AIzAdBPioWZ82m/JihF2GMG62xT5t5owksujK+A+2MCUQRRQnZLGFqba8QlPnjbTtghWUp7BI3O1bt4XrrrsWY2RIPjzzqU8JJcjQOuRsDnJS5w8rDPOq5UvDsccdE4484ohw6mmPM4tKxZJ5Iuus36VLloZly5dB7I6GIvWaTp7JRc4DxheZ84hLYcfO3ZDDFchgedPqMy+RsCKHU7LYUpGzIovVlqbk1buPMNpVYlyPydNY5DD29kL4lkaHzUu4lz1pv3U8j4uju8NDDzwQbr7xhvDwxgcJMw1BXS0Tgro3dENJiJzWGsKxBslcK9dkMARyIQwMQBZDIsuDegy75bUsMZtkF2ple03q4/VIq6aV6Dprqj4oI/MuBoscBHFPPvEwlnd0N4cwlyDLh4YGw2GHrSZMdz6UGLNr9+7Qt2iRhRjXlwKMLAaPGKbcLoAuAiLb5JVsi1nN+Bf1khphrPsw/SKDyhqWTjN+kJccgYWLgD5o/CZ6aQqBQpJmSWPeQU0STGTl99EfoU9B1fYMNEsMP5XyclTyEPpDyyUvJ2Xyyt7UUp6omJ1Db28RjZN54k40z4HWn5aZQA/OqzLlybKt/U6h82Sk8QOTTZa27SKNpLEI/P2RMQZdj16UDn4VqUh5eRl+Hf0uOh1Sn277LPyQCvenOtVgfch2ASpyO8p0OIIDxVH3dhRd75tjYZL0GtqE2WTX5GDsfSpP8Jna2yTQHHDTVHvSAgcD26k2cqD33VTzt2s/0DUvzEy6m/zPMuWJsvriyi9QfQnmQOVgXLcDxWguvmcO9LrMmfHT+YEwZzYzTUP/kH4XnHTkqqd/8QO/Ep73O58IZb6t7OIIOAKzD4G3X3peeNuLzpNh+gbkq/kwY6Z+uZ59m3eLHAFHwBFwBBwBR8ARcAQWLAI5dh7JODEIyougi2pt00AnEpVKjSzEu5TZjPVLQhRzrrEIYwthnORFFItkVsjla7/9zfDZz/57ePCeeyFd+8JwqRb+/pOfDKsOP8JOiCzyt7QIY1G2dUjXMqqQzvVcwfhHkb0KLV0pq60SHt66hTztRsgyBWRrmbDTOudX5KjqEyI42VyppPOKE3JYaeLNC2lcIyy20cT6zBkhUW4Mj1/ZrtDMeQhihd2WNzX8NoRwJQzv2hHuufvucPutPw67tm8NO3dsD1XWEPmqCNR5zjiGHcaruMu8mOXJLKy7u7shibvC0KJBK4scLpbGQhkvZYWr7iOkdxS1RYl5I2Wz9WZt7DVxKsdihbbuyad7BieFqO7h3GV5E+coVwkRvoLznldA2vcURGTLEzhv9g2LOF4Mccw/fSFA3sV23VWje8JuJBZJTVY/c2aOtmp9zIvd4v3XmsZ2pTZVOt/EO/MWR2DeIyAv2Cxp/K7Mjp9APnp3Xk1e75jrUJHGkgvRLGl8idUmL/IMzL7DWknjj9KefIslM2gaWc1zqEnjh7GBb+FMS0bpJYIlkrynTjJqJ20ibaaS7Nr6Ybm/8n4GPhFdnk5wNOn/SlWfbV2FikC+En0QnUnR2iejur5KRa4Lq+PQVtHjfjLpBI4iq6NsI5PFPNa3prrfN6LHtjZMUe7k3vVFEH1hYzI5mHubzI7ptk1nTxPN1UlsJ1oj1nfivotzTTftxJr8otyU+5u5qTPq2wnSuN1KnbxuncBorr1n2mE6b+sWHGnMH0UV/nDTL243X/j4k4/7l/e8OrzmTz4TqhZDat5eZ9+YIzDnEHjlBY8PH/21S/XLofSVvHfvmHObcIMdAUfAEXAEHAFHwBFwBByBA0BAn+A2Cbk0r+nsk9348W5M1RBFg6KkE0SSMJLHSkWoNlUkpNhARN64V1359fAv//LZsHPncFi+bDEEaZ1w0RU8c28MF73gEs4R5nzgXE8YgyguQZ5W8dLV6G7m3U146QoksUhjEafKF4t8PoktdepUXxaRjIpU1lnKSqUijWNI5TLevRofPYplm/hMhWJWJiE9hUe6XxJtQd62qhG5XC0Xw8b77wvrH7g/3HH7reHhDRvxKi4aibposC8sWtQHcY3nswhwSOPSKOcVE2a7m9DPCpndxznJff29ELUQsjjcaC+yu5Z6F4tQ1n4kZk8mle2qy6bqlxDeyo2XOH58LfOKyKZSBL3w7VEqgpv5lxCCes2aVUYmF4ujdj1ztPXoOuzeGQYIBS48dU2Ta58hi5nT7GtdsE05vY0SfIUzlIpUOEtdHAFHYBwCX6PEm9Q8hx9Dqg/HRXxJLk4SexWBKFH6bsuFINI4K1nS+L+yDeRbSePzWtqnW2ydZ7rjDrTfaZkJ7s/kp5O9j06RNBYZOpHIm/tgyk0sJu9wEcPHtyw8RPlFqeqzrhvRj6P/inZKjmKid6CvQ1d2alLm6QSO2dDUU5GwWdNFrh+brZggP1N7v5/19H6eTGZ6b5OtvT9t9zNoqj1l550pbLNrtMt34r5rN+9kdZ1Yc01mgfWZ/FRZPdc6KTN13TqB0Vx7z3Tyusz6uRYcaawrwh9FW/mj7flkr7n8gscfpm8Vv+JD/+Iex7P+dnUDFwoCr3/uOeEff/sVREizjx9+jfS/F8refZ+OgCPgCDgCjoAj4Ag4Ao5AEwF+HdZvxKYxHytUr3xWMhX2u3SmrFlUt1fTUMV4n1r4YkhFO9cYJvBbX/tq+Pd/+38QhISdXrEY0jbxCu7ryYdbbr4xnHv+M8MwIaaLkLqRfK5zDnClguetCOJaMHJVZK+8fUWy7sbrVaGdtb7KJYhaka1qr+AFrK+Kqk2EqtoieaztiXRtivZAH4VpVv8uzhiWZ63YTBHG8mp+ZMvDYdOGDeHBhx4Kmx9+ONx/1zrm0FnADbyQIYMHOKuZCUsjnPs71BMG8NCtai8s09OrqfKEoO4l5POQee+KIC5DNMuzOCGwEzJbZCwHH9uX0C2fksTCLYr2E/eldDIZt8+0Y445FXK7l3DY5mGMlzFbhhCvcm2Wh7UijKmogn0fXuKGZZ2A34yrgYU8qRVG3EwVdmRkh/6RiaskNmZxzhiqbuopfE1ZX+caSzWv6vjf1MzVysziWUdgQSGwld2KPHx6umsRxZ9O8xelqZKr0vz1pPK65AlkXmYiQ+VJe2xaJgny7LpWmYxEj+VM1X5lV+3XqAMfJBI1yljMTDPlJ01T9AiaSA7FI+nnGHMW+jL0tej5aOtn8LJZ94f02eibUXl/Hoi8i8F/grauFecUSajwt7rvvoqKrJ7uPdQJHAusF2Vf9qqzl6eSmdz7dO7NmdzbVHvfn/bp7CnOO5PYxjUmSjtx300090T1nVgzG/Ghf6KF2tT3tKnb36qZvG6dwGiuvWf29zrMyXET/RCZk5vZF6P5I+ln/BF1AWOuvvTcx6658o/fFF7+h58OO4b35Zm5Lyt6X0fAEZgOAr/zymeFP37DJfGHz1t5r+pbly6OgCPgCDgCjoAj4Ag4Ao7AwkSAj5Wb3B4I6FPmqNMBxAjC5gRQhRCiIg55sbxNzm/fIkpHdu8JX/vK18O1115v5SV4qULLckavvH97wsiWneGG624MFz/3tnD0iScZMVwhbLRITXkaK9x0De/efHeveQ3LK1cka/TOlSexiGKlIjVFtKpcryTnFsd2mSt7dOaxRGXxmUl46kbopSI5pzcH2VsOw2MjnJU8Fm666YawY/uOsGXLlrB7107WwRb69nfhPdyVeEHnOIdYIZ0136L+HGcbc+4y1EMvH10NDnTjWdwfCn291i7byiPFJgEuO8RuKxWu8jw2opfw3hbWWVeG//FMY7tQsl/DkGbK4tnrYoQz7dHTWm3yYDZhUN/g4tCQZzeEsa5FrVILJxx3XDhsxTLMAWPq1N/CdhuJzjVRCHDogZHh4dCvs43BU5gKD7sHlKb/koXGv8oGgZBcAdooqkoaieNIIsf6OANd9o6LlZ46AgsPAYWSbiWNB6l7WgrFvaT3pXl9GHkjekFavpD082jWy1jey5W0PSaaI4pIZYVEbr5tY8MsTkVgRjkmZqaZZvuLpJ9tMoxBn051Mamu6XPQ56LHoll5HQV53T0vW7mP+Svo/+GWMTorW/fVbaneQTqCRuGXgYMq+iJElKNjZhrp2in6XEH7od77TO1tiq3PePNswHbGNzkDC+iLI3JYlOzLvb4vfZPZ279eQfWhfk+0t2xv7Xx9z+zd4RzOLVjSWNeMP4J+yh9455O98llPOPnEWz72zvDC3//H8NP1nfCwn8N3hZvuCBwCBPoLPeGT73pFeOWFT9Dq+kPojbxH9a1HF0fAEXAEHAFHwBFwBBwBR2BBISDSLWrcuIg5q4wVE6QiAqMY8acCg5WPKsLUyEP6GpEI4Tg2Mhz+53+uCt//wW1hoL8/LF2yFM/gAQY3whjhpnft2Um45nzYuvXhcOe6O8PgkhWhrPOGmUNn/5qXsQhgSGICUifkZkoQRzJUpkQPYpGcqpdXcXc92Vwew6LHrgjLAn8jaG71Y6B5JcvbdmT3I2F4eE/YtHFjeOD++8ND69eH7ZDFI3t2sJ+GeR7n8w1ztco1IJ9rkLSQxiFfFRSJC1Ydl66RRujvy4WhxX0Qxr2htxcPZNjlop27nKwbiXCRw92sLQyV1zyBuQ1u1WlzvNJkqV6FrSTpH8ftvUKqj+1KNbfOlJYIJ3lhWxkv43x3I5QJU70EAviYo44Iy4b61cvIZJH2XYztpl+tSLhv7RFCXsRxDBsug5Pw4zSSl2i9mKamWDn7oh5ZjURxTDVFbLdMsqXsFJ53BBYqAl9h43+ebl7exXognItG7yp5e2bl2xQuSCsuJG0ljVtDU6uriIkoS8kci94XK+ZAenfGRpElwoin85Qib7zDM71m+we5u7FV1y9eQ50rLA/k30AXoRLdI3wTKOxQYR9F1/4fMmNEor8MvS5T15rVo3tJplLlmZZNmQVEBOs6tn4RItOlmdW9MZHMlr3PxN4m2vPBqp8t2B6s/XZyneyz7ch9mPiYfeg7Ude5ct3m43tmomsy5+oXNGmsq8UfSXfzh9pTyH7pxCNWnff9j/1W49f/7//Lffqb359zF9MNdgTmKgJnHLMmfP53XxvOPN5+51fYmUt5b14/V/fjdjsCjoAj4Ag4Ao6AI+AIOAL7i0DK4xkT1yTk+ChXn+baJ7r7+rFuhhg0spByF4SiCMM87KLI4wrnDV+PB/Gtt94OWTwUVq1cw7nAnDNMyOade0bCti3bQglisq93IIwSovl7N90YznjcE0OFj/Zx0oUgRiGIxRfWKVQI5SwvYSOQrRKSlTaRv9H7WISoCGK4XTyNFfY56dMF8SmitNBTMGK4ioft6Oho2LZtW9i8+eGwbfv2sOmh+8OunTvC9q1bw1ixzPm9eOP25kMv+9HZwwUqRGpqvRrj+/lYWrwzy7BmCP39ebxvu8Jgfx8exilZjPUKmy2CfKwEFc74hNSVXSKL2QCyl1xNKvZSwEl79jWSxa11KqstwUdEs0jonJ2fLJuFizDQmcq66iN1zlaGQN+2Y0/YvGMsHHv8SaFrYCDURvbQ3pV6GZdDnb12sf8uvL2BN3RBxitcdx3yOPFmFnGcXPvmhuLGZFSyaeUeJdqtmiNZrNTKab3y6uPiCDgCTQTuIidS92R0JapvyF+ARmlHGv9R2vgsUoVufmZa5ukVvpHms0mWNFa91rgv22GCfC/1suV+dD06ih4KyRIreuCdhq6bhiFn0oeneVNacWg2HILMYawpUlgPaHn6tpOfUfm76HfQb6UdeGpbmOovpOV9Sc6jc/Yz/tdTvm6KCSJJH7tl8Yx1nU6zH7ZrvdPRW6dYRKT6mkn6zJa9z8TeJtn2QWmaLdgelM12eBE9/6PoW37inr4XKyZIj6D+xAna9qV6rly3+fie2ZfrNKv7Zn+gzGpDZ9I4/jjbzh9pz2aNPx/s7/3Nf3r3q8LzzjktvOWjX/Bw1TMJvM+94BHQhwpve9G54S/e/KJGb6FbxVvQV/CenM4fOQsePwfAEXAEHAFHwBFwBBwBR2CeIaDfiCWkzWyaN1IuU590TF5FOGblUeV0tma9kZRaA0ISD+Mbvvvt8NBDm8PqVSvDkqUrIZN7wuhYOezYuQuSdlcojVUhMfHwHYHchXT8+S9+ETZwZvDyVYeFCp7AZXkDm5dxzcJNE6+a84FhgxF7Ja+0BiHaEIGJB66dQ0ylSN0Goa27qdN5vFse3h4efPDBsGnTw+ER1h/esyfseGR7SiDD/CL4NpvXr7yOhwgpLXK7OFYPg3gN1yG3S+WETRAprk/hFX66r4cw1Ut6IWYL7KUQemBVRcQW8XTeTVjuCmS4zY0dpRKhoME0DzOqVAS3dlLHY5mNUYf3srLkk21qd+1FLbo6MVUv5UUUt6pIa3lgS5P1IXgh73cXe8Ld924Jj2zfA16NMPL174bzzjk1nHXGSaGI13WjTHhq9kp0cDt3WV7WIuNlf0nnMINJEo482Y/mlvIic0xUlj2PkozxsbcNpWOcItY/aqxXOAKOgEJU/68UhotJn5nm9Wa7Os3H5IdkdqLyEjsOfRMqclfybVThjlvlASriGLX9NvpFtM2bWc1NeSO5v2mWkjN3r8+UD1b2ThbiAWUep1rz99BXKjOFqF8UPbz/OxYOcfpvrH95asN3SC+cwh5dVxH2A2k/fcGgVfgJ1pSJHrciiaJUyVwVC5Okl7a0HQyOQJg8gi5P134v6VTX+230mWjfmma27H0m9qb9TUemc49MZ57WPrMF21a75kL5Woy8Bz0hNfYDpM9N8xMlv0tDYaLGfaifK9ftUL5n9gHOhdn1YPxAmBPI8gcSf1aGd/BHkn5p+6fLnvn45Rc87qTGb37si7nPf+fHc2IPbqQjMJcQOO3o1eET77w8nHvm8dHsj5B5L+9F/cHg4gg4Ao6AI+AIOAKOgCPgCCxYBPTpqAg5OL+mWjmtN7JuSnQ0S0Y0CBFRnIQpboRScSzcfPOPwgMbNocVK1aFFXgY5/IFSNTRsFWevVseMQ/dXsjRMt63FQjLvqHesJUzg++7776waBkhqiGMda5x02sWriKvENUiRVXPwbpKdR5xEY/msWKJUNicQczaw8MjRgpvfOAXYXjXrrB9x64wgqfvCERxqVIN+uRbrmcifOVB2wMgWicPWcykeA4rnDUhpgu0DfRAjo7RzgBUPG8v43oLhTDQh/cxffo4q1he1iKnx7Blz+5dRnLbucrYayG7mQ+62HBKDioWmarrIaKYz2V1eDF4mKSYjvN3S1r2viaDxRInF5UWrSeSVioP4JiXd7FUJLX6yEu4xJnL6zaMhdE9HHkK/oN8nHjv5rFQvWFdWDTYH447YlUoQrqXIYf72GsZd2q5b2Gt3TsNkfqQxj29fck67CyRmFLSPtItpY3jEtum2rX1dJhSZa2Y1o0b5AVHwBEQAl9BI2n8UvKPVSVyG7rVcntfeMCE76CRzMsSo/+1t9u4nMa8H/2rtPappG9A/zEtt0vk9SyyLsodZK6PhYOcKqz0R9GIkQjXv0BFoE8k8tiLGKnP/6C7lZkFciU2RNJYxI08aUWMTyQn0BAJY/Vp5zEtUjmKvG7bibyao+izfn3xgB8aE8pFtPxpS2v8gkJLdUeL+rH+7+hb01kvI/0TVO+HdrKYyne1a8jUzZa9z8TeMtucNDude2TSCSZonC3YTmDerK7W/fAB9F9TK59DqmeD7v92oufFG9s17EfdXLluh/I9sx+wLqwhThq3XG/+WPsKf9yeQfXfrlo6dOnnfvd14ZcvPidAHoe7Hmr9fa5lsBcdAUdgSgSG+Gb/e1/57PDuyy9s4BmgjxfuQXV+8TVTDvYOjoAj4Ag4Ao6AI+AIOAKOwDxGIJJwzZRMK3Ec25rc3z7gEclJMX81iMR1d/w0bH54c1i+ZFFYvnyZebgOj4wRCvoRzgjeBSPYFQYgJmvDw5CY9WDBgURmQgLfetttYemq1XgaE4OT8NEjqMJIl/HcHdm6xUIlK9SyaV0exo2wceMm8xiW97BIWwsfjedxt5GxIjohe/mUohv7CoSbjmfwym5xriEHG4zKo7arK88ZvoSNproMwVwcq0Aei2DO430MUTzQB1ms8NOcB8wYhcVW6Gl58ypcs0hSEdoWIhtCWuUG9taIud2bnuWcEKlqSDyKRScntoiZlmh1CWXZ1yIiVtVHBHocJ9K+BrMt8joPOdzNhnVWsvbT36cIhpoIz21I4M2bd4WtO2the4VQ3tQSuTtUaMbpOzy8qxy+efWPwosuOiusXjYEYZ4zPBXSWqR40yAGypubKNyYEu1N0ub9QNM4UT8DfG+tRowfvbfNc46AIzAhAjfSsg0VUfuETK+rMvls9tsUIiG6LG3QA+cr2U4t+Y9RfiN6Zlr/N6QiDfXF/PiwSpvCajJfQ4+MFaT/J5M/FNn/zaKvQdeieswIg19B2xHlL6X+U2gUkWXviYVZkH4ZG0qonrg8scOXUHkX3oe2is4T/pdM5XbyV2bKMTscM6QvQf8K3ZWpU7bV2+lD1L2hpU8snkdG2MrGrExESGf7dCL/+0zyclTvCf1o0/VW+Vo0K8dS+CK6IlvZJj+b9t7pvbXZbtuq6dwjbQdOUTmbsJ3C1FnZ/Dms+h1UXx6RKBLBk1B9MWYzKlmO/iqq96yeGZ2QuXTdDtV7phM4z+s5OnUzziuQ+MPpYTb0Ev6wu4z0r55z9qlr1n3yvY2//coNuQ/+6/+E7Xzr2sURcAT2DQF9a/+K55wd/uj1L2isWb5YfwjU0L9Gf5/3nL+p9g1O7+0IOAKOgCPgCDgCjoAjMM8Q0C/ITaEg3q5JGPOxaswb75fpnMk2h8eMCEr92ysJs1mFOL13/f1hw8bNYTGE8arDDsd7ty/sIkzzxk3bODt3BM6wCnGYI3zzCF662CNSEiJRHrAKC33rD24JN9xwA0QldZCv8gA2FUHK/CJKRVyKsVBOVhTEeipPhQhLuE/rV9DmlEdznMNrIzUPmWReMqpUO13hk/FmTshm8ybG+9k8ivs5z1dkLPNpRhHF5ZK8mzljGRv3ij6nRhp55sd7mbkNqS55+Sr4tf5UYX3mSfZhndXJ5qUhaZcxMq2sc4gVxpr9USdvZ/yrGasdyhKJ7JIHcyP01IuEus5BbEMW6/xlzm+u5wphD3X1vM4u3sW12QkZjzc4c+U5yafBpHDjFq4639MAu0a4/5Fi+OJVt4ZLnnV2WLtyVaiN7Yb4LoWayHUivuKvzCeQnDFdGuUc517DvCGbUeHfToQDlrdrsrrsKJsiWzHhKG9wBBYsAnoz/jd6RQsCIsraSbv6m+goj9yJRA+sX0c1VsEZRAZ+GH0l+h30VlTfSHkc+sto1rP1E5Q/hR5KkVfcb6EiWPREEeH9JfSb6PfQu9BTUXkYy0M2K9r3z7IVhzgvMvfX0E+mdpxMKvs/j4rI2YCKBD0FvQLVXqO8j8xILGTSdeSfnJbPIhW5vAkVbpGMuoq8+skJSvJ6VGt/HL0H1Q/fY1GRzpegKvMTxc6zPp5UcmSSzPir7BdGItD0w3QVqntXRPZ3URGg0ZtchFoZ1Zi1aDuZTXvv9N7a7bdd3XTukXbjpqqbTdhOZetsbNfz/2XoV9ATUwPfRSpdj+rejvVkw4OonuFrVED0Ht0fmUvX7VC9Z/YH1wU1Rj8kXCZAgD+i/oM/EL9O8+/zrd/f/I1Lzy/gddz4yy98J/fRL14bdhMmysURcAQmR0C/8b/kvMeGD7zuueExx9nveKrSL4S/yXvszslHe6sj4Ag4Ao6AI+AIOAKOgCOwwBDgt2X9wmzKi/jUqMb3xbY0nQ46mithN5N088NbwgbODO7vHwjLVq7EC7U3DI8Uw3ZCUj9CmOgy7qw93RCqEK3iWmuiKJlEBCp+vqELz+FKcTiM7BxVxOQmxah1pPqUTKJPgxVbKLE7IT7V3iQlaeiBEK3i3Ws0L40Kq8xCeOPyaRmssWhXRaOWPQrb3JXvCkv76qEHArqfcNM9eBLbmcMMq+ApLC/iUjHxcJZHbx2SO7GKuQWkSsZ2Ws7s1ZJWRyrrcnl9TkffWK9SOka2m602kj7YKMdlndOssNYNgBJudYXkxuM3313AI1qfASaewHXqhuQNvbgXspjzikeLkMeLGNMddvIZw5Zde8JDm3eHkWLNcNSyfZDAec5frgIK9HQYBq/DVi4Owzt3hPUQx9+6/naI4ycQurpHzuEQ79hI3y7sEGlcGhsJtaGhZE9MqG3aVjM5qlwcAUdgZhAQYXBFZmqFArg+U85mf07hQfSoTKXItKnkOjqcg34aFbEoeUKqVmjz8hnq3tqm/lBUiUDcgn4KPSY14GJSaTvZQeUVqLCdbaI9iBR+d2qYfgC8LtW0alzCTzsLMf6JcbV7C39H9jJUD3FJF3okqsf4IDqCjqGvRG9CVSc5N1UrtLw8QFn9RcbLXslFqL5coLlmWr7AAvpB+zm0DxU/IXJNmhXZ8lJUXw6wDxSzjWl+tu29k3trs922VX9H7VT3SNuBU1TONmynMHdWNv8Uq/Rs/g/02RkLj87klb0afUWaRtJ4txr2Q+badTsU75n9gHVhDdHfcC6TIMAfhntQ/aB/DPqfiwf7wgeveF544HPvb/zeqy8KSym7OAKOwKMRkGfxS849M/z44+8K//n+X4mE8Z30fDHvqYtQ5V0cAUfAEXAEHAFHwBFwBBwBRyCDAByhichC/cGuVHWWKp+Wk17TeeVzZQ2SkOzcsTNs2rTJCNfVhJceHBgktHMxbNywMWzbvjM0aiKMkyFdsLXJUH2mLWIXmzSdUl4Wc46wCFCODrZPsUUQ90Lu6tNfqcpwwpZ2MbAOkakw13XWqME216sKNZ2Qo/LMzUMWi0TuI8704GBvWLWiL6xeNRDWrFkc1q5dFo46YkU46siVYfnKpWFgiLDZ2DSKF/FuwmfvGt4TdnJG8S5CX4+MESa7UoZkhUTW5+oCEjXiG3v2piLBx2tCjtMHWyTZtDWvsnXrHQh1PLXLoFABnbrVQ9gCQL5WCnU8gHOl3WEgVw7L8C5esmppaHT3hmKNsNO9i0Kpmg/rN2wP9967MWxY/wje0bIbz+zunvDkc54SXv+GN4QlS5eClT5jT+zq6xtgJZ3pnAsbtuwMP7njrlCE7BfBb2S/ZhDpzjWMZzZrPxIgSCTeF7HsqSPgCMwEAt9k0mJm4pvJD2fKrdlvt1RMhzTWkJ+gZ6N/iK5HJxLNfy76WlSPmtkiV2PImejfoyKQ28lWKj+MihifjYRxtPk9ZJ6K/nesaJOWqPsaqmv2oTbtseoHZF6Mtl5TPcpFTke5nYxI4H9Fkx8UsSVJ9Q2qn6HvRE9DRTDr3oyyiMwLY+EgpF9ijSein0X1wy0rui+1n+ehX882TJCfbXvv5N4m2PK46uneI+MGTbMw27Cdptmzqpu+5PJc9DL0L9Br0J2oCOVPo29E9QUZPd/063OU1jD0sX466Vy7bgf7PTMdDBd0n+bfCgsahX3YPH8U6pt6+mGuH1xheKzU+Iev3WSex+v5Q83FEVjoCPTxCdMvX3x2+O2XXxBOOnJVhONeMh9APwtZPJv+KIn2eeoIOAKOwJxH4DPh2HV4Pp0+5zfiG3AE9hEBKJIzXhPW+5fR9hG3WdR9Hbb4swsQ9Me5ODyOtg0c1Qv5irtQL8SstB/l+8pEYebMXshE2tVPxK3G8Dt2cklJ7Z/CIOP5qnDJebQb8rGbQ20rELQbH94GiRjCsuV4GPcPQVg2wgPrN4bde4qhTL7BuAppBQJSZ+7C6/Jxbk3/zftXhqo+x7xVGovFShjFby7+kh9JYhlktmGnrFNeBKuFccZbWGSm5lF5sB8PWfp05QmnLBKa+m5tTiJvZ5Gw8tzFw1lSLyd1IkOlskMhoRlm6+SZX5hoboWEVohrWSEb9qaJPVaTNNiYpIccATVXYkNM29fRp8DF0Uf0OpsZojqHzXnCe3eDiojjAnvt56IVUJ1hPFZYTEhvPI+xa8vWHeHhraN8tlAPYwwXs7RmxdJw8QteGF586UvC059+bvjOVd8I/+vd7w0PbXgIL+gEs2OOPiLs2rY5dEFK97F4oZALFzz59HDqiUeGWnFbqBJWvFYlTDhblzfz8sPW4hWewwubc52LeIiXdaZ0mTOcUWxu4C5tOAlrbcaIb8vZdYPrt/OrudxhGKpjN4ZGVVn1Ot9a/XSdBYfLIUHgDFb1n4mHBPpZt+hiLNL9oJ+xo+i96D3oNnQuyGEYKRL5RFQk8gOoiJAyOpdkBcYegx6NHo7Ke/Bh9GZ0Dzpd0Q8kYbEaFZkkEnkiT8SVtJ2EnoDykzHcgeq5MIbORtG11r0qe7Wv76H7S5jNtr13cm/AMqnsyz0y6UQTNM42bCcwc05Xb8T6tekO3kL68Q7sZq5dt4P5nukAvPNzCv4MddkXBPhj8Uf0fz5/tD6F9L1D/b0vfOfLnhnefun5jf+87tYc5x6H627X72EujsDCQuDo1UvDm17wtPDmS57WWLlkUL+oSH6K/jkqsjj55EW1Lo6AI+AIOAKOgCPgCDgCjoAjcPAQgPyUR+8jj+z4/+y9CZylR1nvX2ft0+usmWQm62SyQVaCgBjCjgG5CsrmAlxEuIrihoB6rwuK+vd+uIrXv1xF/wIiKuJVUEBEtqAQ9iSEJJB1ZpJJJjOZtXt6Ofv/+3vqrTNvn3T39Ex6Mj09T808p+qtqrfeqt/7ds+c8z3PUxaueXR0lQFl1e3Zsy9MTk4b5CsbiS7hpdsysCwf3ALQVRBQ+wv3SCCws1DshBrhkMtAzAr9BRwFaBUqWqBUKQJswVqVgOGEki4QP9n2HeZa6qu2SrXO+FwN4KhxusDMBrRzZrrOtbkwFsGxDWuEWpGNBIWVdF1NzvYMpirm8bqahc2f+nyu82xsFR6RCO/M+PO3509gfoKuDC5YrL2Gtf5SAWDP8VCNMNoDFfPAlmewgltPdgbCJKGodz60N+zeO2X7FsNbw5kb1ofnXPf88PJXvCJcc+0zw+DoMPC3Gfbt3WNQN+rYZf/mgTA8iocyYacnD8zYFwi6gP6vffOuMDo2HM5cR1DqDuGs2d9Yeha4eS32d4bI5yfuZVfAFVjZCggoyptUdjKmXUxaJs/okzlpz06ZPk9+NEn/Ct+V2ZHG0RcDZCfLvU/3+rNHWtgi2pfb2pdybUda/tE8I0caa6725abtXHNcLnVVJvJM7E5MX4Tgf7hHTEP0OCPXa3uu/GiKJ9t9eyx/Zh6Nriv6XIfGx3h7ebOmbz29mDeG+sbeW9nz+Idf8awnDGDhtm07w5/+yw3hbz7z9XCAfaE8uQIrVQF9UHPdd10cfur7rwn/5bsv7fKhjT61kd2AvQP7Z35W9J8WT66AK+AKuAKugCvgCrgCroArsEgFBDctkauoY8uz6qPOGGD80Hio1/FKrdXwSsVlmXSQcM4HDowDb7WTcAFYzJ65ANA24aPtf/ac18R9VKgx/Ucfh1+SACmhpDlPXsG1AT4bowMY10CugiTH/oBbwK68nu1YkJfz23i1NusCw4LBgs04jjGwAGcMXx3XjIOuRZaOc2F8XUNDYG3OZXtgu16Z+Njy4tU+wkr4Hfc+nlONILLhWk5OufVjDIPDmhSJYS0pTHZK1p4dp3I+V79CAyc+jcU4xXIZT/AKey4DitkrusJxAY3q6FonlPYk9+C2vQ+EqclmmAAIy3f6rPXrwhVXXRl+57d/N2zesiWs3nCaQWhkxHlZIbybNm+D2Gg4tnpNWL16bZiZmgzj+/EqZuLyzJ5A029+++6w9knn4uWs1cT9lRmFkNczFu46rVHztgfLCv7iCrgCroAr4Aq4Aq6AK7BECshx6h+xEYx4LLZn+H+SL5R+lMb03zT+YxyO1H+hsbzNFXhUCjg0flTy8ZMc92V9DW/e3spQr8d+6tLzNp71Jz/3kvAHb3hR98NfuKXwvk9+NXzqG3favkaP8nJ+uiuwLBS45OwNFoL6Vc/7rnAm+4llSf8IfhD7E34uvpEqPXcFXAFXwBVwBVwBV8AVcAVcgcUrkHClOKXcEpSrznKVs+PFj0joYGDl1PQ0YaoVIrmKF2onHJpkD+CDEwY6BYmhu3Yd7TWsT6zkdKxquopd2nXTNTUHhYWenJykL0AYYKnvjwqmKrWzMNI6TnVqEhC2wTVA9rEYPRiB4yzJZ1imF5ixJW1wox69tXO9kvYLBhTb+DQ0CVGdXd46Hx4xricbalZmLerYOzGblF2LVuoN1GZnJW9qHarN9goGVI8W2oTaZr4A4jLe1BWgfKGCldGa8hRzOzBeD3v2s/8yXyx/aIpzWdEZ7Cn9PU+/Nrz8h14SXvrSl9n5WiiRrQ2As0xCWLdsD2rBe0vcEN3DoaFBbFgzMUQvz+wycPzB3fvD/TuHw1mnr+G+MKlCi3vJXsvA6urwMOtBpziSv7oCroAr4Aq4Aq6AK+AKLL0C+t+ltuB5CqZvav4kthAEPov238BS+iKFyXTguSvwWCvg0HiJFOcNo/bX+F3eUP5P8h/AfmKgUr7uh591dQkLD+45GD70+ZvD319/U/jyt7cv0VV9GFfgsVPgrPWrwsufeVX44WdeHZ50yTn5C2tflPdgf8XPgUJeeHIFXAFXwBVwBVwBV8AVcAVcgWNVQNCQc814kfNssgRNe+30WwgAyqtWAHdqmghYnFwBNhaBw9PsazsxOWUQt2TAFy9fBhIoxinWrq09jJXk7YuTrNWpj5LYrxFcHbCPr+aTRaRWTegyTuKwKVe9ldVZ5ZhZodOtEMo5VshL2No4bhG0SF6/Ok91ArWaieYhoK3w2tlpvfbYJ/W1k0w/G53OatcfJZ0rS8cqKQluq5+iXsecgiW8m7mmQLpCY1cJz11j8+nVZUJEI16xgr6VGnG4B0O7WA0TM232K94XHtozDixuhjoX0/U2nbs5vPqVrwrXXXddePq1T7WRm+zTbFfRFDCcsQ1ETwKZH3hol4WaVkc1D+HZPTg4GBoNfW9X81TY71Zo8qAINN+9dVdYv2Y0DKiefaK73KMGoaqH9CChp0B4HoZr3JT0zHhyBVwBV8AVcAVcAVfAFXhUCvwbZwsaK/0Ypv+0/Q62FUtJbO7l2Nuxs7NKBaH5lazsmStwQhRwaLzEsvOGUj/Y/yTjTdgm8tdgr960ftXFv/CSZwTZ9l37woeuvzl8+Iu3hC/fvt3fkiGQp+WpwLkb1oQXXXNZeNnTrwpPu/x8fXqgzyiUDmD/gL2HZ16h2j25Aq6AK+AKuAKugCvgCrgCrsCjUCD/n229SYTr9WCxIGUeHOffRM46b47rNxoN84gt4wWrvYTNQ3gqAmOBTxyG7X/5gqMJGNqY+p8/19U8dGycmFxlvSg3hqscU7uS1akdUznlalNZSdeKZ8U6UGfWppOAr8pkAOuOgePYPY6lczOi3RuFgiV5BnMe4/euRT1VsTVXr4r+vrEXzBdSnqCqcmmm1MJjWB7OlUqZ0NNV8/hV34Fyke2CB0KnWAkzhPjev/9QODjdCQ8fmAx7JwG2un94+5pGANzLrrgivOnNbwrrVo+FpubLzS1Xor+1wLGuKe9tiXD/jvvD1NS0eYqrSqG2V42NhiG+ANDEg9zuGt7DXQsBbr7TYe94I+zki+tn88VfBgFyC3RzoSRKpoiuk9ap9fVSr1+vpldIWqrCuuUrer284Aq4Aq6AK+AKuAKuwCmtgEDwNdhzMxVeS/7jmBwPH8D0nzR5GMc9YyiQ9L+qt2Bf1YEnV+BEKeDQ+DgqzxvVBxn+92S8EbuaXLHpX3Hu6WvPessrnh1kD+0bDx/90m3hn2+4NXz2prvCdEMh7z25AidGAX38ctUFZ4YfeOpl4cXXXG7l3Ey0QfdHsb/DPsHzrW9IeXIFXAFXwBVwBVwBV8AVcAVcgSVSQJ8UzTIOEixOzC+1W0f9B37eFGFns9kyqKvw0QKE0+xtq/DUApBCjPjt2gjyoO1dnJo8N9QOxRlbph4ImcWLNrib9TUOrJGYoHkap4kaBaVec8UKnJ+a8tPXPsUCxZqTZlTgQPM1KMrg2en00dlzpwRA07jK873z9Rohjh/Hyvft2DXyQFXaBYPEtVrV9ipWiGjBaa27WRm2/YQnJqfDvvGZsHeiHiZnCJnN0B06FIHKuCCjO9fEHnzgvjDOftKCv5qDvL1bgOkibt7yoJZkNi73ZNv27YTebocZ9j/WHAeB1etPW4NndydMTx2KnQHRUkjzgUAbiL7v/j1h/ehwqJV1lvaUBixzLf2JKeUc2Xqz6nky9U5npHyerl7tCrgCroAr4Aq4Aq7Aqa6A/uv8Cuxd2Mswvgpp/5U7nVzWn3ZR8RpMHsqeXIETqoBD48dIft683cilbuRNmr4tom+Z/KDsjLVjm1//wqcGWb3Z6l5/892FT3z12+HfvvbtcMeOh+niyRU4vgqsHR0Kz3viReEFT3pcuO5JlwSeyfwFJzj4V+zD2Md5jvlUwpMr4Aq4Aq6AK+AKuAKugCvgChxPBQzQ8dIPjAVTxffMFpiAwVAAYrPZNFAoaCmg2OC4hRlcBAG2s/1tBS3Nm1lYUNzRJnD4OuBMPFnpo2tzXQWQNpjLUYK4Bjo1P80r66eijaVcA1vj4XKcB9XsCaw2gWJL5Bpfx0Ke6qdyCmFdFGGeI2ndSmlcy3NANI4jABz7JZ1652T103V5FAsSF3sexdoPenBwiH2h8dgmFLVCWNfZJ7jBXsN37mmGiUMT4SDAWFsP6z4pWehvtG3g6atQ2gV5UXM8cWBf6LTYY5qyFt5U/G+K4GXLbYtprv/gjofCtvvuCzsJT63rtuk3OFgLa1evtnOn2Jcan+hMI50qzQSpS+HgZD3s3nOAvY1X47Ucvbe13rzZg2QzZRY5nbKqXtZrQrZUVi4VTUl76XX3givgCrgCroAr4Aq4Aq5ACPsQ4UewX8N+BrsUOw87G9Nn7HI4vBP7IPYxrIF5cgVOuAIOjR/jW8AbOL2d+kJmv8Qbs6sovxh7IXsgP1HQTiamfN+u/eGzN98VPofJC3kH4aU8uQKPVoFhvhl/7eXnh2dddUF49lUXhidedDaf//DJwuG0g6JA8b9gn6bJPYoPa+MlV8AVcAVcAVfAFXAFXAFX4PgpoHeLkSNGGMehwTnqe/kcVxfwm/1fesAlnqvRo1jAVeGfgcStFh7G0ZNXF7A3AeTmWau3BPFv1hAvZNOBZXbaQpI5SGiuwdpTOAPIatT55HTtzSc/r7nKquuyy5NdJ1ubALR5xKpSiUE1vHUiS/BYVUrWZgVKGs/0iJpZB5uYOvZ6Zv3idWwOWZvmPzY2YGC4AqitKgw1exULGis0tPYRHp+qsyf0dDh0qI7ndjdsaw7YNQN7M0dKruuwO3NG/QWRaWHendAAKiu8ddk2gdbuVgBq4HHL9lHWeUW0boUK1Pr+B3eEAwf3hzvuvJv6AnMqhTNO3xA2rF8XDuzbA3huAY91hsaO8Ni0kzc32u3Ztz9sOi2GqG7rWwFSlVz69LTIa5Iv09uSpqSkU2LJTlU5DZPqs2bPXAFXwBVwBVwBV8AVcAUOK3APxTcdPvSSK7C8FXBofILvD29Ob2YKsrfxxm0D+fOx78Oee87pa9a95ronB5nS3Q88HP7jlnvDF2/Dbt3qnsimir8cSYH1Y8Phey49L1xz2eZw7WVbwpMuOadb1qcSh5Oipn0J+wT2rzyT3zrc5CVXwBVwBVwBV8AVcAVcAVfAFXgsFTBulxFUg3Jc3ABdlvfI3TyTEl4Vxu0IDtLH/uPPQDH89GH4CkdWJOOY6NgW4CQJNlqJdl1f1UXrF0c2GKqOopPUq282XdXGc6mM4DYBXJtFry521JnqjwdxKZatIr1kA2sO8Wy7nJhwgNOSBMO1UkqWE0K7A3ymrWj7+8pvV33k8QvctsUwH12POtXDgaP3NOeoXK2W2c+4EtasWct+ytpjOIaNnmIbqcZMnRDRbTyKD4W9+xt4D2vfYzx4DSaXKUcxOl29vdK+xwOh2ZgB6hbDhnXrwqHxA3h5t0OZC59//nlh3br11k8sV1OrlqscJ91YI/sVP/jAA2HPw7vDgf37AMYF5lgMmzdvCSOjY+E737499s/WIiG0JiVldAVot8OhqRmgdxxXwFiK2wVV0oVzuR3M86KeJiGFjIXH46xebXG0eQbwalfAFXAFXAFXwBVwBVwBV8AVWPYKODReRreIN7raCP39Mt686X2evJCfjT0He/oFZ542jIXXvuApHIbw8IFD4Ybbtoavfue+8NU77gtfxw5MattZT6eqAhU+Gbh886bw5EvOMXvq488Ll5zziG0S9F5e4dI/i30G+0+evSlyT66AK+AKuAKugCvgCrgCroArsAwU0H/YkyXGZ3yPylSvfL4kGCjvYWOIkEQDyBzbObkTBRllRYCpvHcVFlnNejeKQ2zPygDRHijURW0yEQjboap0MU628TQISVVKKbcDO9e62mFqT32yU2NXGKfGE+rUIOqjcnrXK5CqPyUuboC4WLX5y8M3rUdzLZc4S3+pT+uVh+4gn4hUIaoDtYFQGyAHGhcRY6JdJux0K0xO18PBiSn2KW6EQ7gI1wkxXawOhFaJ6+D1K9CucNOBUNNakZYmSCztO62G7X385re8JbzgBS8I7373u8Pf/e3f0qcbnn7tc8LIyBrO0foQn8nV2bO4QxjrSqVi3uC333FHuOP2W8MtN+mtG18A6BRCbXgobNx0ZpgCYN+zbbtpoXXmNdP6FOBM91Lhx/ePT4TT1uFtTIPmJbjdZQ9kzSP90TzmShq339JzIP6sMsP0+syayFwDep0r4Aq4Aq6AK+AKuAKugCvgCixrBRwaL9PbA8TTe7ObMvsD3tDpXl2NPQ27FrvmtNUjp73omsuDLEvdO+/fXfj6nfeHm+5+INx8D0a+Z3wytXu+ghSoEdLsss0bw1VbzgxXXbApPPHCs8MTLjyrS5hzfZaSTwov/TXsP7EvYDfwfB0g9+QKuAKugCvgCrgCroAr4Aq4AstZgRyQ0xtEs1TQMWVBwl7KKgSJlfh/f4SD1EeYSJ0gJ0RRp5nxonefZqlOJ5OsXdcz4Kjry1MXw0U5wkKdGOdhJ/CienisJYHJNAfl86W4R3J2DlnqaZfOrq/F23wYZ0RRoKnodAnvzFJ1zQLgWNDX6rUYwj0LigpsNuhW5m1SbahsexRrf2DYru0PXOZ9lQIxNZqNsHffVJgBFB9qFcPUdDMcmgbmQs+lZoLiM+xjHBOzAfiWyoSyrlZtf2OtV2GglSttPO208OpXvSpcdNFFYRIP5b9+//vDavYjftnLXmrtSR/BbO2TLACtENjT09Phy1/+snk1b2dPY4WlVrjxiy68MAwCuB/AA3n84MFMD6ahqSRjZGmg26ykfZe1j3WRMVgF8BljetLGCtZLdXHOdqie+WM1YdLSDEEilOY407+/exrHc1fAFXAFXAFXwBVwBVwBV8AVOHkUcGh8ktwr3mDzNjd8NbM/1LR5E3cxmWJXy/VY+ZUXnb2hioUffc4TOYzpgT0HDB7fuu2hcPv2h8Jt5N++b1eY4pvMnpa/Atpz7PyN68Kl550RLj0XI79yyyZ5EHdL9rX2WWvQRwP3YOlZ+QrlG3l+0icbszr7gSvgCrgCroAr4Aq4Aq6AK+AKLF8FjNXxYpAvyzVb1duL/vevgwwQqlqdZwO/CIitLXaIgFFlztOpeeAo4Ks6yxlbZUHTlDS2AmMJOio8c681N48EEDWu+idwnMbozztGZHWluZO8iWOKpSrvZQVF7euy5HrPpIqZQ9QzJZiwmWCr3jKVB4bwKK7iUVwLFXJbMPOfqU+Hyf2TwFn2J56sB7YrFmsOTdYiT2utW6ZrlbhYCS/kdavXhqc9/ZmA4IvDRz/2sXDLN28OTbsJcYZ57ffs2RP+7M/+LDzhCU8In/rUp0KN6//iL/5iuOCCC3r3KPU3MAyBbQJ4r7/++rBz586wffv2oDH0JYDhkeFw0cUXhWarGe66684wMTHRu49Mz+Zo94wXfTFAOijVIeYt9j4eGqj19jO2a2Y3KV0/9p77VbdWsFinKDdgjDACxgkkq0+yuUfxWlfAFXAFXAFXwBVwBVwBV8AVWO4KODRe7ndogfnxxvsOmmV/rW682RsguxKTR7JCWz8Bu/zM9asHsfDC776Uw5jo2926c2/hjh0Phzt37MaUx/KOhw/yxk9v9zw9lgqsHR0KF511mtmFhCFX+WK+ACCraROqudOdVGtP7OSV/nWei71zd/VaV8AVcAVcAVfAFXAFXAFXwBU4GRQw+MaLQTomnGBdD9CpbZ6FqF4Q0dpz7+tU17OsUzpWZ5VjoqR2nQuMtSKtMc/gYXYsX15zwdXJuWvligY2jwQmtR3wfMmG5kV5hNAh2LsjztF50kTQVnMZHasS3rlkexNXCTctUFwqlcN0p2rgdQZ4un9ywsI7N4Cz+8dnQhMC2mIcQWKDrlxI09G1lFTf0jIhyc985veEX//1t4VnPPMZ5hH8nOc8K7zuJ14f7t2q7+1yPqQ2rXVgYCDMsC/xO9/5TmvTy9vf/vbwq7/6q6GN17BArvooCaor6fyvfvWr4Rtf/zpez3vD9Z/7nPXTmI+75JIwNDgYHtixI9x7zz2sh1DWOA/r1LwJcMtS6iBSm77lUsXe59vey2ilMW2uuZulLwLk72M6pFbFCIilVaa74LH0tyHUwZMr4Aq4Aq6AK+AKuAKugCvgCpzUCjg0Pqlv3+zJ80aT70X3PEytkTeBikElj+TLMhM5voy+W87ftL6EhRc8+XFUHU7au2n7rv3h3p17w9aHsJ37wrZd+8L9D+8P9+8+EHbuG+dNor8jPKzY4krrxobCWcD7szesDuduWBs2b8TOWGdexJvPWBtWjQwuNNBDNN6G3ZrLv8V9PLTQSd7mCrgCroAr4Aq4Aq6AK+AKuAInoQICcUzbjBeDxRmoM0iX2rJcK0yc0ECgKkh2fq8hwkWrpE7V1qRydmAeu9lxApnWR4NRMI9giKSFOFYFYaDldWyNClmtEoMJTKbzBBRtfI0xTyra+0ud/ciUxsm34E8MBFW4aYxwzRWszBdtq4OCw0DgdgsY3A4TeCS3uo1w8OA4IapbYZq9gGcaCjktyByBsM0ZWtxlkoKhMQx1dlXqwMD2R9cvVwbC7od3h0OEmh4bGwvPeMbTwz/83w+FN7/5zeELX/iCeQmneQoYp7R58+bwxje+MbzhDW+IoaZx0RUgVuho28OYcqPRCDfddFP4/Oc/b3Vf/OIXmfdBPKZLhN0eDBs2bAj79u0Lt99+G+GrpwwMa5YJEqeytC5yXwTArY1cz0SJcQSPu1xbZqQ3A8b98DjNu5cjkrrqNgkUSycDx+Sznkfa7CHonegFV8AVcAVcAVfAFXAFXAFXwBU4mRRwaHwy3a1jmCtv2PXF6Nsz+1AagjeF+krzhdhFmKCyctmF1Ur5tAvxcpXNlXgT3n1w78HCjocPhAf2HAwP7R8PD+2bMNu1n5zj3fsPhb3spXxopjHXECsFaq62AABAAElEQVSmrsob73Vjw2H9quFwxprRcDp2xtoxjJzypvWrDBSfddqqMDhQPdK6p+igr6jLe1ge5MqtzH3cR9mTK+AKuAKugCvgCrgCroAr4AqsYAXE3DJc2WN64nsG6mhUWZBOZnAud4LAXwK9SSIBRME+G1MvlAWGhXfVpv4GGFXOTlK7YKPaBR7bKlNR1LU5z8ajTnBVWwoLIPc2RNYl6KDzU1J/zVtJ+wprXNXlU0kLzJKVDh+yFy+exZwnuKq9iBXGeYxQzRqoUGCG2WQbFHfvmSJ8c4uw04LDdUIzdzmOemnIZJqOrCvPYJuQLs7EOLa8pJ5xoh3qisBWabB127bwpS99ydb4/Odfx/7Ea8LVV18dPvrRj4Z3vetd4Rvf+Ea4++67w+7duw0KX3rppWHjxo3h53/+58MVV1yhixhYFjDWehR2WuW9e/fauV/5ylfMO/nbt98e7sGbeGhoiDDU42HLlvPpVwgPPvhAeJD9jNmmKJSZKpjb9LR7pWMtwWYOJKZ/AXhsy0NwRRPTvbE/dmx30+psYnO8zNKLA3sWEa73PFLHUD1wPMcQXuUKuAKugCvgCrgCroAr4Aq4AieRAg6NT6KbtZRT5cMBeSXLa1U2K/FGcoSKzdj5Wa7yudjZ2Fm8WT3trNNWB9mRkvZPEjyONhX2H5oKBydnsOnD+aEZ4HKdEGHNMAlknlSZb4SrPNNohkazHeq809c3w1XmPekxp0qpGIDiYQAT8FU+VKuEYb6ZPkT4suFa1crDfEN9bKgWVg0nG7TyaryBFUZ6/aoRYPFQGKXPUSR5Bd+P7cDuw7Zmdq9y7skuck+ugCvgCrgCroAr4Aq4Aq6AK3AKK6D3O8kSIM7DOtXNC+rUME+CJxpA1MkCiXbMlSwHMPJWybxHBZGtnQaqadcfeacygCbCJQRry1iLSgPYWZPajCEPCFiq++H5aGr6TrGNnc0B9pkdx2sRQdk8iONexEXCTY/avEp4FRehxwa5yae6Q7w35D3j1HSYmDwEJJZHcTdMkmuaKaWrK/yWko5loFYd8qoWLZSJAKML2gOZCXbw+lXHbruJFzPv+YDGrXqDsND3hhtvujkMEiZ6YvxAeNrTrmV/4i1heHg4vPWtb7UxdxA+OnoIl8O5555r+xirIQFiweKU5Gl88803hxtuuMG8iNXnphtvDLfedqvNQ97HZ555Zjhz06bwALD43rvvIrR1K5QUl5oJ2v3R9GeZ6iMwVn3vywTo08H72rzEdTM5VptSyu0gO44tOoimWylYrEcgmR1n7dYv607myRVwBVwBV8AVcAVcAVfAFXAFTkIFHBqfhDfteE+ZN+KCm9/K7BGX4w2lvJTPxASRN2Jn9NnpHK/H1g1Uy4PytpUtVWq2eMPOm119AKFvS+dzfWvavhnPm/6Y68MPfVNesDh+yLBU88jGkSe39hCW7cYURjpvOzl+ALsfXQ+Se3IFXAFXwBVwBVwBV8AVcAVcAVfgyAoA48T0erCO8qyQwGpnFPWBDcYXwUIqYojleAm1qZ/1ycrWQoUArgHH9D5KYY0NOKqest5LaQKYMKWOW/Y+DFjc7ESgC28Vg+wwlnIO+RJwBqPtQr2pxfHEZ+Upi6usQi/L03aMd5glqHUZK+FNXMFUX8C096/CSk9NTRDCuYmnbgj72mXTogXM1d6+CoatNSoxhV7ZKnhJa1dbTKphIqy1xL7Hg3j0DgJ+h7ByucKeyXHNOx96MIzvO4DGXIFTdK0b8Sbmfa69v/z3f/8k4aLPCldc+QQLHz06OhrOOusss3SllGs9grPyKh4fHzcIfNddd4WdO3daaGqB8ltuuSXcdttteBez7/LkJJ7MqwwYP0Sfbdu2hnargTYlmwfDoY/u02xL99TWzIuU0ZrMM5x1aC2ax2FQnJRLM52dq1WPgJ4z5cnLOP8sJs3V15Mr4AqcFAro87wN2Uz1GaCi3eWTPufT535KyekkHh3761Wcmr7Ds42yPkdbTul4rHk5rc/ncuIVONLP3Ymfoc/AFXAFXAEUcGjsj8FRKwD81H8Y781swfN5IzpEh3WYQWRy0eO5jPhiQab++bIAtUyxnc14k1zQG+VjTAQns//w8tXxXq6w0LLJnOl4HBPo7TeFitZ/bveoDT38vTFCeHIFXAFXwBVwBVwBV8AVcAVcgSVQgHcXeoNhkI9coE6enWkfWYN1qY86Gh08fF3en8QDgcGsWlWCfgYURRk5UCbQqzrBR8HmeKo6xhMVwNhS1k/n4fRLX4ycQFCxTCe9RSvwCYOuL8Qs/2SDzgKbXEwmCGohpemvfmmune4MHrSdMAMgbrEXMN8RZt0dQPGMrZ1qYHXURfOZxFfYZqZ5ctH0ReFmC6I8R4rrYCKkqIpOVAhnvlwMoK6xH/LIYC2MDA+G2sAAUaVWGcQ+e9MZ4d6t94YHdtwPsG2hU8X2E/7iF29gvaVw1VVXhT1794W779ka1q1bFzbhEbx27VoLK61Q2vIqnp6eNvAt7+NJQPCuXbssfLW8jAWSlbZu3WrA+MD+/Ra+ugUZV2jq9evXh+33bcer+WBoEm5bkbJ037R45Y8waZ3qs7KeIwF53TvzMtbDRDmZwWO1aVDVp5S6xMs98jmkPcFknerJFXAFTioF3sxsfy6b8Q3k1/TN/sc5/r2s7h7yC/raj+Xws5y0JjvxdeR/eSyDPIpzBMnPxr4xzxjHY83zXMqrT1EFjvRzd4rK4st2BVyB5aaAQ+PldkdW2Hz4ECAB2fuXamm8qVU8Lz27eofdb7wlti+X8054Vs7HDqHBfPztLEJ4cgVcAVfAFXAFXAFXwBVwBVyB5a2A+J0BY3Lz7uQdziwvT+r15iaZrYaTEozVHrzWxosiNInu6t2Q3kBpH19xREu8gypAjw0cc06pUghlIG2TzYwHuGaTtmKnCMzsAD8V9QkEq3dbgr7FstCwnVPCc1jesgKxXX3fV+MyqDyXrS+5vIYVXrkNFU6mkMx7pwWKu4ytSFKH16Q3d0rKbX52RDvr0dpUr1Kny2K6Cjqt2lhvheyFnYNzteoTzfYE7vB94lY5dOoToVlijGYlHGzO4HXMdkQjo+HKyx4X1q9dHb75zW+yR3IzDACVdf1/++S/A5S3hSewp/FZhJAWEJbnsECwdFBSuQkA1rHWqbDWKU1NTYU9e/bYOQo9raRw1IcIt61rDA0Nhv379oX9+/cZCNZa5emslehL1FXtD82m0gaJqUywWPe1yFvhmKte3tvUcH15GQvGywwT67k4QlIXmZ7FWV9c4D4ZNOZ8jWJDqeDJFXAFXIHlpYB+bb4B+x3st7D5oDFNnlwBV8AVcAVcAVfAobE/AyedAnwIoq+Pz/0V8pNuNT5hV8AVcAVcAVfAFXAFXAFXwBVwBQ4rIO4mQKjUA3YAujmBndE6OqYT7Kz4kuCxoG0XcJjAn8CvaF/BwG8XEG3YNV5LIFDNMsrJw3eA7+wWgL1ERwbuAi1houUqXrq1Siizx3Cap8Fc+k1ON7gE4zK/BHg11Znpuo2rsYUtBR2VJjALqa0DeSyTaf4dUfIs9ZZIoavJc4LGFrHUCtQuXG3nUk79lePTm40Sx8twOjAXQM71SoxXAqSWOngTc812k/7tSjg0vj9UqwNh44b1Yc/G08PW7TtCAw/hCh6/wyMj4e677w4PP/xw2LhxY7jwggvD2nVrw6pVqwwWCxDPAMOrA9UeIN+2bZvtd6zw1Pv27cfr+CFbg/pqXIHnNG8LUT01SShsvjMtyCtgbI1aYxZGXGumLpm8wK3Mi74vLUfmgYEy4anLdr7AcXZTonZSThr2JdXkrQeMqdQtsWeRXPWyOYboG9EPXQFXwBU4YQp8iis/+4Rd3S/sCrgCroAr4AqcZAo4ND7JbphP1xVwBVwBV8AVcAVcAVfAFXAFXAFXYGUrAIfrQTsDdlQI1ilEcw/YZfyPLCJRnZSIIxRPEFaexfIqFjgu4u1aLBHKGStk1qGTvHvr8iLu4A0LHO4S7rmDsRNuaGf5IOC0UKhyXtXGGB4ZtFDOn/7kJ8ODD+y2OWkPo2aT87qEpi61zLNVY2ta+amlKaacZsYEEM/qJRgZwWcCkim3RdqBRuVcwKhAaQkw3gSIa9zYYs3WXUBYKdZHsKzj5kzDYLg+GGkxSAeCrHm0uvUwM1U0z2CD77SvJ/z0jgceZI1tQkU3AdrtsGp0hLDVzXDf/feFrdu24pk8ElavWWOQeIT9kbX6DjewTsjtKt7D5mkNia9UquZVPIw3s0CuAHETL+NJILHCY09MjMd7xn1TqGqcikMVjSq4DxcA3PIkLjCuHJpxIg4V5q68WIwwucyxAXTOq7FnswF4rtvFG7vL9SJsP6xSvEtSBI0OC22C6VDPoHhz/xcX1NbXPQ7ir66AK3CyK3AbC/hAtojdJ/liLljk/FfSmhe5ZO/mCrgCroAr4Ao8UgGHxo/UxGtcAVfAFXAFXAFXwBVwBVwBV8AVcAVcgROngGAcV+8BOw4M2OHlK3Bs8Jg6wbwE7uCL8yaBwPwfQeRKuRIqA7UwUBvGY3g41IZGzQZHVoXBYSzLa8NjHI+FAdrVV1YZGMTTeCAcmDoYPvIPHw8T9XaYAVQ2ywPMYQiAewj4DDgGcBpUFLSloCnCNXtsuzdhmuP0mSXtSvKmnRWSWpVqs7Wk/tn6AaZtrsEw1iV1TZJUsj2Q07GFzLYrMA6hrbuE4u5gTTypbaoso0JI7ukG4bjZb3l8YjJMTU4DYgvAWzyrdRU6TuIxrDDg1RF0QQ8B54PsP6y9jLW/svYSjvs5l7lnLcJEl0IFz+G2LkTv8fFD7Ns8aR7YbcFcTHsg615qLIa2coX16aoFvI0Fh2UlvhEwQIcK88HxmbwLPI71YH/aAc24hA8S5lpzFTBWeGsLTW3H0lrryARPucTLUiZ3BMYc9KCxypg9f/S1fukkz10BV2AlKPAvLEJ2KqVTcc2n0v31tboCroAr4AosUgGHxosUyru5Aq6AK+AKuAKugCvgCrgCroAr4Aq4Ao+lAgbjBOegoQJ2PWCcHfegMZNSX0vAP3mWpiQwqFDFBo4FJg3kAlgFELW3MHCz3W7iBduI1qiHVhXLcgHO1swEoBJP3mIldMvV0CEkdbHYCldfeUW448abwje/dT/zYwZ46DbZ51hjFrTvLl6zop5d7YWs/YwxgV0lzVEQVu3tTm73IVXpj9YQm+PiKPcWqbZMHAFcaZMaezpYTezXUMzreII6Zl2znog6026EKaZQmQLsAmHLQN9SedKur5cGWhyaOISXcX50CK2SQP5UnTDXbUJZs5vzAOsSGeev9jRutVSWl6/CcTNR8pmZaaByy0JS1/Ewlj6IYUDZTqSPcgPnlDibdtYiKAwNLpjRkB1rP2cleV2XuLbkETQeHqwxpry/5fEcobSur2ONH3M7dfaLppyZbqsAcf/zp/re80dZU/TkCrgCroAr4Aq4Aq6AK+AKuAIntwIOjU/u++ezdwVcAVfAFXAFXAFXwBVwBVwBV8AVWGEKiL/B/AzEJTiXgLH4oix5feIQa4AvnpATQnCQQSxEtQigyLPtbUy9AURBY0CuTOAYEyhulGdCqTEQShWsHvMyILVFexG4XAAud3F1bbfL4fLHPz7ceeXjw4Fde8PW3VO2J3CpMxFmoJkdAGtT+/Ba/Gg8bssAzAH5yzJ/QKoBa80Jg0HbYrVWLVows2sHVqHKLJkqlFO94LLCYQszC5oiRn+CoBbMA5o5UC4BXwvooLJGkRYCqm3mOk3o6dAAHEtgksC3vIZBsKE6iId1bcjmZgCWOaYcNGz3QPtFdzoNPJZbdr7mY2MD521Nug/Y9DRAOks2c82RphZhryliAs+y6KlcsrDg2n8Zb+KKvIs75lVcLjAu17aQ1Zk0YstV9BwZGSDUtfac1hxiWGotWOu1yWbXR+k0lThHjlQjo6eBYZ1i3u3kvecuG+bw2XT25Aq4AidCgTO4qH6L7semTsQEctfUPDSfnZh+hSxlWk7r7F/X8Vq3/gHSuhUiXLtAPBbpNC6ib3IdWMKLHY97RxiNsAGTNtOLmOs6+ug+LVW4dTEVXf8hbKmfdYZ8RNK/8huxXVj8T8ojuhxVxfG4z0c1Ae/sCrgCy1cBh8bL9974zFwBV8AVcAVcAVfAFXAFXAFXwBVwBU5RBRK4M3jHixhq3tszQTzVZywycuMMHuZlE7CUt7E8XQuYeRnL01gGNDZ4LI9ihawWQKbcBg63m3Wzpjxv2Vu3yHGRPl1gahuAedppG8IVl14eDu7cFQo33hb2jDfDDBPbB5RVOGZ5Fre1NzHexwpnPVgbDF0IMbvxMmcmqrliA/osnDl2ZBng1LpKAE+Ffy4ynuWcG4EqOy6Xy6FWq4Ua3rQ1wmwL7mrfZsFXOumvJYOylZq1VdlLuEp4aPMkZiyB4giogcaAXV1ftLQ8UMWrGP9keR1rvdTrzwyhoxVGusON0Dx1jtpKeCqrrou28qjWPsZqn5qasjDVdfZOrtdnbEy1DQ+P2Hm82Hg6T9dluSaJ1i2P4gpwXmvv4OFdwBu70+UedGZCR9cAHBfKhLDms2puh0HkgWohDNfKYe0Q2gwOMgbjMJbNiWtZaGqtROvBdH1LKY9HvSpV6z4kUJy+uNALTa3TsyFyp3rRFTiVFXgei//5nACvoyyodKRUpcPfY4KESn+KfdxKs18Evn4cuxy7BLsYW4WlJKD0eew92CdT5THkL+ecV2fnCQC/foExRmn7Sey12AWY1iDgeAN2PfbH2NECz0e7zmu45q9iSgJ7KWkdz80O7iL/xdRAfjRr1mnXYm/EHo9dhOkeTmG3Ybdgup+fwhZKP0Ljj2UdPkj+AWwQ+xnsVZjus/41a2K3Yzdi/wf7OrZUaQ0DvQF7KbYFG8OUdN91Tdk/Yddji0mP9t6la+S1kS7SZwT7BUw/A+djKW2j8BfY/8YmsZR0X34JewWWfk70bH4L+3VMPyvzpfz1tX79TAl+655fgz0ZG8Lq2J3YuzH1WQzAptui0nfR679iV2amezOD3YrdjP0t9jlsMWmp7/Nirul9XAFX4CRVoHySztun7Qq4Aq6AK+AKuAKugCvgCrgCroAr4AqsXAWAceJxeXCXoF0CxgnedegnH1v1t8RJFt6ZA8FOA6dqoN68jAlRLU/fDiGVe57GZTyNAaVNwHAZazZmgKvyNp4GnA4QshqISn0JiNrSXr2Mob2Nz1h/Wrhw8+ZQ338w7H14b9i7ZyI0poHFfMwt4NhSDmjtsj/wJGMWSnEv5ergEKGTBwzKlpiXPH/l0VwClJboY8ds1lsE7hpEhYyqrHoYNLn60Wb7BqueOVE2EUSM7a/gMWb1ArlA8JmZ0JpWGQ9ngHSpVLa9hgeBzgk8NwHpLfYaxrGafZkL6NKiL/AVfXSdiqAu+wUXB5gf7Z0m+yKzBgHhNrBcY2seI8NAckF6bpTpzLh0w/ByzuBzk4sI2qvBYDShpu0cxuEkbhrhs/EADwDjUrfBspTLgOrYEJgCDh4GB0JYMzYURoZq1OEJDljWnsYd7WUsuC3TfdAE9FRYroeiL9n81CPeP/uiAgfp2UsAWfdWz536JesbyQ9dgVNRgZtY9PdiAqdKP4z9kZUWfnkhzS/OuuhHSxC2P/0CFW/HBM7mS6fT8PLMPkou8JWHaBwuKgkeak5K98Rsztdrqf0ItravdTXH35fZi8hli01Lsc6NXCzNP39dAV6ZUj94Xeyaz+bcd2ACkf1JEPFJmf0E+Z9jb8YmsLnShVSmed5IWXP7v9jjsHzS85TA4Y9R1pj/b77DMZZfwHkfwuZ6pqSh7DnYz2KLeZ6W4t5xKUt5bb5CjaDnv2GCtf3pPCp+F3sx9nxsHyad3o0NY/mkZ1PP7ecw3cdfxuZK+et/hw6bsU9hek7yiX99De7/Cfl/x3Q/b8YeTSpxssb6Dayf3dSo+67M9Iy9E1PfOjZfWur7PN91vN4VcAVWiAL9v3hWyLJ8Ga6AK+AKuAKugCvgCrgCroAr4Aq4Aq7Aya+A2N4sb882AC8zATy1ZRww+iPll6wGoKY69GChAcPoFWvgmL2JEzguleUdjMex9jOuyACseOc2mgScblTNy9igsQAuwxYJfzy8Zk1YvXp12LRhXVjNx9rrKp0w/XALj+NmaAIsBRtn6t0wJZjK3BS+ud2cDtNTB8M0ABbSi0ct0NjycvQEBkzLaxjKyfQVolkG/DVoTARp4K08eZvA3AZ7ApsnLVBU/bRcgVxBYw7JgMzaU9iuzbozcCo9zDNZHfOJc4t4MU9NT1mtefpyLQPUAG2Np2RQ3orsydyN0FgL1HXMi5rcvH2Zcxm9SoypXOPV69Bo9eR8uy86ieMGgL4rD+YszLXKBeYpL+sBCLGFp+ZTHAHiwUox1CrdMDZSwru4QjjqQdvDuExfeYjrnpe4tu6ttDIvY/RM61e7/ii3q9uxZpEZBbFvg8a5Z073U/XpubPT4xA2jr+4Aqe4AntYv+DaD2U6vJL8j7LyQtmrco0CUw/kjlX8TextKuSSvFnvw8axs7GLsQ1YSt9P4VewX08VS5wLRMkDUxArpf0UvowJpAnuCUZeg30Jy/fjcM60VOuUt2fS8AzKgnBKB7FDVgrh4Sw/mkxQXnB3fe4kfkOad/Ed5JdgAr/pev+Nsr5EcB12J7ZQ2kijoKjupZLGvQvTWAKYKfFVIfPe5jdxeFeqPIb8iZzzD9hwdq6uJzgr3Wawc7GnYLqXSnqe3oe9DJsrLdW9m2ts3cPPY/K8Vupit2Fj2DlYSgL2v499DHs/xv8CLD3Eq0znp3ujf8Hfgn0Y+zK2UHocjV/ANmWdbiW/HqtgT8MuxZTUfj2m+y0tjyUJjn8ce2ruZP5Rt/V+h3wz9gSshmkNb8Keiz0P2431p6W+z/3j+7Er4AqsQAV4u+HJFXAFXAFXwBVwBVwBV8AVcAVcAVfAFXAFlpMC+kRUSbl5dVIwgMfHxMnrU3kCx/r0WKGZ9QlixjVV6iXBSYWoNmhIB8sFEw1SEpIaj1sDxk08icvJ07gK7BQsruJ1XLXw1S3gZ0uetsDVdrUcNpy7JWy77bYwNjYcClP7w/D64TCyYSSMH5rGptgiuBUmputhP6Gri+zH22Ci00y6BXmUp7SgozFUZkrk5TCjBVCnBPOMZVvU4WX1IltTLzBcM7CsE4DLrE0sWuGdDSJTXStkkJZygr20Gmym6hGpQ/81Y4S6VgsvhQIfnUjboiaGKcvmpPJMlxDWrCuGpY4ex+rSnjnAp/4c2ykC91RyYjeD2jYOL6rWvKoAX91HRmPtmGA696zK8dhIGVgMgGYOo4SfHh2p4lWsfYtLhP0m7Dbe1PIObymsOABd4bs1cgybnd33jPDGLxBoLvMkJqQ56/70P3M6Th7umrcnV8AVeIQC76UmQWMBm0swwZ750loaXphr1Pn5dBkHv5ar+Brl12ICV/mk35iq/5+YwJPST2O/j03qYAnTKGO9BxO4UmpgP4v9BZZ+NWg+Alq6/oXYkdJSrlPQ7azsgtvJz8nKbyNfDMTPuj8i+3Nq8sBY9+rnsEO5ngKZ78L0hQGl87A/w56NLZR+gkb9yzKO/QamcwQLlbSWN2J5r1hd9/9gSW+KR5X+mN4JGAvq/wgmrfJJ19V9FpBU+kFsI7ZTB7m0lPcuN2yvqOdYaQL7BexfsYcwpauxf8LO1QFJPwM/ihWxz2A/id2DKa3Cfh77LR2QpPfvYUe6N9+nziT+l2Lhoj9kR4dfdO/+FBNE1jX+ChOg1n8+jjZpbk/NnfR3lPWztTdXp+u8DftVTGu4Avtd7PVYf1rK+9w/th+7Aq7AClXAofEKvbG+LFfAFXAFXAFXwBVwBVwBV8AVcAVcgZNcAT4K1qfBYn0J4gkUN/EHksnj2KAxdTi7AiNjf1s1JxkgJTfIqrESNBRMlBdrB8QKYOzI2xjPYIVfVmjoJtC41GRfXyCkoHFJ3sZ1wDF76zaBk/JglTduoY5n8OhYGDtjU6htvzt0Rmp4AR8MY0DX0dGBsHG0hrexwiu3Qr3RDFPsiyzgWOeaM4SrruMp3Gx2wpQ+2jXcaxnwW5+Ban2EkKao8NIlCrqu1jQwqLDVwGHocNrzWIB4dHTUYOkAYa+rtYEwUGXOwPCh8rSNJ1arz1dn59Y066VEqGr7GJa5anw7h1ftgWyJemsnk1fx7gk8qoHjjTp7F+OlXa/jpc3x5OSUesRTdB+s2A11zrGpcNwlzLdgv+6NvILjOovMW9BbHLwQVuFXthooP4yLcY3Q0wN4eFcB8Pg3A6u5b60619Zex9qPmnDXPA9VgL6Fpc68i+3iNodsPtm8NLkeRLZ5xGcteRlze+w5m/W8Zf3ietIKbZn+4gq4AiF8AhEEtM7IxPgx8l/PynNlL6eSn3JL+3n9SFZOmSBZ+vx2D+Xvx3alxlyuX1CCtgJV783q15J/L/bh7Hipsv/BQGl9GlNQ7TMq5JLm8w7sLmwx11+O68wtx2DhD+QqNN//nTtOxXEKr8IE9QXMlZ6F/Rj2NzqYJ+mfhRb2NOxbfX12cPwr2DrsdVnbReTPwK7Pjo8mG6Lzk3InCLJuzx2noq77EuwOTLBYXwR4Jab7mk+Pxb3jXx7bZ/sj+QtTvhF7E/aPWb3mKBiun8MXYjovpYMUfhu7ANM9Uro6Zkd85V9D8+j94hw9/5I6Qd30nF9MWZD/ndjRpEvp/NO5E/4fyv89d5yK+hnXz6CeE0FlpddiAtfSI6Wlvs9pXM9dAVdghStQXuHr8+W5Aq6AK+AKuAKugCvgCrgCroAr4Aq4Aie1AvrE06AxH1kmz0+DeQJ6qqOD2s0RVh876wTlfUlwMO9tLKhYKBC+GDopYFkUOMZTtdgoA44rAGNM+wtn5Yb2/yUsstoUrlr7ExeBv+de8riw/ds3he70SGhP7Qmd/eOEnK6F8gA+shDa4QHwZq3GPse1UALoDg2NhBIAtMh4SvtwNdb8lXqQlvMqwGKFpFZo54pCPOv6zLUDExE8juGoBYFj2fYkzspUWrtCVQ+UG6aHoVp0iciWcM+Cw3MlwlBLQ4PumhNjzKVnPJVdhkvDBl6lr0x7GsvrWKcpVzLtGbTN8YEJ9ksWcVWT9hzWeRRZLklwnHvJyeleFeoHWb98qLuAfQFpeRM3CfE9BRgW8OcekmsvZHkZFwqC3ngtcy19OcD2SBY8xizp2kopVzFn6qVnyjzawT7pSwrpmdMzmE7NRuIMT66AK5ApwE+NhcZ9a3YsWPgb2Hw/Lq/O+ikTAKrnjvWb/Pm54/dRngsY57rYnrjvzVUI9i1l0px+PDeg4Nxncsf9RUE+tT+nvyF3vBzXmZueQfs8IL6Jxj/Od5ijLLCqe3951vYH5P+ETWfHc2XyUO4Hxvl+AoWvy1XIw/f63PFii4KT6R9APZcLzWmCds3rydg92F1YPj1W9+5/cdF+YJzmoXqtYTCrEHwXSJ7vZ+4DtL0q67uKfC22LzueL/sgDXMB49Rfc/gS9tSsQvfpaKHxH3GOoLfSLdjbVFggaU6C+ILj+h+Ezn86ltJS3uc0pueugCtwCihQPgXW6Et0BVwBV8AVcAVcAVfAFXAFXAFXwBVwBU46BfRppz6NFaCTGTBu5yAe5bS/sTgnzqkR5pHH8xb2NhZENKCYhajWvsJtvI0Vqlr7GjeBwoLGgscCxfLa1d6+gr2F0kBo07/ZKYSxdevDmedvCQ9OPhzaoyOhNd7Ce1YTBUDjKStnXfxp2YNXexV3wyCeyPgKA0dZFBB4bJTQ1gZmI84V8NT8BVNVb/sJ67r01RxbAFK1Kww1VRlg1drrfEKslrh+7Qip8wtl6riUXSPl9GEqc6Z2IwLfbBSdaP0qlbk+QlGob32mTrLxAMDMSXOrVAaootKuKZBMEbfiVcNjVh/3F6aedXahtCBloK/uicKGK6cGb2wD0W1gsQFi9nBG1yZ7OSuceATUgsWE/NZeyJwjT+kIrwHGGscuzPi6jkwT6k9ZFc02z/yzxvcCZnu2q4+Wq3Oy8/qH82NX4BRX4L2sP0HjzZS/B5sLOF1AfYJMFHsewior6SfsXOxsbAt2O3akpFDJ8npcl3UcONIJR9n+XfTfkDsnedPmqh5R/F1qFoLGy3Gd+UVIe8HFlH6Ngua8UNKvSUHef8k6nU4uiPf17Hiu7C/nqszV7aasLw1oLKXVMTvqV3lBK9RyDdM/cH+I/TdsEpsr6f7Nlx6re/e3802Aemn9IKb7pPRt7DtWmvtlR1/1ORwvBI01/m/3nTPX4e9R+dGs4fHk+tnfmh0fKRujw3Nznf6cMt94O2L6/+ghaKz0NEzexVM6IC3lfY4j+qsr4AqcEgrM9Y7nlFi4L9IVcAVcAVfAFXAFXAFXwBVwBVwBV8AVWO4K2KfSvMhhVTYL5rX4RJF39VU+zlR9WwCVPmKcZBk+nb1CQcPkwSrAGr2NBWMBjgYlY5hqgWF59uY9juXxW8QNVp7HndK0AdxqAYjbKoUtj7s4HLjv1lCcGQrtg4SjngZoAj/lcVzFu1gTa3cEe3UdPIVbQGMod6Va4hgHIc1bc4YwK9ciBjlPwJk42kBozgVe4q4Mbm7EvoR5LkJok8dxhXDUENIMmOozXkFSXos4H2Xg1yByVtZ+wXMlgWgDzDSmXP1Kc/bvhGpXnszMm7kqZLbmo+u2GhPMU9eIsNaKHJWrNaaJF7DqeS1y89qA3SYwWBBf5xRoF+wtohU4GFA8Q+hv7g33SKGvm5jO1nETsCwP5lYWPlthvDtobddgjE725QDde5uYFqOyZRpFMzz8jOk5y4dBT57G5tWOrGrPTrfzNI4nV8AVmKWAgFXe6/CVHM8FjVWfkgDPXEBRv/m2ZUa2YNpE67MwwcCUlvqz32ekgcn1q+MrueP5itfTIADGL+l503JbZ36ij8sdaM2fyR0vVOzvdzGd57rHaYztqbBArhDLCRovpOcCQ5hX7n/S4XlZpx8lF3D8e+wTmJ5V3a/FpuN97zQXed4ulPRFiQSN712oI239cPxIPyPbOOeOI4yp5q/19dHPyta+uvkOL+xr0O+PxaRv5Drpf0zS4FtZHf+5Ckt5n7NhPXMFXIGVrsCRfimu9PX7+lwBV8AVcAVcAVfAFXAFXAFXwBVwBVyB5auAPp7mY0BBuh7MA9yxTbAB4+QFWikBNamXt3Fim4KYERLHAbRVsNoMHvJZv2BiAVMIY4WnjnsEEwZZXr3N6FFcamgf4wiQizOEiJa3L/sc485q/av0bTSKobLmtDB65oVhfLoeRlfLu3g8TE+zz65dtBCmZ+qEg+YjCKBvB8gqgDxAqOU21Htw7ahoK3+jV7KwsYHgbpNZypNXr5FO6EZV2NNXa4uwWKdyBvNqKLQ2lFn1BrjJVT+VUU716+mh8RL51KBZMkhMKGyTXbJRr2vxCkhF4L6kpsGhGmtSKOjk8cu8uWYRbdRus8/aVSFoLmDMSQaGO0BjweMKHtFElzYgLDDcsnDhAHa0amRl80DWerAWsFghqLUMhaVWLnCusXRPU2jq6NFMo80l5nbQ96LzNXQ+LHV6vhSaWl9MULv6Jesbwg9dAVfgsALvpZi8iF9G+eew5uFmK+WhsfovNp1Dx4swgSblgpGXYJux/qRfQ0uZNuUG20O5njuer8hvDfMEPW++DvPUn8h15qeUh8YP0bCYNet8eXzuwhLk1T2aLx2gYXy+xlx9/tp8xemY029y5hOxtdkI0votmR0iF/AWQP5X7H7saNNS3juB8s5RTOC+I/Q9mrE01PYjjJea5Qmu+zOQVWxMDYvI+6GxQk3zP72jThonQWOdfLzv81FP0E9wBVyB5a+AQ+Plf498hq6AK+AKuAKugCvgCrgCroAr4Aq4AqeoAvqkXUm5gJ3tNSuwiBk4FjwGGAsaK0R1B1uMt7E+fy0AdAUUO7jwFjjR9jVuxxDVLbxV5W0sYKx9hVWO3scKMT2Nl3AlDFDfBYw2GafAPsWnX/ms8J2D1bBh/wHCJ8+EyWm8kaHYAo4gUcap2XGR65WKAqV4yDaaod4cDMWq9i1W2GssW6/WrLJBYcitgLDBWyPjAswcYwaDaStU1K4ugGlr0nE3VLI+sSnrw4HCXufrDBhT0YybC9s4WYdZ/ewge9EcJxvZ588C5NqOEO9r/rLm6MWrrlbSjeFvy+gs7Tpkoh3m1wWmtwG9dj+4t7B8wKwgdxGIG8GwHUNuO2jWFSSmXsBZ4LjFg6H7VJAXOGGqYzsz0LV09TzltTqNrzblh01gWCHPk3exPWMcq86gsc3LTvMXV8AVWFgBeW0K/Chc7DrsBVgKVUzRQlZvUYEkOPQBK83/cjZNv4C9Gls/f7fj3pIPTb3jKK52P33PW0T/5bLO/FTz0HhbvmER5a30SdBYcH++JOD4WCZ5sj4VExg+v+/CIxy/KDP9Q3ED9m7sr7GF0vG6d4uB6fl59X85I992LOXtizxJWuk5vyDrf8Yiz1O3fmh87VGcm+/aP87xuM/563nZFXAFVqACvJ305Aq4Aq6AK+AKuAKugCvgCrgCroAr4Aq4AstWgQzqCTIa1APczYLG7XhsHqK0qV8CgVpTgoOqNHip3AzgqNDFgOMIGfE4xqPVjFDJtq8x8LeFNevToVGf6ll9epIQ1NFm6pNhenI8rB4dCudsOj0UB4dCddVYKA0NGs2ut+phcFBQuA3QJsokobDl8izP5k6xHOpT06Ezg5cw8y7LO1jQFygqKKxw1RBmQkwDUFUWHKbe2ubI1Uav2J5y6pQSFLY+WZ01zPGS7ztH86wqG525p8RMKZrSVhX1102JILmY3SB5eaumwE3tKrS0oLH2JZaXMOuP8Bewzs02s3vDsUJU462sXMBYXsYavAx0N+is8zVWurfZw3B4RjatOLesqC76QkJ6vuRhnAyHZvM+Nmgcl5Gt8PA4XnIFXIFHKCDQ9Y+52lfmyioK/qYkb86FoOGbab8XexM2FzDWL5M7sT/Fvg9TqN7jlQg10UvaF3exaTFzWk7rzK9LEDWl6VRYZK5f0CnFf4zS0eyc366PedIzcyX249hnMX15oT9pztdg78f+CsuHPuewl47nvZtrXr0LPwYFeTovNuXv8dHcU32xZCnSaXMMspT3eY7hvcoVcAVWmgLuabzS7qivxxVwBVwBV8AVcAVcAVfAFXAFXAFXYEUpoE8d9SmkwF4CxwaN+Si6zkepVRnv7iscL8bbWPBQaNVgpoAxAFWQUaGjIavmsaq9dAvsVWxhquXBileuwi0X5XWsY0x9ZQoFrfEa5BdfuCXccuuYQc/h04thct++UB4ohnJJ3sxwDcHiLpPlvC5euSBiI5WtBiGrJ/FaZoFF3KY1JhcSI7b5WUF9OU8exwn8GtzVWDSprZeycuqn+n4QnI5t2PjCaxx7rv69sXMF3ZN80qryVbOAMZ3tj3J5eMsXGRKrkNPyGBb8bRGKug047oFhwlS3obexTjnAmHulvm2gsEJmN4HH8ppWWG7tcSzgbF8EkN66Yn6SWTnV6VCWnqu0l7GAsZ4teRrLU9w8jdVPI2qBMk+ugCtwJAXeS4dXZZ2+n3wME0wWeH05lpL6zZdeQ8M7+hpv5Fjen7dkdit5fp/W4+kktItrpXROKiwiP1Ko3tcwxnJaZ35Jd+cOzs2VF1PM9394MSc8xn0Ocb33Zabn89nYddjzsfOwfHo1B/I0l9d8Pr2Gg+V67/LzPNbyWYs8Uf8JyfdVKPPFpntzHRWqfC3WzdU92uJS3OdHOwc/3xVwBU4SBRwanyQ3yqfpCrgCroAr4Aq4Aq6AK+AKuAKugCtw6iqQsT6DewJ72r94lrcxcK8Kx017Gwu2zre3scFVBtSnkQU6GWAElnYLApgCk00DkPIEbmEJFDcyWNyDxgxQIpz0zGQx1IYZbLoQhobHwkVPeWq46UtfBCwDk2emQgcv5AbeyjXApvZE5lJcu8L15EFcDjVcjOVVO9k6FKrNZhgaGgqVkQiN1deAMRBYkFeAO3kZ94Cw1TOm1kxZybySraS6yE9SW8rj+mN/OsXzeE3tqlA5HSfQah15SffE+mWf7aZPeFNfUxkiq3p7BQ6rLQ+MDQCju4CvYLEAsXkS277GEQJbPW2pj2CxQWbRXFK5Qlhq6iKExtNYXsa6x5qkcs1A5VxKR6pOXsb9z5SOe8A4G0LnpXNzw3nRFXAFHqnA9VRtxTZj8tBUyF+F+JU38BpMSR7GH7fSI19WU/UXuWpBx5di/5Gr6y/ql9mqXGX2Sy5X8+iKO3OnCwRXsMWEAz4nd15/cTmuMz/Hu3IHWof+UeFf4SMmabMp12shb/JctxNW1BcaPpKZJnEJpi89/Cw2iik9D9Ozu18HpOV+7+IsH93rQs9ufmQB9YFcRf5nJVc9Z1HewClJ0/Mw/e44HulY7vPxmIeP6Qq4AstUgfjOaZlOzqflCrgCroAr4Aq4Aq6AK+AKuAKugCvgCrgChyGdAF/yCs17G5tXKIBPdQojLAiYGCHF2YmGBBEjWARhAhktpDFeqh0Dl83QUojqVsP2J9YexRaiGghsYapnJmmfIUT1oVCnrFDV7WY9TE9NhLEzzw6nb7kwNACZ1ZHRUKgChwHShWIL4Kv9jPEmFqQVNDZfY14hvhVRX67faNRDY3ImwmEgc0GgGetqL2NMHrUJ5iqX53Gq00IT5E19+uuSGPl+qU+qS8epbz6Xrklb6xfV7IFUA7VWh8qZ1ilUtMFieQBD/i0EtemdhaWGzioMtfQXRJdXsRl1Bpbpq32PBYw7XYFlwWGFpRaX4LlQPe3pWpbnJ2q9NHe7+7YGNafnKe9lnPbL1vOkej1T6mfDPeKBygb2zBVwBfoV0E/L+3KVP5SV817Gf0PdfND1WtryDj+v5fg/sjHmy86hIf95b7483zlHU/+1XGeN/fjc8XxFAceF9nddjuvMryUPjfULN7/Hcb5ff/lyKvL658Fgf9/H+lj7LD8Du3qBC3+Htv+BpedWXfl6WniuClla7vcuzfPR5Odzcv7ncL6xruhr+GLf8UKH/c/GQvclP44gtbzCBfiH8g1Zeanu8xxDe5Ur4AqsVAXy/3Ct1DX6ulwBV8AVcAVcAVfAFXAFXAFXwBVwBVyBk14BAbsE+QTyepAPL+OFIJ+dx+ojzJwtgwFEOpi3cQaOzdtY4FKerQLHmEFjgeMGexsLHGMCxTNTh6xuBlgsgKzjOvDy7AsvDkNr1uNbVwu1kdWhXKsa/BX0FOjUHxAwIa+BwSwk7mfMvscFPo+GUE7PTIfm9Ezo4HmsJA/lIm0KZ53WkaCwPImtTL9UlyCyHZvbtTXSQawa72Gr04FMbehDJlPSef3JdEwd1MfWEAGs+iYtbZSsX/L2jkBXoDjzEBaYFwSWNzGhwM0EiSk3WXMr8zJO7RauWu2QW/MwZhyNqXlKQ9XNGZY609oenGyOmmtaqAFj5iooLECs50hfQOj/EkIPGB8+1YbxF1fAFTiiAn9Fj/Sb4zrKa7H/kjvrvblyf1FALiV+MsNn0sEC+Q/2tS0GdvWdsuDh52jdl+vxK7nyfMWfoeGRv1QP9z6e6+Q3Wy8tNIdepzkKt1OXB/u/Nkefuary/ep0+NhcnU5A3Qe55kPY9dj/wo6UPk2HqVyni3Ll43nvcpc5oUXtN/yqRczgl3N9bqV8d+74SMXtdDiQ6/RLlBfzvL6Ofp/Avo0pRH3+fizlfWZoT66AK3CqKODQ+FS5075OV8AVcAVcAVfAFXAFXAFXwBVwBVyBk1qBRB0ELw32JdDHR+I90Nea7W2sfpayPAFXQUTDnco1oI6AkDJBY/M2BmAqVHUbb+MWXsTR23gGT+MIjuVh3BJEBh63AMryQO60G2Fqphkqw6Ph0quvDsXB4VAYHCIfCXUclDqEsw4KW20fhbInL+Pqgwm5LgnCah44JRtEPnjwYJg8dCi0gccFPG+1c3JFeyoT5tq8j3ufp8bFqS5BY4OpHGuvX4W0lkGde+V0PLuOq9OtHxh3EFGWUh4Wq/Zw/6hpAsS6pNWY1pknsPYhRlNB4Sa5rAUkjqBYZSCytcnLO/bT/WhSpzkYaCZv6RsDKFetDsT7JmhsQH5xYak1by1J1gPGeo6w/i8g6FLqp8fEHpUkhOeugCuwGAW20+kzWcdB8ndiKdTvNyh/K2ubK5vIVQr+KmztQul5NP5+X4d8uNy+pmM65F+Z8Pe5M+U13e9hmWu2fZzfnK+Yo3w815mHnUn3OaawYJXCSv9RrscrKD8xdzxX8bupzAP8T3I8PlfHE1D3r7lrXkv5SN7iW+iT92LNe8Uez3uXm+YJL8rjWl7m86Wn0/CcXONf58qLKfKvb/jNXMenUv6J3PFcRb6ZF/Jf2hCo/s9cx6W8z7lhvegKuAIrXQGHxiv9Dvv6XAFXwBVwBVwBV8AVcAVcAVfAFXAFVowCCdwJ4gnmyZKHqHmJCvphqhMMTGCQ7oZkZwlhMDOSQIFOhTMWeOxmXqtxf12BzOht3CJstDyNLUw1gNi8jRWaOpm8jOVtDOQ9NDUVRtdvCGdvuZgd/oaBxmyxSd4t1dgnuRBK1aoiTnPNloFawdqMJJMDjukzWIF1sMDJQ5NhcmIiNGcIWc2cK4RjlsmbWHq0WKigakqHIW6sSWtfbK6zks7KU+qHxapP3sUK7y2BTUdE17H2JpZnsfYhjhA+AuE2YLgNKJ6VM3/zKgYUR1CfHQvcSyMmorDUDW4sGUZY6kqZ+gzy675RTh7jvS8HpMlnc7XDbE1am56RRzxDSJmeIbXpGUo6ZKfmRvWiK+AKLEKB9+b6vDpXztfnqnvFm3qlWPidvuP84bUcfATrh8THCkrzY/eXf52KPVmlPlv+NPaM7DifncfB9dg6bKF0PNd5KHdhhVnmH6NjSm/nrJ3ZmfoXS2t+cXbcn72ECkHilASu816oqf5E5f/MheX5rKQvI3wY26yDOZL0en+ufi/lPIw8nvcud9kTXtzCDHTPN8wxkxdS9y+5eoX1zn/JINe0YPFdtH4r1+NPKL8Zm4vfaB7yMD4LS+mdqZDlS3mf+4b2Q1fAFVjJCugfBk+ugCvgCrgCroAr4Aq4Aq6AK+AKuAKugCtwEiggaKdPqwXxBPN6wA9ILG/jKtYo4Q7DR4xlckVhlsWTYi6gmAer8oZVGOUIGiPwhHKGQrsQCIAc2gBc9U9mg3FMBQPqKOZ2YMdd8xzWJ9LnXnxp6E5Ph/vv/E4YO30kHNq7K7QIPV2zc1theIBJcr6uY/O0I8azBQJElbPIdhd4XcSbmfZSfv6Epo6hq0G6dNUclce5ZXPmMKW07sNrOQxEUx87PzsQKE7pcClppRZ5aNPCSSnstoFjaapW89yOIamTJ3CEyNEz2DyHFaoaveVlrPDU2qvYwlGrXjC5I4texppDCxgtaK6w3DamXQNoLJqs+WgBmP5EMajOks6Xacra91rQ2L50kD0/OIn3QlPr2VJ7trz+odKQnrsCrsCRFRCUO4gJwKWkX5F/lw7myT9D/W3YpVn7a8kVGvjd2D2YPtc9DxMMVchrHfOvQLgPOx9TykOlWPPoXwUOfxpT+Fv+tQmnYZ/GBK21j6tAbfK0XUtZv7p1zkZsrnQ81yn9npJd9EpyzUPwVx6yR/KwpUsvqf+bsL/F9Ot9Nab7+u/Yl7E7sEswrVse3/n0Rg4EEpdL0rOo+/eX2YT0TGn+eh5vwh7A1mEXY6/BtNaU/juFyXRAfjzvXe4yy6L4dGZxMyYY+xVMz/3TMP3s6edAaRp7PaZn/mhTmxP0rOhnqYINYO/AfgT7HPZNTNEKrsL+K5b3/v5zjt+D5dNS3uf8uF52BVyBFa6AQ+MVfoN9ea6AK+AKuAKugCvgCrgCroAr4Aq4AitLgQQ1DfwB9QT35BkqaFwB+lUEjbEydSU+2pZHr6DwYSib00ODZfA3QmMG46/CNwtIKgmwChzb5+TKsQRfU7tyS4xXxOO1CPgstmt8vDkUzr748WFqfDLs3bkDZ+M1wsPh4OTBMFQlzDSxqDtdAKg+g+dviUmm/YbliVsmvHSpSrsWQezkmZmJ0Boi0DVEvFKphAHzOMbrmH19zds3zS3Lo1aae0qprDyWtaSUBInx102HuVKsihqpbEjWiKrqzHRuBokFkjWSHUNeO12gMLC3K49ghaA2MAwcBghrL+IIieWZnHkjCxirXjCZc80rWbeG8cvlcijhZRyBM+PRL3kYq30uYJzmLT1k6iazLx3w3OTDm8tjXSBZbT1gbCuOGvirK+AKHLUCAkkCcj+VO1Oeifm9gXNNvaLOEzD6Ejac1T6NXDZX2k6l+gtevifrIIAp0KSxljL9A4Px28IgKr/sDVi/lFyWT7ruSzDBsI35hlz5eK7zT7nOy7GR7Hr6ptJZmH7RS9M8AOVwwfRBWndj0vbcrOf3ksvmSvupfA2me73cktYgKPzWbGKClK/OLKualXU4+k1McDKfjue9y1/nRJbv5OJ/g70N0zOsn2NZf9Kz8SLsy/0NR3H8H/R9MvY+7EpM6erM7GCOlw9Q94Y56lW1VPd5nuG92hVwBVaiAulbMCtxbb4mV8AVcAVcAVfAFXAFXAFXwBVwBVwBV2DFKaBPus14EdQz8AfkS3vRJgAIm4zgj496M5bY8xZNENHEEfTUiBn81OjRQzZ5w8rjlTDJCpVMqGrb25g9jBWmWtYLT01oaoWnbhCmenpyItTrM2GasNKdSi2cc9EloTYyGpoA46GREfYaLhvELgvRQrRlHeAt+NP+iOOWAcFV9jBmF+NQVvxqLQL3WHnfNhvN0MBmGo0wTdjsGSzCbMtUNCQsUC4QLEvllKf6fC49TNsst+NMl6hZnJ9BYYFh2gSrLUw08FbQ1rQT6AUSa3/itDexgeF03NsvOms3WKw9jgWLM5OHMeWuwDLXEGAWPBc0bnMdweZ4LV0/zsXuoa1AM48p3Wu6WFKef260j7GeGXkZz5DrCwhMnWvEfnZedm42pGeugCtw9Aq8t++U/uO+5t7htyhdgv01NtdPIj+t5sX6i+SPwwSY/x1LaZTCD6SDJc4/zHhPxATU+O0xK/EbxELtvoD8E7Na5j44Xuv8Opd7MXZf32X1z4yg6dGmz3LC5difYYKEc6WHqXwHJui3HIFxmvMvU3gq9rFUMUfOP67h49iTsN+Zo11Vx+vezXO5x7xaP3e/jb0Eu2uOq09Q94fYE7BHA4zT0PJmlt66Zv9zm/oo/zT2NOxVmH7e5ktLdZ/nG9/rXQFXYIUpoH8gPbkCroAr4Aq4Aq6AK+AKnOQKfCCcdxu+WY8/yZfh03cFjloB8NGlrwz33X7UJ/oJy0WB25iI/+46hruhN/MCo3LALWM47YYafkJDVdypBkIYrcV8mGPVq139zOs4O1eXzXsMG3QVamVg7Res8MeClEUBXsFbvHpl5XI1lKsD7Kk7ECrVWqgMDIZqbShUBzDlteEwMDgcqlWVB0ON42qtGuoP7w633/S1UG5Ohc7UgTA9vi/UiH9WIgJjQePL8B7WtbUXcJV9jxXwsSjCCVS2kNLMua6TtPYCOFnzFAUmHxwZsj5F5t3FxVr9rUxbXBb98muOh1Y3i8R0tVdyvkagmCRIrD864AVsa2XzHtYxYNfqDeayB7FBZHkRCyZzDBCW97b2YO5kcLiT8zROoartPM5R3pWHMuPq3ALrrVSquirnR6Cv8ZKXsc2ZeeiPTcymGdeRTdlgsWCwPIkFiqcbxJEFCcgmsElsOgtRbeCYEyW/rVkaeFruClzKBP3fxOV+l459fus59UJsC8Zv9XArpvstb88TnU5nAnr+NLf7MMEzhcc9lnQ81qnf+BdgGzDNS3McFTm34QAAQABJREFUxx5t0roFkTW2IPJ2TBCV364nVVrHbM/FzsE2YdLmIewrmKDoYtPxuHeLvfZS9vsNBvutbMA7yC/JDa5nSLB2DBNEvgU7Go3oflRJ19HPlv6/PIXdi92D7cGONi3VfT7a63p/V8AVOIkU4J2WJ1fAFXAFXAFXwBVwBVwBV8AVcAVcAVfAFTiZFBAEVBLMkwNui4/DbW9aQGAdlFAF/GlfYxkOu9HLVh+Z01+MVckOGSAPjgUcFco6QlMG5i9fzlBmexzbifO9aDJmOil63JoXMf0FOivrN4TNVz8l3HXrTXi0tkONPY7Hx/eH0/AWhhLjfaxw1YTCxqu4LXDMH3kBl0rMqdgNZUJZa3IwcMYHZmuuck+2lRRC/UADdlywcbShs/Y61uK7tZqNq5DbttYMItswGoo5M3MzjVbqMA87zrSwFjxvAbfWl87mUUy9gG2h3QCsUtbyzdMYqCtPYCqsH5RWnsHdDPTOaOZ4TFtYaXkSy+vYvIuTp3Lcv9hgM9dosOxSEVCPh7FmqvPiXsYLA2OWYElr4zSbnwCwoLGFNOdZkWdxMkFk8zKmXX16sNgGiGP5qyvgCpxQBQSJZPImXm5pFxOSfXYJJnY81qnfZAJ8sqVMad2fXspBT8BY2utZduOjvPbxuHePckpLfrq+HPBPSz7q/AMK4Otnfil+7pfqPs8/W29xBVyBk14Bh8Yn/S30BbgCroAr4Aq4Aq6AK+AKuAKugCvgCpySCggEsnD43mEQiAdpGfhnexoDj2GnBo3z4DhhVkV8VrmXRD0BqwKTUEnz3tXoVqQTQz8iRbgsIKnJcKaMcyw3cMr58pgFCnc6zTCyZlXYcuEFYdsdhLkmhPXQ6KrQOLiLcNMN84guM0wLollkz95CVxZnKzjcKXCsQ9ZU0HXsALCrVWjqFnda81eyWQBYO6E5lTnhAYt1uryTNWxJEFYVWeoApIssGzxtY2glao5rIpcQDC4ArrIBYeZR4Brq0wPFvWPgr60baAwwVrmd2gDF2r9YwNj2Mc48i0VqW4xt4aflYUx9qSTvbhbNddVXMFnhsG0+mgvX5kUly9N6rF4HqlbGi5aQgHHaB9vCmfMlA4U3l3exTMDYoHE8XaN4cgVcAVfAFXAFXAFXwBVwBVyBFa6AQ+MVfoN9ea6AK+AKuAKugCvgCrgCroAr4Aq4AitTAYFAJcE9eQcL9hGV2eBfXV7GcMZkJYCgRXGmXY62Bow1AMeCiz1vY5FFg7Gx3i4gcMwJRcbn79xJ55EiwIxAM0LUHDRuNUIbN+iRdevDBY+/LNx127dCY3oqVEcGCa7aDVN47A4yn/IAH1UAUwssrNhlEYSt7hQrrJFw1IRo7nbw1WXBEW+DjLm2eRFn1xfUFUXXvsPyPDZ91EbZ1ikArWP6KE8paqJ1pFoBWQ5tfOkmsaR3HDvtZVwQxOWPrddgsuBuBn5pEwAWLI7QWPsTA9AtPLVgeoTAERQzSgaMzcuY6yo8eAWALl0jMI7ezgLxmoyuafPTxHIpAWObPvXSAOdmA8bmkc7ptocxsFh58jJuMpz6qb+llGeHnrkCroAr4Aq4Aq6AK+AKuAKuwMpVwKHxyr23vjJXwBVwBVwBV8AVcAVcAVfAFXAFXIGVroCgHiwzQUFBP8FdeRuXgYHax1gmT+O8t7EYoxiokrJ+cJygco8ZCoYyxlzgOIJiAVYZQFVQlFxAU8fJ01jhnTu1AfN2rY6OhQsuuyJsu+uuUD84EUrVcujMTIcpwPIAHrlVhZW28ZiBJiOOC0DFB7kHixX+uQjgVhO0FcAqF2T6Zudp7m0Aseq1xqLGoaQ/xn8NvFJFsktwXuyhCv3VeqzVjjsQee1NbPNijso1B+JO21ojNI4gtwN5NUgs3bhO3N9YmnBeu24gOYJloDJzF1S2e8hL9DLG31l7SXPzTEtpZ3oKHs8DjONkbV7ZrG3+quZUxj0cwnwuYKwvHaiP5mHLUq6BPLkCroAr4Aq4Aq6AK+AKuAKuwCmhgEPjU+I2+yJdAVfAFXAFXAFXwBVwBVwBV8AVcAVWogIG9TKyF6Fj9DhuCBYLHCvPgLG8kAVLZYKj8Ff9tQMdC4LmPY5ng2PRRP7qHMHFrH+PLhpgNczKQPLExSCQ5jkL5BQsbRCCutMZDu0BvGvxsK3WhsL5l1wWtt45Gcb37wtEnw5VPIobM1NcgHZFZJYLNbMVztUc5VVcYqEd9jjWrsbmaQsoLgCZBXQL9NcZttUxBR2VDBarkgpB5cxMg6y/ytJCGiimt8aIKZZUrxDTBooTvM3AsYXept3guCA51zHwayBYIaYBvpTNe5hz5EVt7YzTyvq2NT6gOcJ2gDHexSXmYf0yYGzAWfSX2dm6OSeuVlWH56l560hVMnkOy9Ke17aHMV8omM68jAWQLTS17i9m52VjaCxProAr4Aq4Aq6AK+AKuAKugCtwaijg0PjUuM++SlfAFXAFXAFXwBVwBVwBV8AVcAVcgRWqADzQkmCfoF8LAioIKDgsUJy8jJOnseqVyuRZ0QoqHxkcAywBrQKswpcp6TwZIwCKBYnlZRwhqO3rCzitAIsFlNtNwlTXFJa5EyrVgXDe464O9997T9iz437m0cLrWMy4HhqMUwYOh06dcTkXK7C/r8YoiFqrrVBkz195AFvRGHOchc2ETngV4/msOrFiE0Wr5qBYUqhrtagvSYBa0DSbe1xOBLNaiwCu2uRdLICr3PpStrWqDUv9lEdgHPvH8NRtwm63bN9imkOrB5k1vmbRtXDURW6WQWYD7hqXa+gE2ucDxlpCL8VlGSfXaSkktcJQCxL3gDHH2ttY+xxzO6x/XHdvJC+4Aq6AK+AKuAKugCvgCrgCrsApooBD41PkRvsyXQFXwBVwBVwBV8AVcAVcAVfAFXAFVrACQEJxQmFFwT8Dx8BA7WWch8bmbQzx7TncynM4ySKWSlkAdH6PY3UWxIznxfDMERjrvAhPI0RNIDVCZCCredzK67ZpZYWrbjeboVyuhjPPuSiMDI6FB7ffE+qTAOZa2cBx10I/N0MVd+lypR1mGgDkYpU5M1M+0ZBncUH7HBcBuiKftgZCW2tdRonlo5ylyGQ5iHVt5mOAVDUq2OLllat1ZHX0FVjWH1UK2BosBvam9Wmc5CFsMFl9OEdlgWBbZwZ/2wLY1Gu4lkJY25hdoG2b6NtaY9W0T17JXZ2nazFmDP0d52Hz0bI00SzZGrIq1eoeycNYsuhLBHlgnKCx6gSUDRrTV9xa58o8uQKugCvgCrgCp6gCX2Ldf5itffcpqoEv2xVwBU5RBRwan6I33pftCrgCroAr4Aq4Aq6AK+AKuAKugCuwchRIkE/QT8SvIH4KDEzexvDIWXsai48mEzU+VnCswNEpRZDJsRFXMoOnEZKWgZ/dDBq3W4RnxlpmDaBxDe/aWhhZf0bYMjwaHrzvnjC5fzeewHgDdxo4ExdCA9jaBDArZLOFvcbDuMX4RdrEYAuEoI4z4Rw8kOGs7FNsaDlwpQjBtWD6cmB9Bcall47obvO2NbQVFzsbQ7m8gVmToKytqS9vMjcDu9B69Yue1dHLWFrE/Yoj9BVAFrI2KGx9deUCGlRYW9muZXs/s6iefrpeBqk1nv5YopxSDxhToVqty4Ax6xUwlsnDeEpexo1YVphqA8b00RcNNJzOzQ2bhvfcFXAFXAFXwBU4lRT4FIuVeXIFXAFX4JRTwKHxKXfLfcGugCvgCrgCroAr4Aq4Aq6AK+AKuAIrUQGDfYBR5YKAYqSCxjOYQWPlmSVP454ORwOOdQENKNQa/3JNQlarHjO4apAz847N4LF55CZv4x44boRKmXDV1ToguRbKAwNh03lbwt7aYHh41w7AaTFU5EkcGsDiOvsZt0ONY7HfImQ0BcouaBNjLY4pdHAz1jbGRZFzVOCILO6JrPXGLY7pIBFoi/sf08eObIBsDRHQaj0GjVlHLJPbmsj50xQQF1i2uuhhbGGqdRNIClEdoW8EycmTWeGrFYa6UiHkNqkj+Gz7JmusCI0TLNZ1GcSuZ511nCVro6yarFv0MubyDGfhpwWIBYvlYZy8jBWWWtBYcFmQ2YY8PGwa3nNXwBVwBVwBV8AVcAVcAVfAFThFFHBofIrcaF+mK+AKuAKugCvgCrgCroAr4Aq4Aq7AKaCA4B/LFK4UDBQUFBstAgvFVA0W05byWYosEhzbOQKoDCJ23OVCBe0vnEHNHuDkOILUCEHN2xgYKnhs3saEqW63GqFZqoeBdg3PY6BwY8A8btecvjEMjY2G/bseCgf27gxlAGwF/FskHLWgKz7CXI4FMQ8tRl7DcFvLiwLWHHcwkfPC/8/edQBIVZ3dbyu9I70jCCg2REVERMUaW1CxYe8lamI0scdoNDGxRRM1v4qCPTYsqIgSlKLYEESKyNK7tGWX7f859707e3d2ZnZ2d2Z3Zvb74Ozt9373vDdvZt6Zex/3qoZxxbJnXmiy4bYn/aLE+I96JVx77M3HlHEe7JMitRGHWeqJxMzjimfm22cZsy5XF3M1Mc2IwUaVhYPoi8eFHmRlY3Uxn6vMPIrp7MfvKxDSJ4Dt+I+hNSsWM81cv5rpn9tN89gXAPYZxkYw5ipjrjxGPgVl6tqk0LYt792OoqEyoAwoA8qAMqAMKAPKgDKgDDQUBlQ0bihHWuepDCgDyoAyoAwoA8qAMqAMKAPKgDKQ8gwY0c9X/igGQhc04iFlUyMaIzRaqtVPgxmJQjhmBxQwod8aodM8/5jiI9tiQK7KpQpJ8TQDMKtlzcpZ73nGGXymcRaEY188zuSW06UFkpbZSDKzsdoYzzguKoF4jGcXd+jWS9q2aSvbNqyR3F82Sf7OndKkEcaGD5wTdWNKyOa/vx11KZRs5nNtsfHNyMLIIBlsBEMKfmEedBX/+J8JMy8sPS6fA3KYj3kErzY2Ii/KrGjsCeR+XYq/+FeM7ajZuXnGMUVkDJyV3QgrjLkFNlziKmTkW8HYE6dBpmnjq7km5XHKNrRIgjGFYPMcYwxX4K8s5ipjbk1NAZl53K7aiMbo1ujZ7NP0rH+UAWVAGVAGlAFlQBlQBpQBZaChMqCicUM98jpvZUAZUAaUAWVAGVAGlAFlQBlQBpSBlGTAin8UA6GXGqNIapRSBkiYtCkJ8aeawrE3HgbCgKVQko2kDDE1sF01BVTAiKoZnkBaCqE0I9N/tnEmVhAX7pLMrCZSXFSI/GzJLAQQlmZlSwZE1jbde0jLVq2x8ni97MrbgOcYs58yycT2zhTDuZo3DdtRp1NMhkDNCaZDPGa9MmxnzXQaRGXyYcRkTNunxhBAgZf98R+X6nJO3gpf+M1czKcUaqxZ0Yw4n1vMlcWYGEKUIc8Iy2hrWvh5ntDslWVkZUoGtqLms5Y9/zEH9Gm2oqa4Tp6MyO6Nh4TnDz1E3FoFwRjZLMFwBhSMucLYPsPYbkdtt6QugFjMcorKXPFsplCxezuMhsqAMqAMKAPKgDKgDCgDyoAy0MAYUNG4gR1wna4yoAwoA8qAMqAMKAOJwEB642w58N/3VHBl+6KfZcH9T1TIiyYx+O4bpFn3zoGqK9/8UFZPmhpI10ek4xHDpPe4U83QK157X9a8Py3mbuw2Yqj0veh00++qSR/Lqjc/ivkY2qEyoAwkLwNGYzRiKuYAcTANQiENWf4fRiJYjYRj9gfhE2pkhe2qKRhDDKWYW1aaCaGSWzcXS4ZFMbZpzkQdtMvAs44zIRSXFmYZwbgkk2UExONmraRNv3ayY0tb2bl9u+Ru24qxiiFOs+80aZydIWmlxZhrkRkrE2J0cSFWMDdqAg4wc6qr5o8vG0M4TYOwTKGZims6BGauGi7Fcmkj1xoh1jQyPhfD/4z0TNNDEfz0xFuIxEWYM/qgQEwBmWKyiSOdnp4h6Vl4bnFmphHrzTbUdnUxBWKK3wzRhr6xT9MvQzMSsz0fjPd+3E6FoRGM0ZxCsFlhDGHY3ZI6z3+WsVlhjPOAojGHYztjNvSTGigDyoAyoAwoA8qAMqAMKAPKQMNkQEXjhnncddbKgDKgDCgDyoAyoAzUKwPpuHne54LTKvhQUlAgi/81UYq351bIj5Ro0rWj7HXbNWbll62X+/OKeheNWw7oG5jf9oVL4yIat+jfOzDGzpVrVTS2J4CGyoAyUM4AxECjlSI0mjF1Se6WHK1FIxyjL7MVNMei0AnxNLBdtV11jJW1XHVclu6JoxSPPQGZK22xTXVGEQRkCMPFWIkLgbjErDbOwqpjPMU4w8vzhGPUQTq7SWNp0rSJtG7fTgqwXXXu9q3ANtmJZbRpEGAboQ+u5k0vLsNW0C2w6hYCL1YhGw3WiK4UjWFQTbn6GC7TeVNMAbeYyqtNI9dsw23S6VJUiNXRRm2FMIzQWxmM7bC5vhhtSyB8p2P1cwa23M7GFtQUjTFrb57YDzqwDTUFdF8s9oRi1KJvBN3BPxs3GcwLlJkKrGHEX64YDqwwxoG2gjHFYisYm22pfcHY1Pe7N0PaATRUBpQBZUAZUAaUAWVAGVAGlIEGzYCKxg368OvklQFlQBlQBpQBZUAZSBwGMho1km4nHyU5E96K2qkeZ5xQQTCOuqFWVAaUAWWgATBAUZGCJ0Pqxd4f5BkFmRlRWBjhmC295wUjAuWRwjHNjucNho2qoVDaVcdpZRBXscTViqXcojk9AyuOIQR74jEEYgi8Rij2xWLGg9MZ3JLarBBOl+xmzaVd06bSCs89pnCcu3OH7CooxJy9Zx7nF2IVMlJcTWz9NVtlQ7DN4LOPrXAL1bUEW1yXUUiG6GwmBOLImxF1GZoVxMjjfEkqucXEDbccLyNLGjfCHOAfRV+uOC7mqmK2g0hstsr2x6vJ6mKMyCHN2Byf2rURgH0xuABh4BnGeG6xFY2NYOxvS01xGe4E+mB/asqAMqAMKAPKgDKgDCgDyoAyoAyQARWN9TxQBpQBZUAZUAaUAWVAGUgYBnqcfny1ROOeZ/4qYXxXR5QBZUAZSEQGjCjoK4NG3PQUzuq5GkI4ZgcUU60Qa1RICLMUS41wTGHVCrVQN+2zjimxGqEWq47TuA10KVbi+uIxV+WWUCRGSCHZE4yxWpcCsk2jLM1Pp1E8LoBYa+pnSDMIxy3atZfiXQWyI3cHVgUXSiG3py7ON9tOFxdBwMU/89xjPOe4sBhpo76CFDidZpZMw0OzGhs+cw7+amJPKIYADaEZT0s29bmiOB0PVKYsnYWVxWxOgZhCMZ9VXEahmCuKKRpTLMY8OT7ThgP2z/EJ8ol/XsQPmeeXMZ+5troRjOG22Y4aYnHwM4wpGNsVxxSSzXOMUc+IxujI9MVO1ZQBZUAZUAaUAWVAGVAGlAFlQBnwGVDRWE8FZUAZUAaUAWVAGVAGlIF6ZaA4f5dwS+nWe/aXzkePkKxWLaRo244qfWrep7u0P3BfUy9vzXpp2qVjlW20gjKgDCgDDZEBI0GaP97KWX9pbNRUsCn0UclwWpgtnZGuJBwzr9KqYzakaOqJzEZahvppVx4HxGOuHobgmkbxGEjn9s4Uixm3aYTIRJrbViOO7aUzuMU0HntAOTcNIi7LmjZvCUEbIimEYTzZGFteF0nhrl3YepqKMP0Qyd+VbwRrrnKmuAuJlxMyAnM6t7fGOJl4HrF55rFpxWcUe2kj/lIIxj8+i7nECMRhxGIrEpvQk2vJmxWMQ4nFGM4rZwRmxGI/NKuLKRhTLObqYkzJCsT5/vOL8yEUuyuMWZftKDYbD/hHTRlQBuLFQBd03NnvPB/hgngNlGD9Jsu8eWzoK20X8IOJ6Z9kYCBZzrGacMkvtvajVg7im4M66Y50Bz+Pz3NaFFSuSWWgvhnoBAe6+k4UIJwfwSHqkqcABwC8JvOcXg0sBN4AqtMXqqvFkgEVjWPJpvalDCgDyoAyoAwoA8qAMlAjBla88q60vvu3wi2qu550ZFSrjXueeaIZqwQryVa99ZH0v2pcjcbWRsqAMqAMNAQGjEboC4XQG331OPLMrbhoQ6qyXGgLXdZTHhGaKBVNWFSrjtHCblnN5w/z2cPl4nE6ViSXQPj1BNu0Ei+keGvzGC9DeYbJo3iM9oib0DjorTw2vkAZpqBsFFeEGY2aAPTdzEiykC7jltRcAYw8rAf2VwFTXEUd5kHo5WTNs4jhL0MjEJs2SPM5xZwHy/y8wMpiKxajH+axH1csRgZzGJixvAijfh6zTZkXUvBlN1xdbFYYQyymYEyB2KCwPM7VxdyuOrDC2G9r+7NjaagMKANxYeBK9Hqb3zNvmg+Oyyix7ZRiFEWpr2vRbbLM+1LM8U/+PCm8DajFnLVp3TKQLOdYTVj5BI3a+A0vQfh0UCc3Iv0bP28mwuFB5ZpUBuqbgQvhwF98J5Yi3D2MQ22RPwvoH6L8Y+RRNI62rxBdaFZtGVDRuLYMantlQBlQBpQBZUAZUAaUgVozsPyV92RviMa0nnhOcTTPNbZbU6+d/D8p3Lo9ah/SsDKted8e0mpQPxPmrVwrW79fKDsWL4NwwJv60VmjDu2k/bD9pHmfHrJr3UbZ8NkcyV+1LrrGQbUad2wvrfcZIK2w2pp9bfluQbX9CeoykOTK7Tb7DcJce0p265am320LlkjuspXekrNATY0oA8pAqjPgCobmahfmksd6vmbqhUz7a19MAKWYK3Xx3/wxoWnjrSRmtmnIwC8MtWU1aqMfbFPNZx0zTgGYIjLCUmxdzdW+pU6eEY8D5b5Y7KdNO3qEJdGMm9XBcNITsn0nDAHGO3pmfKfYS1/NdtFG5LXCricg22cPe6KwJw7b7aUrbD3NtqY96/j9MaTKi7GiEYvpWbBg7B4Hu8KYQrBZYWzFYoTcjtqKxxSMWc56fFtzt6TmtCvQwEHVlAFloCEzwE0aKMTdA/wJqI1ojOZqyoAyoAwoA8pAWAb4VeJlIJRgzEb8oZVaPTOgonE9HwAdXhlQBpQBZUAZUAaUAWVAjJBJobTNvoOkUxRbVLcctLu0HuwtCsh5aZK03ju6BQJ8ZvJ+D/xRmvXsWon24rx8mX/3o/Lj3/8TUTzObtNS9rnvJulz/hjJaMwla+W29YfF8tkY3neLzig6H/zsA9Jyjz6VGhTvzJO5t/5dFj06vkZ3+DNbNpe9/3S99L1krGQ1b1a5f8z3+zsfkoUPPq3icSV2NEMZSF0GjGDoq4ZGMzZ/KFb6c0YYECqRZbYzRl4p7h4wnoVbPdB4zXbVVBoCBl2W0qwVPSusOma+r9tWEo/Rym5bDYXVCMVY/OutLLaCMIVj/kPIUcyqYgrCVlCuEEcdXyxmGy9ewdOAy3aiZXDK+E2R18AXfq0IzJD/nLQVk9kH4wxNeaAeCbX9+aR6Od74qO+a5Y15poRNGMcf8k7hl6uLjWAcaYUxyvh8YyMYsy3amGPod8w+1ZQBZUAZcBiYgvgRTlqjyoAyoAwoA8pAvBg4Dx2PdjrPQXwisB5oB3wCqNUzAyoa1/MB0OGVAWVAGVAGlAFlQBlQBjwGlmOLaorGGdnZ0u3ko2TZ82+GpcZuTU1hdfU7U6sUjdOyMuXwd582z0x2O2X7zGZNTVZm0yay7/03S9eTR8usc2/Ac5ZXulVNvEnXjjLqg/HSeq89AmUUCUoLi4yAzOcyHz3zdVn74fRAeahIGlbCDb7rehn0xyvNMzttncJt2yWrRXMjhNCvIQ/fIV2OHyWzzvud7Fq/yVarMuR8D3vzCel0xCGBuqV4nmdxbp5kt2ll8jjf/R+4RbqecIT876RLpXjHzkBdjSgDykBqM2CEQ189hKYIVdGfL8RGKyoytIKlES2RwdAAwnEWyjOgyVLHpR5sNGFqtH5XVgQNFo8pv7Zv116aNW9uarJ848aNkp/Px36m4bnCDLxerFBMX8oFYJYhZQJPDC4v85yxadYzRvHZi3h//Wwm6Kc3HAVetkBIFpDw5mDj5IZxkMXAf/6xrWNCv43XB+qwQ4LjsJGJ+KGX8sfwEwhYapsxTtGXK4wpAlvBeBeOU/AzjO3zi7nC2Dy/2G9n+vL7RKCmDCgDykAwA7sHZ2haGVAGlAFlQBmoAQN8PjwFYNoGL6j090AnZwfiBwCbnTwbjaYvW1fDGDOgonGMCdXulAFlQBlQBpQBZUAZUAZqxsCKV9+TfbGCl9YDW1RHFo1/ZeqtenuKlOTtMvFIf/a6/dqAYFxSUCDf/fEB8xzknctWSaPd2krHUQfLkEfvlCYdd5Pdhu0vh776mHxw4CmeOuJ0PPzFRwKCcd6a9TLnyttk/aezpWRXgRG8973v99LpyOHS66yTnFaVo/2uPFf2uu0aU0B/5v/5Mfn5udfN9taZLZqZba8P+Odd0rJ/H+P3gU/9RaaffFnljsLk7Akx2grGaz/6TL696T6zBTfVCK7S7nLc4TLopsukcYf20vHwg6XX2SfJT0++FKY3zVYGlIFUZICCpNUxjWbMPybTC61AbLdEttscM80yipGZ0Gi5xxzFY+qwnoSLiJ9GzIiiVjimiMr4DTdcL+0gHNvyqVOnyn9ff92k7R/2z7pcdWwMaV/dNW6aFcsswFjc2jrgu5/HoMxf3sy+jIPMhNFXz7wYRWNPOBZp3qy5tG3bRlauWhHo09Znd2mcAxqzS/41cTMvk+Hl+nW8Gl5N20d5LfrnlXn18BdJ5lh+DfcUgXFszHbUvlgcEIyxDTXjBFcXG8GYYjFg++AQ5aO4o2tcGVAGlAFlQBlQBpQBZUAZiBkDk9ATEcnaOoWzEQ8lGLNKNH05XWk0lgwEvtPFslPtSxlQBpQBZUAZUAaUAWVAGaguA1zZu3nOXNOs0+hDJat1i5BdtNl/T2nZr7cpW/7yuyHruJlth+4tFFFpRTty5aNhY2TRQ88IBWNawcZfZMWr78vkfU8Qbi9NaztksPS74hwTt3+4tXWHw7wfxu5cuUY+OOAkWT1pqlmhW1ZULL/M+V4+PfYCyXnxbdskZJjdrjWe33yDKSstLpapo86WH+59PPA8ZK74XffR5/LB0FNkw+dzTL1uJx0l5CRa63zMYaYqVy5PP+Uy2Tp3YUA12L7gJ1n4j/+T729/MNBdnwtOC8Q1ogwoAw2HAYqJFBWtQFyMOFezFgBczcrn5OYViOwEcv2Q6Xz/+bkFqIPLnxE1zTbIYcRKiqNWMB4wcKC0b9/eiLSeWJsmw4YNk+wsrF12RFRPxC3DdtWeyGvTvJhxJa/X1i9D2qvnlZl+0FcaVgMb8LnJJl5iQswYM2deMVCC6pgE1Onhww6WW/54s/Tu1UNKS5BfWo5SqtdIcytq71nGSCNunomM8Uu56wRgnmHM+fr/rC+WAzfkmWaPAadujgM5BMxW1HCLHJtnFYc5FjxGPFY8ZnaFsQrGZFZNGVAGlAFlQBlQBpQBZSDBGGji+LPOiWs0gRhQ0TiBDoa6ogwoA8qAMqAMKAPKQENnYPkr7xkKvC2q3UfdlDNjt6Yu3LKtym2g2Wq/v/1B0jO9DXZ+hFi65dsF5Z05sV3rNsm3v7s3kLPPvTdKZnNv62pm9rno9EDZgr8+KbvWbgykbaQMd+y/ufEvwtXD4WzwXdcFtohe8LcnZdOsb0NWLd6eK3OuugN6AyUFkf0fvDVkvVCZrQb1M9llWG5WShUhhC19+lVZ8uSLMv/ex+Snp7DKmIqLmjKgDDQ4BswVxhEszbNzcdngylUKlnlYxboTwiRB4TiXIUCxkmKmESxRL3iVayjhktez4YccEuC4CNvmU/xtjueuDx06FFvze9tO82pk4CnF2AKbzzFOk0xs7Z+B/bANEDfpjAzJysyQTCx7zsbW/C4aZWeJh0w/ZJpxwNTN8urjPeL6666Tc845W9rgufVZKGOfGRzPAr6kA0gG/LEToa8sM+76zrtz4Ippu9ratmHIy7sB4uSLK4srrCoGr+SYXFvejXiPNI8Hjw2PEY8VL/UU/SusMnYH07gyoAwkEgP4lYx42y0kklfx9yXW8+altgvATS9ibR3Qobsirrb9d0IHXYHyLxe17TF27WPN425wrXXs3KtWT7E+xzh4LI8dz1WeBw1Bk4nXXHmMuwONgLqyeJzTvEHB61eszoVYv47JLQXeHgA5j4eFvlFR+5Hi7XftPUzwHmJ1Uib4NNU9ZUAZUAaUAWVAGVAGlIFkYGDFa+8HRFKu7K1k+CrUc+wJJnvF65PNs4Qr1QnK4HOSaXx+8cIHnw4qrZhc++FnsumL70xmduuWYsXXjKaNzTbOLCjK3SlLn36lYkMnRTE5Z+JbTk7FaOfRIwIZORPCP7eZlbbNWyTbf/zJ1OdzlMOtvg506Ec2zvjKxBq1bS2HTHhQGrVvE1wFi+pKZc4Vt8n3tz0oPz/7X0+xqFRLM5SBuDMAmUutvhkwBwF/GBrx0l/paoVjK1wGhGM8FcCKl2alqxUvceuHoifBfoxwjD6tONqkaVPZd999zXQpGH/88ceBqY8YgWujrcjbXgQ88gI6561WNnm2DGE69qlm0gB/KNzyNzAUmRn30hR7iXLR2QrRDCkMd+ncyROfMzJN2hOIvXamD9sfRjIiMMYMKxTTdd/CrSy23Lh8U/h1t6Emt+E45zEJCMbg24jFCHkM+ceE1gkNlQFlIBEY4BY69wGfA9sB/vpwOfAqwGeWeL9wRCSM8Tkl3GKHeCBMneDsl/36bHOcUzjcyac4au1SROwYD9nMWoa1nXfw8Acg45/AdGArsBrIBeYA/wFGATUxvGPIFcAbwCpgPcBtU3cC+KAsRwHRWjtUvBF4DvgCoJ9rAfbL/ri6jl8mjgFC2Whk2uPAkKJlNJaNSvxyYdt6X5pCtzwA2bHikV80bgG+AbYBG4AtwBqAb/SPAocD8bJYnmO1PXah5kj/bgT4y+V8gOcBz633gN8DtRU/B6MPe8wZln/ZRKIKewTltu0FVdSNpphj89yeB+QBnOsO4Evg/wCe21XZWahgfTrXr9wE4Y3AXIC/zl4BsN/vgGeAA4BYWizOaXceF/nO8bV8D/ApwNcKr1/k6XvgaoDzrI5x3v8EYnk9HIn+XgB4naJvfJ/iefsz8BKwJxDOzkCBPXa8HlvjsbP5B9lMhEc5+Swf45SF68upUiFaG78rdKSJqj+QKEfKgDKgDCgDyoAyoAwoA8pAnTGQt2INVt5+I7sdMsRsx0yRtGgrvw961h7PG27Wo6tJLH/pHZsdNmzcqb1Q/KXtWLrcbCUdtrJfsOXbH6T9QZ6o0WKP3rL5y7nSauDuktHY+z6/M2eVlO7CnfwI9gtWM/cNUZ4GYaJZ726mpDgvHyuZm2Er7L1C1CzPylu1LiBet8C23NwGuyrjs567nnCEqdZz7K+k+5hjZePnX5mV2WsmT/O2q66qEy1XBuqGgaK6GUZHqYoBIzLyD26bG0GTAiTizKKWa8VNuxLZisNmhSvEzkaZEC6BLKwr4bOOSwGuyjXPAUY/+I/VxAdiFS/uaSOxZMkS+fLLL+W4444zrvXu3Uu6de8uq1au9AY0uV57P4rnCXsxuwNDGdVc38zzj/1ys6rXj9tyX362SYQVK3h9BgbAnLn1tFPHiXPr6QrmlNn8Cm3taGhmWxqOkajAKzjn9uBWrKcozJXc9rnFTBuhGHUM76gfvLI4hCvWJQ0bBgORP6A0DA4ScZa94RQ/uAbfbO+BPOJ0gDfIxwJrgVDGtif4BRQ0orGjUcnWdT84d0a+7cvth7+0JGhfeUGt/sZi3tYBrlqkMHkHgHebCtYYqQN8XIyQgjfrUlyKxtqi0gTg+BCVmyJvjI/HEf4GwNU3rF2Pkj8DzcPWEOmIMh5vgseF4tJOwNq3iPDYZfkZZyJ82I9HCnhMT/Er0MfLQ1SONY98E38VCDVfnmfEkcC1QKi5IrtWFstzLBbHLngyFFHfAniOudYaCZ5vxMk+ENTIfkSroUAHvzXFyM/8eKSgJwp5XOyHqbsjVa6irDvKHwB4DQs2voboH3Ex8BRAEXEHEMr6IdNen/hDhEHAf4GBgGt8fezj4xyE7POfQG0tVue0O4+FcIrn6hSgb5CDvMlA4f8xgNctzv07IJJloDDW18N26PNlgEJusHE8+k/wuvUscAWAT6YVjHOzx26pU7Kfk+9kSzckCGvTbQRhuL6cKiYaC7+D+2zw6eA32QZPiBKgDCgDyoAyoAwoA8qAMlC/DCx/5V0jGtstqpc990bAIbs1df7aDbJ+2uxAfrhIywH8ruHZzpzVNhoxzF22MlBu2zfqwO8inuWtDHcvz9bAT3LxzONQ1qxnV+G8aJlNm8ixc94OVS1sXovde0YlGi/9zyvSuEN72eee35m+uD13x8MPNtj3vpskb816WfPep+b5yxv+90W5ihF2ZC1QBuLGQLQ3dOPmgHZckQFXdOQdZ2inAdHYPLcYQqcRjpFP4bIIt+wodBKNkJedAeBOA4VjwtvO2Vv9e+ihh+HWJDIhnc7/YYGsXr1G1q5bJ507eYuoRh52mEycONGs5A145ThEAbqCoQzrfk1WQKSFkByIV6hcVQITo6FPIxhzskwGZF6T9P44Pjm5lcb1e2SXxhgyypBisRWMKcAbDikKg8cC/JTCCsaMUzimkMxVyGYratTnMeB21qZPG7rOaLyhMlDYUCeewPOmaDYHsB8mNyM+E6BQcDDg/brRWx1IgYQ33BnG0/LRuf1gzAswrtzGKDbl+vGNfljTIJbzbgMn3gOGOc7w88MPgBVjKEpQPOabwm8BCh+jgQ1AJGuPwq+BXk6lFYhz/gOAZk7+1YgvA/7h5LnRO5G4y81AnL/2ZH/bge7AHoAV9xCVE4E/ALcz4dsmhO8Av/bT5yJ82I9HCsY5hRSn7DG22bHmcQg6fg2wHOFdyqys5ri7gJ7AQQDPdRrnOh44HYiFxfIci9Wxc+dF8ZFfZHleWtuCCL/EkpMDAYrtw4FZgFsPyagNnxBkIsDznkYRmv3a1zLzQhnPK/vJisLzl6EqRZHXEXV4zeJryRrPBb4+FwF8HVH4tdeZyxA/GjgGWAxEMh7jD4DufiX2uwRgX/38PAb8gv0owA9vjwM1tXid0xS8Pwe6+I7NRzgNyAIOBfYEaCyfBpAbfEkPabF+HXOQ3sCnAF+z1vAJ1BxDHkceB76W+VrnB/mLAZZfCURjv6ASrws0vhfacz0Pcb4mrFV1ztp6Noy333acBhfyIKspA8qAMqAMKAPKgDKgDCgDCcPAyv9OhkjB73tYenHGCQG/uErXblm9/NX3vLvtgdLQkabd+f3Gs10beP+laivYVP69pXH7tqaBXa3MRMHm8vJwveWvCX1/qmkP+z0xXMvI+c2q0f6Hex+Xab+6WNZO+SzAp+29aZeOsvulZ8pRn74kh08ej2cst7RFGioDdc1AQV0PqONVzUAZqgSETV+gtMImV73a7arNc3ZxW3gHjqJ53jHi3E55J9KsE3jeMW5ndunWU3r27ovbk7jPB3w/b74RUGfP5j0x3rNMkwMPOkgaN26Msb2tqG0Y8LiCQgovWS/4H1TuTDznuE3r1mY76oqqqtcmOI8icSCPgzn9BvLdsQMOsWq5r+nY/roVxoXqXakZ39as2G5FdiMMk09wFnhusc+l4RR8ZjRphVXbjT0+UZdteSzMCmOEdsUyjxltt912k9b0Qa2hMoCzRi3BGOANcgKvdLkU2A04CaAowJv/fwbsS7gT4rURPNA8KsMH6cAKL3sjnw3vcvKPZ0YtLJbz/hP8GOb48hLiXYEhwDnAIQA/zP4FsFzujfi9QFVGP3sBFN5uAihw9QQOAFoBvwdwZQ7YnYhlBVLlkb0Qva08aX4oMBjpfQCKpfTzMKALcBngfqG4CmkrvCJq7FkbQch50q9Ixi8t5V+cvJWAwfVjzSNFOuv3LMTxJm8E0DMQngeMBHYHpgDWTkWks03UMozVORbrY8dptQCeAaw4xtf/5QB95mvrSIBv1jzn8G5uBNAmCGtq7vnCY0KeqzIeI2vP2UgNwqfQxhWM6QvnxnOf5wJfizw/KWxb64XIEzYRIbwYZRSMtwPXA5zbQKA/wPy/Aq79Bok0N6Oa8Xid0zzmfO3zPXoswGvDtcAVAM+/S4AigMbrDo9HqOsMy2P9OmafPIY9GfGN11ge0/2AcwGer/2AdwFr9H2cTVQRcq7dfLjXg1edfJb/C6iOxdvv6viSUnUzU2o2OhllQBlQBpQBZUAZUAaUgaRngIIrt1LucNiB0nn0oUbQLNyyXTqMPEiadOJ9NjxY56VJUc3TXRXcBEJpNNa0G+/XebZr42YTcYVk64OtEyrMbsPvepVt17qNgcyt8xbKzHG/DaSjieSvLW8fTX2uJib4TONOR4+QzsccJp0RunPogrxjv5ok7+97QlTbd0czrtZRBqrBwC/VqKtV65CBMo7FP7j1ZoRJ3NLkSl93hawRQTNwlwtlFDPNSliE3Ko6GyFXHXO7amL4iCOggDZCJyWyaeN6WbV6rXn28MxZs+WUU06lziqNGjWWgw4eJtM+/TRwyw/ZRpilO9bM9tM2QTEX1q9/fxlx6AgZMGAPadmypckrhbO//PKLLMtZJpPfnyxr1q4x+fZP3z595djjjjXzbNGiObK9vg499FD0w3uSWKK2YYO89hrvaVX2g3n9+vWTw0eNks6dO0vHDh0lMytLCgsLZc2atbJy1SpswT1H5s3/oTJv4Mys1AZPo446Rg4deaSUpmXJm5MmyzvvfyRHHXOCXHDRZXLAgcOgsWfLj4uWyKP/fFyenzBRSoqKPVEa47du3UauvPJKOe2006Rv376Bua9du1YWLFhg8MYbb8i0adPorlrqM+AKUak/2+SZIV7pZtXr9CCXcSUw2y2vRmgFlIMRp8DwPpDsFot57wkSrnKIuA/xW5y0jVJwuRWYB1DwoF0E/Bv4hokqjKLNc0F16P/fAYr7dswWiA8HpgGuUdCy99k3IX4isN6t4MfZ538A+vusn9cW4dHAm36awWRgHWC/mJyD+O1AOKM4l+0X8jrwVlDFWPPYFP0PdcY4G/HlTtpGVyEyBlgEUCzGJwIjQj2AMBYWi3Ms1seO87oVsMeOab6mpzLiGH0nD0sA99g7VaKOzkfNr4AD/BYU+ib48VDBQcjs7xfQj0h1Q7W3eecjcpJNICSXjzhpG92OyDiAft7vZ45CyPP6BT8dKuDHwGLgUICvbdd4bv0BoBDP1y+NcxoJTAOqa/E+p3m9PwqYEcKxp5HHGw/2PNgD8WuAhwDXYv06Zt9nAfTL2j2I3G4TTrgW8dOBmQDFZNrlQE3PHdNBLf4kq9+1mHLdNc2su6F0JGVAGVAGlAFlQBlQBpSBeDGA7Tn5ZSpljFtUUzROx833riePlmXjXxe7NTW3j978xdyo5rpjSU6gXvNe3QLxSBFuIW2tYKOnZ+U7Ym9Tp9zWCw7DrSjesXSFlJaUSDpWwTXr3V22zsP9EyowcTaK3stfnGRAAajNvoOkzwWnSf9rzsNOsenSvE8P6XTkIbLqrSlx9iT23ZdISUqd+7FnKOF79H6ZkfBuNlwHfU3WEGC2Q8Y1hHmueGyeb4xbjq5wnI1XJreppnDctHGWjBh1DG6nN0HDIpn5xddmm2VuXb0B19lFS5YasVewHHfk4aPkk08+DSwT4RWSdwyN2YjjVLNmzeTSSy+VgQM9kddWZZienibt27czOGDIEJk5c5a88MJEDMP7dliW1qqlDN5rLxN3/1AAJmjLly8PiNbu1bptm7YyZswYGXrggU5TOAj1O7tRE+mFVdW9+vSTERCDP/l0mkyY+KLk7cw3K4TtSmPLV6fu/eSQkeAHovqsb5fIUcc2kcef+A+uz1mYBEgEBu25t9x3/9/k+YkvwR9PND4Wz4N+9dVXpXnz5o4PXtTO4cgjj5Rrr71W3nnnHTnrrLNk586dlepqRsowwJvyRSkzm9SayERMJ1gwdmf4HySuA+yF7G7EU0E0jsW8HwYXeCcx9j3+3uVFw/59GSXnAicAeJcx2zofhjCSUcR6LkIFCntWNGa1QcA0Rnzju9OxNoFwPBBKMHaqmGe0PutkeG865Rl4V5XngZv8rHMQ3gG4b0V+kQnOcxIvIV7gpBmNNY8Ur/AmZYw+5fvxUMEOZD4O8A1zKbAEiJXV9hyLx7Fjnxc6E+QPAKY66eDoW375kcEF1UzzfLKiMfuiaL0uTB/u+TIFddaEqRcpOxOFrkD8LdKPRmqAMr6WeC4P9uv9A+EbQKTzh+dOsGDsNzfBrfh7iZPBD3bTnHS00Xif07w2hRKMrX88D2YBw/wMzilYNI7165hDne+Px4Dny1+cdHB0FzL4/vSmX3AIwl5ADlDXlqx+1zVPNRqPb55qyoAyoAwoA8qAMqAMKANJzgA2x8xN8ilUcH8FtqimuErrMeZYScvMkO6/xg112PKX3zFhNH/y8eze4jzvO2jzvj2k0W5tIzfDV/z2B9sfzopsnb/Y1N++cKkUbtlm4hSVq+qn9V79Q45ThtVhO3NWmbKs5s2k1aB+Ieu5mT3PPkl6n/9r6XD4QZLVqoVbFDLOraY7QgDud/U4rM52xQy/Om7rbPl2gXx93d0y/8//DPTR+diRgXgyRYolPaXO/WTiPka+bohRP9pNHBnAZSOwspViMcVjCsVcWcwtlgm7ZTW3pt6BW0rcXtmEiO9/0GHSol0XiMbNDD78eLrZutpuXz1tOu6hpeHeYwZ+KNS9p/TdvX+F7Zc5ZsAHREwaYbt27eXW226TgYNw/55LlWEleO9YuXKlfPHFF7Jw4UIpKPDum3N18vDhh8iZZ55pRGBuK12EFcFbt241sEIy+8jftUu2bttmsGNHrpm7HZN6dYsWLeW22++AYHwQxuVtlXTsLZkuK7F6+ss53yBch1XDnE+2EYKPOPoE+es//iltduvqbeMNl7idt+WokI82zILwC35at+8i9z3wMOhobNqaPoxwnCGvvPqa7MJ86MP+EMFfe+21gGDMec+cOdPkTZgwQaZPnx6YO+d04oknyvjx4xlVS10G9HqamMeWH2h5kz2S4Yoq9zkVhiDe3kknYzQW826JiR/lTP4pxAuddLjo/zkFhyLe1EmHij4XKtPJ24q4+/oK/kKBq7LZ2rU3Qvr7IFCV8fOr+8M5vBFUsmedHPZ9iJN2o7sjMczJcNsxOx48zke/FJBofAPmnPEmH9buRcnJwG8BimOxsFicY/E4dgdgch2cCd7vxMNFyU9t7SV04H3owScIxM8K0yE+nJgtkm1xVee/rRcc9kVGKyfzNsTJZyTjtY4ir7WOiFCsjWRPRypEGV+b7o80WldRP1xxPM9pzruq9wH65Qq2g5Dm695aPF7HvLkwyg6A8AEgkoDPqu8BfwbOAw4GXO6RrBNLVr/rhJxYDIJvMWrKgDKgDCgDyoAyoAwoA8nOAL6p70j2Obj+F2zYLBumzcbq1+HSCVtUdz3pKGnUro2pkvNS9KIxv7aumzpDup14FBZwNZI9/3ilfPPb8N/J+QxlK+Tu2vSL8YGDUuxdNelj6XP+GNy/z5ZBN18u397o3tsr9z67XWvpe/EZ5RlBsfVTZ0qLvj1N7j73/E6mn3J5UI3yZLNeXWXY+AfMimuK6JP6jpSibZEPdbuD9pVRk8ebTla/O1U2/O/L8g6DYmals5+X1SLSfZ6ghgmVzI9MSEL5qs6EYGB5iDzNSkAGzF1A/1YggzT84XbVdsvqEmin3K6aq2e5HTUF5SzkMX7cyWMRwf2djEL5cf5c+fGnlSYfvweSTNSZPnOO2Yq5cWPcM8fzhUceMVoWLFxsdGBqwfjvxf2Q9DDv3HHnGeGYadqXc+bIRAimuyD6mgrIa9WqlYwbN0723ntvU+ewkSPlp6VLZfbs2Xiu8jy56eabTf79990vbdtCB8CAkya9Ix9//LHJt4ua/anDP+yveN4F0rwF7pMa59Ig0H4mE7BtdF7+Lk/sRh2uNr7ksivk8COgH0BA7tC1lVx53U1y1dXXVViRbVYbl+L+LQRjiubnX3S5ZDduir4K5P3J78qHH02RfffdT44/4QR57rnnjWBMxx599FHhKmvarFmzzCpirop2rVu3bvLMM8/I6NGjTfapp55qVlBz62q1lGSg4gmQklNMyknlwOufo/B8ZlCd/khvCspLpmQOnK3tvPsFTXhWUDpc8mungG8XfYF5Tp4bpUCy0M0IE1+FfCsEukKZrY53PcnxYfPChfgVlRFq8OuggIW6R0+/OGcrCJ+L+IxAi/II861R+PrKJvwwHjySt88A7w1G5GzEDwVeASYD9DMagR/Vamw5aFnbc4yDx/rYjWSnvvHjwxc2ESGchjLyhQ8ENbYtaElBfqzfA1f0PuTH3eB4JNr5GfxBRE1F/IFOp5znVCcdKRpcbw9UDj5n3fbRvLfx190d/UY15TCe53QOfFvk+xcpmBNUyHNpmZ8Xj9fxQejb5Suaa2wR2tzh+1RfQbL6XV98VXvcUG9I1e5EGygDyoAyoAwoA8qAMqAM1C8D+Ja2lXdEUsm4RTVFY4q9Qx6+3Uxt6w+LZRu3dK6GUdzls3wp9va78lxZM/l/sm7K55V6aNG/t+x7/82B/J+ffhWP3iwNpBc98qz0OvcUs7V0v6vGyaq3P5aNn1X8XscV0fs/eBu0EawYC2Nzb39Qeow9QbKxLWo3bL29+2Vnyk9PvVypdnrjbDl4/N+NYMzC1W9PkbzlayrVC85Y9/EMKfhlqzRq21q6HD8KzzI+VNZ9VHm+bNfrnJMDzTd/OTcQT6JI8cWyKTeJ/FVXKzOQUzlLcxKZAd4VdNeRVNiyGpdM7HhvViFn4hZsIeI9u3eRYSN4TxkZZUXy5ntTzQpkiskUjRnmF+2Sz2Z/I6OPOgLLiEvkwENGytPPTpDc3O3mucfYZTogHDPOxLCDD8aWzYM9Z6Dsvvveu/Lmm29arTgQbt26TZ544kn5wx/+ID169EB9kcMOGwmhdbaJB/5wxbBBGqaXxp2yzTRd0Zjx4Xje8b77D/Xqot7zEKnfew+7Y6CMXJjnPKPt9l358uf7H5JFy9bL5VddA2KysOJ6pIw86lfyxtvvGuGYz4I2zzXmDp/cujstQ7KbZEAYTpMTTzpFpv1vunGvrOxZueba3wRcbdq0qQwdCh98O/vss8022jZtw1V4pjK30F60aJERizPwaIRzzz1XHnjgAVtFw9RiICeFpnMV5tI1ivl8iDreCyWKyvVUJSfKcVehHi+x9iM9BYKZUbZNxGo5UToVad7BIsnD6LM4yn7dauwnnGj8i1sxQpxCiTW8YURlfNPpD3B8hhTHBgC9gWCzxz04/1lkWNH4dMT5ZuD6wvquaMz6wRYvHu/EQEMAu/Ka8/29D34+pzg4GXgfWAnE2nKi7DDSORaui9ocO/4owNomRApsIkLI1z6/6PWKUCeaIh7/sX5FHhsKuz/6aRucZyMIXwHwS7samSsar0MP0cyTA+UB64GOTMD4mghnFLW3hyt08t2xo319Os0D0Xid08sDI0SObEAx59LIr9bZqR6P17HbP4da4YyXyNFk9TuROa3gW2aFlCaUAWVAGVAGlAFlQBlQBpKUgbSfktTxsG6vfONDGfqvP+NRjpnSDKIDbXl1Vhn7Pe9YvEwWPfQMVgdfIRlYxTbqg/Gy8MGnZeWbH5pnCjfHs4W59TNX/Vqxd8t3C+T7O3lfqty4pfOih5+Rgb+7FPf2G8uRUyfKvLseMSuQ81atlXZD95E9b7lKOh5+cOC5xeWty2NcRf39HQ/JAY/wOyke7vXkX7Cd9HD56ckXheOmYb7tsVp48J2/kbZDKIjgm+PmLTL31r+beFV/yqBAcH6cD59XfNgbT8hCzJ/beudia2xy0HJAXxl446XS/ZSjTXd52MZ7KUTyJLSf4TNvskptP3oAAEAASURBVKglLwMpd+1K3kNRPc/NC49/cJvbbh/NMB2g4MqVxxR4Tz3tbElvhFXGeJZxYUGevPb2B5KH25MUi61wzNXGb7//iYw+Dj9kKSuRzMxiORTPQH7jv68ZEZrPPmZfRjz2w0MOPRwZWXCgTLZt2ypvT3rPiNUo9hQXRvx4SUGRTHrnPbnmGoi3MG5/3aZte9m0aVPgAlLGR19yG2isHi4tSxcKuhSJDdDGzC0douv5l2BFcGPUS5efl+XIG5M+wJbYnmDMLbuNaIw041xF/NT4l+WI40+TfgP2RJtM+f2td8ur2LliZ6G3Kpn1Sygacytrf6vrx//1uBGMSS8dNCHjvu25556SlcW5078yyc/P90sqBzt27JDHH39cDsSzl5dihfWSJUsqV9KcVGEgla6nF+KgHBDFgaGYMD2KevVZJSfKwSkUbATsatZggSDKbhKmWk6UnkSadzAHI6LsM7hacD9ueax/fNgdnV8PnAe0dweqYZyiHr+UNAW4OvQ4YBJg7RBE+vqJYoQTbYETBs8/VjxyVeIwYDLQxxmP0eYAf51K8G1sJvAkMAGIleVE2VGkc8ztIlbHzr6G2fcqd4Aq4hTWe1VRp6riKaiwGujqVzwH4W1+nAEF/hOc9HgnXt2oKxrnVLPxMtS3ojF/TBHOKKLWpcXrnF4e5ST4WuF5sLtfv5PTLh6vY/dcLcJYa53xEjmarH4nMqcVfMuskNKEMqAMKAPKgDKgDCgDykBSMoB744uS0vEIThdu3ipcNdvFedZudZ5n7HZNAZirgAfccLERUimYEqFsw2dfyowzfyOlBdwhrKJ994e/SXbrlth+eqxZAbzPvTcK4Vr++o0yD6IwxeBwtvix5yWzaRMZfNd1ZiV1T2yLTYSyotydMu34i2T7wp9DFYfM++Evj0ubfQZKj9OPx46nTWWv264xCFV585y5Mv3UK6Q4Ny9UcaLnpdx5n+iEx8E/ntg7gWZx6Fu7jDMDRsw0f7w7wngvMneGKbBy++oMqLxnj7sQkUZG4J387mRZs2Gb2ZKaq4wpFlt89sVcWbVhu3TrgvtjEI6P+dVpMuGl1yEAl0oGOjbCMXVdxJvh+jlobyygYSa2s3773Q9lR16ht1s0fDC7RnPuqIv/xr76Zq68BWF53bp1Bpu3bDeibmAOFG3TKdymSXFZhuzCrTMKxpwLwXhn+Na0JTQAsyI5XZ5+/hWsmi4zQrFdYUyxmKuHrWhcjIL7H/wXVk4/h3b4UVDn1tJr94Hy1dffen2jPkVqlhnH4fHb2B7bitW++xWC+fPnmy24GzdujCZp8uCDD8pll10mO3fypVTZ7r333sqZmpOKDMxLxUmlwJwqf6AMPymKW9bC/xrE1qg6tJfAqmvGvkYs5k2RNBa2Wyw6iaIPfim4Dwh3vx1XfOGPO6YC7wAUUKuaI38Y8TowDqCdC7ii8Xkm1/vzPoJQIltVYzhdRIyG4nExWuwDnAbQx8OA4PnzPBzu4yiElwP4+VitLRbnmHUilscOHyYCVp15bg60qnmE5xg+cMgtfhcUjW8H8CnG2Jn4a/3j96jZXnaN/vKHAdaqe70qsQ0RRrpOWb+d6nGPxuOc5vbZ0ZrLhzv/eLyOsxynKBrz/EkGS1a/k4Fb4yO+magpA8qAMqAMKAPKgDKgDCQ7AyVS/GOyzyGU/9yi2tqmL7+T3KUrbLJaIQVgblM95bCx8svX86S0mD/Er2jbFiyRr35zl0w94hzJXxPqfgu+bUMJ+OKSP8qsC38v2xf9XKEDPnOYAuzHI8bKlu8XViirlIACseD+J+SDISfJejy7uWSXe4/Qq828hdgS+53+R0i1t47G18sZZ18nM8/7rWxbuLTS8MzIXbZSFj32nOEkfzV3CEs+K5PSKohOvjk1QI95M2R+A5x3Sk2ZB9GInIhYkZUi6vARI6V3334Qd3F/Enhu4ivYihr7EuI2705c9nJ97EC4HbdVX3v7Q9zKbGnQpdcA2XO/YSafZcQOIBfo3mcgdnxojaXK+K0B8P2Cn0yf7Jf9W1D4tfHcXSXywsuvy9RpM2Q+rovbdhaaMlunjKuHuUV0ZlMpLM30fKSfAP2kj+279MZ4WDUNH8syW8j02d/Jdtwm3UbQR4QGjNNXtMlF+ymfzjD9mhXK6Y2kV989vO2pccuU4nKpUbcpHPMWTZosXrwkcHc31InClcWfffZZoIjbUy9YsED+9re/yahRoyQbj2NQa5AMzEuhWfND1oIosCkJ5tw+Sh8ptHVx6ob+AOdVcEUFp0mlaFMnJ9o2TpNaRWMxb/fD9lZ4410kPZGJ84kWN9ZqJtE1vgDVHgBcwfQbpB8DLgMOBvAGZ7aovgrhZIDzicaedSqdiDj7ofFif4aJeX/cek52hef+xoPHXAw2HjgSoLB1KvAEkAME23nIeDM4s4bpWJxjHPoCIJbHzv1i1YMDRGmdo6xXVbXxToVeiA930uTf2nM2UsOQP4Cw1tNGogzd+hujbFOX1WJ9TneL0nle09y665x28bgeuu8zfL9o44yXyNFk9TuROa3gm/tGVqFAE8qAMqAMKAPKgDKgDCgDycNAvqyc21x688uN+4vfhJ0AV7W+mNanSv+WjX9diKrs+1v/IURVtmnG1/LBASdjq9RsaTmwr7Tco48Ubd0OAXWVcBvraM349fwb0mpQP2mxe08p3pkvm2Z/K8U7/FVeSySq+W37YYlMHXU2HmOZLs3RT6s9++ORn8WS+/MKg5J8KA5hjM9cJsIZBe6cCW9JzsS3pEnXTtKsRxdp2r2z7ML22Fu+/QHz3hGuadLkYzvZz5PGWXU0EgO8qXpQpApalhwMlNFN88fz98ILL8It/QyTuX17rqzEVv679x+Eu/x49eJWudly2sYRzvnuR0+UxUpjrjY+5fTz5KNPZpgFxXaLaoZNW3fy6nFBBFYaL1u1yYi6pj/cbmPI1cY0P/ASIf5ad0vTIRhn4S0Uwm1BWbYRqPmMYiOCYxjGO3aDAN6oFXpJl02bf5FN2wu9rbj9crPVNOJGCEZ9m95ZkC8bNm2TDh3hNzzqt8dAU4dCO43PUPY8TZNdBbuEzyKuyu68804ZMmSItG3LnSbFPK/597//vRC5ubkydepUmTx5srz//vuycuXKqrrT8uRngOLpiuSfRmAGYwOx5I9EK2xRKOAF05oryDDPv2KY4mh+GYJfwgRWFAa3N53E+U8s5r3Y8RG/FJJewDInL1Gi9O0/jjMUwU4Dpjt5wVFe+PmGYi3S29U0VOK8ewM8ricDE4DjASvybED8PSCU1SWPXBn9lg/6MgAYB1wLtABoowH6vYWJWlgszrF4HLu1zpwoBGcB+BlbldajyhrRVcA3UeF3pEP96mP8NK8x9vM2Pq3UeqtwjmONvuMTWlQrVcmH+wMZnruJbLE4p6M9th1ARCOHDPdcisfrOPh9pjvGjuZ1yfnwZkI0dZ3pxCyarH7HjIB4d6SicbwZ1v6VAWVAGVAGlAFlQBmoAwYuxxfRiVL2WZqkHVcHwyX9EFx5vBXiBFFjg5Kwbf5igxr34Tcsg7KwY9Eyg9r2Vak9bjHmr1pnUKksqTPKSvJk2/+SegrqvGVgBiJX2oSGyc8AlY1WrVrJr8fwnjnug0PBbdmqtcz5Gr8PMEopawBmeTLvWzKO0MIXgw8f/Stp0a6LrF+7xnu2Mbqi2Ny0FcRXirel2GejqFCWrfnFbIcdEKI5JEfmnyjMuOGvIKZovKsk26wc5rOZKRob8Rjxbn0GeWI1hPAVa5caYdkVlW3cbcO+OcNly1dKh05djVP9+w8w/ZZLQHTYczYnJwdCNFtEtlmzZsmwYcOMMNynT58KlZs3by4nn3yyAZ95PHPmTHnyySdlwgRqDGopysDMFJ1XKkxrb0wiGtFon6DJBt8UL3TKmzrxcNF+QQVRXhGDWtU8GYt5uyIJPdkfWBaFSxRdRgE5wAogD4injUDn7j32i5CeXsWAPVCe7tRx4062ieKdRMYDfzIpkV8j5AXdXWX8AtLhhMl48dgRY1IUpnjEHwCGsoXIvBX4FJjiV+CPI44CXvPTNQ1icY7F49jNcSbE44oPDzLXyQsVpaDOX5bFyp5FR4f6nZ2C8AbgRD/N4GNglZOuSXSJ04jXuIHAD05euOhgFLjne/D5Ga5dXeTH65zuA+d5jSiuYhI8p12b4SSCeYrF9XCp0z+j/YDvg/JCJScjk+f1NuAu4GGgLi1Z/a5Ljmo1lvsCrVVH2lgZUAaUAWVAGVAGlAFloH4ZgGD8Sf16oKMrA3XJQNrXl8sWflFVS34G3BsiyT8bnYFh4KyzzpImTbB614ih1CoA/1nAZvUxVyATGbjPmA7YZwrz+cfcKhrIaNRcxp5zodki2t3OuiQDeokv8halNZGteaXeNtb53vbQdrtoEzJvV2jYcm4tXYrtpiWrpcGu0uwKbbgtNrenzm7eDuVYjYwtsfOK0ipsic1trokC3A4kikq8FcdcdcwVx8VUnu38wYURqsEKlQDXuPV0tLZ48WLZZ5995MILL5RPPvlEikM8eoHPPB4+fLg8//zz8txzzwmfg6yWkgx8npKzSo1J4cIhR0cxlT84dRYgvtVJM5rrpLshTuEtkh0bqdApw9UqYLxYx8piMe/lcMbl4XdIR+PjJahHUeNHgNsAjQDiaW7/FIWmRjHYqUF1KChFsudQaN8yjkG8LfArpwFFwnAWDx5fxmDrgGnA34Gq7GNUcMX7/lU1iKI8FudYPI7dp/D9F8d/97XtZFeIXo1UNOd2hUYREhTk/S2wpBfi/FHKSYA1nk+1NV6n8MknYLcFYpEjbj1uq/Vu5Op1VhrPc5rn6rgoZnKzU2c+4j856Xi8jrejf/Zr7Tc2EiHsijIKxjT8glO+NrG6/ZOsftctS7UYTUXjWpCnTZUBZUAZUAaUAWVAGUgwBvDlkLeh1ZSB1GegVEpfSf1ZNpgZ5mCmyxrMbBvIRC+66KLATLds2SKfz5ghn38OzJhZHpo40jNmyYyZs2XGrC9kxuwvJY/qK0VkCMjjLrhEStOyjCBrnk9cKLL451XeM4Ih3jZp2V6y8HxjirrEdoYUeQH7HORworFbpzSjGVYvQzTObmG2p6ZIzecZE3xWMrHEjotnH3ft0beCSFxY4gnFRb5IbLemNquP8c7cs2cvTzTGPeGNm7iTcGyMW1GPHz9ejjzySGnXrp2ceuqp8sQTTwhXLAfbeeedJ2+++WZwtqZTg4FPUmMaKTuLezGz1hFmR/HvYKf8Jiduo0ttBCF+5SK/ddLB0UOQ8afgzDBpV8hjv7G02s4bV1a503FoGOIXO+lQ0fbIdEU6Ci+fhaoYw7wdTl8UfyMda1YdDdzPiGP4xVREo7Az1a+BX2TJQ4A9XhRt5vlloYJ48Pi+M9AIxK2I5GRXiPZFCr/4CtjiQKx2kdqeY/E4dsWYkvs95Qyk944wTXz4kBsjlNekiPN63Wk4DvEj/DQFt1h8GNiAfh72+2QwFhjipENFeZ1zfzDxIdL0JxEs3uf0rZhkVoSJHoayI53yCU6c0Xi8jtnvXfzjG30YaRNhwrucfJ4DM5x0XUbvcgZLJr8dtxM3qqJx4h4b9UwZUAaUAWVAGVAGlIFqMXAONr/Efen/VauRVlYGkpKBspJiSXsxKV1Xp8MxwBVBainCwODBg2Xo0KGB2Tz44IMyYsSIchx2GOLAYSPlMB8mPvJwpA+X5ye8AIE1w6BT525y7PEnmpW7FGa5infhkmVmJbJZkQwBt22HbpLvC7t5Vuz1w1zk5yJOtN6tm2RCYLZplhlhGGVlmbiXzRXMCItKM41ITJHaXUG86KccT8zGyujuPfvg7l06VhCXP7uYQrHd0ppisVlNjDAzM0u6dO0a4GPDBt5ji71t375d3nrrLbnyyiuld+/eMnDgQPnLX/4iO3aU3xMfPXq0tGnTJvaDa4/1yQBPqG/q0wEdu0oG9kGNDwAr8rkNTkfCFZg+Rvo9t4Ifn4Sw/MXsicLsN9h48WVdCovRWK5T6deIt3LStY3GYt6Pw4l5jiOPIU6BLdQ97Q7I5+eJboC1h2wkjuG3QX3fE5R2kyOQeAsIFolDnRtuO8afdTLOc+JuvpNdIRprHt9G73j3NEah/E2gt5es9LcVcp53cjcj7gp0TlG1o/ugRW1eW/E6drfDL/sLMZ6rfF2PBIKtFzKmAe2AWJt7XvwGnePXeMZ4vcn347UN/owO1vqdpCHkPE/x08HBGGR86GTmIX6zk67vaLzP6b6YIPnhdSrYTkAGr9vWFiLiCvI2P9avY/b7HPCVHQDhG8DxTtpGeXyvBS6xGQgfAvDpt14sWf2uF7KqOygv6mrKgDKgDCgDyoAyoAwoAynCAD7J88Pz4SkyHZ2GMhCOgQ8vlBxuiaeWOgzwJu9VqTOdhj0Tbpdsjc/UnThxok1WCstsDiKM431Mnn76GbkCwqeXI3L5FVfJa/99w+x0zfIlPy2DeIvFGmZzjTLp2aeffDt3fqDcfzwwala0V/77tgwYMEAort57773y2GPUHjwrS/dWNrOT4rIMoUBNM8KviWCF85KlnpiNdFZ2puyxx0D54YcfWOrtGepPxg9MPv9QRE/nA5d947bStbWOHTuauVAQ/uab0HrhwoUL5dZbb5VPP/1UpkyZYobMyMiQo446Sl57jTtXqqUIAxRMgk+7FJlaSkxjJWbRHTgI+Angi/FToD3A1VHuzflcpH8HhLIdyJwA2PdKisLfAfMBCnAUnY4GOBbtF+BrYDQTEYwXMfpGowBHQY8iEMcbBNTUYjVvXo2vASi24MJvxNYHEJ4FkMe5ALnYFzgfaApYewqRZ2wijuFU9E0e9/THuAhhf+BJYCnA+++9AIryXFXOdDGwAugD0Lp5QcS/b6J0G9DKqUXh9iUnHS4aax7pB8/Fp/0BOd9FAH35FlgN8JzcA7gAaA1YuwWRnTZRizAW51i8jh1fR+SHWx7zA8BuAM/ht4AZAF/rBwOnAm0B/IzNvPY6I4yV/Q8dLQN6A3ztWBtvIzEIeZ34LfAiwI9oPM48Tz8CZgM8JwYAnOtowDW+rimOJorVxTnNa/53wNvAFwDPi0MBXhd4ntDygUsBnhPBFuvXMfsvA64HpgG8NvF8fNfHlwh/AvoCJwL2vQJRI3L/lZF6smT1u57oqt6wPBHUlAFlQBlQBpQBZUAZUAZShIElkvPi7tLrHjzfuHxJU4rMTaehDFgG8HNm3ixUSy0GeBN9O9AytabV8GaTnZ0t48aNC0z8888/D7lVcqBCUIR3gL76+mv5/vt5svfeexvVdtSoI6R//z1k0eJF5o7k1m3bZfnyldjyuSdql8nV1/xGXn+d9yhhuGXJu5bB1qVLFxkwaLDJbtm6ncz5+juzetnWKynDvTojRLPHdPMcYpYZXdqv9MOCH6UIS4uzsnDvFY7ecsutcvbZZ5u7bbafUOFtt90WyC4oKJB33+W9uJrbyy+/LGPHjjUdUBA+4ogjInb28ccfS15enjRt6mkp/fvz3r5aCjHw3xSaSypO5R1M6ivgPwBXmJ3jA0EFo/h7OhBJRLkD5X2AYwFreyFCuLYFiVMAXiisUMPLayj7NzLPAJr7hRkIuwGs3wyoqbgXy3lPhx8HAuOBfQDa/j5MIsSfici7MkR+PLLy0elZwCyAnNEoBBGhbDkyWZ9imhW1eZwofrOvcMYyirJXOBUmIc4fCERjseaRvlMUvskfHG+Ocp4PP6tCUIrUncBTFXJrnojFORbPY/caplYMUFBtDFCHOc0HgoDRhzHANUAsRWO+hscDfwKsLUFkpk3EKHwZ/WwAeD709Ps8GiERynh9ugDguZtoFq9zejEm+gJwF8BjzNew+zpG0hh5PBmY7SVD/o3165iDzACGAbxu8jXNj9IUiYlQNg2ZfK3zHKtPS1a/65OzqMZOj6qWVlIGlAFlQBlQBpQBZUAZSAoG7sIvUvHJXQW1pDha6mRNGMD5PWOc5EyrSVttk9AMFMA7/upeLckZOPHEE6V9+/aBWUyYMCEQr07k6af9xUtcNgxcfsUVRsC12z7/6U93ozuWpZttrrm9tXl+MG5J2+cJu+Htd9yFutBCgA0bN8tnn8+sUG9n3i70h1sk6K95i5ZeX7jgmJXGfrhu/QZ5+OFH/HHTZOyZZ8r+Q4YgHd4OPvhg85xhW+PDDz80K51tuibh+++/H2jGbb8HDRoUSIeK9O3bNyAYszwWK51DjaN59cIAV0Z9WC8j66DVYeBZVKYgvChEo83I+zdAUTSSYMymrHs88EdgLRBsRch4HcAvbqJ+ju9XqEuBeQXgGkUDige1sVjNmz5wdd5QgBf/YF+RFbCPEaNYy18v4R2hzmweRhoA8E0P7xqVjCsEeXxvAAYCs4CPAGstEDnJJiKE5NS14LRbFioeax5vxiDDgEi/hipA+XsAj989QCwtFudYPI/dm5gsPyhQMKSA7BrPT459HDDZLYhh/Dn05Z6PTMfDPkGng4EnAAqfoWwjMnmfgj/8SETB2Pocj3Oax4DXrjEAhftg24GMB4H9gEiCsW0X69cx++V7Acd/FAh3DJejbCwwCuDnj0SwZPU7EbgL60Na2BItUAaUAWVAGVAGlAFlQBlISgaelC5Nm0n2Yl1tnJSHT52ukoGS0efICt4QVEs9Bo7FlOJ10yz12ErQGb333nty/PHUNPCwQ6yq7dSpk2zdurXa3rZr107WrFkjXLlM27Jli3TFc4Hz8/NNOg1C8pdffikHHHCASf/yyy9mhfNkR1BlAetdc8018sgjj5h6/HPLLbfI/fffH0gzMmvWLDnooIPYQObOnSsjR46Ubdsq3w9r0aKFLFq0SDp37mzac27cjpvPEg62MWPGyDPPPCMtW7Y0RVztOwQiM7eNdo2+3HzzzSbr22+/lf33398trhRv1aqVrF+/Xho1amTKKAIfe+yxsmzZspB1KTIfcsghpmzz5s1mhfbOnTsr1dWMpGTgGXh9cVJ63nCd3g1THw5wVemXwFKgptYFDXkR5AVpFcD+KMzUxHiPeHegA8CLH4XZ7UCsLJbzpk+8sO4JDALygJ8BcrkJqG/jL6f6AX0B/FrJbCG+AKH3BoZIAlkseWyHefUEegA8N3n+rAO+ACiKxdticY7F89h1BAE8Z3le8PVFcbDyBw1kxtA45mqA52Ep0AtYCcTbOC5FZF5TNgDLgXlAIZBMVptz+g5M9E/+ZBchHOBMnNdZ/riFrz+KyN8DtXmNxPJ1DFeM8RhS4O8F5AA/AnyfcX+EgGTCWbL6nVBEpiWUN+qMMqAMKAPKgDKgDCgDykBMGJggvc9IF3klJp1pJ8pAgjCAb6j/PVeWnZ4g7qgbsWcAly3z7DXebFRLQgYo6i5fvlz43Fza66+/LqeddlqNZ/LKK6/IGWecEWhPcXb8+PGB9PDhw2XatGmSmZlp8vj8ZG79TDH5p59+Eq6w5cpnIwb7rSZNmiSnnHIKVhBXvOfFlc0XXXRRoO+SkhJZu3at8JnBwSt5z8QK4xdffNEI0rbBRx99JLNnzzaCMp+bzBXGo0ePtsUmZP/PPvtshTwmqisasw37CqzGRrqoqEheeukloei8evVqoei+xx57yAUXXCCtW7dmE2OXX365PPXUUzapYfIzcAimwBWLasqAMqAMKAPKQDADv0PG3/3MyQiPD66g6bgxEEk0jtug2rEyEAsGVDSOBYvahzKgDCgDyoAyoAwoAwnIwAvS+yO4VfGOdQL6qS4pA1EykFsmhQPPldX8hbNa6jJwJ6Z2V+pOL7VnxhW89957b2CSFGfffvvtQLq6kWOOOUY++OCDQDOKwa4AzAKuNJ44caIRSAMVw0QoMNOnUCuI2Q+fD9y8efMKrSkuc3Vx8MpcPkeYq4i95ypXaFIpwVXSFG8pWIeymojG7Oevf/2r3HTTTaG6rJRXWloqd955p9xzzz2VyjQjaRn4AZ7vlbTeq+PKgDKgDCgD8WSAug/fJwb6g4xB+IYf1yD+DKhoHH+OdYQ4McBfcqspA8qAMqAMKAPKgDKgDKQmA5di96AtqTk1nVVDY6BUSm9QwbhBHHUugSwEKi4DbRBTT+5JchtorgS2xm2Q3Wfv2vzqhFOmTJGVK8t3UTzwwAMrbd381VdfyX777SePPvqobNjAHRArG1c/jx07VkaNGhVSMGYL9kNBecWKFRU64Ly4YjfYPvnkExk8eLA88cQTYcfduHGjPPDAA7LPPvuEFYyD+61OmltaDxs2zKyuDteOW4Rzy/ChQ4eqYByOpOTN/2fyuq6eKwPKgDKgDMSZgWvRvxWM+eEm9C/X4uyEdq8MKAPJx4CuNE6+Y6YeKwPKgDKgDCgDyoAyEDUDE6TXKemS9mbUDbSiMpCYDLxwjiw7NzFdU6/iwMAz6LNcfYzDANpl6jLQsWNHI9L26tVLcnJy5Mcff5RVq1ZV2o46HAMUiXfffXfp0KGDEZgpIm/fzscyRjaOSxGZbSleU6ieN2+eFBbyNxDxN25HzVXPPXr0kC5duhif161bJ1988YXZYjv+HugIdcwAn1vbE8iv43F1OGVAGVAGlIHEZOBGuPUTwGdSjAQuAKxdh8ijNqFhnTCgK43rhGYdJB4MqGgcD1a1T2VAGVAGlAFlQBlQBhKIgQnS84F0SeeXSDVlIOkYwHLT+dskd9jVsjE36ZxXh2vKwJ5oOM9vrN9Za8qitlMGlIFUZuBOTO7uVJ6gzk0ZUAaUAWWgWgzwET5dQ7T4DnkHAkUhyjQrfgyoaBw/brXnODOg21PHmWDtXhlQBpQBZUAZUAaUgfpmYJwsv6lMyl6sbz90fGWgugxAMF5RIkXHqmBcXeaSvj6fv/YqoIJx0h9KnYAyoAzEgQE+euSROPSrXSoDyoAyoAwkLwPLQ7hOIXksoIJxCHI0SxlQBkIzkBk6W3OVAWVAGVAGlAFlQBlQBlKIgbKdknNBM+nVNk3Sjk2heelUUpuBjRCNjz5fVq1O7Wnq7MIwcBfyTwP4Q2cVj0GCmjKgDCgDPgN/R7hN2VAGlAFlQBlQBhwGXkO8CdAHmA18ALwMrAPU6p6BWRjyQX/YDXU/vI6oDNScAf3yXXPutKUyoAwoA8qAMqAMKANJxcBdItn9pPdEOH16UjmuzjY4BrAyfjlWGEMwXr24wU1eJ+wy8CQSl7kZGlcGlAFloIEzwB9S9QfyGjgPOn1lQBlQBpQBZUAZUAaUgTgwkBGHPrVLZUAZUAaUAWVAGVAGlIEEZGCaSMnesvX1ttKmPX45yOcaqSkDicjA9wVScsSFsionEZ1Tn+qUgS8wGkXjxnU6qg6mDCgDykDiMYDNN8yuC1ch/Cbx3FOPlAFlQBlQBpQBZUAZUAZSgQFdaZwKR1HnoAwoA8qAMqAMKAPKQDUZmCi9zkeTf2G76qbVbKrVlYG4MYAVxuN3SuHVl8saXUEVN5aTruPr4fFDSee1OqwMKAPKQOwZmIEuRwAUkNWUAWVAGVAGlAFlQBlQBpSBmDOgonHMKdUOlQFlQBlQBpQBZUAZSA4GJkqPQXhc6AsQjvdNDo/VyxRmYFupyG/GybLnU3iOOrWaMcDdsfhMsKE1a66tlAFlQBlIegYoEhcB/Lz2Y9LPRiegDCgDyoAyoAwoA8qAMpCwDKQnrGfqmDKgDCgDyoAyoAwoA8pAXBk4V1YseFtyDiiVsutxN3J7XAfTzpWBMAzg3JuYJzv3UME4DEGaXQIKLgEomOjqOj0flAFloCEywAUf9wAqGDfEo69zVgaUAWVAGVAGlAFloA4Z0JXGdUi2DqUMKAPKgDKgDCgDykCiMvCs9OqULfIH+Ifnh6Y1SVQ/1a+UYmBKmZT8CT9e4HabaspAVQzcjAr3AxSO9XtsVWxpuTKgDKQSA3y++6FAcSpNSueiDCgDyoAyoAwoA8qAMpB4DOiX7cQ7JuqRMqAMKAPKgDKgDCgD9cbA89KxQ4Y0vQ66zAXQZbrUmyM6cEoygGcW78LE3hYp+ce5snJOSk5SJxUvBrhL1lTg8HgNoP0qA8qAMpCADOTCJ25LvTQBfVOXlAFlQBlQBpQBZUAZUAZSjAEVjVPsgOp0lAFlQBlQBpQBZUAZiAUDd+Fhx7tL76MgHp+N/o7Gc487x6Jf7aPhMQChOA/nzwwsD31tp2x99XLZsq3hsaAzjhEDvA59A3SKUX/ajTKgDCgDicqA3VXhTDj4SqI6qX4pA8qAMqAMKAPKgDKgDKQWAyoap9bx1NkoA8qAMqAMKAPKgDIQFwawffWATJGR6ZK2FwbYA0Jgf4S7QQxsGpcBtdNkZKAQTm/DucHVUIuAhTg/Zi6RZbPvEmGZmjIQCwZGoJNPAFyS1JQBZUAZSEkGrGD8IGb3u5ScoU5KGVAGlAFlQBlQBpQBZSAhGVDROCEPizqlDCgDyoAyoAwoA8pAcjBwl1mR3LZ5oWQ3Sg6P1ctYM1AqmSWrZFUuzgUVhmNNrvYXjoErUPBvwAor4eppvjKgDCgDycrAR3D8BECfY5ysR1D9jhUD3F2kq99ZAcL5seq4AffDnVu6+PPno2N+aMBcJMvU9XWQLEdK/VQGlAFlQBlQBpQBZUAZUAaUAWVAGVAGlAFlQBmocwb+hhEpGpf6IeMK5UDPAT0HUuEcmIvrWQtATRkIx0AHFAwJVxiUX526QU0TIvlHeGFf1z8lhEfJ78QdDqcLk386DWIG+jqI7jAn+/UuullqLWUgzgykx7l/7V4ZUAaUAWVAGVAGlAFlQBlQBpQBZUAZiDUDN6PDFwDunsWbyWrKgDKgDKQCAzmYBFcY70iFyegcYs4A7+NeDSwC+LiGSFadupH60TJlQBlQBhKdAb3eJfoRUv+SigF9DlRSHS51VhlQBpQBZUAZUAaUAWVAGVAGlAFlAAxQKL4A4HPVTwWY1scvgQQ1ZUAZSFoG1sDzI4FVSTsDdTzeDEzBAEdEOUh16kbZpVZTBpQBZSAhGdDrXUIeFnUqWRlQ0ThZj5z6rQwoA8qAMqAMKAPKgDKgDCgDykDDZoDP+jwTeA04CVDhGCSoKQPKQFIyYAXjn5PSe3W6rhjYvRoDVaduNbrVqsqAMlAPDPC50xP9cTfUw/iJPqRe7xL9CKl/ScVAZlJ5q84qA8qAMqAMKAPKgDKgDCgDyoAyoAwoA+UMFCI6BpgAUEBW4RgkqCkDykBSMGCvVznwliuMVTBOisOmTioDyoAyUOcMTMKIhJoyoAwoA3FngPu9qykDyoAyoAwoA8qAMqAMKAPKgDKgDCgDycoAVxyfAzwG6BbVyXoU1W9loGExYAXjuZj2oYAKxg3r+OtslQFlQBlQBpQBZUAZSEgGVDROyMOiTikDyoAyoAwoA8qAMqAMKAPKgDKgDFSDgVLUvRa4AWBcTRlQBpSBRGXACsaT4SAF49WJ6qj6FTcGOqHnrkDTuI0Qu45j7WsTuNYDyIqdizHpiT866wJkxKQ3r5M2CDjfeFs8OO0Ap9vGwHHyyXM9VhrEbuirdQz8sl3E47izb57f3YFGTCSAxcMf7mDL10ysjm190hTr86o+56JjKwPKgDKgDCgDyoAyoAwoA8qAMqAMKAPKgDKgDDgMHIv4ZoDCjEI50HNAz4FEPAf+iutTLMUpdKeWoAy0g183As8BXwBbAfecXIf0K8AxQCgbjsx3feQjtG35jFOb/xDitOrU9VpU/FtbXyv2Vp4aiegLAOdq/ecuIVxh/xKwJxDO/ogC2+ancJX8/CsQWk4Y3g1E2oHkAJT/E5gObAM4DjmeA/wHGAVUZReggh3zdL8yw8+AEoA/ZJsHXAzE8jGRteH0DvhiOV2IOI08kb83gFWALd+J+H+Bo4BobQQq8pzmvAsA9sV+vgT+DxgNRGsU3m8BvgHsMWJ/fA78x8CjwOFAdSwWx/0sDGiP+7n+4E0Q3gjMBXjc6Scfo/Id8AzAcWtqZ6ChHY/nZrDFwx+3z4v8AfkjknuATwEeU85xF/A9cDVADiLZZSi083ggUkWn7GWnzXFOfm2ud/E4rxzXNKoMKAPKgDKgDCgDyoAyoAwoA8qAMqAMKAPKgDKQSAz0hjNfA7yZZW/cMa5QDvQc0HOgPs4Bex2iYDgGUGsYDFyPae4Aoj3n+MzSZkHUnBZFe4qctOrU9VqU/42Fr+W9ebF2CKYAVc2f4irFxFCi6h+d9j8hHs6uQYE7zmykW4epzB9s3A4UAW6b4Dhft/8AIq0Wvd/pg76eCdjXu9vfL1X0g+KoLBac3oGRrG8Ujbmi+D0nz5YFh4+hTjoQzrqjgAJfcLtQ6SdRr0W4jvx8CoTRvn5CvXaCu4/lcXc5vBsDDQIWAKHmavMKUH4tUBOr6nUQD3/cPv8Op/nZmq9BO59Q4WqU7wuEs0dQYNvNCFcpKJ+vHdvmcqespte7WJ9XjksaVQaUAWVAGVAGlAFlQBlQBpQBZUAZUAaUAWVAGUhUBrLh2N+AUsDebNJQudBzQM+B+joHuPKwJ6DWMBi4E9MMPte4AvEdgKtupwPrgeA6f0aeaycgwZWfRDFg62918t9HnFadul4L72+sfHX7pMCUA1h/GXLV5bfARGAqkAu45f9GOtiqEstY/0rA7YevtXCCJFcYzgyqz5WSXwM8LixzV3SzXx43btccylzRmILYFsD1xcb/FapxNfNixakrBm6CD8uCfF6O9FdA8PHhXH4HhLKOyNwI2Pky5PlK7l4FuBLVPX9ZvgzoD4SyIch0x2dbiozs63ngfwCPmzvea0iHs1gfd5dDrvxdAVhf6OuPwGInz5YxvBqorlX1OoiHP26f/FHBasDOYx7i/wSeAOY7+SzntekgIJTxNWL74PGMxsKJxjW53sX6vIrGf62jDCgDyoAyoAwoA8qAMqAMKAPKgDKgDCgDyoAykEAMHA5flgC8SVXqh/aGlYblN++UC+VCz4HYngP2epOH685NAFe5qTUMBvbCNN1VrF8izbxg4zlxKeCKIpuRbhZc0U9TzLOv0+vD1LHZ0daNl69THF/p84tAS+ucH3ZG+A5g58RwnF9mg6rEMvJnX2ts/wkQjj/2+Sjgjke/2rHAsSzE7wXcfikMhjJXNC5ABfbNbXtfAS4CKKz9DIQT0VAUtcWK0zswossB4zxffw/sAVjj+Xkj4Iqz25EmP8H2NjLcPp9BunlQJR7/CUH1PgmqY5MznHoU8nvaAifshvhHgB2XYi3PqVAW6+PucliKAenDNuA6wF2ZTh/dc4T1FgFpQHWsqtdBPPxx+7Qc58PpM0I4fjHy+KMQW28h4qHOk1iKxq4b0V7vYn1euT5oXBlQBpQBZUAZUAaUAWVAGVAGlAFlQBlQBpQBZSBJGGgCP3nTzt7Qsjf47M0tDctv9CkXyoWeA7E7B7iacvckuU6qm7Fj4P/QlX0dbUS8YxVdX+DUZ7tTw9SPVhhh82jrxsPXszC+nT/DP9OhMNYY+d8Atv7nQfUiiWUUZd338w+Q5vt9ONsTBRQW7Vh/CVfRzz/TqVuC+P4h6gcLgvRnZIh6tc2KJaehxMDzIzh4L8osZwwPD6rLtm45hdNIdjMK3frnBFVuirT9vMZ6vYLK3SRXlK8BbH8UvoMtHsc9mEOK7oODB3bS/0Hc+sjwcKcsmmik1wHbx8Of4D75GhgewdlTUObO8YYQdetTNI71eRViepqlDCgDyoAyoAwoA8qAMqAMKAPKgDKgDCgDyoAykEwMcAXNu4C9qVXqxG2ehuX8KBfKhZ4DNTsHfsa15deAWsNjIA1T5lbS9rXzQBQUcEWmrc/wqjBtohWC2TyauvHyleKtnc9axCMJufTVFZv4vtyLmb6FE8vORzlFLDvOJMTdFZ5+8wrBFKf+XMSzK5SGTrifGaaHqBIsGnNFazwslpwGi4H3VeFwa5Rbnhm652cm0ludcv4AgOdVJEtH4feA7XMd4u45MtQp4/lQ1Y8ubkWdt4EHAZ5LwRaP4x7M4cPBgwalOyBt58vwmqDyqpLhXge2XTz8Ce7zBTtYhJCrwu08fwhRrz5F41ifVyGmp1nKgDKgDCgDyoAyoAwoA8qAMqAMKAPKgDKgDCgDycjAkXB6FmBvbPGmpI1rqFzoOaDnQE3PAYof1wNViVeoopbCDGRgbr0Avtd0BqKxTahkz7sbwjRY7tTheRbJoq0ba1+58rMAsHP5bSQn/TJuY3s3MA44EHAFxFBi2bmo4wrG/0WafUQybo1sfWJ4daTKThlFSNuOnxW4WtG1YNGYxzzWFmtOg8XAAVE4vB51LA+3OfX3cPJZfrxTFil6YlC7A5zKPP7cBtmOR7GymVNenWi8jnswh5FWGVt/+f5g5+RyaMsjhaFeB279ePjj9snXG491VfYrVLBzZNg7qEF9isaxPK+CpqVJZSC1GOAve9SUAWVAGVAGlAFlQBlQBpQBZUAZUAaUgYbEwFRMdhhwAvAZkAbQeINLTRlQBpSBaBhwrxdcWcpnf/YBuOKMoplaw2WAAksOwPeatUAk64LCc4DGTiWu3qwri7WvB8FxdwXvrCgmUoQ6FKgmAF8CFAzD2VkoGA/Ye9pfIM5tpNlHJOsXVBiNX2zytdOOnxX6OulQ0SWhMmuZF09OyfXCKPzjNc5aKxtBONCJ85rIcz4aC663h9OIPvGzmbWzEVkA/A0YBbjnF5IRra6O+/KIXniF25w61ZmD0yzqaKz9ycHIi6IYfU5QnZFB6fpMxvK8qs956NjKQNwZqMsPIXGfTAwHaIe+tgNVfeCI4ZDalTLQ4BjgB/zWwC9hZl5VeZhmmq0MKAPKgDKgDCgDyoAyoAxEzcD7qElwyzqu2hoDNAJovPnJG8RqyoAyoAyEYoDXh9nA48ArgN5DAglqYRnogZL+AEUshhTJuMKzNxBs9f3eUxtfOwdNZkVQujbJrmhMYTnD6WQ/xAcB3O44kgWLh/xxB59vXF1jP/PCNNqFfFdcDVXtKmRyHlXZh6gw3a8UT07D3ZML9s+9vvF+nTVXNOZK2gJbUEWYh/L1QEe/Hl8Lrt2JxBCgrZ/Jc/L3PnIRUnSeDPAz3EognNXFcd+KwakjVGUuNy6HVbWrbnk8/FkepRMbUI/ztJ+lg8/dKLuJW7VYnVdxc1A7VgYSgQEVjb2jsCeCS4DjAL4JNQH4Szt+sFkAcOuEKUAqWRtMZksqTciZi/1Q9QLynnTyNZoYDPBG3MXAIQB/ncgPEzcDfJ3Rqir3atXf31R+7dSW1frk5jr/3OEXmSNrO5F6aK/XrfIvxLG+dt+O4zka2AycWs1jyy+l3DZrJ8DPCGp1x0CivqZrcz5Fy16oa2mi8hHtnP6fvfuAm6So8z8+8+wuOQkSBVlBgoKiYERQQDGjhwoCIiLm/+l5p6eop4jpRE9Q0TuFO45FRBAwoYIBc8BwoIBKkLAgSSVnlt1n/t/v7tSz9fRTPdMz3f08Ez71ev22q6urq6vfHWaerpnZbL3UPmbr1DVfl2Vd7dbl0E+7s/FaNZfnhr8Z8QrFWxSHKPzTl35YGRIDyEGCKQII3CiC0xX/o/gDHAh0ENhCy/yBJL+uPLRDvUFYVFVfN4p2xn+f+3qpKq0WNRRel/2NzUWKJyk6DQJvo+Vx2j2e6SGfbSdedbFmJuOCRP7VKntCojxb5EHI8N6rTlMPwJZJj4pWXhzli2SvVqUwaOwPUcTpPM08VeGB4a3iBcqvpXhxO3we/FLh578nK7Ipe7zqOO4eKB2kVEd/ig4a+3h4EP+RbZBNBglGfanqvBqw3aI7CFQrMFFtc0PXmh9KfE3hN/l+E+cXKA8YO81TPELhnyv7nuLnii0Vw578ya0fKPzTLaOa/AbA4eNHGiwBH5dTFR58WbfdtVU1vb6d77a8XW1OJuNw7fQLOwg2W6vzPn9263cn5ng9990xzvetugx8frrtpyh6Tf4D0+s+rdcVqV9aYFCv6TLnUzeUTvfSQfXotk/Z5Z32MVu3rvm6LOtqty6Hftqt6z7tvgzCuRFM/I2bTyn8QNcPvN6t8N+CyxRx8kMxEgIIjKZA6vq+Qrv6GcUeis0VfobkZ0kkBPIE/lULrlK8TfHQRCUPLl6u+Jzi+Qp/yHWuUpV9XRDthAeNuw2iRtULZ09QzTdHtR+v/Hui+VR2g1RhH2Ubdljnvg7LyiyaDdN+++cB3JB63f/4vVUzNBJNfX3spHi14oeK1IcCvJ7/Xv+C4iTFaoo4zcZxT71mxH2Y7Xwd/bmjh52Ij2UVfYnb66EbuVWrOK9yG2cBAqMgMH8UdqLPfdhR631TsbC9vm9+ixR+03+twi8qj1a8XuFPdPkF6FyFH5bcpBjG5E/3XKTwm40zh3EH6PPQC3xIe+Dzz380+A+j7yj84u9PBTp1W76i1uz/y7WTb45Nvg1LEEAAgaIC43AvHYd9LHq8qTddYJDPjSvV1aPasZ6meyp2U/hvw8cp/OFHEgIIjKbAFdqtX7TjR5p6noRAUYFDVfE/MpUv0Lyfffi5nMPPH/2LRiHN1Rd7DlUHquyrXztDWkMZf2HntlBQwfSzauOf2u28XNOnt/Pv1fQbigvb89nJVVHB7cqvr6hiQCtqtlDW/bBLt3RzVKFu02hTPWfje+OWPa4d1/97zrp3q3xRO9bRdC/FcxTPVSxUxOkQzfgZfvwrYYNy3ON+DmN+84Kd9jPeuG6nMRTXLZLi66XoOt3aLXtedWuf5QgMtcC4Dhr7ZvMVxcL20fu8pocr7mzPx5OPauYExQEKf9L8W4onKubijYU2WyqtorU9YEdCYK4EFrY3fI6mb050otvyxCqzUsS1k888KDa+Vx+vmMzvKksQQGCIBMbtmu52Lx0Fj277OFunZ12WdbU7Wy5zuZ1BOTe6GfgBs3+lyuHkv6W3UzxGsa3iEYqFCg+C+wPIfhg9T0FCAIHBFPDzHw/KeKDCXxy4WuHBhYvbcZemJAT6EfCHjP47WtHn2MsUP43KslkPhKwbFXp+NlIdfY0HEb0PWyiKDBo/XPV83XWq62vV/41ESK9TxoPEqyn8vHORwj9T/aAim/ztwpC83wsVvu5nO3mgu9dUp2mvfcnW/3NU4GM4oSjyXMbHa7No3b9F+bys79tfb4frbK94pcLnxNoKp70V8QcVBuW4L+/cEP/jY1skedB+1ajijVHe2Xg8xX8DdEu+tuN68frd1i26vJ/zqmjb1ENgKAXGddD4aB0t/2HvdKzirctz6X/uVfEhiocpdlfsovAnls5WkBBAoDcBP0RzumrFZMa/3ZbPWIECBNoCfiOafTMKDgIIDK8A1/T0Y4fHdI8yc3VZ1tVumX1l3XoFlqr5P7YjtSU/8PdDrhB+iEpCAIG5E/CDZg8kLVE8oCgyqKFqJAR6FvCzw/h562Ga/2mXVjwgE79OxPkuq5ZaXEdfr8z0aBvNX5QpS836ywWPVtyhOFLxKUU2ZQeDPSD4AcVH2xUfp+m/KY5sz8eTePDQ5Tsrigwar6p6eyoWK65V+DnxbKc6Tcvuy5+jBjwQ/CiF3x91S49Rhfg8zx6fjbXcg8L+IIG/pZ9Kl6rQx/tHiu+3K/gDe89SnNGez7Y7TMe9vQsDMdlKvfB9ze9/O6XHZhb+IjO/JJqPv0EcFU/L+v4RJ7+/LpOqOq/K9IF1ERh4AV/s45Yeqh1+bXunr9f08AIAD6rO+xQ/btd9m6bxoLEfBPhFaZnCNz/nd1WspfiN4hZFKtnfL6bbKRYr/qC4X1E0+dM22yp8A71T4Rdqv4FJ/fGzusodIbnPYf6+UJiZlu1fprkZs730P7uy31j4hWhrxQ2K8xXxC49mp5JfULwtJ7+4+Xh2SsElHM9U3V5t+jlH/EfDIxUbKHxsL1PkHSstmpF67eOMBroUFG3fx8pvsp2cDyk4+5i4LafUch+v1JuSottf3nDiHw9QP0rhNwzXKS5XpD7Z6H6Gviq7/MFfmE8dj7LHzdvolNbWQvd7S8VVCr8Z73TfWKDltorPZ18TvnfsqPC59SdFyljFHVMRm37OfW+01/tD2E+vGx+XUF7V/pc573q5b3k/iqbUPoZtba5GfqFIfVrbr1X+INTDFL6/OOzULfV6DsbthX4VuXfH68X5Mscgbqff/Jpa0b844nP0t4q81/hwn/Brsh9Mdkq+R9qmSN3Qzmwf936vyX6vvbB/3t/UNR1b2foJig0VVyouVnh5KoV7kh8cd7p3huPne6Nfh3pNvXgVuZd28+jXOd4vn4OPUWyluF3h8/tuhVNwi7ezYkmxf4vsY3YbRd9P92Lt3tZlWVe7sXCdxyhsx9so+h47rJOd9nJMipwbZdrPrjub877P+P7f7TVgNvvEthBAAAEE6hfYPdqE30v+IJrPy+6bWeC/eVLJ78VC6jZ4UqRuHX3188lrFH5m4fRPiq8sz+X/479JPWDstK7i/OW5Yv98QtX2Vzy+Xf09mvrbqL9vz4eJ++T3uOu1C96u6VcVfr3ulF6rhZ+NKjxd+Z9F87ORnW3TXvbJz5P895LfCzu9V3Hg8lznf1wvJL9X+laY0fQ0xcvb8z/SdK92Pm9yrhbcqwiDkNtGFYf5uEe7MedZP5t+peLELj05PFr+B+WviOadDX9fOr+5wn/zxfcql8fpufFMh3zcRt69scrzqkNXWIQAAsMo8P/Uab8hcLyjxx34Z9XfW7F2Zr0TNO/2/CZoe8Vf2vMu84vWSxRxWl8zvlH5YWXoi6dLFWcr/GapU3q2FvpF2TfEeH3n3ebHFH4AE6dUXddPPcQo2794u6l8P/2P2/GbDw/wxftu5/9U+I11KD9KeSeX3aRw+Y8VnZKPX1j/XxMV+7Xp5RzZQ9v9ZdSP0B8fw18pPNDXKfXbx05txst6bf9pWjnsQ2p6fJfl7403rnyv28+s3thJBX4zmr0mPMBwmmJrRZyy9cI+ZK+dPbRSmeMWbzOV30aF7nfYfpj6vvE9xSMUqfRfKnRdr+s3Th9R3KII63vqAZkPKPxmrZdUxOYENehtFL0/9nt/OLa9nexxqWr/y553vd63ejkOYR+/rpV8DG1xqyIcYx+nCxSPUTj5nnic4k5FqOPp7xQ7K/JSv+dgaK+sQb/H4BR1wPt3Y+hID9Nwf7KVB3ZPVPiai93+qPmnKLLJA5eud73CgzB5yYPQdytc9z/yKiXKZ+u493tNhv71e+85tm2Sd01/X8v9XufLiiXtuuG4XKr5lylSqej5ENrKHpNu6/fjVeRe2s2jX+dglLo+fa6fpfCDNTvbxPeZflKRfRz214thP0Y+rqnz4F6V573Hzp4LdZ3/YTv9tB/WZYoAAggggMBsCbxPGwrvJT3dtMuG99by7PPBD+es8weVh7aPyKkTiovUrauvh0b9dH+fETqVM/3vqP5flY//fnp3tOyKnPU9YPxgVO/3yi9I1PUAdvDz9LWJOnHRQzUTP+P133jZdJQKQpv+u7uudKgaDtvx9BldNtTJ1OdOaMt/OxVJfh4Z1jk6s8LHo2V+trZLZnl21n8/h7Y8/UamwiHRch/X8IGCTLWpWT/Hi9vbb2rJikwdx70fw4uifuZd45muT812uw7q6E/cpn19/aWuq9DJpysTH4d3hgXR9BWZOu+IlmWzu6rAf4vEbb4xW6k9X+R+d0jUVhXnVU5XKEYAgWEU8OBKuNl0+6RS0f0LD7m+qRXim1TYzqOihnZX/npFWPZ35X+u8MPsUHar8s9XpFJ4COu6HujxC87PFFcrlilCG79QPk7+ltldirDc696huFkRp7L9i9tK5fvtf2jrfcqEffDUL1h+0xb2/bvRcr9xC8kPfV1/UrFFKExMP6Iy1/OLxyaZ5WVsTmi32+0ceZrq+diEPlyovI/vPe0yl/uPiZcqUqlMH1PtZcv6af/JasTnmsP9dzzQnneZH7J2Wh6/gejbbHf+AABAAElEQVRn+2p+KtktfsPha81vfN2f0Df/gbJQEVKRa6fscQvbypvurwVxH90n99v9D/2+S/mDFNkUrrnvaMEiRah/k/JeJ8x7eqail1TEpui57+2GvrovvdzfvG63h/Vl9r/sefc+9S92Lnrf8n4VScHtHFX24HzYll9X7o3mL1d+Q4UHfVxnmeJahe+LYZ0blF9LkU37q6Dfc9BtlTUocwxO0fa9f2UGjX0+ntdux68P5yuuas+77SWKNyji9HbNBNdnxQsy+fgPlx0yyzrNzsZxD9vwfvR6TYZ1+732ul3Tv1affqpw3/wacrbC733i8/Rtms+moudDOHa9DBqHfe7Vq8i9tJtHv872ea8i7K+nPrf9oG1pu/wyTe3tZf0OGhfZxxPa2/impt3eT/drraZre70Y9mNU9j7d7zEpcm74uPXbvtclIYAAAgggMJsCL9TG4vdWfo+Tl/x3Tvy8J6z36ZwV/Bwg1PH7tXVz6rm4SN26+uoPrP9WEfp6i/Kp55yu95aonuu/SxGnd2smtOO/pfPSR7Ug1PP0g4mK81R2UVTvfuX/VTGhyKaNVBDvg9s8LFtJ80cpwnYvSCyvqqhK0yOiPl9asIPx+XR0Zp21Ne9nCcHB7+/+IVMnzL5UGf/9Fur6/N8+LGxPfV772IQ6/nvkEe1l2Ynr+u/AUPdm5dfMVKrjuPdjGJ97wzhobOOfKHxtZNMLVHC7IhyHS5RfJVtJ8z5X7lSEevcqv5Mim56oAh/LUC9M35it2J6Pz8+8e2PV51VOVyhGAIFhFPBD83Cj2aSiHQgPufwg2W1/X+GHwO9QHKcIyS9a1ylcxze+lyvitKtm/qIIyx8SL1R+z/YyL/+SYn1FnB6jmfMVXu5w/Tg9XDNh2ZviBe182f4lmpxWVLb/HpgL/fdx3CFqfUPlz42Wu95R0XJ/Ki2se3hUHmebmlmscL2zFHEqa1P0HAlvIK7VxjePOrCq8i9QhDdNv4uWhWzZPoZ28qZVtO+BFfsek7ORTsvLbn8LbTMMkt6mvN+ohrSeMm9VhHPkN2FBe9rt2ilz3DKbmjH7MJWEweFblH+Rwueqk6f7KFzuvntQZytFnMJD1kkVus5pCu+P0wLFMxX2CPvua6WX1M2m6Llf9v5wrDrtfXgg0/my+1/2vCtz38rsSu5s2MdwDH2Md2jX9h9H/6EIy/wmfpniQ4o1FE6+f35FEer4j+Y4lT0HyxqUPQZFBwnjfQ7545UJLp5+WxG/Nj9J8+F12/ev+PrbWPMPKrzeIkVeCq9dv82rkFNe93Eve02G/vV77+l2TYfj8jX5+BwJ6ZHKXKLwcp/rOyviVPR8CO37+olT3vplvbrdS7t59Ov8dO1c2NfLlX9stLN+bTwzWu56X4+W95rtto/D/noxzMeo7H267vO/bPu9nqvURwABBBBAoIzA6lo5+wG4n6nsYMVTFbsrXqnw+9jw94KnVyrC+zL/fZZK4f1SqLdUlfz3yJ8SlYvUrbOvfn8R9s/99fvVsxTvVRyg+DdFPNDjOt9QNBVxerdmwv5eES/I5FfT/GVRXW87+7eAV/H73/DsKbR7vso+ofBxeb3Cf8vcowjLPT1OkUpHqTDUuyBVocKyqkyPiPp8acH+xcfq6MQ6PqY+xsHC0+8qPqA4SPFBxfcU8XLnX61IpcNUGNf1MTtJ8c+K/RRvVHxSET/Pcn0fv1Sq+rj3Y3iROhb2aVgHjd3/GxSfUxyqeIfC1+0yRdi3e5XfTZGX/lMLQt0wvVhlH1P8j+LaaPktysfnjY97KhW533m9qs+rVF8oQwCBIRQIL/oehKkqxTemX6vReTkNf0Tlvhn6RXTPnDpbqDz00Q+f4uQHdV7fbwhXiRdE+V2UDzfc7AvQw6Nlb4rWCdmy/Qvt5E3L9v8Hatj75oHTzRIbsfuFirD/fuMWJx8bL/MLUSo9Q4Vh3ZdkKpS1KXKObKBt+txwH/4xs/0w6zexXn6XYutQ2J6W7WOmuRmzVbTvN3nu/zEzWl9R0Gl52e3HxyDv+vtqu3/u42OiPna6dsoet2gzyezZKnV/lil2StZY8ZDfy13v25k6/mPH5Y6fZZaF2X3by13nyFBYcNrJxk3E7p3uj2XvD75fuv8PeKNRKrv/Zc+7H6gv7le/961oV3Kz8T76Dzl/yCROCzRzo8L9cPjNfTZ5APlOhZf7D7E4lT0HyxqUPQanaGe8XzboNR2vFYLbecpPJBp4lMrC9bcos9x/PHl9266eWebZzRVh3bz7fmK15UV1H/ey12Tcv37uPUWuaX/AJ3VMfF8KryffzAAWPR/CcS86aFzWq9u9tIhHP84/l4/39W6FP+iQSn7YEzy8n/2mbvt4ghoO2xnG14thPkZl79N1n/9l2+/3nGU9BBBAAAEE+hXw8wS/vwrvbTpNF6veUxWvjurn/f3wBNXx86Bse5MqW1MRp6J16+qr++I+eFAy29/U/I9Ub11FNr1bBaF+p0Fjr/d0hS1C/YuUTz0/fZzKfx/VC/XzpierburvDhXP2jeNvS2nKkyPUDthX318iiQ/awjrHJ2zwl4qXxzVC/VTUz+Xf5GiU/qYFqbWTZX57+r3dmpMy6o87v0Y+nwMfc8+s+/S9Ua366CO/sRtXqYOen4y2oewL/H0r1r+FEWn5Oeo5yji9VJ5nyO7Kz4b1X2D8qlU9H7ndas+r1L9oQwBBIZIYFX1NdyEPLhYVYofcv1DTqPzVH6vwts/K6dOKPYDfdd7QDE/FGr6YsU7FPtFZdms3wyFG/ixmYUP13zY/zdlllXRv0yTM2bL9L+p1sIb49SAR9jYy5UJ+5gdNPYLS1iWGnzzp5m8/O8KD7KEVIVNkXNkbW0wPOQ+Xfm4D6EvqyuzUZiJplX0MWpuRraq9sP+HTNjCysK8paX3b7X97f7fXx/tGJTyX+3VenXFD53/IdTSJ2unTLHLbSfN11XC8I5e3JepXb5iVHdLaO68cDN3lF5nPU5FbZzXLygQL6TjVcvcu67Xpn7g9f3/c774PtmnMrsf9nzror7Vrwvefl4H5+VU+l7Kg/HeOucOv/XrvOTaHnZc7CsQdlj4F0pOkgY7fZU9njlgluerSuf1q63VFPfp0Pye4Kw/gGhMJoe3l7u83b9qLxIts7j7u2XvSbj/vVz7ylyTT+vA9R/a5ntfe+PU9HzIRy3ooPGZb263UuLePTq7PeMPve8rx+NkTL5Xdp1XO/rmWW9zHbbx2F+vbDDsB6jsvdp73vd53/Z9t1HEgIIIIAAArMtsLk2+AXFpCK8twxT/91wieKfFeHvh4dl6vn5Vio9U4XXKEJbYbpzonLRunX11V3y/n1a8VdF6Gs8Xazy/RV56d1aEOpfkVcpKv9cVN/r5Q3OLdCyDyhSlmF739dyf7u3U/Lzo1D/gk4VK1xW1vSIqM9VDhp7F/2MzMcg73j/Tcs+rthCUSR5APKbimCcnd6vZd9SpM5/Fc9IVR33fgwvUm9C//POyxkdbhd0uw7q6E+qTX/p5PJoP8L+3KmyoxWbKYok/w3yLsUNitBGmC5R2ZkK35ecigwau17R+53rVn1euU0SAiMjMH9k9qTYjvjB2L2KNRSPUPgG5RtSlck3zlR6uAr9ou70sxWT3H9/pSVvVPhh3kJFeFPkbyzlJe/L9oo9FN4nz/dyfKvonzbZMZXp/9Zqea126z/osJWzOiw7Tcs+qfBxOFhxoSKk1ZQJg/F+oPxgWKBp1TZ554gHxc9TPF3hvuygcJ+/ozhfMam4rx2aTEtV93Fa45qpu/3s9rLzZbdvyw3ajZ6bbTya97HxG6BeUpnj1m07j4oqdLp+XM3LD3VGyfeCa5bnpv9z6fTZqTm/aQ/3Rt936kp5576312n/yt7fwv70uv9lz7sq7luh70Wnvlek0i3twqWaLk5VUNnd7XLfD0Mqew6WNSh7DMJ+lJ363tvp3uFlL1fMUzxScbHC6dsKfxBpQ8XBCt/T43RIe+abmvpTtP2mqo+7+1HlNdnrtVfE4X5V+k6Hij/WstcqfO/fXHGdos5UpVe//ezV2a+N4Z7f6b2Vz69wHvfbt17XG7bXi6L7N4jHqOx92vte9/lfd/tFjx/1EEAAAQQQ6EXA7z/9fv9tim0Ufs313wt/UPxJ4b8x4nS9Zvy3b7fk920LFf67YyPFHYprFR60yaaidevqq/vj/XxrOzbWdCfFQsVixSUKb7vTs1l/uNFRNL1JFR3dkp/7vb8d62jq98aPVvjZyFWKKxXZD6CqaEZ6l0ocs5nKmn5QnXX0kp5SsLKfkYVj4OP9GIXPVT93ukbhv5WXKIqmX6niPgr/Xbelws8INlP4fL9J8WuFt1k0VXXc+zF8bNFOJup1uw5mqz9fU98cvvfspvC182fFRYpejoOv+aPa4eP5BMWmCt8PfqPw358hvVkZR7dU9H7ndqo+r7r1jeUIDJXA/KHqbTWdvVHN+I3a2ootFNcqqkqTashvKlJp26jww8p/IJrPZv0mMiS/sbwizLSna2j6fMWOCr+p2U7hF+DVFf2mKvvXrQ/99N9vKkPyC0he8hunWxR+M5FNfiPtF7aDFAcqDlf4mDm9WOEXOqcTV0ym/q3SptM54g2+XnG2YivFoxXhRd8vlt9V+Bs+31L4AxBxqrKPcbshX3f7YTt507Lb3yRquMprPjTb73EL6+dNHxUtWBzlU9mrosLtlff5EqdlmrkhLsjk79G8r82JTHlVs93O/bCdfu4PYd1O0372v+x5V8V9q9M+ZZf5Tfft2cLMvO8ltkilcD+Ml5U9B8salD0G8b6UyfuP204pvq+4zxe3K/sP0i8q/kXxHIUHj8MfP7so7/u806Ll//b3Tx3HPe5J2Wuyn2sv3n5e3ube97wUH5PHq1Kn9w55bfRTXtarn216nX6cd442tjjKp7Je7vN3NtIwvl4UcRnUY1T2Ph3ve93nf93tx/tCHgEEEEAAgaoEPPDoOK+qBtWO3wd7oMbRLfVSt46+xv37q2a+FxcMSN4DkD4+VR6j2dq1QTX1/rtvjnM9UzL5Wa/jgpLtxKsP83GP92Mu8n/TRr9a0Yb9rPKsitrq5X7nTdZxXlW0KzSDwNwJzJ+7Tc/Zlj0A60FjJw+4xg8Vlxd2+GcLLfMD+bxPznhQMjuYF5rbMmQ0XSXKd8vG67nuO9uRGhT1J6w8oPgaRVPRS4q3U6Z/3bbZb//9ED6k8MA9zGenfrFJ+bje/yo8aPwwxR6KHyqcXrli0vidphe282FSpU2nc8Tbu0zhT+8dqThAsb7CKXxL7WDlfZz3U/xcEVKVfQxtxtO624+3lcqX3f56UaN+Y1N16ve4devHw6MKfqPdKcXXhT/NmU2+N/mB9Vylbue++9Xv/aHIPvWz/2XPu6ruW0X2z3XuV7S6VO60PPW6UfYcLGtQ9hh04Si8OL6+Uiv54UpI4b4d5k9U5l8Ufs/1csVnFU6HrJgs/yP6O+18P5M6jnvoRxXXZD/XXth+p6lf6zulW6OFW0X5OrNVePXbv36cY5e897ahP7eFzCxMh/H1ogjLoB6jsvfpsO91n/91tx/2gykCCCCAAAIIIIAAAggggAACYyswjoPG39bR9rd9nB6tOGd5rtg//6tqeyrOV7xM8RdF0eSBvpAOVeanYabLNH4Q/V7V/VC7vh/u+dN5/oTVRe24VlM/9Pegca+piv5122aZ/sffnttEG7qyw8biAcJstR+q4BqFByI8AOv5jRThnPDD/WyaDZt4mx6c+EfFWxVPUbhvz1XsovDx9f67389ShPOo7j7W3b52pWMqu33/zFNIPt51pH6OW7d+XBdV8HHvdM/ZLKob3zei4oHOlrk/1LVjZc+7qu5bRfevVbRiD/XKnoNlDcoegx52tWPVTq8rXtHXZ0hXhUx7erGmft/ge/grFB409vuvAxVOpyiWLs/1908dx909GcRrMhbq9q3X+J7oTw9nk19P89JqeQs6lA+6V6rrft8Y0ubKdPpwkpcPShpG637tZuMYlb1Pe9/qPiZ1t9/v8WE9BBBAAAEEEEAAAQQQQAABBEZKwA8txy355w6Obe/0/poeoyjywHVT1dtLMaF4iCJ+kK7ZrunyqIYHra6O5otkN1ClD7YrXqLpcxXxg6T2ooYfbIcHofNCYYFp2f5120TZ/seDfltqY7/I2aDP6fhBcbaaj/UixfsV+yreoPC3dr3eEsWXFNlUt012e2F+qTI/b8f7NPWgxEcUhykWKHz+/lThVHcf625/xV7k/1t2+1dETW8R5VNZf+jiboU/jOFrrdfUy3Hr1nb8U1MLVfm3HVbwdRFSHd+mDm3XMS17f6ijT26z7Hl3fdSxMvetqJlZz5Y9B8salD0GVYFt3KWh+PpLfajpRK3vQeMnK/xLF/5mXxj0XKT8oKVBvSZjp2738nh5/H5pst3IKnFjmbzf5/WShsErtT/Z69sfbkilCRU+PLVgDsqG1bpfqtk4RmXv03Ufk7rb7/fYsB4CCCCAAAIIIIAAAggggAACIyfgh0Djlvwt06+1d/pJmh5SEOBTqhe8jlO+VXC9UM3fPPJgktPzV0xy//1/WvJDhR8yL1Q4PV0RBoM/rXz8ANTLQ3piyGjqgdC8FNoKy8v2L7STNy3b/x+r4SXtxl+UtxGVP0PRbbD8JNXx8fMA+54KDxo7+QMFqW8j1W2zfOP653mKLyv8E9mPUGSTv3H3FsWN7QW7RhXq7mPd7Ue7ksyW3b6/PXVnu+W9k1tYUehzwtf3aYqDVhTN+Dd77ZQ5bjMazxTEA2YvzSzLzobz2OX+sMFcpKxN0T48XRXDulXc34put1u9sufdj7WBqu5b3fpa1/Ky52BZg7LHoCoXf9hr5w6NvaS9zB84Sb0+n6ryBxQ+z1+sCK9j/rWQixWDlgb1moyd1tHMU+KCTN4fDHO6Q3He8tyKf+5v571+3vuk+L1UuDdFTczIVu1VZJszOtFHgd9v3NNezx+Yykv+kNpaeQv7LO93H6u27rP7s7babByjH2tvyrxWVX1MsudG1e3P2sFjQwgggAACCCCAAAIIIIAAAggMm0AYBB22fpft79vUwH3tRo7X9JUdGrTRhxV+YOZ0ncKDub0mP4zxYJTTHorQnufjtKFm3q/YU/FMhbfntOaKyfJ//S2lVPI3oY6NFiyI8s6GfXZ+ff8TpbL9i5pKZsv2/061+vV2y7Z7XGIrfsj0gUR5tuhqFfy4XfgmTXdv5xe1p9lJ3TZhe8uUCfv2vlCYmXrQwQPeTr9ZMVn+b919rLv9aFeS2Sq2//l2y3toGo55dmM+H8KHDr4WLex07ZQ5btEmkll/UOD09hKfG7skazUaj1f5y9vLfq3p4nZ+NiadbIpuv+z9oeh2eq1X9ryr8r7Va9+rql/2HCxrUPYYVOXgdvzaHO4Pcbt7aOZZ7YKjNZ1s5+PJrZr5RrvgHzR9UTu/qD0dtMmgXpNZpyOzBe35J2n6wnb+TE0fbOc9CYP6PpavispD9iHKfDTMFJxW4VXFvbRgd6eq3abcp9tz/gDUPlNLVmY2VvZDK2dL5arYxyqsS+3ELK88G8eo7H26imPS6dyoov1ZPmxsDgEEEEAAAQQQQAABBBBAAAEEhk3gNeqwB3s8AOc4V/ESxULFaortFQcoLlKEOnco/1hFNp2gAtfxQ+FOyQ8i/65wXX/T5V8UYWDX2/QDTn+jIGzPA1ghbadMKL9Z+Z0VHiR18sC2B5PivrruWYo4raqZsM+uu5dit6hCmf5FzSSzVfTf3wK9ROF9s+OzFSGtr4wHlYORp0eFhYnpwZm6N2g+NRgQVi1rU+Qcma+N/aXdLz/gfoXCxzYk7+OJirCPfsAbp7J9jNtK5ato3wNA7v8xqQ2orNPysttfW+3fpPD2fa2+WBGuIU9frwjb/6bycep07ZQ9bvF2UvktVOhvgrnftyv8/6mH88LTlyj8UNnL/SGTrRVx+i/NeFn4Nlm8LM7/rV3vpLiwQL6TjVcvcu5XcX84VtvyfvqDFXEqu/9lz7sq71vxfsX5Ivt4qlawT/wzpHEbzv9I4Tr+4EGcyp6DZQ3KHoNTtDPerxvjnSqYP769rtd3fFGxkcLJ9419FXcqvMy2ayjy0nO1wPU8qOyp7zcPVfSb6jzuVVyTRfrnfc+793S7pm3oOE2xjiKk5yvje7yX+d64iSJO22gmHAMvf5HCx21VxTMVv1B43fAhrU8oH6fU+VSFl7e/TOFtX6TIvkfr5tHvPd7XZzgG3v5/KPZQ7KTw66LfH7lPIb6ufL+p2z4O++vFMB+jMvfpus//Ktrv95xlPQQQQAABBBBAAAEExlVgb+24PxjvOHxcEdhvBBAYP4GXapc9eBsehHWa3qh6fpiYSkUecoX1nqVMGLjy9rz9CxX+Sct4+8doPps+o4K4jgeIvq+4vV3uh6CfV3y3PX+lptn0SxXEbTi/WVSpTP+iZpLZKvrvATEPmod98IPOPyj8oNNlZyuuaOeP0jQv+QHxHYrQzsfyKkblZWyKniO7aHv3Rv3yg+//U/xJ4W9huL8+zu9XhAFPZadSmT5ONdIhU7b9MCibOr+92W7Ly27fb3jCBzds6XPgfIWdw7lwmfJ+eJpNna6dssctu63s/H4qCNe5++n7xQWKuxSh336wv40im8oO3GTbS813sil67pe9P9T1sN77W/a8q+q+lbJ3WZFjfKrq+Vy53ivkpB+p3HWyg8auXuYc9PplDcocg1O0fe+XX8d7TcdrBa/7e8XP2nnfg/+oiO8bfl3fXdEpzdPC6xRuz/FVRZlU93Eve00W6Z/3PwxYZj+w0u2a9uuFX/9tuVRxsSJ+f+D7u+/5qeRthePgqV9f743K3q+83195mQdR45R3PpX18jY63Uu7efQ7aOztbqUIlrGL8z7fj1SE104P0pdJnfZx2F8vhv0YlblP133+V9F+mfOWdRFAAAEEEEAAAQQQQAABBBBAYEwENtd+fkQRHlpmH5b5Idl7FB5kzEtFH3KF9TdQ5mTFXYrs9i5S2csUqQHBVVTuvtypiNfzw1I/5N9V4fQGRVj+5OUlK//ZTFk/sFuiCHWeuXLx8ly//cs0M2O2iv670Z0VP1SE/nvqh71+COxthAefnQaNVa1xvCK0sb0LCqR+bXo5R/ztnq8o/KA29M9Tf/vYHzDYV9Ep9dvHTm3Gy8q0H867Y+IGo3y35a5aZvtef2OFB2vCIHww9gc4fM6spUilzVTY6dope9xS24zLfK86S5Htt818vmyrSKWyAzepNrNlnWyKnvtl7w/HqlM+llV/0zjsa9nzrqr7VuhPPC1yjMsOGnt7/Z6Doa9lDfo9BnmDfKFfnabHa6HPq58pVld4wCz+sJmvv28pNlEUSf+uSuGe86IiK3SoU/dxL3tNFumfdy+8/+p10NiDpGsrfEzCt4Jt69fOrysereiU3qaFtyjC8fD0SsUbFU69DhqX9fI2O91L677H2fIIxbcVf1UsVnxZ8VyF060KG/nDiWVSp30c9teLUThG/d6n6z7/q2i/zHnLuggggAACCCCAAAIIIIAAAgiMhUBqYHIsdjyxk/6Z100VfijuB1oeLL5c4YeZdSX7b6l4lOIuxVUKfxPKD+U6JT+49rcBFipuUPxJ4YfYvaRVVdnfLPGDQT8ITKV++5dqKy6rov9u7+GK7RR3Ki5QeFB1tlJdNnH/19XMQoXPSZ+HFyt6Oc5197Hu9rW7HVPZ7c9T69sqfB1co/D1vkTRLXW7dsoet27bd7+3aYfvU/5m9G2KQUjdbIr0sar7Q5Ft9VOn7Hk3l/etfvY3tU7Zc7CsQdljkNqnXsp8nj9RsVTxO0X2gwoqyk3v15IjFX7t9b3dbQx6GrRr0oPRb1Lcq1izjbeapj4m8xWXKG5SFE1+DfBrwUUKv6cqm6rwquJeWnY/4vX9HtnvPxYoPq44XFE2VbGPVViX3Y9BWb/qY9TvfbqKY9Lp3Kii/UE5ZvQDAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEExlDAg91XKfzBsI+N4f5XtctFv8Fc1fZGuR0PNJ6q8C/uPLnDju6uZT5vHa/sUI9F1QtwjKo3pUUEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAYM4EDtGWPei2TPHIOevF8G+YQeNqj+Gf1JzPyysU/qZ2Nvlbn/72tutMKjZRkGZXgGM0u95sDQEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBCoVOAItfaPiuMU/i8UPPD2JQWpfwEGjfu3S63pb737vHT4/+c+TOGfTvf/If50xdmKsPwzypNmX4BjNPvmbBEBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQqEzA/1d6GHDz9I8K/7/npP4FGDTu3y61pv+f4h8p4vM0lT9Ndfz/yZJmX4BjNPvmbBEBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQqEzgZLW0VHGr4nTFIxSkcgJv0+o/U3y/XDOsHQnMU/4Axc8VtyjCoPHtyp+l+AcFaW4FOEZz68/WEUAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIFSAqtpbQ/4kBAYFoG11dE1hqWzY9pPjtGYHnh2GwEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQqFWhW2tqANnbQaQ8c0Gq01nf3lk48cNoZ+69764B2lW4hgAACCCCAAAIIIIDAgAkc+OUHDmy0Wg9xt5rL7j/1S69Y77YB6yLdQQABBBBAAAEEEEAAAQQQQAABBEoJzC+19pCs3Go2/q3ZnNjR3Z2YXO2nmjBoPCTHjm4igAACCCCAAAIIIDDnAs3GEfp7Ynv3ozW5+g81YdB4zg8KHUAAAQQQQAABBBBAAAEEEEAAgSoFJqpsbGDbajYmQ98mJhvjsc9hh5kigAACCCCAAAIIIIBAOYHWyr8hJidaU39blGuUtRFAAAEEEEAAAQQQQAABBBBAAIHBERiLAVT9BvfUg52l85tjsc+Dc4rREwQQQAABBBBAAAEEhlug2YwHjVf+bTHce0XvEUAAAQQQQAABBBBAAAEEEEAAgZUCYzKA2lwWdnlBozEv5JkigAACCCCAAAIIIIAAAgUEor8hWlN/WxRYjyoIIIAAAggggAACCCCAAAIIIIDAUAiMxaBxq9FYEo7G5LKlq4Y8UwQQQAABBBBAAAEEEECgm4D+nlhlZZ3W1N8WK8vIIYAAAggggAACCCCAAAIIIIAAAsMtMBaDxjpED6w8TM3ogc/KUnIIIIAAAggggAACCCCAQFKg1Zz64On8pUujvy2StSlEAAEEEEAAAQQQQAABBBBAAAEEhk5gLAaNm42V3wZoNRk0HrqzlA4jgAACCCCAAAIIIDCHAs3om8a3Tz7IN43n8FiwaQQQQAABBBBAAAEEEEAAAQQQqEdgLAaNRXffSr7m6ivz5BBAAAEEEEAAAQQQQACBrgJTf0OstcpN93etTQUEEEAAAQQQQAABBBBAAAEEEEBgyATGYtC41WrcG45Lc6KxZsgzRQABBBBAAAEEEEAAAQQ6Cexy3P8taDQbC1xH/7fx0jP234FvGncCYxkCCCCAAAIIIIAAAggggAACCAylwFgMGuvxzj1TR6fVZNB4CoMMAggggAACCCCAAAIIdBLY6iFbrREtX/l3RVRIFgEEEEAAAQQQQAABBBBAAAEEEBh2gfEYNG5OTD3cabYmGTQe9rOW/iOAAAIIIIAAAgggMGsCq601talW9GHUqUIyCCCAAAIIIIAAAggggAACCCCAwPALjMegcaN1RzhUrWZz3ZBnigACCCCAAAIIIIAAAgh0Fpi38u+HZuPOznVZigACCCCAAAIIIIAAAggggAACCAynwNgNGk80GDQezlOVXiOAAAIIIIAAAgggMPsC8xvLpgaNm63m7bPfA7aIAAIIIIAAAggggAACCCCAAAII1C8wFoPGrcbEym8aN1rr1c/KFhBAAAEEEEAAAQQQQGA0BCamBo1b0S8Yjca+sRcIIIAAAggggAACCCCAAAIIIIDACoGxGDRuNFq3hgPebDbWD3mmCCCAAAIIIIAAAggggEAngeZkc4No+W1RniwCCCCAAAIIIIAAAggggAACCCAwMgLjMWi8bPLmqSPWaj50Kk8GAQQQQAABBBBAAAEEEOgkMNGK/n5o/r1TVZYhgAACCCCAAAIIIIAAAggggAACwyowFoPGrebEykHjZvzQZ1gPG/1GAAEEEEAAAQQQQACBWRGIP3TabK38u2JWNs5GEEAAAQQQQAABBBBAAAEEEEAAgdkRGItB44nJB6JvBDQ3nh1atoIAAggggAACCCCAAALDLqD/x3jq74dmoxX9XTHse0b/EUAAAQQQQAABBBBAAAEEEEAAgZUCYzFofOpB69zSajSWtHd7vf1O/8vqKwnIIYAAAggggAACCCCAAAJpgWazuWlY0mo1bwx5pggggAACCCCAAAIIIIAAAggggMAoCYzFoLEOWEvfCrgpHLgFD2449eAnlDFFAAEEEEAAAQQQQAABBGYItBqbhbJmc9kNIc8UAQQQQAABBBBAAAEEEEAAAQQQGCWBcRk09rDx1AOeyYnWw0bpILIvCCCAAAIIIIAAAgggUI9Aq9Gc+tvh/iWNqb8p6tkarSKAAAIIIIAAAggggAACCCCAAAJzIzA+g8bN1jVTxPPmLZzKk0EAAQQQQAABBBBAAAEEEgJ7nHj1as1mY8X/adxqPPjVKz7OoHHCiSIEEEAAAQQQQAABBBBAAAEEEBh+gfEZNG41Fk8drlZry6k8GQQQQAABBBBAAAEEEEAgIbDhmpuu/Luh2bquceSRk4lqFCGAAAIIIIAAAggggAACCCCAAAJDLzA2g8aTjdbicLQmmo2tQ54pAggggAACCCCAAAIIIJASmJhsbBXK9TPVV4c8UwQQQAABBBBAAAEEEEAAAQQQQGDUBMZm0LjZbF0eDp4e+Gwb8kwRQAABBBBAAAEEEEAAgZSA/ljabqq81fjzVJ4MAggggAACCCCAAAIIIIAAAgggMGICYzNo/MCSlYPGzfjhz4gdUHYHAQQQQAABBBBAAAEEqhFoNiemBo1bjcnLqmmVVhBAAAEEEEAAAQQQQAABBBBAAIHBExibQeOvHrzG9eK/q30INtjv9Ls3GbzDQY8QQAABBBBAAAEEEEBgUARazcaOoS+tVuPSkGeKAAIIIIAAAggggAACCCCAAAIIjJrA2Awa68DpOU/j4nAAF7Tm7xTyTBFAAAEEEEAAAQQQQACBrECz1XhsKGu2ll0Y8kwRQAABBBBAAAEEEEAAAQQQQACBURMYp0HjhgaNpx706P813nnUDib7gwACCCCAAAIIIIAAAtUI7Hf6/Y9sNBvruDV9+vTm0w5c84ZqWqYVBBBAAAEEEEAAAQQQQAABBBBAYPAExmrQWPznR4fgqVGeLAIIIIAAAggggAACCCAwJTC/1XzK1EyjFf8dsbKYHAIIIIAAAggggAACCCCAAAIIIDAiAmM1aNxc2vpFOG7NZnNX5ZthnikCCCCAAAIIIIAAAgggsFKgudtUvtn45VSeDAIIIIAAAggggAACCCCAAAIIIDCCAmM1aHzqQate5p+Wax/HDQ46fQk/UT2CJzW7hAACCCCAAAIIIIBAWYFms/XslW1M/nxlnhwCCCCAAAIIIIAAAggggAACCCAwegJjNWisw9fSw5/vhcPYarReFPJMEUAAAQQQQAABBBBAAAELHPilB3bQjxI9wvlWo3H30sZVDBobg4QAAggggAACCCCAAAIIIIAAAiMrMG6Dxg190/hb0dE8UHl+ojoCIYsAAggggAACCCCAwNgLzGu8YqVB6/tn7L/DkpXz5BBAAAEEEEAAAQQQQAABBBBAAIHRExi7QeO7b7/lG/q6wJ0+lM1Gc5sDv3z/C0fvsLJHCCCAAAIIIIAAAggg0I/Afqf/bS39pfC6qXVbjS9M5ckggAACCCCAAAIIIIAAAggggAACIyowlt+yPejLSz6tEeN/WnFMW1e1JltHj+jxZbcQQAABBDICzYmJpcsmWz/78gGrXpJZNGN2v9NvXXd+a8199DMV68xYSAECCCCAwGgKNJt7NZvNl67Yuda1N/zpl1v/+Mg9l47mzrJXCCCAAAIIIIAAAggggAACCCCAwAqBsRw03u/0uzac31j1z9r5dTkREEAAAQTGT0D/P+XSVmvy7ae9fLVj8/Ze/5/lY5vzG9/Rt802zatDOQIIIIDAiAtMtg7+0gGrnjLie8nuIYAAAggggAACCCCAAAIIIIAAAo2x+3lqH/Mz9l/775OTkwdq0OABzgEEEEAAgfET0IeG5jebE5868PT79krt/T7H3bCGBozPYsA4pUMZAgggMCYCrcbnGTAek2PNbiKAAAIIIIAAAggggAACCCCAQGP+uBp8+YDVztG3yB7Xmt94VaPV5GdHx/VEYL8RQGD8BJqNJ2jQ+EmKZqs1cYwAHpdFWHO9Dd6mxVu6XB8wulX/nJatwzwCCCCAwGgK6PVh6bJW64f6bwy+MZp7yF4hgAACCCCAAAIIIIAAAggggAACMwX0TISEAAIIIIDA+Ai85OR7Nl1t1QVXaI/X8F5PLlu252kHrv5j5532OPJH8zd79K7Xhm8ZtyYbrzv1gFX+Z8VS/kUAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAYPQExvLnqUfvMLJHCCCAAAJFBb76yjVv1DeHvxDqNycmDg15Tzd59FOfMzVg3GjddPkdF50ULyePAAIIIIAAAggggAACCCCAAAIIIIAAAggggMCoCTBoPGpHlP1BAAEEEOgqMDm59OSVlZovaBx55NTrYbM1sc/UslbztPPf8IQHp+bJIIAAAggggAACCCCAAAIIIIAAAggggAACCCAwggJTD8lHcN/YJQQQQAABBJICpx24xnmtVuOvXthsNh56wHbvfOLKiq3nT+Wby74+lSeDAAIIIIAAAggggAACCCCAAAIIIIAAAggggMCICjBoPKIHlt1CAAEEEOgo0Go0WudO1ZiY2N35l5xy35bNZnML51Xh7stv++MvnSchgAACCCCAAAIIIIAAAggggAACCCCAAAIIIDDKAgwaj/LRZd8QQAABBHIFNDj807BQ+V2dX3X+xPKp881W41f8NLUlSAgggAACCCCAAAIIIIAAAggggAACCCCAAAKjLsCg8agfYfYPAQQQQCAp8GCjdV5Y0Gw0lv88dbPRnPqZ6sloeajHFAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBURRg0HgUjyr7hAACCCBQQOAbf9JPUN+3omJz831PunODVrOxU1hR/+fx+SHPFAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBURZg0HiUjy77hgACCCCQK3DG/vsv009Q/yFUWLDagsfqG8dTg8bN1uSFYRlTBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQRGWYBB41E+uuwbAggggEBHgVajdXGoMK85sYfyG7Tn7zrtwNUXt/NMEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBAYaQEGjUf68LJzCCCAAAJdBC6Llu8T5S+P8mQRQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEBhpAQaNR/rwsnMIIIAAAl0EpgaHm43m41fWbcWDySuLySGAAAIIIIAAAggggAACCCCAAAIIIIAAAgggMIICDBqP4EFllxBAAAEEiglMthpXpGqq/MpUOWUIIIAAAggggAACCCCAAAIIIIAAAggggAACCIyiAIPGo3hU2ScEEEAAgUIC991z5zXpis3F6XJKEUAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBAYPQEGjUfvmLJHCCCAAAIFBc56zYZ3tRqNW7PVm81li7NlzCOAAAIIIIAAAggggAACCCCAAAIIIIAAAgggMKoCDBqP6pFlvxBAAAEECgq0rstWnJxszijL1mEeAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIFREWDQeFSOJPuBAAIIINCvwA3ZFScn7pxRlq3DPAIIIIAAAggggAACCCCAAAIIIIAAAggggAACoyLAoPGoHEn2AwEEEECgP4FW48Z4Rf1c9d1n7L/R3XEZeQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEERlmAQeNRPrrsGwIIIIBAd4Fm46/TKrVa0+enLWQGAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIHRE2DQePSOKXuEAAIIINCTQOvvcfVmozltPl5GHgEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBURRg0HgUjyr7hAACCCBQWKA5OXHztMrN1vT5aQuZQQABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEBg9AQYNB69Y8oeIYAAAgj0IDA5MXlLXL3Vakybj5eRRwABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEBgFAUYNB7Fo8o+IYAAAggUFphsTd4eV9bPU98Rz5NHAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQGDUBRg0HvUjzP4hgAACCHQUaLbmTRskbjVb0waRO67MQgQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEERkCAQeMROIjsAgIIIIBA/wLN1rJpg8aNRisz33/brIkAAggggAACCCCAAAIIIIAAAggggAACCCCAwDAIMGg8DEeJPiKAAAII1CYwMbnk7mmNTzbumTbPDAIIIIAAAggggAACCCCAAAIIIIAAAggggAACIy7AoPGIH2B2DwEEEECgs8CDC66fPkjcbE6f77w6SxFAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQGHoBBo2H/hCyAwgggAACZQTO2H+HJa1GY1loo9Xkm8bBgikCCCCAAAIIIIAAAggggAACCCCAAAIIIIDAeAgwaDwex5m9RAABBBDoINBsNO4Ni5uN1n0hzxQBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgXEQYNB4HI4y+4gAAggg0E1gSaiwrNV6IOSZIoAAAggggAACCCCAAAIIIIAAAggggAACCCAwDgIMGo/DUWYfEUAAAQQ6CrSmDRS3pgaQO67EQgQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEERkSAQeMROZDsBgIIIIBAKYGpgeKJSb5pXEqSlRFAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQSGToBB46E7ZHQYAQQQQKBqgWazsXRlmwuWrcyTQwABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEBg9AUYNB79Y8weIoAAAgh0EWi1GpOhSrPVYtA4YDBFAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQGAsBBo3H4jCzkwgggAACHQWaKweNJydW5juuw0IEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBEZEYP6I7Ae7gQACCCCAQN8C+qbxKVp5Uzfw4INLb+m7IVZEAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQKBygWblLdJgzwKtRmMjrbRFYsW/6AD9LVFOUUEB2W6sqptnqt8l18szZczWLKBjsY42sU1mM0t1LC7MlA3FrPZndXX00TV0dmCv+5xjuEzH8Pc1OOQ2OSj9yO3gLCyQwVrazHYFNrVMdSYV9yvuUdys4/WApiQEahUY9nOU+0x/pwduxdzkxHv/YlQ915It7/17VmMFBBBAAAEEEEAAAQQQQAABBBAYGAE93PiKopWIr1bRSbW7p2LLXtrqZ51e2p+tutqPdyiytj+Zre2P03a6nTNa/vzEsbh5WI20L49N7E/2XOtn/p8G1UT7u3din2+f7f4OSj9me7/j7clgt8SxKHq+3ah1T1DsELdJHoEqBYb9HFX/B+J+V+UxmY22cCumLCfe+xej6rmWbHnv37MaKyCAAAIIIIAAAggggAACCCCwQmACiLkV0IMNfxp+n5xe7KPlm+Ys61qsdRcqzlTFHyoe2nUFVehnnSLtUmd0BThnRvfYsmcjK7CJ9uwwxcW6fo9V6MvHJAQGSoBzdKAOB52pUkD3XN77VwlKWwgggAACCCCAAAIIIIAAAgggUJkAg8aVUfbd0CFac0HO2vNV7gf7PSU9jFpD8UGtdInipUVW7medIu1SZ3QFOGdG99iyZ2Mj4MHityhO1PXMwPHYHPah2lHO0aE6XHS2oADv/QtCUQ0BBBBAAAEEEEAAAQQQQAABBGZXwIOSpLkVeE2Xzb9OD/M/qqem/j8puybV9QD0ZYrs/+Obu24/6+Q2xoKxEOCcGYvDzE6Oj8CrtKvnKL48PrvMng6ZAOfokB0wuttRgPf+HXlYiAACCCCAAAIIIIAAAggggAACcyXAoPFcyWu7GnjbTZPtunRhSy1/tuI7XeqFxf72eOEB4/ZK/awTtsd0PAU4ZxqN63To39Dh8D9Ey45KLD9JZb9MlIeiTstCnbma3qANL8ps/L7M/GzMDko/ZmNfe93GMVrBHxxymqfw6/zqCv/c78sVmylS6Qi9Jp1R9ANKqQYoQ6CgwLCco9xnCh7QTDXcMiDxLO/9Yw3yCCCAAAIIIIAAAggggAACCCCAAAJTAnpwtEjRKhBfm1qpS0ZtrZrT3i55q/azTl5bg1aufXtHwuMng9bPYetPP+eM1nl+4ljcPGz7XrS/2tfNE/vr6/3VRdugHgJ5AjqPdss5v/bssI7/64L3KO7PWdcfZCIhUImAzjHO0UokaWSUBHRd8N6/5gMqY97712xM8wgggAACCCCAAAIIIIAAAqMr4G8LkuZAQA801tFm90ts+vuJsheqft63wxLV57bI+6Z4hGIDhb/pVklSW2spNlMM/XmrfVig2EKxSiU4iUbUdlOxsWLdxOKBLlKffQ6tVUUn1Y4/SLFxFW0NYhvat/UUvp8UTq6vqPwaLdyBqKL6MRDnadX9UHu+/z002tU5z+pbxPcq/l0d+XhOZx6ZU55brH3s+fxzY1qvtnug2q70tULtVXoPUXsTik0VmygqeY2so83cg17jgqrPUbnUdp4VYajzuMzGvmkb3J+LHOiCdeTJe/+CVqGazCq9n4d2q5qqf5Xfz6vqG+0ggAACCCCAAAIIIIAAAggg0I8AP0/dj1o16xyoZtbINOVvXR6qWKzw/00cko/TYYoPh4LsVA8tXqiyNyryBlQ/pTp3tNf7ox7MHt7POtntel7tbKvJPyqeq3iYYk1FSP5m5d81VndCzAAAIgBJREFUc67i64oztW0VdU+qtJFqvUWxk2JHxUKFVm88qGV/0fT3imNU8AtNSye1ua8aeU2iIR+X/6ft3JtYVqhIbfu4PFvh9ndV+KdiXeZ9uVTTixUXKI7Tdu7WNDep/pO18H2ZCnZd5DIt30eT1yv2VqzqIpVdpen5ipNU72xN+0pqp+fzrOiG1PbTVfcghX128Hoqs81vFSeq3z9xWZGk9V6seo6dFY9WeODAx9HnzIWKU9WePQY6qc++T7wi08mPq+8/1TKfS44nKeZp/npNT1AcreV3ajqVtKySa1TtPF6Nfmiq4RWZe7Q9/+zxtKS6tZ2ng9KPaTusGfXrVZr4+vMvOyxUuMzH4kyFj8ufNP8Q5U9WxOkyLXt7XFBz/gy1n72HeJNbx9tVXys5/9ym2qrsHhj3sd125a8V6m9l95D2vvsaea1iocL/hcQqCqdJLfdr5BWKkxS+N3V8DVCd4FlZm+rD49Rs9j2GP2Swv7c3B6nQOZrtV9u6qtfawve70I86jnWm7ar2jfuzYHW8Zvv+zHv/cELnTHVMKr+f52xqebG2t68yfi+VTcn3/nVe49kOMI8AAggggAACCCCAAAIIIIAAAmMkoIcOv1Vkf5r6MyZQ+dcTyxarLG9A2Ou8ObFOtv0w/7P2dnpeJz5E2p6/gfI5hR96h7a7TX+iuhvH7aTyqrOf4u8F2/UAmgdhZySVF/qJOtV7muI+Rbb/d6rsCTMa7qFA6++suDzRdnZbnr9KsWen5rX8xYm2jlKZv/F3ZmJZdjufVJ0wYNFpUzOWab2+zxmtm/vz1Frm47RUke1rmPeyd87oUKZAddZS/G+HdkJ7S1THH5zQmEg9SW1vntOPVxfdotY/ItHGASp7V6Lc++afHV4vtK98pdeo2ttbEQzD9PawvXiqerWdp4PSj7C/6k+Ra8/3lzcp/A3TYBem54W2ik7VRs8//Rva1rrbJPrgvnwq1PFU86XOv9CW2qn0HhjabfexkteK0Kb6Wuk9RO09XfFHRTjW3aZ+zXl+6E9qWlObqWv7jtT2i5apn7Wfo3FftL1KzzO1lzJJ3u/cjzqOS9i/GvaN+/P0a7LS+3M4btmpjiPv/bMo0bx8Krmfqx2/p8zea38SbWp5VnV6eu+v+pXfz7N9Yh4BBBBAAAEEEEAAAQQQQAABBMZQQA8ddko8zPDDjSeZQ9N9c5Y/L49L9XsezOtnnXj7Wv8zOf3MPqjJzv9S682L2wp5lftn3r7UR7sXah1/g29aUlnXB0eqs53iFkW2n36IuMe0Bnuc0fqvUXggL9t2p/lJ1f+sYkFqcypPPez1sbBrp3bjZZ9Itd2tTO33fJ6FNrVuatD4dpWf1kO/3xPay07VhgfCig7OB4vva531s21VMa926xo0/k+1vVQR9iGefiXuu+pUeo2qvcKDKKpb23k6KP2wtfri47xYER+HTvn3J+rO9qDxWxJ9cJ/flTl/UoPGhc+/tk/l98B2u5W+VrTbrPQeIs+tFLflWHc6Rx7QOsmB4zrabO976tqey0HjQueo++4kl8rPM7WZMkkOGtd1XGrcN+7P6Xt2Jffn5Sdl5h+dIzspUtc97/2H4L1/ndd45lRhFgEEEEAAAQQQQAABBBBAAAEExk1ADx6OTTw48s/wLk9atori5kSdr4U62anq9jyY1886Ybtad89E/8LDsHu07BpFp8HS3UJb8VTrvK5Du25/WYfl58RtOa+6HQeNtXxjxVWJNv2z0S/MttfLvNb3NxI69Td45U3fm9qe2kw97HV/43Y8qJgaCA917tLyGYPsqe3FZVqn5/MsrK91U4PGoT9h6m8Ap879sPwmLU9+S1rlP1CEevHUg/B/UeQNtE77dmXob9mptufBxLgfIV/2m8ahndT0JaHf2nbl16ja7GUQpbbzdFD6YWv15QuK1LFwmT944nM2b3kon7VBY/XF33S+OKdP+4Xzp71vqUHj0OfUdOr8a69fyz2w3XalrxXtNiu9h8j41wlnvz5+T3Gy4jsKf7M4ZXmtymd8cEhllbfZ3vfUtT0ng8bax8LnaJ3nmfqRMskbNK7ruNRyDWnfuD/XeH/2eZlNMue9v359IOviedlUej9Xe5W/91ebtVzjKQ/KEEAAAQQQQAABBBBAAAEEEEBgjAT00GE1xa2K7EPiaQOEWp76hqAHBjdLcan88Yq3Kw5XZNv2/Cfby13nALehac/rhG1r3W8ostv5scqeplj+oFtTfxPsZYrU4PEHQlthqnoPUaR+kvpGle+n2Khd51Wa/kyR3b7nHx/a81TzuQ+OtGxNReqnAj3Q6/9bt++k9f1ztX7on+rjd1V+gGJ7xbMU/67wYGm2rr9t5v+Pd1pSWephb1j3j1p+sGJ9hX+a+IkKl4Xl8TT1f7hN21Z2Ru2UOWc6DRr/QW3vo1jb29R0R8VPFXF/Q37/RL9ekqjr4/hhxTrtNtdQ3tdH1toDe/6/uCtNarPuQWPv308U/6W4UOFvbfv/sF6elK/jGu1lEKW281T7Nij92EV98YcSwrkZpter7NmKcC/0uXBiol6o/6tw3IpO1dZuOe3tmWpDdTdW7Kfw/TRsN57eo/K14nU132nQuNv5V+c9sI7XikrvIbLz61Xs6/yliuX3uOCs+XmKAxXZup4/JNTzVPOVtxnaV9upa2pWB43Vh37O0TrPs5TJjEHjuo6L2q1z37g/13h/DtdVmOpY8t5/xT1uKN/713WNh/ODKQIIIIAAAggggAACCCCAAAIIjLGAHjwcpMg+HPagw8KYRfNPSNTzetMGl+N1nNfyVXPW2yVbN8z3uo7q+yH30YofKsK3Qu9QPvnNVZV7UCu7z8eH7Yep6qQGyj0QtmmoE6Yq86Cot5lt9wuhjqda/o5EHQ+0eR++lVjm9t4Ut9FPXm38a07bH1B5M9umyp6sCJbxPp2n8mn/l7Xm8x722uORibZ3UHlq4P7j2bpF59VeP+dZ3qCxj3Gq3x5ASH3AYtpDR9Xxw9irFbGb80em9kfl/uBEtu5/puqWKdM26hw09rmyTdw/zU99wED5uq7RQoMo7pf6UNt5qrYHpR/+xmj2XLpTZXkf7vF9M1vf81UOGv9V7V3Tjus09S8O3KdIbTcuOzo+n9rHMG/QuOP51163zntgpa8Vsqn8HqI2X5gwn/b6FHur7ucUFyu+qPDrls/xDTJ1Km8ztK9tba34YCbeE5b3M1VbeR9sqPIcrfM8K3Sf0X7WclzUbp37xv05c1LLu7L7c6Zpvx7y3n/Fa9BQvvev6xrPnifMI4AAAggggAACCCCAAAIIIIDAGArowcMPFPGDeud/kqJQ+R8TdRerbNogYryulvUzmNfzOpltbqbtPiYui/Na9hZFdp9Pj+s4rzret2y9D2frhXnV9QPuvynOVRyjOFTx2LDcU83nDRp/Xsuy2/L8u+P1+82rnUsS7f9GZTMGjMM2tOzNiXXcp6eGOp5qPu9h7+vienFe6/wq0fZ/x3V6yautns8ZrZM3aPyGvG1rHX8rO3ucPhfX1/LUwMRtKl8jrhfyKl+guFERt+tvdS//RnKoV3aq9uocND68l/6pL1Vdo4UGUdw3bbO281RtD0o/7lZf4vPI+Y/kHRst8zdkUx92qXLQONufIvNnq18z/p95leUNGnc9/7RunffAxWo/u199v1aorcrvIWrzGYk+LlWZB7yfosh9He9w/lTeZt62qijXPqZcs8et6HzeOVrneVboPqP9rOW4qN069437c+Ykl3dl9+dM03495L3/inv2UL73r+saz54nzCOAAAIIIIAAAggggAACCCCAwJgJ6KGDv8njbxVnH5K+NkWheu9M1PW6z0vVd5mW9TOY1/M6edsP5eqH/19mPxj/kOLPiuw+nx3qeqrl/qZXymanuF6cV/3cAdhQT3VSg8YeIMz2x/PfD+uVmaodf7M31X7ucfP2tI4HM29IrPv6uD9anvewd+u4XpzXOqck2v1iXKeXvNrq+ZzROnmDxo/M27bWOTHR7xPj+lp+WKLOL+M62bzqn5NYZ8dsvTLzar/OQeOFZfrmddW/nq7R9jqFBlHadWs7T9X3Oe+H+rCJInWdT33j2w7ZpHUWJdaby0Hj36s/036WOvRZ5XmDxgtDndRU69V2D1Tblb9WqM3K7yFqc3XFEkXqHHGZvwH+VcU/Kx6VcsyWqV7lbWa3UeW8+lvVoHHyHFX7tZ1ndlD7he4zdRyXWdg37s+Jk13uldyf46bVJu/9V94Hh/K9fx3XeHyOkEcAAQQQQAABBBBAAAEEEEBgkATmD1JnxqAvh2kfswOd96vsjJx9P0XlH1Vkv5Hkb2aek7POnBTrgYp/Qnq3djxZU//fwqsoiiYPHGZtvO71eQ2osjbbV8rr1zPU4LZq9/K+Wl25Ut7g7fkrq8zMabv+P6sv0pLsz3Hnfos7asUWV0fz2ey92QLNZ8+rRJVZKerU7wcSPcgev20SdTYQyMcT5aEo9fPB/n+N/xAqDPD0Lp0ri3vtnzzKXqO9bjJVX90YiPO0in7kXefXpnY8Klsc5ecy63vrpxXH6Xy6u4eOFDn/8myquAfW8VpR+T1EpvfpJPuGXF+WY7u+yvdth1/MrlL+W4pTtW7yQwR1tKntDXLqdo7WeZ4VdqnpuMzFvlVxXyzs1qFiFf3I85uL+/Nh2ledJtMS7/1XcNRxP58GHc1k3zuGRV3f+9d0jYftM0UAAQQQQAABBBBAAAEEEEBgoAQYNJ6lw6EnYPO0qUMTm1umsm/5CVlOWqLy1TLL/P/nbaaHGDdkymd9Vv14pTbq/wP4qT1u3Psdp23jmXb+Ae3jzYnyuooWqOGjFfuU3MDCxPp3a1/+lijPFnngIJuKDBrfp/YnsytG835AOYjpHvU7ey7E/UwNGmuVaSl17rjsHdNqdZ/xoPEwpJt66WSF12gvm82rOyjnaRX9SA1K3KGTs9sAbLdBizy7ouW/U8XbosoPKu/r39eSy69RXKr4lvrqZb2mIuffwkSjVd0DU9d72deKVJtV3ENeJQffV4q8Pm6lev/k0DX7dU3fqePzZ02zqY42s9uoe76qc3RhoqNVnWeJpjsWVX1c5mLfqrgvdkQquLCKfgzE/VnXMu/9px/07Pu91L237P18+ha7zxV971/1Nd69Z9RAAAEEEEAAAQQQQAABBBBAYA4EGDSePXT/NHHq241rqtzf0O0l+SHUaxQf6mWlKuvqQdiE2vukwg+5O6XLtPAWxa6ZStkBztTPoy7QdubrwfnSzLp1znpA/jna5ndLbCTV3/sKtpca3M1+aCDVVLf2U31KtTPbZan9jfuQPU/iZSH/0JApOfXgzjCkO4p0soZrtMhmu9UZlPO0in6kXj/XsLvuH53O29W7IZVc/nZt/0cl2+i0epHzL3W/6WYetpm6J8T3wDpeK2q5h+g43Kvz4QXaMX8YyR+wSp0zYb/j6T9oZg+t+zi1cU28oI424/ZnKV/VOVrnedYTRQ3HZS72rds1mupTT04FK1fRj9S1Nhf3Z977Tz/o2dfGOu7n07dYbK7re/8arvFiPaMWAggggAACCCCAAAIIIIAAArMskHqoMstdGJvNvbbiPX2tHih/RA8xsg9gKt5MbnOLtMQPwbPpChWco/ix4hfq31/Vz9cr323QOPWNKg9Mb6zwT1TWkf5PjV6peHmm8U+qz49V3/t9QJrq74Zqcy212e1biP62WTZdnC1IzD+YKBuXomsTO+rzyce3l/SXXirPYd17Cm57kepVeY0W3GzHaoNynlbRj8WJPfU3lnzPujGxLBQ9ImSGdFrk/KvzHuhrO5vKvlbUdg/RPd/f7vb/mfwuTQ9UvFixm8LnSqe0nhZ+QfGMbKU62sxuY0jm6zzPeiao+LjMxb5VcV/s2S2xQhX9WJxo19fcbN+fee8//UBk/2ap434+fYsz5/p+71/xNT6zZ5QggAACCCCAAAIIIIAAAgggMAACDBrPwkHQw+JNtBl/26jK9HA19lzF2QUb1bOOnlNyHe3Phmrp4ExrKl4+OHyCVnI+TqvGM+38skyZv5GcSg9TYerhqTfiB4BvV/yxHVcntq1FyeSB2Oco3LcXKvyN75Aepcw/Kj4dCnqc3pBT3z/Dd0HOslC8TchE0yKDxlH1Oc0mz5mae3RVov0/qSMHJcpHoajrhxlqukZHwa7KfVic09gOKu80aPz4nPWGpbjr+acdqfMeWMdrRe33EN2P/N8T+DXl07o+19Z09yiepLxfz7Jpd9VdR+vemV3g+TraTG1ngMvqPM/63u2KjstA7lvfKLO/4uKcTc7a/VnXLu/9Zx6EkXjvX9E1PlOHEgQQQAABBBBAAAEEEEAAAQQGQMDfziHVL/AqbSI1QO/BTg8Idos/5HTx9Yny7Kf4QxX/pHVe6nWdZ6ohPTOZlr6rgv9R6DnZjJT66c9p9bTerVrLP2OdTQdkC6L5Zyj/UcVZiisVd6nRXynWUL5T8iD03t6mwoM7/5Go/H61s0GivEiRj1fqIf9bO62s7e2t5Y9O1BnEQeNez5nEblVWdHWipZ0SZVNFst5JsdZUwXBlsg9dU72v/BpNbWTMy/zN9AcSBm9LlC0v0jnnb5julbd8SMqLnH+13QNreq2o7R6iY76qYkfFSxXL/4sK7cNdirMV71b4nPBrpF/LskmLGztmC+toM7uNIZmv7TzrZ/8rPi4DtW/9eMzxOoNwf36VDHjvP/1E0GWyMukGN1Tv/Su+xldCkEMAAQQQQAABBBBAAAEEEEBggAQYNJ6dg/GaxGYu1sOSHRX+GeRu8Rit759Tyyb/H1z+Jm6c8r4FFv+fkHF953tdxw+5s+n2bIHn1T8P4O6fWLZKouw3iTL/DLe/2ZxK2f9PeU1VukOe96YqR2VXqs5fo3kPGme/1fMQlX0oqlM4q7bvU+UzEiscrH3ZPVFuJ3/zLDV4fY3Kf51aZ47Lej1n6uzuuWp8SWYDC2V6cKZs+azKH6vM7xV3Kr9Y8W3FxxXLB3RS6wxYWd6AfdzNuq7ReBtjndd17p9Q9c8HZ9PzdC4dki1U2UKVfT5bPoTzXc+/WbgHVv1aUek9RMd6nuKriqt0fP165A/+nKn4pGJGkpc/ZORB45Tt371CHW263ZDU/jqKJ2Vil7B8EKezcJ513e26jssg7FvXnR/gCvIbhPsz7/1nniND996/rmt8Jg0lCCCAAAIIIIAAAggggAACCAyGAIPGNR8HPWx4hjaR+snhL/a46f9N1J+nssPicj0o0yaT3357gRZMKPx/6z615DrZAVY354GSLeJ2Na/uLP85zu3j8nY+NYj9Li3LfovNg6m/VVu7KZafr5quqThG5fu024oni+KZInl10g/1/y1R9/Xajgfs+0knaCWtPi25/+eq8K2KdbxEUw8uPE1Zfygg9e3YN7T75+oDk/o5z+rqvPpyndr+QqJ9/9/Ue8blml9T859rl2nVxpaK5yv2VfxNMSqprmt0VHyq2o+j1ZBOqxnpJBV+QXGgwvfeD6rGBYodZtQc3YI674GVvlZUfQ9Re34du1nxCMXy1632Yd5f58Je7Xx24g+5xHW9/Ga19Wdn6mjT7Ubpycr7A0px/DBaPqjZOs+zrvtc83GZ033ruvODX2HO7s+6zp8hHt77zzxHhu69f83X+EwhShBAAAEEEEAAAQQQQAABBBBAYLQF9ODIAwetTCzT/MN62XPVX1dxb6Ydt3uNYtqDZs1fnajnuv5mpaeXZLetssLrqK4HiN1ONu5Q2XEKD5K8UXGBIlsnzPubnjOS6n+mwzr++enLFUty6vxZ5dMeSGn+HYm6P8luWHU8oP67RN0fZOsWnVdb/vZq2N/sdFLLrlDc06HOotS2VP/FiXX8M9u5SfU9gJrtw5dyVyiwQO1dnWjT20ieZyp/fqK+B1Zyk+oX6rfqPVKxNNG+r7VzFB9RnKnI835Fbif6XKBtba7Imnv+1UWbVN0jEm2c0219rVPLNap29070J+9XBmo7TwelHz4O6suXEyap4x7KUvevX3U7ptnl2uZuOdvdM1u333m139f5F7an9Wu5B7p9tV31a0Wl9xD1b3uF7z/huIepj78/VPBmhT9U8C8Kfys5VfezwbK9z5W3GdrX9lPX9h1heT9TtVn7Odp2qeU8yzGZcb9TvTqPS137xv155rXpa7Sq+zPv/dO+Q/nev85rvJ97K+sggAACCCCAAAIIIIAAAggggMCQCughw3qK1ECvfwqz56S2vqgID57jqb8pOZVU57M59cI6d01Vbmd6WUd15yt+3WUbYVuePqjwIGlc5gHgVRP9sJkHwuO6RfK3ap1tE+0VGjT2elp/r5zt+luoPSe1Zadzc9rstk/naz3/RPaMpPLaHvbO2FiHAvWjp/NM9WsbNHY31f7bFd1cU8tP1nrTPnjRYbcLL1KbczloXMs1qn1KDSzNGERpH4/aztNB6Ud7P/2zvhcrUudWtuyHqve+RN3zCp9Y7Ypqo/YBOW2j7KBxLffAtnulrxXtNiu9h8jvVYrUYHD2vEjN+xc2Uq+RlbfZ3vfUtT0sg8a1nGfyT5nk3e/qOi517Rv355n37EruzzpveO+/wnak3vvruNZyjbdf0pkggAACCCCAAAIIIIAAAgggMDAClQ+UDMyeDUZHDlI3Vk905eREWZEi/1RhKr0hU7hI80szZfHsWnr4sW5coPwiRaF19FNtrudvZiYfnqo8TpdqZleFf345Tmtp5gVxgfNq2236Z5p7+flu/6zwvlr3ck37TlrfP8X5rUQDn5DXjIf3iXrTitpOz1Hh+xWdbOP1XO8Diidr/dviBQOYX6Q+ddqv1HlW227Iyz9F+Q7Fkh42cqLqvkrrpv4v0R6aGayq2p/artHB2tO5742s/f/RPkvx3S69OVPLn6dIDcT1cs522czgLG6fh7XcA9V25a8VarPSe4jaO0lH41DF/T0elV+q/ku0/gPZ9epoM7uNYZuXie93tZxnRS3qOi6DsG9FDQaxnvzm4v58kCx47z9i7/3rusYH8bqhTwgggAACCCCAAAIIIIAAAgggUJOABhr9bdHsN4j887geMO05ab2m4qpEm0tVNu3nrjW/qyL1jd37VO5vME2r7870uo7qb6Q4UTGpyO7nzSo7WrH856I1PTxR5yudEFR/X8WFitTPBXp73sY7Ff5/apNJywp/09gNqL5/ZtLfjsjuz7uTGyhYqPZ2VHxa8bdE296Wvyn9PcXO3ZpUndq+IdRt29nl6kvh80x1a/2mceibtrOd4jRF+Jns7LH0/HcUe4V16piq/Tn7pnHYH/Wh0mtU7fXyzbvaztNB6Udw9lR98v35RYqvKHyf9q9M+F7r++A+iuUf0tL0rYrsOfntuK0iebUx8N80jvdD/a3sHhi367zaLv1aEbep9iq9h6i9DRVHKG5SZI99PH+Bls/4MFXct5BXvUrbVHupazv1AYfQha5TtVn7OZrthLZZ2XmWY9Lxw3Jap9LjEu9fxfvG/bmm+7OO0/mK+Lp2/h4F7/1XuAz1e38dx9qu8fh6J48AAggggAACCCCAAAIIIIDAXAnog9OkURXQgw0PUmyj8Dd3V1H8XnFp+5srys5Mfa6zgVraXuGfh/a3Os7XNhZrWklSnxaooe0Uj1H42xtXKa5WXKftLNN0aJL2Zb46u6Pi4YrNFNcpLtR+/EXToUz9nDOzsaPql8/5JyrsvKHCD/sXK66U9181HZski1qv0bGBrGBHdSwOVzNHZZo6XefkyzNlIzmr/a/tHqi2K32tUHuV3kPU3jwd1M0VW7RjPU1971/s0Dlwt6Y9pTra7KkDA1pZLrWdZ0V2uc7jMtf7VmT/h7WObIf+/qx94L3/LJyAdV7js9B9NoEAAggggAACCCCAAAIIIIAAAggggAACCCBQpYAeGh+p+ITiLQp/y3gnhQcCc5OWf1KR/Rbax3NXYAECCCCAQM8Cus9yf+5ZjRUQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEECgZwENSnwpMQDsAeG3pBpT+WaK1E8Uj8W3jFMmlCGAAAJ1COhey/25DljaRAABBP5/+3asmjAUhQEYEbcOQl2im+/SoZvYPkffwdfoE7np2Bdo19KCq/hnKSQETBOSJZ/wIzfcJDefcjIcLgECBAgQIECAAAECBAgQIECAAAECBKoCaUq8JfVdw+X4M3lOlskiKZJ9ckrq879zbFW9shEBAgQI9BFIXVWf+wA6lwABAgQIECBAgAABAgQIECBAgAABAgQIEGgnkKbENvlN6o3g/4wbdyW3W4FZBAgQINAkoD43qThGgAABAgQIECBAgAABAgQIECBAgAABAgQIDCKQxsQuuXZsHJc7j+eDLMxFCRAgMHEB9XnifwCPT4AAAQIECBAgQIAAAQIECBAgQIAAAQIExhRIY+IlOSdtdxhfMveQPIy5TvciQIDA1ARSZ9Xnqf3onpcAAQIECBAgQIAAAQIECBDoLDDrfKYTCRAgQIAAgT+BNCeeMnhNNkmRrJPH5JL8JB/JMXnPy/cr3z4ECBAgMIKA+jwCslsQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAjcE7gBZ7HpUnCTMwMAAAAASUVORK5CYII="
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![image.png](attachment:image.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# You can preinstall all prerequisites by uncommenting and running the following two commands:\n",
+    "# import sys\n",
+    "# !{sys.executable} -m pip install adversarial-robustness-toolbox==1.5.1 tensorflow==2.3.1 Keras==2.4.3 matplotlib==3.3.2 ipywidgets==7.6.3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from __future__ import absolute_import, division, print_function, unicode_literals\n",
+    "\n",
+    "import os, sys\n",
+    "from os.path import abspath\n",
+    "\n",
+    "module_path = os.path.abspath(os.path.join('..'))\n",
+    "if module_path not in sys.path:\n",
+    "    sys.path.append(module_path)\n",
+    "\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "\n",
+    "# Disable TensorFlow eager execution:\n",
+    "import tensorflow as tf\n",
+    "if tf.executing_eagerly():\n",
+    "    tf.compat.v1.disable_eager_execution()\n",
+    "\n",
+    "import keras.backend as k\n",
+    "from keras.models import Sequential\n",
+    "from keras.layers import Dense, Flatten, Conv2D, MaxPooling2D, Activation, Dropout\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "from mpl_toolkits import mplot3d\n",
+    "\n",
+    "from art.estimators.classification import KerasClassifier\n",
+    "from art.attacks.poisoning import PoisoningAttackBackdoor\n",
+    "from art.attacks.poisoning.perturbations import add_pattern_bd, add_single_bd, insert_image\n",
+    "from art.utils import load_mnist, preprocess\n",
+    "from art.defences.detector.poison import ActivationDefence\n"
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": "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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### The classification problem: Automatically detect numbers written in a check\n",
+    "![image.png](attachment:image.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "(x_raw, y_raw), (x_raw_test, y_raw_test), min_, max_ = load_mnist(raw=True)\n",
+    "\n",
+    "# Random Selection:\n",
+    "n_train = np.shape(x_raw)[0]\n",
+    "num_selection = 7500\n",
+    "random_selection_indices = np.random.choice(n_train, num_selection)\n",
+    "x_raw = x_raw[random_selection_indices]\n",
+    "y_raw = y_raw[random_selection_indices]\n",
+    "\n",
+    "BACKDOOR_TYPE = \"pattern\" # one of ['pattern', 'pixel', 'image']"
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": "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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Adversary's goal: make some easy money \n",
+    "![image.png](attachment:image.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "max_val = np.max(x_raw)\n",
+    "def add_modification(x):\n",
+    "        if BACKDOOR_TYPE == 'pattern':\n",
+    "            return add_pattern_bd(x, pixel_value=max_val)\n",
+    "        elif BACKDOOR_TYPE == 'pixel':\n",
+    "            return add_single_bd(x, pixel_value=max_val) \n",
+    "        elif BACKDOOR_TYPE == 'image':\n",
+    "            return insert_image(x, backdoor_path='../utils/data/backdoors/alert.png', size=(10,10))\n",
+    "        else:\n",
+    "            raise(\"Unknown backdoor type\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def poison_dataset(x_clean, y_clean, percent_poison, poison_func):\n",
+    "    x_poison = np.copy(x_clean)\n",
+    "    y_poison = np.copy(y_clean)\n",
+    "    is_poison = np.zeros(np.shape(y_poison))\n",
+    "    \n",
+    "    sources=np.arange(10) # 0, 1, 2, 3, ...\n",
+    "    targets=(np.arange(10) + 1) % 10 # 1, 2, 3, 4, ...\n",
+    "    for i, (src, tgt) in enumerate(zip(sources, targets)):\n",
+    "        n_points_in_tgt = np.size(np.where(y_clean == tgt))\n",
+    "        num_poison = round((percent_poison * n_points_in_tgt) / (1 - percent_poison))\n",
+    "        src_imgs = x_clean[y_clean == src]\n",
+    "\n",
+    "        n_points_in_src = np.shape(src_imgs)[0]\n",
+    "        indices_to_be_poisoned = np.random.choice(n_points_in_src, num_poison)\n",
+    "\n",
+    "        imgs_to_be_poisoned = np.copy(src_imgs[indices_to_be_poisoned])\n",
+    "        backdoor_attack = PoisoningAttackBackdoor(poison_func)\n",
+    "        imgs_to_be_poisoned, poison_labels = backdoor_attack.poison(imgs_to_be_poisoned, y=np.ones(num_poison) * tgt)\n",
+    "        x_poison = np.append(x_poison, imgs_to_be_poisoned, axis=0)\n",
+    "        y_poison = np.append(y_poison, poison_labels, axis=0)\n",
+    "        is_poison = np.append(is_poison, np.ones(num_poison))\n",
+    "\n",
+    "    is_poison = is_poison != 0\n",
+    "\n",
+    "    return is_poison, x_poison, y_poison"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Poison training data\n",
+    "percent_poison = .33\n",
+    "(is_poison_train, x_poisoned_raw, y_poisoned_raw) = poison_dataset(x_raw, y_raw, percent_poison, add_modification)\n",
+    "x_train, y_train = preprocess(x_poisoned_raw, y_poisoned_raw)\n",
+    "# Add channel axis:\n",
+    "x_train = np.expand_dims(x_train, axis=3)\n",
+    "\n",
+    "# Poison test data\n",
+    "(is_poison_test, x_poisoned_raw_test, y_poisoned_raw_test) = poison_dataset(x_raw_test, y_raw_test, percent_poison, add_modification)\n",
+    "x_test, y_test = preprocess(x_poisoned_raw_test, y_poisoned_raw_test)\n",
+    "# Add channel axis:\n",
+    "x_test = np.expand_dims(x_test, axis=3)\n",
+    "\n",
+    "# Shuffle training data\n",
+    "n_train = np.shape(y_train)[0]\n",
+    "shuffled_indices = np.arange(n_train)\n",
+    "np.random.shuffle(shuffled_indices)\n",
+    "x_train = x_train[shuffled_indices]\n",
+    "y_train = y_train[shuffled_indices]\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Victim bank trains a neural network"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create Keras convolutional neural network - basic architecture from Keras examples\n",
+    "# Source here: https://github.com/keras-team/keras/blob/master/examples/mnist_cnn.py\n",
+    "\n",
+    "model = Sequential()\n",
+    "model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=x_train.shape[1:]))\n",
+    "model.add(Conv2D(64, (3, 3), activation='relu'))\n",
+    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
+    "model.add(Dropout(0.25))\n",
+    "model.add(Flatten())\n",
+    "model.add(Dense(128, activation='relu'))\n",
+    "model.add(Dropout(0.5))\n",
+    "model.add(Dense(10, activation='softmax'))\n",
+    "\n",
+    "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From /Users/mathieu/Documents/git/emptyvenv/lib/python3.7/site-packages/art/estimators/classification/keras.py:517: Model.fit_generator (from tensorflow.python.keras.engine.training_v1) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Please use Model.fit, which supports generators.\n",
+      "Epoch 1/10\n",
+      "87/87 [==============================] - 11s 123ms/step - batch: 43.0000 - size: 128.0000 - loss: 1.2867 - accuracy: 0.5265\n",
+      "Epoch 2/10\n",
+      "87/87 [==============================] - 10s 120ms/step - batch: 43.0000 - size: 128.0000 - loss: 0.4563 - accuracy: 0.8606\n",
+      "Epoch 3/10\n",
+      "87/87 [==============================] - 11s 125ms/step - batch: 43.0000 - size: 128.0000 - loss: 0.2624 - accuracy: 0.9231\n",
+      "Epoch 4/10\n",
+      "87/87 [==============================] - 11s 127ms/step - batch: 43.0000 - size: 128.0000 - loss: 0.1998 - accuracy: 0.9388\n",
+      "Epoch 5/10\n",
+      "87/87 [==============================] - 11s 123ms/step - batch: 43.0000 - size: 128.0000 - loss: 0.1667 - accuracy: 0.9504\n",
+      "Epoch 6/10\n",
+      "87/87 [==============================] - 11s 124ms/step - batch: 43.0000 - size: 128.0000 - loss: 0.1217 - accuracy: 0.9625\n",
+      "Epoch 7/10\n",
+      "87/87 [==============================] - 11s 128ms/step - batch: 43.0000 - size: 128.0000 - loss: 0.1096 - accuracy: 0.9673\n",
+      "Epoch 8/10\n",
+      "87/87 [==============================] - 11s 128ms/step - batch: 43.0000 - size: 128.0000 - loss: 0.1150 - accuracy: 0.9652\n",
+      "Epoch 9/10\n",
+      "87/87 [==============================] - 11s 123ms/step - batch: 43.0000 - size: 128.0000 - loss: 0.0916 - accuracy: 0.9721\n",
+      "Epoch 10/10\n",
+      "87/87 [==============================] - 11s 127ms/step - batch: 43.0000 - size: 128.0000 - loss: 0.0838 - accuracy: 0.9740\n"
+     ]
+    }
+   ],
+   "source": [
+    "classifier = KerasClassifier(model=model, clip_values=(min_, max_))\n",
+    "classifier.fit(x_train, y_train, nb_epochs=10, batch_size=128)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# The victim bank evaluates the model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Evaluation on clean test samples"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From /Users/mathieu/Documents/git/emptyvenv/lib/python3.7/site-packages/tensorflow/python/keras/engine/training_v1.py:2070: Model.state_updates (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "This property should not be used in TensorFlow 2.0, as updates are applied automatically.\n",
+      "\n",
+      "Clean test set accuracy: 97.67%\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Prediction: 0\n"
+     ]
+    }
+   ],
+   "source": [
+    "clean_x_test = x_test[is_poison_test == 0]\n",
+    "clean_y_test = y_test[is_poison_test == 0]\n",
+    "\n",
+    "clean_preds = np.argmax(classifier.predict(clean_x_test), axis=1)\n",
+    "clean_correct = np.sum(clean_preds == np.argmax(clean_y_test, axis=1))\n",
+    "clean_total = clean_y_test.shape[0]\n",
+    "\n",
+    "clean_acc = clean_correct / clean_total\n",
+    "print(\"\\nClean test set accuracy: %.2f%%\" % (clean_acc * 100))\n",
+    "\n",
+    "# Display image, label, and prediction for a clean sample to show how the poisoned model classifies a clean sample\n",
+    "\n",
+    "c = 0 # class to display\n",
+    "i = 0 # image of the class to display\n",
+    "\n",
+    "c_idx = np.where(np.argmax(clean_y_test,1) == c)[0][i] # index of the image in clean arrays\n",
+    "\n",
+    "plt.imshow(clean_x_test[c_idx].squeeze())\n",
+    "plt.show()\n",
+    "clean_label = c\n",
+    "print(\"Prediction: \" + str(clean_preds[c_idx]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### But the adversary has other plans..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Prediction: 1\n",
+      "\n",
+      " Effectiveness of poison: 96.55%\n"
+     ]
+    }
+   ],
+   "source": [
+    "poison_x_test = x_test[is_poison_test]\n",
+    "poison_y_test = y_test[is_poison_test]\n",
+    "\n",
+    "poison_preds = np.argmax(classifier.predict(poison_x_test), axis=1)\n",
+    "poison_correct = np.sum(poison_preds == np.argmax(poison_y_test, axis=1))\n",
+    "poison_total = poison_y_test.shape[0]\n",
+    "\n",
+    "# Display image, label, and prediction for a poisoned image to see the backdoor working\n",
+    "\n",
+    "c = 1 # class to display\n",
+    "i = 0 # image of the class to display\n",
+    "\n",
+    "c_idx = np.where(np.argmax(poison_y_test,1) == c)[0][i] # index of the image in poison arrays\n",
+    "\n",
+    "plt.imshow(poison_x_test[c_idx].squeeze())\n",
+    "plt.show()\n",
+    "poison_label = c\n",
+    "print(\"Prediction: \" + str(poison_preds[c_idx]))\n",
+    "\n",
+    "poison_acc = poison_correct / poison_total\n",
+    "print(\"\\n Effectiveness of poison: %.2f%%\" % (poison_acc * 100))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Evaluate accuracy on entire test set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      " Overall test set accuracy (i.e. effectiveness of poison): 97.30%\n"
+     ]
+    }
+   ],
+   "source": [
+    "total_correct = clean_correct + poison_correct\n",
+    "total = clean_total + poison_total\n",
+    "\n",
+    "total_acc = total_correct / total\n",
+    "print(\"\\n Overall test set accuracy (i.e. effectiveness of poison): %.2f%%\" % (total_acc * 100))\n"
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": "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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Detect Poison Using Activation Defence\n",
+    "![image.png](attachment:image.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "defence = ActivationDefence(classifier, x_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Analysis completed. Report:\n",
+      "{'Class_0': {'cluster_0': {'ptc_data_in_cluster': 0.67,\n",
+      "                           'suspicious_cluster': False},\n",
+      "             'cluster_1': {'ptc_data_in_cluster': 0.33,\n",
+      "                           'suspicious_cluster': True}},\n",
+      " 'Class_1': {'cluster_0': {'ptc_data_in_cluster': 0.33,\n",
+      "                           'suspicious_cluster': True},\n",
+      "             'cluster_1': {'ptc_data_in_cluster': 0.67,\n",
+      "                           'suspicious_cluster': False}},\n",
+      " 'Class_2': {'cluster_0': {'ptc_data_in_cluster': 0.66,\n",
+      "                           'suspicious_cluster': False},\n",
+      "             'cluster_1': {'ptc_data_in_cluster': 0.34,\n",
+      "                           'suspicious_cluster': True}},\n",
+      " 'Class_3': {'cluster_0': {'ptc_data_in_cluster': 0.67,\n",
+      "                           'suspicious_cluster': False},\n",
+      "             'cluster_1': {'ptc_data_in_cluster': 0.33,\n",
+      "                           'suspicious_cluster': True}},\n",
+      " 'Class_4': {'cluster_0': {'ptc_data_in_cluster': 0.67,\n",
+      "                           'suspicious_cluster': False},\n",
+      "             'cluster_1': {'ptc_data_in_cluster': 0.33,\n",
+      "                           'suspicious_cluster': True}},\n",
+      " 'Class_5': {'cluster_0': {'ptc_data_in_cluster': 0.67,\n",
+      "                           'suspicious_cluster': False},\n",
+      "             'cluster_1': {'ptc_data_in_cluster': 0.33,\n",
+      "                           'suspicious_cluster': True}},\n",
+      " 'Class_6': {'cluster_0': {'ptc_data_in_cluster': 0.67,\n",
+      "                           'suspicious_cluster': False},\n",
+      "             'cluster_1': {'ptc_data_in_cluster': 0.33,\n",
+      "                           'suspicious_cluster': True}},\n",
+      " 'Class_7': {'cluster_0': {'ptc_data_in_cluster': 0.67,\n",
+      "                           'suspicious_cluster': False},\n",
+      "             'cluster_1': {'ptc_data_in_cluster': 0.33,\n",
+      "                           'suspicious_cluster': True}},\n",
+      " 'Class_8': {'cluster_0': {'ptc_data_in_cluster': 0.67,\n",
+      "                           'suspicious_cluster': False},\n",
+      "             'cluster_1': {'ptc_data_in_cluster': 0.33,\n",
+      "                           'suspicious_cluster': True}},\n",
+      " 'Class_9': {'cluster_0': {'ptc_data_in_cluster': 0.67,\n",
+      "                           'suspicious_cluster': False},\n",
+      "             'cluster_1': {'ptc_data_in_cluster': 0.33,\n",
+      "                           'suspicious_cluster': True}},\n",
+      " 'cluster_analysis': 'smaller',\n",
+      " 'clustering_method': 'KMeans',\n",
+      " 'generator': None,\n",
+      " 'nb_clusters': 2,\n",
+      " 'nb_dims': 10,\n",
+      " 'reduce': 'PCA',\n",
+      " 'suspicious_clusters': 10}\n"
+     ]
+    }
+   ],
+   "source": [
+    "report, is_clean_lst = defence.detect_poison(nb_clusters=2,\n",
+    "                                             nb_dims=10,\n",
+    "                                             reduce=\"PCA\")\n",
+    "\n",
+    "print(\"Analysis completed. Report:\")\n",
+    "import pprint\n",
+    "pp = pprint.PrettyPrinter(indent=10)\n",
+    "pprint.pprint(report)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Evaluate Defence"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "------------------- Results using size metric -------------------\n",
+      "class_0\n",
+      "{'FalseNegative': {'denominator': 344, 'numerator': 0, 'rate': 0.0},\n",
+      " 'FalsePositive': {'denominator': 699, 'numerator': 2, 'rate': 0.29},\n",
+      " 'TrueNegative': {'denominator': 699, 'numerator': 697, 'rate': 99.71},\n",
+      " 'TruePositive': {'denominator': 344, 'numerator': 344, 'rate': 100.0}}\n",
+      "class_1\n",
+      "{'FalseNegative': {'denominator': 401, 'numerator': 0, 'rate': 0.0},\n",
+      " 'FalsePositive': {'denominator': 814, 'numerator': 0, 'rate': 0.0},\n",
+      " 'TrueNegative': {'denominator': 814, 'numerator': 814, 'rate': 100.0},\n",
+      " 'TruePositive': {'denominator': 401, 'numerator': 401, 'rate': 100.0}}\n",
+      "class_2\n",
+      "{'FalseNegative': {'denominator': 376, 'numerator': 0, 'rate': 0.0},\n",
+      " 'FalsePositive': {'denominator': 764, 'numerator': 10, 'rate': 1.31},\n",
+      " 'TrueNegative': {'denominator': 764, 'numerator': 754, 'rate': 98.69},\n",
+      " 'TruePositive': {'denominator': 376, 'numerator': 376, 'rate': 100.0}}\n",
+      "class_3\n",
+      "{'FalseNegative': {'denominator': 389, 'numerator': 0, 'rate': 0.0},\n",
+      " 'FalsePositive': {'denominator': 790, 'numerator': 0, 'rate': 0.0},\n",
+      " 'TrueNegative': {'denominator': 790, 'numerator': 790, 'rate': 100.0},\n",
+      " 'TruePositive': {'denominator': 389, 'numerator': 389, 'rate': 100.0}}\n",
+      "class_4\n",
+      "{'FalseNegative': {'denominator': 342, 'numerator': 0, 'rate': 0.0},\n",
+      " 'FalsePositive': {'denominator': 695, 'numerator': 0, 'rate': 0.0},\n",
+      " 'TrueNegative': {'denominator': 695, 'numerator': 695, 'rate': 100.0},\n",
+      " 'TruePositive': {'denominator': 342, 'numerator': 342, 'rate': 100.0}}\n",
+      "class_5\n",
+      "{'FalseNegative': {'denominator': 350, 'numerator': 0, 'rate': 0.0},\n",
+      " 'FalsePositive': {'denominator': 711, 'numerator': 3, 'rate': 0.42},\n",
+      " 'TrueNegative': {'denominator': 711, 'numerator': 708, 'rate': 99.58},\n",
+      " 'TruePositive': {'denominator': 350, 'numerator': 350, 'rate': 100.0}}\n",
+      "class_6\n",
+      "{'FalseNegative': {'denominator': 367, 'numerator': 0, 'rate': 0.0},\n",
+      " 'FalsePositive': {'denominator': 746, 'numerator': 0, 'rate': 0.0},\n",
+      " 'TrueNegative': {'denominator': 746, 'numerator': 746, 'rate': 100.0},\n",
+      " 'TruePositive': {'denominator': 367, 'numerator': 367, 'rate': 100.0}}\n",
+      "class_7\n",
+      "{'FalseNegative': {'denominator': 388, 'numerator': 0, 'rate': 0.0},\n",
+      " 'FalsePositive': {'denominator': 788, 'numerator': 0, 'rate': 0.0},\n",
+      " 'TrueNegative': {'denominator': 788, 'numerator': 788, 'rate': 100.0},\n",
+      " 'TruePositive': {'denominator': 388, 'numerator': 388, 'rate': 100.0}}\n",
+      "class_8\n",
+      "{'FalseNegative': {'denominator': 360, 'numerator': 0, 'rate': 0.0},\n",
+      " 'FalsePositive': {'denominator': 731, 'numerator': 1, 'rate': 0.14},\n",
+      " 'TrueNegative': {'denominator': 731, 'numerator': 730, 'rate': 99.86},\n",
+      " 'TruePositive': {'denominator': 360, 'numerator': 360, 'rate': 100.0}}\n",
+      "class_9\n",
+      "{'FalseNegative': {'denominator': 375, 'numerator': 0, 'rate': 0.0},\n",
+      " 'FalsePositive': {'denominator': 762, 'numerator': 2, 'rate': 0.26},\n",
+      " 'TrueNegative': {'denominator': 762, 'numerator': 760, 'rate': 99.74},\n",
+      " 'TruePositive': {'denominator': 375, 'numerator': 375, 'rate': 100.0}}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Evaluate method when ground truth is known:\n",
+    "print(\"------------------- Results using size metric -------------------\")\n",
+    "is_clean = (is_poison_train == 0)\n",
+    "confusion_matrix = defence.evaluate_defence(is_clean[shuffled_indices])\n",
+    "\n",
+    "import json\n",
+    "jsonObject = json.loads(confusion_matrix)\n",
+    "for label in jsonObject:\n",
+    "    print(label)\n",
+    "    pprint.pprint(jsonObject[label]) "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Visualize Activations"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Get clustering and reduce activations to 3 dimensions using PCA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "[clusters_by_class, _] = defence.cluster_activations()\n",
+    "\n",
+    "defence.set_params(**{'ndims': 3})\n",
+    "[_, red_activations_by_class] = defence.cluster_activations()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Visualize activations colored by clustering"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "c=0\n",
+    "red_activations = red_activations_by_class[c]\n",
+    "clusters = clusters_by_class[c]\n",
+    "fig = plt.figure()\n",
+    "ax = plt.axes(projection='3d')\n",
+    "colors=[\"#0000FF\", \"#00FF00\"]\n",
+    "for i, act in enumerate(red_activations):\n",
+    "    ax.scatter3D(act[0], act[1], act[2], color = colors[clusters[i]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Clusters for class 1.\n",
+      "Note that one of the clusters contains the poisonous data for this class.\n",
+      "Also, legitimate number of data points are less (see relative size of digits)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 1800x1800 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def plot_class_clusters(n_class, n_clusters):\n",
+    "    for q in range(n_clusters):\n",
+    "        plt.figure(1, figsize=(25,25))\n",
+    "        plt.tight_layout()\n",
+    "        plt.subplot(1, n_clusters, q+1)\n",
+    "        plt.title(\"Class \"+ str(n_class)+ \", Cluster \"+ str(q), fontsize=40)\n",
+    "        sprite = sprites_by_class[n_class][q]\n",
+    "        plt.imshow(sprite, interpolation='none')\n",
+    "        \n",
+    "sprites_by_class = defence.visualize_clusters(x_train, save=False)\n",
+    "\n",
+    "# Visualize clusters for class 1\n",
+    "print(\"Clusters for class 1.\")\n",
+    "print(\"Note that one of the clusters contains the poisonous data for this class.\")\n",
+    "print(\"Also, legitimate number of data points are less (see relative size of digits)\")\n",
+    "plot_class_clusters(1, 2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Clusters for class 5:\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 1800x1800 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "print(\"Clusters for class 5:\")\n",
+    "plot_class_clusters(5, 2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "emptyenv",
+   "language": "python",
+   "name": "emptyenv"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/adversarial-robustness-toolbox/notebooks/poisoning_defense_neural_cleanse.ipynb b/adversarial-robustness-toolbox/notebooks/poisoning_defense_neural_cleanse.ipynb
new file mode 100644
index 0000000..8bb1967
--- /dev/null
+++ b/adversarial-robustness-toolbox/notebooks/poisoning_defense_neural_cleanse.ipynb
@@ -0,0 +1,805 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Using ART to Defend against Poisoning Attacks with Neural Cleanse\n",
+    "\n",
+    "Neural Cleanse is a method developed by [Wang et. al. (2019)](https://people.cs.uchicago.edu/~ravenben/publications/pdf/backdoor-sp19.pdf). Using this method, we show how ART can defend against poison input by:\n",
+    "\n",
+    "- filtering out potentially poisonous input\n",
+    "- unlearning the backdoor by retraining\n",
+    "- pruning the neural network of neurons associated with the backdoor\n",
+    "- some combination of the above\n",
+    "\n",
+    "One main distinction is that this method allows us to identify the backdoor pattern, and investigate neurons associated with these backdoors."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using TensorFlow backend.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from __future__ import absolute_import, division, print_function, unicode_literals\n",
+    "\n",
+    "import os, sys\n",
+    "from os.path import abspath\n",
+    "\n",
+    "module_path = os.path.abspath(os.path.join('..'))\n",
+    "if module_path not in sys.path:\n",
+    "    sys.path.append(module_path)\n",
+    "\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "import keras.backend as k\n",
+    "from keras.models import Sequential\n",
+    "from keras.layers import Dense, Flatten, Conv2D, MaxPooling2D, Activation, Dropout\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "from mpl_toolkits import mplot3d\n",
+    "\n",
+    "from art.estimators.classification import KerasClassifier\n",
+    "from art.attacks.poisoning import PoisoningAttackBackdoor\n",
+    "from art.attacks.poisoning.perturbations import add_pattern_bd, add_single_bd, insert_image\n",
+    "from art.utils import load_mnist, preprocess\n",
+    "from art.defences.detector.poison import ActivationDefence\n",
+    "from art.defences.transformer.poisoning import NeuralCleanse\n",
+    "from art.estimators.certification.neural_cleanse import KerasNeuralCleanse\n"
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": "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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### The classification problem: Automatically detect numbers written in a check\n",
+    "![image.png](attachment:image.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "(x_raw, y_raw), (x_raw_test, y_raw_test), min_, max_ = load_mnist(raw=True)\n",
+    "\n",
+    "# Random Selection:\n",
+    "n_train = np.shape(x_raw)[0]\n",
+    "num_selection = 7500\n",
+    "random_selection_indices = np.random.choice(n_train, num_selection)\n",
+    "x_raw = x_raw[random_selection_indices]\n",
+    "y_raw = y_raw[random_selection_indices]\n",
+    "\n",
+    "BACKDOOR_TYPE = \"pattern\" # one of ['pattern', 'pixel', 'image']"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Adversary's goal: make some easy money "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<img src=\"../utils/data/images/zero_to_one.png\" width=400>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import HTML\n",
+    "HTML('<img src=\"../utils/data/images/zero_to_one.png\" width=400>')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "max_val = np.max(x_raw)\n",
+    "def add_modification(x):\n",
+    "        if BACKDOOR_TYPE == 'pattern':\n",
+    "            return add_pattern_bd(x, pixel_value=max_val)\n",
+    "        elif BACKDOOR_TYPE == 'pixel':\n",
+    "            return add_single_bd(x, pixel_value=max_val) \n",
+    "        elif BACKDOOR_TYPE == 'image':\n",
+    "            return insert_image(x, backdoor_path='../utils/data/backdoors/alert.png', size=(10,10))\n",
+    "        else:\n",
+    "            raise(\"Unknown backdoor type\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def poison_dataset(x_clean, y_clean, percent_poison, poison_func):\n",
+    "    x_poison = np.copy(x_clean)\n",
+    "    y_poison = np.copy(y_clean)\n",
+    "    is_poison = np.zeros(np.shape(y_poison))\n",
+    "    \n",
+    "    # sources=np.arange(10) # 0, 1, 2, 3, ...\n",
+    "    # targets=(np.arange(10) + 1) % 10 # 1, 2, 3, 4, ...\n",
+    "    sources = np.array([0])\n",
+    "    targets = np.array([1])\n",
+    "    for i, (src, tgt) in enumerate(zip(sources, targets)):\n",
+    "        n_points_in_tgt = np.size(np.where(y_clean == tgt))\n",
+    "        num_poison = round((percent_poison * n_points_in_tgt) / (1 - percent_poison))\n",
+    "        src_imgs = x_clean[y_clean == src]\n",
+    "\n",
+    "        n_points_in_src = np.shape(src_imgs)[0]\n",
+    "        indices_to_be_poisoned = np.random.choice(n_points_in_src, num_poison)\n",
+    "\n",
+    "        imgs_to_be_poisoned = np.copy(src_imgs[indices_to_be_poisoned])\n",
+    "        backdoor_attack = PoisoningAttackBackdoor(poison_func)\n",
+    "        imgs_to_be_poisoned, poison_labels = backdoor_attack.poison(imgs_to_be_poisoned, y=np.ones(num_poison) * tgt)\n",
+    "        x_poison = np.append(x_poison, imgs_to_be_poisoned, axis=0)\n",
+    "        y_poison = np.append(y_poison, poison_labels, axis=0)\n",
+    "        is_poison = np.append(is_poison, np.ones(num_poison))\n",
+    "\n",
+    "    is_poison = is_poison != 0\n",
+    "\n",
+    "    return is_poison, x_poison, y_poison"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Poison training data\n",
+    "percent_poison = .33\n",
+    "(is_poison_train, x_poisoned_raw, y_poisoned_raw) = poison_dataset(x_raw, y_raw, percent_poison, add_modification)\n",
+    "x_train, y_train = preprocess(x_poisoned_raw, y_poisoned_raw)\n",
+    "# Add channel axis:\n",
+    "x_train = np.expand_dims(x_train, axis=3)\n",
+    "\n",
+    "# Poison test data\n",
+    "(is_poison_test, x_poisoned_raw_test, y_poisoned_raw_test) = poison_dataset(x_raw_test, y_raw_test, percent_poison, add_modification)\n",
+    "x_test, y_test = preprocess(x_poisoned_raw_test, y_poisoned_raw_test)\n",
+    "# Add channel axis:\n",
+    "x_test = np.expand_dims(x_test, axis=3)\n",
+    "\n",
+    "# Shuffle training data\n",
+    "n_train = np.shape(y_train)[0]\n",
+    "shuffled_indices = np.arange(n_train)\n",
+    "np.random.shuffle(shuffled_indices)\n",
+    "x_train = x_train[shuffled_indices]\n",
+    "y_train = y_train[shuffled_indices]\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Victim bank trains a neural network"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From /Users/ebubechuba/anaconda3/envs/art/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
+      "\n",
+      "WARNING:tensorflow:From /Users/ebubechuba/anaconda3/envs/art/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
+      "\n",
+      "WARNING:tensorflow:From /Users/ebubechuba/anaconda3/envs/art/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
+      "\n",
+      "WARNING:tensorflow:From /Users/ebubechuba/anaconda3/envs/art/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3976: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
+      "\n",
+      "WARNING:tensorflow:From /Users/ebubechuba/anaconda3/envs/art/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:133: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n",
+      "\n",
+      "WARNING:tensorflow:From /Users/ebubechuba/anaconda3/envs/art/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
+      "WARNING:tensorflow:From /Users/ebubechuba/anaconda3/envs/art/lib/python3.6/site-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
+      "\n",
+      "WARNING:tensorflow:From /Users/ebubechuba/anaconda3/envs/art/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3295: The name tf.log is deprecated. Please use tf.math.log instead.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Create Keras convolutional neural network - basic architecture from Keras examples\n",
+    "# Source here: https://github.com/keras-team/keras/blob/master/examples/mnist_cnn.py\n",
+    "\n",
+    "model = Sequential()\n",
+    "model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=x_train.shape[1:]))\n",
+    "model.add(Conv2D(64, (3, 3), activation='relu'))\n",
+    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
+    "model.add(Dropout(0.25))\n",
+    "model.add(Flatten())\n",
+    "model.add(Dense(128, activation='relu'))\n",
+    "model.add(Dropout(0.5))\n",
+    "model.add(Dense(10, activation='softmax'))\n",
+    "\n",
+    "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From /Users/ebubechuba/anaconda3/envs/art/lib/python3.6/site-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
+      "Epoch 1/3\n",
+      "61/61 [==============================] - 8s 132ms/step - loss: 0.8180 - acc: 0.7331\n",
+      "Epoch 2/3\n",
+      "61/61 [==============================] - 7s 115ms/step - loss: 0.2557 - acc: 0.9264\n",
+      "Epoch 3/3\n",
+      "61/61 [==============================] - 7s 116ms/step - loss: 0.1681 - acc: 0.9539\n"
+     ]
+    }
+   ],
+   "source": [
+    "classifier = KerasClassifier(model=model, clip_values=(min_, max_))\n",
+    "classifier.fit(x_train, y_train, nb_epochs=3, batch_size=128)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# The victim bank evaluates the model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Evaluation on clean test samples"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Clean test set accuracy: 96.47%\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Prediction: 0\n"
+     ]
+    }
+   ],
+   "source": [
+    "clean_x_test = x_test[is_poison_test == 0]\n",
+    "clean_y_test = y_test[is_poison_test == 0]\n",
+    "\n",
+    "clean_preds = np.argmax(classifier.predict(clean_x_test), axis=1)\n",
+    "clean_correct = np.sum(clean_preds == np.argmax(clean_y_test, axis=1))\n",
+    "clean_total = clean_y_test.shape[0]\n",
+    "\n",
+    "clean_acc = clean_correct / clean_total\n",
+    "print(\"\\nClean test set accuracy: %.2f%%\" % (clean_acc * 100))\n",
+    "\n",
+    "# Display image, label, and prediction for a clean sample to show how the poisoned model classifies a clean sample\n",
+    "\n",
+    "c = 0 # class to display\n",
+    "i = 0 # image of the class to display\n",
+    "\n",
+    "c_idx = np.where(np.argmax(clean_y_test,1) == c)[0][i] # index of the image in clean arrays\n",
+    "\n",
+    "plt.imshow(clean_x_test[c_idx].squeeze())\n",
+    "plt.show()\n",
+    "clean_label = c\n",
+    "print(\"Prediction: \" + str(clean_preds[c_idx]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### But the adversary has other plans..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Prediction: 1\n",
+      "\n",
+      " Effectiveness of poison: 100.00%\n"
+     ]
+    }
+   ],
+   "source": [
+    "poison_x_test = x_test[is_poison_test]\n",
+    "poison_y_test = y_test[is_poison_test]\n",
+    "\n",
+    "poison_preds = np.argmax(classifier.predict(poison_x_test), axis=1)\n",
+    "poison_correct = np.sum(poison_preds == np.argmax(poison_y_test, axis=1))\n",
+    "poison_total = poison_y_test.shape[0]\n",
+    "\n",
+    "# Display image, label, and prediction for a poisoned image to see the backdoor working\n",
+    "\n",
+    "c = 1 # class to display\n",
+    "i = 0 # image of the class to display\n",
+    "\n",
+    "c_idx = np.where(np.argmax(poison_y_test,1) == c)[0][i] # index of the image in poison arrays\n",
+    "\n",
+    "plt.imshow(poison_x_test[c_idx].squeeze())\n",
+    "plt.show()\n",
+    "poison_label = c\n",
+    "print(\"Prediction: \" + str(poison_preds[c_idx]))\n",
+    "\n",
+    "poison_acc = poison_correct / poison_total\n",
+    "print(\"\\n Effectiveness of poison: %.2f%%\" % (poison_acc * 100))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Evaluate accuracy on entire test set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      " Overall test set accuracy: 96.66%\n"
+     ]
+    }
+   ],
+   "source": [
+    "total_correct = clean_correct + poison_correct\n",
+    "total = clean_total + poison_total\n",
+    "\n",
+    "total_acc = total_correct / total\n",
+    "print(\"\\n Overall test set accuracy: %.2f%%\" % (total_acc * 100))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!-- # Detect Poison Using Activation Defence -->\n",
+    "<!-- ![image.png](attachment:image.png) -->"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "cleanse = NeuralCleanse(classifier)\n",
+    "defence_cleanse = cleanse(classifier, steps=10, learning_rate=0.1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Identifying the Backdoor\n",
+    "\n",
+    "Unlike most defenses, part of the procedure for this defense is identifying exactly what the suspected backdoor is for each class. Below is the reverse-engineered backdoor. This will be appended to clean images to mimic backdoor behavior"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Generating backdoor for class 1: 100%|██████████| 10/10 [01:14<00:00,  7.46s/it]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7feb9433c3c8>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPsAAAD4CAYAAAAq5pAIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAUGUlEQVR4nO3dbYxc1XkH8P9/Zt/wev2yGJvFNuCAEbihMbAxEFIUSgOGNAKqpsFSKa1QHDUgkSoqpUQqqO0HhJLQfGgjmWBhUgpKSyioIiGOC6KorcMajLFjbC/GL2sb29iYXdvsy8w8/bBDuoE9zx3PnZk79vn/pNXszjPn3rN355k7O88959DMICKnvlzWHRCRxlCyi0RCyS4SCSW7SCSU7CKRaGnkztrYbh3obOQuK0f68RRVCyZs+2SuiDCXcL5oa61+46NjbthKpao3zRb/qW+FQtXbztIwjmHURiZ9wqVKdpJLAXwfQB7AD83sQe/xHejE5bw2/IBc3t9hqXjCfawUW9vcuI2NVr3tXEeHGy8ND1e97azlpna5cc47Mxws+S9yNrDPjZeOHXPjnvys2W68uP9A1dvO0lpbE4xV/TaeZB7APwK4AcAiAMtILqp2eyJSX2n+Z18CoN/MtpvZKICnANxUm26JSK2lSfa5AHZP+HmgfN9vILmcZB/JvjGMpNidiKSRJtkn+xDgE/+EmdkKM+s1s95WtKfYnYikkSbZBwDMn/DzPAB703VHROolTbK/CmAhyQUk2wDcCuC52nRLRGqt6tKbmRVI3gXgBYyX3laa2aY0nWFrQu1zpPrSW8v8eW58+II5brz1cLg8ltv9rr/zpJptQj25niXHRAnXCJSGhtx4PndWMHbsvOlu2yk7B9x4klxXuCx4spbWAPh/E6eamarObmbPA3g+zTZEpDF0uaxIJJTsIpFQsotEQskuEgklu0gklOwikWjoeHaS7nBPzuvx2xfD45ft0PtuWzt+3I13bPFr5aPnnhGM5bpnuG1LSfViq35cdlpJ1x8UdqerdRc3bQnGOhKuykh7VLxrAEqfX+y2zb2y3o0fue1KNz7jR//jxlOpcv4DndlFIqFkF4mEkl0kEkp2kUgo2UUioWQXiURDS28Gf9pkHjritz+jO9x2pj9c0jr8WXIKW/rdeG5gTzBWrOM01GnlZ53uxrf/2dlu/Oy/TVd6y9KRPwmXx2Y87pfGzvjvhHLqPR+68a2PfNaNX/C1V924y5uF2RkNrTO7SCSU7CKRULKLRELJLhIJJbtIJJTsIpFQsotEoqF1dpjBRsJLQBWdGADg/fAw1vz5C9ymxYQ6eirNvORywjTVw/MTprFuYvkZ/rUVXi39onX+U3/zZf41H6v3PubGe//mz914GrnTnGHix8Pnb53ZRSKhZBeJhJJdJBJKdpFIKNlFIqFkF4mEkl0kEo2ts9dRcfsuN86WhOWgSwm18iyXTU7h6DUXuvGp2+r7FMgvuiAYK/5qa6ptF4984Mbtc58JxjZf9obbNjdlihu//ix/KurZiw65ce/ZlPRc5VnO8uI7w21T/aVJ7gAwhPG+F8ysN832RKR+avGyfo2ZvVeD7YhIHel/dpFIpE12A/BzkutILp/sASSXk+wj2TeGhGvfRaRu0r6Nv8rM9pKcDWA1ybfM7OWJDzCzFQBWAMA0djfxiBGRU1uqM7uZ7S3fHgDwDIAlteiUiNRe1clOspNk10ffA7gOwMZadUxEaivN2/g5AJ7h+JzpLQD+xcx+VpNeBXjLPZeGhxMaJ/yqJ2kdPclpz/7SjZf+8PJU2891dbnxtLV0T0vPmf4D9hwOhuz08BoEAFA8FG5biTS/d36uv3S5HXbG2hfDz+Oqk93MtgMIX7UgIk1FpTeRSCjZRSKhZBeJhJJdJBJKdpFINNUQ13xCOYTTp4VjQ8fctmlLKaeqzn9b68YLv3tZqu23/Oe6VO09pcEhN56bFX4+NfPzobBzt/8AZ8lmc0pvOrOLRELJLhIJJbtIJJTsIpFQsotEQskuEgklu0gkmqrO7tUPAfjLD+foNs1Pm+rGk6Ylrie7yp+WuHXnQX8Dba3BUGH7jip69P/2/k67Gz/3Gb9eXUq1d1/pmH9tRW7OGcFYfs5st21x/4Gq+tQQVQ7H1pldJBJKdpFIKNlFIqFkF4mEkl0kEkp2kUgo2UUi0VR1dkuom6JQCMfyCa9bzthmAMgnxIv97/jbd4xe7y9uW+zw+966I2k56eqr2Uk1/rFp/rZ3fck/bvM2nHCXaibVNQb0r9uAnXyLG+nMLhIJJbtIJJTsIpFQsotEQskuEgklu0gklOwikWiqOntivdiZE7t01K/RW8I84Ulz1rvLA7e3uW1H3ShwaJH/ZxiZfo4bP/2FtxP2EJZf95Yb//uVW9z4Qw/fWvW+s5Rf+Ck3PjJ/phuv53z49ZJ4Zie5kuQBkhsn3NdNcjXJbeVb/8iISOYqeRv/GIClH7vvXgBrzGwhgDXln0WkiSUmu5m9DODj74FvArCq/P0qADfXuF8iUmPVfkA3x8z2AUD5NjihF8nlJPtI9o1hpMrdiUhadf803sxWmFmvmfW2wp+8UETqp9pk30+yBwDKt008FaeIANUn+3MAbi9/fzuAZ2vTHRGpl8Q6O8knAXwBwCySAwDuB/AggB+TvAPALgBfqUVnSt688ABYDNfhbSyhmp0wJz1b/ENh3dPDwTFnnD2A3df52566ww1j6oD/u6WZ49wuXujG//qVT/sbuNT/3cMzt2eLR4+78YFr5rjxs8cuceO5/3r9hPtUqVxHRzDG4fA4/MRkN7NlgdC1ib0Skaahy2VFIqFkF4mEkl0kEkp2kUgo2UUi0WRDXP2laM2JJ5bOvGmoAQzcep4bn/tkfzBWmu8v/zsroQrTccgvOba/Ft43AFS3gO+4/Lvvu/Ff/N5TbvwP/uGeFHvPTmlwyI2fdcVeNz5w4TQ3vuANP14cHHTjntLwcDBmzhTXOrOLRELJLhIJJbtIJJTsIpFQsotEQskuEgklu0gkmqvOnkJSHb1w7WVufEa/337H8vODsQWP7XLbjlzh11xnbPGnwS4e+cCNp7H3y2e78aVP/qUb73nHP27NiufMdeNH/n2WG/+Pex5y43eONN+gUJ3ZRSKhZBeJhJJdJBJKdpFIKNlFIqFkF4mEkl0kEo2vs3tTOieMZ/e0zPPrpljjL7H71c3+dMxPLwpPLbz3zivdtvnh8BhjACi1+K+54cmB0+v5121u/OCXw9cXAEDndn9cdsIi3Jmx7f61EUPf7nTjD+3/ohs/+vu/7cY7n17rxj3e8uE8GE5pndlFIqFkF4mEkl0kEkp2kUgo2UUioWQXiYSSXSQSja+zW30qr4WBPW585IbPuvGn7vZr4cNfDR+qnhcPuW3f/8xMN54bS5gv342mU1wQrtkCQNtRf+/FrvDywUB9rxFIg11dbnzGS/7v9XdX/8KN3/b0hyfcp4/kz1/gxgtv7wjGrBSeXyDxzE5yJckDJDdOuO8BkntIri9/3Zi0HRHJViVv4x8DsHSS+x82s8Xlr+dr2y0RqbXEZDezlwEcbkBfRKSO0nxAdxfJDeW3+cF/SkkuJ9lHsm8MIyl2JyJpVJvsPwBwHoDFAPYB+G7ogWa2wsx6zay3Fe1V7k5E0qoq2c1sv5kVzawE4BEAS2rbLRGptaqSnWTPhB9vAbAx9FgRaQ6JdXaSTwL4AoBZJAcA3A/gCyQXY7wEvAPA1+vYx5po/+mrbnxw2RVuvG0ofH2A0a8md+4bdeOWMJ69nvJHjrvx1mN+vbml31/HPM3a8Unyp3f7D/DmTkgweK0/l/+VL93lxs/H61Xvu+jU0QGg5czw3AreePbEZDezZZPc/WhSOxFpLrpcViQSSnaRSCjZRSKhZBeJhJJdJBKnzJLNac3YdMSNF6eGr/7jiF9aaxlMKAHl/Nfceg5xfWdZuIwDADPf8ockd7a21rI7J6Yl4ek7Er48u3jhOW5T9vtTSdv8+l36ffxm/xq1Kc+Ep6E2SzHEVURODUp2kUgo2UUioWQXiYSSXSQSSnaRSCjZRSKRwVTS9awaV6+04S03PrY0PBX1kYV+rXp0uj8EtnXIPybd/uhc18iX/Cm0p/f7dfRSQhk9aQrvVBKGDhf3+8tse3Lrt7rx8/fMcuOlIx/48YT9t5wzPxjresuf8rHU2hYOjoWPmc7sIpFQsotEQskuEgklu0gklOwikVCyi0RCyS4SiYbW2Uki1xGemrjkjD9OVOf6PUvh7edH/X2z4NeLTzucbsJlb4nf9p++5rYd+OeL3fjCh/yx+mkW4PamRAYAm+4vq1zc0l/1vkvDw35890DV265EYefuYIxeHR2AFcacYPi5qDO7SCSU7CKRULKLRELJLhIJJbtIJJTsIpFQsotEorHj2XM5sCM8/zpGnfohAJTquQCwrzAl/LrYetyvNlvCtPFdG/b7+/abo9j/TjC29Z/8Ocgvuv89Nz508Rlu/LTOxW78+Jnhv/fwDP9cM3PLh248t8UNn7Tys/2x9HY8fFz4QfjJlnhmJzmf5IskN5PcRPLu8v3dJFeT3Fa+nZm0LRHJTiVv4wsAvmVmFwG4AsCdJBcBuBfAGjNbCGBN+WcRaVKJyW5m+8zstfL3QwA2A5gL4CYAq8oPWwXg5np1UkTSO6EP6EieC+ASAGsBzDGzfcD4CwKA2YE2y0n2kewbNf9/MBGpn4qTneRUAE8D+KaZDVbazsxWmFmvmfW28bRq+igiNVBRspNsxXiiP2FmPynfvZ9kTzneA6D6qT5FpO4SS28kCeBRAJvN7HsTQs8BuB3Ag+XbZxP3RgLt4VJMrs0f4loaDpfeclOm+PvO+/Wv0m+Fh4kCQOtgeN/tu/ypfzF0zA0XUkyJnOSCb/zSjQ/dcrkbH5viD889fLV/3EvOaM1Cpz80uPOAP9QzPFj65JY0Rbb/XA8f00rq7FcBuA3AmyTXl++7D+NJ/mOSdwDYBeArFWxLRDKSmOxm9gqA0Mv7tbXtjojUiy6XFYmEkl0kEkp2kUgo2UUioWQXiURjh7haCfgwPIWvFaufmLh0/Lgb3/b4pW6863W/altyjlR3pz8MtP29aW48f9Svw5eO+XFP0rTEnTuOuvEt3/Dr6Lkhf/+lmeFhy/nD/nrQrUMJg3tzCWOHMxwSncbPdvW58evPCg8rNgvnkM7sIpFQsotEQskuEgklu0gklOwikVCyi0RCyS4SiYbW2a1YQtGpKTNhzLln6NYr3DgThpz/8R0vuPGXll4YjI0s9Jcefm+xX6vueWeqG09TZ7cxf8nl/NvhpYMB4KLvTDrb2K8dWexPe9wyHP6bDs/0x8q3rtvmxktOTbnZvbB3fTDm1dEB4N2/+FwwNvbE/wZjOrOLRELJLhIJJbtIJJTsIpFQsotEQskuEgklu0gkaObP3V1L09htl1MT0n7CKTouO62ksfj5Of48AoWBPVXvO2kdgqT5E7w6OuDX0tO0XWtrMGiHJ72AQWd2kUgo2UUioWQXiYSSXSQSSnaRSCjZRSKhZBeJRCXrs88H8DiAMwGUAKwws++TfADA1wAcLD/0PjN7vl4dPaVFWkdPkjQWv7j/oBv3sMV/6tezjp7UPk3bJdeH+13J5BUFAN8ys9dIdgFYR3J1OfawmX2ngm2ISMYqWZ99H4B95e+HSG4GMLfeHROR2jqh/9lJngvgEgBry3fdRXIDyZUkZwbaLCfZR7JvDCOpOisi1as42UlOBfA0gG+a2SCAHwA4D8BijJ/5vztZOzNbYWa9ZtbbivYadFlEqlFRspNsxXiiP2FmPwEAM9tvZkUbX0nuEQBL6tdNEUkrMdlJEsCjADab2fcm3N8z4WG3ANhY++6JSK1U8mn8VQBuA/AmyY8+878PwDKSiwEYgB0Avl6XHjaJ/LTwssvFwUG3ba6z042nmSo6ZlYILwed3NZfDvqet99042nKY0nt07TdaoeCsUo+jX8FwGTjY1VTFzmJ6Ao6kUgo2UUioWQXiYSSXSQSSnaRSCjZRSLRXFNJp5lSOaFt0nLQ7Ei4lHcsXNMtjSRc89/AY3xKob+kM1ta3XjSENlTkaaSFhElu0gslOwikVCyi0RCyS4SCSW7SCSU7CKRaGidneRBADsn3DULwHsN68CJada+NWu/APWtWrXs2zlmNula1g1N9k/snOwzs97MOuBo1r41a78A9a1ajeqb3saLRELJLhKJrJN9Rcb79zRr35q1X4D6Vq2G9C3T/9lFpHGyPrOLSIMo2UUikUmyk1xKcgvJfpL3ZtGHEJI7SL5Jcj3Jvoz7spLkAZIbJ9zXTXI1yW3l20nX2Muobw+Q3FM+dutJ3phR3+aTfJHkZpKbSN5dvj/TY+f0qyHHreH/s5PMA9gK4IsABgC8CmCZmf2qoR0JILkDQK+ZZX4BBsmrARwF8LiZfbp830MADpvZg+UXyplm9ldN0rcHABzNehnv8mpFPROXGQdwM4A/RYbHzunXH6EBxy2LM/sSAP1mtt3MRgE8BeCmDPrR9MzsZQCHP3b3TQBWlb9fhfEnS8MF+tYUzGyfmb1W/n4IwEfLjGd67Jx+NUQWyT4XwO4JPw+gudZ7NwA/J7mO5PKsOzOJOWa2Dxh/8gCYnXF/Pi5xGe9G+tgy401z7KpZ/jytLJJ9svmxmqn+d5WZXQrgBgB3lt+uSmUqWsa7USZZZrwpVLv8eVpZJPsAgPkTfp4HYG8G/ZiUme0t3x4A8Ayabynq/R+toFu+PZBxf36tmZbxnmyZcTTBscty+fMskv1VAAtJLiDZBuBWAM9l0I9PINlZ/uAEJDsBXIfmW4r6OQC3l7+/HcCzGfblNzTLMt6hZcaR8bHLfPlzM2v4F4AbMf6J/NsAvp1FHwL9+hSAN8pfm7LuG4AnMf62bgzj74juAHA6gDUAtpVvu5uobz8C8CaADRhPrJ6M+vZ5jP9ruAHA+vLXjVkfO6dfDTluulxWJBK6gk4kEkp2kUgo2UUioWQXiYSSXSQSSnaRSCjZRSLxf/HjQnXJQgiqAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pattern, mask = defence_cleanse.generate_backdoor(x_test, y_test, np.array([0, 1, 0, 0, 0, 0, 0, 0, 0, 0]))\n",
+    "plt.imshow(np.squeeze(mask * pattern))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Usually `generate_backdoor` is called as a result of calling `mitigate`. During this process, this defense generates a suspected backdoor for each class visualized above. The `mitigate` method also performs the mitigation  types presented below."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Mitigation Types\n",
+    "\n",
+    "There are different mitigation methods that are described below."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Filtering\n",
+    "\n",
+    "Filtering is the process of abstaining from potentially poisonous predictions at runtime. When this method is set, neurons are ranked by their association with the backdoor, and when neural activations are higher than normal, the classifier abstains from predication (output is all zeros)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Generating backdoor for class 0: 100%|██████████| 10/10 [01:10<00:00,  7.04s/it]\n",
+      "Generating backdoor for class 1: 100%|██████████| 10/10 [01:09<00:00,  6.95s/it]\n",
+      "Generating backdoor for class 2: 100%|██████████| 10/10 [01:09<00:00,  7.00s/it]\n",
+      "Generating backdoor for class 3: 100%|██████████| 10/10 [01:10<00:00,  7.02s/it]\n",
+      "Generating backdoor for class 4: 100%|██████████| 10/10 [01:09<00:00,  6.95s/it]\n",
+      "Generating backdoor for class 5: 100%|██████████| 10/10 [01:09<00:00,  6.96s/it]\n",
+      "Generating backdoor for class 6: 100%|██████████| 10/10 [01:09<00:00,  6.97s/it]\n",
+      "Generating backdoor for class 7: 100%|██████████| 10/10 [01:11<00:00,  7.16s/it]\n",
+      "Generating backdoor for class 8: 100%|██████████| 10/10 [01:14<00:00,  7.43s/it]\n",
+      "Generating backdoor for class 9: 100%|██████████| 10/10 [01:11<00:00,  7.17s/it]\n"
+     ]
+    }
+   ],
+   "source": [
+    "defence_cleanse = cleanse(classifier, steps=10, learning_rate=0.1)\n",
+    "defence_cleanse.mitigate(clean_x_test, clean_y_test, mitigation_types=[\"filtering\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Filtered 500/559 poison samples (89.45% effective)\n"
+     ]
+    }
+   ],
+   "source": [
+    "poison_pred = defence_cleanse.predict(poison_x_test)\n",
+    "num_filtered = np.sum(np.all(poison_pred == np.zeros(10), axis=1))\n",
+    "num_poison = len(poison_pred)\n",
+    "effectiveness = float(num_filtered) / num_poison * 100\n",
+    "print(\"Filtered {}/{} poison samples ({:.2f}% effective)\".format(num_filtered, num_poison, effectiveness))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Unlearning\n",
+    "\n",
+    "Unlearning is the process of retraining the backdoors with the correct label for one epoch. This works best for Trojan-style triggers that react to a specific neuron configuration."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Generating backdoor for class 0: 100%|██████████| 10/10 [01:16<00:00,  7.68s/it]\n",
+      "Generating backdoor for class 1: 100%|██████████| 10/10 [01:11<00:00,  7.17s/it]\n",
+      "Generating backdoor for class 2: 100%|██████████| 10/10 [01:13<00:00,  7.39s/it]\n",
+      "Generating backdoor for class 3: 100%|██████████| 10/10 [01:12<00:00,  7.24s/it]\n",
+      "Generating backdoor for class 4: 100%|██████████| 10/10 [01:12<00:00,  7.28s/it]\n",
+      "Generating backdoor for class 5: 100%|██████████| 10/10 [01:09<00:00,  7.00s/it]\n",
+      "Generating backdoor for class 6: 100%|██████████| 10/10 [01:10<00:00,  7.01s/it]\n",
+      "Generating backdoor for class 7: 100%|██████████| 10/10 [01:11<00:00,  7.18s/it]\n",
+      "Generating backdoor for class 8: 100%|██████████| 10/10 [01:11<00:00,  7.13s/it]\n",
+      "Generating backdoor for class 9: 100%|██████████| 10/10 [01:10<00:00,  7.01s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/1\n",
+      "   5/4129 [..............................] - ETA: 1:03 - loss: 2.7731 - acc: 0.6000    "
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4129/4129 [==============================] - 70s 17ms/step - loss: 0.0115 - acc: 0.9981\n"
+     ]
+    }
+   ],
+   "source": [
+    "defence_cleanse = cleanse(classifier, steps=10, learning_rate=0.1)\n",
+    "defence_cleanse.mitigate(clean_x_test, clean_y_test, mitigation_types=[\"unlearning\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      " Effectiveness of poison after unlearning: 5.19% (previously 100.00%)\n",
+      "\n",
+      " Clean test set accuracy: 54.14% (previously 96.47%)\n"
+     ]
+    }
+   ],
+   "source": [
+    "poison_preds = np.argmax(classifier.predict(poison_x_test), axis=1)\n",
+    "poison_correct = np.sum(poison_preds == np.argmax(poison_y_test, axis=1))\n",
+    "poison_total = poison_y_test.shape[0]\n",
+    "new_poison_acc = poison_correct / poison_total\n",
+    "print(\"\\n Effectiveness of poison after unlearning: %.2f%% (previously %.2f%%)\" % (new_poison_acc * 100, poison_acc * 100))\n",
+    "clean_preds = np.argmax(classifier.predict(clean_x_test), axis=1)\n",
+    "clean_correct = np.sum(clean_preds == np.argmax(clean_y_test, axis=1))\n",
+    "clean_total = clean_y_test.shape[0]\n",
+    "\n",
+    "new_clean_acc = clean_correct / clean_total\n",
+    "print(\"\\n Clean test set accuracy: %.2f%% (previously %.2f%%)\" % (new_clean_acc * 100, clean_acc * 100))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Pruning\n",
+    "\n",
+    "Pruning is the process of zero-ing out neurons strongly associated with backdoor behavior until the backdoor is ineffective or 30% of all neurons have been pruned. Be careful as this can negatively affect the accuracy of your model. This works best for fully mitigating the effects of backdoor poisoning attacks."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Generating backdoor for class 0: 100%|██████████| 10/10 [01:15<00:00,  7.57s/it]\n",
+      "Generating backdoor for class 1: 100%|██████████| 10/10 [01:14<00:00,  7.49s/it]\n",
+      "Generating backdoor for class 2: 100%|██████████| 10/10 [01:14<00:00,  7.49s/it]\n",
+      "Generating backdoor for class 3: 100%|██████████| 10/10 [01:16<00:00,  7.61s/it]\n",
+      "Generating backdoor for class 4: 100%|██████████| 10/10 [01:15<00:00,  7.51s/it]\n",
+      "Generating backdoor for class 5: 100%|██████████| 10/10 [01:11<00:00,  7.17s/it]\n",
+      "Generating backdoor for class 6: 100%|██████████| 10/10 [01:11<00:00,  7.19s/it]\n",
+      "Generating backdoor for class 7: 100%|██████████| 10/10 [01:11<00:00,  7.18s/it]\n",
+      "Generating backdoor for class 8: 100%|██████████| 10/10 [01:10<00:00,  7.06s/it]\n",
+      "Generating backdoor for class 9: 100%|██████████| 10/10 [01:10<00:00,  7.02s/it]\n"
+     ]
+    }
+   ],
+   "source": [
+    "defence_cleanse = cleanse(classifier, steps=10, learning_rate=0.1)\n",
+    "defence_cleanse.mitigate(clean_x_test, clean_y_test, mitigation_types=[\"pruning\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      " Effectiveness of poison after pruning: 0.00% (previously 100.00%)\n",
+      "\n",
+      " Clean test set accuracy: 46.46% (previously 96.47%)\n"
+     ]
+    }
+   ],
+   "source": [
+    "poison_preds = np.argmax(classifier.predict(poison_x_test), axis=1)\n",
+    "poison_correct = np.sum(poison_preds == np.argmax(poison_y_test, axis=1))\n",
+    "poison_total = poison_y_test.shape[0]\n",
+    "new_poison_acc = poison_correct / poison_total\n",
+    "print(\"\\n Effectiveness of poison after pruning: %.2f%% (previously %.2f%%)\" % (new_poison_acc * 100, poison_acc * 100))\n",
+    "clean_preds = np.argmax(classifier.predict(clean_x_test), axis=1)\n",
+    "clean_correct = np.sum(clean_preds == np.argmax(clean_y_test, axis=1))\n",
+    "clean_total = clean_y_test.shape[0]\n",
+    "\n",
+    "new_clean_acc = clean_correct / clean_total\n",
+    "print(\"\\n Clean test set accuracy: %.2f%% (previously %.2f%%)\" % (new_clean_acc * 100, clean_acc * 100))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Combination\n",
+    "\n",
+    "Finally, you can also do a combination of any of the above mitigation methods to fit your needs. Just add those types to the `mitigation_types` list."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Generating backdoor for class 0: 100%|██████████| 10/10 [01:12<00:00,  7.25s/it]\n",
+      "Generating backdoor for class 1: 100%|██████████| 10/10 [01:13<00:00,  7.38s/it]\n",
+      "Generating backdoor for class 2: 100%|██████████| 10/10 [01:10<00:00,  7.07s/it]\n",
+      "Generating backdoor for class 3: 100%|██████████| 10/10 [01:10<00:00,  7.04s/it]\n",
+      "Generating backdoor for class 4: 100%|██████████| 10/10 [01:13<00:00,  7.35s/it]\n",
+      "Generating backdoor for class 5: 100%|██████████| 10/10 [01:10<00:00,  7.07s/it]\n",
+      "Generating backdoor for class 6: 100%|██████████| 10/10 [01:10<00:00,  7.04s/it]\n",
+      "Generating backdoor for class 7: 100%|██████████| 10/10 [01:12<00:00,  7.26s/it]\n",
+      "Generating backdoor for class 8: 100%|██████████| 10/10 [01:12<00:00,  7.28s/it]\n",
+      "Generating backdoor for class 9: 100%|██████████| 10/10 [01:10<00:00,  7.09s/it]\n"
+     ]
+    }
+   ],
+   "source": [
+    "defence_cleanse.mitigate(clean_x_test, clean_y_test, mitigation_types=[\"pruning\", \"filtering\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/adversarial-robustness-toolbox/notebooks/poisoning_defense_spectral_signatures.ipynb b/adversarial-robustness-toolbox/notebooks/poisoning_defense_spectral_signatures.ipynb
new file mode 100644
index 0000000..a5e018e
--- /dev/null
+++ b/adversarial-robustness-toolbox/notebooks/poisoning_defense_spectral_signatures.ipynb
@@ -0,0 +1,477 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Using ART to Detect against Poisoning Attacks via Spectral Signatures\n",
+    "\n",
+    "[Tran et. al. (2018)](https://papers.nips.cc/paper/8024-spectral-signatures-in-backdoor-attacks.pdf) developed a method to detect backdoors inputs in training data. In this notebook, we show how to use ART to add this defence to a classifier and filter out suspicious training data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using TensorFlow backend.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from __future__ import absolute_import, division, print_function, unicode_literals\n",
+    "\n",
+    "import os, sys\n",
+    "from os.path import abspath\n",
+    "\n",
+    "module_path = os.path.abspath(os.path.join('..'))\n",
+    "if module_path not in sys.path:\n",
+    "    sys.path.append(module_path)\n",
+    "\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "import keras.backend as k\n",
+    "from keras.models import Sequential\n",
+    "from keras.layers import Dense, Flatten, Conv2D, MaxPooling2D, Activation, Dropout\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "from mpl_toolkits import mplot3d\n",
+    "\n",
+    "import tensorflow as tf\n",
+    "tf.get_logger().setLevel('ERROR')\n",
+    "\n",
+    "from art.estimators.classification import KerasClassifier\n",
+    "from art.attacks.poisoning import PoisoningAttackBackdoor\n",
+    "from art.attacks.poisoning.perturbations import add_pattern_bd, add_single_bd, insert_image\n",
+    "from art.utils import load_mnist, preprocess\n",
+    "from art.defences.detector.poison import SpectralSignatureDefense"
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": "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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### The classification problem: Automatically detect numbers written in a check\n",
+    "![image.png](attachment:image.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "(x_raw, y_raw), (x_raw_test, y_raw_test), min_, max_ = load_mnist(raw=True)\n",
+    "\n",
+    "# Random Selection:\n",
+    "n_train = np.shape(x_raw)[0]\n",
+    "num_selection = 7500\n",
+    "random_selection_indices = np.random.choice(n_train, num_selection)\n",
+    "x_raw = x_raw[random_selection_indices]\n",
+    "y_raw = y_raw[random_selection_indices]\n",
+    "\n",
+    "BACKDOOR_TYPE = \"pattern\" # one of ['pattern', 'pixel', 'image']"
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": "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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Adversary's goal: make some easy money \n",
+    "![image.png](attachment:image.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "max_val = np.max(x_raw)\n",
+    "def add_modification(x):\n",
+    "        if BACKDOOR_TYPE == 'pattern':\n",
+    "            return add_pattern_bd(x, pixel_value=max_val)\n",
+    "        elif BACKDOOR_TYPE == 'pixel':\n",
+    "            return add_single_bd(x, pixel_value=max_val) \n",
+    "        elif BACKDOOR_TYPE == 'image':\n",
+    "            return insert_image(x, backdoor_path='../utils/data/backdoors/alert.png', size=(10,10))\n",
+    "        else:\n",
+    "            raise(\"Unknown backdoor type\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def poison_dataset(x_clean, y_clean, percent_poison, poison_func):\n",
+    "    x_poison = np.copy(x_clean)\n",
+    "    y_poison = np.copy(y_clean)\n",
+    "    is_poison = np.zeros(np.shape(y_poison))\n",
+    "    \n",
+    "    sources = np.array([0]) # np.arange(10) # 0, 1, 2, 3, ...\n",
+    "    targets = np.array([1]) #(np.arange(10) + 1) % 10 # 1, 2, 3, 4, ...\n",
+    "    for i, (src, tgt) in enumerate(zip(sources, targets)):\n",
+    "        n_points_in_tgt = np.size(np.where(y_clean == tgt))\n",
+    "        num_poison = round((percent_poison * n_points_in_tgt) / (1 - percent_poison))\n",
+    "        src_imgs = x_clean[y_clean == src]\n",
+    "\n",
+    "        n_points_in_src = np.shape(src_imgs)[0]\n",
+    "        indices_to_be_poisoned = np.random.choice(n_points_in_src, num_poison)\n",
+    "\n",
+    "        imgs_to_be_poisoned = np.copy(src_imgs[indices_to_be_poisoned])\n",
+    "        backdoor_attack = PoisoningAttackBackdoor(poison_func)\n",
+    "        imgs_to_be_poisoned, poison_labels = backdoor_attack.poison(imgs_to_be_poisoned, y=np.ones(num_poison) * tgt)\n",
+    "        x_poison = np.append(x_poison, imgs_to_be_poisoned, axis=0)\n",
+    "        y_poison = np.append(y_poison, poison_labels, axis=0)\n",
+    "        is_poison = np.append(is_poison, np.ones(num_poison))\n",
+    "\n",
+    "    is_poison = is_poison != 0\n",
+    "\n",
+    "    return is_poison, x_poison, y_poison"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Poison training data\n",
+    "percent_poison = .33\n",
+    "(is_poison_train, x_poisoned_raw, y_poisoned_raw) = poison_dataset(x_raw, y_raw, percent_poison, add_modification)\n",
+    "x_train, y_train = preprocess(x_poisoned_raw, y_poisoned_raw)\n",
+    "# Add channel axis:\n",
+    "x_train = np.expand_dims(x_train, axis=3)\n",
+    "\n",
+    "# Poison test data\n",
+    "(is_poison_test, x_poisoned_raw_test, y_poisoned_raw_test) = poison_dataset(x_raw_test, y_raw_test, percent_poison, add_modification)\n",
+    "x_test, y_test = preprocess(x_poisoned_raw_test, y_poisoned_raw_test)\n",
+    "# Add channel axis:\n",
+    "x_test = np.expand_dims(x_test, axis=3)\n",
+    "\n",
+    "# Shuffle training data\n",
+    "n_train = np.shape(y_train)[0]\n",
+    "shuffled_indices = np.arange(n_train)\n",
+    "np.random.shuffle(shuffled_indices)\n",
+    "x_train = x_train[shuffled_indices]\n",
+    "y_train = y_train[shuffled_indices]\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Victim bank trains a neural network"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create Keras convolutional neural network - basic architecture from Keras examples\n",
+    "# Source here: https://github.com/keras-team/keras/blob/master/examples/mnist_cnn.py\n",
+    "\n",
+    "model = Sequential()\n",
+    "model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=x_train.shape[1:]))\n",
+    "model.add(Conv2D(64, (3, 3), activation='relu'))\n",
+    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
+    "model.add(Dropout(0.25))\n",
+    "model.add(Flatten())\n",
+    "model.add(Dense(128, activation='relu'))\n",
+    "model.add(Dropout(0.5))\n",
+    "model.add(Dense(10, activation='softmax'))\n",
+    "\n",
+    "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/5\n",
+      "61/61 [==============================] - 9s 152ms/step - loss: 0.7828 - acc: 0.7520\n",
+      "Epoch 2/5\n",
+      "61/61 [==============================] - 7s 116ms/step - loss: 0.2486 - acc: 0.9237\n",
+      "Epoch 3/5\n",
+      "61/61 [==============================] - 8s 127ms/step - loss: 0.1518 - acc: 0.9574\n",
+      "Epoch 4/5\n",
+      "61/61 [==============================] - 8s 128ms/step - loss: 0.1204 - acc: 0.9643\n",
+      "Epoch 5/5\n",
+      "61/61 [==============================] - 8s 132ms/step - loss: 0.0983 - acc: 0.9705\n"
+     ]
+    }
+   ],
+   "source": [
+    "classifier = KerasClassifier(model=model, clip_values=(min_, max_))\n",
+    "classifier.fit(x_train, y_train, nb_epochs=5, batch_size=128)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# The victim bank evaluates the model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Evaluation on clean test samples"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Clean test set accuracy: 97.72%\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Prediction: 0\n"
+     ]
+    }
+   ],
+   "source": [
+    "clean_x_test = x_test[is_poison_test == 0]\n",
+    "clean_y_test = y_test[is_poison_test == 0]\n",
+    "\n",
+    "clean_preds = np.argmax(classifier.predict(clean_x_test), axis=1)\n",
+    "clean_correct = np.sum(clean_preds == np.argmax(clean_y_test, axis=1))\n",
+    "clean_total = clean_y_test.shape[0]\n",
+    "\n",
+    "clean_acc = clean_correct / clean_total\n",
+    "print(\"\\nClean test set accuracy: %.2f%%\" % (clean_acc * 100))\n",
+    "\n",
+    "# Display image, label, and prediction for a clean sample to show how the poisoned model classifies a clean sample\n",
+    "\n",
+    "c = 0 # class to display\n",
+    "i = 0 # image of the class to display\n",
+    "\n",
+    "c_idx = np.where(np.argmax(clean_y_test,1) == c)[0][i] # index of the image in clean arrays\n",
+    "\n",
+    "plt.imshow(clean_x_test[c_idx].squeeze())\n",
+    "plt.show()\n",
+    "clean_label = c\n",
+    "print(\"Prediction: \" + str(clean_preds[c_idx]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### But the adversary has other plans..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Prediction: 1\n",
+      "\n",
+      " Effectiveness of poison: 100.00%\n"
+     ]
+    }
+   ],
+   "source": [
+    "poison_x_test = x_test[is_poison_test]\n",
+    "poison_y_test = y_test[is_poison_test]\n",
+    "\n",
+    "poison_preds = np.argmax(classifier.predict(poison_x_test), axis=1)\n",
+    "poison_correct = np.sum(poison_preds == np.argmax(poison_y_test, axis=1))\n",
+    "poison_total = poison_y_test.shape[0]\n",
+    "\n",
+    "# Display image, label, and prediction for a poisoned image to see the backdoor working\n",
+    "\n",
+    "c = 1 # class to display\n",
+    "i = 0 # image of the class to display\n",
+    "\n",
+    "c_idx = np.where(np.argmax(poison_y_test,1) == c)[0][i] # index of the image in poison arrays\n",
+    "\n",
+    "plt.imshow(poison_x_test[c_idx].squeeze())\n",
+    "plt.show()\n",
+    "poison_label = c\n",
+    "print(\"Prediction: \" + str(poison_preds[c_idx]))\n",
+    "\n",
+    "poison_acc = poison_correct / poison_total\n",
+    "print(\"\\n Effectiveness of poison: %.2f%%\" % (poison_acc * 100))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Evaluate accuracy on entire test set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      " Overall test set accuracy (i.e. effectiveness of poison): 97.84%\n"
+     ]
+    }
+   ],
+   "source": [
+    "total_correct = clean_correct + poison_correct\n",
+    "total = clean_total + poison_total\n",
+    "\n",
+    "total_acc = total_correct / total\n",
+    "print(\"\\n Overall test set accuracy (i.e. effectiveness of poison): %.2f%%\" % (total_acc * 100))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "defence = SpectralSignatureDefense(classifier, x_train, y_train, \n",
+    "                                   batch_size=128, eps_multiplier=1, expected_pp_poison=percent_poison)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "report, is_clean_lst = defence.detect_poison(nb_clusters=2,\n",
+    "                                             nb_dims=10,\n",
+    "                                             reduce=\"PCA\")\n",
+    "\n",
+    "print(\"Analysis completed. Report:\")\n",
+    "import pprint\n",
+    "pp = pprint.PrettyPrinter(indent=10)\n",
+    "pprint.pprint(report)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Evaluate Defence"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# Evaluate method when ground truth is known:\n",
+    "print(\"------------------- Results using size metric -------------------\")\n",
+    "is_clean = (is_poison_train == 0)\n",
+    "confusion_matrix = defence.evaluate_defence(is_clean)\n",
+    "\n",
+    "import json\n",
+    "jsonObject = json.loads(confusion_matrix)\n",
+    "for label in jsonObject:\n",
+    "    print(label)\n",
+    "    pprint.pprint(jsonObject[label]) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/adversarial-robustness-toolbox/notebooks/provenance_defence.ipynb b/adversarial-robustness-toolbox/notebooks/provenance_defence.ipynb
new file mode 100644
index 0000000..741a0f8
--- /dev/null
+++ b/adversarial-robustness-toolbox/notebooks/provenance_defence.ipynb
@@ -0,0 +1,466 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Adversarial Robustness Toolbox for Provenance-Based Defenses\n",
+    "\n",
+    "In this notebook we will learn how to use ART to defend against adversarial attacks in IoT settings.\n",
+    "\n",
+    "When data is collected from multiple sources, we can use **provenance features** to track the origin of that data. Using those features, we can defend models against malicious attacks. We will also show how to use the Reject on Negative Impact (RONI) defense method within ART."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from __future__ import absolute_import, division, print_function, unicode_literals\n",
+    "\n",
+    "import os, sys\n",
+    "from os.path import abspath\n",
+    "\n",
+    "module_path = os.path.abspath(os.path.join('..'))\n",
+    "if module_path not in sys.path:\n",
+    "    sys.path.append(module_path)\n",
+    "\n",
+    "from art.attacks.poisoning.poisoning_attack_svm import PoisoningAttackSVM\n",
+    "from art.estimators.classification.scikitlearn import ScikitlearnSVC\n",
+    "from art.defences.detector.poison import ProvenanceDefense, RONIDefense\n",
+    "from art.utils import load_mnist\n",
+    "from sklearn.svm import SVC\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "np.random.seed(301)\n",
+    "\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Set Hyperparameters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "num_training = 40\n",
+    "num_poison = 5\n",
+    "num_valid = 40 # the number of valid examples for the attacker\n",
+    "num_trusted = 25 # the number of trusted data for the defender\n",
+    "num_devices = 4 # last device is inserting poison\n",
+    "kernel = 'linear' # available kernels are 'rbf', 'poly' and 'linear'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load and transform MNIST data\n",
+    "\n",
+    "In this examples we are training a classifer that differentiates between the number 4 and the number 0. The training data is split between the first `num_devices - 1` devices and the poisoned training data is the added to the last device. Quantity fo data and model kernel are specified by hyperparameters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "(x_train, y_train), (x_test, y_test), min_, max_ = load_mnist()\n",
+    "y_train = np.argmax(y_train, axis=1)\n",
+    "y_test = np.argmax(y_test, axis=1)\n",
+    "zero_or_four = np.logical_or(y_train == 4, y_train == 0)\n",
+    "x_train = x_train[zero_or_four]\n",
+    "y_train = y_train[zero_or_four]\n",
+    "tr_labels = np.zeros((y_train.shape[0], 2))\n",
+    "tr_labels[y_train == 0] = np.array([1, 0])\n",
+    "tr_labels[y_train == 4] = np.array([0, 1])\n",
+    "y_train = tr_labels\n",
+    "\n",
+    "\n",
+    "zero_or_four = np.logical_or(y_test == 4, y_test == 0)\n",
+    "x_test = x_test[zero_or_four]\n",
+    "y_test = y_test[zero_or_four]\n",
+    "te_labels = np.zeros((y_test.shape[0], 2))\n",
+    "te_labels[y_test == 0] = np.array([1, 0])\n",
+    "te_labels[y_test == 4] = np.array([0, 1])\n",
+    "y_test = te_labels\n",
+    "\n",
+    "n_samples_train = x_train.shape[0]\n",
+    "n_features_train = x_train.shape[1] * x_train.shape[2] * x_train.shape[3]\n",
+    "n_samples_test = x_test.shape[0]\n",
+    "n_features_test = x_test.shape[1] * x_test.shape[2] * x_test.shape[3]\n",
+    "\n",
+    "x_train = x_train.reshape(n_samples_train, n_features_train)\n",
+    "x_test = x_test.reshape(n_samples_test, n_features_test)\n",
+    "x_train = x_train[:num_training]\n",
+    "y_train = y_train[:num_training]\n",
+    "\n",
+    "trusted_data = x_test[:num_trusted]\n",
+    "trusted_labels = y_test[:num_trusted]\n",
+    "x_test = x_test[num_trusted:]\n",
+    "y_test = y_test[num_trusted:]\n",
+    "valid_data = x_test[:num_valid]\n",
+    "valid_labels = y_test[:num_valid]\n",
+    "x_test = x_test[num_valid:]\n",
+    "y_test = y_test[num_valid:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Add provenance data and poison samples\n",
+    "\n",
+    "*Note:* In real application scenarios, provenance data is also loaded. Provenance data is generated for this experiment for demonstration purposes.\n",
+    "\n",
+    "This code will take longer to run depending on the number of poison samples you allow. Each samples is being generated independently, iteratively maximizing the generalization loss of the original SVM"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# assign random provenance features to the original training points\n",
+    "clean_prov = np.random.randint(num_devices - 1, size=x_train.shape[0])\n",
+    "p_train = np.eye(num_devices)[clean_prov]\n",
+    "\n",
+    "no_defense = ScikitlearnSVC(model=SVC(kernel=kernel), clip_values=(min_, max_))\n",
+    "no_defense.fit(x=x_train, y=y_train)\n",
+    "# poison a predetermined number of points starting at training points\n",
+    "poison_points = np.random.randint(no_defense._model.support_vectors_.shape[0], size=num_poison)\n",
+    "all_poison_init = np.copy(no_defense._model.support_vectors_[poison_points])\n",
+    "poison_labels = np.array([1,1]) - no_defense.predict(all_poison_init)\n",
+    "\n",
+    "\n",
+    "svm_attack = PoisoningAttackSVM(classifier=no_defense, x_train=x_train, y_train=y_train,\n",
+    "                                step=0.1, eps=1.0, x_val=valid_data, y_val=valid_labels, max_iters=200)\n",
+    "\n",
+    "poisoned_data, _ = svm_attack.poison(all_poison_init, y=poison_labels)\n",
+    "\n",
+    "# Stack on poison to data and add provenance of bad actor\n",
+    "all_data = np.vstack([x_train, poisoned_data])\n",
+    "all_labels = np.vstack([y_train, poison_labels])\n",
+    "poison_prov = np.zeros((num_poison, num_devices))\n",
+    "poison_prov[:,num_devices - 1] = 1\n",
+    "all_p = np.vstack([p_train, poison_prov])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Visualize Poison\n",
+    "\n",
+    "By changing the value of `idx` from 0 to `num_poison - 1` you can visualize each poison sample. Notice how they attempt to add features from the other class to confuse the classifier."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQEAAAEbCAYAAADaoTEAAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAASO0lEQVR4nO3df5BdZX3H8fdnN5us+QEkICGE2AhSlbESnC0yQts4mSowIDDVtrFFap2GTsGBGSowtB2wYy1lAGvHFicByq8IZUQUa9pCIy1SHcaAEZKGFmoRQtZEiCFLQshm99s/7olsw93n3N3749zN83nNZPbmfO+595uT3c+eH899jiICM8tXT9UNmFm1HAJmmXMImGXOIWCWOYeAWeYcAmaZcwgchCRdJenmqvtohKQvS/qzqvvImTxOoHtJeg6YD4wAu4A1wKcj4tUq+6pHUgC7gQBeAf4B+ExEjLTwPW4DNkfEn7bqNc17AlPB2RExG3gf8MtAN/8AnFj0ugz4OPAHFfdjDXAITBER8SLwT8B7ACQdLekBSdslPSvp5z9wkq6RdFfxuF/SXZJelrRD0vclzW/wNe6VdIekIUkbJQ002OvTwHfG9PpuSf9WvP9GSR8Z8z63Sfpc8XippM2SLpO0TdKgpE8WtRXA7wCXS3pV0jeb2Z72BofAFCFpEXAm8INi0d3AZuBo4KPA5yUtq7PqBcChwCLgcOAPgdcafI2PAPcAhwEPAF9qsNcTgF8BfiCpD/gm8CBwJPBpYLWkd46z+lFFvwuBTwF/K2luRKwEVgPXRcTsiDi7kV6snEOg+31d0g7gUeDfqf2gLgJOA66IiD0RsR64GTi/zvrD1H743xERIxHxeETsbPA1Ho2INcVx/Z3AiSW9PiHpZ9R+6G8G/h44BZgNXBsReyPi28A/AsvHeY1h4M8jYjgi1gCvAuMFhrXAtKobsFLnRsS/jl0g6Whge0QMjVn8Y6De7vqd1PYC7pF0GHAX8CfUfvuXvcZPxjzeDfRLmhYR+8bp9X0R8WydXl+IiNED3mfhOK/x8gGvv5taiFibeE9gatoCzJM0Z8yytwEvHvjE4jfqZyPiBOADwFnAJybyGi3odZGksd9rk30fX8pqA4fAFBQRLwDfBf6yOPH3XmrHz6sPfK6kD0r6JUm9wE5qu9sjE3mNJj1G7fLm5ZL6JC0FzqZ2rmGitgLHtrA3wyEwlS0HFlP7TXs/cHVEPFTneUcBX6UWAJuonVe4a4KvMWkRsZfaCcYzgJeAvwM+UVxBmKhbgBOKqwxfb2GbWfNgIbPMeU/ALHMOAbPMOQTMMucQMMucQ8Ascw4Bs8w5BMwy5xAwy5xDwCxzDgGzzDkEzDLnEDDLnEPALHMOAbPMVRICkk6X9F/FDLdXVtFDiqTnJD0lab2kdV3Qz63F7LsbxiybJ+khSc8UX+d2WX/XSHqx2IbrJZ1ZYX+LJD0saVMx2/ElxfKu2IaJ/jqyDTs+n0Axw81/A79Obabb7wPLI+I/O9pIQnHTj4GIeKnqXgAk/Sq1CTfviIj903hfR22OwGuLIJ0bEVd0UX/XAK9GxPVV9DSWpAXAgoh4ophO7XHgXOD36IJtmOjvN+nANqxiT+Bk4NmI+FEx68w9wDkV9DFlRMQjwPYDFp8D3F48vp3aN00lxumva0TEYEQ8UTweojbD0kK6ZBsm+uuIKkJgIfDCmL9vpoP/4AYF8KCkx4ubXnSj+RExCLVvImpz+nebiyU9WRwuVHa4MpakxcBJ1OY+7LpteEB/0IFtWEUIqM6ybpvj7NSIeB+1efEuKnZ3bWJuAo4DlgCDwA3VtgOSZgP3AZdGxM6q+zlQnf46sg2rCIHN1ObB3+8YahNddo2I2FJ83UZtAs6Tq+2orq3FseT+Y8ptFffz/0TE1uJmJ6PAKirehsWdkO4DVkfE14rFXbMN6/XXqW1YRQh8Hzhe0tslTQd+m9otrrqCpFn75+KXNAv4ELAhvVYlHqB2izGKr9+osJc32f/DVTiPCrehJFGbqXhTRNw4ptQV23C8/jq1DSuZbbi41PHXQC9wa0T8RcebGIekY6n99ofaHZq+UnV/ku4GlgJHUJt7/2rg68C91G7k8TzwsYio5OTcOP0tpbYbG8BzwIX7j78r6O80ajdIfQrYfyekq6gdd1e+DRP9LacD29BTjptlziMGzTLnEDDLnEPALHMOAbPMOQTMMldpCHTxkFzA/TWrm/vr5t6gs/1VvSfQ1f8RuL9mdXN/3dwbdLC/qkPAzCrW1GAhSacDX6Q28u/miLg29fzpmhH9zPr534d5nT5mTPr92839Naeb++vm3qD1/e1hF3vj9Xof3pt8CExmcpBDNC/er2WTej8zm7zHYi07Y3vdEGjmcMCTg5gdBJoJgakwOYiZlZjWxLoNTQ5SXOpYAdDPzCbezszaoZk9gYYmB4mIlRExEBED3XwixixXzYRAV08OYmaNmfThQETsk3Qx8C+8MTnIxpZ1ZmYd0cw5ASJiDbCmRb2YWQU8YtAscw4Bs8w5BMwy5xAwy5xDwCxzDgGzzDkEzDLnEDDLnEPALHMOAbPMOQTMMucQMMucQ8Ascw4Bs8w5BMwy5xAwy5xDwCxzDgGzzDkEzDLnEDDLnEPALHMOAbPMOQTMMucQMMucQ8Ascw4Bs8w5BMwy5xAwy5xDwCxzDgGzzDV1a3Kzidjyxx9I1h/49HXJ+h07Tk7Wv/mlX0vWD1/1vWQ9V02FgKTngCFgBNgXEQOtaMrMOqcVewIfjIiXWvA6ZlYBnxMwy1yzIRDAg5Iel7SiFQ2ZWWc1ezhwakRskXQk8JCkpyPikbFPKMJhBUA/M5t8OzNrtab2BCJiS/F1G3A/8KbTtxGxMiIGImKgjxnNvJ2ZtcGkQ0DSLElz9j8GPgRsaFVjZtYZzRwOzAful7T/db4SEf/ckq4OUs988ZRk/Z2rdiTroxuebmU7Hbfik99K1n+875Bk/ZxDfpCsf2/525P1WJUsZ2vSIRARPwJObGEvZlYBXyI0y5xDwCxzDgGzzDkEzDLnEDDLnEPALHOeT2ACeo84PFnfsvydyfqtZ305WX/+w/OS9dXvOiZZ73bz+9LjIHaMpoeVvzwyO1l/4WeHJevH8GKynivvCZhlziFgljmHgFnmHAJmmXMImGXOIWCWOYeAWeY8TmACXn/v4mT94cuvT9Z3jI4m6/N6dyfrq5na4wQW96Unpf7pyJxkfdforGR9eK+/nSfDewJmmXMImGXOIWCWOYeAWeYcAmaZcwiYZc4hYJY5X1idgJ69I8n6rkiPAxhGyfr2kYP7Nm2Lp+1N1ocjPU5i12j6DlbT+tL/P1af9wTMMucQMMucQ8Ascw4Bs8w5BMwy5xAwy5xDwCxzHicwAT37SsYBRHr9PdGbrO8tqU91M5T+ndOvfcn6UdNeSdZ7ekr+A6yu0j0BSbdK2iZpw5hl8yQ9JOmZ4uvc9rZpZu3SyOHAbcDpByy7ElgbEccDa4u/m9kUVBoCEfEIsP2AxecAtxePbwfObXFfZtYhkz0xOD8iBgGKr0e2riUz66S2nxiUtAJYAdDPwf0BGbOpaLJ7AlslLQAovm4b74kRsTIiBiJioI/0p8DMrPMmGwIPABcUjy8AvtGadsys00oPByTdDSwFjpC0GbgauBa4V9KngOeBj7WzyW6h19OfVy+bL2A40pk7NPqWZL3nxHcn66M/3JSsl1K6f6LkOnzJ+sNl8y3E9GS9l/T6M2ek5yuw+kpDICKWj1Na1uJezKwCHjZsljmHgFnmHAJmmXMImGXOIWCWOYeAWeY8n8AEDC49NFkfifR18j2R3txDo/3J+rNXpEdcHvvxZLn0Ov6O3z0lWR96W3r9d334mWT9ldH0OIORknEWvaTX7+1JjyNoehzEQcp7AmaZcwiYZc4hYJY5h4BZ5hwCZplzCJhlziFgljmPE5iAuWdsSdaHSzK1bL6AoZF0/TMnPZis796Y/jz+4ukvJeun9T+arM9UX7L+ymj68/zbR9P3VRgp/Z2UHgcw53NzStYvkek4Au8JmGXOIWCWOYeAWeYcAmaZcwiYZc4hYJY5h4BZ5jxOYIzR05Yk63+0+P5kfWg0fZ3+5ZHZyfpwpK+jv7VnZ7J+6lv+J1mf35u+zt6n9O+E7SXjAF4pGQewq2Q+hTKzNJysT9vxWrI+UvLvo+S+CAcr7wmYZc4hYJY5h4BZ5hwCZplzCJhlziFgljmHgFnmPE5gjJ7/+GGy/tmnzkrWbzjxq8n6ntH05/Fn9+5J1vt70tfJ+5S+zl027/+eko/L74n0OIhdJfXdo+n7JszseT1Zf3rvUcn68LyZyXpP2TiAg3S+gDKlewKSbpW0TdKGMcuukfSipPXFnzPb26aZtUsjhwO3AafXWf6FiFhS/FnT2rbMrFNKQyAiHgG2d6AXM6tAMycGL5b0ZHG4MLdlHZlZR002BG4CjgOWAIPADeM9UdIKSeskrRsmfeLHzDpvUiEQEVsjYiQiRoFVwMmJ566MiIGIGOgjfXbYzDpvUiEgacGYv54HbBjvuWbW3UrHCUi6G1gKHCFpM3A1sFTSEiCA54AL29hj55RcJz7mNzYm61ef//vJ+nsuemrCLY01py89juCH096WrPdpJFn/yuplyfrMn6S3z4VXpudbOKx3d7LeXzJfwIbXjknWf1TyXfiO7+Q5DqBMaQhExPI6i29pQy9mVgEPGzbLnEPALHMOAbPMOQTMMucQMMucQ8Asc55PoIUOu/N7yfrmO9vdQfrz/GUW8t2m1v+rd52XrF//0duT9bL5BD566Lpkfe3hv5isW33eEzDLnEPALHMOAbPMOQTMMucQMMucQ8Ascw4Bs8x5nIC9QUrXy+ZbWLs3WT/yt4aS9emk7wuwJ9Lfrr3yfAGT4T0Bs8w5BMwy5xAwy5xDwCxzDgGzzDkEzDLnEDDLnMcJ5KRsHECTBk9N32FqT/Ql6ztH+5P1Q3rS913o8TiBSfGegFnmHAJmmXMImGXOIWCWOYeAWeYcAmaZcwiYZc7jBOwNJfMFlNFIur5tZE6y3q/hdJ10fWZfej4DjyKor3RPQNIiSQ9L2iRpo6RLiuXzJD0k6Zni69z2t2tmrdbI4cA+4LKIeDdwCnCRpBOAK4G1EXE8sLb4u5lNMaUhEBGDEfFE8XgI2AQsBM4B9t9X6nbg3HY1aWbtM6ETg5IWAycBjwHzI2IQakEBHNnq5sys/RoOAUmzgfuASyNi5wTWWyFpnaR1w6RvOGlmnddQCEjqoxYAqyPia8XirZIWFPUFwLZ660bEyogYiIiBPtKfMjOzzmvk6oCAW4BNEXHjmNIDwAXF4wuAb7S+PTNrt0bGCZwKnA88JWl9sewq4FrgXkmfAp4HPtaeFq1lmhwHUGbJGZuS9aGRtyTr06elBxoc3vNast7fuy9ZT6+dr9IQiIhHgfFmo1jW2nbMrNM8bNgscw4Bs8w5BMwy5xAwy5xDwCxzDgGzzHk+AWuZkw/732S9vyc9H8Bbe9Oj0Q/tSY8jmO5xApPiPQGzzDkEzDLnEDDLnEPALHMOAbPMOQTMMucQMMucxwlMJRrvE92FNs8XUOaoaa8k6yfMGEyv35seB/D5bUuT9c1fOj5Zn8PLyXquvCdgljmHgFnmHAJmmXMImGXOIWCWOYeAWeYcAmaZ8ziBqaTicQBlbrosfeuJb335b5L12T2zkvVvrz45WV9wz3eTdavPewJmmXMImGXOIWCWOYeAWeYcAmaZcwiYZc4hYJY5RQevPR+iefF++W7m4+ry+QJs6nos1rIzttf9BivdE5C0SNLDkjZJ2ijpkmL5NZJelLS++HNmqxs3s/ZrZMTgPuCyiHhC0hzgcUkPFbUvRMT17WvPzNqtNAQiYhAYLB4PSdoELGx3Y2bWGRM6MShpMXAS8Fix6GJJT0q6VdLccdZZIWmdpHXDvN5Us2bWeg2HgKTZwH3ApRGxE7gJOA5YQm1P4YZ660XEyogYiIiBPma0oGUza6WGQkBSH7UAWB0RXwOIiK0RMRIRo8AqIP0RLzPrSo1cHRBwC7ApIm4cs3zBmKedB2xofXtm1m6NXB04FTgfeErS+mLZVcBySUuAAJ4DLmxLhweTsnEAZhVo5OrAo0C97941rW/HzDrNw4bNMucQMMucQ8Ascw4Bs8w5BMwy5xAwy5zvO9BJng/AupD3BMwy5xAwy5xDwCxzDgGzzDkEzDLnEDDLnEPALHMdve+ApJ8CPx6z6AjgpY41MHHurznd3F839wat7+8XIuKt9QodDYE3vbm0LiIGKmughPtrTjf31829QWf78+GAWeYcAmaZqzoEVlb8/mXcX3O6ub9u7g062F+l5wTMrHpV7wmYWcUcAmaZcwiYZc4hYJY5h4BZ5v4PPqhQrabQ6K4AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 288x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "idx = 0\n",
+    "plt.matshow(poisoned_data[idx].reshape(28, 28))\n",
+    "plt.title(\"Poison Point\\n\")\n",
+    "plt.matshow(all_poison_init[idx].reshape(28, 28))\n",
+    "plt.title(\"Original Example\\n\")\n",
+    "plt.clim(0,1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice that sometimes the poison appears to be a number 4 with features of the number zero in the background. It may appear as a shadowed zero \"watermarking\" the four. The aim of inserting poisonous samples like these in the training set is to shift the decision boundary so actual 0s to also become classified as 4s. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Train clean classifier and poisoned classifier\n",
+    "perfect_defense = ScikitlearnSVC(model=SVC(kernel=kernel), clip_values=(min_, max_))\n",
+    "perfect_defense.fit(x=x_train, y=y_train)\n",
+    "no_defense.fit(x=all_data, y=all_labels)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Perfect defense accuracy (trusted set) 97.68%\n",
+      "No defense accuracy (trusted set) 77.81%\n"
+     ]
+    }
+   ],
+   "source": [
+    "perf_acc = np.average(np.all(perfect_defense.predict(x_test) == y_test, axis=1)) * 100\n",
+    "no_acc = np.average(np.all(no_defense.predict(x_test) == y_test, axis=1)) * 100\n",
+    "print(\"Perfect defense accuracy (trusted set) {0:.2f}%\".format(perf_acc))\n",
+    "print(\"No defense accuracy (trusted set) {0:.2f}%\".format(no_acc))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Apply Defenses\n",
+    "\n",
+    "We will apply the following defenses to this poisoning attack:\n",
+    "* **Perfect Defense** — All poison is detected and model is trained on clean data.\n",
+    "* **Provenance-Based Defense with Trusted Data** — Poison is detected using the provenance defense algorithm specified above.\n",
+    "* **Provenance-Baseed Defense without Trusted Data** — Assuming no validation data, just check each data segment for suspected poison.\n",
+    "* **RONI Defense w/ Calibration** — Poison is detecting using RONI defense method (see below).\n",
+    "* **RONI Defense w/o Calibration** — Suspicious poison is found by a threshold epsilon value\n",
+    "* **No defense** — Model is trained with poisoned data\n",
+    "\n",
+    "### RONI Defense\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<img src=\"../utils/data/images/roni.gif\">"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import HTML\n",
+    "HTML('<img src=\"../utils/data/images/roni.gif\">')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The [RONI (Reject on Negative Impact) defense method](https://www.usenix.org/legacy/event/leet08/tech/full_papers/nelson/nelson_html/#SECTION00051000000000000000) checks the empirical effect of each point on the performance of the classifier and removes suspicious points. Our is similar except instead of checking each point we check each set of points with the same provenance feature. We evaluate the defense with both the provenance defense and the perfect defense"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%capture\n",
+    "roni_defense = RONIDefense(no_defense, all_data, all_labels, trusted_data, trusted_labels)\n",
+    "roni_defense.detect_poison()\n",
+    "roni_no_cal = RONIDefense(no_defense, all_data, all_labels, trusted_data, trusted_labels)\n",
+    "roni_no_cal.detect_poison()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Provenance Defense"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<img src=\"../utils/data/images/prov_defense.gif\">"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import HTML\n",
+    "HTML('<img src=\"../utils/data/images/prov_defense.gif\">')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The provenenace defense method checks the effect of removing segments of the data that may come a bad actor intentionally poisoning the data. When a sector is found that is potentially poisonous, it is flagged as suspicious.\n",
+    "\n",
+    "In the trusted data version of the algorithm, the defender has some handpicked trusted data to test the performance of the model. In the version of the algorithm without trusted data, a random subset of training points from all segments are used as the test set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%capture\n",
+    "prov_defense_trust = ProvenanceDefense(no_defense, all_data, all_labels, all_p, \n",
+    "                                       x_val=trusted_data, y_val=trusted_labels, eps=0.1)\n",
+    "prov_defense_trust.detect_poison()\n",
+    "prov_defense_no_trust = ProvenanceDefense(no_defense, all_data, all_labels, all_p, eps=0.1)\n",
+    "prov_defense_no_trust.detect_poison()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Evaluate Defenses"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "real_is_clean = np.array([1 if i < num_training else 0 for i in range(len(all_data))])\n",
+    "def evaluate_defense(defense, name):\n",
+    "    print(\"\\nEvaluating results of {} defense...\".format(name))\n",
+    "    pc_tp = np.average(real_is_clean[:num_training] == defense.is_clean_lst[:num_training]) * 100\n",
+    "    pc_tn = np.average(real_is_clean[num_training:] == defense.is_clean_lst[num_training:]) * 100\n",
+    "    print(\"Percent of normal points correctly labeled (True Negative): {0:.2f}%\".format(pc_tp))\n",
+    "    print(\"Percent of poison points correctly labeled (True Positive): {0:.2f}%\".format(pc_tn))\n",
+    "    \n",
+    "    classifier = ScikitlearnSVC(model=SVC(kernel=kernel), clip_values=(min_, max_))\n",
+    "    mask = np.array(defense.is_clean_lst) == 1\n",
+    "    classifier.fit(all_data[mask], all_labels[mask])\n",
+    "    acc = np.average(np.all(classifier.predict(x_test) == y_test, axis=1)) * 100\n",
+    "    print(\"Accuracy of classifier trained with {0:.2f} filter on test set\".format(acc))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Evaluating results of RONI w/o Calibration defense...\n",
+      "Percent of normal points correctly labeled (True Negative): 95.00%\n",
+      "Percent of poison points correctly labeled (True Positive): 0.00%\n",
+      "Accuracy of classifier trained with 82.92 filter on test set\n",
+      "\n",
+      "Evaluating results of RONI w/ Calibration defense...\n",
+      "Percent of normal points correctly labeled (True Negative): 100.00%\n",
+      "Percent of poison points correctly labeled (True Positive): 20.00%\n",
+      "Accuracy of classifier trained with 80.81 filter on test set\n",
+      "\n",
+      "Evaluating results of Provenance Defense w/o Trusted Data defense...\n",
+      "Percent of normal points correctly labeled (True Negative): 70.00%\n",
+      "Percent of poison points correctly labeled (True Positive): 100.00%\n",
+      "Accuracy of classifier trained with 97.84 filter on test set\n",
+      "\n",
+      "Evaluating results of Provenance Defense w/ Trusted Data defense...\n",
+      "Percent of normal points correctly labeled (True Negative): 100.00%\n",
+      "Percent of poison points correctly labeled (True Positive): 100.00%\n",
+      "Accuracy of classifier trained with 97.68 filter on test set\n"
+     ]
+    }
+   ],
+   "source": [
+    "evaluate_defense(roni_no_cal, \"RONI w/o Calibration\")\n",
+    "evaluate_defense(roni_defense, \"RONI w/ Calibration\")\n",
+    "evaluate_defense(prov_defense_no_trust, \"Provenance Defense w/o Trusted Data\")\n",
+    "evaluate_defense(prov_defense_trust, \"Provenance Defense w/ Trusted Data\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In [the paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8473440), we show that with only limited amounts of trusted data, you can still have a very powerful defense able to detect against bad actors. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/adversarial-robustness-toolbox/notebooks/robustness_verification_clique_method_tree_ensembles_gradient_boosted_decision_trees_classifiers.ipynb b/adversarial-robustness-toolbox/notebooks/robustness_verification_clique_method_tree_ensembles_gradient_boosted_decision_trees_classifiers.ipynb
new file mode 100644
index 0000000..6fdc862
--- /dev/null
+++ b/adversarial-robustness-toolbox/notebooks/robustness_verification_clique_method_tree_ensembles_gradient_boosted_decision_trees_classifiers.ipynb
@@ -0,0 +1,280 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Clique Method Robustness Verification for Tree Ensembles and Gradient Boosted Decision Tree Classifiers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from xgboost import XGBClassifier\n",
+    "import lightgbm\n",
+    "import numpy as np\n",
+    "from sklearn.ensemble import ExtraTreesClassifier, RandomForestClassifier, GradientBoostingClassifier\n",
+    "\n",
+    "from art.estimators.classification import XGBoostClassifier, LightGBMClassifier, SklearnClassifier\n",
+    "from art.utils import load_dataset\n",
+    "from art.metrics import RobustnessVerificationTreeModelsCliqueMethod\n",
+    "\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "NB_TRAIN = 100\n",
+    "NB_TEST = 100\n",
+    "\n",
+    "(x_train, y_train), (x_test, y_test), _, _ = load_dataset('mnist')\n",
+    "\n",
+    "n_classes = 10\n",
+    "n_features = 28 * 28\n",
+    "n_train = x_train.shape[0]\n",
+    "n_test = x_test.shape[0]\n",
+    "x_train = x_train.reshape((n_train, n_features))\n",
+    "x_test = x_test.reshape((n_test, n_features))\n",
+    "\n",
+    "x_train = x_train[:NB_TRAIN]\n",
+    "y_train = y_train[:NB_TRAIN]\n",
+    "x_test = x_test[:NB_TEST]\n",
+    "y_test = y_test[:NB_TEST]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# XGBoost"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Average bound: 0.035996093750000006\n",
+      "Verified error at eps: 0.96\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = XGBClassifier(n_estimators=4, max_depth=6)\n",
+    "model.fit(x_train, np.argmax(y_train, axis=1))\n",
+    "\n",
+    "classifier = XGBoostClassifier(model=model, nb_features=n_features, nb_classes=n_classes)\n",
+    "\n",
+    "rt = RobustnessVerificationTreeModelsCliqueMethod(classifier=classifier)\n",
+    "average_bound, verified_error = rt.verify(x=x_test, y=y_test, eps_init=0.3, nb_search_steps=10, max_clique=2,\n",
+    "                                          max_level=2)\n",
+    "\n",
+    "print('Average bound:', average_bound)\n",
+    "print('Verified error at eps:', verified_error)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# LightGBM"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1]\tvalid_0's multi_logloss: 2.25471\n",
+      "Training until validation scores don't improve for 10 rounds.\n",
+      "[2]\tvalid_0's multi_logloss: 2.21845\n",
+      "Did not meet early stopping. Best iteration is:\n",
+      "[2]\tvalid_0's multi_logloss: 2.21845\n",
+      "Average bound: 0.07634765624999999\n",
+      "Verified error at eps: 0.85\n"
+     ]
+    }
+   ],
+   "source": [
+    "train_data = lightgbm.Dataset(x_train, label=np.argmax(y_train, axis=1))\n",
+    "test_data = lightgbm.Dataset(x_test, label=np.argmax(y_test, axis=1))\n",
+    "\n",
+    "parameters = {'objective': 'multiclass',\n",
+    "              'num_class': n_classes,\n",
+    "              'metric': 'multi_logloss',\n",
+    "              'is_unbalance': 'true',\n",
+    "              'boosting': 'gbdt',\n",
+    "              'num_leaves': 5,\n",
+    "              'feature_fraction': 0.5,\n",
+    "              'bagging_fraction': 0.5,\n",
+    "              'bagging_freq': 0,\n",
+    "              'learning_rate': 0.05,\n",
+    "              'verbose': 0}\n",
+    "\n",
+    "model = lightgbm.train(parameters,\n",
+    "                       train_data,\n",
+    "                       valid_sets=test_data,\n",
+    "                       num_boost_round=2,\n",
+    "                       early_stopping_rounds=10)\n",
+    "\n",
+    "classifier = LightGBMClassifier(model=model)\n",
+    "\n",
+    "rt = RobustnessVerificationTreeModelsCliqueMethod(classifier=classifier)\n",
+    "average_bound, verified_error = rt.verify(x=x_test, y=y_test, eps_init=0.3, nb_search_steps=10, max_clique=2,\n",
+    "                                          max_level=2)\n",
+    "\n",
+    "print('Average bound:', average_bound)\n",
+    "print('Verified error at eps:', verified_error)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# GradientBoosting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Average bound: 0.009234374999999996\n",
+      "Verified error at eps: 1.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = GradientBoostingClassifier(n_estimators=4, max_depth=6)\n",
+    "model.fit(x_train, np.argmax(y_train, axis=1))\n",
+    "\n",
+    "classifier = SklearnClassifier(model=model)\n",
+    "\n",
+    "rt = RobustnessVerificationTreeModelsCliqueMethod(classifier=classifier)\n",
+    "average_bound, verified_error = rt.verify(x=x_test, y=y_test, eps_init=0.3, nb_search_steps=10, max_clique=2, \n",
+    "                                          max_level=2)\n",
+    "\n",
+    "print('Average bound:', average_bound)\n",
+    "print('Verified error at eps:', verified_error)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# RandomForest"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Average bound: 0.019962890624999997\n",
+      "Verified error at eps: 1.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = RandomForestClassifier(n_estimators=4, max_depth=6)\n",
+    "model.fit(x_train, np.argmax(y_train, axis=1))\n",
+    "\n",
+    "classifier = SklearnClassifier(model=model)\n",
+    "\n",
+    "rt = RobustnessVerificationTreeModelsCliqueMethod(classifier=classifier)\n",
+    "average_bound, verified_error = rt.verify(x=x_test, y=y_test, eps_init=0.3, nb_search_steps=10, max_clique=2, \n",
+    "                                          max_level=2)\n",
+    "\n",
+    "print('Average bound:', average_bound)\n",
+    "print('Verified error at eps:', verified_error)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# ExtraTrees"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Average bound: 0.041332031250000026\n",
+      "Verified error at eps: 1.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = ExtraTreesClassifier(n_estimators=4, max_depth=6)\n",
+    "model.fit(x_train, np.argmax(y_train, axis=1))\n",
+    "\n",
+    "classifier = SklearnClassifier(model=model)\n",
+    "\n",
+    "rt = RobustnessVerificationTreeModelsCliqueMethod(classifier=classifier)\n",
+    "average_bound, verified_error = rt.verify(x=x_test, y=y_test, eps_init=0.3, nb_search_steps=10, max_clique=2, \n",
+    "                                          max_level=2)\n",
+    "\n",
+    "print('Average bound:', average_bound)\n",
+    "print('Verified error at eps:', verified_error)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/adversarial-robustness-toolbox/pyproject.toml b/adversarial-robustness-toolbox/pyproject.toml
new file mode 100644
index 0000000..f3a8b00
--- /dev/null
+++ b/adversarial-robustness-toolbox/pyproject.toml
@@ -0,0 +1,2 @@
+[tool.black]
+line-length=120
diff --git a/adversarial-robustness-toolbox/readthedocs.yml b/adversarial-robustness-toolbox/readthedocs.yml
new file mode 100644
index 0000000..a0e3677
--- /dev/null
+++ b/adversarial-robustness-toolbox/readthedocs.yml
@@ -0,0 +1,11 @@
+version: 2
+sphinx:
+  configuration: docs/conf.py
+python:
+    version: 3.6
+    install:
+      - method: pip
+        path: .
+        extra_requirements:
+          - docs
+    system_packages: true
diff --git a/adversarial-robustness-toolbox/requirements.txt b/adversarial-robustness-toolbox/requirements.txt
new file mode 100644
index 0000000..0411e7c
--- /dev/null
+++ b/adversarial-robustness-toolbox/requirements.txt
@@ -0,0 +1,57 @@
+# base
+numpy>=1.18.0
+scipy==1.4.1
+matplotlib==3.3.4
+scikit-learn>=0.22.2,==0.23.*
+six==1.15.0
+Pillow==8.1.2
+tqdm==4.59.0
+statsmodels==0.12.1
+pydub==0.25.0
+resampy==0.2.2
+ffmpeg-python==0.2.0
+cma==3.0.3
+pandas==1.1.4
+librosa==0.8.0
+numba~=0.52.0
+opencv-python
+
+# frameworks
+h5py==2.10.0
+# supported versions: (tensorflow==2.2.0 with keras==2.3.1) or (tensorflow==1.15.4 with keras==2.2.5)
+tensorflow>=1.15.4
+keras>=2.2.5
+tensorflow-addons~=0.11.2
+# using mxnet-native for reproducible test results on CI machines without Intel Architecture Processors, but mxnet is fully supported by ART
+mxnet==1.6.0
+--find-links https://download.pytorch.org/whl/torch_stable.html
+torch==1.7.1+cpu -f https://download.pytorch.org/whl/torch_stable.html
+torchaudio==0.7.2 -f https://download.pytorch.org/whl/torch_stable.html
+torchvision==0.8.2+cpu -f https://download.pytorch.org/whl/torch_stable.html
+catboost==0.24.4
+GPy==1.9.9
+lightgbm==3.0.0
+xgboost==1.3.1
+kornia~=0.4.1
+
+# Lingvo ASR dependencies
+# supported versions: (lingvo==0.6.4 with tensorflow-gpu==2.1.0)
+# note: due to conflicts with other TF1/2 version supported by ART, the dependencies are not installed by default
+#tensorflow-gpu==2.1.0
+#lingvo==0.6.4
+
+# other
+pytest~=5.4.1
+pytest-pep8~=1.0.6
+pytest-mock~=3.3.1
+pytest-cov~=2.10.1
+codecov~=2.1.9
+requests~=2.24.0
+
+
+
+# ART
+-e .
+
+#NOTE to contributors: When changing/adding packages, please make sure that the packages are consitent with those
+# present within the Dockerfile
diff --git a/adversarial-robustness-toolbox/run_tests.sh b/adversarial-robustness-toolbox/run_tests.sh
new file mode 100755
index 0000000..3b534b4
--- /dev/null
+++ b/adversarial-robustness-toolbox/run_tests.sh
@@ -0,0 +1,181 @@
+#!/usr/bin/env bash
+exit_code=0
+
+# Set TensorFlow logging to minimum level ERROR
+export TF_CPP_MIN_LOG_LEVEL="3"
+
+# --------------------------------------------------------------------------------------------------------------- TESTS
+
+#NOTE: All the tests should be ran within this loop. All other tests are legacy tests that must be
+# made framework independent to be incorporated within this loop
+frameworkList=("tensorflow" "keras" "pytorch" "scikitlearn" "mxnet" "kerastf")
+framework=$1
+
+if [[ ${framework} != "legacy" ]]
+then
+    echo "#######################################################################"
+    echo "############### Running tests with framework $framework ###############"
+    echo "#######################################################################"
+
+    pytest --cov-report=xml --cov=art --cov-append  -q -vv tests/defences/detector/poison/test_spectral_signature_defense.py --framework=$framework --skip_travis=True --durations=0
+    if [[ $? -ne 0 ]]; then exit_code=1; echo "Failed defences/detector/poison/test_spectral_signature_defense.py tests"; fi
+
+    pytest --cov-report=xml --cov=art --cov-append  -q -vv tests/defences/preprocessor --framework=$framework --skip_travis=True --durations=0
+    if [[ $? -ne 0 ]]; then exit_code=1; echo "Failed defences/preprocessor tests"; fi
+
+    pytest --cov-report=xml --cov=art --cov-append  -q -vv tests/defences/transformer --framework=$framework --skip_travis=True --durations=0
+    if [[ $? -ne 0 ]]; then exit_code=1; echo "Failed defences/transformer tests"; fi
+
+    pytest --cov-report=xml --cov=art --cov-append  -q -vv tests/preprocessing/audio --framework=$framework --skip_travis=True --durations=0
+    if [[ $? -ne 0 ]]; then exit_code=1; echo "Failed preprocessing/audio tests"; fi
+
+    pytest --cov-report=xml --cov=art --cov-append  -q -vv tests/preprocessing/expectation_over_transformation --framework=$framework --skip_travis=True --durations=0
+    if [[ $? -ne 0 ]]; then exit_code=1; echo "Failed preprocessing/expectation_over_transformation tests"; fi
+
+    pytest --cov-report=xml --cov=art --cov-append  -q -vv tests/utils --framework=$framework --skip_travis=True --durations=0
+    if [[ $? -ne 0 ]]; then exit_code=1; echo "Failed utils tests"; fi
+
+    pytest --cov-report=xml --cov=art --cov-append  -q -vv -s tests/attacks/poison/ --framework=$framework  --skip_travis=True --durations=0
+    if [[ $? -ne 0 ]]; then exit_code=1; echo "Failed attacks/poison tests"; fi
+
+    pytest --cov-report=xml --cov=art --cov-append  -q -vv -s tests/attacks/evasion/ --framework=$framework  --skip_travis=True --durations=0
+    if [[ $? -ne 0 ]]; then exit_code=1; echo "Failed attacks/evasion/test_shadow_attack.py"; fi
+
+    pytest --cov-report=xml --cov=art --cov-append  -q -vv tests/estimators/speech_recognition/ --framework=$framework  --skip_travis=True --durations=0
+    if [[ $? -ne 0 ]]; then exit_code=1; echo "Failed estimators/speech_recognition tests"; fi
+
+    pytest --cov-report=xml --cov=art --cov-append  -q -vv tests/attacks/inference/ --framework=$framework --skip_travis=True --durations=0
+    if [[ $? -ne 0 ]]; then exit_code=1; echo "Failed attacks/inference"; fi
+
+    pytest -q -s tests/attacks/evasion/test_brendel_and_bethge.py --framework=$framework --durations=0
+    if [[ $? -ne 0 ]]; then exit_code=1; echo "Failed attacks/evasion/test_brendel_and_bethge.py"; fi
+
+    pytest --cov-report=xml --cov=art --cov-append -q -vv tests/classifiersFrameworks/  --framework=$framework --skip_travis=True --durations=0
+    if [[ $? -ne 0 ]]; then exit_code=1; echo "Failed classifiersFrameworks tests"; fi
+
+    pytest --cov-report=xml --cov=art --cov-append  -q -vv tests/defences/preprocessor/test_spatial_smoothing_pytorch.py  --framework=$framework --skip_travis=True --durations=0
+    if [[ $? -ne 0 ]]; then exit_code=1; echo "Failed defences/preprocessor/test_spatial_smoothing_pytorch.py tests"; fi
+
+    pytest --cov-report=xml --cov=art --cov-append  -q -vv tests/estimators/classification/test_deeplearning_common.py --framework=$framework --skip_travis=True --durations=0
+    if [[ $? -ne 0 ]]; then exit_code=1; echo "Failed estimators/classification/test_deeplearning_common.py $framework"; fi
+
+    pytest --cov-report=xml --cov=art --cov-append  -q -vv tests/estimators/classification/test_deeplearning_specific.py --framework=$framework --skip_travis=True --durations=0
+    if [[ $? -ne 0 ]]; then exit_code=1; echo "Failed estimators/classification tests for framework $framework"; fi
+
+    pytest --cov-report=xml --cov=art --cov-append  -q -vv tests/metrics/privacy --framework=$framework --skip_travis=True --durations=0
+    if [[ $? -ne 0 ]]; then exit_code=1; echo "Failed metrics/privacy tests"; fi
+else
+    declare -a attacks=("tests/attacks/test_adversarial_patch.py" \
+                        "tests/attacks/test_adversarial_embedding.py" \
+                        "tests/attacks/test_backdoor_attack.py" \
+                        "tests/attacks/test_carlini.py" \
+                        "tests/attacks/test_copycat_cnn.py" \
+                        "tests/attacks/test_decision_tree_attack.py" \
+                        "tests/attacks/test_deepfool.py" \
+                        "tests/attacks/test_elastic_net.py" \
+                        "tests/attacks/test_feature_collision.py" \
+                        "tests/attacks/test_functionally_equivalent_extraction.py" \
+                        "tests/attacks/test_hclu.py" \
+                        "tests/attacks/test_input_filter.py" \
+                        "tests/attacks/test_hop_skip_jump.py" \
+                        "tests/attacks/test_iterative_method.py" \
+                        "tests/attacks/test_knockoff_nets.py" \
+                        "tests/attacks/test_newtonfool.py" \
+                        "tests/attacks/test_poisoning_attack_svm.py" \
+                        "tests/attacks/test_projected_gradient_descent.py" \
+                        "tests/attacks/test_saliency_map.py" \
+                        "tests/attacks/test_spatial_transformation.py" \
+                        "tests/attacks/test_universal_perturbation.py" \
+                        "tests/attacks/test_virtual_adversarial.py" \
+                        "tests/attacks/test_zoo.py" \
+                        "tests/attacks/test_pixel_attack.py" \
+                        "tests/attacks/test_threshold_attack.py" \
+                        "tests/attacks/test_wasserstein.py" \
+                        "tests/attacks/test_shapeshifter.py" \
+                        "tests/attacks/test_targeted_universal_perturbation.py" \
+                        "tests/attacks/test_simba.py" )
+
+    declare -a classifiers=("tests/estimators/certification/test_randomized_smoothing.py" \
+                            "tests/estimators/classification/test_blackbox.py" \
+                            "tests/estimators/classification/test_catboost.py" \
+                            "tests/estimators/classification/test_classifier.py" \
+                            "tests/estimators/classification/test_detector_classifier.py" \
+                            "tests/estimators/classification/test_ensemble.py" \
+                            "tests/estimators/classification/test_GPy.py" \
+                            "tests/estimators/classification/test_input_filter.py" \
+                            "tests/estimators/classification/test_lightgbm.py" \
+                            "tests/estimators/classification/test_scikitlearn.py" \
+                            "tests/estimators/classification/test_xgboost.py" )
+
+    declare -a object_detectors=("tests/estimators/object_detection/test_tensorflow_faster_rcnn.py")
+
+    declare -a speech_recognizers=("tests/estimators/speech_recognition/test_pytorch_deep_speech.py")
+
+    declare -a defences=("tests/defences/test_adversarial_trainer.py" \
+                         "tests/defences/test_adversarial_trainer_madry_pgd.py" \
+                         "tests/defences/test_class_labels.py" \
+                         "tests/defences/test_defensive_distillation.py" \
+                         "tests/defences/test_feature_squeezing.py" \
+                         "tests/defences/test_gaussian_augmentation.py" \
+                         "tests/defences/test_gaussian_noise.py" \
+                         "tests/defences/test_high_confidence.py" \
+                         "tests/defences/test_label_smoothing.py" \
+                         "tests/defences/test_neural_cleanse.py" \
+                         "tests/defences/test_pixel_defend.py" \
+                         "tests/defences/test_reverse_sigmoid.py" \
+                         "tests/defences/test_rounded.py" \
+                         "tests/defences/test_thermometer_encoding.py" \
+                         "tests/defences/test_variance_minimization.py" \
+                         "tests/defences/detector/evasion/subsetscanning/test_detector.py" \
+                         "tests/defences/detector/evasion/test_detector.py" \
+                         "tests/defences/detector/poison/test_activation_defence.py" \
+                         "tests/defences/detector/poison/test_clustering_analyzer.py" \
+                         "tests/defences/detector/poison/test_ground_truth_evaluator.py" \
+                         "tests/defences/detector/poison/test_provenance_defence.py" \
+                         "tests/defences/detector/poison/test_roni.py" )
+
+    declare -a metrics=("tests/metrics/test_gradient_check.py" \
+                        "tests/metrics/test_metrics.py" \
+                        "tests/metrics/test_verification_decision_trees.py" )
+
+    declare -a wrappers=("tests/wrappers/test_expectation.py" \
+                         "tests/wrappers/test_query_efficient_bb.py" \
+                         "tests/wrappers/test_wrapper.py" )
+
+    declare -a art=("tests/test_data_generators.py" \
+                    "tests/test_utils.py" \
+                    "tests/test_visualization.py" )
+
+    tests_modules=("attacks" \
+                   "classifiers" \
+                   "object_detectors" \
+                   "speech_recognizers" \
+                   "defences" \
+                   "metrics" \
+                   "wrappers" \
+                   "art" )
+
+    # --------------------------------------------------------------------------------------------------- CODE TO RUN TESTS
+
+    run_test () {
+      test=$1
+      test_file_name="$(echo ${test} | rev | cut -d'/' -f1 | rev)"
+
+      echo $'\n\n'
+      echo "######################################################################"
+      echo ${test}
+      echo "######################################################################"
+      coverage run --append -m unittest -v ${test}
+      if [[ $? -ne 0 ]]; then exit_code=1; echo "Failed $test"; fi
+    }
+
+    for tests_module in "${tests_modules[@]}"; do
+      tests="$tests_module[@]"
+      for test in "${!tests}"; do
+         run_test ${test}
+      done
+    done
+fi
+
+#bash <(curl -s https://codecov.io/bash)
+exit ${exit_code}
diff --git a/adversarial-robustness-toolbox/setup.cfg b/adversarial-robustness-toolbox/setup.cfg
new file mode 100644
index 0000000..32f21f8
--- /dev/null
+++ b/adversarial-robustness-toolbox/setup.cfg
@@ -0,0 +1,15 @@
+[metadata]
+description-file = README.md
+
+[tool:pytest]
+pep8maxlinelength = 120
+pep8ignore = *.py E402 W503 E203 E231 E251 E701
+markers =
+    skip_framework: marks a test to be skipped for specific framework values. Valid values are ("tensorflow" "keras" "mxnet" "pytorch" "scikitlearn")
+    only_with_platform: DEPRECATED only used for legacy tests. Use skip_framework instead. marks a test to be performed only for a specific framework value
+    framework_agnostic: marks a test to be agnostic to frameworks and run only for one default framework
+    skip_travis: Skips a test marked with this decorator if the command line argument skip_travis is set to true
+    skip_module: Skip the test if a module is not available in the current environment
+
+[mypy]
+ignore_missing_imports = True
diff --git a/adversarial-robustness-toolbox/setup.py b/adversarial-robustness-toolbox/setup.py
new file mode 100644
index 0000000..3cc0c84
--- /dev/null
+++ b/adversarial-robustness-toolbox/setup.py
@@ -0,0 +1,108 @@
+import codecs
+import os
+
+from setuptools import find_packages, setup
+
+with open("README.md", "r") as fh:
+    long_description = fh.read()
+
+install_requires = [
+    "numpy>=1.18.0",
+    "scipy>=1.4.1",
+    "matplotlib",
+    "scikit-learn>=0.22.2,==0.23.*",
+    "six",
+    "setuptools",
+    "Pillow",
+    "tqdm",
+    "statsmodels",
+    "pydub",
+    "resampy",
+    "ffmpeg-python",
+    "cma",
+    "mypy",
+    "numba~=0.52.0"
+]
+
+docs_require = [
+    "sphinx >= 1.4",
+    "sphinx_rtd_theme",
+    "sphinx-autodoc-annotation",
+    "sphinx-autodoc-typehints",
+    "matplotlib",
+    "numpy",
+    "scipy>=1.4.1",
+    "six>=1.13.0",
+    "scikit-learn>=0.22.2,==0.23.*",
+    "Pillow>=6.0.0",
+]
+
+
+def read(rel_path):
+    here = os.path.abspath(os.path.dirname(__file__))
+    with codecs.open(os.path.join(here, rel_path), "r", encoding="utf-8") as fp:
+        return fp.read()
+
+
+def get_version(rel_path):
+    for line in read(rel_path).splitlines():
+        if line.startswith("__version__"):
+            delim = '"' if '"' in line else "'"
+            return line.split(delim)[1]
+    raise RuntimeError("Unable to find version string.")
+
+
+setup(
+    name="adversarial-robustness-toolbox",
+    version=get_version("art/__init__.py"),
+    description="Toolbox for adversarial machine learning.",
+    long_description=long_description,
+    long_description_content_type="text/markdown",
+    author="Irina Nicolae",
+    author_email="irinutza.n@gmail.com",
+    maintainer="Beat Buesser",
+    maintainer_email="beat.buesser@ie.ibm.com",
+    url="https://github.com/Trusted-AI/adversarial-robustness-toolbox",
+    license="MIT",
+    install_requires=install_requires,
+    extras_require={
+        "docs": docs_require,
+        "catboost": ["catboost"],
+        "gpy": ["GPy"],
+        "keras": ["keras", "h5py"],
+        "lightgbm": ["lightgbm"],
+        "mxnet": ["mxnet"],
+        "tensorflow": ["tensorflow", "tensorflow_addons", "h5py"],
+        "pytorch": ["torch", "torchvision", "torchaudio"],
+        "xgboost": ["xgboost"],
+        "lingvo_asr": ["tensorflow-gpu==2.1.0", "lingvo==0.6.4"],
+        "all": [
+            "mxnet",
+            "catboost",
+            "lightgbm",
+            "tensorflow",
+            "tensorflow-addons",
+            "h5py",
+            "torch",
+            "torchvision",
+            "xgboost",
+            "pandas",
+            "kornia",
+        ],
+    },
+    classifiers=[
+        "Development Status :: 3 - Alpha",
+        "Intended Audience :: Developers",
+        "Intended Audience :: Education",
+        "Intended Audience :: Science/Research",
+        "License :: OSI Approved :: MIT License",
+        "Programming Language :: Python :: 3.6",
+        "Programming Language :: Python :: 3.7",
+        "Programming Language :: Python :: 3.8",
+        "Topic :: Software Development :: Libraries",
+        "Topic :: Software Development :: Libraries :: Python Modules",
+        "Topic :: Scientific/Engineering :: Artificial Intelligence",
+    ],
+    packages=find_packages(),
+    include_package_data=True,
+)
diff --git a/adversarial-robustness-toolbox/tests/__init__.py b/adversarial-robustness-toolbox/tests/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/adversarial-robustness-toolbox/tests/attacks/__init__.py b/adversarial-robustness-toolbox/tests/attacks/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/adversarial-robustness-toolbox/tests/attacks/conftest.py b/adversarial-robustness-toolbox/tests/attacks/conftest.py
new file mode 100644
index 0000000..7ca8662
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/attacks/conftest.py
@@ -0,0 +1,44 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+import logging
+import pytest
+
+from tests.utils import ARTTestFixtureNotImplemented
+
+logger = logging.getLogger(__name__)
+
+
+@pytest.fixture
+def tabular_dl_estimator_for_attack(framework, tabular_dl_estimator):
+    def _tabular_dl_estimator_for_attack(attack, clipped=True):
+        classifier = tabular_dl_estimator(clipped)
+        classifier_list = [classifier]
+
+        classifier_tested = [
+            potential_classifier
+            for potential_classifier in classifier_list
+            if all(t in type(potential_classifier).__mro__ for t in attack._estimator_requirements)
+        ]
+
+        if len(classifier_tested) == 0:
+            raise ARTTestFixtureNotImplemented(
+                "no estimator available", tabular_dl_estimator.__name__, framework, {"attack": attack}
+            )
+        return classifier_tested[0]
+
+    return _tabular_dl_estimator_for_attack
diff --git a/adversarial-robustness-toolbox/tests/attacks/evasion/__init__.py b/adversarial-robustness-toolbox/tests/attacks/evasion/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/adversarial-robustness-toolbox/tests/attacks/evasion/conftest.py b/adversarial-robustness-toolbox/tests/attacks/evasion/conftest.py
new file mode 100644
index 0000000..608d20c
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/attacks/evasion/conftest.py
@@ -0,0 +1,118 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import numpy as np
+import pytest
+
+from art.estimators.pytorch import PyTorchEstimator
+from art.estimators.speech_recognition.speech_recognizer import SpeechRecognizerMixin
+from art.estimators.tensorflow import TensorFlowV2Estimator
+from tests.utils import ARTTestFixtureNotImplemented
+
+
+@pytest.fixture
+def audio_sample():
+    """
+    Create audio sample.
+    """
+    sample_rate = 16000
+    test_input = np.ones((sample_rate)) * 10e3
+    return test_input
+
+
+@pytest.fixture
+def audio_data():
+    """
+    Create audio fixtures of shape (nb_samples=3,) with elements of variable length.
+    """
+    sample_rate = 16000
+    test_input = np.array(
+        [
+            np.zeros(sample_rate),
+            np.ones(sample_rate * 2) * 2e3,
+            np.ones(sample_rate * 3) * 3e3,
+            np.ones(sample_rate * 3) * 3e3,
+        ],
+        dtype=object,
+    )
+    test_target = ["DUMMY"] * test_input.shape[0]
+    return test_input, test_target
+
+
+@pytest.fixture
+def audio_batch_padded():
+    """
+    Create audio fixtures of shape (batch_size=2,) with elements of variable length.
+    """
+    sample_rate = 16000
+    test_input = np.ones((2, sample_rate)) * 2e3
+    return test_input
+
+
+@pytest.fixture
+def asr_dummy_estimator(framework):
+    def _asr_dummy_estimator(**kwargs):
+        asr_dummy = None
+        if framework in ("tensorflow2v1", "tensorflow2"):
+
+            class TensorFlowV2ASRDummy(TensorFlowV2Estimator, SpeechRecognizerMixin):
+                def get_activations(self):
+                    pass
+
+                def predict(self, x):
+                    pass
+
+                def loss_gradient(self, x, y, **kwargs):
+                    return x
+
+                @property
+                def input_shape(self):
+                    return None
+
+                def compute_loss(self, x, y, **kwargs):
+                    pass
+
+            asr_dummy = TensorFlowV2ASRDummy(channels_first=None, model=None, clip_values=None)
+        if framework == "pytorch":
+
+            class PyTorchASRDummy(PyTorchEstimator, SpeechRecognizerMixin):
+                def get_activations(self):
+                    pass
+
+                def predict(self, x):
+                    pass
+
+                def loss_gradient(self, x, y, **kwargs):
+                    return x
+
+                @property
+                def input_shape(self):
+                    return None
+
+                def compute_loss(self, x, y, **kwargs):
+                    pass
+
+            asr_dummy = PyTorchASRDummy(channels_first=None, model=None, clip_values=None)
+        if asr_dummy is None:
+            raise ARTTestFixtureNotImplemented(
+                "ASR dummy estimator not available for this framework", asr_dummy_estimator.__name__, framework
+            )
+        return asr_dummy
+
+    return _asr_dummy_estimator
diff --git a/adversarial-robustness-toolbox/tests/attacks/evasion/test_adversarial_asr.py b/adversarial-robustness-toolbox/tests/attacks/evasion/test_adversarial_asr.py
new file mode 100644
index 0000000..5751570
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/attacks/evasion/test_adversarial_asr.py
@@ -0,0 +1,79 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+
+import pytest
+from numpy.testing import assert_array_equal
+
+from art.attacks.attack import EvasionAttack
+from art.attacks.evasion.adversarial_asr import CarliniWagnerASR
+from art.attacks.evasion.imperceptible_asr.imperceptible_asr import ImperceptibleASR
+from art.estimators.estimator import BaseEstimator, LossGradientsMixin, NeuralNetworkMixin
+from art.estimators.speech_recognition.speech_recognizer import SpeechRecognizerMixin
+from tests.attacks.utils import backend_test_classifier_type_check_fail
+from tests.utils import ARTTestException
+
+logger = logging.getLogger(__name__)
+
+
+class TestImperceptibleASR:
+    """
+    Test the ImperceptibleASR attack.
+    """
+
+    @pytest.mark.framework_agnostic
+    def test_is_subclass(self, art_warning):
+        try:
+            assert issubclass(CarliniWagnerASR, (ImperceptibleASR, EvasionAttack))
+        except ARTTestException as e:
+            art_warning(e)
+
+    @pytest.mark.skip_framework("tensorflow1", "mxnet", "kerastf", "non_dl_frameworks")
+    def test_implements_abstract_methods(self, art_warning, asr_dummy_estimator):
+        try:
+            CarliniWagnerASR(estimator=asr_dummy_estimator())
+        except ARTTestException as e:
+            art_warning(e)
+
+    @pytest.mark.framework_agnostic
+    def test_classifier_type_check_fail(self, art_warning):
+        try:
+            backend_test_classifier_type_check_fail(
+                CarliniWagnerASR, [NeuralNetworkMixin, LossGradientsMixin, BaseEstimator, SpeechRecognizerMixin]
+            )
+        except ARTTestException as e:
+            art_warning(e)
+
+    @pytest.mark.skip_framework("tensorflow1", "mxnet", "kerastf", "non_dl_frameworks")
+    def test_generate_batch(self, art_warning, mocker, asr_dummy_estimator, audio_data):
+        try:
+            test_input, test_target = audio_data
+
+            # mock _create_adversarial and test if result gets passed unchanged through _create_imperceptible
+            mocker.patch.object(CarliniWagnerASR, "_create_adversarial", return_value=test_input)
+
+            carlini_asr = CarliniWagnerASR(estimator=asr_dummy_estimator())
+            adversarial = carlini_asr._generate_batch(test_input, test_target)
+
+            carlini_asr._create_adversarial.assert_called()
+            for a, t in zip(adversarial, test_input):
+                assert_array_equal(a, t)
+        except ARTTestException as e:
+            art_warning(e)
diff --git a/adversarial-robustness-toolbox/tests/attacks/evasion/test_auto_attack.py b/adversarial-robustness-toolbox/tests/attacks/evasion/test_auto_attack.py
new file mode 100644
index 0000000..2e71a69
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/attacks/evasion/test_auto_attack.py
@@ -0,0 +1,147 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+import logging
+import pytest
+
+import numpy as np
+
+from art.attacks.evasion import AutoAttack
+from art.attacks.evasion.auto_projected_gradient_descent import AutoProjectedGradientDescent
+from art.attacks.evasion.deepfool import DeepFool
+from art.attacks.evasion.square_attack import SquareAttack
+from art.estimators.estimator import BaseEstimator
+from art.estimators.classification.classifier import ClassifierMixin
+
+from tests.attacks.utils import backend_test_classifier_type_check_fail
+from tests.utils import ARTTestException
+
+logger = logging.getLogger(__name__)
+
+
+@pytest.fixture()
+def fix_get_mnist_subset(get_mnist_dataset):
+    (x_train_mnist, y_train_mnist), (x_test_mnist, y_test_mnist) = get_mnist_dataset
+    n_train = 100
+    n_test = 10
+    yield x_train_mnist[:n_train], y_train_mnist[:n_train], x_test_mnist[:n_test], y_test_mnist[:n_test]
+
+
+@pytest.mark.skip_framework("tensorflow1", "keras", "pytorch", "non_dl_frameworks", "mxnet", "kerastf")
+def test_generate_default(art_warning, fix_get_mnist_subset, image_dl_estimator):
+    try:
+        classifier, _ = image_dl_estimator(from_logits=True)
+
+        attack = AutoAttack(
+            estimator=classifier, norm=np.inf, eps=0.3, eps_step=0.1, attacks=None, batch_size=32, estimator_orig=None,
+        )
+
+        (x_train_mnist, y_train_mnist, x_test_mnist, y_test_mnist) = fix_get_mnist_subset
+
+        x_train_mnist_adv = attack.generate(x=x_train_mnist, y=y_train_mnist)
+
+        assert np.mean(np.abs(x_train_mnist_adv - x_train_mnist)) == pytest.approx(0.0292, abs=0.105)
+        assert np.max(np.abs(x_train_mnist_adv - x_train_mnist)) == pytest.approx(0.3, abs=0.05)
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.skip_framework("tensorflow1", "keras", "pytorch", "non_dl_frameworks", "mxnet", "kerastf")
+def test_generate_attacks_and_targeted(art_warning, fix_get_mnist_subset, image_dl_estimator):
+    try:
+        classifier, _ = image_dl_estimator(from_logits=True)
+
+        norm = np.inf
+        eps = 0.3
+        eps_step = 0.1
+        batch_size = 32
+
+        attacks = list()
+        attacks.append(
+            AutoProjectedGradientDescent(
+                estimator=classifier,
+                norm=norm,
+                eps=eps,
+                eps_step=eps_step,
+                max_iter=100,
+                targeted=True,
+                nb_random_init=5,
+                batch_size=batch_size,
+                loss_type="cross_entropy",
+            )
+        )
+        attacks.append(
+            AutoProjectedGradientDescent(
+                estimator=classifier,
+                norm=norm,
+                eps=eps,
+                eps_step=eps_step,
+                max_iter=100,
+                targeted=False,
+                nb_random_init=5,
+                batch_size=batch_size,
+                loss_type="difference_logits_ratio",
+            )
+        )
+        attacks.append(DeepFool(classifier=classifier, max_iter=100, epsilon=1e-6, nb_grads=3, batch_size=batch_size))
+        attacks.append(SquareAttack(estimator=classifier, norm=norm, max_iter=5000, eps=eps, p_init=0.8, nb_restarts=5))
+
+        (x_train_mnist, y_train_mnist, x_test_mnist, y_test_mnist) = fix_get_mnist_subset
+
+        # First test with defined_attack_only=False
+        attack = AutoAttack(
+            estimator=classifier,
+            norm=norm,
+            eps=eps,
+            eps_step=eps_step,
+            attacks=attacks,
+            batch_size=batch_size,
+            estimator_orig=None,
+            targeted=False,
+        )
+
+        x_train_mnist_adv = attack.generate(x=x_train_mnist, y=y_train_mnist)
+
+        assert np.mean(np.abs(x_train_mnist_adv - x_train_mnist)) == pytest.approx(0.0182, abs=0.105)
+        assert np.max(np.abs(x_train_mnist_adv - x_train_mnist)) == pytest.approx(0.3, abs=0.05)
+
+        # Then test with defined_attack_only=True
+        attack = AutoAttack(
+            estimator=classifier,
+            norm=norm,
+            eps=eps,
+            eps_step=eps_step,
+            attacks=attacks,
+            batch_size=batch_size,
+            estimator_orig=None,
+            targeted=True,
+        )
+
+        x_train_mnist_adv = attack.generate(x=x_train_mnist, y=y_train_mnist)
+
+        assert np.mean(x_train_mnist_adv - x_train_mnist) == pytest.approx(0.0179, abs=0.0075)
+        assert np.max(np.abs(x_train_mnist_adv - x_train_mnist)) == pytest.approx(eps, abs=0.005)
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.framework_agnostic
+def test_classifier_type_check_fail(art_warning):
+    try:
+        backend_test_classifier_type_check_fail(AutoAttack, [BaseEstimator, ClassifierMixin])
+    except ARTTestException as e:
+        art_warning(e)
diff --git a/adversarial-robustness-toolbox/tests/attacks/evasion/test_auto_projected_gradient_descent.py b/adversarial-robustness-toolbox/tests/attacks/evasion/test_auto_projected_gradient_descent.py
new file mode 100644
index 0000000..4109b5c
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/attacks/evasion/test_auto_projected_gradient_descent.py
@@ -0,0 +1,75 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+import logging
+import pytest
+
+import numpy as np
+
+from art.attacks.evasion import AutoProjectedGradientDescent
+from art.estimators.estimator import BaseEstimator, LossGradientsMixin
+from art.estimators.classification.classifier import ClassifierMixin
+
+from tests.attacks.utils import backend_test_classifier_type_check_fail
+from tests.utils import ARTTestException
+
+logger = logging.getLogger(__name__)
+
+
+@pytest.fixture()
+def fix_get_mnist_subset(get_mnist_dataset):
+    (x_train_mnist, y_train_mnist), (x_test_mnist, y_test_mnist) = get_mnist_dataset
+    n_train = 100
+    n_test = 10
+    yield x_train_mnist[:n_train], y_train_mnist[:n_train], x_test_mnist[:n_test], y_test_mnist[:n_test]
+
+
+@pytest.mark.skip_framework("tensorflow1", "keras", "pytorch", "non_dl_frameworks", "mxnet", "kerastf")
+def test_generate(art_warning, fix_get_mnist_subset, image_dl_estimator_for_attack):
+    try:
+        classifier = image_dl_estimator_for_attack(AutoProjectedGradientDescent)
+
+        attack = AutoProjectedGradientDescent(
+            estimator=classifier,
+            norm=np.inf,
+            eps=0.3,
+            eps_step=0.1,
+            max_iter=5,
+            targeted=False,
+            nb_random_init=1,
+            batch_size=32,
+            loss_type="cross_entropy",
+        )
+
+        (x_train_mnist, y_train_mnist, x_test_mnist, y_test_mnist) = fix_get_mnist_subset
+
+        x_train_mnist_adv = attack.generate(x=x_train_mnist, y=y_train_mnist)
+
+        assert np.mean(np.abs(x_train_mnist_adv - x_train_mnist)) == pytest.approx(0.0329, abs=0.005)
+        assert np.max(np.abs(x_train_mnist_adv - x_train_mnist)) == pytest.approx(0.3, abs=0.01)
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.framework_agnostic
+def test_classifier_type_check_fail(art_warning):
+    try:
+        backend_test_classifier_type_check_fail(
+            AutoProjectedGradientDescent, [BaseEstimator, LossGradientsMixin, ClassifierMixin]
+        )
+    except ARTTestException as e:
+        art_warning(e)
diff --git a/adversarial-robustness-toolbox/tests/attacks/evasion/test_boundary.py b/adversarial-robustness-toolbox/tests/attacks/evasion/test_boundary.py
new file mode 100644
index 0000000..bdeedb9
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/attacks/evasion/test_boundary.py
@@ -0,0 +1,74 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+
+import pytest
+import logging
+
+from art.attacks.evasion import BoundaryAttack
+from art.estimators.estimator import BaseEstimator
+from art.estimators.classification.classifier import ClassifierMixin
+
+from tests.attacks.utils import backend_targeted_tabular, backend_untargeted_tabular, backend_targeted_images
+from tests.attacks.utils import back_end_untargeted_images, backend_test_classifier_type_check_fail
+from tests.utils import ARTTestException
+
+logger = logging.getLogger(__name__)
+
+
+@pytest.fixture()
+def fix_get_mnist_subset(get_mnist_dataset):
+    (x_train_mnist, y_train_mnist), (x_test_mnist, y_test_mnist) = get_mnist_dataset
+    n_train = 10
+    n_test = 10
+    yield x_train_mnist[:n_train], y_train_mnist[:n_train], x_test_mnist[:n_test], y_test_mnist[:n_test]
+
+
+@pytest.mark.framework_agnostic
+@pytest.mark.parametrize("clipped_classifier, targeted", [(True, True), (True, False), (False, True), (False, False)])
+def test_tabular(art_warning, tabular_dl_estimator, framework, get_iris_dataset, clipped_classifier, targeted):
+    try:
+        classifier = tabular_dl_estimator(clipped=clipped_classifier)
+        attack = BoundaryAttack(classifier, targeted=targeted, max_iter=10)
+        if targeted:
+            backend_targeted_tabular(attack, get_iris_dataset)
+        else:
+            backend_untargeted_tabular(attack, get_iris_dataset, clipped=clipped_classifier)
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.framework_agnostic
+@pytest.mark.parametrize("targeted", [True, False])
+def test_images(art_warning, fix_get_mnist_subset, image_dl_estimator_for_attack, framework, targeted):
+    try:
+        classifier = image_dl_estimator_for_attack(BoundaryAttack)
+        attack = BoundaryAttack(estimator=classifier, targeted=targeted, max_iter=20)
+        if targeted:
+            backend_targeted_images(attack, fix_get_mnist_subset)
+        else:
+            back_end_untargeted_images(attack, fix_get_mnist_subset, framework)
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.framework_agnostic
+def test_classifier_type_check_fail(art_warning):
+    try:
+        backend_test_classifier_type_check_fail(BoundaryAttack, [BaseEstimator, ClassifierMixin])
+    except ARTTestException as e:
+        art_warning(e)
diff --git a/adversarial-robustness-toolbox/tests/attacks/evasion/test_brendel_and_bethge.py b/adversarial-robustness-toolbox/tests/attacks/evasion/test_brendel_and_bethge.py
new file mode 100644
index 0000000..0a0db81
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/attacks/evasion/test_brendel_and_bethge.py
@@ -0,0 +1,63 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+import logging
+
+import numpy as np
+import pytest
+
+from art.attacks.evasion import BrendelBethgeAttack
+from art.estimators.estimator import BaseEstimator, LossGradientsMixin
+from art.estimators.classification.classifier import ClassifierMixin
+
+from tests.attacks.utils import backend_test_classifier_type_check_fail
+from tests.utils import ARTTestException
+
+logger = logging.getLogger(__name__)
+
+
+@pytest.mark.skip_framework("tensorflow1", "keras", "kerastf", "mxnet", "non_dl_frameworks")
+@pytest.mark.parametrize("targeted", [True, False])
+@pytest.mark.parametrize("norm", [1, 2, np.inf, "inf"])
+def test_generate(art_warning, get_default_mnist_subset, image_dl_estimator_for_attack, targeted, norm):
+    try:
+        (x_train_mnist, y_train_mnist), (x_test_mnist, y_test_mnist) = get_default_mnist_subset
+        classifier = image_dl_estimator_for_attack(BrendelBethgeAttack, defended=False, from_logits=True)
+
+        attack = BrendelBethgeAttack(
+            estimator=classifier,
+            norm=norm,
+            targeted=targeted,
+            overshoot=1.1,
+            steps=1,
+            lr=1e-3,
+            lr_decay=0.5,
+            lr_num_decay=20,
+            momentum=0.8,
+            binary_search_steps=1,
+            init_size=5,
+            batch_size=32,
+        )
+
+        attack.generate(x=x_test_mnist[0:1].astype(np.float32), y=y_test_mnist[0:1].astype(np.int32))
+
+    except ARTTestException as e:
+        art_warning(e)
+
+
+def test_classifier_type_check_fail():
+    backend_test_classifier_type_check_fail(BrendelBethgeAttack, [BaseEstimator, LossGradientsMixin, ClassifierMixin])
diff --git a/adversarial-robustness-toolbox/tests/attacks/evasion/test_dpatch.py b/adversarial-robustness-toolbox/tests/attacks/evasion/test_dpatch.py
new file mode 100644
index 0000000..48b650b
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/attacks/evasion/test_dpatch.py
@@ -0,0 +1,145 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+import logging
+
+import numpy as np
+import pytest
+
+from art.attacks.evasion import DPatch
+from art.estimators.estimator import BaseEstimator, LossGradientsMixin
+from art.estimators.object_detection.object_detector import ObjectDetectorMixin
+from tests.attacks.utils import backend_test_classifier_type_check_fail
+from tests.utils import master_seed
+from tests.utils import ARTTestException
+
+logger = logging.getLogger(__name__)
+
+
+@pytest.fixture()
+def fix_get_mnist_subset(get_mnist_dataset):
+    (x_train_mnist, y_train_mnist), (x_test_mnist, y_test_mnist) = get_mnist_dataset
+    n_train = 100
+    n_test = 11
+    yield x_train_mnist[:n_train], y_train_mnist[:n_train], x_test_mnist[:n_test], y_test_mnist[:n_test]
+
+
+@pytest.mark.parametrize("random_location", [True, False])
+@pytest.mark.parametrize("image_format", ["NHWC", "NCHW"])
+@pytest.mark.framework_agnostic
+def test_augment_images_with_patch(art_warning, random_location, image_format, fix_get_mnist_subset):
+    try:
+        (x_train_mnist, y_train_mnist, x_test_mnist, y_test_mnist) = fix_get_mnist_subset
+
+        # TODO this master_seed should be removed as it is already set in conftest.py. expected values will
+        # need to be updated accordingly
+        master_seed()
+
+        if image_format == "NHWC":
+            patch = np.ones(shape=(4, 4, 1)) * 0.5
+            x = x_train_mnist[0:3]
+            channels_first = False
+        elif image_format == "NCHW":
+            patch = np.ones(shape=(1, 4, 4)) * 0.5
+            x = np.transpose(x_train_mnist[0:3], (0, 3, 1, 2))
+            channels_first = True
+
+        patched_images, transformations = DPatch._augment_images_with_patch(
+            x=x, patch=patch, random_location=random_location, channels_first=channels_first
+        )
+
+        if random_location:
+            transformation_expected = {"i_x_1": 0, "i_y_1": 2, "i_x_2": 4, "i_y_2": 6}
+            patched_images_column = [
+                0.0,
+                0.0,
+                0.5,
+                0.5,
+                0.5,
+                0.5,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+            ]
+        else:
+            transformation_expected = {"i_x_1": 0, "i_y_1": 0, "i_x_2": 4, "i_y_2": 4}
+            patched_images_column = [
+                0.5,
+                0.5,
+                0.5,
+                0.5,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+            ]
+
+        assert transformations[1] == transformation_expected
+
+        if image_format == "NCHW":
+            patched_images = np.transpose(patched_images, (0, 2, 3, 1))
+
+        np.testing.assert_array_equal(patched_images[1, 2, :, 0], patched_images_column)
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.framework_agnostic
+def test_classifier_type_check_fail(art_warning):
+    try:
+        backend_test_classifier_type_check_fail(DPatch, [BaseEstimator, LossGradientsMixin, ObjectDetectorMixin])
+    except ARTTestException as e:
+        art_warning(e)
diff --git a/adversarial-robustness-toolbox/tests/attacks/evasion/test_dpatch_robust.py b/adversarial-robustness-toolbox/tests/attacks/evasion/test_dpatch_robust.py
new file mode 100644
index 0000000..f8f41d3
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/attacks/evasion/test_dpatch_robust.py
@@ -0,0 +1,169 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+import logging
+
+import numpy as np
+import pytest
+
+from art.attacks.evasion import RobustDPatch
+from art.estimators.estimator import BaseEstimator, LossGradientsMixin
+from art.estimators.object_detection.object_detector import ObjectDetectorMixin
+
+from tests.attacks.utils import backend_test_classifier_type_check_fail
+from tests.utils import ARTTestException
+
+logger = logging.getLogger(__name__)
+
+
+@pytest.fixture()
+def fix_get_robust_dpatch():
+    from abc import ABC
+
+    class DummyObjectDetector(ObjectDetectorMixin, LossGradientsMixin, BaseEstimator, ABC):
+        def __init__(self):
+            self._clip_values = (0, 1)
+            self.channels_first = False
+            self._input_shape = None
+
+        def loss_gradient(self, x: np.ndarray, y: None, **kwargs):
+            raise NotImplementedError
+
+        def fit(self, x: np.ndarray, y, batch_size: int = 128, nb_epochs: int = 20, **kwargs):
+            raise NotImplementedError
+
+        def predict(self, x: np.ndarray, batch_size: int = 128, **kwargs):
+            return [{"boxes": [], "labels": [], "scores": []}]
+
+        @property
+        def input_shape(self):
+            return self._input_shape
+
+    frcnn = DummyObjectDetector()
+    attack = RobustDPatch(
+        frcnn,
+        patch_shape=(4, 4, 1),
+        patch_location=(2, 2),
+        crop_range=(0, 0),
+        brightness_range=(1.0, 1.0),
+        rotation_weights=(1, 0, 0, 0),
+        sample_size=1,
+        learning_rate=1.0,
+        max_iter=1,
+        batch_size=1,
+    )
+    yield attack
+
+
+@pytest.mark.parametrize("image_format", ["NHWC", "NCHW"])
+@pytest.mark.framework_agnostic
+def test_augment_images_with_patch(art_warning, image_format, fix_get_robust_dpatch):
+    try:
+        attack = fix_get_robust_dpatch
+
+        if image_format == "NHWC":
+            patch = np.ones(shape=(4, 4, 1))
+            x = np.zeros(shape=(1, 10, 10, 1))
+            channels_first = False
+        elif image_format == "NCHW":
+            patch = np.ones(shape=(1, 4, 4))
+            x = np.zeros(shape=(1, 1, 10, 10))
+            channels_first = True
+
+        patched_images, _, transformations = attack._augment_images_with_patch(
+            x=x, patch=patch, channels_first=channels_first
+        )
+
+        transformation_expected = {"crop_x": 0, "crop_y": 0, "rot90": 0, "brightness": 1.0}
+        patch_sum_expected = 16.0
+        complement_sum_expected = 0.0
+
+        if image_format == "NHWC":
+            patch_sum = np.sum(patched_images[0, 2:7, 2:7, :])
+        elif image_format == "NCHW":
+            patch_sum = np.sum(patched_images[0, :, 2:7, 2:7])
+
+        complement_sum = np.sum(patched_images[0]) - patch_sum
+
+        assert transformations == transformation_expected
+
+        assert patch_sum == patch_sum_expected
+
+        assert complement_sum == complement_sum_expected
+
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.framework_agnostic
+def test_apply_patch(art_warning, fix_get_robust_dpatch):
+    try:
+        attack = fix_get_robust_dpatch
+
+        patch = np.ones(shape=(4, 4, 1))
+        x = np.zeros(shape=(1, 10, 10, 1))
+
+        patched_images = attack.apply_patch(x=x, patch_external=patch)
+
+        patch_sum_expected = 16.0
+        complement_sum_expected = 0.0
+
+        patch_sum = np.sum(patched_images[0, 2:7, 2:7, :])
+        complement_sum = np.sum(patched_images[0]) - patch_sum
+
+        assert patch_sum == patch_sum_expected
+
+        assert complement_sum == complement_sum_expected
+
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.framework_agnostic
+def test_untransform_gradients(art_warning, fix_get_robust_dpatch):
+    try:
+        attack = fix_get_robust_dpatch
+
+        gradients = np.zeros(shape=(1, 10, 10, 1))
+        gradients[:, 2:7, 2:7, :] = 1
+
+        crop_x = 1
+        crop_y = 1
+        rot90 = 3
+        brightness = 1.0
+
+        gradients = gradients[:, crop_x : 10 - crop_x, crop_y : 10 - crop_y, :]
+        gradients = np.rot90(gradients, rot90, (1, 2))
+
+        transforms = {"crop_x": crop_x, "crop_y": crop_y, "rot90": rot90, "brightness": brightness}
+
+        gradients = attack._untransform_gradients(gradients=gradients, transforms=transforms, channels_first=False)
+        gradients_sum = np.sum(gradients[0])
+        gradients_sum_expected = 16.0
+
+        assert gradients_sum == gradients_sum_expected
+
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.framework_agnostic
+def test_classifier_type_check_fail(art_warning):
+    try:
+        backend_test_classifier_type_check_fail(RobustDPatch, [BaseEstimator, LossGradientsMixin, ObjectDetectorMixin])
+    except ARTTestException as e:
+        art_warning(e)
diff --git a/adversarial-robustness-toolbox/tests/attacks/evasion/test_fast_gradient.py b/adversarial-robustness-toolbox/tests/attacks/evasion/test_fast_gradient.py
new file mode 100644
index 0000000..2277e2f
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/attacks/evasion/test_fast_gradient.py
@@ -0,0 +1,208 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import numpy as np
+import pytest
+
+from art.attacks.evasion import FastGradientMethod
+from art.estimators.estimator import BaseEstimator, LossGradientsMixin
+
+from tests.attacks.utils import backend_check_adverse_values, backend_test_defended_images
+from tests.attacks.utils import backend_test_random_initialisation_images, backend_targeted_images
+from tests.attacks.utils import backend_targeted_tabular, backend_untargeted_tabular, backend_masked_images
+from tests.attacks.utils import backend_test_classifier_type_check_fail
+from tests.utils import ExpectedValue, ARTTestException
+
+logger = logging.getLogger(__name__)
+
+
+@pytest.fixture()
+def fix_get_mnist_subset(get_mnist_dataset):
+    (x_train_mnist, y_train_mnist), (x_test_mnist, y_test_mnist) = get_mnist_dataset
+    n_train = 100
+    n_test = 11
+    yield x_train_mnist[:n_train], y_train_mnist[:n_train], x_test_mnist[:n_test], y_test_mnist[:n_test]
+
+
+# currently NOT setting this test as framework_agnostic since no tensorflow implementation
+# of the defended classifier exists
+def test_classifier_defended_images(art_warning, fix_get_mnist_subset, image_dl_estimator_for_attack):
+    try:
+        classifier = image_dl_estimator_for_attack(FastGradientMethod, defended=True)
+        attack = FastGradientMethod(classifier, eps=1.0, batch_size=128)
+        backend_test_defended_images(attack, fix_get_mnist_subset)
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.framework_agnostic
+def test_random_initialisation_images(art_warning, fix_get_mnist_subset, image_dl_estimator_for_attack):
+    try:
+        classifier = image_dl_estimator_for_attack(FastGradientMethod)
+        attack = FastGradientMethod(classifier, num_random_init=3)
+        backend_test_random_initialisation_images(attack, fix_get_mnist_subset)
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.framework_agnostic
+def test_targeted_images(art_warning, fix_get_mnist_subset, image_dl_estimator_for_attack):
+    try:
+        classifier = image_dl_estimator_for_attack(FastGradientMethod)
+        attack = FastGradientMethod(classifier, eps=1.0, targeted=True)
+        attack_params = {"minimal": True, "eps_step": 0.01, "eps": 1.0}
+        attack.set_params(**attack_params)
+
+        backend_targeted_images(attack, fix_get_mnist_subset)
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.framework_agnostic
+def test_masked_images(art_warning, fix_get_mnist_subset, image_dl_estimator_for_attack):
+    try:
+        classifier = image_dl_estimator_for_attack(FastGradientMethod)
+        attack = FastGradientMethod(classifier, eps=1.0, num_random_init=1)
+        backend_masked_images(attack, fix_get_mnist_subset)
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.framework_agnostic
+def test_minimal_perturbations_images(art_warning, fix_get_mnist_subset, image_dl_estimator_for_attack):
+    try:
+        classifier = image_dl_estimator_for_attack(FastGradientMethod)
+
+        expected_values = {
+            "x_test_mean": ExpectedValue(0.03896513, 0.01),
+            "x_test_min": ExpectedValue(-0.30000000, 0.00001),
+            "x_test_max": ExpectedValue(0.30000000, 0.00001),
+            "y_test_pred_adv_expected": ExpectedValue(np.asarray([4, 2, 4, 7, 0, 4, 7, 2, 0, 7, 0]), 2),
+        }
+
+        attack = FastGradientMethod(classifier, eps=1.0, batch_size=11)
+
+        # Test eps of float type
+        attack_params = {"minimal": True, "eps_step": 0.1, "eps": 5.0}
+        attack.set_params(**attack_params)
+
+        backend_check_adverse_values(attack, fix_get_mnist_subset, expected_values)
+
+        # Test eps of array type 1
+        (_, _, x_test_mnist, _) = fix_get_mnist_subset
+        eps = np.ones(shape=x_test_mnist.shape) * 5.0
+        eps_step = np.ones_like(eps) * 0.1
+
+        attack_params = {"minimal": True, "eps_step": eps_step, "eps": eps}
+        attack.set_params(**attack_params)
+
+        backend_check_adverse_values(attack, fix_get_mnist_subset, expected_values)
+
+        # Test eps of array type 2
+        eps = np.ones(shape=x_test_mnist.shape[1:]) * 5.0
+        eps_step = np.ones_like(eps) * 0.1
+
+        attack_params = {"minimal": True, "eps_step": eps_step, "eps": eps}
+        attack.set_params(**attack_params)
+
+        backend_check_adverse_values(attack, fix_get_mnist_subset, expected_values)
+
+        # Test eps of array type 3
+        eps = np.ones(shape=x_test_mnist.shape[2:]) * 5.0
+        eps_step = np.ones_like(eps) * 0.1
+
+        attack_params = {"minimal": True, "eps_step": eps_step, "eps": eps}
+        attack.set_params(**attack_params)
+
+        backend_check_adverse_values(attack, fix_get_mnist_subset, expected_values)
+
+        # Test eps of array type 4
+        eps = np.ones(shape=x_test_mnist.shape[3:]) * 5.0
+        eps_step = np.ones_like(eps) * 0.1
+
+        attack_params = {"minimal": True, "eps_step": eps_step, "eps": eps}
+        attack.set_params(**attack_params)
+
+        backend_check_adverse_values(attack, fix_get_mnist_subset, expected_values)
+
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.parametrize("norm", [np.inf, 1, 2])
+@pytest.mark.skip_framework("pytorch")  # temporarily skipping for pytorch until find bug fix in bounded test
+@pytest.mark.framework_agnostic
+def test_norm_images(art_warning, norm, fix_get_mnist_subset, image_dl_estimator_for_attack):
+    try:
+        classifier = image_dl_estimator_for_attack(FastGradientMethod)
+
+        if norm == np.inf:
+            expected_values = {
+                "x_test_mean": ExpectedValue(0.2346725, 0.002),
+                "x_test_min": ExpectedValue(-1.0, 0.00001),
+                "x_test_max": ExpectedValue(1.0, 0.00001),
+                "y_test_pred_adv_expected": ExpectedValue(np.asarray([4, 4, 4, 7, 7, 4, 7, 2, 2, 3, 0]), 2),
+            }
+
+        elif norm == 1:
+            expected_values = {
+                "x_test_mean": ExpectedValue(0.00051374, 0.002),
+                "x_test_min": ExpectedValue(-0.01486498, 0.001),
+                "x_test_max": ExpectedValue(0.014761963, 0.001),
+                "y_test_pred_adv_expected": ExpectedValue(np.asarray([7, 1, 1, 4, 4, 1, 4, 4, 4, 4, 4]), 4),
+            }
+        elif norm == 2:
+            expected_values = {
+                "x_test_mean": ExpectedValue(0.007636416, 0.001),
+                "x_test_min": ExpectedValue(-0.211054801, 0.001),
+                "x_test_max": ExpectedValue(0.209592223, 0.001),
+                "y_test_pred_adv_expected": ExpectedValue(np.asarray([7, 2, 4, 4, 4, 7, 7, 4, 0, 4, 4]), 2),
+            }
+
+        attack = FastGradientMethod(classifier, eps=1.0, norm=norm, batch_size=128)
+
+        backend_check_adverse_values(attack, fix_get_mnist_subset, expected_values)
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.skip_framework("scikitlearn")  # temporarily skipping for scikitlearn until find bug fix in bounded test
+@pytest.mark.parametrize("targeted, clipped", [(True, True), (True, False), (False, True), (False, False)])
+@pytest.mark.framework_agnostic
+def test_tabular(art_warning, tabular_dl_estimator, framework, get_iris_dataset, targeted, clipped):
+    try:
+        classifier = tabular_dl_estimator(clipped=clipped)
+
+        if targeted:
+            attack = FastGradientMethod(classifier, targeted=True, eps=0.1, batch_size=128)
+            backend_targeted_tabular(attack, get_iris_dataset)
+        else:
+            attack = FastGradientMethod(classifier, eps=0.1)
+            backend_untargeted_tabular(attack, get_iris_dataset, clipped=clipped)
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.framework_agnostic
+def test_classifier_type_check_fail(art_warning):
+    try:
+        backend_test_classifier_type_check_fail(FastGradientMethod, [BaseEstimator, LossGradientsMixin])
+    except ARTTestException as e:
+        art_warning(e)
diff --git a/adversarial-robustness-toolbox/tests/attacks/evasion/test_feature_adversaries.py b/adversarial-robustness-toolbox/tests/attacks/evasion/test_feature_adversaries.py
new file mode 100644
index 0000000..93d905c
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/attacks/evasion/test_feature_adversaries.py
@@ -0,0 +1,60 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+import logging
+import numpy as np
+import pytest
+
+from art.attacks.evasion import FeatureAdversaries
+from art.estimators.estimator import BaseEstimator, NeuralNetworkMixin
+
+from tests.attacks.utils import backend_test_classifier_type_check_fail
+from tests.utils import ARTTestException
+
+logger = logging.getLogger(__name__)
+
+
+@pytest.fixture()
+def fix_get_mnist_subset(get_mnist_dataset):
+    (x_train_mnist, y_train_mnist), (x_test_mnist, y_test_mnist) = get_mnist_dataset
+    n_train = 100
+    n_test = 11
+    yield x_train_mnist[:n_train], y_train_mnist[:n_train], x_test_mnist[:n_test], y_test_mnist[:n_test]
+
+
+@pytest.mark.skip_framework("pytorch")
+@pytest.mark.framework_agnostic
+def test_images(art_warning, fix_get_mnist_subset, image_dl_estimator_for_attack):
+    try:
+        (x_train_mnist, y_train_mnist, x_test_mnist, y_test_mnist) = fix_get_mnist_subset
+
+        classifier = image_dl_estimator_for_attack(FeatureAdversaries)
+
+        attack = FeatureAdversaries(classifier, delta=0.2, layer=1, batch_size=32)
+        x_train_mnist_adv = attack.generate(x=x_train_mnist[0:3], y=x_test_mnist[0:3], maxiter=1)
+        assert np.mean(x_train_mnist[0:3]) == pytest.approx(0.13015706282513004, 0.01)
+        assert np.mean(x_train_mnist_adv) == pytest.approx(0.1592448561261751, 0.01)
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.framework_agnostic
+def test_classifier_type_check_fail(art_warning):
+    try:
+        backend_test_classifier_type_check_fail(FeatureAdversaries, [BaseEstimator, NeuralNetworkMixin])
+    except ARTTestException as e:
+        art_warning(e)
diff --git a/adversarial-robustness-toolbox/tests/attacks/evasion/test_frame_saliency.py b/adversarial-robustness-toolbox/tests/attacks/evasion/test_frame_saliency.py
new file mode 100644
index 0000000..6a621fd
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/attacks/evasion/test_frame_saliency.py
@@ -0,0 +1,119 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import numpy as np
+import pytest
+
+from art.attacks.evasion import FastGradientMethod, FrameSaliencyAttack
+from art.estimators.estimator import BaseEstimator, LossGradientsMixin
+
+from tests.utils import ExpectedValue
+from tests.attacks.utils import backend_check_adverse_values, backend_check_adverse_frames
+from tests.attacks.utils import backend_test_classifier_type_check_fail
+from tests.utils import ARTTestException
+
+logger = logging.getLogger(__name__)
+
+
+@pytest.fixture()
+def fix_get_mnist_subset(get_mnist_dataset):
+    (x_train_mnist, y_train_mnist), (x_test_mnist, y_test_mnist) = get_mnist_dataset
+    n_train = 100
+    n_test = 11
+    yield x_train_mnist[:n_train], y_train_mnist[:n_train], x_test_mnist[:n_test], y_test_mnist[:n_test]
+
+
+@pytest.mark.skip_framework("pytorch")
+@pytest.mark.framework_agnostic
+def test_one_shot(art_warning, fix_get_mnist_subset, image_dl_estimator_for_attack):
+    try:
+        classifier = image_dl_estimator_for_attack(FastGradientMethod)
+
+        # for the one-shot method, frame saliency attack should resort to plain FastGradientMethod
+        expected_values = {
+            "x_test_mean": ExpectedValue(0.2346725, 0.002),
+            "x_test_min": ExpectedValue(-1.0, 0.00001),
+            "x_test_max": ExpectedValue(1.0, 0.00001),
+            "y_test_pred_adv_expected": ExpectedValue(np.asarray([4, 4, 4, 7, 7, 4, 7, 2, 2, 3, 0]), 2),
+        }
+
+        attacker = FastGradientMethod(classifier, eps=1.0, batch_size=128)
+        attack = FrameSaliencyAttack(classifier, attacker, "one_shot")
+        backend_check_adverse_values(attack, fix_get_mnist_subset, expected_values)
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.skip_framework("pytorch")
+@pytest.mark.framework_agnostic
+def test_iterative_saliency(art_warning, fix_get_mnist_subset, image_dl_estimator_for_attack):
+    try:
+        classifier = image_dl_estimator_for_attack(FastGradientMethod)
+
+        expected_values_axis_1 = {
+            "nb_perturbed_frames": ExpectedValue(np.asarray([10, 1, 2, 12, 16, 1, 2, 7, 4, 11, 5]), 2)
+        }
+
+        expected_values_axis_2 = {
+            "nb_perturbed_frames": ExpectedValue(np.asarray([11, 1, 2, 6, 14, 2, 2, 13, 4, 8, 4]), 2)
+        }
+
+        attacker = FastGradientMethod(classifier, eps=0.3, batch_size=128)
+        attack = FrameSaliencyAttack(classifier, attacker, "iterative_saliency")
+        backend_check_adverse_frames(attack, fix_get_mnist_subset, expected_values_axis_1)
+
+        # test with non-default frame index:
+        attack = FrameSaliencyAttack(classifier, attacker, "iterative_saliency", frame_index=2)
+        backend_check_adverse_frames(attack, fix_get_mnist_subset, expected_values_axis_2)
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.skip_framework("pytorch")
+@pytest.mark.framework_agnostic
+def test_iterative_saliency_refresh(art_warning, fix_get_mnist_subset, image_dl_estimator_for_attack):
+    try:
+        classifier = image_dl_estimator_for_attack(FastGradientMethod)
+
+        expected_values_axis_1 = {
+            "nb_perturbed_frames": ExpectedValue(np.asarray([5, 1, 3, 10, 8, 1, 3, 8, 4, 7, 7]), 2)
+        }
+
+        expected_values_axis_2 = {
+            "nb_perturbed_frames": ExpectedValue(np.asarray([11, 1, 2, 6, 14, 2, 2, 13, 4, 8, 4]), 2)
+        }
+
+        attacker = FastGradientMethod(classifier, eps=0.3, batch_size=128)
+        attack = FrameSaliencyAttack(classifier, attacker, "iterative_saliency_refresh")
+        backend_check_adverse_frames(attack, fix_get_mnist_subset, expected_values_axis_1)
+
+        # test with non-default frame index:
+        attack = FrameSaliencyAttack(classifier, attacker, "iterative_saliency", frame_index=2)
+        backend_check_adverse_frames(attack, fix_get_mnist_subset, expected_values_axis_2)
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.framework_agnostic
+def test_classifier_type_check_fail(art_warning):
+    try:
+        backend_test_classifier_type_check_fail(FastGradientMethod, [LossGradientsMixin, BaseEstimator])
+    except ARTTestException as e:
+        art_warning(e)
diff --git a/adversarial-robustness-toolbox/tests/attacks/evasion/test_imperceptible_asr.py b/adversarial-robustness-toolbox/tests/attacks/evasion/test_imperceptible_asr.py
new file mode 100644
index 0000000..7d68f20
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/attacks/evasion/test_imperceptible_asr.py
@@ -0,0 +1,338 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+
+import numpy as np
+import pytest
+
+from art.attacks.attack import EvasionAttack
+from art.attacks.evasion.imperceptible_asr.imperceptible_asr import ImperceptibleASR, PsychoacousticMasker
+from tests.utils import ARTTestException
+
+logger = logging.getLogger(__name__)
+
+
+class TestImperceptibleASR:
+    """
+    Test the ImperceptibleASR attack.
+    """
+
+    @pytest.mark.skip_framework("tensorflow1", "tensorflow2", "mxnet", "kerastf", "non_dl_frameworks")
+    def test_is_subclass(self, art_warning):
+        try:
+            assert issubclass(ImperceptibleASR, EvasionAttack)
+        except ARTTestException as e:
+            art_warning(e)
+
+    @pytest.mark.skip_framework("tensorflow1", "tensorflow2", "mxnet", "kerastf", "non_dl_frameworks")
+    def test_implements_abstract_methods(self, art_warning, asr_dummy_estimator):
+        try:
+            ImperceptibleASR(estimator=asr_dummy_estimator(), masker=PsychoacousticMasker())
+        except ARTTestException as e:
+            art_warning(e)
+
+    @pytest.mark.skip_framework("tensorflow1", "tensorflow2", "mxnet", "kerastf", "non_dl_frameworks")
+    def test_generate(self, art_warning, mocker, asr_dummy_estimator, audio_data):
+        try:
+            test_input, test_target = audio_data
+
+            mocker.patch.object(ImperceptibleASR, "_generate_batch")
+
+            imperceptible_asr = ImperceptibleASR(estimator=asr_dummy_estimator(), masker=PsychoacousticMasker())
+            _ = imperceptible_asr.generate(test_input, test_target)
+
+            imperceptible_asr._generate_batch.assert_called()
+        except ARTTestException as e:
+            art_warning(e)
+
+    @pytest.mark.skip_framework("tensorflow1", "tensorflow2", "mxnet", "kerastf", "non_dl_frameworks")
+    def test_generate_batch(self, art_warning, mocker, asr_dummy_estimator, audio_data):
+        try:
+            test_input, test_target = audio_data
+
+            mocker.patch.object(ImperceptibleASR, "_create_adversarial")
+            mocker.patch.object(ImperceptibleASR, "_create_imperceptible")
+
+            imperceptible_asr = ImperceptibleASR(estimator=asr_dummy_estimator(), masker=PsychoacousticMasker())
+            _ = imperceptible_asr._generate_batch(test_input, test_target)
+
+            imperceptible_asr._create_adversarial.assert_called()
+            imperceptible_asr._create_imperceptible.assert_called()
+        except ARTTestException as e:
+            art_warning(e)
+
+    @pytest.mark.skip_framework("tensorflow1", "tensorflow2", "mxnet", "kerastf", "non_dl_frameworks")
+    def test_create_adversarial(self, art_warning, mocker, asr_dummy_estimator, audio_data):
+        try:
+            test_input, test_target = audio_data
+
+            estimator = asr_dummy_estimator()
+            mocker.patch.object(estimator, "predict", return_value=test_target)
+            mocker.patch.object(
+                ImperceptibleASR,
+                "_loss_gradient_masking_threshold",
+                return_value=(np.zeros_like(audio_data), [0] * test_input.shape[0]),
+            )
+
+            # learning rate of zero ensures that adversarial example equals test input
+            imperceptible_asr = ImperceptibleASR(
+                estimator=estimator, masker=PsychoacousticMasker(), max_iter_1=15, learning_rate_1=0.5
+            )
+            # learning rate of zero ensures that adversarial example equals test input
+            imperceptible_asr.learning_rate_1 = 0
+            adversarial = imperceptible_asr._create_adversarial(test_input, test_target)
+
+            # test shape and adversarial example result
+            assert [x.shape for x in test_input] == [a.shape for a in adversarial]
+            assert [(a - x).sum() for a, x in zip(adversarial, test_input)] == [0.0] * test_input.shape[0]
+        except ARTTestException as e:
+            art_warning(e)
+
+    @pytest.mark.skip_framework("tensorflow1", "tensorflow2", "mxnet", "kerastf", "non_dl_frameworks")
+    def test_create_imperceptible(self, art_warning, mocker, asr_dummy_estimator, audio_data):
+        try:
+            test_input, test_target = audio_data
+            test_adversarial = test_input
+
+            estimator = asr_dummy_estimator()
+            mocker.patch.object(estimator, "predict", return_value=test_target)
+            mocker.patch.object(
+                ImperceptibleASR,
+                "_loss_gradient_masking_threshold",
+                return_value=(np.zeros_like(test_input), [0] * test_input.shape[0]),
+            )
+
+            imperceptible_asr = ImperceptibleASR(
+                estimator=estimator, masker=PsychoacousticMasker(), max_iter_2=25, learning_rate_2=0.5
+            )
+            # learning rate of zero ensures that adversarial example equals test input
+            imperceptible_asr.learning_rate_2 = 0
+            adversarial = imperceptible_asr._create_imperceptible(test_input, test_adversarial, test_target)
+
+            # test shape and adversarial example result
+            assert [x.shape for x in test_input] == [a.shape for a in adversarial]
+            assert [(x - a).sum() for a, x in zip(adversarial, test_input)] == [0.0] * test_input.shape[0]
+        except ARTTestException as e:
+            art_warning(e)
+
+    @pytest.mark.skip_framework("tensorflow", "mxnet", "kerastf", "non_dl_frameworks")
+    def test_loss_gradient_masking_threshold(self, art_warning, asr_dummy_estimator, audio_data):
+        try:
+            test_input, _ = audio_data
+            test_delta = test_input * 0
+
+            imperceptible_asr = ImperceptibleASR(estimator=asr_dummy_estimator(), masker=PsychoacousticMasker())
+
+            masking_threshold, psd_maximum = imperceptible_asr._stabilized_threshold_and_psd_maximum(test_input)
+            loss_gradient, loss = imperceptible_asr._loss_gradient_masking_threshold(
+                test_delta, test_input, masking_threshold, psd_maximum
+            )
+
+            assert [g.shape for g in loss_gradient] == [d.shape for d in test_delta]
+            assert loss.ndim == 1 and loss.shape == test_delta.shape
+        except ARTTestException as e:
+            art_warning(e)
+
+    @pytest.mark.skip_framework("pytorch", "tensorflow1", "tensorflow2", "mxnet", "kerastf", "non_dl_frameworks")
+    def test_loss_gradient_masking_threshold_tf(self, art_warning, asr_dummy_estimator, audio_batch_padded):
+        try:
+            import tensorflow.compat.v1 as tf1
+
+            tf1.reset_default_graph()
+
+            test_delta = audio_batch_padded
+            test_psd_maximum = np.ones((test_delta.shape[0]))
+            test_masking_threshold = np.zeros((test_delta.shape[0], 1025, 28))
+
+            imperceptible_asr = ImperceptibleASR(estimator=asr_dummy_estimator(), masker=PsychoacousticMasker())
+            feed_dict = {
+                imperceptible_asr._delta: test_delta,
+                imperceptible_asr._power_spectral_density_maximum_tf: test_psd_maximum,
+                imperceptible_asr._masking_threshold_tf: test_masking_threshold,
+            }
+            with tf1.Session() as sess:
+                loss_gradient, loss = sess.run(imperceptible_asr._loss_gradient_masking_threshold_op_tf, feed_dict)
+
+            assert loss_gradient.shape == test_delta.shape
+            assert loss.ndim == 1 and loss.shape[0] == test_delta.shape[0]
+        except ARTTestException as e:
+            art_warning(e)
+
+    @pytest.mark.skip_framework("tensorflow", "mxnet", "kerastf", "non_dl_frameworks")
+    def test_loss_gradient_masking_threshold_torch(self, art_warning, asr_dummy_estimator, audio_batch_padded):
+        try:
+            test_delta = audio_batch_padded
+            test_psd_maximum = np.ones((test_delta.shape[0], 1, 1))
+            test_masking_threshold = np.zeros((test_delta.shape[0], 1025, 28))
+
+            imperceptible_asr = ImperceptibleASR(estimator=asr_dummy_estimator(), masker=PsychoacousticMasker())
+            loss_gradient, loss = imperceptible_asr._loss_gradient_masking_threshold_torch(
+                test_delta, test_psd_maximum, test_masking_threshold
+            )
+
+            assert loss_gradient.shape == test_delta.shape
+            assert loss.ndim == 1 and loss.shape[0] == test_delta.shape[0]
+        except ARTTestException as e:
+            art_warning(e)
+
+    @pytest.mark.skip_framework("pytorch", "tensorflow1", "tensorflow2", "mxnet", "kerastf", "non_dl_frameworks")
+    def test_approximate_power_spectral_density_tf(self, art_warning, asr_dummy_estimator, audio_batch_padded):
+        try:
+            import tensorflow.compat.v1 as tf1
+
+            tf1.reset_default_graph()
+
+            test_delta = audio_batch_padded
+            test_psd_maximum = np.ones((test_delta.shape[0]))
+
+            masker = PsychoacousticMasker()
+            imperceptible_asr = ImperceptibleASR(estimator=asr_dummy_estimator(), masker=masker)
+            feed_dict = {
+                imperceptible_asr._delta: test_delta,
+                imperceptible_asr._power_spectral_density_maximum_tf: test_psd_maximum,
+            }
+
+            approximate_psd_tf = imperceptible_asr._approximate_power_spectral_density_tf(
+                imperceptible_asr._delta, imperceptible_asr._power_spectral_density_maximum_tf
+            )
+            with tf1.Session() as sess:
+                psd_approximated = sess.run(approximate_psd_tf, feed_dict)
+
+            assert psd_approximated.ndim == 3
+            assert psd_approximated.shape[0] == test_delta.shape[0]  # batch_size
+            assert psd_approximated.shape[1] == masker.window_size // 2 + 1
+        except ARTTestException as e:
+            art_warning(e)
+
+    @pytest.mark.skip_framework("tensorflow", "mxnet", "kerastf", "non_dl_frameworks")
+    def test_approximate_power_spectral_density_torch(self, art_warning, asr_dummy_estimator, audio_batch_padded):
+        try:
+            import torch
+
+            test_delta = audio_batch_padded
+            test_psd_maximum = np.ones((test_delta.shape[0], 1, 1))
+
+            masker = PsychoacousticMasker()
+            imperceptible_asr = ImperceptibleASR(estimator=asr_dummy_estimator(), masker=masker)
+            approximate_psd_torch = imperceptible_asr._approximate_power_spectral_density_torch(
+                torch.from_numpy(test_delta), torch.from_numpy(test_psd_maximum)
+            )
+            psd_approximated = approximate_psd_torch.numpy()
+
+            assert psd_approximated.ndim == 3
+            assert psd_approximated.shape[0] == test_delta.shape[0]  # batch_size
+            assert psd_approximated.shape[1] == masker.window_size // 2 + 1
+        except ARTTestException as e:
+            art_warning(e)
+
+
+class TestPsychoacousticMasker:
+    """
+    Test the PsychoacousticMasker.
+    """
+
+    @pytest.mark.framework_agnostic
+    def test_power_spectral_density(self, art_warning, audio_sample):
+        try:
+            test_input = audio_sample
+
+            masker = PsychoacousticMasker()
+            psd_matrix, psd_max = masker.power_spectral_density(test_input)
+
+            assert psd_matrix.shape == (masker.window_size // 2 + 1, 28)
+            assert np.floor(psd_max) == 78.0
+        except ARTTestException as e:
+            art_warning(e)
+
+    @pytest.mark.framework_agnostic
+    def test_find_maskers(self, art_warning):
+        try:
+            test_psd_vector = np.array([2, 10, 96, 90, 35, 40, 36, 60, 55, 91, 40])
+
+            masker = PsychoacousticMasker()
+            maskers, masker_idx = masker.find_maskers(test_psd_vector)
+
+            # test masker_idx shape and first, last maskers
+            assert masker_idx.tolist() == [2, 5, 7, 9]
+            np.testing.assert_array_equal(
+                maskers[[0, -1]], 10 * np.log10(np.sum(10 ** np.array([[1.0, 9.6, 9.0], [5.5, 9.1, 4.0]]), axis=1))
+            )
+        except ARTTestException as e:
+            art_warning(e)
+
+    @pytest.mark.framework_agnostic
+    def test_filter_maskers(self, art_warning):
+        try:
+            test_psd_vector = np.array([2, 10, 96, 90, 35, 40, 36, 60, 55, 91, 40])
+            test_masker_idx = np.array([2, 5, 7, 9])
+            test_maskers = test_psd_vector[test_masker_idx]
+
+            masker = PsychoacousticMasker()
+            maskers, masker_idx = masker.filter_maskers(test_maskers, test_masker_idx)
+
+            assert masker_idx.tolist() == [2]
+            assert maskers.tolist() == [96]
+        except ARTTestException as e:
+            art_warning(e)
+
+    @pytest.mark.framework_agnostic
+    def test_calculate_individual_threshold(self, art_warning, mocker):
+        try:
+            test_masker_idx = np.array([2, 5, 7, 9])
+            test_maskers = np.array([96, 40, 60, 9])
+
+            masker = PsychoacousticMasker()
+            threshold = masker.calculate_individual_threshold(test_maskers, test_masker_idx)
+
+            assert threshold.shape == test_masker_idx.shape + (masker.window_size // 2 + 1,)
+        except ARTTestException as e:
+            art_warning(e)
+
+    @pytest.mark.framework_agnostic
+    def test_calculate_global_threshold(self, art_warning, mocker):
+        try:
+            test_threshold = np.array([[0, 10, 20], [10, 0, 20]])
+
+            mocker.patch(
+                "art.attacks.evasion.imperceptible_asr.imperceptible_asr."
+                "PsychoacousticMasker.absolute_threshold_hearing",
+                new_callable=mocker.PropertyMock,
+                return_value=np.zeros(test_threshold.shape[1]),
+            )
+
+            masker = PsychoacousticMasker()
+            threshold = masker.calculate_global_threshold(test_threshold)
+
+            assert threshold.tolist() == (10 * np.log10([12, 12, 201])).tolist()
+        except ARTTestException as e:
+            art_warning(e)
+
+    @pytest.mark.framework_agnostic
+    def test_calculate_threshold_and_psd_maximum(self, art_warning, audio_sample):
+        try:
+            test_input = audio_sample
+
+            masker = PsychoacousticMasker()
+            threshold, psd_max = masker.calculate_threshold_and_psd_maximum(test_input)
+
+            assert threshold.shape == (masker.window_size // 2 + 1, 28)
+            assert np.floor(psd_max) == 78.0
+        except ARTTestException as e:
+            art_warning(e)
diff --git a/adversarial-robustness-toolbox/tests/attacks/evasion/test_imperceptible_asr_pytorch.json b/adversarial-robustness-toolbox/tests/attacks/evasion/test_imperceptible_asr_pytorch.json
new file mode 100644
index 0000000..98de373
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/attacks/evasion/test_imperceptible_asr_pytorch.json
@@ -0,0 +1,202 @@
+{
+    "test_imperceptible_asr_pytorch[cpu-False]": [
+        [
+            -0.0010376293,
+            -0.0010681478,
+            -0.0010986663,
+            -0.0011291848,
+            -0.0011291848,
+            -0.0011291848,
+            -0.0011902219,
+            -0.0011597034,
+            -0.0011902219,
+            -0.0011291848,
+            -0.0011291848,
+            -0.0010681478,
+            -0.00091555528
+        ],
+        [
+            -0.00018311106,
+            -0.00012207404,
+            -6.1037019e-05,
+            0.0,
+            3.0518509e-05,
+            0.0,
+            -3.0518509e-05,
+            0.0,
+            0.0,
+            9.1555528e-05,
+            0.00021362957,
+            0.0003357036,
+            0.00042725913,
+            0.00045777764,
+            -0.00018311106
+        ],
+        [
+            -0.00082399976,
+            -0.00070192572,
+            -0.00054933317,
+            -0.00042725913,
+            -0.00036622211,
+            -0.00027466659,
+            -0.00021362957,
+            0.00054933317,
+            0.00057985168,
+            0.00061037019,
+            0.00067140721,
+            0.00070192572,
+            0.00067140721,
+            -0.00015259255
+        ]
+    ],
+    "test_imperceptible_asr_pytorch[cpu-True]": [
+        [
+            -0.0010376293,
+            -0.0010681478,
+            -0.0010986663,
+            -0.0011291848,
+            -0.0011291848,
+            -0.0011291848,
+            -0.0011902219,
+            -0.0011597034,
+            -0.0011902219,
+            -0.0011291848,
+            -0.0011291848,
+            -0.0010681478,
+            -0.00091555528
+        ],
+        [
+            -0.00018311106,
+            -0.00012207404,
+            -6.1037019e-05,
+            0.0,
+            3.0518509e-05,
+            0.0,
+            -3.0518509e-05,
+            0.0,
+            0.0,
+            9.1555528e-05,
+            0.00021362957,
+            0.0003357036,
+            0.00042725913,
+            0.00045777764,
+            -0.00018311106
+        ],
+        [
+            -0.00082399976,
+            -0.00070192572,
+            -0.00054933317,
+            -0.00042725913,
+            -0.00036622211,
+            -0.00027466659,
+            -0.00021362957,
+            0.00054933317,
+            0.00057985168,
+            0.00061037019,
+            0.00067140721,
+            0.00070192572,
+            0.00067140721,
+            -0.00015259255
+        ]
+    ],
+    "test_imperceptible_asr_pytorch[gpu-False]": [
+        [
+            -0.0010376293,
+            -0.0010681478,
+            -0.0010986663,
+            -0.0011291848,
+            -0.0011291848,
+            -0.0011291848,
+            -0.0011902219,
+            -0.0011597034,
+            -0.0011902219,
+            -0.0011291848,
+            -0.0011291848,
+            -0.0010681478,
+            -0.00091555528
+        ],
+        [
+            -0.00018311106,
+            -0.00012207404,
+            -6.1037019e-05,
+            0.0,
+            3.0518509e-05,
+            0.0,
+            -3.0518509e-05,
+            0.0,
+            0.0,
+            9.1555528e-05,
+            0.00021362957,
+            0.0003357036,
+            0.00042725913,
+            0.00045777764,
+            -0.00018311106
+        ],
+        [
+            -0.00082399976,
+            -0.00070192572,
+            -0.00054933317,
+            -0.00042725913,
+            -0.00036622211,
+            -0.00027466659,
+            -0.00021362957,
+            0.00054933317,
+            0.00057985168,
+            0.00061037019,
+            0.00067140721,
+            0.00070192572,
+            0.00067140721,
+            -0.00015259255
+        ]
+    ],
+    "test_imperceptible_asr_pytorch[gpu-True]": [
+        [
+            -0.0010376293,
+            -0.0010681478,
+            -0.0010986663,
+            -0.0011291848,
+            -0.0011291848,
+            -0.0011291848,
+            -0.0011902219,
+            -0.0011597034,
+            -0.0011902219,
+            -0.0011291848,
+            -0.0011291848,
+            -0.0010681478,
+            -0.00091555528
+        ],
+        [
+            -0.00018311106,
+            -0.00012207404,
+            -6.1037019e-05,
+            0.0,
+            3.0518509e-05,
+            0.0,
+            -3.0518509e-05,
+            0.0,
+            0.0,
+            9.1555528e-05,
+            0.00021362957,
+            0.0003357036,
+            0.00042725913,
+            0.00045777764,
+            -0.00018311106
+        ],
+        [
+            -0.00082399976,
+            -0.00070192572,
+            -0.00054933317,
+            -0.00042725913,
+            -0.00036622211,
+            -0.00027466659,
+            -0.00021362957,
+            0.00054933317,
+            0.00057985168,
+            0.00061037019,
+            0.00067140721,
+            0.00070192572,
+            0.00067140721,
+            -0.00015259255
+        ]
+    ]
+}
\ No newline at end of file
diff --git a/adversarial-robustness-toolbox/tests/attacks/evasion/test_imperceptible_asr_pytorch.py b/adversarial-robustness-toolbox/tests/attacks/evasion/test_imperceptible_asr_pytorch.py
new file mode 100644
index 0000000..7f133df
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/attacks/evasion/test_imperceptible_asr_pytorch.py
@@ -0,0 +1,117 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+
+import numpy as np
+import pytest
+
+from art.config import ART_NUMPY_DTYPE
+from tests.utils import ARTTestException
+
+logger = logging.getLogger(__name__)
+
+
+@pytest.mark.skip_module("apex.amp", "deepspeech_pytorch", "torchaudio")
+@pytest.mark.skip_framework("tensorflow", "keras", "kerastf", "mxnet", "non_dl_frameworks")
+@pytest.mark.parametrize("use_amp", [False, True])
+@pytest.mark.parametrize("device_type", ["cpu", "gpu"])
+def test_imperceptible_asr_pytorch(art_warning, expected_values, use_amp, device_type):
+    # Only import if deepspeech_pytorch module is available
+    import torch
+
+    from art.estimators.speech_recognition.pytorch_deep_speech import PyTorchDeepSpeech
+    from art.attacks.evasion.imperceptible_asr.imperceptible_asr_pytorch import ImperceptibleASRPyTorch
+    from art.preprocessing.audio import LFilterPyTorch
+
+    try:
+        # Load data for testing
+        expected_data = expected_values()
+
+        x1 = expected_data[0]
+        x2 = expected_data[1]
+        x3 = expected_data[2]
+
+        # Create signal data
+        x = np.array(
+            [
+                np.array(x1 * 100, dtype=ART_NUMPY_DTYPE),
+                np.array(x2 * 100, dtype=ART_NUMPY_DTYPE),
+                np.array(x3 * 100, dtype=ART_NUMPY_DTYPE),
+            ]
+        )
+
+        # Create labels
+        y = np.array(["S", "I", "GD"])
+
+        # Create DeepSpeech estimator with preprocessing
+        numerator_coef = np.array([0.0000001, 0.0000002, -0.0000001, -0.0000002])
+        denominator_coef = np.array([1.0, 0.0, 0.0, 0.0])
+        audio_filter = LFilterPyTorch(
+            numerator_coef=numerator_coef, denominator_coef=denominator_coef, device_type=device_type
+        )
+
+        speech_recognizer = PyTorchDeepSpeech(
+            pretrained_model="librispeech",
+            device_type=device_type,
+            use_amp=use_amp,
+            preprocessing_defences=audio_filter,
+        )
+
+        # Create attack
+        asr_attack = ImperceptibleASRPyTorch(
+            estimator=speech_recognizer,
+            eps=0.001,
+            max_iter_1=5,
+            max_iter_2=5,
+            learning_rate_1=0.00001,
+            learning_rate_2=0.001,
+            optimizer_1=torch.optim.SGD,
+            optimizer_2=torch.optim.SGD,
+            global_max_length=2000,
+            initial_rescale=1.0,
+            decrease_factor_eps=0.8,
+            num_iter_decrease_eps=5,
+            alpha=0.01,
+            increase_factor_alpha=1.2,
+            num_iter_increase_alpha=5,
+            decrease_factor_alpha=0.8,
+            num_iter_decrease_alpha=5,
+            batch_size=2,
+            use_amp=use_amp,
+            opt_level="O1",
+        )
+
+        # Test transcription output
+        transcriptions_preprocessing = speech_recognizer.predict(x, batch_size=2, transcription_output=True)
+
+        expected_transcriptions = np.array(["", "", ""])
+
+        assert (expected_transcriptions == transcriptions_preprocessing).all()
+
+        # Generate attack
+        x_adv_preprocessing = asr_attack.generate(x, y)
+
+        # Test shape
+        assert x_adv_preprocessing[0].shape == x[0].shape
+        assert x_adv_preprocessing[1].shape == x[1].shape
+        assert x_adv_preprocessing[2].shape == x[2].shape
+
+    except ARTTestException as e:
+        art_warning(e)
diff --git a/adversarial-robustness-toolbox/tests/attacks/evasion/test_shadow_attack.py b/adversarial-robustness-toolbox/tests/attacks/evasion/test_shadow_attack.py
new file mode 100644
index 0000000..6d56148
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/attacks/evasion/test_shadow_attack.py
@@ -0,0 +1,133 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+import logging
+import pytest
+
+import numpy as np
+
+from art.attacks.evasion import ShadowAttack
+from art.estimators.estimator import BaseEstimator, LossGradientsMixin
+from art.estimators.classification.classifier import ClassifierMixin
+
+from tests.attacks.utils import backend_test_classifier_type_check_fail
+from tests.utils import ARTTestException
+
+logger = logging.getLogger(__name__)
+
+
+@pytest.fixture()
+def fix_get_mnist_subset(get_mnist_dataset):
+    (x_train_mnist, y_train_mnist), (x_test_mnist, y_test_mnist) = get_mnist_dataset
+    n_train = 100
+    n_test = 11
+    yield x_train_mnist[:n_train], y_train_mnist[:n_train], x_test_mnist[:n_test], y_test_mnist[:n_test]
+
+
+@pytest.mark.only_with_platform("pytorch")
+def test_generate(art_warning, fix_get_mnist_subset, image_dl_estimator_for_attack):
+    try:
+        classifier = image_dl_estimator_for_attack(ShadowAttack)
+        attack = ShadowAttack(
+            estimator=classifier,
+            sigma=0.5,
+            nb_steps=3,
+            learning_rate=0.1,
+            lambda_tv=0.3,
+            lambda_c=1.0,
+            lambda_s=0.5,
+            batch_size=32,
+            targeted=True,
+        )
+
+        (x_train_mnist, y_train_mnist, x_test_mnist, y_test_mnist) = fix_get_mnist_subset
+
+        x_train_mnist_adv = attack.generate(x=x_train_mnist[0:1], y=y_train_mnist[0:1])
+
+        assert np.max(np.abs(x_train_mnist_adv - x_train_mnist[0:1])) == pytest.approx(0.38116083, abs=0.06)
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.only_with_platform("pytorch")
+def test_get_regularisation_loss_gradients(art_warning, fix_get_mnist_subset, image_dl_estimator_for_attack):
+    try:
+        classifier = image_dl_estimator_for_attack(ShadowAttack)
+
+        attack = ShadowAttack(
+            estimator=classifier,
+            sigma=0.5,
+            nb_steps=3,
+            learning_rate=0.1,
+            lambda_tv=0.3,
+            lambda_c=1.0,
+            lambda_s=0.5,
+            batch_size=32,
+            targeted=True,
+        )
+
+        (x_train_mnist, _, _, _) = fix_get_mnist_subset
+
+        gradients = attack._get_regularisation_loss_gradients(x_train_mnist[0:1])
+
+        gradients_expected = np.array(
+            [
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                -0.27294118,
+                -0.36906054,
+                0.83799828,
+                0.40741005,
+                0.65682181,
+                -0.13141348,
+                -0.39729583,
+                -0.12235294,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+            ]
+        )
+
+        if attack.framework == "pytorch":
+            np.testing.assert_array_almost_equal(gradients[0, 0, 14, :], gradients_expected, decimal=3)
+        else:
+            np.testing.assert_array_almost_equal(gradients[0, 14, :, 0], gradients_expected, decimal=3)
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.only_with_platform("pytorch")
+def test_classifier_type_check_fail(art_warning):
+    try:
+        backend_test_classifier_type_check_fail(ShadowAttack, [BaseEstimator, LossGradientsMixin, ClassifierMixin])
+    except ARTTestException as e:
+        art_warning(e)
diff --git a/adversarial-robustness-toolbox/tests/attacks/evasion/test_square_attack.py b/adversarial-robustness-toolbox/tests/attacks/evasion/test_square_attack.py
new file mode 100644
index 0000000..3c99fc4
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/attacks/evasion/test_square_attack.py
@@ -0,0 +1,71 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+import logging
+import pytest
+
+import numpy as np
+
+from art.attacks.evasion import SquareAttack
+from art.estimators.estimator import BaseEstimator, NeuralNetworkMixin
+from art.estimators.classification.classifier import ClassifierMixin
+
+from tests.attacks.utils import backend_test_classifier_type_check_fail
+from tests.utils import ARTTestException
+
+logger = logging.getLogger(__name__)
+
+
+@pytest.fixture()
+def fix_get_mnist_subset(get_mnist_dataset):
+    (x_train_mnist, y_train_mnist), (x_test_mnist, y_test_mnist) = get_mnist_dataset
+    n_train = 100
+    n_test = 10
+    yield x_train_mnist[:n_train], y_train_mnist[:n_train], x_test_mnist[:n_test], y_test_mnist[:n_test]
+
+
+@pytest.mark.framework_agnostic
+@pytest.mark.parametrize("norm", [2, "inf"])
+def test_generate(art_warning, fix_get_mnist_subset, image_dl_estimator_for_attack, norm):
+    try:
+        classifier = image_dl_estimator_for_attack(SquareAttack)
+
+        attack = SquareAttack(estimator=classifier, norm=norm, max_iter=5, eps=0.3, p_init=0.8, nb_restarts=1)
+
+        (x_train_mnist, y_train_mnist, x_test_mnist, y_test_mnist) = fix_get_mnist_subset
+
+        x_train_mnist_adv = attack.generate(x=x_train_mnist, y=y_train_mnist)
+
+        if norm == "inf":
+            expected_mean = 0.053533513
+            expected_max = 0.3
+        elif norm == 2:
+            expected_mean = 0.00073682
+            expected_max = 0.25
+
+        assert np.mean(np.abs(x_train_mnist_adv - x_train_mnist)) == pytest.approx(expected_mean, abs=0.025)
+        assert np.max(np.abs(x_train_mnist_adv - x_train_mnist)) == pytest.approx(expected_max, abs=0.05)
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.framework_agnostic
+def test_classifier_type_check_fail(art_warning):
+    try:
+        backend_test_classifier_type_check_fail(SquareAttack, [BaseEstimator, ClassifierMixin, NeuralNetworkMixin])
+    except ARTTestException as e:
+        art_warning(e)
diff --git a/adversarial-robustness-toolbox/tests/attacks/inference/__init__.py b/adversarial-robustness-toolbox/tests/attacks/inference/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/adversarial-robustness-toolbox/tests/attacks/inference/attribute_inference/__init__.py b/adversarial-robustness-toolbox/tests/attacks/inference/attribute_inference/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/adversarial-robustness-toolbox/tests/attacks/inference/attribute_inference/test_baseline.py b/adversarial-robustness-toolbox/tests/attacks/inference/attribute_inference/test_baseline.py
new file mode 100644
index 0000000..1a81395
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/attacks/inference/attribute_inference/test_baseline.py
@@ -0,0 +1,110 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2021
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import pytest
+
+import numpy as np
+
+from art.attacks.inference.attribute_inference.black_box import AttributeInferenceBlackBox
+from art.attacks.inference.attribute_inference.baseline import AttributeInferenceBaseline
+
+from tests.utils import ARTTestException
+
+logger = logging.getLogger(__name__)
+
+
+@pytest.mark.skipMlFramework("dl_frameworks")
+def test_black_box_baseline(art_warning, decision_tree_estimator, get_iris_dataset):
+    try:
+        attack_feature = 2  # petal length
+
+        # need to transform attacked feature into categorical
+        def transform_feature(x):
+            x[x > 0.5] = 2.0
+            x[(x > 0.2) & (x <= 0.5)] = 1.0
+            x[x <= 0.2] = 0.0
+
+        values = [0.0, 1.0, 2.0]
+
+        (x_train_iris, y_train_iris), (x_test_iris, y_test_iris) = get_iris_dataset
+        # training data without attacked feature
+        x_train_for_attack = np.delete(x_train_iris, attack_feature, 1)
+        # only attacked feature
+        x_train_feature = x_train_iris[:, attack_feature].copy().reshape(-1, 1)
+        transform_feature(x_train_feature)
+        # training data with attacked feature (after transformation)
+        x_train = np.concatenate((x_train_for_attack[:, :attack_feature], x_train_feature), axis=1)
+        x_train = np.concatenate((x_train, x_train_for_attack[:, attack_feature:]), axis=1)
+
+        # test data without attacked feature
+        x_test_for_attack = np.delete(x_test_iris, attack_feature, 1)
+        # only attacked feature
+        x_test_feature = x_test_iris[:, attack_feature].copy().reshape(-1, 1)
+        transform_feature(x_test_feature)
+
+        classifier = decision_tree_estimator()
+
+        attack = AttributeInferenceBlackBox(classifier, attack_feature=attack_feature)
+        # get original model's predictions
+        x_train_predictions = np.array([np.argmax(arr) for arr in classifier.predict(x_train_iris)]).reshape(-1, 1)
+        x_test_predictions = np.array([np.argmax(arr) for arr in classifier.predict(x_test_iris)]).reshape(-1, 1)
+        # train attack model
+        attack.fit(x_train)
+        # infer attacked feature
+        inferred_train = attack.infer(x_train_for_attack, x_train_predictions, values=values)
+        inferred_test = attack.infer(x_test_for_attack, x_test_predictions, values=values)
+        # check accuracy
+        # train_acc = np.sum(inferred_train == x_train_feature.reshape(1, -1)) / len(inferred_train)
+        test_acc = np.sum(inferred_test == x_test_feature.reshape(1, -1)) / len(inferred_test)
+
+        baseline_attack = AttributeInferenceBaseline(attack_feature=attack_feature)
+        # train attack model
+        baseline_attack.fit(x_train)
+        # infer attacked feature
+        baseline_inferred_train = baseline_attack.infer(x_train_for_attack, values=values)
+        baseline_inferred_test = baseline_attack.infer(x_test_for_attack, values=values)
+        # check accuracy
+        # baseline_train_acc = np.sum(baseline_inferred_train == x_train_feature.reshape(1, -1)) / len(
+        #     baseline_inferred_train
+        # )
+        baseline_test_acc = np.sum(baseline_inferred_test == x_test_feature.reshape(1, -1)) / len(
+            baseline_inferred_test
+        )
+
+        assert test_acc > baseline_test_acc
+
+    except ARTTestException as e:
+        art_warning(e)
+
+
+def test_errors(art_warning, get_iris_dataset):
+    try:
+        (x_train, y_train), (_, _) = get_iris_dataset
+
+        with pytest.raises(ValueError):
+            AttributeInferenceBaseline(attack_feature="a")
+        with pytest.raises(ValueError):
+            AttributeInferenceBaseline(attack_feature=-3)
+        attack = AttributeInferenceBaseline(attack_feature=8)
+        with pytest.raises(ValueError):
+            attack.fit(x_train)
+        _ = AttributeInferenceBaseline()
+    except ARTTestException as e:
+        art_warning(e)
diff --git a/adversarial-robustness-toolbox/tests/attacks/inference/attribute_inference/test_black_box.py b/adversarial-robustness-toolbox/tests/attacks/inference/attribute_inference/test_black_box.py
new file mode 100644
index 0000000..2ccfd02
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/attacks/inference/attribute_inference/test_black_box.py
@@ -0,0 +1,312 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import pytest
+
+import numpy as np
+import torch.nn as nn
+import torch.optim as optim
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.preprocessing import StandardScaler
+
+from art.attacks.inference.attribute_inference.black_box import AttributeInferenceBlackBox
+from art.estimators.classification.pytorch import PyTorchClassifier
+from art.estimators.estimator import BaseEstimator
+from art.estimators.classification import ClassifierMixin
+from art.estimators.classification.scikitlearn import ScikitlearnDecisionTreeClassifier
+
+from tests.attacks.utils import backend_test_classifier_type_check_fail
+from tests.utils import ARTTestException
+
+logger = logging.getLogger(__name__)
+
+
+@pytest.mark.skip_framework("dl_frameworks")
+def test_black_box(art_warning, decision_tree_estimator, get_iris_dataset):
+    try:
+        attack_feature = 2  # petal length
+
+        # need to transform attacked feature into categorical
+        def transform_feature(x):
+            x[x > 0.5] = 2.0
+            x[(x > 0.2) & (x <= 0.5)] = 1.0
+            x[x <= 0.2] = 0.0
+
+        values = [0.0, 1.0, 2.0]
+
+        (x_train_iris, y_train_iris), (x_test_iris, y_test_iris) = get_iris_dataset
+        # training data without attacked feature
+        x_train_for_attack = np.delete(x_train_iris, attack_feature, 1)
+        # only attacked feature
+        x_train_feature = x_train_iris[:, attack_feature].copy().reshape(-1, 1)
+        transform_feature(x_train_feature)
+        # training data with attacked feature (after transformation)
+        x_train = np.concatenate((x_train_for_attack[:, :attack_feature], x_train_feature), axis=1)
+        x_train = np.concatenate((x_train, x_train_for_attack[:, attack_feature:]), axis=1)
+
+        # test data without attacked feature
+        x_test_for_attack = np.delete(x_test_iris, attack_feature, 1)
+        # only attacked feature
+        x_test_feature = x_test_iris[:, attack_feature].copy().reshape(-1, 1)
+        transform_feature(x_test_feature)
+
+        classifier = decision_tree_estimator()
+
+        attack = AttributeInferenceBlackBox(classifier, attack_feature=attack_feature)
+        # get original model's predictions
+        x_train_predictions = np.array([np.argmax(arr) for arr in classifier.predict(x_train_iris)]).reshape(-1, 1)
+        x_test_predictions = np.array([np.argmax(arr) for arr in classifier.predict(x_test_iris)]).reshape(-1, 1)
+        # train attack model
+        attack.fit(x_train)
+        # infer attacked feature
+        inferred_train = attack.infer(x_train_for_attack, x_train_predictions, values=values)
+        inferred_test = attack.infer(x_test_for_attack, x_test_predictions, values=values)
+        # check accuracy
+        train_acc = np.sum(inferred_train == x_train_feature.reshape(1, -1)) / len(inferred_train)
+        test_acc = np.sum(inferred_test == x_test_feature.reshape(1, -1)) / len(inferred_test)
+        assert train_acc == pytest.approx(0.8285, abs=0.03)
+        assert test_acc == pytest.approx(0.8888, abs=0.03)
+
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.skip_framework("dl_frameworks")
+def test_black_box_with_model(art_warning, decision_tree_estimator, get_iris_dataset):
+    try:
+        attack_feature = 2  # petal length
+
+        # need to transform attacked feature into categorical
+        def transform_feature(x):
+            x[x > 0.5] = 2.0
+            x[(x > 0.2) & (x <= 0.5)] = 1.0
+            x[x <= 0.2] = 0.0
+
+        values = [0.0, 1.0, 2.0]
+
+        (x_train_iris, y_train_iris), (x_test_iris, y_test_iris) = get_iris_dataset
+        # training data without attacked feature
+        x_train_for_attack = np.delete(x_train_iris, attack_feature, 1)
+        # only attacked feature
+        x_train_feature = x_train_iris[:, attack_feature].copy().reshape(-1, 1)
+        transform_feature(x_train_feature)
+        # training data with attacked feature (after transformation)
+        x_train = np.concatenate((x_train_for_attack[:, :attack_feature], x_train_feature), axis=1)
+        x_train = np.concatenate((x_train, x_train_for_attack[:, attack_feature:]), axis=1)
+
+        # test data without attacked feature
+        x_test_for_attack = np.delete(x_test_iris, attack_feature, 1)
+        # only attacked feature
+        x_test_feature = x_test_iris[:, attack_feature].copy().reshape(-1, 1)
+        transform_feature(x_test_feature)
+
+        model = nn.Linear(4, 3)
+
+        # Define a loss function and optimizer
+        loss_fn = nn.CrossEntropyLoss()
+        optimizer = optim.Adam(model.parameters(), lr=0.01)
+        attack_model = PyTorchClassifier(
+            model=model, clip_values=(0, 1), loss=loss_fn, optimizer=optimizer, input_shape=(4,), nb_classes=3
+        )
+
+        classifier = decision_tree_estimator()
+
+        attack = AttributeInferenceBlackBox(classifier, attack_model=attack_model, attack_feature=attack_feature)
+        # get original model's predictions
+        x_train_predictions = np.array([np.argmax(arr) for arr in classifier.predict(x_train_iris)]).reshape(-1, 1)
+        x_test_predictions = np.array([np.argmax(arr) for arr in classifier.predict(x_test_iris)]).reshape(-1, 1)
+        # train attack model
+        attack.fit(x_train)
+        # infer attacked feature
+        inferred_train = attack.infer(x_train_for_attack, x_train_predictions, values=values)
+        inferred_test = attack.infer(x_test_for_attack, x_test_predictions, values=values)
+        # check accuracy
+        train_acc = np.sum(inferred_train == x_train_feature.reshape(1, -1)) / len(inferred_train)
+        test_acc = np.sum(inferred_test == x_test_feature.reshape(1, -1)) / len(inferred_test)
+        # assert train_acc == pytest.approx(0.5523, abs=0.03)
+        # assert test_acc == pytest.approx(0.5777, abs=0.03)
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.skip_framework("dl_frameworks")
+def test_black_box_one_hot(art_warning, get_iris_dataset):
+    try:
+        attack_feature = 2  # petal length
+
+        # need to transform attacked feature into categorical
+        def transform_feature(x):
+            x[x > 0.5] = 2
+            x[(x > 0.2) & (x <= 0.5)] = 1
+            x[x <= 0.2] = 0
+
+        (x_train_iris, y_train_iris), (x_test_iris, y_test_iris) = get_iris_dataset
+        # training data without attacked feature
+        x_train_for_attack = np.delete(x_train_iris, attack_feature, 1)
+        # only attacked feature
+        x_train_feature = x_train_iris[:, attack_feature].copy().reshape(-1, 1)
+        transform_feature(x_train_feature)
+        # transform to one-hot encoding
+        train_one_hot = np.zeros((x_train_feature.size, int(x_train_feature.max()) + 1))
+        train_one_hot[np.arange(x_train_feature.size), x_train_feature.reshape(1, -1).astype(int)] = 1
+        # training data with attacked feature (after transformation)
+        x_train = np.concatenate((x_train_for_attack[:, :attack_feature], train_one_hot), axis=1)
+        x_train = np.concatenate((x_train, x_train_for_attack[:, attack_feature:]), axis=1)
+
+        y_train = np.array([np.argmax(y) for y in y_train_iris]).reshape(-1, 1)
+
+        # test data without attacked feature
+        x_test_for_attack = np.delete(x_test_iris, attack_feature, 1)
+        # only attacked feature
+        x_test_feature = x_test_iris[:, attack_feature].copy().reshape(-1, 1)
+        transform_feature(x_test_feature)
+        # transform to one-hot encoding
+        test_one_hot = np.zeros((x_test_feature.size, int(x_test_feature.max()) + 1))
+        test_one_hot[np.arange(x_test_feature.size), x_test_feature.reshape(1, -1).astype(int)] = 1
+        # test data with attacked feature (after transformation)
+        x_test = np.concatenate((x_test_for_attack[:, :attack_feature], test_one_hot), axis=1)
+        x_test = np.concatenate((x_test, x_test_for_attack[:, attack_feature:]), axis=1)
+
+        tree = DecisionTreeClassifier()
+        tree.fit(x_train, y_train)
+        classifier = ScikitlearnDecisionTreeClassifier(tree)
+
+        attack = AttributeInferenceBlackBox(classifier, attack_feature=slice(attack_feature, attack_feature + 3))
+        # get original model's predictions
+        x_train_predictions = np.array([np.argmax(arr) for arr in classifier.predict(x_train)]).reshape(-1, 1)
+        x_test_predictions = np.array([np.argmax(arr) for arr in classifier.predict(x_test)]).reshape(-1, 1)
+        # train attack model
+        attack.fit(x_train)
+        # infer attacked feature
+        inferred_train = attack.infer(x_train_for_attack, x_train_predictions)
+        inferred_test = attack.infer(x_test_for_attack, x_test_predictions)
+        # check accuracy
+        train_acc = np.sum(np.all(inferred_train == train_one_hot, axis=1)) / len(inferred_train)
+        test_acc = np.sum(np.all(inferred_test == test_one_hot, axis=1)) / len(inferred_test)
+        assert pytest.approx(0.9145, abs=0.03) == train_acc
+        assert pytest.approx(0.9333, abs=0.03) == test_acc
+
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.skip_framework("dl_frameworks")
+def test_black_box_one_hot_float(art_warning, get_iris_dataset):
+    try:
+        attack_feature = 2  # petal length
+
+        # need to transform attacked feature into categorical
+        def transform_feature(x):
+            x[x > 0.5] = 2
+            x[(x > 0.2) & (x <= 0.5)] = 1
+            x[x <= 0.2] = 0
+
+        (x_train_iris, y_train_iris), (x_test_iris, y_test_iris) = get_iris_dataset
+        # training data without attacked feature
+        x_train_for_attack = np.delete(x_train_iris, attack_feature, 1)
+        # only attacked feature
+        x_train_feature = x_train_iris[:, attack_feature].copy().reshape(-1, 1)
+        transform_feature(x_train_feature)
+        # transform to one-hot encoding
+        num_columns = int(x_train_feature.max()) + 1
+        train_one_hot = np.zeros((x_train_feature.size, num_columns))
+        train_one_hot[np.arange(x_train_feature.size), x_train_feature.reshape(1, -1).astype(int)] = 1
+        # training data with attacked feature (after transformation)
+        x_train = np.concatenate((x_train_for_attack[:, :attack_feature], train_one_hot), axis=1)
+        x_train = np.concatenate((x_train, x_train_for_attack[:, attack_feature:]), axis=1)
+
+        y_train = np.array([np.argmax(y) for y in y_train_iris]).reshape(-1, 1)
+
+        # test data without attacked feature
+        x_test_for_attack = np.delete(x_test_iris, attack_feature, 1)
+        # only attacked feature
+        x_test_feature = x_test_iris[:, attack_feature].copy().reshape(-1, 1)
+        transform_feature(x_test_feature)
+        # transform to one-hot encoding
+        test_one_hot = np.zeros((x_test_feature.size, int(x_test_feature.max()) + 1))
+        test_one_hot[np.arange(x_test_feature.size), x_test_feature.reshape(1, -1).astype(int)] = 1
+        # test data with attacked feature (after transformation)
+        x_test = np.concatenate((x_test_for_attack[:, :attack_feature], test_one_hot), axis=1)
+        x_test = np.concatenate((x_test, x_test_for_attack[:, attack_feature:]), axis=1)
+
+        # scale before training
+        scaler = StandardScaler().fit(x_train)
+        x_test = scaler.transform(x_test).astype(np.float32)
+        x_train = scaler.transform(x_train).astype(np.float32)
+        # derive dataset for attack (after scaling)
+        attack_feature = slice(attack_feature, attack_feature + 3)
+        x_train_for_attack = np.delete(x_train, attack_feature, 1)
+        x_test_for_attack = np.delete(x_test, attack_feature, 1)
+        train_one_hot = x_train[:, attack_feature]
+        test_one_hot = x_test[:, attack_feature]
+
+        tree = DecisionTreeClassifier()
+        tree.fit(x_train, y_train)
+        classifier = ScikitlearnDecisionTreeClassifier(tree)
+
+        attack = AttributeInferenceBlackBox(classifier, attack_feature=attack_feature)
+        # get original model's predictions
+        x_train_predictions = np.array([np.argmax(arr) for arr in classifier.predict(x_train)]).reshape(-1, 1)
+        x_test_predictions = np.array([np.argmax(arr) for arr in classifier.predict(x_test)]).reshape(-1, 1)
+        # train attack model
+        attack.fit(x_train)
+        # infer attacked feature
+        values = [[-0.6324555, 1.5811388], [-0.4395245, 2.2751858], [-1.1108746, 0.9001915]]
+        inferred_train = attack.infer(x_train_for_attack, x_train_predictions, values=values)
+        inferred_test = attack.infer(x_test_for_attack, x_test_predictions, values=values)
+        # check accuracy
+        train_acc = np.sum(
+            np.all(np.around(inferred_train, decimals=3) == np.around(train_one_hot, decimals=3), axis=1)
+        ) / len(inferred_train)
+        test_acc = np.sum(
+            np.all(np.around(inferred_test, decimals=3) == np.around(test_one_hot, decimals=3), axis=1)
+        ) / len(inferred_test)
+        assert pytest.approx(0.9145, abs=0.03) == train_acc
+        assert pytest.approx(0.9333, abs=0.03) == test_acc
+
+    except ARTTestException as e:
+        art_warning(e)
+
+
+def test_errors(art_warning, tabular_dl_estimator_for_attack, get_iris_dataset):
+    try:
+        classifier = tabular_dl_estimator_for_attack(AttributeInferenceBlackBox)
+        (x_train, y_train), (x_test, y_test) = get_iris_dataset
+
+        with pytest.raises(ValueError):
+            AttributeInferenceBlackBox(classifier, attack_feature="a")
+        with pytest.raises(ValueError):
+            AttributeInferenceBlackBox(classifier, attack_feature=-3)
+        attack = AttributeInferenceBlackBox(classifier, attack_feature=8)
+        with pytest.raises(ValueError):
+            attack.fit(x_train)
+        attack = AttributeInferenceBlackBox(classifier)
+        with pytest.raises(ValueError):
+            attack.fit(np.delete(x_train, 1, 1))
+        with pytest.raises(ValueError):
+            attack.infer(x_train, y_test)
+        with pytest.raises(ValueError):
+            attack.infer(x_train, y_train)
+    except ARTTestException as e:
+        art_warning(e)
+
+
+def test_classifier_type_check_fail():
+    backend_test_classifier_type_check_fail(AttributeInferenceBlackBox, (BaseEstimator, ClassifierMixin))
diff --git a/adversarial-robustness-toolbox/tests/attacks/inference/attribute_inference/test_white_box_decision_tree.py b/adversarial-robustness-toolbox/tests/attacks/inference/attribute_inference/test_white_box_decision_tree.py
new file mode 100644
index 0000000..d3b4878
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/attacks/inference/attribute_inference/test_white_box_decision_tree.py
@@ -0,0 +1,65 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import pytest
+
+import numpy as np
+
+from art.attacks.inference.attribute_inference.white_box_decision_tree import AttributeInferenceWhiteBoxDecisionTree
+from art.estimators.classification.scikitlearn import ScikitlearnDecisionTreeClassifier
+
+from tests.attacks.utils import backend_test_classifier_type_check_fail
+from tests.utils import ARTTestException
+
+logger = logging.getLogger(__name__)
+
+
+@pytest.mark.skip_framework("dl_frameworks")
+def test_white_box(art_warning, decision_tree_estimator, get_iris_dataset):
+    try:
+        attack_feature = 2  # petal length
+        values = [0.14, 0.42, 0.71]  # rounded down
+        priors = [50 / 150, 54 / 150, 46 / 150]
+
+        (x_train_iris, y_train_iris), (x_test_iris, y_test_iris) = get_iris_dataset
+        x_train_for_attack = np.delete(x_train_iris, attack_feature, 1)
+        x_train_feature = x_train_iris[:, attack_feature]
+        x_test_for_attack = np.delete(x_test_iris, attack_feature, 1)
+        x_test_feature = x_test_iris[:, attack_feature]
+
+        classifier = decision_tree_estimator()
+
+        attack = AttributeInferenceWhiteBoxDecisionTree(classifier, attack_feature=attack_feature)
+        x_train_predictions = np.array([np.argmax(arr) for arr in classifier.predict(x_train_iris)]).reshape(-1, 1)
+        x_test_predictions = np.array([np.argmax(arr) for arr in classifier.predict(x_test_iris)]).reshape(-1, 1)
+        inferred_train = attack.infer(x_train_for_attack, x_train_predictions, values=values, priors=priors)
+        inferred_test = attack.infer(x_test_for_attack, x_test_predictions, values=values, priors=priors)
+        train_diff = np.abs(inferred_train - x_train_feature.reshape(1, -1))
+        test_diff = np.abs(inferred_test - x_test_feature.reshape(1, -1))
+        assert np.sum(train_diff) / len(inferred_train) == pytest.approx(0.2108, abs=0.03)
+        assert np.sum(test_diff) / len(inferred_test) == pytest.approx(0.1988, abs=0.03)
+    except ARTTestException as e:
+        art_warning(e)
+
+
+def test_classifier_type_check_fail():
+    backend_test_classifier_type_check_fail(
+        AttributeInferenceWhiteBoxDecisionTree, (ScikitlearnDecisionTreeClassifier,)
+    )
diff --git a/adversarial-robustness-toolbox/tests/attacks/inference/attribute_inference/test_white_box_lifestyle_decision_tree.py b/adversarial-robustness-toolbox/tests/attacks/inference/attribute_inference/test_white_box_lifestyle_decision_tree.py
new file mode 100644
index 0000000..be62536
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/attacks/inference/attribute_inference/test_white_box_lifestyle_decision_tree.py
@@ -0,0 +1,67 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import pytest
+
+import numpy as np
+
+from art.attacks.inference.attribute_inference.white_box_lifestyle_decision_tree import (
+    AttributeInferenceWhiteBoxLifestyleDecisionTree,
+)
+from art.estimators.classification.scikitlearn import ScikitlearnDecisionTreeClassifier
+
+from tests.attacks.utils import backend_test_classifier_type_check_fail
+from tests.utils import ARTTestException
+
+logger = logging.getLogger(__name__)
+
+
+@pytest.mark.skip_framework("dl_frameworks")
+def test_white_box_lifestyle(art_warning, decision_tree_estimator, get_iris_dataset):
+    try:
+        attack_feature = 2  # petal length
+        values = [0.14, 0.42, 0.71]  # rounded down
+        priors = [50 / 150, 54 / 150, 46 / 150]
+
+        (x_train_iris, y_train_iris), (x_test_iris, y_test_iris) = get_iris_dataset
+        x_train_for_attack = np.delete(x_train_iris, attack_feature, 1)
+        x_train_feature = x_train_iris[:, attack_feature]
+        x_test_for_attack = np.delete(x_test_iris, attack_feature, 1)
+        x_test_feature = x_test_iris[:, attack_feature]
+
+        classifier = decision_tree_estimator()
+        attack = AttributeInferenceWhiteBoxLifestyleDecisionTree(classifier, attack_feature=attack_feature)
+        x_train_predictions = np.array([np.argmax(arr) for arr in classifier.predict(x_train_iris)]).reshape(-1, 1)
+        x_test_predictions = np.array([np.argmax(arr) for arr in classifier.predict(x_test_iris)]).reshape(-1, 1)
+        inferred_train = attack.infer(x_train_for_attack, x_train_predictions, values=values, priors=priors)
+        inferred_test = attack.infer(x_test_for_attack, x_test_predictions, values=values, priors=priors)
+        train_diff = np.abs(inferred_train - x_train_feature.reshape(1, -1))
+        test_diff = np.abs(inferred_test - x_test_feature.reshape(1, -1))
+        assert np.sum(train_diff) / len(inferred_train) == pytest.approx(0.3357, abs=0.03)
+        assert np.sum(test_diff) / len(inferred_test) == pytest.approx(0.3149, abs=0.03)
+        # assert np.sum(train_diff) / len(inferred_train) < np.sum(test_diff) / len(inferred_test)
+    except ARTTestException as e:
+        art_warning(e)
+
+
+def test_classifier_type_check_fail():
+    backend_test_classifier_type_check_fail(
+        AttributeInferenceWhiteBoxLifestyleDecisionTree, (ScikitlearnDecisionTreeClassifier,)
+    )
diff --git a/adversarial-robustness-toolbox/tests/attacks/inference/membership_inference/__init__.py b/adversarial-robustness-toolbox/tests/attacks/inference/membership_inference/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/adversarial-robustness-toolbox/tests/attacks/inference/membership_inference/test_black_box.py b/adversarial-robustness-toolbox/tests/attacks/inference/membership_inference/test_black_box.py
new file mode 100644
index 0000000..20cd72d
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/attacks/inference/membership_inference/test_black_box.py
@@ -0,0 +1,188 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import pytest
+
+import keras
+
+from art.attacks.inference.membership_inference.black_box import MembershipInferenceBlackBox
+from art.estimators.classification.keras import KerasClassifier
+from art.estimators.estimator import BaseEstimator
+from art.estimators.classification.classifier import ClassifierMixin
+
+from tests.attacks.utils import backend_test_classifier_type_check_fail
+from tests.utils import ARTTestException
+
+logger = logging.getLogger(__name__)
+attack_train_ratio = 0.5
+num_classes_iris = 3
+num_classes_mnist = 10
+
+
+def test_black_box_image(art_warning, get_default_mnist_subset, image_dl_estimator_for_attack):
+    try:
+        classifier = image_dl_estimator_for_attack(MembershipInferenceBlackBox)
+        attack = MembershipInferenceBlackBox(classifier)
+        backend_check_membership_accuracy(attack, get_default_mnist_subset, attack_train_ratio, 0.03)
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.parametrize("model_type", ["nn", "rf", "gb"])
+def test_black_box_tabular(art_warning, model_type, tabular_dl_estimator_for_attack, get_iris_dataset):
+    try:
+        classifier = tabular_dl_estimator_for_attack(MembershipInferenceBlackBox)
+        attack = MembershipInferenceBlackBox(classifier, attack_model_type=model_type)
+        backend_check_membership_accuracy(attack, get_iris_dataset, attack_train_ratio, 0.08)
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.parametrize("model_type", ["nn", "rf", "gb"])
+def test_black_box_loss_tabular(art_warning, model_type, tabular_dl_estimator_for_attack, get_iris_dataset):
+    try:
+        classifier = tabular_dl_estimator_for_attack(MembershipInferenceBlackBox)
+        if type(classifier).__name__ == "PyTorchClassifier" or type(classifier).__name__ == "TensorFlowV2Classifier":
+            attack = MembershipInferenceBlackBox(classifier, input_type="loss", attack_model_type=model_type)
+            backend_check_membership_accuracy(attack, get_iris_dataset, attack_train_ratio, 0.15)
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.skip_framework("tensorflow", "pytorch", "scikitlearn", "mxnet", "kerastf")
+@pytest.mark.skipif(keras.__version__.startswith("2.2"), reason="requires Keras 2.3.0 or higher")
+def test_black_box_keras_loss(art_warning, get_iris_dataset):
+    try:
+        (x_train, y_train), (_, _) = get_iris_dataset
+
+        # This test creates a framework-specific (keras) model because it needs to check both the case of a string-based
+        # loss and a class-based loss, and therefore cannot use the generic fixture get_tabular_classifier_list
+        model = keras.models.Sequential()
+        model.add(keras.layers.Dense(8, input_dim=4, activation="relu"))
+        model.add(keras.layers.Dense(3, activation="softmax"))
+        model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])
+        model.fit(x_train, y_train, epochs=150, batch_size=10)
+
+        classifier = KerasClassifier(model)
+        attack = MembershipInferenceBlackBox(classifier, input_type="loss")
+        backend_check_membership_accuracy(attack, get_iris_dataset, attack_train_ratio, 0.15)
+
+        model2 = keras.models.Sequential()
+        model2.add(keras.layers.Dense(12, input_dim=4, activation="relu"))
+        model2.add(keras.layers.Dense(3, activation="softmax"))
+        model2.compile(loss=keras.losses.CategoricalCrossentropy(), optimizer="adam", metrics=["accuracy"])
+        model2.fit(x_train, y_train, epochs=150, batch_size=10)
+
+        classifier = KerasClassifier(model2)
+        attack = MembershipInferenceBlackBox(classifier, input_type="loss")
+        backend_check_membership_accuracy(attack, get_iris_dataset, attack_train_ratio, 0.15)
+    except ARTTestException as e:
+        art_warning(e)
+
+
+def test_black_box_tabular_rf(art_warning, tabular_dl_estimator_for_attack, get_iris_dataset):
+    try:
+        classifier = tabular_dl_estimator_for_attack(MembershipInferenceBlackBox)
+        attack = MembershipInferenceBlackBox(classifier, attack_model_type="rf")
+        backend_check_membership_accuracy(attack, get_iris_dataset, attack_train_ratio, 0.1)
+    except ARTTestException as e:
+        art_warning(e)
+
+
+def test_black_box_tabular_gb(art_warning, tabular_dl_estimator_for_attack, get_iris_dataset):
+    try:
+        classifier = tabular_dl_estimator_for_attack(MembershipInferenceBlackBox)
+        attack = MembershipInferenceBlackBox(classifier, attack_model_type="gb")
+        # train attack model using only attack_train_ratio of data
+        backend_check_membership_accuracy(attack, get_iris_dataset, attack_train_ratio, 0.03)
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.skip_framework("tensorflow", "keras", "scikitlearn", "mxnet", "kerastf")
+def test_black_box_with_model(art_warning, tabular_dl_estimator_for_attack, estimator_for_attack, get_iris_dataset):
+    try:
+        classifier = tabular_dl_estimator_for_attack(MembershipInferenceBlackBox)
+        attack_model = estimator_for_attack(num_features=2 * num_classes_iris)
+        print(type(attack_model).__name__)
+        attack = MembershipInferenceBlackBox(classifier, attack_model=attack_model)
+        backend_check_membership_accuracy(attack, get_iris_dataset, attack_train_ratio, 0.03)
+    except ARTTestException as e:
+        art_warning(e)
+
+
+def test_errors(art_warning, tabular_dl_estimator_for_attack, get_iris_dataset):
+    try:
+        classifier = tabular_dl_estimator_for_attack(MembershipInferenceBlackBox)
+        (x_train, y_train), (x_test, y_test) = get_iris_dataset
+
+        with pytest.raises(ValueError):
+            MembershipInferenceBlackBox(classifier, attack_model_type="a")
+        with pytest.raises(ValueError):
+            MembershipInferenceBlackBox(classifier, input_type="a")
+        attack = MembershipInferenceBlackBox(classifier)
+        with pytest.raises(ValueError):
+            attack.fit(x_train, y_test, x_test, y_test)
+        with pytest.raises(ValueError):
+            attack.fit(x_train, y_train, x_test, y_train)
+        with pytest.raises(ValueError):
+            attack.infer(x_train, y_test)
+    except ARTTestException as e:
+        art_warning(e)
+
+
+def test_classifier_type_check_fail(art_warning):
+    try:
+        backend_test_classifier_type_check_fail(MembershipInferenceBlackBox, [BaseEstimator, ClassifierMixin])
+    except ARTTestException as e:
+        art_warning(e)
+
+
+def backend_check_membership_accuracy_no_fit(attack, dataset, approx):
+    (x_train, y_train), (x_test, y_test) = dataset
+    # infer attacked feature
+    inferred_train = attack.infer(x_train, y_train)
+    inferred_test = attack.infer(x_test, y_test)
+    # check accuracy
+    backend_check_accuracy(inferred_train, inferred_test, approx)
+
+
+def backend_check_membership_accuracy(attack, dataset, attack_train_ratio, approx):
+    (x_train, y_train), (x_test, y_test) = dataset
+    attack_train_size = int(len(x_train) * attack_train_ratio)
+    attack_test_size = int(len(x_test) * attack_train_ratio)
+
+    # train attack model using only attack_train_ratio of data
+    attack.fit(
+        x_train[:attack_train_size], y_train[:attack_train_size], x_test[:attack_test_size], y_test[:attack_test_size]
+    )
+
+    # infer attacked feature on remainder of data
+    inferred_train = attack.infer(x_train[attack_train_size:], y_train[attack_train_size:])
+    inferred_test = attack.infer(x_test[attack_test_size:], y_test[attack_test_size:])
+
+    # check accuracy
+    backend_check_accuracy(inferred_train, inferred_test, approx)
+
+
+def backend_check_accuracy(inferred_train, inferred_test, approx):
+    train_pos = sum(inferred_train) / len(inferred_train)
+    test_pos = sum(inferred_test) / len(inferred_test)
+    assert train_pos > test_pos or train_pos == pytest.approx(test_pos, abs=approx) or test_pos == 1
diff --git a/adversarial-robustness-toolbox/tests/attacks/inference/membership_inference/test_black_box_rule_based.py b/adversarial-robustness-toolbox/tests/attacks/inference/membership_inference/test_black_box_rule_based.py
new file mode 100644
index 0000000..422c5f8
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/attacks/inference/membership_inference/test_black_box_rule_based.py
@@ -0,0 +1,73 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import pytest
+
+from art.attacks.inference.membership_inference.black_box_rule_based import MembershipInferenceBlackBoxRuleBased
+from art.estimators.estimator import BaseEstimator
+from art.estimators.classification.classifier import ClassifierMixin
+
+from tests.attacks.utils import backend_test_classifier_type_check_fail
+from tests.utils import ARTTestException
+
+logger = logging.getLogger(__name__)
+attack_train_ratio = 0.5
+num_classes_iris = 3
+num_classes_mnist = 10
+
+
+def test_rule_based_image(art_warning, get_default_mnist_subset, image_dl_estimator_for_attack):
+    try:
+        classifier = image_dl_estimator_for_attack(MembershipInferenceBlackBoxRuleBased)
+        attack = MembershipInferenceBlackBoxRuleBased(classifier)
+        backend_check_membership_accuracy_no_fit(attack, get_default_mnist_subset, 0.8)
+    except ARTTestException as e:
+        art_warning(e)
+
+
+def test_rule_based_tabular(art_warning, get_iris_dataset, tabular_dl_estimator_for_attack):
+    try:
+        classifier = tabular_dl_estimator_for_attack(MembershipInferenceBlackBoxRuleBased)
+        attack = MembershipInferenceBlackBoxRuleBased(classifier)
+        backend_check_membership_accuracy_no_fit(attack, get_iris_dataset, 0.06)
+    except ARTTestException as e:
+        art_warning(e)
+
+
+def test_classifier_type_check_fail(art_warning):
+    try:
+        backend_test_classifier_type_check_fail(MembershipInferenceBlackBoxRuleBased, [BaseEstimator, ClassifierMixin])
+    except ARTTestException as e:
+        art_warning(e)
+
+
+def backend_check_membership_accuracy_no_fit(attack, dataset, approx):
+    (x_train, y_train), (x_test, y_test) = dataset
+    # infer attacked feature
+    inferred_train = attack.infer(x_train, y_train)
+    inferred_test = attack.infer(x_test, y_test)
+    # check accuracy
+    backend_check_accuracy(inferred_train, inferred_test, approx)
+
+
+def backend_check_accuracy(inferred_train, inferred_test, approx):
+    train_pos = sum(inferred_train) / len(inferred_train)
+    test_pos = sum(inferred_test) / len(inferred_test)
+    assert train_pos > test_pos or train_pos == pytest.approx(test_pos, abs=approx) or test_pos == 1
diff --git a/adversarial-robustness-toolbox/tests/attacks/inference/membership_inference/test_label_only_boundary_distance.py b/adversarial-robustness-toolbox/tests/attacks/inference/membership_inference/test_label_only_boundary_distance.py
new file mode 100644
index 0000000..f755172
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/attacks/inference/membership_inference/test_label_only_boundary_distance.py
@@ -0,0 +1,75 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import pytest
+
+from art.attacks.inference.membership_inference.label_only_boundary_distance import LabelOnlyDecisionBoundary
+from art.estimators.estimator import BaseEstimator
+from art.estimators.classification.classifier import ClassifierMixin
+
+from tests.attacks.utils import backend_test_classifier_type_check_fail
+from tests.utils import ARTTestException
+
+logger = logging.getLogger(__name__)
+
+attack_train_ratio = 0.5
+
+
+def test_label_only_boundary_distance_image(art_warning, get_default_mnist_subset, image_dl_estimator_for_attack):
+    try:
+        classifier = image_dl_estimator_for_attack(LabelOnlyDecisionBoundary)
+        attack = LabelOnlyDecisionBoundary(classifier, distance_threshold_tau=0.5)
+        backend_check_membership_accuracy(attack, get_default_mnist_subset, attack_train_ratio, 0.03)
+    except ARTTestException as e:
+        art_warning(e)
+
+
+def test_classifier_type_check_fail(art_warning):
+    try:
+        backend_test_classifier_type_check_fail(LabelOnlyDecisionBoundary, [BaseEstimator, ClassifierMixin])
+    except ARTTestException as e:
+        art_warning(e)
+
+
+def backend_check_membership_accuracy(attack, dataset, attack_train_ratio, approx):
+    (x_train, y_train), (x_test, y_test) = dataset
+    attack_train_size = int(len(x_train) * attack_train_ratio)
+    attack_test_size = int(len(x_test) * attack_train_ratio)
+
+    # infer attacked feature on remainder of data
+    kwargs = {
+        "norm": 2,
+        "max_iter": 2,
+        "max_eval": 4,
+        "init_eval": 1,
+        "init_size": 1,
+        "verbose": False,
+    }
+    inferred_train = attack.infer(x_train[attack_train_size:], y_train[attack_train_size:], **kwargs)
+    inferred_test = attack.infer(x_test[attack_test_size:], y_test[attack_test_size:], **kwargs)
+
+    # check accuracy
+    backend_check_accuracy(inferred_train, inferred_test, approx)
+
+
+def backend_check_accuracy(inferred_train, inferred_test, approx):
+    train_pos = sum(inferred_train) / len(inferred_train)
+    test_pos = sum(inferred_test) / len(inferred_test)
+    assert train_pos > test_pos or train_pos == pytest.approx(test_pos, abs=approx) or test_pos == 1
diff --git a/adversarial-robustness-toolbox/tests/attacks/inference/membership_inference/test_label_only_gap_attack.py b/adversarial-robustness-toolbox/tests/attacks/inference/membership_inference/test_label_only_gap_attack.py
new file mode 100644
index 0000000..18587b8
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/attacks/inference/membership_inference/test_label_only_gap_attack.py
@@ -0,0 +1,73 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import pytest
+
+from art.attacks.inference.membership_inference.label_only_gap_attack import LabelOnlyGapAttack
+from art.estimators.estimator import BaseEstimator
+from art.estimators.classification.classifier import ClassifierMixin
+
+from tests.attacks.utils import backend_test_classifier_type_check_fail
+from tests.utils import ARTTestException
+
+logger = logging.getLogger(__name__)
+attack_train_ratio = 0.5
+num_classes_iris = 3
+num_classes_mnist = 10
+
+
+def test_rule_based_image(art_warning, get_default_mnist_subset, image_dl_estimator_for_attack):
+    try:
+        classifier = image_dl_estimator_for_attack(LabelOnlyGapAttack)
+        attack = LabelOnlyGapAttack(classifier)
+        backend_check_membership_accuracy_no_fit(attack, get_default_mnist_subset, 0.8)
+    except ARTTestException as e:
+        art_warning(e)
+
+
+def test_rule_based_tabular(art_warning, get_iris_dataset, tabular_dl_estimator_for_attack):
+    try:
+        classifier = tabular_dl_estimator_for_attack(LabelOnlyGapAttack)
+        attack = LabelOnlyGapAttack(classifier)
+        backend_check_membership_accuracy_no_fit(attack, get_iris_dataset, 0.06)
+    except ARTTestException as e:
+        art_warning(e)
+
+
+def test_classifier_type_check_fail(art_warning):
+    try:
+        backend_test_classifier_type_check_fail(LabelOnlyGapAttack, [BaseEstimator, ClassifierMixin])
+    except ARTTestException as e:
+        art_warning(e)
+
+
+def backend_check_membership_accuracy_no_fit(attack, dataset, approx):
+    (x_train, y_train), (x_test, y_test) = dataset
+    # infer attacked feature
+    inferred_train = attack.infer(x_train, y_train)
+    inferred_test = attack.infer(x_test, y_test)
+    # check accuracy
+    backend_check_accuracy(inferred_train, inferred_test, approx)
+
+
+def backend_check_accuracy(inferred_train, inferred_test, approx):
+    train_pos = sum(inferred_train) / len(inferred_train)
+    test_pos = sum(inferred_test) / len(inferred_test)
+    assert train_pos > test_pos or train_pos == pytest.approx(test_pos, abs=approx) or test_pos == 1
diff --git a/adversarial-robustness-toolbox/tests/attacks/inference/model_inversion/__init__.py b/adversarial-robustness-toolbox/tests/attacks/inference/model_inversion/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/adversarial-robustness-toolbox/tests/attacks/inference/model_inversion/test_mi_face.py b/adversarial-robustness-toolbox/tests/attacks/inference/model_inversion/test_mi_face.py
new file mode 100644
index 0000000..74f31d1
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/attacks/inference/model_inversion/test_mi_face.py
@@ -0,0 +1,90 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import pytest
+
+import numpy as np
+
+from art.attacks.inference.model_inversion import MIFace
+from art.estimators.estimator import BaseEstimator
+from art.estimators.classification.classifier import ClassifierMixin, ClassGradientsMixin
+
+from tests.attacks.utils import backend_test_classifier_type_check_fail
+from tests.utils import ARTTestException
+
+logger = logging.getLogger(__name__)
+
+
+@pytest.fixture()
+def fix_get_mnist_subset(get_mnist_dataset):
+    (x_train_mnist, y_train_mnist), (x_test_mnist, y_test_mnist) = get_mnist_dataset
+    n_train = 100
+    n_test = 11
+    yield x_train_mnist[:n_train], y_train_mnist[:n_train], x_test_mnist[:n_test], y_test_mnist[:n_test]
+
+
+def backend_check_inferred_values(attack, mnist_dataset, classifier):
+    # We assert that, when starting with zero information about the inputs x, we are able to infer - for each
+    # class - a representative sample that is classified by the classifier as belonging to that class:
+
+    x_train_infer_from_zero = attack.infer(None, y=np.arange(10))
+    preds = np.argmax(classifier.predict(x_train_infer_from_zero), axis=1)
+    np.testing.assert_array_equal(preds, np.arange(10))
+
+    # Next we would like to assert that, when starting with blurry training instances, the inference attack will result
+    # in instances that more closely resemble the original instances. However, this turns out not to be the case in
+    # this scenario:
+
+    (x_train_mnist, y_train_mnist, _, _) = mnist_dataset
+    x_original = x_train_mnist[:10]
+    x_noisy = np.clip(x_original + np.random.uniform(-0.01, 0.01, x_original.shape), 0, 1)
+    x_train_infer_from_noisy = attack.infer(x_noisy, y=y_train_mnist[:10])
+
+    diff_noisy = np.mean(np.reshape(np.abs(x_original - x_noisy), (len(x_original), -1)), axis=1)
+    diff_inferred = np.mean(np.reshape(np.abs(x_original - x_train_infer_from_noisy), (len(x_original), -1)), axis=1)
+
+    np.testing.assert_array_less(diff_noisy, diff_inferred)
+
+
+@pytest.mark.framework_agnostic
+def test_miface(art_warning, fix_get_mnist_subset, image_dl_estimator_for_attack):
+    try:
+        classifier = image_dl_estimator_for_attack(MIFace)
+
+        # for the one-shot method, frame saliency attack should resort to plain FastGradientMethod
+        # expected_values = {
+        #     "x_test_mean": ExpectedValue(0.2346725, 0.002),
+        #     "x_test_min": ExpectedValue(-1.0, 0.00001),
+        #     "x_test_max": ExpectedValue(1.0, 0.00001),
+        #     "y_test_pred_adv_expected": ExpectedValue(np.asarray([4, 4, 4, 7, 7, 4, 7, 2, 2, 3, 0]), 2),
+        # }
+
+        attack = MIFace(classifier, max_iter=150, batch_size=3)
+        backend_check_inferred_values(attack, fix_get_mnist_subset, classifier)
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.framework_agnostic
+def test_classifier_type_check_fail(art_warning):
+    try:
+        backend_test_classifier_type_check_fail(MIFace, [BaseEstimator, ClassifierMixin, ClassGradientsMixin])
+    except ARTTestException as e:
+        art_warning(e)
diff --git a/adversarial-robustness-toolbox/tests/attacks/inference/test_reconstruction.py b/adversarial-robustness-toolbox/tests/attacks/inference/test_reconstruction.py
new file mode 100644
index 0000000..e8d57e9
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/attacks/inference/test_reconstruction.py
@@ -0,0 +1,80 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+
+import numpy as np
+from sklearn.naive_bayes import GaussianNB
+from sklearn.linear_model.logistic import LogisticRegression
+
+from art.attacks.inference.reconstruction import DatabaseReconstruction
+from art.estimators.classification.scikitlearn import ScikitlearnGaussianNB, ScikitlearnLogisticRegression
+
+
+logger = logging.getLogger(__name__)
+
+
+def test_database_reconstruction_gaussian_nb(get_iris_dataset):
+    (x_train_iris, y_train_iris), (x_test_iris, y_test_iris) = get_iris_dataset
+    y_train_iris = np.array([np.argmax(y) for y in y_train_iris])
+    y_test_iris = np.array([np.argmax(y) for y in y_test_iris])
+
+    x_private = x_test_iris[0, :].reshape(1, -1)
+    y_private = y_test_iris[0]
+
+    x_input = np.vstack((x_train_iris, x_private))
+    y_input = np.hstack((y_train_iris, y_private))
+
+    nb_private = GaussianNB()
+    nb_private.fit(x_input, y_input)
+    estimator_private = ScikitlearnGaussianNB(model=nb_private)
+
+    recon = DatabaseReconstruction(estimator=estimator_private)
+    x_recon, y_recon = recon.reconstruct(x_train_iris, y_train_iris)
+
+    assert x_recon is not None
+    assert x_recon.shape == (1, 4)
+    assert y_recon.shape == (1, 3)
+    assert np.isclose(x_recon, x_private).all()
+    assert np.argmax(y_recon, axis=1) == y_private
+
+
+def test_database_reconstruction_logistic_regression(get_iris_dataset):
+    (x_train_iris, y_train_iris), (x_test_iris, y_test_iris) = get_iris_dataset
+    y_train_iris = np.array([np.argmax(y) for y in y_train_iris])
+    y_test_iris = np.array([np.argmax(y) for y in y_test_iris])
+
+    x_private = x_test_iris[0, :].reshape(1, -1)
+    y_private = y_test_iris[0]
+
+    x_input = np.vstack((x_train_iris, x_private))
+    y_input = np.hstack((y_train_iris, y_private))
+
+    nb_private = LogisticRegression()
+    nb_private.fit(x_input, y_input)
+    estimator_private = ScikitlearnLogisticRegression(model=nb_private)
+
+    recon = DatabaseReconstruction(estimator=estimator_private)
+    x_recon, y_recon = recon.reconstruct(x_train_iris, y_train_iris)
+
+    assert x_recon is not None
+    assert x_recon.shape == (1, 4)
+    assert y_recon.shape == (1, 3)
+    assert np.isclose(x_recon, x_private, rtol=0.05).all()
+    assert np.argmax(y_recon, axis=1) == y_private
diff --git a/adversarial-robustness-toolbox/tests/attacks/poison/__init__.py b/adversarial-robustness-toolbox/tests/attacks/poison/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/adversarial-robustness-toolbox/tests/attacks/poison/test_bullseye_polytope_attack.py b/adversarial-robustness-toolbox/tests/attacks/poison/test_bullseye_polytope_attack.py
new file mode 100644
index 0000000..977b392
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/attacks/poison/test_bullseye_polytope_attack.py
@@ -0,0 +1,81 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2021
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import numpy as np
+import pytest
+
+from art.attacks.poisoning import BullseyePolytopeAttackPyTorch
+
+from tests.utils import ARTTestException
+
+logger = logging.getLogger(__name__)
+
+
+@pytest.mark.skip_framework("non_dl_frameworks", "tensorflow", "mxnet", "keras", "kerastf")
+def test_poison(art_warning, get_default_mnist_subset, image_dl_estimator):
+    try:
+        (x_train, y_train), (_, _) = get_default_mnist_subset
+        classifier, _ = image_dl_estimator(functional=True)
+        target = np.expand_dims(x_train[3], 0)
+        attack = BullseyePolytopeAttackPyTorch(classifier, target, len(classifier.layer_names) - 2)
+        poison_data, poison_labels = attack.poison(x_train[5:10], y_train[5:10])
+
+        np.testing.assert_equal(poison_data.shape, x_train[5:10].shape)
+        np.testing.assert_equal(poison_labels.shape, y_train[5:10].shape)
+
+        with pytest.raises(AssertionError):
+            np.testing.assert_equal(poison_data, x_train[5:10])
+
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.skip_framework("non_dl_frameworks", "tensorflow", "mxnet", "keras", "kerastf")
+def test_failure_modes(art_warning, get_default_mnist_subset, image_dl_estimator):
+    try:
+        (x_train, y_train), (_, _) = get_default_mnist_subset
+        classifier, _ = image_dl_estimator(functional=True)
+        target = np.expand_dims(x_train[3], 0)
+        with pytest.raises(ValueError):
+            attack = BullseyePolytopeAttackPyTorch(classifier, target, len(classifier.layer_names) - 2,
+                                                   learning_rate=-1)
+        with pytest.raises(ValueError):
+            attack = BullseyePolytopeAttackPyTorch(classifier, target, len(classifier.layer_names) - 2, max_iter=-1)
+        with pytest.raises(TypeError):
+            attack = BullseyePolytopeAttackPyTorch(classifier, target, 2.5)
+        with pytest.raises(ValueError):
+            attack = BullseyePolytopeAttackPyTorch(classifier, target, len(classifier.layer_names) - 2,
+                                                   opt="new optimizer")
+        with pytest.raises(ValueError):
+            attack = BullseyePolytopeAttackPyTorch(classifier, target, len(classifier.layer_names) - 2, momentum=1.2)
+        with pytest.raises(ValueError):
+            attack = BullseyePolytopeAttackPyTorch(classifier, target, len(classifier.layer_names) - 2, decay_iter=-1)
+        with pytest.raises(ValueError):
+            attack = BullseyePolytopeAttackPyTorch(classifier, target, len(classifier.layer_names) - 2, epsilon=-1)
+        with pytest.raises(ValueError):
+            attack = BullseyePolytopeAttackPyTorch(classifier, target, len(classifier.layer_names) - 2, dropout=2)
+        with pytest.raises(ValueError):
+            attack = BullseyePolytopeAttackPyTorch(classifier, target, len(classifier.layer_names) - 2, net_repeat=-1)
+        with pytest.raises(ValueError):
+            attack = BullseyePolytopeAttackPyTorch(classifier, target, -1)
+        with pytest.raises(ValueError):
+            attack = BullseyePolytopeAttackPyTorch(classifier, target, len(classifier.layer_names) - 2, decay_coeff=2)
+    except ARTTestException as e:
+        art_warning(e)
diff --git a/adversarial-robustness-toolbox/tests/attacks/poison/test_clean_label_backdoor_attack.py b/adversarial-robustness-toolbox/tests/attacks/poison/test_clean_label_backdoor_attack.py
new file mode 100644
index 0000000..6436bbd
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/attacks/poison/test_clean_label_backdoor_attack.py
@@ -0,0 +1,60 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import numpy as np
+import pytest
+
+from art.attacks.poisoning import PoisoningAttackCleanLabelBackdoor, PoisoningAttackBackdoor
+from art.attacks.poisoning.perturbations import add_pattern_bd
+from art.utils import to_categorical
+
+from tests.utils import ARTTestException
+
+logger = logging.getLogger(__name__)
+
+
+@pytest.mark.skip_framework("non_dl_frameworks", "pytorch", "mxnet")
+def test_poison(art_warning, get_default_mnist_subset, image_dl_estimator):
+    try:
+        (x_train, y_train), (_, _) = get_default_mnist_subset
+        classifier, _ = image_dl_estimator()
+        target = to_categorical([9], 10)[0]
+        backdoor = PoisoningAttackBackdoor(add_pattern_bd)
+        attack = PoisoningAttackCleanLabelBackdoor(backdoor, classifier, target)
+        poison_data, poison_labels = attack.poison(x_train, y_train)
+
+        np.testing.assert_equal(poison_data.shape, x_train.shape)
+        np.testing.assert_equal(poison_labels.shape, y_train.shape)
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.parametrize("params", [dict(pp_poison=-0.2), dict(pp_poison=1.2)])
+@pytest.mark.skip_framework("non_dl_frameworks", "pytorch", "mxnet")
+def test_failure_modes(art_warning, get_default_mnist_subset, image_dl_estimator, params):
+    try:
+        (x_train, y_train), (_, _) = get_default_mnist_subset
+        classifier, _ = image_dl_estimator()
+        target = to_categorical([9], 10)[0]
+        backdoor = PoisoningAttackBackdoor(add_pattern_bd)
+        with pytest.raises(ValueError):
+            attack = PoisoningAttackCleanLabelBackdoor(backdoor, classifier, target, **params)
+    except ARTTestException as e:
+        art_warning(e)
diff --git a/adversarial-robustness-toolbox/tests/attacks/test_adversarial_embedding.py b/adversarial-robustness-toolbox/tests/attacks/test_adversarial_embedding.py
new file mode 100644
index 0000000..8dbfbc3
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/attacks/test_adversarial_embedding.py
@@ -0,0 +1,146 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import os
+import unittest
+
+import numpy as np
+
+from art.attacks.poisoning.backdoor_attack import PoisoningAttackBackdoor
+from art.attacks.poisoning.adversarial_embedding_attack import PoisoningAttackAdversarialEmbedding
+from art.attacks.poisoning.perturbations import add_pattern_bd
+from art.utils import load_dataset
+
+from tests.utils import master_seed, get_image_classifier_kr_tf
+
+os.environ["KMP_DUPLICATE_LIB_OK"] = "True"
+logger = logging.getLogger(__name__)
+
+BATCH_SIZE = 100
+NB_TRAIN = 5000
+NB_TEST = 10
+NB_EPOCHS = 1
+
+
+class TestAdversarialEmbedding(unittest.TestCase):
+    """
+    A unittest class for testing Randomized Smoothing as a post-processing step for classifiers.
+    """
+
+    @classmethod
+    def setUpClass(cls):
+        # Get MNIST
+        (x_train, y_train), (x_test, y_test), _, _ = load_dataset("mnist")
+        x_train, y_train = x_train[:NB_TRAIN], y_train[:NB_TRAIN]
+        x_test, y_test = x_test[:NB_TEST], y_test[:NB_TEST]
+        cls.mnist = (x_train, y_train), (x_test, y_test)
+
+    def setUp(self):
+        master_seed(seed=301)
+
+    def test_keras(self):
+        """
+        Test with a KerasClassifier.
+        :return:
+        """
+        # Build KerasClassifier
+        krc = get_image_classifier_kr_tf(loss_type="label")
+
+        # Get MNIST
+        (x_train, y_train), (_, _) = self.mnist
+        target_idx = 9
+        target = np.zeros(10)
+        target[target_idx] = 1
+        target2 = np.zeros(10)
+        target2[(target_idx + 1) % 10] = 1
+
+        backdoor = PoisoningAttackBackdoor(add_pattern_bd)
+
+        emb_attack = PoisoningAttackAdversarialEmbedding(krc, backdoor, 2, target)
+        classifier = emb_attack.poison_estimator(x_train, y_train, nb_epochs=NB_EPOCHS)
+
+        data, labels, bd = emb_attack.get_training_data()
+        self.assertEqual(x_train.shape, data.shape)
+        self.assertEqual(y_train.shape, labels.shape)
+        self.assertEqual(bd.shape, (len(x_train), 2))
+
+        # Assert successful cloning of classifier model
+        self.assertTrue(classifier is not krc)
+
+        emb_attack2 = PoisoningAttackAdversarialEmbedding(krc, backdoor, 2, [(target, target2)])
+        _ = emb_attack2.poison_estimator(x_train, y_train, nb_epochs=NB_EPOCHS)
+
+        data, labels, bd = emb_attack2.get_training_data()
+        self.assertEqual(x_train.shape, data.shape)
+        self.assertEqual(y_train.shape, labels.shape)
+        self.assertEqual(bd.shape, (len(x_train), 2))
+
+        _ = PoisoningAttackAdversarialEmbedding(krc, backdoor, 2, [(target, target2)], pp_poison=[0.4])
+
+    def test_errors(self):
+        krc = get_image_classifier_kr_tf(loss_type="function")
+        krc_valid = get_image_classifier_kr_tf(loss_type="label")
+        backdoor = PoisoningAttackBackdoor(add_pattern_bd)
+        target_idx = 9
+        target = np.zeros(10)
+        target[target_idx] = 1
+        target2 = np.zeros(10)
+        target2[(target_idx + 1) % 10] = 1
+
+        # invalid loss function
+        with self.assertRaises(TypeError):
+            _ = PoisoningAttackAdversarialEmbedding(krc, backdoor, 2, target)
+
+        # feature layer not real name
+        with self.assertRaises(ValueError):
+            _ = PoisoningAttackAdversarialEmbedding(krc_valid, backdoor, "not a layer", target)
+
+        # feature layer out of range
+        with self.assertRaises(ValueError):
+            _ = PoisoningAttackAdversarialEmbedding(krc_valid, backdoor, 20, target)
+
+        # target misshaped
+        with self.assertRaises(ValueError):
+            _ = PoisoningAttackAdversarialEmbedding(krc_valid, backdoor, 20, np.expand_dims(target, axis=0))
+
+        with self.assertRaises(ValueError):
+            _ = PoisoningAttackAdversarialEmbedding(krc_valid, backdoor, 20, [target])
+
+        with self.assertRaises(ValueError):
+            _ = PoisoningAttackAdversarialEmbedding(krc_valid, backdoor, 20, target, regularization=-1)
+
+        with self.assertRaises(ValueError):
+            _ = PoisoningAttackAdversarialEmbedding(krc_valid, backdoor, 20, target, discriminator_layer_1=-1)
+
+        with self.assertRaises(ValueError):
+            _ = PoisoningAttackAdversarialEmbedding(krc_valid, backdoor, 20, target, discriminator_layer_2=-1)
+
+        with self.assertRaises(ValueError):
+            _ = PoisoningAttackAdversarialEmbedding(krc_valid, backdoor, 20, target, pp_poison=-1)
+
+        with self.assertRaises(ValueError):
+            _ = PoisoningAttackAdversarialEmbedding(krc_valid, backdoor, 20, [(target, target2)], pp_poison=[])
+
+        with self.assertRaises(ValueError):
+            _ = PoisoningAttackAdversarialEmbedding(krc_valid, backdoor, 20, [(target, target2)], pp_poison=[-1])
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/adversarial-robustness-toolbox/tests/attacks/test_adversarial_patch.py b/adversarial-robustness-toolbox/tests/attacks/test_adversarial_patch.py
new file mode 100644
index 0000000..6df5af0
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/attacks/test_adversarial_patch.py
@@ -0,0 +1,291 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import unittest
+
+import numpy as np
+import keras
+import tensorflow as tf
+
+from art.attacks.evasion.adversarial_patch.adversarial_patch import AdversarialPatch, AdversarialPatchNumpy
+from art.estimators.estimator import BaseEstimator, NeuralNetworkMixin
+from art.estimators.classification.classifier import ClassifierMixin
+
+from tests.utils import TestBase, master_seed
+from tests.utils import get_image_classifier_tf, get_image_classifier_kr
+from tests.utils import get_tabular_classifier_kr, get_image_classifier_pt
+from tests.attacks.utils import backend_test_classifier_type_check_fail
+
+logger = logging.getLogger(__name__)
+
+
+class TestAdversarialPatch(TestBase):
+    """
+    A unittest class for testing Adversarial Patch attack.
+    """
+
+    @classmethod
+    def setUpClass(cls):
+        master_seed(seed=1234)
+        super().setUpClass()
+
+        cls.n_train = 1
+        cls.n_test = 1
+        cls.x_train_mnist = cls.x_train_mnist[0 : cls.n_train]
+        cls.y_train_mnist = cls.y_train_mnist[0 : cls.n_train]
+        cls.x_test_mnist = cls.x_test_mnist[0 : cls.n_test]
+        cls.y_test_mnist = cls.y_test_mnist[0 : cls.n_test]
+
+    def setUp(self):
+        master_seed(seed=1234)
+        super().setUp()
+
+    def test_2_tensorflow_numpy(self):
+        """
+        First test with the TensorFlowClassifier.
+        :return:
+        """
+        import tensorflow as tf
+
+        tfc, sess = get_image_classifier_tf(from_logits=True)
+
+        attack_ap = AdversarialPatchNumpy(
+            tfc, rotation_max=0.5, scale_min=0.4, scale_max=0.41, learning_rate=5.0, batch_size=10, max_iter=5,
+        )
+
+        target = np.zeros(self.x_train_mnist.shape[0])
+        patch_adv, _ = attack_ap.generate(self.x_train_mnist, target, shuffle=False)
+
+        if tf.__version__[0] == "2":
+            self.assertAlmostEqual(patch_adv[8, 8, 0], 0.67151666, delta=0.05)
+            self.assertAlmostEqual(patch_adv[14, 14, 0], 0.6292826, delta=0.05)
+            self.assertAlmostEqual(float(np.sum(patch_adv)), 424.31439208984375, delta=1.0)
+        else:
+            self.assertAlmostEqual(patch_adv[8, 8, 0], 0.67151666, delta=0.05)
+            self.assertAlmostEqual(patch_adv[14, 14, 0], 0.6292826, delta=0.05)
+            self.assertAlmostEqual(float(np.sum(patch_adv)), 424.31439208984375, delta=1.0)
+
+        # insert_transformed_patch
+        x_out = attack_ap.insert_transformed_patch(
+            self.x_train_mnist[0], np.ones((14, 14, 1)), np.asarray([[2, 13], [2, 18], [12, 22], [8, 13]])
+        )
+        x_out_expexted = np.array(
+            [
+                0.0,
+                0.0,
+                1.0,
+                1.0,
+                1.0,
+                1.0,
+                1.0,
+                1.0,
+                1.0,
+                0.84313726,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.1764706,
+                0.7294118,
+                0.99215686,
+                0.99215686,
+                0.5882353,
+                0.10588235,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+            ],
+            dtype=np.float32,
+        )
+        np.testing.assert_almost_equal(x_out[15, :, 0], x_out_expexted, decimal=3)
+
+        if sess is not None:
+            sess.close()
+
+    @unittest.skipIf(int(tf.__version__.split(".")[0]) != 2, reason="Skip unittests if not TensorFlow>=2.0.")
+    def test_3_tensorflow_v2_framework(self):
+        """
+        First test with the TensorFlowClassifier.
+        :return:
+        """
+        tfc, _ = get_image_classifier_tf(from_logits=True)
+
+        attack_ap = AdversarialPatch(
+            tfc,
+            rotation_max=0.5,
+            scale_min=0.4,
+            scale_max=0.41,
+            learning_rate=5.0,
+            batch_size=10,
+            max_iter=10,
+            patch_shape=(28, 28, 1),
+        )
+
+        target = np.zeros(self.x_train_mnist.shape[0])
+        patch_adv, _ = attack_ap.generate(self.x_train_mnist, target, shuffle=False)
+
+        self.assertAlmostEqual(patch_adv[8, 8, 0], 1.0, delta=0.05)
+        self.assertAlmostEqual(patch_adv[14, 14, 0], 0.0, delta=0.05)
+        self.assertAlmostEqual(float(np.sum(patch_adv)), 377.415771484375, delta=1.0)
+
+        # insert_transformed_patch
+        x_out = attack_ap.insert_transformed_patch(
+            self.x_train_mnist[0], np.ones((14, 14, 1)), np.asarray([[2, 13], [2, 18], [12, 22], [8, 13]])
+        )
+        x_out_expexted = np.array(
+            [
+                0.0,
+                0.0,
+                1.0,
+                1.0,
+                1.0,
+                1.0,
+                1.0,
+                1.0,
+                1.0,
+                0.84313726,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.1764706,
+                0.7294118,
+                0.99215686,
+                0.99215686,
+                0.5882353,
+                0.10588235,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+            ],
+            dtype=np.float32,
+        )
+        np.testing.assert_almost_equal(x_out[15, :, 0], x_out_expexted, decimal=3)
+
+    @unittest.skipIf(
+        int(keras.__version__.split(".")[0]) == 2 and int(keras.__version__.split(".")[1]) < 3,
+        reason="Skip unittests if not Keras>=2.3.",
+    )
+    def test_6_keras(self):
+        """
+        Second test with the KerasClassifier.
+        :return:
+        """
+        krc = get_image_classifier_kr(from_logits=True)
+
+        attack_ap = AdversarialPatch(
+            krc, rotation_max=0.5, scale_min=0.4, scale_max=0.41, learning_rate=5.0, batch_size=10, max_iter=5
+        )
+
+        target = np.zeros(self.x_train_mnist.shape[0])
+        patch_adv, _ = attack_ap.generate(self.x_train_mnist, target)
+
+        self.assertAlmostEqual(patch_adv[8, 8, 0], 0.67151666, delta=0.05)
+        self.assertAlmostEqual(patch_adv[14, 14, 0], 0.6292826, delta=0.05)
+        self.assertAlmostEqual(float(np.sum(patch_adv)), 424.31439208984375, delta=1.0)
+
+        # insert_transformed_patch
+        x_out = attack_ap.insert_transformed_patch(
+            self.x_train_mnist[0], np.ones((14, 14, 1)), np.asarray([[2, 13], [2, 18], [12, 22], [8, 13]])
+        )
+        x_out_expexted = np.array(
+            [
+                0.0,
+                0.0,
+                1.0,
+                1.0,
+                1.0,
+                1.0,
+                1.0,
+                1.0,
+                1.0,
+                0.84313726,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.1764706,
+                0.7294118,
+                0.99215686,
+                0.99215686,
+                0.5882353,
+                0.10588235,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+            ],
+            dtype=np.float32,
+        )
+        np.testing.assert_almost_equal(x_out[15, :, 0], x_out_expexted, decimal=3)
+
+    def test_4_pytorch(self):
+        """
+        Third test with the PyTorchClassifier.
+        :return:
+        """
+        ptc = get_image_classifier_pt(from_logits=True)
+
+        x_train = np.reshape(self.x_train_mnist, (self.n_train, 1, 28, 28)).astype(np.float32)
+
+        attack_ap = AdversarialPatch(
+            ptc, rotation_max=0.5, scale_min=0.4, scale_max=0.41, learning_rate=5.0, batch_size=10, max_iter=5
+        )
+
+        target = np.zeros(self.x_train_mnist.shape[0])
+        patch_adv, _ = attack_ap.generate(x_train, target)
+
+        self.assertAlmostEqual(patch_adv[0, 8, 8], 0.6715167, delta=0.05)
+        self.assertAlmostEqual(patch_adv[0, 14, 14], 0.6292826, delta=0.05)
+        self.assertAlmostEqual(float(np.sum(patch_adv)), 424.31439208984375, delta=1.0)
+
+    def test_5_failure_feature_vectors(self):
+        classifier = get_tabular_classifier_kr()
+        classifier._clip_values = (0, 1)
+
+        # Assert that value error is raised for feature vectors
+        with self.assertRaises(ValueError) as context:
+            _ = AdversarialPatch(classifier=classifier)
+
+        self.assertIn(
+            "Unexpected input_shape in estimator detected. AdversarialPatch is expecting images or videos as input.",
+            str(context.exception),
+        )
+
+    def test_1_classifier_type_check_fail(self):
+        backend_test_classifier_type_check_fail(AdversarialPatch, [BaseEstimator, NeuralNetworkMixin, ClassifierMixin])
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/adversarial-robustness-toolbox/tests/attacks/test_backdoor_attack.py b/adversarial-robustness-toolbox/tests/attacks/test_backdoor_attack.py
new file mode 100644
index 0000000..4971430
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/attacks/test_backdoor_attack.py
@@ -0,0 +1,198 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import os
+import logging
+import unittest
+
+import numpy as np
+
+from art.attacks.poisoning.backdoor_attack import PoisoningAttackBackdoor
+from art.attacks.poisoning.perturbations import add_pattern_bd, add_single_bd, insert_image
+from art.utils import to_categorical
+
+from tests.utils import TestBase, master_seed, get_image_classifier_kr
+
+logger = logging.getLogger(__name__)
+
+PP_POISON = 0.33
+NB_EPOCHS = 3
+
+
+class TestBackdoorAttack(TestBase):
+    """
+    A unittest class for testing Backdoor Poisoning attack.
+    """
+
+    def setUp(self):
+        master_seed(seed=301)
+        self.backdoor_path = os.path.join(
+            os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(__file__)))),
+            "utils",
+            "data",
+            "backdoors",
+            "alert.png",
+        )
+        super().setUp()
+
+    @staticmethod
+    def poison_dataset(x_clean, y_clean, poison_func):
+        x_poison = np.copy(x_clean)
+        y_poison = np.copy(y_clean)
+        is_poison = np.zeros(np.shape(y_poison)[0])
+
+        for i in range(10):
+            src = i
+            tgt = (i + 1) % 10
+            n_points_in_tgt = np.round(np.sum(np.argmax(y_clean, axis=1) == tgt))
+            num_poison = int((PP_POISON * n_points_in_tgt) / (1 - PP_POISON))
+            src_imgs = np.copy(x_clean[np.argmax(y_clean, axis=1) == src])
+
+            n_points_in_src = np.shape(src_imgs)[0]
+            if num_poison:
+                indices_to_be_poisoned = np.random.choice(n_points_in_src, num_poison)
+
+                imgs_to_be_poisoned = src_imgs[indices_to_be_poisoned]
+                backdoor_attack = PoisoningAttackBackdoor(poison_func)
+                poison_images, poison_labels = backdoor_attack.poison(
+                    imgs_to_be_poisoned, y=to_categorical(np.ones(num_poison) * tgt, 10)
+                )
+                x_poison = np.append(x_poison, poison_images, axis=0)
+                y_poison = np.append(y_poison, poison_labels, axis=0)
+                is_poison = np.append(is_poison, np.ones(num_poison))
+
+        is_poison = is_poison != 0
+
+        return is_poison, x_poison, y_poison
+
+    def poison_func_1(self, x):
+        max_val = np.max(self.x_train_mnist)
+        return np.expand_dims(add_pattern_bd(x.squeeze(3), pixel_value=max_val), axis=3)
+
+    def poison_func_2(self, x):
+        max_val = np.max(self.x_train_mnist)
+        return np.expand_dims(add_single_bd(x.squeeze(3), pixel_value=max_val), axis=3)
+
+    def poison_func_3(self, x):
+        return insert_image(x, backdoor_path=self.backdoor_path, size=(5, 5), random=False, x_shift=3, y_shift=3)
+
+    def poison_func_4(self, x):
+        return insert_image(x, backdoor_path=self.backdoor_path, size=(5, 5), random=True)
+
+    def poison_func_5(self, x):
+        return insert_image(x, backdoor_path=self.backdoor_path, random=True, size=(100, 100))
+
+    def poison_func_6(self, x):
+        return insert_image(x, backdoor_path=self.backdoor_path, random=True, size=(100, 100))
+
+    def test_backdoor_pattern(self):
+        """
+        Test the backdoor attack with a pattern-based perturbation can be trained on classifier
+        """
+
+        krc = get_image_classifier_kr()
+        (is_poison_train, x_poisoned_raw, y_poisoned_raw) = self.poison_dataset(
+            self.x_train_mnist, self.y_train_mnist, self.poison_func_1
+        )
+        # Shuffle training data
+        n_train = np.shape(y_poisoned_raw)[0]
+        shuffled_indices = np.arange(n_train)
+        np.random.shuffle(shuffled_indices)
+        x_train = x_poisoned_raw[shuffled_indices]
+        y_train = y_poisoned_raw[shuffled_indices]
+
+        krc.fit(x_train, y_train, nb_epochs=NB_EPOCHS, batch_size=32)
+
+    def test_backdoor_pixel(self):
+        """
+        Test the backdoor attack with a pixel-based perturbation can be trained on classifier
+        """
+
+        krc = get_image_classifier_kr()
+        (is_poison_train, x_poisoned_raw, y_poisoned_raw) = self.poison_dataset(
+            self.x_train_mnist, self.y_train_mnist, self.poison_func_2
+        )
+
+        # Shuffle training data
+        n_train = np.shape(y_poisoned_raw)[0]
+        shuffled_indices = np.arange(n_train)
+        np.random.shuffle(shuffled_indices)
+        x_train = x_poisoned_raw[shuffled_indices]
+        y_train = y_poisoned_raw[shuffled_indices]
+
+        krc.fit(x_train, y_train, nb_epochs=NB_EPOCHS, batch_size=32)
+
+    def test_backdoor_image(self):
+        """
+        Test the backdoor attack with a image-based perturbation can be trained on classifier
+        """
+        krc = get_image_classifier_kr()
+        (is_poison_train, x_poisoned_raw, y_poisoned_raw) = self.poison_dataset(
+            self.x_train_mnist, self.y_train_mnist, self.poison_func_3
+        )
+
+        # Shuffle training data
+        n_train = np.shape(y_poisoned_raw)[0]
+        shuffled_indices = np.arange(n_train)
+        np.random.shuffle(shuffled_indices)
+        x_train = x_poisoned_raw[shuffled_indices]
+        y_train = y_poisoned_raw[shuffled_indices]
+
+        krc.fit(x_train, y_train, nb_epochs=NB_EPOCHS, batch_size=32)
+
+    def test_multiple_perturbations(self):
+        """
+        Test using multiple perturbation functions in the same attack can be trained on classifier
+        """
+
+        krc = get_image_classifier_kr()
+        (is_poison_train, x_poisoned_raw, y_poisoned_raw) = self.poison_dataset(
+            self.x_train_mnist, self.y_train_mnist, [self.poison_func_4, self.poison_func_1]
+        )
+
+        # Shuffle training data
+        n_train = np.shape(y_poisoned_raw)[0]
+        shuffled_indices = np.arange(n_train)
+        np.random.shuffle(shuffled_indices)
+        x_train = x_poisoned_raw[shuffled_indices]
+        y_train = y_poisoned_raw[shuffled_indices]
+
+        krc.fit(x_train, y_train, nb_epochs=NB_EPOCHS, batch_size=32)
+
+    def test_image_failure_modes(self):
+        """
+        Tests failure modes for image perturbation functions
+        """
+        backdoor_attack = PoisoningAttackBackdoor(self.poison_func_5)
+        adv_target = np.argmax(self.y_train_mnist) + 1 % 10
+        with self.assertRaises(ValueError) as context:
+            backdoor_attack.poison(self.x_train_mnist, y=adv_target)
+
+        self.assertIn("Backdoor does not fit inside original image", str(context.exception))
+
+        backdoor_attack = PoisoningAttackBackdoor(self.poison_func_6)
+
+        with self.assertRaises(ValueError) as context:
+            backdoor_attack.poison(np.zeros(5), y=np.ones(5))
+
+        self.assertIn("Invalid array shape", str(context.exception))
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/adversarial-robustness-toolbox/tests/attacks/test_carlini.py b/adversarial-robustness-toolbox/tests/attacks/test_carlini.py
new file mode 100644
index 0000000..d0fdd09
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/attacks/test_carlini.py
@@ -0,0 +1,593 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import unittest
+import keras
+import keras.backend as k
+import numpy as np
+
+from art.attacks.evasion.carlini import CarliniL2Method, CarliniLInfMethod
+from art.estimators.classification.keras import KerasClassifier
+from art.estimators.estimator import BaseEstimator
+from art.estimators.classification.classifier import ClassGradientsMixin
+from art.utils import random_targets, to_categorical
+
+from tests.utils import TestBase, master_seed
+from tests.utils import get_image_classifier_tf, get_image_classifier_kr, get_image_classifier_pt
+from tests.utils import get_tabular_classifier_tf, get_tabular_classifier_kr, get_tabular_classifier_pt
+from tests.attacks.utils import backend_test_classifier_type_check_fail
+
+logger = logging.getLogger(__name__)
+
+
+class TestCarlini(TestBase):
+    """
+    A unittest class for testing the Carlini L2 attack.
+    """
+
+    @classmethod
+    def setUpClass(cls):
+        super().setUpClass()
+
+        cls.n_train = 10
+        cls.n_test = 10
+        cls.x_train_mnist = cls.x_train_mnist[0 : cls.n_train]
+        cls.y_train_mnist = cls.y_train_mnist[0 : cls.n_train]
+        cls.x_test_mnist = cls.x_test_mnist[0 : cls.n_test]
+        cls.y_test_mnist = cls.y_test_mnist[0 : cls.n_test]
+
+    def setUp(self):
+        master_seed(seed=1234)
+        super().setUp()
+
+    def test_tensorflow_failure_attack_L2(self):
+        """
+        Test the corner case when attack is failed.
+        :return:
+        """
+        x_test_original = self.x_test_mnist.copy()
+
+        # Build TensorFlowClassifier
+        tfc, sess = get_image_classifier_tf(from_logits=True)
+
+        # Failure attack
+        cl2m = CarliniL2Method(
+            classifier=tfc, targeted=True, max_iter=0, binary_search_steps=0, learning_rate=0, initial_const=1
+        )
+        params = {"y": random_targets(self.y_test_mnist, tfc.nb_classes)}
+        x_test_adv = cl2m.generate(self.x_test_mnist, **params)
+        self.assertLessEqual(np.amax(x_test_adv), 1.0)
+        self.assertGreaterEqual(np.amin(x_test_adv), 0.0)
+        np.testing.assert_array_almost_equal(self.x_test_mnist, x_test_adv, decimal=3)
+
+        # Check that x_test has not been modified by attack and classifier
+        self.assertAlmostEqual(float(np.max(np.abs(x_test_original - self.x_test_mnist))), 0.0, delta=0.00001)
+
+        # Clean-up session
+        if sess is not None:
+            sess.close()
+
+    def test_tensorflow_mnist_L2(self):
+        """
+        First test with the TensorFlowClassifier.
+        :return:
+        """
+        x_test_original = self.x_test_mnist.copy()
+
+        # Build TensorFlowClassifier
+        tfc, sess = get_image_classifier_tf(from_logits=True)
+
+        # First attack
+        cl2m = CarliniL2Method(classifier=tfc, targeted=True, max_iter=10)
+        params = {"y": random_targets(self.y_test_mnist, tfc.nb_classes)}
+        x_test_adv = cl2m.generate(self.x_test_mnist, **params)
+        self.assertFalse((self.x_test_mnist == x_test_adv).all())
+        self.assertLessEqual(np.amax(x_test_adv), 1.0)
+        self.assertGreaterEqual(np.amin(x_test_adv), 0.0)
+        target = np.argmax(params["y"], axis=1)
+        y_pred_adv = np.argmax(tfc.predict(x_test_adv), axis=1)
+        logger.debug("CW2 Target: %s", target)
+        logger.debug("CW2 Actual: %s", y_pred_adv)
+        logger.info("CW2 Success Rate: %.2f", (np.sum(target == y_pred_adv) / float(len(target))))
+        self.assertTrue((target == y_pred_adv).any())
+
+        # Second attack, no batching
+        cl2m = CarliniL2Method(classifier=tfc, targeted=False, max_iter=10, batch_size=1)
+        x_test_adv = cl2m.generate(self.x_test_mnist)
+        self.assertLessEqual(np.amax(x_test_adv), 1.0)
+        self.assertGreaterEqual(np.amin(x_test_adv), 0.0)
+        target = np.argmax(params["y"], axis=1)
+        y_pred_adv = np.argmax(tfc.predict(x_test_adv), axis=1)
+        logger.debug("CW2 Target: %s", target)
+        logger.debug("CW2 Actual: %s", y_pred_adv)
+        logger.info("CW2 Success Rate: %.2f", (np.sum(target == y_pred_adv) / float(len(target))))
+        self.assertTrue((target != y_pred_adv).any())
+
+        # Check that x_test has not been modified by attack and classifier
+        self.assertAlmostEqual(float(np.max(np.abs(x_test_original - self.x_test_mnist))), 0.0, delta=0.00001)
+
+        # Clean-up session
+        if sess is not None:
+            sess.close()
+
+    # @unittest.skipIf(
+    #     not (int(keras.__version__.split(".")[0]) == 2 and int(keras.__version__.split(".")[1]) >= 3),
+    #     reason="Minimal version of Keras or TensorFlow required.",
+    # )
+    # def test_keras_mnist_L2(self):
+    #     """
+    #     Second test with the KerasClassifier.
+    #     :return:
+    #     """
+    #     x_test_original = self.x_test_mnist.copy()
+    #
+    #     # Build KerasClassifier
+    #     krc = get_image_classifier_kr(from_logits=True)
+    #
+    #     # First attack
+    #     cl2m = CarliniL2Method(classifier=krc, targeted=True, max_iter=10)
+    #     y_target = [6, 6, 7, 4, 9, 7, 9, 0, 1, 0]
+    #     x_test_adv = cl2m.generate(self.x_test_mnist, y=to_categorical(y_target, nb_classes=10))
+    #     self.assertFalse((self.x_test_mnist == x_test_adv).all())
+    #     self.assertLessEqual(np.amax(x_test_adv), 1.0)
+    #     self.assertGreaterEqual(np.amin(x_test_adv), 0.0)
+    #     y_pred_adv = np.argmax(krc.predict(x_test_adv), axis=1)
+    #     logger.debug("CW2 Target: %s", y_target)
+    #     logger.debug("CW2 Actual: %s", y_pred_adv)
+    #     logger.info("CW2 Success Rate: %.2f", (np.sum(y_target == y_pred_adv) / float(len(y_target))))
+    #     self.assertTrue((y_target == y_pred_adv).any())
+    #
+    #     # Second attack
+    #     cl2m = CarliniL2Method(classifier=krc, targeted=False, max_iter=10)
+    #     x_test_adv = cl2m.generate(self.x_test_mnist)
+    #     self.assertLessEqual(np.amax(x_test_adv), 1.0)
+    #     self.assertGreaterEqual(np.amin(x_test_adv), 0.0)
+    #     y_pred_adv = np.argmax(krc.predict(x_test_adv), axis=1)
+    #     logger.debug("CW2 Target: %s", y_target)
+    #     logger.debug("CW2 Actual: %s", y_pred_adv)
+    #     logger.info("CW2 Success Rate: %.2f", (np.sum(y_target != y_pred_adv) / float(len(y_target))))
+    #     self.assertTrue((y_target != y_pred_adv).any())
+    #
+    #     # Check that x_test has not been modified by attack and classifier
+    #     self.assertAlmostEqual(float(np.max(np.abs(x_test_original - self.x_test_mnist))), 0.0, delta=0.00001)
+    #
+    #     # Clean-up
+    #     k.clear_session()
+    #
+    # def test_pytorch_mnist_L2(self):
+    #     """
+    #     Third test with the PyTorchClassifier.
+    #     :return:
+    #     """
+    #     x_test = np.reshape(self.x_test_mnist, (self.x_test_mnist.shape[0], 1, 28, 28)).astype(np.float32)
+    #     x_test_original = x_test.copy()
+    #
+    #     # Build PyTorchClassifier
+    #     ptc = get_image_classifier_pt(from_logits=True)
+    #
+    #     # First attack
+    #     cl2m = CarliniL2Method(classifier=ptc, targeted=True, max_iter=10)
+    #     params = {"y": random_targets(self.y_test_mnist, ptc.nb_classes)}
+    #     x_test_adv = cl2m.generate(x_test, **params)
+    #     self.assertFalse((x_test == x_test_adv).all())
+    #     self.assertLessEqual(np.amax(x_test_adv), 1.0)
+    #     self.assertGreaterEqual(np.amin(x_test_adv), 0.0)
+    #     target = np.argmax(params["y"], axis=1)
+    #     y_pred_adv = np.argmax(ptc.predict(x_test_adv), axis=1)
+    #     self.assertTrue((target == y_pred_adv).any())
+    #     logger.info("CW2 Success Rate: %.2f", (sum(target == y_pred_adv) / float(len(target))))
+    #
+    #     # Second attack
+    #     cl2m = CarliniL2Method(classifier=ptc, targeted=False, max_iter=10)
+    #     x_test_adv = cl2m.generate(x_test)
+    #     self.assertLessEqual(np.amax(x_test_adv), 1.0)
+    #     self.assertGreaterEqual(np.amin(x_test_adv), 0.0)
+    #     target = np.argmax(params["y"], axis=1)
+    #     y_pred_adv = np.argmax(ptc.predict(x_test_adv), axis=1)
+    #     self.assertTrue((target != y_pred_adv).any())
+    #     logger.info("CW2 Success Rate: %.2f", (sum(target != y_pred_adv) / float(len(target))))
+    #
+    #     # Check that x_test has not been modified by attack and classifier
+    #     self.assertAlmostEqual(float(np.max(np.abs(x_test_original - x_test))), 0.0, delta=0.00001)
+
+    def test_classifier_type_check_fail_L2(self):
+        backend_test_classifier_type_check_fail(CarliniL2Method, [BaseEstimator, ClassGradientsMixin])
+
+    # def test_keras_iris_clipped_L2(self):
+    #     classifier = get_tabular_classifier_kr()
+    #     attack = CarliniL2Method(classifier, targeted=False, max_iter=10)
+    #     x_test_adv = attack.generate(self.x_test_iris)
+    #     self.assertFalse((self.x_test_iris == x_test_adv).all())
+    #     self.assertLessEqual(np.amax(x_test_adv), 1.0)
+    #     self.assertGreaterEqual(np.amin(x_test_adv), 0.0)
+    #
+    #     predictions_adv = np.argmax(classifier.predict(x_test_adv), axis=1)
+    #     self.assertFalse((np.argmax(self.y_test_iris, axis=1) == predictions_adv).all())
+    #     accuracy = np.sum(predictions_adv == np.argmax(self.y_test_iris, axis=1)) / self.y_test_iris.shape[0]
+    #     logger.info("Accuracy on Iris with C&W adversarial examples: %.2f%%", (accuracy * 100))
+    #
+    # def test_keras_iris_unbounded_L2(self):
+    #     classifier = get_tabular_classifier_kr()
+    #
+    #     # Recreate a classifier without clip values
+    #     classifier = KerasClassifier(model=classifier._model, use_logits=False, channels_first=True)
+    #     attack = CarliniL2Method(classifier, targeted=False, max_iter=10)
+    #     x_test_adv = attack.generate(self.x_test_iris)
+    #     self.assertFalse((self.x_test_iris == x_test_adv).all())
+    #
+    #     predictions_adv = np.argmax(classifier.predict(x_test_adv), axis=1)
+    #     self.assertFalse((np.argmax(self.y_test_iris, axis=1) == predictions_adv).all())
+    #     accuracy = np.sum(predictions_adv == np.argmax(self.y_test_iris, axis=1)) / self.y_test_iris.shape[0]
+    #     logger.info("Accuracy on Iris with C&W adversarial examples: %.2f%%", (accuracy * 100))
+
+    # def test_tensorflow_iris_L2(self):
+    #     classifier, _ = get_tabular_classifier_tf()
+    #
+    #     # Test untargeted attack
+    #     attack = CarliniL2Method(classifier, targeted=False, max_iter=10)
+    #     x_test_adv = attack.generate(self.x_test_iris)
+    #     self.assertFalse((self.x_test_iris == x_test_adv).all())
+    #     self.assertLessEqual(np.amax(x_test_adv), 1.0)
+    #     self.assertGreaterEqual(np.amin(x_test_adv), 0.0)
+    #
+    #     predictions_adv = np.argmax(classifier.predict(x_test_adv), axis=1)
+    #     self.assertFalse((np.argmax(self.y_test_iris, axis=1) == predictions_adv).all())
+    #     accuracy = np.sum(predictions_adv == np.argmax(self.y_test_iris, axis=1)) / self.y_test_iris.shape[0]
+    #     logger.info("Accuracy on Iris with C&W adversarial examples: %.2f%%", (accuracy * 100))
+    #
+    #     # Test targeted attack
+    #     targets = random_targets(self.y_test_iris, nb_classes=3)
+    #     attack = CarliniL2Method(classifier, targeted=True, max_iter=10)
+    #     x_test_adv = attack.generate(self.x_test_iris, **{"y": targets})
+    #     self.assertFalse((self.x_test_iris == x_test_adv).all())
+    #     self.assertLessEqual(np.amax(x_test_adv), 1.0)
+    #     self.assertGreaterEqual(np.amin(x_test_adv), 0.0)
+    #
+    #     predictions_adv = np.argmax(classifier.predict(x_test_adv), axis=1)
+    #     self.assertTrue((np.argmax(targets, axis=1) == predictions_adv).any())
+    #     accuracy = np.sum(predictions_adv == np.argmax(targets, axis=1)) / self.y_test_iris.shape[0]
+    #     logger.info("Success rate of targeted C&W on Iris: %.2f%%", (accuracy * 100))
+
+    # def test_pytorch_iris_L2(self):
+    #     classifier = get_tabular_classifier_pt()
+    #     attack = CarliniL2Method(classifier, targeted=False, max_iter=10)
+    #     x_test_adv = attack.generate(self.x_test_iris)
+    #     self.assertFalse((self.x_test_iris == x_test_adv).all())
+    #     self.assertLessEqual(np.amax(x_test_adv), 1.0)
+    #     self.assertGreaterEqual(np.amin(x_test_adv), 0.0)
+    #
+    #     predictions_adv = np.argmax(classifier.predict(x_test_adv), axis=1)
+    #     self.assertFalse((np.argmax(self.y_test_iris, axis=1) == predictions_adv).all())
+    #     accuracy = np.sum(predictions_adv == np.argmax(self.y_test_iris, axis=1)) / self.y_test_iris.shape[0]
+    #     logger.info("Accuracy on Iris with C&W adversarial examples: %.2f%%", (accuracy * 100))
+
+    # def test_scikitlearn_L2(self):
+    #     from sklearn.linear_model import LogisticRegression
+    #     from sklearn.svm import SVC, LinearSVC
+    #
+    #     from art.estimators.classification.scikitlearn import SklearnClassifier
+    #
+    #     scikitlearn_test_cases = [
+    #         LogisticRegression(solver="lbfgs", multi_class="auto"),
+    #         SVC(gamma="auto"),
+    #         LinearSVC(),
+    #     ]
+    #
+    #     x_test_original = self.x_test_iris.copy()
+    #
+    #     for model in scikitlearn_test_cases:
+    #         classifier = SklearnClassifier(model=model, clip_values=(0, 1))
+    #         classifier.fit(x=self.x_test_iris, y=self.y_test_iris)
+    #
+    #         # Test untargeted attack
+    #         attack = CarliniL2Method(classifier, targeted=False, max_iter=2)
+    #         x_test_adv = attack.generate(self.x_test_iris)
+    #         self.assertFalse((self.x_test_iris == x_test_adv).all())
+    #         self.assertLessEqual(np.amax(x_test_adv), 1.0)
+    #         self.assertGreaterEqual(np.amin(x_test_adv), 0.0)
+    #
+    #         predictions_adv = np.argmax(classifier.predict(x_test_adv), axis=1)
+    #         self.assertFalse((np.argmax(self.y_test_iris, axis=1) == predictions_adv).all())
+    #         accuracy = np.sum(predictions_adv == np.argmax(self.y_test_iris, axis=1)) / self.y_test_iris.shape[0]
+    #         logger.info(
+    #             "Accuracy of " + classifier.__class__.__name__ + " on Iris with C&W adversarial examples: " "%.2f%%",
+    #             (accuracy * 100),
+    #         )
+    #
+    #         # Test targeted attack
+    #         targets = random_targets(self.y_test_iris, nb_classes=3)
+    #         attack = CarliniL2Method(classifier, targeted=True, max_iter=2)
+    #         x_test_adv = attack.generate(self.x_test_iris, **{"y": targets})
+    #         self.assertFalse((self.x_test_iris == x_test_adv).all())
+    #         self.assertLessEqual(np.amax(x_test_adv), 1.0)
+    #         self.assertGreaterEqual(np.amin(x_test_adv), 0.0)
+    #
+    #         predictions_adv = np.argmax(classifier.predict(x_test_adv), axis=1)
+    #         self.assertTrue((np.argmax(targets, axis=1) == predictions_adv).any())
+    #         accuracy = np.sum(predictions_adv == np.argmax(targets, axis=1)) / self.y_test_iris.shape[0]
+    #         logger.info(
+    #             "Success rate of " + classifier.__class__.__name__ + " on targeted C&W on Iris: %.2f%%",
+    #             (accuracy * 100),
+    #         )
+    #
+    #         # Check that x_test has not been modified by attack and classifier
+    #         self.assertAlmostEqual(float(np.max(np.abs(x_test_original - self.x_test_iris))), 0.0, delta=0.00001)
+
+    """
+    A unittest class for testing the Carlini LInf attack.
+    """
+
+    def test_tensorflow_failure_attack_LInf(self):
+        """
+        Test the corner case when attack is failed.
+        :return:
+        """
+        # Build TensorFlowClassifier
+        tfc, sess = get_image_classifier_tf(from_logits=True)
+
+        # Failure attack
+        clinfm = CarliniLInfMethod(classifier=tfc, targeted=True, max_iter=0, learning_rate=0, eps=0.5)
+        params = {"y": random_targets(self.y_test_mnist, tfc.nb_classes)}
+        x_test_adv = clinfm.generate(self.x_test_mnist, **params)
+        self.assertLessEqual(np.amax(x_test_adv), 1.0)
+        self.assertGreaterEqual(np.amin(x_test_adv), 0.0)
+        self.assertTrue(np.allclose(self.x_test_mnist, x_test_adv, atol=1e-3))
+
+        # Clean-up session
+        if sess is not None:
+            sess.close()
+
+    def test_tensorflow_mnist_LInf(self):
+        """
+        First test with the TensorFlowClassifier.
+        :return:
+        """
+        # Build TensorFlowClassifier
+        tfc, sess = get_image_classifier_tf(from_logits=True)
+
+        # First attack
+        clinfm = CarliniLInfMethod(classifier=tfc, targeted=True, max_iter=10, eps=0.5)
+        params = {"y": random_targets(self.y_test_mnist, tfc.nb_classes)}
+        x_test_adv = clinfm.generate(self.x_test_mnist, **params)
+        self.assertFalse((self.x_test_mnist == x_test_adv).all())
+        self.assertLessEqual(np.amax(x_test_adv), 1.0)
+        self.assertGreaterEqual(np.amin(x_test_adv), 0.0)
+        target = np.argmax(params["y"], axis=1)
+        y_pred_adv = np.argmax(tfc.predict(x_test_adv), axis=1)
+        logger.debug("CW0 Target: %s", target)
+        logger.debug("CW0 Actual: %s", y_pred_adv)
+        logger.info("CW0 Success Rate: %.2f", (np.sum(target == y_pred_adv) / float(len(target))))
+        self.assertTrue((target == y_pred_adv).any())
+
+        # Second attack, no batching
+        clinfm = CarliniLInfMethod(classifier=tfc, targeted=False, max_iter=10, eps=0.5, batch_size=1)
+        x_test_adv = clinfm.generate(self.x_test_mnist)
+        self.assertLessEqual(np.amax(x_test_adv), 1.0)
+        self.assertGreaterEqual(np.amin(x_test_adv), -1e-6)
+        target = np.argmax(params["y"], axis=1)
+        y_pred_adv = np.argmax(tfc.predict(x_test_adv), axis=1)
+        logger.debug("CW0 Target: %s", target)
+        logger.debug("CW0 Actual: %s", y_pred_adv)
+        logger.info("CW0 Success Rate: %.2f", (np.sum(target != y_pred_adv) / float(len(target))))
+        self.assertTrue((target != y_pred_adv).any())
+
+        # Clean-up session
+        if sess is not None:
+            sess.close()
+
+    # @unittest.skipIf(
+    #     not (int(keras.__version__.split(".")[0]) == 2 and int(keras.__version__.split(".")[1]) >= 3),
+    #     reason="Keras 2.3 or later or TensorFlow-Keras required to support selected combination of loss "
+    #     "function and logits.",
+    # )
+    # def test_keras_mnist_LInf(self):
+    #     """
+    #     Second test with the KerasClassifier.
+    #     :return:
+    #     """
+    #     # Build KerasClassifier
+    #     krc = get_image_classifier_kr(from_logits=True)
+    #
+    #     # First attack
+    #     clinfm = CarliniLInfMethod(classifier=krc, targeted=True, max_iter=10, eps=0.5)
+    #     params = {"y": random_targets(self.y_test_mnist, krc.nb_classes)}
+    #     x_test_adv = clinfm.generate(self.x_test_mnist, **params)
+    #     self.assertFalse((self.x_test_mnist == x_test_adv).all())
+    #     self.assertLessEqual(np.amax(x_test_adv), 1.000001)
+    #     self.assertGreaterEqual(np.amin(x_test_adv), -1e-6)
+    #     target = np.argmax(params["y"], axis=1)
+    #     y_pred_adv = np.argmax(krc.predict(x_test_adv), axis=1)
+    #     logger.debug("CW0 Target: %s", target)
+    #     logger.debug("CW0 Actual: %s", y_pred_adv)
+    #     logger.info("CW0 Success Rate: %.2f", (np.sum(target == y_pred_adv) / float(len(target))))
+    #     self.assertTrue((target == y_pred_adv).any())
+    #
+    #     # Second attack
+    #     clinfm = CarliniLInfMethod(classifier=krc, targeted=False, max_iter=10, eps=0.5)
+    #     x_test_adv = clinfm.generate(self.x_test_mnist)
+    #     self.assertLessEqual(np.amax(x_test_adv), 1.000001)
+    #     self.assertGreaterEqual(np.amin(x_test_adv), -1e-6)
+    #     target = np.argmax(params["y"], axis=1)
+    #     y_pred_adv = np.argmax(krc.predict(x_test_adv), axis=1)
+    #     logger.debug("CW0 Target: %s", target)
+    #     logger.debug("CW0 Actual: %s", y_pred_adv)
+    #     logger.info("CW0 Success Rate: %.2f", (np.sum(target != y_pred_adv) / float(len(target))))
+    #     self.assertTrue((target != y_pred_adv).any())
+    #
+    #     # Clean-up
+    #     k.clear_session()
+    #
+    # def test_pytorch_mnist_LInf(self):
+    #     """
+    #     Third test with the PyTorchClassifier.
+    #     :return:
+    #     """
+    #     x_test = np.reshape(self.x_test_mnist, (self.x_test_mnist.shape[0], 1, 28, 28)).astype(np.float32)
+    #
+    #     # Build PyTorchClassifier
+    #     ptc = get_image_classifier_pt(from_logits=True)
+    #
+    #     # First attack
+    #     clinfm = CarliniLInfMethod(classifier=ptc, targeted=True, max_iter=10, eps=0.5)
+    #     params = {"y": random_targets(self.y_test_mnist, ptc.nb_classes)}
+    #     x_test_adv = clinfm.generate(x_test, **params)
+    #     self.assertFalse((x_test == x_test_adv).all())
+    #     self.assertLessEqual(np.amax(x_test_adv), 1.0 + 1e-6)
+    #     self.assertGreaterEqual(np.amin(x_test_adv), -1e-6)
+    #     target = np.argmax(params["y"], axis=1)
+    #     y_pred_adv = np.argmax(ptc.predict(x_test_adv), axis=1)
+    #     self.assertTrue((target == y_pred_adv).any())
+    #
+    #     # Second attack
+    #     clinfm = CarliniLInfMethod(classifier=ptc, targeted=False, max_iter=10, eps=0.5)
+    #     x_test_adv = clinfm.generate(x_test)
+    #     self.assertLessEqual(np.amax(x_test_adv), 1.0 + 1e-6)
+    #     self.assertGreaterEqual(np.amin(x_test_adv), -1e-6)
+    #
+    #     target = np.argmax(params["y"], axis=1)
+    #     y_pred_adv = np.argmax(ptc.predict(x_test_adv), axis=1)
+    #     self.assertTrue((target != y_pred_adv).any())
+
+    def test_classifier_type_check_fail_LInf(self):
+        backend_test_classifier_type_check_fail(CarliniLInfMethod, [BaseEstimator, ClassGradientsMixin])
+
+    # def test_keras_iris_clipped_LInf(self):
+    #     classifier = get_tabular_classifier_kr()
+    #     attack = CarliniLInfMethod(classifier, targeted=False, max_iter=10, eps=0.5)
+    #     x_test_adv = attack.generate(self.x_test_iris)
+    #     self.assertFalse((self.x_test_iris == x_test_adv).all())
+    #     self.assertLessEqual(np.amax(x_test_adv), 1.0)
+    #     self.assertGreaterEqual(np.amin(x_test_adv), 0.0)
+    #
+    #     predictions_adv = np.argmax(classifier.predict(x_test_adv), axis=1)
+    #     self.assertFalse((np.argmax(self.y_test_iris, axis=1) == predictions_adv).all())
+    #     accuracy = np.sum(predictions_adv == np.argmax(self.y_test_iris, axis=1)) / self.y_test_iris.shape[0]
+    #     logger.info("Accuracy on Iris with C&W adversarial examples: %.2f%%", (accuracy * 100))
+    #
+    # def test_keras_iris_unbounded_LInf(self):
+    #     classifier = get_tabular_classifier_kr()
+    #
+    #     # Recreate a classifier without clip values
+    #     classifier = KerasClassifier(model=classifier._model, use_logits=False, channels_first=True)
+    #     attack = CarliniLInfMethod(classifier, targeted=False, max_iter=10, eps=1)
+    #     x_test_adv = attack.generate(self.x_test_iris)
+    #     self.assertFalse((self.x_test_iris == x_test_adv).all())
+    #
+    #     predictions_adv = np.argmax(classifier.predict(x_test_adv), axis=1)
+    #     self.assertFalse((np.argmax(self.y_test_iris, axis=1) == predictions_adv).all())
+    #     accuracy = np.sum(predictions_adv == np.argmax(self.y_test_iris, axis=1)) / self.y_test_iris.shape[0]
+    #     logger.info("Accuracy on Iris with C&W adversarial examples: %.2f%%", (accuracy * 100))
+
+    # def test_tensorflow_iris_LInf(self):
+    #     classifier, _ = get_tabular_classifier_tf()
+    #
+    #     # Test untargeted attack
+    #     attack = CarliniLInfMethod(classifier, targeted=False, max_iter=10, eps=0.5)
+    #     x_test_adv = attack.generate(self.x_test_iris)
+    #     self.assertFalse((self.x_test_iris == x_test_adv).all())
+    #     self.assertLessEqual(np.amax(x_test_adv), 1.0)
+    #     self.assertGreaterEqual(np.amin(x_test_adv), 0.0)
+    #
+    #     predictions_adv = np.argmax(classifier.predict(x_test_adv), axis=1)
+    #     self.assertFalse((np.argmax(self.y_test_iris, axis=1) == predictions_adv).all())
+    #     accuracy = np.sum(predictions_adv == np.argmax(self.y_test_iris, axis=1)) / self.y_test_iris.shape[0]
+    #     logger.info("Accuracy on Iris with C&W adversarial examples: %.2f%%", (accuracy * 100))
+    #
+    #     # Test targeted attack
+    #     targets = random_targets(self.y_test_iris, nb_classes=3)
+    #     attack = CarliniLInfMethod(classifier, targeted=True, max_iter=10, eps=0.5)
+    #     x_test_adv = attack.generate(self.x_test_iris, **{"y": targets})
+    #     self.assertFalse((self.x_test_iris == x_test_adv).all())
+    #     self.assertLessEqual(np.amax(x_test_adv), 1.0)
+    #     self.assertGreaterEqual(np.amin(x_test_adv), 0.0)
+    #
+    #     predictions_adv = np.argmax(classifier.predict(x_test_adv), axis=1)
+    #     self.assertTrue((np.argmax(targets, axis=1) == predictions_adv).any())
+    #     accuracy = np.sum(predictions_adv == np.argmax(targets, axis=1)) / self.y_test_iris.shape[0]
+    #     logger.info("Success rate of targeted C&W on Iris: %.2f%%", (accuracy * 100))
+
+    # def test_pytorch_iris_LInf(self):
+    #     classifier = get_tabular_classifier_pt()
+    #     attack = CarliniLInfMethod(classifier, targeted=False, max_iter=10, eps=0.5)
+    #     x_test_adv = attack.generate(self.x_test_iris.astype(np.float32))
+    #     self.assertFalse((self.x_test_iris == x_test_adv).all())
+    #     self.assertLessEqual(np.amax(x_test_adv), 1.0)
+    #     self.assertGreaterEqual(np.amin(x_test_adv), 0.0)
+    #
+    #     predictions_adv = np.argmax(classifier.predict(x_test_adv), axis=1)
+    #     self.assertFalse((np.argmax(self.y_test_iris, axis=1) == predictions_adv).all())
+    #     accuracy = np.sum(predictions_adv == np.argmax(self.y_test_iris, axis=1)) / self.y_test_iris.shape[0]
+    #     logger.info("Accuracy on Iris with C&W adversarial examples: %.2f%%", (accuracy * 100))
+
+    # def test_scikitlearn_LInf(self):
+    #     from sklearn.linear_model import LogisticRegression
+    #     from sklearn.svm import SVC, LinearSVC
+    #
+    #     from art.estimators.classification.scikitlearn import SklearnClassifier
+    #
+    #     scikitlearn_test_cases = [
+    #         LogisticRegression(solver="lbfgs", multi_class="auto"),
+    #         SVC(gamma="auto"),
+    #         LinearSVC(),
+    #     ]
+    #
+    #     x_test_original = self.x_test_iris.copy()
+    #
+    #     for model in scikitlearn_test_cases:
+    #         classifier = SklearnClassifier(model=model, clip_values=(0, 1))
+    #         classifier.fit(x=self.x_test_iris, y=self.y_test_iris)
+    #
+    #         # Test untargeted attack
+    #         attack = CarliniLInfMethod(classifier, targeted=False, max_iter=10, eps=0.5)
+    #         x_test_adv = attack.generate(self.x_test_iris)
+    #         self.assertFalse((self.x_test_iris == x_test_adv).all())
+    #         self.assertLessEqual(np.amax(x_test_adv), 1.0)
+    #         self.assertGreaterEqual(np.amin(x_test_adv), 0.0)
+    #
+    #         predictions_adv = np.argmax(classifier.predict(x_test_adv), axis=1)
+    #         self.assertFalse((np.argmax(self.y_test_iris, axis=1) == predictions_adv).all())
+    #         accuracy = np.sum(predictions_adv == np.argmax(self.y_test_iris, axis=1)) / self.y_test_iris.shape[0]
+    #         logger.info(
+    #             "Accuracy of " + classifier.__class__.__name__ + " on Iris with C&W adversarial examples: " "%.2f%%",
+    #             (accuracy * 100),
+    #         )
+    #
+    #         # Test targeted attack
+    #         targets = random_targets(self.y_test_iris, nb_classes=3)
+    #         attack = CarliniLInfMethod(classifier, targeted=True, max_iter=10, eps=0.5)
+    #         x_test_adv = attack.generate(self.x_test_iris, **{"y": targets})
+    #         self.assertFalse((self.x_test_iris == x_test_adv).all())
+    #         self.assertLessEqual(np.amax(x_test_adv), 1.0)
+    #         self.assertGreaterEqual(np.amin(x_test_adv), 0.0)
+    #
+    #         predictions_adv = np.argmax(classifier.predict(x_test_adv), axis=1)
+    #         self.assertTrue((np.argmax(targets, axis=1) == predictions_adv).any())
+    #         accuracy = np.sum(predictions_adv == np.argmax(targets, axis=1)) / self.y_test_iris.shape[0]
+    #         logger.info(
+    #             "Success rate of " + classifier.__class__.__name__ + " on targeted C&W on Iris: %.2f%%",
+    #             (accuracy * 100),
+    #         )
+    #
+    #         # Check that x_test has not been modified by attack and classifier
+    #         self.assertAlmostEqual(float(np.max(np.abs(x_test_original - self.x_test_iris))), 0.0, delta=0.00001)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/adversarial-robustness-toolbox/tests/attacks/test_copycat_cnn.py b/adversarial-robustness-toolbox/tests/attacks/test_copycat_cnn.py
new file mode 100644
index 0000000..2fc0404
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/attacks/test_copycat_cnn.py
@@ -0,0 +1,402 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import unittest
+
+import keras
+import keras.backend as k
+import numpy as np
+import tensorflow as tf
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from keras.layers import Conv2D, Dense, Flatten, MaxPooling2D
+from keras.models import Sequential
+
+from art.attacks.extraction.copycat_cnn import CopycatCNN
+from art.estimators.classification.keras import KerasClassifier
+from art.estimators.classification.pytorch import PyTorchClassifier
+from art.estimators.classification.tensorflow import TensorFlowClassifier
+from tests.utils import (
+    TestBase,
+    get_image_classifier_kr,
+    get_image_classifier_pt,
+    get_image_classifier_tf,
+    get_tabular_classifier_kr,
+    get_tabular_classifier_pt,
+    get_tabular_classifier_tf,
+    master_seed,
+)
+
+logger = logging.getLogger(__name__)
+
+NB_EPOCHS = 20
+NB_STOLEN = 1000
+
+
+class TestCopycatCNN(TestBase):
+    """
+    A unittest class for testing the CopycatCNN attack.
+    """
+
+    @classmethod
+    def setUpClass(cls):
+        master_seed(seed=1234)
+        super().setUpClass()
+
+    @unittest.skipIf(tf.__version__[0] == "2", reason="Skip unittests for TensorFlow v2.")
+    def test_tensorflow_classifier(self):
+        """
+        First test with the TensorFlowClassifier.
+        :return:
+        """
+        # Build TensorFlowClassifiers
+        victim_tfc, sess = get_image_classifier_tf()
+
+        # Define input and output placeholders
+        input_ph = tf.placeholder(tf.float32, shape=[None, 28, 28, 1])
+        output_ph = tf.placeholder(tf.int32, shape=[None, 10])
+
+        # Define the tensorflow graph
+        conv = tf.layers.conv2d(input_ph, 1, 7, activation=tf.nn.relu)
+        conv = tf.layers.max_pooling2d(conv, 4, 4)
+        flattened = tf.layers.flatten(conv)
+
+        # Logits layer
+        logits = tf.layers.dense(flattened, 10)
+
+        # Train operator
+        loss = tf.reduce_mean(tf.losses.softmax_cross_entropy(logits=logits, onehot_labels=output_ph))
+        optimizer = tf.train.AdamOptimizer(learning_rate=0.001)
+        train = optimizer.minimize(loss)
+
+        # TensorFlow session and initialization
+        sess.run(tf.global_variables_initializer())
+
+        # Create the classifier
+        thieved_tfc = TensorFlowClassifier(
+            clip_values=(0, 1),
+            input_ph=input_ph,
+            output=logits,
+            labels_ph=output_ph,
+            train=train,
+            loss=loss,
+            learning=None,
+            sess=sess,
+        )
+
+        # Create attack
+        copycat_cnn = CopycatCNN(
+            classifier=victim_tfc,
+            batch_size_query=self.batch_size,
+            batch_size_fit=self.batch_size,
+            nb_epochs=NB_EPOCHS,
+            nb_stolen=NB_STOLEN,
+        )
+        thieved_tfc = copycat_cnn.extract(x=self.x_train_mnist, thieved_classifier=thieved_tfc)
+
+        victim_preds = np.argmax(victim_tfc.predict(x=self.x_train_mnist[:100]), axis=1)
+        thieved_preds = np.argmax(thieved_tfc.predict(x=self.x_train_mnist[:100]), axis=1)
+        acc = np.sum(victim_preds == thieved_preds) / len(victim_preds)
+
+        self.assertGreater(acc, 0.3)
+
+        # Clean-up session
+        if sess is not None:
+            sess.close()
+            tf.reset_default_graph()
+
+    def test_keras_classifier(self):
+        """
+        Second test with the KerasClassifier.
+        :return:
+        """
+        # Build KerasClassifier
+        victim_krc = get_image_classifier_kr()
+
+        # Create simple CNN
+        model = Sequential()
+        model.add(Conv2D(1, kernel_size=(7, 7), activation="relu", input_shape=(28, 28, 1)))
+        model.add(MaxPooling2D(pool_size=(4, 4)))
+        model.add(Flatten())
+        model.add(Dense(10, activation="softmax"))
+        loss = keras.losses.categorical_crossentropy
+        model.compile(loss=loss, optimizer=keras.optimizers.Adam(lr=0.001), metrics=["accuracy"])
+
+        # Get classifier
+        thieved_krc = KerasClassifier(model, clip_values=(0, 1), use_logits=False)
+
+        # Create attack
+        copycat_cnn = CopycatCNN(
+            classifier=victim_krc,
+            batch_size_fit=self.batch_size,
+            batch_size_query=self.batch_size,
+            nb_epochs=NB_EPOCHS,
+            nb_stolen=NB_STOLEN,
+        )
+        thieved_krc = copycat_cnn.extract(x=self.x_train_mnist, thieved_classifier=thieved_krc)
+
+        victim_preds = np.argmax(victim_krc.predict(x=self.x_train_mnist[:100]), axis=1)
+        thieved_preds = np.argmax(thieved_krc.predict(x=self.x_train_mnist[:100]), axis=1)
+        acc = np.sum(victim_preds == thieved_preds) / len(victim_preds)
+
+        self.assertGreater(acc, 0.3)
+
+        # Clean-up
+        k.clear_session()
+
+    def test_pytorch_classifier(self):
+        """
+        Third test with the PyTorchClassifier.
+        :return:
+        """
+        x_train = np.reshape(self.x_train_mnist, (self.x_train_mnist.shape[0], 1, 28, 28)).astype(np.float32)
+
+        # Build PyTorchClassifier
+        victim_ptc = get_image_classifier_pt()
+
+        class Model(nn.Module):
+            """
+            Create model for pytorch.
+            """
+
+            def __init__(self):
+                super(Model, self).__init__()
+
+                self.conv = nn.Conv2d(in_channels=1, out_channels=1, kernel_size=7)
+                self.pool = nn.MaxPool2d(4, 4)
+                self.fullyconnected = nn.Linear(25, 10)
+
+            # pylint: disable=W0221
+            # disable pylint because of API requirements for function
+            def forward(self, x):
+                """
+                Forward function to evaluate the model
+
+                :param x: Input to the model
+                :return: Prediction of the model
+                """
+                x = self.conv(x)
+                x = torch.nn.functional.relu(x)
+                x = self.pool(x)
+                x = x.reshape(-1, 25)
+                x = self.fullyconnected(x)
+                x = torch.nn.functional.softmax(x, dim=1)
+
+                return x
+
+        # Define the network
+        model = Model()
+
+        # Define a loss function and optimizer
+        loss_fn = nn.CrossEntropyLoss()
+        optimizer = optim.Adam(model.parameters(), lr=0.01)
+
+        # Get classifier
+        thieved_ptc = PyTorchClassifier(
+            model=model, loss=loss_fn, optimizer=optimizer, input_shape=(1, 28, 28), nb_classes=10, clip_values=(0, 1)
+        )
+
+        # Create attack
+        copycat_cnn = CopycatCNN(
+            classifier=victim_ptc,
+            batch_size_fit=self.batch_size,
+            batch_size_query=self.batch_size,
+            nb_epochs=NB_EPOCHS,
+            nb_stolen=NB_STOLEN,
+        )
+
+        thieved_ptc = copycat_cnn.extract(x=x_train, thieved_classifier=thieved_ptc)
+        victim_preds = np.argmax(victim_ptc.predict(x=x_train[:100]), axis=1)
+        thieved_preds = np.argmax(thieved_ptc.predict(x=x_train[:100]), axis=1)
+
+        acc = np.sum(victim_preds == thieved_preds) / len(victim_preds)
+
+        self.assertGreater(acc, 0.3)
+
+
+class TestCopycatCNNVectors(TestBase):
+    @classmethod
+    def setUpClass(cls):
+        super().setUpClass()
+
+    @unittest.skipIf(tf.__version__[0] == "2", reason="Skip unittests for TensorFlow v2.")
+    def test_tensorflow_iris(self):
+        """
+        First test for TensorFlow.
+        :return:
+        """
+        # Get the TensorFlow classifier
+        victim_tfc, sess = get_tabular_classifier_tf()
+
+        # Define input and output placeholders
+        input_ph = tf.placeholder(tf.float32, shape=[None, 4])
+        output_ph = tf.placeholder(tf.int32, shape=[None, 3])
+
+        # Define the tensorflow graph
+        dense1 = tf.layers.dense(input_ph, 10)
+        dense2 = tf.layers.dense(dense1, 10)
+        logits = tf.layers.dense(dense2, 3)
+
+        # Train operator
+        loss = tf.reduce_mean(tf.losses.softmax_cross_entropy(logits=logits, onehot_labels=output_ph))
+        optimizer = tf.train.AdamOptimizer(learning_rate=0.001)
+        train = optimizer.minimize(loss)
+
+        # TensorFlow session and initialization
+        sess.run(tf.global_variables_initializer())
+
+        # Train the classifier
+        thieved_tfc = TensorFlowClassifier(
+            clip_values=(0, 1),
+            input_ph=input_ph,
+            output=logits,
+            labels_ph=output_ph,
+            train=train,
+            loss=loss,
+            learning=None,
+            sess=sess,
+            channels_first=True,
+        )
+
+        # Create attack
+        copycat_cnn = CopycatCNN(
+            classifier=victim_tfc,
+            batch_size_fit=self.batch_size,
+            batch_size_query=self.batch_size,
+            nb_epochs=NB_EPOCHS,
+            nb_stolen=NB_STOLEN,
+        )
+        thieved_tfc = copycat_cnn.extract(x=self.x_train_iris, thieved_classifier=thieved_tfc)
+
+        victim_preds = np.argmax(victim_tfc.predict(x=self.x_train_iris[:100]), axis=1)
+        thieved_preds = np.argmax(thieved_tfc.predict(x=self.x_train_iris[:100]), axis=1)
+        acc = np.sum(victim_preds == thieved_preds) / len(victim_preds)
+
+        self.assertGreater(acc, 0.3)
+
+        # Clean-up session
+        if sess is not None:
+            sess.close()
+            tf.reset_default_graph()
+
+    def test_keras_iris(self):
+        """
+        Second test for Keras.
+        :return:
+        """
+        # Build KerasClassifier
+        victim_krc = get_tabular_classifier_kr()
+
+        # Create simple CNN
+        model = Sequential()
+        model.add(Dense(10, input_shape=(4,), activation="relu"))
+        model.add(Dense(10, activation="relu"))
+        model.add(Dense(3, activation="softmax"))
+        model.compile(loss="categorical_crossentropy", optimizer=keras.optimizers.Adam(lr=0.001), metrics=["accuracy"])
+
+        # Get classifier
+        thieved_krc = KerasClassifier(model, clip_values=(0, 1), use_logits=False, channels_first=True)
+
+        # Create attack
+        copycat_cnn = CopycatCNN(
+            classifier=victim_krc,
+            batch_size_fit=self.batch_size,
+            batch_size_query=self.batch_size,
+            nb_epochs=NB_EPOCHS,
+            nb_stolen=NB_STOLEN,
+        )
+        thieved_krc = copycat_cnn.extract(x=self.x_train_iris, thieved_classifier=thieved_krc)
+
+        victim_preds = np.argmax(victim_krc.predict(x=self.x_train_iris[:100]), axis=1)
+        thieved_preds = np.argmax(thieved_krc.predict(x=self.x_train_iris[:100]), axis=1)
+        acc = np.sum(victim_preds == thieved_preds) / len(victim_preds)
+
+        self.assertGreater(acc, 0.3)
+
+        # Clean-up
+        k.clear_session()
+
+    def test_pytorch_iris(self):
+        """
+        Third test for PyTorch.
+        :return:
+        """
+        # Build PyTorchClassifier
+        victim_ptc = get_tabular_classifier_pt()
+
+        class Model(nn.Module):
+            """
+            Create Iris model for PyTorch.
+            """
+
+            def __init__(self):
+                super(Model, self).__init__()
+
+                self.fully_connected1 = nn.Linear(4, 10)
+                self.fully_connected2 = nn.Linear(10, 10)
+                self.fully_connected3 = nn.Linear(10, 3)
+
+            # pylint: disable=W0221
+            # disable pylint because of API requirements for function
+            def forward(self, x):
+                x = self.fully_connected1(x)
+                x = self.fully_connected2(x)
+                logit_output = self.fully_connected3(x)
+
+                return logit_output
+
+        # Define the network
+        model = Model()
+
+        # Define a loss function and optimizer
+        loss_fn = nn.CrossEntropyLoss()
+        optimizer = optim.Adam(model.parameters(), lr=0.001)
+
+        # Get classifier
+        thieved_ptc = PyTorchClassifier(
+            model=model,
+            loss=loss_fn,
+            optimizer=optimizer,
+            input_shape=(4,),
+            nb_classes=3,
+            clip_values=(0, 1),
+            channels_first=True,
+        )
+
+        # Create attack
+        copycat_cnn = CopycatCNN(
+            classifier=victim_ptc,
+            batch_size_fit=self.batch_size,
+            batch_size_query=self.batch_size,
+            nb_epochs=NB_EPOCHS,
+            nb_stolen=NB_STOLEN,
+        )
+        thieved_ptc = copycat_cnn.extract(x=self.x_train_iris, thieved_classifier=thieved_ptc)
+
+        victim_preds = np.argmax(victim_ptc.predict(x=self.x_train_iris[:100]), axis=1)
+        thieved_preds = np.argmax(thieved_ptc.predict(x=self.x_train_iris[:100]), axis=1)
+        acc = np.sum(victim_preds == thieved_preds) / len(victim_preds)
+
+        self.assertGreater(acc, 0.3)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/adversarial-robustness-toolbox/tests/attacks/test_decision_tree_attack.py b/adversarial-robustness-toolbox/tests/attacks/test_decision_tree_attack.py
new file mode 100644
index 0000000..81f951a
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/attacks/test_decision_tree_attack.py
@@ -0,0 +1,73 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import unittest
+
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.datasets import load_digits
+import numpy as np
+
+from art.attacks.evasion.decision_tree_attack import DecisionTreeAttack
+from art.estimators.classification.scikitlearn import SklearnClassifier
+from art.estimators.classification.scikitlearn import ScikitlearnDecisionTreeClassifier
+
+from tests.utils import TestBase, master_seed
+from tests.attacks.utils import backend_test_classifier_type_check_fail
+
+logger = logging.getLogger(__name__)
+
+
+class TestDecisionTreeAttack(TestBase):
+    """
+    A unittest class for testing the decision tree attack.
+    """
+
+    @classmethod
+    def setUpClass(cls):
+        master_seed(seed=1234)
+        super().setUpClass()
+
+        # Get MNIST
+        digits = load_digits()
+        cls.X = digits.data
+        cls.y = digits.target
+
+    def test_scikitlearn(self):
+        clf = DecisionTreeClassifier()
+        x_original = self.X.copy()
+        clf.fit(self.X, self.y)
+        clf_art = SklearnClassifier(clf)
+        attack = DecisionTreeAttack(clf_art)
+        adv = attack.generate(self.X[:25])
+        # all crafting should succeed
+        self.assertTrue(np.sum(clf.predict(adv) == clf.predict(self.X[:25])) == 0)
+        targets = np.array([1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6, 7, 7, 7, 8, 8, 8, 9])
+        adv = attack.generate(self.X[:25], targets)
+        # all targeted crafting should succeed as well
+        self.assertTrue(np.sum(clf.predict(adv) == targets) == 25.0)
+        # Check that X has not been modified by attack and classifier
+        self.assertAlmostEqual(float(np.max(np.abs(x_original - self.X))), 0.0, delta=0.00001)
+
+    def test_classifier_type_check_fail(self):
+        backend_test_classifier_type_check_fail(DecisionTreeAttack, [ScikitlearnDecisionTreeClassifier])
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/adversarial-robustness-toolbox/tests/attacks/test_deepfool.py b/adversarial-robustness-toolbox/tests/attacks/test_deepfool.py
new file mode 100644
index 0000000..2bce094
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/attacks/test_deepfool.py
@@ -0,0 +1,255 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import unittest
+
+import keras
+import numpy as np
+
+from art.attacks.evasion.deepfool import DeepFool
+from art.estimators.estimator import BaseEstimator
+from art.estimators.classification.classifier import ClassGradientsMixin
+from art.estimators.classification.keras import KerasClassifier
+from art.utils import get_labels_np_array
+from tests.attacks.utils import backend_test_classifier_type_check_fail
+from tests.utils import (
+    TestBase,
+    get_image_classifier_kr,
+    get_image_classifier_pt,
+    get_image_classifier_tf,
+    get_tabular_classifier_kr,
+    get_tabular_classifier_pt,
+    get_tabular_classifier_tf,
+)
+
+logger = logging.getLogger(__name__)
+
+
+class TestDeepFool(TestBase):
+    @classmethod
+    def setUpClass(cls):
+        super().setUpClass()
+
+        cls.n_train = 100
+        cls.n_test = 11
+        cls.x_train_mnist = cls.x_train_mnist[0 : cls.n_train]
+        cls.y_train_mnist = cls.y_train_mnist[0 : cls.n_train]
+        cls.x_test_mnist = cls.x_test_mnist[0 : cls.n_test]
+        cls.y_test_mnist = cls.y_test_mnist[0 : cls.n_test]
+
+    @unittest.skipIf(
+        not (int(keras.__version__.split(".")[0]) == 2 and int(keras.__version__.split(".")[1]) >= 3),
+        reason="Minimal version of Keras or TensorFlow required.",
+    )
+    def test_8_keras_mnist(self):
+        x_test_original = self.x_test_mnist.copy()
+
+        # Keras classifier
+        classifier = get_image_classifier_kr(from_logits=True)
+
+        scores = classifier._model.evaluate(self.x_train_mnist, self.y_train_mnist)
+        logger.info("[Keras, MNIST] Accuracy on training set: %.2f%%", (scores[1] * 100))
+        scores = classifier._model.evaluate(self.x_test_mnist, self.y_test_mnist)
+        logger.info("[Keras, MNIST] Accuracy on test set: %.2f%%", (scores[1] * 100))
+
+        attack = DeepFool(classifier, max_iter=5, batch_size=11)
+        x_train_adv = attack.generate(self.x_train_mnist)
+        x_test_adv = attack.generate(self.x_test_mnist)
+
+        self.assertFalse((self.x_train_mnist == x_train_adv).all())
+        self.assertFalse((self.x_test_mnist == x_test_adv).all())
+
+        train_y_pred = get_labels_np_array(classifier.predict(x_train_adv))
+        test_y_pred = get_labels_np_array(classifier.predict(x_test_adv))
+
+        self.assertFalse((self.y_train_mnist == train_y_pred).all())
+        self.assertFalse((self.y_test_mnist == test_y_pred).all())
+
+        sum_0 = np.sum(np.argmax(train_y_pred, axis=1) == np.argmax(self.y_train_mnist, axis=1))
+        accuracy_0 = sum_0 / self.y_train_mnist.shape[0]
+        logger.info("Accuracy on adversarial train examples: %.2f%%", (accuracy_0 * 100))
+
+        sum_1 = np.sum(np.argmax(test_y_pred, axis=1) == np.argmax(self.y_test_mnist, axis=1))
+        accuracy_1 = sum_1 / self.y_test_mnist.shape[0]
+        logger.info("Accuracy on adversarial test examples: %.2f%%", (accuracy_1 * 100))
+
+        # Check that x_test has not been modified by attack and classifier
+        self.assertAlmostEqual(float(np.max(np.abs(x_test_original - self.x_test_mnist))), 0.0, delta=0.00001)
+
+    def test_3_tensorflow_mnist(self):
+        x_test_original = self.x_test_mnist.copy()
+
+        # Create basic CNN on MNIST using TensorFlow
+        classifier, sess = get_image_classifier_tf(from_logits=True)
+
+        scores = get_labels_np_array(classifier.predict(self.x_train_mnist))
+        sum2 = np.sum(np.argmax(scores, axis=1) == np.argmax(self.y_train_mnist, axis=1))
+        accuracy = sum2 / self.y_train_mnist.shape[0]
+        logger.info("[TF, MNIST] Accuracy on training set: %.2f%%", (accuracy * 100))
+
+        scores = get_labels_np_array(classifier.predict(self.x_test_mnist))
+        sum3 = np.sum(np.argmax(scores, axis=1) == np.argmax(self.y_test_mnist, axis=1))
+        accuracy = sum3 / self.y_test_mnist.shape[0]
+        logger.info("[TF, MNIST] Accuracy on test set: %.2f%%", (accuracy * 100))
+
+        attack = DeepFool(classifier, max_iter=5, batch_size=11)
+        x_train_adv = attack.generate(self.x_train_mnist)
+        x_test_adv = attack.generate(self.x_test_mnist)
+
+        self.assertFalse((self.x_train_mnist == x_train_adv).all())
+        self.assertFalse((self.x_test_mnist == x_test_adv).all())
+
+        train_y_pred = get_labels_np_array(classifier.predict(x_train_adv))
+        test_y_pred = get_labels_np_array(classifier.predict(x_test_adv))
+
+        self.assertFalse((self.y_train_mnist == train_y_pred).all())
+        self.assertFalse((self.y_test_mnist == test_y_pred).all())
+
+        sum4 = np.sum(np.argmax(train_y_pred, axis=1) == np.argmax(self.y_train_mnist, axis=1))
+        accuracy = sum4 / self.y_train_mnist.shape[0]
+        logger.info("Accuracy on adversarial train examples: %.2f%%", (accuracy * 100))
+
+        sum5 = np.sum(np.argmax(test_y_pred, axis=1) == np.argmax(self.y_test_mnist, axis=1))
+        accuracy = sum5 / self.y_test_mnist.shape[0]
+        logger.info("Accuracy on adversarial test examples: %.2f%%", (accuracy * 100))
+
+        # Check that x_test has not been modified by attack and classifier
+        self.assertAlmostEqual(float(np.max(np.abs(x_test_original - self.x_test_mnist))), 0.0, delta=0.00001)
+
+    def test_5_pytorch_mnist(self):
+        x_train = np.reshape(self.x_train_mnist, (self.x_train_mnist.shape[0], 1, 28, 28)).astype(np.float32)
+        x_test = np.reshape(self.x_test_mnist, (self.x_test_mnist.shape[0], 1, 28, 28)).astype(np.float32)
+        x_test_original = x_test.copy()
+
+        # Create basic PyTorch model
+        classifier = get_image_classifier_pt(from_logits=True)
+
+        scores = get_labels_np_array(classifier.predict(x_train))
+        sum6 = np.sum(np.argmax(scores, axis=1) == np.argmax(self.y_train_mnist, axis=1))
+        accuracy = sum6 / self.y_train_mnist.shape[0]
+        logger.info("[PyTorch, MNIST] Accuracy on training set: %.2f%%", (accuracy * 100))
+
+        scores = get_labels_np_array(classifier.predict(x_test))
+        sum7 = np.sum(np.argmax(scores, axis=1) == np.argmax(self.y_test_mnist, axis=1))
+        accuracy = sum7 / self.y_test_mnist.shape[0]
+        logger.info("[PyTorch, MNIST] Accuracy on test set: %.2f%%", (accuracy * 100))
+
+        attack = DeepFool(classifier, max_iter=5, batch_size=11)
+        x_train_adv = attack.generate(x_train)
+        x_test_adv = attack.generate(x_test)
+
+        self.assertFalse((x_train == x_train_adv).all())
+        self.assertFalse((x_test == x_test_adv).all())
+
+        train_y_pred = get_labels_np_array(classifier.predict(x_train_adv))
+        test_y_pred = get_labels_np_array(classifier.predict(x_test_adv))
+
+        self.assertFalse((self.y_train_mnist == train_y_pred).all())
+        self.assertFalse((self.y_test_mnist == test_y_pred).all())
+
+        sum8 = np.sum(np.argmax(train_y_pred, axis=1) == np.argmax(self.y_train_mnist, axis=1))
+        accuracy = sum8 / self.y_train_mnist.shape[0]
+        logger.info("Accuracy on adversarial train examples: %.2f%%", (accuracy * 100))
+
+        sum9 = np.sum(np.argmax(test_y_pred, axis=1) == np.argmax(self.y_test_mnist, axis=1))
+        accuracy = sum9 / self.y_test_mnist.shape[0]
+        logger.info("Accuracy on adversarial test examples: %.2f%%", (accuracy * 100))
+
+        # Check that x_test has not been modified by attack and classifier
+        self.assertAlmostEqual(float(np.max(np.abs(x_test_original - x_test))), 0.0, delta=0.00001)
+
+    @unittest.skipIf(
+        not (int(keras.__version__.split(".")[0]) == 2 and int(keras.__version__.split(".")[1]) >= 3),
+        reason="Minimal version of Keras or TensorFlow required.",
+    )
+    def test_9_keras_mnist_partial_grads(self):
+        classifier = get_image_classifier_kr(from_logits=True)
+        attack = DeepFool(classifier, max_iter=2, nb_grads=3)
+        x_test_adv = attack.generate(self.x_test_mnist)
+        self.assertFalse((self.x_test_mnist == x_test_adv).all())
+
+        test_y_pred = get_labels_np_array(classifier.predict(x_test_adv))
+        self.assertFalse((self.y_test_mnist == test_y_pred).all())
+        sum10 = np.sum(np.argmax(test_y_pred, axis=1) == np.argmax(self.y_test_mnist, axis=1))
+        accuracy = sum10 / self.y_test_mnist.shape[0]
+        logger.info("Accuracy on adversarial test examples: %.2f%%", (accuracy * 100))
+
+    def test_1_classifier_type_check_fail(self):
+        backend_test_classifier_type_check_fail(DeepFool, [BaseEstimator, ClassGradientsMixin])
+
+    def test_6_keras_iris_clipped(self):
+        classifier = get_tabular_classifier_kr()
+
+        attack = DeepFool(classifier, max_iter=5)
+        x_test_adv = attack.generate(self.x_test_iris)
+        self.assertFalse((self.x_test_iris == x_test_adv).all())
+        self.assertLessEqual(np.amax(x_test_adv), 1.0)
+        self.assertGreaterEqual(np.amin(x_test_adv), 0.0)
+
+        predictions_adv = np.argmax(classifier.predict(x_test_adv), axis=1)
+        self.assertFalse((np.argmax(self.y_test_iris, axis=1) == predictions_adv).all())
+        accuracy = np.sum(predictions_adv == np.argmax(self.y_test_iris, axis=1)) / self.y_test_iris.shape[0]
+        logger.info("Accuracy on Iris with DeepFool adversarial examples: %.2f%%", (accuracy * 100))
+
+    def test_7_keras_iris_unbounded(self):
+        classifier = get_tabular_classifier_kr()
+
+        # Recreate a classifier without clip values
+        classifier = KerasClassifier(model=classifier._model, use_logits=False, channels_first=True)
+        attack = DeepFool(classifier, max_iter=5, batch_size=128)
+        x_test_adv = attack.generate(self.x_test_iris)
+        self.assertFalse((self.x_test_iris == x_test_adv).all())
+
+        predictions_adv = np.argmax(classifier.predict(x_test_adv), axis=1)
+        self.assertFalse((np.argmax(self.y_test_iris, axis=1) == predictions_adv).all())
+        accuracy = np.sum(predictions_adv == np.argmax(self.y_test_iris, axis=1)) / self.y_test_iris.shape[0]
+        logger.info("Accuracy on Iris with DeepFool adversarial examples: %.2f%%", (accuracy * 100))
+
+    def test_2_tensorflow_iris(self):
+        classifier, _ = get_tabular_classifier_tf()
+
+        attack = DeepFool(classifier, max_iter=5, batch_size=128)
+        x_test_adv = attack.generate(self.x_test_iris)
+        self.assertFalse((self.x_test_iris == x_test_adv).all())
+        self.assertLessEqual(np.amax(x_test_adv), 1.0)
+        self.assertGreaterEqual(np.amin(x_test_adv), 0.0)
+
+        predictions_adv = np.argmax(classifier.predict(x_test_adv), axis=1)
+        self.assertFalse((np.argmax(self.y_test_iris, axis=1) == predictions_adv).all())
+        accuracy = np.sum(predictions_adv == np.argmax(self.y_test_iris, axis=1)) / self.y_test_iris.shape[0]
+        logger.info("Accuracy on Iris with DeepFool adversarial examples: %.2f%%", (accuracy * 100))
+
+    def test_4_pytorch_iris(self):
+        classifier = get_tabular_classifier_pt()
+
+        attack = DeepFool(classifier, max_iter=5, batch_size=128)
+        x_test_adv = attack.generate(self.x_test_iris)
+        self.assertFalse((self.x_test_iris == x_test_adv).all())
+        self.assertLessEqual(np.amax(x_test_adv), 1.0)
+        self.assertGreaterEqual(np.amin(x_test_adv), 0.0)
+
+        predictions_adv = np.argmax(classifier.predict(x_test_adv), axis=1)
+        self.assertFalse((np.argmax(self.y_test_iris, axis=1) == predictions_adv).all())
+        accuracy = np.sum(predictions_adv == np.argmax(self.y_test_iris, axis=1)) / self.y_test_iris.shape[0]
+        logger.info("Accuracy on Iris with DeepFool adversarial examples: %.2f%%", (accuracy * 100))
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/adversarial-robustness-toolbox/tests/attacks/test_elastic_net.py b/adversarial-robustness-toolbox/tests/attacks/test_elastic_net.py
new file mode 100644
index 0000000..64ce52b
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/attacks/test_elastic_net.py
@@ -0,0 +1,793 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import unittest
+
+import keras.backend as k
+import numpy as np
+
+from art.attacks.evasion.elastic_net import ElasticNet
+from art.estimators.estimator import BaseEstimator
+from art.estimators.classification.classifier import ClassGradientsMixin
+from art.estimators.classification.keras import KerasClassifier
+from art.utils import random_targets, to_categorical
+from tests.attacks.utils import backend_test_classifier_type_check_fail
+from tests.utils import (
+    TestBase,
+    get_image_classifier_kr,
+    get_image_classifier_pt,
+    get_image_classifier_tf,
+    get_tabular_classifier_kr,
+    get_tabular_classifier_pt,
+    get_tabular_classifier_tf,
+    master_seed,
+)
+
+logger = logging.getLogger(__name__)
+
+
+class TestElasticNet(TestBase):
+    """
+    A unittest class for testing the ElasticNet attack.
+    """
+
+    @classmethod
+    def setUpClass(cls):
+        master_seed(seed=1234)
+        super().setUpClass()
+
+        cls.n_train = 500
+        cls.n_test = 10
+        cls.x_train_mnist = cls.x_train_mnist[0 : cls.n_train]
+        cls.y_train_mnist = cls.y_train_mnist[0 : cls.n_train]
+        cls.x_test_mnist = cls.x_test_mnist[0 : cls.n_test]
+        cls.y_test_mnist = cls.y_test_mnist[0 : cls.n_test]
+
+    def setUp(self):
+        master_seed(seed=1234)
+        super().setUp()
+
+    def test_2_tensorflow_failure_attack(self):
+        """
+        Test the corner case when attack fails.
+        :return:
+        """
+        # Build TensorFlowClassifier
+        tfc, sess = get_image_classifier_tf()
+
+        # Failure attack
+        ead = ElasticNet(
+            classifier=tfc, targeted=True, max_iter=0, binary_search_steps=0, learning_rate=0, initial_const=1
+        )
+        params = {"y": random_targets(self.y_test_mnist, tfc.nb_classes)}
+        x_test_adv = ead.generate(self.x_test_mnist, **params)
+        self.assertLessEqual(np.amax(x_test_adv), 1.0)
+        self.assertGreaterEqual(np.amin(x_test_adv), 0.0)
+        np.testing.assert_almost_equal(self.x_test_mnist, x_test_adv, 3)
+
+        # Clean-up session
+        if sess is not None:
+            sess.close()
+
+    def test_4_tensorflow_mnist(self):
+        """
+        First test with the TensorFlowClassifier.
+        :return:
+        """
+        x_test_original = self.x_test_mnist.copy()
+
+        # Build TensorFlowClassifier
+        tfc, sess = get_image_classifier_tf(from_logits=True)
+
+        # First attack
+        ead = ElasticNet(classifier=tfc, targeted=True, max_iter=2)
+        params = {"y": random_targets(self.y_test_mnist, tfc.nb_classes)}
+        x_test_adv = ead.generate(self.x_test_mnist, **params)
+        expected_x_test_adv = np.asarray(
+            [
+                0.45704955,
+                0.43627003,
+                0.57238287,
+                1.0,
+                0.11541145,
+                0.12619308,
+                0.48318917,
+                0.3457903,
+                0.17863746,
+                0.09060935,
+                0.0,
+                0.00963121,
+                0.0,
+                0.04749763,
+                0.4058206,
+                0.17860745,
+                0.0,
+                0.9153206,
+                0.84564775,
+                0.20603634,
+                0.10586322,
+                0.00947509,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+            ]
+        )
+        np.testing.assert_array_almost_equal(x_test_adv[0, 14, :, 0], expected_x_test_adv, decimal=6)
+        self.assertLessEqual(np.amax(x_test_adv), 1.0)
+        self.assertGreaterEqual(np.amin(x_test_adv), 0.0)
+        target = np.argmax(params["y"], axis=1)
+        y_pred_adv = np.argmax(tfc.predict(x_test_adv), axis=1)
+        logger.debug("EAD target: %s", target)
+        logger.debug("EAD actual: %s", y_pred_adv)
+        logger.info("EAD success rate on MNIST: %.2f%%", (100 * sum(target == y_pred_adv) / len(target)))
+        self.assertTrue((target == y_pred_adv).any())
+
+        # Second attack
+        ead = ElasticNet(classifier=tfc, targeted=False, max_iter=2)
+        params = {"y": random_targets(self.y_test_mnist, tfc.nb_classes)}
+        x_test_adv = ead.generate(self.x_test_mnist, **params)
+        self.assertLessEqual(np.amax(x_test_adv), 1.0)
+        self.assertGreaterEqual(np.amin(x_test_adv), 0.0)
+        target = np.argmax(params["y"], axis=1)
+        y_pred_adv = np.argmax(tfc.predict(x_test_adv), axis=1)
+        logger.debug("EAD target: %s", target)
+        logger.debug("EAD actual: %s", y_pred_adv)
+        logger.info("EAD success rate on MNIST: %.2f%%", (100 * sum(target != y_pred_adv) / float(len(target))))
+        np.testing.assert_array_equal(y_pred_adv, np.asarray([7, 1, 1, 4, 4, 1, 4, 4, 4, 4]))
+
+        # Third attack
+        ead = ElasticNet(classifier=tfc, targeted=False, max_iter=2)
+        params = {}
+        x_test_adv = ead.generate(self.x_test_mnist, **params)
+        expected_x_test_adv = np.asarray(
+            [
+                0.22866514,
+                0.21826893,
+                0.22902338,
+                0.06268515,
+                0.0,
+                0.0,
+                0.04822975,
+                0.0,
+                0.0,
+                0.0,
+                0.05555382,
+                0.0,
+                0.0,
+                0.0,
+                0.38986346,
+                0.10653087,
+                0.32385707,
+                0.98043066,
+                0.75790393,
+                0.16486718,
+                0.16069527,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+            ]
+        )
+        np.testing.assert_array_almost_equal(x_test_adv[0, 14, :, 0], expected_x_test_adv, decimal=6)
+        self.assertLessEqual(np.amax(x_test_adv), 1.0)
+        self.assertGreaterEqual(np.amin(x_test_adv), 0.0)
+        y_pred = np.argmax(tfc.predict(self.x_test_mnist), axis=1)
+        y_pred_adv = np.argmax(tfc.predict(x_test_adv), axis=1)
+        logger.debug("EAD target: %s", y_pred)
+        logger.debug("EAD actual: %s", y_pred_adv)
+        logger.info("EAD success rate: %.2f%%", (100 * sum(y_pred != y_pred_adv) / float(len(y_pred))))
+        np.testing.assert_array_equal(y_pred_adv, np.asarray([0, 4, 7, 9, 0, 7, 7, 3, 0, 7]))
+
+        # First attack without batching
+        ead_wob = ElasticNet(classifier=tfc, targeted=True, max_iter=2, batch_size=1)
+        params = {"y": random_targets(self.y_test_mnist, tfc.nb_classes)}
+        x_test_adv = ead_wob.generate(self.x_test_mnist, **params)
+        expected_x_test_adv = np.asarray(
+            [
+                0.3287169,
+                0.31374657,
+                0.42853343,
+                0.8994576,
+                0.19850709,
+                0.11997936,
+                0.5622535,
+                0.43854535,
+                0.19387433,
+                0.12516324,
+                0.0,
+                0.10933565,
+                0.02162433,
+                0.07120894,
+                0.95224255,
+                0.3072921,
+                0.48966524,
+                1.0,
+                0.3814998,
+                0.15782641,
+                0.52283823,
+                0.12852049,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+            ]
+        )
+        np.testing.assert_array_almost_equal(x_test_adv[0, 14, :, 0], expected_x_test_adv, decimal=6)
+        self.assertLessEqual(np.amax(x_test_adv), 1.0)
+        self.assertGreaterEqual(np.amin(x_test_adv), 0.0)
+        target = np.argmax(params["y"], axis=1)
+        y_pred_adv = np.argmax(tfc.predict(x_test_adv), axis=1)
+        logger.debug("EAD target: %s", target)
+        logger.debug("EAD actual: %s", y_pred_adv)
+        logger.info("EAD success rate: %.2f%%", (100 * sum(target == y_pred_adv) / float(len(target))))
+        self.assertTrue((target == y_pred_adv).any())
+
+        # Second attack without batching
+        ead_wob = ElasticNet(classifier=tfc, targeted=False, max_iter=2, batch_size=1)
+        params = {"y": random_targets(self.y_test_mnist, tfc.nb_classes)}
+        x_test_adv = ead_wob.generate(self.x_test_mnist, **params)
+        self.assertLessEqual(np.amax(x_test_adv), 1.0)
+        self.assertGreaterEqual(np.amin(x_test_adv), 0.0)
+        target = np.argmax(params["y"], axis=1)
+        y_pred_adv = np.argmax(tfc.predict(x_test_adv), axis=1)
+        logger.debug("EAD target: %s", target)
+        logger.debug("EAD actual: %s", y_pred_adv)
+        logger.info("EAD success rate: %.2f%%", (100 * sum(target != y_pred_adv) / float(len(target))))
+        np.testing.assert_array_equal(y_pred_adv, np.asarray([7, 1, 1, 4, 4, 1, 4, 4, 4, 4]))
+
+        # Check that x_test has not been modified by attack and classifier
+        self.assertAlmostEqual(float(np.max(np.abs(x_test_original - self.x_test_mnist))), 0.0, delta=0.00001)
+
+        # Close session
+        if sess is not None:
+            sess.close()
+
+    def test_9a_keras_mnist(self):
+        """
+        Second test with the KerasClassifier.
+        :return:
+        """
+        x_test_original = self.x_test_mnist.copy()
+
+        # Build KerasClassifier
+        krc = get_image_classifier_kr()
+
+        # First attack
+        ead = ElasticNet(classifier=krc, targeted=True, max_iter=2)
+        y_target = to_categorical(np.asarray([6, 6, 7, 4, 9, 7, 9, 0, 1, 0]), nb_classes=10)
+        x_test_adv = ead.generate(self.x_test_mnist, y=y_target)
+        expected_x_test_adv = np.asarray(
+            [
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.00183569,
+                0.0,
+                0.0,
+                0.49765405,
+                1.0,
+                0.6467149,
+                0.0033755,
+                0.0052456,
+                0.0,
+                0.01104407,
+                0.00495547,
+                0.02747423,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+            ]
+        )
+        np.testing.assert_array_almost_equal(x_test_adv[2, 14, :, 0], expected_x_test_adv, decimal=6)
+        self.assertLessEqual(np.amax(x_test_adv), 1.0)
+        self.assertGreaterEqual(np.amin(x_test_adv), 0.0)
+        target = np.argmax(y_target, axis=1)
+        y_pred_adv = np.argmax(krc.predict(x_test_adv), axis=1)
+        logger.debug("EAD target: %s", target)
+        logger.debug("EAD actual: %s", y_pred_adv)
+        logger.info("EAD success rate: %.2f%%", (100 * sum(target == y_pred_adv) / float(len(target))))
+        self.assertTrue((target == y_pred_adv).any())
+
+        # Second attack
+        ead = ElasticNet(classifier=krc, targeted=False, max_iter=2)
+        y_target = to_categorical(np.asarray([9, 5, 6, 7, 1, 6, 1, 5, 8, 5]), nb_classes=10)
+        x_test_adv = ead.generate(self.x_test_mnist, y=y_target)
+        self.assertLessEqual(np.amax(x_test_adv), 1.0)
+        self.assertGreaterEqual(np.amin(x_test_adv), 0.0)
+        y_pred_adv = np.argmax(krc.predict(x_test_adv), axis=1)
+        logger.debug("EAD target: %s", y_target)
+        logger.debug("EAD actual: %s", y_pred_adv)
+        logger.info("EAD success rate: %.2f", (100 * sum(target != y_pred_adv) / float(len(target))))
+        self.assertTrue((target != y_pred_adv).any())
+        np.testing.assert_array_equal(y_pred_adv, np.asarray([7, 1, 1, 4, 4, 1, 4, 4, 4, 4]))
+
+        # Check that x_test has not been modified by attack and classifier
+        self.assertAlmostEqual(float(np.max(np.abs(x_test_original - self.x_test_mnist))), 0.0, delta=0.00001)
+
+        k.clear_session()
+
+    def test_6_pytorch_mnist(self):
+        """
+        Third test with the PyTorchClassifier.
+        :return:
+        """
+        x_test = np.reshape(self.x_test_mnist, (self.x_test_mnist.shape[0], 1, 28, 28)).astype(np.float32)
+        x_test_original = x_test.copy()
+
+        # Build PyTorchClassifier
+        ptc = get_image_classifier_pt(from_logits=False)
+
+        # First attack
+        ead = ElasticNet(classifier=ptc, targeted=True, max_iter=2)
+        params = {"y": random_targets(self.y_test_mnist, ptc.nb_classes)}
+        x_test_adv = ead.generate(x_test, **params)
+        expected_x_test_adv = np.asarray(
+            [
+                0.01678124,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.00665895,
+                0.0,
+                0.11374763,
+                0.36250514,
+                0.5472948,
+                0.9308808,
+                1.0,
+                0.99920374,
+                0.86274165,
+                0.6346757,
+                0.5597227,
+                0.24191494,
+                0.25882354,
+                0.0091916,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+            ]
+        )
+        np.testing.assert_array_almost_equal(x_test_adv[2, 0, :, 14], expected_x_test_adv, decimal=6)
+        self.assertLessEqual(np.amax(x_test_adv), 1.0)
+        self.assertGreaterEqual(np.amin(x_test_adv), 0.0)
+        target = np.argmax(params["y"], axis=1)
+        y_pred_adv = np.argmax(ptc.predict(x_test_adv), axis=1)
+        self.assertTrue((target == y_pred_adv).any())
+
+        # Second attack
+        ead = ElasticNet(classifier=ptc, targeted=False, max_iter=2)
+        params = {"y": random_targets(self.y_test_mnist, ptc.nb_classes)}
+        x_test_adv = ead.generate(x_test, **params)
+        self.assertLessEqual(np.amax(x_test_adv), 1.0)
+        self.assertGreaterEqual(np.amin(x_test_adv), 0.0)
+        target = np.argmax(params["y"], axis=1)
+        y_pred_adv = np.argmax(ptc.predict(x_test_adv), axis=1)
+        self.assertTrue((target != y_pred_adv).any())
+        np.testing.assert_array_equal(y_pred_adv, np.asarray([7, 1, 1, 4, 4, 1, 4, 4, 4, 4]))
+
+        # Check that x_test has not been modified by attack and classifier
+        self.assertAlmostEqual(float(np.max(np.abs(x_test_original - x_test))), 0.0, delta=0.00001)
+
+    def test_1_classifier_type_check_fail(self):
+        backend_test_classifier_type_check_fail(ElasticNet, [BaseEstimator, ClassGradientsMixin])
+
+    def test_8_keras_iris_clipped(self):
+        classifier = get_tabular_classifier_kr()
+        attack = ElasticNet(classifier, targeted=False, max_iter=10)
+        x_test_adv = attack.generate(self.x_test_iris)
+        expected_x_test_adv = np.asarray([0.85931635, 0.44633555, 0.65658355, 0.23840423])
+        np.testing.assert_array_almost_equal(x_test_adv[0, :], expected_x_test_adv, decimal=6)
+        self.assertLessEqual(np.amax(x_test_adv), 1.0)
+        self.assertGreaterEqual(np.amin(x_test_adv), 0.0)
+        predictions_adv = np.argmax(classifier.predict(x_test_adv), axis=1)
+        np.testing.assert_array_equal(
+            predictions_adv,
+            np.asarray(
+                [
+                    1,
+                    1,
+                    1,
+                    2,
+                    1,
+                    1,
+                    1,
+                    2,
+                    1,
+                    2,
+                    1,
+                    1,
+                    1,
+                    2,
+                    1,
+                    1,
+                    2,
+                    2,
+                    1,
+                    1,
+                    1,
+                    1,
+                    1,
+                    1,
+                    1,
+                    1,
+                    1,
+                    1,
+                    2,
+                    1,
+                    2,
+                    1,
+                    2,
+                    1,
+                    0,
+                    1,
+                    1,
+                    1,
+                    2,
+                    0,
+                    2,
+                    2,
+                    1,
+                    1,
+                    2,
+                ]
+            ),
+        )
+        accuracy = 1.0 - np.sum(predictions_adv == np.argmax(self.y_test_iris, axis=1)) / self.y_test_iris.shape[0]
+        logger.info("EAD success rate on Iris: %.2f%%", (accuracy * 100))
+
+    def test_9_keras_iris_unbounded(self):
+        classifier = get_tabular_classifier_kr()
+
+        # Recreate a classifier without clip values
+        classifier = KerasClassifier(model=classifier._model, use_logits=False, channels_first=True)
+        attack = ElasticNet(classifier, targeted=False, max_iter=10)
+        x_test_adv = attack.generate(self.x_test_iris)
+        expected_x_test_adv = np.asarray([0.85931635, 0.44633555, 0.65658355, 0.23840423])
+        np.testing.assert_array_almost_equal(x_test_adv[0, :], expected_x_test_adv, decimal=6)
+        predictions_adv = np.argmax(classifier.predict(x_test_adv), axis=1)
+        np.testing.assert_array_equal(
+            predictions_adv,
+            np.asarray(
+                [
+                    1,
+                    1,
+                    1,
+                    2,
+                    1,
+                    1,
+                    1,
+                    2,
+                    1,
+                    2,
+                    1,
+                    1,
+                    1,
+                    2,
+                    1,
+                    1,
+                    2,
+                    2,
+                    1,
+                    1,
+                    1,
+                    1,
+                    1,
+                    1,
+                    1,
+                    1,
+                    1,
+                    1,
+                    2,
+                    1,
+                    2,
+                    1,
+                    2,
+                    1,
+                    0,
+                    1,
+                    1,
+                    1,
+                    2,
+                    0,
+                    2,
+                    2,
+                    1,
+                    1,
+                    2,
+                ]
+            ),
+        )
+        accuracy = 1.0 - np.sum(predictions_adv == np.argmax(self.y_test_iris, axis=1)) / self.y_test_iris.shape[0]
+        logger.info("EAD success rate on Iris: %.2f%%", (accuracy * 100))
+
+    def test_3_tensorflow_iris(self):
+        classifier, _ = get_tabular_classifier_tf()
+
+        # Test untargeted attack
+        attack = ElasticNet(classifier, targeted=False, max_iter=10)
+        x_test_adv = attack.generate(self.x_test_iris)
+        expected_x_test_adv = np.asarray([0.8479195, 0.42525578, 0.70166135, 0.28664514])
+        np.testing.assert_array_almost_equal(x_test_adv[0, :], expected_x_test_adv, decimal=6)
+        self.assertLessEqual(np.amax(x_test_adv), 1.0)
+        self.assertGreaterEqual(np.amin(x_test_adv), 0.0)
+
+        predictions_adv = np.argmax(classifier.predict(x_test_adv), axis=1)
+        np.testing.assert_array_equal(
+            predictions_adv,
+            np.asarray(
+                [
+                    1,
+                    2,
+                    2,
+                    2,
+                    1,
+                    1,
+                    1,
+                    2,
+                    1,
+                    2,
+                    1,
+                    1,
+                    1,
+                    2,
+                    2,
+                    2,
+                    2,
+                    2,
+                    1,
+                    1,
+                    1,
+                    1,
+                    1,
+                    1,
+                    1,
+                    2,
+                    2,
+                    2,
+                    2,
+                    2,
+                    2,
+                    1,
+                    2,
+                    1,
+                    0,
+                    2,
+                    2,
+                    1,
+                    2,
+                    0,
+                    2,
+                    2,
+                    1,
+                    1,
+                    2,
+                ]
+            ),
+        )
+        accuracy = 1.0 - np.sum(predictions_adv == np.argmax(self.y_test_iris, axis=1)) / self.y_test_iris.shape[0]
+        logger.info("EAD success rate on Iris: %.2f%%", (accuracy * 100))
+
+        # Test targeted attack
+        targets = random_targets(self.y_test_iris, nb_classes=3)
+        attack = ElasticNet(classifier, targeted=True, max_iter=10)
+        x_test_adv = attack.generate(self.x_test_iris, **{"y": targets})
+        expected_x_test_adv = np.asarray([0.8859426, 0.51877, 0.5014498, 0.05447771])
+        np.testing.assert_array_almost_equal(x_test_adv[0, :], expected_x_test_adv, decimal=6)
+        self.assertLessEqual(np.amax(x_test_adv), 1.0)
+        self.assertGreaterEqual(np.amin(x_test_adv), 0.0)
+
+        predictions_adv = np.argmax(classifier.predict(x_test_adv), axis=1)
+        np.testing.assert_array_equal(
+            predictions_adv,
+            np.asarray(
+                [
+                    0,
+                    0,
+                    0,
+                    2,
+                    2,
+                    2,
+                    2,
+                    2,
+                    2,
+                    2,
+                    2,
+                    2,
+                    0,
+                    2,
+                    0,
+                    0,
+                    2,
+                    2,
+                    0,
+                    2,
+                    2,
+                    2,
+                    2,
+                    2,
+                    2,
+                    0,
+                    0,
+                    0,
+                    2,
+                    0,
+                    2,
+                    2,
+                    2,
+                    2,
+                    2,
+                    0,
+                    0,
+                    0,
+                    2,
+                    2,
+                    2,
+                    2,
+                    2,
+                    0,
+                    2,
+                ]
+            ),
+        )
+
+        accuracy = np.sum(predictions_adv == np.argmax(targets, axis=1)) / self.y_test_iris.shape[0]
+        logger.info("Targeted EAD success rate on Iris: %.2f%%", (accuracy * 100))
+
+    def test_5_pytorch_iris(self):
+        classifier = get_tabular_classifier_pt()
+        attack = ElasticNet(classifier, targeted=False, max_iter=10)
+        x_test_adv = attack.generate(self.x_test_iris.astype(np.float32))
+        expected_x_test_adv = np.asarray([0.8479194, 0.42525578, 0.70166135, 0.28664517])
+        np.testing.assert_array_almost_equal(x_test_adv[0, :], expected_x_test_adv, decimal=6)
+        self.assertLessEqual(np.amax(x_test_adv), 1.0)
+        self.assertGreaterEqual(np.amin(x_test_adv), 0.0)
+
+        predictions_adv = np.argmax(classifier.predict(x_test_adv), axis=1)
+        np.testing.assert_array_equal(
+            predictions_adv,
+            np.asarray(
+                [
+                    1,
+                    2,
+                    2,
+                    2,
+                    1,
+                    1,
+                    1,
+                    2,
+                    1,
+                    2,
+                    1,
+                    1,
+                    1,
+                    2,
+                    2,
+                    2,
+                    2,
+                    2,
+                    1,
+                    1,
+                    1,
+                    1,
+                    1,
+                    1,
+                    1,
+                    2,
+                    2,
+                    2,
+                    2,
+                    2,
+                    2,
+                    1,
+                    2,
+                    1,
+                    0,
+                    2,
+                    2,
+                    1,
+                    2,
+                    0,
+                    2,
+                    2,
+                    1,
+                    1,
+                    2,
+                ]
+            ),
+        )
+
+        accuracy = 1.0 - np.sum(predictions_adv == np.argmax(self.y_test_iris, axis=1)) / self.y_test_iris.shape[0]
+        logger.info("EAD success rate on Iris: %.2f%%", (accuracy * 100))
+
+    def test_7_scikitlearn(self):
+        from sklearn.linear_model import LogisticRegression
+        from sklearn.svm import SVC, LinearSVC
+
+        from art.estimators.classification.scikitlearn import SklearnClassifier
+
+        scikitlearn_test_cases = [
+            LogisticRegression(solver="lbfgs", multi_class="auto"),
+            SVC(gamma="auto"),
+            LinearSVC(),
+        ]
+
+        x_test_original = self.x_test_iris.copy()
+
+        for model in scikitlearn_test_cases:
+            classifier = SklearnClassifier(model=model, clip_values=(0, 1))
+            classifier.fit(x=self.x_test_iris, y=self.y_test_iris)
+
+            # Test untargeted attack
+            attack = ElasticNet(classifier, targeted=False, max_iter=2)
+            x_test_adv = attack.generate(self.x_test_iris)
+            self.assertFalse((self.x_test_iris == x_test_adv).all())
+            self.assertLessEqual(np.amax(x_test_adv), 1.0)
+            self.assertGreaterEqual(np.amin(x_test_adv), 0.0)
+
+            predictions_adv = np.argmax(classifier.predict(x_test_adv), axis=1)
+            self.assertFalse((np.argmax(self.y_test_iris, axis=1) == predictions_adv).all())
+            accuracy = 1.0 - np.sum(predictions_adv == np.argmax(self.y_test_iris, axis=1)) / self.y_test_iris.shape[0]
+            logger.info("EAD success rate  of " + classifier.__class__.__name__ + " on Iris: %.2f%%", (accuracy * 100))
+
+            # Test targeted attack
+            targets = random_targets(self.y_test_iris, nb_classes=3)
+            attack = ElasticNet(classifier, targeted=True, max_iter=2)
+            x_test_adv = attack.generate(self.x_test_iris, **{"y": targets})
+            self.assertFalse((self.x_test_iris == x_test_adv).all())
+            self.assertLessEqual(np.amax(x_test_adv), 1.0)
+            self.assertGreaterEqual(np.amin(x_test_adv), 0.0)
+
+            predictions_adv = np.argmax(classifier.predict(x_test_adv), axis=1)
+            self.assertTrue((np.argmax(targets, axis=1) == predictions_adv).any())
+            accuracy = np.sum(predictions_adv == np.argmax(targets, axis=1)) / self.y_test_iris.shape[0]
+            logger.info(
+                "Targeted EAD success rate of " + classifier.__class__.__name__ + " on Iris: %.2f%%", (accuracy * 100)
+            )
+
+            # Check that x_test has not been modified by attack and classifier
+            self.assertAlmostEqual(float(np.max(np.abs(x_test_original - self.x_test_iris))), 0.0, delta=0.00001)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/adversarial-robustness-toolbox/tests/attacks/test_feature_collision.py b/adversarial-robustness-toolbox/tests/attacks/test_feature_collision.py
new file mode 100644
index 0000000..20c97d1
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/attacks/test_feature_collision.py
@@ -0,0 +1,94 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import unittest
+
+import numpy as np
+
+from art.attacks.poisoning.feature_collision_attack import FeatureCollisionAttack
+
+from tests.utils import TestBase, master_seed, get_image_classifier_kr  # , get_image_classifier_tf
+
+logger = logging.getLogger(__name__)
+
+NB_EPOCHS = 3
+
+
+class TestFeatureCollision(TestBase):
+    """
+    A unittest class for testing Feature Collision attack.
+    """
+
+    @classmethod
+    def setUpClass(cls):
+        master_seed(seed=1234)
+        super().setUpClass()
+
+        cls.n_train = 10
+        cls.n_test = 10
+        cls.x_train_mnist = cls.x_train_mnist[0 : cls.n_train]
+        cls.y_train_mnist = cls.y_train_mnist[0 : cls.n_train]
+        cls.x_test_mnist = cls.x_test_mnist[0 : cls.n_test]
+        cls.y_test_mnist = cls.y_test_mnist[0 : cls.n_test]
+
+    def setUp(self):
+        master_seed(seed=301)
+        super().setUp()
+
+    @staticmethod
+    def poison_dataset(classifier, x_clean, y_clean):
+        x_poison = np.copy(x_clean)
+        y_poison = np.copy(y_clean)
+        base = np.expand_dims(x_clean[0], axis=0)
+        target = np.expand_dims(x_clean[1], axis=0)
+        feature_layer = classifier.layer_names[-1]
+        attack = FeatureCollisionAttack(classifier, target, feature_layer, max_iter=1)
+        attack, attack_label = attack.poison(base)
+        x_poison = np.append(x_poison, attack, axis=0)
+        y_poison = np.append(y_poison, attack_label, axis=0)
+
+        return x_poison, y_poison
+
+    # def test_tensorflow(self):
+    #     """
+    #     First test with the TensorFlowClassifier.
+    #     :return:
+    #     """
+    #
+    #     tfc, sess = get_image_classifier_tf()
+    #     x_adv, y_adv = self.poison_dataset(tfc, self.x_train_mnist, self.y_train_mnist)
+    #     tfc.fit(x_adv, y_adv, nb_epochs=NB_EPOCHS, batch_size=32)
+    #
+    #     if sess is not None:
+    #         sess.close()
+
+    def test_keras(self):
+        """
+        Test working keras implementation.
+        :return:
+        """
+
+        krc = get_image_classifier_kr()
+        x_adv, y_adv = self.poison_dataset(krc, self.x_train_mnist, self.y_train_mnist)
+        krc.fit(x_adv, y_adv, nb_epochs=NB_EPOCHS, batch_size=32)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/adversarial-robustness-toolbox/tests/attacks/test_functionally_equivalent_extraction.py b/adversarial-robustness-toolbox/tests/attacks/test_functionally_equivalent_extraction.py
new file mode 100644
index 0000000..410f574
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/attacks/test_functionally_equivalent_extraction.py
@@ -0,0 +1,938 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import unittest
+
+from os.path import dirname, join
+import numpy as np
+
+import tensorflow as tf
+
+tf.compat.v1.disable_eager_execution()
+from tensorflow.keras.models import load_model
+
+from art.attacks.extraction.functionally_equivalent_extraction import FunctionallyEquivalentExtraction
+from art.estimators.classification.keras import KerasClassifier
+from art.estimators.estimator import BaseEstimator, NeuralNetworkMixin
+from art.estimators.classification.classifier import ClassifierMixin
+
+from tests.utils import TestBase, master_seed
+from tests.attacks.utils import backend_test_classifier_type_check_fail
+
+logger = logging.getLogger(__name__)
+
+
+@unittest.skip("Needs update of critical points.")
+@unittest.skipIf(
+    tf.__version__[0] != "2" or (tf.__version__[0] == "1" and tf.__version__.split(".")[1] != "15"),
+    reason="Skip unittests if not TensorFlow v2 or 1.15 because of pre-trained model.",
+)
+class TestFunctionallyEquivalentExtraction(TestBase):
+    @classmethod
+    def setUpClass(cls):
+        master_seed(seed=1234, set_tensorflow=True)
+        super().setUpClass()
+
+        cls.n_train = 100
+        cls.n_test = 11
+        cls.x_train_mnist = cls.x_train_mnist[0 : cls.n_train]
+        cls.y_train_mnist = cls.y_train_mnist[0 : cls.n_train]
+        cls.x_test_mnist = cls.x_test_mnist[0 : cls.n_test]
+        cls.y_test_mnist = cls.y_test_mnist[0 : cls.n_test]
+
+        model = load_model(
+            join(
+                *[
+                    dirname(dirname(dirname(__file__))),
+                    "utils",
+                    "data",
+                    "test_models",
+                    "model_test_functionally_equivalent_extraction.h5",
+                ]
+            )
+        )
+
+        np.random.seed(0)
+        num_neurons = 16
+        img_rows = 28
+        img_cols = 28
+        num_channels = 1
+
+        x_train = cls.x_train_mnist.reshape(cls.n_train, img_rows, img_cols, num_channels)
+        x_test = cls.x_test_mnist.reshape(cls.n_test, img_rows, img_cols, num_channels)
+
+        x_train = x_train.reshape((x_train.shape[0], num_channels * img_rows * img_cols)).astype("float64")
+        x_test = x_test.reshape((x_test.shape[0], num_channels * img_rows * img_cols)).astype("float64")
+
+        mean = np.mean(x_train)
+        std = np.std(x_train)
+
+        x_test = (x_test - mean) / std
+
+        classifier = KerasClassifier(model=model, use_logits=True, clip_values=(0, 1))
+
+        cls.fee = FunctionallyEquivalentExtraction(classifier=classifier, num_neurons=num_neurons)
+        cls.fee.extract(x_test[0:100])
+
+    def setUp(self):
+        master_seed(seed=1234, set_tensorflow=True)
+        super().setUp()
+
+    def test_critical_points(self):
+        critical_points_expected_15 = np.array(
+            [
+                [
+                    3.61953106e00,
+                    9.77733178e-01,
+                    3.03710564e00,
+                    3.88522344e00,
+                    -3.42297003e00,
+                    -1.13835691e00,
+                    -1.99857599e00,
+                    -3.46220468e-01,
+                    -3.59475588e00,
+                    5.51705510e00,
+                    -3.19797872e00,
+                    -2.04326002e00,
+                    1.05123266e00,
+                    -4.06901743e00,
+                    -1.20838338e00,
+                    -2.89548673e00,
+                    6.98455648e00,
+                    2.85218553e00,
+                    8.94698139e-02,
+                    -2.37621231e00,
+                    1.10785852e00,
+                    2.23015480e00,
+                    2.80221937e00,
+                    -8.44071720e-01,
+                    -4.29867814e00,
+                    -1.89193948e00,
+                    -2.02601143e00,
+                    2.32254653e00,
+                    5.46957626e00,
+                    -1.09054547e00,
+                    1.97730390e00,
+                    7.13198416e00,
+                    -3.48566995e00,
+                    4.56309251e00,
+                    -3.66508619e00,
+                    2.45678983e-01,
+                    1.18692621e00,
+                    1.24711887e00,
+                    -3.64649874e00,
+                    -2.60243153e00,
+                    -3.64646660e00,
+                    -1.47897557e-01,
+                    -4.22195494e-01,
+                    1.06113047e01,
+                    4.82448414e00,
+                    -2.42173234e00,
+                    1.11818199e-02,
+                    4.65699866e00,
+                    -1.49483467e00,
+                    -4.83696263e-01,
+                    -6.94802825e-01,
+                    3.76123427e00,
+                    -3.81138399e00,
+                    -2.44772137e00,
+                    1.80214210e00,
+                    1.64008567e00,
+                    9.98667003e-01,
+                    -1.13632143e00,
+                    3.14954375e00,
+                    7.93954578e00,
+                    9.08789028e-01,
+                    -1.11114990e00,
+                    2.12722866e00,
+                    -3.82389751e00,
+                    -2.73941016e00,
+                    -2.74131238e-01,
+                    -1.16791406e01,
+                    -4.02691717e00,
+                    -2.26112102e00,
+                    -5.21371365e00,
+                    -3.28863610e00,
+                    -1.57028321e00,
+                    -5.25291961e00,
+                    -2.81473806e00,
+                    -1.68024547e00,
+                    -5.85965502e-01,
+                    3.61981141e00,
+                    9.23169673e-02,
+                    -2.29606074e-01,
+                    4.43114931e-01,
+                    -2.80427895e-01,
+                    -3.05502037e00,
+                    1.91036227e-02,
+                    -3.34978609e-01,
+                    -3.84499306e00,
+                    5.26390356e00,
+                    5.38611250e00,
+                    -2.63643293e00,
+                    -2.00973074e00,
+                    -2.36234227e00,
+                    2.31791770e00,
+                    -2.90647524e00,
+                    -6.57321096e-01,
+                    -2.36517907e00,
+                    5.54615295e-01,
+                    -6.27427313e00,
+                    5.17139277e00,
+                    -1.96714440e00,
+                    3.59945621e-01,
+                    -4.24878604e-01,
+                    -1.08202458e00,
+                    -4.80186427e00,
+                    2.37278089e00,
+                    -1.07572442e00,
+                    -1.18075753e-01,
+                    -1.17477993e00,
+                    -2.93162165e00,
+                    1.08992730e00,
+                    5.54290231e00,
+                    7.98407506e-01,
+                    -3.66473517e00,
+                    8.67953522e00,
+                    -4.19382044e00,
+                    -4.08782220e00,
+                    9.82618000e00,
+                    -7.69520713e-01,
+                    -4.73994274e00,
+                    -2.81408385e00,
+                    2.04409418e00,
+                    1.66265593e00,
+                    -2.93741552e00,
+                    5.99230900e00,
+                    -1.73108306e00,
+                    -3.28289962e00,
+                    3.04322254e00,
+                    5.02137877e00,
+                    -3.61579148e00,
+                    -3.60298823e00,
+                    4.68144302e00,
+                    -7.55810404e00,
+                    -5.54235927e00,
+                    4.30331267e00,
+                    -8.89694006e-01,
+                    -9.95076143e-01,
+                    7.36865058e-01,
+                    8.20305695e-02,
+                    -4.47623746e00,
+                    4.75655495e00,
+                    5.55126730e00,
+                    -2.94169700e-01,
+                    -1.31565371e00,
+                    9.54222010e00,
+                    -9.08849702e-01,
+                    -3.74910292e-01,
+                    3.80123979e00,
+                    6.66898337e00,
+                    5.28420510e00,
+                    1.10982206e-01,
+                    -1.16276421e-01,
+                    -5.82332350e00,
+                    -1.28205374e00,
+                    -1.55599314e00,
+                    -4.66205671e00,
+                    5.71610805e00,
+                    -3.18101923e00,
+                    -2.73180879e00,
+                    2.55005165e00,
+                    3.96954509e00,
+                    7.24416286e-01,
+                    1.02980621e01,
+                    -7.88544755e-01,
+                    2.93612566e00,
+                    2.02170626e00,
+                    5.67092866e00,
+                    7.48089944e-01,
+                    3.92145589e-01,
+                    -4.68662954e00,
+                    -5.93709701e-01,
+                    6.64027217e00,
+                    -1.27973863e00,
+                    2.97883110e00,
+                    1.27642013e00,
+                    4.21654506e00,
+                    -3.78209823e00,
+                    8.09590708e00,
+                    -4.29526503e00,
+                    -2.22566713e00,
+                    2.96030699e00,
+                    6.98973613e-01,
+                    3.24672410e00,
+                    -2.28418990e00,
+                    -1.66599664e00,
+                    -5.96027162e-01,
+                    3.88214888e00,
+                    3.31149846e00,
+                    1.49757160e00,
+                    -3.66419049e00,
+                    3.82181754e00,
+                    1.38112419e-01,
+                    6.94779206e00,
+                    6.54329012e00,
+                    -9.26489313e-01,
+                    -1.62009512e00,
+                    -4.52985187e00,
+                    -3.53512243e-02,
+                    -1.65790094e00,
+                    2.17052203e00,
+                    2.61034940e-01,
+                    7.56353874e-01,
+                    5.47853217e00,
+                    -4.01821256e00,
+                    1.44572322e00,
+                    -4.79746586e-01,
+                    3.47357980e00,
+                    6.02979833e00,
+                    -2.79622692e00,
+                    1.69161006e00,
+                    -4.23976729e-02,
+                    -2.83040527e00,
+                    8.38686737e-01,
+                    2.03506626e00,
+                    1.92358357e00,
+                    1.44131202e-02,
+                    -9.99430943e-02,
+                    -5.40948077e00,
+                    -1.80337181e00,
+                    2.14607550e00,
+                    3.85151903e00,
+                    6.16199609e-01,
+                    3.65155968e-01,
+                    -6.86530386e-02,
+                    4.37920573e-01,
+                    1.64040341e00,
+                    -6.59215215e00,
+                    -1.73270323e00,
+                    9.93275152e-01,
+                    -3.73550020e00,
+                    6.74519312e00,
+                    3.12660362e-02,
+                    5.84485063e00,
+                    -4.49976578e00,
+                    -4.02337192e00,
+                    3.29641448e-01,
+                    -6.11525876e00,
+                    -3.19811199e-01,
+                    1.15945105e00,
+                    5.44615523e00,
+                    6.57571553e-01,
+                    -1.19802935e00,
+                    -3.59314573e00,
+                    6.02466561e00,
+                    -3.47917071e00,
+                    -4.20072539e00,
+                    -4.51866361e00,
+                    4.03811078e00,
+                    -3.69489996e00,
+                    -1.78012256e00,
+                    1.61533135e00,
+                    -1.61852848e00,
+                    -4.10470488e00,
+                    3.45463564e00,
+                    3.56905786e00,
+                    3.97554912e00,
+                    2.66454239e00,
+                    2.25804254e00,
+                    -6.21473638e00,
+                    5.76899253e00,
+                    -2.08408059e-01,
+                    7.83228855e-01,
+                    4.94838720e00,
+                    4.38791606e00,
+                    1.12105376e00,
+                    1.09827474e00,
+                    -2.38398204e00,
+                    -1.80753680e00,
+                    -3.13452494e00,
+                    -2.27719704e00,
+                    -3.38822700e00,
+                    -9.17931670e-01,
+                    4.17912953e00,
+                    1.27364259e01,
+                    -2.03530245e00,
+                    -3.29038740e00,
+                    5.31179109e00,
+                    -1.82267486e00,
+                    -2.96119740e00,
+                    1.31020764e00,
+                    -4.94302867e00,
+                    -1.16514227e00,
+                    1.72064832e00,
+                    2.72220374e-01,
+                    2.50415711e00,
+                    -4.29456275e-01,
+                    1.59994399e00,
+                    1.39253228e00,
+                    2.22505196e00,
+                    -5.05846429e00,
+                    -4.35255236e00,
+                    4.50001673e-01,
+                    -4.27252846e00,
+                    -2.87526989e-01,
+                    3.17137548e00,
+                    4.66601910e00,
+                    -5.13815490e00,
+                    -3.48299127e00,
+                    2.41422025e00,
+                    -1.46361301e00,
+                    -6.49063866e-01,
+                    1.92294782e00,
+                    -3.47120162e00,
+                    -2.86761934e00,
+                    -1.45476737e00,
+                    -4.17669035e00,
+                    -4.01483069e00,
+                    3.30219967e00,
+                    -2.59101087e-01,
+                    -4.75482758e00,
+                    -2.24586949e00,
+                    -5.68236958e00,
+                    -3.01268930e00,
+                    8.22969417e00,
+                    7.26630125e-01,
+                    1.71985527e00,
+                    -9.85474778e-01,
+                    9.69749700e-01,
+                    2.67490406e00,
+                    -4.33992693e00,
+                    -4.07251552e-01,
+                    6.08129826e00,
+                    -3.20237632e00,
+                    -2.92346407e00,
+                    -2.01013404e00,
+                    1.32121409e00,
+                    1.15139410e00,
+                    3.77379044e00,
+                    1.63111624e00,
+                    -3.99098443e-01,
+                    7.15579205e00,
+                    2.03479958e00,
+                    -4.87601164e00,
+                    1.05765834e01,
+                    5.69732614e00,
+                    1.18778294e-01,
+                    2.86462296e-01,
+                    2.49353875e00,
+                    -6.36657921e-02,
+                    1.08570479e00,
+                    4.74854161e00,
+                    -4.63241582e00,
+                    -6.83954662e-01,
+                    4.65345281e00,
+                    1.33951496e00,
+                    2.90639747e00,
+                    -1.72986262e00,
+                    -1.56536140e00,
+                    -8.05650496e00,
+                    -4.82346198e00,
+                    3.39824919e-01,
+                    3.78664395e00,
+                    2.41632152e00,
+                    -1.26309772e00,
+                    -2.49517893e00,
+                    2.20951730e00,
+                    -3.85151265e-01,
+                    4.81240175e00,
+                    4.85709334e-02,
+                    -7.60618498e00,
+                    -5.42914323e00,
+                    5.42941370e00,
+                    -3.93630082e00,
+                    3.67290378e00,
+                    -1.04039267e00,
+                    2.71366140e-01,
+                    -1.81908310e-01,
+                    4.73638654e00,
+                    -5.89365669e-01,
+                    -3.20289542e-01,
+                    -6.35077950e00,
+                    5.36441669e-01,
+                    9.38127137e-01,
+                    1.21089054e00,
+                    4.44570135e00,
+                    1.05628764e00,
+                    9.13779419e-01,
+                    6.46336488e00,
+                    -5.53683667e00,
+                    -1.13017499e00,
+                    3.97816303e00,
+                    3.43531407e00,
+                    3.51956691e00,
+                    1.54150627e00,
+                    1.65980399e00,
+                    4.09252687e00,
+                    4.47248858e-01,
+                    9.71886644e-01,
+                    -1.03825118e00,
+                    -2.35130810e-01,
+                    -5.97346695e00,
+                    4.64660911e00,
+                    -3.43276914e-01,
+                    7.65585441e00,
+                    -5.17010009e-01,
+                    1.28424404e00,
+                    -6.57013775e-01,
+                    -2.72570553e00,
+                    3.09863582e00,
+                    8.26999588e00,
+                    1.08360782e00,
+                    2.97499462e-01,
+                    -5.28765957e-01,
+                    -7.96130693e00,
+                    -1.80771840e00,
+                    1.74322693e00,
+                    4.46006209e00,
+                    1.96673988e00,
+                    -1.26500012e00,
+                    -2.62521339e-01,
+                    4.43172806e00,
+                    -8.59953375e-01,
+                    -2.79203135e00,
+                    3.97136669e00,
+                    4.83725475e00,
+                    -2.36000818e-01,
+                    -2.54368931e00,
+                    -6.09494471e00,
+                    2.97887357e00,
+                    -3.11669990e00,
+                    -7.49438171e00,
+                    7.68609007e00,
+                    4.24065149e00,
+                    -3.50205849e00,
+                    -4.14267291e00,
+                    1.29406661e00,
+                    -3.29221719e00,
+                    4.91285113e00,
+                    2.49242470e00,
+                    3.03079368e00,
+                    -1.16511988e00,
+                    1.75569959e-01,
+                    3.69572816e00,
+                    -2.23354575e00,
+                    -1.08249093e00,
+                    3.79457820e00,
+                    2.46730808e00,
+                    -5.62046536e00,
+                    -1.63213742e00,
+                    1.80517373e00,
+                    -1.58217893e00,
+                    7.70526692e00,
+                    -1.45138939e00,
+                    -1.02637577e00,
+                    1.83421798e00,
+                    1.20008006e00,
+                    -3.70929508e-01,
+                    -2.06747283e00,
+                    1.05799974e00,
+                    4.50025041e00,
+                    8.99414047e-01,
+                    -3.81032447e00,
+                    6.64691827e00,
+                    -6.68286008e00,
+                    -5.33754112e00,
+                    4.20039092e00,
+                    1.15777816e00,
+                    -1.79904165e00,
+                    -2.25318912e00,
+                    8.56072151e00,
+                    -1.74587332e00,
+                    2.27772815e00,
+                    1.18619882e00,
+                    1.17419760e00,
+                    1.12252724e00,
+                    2.41046828e00,
+                    -1.27854741e00,
+                    -1.63751443e00,
+                    -4.36138109e00,
+                    -3.99645147e00,
+                    2.61707008e-01,
+                    1.77727481e00,
+                    2.58218034e00,
+                    -3.34194564e00,
+                    -5.45410857e00,
+                    -1.10816013e01,
+                    3.77134811e00,
+                    -5.53653174e-01,
+                    -7.50458024e-01,
+                    1.83105453e00,
+                    -6.35106143e00,
+                    -2.32310964e-01,
+                    8.36876665e00,
+                    2.73772575e00,
+                    2.42717722e00,
+                    -7.06580844e00,
+                    8.30491238e00,
+                    -4.67310265e00,
+                    4.82361105e00,
+                    -6.71576571e00,
+                    6.02101751e00,
+                    6.24969448e00,
+                    -2.98703859e00,
+                    6.14207232e-01,
+                    1.78015104e00,
+                    -2.06596331e00,
+                    -4.34009099e00,
+                    -2.43064707e00,
+                    2.03098762e00,
+                    -9.89714067e-01,
+                    -2.70977210e00,
+                    2.74338316e00,
+                    1.89889595e00,
+                    -2.55656260e00,
+                    -4.70778279e00,
+                    3.13221251e00,
+                    -2.32580294e00,
+                    3.85278333e-02,
+                    5.55167173e00,
+                    3.21784728e-01,
+                    -4.92260843e00,
+                    -5.54069995e-01,
+                    -2.40504807e00,
+                    7.15357191e00,
+                    -8.09982416e-01,
+                    -5.25778915e-01,
+                    -7.71322963e-01,
+                    -4.04571082e-02,
+                    -7.44434946e00,
+                    -5.12893117e00,
+                    -7.11996760e-01,
+                    1.52709995e00,
+                    1.20660824e00,
+                    -3.94659988e00,
+                    -6.15942263e00,
+                    -3.24356676e00,
+                    -2.71168115e00,
+                    2.23742176e00,
+                    -2.15833449e00,
+                    3.28171007e00,
+                    -9.01288903e-01,
+                    -3.36544690e00,
+                    -4.90099212e-01,
+                    -5.28357599e00,
+                    2.83366162e00,
+                    -1.94060483e00,
+                    -1.96470570e00,
+                    -1.56417735e00,
+                    -5.63317405e00,
+                    -1.52587686e00,
+                    -2.94973969e00,
+                    -1.71309668e00,
+                    -3.43045944e-01,
+                    -2.89876104e00,
+                    -2.06482721e00,
+                    4.84964575e00,
+                    1.41788617e00,
+                    4.07125067e00,
+                    9.04277262e-01,
+                    4.09024059e00,
+                    -5.57238878e00,
+                    1.58954316e00,
+                    -1.10885879e-01,
+                    -2.21962753e00,
+                    -3.10507445e00,
+                    -4.85573938e00,
+                    5.55346782e00,
+                    -4.46137455e00,
+                    6.53561699e00,
+                    -4.18305953e00,
+                    -3.33538699e00,
+                    1.07412314e00,
+                    -3.21736541e00,
+                    4.22297199e00,
+                    -1.33947330e00,
+                    2.06426759e00,
+                    -5.54850513e00,
+                    2.50551073e00,
+                    2.09512318e00,
+                    -3.22334697e00,
+                    1.08998132e01,
+                    2.11009614e00,
+                    9.43857355e00,
+                    6.67997823e00,
+                    -2.56444394e00,
+                    -1.56702883e00,
+                    -8.01844888e-01,
+                    -6.53025150e00,
+                    -3.07115943e00,
+                    1.54471353e-01,
+                    4.81876388e00,
+                    -3.13769415e00,
+                    4.56491640e00,
+                    -6.82529587e00,
+                    -2.94109962e00,
+                    -2.92035453e00,
+                    2.23157087e00,
+                    1.22495482e00,
+                    3.27356600e00,
+                    2.78216232e00,
+                    1.39149304e00,
+                    1.12641226e00,
+                    3.13438737e00,
+                    -1.44455956e00,
+                    3.45329504e00,
+                    -7.25452537e00,
+                    5.16350338e-01,
+                    -1.52840925e00,
+                    3.89239288e-01,
+                    3.57665297e00,
+                    4.23851729e-01,
+                    2.51386164e00,
+                    5.55541927e00,
+                    -3.65730975e-02,
+                    4.97351340e00,
+                    -2.21492629e00,
+                    2.06160783e-01,
+                    -3.43932949e00,
+                    3.46787764e00,
+                    1.50062470e00,
+                    -3.63420781e00,
+                    7.16921221e-01,
+                    3.67330490e00,
+                    -1.89513701e00,
+                    -4.99527599e00,
+                    1.11835198e00,
+                    -6.81027303e00,
+                    2.85916379e00,
+                    -1.23450647e00,
+                    -1.60211378e00,
+                    3.73671094e00,
+                    -4.02548447e00,
+                    6.06862004e00,
+                    -1.19202728e00,
+                    -2.41783262e00,
+                    3.74904207e00,
+                    2.45508616e00,
+                    9.16190491e00,
+                    -2.04793984e00,
+                    -2.85129492e-01,
+                    -4.08466337e00,
+                    -1.34825047e00,
+                    -2.80827325e00,
+                    -2.43332648e00,
+                    -6.90362325e00,
+                    6.92712787e00,
+                    -5.88185198e00,
+                    -1.13563946e01,
+                    -4.22056384e00,
+                    -3.26737627e00,
+                    -4.22009802e00,
+                    5.09351493e00,
+                    8.23654694e-01,
+                    8.38630810e-03,
+                    3.74246157e00,
+                    2.14720496e00,
+                    2.81112013e00,
+                    -5.53460662e00,
+                    -2.43520405e00,
+                    3.62002815e00,
+                    -9.93353240e00,
+                    -5.95111730e00,
+                    3.50146440e00,
+                    -1.58161073e00,
+                    1.32153944e00,
+                    3.46545576e00,
+                    -4.14140504e00,
+                    1.80779810e00,
+                    5.12518371e00,
+                    5.06350579e-01,
+                    -5.12143943e00,
+                    3.05075730e00,
+                    1.52664403e00,
+                    1.17840650e00,
+                    1.52245045e00,
+                    -1.11987154e01,
+                    3.52537880e00,
+                    6.58677184e00,
+                    1.04950075e00,
+                    7.26431734e-01,
+                    3.78884361e00,
+                    -6.88274613e-01,
+                    2.91277585e00,
+                    -5.39988722e-01,
+                    -4.86762086e00,
+                    -5.85324299e00,
+                    -4.79646945e00,
+                    -5.12261654e00,
+                    -3.76122380e00,
+                    5.91361431e00,
+                    3.95099716e00,
+                    -1.00882397e00,
+                    -1.12282264e00,
+                    -1.53472669e-01,
+                    -1.42612392e00,
+                    1.01808498e00,
+                    3.89284850e00,
+                    -7.95528695e-01,
+                    -1.52721085e00,
+                    5.56588266e00,
+                    -2.66966726e00,
+                    1.07227282e00,
+                    1.17704332e00,
+                    2.19578871e-01,
+                    -3.14188532e-01,
+                    -3.56008185e00,
+                    -1.10180252e00,
+                    1.67156722e00,
+                    1.65997958e00,
+                    1.59415822e00,
+                    -3.66572332e00,
+                    -4.48543103e00,
+                    2.70453532e00,
+                    1.23141468e00,
+                    -1.01656226e00,
+                    4.45616246e00,
+                    4.62624155e00,
+                    1.06641760e01,
+                    1.35086342e00,
+                    -2.94979670e00,
+                    -2.91476126e00,
+                    -9.35116602e-01,
+                    2.06360252e00,
+                    -9.10136499e00,
+                    5.81008956e00,
+                    -1.62736303e00,
+                    -1.25060209e00,
+                    -2.87164090e00,
+                    -5.45701288e-01,
+                    -7.51629139e-01,
+                    -9.38791436e-01,
+                    2.34097570e00,
+                    -2.84663470e00,
+                    -3.87224043e00,
+                    1.62309927e00,
+                    5.67813073e-01,
+                    3.81686799e-01,
+                    2.51854400e00,
+                    -4.86569414e00,
+                    -4.26029143e00,
+                    6.13481084e00,
+                    -4.95681203e00,
+                    -4.50729853e00,
+                    2.67671425e00,
+                    1.10979053e-01,
+                    -9.80886696e-02,
+                    -1.40850133e00,
+                    2.61885371e00,
+                    -2.60370423e00,
+                    5.83765852e00,
+                    -2.83363576e00,
+                    -7.32202969e-01,
+                    5.99369850e00,
+                    -1.07059637e00,
+                    7.54395772e00,
+                    1.34653938e00,
+                    5.18724237e00,
+                    -7.20618474e00,
+                    1.15357476e00,
+                    -6.15439595e00,
+                    4.00557024e00,
+                    -6.54318747e00,
+                    1.40767219e00,
+                    -3.25250711e-01,
+                    -6.16784426e00,
+                    -5.85228332e00,
+                    -2.92134516e-01,
+                    6.75744660e00,
+                    -3.20462659e-01,
+                    4.23922397e00,
+                    -9.29443606e-01,
+                    3.45086639e00,
+                    -8.67499798e00,
+                    -2.01999643e00,
+                    3.95956040e00,
+                    8.79209638e-02,
+                    -3.11761297e-01,
+                    -9.54823660e-01,
+                    3.36900880e00,
+                    1.05584820e00,
+                    1.90557798e-01,
+                    4.35153735e00,
+                    2.07445269e00,
+                    3.28100342e-01,
+                    6.04041984e00,
+                    -1.15367544e00,
+                    1.27468974e00,
+                    -2.86660450e00,
+                    -1.20727102e00,
+                    6.11895125e00,
+                    -2.82027924e00,
+                    -6.04291722e00,
+                    3.81097996e00,
+                    9.10548304e-01,
+                    8.94829367e-01,
+                    4.36403895e-01,
+                    -1.03365614e00,
+                ]
+            ]
+        )
+        np.testing.assert_array_almost_equal(self.fee.critical_points[15], critical_points_expected_15)
+
+    def test_layer_0_biases(self):
+        layer_0_biases_expected = np.array(
+            [
+                [3.52880724],
+                [1.04879517],
+                [1.50037751],
+                [1.28102357],
+                [-0.12998148],
+                [1.31377369],
+                [-0.37855184],
+                [0.31751928],
+                [-0.83950368],
+                [1.00915159],
+                [-0.22809063],
+                [-0.09700302],
+                [0.20176007],
+                [-0.48283775],
+                [0.15261177],
+                [0.40842637],
+            ]
+        )
+        np.testing.assert_array_almost_equal(self.fee.b_0, layer_0_biases_expected)
+
+    def test_layer_1_biases(self):
+        layer_1_biases_expected = np.array(
+            [
+                [0.3580238],
+                [0.16528493],
+                [-0.4548632],
+                [-1.52886227],
+                [0.23741153],
+                [-1.2571574],
+                [-0.75966823],
+                [-1.02489274],
+                [-0.48252173],
+                [1.92286191],
+            ]
+        )
+        np.testing.assert_array_almost_equal(self.fee.b_1, layer_1_biases_expected, decimal=4)
+
+    def test_classifier_type_check_fail(self):
+        backend_test_classifier_type_check_fail(
+            FunctionallyEquivalentExtraction, [BaseEstimator, NeuralNetworkMixin, ClassifierMixin]
+        )
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/adversarial-robustness-toolbox/tests/attacks/test_hclu.py b/adversarial-robustness-toolbox/tests/attacks/test_hclu.py
new file mode 100644
index 0000000..45c4050
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/attacks/test_hclu.py
@@ -0,0 +1,83 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2019
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import unittest
+
+import numpy as np
+import GPy
+
+from art.attacks.evasion.hclu import HighConfidenceLowUncertainty
+from art.estimators.classification.GPy import GPyGaussianProcessClassifier
+
+from tests.utils import TestBase
+from tests.attacks.utils import backend_test_classifier_type_check_fail
+
+logger = logging.getLogger(__name__)
+
+
+class TestHCLU(TestBase):
+    @classmethod
+    def setUpClass(cls):
+        super().setUpClass()
+
+        # change iris to a binary problem
+        cls.x_train = cls.x_train_iris[0:35]
+        cls.y_train = cls.y_train_iris[0:35, 1]
+        cls.x_test = cls.x_train
+        cls.y_test = cls.y_train
+
+    def test_GPy(self):
+        x_test_original = self.x_test.copy()
+        # set up GPclassifier
+        gpkern = GPy.kern.RBF(np.shape(self.x_train)[1])
+        m = GPy.models.GPClassification(self.x_train, self.y_train.reshape(-1, 1), kernel=gpkern)
+        m.inference_method = GPy.inference.latent_function_inference.laplace.Laplace()
+        m.optimize(messages=True, optimizer="lbfgs")
+        # get ART classifier + clean accuracy
+        m_art = GPyGaussianProcessClassifier(m)
+        clean_acc = np.mean(np.argmin(m_art.predict(self.x_test), axis=1) == self.y_test)
+        # get adversarial examples, accuracy, and uncertainty
+        attack = HighConfidenceLowUncertainty(m_art, conf=0.9, min_val=-0.0, max_val=1.0)
+        adv = attack.generate(self.x_test)
+        adv_acc = np.mean(np.argmin(m_art.predict(adv), axis=1) == self.y_test)
+        unc_f = m_art.predict_uncertainty(adv)
+        # not all attacks succeed due to the decision surface landscape of GP, some should
+        self.assertGreater(clean_acc, adv_acc)
+
+        # now take into account uncertainty
+        attack = HighConfidenceLowUncertainty(m_art, unc_increase=0.9, conf=0.9, min_val=0.0, max_val=1.0)
+        adv = attack.generate(self.x_test)
+        adv_acc = np.mean(np.argmin(m_art.predict(adv), axis=1) == self.y_test)
+        unc_o = m_art.predict_uncertainty(adv)
+        # same above
+        self.assertGreater(clean_acc, adv_acc)
+        # uncertainty should indeed be lower when used as a constraint
+        # however, same as above, crafting might fail
+        self.assertGreater(np.mean(unc_f > unc_o), 0.65)
+
+        # Check that x_test has not been modified by attack and classifier
+        self.assertAlmostEqual(float(np.max(np.abs(x_test_original - self.x_test))), 0.0, delta=0.00001)
+
+    def test_classifier_type_check_fail(self):
+        backend_test_classifier_type_check_fail(HighConfidenceLowUncertainty, [GPyGaussianProcessClassifier])
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/adversarial-robustness-toolbox/tests/attacks/test_hop_skip_jump.py b/adversarial-robustness-toolbox/tests/attacks/test_hop_skip_jump.py
new file mode 100644
index 0000000..2dcf4fd
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/attacks/test_hop_skip_jump.py
@@ -0,0 +1,767 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import unittest
+import keras.backend as k
+import numpy as np
+
+from art.attacks.evasion.hop_skip_jump import HopSkipJump
+from art.estimators.estimator import BaseEstimator
+from art.estimators.classification import ClassifierMixin
+from art.estimators.classification.keras import KerasClassifier
+from art.utils import random_targets
+
+from tests.utils import TestBase
+from tests.utils import get_image_classifier_tf, get_image_classifier_kr, get_image_classifier_pt
+from tests.utils import get_tabular_classifier_tf, get_tabular_classifier_kr
+from tests.utils import get_tabular_classifier_pt, master_seed
+from tests.attacks.utils import backend_test_classifier_type_check_fail
+
+logger = logging.getLogger(__name__)
+
+
+class TestHopSkipJump(TestBase):
+    """
+    A unittest class for testing the HopSkipJump attack.
+    """
+
+    @classmethod
+    def setUpClass(cls):
+        master_seed(seed=1234, set_tensorflow=True, set_torch=True)
+        super().setUpClass()
+
+        cls.n_train = 100
+        cls.n_test = 10
+        cls.x_train_mnist = cls.x_train_mnist[0 : cls.n_train]
+        cls.y_train_mnist = cls.y_train_mnist[0 : cls.n_train]
+        cls.x_test_mnist = cls.x_test_mnist[0 : cls.n_test]
+        cls.y_test_mnist = cls.y_test_mnist[0 : cls.n_test]
+
+    def setUp(self):
+        master_seed(seed=1234, set_tensorflow=True, set_torch=True)
+        super().setUp()
+
+    def test_3_tensorflow_mnist(self):
+        """
+        First test with the TensorFlowClassifier.
+        :return:
+        """
+        x_test_original = self.x_test_mnist.copy()
+
+        # Build TensorFlowClassifier
+        tfc, sess = get_image_classifier_tf()
+
+        # First targeted attack and norm=2
+        hsj = HopSkipJump(classifier=tfc, targeted=True, max_iter=2, max_eval=100, init_eval=10)
+        params = {"y": random_targets(self.y_test_mnist, tfc.nb_classes)}
+        x_test_adv = hsj.generate(self.x_test_mnist, **params)
+
+        self.assertFalse((self.x_test_mnist == x_test_adv).all())
+        self.assertTrue((x_test_adv <= 1.0001).all())
+        self.assertTrue((x_test_adv >= -0.0001).all())
+
+        target = np.argmax(params["y"], axis=1)
+        y_pred_adv = np.argmax(tfc.predict(x_test_adv), axis=1)
+        self.assertTrue((target == y_pred_adv).any())
+
+        # Test the masking 1
+        mask = np.random.binomial(n=1, p=0.5, size=np.prod(self.x_test_mnist.shape))
+        mask = mask.reshape(self.x_test_mnist.shape)
+
+        params.update(mask=mask)
+        x_test_adv = hsj.generate(self.x_test_mnist, **params)
+        mask_diff = (1 - mask) * (x_test_adv - self.x_test_mnist)
+        self.assertAlmostEqual(float(np.max(np.abs(mask_diff))), 0.0, delta=0.00001)
+
+        unmask_diff = mask * (x_test_adv - self.x_test_mnist)
+        self.assertGreater(float(np.sum(np.abs(unmask_diff))), 0.0)
+
+        # Test the masking 2
+        mask = np.random.binomial(n=1, p=0.5, size=np.prod(self.x_test_mnist.shape[1:]))
+        mask = mask.reshape(self.x_test_mnist.shape[1:])
+
+        params.update(mask=mask)
+        x_test_adv = hsj.generate(self.x_test_mnist, **params)
+        mask_diff = (1 - mask) * (x_test_adv - self.x_test_mnist)
+        self.assertAlmostEqual(float(np.max(np.abs(mask_diff))), 0.0, delta=0.00001)
+
+        unmask_diff = mask * (x_test_adv - self.x_test_mnist)
+        self.assertGreater(float(np.sum(np.abs(unmask_diff))), 0.0)
+
+        # First targeted attack and norm=np.inf
+        hsj = HopSkipJump(classifier=tfc, targeted=True, max_iter=2, max_eval=100, init_eval=10, norm=np.Inf)
+        params = {"y": random_targets(self.y_test_mnist, tfc.nb_classes)}
+        x_test_adv = hsj.generate(self.x_test_mnist, **params)
+
+        self.assertFalse((self.x_test_mnist == x_test_adv).all())
+        self.assertTrue((x_test_adv <= 1.0001).all())
+        self.assertTrue((x_test_adv >= -0.0001).all())
+
+        target = np.argmax(params["y"], axis=1)
+        y_pred_adv = np.argmax(tfc.predict(x_test_adv), axis=1)
+        self.assertTrue((target == y_pred_adv).any())
+
+        # Test the masking 1
+        mask = np.random.binomial(n=1, p=0.5, size=np.prod(self.x_test_mnist.shape))
+        mask = mask.reshape(self.x_test_mnist.shape)
+
+        params.update(mask=mask)
+        x_test_adv = hsj.generate(self.x_test_mnist, **params)
+        mask_diff = (1 - mask) * (x_test_adv - self.x_test_mnist)
+        self.assertAlmostEqual(float(np.max(np.abs(mask_diff))), 0.0, delta=0.00001)
+
+        unmask_diff = mask * (x_test_adv - self.x_test_mnist)
+        self.assertGreater(float(np.sum(np.abs(unmask_diff))), 0.0)
+
+        # Test the masking 2
+        mask = np.random.binomial(n=1, p=0.5, size=np.prod(self.x_test_mnist.shape[1:]))
+        mask = mask.reshape(self.x_test_mnist.shape[1:])
+
+        params.update(mask=mask)
+        x_test_adv = hsj.generate(self.x_test_mnist, **params)
+        mask_diff = (1 - mask) * (x_test_adv - self.x_test_mnist)
+        self.assertAlmostEqual(float(np.max(np.abs(mask_diff))), 0.0, delta=0.00001)
+
+        unmask_diff = mask * (x_test_adv - self.x_test_mnist)
+        self.assertGreater(float(np.sum(np.abs(unmask_diff))), 0.0)
+
+        # Second untargeted attack and norm=2
+        hsj = HopSkipJump(classifier=tfc, targeted=False, max_iter=2, max_eval=100, init_eval=10)
+        x_test_adv = hsj.generate(self.x_test_mnist)
+
+        self.assertFalse((self.x_test_mnist == x_test_adv).all())
+        self.assertTrue((x_test_adv <= 1.0001).all())
+        self.assertTrue((x_test_adv >= -0.0001).all())
+
+        y_pred = np.argmax(tfc.predict(self.x_test_mnist), axis=1)
+        y_pred_adv = np.argmax(tfc.predict(x_test_adv), axis=1)
+        self.assertTrue((y_pred != y_pred_adv).any())
+
+        # Test the masking 1
+        mask = np.random.binomial(n=1, p=0.5, size=np.prod(self.x_test_mnist.shape))
+        mask = mask.reshape(self.x_test_mnist.shape)
+
+        x_test_adv = hsj.generate(self.x_test_mnist, mask=mask)
+        mask_diff = (1 - mask) * (x_test_adv - self.x_test_mnist)
+        self.assertAlmostEqual(float(np.max(np.abs(mask_diff))), 0.0, delta=0.00001)
+
+        unmask_diff = mask * (x_test_adv - self.x_test_mnist)
+        self.assertGreater(float(np.sum(np.abs(unmask_diff))), 0.0)
+
+        # Test the masking 2
+        mask = np.random.binomial(n=1, p=0.5, size=np.prod(self.x_test_mnist.shape[1:]))
+        mask = mask.reshape(self.x_test_mnist.shape[1:])
+
+        x_test_adv = hsj.generate(self.x_test_mnist, mask=mask)
+        mask_diff = (1 - mask) * (x_test_adv - self.x_test_mnist)
+        self.assertAlmostEqual(float(np.max(np.abs(mask_diff))), 0.0, delta=0.00001)
+
+        unmask_diff = mask * (x_test_adv - self.x_test_mnist)
+        self.assertGreater(float(np.sum(np.abs(unmask_diff))), 0.0)
+
+        # Second untargeted attack and norm=np.inf
+        hsj = HopSkipJump(classifier=tfc, targeted=False, max_iter=2, max_eval=100, init_eval=10, norm=np.Inf)
+        x_test_adv = hsj.generate(self.x_test_mnist)
+
+        self.assertFalse((self.x_test_mnist == x_test_adv).all())
+        self.assertTrue((x_test_adv <= 1.0001).all())
+        self.assertTrue((x_test_adv >= -0.0001).all())
+
+        y_pred = np.argmax(tfc.predict(self.x_test_mnist), axis=1)
+        y_pred_adv = np.argmax(tfc.predict(x_test_adv), axis=1)
+        self.assertTrue((y_pred != y_pred_adv).any())
+
+        # Test the masking 1
+        mask = np.random.binomial(n=1, p=0.5, size=np.prod(self.x_test_mnist.shape))
+        mask = mask.reshape(self.x_test_mnist.shape)
+
+        x_test_adv = hsj.generate(self.x_test_mnist, mask=mask)
+        mask_diff = (1 - mask) * (x_test_adv - self.x_test_mnist)
+        self.assertAlmostEqual(float(np.max(np.abs(mask_diff))), 0.0, delta=0.00001)
+
+        unmask_diff = mask * (x_test_adv - self.x_test_mnist)
+        self.assertGreater(float(np.sum(np.abs(unmask_diff))), 0.0)
+
+        # Test the masking 2
+        mask = np.random.binomial(n=1, p=0.5, size=np.prod(self.x_test_mnist.shape[1:]))
+        mask = mask.reshape(self.x_test_mnist.shape[1:])
+
+        x_test_adv = hsj.generate(self.x_test_mnist, mask=mask)
+        mask_diff = (1 - mask) * (x_test_adv - self.x_test_mnist)
+        self.assertAlmostEqual(float(np.max(np.abs(mask_diff))), 0.0, delta=0.00001)
+
+        unmask_diff = mask * (x_test_adv - self.x_test_mnist)
+        self.assertGreater(float(np.sum(np.abs(unmask_diff))), 0.0)
+
+        # Check that x_test has not been modified by attack and classifier
+        self.assertAlmostEqual(float(np.max(np.abs(x_test_original - self.x_test_mnist))), 0.0, delta=0.00001)
+
+        # Clean-up session
+        if sess is not None:
+            sess.close()
+
+    def test_8_keras_mnist(self):
+        """
+        Second test with the KerasClassifier.
+        :return:
+        """
+        x_test_original = self.x_test_mnist.copy()
+
+        # Build KerasClassifier
+        krc = get_image_classifier_kr()
+
+        # First targeted attack and norm=2
+        hsj = HopSkipJump(classifier=krc, targeted=True, max_iter=2, max_eval=100, init_eval=10)
+        params = {"y": random_targets(self.y_test_mnist, krc.nb_classes)}
+        x_test_adv = hsj.generate(self.x_test_mnist, **params)
+
+        self.assertFalse((self.x_test_mnist == x_test_adv).all())
+        self.assertTrue((x_test_adv <= 1.0001).all())
+        self.assertTrue((x_test_adv >= -0.0001).all())
+
+        target = np.argmax(params["y"], axis=1)
+        y_pred_adv = np.argmax(krc.predict(x_test_adv), axis=1)
+        self.assertTrue((target == y_pred_adv).any())
+
+        # Test the masking 1
+        mask = np.random.binomial(n=1, p=0.5, size=np.prod(self.x_test_mnist.shape))
+        mask = mask.reshape(self.x_test_mnist.shape)
+
+        params.update(mask=mask)
+        x_test_adv = hsj.generate(self.x_test_mnist, **params)
+        mask_diff = (1 - mask) * (x_test_adv - self.x_test_mnist)
+        self.assertAlmostEqual(float(np.max(np.abs(mask_diff))), 0.0, delta=0.00001)
+
+        unmask_diff = mask * (x_test_adv - self.x_test_mnist)
+        self.assertGreater(float(np.sum(np.abs(unmask_diff))), 0.0)
+
+        # Test the masking 2
+        mask = np.random.binomial(n=1, p=0.5, size=np.prod(self.x_test_mnist.shape[1:]))
+        mask = mask.reshape(self.x_test_mnist.shape[1:])
+
+        params.update(mask=mask)
+        x_test_adv = hsj.generate(self.x_test_mnist, **params)
+        mask_diff = (1 - mask) * (x_test_adv - self.x_test_mnist)
+        self.assertAlmostEqual(float(np.max(np.abs(mask_diff))), 0.0, delta=0.00001)
+
+        unmask_diff = mask * (x_test_adv - self.x_test_mnist)
+        self.assertGreater(float(np.sum(np.abs(unmask_diff))), 0.0)
+
+        # First targeted attack and norm=np.inf
+        hsj = HopSkipJump(classifier=krc, targeted=True, max_iter=2, max_eval=100, init_eval=10, norm=np.Inf)
+        params = {"y": random_targets(self.y_test_mnist, krc.nb_classes)}
+        x_test_adv = hsj.generate(self.x_test_mnist, **params)
+
+        self.assertFalse((self.x_test_mnist == x_test_adv).all())
+        self.assertTrue((x_test_adv <= 1.0001).all())
+        self.assertTrue((x_test_adv >= -0.0001).all())
+
+        target = np.argmax(params["y"], axis=1)
+        y_pred_adv = np.argmax(krc.predict(x_test_adv), axis=1)
+        self.assertTrue((target == y_pred_adv).any())
+
+        # Test the masking 1
+        mask = np.random.binomial(n=1, p=0.5, size=np.prod(self.x_test_mnist.shape))
+        mask = mask.reshape(self.x_test_mnist.shape)
+
+        params.update(mask=mask)
+        x_test_adv = hsj.generate(self.x_test_mnist, **params)
+        mask_diff = (1 - mask) * (x_test_adv - self.x_test_mnist)
+        self.assertAlmostEqual(float(np.max(np.abs(mask_diff))), 0.0, delta=0.00001)
+
+        unmask_diff = mask * (x_test_adv - self.x_test_mnist)
+        self.assertGreater(float(np.sum(np.abs(unmask_diff))), 0.0)
+
+        # Test the masking 2
+        mask = np.random.binomial(n=1, p=0.5, size=np.prod(self.x_test_mnist.shape[1:]))
+        mask = mask.reshape(self.x_test_mnist.shape[1:])
+
+        params.update(mask=mask)
+        x_test_adv = hsj.generate(self.x_test_mnist, **params)
+        mask_diff = (1 - mask) * (x_test_adv - self.x_test_mnist)
+        self.assertAlmostEqual(float(np.max(np.abs(mask_diff))), 0.0, delta=0.00001)
+
+        unmask_diff = mask * (x_test_adv - self.x_test_mnist)
+        self.assertGreater(float(np.sum(np.abs(unmask_diff))), 0.0)
+
+        # Second untargeted attack and norm=2
+        hsj = HopSkipJump(classifier=krc, targeted=False, max_iter=2, max_eval=100, init_eval=10)
+        x_test_adv = hsj.generate(self.x_test_mnist)
+
+        self.assertFalse((self.x_test_mnist == x_test_adv).all())
+        self.assertTrue((x_test_adv <= 1.0001).all())
+        self.assertTrue((x_test_adv >= -0.0001).all())
+
+        y_pred = np.argmax(krc.predict(self.x_test_mnist), axis=1)
+        y_pred_adv = np.argmax(krc.predict(x_test_adv), axis=1)
+        self.assertTrue((y_pred != y_pred_adv).any())
+
+        # Test the masking 1
+        mask = np.random.binomial(n=1, p=0.5, size=np.prod(self.x_test_mnist.shape))
+        mask = mask.reshape(self.x_test_mnist.shape)
+
+        x_test_adv = hsj.generate(self.x_test_mnist, mask=mask)
+        mask_diff = (1 - mask) * (x_test_adv - self.x_test_mnist)
+        self.assertAlmostEqual(float(np.max(np.abs(mask_diff))), 0.0, delta=0.00001)
+
+        unmask_diff = mask * (x_test_adv - self.x_test_mnist)
+        self.assertGreater(float(np.sum(np.abs(unmask_diff))), 0.0)
+
+        # Test the masking 2
+        mask = np.random.binomial(n=1, p=0.5, size=np.prod(self.x_test_mnist.shape[1:]))
+        mask = mask.reshape(self.x_test_mnist.shape[1:])
+
+        x_test_adv = hsj.generate(self.x_test_mnist, mask=mask)
+        mask_diff = (1 - mask) * (x_test_adv - self.x_test_mnist)
+        self.assertAlmostEqual(float(np.max(np.abs(mask_diff))), 0.0, delta=0.00001)
+
+        unmask_diff = mask * (x_test_adv - self.x_test_mnist)
+        self.assertGreater(float(np.sum(np.abs(unmask_diff))), 0.0)
+
+        # Second untargeted attack and norm=np.inf
+        hsj = HopSkipJump(classifier=krc, targeted=False, max_iter=2, max_eval=100, init_eval=10, norm=np.Inf)
+        x_test_adv = hsj.generate(self.x_test_mnist)
+
+        self.assertFalse((self.x_test_mnist == x_test_adv).all())
+        self.assertTrue((x_test_adv <= 1.0001).all())
+        self.assertTrue((x_test_adv >= -0.0001).all())
+
+        y_pred = np.argmax(krc.predict(self.x_test_mnist), axis=1)
+        y_pred_adv = np.argmax(krc.predict(x_test_adv), axis=1)
+        self.assertTrue((y_pred != y_pred_adv).any())
+
+        # Test the masking 1
+        mask = np.random.binomial(n=1, p=0.5, size=np.prod(self.x_test_mnist.shape))
+        mask = mask.reshape(self.x_test_mnist.shape)
+
+        x_test_adv = hsj.generate(self.x_test_mnist, mask=mask)
+        mask_diff = (1 - mask) * (x_test_adv - self.x_test_mnist)
+        self.assertAlmostEqual(float(np.max(np.abs(mask_diff))), 0.0, delta=0.00001)
+
+        unmask_diff = mask * (x_test_adv - self.x_test_mnist)
+        self.assertGreater(float(np.sum(np.abs(unmask_diff))), 0.0)
+
+        # Test the masking 2
+        mask = np.random.binomial(n=1, p=0.5, size=np.prod(self.x_test_mnist.shape[1:]))
+        mask = mask.reshape(self.x_test_mnist.shape[1:])
+
+        x_test_adv = hsj.generate(self.x_test_mnist, mask=mask)
+        mask_diff = (1 - mask) * (x_test_adv - self.x_test_mnist)
+        self.assertAlmostEqual(float(np.max(np.abs(mask_diff))), 0.0, delta=0.00001)
+
+        unmask_diff = mask * (x_test_adv - self.x_test_mnist)
+        self.assertGreater(float(np.sum(np.abs(unmask_diff))), 0.0)
+
+        # Check that x_test has not been modified by attack and classifier
+        self.assertAlmostEqual(float(np.max(np.abs(x_test_original - self.x_test_mnist))), 0.0, delta=0.00001)
+
+        # Clean-up session
+        k.clear_session()
+
+    def test_4_pytorch_classifier(self):
+        """
+        Third test with the PyTorchClassifier.
+        :return:
+        """
+        x_test = np.swapaxes(self.x_test_mnist, 1, 3).astype(np.float32)
+        x_test_original = x_test.copy()
+
+        # Build PyTorchClassifier
+        ptc = get_image_classifier_pt()
+
+        # First targeted attack and norm=2
+        hsj = HopSkipJump(classifier=ptc, targeted=True, max_iter=2, max_eval=100, init_eval=10)
+        params = {"y": random_targets(self.y_test_mnist, ptc.nb_classes)}
+        x_test_adv = hsj.generate(x_test, **params)
+
+        self.assertFalse((x_test == x_test_adv).all())
+        self.assertTrue((x_test_adv <= 1.0001).all())
+        self.assertTrue((x_test_adv >= -0.0001).all())
+
+        target = np.argmax(params["y"], axis=1)
+        y_pred_adv = np.argmax(ptc.predict(x_test_adv), axis=1)
+        self.assertTrue((target == y_pred_adv).any())
+
+        # Test the masking 1
+        mask = np.random.binomial(n=1, p=0.5, size=np.prod(x_test.shape))
+        mask = mask.reshape(x_test.shape)
+
+        params.update(mask=mask)
+        x_test_adv = hsj.generate(x_test, **params)
+        mask_diff = (1 - mask) * (x_test_adv - x_test)
+        self.assertAlmostEqual(float(np.max(np.abs(mask_diff))), 0.0, delta=0.00001)
+
+        unmask_diff = mask * (x_test_adv - x_test)
+        self.assertGreater(float(np.sum(np.abs(unmask_diff))), 0.0)
+
+        # Test the masking 2
+        mask = np.random.binomial(n=1, p=0.5, size=np.prod(x_test.shape[1:]))
+        mask = mask.reshape(x_test.shape[1:])
+
+        params.update(mask=mask)
+        x_test_adv = hsj.generate(x_test, **params)
+        mask_diff = (1 - mask) * (x_test_adv - x_test)
+        self.assertAlmostEqual(float(np.max(np.abs(mask_diff))), 0.0, delta=0.00001)
+
+        unmask_diff = mask * (x_test_adv - x_test)
+        self.assertGreater(float(np.sum(np.abs(unmask_diff))), 0.0)
+
+        # First targeted attack and norm=np.inf
+        hsj = HopSkipJump(classifier=ptc, targeted=True, max_iter=5, max_eval=100, init_eval=10, norm=np.Inf)
+        params = {"y": random_targets(self.y_test_mnist, ptc.nb_classes)}
+        x_test_adv = hsj.generate(x_test, **params)
+
+        self.assertFalse((x_test == x_test_adv).all())
+        self.assertTrue((x_test_adv <= 1.0001).all())
+        self.assertTrue((x_test_adv >= -0.0001).all())
+
+        target = np.argmax(params["y"], axis=1)
+        y_pred_adv = np.argmax(ptc.predict(x_test_adv), axis=1)
+        self.assertTrue((target == y_pred_adv).any())
+
+        # Test the masking 1
+        mask = np.random.binomial(n=1, p=0.5, size=np.prod(x_test.shape))
+        mask = mask.reshape(x_test.shape)
+
+        params.update(mask=mask)
+        x_test_adv = hsj.generate(x_test, **params)
+        mask_diff = (1 - mask) * (x_test_adv - x_test)
+        self.assertAlmostEqual(float(np.max(np.abs(mask_diff))), 0.0, delta=0.00001)
+
+        unmask_diff = mask * (x_test_adv - x_test)
+        self.assertGreater(float(np.sum(np.abs(unmask_diff))), 0.0)
+
+        # Test the masking 2
+        mask = np.random.binomial(n=1, p=0.5, size=np.prod(x_test.shape[1:]))
+        mask = mask.reshape(x_test.shape[1:])
+
+        params.update(mask=mask)
+        x_test_adv = hsj.generate(x_test, **params)
+        mask_diff = (1 - mask) * (x_test_adv - x_test)
+        self.assertAlmostEqual(float(np.max(np.abs(mask_diff))), 0.0, delta=0.00001)
+
+        unmask_diff = mask * (x_test_adv - x_test)
+        self.assertGreater(float(np.sum(np.abs(unmask_diff))), 0.0)
+
+        # Second untargeted attack and norm=2
+        hsj = HopSkipJump(classifier=ptc, targeted=False, max_iter=2, max_eval=100, init_eval=10)
+        x_test_adv = hsj.generate(x_test)
+
+        self.assertFalse((x_test == x_test_adv).all())
+        self.assertTrue((x_test_adv <= 1.0001).all())
+        self.assertTrue((x_test_adv >= -0.0001).all())
+
+        y_pred = np.argmax(ptc.predict(x_test), axis=1)
+        y_pred_adv = np.argmax(ptc.predict(x_test_adv), axis=1)
+        self.assertTrue((y_pred != y_pred_adv).any())
+
+        # Test the masking 1
+        mask = np.random.binomial(n=1, p=0.5, size=np.prod(x_test.shape))
+        mask = mask.reshape(x_test.shape)
+
+        x_test_adv = hsj.generate(x_test, mask=mask)
+        mask_diff = (1 - mask) * (x_test_adv - x_test)
+        self.assertAlmostEqual(float(np.max(np.abs(mask_diff))), 0.0, delta=0.00001)
+
+        unmask_diff = mask * (x_test_adv - x_test)
+        self.assertGreater(float(np.sum(np.abs(unmask_diff))), 0.0)
+
+        # Test the masking 2
+        mask = np.random.binomial(n=1, p=0.5, size=np.prod(x_test.shape[1:]))
+        mask = mask.reshape(x_test.shape[1:])
+
+        x_test_adv = hsj.generate(x_test, mask=mask)
+        mask_diff = (1 - mask) * (x_test_adv - x_test)
+        self.assertAlmostEqual(float(np.max(np.abs(mask_diff))), 0.0, delta=0.00001)
+
+        unmask_diff = mask * (x_test_adv - x_test)
+        self.assertGreater(float(np.sum(np.abs(unmask_diff))), 0.0)
+
+        # Second untargeted attack and norm=np.inf
+        hsj = HopSkipJump(classifier=ptc, targeted=False, max_iter=2, max_eval=100, init_eval=10, norm=np.Inf)
+        x_test_adv = hsj.generate(x_test)
+
+        self.assertFalse((x_test == x_test_adv).all())
+        self.assertTrue((x_test_adv <= 1.0001).all())
+        self.assertTrue((x_test_adv >= -0.0001).all())
+
+        y_pred = np.argmax(ptc.predict(x_test), axis=1)
+        y_pred_adv = np.argmax(ptc.predict(x_test_adv), axis=1)
+        self.assertTrue((y_pred != y_pred_adv).any())
+
+        # Test the masking 1
+        mask = np.random.binomial(n=1, p=0.5, size=np.prod(x_test.shape))
+        mask = mask.reshape(x_test.shape)
+
+        x_test_adv = hsj.generate(x_test, mask=mask)
+        mask_diff = (1 - mask) * (x_test_adv - x_test)
+        self.assertAlmostEqual(float(np.max(np.abs(mask_diff))), 0.0, delta=0.00001)
+
+        unmask_diff = mask * (x_test_adv - x_test)
+        self.assertGreater(float(np.sum(np.abs(unmask_diff))), 0.0)
+
+        # Test the masking 2
+        mask = np.random.binomial(n=1, p=0.5, size=np.prod(x_test.shape[1:]))
+        mask = mask.reshape(x_test.shape[1:])
+
+        x_test_adv = hsj.generate(x_test, mask=mask)
+        mask_diff = (1 - mask) * (x_test_adv - x_test)
+        self.assertAlmostEqual(float(np.max(np.abs(mask_diff))), 0.0, delta=0.00001)
+
+        unmask_diff = mask * (x_test_adv - x_test)
+        self.assertGreater(float(np.sum(np.abs(unmask_diff))), 0.0)
+
+        # Check that x_test has not been modified by attack and classifier
+        self.assertAlmostEqual(float(np.max(np.abs(x_test_original - x_test))), 0.0, delta=0.00001)
+
+    def test_5_pytorch_resume(self):
+        x_test = np.reshape(self.x_test_mnist, (self.x_test_mnist.shape[0], 1, 28, 28)).astype(np.float32)
+
+        # Build PyTorchClassifier
+        ptc = get_image_classifier_pt()
+
+        # HSJ attack
+        hsj = HopSkipJump(classifier=ptc, targeted=True, max_iter=10, max_eval=100, init_eval=10)
+
+        params = {"y": self.y_test_mnist[2:3], "x_adv_init": x_test[2:3]}
+        x_test_adv1 = hsj.generate(x_test[0:1], **params)
+        diff1 = np.linalg.norm(x_test_adv1 - x_test)
+
+        params.update(resume=True, x_adv_init=x_test_adv1)
+        x_test_adv2 = hsj.generate(x_test[0:1], **params)
+        params.update(x_adv_init=x_test_adv2)
+        x_test_adv2 = hsj.generate(x_test[0:1], **params)
+        diff2 = np.linalg.norm(x_test_adv2 - x_test)
+
+        self.assertGreater(diff1, diff2)
+
+    def test_7_keras_iris_clipped(self):
+        classifier = get_tabular_classifier_kr()
+
+        # Norm=2
+        attack = HopSkipJump(classifier, targeted=False, max_iter=2, max_eval=100, init_eval=10)
+        x_test_adv = attack.generate(self.x_test_iris)
+        self.assertFalse((self.x_test_iris == x_test_adv).all())
+        self.assertTrue((x_test_adv <= 1).all())
+        self.assertTrue((x_test_adv >= 0).all())
+
+        preds_adv = np.argmax(classifier.predict(x_test_adv), axis=1)
+        self.assertFalse((np.argmax(self.y_test_iris, axis=1) == preds_adv).all())
+        acc = np.sum(preds_adv == np.argmax(self.y_test_iris, axis=1)) / self.y_test_iris.shape[0]
+        logger.info("Accuracy on Iris with HopSkipJump adversarial examples: %.2f%%", (acc * 100))
+
+        # Norm=np.inf
+        attack = HopSkipJump(classifier, targeted=False, max_iter=2, max_eval=100, init_eval=10, norm=np.Inf)
+        x_test_adv = attack.generate(self.x_test_iris)
+        self.assertFalse((self.x_test_iris == x_test_adv).all())
+        self.assertTrue((x_test_adv <= 1).all())
+        self.assertTrue((x_test_adv >= 0).all())
+
+        preds_adv = np.argmax(classifier.predict(x_test_adv), axis=1)
+        self.assertFalse((np.argmax(self.y_test_iris, axis=1) == preds_adv).all())
+        acc = np.sum(preds_adv == np.argmax(self.y_test_iris, axis=1)) / self.y_test_iris.shape[0]
+        logger.info("Accuracy on Iris with HopSkipJump adversarial examples: %.2f%%", (acc * 100))
+
+        # Clean-up session
+        k.clear_session()
+
+    def test_7_keras_iris_unbounded(self):
+        classifier = get_tabular_classifier_kr()
+
+        # Recreate a classifier without clip values
+        classifier = KerasClassifier(model=classifier._model, use_logits=False, channels_first=True)
+
+        # Norm=2
+        attack = HopSkipJump(classifier, targeted=False, max_iter=2, max_eval=100, init_eval=10)
+        x_test_adv = attack.generate(self.x_test_iris)
+        self.assertFalse((self.x_test_iris == x_test_adv).all())
+
+        preds_adv = np.argmax(classifier.predict(x_test_adv), axis=1)
+        self.assertFalse((np.argmax(self.y_test_iris, axis=1) == preds_adv).all())
+        acc = np.sum(preds_adv == np.argmax(self.y_test_iris, axis=1)) / self.y_test_iris.shape[0]
+        logger.info("Accuracy on Iris with HopSkipJump adversarial examples: %.2f%%", (acc * 100))
+
+        # Norm=np.inf
+        attack = HopSkipJump(classifier, targeted=False, max_iter=2, max_eval=100, init_eval=10, norm=np.Inf)
+        x_test_adv = attack.generate(self.x_test_iris)
+        self.assertFalse((self.x_test_iris == x_test_adv).all())
+
+        preds_adv = np.argmax(classifier.predict(x_test_adv), axis=1)
+        self.assertFalse((np.argmax(self.y_test_iris, axis=1) == preds_adv).all())
+        acc = np.sum(preds_adv == np.argmax(self.y_test_iris, axis=1)) / self.y_test_iris.shape[0]
+        logger.info("Accuracy on Iris with HopSkipJump adversarial examples: %.2f%%", (acc * 100))
+
+        # Clean-up session
+        k.clear_session()
+
+    def test_2_tensorflow_iris(self):
+        classifier, sess = get_tabular_classifier_tf()
+
+        # Test untargeted attack and norm=2
+        attack = HopSkipJump(classifier, targeted=False, max_iter=2, max_eval=100, init_eval=10)
+        x_test_adv = attack.generate(self.x_test_iris)
+        self.assertFalse((self.x_test_iris == x_test_adv).all())
+        self.assertTrue((x_test_adv <= 1).all())
+        self.assertTrue((x_test_adv >= 0).all())
+
+        preds_adv = np.argmax(classifier.predict(x_test_adv), axis=1)
+        self.assertFalse((np.argmax(self.y_test_iris, axis=1) == preds_adv).all())
+        acc = np.sum(preds_adv == np.argmax(self.y_test_iris, axis=1)) / self.y_test_iris.shape[0]
+        logger.info("Accuracy on Iris with HopSkipJump adversarial examples: %.2f%%", (acc * 100))
+
+        # Test untargeted attack and norm=np.inf
+        attack = HopSkipJump(classifier, targeted=False, max_iter=2, max_eval=100, init_eval=10, norm=np.Inf)
+        x_test_adv = attack.generate(self.x_test_iris)
+        self.assertFalse((self.x_test_iris == x_test_adv).all())
+        self.assertTrue((x_test_adv <= 1).all())
+        self.assertTrue((x_test_adv >= 0).all())
+
+        preds_adv = np.argmax(classifier.predict(x_test_adv), axis=1)
+        self.assertFalse((np.argmax(self.y_test_iris, axis=1) == preds_adv).all())
+        acc = np.sum(preds_adv == np.argmax(self.y_test_iris, axis=1)) / self.y_test_iris.shape[0]
+        logger.info("Accuracy on Iris with HopSkipJump adversarial examples: %.2f%%", (acc * 100))
+
+        # Test targeted attack and norm=2
+        targets = random_targets(self.y_test_iris, nb_classes=3)
+        attack = HopSkipJump(classifier, targeted=True, max_iter=2, max_eval=100, init_eval=10)
+        x_test_adv = attack.generate(self.x_test_iris, **{"y": targets})
+        self.assertFalse((self.x_test_iris == x_test_adv).all())
+        self.assertTrue((x_test_adv <= 1).all())
+        self.assertTrue((x_test_adv >= 0).all())
+
+        preds_adv = np.argmax(classifier.predict(x_test_adv), axis=1)
+        self.assertTrue((np.argmax(targets, axis=1) == preds_adv).any())
+        acc = np.sum(preds_adv == np.argmax(targets, axis=1)) / self.y_test_iris.shape[0]
+        logger.info("Success rate of targeted HopSkipJump on Iris: %.2f%%", (acc * 100))
+
+        # Test targeted attack and norm=np.inf
+        targets = random_targets(self.y_test_iris, nb_classes=3)
+        attack = HopSkipJump(classifier, targeted=True, max_iter=2, max_eval=100, init_eval=10, norm=np.Inf)
+        x_test_adv = attack.generate(self.x_test_iris, **{"y": targets})
+        self.assertFalse((self.x_test_iris == x_test_adv).all())
+        self.assertTrue((x_test_adv <= 1).all())
+        self.assertTrue((x_test_adv >= 0).all())
+
+        preds_adv = np.argmax(classifier.predict(x_test_adv), axis=1)
+        self.assertTrue((np.argmax(targets, axis=1) == preds_adv).any())
+        acc = np.sum(preds_adv == np.argmax(targets, axis=1)) / self.y_test_iris.shape[0]
+        logger.info("Success rate of targeted HopSkipJump on Iris: %.2f%%", (acc * 100))
+
+        # Clean-up session
+        if sess is not None:
+            sess.close()
+
+    def test_4_pytorch_iris(self):
+        classifier = get_tabular_classifier_pt()
+        x_test = self.x_test_iris.astype(np.float32)
+
+        # Norm=2
+        attack = HopSkipJump(classifier, targeted=False, max_iter=2, max_eval=100, init_eval=10)
+        x_test_adv = attack.generate(x_test)
+        self.assertFalse((x_test == x_test_adv).all())
+        self.assertTrue((x_test_adv <= 1).all())
+        self.assertTrue((x_test_adv >= 0).all())
+
+        preds_adv = np.argmax(classifier.predict(x_test_adv), axis=1)
+        self.assertFalse((np.argmax(self.y_test_iris, axis=1) == preds_adv).all())
+        acc = np.sum(preds_adv == np.argmax(self.y_test_iris, axis=1)) / self.y_test_iris.shape[0]
+        logger.info("Accuracy on Iris with HopSkipJump adversarial examples: %.2f%%", (acc * 100))
+
+        # Norm=np.inf
+        attack = HopSkipJump(classifier, targeted=False, max_iter=2, max_eval=100, init_eval=10, norm=np.Inf)
+        x_test_adv = attack.generate(x_test)
+        self.assertFalse((x_test == x_test_adv).all())
+        self.assertTrue((x_test_adv <= 1).all())
+        self.assertTrue((x_test_adv >= 0).all())
+
+        preds_adv = np.argmax(classifier.predict(x_test_adv), axis=1)
+        self.assertFalse((np.argmax(self.y_test_iris, axis=1) == preds_adv).all())
+        acc = np.sum(preds_adv == np.argmax(self.y_test_iris, axis=1)) / self.y_test_iris.shape[0]
+        logger.info("Accuracy on Iris with HopSkipJump adversarial examples: %.2f%%", (acc * 100))
+
+    def test_6_scikitlearn(self):
+        from sklearn.linear_model import LogisticRegression
+        from sklearn.svm import SVC, LinearSVC
+        from sklearn.tree import DecisionTreeClassifier, ExtraTreeClassifier
+        from sklearn.ensemble import AdaBoostClassifier, BaggingClassifier, ExtraTreesClassifier
+        from sklearn.ensemble import GradientBoostingClassifier, RandomForestClassifier
+
+        from art.estimators.classification.scikitlearn import SklearnClassifier
+
+        scikitlearn_test_cases = [
+            DecisionTreeClassifier(),
+            ExtraTreeClassifier(),
+            AdaBoostClassifier(),
+            BaggingClassifier(),
+            ExtraTreesClassifier(n_estimators=10),
+            GradientBoostingClassifier(n_estimators=10),
+            RandomForestClassifier(n_estimators=10),
+            LogisticRegression(solver="lbfgs", multi_class="auto"),
+            SVC(gamma="auto"),
+            LinearSVC(),
+        ]
+
+        x_test_original = self.x_test_iris.copy()
+
+        for model in scikitlearn_test_cases:
+            classifier = SklearnClassifier(model=model, clip_values=(0, 1))
+            classifier.fit(x=self.x_test_iris, y=self.y_test_iris)
+
+            # Norm=2
+            attack = HopSkipJump(classifier, targeted=False, max_iter=2, max_eval=100, init_eval=10)
+            x_test_adv = attack.generate(self.x_test_iris)
+            self.assertFalse((self.x_test_iris == x_test_adv).all())
+            self.assertTrue((x_test_adv <= 1).all())
+            self.assertTrue((x_test_adv >= 0).all())
+
+            preds_adv = np.argmax(classifier.predict(x_test_adv), axis=1)
+            self.assertFalse((np.argmax(self.y_test_iris, axis=1) == preds_adv).all())
+            acc = np.sum(preds_adv == np.argmax(self.y_test_iris, axis=1)) / self.y_test_iris.shape[0]
+            logger.info(
+                "Accuracy of " + classifier.__class__.__name__ + " on Iris with HopSkipJump adversarial "
+                "examples: %.2f%%",
+                (acc * 100),
+            )
+
+            # Norm=np.inf
+            attack = HopSkipJump(classifier, targeted=False, max_iter=2, max_eval=100, init_eval=10, norm=np.Inf)
+            x_test_adv = attack.generate(self.x_test_iris)
+            self.assertFalse((self.x_test_iris == x_test_adv).all())
+            self.assertTrue((x_test_adv <= 1).all())
+            self.assertTrue((x_test_adv >= 0).all())
+
+            preds_adv = np.argmax(classifier.predict(x_test_adv), axis=1)
+            self.assertFalse((np.argmax(self.y_test_iris, axis=1) == preds_adv).all())
+            acc = np.sum(preds_adv == np.argmax(self.y_test_iris, axis=1)) / self.y_test_iris.shape[0]
+            logger.info(
+                "Accuracy of " + classifier.__class__.__name__ + " on Iris with HopSkipJump adversarial "
+                "examples: %.2f%%",
+                (acc * 100),
+            )
+
+            # Check that x_test has not been modified by attack and classifier
+            self.assertAlmostEqual(float(np.max(np.abs(x_test_original - self.x_test_iris))), 0.0, delta=0.00001)
+
+    def test_1_classifier_type_check_fail(self):
+        backend_test_classifier_type_check_fail(HopSkipJump, [BaseEstimator, ClassifierMixin])
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/adversarial-robustness-toolbox/tests/attacks/test_input_filter.py b/adversarial-robustness-toolbox/tests/attacks/test_input_filter.py
new file mode 100644
index 0000000..f2c711b
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/attacks/test_input_filter.py
@@ -0,0 +1,249 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import unittest
+import numpy as np
+import pandas as pd
+
+from art.attacks.evasion.projected_gradient_descent.projected_gradient_descent import ProjectedGradientDescent
+from art.estimators.classification.keras import KerasClassifier
+from art.estimators.estimator import BaseEstimator, LossGradientsMixin
+from art.utils import load_dataset, get_labels_np_array
+
+from tests.utils import get_image_classifier_tf, get_image_classifier_pt
+from tests.utils import get_tabular_classifier_tf, get_tabular_classifier_kr
+from tests.utils import get_tabular_classifier_pt, master_seed
+from tests.attacks.utils import backend_test_classifier_type_check_fail
+
+logger = logging.getLogger(__name__)
+
+BATCH_SIZE = 10
+NB_TRAIN = 10
+NB_TEST = 11
+
+
+class TestInputFilter(unittest.TestCase):
+    """
+    A unittest class for testing the input filtering using
+    PGD tests.
+    """
+
+    @classmethod
+    def setUpClass(cls):
+        # MNIST
+        (x_train, y_train), (x_test, y_test), _, _ = load_dataset("mnist")
+        x_train = list(x_train[:NB_TRAIN])
+        y_train = list(y_train[:NB_TRAIN])
+        x_test = list(x_test[:NB_TEST])
+        y_test = list(y_test[:NB_TEST])
+        cls.mnist = (x_train, y_train), (x_test, y_test)
+
+        # Iris
+        (x_train, y_train), (x_test, y_test), _, _ = load_dataset("iris")
+        x_train = pd.DataFrame(x_train)
+        y_train = pd.DataFrame(y_train)
+        x_test = pd.DataFrame(x_test)
+        y_test = pd.DataFrame(y_test)
+        cls.iris = (x_train, y_train), (x_test, y_test)
+
+    def setUp(self):
+        master_seed(1234)
+
+    def test_2_tensorflow_mnist(self):
+        (x_train, y_train), (x_test, y_test) = self.mnist
+        classifier, sess = get_image_classifier_tf()
+
+        scores = get_labels_np_array(classifier.predict(x_train))
+        acc = np.sum(np.argmax(scores, axis=1) == np.argmax(y_train, axis=1)) / len(y_train)
+        logger.info("[TF, MNIST] Accuracy on training set: %.2f%%", acc * 100)
+
+        scores = get_labels_np_array(classifier.predict(x_test))
+        acc = np.sum(np.argmax(scores, axis=1) == np.argmax(np.array(y_test), axis=1)) / len(y_test)
+        logger.info("[TF, MNIST] Accuracy on test set: %.2f%%", acc * 100)
+
+        self._test_backend_mnist(classifier, x_train, y_train, x_test, y_test)
+
+    def test_4_pytorch_mnist(self):
+        (x_train, y_train), (x_test, y_test) = self.mnist
+        x_train = np.swapaxes(x_train, 1, 3).astype(np.float32)
+        x_test = np.swapaxes(x_test, 1, 3).astype(np.float32)
+        classifier = get_image_classifier_pt()
+
+        scores = get_labels_np_array(classifier.predict(x_train))
+        acc = np.sum(np.argmax(scores, axis=1) == np.argmax(y_train, axis=1)) / len(y_train)
+        logger.info("[PyTorch, MNIST] Accuracy on training set: %.2f%%", acc * 100)
+
+        scores = get_labels_np_array(classifier.predict(x_test))
+        acc = np.sum(np.argmax(scores, axis=1) == np.argmax(np.array(y_test), axis=1)) / len(y_test)
+        logger.info("[PyTorch, MNIST] Accuracy on test set: %.2f%%", acc * 100)
+
+        self._test_backend_mnist(classifier, x_train, y_train, x_test, y_test)
+
+    def _test_backend_mnist(self, classifier, x_train, y_train, x_test, y_test):
+        x_test_original = x_test.copy()
+
+        # Test PGD with np.inf norm
+        attack = ProjectedGradientDescent(classifier, eps=1, eps_step=0.1, max_iter=5)
+        x_train_adv = attack.generate(x_train)
+        x_test_adv = attack.generate(x_test)
+
+        self.assertFalse((x_train == x_train_adv).all())
+        self.assertFalse((x_test == x_test_adv).all())
+
+        train_y_pred = get_labels_np_array(classifier.predict(x_train_adv))
+        test_y_pred = get_labels_np_array(classifier.predict(x_test_adv))
+
+        self.assertFalse((y_train == train_y_pred).all())
+        self.assertFalse((y_test == test_y_pred).all())
+
+        acc = np.sum(np.argmax(train_y_pred, axis=1) == np.argmax(y_train, axis=1)) / len(y_train)
+        logger.info("Accuracy on adversarial train examples: %.2f%%", acc * 100)
+
+        acc = np.sum(np.argmax(test_y_pred, axis=1) == np.argmax(np.array(y_test), axis=1)) / len(y_test)
+        logger.info("Accuracy on adversarial test examples: %.2f%%", acc * 100)
+
+        # Test PGD with 3 random initialisations
+        attack = ProjectedGradientDescent(classifier, num_random_init=3, max_iter=5)
+        x_train_adv = attack.generate(x_train)
+        x_test_adv = attack.generate(x_test)
+
+        self.assertFalse((x_train == x_train_adv).all())
+        self.assertFalse((x_test == x_test_adv).all())
+
+        train_y_pred = get_labels_np_array(classifier.predict(x_train_adv))
+        test_y_pred = get_labels_np_array(classifier.predict(x_test_adv))
+
+        self.assertFalse((y_train == train_y_pred).all())
+        self.assertFalse((y_test == test_y_pred).all())
+
+        acc = np.sum(np.argmax(train_y_pred, axis=1) == np.argmax(y_train, axis=1)) / len(y_train)
+        logger.info("Accuracy on adversarial train examples with 3 random initialisations: %.2f%%", acc * 100)
+
+        acc = np.sum(np.argmax(test_y_pred, axis=1) == np.argmax(np.array(y_test), axis=1)) / len(y_test)
+        logger.info("Accuracy on adversarial test examples with 3 random initialisations: %.2f%%", acc * 100)
+
+        # Check that x_test has not been modified by attack and classifier
+        self.assertAlmostEqual(float(np.max(np.abs(np.array(x_test_original) - np.array(x_test)))), 0.0, delta=0.00001)
+
+    def test_1_classifier_type_check_fail(self):
+        backend_test_classifier_type_check_fail(ProjectedGradientDescent, [BaseEstimator, LossGradientsMixin])
+
+    def test_6_keras_iris_clipped(self):
+        (_, _), (x_test, y_test) = self.iris
+        classifier = get_tabular_classifier_kr()
+
+        # Test untargeted attack
+        attack = ProjectedGradientDescent(classifier, eps=1, eps_step=0.1, max_iter=5)
+        x_test_adv = attack.generate(x_test)
+        self.assertFalse((np.array(x_test) == x_test_adv).all())
+        self.assertTrue((x_test_adv <= 1).all())
+        self.assertTrue((x_test_adv >= 0).all())
+
+        preds_adv = np.argmax(classifier.predict(x_test_adv), axis=1)
+        self.assertFalse((np.argmax(np.array(y_test), axis=1) == preds_adv).all())
+        acc = np.sum(preds_adv == np.argmax(np.array(y_test), axis=1)) / len(y_test)
+        logger.info("Accuracy on Iris with PGD adversarial examples: %.2f%%", (acc * 100))
+
+    def test_7_keras_iris_unbounded(self):
+        (_, _), (x_test, y_test) = self.iris
+        classifier = get_tabular_classifier_kr()
+
+        # Recreate a classifier without clip values
+        classifier = KerasClassifier(model=classifier._model, use_logits=False, channels_first=True)
+        attack = ProjectedGradientDescent(classifier, eps=1, eps_step=0.2, max_iter=5)
+        x_test_adv = attack.generate(x_test)
+        self.assertFalse((np.array(x_test) == x_test_adv).all())
+        self.assertTrue((x_test_adv > 1).any())
+        self.assertTrue((x_test_adv < 0).any())
+
+        preds_adv = np.argmax(classifier.predict(x_test_adv), axis=1)
+        self.assertFalse((np.argmax(np.array(y_test), axis=1) == preds_adv).all())
+        acc = np.sum(preds_adv == np.argmax(np.array(y_test), axis=1)) / len(y_test)
+        logger.info("Accuracy on Iris with PGD adversarial examples: %.2f%%", (acc * 100))
+
+    def test_2_tensorflow_iris(self):
+        (_, _), (x_test, y_test) = self.iris
+        classifier, _ = get_tabular_classifier_tf()
+
+        # Test untargeted attack
+        attack = ProjectedGradientDescent(classifier, eps=1, eps_step=0.1, max_iter=5)
+        x_test_adv = attack.generate(x_test)
+        self.assertFalse((np.array(x_test) == x_test_adv).all())
+        self.assertTrue((x_test_adv <= 1).all())
+        self.assertTrue((x_test_adv >= 0).all())
+
+        preds_adv = np.argmax(classifier.predict(x_test_adv), axis=1)
+        self.assertFalse((np.argmax(np.array(y_test), axis=1) == preds_adv).all())
+        acc = np.sum(preds_adv == np.argmax(np.array(y_test), axis=1)) / len(y_test)
+        logger.info("Accuracy on Iris with PGD adversarial examples: %.2f%%", (acc * 100))
+
+    def test_3_pytorch_iris_pt(self):
+        (_, _), (x_test, y_test) = self.iris
+        classifier = get_tabular_classifier_pt()
+
+        # Test untargeted attack
+        attack = ProjectedGradientDescent(classifier, eps=1, eps_step=0.1, max_iter=5)
+        x_test_adv = attack.generate(x_test)
+        self.assertFalse((np.array(x_test) == x_test_adv).all())
+        self.assertTrue((x_test_adv <= 1).all())
+        self.assertTrue((x_test_adv >= 0).all())
+
+        preds_adv = np.argmax(classifier.predict(x_test_adv), axis=1)
+        self.assertFalse((np.argmax(np.array(y_test), axis=1) == preds_adv).all())
+        acc = np.sum(preds_adv == np.argmax(np.array(y_test), axis=1)) / len(y_test)
+        logger.info("Accuracy on Iris with PGD adversarial examples: %.2f%%", (acc * 100))
+
+    def test_5_scikitlearn(self):
+        from sklearn.linear_model import LogisticRegression
+        from sklearn.svm import SVC, LinearSVC
+
+        from art.estimators.classification.scikitlearn import ScikitlearnLogisticRegression, ScikitlearnSVC
+
+        scikitlearn_test_cases = {
+            LogisticRegression: ScikitlearnLogisticRegression,
+            SVC: ScikitlearnSVC,
+            LinearSVC: ScikitlearnSVC,
+        }
+
+        (_, _), (x_test, y_test) = self.iris
+
+        for (model_class, classifier_class) in scikitlearn_test_cases.items():
+            model = model_class()
+            classifier = classifier_class(model=model, clip_values=(0, 1))
+            classifier.fit(x=x_test, y=y_test)
+
+            # Test untargeted attack
+            attack = ProjectedGradientDescent(classifier, eps=1, eps_step=0.1, max_iter=5)
+            x_test_adv = attack.generate(x_test)
+            self.assertFalse((np.array(x_test) == x_test_adv).all())
+            self.assertTrue((x_test_adv <= 1).all())
+            self.assertTrue((x_test_adv >= 0).all())
+
+            preds_adv = np.argmax(classifier.predict(x_test_adv), axis=1)
+            self.assertFalse((np.argmax(np.array(y_test), axis=1) == preds_adv).all())
+            acc = np.sum(preds_adv == np.argmax(np.array(y_test), axis=1)) / len(y_test)
+            logger.info(
+                "Accuracy of " + classifier.__class__.__name__ + " on Iris with PGD adversarial examples: " "%.2f%%",
+                (acc * 100),
+            )
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/adversarial-robustness-toolbox/tests/attacks/test_iterative_method.py b/adversarial-robustness-toolbox/tests/attacks/test_iterative_method.py
new file mode 100644
index 0000000..93be3cc
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/attacks/test_iterative_method.py
@@ -0,0 +1,367 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import unittest
+
+import numpy as np
+
+from art.attacks.evasion.iterative_method import BasicIterativeMethod
+from art.estimators.classification.keras import KerasClassifier
+from art.estimators.estimator import BaseEstimator, LossGradientsMixin
+from art.utils import get_labels_np_array, random_targets
+from tests.attacks.utils import backend_test_classifier_type_check_fail
+from tests.utils import (
+    TestBase,
+    get_image_classifier_kr,
+    get_image_classifier_pt,
+    get_image_classifier_tf,
+    get_tabular_classifier_kr,
+    get_tabular_classifier_pt,
+    get_tabular_classifier_tf,
+)
+
+logger = logging.getLogger(__name__)
+
+
+class TestIterativeAttack(TestBase):
+    @classmethod
+    def setUpClass(cls):
+        super().setUpClass()
+
+        cls.n_train = 100
+        cls.n_test = 11
+        cls.x_train_mnist = cls.x_train_mnist[0 : cls.n_train]
+        cls.y_train_mnist = cls.y_train_mnist[0 : cls.n_train]
+        cls.x_test_mnist = cls.x_test_mnist[0 : cls.n_test]
+        cls.y_test_mnist = cls.y_test_mnist[0 : cls.n_test]
+
+    def test_9b_keras_mnist(self):
+        classifier = get_image_classifier_kr()
+
+        scores = classifier._model.evaluate(self.x_train_mnist, self.y_train_mnist)
+        logger.info("[Keras, MNIST] Accuracy on training set: %.2f%%", (scores[1] * 100))
+        scores = classifier._model.evaluate(self.x_test_mnist, self.y_test_mnist)
+        logger.info("[Keras, MNIST] Accuracy on test set: %.2f%%", (scores[1] * 100))
+
+        self._test_backend_mnist(
+            classifier, self.x_train_mnist, self.y_train_mnist, self.x_test_mnist, self.y_test_mnist
+        )
+
+    def test_3_tensorflow_mnist(self):
+        classifier, sess = get_image_classifier_tf()
+
+        scores = get_labels_np_array(classifier.predict(self.x_train_mnist))
+        acc = np.sum(np.argmax(scores, axis=1) == np.argmax(self.y_train_mnist, axis=1)) / self.y_train_mnist.shape[0]
+        logger.info("[TF, MNIST] Accuracy on training set: %.2f%%", (acc * 100))
+
+        scores = get_labels_np_array(classifier.predict(self.x_test_mnist))
+        acc = np.sum(np.argmax(scores, axis=1) == np.argmax(self.y_test_mnist, axis=1)) / self.y_test_mnist.shape[0]
+        logger.info("[TF, MNIST] Accuracy on test set: %.2f%%", (acc * 100))
+
+        self._test_backend_mnist(
+            classifier, self.x_train_mnist, self.y_train_mnist, self.x_test_mnist, self.y_test_mnist
+        )
+
+    def test_6_pytorch_mnist(self):
+        classifier = get_image_classifier_pt()
+        x_train = np.swapaxes(self.x_train_mnist, 1, 3).astype(np.float32)
+        x_test = np.swapaxes(self.x_test_mnist, 1, 3).astype(np.float32)
+
+        scores = get_labels_np_array(classifier.predict(x_train))
+        acc = np.sum(np.argmax(scores, axis=1) == np.argmax(self.y_train_mnist, axis=1)) / self.y_train_mnist.shape[0]
+        logger.info("[PyTorch, MNIST] Accuracy on training set: %.2f%%", (acc * 100))
+
+        scores = get_labels_np_array(classifier.predict(x_test))
+        acc = np.sum(np.argmax(scores, axis=1) == np.argmax(self.y_test_mnist, axis=1)) / self.y_test_mnist.shape[0]
+        logger.info("[PyTorch, MNIST] Accuracy on test set: %.2f%%", (acc * 100))
+
+        self._test_backend_mnist(classifier, x_train, self.y_train_mnist, x_test, self.y_test_mnist)
+
+    def _test_backend_mnist(self, classifier, x_train, y_train, x_test, y_test):
+        x_test_original = x_test.copy()
+
+        # Test BIM with np.inf norm
+        attack = BasicIterativeMethod(classifier, eps=1.0, eps_step=0.1, batch_size=128)
+        x_train_adv = attack.generate(x_train)
+        x_test_adv = attack.generate(x_test)
+
+        self.assertFalse((x_train == x_train_adv).all())
+        self.assertFalse((x_test == x_test_adv).all())
+
+        train_y_pred = get_labels_np_array(classifier.predict(x_train_adv))
+        test_y_pred = get_labels_np_array(classifier.predict(x_test_adv))
+
+        self.assertFalse((y_train == train_y_pred).all())
+        self.assertFalse((y_test == test_y_pred).all())
+
+        acc = np.sum(np.argmax(train_y_pred, axis=1) == np.argmax(y_train, axis=1)) / y_train.shape[0]
+        logger.info("Accuracy on adversarial train examples: %.2f%%", (acc * 100))
+
+        acc = np.sum(np.argmax(test_y_pred, axis=1) == np.argmax(y_test, axis=1)) / y_test.shape[0]
+        logger.info("Accuracy on adversarial test examples: %.2f%%", (acc * 100))
+
+        # Check that x_test has not been modified by attack and classifier
+        self.assertAlmostEqual(float(np.max(np.abs(x_test_original - x_test))), 0.0, delta=0.00001)
+
+        # Test eps of array type 1
+        eps = np.ones(shape=x_test.shape) * 1.0
+        eps_step = np.ones_like(eps) * 0.1
+
+        attack_params = {"eps_step": eps_step, "eps": eps}
+        attack.set_params(**attack_params)
+
+        x_test_adv = attack.generate(x_test)
+        self.assertFalse((x_test == x_test_adv).all())
+
+        test_y_pred = get_labels_np_array(classifier.predict(x_test_adv))
+        self.assertFalse((y_test == test_y_pred).all())
+
+        # Test eps of array type 2
+        eps = np.ones(shape=x_test.shape[1:]) * 1.0
+        eps_step = np.ones_like(eps) * 0.1
+
+        attack_params = {"eps_step": eps_step, "eps": eps}
+        attack.set_params(**attack_params)
+
+        x_test_adv = attack.generate(x_test)
+        self.assertFalse((x_test == x_test_adv).all())
+
+        test_y_pred = get_labels_np_array(classifier.predict(x_test_adv))
+        self.assertFalse((y_test == test_y_pred).all())
+
+        # Test eps of array type 3
+        eps = np.ones(shape=x_test.shape[2:]) * 1.0
+        eps_step = np.ones_like(eps) * 0.1
+
+        attack_params = {"eps_step": eps_step, "eps": eps}
+        attack.set_params(**attack_params)
+
+        x_test_adv = attack.generate(x_test)
+        self.assertFalse((x_test == x_test_adv).all())
+
+        test_y_pred = get_labels_np_array(classifier.predict(x_test_adv))
+        self.assertFalse((y_test == test_y_pred).all())
+
+        # Test eps of array type 4
+        eps = np.ones(shape=x_test.shape[3:]) * 1.0
+        eps_step = np.ones_like(eps) * 0.1
+
+        attack_params = {"eps_step": eps_step, "eps": eps}
+        attack.set_params(**attack_params)
+
+        x_test_adv = attack.generate(x_test)
+        self.assertFalse((x_test == x_test_adv).all())
+
+        test_y_pred = get_labels_np_array(classifier.predict(x_test_adv))
+        self.assertFalse((y_test == test_y_pred).all())
+
+    def _test_mnist_targeted(self, classifier, x_test):
+        x_test_original = x_test.copy()
+
+        # Test FGSM with np.inf norm
+        attack = BasicIterativeMethod(classifier, eps=1.0, eps_step=0.01, targeted=True, batch_size=128)
+        # y_test_adv = to_categorical((np.argmax(y_test, axis=1) + 1)  % 10, 10)
+        pred_sort = classifier.predict(x_test).argsort(axis=1)
+        y_test_adv = np.zeros((x_test.shape[0], 10))
+        for i in range(x_test.shape[0]):
+            y_test_adv[i, pred_sort[i, -2]] = 1.0
+        x_test_adv = attack.generate(x_test, y=y_test_adv)
+
+        self.assertFalse((x_test == x_test_adv).all())
+
+        test_y_pred = get_labels_np_array(classifier.predict(x_test_adv))
+
+        self.assertEqual(y_test_adv.shape, test_y_pred.shape)
+        # This doesn't work all the time, especially with small networks
+        self.assertGreaterEqual((y_test_adv == test_y_pred).sum(), x_test.shape[0] // 2)
+
+        # Check that x_test has not been modified by attack and classifier
+        self.assertAlmostEqual(float(np.max(np.abs(x_test_original - x_test))), 0.0, delta=0.00001)
+
+    def test_9c_keras_mnist_targeted(self):
+        classifier = get_image_classifier_kr()
+        self._test_mnist_targeted(classifier, self.x_test_mnist)
+
+    def test_4_tensorflow_mnist_targeted(self):
+        classifier, sess = get_image_classifier_tf()
+        self._test_mnist_targeted(classifier, self.x_test_mnist)
+
+    def test_7_pytorch_mnist_targeted(self):
+        classifier = get_image_classifier_pt()
+        x_test = np.swapaxes(self.x_test_mnist, 1, 3).astype(np.float32)
+        self._test_mnist_targeted(classifier, x_test)
+
+    def test_1_classifier_type_check_fail(self):
+        backend_test_classifier_type_check_fail(BasicIterativeMethod, [BaseEstimator, LossGradientsMixin])
+
+    def test_9_keras_iris_clipped(self):
+        classifier = get_tabular_classifier_kr()
+
+        # Test untargeted attack
+        attack = BasicIterativeMethod(classifier, eps=1.0, eps_step=0.1, batch_size=128)
+        x_test_adv = attack.generate(self.x_test_iris)
+        self.assertFalse((self.x_test_iris == x_test_adv).all())
+        self.assertTrue((x_test_adv <= 1).all())
+        self.assertTrue((x_test_adv >= 0).all())
+
+        preds_adv = np.argmax(classifier.predict(x_test_adv), axis=1)
+        self.assertFalse((np.argmax(self.y_test_iris, axis=1) == preds_adv).all())
+        acc = np.sum(preds_adv == np.argmax(self.y_test_iris, axis=1)) / self.y_test_iris.shape[0]
+        logger.info("Accuracy on Iris with BIM adversarial examples: %.2f%%", (acc * 100))
+
+        # Test targeted attack
+        targets = random_targets(self.y_test_iris, nb_classes=3)
+        attack = BasicIterativeMethod(classifier, targeted=True, eps=1.0, eps_step=0.1)
+        x_test_adv = attack.generate(self.x_test_iris, **{"y": targets})
+        self.assertFalse((self.x_test_iris == x_test_adv).all())
+        self.assertTrue((x_test_adv <= 1).all())
+        self.assertTrue((x_test_adv >= 0).all())
+
+        preds_adv = np.argmax(classifier.predict(x_test_adv), axis=1)
+        self.assertTrue((np.argmax(targets, axis=1) == preds_adv).any())
+        acc = np.sum(preds_adv == np.argmax(targets, axis=1)) / self.y_test_iris.shape[0]
+        logger.info("Success rate of targeted BIM on Iris: %.2f%%", (acc * 100))
+
+    def test_9a_keras_iris_unbounded(self):
+        classifier = get_tabular_classifier_kr()
+
+        # Recreate a classifier without clip values
+        classifier = KerasClassifier(model=classifier._model, use_logits=False, channels_first=True)
+        attack = BasicIterativeMethod(classifier, eps=1.0, eps_step=0.2, batch_size=128)
+        x_test_adv = attack.generate(self.x_test_iris)
+        self.assertFalse((self.x_test_iris == x_test_adv).all())
+        self.assertTrue((x_test_adv > 1).any())
+        self.assertTrue((x_test_adv < 0).any())
+
+        preds_adv = np.argmax(classifier.predict(x_test_adv), axis=1)
+        self.assertFalse((np.argmax(self.y_test_iris, axis=1) == preds_adv).all())
+        acc = np.sum(preds_adv == np.argmax(self.y_test_iris, axis=1)) / self.y_test_iris.shape[0]
+        logger.info("Accuracy on Iris with BIM adversarial examples: %.2f%%", (acc * 100))
+
+    def test_2_tensorflow_iris(self):
+        classifier, _ = get_tabular_classifier_tf()
+
+        # Test untargeted attack
+        attack = BasicIterativeMethod(classifier, eps=1.0, eps_step=0.1, max_iter=5)
+        x_test_adv = attack.generate(self.x_test_iris)
+        self.assertFalse((self.x_test_iris == x_test_adv).all())
+        self.assertTrue((x_test_adv <= 1).all())
+        self.assertTrue((x_test_adv >= 0).all())
+
+        preds_adv = np.argmax(classifier.predict(x_test_adv), axis=1)
+        self.assertFalse((np.argmax(self.y_test_iris, axis=1) == preds_adv).all())
+        acc = np.sum(preds_adv == np.argmax(self.y_test_iris, axis=1)) / self.y_test_iris.shape[0]
+        logger.info("Accuracy on Iris with BIM adversarial examples: %.2f%%", (acc * 100))
+
+        # Test targeted attack
+        targets = random_targets(self.y_test_iris, nb_classes=3)
+        attack = BasicIterativeMethod(classifier, targeted=True, eps=1.0, eps_step=0.1, max_iter=5)
+        x_test_adv = attack.generate(self.x_test_iris, **{"y": targets})
+        self.assertFalse((self.x_test_iris == x_test_adv).all())
+        self.assertTrue((x_test_adv <= 1).all())
+        self.assertTrue((x_test_adv >= 0).all())
+
+        preds_adv = np.argmax(classifier.predict(x_test_adv), axis=1)
+        self.assertTrue((np.argmax(targets, axis=1) == preds_adv).any())
+        acc = np.sum(preds_adv == np.argmax(targets, axis=1)) / self.y_test_iris.shape[0]
+        logger.info("Success rate of targeted BIM on Iris: %.2f%%", (acc * 100))
+
+    def test_5_pytorch_iris(self):
+        classifier = get_tabular_classifier_pt()
+
+        # Test untargeted attack
+        attack = BasicIterativeMethod(classifier, eps=1.0, eps_step=0.1)
+        x_test_adv = attack.generate(self.x_test_iris)
+        self.assertFalse((self.x_test_iris == x_test_adv).all())
+        self.assertTrue((x_test_adv <= 1).all())
+        self.assertTrue((x_test_adv >= 0).all())
+
+        preds_adv = np.argmax(classifier.predict(x_test_adv), axis=1)
+        self.assertFalse((np.argmax(self.y_test_iris, axis=1) == preds_adv).all())
+        acc = np.sum(preds_adv == np.argmax(self.y_test_iris, axis=1)) / self.y_test_iris.shape[0]
+        logger.info("Accuracy on Iris with BIM adversarial examples: %.2f%%", (acc * 100))
+
+        # Test targeted attack
+        targets = random_targets(self.y_test_iris, nb_classes=3)
+        attack = BasicIterativeMethod(classifier, targeted=True, eps=1.0, eps_step=0.1, batch_size=128)
+        x_test_adv = attack.generate(self.x_test_iris, **{"y": targets})
+        self.assertFalse((self.x_test_iris == x_test_adv).all())
+        self.assertTrue((x_test_adv <= 1).all())
+        self.assertTrue((x_test_adv >= 0).all())
+
+        preds_adv = np.argmax(classifier.predict(x_test_adv), axis=1)
+        self.assertTrue((np.argmax(targets, axis=1) == preds_adv).any())
+        acc = np.sum(preds_adv == np.argmax(targets, axis=1)) / self.y_test_iris.shape[0]
+        logger.info("Success rate of targeted BIM on Iris: %.2f%%", (acc * 100))
+
+    def test_8_scikitlearn(self):
+        from sklearn.linear_model import LogisticRegression
+        from sklearn.svm import SVC, LinearSVC
+
+        from art.estimators.classification.scikitlearn import SklearnClassifier
+
+        scikitlearn_test_cases = [
+            LogisticRegression(solver="lbfgs", multi_class="auto"),
+            SVC(gamma="auto"),
+            LinearSVC(),
+        ]
+
+        x_test_original = self.x_test_iris.copy()
+
+        for model in scikitlearn_test_cases:
+            classifier = SklearnClassifier(model=model, clip_values=(0, 1))
+            classifier.fit(x=self.x_test_iris, y=self.y_test_iris)
+
+            # Test untargeted attack
+            attack = BasicIterativeMethod(classifier, eps=1.0, eps_step=0.1, max_iter=5)
+            x_test_adv = attack.generate(self.x_test_iris)
+            self.assertFalse((self.x_test_iris == x_test_adv).all())
+            self.assertTrue((x_test_adv <= 1).all())
+            self.assertTrue((x_test_adv >= 0).all())
+
+            preds_adv = np.argmax(classifier.predict(x_test_adv), axis=1)
+            self.assertFalse((np.argmax(self.y_test_iris, axis=1) == preds_adv).all())
+            acc = np.sum(preds_adv == np.argmax(self.y_test_iris, axis=1)) / self.y_test_iris.shape[0]
+            logger.info(
+                "Accuracy of " + classifier.__class__.__name__ + " on Iris with BIM adversarial examples: " "%.2f%%",
+                (acc * 100),
+            )
+
+            # Test targeted attack
+            targets = random_targets(self.y_test_iris, nb_classes=3)
+            attack = BasicIterativeMethod(classifier, targeted=True, eps=1.0, eps_step=0.1, batch_size=128, max_iter=5)
+            x_test_adv = attack.generate(self.x_test_iris, **{"y": targets})
+            self.assertFalse((self.x_test_iris == x_test_adv).all())
+            self.assertTrue((x_test_adv <= 1).all())
+            self.assertTrue((x_test_adv >= 0).all())
+
+            preds_adv = np.argmax(classifier.predict(x_test_adv), axis=1)
+            self.assertTrue((np.argmax(targets, axis=1) == preds_adv).any())
+            acc = np.sum(preds_adv == np.argmax(targets, axis=1)) / self.y_test_iris.shape[0]
+            logger.info(
+                "Success rate of " + classifier.__class__.__name__ + " on targeted BIM on Iris: %.2f%%", (acc * 100)
+            )
+
+            # Check that x_test has not been modified by attack and classifier
+            self.assertAlmostEqual(float(np.max(np.abs(x_test_original - self.x_test_iris))), 0.0, delta=0.00001)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/adversarial-robustness-toolbox/tests/attacks/test_knockoff_nets.py b/adversarial-robustness-toolbox/tests/attacks/test_knockoff_nets.py
new file mode 100644
index 0000000..809716a
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/attacks/test_knockoff_nets.py
@@ -0,0 +1,356 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import unittest
+
+import numpy as np
+import keras.backend as k
+
+from art.attacks.extraction.knockoff_nets import KnockoffNets
+from art.estimators.estimator import BaseEstimator
+from art.estimators.classification.classifier import ClassifierMixin
+
+from tests.utils import TestBase, master_seed
+from tests.utils import get_image_classifier_tf, get_image_classifier_kr, get_image_classifier_pt
+from tests.utils import get_tabular_classifier_tf, get_tabular_classifier_kr, get_tabular_classifier_pt
+from tests.attacks.utils import backend_test_classifier_type_check_fail
+
+logger = logging.getLogger(__name__)
+
+BATCH_SIZE = 10
+NB_TRAIN = 100
+NB_EPOCHS = 10
+NB_STOLEN = 100
+
+
+class TestKnockoffNets(TestBase):
+    """
+    A unittest class for testing the KnockoffNets attack.
+    """
+
+    @classmethod
+    def setUpClass(cls):
+        master_seed(seed=1234, set_tensorflow=True)
+        super().setUpClass()
+
+    def setUp(self):
+        super().setUp()
+
+    def test_3_tensorflow_classifier(self):
+        """
+        First test with the TensorFlowClassifier.
+        :return:
+        """
+        # Build TensorFlowClassifier
+        victim_tfc, sess = get_image_classifier_tf()
+
+        # Create the thieved classifier
+        thieved_tfc, _ = get_image_classifier_tf(load_init=False, sess=sess)
+
+        # Create random attack
+        attack = KnockoffNets(
+            classifier=victim_tfc,
+            batch_size_fit=BATCH_SIZE,
+            batch_size_query=BATCH_SIZE,
+            nb_epochs=NB_EPOCHS,
+            nb_stolen=NB_STOLEN,
+            sampling_strategy="random",
+        )
+        thieved_tfc = attack.extract(x=self.x_train_mnist, thieved_classifier=thieved_tfc)
+
+        victim_preds = np.argmax(victim_tfc.predict(x=self.x_train_mnist), axis=1)
+        thieved_preds = np.argmax(thieved_tfc.predict(x=self.x_train_mnist), axis=1)
+        acc = np.sum(victim_preds == thieved_preds) / len(victim_preds)
+
+        self.assertGreater(acc, 0.3)
+
+        # Create adaptive attack
+        attack = KnockoffNets(
+            classifier=victim_tfc,
+            batch_size_fit=BATCH_SIZE,
+            batch_size_query=BATCH_SIZE,
+            nb_epochs=NB_EPOCHS,
+            nb_stolen=NB_STOLEN,
+            sampling_strategy="adaptive",
+            reward="all",
+        )
+        thieved_tfc = attack.extract(x=self.x_train_mnist, y=self.y_train_mnist, thieved_classifier=thieved_tfc)
+
+        victim_preds = np.argmax(victim_tfc.predict(x=self.x_train_mnist), axis=1)
+        thieved_preds = np.argmax(thieved_tfc.predict(x=self.x_train_mnist), axis=1)
+        acc = np.sum(victim_preds == thieved_preds) / len(victim_preds)
+
+        self.assertGreater(acc, 0.4)
+
+        # Clean-up session
+        if sess is not None:
+            sess.close()
+
+    def test_7_keras_classifier(self):
+        """
+        Second test with the KerasClassifier.
+        :return:
+        """
+        # Build KerasClassifier
+        victim_krc = get_image_classifier_kr()
+
+        # Create the thieved classifier
+        thieved_krc = get_image_classifier_kr(load_init=False)
+
+        # Create random attack
+        attack = KnockoffNets(
+            classifier=victim_krc,
+            batch_size_fit=BATCH_SIZE,
+            batch_size_query=BATCH_SIZE,
+            nb_epochs=NB_EPOCHS,
+            nb_stolen=NB_STOLEN,
+            sampling_strategy="random",
+        )
+        thieved_krc = attack.extract(x=self.x_train_mnist, thieved_classifier=thieved_krc)
+
+        victim_preds = np.argmax(victim_krc.predict(x=self.x_train_mnist), axis=1)
+        thieved_preds = np.argmax(thieved_krc.predict(x=self.x_train_mnist), axis=1)
+        acc = np.sum(victim_preds == thieved_preds) / len(victim_preds)
+
+        self.assertGreater(acc, 0.3)
+
+        # Create adaptive attack
+        attack = KnockoffNets(
+            classifier=victim_krc,
+            batch_size_fit=BATCH_SIZE,
+            batch_size_query=BATCH_SIZE,
+            nb_epochs=NB_EPOCHS,
+            nb_stolen=NB_STOLEN,
+            sampling_strategy="adaptive",
+            reward="all",
+        )
+        thieved_krc = attack.extract(x=self.x_train_mnist, y=self.y_train_mnist, thieved_classifier=thieved_krc)
+
+        victim_preds = np.argmax(victim_krc.predict(x=self.x_train_mnist), axis=1)
+        thieved_preds = np.argmax(thieved_krc.predict(x=self.x_train_mnist), axis=1)
+        acc = np.sum(victim_preds == thieved_preds) / len(victim_preds)
+
+        self.assertGreater(acc, 0.4)
+
+        # Clean-up
+        k.clear_session()
+
+    def test_5_pytorch_classifier(self):
+        """
+        Third test with the PyTorchClassifier.
+        :return:
+        """
+        self.x_train_mnist = np.reshape(self.x_train_mnist, (self.x_train_mnist.shape[0], 1, 28, 28)).astype(np.float32)
+
+        # Build PyTorchClassifier
+        victim_ptc = get_image_classifier_pt()
+
+        # Create the thieved classifier
+        thieved_ptc = get_image_classifier_pt(load_init=False)
+
+        # Create random attack
+        attack = KnockoffNets(
+            classifier=victim_ptc,
+            batch_size_fit=BATCH_SIZE,
+            batch_size_query=BATCH_SIZE,
+            nb_epochs=NB_EPOCHS,
+            nb_stolen=NB_STOLEN,
+            sampling_strategy="random",
+        )
+
+        thieved_ptc = attack.extract(x=self.x_train_mnist, thieved_classifier=thieved_ptc)
+
+        victim_preds = np.argmax(victim_ptc.predict(x=self.x_train_mnist), axis=1)
+        thieved_preds = np.argmax(thieved_ptc.predict(x=self.x_train_mnist), axis=1)
+        acc = np.sum(victim_preds == thieved_preds) / len(victim_preds)
+
+        self.assertGreater(acc, 0.3)
+
+        # Create adaptive attack
+        attack = KnockoffNets(
+            classifier=victim_ptc,
+            batch_size_fit=BATCH_SIZE,
+            batch_size_query=BATCH_SIZE,
+            nb_epochs=NB_EPOCHS,
+            nb_stolen=NB_STOLEN,
+            sampling_strategy="adaptive",
+            reward="all",
+        )
+        thieved_ptc = attack.extract(x=self.x_train_mnist, y=self.y_train_mnist, thieved_classifier=thieved_ptc)
+
+        victim_preds = np.argmax(victim_ptc.predict(x=self.x_train_mnist), axis=1)
+        thieved_preds = np.argmax(thieved_ptc.predict(x=self.x_train_mnist), axis=1)
+        acc = np.sum(victim_preds == thieved_preds) / len(victim_preds)
+
+        self.assertGreater(acc, 0.4)
+
+        self.x_train_mnist = np.reshape(self.x_train_mnist, (self.x_train_mnist.shape[0], 28, 28, 1)).astype(np.float32)
+
+    def test_1_classifier_type_check_fail(self):
+        backend_test_classifier_type_check_fail(KnockoffNets, [BaseEstimator, ClassifierMixin])
+
+    def test_2_tensorflow_iris(self):
+        """
+        First test for TensorFlow.
+        :return:
+        """
+        # Get the TensorFlow classifier
+        victim_tfc, sess = get_tabular_classifier_tf()
+
+        # Create the thieved classifier
+        thieved_tfc, _ = get_tabular_classifier_tf(load_init=False, sess=sess)
+
+        # Create random attack
+        attack = KnockoffNets(
+            classifier=victim_tfc,
+            batch_size_fit=BATCH_SIZE,
+            batch_size_query=BATCH_SIZE,
+            nb_epochs=NB_EPOCHS,
+            nb_stolen=NB_STOLEN,
+            sampling_strategy="random",
+        )
+        thieved_tfc = attack.extract(x=self.x_train_iris, thieved_classifier=thieved_tfc)
+
+        victim_preds = np.argmax(victim_tfc.predict(x=self.x_train_iris), axis=1)
+        thieved_preds = np.argmax(thieved_tfc.predict(x=self.x_train_iris), axis=1)
+        acc = np.sum(victim_preds == thieved_preds) / len(victim_preds)
+
+        self.assertGreater(acc, 0.3)
+
+        # Create adaptive attack
+        attack = KnockoffNets(
+            classifier=victim_tfc,
+            batch_size_fit=BATCH_SIZE,
+            batch_size_query=BATCH_SIZE,
+            nb_epochs=NB_EPOCHS,
+            nb_stolen=NB_STOLEN,
+            sampling_strategy="adaptive",
+            reward="all",
+        )
+        thieved_tfc = attack.extract(x=self.x_train_iris, y=self.y_train_iris, thieved_classifier=thieved_tfc)
+
+        victim_preds = np.argmax(victim_tfc.predict(x=self.x_train_iris), axis=1)
+        thieved_preds = np.argmax(thieved_tfc.predict(x=self.x_train_iris), axis=1)
+        acc = np.sum(victim_preds == thieved_preds) / len(victim_preds)
+
+        self.assertGreater(acc, 0.4)
+
+        # Clean-up session
+        if sess is not None:
+            sess.close()
+
+    def test_6_keras_iris(self):
+        """
+        Second test for Keras.
+        :return:
+        """
+        # Build KerasClassifier
+        victim_krc = get_tabular_classifier_kr()
+
+        # Create the thieved classifier
+        thieved_krc = get_tabular_classifier_kr(load_init=False)
+
+        # Create random attack
+        attack = KnockoffNets(
+            classifier=victim_krc,
+            batch_size_fit=BATCH_SIZE,
+            batch_size_query=BATCH_SIZE,
+            nb_epochs=NB_EPOCHS,
+            nb_stolen=NB_STOLEN,
+            sampling_strategy="random",
+        )
+        thieved_krc = attack.extract(x=self.x_train_iris, thieved_classifier=thieved_krc)
+
+        victim_preds = np.argmax(victim_krc.predict(x=self.x_train_iris), axis=1)
+        thieved_preds = np.argmax(thieved_krc.predict(x=self.x_train_iris), axis=1)
+        acc = np.sum(victim_preds == thieved_preds) / len(victim_preds)
+
+        self.assertGreater(acc, 0.3)
+
+        # Create adaptive attack
+        attack = KnockoffNets(
+            classifier=victim_krc,
+            batch_size_fit=BATCH_SIZE,
+            batch_size_query=BATCH_SIZE,
+            nb_epochs=NB_EPOCHS,
+            nb_stolen=NB_STOLEN,
+            sampling_strategy="adaptive",
+            reward="all",
+        )
+        thieved_krc = attack.extract(x=self.x_train_iris, y=self.y_train_iris, thieved_classifier=thieved_krc)
+
+        victim_preds = np.argmax(victim_krc.predict(x=self.x_train_iris), axis=1)
+        thieved_preds = np.argmax(thieved_krc.predict(x=self.x_train_iris), axis=1)
+        acc = np.sum(victim_preds == thieved_preds) / len(victim_preds)
+
+        self.assertGreater(acc, 0.33)
+
+        # Clean-up
+        k.clear_session()
+
+    def test_4_pytorch_iris(self):
+        """
+        Third test for PyTorch.
+        :return:
+        """
+        # Build PyTorchClassifier
+        victim_ptc = get_tabular_classifier_pt()
+
+        # Create the thieved classifier
+        thieved_ptc = get_tabular_classifier_pt(load_init=False)
+
+        # Create random attack
+        attack = KnockoffNets(
+            classifier=victim_ptc,
+            batch_size_fit=BATCH_SIZE,
+            batch_size_query=BATCH_SIZE,
+            nb_epochs=NB_EPOCHS,
+            nb_stolen=NB_STOLEN,
+            sampling_strategy="random",
+        )
+        thieved_ptc = attack.extract(x=self.x_train_iris, thieved_classifier=thieved_ptc)
+
+        victim_preds = np.argmax(victim_ptc.predict(x=self.x_train_iris), axis=1)
+        thieved_preds = np.argmax(thieved_ptc.predict(x=self.x_train_iris), axis=1)
+        acc = np.sum(victim_preds == thieved_preds) / len(victim_preds)
+
+        self.assertGreater(acc, 0.3)
+
+        # Create adaptive attack
+        attack = KnockoffNets(
+            classifier=victim_ptc,
+            batch_size_fit=BATCH_SIZE,
+            batch_size_query=BATCH_SIZE,
+            nb_epochs=NB_EPOCHS,
+            nb_stolen=NB_STOLEN,
+            sampling_strategy="adaptive",
+            reward="all",
+        )
+        thieved_ptc = attack.extract(x=self.x_train_iris, y=self.y_train_iris, thieved_classifier=thieved_ptc)
+
+        victim_preds = np.argmax(victim_ptc.predict(x=self.x_train_iris), axis=1)
+        thieved_preds = np.argmax(thieved_ptc.predict(x=self.x_train_iris), axis=1)
+        acc = np.sum(victim_preds == thieved_preds) / len(victim_preds)
+
+        self.assertGreater(acc, 0.4)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/adversarial-robustness-toolbox/tests/attacks/test_newtonfool.py b/adversarial-robustness-toolbox/tests/attacks/test_newtonfool.py
new file mode 100644
index 0000000..e8204da
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/attacks/test_newtonfool.py
@@ -0,0 +1,228 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import unittest
+
+import numpy as np
+
+from art.attacks.evasion.newtonfool import NewtonFool
+from art.estimators.classification.classifier import ClassGradientsMixin
+from art.estimators.classification.keras import KerasClassifier
+from art.estimators.estimator import BaseEstimator
+from tests.attacks.utils import backend_test_classifier_type_check_fail
+from tests.utils import (
+    TestBase,
+    get_image_classifier_kr,
+    get_image_classifier_pt,
+    get_image_classifier_tf,
+    get_tabular_classifier_kr,
+    get_tabular_classifier_pt,
+    get_tabular_classifier_tf,
+)
+
+logger = logging.getLogger(__name__)
+
+
+class TestNewtonFool(TestBase):
+    """
+    A unittest class for testing the NewtonFool attack.
+    """
+
+    @classmethod
+    def setUpClass(cls):
+        super().setUpClass()
+
+    def test_3_tensorflow_mnist(self):
+        """
+        First test with the TensorFlowClassifier.
+        :return:
+        """
+        x_test_original = self.x_test_mnist.copy()
+
+        # Build TensorFlowClassifier
+        tfc, sess = get_image_classifier_tf()
+
+        # Attack
+        nf = NewtonFool(tfc, max_iter=5, batch_size=100)
+        x_test_adv = nf.generate(self.x_test_mnist)
+
+        self.assertFalse((self.x_test_mnist == x_test_adv).all())
+
+        y_pred = tfc.predict(self.x_test_mnist)
+        y_pred_adv = tfc.predict(x_test_adv)
+        y_pred_bool = y_pred.max(axis=1, keepdims=1) == y_pred
+        y_pred_max = y_pred.max(axis=1)
+        y_pred_adv_max = y_pred_adv[y_pred_bool]
+        self.assertTrue((y_pred_max >= 0.9 * y_pred_adv_max).all())
+
+        # Check that x_test has not been modified by attack and classifier
+        self.assertAlmostEqual(float(np.max(np.abs(x_test_original - self.x_test_mnist))), 0.0, delta=0.00001)
+
+    def test_9_keras_mnist(self):
+        """
+        Second test with the KerasClassifier.
+        :return:
+        """
+        x_test_original = self.x_test_mnist.copy()
+
+        # Build KerasClassifier
+        krc = get_image_classifier_kr()
+
+        # Attack
+        nf = NewtonFool(krc, max_iter=5, batch_size=100)
+        x_test_adv = nf.generate(self.x_test_mnist)
+
+        self.assertFalse((self.x_test_mnist == x_test_adv).all())
+
+        y_pred = krc.predict(self.x_test_mnist)
+        y_pred_adv = krc.predict(x_test_adv)
+        y_pred_bool = y_pred.max(axis=1, keepdims=1) == y_pred
+        y_pred_max = y_pred.max(axis=1)
+        y_pred_adv_max = y_pred_adv[y_pred_bool]
+        self.assertTrue((y_pred_max >= 0.9 * y_pred_adv_max).all())
+
+        # Check that x_test has not been modified by attack and classifier
+        self.assertAlmostEqual(float(np.max(np.abs(x_test_original - self.x_test_mnist))), 0.0, delta=0.00001)
+
+    def test_5_pytorch_mnist(self):
+        """
+        Third test with the PyTorchClassifier.
+        :return:
+        """
+        x_test = np.swapaxes(self.x_test_mnist, 1, 3).astype(np.float32)
+        x_test_original = x_test.copy()
+
+        # Build PyTorchClassifier
+        ptc = get_image_classifier_pt()
+
+        # Attack
+        nf = NewtonFool(ptc, max_iter=5, batch_size=100)
+        x_test_adv = nf.generate(x_test)
+
+        self.assertFalse((x_test == x_test_adv).all())
+
+        y_pred = ptc.predict(x_test)
+        y_pred_adv = ptc.predict(x_test_adv)
+        y_pred_bool = y_pred.max(axis=1, keepdims=1) == y_pred
+        y_pred_max = y_pred.max(axis=1)
+        y_pred_adv_max = y_pred_adv[y_pred_bool]
+        self.assertTrue((y_pred_max >= 0.9 * y_pred_adv_max).all())
+
+        # Check that x_test has not been modified by attack and classifier
+        self.assertAlmostEqual(float(np.max(np.abs(x_test_original - x_test))), 0.0, delta=0.00001)
+
+    def test_7_keras_iris_clipped(self):
+        classifier = get_tabular_classifier_kr()
+
+        attack = NewtonFool(classifier, max_iter=5)
+        x_test_adv = attack.generate(self.x_test_iris)
+        self.assertFalse((self.x_test_iris == x_test_adv).all())
+        self.assertTrue((x_test_adv <= 1).all())
+        self.assertTrue((x_test_adv >= 0).all())
+
+        preds_adv = np.argmax(classifier.predict(x_test_adv), axis=1)
+        self.assertFalse((np.argmax(self.y_test_iris, axis=1) == preds_adv).all())
+        acc = np.sum(preds_adv == np.argmax(self.y_test_iris, axis=1)) / self.y_test_iris.shape[0]
+        logger.info("Accuracy on Iris with NewtonFool adversarial examples: %.2f%%", (acc * 100))
+
+    def test_8_keras_iris_unbounded(self):
+        classifier = get_tabular_classifier_kr()
+
+        # Recreate a classifier without clip values
+        classifier = KerasClassifier(model=classifier._model, use_logits=False, channels_first=True)
+        attack = NewtonFool(classifier, max_iter=5, batch_size=128)
+        x_test_adv = attack.generate(self.x_test_iris)
+        self.assertFalse((self.x_test_iris == x_test_adv).all())
+
+        preds_adv = np.argmax(classifier.predict(x_test_adv), axis=1)
+        self.assertFalse((np.argmax(self.y_test_iris, axis=1) == preds_adv).all())
+        acc = np.sum(preds_adv == np.argmax(self.y_test_iris, axis=1)) / self.y_test_iris.shape[0]
+        logger.info("Accuracy on Iris with NewtonFool adversarial examples: %.2f%%", (acc * 100))
+
+    def test_2_tensorflow_iris(self):
+        classifier, _ = get_tabular_classifier_tf()
+
+        attack = NewtonFool(classifier, max_iter=5, batch_size=128)
+        x_test_adv = attack.generate(self.x_test_iris)
+        self.assertFalse((self.x_test_iris == x_test_adv).all())
+        self.assertTrue((x_test_adv <= 1).all())
+        self.assertTrue((x_test_adv >= 0).all())
+
+        preds_adv = np.argmax(classifier.predict(x_test_adv), axis=1)
+        self.assertFalse((np.argmax(self.y_test_iris, axis=1) == preds_adv).all())
+        acc = np.sum(preds_adv == np.argmax(self.y_test_iris, axis=1)) / self.y_test_iris.shape[0]
+        logger.info("Accuracy on Iris with NewtonFool adversarial examples: %.2f%%", (acc * 100))
+
+    def test_4_pytorch_iris(self):
+        classifier = get_tabular_classifier_pt()
+
+        attack = NewtonFool(classifier, max_iter=5, batch_size=128)
+        x_test_adv = attack.generate(self.x_test_iris)
+        self.assertFalse((self.x_test_iris == x_test_adv).all())
+        self.assertTrue((x_test_adv <= 1).all())
+        self.assertTrue((x_test_adv >= 0).all())
+
+        preds_adv = np.argmax(classifier.predict(x_test_adv), axis=1)
+        self.assertFalse((np.argmax(self.y_test_iris, axis=1) == preds_adv).all())
+        acc = np.sum(preds_adv == np.argmax(self.y_test_iris, axis=1)) / self.y_test_iris.shape[0]
+        logger.info("Accuracy on Iris with NewtonFool adversarial examples: %.2f%%", (acc * 100))
+
+    def test_6_scikitlearn(self):
+        from sklearn.linear_model import LogisticRegression
+        from sklearn.svm import SVC, LinearSVC
+
+        from art.estimators.classification.scikitlearn import SklearnClassifier
+
+        scikitlearn_test_cases = [
+            LogisticRegression(solver="lbfgs", multi_class="auto"),
+            SVC(gamma="auto"),
+            LinearSVC(),
+        ]
+
+        x_test_original = self.x_test_iris.copy()
+
+        for model in scikitlearn_test_cases:
+            classifier = SklearnClassifier(model=model, clip_values=(0, 1))
+            classifier.fit(x=self.x_test_iris, y=self.y_test_iris)
+
+            attack = NewtonFool(classifier, max_iter=5, batch_size=128)
+            x_test_adv = attack.generate(self.x_test_iris)
+            self.assertFalse((self.x_test_iris == x_test_adv).all())
+            self.assertTrue((x_test_adv <= 1).all())
+            self.assertTrue((x_test_adv >= 0).all())
+
+            preds_adv = np.argmax(classifier.predict(x_test_adv), axis=1)
+            self.assertFalse((np.argmax(self.y_test_iris, axis=1) == preds_adv).all())
+            acc = np.sum(preds_adv == np.argmax(self.y_test_iris, axis=1)) / self.y_test_iris.shape[0]
+            logger.info(
+                "Accuracy of " + classifier.__class__.__name__ + " on Iris with NewtonFool adversarial examples"
+                ": %.2f%%",
+                (acc * 100),
+            )
+
+            # Check that x_test has not been modified by attack and classifier
+            self.assertAlmostEqual(float(np.max(np.abs(x_test_original - self.x_test_iris))), 0.0, delta=0.00001)
+
+    def test_1_classifier_type_check_fail(self):
+        backend_test_classifier_type_check_fail(NewtonFool, [BaseEstimator, ClassGradientsMixin])
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/adversarial-robustness-toolbox/tests/attacks/test_pixel_attack.py b/adversarial-robustness-toolbox/tests/attacks/test_pixel_attack.py
new file mode 100644
index 0000000..da5b9f2
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/attacks/test_pixel_attack.py
@@ -0,0 +1,158 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module tests the Pixel Attack.
+The Pixel Attack is a generalisation of One Pixel Attack.
+
+| One Pixel Attack Paper link:
+    https://ieeexplore.ieee.org/abstract/document/8601309/citations#citations
+    (arXiv link: https://arxiv.org/pdf/1710.08864.pdf)
+| Pixel Attack Paper link:
+    https://arxiv.org/abs/1906.06026
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import unittest
+
+import numpy as np
+
+from art.attacks.evasion.pixel_threshold import PixelAttack
+from art.estimators.estimator import BaseEstimator, NeuralNetworkMixin
+from art.estimators.classification.classifier import ClassifierMixin
+from art.utils import get_labels_np_array
+
+from tests.utils import TestBase
+from tests.utils import get_image_classifier_tf, get_image_classifier_kr, get_image_classifier_pt
+from tests.attacks.utils import backend_test_classifier_type_check_fail
+
+logger = logging.getLogger(__name__)
+
+
+class TestPixelAttack(TestBase):
+    """
+    A unittest class for testing the Pixel Attack.
+
+    This module tests the Pixel Attack.
+    The Pixel Attack is a generalisation of One Pixel Attack.
+
+    | One Pixel Attack Paper link:
+        https://ieeexplore.ieee.org/abstract/document/8601309/citations#citations
+        (arXiv link: https://arxiv.org/pdf/1710.08864.pdf)
+    | Pixel Attack Paper link:
+        https://arxiv.org/abs/1906.06026
+    """
+
+    @classmethod
+    def setUpClass(cls):
+        super().setUpClass()
+
+        cls.n_test = 2
+        cls.x_test_mnist = cls.x_test_mnist[0 : cls.n_test]
+        cls.y_test_mnist = cls.y_test_mnist[0 : cls.n_test]
+
+    def test_6_keras_mnist(self):
+        """
+        Test with the KerasClassifier. (Untargeted Attack)
+        :return:
+        """
+
+        classifier = get_image_classifier_kr()
+        self._test_attack(classifier, self.x_test_mnist, self.y_test_mnist, False)
+
+    def test_2_tensorflow_mnist(self):
+        """
+        Test with the TensorFlowClassifier. (Untargeted Attack)
+        :return:
+        """
+        classifier, sess = get_image_classifier_tf()
+        self._test_attack(classifier, self.x_test_mnist, self.y_test_mnist, False)
+
+    def test_4_pytorch_mnist(self):
+        """
+        Test with the PyTorchClassifier. (Untargeted Attack)
+        :return:
+        """
+        x_test = np.reshape(self.x_test_mnist, (self.x_test_mnist.shape[0], 1, 28, 28)).astype(np.float32)
+        classifier = get_image_classifier_pt()
+        self._test_attack(classifier, x_test, self.y_test_mnist, False)
+
+    def test_7_keras_mnist_targeted(self):
+        """
+        Test with the KerasClassifier. (Targeted Attack)
+        :return:
+        """
+        classifier = get_image_classifier_kr()
+        self._test_attack(classifier, self.x_test_mnist, self.y_test_mnist, True)
+
+    def test_3_tensorflow_mnist_targeted(self):
+        """
+        Test with the TensorFlowClassifier. (Targeted Attack)
+        :return:
+        """
+        classifier, sess = get_image_classifier_tf()
+        self._test_attack(classifier, self.x_test_mnist, self.y_test_mnist, True)
+
+    def test_5_pytorch_mnist_targeted(self):
+        """
+        Test with the PyTorchClassifier. (Targeted Attack)
+        :return:
+        """
+        x_test = np.reshape(self.x_test_mnist, (self.x_test_mnist.shape[0], 1, 28, 28)).astype(np.float32)
+        classifier = get_image_classifier_pt()
+        self._test_attack(classifier, x_test, self.y_test_mnist, True)
+
+    def _test_attack(self, classifier, x_test, y_test, targeted):
+        """
+        Test with the Pixel Attack
+        :return:
+        """
+        x_test_original = x_test.copy()
+
+        if targeted:
+            # Generate random target classes
+            class_y_test = np.argmax(y_test, axis=1)
+            nb_classes = np.unique(class_y_test).shape[0]
+            targets = np.random.randint(nb_classes, size=self.n_test)
+            for i in range(self.n_test):
+                if class_y_test[i] == targets[i]:
+                    targets[i] -= 1
+        else:
+            targets = y_test
+
+        for es in [1]:  # Option 0 is not easy to reproduce reliably, we should consider it at a later time
+            df = PixelAttack(classifier, th=64, es=es, targeted=targeted)
+            x_test_adv = df.generate(x_test_original, targets, max_iter=10)
+
+            np.testing.assert_raises(AssertionError, np.testing.assert_array_equal, x_test, x_test_adv)
+            self.assertFalse((0.0 == x_test_adv).all())
+
+            y_pred = get_labels_np_array(classifier.predict(x_test_adv))
+
+            accuracy = np.sum(np.argmax(y_pred, axis=1) == np.argmax(self.y_test_mnist, axis=1)) / self.n_test
+            logger.info("Accuracy on adversarial examples: %.2f%%", (accuracy * 100))
+
+        # Check that x_test has not been modified by attack and classifier
+        self.assertAlmostEqual(float(np.max(np.abs(x_test_original - x_test))), 0.0, delta=0.00001)
+
+    def test_1_classifier_type_check_fail(self):
+        backend_test_classifier_type_check_fail(PixelAttack, [BaseEstimator, NeuralNetworkMixin, ClassifierMixin])
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/adversarial-robustness-toolbox/tests/attacks/test_poisoning_attack_svm.py b/adversarial-robustness-toolbox/tests/attacks/test_poisoning_attack_svm.py
new file mode 100644
index 0000000..03e4afe
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/attacks/test_poisoning_attack_svm.py
@@ -0,0 +1,184 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2019
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import unittest
+
+import numpy as np
+from sklearn.svm import LinearSVC, NuSVC, SVC
+
+from art.attacks.poisoning import PoisoningAttackSVM
+from art.estimators.classification.scikitlearn import SklearnClassifier, ScikitlearnSVC
+from art.utils import load_iris
+
+from tests.utils import master_seed
+from tests.attacks.utils import backend_test_classifier_type_check_fail
+
+logger = logging.getLogger(__name__)
+
+BATCH_SIZE = 10
+NB_TRAIN = 10
+NB_VALID = 10
+NB_TEST = 10
+
+
+class TestSVMAttack(unittest.TestCase):
+    """
+    A unittest class for testing Poisoning Attack on SVMs.
+    """
+
+    @classmethod
+    def setUpClass(cls):
+        master_seed(seed=1234)
+        cls.setUpIRIS()
+
+    @staticmethod
+    def find_duplicates(x_train):
+        """
+        Returns an array of booleans that is true if that element was previously in the array
+
+        :param x_train: training data
+        :type x_train: `np.ndarray`
+        :return: duplicates array
+        :rtype: `np.ndarray`
+        """
+        dup = np.zeros(x_train.shape[0])
+        for idx, x in enumerate(x_train):
+            dup[idx] = np.isin(x_train[:idx], x).all(axis=1).any()
+        return dup
+
+    @classmethod
+    def setUpIRIS(cls):
+        (x_train, y_train), (x_test, y_test), min_, max_ = load_iris()
+        # Naturally IRIS has labels 0, 1, and 2. For binary classification use only classes 1 and 2.
+        no_zero = np.where(np.argmax(y_train, axis=1) != 0)
+        x_train = x_train[no_zero, :2][0]
+        y_train = y_train[no_zero]
+        no_zero = np.where(np.argmax(y_test, axis=1) != 0)
+        x_test = x_test[no_zero, :2][0]
+        y_test = y_test[no_zero]
+        labels = np.zeros((y_train.shape[0], 2))
+        labels[np.argmax(y_train, axis=1) == 2] = np.array([1, 0])
+        labels[np.argmax(y_train, axis=1) == 1] = np.array([0, 1])
+        y_train = labels
+        te_labels = np.zeros((y_test.shape[0], 2))
+        te_labels[np.argmax(y_test, axis=1) == 2] = np.array([1, 0])
+        te_labels[np.argmax(y_test, axis=1) == 1] = np.array([0, 1])
+        y_test = te_labels
+        n_sample = len(x_train)
+
+        order = np.random.permutation(n_sample)
+        x_train = x_train[order]
+        y_train = y_train[order].astype(np.float)
+
+        x_train = x_train[: int(0.9 * n_sample)]
+        y_train = y_train[: int(0.9 * n_sample)]
+        train_dups = cls.find_duplicates(x_train)
+        x_train = x_train[np.logical_not(train_dups)]
+        y_train = y_train[np.logical_not(train_dups)]
+        test_dups = cls.find_duplicates(x_test)
+        x_test = x_test[np.logical_not(test_dups)]
+        y_test = y_test[np.logical_not(test_dups)]
+        cls.iris = (x_train, y_train), (x_test, y_test), min_, max_
+
+    def setUp(self):
+        super().setUp()
+
+    # def test_linearSVC(self):
+    #     """
+    #     Test using a attack on LinearSVC
+    #     """
+    #     (x_train, y_train), (x_test, y_test), min_, max_ = self.iris
+    #     x_test_original = x_test.copy()
+    #
+    #     # Build Scikitlearn Classifier
+    #     clip_values = (min_, max_)
+    #     clean = SklearnClassifier(model=LinearSVC(), clip_values=clip_values)
+    #     clean.fit(x_train, y_train)
+    #     poison = SklearnClassifier(model=LinearSVC(), clip_values=clip_values)
+    #     poison.fit(x_train, y_train)
+    #     attack = PoisoningAttackSVM(poison, 0.01, 1.0, x_train, y_train, x_test, y_test, 100)
+    #     attack_y = np.array([1, 1]) - y_train[0]
+    #     attack_point, _ = attack.poison(np.array([x_train[0]]), y=np.array([attack_y]))
+    #     poison.fit(x=np.vstack([x_train, attack_point]), y=np.vstack([y_train, np.copy(y_train[0].reshape((1, 2)))]))
+    #
+    #     acc = np.average(np.all(clean.predict(x_test) == y_test, axis=1)) * 100
+    #     poison_acc = np.average(np.all(poison.predict(x_test) == y_test, axis=1)) * 100
+    #     logger.info("Clean Accuracy {}%".format(acc))
+    #     logger.info("Poison Accuracy {}%".format(poison_acc))
+    #     self.assertGreaterEqual(acc, poison_acc)
+    #
+    #     # Check that x_test has not been modified by attack and classifier
+    #     self.assertAlmostEqual(float(np.max(np.abs(x_test_original - x_test))), 0.0, delta=0.00001)
+
+    def test_unsupported_kernel(self):
+        (x_train, y_train), (x_test, y_test), min_, max_ = self.iris
+        model = SVC(kernel="sigmoid", gamma="auto")
+        with self.assertRaises(TypeError):
+            _ = PoisoningAttackSVM(
+                classifier=model, step=0.01, eps=1.0, x_train=x_train, y_train=y_train, x_val=x_test, y_val=y_test
+            )
+
+    def test_unsupported_SVC(self):
+        (x_train, y_train), (x_test, y_test), _, _ = self.iris
+        model = NuSVC()
+        with self.assertRaises(TypeError):
+            _ = PoisoningAttackSVM(
+                classifier=model, step=0.01, eps=1.0, x_train=x_train, y_train=y_train, x_val=x_test, y_val=y_test
+            )
+
+    def test_SVC_kernels(self):
+        """
+        First test with the TensorFlowClassifier.
+        :return:
+        """
+        # Get MNIST
+        (x_train, y_train), (x_test, y_test), min_, max_ = self.iris
+        x_test_original = x_test.copy()
+
+        # Build Scikitlearn Classifier
+        clip_values = (min_, max_)
+        for kernel in ["linear"]:  # ["linear", "poly", "rbf"]
+            clean = SklearnClassifier(model=SVC(kernel=kernel, gamma="auto"), clip_values=clip_values)
+            clean.fit(x_train, y_train)
+            poison = SklearnClassifier(model=SVC(kernel=kernel, gamma="auto"), clip_values=clip_values)
+            poison.fit(x_train, y_train)
+            attack = PoisoningAttackSVM(poison, 0.01, 1.0, x_train, y_train, x_test, y_test, 100)
+            attack_y = np.array([1, 1]) - y_train[0]
+            attack_point, _ = attack.poison(np.array([x_train[0]]), y=np.array([attack_y]))
+            poison.fit(
+                x=np.vstack([x_train, attack_point]),
+                y=np.vstack([y_train, np.array([1, 1]) - np.copy(y_train[0].reshape((1, 2)))]),
+            )
+
+            acc = np.average(np.all(clean.predict(x_test) == y_test, axis=1)) * 100
+            poison_acc = np.average(np.all(poison.predict(x_test) == y_test, axis=1)) * 100
+            logger.info("Clean Accuracy {}%".format(acc))
+            logger.info("Poison Accuracy {}%".format(poison_acc))
+            self.assertGreaterEqual(acc, poison_acc)
+
+            # Check that x_test has not been modified by attack and classifier
+            self.assertAlmostEqual(float(np.max(np.abs(x_test_original - x_test))), 0.0, delta=0.00001)
+
+    def test_classifier_type_check_fail(self):
+        backend_test_classifier_type_check_fail(PoisoningAttackSVM, [ScikitlearnSVC])
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/adversarial-robustness-toolbox/tests/attacks/test_projected_gradient_descent.py b/adversarial-robustness-toolbox/tests/attacks/test_projected_gradient_descent.py
new file mode 100644
index 0000000..f7b72ee
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/attacks/test_projected_gradient_descent.py
@@ -0,0 +1,721 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import unittest
+
+import numpy as np
+import tensorflow as tf
+
+from art.attacks.evasion.projected_gradient_descent.projected_gradient_descent import ProjectedGradientDescent
+from art.attacks.evasion.projected_gradient_descent.projected_gradient_descent_numpy import (
+    ProjectedGradientDescentNumpy,
+)
+from art.estimators.classification import KerasClassifier
+from art.estimators.estimator import BaseEstimator, LossGradientsMixin
+from art.utils import get_labels_np_array, random_targets
+from tests.attacks.utils import backend_test_classifier_type_check_fail
+from tests.utils import (
+    TestBase,
+    get_image_classifier_kr,
+    get_image_classifier_pt,
+    get_image_classifier_tf,
+    get_tabular_classifier_kr,
+    get_tabular_classifier_pt,
+    get_tabular_classifier_tf,
+    master_seed,
+)
+
+logger = logging.getLogger(__name__)
+
+
+class TestPGD(TestBase):
+    @classmethod
+    def setUpClass(cls):
+        super().setUpClass()
+
+        cls.n_train = 10
+        cls.n_test = 10
+        cls.x_train_mnist = cls.x_train_mnist[0 : cls.n_train]
+        cls.y_train_mnist = cls.y_train_mnist[0 : cls.n_train]
+        cls.x_test_mnist = cls.x_test_mnist[0 : cls.n_test]
+        cls.y_test_mnist = cls.y_test_mnist[0 : cls.n_test]
+
+    def test_9a_keras_mnist(self):
+        classifier = get_image_classifier_kr()
+
+        scores = classifier._model.evaluate(self.x_train_mnist, self.y_train_mnist)
+        logger.info("[Keras, MNIST] Accuracy on training set: %.2f%%", scores[1] * 100)
+        scores = classifier._model.evaluate(self.x_test_mnist, self.y_test_mnist)
+        logger.info("[Keras, MNIST] Accuracy on test set: %.2f%%", scores[1] * 100)
+
+        self._test_backend_mnist(
+            classifier, self.x_train_mnist, self.y_train_mnist, self.x_test_mnist, self.y_test_mnist
+        )
+
+    def test_3_tensorflow_mnist(self):
+        classifier, sess = get_image_classifier_tf()
+
+        scores = get_labels_np_array(classifier.predict(self.x_train_mnist))
+        acc = np.sum(np.argmax(scores, axis=1) == np.argmax(self.y_train_mnist, axis=1)) / self.y_train_mnist.shape[0]
+        logger.info("[TF, MNIST] Accuracy on training set: %.2f%%", acc * 100)
+
+        scores = get_labels_np_array(classifier.predict(self.x_test_mnist))
+        acc = np.sum(np.argmax(scores, axis=1) == np.argmax(self.y_test_mnist, axis=1)) / self.y_test_mnist.shape[0]
+        logger.info("[TF, MNIST] Accuracy on test set: %.2f%%", acc * 100)
+
+        self._test_backend_mnist(
+            classifier, self.x_train_mnist, self.y_train_mnist, self.x_test_mnist, self.y_test_mnist
+        )
+
+    def test_5_pytorch_mnist(self):
+        x_train_mnist = np.swapaxes(self.x_train_mnist, 1, 3).astype(np.float32)
+        x_test_mnist = np.swapaxes(self.x_test_mnist, 1, 3).astype(np.float32)
+        classifier = get_image_classifier_pt()
+
+        scores = get_labels_np_array(classifier.predict(x_train_mnist))
+        acc = np.sum(np.argmax(scores, axis=1) == np.argmax(self.y_train_mnist, axis=1)) / self.y_train_mnist.shape[0]
+        logger.info("[PyTorch, MNIST] Accuracy on training set: %.2f%%", acc * 100)
+
+        scores = get_labels_np_array(classifier.predict(x_test_mnist))
+        acc = np.sum(np.argmax(scores, axis=1) == np.argmax(self.y_test_mnist, axis=1)) / self.y_test_mnist.shape[0]
+        logger.info("[PyTorch, MNIST] Accuracy on test set: %.2f%%", acc * 100)
+
+        self._test_backend_mnist(classifier, x_train_mnist, self.y_train_mnist, x_test_mnist, self.y_test_mnist)
+
+        # Test with clip values of array type
+        classifier.set_params(clip_values=(np.zeros_like(x_test_mnist[0]), np.ones_like(x_test_mnist[0])))
+        self._test_backend_mnist(classifier, x_train_mnist, self.y_train_mnist, x_test_mnist, self.y_test_mnist)
+
+        classifier.set_params(clip_values=(np.zeros_like(x_test_mnist[0][0]), np.ones_like(x_test_mnist[0][0])))
+        self._test_backend_mnist(classifier, x_train_mnist, self.y_train_mnist, x_test_mnist, self.y_test_mnist)
+
+        classifier.set_params(clip_values=(np.zeros_like(x_test_mnist[0][0][0]), np.ones_like(x_test_mnist[0][0][0])))
+        self._test_backend_mnist(classifier, x_train_mnist, self.y_train_mnist, x_test_mnist, self.y_test_mnist)
+
+    def _test_backend_mnist(self, classifier, x_train, y_train, x_test, y_test):
+        x_test_original = x_test.copy()
+
+        # Test PGD with np.inf norm
+        attack = ProjectedGradientDescent(classifier, eps=1.0, eps_step=0.1)
+        x_train_adv = attack.generate(x_train)
+        x_test_adv = attack.generate(x_test)
+
+        self.assertFalse((x_train == x_train_adv).all())
+        self.assertFalse((x_test == x_test_adv).all())
+
+        train_y_pred = get_labels_np_array(classifier.predict(x_train_adv))
+        test_y_pred = get_labels_np_array(classifier.predict(x_test_adv))
+
+        self.assertFalse((y_train == train_y_pred).all())
+        self.assertFalse((y_test == test_y_pred).all())
+
+        acc = np.sum(np.argmax(train_y_pred, axis=1) == np.argmax(y_train, axis=1)) / y_train.shape[0]
+        logger.info("Accuracy on adversarial train examples: %.2f%%", acc * 100)
+
+        acc = np.sum(np.argmax(test_y_pred, axis=1) == np.argmax(y_test, axis=1)) / y_test.shape[0]
+        logger.info("Accuracy on adversarial test examples: %.2f%%", acc * 100)
+
+        # Test PGD with 3 random initialisations
+        attack = ProjectedGradientDescent(classifier, num_random_init=3)
+        x_train_adv = attack.generate(x_train)
+        x_test_adv = attack.generate(x_test)
+
+        self.assertFalse((x_train == x_train_adv).all())
+        self.assertFalse((x_test == x_test_adv).all())
+
+        train_y_pred = get_labels_np_array(classifier.predict(x_train_adv))
+        test_y_pred = get_labels_np_array(classifier.predict(x_test_adv))
+
+        self.assertFalse((y_train == train_y_pred).all())
+        self.assertFalse((y_test == test_y_pred).all())
+
+        acc = np.sum(np.argmax(train_y_pred, axis=1) == np.argmax(y_train, axis=1)) / y_train.shape[0]
+        logger.info("Accuracy on adversarial train examples with 3 random initialisations: %.2f%%", acc * 100)
+
+        acc = np.sum(np.argmax(test_y_pred, axis=1) == np.argmax(y_test, axis=1)) / y_test.shape[0]
+        logger.info("Accuracy on adversarial test examples with 3 random initialisations: %.2f%%", acc * 100)
+
+        # Check that x_test has not been modified by attack and classifier
+        self.assertAlmostEqual(float(np.max(np.abs(x_test_original - x_test))), 0.0, delta=0.00001)
+
+        # Test the masking
+        attack = ProjectedGradientDescent(classifier, num_random_init=1)
+        mask = np.random.binomial(n=1, p=0.5, size=np.prod(x_test.shape))
+        mask = mask.reshape(x_test.shape).astype(np.float32)
+
+        x_test_adv = attack.generate(x_test, mask=mask)
+        mask_diff = (1 - mask) * (x_test_adv - x_test)
+        self.assertAlmostEqual(float(np.max(np.abs(mask_diff))), 0.0, delta=0.00001)
+
+        # Test eps of array type 1
+        attack = ProjectedGradientDescent(classifier, eps=1.0, eps_step=0.1)
+
+        eps = np.ones(shape=x_test.shape) * 1.0
+        eps_step = np.ones_like(eps) * 0.1
+
+        attack_params = {"eps_step": eps_step, "eps": eps}
+        attack.set_params(**attack_params)
+
+        x_test_adv = attack.generate(x_test)
+        self.assertFalse((x_test == x_test_adv).all())
+
+        test_y_pred = get_labels_np_array(classifier.predict(x_test_adv))
+        self.assertFalse((y_test == test_y_pred).all())
+
+        # Test eps of array type 2
+        eps = np.ones(shape=x_test.shape[1:]) * 1.0
+        eps_step = np.ones_like(eps) * 0.1
+
+        attack_params = {"eps_step": eps_step, "eps": eps}
+        attack.set_params(**attack_params)
+
+        x_test_adv = attack.generate(x_test)
+        self.assertFalse((x_test == x_test_adv).all())
+
+        test_y_pred = get_labels_np_array(classifier.predict(x_test_adv))
+        self.assertFalse((y_test == test_y_pred).all())
+
+        # Test eps of array type 3
+        eps = np.ones(shape=x_test.shape[2:]) * 1.0
+        eps_step = np.ones_like(eps) * 0.1
+
+        attack_params = {"eps_step": eps_step, "eps": eps}
+        attack.set_params(**attack_params)
+
+        x_test_adv = attack.generate(x_test)
+        self.assertFalse((x_test == x_test_adv).all())
+
+        test_y_pred = get_labels_np_array(classifier.predict(x_test_adv))
+        self.assertFalse((y_test == test_y_pred).all())
+
+        # Test eps of array type 4
+        eps = np.ones(shape=x_test.shape[3:]) * 1.0
+        eps_step = np.ones_like(eps) * 0.1
+
+        attack_params = {"eps_step": eps_step, "eps": eps}
+        attack.set_params(**attack_params)
+
+        x_test_adv = attack.generate(x_test)
+        self.assertFalse((x_test == x_test_adv).all())
+
+        test_y_pred = get_labels_np_array(classifier.predict(x_test_adv))
+        self.assertFalse((y_test == test_y_pred).all())
+
+    def test_1_classifier_type_check_fail(self):
+        backend_test_classifier_type_check_fail(ProjectedGradientDescent, [BaseEstimator, LossGradientsMixin])
+
+    def test_8_keras_iris_clipped(self):
+        classifier = get_tabular_classifier_kr()
+
+        # Test untargeted attack
+        attack = ProjectedGradientDescent(classifier, eps=1.0, eps_step=0.1, max_iter=5)
+        x_test_adv = attack.generate(self.x_test_iris)
+        self.assertFalse((self.x_test_iris == x_test_adv).all())
+        self.assertTrue((x_test_adv <= 1).all())
+        self.assertTrue((x_test_adv >= 0).all())
+
+        preds_adv = np.argmax(classifier.predict(x_test_adv), axis=1)
+        self.assertFalse((np.argmax(self.y_test_iris, axis=1) == preds_adv).all())
+        acc = np.sum(preds_adv == np.argmax(self.y_test_iris, axis=1)) / self.y_test_iris.shape[0]
+        logger.info("Accuracy on Iris with PGD adversarial examples: %.2f%%", (acc * 100))
+
+        # Test targeted attack
+        targets = random_targets(self.y_test_iris, nb_classes=3)
+        attack = ProjectedGradientDescent(classifier, targeted=True, eps=1.0, eps_step=0.1, max_iter=5)
+        x_test_adv = attack.generate(self.x_test_iris, **{"y": targets})
+        self.assertFalse((self.x_test_iris == x_test_adv).all())
+        self.assertTrue((x_test_adv <= 1).all())
+        self.assertTrue((x_test_adv >= 0).all())
+
+        preds_adv = np.argmax(classifier.predict(x_test_adv), axis=1)
+        self.assertTrue((np.argmax(targets, axis=1) == preds_adv).any())
+        acc = np.sum(preds_adv == np.argmax(targets, axis=1)) / self.y_test_iris.shape[0]
+        logger.info("Success rate of targeted PGD on Iris: %.2f%%", (acc * 100))
+
+    def test_keras_9_iris_unbounded(self):
+        classifier = get_tabular_classifier_kr()
+
+        # Recreate a classifier without clip values
+        classifier = KerasClassifier(model=classifier._model, use_logits=False, channels_first=True)
+        attack = ProjectedGradientDescent(classifier, eps=1.0, eps_step=0.2, max_iter=5)
+        x_test_adv = attack.generate(self.x_test_iris)
+        self.assertFalse((self.x_test_iris == x_test_adv).all())
+        self.assertTrue((x_test_adv > 1).any())
+        self.assertTrue((x_test_adv < 0).any())
+
+        preds_adv = np.argmax(classifier.predict(x_test_adv), axis=1)
+        self.assertFalse((np.argmax(self.y_test_iris, axis=1) == preds_adv).all())
+        acc = np.sum(preds_adv == np.argmax(self.y_test_iris, axis=1)) / self.y_test_iris.shape[0]
+        logger.info("Accuracy on Iris with PGD adversarial examples: %.2f%%", (acc * 100))
+
+    def test_2_tensorflow_iris(self):
+        classifier, _ = get_tabular_classifier_tf()
+
+        # Test untargeted attack
+        attack = ProjectedGradientDescent(classifier, eps=1.0, eps_step=0.1, max_iter=5)
+        x_test_adv = attack.generate(self.x_test_iris)
+        self.assertFalse((self.x_test_iris == x_test_adv).all())
+        self.assertTrue((x_test_adv <= 1).all())
+        self.assertTrue((x_test_adv >= 0).all())
+
+        preds_adv = np.argmax(classifier.predict(x_test_adv), axis=1)
+        self.assertFalse((np.argmax(self.y_test_iris, axis=1) == preds_adv).all())
+        acc = np.sum(preds_adv == np.argmax(self.y_test_iris, axis=1)) / self.y_test_iris.shape[0]
+        logger.info("Accuracy on Iris with PGD adversarial examples: %.2f%%", (acc * 100))
+
+        # Test targeted attack
+        targets = random_targets(self.y_test_iris, nb_classes=3)
+        attack = ProjectedGradientDescent(classifier, targeted=True, eps=1.0, eps_step=0.1, max_iter=5)
+        x_test_adv = attack.generate(self.x_test_iris, **{"y": targets})
+        self.assertFalse((self.x_test_iris == x_test_adv).all())
+        self.assertTrue((x_test_adv <= 1).all())
+        self.assertTrue((x_test_adv >= 0).all())
+
+        preds_adv = np.argmax(classifier.predict(x_test_adv), axis=1)
+        self.assertTrue((np.argmax(targets, axis=1) == preds_adv).any())
+        acc = np.sum(preds_adv == np.argmax(targets, axis=1)) / self.y_test_iris.shape[0]
+        logger.info("Success rate of targeted PGD on Iris: %.2f%%", (acc * 100))
+
+    def test_4_pytorch_iris_pt(self):
+        classifier = get_tabular_classifier_pt()
+
+        # Test untargeted attack
+        attack = ProjectedGradientDescent(classifier, eps=1.0, eps_step=0.1, max_iter=5)
+        x_test_adv = attack.generate(self.x_test_iris)
+        self.assertFalse((self.x_test_iris == x_test_adv).all())
+        self.assertTrue((x_test_adv <= 1).all())
+        self.assertTrue((x_test_adv >= 0).all())
+
+        preds_adv = np.argmax(classifier.predict(x_test_adv), axis=1)
+        self.assertFalse((np.argmax(self.y_test_iris, axis=1) == preds_adv).all())
+        acc = np.sum(preds_adv == np.argmax(self.y_test_iris, axis=1)) / self.y_test_iris.shape[0]
+        logger.info("Accuracy on Iris with PGD adversarial examples: %.2f%%", (acc * 100))
+
+        # Test targeted attack
+        targets = random_targets(self.y_test_iris, nb_classes=3)
+        attack = ProjectedGradientDescent(classifier, targeted=True, eps=1.0, eps_step=0.1, max_iter=5)
+        x_test_adv = attack.generate(self.x_test_iris, **{"y": targets})
+        self.assertFalse((self.x_test_iris == x_test_adv).all())
+        self.assertTrue((x_test_adv <= 1).all())
+        self.assertTrue((x_test_adv >= 0).all())
+
+        preds_adv = np.argmax(classifier.predict(x_test_adv), axis=1)
+        self.assertTrue((np.argmax(targets, axis=1) == preds_adv).any())
+        acc = np.sum(preds_adv == np.argmax(targets, axis=1)) / self.y_test_iris.shape[0]
+        logger.info("Success rate of targeted PGD on Iris: %.2f%%", (acc * 100))
+
+    def test_7_scikitlearn(self):
+        from sklearn.linear_model import LogisticRegression
+        from sklearn.svm import SVC, LinearSVC
+
+        from art.estimators.classification.scikitlearn import SklearnClassifier
+
+        scikitlearn_test_cases = [
+            LogisticRegression(solver="lbfgs", multi_class="auto"),
+            SVC(gamma="auto"),
+            LinearSVC(),
+        ]
+
+        x_test_original = self.x_test_iris.copy()
+
+        for model in scikitlearn_test_cases:
+            classifier = SklearnClassifier(model=model, clip_values=(0, 1))
+            classifier.fit(x=self.x_test_iris, y=self.y_test_iris)
+
+            # Test untargeted attack
+            attack = ProjectedGradientDescent(classifier, eps=1.0, eps_step=0.1, max_iter=5)
+            x_test_adv = attack.generate(self.x_test_iris)
+            self.assertFalse((self.x_test_iris == x_test_adv).all())
+            self.assertTrue((x_test_adv <= 1).all())
+            self.assertTrue((x_test_adv >= 0).all())
+
+            preds_adv = np.argmax(classifier.predict(x_test_adv), axis=1)
+            self.assertFalse((np.argmax(self.y_test_iris, axis=1) == preds_adv).all())
+            acc = np.sum(preds_adv == np.argmax(self.y_test_iris, axis=1)) / self.y_test_iris.shape[0]
+            logger.info(
+                "Accuracy of " + classifier.__class__.__name__ + " on Iris with PGD adversarial examples: " "%.2f%%",
+                (acc * 100),
+            )
+
+            # Test targeted attack
+            targets = random_targets(self.y_test_iris, nb_classes=3)
+            attack = ProjectedGradientDescent(classifier, targeted=True, eps=1.0, eps_step=0.1, max_iter=5)
+            x_test_adv = attack.generate(self.x_test_iris, **{"y": targets})
+            self.assertFalse((self.x_test_iris == x_test_adv).all())
+            self.assertTrue((x_test_adv <= 1).all())
+            self.assertTrue((x_test_adv >= 0).all())
+
+            preds_adv = np.argmax(classifier.predict(x_test_adv), axis=1)
+            self.assertTrue((np.argmax(targets, axis=1) == preds_adv).any())
+            acc = np.sum(preds_adv == np.argmax(targets, axis=1)) / self.y_test_iris.shape[0]
+            logger.info(
+                "Success rate of " + classifier.__class__.__name__ + " on targeted PGD on Iris: %.2f%%", (acc * 100)
+            )
+
+            # Check that x_test has not been modified by attack and classifier
+            self.assertAlmostEqual(float(np.max(np.abs(x_test_original - self.x_test_iris))), 0.0, delta=0.00001)
+
+    @unittest.skipIf(tf.__version__[0] != "2", "")
+    def test_4_framework_tensorflow_v2_mnist(self):
+        classifier, _ = get_image_classifier_tf()
+        self._test_framework_vs_numpy(classifier)
+
+    def test_6_framework_pytorch_mnist(self):
+        self.x_train_mnist = np.swapaxes(self.x_train_mnist, 1, 3).astype(np.float32)
+        self.x_test_mnist = np.swapaxes(self.x_test_mnist, 1, 3).astype(np.float32)
+
+        classifier = get_image_classifier_pt()
+        self._test_framework_vs_numpy(classifier)
+
+        self.x_train_mnist = np.swapaxes(self.x_train_mnist, 1, 3).astype(np.float32)
+        self.x_test_mnist = np.swapaxes(self.x_test_mnist, 1, 3).astype(np.float32)
+
+    def _test_framework_vs_numpy(self, classifier):
+        # Test PGD with np.inf norm
+        attack_np = ProjectedGradientDescentNumpy(
+            classifier,
+            eps=1.0,
+            eps_step=0.1,
+            max_iter=5,
+            norm=np.inf,
+            targeted=False,
+            num_random_init=0,
+            batch_size=3,
+            random_eps=False,
+        )
+        x_train_adv_np = attack_np.generate(self.x_train_mnist)
+        x_test_adv_np = attack_np.generate(self.x_test_mnist)
+
+        attack_fw = ProjectedGradientDescent(
+            classifier,
+            eps=1.0,
+            eps_step=0.1,
+            max_iter=5,
+            norm=np.inf,
+            targeted=False,
+            num_random_init=0,
+            batch_size=3,
+            random_eps=False,
+        )
+        x_train_adv_fw = attack_fw.generate(self.x_train_mnist)
+        x_test_adv_fw = attack_fw.generate(self.x_test_mnist)
+
+        # Test
+        self.assertAlmostEqual(
+            np.mean(x_train_adv_np - self.x_train_mnist), np.mean(x_train_adv_fw - self.x_train_mnist), places=6
+        )
+        self.assertAlmostEqual(
+            np.mean(x_test_adv_np - self.x_test_mnist), np.mean(x_test_adv_fw - self.x_test_mnist), places=6
+        )
+
+        # Test PGD with L1 norm
+        attack_np = ProjectedGradientDescentNumpy(
+            classifier,
+            eps=1.0,
+            eps_step=0.1,
+            max_iter=5,
+            norm=1,
+            targeted=False,
+            num_random_init=0,
+            batch_size=3,
+            random_eps=False,
+        )
+        x_train_adv_np = attack_np.generate(self.x_train_mnist)
+        x_test_adv_np = attack_np.generate(self.x_test_mnist)
+
+        attack_fw = ProjectedGradientDescent(
+            classifier,
+            eps=1.0,
+            eps_step=0.1,
+            max_iter=5,
+            norm=1,
+            targeted=False,
+            num_random_init=0,
+            batch_size=3,
+            random_eps=False,
+        )
+        x_train_adv_fw = attack_fw.generate(self.x_train_mnist)
+        x_test_adv_fw = attack_fw.generate(self.x_test_mnist)
+
+        # Test
+        self.assertAlmostEqual(
+            np.mean(x_train_adv_np - self.x_train_mnist), np.mean(x_train_adv_fw - self.x_train_mnist), places=6
+        )
+        self.assertAlmostEqual(
+            np.mean(x_test_adv_np - self.x_test_mnist), np.mean(x_test_adv_fw - self.x_test_mnist), places=6
+        )
+
+        # Test PGD with L2 norm
+        attack_np = ProjectedGradientDescentNumpy(
+            classifier,
+            eps=1.0,
+            eps_step=0.1,
+            max_iter=5,
+            norm=2,
+            targeted=False,
+            num_random_init=0,
+            batch_size=3,
+            random_eps=False,
+        )
+        x_train_adv_np = attack_np.generate(self.x_train_mnist)
+        x_test_adv_np = attack_np.generate(self.x_test_mnist)
+
+        attack_fw = ProjectedGradientDescent(
+            classifier,
+            eps=1.0,
+            eps_step=0.1,
+            max_iter=5,
+            norm=2,
+            targeted=False,
+            num_random_init=0,
+            batch_size=3,
+            random_eps=False,
+        )
+        x_train_adv_fw = attack_fw.generate(self.x_train_mnist)
+        x_test_adv_fw = attack_fw.generate(self.x_test_mnist)
+
+        # Test
+        self.assertAlmostEqual(
+            np.mean(x_train_adv_np - self.x_train_mnist), np.mean(x_train_adv_fw - self.x_train_mnist), places=6
+        )
+        self.assertAlmostEqual(
+            np.mean(x_test_adv_np - self.x_test_mnist), np.mean(x_test_adv_fw - self.x_test_mnist), places=6
+        )
+
+        # Test PGD with True targeted
+        attack_np = ProjectedGradientDescentNumpy(
+            classifier,
+            eps=1.0,
+            eps_step=0.1,
+            max_iter=5,
+            norm=np.inf,
+            targeted=True,
+            num_random_init=0,
+            batch_size=3,
+            random_eps=False,
+        )
+        x_train_adv_np = attack_np.generate(self.x_train_mnist, self.y_train_mnist)
+        x_test_adv_np = attack_np.generate(self.x_test_mnist, self.y_test_mnist)
+
+        attack_fw = ProjectedGradientDescent(
+            classifier,
+            eps=1.0,
+            eps_step=0.1,
+            max_iter=5,
+            norm=np.inf,
+            targeted=True,
+            num_random_init=0,
+            batch_size=3,
+            random_eps=False,
+        )
+        x_train_adv_fw = attack_fw.generate(self.x_train_mnist, self.y_train_mnist)
+        x_test_adv_fw = attack_fw.generate(self.x_test_mnist, self.y_test_mnist)
+
+        # Test
+        self.assertAlmostEqual(
+            np.mean(x_train_adv_np - self.x_train_mnist), np.mean(x_train_adv_fw - self.x_train_mnist), places=6
+        )
+        self.assertAlmostEqual(
+            np.mean(x_test_adv_np - self.x_test_mnist), np.mean(x_test_adv_fw - self.x_test_mnist), places=6
+        )
+
+        # Test PGD with num_random_init=2
+        master_seed(1234)
+        attack_np = ProjectedGradientDescentNumpy(
+            classifier,
+            eps=1.0,
+            eps_step=0.1,
+            max_iter=5,
+            norm=np.inf,
+            targeted=False,
+            num_random_init=2,
+            batch_size=3,
+            random_eps=False,
+        )
+        x_train_adv_np = attack_np.generate(self.x_train_mnist)
+        x_test_adv_np = attack_np.generate(self.x_test_mnist)
+
+        master_seed(1234)
+        attack_fw = ProjectedGradientDescent(
+            classifier,
+            eps=1.0,
+            eps_step=0.1,
+            max_iter=5,
+            norm=np.inf,
+            targeted=False,
+            num_random_init=2,
+            batch_size=3,
+            random_eps=False,
+        )
+        x_train_adv_fw = attack_fw.generate(self.x_train_mnist)
+        x_test_adv_fw = attack_fw.generate(self.x_test_mnist)
+
+        # Test
+        self.assertAlmostEqual(
+            np.mean(x_train_adv_np - self.x_train_mnist), np.mean(x_train_adv_fw - self.x_train_mnist), places=6
+        )
+        self.assertAlmostEqual(
+            np.mean(x_test_adv_np - self.x_test_mnist), np.mean(x_test_adv_fw - self.x_test_mnist), places=6
+        )
+
+        # Test PGD with random_eps=True
+        master_seed(1234)
+        attack_np = ProjectedGradientDescentNumpy(
+            classifier,
+            eps=1.0,
+            eps_step=0.1,
+            max_iter=5,
+            norm=np.inf,
+            targeted=False,
+            num_random_init=0,
+            batch_size=3,
+            random_eps=True,
+        )
+        x_train_adv_np = attack_np.generate(self.x_train_mnist)
+        x_test_adv_np = attack_np.generate(self.x_test_mnist)
+
+        master_seed(1234)
+        attack_fw = ProjectedGradientDescent(
+            classifier,
+            eps=1.0,
+            eps_step=0.1,
+            max_iter=5,
+            norm=np.inf,
+            targeted=False,
+            num_random_init=0,
+            batch_size=3,
+            random_eps=True,
+        )
+        x_train_adv_fw = attack_fw.generate(self.x_train_mnist)
+        x_test_adv_fw = attack_fw.generate(self.x_test_mnist)
+
+        # Test
+        self.assertAlmostEqual(
+            np.mean(x_train_adv_np - self.x_train_mnist), np.mean(x_train_adv_fw - self.x_train_mnist), places=6
+        )
+        self.assertAlmostEqual(
+            np.mean(x_test_adv_np - self.x_test_mnist), np.mean(x_test_adv_fw - self.x_test_mnist), places=6
+        )
+
+        # Test the masking 1
+        master_seed(1234)
+        attack_np = ProjectedGradientDescentNumpy(
+            classifier,
+            eps=1.0,
+            eps_step=0.1,
+            max_iter=5,
+            norm=np.inf,
+            targeted=False,
+            num_random_init=1,
+            batch_size=3,
+            random_eps=True,
+        )
+
+        mask = np.random.binomial(n=1, p=0.5, size=np.prod(self.x_train_mnist.shape))
+        mask = mask.reshape(self.x_train_mnist.shape).astype(np.float32)
+        x_train_adv_np = attack_np.generate(self.x_train_mnist, mask=mask)
+
+        mask = np.random.binomial(n=1, p=0.5, size=np.prod(self.x_test_mnist.shape))
+        mask = mask.reshape(self.x_test_mnist.shape).astype(np.float32)
+        x_test_adv_np = attack_np.generate(self.x_test_mnist, mask=mask)
+
+        master_seed(1234)
+        attack_fw = ProjectedGradientDescent(
+            classifier,
+            eps=1.0,
+            eps_step=0.1,
+            max_iter=5,
+            norm=np.inf,
+            targeted=False,
+            num_random_init=1,
+            batch_size=3,
+            random_eps=True,
+        )
+
+        mask = np.random.binomial(n=1, p=0.5, size=np.prod(self.x_train_mnist.shape))
+        mask = mask.reshape(self.x_train_mnist.shape).astype(np.float32)
+        x_train_adv_fw = attack_fw.generate(self.x_train_mnist, mask=mask)
+
+        mask = np.random.binomial(n=1, p=0.5, size=np.prod(self.x_test_mnist.shape))
+        mask = mask.reshape(self.x_test_mnist.shape).astype(np.float32)
+        x_test_adv_fw = attack_fw.generate(self.x_test_mnist, mask=mask)
+
+        # Test
+        self.assertAlmostEqual(
+            np.mean(x_train_adv_np - self.x_train_mnist), np.mean(x_train_adv_fw - self.x_train_mnist), places=6
+        )
+        self.assertAlmostEqual(
+            np.mean(x_test_adv_np - self.x_test_mnist), np.mean(x_test_adv_fw - self.x_test_mnist), places=6
+        )
+
+        # Test the masking 2
+        master_seed(1234)
+        attack_np = ProjectedGradientDescentNumpy(
+            classifier,
+            eps=1.0,
+            eps_step=0.1,
+            max_iter=5,
+            norm=np.inf,
+            targeted=False,
+            num_random_init=1,
+            batch_size=3,
+            random_eps=True,
+        )
+
+        mask = np.random.binomial(n=1, p=0.5, size=np.prod(self.x_train_mnist.shape[1:]))
+        mask = mask.reshape(self.x_train_mnist.shape[1:]).astype(np.float32)
+        x_train_adv_np = attack_np.generate(self.x_train_mnist, mask=mask)
+
+        mask = np.random.binomial(n=1, p=0.5, size=np.prod(self.x_test_mnist.shape[1:]))
+        mask = mask.reshape(self.x_test_mnist.shape[1:]).astype(np.float32)
+        x_test_adv_np = attack_np.generate(self.x_test_mnist, mask=mask)
+
+        master_seed(1234)
+        attack_fw = ProjectedGradientDescent(
+            classifier,
+            eps=1.0,
+            eps_step=0.1,
+            max_iter=5,
+            norm=np.inf,
+            targeted=False,
+            num_random_init=1,
+            batch_size=3,
+            random_eps=True,
+        )
+
+        mask = np.random.binomial(n=1, p=0.5, size=np.prod(self.x_train_mnist.shape[1:]))
+        mask = mask.reshape(self.x_train_mnist.shape[1:]).astype(np.float32)
+        x_train_adv_fw = attack_fw.generate(self.x_train_mnist, mask=mask)
+
+        mask = np.random.binomial(n=1, p=0.5, size=np.prod(self.x_test_mnist.shape[1:]))
+        mask = mask.reshape(self.x_test_mnist.shape[1:]).astype(np.float32)
+        x_test_adv_fw = attack_fw.generate(self.x_test_mnist, mask=mask)
+
+        # Test
+        self.assertAlmostEqual(
+            np.mean(x_train_adv_np - self.x_train_mnist), np.mean(x_train_adv_fw - self.x_train_mnist), places=6
+        )
+        self.assertAlmostEqual(
+            np.mean(x_test_adv_np - self.x_test_mnist), np.mean(x_test_adv_fw - self.x_test_mnist), places=6
+        )
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/adversarial-robustness-toolbox/tests/attacks/test_saliency_map.py b/adversarial-robustness-toolbox/tests/attacks/test_saliency_map.py
new file mode 100644
index 0000000..5880436
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/attacks/test_saliency_map.py
@@ -0,0 +1,298 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import unittest
+
+import numpy as np
+
+from art.attacks.evasion.saliency_map import SaliencyMapMethod
+from art.estimators.classification.classifier import ClassGradientsMixin
+from art.estimators.classification.keras import KerasClassifier
+from art.estimators.estimator import BaseEstimator
+from art.utils import get_labels_np_array, to_categorical
+from tests.attacks.utils import backend_test_classifier_type_check_fail
+from tests.utils import (
+    TestBase,
+    get_image_classifier_kr,
+    get_image_classifier_pt,
+    get_image_classifier_tf,
+    get_tabular_classifier_kr,
+    get_tabular_classifier_pt,
+    get_tabular_classifier_tf,
+)
+
+logger = logging.getLogger(__name__)
+
+
+class TestSaliencyMap(TestBase):
+    @classmethod
+    def setUpClass(cls):
+        super().setUpClass()
+
+        cls.n_train = 100
+        cls.n_test = 2
+        cls.x_train_mnist = cls.x_train_mnist[0 : cls.n_train]
+        cls.y_train_mnist = cls.y_train_mnist[0 : cls.n_train]
+        cls.x_test_mnist = cls.x_test_mnist[0 : cls.n_test]
+        cls.y_test_mnist = cls.y_test_mnist[0 : cls.n_test]
+
+    def test_9_keras_mnist(self):
+        x_test_original = self.x_test_mnist.copy()
+
+        # Keras classifier
+        classifier = get_image_classifier_kr()
+
+        scores = classifier._model.evaluate(self.x_train_mnist, self.y_train_mnist)
+        logger.info("[Keras, MNIST] Accuracy on training set: %.2f%%", (scores[1] * 100))
+
+        scores = classifier._model.evaluate(self.x_test_mnist, self.y_test_mnist)
+        logger.info("[Keras, MNIST] Accuracy on test set: %.2f%%", (scores[1] * 100))
+
+        # targeted
+
+        # Generate random target classes
+        nb_classes = np.unique(np.argmax(self.y_test_mnist, axis=1)).shape[0]
+        targets = np.random.randint(nb_classes, size=self.n_test)
+        while (targets == np.argmax(self.y_test_mnist, axis=1)).any():
+            targets = np.random.randint(nb_classes, size=self.n_test)
+
+        # Perform attack
+        df = SaliencyMapMethod(classifier, theta=1, batch_size=100)
+        x_test_adv = df.generate(self.x_test_mnist, y=to_categorical(targets, nb_classes))
+
+        self.assertFalse((self.x_test_mnist == x_test_adv).all())
+        self.assertFalse((0.0 == x_test_adv).all())
+
+        y_pred = get_labels_np_array(classifier.predict(x_test_adv))
+        self.assertFalse((self.y_test_mnist == y_pred).all())
+
+        accuracy = np.sum(np.argmax(y_pred, axis=1) == np.argmax(self.y_test_mnist, axis=1)) / self.n_test
+        logger.info("Accuracy on adversarial examples: %.2f%%", (accuracy * 100))
+
+        # untargeted
+        df = SaliencyMapMethod(classifier, theta=1, batch_size=100)
+        x_test_adv = df.generate(self.x_test_mnist)
+
+        self.assertFalse((self.x_test_mnist == x_test_adv).all())
+        self.assertFalse((0.0 == x_test_adv).all())
+
+        y_pred = get_labels_np_array(classifier.predict(x_test_adv))
+        self.assertFalse((self.y_test_mnist == y_pred).all())
+
+        accuracy = np.sum(np.argmax(y_pred, axis=1) == np.argmax(self.y_test_mnist, axis=1)) / self.n_test
+        logger.info("Accuracy on adversarial examples: %.2f%%", (accuracy * 100))
+
+        # Check that x_test has not been modified by attack and classifier
+        self.assertAlmostEqual(float(np.max(np.abs(x_test_original - self.x_test_mnist))), 0.0, delta=0.00001)
+
+    def test_3_tensorflow_mnist(self):
+        x_test_original = self.x_test_mnist.copy()
+
+        # Create basic CNN on MNIST using TensorFlow
+        classifier, sess = get_image_classifier_tf()
+
+        scores = get_labels_np_array(classifier.predict(self.x_train_mnist))
+        accuracy = np.sum(np.argmax(scores, axis=1) == np.argmax(self.y_train_mnist, axis=1)) / self.n_train
+        logger.info("[TF, MNIST] Accuracy on training set: %.2f%%", (accuracy * 100))
+
+        scores = get_labels_np_array(classifier.predict(self.x_test_mnist))
+        accuracy = np.sum(np.argmax(scores, axis=1) == np.argmax(self.y_test_mnist, axis=1)) / self.n_train
+        logger.info("[TF, MNIST] Accuracy on test set: %.2f%%", (accuracy * 100))
+
+        # targeted
+        # Generate random target classes
+        nb_classes = np.unique(np.argmax(self.y_test_mnist, axis=1)).shape[0]
+        targets = np.random.randint(nb_classes, size=self.n_test)
+        while (targets == np.argmax(self.y_test_mnist, axis=1)).any():
+            targets = np.random.randint(nb_classes, size=self.n_test)
+
+        # Perform attack
+        df = SaliencyMapMethod(classifier, theta=1, batch_size=100)
+        x_test_adv = df.generate(self.x_test_mnist, y=to_categorical(targets, nb_classes))
+
+        self.assertFalse((self.x_test_mnist == x_test_adv).all())
+        self.assertFalse((0.0 == x_test_adv).all())
+
+        y_pred = get_labels_np_array(classifier.predict(x_test_adv))
+        self.assertFalse((self.y_test_mnist == y_pred).all())
+
+        accuracy = np.sum(np.argmax(y_pred, axis=1) == np.argmax(self.y_test_mnist, axis=1)) / self.n_test
+        logger.info("Accuracy on adversarial examples: %.2f%%", (accuracy * 100))
+
+        # untargeted
+        df = SaliencyMapMethod(classifier, theta=1, batch_size=100)
+        x_test_adv = df.generate(self.x_test_mnist)
+
+        self.assertFalse((self.x_test_mnist == x_test_adv).all())
+        self.assertFalse((0.0 == x_test_adv).all())
+
+        y_pred = get_labels_np_array(classifier.predict(x_test_adv))
+        self.assertFalse((self.y_test_mnist == y_pred).all())
+
+        accuracy = np.sum(np.argmax(y_pred, axis=1) == np.argmax(self.y_test_mnist, axis=1)) / self.n_test
+        logger.info("Accuracy on adversarial examples: %.2f%%", (accuracy * 100))
+
+        # Check that x_test has not been modified by attack and classifier
+        self.assertAlmostEqual(float(np.max(np.abs(x_test_original - self.x_test_mnist))), 0.0, delta=0.00001)
+
+    def test_5_pytorch_mnist(self):
+        x_train_mnist = np.swapaxes(self.x_train_mnist, 1, 3).astype(np.float32)
+        x_test_mnist = np.swapaxes(self.x_test_mnist, 1, 3).astype(np.float32)
+        x_test_original = x_test_mnist.copy()
+
+        # Create basic PyTorch model
+        classifier = get_image_classifier_pt()
+
+        scores = get_labels_np_array(classifier.predict(x_train_mnist))
+        accuracy = np.sum(np.argmax(scores, axis=1) == np.argmax(self.y_train_mnist, axis=1)) / self.n_train
+        logger.info("[PyTorch, MNIST] Accuracy on training set: %.2f%%", (accuracy * 100))
+
+        scores = get_labels_np_array(classifier.predict(x_test_mnist))
+        accuracy = np.sum(np.argmax(scores, axis=1) == np.argmax(self.y_test_mnist, axis=1)) / self.n_test
+        logger.info("\n[PyTorch, MNIST] Accuracy on test set: %.2f%%", (accuracy * 100))
+
+        # targeted
+        # Generate random target classes
+        nb_classes = np.unique(np.argmax(self.y_test_mnist, axis=1)).shape[0]
+        targets = np.random.randint(nb_classes, size=self.n_test)
+        while (targets == np.argmax(self.y_test_mnist, axis=1)).any():
+            targets = np.random.randint(nb_classes, size=self.n_test)
+
+        # Perform attack
+        df = SaliencyMapMethod(classifier, theta=1, batch_size=100)
+        x_test_mnist_adv = df.generate(x_test_mnist, y=to_categorical(targets, nb_classes))
+
+        self.assertFalse((x_test_mnist == x_test_mnist_adv).all())
+        self.assertFalse((0.0 == x_test_mnist_adv).all())
+
+        y_pred = get_labels_np_array(classifier.predict(x_test_mnist_adv))
+        self.assertFalse((self.y_test_mnist == y_pred).all())
+
+        accuracy = np.sum(np.argmax(y_pred, axis=1) == np.argmax(self.y_test_mnist, axis=1)) / self.n_test
+        logger.info("Accuracy on adversarial examples: %.2f%%", (accuracy * 100))
+
+        # untargeted
+        df = SaliencyMapMethod(classifier, theta=1, batch_size=100)
+        x_test_mnist_adv = df.generate(x_test_mnist)
+
+        self.assertFalse((x_test_mnist == x_test_mnist_adv).all())
+        self.assertFalse((0.0 == x_test_mnist_adv).all())
+
+        y_pred = get_labels_np_array(classifier.predict(x_test_mnist_adv))
+        self.assertFalse((self.y_test_mnist == y_pred).all())
+
+        accuracy = np.sum(np.argmax(y_pred, axis=1) == np.argmax(self.y_test_mnist, axis=1)) / self.n_test
+        logger.info("Accuracy on adversarial examples: %.2f%%", (accuracy * 100))
+
+        # Check that x_test has not been modified by attack and classifier
+        self.assertAlmostEqual(float(np.max(np.abs(x_test_original - x_test_mnist))), 0.0, delta=0.00001)
+
+    def test_7_keras_iris_vector_clipped(self):
+        classifier = get_tabular_classifier_kr()
+
+        attack = SaliencyMapMethod(classifier, theta=1)
+        x_test_iris_adv = attack.generate(self.x_test_iris)
+        self.assertFalse((self.x_test_iris == x_test_iris_adv).all())
+        self.assertTrue((x_test_iris_adv <= 1).all())
+        self.assertTrue((x_test_iris_adv >= 0).all())
+
+        preds_adv = np.argmax(classifier.predict(x_test_iris_adv), axis=1)
+        self.assertFalse((np.argmax(self.y_test_iris, axis=1) == preds_adv).all())
+        accuracy = np.sum(preds_adv == np.argmax(self.y_test_iris, axis=1)) / self.y_test_iris.shape[0]
+        logger.info("Accuracy on Iris with JSMA adversarial examples: %.2f%%", (accuracy * 100))
+
+    def test_8_keras_iris_vector_unbounded(self):
+        classifier = get_tabular_classifier_kr()
+
+        # Recreate a classifier without clip values
+        classifier = KerasClassifier(model=classifier._model, use_logits=False, channels_first=True)
+        attack = SaliencyMapMethod(classifier, theta=1)
+        x_test_iris_adv = attack.generate(self.x_test_iris)
+        self.assertFalse((self.x_test_iris == x_test_iris_adv).all())
+
+    def test_2_tensorflow_iris_vector(self):
+        classifier, _ = get_tabular_classifier_tf()
+
+        attack = SaliencyMapMethod(classifier, theta=1)
+        x_test_iris_adv = attack.generate(self.x_test_iris)
+        self.assertFalse((self.x_test_iris == x_test_iris_adv).all())
+        self.assertTrue((x_test_iris_adv <= 1).all())
+        self.assertTrue((x_test_iris_adv >= 0).all())
+
+        preds_adv = np.argmax(classifier.predict(x_test_iris_adv), axis=1)
+        self.assertFalse((np.argmax(self.y_test_iris, axis=1) == preds_adv).all())
+        accuracy = np.sum(preds_adv == np.argmax(self.y_test_iris, axis=1)) / self.y_test_iris.shape[0]
+        logger.info("Accuracy on Iris with JSMA adversarial examples: %.2f%%", (accuracy * 100))
+
+    def test_4_pytorch_iris_vector(self):
+        classifier = get_tabular_classifier_pt()
+
+        attack = SaliencyMapMethod(classifier, theta=1)
+        x_test_iris_adv = attack.generate(self.x_test_iris)
+        self.assertFalse((self.x_test_iris == x_test_iris_adv).all())
+        self.assertTrue((x_test_iris_adv <= 1).all())
+        self.assertTrue((x_test_iris_adv >= 0).all())
+
+        preds_adv = np.argmax(classifier.predict(x_test_iris_adv), axis=1)
+        self.assertFalse((np.argmax(self.y_test_iris, axis=1) == preds_adv).all())
+        accuracy = np.sum(preds_adv == np.argmax(self.y_test_iris, axis=1)) / self.y_test_iris.shape[0]
+        logger.info("Accuracy on Iris with JSMA adversarial examples: %.2f%%", (accuracy * 100))
+
+    def test_6_scikitlearn(self):
+        from sklearn.linear_model import LogisticRegression
+        from sklearn.svm import SVC, LinearSVC
+
+        from art.estimators.classification.scikitlearn import SklearnClassifier
+
+        scikitlearn_test_cases = [
+            LogisticRegression(solver="lbfgs", multi_class="auto"),
+            SVC(gamma="auto"),
+            LinearSVC(),
+        ]
+
+        x_test_original = self.x_test_iris.copy()
+
+        for model in scikitlearn_test_cases:
+            classifier = SklearnClassifier(model=model, clip_values=(0, 1))
+            classifier.fit(x=self.x_test_iris, y=self.y_test_iris)
+
+            attack = SaliencyMapMethod(classifier, theta=1, batch_size=128)
+            x_test_iris_adv = attack.generate(self.x_test_iris)
+            self.assertFalse((self.x_test_iris == x_test_iris_adv).all())
+            self.assertTrue((x_test_iris_adv <= 1).all())
+            self.assertTrue((x_test_iris_adv >= 0).all())
+
+            preds_adv = np.argmax(classifier.predict(x_test_iris_adv), axis=1)
+            self.assertFalse((np.argmax(self.y_test_iris, axis=1) == preds_adv).all())
+            accuracy = np.sum(preds_adv == np.argmax(self.y_test_iris, axis=1)) / self.y_test_iris.shape[0]
+            logger.info(
+                "Accuracy of " + classifier.__class__.__name__ + " on Iris with JSMA adversarial examples: " "%.2f%%",
+                (accuracy * 100),
+            )
+
+            # Check that x_test has not been modified by attack and classifier
+            self.assertAlmostEqual(float(np.max(np.abs(x_test_original - self.x_test_iris))), 0.0, delta=0.00001)
+
+    def test_1_classifier_type_check_fail(self):
+        backend_test_classifier_type_check_fail(SaliencyMapMethod, [BaseEstimator, ClassGradientsMixin])
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/adversarial-robustness-toolbox/tests/attacks/test_shapeshifter.py b/adversarial-robustness-toolbox/tests/attacks/test_shapeshifter.py
new file mode 100644
index 0000000..1b48706
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/attacks/test_shapeshifter.py
@@ -0,0 +1,239 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import unittest
+import importlib
+
+import tensorflow as tf
+import numpy as np
+
+from tests.utils import TestBase, master_seed
+
+object_detection_spec = importlib.util.find_spec("object_detection")
+object_detection_found = object_detection_spec is not None
+
+logger = logging.getLogger(__name__)
+
+
+@unittest.skipIf(
+    not object_detection_found,
+    reason="Skip unittests if object detection module is not found because of pre-trained model.",
+)
+@unittest.skipIf(
+    tf.__version__[0] == "2" or (tf.__version__[0] == "1" and tf.__version__.split(".")[1] != "15"),
+    reason="Skip unittests if not TensorFlow v1.15 because of pre-trained model.",
+)
+class TestShapeShifter(TestBase):
+    @classmethod
+    def setUpClass(cls):
+        master_seed(seed=1234, set_tensorflow=True)
+        super().setUpClass()
+
+        cls.n_test = 10
+        cls.x_test_mnist = cls.x_test_mnist[0 : cls.n_test]
+        cls.y_test_mnist = cls.y_test_mnist[0 : cls.n_test]
+
+    def test_image_as_input(self):
+        self._test_image_as_input(True)
+        self._test_image_as_input(False)
+
+    def _test_image_as_input(self, sign_gradients):
+        # We must start a new graph
+        tf.reset_default_graph()
+
+        # Only import if object detection module is available
+        from art.estimators.object_detection.tensorflow_faster_rcnn import TensorFlowFasterRCNN
+        from art.attacks.evasion.shapeshifter import ShapeShifter
+
+        # Define object detector
+        images = tf.Variable(initial_value=np.zeros([1, 28, 28, 1]), dtype=tf.float32)
+        obj_dec = TensorFlowFasterRCNN(images=images)
+
+        # Create labels
+        result = obj_dec.predict(self.x_test_mnist[:1].astype(np.float32))
+
+        groundtruth_boxes_list = [result["detection_boxes"][i] for i in range(1)]
+        groundtruth_classes_list = [result["detection_classes"][i] for i in range(1)]
+        groundtruth_weights_list = [np.ones_like(r) for r in groundtruth_classes_list]
+
+        y = {
+            "groundtruth_boxes_list": groundtruth_boxes_list,
+            "groundtruth_classes_list": groundtruth_classes_list,
+            "groundtruth_weights_list": groundtruth_weights_list,
+        }
+
+        # Define attack
+        attack = ShapeShifter(
+            estimator=obj_dec,
+            random_transform=lambda x: x + 1e-10,
+            box_classifier_weight=1.0,
+            box_localizer_weight=1.0,
+            rpn_classifier_weight=1.0,
+            rpn_localizer_weight=1.0,
+            box_iou_threshold=0.3,
+            box_victim_weight=1.0,
+            box_target_weight=1.0,
+            box_victim_cw_weight=1.0,
+            box_victim_cw_confidence=1.0,
+            box_target_cw_weight=1.0,
+            box_target_cw_confidence=1.0,
+            rpn_iou_threshold=0.3,
+            rpn_background_weight=1.0,
+            rpn_foreground_weight=1.0,
+            rpn_cw_weight=1.0,
+            rpn_cw_confidence=1.0,
+            similarity_weight=1.0,
+            learning_rate=0.1,
+            optimizer="RMSPropOptimizer",
+            momentum=0.01,
+            decay=0.01,
+            sign_gradients=sign_gradients,
+            random_size=2,
+            max_iter=2,
+            texture_as_input=False,
+            use_spectral=False,
+            soft_clip=True,
+        )
+
+        # Targeted attack
+        adv_x = attack.generate(x=self.x_test_mnist[:1].astype(np.float32), label=y, target_class=2, victim_class=5)
+
+        self.assertTrue(adv_x.shape == (1, 28, 28, 1))
+        self.assertTrue((adv_x >= 0).all())
+        self.assertTrue((adv_x <= 1).all())
+
+        # Untargeted attack
+        adv_x = attack.generate(x=self.x_test_mnist[:1].astype(np.float32), label=y, victim_class=8)
+
+        self.assertTrue(adv_x.shape == (1, 28, 28, 1))
+        self.assertTrue((adv_x >= 0).all())
+        self.assertTrue((adv_x <= 1).all())
+
+    def test_texture_as_input(self):
+        self._test_texture_as_input(True, True, True)
+        self._test_texture_as_input(True, True, False)
+        self._test_texture_as_input(True, False, True)
+        self._test_texture_as_input(True, False, False)
+        self._test_texture_as_input(False, True, True)
+        self._test_texture_as_input(False, True, False)
+        self._test_texture_as_input(False, False, True)
+        self._test_texture_as_input(False, False, False)
+
+    def _test_texture_as_input(self, sign_gradients, use_spectral, soft_clip):
+        # We must start a new graph
+        tf.reset_default_graph()
+
+        # Only import if object detection module is available
+        from art.estimators.object_detection.tensorflow_faster_rcnn import TensorFlowFasterRCNN
+        from art.attacks.evasion.shapeshifter import ShapeShifter
+
+        # Define object detector
+        images = tf.Variable(initial_value=np.zeros([1, 28, 28, 1]), dtype=tf.float32)
+        obj_dec = TensorFlowFasterRCNN(images=images)
+
+        # Create labels
+        result = obj_dec.predict(self.x_test_mnist[:1].astype(np.float32))
+
+        groundtruth_boxes_list = [result["detection_boxes"][i] for i in range(1)]
+        groundtruth_classes_list = [result["detection_classes"][i] for i in range(1)]
+        groundtruth_weights_list = [np.ones_like(r) for r in groundtruth_classes_list]
+
+        y = {
+            "groundtruth_boxes_list": groundtruth_boxes_list,
+            "groundtruth_classes_list": groundtruth_classes_list,
+            "groundtruth_weights_list": groundtruth_weights_list,
+        }
+
+        # Define random transform
+        def random_transform(x):
+            background = np.random.rand(*x.shape)
+            image_frame = np.random.rand(*(list(x.shape[:-1]) + [4]))
+
+            y_ = y.copy()
+            y_["groundtruth_boxes_list"][0] = y_["groundtruth_boxes_list"][0] + np.random.rand()
+            y_["groundtruth_weights_list"][0] = y_["groundtruth_weights_list"][0] + np.random.rand()
+
+            return background, image_frame, y_
+
+        # Define attack
+        attack = ShapeShifter(
+            estimator=obj_dec,
+            random_transform=random_transform,
+            box_classifier_weight=1.0,
+            box_localizer_weight=1.0,
+            rpn_classifier_weight=1.0,
+            rpn_localizer_weight=1.0,
+            box_iou_threshold=0.3,
+            box_victim_weight=1.0,
+            box_target_weight=1.0,
+            box_victim_cw_weight=1.0,
+            box_victim_cw_confidence=1.0,
+            box_target_cw_weight=1.0,
+            box_target_cw_confidence=1.0,
+            rpn_iou_threshold=0.3,
+            rpn_background_weight=1.0,
+            rpn_foreground_weight=1.0,
+            rpn_cw_weight=1.0,
+            rpn_cw_confidence=1.0,
+            similarity_weight=1.0,
+            learning_rate=0.1,
+            optimizer="MomentumOptimizer",
+            momentum=0.01,
+            decay=0.01,
+            sign_gradients=sign_gradients,
+            random_size=2,
+            max_iter=2,
+            texture_as_input=True,
+            use_spectral=use_spectral,
+            soft_clip=soft_clip,
+        )
+
+        # Define rendering function
+        def rendering_function(background_phd, image_frame_phd, current_texture):
+            current_image = background_phd + current_texture
+            current_image = tf.clip_by_value(current_image, 0, 1)
+
+            return current_image
+
+        # Targeted attack
+        adv_x = attack.generate(
+            x=self.x_test_mnist[:1].astype(np.float32),
+            label=y,
+            target_class=2,
+            victim_class=5,
+            rendering_function=rendering_function,
+        )
+
+        self.assertTrue(adv_x.shape == (1, 28, 28, 1))
+
+        # Untargeted attack
+        adv_x = attack.generate(
+            x=self.x_test_mnist[:1].astype(np.float32),
+            label=y,
+            target_class=8,
+            victim_class=8,
+            rendering_function=rendering_function,
+        )
+
+        self.assertTrue(adv_x.shape == (1, 28, 28, 1))
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/adversarial-robustness-toolbox/tests/attacks/test_simba.py b/adversarial-robustness-toolbox/tests/attacks/test_simba.py
new file mode 100644
index 0000000..6318b0f
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/attacks/test_simba.py
@@ -0,0 +1,153 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import unittest
+
+import numpy as np
+
+from art.attacks.evasion.simba import SimBA
+from art.estimators.estimator import BaseEstimator, NeuralNetworkMixin
+from art.estimators.classification.classifier import ClassifierMixin
+from art.utils import get_labels_np_array
+
+from tests.utils import TestBase
+from tests.utils import get_image_classifier_tf, get_image_classifier_kr, get_image_classifier_pt
+from tests.attacks.utils import backend_test_classifier_type_check_fail
+
+logger = logging.getLogger(__name__)
+
+
+class TestSimBA(TestBase):
+    """
+    A unittest class for testing the Simple Black-box Adversarial Attacks (SimBA).
+
+    This module tests SimBA.
+    Note: SimBA runs only in Keras and TensorFlow (not in PyTorch)
+    This is due to the channel first format in PyTorch.
+
+    | Paper link: https://arxiv.org/abs/1905.07121
+    """
+
+    @classmethod
+    def setUpClass(cls):
+        super().setUpClass()
+
+        cls.n_test = 2
+        cls.x_test_mnist = cls.x_test_mnist[0 : cls.n_test]
+        cls.y_test_mnist = cls.y_test_mnist[0 : cls.n_test]
+
+    def test_5_keras_mnist(self):
+        """
+        Test with the KerasClassifier. (Untargeted Attack)
+        :return:
+        """
+        classifier = get_image_classifier_kr()
+        self._test_attack(classifier, self.x_test_mnist, self.y_test_mnist, False)
+
+    def test_2_tensorflow_mnist(self):
+        """
+        Test with the TensorFlowClassifier. (Untargeted Attack)
+        :return:
+        """
+        classifier, sess = get_image_classifier_tf()
+        self._test_attack(classifier, self.x_test_mnist, self.y_test_mnist, False)
+
+    def test_3_pytorch_mnist(self):
+        """
+        Test with the PyTorchClassifier. (Untargeted Attack)
+        :return:
+        """
+        x_test = np.reshape(self.x_test_mnist, (self.x_test_mnist.shape[0], 1, 28, 28)).astype(np.float32)
+        classifier = get_image_classifier_pt()
+        self._test_attack(classifier, x_test, self.y_test_mnist, False)
+
+    def test_6_keras_mnist_targeted(self):
+        """
+        Test with the KerasClassifier. (Targeted Attack)
+        :return:
+        """
+        classifier = get_image_classifier_kr()
+        self._test_attack(classifier, self.x_test_mnist, self.y_test_mnist, True)
+
+    def test_2_tensorflow_mnist_targeted(self):
+        """
+        Test with the TensorFlowClassifier. (Targeted Attack)
+        :return:
+        """
+        classifier, sess = get_image_classifier_tf()
+        self._test_attack(classifier, self.x_test_mnist, self.y_test_mnist, True)
+
+    # SimBA is not avaialbe for PyTorch
+    def test_4_pytorch_mnist_targeted(self):
+        """
+        Test with the PyTorchClassifier. (Targeted Attack)
+        :return:
+        """
+        x_test = np.reshape(self.x_test_mnist, (self.x_test_mnist.shape[0], 1, 28, 28)).astype(np.float32)
+        classifier = get_image_classifier_pt()
+        self._test_attack(classifier, x_test, self.y_test_mnist, True)
+
+    def _test_attack(self, classifier, x_test, y_test, targeted):
+        """
+        Test with SimBA
+        :return:
+        """
+        x_test_original = x_test.copy()
+
+        # set the targeted label
+        if targeted:
+            y_target = np.zeros(10)
+            y_target[8] = 1.0
+
+        df = SimBA(classifier, attack="dct", targeted=targeted)
+
+        x_i = x_test_original[0][None, ...]
+        if targeted:
+            x_test_adv = df.generate(x_i, y=y_target.reshape(1, 10))
+        else:
+            x_test_adv = df.generate(x_i)
+
+        for i in range(1, len(x_test_original)):
+            x_i = x_test_original[i][None, ...]
+            if targeted:
+                tmp_x_test_adv = df.generate(x_i, y=y_target.reshape(1, 10))
+                x_test_adv = np.concatenate([x_test_adv, tmp_x_test_adv])
+            else:
+                tmp_x_test_adv = df.generate(x_i)
+                x_test_adv = np.concatenate([x_test_adv, tmp_x_test_adv])
+
+        self.assertFalse((x_test == x_test_adv).all())
+        self.assertFalse((0.0 == x_test_adv).all())
+
+        y_pred = get_labels_np_array(classifier.predict(x_test_adv))
+        self.assertFalse((y_test == y_pred).all())
+
+        accuracy = np.sum(np.argmax(y_pred, axis=1) == np.argmax(self.y_test_mnist, axis=1)) / self.n_test
+        logger.info("Accuracy on adversarial examples: %.2f%%", (accuracy * 100))
+
+        # Check that x_test has not been modified by attack and classifier
+        self.assertAlmostEqual(float(np.max(np.abs(x_test_original - x_test))), 0.0, delta=0.00001)
+
+    def test_1_classifier_type_check_fail(self):
+        backend_test_classifier_type_check_fail(SimBA, (BaseEstimator, ClassifierMixin, NeuralNetworkMixin))
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/adversarial-robustness-toolbox/tests/attacks/test_spatial_transformation.py b/adversarial-robustness-toolbox/tests/attacks/test_spatial_transformation.py
new file mode 100644
index 0000000..47d7051
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/attacks/test_spatial_transformation.py
@@ -0,0 +1,168 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import unittest
+
+import keras.backend as k
+import numpy as np
+
+from art.attacks.evasion.spatial_transformation import SpatialTransformation
+from art.estimators.estimator import BaseEstimator, NeuralNetworkMixin
+
+from tests.utils import TestBase
+from tests.utils import get_image_classifier_tf, get_image_classifier_kr
+from tests.utils import get_image_classifier_pt, get_tabular_classifier_kr
+from tests.attacks.utils import backend_test_classifier_type_check_fail
+
+logger = logging.getLogger(__name__)
+
+
+class TestSpatialTransformation(TestBase):
+    """
+    A unittest class for testing Spatial attack.
+    """
+
+    @classmethod
+    def setUpClass(cls):
+        super().setUpClass()
+
+        cls.n_train = 100
+        cls.n_test = 10
+        cls.x_train_mnist = cls.x_train_mnist[0 : cls.n_train]
+        cls.y_train_mnist = cls.y_train_mnist[0 : cls.n_train]
+        cls.x_test_mnist = cls.x_test_mnist[0 : cls.n_test]
+        cls.y_test_mnist = cls.y_test_mnist[0 : cls.n_test]
+
+    def test_2_tensorflow_classifier(self):
+        """
+        First test with the TensorFlowClassifier.
+        :return:
+        """
+        x_test_original = self.x_test_mnist.copy()
+
+        # Build TensorFlowClassifier
+        tfc, sess = get_image_classifier_tf()
+
+        # Attack
+        attack_st = SpatialTransformation(
+            tfc, max_translation=10.0, num_translations=3, max_rotation=30.0, num_rotations=3
+        )
+        x_train_adv = attack_st.generate(self.x_train_mnist)
+
+        self.assertAlmostEqual(x_train_adv[0, 8, 13, 0], 0.49004024, delta=0.01)
+        self.assertAlmostEqual(attack_st.fooling_rate, 0.71, delta=0.02)
+
+        self.assertEqual(attack_st.attack_trans_x, 3)
+        self.assertEqual(attack_st.attack_trans_y, 3)
+        self.assertEqual(attack_st.attack_rot, 30.0)
+
+        x_test_adv = attack_st.generate(self.x_test_mnist)
+
+        self.assertAlmostEqual(x_test_adv[0, 14, 14, 0], 0.013572651, delta=0.01)
+
+        # Check that x_test has not been modified by attack and classifier
+        self.assertAlmostEqual(float(np.max(np.abs(x_test_original - self.x_test_mnist))), 0.0, delta=0.00001)
+
+        if sess is not None:
+            sess.close()
+
+    def test_4_keras_classifier(self):
+        """
+        Second test with the KerasClassifier.
+        :return:
+        """
+        x_test_original = self.x_test_mnist.copy()
+
+        # Build KerasClassifier
+        krc = get_image_classifier_kr()
+
+        # Attack
+        attack_st = SpatialTransformation(
+            krc, max_translation=10.0, num_translations=3, max_rotation=30.0, num_rotations=3
+        )
+        x_train_adv = attack_st.generate(self.x_train_mnist)
+
+        self.assertAlmostEqual(x_train_adv[0, 8, 13, 0], 0.49004024, delta=0.01)
+        self.assertAlmostEqual(attack_st.fooling_rate, 0.71, delta=0.02)
+
+        self.assertEqual(attack_st.attack_trans_x, 3)
+        self.assertEqual(attack_st.attack_trans_y, 3)
+        self.assertEqual(attack_st.attack_rot, 30.0)
+
+        x_test_adv = attack_st.generate(self.x_test_mnist)
+
+        self.assertAlmostEqual(x_test_adv[0, 14, 14, 0], 0.013572651, delta=0.01)
+
+        # Check that x_test has not been modified by attack and classifier
+        self.assertAlmostEqual(float(np.max(np.abs(x_test_original - self.x_test_mnist))), 0.0, delta=0.00001)
+
+        k.clear_session()
+
+    def test_3_pytorch_classifier(self):
+        """
+        Third test with the PyTorchClassifier.
+        :return:
+        """
+        x_train_mnist = np.reshape(self.x_train_mnist, (self.x_train_mnist.shape[0], 1, 28, 28)).astype(np.float32)
+        x_test_mnist = np.reshape(self.x_test_mnist, (self.x_test_mnist.shape[0], 1, 28, 28)).astype(np.float32)
+        x_test_original = x_test_mnist.copy()
+
+        # Build PyTorchClassifier
+        ptc = get_image_classifier_pt(from_logits=True)
+
+        # Attack
+        attack_st = SpatialTransformation(
+            ptc, max_translation=10.0, num_translations=3, max_rotation=30.0, num_rotations=3
+        )
+        x_train__mnistadv = attack_st.generate(x_train_mnist)
+
+        self.assertAlmostEqual(x_train__mnistadv[0, 0, 13, 18], 0.627451, delta=0.01)
+        self.assertAlmostEqual(attack_st.fooling_rate, 0.57, delta=0.03)
+
+        self.assertEqual(attack_st.attack_trans_x, 0)
+        self.assertEqual(attack_st.attack_trans_y, 3)
+        self.assertEqual(attack_st.attack_rot, 0.0)
+
+        x_test_adv = attack_st.generate(x_test_mnist)
+
+        self.assertLessEqual(abs(x_test_adv[0, 0, 14, 14] - 0.008591662), 0.01)
+
+        # Check that x_test has not been modified by attack and classifier
+        self.assertAlmostEqual(float(np.max(np.abs(x_test_original - x_test_mnist))), 0.0, delta=0.00001)
+
+    def test_5_failure_feature_vectors(self):
+        attack_params = {"max_translation": 10.0, "num_translations": 3, "max_rotation": 30.0, "num_rotations": 3}
+        classifier = get_tabular_classifier_kr()
+        attack = SpatialTransformation(classifier=classifier)
+        attack.set_params(**attack_params)
+        data = np.random.rand(10, 4)
+
+        # Assert that value error is raised for feature vectors
+        with self.assertRaises(ValueError) as context:
+            attack.generate(data)
+
+        self.assertIn("Feature vectors detected.", str(context.exception))
+
+    def test_1_classifier_type_check_fail(self):
+        backend_test_classifier_type_check_fail(SpatialTransformation, [BaseEstimator, NeuralNetworkMixin])
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/adversarial-robustness-toolbox/tests/attacks/test_targeted_universal_perturbation.py b/adversarial-robustness-toolbox/tests/attacks/test_targeted_universal_perturbation.py
new file mode 100644
index 0000000..0ed8658
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/attacks/test_targeted_universal_perturbation.py
@@ -0,0 +1,168 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import unittest
+
+import numpy as np
+
+from art.attacks.evasion.targeted_universal_perturbation import TargetedUniversalPerturbation
+from art.estimators.classification.classifier import ClassifierMixin
+from art.estimators.estimator import BaseEstimator
+from tests.attacks.utils import backend_test_classifier_type_check_fail
+from tests.utils import (
+    TestBase,
+    get_image_classifier_kr,
+    get_image_classifier_pt,
+    get_image_classifier_tf,
+)
+
+logger = logging.getLogger(__name__)
+
+
+class TestTargetedUniversalPerturbation(TestBase):
+    """
+    A unittest class for testing the TargetedUniversalPerturbation attack.
+
+    This module tests the Targeted Universal Perturbation.
+
+    | Paper link: https://arxiv.org/abs/1911.06502)
+    """
+
+    @classmethod
+    def setUpClass(cls):
+        super().setUpClass()
+
+        cls.n_train = 500
+        cls.n_test = 10
+        cls.x_train_mnist = cls.x_train_mnist[0 : cls.n_train]
+        cls.y_train_mnist = cls.y_train_mnist[0 : cls.n_train]
+        cls.x_test_mnist = cls.x_test_mnist[0 : cls.n_test]
+        cls.y_test_mnist = cls.y_test_mnist[0 : cls.n_test]
+
+    def test_2_tensorflow_mnist(self):
+        """
+        First test with the TensorFlowClassifier.
+        :return:
+        """
+        x_test_original = self.x_test_mnist.copy()
+
+        # Build TensorFlowClassifier
+        tfc, sess = get_image_classifier_tf()
+
+        # set target label
+        target = 0
+        y_target = np.zeros([len(self.x_train_mnist), 10])
+        for i in range(len(self.x_train_mnist)):
+            y_target[i, target] = 1.0
+
+        # Attack
+        up = TargetedUniversalPerturbation(
+            tfc, max_iter=1, attacker="fgsm", attacker_params={"eps": 0.3, "targeted": True}
+        )
+        x_train_adv = up.generate(self.x_train_mnist, y=y_target)
+        self.assertTrue((up.fooling_rate >= 0.2) or not up.converged)
+
+        x_test_adv = self.x_test_mnist + up.noise
+        self.assertFalse((self.x_test_mnist == x_test_adv).all())
+
+        train_y_pred = np.argmax(tfc.predict(x_train_adv), axis=1)
+        test_y_pred = np.argmax(tfc.predict(x_test_adv), axis=1)
+        self.assertFalse((np.argmax(self.y_test_mnist, axis=1) == test_y_pred).all())
+        self.assertFalse((np.argmax(self.y_train_mnist, axis=1) == train_y_pred).all())
+
+        # Check that x_test has not been modified by attack and classifier
+        self.assertAlmostEqual(float(np.max(np.abs(x_test_original - self.x_test_mnist))), 0.0, delta=0.00001)
+
+    def test_4_keras_mnist(self):
+        """
+        Second test with the KerasClassifier.
+        :return:
+        """
+        x_test_original = self.x_test_mnist.copy()
+
+        # Build KerasClassifier
+        krc = get_image_classifier_kr()
+
+        # set target label
+        target = 0
+        y_target = np.zeros([len(self.x_train_mnist), 10])
+        for i in range(len(self.x_train_mnist)):
+            y_target[i, target] = 1.0
+
+        # Attack
+        up = TargetedUniversalPerturbation(
+            krc, max_iter=1, attacker="fgsm", attacker_params={"eps": 0.3, "targeted": True}
+        )
+        x_train_adv = up.generate(self.x_train_mnist, y=y_target)
+        self.assertTrue((up.fooling_rate >= 0.2) or not up.converged)
+
+        x_test_adv = self.x_test_mnist + up.noise
+        self.assertFalse((self.x_test_mnist == x_test_adv).all())
+
+        train_y_pred = np.argmax(krc.predict(x_train_adv), axis=1)
+        test_y_pred = np.argmax(krc.predict(x_test_adv), axis=1)
+        self.assertFalse((np.argmax(self.y_test_mnist, axis=1) == test_y_pred).all())
+        self.assertFalse((np.argmax(self.y_train_mnist, axis=1) == train_y_pred).all())
+
+        # Check that x_test has not been modified by attack and classifier
+        self.assertAlmostEqual(float(np.max(np.abs(x_test_original - self.x_test_mnist))), 0.0, delta=0.00001)
+
+    def test_3_pytorch_mnist(self):
+        """
+        Third test with the PyTorchClassifier.
+        :return:
+        """
+        x_train_mnist = np.swapaxes(self.x_train_mnist, 1, 3).astype(np.float32)
+        x_test_mnist = np.swapaxes(self.x_test_mnist, 1, 3).astype(np.float32)
+        x_test_original = x_test_mnist.copy()
+
+        # Build PyTorchClassifier
+        ptc = get_image_classifier_pt()
+
+        # set target label
+        target = 0
+        y_target = np.zeros([len(self.x_train_mnist), 10])
+        for i in range(len(self.x_train_mnist)):
+            y_target[i, target] = 1.0
+
+        # Attack
+        up = TargetedUniversalPerturbation(
+            ptc, max_iter=1, attacker="fgsm", attacker_params={"eps": 0.3, "targeted": True}
+        )
+        x_train_mnist_adv = up.generate(x_train_mnist, y=y_target)
+        self.assertTrue((up.fooling_rate >= 0.2) or not up.converged)
+
+        x_test_mnist_adv = x_test_mnist + up.noise
+        self.assertFalse((x_test_mnist == x_test_mnist_adv).all())
+
+        train_y_pred = np.argmax(ptc.predict(x_train_mnist_adv), axis=1)
+        test_y_pred = np.argmax(ptc.predict(x_test_mnist_adv), axis=1)
+        self.assertFalse((np.argmax(self.y_test_mnist, axis=1) == test_y_pred).all())
+        self.assertFalse((np.argmax(self.y_train_mnist, axis=1) == train_y_pred).all())
+
+        # Check that x_test has not been modified by attack and classifier
+        self.assertAlmostEqual(float(np.max(np.abs(x_test_original - x_test_mnist))), 0.0, delta=0.00001)
+
+    def test_1_classifier_type_check_fail(self):
+        backend_test_classifier_type_check_fail(TargetedUniversalPerturbation, (BaseEstimator, ClassifierMixin))
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/adversarial-robustness-toolbox/tests/attacks/test_threshold_attack.py b/adversarial-robustness-toolbox/tests/attacks/test_threshold_attack.py
new file mode 100644
index 0000000..f7c5ac4
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/attacks/test_threshold_attack.py
@@ -0,0 +1,149 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+This module tests the Threshold Attack.
+
+| Paper link:
+    https://arxiv.org/abs/1906.06026
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import unittest
+
+import numpy as np
+
+from art.attacks.evasion.pixel_threshold import ThresholdAttack
+from art.estimators.estimator import BaseEstimator, NeuralNetworkMixin
+from art.estimators.classification.classifier import ClassifierMixin
+from art.utils import get_labels_np_array
+
+from tests.utils import TestBase
+from tests.utils import get_image_classifier_tf, get_image_classifier_kr, get_image_classifier_pt
+from tests.attacks.utils import backend_test_classifier_type_check_fail
+
+logger = logging.getLogger(__name__)
+
+
+class TestThresholdAttack(TestBase):
+    """
+    A unittest class for testing the Threshold Attack.
+
+    This module tests the Threshold Attack.
+
+    | Paper link:
+        https://arxiv.org/abs/1906.06026
+    """
+
+    @classmethod
+    def setUpClass(cls):
+        super().setUpClass()
+
+        cls.n_test = 2
+        cls.x_test_mnist = cls.x_test_mnist[0 : cls.n_test]
+        cls.y_test_mnist = cls.y_test_mnist[0 : cls.n_test]
+
+    def test_6_keras_mnist(self):
+        """
+        Test with the KerasClassifier. (Untargeted Attack)
+        :return:
+        """
+        classifier = get_image_classifier_kr()
+        self._test_attack(classifier, self.x_test_mnist, self.y_test_mnist, False)
+
+    def test_2_tensorflow_mnist(self):
+        """
+        Test with the TensorFlowClassifier. (Untargeted Attack)
+        :return:
+        """
+        classifier, sess = get_image_classifier_tf()
+        self._test_attack(classifier, self.x_test_mnist, self.y_test_mnist, False)
+
+    def test_4_pytorch_mnist(self):
+        """
+        Test with the PyTorchClassifier. (Untargeted Attack)
+        :return:
+        """
+        x_test = np.reshape(self.x_test_mnist, (self.x_test_mnist.shape[0], 1, 28, 28)).astype(np.float32)
+        classifier = get_image_classifier_pt()
+        self._test_attack(classifier, x_test, self.y_test_mnist, False)
+
+    def test_7_keras_mnist_targeted(self):
+        """
+        Test with the KerasClassifier. (Targeted Attack)
+        :return:
+        """
+        classifier = get_image_classifier_kr()
+        self._test_attack(classifier, self.x_test_mnist, self.y_test_mnist, True)
+
+    def test_3_tensorflow_mnist_targeted(self):
+        """
+        Test with the TensorFlowClassifier. (Targeted Attack)
+        :return:
+        """
+        classifier, sess = get_image_classifier_tf()
+        self._test_attack(classifier, self.x_test_mnist, self.y_test_mnist, True)
+
+    def test_5_pytorch_mnist_targeted(self):
+        """
+        Test with the PyTorchClassifier. (Targeted Attack)
+        :return:
+        """
+        x_test = np.reshape(self.x_test_mnist, (self.x_test_mnist.shape[0], 1, 28, 28)).astype(np.float32)
+        classifier = get_image_classifier_pt()
+        self._test_attack(classifier, x_test, self.y_test_mnist, True)
+
+    def _test_attack(self, classifier, x_test, y_test, targeted):
+        """
+        Test with the Threshold Attack
+        :return:
+        """
+        x_test_original = x_test.copy()
+
+        if targeted:
+            # Generate random target classes
+            class_y_test = np.argmax(y_test, axis=1)
+            nb_classes = np.unique(class_y_test).shape[0]
+            targets = np.random.randint(nb_classes, size=self.n_test)
+            for i in range(self.n_test):
+                if class_y_test[i] == targets[i]:
+                    targets[i] -= 1
+        else:
+            targets = y_test
+
+        for es in [1]:  # Option 0 is not easy to reproduce reliably, we should consider it at a later time
+            df = ThresholdAttack(classifier, th=128, es=es, targeted=targeted)
+            x_test_adv = df.generate(x_test_original, targets, max_iter=10)
+
+            np.testing.assert_raises(AssertionError, np.testing.assert_array_equal, x_test, x_test_adv)
+            self.assertFalse((0.0 == x_test_adv).all())
+
+            y_pred = get_labels_np_array(classifier.predict(x_test_adv))
+
+            accuracy = np.sum(np.argmax(y_pred, axis=1) == np.argmax(self.y_test_mnist, axis=1)) / self.n_test
+            logger.info("Accuracy on adversarial examples: %.2f%%", (accuracy * 100))
+
+        # Check that x_test has not been modified by attack and classifier
+        self.assertAlmostEqual(float(np.max(np.abs(x_test_original - x_test))), 0.0, delta=0.00001)
+
+    def test_1_classifier_type_check_fail(self):
+        backend_test_classifier_type_check_fail(ThresholdAttack, [BaseEstimator, NeuralNetworkMixin, ClassifierMixin])
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/adversarial-robustness-toolbox/tests/attacks/test_universal_perturbation.py b/adversarial-robustness-toolbox/tests/attacks/test_universal_perturbation.py
new file mode 100644
index 0000000..61e0ad4
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/attacks/test_universal_perturbation.py
@@ -0,0 +1,210 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import unittest
+
+import numpy as np
+
+from art.attacks.evasion.universal_perturbation import UniversalPerturbation
+from art.estimators.classification.classifier import ClassifierMixin
+from art.estimators.classification.keras import KerasClassifier
+from art.estimators.estimator import BaseEstimator
+from tests.attacks.utils import backend_test_classifier_type_check_fail
+from tests.utils import (
+    TestBase,
+    get_image_classifier_kr,
+    get_image_classifier_pt,
+    get_image_classifier_tf,
+    get_tabular_classifier_kr,
+    get_tabular_classifier_pt,
+    get_tabular_classifier_tf,
+)
+
+logger = logging.getLogger(__name__)
+
+
+class TestUniversalPerturbation(TestBase):
+    """
+    A unittest class for testing the UniversalPerturbation attack.
+    """
+
+    @classmethod
+    def setUpClass(cls):
+        super().setUpClass()
+
+        cls.n_train = 500
+        cls.n_test = 10
+        cls.x_train_mnist = cls.x_train_mnist[0 : cls.n_train]
+        cls.y_train_mnist = cls.y_train_mnist[0 : cls.n_train]
+        cls.x_test_mnist = cls.x_test_mnist[0 : cls.n_test]
+        cls.y_test_mnist = cls.y_test_mnist[0 : cls.n_test]
+
+    def test_3_tensorflow_mnist(self):
+        """
+        First test with the TensorFlowClassifier.
+        :return:
+        """
+        x_test_original = self.x_test_mnist.copy()
+
+        # Build TensorFlowClassifier
+        tfc, sess = get_image_classifier_tf()
+
+        # Attack
+        up = UniversalPerturbation(tfc, max_iter=1, attacker="newtonfool", attacker_params={"max_iter": 5})
+        x_train_adv = up.generate(self.x_train_mnist)
+        self.assertTrue((up.fooling_rate >= 0.2) or not up.converged)
+
+        x_test_adv = self.x_test_mnist + up.noise
+        self.assertFalse((self.x_test_mnist == x_test_adv).all())
+
+        train_y_pred = np.argmax(tfc.predict(x_train_adv), axis=1)
+        test_y_pred = np.argmax(tfc.predict(x_test_adv), axis=1)
+        self.assertFalse((np.argmax(self.y_test_mnist, axis=1) == test_y_pred).all())
+        self.assertFalse((np.argmax(self.y_train_mnist, axis=1) == train_y_pred).all())
+
+        # Check that x_test has not been modified by attack and classifier
+        self.assertAlmostEqual(float(np.max(np.abs(x_test_original - self.x_test_mnist))), 0.0, delta=0.00001)
+
+    def test_8_keras_mnist(self):
+        """
+        Second test with the KerasClassifier.
+        :return:
+        """
+        x_test_original = self.x_test_mnist.copy()
+
+        # Build KerasClassifier
+        krc = get_image_classifier_kr()
+
+        # Attack
+        up = UniversalPerturbation(krc, max_iter=1, attacker="ead", attacker_params={"max_iter": 2, "targeted": False})
+        x_train_adv = up.generate(self.x_train_mnist)
+        self.assertTrue((up.fooling_rate >= 0.2) or not up.converged)
+
+        x_test_adv = self.x_test_mnist + up.noise
+        self.assertFalse((self.x_test_mnist == x_test_adv).all())
+
+        train_y_pred = np.argmax(krc.predict(x_train_adv), axis=1)
+        test_y_pred = np.argmax(krc.predict(x_test_adv), axis=1)
+        self.assertFalse((np.argmax(self.y_test_mnist, axis=1) == test_y_pred).all())
+        self.assertFalse((np.argmax(self.y_train_mnist, axis=1) == train_y_pred).all())
+
+        # Check that x_test has not been modified by attack and classifier
+        self.assertAlmostEqual(float(np.max(np.abs(x_test_original - self.x_test_mnist))), 0.0, delta=0.00001)
+
+    def test_5_pytorch_mnist(self):
+        """
+        Third test with the PyTorchClassifier.
+        :return:
+        """
+        x_train_mnist = np.swapaxes(self.x_train_mnist, 1, 3).astype(np.float32)
+        x_test_mnist = np.swapaxes(self.x_test_mnist, 1, 3).astype(np.float32)
+        x_test_original = x_test_mnist.copy()
+
+        # Build PyTorchClassifier
+        ptc = get_image_classifier_pt()
+
+        # Attack
+        up = UniversalPerturbation(ptc, max_iter=1, attacker="newtonfool", attacker_params={"max_iter": 5})
+        x_train_mnist_adv = up.generate(x_train_mnist)
+        self.assertTrue((up.fooling_rate >= 0.2) or not up.converged)
+
+        x_test_mnist_adv = x_test_mnist + up.noise
+        self.assertFalse((x_test_mnist == x_test_mnist_adv).all())
+
+        train_y_pred = np.argmax(ptc.predict(x_train_mnist_adv), axis=1)
+        test_y_pred = np.argmax(ptc.predict(x_test_mnist_adv), axis=1)
+        self.assertFalse((np.argmax(self.y_test_mnist, axis=1) == test_y_pred).all())
+        self.assertFalse((np.argmax(self.y_train_mnist, axis=1) == train_y_pred).all())
+
+        # Check that x_test has not been modified by attack and classifier
+        self.assertAlmostEqual(float(np.max(np.abs(x_test_original - x_test_mnist))), 0.0, delta=0.00001)
+
+    def test_6_keras_iris_clipped(self):
+        classifier = get_tabular_classifier_kr()
+
+        # Test untargeted attack
+        attack_params = {"max_iter": 1, "attacker": "newtonfool", "attacker_params": {"max_iter": 5}}
+        attack = UniversalPerturbation(classifier)
+        attack.set_params(**attack_params)
+        x_test_iris_adv = attack.generate(self.x_test_iris)
+        self.assertFalse((self.x_test_iris == x_test_iris_adv).all())
+        self.assertTrue((x_test_iris_adv <= 1).all())
+        self.assertTrue((x_test_iris_adv >= 0).all())
+
+        preds_adv = np.argmax(classifier.predict(x_test_iris_adv), axis=1)
+        self.assertFalse((np.argmax(self.y_test_iris, axis=1) == preds_adv).all())
+        acc = np.sum(preds_adv == np.argmax(self.y_test_iris, axis=1)) / self.y_test_iris.shape[0]
+        logger.info("Accuracy on Iris with universal adversarial examples: %.2f%%", (acc * 100))
+
+    def test_7_keras_iris_unbounded(self):
+        classifier = get_tabular_classifier_kr()
+
+        # Recreate a classifier without clip values
+        classifier = KerasClassifier(model=classifier._model, use_logits=False, channels_first=True)
+        attack_params = {"max_iter": 1, "attacker": "newtonfool", "attacker_params": {"max_iter": 5}}
+        attack = UniversalPerturbation(classifier)
+        attack.set_params(**attack_params)
+        x_test_iris_adv = attack.generate(self.x_test_iris)
+        self.assertFalse((self.x_test_iris == x_test_iris_adv).all())
+
+        preds_adv = np.argmax(classifier.predict(x_test_iris_adv), axis=1)
+        self.assertFalse((np.argmax(self.y_test_iris, axis=1) == preds_adv).all())
+        acc = np.sum(preds_adv == np.argmax(self.y_test_iris, axis=1)) / self.y_test_iris.shape[0]
+        logger.info("Accuracy on Iris with universal adversarial examples: %.2f%%", (acc * 100))
+
+    def test_2_tensorflow_iris(self):
+        classifier, _ = get_tabular_classifier_tf()
+
+        # Test untargeted attack
+        attack_params = {"max_iter": 1, "attacker": "ead", "attacker_params": {"max_iter": 5, "targeted": False}}
+        attack = UniversalPerturbation(classifier)
+        attack.set_params(**attack_params)
+        x_test_iris_adv = attack.generate(self.x_test_iris)
+        self.assertFalse((self.x_test_iris == x_test_iris_adv).all())
+        self.assertTrue((x_test_iris_adv <= 1).all())
+        self.assertTrue((x_test_iris_adv >= 0).all())
+
+        preds_adv = np.argmax(classifier.predict(x_test_iris_adv), axis=1)
+        self.assertFalse((np.argmax(self.y_test_iris, axis=1) == preds_adv).all())
+        acc = np.sum(preds_adv == np.argmax(self.y_test_iris, axis=1)) / self.y_test_iris.shape[0]
+        logger.info("Accuracy on Iris with universal adversarial examples: %.2f%%", (acc * 100))
+
+    def test_4_pytorch_iris(self):
+        classifier = get_tabular_classifier_pt()
+
+        attack_params = {"max_iter": 1, "attacker": "ead", "attacker_params": {"max_iter": 5, "targeted": False}}
+        attack = UniversalPerturbation(classifier)
+        attack.set_params(**attack_params)
+        x_test_iris_adv = attack.generate(self.x_test_iris)
+        self.assertFalse((self.x_test_iris == x_test_iris_adv).all())
+        self.assertTrue((x_test_iris_adv <= 1).all())
+        self.assertTrue((x_test_iris_adv >= 0).all())
+
+        preds_adv = np.argmax(classifier.predict(x_test_iris_adv), axis=1)
+        self.assertFalse((np.argmax(self.y_test_iris, axis=1) == preds_adv).all())
+        acc = np.sum(preds_adv == np.argmax(self.y_test_iris, axis=1)) / self.y_test_iris.shape[0]
+        logger.info("Accuracy on Iris with universal adversarial examples: %.2f%%", (acc * 100))
+
+    def test_1_classifier_type_check_fail(self):
+        backend_test_classifier_type_check_fail(UniversalPerturbation, [BaseEstimator, ClassifierMixin])
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/adversarial-robustness-toolbox/tests/attacks/test_virtual_adversarial.py b/adversarial-robustness-toolbox/tests/attacks/test_virtual_adversarial.py
new file mode 100644
index 0000000..84181f5
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/attacks/test_virtual_adversarial.py
@@ -0,0 +1,201 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import unittest
+import numpy as np
+
+from art.attacks.evasion.virtual_adversarial import VirtualAdversarialMethod
+from art.estimators.classification.keras import KerasClassifier
+from art.estimators.estimator import BaseEstimator
+from art.estimators.classification.classifier import ClassifierMixin
+from art.utils import get_labels_np_array
+
+from tests.utils import TestBase
+from tests.utils import get_image_classifier_tf, get_image_classifier_kr, get_image_classifier_pt
+from tests.utils import get_tabular_classifier_tf, get_tabular_classifier_kr, get_tabular_classifier_pt
+from tests.attacks.utils import backend_test_classifier_type_check_fail
+
+logger = logging.getLogger(__name__)
+
+
+class TestVirtualAdversarial(TestBase):
+    @classmethod
+    def setUpClass(cls):
+        super().setUpClass()
+
+        cls.n_train = 100
+        cls.n_test = 10
+        cls.x_train_mnist = cls.x_train_mnist[0 : cls.n_train]
+        cls.y_train_mnist = cls.y_train_mnist[0 : cls.n_train]
+        cls.x_test_mnist = cls.x_test_mnist[0 : cls.n_test]
+        cls.y_test_mnist = cls.y_test_mnist[0 : cls.n_test]
+
+    def test_8_keras_mnist(self):
+        classifier = get_image_classifier_kr()
+
+        scores = classifier._model.evaluate(self.x_train_mnist, self.y_train_mnist)
+        logging.info("[Keras, MNIST] Accuracy on training set: %.2f%%", (scores[1] * 100))
+        scores = classifier._model.evaluate(self.x_test_mnist, self.y_test_mnist)
+        logging.info("[Keras, MNIST] Accuracy on test set: %.2f%%", (scores[1] * 100))
+
+        self._test_backend_mnist(classifier, self.x_test_mnist, self.y_test_mnist)
+
+    def test_3_tensorflow_mnist(self):
+        classifier, sess = get_image_classifier_tf(from_logits=False)
+
+        scores = get_labels_np_array(classifier.predict(self.x_train_mnist))
+        acc = np.sum(np.argmax(scores, axis=1) == np.argmax(self.y_train_mnist, axis=1)) / self.y_train_mnist.shape[0]
+        logger.info("[TF, MNIST] Accuracy on training set: %.2f%%", (acc * 100))
+
+        scores = get_labels_np_array(classifier.predict(self.x_test_mnist))
+        acc = np.sum(np.argmax(scores, axis=1) == np.argmax(self.y_test_mnist, axis=1)) / self.y_test_mnist.shape[0]
+        logger.info("[TF, MNIST] Accuracy on test set: %.2f%%", (acc * 100))
+
+        self._test_backend_mnist(classifier, self.x_test_mnist, self.y_test_mnist)
+
+    def test_5_pytorch_mnist(self):
+        x_train_mnist = np.swapaxes(self.x_train_mnist, 1, 3).astype(np.float32)
+        x_test_mnist = np.swapaxes(self.x_test_mnist, 1, 3).astype(np.float32)
+        classifier = get_image_classifier_pt()
+
+        scores = get_labels_np_array(classifier.predict(x_train_mnist))
+        acc = np.sum(np.argmax(scores, axis=1) == np.argmax(self.y_train_mnist, axis=1)) / self.y_train_mnist.shape[0]
+        logger.info("[PyTorch, MNIST] Accuracy on training set: %.2f%%", (acc * 100))
+
+        scores = get_labels_np_array(classifier.predict(x_test_mnist))
+        acc = np.sum(np.argmax(scores, axis=1) == np.argmax(self.y_test_mnist, axis=1)) / self.y_test_mnist.shape[0]
+        logger.info("[PyTorch, MNIST] Accuracy on test set: %.2f%%", (acc * 100))
+
+        self._test_backend_mnist(classifier, x_test_mnist, self.y_test_mnist)
+
+    def _test_backend_mnist(self, classifier, x_test, y_test):
+        x_test_original = x_test.copy()
+
+        df = VirtualAdversarialMethod(classifier, batch_size=100, max_iter=2)
+
+        x_test_adv = df.generate(x_test)
+
+        self.assertFalse((x_test == x_test_adv).all())
+
+        y_pred = get_labels_np_array(classifier.predict(x_test_adv))
+        self.assertFalse((y_test == y_pred).all())
+
+        acc = np.sum(np.argmax(y_pred, axis=1) == np.argmax(y_test, axis=1)) / y_test.shape[0]
+        logger.info("Accuracy on adversarial examples: %.2f%%", (acc * 100))
+
+        # Check that x_test has not been modified by attack and classifier
+        self.assertAlmostEqual(float(np.max(np.abs(x_test_original - x_test))), 0.0, delta=0.00001)
+
+    def test_6_keras_iris_clipped(self):
+        classifier = get_tabular_classifier_kr()
+
+        # Test untargeted attack
+        attack = VirtualAdversarialMethod(classifier, eps=0.1)
+        x_test_iris_adv = attack.generate(self.x_test_iris)
+        self.assertFalse((self.x_test_iris == x_test_iris_adv).all())
+        self.assertTrue((x_test_iris_adv <= 1).all())
+        self.assertTrue((x_test_iris_adv >= 0).all())
+
+        preds_adv = np.argmax(classifier.predict(x_test_iris_adv), axis=1)
+        self.assertFalse((np.argmax(self.y_test_iris, axis=1) == preds_adv).all())
+        acc = np.sum(preds_adv == np.argmax(self.y_test_iris, axis=1)) / self.y_test_iris.shape[0]
+        logger.info("Accuracy on Iris with VAT adversarial examples: %.2f%%", (acc * 100))
+
+    def test_7_keras_iris_unbounded(self):
+        classifier = get_tabular_classifier_kr()
+
+        # Recreate a classifier without clip values
+        classifier = KerasClassifier(model=classifier._model, use_logits=False, channels_first=True)
+        attack = VirtualAdversarialMethod(classifier, eps=1)
+        x_test_iris_adv = attack.generate(self.x_test_iris)
+        self.assertFalse((self.x_test_iris == x_test_iris_adv).all())
+        self.assertTrue((x_test_iris_adv > 1).any())
+        self.assertTrue((x_test_iris_adv < 0).any())
+
+        preds_adv = np.argmax(classifier.predict(x_test_iris_adv), axis=1)
+        self.assertFalse((np.argmax(self.y_test_iris, axis=1) == preds_adv).all())
+        acc = np.sum(preds_adv == np.argmax(self.y_test_iris, axis=1)) / self.y_test_iris.shape[0]
+        logger.info("Accuracy on Iris with VAT adversarial examples: %.2f%%", (acc * 100))
+
+    # def test_iris_tf(self):
+    #     classifier, _ = get_iris_classifier_tf()
+    #
+    #     attack = VirtualAdversarialMethod(classifier, eps=.1)
+    #     x_test_adv = attack.generate(x_test)
+    #     #print(np.min(x_test_adv), np.max(x_test_adv), np.min(x_test), np.max(x_test))
+    #     self.assertFalse((x_test == x_test_adv).all())
+    #     self.assertTrue((x_test_adv <= 1).all())
+    #     self.assertTrue((x_test_adv >= 0).all())
+    #
+    #     preds_adv = np.argmax(classifier.predict(x_test_adv), axis=1)
+    #     self.assertFalse((np.argmax(y_test, axis=1) == preds_adv).all())
+    #     acc = np.sum(preds_adv == np.argmax(y_test, axis=1)) / y_test.shape[0]
+    #     logger.info('Accuracy on Iris with VAT adversarial examples: %.2f%%', (acc * 100))
+
+    # def test_iris_pt(self):
+    #     (_, _), (x_test, y_test) = self.iris
+    #     classifier = get_iris_classifier_pt()
+    #
+    #     attack = VirtualAdversarialMethod(classifier, eps=.1)
+    #     x_test_adv = attack.generate(x_test.astype(np.float32))
+    #     #print(np.min(x_test_adv),  np.max(x_test_adv), np.min(x_test), np.max(x_test))
+    #     self.assertFalse((x_test == x_test_adv).all())
+    #     self.assertTrue((x_test_adv <= 1).all())
+    #     self.assertTrue((x_test_adv >= 0).all())
+    #
+    #     preds_adv = np.argmax(classifier.predict(x_test_adv), axis=1)
+    #     self.assertFalse((np.argmax(y_test, axis=1) == preds_adv).all())
+    #     acc = np.sum(preds_adv == np.argmax(y_test, axis=1)) / y_test.shape[0]
+    #     logger.info('Accuracy on Iris with VAT adversarial examples: %.2f%%', (acc * 100))
+
+    def test_2_tensorflow_iris(self):
+        classifier, _ = get_tabular_classifier_tf()
+
+        attack = VirtualAdversarialMethod(classifier, eps=0.1)
+
+        with self.assertRaises(TypeError) as context:
+            _ = attack.generate(self.x_test_iris)
+
+        self.assertIn(
+            "This attack requires a classifier predicting probabilities in the range [0, 1] as output."
+            "Values smaller than 0.0 or larger than 1.0 have been detected.",
+            str(context.exception),
+        )
+
+    def test_4_pytorch_iris(self):
+        classifier = get_tabular_classifier_pt()
+
+        attack = VirtualAdversarialMethod(classifier, eps=0.1)
+
+        with self.assertRaises(TypeError) as context:
+            _ = attack.generate(self.x_test_iris.astype(np.float32))
+
+        self.assertIn(
+            "This attack requires a classifier predicting probabilities in the range [0, 1] as output."
+            "Values smaller than 0.0 or larger than 1.0 have been detected.",
+            str(context.exception),
+        )
+
+    def test_1_classifier_type_check_fail(self):
+        backend_test_classifier_type_check_fail(VirtualAdversarialMethod, [BaseEstimator, ClassifierMixin])
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/adversarial-robustness-toolbox/tests/attacks/test_wasserstein.py b/adversarial-robustness-toolbox/tests/attacks/test_wasserstein.py
new file mode 100644
index 0000000..5fcef8c
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/attacks/test_wasserstein.py
@@ -0,0 +1,493 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import unittest
+
+import numpy as np
+
+from art.attacks.evasion.wasserstein import Wasserstein
+from art.estimators.estimator import BaseEstimator, LossGradientsMixin
+from art.estimators.classification.classifier import ClassifierMixin
+from art.utils import get_labels_np_array
+
+from tests.utils import TestBase
+from tests.utils import get_image_classifier_tf
+from tests.attacks.utils import backend_test_classifier_type_check_fail
+
+logger = logging.getLogger(__name__)
+
+
+class TestWasserstein(TestBase):
+    @classmethod
+    def setUpClass(cls):
+        super().setUpClass()
+
+        cls.n_train = 10
+        cls.n_test = 10
+        cls.x_train_mnist = cls.x_train_mnist[0 : cls.n_train]
+        cls.y_train_mnist = cls.y_train_mnist[0 : cls.n_train]
+        cls.x_test_mnist = cls.x_test_mnist[0 : cls.n_test]
+        cls.y_test_mnist = cls.y_test_mnist[0 : cls.n_test]
+
+    # def test_keras_mnist(self):
+    #     classifier = get_image_classifier_kr()
+    #
+    #     scores = classifier._model.evaluate(self.x_train_mnist, self.y_train_mnist)
+    #     logger.info("[Keras, MNIST] Accuracy on training set: %.2f%%", scores[1] * 100)
+    #     scores = classifier._model.evaluate(self.x_test_mnist, self.y_test_mnist)
+    #     logger.info("[Keras, MNIST] Accuracy on test set: %.2f%%", scores[1] * 100)
+    #
+    #     self._test_backend_mnist(
+    #         classifier, self.x_train_mnist, self.y_train_mnist, self.x_test_mnist, self.y_test_mnist
+    #     )
+
+    def test_tensorflow_mnist(self):
+        classifier, sess = get_image_classifier_tf()
+
+        scores = get_labels_np_array(classifier.predict(self.x_train_mnist))
+        acc = np.sum(np.argmax(scores, axis=1) == np.argmax(self.y_train_mnist, axis=1)) / self.y_train_mnist.shape[0]
+        logger.info("[TF, MNIST] Accuracy on training set: %.2f%%", acc * 100)
+
+        scores = get_labels_np_array(classifier.predict(self.x_test_mnist))
+        acc = np.sum(np.argmax(scores, axis=1) == np.argmax(self.y_test_mnist, axis=1)) / self.y_test_mnist.shape[0]
+        logger.info("[TF, MNIST] Accuracy on test set: %.2f%%", acc * 100)
+
+        self._test_backend_mnist(
+            classifier, self.x_train_mnist, self.y_train_mnist, self.x_test_mnist, self.y_test_mnist
+        )
+
+    # def test_pytorch_mnist(self):
+    #     x_train_mnist = np.swapaxes(self.x_train_mnist, 1, 3).astype(np.float32)
+    #     x_test_mnist = np.swapaxes(self.x_test_mnist, 1, 3).astype(np.float32)
+    #     classifier = get_image_classifier_pt()
+    #
+    #     scores = get_labels_np_array(classifier.predict(x_train_mnist))
+    #     acc = np.sum(np.argmax(scores, axis=1) == np.argmax(self.y_train_mnist, axis=1)) / self.y_train_mnist.shape[0]
+    #     logger.info("[PyTorch, MNIST] Accuracy on training set: %.2f%%", acc * 100)
+    #
+    #     scores = get_labels_np_array(classifier.predict(x_test_mnist))
+    #     acc = np.sum(np.argmax(scores, axis=1) == np.argmax(self.y_test_mnist, axis=1)) / self.y_test_mnist.shape[0]
+    #     logger.info("[PyTorch, MNIST] Accuracy on test set: %.2f%%", acc * 100)
+    #
+    #     self._test_backend_mnist(classifier, x_train_mnist, self.y_train_mnist, x_test_mnist, self.y_test_mnist)
+
+    def _test_backend_mnist(self, classifier, x_train, y_train, x_test, y_test):
+
+        base_success_rate = 0.1
+        num_iter = 5
+        regularization = 100
+        batch_size = 5
+        eps = 0.3
+
+        # Test Wasserstein with wasserstein ball and wasserstein norm
+        attack = Wasserstein(
+            classifier,
+            regularization=regularization,
+            max_iter=num_iter,
+            conjugate_sinkhorn_max_iter=num_iter,
+            projected_sinkhorn_max_iter=num_iter,
+            norm="wasserstein",
+            ball="wasserstein",
+            targeted=False,
+            p=2,
+            eps_iter=2,
+            eps_factor=1.05,
+            eps=eps,
+            eps_step=0.1,
+            kernel_size=5,
+            batch_size=batch_size,
+        )
+
+        x_train_adv = attack.generate(x_train)
+        x_test_adv = attack.generate(x_test)
+
+        self.assertFalse((x_train_adv == x_train).all())
+        self.assertFalse((x_test_adv == x_test).all())
+
+        train_y_pred = get_labels_np_array(classifier.predict(x_train_adv)).astype(float)
+        test_y_pred = get_labels_np_array(classifier.predict(x_test_adv)).astype(float)
+
+        train_success_rate = (
+            np.sum(np.argmax(train_y_pred, axis=1) != np.argmax(classifier.predict(x_train), axis=1)) / y_train.shape[0]
+        )
+        self.assertGreaterEqual(train_success_rate, base_success_rate)
+
+        test_success_rate = (
+            np.sum(np.argmax(test_y_pred, axis=1) != np.argmax(classifier.predict(x_test), axis=1)) / y_test.shape[0]
+        )
+        self.assertGreaterEqual(test_success_rate, base_success_rate)
+
+        # # Test Wasserstein with wasserstein ball and l2 norm
+        # attack = Wasserstein(
+        #     classifier,
+        #     regularization=regularization,
+        #     max_iter=num_iter,
+        #     conjugate_sinkhorn_max_iter=num_iter,
+        #     projected_sinkhorn_max_iter=num_iter,
+        #     norm="2",
+        #     ball="wasserstein",
+        #     targeted=False,
+        #     p=2,
+        #     eps_iter=2,
+        #     eps_factor=1.05,
+        #     eps=eps,
+        #     eps_step=0.1,
+        #     kernel_size=5,
+        #     batch_size=batch_size,
+        # )
+        #
+        # x_train_adv = attack.generate(x_train)
+        # x_test_adv = attack.generate(x_test)
+        #
+        # train_y_pred = get_labels_np_array(classifier.predict(x_train_adv)).astype(float)
+        # test_y_pred = get_labels_np_array(classifier.predict(x_test_adv)).astype(float)
+        #
+        # train_success_rate = (
+        #     np.sum(np.argmax(train_y_pred, axis=1) != np.argmax(classifier.predict(x_train), axis=1))
+        #     / y_train.shape[0]
+        # )
+        # self.assertGreaterEqual(train_success_rate, base_success_rate)
+        #
+        # test_success_rate = (
+        #     np.sum(np.argmax(test_y_pred, axis=1) != np.argmax(classifier.predict(x_test), axis=1)) / y_test.shape[0]
+        # )
+        # self.assertGreaterEqual(test_success_rate, 0)
+        #
+        # # Test Wasserstein with wasserstein ball and inf norm
+        # attack = Wasserstein(
+        #     classifier,
+        #     regularization=regularization,
+        #     max_iter=num_iter,
+        #     conjugate_sinkhorn_max_iter=num_iter,
+        #     projected_sinkhorn_max_iter=num_iter,
+        #     norm="inf",
+        #     ball="wasserstein",
+        #     targeted=False,
+        #     p=2,
+        #     eps_iter=2,
+        #     eps_factor=1.05,
+        #     eps=eps,
+        #     eps_step=0.1,
+        #     kernel_size=5,
+        #     batch_size=batch_size,
+        # )
+        #
+        # x_train_adv = attack.generate(x_train)
+        # x_test_adv = attack.generate(x_test)
+        #
+        # train_y_pred = get_labels_np_array(classifier.predict(x_train_adv)).astype(float)
+        # test_y_pred = get_labels_np_array(classifier.predict(x_test_adv)).astype(float)
+        #
+        # train_success_rate = (
+        #     np.sum(np.argmax(train_y_pred, axis=1) != np.argmax(classifier.predict(x_train), axis=1))
+        #     / y_train.shape[0]
+        # )
+        # self.assertGreaterEqual(train_success_rate, base_success_rate)
+        #
+        # test_success_rate = (
+        #     np.sum(np.argmax(test_y_pred, axis=1) != np.argmax(classifier.predict(x_test), axis=1)) / y_test.shape[0]
+        # )
+        # self.assertGreaterEqual(test_success_rate, 0)
+        #
+        # # Test Wasserstein with wasserstein ball and l1 norm
+        # attack = Wasserstein(
+        #     classifier,
+        #     regularization=regularization,
+        #     max_iter=num_iter,
+        #     conjugate_sinkhorn_max_iter=num_iter,
+        #     projected_sinkhorn_max_iter=num_iter,
+        #     norm="1",
+        #     ball="wasserstein",
+        #     targeted=False,
+        #     p=2,
+        #     eps_iter=2,
+        #     eps_factor=1.05,
+        #     eps=eps,
+        #     eps_step=0.1,
+        #     kernel_size=5,
+        #     batch_size=batch_size,
+        # )
+        #
+        # x_train_adv = attack.generate(x_train)
+        # x_test_adv = attack.generate(x_test)
+        #
+        # train_y_pred = get_labels_np_array(classifier.predict(x_train_adv)).astype(float)
+        # test_y_pred = get_labels_np_array(classifier.predict(x_test_adv)).astype(float)
+        #
+        # train_success_rate = (
+        #     np.sum(np.argmax(train_y_pred, axis=1) != np.argmax(classifier.predict(x_train), axis=1))
+        #     / y_train.shape[0]
+        # )
+        # self.assertGreaterEqual(train_success_rate, base_success_rate)
+        #
+        # test_success_rate = (
+        #     np.sum(np.argmax(test_y_pred, axis=1) != np.argmax(classifier.predict(x_test), axis=1)) / y_test.shape[0]
+        # )
+        # self.assertGreaterEqual(test_success_rate, 0)
+        #
+        # # Test Wasserstein with l2 ball and wasserstein norm
+        # attack = Wasserstein(
+        #     classifier,
+        #     regularization=regularization,
+        #     max_iter=num_iter,
+        #     conjugate_sinkhorn_max_iter=num_iter,
+        #     projected_sinkhorn_max_iter=num_iter,
+        #     norm="wasserstein",
+        #     ball="2",
+        #     targeted=False,
+        #     p=2,
+        #     eps_iter=2,
+        #     eps_factor=1.05,
+        #     eps=eps,
+        #     eps_step=0.05,
+        #     kernel_size=5,
+        #     batch_size=batch_size,
+        # )
+        #
+        # x_train_adv = attack.generate(x_train)
+        # x_test_adv = attack.generate(x_test)
+        #
+        # train_y_pred = get_labels_np_array(classifier.predict(x_train_adv)).astype(float)
+        # test_y_pred = get_labels_np_array(classifier.predict(x_test_adv)).astype(float)
+        #
+        # train_success_rate = (
+        #     np.sum(np.argmax(train_y_pred, axis=1) != np.argmax(classifier.predict(x_train), axis=1))
+        #     / y_train.shape[0]
+        # )
+        # self.assertGreaterEqual(train_success_rate, 0)
+        #
+        # test_success_rate = (
+        #     np.sum(np.argmax(test_y_pred, axis=1) != np.argmax(classifier.predict(x_test), axis=1)) / y_test.shape[0]
+        # )
+        # self.assertGreaterEqual(test_success_rate, 0)
+        #
+        # # Test Wasserstein with l1 ball and wasserstein norm
+        # attack = Wasserstein(
+        #     classifier,
+        #     regularization=regularization,
+        #     max_iter=num_iter,
+        #     conjugate_sinkhorn_max_iter=num_iter,
+        #     projected_sinkhorn_max_iter=num_iter,
+        #     norm="wasserstein",
+        #     ball="1",
+        #     targeted=False,
+        #     p=2,
+        #     eps_iter=2,
+        #     eps_factor=1.05,
+        #     eps=eps,
+        #     eps_step=0.1,
+        #     kernel_size=5,
+        #     batch_size=batch_size,
+        # )
+        #
+        # x_train_adv = attack.generate(x_train)
+        # x_test_adv = attack.generate(x_test)
+        #
+        # train_y_pred = get_labels_np_array(classifier.predict(x_train_adv)).astype(float)
+        # test_y_pred = get_labels_np_array(classifier.predict(x_test_adv)).astype(float)
+        #
+        # train_success_rate = (
+        #     np.sum(np.argmax(train_y_pred, axis=1) != np.argmax(classifier.predict(x_train), axis=1))
+        #     / y_train.shape[0]
+        # )
+        # self.assertGreaterEqual(train_success_rate, 0)
+        #
+        # test_success_rate = (
+        #     np.sum(np.argmax(test_y_pred, axis=1) != np.argmax(classifier.predict(x_test), axis=1)) / y_test.shape[0]
+        # )
+        # self.assertGreaterEqual(test_success_rate, 0)
+        #
+        # # Test Wasserstein with inf ball and Wasserstein norm
+        # attack = Wasserstein(
+        #     classifier,
+        #     regularization=regularization,
+        #     max_iter=num_iter,
+        #     conjugate_sinkhorn_max_iter=num_iter,
+        #     projected_sinkhorn_max_iter=num_iter,
+        #     norm="wasserstein",
+        #     ball="inf",
+        #     targeted=False,
+        #     p=2,
+        #     eps_iter=2,
+        #     eps_factor=1.05,
+        #     eps=eps,
+        #     eps_step=0.1,
+        #     kernel_size=5,
+        #     batch_size=batch_size,
+        # )
+        #
+        # x_train_adv = attack.generate(x_train)
+        # x_test_adv = attack.generate(x_test)
+        #
+        # train_y_pred = get_labels_np_array(classifier.predict(x_train_adv)).astype(float)
+        # test_y_pred = get_labels_np_array(classifier.predict(x_test_adv)).astype(float)
+        #
+        # train_success_rate = (
+        #     np.sum(np.argmax(train_y_pred, axis=1) != np.argmax(classifier.predict(x_train), axis=1))
+        #     / y_train.shape[0]
+        # )
+        # self.assertGreaterEqual(train_success_rate, base_success_rate)
+        #
+        # test_success_rate = (
+        #     np.sum(np.argmax(test_y_pred, axis=1) != np.argmax(classifier.predict(x_test), axis=1)) / y_test.shape[0]
+        # )
+        # self.assertGreaterEqual(test_success_rate, base_success_rate)
+        #
+        # # Test Wasserstein with targeted attack
+        # master_seed(1234)
+        # attack = Wasserstein(
+        #     classifier,
+        #     regularization=regularization,
+        #     max_iter=num_iter,
+        #     conjugate_sinkhorn_max_iter=num_iter,
+        #     projected_sinkhorn_max_iter=num_iter,
+        #     norm="wasserstein",
+        #     ball="wasserstein",
+        #     targeted=True,
+        #     p=2,
+        #     eps_iter=2,
+        #     eps_factor=1.05,
+        #     eps=eps,
+        #     eps_step=0.1,
+        #     kernel_size=5,
+        #     batch_size=batch_size,
+        # )
+        #
+        # train_y_rand = random_targets(y_train, nb_classes=10)
+        # test_y_rand = random_targets(y_test, nb_classes=10)
+        #
+        # x_train_adv = attack.generate(x_train, train_y_rand)
+        # x_test_adv = attack.generate(x_test, test_y_rand)
+        #
+        # train_y_pred = get_labels_np_array(classifier.predict(x_train_adv)).astype(float)
+        # test_y_pred = get_labels_np_array(classifier.predict(x_test_adv)).astype(float)
+        #
+        # train_success_rate = (
+        #     np.sum(np.argmax(train_y_pred, axis=1) == np.argmax(train_y_rand, axis=1)) / y_train.shape[0]
+        # )
+        # self.assertGreaterEqual(train_success_rate, base_success_rate)
+        #
+        # test_success_rate = np.sum(np.argmax(test_y_pred, axis=1) == np.argmax(test_y_rand, axis=1)) / y_test.shape[0]
+        # self.assertGreaterEqual(test_success_rate, 0)
+        #
+        # # Test Wasserstein with p-wasserstein=1 and kernel_size=3
+        # attack = Wasserstein(
+        #     classifier,
+        #     regularization=regularization,
+        #     max_iter=num_iter,
+        #     conjugate_sinkhorn_max_iter=num_iter,
+        #     projected_sinkhorn_max_iter=num_iter,
+        #     norm="wasserstein",
+        #     ball="wasserstein",
+        #     targeted=False,
+        #     p=1,
+        #     eps_iter=2,
+        #     eps_factor=1.05,
+        #     eps=eps,
+        #     eps_step=0.1,
+        #     kernel_size=3,
+        #     batch_size=batch_size,
+        # )
+        #
+        # x_train_adv = attack.generate(x_train)
+        # x_test_adv = attack.generate(x_test)
+        #
+        # train_y_pred = get_labels_np_array(classifier.predict(x_train_adv)).astype(float)
+        # test_y_pred = get_labels_np_array(classifier.predict(x_test_adv)).astype(float)
+        #
+        # train_success_rate = (
+        #     np.sum(np.argmax(train_y_pred, axis=1) != np.argmax(classifier.predict(x_train), axis=1))
+        #     / y_train.shape[0]
+        # )
+        # self.assertTrue(train_success_rate >= base_success_rate)
+        #
+        # test_success_rate = (
+        #     np.sum(np.argmax(test_y_pred, axis=1) != np.argmax(classifier.predict(x_test), axis=1)) / y_test.shape[0]
+        # )
+        # self.assertTrue(test_success_rate >= base_success_rate)
+
+    def test_classifier_type_check_fail(self):
+        backend_test_classifier_type_check_fail(Wasserstein, (BaseEstimator, LossGradientsMixin, ClassifierMixin))
+
+    def test_unsquared_images(self):
+        from art.estimators.estimator import (
+            BaseEstimator,
+            LossGradientsMixin,
+            NeuralNetworkMixin,
+        )
+
+        from art.estimators.classification.classifier import (
+            ClassGradientsMixin,
+            ClassifierMixin,
+        )
+
+        class DummyClassifier(
+            ClassGradientsMixin, ClassifierMixin, NeuralNetworkMixin, LossGradientsMixin, BaseEstimator
+        ):
+            estimator_params = (
+                BaseEstimator.estimator_params + NeuralNetworkMixin.estimator_params + ClassifierMixin.estimator_params
+            )
+
+            def __init__(self):
+                super(DummyClassifier, self).__init__(model=None, clip_values=None, channels_first=True)
+                self._nb_classes = 10
+
+            def class_gradient(self):
+                return None
+
+            def fit(self):
+                pass
+
+            def loss_gradient(self, x, y):
+                return np.random.normal(size=(1, 3, 33, 32))
+
+            def predict(self, x, batch_size=1):
+                return np.array([[0, 1, 0, 0, 0, 0, 0, 0, 0, 0]])
+
+            def get_activations(self):
+                return None
+
+            def save(self):
+                pass
+
+            def compute_loss(self, x, y, **kwargs):
+                pass
+
+            def input_shape(self):
+                pass
+
+        classifier = DummyClassifier()
+        attack = Wasserstein(
+            classifier,
+            regularization=1,
+            kernel_size=3,
+            max_iter=1,
+            conjugate_sinkhorn_max_iter=10,
+            projected_sinkhorn_max_iter=10,
+        )
+
+        x = np.random.normal(size=(1, 3, 33, 32))
+        x_adv = attack.generate(x)
+
+        self.assertTrue(x_adv.shape == x.shape)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/adversarial-robustness-toolbox/tests/attacks/test_zoo.py b/adversarial-robustness-toolbox/tests/attacks/test_zoo.py
new file mode 100644
index 0000000..c750faa
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/attacks/test_zoo.py
@@ -0,0 +1,229 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import unittest
+
+import keras.backend as k
+import numpy as np
+
+from art.attacks.evasion.zoo import ZooAttack
+from art.estimators.estimator import BaseEstimator
+from art.estimators.classification.classifier import ClassifierMixin
+from art.utils import random_targets
+
+from tests.utils import TestBase, get_image_classifier_kr, get_image_classifier_pt
+from tests.utils import get_image_classifier_tf, master_seed
+from tests.attacks.utils import backend_test_classifier_type_check_fail
+
+logger = logging.getLogger(__name__)
+
+
+class TestZooAttack(TestBase):
+    """
+    A unittest class for testing the ZOO attack.
+    """
+
+    @classmethod
+    def setUpClass(cls):
+        master_seed(seed=1234)
+        super().setUpClass()
+
+        cls.n_train = 1
+        cls.n_test = 1
+        cls.x_train_mnist = cls.x_train_mnist[0 : cls.n_train]
+        cls.y_train_mnist = cls.y_train_mnist[0 : cls.n_train]
+        cls.x_test_mnist = cls.x_test_mnist[0 : cls.n_test]
+        cls.y_test_mnist = cls.y_test_mnist[0 : cls.n_test]
+
+    def test_2_tensorflow_failure_attack(self):
+        """
+        Test the corner case when attack fails.
+        :return:
+        """
+        x_test_original = self.x_test_mnist.copy()
+
+        # Build TensorFlowClassifier
+        tfc, sess = get_image_classifier_tf()
+
+        # Failure attack
+        zoo = ZooAttack(classifier=tfc, max_iter=0, binary_search_steps=0, learning_rate=0)
+        x_test_mnist_adv = zoo.generate(self.x_test_mnist)
+        self.assertLessEqual(np.amax(x_test_mnist_adv), 1.0)
+        self.assertGreaterEqual(np.amin(x_test_mnist_adv), 0.0)
+        np.testing.assert_almost_equal(self.x_test_mnist, x_test_mnist_adv, 3)
+
+        # Check that x_test has not been modified by attack and classifier
+        self.assertAlmostEqual(float(np.max(np.abs(x_test_original - self.x_test_mnist))), 0.0, delta=0.00001)
+
+        # Clean-up session
+        if sess is not None:
+            sess.close()
+
+    def test_3_tensorflow_mnist(self):
+        """
+        First test with the TensorFlowClassifier.
+        :return:
+        """
+        x_test_original = self.x_test_mnist.copy()
+
+        # Build TensorFlowClassifier
+        tfc, sess = get_image_classifier_tf()
+
+        # Targeted attack
+        zoo = ZooAttack(classifier=tfc, targeted=True, max_iter=30, binary_search_steps=8, batch_size=128)
+        params = {"y": random_targets(self.y_test_mnist, tfc.nb_classes)}
+        x_test_mnist_adv = zoo.generate(self.x_test_mnist, **params)
+        self.assertFalse((self.x_test_mnist == x_test_mnist_adv).all())
+        self.assertLessEqual(np.amax(x_test_mnist_adv), 1.0)
+        self.assertGreaterEqual(np.amin(x_test_mnist_adv), 0.0)
+        target = np.argmax(params["y"], axis=1)
+        y_pred_adv = np.argmax(tfc.predict(x_test_mnist_adv), axis=1)
+        logger.debug("ZOO target: %s", target)
+        logger.debug("ZOO actual: %s", y_pred_adv)
+        logger.info("ZOO success rate on MNIST: %.2f", (sum(target == y_pred_adv) / float(len(target))))
+
+        # Untargeted attack
+        zoo = ZooAttack(classifier=tfc, targeted=False, max_iter=10, binary_search_steps=3)
+        x_test_mnist_adv = zoo.generate(self.x_test_mnist)
+        # self.assertFalse((x_test == x_test_adv).all())
+        self.assertLessEqual(np.amax(x_test_mnist_adv), 1.0)
+        self.assertGreaterEqual(np.amin(x_test_mnist_adv), 0.0)
+        y_pred = np.argmax(tfc.predict(self.x_test_mnist), axis=1)
+        y_pred_adv = np.argmax(tfc.predict(x_test_mnist_adv), axis=1)
+        logger.debug("ZOO actual: %s", y_pred_adv)
+        logger.info("ZOO success rate on MNIST: %.2f", (sum(y_pred != y_pred_adv) / float(len(y_pred))))
+
+        # Check that x_test has not been modified by attack and classifier
+        self.assertAlmostEqual(float(np.max(np.abs(x_test_original - self.x_test_mnist))), 0.0, delta=0.00001)
+
+        # Clean-up session
+        if sess is not None:
+            sess.close()
+
+    def test_5_keras_mnist(self):
+        """
+        Second test with the KerasClassifier.
+        :return:
+        """
+        x_test_original = self.x_test_mnist.copy()
+
+        # Build KerasClassifier
+        krc = get_image_classifier_kr()
+
+        # Targeted attack
+        # zoo = ZooAttack(classifier=krc, targeted=True, batch_size=5)
+        # params = {'y': random_targets(self.y_test, krc.nb_classes)}
+        # x_test_adv = zoo.generate(self.x_test, **params)
+        #
+        # self.assertFalse((self.x_test == x_test_adv).all())
+        # self.assertLessEqual(np.amax(x_test_adv), 1.0)
+        # self.assertGreaterEqual(np.amin(x_test_adv), 0.0)
+        # target = np.argmax(params['y'], axis=1)
+        # y_pred_adv = np.argmax(krc.predict(x_test_adv), axis=1)
+        # logger.debug('ZOO target: %s', target)
+        # logger.debug('ZOO actual: %s', y_pred_adv)
+        # logger.info('ZOO success rate on MNIST: %.2f', (sum(target == y_pred_adv) / float(len(target))))
+
+        # Untargeted attack
+        # zoo = ZooAttack(classifier=krc, targeted=False, max_iter=20)
+        zoo = ZooAttack(classifier=krc, targeted=False, batch_size=5, max_iter=10, binary_search_steps=3)
+        # x_test_adv = zoo.generate(x_test)
+        params = {"y": random_targets(self.y_test_mnist, krc.nb_classes)}
+        x_test_mnist_adv = zoo.generate(self.x_test_mnist, **params)
+
+        # x_test_adv_true = [0.00000000e+00, 2.50167388e-04, 1.50529508e-04, 4.69674182e-04,
+        #                    0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
+        #                    1.67321396e-05, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
+        #                    0.00000000e+00, 2.08451956e-06, 0.00000000e+00, 0.00000000e+00,
+        #                    2.53360748e-01, 9.60119188e-01, 9.85227525e-01, 2.53600776e-01,
+        #                    0.00000000e+00, 0.00000000e+00, 5.23251540e-04, 0.00000000e+00,
+        #                    0.00000000e+00, 0.00000000e+00, 1.08632184e-05, 0.00000000e+00]
+        #
+        # for i in range(14):
+        #     self.assertAlmostEqual(x_test_adv_true[i], x_test_adv[0, 14, i, 0])
+
+        # self.assertFalse((x_test == x_test_adv).all())
+        self.assertLessEqual(np.amax(x_test_mnist_adv), 1.0)
+        self.assertGreaterEqual(np.amin(x_test_mnist_adv), 0.0)
+        y_pred_adv = np.argmax(krc.predict(x_test_mnist_adv), axis=1)
+        y_pred = np.argmax(krc.predict(self.x_test_mnist), axis=1)
+        logger.debug("ZOO actual: %s", y_pred_adv)
+        logger.info("ZOO success rate on MNIST: %.2f", (sum(y_pred != y_pred_adv) / float(len(y_pred))))
+
+        # Check that x_test has not been modified by attack and classifier
+        self.assertAlmostEqual(float(np.max(np.abs(x_test_original - self.x_test_mnist))), 0.0, delta=0.00001)
+
+        # Clean-up
+        k.clear_session()
+
+    def test_4_pytorch_mnist(self):
+        """
+        Third test with the PyTorchClassifier.
+        :return:
+        """
+        # Build PyTorchClassifier
+        ptc = get_image_classifier_pt()
+
+        # Get MNIST
+        x_test_mnist = np.swapaxes(self.x_test_mnist, 1, 3).astype(np.float32)
+        x_test_original = x_test_mnist.copy()
+
+        # First attack
+        # zoo = ZooAttack(classifier=ptc, targeted=True, max_iter=10, binary_search_steps=10)
+        # params = {'y': random_targets(self.y_test, ptc.nb_classes)}
+        # x_test_adv = zoo.generate(x_test, **params)
+        # self.assertFalse((x_test == x_test_adv).all())
+        # self.assertLessEqual(np.amax(x_test_adv), 1.0)
+        # self.assertGreaterEqual(np.amin(x_test_adv), 0.0)
+        # target = np.argmax(params['y'], axis=1)
+        # y_pred_adv = np.argmax(ptc.predict(x_test_adv), axis=1)
+        # logger.debug('ZOO target: %s', target)
+        # logger.debug('ZOO actual: %s', y_pred_adv)
+        # logger.info('ZOO success rate on MNIST: %.2f', (sum(target != y_pred_adv) / float(len(target))))
+
+        # Second attack
+        zoo = ZooAttack(
+            classifier=ptc,
+            targeted=False,
+            learning_rate=1e-2,
+            max_iter=10,
+            binary_search_steps=3,
+            abort_early=False,
+            use_resize=False,
+            use_importance=False,
+        )
+        x_test_mnist_adv = zoo.generate(x_test_mnist)
+        self.assertLessEqual(np.amax(x_test_mnist_adv), 1.0)
+        self.assertGreaterEqual(np.amin(x_test_mnist_adv), 0.0)
+
+        # print(x_test[0, 0, 14, :])
+        # print(x_test_adv[0, 0, 14, :])
+        # print(np.amax(x_test - x_test_adv))
+        # x_test_adv_expected = []
+
+        # Check that x_test has not been modified by attack and classifier
+        self.assertAlmostEqual(float(np.max(np.abs(x_test_original - x_test_mnist))), 0.0, delta=0.00001)
+
+    def test_1_classifier_type_check_fail(self):
+        backend_test_classifier_type_check_fail(ZooAttack, [BaseEstimator, ClassifierMixin])
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/adversarial-robustness-toolbox/tests/attacks/utils.py b/adversarial-robustness-toolbox/tests/attacks/utils.py
new file mode 100644
index 0000000..3780f0c
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/attacks/utils.py
@@ -0,0 +1,214 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+import pytest
+import numpy as np
+import logging
+
+import keras.backend as k
+
+from art.utils import random_targets, get_labels_np_array
+from art.exceptions import EstimatorError
+
+from tests.utils import check_adverse_example_x, check_adverse_predicted_sample_y
+
+logger = logging.getLogger(__name__)
+
+
+def backend_targeted_images(attack, fix_get_mnist_subset):
+    (x_train_mnist, y_train_mnist, x_test_mnist, y_test_mnist) = fix_get_mnist_subset
+    targets = random_targets(y_test_mnist, attack.estimator.nb_classes)
+    x_test_adv = attack.generate(x_test_mnist, y=targets)
+    assert bool((x_test_mnist == x_test_adv).all()) is False
+
+    y_test_pred_adv = get_labels_np_array(attack.estimator.predict(x_test_adv))
+
+    assert targets.shape == y_test_pred_adv.shape
+    assert (targets == y_test_pred_adv).sum() >= (x_test_mnist.shape[0] // 2)
+
+    check_adverse_example_x(x_test_adv, x_test_mnist)
+
+    y_pred_adv = np.argmax(attack.estimator.predict(x_test_adv), axis=1)
+
+    target = np.argmax(targets, axis=1)
+    assert (target == y_pred_adv).any()
+
+
+def backend_test_defended_images(attack, mnist_dataset):
+    (x_train_mnist, y_train_mnist, x_test_mnist, y_test_mnist) = mnist_dataset
+    x_train_adv = attack.generate(x_train_mnist)
+
+    check_adverse_example_x(x_train_adv, x_train_mnist)
+
+    y_train_pred_adv = get_labels_np_array(attack.estimator.predict(x_train_adv))
+    y_train_labels = get_labels_np_array(y_train_mnist)
+
+    check_adverse_predicted_sample_y(y_train_pred_adv, y_train_labels)
+
+    x_test_adv = attack.generate(x_test_mnist)
+    check_adverse_example_x(x_test_adv, x_test_mnist)
+
+    y_test_pred_adv = get_labels_np_array(attack.estimator.predict(x_test_adv))
+    check_adverse_predicted_sample_y(y_test_pred_adv, y_test_mnist)
+
+
+def backend_test_random_initialisation_images(attack, mnist_dataset):
+    (x_train_mnist, y_train_mnist, x_test_mnist, y_test_mnist) = mnist_dataset
+    x_test_adv = attack.generate(x_test_mnist)
+    assert bool((x_test_mnist == x_test_adv).all()) is False
+
+
+def backend_check_adverse_values(attack, mnist_dataset, expected_values):
+    (x_train_mnist, y_train_mnist, x_test_mnist, y_test_mnist) = mnist_dataset
+    x_test_adv = attack.generate(x_test_mnist)
+    y_test_pred_adv_matrix = attack.estimator.predict(x_test_adv)
+    y_test_pred_adv = np.argmax(y_test_pred_adv_matrix, axis=1)
+
+    if "x_test_mean" in expected_values:
+        np.testing.assert_array_almost_equal(
+            float(np.mean(x_test_adv - x_test_mnist)),
+            expected_values["x_test_mean"].value,
+            decimal=expected_values["x_test_mean"].decimals,
+        )
+    if "x_test_min" in expected_values:
+        # utils_test.assert_almost_equal_min(x_test_mnist, x_test_adv,
+        # expected_values["x_test_min"].value, decimal=expected_values["x_test_min"].decimals)
+        np.testing.assert_array_almost_equal(
+            float(np.min(x_test_adv - x_test_mnist)),
+            expected_values["x_test_min"].value,
+            decimal=expected_values["x_test_min"].decimals,
+        )
+    if "x_test_max" in expected_values:
+        np.testing.assert_array_almost_equal(
+            float(np.max(x_test_adv - x_test_mnist)),
+            expected_values["x_test_max"].value,
+            decimal=expected_values["x_test_max"].decimals,
+        )
+    if "y_test_pred_adv_expected_matrix" in expected_values:
+        np.testing.assert_array_almost_equal(
+            y_test_pred_adv_matrix,
+            expected_values["y_test_pred_adv_expected_matrix"].value,
+            decimal=expected_values["y_test_pred_adv_expected"].decimals,
+        )
+    if "y_test_pred_adv_expected" in expected_values:
+        np.testing.assert_array_equal(y_test_pred_adv, expected_values["y_test_pred_adv_expected"].value)
+
+
+def backend_check_adverse_frames(attack, mnist_dataset, expected_values):
+    (x_train_mnist, y_train_mnist, x_test_mnist, y_test_mnist) = mnist_dataset
+    x_test_adv = attack.generate(x_test_mnist)
+
+    x_diff = x_test_adv - x_test_mnist
+    x_diff = np.swapaxes(x_diff, 1, attack.frame_index)
+    x_diff = np.reshape(x_diff, x_diff.shape[:2] + (np.prod(x_diff.shape[2:]),))
+    x_diff_norm = np.round(np.linalg.norm(x_diff, axis=-1), decimals=4)
+    x_diff_norm = np.linalg.norm(x_diff_norm, ord=0, axis=-1)
+
+    np.testing.assert_array_equal(x_diff_norm, expected_values["nb_perturbed_frames"].value)
+
+
+def backend_test_classifier_type_check_fail(attack, classifier_expected_list=[], classifier=None):
+
+    assert len(classifier_expected_list) == len(attack._estimator_requirements)
+
+    for cls in classifier_expected_list:
+        assert cls in classifier_expected_list
+
+    for cls in attack._estimator_requirements:
+        assert cls in classifier_expected_list
+
+    # Use a useless test classifier to test basic classifier properties
+    class ClassifierNoAPI:
+        pass
+
+    classifier = ClassifierNoAPI
+
+    with pytest.raises(EstimatorError) as exception:
+        _ = attack(classifier)
+
+    for classifier_expected in classifier_expected_list:
+        assert classifier_expected in exception.value.class_expected_list
+
+
+def backend_targeted_tabular(attack, fix_get_iris):
+    (_, _), (x_test_iris, y_test_iris) = fix_get_iris
+
+    targets = random_targets(y_test_iris, nb_classes=3)
+    x_test_adv = attack.generate(x_test_iris, **{"y": targets})
+
+    check_adverse_example_x(x_test_adv, x_test_iris)
+
+    y_pred_adv = np.argmax(attack.estimator.predict(x_test_adv), axis=1)
+    target = np.argmax(targets, axis=1)
+    assert (target == y_pred_adv).any()
+
+    accuracy = np.sum(y_pred_adv == target) / y_test_iris.shape[0]
+    logger.info("Success rate of targeted boundary on Iris: %.2f%%", (accuracy * 100))
+
+
+def back_end_untargeted_images(attack, fix_get_mnist_subset, fix_framework):
+    (x_train_mnist, y_train_mnist, x_test_mnist, y_test_mnist) = fix_get_mnist_subset
+
+    x_test_adv = attack.generate(x_test_mnist)
+
+    check_adverse_example_x(x_test_adv, x_test_mnist)
+
+    y_pred = np.argmax(attack.estimator.predict(x_test_mnist), axis=1)
+    y_pred_adv = np.argmax(attack.estimator.predict(x_test_adv), axis=1)
+    assert (y_pred != y_pred_adv).any()
+
+    if fix_framework in ["keras"]:
+        k.clear_session()
+
+
+def backend_untargeted_tabular(attack, iris_dataset, clipped):
+    (_, _), (x_test_iris, y_test_iris) = iris_dataset
+
+    x_test_adv = attack.generate(x_test_iris)
+
+    # TODO remove that platform specific case
+    # if framework in ["scikitlearn"]:
+    #     np.testing.assert_array_almost_equal(np.abs(x_test_adv - x_test_iris), .1, decimal=5)
+
+    check_adverse_example_x(x_test_adv, x_test_iris)
+    # utils_test.check_adverse_example_x(x_test_adv, x_test_iris, bounded=clipped)
+
+    y_pred_test_adv = np.argmax(attack.estimator.predict(x_test_adv), axis=1)
+    y_test_true = np.argmax(y_test_iris, axis=1)
+
+    # assert (y_test_true == y_pred_test_adv).any(), "An untargeted attack should have changed SOME predictions"
+    assert (
+        bool((y_test_true == y_pred_test_adv).all()) is False
+    ), "An untargeted attack should NOT have changed all predictions"
+
+    accuracy = np.sum(y_pred_test_adv == y_test_true) / y_test_true.shape[0]
+    logger.info(
+        "Accuracy of " + attack.estimator.__class__.__name__ + " on Iris with FGM adversarial examples: " "%.2f%%",
+        (accuracy * 100),
+    )
+
+
+def backend_masked_images(attack, fix_get_mnist_subset):
+    (x_train_mnist, y_train_mnist, x_test_mnist, y_test_mnist) = fix_get_mnist_subset
+
+    # generate a random mask:
+    mask = np.random.binomial(n=1, p=0.5, size=np.prod(x_test_mnist.shape))
+    mask = mask.reshape(x_test_mnist.shape).astype(np.float32)
+
+    x_test_adv = attack.generate(x_test_mnist, mask=mask)
+    mask_diff = (1 - mask) * (x_test_adv - x_test_mnist)
+    np.testing.assert_array_almost_equal(mask_diff, np.zeros(mask_diff.shape), decimal=3)
diff --git a/adversarial-robustness-toolbox/tests/classifiersFrameworks/test_pytorch.py b/adversarial-robustness-toolbox/tests/classifiersFrameworks/test_pytorch.py
new file mode 100644
index 0000000..dff01b9
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/classifiersFrameworks/test_pytorch.py
@@ -0,0 +1,223 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+import numpy as np
+import pytest
+import torch
+import torch.nn as nn
+
+from art.estimators.classification.pytorch import PyTorchClassifier
+from art.defences.preprocessor.spatial_smoothing import SpatialSmoothing
+from art.defences.preprocessor.spatial_smoothing_pytorch import SpatialSmoothingPyTorch
+from art.attacks.evasion import FastGradientMethod
+
+from tests.attacks.utils import backend_test_defended_images
+from tests.utils import ARTTestException
+
+
+@pytest.fixture()
+def fix_get_mnist_subset(get_mnist_dataset):
+    (x_train_mnist, y_train_mnist), (x_test_mnist, y_test_mnist) = get_mnist_dataset
+    n_train = 100
+    n_test = 11
+    yield x_train_mnist[:n_train], y_train_mnist[:n_train], x_test_mnist[:n_test], y_test_mnist[:n_test]
+
+
+# A generic test for various preprocessing_defences, forward pass.
+def _test_preprocessing_defences_forward(
+    get_default_mnist_subset, image_dl_estimator, device_type, preprocessing_defences
+):
+    (_, _), (x_test_mnist, y_test_mnist) = get_default_mnist_subset
+
+    classifier_, _ = image_dl_estimator()
+
+    clip_values = (0, 1)
+    criterion = nn.CrossEntropyLoss()
+    classifier = PyTorchClassifier(
+        clip_values=clip_values,
+        model=classifier_.model,
+        preprocessing_defences=preprocessing_defences,
+        loss=criterion,
+        input_shape=(1, 28, 28),
+        nb_classes=10,
+        device_type=device_type,
+    )
+
+    with torch.no_grad():
+        predictions_classifier = classifier.predict(x_test_mnist)
+
+    # Apply the same defences by hand
+    x_test_defense = x_test_mnist
+    for defence in preprocessing_defences:
+        x_test_defense, _ = defence(x_test_defense, y_test_mnist)
+
+    x_test_defense = torch.tensor(x_test_defense)
+    with torch.no_grad():
+        predictions_check = classifier_.model(x_test_defense)
+    predictions_check = predictions_check.cpu().numpy()
+
+    # Check that the prediction results match
+    np.testing.assert_array_almost_equal(predictions_classifier, predictions_check, decimal=4)
+
+
+# A generic test for various preprocessing_defences, backward pass.
+def _test_preprocessing_defences_backward(
+    get_default_mnist_subset, image_dl_estimator, device_type, preprocessing_defences
+):
+    (_, _), (x_test_mnist, y_test_mnist) = get_default_mnist_subset
+
+    classifier_, _ = image_dl_estimator()
+
+    clip_values = (0, 1)
+    criterion = nn.CrossEntropyLoss()
+    classifier = PyTorchClassifier(
+        clip_values=clip_values,
+        model=classifier_.model,
+        preprocessing_defences=preprocessing_defences,
+        loss=criterion,
+        input_shape=(1, 28, 28),
+        nb_classes=10,
+        device_type=device_type,
+    )
+
+    # The efficient defence-chaining.
+    pseudo_gradients = np.random.randn(*x_test_mnist.shape)
+    gradients_in_chain = classifier._apply_preprocessing_gradient(x_test_mnist, pseudo_gradients)
+
+    # Apply the same backward pass one by one.
+    x = x_test_mnist
+    x_intermediates = [x]
+    for preprocess in classifier.preprocessing_operations[:-1]:
+        x = preprocess(x)[0]
+        x_intermediates.append(x)
+
+    gradients = pseudo_gradients
+    for preprocess, x in zip(classifier.preprocessing_operations[::-1], x_intermediates[::-1]):
+        gradients = preprocess.estimate_gradient(x, gradients)
+
+    np.testing.assert_array_almost_equal(gradients_in_chain, gradients, decimal=4)
+
+
+@pytest.mark.only_with_platform("pytorch")
+@pytest.mark.parametrize("device_type", ["cpu", "gpu"])
+def test_nodefence(art_warning, get_default_mnist_subset, image_dl_estimator, device_type):
+    try:
+        preprocessing_defences = []
+        _test_preprocessing_defences_forward(
+            get_default_mnist_subset, image_dl_estimator, device_type, preprocessing_defences
+        )
+        _test_preprocessing_defences_backward(
+            get_default_mnist_subset, image_dl_estimator, device_type, preprocessing_defences
+        )
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.only_with_platform("pytorch")
+@pytest.mark.parametrize("device_type", ["cpu", "gpu"])
+def test_defence_pytorch(art_warning, get_default_mnist_subset, image_dl_estimator, device_type):
+    try:
+        smooth_3x3 = SpatialSmoothingPyTorch(window_size=3, channels_first=True, device_type=device_type)
+        preprocessing_defences = [smooth_3x3]
+        _test_preprocessing_defences_forward(
+            get_default_mnist_subset, image_dl_estimator, device_type, preprocessing_defences
+        )
+        _test_preprocessing_defences_backward(
+            get_default_mnist_subset, image_dl_estimator, device_type, preprocessing_defences
+        )
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.only_with_platform("pytorch")
+@pytest.mark.parametrize("device_type", ["cpu", "gpu"])
+def test_defence_non_pytorch(art_warning, get_default_mnist_subset, image_dl_estimator, device_type):
+    try:
+        smooth_3x3 = SpatialSmoothing(window_size=3, channels_first=True)
+        preprocessing_defences = [smooth_3x3]
+        _test_preprocessing_defences_forward(
+            get_default_mnist_subset, image_dl_estimator, device_type, preprocessing_defences
+        )
+        _test_preprocessing_defences_backward(
+            get_default_mnist_subset, image_dl_estimator, device_type, preprocessing_defences
+        )
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.xfail(reason="Preprocessing-defence chaining only supports defences implemented in PyTorch.")
+@pytest.mark.only_with_platform("pytorch")
+@pytest.mark.parametrize("device_type", ["cpu", "gpu"])
+def test_defences_pytorch_and_nonpytorch(art_warning, get_default_mnist_subset, image_dl_estimator, device_type):
+    try:
+        smooth_3x3_nonpth = SpatialSmoothing(window_size=3, channels_first=True)
+        smooth_3x3_pth = SpatialSmoothingPyTorch(window_size=3, channels_first=True, device_type=device_type)
+        preprocessing_defences = [smooth_3x3_nonpth, smooth_3x3_pth]
+        _test_preprocessing_defences_forward(
+            get_default_mnist_subset, image_dl_estimator, device_type, preprocessing_defences
+        )
+        _test_preprocessing_defences_backward(
+            get_default_mnist_subset, image_dl_estimator, device_type, preprocessing_defences
+        )
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.only_with_platform("pytorch")
+@pytest.mark.parametrize("device_type", ["cpu", "gpu"])
+def test_defences_chaining(art_warning, get_default_mnist_subset, image_dl_estimator, device_type):
+    try:
+        smooth_3x3 = SpatialSmoothingPyTorch(window_size=3, channels_first=True, device_type=device_type)
+        smooth_5x5 = SpatialSmoothingPyTorch(window_size=5, channels_first=True, device_type=device_type)
+        smooth_7x7 = SpatialSmoothingPyTorch(window_size=7, channels_first=True, device_type=device_type)
+        preprocessing_defences = [smooth_3x3, smooth_5x5, smooth_7x7]
+        _test_preprocessing_defences_forward(
+            get_default_mnist_subset, image_dl_estimator, device_type, preprocessing_defences
+        )
+        _test_preprocessing_defences_backward(
+            get_default_mnist_subset, image_dl_estimator, device_type, preprocessing_defences
+        )
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.only_with_platform("pytorch")
+@pytest.mark.parametrize("device_type", ["cpu", "gpu"])
+def test_fgsm_defences(art_warning, fix_get_mnist_subset, image_dl_estimator, device_type):
+    try:
+        clip_values = (0, 1)
+        smooth_3x3 = SpatialSmoothingPyTorch(window_size=3, channels_first=True, device_type=device_type)
+        smooth_5x5 = SpatialSmoothingPyTorch(window_size=5, channels_first=True, device_type=device_type)
+        smooth_7x7 = SpatialSmoothingPyTorch(window_size=7, channels_first=True, device_type=device_type)
+        classifier_, _ = image_dl_estimator()
+
+        criterion = nn.CrossEntropyLoss()
+        classifier = PyTorchClassifier(
+            clip_values=clip_values,
+            model=classifier_.model,
+            preprocessing_defences=[smooth_3x3, smooth_5x5, smooth_7x7],
+            loss=criterion,
+            input_shape=(1, 28, 28),
+            nb_classes=10,
+            device_type=device_type,
+        )
+        assert len(classifier.preprocessing_defences) == 3
+
+        attack = FastGradientMethod(classifier, eps=1.0, batch_size=128)
+        backend_test_defended_images(attack, fix_get_mnist_subset)
+    except ARTTestException as e:
+        art_warning(e)
diff --git a/adversarial-robustness-toolbox/tests/classifiersFrameworks/test_tensorflow.py b/adversarial-robustness-toolbox/tests/classifiersFrameworks/test_tensorflow.py
new file mode 100644
index 0000000..6c8ddd5
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/classifiersFrameworks/test_tensorflow.py
@@ -0,0 +1,217 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+import numpy as np
+import pytest
+
+import tensorflow as tf
+
+from art.estimators.classification.tensorflow import TensorFlowV2Classifier
+from art.defences.preprocessor.spatial_smoothing import SpatialSmoothing
+from art.defences.preprocessor.spatial_smoothing_tensorflow import SpatialSmoothingTensorFlowV2
+from art.attacks.evasion import FastGradientMethod
+
+from tests.attacks.utils import backend_test_defended_images
+from tests.utils import ARTTestException
+
+
+@pytest.fixture()
+def fix_get_mnist_subset(get_mnist_dataset):
+    (x_train_mnist, y_train_mnist), (x_test_mnist, y_test_mnist) = get_mnist_dataset
+    n_train = 100
+    n_test = 11
+    yield x_train_mnist[:n_train], y_train_mnist[:n_train], x_test_mnist[:n_test], y_test_mnist[:n_test]
+
+
+# A generic test for various preprocessing_defences, forward pass.
+def _test_preprocessing_defences_forward(
+    get_default_mnist_subset, image_dl_estimator, device_type, preprocessing_defences
+):
+    (_, _), (x_test_mnist, y_test_mnist) = get_default_mnist_subset
+
+    classifier_, _ = image_dl_estimator()
+
+    clip_values = (0, 1)
+    loss_object = tf.keras.losses.CategoricalCrossentropy(from_logits=True)
+    classifier = TensorFlowV2Classifier(
+        clip_values=clip_values,
+        model=classifier_.model,
+        preprocessing_defences=preprocessing_defences,
+        loss_object=loss_object,
+        input_shape=(28, 28, 1),
+        nb_classes=10,
+    )
+
+    predictions_classifier = classifier.predict(x_test_mnist)
+
+    # Apply the same defences by hand
+    x_test_defense = x_test_mnist
+    for defence in preprocessing_defences:
+        x_test_defense, _ = defence(x_test_defense, y_test_mnist)
+
+    x_test_defense = tf.convert_to_tensor(x_test_defense)
+    predictions_check = classifier_.model(x_test_defense)
+    predictions_check = predictions_check.cpu().numpy()
+
+    # Check that the prediction results match
+    np.testing.assert_array_almost_equal(predictions_classifier, predictions_check, decimal=4)
+
+
+# A generic test for various preprocessing_defences, backward pass.
+def _test_preprocessing_defences_backward(
+    get_default_mnist_subset, image_dl_estimator, device_type, preprocessing_defences
+):
+    (_, _), (x_test_mnist, y_test_mnist) = get_default_mnist_subset
+
+    classifier_, _ = image_dl_estimator()
+
+    clip_values = (0, 1)
+    loss_object = tf.keras.losses.CategoricalCrossentropy(from_logits=True)
+    classifier = TensorFlowV2Classifier(
+        clip_values=clip_values,
+        model=classifier_.model,
+        preprocessing_defences=preprocessing_defences,
+        loss_object=loss_object,
+        input_shape=(28, 28, 1),
+        nb_classes=10,
+    )
+
+    # The efficient defence-chaining.
+    pseudo_gradients = np.random.randn(*x_test_mnist.shape)
+    gradients_in_chain = classifier._apply_preprocessing_gradient(x_test_mnist, pseudo_gradients)
+
+    # Apply the same backward pass one by one.
+    x = x_test_mnist
+    x_intermediates = [x]
+    for preprocess in classifier.preprocessing_operations[:-1]:
+        x = preprocess(x)[0]
+        x_intermediates.append(x)
+
+    gradients = pseudo_gradients
+    for preprocess, x in zip(classifier.preprocessing_operations[::-1], x_intermediates[::-1]):
+        gradients = preprocess.estimate_gradient(x, gradients)
+
+    np.testing.assert_array_almost_equal(gradients_in_chain, gradients, decimal=4)
+
+
+@pytest.mark.only_with_platform("tensorflow2")
+def test_nodefence(art_warning, get_default_mnist_subset, image_dl_estimator):
+    try:
+        preprocessing_defences = []
+        device_type = None
+        _test_preprocessing_defences_forward(
+            get_default_mnist_subset, image_dl_estimator, device_type, preprocessing_defences
+        )
+        _test_preprocessing_defences_backward(
+            get_default_mnist_subset, image_dl_estimator, device_type, preprocessing_defences
+        )
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.only_with_platform("tensorflow2")
+def test_defence_tensorflow(art_warning, get_default_mnist_subset, image_dl_estimator):
+    try:
+        smooth_3x3 = SpatialSmoothingTensorFlowV2(window_size=3, channels_first=False)
+        preprocessing_defences = [smooth_3x3]
+        device_type = None
+        _test_preprocessing_defences_forward(
+            get_default_mnist_subset, image_dl_estimator, device_type, preprocessing_defences
+        )
+        _test_preprocessing_defences_backward(
+            get_default_mnist_subset, image_dl_estimator, device_type, preprocessing_defences
+        )
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.only_with_platform("tensorflow2")
+def test_defence_non_tensorflow(art_warning, get_default_mnist_subset, image_dl_estimator):
+    try:
+        smooth_3x3 = SpatialSmoothing(window_size=3, channels_first=False)
+        preprocessing_defences = [smooth_3x3]
+        device_type = None
+        _test_preprocessing_defences_forward(
+            get_default_mnist_subset, image_dl_estimator, device_type, preprocessing_defences
+        )
+        _test_preprocessing_defences_backward(
+            get_default_mnist_subset, image_dl_estimator, device_type, preprocessing_defences
+        )
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.xfail(reason="Preprocessing-defence chaining only supports defences implemented in TensorFlow v2.")
+@pytest.mark.only_with_platform("tensorflow2")
+def test_defences_tensorflow_and_nontensorflow(art_warning, get_default_mnist_subset, image_dl_estimator, device_type):
+    try:
+        smooth_3x3_nonpth = SpatialSmoothing(window_size=3, channels_first=False)
+        smooth_3x3_pth = SpatialSmoothingTensorFlowV2(window_size=3, channels_first=False)
+        preprocessing_defences = [smooth_3x3_nonpth, smooth_3x3_pth]
+        device_type = None
+        _test_preprocessing_defences_forward(
+            get_default_mnist_subset, image_dl_estimator, device_type, preprocessing_defences
+        )
+        _test_preprocessing_defences_backward(
+            get_default_mnist_subset, image_dl_estimator, device_type, preprocessing_defences
+        )
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.only_with_platform("tensorflow2")
+def test_defences_chaining(art_warning, get_default_mnist_subset, image_dl_estimator):
+    try:
+        smooth_3x3 = SpatialSmoothingTensorFlowV2(window_size=3, channels_first=False)
+        smooth_5x5 = SpatialSmoothingTensorFlowV2(window_size=5, channels_first=False)
+        smooth_7x7 = SpatialSmoothingTensorFlowV2(window_size=7, channels_first=False)
+        preprocessing_defences = [smooth_3x3, smooth_5x5, smooth_7x7]
+        device_type = None
+        _test_preprocessing_defences_forward(
+            get_default_mnist_subset, image_dl_estimator, device_type, preprocessing_defences
+        )
+        _test_preprocessing_defences_backward(
+            get_default_mnist_subset, image_dl_estimator, device_type, preprocessing_defences
+        )
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.only_with_platform("tensorflow2")
+def test_fgsm_defences(art_warning, fix_get_mnist_subset, image_dl_estimator):
+    try:
+        clip_values = (0, 1)
+        smooth_3x3 = SpatialSmoothingTensorFlowV2(window_size=3, channels_first=False)
+        smooth_5x5 = SpatialSmoothingTensorFlowV2(window_size=5, channels_first=False)
+        smooth_7x7 = SpatialSmoothingTensorFlowV2(window_size=7, channels_first=False)
+        classifier_, _ = image_dl_estimator()
+
+        loss_object = tf.keras.losses.CategoricalCrossentropy(from_logits=True)
+        classifier = TensorFlowV2Classifier(
+            clip_values=clip_values,
+            model=classifier_.model,
+            preprocessing_defences=[smooth_3x3, smooth_5x5, smooth_7x7],
+            loss_object=loss_object,
+            input_shape=(28, 28, 1),
+            nb_classes=10,
+        )
+        assert len(classifier.preprocessing_defences) == 3
+
+        attack = FastGradientMethod(classifier, eps=1.0, batch_size=128)
+        backend_test_defended_images(attack, fix_get_mnist_subset)
+    except ARTTestException as e:
+        art_warning(e)
diff --git a/adversarial-robustness-toolbox/tests/defences/__init__.py b/adversarial-robustness-toolbox/tests/defences/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/adversarial-robustness-toolbox/tests/defences/detector/__init__.py b/adversarial-robustness-toolbox/tests/defences/detector/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/adversarial-robustness-toolbox/tests/defences/detector/evasion/__init__.py b/adversarial-robustness-toolbox/tests/defences/detector/evasion/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/adversarial-robustness-toolbox/tests/defences/detector/evasion/subsetscanning/__init__.py b/adversarial-robustness-toolbox/tests/defences/detector/evasion/subsetscanning/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/adversarial-robustness-toolbox/tests/defences/detector/evasion/subsetscanning/test_detector.py b/adversarial-robustness-toolbox/tests/defences/detector/evasion/subsetscanning/test_detector.py
new file mode 100644
index 0000000..f2f5b6e
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/defences/detector/evasion/subsetscanning/test_detector.py
@@ -0,0 +1,86 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+A unittest class for testing the subset scanning detector.
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import unittest
+
+import keras.backend as k
+import numpy as np
+
+from art.attacks.evasion.fast_gradient import FastGradientMethod
+from art.defences.detector.evasion.subsetscanning import SubsetScanningDetector
+from art.utils import load_dataset
+
+from tests.utils import master_seed, get_image_classifier_kr
+
+logger = logging.getLogger(__name__)
+
+BATCH_SIZE = 100
+NB_TRAIN = 100
+NB_TEST = 100
+
+
+class TestSubsetScanningDetector(unittest.TestCase):
+    """
+    A unittest class for testing the subset scanning detector.
+    """
+
+    def setUp(self):
+        master_seed(seed=1234)
+
+    def tearDown(self):
+        k.clear_session()
+
+    def test_subsetscan_detector(self):
+        (x_train, y_train), (x_test, y_test), _, _ = load_dataset("mnist")
+        x_train, y_train = x_train[:NB_TRAIN], y_train[:NB_TRAIN]
+        x_test, y_test = x_test[:NB_TEST], y_test[:NB_TEST]
+
+        # Keras classifier
+        classifier = get_image_classifier_kr()
+
+        # Generate adversarial samples:
+        attacker = FastGradientMethod(classifier, eps=0.5)
+        x_train_adv = attacker.generate(x_train)
+        x_test_adv = attacker.generate(x_test)
+
+        # Compile training data for detector:
+        x_train_detector = np.concatenate((x_train, x_train_adv), axis=0)
+
+        bgd = x_train
+        clean = x_test
+        anom = x_test_adv
+
+        detector = SubsetScanningDetector(classifier, bgd, layer=1)
+
+        _, _, dpwr = detector.scan(clean, clean)
+        self.assertAlmostEqual(dpwr, 0.5)
+
+        _, _, dpwr = detector.scan(clean, anom)
+        self.assertGreater(dpwr, 0.5)
+
+        _, _, dpwr = detector.scan(clean, x_train_detector, 85, 15)
+        self.assertGreater(dpwr, 0.5)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/adversarial-robustness-toolbox/tests/defences/detector/evasion/test_detector.py b/adversarial-robustness-toolbox/tests/defences/detector/evasion/test_detector.py
new file mode 100644
index 0000000..fad8639
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/defences/detector/evasion/test_detector.py
@@ -0,0 +1,168 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import unittest
+
+import keras
+import keras.backend as k
+from keras.layers import Dense, Flatten, Conv2D, MaxPooling2D
+from keras.models import Sequential
+import numpy as np
+
+from art.attacks.evasion.fast_gradient import FastGradientMethod
+from art.estimators.classification.keras import KerasClassifier
+from art.defences.detector.evasion import BinaryInputDetector, BinaryActivationDetector
+from art.utils import load_mnist
+
+from tests.utils import master_seed, get_image_classifier_kr
+
+logger = logging.getLogger(__name__)
+
+BATCH_SIZE, NB_TRAIN, NB_TEST = 100, 1000, 10
+
+
+class TestBinaryInputDetector(unittest.TestCase):
+    """
+    A unittest class for testing the binary input detector.
+    """
+
+    def setUp(self):
+        master_seed(seed=1234)
+
+    def tearDown(self):
+        k.clear_session()
+
+    def test_binary_input_detector(self):
+        """
+        Test the binary input detector end-to-end.
+        :return:
+        """
+        # Get MNIST
+        nb_train, nb_test = 1000, 10
+        (x_train, y_train), (x_test, y_test), _, _ = load_mnist()
+        x_train, y_train = x_train[:NB_TRAIN], y_train[:NB_TRAIN]
+        x_test, y_test = x_test[:NB_TEST], y_test[:NB_TEST]
+
+        # Keras classifier
+        classifier = get_image_classifier_kr()
+
+        # Generate adversarial samples:
+        attacker = FastGradientMethod(classifier, eps=0.1)
+        x_train_adv = attacker.generate(x_train[:nb_train])
+        x_test_adv = attacker.generate(x_test[:nb_test])
+
+        # Compile training data for detector:
+        x_train_detector = np.concatenate((x_train[:nb_train], x_train_adv), axis=0)
+        y_train_detector = np.concatenate((np.array([[1, 0]] * nb_train), np.array([[0, 1]] * nb_train)), axis=0)
+
+        # Create a simple CNN for the detector
+        input_shape = x_train.shape[1:]
+        model = Sequential()
+        model.add(Conv2D(4, kernel_size=(5, 5), activation="relu", input_shape=input_shape))
+        model.add(MaxPooling2D(pool_size=(2, 2)))
+        model.add(Flatten())
+        model.add(Dense(2, activation="softmax"))
+        model.compile(
+            loss=keras.losses.categorical_crossentropy, optimizer=keras.optimizers.Adam(lr=0.01), metrics=["accuracy"]
+        )
+
+        # Create detector and train it:
+        detector = BinaryInputDetector(KerasClassifier(model=model, clip_values=(0, 1), use_logits=False))
+        detector.fit(x_train_detector, y_train_detector, nb_epochs=2, batch_size=128)
+
+        # Apply detector on clean and adversarial test data:
+        test_detection = np.argmax(detector.predict(x_test), axis=1)
+        test_adv_detection = np.argmax(detector.predict(x_test_adv), axis=1)
+
+        # Assert there is at least one true positive and negative:
+        nb_true_positives = len(np.where(test_adv_detection == 1)[0])
+        nb_true_negatives = len(np.where(test_detection == 0)[0])
+        logger.debug("Number of true positives detected: %i", nb_true_positives)
+        logger.debug("Number of true negatives detected: %i", nb_true_negatives)
+        self.assertGreater(nb_true_positives, 0)
+        self.assertGreater(nb_true_negatives, 0)
+
+
+class TestBinaryActivationDetector(unittest.TestCase):
+    """
+    A unittest class for testing the binary activation detector.
+    """
+
+    def setUp(self):
+        master_seed(seed=1234)
+
+    def tearDown(self):
+        k.clear_session()
+
+    def test_binary_activation_detector(self):
+        """
+        Test the binary activation detector end-to-end.
+        :return:
+        """
+        # Get MNIST
+        (x_train, y_train), (x_test, y_test), _, _ = load_mnist()
+        x_train, y_train = x_train[:NB_TRAIN], y_train[:NB_TRAIN]
+        x_test, y_test = x_test[:NB_TEST], y_test[:NB_TEST]
+
+        # Keras classifier
+        classifier = get_image_classifier_kr()
+
+        # Generate adversarial samples:
+        attacker = FastGradientMethod(classifier, eps=0.1)
+        x_train_adv = attacker.generate(x_train[:NB_TRAIN])
+        x_test_adv = attacker.generate(x_test[:NB_TRAIN])
+
+        # Compile training data for detector:
+        x_train_detector = np.concatenate((x_train[:NB_TRAIN], x_train_adv), axis=0)
+        y_train_detector = np.concatenate((np.array([[1, 0]] * NB_TRAIN), np.array([[0, 1]] * NB_TRAIN)), axis=0)
+
+        # Create a simple CNN for the detector
+        activation_shape = classifier.get_activations(x_test[:1], 0, batch_size=128).shape[1:]
+        number_outputs = 2
+        model = Sequential()
+        model.add(MaxPooling2D(pool_size=(2, 2), input_shape=activation_shape))
+        model.add(Flatten())
+        model.add(Dense(number_outputs, activation="softmax"))
+        model.compile(
+            loss=keras.losses.categorical_crossentropy, optimizer=keras.optimizers.Adam(lr=0.01), metrics=["accuracy"]
+        )
+
+        # Create detector and train it.
+        # Detector consider activations at layer=0:
+        detector = BinaryActivationDetector(
+            classifier=classifier, detector=KerasClassifier(model=model, clip_values=(0, 1), use_logits=False), layer=0
+        )
+        detector.fit(x_train_detector, y_train_detector, nb_epochs=2, batch_size=128)
+
+        # Apply detector on clean and adversarial test data:
+        test_detection = np.argmax(detector.predict(x_test), axis=1)
+        test_adv_detection = np.argmax(detector.predict(x_test_adv), axis=1)
+
+        # Assert there is at least one true positive and negative
+        nb_true_positives = len(np.where(test_adv_detection == 1)[0])
+        nb_true_negatives = len(np.where(test_detection == 0)[0])
+        logger.debug("Number of true positives detected: %i", nb_true_positives)
+        logger.debug("Number of true negatives detected: %i", nb_true_negatives)
+        self.assertGreater(nb_true_positives, 0)
+        self.assertGreater(nb_true_negatives, 0)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/adversarial-robustness-toolbox/tests/defences/detector/poison/__init__.py b/adversarial-robustness-toolbox/tests/defences/detector/poison/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/adversarial-robustness-toolbox/tests/defences/detector/poison/test_activation_defence.py b/adversarial-robustness-toolbox/tests/defences/detector/poison/test_activation_defence.py
new file mode 100644
index 0000000..7c2894a
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/defences/detector/poison/test_activation_defence.py
@@ -0,0 +1,309 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import unittest
+
+from keras_preprocessing.image import ImageDataGenerator
+import numpy as np
+
+from art.data_generators import KerasDataGenerator
+from art.defences.detector.poison import ActivationDefence
+from art.utils import load_mnist
+from art.visualization import convert_to_rgb
+
+from tests.utils import master_seed
+
+logger = logging.getLogger(__name__)
+
+NB_TRAIN, NB_TEST, BATCH_SIZE = 300, 10, 128
+
+
+class TestActivationDefence(unittest.TestCase):
+    @classmethod
+    def setUpClass(cls):
+
+        (x_train, y_train), (x_test, y_test), min_, max_ = load_mnist()
+        x_train, y_train = x_train[:NB_TRAIN], y_train[:NB_TRAIN]
+        cls.mnist = (x_train, y_train), (x_test, y_test), (min_, max_)
+
+        # Create simple keras model
+        import tensorflow as tf
+
+        tf_version = [int(v) for v in tf.__version__.split(".")]
+        if tf_version[0] == 2 and tf_version[1] >= 3:
+            tf.compat.v1.disable_eager_execution()
+            from tensorflow.keras.models import Sequential
+            from tensorflow.keras.layers import Dense, Flatten, Conv2D, MaxPooling2D
+        else:
+            from keras.models import Sequential
+            from keras.layers import Dense, Flatten, Conv2D, MaxPooling2D
+
+        model = Sequential()
+        model.add(Conv2D(32, kernel_size=(3, 3), activation="relu", input_shape=x_train.shape[1:]))
+        model.add(MaxPooling2D(pool_size=(3, 3)))
+        model.add(Flatten())
+        model.add(Dense(10, activation="softmax"))
+
+        model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])
+
+        from art.estimators.classification.keras import KerasClassifier
+
+        cls.classifier = KerasClassifier(model=model, clip_values=(min_, max_))
+
+        cls.classifier.fit(x_train, y_train, nb_epochs=1, batch_size=128)
+
+        cls.defence = ActivationDefence(cls.classifier, x_train, y_train)
+
+        datagen = ImageDataGenerator()
+        datagen.fit(x_train)
+
+        data_gen = KerasDataGenerator(
+            datagen.flow(x_train, y_train, batch_size=NB_TRAIN), size=NB_TRAIN, batch_size=NB_TRAIN
+        )
+
+        cls.defence_gen = ActivationDefence(cls.classifier, None, None, generator=data_gen)
+
+    def setUp(self):
+        # Set master seed
+        master_seed(1234)
+
+    @unittest.expectedFailure
+    def test_wrong_parameters_1(self):
+        self.defence.set_params(nb_clusters=0)
+
+    @unittest.expectedFailure
+    def test_wrong_parameters_2(self):
+        self.defence.set_params(clustering_method="what")
+
+    @unittest.expectedFailure
+    def test_wrong_parameters_3(self):
+        self.defence.set_params(reduce="what")
+
+    @unittest.expectedFailure
+    def test_wrong_parameters_4(self):
+        self.defence.set_params(cluster_analysis="what")
+
+    def test_activations(self):
+        (x_train, _), (_, _), (_, _) = self.mnist
+        activations = self.defence._get_activations()
+        self.assertEqual(len(x_train), len(activations))
+
+    def test_output_clusters(self):
+        # Get MNIST
+        (x_train, _), (_, _), (_, _) = self.mnist
+
+        n_classes = self.classifier.nb_classes
+        for nb_clusters in range(2, 5):
+            clusters_by_class, _ = self.defence.cluster_activations(nb_clusters=nb_clusters)
+
+            # Verify expected number of classes
+            self.assertEqual(np.shape(clusters_by_class)[0], n_classes)
+            # Check we get the expected number of clusters:
+            found_clusters = len(np.unique(clusters_by_class[0]))
+            self.assertEqual(found_clusters, nb_clusters)
+            # Check right amount of data
+            n_dp = 0
+            for i in range(0, n_classes):
+                n_dp += len(clusters_by_class[i])
+            self.assertEqual(len(x_train), n_dp)
+
+    def test_detect_poison(self):
+        # Get MNIST
+        (x_train, _), (_, _), (_, _) = self.mnist
+
+        _, is_clean_lst = self.defence.detect_poison(nb_clusters=2, nb_dims=10, reduce="PCA")
+        sum_clean1 = sum(is_clean_lst)
+
+        _, is_clean_lst_gen = self.defence_gen.detect_poison(nb_clusters=2, nb_dims=10, reduce="PCA")
+        sum_clean1_gen = sum(is_clean_lst_gen)
+
+        # Check number of items in is_clean
+        self.assertEqual(len(x_train), len(is_clean_lst))
+        self.assertEqual(len(x_train), len(is_clean_lst_gen))
+
+        # Test right number of clusters
+        found_clusters = len(np.unique(self.defence.clusters_by_class[0]))
+        found_clusters_gen = len(np.unique(self.defence_gen.clusters_by_class[0]))
+        self.assertEqual(found_clusters, 2)
+        self.assertEqual(found_clusters_gen, 2)
+
+        _, is_clean_lst = self.defence.detect_poison(
+            nb_clusters=3, nb_dims=10, reduce="PCA", cluster_analysis="distance"
+        )
+        _, is_clean_lst_gen = self.defence_gen.detect_poison(
+            nb_clusters=3, nb_dims=10, reduce="PCA", cluster_analysis="distance"
+        )
+        self.assertEqual(len(x_train), len(is_clean_lst))
+        self.assertEqual(len(x_train), len(is_clean_lst_gen))
+
+        # Test change of state to new number of clusters:
+        found_clusters = len(np.unique(self.defence.clusters_by_class[0]))
+        found_clusters_gen = len(np.unique(self.defence_gen.clusters_by_class[0]))
+        self.assertEqual(found_clusters, 3)
+        self.assertEqual(found_clusters_gen, 3)
+
+        # Test clean data has changed
+        sum_clean2 = sum(is_clean_lst)
+        sum_clean2_gen = sum(is_clean_lst_gen)
+        self.assertNotEqual(sum_clean1, sum_clean2)
+        self.assertNotEqual(sum_clean1_gen, sum_clean2_gen)
+
+        kwargs = {"nb_clusters": 2, "nb_dims": 10, "reduce": "PCA", "cluster_analysis": "distance"}
+        _, is_clean_lst = self.defence.detect_poison(**kwargs)
+        _, is_clean_lst_gen = self.defence_gen.detect_poison(**kwargs)
+        sum_dist = sum(is_clean_lst)
+        sum_dist_gen = sum(is_clean_lst_gen)
+        kwargs = {"nb_clusters": 2, "nb_dims": 10, "reduce": "PCA", "cluster_analysis": "smaller"}
+        _, is_clean_lst = self.defence.detect_poison(**kwargs)
+        _, is_clean_lst_gen = self.defence_gen.detect_poison(**kwargs)
+        sum_size = sum(is_clean_lst)
+        sum_size_gen = sum(is_clean_lst_gen)
+        self.assertNotEqual(sum_dist, sum_size)
+        self.assertNotEqual(sum_dist_gen, sum_size_gen)
+
+    def test_evaluate_defense(self):
+        # Get MNIST
+        (x_train, _), (_, _), (_, _) = self.mnist
+
+        kwargs = {"nb_clusters": 2, "nb_dims": 10, "reduce": "PCA"}
+        _, _ = self.defence.detect_poison(**kwargs)
+        _, _ = self.defence_gen.detect_poison(**kwargs)
+        is_clean = np.zeros(len(x_train))
+        self.defence.evaluate_defence(is_clean)
+        self.defence_gen.evaluate_defence(is_clean)
+
+    def test_analyze_cluster(self):
+        # Get MNIST
+        (x_train, _), (_, _), (_, _) = self.mnist
+
+        self.defence.analyze_clusters(cluster_analysis="relative-size")
+        self.defence_gen.analyze_clusters(cluster_analysis="relative-size")
+
+        self.defence.analyze_clusters(cluster_analysis="silhouette-scores")
+        self.defence_gen.analyze_clusters(cluster_analysis="silhouette-scores")
+
+        report, dist_clean_by_class = self.defence.analyze_clusters(cluster_analysis="distance")
+        report_gen, dist_clean_by_class_gen = self.defence_gen.analyze_clusters(cluster_analysis="distance")
+        n_classes = self.classifier.nb_classes
+        self.assertEqual(n_classes, len(dist_clean_by_class))
+        self.assertEqual(n_classes, len(dist_clean_by_class_gen))
+
+        # Check right amount of data
+        n_dp = 0
+        n_dp_gen = 0
+        for i in range(0, n_classes):
+            n_dp += len(dist_clean_by_class[i])
+            n_dp_gen += len(dist_clean_by_class_gen[i])
+        self.assertEqual(len(x_train), n_dp)
+        self.assertEqual(len(x_train), n_dp_gen)
+
+        report, sz_clean_by_class = self.defence.analyze_clusters(cluster_analysis="smaller")
+        report_gen, sz_clean_by_class_gen = self.defence_gen.analyze_clusters(cluster_analysis="smaller")
+        n_classes = self.classifier.nb_classes
+        self.assertEqual(n_classes, len(sz_clean_by_class))
+        self.assertEqual(n_classes, len(sz_clean_by_class_gen))
+
+        # Check right amount of data
+        n_dp = 0
+        n_dp_gen = 0
+        sum_sz = 0
+        sum_sz_gen = 0
+        sum_dis = 0
+        sum_dis_gen = 0
+
+        for i in range(0, n_classes):
+            n_dp += len(sz_clean_by_class[i])
+            n_dp_gen += len(sz_clean_by_class_gen[i])
+            sum_sz += sum(sz_clean_by_class[i])
+            sum_sz_gen += sum(sz_clean_by_class_gen[i])
+            sum_dis += sum(dist_clean_by_class[i])
+            sum_dis_gen += sum(dist_clean_by_class_gen[i])
+        self.assertEqual(len(x_train), n_dp)
+        self.assertEqual(len(x_train), n_dp_gen)
+
+        # Very unlikely that they are the same
+        self.assertNotEqual(sum_dis, sum_sz, msg="This is very unlikely to happen... there may be an error")
+        self.assertNotEqual(sum_dis_gen, sum_sz_gen, msg="This is very unlikely to happen... there may be an error")
+
+    def test_plot_clusters(self):
+        self.defence.detect_poison(nb_clusters=2, nb_dims=10, reduce="PCA")
+        self.defence_gen.detect_poison(nb_clusters=2, nb_dims=10, reduce="PCA")
+        self.defence.plot_clusters(save=False)
+        self.defence_gen.plot_clusters(save=False)
+
+    def test_pickle(self):
+
+        # Test pickle and unpickle:
+        filename = "test_pickle.h5"
+        ActivationDefence._pickle_classifier(self.classifier, filename)
+        loaded = ActivationDefence._unpickle_classifier(filename)
+
+        np.testing.assert_equal(self.classifier._clip_values, loaded._clip_values)
+        self.assertEqual(self.classifier._channels_first, loaded._channels_first)
+        self.assertEqual(self.classifier._use_logits, loaded._use_logits)
+        self.assertEqual(self.classifier._input_layer, loaded._input_layer)
+
+        ActivationDefence._remove_pickle(filename)
+
+    def test_fix_relabel_poison(self):
+        (x_train, y_train), (_, _), (_, _) = self.mnist
+        x_poison = x_train[:100]
+        y_fix = y_train[:100]
+
+        test_set_split = 0.7
+        n_train = int(len(x_poison) * test_set_split)
+        x_test = x_poison[n_train:]
+        y_test = y_fix[n_train:]
+
+        predictions = np.argmax(self.classifier.predict(x_test), axis=1)
+        ini_miss = 1 - np.sum(predictions == np.argmax(y_test, axis=1)) / y_test.shape[0]
+
+        improvement, new_classifier = ActivationDefence.relabel_poison_ground_truth(
+            self.classifier,
+            x_poison,
+            y_fix,
+            test_set_split=test_set_split,
+            tolerable_backdoor=0.01,
+            max_epochs=5,
+            batch_epochs=10,
+        )
+
+        predictions = np.argmax(new_classifier.predict(x_test), axis=1)
+        final_miss = 1 - np.sum(predictions == np.argmax(y_test, axis=1)) / y_test.shape[0]
+
+        self.assertEqual(improvement, ini_miss - final_miss)
+
+        # Other method (since it's cross validation we can't assert to a concrete number).
+        improvement, _ = ActivationDefence.relabel_poison_cross_validation(
+            self.classifier, x_poison, y_fix, n_splits=2, tolerable_backdoor=0.01, max_epochs=5, batch_epochs=10
+        )
+        self.assertGreaterEqual(improvement, 0)
+
+    def test_visualizations(self):
+        # test that visualization doesn't error in grayscale and RGB settings
+        (x_train, _), (_, _), (_, _) = self.mnist
+        self.defence.visualize_clusters(x_train)
+
+        x_train_rgb = convert_to_rgb(x_train)
+        self.defence.visualize_clusters(x_train_rgb)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/adversarial-robustness-toolbox/tests/defences/detector/poison/test_clustering_analyzer.py b/adversarial-robustness-toolbox/tests/defences/detector/poison/test_clustering_analyzer.py
new file mode 100644
index 0000000..64655a3
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/defences/detector/poison/test_clustering_analyzer.py
@@ -0,0 +1,229 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import unittest
+
+import numpy as np
+
+from art.defences.detector.poison import ClusteringAnalyzer
+
+logger = logging.getLogger(__name__)
+
+
+class TestActivationDefence(unittest.TestCase):
+    def test_size_analyzer(self):
+        nb_clusters = 2
+        nb_classes = 3
+        clusters_by_class = [[[] for x in range(nb_clusters)] for y in range(nb_classes)]
+
+        clusters_by_class[0] = [0, 1, 1, 1, 1]  # Class 0
+        clusters_by_class[1] = [1, 0, 0, 0, 0]  # Class 1
+        clusters_by_class[2] = [0, 0, 0, 0, 1]  # Class 2
+        analyzer = ClusteringAnalyzer()
+        assigned_clean_by_class, poison_clusters, report = analyzer.analyze_by_size(clusters_by_class)
+
+        # print("clusters_by_class")
+        # print(clusters_by_class)
+        # print("assigned_clean_by_class")
+        # print(assigned_clean_by_class)
+        # print("poison_clusters")
+        # print(poison_clusters)
+
+        clean = 0
+        poison = 1
+        # For class 0, cluster 0 should be marked as poison.
+        self.assertEqual(poison_clusters[0][0], poison)
+        # For class 0, cluster 1 should be marked as clean.
+        self.assertEqual(poison_clusters[0][1], clean)
+        self.assertEqual(report["Class_0"]["cluster_0"]["suspicious_cluster"], True)
+        self.assertEqual(report["Class_0"]["cluster_1"]["suspicious_cluster"], False)
+        total = len(clusters_by_class[0])
+        c1 = sum(clusters_by_class[0])
+        self.assertEqual(report["Class_0"]["cluster_0"]["ptc_data_in_cluster"], (total - c1) / total)
+        self.assertEqual(report["Class_0"]["cluster_1"]["ptc_data_in_cluster"], c1 / total)
+
+        # Inverse relations for class 1
+        self.assertEqual(poison_clusters[1][0], clean)
+        self.assertEqual(poison_clusters[1][1], poison)
+        self.assertEqual(report["Class_1"]["cluster_0"]["suspicious_cluster"], False)
+        self.assertEqual(report["Class_1"]["cluster_1"]["suspicious_cluster"], True)
+        total = len(clusters_by_class[1])
+        c1 = sum(clusters_by_class[1])
+        self.assertEqual(report["Class_1"]["cluster_0"]["ptc_data_in_cluster"], (total - c1) / total)
+        self.assertEqual(report["Class_1"]["cluster_1"]["ptc_data_in_cluster"], c1 / total)
+
+        self.assertEqual(poison_clusters[2][0], clean)
+        self.assertEqual(poison_clusters[2][1], poison)
+        self.assertEqual(report["Class_2"]["cluster_0"]["suspicious_cluster"], False)
+        self.assertEqual(report["Class_2"]["cluster_1"]["suspicious_cluster"], True)
+        total = len(clusters_by_class[2])
+        c1 = sum(clusters_by_class[2])
+        self.assertEqual(report["Class_2"]["cluster_0"]["ptc_data_in_cluster"], (total - c1) / total)
+        self.assertEqual(report["Class_2"]["cluster_1"]["ptc_data_in_cluster"], c1 / total)
+
+        poison = 0
+        self.assertEqual(assigned_clean_by_class[0][0], poison)
+        self.assertEqual(assigned_clean_by_class[1][0], poison)
+        self.assertEqual(assigned_clean_by_class[2][4], poison)
+
+    def test_size_analyzer_three(self):
+        nb_clusters = 3
+        nb_classes = 3
+        clusters_by_class = [[[] for x in range(nb_clusters)] for y in range(nb_classes)]
+
+        clusters_by_class[0] = [0, 1, 1, 2, 2]  # Class 0
+        clusters_by_class[1] = [1, 0, 0, 2, 2]  # Class 1
+        clusters_by_class[2] = [0, 0, 0, 2, 1, 1]  # Class 2
+        analyzer = ClusteringAnalyzer()
+        assigned_clean_by_class, poison_clusters, report = analyzer.analyze_by_size(clusters_by_class)
+
+        # print("clusters_by_class")
+        # print(clusters_by_class)
+        # print("assigned_clean_by_class")
+        # print(assigned_clean_by_class)
+        # print("poison_clusters")
+        # print(poison_clusters)
+
+        clean = 0
+        poison = 1
+        # For class 0, cluster 0 should be marked as poison.
+        self.assertEqual(poison_clusters[0][0], poison)
+        # For class 0, cluster 1 and 2 should be marked as clean.
+        self.assertEqual(poison_clusters[0][1], clean)
+        self.assertEqual(poison_clusters[0][2], clean)
+        self.assertEqual(report["Class_0"]["cluster_0"]["suspicious_cluster"], True)
+        self.assertEqual(report["Class_0"]["cluster_1"]["suspicious_cluster"], False)
+        self.assertEqual(report["Class_0"]["cluster_2"]["suspicious_cluster"], False)
+        total = len(clusters_by_class[0])
+        counts = np.bincount(clusters_by_class[0])
+        self.assertEqual(report["Class_0"]["cluster_0"]["ptc_data_in_cluster"], counts[0] / total)
+        self.assertEqual(report["Class_0"]["cluster_1"]["ptc_data_in_cluster"], counts[1] / total)
+        self.assertEqual(report["Class_0"]["cluster_2"]["ptc_data_in_cluster"], counts[2] / total)
+
+        self.assertEqual(poison_clusters[1][0], clean)
+        self.assertEqual(poison_clusters[1][1], poison)
+        self.assertEqual(poison_clusters[1][2], clean)
+        self.assertEqual(report["Class_1"]["cluster_0"]["suspicious_cluster"], False)
+        self.assertEqual(report["Class_1"]["cluster_1"]["suspicious_cluster"], True)
+        self.assertEqual(report["Class_1"]["cluster_2"]["suspicious_cluster"], False)
+        total = len(clusters_by_class[1])
+        counts = np.bincount(clusters_by_class[1])
+        self.assertEqual(report["Class_1"]["cluster_0"]["ptc_data_in_cluster"], counts[0] / total)
+        self.assertEqual(report["Class_1"]["cluster_1"]["ptc_data_in_cluster"], counts[1] / total)
+        self.assertEqual(report["Class_1"]["cluster_2"]["ptc_data_in_cluster"], counts[2] / total)
+
+        self.assertEqual(poison_clusters[2][0], clean)
+        self.assertEqual(poison_clusters[2][1], clean)
+        self.assertEqual(poison_clusters[2][2], poison)
+        self.assertEqual(report["Class_2"]["cluster_0"]["suspicious_cluster"], False)
+        self.assertEqual(report["Class_2"]["cluster_1"]["suspicious_cluster"], False)
+        self.assertEqual(report["Class_2"]["cluster_2"]["suspicious_cluster"], True)
+        total = len(clusters_by_class[2])
+        counts = np.bincount(clusters_by_class[2])
+        self.assertEqual(report["Class_2"]["cluster_0"]["ptc_data_in_cluster"], round(counts[0] / total, 2))
+        self.assertEqual(report["Class_2"]["cluster_1"]["ptc_data_in_cluster"], round(counts[1] / total, 2))
+        self.assertEqual(report["Class_2"]["cluster_2"]["ptc_data_in_cluster"], round(counts[2] / total, 2))
+
+        poison = 0
+        self.assertEqual(assigned_clean_by_class[0][0], poison)
+        self.assertEqual(assigned_clean_by_class[1][0], poison)
+        self.assertEqual(assigned_clean_by_class[2][3], poison)
+
+    def test_relative_size_analyzer(self):
+        nb_clusters = 2
+        nb_classes = 4
+        clusters_by_class = [[[] for x in range(nb_clusters)] for y in range(nb_classes)]
+
+        clusters_by_class[0] = [0, 1, 1, 1, 1]  # Class 0
+        clusters_by_class[1] = [1, 0, 0, 0, 0]  # Class 1
+        clusters_by_class[2] = [0, 0, 0, 0, 1]  # Class 2
+        clusters_by_class[3] = [0, 0, 1, 1, 1]  # Class 3
+        analyzer = ClusteringAnalyzer()
+        assigned_clean_by_class, poison_clusters, report = analyzer.analyze_by_relative_size(clusters_by_class)
+
+        # print("clusters_by_class")
+        # print(clusters_by_class)
+        # print("assigned_clean_by_class")
+        # print(assigned_clean_by_class)
+        # print("poison_clusters")
+        # print(poison_clusters)
+
+        clean = 0
+        poison = 1
+        # For class 0, cluster 0 should be marked as poison.
+        self.assertEqual(poison_clusters[0][0], poison)
+        # For class 0, cluster 1 should be marked as clean.
+        self.assertEqual(poison_clusters[0][1], clean)
+        self.assertEqual(report["Class_0"]["cluster_0"]["suspicious_cluster"], True)
+        self.assertEqual(report["Class_0"]["cluster_1"]["suspicious_cluster"], False)
+        total = len(clusters_by_class[0])
+        c1 = sum(clusters_by_class[0])
+        self.assertEqual(report["Class_0"]["cluster_0"]["ptc_data_in_cluster"], round((total - c1) / total, 2))
+        self.assertEqual(report["Class_0"]["cluster_1"]["ptc_data_in_cluster"], round(c1 / total, 2))
+
+        # Inverse relations for class 1
+        self.assertEqual(poison_clusters[1][0], clean)
+        self.assertEqual(poison_clusters[1][1], poison)
+        self.assertEqual(report["Class_1"]["cluster_0"]["suspicious_cluster"], False)
+        self.assertEqual(report["Class_1"]["cluster_1"]["suspicious_cluster"], True)
+        total = len(clusters_by_class[1])
+        c1 = sum(clusters_by_class[1])
+        self.assertEqual(report["Class_1"]["cluster_0"]["ptc_data_in_cluster"], round((total - c1) / total, 2))
+        self.assertEqual(report["Class_1"]["cluster_1"]["ptc_data_in_cluster"], round(c1 / total, 2))
+
+        self.assertEqual(poison_clusters[2][0], clean)
+        self.assertEqual(poison_clusters[2][1], poison)
+        self.assertEqual(report["Class_2"]["cluster_0"]["suspicious_cluster"], False)
+        self.assertEqual(report["Class_2"]["cluster_1"]["suspicious_cluster"], True)
+        total = len(clusters_by_class[2])
+        c1 = sum(clusters_by_class[2])
+        self.assertEqual(report["Class_2"]["cluster_0"]["ptc_data_in_cluster"], round((total - c1) / total, 2))
+        self.assertEqual(report["Class_2"]["cluster_1"]["ptc_data_in_cluster"], round(c1 / total, 2))
+
+        self.assertEqual(poison_clusters[3][0], clean)
+        self.assertEqual(poison_clusters[3][1], clean)
+        self.assertEqual(report["Class_3"]["cluster_0"]["suspicious_cluster"], False)
+        self.assertEqual(report["Class_3"]["cluster_1"]["suspicious_cluster"], False)
+        total = len(clusters_by_class[3])
+        c1 = sum(clusters_by_class[3])
+        self.assertEqual(report["Class_3"]["cluster_0"]["ptc_data_in_cluster"], round((total - c1) / total, 2))
+        self.assertEqual(report["Class_3"]["cluster_1"]["ptc_data_in_cluster"], round(c1 / total, 2))
+
+        poison = 0
+        self.assertEqual(assigned_clean_by_class[0][0], poison)
+        self.assertEqual(assigned_clean_by_class[1][0], poison)
+        self.assertEqual(assigned_clean_by_class[2][4], poison)
+        self.assertEqual(sum(assigned_clean_by_class[3]), len(assigned_clean_by_class[3]))
+
+    @unittest.expectedFailure
+    def test_relative_size_analyzer_three(self):
+        nb_clusters = 3
+        nb_classes = 3
+        clusters_by_class = [[[] for x in range(nb_clusters)] for y in range(nb_classes)]
+
+        clusters_by_class[0] = [0, 1, 1, 2, 2]  # Class 0
+        clusters_by_class[1] = [1, 0, 0, 2, 2]  # Class 1
+        clusters_by_class[2] = [0, 0, 0, 2, 1, 1]  # Class 2
+        analyzer = ClusteringAnalyzer()
+        analyzer.analyze_clusters(clusters_by_class)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/adversarial-robustness-toolbox/tests/defences/detector/poison/test_ground_truth_evaluator.py b/adversarial-robustness-toolbox/tests/defences/detector/poison/test_ground_truth_evaluator.py
new file mode 100644
index 0000000..3d50ba2
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/defences/detector/poison/test_ground_truth_evaluator.py
@@ -0,0 +1,222 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import json
+import logging
+import pprint
+import unittest
+
+from art.defences.detector.poison import GroundTruthEvaluator
+
+from tests.utils import master_seed
+
+logger = logging.getLogger(__name__)
+
+
+class TestGroundTruth(unittest.TestCase):
+    def setUp(self):
+        master_seed(seed=1234)
+
+    @classmethod
+    def setUpClass(cls):
+        cls.evaluator = GroundTruthEvaluator()
+        cls.n_classes = 3
+        cls.n_dp = 10
+        cls.n_dp_mix = 5
+
+        cls.is_clean_all_clean = [[] for _ in range(cls.n_classes)]
+        cls.is_clean_all_poison = [[] for _ in range(cls.n_classes)]
+        cls.is_clean_mixed = [[] for _ in range(cls.n_classes)]
+        cls.is_clean_comp_mix = [[] for _ in range(cls.n_classes)]
+
+        for i in range(cls.n_classes):
+            cls.is_clean_all_clean[i] = [1] * cls.n_dp
+            cls.is_clean_all_poison[i] = [0] * cls.n_dp
+            cls.is_clean_mixed[i] = [1, 0, 0, 1, 0, 1, 1, 1, 0, 0]
+            cls.is_clean_comp_mix[i] = [0, 1, 1, 0, 1, 0, 0, 0, 1, 1]
+
+    def test_analyze_correct_all_clean(self):
+        # perfect detection all data is actually clean:
+        errors_by_class, conf_matrix_json = self.evaluator.analyze_correctness(
+            self.is_clean_all_clean, self.is_clean_all_clean
+        )
+
+        json_object = json.loads(conf_matrix_json)
+        self.assertEqual(len(json_object.keys()), self.n_classes)
+        self.assertEqual(len(errors_by_class), self.n_classes)
+
+        # print(json_object)
+        for i in range(self.n_classes):
+            res_class_i = json_object["class_" + str(i)]
+            self.assertEqual(res_class_i["TruePositive"]["rate"], "N/A")
+            self.assertEqual(res_class_i["TrueNegative"]["rate"], 100)
+            self.assertEqual(res_class_i["FalseNegative"]["rate"], "N/A")
+            self.assertEqual(res_class_i["FalsePositive"]["rate"], 0)
+
+            self.assertEqual(res_class_i["TruePositive"]["numerator"], 0)
+            self.assertEqual(res_class_i["TruePositive"]["denominator"], 0)
+
+            self.assertEqual(res_class_i["TrueNegative"]["numerator"], self.n_dp)
+            self.assertEqual(res_class_i["TrueNegative"]["denominator"], self.n_dp)
+
+            self.assertEqual(res_class_i["FalseNegative"]["numerator"], 0)
+            self.assertEqual(res_class_i["FalseNegative"]["denominator"], 0)
+
+            self.assertEqual(res_class_i["FalsePositive"]["numerator"], 0)
+            self.assertEqual(res_class_i["FalsePositive"]["denominator"], self.n_dp)
+
+            # all errors_by_class should be 1 (errors_by_class[i] = 1 if marked clean, is clean)
+            for item in errors_by_class[i]:
+                self.assertEqual(item, 1)
+
+    def test_analyze_correct_all_poison(self):
+        # perfect detection all data is actually poison
+        errors_by_class, conf_matrix_json = self.evaluator.analyze_correctness(
+            self.is_clean_all_poison, self.is_clean_all_poison
+        )
+
+        json_object = json.loads(conf_matrix_json)
+        self.assertEqual(len(json_object.keys()), self.n_classes)
+        self.assertEqual(len(errors_by_class), self.n_classes)
+
+        # print(json_object)
+        for i in range(self.n_classes):
+            res_class_i = json_object["class_" + str(i)]
+            self.assertEqual(res_class_i["TruePositive"]["rate"], 100)
+            self.assertEqual(res_class_i["TrueNegative"]["rate"], "N/A")
+            self.assertEqual(res_class_i["FalseNegative"]["rate"], 0)
+            self.assertEqual(res_class_i["FalsePositive"]["rate"], "N/A")
+
+            self.assertEqual(res_class_i["TruePositive"]["numerator"], self.n_dp)
+            self.assertEqual(res_class_i["TruePositive"]["denominator"], self.n_dp)
+
+            self.assertEqual(res_class_i["TrueNegative"]["numerator"], 0)
+            self.assertEqual(res_class_i["TrueNegative"]["denominator"], 0)
+
+            self.assertEqual(res_class_i["FalseNegative"]["numerator"], 0)
+            self.assertEqual(res_class_i["FalseNegative"]["denominator"], self.n_dp)
+
+            self.assertEqual(res_class_i["FalsePositive"]["numerator"], 0)
+            self.assertEqual(res_class_i["FalsePositive"]["denominator"], 0)
+
+            # all errors_by_class should be 0 (all_errors_by_class[i] = 0 if marked poison, is poison)
+            for item in errors_by_class[i]:
+                self.assertEqual(item, 0)
+
+    def test_analyze_correct_mixed(self):
+        # perfect detection mixed
+        errors_by_class, conf_matrix_json = self.evaluator.analyze_correctness(self.is_clean_mixed, self.is_clean_mixed)
+
+        json_object = json.loads(conf_matrix_json)
+        self.assertEqual(len(json_object.keys()), self.n_classes)
+        self.assertEqual(len(errors_by_class), self.n_classes)
+
+        # print(json_object)
+        for i in range(self.n_classes):
+            res_class_i = json_object["class_" + str(i)]
+            self.assertEqual(res_class_i["TruePositive"]["rate"], 100)
+            self.assertEqual(res_class_i["TrueNegative"]["rate"], 100)
+            self.assertEqual(res_class_i["FalseNegative"]["rate"], 0)
+            self.assertEqual(res_class_i["FalsePositive"]["rate"], 0)
+
+            self.assertEqual(res_class_i["TruePositive"]["numerator"], self.n_dp_mix)
+            self.assertEqual(res_class_i["TruePositive"]["denominator"], self.n_dp_mix)
+
+            self.assertEqual(res_class_i["TrueNegative"]["numerator"], self.n_dp_mix)
+            self.assertEqual(res_class_i["TrueNegative"]["denominator"], self.n_dp_mix)
+
+            self.assertEqual(res_class_i["FalseNegative"]["numerator"], 0)
+            self.assertEqual(res_class_i["FalseNegative"]["denominator"], self.n_dp_mix)
+
+            self.assertEqual(res_class_i["FalsePositive"]["numerator"], 0)
+            self.assertEqual(res_class_i["FalsePositive"]["denominator"], self.n_dp_mix)
+
+            # all errors_by_class should be 1 (errors_by_class[i] = 1 if marked clean, is clean)
+            for j, item in enumerate(errors_by_class[i]):
+                self.assertEqual(item, self.is_clean_mixed[i][j])
+
+    def test_analyze_fully_misclassified(self):
+        # Completely wrong
+        # order parameters: analyze_correctness(assigned_clean_by_class, is_clean_by_class)
+        errors_by_class, conf_matrix_json = self.evaluator.analyze_correctness(
+            self.is_clean_all_clean, self.is_clean_all_poison
+        )
+
+        json_object = json.loads(conf_matrix_json)
+        self.assertEqual(len(json_object.keys()), self.n_classes)
+        self.assertEqual(len(errors_by_class), self.n_classes)
+
+        print(json_object)
+        for i in range(self.n_classes):
+            res_class_i = json_object["class_" + str(i)]
+            self.assertEqual(res_class_i["TruePositive"]["rate"], 0)
+            self.assertEqual(res_class_i["TrueNegative"]["rate"], "N/A")
+            self.assertEqual(res_class_i["FalseNegative"]["rate"], 100)
+            self.assertEqual(res_class_i["FalsePositive"]["rate"], "N/A")
+
+            self.assertEqual(res_class_i["TruePositive"]["numerator"], 0)
+            self.assertEqual(res_class_i["TruePositive"]["denominator"], self.n_dp)
+
+            self.assertEqual(res_class_i["TrueNegative"]["numerator"], 0)
+            self.assertEqual(res_class_i["TrueNegative"]["denominator"], 0)
+
+            self.assertEqual(res_class_i["FalseNegative"]["numerator"], self.n_dp)
+            self.assertEqual(res_class_i["FalseNegative"]["denominator"], self.n_dp)
+
+            self.assertEqual(res_class_i["FalsePositive"]["numerator"], 0)
+            self.assertEqual(res_class_i["FalsePositive"]["denominator"], 0)
+
+            # all errors_by_class should be 3 (all_errors_by_class[i] = 3 marked clean, is poison)
+            for item in errors_by_class[i]:
+                self.assertEqual(item, 3)
+
+    def test_analyze_fully_misclassified_rev(self):
+        # Completely wrong
+        # order parameters: analyze_correctness(assigned_clean_by_class, is_clean_by_class)
+        errors_by_class, conf_matrix_json = self.evaluator.analyze_correctness(
+            self.is_clean_all_poison, self.is_clean_all_clean
+        )
+
+        json_object = json.loads(conf_matrix_json)
+        self.assertEqual(len(json_object.keys()), self.n_classes)
+        self.assertEqual(len(errors_by_class), self.n_classes)
+
+        pprint.pprint(json_object)
+        for i in range(self.n_classes):
+            res_class_i = json_object["class_" + str(i)]
+            self.assertEqual(res_class_i["TruePositive"]["rate"], "N/A")
+            self.assertEqual(res_class_i["TrueNegative"]["rate"], 0)
+            self.assertEqual(res_class_i["FalseNegative"]["rate"], "N/A")
+            self.assertEqual(res_class_i["FalsePositive"]["rate"], 100)
+
+            self.assertEqual(res_class_i["TruePositive"]["numerator"], 0)
+            self.assertEqual(res_class_i["TruePositive"]["denominator"], 0)
+
+            self.assertEqual(res_class_i["TrueNegative"]["numerator"], 0)
+            self.assertEqual(res_class_i["TrueNegative"]["denominator"], self.n_dp)
+
+            self.assertEqual(res_class_i["FalseNegative"]["numerator"], 0)
+            self.assertEqual(res_class_i["FalseNegative"]["denominator"], 0)
+
+            self.assertEqual(res_class_i["FalsePositive"]["numerator"], self.n_dp)
+            self.assertEqual(res_class_i["FalsePositive"]["denominator"], self.n_dp)
+
+            # all errors_by_class should be 3 (all_errors_by_class[i] = 2 if marked poison, is clean)
+            for item in errors_by_class[i]:
+                self.assertEqual(item, 2)
diff --git a/adversarial-robustness-toolbox/tests/defences/detector/poison/test_provenance_defence.py b/adversarial-robustness-toolbox/tests/defences/detector/poison/test_provenance_defence.py
new file mode 100644
index 0000000..a0d50f5
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/defences/detector/poison/test_provenance_defence.py
@@ -0,0 +1,179 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import unittest
+
+import numpy as np
+from sklearn.svm import SVC
+
+from art.attacks.poisoning.poisoning_attack_svm import PoisoningAttackSVM
+from art.estimators.classification.scikitlearn import SklearnClassifier, ScikitlearnSVC
+from art.defences.detector.poison.provenance_defense import ProvenanceDefense
+from art.utils import load_mnist
+
+from tests.utils import master_seed
+
+logger = logging.getLogger(__name__)
+
+NB_TRAIN = 40
+NB_POISON = 5
+NB_VALID = 10
+NB_TRUSTED = 10
+NB_DEVICES = 4
+kernel = "linear"
+
+
+class TestProvenanceDefence(unittest.TestCase):
+    @classmethod
+    def setUpClass(cls):
+        master_seed(seed=301)
+        (x_train, y_train), (x_test, y_test), min_, max_ = load_mnist()
+        y_train = np.argmax(y_train, axis=1)
+        y_test = np.argmax(y_test, axis=1)
+        zero_or_four = np.logical_or(y_train == 4, y_train == 0, y_train == 9)
+        x_train = x_train[zero_or_four]
+        y_train = y_train[zero_or_four]
+        tr_labels = np.zeros((y_train.shape[0], 2))
+        tr_labels[y_train == 0] = np.array([1, 0])
+        tr_labels[y_train == 4] = np.array([0, 1])
+        y_train = tr_labels
+
+        zero_or_four = np.logical_or(y_test == 4, y_test == 0)
+        x_test = x_test[zero_or_four]
+        y_test = y_test[zero_or_four]
+        te_labels = np.zeros((y_test.shape[0], 2))
+        te_labels[y_test == 0] = np.array([1, 0])
+        te_labels[y_test == 4] = np.array([0, 1])
+        y_test = te_labels
+
+        n_samples_train = x_train.shape[0]
+        n_features_train = x_train.shape[1] * x_train.shape[2] * x_train.shape[3]
+        n_samples_test = x_test.shape[0]
+        n_features_test = x_test.shape[1] * x_test.shape[2] * x_test.shape[3]
+
+        x_train = x_train.reshape(n_samples_train, n_features_train)
+        x_test = x_test.reshape(n_samples_test, n_features_test)
+        x_train = x_train[:NB_TRAIN]
+        y_train = y_train[:NB_TRAIN]
+
+        trusted_data = x_test[:NB_TRUSTED]
+        trusted_labels = y_test[:NB_TRUSTED]
+        x_test = x_test[NB_TRUSTED:]
+        y_test = y_test[NB_TRUSTED:]
+        valid_data = x_test[:NB_VALID]
+        valid_labels = y_test[:NB_VALID]
+        x_test = x_test[NB_VALID:]
+        y_test = y_test[NB_VALID:]
+
+        clean_prov = np.random.randint(NB_DEVICES - 1, size=x_train.shape[0])
+        p_train = np.eye(NB_DEVICES)[clean_prov]
+
+        no_defense = ScikitlearnSVC(model=SVC(kernel=kernel, gamma="auto"), clip_values=(min_, max_))
+        no_defense.fit(x=x_train, y=y_train)
+        poison_points = np.random.randint(no_defense._model.support_vectors_.shape[0], size=NB_POISON)
+        all_poison_init = np.copy(no_defense._model.support_vectors_[poison_points])
+        poison_labels = np.array([1, 1]) - no_defense.predict(all_poison_init)
+
+        svm_attack = PoisoningAttackSVM(
+            classifier=no_defense,
+            x_train=x_train,
+            y_train=y_train,
+            step=0.1,
+            eps=1.0,
+            x_val=valid_data,
+            y_val=valid_labels,
+        )
+
+        poisoned_data, _ = svm_attack.poison(all_poison_init, y=poison_labels)
+
+        # Stack on poison to data and add provenance of bad actor
+        all_data = np.vstack([x_train, poisoned_data])
+        all_labels = np.vstack([y_train, poison_labels])
+        poison_prov = np.zeros((NB_POISON, NB_DEVICES))
+        poison_prov[:, NB_DEVICES - 1] = 1
+        all_p = np.vstack([p_train, poison_prov])
+
+        model = SVC(kernel=kernel, gamma="auto")
+        cls.mnist = (
+            (all_data, all_labels, all_p),
+            (x_test, y_test),
+            (trusted_data, trusted_labels),
+            (valid_data, valid_labels),
+            (min_, max_),
+        )
+        cls.classifier = SklearnClassifier(model=model, clip_values=(min_, max_))
+
+        cls.classifier.fit(all_data, all_labels)
+        cls.defence_trust = ProvenanceDefense(
+            cls.classifier, all_data, all_labels, all_p, x_val=trusted_data, y_val=trusted_labels, eps=0.1,
+        )
+        cls.defence_no_trust = ProvenanceDefense(cls.classifier, all_data, all_labels, all_p, eps=0.1)
+
+    def setUp(self):
+        master_seed(seed=301)
+
+    def test_wrong_parameters_1(self):
+        self.assertRaises(ValueError, self.defence_no_trust.set_params, eps=-2.0)
+        self.assertRaises(ValueError, self.defence_trust.set_params, eps=-2.0)
+
+    def test_wrong_parameters_2(self):
+        self.assertRaises(ValueError, self.defence_no_trust.set_params, pp_valid=-0.1)
+        self.assertRaises(ValueError, self.defence_trust.set_params, eps=-0.1)
+
+    def test_wrong_parameters_3(self):
+        (all_data, _, _), (_, y_test), (_, _), (_, _), (_, _) = self.mnist
+        self.assertRaises(
+            ValueError, self.defence_no_trust.set_params, x_train=-all_data, y_train=y_test,
+        )
+        self.assertRaises(ValueError, self.defence_trust.set_params, x_train=-all_data, y_train=y_test)
+
+    def test_wrong_parameters_4(self):
+        (_, _, p_train), (x_test, y_test), (_, _), (_, _), (_, _) = self.mnist
+        self.assertRaises(
+            ValueError, self.defence_no_trust.set_params, x_train=-x_test, y_train=y_test, p_train=p_train,
+        )
+        self.assertRaises(
+            ValueError, self.defence_trust.set_params, x_train=-x_test, y_train=y_test, p_train=p_train,
+        )
+
+    def test_detect_poison(self):
+        _, clean_trust = self.defence_trust.detect_poison()
+        _, clean_no_trust = self.defence_no_trust.detect_poison()
+        real_clean = np.array([1 if i < NB_TRAIN else 0 for i in range(NB_TRAIN + NB_POISON)])
+        pc_tp_trust = np.average(real_clean[:NB_TRAIN] == clean_trust[:NB_TRAIN])
+        pc_tn_trust = np.average(real_clean[NB_TRAIN:] == clean_trust[NB_TRAIN:])
+        self.assertGreaterEqual(pc_tn_trust, 0.7)
+        self.assertGreaterEqual(pc_tp_trust, 0.7)
+
+        pc_tp_no_trust = np.average(real_clean[:NB_TRAIN] == clean_no_trust[:NB_TRAIN])
+        pc_tn_no_trust = np.average(real_clean[NB_TRAIN:] == clean_no_trust[NB_TRAIN:])
+        self.assertGreaterEqual(pc_tn_no_trust, 0.7)
+        self.assertGreaterEqual(pc_tp_no_trust, 0.7)
+
+    def test_evaluate_defense(self):
+        real_clean = np.array([1 if i < NB_TRAIN else 0 for i in range(NB_TRAIN + NB_POISON)])
+        self.defence_no_trust.detect_poison()
+        self.defence_trust.detect_poison()
+        logger.info(self.defence_trust.evaluate_defence(real_clean))
+        logger.info(self.defence_no_trust.evaluate_defence(real_clean))
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/adversarial-robustness-toolbox/tests/defences/detector/poison/test_roni.py b/adversarial-robustness-toolbox/tests/defences/detector/poison/test_roni.py
new file mode 100644
index 0000000..0f062fc
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/defences/detector/poison/test_roni.py
@@ -0,0 +1,159 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import unittest
+
+import numpy as np
+from sklearn.svm import SVC
+
+from art.attacks.poisoning.poisoning_attack_svm import PoisoningAttackSVM
+from art.estimators.classification.scikitlearn import SklearnClassifier, ScikitlearnSVC
+from art.defences.detector.poison.roni import RONIDefense
+from art.utils import load_mnist
+
+from tests.utils import master_seed
+
+logger = logging.getLogger(__name__)
+
+NB_TRAIN, NB_POISON, NB_VALID, NB_TRUSTED = 40, 5, 40, 15
+kernel = "linear"
+
+
+class TestRONI(unittest.TestCase):
+    @classmethod
+    def setUpClass(cls):
+        master_seed(seed=1234)
+        (x_train, y_train), (x_test, y_test), min_, max_ = load_mnist()
+        y_train = np.argmax(y_train, axis=1)
+        y_test = np.argmax(y_test, axis=1)
+        zero_or_four = np.logical_or(y_train == 4, y_train == 0)
+        x_train = x_train[zero_or_four]
+        y_train = y_train[zero_or_four]
+        tr_labels = np.zeros((y_train.shape[0], 2))
+        tr_labels[y_train == 0] = np.array([1, 0])
+        tr_labels[y_train == 4] = np.array([0, 1])
+        y_train = tr_labels
+
+        zero_or_four = np.logical_or(y_test == 4, y_test == 0)
+        x_test = x_test[zero_or_four]
+        y_test = y_test[zero_or_four]
+        te_labels = np.zeros((y_test.shape[0], 2))
+        te_labels[y_test == 0] = np.array([1, 0])
+        te_labels[y_test == 4] = np.array([0, 1])
+        y_test = te_labels
+
+        n_samples_train = x_train.shape[0]
+        n_features_train = x_train.shape[1] * x_train.shape[2] * x_train.shape[3]
+        n_samples_test = x_test.shape[0]
+        n_features_test = x_test.shape[1] * x_test.shape[2] * x_test.shape[3]
+
+        x_train = x_train.reshape(n_samples_train, n_features_train)
+        x_test = x_test.reshape(n_samples_test, n_features_test)
+        x_train = x_train[:NB_TRAIN]
+        y_train = y_train[:NB_TRAIN]
+
+        trusted_data = x_test[:NB_TRUSTED]
+        trusted_labels = y_test[:NB_TRUSTED]
+        x_test = x_test[NB_TRUSTED:]
+        y_test = y_test[NB_TRUSTED:]
+        valid_data = x_test[:NB_VALID]
+        valid_labels = y_test[:NB_VALID]
+        x_test = x_test[NB_VALID:]
+        y_test = y_test[NB_VALID:]
+
+        no_defense = ScikitlearnSVC(model=SVC(kernel=kernel, gamma="auto"), clip_values=(min_, max_))
+        no_defense.fit(x=x_train, y=y_train)
+        poison_points = np.random.randint(no_defense._model.support_vectors_.shape[0], size=NB_POISON)
+        all_poison_init = np.copy(no_defense._model.support_vectors_[poison_points])
+        poison_labels = np.array([1, 1]) - no_defense.predict(all_poison_init)
+
+        svm_attack = PoisoningAttackSVM(
+            classifier=no_defense,
+            x_train=x_train,
+            y_train=y_train,
+            step=0.1,
+            eps=1.0,
+            x_val=valid_data,
+            y_val=valid_labels,
+            max_iter=200,
+        )
+
+        poisoned_data, _ = svm_attack.poison(all_poison_init, y=poison_labels)
+
+        # Stack on poison to data and add provenance of bad actor
+        all_data = np.vstack([x_train, poisoned_data])
+        all_labels = np.vstack([y_train, poison_labels])
+
+        model = SVC(kernel=kernel, gamma="auto")
+        cls.mnist = (
+            (all_data, all_labels),
+            (x_test, y_test),
+            (trusted_data, trusted_labels),
+            (valid_data, valid_labels),
+            (min_, max_),
+        )
+        cls.classifier = SklearnClassifier(model=model, clip_values=(min_, max_))
+
+        cls.classifier.fit(all_data, all_labels)
+        cls.defense_cal = RONIDefense(
+            cls.classifier, all_data, all_labels, trusted_data, trusted_labels, eps=0.1, calibrated=True,
+        )
+        cls.defence_no_cal = RONIDefense(
+            cls.classifier, all_data, all_labels, trusted_data, trusted_labels, eps=0.1, calibrated=False,
+        )
+
+    def setUp(self):
+        master_seed(seed=1234)
+
+    def test_wrong_parameters_1(self):
+        self.assertRaises(ValueError, self.defence_no_cal.set_params, eps=-2.0)
+        self.assertRaises(ValueError, self.defense_cal.set_params, eps=-2.0)
+
+    def test_wrong_parameters_2(self):
+        (all_data, _), (_, y_test), (_, _), (_, _), (_, _) = self.mnist
+        self.assertRaises(
+            ValueError, self.defence_no_cal.set_params, x_train=-all_data, y_train=y_test,
+        )
+        self.assertRaises(ValueError, self.defense_cal.set_params, x_train=-all_data, y_train=y_test)
+
+    def test_detect_poison(self):
+        _, clean_trust = self.defense_cal.detect_poison()
+        _, clean_no_trust = self.defence_no_cal.detect_poison()
+        real_clean = np.array([1 if i < NB_TRAIN else 0 for i in range(NB_TRAIN + NB_POISON)])
+        pc_tp_cal = np.average(real_clean[:NB_TRAIN] == clean_trust[:NB_TRAIN])
+        pc_tn_cal = np.average(real_clean[NB_TRAIN:] == clean_trust[NB_TRAIN:])
+        self.assertGreaterEqual(pc_tn_cal, 0)
+        self.assertGreaterEqual(pc_tp_cal, 0.7)
+
+        pc_tp_no_cal = np.average(real_clean[:NB_TRAIN] == clean_no_trust[:NB_TRAIN])
+        pc_tn_no_cal = np.average(real_clean[NB_TRAIN:] == clean_no_trust[NB_TRAIN:])
+        self.assertGreaterEqual(pc_tn_no_cal, 0)
+        self.assertGreaterEqual(pc_tp_no_cal, 0.7)
+
+    def test_evaluate_defense(self):
+        real_clean = np.array([1 if i < NB_TRAIN else 0 for i in range(NB_TRAIN + NB_POISON)])
+        self.defence_no_cal.detect_poison()
+        self.defense_cal.detect_poison()
+        logger.info(self.defense_cal.evaluate_defence(real_clean))
+        logger.info(self.defence_no_cal.evaluate_defence(real_clean))
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/adversarial-robustness-toolbox/tests/defences/detector/poison/test_spectral_signature_defense.py b/adversarial-robustness-toolbox/tests/defences/detector/poison/test_spectral_signature_defense.py
new file mode 100644
index 0000000..91fad82
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/defences/detector/poison/test_spectral_signature_defense.py
@@ -0,0 +1,87 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import pytest
+import numpy as np
+
+from art.defences.detector.poison import SpectralSignatureDefense
+
+from tests.utils import ARTTestException
+
+logger = logging.getLogger(__name__)
+
+
+NB_TRAIN, NB_TEST, BATCH_SIZE, EPS_MULTIPLIER, UB_PCT_POISON = 300, 10, 128, 1.5, 0.2
+
+
+@pytest.mark.parametrize("params", [dict(batch_size=-1), dict(eps_multiplier=-1.0), dict(expected_pp_poison=2.0)])
+def test_wrong_parameters(params, art_warning, get_default_mnist_subset, image_dl_estimator):
+    try:
+        (x_train_mnist, y_train_mnist), (_, _) = get_default_mnist_subset
+
+        classifier, _ = image_dl_estimator()
+
+        classifier.fit(x_train_mnist[:NB_TRAIN], y_train_mnist[:NB_TRAIN], nb_epochs=1)
+        with pytest.raises(ValueError):
+            defence = SpectralSignatureDefense(classifier, x_train_mnist[:NB_TRAIN], y_train_mnist[:NB_TRAIN], **params)
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.skip_framework("non_dl_frameworks", "mxnet")
+def test_detect_poison(art_warning, get_default_mnist_subset, image_dl_estimator):
+    try:
+        (x_train_mnist, y_train_mnist), (_, _) = get_default_mnist_subset
+
+        classifier, _ = image_dl_estimator()
+
+        classifier.fit(x_train_mnist[:NB_TRAIN], y_train_mnist[:NB_TRAIN], nb_epochs=1)
+        defence = SpectralSignatureDefense(
+            classifier,
+            x_train_mnist[:NB_TRAIN],
+            y_train_mnist[:NB_TRAIN],
+            batch_size=BATCH_SIZE,
+            eps_multiplier=EPS_MULTIPLIER,
+            expected_pp_poison=UB_PCT_POISON,
+        )
+        report, is_clean = defence.detect_poison()
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.skip_framework("non_dl_frameworks", "mxnet")
+def test_evaluate_defense(art_warning, get_default_mnist_subset, image_dl_estimator):
+    try:
+        (x_train_mnist, y_train_mnist), (_, _) = get_default_mnist_subset
+
+        classifier, _ = image_dl_estimator()
+
+        classifier.fit(x_train_mnist[:NB_TRAIN], y_train_mnist[:NB_TRAIN], nb_epochs=1)
+        defence = SpectralSignatureDefense(
+            classifier,
+            x_train_mnist[:NB_TRAIN],
+            y_train_mnist[:NB_TRAIN],
+            batch_size=BATCH_SIZE,
+            eps_multiplier=EPS_MULTIPLIER,
+            expected_pp_poison=UB_PCT_POISON,
+        )
+        res = defence.evaluate_defence(np.zeros(NB_TRAIN))
+    except ARTTestException as e:
+        art_warning(e)
diff --git a/adversarial-robustness-toolbox/tests/defences/preprocessor/conftest.py b/adversarial-robustness-toolbox/tests/defences/preprocessor/conftest.py
new file mode 100644
index 0000000..cabe493
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/defences/preprocessor/conftest.py
@@ -0,0 +1,57 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+import numpy as np
+import pytest
+
+
+@pytest.fixture
+def image_batch(channels_first):
+    """
+    Image fixture of shape NHWC and NCHW.
+    """
+    test_input = np.repeat(np.array(range(6)).reshape(6, 1), 24, axis=1).reshape((2, 3, 4, 6))
+    if not channels_first:
+        test_input = np.transpose(test_input, (0, 2, 3, 1))
+    test_output = test_input.copy()
+    return test_input, test_output
+
+
+@pytest.fixture
+def image_batch_small():
+    """Create image fixture of shape (batch_size, channels, width, height)."""
+    return np.zeros((2, 1, 4, 4))
+
+
+@pytest.fixture
+def video_batch(channels_first):
+    """
+    Video fixture of shape NFHWC and NCFHW.
+    """
+    test_input = np.repeat(np.array(range(6)).reshape(6, 1), 24, axis=1).reshape((1, 3, 2, 4, 6))
+    if not channels_first:
+        test_input = np.transpose(test_input, (0, 2, 3, 4, 1))
+    test_output = test_input.copy()
+    return test_input, test_output
+
+
+@pytest.fixture
+def tabular_batch():
+    """
+    Create tabular data fixture of shape (batch_size, features).
+    """
+    return np.zeros((2, 4))
diff --git a/adversarial-robustness-toolbox/tests/defences/preprocessor/test_inverse_gan.py b/adversarial-robustness-toolbox/tests/defences/preprocessor/test_inverse_gan.py
new file mode 100644
index 0000000..a8edc0d
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/defences/preprocessor/test_inverse_gan.py
@@ -0,0 +1,62 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import pytest
+import logging
+import numpy as np
+
+from art.defences.preprocessor.inverse_gan import InverseGAN
+from art.attacks.evasion import FastGradientMethod
+
+from tests.utils import get_gan_inverse_gan_ft
+from tests.utils import ARTTestException
+
+
+@pytest.fixture()
+def fix_get_mnist_subset(get_mnist_dataset):
+    (x_train_mnist, y_train_mnist), (x_test_mnist, y_test_mnist) = get_mnist_dataset
+    n_train = 50
+    n_test = 50
+    yield x_train_mnist[:n_train], y_train_mnist[:n_train], x_test_mnist[:n_test], y_test_mnist[:n_test]
+
+
+@pytest.mark.skip_framework("keras", "pytorch", "scikitlearn", "mxnet", "kerastf")
+def test_inverse_gan(art_warning, fix_get_mnist_subset, image_dl_estimator_for_attack):
+    try:
+        (x_train_mnist, y_train_mnist, x_test_mnist, y_test_mnist) = fix_get_mnist_subset
+
+        gan, inverse_gan, sess = get_gan_inverse_gan_ft()
+        if gan is None:
+            logging.warning("Couldn't perform  this test because no gan is defined for this framework configuration")
+            return
+
+        classifier = image_dl_estimator_for_attack(FastGradientMethod)
+
+        attack = FastGradientMethod(classifier, eps=0.2)
+        x_test_adv = attack.generate(x=x_test_mnist)
+
+        inverse_gan = InverseGAN(sess=sess, gan=gan, inverse_gan=inverse_gan)
+
+        x_test_defended = inverse_gan(x_test_adv, maxiter=1)
+
+        np.testing.assert_array_almost_equal(
+            float(np.mean(x_test_defended - x_test_adv)), 0.08818667382001877, decimal=0.01,
+        )
+    except ARTTestException as e:
+        art_warning(e)
diff --git a/adversarial-robustness-toolbox/tests/defences/preprocessor/test_jpeg_compression.py b/adversarial-robustness-toolbox/tests/defences/preprocessor/test_jpeg_compression.py
new file mode 100644
index 0000000..1315ea0
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/defences/preprocessor/test_jpeg_compression.py
@@ -0,0 +1,150 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+
+import numpy as np
+import pytest
+from numpy.testing import assert_array_equal
+
+from art.config import ART_NUMPY_DTYPE
+from art.defences.preprocessor import JpegCompression
+from tests.utils import ARTTestException
+
+logger = logging.getLogger(__name__)
+
+
+class DataGenerator:
+    """
+    Create image or video batch.
+    """
+
+    def __init__(self, channels_first, channels, image_data=True, batch_size=2):
+        self.channels_first = channels_first
+        self.channels = (channels,)
+        self.batch_size = batch_size
+        self.image_data = image_data
+
+    def get_data(self):
+        temporal_index = () if self.image_data else (2,)
+        if self.channels_first:
+            data_shape = (self.batch_size,) + self.channels + temporal_index + (8, 12)
+        else:
+            data_shape = (self.batch_size,) + temporal_index + (8, 12) + self.channels
+        return (255 * np.ones(data_shape)).astype(ART_NUMPY_DTYPE)
+
+
+@pytest.fixture(params=[1, 2, 3, 5], ids=["grayscale", "grayscale-2", "RGB", "grayscale-5"])
+def image_batch(request, channels_first):
+    """
+    Image fixtures of shape NHWC and NCHW.
+    """
+    channels = request.param
+    image_input = DataGenerator(channels_first, channels)
+    test_input = image_input.get_data()
+    test_output = test_input.copy()
+    return test_input, test_output
+
+
+@pytest.fixture(params=[1, 2, 3, 5], ids=["grayscale", "grayscale-2", "RGB", "grayscale-5"])
+def video_batch(request, channels_first):
+    """
+    Video fixtures of shape NFHWC and NCFHW.
+    """
+    channels = request.param
+    video_input = DataGenerator(channels_first, channels, image_data=False)
+    test_input = video_input.get_data()
+    test_output = test_input.copy()
+    return test_input, test_output
+
+
+@pytest.mark.framework_agnostic
+@pytest.mark.parametrize("channels_first", [True, False])
+def test_jpeg_compression_image_data(art_warning, image_batch, channels_first, framework):
+    try:
+        test_input, test_output = image_batch
+        jpeg_compression = JpegCompression(clip_values=(0, 255), channels_first=channels_first)
+
+        assert_array_equal(jpeg_compression(test_input)[0], test_output)
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.parametrize("channels_first", [True, False])
+@pytest.mark.framework_agnostic
+def test_jpeg_compression_video_data(art_warning, video_batch, channels_first):
+    try:
+        test_input, test_output = video_batch
+        jpeg_compression = JpegCompression(clip_values=(0, 255), channels_first=channels_first)
+
+        assert_array_equal(jpeg_compression(test_input)[0], test_output)
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.parametrize("channels_first", [False])
+@pytest.mark.framework_agnostic
+def test_jpeg_compress(art_warning, image_batch, channels_first):
+    try:
+        test_input, test_output = image_batch
+        # Run only for grayscale [1] and RGB [3] data because testing `_compress` which is applied internally only to
+        # either grayscale or RGB data.
+        if test_input.shape[-1] in [1, 3]:
+            jpeg_compression = JpegCompression(clip_values=(0, 255))
+
+            image_mode = "RGB" if test_input.shape[-1] == 3 else "L"
+            test_single_input = np.squeeze(test_input[0]).astype(np.uint8)
+            test_single_output = np.squeeze(test_output[0]).astype(np.uint8)
+
+            assert_array_equal(jpeg_compression._compress(test_single_input, image_mode), test_single_output)
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.framework_agnostic
+def test_non_spatial_data_error(art_warning, tabular_batch):
+    try:
+        test_input = tabular_batch
+        jpeg_compression = JpegCompression(clip_values=(0, 255), channels_first=True)
+
+        exc_msg = "Unrecognized input dimension. JPEG compression can only be applied to image and video data."
+        with pytest.raises(ValueError, match=exc_msg):
+            jpeg_compression(test_input)
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.framework_agnostic
+def test_negative_clip_values_error(art_warning):
+    try:
+        exc_msg = "'clip_values' min value must be 0."
+        with pytest.raises(ValueError, match=exc_msg):
+            JpegCompression(clip_values=(-1, 255), channels_first=True)
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.framework_agnostic
+def test_maximum_clip_values_error(art_warning):
+    try:
+        exc_msg = "'clip_values' max value must be either 1 or 255."
+        with pytest.raises(ValueError, match=exc_msg):
+            JpegCompression(clip_values=(0, 2), channels_first=True)
+    except ARTTestException as e:
+        art_warning(e)
diff --git a/adversarial-robustness-toolbox/tests/defences/preprocessor/test_mp3_compression.py b/adversarial-robustness-toolbox/tests/defences/preprocessor/test_mp3_compression.py
new file mode 100644
index 0000000..eb91e0b
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/defences/preprocessor/test_mp3_compression.py
@@ -0,0 +1,93 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+
+import numpy as np
+import pytest
+from numpy.testing import assert_array_equal
+
+from art.defences.preprocessor import Mp3Compression
+
+from tests.utils import ARTTestException
+
+logger = logging.getLogger(__name__)
+
+
+class AudioInput:
+    """
+    Create audio batch.
+    """
+
+    def __init__(self, channels_first, channels, sample_rate=44100, batch_size=2):
+        self.channels_first = channels_first
+        self.channels = channels
+        self.sample_rate = sample_rate
+        self.batch_size = batch_size
+
+    def get_data(self):
+        if self.channels_first:
+            return np.zeros((self.batch_size, self.channels, self.sample_rate), dtype=np.int16)
+        else:
+            return np.zeros((self.batch_size, self.sample_rate, self.channels), dtype=np.int16)
+
+
+@pytest.fixture(params=[1, 2], ids=["mono", "stereo"])
+def audio_batch(request, channels_first):
+    """
+    Audio fixtures of shape `(batch_size, channels, samples)` or `(batch_size, samples, channels)`.
+    """
+    channels = request.param
+    audio_input = AudioInput(channels_first, channels)
+    test_input = audio_input.get_data()
+    test_output = test_input.copy()
+    return test_input, test_output, audio_input.sample_rate
+
+
+def test_sample_rate_error(art_warning):
+    try:
+        exc_msg = "Sample rate be must a positive integer."
+        with pytest.raises(ValueError, match=exc_msg):
+            Mp3Compression(sample_rate=0)
+    except ARTTestException as e:
+        art_warning(e)
+
+
+def test_non_temporal_data_error(art_warning, image_batch_small):
+    try:
+        test_input = image_batch_small
+        mp3compression = Mp3Compression(sample_rate=16000)
+
+        exc_msg = "Mp3 compression can only be applied to temporal data across at least one channel."
+        with pytest.raises(ValueError, match=exc_msg):
+            mp3compression(test_input)
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.parametrize("channels_first", [True, False])
+@pytest.mark.skip_framework("keras", "pytorch", "scikitlearn", "mxnet")
+def test_mp3_compresssion(art_warning, audio_batch, channels_first):
+    try:
+        test_input, test_output, sample_rate = audio_batch
+        mp3compression = Mp3Compression(sample_rate=sample_rate, channels_first=channels_first)
+
+        assert_array_equal(mp3compression(test_input)[0], test_output)
+    except ARTTestException as e:
+        art_warning(e)
diff --git a/adversarial-robustness-toolbox/tests/defences/preprocessor/test_resample.py b/adversarial-robustness-toolbox/tests/defences/preprocessor/test_resample.py
new file mode 100644
index 0000000..96ff600
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/defences/preprocessor/test_resample.py
@@ -0,0 +1,86 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+import logging
+
+import numpy as np
+import pytest
+import resampy
+
+from art.defences.preprocessor import Resample
+from tests.utils import ARTTestException
+
+logger = logging.getLogger(__name__)
+
+
+@pytest.fixture(params=[1, 2], ids=["one_channel", "two_channel"])
+def audio_batch(request):
+    """
+    Create audio fixtures of shape (batch_size=2, channels={1,2}, samples).
+    """
+    sample_rate_orig = 16000
+    sample_rate_new = 8000
+    test_input = np.zeros((2, request.param, sample_rate_orig), dtype=np.int16)
+    test_output = np.zeros((2, request.param, sample_rate_new), dtype=np.int16)
+    return test_input, test_output, sample_rate_orig, sample_rate_new
+
+
+@pytest.mark.framework_agnostic
+def test_sample_rate_original_error(art_warning):
+    try:
+        exc_msg = "Original sampling rate be must a positive integer."
+        with pytest.raises(ValueError, match=exc_msg):
+            Resample(sr_original=0, sr_new=16000)
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.framework_agnostic
+def test_sample_rate_new_error(art_warning):
+    try:
+        exc_msg = "New sampling rate be must a positive integer."
+        with pytest.raises(ValueError, match=exc_msg):
+            Resample(sr_original=16000, sr_new=0)
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.framework_agnostic
+def test_non_temporal_data_error(art_warning, image_batch_small):
+    try:
+        test_input = image_batch_small
+        resample = Resample(16000, 16000)
+
+        exc_msg = "Resampling can only be applied to temporal data across at least one channel."
+        with pytest.raises(ValueError, match=exc_msg):
+            resample(test_input)
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.framework_agnostic
+def test_resample(art_warning, audio_batch, mocker):
+    try:
+        test_input, test_output, sr_orig, sr_new = audio_batch
+
+        mocker.patch("resampy.resample", autospec=True)
+        resampy.resample.return_value = test_input[:, :, :sr_new]
+
+        resampler = Resample(sr_original=sr_orig, sr_new=sr_new, channels_first=True)
+        assert resampler(test_input)[0].shape == test_output.shape
+    except ARTTestException as e:
+        art_warning(e)
diff --git a/adversarial-robustness-toolbox/tests/defences/preprocessor/test_spatial_smoothing.py b/adversarial-robustness-toolbox/tests/defences/preprocessor/test_spatial_smoothing.py
new file mode 100644
index 0000000..1fe428a
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/defences/preprocessor/test_spatial_smoothing.py
@@ -0,0 +1,109 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+
+import numpy as np
+import pytest
+from numpy.testing import assert_array_equal
+
+from art.defences.preprocessor import SpatialSmoothing
+from tests.utils import ARTTestException
+
+logger = logging.getLogger(__name__)
+
+
+@pytest.mark.framework_agnostic
+def test_spatial_smoothing_median_filter_call(art_warning):
+    try:
+        test_input = np.array([[[[1, 2], [3, 4]]]])
+        test_output = np.array([[[[1, 2], [3, 3]]]])
+        spatial_smoothing = SpatialSmoothing(channels_first=True, window_size=2)
+
+        assert_array_equal(spatial_smoothing(test_input)[0], test_output)
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.parametrize("channels_first", [True, False])
+@pytest.mark.parametrize("window_size", [1, 2, 10])
+@pytest.mark.framework_agnostic
+def test_spatial_smoothing_image_data(art_warning, image_batch, channels_first, window_size):
+    try:
+        test_input, test_output = image_batch
+        spatial_smoothing = SpatialSmoothing(channels_first=channels_first, window_size=window_size)
+
+        assert_array_equal(spatial_smoothing(test_input)[0], test_output)
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.parametrize("channels_first", [True, False])
+@pytest.mark.framework_agnostic
+def test_spatial_smoothing_video_data(art_warning, video_batch, channels_first):
+    try:
+        test_input, test_output = video_batch
+        spatial_smoothing = SpatialSmoothing(channels_first=channels_first, window_size=2)
+
+        assert_array_equal(spatial_smoothing(test_input)[0], test_output)
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.framework_agnostic
+def test_non_spatial_data_error(art_warning, tabular_batch):
+    try:
+        test_input = tabular_batch
+        spatial_smoothing = SpatialSmoothing(channels_first=True)
+
+        exc_msg = "Unrecognized input dimension. Spatial smoothing can only be applied to image and video data."
+        with pytest.raises(ValueError, match=exc_msg):
+            spatial_smoothing(test_input)
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.framework_agnostic
+def test_window_size_error(art_warning):
+    try:
+        exc_msg = "Sliding window size must be a positive integer."
+        with pytest.raises(ValueError, match=exc_msg):
+            SpatialSmoothing(window_size=0)
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.framework_agnostic
+def test_triple_clip_values_error(art_warning):
+    try:
+        exc_msg = "'clip_values' should be a tuple of 2 floats or arrays containing the allowed data range."
+        with pytest.raises(ValueError, match=exc_msg):
+            SpatialSmoothing(clip_values=(0, 1, 2))
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.framework_agnostic
+def test_relation_clip_values_error(art_warning):
+    try:
+        exc_msg = "Invalid 'clip_values': min >= max."
+        with pytest.raises(ValueError, match=exc_msg):
+            SpatialSmoothing(clip_values=(1, 0))
+    except ARTTestException as e:
+        art_warning(e)
diff --git a/adversarial-robustness-toolbox/tests/defences/preprocessor/test_spatial_smoothing_pytorch.py b/adversarial-robustness-toolbox/tests/defences/preprocessor/test_spatial_smoothing_pytorch.py
new file mode 100644
index 0000000..6f2341d
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/defences/preprocessor/test_spatial_smoothing_pytorch.py
@@ -0,0 +1,138 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+
+import numpy as np
+from numpy.testing import assert_array_equal
+import pytest
+
+from art.defences.preprocessor.spatial_smoothing_pytorch import SpatialSmoothingPyTorch
+from tests.utils import ARTTestException
+
+logger = logging.getLogger(__name__)
+
+
+@pytest.mark.xfail(
+    reason="""a) SciPy's "reflect" padding mode is not supported in PyTorch. The "reflect" model in PyTorch maps
+    to the "mirror" mode in SciPy; b) torch.median() takes the smaller value when the window size is even."""
+)
+@pytest.mark.only_with_platform("pytorch")
+def test_spatial_smoothing_median_filter_call(art_warning):
+    try:
+        test_input = np.array([[[[1, 2], [3, 4]]]])
+        test_output = np.array([[[[1, 2], [3, 3]]]])
+        spatial_smoothing = SpatialSmoothingPyTorch(channels_first=True, window_size=2)
+
+        assert_array_equal(spatial_smoothing(test_input)[0], test_output)
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.only_with_platform("pytorch")
+def test_spatial_smoothing_median_filter_call_expected_behavior(art_warning):
+    try:
+        test_input = np.array([[[[1, 2], [3, 4]]]])
+        test_output = np.array([[[[2, 2], [2, 2]]]])
+        spatial_smoothing = SpatialSmoothingPyTorch(channels_first=True, window_size=2)
+
+        assert_array_equal(spatial_smoothing(test_input)[0], test_output)
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.only_with_platform("pytorch")
+@pytest.mark.parametrize("channels_first", [True, False])
+@pytest.mark.parametrize(
+    "window_size",
+    [
+        1,
+        2,
+        pytest.param(
+            10,
+            marks=pytest.mark.xfail(
+                reason="Window size of 10 fails, because PyTorch requires that Padding size should be less than "
+                "the corresponding input dimension."
+            ),
+        ),
+    ],
+)
+def test_spatial_smoothing_image_data(art_warning, image_batch, channels_first, window_size):
+    try:
+        test_input, test_output = image_batch
+        spatial_smoothing = SpatialSmoothingPyTorch(channels_first=channels_first, window_size=window_size)
+
+        assert_array_equal(spatial_smoothing(test_input)[0], test_output)
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.only_with_platform("pytorch")
+@pytest.mark.parametrize("channels_first", [True, False])
+def test_spatial_smoothing_video_data(art_warning, video_batch, channels_first):
+    try:
+        test_input, test_output = video_batch
+        spatial_smoothing = SpatialSmoothingPyTorch(channels_first=channels_first, window_size=2)
+
+        assert_array_equal(spatial_smoothing(test_input)[0], test_output)
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.only_with_platform("pytorch")
+def test_non_spatial_data_error(art_warning, tabular_batch):
+    try:
+        test_input = tabular_batch
+        spatial_smoothing = SpatialSmoothingPyTorch(channels_first=True)
+
+        exc_msg = "Unrecognized input dimension. Spatial smoothing can only be applied to image and video data."
+        with pytest.raises(ValueError, match=exc_msg):
+            spatial_smoothing(test_input)
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.only_with_platform("pytorch")
+def test_window_size_error(art_warning):
+    try:
+        exc_msg = "Sliding window size must be a positive integer."
+        with pytest.raises(ValueError, match=exc_msg):
+            SpatialSmoothingPyTorch(window_size=0)
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.only_with_platform("pytorch")
+def test_triple_clip_values_error(art_warning):
+    try:
+        exc_msg = "'clip_values' should be a tuple of 2 floats or arrays containing the allowed data range."
+        with pytest.raises(ValueError, match=exc_msg):
+            SpatialSmoothingPyTorch(clip_values=(0, 1, 2))
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.only_with_platform("pytorch")
+def test_relation_clip_values_error(art_warning):
+    try:
+        exc_msg = "Invalid 'clip_values': min >= max."
+        with pytest.raises(ValueError, match=exc_msg):
+            SpatialSmoothingPyTorch(clip_values=(1, 0))
+    except ARTTestException as e:
+        art_warning(e)
diff --git a/adversarial-robustness-toolbox/tests/defences/preprocessor/test_spatial_smoothing_tensorflow.py b/adversarial-robustness-toolbox/tests/defences/preprocessor/test_spatial_smoothing_tensorflow.py
new file mode 100644
index 0000000..6bf9e3f
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/defences/preprocessor/test_spatial_smoothing_tensorflow.py
@@ -0,0 +1,145 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+
+import numpy as np
+from numpy.testing import assert_array_equal
+import pytest
+
+from art.defences.preprocessor.spatial_smoothing_tensorflow import SpatialSmoothingTensorFlowV2
+from tests.utils import ARTTestException
+
+logger = logging.getLogger(__name__)
+
+
+@pytest.mark.xfail()
+@pytest.mark.only_with_platform("tensorflow2")
+def test_spatial_smoothing_median_filter_call(art_warning):
+    try:
+        test_input = np.array([[[[1], [2]], [[3], [4]]]])
+        test_output = np.array([[[[1], [2]], [[3], [3]]]])
+        spatial_smoothing = SpatialSmoothingTensorFlowV2(channels_first=False, window_size=2)
+
+        assert_array_equal(spatial_smoothing(test_input)[0], test_output)
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.only_with_platform("tensorflow2")
+def test_spatial_smoothing_median_filter_call_expected_behavior(art_warning):
+    try:
+        test_input = np.array([[[[1], [2]], [[3], [4]]]])
+        test_output = np.array([[[[2], [2]], [[2], [2]]]])
+        spatial_smoothing = SpatialSmoothingTensorFlowV2(channels_first=False, window_size=2)
+
+        assert_array_equal(spatial_smoothing(test_input)[0], test_output)
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.parametrize("channels_first", [True, False])
+@pytest.mark.parametrize(
+    "window_size",
+    [
+        1,
+        2,
+        pytest.param(
+            10,
+            marks=pytest.mark.xfail(
+                reason="Window size of 10 fails, because TensorFlow requires that Padding size should be less than "
+                "the corresponding input dimension."
+            ),
+        ),
+    ],
+)
+@pytest.mark.only_with_platform("tensorflow2")
+def test_spatial_smoothing_image_data(art_warning, image_batch, channels_first, window_size):
+    try:
+        test_input, test_output = image_batch
+
+        if channels_first:
+            exc_msg = "Only channels last input data is supported"
+            with pytest.raises(ValueError, match=exc_msg):
+                _ = SpatialSmoothingTensorFlowV2(channels_first=channels_first, window_size=window_size)
+        else:
+            spatial_smoothing = SpatialSmoothingTensorFlowV2(channels_first=channels_first, window_size=window_size)
+            assert_array_equal(spatial_smoothing(test_input)[0], test_output)
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.only_with_platform("tensorflow2")
+@pytest.mark.parametrize("channels_first", [True, False])
+def test_spatial_smoothing_video_data(art_warning, video_batch, channels_first):
+    try:
+        test_input, test_output = video_batch
+
+        if channels_first:
+            exc_msg = "Only channels last input data is supported"
+            with pytest.raises(ValueError, match=exc_msg):
+                _ = SpatialSmoothingTensorFlowV2(channels_first=channels_first, window_size=2)
+        else:
+            spatial_smoothing = SpatialSmoothingTensorFlowV2(channels_first=channels_first, window_size=2)
+            assert_array_equal(spatial_smoothing(test_input)[0], test_output)
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.only_with_platform("tensorflow", "tensorflow2v1")
+def test_non_spatial_data_error(art_warning, tabular_batch):
+    try:
+        test_input = tabular_batch
+        spatial_smoothing = SpatialSmoothingTensorFlowV2(channels_first=False)
+
+        exc_msg = "Unrecognized input dimension. Spatial smoothing can only be applied to image"
+        with pytest.raises(ValueError, match=exc_msg):
+            spatial_smoothing(test_input)
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.only_with_platform("tensorflow2")
+def test_window_size_error(art_warning):
+    try:
+        exc_msg = "Sliding window size must be a positive integer."
+        with pytest.raises(ValueError, match=exc_msg):
+            SpatialSmoothingTensorFlowV2(window_size=0)
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.only_with_platform("tensorflow2")
+def test_triple_clip_values_error(art_warning):
+    try:
+        exc_msg = "'clip_values' should be a tuple of 2 floats or arrays containing the allowed data range."
+        with pytest.raises(ValueError, match=exc_msg):
+            SpatialSmoothingTensorFlowV2(clip_values=(0, 1, 2))
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.only_with_platform("tensorflow2")
+def test_relation_clip_values_error(art_warning):
+    try:
+        exc_msg = "Invalid 'clip_values': min >= max."
+        with pytest.raises(ValueError, match=exc_msg):
+            SpatialSmoothingTensorFlowV2(clip_values=(1, 0))
+    except ARTTestException as e:
+        art_warning(e)
diff --git a/adversarial-robustness-toolbox/tests/defences/preprocessor/test_video_compression.py b/adversarial-robustness-toolbox/tests/defences/preprocessor/test_video_compression.py
new file mode 100644
index 0000000..f218899
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/defences/preprocessor/test_video_compression.py
@@ -0,0 +1,87 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+
+import numpy as np
+import pytest
+from numpy.testing import assert_array_equal
+
+from art.defences.preprocessor import VideoCompression
+
+from tests.utils import ARTTestException
+
+logger = logging.getLogger(__name__)
+
+
+@pytest.fixture
+def video_batch(channels_first):
+    """
+    Video fixture of shape NFHWC and NCFHW.
+    """
+    test_input = np.stack((np.zeros((3, 25, 4, 6)), np.ones((3, 25, 4, 6))))
+    if not channels_first:
+        test_input = np.transpose(test_input, (0, 2, 3, 4, 1))
+    test_output = test_input.copy()
+    return test_input, test_output
+
+
+@pytest.mark.parametrize("channels_first", [True, False])
+@pytest.mark.skip_framework("keras", "pytorch", "scikitlearn", "mxnet")
+def test_video_compresssion(art_warning, video_batch, channels_first):
+    try:
+        test_input, test_output = video_batch
+        video_compression = VideoCompression(video_format="mp4", constant_rate_factor=0, channels_first=channels_first)
+
+        assert_array_equal(video_compression(test_input)[0], test_output)
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.skip_framework("keras", "pytorch", "scikitlearn", "mxnet")
+def test_compress_video_call(art_warning):
+    try:
+        test_input = np.arange(12).reshape((1, 3, 1, 2, 2))
+        video_compression = VideoCompression(video_format="mp4", constant_rate_factor=50, channels_first=True)
+
+        assert np.any(np.not_equal(video_compression(test_input)[0], test_input))
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.parametrize("constant_rate_factor", [-1, 52])
+def test_constant_rate_factor_error(art_warning, constant_rate_factor):
+    try:
+        exc_msg = r"Constant rate factor must be an integer in the range \[0, 51\]."
+        with pytest.raises(ValueError, match=exc_msg):
+            VideoCompression(video_format="", constant_rate_factor=constant_rate_factor)
+    except ARTTestException as e:
+        art_warning(e)
+
+
+def test_non_spatio_temporal_data_error(art_warning, image_batch_small):
+    try:
+        test_input = image_batch_small
+        video_compression = VideoCompression(video_format="")
+
+        exc_msg = "Video compression can only be applied to spatio-temporal data."
+        with pytest.raises(ValueError, match=exc_msg):
+            video_compression(test_input)
+    except ARTTestException as e:
+        art_warning(e)
diff --git a/adversarial-robustness-toolbox/tests/defences/test_adversarial_trainer.py b/adversarial-robustness-toolbox/tests/defences/test_adversarial_trainer.py
new file mode 100644
index 0000000..7076b41
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/defences/test_adversarial_trainer.py
@@ -0,0 +1,165 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import unittest
+
+import numpy as np
+
+from art.attacks.evasion.fast_gradient import FastGradientMethod
+from art.attacks.evasion.deepfool import DeepFool
+from art.data_generators import DataGenerator
+from art.defences.trainer.adversarial_trainer import AdversarialTrainer
+from art.utils import load_mnist
+
+from tests.utils import master_seed, get_image_classifier_tf
+
+logger = logging.getLogger(__name__)
+
+BATCH_SIZE = 10
+NB_TRAIN = 100
+NB_TEST = 100
+
+
+class TestAdversarialTrainer(unittest.TestCase):
+    """
+    Test cases for the AdversarialTrainer class.
+    """
+
+    @classmethod
+    def setUpClass(cls):
+        # MNIST
+        (x_train, y_train), (x_test, y_test), _, _ = load_mnist()
+        x_train, y_train, x_test, y_test = (
+            x_train[:NB_TRAIN],
+            y_train[:NB_TRAIN],
+            x_test[:NB_TEST],
+            y_test[:NB_TEST],
+        )
+        cls.mnist = ((x_train, y_train), (x_test, y_test))
+
+        cls.classifier, _ = get_image_classifier_tf()
+
+    def setUp(self):
+        master_seed(seed=1234)
+
+    def test_classifier_match(self):
+        attack = FastGradientMethod(self.classifier)
+        adv_trainer = AdversarialTrainer(self.classifier, attack)
+
+        self.assertEqual(len(adv_trainer.attacks), 1)
+        self.assertEqual(adv_trainer.attacks[0].estimator, adv_trainer.get_classifier())
+
+    def test_fit_predict(self):
+        (x_train, y_train), (x_test, y_test) = self.mnist
+        x_test_original = x_test.copy()
+
+        attack = FastGradientMethod(self.classifier)
+        x_test_adv = attack.generate(x_test)
+        predictions = np.argmax(self.classifier.predict(x_test_adv), axis=1)
+        accuracy = np.sum(predictions == np.argmax(y_test, axis=1)) / NB_TEST
+
+        adv_trainer = AdversarialTrainer(self.classifier, attack)
+        adv_trainer.fit(x_train, y_train, nb_epochs=5, batch_size=128)
+
+        predictions_new = np.argmax(adv_trainer.predict(x_test_adv), axis=1)
+        accuracy_new = np.sum(predictions_new == np.argmax(y_test, axis=1)) / NB_TEST
+
+        self.assertEqual(accuracy_new, 0.12)
+        self.assertEqual(accuracy, 0.13)
+
+        # Check that x_test has not been modified by attack and classifier
+        self.assertAlmostEqual(float(np.max(np.abs(x_test_original - x_test))), 0.0, delta=0.00001)
+
+    def test_two_attacks(self):
+        (x_train, y_train), (x_test, y_test) = self.mnist
+        x_test_original = x_test.copy()
+
+        attack1 = FastGradientMethod(estimator=self.classifier, batch_size=16)
+        attack2 = DeepFool(classifier=self.classifier, max_iter=5, batch_size=16)
+        x_test_adv = attack1.generate(x_test)
+        predictions = np.argmax(self.classifier.predict(x_test_adv), axis=1)
+        accuracy = np.sum(predictions == np.argmax(y_test, axis=1)) / NB_TEST
+
+        adv_trainer = AdversarialTrainer(self.classifier, attacks=[attack1, attack2])
+        adv_trainer.fit(x_train, y_train, nb_epochs=2, batch_size=16)
+
+        predictions_new = np.argmax(adv_trainer.predict(x_test_adv), axis=1)
+        accuracy_new = np.sum(predictions_new == np.argmax(y_test, axis=1)) / NB_TEST
+
+        self.assertEqual(accuracy_new, 0.14)
+        self.assertEqual(accuracy, 0.13)
+
+        # Check that x_test has not been modified by attack and classifier
+        self.assertAlmostEqual(float(np.max(np.abs(x_test_original - x_test))), 0.0, delta=0.00001)
+
+    def test_two_attacks_with_generator(self):
+        (x_train, y_train), (x_test, y_test) = self.mnist
+        x_train_original = x_train.copy()
+        x_test_original = x_test.copy()
+
+        class MyDataGenerator(DataGenerator):
+            def __init__(self, x, y, size, batch_size):
+                super().__init__(size=size, batch_size=batch_size)
+                self.x = x
+                self.y = y
+                self._size = size
+                self._batch_size = batch_size
+
+            def get_batch(self):
+                ids = np.random.choice(self.size, size=min(self.size, self.batch_size), replace=False)
+                return self.x[ids], self.y[ids]
+
+        generator = MyDataGenerator(x_train, y_train, size=x_train.shape[0], batch_size=16)
+
+        attack1 = FastGradientMethod(estimator=self.classifier, batch_size=16)
+        attack2 = DeepFool(classifier=self.classifier, max_iter=5, batch_size=16)
+        x_test_adv = attack1.generate(x_test)
+        predictions = np.argmax(self.classifier.predict(x_test_adv), axis=1)
+        accuracy = np.sum(predictions == np.argmax(y_test, axis=1)) / NB_TEST
+
+        adv_trainer = AdversarialTrainer(self.classifier, attacks=[attack1, attack2])
+        adv_trainer.fit_generator(generator, nb_epochs=3)
+
+        predictions_new = np.argmax(adv_trainer.predict(x_test_adv), axis=1)
+        accuracy_new = np.sum(predictions_new == np.argmax(y_test, axis=1)) / NB_TEST
+
+        self.assertAlmostEqual(accuracy_new, 0.38, delta=0.02)
+        self.assertAlmostEqual(accuracy, 0.1, delta=0.0)
+
+        # Check that x_train and x_test has not been modified by attack and classifier
+        self.assertAlmostEqual(float(np.max(np.abs(x_train_original - x_train))), 0.0, delta=0.00001)
+        self.assertAlmostEqual(float(np.max(np.abs(x_test_original - x_test))), 0.0, delta=0.00001)
+
+    def test_targeted_attack_error(self):
+        """
+        Test the adversarial trainer using a targeted attack, which will currently result in a NotImplementError.
+
+        :return: None
+        """
+        (x_train, y_train), (_, _) = self.mnist
+        params = {"nb_epochs": 2, "batch_size": BATCH_SIZE}
+
+        adv = FastGradientMethod(self.classifier, targeted=True)
+        adv_trainer = AdversarialTrainer(self.classifier, attacks=adv)
+        self.assertRaises(NotImplementedError, adv_trainer.fit, x_train, y_train, **params)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/adversarial-robustness-toolbox/tests/defences/test_adversarial_trainer_FBF.py b/adversarial-robustness-toolbox/tests/defences/test_adversarial_trainer_FBF.py
new file mode 100644
index 0000000..a139358
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/defences/test_adversarial_trainer_FBF.py
@@ -0,0 +1,75 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import pytest
+import logging
+import numpy as np
+
+from art.defences.trainer import AdversarialTrainerFBFPyTorch
+
+
+@pytest.fixture()
+def get_adv_trainer(framework, image_dl_estimator):
+    def _get_adv_trainer():
+
+        if framework == "keras":
+            trainer = None
+        if framework in ["tensorflow", "tensorflow2v1"]:
+            trainer = None
+        if framework == "pytorch":
+            classifier = image_dl_estimator()[0][0]
+            trainer = AdversarialTrainerFBFPyTorch(classifier)
+        if framework == "scikitlearn":
+            trainer = None
+
+        return trainer
+
+    return _get_adv_trainer
+
+
+@pytest.fixture()
+def fix_get_mnist_subset(get_mnist_dataset):
+    (x_train_mnist, y_train_mnist), (x_test_mnist, y_test_mnist) = get_mnist_dataset
+    n_train = 100
+    n_test = 100
+    yield x_train_mnist[:n_train], y_train_mnist[:n_train], x_test_mnist[:n_test], y_test_mnist[:n_test]
+
+
+def test_adversarial_trainer_fbf_pytorch_fit_and_predict(get_adv_trainer, fix_get_mnist_subset):
+    (x_train_mnist, y_train_mnist, x_test_mnist, y_test_mnist) = fix_get_mnist_subset
+    x_test_mnist_original = x_test_mnist.copy()
+
+    trainer = get_adv_trainer()
+    if trainer is None:
+        logging.warning("Couldn't perform  this test because no gan is defined for this framework configuration")
+        return
+
+    predictions = np.argmax(trainer.predict(x_test_mnist), axis=1)
+    accuracy = np.sum(predictions == np.argmax(y_test_mnist, axis=1)) / x_test_mnist.shape[0]
+
+    trainer.fit(x_train_mnist, y_train_mnist, nb_epochs=5)
+    predictions_new = np.argmax(trainer.predict(x_test_mnist), axis=1)
+    accuracy_new = np.sum(predictions_new == np.argmax(y_test_mnist, axis=1)) / x_test_mnist.shape[0]
+
+    np.testing.assert_array_almost_equal(
+        float(np.mean(x_test_mnist_original - x_test_mnist)), 0.0, decimal=4,
+    )
+
+    np.testing.assert_array_almost_equal(accuracy, 0.32, decimal=4)
+    np.testing.assert_array_almost_equal(accuracy_new, 0.14, decimal=4)
diff --git a/adversarial-robustness-toolbox/tests/defences/test_adversarial_trainer_madry_pgd.py b/adversarial-robustness-toolbox/tests/defences/test_adversarial_trainer_madry_pgd.py
new file mode 100644
index 0000000..7cddc34
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/defences/test_adversarial_trainer_madry_pgd.py
@@ -0,0 +1,76 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import unittest
+
+import numpy as np
+
+from art.defences.trainer.adversarial_trainer_madry_pgd import AdversarialTrainerMadryPGD
+from art.utils import load_mnist
+
+from tests.utils import master_seed, get_image_classifier_tf
+
+logger = logging.getLogger(__name__)
+
+BATCH_SIZE = 10
+NB_TRAIN = 100
+NB_TEST = 100
+
+
+class TestAdversarialTrainerMadryPGD(unittest.TestCase):
+    """
+    Test cases for the AdversarialTrainerMadryPGD class.
+    """
+
+    @classmethod
+    def setUpClass(cls):
+        # MNIST
+        (x_train, y_train), (x_test, y_test), _, _ = load_mnist()
+        x_train, y_train, x_test, y_test = (
+            x_train[:NB_TRAIN],
+            y_train[:NB_TRAIN],
+            x_test[:NB_TEST],
+            y_test[:NB_TEST],
+        )
+        cls.mnist = ((x_train, y_train), (x_test, y_test))
+
+        cls.classifier, _ = get_image_classifier_tf()
+
+    def setUp(self):
+        master_seed(seed=1234)
+
+    def test_fit_predict(self):
+        (x_train, y_train), (x_test, y_test) = self.mnist
+        x_test_original = x_test.copy()
+
+        adv_trainer = AdversarialTrainerMadryPGD(self.classifier, nb_epochs=1, batch_size=128)
+        adv_trainer.fit(x_train, y_train)
+
+        predictions_new = np.argmax(adv_trainer.trainer.get_classifier().predict(x_test), axis=1)
+        accuracy_new = np.sum(predictions_new == np.argmax(y_test, axis=1)) / NB_TEST
+
+        self.assertEqual(accuracy_new, 0.38)
+
+        # Check that x_test has not been modified by attack and classifier
+        self.assertAlmostEqual(float(np.max(np.abs(x_test_original - x_test))), 0.0, delta=0.00001)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/adversarial-robustness-toolbox/tests/defences/test_class_labels.py b/adversarial-robustness-toolbox/tests/defences/test_class_labels.py
new file mode 100644
index 0000000..9594d33
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/defences/test_class_labels.py
@@ -0,0 +1,96 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+import logging
+import unittest
+
+import numpy as np
+
+from art.utils import load_dataset
+from art.defences.postprocessor import ClassLabels
+
+from tests.utils import master_seed, get_image_classifier_kr_tf, get_image_classifier_kr_tf_binary
+
+logger = logging.getLogger(__name__)
+
+
+class TestClassLabels(unittest.TestCase):
+    """
+    A unittest class for testing the ClassLabels postprocessor.
+    """
+
+    @classmethod
+    def setUpClass(cls):
+        (x_train, y_train), (x_test, y_test), _, _ = load_dataset("mnist")
+        cls.mnist = (x_train, y_train), (x_test, y_test)
+
+    def setUp(self):
+        master_seed(seed=1234)
+
+    def test_class_labels(self):
+        """
+        Test class labels.
+        """
+        (_, _), (x_test, _) = self.mnist
+        classifier = get_image_classifier_kr_tf()
+        preds = classifier.predict(x_test[0:1])
+        postprocessor = ClassLabels()
+        post_preds = postprocessor(preds=preds)
+
+        classifier_prediction_expected = np.asarray(
+            [
+                [
+                    0.12109935,
+                    0.0498215,
+                    0.0993958,
+                    0.06410096,
+                    0.11366928,
+                    0.04645343,
+                    0.06419807,
+                    0.30685693,
+                    0.07616714,
+                    0.05823757,
+                ]
+            ],
+            dtype=np.float32,
+        )
+        post_classifier_prediction_expected = np.asarray(
+            [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0]], dtype=np.float32
+        )
+
+        np.testing.assert_array_almost_equal(preds, classifier_prediction_expected, decimal=4)
+        np.testing.assert_array_almost_equal(post_preds, post_classifier_prediction_expected, decimal=4)
+
+    def test_class_labels_binary(self):
+        """
+        Test class labels for binary classifier.
+        """
+        (_, _), (x_test, _) = self.mnist
+        classifier = get_image_classifier_kr_tf_binary()
+        preds = classifier.predict(x_test[0:1])
+        postprocessor = ClassLabels()
+        post_preds = postprocessor(preds=preds)
+
+        classifier_prediction_expected = np.asarray([[0.5301345]], dtype=np.float32)
+        post_classifier_prediction_expected = np.asarray([[1.0]], dtype=np.float32)
+
+        np.testing.assert_array_almost_equal(preds, classifier_prediction_expected, decimal=4)
+        np.testing.assert_array_almost_equal(post_preds, post_classifier_prediction_expected, decimal=4)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/adversarial-robustness-toolbox/tests/defences/test_defensive_distillation.py b/adversarial-robustness-toolbox/tests/defences/test_defensive_distillation.py
new file mode 100644
index 0000000..b373b27
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/defences/test_defensive_distillation.py
@@ -0,0 +1,256 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import unittest
+
+import numpy as np
+
+from art.defences.transformer.evasion import DefensiveDistillation
+
+from tests.utils import master_seed, TestBase
+from tests.utils import get_image_classifier_tf, get_image_classifier_pt, get_image_classifier_kr
+from tests.utils import get_tabular_classifier_tf, get_tabular_classifier_kr, get_tabular_classifier_pt
+
+logger = logging.getLogger(__name__)
+
+BATCH_SIZE = 100
+NB_EPOCHS = 30
+
+
+def cross_entropy(prob1, prob2, eps=1e-10):
+    """
+    Compute cross-entropy between two probability distributions.
+
+    :param prob1: First probability distribution.
+    :type prob1: `np.ndarray`
+    :param prob2: Second probability distribution.
+    :type prob2: `np.ndarray`
+    :param eps: A small amount to avoid the possibility of having a log of zero.
+    :type eps: `float`
+    :return: Cross entropy.
+    :rtype: `float`
+    """
+    prob1 = np.clip(prob1, eps, 1.0 - eps)
+    size = prob1.shape[0]
+    result = -np.sum(prob2 * np.log(prob1 + eps)) / size
+
+    return result
+
+
+class TestDefensiveDistillation(TestBase):
+    """
+    A unittest class for testing the DefensiveDistillation transformer on image data.
+    """
+
+    @classmethod
+    def setUpClass(cls):
+        master_seed(seed=1234, set_tensorflow=True)
+        super().setUpClass()
+
+    def setUp(self):
+        super().setUp()
+
+    def test_1_tensorflow_classifier(self):
+        """
+        First test with the TensorFlowClassifier.
+        :return:
+        """
+        # Create the trained classifier
+        trained_classifier, sess = get_image_classifier_tf()
+
+        # Create the modified classifier
+        transformed_classifier, _ = get_image_classifier_tf(load_init=False, sess=sess)
+
+        # Create defensive distillation transformer
+        transformer = DefensiveDistillation(classifier=trained_classifier, batch_size=BATCH_SIZE, nb_epochs=NB_EPOCHS)
+
+        # Perform the transformation
+        transformed_classifier = transformer(x=self.x_train_mnist, transformed_classifier=transformed_classifier)
+
+        # Compare the 2 outputs
+        preds1 = trained_classifier.predict(x=self.x_train_mnist, batch_size=BATCH_SIZE)
+
+        preds2 = transformed_classifier.predict(x=self.x_train_mnist, batch_size=BATCH_SIZE)
+
+        preds1 = np.argmax(preds1, axis=1)
+        preds2 = np.argmax(preds2, axis=1)
+        acc = np.sum(preds1 == preds2) / len(preds1)
+
+        self.assertGreater(acc, 0.5)
+
+        ce = cross_entropy(preds1, preds2)
+
+        self.assertLess(ce, 10)
+        self.assertGreaterEqual(ce, 0)
+
+        # Clean-up session
+        if sess is not None:
+            sess.close()
+
+    def test_3_pytorch_classifier(self):
+        """
+        Second test with the PyTorchClassifier.
+        :return:
+        """
+        self.x_train_mnist = np.reshape(self.x_train_mnist, (self.x_train_mnist.shape[0], 1, 28, 28)).astype(np.float32)
+
+        # Create the trained classifier
+        trained_classifier = get_image_classifier_pt()
+
+        # Create the modified classifier
+        transformed_classifier = get_image_classifier_pt(load_init=False)
+
+        # Create defensive distillation transformer
+        transformer = DefensiveDistillation(classifier=trained_classifier, batch_size=BATCH_SIZE, nb_epochs=NB_EPOCHS)
+
+        # Perform the transformation
+        transformed_classifier = transformer(x=self.x_train_mnist, transformed_classifier=transformed_classifier)
+
+        # Compare the 2 outputs
+        preds1 = trained_classifier.predict(x=self.x_train_mnist, batch_size=BATCH_SIZE)
+
+        preds2 = transformed_classifier.predict(x=self.x_train_mnist, batch_size=BATCH_SIZE)
+
+        preds1 = np.argmax(preds1, axis=1)
+        preds2 = np.argmax(preds2, axis=1)
+        acc = np.sum(preds1 == preds2) / len(preds1)
+
+        self.assertGreater(acc, 0.5)
+
+        ce = cross_entropy(preds1, preds2)
+
+        self.assertLess(ce, 10)
+        self.assertGreaterEqual(ce, 0)
+
+        self.x_train_mnist = np.reshape(self.x_train_mnist, (self.x_train_mnist.shape[0], 28, 28, 1)).astype(np.float32)
+
+    def test_5_keras_classifier(self):
+        """
+        Third test with the KerasClassifier.
+        :return:
+        """
+        # Create the trained classifier
+        trained_classifier = get_image_classifier_kr()
+
+        # Create the modified classifier
+        transformed_classifier = get_image_classifier_kr(load_init=False)
+
+        # Create defensive distillation transformer
+        transformer = DefensiveDistillation(classifier=trained_classifier, batch_size=BATCH_SIZE, nb_epochs=NB_EPOCHS)
+
+        # Perform the transformation
+        transformed_classifier = transformer(x=self.x_train_mnist, transformed_classifier=transformed_classifier)
+
+        # Compare the 2 outputs
+        preds1 = trained_classifier.predict(x=self.x_train_mnist, batch_size=BATCH_SIZE)
+
+        preds2 = transformed_classifier.predict(x=self.x_train_mnist, batch_size=BATCH_SIZE)
+
+        preds1 = np.argmax(preds1, axis=1)
+        preds2 = np.argmax(preds2, axis=1)
+        acc = np.sum(preds1 == preds2) / len(preds1)
+
+        self.assertGreater(acc, 0.5)
+
+        ce = cross_entropy(preds1, preds2)
+
+        self.assertLess(ce, 10)
+        self.assertGreaterEqual(ce, 0)
+
+    def test_2_tensorflow_iris(self):
+        """
+        First test for TensorFlow.
+        :return:
+        """
+        # Create the trained classifier
+        trained_classifier, sess = get_tabular_classifier_tf()
+
+        # Create the modified classifier
+        transformed_classifier, _ = get_tabular_classifier_tf(load_init=False, sess=sess)
+
+        # Create defensive distillation transformer
+        transformer = DefensiveDistillation(classifier=trained_classifier, batch_size=BATCH_SIZE, nb_epochs=NB_EPOCHS)
+
+        # Perform the transformation
+        with self.assertRaises(ValueError) as context:
+            _ = transformer(x=self.x_train_iris, transformed_classifier=transformed_classifier)
+
+        self.assertIn("The input trained classifier do not produce probability outputs.", str(context.exception))
+
+        # Clean-up session
+        if sess is not None:
+            sess.close()
+
+    def test_6_keras_iris(self):
+        """
+        Second test for Keras.
+        :return:
+        """
+        # Create the trained classifier
+        trained_classifier = get_tabular_classifier_kr()
+
+        # Create the modified classifier
+        transformed_classifier = get_tabular_classifier_kr(load_init=False)
+
+        # Create defensive distillation transformer
+        transformer = DefensiveDistillation(classifier=trained_classifier, batch_size=BATCH_SIZE, nb_epochs=NB_EPOCHS)
+
+        # Perform the transformation
+        transformed_classifier = transformer(x=self.x_train_iris, transformed_classifier=transformed_classifier)
+
+        # Compare the 2 outputs
+        preds1 = trained_classifier.predict(x=self.x_train_iris, batch_size=BATCH_SIZE)
+
+        preds2 = transformed_classifier.predict(x=self.x_train_iris, batch_size=BATCH_SIZE)
+
+        preds1 = np.argmax(preds1, axis=1)
+        preds2 = np.argmax(preds2, axis=1)
+        acc = np.sum(preds1 == preds2) / len(preds1)
+
+        self.assertGreater(acc, 0.2)
+
+        ce = cross_entropy(preds1, preds2)
+
+        self.assertLess(ce, 20)
+        self.assertGreaterEqual(ce, 0)
+
+    def test_4_pytorch_iris(self):
+        """
+        Third test for PyTorch.
+        :return:
+        """
+        # Create the trained classifier
+        trained_classifier = get_tabular_classifier_pt()
+
+        # Create the modified classifier
+        transformed_classifier = get_tabular_classifier_pt(load_init=False)
+
+        # Create defensive distillation transformer
+        transformer = DefensiveDistillation(classifier=trained_classifier, batch_size=BATCH_SIZE, nb_epochs=NB_EPOCHS)
+
+        # Perform the transformation
+        with self.assertRaises(ValueError) as context:
+            _ = transformer(x=self.x_train_iris, transformed_classifier=transformed_classifier)
+
+        self.assertIn("The input trained classifier do not produce probability outputs.", str(context.exception))
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/adversarial-robustness-toolbox/tests/defences/test_feature_squeezing.py b/adversarial-robustness-toolbox/tests/defences/test_feature_squeezing.py
new file mode 100644
index 0000000..17b440b
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/defences/test_feature_squeezing.py
@@ -0,0 +1,74 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import unittest
+
+import numpy as np
+
+from art.defences.preprocessor import FeatureSqueezing
+
+from tests.utils import master_seed
+
+logger = logging.getLogger(__name__)
+
+
+class TestFeatureSqueezing(unittest.TestCase):
+    def setUp(self):
+        master_seed(seed=1234)
+
+    def test_ones(self):
+        m, n = 10, 2
+        x = np.ones((m, n))
+
+        for depth in range(1, 50):
+            preproc = FeatureSqueezing(clip_values=(0, 1), bit_depth=depth)
+            x_squeezed, _ = preproc(x)
+            self.assertTrue((x_squeezed == 1).all())
+
+    def test_random(self):
+        m, n = 1000, 20
+        x = np.random.rand(m, n)
+        x_original = x.copy()
+        x_zero = np.where(x < 0.5)
+        x_one = np.where(x >= 0.5)
+
+        preproc = FeatureSqueezing(clip_values=(0, 1), bit_depth=1)
+        x_squeezed, _ = preproc(x)
+        self.assertTrue((x_squeezed[x_zero] == 0.0).all())
+        self.assertTrue((x_squeezed[x_one] == 1.0).all())
+
+        preproc = FeatureSqueezing(clip_values=(0, 1), bit_depth=2)
+        x_squeezed, _ = preproc(x)
+        self.assertFalse(np.logical_and(0.0 < x_squeezed, x_squeezed < 0.33).any())
+        self.assertFalse(np.logical_and(0.34 < x_squeezed, x_squeezed < 0.66).any())
+        self.assertFalse(np.logical_and(0.67 < x_squeezed, x_squeezed < 1.0).any())
+        # Check that x has not been modified by attack and classifier
+        self.assertAlmostEqual(float(np.max(np.abs(x_original - x))), 0.0, delta=0.00001)
+
+    def test_data_range(self):
+        x = np.arange(5)
+        preproc = FeatureSqueezing(clip_values=(0, 4), bit_depth=2)
+        x_squeezed, _ = preproc(x)
+        self.assertTrue(np.array_equal(x, np.arange(5)))
+        self.assertTrue(np.allclose(x_squeezed, [0, 1.33, 2.67, 2.67, 4], atol=1e-1))
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/adversarial-robustness-toolbox/tests/defences/test_gaussian_augmentation.py b/adversarial-robustness-toolbox/tests/defences/test_gaussian_augmentation.py
new file mode 100644
index 0000000..def5079
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/defences/test_gaussian_augmentation.py
@@ -0,0 +1,97 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import unittest
+
+import numpy as np
+
+from art.defences.preprocessor import GaussianAugmentation
+
+from tests.utils import master_seed
+
+logger = logging.getLogger(__name__)
+
+
+class TestGaussianAugmentation(unittest.TestCase):
+    def setUp(self):
+        # Set master seed
+        master_seed(seed=1234)
+
+    def test_small_size(self):
+        x = np.arange(15).reshape((5, 3))
+        ga = GaussianAugmentation(ratio=0.4)
+        x_new, _ = ga(x)
+        self.assertEqual(x_new.shape, (7, 3))
+
+    def test_double_size(self):
+        x = np.arange(12).reshape((4, 3))
+        x_original = x.copy()
+        ga = GaussianAugmentation()
+        x_new, _ = ga(x)
+        self.assertEqual(x_new.shape[0], 2 * x.shape[0])
+        # Check that x has not been modified by attack and classifier
+        self.assertAlmostEqual(float(np.max(np.abs(x_original - x))), 0.0, delta=0.00001)
+
+    def test_multiple_size(self):
+        x = np.arange(12).reshape((4, 3))
+        x_original = x.copy()
+        ga = GaussianAugmentation(ratio=3.5)
+        x_new, _ = ga(x)
+        self.assertEqual(int(4.5 * x.shape[0]), x_new.shape[0])
+        # Check that x has not been modified by attack and classifier
+        self.assertAlmostEqual(float(np.max(np.abs(x_original - x))), 0.0, delta=0.00001)
+
+    def test_labels(self):
+        x = np.arange(12).reshape((4, 3))
+        y = np.arange(8).reshape((4, 2))
+
+        ga = GaussianAugmentation()
+        x_new, new_y = ga(x, y)
+        self.assertTrue(x_new.shape[0] == new_y.shape[0] == 8)
+        self.assertEqual(x_new.shape[1:], x.shape[1:])
+        self.assertEqual(new_y.shape[1:], y.shape[1:])
+
+    def test_no_augmentation(self):
+        x = np.arange(12).reshape((4, 3))
+        ga = GaussianAugmentation(augmentation=False)
+        x_new, _ = ga(x)
+        self.assertEqual(x.shape, x_new.shape)
+        self.assertFalse((x == x_new).all())
+
+    def test_failure_augmentation_fit_predict(self):
+        # Assert that value error is raised
+        with self.assertRaises(ValueError) as context:
+            _ = GaussianAugmentation(augmentation=True, apply_fit=False, apply_predict=True)
+
+        self.assertTrue(
+            "If `augmentation` is `True`, then `apply_fit` must be `True` and `apply_predict`"
+            " must be `False`." in str(context.exception)
+        )
+        with self.assertRaises(ValueError) as context:
+            _ = GaussianAugmentation(augmentation=True, apply_fit=False, apply_predict=False)
+
+        self.assertIn(
+            "If `augmentation` is `True`, then `apply_fit` and `apply_predict` can't be both `False`.",
+            str(context.exception),
+        )
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/adversarial-robustness-toolbox/tests/defences/test_gaussian_noise.py b/adversarial-robustness-toolbox/tests/defences/test_gaussian_noise.py
new file mode 100644
index 0000000..6f2278e
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/defences/test_gaussian_noise.py
@@ -0,0 +1,97 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+import logging
+import unittest
+
+import numpy as np
+
+from art.defences.postprocessor import GaussianNoise
+from art.utils import load_dataset
+
+from tests.utils import master_seed, get_image_classifier_kr_tf, get_image_classifier_kr_tf_binary
+
+logger = logging.getLogger(__name__)
+
+
+class TestGaussianNoise(unittest.TestCase):
+    """
+    A unittest class for testing the GaussianNoise postprocessor.
+    """
+
+    @classmethod
+    def setUpClass(cls):
+        (x_train, y_train), (x_test, y_test), _, _ = load_dataset("mnist")
+        cls.mnist = (x_train, y_train), (x_test, y_test)
+
+    def setUp(self):
+        master_seed(seed=1234)
+
+    def test_gaussian_noise(self):
+        """
+        Test Gaussian noise.
+        """
+        (_, _), (x_test, _) = self.mnist
+        classifier = get_image_classifier_kr_tf()
+        preds = classifier.predict(x_test[0:1])
+        postprocessor = GaussianNoise(scale=0.1)
+        post_preds = postprocessor(preds=preds)
+
+        classifier_prediction_expected = np.asarray(
+            [
+                [
+                    0.12109935,
+                    0.0498215,
+                    0.0993958,
+                    0.06410096,
+                    0.11366928,
+                    0.04645343,
+                    0.06419807,
+                    0.30685693,
+                    0.07616714,
+                    0.05823757,
+                ]
+            ],
+            dtype=np.float32,
+        )
+        post_classifier_prediction_expected = np.asarray(
+            [[0.15412168, 0.0, 0.2222987, 0.03007976, 0.0381179, 0.12382449, 0.13755375, 0.22279163, 0.07121207, 0.0]],
+            dtype=np.float32,
+        )
+
+        np.testing.assert_array_almost_equal(preds, classifier_prediction_expected, decimal=4)
+        np.testing.assert_array_almost_equal(post_preds, post_classifier_prediction_expected, decimal=4)
+
+    def test_gaussian_noise_binary(self):
+        """
+        Test Gaussian noise for binary classifier.
+        """
+        (_, _), (x_test, _) = self.mnist
+        classifier = get_image_classifier_kr_tf_binary()
+        preds = classifier.predict(x_test[0:1])
+        postprocessor = GaussianNoise(scale=0.1)
+        post_preds = postprocessor(preds=preds)
+
+        classifier_prediction_expected = np.asarray([[0.5301345]], dtype=np.float32)
+        post_classifier_prediction_expected = np.asarray([[0.577278]], dtype=np.float32)
+
+        np.testing.assert_array_almost_equal(preds, classifier_prediction_expected, decimal=4)
+        np.testing.assert_array_almost_equal(post_preds, post_classifier_prediction_expected, decimal=4)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/adversarial-robustness-toolbox/tests/defences/test_high_confidence.py b/adversarial-robustness-toolbox/tests/defences/test_high_confidence.py
new file mode 100644
index 0000000..2b36dde
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/defences/test_high_confidence.py
@@ -0,0 +1,146 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+import logging
+import unittest
+
+import numpy as np
+
+from art.defences.postprocessor import HighConfidence
+from art.utils import load_dataset
+
+from tests.utils import master_seed, get_image_classifier_kr_tf, get_image_classifier_kr_tf_binary
+
+logger = logging.getLogger(__name__)
+
+
+class TestHighConfidence(unittest.TestCase):
+    """
+    A unittest class for testing the HighConfidence postprocessor.
+    """
+
+    @classmethod
+    def setUpClass(cls):
+        (x_train, y_train), (x_test, y_test), _, _ = load_dataset("mnist")
+        cls.mnist = (x_train, y_train), (x_test, y_test)
+
+    def setUp(self):
+        master_seed(seed=1234)
+
+    def test_decimals_0_1(self):
+        """
+        Test with cutoff of 0.1.
+        """
+        (_, _), (x_test, _) = self.mnist
+        classifier = get_image_classifier_kr_tf()
+        preds = classifier.predict(x_test[0:1])
+        postprocessor = HighConfidence(cutoff=0.1)
+        post_preds = postprocessor(preds=preds)
+
+        classifier_prediction_expected = np.asarray(
+            [
+                [
+                    0.12109935,
+                    0.0498215,
+                    0.0993958,
+                    0.06410096,
+                    0.11366928,
+                    0.04645343,
+                    0.06419807,
+                    0.30685693,
+                    0.07616714,
+                    0.05823757,
+                ]
+            ],
+            dtype=np.float32,
+        )
+        post_classifier_prediction_expected = np.asarray(
+            [[0.12109935, 0.0, 0.0, 0.0, 0.11366928, 0.0, 0.0, 0.30685693, 0.0, 0.0]], dtype=np.float32
+        )
+
+        np.testing.assert_array_almost_equal(preds, classifier_prediction_expected, decimal=4)
+        np.testing.assert_array_almost_equal(post_preds, post_classifier_prediction_expected, decimal=4)
+
+    def test_decimals_0_2(self):
+        """
+        Test with cutoff of 0.2.
+        """
+        (_, _), (x_test, _) = self.mnist
+        classifier = get_image_classifier_kr_tf()
+        preds = classifier.predict(x_test[0:1])
+        postprocessor = HighConfidence(cutoff=0.2)
+        post_preds = postprocessor(preds=preds)
+
+        classifier_prediction_expected = np.asarray(
+            [
+                [
+                    0.12109935,
+                    0.0498215,
+                    0.0993958,
+                    0.06410096,
+                    0.11366928,
+                    0.04645343,
+                    0.06419807,
+                    0.30685693,
+                    0.07616714,
+                    0.05823757,
+                ]
+            ],
+            dtype=np.float32,
+        )
+        post_classifier_prediction_expected = np.asarray(
+            [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.30685693, 0.0, 0.0]], dtype=np.float32
+        )
+
+        np.testing.assert_array_almost_equal(preds, classifier_prediction_expected, decimal=4)
+        np.testing.assert_array_almost_equal(post_preds, post_classifier_prediction_expected, decimal=4)
+
+    def test_binary_decimals_0_5(self):
+        """
+        Test with cutoff of 0.5 for binary classifier.
+        """
+        (_, _), (x_test, _) = self.mnist
+        classifier = get_image_classifier_kr_tf_binary()
+        preds = classifier.predict(x_test[0:1])
+        postprocessor = HighConfidence(cutoff=0.5)
+        post_preds = postprocessor(preds=preds)
+
+        classifier_prediction_expected = np.asarray([[0.5301345]], dtype=np.float32)
+        post_classifier_prediction_expected = np.asarray([[0.5301345]], dtype=np.float32)
+
+        np.testing.assert_array_almost_equal(preds, classifier_prediction_expected, decimal=4)
+        np.testing.assert_array_almost_equal(post_preds, post_classifier_prediction_expected, decimal=4)
+
+    def test_binary_decimals_0_6(self):
+        """
+        Test with cutoff of 0.6 for binary classifier.
+        """
+        (_, _), (x_test, _) = self.mnist
+        classifier = get_image_classifier_kr_tf_binary()
+        preds = classifier.predict(x_test[0:1])
+        postprocessor = HighConfidence(cutoff=0.6)
+        post_preds = postprocessor(preds=preds)
+
+        classifier_prediction_expected = np.asarray([[0.5301345]], dtype=np.float32)
+        post_classifier_prediction_expected = np.asarray([[0.0]], dtype=np.float32)
+
+        np.testing.assert_array_almost_equal(preds, classifier_prediction_expected, decimal=4)
+        np.testing.assert_array_almost_equal(post_preds, post_classifier_prediction_expected, decimal=4)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/adversarial-robustness-toolbox/tests/defences/test_label_smoothing.py b/adversarial-robustness-toolbox/tests/defences/test_label_smoothing.py
new file mode 100644
index 0000000..67f5afb
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/defences/test_label_smoothing.py
@@ -0,0 +1,59 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import unittest
+
+import numpy as np
+
+from art.defences.preprocessor import LabelSmoothing
+
+from tests.utils import master_seed
+
+logger = logging.getLogger(__name__)
+
+
+class TestLabelSmoothing(unittest.TestCase):
+    def setUp(self):
+        master_seed(seed=1234)
+
+    def test_default(self):
+        m, n = 1000, 20
+        y = np.zeros((m, n))
+        y[(range(m), np.random.choice(range(n), m))] = 1.0
+
+        ls = LabelSmoothing()
+        _, y_smooth = ls(None, y)
+        self.assertTrue(np.isclose(np.sum(y_smooth, axis=1), np.ones(m)).all())
+        self.assertTrue((np.max(y_smooth, axis=1) == np.ones(m) * 0.9).all())
+
+    def test_customizing(self):
+        m, n = 1000, 20
+        y = np.zeros((m, n))
+        y[(range(m), np.random.choice(range(n), m))] = 1.0
+
+        ls = LabelSmoothing(max_value=1.0 / n)
+        _, y_smooth = ls(None, y)
+        self.assertTrue(np.isclose(np.sum(y_smooth, axis=1), np.ones(m)).all())
+        self.assertTrue((np.max(y_smooth, axis=1) == np.ones(m) / n).all())
+        self.assertTrue(np.isclose(y_smooth, np.ones((m, n)) / n).all())
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/adversarial-robustness-toolbox/tests/defences/test_neural_cleanse.py b/adversarial-robustness-toolbox/tests/defences/test_neural_cleanse.py
new file mode 100644
index 0000000..5793c9d
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/defences/test_neural_cleanse.py
@@ -0,0 +1,76 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2019
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import os
+import unittest
+import keras
+
+from art.defences.transformer.poisoning import NeuralCleanse
+from art.utils import load_dataset
+
+from tests.utils import master_seed, get_image_classifier_kr
+
+os.environ["KMP_DUPLICATE_LIB_OK"] = "True"
+logger = logging.getLogger(__name__)
+
+BATCH_SIZE = 100
+NB_TRAIN = 5000
+NB_TEST = 10
+
+
+class TestNeuralCleanse(unittest.TestCase):
+    """
+    A unittest class for testing Randomized Smoothing as a post-processing step for classifiers.
+    """
+
+    @classmethod
+    def setUpClass(cls):
+        # Get MNIST
+        (x_train, y_train), (x_test, y_test), _, _ = load_dataset("mnist")
+        x_train, y_train = x_train[:NB_TRAIN], y_train[:NB_TRAIN]
+        x_test, y_test = x_test[:NB_TEST], y_test[:NB_TEST]
+        cls.mnist = (x_train, y_train), (x_test, y_test)
+
+    def setUp(self):
+        master_seed(seed=1234)
+
+    def test_keras(self):
+        """
+        Test with a KerasClassifier.
+        :return:
+        """
+        if keras.__version__ != "2.2.4":
+            self.assertRaises(NotImplementedError)
+        else:
+            # Build KerasClassifier
+            krc = get_image_classifier_kr()
+
+            # Get MNIST
+            (x_train, y_train), (x_test, y_test) = self.mnist
+
+            krc.fit(x_train, y_train, nb_epochs=1)
+
+            cleanse = NeuralCleanse(krc)
+            defense_cleanse = cleanse(krc, steps=2)
+            defense_cleanse.mitigate(x_test, y_test, mitigation_types=["filtering", "pruning", "unlearning"])
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/adversarial-robustness-toolbox/tests/defences/test_pixel_defend.py b/adversarial-robustness-toolbox/tests/defences/test_pixel_defend.py
new file mode 100644
index 0000000..93c53de
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/defences/test_pixel_defend.py
@@ -0,0 +1,109 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import unittest
+
+import numpy as np
+import torch.nn as nn
+import torch.optim as optim
+
+from art.estimators.classification.pytorch import PyTorchClassifier
+from art.defences.preprocessor import PixelDefend
+from art.utils import load_mnist
+
+from tests.utils import master_seed
+
+logger = logging.getLogger(__name__)
+
+
+class ModelImage(nn.Module):
+    def __init__(self):
+        super(ModelImage, self).__init__()
+        self.fc = nn.Linear(25, 6400)
+
+    def forward(self, x):
+        x = x.view(-1, 25)
+        logit_output = self.fc(x)
+        logit_output = logit_output.view(-1, 5, 5, 1, 256)
+
+        return logit_output
+
+
+class Model(nn.Module):
+    def __init__(self):
+        super(Model, self).__init__()
+        self.fc = nn.Linear(4, 1024)
+
+    def forward(self, x):
+        x = x.view(-1, 4)
+        logit_output = self.fc(x)
+        logit_output = logit_output.view(-1, 4, 256)
+
+        return logit_output
+
+
+class TestPixelDefend(unittest.TestCase):
+    def setUp(self):
+        # Set master seed
+        master_seed(seed=1234)
+
+    def test_one_channel(self):
+        (x_train, _), (_, _), _, _ = load_mnist()
+        x_train = x_train[:2, 10:15, 15:20, :]
+        x_train = x_train.astype(np.float32)
+        x_train_original = x_train.copy()
+
+        # Define the network
+        model = ModelImage()
+        loss_fn = nn.CrossEntropyLoss()
+        optimizer = optim.Adam(model.parameters(), lr=0.01)
+        self.pixelcnn = PyTorchClassifier(
+            model=model, loss=loss_fn, optimizer=optimizer, input_shape=(1, 28, 28), nb_classes=10, clip_values=(0, 1)
+        )
+        preprocess = PixelDefend(eps=5, pixel_cnn=self.pixelcnn)
+        x_defended, _ = preprocess(x_train)
+
+        self.assertEqual(x_defended.shape, x_train.shape)
+        self.assertTrue((x_defended <= 1.0).all())
+        self.assertTrue((x_defended >= 0.0).all())
+
+        # Check that x_train has not been modified by attack and classifier
+        self.assertAlmostEqual(float(np.max(np.abs(x_train_original - x_train))), 0.0, delta=0.00001)
+
+    def test_feature_vectors(self):
+        # Define the network
+        model = Model()
+        loss_fn = nn.CrossEntropyLoss()
+        optimizer = optim.Adam(model.parameters(), lr=0.01)
+        pixel_cnn = PyTorchClassifier(
+            model=model, loss=loss_fn, optimizer=optimizer, input_shape=(4,), nb_classes=2, clip_values=(0, 1)
+        )
+
+        x = np.random.rand(5, 4).astype(np.float32)
+        preprocess = PixelDefend(eps=5, pixel_cnn=pixel_cnn)
+        x_defended, _ = preprocess(x)
+
+        self.assertEqual(x_defended.shape, x.shape)
+        self.assertTrue((x_defended <= 1.0).all())
+        self.assertTrue((x_defended >= 0.0).all())
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/adversarial-robustness-toolbox/tests/defences/test_reverse_sigmoid.py b/adversarial-robustness-toolbox/tests/defences/test_reverse_sigmoid.py
new file mode 100644
index 0000000..aad4768
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/defences/test_reverse_sigmoid.py
@@ -0,0 +1,238 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+import logging
+import unittest
+
+import numpy as np
+
+from art.utils import load_dataset
+from art.defences.postprocessor import ReverseSigmoid
+
+from tests.utils import master_seed, get_image_classifier_kr_tf, get_image_classifier_kr_tf_binary
+
+logger = logging.getLogger(__name__)
+
+
+class TestReverseSigmoid(unittest.TestCase):
+    """
+    A unittest class for testing the ReverseSigmoid postprocessor.
+    """
+
+    @classmethod
+    def setUpClass(cls):
+        (x_train, y_train), (x_test, y_test), _, _ = load_dataset("mnist")
+        cls.mnist = (x_train, y_train), (x_test, y_test)
+
+    def setUp(self):
+        master_seed(1234)
+
+    def test_reverse_sigmoid(self):
+        """
+        Test reverse sigmoid.
+        """
+        (_, _), (x_test, _) = self.mnist
+        classifier = get_image_classifier_kr_tf()
+        preds = classifier.predict(x_test[0:1])
+        postprocessor = ReverseSigmoid(beta=1.0, gamma=0.1)
+        post_preds = postprocessor(preds=preds)
+
+        classifier_prediction_expected = np.asarray(
+            [
+                [
+                    0.12109935,
+                    0.0498215,
+                    0.0993958,
+                    0.06410096,
+                    0.11366928,
+                    0.04645343,
+                    0.06419807,
+                    0.30685693,
+                    0.07616714,
+                    0.05823757,
+                ]
+            ],
+            dtype=np.float32,
+        )
+        post_classifier_prediction_expected = np.asarray(
+            [
+                [
+                    0.10733664,
+                    0.07743666,
+                    0.09712707,
+                    0.08230411,
+                    0.10377649,
+                    0.0764482,
+                    0.08234023,
+                    0.20600921,
+                    0.08703023,
+                    0.08019119,
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        np.testing.assert_array_almost_equal(preds, classifier_prediction_expected, decimal=4)
+        np.testing.assert_array_almost_equal(post_preds, post_classifier_prediction_expected, decimal=4)
+
+    def test_reverse_sigmoid_beta(self):
+        """
+        Test reverse sigmoid parameter beta.
+        """
+        (_, _), (x_test, _) = self.mnist
+        classifier = get_image_classifier_kr_tf()
+        preds = classifier.predict(x_test[0:1])
+        postprocessor = ReverseSigmoid(beta=0.75, gamma=0.1)
+        post_preds = postprocessor(preds=preds)
+
+        classifier_prediction_expected = np.asarray(
+            [
+                [
+                    0.12109935,
+                    0.0498215,
+                    0.0993958,
+                    0.06410096,
+                    0.11366928,
+                    0.04645343,
+                    0.06419807,
+                    0.30685693,
+                    0.07616714,
+                    0.05823757,
+                ]
+            ],
+            dtype=np.float32,
+        )
+        post_classifier_prediction_expected = np.asarray(
+            [
+                [
+                    0.1097239,
+                    0.07264659,
+                    0.09752058,
+                    0.07914664,
+                    0.10549247,
+                    0.07124537,
+                    0.07919333,
+                    0.22350204,
+                    0.08514594,
+                    0.07638316,
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        np.testing.assert_array_almost_equal(preds, classifier_prediction_expected, decimal=4)
+        np.testing.assert_array_almost_equal(post_preds, post_classifier_prediction_expected, decimal=4)
+
+    def test_reverse_sigmoid_gamma(self):
+        """
+        Test reverse sigmoid parameter gamma.
+        """
+        (_, _), (x_test, _) = self.mnist
+        classifier = get_image_classifier_kr_tf()
+        preds = classifier.predict(x_test[0:1])
+        postprocessor = ReverseSigmoid(beta=1.0, gamma=0.5)
+        post_preds = postprocessor(preds=preds)
+
+        classifier_prediction_expected = np.asarray(
+            [
+                [
+                    0.12109935,
+                    0.0498215,
+                    0.0993958,
+                    0.06410096,
+                    0.11366928,
+                    0.04645343,
+                    0.06419807,
+                    0.30685693,
+                    0.07616714,
+                    0.05823757,
+                ]
+            ],
+            dtype=np.float32,
+        )
+        post_classifier_prediction_expected = np.asarray(
+            [
+                [
+                    0.09699764,
+                    0.10062696,
+                    0.09689676,
+                    0.09873781,
+                    0.0968849,
+                    0.10121989,
+                    0.0987279,
+                    0.11275949,
+                    0.09774373,
+                    0.09940492,
+                ]
+            ],
+            dtype=np.float32,
+        )
+
+        np.testing.assert_array_almost_equal(preds, classifier_prediction_expected, decimal=4)
+        np.testing.assert_array_almost_equal(post_preds, post_classifier_prediction_expected, decimal=4)
+
+    def test_reverse_sigmoid_binary(self):
+        """
+        Test reverse sigmoid for binary classifier.
+        """
+        (_, _), (x_test, _) = self.mnist
+        classifier = get_image_classifier_kr_tf_binary()
+        preds = classifier.predict(x_test[0:1])
+        postprocessor = ReverseSigmoid(beta=1.0, gamma=0.1)
+        post_preds = postprocessor(preds=preds)
+
+        classifier_prediction_expected = np.asarray([[0.5301345]], dtype=np.float32)
+        post_classifier_prediction_expected = np.asarray([[0.52711743]], dtype=np.float32)
+
+        np.testing.assert_array_almost_equal(preds, classifier_prediction_expected, decimal=4)
+        np.testing.assert_array_almost_equal(post_preds, post_classifier_prediction_expected, decimal=4)
+
+    def test_reverse_sigmoid_beta_binary(self):
+        """
+        Test reverse sigmoid parameter beta for binary classifier
+        """
+        (_, _), (x_test, _) = self.mnist
+        classifier = get_image_classifier_kr_tf_binary()
+        preds = classifier.predict(x_test[0:1])
+        postprocessor = ReverseSigmoid(beta=0.75, gamma=0.1)
+        post_preds = postprocessor(preds=preds)
+
+        classifier_prediction_expected = np.asarray([[0.5301345]], dtype=np.float32)
+        post_classifier_prediction_expected = np.asarray([[0.5278717]], dtype=np.float32)
+
+        np.testing.assert_array_almost_equal(preds, classifier_prediction_expected, decimal=4)
+        np.testing.assert_array_almost_equal(post_preds, post_classifier_prediction_expected, decimal=4)
+
+    def test_reverse_sigmoid_gamma_binary(self):
+        """
+        Test reverse sigmoid parameter gamma for binary classifier
+        """
+        (_, _), (x_test, _) = self.mnist
+        classifier = get_image_classifier_kr_tf_binary()
+        preds = classifier.predict(x_test[0:1])
+        postprocessor = ReverseSigmoid(beta=1.0, gamma=0.5)
+        post_preds = postprocessor(preds=preds)
+
+        classifier_prediction_expected = np.asarray([[0.5301345]], dtype=np.float32)
+        post_classifier_prediction_expected = np.asarray([[0.51505363]], dtype=np.float32)
+
+        np.testing.assert_array_almost_equal(preds, classifier_prediction_expected, decimal=4)
+        np.testing.assert_array_almost_equal(post_preds, post_classifier_prediction_expected, decimal=4)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/adversarial-robustness-toolbox/tests/defences/test_rounded.py b/adversarial-robustness-toolbox/tests/defences/test_rounded.py
new file mode 100644
index 0000000..b420d01
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/defences/test_rounded.py
@@ -0,0 +1,75 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+import logging
+import unittest
+
+import numpy as np
+
+from art.defences.postprocessor import Rounded
+from art.utils import load_dataset
+
+from tests.utils import master_seed, get_image_classifier_kr
+
+logger = logging.getLogger(__name__)
+
+
+class TestRounded(unittest.TestCase):
+    """
+    A unittest class for testing the Rounded postprocessor.
+    """
+
+    @classmethod
+    def setUpClass(cls):
+        (x_train, y_train), (x_test, y_test), _, _ = load_dataset("mnist")
+        cls.mnist = (x_train, y_train), (x_test, y_test)
+        cls.classifier = get_image_classifier_kr()
+
+    def setUp(self):
+        master_seed(seed=1234)
+
+    def test_decimals_2(self):
+        """
+        Test with 2 decimal places.
+        """
+        (_, _), (x_test, _) = self.mnist
+        preds = self.classifier.predict(x_test[0:1])
+        postprocessor = Rounded(decimals=2)
+        post_preds = postprocessor(preds=preds)
+
+        expected_predictions = np.asarray(
+            [[0.12, 0.05, 0.1, 0.06, 0.11, 0.05, 0.06, 0.31, 0.08, 0.06]], dtype=np.float32
+        )
+        np.testing.assert_array_equal(post_preds, expected_predictions)
+
+    def test_decimals_3(self):
+        """
+        Test with 3 decimal places.
+        """
+        (_, _), (x_test, _) = self.mnist
+        preds = self.classifier.predict(x_test[0:1])
+        postprocessor = Rounded(decimals=3)
+        post_preds = postprocessor(preds=preds)
+
+        expected_predictions = np.asarray(
+            [[0.121, 0.05, 0.099, 0.064, 0.114, 0.046, 0.064, 0.307, 0.076, 0.058]], dtype=np.float32
+        )
+        np.testing.assert_array_equal(post_preds, expected_predictions)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/adversarial-robustness-toolbox/tests/defences/test_thermometer_encoding.py b/adversarial-robustness-toolbox/tests/defences/test_thermometer_encoding.py
new file mode 100644
index 0000000..1fd9894
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/defences/test_thermometer_encoding.py
@@ -0,0 +1,131 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import unittest
+
+import numpy as np
+
+from art.defences.preprocessor import ThermometerEncoding
+
+from tests.utils import master_seed
+
+logger = logging.getLogger(__name__)
+
+
+class TestThermometerEncoding(unittest.TestCase):
+    def setUp(self):
+        # Set master seed
+        master_seed(seed=1234)
+
+    def test_channel_last(self):
+        # Test data
+        x = np.array(
+            [
+                [
+                    [[0.2, 0.6, 0.8], [0.9, 0.4, 0.3], [0.2, 0.8, 0.5]],
+                    [[0.2, 0.6, 0.8], [0.9, 0.4, 0.3], [0.2, 0.8, 0.5]],
+                ],
+                [
+                    [[0.2, 0.6, 0.8], [0.9, 0.4, 0.3], [0.2, 0.8, 0.5]],
+                    [[0.2, 0.6, 0.8], [0.9, 0.4, 0.3], [0.2, 0.8, 0.5]],
+                ],
+            ]
+        )
+
+        # Create an instance of ThermometerEncoding
+        th_encoder = ThermometerEncoding(clip_values=(0, 1), num_space=4)
+
+        # Preprocess
+        x_preproc, _ = th_encoder(x)
+
+        # Test
+        self.assertEqual(x_preproc.shape, (2, 2, 3, 12))
+
+        true_value = np.array(
+            [
+                [
+                    [
+                        [1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1],
+                        [1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0],
+                        [1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0],
+                    ],
+                    [
+                        [1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1],
+                        [1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0],
+                        [1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0],
+                    ],
+                ],
+                [
+                    [
+                        [1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1],
+                        [1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0],
+                        [1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0],
+                    ],
+                    [
+                        [1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1],
+                        [1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0],
+                        [1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0],
+                    ],
+                ],
+            ]
+        )
+        self.assertTrue((x_preproc == true_value).all())
+
+        # Create an instance of ThermometerEncoding
+        th_encoder_scaled = ThermometerEncoding(clip_values=(-10, 10), num_space=4)
+        x_preproc_scaled, _ = th_encoder_scaled(20 * x - 10)
+        self.assertTrue((x_preproc_scaled == true_value).all())
+
+    def test_channel_first(self):
+        x = np.random.rand(5, 2, 28, 28)
+        x_copy = x.copy()
+        num_space = 5
+        encoder = ThermometerEncoding(clip_values=(0, 1), num_space=num_space, channels_first=True)
+        x_encoded, _ = encoder(x)
+        self.assertTrue((x == x_copy).all())
+        self.assertEqual(x_encoded.shape, (5, 10, 28, 28))
+
+    def test_estimate_gradient(self):
+        num_space = 5
+        encoder = ThermometerEncoding(clip_values=(0, 1), num_space=num_space)
+        encoder_cf = ThermometerEncoding(clip_values=(0, 1), num_space=num_space, channels_first=True)
+        x = np.random.uniform(size=(5, 28, 28, 1))
+        x_cf = np.transpose(x, (0, 3, 1, 2))
+        grad = np.ones((5, 28, 28, num_space))
+        grad_cf = np.transpose(grad, (0, 3, 1, 2))
+        estimated_grads = encoder.estimate_gradient(grad=grad, x=x)
+        estimated_grads_cf = encoder_cf.estimate_gradient(grad=grad_cf, x=x_cf)
+        self.assertEqual(estimated_grads.shape, x.shape)
+        self.assertEqual(estimated_grads_cf.shape, x_cf.shape)
+        self.assertTrue((estimated_grads == np.transpose(estimated_grads_cf, (0, 2, 3, 1))).all())
+
+    def test_feature_vectors(self):
+        x = np.random.rand(10, 4)
+        x_original = x.copy()
+        num_space = 5
+        encoder = ThermometerEncoding(clip_values=(0, 1), num_space=num_space, channels_first=True)
+        x_encoded, _ = encoder(x)
+        self.assertEqual(x_encoded.shape, (10, 20))
+        # Check that x has not been modified by attack and classifier
+        self.assertAlmostEqual(float(np.max(np.abs(x_original - x))), 0.0, delta=0.00001)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/adversarial-robustness-toolbox/tests/defences/test_variance_minimization.py b/adversarial-robustness-toolbox/tests/defences/test_variance_minimization.py
new file mode 100644
index 0000000..d570934
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/defences/test_variance_minimization.py
@@ -0,0 +1,72 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import unittest
+
+import numpy as np
+
+from art.defences.preprocessor import TotalVarMin
+
+from tests.utils import master_seed
+
+logger = logging.getLogger(__name__)
+
+
+class TestTotalVarMin(unittest.TestCase):
+    def setUp(self):
+        # Set master seed
+        master_seed(seed=1234)
+
+    def test_one_channel(self):
+        clip_values = (0, 1)
+        x = np.random.rand(2, 28, 28, 1)
+        preprocess = TotalVarMin(clip_values=(0, 1))
+        x_preprocessed, _ = preprocess(x)
+        self.assertEqual(x_preprocessed.shape, x.shape)
+        self.assertTrue((x_preprocessed >= clip_values[0]).all())
+        self.assertTrue((x_preprocessed <= clip_values[1]).all())
+        self.assertFalse((x_preprocessed == x).all())
+
+    def test_three_channels(self):
+        clip_values = (0, 1)
+        x = np.random.rand(2, 32, 32, 3)
+        x_original = x.copy()
+        preprocess = TotalVarMin(clip_values=clip_values)
+        x_preprocessed, _ = preprocess(x)
+        self.assertEqual(x_preprocessed.shape, x.shape)
+        self.assertTrue((x_preprocessed >= clip_values[0]).all())
+        self.assertTrue((x_preprocessed <= clip_values[1]).all())
+        self.assertFalse((x_preprocessed == x).all())
+        # Check that x has not been modified by attack and classifier
+        self.assertAlmostEqual(float(np.max(np.abs(x_original - x))), 0.0, delta=0.00001)
+
+    def test_failure_feature_vectors(self):
+        x = np.random.rand(10, 3)
+        preprocess = TotalVarMin()
+
+        # Assert that value error is raised for feature vectors
+        with self.assertRaises(ValueError) as context:
+            preprocess(x)
+
+        self.assertIn("Feature vectors detected.", str(context.exception))
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/adversarial-robustness-toolbox/tests/defences/transformer/__init__.py b/adversarial-robustness-toolbox/tests/defences/transformer/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/adversarial-robustness-toolbox/tests/defences/transformer/poisoning/__init__.py b/adversarial-robustness-toolbox/tests/defences/transformer/poisoning/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/adversarial-robustness-toolbox/tests/defences/transformer/poisoning/test_strip.py b/adversarial-robustness-toolbox/tests/defences/transformer/poisoning/test_strip.py
new file mode 100644
index 0000000..d9d3d8e
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/defences/transformer/poisoning/test_strip.py
@@ -0,0 +1,42 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import pytest
+
+from art.defences.transformer.poisoning import STRIP
+
+from tests.utils import ARTTestException
+
+logger = logging.getLogger(__name__)
+
+
+@pytest.mark.framework_agnostic
+def test_strip(art_warning, get_default_mnist_subset, image_dl_estimator):
+    try:
+        (x_train_mnist, y_train_mnist), (x_test_mnist, y_test_mnist) = get_default_mnist_subset
+
+        classifier, _ = image_dl_estimator()
+
+        classifier.fit(x_train_mnist, y_train_mnist, nb_epochs=1)
+        strip = STRIP(classifier)
+        defense_cleanse = strip()
+        defense_cleanse.mitigate(x_test_mnist)
+    except ARTTestException as e:
+        art_warning(e)
diff --git a/adversarial-robustness-toolbox/tests/estimators/__init__.py b/adversarial-robustness-toolbox/tests/estimators/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/adversarial-robustness-toolbox/tests/estimators/certification/__init__.py b/adversarial-robustness-toolbox/tests/estimators/certification/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/adversarial-robustness-toolbox/tests/estimators/certification/test_randomized_smoothing.py b/adversarial-robustness-toolbox/tests/estimators/certification/test_randomized_smoothing.py
new file mode 100644
index 0000000..e6bfc20
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/estimators/certification/test_randomized_smoothing.py
@@ -0,0 +1,170 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2019
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import os
+import unittest
+
+import numpy as np
+
+from art.attacks.evasion.fast_gradient import FastGradientMethod
+from art.utils import load_dataset, random_targets, compute_accuracy
+from art.estimators.certification.randomized_smoothing import PyTorchRandomizedSmoothing
+
+from tests.utils import master_seed, get_image_classifier_pt, get_tabular_classifier_pt
+
+os.environ["KMP_DUPLICATE_LIB_OK"] = "True"
+logger = logging.getLogger(__name__)
+
+BATCH_SIZE = 100
+NB_TRAIN = 5000
+NB_TEST = 10
+
+
+class TestRandomizedSmoothing(unittest.TestCase):
+    """
+    A unittest class for testing Randomized Smoothing as a post-processing step for classifiers.
+    """
+
+    @classmethod
+    def setUpClass(cls):
+        # Get MNIST
+        (x_train, y_train), (x_test, y_test), _, _ = load_dataset("mnist")
+        x_train, y_train = x_train[:NB_TRAIN], y_train[:NB_TRAIN]
+        x_test, y_test = x_test[:NB_TEST], y_test[:NB_TEST]
+        cls.mnist = (x_train, y_train), (x_test, y_test)
+
+    def setUp(self):
+        master_seed(seed=1234)
+
+    def test_ptclassifier(self):
+        """
+        Test with a KerasClassifier.
+        :return:
+        """
+        # Build KerasClassifier
+        ptc = get_image_classifier_pt()
+
+        # Get MNIST
+        (_, _), (x_test, y_test) = self.mnist
+
+        x_test = x_test.transpose(0, 3, 1, 2).astype(np.float32)
+
+        # First FGSM attack:
+        fgsm = FastGradientMethod(estimator=ptc, targeted=True)
+        params = {"y": random_targets(y_test, ptc.nb_classes)}
+        x_test_adv = fgsm.generate(x_test, **params)
+
+        # Initialize RS object and attack with FGSM
+        rs = PyTorchRandomizedSmoothing(
+            model=ptc.model,
+            loss=ptc._loss,
+            input_shape=ptc.input_shape,
+            nb_classes=ptc.nb_classes,
+            channels_first=ptc.channels_first,
+            clip_values=ptc.clip_values,
+            sample_size=100,
+            scale=0.01,
+            alpha=0.001,
+        )
+        fgsm_with_rs = FastGradientMethod(estimator=rs, targeted=True)
+        x_test_adv_with_rs = fgsm_with_rs.generate(x_test, **params)
+
+        # Compare results
+        # check shapes are equal and values are within a certain range
+        self.assertEqual(x_test_adv.shape, x_test_adv_with_rs.shape)
+        self.assertTrue((np.abs(x_test_adv - x_test_adv_with_rs) < 0.75).all())
+
+        # Check basic functionality of RS object
+        # check predict
+        y_test_smooth = rs.predict(x=x_test)
+        y_test_base = ptc.predict(x=x_test)
+        self.assertEqual(y_test_smooth.shape, y_test.shape)
+        self.assertTrue((np.sum(y_test_smooth, axis=1) <= np.ones((NB_TEST,))).all())
+        self.assertTrue((np.argmax(y_test_smooth, axis=1) == np.argmax(y_test_base, axis=1)).all())
+
+        # check certification
+        pred, radius = rs.certify(x=x_test, n=250)
+        self.assertEqual(len(pred), NB_TEST)
+        self.assertEqual(len(radius), NB_TEST)
+        self.assertTrue((radius <= 1).all())
+        self.assertTrue((pred < y_test.shape[1]).all())
+
+
+class TestRandomizedSmoothingVectors(unittest.TestCase):
+    @classmethod
+    def setUpClass(cls):
+        # Get Iris
+        (x_train, y_train), (x_test, y_test), _, _ = load_dataset("iris")
+        cls.iris = (x_train, y_train), (x_test, y_test)
+
+    def setUp(self):
+        master_seed(seed=1234)
+
+    def test_iris_clipped(self):
+        (_, _), (x_test, y_test) = self.iris
+
+        ptc = get_tabular_classifier_pt()
+        rs = PyTorchRandomizedSmoothing(
+            model=ptc.model,
+            loss=ptc._loss,
+            input_shape=ptc.input_shape,
+            nb_classes=ptc.nb_classes,
+            channels_first=ptc.channels_first,
+            clip_values=ptc.clip_values,
+            sample_size=100,
+            scale=0.01,
+            alpha=0.001,
+        )
+
+        # Test untargeted attack
+        attack = FastGradientMethod(ptc, eps=0.1)
+        x_test_adv = attack.generate(x_test)
+        self.assertFalse((x_test == x_test_adv).all())
+        self.assertTrue((x_test_adv <= 1).all())
+        self.assertTrue((x_test_adv >= 0).all())
+
+        preds_smooth = np.argmax(rs.predict(x_test_adv), axis=1)
+        self.assertFalse((np.argmax(y_test, axis=1) == preds_smooth).all())
+
+        pred = rs.predict(x_test)
+        pred2 = rs.predict(x_test_adv)
+        acc, cov = compute_accuracy(pred, y_test)
+        acc2, cov2 = compute_accuracy(pred2, y_test)
+        logger.info("Accuracy on Iris with smoothing on adversarial examples: %.2f%%", (acc * 100))
+        logger.info("Coverage on Iris with smoothing on adversarial examples: %.2f%%", (cov * 100))
+        logger.info("Accuracy on Iris with smoothing: %.2f%%", (acc2 * 100))
+        logger.info("Coverage on Iris with smoothing: %.2f%%", (cov2 * 100))
+
+        # Check basic functionality of RS object
+        # check predict
+        y_test_smooth = rs.predict(x=x_test)
+        self.assertEqual(y_test_smooth.shape, y_test.shape)
+        self.assertTrue((np.sum(y_test_smooth, axis=1) <= 1).all())
+
+        # check certification
+        pred, radius = rs.certify(x=x_test, n=250)
+        self.assertEqual(len(pred), len(x_test))
+        self.assertEqual(len(radius), len(x_test))
+        self.assertTrue((radius <= 1).all())
+        self.assertTrue((pred < y_test.shape[1]).all())
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/adversarial-robustness-toolbox/tests/estimators/classification/__init__.py b/adversarial-robustness-toolbox/tests/estimators/classification/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/adversarial-robustness-toolbox/tests/estimators/classification/test_GPy.py b/adversarial-robustness-toolbox/tests/estimators/classification/test_GPy.py
new file mode 100644
index 0000000..859d76a
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/estimators/classification/test_GPy.py
@@ -0,0 +1,97 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import unittest
+
+import numpy as np
+import GPy
+
+from art.estimators.classification.GPy import GPyGaussianProcessClassifier
+
+from tests.utils import TestBase, master_seed
+
+logger = logging.getLogger(__name__)
+
+
+class TestGPyGaussianProcessClassifier(TestBase):
+    """
+    This class tests the GPy Gaussian Process classifier.
+    """
+
+    @classmethod
+    def setUpClass(cls):
+        master_seed(seed=1234)
+        super().setUpClass()
+
+        # change iris to binary problem, so it is learnable for GPC
+        cls.y_train_iris_binary = cls.y_train_iris[:, 1]
+        cls.y_test_iris_binary = cls.y_test_iris[:, 1]
+
+        # set up GPclassifier
+        gpkern = GPy.kern.RBF(np.shape(cls.x_train_iris)[1])
+        m = GPy.models.GPClassification(cls.x_train_iris, cls.y_train_iris_binary.reshape(-1, 1), kernel=gpkern)
+        m.inference_method = GPy.inference.latent_function_inference.laplace.Laplace()
+        m.optimize(messages=True, optimizer="lbfgs")
+
+        # get ART classifier + clean accuracy
+        cls.classifier = GPyGaussianProcessClassifier(m)
+
+    def setUp(self):
+        master_seed(seed=1234)
+        super().setUp()
+
+    def test_predict(self):
+        # predictions should be correct
+        self.assertTrue(
+            np.mean((self.classifier.predict(self.x_test_iris[:3])[:, 0] > 0.5) == self.y_test_iris_binary[:3]) > 0.6
+        )
+        outlier = np.ones(np.shape(self.x_test_iris[:3])) * 10.0
+        # output for random points should be 0.5 (as classifier is uncertain)
+        self.assertTrue(np.sum(self.classifier.predict(outlier).flatten() == 0.5) == 6.0)
+
+    def test_predict_unc(self):
+        outlier = np.ones(np.shape(self.x_test_iris[:3])) * (np.max(self.x_test_iris.flatten()) * 10.0)
+        # uncertainty should increase as we go deeper into data
+        self.assertTrue(
+            np.mean(
+                self.classifier.predict_uncertainty(outlier) > self.classifier.predict_uncertainty(self.x_test_iris[:3])
+            )
+            == 1.0
+        )
+
+    def test_loss_gradient(self):
+        grads = self.classifier.loss_gradient(self.x_test_iris[0:1], self.y_test_iris_binary[0:1])
+        # grads with given seed should be [[-2.25244234e-11 -5.63282695e-11  1.74214328e-11 -1.21877914e-11]]
+        # we test roughly: amount of positive/negative and largest gradient
+        self.assertTrue(np.sum(grads < 0.0) == 3.0)
+        self.assertTrue(np.sum(grads > 0.0) == 1.0)
+        self.assertTrue(np.argmax(grads) == 2)
+
+    def test_class_gradient(self):
+        grads = self.classifier.class_gradient(self.x_test_iris[0:1], int(self.y_test_iris_binary[0:1]))
+        # grads with given seed should be [[[2.25244234e-11  5.63282695e-11 -1.74214328e-11  1.21877914e-11]]]
+        # we test roughly: amount of positive/negative and largest gradient
+        self.assertTrue(np.sum(grads < 0.0) == 1.0)
+        self.assertTrue(np.sum(grads > 0.0) == 3.0)
+        self.assertTrue(np.argmax(grads) == 1)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/adversarial-robustness-toolbox/tests/estimators/classification/test_blackbox.py b/adversarial-robustness-toolbox/tests/estimators/classification/test_blackbox.py
new file mode 100644
index 0000000..c9c24fe
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/estimators/classification/test_blackbox.py
@@ -0,0 +1,99 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import tempfile
+import unittest
+
+import numpy as np
+
+from art.defences.preprocessor import FeatureSqueezing, JpegCompression, SpatialSmoothing
+
+from tests.utils import TestBase, get_classifier_bb
+
+logger = logging.getLogger(__name__)
+
+
+class TestBlackBoxClassifier(TestBase):
+    """
+    This class tests the BlackBox classifier.
+    """
+
+    @classmethod
+    def setUpClass(cls):
+        super().setUpClass()
+        cls.test_dir = tempfile.mkdtemp()
+
+    def test_fit(self):
+        classifier = get_classifier_bb()
+        self.assertRaises(
+            NotImplementedError,
+            lambda: classifier.fit(self.x_train_mnist, self.y_train_mnist, batch_size=self.batch_size, nb_epochs=2),
+        )
+
+    def test_shapes(self):
+        classifier = get_classifier_bb()
+        predictions = classifier.predict(self.x_test_mnist)
+        self.assertEqual(predictions.shape, self.y_test_mnist.shape)
+        self.assertEqual(classifier.nb_classes, 10)
+        self.assertEqual(predictions.shape, self.y_test_mnist.shape)
+
+    def test_defences_predict(self):
+        clip_values = (0, 1)
+        fs = FeatureSqueezing(clip_values=clip_values, bit_depth=2)
+        jpeg = JpegCompression(clip_values=clip_values, apply_predict=True)
+        smooth = SpatialSmoothing()
+        classifier = get_classifier_bb(defences=[fs, jpeg, smooth])
+        self.assertEqual(len(classifier.preprocessing_defences), 3)
+
+        predictions_classifier = classifier.predict(self.x_test_mnist)
+
+        # Apply the same defences by hand
+        x_test_defense = self.x_test_mnist
+        x_test_defense, _ = fs(x_test_defense, self.y_test_mnist)
+        x_test_defense, _ = jpeg(x_test_defense, self.y_test_mnist)
+        x_test_defense, _ = smooth(x_test_defense, self.y_test_mnist)
+        classifier = get_classifier_bb()
+        predictions_check = classifier.predict(x_test_defense)
+
+        # Check that the prediction results match
+        np.testing.assert_array_almost_equal(predictions_classifier, predictions_check, decimal=4)
+
+    def test_save(self):
+        path = "tmp"
+        filename = "model.h5"
+
+        classifier = get_classifier_bb()
+
+        self.assertRaises(NotImplementedError, lambda: classifier.save(filename, path=path))
+
+    def test_repr(self):
+        classifier = get_classifier_bb()
+        repr_ = repr(classifier)
+
+        self.assertIn("BlackBoxClassifier", repr_)
+        self.assertIn("clip_values=[  0. 255.]", repr_)
+        self.assertIn("defences=None", repr_)
+        self.assertIn(
+            "preprocessing=StandardisationMeanStd(mean=0.0, std=1.0, apply_fit=True, apply_predict=True)", repr_
+        )
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/adversarial-robustness-toolbox/tests/estimators/classification/test_catboost.py b/adversarial-robustness-toolbox/tests/estimators/classification/test_catboost.py
new file mode 100644
index 0000000..597193b
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/estimators/classification/test_catboost.py
@@ -0,0 +1,51 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+import logging
+import unittest
+
+from catboost import CatBoostClassifier
+import numpy as np
+
+from art.estimators.classification.catboost import CatBoostARTClassifier
+
+from tests.utils import TestBase, master_seed
+
+logger = logging.getLogger(__name__)
+
+
+class TestCatBoostClassifier(TestBase):
+    @classmethod
+    def setUpClass(cls):
+        master_seed(seed=1234)
+        super().setUpClass()
+
+        cls.y_train_iris_index = np.argmax(cls.y_train_iris, axis=1)
+        cls.y_test_iris_index = np.argmax(cls.y_test_iris, axis=1)
+
+        model = CatBoostClassifier(custom_loss=["Accuracy"], random_seed=42, logging_level="Silent")
+        cls.classifier = CatBoostARTClassifier(model=model)
+        cls.classifier.fit(cls.x_train_iris, cls.y_train_iris_index, cat_features=[], eval_set=[])
+
+    def test_predict(self):
+        y_predicted = self.classifier.predict(self.x_test_iris[0:1])
+        y_expected = np.asarray([[0.00106307, 0.00118028, 0.99775665]])
+        np.testing.assert_array_almost_equal(y_predicted, y_expected, decimal=4)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/adversarial-robustness-toolbox/tests/estimators/classification/test_classifier.py b/adversarial-robustness-toolbox/tests/estimators/classification/test_classifier.py
new file mode 100644
index 0000000..8f9ac81
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/estimators/classification/test_classifier.py
@@ -0,0 +1,157 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import unittest
+
+import numpy as np
+
+from art.estimators.classification.classifier import ClassGradientsMixin, ClassifierMixin
+from art.estimators.estimator import BaseEstimator, LossGradientsMixin, NeuralNetworkMixin
+from tests.utils import TestBase, master_seed
+
+logger = logging.getLogger(__name__)
+
+
+class ClassifierInstance(ClassifierMixin, BaseEstimator):
+    estimator_params = BaseEstimator.estimator_params + ClassifierMixin.estimator_params
+
+    def __init__(self, clip_values=None, channels_first=True):
+        super(ClassifierInstance, self).__init__(model=None, clip_values=clip_values)
+
+    def fit(self, x, y, **kwargs):
+        pass
+
+    def predict(self, x, **kwargs):
+        pass
+
+    def nb_classes(self):
+        pass
+
+    def save(self, filename, path=None):
+        pass
+
+    def input_shape(self):
+        pass
+
+
+class ClassifierNeuralNetworkInstance(
+    ClassGradientsMixin, ClassifierMixin, NeuralNetworkMixin, LossGradientsMixin, BaseEstimator
+):
+    estimator_params = (
+        BaseEstimator.estimator_params + NeuralNetworkMixin.estimator_params + ClassifierMixin.estimator_params
+    )
+
+    def __init__(self, clip_values, channels_first=True):
+        super(ClassifierNeuralNetworkInstance, self).__init__(
+            model=None, clip_values=clip_values, channels_first=channels_first
+        )
+
+    def class_gradient(self, x, label=None, **kwargs):
+        pass
+
+    def fit(self, x, y, batch_size=128, nb_epochs=20, **kwargs):
+        pass
+
+    def get_activations(self, x, layer, batch_size):
+        pass
+
+    def compute_loss(self, x, y, **kwargs):
+        pass
+
+    def loss_gradient(self, x, y, **kwargs):
+        pass
+
+    def predict(self, x, batch_size=128, **kwargs):
+        pass
+
+    def nb_classes(self):
+        pass
+
+    def save(self, filename, path=None):
+        pass
+
+    def layer_names(self):
+        pass
+
+    def input_shape(self):
+        pass
+
+
+class TestClassifier(TestBase):
+    @classmethod
+    def setUpClass(cls):
+        master_seed(seed=1234)
+        super().setUpClass()
+
+    def setUp(self):
+        master_seed(seed=1234)
+        super().setUp()
+
+    def test_preprocessing_normalisation(self):
+        classifier = ClassifierInstance()
+
+        x = np.random.rand(2, 3)
+        x_new, _ = classifier._apply_preprocessing(x=x, y=None, fit=False)
+        x_new_expected = np.asarray([[0.19151945, 0.62210877, 0.43772774], [0.78535858, 0.77997581, 0.27259261]])
+        np.testing.assert_array_almost_equal(x_new, x_new_expected)
+
+    def test_repr(self):
+        classifier = ClassifierInstance()
+
+        repr_ = repr(classifier)
+        self.assertIn("ClassifierInstance", repr_)
+        self.assertIn("clip_values=None", repr_)
+        self.assertIn("defences=None", repr_)
+        self.assertIn(
+            "preprocessing=StandardisationMeanStd(mean=0.0, std=1.0, apply_fit=True, apply_predict=True)", repr_
+        )
+
+
+class TestClassifierNeuralNetwork(TestBase):
+    @classmethod
+    def setUpClass(cls):
+        master_seed(seed=1234)
+        super().setUpClass()
+
+    def setUp(self):
+        master_seed(seed=1234)
+        super().setUp()
+
+    def test_preprocessing_normalisation(self):
+        classifier = ClassifierNeuralNetworkInstance((0, 1))
+        x = np.random.rand(2, 3)
+        x_new_expected = np.asarray([[0.19151945, 0.62210877, 0.43772774], [0.78535858, 0.77997581, 0.27259261]])
+        x_new, _ = classifier._apply_preprocessing(x, y=None, fit=False)
+        np.testing.assert_array_almost_equal(x_new, x_new_expected, decimal=4)
+
+    def test_repr(self):
+        classifier = ClassifierNeuralNetworkInstance((0, 1))
+        repr_ = repr(classifier)
+        self.assertIn("ClassifierNeuralNetworkInstance", repr_)
+        self.assertIn(f"channels_first=True", repr_)
+        self.assertIn("clip_values=[0. 1.]", repr_)
+        self.assertIn("defences=None", repr_)
+        self.assertIn(
+            "preprocessing=StandardisationMeanStd(mean=0.0, std=1.0, apply_fit=True, apply_predict=True)", repr_
+        )
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/adversarial-robustness-toolbox/tests/estimators/classification/test_deeplearning_common.json b/adversarial-robustness-toolbox/tests/estimators/classification/test_deeplearning_common.json
new file mode 100644
index 0000000..d48c110
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/estimators/classification/test_deeplearning_common.json
@@ -0,0 +1,3181 @@
+{
+    "test_class_gradient": [
+        [
+            [
+                -0.03347399,
+                -0.03195872,
+                -0.02650188,
+                0.04111874,
+                0.08676253,
+                0.03339913,
+                0.06925241,
+                0.09387045,
+                0.15184258,
+                -0.00684002,
+                0.05070481,
+                0.01409407,
+                -0.03632583,
+                0.00151133,
+                0.05102589,
+                0.00766463,
+                -0.00898967,
+                0.00232938,
+                -0.00617045,
+                -0.00201032,
+                0.00410065,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0
+            ],
+            4
+        ],
+        [
+            [
+                -0.09723657,
+                -0.00240533,
+                0.02445251,
+                -0.00035474,
+                0.04765627,
+                0.04286841,
+                0.07209076,
+                0.0,
+                0.0,
+                -0.07938144,
+                -0.00142567,
+                0.02882954,
+                -0.00049514,
+                0.04170151,
+                0.05102589,
+                0.09544909,
+                -0.04401167,
+                -0.06158172,
+                0.03359772,
+                -0.00838454,
+                0.01722163,
+                -0.13376027,
+                0.08206709,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0
+            ],
+            4
+        ],
+        [
+            [
+                -0.03347399,
+                -0.03195872,
+                -0.02650188,
+                0.04111874,
+                0.08676253,
+                0.03339913,
+                0.06925241,
+                0.09387045,
+                0.15184258,
+                -0.00684002,
+                0.05070481,
+                0.01409407,
+                -0.03632583,
+                0.00151133,
+                0.05102589,
+                0.00766463,
+                -0.00898967,
+                0.00232938,
+                -0.00617045,
+                -0.00201032,
+                0.00410065,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0
+            ],
+            4
+        ],
+        [
+            [
+                -0.09723657,
+                -0.00240533,
+                0.02445251,
+                -0.00035474,
+                0.04765627,
+                0.04286841,
+                0.07209076,
+                0.0,
+                0.0,
+                -0.07938144,
+                -0.00142567,
+                0.02882954,
+                -0.00049514,
+                0.04170151,
+                0.05102589,
+                0.09544909,
+                -0.04401167,
+                -0.06158172,
+                0.03359772,
+                -0.00838454,
+                0.01722163,
+                -0.13376027,
+                0.08206709,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0
+            ],
+            4
+        ],
+        [
+            [
+                0.06860766,
+                0.065502,
+                0.08539103,
+                0.13868105,
+                -0.05520725,
+                -0.18788849,
+                0.02264893,
+                0.02980516,
+                0.2226511,
+                0.11288887,
+                -0.00678776,
+                0.02045561,
+                -0.03120914,
+                0.00642691,
+                0.08449504,
+                0.02848018,
+                -0.03251382,
+                0.00854315,
+                -0.02354656,
+                -0.00767687,
+                0.01565931,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0
+            ],
+            4
+        ],
+        [
+            [
+                -0.0487146,
+                -0.0171556,
+                -0.03161772,
+                -0.0420007,
+                0.03360246,
+                -0.01864819,
+                0.00315916,
+                0.0,
+                0.0,
+                -0.07631349,
+                -0.00374462,
+                0.04229517,
+                -0.01131879,
+                0.05044588,
+                0.08449504,
+                0.12417868,
+                0.07536847,
+                0.03906382,
+                0.09467953,
+                0.00543209,
+                -0.00504872,
+                -0.03366479,
+                -0.00385999,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0
+            ],
+            4
+        ],
+        [
+            3,
+            4,
+            4,
+            0,
+            1,
+            1,
+            1,
+            2,
+            3,
+            4,
+            4,
+            2,
+            2,
+            0,
+            0,
+            4,
+            0,
+            1,
+            2,
+            0,
+            3,
+            4,
+            2,
+            2,
+            3,
+            3,
+            0,
+            1,
+            3,
+            0,
+            3,
+            2,
+            3,
+            4,
+            1,
+            3,
+            3,
+            3,
+            2,
+            1,
+            3,
+            4,
+            2,
+            3,
+            4,
+            1,
+            4,
+            0,
+            4,
+            1,
+            1,
+            4,
+            1,
+            4,
+            0,
+            1,
+            0,
+            0,
+            4,
+            0,
+            4,
+            2,
+            3,
+            1,
+            2,
+            2,
+            4,
+            3,
+            4,
+            2,
+            2,
+            4,
+            4,
+            2,
+            1,
+            3,
+            2,
+            1,
+            4,
+            1,
+            0,
+            1,
+            2,
+            1,
+            2,
+            1,
+            2,
+            1,
+            1,
+            4,
+            1,
+            2,
+            4,
+            0,
+            4,
+            1,
+            2,
+            1,
+            1,
+            3
+        ]
+    ],
+    "test_loss_gradient": [
+        [
+            [
+                0.00210803,
+                0.00213919,
+                0.0052098,
+                0.00548,
+                -0.0023059,
+                0.00432076,
+                0.00274945,
+                0.0,
+                0.0,
+                -0.0583441,
+                -0.00616604,
+                0.05262191,
+                -0.02373985,
+                0.05273107,
+                0.10711591,
+                0.12773865,
+                0.06892889,
+                0.01337799,
+                0.1003202,
+                0.01681095,
+                -0.00028647,
+                -0.05588859,
+                0.01474165,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0
+            ],
+            4
+        ],
+        [
+            [
+                0.0559206,
+                0.05338925,
+                0.0648919,
+                0.07925165,
+                -0.04029291,
+                -0.11281465,
+                0.01850601,
+                0.00325053,
+                0.08163194,
+                0.03333949,
+                0.031766,
+                -0.02420464,
+                -0.07815557,
+                -0.04698735,
+                0.10711591,
+                0.04086433,
+                -0.03441072,
+                0.01071284,
+                -0.04229196,
+                -0.01386157,
+                0.02827487,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0
+            ],
+            4
+        ]
+    ],
+    "test_predict": [
+        [
+            0.15710345,
+            -0.73106134,
+            -0.04039804,
+            -0.47904843,
+            0.09378531,
+            -0.80105764,
+            -0.47753483,
+            1.0868737,
+            -0.3065778,
+            -0.57497704
+        ]
+    ],
+    "test_repr_tensorflow2": [
+        "TensorFlowV2Classifier",
+        "model=",
+        "nb_classes=10",
+        "input_shape=(28, 28, 1)",
+        "loss_object=<tensorflow.python.keras.losses.SparseCategoricalCrossentropy",
+        "train_step=<function get_image_classifier_tf_v2.<locals>.train_step",
+        "channels_first=False, ",
+        "clip_values=array([0., 1.], dtype=float32), ",
+        "preprocessing_defences=None, postprocessing_defences=None, preprocessing=StandardisationMeanStdTensorFlow(mean=0.0, std=1.0, apply_fit=True, apply_predict=True)"
+    ],
+    "test_repr_tensorflow1": [
+        "TensorFlowClassifier",
+        "input_ph=<tf.Tensor 'Placeholder:0' shape=(?, 28, 28, 1) dtype=float32>",
+        "output=<tf.Tensor 'dense/BiasAdd:0' shape=(?, 10) dtype=float32>",
+        "labels_ph=<tf.Tensor 'Placeholder_1:0' shape=(?, 10) dtype=float32>",
+        "train=<tf.Operation 'Adam' type=NoOp>",
+        "loss=<tf.Tensor 'Mean:0' shape=() dtype=float32>",
+        "learning=None",
+        "sess=<tensorflow.python.client.session.Session object",
+        "channels_first=False",
+        "clip_values=array([0., 1.], dtype=float32)",
+        "preprocessing_defences=None, postprocessing_defences=None",
+        "preprocessing=StandardisationMeanStd(mean=0.0, std=1.0, apply_fit=True, apply_predict=True)"
+    ],
+    "test_repr_pytorch": [
+        "art.estimators.classification.pytorch.PyTorchClassifier",
+        "(conv): Conv2d(1, 1, kernel_size=(7, 7), stride=(1, 1))",
+        "(pool): MaxPool2d(kernel_size=4, stride=4, padding=0, dilation=1, ceil_mode=False)",
+        "(fullyconnected): Linear(in_features=25, out_features=10, bias=True)",
+        "loss=CrossEntropyLoss(), optimizer=Adam",
+        "input_shape=(1, 28, 28), nb_classes=10",
+        "clip_values=array([0., 1.], dtype=float32",
+        "preprocessing_defences=None, postprocessing_defences=None, preprocessing=StandardisationMeanStdPyTorch(mean=0.0, std=1.0, apply_fit=True, apply_predict=True, device=cpu)"
+    ],
+    "test_repr_keras": [
+        "art.estimators.classification.keras.KerasClassifier",
+        "use_logits=True",
+        "channels_first=False",
+        "clip_values=array([0., 1.], dtype=float32)",
+        "preprocessing_defences=None",
+        "preprocessing=StandardisationMeanStd(mean=0.0, std=1.0, apply_fit=True, apply_predict=True)",
+        "input_layer=0, output_layer=0"
+    ],
+    "test_repr_kerastf": [
+        "art.estimators.classification.keras.KerasClassifier",
+        "use_logits=True",
+        "channels_first=False",
+        "clip_values=array([0., 1.], dtype=float32)",
+        "preprocessing_defences=None",
+        "preprocessing=StandardisationMeanStd(mean=0.0, std=1.0, apply_fit=True, apply_predict=True)",
+        "input_layer=0, output_layer=0"
+    ],
+    "test_repr_mxnet": [
+        "art.estimators.classification.mxnet.MXClassifier",
+        "input_shape=(1, 28, 28), nb_classes=10",
+        "channels_first=True, clip_values=array([0., 1.], dtype=float32)",
+        "defences=None, preprocessing=StandardisationMeanStd(mean=0.0, std=1.0, apply_fit=True, apply_predict=True)"
+    ],
+    "test_predict_mxnet": [
+        [
+            0.2463037669658661,
+            -0.8145769834518433,
+            -0.012770920991897583,
+            -0.4897285997867584,
+            0.19233708083629608,
+            -0.8189597129821777,
+            -0.4701957106590271,
+            1.0267436504364014,
+            -0.2264045774936676,
+            -0.6284593343734741
+        ]
+    ],
+    "test_loss_gradient_mxnet": [
+        [
+            [
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                -0.0632929652929306,
+                -0.009614026173949242,
+                0.07504866272211075,
+                -0.039112482219934464,
+                0.06923949718475342,
+                0.1398390680551529,
+                0.12718409299850464,
+                0.02408408373594284,
+                -0.020145107060670853,
+                0.0843563973903656,
+                0.005544796586036682,
+                0.008247891440987587,
+                -0.10491124540567398,
+                0.050626058131456375,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0
+            ],
+            4
+        ],
+        [
+            [
+                0.0,
+                0.0,
+                0.0031933714635670185,
+                0.022913113236427307,
+                0.013333073817193508,
+                -0.01157569419592619,
+                0.017699453979730606,
+                0.014487833715975285,
+                0.031547531485557556,
+                -0.02010086551308632,
+                0.06656481325626373,
+                -0.04619080200791359,
+                -0.10763540863990784,
+                -0.0736103355884552,
+                0.1398390680551529,
+                0.04002942889928818,
+                0.0030015036463737488,
+                0.00586030725389719,
+                -0.06693753600120544,
+                -0.02211461216211319,
+                0.04510943591594696,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0
+            ],
+            4
+        ]
+    ],
+    "test_class_gradient_tensorflow2": [
+        [
+            [
+                -0.03347398713231087,
+                -0.031958721578121185,
+                -0.026501882821321487,
+                0.041118744760751724,
+                0.08676252514123917,
+                0.03339913487434387,
+                0.06925240904092789,
+                0.09387044608592987,
+                0.15184257924556732,
+                -0.006840024143457413,
+                0.05070481449365616,
+                0.014094067737460136,
+                -0.036325834691524506,
+                0.0015113259432837367,
+                0.051025886088609695,
+                0.007664630189538002,
+                -0.008989665657281876,
+                0.002329375362023711,
+                -0.0061704539693892,
+                -0.0020103193819522858,
+                0.004100654274225235,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0
+            ],
+            4
+        ],
+        [
+            [
+                -0.09723656624555588,
+                -0.002405333798378706,
+                0.024452514946460724,
+                -0.00035474030300974846,
+                0.04765626788139343,
+                0.04286840930581093,
+                0.0720907598733902,
+                0.0,
+                0.0,
+                -0.07938143610954285,
+                -0.0014256746508181095,
+                0.02882954478263855,
+                -0.0004951448645442724,
+                0.04170151427388191,
+                0.051025886088609695,
+                0.09544909000396729,
+                -0.0440116748213768,
+                -0.06158171966671944,
+                0.03359771892428398,
+                -0.008384542539715767,
+                0.017221633344888687,
+                -0.1337602734565735,
+                0.0820670947432518,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0
+            ],
+            4
+        ],
+        [
+            [
+                -0.03347398713231087,
+                -0.031958721578121185,
+                -0.026501882821321487,
+                0.041118744760751724,
+                0.08676252514123917,
+                0.03339913487434387,
+                0.06925240904092789,
+                0.09387044608592987,
+                0.15184257924556732,
+                -0.006840024143457413,
+                0.05070481449365616,
+                0.014094067737460136,
+                -0.036325834691524506,
+                0.0015113259432837367,
+                0.051025886088609695,
+                0.007664630189538002,
+                -0.008989665657281876,
+                0.002329375362023711,
+                -0.0061704539693892,
+                -0.0020103193819522858,
+                0.004100654274225235,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0
+            ],
+            4
+        ],
+        [
+            [
+                -0.09723656624555588,
+                -0.002405333798378706,
+                0.024452514946460724,
+                -0.00035474030300974846,
+                0.04765626788139343,
+                0.04286840930581093,
+                0.0720907598733902,
+                0.0,
+                0.0,
+                -0.07938143610954285,
+                -0.0014256746508181095,
+                0.02882954478263855,
+                -0.0004951448645442724,
+                0.04170151427388191,
+                0.051025886088609695,
+                0.09544909000396729,
+                -0.0440116748213768,
+                -0.06158171966671944,
+                0.03359771892428398,
+                -0.008384542539715767,
+                0.017221633344888687,
+                -0.1337602734565735,
+                0.0820670947432518,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0
+            ],
+            4
+        ],
+        [
+            [
+                0.06860765814781189,
+                0.06550200283527374,
+                0.08539102971553802,
+                0.1386810541152954,
+                -0.055207252502441406,
+                -0.1878884881734848,
+                0.022648930549621582,
+                0.029805157333612442,
+                0.22265109419822693,
+                0.1128888726234436,
+                -0.006787760183215141,
+                0.020455610007047653,
+                -0.03120914287865162,
+                0.006426911801099777,
+                0.08449503779411316,
+                0.028480183333158493,
+                -0.0325138159096241,
+                0.008543146774172783,
+                -0.023546557873487473,
+                -0.007676873821765184,
+                0.015659306198358536,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0
+            ],
+            4
+        ],
+        [
+            [
+                -0.04871459677815437,
+                -0.01715559884905815,
+                -0.031617723405361176,
+                -0.042000703513622284,
+                0.033602457493543625,
+                -0.018648194149136543,
+                0.0031591616570949554,
+                0.0,
+                0.0,
+                -0.07631348818540573,
+                -0.003744617337360978,
+                0.042295172810554504,
+                -0.011318785138428211,
+                0.050445880740880966,
+                0.08449503779411316,
+                0.1241786777973175,
+                0.0753684714436531,
+                0.039063822478055954,
+                0.09467952698469162,
+                0.0054320949129760265,
+                -0.005048719234764576,
+                -0.03366478532552719,
+                -0.0038599905092269182,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0
+            ],
+            4
+        ],
+        [
+            3,
+            4,
+            4,
+            0,
+            1,
+            1,
+            1,
+            2,
+            3,
+            4,
+            4,
+            2,
+            2,
+            0,
+            0,
+            4,
+            0,
+            1,
+            2,
+            0,
+            3,
+            4,
+            2,
+            2,
+            3,
+            3,
+            0,
+            1,
+            3,
+            0,
+            3,
+            2,
+            3,
+            4,
+            1,
+            3,
+            3,
+            3,
+            2,
+            1,
+            3,
+            4,
+            2,
+            3,
+            4,
+            1,
+            4,
+            0,
+            4,
+            1,
+            1,
+            4,
+            1,
+            4,
+            0,
+            1,
+            0,
+            0,
+            4,
+            0,
+            4,
+            2,
+            3,
+            1,
+            2,
+            2,
+            4,
+            3,
+            4,
+            2,
+            2,
+            4,
+            4,
+            2,
+            1,
+            3,
+            2,
+            1,
+            4,
+            1,
+            0,
+            1,
+            2,
+            1,
+            2,
+            1,
+            2,
+            1,
+            1,
+            4,
+            1,
+            2,
+            4,
+            0,
+            4,
+            1,
+            2,
+            1,
+            1,
+            3
+        ]
+    ],
+    "test_class_gradient_mxnet": [
+        [
+            [
+                0.0,
+                0.0,
+                0.009414948523044586,
+                0.06755423545837402,
+                0.03930961340665817,
+                -0.03412837162613869,
+                0.052182916551828384,
+                0.0689273327589035,
+                0.166815385222435,
+                0.028423108160495758,
+                0.03483463078737259,
+                0.014094067737460136,
+                -0.036325834691524506,
+                0.0015113260596990585,
+                0.0510258823633194,
+                0.007664630189538002,
+                -0.008989665657281876,
+                0.002329375362023711,
+                -0.0061704539693892,
+                -0.0020103193819522858,
+                0.004100654274225235,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0
+            ],
+            4
+        ],
+        [
+            [
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                -0.07938143610954285,
+                -0.0014256746508181095,
+                0.02882954478263855,
+                -0.0004951448645442724,
+                0.04170151427388191,
+                0.0510258823633194,
+                0.09544909000396729,
+                -0.0440116748213768,
+                -0.06158171966671944,
+                0.03359771892428398,
+                -0.008384542539715767,
+                0.017221633344888687,
+                -0.1337602734565735,
+                0.0820670947432518,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0
+            ],
+            4
+        ],
+        [
+            [
+                0.0,
+                0.0,
+                0.009414948523044586,
+                0.06755423545837402,
+                0.03930961340665817,
+                -0.03412837162613869,
+                0.052182916551828384,
+                0.0689273327589035,
+                0.166815385222435,
+                0.028423108160495758,
+                0.03483463078737259,
+                0.014094067737460136,
+                -0.036325834691524506,
+                0.0015113260596990585,
+                0.0510258823633194,
+                0.007664630189538002,
+                -0.008989665657281876,
+                0.002329375362023711,
+                -0.0061704539693892,
+                -0.0020103193819522858,
+                0.004100654274225235,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0
+            ],
+            4
+        ],
+        [
+            [
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                -0.07938143610954285,
+                -0.0014256746508181095,
+                0.02882954478263855,
+                -0.0004951448645442724,
+                0.04170151427388191,
+                0.0510258823633194,
+                0.09544909000396729,
+                -0.0440116748213768,
+                -0.06158171966671944,
+                0.03359771892428398,
+                -0.008384542539715767,
+                0.017221633344888687,
+                -0.1337602734565735,
+                0.0820670947432518,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0
+            ],
+            4
+        ],
+        [
+            [
+                0.0,
+                0.0,
+                0.011776568368077278,
+                0.08449935913085938,
+                0.04916992783546448,
+                -0.04268903657793999,
+                0.06527233123779297,
+                0.08654987812042236,
+                0.188588485121727,
+                0.032666485756635666,
+                0.02931634895503521,
+                0.020455610007047653,
+                -0.03120914101600647,
+                0.006426911801099777,
+                0.08449503779411316,
+                0.028480183333158493,
+                -0.0325138159096241,
+                0.008543146774172783,
+                -0.023546557873487473,
+                -0.007676873821765184,
+                0.015659306198358536,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0
+            ],
+            4
+        ],
+        [
+            [
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                -0.07631348818540573,
+                -0.003744617337360978,
+                0.042295172810554504,
+                -0.011318785138428211,
+                0.050445880740880966,
+                0.08449503779411316,
+                0.1241786777973175,
+                0.07536846399307251,
+                0.03906382620334625,
+                0.09467952698469162,
+                0.005432095378637314,
+                -0.005048719234764576,
+                -0.03366478532552719,
+                -0.0038599905092269182,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0
+            ],
+            4
+        ],
+        [
+            3,
+            4,
+            4,
+            0,
+            1,
+            1,
+            1,
+            2,
+            3,
+            4,
+            4,
+            2,
+            2,
+            0,
+            0,
+            4,
+            0,
+            1,
+            2,
+            0,
+            3,
+            4,
+            2,
+            2,
+            3,
+            3,
+            0,
+            1,
+            3,
+            0,
+            3,
+            2,
+            3,
+            4,
+            1,
+            3,
+            3,
+            3,
+            2,
+            1,
+            3,
+            4,
+            2,
+            3,
+            4,
+            1,
+            4,
+            0,
+            4,
+            1,
+            1,
+            4,
+            1,
+            4,
+            0,
+            1,
+            0,
+            0,
+            4,
+            0,
+            4,
+            2,
+            3,
+            1,
+            2,
+            2,
+            4,
+            3,
+            4,
+            2,
+            2,
+            4,
+            4,
+            2,
+            1,
+            3,
+            2,
+            1,
+            4,
+            1,
+            0,
+            1,
+            2,
+            1,
+            2,
+            1,
+            2,
+            1,
+            1,
+            4,
+            1,
+            2,
+            4,
+            0,
+            4,
+            1,
+            2,
+            1,
+            1,
+            3
+        ]
+    ],
+    "test_loss_functions[categorical_crossentropy-True]": [
+        [
+            7,
+            1,
+            1,
+            4,
+            4,
+            1,
+            4,
+            4,
+            4,
+            4,
+            4,
+            4,
+            4,
+            4,
+            1,
+            4,
+            4,
+            7,
+            8,
+            4,
+            4,
+            4,
+            4,
+            3,
+            4,
+            4,
+            7,
+            4,
+            4,
+            4,
+            7,
+            4,
+            3,
+            4,
+            7,
+            0,
+            7,
+            7,
+            1,
+            1,
+            7,
+            7,
+            4,
+            0,
+            1,
+            4,
+            4,
+            4,
+            4,
+            4,
+            4,
+            7,
+            4,
+            4,
+            4,
+            4,
+            4,
+            1,
+            4,
+            4,
+            7,
+            4,
+            4,
+            4,
+            4,
+            4,
+            4,
+            4,
+            3,
+            0,
+            7,
+            4,
+            0,
+            1,
+            7,
+            4,
+            4,
+            7,
+            4,
+            4,
+            4,
+            4,
+            4,
+            7,
+            4,
+            4,
+            4,
+            4,
+            4,
+            1,
+            4,
+            4,
+            4,
+            4,
+            4,
+            4,
+            4,
+            4,
+            4,
+            4
+        ],
+        [
+            0.0,
+            0.0,
+            0.0,
+            0.08147776,
+            0.01847786,
+            0.07045883,
+            -0.00269106,
+            -0.03189164,
+            0.01643312,
+            0.1185048,
+            0.02166386,
+            0.00905327,
+            0.06592228,
+            0.04471018,
+            -0.02879605,
+            0.04668707,
+            0.06856851,
+            0.06857751,
+            0.00657996,
+            0.0,
+            0.0,
+            0.0,
+            0.0,
+            0.0,
+            0.0,
+            0.0,
+            0.0,
+            0.0
+        ],
+        {
+            "categorical_hinge": [
+                0.0,
+                0.0,
+                0.0,
+                -0.00644846,
+                -0.00274792,
+                -0.01334668,
+                -0.01417109,
+                0.03608133,
+                0.00670766,
+                0.02741555,
+                -0.00938758,
+                0.002172,
+                0.01400783,
+                -0.00224488,
+                -0.01445661,
+                0.01775403,
+                0.00643058,
+                0.00382644,
+                -0.01077214,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0
+            ],
+            "categorical_crossentropy": [
+                0.0,
+                0.0,
+                0.0,
+                -0.09573442,
+                -0.0089094,
+                0.01402334,
+                0.0258659,
+                0.08960329,
+                0.10324767,
+                0.10624839,
+                0.06578761,
+                -0.00018638,
+                -0.01345262,
+                -0.08770822,
+                -0.04990875,
+                0.04288402,
+                -0.06845165,
+                -0.08588978,
+                -0.08277036,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0
+            ],
+            "sparse_categorical_crossentropy": [
+                0.0,
+                0.0,
+                0.0,
+                -0.09573442,
+                -0.0089094,
+                0.01402334,
+                0.0258659,
+                0.08960329,
+                0.10324767,
+                0.10624839,
+                0.06578761,
+                -0.00018638,
+                -0.01345262,
+                -0.08770822,
+                -0.04990875,
+                0.04288402,
+                -0.06845165,
+                -0.08588978,
+                -0.08277036,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0
+            ],
+            "kullback_leibler_divergence": [
+                0.0,
+                0.0,
+                0.0,
+                -0.09573442,
+                -0.0089094,
+                0.01402334,
+                0.0258659,
+                0.08960329,
+                0.10324767,
+                0.10624839,
+                0.06578761,
+                -0.00018638,
+                -0.01345262,
+                -0.08770822,
+                -0.04990875,
+                0.04288402,
+                -0.06845165,
+                -0.08588978,
+                -0.08277036,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0
+            ]
+        }
+    ],
+    "test_loss_functions[categorical_crossentropy-False]": [
+        [
+            7,
+            1,
+            1,
+            4,
+            4,
+            1,
+            4,
+            4,
+            4,
+            4,
+            4,
+            4,
+            4,
+            4,
+            1,
+            4,
+            4,
+            7,
+            8,
+            4,
+            4,
+            4,
+            4,
+            3,
+            4,
+            4,
+            7,
+            4,
+            4,
+            4,
+            7,
+            4,
+            3,
+            4,
+            7,
+            0,
+            7,
+            7,
+            1,
+            1,
+            7,
+            7,
+            4,
+            0,
+            1,
+            4,
+            4,
+            4,
+            4,
+            4,
+            4,
+            7,
+            4,
+            4,
+            4,
+            4,
+            4,
+            1,
+            4,
+            4,
+            7,
+            4,
+            4,
+            4,
+            4,
+            4,
+            4,
+            4,
+            3,
+            0,
+            7,
+            4,
+            0,
+            1,
+            7,
+            4,
+            4,
+            7,
+            4,
+            4,
+            4,
+            4,
+            4,
+            7,
+            4,
+            4,
+            4,
+            4,
+            4,
+            1,
+            4,
+            4,
+            4,
+            4,
+            4,
+            4,
+            4,
+            4,
+            4,
+            4
+        ],
+        [
+            0.0,
+            0.0,
+            0.0,
+            0.0023582461,
+            0.00048802234,
+            0.0016699843,
+            -6.4777887e-05,
+            -0.0014215634,
+            -0.00013359448,
+            0.0020448549,
+            0.00028171093,
+            0.00019665064,
+            0.0015335126,
+            0.0017000455,
+            -0.00020136381,
+            0.00064588618,
+            0.0020524357,
+            0.002199081,
+            0.00083692279,
+            0.0,
+            0.0,
+            0.0,
+            0.0,
+            0.0,
+            0.0,
+            0.0,
+            0.0,
+            0.0
+        ],
+        {
+            "categorical_hinge": [
+                0.0,
+                0.0,
+                0.0,
+                -0.00644846,
+                -0.00274792,
+                -0.01334668,
+                -0.01417109,
+                0.03608133,
+                0.00670766,
+                0.02741555,
+                -0.00938758,
+                0.002172,
+                0.01400783,
+                -0.00224488,
+                -0.01445661,
+                0.01775403,
+                0.00643058,
+                0.00382644,
+                -0.01077214,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0
+            ],
+            "categorical_crossentropy": [
+                0.0,
+                0.0,
+                0.0,
+                -0.09573442,
+                -0.0089094,
+                0.01402334,
+                0.0258659,
+                0.08960329,
+                0.10324767,
+                0.10624839,
+                0.06578761,
+                -0.00018638,
+                -0.01345262,
+                -0.08770822,
+                -0.04990875,
+                0.04288402,
+                -0.06845165,
+                -0.08588978,
+                -0.08277036,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0
+            ],
+            "sparse_categorical_crossentropy": [
+                0.0,
+                0.0,
+                0.0,
+                -0.09573442,
+                -0.0089094,
+                0.01402334,
+                0.0258659,
+                0.08960329,
+                0.10324767,
+                0.10624839,
+                0.06578761,
+                -0.00018638,
+                -0.01345262,
+                -0.08770822,
+                -0.04990875,
+                0.04288402,
+                -0.06845165,
+                -0.08588978,
+                -0.08277036,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0
+            ],
+            "kullback_leibler_divergence": [
+                0.0,
+                0.0,
+                0.0,
+                -0.09573442,
+                -0.0089094,
+                0.01402334,
+                0.0258659,
+                0.08960329,
+                0.10324767,
+                0.10624839,
+                0.06578761,
+                -0.00018638,
+                -0.01345262,
+                -0.08770822,
+                -0.04990875,
+                0.04288402,
+                -0.06845165,
+                -0.08588978,
+                -0.08277036,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0
+            ]
+        }
+    ],
+    "test_loss_functions[categorical_hinge-False]": [
+        [
+            7,
+            1,
+            1,
+            4,
+            4,
+            1,
+            4,
+            4,
+            4,
+            4,
+            4,
+            4,
+            4,
+            4,
+            1,
+            4,
+            4,
+            7,
+            8,
+            4,
+            4,
+            4,
+            4,
+            3,
+            4,
+            4,
+            7,
+            4,
+            4,
+            4,
+            7,
+            4,
+            3,
+            4,
+            7,
+            0,
+            7,
+            7,
+            1,
+            1,
+            7,
+            7,
+            4,
+            0,
+            1,
+            4,
+            4,
+            4,
+            4,
+            4,
+            4,
+            7,
+            4,
+            4,
+            4,
+            4,
+            4,
+            1,
+            4,
+            4,
+            7,
+            4,
+            4,
+            4,
+            4,
+            4,
+            4,
+            4,
+            3,
+            0,
+            7,
+            4,
+            0,
+            1,
+            7,
+            4,
+            4,
+            7,
+            4,
+            4,
+            4,
+            4,
+            4,
+            7,
+            4,
+            4,
+            4,
+            4,
+            4,
+            1,
+            4,
+            4,
+            4,
+            4,
+            4,
+            4,
+            4,
+            4,
+            4,
+            4
+        ],
+        [
+            0.0,
+            0.0,
+            0.0,
+            0.0023582461,
+            0.00048802234,
+            0.0016699843,
+            -6.4777887e-05,
+            -0.0014215634,
+            -0.00013359448,
+            0.0020448549,
+            0.00028171093,
+            0.00019665064,
+            0.0015335126,
+            0.0017000455,
+            -0.00020136381,
+            0.00064588618,
+            0.0020524357,
+            0.002199081,
+            0.00083692279,
+            0.0,
+            0.0,
+            0.0,
+            0.0,
+            0.0,
+            0.0,
+            0.0,
+            0.0,
+            0.0
+        ],
+        {
+            "categorical_hinge": [
+                0.0,
+                0.0,
+                0.0,
+                -0.00644846,
+                -0.00274792,
+                -0.01334668,
+                -0.01417109,
+                0.03608133,
+                0.00670766,
+                0.02741555,
+                -0.00938758,
+                0.002172,
+                0.01400783,
+                -0.00224488,
+                -0.01445661,
+                0.01775403,
+                0.00643058,
+                0.00382644,
+                -0.01077214,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0
+            ],
+            "categorical_crossentropy": [
+                0.0,
+                0.0,
+                0.0,
+                -0.09573442,
+                -0.0089094,
+                0.01402334,
+                0.0258659,
+                0.08960329,
+                0.10324767,
+                0.10624839,
+                0.06578761,
+                -0.00018638,
+                -0.01345262,
+                -0.08770822,
+                -0.04990875,
+                0.04288402,
+                -0.06845165,
+                -0.08588978,
+                -0.08277036,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0
+            ],
+            "sparse_categorical_crossentropy": [
+                0.0,
+                0.0,
+                0.0,
+                -0.09573442,
+                -0.0089094,
+                0.01402334,
+                0.0258659,
+                0.08960329,
+                0.10324767,
+                0.10624839,
+                0.06578761,
+                -0.00018638,
+                -0.01345262,
+                -0.08770822,
+                -0.04990875,
+                0.04288402,
+                -0.06845165,
+                -0.08588978,
+                -0.08277036,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0
+            ],
+            "kullback_leibler_divergence": [
+                0.0,
+                0.0,
+                0.0,
+                -0.09573442,
+                -0.0089094,
+                0.01402334,
+                0.0258659,
+                0.08960329,
+                0.10324767,
+                0.10624839,
+                0.06578761,
+                -0.00018638,
+                -0.01345262,
+                -0.08770822,
+                -0.04990875,
+                0.04288402,
+                -0.06845165,
+                -0.08588978,
+                -0.08277036,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0
+            ]
+        }
+    ],
+    "test_loss_functions[sparse_categorical_crossentropy-True]": [
+        [
+            7,
+            1,
+            1,
+            4,
+            4,
+            1,
+            4,
+            4,
+            4,
+            4,
+            4,
+            4,
+            4,
+            4,
+            1,
+            4,
+            4,
+            7,
+            8,
+            4,
+            4,
+            4,
+            4,
+            3,
+            4,
+            4,
+            7,
+            4,
+            4,
+            4,
+            7,
+            4,
+            3,
+            4,
+            7,
+            0,
+            7,
+            7,
+            1,
+            1,
+            7,
+            7,
+            4,
+            0,
+            1,
+            4,
+            4,
+            4,
+            4,
+            4,
+            4,
+            7,
+            4,
+            4,
+            4,
+            4,
+            4,
+            1,
+            4,
+            4,
+            7,
+            4,
+            4,
+            4,
+            4,
+            4,
+            4,
+            4,
+            3,
+            0,
+            7,
+            4,
+            0,
+            1,
+            7,
+            4,
+            4,
+            7,
+            4,
+            4,
+            4,
+            4,
+            4,
+            7,
+            4,
+            4,
+            4,
+            4,
+            4,
+            1,
+            4,
+            4,
+            4,
+            4,
+            4,
+            4,
+            4,
+            4,
+            4,
+            4
+        ],
+        [
+            0.0,
+            0.0,
+            0.0,
+            0.08147776,
+            0.01847786,
+            0.07045883,
+            -0.00269106,
+            -0.03189164,
+            0.01643312,
+            0.1185048,
+            0.02166386,
+            0.00905327,
+            0.06592228,
+            0.04471018,
+            -0.02879605,
+            0.04668707,
+            0.06856851,
+            0.06857751,
+            0.00657996,
+            0.0,
+            0.0,
+            0.0,
+            0.0,
+            0.0,
+            0.0,
+            0.0,
+            0.0,
+            0.0
+        ],
+        {
+            "categorical_hinge": [
+                0.0,
+                0.0,
+                0.0,
+                -0.00644846,
+                -0.00274792,
+                -0.01334668,
+                -0.01417109,
+                0.03608133,
+                0.00670766,
+                0.02741555,
+                -0.00938758,
+                0.002172,
+                0.01400783,
+                -0.00224488,
+                -0.01445661,
+                0.01775403,
+                0.00643058,
+                0.00382644,
+                -0.01077214,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0
+            ],
+            "categorical_crossentropy": [
+                0.0,
+                0.0,
+                0.0,
+                -0.09573442,
+                -0.0089094,
+                0.01402334,
+                0.0258659,
+                0.08960329,
+                0.10324767,
+                0.10624839,
+                0.06578761,
+                -0.00018638,
+                -0.01345262,
+                -0.08770822,
+                -0.04990875,
+                0.04288402,
+                -0.06845165,
+                -0.08588978,
+                -0.08277036,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0
+            ],
+            "sparse_categorical_crossentropy": [
+                0.0,
+                0.0,
+                0.0,
+                -0.09573442,
+                -0.0089094,
+                0.01402334,
+                0.0258659,
+                0.08960329,
+                0.10324767,
+                0.10624839,
+                0.06578761,
+                -0.00018638,
+                -0.01345262,
+                -0.08770822,
+                -0.04990875,
+                0.04288402,
+                -0.06845165,
+                -0.08588978,
+                -0.08277036,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0
+            ],
+            "kullback_leibler_divergence": [
+                0.0,
+                0.0,
+                0.0,
+                -0.09573442,
+                -0.0089094,
+                0.01402334,
+                0.0258659,
+                0.08960329,
+                0.10324767,
+                0.10624839,
+                0.06578761,
+                -0.00018638,
+                -0.01345262,
+                -0.08770822,
+                -0.04990875,
+                0.04288402,
+                -0.06845165,
+                -0.08588978,
+                -0.08277036,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0
+            ]
+        }
+    ],
+    "test_loss_functions[sparse_categorical_crossentropy-False]": [
+        [
+            7,
+            1,
+            1,
+            4,
+            4,
+            1,
+            4,
+            4,
+            4,
+            4,
+            4,
+            4,
+            4,
+            4,
+            1,
+            4,
+            4,
+            7,
+            8,
+            4,
+            4,
+            4,
+            4,
+            3,
+            4,
+            4,
+            7,
+            4,
+            4,
+            4,
+            7,
+            4,
+            3,
+            4,
+            7,
+            0,
+            7,
+            7,
+            1,
+            1,
+            7,
+            7,
+            4,
+            0,
+            1,
+            4,
+            4,
+            4,
+            4,
+            4,
+            4,
+            7,
+            4,
+            4,
+            4,
+            4,
+            4,
+            1,
+            4,
+            4,
+            7,
+            4,
+            4,
+            4,
+            4,
+            4,
+            4,
+            4,
+            3,
+            0,
+            7,
+            4,
+            0,
+            1,
+            7,
+            4,
+            4,
+            7,
+            4,
+            4,
+            4,
+            4,
+            4,
+            7,
+            4,
+            4,
+            4,
+            4,
+            4,
+            1,
+            4,
+            4,
+            4,
+            4,
+            4,
+            4,
+            4,
+            4,
+            4,
+            4
+        ],
+        [
+            0.0,
+            0.0,
+            0.0,
+            0.0023582461,
+            0.00048802234,
+            0.0016699843,
+            -6.4777887e-05,
+            -0.0014215634,
+            -0.00013359448,
+            0.0020448549,
+            0.00028171093,
+            0.00019665064,
+            0.0015335126,
+            0.0017000455,
+            -0.00020136381,
+            0.00064588618,
+            0.0020524357,
+            0.002199081,
+            0.00083692279,
+            0.0,
+            0.0,
+            0.0,
+            0.0,
+            0.0,
+            0.0,
+            0.0,
+            0.0,
+            0.0
+        ],
+        {
+            "categorical_hinge": [
+                0.0,
+                0.0,
+                0.0,
+                -0.00644846,
+                -0.00274792,
+                -0.01334668,
+                -0.01417109,
+                0.03608133,
+                0.00670766,
+                0.02741555,
+                -0.00938758,
+                0.002172,
+                0.01400783,
+                -0.00224488,
+                -0.01445661,
+                0.01775403,
+                0.00643058,
+                0.00382644,
+                -0.01077214,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0
+            ],
+            "categorical_crossentropy": [
+                0.0,
+                0.0,
+                0.0,
+                -0.09573442,
+                -0.0089094,
+                0.01402334,
+                0.0258659,
+                0.08960329,
+                0.10324767,
+                0.10624839,
+                0.06578761,
+                -0.00018638,
+                -0.01345262,
+                -0.08770822,
+                -0.04990875,
+                0.04288402,
+                -0.06845165,
+                -0.08588978,
+                -0.08277036,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0
+            ],
+            "sparse_categorical_crossentropy": [
+                0.0,
+                0.0,
+                0.0,
+                -0.09573442,
+                -0.0089094,
+                0.01402334,
+                0.0258659,
+                0.08960329,
+                0.10324767,
+                0.10624839,
+                0.06578761,
+                -0.00018638,
+                -0.01345262,
+                -0.08770822,
+                -0.04990875,
+                0.04288402,
+                -0.06845165,
+                -0.08588978,
+                -0.08277036,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0
+            ],
+            "kullback_leibler_divergence": [
+                0.0,
+                0.0,
+                0.0,
+                -0.09573442,
+                -0.0089094,
+                0.01402334,
+                0.0258659,
+                0.08960329,
+                0.10324767,
+                0.10624839,
+                0.06578761,
+                -0.00018638,
+                -0.01345262,
+                -0.08770822,
+                -0.04990875,
+                0.04288402,
+                -0.06845165,
+                -0.08588978,
+                -0.08277036,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0
+            ]
+        }
+    ],
+    "test_loss_functions[kullback_leibler_divergence-False]": [
+        [
+            7,
+            1,
+            1,
+            4,
+            4,
+            1,
+            4,
+            4,
+            4,
+            4,
+            4,
+            4,
+            4,
+            4,
+            1,
+            4,
+            4,
+            7,
+            8,
+            4,
+            4,
+            4,
+            4,
+            3,
+            4,
+            4,
+            7,
+            4,
+            4,
+            4,
+            7,
+            4,
+            3,
+            4,
+            7,
+            0,
+            7,
+            7,
+            1,
+            1,
+            7,
+            7,
+            4,
+            0,
+            1,
+            4,
+            4,
+            4,
+            4,
+            4,
+            4,
+            7,
+            4,
+            4,
+            4,
+            4,
+            4,
+            1,
+            4,
+            4,
+            7,
+            4,
+            4,
+            4,
+            4,
+            4,
+            4,
+            4,
+            3,
+            0,
+            7,
+            4,
+            0,
+            1,
+            7,
+            4,
+            4,
+            7,
+            4,
+            4,
+            4,
+            4,
+            4,
+            7,
+            4,
+            4,
+            4,
+            4,
+            4,
+            1,
+            4,
+            4,
+            4,
+            4,
+            4,
+            4,
+            4,
+            4,
+            4,
+            4
+        ],
+        [
+            0.0,
+            0.0,
+            0.0,
+            0.0023582461,
+            0.00048802234,
+            0.0016699843,
+            -6.4777887e-05,
+            -0.0014215634,
+            -0.00013359448,
+            0.0020448549,
+            0.00028171093,
+            0.00019665064,
+            0.0015335126,
+            0.0017000455,
+            -0.00020136381,
+            0.00064588618,
+            0.0020524357,
+            0.002199081,
+            0.00083692279,
+            0.0,
+            0.0,
+            0.0,
+            0.0,
+            0.0,
+            0.0,
+            0.0,
+            0.0,
+            0.0
+        ],
+        {
+            "categorical_hinge": [
+                0.0,
+                0.0,
+                0.0,
+                -0.00644846,
+                -0.00274792,
+                -0.01334668,
+                -0.01417109,
+                0.03608133,
+                0.00670766,
+                0.02741555,
+                -0.00938758,
+                0.002172,
+                0.01400783,
+                -0.00224488,
+                -0.01445661,
+                0.01775403,
+                0.00643058,
+                0.00382644,
+                -0.01077214,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0
+            ],
+            "categorical_crossentropy": [
+                0.0,
+                0.0,
+                0.0,
+                -0.09573442,
+                -0.0089094,
+                0.01402334,
+                0.0258659,
+                0.08960329,
+                0.10324767,
+                0.10624839,
+                0.06578761,
+                -0.00018638,
+                -0.01345262,
+                -0.08770822,
+                -0.04990875,
+                0.04288402,
+                -0.06845165,
+                -0.08588978,
+                -0.08277036,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0
+            ],
+            "sparse_categorical_crossentropy": [
+                0.0,
+                0.0,
+                0.0,
+                -0.09573442,
+                -0.0089094,
+                0.01402334,
+                0.0258659,
+                0.08960329,
+                0.10324767,
+                0.10624839,
+                0.06578761,
+                -0.00018638,
+                -0.01345262,
+                -0.08770822,
+                -0.04990875,
+                0.04288402,
+                -0.06845165,
+                -0.08588978,
+                -0.08277036,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0
+            ],
+            "kullback_leibler_divergence": [
+                0.0,
+                0.0,
+                0.0,
+                -0.09573442,
+                -0.0089094,
+                0.01402334,
+                0.0258659,
+                0.08960329,
+                0.10324767,
+                0.10624839,
+                0.06578761,
+                -0.00018638,
+                -0.01345262,
+                -0.08770822,
+                -0.04990875,
+                0.04288402,
+                -0.06845165,
+                -0.08588978,
+                -0.08277036,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0
+            ]
+        }
+    ],
+    "test_loss_functions[categorical_hinge-True]": [
+        [
+            7,
+            1,
+            1,
+            4,
+            4,
+            1,
+            4,
+            4,
+            4,
+            4,
+            4,
+            4,
+            4,
+            4,
+            1,
+            4,
+            4,
+            7,
+            8,
+            4,
+            4,
+            4,
+            4,
+            3,
+            4,
+            4,
+            7,
+            4,
+            4,
+            4,
+            7,
+            4,
+            3,
+            4,
+            7,
+            0,
+            7,
+            7,
+            1,
+            1,
+            7,
+            7,
+            4,
+            0,
+            1,
+            4,
+            4,
+            4,
+            4,
+            4,
+            4,
+            7,
+            4,
+            4,
+            4,
+            4,
+            4,
+            1,
+            4,
+            4,
+            7,
+            4,
+            4,
+            4,
+            4,
+            4,
+            4,
+            4,
+            3,
+            0,
+            7,
+            4,
+            0,
+            1,
+            7,
+            4,
+            4,
+            7,
+            4,
+            4,
+            4,
+            4,
+            4,
+            7,
+            4,
+            4,
+            4,
+            4,
+            4,
+            1,
+            4,
+            4,
+            4,
+            4,
+            4,
+            4,
+            4,
+            4,
+            4,
+            4
+        ],
+        [
+            0.0,
+            0.0,
+            0.0,
+            0.08147776,
+            0.01847786,
+            0.07045883,
+            -0.00269106,
+            -0.03189164,
+            0.01643312,
+            0.1185048,
+            0.02166386,
+            0.00905327,
+            0.06592228,
+            0.04471018,
+            -0.02879605,
+            0.04668707,
+            0.06856851,
+            0.06857751,
+            0.00657996,
+            0.0,
+            0.0,
+            0.0,
+            0.0,
+            0.0,
+            0.0,
+            0.0,
+            0.0,
+            0.0
+        ],
+        {
+            "categorical_hinge": [
+                0.0,
+                0.0,
+                0.0,
+                -0.00644846,
+                -0.00274792,
+                -0.01334668,
+                -0.01417109,
+                0.03608133,
+                0.00670766,
+                0.02741555,
+                -0.00938758,
+                0.002172,
+                0.01400783,
+                -0.00224488,
+                -0.01445661,
+                0.01775403,
+                0.00643058,
+                0.00382644,
+                -0.01077214,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0
+            ],
+            "categorical_crossentropy": [
+                0.0,
+                0.0,
+                0.0,
+                -0.09573442,
+                -0.0089094,
+                0.01402334,
+                0.0258659,
+                0.08960329,
+                0.10324767,
+                0.10624839,
+                0.06578761,
+                -0.00018638,
+                -0.01345262,
+                -0.08770822,
+                -0.04990875,
+                0.04288402,
+                -0.06845165,
+                -0.08588978,
+                -0.08277036,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0
+            ],
+            "sparse_categorical_crossentropy": [
+                0.0,
+                0.0,
+                0.0,
+                -0.09573442,
+                -0.0089094,
+                0.01402334,
+                0.0258659,
+                0.08960329,
+                0.10324767,
+                0.10624839,
+                0.06578761,
+                -0.00018638,
+                -0.01345262,
+                -0.08770822,
+                -0.04990875,
+                0.04288402,
+                -0.06845165,
+                -0.08588978,
+                -0.08277036,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0
+            ],
+            "kullback_leibler_divergence": [
+                0.0,
+                0.0,
+                0.0,
+                -0.09573442,
+                -0.0089094,
+                0.01402334,
+                0.0258659,
+                0.08960329,
+                0.10324767,
+                0.10624839,
+                0.06578761,
+                -0.00018638,
+                -0.01345262,
+                -0.08770822,
+                -0.04990875,
+                0.04288402,
+                -0.06845165,
+                -0.08588978,
+                -0.08277036,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0
+            ]
+        }
+    ],
+    "test_loss_functions[kullback_leibler_divergence-True]": [
+        [
+            7,
+            1,
+            1,
+            4,
+            4,
+            1,
+            4,
+            4,
+            4,
+            4,
+            4,
+            4,
+            4,
+            4,
+            1,
+            4,
+            4,
+            7,
+            8,
+            4,
+            4,
+            4,
+            4,
+            3,
+            4,
+            4,
+            7,
+            4,
+            4,
+            4,
+            7,
+            4,
+            3,
+            4,
+            7,
+            0,
+            7,
+            7,
+            1,
+            1,
+            7,
+            7,
+            4,
+            0,
+            1,
+            4,
+            4,
+            4,
+            4,
+            4,
+            4,
+            7,
+            4,
+            4,
+            4,
+            4,
+            4,
+            1,
+            4,
+            4,
+            7,
+            4,
+            4,
+            4,
+            4,
+            4,
+            4,
+            4,
+            3,
+            0,
+            7,
+            4,
+            0,
+            1,
+            7,
+            4,
+            4,
+            7,
+            4,
+            4,
+            4,
+            4,
+            4,
+            7,
+            4,
+            4,
+            4,
+            4,
+            4,
+            1,
+            4,
+            4,
+            4,
+            4,
+            4,
+            4,
+            4,
+            4,
+            4,
+            4
+        ],
+        [
+            0.0,
+            0.0,
+            0.0,
+            0.08147776,
+            0.01847786,
+            0.07045883,
+            -0.00269106,
+            -0.03189164,
+            0.01643312,
+            0.1185048,
+            0.02166386,
+            0.00905327,
+            0.06592228,
+            0.04471018,
+            -0.02879605,
+            0.04668707,
+            0.06856851,
+            0.06857751,
+            0.00657996,
+            0.0,
+            0.0,
+            0.0,
+            0.0,
+            0.0,
+            0.0,
+            0.0,
+            0.0,
+            0.0
+        ],
+        {
+            "categorical_hinge": [
+                0.0,
+                0.0,
+                0.0,
+                -0.00644846,
+                -0.00274792,
+                -0.01334668,
+                -0.01417109,
+                0.03608133,
+                0.00670766,
+                0.02741555,
+                -0.00938758,
+                0.002172,
+                0.01400783,
+                -0.00224488,
+                -0.01445661,
+                0.01775403,
+                0.00643058,
+                0.00382644,
+                -0.01077214,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0
+            ],
+            "categorical_crossentropy": [
+                0.0,
+                0.0,
+                0.0,
+                -0.09573442,
+                -0.0089094,
+                0.01402334,
+                0.0258659,
+                0.08960329,
+                0.10324767,
+                0.10624839,
+                0.06578761,
+                -0.00018638,
+                -0.01345262,
+                -0.08770822,
+                -0.04990875,
+                0.04288402,
+                -0.06845165,
+                -0.08588978,
+                -0.08277036,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0
+            ],
+            "sparse_categorical_crossentropy": [
+                0.0,
+                0.0,
+                0.0,
+                -0.09573442,
+                -0.0089094,
+                0.01402334,
+                0.0258659,
+                0.08960329,
+                0.10324767,
+                0.10624839,
+                0.06578761,
+                -0.00018638,
+                -0.01345262,
+                -0.08770822,
+                -0.04990875,
+                0.04288402,
+                -0.06845165,
+                -0.08588978,
+                -0.08277036,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0
+            ],
+            "kullback_leibler_divergence": [
+                0.0,
+                0.0,
+                0.0,
+                -0.09573442,
+                -0.0089094,
+                0.01402334,
+                0.0258659,
+                0.08960329,
+                0.10324767,
+                0.10624839,
+                0.06578761,
+                -0.00018638,
+                -0.01345262,
+                -0.08770822,
+                -0.04990875,
+                0.04288402,
+                -0.06845165,
+                -0.08588978,
+                -0.08277036,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0
+            ]
+        }
+    ],
+    "test_repr_scikitlearn": [],
+    "test_predict_scikitlearn": ""
+}
diff --git a/adversarial-robustness-toolbox/tests/estimators/classification/test_deeplearning_common.py b/adversarial-robustness-toolbox/tests/estimators/classification/test_deeplearning_common.py
new file mode 100644
index 0000000..5339543
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/estimators/classification/test_deeplearning_common.py
@@ -0,0 +1,483 @@
+import logging
+import os
+from os import listdir, path
+import pickle
+import tempfile
+
+import keras
+import numpy as np
+import pytest
+from tensorflow.keras.callbacks import LearningRateScheduler
+
+from art.defences.preprocessor import FeatureSqueezing, JpegCompression, SpatialSmoothing
+from tests.utils import ARTTestException, ARTTestFixtureNotImplemented
+
+logger = logging.getLogger(__name__)
+
+
+@pytest.mark.skip_framework("non_dl_frameworks", "tensorflow2")
+def test_layers(art_warning, get_default_mnist_subset, framework, image_dl_estimator):
+    try:
+        classifier, _ = image_dl_estimator(from_logits=True)
+
+        (_, _), (x_test_mnist, y_test_mnist) = get_default_mnist_subset
+
+        batch_size = 128
+        for i, name in enumerate(classifier.layer_names):
+            activation_i = classifier.get_activations(x_test_mnist, i, batch_size=batch_size)
+            activation_name = classifier.get_activations(x_test_mnist, name, batch_size=batch_size)
+            np.testing.assert_array_equal(activation_name, activation_i)
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.skip_framework("non_dl_frameworks")
+def test_loss_gradient_with_wildcard(art_warning, image_dl_estimator):
+    try:
+        classifier, _ = image_dl_estimator(wildcard=True)
+        shapes = [(1, 10, 1), (1, 20, 1)]
+        for shape in shapes:
+            x = np.random.normal(size=shape)
+            loss_gradient = classifier.loss_gradient(x, y=[1])
+            assert loss_gradient.shape == shape
+
+            class_gradient = classifier.class_gradient(x, 0)
+            assert class_gradient[0].shape == shape
+
+    except ARTTestException as e:
+        art_warning(e)
+
+
+# Note: because mxnet only supports 1 concurrent version of a model if we fit that model, all expected values will
+# change for all other tests using that fitted model
+@pytest.mark.skip_framework("mxnet", "non_dl_frameworks")
+def test_fit(art_warning, get_default_mnist_subset, default_batch_size, image_dl_estimator):
+    try:
+        (x_train_mnist, y_train_mnist), (x_test_mnist, y_test_mnist) = get_default_mnist_subset
+
+        labels = np.argmax(y_test_mnist, axis=1)
+        classifier, sess = image_dl_estimator(from_logits=True)
+
+        accuracy = np.sum(np.argmax(classifier.predict(x_test_mnist), axis=1) == labels) / x_test_mnist.shape[0]
+        np.testing.assert_array_almost_equal(accuracy, 0.32, decimal=0.06)
+
+        classifier.fit(x_train_mnist, y_train_mnist, batch_size=default_batch_size, nb_epochs=2)
+        accuracy_2 = np.sum(np.argmax(classifier.predict(x_test_mnist), axis=1) == labels) / x_test_mnist.shape[0]
+        np.testing.assert_array_almost_equal(accuracy_2, 0.73, decimal=0.06)
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.skip_framework("non_dl_frameworks")
+@pytest.mark.skipif(keras.__version__.startswith("2.2"), reason="requires Keras 2.3.0 or higher")
+def test_predict(
+    art_warning, framework, get_default_mnist_subset, image_dl_estimator, expected_values, store_expected_values
+):
+    try:
+        (_, _), (x_test_mnist, y_test_mnist) = get_default_mnist_subset
+
+        classifier, _ = image_dl_estimator(from_logits=True)
+
+        y_predicted = classifier.predict(x_test_mnist[0:1])
+        np.testing.assert_array_almost_equal(y_predicted, expected_values(), decimal=4)
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.skip_framework("non_dl_frameworks")
+def test_shapes(art_warning, get_default_mnist_subset, image_dl_estimator):
+    try:
+        (_, _), (x_test_mnist, y_test_mnist) = get_default_mnist_subset
+        classifier, sess = image_dl_estimator(from_logits=True)
+
+        predictions = classifier.predict(x_test_mnist)
+        assert predictions.shape == y_test_mnist.shape
+
+        assert classifier.nb_classes == 10
+
+        class_gradients = classifier.class_gradient(x_test_mnist[:11])
+        assert class_gradients.shape == tuple([11, 10] + list(x_test_mnist[1].shape))
+
+        loss_gradients = classifier.loss_gradient(x_test_mnist[:11], y_test_mnist[:11])
+        assert loss_gradients.shape == x_test_mnist[:11].shape
+
+    except ARTTestException as e:
+        art_warning(e)
+
+
+# TODO skipping with kerastf because overall tests are taking too long to run - unskip once tests run under limit time
+@pytest.mark.skip_framework("kerastf", "non_dl_frameworks")
+@pytest.mark.parametrize("from_logits", [True, False])
+@pytest.mark.parametrize(
+    "loss_name",
+    ["categorical_crossentropy", "categorical_hinge", "sparse_categorical_crossentropy", "kullback_leibler_divergence"],
+)
+def test_loss_functions(
+    art_warning,
+    image_dl_estimator,
+    get_default_mnist_subset,
+    loss_name,
+    supported_losses_proba,
+    supported_losses_logit,
+    store_expected_values,
+    supported_losses_types,
+    from_logits,
+    expected_values,
+):
+    # prediction and class_gradient should be independent of logits/probabilities and of loss function
+
+    try:
+        (_, _), (x_test_mnist, y_test_mnist) = get_default_mnist_subset
+
+        if from_logits:
+            supported_losses = supported_losses_logit()
+        else:
+            supported_losses = supported_losses_proba()
+
+        for loss_type in supported_losses_types():
+            (y_test_pred_exp, class_gradient_exp, loss_grad_exp) = expected_values()
+            # store_expected_values(expected_values)
+
+            if loss_name + "_" + loss_type in supported_losses:
+                classifier, _ = image_dl_estimator(loss_name=loss_name, loss_type=loss_type, from_logits=from_logits)
+
+                y_test_pred_exp = np.argmax(classifier.predict(x=x_test_mnist), axis=1)
+                np.testing.assert_array_equal(y_test_pred_exp, y_test_pred_exp)
+
+                class_gradient = classifier.class_gradient(x_test_mnist, label=5)
+                # np.testing.assert_array_almost_equal(class_gradient[99, 0, 14, :, 0], class_gradient_prob_exp)
+                np.testing.assert_array_almost_equal(class_gradient[99, 0, 14, :, 0], class_gradient_exp)
+
+                loss_gradient_value = classifier.loss_gradient(x=x_test_mnist, y=y_test_mnist)
+                np.testing.assert_array_almost_equal(loss_gradient_value[99, 14, :, 0], loss_grad_exp[loss_name])
+
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.skip_framework("non_dl_frameworks")
+def test_pickle(art_warning, image_dl_estimator, image_dl_estimator_defended, tmp_path):
+    try:
+        full_path = os.path.join(tmp_path, "my_classifier.p")
+
+        classifier, _ = image_dl_estimator(functional=True)
+        with open(full_path, "wb") as save_file:
+            pickle.dump(classifier, save_file)
+
+        with open(full_path, "rb") as load_file:
+            loaded = pickle.load(load_file)
+
+        assert (classifier._clip_values == loaded._clip_values).all()
+        assert classifier._channels_first == loaded._channels_first
+        assert classifier._use_logits == loaded._use_logits
+        assert classifier._input_layer == loaded._input_layer
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.skip_framework("non_dl_frameworks")
+def test_functional_model(art_warning, image_dl_estimator):
+    try:
+        # Need to update the functional_model code to produce a model with more than one input and output layers...
+        classifier, _ = image_dl_estimator(functional=True, input_layer=1, output_layer=1)
+        assert "input1" in classifier._input.name
+        assert "output1" in classifier._output.name
+
+        classifier, _ = image_dl_estimator(functional=True, input_layer=0, output_layer=0)
+        assert "input0" in classifier._input.name
+        assert "output0" in classifier._output.name
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.skip_framework("mxnet", "tensorflow", "pytorch", "non_dl_frameworks")
+def test_fit_kwargs(art_warning, image_dl_estimator, get_default_mnist_subset, default_batch_size):
+    try:
+        (x_train_mnist, y_train_mnist), (_, _) = get_default_mnist_subset
+
+        def get_lr(_):
+            return 0.01
+
+        # Test a valid callback
+        classifier, _ = image_dl_estimator(from_logits=True)
+        kwargs = {"callbacks": [LearningRateScheduler(get_lr)]}
+        classifier.fit(x_train_mnist, y_train_mnist, batch_size=default_batch_size, nb_epochs=1, **kwargs)
+
+        # Test failure for invalid parameters
+        kwargs = {"epochs": 1}
+        with pytest.raises(TypeError) as exception:
+            classifier.fit(x_train_mnist, y_train_mnist, batch_size=default_batch_size, nb_epochs=1, **kwargs)
+
+        assert "multiple values for keyword argument" in str(exception)
+
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.skip_framework("non_dl_frameworks")
+def test_defences_predict(art_warning, get_default_mnist_subset, image_dl_estimator_defended, image_dl_estimator):
+    try:
+        (_, _), (x_test_mnist, y_test_mnist) = get_default_mnist_subset
+
+        classifier, _ = image_dl_estimator_defended(
+            defenses=["FeatureSqueezing", "JpegCompression", "SpatialSmoothing"]
+        )
+        assert len(classifier.preprocessing_defences) == 3
+
+        predictions_classifier = classifier.predict(x_test_mnist)
+
+        # Apply the same defences by hand
+        x_test_defense = x_test_mnist
+        clip_values = (0, 1)
+        fs = FeatureSqueezing(clip_values=clip_values, bit_depth=2)
+        x_test_defense, _ = fs(x_test_defense, y_test_mnist)
+        jpeg = JpegCompression(clip_values=clip_values, apply_predict=True)
+        x_test_defense, _ = jpeg(x_test_defense, y_test_mnist)
+        smooth = SpatialSmoothing()
+        x_test_defense, _ = smooth(x_test_defense, y_test_mnist)
+        # classifier, _ = get_image_classifier_list(one_classifier=True, from_logits=True)
+        classifier, _ = image_dl_estimator()
+        predictions_check = classifier._model.predict(x_test_defense)
+
+        # Check that the prediction results match
+        np.testing.assert_array_almost_equal(predictions_classifier, predictions_check, decimal=4)
+    except ARTTestException as e:
+        art_warning(e)
+
+
+# Note: because mxnet only supports 1 concurrent version of a model if we fit that model, all expected values will
+# change for all other tests using that fitted model
+@pytest.mark.skip_framework("mxnet", "non_dl_frameworks", "tensorflow2")
+def test_fit_image_generator(
+    art_warning, framework, image_dl_estimator, image_data_generator, get_default_mnist_subset
+):
+    try:
+        classifier, sess = image_dl_estimator(from_logits=True)
+        (_, _), (x_test_mnist, y_test_mnist) = get_default_mnist_subset
+
+        true_class = np.argmax(y_test_mnist, axis=1)
+
+        predictions = classifier.predict(x_test_mnist)
+        prediction_class = np.argmax(predictions, axis=1)
+        pre_fit_accuracy = np.sum(prediction_class == true_class) / x_test_mnist.shape[0]
+
+        assert pre_fit_accuracy == pytest.approx(0.32, abs=0.01)
+
+        data_gen = image_data_generator(sess=sess)
+        classifier.fit_generator(generator=data_gen, nb_epochs=2)
+        predictions = classifier.predict(x_test_mnist)
+        prediction_class = np.argmax(predictions, axis=1)
+        post_fit_accuracy = np.sum(prediction_class == true_class) / x_test_mnist.shape[0]
+
+        np.testing.assert_array_almost_equal(post_fit_accuracy, 0.68, decimal=0.06)
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.skip_framework("non_dl_frameworks")
+@pytest.mark.skipif(keras.__version__.startswith("2.2"), reason="requires Keras 2.3.0 or higher")
+def test_loss_gradient(
+    art_warning,
+    framework,
+    get_default_mnist_subset,
+    image_dl_estimator,
+    expected_values,
+    mnist_shape,
+    store_expected_values,
+):
+    try:
+        (expected_gradients_1, expected_gradients_2) = expected_values()
+
+        (_, _), (x_test_mnist, y_test_mnist) = get_default_mnist_subset
+        classifier, _ = image_dl_estimator(from_logits=True)
+
+        gradients = classifier.loss_gradient(x_test_mnist, y_test_mnist)
+
+        assert gradients.shape == (x_test_mnist.shape[0],) + mnist_shape
+
+        if mnist_shape[0] == 1:
+            sub_gradients = gradients[0, 0, :, 14]
+        else:
+            sub_gradients = gradients[0, :, 14, 0]
+
+        np.testing.assert_array_almost_equal(
+            sub_gradients, expected_gradients_1[0], decimal=expected_gradients_1[1],
+        )
+
+        if mnist_shape[0] == 1:
+            sub_gradients = gradients[0, 0, 14, :]
+        else:
+            sub_gradients = gradients[0, 14, :, 0]
+
+        np.testing.assert_array_almost_equal(
+            sub_gradients, expected_gradients_2[0], decimal=expected_gradients_2[1],
+        )
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.skip_framework("non_dl_frameworks")
+def test_nb_classes(art_warning, image_dl_estimator):
+    try:
+        classifier, _ = image_dl_estimator(from_logits=True)
+        assert classifier.nb_classes == 10
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.skip_framework("non_dl_frameworks")
+def test_input_shape(art_warning, image_dl_estimator, mnist_shape):
+    try:
+        classifier, _ = image_dl_estimator(from_logits=True)
+        assert classifier.input_shape == mnist_shape
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.skip_framework("non_dl_frameworks")
+def test_save(art_warning, image_dl_estimator):
+    try:
+        classifier, _ = image_dl_estimator(from_logits=True)
+        t_file = tempfile.NamedTemporaryFile()
+        model_path = t_file.name
+        t_file.close()
+        filename = "model_to_save"
+        classifier.save(filename, path=model_path)
+
+        assert path.exists(model_path)
+
+        created_model = False
+
+        for file in listdir(model_path):
+            if filename in file:
+                created_model = True
+        assert created_model
+
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.skip_framework("non_dl_frameworks")
+def test_repr(art_warning, image_dl_estimator, framework, expected_values, store_expected_values):
+    try:
+        classifier, _ = image_dl_estimator(from_logits=True)
+        repr_ = repr(classifier)
+        for message in expected_values():
+            assert message in repr_, "{0}: was not contained within repr".format(message)
+
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.skip_framework("tensorflow", "non_dl_frameworks")
+def test_save(art_warning, image_dl_estimator, get_default_mnist_subset, tmp_path):
+    try:
+        classifier, _ = image_dl_estimator()
+        (x_train_mnist, y_train_mnist), (_, _) = get_default_mnist_subset
+        classifier.fit(x_train_mnist, y_train_mnist, batch_size=128, nb_epochs=2)
+        full_path = tmp_path / "sub"
+        full_path.mkdir()
+
+        assert not os.listdir(full_path._str)
+        classifier.save("modelFile", path=full_path._str)
+        assert os.listdir(full_path._str)
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.skip_framework("mxnet", "non_dl_frameworks")
+@pytest.mark.skipif(keras.__version__.startswith("2.2"), reason="requires Keras 2.3.0 or higher")
+def test_class_gradient(
+    art_warning,
+    framework,
+    image_dl_estimator,
+    get_default_mnist_subset,
+    mnist_shape,
+    store_expected_values,
+    expected_values,
+):
+    try:
+        (_, _), (x_test_mnist, y_test_mnist) = get_default_mnist_subset
+
+        classifier, _ = image_dl_estimator(from_logits=True)
+
+        (
+            grad_1_all_labels,
+            grad_2_all_labels,
+            grad_1_label5,
+            grad_2_label5,
+            grad_1_labelArray,
+            grad_2_labelArray,
+            labels_list,
+        ) = expected_values()
+
+        labels = np.array(labels_list, dtype=object)
+
+        # TODO we should consider checking channel independent columns to make this test truly framework independent
+        def get_gradient1_column(gradients):
+            if mnist_shape[0] == 1:
+                return gradients[0, 5, 0, 14, :]  # expected_gradients_1_all_labels
+            else:
+                return gradients[0, 5, 14, :, 0]
+
+        def get_gradient2_column(gradients):
+            if mnist_shape[0] == 1:
+                return gradients[0, 5, 0, :, 14]  # expected_gradients_2_all_labels
+            else:
+                return gradients[0, 5, :, 14, 0]
+
+        def get_gradient3_column(gradients):
+            if mnist_shape[0] == 1:
+                return gradients[0, 0, 0, 14, :]  # expected_gradients_1_label5
+            else:
+                return gradients[0, 0, 14, :, 0]
+
+        def get_gradient4_column(gradients):
+            if mnist_shape[0] == 1:
+                return gradients[0, 0, 0, :, 14]  # expected_gradients_2_all_labels
+            else:
+                return gradients[0, 0, :, 14, 0]
+
+        # Test all gradients label
+        gradients = classifier.class_gradient(x_test_mnist)
+
+        new_shape = (x_test_mnist.shape[0], 10,) + mnist_shape
+        assert gradients.shape == new_shape
+
+        sub_gradients2 = get_gradient2_column(gradients)
+        np.testing.assert_array_almost_equal(
+            sub_gradients2, grad_2_all_labels[0], decimal=4,
+        )
+
+        # Test 1 gradient label = 5
+        gradients = classifier.class_gradient(x_test_mnist, label=5)
+
+        assert gradients.shape == (x_test_mnist.shape[0], 1,) + mnist_shape
+
+        sub_gradients2 = get_gradient3_column(gradients)
+        np.testing.assert_array_almost_equal(
+            sub_gradients2, grad_1_label5[0], decimal=4,
+        )
+
+        sub_gradients4 = get_gradient4_column(gradients)
+        np.testing.assert_array_almost_equal(
+            sub_gradients4, grad_2_label5[0], decimal=4,
+        )
+
+        # # Test a set of gradients label = array
+        gradients = classifier.class_gradient(x_test_mnist, label=labels)
+
+        new_shape = (x_test_mnist.shape[0], 1,) + mnist_shape
+        assert gradients.shape == new_shape
+
+        sub_gradients5 = get_gradient3_column(gradients)
+        np.testing.assert_array_almost_equal(
+            sub_gradients5, grad_1_labelArray[0], decimal=4,
+        )
+
+        sub_gradients6 = get_gradient4_column(gradients)
+        np.testing.assert_array_almost_equal(
+            sub_gradients6, grad_2_labelArray[0], decimal=4,
+        )
+    except ARTTestException as e:
+        art_warning(e)
diff --git a/adversarial-robustness-toolbox/tests/estimators/classification/test_deeplearning_specific.json b/adversarial-robustness-toolbox/tests/estimators/classification/test_deeplearning_specific.json
new file mode 100644
index 0000000..e25a65f
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/estimators/classification/test_deeplearning_specific.json
@@ -0,0 +1,126 @@
+{
+    "test_loss_gradient_amp[cpu]": [
+        [
+            2.1159649e-05,
+            2.1398067e-05,
+            5.2034855e-05,
+            5.4776669e-05,
+            -2.3066998e-05,
+            4.3153763e-05,
+            2.7418137e-05,
+            0.0,
+            -0.0,
+            -0.00058364868,
+            -6.1690807e-05,
+            0.00052642822,
+            -0.0002373457,
+            0.00052785873,
+            0.0010719299,
+            0.0012779236,
+            0.00068902969,
+            0.0001333952,
+            0.0010032654,
+            0.00016820431,
+            -2.8014183e-06,
+            -0.00055932999,
+            0.00014781952,
+            -0.0,
+            0.0,
+            -0.0,
+            0.0,
+            -0.0
+        ],
+        [
+            0.00055932999,
+            0.00053405762,
+            0.00064897537,
+            0.00079298019,
+            -0.00040316582,
+            -0.0011281967,
+            0.00018537045,
+            3.2603741e-05,
+            0.00081682205,
+            0.00033354759,
+            0.00031805038,
+            -0.00024235249,
+            -0.00078201294,
+            -0.00047016144,
+            0.0010719299,
+            0.00040864944,
+            -0.00034427643,
+            0.00010716915,
+            -0.00042295456,
+            -0.0001386404,
+            0.00028300285,
+            -0.0,
+            0.0,
+            0.0,
+            0.0,
+            0.0,
+            0.0,
+            0.0
+        ]
+    ],
+    "test_loss_gradient_amp[gpu]": [
+        [
+            2.1159649e-05,
+            2.1398067e-05,
+            5.2034855e-05,
+            5.4776669e-05,
+            -2.3066998e-05,
+            4.3153763e-05,
+            2.7418137e-05,
+            0.0,
+            -0.0,
+            -0.00058364868,
+            -6.1690807e-05,
+            0.00052642822,
+            -0.0002373457,
+            0.00052785873,
+            0.0010719299,
+            0.0012779236,
+            0.00068902969,
+            0.0001333952,
+            0.0010032654,
+            0.00016820431,
+            -2.8014183e-06,
+            -0.00055932999,
+            0.00014781952,
+            -0.0,
+            0.0,
+            -0.0,
+            0.0,
+            -0.0
+        ],
+        [
+            0.00055932999,
+            0.00053405762,
+            0.00064897537,
+            0.00079298019,
+            -0.00040316582,
+            -0.0011281967,
+            0.00018537045,
+            3.2603741e-05,
+            0.00081682205,
+            0.00033354759,
+            0.00031805038,
+            -0.00024235249,
+            -0.00078201294,
+            -0.00047016144,
+            0.0010719299,
+            0.00040864944,
+            -0.00034427643,
+            0.00010716915,
+            -0.00042295456,
+            -0.0001386404,
+            0.00028300285,
+            -0.0,
+            0.0,
+            0.0,
+            0.0,
+            0.0,
+            0.0,
+            0.0
+        ]
+    ]
+}
\ No newline at end of file
diff --git a/adversarial-robustness-toolbox/tests/estimators/classification/test_deeplearning_specific.py b/adversarial-robustness-toolbox/tests/estimators/classification/test_deeplearning_specific.py
new file mode 100644
index 0000000..a97137a
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/estimators/classification/test_deeplearning_specific.py
@@ -0,0 +1,228 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+import os
+import logging
+import numpy as np
+import pytest
+import pickle
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+
+from art.estimators.classification.pytorch import PyTorchClassifier
+
+from tests.utils import ARTTestException
+
+logger = logging.getLogger(__name__)
+
+
+class Model(nn.Module):
+    def __init__(self):
+        super(Model, self).__init__()
+        self.conv = nn.Conv2d(1, 2, 5)
+        self.pool = nn.MaxPool2d(2, 2)
+        self.fc = nn.Linear(288, 10)
+
+    def forward(self, x):
+        x = self.pool(F.relu(self.conv(x)))
+        x = x.view(-1, 288)
+        logit_output = self.fc(x)
+        return logit_output
+
+
+@pytest.mark.only_with_platform("pytorch")
+def test_device(art_warning):
+    try:
+
+        class Flatten(nn.Module):
+            def forward(self, x):
+                n, _, _, _ = x.size()
+                result = x.view(n, -1)
+
+                return result
+
+        # Define the network
+        model = nn.Sequential(nn.Conv2d(1, 2, 5), nn.ReLU(), nn.MaxPool2d(2, 2), Flatten(), nn.Linear(288, 10))
+
+        # Define a loss function and optimizer
+        loss_fn = nn.CrossEntropyLoss()
+        optimizer = optim.Adam(model.parameters(), lr=0.01)
+
+        # First test cpu
+        classifier_cpu = PyTorchClassifier(
+            model=model,
+            clip_values=(0, 1),
+            loss=loss_fn,
+            optimizer=optimizer,
+            input_shape=(1, 28, 28),
+            nb_classes=10,
+            device_type="cpu",
+        )
+
+        assert classifier_cpu._device == torch.device("cpu")
+        assert classifier_cpu._device != torch.device("cuda")
+
+        # Then test gpu
+        if torch.cuda.device_count() >= 2:
+            with torch.cuda.device(0):
+                classifier_gpu0 = PyTorchClassifier(
+                    model=model,
+                    clip_values=(0, 1),
+                    loss=loss_fn,
+                    optimizer=optimizer,
+                    input_shape=(1, 28, 28),
+                    nb_classes=10,
+                )
+                assert classifier_gpu0._device == torch.device("cuda:0")
+                assert classifier_gpu0._device != torch.device("cuda:1")
+
+            with torch.cuda.device(1):
+                classifier_gpu1 = PyTorchClassifier(
+                    model=model,
+                    clip_values=(0, 1),
+                    loss=loss_fn,
+                    optimizer=optimizer,
+                    input_shape=(1, 28, 28),
+                    nb_classes=10,
+                )
+                assert classifier_gpu1._device == torch.device("cuda:1")
+                assert classifier_gpu1._device != torch.device("cuda:0")
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.only_with_platform("pytorch")
+def test_pickle(art_warning, get_default_mnist_subset, image_dl_estimator):
+    try:
+        (x_train_mnist, y_train_mnist), (x_test_mnist, y_test_mnist) = get_default_mnist_subset
+
+        from art import config
+
+        full_path = os.path.join(config.ART_DATA_PATH, "my_classifier")
+        folder = os.path.split(full_path)[0]
+        if not os.path.exists(folder):
+            os.makedirs(folder)
+
+        # The model used within the common ART pytorch get_image_classifier_list does not support pickling
+        model = Model()
+        loss_fn = nn.CrossEntropyLoss()
+        optimizer = optim.Adam(model.parameters(), lr=0.01)
+        myclassifier_2 = PyTorchClassifier(
+            model=model, clip_values=(0, 1), loss=loss_fn, optimizer=optimizer, input_shape=(1, 28, 28), nb_classes=10
+        )
+        myclassifier_2.fit(x_train_mnist, y_train_mnist, batch_size=100, nb_epochs=1)
+
+        pickle.dump(myclassifier_2, open(full_path, "wb"))
+
+        with open(full_path, "rb") as f:
+            loaded_model = pickle.load(f)
+            np.testing.assert_equal(myclassifier_2._clip_values, loaded_model._clip_values)
+            assert myclassifier_2._channels_first == loaded_model._channels_first
+            assert set(myclassifier_2.__dict__.keys()) == set(loaded_model.__dict__.keys())
+
+        # Test predict
+        predictions_1 = myclassifier_2.predict(x_test_mnist)
+        accuracy_1 = np.sum(np.argmax(predictions_1, axis=1) == np.argmax(y_test_mnist, axis=1)) / y_test_mnist.shape[0]
+        predictions_2 = loaded_model.predict(x_test_mnist)
+        accuracy_2 = np.sum(np.argmax(predictions_2, axis=1) == np.argmax(y_test_mnist, axis=1)) / y_test_mnist.shape[0]
+        assert accuracy_1 == accuracy_2
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.skip_module("apex.amp")
+@pytest.mark.skip_framework("tensorflow", "tensorflow2v1", "keras", "kerastf", "mxnet", "non_dl_frameworks")
+@pytest.mark.parametrize("device_type", ["cpu", "gpu"])
+def test_loss_gradient_amp(
+    art_warning, get_default_mnist_subset, image_dl_estimator, expected_values, mnist_shape, device_type,
+):
+    import torch
+    import torch.nn as nn
+
+    from art.estimators.classification.pytorch import PyTorchClassifier
+
+    try:
+        (expected_gradients_1, expected_gradients_2) = expected_values()
+
+        (_, _), (x_test_mnist, y_test_mnist) = get_default_mnist_subset
+
+        classifier, _ = image_dl_estimator(from_logits=True)
+        optimizer = torch.optim.Adam(classifier.model.parameters(), lr=0.01)
+
+        # Redefine the classifier with amp
+        clip_values = (0, 1)
+        criterion = nn.CrossEntropyLoss()
+        classifier = PyTorchClassifier(
+            clip_values=clip_values,
+            model=classifier.model,
+            preprocessing_defences=[],
+            loss=criterion,
+            input_shape=(1, 28, 28),
+            nb_classes=10,
+            device_type=device_type,
+            optimizer=optimizer,
+            use_amp=True,
+            loss_scale=1.0,
+        )
+
+        # Compute loss gradients
+        gradients = classifier.loss_gradient(x_test_mnist, y_test_mnist)
+
+        # Test shape
+        assert gradients.shape == (x_test_mnist.shape[0],) + mnist_shape
+
+        # First test of gradients
+        sub_gradients = gradients[0, 0, :, 14]
+
+        np.testing.assert_array_almost_equal(
+            sub_gradients, expected_gradients_1, decimal=4,
+        )
+
+        # Second test of gradients
+        sub_gradients = gradients[0, 0, 14, :]
+
+        np.testing.assert_array_almost_equal(
+            sub_gradients, expected_gradients_2, decimal=4,
+        )
+
+        # Compute loss gradients with framework
+        gradients = classifier.loss_gradient_framework(
+            torch.tensor(x_test_mnist).to(classifier.device), torch.tensor(y_test_mnist).to(classifier.device)
+        )
+        gradients = gradients.cpu().numpy()
+
+        # Test shape
+        assert gradients.shape == (x_test_mnist.shape[0],) + mnist_shape
+
+        # First test of gradients
+        sub_gradients = gradients[0, 0, :, 14]
+
+        np.testing.assert_array_almost_equal(
+            sub_gradients, expected_gradients_1, decimal=4,
+        )
+
+        # Second test of gradients
+        sub_gradients = gradients[0, 0, 14, :]
+
+        np.testing.assert_array_almost_equal(
+            sub_gradients, expected_gradients_2, decimal=4,
+        )
+
+    except ARTTestException as e:
+        art_warning(e)
diff --git a/adversarial-robustness-toolbox/tests/estimators/classification/test_detector_classifier.py b/adversarial-robustness-toolbox/tests/estimators/classification/test_detector_classifier.py
new file mode 100644
index 0000000..195b287
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/estimators/classification/test_detector_classifier.py
@@ -0,0 +1,165 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import os
+import tempfile
+import logging
+import unittest
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.optim as optim
+
+from art.estimators.classification.pytorch import PyTorchClassifier
+from art.estimators.classification.detector_classifier import DetectorClassifier
+
+from tests.utils import TestBase, get_image_classifier_pt
+
+
+logger = logging.getLogger(__name__)
+
+
+class Model(nn.Module):
+    def __init__(self, model):
+        super(Model, self).__init__()
+        self.model = model
+
+    def forward(self, x):
+        x = self.model(x)
+        x = x - 100000
+
+        return x
+
+
+class Flatten(nn.Module):
+    def forward(self, x):
+        n, _, _, _ = x.size()
+        result = x.view(n, -1)
+
+        return result
+
+
+class TestDetectorClassifier(TestBase):
+    """
+    This class tests the Detector Classifier.
+    """
+
+    @classmethod
+    def setUpClass(cls):
+        super().setUpClass()
+
+        cls.x_train_mnist = np.reshape(cls.x_train_mnist, (cls.x_train_mnist.shape[0], 1, 28, 28)).astype(np.float32)
+        cls.x_test_mnist = np.reshape(cls.x_test_mnist, (cls.x_test_mnist.shape[0], 1, 28, 28)).astype(np.float32)
+
+        # Define the internal classifier
+        classifier = get_image_classifier_pt()
+
+        # Define the internal detector
+        conv = nn.Conv2d(1, 16, 5)
+        linear = nn.Linear(2304, 1)
+        torch.nn.init.xavier_uniform_(conv.weight)
+        torch.nn.init.xavier_uniform_(linear.weight)
+        model = nn.Sequential(conv, nn.ReLU(), nn.MaxPool2d(2, 2), Flatten(), linear)
+        model = Model(model)
+        loss_fn = nn.CrossEntropyLoss()
+        optimizer = optim.Adam(model.parameters(), lr=0.01)
+        detector = PyTorchClassifier(
+            model=model, loss=loss_fn, optimizer=optimizer, input_shape=(1, 28, 28), nb_classes=1, clip_values=(0, 1)
+        )
+
+        # Define the detector-classifier
+        cls.detector_classifier = DetectorClassifier(classifier=classifier, detector=detector)
+
+        cls.x_train_mnist = np.reshape(cls.x_train_mnist, (cls.x_train_mnist.shape[0], 28, 28, 1)).astype(np.float32)
+        cls.x_test_mnist = np.reshape(cls.x_test_mnist, (cls.x_test_mnist.shape[0], 28, 28, 1)).astype(np.float32)
+
+    def setUp(self):
+        self.x_train_mnist = np.reshape(self.x_train_mnist, (self.x_train_mnist.shape[0], 1, 28, 28)).astype(np.float32)
+        self.x_test_mnist = np.reshape(self.x_test_mnist, (self.x_test_mnist.shape[0], 1, 28, 28)).astype(np.float32)
+        super().setUp()
+
+    def tearDown(self):
+        self.x_train_mnist = np.reshape(self.x_train_mnist, (self.x_train_mnist.shape[0], 28, 28, 1)).astype(np.float32)
+        self.x_test_mnist = np.reshape(self.x_test_mnist, (self.x_test_mnist.shape[0], 28, 28, 1)).astype(np.float32)
+        super().tearDown()
+
+    def test_predict(self):
+        predictions = self.detector_classifier.predict(x=self.x_test_mnist[0:1])
+        predictions_expected = 7
+        self.assertEqual(predictions.shape, (1, 11))
+        self.assertEqual(np.argmax(predictions, axis=1)[0], predictions_expected)
+
+    def test_nb_classes(self):
+        self.assertEqual(self.detector_classifier.nb_classes, 11)
+
+    def test_input_shape(self):
+        self.assertEqual(self.detector_classifier.input_shape, (1, 28, 28))
+
+    def test_class_gradient_1(self):
+        # Test label = None
+        gradients = self.detector_classifier.class_gradient(x=self.x_test_mnist[0:1], label=None)
+        self.assertEqual(gradients.shape, (1, 11, 1, 28, 28))
+
+    def test_class_gradient_2(self):
+        # Test label = 5
+        gradients = self.detector_classifier.class_gradient(x=self.x_test_mnist, label=5)
+        self.assertEqual(gradients.shape, (self.n_test, 1, 1, 28, 28))
+
+    def test_class_gradient_3(self):
+        # Test label = 10 (detector classifier has 11 classes (10 + 1))
+        gradients = self.detector_classifier.class_gradient(x=self.x_test_mnist, label=10)
+        self.assertEqual(gradients.shape, (self.n_test, 1, 1, 28, 28))
+
+    def test_class_gradient_4(self):
+        # Test label = array
+        n_test_local = 2
+        label = np.array([2, 10])
+        gradients = self.detector_classifier.class_gradient(x=self.x_test_mnist[0:n_test_local], label=label)
+        self.assertEqual(gradients.shape, (n_test_local, 1, 1, 28, 28))
+
+    def test_save(self):
+        model = self.detector_classifier
+        t_file = tempfile.NamedTemporaryFile()
+        full_path = t_file.name
+        t_file.close()
+        base_name = os.path.basename(full_path)
+        dir_name = os.path.dirname(full_path)
+        model.save(base_name, path=dir_name)
+
+        self.assertTrue(os.path.exists(full_path + "_classifier.optimizer"))
+        self.assertTrue(os.path.exists(full_path + "_classifier.model"))
+        os.remove(full_path + "_classifier.optimizer")
+        os.remove(full_path + "_classifier.model")
+
+        self.assertTrue(os.path.exists(full_path + "_detector.optimizer"))
+        self.assertTrue(os.path.exists(full_path + "_detector.model"))
+        os.remove(full_path + "_detector.optimizer")
+        os.remove(full_path + "_detector.model")
+
+    def test_repr(self):
+        repr_ = repr(self.detector_classifier)
+        self.assertIn("art.estimators.classification.detector_classifier.DetectorClassifier", repr_)
+        self.assertIn(
+            "preprocessing=StandardisationMeanStd(mean=0.0, std=1.0, apply_fit=True, apply_predict=True)", repr_
+        )
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/adversarial-robustness-toolbox/tests/estimators/classification/test_ensemble.py b/adversarial-robustness-toolbox/tests/estimators/classification/test_ensemble.py
new file mode 100644
index 0000000..9fe4e65
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/estimators/classification/test_ensemble.py
@@ -0,0 +1,264 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import unittest
+
+import keras.backend as k
+import numpy as np
+
+from art.estimators.classification.ensemble import EnsembleClassifier
+from tests.utils import TestBase, get_image_classifier_kr
+
+logger = logging.getLogger(__name__)
+
+
+class TestEnsembleClassifier(TestBase):
+    """
+    This class tests the ensemble classifier.
+    """
+
+    @classmethod
+    def setUpClass(cls):
+        super().setUpClass()
+
+        # Use twice the same classifier for unit-testing, in application they would be different
+        classifier_1 = get_image_classifier_kr()
+        classifier_2 = get_image_classifier_kr()
+        cls.ensemble = EnsembleClassifier(classifiers=[classifier_1, classifier_2], clip_values=(0, 1))
+
+    @classmethod
+    def tearDownClass(cls):
+        k.clear_session()
+
+    def test_fit(self):
+        with self.assertRaises(NotImplementedError):
+            self.ensemble.fit(self.x_train_mnist, self.y_train_mnist)
+
+    def test_fit_generator(self):
+        with self.assertRaises(NotImplementedError):
+            self.ensemble.fit_generator(None)
+
+    def test_layers(self):
+        with self.assertRaises(NotImplementedError):
+            self.ensemble.get_activations(self.x_test_mnist, layer=2)
+
+    def test_predict(self):
+        predictions = self.ensemble.predict(self.x_test_mnist, raw=False)
+        self.assertTrue(predictions.shape, (self.n_test, 10))
+
+        expected_predictions_1 = np.asarray(
+            [
+                0.12109935,
+                0.0498215,
+                0.0993958,
+                0.06410097,
+                0.11366927,
+                0.04645343,
+                0.06419807,
+                0.30685693,
+                0.07616713,
+                0.05823759,
+            ]
+        )
+        np.testing.assert_array_almost_equal(predictions[0, :], expected_predictions_1, decimal=4)
+
+        predictions_raw = self.ensemble.predict(self.x_test_mnist, raw=True)
+        self.assertEqual(predictions_raw.shape, (2, self.n_test, 10))
+
+        expected_predictions_2 = np.asarray(
+            [
+                0.06054967,
+                0.02491075,
+                0.0496979,
+                0.03205048,
+                0.05683463,
+                0.02322672,
+                0.03209903,
+                0.15342847,
+                0.03808356,
+                0.02911879,
+            ]
+        )
+        np.testing.assert_array_almost_equal(predictions_raw[0, 0, :], expected_predictions_2, decimal=4)
+
+    def test_loss_gradient(self):
+        gradients = self.ensemble.loss_gradient(self.x_test_mnist, self.y_test_mnist, raw=False)
+        self.assertEqual(gradients.shape, (self.n_test, 28, 28, 1))
+
+        expected_predictions_1 = np.asarray(
+            [
+                0.0559206,
+                0.05338925,
+                0.0648919,
+                0.07925165,
+                -0.04029291,
+                -0.11281465,
+                0.01850601,
+                0.00325054,
+                0.08163195,
+                0.03333949,
+                0.031766,
+                -0.02420463,
+                -0.07815556,
+                -0.04698735,
+                0.10711591,
+                0.04086434,
+                -0.03441073,
+                0.01071284,
+                -0.04229195,
+                -0.01386157,
+                0.02827487,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+            ]
+        )
+        np.testing.assert_array_almost_equal(gradients[0, 14, :, 0], expected_predictions_1, decimal=4)
+
+        gradients_2 = self.ensemble.loss_gradient(self.x_test_mnist, self.y_test_mnist, raw=True)
+        self.assertEqual(gradients_2.shape, (2, self.n_test, 28, 28, 1))
+
+        expected_predictions_2 = np.asarray(
+            [
+                -0.02444103,
+                -0.06092717,
+                -0.0449727,
+                0.00737736,
+                -0.0462507,
+                -0.06225448,
+                -0.08359106,
+                -0.00270847,
+                -0.009243,
+                -0.00214317,
+                -0.04728884,
+                0.00369186,
+                0.02211389,
+                0.02094269,
+                0.00219593,
+                -0.02638348,
+                0.00148741,
+                -0.004582,
+                -0.00621604,
+                0.01604268,
+                0.0174383,
+                -0.01077293,
+                -0.00548703,
+                -0.01247547,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+            ]
+        )
+        np.testing.assert_array_almost_equal(gradients_2[0, 5, 14, :, 0], expected_predictions_2, decimal=4)
+
+    def test_class_gradient(self):
+        gradients = self.ensemble.class_gradient(self.x_test_mnist, None, raw=False)
+        self.assertEqual(gradients.shape, (self.n_test, 10, 28, 28, 1))
+
+        expected_predictions_1 = np.asarray(
+            [
+                -1.0557447e-03,
+                -1.0079544e-03,
+                -7.7426434e-04,
+                1.7387432e-03,
+                2.1773507e-03,
+                5.0880699e-05,
+                1.6497371e-03,
+                2.6113100e-03,
+                6.0904310e-03,
+                4.1080985e-04,
+                2.5268078e-03,
+                -3.6661502e-04,
+                -3.0568996e-03,
+                -1.1665225e-03,
+                3.8904310e-03,
+                3.1726385e-04,
+                1.3203260e-03,
+                -1.1720930e-04,
+                -1.4315104e-03,
+                -4.7676818e-04,
+                9.7251288e-04,
+                0.0000000e00,
+                0.0000000e00,
+                0.0000000e00,
+                0.0000000e00,
+                0.0000000e00,
+                0.0000000e00,
+                0.0000000e00,
+            ]
+        )
+        np.testing.assert_array_almost_equal(gradients[0, 5, 14, :, 0], expected_predictions_1, decimal=4)
+
+        gradients_2 = self.ensemble.class_gradient(self.x_test_mnist, raw=True)
+        self.assertEqual(gradients_2.shape, (2, self.n_test, 10, 28, 28, 1))
+
+        expected_predictions_2 = np.asarray(
+            [
+                -5.2787235e-04,
+                -5.0397718e-04,
+                -3.8713217e-04,
+                8.6937158e-04,
+                1.0886753e-03,
+                2.5440349e-05,
+                8.2486856e-04,
+                1.3056550e-03,
+                3.0452155e-03,
+                2.0540493e-04,
+                1.2634039e-03,
+                -1.8330751e-04,
+                -1.5284498e-03,
+                -5.8326125e-04,
+                1.9452155e-03,
+                1.5863193e-04,
+                6.6016300e-04,
+                -5.8604652e-05,
+                -7.1575522e-04,
+                -2.3838409e-04,
+                4.8625644e-04,
+                0.0000000e00,
+                0.0000000e00,
+                0.0000000e00,
+                0.0000000e00,
+                0.0000000e00,
+                0.0000000e00,
+                0.0000000e00,
+            ]
+        )
+        np.testing.assert_array_almost_equal(gradients_2[0, 0, 5, 14, :, 0], expected_predictions_2, decimal=4)
+
+    def test_repr(self):
+        repr_ = repr(self.ensemble)
+        self.assertIn("art.estimators.classification.ensemble.EnsembleClassifier", repr_)
+        self.assertIn("classifier_weights=array([0.5, 0.5])", repr_)
+        self.assertIn(
+            f"channels_first=False, clip_values=array([0., 1.], dtype=float32), "
+            "preprocessing_defences=None, postprocessing_defences=None, preprocessing=StandardisationMeanStd(mean=0.0, "
+            "std=1.0, apply_fit=True, apply_predict=True)",
+            repr_,
+        )
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/adversarial-robustness-toolbox/tests/estimators/classification/test_input_filter.py b/adversarial-robustness-toolbox/tests/estimators/classification/test_input_filter.py
new file mode 100644
index 0000000..b02c4d6
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/estimators/classification/test_input_filter.py
@@ -0,0 +1,371 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+
+import logging
+import unittest
+
+import numpy as np
+
+from tests.utils import TestBase, master_seed, get_image_classifier_kr_tf
+
+logger = logging.getLogger(__name__)
+
+BATCH_SIZE = 10
+NB_TRAIN = 500
+NB_TEST = 100
+
+
+class TestInputFilter(TestBase):
+    @classmethod
+    def setUpClass(cls):
+        master_seed(seed=1234)
+        super().setUpClass()
+
+    def setUp(self):
+        master_seed(1234)
+        super().setUp()
+
+    def test_fit(self):
+        labels = np.argmax(self.y_test_mnist, axis=1)
+        classifier = get_image_classifier_kr_tf()
+
+        acc = np.sum(np.argmax(classifier.predict(self.x_test_mnist), axis=1) == labels) / NB_TEST
+        logger.info("Accuracy: %.2f%%", (acc * 100))
+
+        classifier.fit(self.x_train_mnist, self.y_train_mnist, batch_size=BATCH_SIZE, nb_epochs=2)
+        acc2 = np.sum(np.argmax(classifier.predict(self.x_test_mnist), axis=1) == labels) / NB_TEST
+        logger.info("Accuracy: %.2f%%", (acc2 * 100))
+
+        self.assertEqual(acc, 0.32)
+        self.assertEqual(acc2, 0.73)
+
+        classifier.fit(self.x_train_mnist, y=self.y_train_mnist, batch_size=BATCH_SIZE, nb_epochs=2)
+        classifier.fit(x=self.x_train_mnist, y=self.y_train_mnist, batch_size=BATCH_SIZE, nb_epochs=2)
+
+    # def test_class_gradient(self):
+    #     classifier = get_image_classifier_kr_tf()
+    #
+    #     # Test all gradients label
+    #     gradients = classifier.class_gradient(self.x_test_mnist)
+    #
+    #     self.assertTrue(gradients.shape == (NB_TEST, 10, 28, 28, 1))
+    #
+    #     expected_gradients_1 = np.asarray(
+    #         [
+    #             -1.0557447e-03,
+    #             -1.0079544e-03,
+    #             -7.7426434e-04,
+    #             1.7387432e-03,
+    #             2.1773507e-03,
+    #             5.0880699e-05,
+    #             1.6497371e-03,
+    #             2.6113100e-03,
+    #             6.0904310e-03,
+    #             4.1080985e-04,
+    #             2.5268078e-03,
+    #             -3.6661502e-04,
+    #             -3.0568996e-03,
+    #             -1.1665225e-03,
+    #             3.8904310e-03,
+    #             3.1726385e-04,
+    #             1.3203260e-03,
+    #             -1.1720930e-04,
+    #             -1.4315104e-03,
+    #             -4.7676818e-04,
+    #             9.7251288e-04,
+    #             0.0000000e00,
+    #             0.0000000e00,
+    #             0.0000000e00,
+    #             0.0000000e00,
+    #             0.0000000e00,
+    #             0.0000000e00,
+    #             0.0000000e00,
+    #         ]
+    #     )
+    #     np.testing.assert_array_almost_equal(gradients[0, 5, 14, :, 0], expected_gradients_1, decimal=4)
+    #
+    #     expected_gradients_2 = np.asarray(
+    #         [
+    #             -0.00367321,
+    #             -0.0002892,
+    #             0.00037825,
+    #             -0.00053344,
+    #             0.00192121,
+    #             0.00112047,
+    #             0.0023135,
+    #             0.0,
+    #             0.0,
+    #             -0.00391743,
+    #             -0.0002264,
+    #             0.00238103,
+    #             -0.00073711,
+    #             0.00270405,
+    #             0.00389043,
+    #             0.00440818,
+    #             -0.00412769,
+    #             -0.00441794,
+    #             0.00081916,
+    #             -0.00091284,
+    #             0.00119645,
+    #             -0.00849089,
+    #             0.00547925,
+    #             0.0,
+    #             0.0,
+    #             0.0,
+    #             0.0,
+    #             0.0,
+    #         ]
+    #     )
+    #     np.testing.assert_array_almost_equal(gradients[0, 5, :, 14, 0], expected_gradients_2, decimal=4)
+    #
+    #     # Test 1 gradient label = 5
+    #     gradients = classifier.class_gradient(self.x_test_mnist, label=5)
+    #
+    #     self.assertTrue(gradients.shape == (NB_TEST, 1, 28, 28, 1))
+    #
+    #     expected_gradients_1 = np.asarray(
+    #         [
+    #             -1.0557447e-03,
+    #             -1.0079544e-03,
+    #             -7.7426434e-04,
+    #             1.7387432e-03,
+    #             2.1773507e-03,
+    #             5.0880699e-05,
+    #             1.6497371e-03,
+    #             2.6113100e-03,
+    #             6.0904310e-03,
+    #             4.1080985e-04,
+    #             2.5268078e-03,
+    #             -3.6661502e-04,
+    #             -3.0568996e-03,
+    #             -1.1665225e-03,
+    #             3.8904310e-03,
+    #             3.1726385e-04,
+    #             1.3203260e-03,
+    #             -1.1720930e-04,
+    #             -1.4315104e-03,
+    #             -4.7676818e-04,
+    #             9.7251288e-04,
+    #             0.0000000e00,
+    #             0.0000000e00,
+    #             0.0000000e00,
+    #             0.0000000e00,
+    #             0.0000000e00,
+    #             0.0000000e00,
+    #             0.0000000e00,
+    #         ]
+    #     )
+    #     np.testing.assert_array_almost_equal(gradients[0, 0, 14, :, 0], expected_gradients_1, decimal=4)
+    #
+    #     expected_gradients_2 = np.asarray(
+    #         [
+    #             -0.00367321,
+    #             -0.0002892,
+    #             0.00037825,
+    #             -0.00053344,
+    #             0.00192121,
+    #             0.00112047,
+    #             0.0023135,
+    #             0.0,
+    #             0.0,
+    #             -0.00391743,
+    #             -0.0002264,
+    #             0.00238103,
+    #             -0.00073711,
+    #             0.00270405,
+    #             0.00389043,
+    #             0.00440818,
+    #             -0.00412769,
+    #             -0.00441794,
+    #             0.00081916,
+    #             -0.00091284,
+    #             0.00119645,
+    #             -0.00849089,
+    #             0.00547925,
+    #             0.0,
+    #             0.0,
+    #             0.0,
+    #             0.0,
+    #             0.0,
+    #         ]
+    #     )
+    #     np.testing.assert_array_almost_equal(gradients[0, 0, :, 14, 0], expected_gradients_2, decimal=4)
+    #
+    #     # Test a set of gradients label = array
+    #     label = np.random.randint(5, size=NB_TEST)
+    #     gradients = classifier.class_gradient(self.x_test_mnist, label=label)
+    #
+    #     self.assertTrue(gradients.shape == (NB_TEST, 1, 28, 28, 1))
+    #
+    #     expected_gradients_1 = np.asarray(
+    #         [
+    #             5.0867125e-03,
+    #             4.8564528e-03,
+    #             6.1040390e-03,
+    #             8.6531248e-03,
+    #             -6.0958797e-03,
+    #             -1.4114540e-02,
+    #             -7.1085989e-04,
+    #             -5.0330814e-04,
+    #             1.2943064e-02,
+    #             8.2416134e-03,
+    #             -1.9859476e-04,
+    #             -9.8109958e-05,
+    #             -3.8902222e-03,
+    #             -1.2945873e-03,
+    #             7.5137997e-03,
+    #             1.7720886e-03,
+    #             3.1399424e-04,
+    #             2.3657181e-04,
+    #             -3.0891625e-03,
+    #             -1.0211229e-03,
+    #             2.0828887e-03,
+    #             0.0000000e00,
+    #             0.0000000e00,
+    #             0.0000000e00,
+    #             0.0000000e00,
+    #             0.0000000e00,
+    #             0.0000000e00,
+    #             0.0000000e00,
+    #         ]
+    #     )
+    #     np.testing.assert_array_almost_equal(gradients[0, 0, 14, :, 0], expected_gradients_1, decimal=4)
+    #
+    #     expected_gradients_2 = np.asarray(
+    #         [
+    #             -0.00195835,
+    #             -0.00134457,
+    #             -0.00307221,
+    #             -0.00340564,
+    #             0.00175022,
+    #             -0.00239714,
+    #             -0.00122619,
+    #             0.0,
+    #             0.0,
+    #             -0.00520899,
+    #             -0.00046105,
+    #             0.00414874,
+    #             -0.00171095,
+    #             0.00429184,
+    #             0.0075138,
+    #             0.00792442,
+    #             0.0019566,
+    #             0.00035517,
+    #             0.00504575,
+    #             -0.00037397,
+    #             0.00022343,
+    #             -0.00530034,
+    #             0.0020528,
+    #             0.0,
+    #             0.0,
+    #             0.0,
+    #             0.0,
+    #             0.0,
+    #         ]
+    #     )
+    #     np.testing.assert_array_almost_equal(gradients[0, 0, :, 14, 0], expected_gradients_2, decimal=4)
+    #
+    # def test_loss_gradient(self):
+    #     classifier = get_image_classifier_kr_tf()
+    #
+    #     # Test gradient
+    #     gradients = classifier.loss_gradient(x=self.x_test_mnist, y=self.y_test_mnist)
+    #
+    #     self.assertTrue(gradients.shape == (NB_TEST, 28, 28, 1))
+    #
+    #     expected_gradients_1 = np.asarray(
+    #         [
+    #             0.0559206,
+    #             0.05338925,
+    #             0.0648919,
+    #             0.07925165,
+    #             -0.04029291,
+    #             -0.11281465,
+    #             0.01850601,
+    #             0.00325054,
+    #             0.08163195,
+    #             0.03333949,
+    #             0.031766,
+    #             -0.02420463,
+    #             -0.07815556,
+    #             -0.04698735,
+    #             0.10711591,
+    #             0.04086434,
+    #             -0.03441073,
+    #             0.01071284,
+    #             -0.04229195,
+    #             -0.01386157,
+    #             0.02827487,
+    #             0.0,
+    #             0.0,
+    #             0.0,
+    #             0.0,
+    #             0.0,
+    #             0.0,
+    #             0.0,
+    #         ]
+    #     )
+    #     np.testing.assert_array_almost_equal(gradients[0, 14, :, 0], expected_gradients_1, decimal=4)
+    #
+    #     expected_gradients_2 = np.asarray(
+    #         [
+    #             0.00210803,
+    #             0.00213919,
+    #             0.00520981,
+    #             0.00548001,
+    #             -0.0023059,
+    #             0.00432077,
+    #             0.00274945,
+    #             0.0,
+    #             0.0,
+    #             -0.0583441,
+    #             -0.00616604,
+    #             0.0526219,
+    #             -0.02373985,
+    #             0.05273106,
+    #             0.10711591,
+    #             0.12773865,
+    #             0.0689289,
+    #             0.01337799,
+    #             0.10032021,
+    #             0.01681096,
+    #             -0.00028647,
+    #             -0.05588859,
+    #             0.01474165,
+    #             0.0,
+    #             0.0,
+    #             0.0,
+    #             0.0,
+    #             0.0,
+    #         ]
+    #     )
+    #     np.testing.assert_array_almost_equal(gradients[0, :, 14, 0], expected_gradients_2, decimal=4)
+
+    def test_layers(self):
+        classifier = get_image_classifier_kr_tf()
+        self.assertEqual(len(classifier.layer_names), 3)
+
+        layer_names = classifier.layer_names
+        for i, name in enumerate(layer_names):
+            act_i = classifier.get_activations(self.x_test_mnist, i, batch_size=128)
+            act_name = classifier.get_activations(self.x_test_mnist, name, batch_size=128)
+            np.testing.assert_array_equal(act_name, act_i)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/adversarial-robustness-toolbox/tests/estimators/classification/test_lightgbm.py b/adversarial-robustness-toolbox/tests/estimators/classification/test_lightgbm.py
new file mode 100644
index 0000000..69e81db
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/estimators/classification/test_lightgbm.py
@@ -0,0 +1,56 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import unittest
+
+import lightgbm as lgb
+import numpy as np
+
+from art.estimators.classification.lightgbm import LightGBMClassifier
+
+from tests.utils import TestBase, master_seed
+
+logger = logging.getLogger(__name__)
+
+
+class TestLightGBMClassifier(TestBase):
+    @classmethod
+    def setUpClass(cls):
+        master_seed(seed=1234)
+        super().setUpClass()
+
+        cls.y_train_iris_index = np.argmax(cls.y_train_iris, axis=1)
+        cls.y_test_iris_index = np.argmax(cls.y_test_iris, axis=1)
+
+        num_round = 10
+        param = {"objective": "multiclass", "metric": "multi_logloss", "num_class": 3}
+        train_data = lgb.Dataset(cls.x_train_iris, label=cls.y_train_iris_index)
+        model = lgb.train(param, train_data, num_round, valid_sets=[train_data])
+
+        cls.classifier = LightGBMClassifier(model=model)
+
+    def test_predict(self):
+        y_predicted = self.classifier.predict(self.x_test_iris[0:1])
+        y_expected = np.asarray([[0.1083, 0.1255, 0.7663]])
+        np.testing.assert_array_almost_equal(y_predicted, y_expected, decimal=4)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/adversarial-robustness-toolbox/tests/estimators/classification/test_scikitlearn.py b/adversarial-robustness-toolbox/tests/estimators/classification/test_scikitlearn.py
new file mode 100644
index 0000000..6751f4c
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/estimators/classification/test_scikitlearn.py
@@ -0,0 +1,463 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import unittest
+
+import numpy as np
+
+from sklearn.tree import DecisionTreeClassifier, ExtraTreeClassifier
+from sklearn.ensemble import (
+    AdaBoostClassifier,
+    BaggingClassifier,
+    ExtraTreesClassifier,
+    GradientBoostingClassifier,
+    RandomForestClassifier,
+)
+from sklearn.linear_model import LogisticRegression
+from sklearn.svm import SVC, LinearSVC
+from sklearn.decomposition import PCA
+from sklearn.pipeline import Pipeline
+
+from art.estimators.classification.scikitlearn import (
+    ScikitlearnDecisionTreeClassifier,
+    ScikitlearnExtraTreeClassifier,
+    ScikitlearnAdaBoostClassifier,
+    ScikitlearnBaggingClassifier,
+    ScikitlearnExtraTreesClassifier,
+    ScikitlearnGradientBoostingClassifier,
+    ScikitlearnRandomForestClassifier,
+    ScikitlearnLogisticRegression,
+    ScikitlearnSVC,
+)
+from art.estimators.classification.scikitlearn import SklearnClassifier
+
+from tests.utils import TestBase, master_seed
+
+logger = logging.getLogger(__name__)
+
+
+class TestScikitlearnDecisionTreeClassifier(TestBase):
+    @classmethod
+    def setUpClass(cls):
+        master_seed(seed=1234)
+        super().setUpClass()
+
+        cls.sklearn_model = DecisionTreeClassifier()
+        cls.classifier = ScikitlearnDecisionTreeClassifier(model=cls.sklearn_model)
+        cls.classifier.fit(x=cls.x_train_iris, y=cls.y_train_iris)
+
+    def test_type(self):
+        self.assertIsInstance(self.classifier, type(SklearnClassifier(model=self.sklearn_model)))
+
+    def test_predict(self):
+        y_predicted = self.classifier.predict(self.x_test_iris[0:1])
+        y_expected = np.asarray([[0.0, 0.0, 1.0]])
+        np.testing.assert_array_almost_equal(y_predicted, y_expected, decimal=4)
+
+
+class TestScikitlearnExtraTreeClassifier(TestBase):
+    @classmethod
+    def setUpClass(cls):
+        master_seed(seed=1234)
+        super().setUpClass()
+
+        cls.sklearn_model = ExtraTreeClassifier()
+        cls.classifier = ScikitlearnExtraTreeClassifier(model=cls.sklearn_model)
+        cls.classifier.fit(x=cls.x_train_iris, y=cls.y_train_iris)
+
+    def test_type(self):
+        self.assertIsInstance(self.classifier, type(SklearnClassifier(model=self.sklearn_model)))
+
+    def test_predict(self):
+        y_predicted = self.classifier.predict(self.x_test_iris[0:1])
+        y_expected = np.asarray([[0.0, 0.0, 1.0]])
+        np.testing.assert_array_almost_equal(y_predicted, y_expected, decimal=4)
+
+
+class TestScikitlearnAdaBoostClassifier(TestBase):
+    @classmethod
+    def setUpClass(cls):
+        master_seed(seed=1234)
+        super().setUpClass()
+
+        cls.sklearn_model = AdaBoostClassifier()
+        cls.classifier = ScikitlearnAdaBoostClassifier(model=cls.sklearn_model)
+        cls.classifier.fit(x=cls.x_train_iris, y=cls.y_train_iris)
+
+    def test_type(self):
+        self.assertIsInstance(self.classifier, type(SklearnClassifier(model=self.sklearn_model)))
+
+    def test_predict(self):
+        y_predicted = self.classifier.predict(self.x_test_iris[0:1])
+        y_expected = np.asarray([[3.07686594e-16, 2.23540978e-02, 9.77645902e-01]])
+        np.testing.assert_array_almost_equal(y_predicted, y_expected, decimal=4)
+
+
+class TestScikitlearnBaggingClassifier(TestBase):
+    @classmethod
+    def setUpClass(cls):
+        master_seed(seed=1234)
+        super().setUpClass()
+
+        cls.sklearn_model = BaggingClassifier()
+        cls.classifier = ScikitlearnBaggingClassifier(model=cls.sklearn_model)
+        cls.classifier.fit(x=cls.x_train_iris, y=cls.y_train_iris)
+
+    def test_type(self):
+        self.assertIsInstance(self.classifier, type(SklearnClassifier(model=self.sklearn_model)))
+
+    def test_predict(self):
+        y_predicted = self.classifier.predict(self.x_test_iris[0:1])
+        y_expected = np.asarray([[0.0, 0.0, 1.0]])
+        np.testing.assert_array_almost_equal(y_predicted, y_expected, decimal=4)
+
+
+class TestScikitlearnExtraTreesClassifier(TestBase):
+    @classmethod
+    def setUpClass(cls):
+        master_seed(seed=1234)
+        super().setUpClass()
+
+        cls.sklearn_model = ExtraTreesClassifier(n_estimators=10)
+        cls.classifier = ScikitlearnExtraTreesClassifier(model=cls.sklearn_model)
+        cls.classifier.fit(x=cls.x_train_iris, y=cls.y_train_iris)
+
+    def test_type(self):
+        self.assertIsInstance(self.classifier, type(SklearnClassifier(model=self.sklearn_model)))
+
+    def test_predict(self):
+        y_predicted = self.classifier.predict(self.x_test_iris[0:1])
+        y_expected = np.asarray([[0.0, 0.0, 1.0]])
+        np.testing.assert_array_almost_equal(y_predicted, y_expected, decimal=4)
+
+
+class TestScikitlearnGradientBoostingClassifier(TestBase):
+    @classmethod
+    def setUpClass(cls):
+        master_seed(seed=1234)
+        super().setUpClass()
+
+        cls.sklearn_model = GradientBoostingClassifier(n_estimators=100)
+        cls.classifier = ScikitlearnGradientBoostingClassifier(model=cls.sklearn_model)
+        cls.classifier.fit(x=cls.x_train_iris, y=cls.y_train_iris)
+
+    def test_type(self):
+        self.assertIsInstance(self.classifier, type(SklearnClassifier(model=self.sklearn_model)))
+
+    def test_predict(self):
+        y_predicted = self.classifier.predict(self.x_test_iris[0:1])
+        y_expected = np.asarray([[1.00105813e-05, 2.07276221e-05, 9.99969262e-01]])
+        np.testing.assert_array_almost_equal(y_predicted, y_expected, decimal=4)
+
+
+class TestScikitlearnRandomForestClassifier(TestBase):
+    @classmethod
+    def setUpClass(cls):
+        master_seed(seed=1234)
+        super().setUpClass()
+
+        cls.sklearn_model = RandomForestClassifier(n_estimators=10)
+        cls.classifier = ScikitlearnRandomForestClassifier(model=cls.sklearn_model)
+        cls.classifier.fit(x=cls.x_train_iris, y=cls.y_train_iris)
+
+    def test_type(self):
+        self.assertIsInstance(self.classifier, type(SklearnClassifier(model=self.sklearn_model)))
+
+    def test_predict(self):
+        y_predicted = self.classifier.predict(self.x_test_iris[11:12])
+        y_expected = np.asarray([[0.9, 0.1, 0.0]])
+        np.testing.assert_array_almost_equal(y_predicted, y_expected, decimal=4)
+
+
+class TestScikitlearnLogisticRegression(TestBase):
+    @classmethod
+    def setUpClass(cls):
+        master_seed(seed=1234)
+        super().setUpClass()
+
+        cls.sklearn_model = LogisticRegression(
+            verbose=0, C=1, solver="newton-cg", dual=False, fit_intercept=True, multi_class="ovr"
+        )
+        cls.classifier = ScikitlearnLogisticRegression(model=cls.sklearn_model)
+        cls.classifier.fit(x=cls.x_train_iris, y=cls.y_train_iris)
+
+    def test_type(self):
+        self.assertIsInstance(self.classifier, type(SklearnClassifier(model=self.sklearn_model)))
+
+    def test_predict(self):
+        y_predicted = self.classifier.predict(self.x_test_iris[0:1])
+        y_expected = np.asarray([[0.07809449, 0.36258262, 0.55932295]])
+        np.testing.assert_array_almost_equal(y_predicted, y_expected, decimal=4)
+
+    def test_class_gradient_none_1(self):
+        grad_predicted = self.classifier.class_gradient(self.x_test_iris[0:1], label=None)
+        grad_expected = [
+            [
+                [-1.97934151, 1.36346793, -6.29719639, -2.61386204],
+                [-0.56940532, -0.71100581, -1.00625587, -0.68006182],
+                [0.64548057, 0.27053964, 1.5315429, 0.80580771],
+            ]
+        ]
+        np.testing.assert_array_almost_equal(grad_predicted, grad_expected, decimal=4)
+
+    def test_class_gradient_none_2(self):
+        grad_predicted = self.classifier.class_gradient(self.x_test_iris[0:2], label=None)
+        grad_expected = [
+            [
+                [-1.97934151, 1.36346793, -6.29719639, -2.61386204],
+                [-0.56940532, -0.71100581, -1.00625587, -0.68006182],
+                [0.64548057, 0.27053964, 1.5315429, 0.80580771],
+            ],
+            [
+                [-1.92147708, 1.3512013, -6.13324356, -2.53924561],
+                [-0.51154077, -0.72327244, -0.84230322, -0.60544527],
+                [0.70334512, 0.25827295, 1.69549561, 0.88042426],
+            ],
+        ]
+        np.testing.assert_array_almost_equal(grad_predicted, grad_expected, decimal=4)
+
+    def test_class_gradient_int_1(self):
+        grad_predicted = self.classifier.class_gradient(self.x_test_iris[0:1], label=1)
+        grad_expected = [[[-0.56940532, -0.71100581, -1.00625587, -0.68006182]]]
+
+        for i_shape in range(4):
+            self.assertAlmostEqual(grad_predicted[0, 0, i_shape], grad_expected[0][0][i_shape], 3)
+
+    def test_class_gradient_int_2(self):
+        grad_predicted = self.classifier.class_gradient(self.x_test_iris[0:2], label=1)
+        grad_expected = [
+            [[-0.56940532, -0.71100581, -1.00625587, -0.68006182]],
+            [[-0.51154077, -0.72327244, -0.84230322, -0.60544527]],
+        ]
+        np.testing.assert_array_almost_equal(grad_predicted, grad_expected, decimal=4)
+
+    def test_class_gradient_list_1(self):
+        grad_predicted = self.classifier.class_gradient(self.x_test_iris[0:1], label=[1])
+        grad_expected = [[[-0.56940532, -0.71100581, -1.00625587, -0.68006182]]]
+
+        for i_shape in range(4):
+            self.assertAlmostEqual(grad_predicted[0, 0, i_shape], grad_expected[0][0][i_shape], 3)
+
+    def test_class_gradient_list_2(self):
+        grad_predicted = self.classifier.class_gradient(self.x_test_iris[0:2], label=[1, 2])
+        grad_expected = [
+            [[-0.56940532, -0.71100581, -1.00625587, -0.68006182]],
+            [[0.70334512, 0.25827295, 1.69549561, 0.88042426]],
+        ]
+        np.testing.assert_array_almost_equal(grad_predicted, grad_expected, decimal=4)
+
+    def test_class_gradient_label_wrong_type(self):
+
+        with self.assertRaises(TypeError) as context:
+            _ = self.classifier.class_gradient(self.x_test_iris[0:2], label=np.asarray([0, 1, 0]))
+
+        self.assertIn(
+            "Unrecognized type for argument `label` with type <class 'numpy.ndarray'>", str(context.exception)
+        )
+
+    def test_loss_gradient(self):
+        grad_predicted = self.classifier.loss_gradient(self.x_test_iris[0:1], self.y_test_iris[0:1])
+        grad_expected = np.asarray([[-2.5487468, 0.6524621, -7.3034525, -3.2939239]])
+        np.testing.assert_array_almost_equal(grad_predicted, grad_expected, decimal=4)
+
+
+class TestScikitlearnBinaryLogisticRegression(TestBase):
+    @classmethod
+    def setUpClass(cls):
+        master_seed(seed=1234)
+        super().setUpClass()
+
+        binary_class_index = np.argmax(cls.y_train_iris, axis=1) < 2
+        x_train_binary = cls.x_train_iris[
+            binary_class_index,
+        ]
+        y_train_binary = cls.y_train_iris[binary_class_index,][:, [0, 1]]
+
+        cls.sklearn_model = LogisticRegression(
+            verbose=0, C=1, solver="newton-cg", dual=False, fit_intercept=True, multi_class="ovr"
+        )
+        cls.classifier = ScikitlearnLogisticRegression(model=cls.sklearn_model)
+        cls.classifier.fit(x=x_train_binary, y=y_train_binary)
+
+    def test_type(self):
+        self.assertIsInstance(self.classifier, type(SklearnClassifier(model=self.sklearn_model)))
+
+    def test_class_gradient(self):
+        grad_predicted = self.classifier.class_gradient(self.x_test_iris[0:1], label=None)
+        grad_expected = np.asarray(
+            [[[-0.1428355, 0.12111039, -0.45059183, -0.17579888], [0.1428355, -0.12111039, 0.45059183, 0.17579888]]]
+        )
+        np.testing.assert_array_almost_equal(grad_predicted, grad_expected, decimal=3)
+
+    def test_loss_gradient(self):
+        binary_class_index = np.argmax(self.y_test_iris, axis=1) < 2
+        x_test_binary = self.x_test_iris[
+            binary_class_index,
+        ]
+        y_test_binary = self.y_test_iris[binary_class_index,][:, [0, 1]]
+
+        grad_predicted = self.classifier.loss_gradient(x_test_binary[0:1], y_test_binary[0:1])
+        grad_expected = np.asarray([[-0.25267282, 0.21424159, -0.79708695, -0.31098431]])
+        np.testing.assert_array_almost_equal(grad_predicted, grad_expected, decimal=4)
+
+
+class TestScikitlearnSVCSVC(TestBase):
+    @classmethod
+    def setUpClass(cls):
+        master_seed(seed=1234)
+        super().setUpClass()
+
+        cls.sklearn_model = SVC(gamma="auto")
+        cls.classifier = ScikitlearnSVC(model=cls.sklearn_model)
+        cls.classifier.fit(x=cls.x_train_iris, y=cls.y_train_iris)
+
+    def test_type(self):
+        self.assertIsInstance(self.classifier, type(SklearnClassifier(model=self.sklearn_model)))
+
+    def test_predict(self):
+        y_predicted = self.classifier.predict(self.x_test_iris[0:1])
+        y_expected = np.asarray([[0.0, 0.0, 1.0]])
+        np.testing.assert_array_almost_equal(y_predicted, y_expected, decimal=4)
+
+    def test_loss_gradient(self):
+        grad_predicted = self.classifier.loss_gradient(self.x_test_iris[0:1], self.y_test_iris[0:1])
+        grad_expected = np.asarray([[-2.8753524, 0.31140438, -7.889445, -3.8314016]])
+        np.testing.assert_array_almost_equal(grad_predicted, grad_expected, decimal=4)
+
+    def test_class_gradient_none_1(self):
+        grad_predicted = self.classifier.class_gradient(self.x_test_iris[0:1], label=None)
+        grad_expected = [
+            [
+                [-1.52398277, 1.6984953, -6.05832438, -2.45788848],
+                [-0.43530558, -1.38692786, 0.41607214, -0.15109791],
+                [1.95928835, -0.31156744, 5.64225224, 2.6089864],
+            ]
+        ]
+        np.testing.assert_array_almost_equal(grad_predicted, grad_expected, decimal=4)
+
+    def test_class_gradient_none_2(self):
+        grad_predicted = self.classifier.class_gradient(self.x_test_iris[0:2], label=None)
+        grad_expected = [
+            [
+                [-1.52398277, 1.6984953, -6.05832438, -2.45788848],
+                [-0.43530558, -1.38692786, 0.41607214, -0.15109791],
+                [1.95928835, -0.31156744, 5.64225224, 2.6089864],
+            ],
+            [
+                [-1.52592969, 1.67535916, -6.2138464, -2.57887417],
+                [-0.43875292, -1.38381583, 0.48486185, -0.1011285],
+                [1.96468261, -0.29154334, 5.72898455, 2.68000267],
+            ],
+        ]
+        np.testing.assert_array_almost_equal(grad_predicted, grad_expected, decimal=4)
+
+    def test_class_gradient_int_1(self):
+        grad_predicted = self.classifier.class_gradient(self.x_test_iris[0:1], label=1)
+        grad_expected = [[[-0.43530558, -1.38692786, 0.41607214, -0.15109791]]]
+        np.testing.assert_array_almost_equal(grad_predicted, grad_expected, decimal=4)
+
+    def test_class_gradient_int_2(self):
+        grad_predicted = self.classifier.class_gradient(self.x_test_iris[0:2], label=1)
+        grad_expected = [
+            [[-0.43530558, -1.38692786, 0.41607214, -0.15109791]],
+            [[-0.43875292, -1.38381583, 0.48486185, -0.1011285]],
+        ]
+        np.testing.assert_array_almost_equal(grad_predicted, grad_expected, decimal=4)
+
+    def test_class_gradient_list_1(self):
+        grad_predicted = self.classifier.class_gradient(self.x_test_iris[0:1], label=[1])
+        grad_expected = [[[-0.43530558, -1.38692786, 0.41607214, -0.15109791]]]
+        np.testing.assert_array_almost_equal(grad_predicted, grad_expected, decimal=4)
+
+    def test_class_gradient_list_2(self):
+        grad_predicted = self.classifier.class_gradient(self.x_test_iris[0:2], label=[1, 2])
+        grad_expected = [
+            [[-0.43530558, -1.38692786, 0.41607214, -0.15109791]],
+            [[1.96468261, -0.29154334, 5.72898455, 2.68000267]],
+        ]
+        np.testing.assert_array_almost_equal(grad_predicted, grad_expected, decimal=4)
+
+    def test_class_gradient_label_wrong_type(self):
+        with self.assertRaises(TypeError) as context:
+            _ = self.classifier.class_gradient(self.x_test_iris[0:2], label=np.asarray([0, 1, 0]))
+
+        self.assertIn(
+            "Unrecognized type for argument `label` with type <class 'numpy.ndarray'>", str(context.exception)
+        )
+
+
+class TestScikitlearnSVCLinearSVC(TestBase):
+    @classmethod
+    def setUpClass(cls):
+        master_seed(seed=1234)
+        super().setUpClass()
+
+        cls.sklearn_model = LinearSVC()
+        cls.classifier = ScikitlearnSVC(model=cls.sklearn_model)
+        cls.classifier.fit(x=cls.x_train_iris, y=cls.y_train_iris)
+
+    def test_type(self):
+        self.assertIsInstance(self.classifier, type(SklearnClassifier(model=self.sklearn_model)))
+
+    def test_predict(self):
+        y_predicted = self.classifier.predict(self.x_test_iris[0:1])
+        y_expected = np.asarray([[0.0, 0.0, 1.0]])
+        np.testing.assert_array_almost_equal(y_predicted, y_expected, decimal=4)
+
+    def test_loss_gradient(self):
+        grad_predicted = self.classifier.loss_gradient(self.x_test_iris[0:1], self.y_test_iris[0:1])
+        grad_expected = np.asarray([[0.38537693, 0.5659405, -3.600912, -2.338979]])
+        np.testing.assert_array_almost_equal(grad_predicted, grad_expected, decimal=4)
+
+    def test_class_gradient(self):
+        grad_predicted = self.classifier.class_gradient(self.x_test_iris[0:1], label=None)
+        grad_expected = [
+            [
+                [-0.34997019, 1.61489704, -3.49002061, -1.46298544],
+                [-0.11249995, -2.52947052, 0.7052329, -0.44872424],
+                [-0.3853818, -0.5659519, 3.60090744, 2.33898192],
+            ]
+        ]
+        np.testing.assert_array_almost_equal(grad_predicted, grad_expected, decimal=4)
+
+
+class TestScikitlearnPipeline(TestBase):
+    @classmethod
+    def setUpClass(cls):
+        master_seed(seed=1234)
+        super().setUpClass()
+
+        svc = SVC(C=1.0, kernel="rbf", gamma="auto")
+        pca = PCA()
+        sklearn_model = Pipeline(steps=[("pca", pca), ("svc", svc)])
+        cls.classifier = SklearnClassifier(model=sklearn_model)
+        cls.classifier.fit(x=cls.x_train_iris, y=cls.y_train_iris)
+
+    def test_predict(self):
+        y_predicted = self.classifier.predict(self.x_test_iris[0:1])
+        y_expected = np.asarray([[0.0, 0.0, 1.0]])
+        np.testing.assert_array_almost_equal(y_predicted, y_expected, decimal=4)
+
+    def test_input_shape(self):
+        self.assertEqual(self.classifier.input_shape, (4,))
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/adversarial-robustness-toolbox/tests/estimators/classification/test_xgboost.py b/adversarial-robustness-toolbox/tests/estimators/classification/test_xgboost.py
new file mode 100644
index 0000000..8ec2657
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/estimators/classification/test_xgboost.py
@@ -0,0 +1,91 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import unittest
+
+import xgboost as xgb
+import numpy as np
+
+from art.estimators.classification.xgboost import XGBoostClassifier
+
+from tests.utils import TestBase, master_seed
+
+logger = logging.getLogger(__name__)
+
+
+class TestXGBoostClassifierBoosterSoftprob(TestBase):
+    @classmethod
+    def setUpClass(cls):
+        master_seed(seed=1234)
+        super().setUpClass()
+
+        num_round = 10
+        param = {"objective": "multi:softprob", "metric": "multi_logloss", "num_class": 3}
+        train_data = xgb.DMatrix(cls.x_train_iris, label=np.argmax(cls.y_train_iris, axis=1))
+        eval_list = [(train_data, "train")]
+        model = xgb.train(param, train_data, num_round, eval_list)
+
+        cls.classifier = XGBoostClassifier(model=model, nb_classes=3)
+
+    def test_predict(self):
+        y_predicted = self.classifier.predict(self.x_test_iris[0:1])
+        y_expected = np.asarray([[0.0236, 0.0268, 0.9496]])
+        np.testing.assert_array_almost_equal(y_predicted, y_expected, decimal=4)
+
+
+class TestXGBoostClassifierBoosterSoftmax(TestBase):
+    @classmethod
+    def setUpClass(cls):
+        master_seed(seed=1234)
+        super().setUpClass()
+
+        num_round = 10
+        param = {"objective": "multi:softmax", "metric": "multi_logloss", "num_class": 3}
+        train_data = xgb.DMatrix(cls.x_train_iris, label=np.argmax(cls.y_train_iris, axis=1))
+        eval_list = [(train_data, "train")]
+        model = xgb.train(param, train_data, num_round, eval_list)
+
+        cls.classifier = XGBoostClassifier(model=model, nb_classes=3)
+
+    def test_predict(self):
+        y_predicted = self.classifier.predict(self.x_test_iris[0:1])
+        y_expected = np.asarray([[0.0, 0.0, 1.0]])
+        np.testing.assert_array_almost_equal(y_predicted, y_expected, decimal=4)
+
+
+class TestXGBoostClassifierPythonAPI(TestBase):
+    @classmethod
+    def setUpClass(cls):
+        master_seed(seed=1234)
+        super().setUpClass()
+
+        model = xgb.XGBClassifier(n_estimators=30, max_depth=5)
+        model.fit(cls.x_train_iris, np.argmax(cls.y_train_iris, axis=1))
+
+        cls.classifier = XGBoostClassifier(model=model)
+
+    def test_predict(self):
+        y_predicted = self.classifier.predict(self.x_test_iris[0:1])
+        y_expected = np.asarray([[0.00280161, 0.00359648, 0.99360186]])
+        np.testing.assert_array_almost_equal(y_predicted, y_expected, decimal=4)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/adversarial-robustness-toolbox/tests/estimators/object_detection/__init__.py b/adversarial-robustness-toolbox/tests/estimators/object_detection/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/adversarial-robustness-toolbox/tests/estimators/object_detection/test_tensorflow_faster_rcnn.py b/adversarial-robustness-toolbox/tests/estimators/object_detection/test_tensorflow_faster_rcnn.py
new file mode 100644
index 0000000..e1913f6
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/estimators/object_detection/test_tensorflow_faster_rcnn.py
@@ -0,0 +1,249 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import unittest
+import importlib
+
+import numpy as np
+import tensorflow as tf
+
+from tests.utils import TestBase, master_seed
+
+object_detection_spec = importlib.util.find_spec("object_detection")
+object_detection_found = object_detection_spec is not None
+
+logger = logging.getLogger(__name__)
+
+
+@unittest.skipIf(
+    not object_detection_found,
+    reason="Skip unittests if object detection module is not found because of pre-trained model.",
+)
+@unittest.skipIf(
+    tf.__version__[0] == "2" or (tf.__version__[0] == "1" and tf.__version__.split(".")[1] != "15"),
+    reason="Skip unittests if not TensorFlow v1.15 because of pre-trained model.",
+)
+class TestTensorFlowFasterRCNN(TestBase):
+    """
+    This class tests the TensorFlowFasterRCNN object detector.
+    """
+
+    @classmethod
+    def setUpClass(cls):
+        master_seed(seed=1234, set_tensorflow=True)
+        super().setUpClass()
+
+        cls.n_test = 10
+        cls.x_test_mnist = cls.x_test_mnist[0 : cls.n_test]
+        cls.y_test_mnist = cls.y_test_mnist[0 : cls.n_test]
+
+        # Only import if object detection module is available
+        from art.estimators.object_detection.tensorflow_faster_rcnn import TensorFlowFasterRCNN
+
+        # Define object detector
+        images = tf.placeholder(tf.float32, shape=[2, 28, 28, 1])
+        cls.obj_dec = TensorFlowFasterRCNN(images=images)
+
+    def test_predict(self):
+        result = self.obj_dec.predict(self.x_test_mnist)
+
+        self.assertTrue(
+            list(result.keys())
+            == [
+                "detection_boxes",
+                "detection_scores",
+                "detection_classes",
+                "detection_multiclass_scores",
+                "detection_anchor_indices",
+                "num_detections",
+                "raw_detection_boxes",
+                "raw_detection_scores",
+            ]
+        )
+
+        self.assertTrue(result["detection_boxes"].shape == (10, 300, 4))
+        expected_detection_boxes = np.asarray([0.65566427, 0.0, 1.0, 0.9642794])
+        np.testing.assert_array_almost_equal(result["detection_boxes"][0, 2, :], expected_detection_boxes, decimal=6)
+
+        self.assertTrue(result["detection_scores"].shape == (10, 300))
+        expected_detection_scores = np.asarray(
+            [
+                6.02739106e-04,
+                3.72770795e-04,
+                2.96768820e-04,
+                2.12859799e-04,
+                1.72638058e-04,
+                1.51401327e-04,
+                1.47289087e-04,
+                1.25616702e-04,
+                1.19876706e-04,
+                1.06633954e-04,
+            ]
+        )
+        np.testing.assert_array_almost_equal(result["detection_scores"][0, :10], expected_detection_scores, decimal=6)
+
+        self.assertTrue(result["detection_classes"].shape == (10, 300))
+        expected_detection_classes = np.asarray([81.0, 71.0, 66.0, 15.0, 63.0, 71.0, 66.0, 84.0, 64.0, 37.0])
+        np.testing.assert_array_almost_equal(result["detection_classes"][0, :10], expected_detection_classes, decimal=6)
+
+        self.assertTrue(result["detection_multiclass_scores"].shape == (10, 300, 91))
+        expected_detection_multiclass_scores = np.asarray(
+            [
+                9.9915493e-01,
+                1.5380951e-05,
+                3.2381786e-06,
+                2.3546692e-05,
+                1.0490003e-06,
+                2.9198272e-05,
+                1.9808563e-06,
+                6.0102529e-06,
+                8.9344621e-06,
+                2.8579292e-05,
+            ]
+        )
+        np.testing.assert_array_almost_equal(
+            result["detection_multiclass_scores"][0, 2, :10], expected_detection_multiclass_scores, decimal=6
+        )
+
+        self.assertTrue(result["detection_anchor_indices"].shape == (10, 300))
+        expected_detection_anchor_indices = np.asarray([22.0, 22.0, 4.0, 35.0, 61.0, 49.0, 16.0, 22.0, 16.0, 61.0])
+        np.testing.assert_array_almost_equal(
+            result["detection_anchor_indices"][0, :10], expected_detection_anchor_indices, decimal=6
+        )
+
+        self.assertTrue(result["num_detections"].shape == (10,))
+        expected_num_detections = np.asarray([300.0, 300.0, 300.0, 300.0, 300.0, 300.0, 300.0, 300.0, 300.0, 300.0])
+        np.testing.assert_array_almost_equal(result["num_detections"], expected_num_detections, decimal=6)
+
+        self.assertTrue(result["raw_detection_boxes"].shape == (10, 300, 4))
+        expected_raw_detection_boxes = np.asarray([0.05784893, 0.05130966, 0.41411403, 0.95867515])
+        np.testing.assert_array_almost_equal(
+            result["raw_detection_boxes"][0, 2, :], expected_raw_detection_boxes, decimal=6
+        )
+
+        self.assertTrue(result["raw_detection_scores"].shape == (10, 300, 91))
+        expected_raw_detection_scores = np.asarray(
+            [
+                9.9981636e-01,
+                2.3866653e-06,
+                2.2101715e-06,
+                1.3920785e-05,
+                9.3873712e-07,
+                4.0993282e-06,
+                3.3591269e-07,
+                6.7879691e-06,
+                2.8425752e-06,
+                9.0685753e-06,
+            ]
+        )
+        np.testing.assert_array_almost_equal(
+            result["raw_detection_scores"][0, 2, :10], expected_raw_detection_scores, decimal=6
+        )
+
+    def test_loss_gradient(self):
+        # Create labels
+        result = self.obj_dec.predict(self.x_test_mnist[:2])
+
+        groundtruth_boxes_list = [result["detection_boxes"][i] for i in range(2)]
+        groundtruth_classes_list = [result["detection_classes"][i] for i in range(2)]
+        groundtruth_weights_list = [np.ones_like(r) for r in groundtruth_classes_list]
+
+        y = {
+            "groundtruth_boxes_list": groundtruth_boxes_list,
+            "groundtruth_classes_list": groundtruth_classes_list,
+            "groundtruth_weights_list": groundtruth_weights_list,
+        }
+
+        # Compute gradients
+        grads = self.obj_dec.loss_gradient(self.x_test_mnist[:2], y)
+
+        self.assertTrue(grads.shape == (2, 28, 28, 1))
+
+        expected_gradients1 = np.asarray(
+            [
+                [-6.1982083e-03],
+                [9.2188769e-04],
+                [2.2715484e-03],
+                [3.0439291e-03],
+                [3.9350586e-03],
+                [1.3214475e-03],
+                [-1.9790903e-03],
+                [-1.8616641e-03],
+                [-1.7762191e-03],
+                [-2.4208077e-03],
+                [-2.1795963e-03],
+                [-1.3475846e-03],
+                [-1.7141351e-04],
+                [5.3379539e-04],
+                [6.1705662e-04],
+                [9.1885449e-05],
+                [-2.4936342e-04],
+                [-7.8056828e-04],
+                [-2.4509570e-04],
+                [-1.3246380e-04],
+                [-6.9344416e-04],
+                [-2.8356430e-04],
+                [1.1605137e-03],
+                [2.7452575e-03],
+                [2.9905243e-03],
+                [2.2033940e-03],
+                [1.7121597e-03],
+                [8.4455572e-03],
+            ]
+        )
+        np.testing.assert_array_almost_equal(grads[0, 0, :, :], expected_gradients1, decimal=2)
+
+        expected_gradients2 = np.asarray(
+            [
+                [-8.14103708e-03],
+                [-5.78497676e-03],
+                [-1.93702651e-03],
+                [-1.10854053e-04],
+                [-3.13712610e-03],
+                [-2.40660645e-03],
+                [-2.33814842e-03],
+                [-1.18874465e-04],
+                [-8.61960289e-05],
+                [-8.44302267e-05],
+                [1.16928865e-03],
+                [8.52172205e-04],
+                [1.50172669e-03],
+                [9.76039213e-04],
+                [6.99639553e-04],
+                [1.55441079e-03],
+                [1.99828879e-03],
+                [2.53868615e-03],
+                [3.47398920e-03],
+                [3.55495396e-03],
+                [3.40546807e-03],
+                [5.23657538e-03],
+                [9.50821862e-03],
+                [8.31787288e-03],
+                [4.75075701e-03],
+                [8.02019704e-03],
+                [1.00337435e-02],
+                [6.10247999e-03],
+            ]
+        )
+        np.testing.assert_array_almost_equal(grads[1, :, 0, :], expected_gradients2, decimal=2)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/adversarial-robustness-toolbox/tests/estimators/speech_recognition/__init__.py b/adversarial-robustness-toolbox/tests/estimators/speech_recognition/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/adversarial-robustness-toolbox/tests/estimators/speech_recognition/conftest.py b/adversarial-robustness-toolbox/tests/estimators/speech_recognition/conftest.py
new file mode 100644
index 0000000..fcb256e
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/estimators/speech_recognition/conftest.py
@@ -0,0 +1,51 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import numpy as np
+import pytest
+
+
+@pytest.fixture
+def audio_data():
+    """
+    Create audio fixtures of shape (nb_samples=3,) with elements of variable length.
+    """
+    sample_rate = 16000
+    test_input = np.array(
+        [
+            np.zeros(sample_rate),
+            np.ones(sample_rate * 2) * 2e3,
+            np.ones(sample_rate * 3) * 3e3,
+            np.ones(sample_rate * 3) * 3e3,
+        ],
+        dtype=object,
+    )
+    return test_input
+
+
+@pytest.fixture
+def audio_batch_padded():
+    """
+    Create audio fixtures of shape (batch_size=2,) with elements of variable length.
+    """
+    sample_rate = 16000
+    frequency_length = (sample_rate // 2 + 1) // 240 * 3
+    test_input = np.zeros((2, sample_rate))
+    test_mask_frequency = np.ones((2, frequency_length, 80))
+    return test_input, test_mask_frequency
diff --git a/adversarial-robustness-toolbox/tests/estimators/speech_recognition/test_pytorch_deep_speech.json b/adversarial-robustness-toolbox/tests/estimators/speech_recognition/test_pytorch_deep_speech.json
new file mode 100644
index 0000000..36d85a3
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/estimators/speech_recognition/test_pytorch_deep_speech.json
@@ -0,0 +1,642 @@
+{
+    "test_pytorch_deep_speech[cpu-False]": [
+        [
+            -0.0010376293,
+            -0.0010681478,
+            -0.0010986663,
+            -0.0011291848,
+            -0.0011291848,
+            -0.0011291848,
+            -0.0011902219,
+            -0.0011597034,
+            -0.0011902219,
+            -0.0011291848,
+            -0.0011291848,
+            -0.0010681478,
+            -0.00091555528
+        ],
+        [
+            -0.00018311106,
+            -0.00012207404,
+            -6.1037019e-05,
+            0.0,
+            3.0518509e-05,
+            0.0,
+            -3.0518509e-05,
+            0.0,
+            0.0,
+            9.1555528e-05,
+            0.00021362957,
+            0.0003357036,
+            0.00042725913,
+            0.00045777764,
+            -0.00018311106
+        ],
+        [
+            -0.00082399976,
+            -0.00070192572,
+            -0.00054933317,
+            -0.00042725913,
+            -0.00036622211,
+            -0.00027466659,
+            -0.00021362957,
+            0.00054933317,
+            0.00057985168,
+            0.00061037019,
+            0.00067140721,
+            0.00070192572,
+            0.00067140721,
+            -0.00015259255
+        ],
+        [
+            5,
+            5,
+            5
+        ],
+        [
+            "",
+            "",
+            ""
+        ],
+        [
+            ""
+        ],
+        [
+            1.0,
+            7.0154901e-14,
+            1.9170589e-13,
+            8.2194836e-13,
+            8.9967915e-13,
+            1.8518193e-12,
+            1.7883164e-10,
+            1.8951663e-12,
+            1.8818237e-13,
+            3.2806991e-12,
+            3.5664666e-16,
+            3.3147299e-14,
+            2.3439516e-13,
+            8.4845603e-12,
+            1.2017718e-13,
+            1.1180213e-12,
+            6.5572378e-15,
+            3.0194697e-12,
+            4.9065188e-15,
+            1.9765363e-13,
+            4.1670646e-11,
+            2.6884213e-12,
+            1.1436632e-13,
+            7.1931783e-15,
+            2.8135227e-11,
+            4.5599673e-14,
+            6.4587983e-13,
+            2.4159567e-15,
+            4.6668241e-13
+        ],
+        [
+            -3482.77892371,
+            665.64673575,
+            -116.24408896,
+            265.93803869,
+            1667.02236699,
+            688.33557577,
+            1455.14911883,
+            -3524.90476617,
+            -4082.06471587,
+            -8802.39419605,
+            -277.74274789,
+            2034.54679277,
+            -428.53153241,
+            4114.63683848,
+            1722.53840709,
+            -513.68916798,
+            1159.88786568,
+            7072.47761446,
+            -1963.71829047,
+            382.65287411
+        ],
+        [
+            20992.44844133,
+            3048.78701634,
+            -7849.13725934,
+            15557.59663939,
+            -15760.10725159,
+            -18422.9438386,
+            19132.22699435,
+            6508.51437337,
+            26292.5249963,
+            4232.62414548,
+            -31128.82664215,
+            -2894.85284984,
+            13008.74538039,
+            13845.08921681,
+            17657.67725957,
+            8807.42144017,
+            -16477.89414508,
+            -6977.8092622,
+            -17914.22352666,
+            4086.51150059
+        ],
+        [
+            -1693.10472689,
+            6711.39788693,
+            16480.14166546,
+            -3786.95541286,
+            16448.3969823,
+            -15702.45621671,
+            -26162.89260564,
+            -8979.81601681,
+            9657.87483965,
+            13955.78845296,
+            -7552.01438108,
+            14170.60635269,
+            -24434.37243957,
+            5502.81163675,
+            7171.56926943,
+            -6154.06511686,
+            2483.93980406,
+            -7244.24618697,
+            -10798.70438903,
+            -11129.57632319
+        ]
+    ],
+    "test_pytorch_deep_speech[cpu-True]": [
+        [
+            -0.0010376293,
+            -0.0010681478,
+            -0.0010986663,
+            -0.0011291848,
+            -0.0011291848,
+            -0.0011291848,
+            -0.0011902219,
+            -0.0011597034,
+            -0.0011902219,
+            -0.0011291848,
+            -0.0011291848,
+            -0.0010681478,
+            -0.00091555528
+        ],
+        [
+            -0.00018311106,
+            -0.00012207404,
+            -6.1037019e-05,
+            0.0,
+            3.0518509e-05,
+            0.0,
+            -3.0518509e-05,
+            0.0,
+            0.0,
+            9.1555528e-05,
+            0.00021362957,
+            0.0003357036,
+            0.00042725913,
+            0.00045777764,
+            -0.00018311106
+        ],
+        [
+            -0.00082399976,
+            -0.00070192572,
+            -0.00054933317,
+            -0.00042725913,
+            -0.00036622211,
+            -0.00027466659,
+            -0.00021362957,
+            0.00054933317,
+            0.00057985168,
+            0.00061037019,
+            0.00067140721,
+            0.00070192572,
+            0.00067140721,
+            -0.00015259255
+        ],
+        [
+            5,
+            5,
+            5
+        ],
+        [
+            "",
+            "",
+            ""
+        ],
+        [
+            ""
+        ],
+        [
+            1.0,
+            7.0154901e-14,
+            1.9170589e-13,
+            8.2194836e-13,
+            8.9967915e-13,
+            1.8518193e-12,
+            1.7883164e-10,
+            1.8951663e-12,
+            1.8818237e-13,
+            3.2806991e-12,
+            3.5664666e-16,
+            3.3147299e-14,
+            2.3439516e-13,
+            8.4845603e-12,
+            1.2017718e-13,
+            1.1180213e-12,
+            6.5572378e-15,
+            3.0194697e-12,
+            4.9065188e-15,
+            1.9765363e-13,
+            4.1670646e-11,
+            2.6884213e-12,
+            1.1436632e-13,
+            7.1931783e-15,
+            2.8135227e-11,
+            4.5599673e-14,
+            6.4587983e-13,
+            2.4159567e-15,
+            4.6668241e-13
+        ],
+        [
+            -3482.77892371,
+            665.64673575,
+            -116.24408896,
+            265.93803869,
+            1667.02236699,
+            688.33557577,
+            1455.14911883,
+            -3524.90476617,
+            -4082.06471587,
+            -8802.39419605,
+            -277.74274789,
+            2034.54679277,
+            -428.53153241,
+            4114.63683848,
+            1722.53840709,
+            -513.68916798,
+            1159.88786568,
+            7072.47761446,
+            -1963.71829047,
+            382.65287411
+        ],
+        [
+            20992.44844133,
+            3048.78701634,
+            -7849.13725934,
+            15557.59663939,
+            -15760.10725159,
+            -18422.9438386,
+            19132.22699435,
+            6508.51437337,
+            26292.5249963,
+            4232.62414548,
+            -31128.82664215,
+            -2894.85284984,
+            13008.74538039,
+            13845.08921681,
+            17657.67725957,
+            8807.42144017,
+            -16477.89414508,
+            -6977.8092622,
+            -17914.22352666,
+            4086.51150059
+        ],
+        [
+            -1693.10472689,
+            6711.39788693,
+            16480.14166546,
+            -3786.95541286,
+            16448.3969823,
+            -15702.45621671,
+            -26162.89260564,
+            -8979.81601681,
+            9657.87483965,
+            13955.78845296,
+            -7552.01438108,
+            14170.60635269,
+            -24434.37243957,
+            5502.81163675,
+            7171.56926943,
+            -6154.06511686,
+            2483.93980406,
+            -7244.24618697,
+            -10798.70438903,
+            -11129.57632319
+        ]
+    ],
+    "test_pytorch_deep_speech[gpu-False]": [
+        [
+            -0.0010376293,
+            -0.0010681478,
+            -0.0010986663,
+            -0.0011291848,
+            -0.0011291848,
+            -0.0011291848,
+            -0.0011902219,
+            -0.0011597034,
+            -0.0011902219,
+            -0.0011291848,
+            -0.0011291848,
+            -0.0010681478,
+            -0.00091555528
+        ],
+        [
+            -0.00018311106,
+            -0.00012207404,
+            -6.1037019e-05,
+            0.0,
+            3.0518509e-05,
+            0.0,
+            -3.0518509e-05,
+            0.0,
+            0.0,
+            9.1555528e-05,
+            0.00021362957,
+            0.0003357036,
+            0.00042725913,
+            0.00045777764,
+            -0.00018311106
+        ],
+        [
+            -0.00082399976,
+            -0.00070192572,
+            -0.00054933317,
+            -0.00042725913,
+            -0.00036622211,
+            -0.00027466659,
+            -0.00021362957,
+            0.00054933317,
+            0.00057985168,
+            0.00061037019,
+            0.00067140721,
+            0.00070192572,
+            0.00067140721,
+            -0.00015259255
+        ],
+        [
+            5,
+            5,
+            5
+        ],
+        [
+            "",
+            "",
+            ""
+        ],
+        [
+            ""
+        ],
+        [
+            1.0,
+            7.0154901e-14,
+            1.9170589e-13,
+            8.2194836e-13,
+            8.9967915e-13,
+            1.8518193e-12,
+            1.7883164e-10,
+            1.8951663e-12,
+            1.8818237e-13,
+            3.2806991e-12,
+            3.5664666e-16,
+            3.3147299e-14,
+            2.3439516e-13,
+            8.4845603e-12,
+            1.2017718e-13,
+            1.1180213e-12,
+            6.5572378e-15,
+            3.0194697e-12,
+            4.9065188e-15,
+            1.9765363e-13,
+            4.1670646e-11,
+            2.6884213e-12,
+            1.1436632e-13,
+            7.1931783e-15,
+            2.8135227e-11,
+            4.5599673e-14,
+            6.4587983e-13,
+            2.4159567e-15,
+            4.6668241e-13
+        ],
+        [
+            -3482.77892371,
+            665.64673575,
+            -116.24408896,
+            265.93803869,
+            1667.02236699,
+            688.33557577,
+            1455.14911883,
+            -3524.90476617,
+            -4082.06471587,
+            -8802.39419605,
+            -277.74274789,
+            2034.54679277,
+            -428.53153241,
+            4114.63683848,
+            1722.53840709,
+            -513.68916798,
+            1159.88786568,
+            7072.47761446,
+            -1963.71829047,
+            382.65287411
+        ],
+        [
+            20992.44844133,
+            3048.78701634,
+            -7849.13725934,
+            15557.59663939,
+            -15760.10725159,
+            -18422.9438386,
+            19132.22699435,
+            6508.51437337,
+            26292.5249963,
+            4232.62414548,
+            -31128.82664215,
+            -2894.85284984,
+            13008.74538039,
+            13845.08921681,
+            17657.67725957,
+            8807.42144017,
+            -16477.89414508,
+            -6977.8092622,
+            -17914.22352666,
+            4086.51150059
+        ],
+        [
+            -1693.10472689,
+            6711.39788693,
+            16480.14166546,
+            -3786.95541286,
+            16448.3969823,
+            -15702.45621671,
+            -26162.89260564,
+            -8979.81601681,
+            9657.87483965,
+            13955.78845296,
+            -7552.01438108,
+            14170.60635269,
+            -24434.37243957,
+            5502.81163675,
+            7171.56926943,
+            -6154.06511686,
+            2483.93980406,
+            -7244.24618697,
+            -10798.70438903,
+            -11129.57632319
+        ]
+    ],
+    "test_pytorch_deep_speech[gpu-True]": [
+        [
+            -0.0010376293,
+            -0.0010681478,
+            -0.0010986663,
+            -0.0011291848,
+            -0.0011291848,
+            -0.0011291848,
+            -0.0011902219,
+            -0.0011597034,
+            -0.0011902219,
+            -0.0011291848,
+            -0.0011291848,
+            -0.0010681478,
+            -0.00091555528
+        ],
+        [
+            -0.00018311106,
+            -0.00012207404,
+            -6.1037019e-05,
+            0.0,
+            3.0518509e-05,
+            0.0,
+            -3.0518509e-05,
+            0.0,
+            0.0,
+            9.1555528e-05,
+            0.00021362957,
+            0.0003357036,
+            0.00042725913,
+            0.00045777764,
+            -0.00018311106
+        ],
+        [
+            -0.00082399976,
+            -0.00070192572,
+            -0.00054933317,
+            -0.00042725913,
+            -0.00036622211,
+            -0.00027466659,
+            -0.00021362957,
+            0.00054933317,
+            0.00057985168,
+            0.00061037019,
+            0.00067140721,
+            0.00070192572,
+            0.00067140721,
+            -0.00015259255
+        ],
+        [
+            5,
+            5,
+            5
+        ],
+        [
+            "",
+            "",
+            ""
+        ],
+        [
+            ""
+        ],
+        [
+            1.0,
+            7.0154901e-14,
+            1.9170589e-13,
+            8.2194836e-13,
+            8.9967915e-13,
+            1.8518193e-12,
+            1.7883164e-10,
+            1.8951663e-12,
+            1.8818237e-13,
+            3.2806991e-12,
+            3.5664666e-16,
+            3.3147299e-14,
+            2.3439516e-13,
+            8.4845603e-12,
+            1.2017718e-13,
+            1.1180213e-12,
+            6.5572378e-15,
+            3.0194697e-12,
+            4.9065188e-15,
+            1.9765363e-13,
+            4.1670646e-11,
+            2.6884213e-12,
+            1.1436632e-13,
+            7.1931783e-15,
+            2.8135227e-11,
+            4.5599673e-14,
+            6.4587983e-13,
+            2.4159567e-15,
+            4.6668241e-13
+        ],
+        [
+            -3489.1,
+            663.0,
+            -126.6,
+            260.8,
+            1669.0,
+            685.9,
+            1465.2,
+            -3517.0,
+            -4065.3,
+            -8802.5,
+            -282.0,
+            2038.7,
+            -426.0,
+            4112.7,
+            1713.5,
+            -512.7,
+            1163.1,
+            7071.7,
+            -1979.1,
+            366.0
+        ],
+        [
+            20948.1,
+            3055.3,
+            -7749.6,
+            15617.4,
+            -15755.1,
+            -18439.7,
+            19085.3,
+            6497.6,
+            26328.1,
+            4310.3,
+            -31097.6,
+            -2928.2,
+            12917.2,
+            13846.1,
+            17656.3,
+            8773.8,
+            -16428.4,
+            -7004.6,
+            -17929.5,
+            4090.1
+        ],
+        [
+            -1646.1,
+            6674.9,
+            16426.1,
+            -3735.2,
+            16494.4,
+            -15657.4,
+            -26035.1,
+            -8909.6,
+            9641.5,
+            13939.9,
+            -7570.7,
+            14150.3,
+            -24358.6,
+            5513.7,
+            7114.5,
+            -6168.0,
+            2496.4,
+            -7189.0,
+            -10807.5,
+            -11125.1
+        ]
+    ]
+}
\ No newline at end of file
diff --git a/adversarial-robustness-toolbox/tests/estimators/speech_recognition/test_pytorch_deep_speech.py b/adversarial-robustness-toolbox/tests/estimators/speech_recognition/test_pytorch_deep_speech.py
new file mode 100644
index 0000000..1e40126
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/estimators/speech_recognition/test_pytorch_deep_speech.py
@@ -0,0 +1,114 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+
+import numpy as np
+import pytest
+
+from art.config import ART_NUMPY_DTYPE
+from tests.utils import ARTTestException
+
+logger = logging.getLogger(__name__)
+
+
+@pytest.mark.skip_module("apex.amp", "deepspeech_pytorch")
+@pytest.mark.skip_framework("tensorflow", "tensorflow2v1", "keras", "kerastf", "mxnet", "non_dl_frameworks")
+@pytest.mark.parametrize("use_amp", [False, True])
+@pytest.mark.parametrize("device_type", ["cpu", "gpu"])
+def test_pytorch_deep_speech(art_warning, expected_values, use_amp, device_type):
+    # Only import if deepspeech_pytorch module is available
+    import torch
+
+    from art.estimators.speech_recognition.pytorch_deep_speech import PyTorchDeepSpeech
+
+    try:
+        # Load data for testing
+        expected_data = expected_values()
+
+        x1 = expected_data[0]
+        x2 = expected_data[1]
+        x3 = expected_data[2]
+        expected_sizes = expected_data[3]
+        expected_transcriptions1 = expected_data[4]
+        expected_transcriptions2 = expected_data[5]
+        expected_probs = expected_data[6]
+        expected_gradients1 = expected_data[7]
+        expected_gradients2 = expected_data[8]
+        expected_gradients3 = expected_data[9]
+
+        # Create signal data
+        x = np.array(
+            [
+                np.array(x1 * 100, dtype=ART_NUMPY_DTYPE),
+                np.array(x2 * 100, dtype=ART_NUMPY_DTYPE),
+                np.array(x3 * 100, dtype=ART_NUMPY_DTYPE),
+            ]
+        )
+
+        # Create labels
+        y = np.array(["SIX", "HI", "GOOD"])
+
+        # Test probability outputs
+        speech_recognizer = PyTorchDeepSpeech(pretrained_model="librispeech", device_type=device_type, use_amp=use_amp)
+        probs, sizes = speech_recognizer.predict(x, batch_size=2)
+
+        np.testing.assert_array_almost_equal(probs[1][1], expected_probs, decimal=3)
+        np.testing.assert_array_almost_equal(sizes, expected_sizes)
+
+        # Test transcription outputs
+        transcriptions = speech_recognizer.predict(x, batch_size=2, transcription_output=True)
+
+        assert (expected_transcriptions1 == transcriptions).all()
+
+        # Test transcription outputs, corner case
+        transcriptions = speech_recognizer.predict(np.array([x[0]]), batch_size=2, transcription_output=True)
+
+        assert (expected_transcriptions2 == transcriptions).all()
+
+        # Now test loss gradients
+        # Compute gradients
+        grads = speech_recognizer.loss_gradient(x, y)
+
+        assert grads[0].shape == (1300,)
+        assert grads[1].shape == (1500,)
+        assert grads[2].shape == (1400,)
+
+        np.testing.assert_array_almost_equal(grads[0][0:20], expected_gradients1, decimal=-2)
+        np.testing.assert_array_almost_equal(grads[1][0:20], expected_gradients2, decimal=-2)
+        np.testing.assert_array_almost_equal(grads[2][0:20], expected_gradients3, decimal=-2)
+
+        # Now test fit function
+        # Create the optimizer
+        parameters = speech_recognizer.model.parameters()
+        speech_recognizer._optimizer = torch.optim.SGD(parameters, lr=0.01)
+
+        # Before train
+        transcriptions1 = speech_recognizer.predict(x, batch_size=2, transcription_output=True)
+
+        # Train the estimator
+        speech_recognizer.fit(x=x, y=y, batch_size=2, nb_epochs=5)
+
+        # After train
+        transcriptions2 = speech_recognizer.predict(x, batch_size=2, transcription_output=True)
+
+        assert not ((transcriptions1 == transcriptions2).all())
+
+    except ARTTestException as e:
+        art_warning(e)
diff --git a/adversarial-robustness-toolbox/tests/estimators/speech_recognition/test_tensorflow_lingvo.py b/adversarial-robustness-toolbox/tests/estimators/speech_recognition/test_tensorflow_lingvo.py
new file mode 100644
index 0000000..5bd33ee
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/estimators/speech_recognition/test_tensorflow_lingvo.py
@@ -0,0 +1,318 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+
+import numpy as np
+import pytest
+from numpy.testing import assert_allclose, assert_array_equal
+from scipy.io.wavfile import read
+
+from art.estimators.speech_recognition.speech_recognizer import SpeechRecognizerMixin
+from art.estimators.speech_recognition.tensorflow_lingvo import TensorFlowLingvoASR
+from art.estimators.tensorflow import TensorFlowV2Estimator
+from art.utils import get_file
+from tests.utils import ARTTestException
+
+logger = logging.getLogger(__name__)
+
+
+class TestTensorFlowLingvoASR:
+    """
+    Test the TensorFlowLingvoASR estimator.
+    """
+
+    @pytest.mark.skip_module("lingvo")
+    @pytest.mark.skip_framework("pytorch", "tensorflow1", "tensorflow2", "mxnet", "kerastf", "non_dl_frameworks")
+    def test_is_subclass(self, art_warning):
+        try:
+            assert issubclass(TensorFlowLingvoASR, (SpeechRecognizerMixin, TensorFlowV2Estimator))
+        except ARTTestException as e:
+            art_warning(e)
+
+    @pytest.mark.skip_module("lingvo")
+    @pytest.mark.skip_framework("pytorch", "tensorflow1", "tensorflow2", "mxnet", "kerastf", "non_dl_frameworks")
+    def test_implements_abstract_methods(self, art_warning):
+        try:
+            import tensorflow.compat.v1 as tf1
+
+            TensorFlowLingvoASR()
+        except ARTTestException as e:
+            art_warning(e)
+
+    @pytest.mark.skip_module("lingvo")
+    @pytest.mark.skip_framework("pytorch", "tensorflow1", "tensorflow2", "mxnet", "kerastf", "non_dl_frameworks")
+    def test_load_model(self, art_warning):
+        try:
+            import tensorflow.compat.v1 as tf1
+
+            TensorFlowLingvoASR()
+            graph = tf1.get_default_graph()
+            assert graph.get_operations()
+        except ARTTestException as e:
+            art_warning(e)
+
+    @pytest.mark.skip_module("lingvo")
+    @pytest.mark.skip_framework("pytorch", "tensorflow1", "tensorflow2", "mxnet", "kerastf", "non_dl_frameworks")
+    def test_create_decoder_input(self, art_warning, audio_batch_padded):
+        try:
+            import tensorflow.compat.v1 as tf1
+
+            test_input, test_mask_frequency = audio_batch_padded
+            test_target_dummy = np.array(["DUMMY"] * test_input.shape[0])
+
+            lingvo = TensorFlowLingvoASR()
+            decoder_input_tf = lingvo._create_decoder_input(lingvo._x_padded, lingvo._y_target, lingvo._mask_frequency)
+
+            decoder_input = lingvo._sess.run(
+                decoder_input_tf,
+                {
+                    lingvo._x_padded: test_input,
+                    lingvo._y_target: test_target_dummy,
+                    lingvo._mask_frequency: test_mask_frequency,
+                },
+            )
+            assert set(decoder_input.keys()).issuperset({"src", "tgt", "sample_ids"})
+            assert set(decoder_input.src.keys()).issuperset({"src_inputs", "paddings"})
+            assert set(decoder_input.tgt.keys()).issuperset({"ids", "labels", "paddings", "weights"})
+        except ARTTestException as e:
+            art_warning(e)
+
+    @pytest.mark.skip_module("lingvo")
+    @pytest.mark.skip_framework("pytorch", "tensorflow1", "tensorflow2", "mxnet", "kerastf", "non_dl_frameworks")
+    def test_create_log_mel_features(self, art_warning, audio_batch_padded):
+        try:
+            import tensorflow.compat.v1 as tf1
+
+            test_input, _ = audio_batch_padded
+            lingvo = TensorFlowLingvoASR()
+            features_tf = lingvo._create_log_mel_features(lingvo._x_padded)
+
+            features = lingvo._sess.run(features_tf, {lingvo._x_padded: test_input})
+            assert features.shape[2] == 80
+            assert len(features.shape) == 4
+        except ARTTestException as e:
+            art_warning(e)
+
+    @pytest.mark.skip_module("lingvo")
+    @pytest.mark.skip_framework("pytorch", "tensorflow1", "tensorflow2", "mxnet", "kerastf", "non_dl_frameworks")
+    def test_pad_audio_input(self, art_warning):
+        try:
+            import tensorflow.compat.v1 as tf1
+
+            test_input = np.array([np.array([1]), np.array([2] * 480)], dtype=object)
+            test_mask = np.array([[True] + [False] * 479, [True] * 480])
+            test_output = np.array([[1] + [0] * 479, [2] * 480])
+
+            lingvo = TensorFlowLingvoASR()
+            output, mask, mask_freq = lingvo._pad_audio_input(test_input)
+            assert_array_equal(test_output, output)
+            assert_array_equal(test_mask, mask)
+        except ARTTestException as e:
+            art_warning(e)
+
+    @pytest.mark.skip_module("lingvo")
+    @pytest.mark.skip_framework("pytorch", "tensorflow1", "tensorflow2", "mxnet", "kerastf", "non_dl_frameworks")
+    def test_predict_batch(self, art_warning, audio_batch_padded):
+        try:
+            import tensorflow.compat.v1 as tf1
+
+            test_input, test_mask_frequency = audio_batch_padded
+            test_target_dummy = np.array(["DUMMY"] * test_input.shape[0])
+
+            lingvo = TensorFlowLingvoASR()
+            feed_dict = {
+                lingvo._x_padded: test_input,
+                lingvo._y_target: test_target_dummy,
+                lingvo._mask_frequency: test_mask_frequency,
+            }
+            predictions = lingvo._sess.run(lingvo._predict_batch_op, feed_dict)
+            assert set(predictions.keys()).issuperset(
+                {
+                    "target_ids",
+                    "target_labels",
+                    "target_weights",
+                    "target_paddings",
+                    "transcripts",
+                    "topk_decoded",
+                    "topk_ids",
+                    "topk_lens",
+                    "topk_scores",
+                    "norm_wer_errors",
+                    "norm_wer_words",
+                    "utt_id",
+                }
+            )
+        except ARTTestException as e:
+            art_warning(e)
+
+    @pytest.mark.skip_module("lingvo")
+    @pytest.mark.skip_framework("pytorch", "tensorflow1", "tensorflow2", "mxnet", "kerastf", "non_dl_frameworks")
+    def test_predict(self, art_warning, audio_data):
+        try:
+            import tensorflow.compat.v1 as tf1
+
+            test_input = audio_data
+
+            lingvo = TensorFlowLingvoASR()
+            predictions = lingvo.predict(test_input, batch_size=2)
+            assert predictions.shape == test_input.shape
+            assert isinstance(predictions[0], np.str_)
+        except ARTTestException as e:
+            art_warning(e)
+
+    @pytest.mark.skip_module("lingvo")
+    @pytest.mark.skip_framework("pytorch", "tensorflow1", "tensorflow2", "mxnet", "kerastf", "non_dl_frameworks")
+    def test_loss_gradient_tensor(self, art_warning, audio_batch_padded):
+        try:
+            import tensorflow.compat.v1 as tf1
+
+            test_input, test_mask_frequency = audio_batch_padded
+            test_target_dummy = np.array(["DUMMY"] * test_input.shape[0])
+
+            lingvo = TensorFlowLingvoASR()
+            feed_dict = {
+                lingvo._x_padded: test_input,
+                lingvo._y_target: test_target_dummy,
+                lingvo._mask_frequency: test_mask_frequency,
+            }
+            loss_gradient = lingvo._sess.run(lingvo._loss_gradient_op, feed_dict)
+            assert test_input.shape == loss_gradient.shape
+            assert loss_gradient.sum() == 0.0
+        except ARTTestException as e:
+            art_warning(e)
+
+    @pytest.mark.skip_module("lingvo")
+    @pytest.mark.skip_framework("pytorch", "tensorflow1", "tensorflow2", "mxnet", "kerastf", "non_dl_frameworks")
+    @pytest.mark.parametrize("batch_mode", [True, False])
+    def test_loss_gradient_batch_mode(self, art_warning, batch_mode, audio_data):
+        try:
+            import tensorflow.compat.v1 as tf1
+
+            test_input = audio_data
+            test_target = np.array(["This", "is", "a dummy", "a dummy"])
+
+            lingvo = TensorFlowLingvoASR()
+
+            if batch_mode:
+                gradients = lingvo._loss_gradient_per_batch(test_input, test_target)
+            else:
+                gradients = lingvo._loss_gradient_per_sequence(test_input, test_target)
+            gradients_abs_sum = np.array([np.abs(g).sum() for g in gradients], dtype=object)
+
+            # test shape, equal inputs have equal gradients, non-zero inputs have non-zero gradient sums
+            assert [x.shape for x in test_input] == [g.shape for g in gradients]
+            assert_allclose(np.abs(gradients[2]).sum(), np.abs(gradients[3]).sum(), rtol=1e-01)
+            assert_array_equal(gradients_abs_sum > 0, [False, True, True, True])
+        except ARTTestException as e:
+            art_warning(e)
+
+
+class TestTensorFlowLingvoASRLibriSpeechSamples:
+    # specify LibriSpeech samples for download and with transcriptions
+    samples = {
+        "3575-170457-0013.wav": {
+            "uri": (
+                "https://github.com/tensorflow/cleverhans/blob/6ef939059172901db582c7702eb803b7171e3db5/"
+                "examples/adversarial_asr/LibriSpeech/test-clean/3575/170457/3575-170457-0013.wav?raw=true"
+            ),
+            "transcript": (
+                "THE MORE SHE IS ENGAGED IN HER PROPER DUTIES THE LAST LEISURE WILL SHE HAVE FOR IT EVEN AS"
+                " AN ACCOMPLISHMENT AND A RECREATION"
+            ),
+        },
+        "5105-28241-0006.wav": {
+            "uri": (
+                "https://github.com/tensorflow/cleverhans/blob/6ef939059172901db582c7702eb803b7171e3db5/"
+                "examples/adversarial_asr/LibriSpeech/test-clean/5105/28241/5105-28241-0006.wav?raw=true"
+            ),
+            "transcript": (
+                "THE LOG AND THE COMPASS THEREFORE WERE ABLE TO BE CALLED UPON TO DO THE WORK OF THE SEXTANT WHICH"
+                " HAD BECOME UTTERLY USELESS"
+            ),
+        },
+        "2300-131720-0015.wav": {
+            "uri": (
+                "https://github.com/tensorflow/cleverhans/blob/6ef939059172901db582c7702eb803b7171e3db5/"
+                "examples/adversarial_asr/LibriSpeech/test-clean/2300/131720/2300-131720-0015.wav?raw=true"
+            ),
+            "transcript": (
+                "HE OBTAINED THE DESIRED SPEED AND LOAD WITH A FRICTION BRAKE ALSO REGULATOR OF SPEED BUT WAITED FOR AN"
+                " INDICATOR TO VERIFY IT"
+            ),
+        },
+    }
+
+    @pytest.mark.skip_module("lingvo")
+    @pytest.mark.skip_framework("pytorch", "tensorflow1", "tensorflow2", "mxnet", "kerastf", "non_dl_frameworks")
+    def test_predict(self, art_warning):
+        try:
+            import tensorflow.compat.v1 as tf1
+
+            transcripts = list()
+            audios = list()
+            for filename, sample in self.samples.items():
+                file_path = get_file(filename, sample["uri"])
+                _, audio = read(file_path)
+                audios.append(audio)
+                transcripts.append(sample["transcript"])
+
+            audio_batch = np.array(audios, dtype=object)
+
+            lingvo = TensorFlowLingvoASR()
+            prediction = lingvo.predict(audio_batch, batch_size=1)
+            assert prediction[0] == transcripts[0]
+        except ARTTestException as e:
+            art_warning(e)
+
+    @pytest.mark.skip_module("lingvo")
+    @pytest.mark.skip_framework("pytorch", "tensorflow1", "tensorflow2", "mxnet", "kerastf", "non_dl_frameworks")
+    @pytest.mark.xfail(reason="Known issue that needs further investigation")
+    def test_loss_gradient(self, art_warning):
+        try:
+            import tensorflow.compat.v1 as tf1
+
+            transcripts = list()
+            audios = list()
+            for filename, sample in self.samples.items():
+                file_path = get_file(filename, sample["uri"])
+                _, audio = read(file_path)
+                audios.append(audio)
+                transcripts.append(sample["transcript"])
+
+            audio_batch = np.array(audios, dtype=object)
+            target_batch = np.array(transcripts)
+
+            lingvo = TensorFlowLingvoASR()
+            gradient_batch = lingvo._loss_gradient_per_batch(audio_batch, target_batch)
+            gradient_sequence = lingvo._loss_gradient_per_sequence(audio_batch, target_batch)
+
+            gradient_batch_sum = np.array([np.abs(gb).sum() for gb in gradient_batch], dtype=object)
+            gradient_sequence_sum = np.array([np.abs(gs).sum() for gs in gradient_sequence], dtype=object)
+
+            # test loss gradients per batch and per sequence are the same
+            assert_allclose(gradient_sequence_sum, gradient_batch_sum, rtol=1e-05)
+            # test gradient_batch, gradient_sequence and audios items have same shapes
+            assert (
+                [gb.shape for gb in gradient_batch]
+                == [gs.shape for gs in gradient_sequence]
+                == [a.shape for a in audios]
+            )
+        except ARTTestException as e:
+            art_warning(e)
diff --git a/adversarial-robustness-toolbox/tests/metrics/__init__.py b/adversarial-robustness-toolbox/tests/metrics/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/adversarial-robustness-toolbox/tests/metrics/privacy/__init__.py b/adversarial-robustness-toolbox/tests/metrics/privacy/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/adversarial-robustness-toolbox/tests/metrics/privacy/test_membership_leakage.py b/adversarial-robustness-toolbox/tests/metrics/privacy/test_membership_leakage.py
new file mode 100644
index 0000000..9e81f4c
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/metrics/privacy/test_membership_leakage.py
@@ -0,0 +1,105 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import pytest
+import numpy as np
+import random
+
+from art.metrics import PDTP
+from tests.utils import ARTTestException
+
+logger = logging.getLogger(__name__)
+
+
+@pytest.mark.skip_framework("dl_frameworks")
+def test_membership_leakage_decision_tree(art_warning, decision_tree_estimator, get_iris_dataset):
+    try:
+        classifier = decision_tree_estimator()
+        extra_classifier = decision_tree_estimator()
+        (x_train, y_train), _ = get_iris_dataset
+        prev = classifier.model.tree_
+        leakage = PDTP(classifier, extra_classifier, x_train, y_train)
+        logger.info("Average PDTP leakage: %.2f", (np.average(leakage)))
+        logger.info("Max PDTP leakage: %.2f", (np.max(leakage)))
+        assert classifier.model.tree_ == prev
+        assert np.all(leakage >= 1.0)
+        assert leakage.shape[0] == x_train.shape[0]
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.skip_framework("keras", "kerastf", "tensorflow1", "mxnet")
+def test_membership_leakage_tabular(art_warning, tabular_dl_estimator, get_iris_dataset):
+    try:
+        classifier = tabular_dl_estimator()
+        extra_classifier = tabular_dl_estimator()
+        (x_train, y_train), _ = get_iris_dataset
+        leakage = PDTP(classifier, extra_classifier, x_train, y_train)
+        logger.info("Average PDTP leakage: %.2f", (np.average(leakage)))
+        logger.info("Max PDTP leakage: %.2f", (np.max(leakage)))
+        assert np.all(leakage >= 1.0)
+        assert leakage.shape[0] == x_train.shape[0]
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.skip_framework("keras", "kerastf", "tensorflow1", "mxnet")
+def test_membership_leakage_image(art_warning, image_dl_estimator, get_default_mnist_subset):
+    try:
+        classifier, _ = image_dl_estimator()
+        extra_classifier, _ = image_dl_estimator()
+        (x_train, y_train), _ = get_default_mnist_subset
+        indexes = random.sample(range(x_train.shape[0]), 100)
+        leakage = PDTP(classifier, extra_classifier, x_train, y_train, indexes=indexes, num_iter=1)
+        logger.info("Average PDTP leakage: %.2f", (np.average(leakage)))
+        logger.info("Max PDTP leakage: %.2f", (np.max(leakage)))
+        assert np.all(leakage >= 1.0)
+        assert leakage.shape[0] == len(indexes)
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.skip_framework("keras", "kerastf", "tensorflow1", "mxnet")
+def test_errors(art_warning, tabular_dl_estimator, get_iris_dataset, image_data_generator):
+    try:
+        classifier = tabular_dl_estimator()
+        not_classifier = image_data_generator()
+        (x_train, y_train), (x_test, y_test) = get_iris_dataset
+        with pytest.raises(ValueError):
+            PDTP(not_classifier, classifier, x_train, y_train)
+        with pytest.raises(ValueError):
+            PDTP(classifier, not_classifier, x_train, y_train)
+        with pytest.raises(ValueError):
+            PDTP(classifier, classifier, np.delete(x_train, 1, 1), y_train)
+        with pytest.raises(ValueError):
+            PDTP(classifier, classifier, x_train, y_test)
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.skip_framework("pytorch", "tensorflow", "scikitlearn")
+def test_not_implemented(art_warning, tabular_dl_estimator, get_iris_dataset, image_data_generator):
+    try:
+        classifier = tabular_dl_estimator()
+        (x_train, y_train), _ = get_iris_dataset
+        with pytest.raises(ValueError):
+            PDTP(classifier, classifier, x_train, y_train)
+    except ARTTestException as e:
+        art_warning(e)
diff --git a/adversarial-robustness-toolbox/tests/metrics/test_gradient_check.py b/adversarial-robustness-toolbox/tests/metrics/test_gradient_check.py
new file mode 100644
index 0000000..b3163a1
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/metrics/test_gradient_check.py
@@ -0,0 +1,72 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import unittest
+
+import numpy as np
+
+from art.metrics.gradient_check import loss_gradient_check
+from art.utils import load_mnist
+
+from tests.utils import TestBase, master_seed, get_image_classifier_kr
+
+logger = logging.getLogger(__name__)
+
+BATCH_SIZE = 10
+NB_TRAIN = 100
+NB_TEST = 100
+
+
+class TestGradientCheck(TestBase):
+    def setUp(self):
+        master_seed(seed=42)
+        super().setUp()
+
+    def test_loss_gradient_check(self):
+        (x_train, y_train), (x_test, y_test), _, _ = load_mnist()
+        x_train, y_train = x_train[:NB_TRAIN], y_train[:NB_TRAIN]
+        x_test, y_test = x_test[:NB_TEST], y_test[:NB_TEST]
+
+        # Get classifier and train like normal
+        classifier = get_image_classifier_kr()
+        classifier.fit(x_train, y_train, batch_size=BATCH_SIZE, nb_epochs=2)
+
+        # Now check that the function detects bad gradients
+        # Set the weights of the convolution layer to zero
+        weights = classifier._model.layers[0].get_weights()
+        new_weights = [np.zeros(w.shape) for w in weights]
+        classifier._model.layers[0].set_weights(new_weights)
+        is_bad = loss_gradient_check(classifier, x_test, y_test)
+
+        self.assertTrue(np.all(np.any(is_bad, 1)))
+
+        # Set the weights of the convolution layer to nan
+        weights = classifier._model.layers[0].get_weights()
+        new_weights = [np.empty(w.shape) for w in weights]
+        for i in range(len(new_weights)):
+            new_weights[i][:] = np.nan
+        classifier._model.layers[0].set_weights(new_weights)
+        is_bad = loss_gradient_check(classifier, x_test, y_test)
+
+        self.assertTrue(np.all(np.any(is_bad, 1)))
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/adversarial-robustness-toolbox/tests/metrics/test_metrics.py b/adversarial-robustness-toolbox/tests/metrics/test_metrics.py
new file mode 100644
index 0000000..d4d6de8
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/metrics/test_metrics.py
@@ -0,0 +1,402 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import unittest
+
+import numpy as np
+import tensorflow as tf
+import torch.nn as nn
+import torch.nn.functional as f
+import torch.optim as optim
+
+from art.estimators.classification.keras import KerasClassifier
+from art.estimators.classification.pytorch import PyTorchClassifier
+from art.estimators.classification.tensorflow import TensorFlowClassifier
+from art.metrics.metrics import empirical_robustness, clever_t, clever_u, clever, loss_sensitivity, wasserstein_distance
+from art.utils import load_mnist
+
+from tests.utils import master_seed
+
+logger = logging.getLogger(__name__)
+
+BATCH_SIZE = 10
+NB_TRAIN = 100
+NB_TEST = 100
+
+R_L1 = 40
+R_L2 = 2
+R_LI = 0.1
+
+
+class TestMetrics(unittest.TestCase):
+    def setUp(self):
+        master_seed(seed=42)
+
+    def test_emp_robustness_mnist(self):
+        (x_train, y_train), (_, _), _, _ = load_mnist()
+        x_train, y_train = x_train[:NB_TRAIN], y_train[:NB_TRAIN]
+
+        # Get classifier
+        classifier = self._cnn_mnist_k([28, 28, 1])
+        classifier.fit(x_train, y_train, batch_size=BATCH_SIZE, nb_epochs=2, verbose=0)
+
+        # Compute minimal perturbations
+        params = {"eps_step": 1.0, "eps": 1.0}
+        emp_robust = empirical_robustness(classifier, x_train, str("fgsm"), params)
+        self.assertAlmostEqual(emp_robust, 1.000369094488189, 3)
+
+        params = {"eps_step": 0.1, "eps": 0.2}
+        emp_robust = empirical_robustness(classifier, x_train, str("fgsm"), params)
+        self.assertLessEqual(emp_robust, 0.65)
+
+    def test_loss_sensitivity(self):
+        (x_train, y_train), (_, _), _, _ = load_mnist()
+        x_train, y_train = x_train[:NB_TRAIN], y_train[:NB_TRAIN]
+
+        # Get classifier
+        classifier = self._cnn_mnist_k([28, 28, 1])
+        classifier.fit(x_train, y_train, batch_size=BATCH_SIZE, nb_epochs=2, verbose=0)
+
+        l = loss_sensitivity(classifier, x_train, y_train)
+        self.assertGreaterEqual(l, 0)
+
+    # def testNearestNeighborDist(self):
+    #     # Get MNIST
+    #     (x_train, y_train), (_, _), _, _ = load_mnist()
+    #     x_train, y_train = x_train[:NB_TRAIN], y_train[:NB_TRAIN]
+    #
+    #     # Get classifier
+    #     classifier = self._cnn_mnist_k([28, 28, 1])
+    #     classifier.fit(x_train, y_train, batch_size=BATCH_SIZE, nb_epochs=2)
+    #
+    #     dist = nearest_neighbour_dist(classifier, x_train, x_train, str('fgsm'))
+    #     self.assertGreaterEqual(dist, 0)
+
+    @staticmethod
+    def _cnn_mnist_k(input_shape):
+        import tensorflow as tf
+
+        tf_version = [int(v) for v in tf.__version__.split(".")]
+        if tf_version[0] == 2 and tf_version[1] >= 3:
+            is_tf23_keras24 = True
+            tf.compat.v1.disable_eager_execution()
+            from tensorflow import keras
+            from tensorflow.keras.models import Sequential
+            from tensorflow.keras.layers import Dense, Flatten, Conv2D, MaxPooling2D
+        else:
+            is_tf23_keras24 = False
+            import keras
+            from keras.models import Sequential
+            from keras.layers import Dense, Flatten, Conv2D, MaxPooling2D
+
+        # Create simple CNN
+        model = Sequential()
+        model.add(Conv2D(4, kernel_size=(5, 5), activation="relu", input_shape=input_shape))
+        model.add(MaxPooling2D(pool_size=(2, 2)))
+        model.add(Flatten())
+        model.add(Dense(10, activation="softmax"))
+
+        model.compile(
+            loss=keras.losses.categorical_crossentropy, optimizer=keras.optimizers.Adam(lr=0.01), metrics=["accuracy"]
+        )
+
+        classifier = KerasClassifier(model=model, clip_values=(0, 1), use_logits=False)
+        return classifier
+
+    @staticmethod
+    def _create_tfclassifier():
+        """
+        To create a simple TensorFlowClassifier for testing.
+        :return:
+        """
+        import tensorflow as tf
+
+        # Define input and output placeholders
+        input_ph = tf.placeholder(tf.float32, shape=[None, 28, 28, 1])
+        labels_ph = tf.placeholder(tf.int32, shape=[None, 10])
+
+        # Define the TensorFlow graph
+        conv = tf.layers.conv2d(input_ph, 4, 5, activation=tf.nn.relu)
+        conv = tf.layers.max_pooling2d(conv, 2, 2)
+        fc = tf.layers.flatten(conv)
+
+        # Logits layer
+        logits = tf.layers.dense(fc, 10)
+
+        # Train operator
+        loss = tf.reduce_mean(tf.losses.softmax_cross_entropy(logits=logits, onehot_labels=labels_ph))
+        optimizer = tf.train.AdamOptimizer(learning_rate=0.01)
+        train = optimizer.minimize(loss)
+
+        # TensorFlow session and initialization
+        sess = tf.Session()
+        sess.run(tf.global_variables_initializer())
+
+        # Create the classifier
+        tfc = TensorFlowClassifier(
+            input_ph=input_ph,
+            output=logits,
+            labels_ph=labels_ph,
+            train=train,
+            loss=loss,
+            learning=None,
+            sess=sess,
+            clip_values=(0, 1),
+        )
+
+        return tfc
+
+    @staticmethod
+    def _create_krclassifier():
+        """
+        To create a simple KerasClassifier for testing.
+        :return:
+        """
+        import tensorflow as tf
+
+        tf_version = [int(v) for v in tf.__version__.split(".")]
+        if tf_version[0] == 2 and tf_version[1] >= 3:
+            # is_tf23_keras24 = True
+            tf.compat.v1.disable_eager_execution()
+            from tensorflow import keras
+            from tensorflow.keras.models import Sequential
+            from tensorflow.keras.layers import Dense, Flatten, Conv2D, MaxPooling2D
+        else:
+            import keras
+            from keras.models import Sequential
+            from keras.layers import Dense, Flatten, Conv2D, MaxPooling2D
+
+        # Create simple CNN
+        model = Sequential()
+        model.add(Conv2D(4, kernel_size=(5, 5), activation="relu", input_shape=(28, 28, 1)))
+        model.add(MaxPooling2D(pool_size=(2, 2)))
+        model.add(Flatten())
+        model.add(Dense(10, activation="softmax"))
+
+        model.compile(
+            loss=keras.losses.categorical_crossentropy, optimizer=keras.optimizers.Adam(lr=0.01), metrics=["accuracy"]
+        )
+
+        # Get the classifier
+        krc = KerasClassifier(model=model, clip_values=(0, 1), use_logits=False)
+
+        return krc
+
+    @staticmethod
+    def _create_ptclassifier():
+        """
+        To create a simple PyTorchClassifier for testing.
+        :return:
+        """
+
+        class Model(nn.Module):
+            def __init__(self):
+                super(Model, self).__init__()
+                self.conv = nn.Conv2d(1, 16, 5)
+                self.pool = nn.MaxPool2d(2, 2)
+                self.fc = nn.Linear(2304, 10)
+
+            def forward(self, x):
+                x = self.pool(f.relu(self.conv(x)))
+                x = x.view(-1, 2304)
+                logit_output = self.fc(x)
+
+                return logit_output
+
+        # Define the network
+        model = Model()
+
+        # Define a loss function and optimizer
+        loss_fn = nn.CrossEntropyLoss()
+        optimizer = optim.Adam(model.parameters(), lr=0.01)
+
+        # Get classifier
+        ptc = PyTorchClassifier(
+            model=model, loss=loss_fn, optimizer=optimizer, input_shape=(1, 28, 28), nb_classes=10, clip_values=(0, 1)
+        )
+
+        return ptc
+
+    @unittest.skipIf(tf.__version__[0] == "2", reason="Skip unittests for TensorFlow v2.")
+    def test_2_clever_tf(self):
+        """
+        Test with TensorFlow.
+        :return:
+        """
+        # Get MNIST
+        batch_size, nb_train, nb_test = 100, 1000, 10
+        (x_train, y_train), (x_test, y_test), _, _ = load_mnist()
+        x_train, y_train = x_train[:nb_train], y_train[:nb_train]
+        x_test, y_test = x_test[:nb_test], y_test[:nb_test]
+
+        # Get the classifier
+        tfc = self._create_tfclassifier()
+        tfc.fit(x_train, y_train, batch_size=batch_size, nb_epochs=1)
+
+        # TODO Need to configure r
+        # Test targeted clever
+        res0 = clever_t(tfc, x_test[-1], 2, 10, 5, R_L1, norm=1, pool_factor=3)
+        res1 = clever_t(tfc, x_test[-1], 2, 10, 5, R_L2, norm=2, pool_factor=3)
+        res2 = clever_t(tfc, x_test[-1], 2, 10, 5, R_LI, norm=np.inf, pool_factor=3)
+        logger.info("Targeted TensorFlow: %f %f %f", res0, res1, res2)
+        self.assertNotEqual(res0, res1)
+        self.assertNotEqual(res1, res2)
+        self.assertNotEqual(res2, res0)
+
+        # Test untargeted clever
+        res0 = clever_u(tfc, x_test[-1], 10, 5, R_L1, norm=1, pool_factor=3)
+        res1 = clever_u(tfc, x_test[-1], 10, 5, R_L2, norm=2, pool_factor=3)
+        res2 = clever_u(tfc, x_test[-1], 10, 5, R_LI, norm=np.inf, pool_factor=3)
+        logger.info("Untargeted TensorFlow: %f %f %f", res0, res1, res2)
+        self.assertNotEqual(res0, res1)
+        self.assertNotEqual(res1, res2)
+        self.assertNotEqual(res2, res0)
+
+    def test_clever_kr(self):
+        """
+        Test with keras.
+        :return:
+        """
+        # Get MNIST
+        batch_size, nb_train, nb_test = 100, 1000, 10
+        (x_train, y_train), (x_test, y_test), _, _ = load_mnist()
+        x_train, y_train = x_train[:nb_train], y_train[:nb_train]
+        x_test, y_test = x_test[:nb_test], y_test[:nb_test]
+
+        # Get the classifier
+        krc = self._create_krclassifier()
+        krc.fit(x_train, y_train, batch_size=batch_size, nb_epochs=1, verbose=0)
+
+        # Test targeted clever
+        res0 = clever_t(krc, x_test[-1], 2, 10, 5, R_L1, norm=1, pool_factor=3)
+        res1 = clever_t(krc, x_test[-1], 2, 10, 5, R_L2, norm=2, pool_factor=3)
+        res2 = clever_t(krc, x_test[-1], 2, 10, 5, R_LI, norm=np.inf, pool_factor=3)
+        logger.info("Targeted Keras: %f %f %f", res0, res1, res2)
+        self.assertNotEqual(res0, res1)
+        self.assertNotEqual(res1, res2)
+        self.assertNotEqual(res2, res0)
+
+        # Test untargeted clever
+        res0 = clever_u(krc, x_test[-1], 10, 5, R_L1, norm=1, pool_factor=3)
+        res1 = clever_u(krc, x_test[-1], 10, 5, R_L2, norm=2, pool_factor=3)
+        res2 = clever_u(krc, x_test[-1], 10, 5, R_LI, norm=np.inf, pool_factor=3)
+        logger.info("Untargeted Keras: %f %f %f", res0, res1, res2)
+        self.assertNotEqual(res0, res1)
+        self.assertNotEqual(res1, res2)
+        self.assertNotEqual(res2, res0)
+
+    def test_3_clever_pt(self):
+        """
+        Test with pytorch.
+        :return:
+        """
+        # Get MNIST
+        batch_size, nb_train, nb_test = 100, 1000, 10
+        (x_train, y_train), (x_test, y_test), _, _ = load_mnist()
+        x_train, y_train = x_train[:nb_train], y_train[:nb_train]
+        x_test, y_test = x_test[:nb_test], y_test[:nb_test]
+        x_train = np.swapaxes(x_train, 1, 3).astype(np.float32)
+        x_test = np.swapaxes(x_test, 1, 3).astype(np.float32)
+
+        # Get the classifier
+        ptc = self._create_ptclassifier()
+        ptc.fit(x_train, y_train, batch_size=batch_size, nb_epochs=1)
+
+        # Test targeted clever
+        res0 = clever_t(ptc, x_test[-1], 2, 10, 5, R_L1, norm=1, pool_factor=3)
+        res1 = clever_t(ptc, x_test[-1], 2, 10, 5, R_L2, norm=2, pool_factor=3)
+        res2 = clever_t(ptc, x_test[-1], 2, 10, 5, R_LI, norm=np.inf, pool_factor=3)
+        logger.info("Targeted PyTorch: %f %f %f", res0, res1, res2)
+        self.assertNotEqual(res0, res1)
+        self.assertNotEqual(res1, res2)
+        self.assertNotEqual(res2, res0)
+
+        # Test untargeted clever
+        res0 = clever_u(ptc, x_test[-1], 10, 5, R_L1, norm=1, pool_factor=3)
+        res1 = clever_u(ptc, x_test[-1], 10, 5, R_L2, norm=2, pool_factor=3)
+        res2 = clever_u(ptc, x_test[-1], 10, 5, R_LI, norm=np.inf, pool_factor=3)
+        logger.info("Untargeted PyTorch: %f %f %f", res0, res1, res2)
+        self.assertNotEqual(res0, res1)
+        self.assertNotEqual(res1, res2)
+        self.assertNotEqual(res2, res0)
+
+    def test_clever_l2_no_target(self):
+        batch_size = 100
+        (x_train, y_train), (x_test, _), _, _ = load_mnist()
+
+        # Get the classifier
+        krc = self._create_krclassifier()
+        krc.fit(x_train, y_train, batch_size=batch_size, nb_epochs=2, verbose=0)
+
+        scores = clever(krc, x_test[0], 5, 5, 3, 2, target=None, c_init=1, pool_factor=10)
+        logger.info("Clever scores for n-1 classes: %s %s", str(scores), str(scores.shape))
+        self.assertEqual(scores.shape, (krc.nb_classes - 1,))
+
+    def test_clever_l2_no_target_sorted(self):
+        batch_size = 100
+        (x_train, y_train), (x_test, _), _, _ = load_mnist()
+
+        # Get the classifier
+        krc = self._create_krclassifier()
+        krc.fit(x_train, y_train, batch_size=batch_size, nb_epochs=2, verbose=0)
+
+        scores = clever(krc, x_test[0], 5, 5, 3, 2, target=None, target_sort=True, c_init=1, pool_factor=10)
+        logger.info("Clever scores for n-1 classes: %s %s", str(scores), str(scores.shape))
+        # Should approx. be in decreasing value
+        self.assertEqual(scores.shape, (krc.nb_classes - 1,))
+
+    def test_clever_l2_same_target(self):
+        batch_size = 100
+        (x_train, y_train), (x_test, _), _, _ = load_mnist()
+
+        # Get the classifier
+        krc = self._create_krclassifier()
+        krc.fit(x_train, y_train, batch_size=batch_size, nb_epochs=2, verbose=0)
+
+        scores = clever(krc, x_test[0], 5, 5, 3, 2, target=np.argmax(krc.predict(x_test[:1])), c_init=1, pool_factor=10)
+        self.assertIsNone(scores[0], msg="Clever scores for the predicted class should be `None`.")
+
+    def test_1_wasserstein_distance(self):
+        nb_train = 1000
+        nb_test = 100
+        batch_size = 3
+        (x_train, y_train), (x_test, y_test), _, _ = load_mnist()
+
+        x_train = x_train[0:nb_train]
+        x_test = x_test[0:nb_test]
+        weights = np.ones_like(x_train)
+
+        wd_0 = wasserstein_distance(x_train[:batch_size], x_train[:batch_size])
+        wd_1 = wasserstein_distance(x_train[:batch_size], x_test[:batch_size])
+        wd_2 = wasserstein_distance(
+            x_train[:batch_size], x_train[:batch_size], weights[:batch_size], weights[:batch_size]
+        )
+
+        np.testing.assert_array_equal(wd_0, np.asarray([0.0, 0.0, 0.0]))
+        np.testing.assert_array_almost_equal(wd_1, np.asarray([0.04564, 0.01235, 0.04787]), decimal=4)
+
+        np.testing.assert_array_equal(x_train.shape, np.asarray([nb_train, 28, 28, 1]))
+        np.testing.assert_array_equal(x_test.shape, np.asarray([nb_test, 28, 28, 1]))
+
+        np.testing.assert_array_equal(wd_2, np.asarray([0.0, 0.0, 0.0]))
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/adversarial-robustness-toolbox/tests/metrics/test_verification_decision_trees.py b/adversarial-robustness-toolbox/tests/metrics/test_verification_decision_trees.py
new file mode 100644
index 0000000..561aee0
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/metrics/test_verification_decision_trees.py
@@ -0,0 +1,153 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2019
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import unittest
+
+from xgboost import XGBClassifier
+import lightgbm
+import numpy as np
+from sklearn.ensemble import ExtraTreesClassifier, RandomForestClassifier, GradientBoostingClassifier
+
+from art.estimators.classification.xgboost import XGBoostClassifier
+from art.estimators.classification.lightgbm import LightGBMClassifier
+from art.estimators.classification.scikitlearn import SklearnClassifier
+from art.utils import load_dataset
+from art.metrics.verification_decisions_trees import RobustnessVerificationTreeModelsCliqueMethod
+
+from tests.utils import master_seed
+
+logger = logging.getLogger(__name__)
+
+NB_TRAIN = 100
+NB_TEST = 100
+
+
+class TestMetricsTrees(unittest.TestCase):
+    @classmethod
+    def setUpClass(cls):
+        (x_train, y_train), (x_test, y_test), _, _ = load_dataset("mnist")
+
+        cls.n_classes = 10
+        cls.n_features = 28 * 28
+        n_train = x_train.shape[0]
+        n_test = x_test.shape[0]
+        x_train = x_train.reshape((n_train, cls.n_features))
+        x_test = x_test.reshape((n_test, cls.n_features))
+
+        cls.x_train = x_train[:NB_TRAIN]
+        cls.y_train = y_train[:NB_TRAIN]
+        cls.x_test = x_test[:NB_TEST]
+        cls.y_test = y_test[:NB_TEST]
+
+    @classmethod
+    def setUp(cls):
+        master_seed(seed=42)
+
+    def test_XGBoost(self):
+        model = XGBClassifier(n_estimators=4, max_depth=6)
+        model.fit(self.x_train, np.argmax(self.y_train, axis=1))
+
+        classifier = XGBoostClassifier(model=model, nb_features=self.n_features, nb_classes=self.n_classes)
+
+        rt = RobustnessVerificationTreeModelsCliqueMethod(classifier=classifier)
+        average_bound, verified_error = rt.verify(
+            x=self.x_test, y=self.y_test, eps_init=0.3, nb_search_steps=10, max_clique=2, max_level=2
+        )
+
+        self.assertEqual(average_bound, 0.03186914062500001)
+        self.assertEqual(verified_error, 0.99)
+
+    def test_LightGBM(self):
+        train_data = lightgbm.Dataset(self.x_train, label=np.argmax(self.y_train, axis=1))
+        test_data = lightgbm.Dataset(self.x_test, label=np.argmax(self.y_test, axis=1))
+
+        parameters = {
+            "objective": "multiclass",
+            "num_class": self.n_classes,
+            "metric": "multi_logloss",
+            "is_unbalance": "true",
+            "boosting": "gbdt",
+            "num_leaves": 5,
+            "feature_fraction": 0.5,
+            "bagging_fraction": 0.5,
+            "bagging_freq": 0,
+            "learning_rate": 0.05,
+            "verbose": 0,
+        }
+
+        model = lightgbm.train(
+            parameters, train_data, valid_sets=test_data, num_boost_round=2, early_stopping_rounds=10
+        )
+
+        classifier = LightGBMClassifier(model=model)
+
+        rt = RobustnessVerificationTreeModelsCliqueMethod(classifier=classifier)
+        average_bound, verified_error = rt.verify(
+            x=self.x_test, y=self.y_test, eps_init=0.3, nb_search_steps=10, max_clique=2, max_level=2
+        )
+
+        self.assertEqual(average_bound, 0.047742187500000005)
+        self.assertEqual(verified_error, 0.94)
+
+    def test_GradientBoosting(self):
+        model = GradientBoostingClassifier(n_estimators=4, max_depth=6)
+        model.fit(self.x_train, np.argmax(self.y_train, axis=1))
+
+        classifier = SklearnClassifier(model=model)
+
+        rt = RobustnessVerificationTreeModelsCliqueMethod(classifier=classifier)
+        average_bound, verified_error = rt.verify(
+            x=self.x_test, y=self.y_test, eps_init=0.3, nb_search_steps=10, max_clique=2, max_level=2
+        )
+
+        self.assertAlmostEqual(average_bound, 0.009, delta=0.0002)
+        self.assertEqual(verified_error, 1.0)
+
+    def test_RandomForest(self):
+        model = RandomForestClassifier(n_estimators=4, max_depth=6)
+        model.fit(self.x_train, np.argmax(self.y_train, axis=1))
+
+        classifier = SklearnClassifier(model=model)
+
+        rt = RobustnessVerificationTreeModelsCliqueMethod(classifier=classifier)
+        average_bound, verified_error = rt.verify(
+            x=self.x_test, y=self.y_test, eps_init=0.3, nb_search_steps=10, max_clique=2, max_level=2
+        )
+
+        self.assertEqual(average_bound, 0.016482421874999993)
+        self.assertEqual(verified_error, 1.0)
+
+    def test_ExtraTrees(self):
+        model = ExtraTreesClassifier(n_estimators=4, max_depth=6)
+        model.fit(self.x_train, np.argmax(self.y_train, axis=1))
+
+        classifier = SklearnClassifier(model=model)
+
+        rt = RobustnessVerificationTreeModelsCliqueMethod(classifier=classifier)
+        average_bound, verified_error = rt.verify(
+            x=self.x_test, y=self.y_test, eps_init=0.3, nb_search_steps=10, max_clique=2, max_level=2
+        )
+
+        self.assertEqual(average_bound, 0.05406445312499999)
+        self.assertEqual(verified_error, 0.96)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/adversarial-robustness-toolbox/tests/preprocessing/__init__.py b/adversarial-robustness-toolbox/tests/preprocessing/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/adversarial-robustness-toolbox/tests/preprocessing/audio/__init__.py b/adversarial-robustness-toolbox/tests/preprocessing/audio/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/adversarial-robustness-toolbox/tests/preprocessing/audio/test_l_filter.json b/adversarial-robustness-toolbox/tests/preprocessing/audio/test_l_filter.json
new file mode 100644
index 0000000..c4da17d
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/preprocessing/audio/test_l_filter.json
@@ -0,0 +1,282 @@
+{
+    "test_audio_filter[False]": [
+        [
+            -0.0010376293,
+            -0.0010681478,
+            -0.0010986663,
+            -0.0011291848,
+            -0.0011291848,
+            -0.0011291848,
+            -0.0011902219,
+            -0.0011597034,
+            -0.0011902219,
+            -0.0011291848,
+            -0.0011291848,
+            -0.0010681478,
+            -0.00091555528
+        ],
+        [
+            -0.00018311106,
+            -0.00012207404,
+            -6.1037019e-05,
+            0.0,
+            3.0518509e-05,
+            0.0,
+            -3.0518509e-05,
+            0.0,
+            0.0,
+            9.1555528e-05,
+            0.00021362957,
+            0.0003357036,
+            0.00042725913,
+            0.00045777764,
+            -0.00018311106
+        ],
+        [
+            -0.00082399976,
+            -0.00070192572,
+            -0.00054933317,
+            -0.00042725913,
+            -0.00036622211,
+            -0.00027466659,
+            -0.00021362957,
+            0.00054933317,
+            0.00057985168,
+            0.00061037019,
+            0.00067140721,
+            0.00070192572,
+            0.00067140721,
+            -0.00015259255
+        ],
+        [
+            -0.000103762934,
+            -0.000303964334,
+            -0.000158207942,
+            0.000130204164,
+            0.000140768461,
+            4.04138154e-06,
+            -0.000100820056,
+            -6.26970723e-05,
+            2.87954499e-05,
+            5.93094592e-05,
+            2.27166784e-05,
+            -1.3271565e-05,
+            4.35872198e-06,
+            4.02366786e-05,
+            -9.17821035e-06,
+            -4.9518876e-05,
+            -2.67004216e-05,
+            5.93773893e-06,
+            2.11202023e-05,
+            6.83116525e-07,
+            -2.4038749e-05,
+            -1.23528616e-05,
+            1.12255238e-05,
+            2.44062358e-05,
+            1.74439847e-05,
+            2.00138729e-05
+        ],
+        [
+            -1.8311106e-05,
+            -4.6998506e-05,
+            -2.0142215e-06,
+            5.8247628e-05,
+            4.7149268e-05,
+            -3.0724209e-06,
+            -4.3240292e-05,
+            -2.5821355e-05,
+            1.9835043e-05,
+            3.8318274e-05,
+            4.0220264e-05,
+            4.3689197e-05,
+            3.8430262e-05,
+            2.1895425e-05,
+            -6.7816509e-05,
+            -0.00020132199,
+            -9.0355054e-05,
+            0.00012097351,
+            0.00013216017,
+            2.0204312e-05,
+            -7.1746785e-05,
+            -5.7854388e-05,
+            7.0203455e-06,
+            4.8404847e-05,
+            3.145442e-05,
+            1.9199029e-05,
+            3.6422553e-05,
+            4.820884e-05,
+            3.1506053e-05,
+            -6.8804489e-05
+        ],
+        [
+            -8.23999726e-05,
+            -0.000226752527,
+            -6.55232361e-05,
+            0.000189938044,
+            0.000164608602,
+            4.64848699e-06,
+            -8.00448834e-05,
+            5.36849875e-05,
+            0.000260933652,
+            0.000154619047,
+            -9.38530357e-05,
+            -0.000113907212,
+            -3.98988004e-06,
+            -1.33394606e-05,
+            -0.00027235056,
+            -0.000321181869,
+            3.67591638e-05,
+            0.000284018839,
+            0.000162287542,
+            -6.42566083e-05,
+            -0.000110090376,
+            7.82894858e-05,
+            0.000295048871,
+            0.000155844362,
+            -0.000114051938,
+            -0.000125901017,
+            2.77904201e-06,
+            -2.33865194e-06
+        ]
+    ],
+    "test_audio_filter[True]": [
+        [
+            -0.0010376293,
+            -0.0010681478,
+            -0.0010986663,
+            -0.0011291848,
+            -0.0011291848,
+            -0.0011291848,
+            -0.0011902219,
+            -0.0011597034,
+            -0.0011902219,
+            -0.0011291848,
+            -0.0011291848,
+            -0.0010681478,
+            -0.00091555528
+        ],
+        [
+            -0.00018311106,
+            -0.00012207404,
+            -6.1037019e-05,
+            0.0,
+            3.0518509e-05,
+            0.0,
+            -3.0518509e-05,
+            0.0,
+            0.0,
+            9.1555528e-05,
+            0.00021362957,
+            0.0003357036,
+            0.00042725913,
+            0.00045777764,
+            -0.00018311106
+        ],
+        [
+            -0.00082399976,
+            -0.00070192572,
+            -0.00054933317,
+            -0.00042725913,
+            -0.00036622211,
+            -0.00027466659,
+            -0.00021362957,
+            0.00054933317,
+            0.00057985168,
+            0.00061037019,
+            0.00067140721,
+            0.00070192572,
+            0.00067140721,
+            -0.00015259255
+        ],
+        [
+            -0.00010376293,
+            -0.00031434064,
+            -0.00021973327,
+            -1.8311101e-05,
+            -1.5259249e-05,
+            -6.1036999e-06,
+            -6.1037099e-06,
+            -1.525928e-05,
+            -6.1037199e-06,
+            3.0518599e-06,
+            1.220743e-05,
+            1.8311121e-05,
+            3.3570352e-05,
+            4.5777753e-05,
+            -9.1555521e-06,
+            -3.6622205e-05,
+            -1.8311101e-05,
+            -1.5259249e-05,
+            -6.1036999e-06,
+            -6.1037099e-06,
+            -1.525928e-05,
+            -6.1037199e-06,
+            3.0518599e-06,
+            1.220743e-05,
+            1.8311121e-05,
+            3.3570352e-05
+        ],
+        [
+            -1.8311106e-05,
+            -4.8829617e-05,
+            -1.2207404e-05,
+            3.6622212e-05,
+            3.3570363e-05,
+            1.8311106e-05,
+            -6.1037017e-06,
+            -1.2207403e-05,
+            3.0518509e-06,
+            1.5259255e-05,
+            3.9674062e-05,
+            6.7140718e-05,
+            7.0192567e-05,
+            5.4933316e-05,
+            -3.6622212e-05,
+            -0.00018616291,
+            -0.00012207404,
+            2.4414809e-05,
+            3.6622212e-05,
+            3.3570363e-05,
+            1.8311106e-05,
+            -6.1037017e-06,
+            -1.2207403e-05,
+            3.0518509e-06,
+            1.5259255e-05,
+            3.9674062e-05,
+            6.7140718e-05,
+            7.0192567e-05,
+            5.4933316e-05,
+            -3.6622212e-05
+        ],
+        [
+            -8.2399973e-05,
+            -0.00023499252,
+            -0.00011291848,
+            8.239998e-05,
+            7.3244424e-05,
+            5.1881467e-05,
+            4.5777761e-05,
+            0.00011291848,
+            0.00024414808,
+            0.00016479995,
+            2.1362957e-05,
+            2.7466658e-05,
+            1.8311106e-05,
+            -8.5451829e-05,
+            -0.00032044435,
+            -0.00035401472,
+            -8.2399973e-05,
+            8.239998e-05,
+            7.3244424e-05,
+            5.1881467e-05,
+            4.5777761e-05,
+            0.00011291848,
+            0.00024414808,
+            0.00016479995,
+            2.1362957e-05,
+            2.7466658e-05,
+            1.8311106e-05,
+            -8.5451829e-05
+        ]
+    ]
+}
\ No newline at end of file
diff --git a/adversarial-robustness-toolbox/tests/preprocessing/audio/test_l_filter.py b/adversarial-robustness-toolbox/tests/preprocessing/audio/test_l_filter.py
new file mode 100644
index 0000000..7d5fdb3
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/preprocessing/audio/test_l_filter.py
@@ -0,0 +1,117 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+
+import numpy as np
+import pytest
+
+from art.preprocessing.audio import LFilter
+from tests.utils import ARTTestException
+
+logger = logging.getLogger(__name__)
+
+
+@pytest.mark.framework_agnostic
+@pytest.mark.parametrize("fir_filter", [False, True])
+def test_audio_filter(fir_filter, art_warning, expected_values):
+    try:
+        # Load data for testing
+        expected_data = expected_values()
+
+        x1 = expected_data[0]
+        x2 = expected_data[1]
+        x3 = expected_data[2]
+        result_0 = expected_data[3]
+        result_1 = expected_data[4]
+        result_2 = expected_data[5]
+
+        # Create signal data
+        x = np.array([np.array(x1 * 2), np.array(x2 * 2), np.array(x3 * 2)])
+
+        # Filter params
+        numerator_coef = np.array([0.1, 0.2, -0.1, -0.2])
+
+        if fir_filter:
+            denominator_coef = np.array([1.0])
+        else:
+            denominator_coef = np.array([1.0, 0.1, 0.3, 0.4])
+
+        # Create filter
+        audio_filter = LFilter(numerator_coef=numerator_coef, denominator_coef=denominator_coef)
+
+        # Apply filter
+        result = audio_filter(x)
+
+        # Test
+        assert result[1] is None
+        np.testing.assert_array_almost_equal(result_0, result[0][0], decimal=0)
+        np.testing.assert_array_almost_equal(result_1, result[0][1], decimal=0)
+        np.testing.assert_array_almost_equal(result_2, result[0][2], decimal=0)
+
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.framework_agnostic
+def test_default(art_warning):
+    try:
+        # Small data for testing
+        x = np.array([[0.37, 0.68, 0.63, 0.48, 0.48, 0.18, 0.19]])
+
+        # Create filter
+        audio_filter = LFilter()
+
+        # Apply filter
+        result = audio_filter(x)
+
+        # Test
+        assert result[1] is None
+        np.testing.assert_array_almost_equal(x, result[0], decimal=0)
+
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.framework_agnostic
+def test_triple_clip_values_error(art_warning):
+    try:
+        exc_msg = "`clip_values` should be a tuple of 2 floats containing the allowed data range."
+        with pytest.raises(ValueError, match=exc_msg):
+            LFilter(
+                numerator_coef=np.array([0.1, 0.2, 0.3]),
+                denominator_coef=np.array([0.1, 0.2, 0.3]),
+                clip_values=(0, 1, 2),
+            )
+
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.framework_agnostic
+def test_relation_clip_values_error(art_warning):
+    try:
+        exc_msg = "Invalid `clip_values`: min >= max."
+        with pytest.raises(ValueError, match=exc_msg):
+            LFilter(
+                numerator_coef=np.array([0.1, 0.2, 0.3]), denominator_coef=np.array([0.1, 0.2, 0.3]), clip_values=(1, 0)
+            )
+
+    except ARTTestException as e:
+        art_warning(e)
diff --git a/adversarial-robustness-toolbox/tests/preprocessing/audio/test_l_filter_pytorch.json b/adversarial-robustness-toolbox/tests/preprocessing/audio/test_l_filter_pytorch.json
new file mode 100644
index 0000000..c4da17d
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/preprocessing/audio/test_l_filter_pytorch.json
@@ -0,0 +1,282 @@
+{
+    "test_audio_filter[False]": [
+        [
+            -0.0010376293,
+            -0.0010681478,
+            -0.0010986663,
+            -0.0011291848,
+            -0.0011291848,
+            -0.0011291848,
+            -0.0011902219,
+            -0.0011597034,
+            -0.0011902219,
+            -0.0011291848,
+            -0.0011291848,
+            -0.0010681478,
+            -0.00091555528
+        ],
+        [
+            -0.00018311106,
+            -0.00012207404,
+            -6.1037019e-05,
+            0.0,
+            3.0518509e-05,
+            0.0,
+            -3.0518509e-05,
+            0.0,
+            0.0,
+            9.1555528e-05,
+            0.00021362957,
+            0.0003357036,
+            0.00042725913,
+            0.00045777764,
+            -0.00018311106
+        ],
+        [
+            -0.00082399976,
+            -0.00070192572,
+            -0.00054933317,
+            -0.00042725913,
+            -0.00036622211,
+            -0.00027466659,
+            -0.00021362957,
+            0.00054933317,
+            0.00057985168,
+            0.00061037019,
+            0.00067140721,
+            0.00070192572,
+            0.00067140721,
+            -0.00015259255
+        ],
+        [
+            -0.000103762934,
+            -0.000303964334,
+            -0.000158207942,
+            0.000130204164,
+            0.000140768461,
+            4.04138154e-06,
+            -0.000100820056,
+            -6.26970723e-05,
+            2.87954499e-05,
+            5.93094592e-05,
+            2.27166784e-05,
+            -1.3271565e-05,
+            4.35872198e-06,
+            4.02366786e-05,
+            -9.17821035e-06,
+            -4.9518876e-05,
+            -2.67004216e-05,
+            5.93773893e-06,
+            2.11202023e-05,
+            6.83116525e-07,
+            -2.4038749e-05,
+            -1.23528616e-05,
+            1.12255238e-05,
+            2.44062358e-05,
+            1.74439847e-05,
+            2.00138729e-05
+        ],
+        [
+            -1.8311106e-05,
+            -4.6998506e-05,
+            -2.0142215e-06,
+            5.8247628e-05,
+            4.7149268e-05,
+            -3.0724209e-06,
+            -4.3240292e-05,
+            -2.5821355e-05,
+            1.9835043e-05,
+            3.8318274e-05,
+            4.0220264e-05,
+            4.3689197e-05,
+            3.8430262e-05,
+            2.1895425e-05,
+            -6.7816509e-05,
+            -0.00020132199,
+            -9.0355054e-05,
+            0.00012097351,
+            0.00013216017,
+            2.0204312e-05,
+            -7.1746785e-05,
+            -5.7854388e-05,
+            7.0203455e-06,
+            4.8404847e-05,
+            3.145442e-05,
+            1.9199029e-05,
+            3.6422553e-05,
+            4.820884e-05,
+            3.1506053e-05,
+            -6.8804489e-05
+        ],
+        [
+            -8.23999726e-05,
+            -0.000226752527,
+            -6.55232361e-05,
+            0.000189938044,
+            0.000164608602,
+            4.64848699e-06,
+            -8.00448834e-05,
+            5.36849875e-05,
+            0.000260933652,
+            0.000154619047,
+            -9.38530357e-05,
+            -0.000113907212,
+            -3.98988004e-06,
+            -1.33394606e-05,
+            -0.00027235056,
+            -0.000321181869,
+            3.67591638e-05,
+            0.000284018839,
+            0.000162287542,
+            -6.42566083e-05,
+            -0.000110090376,
+            7.82894858e-05,
+            0.000295048871,
+            0.000155844362,
+            -0.000114051938,
+            -0.000125901017,
+            2.77904201e-06,
+            -2.33865194e-06
+        ]
+    ],
+    "test_audio_filter[True]": [
+        [
+            -0.0010376293,
+            -0.0010681478,
+            -0.0010986663,
+            -0.0011291848,
+            -0.0011291848,
+            -0.0011291848,
+            -0.0011902219,
+            -0.0011597034,
+            -0.0011902219,
+            -0.0011291848,
+            -0.0011291848,
+            -0.0010681478,
+            -0.00091555528
+        ],
+        [
+            -0.00018311106,
+            -0.00012207404,
+            -6.1037019e-05,
+            0.0,
+            3.0518509e-05,
+            0.0,
+            -3.0518509e-05,
+            0.0,
+            0.0,
+            9.1555528e-05,
+            0.00021362957,
+            0.0003357036,
+            0.00042725913,
+            0.00045777764,
+            -0.00018311106
+        ],
+        [
+            -0.00082399976,
+            -0.00070192572,
+            -0.00054933317,
+            -0.00042725913,
+            -0.00036622211,
+            -0.00027466659,
+            -0.00021362957,
+            0.00054933317,
+            0.00057985168,
+            0.00061037019,
+            0.00067140721,
+            0.00070192572,
+            0.00067140721,
+            -0.00015259255
+        ],
+        [
+            -0.00010376293,
+            -0.00031434064,
+            -0.00021973327,
+            -1.8311101e-05,
+            -1.5259249e-05,
+            -6.1036999e-06,
+            -6.1037099e-06,
+            -1.525928e-05,
+            -6.1037199e-06,
+            3.0518599e-06,
+            1.220743e-05,
+            1.8311121e-05,
+            3.3570352e-05,
+            4.5777753e-05,
+            -9.1555521e-06,
+            -3.6622205e-05,
+            -1.8311101e-05,
+            -1.5259249e-05,
+            -6.1036999e-06,
+            -6.1037099e-06,
+            -1.525928e-05,
+            -6.1037199e-06,
+            3.0518599e-06,
+            1.220743e-05,
+            1.8311121e-05,
+            3.3570352e-05
+        ],
+        [
+            -1.8311106e-05,
+            -4.8829617e-05,
+            -1.2207404e-05,
+            3.6622212e-05,
+            3.3570363e-05,
+            1.8311106e-05,
+            -6.1037017e-06,
+            -1.2207403e-05,
+            3.0518509e-06,
+            1.5259255e-05,
+            3.9674062e-05,
+            6.7140718e-05,
+            7.0192567e-05,
+            5.4933316e-05,
+            -3.6622212e-05,
+            -0.00018616291,
+            -0.00012207404,
+            2.4414809e-05,
+            3.6622212e-05,
+            3.3570363e-05,
+            1.8311106e-05,
+            -6.1037017e-06,
+            -1.2207403e-05,
+            3.0518509e-06,
+            1.5259255e-05,
+            3.9674062e-05,
+            6.7140718e-05,
+            7.0192567e-05,
+            5.4933316e-05,
+            -3.6622212e-05
+        ],
+        [
+            -8.2399973e-05,
+            -0.00023499252,
+            -0.00011291848,
+            8.239998e-05,
+            7.3244424e-05,
+            5.1881467e-05,
+            4.5777761e-05,
+            0.00011291848,
+            0.00024414808,
+            0.00016479995,
+            2.1362957e-05,
+            2.7466658e-05,
+            1.8311106e-05,
+            -8.5451829e-05,
+            -0.00032044435,
+            -0.00035401472,
+            -8.2399973e-05,
+            8.239998e-05,
+            7.3244424e-05,
+            5.1881467e-05,
+            4.5777761e-05,
+            0.00011291848,
+            0.00024414808,
+            0.00016479995,
+            2.1362957e-05,
+            2.7466658e-05,
+            1.8311106e-05,
+            -8.5451829e-05
+        ]
+    ]
+}
\ No newline at end of file
diff --git a/adversarial-robustness-toolbox/tests/preprocessing/audio/test_l_filter_pytorch.py b/adversarial-robustness-toolbox/tests/preprocessing/audio/test_l_filter_pytorch.py
new file mode 100644
index 0000000..636082a
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/preprocessing/audio/test_l_filter_pytorch.py
@@ -0,0 +1,128 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+
+import numpy as np
+import pytest
+
+from art.preprocessing.audio import LFilterPyTorch
+from art.config import ART_NUMPY_DTYPE
+from tests.utils import ARTTestException
+
+logger = logging.getLogger(__name__)
+
+
+@pytest.mark.skip_module("torchaudio")
+@pytest.mark.skip_framework("tensorflow", "tensorflow2v1", "keras", "kerastf", "mxnet", "non_dl_frameworks")
+@pytest.mark.parametrize("fir_filter", [False, True])
+def test_audio_filter(fir_filter, art_warning, expected_values):
+    try:
+        # Load data for testing
+        expected_data = expected_values()
+
+        x1 = expected_data[0]
+        x2 = expected_data[1]
+        x3 = expected_data[2]
+        result_0 = expected_data[3]
+        result_1 = expected_data[4]
+        result_2 = expected_data[5]
+
+        # Create signal data
+        x = np.array(
+            [
+                np.array(x1 * 2, dtype=ART_NUMPY_DTYPE),
+                np.array(x2 * 2, dtype=ART_NUMPY_DTYPE),
+                np.array(x3 * 2, dtype=ART_NUMPY_DTYPE),
+            ]
+        )
+
+        # Filter params
+        numerator_coef = np.array([0.1, 0.2, -0.1, -0.2])
+
+        if fir_filter:
+            denominator_coef = np.array([1.0, 0.0, 0.0, 0.0])
+        else:
+            denominator_coef = np.array([1.0, 0.1, 0.3, 0.4])
+
+        # Create filter
+        audio_filter = LFilterPyTorch(numerator_coef=numerator_coef, denominator_coef=denominator_coef)
+
+        # Apply filter
+        result = audio_filter(x)
+
+        # Test
+        assert result[1] is None
+        np.testing.assert_array_almost_equal(result_0, result[0][0], decimal=0)
+        np.testing.assert_array_almost_equal(result_1, result[0][1], decimal=0)
+        np.testing.assert_array_almost_equal(result_2, result[0][2], decimal=0)
+
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.skip_module("torchaudio")
+@pytest.mark.skip_framework("tensorflow", "tensorflow2v1", "keras", "kerastf", "mxnet", "non_dl_frameworks")
+def test_default(art_warning):
+    try:
+        # Small data for testing
+        x = np.array([[0.37, 0.68, 0.63, 0.48, 0.48, 0.18, 0.19]], dtype=ART_NUMPY_DTYPE)
+
+        # Create filter
+        audio_filter = LFilterPyTorch()
+
+        # Apply filter
+        result = audio_filter(x)
+
+        # Test
+        assert result[1] is None
+        np.testing.assert_array_almost_equal(x, result[0], decimal=0)
+
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.skip_module("torchaudio")
+@pytest.mark.skip_framework("tensorflow", "tensorflow2v1", "keras", "kerastf", "mxnet", "non_dl_frameworks")
+def test_triple_clip_values_error(art_warning):
+    try:
+        exc_msg = "`clip_values` should be a tuple of 2 floats containing the allowed data range."
+        with pytest.raises(ValueError, match=exc_msg):
+            LFilterPyTorch(
+                numerator_coef=np.array([0.1, 0.2, 0.3]),
+                denominator_coef=np.array([0.1, 0.2, 0.3]),
+                clip_values=(0, 1, 2),
+            )
+
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.skip_module("torchaudio")
+@pytest.mark.skip_framework("tensorflow", "tensorflow2v1", "keras", "kerastf", "mxnet", "non_dl_frameworks")
+def test_relation_clip_values_error(art_warning):
+    try:
+        exc_msg = "Invalid `clip_values`: min >= max."
+        with pytest.raises(ValueError, match=exc_msg):
+            LFilterPyTorch(
+                numerator_coef=np.array([0.1, 0.2, 0.3]), denominator_coef=np.array([0.1, 0.2, 0.3]), clip_values=(1, 0)
+            )
+
+    except ARTTestException as e:
+        art_warning(e)
diff --git a/adversarial-robustness-toolbox/tests/preprocessing/expectation_over_transformation/__init__.py b/adversarial-robustness-toolbox/tests/preprocessing/expectation_over_transformation/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/adversarial-robustness-toolbox/tests/preprocessing/expectation_over_transformation/natural_corruptions/__init__.py b/adversarial-robustness-toolbox/tests/preprocessing/expectation_over_transformation/natural_corruptions/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/adversarial-robustness-toolbox/tests/preprocessing/expectation_over_transformation/natural_corruptions/test_brightness.py b/adversarial-robustness-toolbox/tests/preprocessing/expectation_over_transformation/natural_corruptions/test_brightness.py
new file mode 100644
index 0000000..709e2d6
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/preprocessing/expectation_over_transformation/natural_corruptions/test_brightness.py
@@ -0,0 +1,147 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2021
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+import logging
+
+import numpy as np
+import pytest
+
+from tests.utils import ARTTestException
+
+logger = logging.getLogger(__name__)
+
+
+@pytest.fixture()
+def fix_get_mnist_subset(get_mnist_dataset):
+    (x_train_mnist, y_train_mnist), (x_test_mnist, y_test_mnist) = get_mnist_dataset
+    n_train = 10
+    n_test = 10
+    yield x_train_mnist[:n_train], y_train_mnist[:n_train], x_test_mnist[:n_test], y_test_mnist[:n_test]
+
+
+@pytest.mark.only_with_platform("pytorch")
+def test_eot_brightness_pytorch(art_warning, fix_get_mnist_subset):
+    try:
+        import torch
+        from art.preprocessing.expectation_over_transformation.natural_corruptions.brightness.pytorch import (
+            EoTBrightnessPyTorch,
+        )
+
+        x_train_mnist, y_train_mnist, _, _ = fix_get_mnist_subset
+        x_train_mnist = np.transpose(x_train_mnist, (0, 2, 3, 1))  # transpose to NHWC
+
+        nb_samples = 3
+
+        eot = EoTBrightnessPyTorch(nb_samples=nb_samples, delta=(0.2, 0.2), clip_values=(0.0, 1.0))
+        x_eot, y_eot = eot.forward(x=torch.from_numpy(x_train_mnist), y=torch.from_numpy(y_train_mnist))
+
+        assert x_eot.shape[0] == nb_samples * x_train_mnist.shape[0]
+        assert y_eot.shape[0] == nb_samples * y_train_mnist.shape[0]
+
+        x_eot_expected = np.array(
+            [
+                0.2,
+                0.2,
+                0.2,
+                0.2,
+                0.2,
+                0.2,
+                0.2,
+                0.2,
+                0.2,
+                0.2,
+                0.2,
+                0.2,
+                0.2,
+                0.5176471,
+                1.0,
+                1.0,
+                1.0,
+                0.6666667,
+                0.29803923,
+                0.2,
+                0.2,
+                0.2,
+                0.2,
+                0.2,
+                0.2,
+                0.2,
+                0.2,
+                0.2,
+            ]
+        )
+
+        np.testing.assert_almost_equal(x_eot.numpy()[0, 14, :, 0], x_eot_expected)
+
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.only_with_platform("tensorflow2")
+def test_eot_brightness_tensorflow_v2(art_warning, fix_get_mnist_subset):
+    try:
+        from art.preprocessing.expectation_over_transformation.natural_corruptions.brightness.tensorflow import (
+            EoTBrightnessTensorFlow,
+        )
+
+        x_train_mnist, y_train_mnist, _, _ = fix_get_mnist_subset
+
+        nb_samples = 3
+
+        eot = EoTBrightnessTensorFlow(nb_samples=nb_samples, delta=(0.2, 0.2), clip_values=(0.0, 1.0))
+        x_eot, y_eot = eot.forward(x=x_train_mnist, y=y_train_mnist)
+
+        assert x_eot.shape[0] == nb_samples * x_train_mnist.shape[0]
+        assert y_eot.shape[0] == nb_samples * y_train_mnist.shape[0]
+
+        x_eot_expected = np.array(
+            [
+                0.2,
+                0.2,
+                0.2,
+                0.2,
+                0.2,
+                0.2,
+                0.2,
+                0.2,
+                0.2,
+                0.2,
+                0.2,
+                0.2,
+                0.2,
+                0.5176471,
+                1.0,
+                1.0,
+                1.0,
+                0.6666667,
+                0.29803923,
+                0.2,
+                0.2,
+                0.2,
+                0.2,
+                0.2,
+                0.2,
+                0.2,
+                0.2,
+                0.2,
+            ]
+        )
+
+        np.testing.assert_almost_equal(x_eot.numpy()[0, 14, :, 0], x_eot_expected)
+
+    except ARTTestException as e:
+        art_warning(e)
diff --git a/adversarial-robustness-toolbox/tests/preprocessing/expectation_over_transformation/natural_corruptions/test_contrast.py b/adversarial-robustness-toolbox/tests/preprocessing/expectation_over_transformation/natural_corruptions/test_contrast.py
new file mode 100644
index 0000000..ed95dd7
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/preprocessing/expectation_over_transformation/natural_corruptions/test_contrast.py
@@ -0,0 +1,147 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2021
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+import logging
+
+import numpy as np
+import pytest
+
+from tests.utils import ARTTestException
+
+logger = logging.getLogger(__name__)
+
+
+@pytest.fixture()
+def fix_get_mnist_subset(get_mnist_dataset):
+    (x_train_mnist, y_train_mnist), (x_test_mnist, y_test_mnist) = get_mnist_dataset
+    n_train = 10
+    n_test = 10
+    yield x_train_mnist[:n_train], y_train_mnist[:n_train], x_test_mnist[:n_test], y_test_mnist[:n_test]
+
+
+@pytest.mark.only_with_platform("pytorch")
+def test_eot_contrast_pytorch(art_warning, fix_get_mnist_subset):
+    try:
+        import torch
+        from art.preprocessing.expectation_over_transformation.natural_corruptions.contrast.pytorch import (
+            EoTContrastPyTorch,
+        )
+
+        x_train_mnist, y_train_mnist, _, _ = fix_get_mnist_subset
+        x_train_mnist = np.transpose(x_train_mnist, (0, 2, 3, 1))  # transpose to NHWC
+
+        nb_samples = 3
+
+        eot = EoTContrastPyTorch(nb_samples=nb_samples, contrast_factor=(0.2, 0.2), clip_values=(0.0, 1.0))
+        x_eot, y_eot = eot.forward(x=torch.from_numpy(x_train_mnist), y=torch.from_numpy(y_train_mnist))
+
+        assert x_eot.shape[0] == nb_samples * x_train_mnist.shape[0]
+        assert y_eot.shape[0] == nb_samples * y_train_mnist.shape[0]
+
+        x_eot_expected = np.array(
+            [
+                0.11014406,
+                0.11014406,
+                0.11014406,
+                0.11014406,
+                0.11014406,
+                0.11014406,
+                0.11014406,
+                0.11014406,
+                0.11014406,
+                0.11014406,
+                0.11014406,
+                0.11014406,
+                0.11014406,
+                0.17367348,
+                0.29837936,
+                0.30857542,
+                0.30857542,
+                0.20347738,
+                0.1297519,
+                0.11014406,
+                0.11014406,
+                0.11014406,
+                0.11014406,
+                0.11014406,
+                0.11014406,
+                0.11014406,
+                0.11014406,
+                0.11014406,
+            ]
+        )
+
+        np.testing.assert_almost_equal(x_eot.numpy()[0, 14, :, 0], x_eot_expected)
+
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.only_with_platform("tensorflow2")
+def test_eot_contrast_tensorflow_v2(art_warning, fix_get_mnist_subset):
+    try:
+        from art.preprocessing.expectation_over_transformation.natural_corruptions.contrast.tensorflow import (
+            EoTContrastTensorFlow,
+        )
+
+        x_train_mnist, y_train_mnist, _, _ = fix_get_mnist_subset
+
+        nb_samples = 3
+
+        eot = EoTContrastTensorFlow(nb_samples=nb_samples, contrast_factor=(0.2, 0.2), clip_values=(0.0, 1.0))
+        x_eot, y_eot = eot.forward(x=x_train_mnist, y=y_train_mnist)
+
+        assert x_eot.shape[0] == nb_samples * x_train_mnist.shape[0]
+        assert y_eot.shape[0] == nb_samples * y_train_mnist.shape[0]
+
+        x_eot_expected = np.array(
+            [
+                0.11014406,
+                0.11014406,
+                0.11014406,
+                0.11014406,
+                0.11014406,
+                0.11014406,
+                0.11014406,
+                0.11014406,
+                0.11014406,
+                0.11014406,
+                0.11014406,
+                0.11014406,
+                0.11014406,
+                0.17367348,
+                0.29837936,
+                0.30857542,
+                0.30857542,
+                0.20347738,
+                0.1297519,
+                0.11014406,
+                0.11014406,
+                0.11014406,
+                0.11014406,
+                0.11014406,
+                0.11014406,
+                0.11014406,
+                0.11014406,
+                0.11014406,
+            ]
+        )
+
+        np.testing.assert_almost_equal(x_eot.numpy()[0, 14, :, 0], x_eot_expected)
+
+    except ARTTestException as e:
+        art_warning(e)
diff --git a/adversarial-robustness-toolbox/tests/preprocessing/expectation_over_transformation/natural_corruptions/test_gaussian_noise.py b/adversarial-robustness-toolbox/tests/preprocessing/expectation_over_transformation/natural_corruptions/test_gaussian_noise.py
new file mode 100644
index 0000000..c98cfb9
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/preprocessing/expectation_over_transformation/natural_corruptions/test_gaussian_noise.py
@@ -0,0 +1,77 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2021
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+import logging
+
+import numpy as np
+import pytest
+
+from tests.utils import ARTTestException
+
+logger = logging.getLogger(__name__)
+
+
+@pytest.fixture()
+def fix_get_mnist_subset(get_mnist_dataset):
+    (x_train_mnist, y_train_mnist), (x_test_mnist, y_test_mnist) = get_mnist_dataset
+    n_train = 10
+    n_test = 10
+    yield x_train_mnist[:n_train], y_train_mnist[:n_train], x_test_mnist[:n_test], y_test_mnist[:n_test]
+
+
+@pytest.mark.only_with_platform("pytorch")
+def test_eot_gaussian_noise_pytorch(art_warning, fix_get_mnist_subset):
+    try:
+        import torch
+        from art.preprocessing.expectation_over_transformation.natural_corruptions.gaussian_noise.pytorch import (
+            EoTGaussianNoisePyTorch,
+        )
+
+        x_train_mnist, y_train_mnist, _, _ = fix_get_mnist_subset
+        x_train_mnist = np.transpose(x_train_mnist, (0, 2, 3, 1))  # transpose to NHWC
+
+        nb_samples = 3
+
+        eot = EoTGaussianNoisePyTorch(nb_samples=nb_samples, std=(0.2, 0.2), clip_values=(0.0, 1.0))
+        x_eot, y_eot = eot.forward(x=torch.from_numpy(x_train_mnist), y=torch.from_numpy(y_train_mnist))
+
+        assert x_eot.shape[0] == nb_samples * x_train_mnist.shape[0]
+        assert y_eot.shape[0] == nb_samples * y_train_mnist.shape[0]
+
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.only_with_platform("tensorflow2")
+def test_eot_gaussian_noise_tensorflow_v2(art_warning, fix_get_mnist_subset):
+    try:
+        from art.preprocessing.expectation_over_transformation.natural_corruptions.gaussian_noise.tensorflow import (
+            EoTGaussianNoiseTensorFlow,
+        )
+
+        x_train_mnist, y_train_mnist, _, _ = fix_get_mnist_subset
+
+        nb_samples = 3
+
+        eot = EoTGaussianNoiseTensorFlow(nb_samples=nb_samples, std=(0.2, 0.2), clip_values=(0.0, 1.0))
+        x_eot, y_eot = eot.forward(x=x_train_mnist, y=y_train_mnist)
+
+        assert x_eot.shape[0] == nb_samples * x_train_mnist.shape[0]
+        assert y_eot.shape[0] == nb_samples * y_train_mnist.shape[0]
+
+    except ARTTestException as e:
+        art_warning(e)
diff --git a/adversarial-robustness-toolbox/tests/preprocessing/expectation_over_transformation/natural_corruptions/test_shot_noise.py b/adversarial-robustness-toolbox/tests/preprocessing/expectation_over_transformation/natural_corruptions/test_shot_noise.py
new file mode 100644
index 0000000..13d4bc3
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/preprocessing/expectation_over_transformation/natural_corruptions/test_shot_noise.py
@@ -0,0 +1,77 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2021
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+import logging
+
+import numpy as np
+import pytest
+
+from tests.utils import ARTTestException
+
+logger = logging.getLogger(__name__)
+
+
+@pytest.fixture()
+def fix_get_mnist_subset(get_mnist_dataset):
+    (x_train_mnist, y_train_mnist), (x_test_mnist, y_test_mnist) = get_mnist_dataset
+    n_train = 10
+    n_test = 10
+    yield x_train_mnist[:n_train], y_train_mnist[:n_train], x_test_mnist[:n_test], y_test_mnist[:n_test]
+
+
+@pytest.mark.only_with_platform("pytorch")
+def test_eot_shot_noise_pytorch(art_warning, fix_get_mnist_subset):
+    try:
+        import torch
+        from art.preprocessing.expectation_over_transformation.natural_corruptions.shot_noise.pytorch import (
+            EoTShotNoisePyTorch,
+        )
+
+        x_train_mnist, y_train_mnist, _, _ = fix_get_mnist_subset
+        x_train_mnist = np.transpose(x_train_mnist, (0, 2, 3, 1))  # transpose to NHWC
+
+        nb_samples = 3
+
+        eot = EoTShotNoisePyTorch(nb_samples=nb_samples, lam=(0.2, 0.2), clip_values=(0.0, 1.0))
+        x_eot, y_eot = eot.forward(x=torch.from_numpy(x_train_mnist), y=torch.from_numpy(y_train_mnist))
+
+        assert x_eot.shape[0] == nb_samples * x_train_mnist.shape[0]
+        assert y_eot.shape[0] == nb_samples * y_train_mnist.shape[0]
+
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.only_with_platform("tensorflow2")
+def test_eot_shot_noise_tensorflow_v2(art_warning, fix_get_mnist_subset):
+    try:
+        from art.preprocessing.expectation_over_transformation.natural_corruptions.shot_noise.tensorflow import (
+            EoTShotNoiseTensorFlow,
+        )
+
+        x_train_mnist, y_train_mnist, _, _ = fix_get_mnist_subset
+
+        nb_samples = 3
+
+        eot = EoTShotNoiseTensorFlow(nb_samples=nb_samples, lam=(0.2, 0.2), clip_values=(0.0, 1.0))
+        x_eot, y_eot = eot.forward(x=x_train_mnist, y=y_train_mnist)
+
+        assert x_eot.shape[0] == nb_samples * x_train_mnist.shape[0]
+        assert y_eot.shape[0] == nb_samples * y_train_mnist.shape[0]
+
+    except ARTTestException as e:
+        art_warning(e)
diff --git a/adversarial-robustness-toolbox/tests/preprocessing/expectation_over_transformation/natural_corruptions/test_zoom_blur.py b/adversarial-robustness-toolbox/tests/preprocessing/expectation_over_transformation/natural_corruptions/test_zoom_blur.py
new file mode 100644
index 0000000..e618b68
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/preprocessing/expectation_over_transformation/natural_corruptions/test_zoom_blur.py
@@ -0,0 +1,156 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2021
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+import logging
+
+import numpy as np
+import pytest
+
+from tests.utils import ARTTestException
+
+logger = logging.getLogger(__name__)
+
+
+@pytest.fixture()
+def fix_get_mnist_subset(get_mnist_dataset):
+    (x_train_mnist, y_train_mnist), (x_test_mnist, y_test_mnist) = get_mnist_dataset
+    n_train = 10
+    n_test = 10
+    yield x_train_mnist[:n_train], y_train_mnist[:n_train], x_test_mnist[:n_test], y_test_mnist[:n_test]
+
+
+@pytest.mark.only_with_platform("pytorch")
+def test_eot_zoom_blur_pytorch(art_warning, fix_get_mnist_subset):
+    try:
+        import torchvision
+
+        if "+" in torchvision.__version__:
+            torchvision_version = torchvision.__version__.split("+")[0]
+        else:
+            torchvision_version = torchvision.__version__
+        torchvision_version = list(map(int, torchvision_version.lower().split(".")))
+
+        if torchvision_version[0] >= 0 and torchvision_version[1] >= 8:
+            import torch
+            from art.preprocessing.expectation_over_transformation.natural_corruptions.zoom_blur.pytorch import (
+                EoTZoomBlurPyTorch,
+            )
+
+            x_train_mnist, y_train_mnist, _, _ = fix_get_mnist_subset
+            x_train_mnist = np.transpose(x_train_mnist, (0, 2, 3, 1))  # transpose to NHWC
+
+            nb_samples = 3
+
+            eot = EoTZoomBlurPyTorch(nb_samples=nb_samples, zoom=(1.5, 1.5), clip_values=(0.0, 1.0))
+            x_eot, y_eot = eot.forward(x=torch.from_numpy(x_train_mnist), y=torch.from_numpy(y_train_mnist))
+
+            assert x_eot.shape[0] == nb_samples * x_train_mnist.shape[0]
+            assert y_eot.shape[0] == nb_samples * y_train_mnist.shape[0]
+
+            x_eot_expected = np.array(
+                [
+                    0.0000000e00,
+                    0.0000000e00,
+                    0.0000000e00,
+                    0.0000000e00,
+                    0.0000000e00,
+                    0.0000000e00,
+                    0.0000000e00,
+                    0.0000000e00,
+                    0.0000000e00,
+                    0.0000000e00,
+                    9.3583600e-05,
+                    6.7037535e-03,
+                    7.0553131e-02,
+                    4.1708022e-01,
+                    8.4442711e-01,
+                    9.2741704e-01,
+                    8.8823336e-01,
+                    6.6305310e-01,
+                    3.1785199e-01,
+                    1.1077325e-01,
+                    2.7649008e-02,
+                    4.9111363e-03,
+                    0.0000000e00,
+                    0.0000000e00,
+                    0.0000000e00,
+                    0.0000000e00,
+                    0.0000000e00,
+                    0.0000000e00,
+                ]
+            )
+
+            np.testing.assert_almost_equal(x_eot.numpy()[0, 14, :, 0], x_eot_expected)
+
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.only_with_platform("tensorflow2")
+def test_eot_zoom_blur_tensorflow_v2(art_warning, fix_get_mnist_subset):
+    try:
+        from art.preprocessing.expectation_over_transformation.natural_corruptions.zoom_blur.tensorflow import (
+            EoTZoomBlurTensorFlow,
+        )
+
+        x_train_mnist, y_train_mnist, _, _ = fix_get_mnist_subset
+
+        nb_samples = 3
+
+        eot = EoTZoomBlurTensorFlow(nb_samples=nb_samples, zoom=(1.5, 1.5), clip_values=(0.0, 1.0))
+        x_eot, y_eot = eot.forward(x=x_train_mnist, y=y_train_mnist)
+
+        assert x_eot.shape[0] == nb_samples * x_train_mnist.shape[0]
+        assert y_eot.shape[0] == nb_samples * y_train_mnist.shape[0]
+
+        x_eot_expected = np.array(
+            [
+                0.0000000e00,
+                0.0000000e00,
+                0.0000000e00,
+                0.0000000e00,
+                0.0000000e00,
+                0.0000000e00,
+                0.0000000e00,
+                0.0000000e00,
+                0.0000000e00,
+                0.0000000e00,
+                9.3583600e-05,
+                6.7037535e-03,
+                7.0553131e-02,
+                4.1708022e-01,
+                8.4442711e-01,
+                9.2741704e-01,
+                8.8823336e-01,
+                6.6305310e-01,
+                3.1785199e-01,
+                1.1077325e-01,
+                2.7649008e-02,
+                4.9111363e-03,
+                0.0000000e00,
+                0.0000000e00,
+                0.0000000e00,
+                0.0000000e00,
+                0.0000000e00,
+                0.0000000e00,
+            ]
+        )
+
+        np.testing.assert_almost_equal(x_eot.numpy()[0, 14, :, 0], x_eot_expected)
+
+    except ARTTestException as e:
+        art_warning(e)
diff --git a/adversarial-robustness-toolbox/tests/preprocessing/expectation_over_transformation/test_image_rotation.py b/adversarial-robustness-toolbox/tests/preprocessing/expectation_over_transformation/test_image_rotation.py
new file mode 100644
index 0000000..2460d47
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/preprocessing/expectation_over_transformation/test_image_rotation.py
@@ -0,0 +1,88 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+import logging
+
+import numpy as np
+import pytest
+
+from art.preprocessing.expectation_over_transformation.image_rotation.tensorflow import EoTImageRotationTensorFlow
+from tests.utils import ARTTestException
+
+logger = logging.getLogger(__name__)
+
+
+@pytest.fixture()
+def fix_get_mnist_subset(get_mnist_dataset):
+    (x_train_mnist, y_train_mnist), (x_test_mnist, y_test_mnist) = get_mnist_dataset
+    n_train = 10
+    n_test = 10
+    yield x_train_mnist[:n_train], y_train_mnist[:n_train], x_test_mnist[:n_test], y_test_mnist[:n_test]
+
+
+@pytest.mark.only_with_platform("tensorflow2")
+def test_eot_image_rotation_classification_tensorflow_v2(art_warning, fix_get_mnist_subset):
+    try:
+        x_train_mnist, y_train_mnist, _, _ = fix_get_mnist_subset
+
+        nb_samples = 3
+
+        eot = EoTImageRotationTensorFlow(
+            nb_samples=nb_samples, angles=(45.0, 45.0), clip_values=(0.0, 1.0), label_type="classification"
+        )
+        x_eot, y_eot = eot.forward(x=x_train_mnist, y=y_train_mnist)
+
+        assert x_eot.shape[0] == nb_samples * x_train_mnist.shape[0]
+        assert y_eot.shape[0] == nb_samples * y_train_mnist.shape[0]
+
+        x_eot_expected = np.array(
+            [
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.07058824,
+                0.3137255,
+                0.6117647,
+                0.05490196,
+                0.0,
+                0.54509807,
+                0.04313726,
+                0.13725491,
+                0.31764707,
+                0.9411765,
+                0.1764706,
+                0.0627451,
+                0.3647059,
+                0.0,
+                0.99215686,
+                0.99215686,
+                0.98039216,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+            ]
+        )
+
+        np.testing.assert_almost_equal(x_eot.numpy()[0, 14, :, 0], x_eot_expected)
+
+    except ARTTestException as e:
+        art_warning(e)
diff --git a/adversarial-robustness-toolbox/tests/preprocessing/test_standardisation_mean_std.py b/adversarial-robustness-toolbox/tests/preprocessing/test_standardisation_mean_std.py
new file mode 100644
index 0000000..b4243bc
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/preprocessing/test_standardisation_mean_std.py
@@ -0,0 +1,93 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+
+import numpy as np
+import pytest
+
+from art.preprocessing.standardisation_mean_std import (
+    StandardisationMeanStd,
+    StandardisationMeanStdPyTorch,
+    StandardisationMeanStdTensorFlow,
+)
+from art.preprocessing.standardisation_mean_std.utils import broadcastable_mean_std
+from tests.utils import ARTTestException
+
+logger = logging.getLogger(__name__)
+
+
+@pytest.fixture(params=[True, False], ids=["channels_first", "channels_last"])
+def image_batch(request):
+    """
+    Create image fixture of shape NFHWC and NCFHW.
+    """
+    channels_first = request.param
+    test_input = np.ones((2, 3, 32, 32))
+    if not channels_first:
+        test_input = np.transpose(test_input, (0, 2, 3, 1))
+    test_mean = [0] * 3
+    test_std = [1] * 3
+    test_output = test_input.copy()
+    return test_input, test_output, test_mean, test_std
+
+
+@pytest.mark.framework_agnostic
+def test_broadcastable_mean_std(art_warning):
+    try:
+        mean, std = broadcastable_mean_std(np.ones((1, 3, 20, 20)), np.ones(3), np.ones(3))
+        assert mean.shape == std.shape == (1, 3, 1, 1)
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.framework_agnostic
+def test_standardisation_mean_std(art_warning, image_batch):
+    try:
+        x, x_expected, mean, std = image_batch
+        standard = StandardisationMeanStd(mean=mean, std=std)
+
+        x_preprocessed, _ = standard(x=x, y=None)
+        np.testing.assert_array_equal(x_preprocessed, x_expected)
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.only_with_platform("pytorch")
+def test_standardisation_mean_std_pytorch(art_warning, image_batch):
+    try:
+        x, x_expected, mean, std = image_batch
+        standard = StandardisationMeanStdPyTorch(mean=mean, std=std)
+
+        x_preprocessed, _ = standard(x=x, y=None)
+        np.testing.assert_array_equal(x_preprocessed, x_expected)
+    except ARTTestException as e:
+        art_warning(e)
+
+
+@pytest.mark.only_with_platform("tensorflow2")
+def test_standardisation_mean_std_tensorflow_v2(art_warning, image_batch):
+    try:
+        x, x_expected, mean, std = image_batch
+        standard = StandardisationMeanStdTensorFlow(mean=mean, std=std)
+
+        x_preprocessed, _ = standard(x=x, y=None)
+        np.testing.assert_array_equal(x_preprocessed, x_expected)
+    except ARTTestException as e:
+        art_warning(e)
diff --git a/adversarial-robustness-toolbox/tests/test_data_generators.py b/adversarial-robustness-toolbox/tests/test_data_generators.py
new file mode 100644
index 0000000..a70c5aa
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/test_data_generators.py
@@ -0,0 +1,364 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import unittest
+
+import tensorflow as tf
+import numpy as np
+from keras.preprocessing.image import ImageDataGenerator
+
+from art.data_generators import KerasDataGenerator, PyTorchDataGenerator, MXDataGenerator, TensorFlowDataGenerator
+from art.data_generators import TensorFlowV2DataGenerator
+
+from tests.utils import master_seed
+
+logger = logging.getLogger(__name__)
+
+
+class TestKerasDataGenerator(unittest.TestCase):
+    def setUp(self):
+        import keras
+
+        master_seed(seed=42)
+
+        class DummySequence(keras.utils.Sequence):
+            def __init__(self):
+                self._size = 5
+                self._x = np.random.rand(self._size, 28, 28, 1)
+                self._y = np.random.randint(0, high=10, size=(self._size, 10))
+
+            def __len__(self):
+                return self._size
+
+            def __getitem__(self, idx):
+                return self._x[idx], self._y[idx]
+
+        sequence = DummySequence()
+        self.data_gen = KerasDataGenerator(sequence, size=5, batch_size=1)
+
+    def tearDown(self):
+        import keras.backend as k
+
+        k.clear_session()
+
+    def test_gen_interface(self):
+        gen = self._dummy_gen()
+        data_gen = KerasDataGenerator(gen, size=None, batch_size=5)
+
+        x, y = data_gen.get_batch()
+
+        # Check return types
+        self.assertTrue(isinstance(x, np.ndarray))
+        self.assertTrue(isinstance(y, np.ndarray))
+
+        # Check shapes
+        self.assertEqual(x.shape, (5, 28, 28, 1))
+        self.assertEqual(y.shape, (5, 10))
+
+    def test_gen_keras_specific(self):
+        gen = self._dummy_gen()
+        data_gen = KerasDataGenerator(gen, size=None, batch_size=5)
+
+        iter_ = iter(data_gen.iterator)
+        x, y = next(iter_)
+
+        # Check return types
+        self.assertTrue(isinstance(x, np.ndarray))
+        self.assertTrue(isinstance(y, np.ndarray))
+
+        # Check shapes
+        self.assertEqual(x.shape, (5, 28, 28, 1))
+        self.assertEqual(y.shape, (5, 10))
+
+    def test_sequence_keras_specific(self):
+        iter_ = iter(self.data_gen.iterator)
+        x, y = next(iter_)
+
+        # Check return types
+        self.assertTrue(isinstance(x, np.ndarray))
+        self.assertTrue(isinstance(y, np.ndarray))
+
+        # Check shapes
+        self.assertEqual(x.shape, (28, 28, 1))
+        self.assertEqual(y.shape, (10,))
+
+    def test_sequence_interface(self):
+        x, y = self.data_gen.get_batch()
+
+        # Check return types
+        self.assertTrue(isinstance(x, np.ndarray))
+        self.assertTrue(isinstance(y, np.ndarray))
+
+        # Check shapes
+        self.assertEqual(x.shape, (28, 28, 1))
+        self.assertEqual(y.shape, (10,))
+
+    def test_imagedatagen_interface(self):
+        train_size, batch_size = 20, 5
+        x_train, y_train = np.random.rand(train_size, 28, 28, 1), np.random.randint(0, 2, size=(train_size, 10))
+
+        datagen = ImageDataGenerator(
+            width_shift_range=0.075,
+            height_shift_range=0.075,
+            rotation_range=12,
+            shear_range=0.075,
+            zoom_range=0.05,
+            fill_mode="constant",
+            cval=0,
+        )
+        datagen.fit(x_train)
+
+        # Create wrapper and get batch
+        data_gen = KerasDataGenerator(
+            datagen.flow(x_train, y_train, batch_size=batch_size), size=None, batch_size=batch_size
+        )
+        x, y = data_gen.get_batch()
+
+        # Check return types
+        self.assertTrue(isinstance(x, np.ndarray))
+        self.assertTrue(isinstance(y, np.ndarray))
+
+        # Check shapes
+        self.assertEqual(x.shape, (batch_size, 28, 28, 1))
+        self.assertEqual(y.shape, (batch_size, 10))
+
+    def test_imagedatagen_keras_specific(self):
+        train_size, batch_size = 20, 5
+        x_train, y_train = np.random.rand(train_size, 28, 28, 1), np.random.randint(0, 2, size=(train_size, 10))
+
+        datagen = ImageDataGenerator(
+            width_shift_range=0.075,
+            height_shift_range=0.075,
+            rotation_range=12,
+            shear_range=0.075,
+            zoom_range=0.05,
+            fill_mode="constant",
+            cval=0,
+        )
+        datagen.fit(x_train)
+
+        # Create wrapper and get batch
+        data_gen = KerasDataGenerator(
+            datagen.flow(x_train, y_train, batch_size=batch_size), size=None, batch_size=batch_size
+        )
+        x, y = next(data_gen.iterator)
+
+        # Check return types
+        self.assertTrue(isinstance(x, np.ndarray))
+        self.assertTrue(isinstance(y, np.ndarray))
+
+        # Check shapes
+        self.assertEqual(x.shape, (batch_size, 28, 28, 1))
+        self.assertEqual(y.shape, (batch_size, 10))
+
+    @staticmethod
+    def _dummy_gen(size=5):
+        yield np.random.rand(size, 28, 28, 1), np.random.randint(low=0, high=10, size=(size, 10))
+
+
+class TestPyTorchGenerator(unittest.TestCase):
+    def setUp(self):
+        import torch
+        from torch.utils.data import DataLoader
+
+        master_seed(seed=42)
+
+        class DummyDataset(torch.utils.data.Dataset):
+            def __init__(self):
+                self._size = 10
+                self._x = np.random.rand(self._size, 1, 5, 5)
+                self._y = np.random.randint(0, high=10, size=self._size)
+
+            def __len__(self):
+                return self._size
+
+            def __getitem__(self, idx):
+                return self._x[idx], self._y[idx]
+
+        dataset = DummyDataset()
+        data_loader = DataLoader(dataset=dataset, batch_size=5, shuffle=True)
+        self.data_gen = PyTorchDataGenerator(data_loader, size=10, batch_size=5)
+
+    def test_gen_interface(self):
+        x, y = self.data_gen.get_batch()
+
+        # Check return types
+        self.assertTrue(isinstance(x, np.ndarray))
+        self.assertTrue(isinstance(y, np.ndarray))
+
+        # Check shapes
+        self.assertEqual(x.shape, (5, 1, 5, 5))
+        self.assertEqual(y.shape, (5,))
+
+    def test_pytorch_specific(self):
+        import torch
+
+        iter_ = iter(self.data_gen.iterator)
+        x, y = next(iter_)
+
+        # Check return types
+        self.assertTrue(isinstance(x, torch.Tensor))
+        self.assertTrue(isinstance(y, torch.Tensor))
+
+        # Check shapes
+        self.assertEqual(x.shape, (5, 1, 5, 5))
+        self.assertEqual(y.shape, (5,))
+
+
+class TestMXGenerator(unittest.TestCase):
+    def setUp(self):
+        import mxnet as mx
+
+        master_seed(seed=42, set_mxnet=True)
+
+        x = mx.random.uniform(shape=(10, 1, 5, 5))
+        y = mx.random.uniform(shape=10)
+        dataset = mx.gluon.data.dataset.ArrayDataset(x, y)
+
+        data_loader = mx.gluon.data.DataLoader(dataset, batch_size=5, shuffle=True)
+        self.data_gen = MXDataGenerator(data_loader, size=10, batch_size=5)
+
+    def test_gen_interface(self):
+        x, y = self.data_gen.get_batch()
+
+        # Check return types
+        self.assertTrue(isinstance(x, np.ndarray))
+        self.assertTrue(isinstance(y, np.ndarray))
+
+        # Check shapes
+        self.assertEqual(x.shape, (5, 1, 5, 5))
+        self.assertEqual(y.shape, (5,))
+
+    def test_mxnet_specific(self):
+        import mxnet as mx
+
+        iter_ = iter(self.data_gen.iterator)
+        x, y = next(iter_)
+
+        # Check return types
+        self.assertTrue(isinstance(x, mx.ndarray.NDArray))
+        self.assertTrue(isinstance(y, mx.ndarray.NDArray))
+
+        # Check shapes
+        self.assertEqual(x.shape, (5, 1, 5, 5))
+        self.assertEqual(y.shape, (5,))
+
+
+@unittest.skipIf(tf.__version__[0] == "2", reason="Skip unittests for TensorFlow v2.")
+class TestTensorFlowDataGenerator(unittest.TestCase):
+    def setUp(self):
+        master_seed(seed=42)
+
+        def generator(batch_size=5):
+            while True:
+                yield np.random.rand(batch_size, 5, 5, 1), np.random.randint(0, 10, size=10 * batch_size).reshape(
+                    batch_size, -1
+                )
+
+        self.sess = tf.Session()
+        self.dataset = tf.data.Dataset.from_generator(generator, (tf.float32, tf.int32))
+
+    def tearDown(self):
+        self.sess.close()
+
+    def test_init(self):
+        iter_ = tf.compat.v1.data.make_initializable_iterator(self.dataset)
+        data_gen = TensorFlowDataGenerator(
+            sess=self.sess, iterator=iter_, iterator_type="initializable", iterator_arg={}, size=10, batch_size=5
+        )
+        x, y = data_gen.get_batch()
+
+        # Check return types
+        self.assertTrue(isinstance(x, np.ndarray))
+        self.assertTrue(isinstance(y, np.ndarray))
+
+        # Check shapes
+        self.assertEqual(x.shape, (5, 5, 5, 1))
+        self.assertEqual(y.shape, (5, 10))
+
+    def test_reinit(self):
+        iter_ = tf.data.Iterator.from_structure(
+            tf.compat.v1.data.get_output_types(self.dataset), tf.compat.v1.data.get_output_shapes(self.dataset)
+        )
+        init_op = iter_.make_initializer(self.dataset)
+        data_gen = TensorFlowDataGenerator(
+            sess=self.sess, iterator=iter_, iterator_type="reinitializable", iterator_arg=init_op, size=10, batch_size=5
+        )
+        x, y = data_gen.get_batch()
+
+        # Check return types
+        self.assertTrue(isinstance(x, np.ndarray))
+        self.assertTrue(isinstance(y, np.ndarray))
+
+        # Check shapes
+        self.assertEqual(x.shape, (5, 5, 5, 1))
+        self.assertEqual(y.shape, (5, 10))
+
+    def test_feedable(self):
+        handle = tf.placeholder(tf.string, shape=[])
+        iter_ = tf.data.Iterator.from_string_handle(
+            handle, tf.compat.v1.data.get_output_types(self.dataset), tf.compat.v1.data.get_output_shapes(self.dataset)
+        )
+        feed_iterator = tf.compat.v1.data.make_initializable_iterator(self.dataset)
+        feed_handle = self.sess.run(feed_iterator.string_handle())
+        data_gen = TensorFlowDataGenerator(
+            sess=self.sess,
+            iterator=iter_,
+            iterator_type="feedable",
+            iterator_arg=(feed_iterator, {handle: feed_handle}),
+            size=10,
+            batch_size=5,
+        )
+        x, y = data_gen.get_batch()
+
+        # Check return types
+        self.assertTrue(isinstance(x, np.ndarray))
+        self.assertTrue(isinstance(y, np.ndarray))
+
+        # Check shapes
+        self.assertEqual(x.shape, (5, 5, 5, 1))
+        self.assertEqual(y.shape, (5, 10))
+
+
+@unittest.skipIf(tf.__version__[0] == "1", reason="Skip unittests for TensorFlow v1.")
+class TestTensorFlowV2DataGenerator(unittest.TestCase):
+    def setUp(self):
+        master_seed(seed=42)
+        self.batch_size = 5
+        x = np.random.rand(self.batch_size, 5, 5, 1)
+        y = np.random.randint(0, 10, size=10 * self.batch_size).reshape(self.batch_size, -1)
+
+        self.dataset = tf.data.Dataset.from_tensor_slices((x, y)).shuffle(100).batch(self.batch_size)
+
+    def test_init(self):
+        data_gen = TensorFlowV2DataGenerator(iterator=self.dataset, size=5, batch_size=self.batch_size)
+        x, y = data_gen.get_batch()
+
+        # Check return types
+        self.assertTrue(isinstance(x, np.ndarray))
+        self.assertTrue(isinstance(y, np.ndarray))
+
+        # Check shapes
+        self.assertEqual(x.shape, (5, 5, 5, 1))
+        self.assertEqual(y.shape, (5, 10))
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/adversarial-robustness-toolbox/tests/test_utils.py b/adversarial-robustness-toolbox/tests/test_utils.py
new file mode 100644
index 0000000..cb2d51b
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/test_utils.py
@@ -0,0 +1,410 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import unittest
+
+import numpy as np
+import tensorflow as tf
+
+from art.utils import projection, random_sphere, to_categorical, least_likely_class, check_and_transform_label_format
+from art.utils import load_iris, load_mnist
+from art.utils import second_most_likely_class, random_targets, get_label_conf, get_labels_np_array, preprocess
+from art.utils import compute_success_array, compute_success
+from art.utils import segment_by_class, performance_diff
+from art.utils import is_probability
+
+from tests.utils import master_seed
+
+logger = logging.getLogger(__name__)
+
+BATCH_SIZE = 10
+NB_TRAIN = 100
+NB_TEST = 100
+
+
+class TestUtils(unittest.TestCase):
+    def setUp(self):
+        master_seed(seed=1234)
+
+    def test_master_seed_mx(self):
+        import mxnet as mx
+
+        master_seed(seed=1234, set_mxnet=True)
+        x = mx.nd.random.uniform(0, 1, shape=(10,)).asnumpy()
+        y = mx.nd.random.uniform(0, 1, shape=(10,)).asnumpy()
+
+        master_seed(seed=1234, set_mxnet=True)
+        z = mx.nd.random.uniform(0, 1, shape=(10,)).asnumpy()
+        self.assertFalse((x == y).any())
+        self.assertTrue((x == z).all())
+
+    def test_master_seed_pytorch(self):
+        import torch
+
+        master_seed(seed=1234, set_torch=True)
+        x = torch.randn(5, 5)
+        y = torch.randn(5, 5)
+
+        master_seed(seed=1234, set_torch=True)
+        z = torch.randn(5, 5)
+        self.assertFalse((x == y).any())
+        self.assertTrue((x == z).all())
+
+    def test_master_seed_py(self):
+        import random
+
+        master_seed(seed=1234)
+        x = random.getrandbits(128)
+        y = random.getrandbits(128)
+
+        master_seed(seed=1234)
+        z = random.getrandbits(128)
+        self.assertNotEqual(x, y)
+        self.assertEqual(z, x)
+
+    def test_master_seed_np(self):
+        master_seed(seed=1234)
+        x = np.random.uniform(size=10)
+        y = np.random.uniform(size=10)
+
+        master_seed(seed=1234)
+        z = np.random.uniform(size=10)
+        self.assertTrue((x != y).any())
+        self.assertTrue((z == x).all())
+
+    @unittest.skipIf(tf.__version__[0] != "1", reason="Skip unittests if not TensorFlow v1.")
+    def test_master_seed_tf(self):
+        tf.reset_default_graph()
+        master_seed(seed=1234, set_tensorflow=True)
+        with tf.Session() as sess:
+            x = tf.random_uniform(shape=(1, 10))
+            y = tf.random_uniform(shape=(1, 10))
+            xv, yv = sess.run([x, y])
+
+        tf.reset_default_graph()
+        master_seed(seed=1234, set_tensorflow=True)
+        with tf.Session() as sess:
+            z = tf.random_uniform(shape=(1, 10))
+            zv = sess.run([z])[0]
+
+        self.assertTrue((xv != yv).any())
+        np.testing.assert_array_almost_equal(zv, xv, decimal=4)
+
+    @unittest.skipIf(tf.__version__[0] != "2", reason="Skip unittests if not TensorFlow v2.")
+    def test_master_seed_tf_v2(self):
+        master_seed(seed=1234, set_tensorflow=True)
+        x = tf.random.uniform(shape=(1, 10))
+        y = tf.random.uniform(shape=(1, 10))
+        xv, yv = x.numpy(), y.numpy()
+
+        master_seed(seed=1234, set_tensorflow=True)
+        z = tf.random.uniform(shape=(1, 10))
+        zv = z.numpy()
+
+        self.assertTrue((xv != yv).any())
+        np.testing.assert_array_almost_equal(zv, xv, decimal=4)
+
+    def test_projection(self):
+        # Get MNIST
+        (x, _), (_, _), _, _ = load_mnist()
+
+        # Probably don't need to test everything
+        x = x[:100]
+        t = tuple(range(1, len(x.shape)))
+        rand_sign = 1 - 2 * np.random.randint(0, 2, size=x.shape)
+
+        x_proj = projection(rand_sign * x, 3.14159, 1)
+        self.assertEqual(x.shape, x_proj.shape)
+        self.assertTrue(np.allclose(np.sum(np.abs(x_proj), axis=t), 3.14159, atol=10e-8))
+
+        x_proj = projection(rand_sign * x, 3.14159, 2)
+        self.assertEqual(x.shape, x_proj.shape)
+        self.assertTrue(np.allclose(np.sqrt(np.sum(x_proj ** 2, axis=t)), 3.14159, atol=10e-8))
+
+        x_proj = projection(rand_sign * x, 0.314159, np.inf)
+        self.assertEqual(x.shape, x_proj.shape)
+        self.assertEqual(x_proj.min(), -0.314159)
+        self.assertEqual(x_proj.max(), 0.314159)
+
+        x_proj = projection(rand_sign * x, 3.14159, np.inf)
+        self.assertEqual(x.shape, x_proj.shape)
+        self.assertEqual(x_proj.min(), -1.0)
+        self.assertEqual(x_proj.max(), 1.0)
+
+    def test_random_sphere(self):
+        x = random_sphere(10, 10, 1, 1)
+        self.assertEqual(x.shape, (10, 10))
+        self.assertTrue(np.all(np.sum(np.abs(x), axis=1) <= 1.0))
+
+        x = random_sphere(10, 10, 1, 2)
+        self.assertTrue(np.all(np.linalg.norm(x, axis=1) < 1.0))
+
+        x = random_sphere(10, 10, 1, np.inf)
+        self.assertTrue(np.all(np.abs(x) < 1.0))
+
+    def test_to_categorical(self):
+        y = np.array([3, 1, 4, 1, 5, 9])
+        y_ = to_categorical(y)
+        self.assertEqual(y_.shape, (6, 10))
+        self.assertTrue(np.all(y_.argmax(axis=1) == y))
+        self.assertTrue(np.all(np.logical_or(y_ == 0.0, y_ == 1.0)))
+
+        y_ = to_categorical(y, 20)
+        self.assertEqual(y_.shape, (6, 20))
+
+    def test_check_and_transform_label_format(self):
+        labels_expected = np.array([[0, 0, 0, 1, 0], [0, 1, 0, 0, 0], [0, 0, 0, 0, 1]])
+
+        # test input shape (nb_samples,)
+        labels = np.array([3, 1, 4])
+        labels_transformed = check_and_transform_label_format(labels)
+        np.testing.assert_array_equal(labels_transformed, labels_expected)
+
+        # test input shape (nb_samples, 1)
+        labels = np.array([[3], [1], [4]])
+        labels_transformed = check_and_transform_label_format(labels)
+        np.testing.assert_array_equal(labels_transformed, labels_expected)
+
+        # test input shape (nb_samples, nb_classes)
+        labels = np.array([[0, 0, 0, 1, 0], [0, 1, 0, 0, 0], [0, 0, 0, 0, 1]])
+        labels_transformed = check_and_transform_label_format(labels)
+        np.testing.assert_array_equal(labels_transformed, labels_expected)
+
+        # test input shape (nb_samples, nb_classes) with return_one_hot=False
+        labels = np.array([[0, 0, 0, 1, 0], [0, 1, 0, 0, 0], [0, 0, 0, 0, 1]])
+        labels_transformed = check_and_transform_label_format(labels, return_one_hot=False)
+        np.testing.assert_array_equal(labels_transformed, np.argmax(labels_expected, axis=1))
+
+        # ValueError for len(labels.shape) > 2
+        labels = np.array([[[0, 0, 0, 1, 0], [0, 1, 0, 0, 0], [0, 0, 0, 0, 1]]])
+        with self.assertRaises(ValueError):
+            check_and_transform_label_format(labels)
+
+    def test_random_targets(self):
+        y = np.array([3, 1, 4, 1, 5, 9])
+        y_ = to_categorical(y)
+
+        random_y = random_targets(y, 10)
+        self.assertTrue(np.all(y != random_y.argmax(axis=1)))
+
+        random_y = random_targets(y_, 10)
+        self.assertTrue(np.all(y != random_y.argmax(axis=1)))
+
+    def test_least_likely_class(self):
+        class DummyClassifier:
+            @property
+            def nb_classes(self):
+                return 4
+
+            def predict(self, x):
+                fake_preds = [0.1, 0.2, 0.05, 0.65]
+                return np.array([fake_preds] * x.shape[0])
+
+        batch_size = 5
+        x = np.random.rand(batch_size, 10, 10, 1)
+        classifier = DummyClassifier()
+        predictions = least_likely_class(x, classifier)
+        self.assertEqual(predictions.shape, (batch_size, classifier.nb_classes))
+
+        expected_predictions = np.array([[0, 0, 1, 0]] * batch_size)
+        self.assertTrue((predictions == expected_predictions).all())
+
+    def test_second_most_likely_class(self):
+        class DummyClassifier:
+            @property
+            def nb_classes(self):
+                return 4
+
+            def predict(self, x):
+                fake_predictions = [0.1, 0.2, 0.05, 0.65]
+                return np.array([fake_predictions] * x.shape[0])
+
+        batch_size = 5
+        x = np.random.rand(batch_size, 10, 10, 1)
+        classifier = DummyClassifier()
+        predictions = second_most_likely_class(x, classifier)
+        self.assertEqual(predictions.shape, (batch_size, classifier.nb_classes))
+
+        expected_predictions = np.array([[0, 1, 0, 0]] * batch_size)
+        self.assertTrue((predictions == expected_predictions).all())
+
+    def test_get_label_conf(self):
+        y = np.array([3, 1, 4, 1, 5, 9])
+        y_ = to_categorical(y)
+
+        logits = np.random.normal(10 * y_, scale=0.1)
+        ps = (np.exp(logits).T / np.sum(np.exp(logits), axis=1)).T
+        c, l = get_label_conf(ps)
+
+        self.assertEqual(c.shape, y.shape)
+        self.assertEqual(l.shape, y.shape)
+
+        self.assertTrue(np.all(l == y))
+        self.assertTrue(np.allclose(c, 0.99, atol=1e-2))
+
+    def test_get_labels_np_array(self):
+        y = np.array([3, 1, 4, 1, 5, 9])
+        y_ = to_categorical(y)
+
+        logits = np.random.normal(1 * y_, scale=0.1)
+        ps = (np.exp(logits).T / np.sum(np.exp(logits), axis=1)).T
+
+        labels = get_labels_np_array(ps)
+        self.assertEqual(labels.shape, y_.shape)
+        self.assertTrue(np.all(labels == y_))
+
+    def test_compute_success_array(self):
+        class DummyClassifier:
+            def predict(self, x, batch_size):
+                return x
+
+        classifier = DummyClassifier()
+        x_clean = np.array([[0, 1], [1, 0]])
+        x_adv = np.array([[1, 0], [0, 1]])
+        labels = np.array([[1, 0], [0, 1]])
+
+        attack_success_targeted = compute_success_array(classifier, x_clean, labels, x_adv, targeted=True)
+        attack_success_untargeted = compute_success_array(classifier, x_clean, labels, x_adv, targeted=False)
+
+        self.assertTrue((attack_success_targeted == np.array([True, True])).all())
+        self.assertTrue((attack_success_untargeted == np.array([True, True])).all())
+
+    def test_compute_success(self):
+        class DummyClassifier:
+            def predict(self, x, batch_size):
+                return x
+
+        classifier = DummyClassifier()
+        x_clean = np.array([[0, 1], [1, 0]])
+        x_adv = np.array([[1, 0], [0, 1]])
+        labels = np.array([[1, 0], [0, 1]])
+
+        attack_success_targeted = compute_success(classifier, x_clean, labels, x_adv, targeted=True)
+        attack_success_untargeted = compute_success(classifier, x_clean, labels, x_adv, targeted=False)
+
+        self.assertEqual(attack_success_targeted, 1.0)
+        self.assertEqual(attack_success_untargeted, 1.0)
+
+    def test_preprocess(self):
+        (x, y), (_, _), _, _ = load_mnist()
+
+        x = (255 * x).astype("int")[:100]
+        y = np.argmax(y, axis=1)[:100]
+
+        x_, y_ = preprocess(x, y, clip_values=(0, 255))
+        self.assertEqual(x_.shape, x.shape)
+        self.assertEqual(y_.shape, (y.shape[0], 10))
+        self.assertEqual(x_.max(), 1.0)
+        self.assertEqual(x_.min(), 0)
+
+        (x, y), (_, _), _, _ = load_mnist()
+
+        x = (5 * x).astype("int")[:100]
+        y = np.argmax(y, axis=1)[:100]
+        x_, y_ = preprocess(x, y, nb_classes=20, clip_values=(0, 5))
+        self.assertEqual(x_.shape, x.shape)
+        self.assertEqual(y_.shape, (y.shape[0], 20))
+        self.assertEqual(x_.max(), 1.0)
+        self.assertEqual(x_.min(), 0)
+
+    def test_iris(self):
+        (x_train, y_train), (x_test, y_test), min_, max_ = load_iris()
+
+        self.assertTrue((min_ == 0).all())
+        self.assertTrue((max_ == 1).all())
+        self.assertEqual(x_train.shape[0], y_train.shape[0])
+        self.assertEqual(x_test.shape[0], y_test.shape[0])
+        train_labels = np.argmax(y_train, axis=1)
+        self.assertEqual(np.setdiff1d(train_labels, np.array([0, 1, 2])).shape, (0,))
+        test_labels = np.argmax(y_test, axis=1)
+        self.assertEqual(np.setdiff1d(test_labels, np.array([0, 1, 2])).shape, (0,))
+
+        (x_train, y_train), (x_test, y_test), _, _ = load_iris(test_set=0)
+        self.assertEqual(x_train.shape[0], 150)
+        self.assertEqual(y_train.shape[0], 150)
+        self.assertIs(x_test, None)
+        self.assertIs(y_test, None)
+
+    def test_segment_by_class(self):
+        data = np.array([[3, 2], [9, 2], [4, 0], [9, 0]])
+        classes = to_categorical(np.array([2, 1, 0, 1]))
+        num_classes = 3
+        segments = segment_by_class(data, classes, num_classes)
+        self.assertEqual(len(segments), num_classes)
+        self.assertEqual(len(segments[1]), 2)
+        self.assertTrue(np.all(np.equal(segments[0], np.array([data[2]]))))
+        self.assertTrue(np.all(np.equal(segments[1], np.array([data[1], data[3]]))))
+        self.assertTrue(np.all(np.equal(segments[2], np.array([data[0]]))))
+
+        num_classes = 4
+        segments = segment_by_class(data, classes, num_classes)
+        self.assertEqual(len(segments), num_classes)
+
+    def test_performance_diff(self):
+        from art.estimators.classification.scikitlearn import SklearnClassifier
+        from sklearn.svm import SVC
+
+        (x_train, y_train), (x_test, y_test), min_, max_ = load_iris()
+
+        full_model = SklearnClassifier(model=SVC(kernel="linear", gamma="auto"), clip_values=(min_, max_))
+        full_model.fit(x_train, y_train)
+
+        limited_model = SklearnClassifier(model=SVC(kernel="linear", gamma="auto"), clip_values=(min_, max_))
+        limited_model.fit(x_train[:10], y_train[:10])
+
+        self.assertEqual(
+            performance_diff(full_model, limited_model, x_test[:20], y_test[:20], perf_function="accuracy"), 0.35
+        )
+        self.assertEqual(performance_diff(full_model, limited_model, x_test[:20], y_test[:20]), 0.35)
+        diff = performance_diff(
+            full_model, limited_model, x_test[:20], y_test[:20], perf_function="f1", average="weighted"
+        )
+        self.assertGreater(diff, 0.43)
+        self.assertLess(diff, 0.44)
+
+        def first_class(true_labels, model_labels, idx=0):
+            return np.average(np.argmax(model_labels, axis=1) == idx)
+
+        self.assertEqual(
+            performance_diff(full_model, limited_model, x_test, y_test, perf_function=first_class), 1.0 / 3
+        )
+        self.assertEqual(
+            performance_diff(full_model, limited_model, x_test, y_test, perf_function=first_class, idx=1), -1.0 / 3
+        )
+
+    def test_is_probability(self):
+        probabilities = np.array([0.1, 0.3, 0.6])
+        self.assertTrue(is_probability(probabilities))
+
+        not_probabilities = np.array([0.1, 0.3, 0.8])
+        self.assertFalse(is_probability(not_probabilities))
+
+        not_probabilities = np.array([0.1, 0.3, 1.8])
+        self.assertFalse(is_probability(not_probabilities))
+
+        not_probabilities = np.array([-1.1, 0.3, 1.8])
+        self.assertFalse(is_probability(not_probabilities))
+
+        not_probabilities = np.array([-1.1, 0.3, 0.7])
+        self.assertFalse(is_probability(not_probabilities))
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/adversarial-robustness-toolbox/tests/test_visualization.py b/adversarial-robustness-toolbox/tests/test_visualization.py
new file mode 100644
index 0000000..baa2838
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/test_visualization.py
@@ -0,0 +1,130 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import os.path
+import unittest
+
+import numpy as np
+
+from art import config
+from art.utils import load_mnist, load_cifar10
+from art.visualization import create_sprite, convert_to_rgb, save_image, plot_3d
+
+from tests.utils import master_seed
+
+logger = logging.getLogger(__name__)
+
+
+class TestVisualization(unittest.TestCase):
+    def setUp(self):
+        master_seed(seed=42)
+
+    def test_save_image(self):
+        (x, _), (_, _), _, _ = load_mnist(raw=True)
+
+        f_name = "image1.png"
+        save_image(x[0], f_name)
+        path = os.path.join(config.ART_DATA_PATH, f_name)
+        self.assertTrue(os.path.isfile(path))
+        os.remove(path)
+
+        f_name = "image2.jpg"
+        save_image(x[1], f_name)
+        path = os.path.join(config.ART_DATA_PATH, f_name)
+        self.assertTrue(os.path.isfile(path))
+        os.remove(path)
+
+        folder = "images123456"
+        f_name_with_dir = os.path.join(folder, "image3.png")
+        save_image(x[3], f_name_with_dir)
+        path = os.path.join(config.ART_DATA_PATH, f_name_with_dir)
+        self.assertTrue(os.path.isfile(path))
+        os.remove(path)
+        os.rmdir(os.path.split(path)[0])  # Remove also test folder
+
+        folder = os.path.join("images123456", "inner")
+        f_name_with_dir = os.path.join(folder, "image4.png")
+        save_image(x[3], f_name_with_dir)
+        path_nested = os.path.join(config.ART_DATA_PATH, f_name_with_dir)
+        self.assertTrue(os.path.isfile(path_nested))
+        os.remove(path_nested)
+        os.rmdir(os.path.split(path_nested)[0])  # Remove inner test folder
+        os.rmdir(os.path.split(path)[0])  # Remove also test folder
+
+    def test_convert_gray_to_rgb(self):
+        # Get MNIST
+        (x, _), (_, _), _, _ = load_mnist(raw=True)
+        n = 100
+        x = x[:n]
+
+        # Test RGB
+        x_rgb = convert_to_rgb(x)
+        s_original = np.shape(x)
+        s_new = np.shape(x_rgb)
+
+        self.assertEqual(s_new[0], s_original[0])
+        self.assertEqual(s_new[1], s_original[1])
+        self.assertEqual(s_new[2], s_original[2])
+        self.assertEqual(s_new[3], 3)  # Should have added 3 channels
+
+    def test_sprites_gray(self):
+        # Get MNIST
+        (x, _), (_, _), _, _ = load_mnist(raw=True)
+        n = 100
+        x = x[:n]
+
+        sprite = create_sprite(x)
+        f_name = "test_sprite_mnist.png"
+        path = os.path.join(config.ART_DATA_PATH, f_name)
+        save_image(sprite, path)
+        self.assertTrue(os.path.isfile(path))
+
+        os.remove(path)  # Remove data added
+
+    def test_sprites_color(self):
+        (x, _), (_, _), _, _ = load_cifar10(raw=True)
+        n = 500
+        x = x[:n]
+
+        sprite = create_sprite(x)
+        f_name = "test_cifar.jpg"
+        path = os.path.join(config.ART_DATA_PATH, f_name)
+        save_image(sprite, path)
+        self.assertTrue(os.path.isfile(path))
+
+        os.remove(path)  # Remove data added
+
+    @unittest.expectedFailure
+    def test_3D_plot_fail(self):
+        points = [[1, 1, 1], [2, 2, 2], [3, 3, 3]]
+        labels = [1, 1, 3]
+
+        # Shouldn't work because labels don't start in zero.
+        plot_3d(points, labels, save=False)
+
+    def test_3D_plot(self):
+        points = [[1, 1, 1], [2, 2, 2], [3, 3, 3]]
+        labels = [0, 1, 1]
+
+        plot_3d(points, labels, save=False)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/adversarial-robustness-toolbox/tests/utils.py b/adversarial-robustness-toolbox/tests/utils.py
new file mode 100644
index 0000000..4eda69f
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/utils.py
@@ -0,0 +1,1655 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+Module providing convenience functions specifically for unit tests.
+"""
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import json
+import logging
+import os
+import pickle
+import time
+import unittest
+import warnings
+
+import numpy as np
+
+from art.estimators.encoding.tensorflow import TensorFlowEncoder
+from art.estimators.generation.tensorflow import TensorFlowGenerator
+from art.utils import load_dataset
+
+logger = logging.getLogger(__name__)
+
+# ----------------------------------------------------------------------------------------------------- TEST BASE CLASS
+art_supported_frameworks = ["keras", "tensorflow", "tensorflow2v1", "pytorch", "scikitlearn"]
+
+
+class TestBase(unittest.TestCase):
+    """
+    This class implements the base class for all unit tests.
+    """
+
+    @classmethod
+    def setUpClass(cls):
+        master_seed(1234)
+
+        cls.n_train = 1000
+        cls.n_test = 100
+        cls.batch_size = 16
+
+        cls.create_image_dataset(n_train=cls.n_train, n_test=cls.n_test)
+
+        (x_train_iris, y_train_iris), (x_test_iris, y_test_iris), _, _ = load_dataset("iris")
+
+        cls.x_train_iris = x_train_iris
+        cls.y_train_iris = y_train_iris
+        cls.x_test_iris = x_test_iris
+        cls.y_test_iris = y_test_iris
+
+        cls._x_train_iris_original = cls.x_train_iris.copy()
+        cls._y_train_iris_original = cls.y_train_iris.copy()
+        cls._x_test_iris_original = cls.x_test_iris.copy()
+        cls._y_test_iris_original = cls.y_test_iris.copy()
+
+        import warnings
+
+        # Filter warning for scipy, removed with scipy 1.4
+        warnings.filterwarnings("ignore", ".*the output shape of zoom.*")
+
+    @classmethod
+    def create_image_dataset(cls, n_train, n_test):
+        (x_train_mnist, y_train_mnist), (x_test_mnist, y_test_mnist), _, _ = load_dataset("mnist")
+        cls.x_train_mnist = x_train_mnist[:n_train]
+        cls.y_train_mnist = y_train_mnist[:n_train]
+        cls.x_test_mnist = x_test_mnist[:n_test]
+        cls.y_test_mnist = y_test_mnist[:n_test]
+
+        cls._x_train_mnist_original = cls.x_train_mnist.copy()
+        cls._y_train_mnist_original = cls.y_train_mnist.copy()
+        cls._x_test_mnist_original = cls.x_test_mnist.copy()
+        cls._y_test_mnist_original = cls.y_test_mnist.copy()
+
+    def setUp(self):
+        self.time_start = time.time()
+        print("\n\n\n----------------------------------------------------------------------")
+
+    def tearDown(self):
+        time_end = time.time() - self.time_start
+        test_name = ".".join(self.id().split(" ")[0].split(".")[-2:])
+        logger.info("%s: completed in %.3f seconds" % (test_name, time_end))
+
+        # Check that the test data has not been modified, only catches changes in attack.generate if self has been used
+        np.testing.assert_array_almost_equal(
+            self._x_train_mnist_original[0 : self.n_train], self.x_train_mnist, decimal=3
+        )
+        np.testing.assert_array_almost_equal(
+            self._y_train_mnist_original[0 : self.n_train], self.y_train_mnist, decimal=3
+        )
+        np.testing.assert_array_almost_equal(self._x_test_mnist_original[0 : self.n_test], self.x_test_mnist, decimal=3)
+        np.testing.assert_array_almost_equal(self._y_test_mnist_original[0 : self.n_test], self.y_test_mnist, decimal=3)
+
+        np.testing.assert_array_almost_equal(self._x_train_iris_original, self.x_train_iris, decimal=3)
+        np.testing.assert_array_almost_equal(self._y_train_iris_original, self.y_train_iris, decimal=3)
+        np.testing.assert_array_almost_equal(self._x_test_iris_original, self.x_test_iris, decimal=3)
+        np.testing.assert_array_almost_equal(self._y_test_iris_original, self.y_test_iris, decimal=3)
+
+
+class ExpectedValue:
+    def __init__(self, value, decimals):
+        self.value = value
+        self.decimals = decimals
+
+
+# ----------------------------------------------------------------------------------------------- TEST MODELS FOR MNIST
+
+
+def check_adverse_example_x(x_adv, x_original, max=1.0, min=0.0, bounded=True):
+    """
+    Performs basic checks on generated adversarial inputs (whether x_test or x_train)
+    :param x_adv:
+    :param x_original:
+    :param max:
+    :param min:
+    :param bounded:
+    :return:
+    """
+    assert bool((x_original == x_adv).all()) is False, "x_test_adv should have been different from x_test"
+
+    if bounded:
+        assert np.amax(x_adv) <= max, "x_test_adv values should have all been below {0}".format(max)
+        assert np.amin(x_adv) >= min, "x_test_adv values should have all been above {0}".format(min)
+    else:
+        assert (x_adv > max).any(), "some x_test_adv values should have been above 1".format(max)
+        assert (x_adv < min).any(), " some x_test_adv values should have all been below {0}".format(min)
+
+
+def check_adverse_predicted_sample_y(y_pred_adv, y_non_adv):
+    assert bool((y_non_adv == y_pred_adv).all()) is False, "Adverse predicted sample was not what was expected"
+
+
+def is_valid_framework(framework):
+    if framework not in art_supported_frameworks:
+        raise Exception(
+            "Framework value {0} is unsupported. Please use one of these valid values: {1}".format(
+                framework, " ".join(art_supported_frameworks)
+            )
+        )
+    return True
+
+
+def _tf_weights_loader(dataset, weights_type, layer="DENSE", tf_version=1):
+    filename = str(weights_type) + "_" + str(layer) + "_" + str(dataset) + ".npy"
+
+    # pylint: disable=W0613
+    # disable pylint because of API requirements for function
+    if tf_version == 1:
+
+        def _tf_initializer(_, dtype, partition_info):
+            import tensorflow as tf
+
+            weights = np.load(
+                os.path.join(os.path.dirname(os.path.dirname(__file__)), "utils/resources/models", filename)
+            )
+            return tf.constant(weights, dtype)
+
+    elif tf_version == 2:
+
+        def _tf_initializer(_, dtype):
+            import tensorflow as tf
+
+            weights = np.load(
+                os.path.join(os.path.dirname(os.path.dirname(__file__)), "utils/resources/models", filename)
+            )
+            return tf.constant(weights, dtype)
+
+    else:
+        raise ValueError("The TensorFlow version tf_version has to be either 1 or 2.")
+
+    return _tf_initializer
+
+
+def _kr_weights_loader(dataset, weights_type, layer="DENSE"):
+    import keras.backend as k
+
+    filename = str(weights_type) + "_" + str(layer) + "_" + str(dataset) + ".npy"
+
+    def _kr_initializer(_, dtype=None):
+        weights = np.load(os.path.join(os.path.dirname(os.path.dirname(__file__)), "utils/resources/models", filename))
+        return k.variable(value=weights, dtype=dtype)
+
+    return _kr_initializer
+
+
+def _kr_tf_weights_loader(dataset, weights_type, layer="DENSE"):
+    filename = str(weights_type) + "_" + str(layer) + "_" + str(dataset) + ".npy"
+    weights = np.load(os.path.join(os.path.dirname(os.path.dirname(__file__)), "utils/resources/models", filename))
+    return weights
+
+
+def get_image_classifier_tf(from_logits=False, load_init=True, sess=None):
+    import tensorflow as tf
+
+    if tf.__version__[0] == "2":
+        # sess is not required but set to None to return 2 values for v1 and v2
+        classifier, sess = get_image_classifier_tf_v2(from_logits=from_logits), None
+    else:
+        classifier, sess = get_image_classifier_tf_v1(from_logits=from_logits, load_init=load_init, sess=sess)
+    return classifier, sess
+
+
+def get_image_classifier_tf_v1(from_logits=False, load_init=True, sess=None):
+    """
+    Standard TensorFlow classifier for unit testing.
+
+    The following hyper-parameters were used to obtain the weights and biases:
+    learning_rate: 0.01
+    batch size: 10
+    number of epochs: 2
+    optimizer: tf.train.AdamOptimizer
+
+    :param from_logits: Flag if model should predict logits (True) or probabilities (False).
+    :type from_logits: `bool`
+    :param load_init: Load the initial weights if True.
+    :type load_init: `bool`
+    :param sess: Computation session.
+    :type sess: `tf.Session`
+    :return: TensorFlowClassifier, tf.Session()
+    """
+    # pylint: disable=E0401
+    import tensorflow as tf
+
+    if tf.__version__[0] == "2":
+        tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
+        import tensorflow.compat.v1 as tf
+
+        tf.disable_eager_execution()
+    from art.estimators.classification.tensorflow import TensorFlowClassifier
+
+    # Define input and output placeholders
+    input_ph = tf.placeholder(tf.float32, shape=[None, 28, 28, 1])
+    output_ph = tf.placeholder(tf.float32, shape=[None, 10])
+
+    # Define the TensorFlow graph
+    if load_init:
+        conv = tf.layers.conv2d(
+            input_ph,
+            1,
+            7,
+            activation=tf.nn.relu,
+            kernel_initializer=_tf_weights_loader("MNIST", "W", "CONV2D"),
+            bias_initializer=_tf_weights_loader("MNIST", "B", "CONV2D"),
+        )
+    else:
+        conv = tf.layers.conv2d(input_ph, 1, 7, activation=tf.nn.relu)
+
+    conv = tf.layers.max_pooling2d(conv, 4, 4)
+    flattened = tf.layers.flatten(conv)
+
+    # Logits layer
+    if load_init:
+        logits = tf.layers.dense(
+            flattened,
+            10,
+            kernel_initializer=_tf_weights_loader("MNIST", "W", "DENSE"),
+            bias_initializer=_tf_weights_loader("MNIST", "B", "DENSE"),
+        )
+    else:
+        logits = tf.layers.dense(flattened, 10)
+
+    # probabilities
+    probabilities = tf.keras.activations.softmax(x=logits)
+
+    # Train operator
+    loss = tf.reduce_mean(
+        tf.losses.softmax_cross_entropy(logits=logits, onehot_labels=output_ph, reduction=tf.losses.Reduction.SUM)
+    )
+    optimizer = tf.train.AdamOptimizer(learning_rate=0.01)
+    train = optimizer.minimize(loss)
+
+    # TensorFlow session and initialization
+    if sess is None:
+        sess = tf.Session()
+    elif not isinstance(sess, tf.Session):
+        raise TypeError("An instance of `tf.Session` should be passed to `sess`.")
+
+    sess.run(tf.global_variables_initializer())
+
+    # Create the classifier
+    if from_logits:
+        tfc = TensorFlowClassifier(
+            clip_values=(0, 1),
+            input_ph=input_ph,
+            output=logits,
+            labels_ph=output_ph,
+            train=train,
+            loss=loss,
+            learning=None,
+            sess=sess,
+        )
+    else:
+        tfc = TensorFlowClassifier(
+            clip_values=(0, 1),
+            input_ph=input_ph,
+            output=probabilities,
+            labels_ph=output_ph,
+            train=train,
+            loss=loss,
+            learning=None,
+            sess=sess,
+        )
+
+    return tfc, sess
+
+
+def get_image_classifier_tf_v2(from_logits=False):
+    """
+    Standard TensorFlow v2 classifier for unit testing.
+
+    The following hyper-parameters were used to obtain the weights and biases:
+    learning_rate: 0.01
+    batch size: 10
+    number of epochs: 2
+    optimizer: tf.train.AdamOptimizer
+
+    :return: TensorFlowV2Classifier
+    """
+    # pylint: disable=E0401
+    import tensorflow as tf
+    from tensorflow.keras import Model
+    from tensorflow.keras.models import Sequential
+    from tensorflow.keras.layers import Dense, Flatten, Conv2D, MaxPool2D
+    from art.estimators.classification.tensorflow import TensorFlowV2Classifier
+
+    if tf.__version__[0] != "2":
+        raise ImportError("This function requires TensorFlow v2.")
+
+    optimizer = tf.keras.optimizers.Adam(learning_rate=0.01)
+
+    def train_step(model, images, labels):
+        with tf.GradientTape() as tape:
+            predictions = model(images, training=True)
+            loss = loss_object(labels, predictions)
+        gradients = tape.gradient(loss, model.trainable_variables)
+        optimizer.apply_gradients(zip(gradients, model.trainable_variables))
+
+    model = Sequential()
+    model.add(
+        Conv2D(
+            filters=1,
+            kernel_size=7,
+            activation="relu",
+            kernel_initializer=_tf_weights_loader("MNIST", "W", "CONV2D", 2),
+            bias_initializer=_tf_weights_loader("MNIST", "B", "CONV2D", 2),
+            input_shape=(28, 28, 1),
+        )
+    )
+    model.add(MaxPool2D(pool_size=(4, 4), strides=(4, 4), padding="valid", data_format=None))
+    model.add(Flatten())
+    if from_logits:
+        model.add(
+            Dense(
+                10,
+                activation="linear",
+                kernel_initializer=_tf_weights_loader("MNIST", "W", "DENSE", 2),
+                bias_initializer=_tf_weights_loader("MNIST", "B", "DENSE", 2),
+            )
+        )
+    else:
+        model.add(
+            Dense(
+                10,
+                activation="softmax",
+                kernel_initializer=_tf_weights_loader("MNIST", "W", "DENSE", 2),
+                bias_initializer=_tf_weights_loader("MNIST", "B", "DENSE", 2),
+            )
+        )
+
+    loss_object = tf.keras.losses.SparseCategoricalCrossentropy(
+        from_logits=from_logits, reduction=tf.keras.losses.Reduction.SUM
+    )
+
+    model.compile(optimizer=optimizer, loss=loss_object)
+
+    # Create the classifier
+    tfc = TensorFlowV2Classifier(
+        model=model,
+        loss_object=loss_object,
+        train_step=train_step,
+        nb_classes=10,
+        input_shape=(28, 28, 1),
+        clip_values=(0, 1),
+    )
+
+    return tfc
+
+
+def get_image_classifier_kr(
+    loss_name="categorical_crossentropy", loss_type="function_losses", from_logits=False, load_init=True
+):
+    """
+    Standard Keras classifier for unit testing
+
+    The weights and biases are identical to the TensorFlow model in get_classifier_tf().
+
+    :param loss_name: The name of the loss function.
+    :type loss_name: `str`
+    :param loss_type: The type of loss function definitions: label (loss function defined by string of its name),
+                      function_losses (loss function imported from keras.losses), function_backend (loss function
+                      imported from keras.backend)
+    :type loss_type: `str`
+    :param from_logits: Flag if model should predict logits (True) or probabilities (False).
+    :type from_logits: `bool`
+    :param load_init: Load the initial weights if True.
+    :type load_init: `bool`
+    :return: KerasClassifier, tf.Session()
+    """
+    import tensorflow as tf
+
+    tf_version = [int(v) for v in tf.__version__.split(".")]
+    if tf_version[0] == 2 and tf_version[1] >= 3:
+        is_tf23_keras24 = True
+        tf.compat.v1.disable_eager_execution()
+        from tensorflow import keras
+        from tensorflow.keras.models import Sequential
+        from tensorflow.keras.layers import Dense, Flatten, Conv2D, MaxPooling2D
+    else:
+        is_tf23_keras24 = False
+        import keras
+        from keras.models import Sequential
+        from keras.layers import Dense, Flatten, Conv2D, MaxPooling2D
+
+    from art.estimators.classification.keras import KerasClassifier
+
+    # Create simple CNN
+    model = Sequential()
+
+    if load_init:
+        if is_tf23_keras24:
+            model.add(
+                Conv2D(
+                    1,
+                    kernel_size=(7, 7),
+                    activation="relu",
+                    input_shape=(28, 28, 1),
+                    kernel_initializer=_tf_weights_loader("MNIST", "W", "CONV2D", 2),
+                    bias_initializer=_tf_weights_loader("MNIST", "B", "CONV2D", 2),
+                )
+            )
+        else:
+            model.add(
+                Conv2D(
+                    1,
+                    kernel_size=(7, 7),
+                    activation="relu",
+                    input_shape=(28, 28, 1),
+                    kernel_initializer=_kr_weights_loader("MNIST", "W", "CONV2D"),
+                    bias_initializer=_kr_weights_loader("MNIST", "B", "CONV2D"),
+                )
+            )
+    else:
+        model.add(Conv2D(1, kernel_size=(7, 7), activation="relu", input_shape=(28, 28, 1)))
+
+    model.add(MaxPooling2D(pool_size=(4, 4)))
+    model.add(Flatten())
+
+    if from_logits:
+        if load_init:
+            if is_tf23_keras24:
+                model.add(
+                    Dense(
+                        10,
+                        activation="linear",
+                        kernel_initializer=_tf_weights_loader("MNIST", "W", "DENSE", 2),
+                        bias_initializer=_tf_weights_loader("MNIST", "B", "DENSE", 2),
+                    )
+                )
+            else:
+                model.add(
+                    Dense(
+                        10,
+                        activation="linear",
+                        kernel_initializer=_kr_weights_loader("MNIST", "W", "DENSE"),
+                        bias_initializer=_kr_weights_loader("MNIST", "B", "DENSE"),
+                    )
+                )
+        else:
+            model.add(Dense(10, activation="linear"))
+    else:
+        if load_init:
+            if is_tf23_keras24:
+                model.add(
+                    Dense(
+                        10,
+                        activation="softmax",
+                        kernel_initializer=_tf_weights_loader("MNIST", "W", "DENSE", 2),
+                        bias_initializer=_tf_weights_loader("MNIST", "B", "DENSE", 2),
+                    )
+                )
+            else:
+                model.add(
+                    Dense(
+                        10,
+                        activation="softmax",
+                        kernel_initializer=_kr_weights_loader("MNIST", "W", "DENSE"),
+                        bias_initializer=_kr_weights_loader("MNIST", "B", "DENSE"),
+                    )
+                )
+        else:
+            model.add(Dense(10, activation="softmax"))
+
+    if loss_name == "categorical_hinge":
+        if loss_type == "label":
+            raise AttributeError("This combination of loss function options is not supported.")
+        elif loss_type == "function_losses":
+            loss = keras.losses.categorical_hinge
+    elif loss_name == "categorical_crossentropy":
+        if loss_type == "label":
+            if from_logits:
+                raise AttributeError("This combination of loss function options is not supported.")
+            else:
+                loss = loss_name
+        elif loss_type == "function_losses":
+            if from_logits:
+                if int(keras.__version__.split(".")[0]) == 2 and int(keras.__version__.split(".")[1]) >= 3:
+
+                    def categorical_crossentropy(y_true, y_pred):
+                        return keras.losses.categorical_crossentropy(y_true, y_pred, from_logits=True)
+
+                    loss = categorical_crossentropy
+                else:
+                    raise NotImplementedError("This combination of loss function options is not supported.")
+            else:
+                loss = keras.losses.categorical_crossentropy
+        elif loss_type == "function_backend":
+            if from_logits:
+
+                def categorical_crossentropy(y_true, y_pred):
+                    return keras.backend.categorical_crossentropy(y_true, y_pred, from_logits=True)
+
+                loss = categorical_crossentropy
+            else:
+                loss = keras.backend.categorical_crossentropy
+    elif loss_name == "sparse_categorical_crossentropy":
+        if loss_type == "label":
+            if from_logits:
+                raise AttributeError("This combination of loss function options is not supported.")
+            else:
+                loss = loss_name
+        elif loss_type == "function_losses":
+            if from_logits:
+                if int(keras.__version__.split(".")[0]) == 2 and int(keras.__version__.split(".")[1]) >= 3:
+
+                    def sparse_categorical_crossentropy(y_true, y_pred):
+                        return keras.losses.sparse_categorical_crossentropy(y_true, y_pred, from_logits=True)
+
+                    loss = sparse_categorical_crossentropy
+                else:
+                    raise AttributeError("This combination of loss function options is not supported.")
+            else:
+                loss = keras.losses.sparse_categorical_crossentropy
+        elif loss_type == "function_backend":
+            if from_logits:
+
+                def sparse_categorical_crossentropy(y_true, y_pred):
+                    return keras.backend.sparse_categorical_crossentropy(y_true, y_pred, from_logits=True)
+
+                loss = sparse_categorical_crossentropy
+            else:
+                loss = keras.backend.sparse_categorical_crossentropy
+    elif loss_name == "kullback_leibler_divergence":
+        if loss_type == "label":
+            raise AttributeError("This combination of loss function options is not supported.")
+        elif loss_type == "function_losses":
+            loss = keras.losses.kullback_leibler_divergence
+        elif loss_type == "function_backend":
+            raise AttributeError("This combination of loss function options is not supported.")
+    elif loss_name == "cosine_similarity":
+        if loss_type == "label":
+            loss = loss_name
+        elif loss_type == "function_losses":
+            loss = keras.losses.cosine_similarity
+        elif loss_type == "function_backend":
+            loss = keras.backend.cosine_similarity
+
+    else:
+        raise ValueError("Loss name not recognised.")
+
+    model.compile(loss=loss, optimizer=keras.optimizers.Adam(lr=0.01), metrics=["accuracy"])
+
+    # Get classifier
+    krc = KerasClassifier(model, clip_values=(0, 1), use_logits=from_logits)
+
+    return krc
+
+
+def get_image_classifier_kr_functional(input_layer=1, output_layer=1):
+    from art.estimators.classification.keras import KerasClassifier
+    import keras
+    from keras.layers import Conv2D, Dense, Dropout, Flatten, Input, MaxPooling2D
+    from keras.models import Model
+
+    def _functional_model():
+        in_layer = Input(shape=(28, 28, 1), name="input0")
+        layer = Conv2D(32, kernel_size=(3, 3), activation="relu")(in_layer)
+        layer = Conv2D(64, (3, 3), activation="relu")(layer)
+        layer = MaxPooling2D(pool_size=(2, 2))(layer)
+        layer = Dropout(0.25)(layer)
+        layer = Flatten()(layer)
+        layer = Dense(128, activation="relu")(layer)
+        layer = Dropout(0.5)(layer)
+        out_layer = Dense(10, activation="softmax", name="output0")(layer)
+
+        in_layer_2 = Input(shape=(28, 28, 1), name="input1")
+        layer = Conv2D(32, kernel_size=(3, 3), activation="relu")(in_layer_2)
+        layer = Conv2D(64, (3, 3), activation="relu")(layer)
+        layer = MaxPooling2D(pool_size=(2, 2))(layer)
+        layer = Dropout(0.25)(layer)
+        layer = Flatten()(layer)
+        layer = Dense(128, activation="relu")(layer)
+        layer = Dropout(0.5)(layer)
+        out_layer_2 = Dense(10, activation="softmax", name="output1")(layer)
+
+        model = Model(inputs=[in_layer, in_layer_2], outputs=[out_layer, out_layer_2])
+
+        model.compile(
+            loss=keras.losses.categorical_crossentropy,
+            optimizer=keras.optimizers.Adadelta(),
+            metrics=["accuracy"],
+            loss_weights=[1.0, 1.0],
+        )
+
+        return model
+
+    functional_model = _functional_model()
+
+    return KerasClassifier(functional_model, clip_values=(0, 1), input_layer=input_layer, output_layer=output_layer)
+
+
+def get_image_classifier_kr_tf_functional(input_layer=1, output_layer=1):
+    """
+       Standard Keras_tf classifier for unit testing built with a functional model
+
+       :return: KerasClassifier
+       """
+    import tensorflow as tf
+
+    if tf.__version__[0] == "2":
+        tf.compat.v1.disable_eager_execution()
+    from tensorflow.keras.layers import Conv2D, Dense, Dropout, Flatten, Input, MaxPooling2D
+    from tensorflow.keras.models import Model
+    from art.estimators.classification.keras import KerasClassifier
+
+    def functional_model():
+        in_layer = Input(shape=(28, 28, 1), name="input0")
+        layer = Conv2D(32, kernel_size=(3, 3), activation="relu")(in_layer)
+        layer = Conv2D(64, (3, 3), activation="relu")(layer)
+        layer = MaxPooling2D(pool_size=(2, 2))(layer)
+        layer = Dropout(0.25)(layer)
+        layer = Flatten()(layer)
+        layer = Dense(128, activation="relu")(layer)
+        layer = Dropout(0.5)(layer)
+        out_layer = Dense(10, activation="softmax", name="output0")(layer)
+
+        in_layer_2 = Input(shape=(28, 28, 1), name="input1")
+        layer = Conv2D(32, kernel_size=(3, 3), activation="relu")(in_layer_2)
+        layer = Conv2D(64, (3, 3), activation="relu")(layer)
+        layer = MaxPooling2D(pool_size=(2, 2))(layer)
+        layer = Dropout(0.25)(layer)
+        layer = Flatten()(layer)
+        layer = Dense(128, activation="relu")(layer)
+        layer = Dropout(0.5)(layer)
+        out_layer_2 = Dense(10, activation="softmax", name="output1")(layer)
+
+        model = Model(inputs=[in_layer, in_layer_2], outputs=[out_layer, out_layer_2])
+
+        model.compile(
+            loss=tf.keras.losses.categorical_crossentropy,
+            optimizer=tf.keras.optimizers.Adadelta(),
+            metrics=["accuracy"],
+            loss_weights=[1.0, 1.0],
+        )
+
+        return model
+
+    return KerasClassifier(functional_model(), clip_values=(0, 1), input_layer=input_layer, output_layer=output_layer)
+
+
+def get_image_classifier_kr_tf(loss_name="categorical_crossentropy", loss_type="function", from_logits=False):
+    """
+    Standard Keras classifier for unit testing
+
+    The weights and biases are identical to the TensorFlow model in get_classifier_tf().
+
+    :param loss_name: The name of the loss function.
+    :type loss_name: `str`
+    :param loss_type: The type of loss function definitions: label (loss function defined by string of its name),
+                      function_losses (loss function), class (loss function generator)
+    :type loss_type: `str`
+    :param from_logits: Flag if model should predict logits (True) or probabilities (False).
+    :type from_logits: `bool`
+
+
+    :return: KerasClassifier
+    """
+    # pylint: disable=E0401
+    import tensorflow as tf
+
+    if tf.__version__[0] == "2":
+        tf.compat.v1.disable_eager_execution()
+    from tensorflow.keras.models import Sequential
+    from tensorflow.keras.layers import Dense, Flatten, Conv2D, MaxPooling2D
+
+    from art.estimators.classification.keras import KerasClassifier
+
+    # Create simple CNN
+    model = Sequential()
+    model.add(Conv2D(1, kernel_size=(7, 7), activation="relu", input_shape=(28, 28, 1)))
+    model.layers[-1].set_weights(
+        [_kr_tf_weights_loader("MNIST", "W", "CONV2D"), _kr_tf_weights_loader("MNIST", "B", "CONV2D")]
+    )
+    model.add(MaxPooling2D(pool_size=(4, 4)))
+    model.add(Flatten())
+
+    if from_logits:
+        model.add(Dense(10, activation="linear"))
+    else:
+        model.add(Dense(10, activation="softmax"))
+
+    model.layers[-1].set_weights(
+        [_kr_tf_weights_loader("MNIST", "W", "DENSE"), _kr_tf_weights_loader("MNIST", "B", "DENSE")]
+    )
+
+    if loss_name == "categorical_hinge":
+        if loss_type == "label":
+            loss = loss_name
+        elif loss_type == "function":
+            loss = tf.keras.losses.categorical_hinge
+        elif loss_type == "class":
+            try:
+                reduction = tf.keras.losses.Reduction.NONE
+            except AttributeError:
+                try:
+                    reduction = tf.losses.Reduction.NONE
+                except AttributeError:
+                    try:
+                        reduction = tf.python.keras.utils.losses_utils.ReductionV2.NONE
+                    except AttributeError:
+                        raise ImportError("This combination of loss function options is not supported.")
+            loss = tf.keras.losses.CategoricalHinge(reduction=reduction)
+    elif loss_name == "categorical_crossentropy":
+        if loss_type == "label":
+            if from_logits:
+                raise AttributeError
+            else:
+                loss = loss_name
+        elif loss_type == "function":
+            if from_logits:
+
+                def categorical_crossentropy(y_true, y_pred):
+                    return tf.keras.losses.categorical_crossentropy(y_true, y_pred, from_logits=True)
+
+                loss = categorical_crossentropy
+            else:
+                loss = tf.keras.losses.categorical_crossentropy
+        elif loss_type == "class":
+            try:
+                reduction = tf.keras.losses.Reduction.NONE
+            except AttributeError:
+                try:
+                    reduction = tf.losses.Reduction.NONE
+                except AttributeError:
+                    try:
+                        reduction = tf.python.keras.utils.losses_utils.ReductionV2.NONE
+                    except AttributeError:
+                        raise ImportError("This combination of loss function options is not supported.")
+            loss = tf.keras.losses.CategoricalCrossentropy(from_logits=from_logits, reduction=reduction)
+    elif loss_name == "sparse_categorical_crossentropy":
+        if loss_type == "label":
+            if from_logits:
+                raise AttributeError
+            else:
+                loss = loss_name
+        elif loss_type == "function":
+            if from_logits:
+
+                def sparse_categorical_crossentropy(y_true, y_pred):
+                    return tf.keras.losses.sparse_categorical_crossentropy(y_true, y_pred, from_logits=True)
+
+                loss = sparse_categorical_crossentropy
+            else:
+                loss = tf.keras.losses.sparse_categorical_crossentropy
+        elif loss_type == "class":
+            try:
+                reduction = tf.keras.losses.Reduction.NONE
+            except AttributeError:
+                try:
+                    reduction = tf.losses.Reduction.NONE
+                except AttributeError:
+                    try:
+                        reduction = tf.python.keras.utils.losses_utils.ReductionV2.NONE
+                    except AttributeError:
+                        raise ImportError("This combination of loss function options is not supported.")
+            loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=from_logits, reduction=reduction)
+    elif loss_name == "kullback_leibler_divergence":
+        if loss_type == "label":
+            loss = loss_name
+        elif loss_type == "function":
+            loss = tf.keras.losses.kullback_leibler_divergence
+        elif loss_type == "class":
+            try:
+                reduction = tf.keras.losses.Reduction.NONE
+            except AttributeError:
+                try:
+                    reduction = tf.losses.Reduction.NONE
+                except AttributeError:
+                    try:
+                        reduction = tf.python.keras.utils.losses_utils.ReductionV2.NONE
+                    except AttributeError:
+                        raise ImportError("This combination of loss function options is not supported.")
+            loss = tf.keras.losses.KLDivergence(reduction=reduction)
+    elif loss_name == "cosine_similarity":
+        if loss_type == "label":
+            loss = loss_name
+        elif loss_type == "function":
+            loss = tf.keras.losses.cosine_similarity
+        elif loss_type == "class":
+            try:
+                reduction = tf.keras.losses.Reduction.NONE
+            except AttributeError:
+                try:
+                    reduction = tf.losses.Reduction.NONE
+                except AttributeError:
+                    try:
+                        reduction = tf.python.keras.utils.losses_utils.ReductionV2.NONE
+                    except AttributeError:
+                        raise ImportError("This combination of loss function options is not supported.")
+            loss = tf.keras.losses.CosineSimilarity(reduction=reduction)
+
+    else:
+        raise ValueError("Loss name not recognised.")
+
+    model.compile(loss=loss, optimizer=tf.keras.optimizers.Adam(lr=0.01), metrics=["accuracy"])
+
+    # Get classifier
+    krc = KerasClassifier(model, clip_values=(0, 1), use_logits=from_logits)
+
+    return krc
+
+
+def get_image_classifier_kr_tf_binary():
+    """
+    Standard TensorFlow-Keras binary classifier for unit testing
+
+    :return: KerasClassifier
+    """
+    # pylint: disable=E0401
+    import tensorflow as tf
+
+    if tf.__version__[0] == "2":
+        tf.compat.v1.disable_eager_execution()
+    from tensorflow.keras.models import Sequential
+    from tensorflow.keras.layers import Dense, Flatten, Conv2D, MaxPooling2D
+
+    from art.estimators.classification.keras import KerasClassifier
+
+    # Create simple CNN
+    model = Sequential()
+    model.add(Conv2D(1, kernel_size=(7, 7), activation="relu", input_shape=(28, 28, 1)))
+    model.layers[-1].set_weights(
+        [_kr_tf_weights_loader("MNIST_BINARY", "W", "CONV2D"), _kr_tf_weights_loader("MNIST_BINARY", "B", "CONV2D")]
+    )
+    model.add(MaxPooling2D(pool_size=(4, 4)))
+    model.add(Flatten())
+    model.add(Dense(1, activation="sigmoid"))
+
+    model.layers[-1].set_weights(
+        [_kr_tf_weights_loader("MNIST_BINARY", "W", "DENSE"), _kr_tf_weights_loader("MNIST_BINARY", "B", "DENSE")]
+    )
+
+    model.compile(loss="binary_crossentropy", optimizer=tf.keras.optimizers.Adam(lr=0.01), metrics=["accuracy"])
+
+    # Get classifier
+    krc = KerasClassifier(model, clip_values=(0, 1), use_logits=False)
+
+    return krc
+
+
+def get_image_classifier_kr_tf_with_wildcard():
+    """
+    Standard TensorFlow-Keras binary classifier for unit testing
+
+    :return: KerasClassifier
+    """
+    # pylint: disable=E0401
+    import tensorflow as tf
+
+    if tf.__version__[0] == "2":
+        tf.compat.v1.disable_eager_execution()
+    from tensorflow.keras.models import Sequential
+    from tensorflow.keras.layers import Dense, Conv1D, LSTM
+
+    from art.estimators.classification.keras import KerasClassifier
+
+    # Create simple CNN
+    model = Sequential()
+    model.add(Conv1D(1, 3, activation="relu", input_shape=(None, 1)))
+    model.add(LSTM(4))
+    model.add(Dense(2, activation="softmax"))
+    model.compile(loss="binary_crossentropy", optimizer="adam")
+
+    # Get classifier
+    krc = KerasClassifier(model)
+
+    return krc
+
+
+def get_image_classifier_pt(from_logits=False, load_init=True):
+    """
+    Standard PyTorch classifier for unit testing.
+
+    :param from_logits: Flag if model should predict logits (True) or probabilities (False).
+    :type from_logits: `bool`
+    :param load_init: Load the initial weights if True.
+    :type load_init: `bool`
+    :return: PyTorchClassifier
+    """
+    import torch
+    import torch.nn as nn
+    import torch.optim as optim
+    from art.estimators.classification.pytorch import PyTorchClassifier
+
+    class Model(torch.nn.Module):
+        """
+        Create model for pytorch.
+
+        The weights and biases are identical to the TensorFlow model in get_classifier_tf().
+        """
+
+        def __init__(self):
+            super(Model, self).__init__()
+
+            self.conv = torch.nn.Conv2d(in_channels=1, out_channels=1, kernel_size=7)
+            self.relu = torch.nn.ReLU()
+            self.pool = torch.nn.MaxPool2d(4, 4)
+            self.fullyconnected = torch.nn.Linear(25, 10)
+
+            if load_init:
+                w_conv2d = np.load(
+                    os.path.join(
+                        os.path.dirname(os.path.dirname(__file__)), "utils/resources/models", "W_CONV2D_MNIST.npy"
+                    )
+                )
+                b_conv2d = np.load(
+                    os.path.join(
+                        os.path.dirname(os.path.dirname(__file__)), "utils/resources/models", "B_CONV2D_MNIST.npy"
+                    )
+                )
+                w_dense = np.load(
+                    os.path.join(
+                        os.path.dirname(os.path.dirname(__file__)), "utils/resources/models", "W_DENSE_MNIST.npy"
+                    )
+                )
+                b_dense = np.load(
+                    os.path.join(
+                        os.path.dirname(os.path.dirname(__file__)), "utils/resources/models", "B_DENSE_MNIST.npy"
+                    )
+                )
+
+                w_conv2d_pt = w_conv2d.reshape((1, 1, 7, 7))
+
+                self.conv.weight = torch.nn.Parameter(torch.Tensor(w_conv2d_pt))
+                self.conv.bias = torch.nn.Parameter(torch.Tensor(b_conv2d))
+                self.fullyconnected.weight = torch.nn.Parameter(torch.Tensor(np.transpose(w_dense)))
+                self.fullyconnected.bias = torch.nn.Parameter(torch.Tensor(b_dense))
+
+        # pylint: disable=W0221
+        # disable pylint because of API requirements for function
+        def forward(self, x):
+            """
+            Forward function to evaluate the model
+            :param x: Input to the model
+            :return: Prediction of the model
+            """
+            x = self.conv(x)
+            x = self.relu(x)
+            x = self.pool(x)
+            x = x.reshape(-1, 25)
+            x = self.fullyconnected(x)
+            if not from_logits:
+                x = torch.nn.functional.softmax(x, dim=1)
+            return x
+
+    # Define the network
+    model = Model()
+
+    # Define a loss function and optimizer
+    loss_fn = torch.nn.CrossEntropyLoss(reduction="sum")
+    optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
+
+    # Get classifier
+    ptc = PyTorchClassifier(
+        model=model, loss=loss_fn, optimizer=optimizer, input_shape=(1, 28, 28), nb_classes=10, clip_values=(0, 1)
+    )
+
+    return ptc
+
+
+def get_classifier_bb(defences=None):
+    """
+    Standard BlackBox classifier for unit testing
+
+    :return: BlackBoxClassifier
+    """
+    from art.estimators.classification.blackbox import BlackBoxClassifier
+    from art.utils import to_categorical
+
+    # define black-box classifier
+    def predict(x):
+        with open(
+            os.path.join(os.path.dirname(os.path.dirname(__file__)), "utils/data/mnist", "api_output.txt")
+        ) as json_file:
+            predictions = json.load(json_file)
+        return to_categorical(predictions["values"][: len(x)], nb_classes=10)
+
+    bbc = BlackBoxClassifier(predict, (28, 28, 1), 10, clip_values=(0, 255), preprocessing_defences=defences)
+    return bbc
+
+
+def get_image_classifier_mxnet_custom_ini():
+    import mxnet
+
+    w_conv2d = np.load(
+        os.path.join(os.path.dirname(os.path.dirname(__file__)), "utils/resources/models", "W_CONV2D_MNIST.npy")
+    )
+    b_conv2d = np.load(
+        os.path.join(os.path.dirname(os.path.dirname(__file__)), "utils/resources/models", "B_CONV2D_MNIST.npy")
+    )
+    w_dense = np.load(
+        os.path.join(os.path.dirname(os.path.dirname(__file__)), "utils/resources/models", "W_DENSE_MNIST.npy")
+    )
+    b_dense = np.load(
+        os.path.join(os.path.dirname(os.path.dirname(__file__)), "utils/resources/models", "B_DENSE_MNIST.npy")
+    )
+
+    w_conv2d_mx = w_conv2d.reshape((1, 1, 7, 7))
+
+    alias = mxnet.registry.get_alias_func(mxnet.initializer.Initializer, "initializer")
+
+    @mxnet.init.register
+    @alias("mm_init")
+    class CustomInit(mxnet.init.Initializer):
+        def __init__(self):
+            super(CustomInit, self).__init__()
+            self.params = dict()
+            self.params["conv0_weight"] = w_conv2d_mx
+            self.params["conv0_bias"] = b_conv2d
+            self.params["dense0_weight"] = np.transpose(w_dense)
+            self.params["dense0_bias"] = b_dense
+
+        def _init_weight(self, name, arr):
+            arr[:] = self.params[name]
+
+        def _init_bias(self, name, arr):
+            arr[:] = self.params[name]
+
+    return CustomInit()
+
+
+def get_gan_inverse_gan_ft():
+    import tensorflow as tf
+    from utils.resources.create_inverse_gan_models import build_gan_graph, build_inverse_gan_graph
+
+    if tf.__version__[0] == "2":
+        return None, None, None
+    else:
+
+        lr = 0.0002
+        latent_enc_len = 100
+
+        gen_tf, z_ph, gen_loss, gen_opt_tf, disc_loss_tf, disc_opt_tf, x_ph = build_gan_graph(lr, latent_enc_len)
+
+        enc_tf, image_to_enc_ph, latent_enc_loss, enc_opt = build_inverse_gan_graph(lr, gen_tf, z_ph, latent_enc_len)
+
+        sess = tf.Session()
+        sess.run(tf.global_variables_initializer())
+
+        gan = TensorFlowGenerator(input_ph=z_ph, model=gen_tf, sess=sess,)
+
+        inverse_gan = TensorFlowEncoder(input_ph=image_to_enc_ph, model=enc_tf, sess=sess,)
+        return gan, inverse_gan, sess
+
+
+# ------------------------------------------------------------------------------------------------ TEST MODELS FOR IRIS
+
+
+def get_tabular_classifier_tf(load_init=True, sess=None):
+    import tensorflow as tf
+
+    if tf.__version__[0] == "2":
+        # sess is not required but set to None to return 2 values for v1 and v2
+        classifier, sess = get_tabular_classifier_tf_v2(), None
+    else:
+        classifier, sess = get_tabular_classifier_tf_v1(load_init=load_init, sess=sess)
+    return classifier, sess
+
+
+def get_tabular_classifier_tf_v1(load_init=True, sess=None):
+    """
+    Standard TensorFlow classifier for unit testing.
+
+    The following hyper-parameters were used to obtain the weights and biases:
+
+    * learning_rate: 0.01
+    * batch size: 5
+    * number of epochs: 200
+    * optimizer: tf.train.AdamOptimizer
+
+    The model is trained of 70% of the dataset, and 30% of the training set is used as validation split.
+
+    :param load_init: Load the initial weights if True.
+    :type load_init: `bool`
+    :param sess: Computation session.
+    :type sess: `tf.Session`
+    :return: The trained model for Iris dataset and the session.
+    :rtype: `tuple(TensorFlowClassifier, tf.Session)`
+    """
+    import tensorflow as tf
+
+    if tf.__version__[0] == "2":
+        # pylint: disable=E0401
+        import tensorflow.compat.v1 as tf
+
+        tf.disable_eager_execution()
+    from art.estimators.classification.tensorflow import TensorFlowClassifier
+
+    # Define input and output placeholders
+    input_ph = tf.placeholder(tf.float32, shape=[None, 4])
+    output_ph = tf.placeholder(tf.int32, shape=[None, 3])
+
+    # Define the TensorFlow graph
+    if load_init:
+        dense1 = tf.layers.dense(
+            input_ph,
+            10,
+            kernel_initializer=_tf_weights_loader("IRIS", "W", "DENSE1"),
+            bias_initializer=_tf_weights_loader("IRIS", "B", "DENSE1"),
+        )
+        dense2 = tf.layers.dense(
+            dense1,
+            10,
+            kernel_initializer=_tf_weights_loader("IRIS", "W", "DENSE2"),
+            bias_initializer=_tf_weights_loader("IRIS", "B", "DENSE2"),
+        )
+        logits = tf.layers.dense(
+            dense2,
+            3,
+            kernel_initializer=_tf_weights_loader("IRIS", "W", "DENSE3"),
+            bias_initializer=_tf_weights_loader("IRIS", "B", "DENSE3"),
+        )
+    else:
+        dense1 = tf.layers.dense(input_ph, 10)
+        dense2 = tf.layers.dense(dense1, 10)
+        logits = tf.layers.dense(dense2, 3)
+
+    # Train operator
+    loss = tf.reduce_mean(tf.losses.softmax_cross_entropy(logits=logits, onehot_labels=output_ph))
+    optimizer = tf.train.AdamOptimizer(learning_rate=0.01)
+    train = optimizer.minimize(loss)
+
+    # TensorFlow session and initialization
+    if sess is None:
+        sess = tf.Session()
+    elif not isinstance(sess, tf.Session):
+        raise TypeError("An instance of `tf.Session` should be passed to `sess`.")
+
+    sess.run(tf.global_variables_initializer())
+
+    # Train the classifier
+    tfc = TensorFlowClassifier(
+        clip_values=(0, 1),
+        input_ph=input_ph,
+        output=logits,
+        labels_ph=output_ph,
+        train=train,
+        loss=loss,
+        learning=None,
+        sess=sess,
+        channels_first=True,
+    )
+
+    return tfc, sess
+
+
+def get_tabular_classifier_tf_v2():
+    """
+    Standard TensorFlow v2 classifier for unit testing.
+
+    The following hyper-parameters were used to obtain the weights and biases:
+
+    * learning_rate: 0.01
+    * batch size: 5
+    * number of epochs: 200
+    * optimizer: tf.train.AdamOptimizer
+
+    The model is trained of 70% of the dataset, and 30% of the training set is used as validation split.
+
+    :return: The trained model for Iris dataset and the session.
+    :rtype: `TensorFlowV2Classifier`
+    """
+    # pylint: disable=E0401
+    import tensorflow as tf
+    from tensorflow.keras import Model
+    from tensorflow.keras.layers import Dense
+    from art.estimators.classification.tensorflow import TensorFlowV2Classifier
+
+    if tf.__version__[0] != "2":
+        raise ImportError("This function requires TensorFlow v2.")
+
+    class TensorFlowModel(Model):
+        """
+        Standard TensorFlow model for unit testing
+        """
+
+        def __init__(self):
+            super(TensorFlowModel, self).__init__()
+            self.dense1 = Dense(
+                10,
+                activation="linear",
+                kernel_initializer=_tf_weights_loader("IRIS", "W", "DENSE1", tf_version=2),
+                bias_initializer=_tf_weights_loader("IRIS", "B", "DENSE1", tf_version=2),
+            )
+            self.dense2 = Dense(
+                10,
+                activation="linear",
+                kernel_initializer=_tf_weights_loader("IRIS", "W", "DENSE2", tf_version=2),
+                bias_initializer=_tf_weights_loader("IRIS", "B", "DENSE2", tf_version=2),
+            )
+            self.logits = Dense(
+                3,
+                activation="linear",
+                kernel_initializer=_tf_weights_loader("IRIS", "W", "DENSE3", tf_version=2),
+                bias_initializer=_tf_weights_loader("IRIS", "B", "DENSE3", tf_version=2),
+            )
+
+        def call(self, x):
+            """
+            Call function to evaluate the model
+
+            :param x: Input to the model
+            :return: Prediction of the model
+            """
+            x = self.dense1(x)
+            x = self.dense2(x)
+            x = self.logits(x)
+            return x
+
+    optimizer = tf.keras.optimizers.Adam(learning_rate=0.01)
+
+    def train_step(model, images, labels):
+        with tf.GradientTape() as tape:
+            predictions = model(images, training=True)
+            loss = loss_object(labels, predictions)
+        gradients = tape.gradient(loss, model.trainable_variables)
+        optimizer.apply_gradients(zip(gradients, model.trainable_variables))
+
+    model = TensorFlowModel()
+    loss_object = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
+
+    # Create the classifier
+    tfc = TensorFlowV2Classifier(
+        model=model, loss_object=loss_object, train_step=train_step, nb_classes=3, input_shape=(4,), clip_values=(0, 1)
+    )
+
+    return tfc
+
+
+def get_tabular_classifier_scikit_list(clipped=False, model_list_names=None):
+    from art.estimators.classification.scikitlearn import (
+        ScikitlearnDecisionTreeClassifier,
+        # ScikitlearnExtraTreeClassifier,
+        ScikitlearnAdaBoostClassifier,
+        ScikitlearnBaggingClassifier,
+        ScikitlearnExtraTreesClassifier,
+        ScikitlearnGradientBoostingClassifier,
+        ScikitlearnRandomForestClassifier,
+        ScikitlearnLogisticRegression,
+        ScikitlearnSVC,
+    )
+
+    available_models = {
+        "decisionTreeClassifier": ScikitlearnDecisionTreeClassifier,
+        # "extraTreeClassifier": ScikitlearnExtraTreeClassifier,
+        "adaBoostClassifier": ScikitlearnAdaBoostClassifier,
+        "baggingClassifier": ScikitlearnBaggingClassifier,
+        "extraTreesClassifier": ScikitlearnExtraTreesClassifier,
+        "gradientBoostingClassifier": ScikitlearnGradientBoostingClassifier,
+        "randomForestClassifier": ScikitlearnRandomForestClassifier,
+        "logisticRegression": ScikitlearnLogisticRegression,
+        "svc": ScikitlearnSVC,
+        "linearSVC": ScikitlearnSVC,
+    }
+
+    if model_list_names is None:
+        model_dict_names = available_models
+    else:
+        model_dict_names = dict()
+        for name in model_list_names:
+            model_dict_names[name] = available_models[name]
+
+    classifier_list = list()
+
+    if clipped:
+        for model_name, model_class in model_dict_names.items():
+            model = pickle.load(
+                open(
+                    os.path.join(
+                        os.path.dirname(os.path.dirname(__file__)),
+                        "utils/resources/models/scikit/",
+                        "scikit-" + model_name + "-iris-clipped.pickle",
+                    ),
+                    "rb",
+                )
+            )
+            classifier_list.append(model_class(model=model, clip_values=(0, 1)))
+    else:
+        for model_name, model_class in model_dict_names.items():
+            model = pickle.load(
+                open(
+                    os.path.join(
+                        os.path.dirname(os.path.dirname(__file__)),
+                        "utils/resources/models/scikit/",
+                        "scikit-" + model_name + "-iris-unclipped.pickle",
+                    ),
+                    "rb",
+                )
+            )
+            classifier_list.append(model_class(model=model, clip_values=None))
+
+    return classifier_list
+
+
+def get_tabular_classifier_kr(load_init=True):
+    """
+    Standard Keras classifier for unit testing on Iris dataset. The weights and biases are identical to the TensorFlow
+    model in `get_iris_classifier_tf`.
+
+    :param load_init: Load the initial weights if True.
+    :type load_init: `bool`
+    :return: The trained model for Iris dataset and the session.
+    :rtype: `tuple(KerasClassifier, tf.Session)`
+    """
+    import tensorflow as tf
+
+    tf_version = [int(v) for v in tf.__version__.split(".")]
+    if tf_version[0] == 2 and tf_version[1] >= 3:
+        is_tf23_keras24 = True
+        tf.compat.v1.disable_eager_execution()
+        from tensorflow import keras
+        from tensorflow.keras.models import Sequential
+        from tensorflow.keras.layers import Dense
+    else:
+        is_tf23_keras24 = False
+        import keras
+        from keras.models import Sequential
+        from keras.layers import Dense
+
+    from art.estimators.classification.keras import KerasClassifier
+
+    # Create simple CNN
+    model = Sequential()
+
+    if load_init:
+        if is_tf23_keras24:
+            model.add(
+                Dense(
+                    10,
+                    input_shape=(4,),
+                    activation="relu",
+                    kernel_initializer=_tf_weights_loader("IRIS", "W", "DENSE1", 2),
+                    bias_initializer=_tf_weights_loader("IRIS", "B", "DENSE1", 2),
+                )
+            )
+            model.add(
+                Dense(
+                    10,
+                    activation="relu",
+                    kernel_initializer=_tf_weights_loader("IRIS", "W", "DENSE2", 2),
+                    bias_initializer=_tf_weights_loader("IRIS", "B", "DENSE2", 2),
+                )
+            )
+            model.add(
+                Dense(
+                    3,
+                    activation="softmax",
+                    kernel_initializer=_tf_weights_loader("IRIS", "W", "DENSE3", 2),
+                    bias_initializer=_tf_weights_loader("IRIS", "B", "DENSE3", 2),
+                )
+            )
+        else:
+            model.add(
+                Dense(
+                    10,
+                    input_shape=(4,),
+                    activation="relu",
+                    kernel_initializer=_kr_weights_loader("IRIS", "W", "DENSE1"),
+                    bias_initializer=_kr_weights_loader("IRIS", "B", "DENSE1"),
+                )
+            )
+            model.add(
+                Dense(
+                    10,
+                    activation="relu",
+                    kernel_initializer=_kr_weights_loader("IRIS", "W", "DENSE2"),
+                    bias_initializer=_kr_weights_loader("IRIS", "B", "DENSE2"),
+                )
+            )
+            model.add(
+                Dense(
+                    3,
+                    activation="softmax",
+                    kernel_initializer=_kr_weights_loader("IRIS", "W", "DENSE3"),
+                    bias_initializer=_kr_weights_loader("IRIS", "B", "DENSE3"),
+                )
+            )
+    else:
+        model.add(Dense(10, input_shape=(4,), activation="relu"))
+        model.add(Dense(10, activation="relu"))
+        model.add(Dense(3, activation="softmax"))
+
+    model.compile(loss="categorical_crossentropy", optimizer=keras.optimizers.Adam(lr=0.001), metrics=["accuracy"])
+
+    # Get classifier
+    krc = KerasClassifier(model, clip_values=(0, 1), use_logits=False, channels_first=True)
+
+    return krc
+
+
+class ARTTestException(Exception):
+    def __init__(self, message):
+        super().__init__(message)
+
+
+class ARTTestFixtureNotImplemented(ARTTestException):
+    def __init__(self, message, fixture_name, framework, parameters_dict=""):
+        super().__init__(
+            "Could NOT run test for framework: {0} due to fixture: {1}. Message was: '"
+            "{2}' for the following parameters: {3}".format(framework, fixture_name, message, parameters_dict)
+        )
+
+
+def get_tabular_classifier_pt(load_init=True):
+    """
+    Standard PyTorch classifier for unit testing on Iris dataset.
+
+    :param load_init: Load the initial weights if True.
+    :type load_init: `bool`
+    :return: Trained model for Iris dataset.
+    :rtype: :class:`.PyTorchClassifier`
+    """
+    import torch
+
+    from art.estimators.classification.pytorch import PyTorchClassifier
+
+    class Model(torch.nn.Module):
+        """
+        Create Iris model for PyTorch.
+
+        The weights and biases are identical to the TensorFlow model in `get_iris_classifier_tf`.
+        """
+
+        def __init__(self):
+            super(Model, self).__init__()
+
+            self.fully_connected1 = torch.nn.Linear(4, 10)
+            self.fully_connected2 = torch.nn.Linear(10, 10)
+            self.fully_connected3 = torch.nn.Linear(10, 3)
+
+            if load_init:
+                w_dense1 = np.load(
+                    os.path.join(
+                        os.path.dirname(os.path.dirname(__file__)), "utils/resources/models", "W_DENSE1_IRIS.npy"
+                    )
+                )
+                b_dense1 = np.load(
+                    os.path.join(
+                        os.path.dirname(os.path.dirname(__file__)), "utils/resources/models", "B_DENSE1_IRIS.npy"
+                    )
+                )
+                w_dense2 = np.load(
+                    os.path.join(
+                        os.path.dirname(os.path.dirname(__file__)), "utils/resources/models", "W_DENSE2_IRIS.npy"
+                    )
+                )
+                b_dense2 = np.load(
+                    os.path.join(
+                        os.path.dirname(os.path.dirname(__file__)), "utils/resources/models", "B_DENSE2_IRIS.npy"
+                    )
+                )
+                w_dense3 = np.load(
+                    os.path.join(
+                        os.path.dirname(os.path.dirname(__file__)), "utils/resources/models", "W_DENSE3_IRIS.npy"
+                    )
+                )
+                b_dense3 = np.load(
+                    os.path.join(
+                        os.path.dirname(os.path.dirname(__file__)), "utils/resources/models", "B_DENSE3_IRIS.npy"
+                    )
+                )
+
+                self.fully_connected1.weight = torch.nn.Parameter(torch.Tensor(np.transpose(w_dense1)))
+                self.fully_connected1.bias = torch.nn.Parameter(torch.Tensor(b_dense1))
+                self.fully_connected2.weight = torch.nn.Parameter(torch.Tensor(np.transpose(w_dense2)))
+                self.fully_connected2.bias = torch.nn.Parameter(torch.Tensor(b_dense2))
+                self.fully_connected3.weight = torch.nn.Parameter(torch.Tensor(np.transpose(w_dense3)))
+                self.fully_connected3.bias = torch.nn.Parameter(torch.Tensor(b_dense3))
+
+        # pylint: disable=W0221
+        # disable pylint because of API requirements for function
+        def forward(self, x):
+            x = self.fully_connected1(x)
+            x = self.fully_connected2(x)
+            logit_output = self.fully_connected3(x)
+
+            return logit_output
+
+    # Define the network
+    model = Model()
+
+    # Define a loss function and optimizer
+    loss_fn = torch.nn.CrossEntropyLoss()
+    optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
+
+    # Get classifier
+    ptc = PyTorchClassifier(
+        model=model,
+        loss=loss_fn,
+        optimizer=optimizer,
+        input_shape=(4,),
+        nb_classes=3,
+        clip_values=(0, 1),
+        channels_first=True,
+    )
+
+    return ptc
+
+
+def get_attack_classifier_pt(num_features):
+    """
+    PyTorch classifier for testing membership inference attacks.
+
+    :param num_features: The number of features in the attack model.
+    :type num_features: `int`
+    :return: Model for attack.
+    :rtype: :class:`.PyTorchClassifier`
+    """
+    import torch.nn as nn
+    import torch.optim as optim
+    from art.estimators.classification.pytorch import PyTorchClassifier
+
+    class AttackModel(nn.Module):
+        def __init__(self, num_features):
+            super(AttackModel, self).__init__()
+            self.layer = nn.Linear(num_features, 1)
+            self.output = nn.Sigmoid()
+
+        def forward(self, x):
+            return self.output(self.layer(x))
+
+    # Create model
+    model = AttackModel(num_features)
+
+    # Define a loss function and optimizer
+    loss_fn = nn.BCELoss()
+    optimizer = optim.Adam(model.parameters(), lr=0.0001)
+    attack_model = PyTorchClassifier(
+        model=model, loss=loss_fn, optimizer=optimizer, input_shape=(num_features,), nb_classes=1
+    )
+
+    return attack_model
+
+
+# -------------------------------------------------------------------------------------------- RANDOM NUMBER GENERATORS
+
+
+def master_seed(seed=1234, set_random=True, set_numpy=True, set_tensorflow=False, set_mxnet=False, set_torch=False):
+    """
+    Set the seed for all random number generators used in the library. This ensures experiments reproducibility and
+    stable testing.
+
+    :param seed: The value to be seeded in the random number generators.
+    :type seed: `int`
+    :param set_random: The flag to set seed for `random`.
+    :type set_random: `bool`
+    :param set_numpy: The flag to set seed for `numpy`.
+    :type set_numpy: `bool`
+    :param set_tensorflow: The flag to set seed for `tensorflow`.
+    :type set_tensorflow: `bool`
+    :param set_mxnet: The flag to set seed for `mxnet`.
+    :type set_mxnet: `bool`
+    :param set_torch: The flag to set seed for `torch`.
+    :type set_torch: `bool`
+    """
+    import numbers
+
+    if not isinstance(seed, numbers.Integral):
+        raise TypeError("The seed for random number generators has to be an integer.")
+
+    # Set Python seed
+    if set_random:
+        import random
+
+        random.seed(seed)
+
+    # Set Numpy seed
+    if set_numpy:
+        np.random.seed(seed)
+        np.random.RandomState(seed)
+
+    # Now try to set seed for all specific frameworks
+    if set_tensorflow:
+        try:
+            import tensorflow as tf
+
+            logger.info("Setting random seed for TensorFlow.")
+            if tf.__version__[0] == "2":
+                tf.random.set_seed(seed)
+            else:
+                tf.set_random_seed(seed)
+        except ImportError:
+            logger.info("Could not set random seed for TensorFlow.")
+
+    if set_mxnet:
+        try:
+            import mxnet as mx
+
+            logger.info("Setting random seed for MXNet.")
+            mx.random.seed(seed)
+        except ImportError:
+            logger.info("Could not set random seed for MXNet.")
+
+    if set_torch:
+        try:
+            logger.info("Setting random seed for PyTorch.")
+            import torch
+
+            torch.manual_seed(seed)
+            if torch.cuda.is_available():
+                torch.cuda.manual_seed_all(seed)
+        except ImportError:
+            logger.info("Could not set random seed for PyTorch.")
diff --git a/adversarial-robustness-toolbox/tests/utils/test_utils.py b/adversarial-robustness-toolbox/tests/utils/test_utils.py
new file mode 100644
index 0000000..e263bde
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/utils/test_utils.py
@@ -0,0 +1,142 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+
+import pytest
+
+from art.utils import Deprecated, deprecated, deprecated_keyword_arg
+
+logger = logging.getLogger(__name__)
+
+
+class TestDeprecated:
+    """
+    Test the deprecation decorator functions and methods.
+    """
+
+    def test_deprecated_simple(self):
+        @deprecated("1.3.0")
+        def simple_addition(a, b):
+            return a + b
+
+        with pytest.deprecated_call():
+            simple_addition(1, 2)
+
+    def test_deprecated_reason_keyword(self, recwarn):
+        @deprecated("1.3.0", reason="With some reason message.")
+        def simple_addition(a, b):
+            return a + b
+
+        warn_msg_expected = (
+            "Function 'simple_addition' is deprecated and will be removed in future release 1.3.0."
+            "\nWith some reason message."
+        )
+
+        simple_addition(1, 2)
+        warn_obj = recwarn.pop(DeprecationWarning)
+        assert str(warn_obj.message) == warn_msg_expected
+
+    def test_deprecated_replaced_by_keyword(self, recwarn):
+        @deprecated("1.3.0", replaced_by="sum")
+        def simple_addition(a, b):
+            return a + b
+
+        warn_msg_expected = (
+            "Function 'simple_addition' is deprecated and will be removed in future release 1.3.0."
+            " It will be replaced by 'sum'."
+        )
+
+        simple_addition(1, 2)
+        warn_obj = recwarn.pop(DeprecationWarning)
+        assert str(warn_obj.message) == warn_msg_expected
+
+
+class TestDeprecatedKeyword:
+    """
+    Test the deprecation decorator for keyword arguments.
+    """
+
+    def test_deprecated_keyword_used(self):
+        @deprecated_keyword_arg("a", "1.3.0")
+        def simple_addition(a=Deprecated, b=1):
+            result = a if a is Deprecated else a + b
+            return result
+
+        with pytest.deprecated_call():
+            simple_addition(a=1)
+
+    def test_deprecated_keyword_not_used(self, recwarn):
+        @deprecated_keyword_arg("b", "1.3.0")
+        def simple_addition(a, b=Deprecated):
+            result = a if b is Deprecated else a + b
+            return result
+
+        simple_addition(1)
+        assert len(recwarn) == 0
+
+    def test_reason(self, recwarn):
+        @deprecated_keyword_arg("a", "1.3.0", reason="With some reason message.")
+        def simple_addition(a=Deprecated, b=1):
+            result = a if a is Deprecated else a + b
+            return result
+
+        warn_msg_expected = (
+            "Keyword argument 'a' in 'simple_addition' is deprecated and will be removed in future release 1.3.0."
+            "\nWith some reason message."
+        )
+
+        simple_addition(a=1)
+        warn_obj = recwarn.pop(DeprecationWarning)
+        assert str(warn_obj.message) == warn_msg_expected
+
+    def test_replaced_by(self, recwarn):
+        @deprecated_keyword_arg("b", "1.3.0", replaced_by="c")
+        def simple_addition(a=1, b=Deprecated, c=1):
+            result = a + c if b is Deprecated else a + b
+            return result
+
+        warn_msg_expected = (
+            "Keyword argument 'b' in 'simple_addition' is deprecated and will be removed in future release 1.3.0."
+            " It will be replaced by 'c'."
+        )
+
+        simple_addition(a=1, b=1)
+        warn_obj = recwarn.pop(DeprecationWarning)
+        assert str(warn_obj.message) == warn_msg_expected
+
+    def test_replaced_by_keyword_missing_signature_error(self, recwarn):
+        @deprecated_keyword_arg("b", "1.3.0", replaced_by="c")
+        def simple_addition(a=1, b=Deprecated):
+            result = a if b is Deprecated else a + b
+            return result
+
+        exc_msg = "Deprecated keyword replacement not found in function signature."
+        with pytest.raises(ValueError, match=exc_msg):
+            simple_addition(a=1)
+
+    def test_deprecated_keyword_default_value_error(self):
+        @deprecated_keyword_arg("a", "1.3.0")
+        def simple_addition(a=None, b=1):
+            result = a if a is None else a + b
+            return result
+
+        exc_msg = "Deprecated keyword argument must default to the Decorator singleton."
+        with pytest.raises(ValueError, match=exc_msg):
+            simple_addition(a=1)
diff --git a/adversarial-robustness-toolbox/tests/wrappers/__init__.py b/adversarial-robustness-toolbox/tests/wrappers/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/adversarial-robustness-toolbox/tests/wrappers/test_expectation.py b/adversarial-robustness-toolbox/tests/wrappers/test_expectation.py
new file mode 100644
index 0000000..259c7ad
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/wrappers/test_expectation.py
@@ -0,0 +1,145 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import unittest
+import numpy as np
+
+from art.attacks.evasion.fast_gradient import FastGradientMethod
+from art.estimators.classification.keras import KerasClassifier
+from art.utils import load_dataset, random_targets
+from art.wrappers.expectation import ExpectationOverTransformations
+from tests.utils import master_seed, get_image_classifier_kr, get_tabular_classifier_kr
+
+logger = logging.getLogger(__name__)
+
+BATCH_SIZE = 100
+NB_TRAIN = 5000
+NB_TEST = 10
+
+
+class TestExpectationOverTransformations(unittest.TestCase):
+    """
+    A unittest class for testing the Expectation over Transformations in attacks.
+    """
+
+    @classmethod
+    def setUpClass(cls):
+        (x_train, y_train), (x_test, y_test), _, _ = load_dataset("mnist")
+        x_train, y_train = x_train[:NB_TRAIN], y_train[:NB_TRAIN]
+        x_test, y_test = x_test[:NB_TEST], y_test[:NB_TEST]
+        cls.mnist = (x_train, y_train), (x_test, y_test)
+
+    def setUp(self):
+        master_seed(seed=1234)
+
+    def test_krclassifier(self):
+        """
+        Test with a KerasClassifier.
+        :return:
+        """
+        # Build KerasClassifier
+        krc = get_image_classifier_kr()
+
+        # Get MNIST
+        (_, _), (x_test, y_test) = self.mnist
+
+        # First attack (without EoT):
+        fgsm = FastGradientMethod(estimator=krc, targeted=True)
+        params = {"y": random_targets(y_test, krc.nb_classes)}
+        x_test_adv = fgsm.generate(x_test, **params)
+
+        # Second attack (with EoT):
+        def t(x):
+            return x
+
+        def transformation():
+            while True:
+                yield t
+
+        eot = ExpectationOverTransformations(classifier=krc, sample_size=1, transformation=transformation)
+        fgsm_with_eot = FastGradientMethod(estimator=eot, targeted=True)
+        x_test_adv_with_eot = fgsm_with_eot.generate(x_test, **params)
+
+        self.assertTrue((np.abs(x_test_adv - x_test_adv_with_eot) < 0.001).all())
+
+
+class TestExpectationVectors(unittest.TestCase):
+    @classmethod
+    def setUpClass(cls):
+        # Get Iris
+        (x_train, y_train), (x_test, y_test), _, _ = load_dataset("iris")
+        cls.iris = (x_train, y_train), (x_test, y_test)
+
+    def setUp(self):
+        master_seed(seed=1234)
+
+    def test_iris_clipped(self):
+        (_, _), (x_test, y_test) = self.iris
+
+        def t(x):
+            return x
+
+        def transformation():
+            while True:
+                yield t
+
+        classifier = get_tabular_classifier_kr()
+        classifier = ExpectationOverTransformations(classifier, sample_size=1, transformation=transformation)
+
+        # Test untargeted attack
+        attack = FastGradientMethod(classifier, eps=0.1)
+        x_test_adv = attack.generate(x_test)
+        self.assertFalse((x_test == x_test_adv).all())
+        self.assertTrue((x_test_adv <= 1).all())
+        self.assertTrue((x_test_adv >= 0).all())
+
+        preds_adv = np.argmax(classifier.predict(x_test_adv), axis=1)
+        self.assertFalse((np.argmax(y_test, axis=1) == preds_adv).all())
+        acc = np.sum(preds_adv == np.argmax(y_test, axis=1)) / y_test.shape[0]
+        logger.info("Accuracy on Iris with limited query info: %.2f%%", (acc * 100))
+
+    def test_iris_unbounded(self):
+        (_, _), (x_test, y_test) = self.iris
+        classifier = get_tabular_classifier_kr()
+
+        def t(x):
+            return x
+
+        def transformation():
+            while True:
+                yield t
+
+        # Recreate a classifier without clip values
+        classifier = KerasClassifier(model=classifier._model, use_logits=False, channels_first=True)
+        classifier = ExpectationOverTransformations(classifier, sample_size=1, transformation=transformation)
+        attack = FastGradientMethod(classifier, eps=1)
+        x_test_adv = attack.generate(x_test)
+        self.assertFalse((x_test == x_test_adv).all())
+        self.assertTrue((x_test_adv > 1).any())
+        self.assertTrue((x_test_adv < 0).any())
+
+        preds_adv = np.argmax(classifier.predict(x_test_adv), axis=1)
+        self.assertFalse((np.argmax(y_test, axis=1) == preds_adv).all())
+        acc = np.sum(preds_adv == np.argmax(y_test, axis=1)) / y_test.shape[0]
+        logger.info("Accuracy on Iris with limited query info: %.2f%%", (acc * 100))
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/adversarial-robustness-toolbox/tests/wrappers/test_query_efficient_bb.py b/adversarial-robustness-toolbox/tests/wrappers/test_query_efficient_bb.py
new file mode 100644
index 0000000..1a86dfe
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/wrappers/test_query_efficient_bb.py
@@ -0,0 +1,169 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import unittest
+
+import keras.backend as k
+import numpy as np
+
+from art.attacks.evasion.fast_gradient import FastGradientMethod
+from art.estimators.classification.keras import KerasClassifier
+from art.defences.preprocessor import FeatureSqueezing
+from art.utils import load_dataset, get_labels_np_array
+from art.wrappers.query_efficient_bb import QueryEfficientBBGradientEstimation
+
+from tests.utils import master_seed, get_image_classifier_kr, get_tabular_classifier_kr
+
+logger = logging.getLogger(__name__)
+
+BATCH_SIZE = 10
+NB_TRAIN = 100
+NB_TEST = 11
+
+
+class TestWrappingClassifierAttack(unittest.TestCase):
+    @classmethod
+    def setUpClass(cls):
+        (x_train, y_train), (x_test, y_test), _, _ = load_dataset("mnist")
+        x_train, y_train, x_test, y_test = x_train[:NB_TRAIN], y_train[:NB_TRAIN], x_test[:NB_TEST], y_test[:NB_TEST]
+        cls.mnist = (x_train, y_train), (x_test, y_test)
+
+        # Keras classifier
+        cls.classifier_k = get_image_classifier_kr()
+
+    def setUp(self):
+        master_seed(seed=1234)
+
+    @classmethod
+    def tearDownClass(cls):
+        k.clear_session()
+
+    def test_without_defences(self):
+        (x_train, y_train), (x_test, y_test) = self.mnist
+
+        # Get the ready-trained Keras model and wrap it in query efficient gradient estimator wrapper
+        classifier = QueryEfficientBBGradientEstimation(self.classifier_k, 20, 1 / 64.0, round_samples=1 / 255.0)
+
+        attack = FastGradientMethod(classifier, eps=1)
+        x_train_adv = attack.generate(x_train)
+        x_test_adv = attack.generate(x_test)
+
+        self.assertFalse((x_train == x_train_adv).all())
+        self.assertFalse((x_test == x_test_adv).all())
+
+        train_y_pred = get_labels_np_array(classifier.predict(x_train_adv))
+        test_y_pred = get_labels_np_array(classifier.predict(x_test_adv))
+
+        self.assertFalse((y_train == train_y_pred).all())
+        self.assertFalse((y_test == test_y_pred).all())
+
+        preds = classifier.predict(x_train_adv)
+        acc = np.sum(np.argmax(preds, axis=1) == np.argmax(y_train, axis=1)) / y_train.shape[0]
+        logger.info("Accuracy on adversarial train examples with limited query info: %.2f%%", (acc * 100))
+
+        preds = classifier.predict(x_test_adv)
+        acc = np.sum(np.argmax(preds, axis=1) == np.argmax(y_test, axis=1)) / y_test.shape[0]
+        logger.info("Accuracy on adversarial test examples with limited query info: %.2f%%", (acc * 100))
+
+    def test_with_defences(self):
+        (x_train, y_train), (x_test, y_test) = self.mnist
+
+        # Get the ready-trained Keras model
+        model = self.classifier_k._model
+        fs = FeatureSqueezing(bit_depth=1, clip_values=(0, 1))
+        classifier = KerasClassifier(model=model, clip_values=(0, 1), preprocessing_defences=fs)
+        # Wrap the classifier
+        classifier = QueryEfficientBBGradientEstimation(classifier, 20, 1 / 64.0, round_samples=1 / 255.0)
+
+        attack = FastGradientMethod(classifier, eps=1)
+        x_train_adv = attack.generate(x_train)
+        x_test_adv = attack.generate(x_test)
+
+        self.assertFalse((x_train == x_train_adv).all())
+        self.assertFalse((x_test == x_test_adv).all())
+
+        train_y_pred = get_labels_np_array(classifier.predict(x_train_adv))
+        test_y_pred = get_labels_np_array(classifier.predict(x_test_adv))
+
+        self.assertFalse((y_train == train_y_pred).all())
+        self.assertFalse((y_test == test_y_pred).all())
+
+        preds = classifier.predict(x_train_adv)
+        acc = np.sum(np.argmax(preds, axis=1) == np.argmax(y_train, axis=1)) / y_train.shape[0]
+        logger.info(
+            "Accuracy on adversarial train examples with feature squeezing and limited query info: %.2f%%", (acc * 100)
+        )
+
+        preds = classifier.predict(x_test_adv)
+        acc = np.sum(np.argmax(preds, axis=1) == np.argmax(y_test, axis=1)) / y_test.shape[0]
+        logger.info(
+            "Accuracy on adversarial test examples with feature squeezing and limited query info: %.2f%%", (acc * 100)
+        )
+
+
+class TestQueryEfficientVectors(unittest.TestCase):
+    @classmethod
+    def setUpClass(cls):
+        # Get Iris
+        (x_train, y_train), (x_test, y_test), _, _ = load_dataset("iris")
+        cls.iris = (x_train, y_train), (x_test, y_test)
+
+    def setUp(self):
+        master_seed(seed=1234)
+
+    def test_iris_clipped(self):
+        (_, _), (x_test, y_test) = self.iris
+
+        classifier = get_tabular_classifier_kr()
+        classifier = QueryEfficientBBGradientEstimation(classifier, 20, 1 / 64.0, round_samples=1 / 255.0)
+
+        # Test untargeted attack
+        attack = FastGradientMethod(classifier, eps=0.1)
+        x_test_adv = attack.generate(x_test)
+        self.assertFalse((x_test == x_test_adv).all())
+        self.assertTrue((x_test_adv <= 1).all())
+        self.assertTrue((x_test_adv >= 0).all())
+
+        preds_adv = np.argmax(classifier.predict(x_test_adv), axis=1)
+        self.assertFalse((np.argmax(y_test, axis=1) == preds_adv).all())
+        acc = np.sum(preds_adv == np.argmax(y_test, axis=1)) / y_test.shape[0]
+        logger.info("Accuracy on Iris with limited query info: %.2f%%", (acc * 100))
+
+    def test_iris_unbounded(self):
+        (_, _), (x_test, y_test) = self.iris
+        classifier = get_tabular_classifier_kr()
+
+        # Recreate a classifier without clip values
+        classifier = KerasClassifier(model=classifier._model, use_logits=False, channels_first=True)
+        classifier = QueryEfficientBBGradientEstimation(classifier, 20, 1 / 64.0, round_samples=1 / 255.0)
+        attack = FastGradientMethod(classifier, eps=1)
+        x_test_adv = attack.generate(x_test)
+        self.assertFalse((x_test == x_test_adv).all())
+        self.assertTrue((x_test_adv > 1).any())
+        self.assertTrue((x_test_adv < 0).any())
+
+        preds_adv = np.argmax(classifier.predict(x_test_adv), axis=1)
+        self.assertFalse((np.argmax(y_test, axis=1) == preds_adv).all())
+        acc = np.sum(preds_adv == np.argmax(y_test, axis=1)) / y_test.shape[0]
+        logger.info("Accuracy on Iris with limited query info: %.2f%%", (acc * 100))
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/adversarial-robustness-toolbox/tests/wrappers/test_wrapper.py b/adversarial-robustness-toolbox/tests/wrappers/test_wrapper.py
new file mode 100644
index 0000000..66c5ef7
--- /dev/null
+++ b/adversarial-robustness-toolbox/tests/wrappers/test_wrapper.py
@@ -0,0 +1,133 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import unittest
+
+import keras.backend as k
+import numpy as np
+
+from art.wrappers.wrapper import ClassifierWrapper
+from art.utils import load_mnist
+
+from tests.utils import master_seed, get_image_classifier_kr
+
+logger = logging.getLogger(__name__)
+
+BATCH_SIZE = 10
+NB_TRAIN = 500
+NB_TEST = 100
+
+
+class TestMixinWKerasClassifier(unittest.TestCase):
+    @classmethod
+    def setUpClass(cls):
+        k.clear_session()
+
+        # Get MNIST
+        (x_train, y_train), (x_test, y_test), _, _ = load_mnist()
+        x_train, y_train, x_test, y_test = x_train[:NB_TRAIN], y_train[:NB_TRAIN], x_test[:NB_TEST], y_test[:NB_TEST]
+        cls.mnist = (x_train, y_train), (x_test, y_test)
+
+        # Load small Keras model
+        cls.model_mnist = get_image_classifier_kr()
+
+    @classmethod
+    def tearDownClass(cls):
+        k.clear_session()
+
+    def setUp(self):
+        master_seed(seed=1234)
+
+    def test_shapes(self):
+        x_test, y_test = self.mnist[1]
+        classifier = ClassifierWrapper(self.model_mnist)
+
+        preds = classifier.predict(self.mnist[1][0])
+        self.assertEqual(preds.shape, y_test.shape)
+
+        self.assertEqual(classifier.nb_classes, 10)
+
+        class_grads = classifier.class_gradient(x_test[:11])
+        self.assertEqual(class_grads.shape, tuple([11, 10] + list(x_test[1].shape)))
+
+        loss_grads = classifier.loss_gradient(x_test[:11], y_test[:11])
+        self.assertEqual(loss_grads.shape, x_test[:11].shape)
+
+    def test_class_gradient(self):
+        (_, _), (x_test, _) = self.mnist
+        classifier = ClassifierWrapper(self.model_mnist)
+
+        # Test all gradients label
+        grads = classifier.class_gradient(x_test)
+
+        self.assertTrue(np.array(grads.shape == (NB_TEST, 10, 28, 28, 1)).all())
+        self.assertNotEqual(np.sum(grads), 0)
+
+        # Test 1 gradient label = 5
+        grads = classifier.class_gradient(x_test, label=5)
+
+        self.assertTrue(np.array(grads.shape == (NB_TEST, 1, 28, 28, 1)).all())
+        self.assertNotEqual(np.sum(grads), 0)
+
+        # Test a set of gradients label = array
+        label = np.random.randint(5, size=NB_TEST)
+        grads = classifier.class_gradient(x_test, label=label)
+
+        self.assertTrue(np.array(grads.shape == (NB_TEST, 1, 28, 28, 1)).all())
+        self.assertNotEqual(np.sum(grads), 0)
+
+    def test_loss_gradient(self):
+        (_, _), (x_test, y_test) = self.mnist
+        classifier = ClassifierWrapper(self.model_mnist)
+
+        # Test gradient
+        grads = classifier.loss_gradient(x_test, y_test)
+
+        self.assertTrue(np.array(grads.shape == (NB_TEST, 28, 28, 1)).all())
+        self.assertNotEqual(np.sum(grads), 0)
+
+    def test_layers(self):
+        (_, _), (x_test, _), _, _ = load_mnist()
+        x_test = x_test[:NB_TEST]
+
+        classifier = ClassifierWrapper(self.model_mnist)
+        self.assertEqual(len(classifier.layer_names), 3)
+
+        layer_names = classifier.layer_names
+        for i, name in enumerate(layer_names):
+            act_i = classifier.get_activations(x_test, i, batch_size=128)
+            act_name = classifier.get_activations(x_test, name, batch_size=128)
+            self.assertAlmostEqual(np.sum(act_name - act_i), 0)
+
+    def test_save(self):
+        import os
+
+        path = "tmp"
+        filename = "model.h5"
+        classifier = ClassifierWrapper(self.model_mnist)
+        classifier.save(filename, path=path)
+        self.assertTrue(os.path.isfile(os.path.join(path, filename)))
+
+        # Remove saved file
+        os.remove(os.path.join(path, filename))
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/adversarial-robustness-toolbox/utils/data/backdoors/alert.png b/adversarial-robustness-toolbox/utils/data/backdoors/alert.png
new file mode 100644
index 0000000..821268e
Binary files /dev/null and b/adversarial-robustness-toolbox/utils/data/backdoors/alert.png differ
diff --git a/adversarial-robustness-toolbox/utils/data/images/adv_embedding_arch.png b/adversarial-robustness-toolbox/utils/data/images/adv_embedding_arch.png
new file mode 100644
index 0000000..d5de9d0
Binary files /dev/null and b/adversarial-robustness-toolbox/utils/data/images/adv_embedding_arch.png differ
diff --git a/adversarial-robustness-toolbox/utils/data/images/adversarial_audio_waveform.png b/adversarial-robustness-toolbox/utils/data/images/adversarial_audio_waveform.png
new file mode 100644
index 0000000..3c9dd32
Binary files /dev/null and b/adversarial-robustness-toolbox/utils/data/images/adversarial_audio_waveform.png differ
diff --git a/adversarial-robustness-toolbox/utils/data/images/adversarial_basketball.gif b/adversarial-robustness-toolbox/utils/data/images/adversarial_basketball.gif
new file mode 100644
index 0000000..84ced67
Binary files /dev/null and b/adversarial-robustness-toolbox/utils/data/images/adversarial_basketball.gif differ
diff --git a/adversarial-robustness-toolbox/utils/data/images/adversarial_basketball_sparse.gif b/adversarial-robustness-toolbox/utils/data/images/adversarial_basketball_sparse.gif
new file mode 100644
index 0000000..c05cc0a
Binary files /dev/null and b/adversarial-robustness-toolbox/utils/data/images/adversarial_basketball_sparse.gif differ
diff --git a/adversarial-robustness-toolbox/utils/data/images/adversarial_patch.png b/adversarial-robustness-toolbox/utils/data/images/adversarial_patch.png
new file mode 100644
index 0000000..e1eb5cd
Binary files /dev/null and b/adversarial-robustness-toolbox/utils/data/images/adversarial_patch.png differ
diff --git a/adversarial-robustness-toolbox/utils/data/images/adversarial_training.png b/adversarial-robustness-toolbox/utils/data/images/adversarial_training.png
new file mode 100644
index 0000000..68e4455
Binary files /dev/null and b/adversarial-robustness-toolbox/utils/data/images/adversarial_training.png differ
diff --git a/adversarial-robustness-toolbox/utils/data/images/basketball.gif b/adversarial-robustness-toolbox/utils/data/images/basketball.gif
new file mode 100644
index 0000000..76bbb47
Binary files /dev/null and b/adversarial-robustness-toolbox/utils/data/images/basketball.gif differ
diff --git a/adversarial-robustness-toolbox/utils/data/images/gaussian_process_hclu.png b/adversarial-robustness-toolbox/utils/data/images/gaussian_process_hclu.png
new file mode 100644
index 0000000..e87d477
Binary files /dev/null and b/adversarial-robustness-toolbox/utils/data/images/gaussian_process_hclu.png differ
diff --git a/adversarial-robustness-toolbox/utils/data/images/poisoning.png b/adversarial-robustness-toolbox/utils/data/images/poisoning.png
new file mode 100644
index 0000000..fd5ceb1
Binary files /dev/null and b/adversarial-robustness-toolbox/utils/data/images/poisoning.png differ
diff --git a/adversarial-robustness-toolbox/utils/data/images/prov_defense.gif b/adversarial-robustness-toolbox/utils/data/images/prov_defense.gif
new file mode 100644
index 0000000..fe0f95c
Binary files /dev/null and b/adversarial-robustness-toolbox/utils/data/images/prov_defense.gif differ
diff --git a/adversarial-robustness-toolbox/utils/data/images/randomised_smoothing.png b/adversarial-robustness-toolbox/utils/data/images/randomised_smoothing.png
new file mode 100644
index 0000000..6750840
Binary files /dev/null and b/adversarial-robustness-toolbox/utils/data/images/randomised_smoothing.png differ
diff --git a/adversarial-robustness-toolbox/utils/data/images/roni.gif b/adversarial-robustness-toolbox/utils/data/images/roni.gif
new file mode 100644
index 0000000..41ae46c
Binary files /dev/null and b/adversarial-robustness-toolbox/utils/data/images/roni.gif differ
diff --git a/adversarial-robustness-toolbox/utils/data/images/svc.png b/adversarial-robustness-toolbox/utils/data/images/svc.png
new file mode 100644
index 0000000..9386056
Binary files /dev/null and b/adversarial-robustness-toolbox/utils/data/images/svc.png differ
diff --git a/adversarial-robustness-toolbox/utils/data/images/svm_poly.gif b/adversarial-robustness-toolbox/utils/data/images/svm_poly.gif
new file mode 100644
index 0000000..61399b3
Binary files /dev/null and b/adversarial-robustness-toolbox/utils/data/images/svm_poly.gif differ
diff --git a/adversarial-robustness-toolbox/utils/data/images/zero_to_nine.png b/adversarial-robustness-toolbox/utils/data/images/zero_to_nine.png
new file mode 100644
index 0000000..0535524
Binary files /dev/null and b/adversarial-robustness-toolbox/utils/data/images/zero_to_nine.png differ
diff --git a/adversarial-robustness-toolbox/utils/data/images/zero_to_one.png b/adversarial-robustness-toolbox/utils/data/images/zero_to_one.png
new file mode 100644
index 0000000..337f533
Binary files /dev/null and b/adversarial-robustness-toolbox/utils/data/images/zero_to_one.png differ
diff --git a/adversarial-robustness-toolbox/utils/data/mnist/api_output.txt b/adversarial-robustness-toolbox/utils/data/mnist/api_output.txt
new file mode 100644
index 0000000..4abe41e
--- /dev/null
+++ b/adversarial-robustness-toolbox/utils/data/mnist/api_output.txt
@@ -0,0 +1 @@
+{"values": [7, 2, 1, 0, 4, 1, 4, 9, 6, 9, 0, 6, 9, 0, 1, 5, 9, 7, 3, 4, 9, 6, 6, 5, 4, 0, 7, 4, 0, 1, 3, 1, 3, 6, 7, 2, 7, 1, 2, 1, 1, 7, 4, 2, 3, 5, 1, 2, 4, 4, 6, 3, 5, 5, 6, 0, 4, 1, 9, 5, 7, 8, 9, 2, 7, 4, 6, 4, 3, 0, 7, 0, 2, 9, 1, 7, 3, 2, 9, 7, 7, 6, 2, 7, 8, 4, 7, 3, 6, 1, 3, 6, 4, 3, 1, 4, 1, 7, 6, 9, 6, 0, 5, 4, 9, 9, 2, 1, 9, 4, 8, 7, 3, 9, 7, 4, 4, 4, 9, 2, 5, 4, 7, 6, 4, 9, 0, 5, 8, 5, 6, 6, 5, 7, 8, 1, 0, 1, 6, 4, 6, 7, 3, 1, 7, 1, 8, 2, 0, 9, 9, 9, 5, 5, 1, 5, 6, 0, 3, 4, 4, 6, 5, 4, 6, 5, 4, 5, 1, 4, 4, 7, 2, 3, 2, 7, 1, 8, 1, 8, 1, 8, 5, 0, 8, 9, 2, 5, 0, 1, 1, 1, 0, 9, 0, 3, 1, 6, 4, 2, 3, 6, 1, 1, 1, 3, 9, 5, 2, 9, 4, 5, 9, 3, 9, 0, 3, 6, 5, 5, 7, 2, 2, 7, 1, 2, 8, 4, 1, 7, 3, 3, 8, 7, 7, 9, 2, 2, 4, 1, 5, 5, 8, 7, 2, 3, 0, 2, 4, 2, 4, 1, 9, 5, 7, 7, 2, 8, 2, 0, 8, 5, 7, 7, 9, 1, 8, 1, 8, 0, 3, 0, 1, 9, 9, 4, 1, 8, 2, 1, 2, 9, 7, 5, 9, 2, 6, 4, 1, 5, 8, 2, 9, 2, 0, 4, 0, 0, 2, 8, 1, 7, 1, 2, 4, 0, 2, 7, 4, 3, 3, 0, 0, 5, 1, 9, 6, 5, 2, 5, 7, 7, 9, 3, 0, 4, 2, 0, 7, 1, 1, 2, 1, 5, 3, 3, 9, 7, 8, 6, 3, 6, 1, 3, 8, 1, 0, 5, 1, 3, 1, 5, 0, 6, 1, 8, 5, 1, 7, 4, 4, 6, 2, 2, 5, 0, 6, 5, 6, 3, 7, 2, 0, 8, 8, 5, 4, 1, 1, 4, 0, 7, 3, 7, 6, 1, 6, 2, 1, 9, 2, 2, 6, 1, 9, 5, 2, 5, 4, 4, 2, 8, 3, 5, 2, 4, 5, 0, 3, 1, 7, 7, 5, 7, 9, 7, 1, 9, 2, 1, 4, 2, 9, 2, 0, 4, 9, 1, 4, 8, 1, 8, 4, 5, 9, 7, 8, 3, 7, 6, 0, 0, 3, 0, 2, 0, 6, 4, 8, 5, 3, 3, 2, 3, 9, 1, 2, 6, 8, 0, 5, 6, 6, 6, 3, 8, 8, 2, 7, 5, 8, 9, 6, 1, 8, 4, 1, 2, 5, 5, 1, 9, 7, 5, 4, 0, 8, 9, 9, 1, 0, 5, 2, 3, 7, 2, 9, 4, 0, 6, 3, 9, 5, 2, 1, 3, 1, 5, 6, 5, 7, 4, 2, 2, 6, 3, 2, 6, 5, 4, 8, 9, 7, 1, 3, 0, 3, 8, 2, 1, 4, 6, 4, 4, 6, 4, 2, 1, 8, 2, 5, 4, 2, 3, 4, 0, 0, 2, 3, 2, 7, 1, 0, 8, 7, 4, 4, 7, 9, 6, 9, 0, 9, 8, 0, 9, 6, 0, 6, 3, 5, 4, 8, 3, 3, 9, 3, 3, 2, 7, 8, 0, 2, 2, 1, 7, 0, 6, 5, 4, 3, 3, 0, 9, 6, 3, 8, 0, 9, 9, 6, 8, 6, 8, 5, 7, 8, 6, 0, 2, 4, 0, 2, 2, 3, 1, 9, 7, 5, 8, 0, 8, 4, 6, 2, 6, 7, 9, 3, 2, 9, 8, 2, 2, 9, 2, 7, 3, 5, 9, 1, 8, 0, 2, 0, 5, 2, 1, 3, 7, 6, 7, 1, 2, 5, 8, 0, 3, 7, 8, 4, 0, 9, 1, 8, 6, 7, 7, 4, 3, 4, 9, 1, 9, 5, 1, 7, 3, 9, 7, 6, 9, 1, 3, 2, 8, 3, 3, 6, 7, 2, 4, 5, 8, 5, 1, 1, 4, 4, 3, 1, 0, 7, 7, 0, 7, 9, 9, 4, 8, 5, 5, 4, 0, 8, 2, 1, 6, 8, 4, 8, 0, 4, 0, 6, 1, 7, 3, 2, 6, 7, 2, 6, 9, 3, 1, 4, 6, 2, 5, 9, 2, 0, 6, 2, 1, 7, 3, 4, 1, 0, 5, 4, 3, 1, 1, 7, 4, 9, 9, 4, 8, 4, 0, 2, 4, 5, 1, 1, 6, 4, 7, 1, 9, 4, 2, 4, 1, 5, 5, 3, 8, 3, 1, 4, 5, 6, 8, 9, 4, 1, 5, 3, 8, 0, 3, 2, 5, 1, 2, 8, 3, 4, 4, 0, 8, 8, 3, 3, 1, 2, 3, 5, 9, 6, 3, 2, 6, 1, 3, 6, 0, 7, 2, 1, 7, 1, 4, 2, 4, 2, 1, 7, 9, 6, 1, 1, 2, 4, 3, 1, 7, 7, 4, 7, 0, 7, 3, 1, 3, 1, 0, 7, 7, 0, 3, 5, 5, 2, 7, 6, 6, 9, 2, 8, 3, 5, 2, 2, 5, 6, 0, 8, 2, 4, 2, 8, 8, 8, 8, 7, 4, 7, 3, 0, 6, 6, 3, 2, 1, 5, 2, 2, 9, 3, 0, 0, 5, 7, 8, 1, 4, 4, 6, 0, 2, 9, 1, 4, 7, 4, 7, 3, 9, 8, 8, 4, 7, 1, 2, 1, 2, 2, 3, 2, 3, 2, 3, 9, 1, 7, 4, 0, 3, 5, 5, 8, 6, 5, 2, 6, 7, 6, 6, 3, 2, 7, 8, 1, 1, 2, 4, 6, 4, 9, 5, 2, 3, 3, 4, 7, 8, 9, 1, 1, 0, 9, 1, 4, 4, 5, 4, 0, 6, 2, 2, 3, 1, 5, 1, 2, 0, 2, 8, 1, 2, 6, 7, 1, 6, 2, 3, 9, 0, 1, 2, 2, 0, 8, 9, 9, 0, 2, 5, 1, 9, 7, 8, 1, 0, 4, 1, 7, 9, 5, 4, 2, 6, 8, 1, 3, 7, 5, 4, 4, 1, 8, 1, 3, 8, 1, 2, 5, 8, 0, 6, 2, 1, 1, 1, 1, 5, 3, 4, 8, 9, 5, 0, 9, 2, 2, 4, 8, 2, 1, 7, 2, 4, 9, 4, 4, 0, 7, 9, 2, 2, 3, 3, 4, 3, 5, 7, 3, 5, 8, 1, 2, 4, 4, 6, 4, 9, 5, 1, 0, 6, 9, 5, 9, 5, 9, 7, 3, 8, 0, 3, 7, 1, 3, 6, 7, 2, 5, 9, 7, 9, 6, 3, 6, 3, 7, 4, 6, 5, 8, 5, 4, 7, 8, 2, 8, 0, 7, 6, 8, 8, 7, 3, 7, 1, 9, 5, 2, 7, 3, 5, 1, 1, 2, 1, 4, 7, 4, 7, 5, 4, 5, 4, 0, 8, 3, 6, 9, 6, 0, 2, 7, 4, 4, 4, 4, 6, 6, 4, 7, 9, 3, 4, 5, 5, 8, 7, 3, 7, 2, 7, 0, 2, 4, 1, 1, 1, 6, 9, 2, 8, 7, 2, 0, 1, 5, 0, 4, 1, 8, 0, 6, 0, 4, 6, 8, 1, 5, 0, 2, 3, 7, 2, 3, 6, 2, 1, 6, 1, 1, 3, 7, 9, 0, 8, 0, 5, 4, 0, 2, 8, 2, 2, 9, 8, 4, 0, 4, 5, 5, 5, 1, 2, 1, 3, 1, 7, 9, 5, 7, 2, 0, 5, 5, 8, 6, 2, 5, 6, 1, 9, 2, 1, 5, 8, 1, 0, 2, 4, 4, 3, 6, 8, 8, 2, 4, 0, 5, 0, 4, 4, 7, 9, 3, 4, 1, 5, 9, 2, 3, 5, 8, 8, 0, 4, 3, 3, 6, 6, 0, 1, 6, 0, 3, 5, 4, 4, 1, 2, 9, 1, 4, 6, 9, 9, 3, 9, 8, 4, 4, 3, 1, 3, 1, 3, 8, 7, 9, 4, 8, 8, 2, 9, 7, 1, 4, 5, 6, 0, 5, 2, 2, 6, 1, 5, 5, 2, 4, 9, 6, 2, 7, 7, 2, 2, 1, 1, 2, 8, 3, 7, 2, 4, 1, 7, 1, 7, 6, 7, 2, 2, 7, 3, 1, 7, 5, 8, 2, 6, 2, 2, 5, 6, 6, 0, 9, 2, 4, 3, 3, 9, 7, 6, 6, 8, 0, 4, 1, 3, 8, 2, 9, 1, 8, 0, 6, 7, 2, 1, 0, 5, 5, 2, 0, 2, 6, 0, 2, 9, 4, 8, 0, 9, 9, 4, 6, 5, 4, 9, 1, 8, 3, 4, 9, 4, 1, 2, 2, 1, 1, 9, 6, 4, 0, 9, 4, 8, 3, 8, 7, 0, 2, 5, 1, 9, 6, 2, 9, 4, 0, 9, 6, 0, 6, 2, 5, 4, 2, 3, 8, 4, 5, 5, 0, 5, 8, 5, 3, 5, 8, 6, 5, 7, 6, 3, 3, 9, 6, 1, 1, 2, 9, 0, 4, 3, 3, 6, 9, 5, 0, 3, 7, 7, 7, 8, 1, 9, 8, 3, 0, 7, 2, 7, 9, 4, 5, 4, 9, 3, 2, 1, 4, 0, 2, 3, 7, 5, 9, 8, 8, 0, 0, 6, 1, 4, 7, 3, 9, 0, 0, 0, 6, 6, 2, 3, 7, 8, 4, 7, 7, 9, 2, 4, 1, 6, 5, 2, 4, 8, 9, 1, 8, 4, 0, 7, 8, 4, 8, 7, 7, 0, 7, 8, 8, 4, 0, 4, 8, 8, 2, 4, 7, 6, 6, 6, 4, 2, 1, 8, 8, 2, 3, 6, 3, 0, 0, 3, 7, 6, 9, 7, 9, 9, 5, 4, 3, 3, 6, 1, 2, 3, 7, 3, 3, 6, 0, 3, 3, 8, 4, 3, 6, 3, 5, 0, 2, 6, 9, 0, 7, 4, 6, 9, 3, 5, 1, 9, 6, 1, 4, 5, 4, 5, 0, 5, 9, 5, 2, 1, 2, 9, 1, 9, 9, 4, 0, 8, 4, 5, 2, 9, 2, 1, 2, 1, 7, 3, 6, 8, 8, 4, 9, 1, 9, 8, 5, 7, 5, 1, 1, 8, 6, 5, 2, 4, 4, 7, 2, 3, 5, 6, 5, 8, 6, 2, 3, 1, 0, 5, 8, 9, 2, 9, 6, 7, 0, 4, 8, 7, 1, 7, 4, 1, 0, 5, 7, 2, 0, 0, 9, 1, 7, 0, 7, 8, 4, 7, 2, 0, 4, 6, 0, 3, 1, 1, 3, 3, 5, 6, 7, 4, 1, 5, 3, 0, 8, 7, 3, 9, 6, 9, 3, 5, 0, 2, 7, 4, 5, 1, 2, 5, 8, 0, 8, 8, 1, 5, 0, 3, 0, 3, 1, 4, 0, 3, 7, 2, 4, 6, 8, 0, 7, 0, 4, 3, 1, 4, 8, 7, 7, 1, 4, 9, 9, 3, 7, 1, 7, 9, 0, 2, 0, 3, 3, 7, 6, 9, 2, 3, 3, 7, 7, 0, 0, 7, 5, 2, 9, 5, 7, 4, 4, 2, 6, 6, 1, 9, 6, 8, 2, 9, 0, 8, 7, 1, 1, 6, 3, 5, 1, 1, 1, 3, 1, 2, 3, 0, 2, 0, 1, 3, 5, 5, 7, 4, 7, 9, 6, 9, 6, 8, 3, 4, 6, 8, 5, 1, 4, 2, 4, 4, 5, 1, 4, 9, 0, 2, 4, 9, 5, 7, 1, 8, 3, 5, 6, 9, 8, 7, 1, 1, 6, 7, 6, 3, 2, 2, 0, 8, 9, 2, 5, 1, 0, 8, 1, 4, 5, 7, 9, 6, 9, 0, 6, 1, 5, 5, 8, 3, 8, 2, 6, 5, 0, 7, 4, 6, 1, 3, 4, 7, 3, 2, 3, 4, 4, 5, 2, 7, 1, 7, 2, 6, 6, 1, 0, 8, 8, 6, 0, 1, 8, 2, 5, 7, 7, 6, 5, 3, 5, 8, 4, 2, 4, 0, 8, 8, 3, 4, 9, 2, 7, 5, 8, 6, 5, 6, 0, 8, 6, 7, 3, 6, 4, 9, 4, 6, 5, 3, 0, 4, 1, 0, 1, 4, 6, 2, 8, 1, 1, 0, 6, 3, 9, 5, 6, 5, 6, 5, 8, 4, 6, 4, 3, 9, 1, 3, 4, 1, 9, 1, 2, 1, 1, 9, 3, 5, 4, 0, 9, 3, 6, 1, 7, 5, 5, 3, 3, 0, 1, 3, 7, 5, 8, 6, 4, 1, 0, 8, 7, 3, 4, 6, 7, 4, 8, 1, 8, 9, 9, 2, 8, 6, 2, 7, 0, 0, 6, 7, 5, 8, 6, 0, 9, 3, 4, 1, 3, 5, 4, 3, 3, 5, 5, 6, 3, 0, 2, 3, 4, 2, 3, 0, 9, 9, 4, 7, 2, 8, 4, 7, 0, 6, 0, 9, 5, 2, 8, 5, 7, 5, 0, 8, 2, 7, 2, 8, 2, 5, 5, 7, 6, 4, 0, 8, 4, 8, 2, 7, 4, 5, 2, 0, 3, 2, 9, 6, 7, 2, 5, 1, 1, 1, 2, 3, 6, 7, 8, 7, 6, 4, 8, 9, 4, 8, 6, 3, 8, 3, 1, 0, 6, 2, 2, 5, 6, 4, 5, 8, 1, 4, 1, 7, 8, 4, 6, 1, 8, 4, 5, 1, 2, 8, 0, 8, 5, 9, 2, 4, 2, 5, 3, 7, 0, 1, 0, 2, 5, 7, 6, 7, 9, 4, 2, 6, 2, 4, 4, 8, 0, 4, 4, 5, 8, 0, 6, 8, 9, 8, 5, 6, 9, 0, 4, 8, 7, 1, 3, 4, 5, 8, 0, 9, 1, 3, 3, 6, 4, 8, 7, 1, 0, 5, 7, 1, 7, 5, 2, 7, 9, 1, 8, 5, 2, 4, 9, 4, 7, 2, 2, 3, 4, 9, 1, 9, 2, 1, 7, 9, 4, 4, 6, 6, 7, 2, 7, 8, 0, 1, 9, 7, 1, 1, 7, 5, 3, 3, 5, 1, 3, 7, 6, 1, 3, 8, 7, 5, 9, 6, 0, 0, 2, 8, 8, 7, 3, 7, 1, 3, 0, 8, 4, 4, 3, 8, 9, 2, 3, 9, 7, 1, 1, 7, 0, 4, 9, 6, 5, 9, 1, 2, 0, 2, 0, 0, 4, 6, 7, 0, 7, 1, 4, 6, 4, 5, 4, 9, 9, 1, 7, 9, 5, 3, 3, 8, 2, 3, 6, 2, 2, 1, 1, 1, 1, 1, 6, 4, 8, 4, 3, 7, 1, 6, 4, 5, 0, 4, 7, 4, 2, 4, 0, 7, 0, 1, 9, 8, 8, 6, 0, 0, 4, 1, 6, 8, 2, 2, 3, 3, 4, 3, 2, 2, 1, 7, 5, 4, 4, 0, 4, 3, 1, 7, 3, 1, 0, 1, 2, 5, 4, 2, 1, 0, 1, 8, 9, 1, 4, 8, 3, 3, 9, 3, 6, 2, 8, 3, 2, 1, 1, 0, 4, 2, 9, 2, 4, 3, 7, 9, 1, 5, 2, 4, 9, 0, 3, 8, 5, 3, 6, 0, 9, 4, 6, 2, 5, 0, 2, 7, 4, 6, 6, 8, 6, 6, 8, 6, 9, 1, 7, 2, 5, 9, 9, 0, 7, 2, 7, 6, 7, 0, 6, 5, 4, 4, 7, 2, 0, 9, 9, 2, 2, 9, 4, 4, 2, 3, 3, 2, 1, 7, 0, 7, 6, 4, 1, 3, 8, 7, 4, 5, 9, 2, 5, 1, 8, 7, 3, 7, 1, 5, 5, 0, 4, 1, 4, 0, 6, 3, 5, 6, 0, 4, 9, 7, 5, 1, 6, 8, 9, 5, 5, 7, 4, 3, 8, 3, 8, 1, 5, 3, 5, 0, 5, 5, 2, 8, 6, 7, 7, 7, 3, 7, 0, 5, 9, 0, 2, 5, 5, 3, 1, 7, 7, 8, 6, 5, 9, 3, 8, 9, 5, 3, 7, 9, 1, 7, 0, 0, 3, 7, 2, 5, 2, 1, 8, 6, 2, 9, 5, 7, 5, 4, 8, 6, 2, 5, 1, 4, 8, 4, 5, 8, 3, 0, 6, 2, 7, 3, 3, 2, 1, 0, 7, 3, 4, 0, 3, 9, 3, 2, 8, 9, 0, 3, 8, 0, 7, 6, 5, 4, 7, 3, 0, 0, 8, 6, 2, 5, 1, 1, 0, 0, 4, 4, 0, 1, 2, 3, 2, 7, 7, 8, 5, 2, 5, 7, 6, 9, 1, 4, 1, 6, 4, 2, 4, 3, 5, 4, 3, 9, 5, 0, 1, 5, 3, 8, 9, 1, 9, 7, 9, 5, 5, 2, 7, 4, 6, 0, 1, 1, 1, 0, 4, 4, 7, 6, 3, 0, 0, 4, 3, 0, 6, 1, 9, 6, 1, 3, 8, 1, 2, 5, 6, 2, 7, 3, 6, 0, 1, 9, 7, 6, 6, 8, 9, 2, 9, 5, 8, 3, 1, 0, 0, 7, 6, 6, 2, 1, 6, 9, 3, 1, 5, 6, 4, 0, 6, 0, 0, 0, 6, 3, 5, 9, 6, 4, 5, 5, 8, 5, 3, 0, 4, 0, 2, 9, 6, 8, 2, 3, 1, 2, 1, 1, 5, 6, 9, 8, 0, 6, 6, 5, 5, 3, 8, 6, 2, 1, 4, 5, 4, 3, 7, 8, 5, 0, 4, 3, 5, 1, 1, 0, 4, 4, 7, 0, 1, 7, 0, 1, 6, 1, 4, 5, 6, 6, 5, 7, 8, 4, 4, 7, 2, 5, 3, 7, 0, 7, 7, 9, 6, 4, 2, 8, 5, 7, 8, 3, 9, 5, 8, 9, 9, 8, 6, 2, 8, 4, 2, 3, 6, 1, 1, 8, 9, 3, 4, 0, 7, 9, 6, 7, 1, 4, 1, 3, 4, 9, 3, 1, 4, 7, 7, 4, 7, 2, 9, 3, 0, 8, 0, 8, 4, 0, 4, 4, 1, 5, 2, 8, 3, 4, 9, 5, 2, 8, 1, 5, 3, 7, 9, 4, 2, 5, 6, 2, 5, 9, 3, 0, 9, 2, 1, 9, 5, 3, 0, 6, 9, 8, 4, 0, 4, 5, 2, 9, 0, 1, 0, 7, 1, 6, 5, 8, 1, 5, 3, 5, 0, 3, 5, 5, 9, 2, 8, 7, 0, 4, 9, 1, 9, 7, 7, 5, 5, 2, 0, 9, 1, 8, 6, 2, 3, 7, 6, 2, 1, 9, 1, 3, 5, 5, 0, 3, 8, 3, 3, 7, 6, 6, 0, 1, 4, 0, 6, 9, 8, 1, 2, 1, 9, 5, 9, 7, 3, 7, 8, 0, 1, 3, 0, 4, 6, 1, 0, 2, 5, 8, 4, 4, 1, 1, 5, 4, 6, 6, 0, 6, 9, 2, 6, 2, 7, 1, 7, 9, 4, 0, 0, 3, 8, 2, 2, 3, 1, 6, 0, 5, 7, 7, 9, 2, 6, 7, 7, 7, 2, 6, 8, 8, 4, 6, 8, 4, 1, 2, 8, 2, 3, 9, 4, 0, 3, 7, 3, 2, 3, 3, 7, 3, 4, 0, 6, 2, 0, 8, 1, 5, 3, 5, 4, 1, 7, 1, 5, 7, 5, 7, 3, 2, 2, 7, 5, 7, 5, 7, 8, 5, 6, 4, 2, 9, 6, 5, 3, 6, 7, 4, 1, 7, 1, 5, 2, 3, 0, 3, 2, 4, 2, 6, 1, 4, 3, 8, 0, 6, 2, 1, 6, 5, 3, 9, 1, 9, 3, 2, 1, 8, 4, 4, 6, 5, 8, 6, 4, 7, 7, 8, 6, 9, 7, 1, 9, 4, 0, 5, 4, 6, 4, 1, 2, 3, 0, 0, 2, 6, 6, 5, 7, 0, 8, 6, 4, 4, 9, 0, 7, 3, 4, 2, 1, 8, 8, 5, 9, 2, 7, 1, 8, 8, 3, 2, 7, 6, 0, 1, 2, 7, 1, 0, 8, 3, 6, 0, 5, 3, 6, 2, 8, 7, 0, 1, 4, 2, 1, 1, 4, 4, 4, 4, 7, 1, 6, 2, 3, 9, 0, 0, 1, 8, 8, 4, 3, 4, 2, 0, 6, 1, 6, 1, 2, 2, 2, 1, 2, 3, 7, 8, 1, 0, 0, 2, 1, 0, 6, 0, 1, 6, 2, 5, 1, 7, 4, 8, 2, 1, 4, 3, 7, 3, 9, 9, 4, 5, 3, 4, 7, 2, 7, 5, 7, 0, 4, 3, 3, 2, 6, 7, 6, 0, 0, 6, 7, 7, 0, 5, 5, 8, 1, 0, 7, 0, 2, 8, 1, 5, 0, 8, 8, 0, 3, 2, 7, 2, 2, 6, 4, 7, 5, 5, 5, 2, 9, 2, 8, 4, 6, 8, 6, 5, 0, 0, 8, 7, 6, 1, 7, 1, 1, 2, 7, 4, 0, 0, 7, 7, 6, 3, 8, 6, 4, 2, 0, 9, 4, 0, 5, 7, 8, 2, 7, 4, 7, 1, 1, 3, 6, 6, 2, 9, 1, 9, 4, 8, 3, 6, 9, 5, 9, 6, 2, 4, 6, 7, 7, 0, 6, 6, 9, 4, 8, 3, 5, 3, 4, 9, 0, 0, 5, 2, 5, 0, 7, 1, 1, 1, 0, 7, 6, 7, 9, 6, 6, 4, 1, 4, 3, 1, 1, 2, 2, 4, 1, 0, 8, 7, 6, 3, 4, 0, 0, 6, 3, 3, 0, 7, 1, 7, 1, 1, 3, 1, 0, 9, 9, 7, 5, 4, 1, 4, 8, 9, 5, 3, 5, 1, 9, 8, 2, 7, 3, 9, 9, 0, 1, 0, 2, 9, 3, 9, 3, 3, 6, 2, 9, 9, 8, 3, 7, 4, 0, 4, 7, 8, 4, 9, 8, 1, 9, 7, 5, 9, 2, 8, 2, 2, 0, 2, 2, 3, 8, 4, 6, 8, 4, 8, 2, 4, 6, 7, 9, 3, 3, 9, 4, 3, 1, 4, 4, 7, 0, 5, 9, 6, 0, 4, 4, 4, 4, 6, 1, 2, 3, 2, 5, 4, 5, 9, 6, 8, 5, 6, 0, 5, 6, 4, 1, 8, 6, 5, 2, 5, 4, 5, 5, 4, 7, 4, 0, 7, 8, 2, 2, 3, 7, 0, 1, 8, 0, 7, 1, 9, 8, 7, 5, 5, 9, 1, 7, 5, 4, 3, 1, 2, 2, 1, 6, 6, 7, 1, 1, 4, 0, 7, 4, 2, 4, 0, 6, 4, 7, 6, 9, 5, 3, 4, 6, 5, 0, 1, 8, 8, 2, 3, 3, 5, 7, 8, 0, 8, 5, 7, 1, 1, 0, 1, 3, 7, 8, 5, 0, 7, 1, 1, 0, 1, 1, 4, 5, 2, 7, 6, 2, 3, 0, 2, 8, 5, 4, 6, 9, 7, 2, 1, 3, 6, 4, 1, 8, 2, 4, 0, 5, 1, 0, 2, 2, 6, 4, 4, 3, 4, 6, 1, 6, 5, 7, 9, 2, 0, 2, 6, 0, 1, 4, 3, 3, 2, 8, 8, 0, 8, 8, 9, 0, 9, 6, 7, 6, 3, 9, 3, 9, 7, 7, 7, 4, 9, 0, 6, 4, 8, 4, 2, 7, 2, 8, 1, 0, 0, 7, 8, 3, 3, 3, 1, 3, 7, 6, 1, 3, 1, 6, 6, 5, 2, 4, 7, 5, 9, 5, 3, 4, 9, 9, 1, 6, 5, 0, 1, 3, 2, 0, 3, 4, 8, 2, 2, 0, 2, 8, 1, 5, 1, 2, 8, 8, 9, 1, 2, 1, 3, 5, 1, 0, 9, 4, 4, 8, 3, 2, 5, 9, 7, 6, 0, 2, 0, 0, 0, 8, 8, 3, 1, 5, 3, 3, 8, 5, 1, 8, 4, 0, 4, 9, 4, 6, 2, 3, 3, 5, 6, 4, 8, 0, 9, 2, 8, 3, 6, 7, 5, 1, 2, 9, 4, 9, 1, 2, 8, 6, 0, 3, 0, 4, 1, 1, 5, 7, 0, 9, 9, 1, 9, 5, 9, 2, 5, 0, 4, 1, 0, 8, 4, 0, 8, 9, 8, 9, 4, 2, 5, 7, 9, 8, 9, 8, 0, 9, 9, 6, 8, 9, 9, 5, 9, 8, 6, 1, 0, 3, 3, 5, 2, 1, 6, 3, 0, 2, 8, 3, 5, 6, 2, 3, 0, 2, 2, 6, 4, 3, 5, 5, 1, 7, 2, 1, 6, 9, 1, 3, 9, 5, 5, 1, 6, 2, 2, 8, 6, 7, 1, 4, 6, 0, 6, 0, 5, 3, 2, 8, 3, 6, 8, 9, 2, 5, 3, 5, 5, 4, 5, 2, 0, 5, 6, 2, 2, 8, 3, 9, 4, 5, 7, 9, 4, 6, 7, 1, 3, 1, 3, 6, 6, 0, 9, 0, 1, 9, 4, 2, 8, 8, 0, 1, 6, 9, 7, 5, 5, 4, 7, 4, 9, 4, 4, 3, 6, 3, 1, 1, 7, 6, 9, 1, 8, 4, 1, 1, 9, 9, 9, 3, 6, 8, 1, 6, 0, 4, 1, 3, 7, 7, 4, 9, 5, 1, 0, 0, 1, 1, 6, 2, 1, 9, 8, 4, 0, 5, 6, 4, 9, 0, 7, 1, 6, 5, 7, 5, 2, 5, 1, 8, 5, 4, 7, 0, 5, 7, 2, 2, 5, 3, 1, 0, 4, 5, 7, 1, 0, 5, 1, 7, 0, 0, 6, 0, 7, 3, 1, 8, 3, 9, 7, 0, 0, 8, 4, 5, 9, 8, 3, 2, 7, 2, 9, 7, 2, 1, 1, 3, 7, 5, 3, 1, 9, 8, 2, 2, 2, 8, 8, 5, 7, 3, 8, 9, 8, 8, 6, 8, 2, 3, 9, 7, 1, 6, 2, 9, 2, 8, 8, 1, 6, 2, 8, 7, 9, 1, 8, 0, 1, 7, 2, 0, 7, 1, 1, 4, 0, 2, 0, 9, 8, 6, 2, 3, 0, 3, 8, 0, 2, 1, 1, 1, 1, 4, 2, 9, 7, 6, 5, 1, 1, 2, 1, 9, 9, 9, 1, 0, 2, 0, 2, 1, 1, 4, 6, 4, 1, 5, 4, 9, 7, 7, 7, 5, 6, 2, 2, 2, 8, 0, 6, 9, 5, 1, 9, 7, 7, 1, 4, 8, 5, 3, 4, 3, 4, 7, 7, 5, 0, 7, 4, 8, 8, 1, 5, 3, 9, 5, 9, 7, 6, 9, 0, 3, 6, 3, 9, 8, 2, 8, 1, 2, 8, 6, 8, 5, 5, 2, 9, 4, 4, 2, 5, 1, 5, 1, 4, 4, 1, 4, 4, 3, 5, 9, 1, 2, 2, 3, 3, 0, 2, 9, 0, 0, 9, 9, 6, 0, 9, 3, 7, 8, 4, 1, 9, 9, 7, 2, 7, 9, 9, 9, 9, 5, 1, 1, 8, 7, 5, 1, 9, 5, 5, 5, 4, 9, 5, 9, 3, 1, 9, 0, 9, 7, 5, 4, 9, 2, 0, 1, 0, 5, 1, 4, 9, 3, 3, 6, 1, 5, 2, 5, 2, 2, 0, 7, 2, 6, 6, 0, 1, 2, 0, 3, 0, 2, 9, 5, 7, 9, 5, 5, 0, 8, 9, 5, 0, 3, 2, 5, 4, 0, 8, 8, 4, 6, 8, 8, 4, 5, 4, 5, 5, 4, 9, 2, 2, 1, 2, 6, 8, 8, 7, 0, 3, 6, 6, 4, 3, 8, 8, 7, 2, 2, 0, 0, 4, 3, 9, 9, 1, 9, 8, 6, 6, 4, 2, 6, 9, 2, 8, 5, 4, 5, 7, 9, 4, 9, 2, 1, 8, 3, 4, 0, 7, 8, 7, 9, 2, 4, 6, 5, 6, 2, 3, 4, 2, 6, 0, 0, 0, 1, 2, 8, 7, 9, 8, 2, 0, 4, 7, 7, 5, 0, 5, 6, 4, 6, 7, 4, 3, 0, 7, 5, 0, 7, 4, 2, 6, 8, 9, 9, 4, 2, 4, 6, 7, 8, 7, 6, 9, 4, 1, 3, 7, 3, 0, 8, 7, 8, 6, 1, 3, 9, 2, 2, 9, 2, 1, 8, 3, 2, 9, 6, 8, 4, 0, 1, 2, 8, 4, 5, 7, 7, 8, 1, 1, 3, 0, 3, 5, 7, 0, 3, 1, 8, 3, 5, 3, 1, 7, 7, 3, 0, 8, 4, 8, 2, 6, 5, 2, 9, 7, 3, 9, 0, 9, 9, 6, 4, 2, 9, 7, 2, 1, 1, 6, 7, 4, 7, 5, 9, 2, 8, 2, 1, 2, 4, 5, 7, 6, 1, 3, 2, 5, 9, 9, 3, 6, 1, 1, 4, 6, 9, 7, 2, 1, 5, 1, 4, 6, 3, 4, 1, 1, 0, 3, 1, 6, 8, 4, 9, 0, 7, 3, 0, 4, 9, 0, 6, 6, 6, 3, 6, 7, 7, 2, 8, 6, 0, 8, 3, 0, 2, 9, 8, 5, 2, 5, 3, 9, 8, 0, 0, 1, 9, 5, 1, 3, 9, 6, 0, 1, 4, 1, 7, 1, 2, 3, 7, 9, 7, 4, 4, 9, 3, 9, 2, 8, 2, 7, 1, 8, 0, 9, 1, 0, 1, 7, 7, 9, 6, 9, 9, 9, 2, 1, 6, 1, 3, 5, 2, 1, 9, 7, 6, 4, 5, 7, 6, 6, 9, 9, 6, 3, 6, 2, 9, 8, 1, 2, 2, 5, 5, 2, 3, 7, 2, 1, 0, 1, 0, 4, 5, 2, 8, 2, 8, 3, 5, 1, 7, 7, 1, 1, 2, 4, 7, 8, 4, 0, 5, 0, 7, 8, 8, 4, 7, 7, 8, 5, 8, 6, 9, 8, 1, 3, 8, 0, 3, 1, 7, 8, 5, 6, 1, 6, 5, 7, 4, 9, 3, 5, 4, 7, 1, 2, 0, 8, 1, 6, 0, 7, 3, 4, 7, 8, 9, 6, 0, 8, 6, 4, 8, 7, 7, 9, 3, 8, 6, 9, 7, 2, 3, 4, 0, 2, 1, 0, 5, 5, 5, 7, 2, 4, 0, 7, 2, 8, 3, 0, 8, 7, 8, 4, 0, 8, 4, 4, 5, 3, 5, 6, 6, 3, 0, 9, 3, 2, 6, 8, 9, 3, 4, 9, 5, 8, 9, 1, 2, 8, 8, 6, 8, 1, 3, 7, 9, 0, 1, 1, 4, 7, 0, 9, 1, 7, 4, 5, 7, 1, 2, 1, 1, 3, 0, 6, 4, 1, 2, 4, 8, 7, 6, 6, 9, 3, 1, 0, 5, 2, 6, 0, 5, 4, 3, 8, 4, 6, 6, 2, 7, 9, 5, 1, 3, 2, 4, 3, 6, 1, 4, 4, 4, 7, 6, 5, 4, 1, 4, 9, 2, 7, 8, 0, 1, 3, 6, 1, 3, 4, 1, 1, 1, 5, 6, 0, 7, 0, 7, 2, 3, 2, 5, 2, 2, 9, 4, 9, 8, 1, 3, 1, 5, 1, 2, 7, 4, 0, 0, 0, 8, 2, 2, 9, 2, 2, 1, 9, 9, 2, 7, 5, 1, 3, 4, 9, 4, 1, 8, 5, 6, 2, 8, 3, 1, 2, 8, 4, 9, 9, 2, 7, 0, 7, 7, 2, 3, 2, 4, 0, 3, 9, 9, 8, 4, 1, 0, 6, 0, 4, 6, 8, 6, 1, 1, 9, 8, 9, 2, 3, 5, 5, 9, 4, 2, 1, 9, 4, 3, 9, 6, 0, 4, 0, 6, 0, 1, 2, 3, 4, 7, 8, 9, 0, 1, 2, 3, 4, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 8, 3, 4, 7, 8, 6, 2, 4, 0, 9, 7, 1, 9, 3, 8, 4, 7, 3, 0, 9, 1, 4, 5, 4, 6, 2, 0, 6, 2, 1, 1, 1, 1, 7, 2, 4, 7, 5, 2, 4, 4, 5, 8, 4, 2, 9, 7, 0, 0, 7, 5, 1, 1, 7, 6, 6, 6, 8, 2, 2, 7, 7, 4, 0, 2, 4, 2, 1, 8, 9, 6, 1, 0, 5, 4, 6, 9, 8, 0, 4, 0, 8, 3, 9, 6, 3, 0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 5, 1, 2, 3, 4, 5, 6, 7, 8, 5, 4, 8, 7, 4, 7, 7, 3, 9, 4, 8, 3, 1, 5, 8, 2, 7, 4, 2, 1, 5, 4, 5, 5, 8, 6, 4, 4, 4, 1, 8, 7, 5, 5, 1, 6, 7, 1, 3, 6, 3, 3, 2, 2, 6, 9, 9, 6, 5, 5, 3, 3, 8, 1, 6, 5, 6, 8, 1, 9, 7, 6, 8, 3, 7, 4, 7, 0, 9, 0, 0, 3, 7, 9, 3, 0, 2, 0, 1, 0, 1, 0, 4, 0, 1, 0, 4, 7, 9, 6, 2, 6, 2, 2, 9, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 8, 0, 5, 6, 6, 0, 8, 0, 2, 3, 7, 9, 4, 7, 1, 9, 1, 7, 1, 4, 0, 0, 4, 1, 7, 5, 7, 1, 3, 3, 3, 6, 6, 9, 7, 4, 3, 0, 2, 5, 2, 6, 0, 8, 9, 4, 3, 5, 4, 8, 1, 5, 9, 0, 6, 4, 3, 6, 3, 3, 8, 1, 4, 7, 5, 7, 2, 2, 0, 0, 1, 7, 7, 9, 5, 9, 8, 9, 6, 8, 8, 2, 3, 6, 1, 2, 9, 8, 9, 5, 2, 6, 2, 4, 8, 4, 6, 5, 0, 1, 5, 6, 7, 8, 9, 0, 1, 2, 3, 6, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 7, 4, 2, 0, 9, 0, 1, 5, 8, 8, 0, 2, 7, 8, 4, 4, 6, 1, 0, 4, 5, 3, 9, 4, 2, 0, 5, 0, 1, 3, 2, 9, 8, 6, 0, 1, 1, 8, 0, 4, 7, 7, 6, 3, 6, 0, 7, 3, 5, 4, 2, 4, 1, 8, 3, 5, 6, 7, 0, 6, 7, 1, 2, 5, 8, 1, 9, 3, 8, 2, 3, 7, 6, 7, 1, 4, 6, 2, 9, 3, 0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 5, 0, 1, 2, 8, 9, 1, 4, 0, 9, 5, 0, 8, 0, 7, 7, 1, 1, 2, 9, 3, 6, 7, 2, 3, 8, 1, 2, 9, 8, 8, 7, 1, 7, 1, 1, 0, 3, 4, 2, 6, 4, 7, 4, 2, 7, 4, 9, 1, 0, 6, 8, 5, 5, 5, 3, 5, 9, 7, 4, 8, 5, 9, 6, 9, 3, 0, 3, 8, 9, 1, 8, 1, 6, 0, 0, 1, 2, 3, 4, 5, 6, 9, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 3, 5, 3, 2, 9, 3, 2, 1, 4, 5, 5, 3, 3, 2, 1, 3, 9, 7, 2, 5, 2, 8, 9, 1, 8, 8, 7, 8, 1, 0, 0, 7, 7, 8, 7, 5, 0, 6, 1, 5, 7, 4, 6, 1, 2, 5, 0, 7, 9, 9, 0, 3, 8, 4, 4, 8, 1, 8, 6, 5, 9, 0, 0, 0, 3, 7, 1, 6, 4, 2, 6, 6, 0, 4, 5, 4, 1, 3, 8, 6, 3, 9, 9, 5, 9, 3, 7, 8, 5, 6, 4, 7, 6, 2, 2, 0, 5, 4, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 7, 6, 6, 0, 1, 2, 3, 4, 5, 6, 8, 7, 1, 3, 2, 6, 0, 7, 5, 9, 9, 6, 0, 7, 4, 1, 3, 2, 1, 2, 3, 8, 3, 2, 6, 5, 6, 8, 2, 7, 4, 8, 1, 8, 0, 5, 3, 9, 4, 1, 9, 2, 1, 9, 6, 7, 9, 0, 4, 6, 1, 7, 3, 8, 7, 2, 9, 6, 5, 8, 3, 9, 0, 5, 7, 1, 6, 1, 0, 9, 3, 3, 4, 4, 0, 6, 2, 5, 4, 2, 3, 4, 6, 0, 0, 2, 0, 1, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 7, 5, 6, 7, 8, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 8, 7, 1, 3, 7, 5, 2, 8, 0, 7, 5, 9, 9, 0, 9, 1, 1, 5, 8, 8, 6, 3, 2, 1, 8, 3, 2, 6, 5, 6, 5, 0, 1, 0, 5, 3, 1, 9, 2, 1, 9, 6, 0, 4, 6, 1, 7, 3, 8, 7, 2, 9, 6, 5, 8, 3, 3, 7, 1, 6, 1, 0, 9, 6, 2, 5, 4, 2, 3, 4, 4, 6, 0, 0, 2, 0, 1, 2, 3, 9, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 8, 4, 5, 6, 7, 8, 9, 8, 6, 5, 0, 6, 8, 9, 4, 1, 9, 5, 8, 0, 4, 8, 9, 1, 4, 0, 5, 5, 2, 1, 5, 4, 0, 7, 6, 0, 1, 7, 0, 6, 8, 9, 5, 1, 7, 9, 8, 6, 0, 8, 1, 7, 7, 1, 3, 2, 3, 1, 4, 2, 0, 0, 7, 8, 4, 6, 4, 9, 3, 8, 4, 7, 2, 5, 6, 5, 6, 9, 6, 3, 0, 2, 4, 6, 9, 0, 2, 5, 5, 1, 5, 8, 9, 7, 8, 7, 2, 2, 5, 7, 9, 8, 2, 1, 5, 1, 3, 0, 1, 2, 8, 4, 5, 6, 7, 8, 3, 0, 1, 2, 3, 4, 5, 6, 7, 8, 5, 0, 1, 2, 3, 4, 5, 6, 7, 8, 0, 1, 2, 6, 5, 3, 0, 7, 0, 4, 1, 4, 3, 6, 7, 2, 3, 1, 2, 1, 2, 0, 6, 0, 1, 3, 0, 2, 7, 5, 7, 6, 2, 9, 1, 9, 0, 6, 0, 6, 0, 2, 0, 6, 1, 5, 8, 4, 3, 0, 1, 5, 4, 4, 8, 5, 7, 5, 7, 8, 3, 4, 8, 8, 5, 2, 0, 7, 1, 3, 8, 1, 0, 7, 5, 3, 6, 3, 4, 7, 7, 0, 2, 3, 4, 4, 3, 8, 6, 2, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 8, 3, 9, 5, 5, 2, 6, 8, 4, 9, 1, 7, 1, 2, 3, 5, 9, 6, 9, 1, 1, 1, 2, 9, 5, 6, 8, 1, 2, 0, 7, 7, 5, 8, 2, 9, 8, 9, 0, 4, 6, 7, 1, 3, 4, 5, 6, 0, 3, 6, 8, 7, 0, 4, 2, 7, 4, 7, 5, 4, 3, 4, 2, 8, 1, 5, 1, 2, 0, 2, 5, 6, 4, 3, 0, 0, 0, 3, 3, 5, 7, 0, 6, 4, 8, 8, 6, 3, 4, 6, 9, 9, 8, 2, 7, 7, 1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 0, 1, 2, 3, 4, 5, 6, 7, 8, 2, 1, 7, 2, 5, 0, 8, 0, 2, 7, 6, 8, 3, 6, 0, 2, 7, 6, 6, 1, 2, 8, 8, 7, 7, 4, 7, 7, 3, 7, 4, 5, 4, 3, 3, 8, 4, 1, 1, 9, 7, 4, 3, 7, 3, 3, 0, 2, 5, 5, 6, 6, 3, 5, 6, 2, 9, 9, 8, 4, 1, 0, 6, 0, 9, 6, 8, 8, 5, 6, 1, 1, 9, 8, 9, 2, 3, 5, 5, 9, 4, 2, 1, 9, 2, 9, 2, 0, 6, 6, 4, 0, 0, 1, 2, 3, 4, 7, 8, 9, 0, 1, 2, 3, 7, 8, 9, 0, 1, 2, 3, 4, 7, 8, 9, 7, 3, 0, 3, 1, 8, 7, 6, 4, 0, 2, 6, 8, 3, 2, 8, 1, 2, 0, 7, 1, 0, 4, 4, 5, 8, 0, 6, 2, 3, 1, 5, 1, 8, 5, 9, 4, 0, 7, 5, 8, 8, 3, 8, 9, 2, 6, 2, 5, 3, 1, 7, 3, 0, 1, 9, 9, 6, 0, 5, 9, 2, 8, 1, 4, 3, 5, 2, 9, 2, 5, 8, 9, 5, 0, 1, 2, 4, 5, 6, 5, 1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4, 5, 1, 0, 4, 5, 6, 6, 3, 4, 4, 2, 8, 1, 0, 2, 4, 9, 7, 2, 3, 3, 9, 2, 0, 4, 3, 3, 7, 1, 5, 2, 3, 7, 7, 8, 4, 0, 2, 4, 0, 2, 4, 7, 8, 0, 7, 5, 6, 9, 3, 2, 8, 6, 7, 3, 7, 5, 1, 0, 7, 1, 6, 7, 2, 5, 7, 9, 5, 8, 6, 2, 6, 2, 8, 1, 7, 5, 0, 1, 1, 3, 7, 4, 9, 1, 8, 6, 8, 5, 0, 1, 2, 3, 4, 5, 6, 7, 5, 9, 0, 1, 2, 3, 4, 7, 8, 4, 4, 1, 7, 8, 9, 9, 8, 9, 8, 4, 1, 7, 7, 3, 3, 7, 6, 6, 6, 1, 9, 0, 1, 7, 6, 3, 2, 1, 7, 1, 3, 9, 1, 7, 6, 8, 4, 1, 4, 3, 6, 4, 6, 1, 4, 4, 7, 2, 4, 4, 0, 1, 2, 3, 4, 5, 6, 7, 8, 8, 0, 1, 2, 3, 4, 5, 6, 9, 0, 1, 2, 3, 4, 7, 8, 1, 3, 5, 1, 7, 7, 2, 1, 4, 8, 3, 4, 4, 3, 0, 7, 4, 1, 8, 3, 4, 9, 1, 6, 0, 1, 0, 0, 2, 8, 7, 1, 1, 4, 0, 4, 7, 3, 6, 5, 0, 3, 7, 4, 0, 6, 9, 2, 6, 5, 8, 6, 9, 0, 4, 0, 6, 1, 4, 4, 0, 9, 5, 1, 3, 7, 6, 4, 3, 0, 2, 2, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 4, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 2, 1, 7, 2, 5, 0, 8, 0, 2, 7, 8, 8, 3, 0, 6, 0, 2, 7, 6, 4, 1, 2, 8, 8, 7, 7, 4, 7, 7, 3, 7, 4, 5, 4, 3, 3, 8, 4, 5, 4, 1, 1, 9, 7, 4, 3, 7, 3, 3, 0, 2, 5, 5, 6, 3, 1, 5, 2, 5, 9, 9, 8, 4, 1, 0, 6, 0, 9, 6, 8, 8, 5, 6, 1, 1, 9, 8, 9, 6, 3, 5, 5, 9, 4, 2, 1, 9, 4, 9, 1, 3, 9, 2, 0, 6, 0, 4, 0, 4, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 3, 8, 0, 7, 1, 0, 7, 5, 5, 6, 9, 0, 1, 0, 0, 8, 3, 4, 3, 1, 5, 0, 0, 9, 6, 3, 4, 9, 3, 7, 6, 9, 2, 4, 5, 7, 2, 6, 4, 9, 4, 9, 4, 1, 2, 2, 5, 8, 1, 3, 2, 9, 4, 3, 8, 2, 2, 1, 2, 8, 6, 5, 1, 6, 7, 2, 1, 3, 9, 3, 8, 7, 5, 7, 0, 7, 4, 8, 8, 5, 0, 6, 6, 3, 7, 6, 9, 9, 4, 8, 4, 1, 0, 6, 6, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 4, 7, 4, 0, 4, 0, 1, 7, 9, 5, 1, 4, 2, 8, 9, 4, 3, 7, 8, 2, 4, 4, 3, 3, 6, 9, 9, 5, 8, 6, 7, 0, 6, 8, 2, 6, 3, 9, 3, 2, 7, 6, 1, 7, 4, 8, 8, 9, 0, 3, 3, 9, 0, 5, 2, 9, 4, 1, 0, 3, 7, 5, 8, 7, 7, 8, 2, 9, 7, 1, 2, 6, 4, 2, 5, 2, 3, 6, 6, 5, 0, 0, 2, 8, 1, 6, 1, 0, 4, 3, 1, 6, 1, 9, 0, 1, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 8, 4, 0, 0, 7, 2, 4, 3, 8, 6, 6, 3, 2, 6, 3, 3, 0, 1, 4, 7, 8, 0, 3, 1, 9, 0, 1, 9, 1, 2, 7, 0, 1, 5, 8, 2, 9, 2, 7, 6, 5, 5, 9, 9, 8, 2, 9, 1, 5, 2, 3, 4, 3, 1, 9, 0, 9, 3, 6, 8, 7, 0, 1, 0, 5, 8, 2, 7, 7, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 7, 4, 8, 1, 5, 6, 5, 7, 2, 8, 6, 3, 3, 8, 6, 5, 4, 0, 9, 1, 7, 2, 9, 1, 5, 1, 3, 2, 2, 3, 0, 6, 4, 3, 7, 6, 9, 0, 4, 8, 1, 4, 0, 6, 1, 2, 6, 9, 2, 2, 3, 5, 5, 1, 0, 7, 7, 9, 6, 2, 9, 4, 7, 0, 2, 3, 4, 0, 0, 8, 8, 8, 5, 1, 3, 7, 4, 9, 8, 8, 9, 0, 9, 8, 9, 0, 2, 6, 5, 6, 7, 4, 7, 5, 4, 1, 3, 5, 3, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 6, 0, 1, 2, 4, 5, 6, 7, 8, 1, 7, 2, 4, 1, 4, 1, 4, 4, 6, 8, 4, 5, 3, 1, 8, 8, 3, 3, 5, 6, 7, 0, 6, 1, 6, 8, 7, 0, 1, 5, 0, 8, 6, 0, 1, 5, 8, 4, 2, 3, 5, 7, 6, 9, 1, 9, 0, 6, 7, 1, 2, 3, 9, 2, 4, 5, 5, 3, 7, 5, 3, 1, 8, 2, 2, 3, 0, 2, 4, 4, 9, 7, 0, 2, 7, 4, 9, 9, 2, 5, 9, 8, 3, 8, 6, 7, 0, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 0, 7, 2, 6, 5, 5, 3, 7, 8, 6, 6, 6, 6, 4, 3, 8, 8, 3, 0, 1, 9, 0, 5, 4, 1, 9, 1, 2, 7, 0, 1, 3, 8, 2, 9, 2, 7, 4, 2, 6, 5, 5, 9, 4, 1, 1, 5, 7, 6, 8, 2, 9, 4, 3, 1, 9, 0, 9, 3, 6, 8, 7, 0, 1, 0, 5, 8, 2, 7, 7, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 2, 1, 2, 1, 3, 9, 9, 8, 5, 3, 7, 0, 7, 7, 5, 7, 9, 9, 4, 7, 0, 3, 4, 1, 5, 8, 1, 4, 8, 4, 1, 8, 6, 6, 4, 6, 0, 5, 5, 3, 3, 5, 7, 2, 5, 9, 6, 9, 2, 6, 2, 1, 2, 0, 8, 3, 8, 3, 0, 8, 7, 4, 9, 5, 0, 9, 7, 0, 0, 4, 6, 0, 9, 1, 6, 2, 7, 6, 8, 3, 5, 2, 1, 8, 3, 8, 6, 1, 0, 2, 1, 4, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 7, 6, 4, 7, 6, 2, 3, 4, 8, 7, 8, 6, 9, 8, 3, 2, 2, 8, 4, 8, 5, 6, 5, 0, 2, 0, 1, 1, 2, 9, 6, 8, 2, 1, 0, 6, 5, 2, 9, 7, 6, 3, 9, 2, 7, 1, 8, 3, 8, 1, 9, 5, 5, 0, 1, 8, 9, 8, 2, 6, 0, 4, 5, 0, 2, 1, 8, 6, 7, 5, 9, 9, 3, 0, 3, 1, 4, 4, 0, 4, 9, 0, 1, 2, 3, 5, 6, 7, 8, 0, 1, 2, 2, 5, 0, 7, 8, 9, 0, 1, 2, 2, 5, 6, 7, 8, 9, 9, 7, 0, 9, 0, 1, 5, 8, 8, 0, 9, 3, 2, 7, 8, 4, 6, 1, 0, 4, 9, 4, 4, 0, 5, 0, 1, 6, 9, 3, 2, 9, 1, 6, 0, 8, 1, 8, 7, 7, 6, 3, 6, 0, 7, 2, 4, 1, 7, 0, 6, 7, 1, 2, 5, 8, 1, 8, 2, 8, 7, 6, 8, 7, 8, 6, 2, 9, 3, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 6, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 8, 9, 5, 7, 0, 3, 1, 6, 8, 4, 1, 5, 6, 4, 2, 7, 8, 1, 3, 4, 3, 4, 7, 2, 0, 5, 0, 8, 9, 2, 3, 2, 3, 5, 5, 7, 8, 4, 9, 9, 7, 1, 1, 9, 0, 7, 8, 3, 4, 8, 6, 3, 8, 0, 9, 6, 2, 8, 0, 1, 0, 6, 2, 3, 8, 9, 0, 7, 2, 3, 4, 5, 5, 2, 8, 5, 4, 6, 6, 6, 7, 9, 1, 8, 2, 1, 5, 3, 4, 7, 9, 4, 0, 0, 0, 1, 2, 3, 4, 8, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 6, 5, 6, 9, 0, 1, 3, 1, 5, 1, 2, 4, 9, 8, 6, 6, 8, 0, 1, 1, 9, 2, 6, 6, 8, 7, 4, 2, 9, 7, 0, 2, 1, 0, 3, 6, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 8, 6, 5, 9, 7, 0, 2, 3, 4, 3, 8, 5, 1, 5, 2, 3, 0, 1, 2, 1, 3, 2, 6, 5, 3, 0, 7, 2, 7, 4, 6, 4, 0, 5, 9, 9, 5, 9, 5, 3, 1, 7, 4, 7, 6, 5, 4, 0, 0, 0, 6, 2, 0, 6, 3, 7, 7, 4, 4, 3, 9, 2, 8, 9, 6, 0, 9, 5, 3, 8, 8, 7, 1, 4, 0, 4, 8, 5, 2, 3, 9, 0, 1, 9, 1, 5, 1, 7, 4, 8, 6, 2, 1, 6, 8, 8, 0, 1, 2, 5, 4, 7, 8, 9, 0, 1, 2, 3, 4, 6, 7, 8, 9, 0, 1, 2, 3, 4, 7, 8, 9, 1, 4, 5, 3, 5, 0, 9, 5, 4, 5, 0, 8, 4, 6, 7, 0, 7, 7, 1, 6, 9, 1, 3, 6, 2, 3, 5, 2, 3, 8, 9, 5, 8, 8, 7, 1, 7, 1, 1, 0, 3, 4, 2, 6, 4, 7, 4, 2, 7, 4, 2, 9, 2, 7, 9, 2, 1, 0, 6, 5, 3, 4, 8, 5, 7, 6, 9, 0, 6, 3, 0, 6, 1, 6, 0, 0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 7, 8, 9, 0, 1, 2, 3, 4, 7, 2, 5, 1, 6, 4, 3, 9, 9, 0, 9, 7, 1, 6, 4, 3, 6, 2, 0, 9, 8, 6, 5, 7, 0, 0, 1, 7, 4, 3, 2, 4, 1, 3, 7, 6, 4, 7, 7, 7, 9, 8, 4, 3, 6, 2, 6, 3, 5, 8, 0, 5, 4, 7, 1, 3, 1, 7, 9, 6, 2, 0, 9, 1, 7, 3, 3, 9, 1, 6, 4, 3, 9, 8, 2, 1, 8, 6, 4, 1, 5, 5, 6, 5, 0, 1, 2, 3, 4, 5, 6, 7, 0, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 6, 9, 7, 0, 2, 3, 4, 3, 8, 5, 1, 3, 0, 1, 2, 1, 3, 2, 0, 7, 2, 6, 4, 0, 5, 9, 5, 8, 9, 5, 3, 1, 7, 4, 7, 0, 0, 6, 6, 6, 3, 7, 9, 2, 6, 5, 8, 7, 1, 4, 0, 4, 8, 5, 2, 3, 9, 0, 1, 9, 1, 5, 1, 7, 6, 1, 2, 1, 6, 8, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 0, 1, 2, 3, 5, 6, 7, 8, 1, 0, 4, 5, 6, 6, 3, 4, 4, 2, 8, 1, 0, 6, 4, 9, 7, 2, 9, 2, 0, 9, 3, 3, 9, 1, 5, 2, 3, 1, 6, 7, 3, 7, 8, 4, 0, 2, 4, 0, 2, 4, 7, 8, 0, 7, 0, 6, 9, 3, 2, 4, 8, 6, 0, 5, 7, 5, 1, 0, 8, 1, 6, 7, 2, 9, 7, 9, 5, 6, 5, 2, 6, 2, 8, 1, 7, 5, 5, 7, 3, 5, 0, 1, 1, 3, 8, 4, 9, 4, 5, 1, 8, 6, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 3, 5, 3, 2, 9, 3, 2, 1, 4, 5, 5, 2, 3, 2, 1, 3, 9, 7, 2, 1, 2, 8, 9, 1, 8, 8, 7, 8, 1, 0, 0, 6, 7, 7, 8, 7, 5, 0, 6, 1, 5, 7, 4, 6, 1, 2, 5, 0, 7, 9, 9, 0, 3, 4, 4, 8, 4, 1, 8, 6, 5, 9, 0, 0, 0, 3, 7, 1, 6, 4, 6, 0, 4, 5, 4, 1, 3, 8, 6, 3, 9, 9, 5, 9, 3, 7, 8, 5, 6, 4, 7, 6, 2, 2, 0, 9, 4, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 6, 4, 2, 6, 4, 7, 5, 5, 4, 7, 2, 9, 3, 9, 3, 8, 2, 0, 9, 5, 6, 0, 1, 0, 6, 5, 3, 5, 3, 8, 0, 0, 3, 4, 1, 6, 3, 0, 8, 3, 0, 6, 2, 7, 8, 1, 7, 1, 3, 8, 5, 4, 2, 0, 9, 7, 6, 7, 4, 1, 6, 2, 6, 7, 1, 9, 8, 0, 6, 9, 4, 9, 9, 6, 2, 3, 7, 1, 9, 2, 2, 5, 3, 7, 8, 0, 1, 2, 3, 4, 7, 8, 9, 0, 1, 2, 3, 4, 7, 8, 9, 0, 1, 7, 8, 9, 8, 9, 2, 6, 1, 3, 5, 4, 8, 2, 6, 4, 3, 4, 5, 9, 2, 0, 3, 9, 4, 9, 7, 3, 8, 7, 4, 4, 9, 8, 5, 8, 2, 6, 6, 2, 3, 1, 3, 2, 7, 3, 1, 9, 0, 1, 1, 3, 5, 0, 7, 8, 1, 5, 1, 4, 6, 0, 0, 4, 9, 1, 6, 6, 9, 0, 7, 6, 1, 1, 0, 1, 2, 3, 4, 2, 2, 3, 4, 5, 6, 2, 5, 1, 2, 2, 8, 6, 2, 9, 2, 1, 4, 3, 9, 6, 1, 7, 2, 4, 4, 5, 2, 0, 0, 1, 6, 6, 8, 2, 7, 2, 2, 4, 2, 1, 6, 1, 0, 6, 9, 8, 3, 9, 6, 3, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 6, 8, 9, 9, 0, 1, 2, 4, 4, 3, 7, 4, 4, 4, 0, 3, 8, 7, 5, 8, 2, 1, 7, 5, 3, 8, 5, 2, 5, 1, 1, 6, 2, 1, 3, 8, 6, 4, 2, 6, 2, 5, 5, 0, 2, 8, 0, 6, 8, 1, 7, 9, 1, 9, 2, 6, 7, 6, 6, 8, 7, 4, 9, 2, 1, 3, 3, 0, 5, 5, 8, 0, 3, 7, 9, 7, 0, 2, 7, 4, 1, 7, 8, 0, 3, 5, 3, 6, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 7, 8, 9, 6, 4, 2, 6, 4, 7, 8, 4, 2, 9, 3, 9, 3, 0, 0, 1, 0, 4, 2, 6, 3, 5, 3, 0, 3, 4, 1, 5, 3, 0, 8, 3, 0, 6, 1, 7, 8, 0, 9, 2, 6, 7, 1, 9, 6, 9, 4, 9, 9, 6, 7, 1, 2, 5, 3, 7, 8, 0, 1, 2, 4, 5, 6, 7, 8, 9, 0, 1, 3, 4, 5, 6, 7, 5, 0, 1, 3, 4, 7, 8, 9, 7, 5, 5, 1, 9, 9, 7, 1, 0, 0, 5, 9, 7, 1, 7, 2, 2, 3, 6, 8, 3, 2, 0, 0, 6, 1, 7, 5, 3, 6, 2, 9, 4, 8, 8, 7, 1, 0, 8, 7, 7, 5, 8, 5, 3, 4, 6, 1, 1, 5, 5, 0, 7, 2, 3, 6, 4, 1, 2, 4, 1, 5, 4, 2, 0, 4, 8, 6, 1, 9, 0, 2, 5, 6, 9, 3, 6, 3, 6, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 6, 9, 0, 1, 2, 3, 5, 6, 7, 8, 1, 0, 9, 5, 7, 5, 1, 8, 6, 9, 0, 4, 1, 9, 3, 8, 4, 4, 7, 0, 1, 9, 2, 8, 7, 8, 2, 5, 9, 6, 0, 6, 3, 5, 3, 3, 3, 9, 6, 1, 1, 0, 6, 1, 0, 0, 6, 2, 1, 1, 3, 2, 7, 7, 8, 8, 7, 8, 4, 6, 0, 2, 0, 7, 0, 3, 6, 8, 7, 1, 5, 9, 9, 3, 7, 2, 4, 9, 4, 3, 6, 2, 2, 5, 3, 2, 5, 5, 9, 4, 1, 7, 2, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 0, 1, 2, 7, 5, 3, 4, 4, 0, 0, 6, 9, 6, 6, 5, 7, 2, 3, 4, 4, 9, 1, 4, 0, 7, 7, 5, 7, 2, 3, 1, 4, 4, 0, 9, 9, 6, 1, 8, 3, 3, 7, 3, 9, 8, 8, 4, 7, 7, 6, 2, 1, 9, 8, 7, 8, 8, 7, 2, 2, 3, 9, 3, 3, 5, 5, 0, 7, 4, 5, 6, 5, 1, 4, 1, 1, 2, 8, 2, 6, 1, 5, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 8, 4, 5, 6, 7, 8, 8, 0, 6, 0, 8, 2, 3, 7, 9, 4, 7, 1, 7, 7, 7, 1, 4, 0, 0, 1, 7, 5, 7, 1, 3, 3, 3, 1, 6, 9, 7, 1, 2, 0, 2, 6, 0, 8, 9, 4, 3, 5, 4, 8, 1, 5, 9, 0, 6, 5, 3, 8, 1, 4, 7, 5, 2, 0, 0, 1, 7, 8, 7, 6, 8, 8, 2, 3, 5, 1, 8, 9, 5, 2, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 6, 6, 7, 8, 9, 7, 4, 6, 1, 4, 0, 4, 7, 3, 7, 1, 2, 7, 5, 8, 6, 3, 2, 2, 0, 5, 5, 6, 0, 3, 8, 1, 0, 3, 0, 4, 7, 4, 9, 0, 9, 0, 7, 1, 7, 1, 6, 6, 0, 6, 0, 8, 7, 6, 4, 9, 9, 5, 3, 7, 4, 3, 0, 4, 6, 6, 1, 1, 3, 2, 1, 0, 0, 1, 2, 3, 4, 7, 8, 4, 0, 1, 2, 3, 4, 5, 6, 7, 8, 0, 1, 2, 3, 4, 7, 8, 9, 0, 8, 3, 9, 5, 5, 2, 6, 8, 4, 1, 7, 1, 7, 3, 5, 6, 9, 1, 1, 1, 2, 1, 2, 0, 7, 7, 5, 8, 2, 9, 8, 6, 7, 3, 4, 6, 8, 7, 0, 4, 2, 7, 7, 5, 4, 3, 4, 2, 8, 1, 5, 1, 0, 2, 3, 3, 5, 7, 0, 6, 8, 0, 3, 4, 4, 6, 2, 7, 7, 1, 0, 1, 7, 8, 4, 0, 1, 2, 7, 4, 5, 6, 7, 8, 0, 1, 2, 3, 4, 7, 8, 9, 7, 8, 6, 4, 1, 9, 2, 8, 4, 4, 7, 0, 1, 9, 2, 8, 7, 8, 2, 6, 0, 6, 5, 3, 5, 8, 9, 1, 4, 0, 6, 1, 0, 0, 6, 2, 1, 1, 7, 7, 8, 4, 6, 0, 7, 0, 3, 6, 8, 7, 1, 3, 2, 4, 9, 4, 2, 6, 4, 1, 7, 2, 6, 6, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6]}
\ No newline at end of file
diff --git a/adversarial-robustness-toolbox/utils/data/tesseract/assent.png b/adversarial-robustness-toolbox/utils/data/tesseract/assent.png
new file mode 100644
index 0000000..49d58d7
Binary files /dev/null and b/adversarial-robustness-toolbox/utils/data/tesseract/assent.png differ
diff --git a/adversarial-robustness-toolbox/utils/data/tesseract/assent_adversarial.png b/adversarial-robustness-toolbox/utils/data/tesseract/assent_adversarial.png
new file mode 100644
index 0000000..dca7f23
Binary files /dev/null and b/adversarial-robustness-toolbox/utils/data/tesseract/assent_adversarial.png differ
diff --git a/adversarial-robustness-toolbox/utils/data/tesseract/assent_benign.png b/adversarial-robustness-toolbox/utils/data/tesseract/assent_benign.png
new file mode 100644
index 0000000..4fa54d3
Binary files /dev/null and b/adversarial-robustness-toolbox/utils/data/tesseract/assent_benign.png differ
diff --git a/adversarial-robustness-toolbox/utils/data/tesseract/dissent.png b/adversarial-robustness-toolbox/utils/data/tesseract/dissent.png
new file mode 100644
index 0000000..3c76db0
Binary files /dev/null and b/adversarial-robustness-toolbox/utils/data/tesseract/dissent.png differ
diff --git a/adversarial-robustness-toolbox/utils/data/test_models/model_test_functionally_equivalent_extraction.h5 b/adversarial-robustness-toolbox/utils/data/test_models/model_test_functionally_equivalent_extraction.h5
new file mode 100644
index 0000000..d3d35d1
Binary files /dev/null and b/adversarial-robustness-toolbox/utils/data/test_models/model_test_functionally_equivalent_extraction.h5 differ
diff --git a/adversarial-robustness-toolbox/utils/mlops/README.md b/adversarial-robustness-toolbox/utils/mlops/README.md
new file mode 100644
index 0000000..72c1a7b
--- /dev/null
+++ b/adversarial-robustness-toolbox/utils/mlops/README.md
@@ -0,0 +1,14 @@
+# MLOps
+
+This directory will contain samples and examples to show how to leverage ART for MLOps, in Machine Learning Platforms and Pipelines.
+
+The will contain designs, specifications, shared code and processes that meet the following goals:
+* Enable integration between MLOps Pipelines and ART e.g. Kubeflow Pipelines working with ART
+* Enable ART to be surfaced in ML Platforms e.g. OpenScale, KFServing
+* Enable support structure for this code to be maintained and tested
+
+Samples:
+
+[Kubeflow](kubeflow/)
+
+<img src="https://www.kubeflow.org/images/logo.svg" width="100">
diff --git a/adversarial-robustness-toolbox/utils/mlops/kubeflow/README.md b/adversarial-robustness-toolbox/utils/mlops/kubeflow/README.md
new file mode 100644
index 0000000..af97e3f
--- /dev/null
+++ b/adversarial-robustness-toolbox/utils/mlops/kubeflow/README.md
@@ -0,0 +1,69 @@
+# Kubeflow Pipeline Components for ART
+
+Kubeflow pipeline components are implementations of Kubeflow pipeline tasks. Each task takes
+one or more [artifacts](https://www.kubeflow.org/docs/pipelines/overview/concepts/output-artifact/)
+as input and may produce one or more
+[artifacts](https://www.kubeflow.org/docs/pipelines/overview/concepts/output-artifact/) as output.
+
+
+**Example: ART Components**
+* [Adversarial Robustness Evaluation - FGSM - PyTorch](robustness_evaluation_fgsm_pytorch)
+
+Each task usually includes two parts:
+
+Each component has a component.yaml which will describe the functionality exposed by it, for e.g.
+
+```
+name: 'Adversarial Robustness Evaluation'
+description: |
+  Evaluate the adversarial robustness using the Fast Gradient Sign Method of the Adversarial Robustness Toolbox (ART).
+  This is a simple demonstration of how to create evaluations using adversarial methods, and generally it is not recommended
+  to evaluate adversarial robustness solely using the Fast Gradient Sign Method. To create a thorough robustness assessment,
+  please refer to https://arxiv.org/abs/1902.06705 which provides widely accepted guidelines on different combinations of attacks.
+metadata:
+  annotations: {platform: 'OpenSource'}
+inputs:
+  - {name: model_id,                       description: 'Required. Training model ID', default: 'training-dummy'}
+  - {name: epsilon,                        description: 'Required. Epsilon value for the FGSM attack', default: '0.2'}
+  - {name: model_class_file,               description: 'Required. pytorch model class file'}
+  - {name: model_class_name,               description: 'Required. pytorch model class name', default: 'model'}
+  - {name: feature_testset_path,           description: 'Required. Feature test dataset path in the data bucket'}
+  - {name: label_testset_path,             description: 'Required. Label test dataset path in the data bucket'}
+  - {name: loss_fn,                        description: 'Required. PyTorch model loss function'}
+  - {name: optimizer,                      description: 'Required. PyTorch model optimizer'}
+  - {name: clip_values,                    description: 'Required. PyTorch model clip_values allowed for features (min, max)'}
+  - {name: nb_classes,                     description: 'Required. The number of classes of the model'}
+  - {name: input_shape,                    description: 'Required. The shape of one input instance for the pytorch model'}
+  - {name: data_bucket_name,               description: 'Bucket that has the processed data',  default: 'training-data'}
+  - {name: result_bucket_name,             description: 'Bucket that has the training results', default: 'training-result'}
+  - {name: adversarial_accuracy_threshold, description: 'Model accuracy threshold on adversarial samples', default: '0.2'}
+outputs:
+  - {name: metric_path,                    description: 'Path for robustness check output'}
+  - {name: robust_status,                  description: 'Path for robustness status output'}
+implementation:
+  container:
+    image: aipipeline/robustness-evaluation:pytorch
+    command: ['python']
+    args: [
+      -u, robustness_evaluation_fgsm_pytorch.py,
+      --model_id, {inputValue: model_id},
+      --model_class_file, {inputValue: model_class_file},
+      --model_class_name, {inputValue: model_class_name},
+      --feature_testset_path, {inputValue: feature_testset_path},
+      --label_testset_path, {inputValue: label_testset_path},
+      --epsilon, {inputValue: epsilon},
+      --loss_fn, {inputValue: loss_fn},
+      --optimizer, {inputValue: optimizer},
+      --clip_values, {inputValue: clip_values},
+      --nb_classes, {inputValue: nb_classes},
+      --input_shape, {inputValue: input_shape},
+      --metric_path, {outputPath: metric_path},
+      --robust_status, {outputPath: robust_status},
+      --data_bucket_name, {inputValue: data_bucket_name},
+      --result_bucket_name, {inputValue: result_bucket_name},
+      --adversarial_accuracy_threshold, {inputValue: adversarial_accuracy_threshold}
+    ]
+```
+
+See how to [use the Kubeflow Pipelines SDK](https://www.kubeflow.org/docs/pipelines/sdk/sdk-overview/)
+and [build your own components](https://www.kubeflow.org/docs/pipelines/sdk/build-component/).
diff --git a/adversarial-robustness-toolbox/utils/mlops/kubeflow/robustness_evaluation_fgsm_pytorch/Dockerfile b/adversarial-robustness-toolbox/utils/mlops/kubeflow/robustness_evaluation_fgsm_pytorch/Dockerfile
new file mode 100644
index 0000000..032ea7a
--- /dev/null
+++ b/adversarial-robustness-toolbox/utils/mlops/kubeflow/robustness_evaluation_fgsm_pytorch/Dockerfile
@@ -0,0 +1,10 @@
+FROM pytorch/pytorch:latest
+
+RUN pip install adversarial-robustness-toolbox pandas minio flask-cors Pillow torchsummary
+
+ENV APP_HOME /app
+COPY src $APP_HOME
+WORKDIR $APP_HOME
+
+ENTRYPOINT ["python"]
+CMD ["robustness_evaluation_fgsm_pytorch.py"]
diff --git a/adversarial-robustness-toolbox/utils/mlops/kubeflow/robustness_evaluation_fgsm_pytorch/component.yaml b/adversarial-robustness-toolbox/utils/mlops/kubeflow/robustness_evaluation_fgsm_pytorch/component.yaml
new file mode 100644
index 0000000..25db9fa
--- /dev/null
+++ b/adversarial-robustness-toolbox/utils/mlops/kubeflow/robustness_evaluation_fgsm_pytorch/component.yaml
@@ -0,0 +1,66 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2019
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+name: 'Adversarial Robustness Evaluation'
+description: |
+  Evaluate the adversarial robustness using the Fast Gradient Sign Method of the Adversarial Robustness Toolbox (ART).
+  This is a simple demonstration of how to create evaluations using adversarial methods, and generally it is not recommended
+  to evaluate adversarial robustness solely using the Fast Gradient Sign Method. To create a thorough robustness assessment,
+  please refer to https://arxiv.org/abs/1902.06705 which provides widely accepted guidelines on different combinations of attacks.
+metadata:
+  annotations: {platform: 'OpenSource'}
+inputs:
+  - {name: model_id,                       description: 'Required. Training model ID', default: 'training-dummy'}
+  - {name: epsilon,                        description: 'Required. Epsilon value for the FGSM attack', default: '0.2'}
+  - {name: model_class_file,               description: 'Required. pytorch model class file'}
+  - {name: model_class_name,               description: 'Required. pytorch model class name', default: 'model'}
+  - {name: feature_testset_path,           description: 'Required. Feature test dataset path in the data bucket'}
+  - {name: label_testset_path,             description: 'Required. Label test dataset path in the data bucket'}
+  - {name: loss_fn,                        description: 'Required. PyTorch model loss function'}
+  - {name: optimizer,                      description: 'Required. PyTorch model optimizer'}
+  - {name: clip_values,                    description: 'Required. PyTorch model clip_values allowed for features (min, max)'}
+  - {name: nb_classes,                     description: 'Required. The number of classes of the model'}
+  - {name: input_shape,                    description: 'Required. The shape of one input instance for the pytorch model'}
+  - {name: data_bucket_name,               description: 'Bucket that has the processed data',  default: 'training-data'}
+  - {name: result_bucket_name,             description: 'Bucket that has the training results', default: 'training-result'}
+  - {name: adversarial_accuracy_threshold, description: 'Model accuracy threshold on adversarial samples', default: '0.2'}
+outputs:
+  - {name: metric_path,                    description: 'Path for robustness check output'}
+  - {name: robust_status,                  description: 'Path for robustness status output'}
+implementation:
+  container:
+    image: aipipeline/robustness-evaluation:pytorch
+    command: ['python']
+    args: [
+      -u, robustness_evaluation_fgsm_pytorch.py,
+      --model_id, {inputValue: model_id},
+      --model_class_file, {inputValue: model_class_file},
+      --model_class_name, {inputValue: model_class_name},
+      --feature_testset_path, {inputValue: feature_testset_path},
+      --label_testset_path, {inputValue: label_testset_path},
+      --epsilon, {inputValue: epsilon},
+      --loss_fn, {inputValue: loss_fn},
+      --optimizer, {inputValue: optimizer},
+      --clip_values, {inputValue: clip_values},
+      --nb_classes, {inputValue: nb_classes},
+      --input_shape, {inputValue: input_shape},
+      --metric_path, {outputPath: metric_path},
+      --robust_status, {outputPath: robust_status},
+      --data_bucket_name, {inputValue: data_bucket_name},
+      --result_bucket_name, {inputValue: result_bucket_name},
+      --adversarial_accuracy_threshold, {inputValue: adversarial_accuracy_threshold}
+    ]
diff --git a/adversarial-robustness-toolbox/utils/mlops/kubeflow/robustness_evaluation_fgsm_pytorch/src/model_files/__init__.py b/adversarial-robustness-toolbox/utils/mlops/kubeflow/robustness_evaluation_fgsm_pytorch/src/model_files/__init__.py
new file mode 100644
index 0000000..a39dc60
--- /dev/null
+++ b/adversarial-robustness-toolbox/utils/mlops/kubeflow/robustness_evaluation_fgsm_pytorch/src/model_files/__init__.py
@@ -0,0 +1,17 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2019
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
diff --git a/adversarial-robustness-toolbox/utils/mlops/kubeflow/robustness_evaluation_fgsm_pytorch/src/robustness.py b/adversarial-robustness-toolbox/utils/mlops/kubeflow/robustness_evaluation_fgsm_pytorch/src/robustness.py
new file mode 100644
index 0000000..ceb06ba
--- /dev/null
+++ b/adversarial-robustness-toolbox/utils/mlops/kubeflow/robustness_evaluation_fgsm_pytorch/src/robustness.py
@@ -0,0 +1,123 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2019
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""Robustness evaluation module."""
+
+import zipfile
+import importlib
+import re
+
+import numpy as np
+from minio import Minio
+
+import torch
+import torch.utils.data
+
+from art.estimators.classification import PyTorchClassifier
+from art.attacks.evasion.fast_gradient import FastGradientMethod
+
+from robustness_util import get_metrics
+
+
+def robustness_evaluation(
+    object_storage_url,
+    object_storage_username,
+    object_storage_password,
+    data_bucket_name,
+    result_bucket_name,
+    model_id,
+    feature_testset_path="processed_data/X_test.npy",
+    label_testset_path="processed_data/y_test.npy",
+    clip_values=(0, 1),
+    nb_classes=2,
+    input_shape=(1, 3, 64, 64),
+    model_class_file="model.py",
+    model_class_name="model",
+    LossFn="",
+    Optimizer="",
+    epsilon=0.2,
+):
+
+    url = re.compile(r"https?://")
+    cos = Minio(
+        url.sub("", object_storage_url),
+        access_key=object_storage_username,
+        secret_key=object_storage_password,
+        secure=False,
+    )
+
+    dataset_filenamex = "X_test.npy"
+    dataset_filenamey = "y_test.npy"
+    weights_filename = "model.pt"
+    model_files = model_id + "/_submitted_code/model.zip"
+
+    cos.fget_object(data_bucket_name, feature_testset_path, dataset_filenamex)
+    cos.fget_object(data_bucket_name, label_testset_path, dataset_filenamey)
+    cos.fget_object(result_bucket_name, model_id + "/" + weights_filename, weights_filename)
+    cos.fget_object(result_bucket_name, model_files, "model.zip")
+
+    # Load PyTorch model definition from the source code.
+    zip_ref = zipfile.ZipFile("model.zip", "r")
+    zip_ref.extractall("model_files")
+    zip_ref.close()
+
+    modulename = "model_files." + model_class_file.split(".")[0].replace("-", "_")
+
+    """
+    We required users to define where the model class is located or follow
+    some naming convention we have provided.
+    """
+    model_class = getattr(importlib.import_module(modulename), model_class_name)
+
+    # load & compile model
+    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+    model = model_class().to(device)
+    model.load_state_dict(torch.load(weights_filename, map_location=device))
+
+    # Define Loss and optimizer function for the PyTorch model
+    if LossFn:
+        loss_fn = eval(LossFn)
+    else:
+        loss_fn = torch.nn.CrossEntropyLoss()
+    if Optimizer:
+        optimizer = eval(Optimizer)
+    else:
+        optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
+
+    # create pytorch classifier
+    classifier = PyTorchClassifier(
+        model=model,
+        loss=loss_fn,
+        optimizer=optimizer,
+        input_shape=input_shape,
+        nb_classes=nb_classes,
+        clip_values=clip_values,
+    )
+
+    # load test dataset
+    x = np.load(dataset_filenamex)
+    y = np.load(dataset_filenamey)
+
+    # craft adversarial samples using FGSM
+    crafter = FastGradientMethod(classifier, eps=epsilon)
+    x_samples = crafter.generate(x)
+
+    # obtain all metrics (robustness score, perturbation metric, reduction in confidence)
+    metrics, y_pred_orig, y_pred_adv = get_metrics(model, x, x_samples, y)
+
+    print("metrics:", metrics)
+    return metrics
diff --git a/adversarial-robustness-toolbox/utils/mlops/kubeflow/robustness_evaluation_fgsm_pytorch/src/robustness_evaluation_fgsm_pytorch.py b/adversarial-robustness-toolbox/utils/mlops/kubeflow/robustness_evaluation_fgsm_pytorch/src/robustness_evaluation_fgsm_pytorch.py
new file mode 100644
index 0000000..aa7309c
--- /dev/null
+++ b/adversarial-robustness-toolbox/utils/mlops/kubeflow/robustness_evaluation_fgsm_pytorch/src/robustness_evaluation_fgsm_pytorch.py
@@ -0,0 +1,141 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2019
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+import json
+import argparse
+import os
+
+from robustness import robustness_evaluation
+
+
+def get_secret(path, default=""):
+    try:
+        with open(path, "r") as f:
+            cred = f.readline().strip("'")
+        f.close()
+        return cred
+    except Exception:
+        return default
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--epsilon", type=float, help="Epsilon value for the FGSM attack", default=0.2)
+    parser.add_argument("--model_id", type=str, help="Training model id", default="training-dummy")
+    parser.add_argument(
+        "--metric_path", type=str, help="Path for robustness check output", default="/tmp/robustness.txt"
+    )
+    parser.add_argument(
+        "--robust_status", type=str, help="Path for robustness status output", default="/tmp/status.txt"
+    )
+    parser.add_argument("--model_class_file", type=str, help="pytorch model class file", default="model.py")
+    parser.add_argument("--model_class_name", type=str, help="pytorch model class name", default="model")
+    parser.add_argument(
+        "--loss_fn", type=str, help="PyTorch model loss function", default="torch.nn.CrossEntropyLoss()"
+    )
+    parser.add_argument(
+        "--optimizer",
+        type=str,
+        help="pytorch model optimizer",
+        default="torch.optim.Adam(model.parameters(), lr=0.001)",
+    )
+    parser.add_argument(
+        "--clip_values", type=str, help="pytorch model clip_values allowed for features (min, max)", default="(0,1)"
+    )
+    parser.add_argument("--nb_classes", type=int, help="The number of classes of the model", default=2)
+    parser.add_argument(
+        "--input_shape", type=str, help="The shape of one input instance for the pytorch model", default="(1,3,64,64)"
+    )
+    parser.add_argument(
+        "--feature_testset_path",
+        type=str,
+        help="Feature test dataset path in the data bucket",
+        default="processed_data/X_test.npy",
+    )
+    parser.add_argument(
+        "--label_testset_path",
+        type=str,
+        help="Label test dataset path in the data bucket",
+        default="processed_data/y_test.npy",
+    )
+    parser.add_argument(
+        "--data_bucket_name", type=str, help="Bucket that has the processed data", default="training-data"
+    )
+    parser.add_argument(
+        "--result_bucket_name", type=str, help="Bucket that has the training results", default="training-result"
+    )
+    parser.add_argument(
+        "--adversarial_accuracy_threshold",
+        type=float,
+        help="Model accuracy threshold on adversarial samples",
+        default=0.2,
+    )
+    args = parser.parse_args()
+
+    epsilon = args.epsilon
+    metric_path = args.metric_path
+    model_id = args.model_id
+    robust_status = args.robust_status
+    model_class_file = args.model_class_file
+    model_class_name = args.model_class_name
+    LossFn = args.loss_fn
+    Optimizer = args.optimizer
+    nb_classes = args.nb_classes
+    feature_testset_path = args.feature_testset_path
+    label_testset_path = args.label_testset_path
+    data_bucket_name = args.data_bucket_name
+    result_bucket_name = args.result_bucket_name
+    clip_values = eval(args.clip_values)
+    input_shape = eval(args.input_shape)
+    adversarial_accuracy_threshold = args.adversarial_accuracy_threshold
+
+    object_storage_url = get_secret("/app/secrets/s3_url", "minio-service.kubeflow:9000")
+    object_storage_username = get_secret("/app/secrets/s3_access_key_id", "minio")
+    object_storage_password = get_secret("/app/secrets/s3_secret_access_key", "minio123")
+
+    metrics = robustness_evaluation(
+        object_storage_url,
+        object_storage_username,
+        object_storage_password,
+        data_bucket_name,
+        result_bucket_name,
+        model_id,
+        feature_testset_path=feature_testset_path,
+        label_testset_path=label_testset_path,
+        clip_values=clip_values,
+        nb_classes=nb_classes,
+        input_shape=input_shape,
+        model_class_file=model_class_file,
+        model_class_name=model_class_name,
+        LossFn=LossFn,
+        Optimizer=Optimizer,
+        epsilon=epsilon,
+    )
+
+    if not os.path.exists(os.path.dirname(metric_path)):
+        os.makedirs(os.path.dirname(metric_path))
+    with open(metric_path, "w") as report:
+        report.write(json.dumps(metrics))
+
+    robust = "true"
+    if metrics["model accuracy on adversarial samples"] < adversarial_accuracy_threshold:
+        robust = "false"
+
+    if not os.path.exists(os.path.dirname(robust_status)):
+        os.makedirs(os.path.dirname(robust_status))
+    with open(robust_status, "w") as report:
+        report.write(robust)
diff --git a/adversarial-robustness-toolbox/utils/mlops/kubeflow/robustness_evaluation_fgsm_pytorch/src/robustness_util.py b/adversarial-robustness-toolbox/utils/mlops/kubeflow/robustness_evaluation_fgsm_pytorch/src/robustness_util.py
new file mode 100644
index 0000000..82c873c
--- /dev/null
+++ b/adversarial-robustness-toolbox/utils/mlops/kubeflow/robustness_evaluation_fgsm_pytorch/src/robustness_util.py
@@ -0,0 +1,96 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2019
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+
+import numpy as np
+import numpy.linalg as la
+
+import torch
+import torch.utils.data
+from torch.autograd import Variable
+
+
+def get_metrics(model, x_original, x_adv_samples, y):
+    model_accuracy_on_non_adversarial_samples, y_pred = evaluate(model, x_original, y)
+    model_accuracy_on_adversarial_samples, y_pred_adv = evaluate(model, x_adv_samples, y)
+
+    pert_metric = get_perturbation_metric(x_original, x_adv_samples, y_pred, y_pred_adv, ord=2)
+    conf_metric = get_confidence_metric(y_pred, y_pred_adv)
+
+    data = {
+        "model accuracy on test data": float(model_accuracy_on_non_adversarial_samples),
+        "model accuracy on adversarial samples": float(model_accuracy_on_adversarial_samples),
+        "confidence reduced on correctly classified adv_samples": float(conf_metric),
+        "average perturbation on misclassified adv_samples": float(pert_metric),
+    }
+    return data, y_pred, y_pred_adv
+
+
+# Compute the accuaracy and predicted label using the given test dataset
+def evaluate(model, X_test, y_test):
+    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+    test = torch.utils.data.TensorDataset(
+        Variable(torch.FloatTensor(X_test.astype("float32"))), Variable(torch.LongTensor(y_test.astype("float32")))
+    )
+    test_loader = torch.utils.data.DataLoader(test, batch_size=64, shuffle=False)
+    model.eval()
+    correct = 0
+    accuracy = 0
+    y_pred = []
+    with torch.no_grad():
+        for images, labels in test_loader:
+            images = images.to(device)
+            labels = labels.to(device)
+            outputs = model(images)
+            _, predicted = torch.max(outputs.data, 1)
+            predictions = torch.softmax(outputs.data, dim=1).detach().numpy()
+            correct += predicted.eq(labels.data.view_as(predicted)).sum().item()
+            y_pred += predictions.tolist()
+        accuracy = 1.0 * correct / len(test_loader.dataset)
+    y_pred = np.array(y_pred)
+    return accuracy, y_pred
+
+
+def get_perturbation_metric(x_original, x_adv, y_pred, y_pred_adv, ord=2):
+    idxs = np.argmax(y_pred_adv, axis=1) != np.argmax(y_pred, axis=1)
+
+    if np.sum(idxs) == 0.0:
+        return 0
+
+    perts_norm = la.norm((x_adv - x_original).reshape(x_original.shape[0], -1), ord, axis=1)
+    perts_norm = perts_norm[idxs]
+
+    return np.mean(perts_norm / la.norm(x_original[idxs].reshape(np.sum(idxs), -1), ord, axis=1))
+
+
+# This computes the change in confidence for all images in the test set
+def get_confidence_metric(y_pred, y_pred_adv):
+    y_classidx = np.argmax(y_pred, axis=1)
+    y_classconf = y_pred[np.arange(y_pred.shape[0]), y_classidx]
+
+    y_adv_classidx = np.argmax(y_pred_adv, axis=1)
+    y_adv_classconf = y_pred_adv[np.arange(y_pred_adv.shape[0]), y_adv_classidx]
+
+    idxs = y_classidx == y_adv_classidx
+
+    if np.sum(idxs) == 0.0:
+        return 0
+
+    idxnonzero = y_classconf != 0
+    idxs = idxs & idxnonzero
+
+    return np.mean((y_classconf[idxs] - y_adv_classconf[idxs]) / y_classconf[idxs])
diff --git a/adversarial-robustness-toolbox/utils/resources/create_inverse_gan_models.py b/adversarial-robustness-toolbox/utils/resources/create_inverse_gan_models.py
new file mode 100644
index 0000000..204dc25
--- /dev/null
+++ b/adversarial-robustness-toolbox/utils/resources/create_inverse_gan_models.py
@@ -0,0 +1,300 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+import logging
+import time
+import os
+
+import numpy as np
+import tensorflow as tf
+
+from art.utils import load_mnist
+
+logging.root.setLevel(logging.NOTSET)
+logging.basicConfig(level=logging.NOTSET)
+logger = logging.getLogger(__name__)
+
+logger.setLevel(logging.INFO)
+
+
+def create_generator_layers(x):
+    with tf.variable_scope("generator", reuse=tf.AUTO_REUSE):
+        x_reshaped = tf.reshape(x, [-1, 1, 1, x.get_shape()[1]])
+        # 1rst HIDDEN LAYER
+        conv1 = tf.layers.conv2d_transpose(x_reshaped, 1024, [4, 4], strides=(1, 1), padding="valid")
+        normalized1 = tf.layers.batch_normalization(conv1)
+        lrelu1 = tf.nn.leaky_relu(normalized1)
+
+        # 2nd HIDDEN LAYER
+        conv2 = tf.layers.conv2d_transpose(lrelu1, 512, [4, 4], strides=(2, 2), padding="same")
+        normalized2 = tf.layers.batch_normalization(conv2)
+        lrelu2 = tf.nn.leaky_relu(normalized2)
+
+        # 3rd HIDDEN LAYER
+        conv3 = tf.layers.conv2d_transpose(lrelu2, 256, [4, 4], strides=(2, 2), padding="same")
+        normalized3 = tf.layers.batch_normalization(conv3)
+        lrelu3 = tf.nn.leaky_relu(normalized3)
+
+        # 4th HIDDEN LAYER
+        conv4 = tf.layers.conv2d_transpose(lrelu3, 128, [4, 4], strides=(2, 2), padding="same")
+        normalized4 = tf.layers.batch_normalization(conv4)
+        lrelu4 = tf.nn.leaky_relu(normalized4)
+
+        # OUTPUT LAYER
+        conv5 = tf.layers.conv2d_transpose(lrelu4, 1, [4, 4], strides=(2, 2), padding="same")
+        output = tf.nn.tanh(conv5, name="output_non_normalized")
+
+        # denormalizing images
+        output_resized = tf.image.resize_images(output, [28, 28])
+        return tf.add(tf.multiply(output_resized, 0.5), 0.5, name="output")
+
+
+def create_discriminator_layers(x):
+    with tf.variable_scope("discriminator", reuse=tf.AUTO_REUSE):
+        # normalizing images
+        x_resized = tf.image.resize_images(x, [64, 64])
+        x_resized_normalised = (x_resized - 0.5) / 0.5  # normalization; range: -1 ~ 1
+
+        # 1rst HIDDEN LAYER
+        conv1 = tf.layers.conv2d(x_resized_normalised, 128, [4, 4], strides=(2, 2), padding="same")
+        lrelu1 = tf.nn.leaky_relu(conv1)
+
+        # 2nd HIDDEN LAYER
+        conv2 = tf.layers.conv2d(lrelu1, 256, [4, 4], strides=(2, 2), padding="same")
+        normalized2 = tf.layers.batch_normalization(conv2)
+        lrelu2 = tf.nn.leaky_relu(normalized2)
+
+        # 3rd HIDDEN LAYER
+        conv3 = tf.layers.conv2d(lrelu2, 512, [4, 4], strides=(2, 2), padding="same")
+        normalized3 = tf.layers.batch_normalization(conv3)
+        lrelu3 = tf.nn.leaky_relu(normalized3)
+
+        # 4th HIDDEN LAYER
+        conv4 = tf.layers.conv2d(lrelu3, 1024, [4, 4], strides=(2, 2), padding="same")
+        normalized4 = tf.layers.batch_normalization(conv4)
+        lrelu4 = tf.nn.leaky_relu(normalized4)
+
+        # OUTPUT LAYER
+        logits = tf.layers.conv2d(lrelu4, 1, [4, 4], strides=(1, 1), padding="valid")
+        output = tf.nn.sigmoid(logits)
+
+        return output, logits
+
+
+def create_encoder_layers2(x, net_dim=64, latent_dim=128, reuse=False):
+    with tf.variable_scope("encoder", reuse=tf.AUTO_REUSE):
+        conv1 = tf.layers.conv2d(x, filters=net_dim, kernel_size=5, strides=(2, 2), padding="same", name="conv1")
+        normalized1 = tf.layers.batch_normalization(conv1, name="normalization1")
+
+        lrelu1 = tf.nn.leaky_relu(normalized1)
+
+        conv2 = tf.layers.conv2d(
+            lrelu1, filters=2 * net_dim, kernel_size=5, strides=(2, 2), padding="same", name="conv2"
+        )
+
+        normalized2 = tf.layers.batch_normalization(conv2, name="normalization2")
+        lrelu2 = tf.nn.leaky_relu(normalized2)
+
+        conv3 = tf.layers.conv2d(
+            lrelu2, filters=4 * net_dim, kernel_size=5, strides=(2, 2), padding="same", name="conv3"
+        )
+
+        normalized3 = tf.layers.batch_normalization(conv3, name="normalization3")
+        lrelu3 = tf.nn.leaky_relu(normalized3)
+
+        reshaped = tf.reshape(lrelu3, [-1, 4 * 4 * 4 * net_dim])
+
+        z = tf.contrib.layers.fully_connected(reshaped, latent_dim)
+
+        return z
+
+
+def load_model(sess, model_name, model_path):
+    saver = tf.train.import_meta_graph(os.path.join(model_path, model_name + ".meta"))
+    saver.restore(sess, os.path.join(model_path, model_name))
+
+    graph = tf.get_default_graph()
+    generator_tf = graph.get_tensor_by_name("generator/output:0")
+    image_to_encode_ph = graph.get_tensor_by_name("image_to_encode_input:0")
+    encoder_tf = graph.get_tensor_by_name("encoder_1/fully_connected/Relu:0")
+    z_ph = graph.get_tensor_by_name("z_input:0")
+
+    return generator_tf, encoder_tf, z_ph, image_to_encode_ph
+
+
+def predict(sess, batch_size, generator_tf, z):
+    z_ = np.random.normal(0, 1, (batch_size, 100))
+    return sess.run([generator_tf], {z: z_})[0]
+
+
+def train_models(
+    sess, x_train, gen_loss, gen_opt_tf, disc_loss_tf, disc_opt_tf, x_ph, z_ph, latent_encoder_loss, encoder_optimizer
+):
+    train_epoch = 3
+    latent_encoding_length = z_ph.get_shape()[1]
+    batch_size = x_train.shape[0]
+    # training-loop
+    np.random.seed(int(time.time()))
+    logging.info("Starting training")
+
+    for epoch in range(train_epoch):
+        gen_losses = []
+        disc_losses = []
+        epoch_start_time = time.time()
+        for minibatch_count in range(x_train.shape[0] // batch_size):
+            # update discriminator
+            x_ = x_train[minibatch_count * batch_size : (minibatch_count + 1) * batch_size]
+            z_ = np.random.normal(0, 1, (batch_size, latent_encoding_length))
+
+            loss_d_, _ = sess.run([disc_loss_tf, disc_opt_tf], {x_ph: x_, z_ph: z_})
+            disc_losses.append(loss_d_)
+
+            # update generator
+            z_ = np.random.normal(0, 1, (batch_size, latent_encoding_length))
+            loss_g_, _ = sess.run([gen_loss, gen_opt_tf], {z_ph: z_, x_ph: x_})
+            gen_losses.append(loss_g_)
+
+        epoch_end_time = time.time()
+        per_epoch_ptime = epoch_end_time - epoch_start_time
+        logging.info(
+            "[{0}/{1}] - epoch_time: {2} loss_discriminator: {3}, loss_generator: {4}".format(
+                (epoch + 1),
+                train_epoch,
+                round(per_epoch_ptime, 2),
+                round(np.mean(disc_losses), 2),
+                round(np.mean(gen_losses), 2),
+            )
+        )
+
+    # Training inverse gan encoder
+    for epoch in range(train_epoch):
+        encoder_losses = []
+        epoch_start_time = time.time()
+        for minibatch_count in range(x_train.shape[0] // batch_size):
+            z_ = np.random.normal(0, 1, (batch_size, latent_encoding_length))
+            loss_encoder_value, _ = sess.run([latent_encoder_loss, encoder_optimizer], {z_ph: z_})
+            encoder_losses.append(loss_encoder_value)
+
+        epoch_end_time = time.time()
+        per_epoch_ptime = epoch_end_time - epoch_start_time
+        logging.info(
+            "[{0}/{1}] - epoch_time: {2} loss_encoder: {3}".format(
+                (epoch + 1), train_epoch, per_epoch_ptime, round(np.mean(encoder_losses), 3)
+            )
+        )
+
+    logging.info("Training finish!... save training results")
+
+
+def build_gan_graph(learning_rate, latent_encoding_length, batch_size=None):
+    if batch_size is None:
+        batch_size = 200
+    # INPUT VARIABLES
+    x_ph = tf.placeholder(tf.float32, shape=(None, 28, 28, 1))
+    z_ph = tf.placeholder(tf.float32, shape=(None, latent_encoding_length), name="z_input")
+
+    # Building Generator and Discriminator
+    generator_tf = create_generator_layers(z_ph)
+    disc_real_tf, disc_real_logits_tf = create_discriminator_layers(x_ph)
+    disc_fake_tf, disc_fake_logits_tf = create_discriminator_layers(generator_tf)
+
+    # CREATE LOSSES
+    disc_loss_real_tf = tf.losses.sigmoid_cross_entropy(
+        multi_class_labels=tf.ones([batch_size, 1, 1, 1]), logits=disc_real_logits_tf
+    )
+
+    disc_loss_fake_tf = tf.losses.sigmoid_cross_entropy(
+        multi_class_labels=tf.zeros([batch_size, 1, 1, 1]), logits=disc_fake_logits_tf
+    )
+    disc_loss_tf = disc_loss_real_tf + disc_loss_fake_tf
+    gen_loss = tf.losses.sigmoid_cross_entropy(
+        multi_class_labels=tf.ones([batch_size, 1, 1, 1]), logits=disc_fake_logits_tf
+    )
+
+    # CREATE OPTIMIZERS
+    # We only want generator variables to be trained when running the generator and not discriminator variables etc.
+    trainable_variables = tf.trainable_variables()
+    disc_trainable_vars = [var for var in trainable_variables if var.name.startswith("discriminator")]
+    gen_trainable_vars = [var for var in trainable_variables if var.name.startswith("generator")]
+
+    # CREATE OPTIMIZERS
+    disc_opt_tf = tf.train.AdamOptimizer(learning_rate, beta1=0.5).minimize(disc_loss_tf, var_list=disc_trainable_vars)
+    gen_opt_tf = tf.train.AdamOptimizer(learning_rate, beta1=0.5).minimize(gen_loss, var_list=gen_trainable_vars)
+
+    return generator_tf, z_ph, gen_loss, gen_opt_tf, disc_loss_tf, disc_opt_tf, x_ph
+
+
+def build_inverse_gan_graph(learning_rate, generator_tf, z_ph, latent_encoding_length):
+    z_ts = create_encoder_layers2(generator_tf, net_dim=64, latent_dim=latent_encoding_length)
+
+    # Reusing exisint nodes with a different input in order to call at inference time
+    image_to_encode_ph = tf.placeholder(tf.float32, shape=(None, 28, 28, 1), name="image_to_encode_input")
+    encoder_tf = create_encoder_layers2(image_to_encode_ph, net_dim=64, latent_dim=latent_encoding_length)
+
+    # CREATE LOSSES
+    latent_encoder_loss = tf.reduce_mean(tf.square(z_ts - z_ph), axis=[1])
+
+    # CREATE OPTIMIZERS
+    trainable_variables = tf.trainable_variables()
+    encoder_trainable_vars = [var for var in trainable_variables if var.name.startswith("encoder")]
+
+    encoder_optimizer = tf.train.AdamOptimizer(learning_rate, beta1=0.5).minimize(
+        latent_encoder_loss, var_list=encoder_trainable_vars
+    )
+
+    return encoder_tf, image_to_encode_ph, latent_encoder_loss, encoder_optimizer
+
+
+def main():
+    model_name = "model-dcgan"
+
+    root = "../utils/resources/models/tensorflow1/"
+
+    if not os.path.isdir(root):
+        os.mkdir(root)
+
+    model_path = root
+
+    # STEP 0
+    logging.info("Loading a Dataset")
+    (x_train_original, y_train_original), (_, _), _, _ = load_mnist()
+
+    batch_size = 100
+
+    (x_train, y_train) = (x_train_original[:batch_size], y_train_original[:batch_size])
+
+    lr = 0.0002
+    latent_enc_len = 100
+
+    gen_tf, z_ph, gen_loss, gen_opt_tf, disc_loss_tf, disc_opt_tf, x_ph = build_gan_graph(
+        lr, latent_enc_len, batch_size
+    )
+    enc_tf, image_to_enc_ph, latent_enc_loss, enc_opt = build_inverse_gan_graph(lr, gen_tf, z_ph, latent_enc_len)
+
+    sess = tf.Session()
+    sess.run(tf.global_variables_initializer())
+
+    train_models(sess, x_train, gen_loss, gen_opt_tf, disc_loss_tf, disc_opt_tf, x_ph, z_ph, latent_enc_loss, enc_opt)
+
+    saver = tf.train.Saver()
+    saver.save(sess, os.path.join(model_path, model_name))
+
+    sess.close()
+
+
+if __name__ == "__main__":
+    main()
diff --git a/adversarial-robustness-toolbox/utils/resources/create_model_weights.py b/adversarial-robustness-toolbox/utils/resources/create_model_weights.py
new file mode 100644
index 0000000..a9e3550
--- /dev/null
+++ b/adversarial-robustness-toolbox/utils/resources/create_model_weights.py
@@ -0,0 +1,136 @@
+# MIT License
+#
+# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2019
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+# Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+import tensorflow as tf
+import os
+import pickle
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Dense, Flatten, Conv2D, MaxPooling2D
+from sklearn.linear_model import LogisticRegression
+from art.estimators.classification import SklearnClassifier
+from sklearn.svm import SVC, LinearSVC
+from sklearn.tree import DecisionTreeClassifier, ExtraTreeClassifier
+from sklearn.ensemble import AdaBoostClassifier, BaggingClassifier, ExtraTreesClassifier
+from sklearn.ensemble import GradientBoostingClassifier, RandomForestClassifier
+import numpy as np
+
+from tests.utils import load_dataset, master_seed
+
+
+def main_mnist_binary():
+    master_seed(1234)
+
+    model = Sequential()
+    model.add(Conv2D(1, kernel_size=(7, 7), activation="relu", input_shape=(28, 28, 1)))
+    model.add(MaxPooling2D(pool_size=(4, 4)))
+    model.add(Flatten())
+    model.add(Dense(1, activation="sigmoid"))
+
+    model.compile(loss="binary_crossentropy", optimizer=tf.keras.optimizers.Adam(lr=0.01), metrics=["accuracy"])
+
+    (x_train, y_train), (_, _), _, _ = load_dataset("mnist")
+
+    y_train = np.argmax(y_train, axis=1)
+    y_train[y_train < 5] = 0
+    y_train[y_train >= 5] = 1
+
+    model.fit(x_train, y_train, batch_size=128, epochs=10)
+
+    w_0, b_0 = model.layers[0].get_weights()
+    w_3, b_3 = model.layers[3].get_weights()
+
+    np.save(
+        os.path.join(
+            os.path.dirname(os.path.dirname(__file__)), "utils/resources/models/scikit/", "W_CONV2D_MNIST_BINARY"
+        ),
+        w_0,
+    )
+    np.save(
+        os.path.join(
+            os.path.dirname(os.path.dirname(__file__)), "utils/resources/models/scikit/" "B_CONV2D_MNIST_BINARY"
+        ),
+        b_0,
+    )
+    np.save(
+        os.path.join(
+            os.path.dirname(os.path.dirname(__file__)), "utils/resources/models/scikit/" "W_DENSE_MNIST_BINARY"
+        ),
+        w_3,
+    )
+    np.save(
+        os.path.join(
+            os.path.dirname(os.path.dirname(__file__)), "utils/resources/models/scikit/" "B_DENSE_MNIST_BINARY"
+        ),
+        b_3,
+    )
+
+
+def create_scikit_model_weights():
+    master_seed(1234)
+
+    model_list = {
+        "decisionTreeClassifier": DecisionTreeClassifier(),
+        "extraTreeClassifier": ExtraTreeClassifier(),
+        "adaBoostClassifier": AdaBoostClassifier(),
+        "baggingClassifier": BaggingClassifier(),
+        "extraTreesClassifier": ExtraTreesClassifier(n_estimators=10),
+        "gradientBoostingClassifier": GradientBoostingClassifier(n_estimators=10),
+        "randomForestClassifier": RandomForestClassifier(n_estimators=10),
+        "logisticRegression": LogisticRegression(solver="lbfgs", multi_class="auto"),
+        "svc": SVC(gamma="auto"),
+        "linearSVC": LinearSVC(),
+    }
+
+    clipped_models = {
+        model_name: SklearnClassifier(model=model, clip_values=(0, 1)) for model_name, model in model_list.items()
+    }
+    unclipped_models = {model_name: SklearnClassifier(model=model) for model_name, model in model_list.items()}
+
+    (x_train_iris, y_train_iris), (_, _), _, _ = load_dataset("iris")
+
+    for model_name, model in clipped_models.items():
+        model.fit(x=x_train_iris, y=y_train_iris)
+        pickle.dump(
+            model,
+            open(
+                os.path.join(
+                    os.path.dirname(os.path.dirname(__file__)),
+                    "utils/resources/models/scikit/",
+                    model_name + "iris_clipped.sav",
+                ),
+                "wb",
+            ),
+        )
+
+    for model_name, model in unclipped_models.items():
+        model.fit(x=x_train_iris, y=y_train_iris)
+        pickle.dump(
+            model,
+            open(
+                os.path.join(
+                    os.path.dirname(os.path.dirname(__file__)),
+                    "utils/resources/models/scikit/",
+                    model_name + "iris_unclipped.sav",
+                ),
+                "wb",
+            ),
+        )
+
+
+if __name__ == "__main__":
+    main_mnist_binary()
+    create_scikit_model_weights()
diff --git a/adversarial-robustness-toolbox/utils/resources/models/scikit/scikit-adaBoostClassifier-iris-clipped.pickle b/adversarial-robustness-toolbox/utils/resources/models/scikit/scikit-adaBoostClassifier-iris-clipped.pickle
new file mode 100644
index 0000000..28089b4
Binary files /dev/null and b/adversarial-robustness-toolbox/utils/resources/models/scikit/scikit-adaBoostClassifier-iris-clipped.pickle differ
diff --git a/adversarial-robustness-toolbox/utils/resources/models/scikit/scikit-adaBoostClassifier-iris-unclipped.pickle b/adversarial-robustness-toolbox/utils/resources/models/scikit/scikit-adaBoostClassifier-iris-unclipped.pickle
new file mode 100644
index 0000000..7cc6d51
Binary files /dev/null and b/adversarial-robustness-toolbox/utils/resources/models/scikit/scikit-adaBoostClassifier-iris-unclipped.pickle differ
diff --git a/adversarial-robustness-toolbox/utils/resources/models/scikit/scikit-baggingClassifier-iris-clipped.pickle b/adversarial-robustness-toolbox/utils/resources/models/scikit/scikit-baggingClassifier-iris-clipped.pickle
new file mode 100644
index 0000000..9feb220
Binary files /dev/null and b/adversarial-robustness-toolbox/utils/resources/models/scikit/scikit-baggingClassifier-iris-clipped.pickle differ
diff --git a/adversarial-robustness-toolbox/utils/resources/models/scikit/scikit-baggingClassifier-iris-unclipped.pickle b/adversarial-robustness-toolbox/utils/resources/models/scikit/scikit-baggingClassifier-iris-unclipped.pickle
new file mode 100644
index 0000000..ff9d2ba
Binary files /dev/null and b/adversarial-robustness-toolbox/utils/resources/models/scikit/scikit-baggingClassifier-iris-unclipped.pickle differ
diff --git a/adversarial-robustness-toolbox/utils/resources/models/scikit/scikit-decisionTreeClassifier-iris-clipped.pickle b/adversarial-robustness-toolbox/utils/resources/models/scikit/scikit-decisionTreeClassifier-iris-clipped.pickle
new file mode 100644
index 0000000..3ad7087
Binary files /dev/null and b/adversarial-robustness-toolbox/utils/resources/models/scikit/scikit-decisionTreeClassifier-iris-clipped.pickle differ
diff --git a/adversarial-robustness-toolbox/utils/resources/models/scikit/scikit-decisionTreeClassifier-iris-unclipped.pickle b/adversarial-robustness-toolbox/utils/resources/models/scikit/scikit-decisionTreeClassifier-iris-unclipped.pickle
new file mode 100644
index 0000000..3ad7087
Binary files /dev/null and b/adversarial-robustness-toolbox/utils/resources/models/scikit/scikit-decisionTreeClassifier-iris-unclipped.pickle differ
diff --git a/adversarial-robustness-toolbox/utils/resources/models/scikit/scikit-extraTreeClassifier-iris-clipped.pickle b/adversarial-robustness-toolbox/utils/resources/models/scikit/scikit-extraTreeClassifier-iris-clipped.pickle
new file mode 100644
index 0000000..f5b942e
Binary files /dev/null and b/adversarial-robustness-toolbox/utils/resources/models/scikit/scikit-extraTreeClassifier-iris-clipped.pickle differ
diff --git a/adversarial-robustness-toolbox/utils/resources/models/scikit/scikit-extraTreeClassifier-iris-unclipped.pickle b/adversarial-robustness-toolbox/utils/resources/models/scikit/scikit-extraTreeClassifier-iris-unclipped.pickle
new file mode 100644
index 0000000..843d9a3
Binary files /dev/null and b/adversarial-robustness-toolbox/utils/resources/models/scikit/scikit-extraTreeClassifier-iris-unclipped.pickle differ
diff --git a/adversarial-robustness-toolbox/utils/resources/models/scikit/scikit-extraTreesClassifier-iris-clipped.pickle b/adversarial-robustness-toolbox/utils/resources/models/scikit/scikit-extraTreesClassifier-iris-clipped.pickle
new file mode 100644
index 0000000..5ad5f46
Binary files /dev/null and b/adversarial-robustness-toolbox/utils/resources/models/scikit/scikit-extraTreesClassifier-iris-clipped.pickle differ
diff --git a/adversarial-robustness-toolbox/utils/resources/models/scikit/scikit-extraTreesClassifier-iris-unclipped.pickle b/adversarial-robustness-toolbox/utils/resources/models/scikit/scikit-extraTreesClassifier-iris-unclipped.pickle
new file mode 100644
index 0000000..71629f2
Binary files /dev/null and b/adversarial-robustness-toolbox/utils/resources/models/scikit/scikit-extraTreesClassifier-iris-unclipped.pickle differ
diff --git a/adversarial-robustness-toolbox/utils/resources/models/scikit/scikit-gradientBoostingClassifier-iris-clipped.pickle b/adversarial-robustness-toolbox/utils/resources/models/scikit/scikit-gradientBoostingClassifier-iris-clipped.pickle
new file mode 100644
index 0000000..6336506
Binary files /dev/null and b/adversarial-robustness-toolbox/utils/resources/models/scikit/scikit-gradientBoostingClassifier-iris-clipped.pickle differ
diff --git a/adversarial-robustness-toolbox/utils/resources/models/scikit/scikit-gradientBoostingClassifier-iris-unclipped.pickle b/adversarial-robustness-toolbox/utils/resources/models/scikit/scikit-gradientBoostingClassifier-iris-unclipped.pickle
new file mode 100644
index 0000000..9ec9a38
Binary files /dev/null and b/adversarial-robustness-toolbox/utils/resources/models/scikit/scikit-gradientBoostingClassifier-iris-unclipped.pickle differ
diff --git a/adversarial-robustness-toolbox/utils/resources/models/scikit/scikit-linearSVC-iris-clipped.pickle b/adversarial-robustness-toolbox/utils/resources/models/scikit/scikit-linearSVC-iris-clipped.pickle
new file mode 100644
index 0000000..92d5484
Binary files /dev/null and b/adversarial-robustness-toolbox/utils/resources/models/scikit/scikit-linearSVC-iris-clipped.pickle differ
diff --git a/adversarial-robustness-toolbox/utils/resources/models/scikit/scikit-linearSVC-iris-unclipped.pickle b/adversarial-robustness-toolbox/utils/resources/models/scikit/scikit-linearSVC-iris-unclipped.pickle
new file mode 100644
index 0000000..6fbd6e6
Binary files /dev/null and b/adversarial-robustness-toolbox/utils/resources/models/scikit/scikit-linearSVC-iris-unclipped.pickle differ
diff --git a/adversarial-robustness-toolbox/utils/resources/models/scikit/scikit-logisticRegression-iris-clipped.pickle b/adversarial-robustness-toolbox/utils/resources/models/scikit/scikit-logisticRegression-iris-clipped.pickle
new file mode 100644
index 0000000..8a333a8
Binary files /dev/null and b/adversarial-robustness-toolbox/utils/resources/models/scikit/scikit-logisticRegression-iris-clipped.pickle differ
diff --git a/adversarial-robustness-toolbox/utils/resources/models/scikit/scikit-logisticRegression-iris-unclipped.pickle b/adversarial-robustness-toolbox/utils/resources/models/scikit/scikit-logisticRegression-iris-unclipped.pickle
new file mode 100644
index 0000000..8a333a8
Binary files /dev/null and b/adversarial-robustness-toolbox/utils/resources/models/scikit/scikit-logisticRegression-iris-unclipped.pickle differ
diff --git a/adversarial-robustness-toolbox/utils/resources/models/scikit/scikit-randomForestClassifier-iris-clipped.pickle b/adversarial-robustness-toolbox/utils/resources/models/scikit/scikit-randomForestClassifier-iris-clipped.pickle
new file mode 100644
index 0000000..52b4eb0
Binary files /dev/null and b/adversarial-robustness-toolbox/utils/resources/models/scikit/scikit-randomForestClassifier-iris-clipped.pickle differ
diff --git a/adversarial-robustness-toolbox/utils/resources/models/scikit/scikit-randomForestClassifier-iris-unclipped.pickle b/adversarial-robustness-toolbox/utils/resources/models/scikit/scikit-randomForestClassifier-iris-unclipped.pickle
new file mode 100644
index 0000000..8d45cd0
Binary files /dev/null and b/adversarial-robustness-toolbox/utils/resources/models/scikit/scikit-randomForestClassifier-iris-unclipped.pickle differ
diff --git a/adversarial-robustness-toolbox/utils/resources/models/scikit/scikit-svc-iris-clipped.pickle b/adversarial-robustness-toolbox/utils/resources/models/scikit/scikit-svc-iris-clipped.pickle
new file mode 100644
index 0000000..be07ee0
Binary files /dev/null and b/adversarial-robustness-toolbox/utils/resources/models/scikit/scikit-svc-iris-clipped.pickle differ
diff --git a/adversarial-robustness-toolbox/utils/resources/models/scikit/scikit-svc-iris-unclipped.pickle b/adversarial-robustness-toolbox/utils/resources/models/scikit/scikit-svc-iris-unclipped.pickle
new file mode 100644
index 0000000..be07ee0
Binary files /dev/null and b/adversarial-robustness-toolbox/utils/resources/models/scikit/scikit-svc-iris-unclipped.pickle differ